Repository: harvardinformatics/bioinformatics-coffee-hour
Branch: master
Commit: 17531934f410
Files: 107
Total size: 3.8 MB

Directory structure:
gitextract_0onxtofe/

├── LICENSE
├── README.md
├── bedtools/
│   ├── binder/
│   │   └── environment.yml
│   ├── data/
│   │   ├── H1-hESC-H3K27Ac_chr22.bed
│   │   ├── hg38.genome
│   │   ├── human_enhancers_chr22.bed
│   │   └── ucscGenes_chr22.bed
│   └── index.ipynb
├── differential-expression-analysis/
│   ├── binder/
│   │   └── Dockerfile
│   ├── data/
│   │   ├── dme_elev_samples.tab
│   │   └── dme_elevgrad_rsem_bt2_gene_counts.matrix.bz2
│   └── index.Rmd
├── enrichment-analysis/
│   ├── binder/
│   │   └── Dockerfile
│   ├── data/
│   │   ├── dme_mapk_ensembl_geneids.txt
│   │   └── heatshockprotein_ensembl_gene_ids.txt
│   └── index.Rmd
├── ggplot-basics/
│   ├── binder/
│   │   └── Dockerfile
│   └── index.Rmd
├── intro-to-awk/
│   ├── binder/
│   │   └── environment.yml
│   ├── data/
│   │   ├── Homo_sapiens.GRCh38.subset.gff3
│   │   ├── Homo_sapiens_ucscGenes.subset.bed
│   │   ├── enhancers.csv
│   │   ├── example.fastq
│   │   └── hg38.genome
│   └── index.ipynb
├── intro-to-r/
│   ├── binder/
│   │   └── Dockerfile
│   ├── data/
│   │   └── mbta_bus.tsv
│   ├── index.Rmd
│   └── instructor_copy.Rmd
├── intro_awk_spring2021/
│   ├── .ipynb_checkpoints/
│   │   └── index-checkpoint.ipynb
│   ├── binder/
│   │   ├── .ipynb_checkpoints/
│   │   │   └── environment-checkpoint.yml
│   │   └── environment.yml
│   ├── data/
│   │   ├── .ipynb_checkpoints/
│   │   │   ├── Homo_sapiens.GRCh38.subset-checkpoint.gff3
│   │   │   ├── Homo_sapiens_ucscGenes.subset-checkpoint.bed
│   │   │   ├── example-checkpoint.fastq
│   │   │   └── hg38-checkpoint.genome
│   │   ├── Homo_sapiens.GRCh38.subset.gff3
│   │   ├── Homo_sapiens_ucscGenes.subset.bed
│   │   ├── dmel-subset-chromosomes.fasta
│   │   ├── enhancers.csv
│   │   ├── example.fastq
│   │   ├── hg38.genome
│   │   └── sample.txt
│   └── index.ipynb
├── intro_data_science/
│   ├── part_1/
│   │   ├── binder/
│   │   │   └── requirements.txt
│   │   ├── ds_part_1.ipynb
│   │   └── ds_part_1_complete.ipynb
│   ├── part_2/
│   │   ├── binder/
│   │   │   └── requirements.txt
│   │   ├── ds_part_2.ipynb
│   │   └── ds_part_2_complete.ipynb
│   ├── part_3/
│   │   ├── binder/
│   │   │   └── requirements.txt
│   │   ├── data/
│   │   │   ├── complete/
│   │   │   │   └── eur_data_final.csv
│   │   │   ├── gdp_2016.csv
│   │   │   ├── misc_data.xlsx
│   │   │   ├── out/
│   │   │   │   ├── eur_data_sorted.csv
│   │   │   │   ├── eur_data_sorted.html
│   │   │   │   ├── eur_data_sorted.json
│   │   │   │   └── eur_data_sorted.xlsx
│   │   │   ├── unemployment_2016.csv
│   │   │   └── ~$misc_data.xlsx
│   │   ├── ds_part_3.ipynb
│   │   └── ds_part_3_complete.ipynb
│   └── part_4/
│       ├── binder/
│       │   └── requirements.txt
│       ├── data/
│       │   └── complete/
│       │       ├── eur_data_final.csv
│       │       └── un_world_data.csv
│       ├── ds_part_4.ipynb
│       └── ds_part_4_complete.ipynb
├── samtools/
│   ├── binder/
│   │   └── environment.yml
│   ├── data/
│   │   ├── file.sorted.bam
│   │   └── subset.bed
│   ├── index.ipynb
│   └── introduction.md
├── singularity/
│   ├── images/
│   │   ├── singularity_container.drawio
│   │   ├── singularity_image.drawio
│   │   └── trinity.drawio
│   ├── part1.html
│   └── part1.md
├── snakemake/
│   ├── LICENSE
│   ├── README.md
│   ├── Snakefile
│   ├── binder/
│   │   ├── README.md
│   │   └── environment.yml
│   ├── data/
│   │   └── .gitignore
│   └── index.ipynb
├── taste-of-conda/
│   ├── binder/
│   │   └── environment.yml
│   └── index.ipynb
├── tidyverse/
│   ├── part1/
│   │   ├── binder/
│   │   │   └── Dockerfile
│   │   └── index.Rmd
│   ├── part1_winter2021/
│   │   ├── binder/
│   │   │   └── Dockerfile
│   │   └── index.Rmd
│   ├── part2/
│   │   ├── binder/
│   │   │   └── Dockerfile
│   │   └── index.Rmd
│   └── part2_winter2021/
│       ├── binder/
│       │   └── Dockerfile
│       ├── co-est2019-annres.csv.xz
│       ├── mask-use-by-county.csv
│       ├── tidyverse_part2_winter2021.Rmd
│       └── tidyverse_part2_winter2021.nb.html
├── tidyverse_covid_data/
│   ├── part1/
│   │   ├── binder/
│   │   │   └── Dockerfile
│   │   ├── tidyverse_1.R
│   │   └── tidyverse_1.Rmd
│   └── part2/
│       ├── binder/
│       │   └── Dockerfile
│       ├── co-est2019-annres.csv.xz
│       ├── mask-use-by-county.csv
│       ├── tidyverse_2.R
│       └── tidyverse_2.Rmd
└── unix-pipes/
    ├── binder/
    │   └── environment.yml
    └── index.ipynb

================================================
FILE CONTENTS
================================================

================================================
FILE: LICENSE
================================================
MIT License

Copyright (c) 2020 President and Fellows of Harvard College

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.


================================================
FILE: README.md
================================================
# Bioinformatics Coffee Hour

| Date | Topic | Launch |
| --- | --- | --- |
| Tues, April 14, 2020 | [Intro to AWK](/intro-to-awk/index.ipynb) | [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/harvardinformatics/bioinformatics-coffee-hour/2020-04-14_intro-to-awk?urlpath=lab/tree/intro-to-awk/index.ipynb) |
| Tues, April 21, 2020 | [Differential Expression Analysis with limma-voom](/differential-expression-analysis/index.Rmd) | [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/harvardinformatics/bioinformatics-coffee-hour/2020-04-21_differential-expression-analysis?urlpath=rstudio) |
| Tues, April 28, 2020 | [A Taste of Conda](/taste-of-conda/index.ipynb) | [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/harvardinformatics/bioinformatics-coffee-hour/2020-04-28_taste-of-conda?urlpath=lab/tree/taste-of-conda/index.ipynb) |
| Tues, May 5, 2020 | [Tidyverse (part 1)](/tidyverse/part1/index.Rmd) | [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/harvardinformatics/bioinformatics-coffee-hour/2020-05-05_tidyverse_part1?urlpath=rstudio) |
| Tues, May 12, 2020 | [Tidyverse (part 2)](/tidyverse/part2/index.Rmd) | [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/harvardinformatics/bioinformatics-coffee-hour/2020-05-12_tidyverse_part2?urlpath=rstudio) |
| Tues, May 19, 2020 | [Intro to bedtools](/bedtools/index.ipynb) | [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/harvardinformatics/bioinformatics-coffee-hour/2020-05-19_bedtools?urlpath=lab/tree/bedtools/index.ipynb) |
| Tues, May 26, 2020 | [Snakemake](/snakemake/index.ipynb) | [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/harvardinformatics/bioinformatics-coffee-hour/2020-05-26_snakemake?urlpath=lab/tree/snakemake/index.ipynb) |
| Tues, June 16, 2020 | [Unix Pipes](/unix-pipes/index.ipynb) | [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/harvardinformatics/bioinformatics-coffee-hour/2020-06-16_unix-pipes?urlpath=lab/tree/unix-pipes/index.ipynb) |
| Tues, June 23, 2020 | [Samtools](/samtools/index.ipynb) | [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/harvardinformatics/bioinformatics-coffee-hour/2020-06-23_samtools?urlpath=lab/tree/samtools/index.ipynb) |
| Tues, June 30, 2020 | [Enrichment Analysis for Bulk RNA-Seq with Camera and ROAST](/enrichment-analysis/index.Rmd) | [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/harvardinformatics/bioinformatics-coffee-hour/2020-06-30_enrichment-analysis?urlpath=rstudio) |
| Tues, Aug 4, 2020 | [Intro to R](/intro-to-r/index.Rmd) | [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/harvardinformatics/bioinformatics-coffee-hour/2020-08-04_intro-to-r?urlpath=rstudio) |
| Tues, Aug 11, 2020 | [Tidyverse with covid data 1](/tidyverse_covid_data/part1/tidyverse_1.Rmd) | [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/harvardinformatics/bioinformatics-coffee-hour/2020-08-11_tidyverse-with-covid-data-1?urlpath=rstudio) |
| Tues, Aug 18, 2020 | [Tidyverse with covid data 2](/tidyverse_covid_data/part2/tidyverse_2.Rmd) | [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/harvardinformatics/bioinformatics-coffee-hour/2020-08-18_tidyverse-with-covid-data-2?urlpath=rstudio) |
| Tues, Aug 25, 2020 | [ggplot basics](/ggplot-basics/index.Rmd) | [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/harvardinformatics/bioinformatics-coffee-hour/2020-08-25_ggplot-basics?urlpath=rstudio) |
| Tues, January 19, 2021 | [Intro to Data Science - Part 1](/intro_data_science/part_1/ds_part_1.ipynb) | [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/harvardinformatics/bioinformatics-coffee-hour/2021-01-19_intro_data_science_1?urlpath=lab/tree/intro_data_science/part_1/ds_part_1.ipynb) |
| Tues, January 26, 2021 | [Intro to Data Science - Part 2](/intro_data_science/part_2/ds_part_2.ipynb) | [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/harvardinformatics/bioinformatics-coffee-hour/2021-01-26_intro_data_science_2?urlpath=lab/tree/intro_data_science/part_2/ds_part_2.ipynb) |
| Tues, February 2, 2021 | [Intro to Data Science - Part 3](/intro_data_science/part_3/ds_part_3.ipynb) | [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/harvardinformatics/bioinformatics-coffee-hour/2021-02-02_intro_data_science_3?urlpath=lab/tree/intro_data_science/part_3/ds_part_3.ipynb) |
| Tues, February 9, 2021 | [Intro to Data Science - Part 4](/intro_data_science/part_4/ds_part_4.ipynb) | [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/harvardinformatics/bioinformatics-coffee-hour/2021-02-09_intro_data_science_4?urlpath=lab/tree/intro_data_science/part_4/ds_part_4.ipynb) |
| Tues, March 2, 2021 | [Tidyverse (part 1) Winter 2021](/tidyverse/part1/index.Rmd) | [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/harvardinformatics/bioinformatics-coffee-hour/2021-03-02_tidyverse_part1?urlpath=rstudio) |
| Tues, March 9, 2021 | [Tidyverse (part 2) Winter 2021](/tidyverse/part2_winter2021/tidyverse_part2_winter2021.Rmd) | [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/harvardinformatics/bioinformatics-coffee-hour/2021-03-09_tidyverse_part2?urlpath=rstudio) |
| Tues, April 20, 2021 | [Intro to AWK and Grep Spring 2021](/intro_awk_spring2021/index.ipynb) | [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/harvardinformatics/bioinformatics-coffee-hour/2021-04-20_intro_awk?urlpath=lab/tree/intro_awk_spring2021/index.ipynb) |
| Tues, April 27, 2021 | [Singularity (part 1): Finding and Running Containers](/singularity/part1.md)


================================================
FILE: bedtools/binder/environment.yml
================================================
channels:
  - conda-forge
  - bioconda
  - defaults
dependencies:
  - bash_kernel
  - bedtools=2.29.2


================================================
FILE: bedtools/data/H1-hESC-H3K27Ac_chr22.bed
================================================
chr22	16846128	16866063	.	295
chr22	16849975	16850195	.	703
chr22	16850703	16850930	.	1000
chr22	16861726	16861890	.	837
chr22	17047932	17048173	.	722
chr22	17306180	17306362	.	857
chr22	17535824	17546861	.	281
chr22	17573289	17612560	.	288
chr22	17617573	17634154	.	297
chr22	17639228	17640854	.	594
chr22	17651864	17653784	.	598
chr22	17667916	17686364	.	521
chr22	17674950	17675707	.	707
chr22	17678957	17680889	.	1000
chr22	17681072	17681213	.	1000
chr22	17695293	17695896	.	495
chr22	17698340	17699734	.	407
chr22	17708995	17726307	.	341
chr22	17736801	17743349	.	625
chr22	17743875	17751576	.	625
chr22	17753338	17754400	.	766
chr22	17766210	17774384	.	292
chr22	17778642	17802961	.	281
chr22	17788505	17788634	.	957
chr22	17790395	17790912	.	519
chr22	17790463	17790589	.	914
chr22	17819638	17820672	.	398
chr22	17838213	18003151	.	335
chr22	17847872	17848297	.	667
chr22	17848575	17851798	.	1000
chr22	17859322	17859430	.	1000
chr22	17860044	17860967	.	755
chr22	17861170	17861746	.	713
chr22	17867255	17867642	.	655
chr22	17902253	17902355	.	1000
chr22	17903643	17903828	.	846
chr22	17909361	17909563	.	751
chr22	17930036	17930459	.	651
chr22	17963184	17963387	.	748
chr22	17971197	17971631	.	693
chr22	18015938	18016806	.	450
chr22	18020970	18022774	.	392
chr22	18026816	18056024	.	327
chr22	18041519	18044898	.	596
chr22	18049468	18049844	.	690
chr22	18050131	18050360	.	682
chr22	18050888	18051455	.	667
chr22	18056377	18057052	.	472
chr22	18063774	18064791	.	402
chr22	18065891	18066447	.	480
chr22	18070477	18071089	.	503
chr22	18072197	18074022	.	390
chr22	18086083	18087196	.	396
chr22	18110918	18112258	.	1000
chr22	18119703	18123278	.	983
chr22	18161634	18161752	.	964
chr22	18188968	18190408	.	383
chr22	18206524	18235419	.	285
chr22	18236445	18237620	.	384
chr22	18255747	18257600	.	576
chr22	18256484	18256718	.	905
chr22	18256895	18257107	.	907
chr22	18257137	18257267	.	1000
chr22	18267051	18281658	.	320
chr22	18286923	18287900	.	483
chr22	18297863	18299181	.	391
chr22	18302879	18330299	.	288
chr22	18377158	18415796	.	307
chr22	18419875	18439132	.	330
chr22	18439474	18460598	.	314
chr22	18483896	18484765	.	1000
chr22	18560313	18560422	.	1000
chr22	18560331	18560545	.	755
chr22	18560660	18561449	.	962
chr22	18604230	18605238	.	404
chr22	18718146	18718292	.	1000
chr22	18876360	18876975	.	489
chr22	18876660	18876984	.	605
chr22	18876760	18876992	.	676
chr22	18878238	18878848	.	1000
chr22	18879886	18880128	.	1000
chr22	18880556	18880895	.	931
chr22	18881929	18882179	.	1000
chr22	18883666	18883770	.	1000
chr22	18884043	18884168	.	920
chr22	18893121	18893822	.	461
chr22	18900742	18900998	.	659
chr22	18907281	18907801	.	502
chr22	18907301	18907401	.	1000
chr22	18911818	18919831	.	784
chr22	18921106	18922484	.	506
chr22	18923452	18923997	.	515
chr22	18926058	18926176	.	964
chr22	18935176	18945475	.	282
chr22	18935447	18935560	.	1000
chr22	18935655	18935869	.	827
chr22	18935873	18935985	.	1000
chr22	18952384	18968131	.	276
chr22	18978030	18988144	.	281
chr22	19023738	19026095	.	390
chr22	19040443	19063280	.	306
chr22	19088604	19119947	.	331
chr22	19092021	19092427	.	652
chr22	19097321	19097538	.	711
chr22	19109205	19110085	.	960
chr22	19110512	19110746	.	938
chr22	19131778	19132114	.	1000
chr22	19153745	19177358	.	444
chr22	19188646	19190188	.	394
chr22	19195700	19216040	.	329
chr22	19197845	19197950	.	1000
chr22	19201513	19201697	.	892
chr22	19204195	19204427	.	709
chr22	19253116	19288464	.	300
chr22	19273182	19273643	.	1000
chr22	19274064	19275659	.	494
chr22	19278727	19278827	.	1000
chr22	19279804	19279973	.	818
chr22	19334044	19334576	.	509
chr22	19336737	19350056	.	299
chr22	19389305	19404840	.	299
chr22	19405354	19425112	.	359
chr22	19410269	19410369	.	1000
chr22	19410703	19410906	.	748
chr22	19417307	19421222	.	616
chr22	19434330	19436322	.	931
chr22	19465158	19468827	.	1000
chr22	19479273	19513263	.	285
chr22	19542526	19542637	.	1000
chr22	19586300	19587331	.	399
chr22	19592902	19603477	.	317
chr22	19608472	19609960	.	560
chr22	19679560	19679808	.	769
chr22	19679699	19679818	.	958
chr22	19692121	19693889	.	525
chr22	19778616	19820959	.	297
chr22	19799693	19799833	.	896
chr22	19811163	19811386	.	731
chr22	19823001	19909709	.	325
chr22	19929629	19930860	.	495
chr22	19937259	20010920	.	343
chr22	19950145	19950376	.	914
chr22	19950802	19950950	.	909
chr22	20013136	20030424	.	277
chr22	20035500	20151405	.	340
chr22	20066277	20070013	.	718
chr22	20072574	20072895	.	658
chr22	20087969	20088187	.	708
chr22	20089831	20090029	.	880
chr22	20103392	20107026	.	914
chr22	20107461	20107570	.	1000
chr22	20107659	20107842	.	853
chr22	20115059	20115324	.	643
chr22	20115505	20115720	.	752
chr22	20123657	20123766	.	1000
chr22	20132503	20132718	.	716
chr22	20152168	20259365	.	302
chr22	20303364	20305168	.	388
chr22	20306733	20308884	.	411
chr22	20326078	20326227	.	1000
chr22	20747879	20748834	.	711
chr22	20771845	20782827	.	303
chr22	20810818	20811884	.	406
chr22	20812810	20814147	.	394
chr22	20843076	20843668	.	475
chr22	20850204	20850526	.	1000
chr22	20851190	20851312	.	1000
chr22	20857339	20873174	.	312
chr22	20861747	20862572	.	824
chr22	20863412	20863762	.	662
chr22	20879749	20892778	.	340
chr22	20884742	20884885	.	989
chr22	20886960	20887144	.	977
chr22	20898333	20915030	.	328
chr22	20917448	20948974	.	319
chr22	20956581	20967725	.	303
chr22	20979336	21012452	.	283
chr22	21056144	21057322	.	423
chr22	21057113	21057329	.	713
chr22	21103262	21104302	.	397
chr22	21104717	21104999	.	669
chr22	21104730	21105241	.	508
chr22	21119342	21120344	.	405
chr22	21129107	21146106	.	300
chr22	21148469	21149212	.	466
chr22	21151608	21151832	.	694
chr22	21158141	21159177	.	413
chr22	21158969	21159197	.	685
chr22	21211335	21214026	.	436
chr22	21212316	21212534	.	958
chr22	21213398	21213501	.	1000
chr22	21220011	21222374	.	406
chr22	21240025	21240240	.	716
chr22	21270705	21272892	.	1000
chr22	21273319	21273546	.	687
chr22	21275195	21275351	.	871
chr22	21279013	21279139	.	914
chr22	21285406	21285936	.	525
chr22	21286847	21313108	.	313
chr22	21291729	21292095	.	704
chr22	21300608	21300718	.	1000
chr22	21301260	21301416	.	921
chr22	21323969	21378797	.	325
chr22	21333308	21333514	.	853
chr22	21333897	21334118	.	842
chr22	21335549	21337055	.	845
chr22	21355063	21357183	.	709
chr22	21363912	21365364	.	553
chr22	21797119	21798545	.	533
chr22	21802209	21804193	.	396
chr22	21809730	21810330	.	471
chr22	21921797	21922925	.	1000
chr22	21926773	21929001	.	420
chr22	21936982	21938209	.	382
chr22	21945343	21945861	.	503
chr22	21955943	21971241	.	306
chr22	21972578	21989164	.	344
chr22	21978272	21979522	.	515
chr22	21982299	21982849	.	498
chr22	21983464	21984505	.	891
chr22	21996096	21996323	.	721
chr22	21996684	21996796	.	1000
chr22	22004734	22009010	.	546
chr22	22010296	22013520	.	457
chr22	22020266	22020948	.	846
chr22	22046509	22060116	.	298
chr22	22079431	22100078	.	293
chr22	22080193	22081145	.	491
chr22	22088813	22091105	.	471
chr22	22093784	22095185	.	389
chr22	22103688	22129118	.	284
chr22	22151630	22163383	.	279
chr22	22170523	22201957	.	287
chr22	22172749	22173368	.	512
chr22	22191517	22192444	.	491
chr22	22209484	22227169	.	326
chr22	22264055	22344964	.	312
chr22	22271999	22272146	.	914
chr22	22272214	22272317	.	1000
chr22	22272456	22272568	.	1000
chr22	22275213	22278033	.	700
chr22	22281323	22281707	.	679
chr22	22384793	22394870	.	280
chr22	22385307	22385416	.	1000
chr22	22670074	22672050	.	397
chr22	22813717	22814465	.	714
chr22	22814767	22815023	.	689
chr22	22839297	22844312	.	301
chr22	22862782	22862949	.	965
chr22	23371407	23376888	.	299
chr22	23411812	23471758	.	307
chr22	23481296	23649321	.	362
chr22	23559141	23559258	.	971
chr22	23576250	23576474	.	729
chr22	23580772	23590814	.	749
chr22	23593534	23594088	.	721
chr22	23602376	23602573	.	805
chr22	23602786	23602919	.	992
chr22	23604165	23604371	.	777
chr22	23609041	23609159	.	964
chr22	23609065	23609295	.	714
chr22	23610160	23610347	.	922
chr22	23736761	23747803	.	281
chr22	23874917	23875717	.	474
chr22	23878003	23878110	.	1000
chr22	23933375	23934521	.	396
chr22	24013062	24023096	.	300
chr22	24023478	24036105	.	300
chr22	24040000	24050797	.	302
chr22	24058895	24059952	.	806
chr22	24092450	24095447	.	515
chr22	24104778	24105792	.	487
chr22	24107257	24107992	.	480
chr22	24108999	24111054	.	403
chr22	24123456	24124481	.	400
chr22	24128298	24130301	.	1000
chr22	24131389	24132721	.	394
chr22	24134620	24135923	.	393
chr22	24142861	24144004	.	397
chr22	24144277	24145718	.	405
chr22	24158002	24159024	.	401
chr22	24161313	24193849	.	342
chr22	24163255	24163364	.	1000
chr22	24171193	24171627	.	675
chr22	24180903	24181326	.	909
chr22	24181665	24181773	.	1000
chr22	24188801	24188916	.	1000
chr22	24198925	24199032	.	1000
chr22	24199211	24199731	.	966
chr22	24199995	24200126	.	945
chr22	24200241	24200538	.	959
chr22	24215947	24224051	.	292
chr22	24234838	24239204	.	802
chr22	24240096	24240305	.	731
chr22	24255457	24256336	.	1000
chr22	24297002	24297140	.	963
chr22	24298777	24299006	.	887
chr22	24372408	24372642	.	938
chr22	24373074	24373466	.	1000
chr22	24373507	24373650	.	880
chr22	24383543	24384949	.	516
chr22	24398117	24592198	.	305
chr22	24498760	24498867	.	1000
chr22	24514393	24514522	.	897
chr22	24514407	24514932	.	706
chr22	24525425	24525644	.	706
chr22	24525979	24526108	.	957
chr22	24525985	24526201	.	713
chr22	24526792	24526900	.	1000
chr22	24585492	24585705	.	721
chr22	24666356	24668086	.	1000
chr22	24685989	24686791	.	483
chr22	24717757	24728383	.	297
chr22	24728411	24745114	.	347
chr22	24734250	24734432	.	814
chr22	24734683	24735506	.	769
chr22	24735643	24735754	.	1000
chr22	24754939	24755461	.	500
chr22	24755063	24756078	.	402
chr22	24757981	24758574	.	501
chr22	24758784	24759299	.	505
chr22	24781223	24850303	.	318
chr22	24861766	24862288	.	515
chr22	24889709	24923347	.	284
chr22	24934960	24999994	.	332
chr22	24950322	24950544	.	944
chr22	24950630	24951088	.	1000
chr22	24951213	24952731	.	1000
chr22	24988551	24988808	.	687
chr22	24989299	24989460	.	946
chr22	25079483	25098398	.	370
chr22	25084127	25084394	.	727
chr22	25086731	25087192	.	747
chr22	25102150	25102697	.	514
chr22	25120458	25130276	.	296
chr22	25153765	25154809	.	403
chr22	25157628	25159014	.	386
chr22	25195835	25240004	.	288
chr22	25231160	25231322	.	990
chr22	25231410	25231559	.	1000
chr22	25258005	25268039	.	300
chr22	25290413	25291481	.	500
chr22	25321347	25322611	.	554
chr22	25334390	25572903	.	321
chr22	25363082	25363186	.	1000
chr22	25363467	25363614	.	1000
chr22	25363918	25364869	.	811
chr22	25365804	25366451	.	678
chr22	25372062	25372270	.	809
chr22	25373638	25373754	.	978
chr22	25378329	25378556	.	756
chr22	25390676	25390990	.	693
chr22	25391320	25393626	.	581
chr22	25391733	25391848	.	985
chr22	25392147	25392376	.	921
chr22	25392378	25392599	.	842
chr22	25392844	25393173	.	859
chr22	25402503	25402803	.	951
chr22	25402905	25403187	.	973
chr22	25505988	25507808	.	784
chr22	25511156	25511361	.	742
chr22	25539035	25539307	.	688
chr22	25556519	25556722	.	748
chr22	25586369	25600591	.	297
chr22	25747992	25809688	.	314
chr22	25757940	25758053	.	1000
chr22	25775871	25776024	.	936
chr22	25781750	25783611	.	674
chr22	25782162	25782283	.	945
chr22	25782479	25783323	.	966
chr22	25833402	25834434	.	489
chr22	25843463	25844543	.	497
chr22	25873656	25873757	.	1000
chr22	25886366	25895154	.	298
chr22	25903856	25904049	.	858
chr22	25910192	25910837	.	498
chr22	25939721	25946584	.	300
chr22	25959915	25969748	.	297
chr22	25960598	25960846	.	706
chr22	26015702	26015948	.	964
chr22	26022735	26028532	.	306
chr22	26036825	26037863	.	397
chr22	26041021	26041646	.	459
chr22	26071085	26101031	.	292
chr22	26104812	26128110	.	276
chr22	26170155	26171805	.	385
chr22	26199126	26205787	.	293
chr22	26234468	26246280	.	281
chr22	26286257	26286773	.	504
chr22	26291290	26296301	.	301
chr22	26299039	26299255	.	749
chr22	26323899	26324198	.	954
chr22	26334631	26342884	.	289
chr22	26353852	26377649	.	303
chr22	26366117	26368088	.	571
chr22	26368348	26368464	.	1000
chr22	26427494	26436110	.	291
chr22	26458211	26467977	.	295
chr22	26497096	26502508	.	300
chr22	26621689	26629535	.	296
chr22	26634220	26645457	.	280
chr22	26667695	26676668	.	292
chr22	26689733	26692573	.	375
chr22	26717309	26717516	.	737
chr22	26723456	26723660	.	745
chr22	26747023	26761397	.	274
chr22	26770351	26780934	.	282
chr22	26800673	26802117	.	377
chr22	26824052	26825084	.	399
chr22	26824530	26825134	.	469
chr22	26833084	26834148	.	399
chr22	26834948	26851016	.	314
chr22	26855660	26863945	.	295
chr22	26876652	26882077	.	596
chr22	26889308	26890619	.	392
chr22	26897783	26900455	.	417
chr22	26899323	26899431	.	1000
chr22	26899446	26899555	.	1000
chr22	26907519	26909390	.	668
chr22	26922592	26990700	.	377
chr22	26945067	26945233	.	1000
chr22	26948474	26948685	.	726
chr22	26962653	26966998	.	753
chr22	26975185	26975303	.	964
chr22	26975484	26975688	.	745
chr22	26976752	26976865	.	1000
chr22	26977035	26977231	.	967
chr22	26996494	27022403	.	282
chr22	27027359	27090433	.	400
chr22	27095171	27095287	.	978
chr22	27102635	27121337	.	297
chr22	27125158	27131927	.	302
chr22	27138668	27153935	.	339
chr22	27171399	27214448	.	291
chr22	27210798	27215881	.	301
chr22	27278837	27309132	.	277
chr22	27341369	27359713	.	279
chr22	27395737	27410591	.	303
chr22	27422278	27422432	.	880
chr22	27449391	27470234	.	282
chr22	27474870	27516049	.	280
chr22	27528441	27549365	.	281
chr22	27595766	27605119	.	297
chr22	27635088	27635642	.	496
chr22	27651302	27669294	.	278
chr22	27706666	27712045	.	298
chr22	27718359	27734120	.	280
chr22	27748784	27748907	.	932
chr22	27768324	27769425	.	399
chr22	27774641	27775655	.	403
chr22	27800922	27818350	.	305
chr22	27832252	27832768	.	504
chr22	27832592	27832745	.	885
chr22	27841369	27851314	.	296
chr22	27856298	27859339	.	446
chr22	27874412	27880124	.	299
chr22	27887733	27898616	.	279
chr22	27925013	27940197	.	278
chr22	27946261	27947262	.	406
chr22	27951881	27953549	.	373
chr22	27956444	27957479	.	398
chr22	27966561	27976681	.	281
chr22	27978190	27995497	.	277
chr22	27995753	28043530	.	290
chr22	28055522	28190000	.	318
chr22	28065715	28065945	.	917
chr22	28066838	28067717	.	1000
chr22	28077485	28077599	.	992
chr22	28124871	28125340	.	704
chr22	28125541	28125660	.	1000
chr22	28145080	28145217	.	1000
chr22	28157015	28157219	.	745
chr22	28195061	28208496	.	284
chr22	28214472	28304615	.	314
chr22	28313796	28317074	.	567
chr22	28326587	28326880	.	943
chr22	28352507	28433142	.	303
chr22	28441564	28448575	.	291
chr22	28465612	28477064	.	278
chr22	28496349	28513667	.	296
chr22	28524971	28526048	.	490
chr22	28530342	28539768	.	297
chr22	28583446	28584048	.	509
chr22	28711250	28716270	.	301
chr22	28711897	28712059	.	845
chr22	28772743	28772983	.	724
chr22	28837663	28839652	.	399
chr22	28865062	28865202	.	896
chr22	28961875	28962419	.	502
chr22	28978240	28979349	.	397
chr22	29025504	29026881	.	478
chr22	29125712	29143485	.	349
chr22	29130531	29130743	.	760
chr22	29137433	29137759	.	961
chr22	29137906	29138669	.	928
chr22	29138891	29139087	.	808
chr22	29155811	29157036	.	1000
chr22	29157461	29157684	.	731
chr22	29157564	29157691	.	908
chr22	29168597	29169051	.	1000
chr22	29169288	29169494	.	739
chr22	29183976	29201905	.	379
chr22	29188743	29188976	.	741
chr22	29194414	29197937	.	780
chr22	29214039	29219625	.	300
chr22	29225002	29226308	.	381
chr22	29227004	29228221	.	383
chr22	29269285	29462725	.	340
chr22	29278667	29279305	.	856
chr22	29279866	29280094	.	753
chr22	29288168	29288349	.	904
chr22	29291091	29291321	.	680
chr22	29292365	29294136	.	682
chr22	29301822	29301927	.	1000
chr22	29331438	29331601	.	889
chr22	29335961	29336189	.	753
chr22	29379222	29379391	.	910
chr22	29379443	29379671	.	890
chr22	29383903	29387116	.	693
chr22	29391570	29391756	.	884
chr22	29393498	29393604	.	1000
chr22	29394799	29394996	.	805
chr22	29395381	29396197	.	860
chr22	29396491	29396917	.	1000
chr22	29450258	29450477	.	706
chr22	29492163	29493310	.	389
chr22	29497612	29498670	.	393
chr22	29507731	29513245	.	302
chr22	29527184	29558521	.	299
chr22	29586000	29586201	.	754
chr22	29590998	29591822	.	475
chr22	29594964	29618109	.	309
chr22	29646300	29715631	.	404
chr22	29653731	29653964	.	674
chr22	29661374	29667114	.	1000
chr22	29706362	29706464	.	1000
chr22	29706830	29706946	.	978
chr22	29717326	29757795	.	291
chr22	29762117	29786218	.	283
chr22	29865998	29866506	.	525
chr22	29873088	29878165	.	301
chr22	29877146	29877655	.	524
chr22	29899678	29900373	.	464
chr22	29916853	29918071	.	390
chr22	29949144	29949668	.	767
chr22	29949678	29950462	.	838
chr22	29966221	29967734	.	383
chr22	29974535	29975156	.	461
chr22	29975464	29977501	.	1000
chr22	29999004	29999184	.	865
chr22	29999545	30000234	.	839
chr22	30011779	30038456	.	297
chr22	30047323	30047991	.	487
chr22	30053783	30054464	.	492
chr22	30063018	30115200	.	293
chr22	30129698	30130287	.	503
chr22	30130844	30132162	.	391
chr22	30142572	30143760	.	395
chr22	30161653	30165107	.	842
chr22	30176843	30177356	.	506
chr22	30186846	30204728	.	319
chr22	30195181	30195597	.	678
chr22	30197496	30197596	.	1000
chr22	30199765	30199988	.	766
chr22	30208809	30215795	.	291
chr22	30219513	30244434	.	278
chr22	30267425	30269448	.	392
chr22	30279135	30279542	.	995
chr22	30300068	30322249	.	285
chr22	30339090	30347178	.	292
chr22	30370000	30371004	.	405
chr22	30374622	30444811	.	293
chr22	30470272	30478572	.	287
chr22	30582017	30667997	.	295
chr22	30670403	30731753	.	302
chr22	30685962	30686086	.	989
chr22	30723333	30723435	.	1000
chr22	30746777	30778155	.	378
chr22	30751688	30753801	.	1000
chr22	30764409	30764604	.	811
chr22	30769892	30770130	.	991
chr22	30770305	30770451	.	866
chr22	30771471	30771686	.	716
chr22	30782371	30784117	.	409
chr22	30788408	30789959	.	388
chr22	30798329	30798442	.	1000
chr22	30813359	30833891	.	395
chr22	30817650	30820813	.	854
chr22	30821782	30822042	.	921
chr22	30851619	30880617	.	276
chr22	30880932	30894801	.	275
chr22	30947649	30973416	.	300
chr22	30976337	30993157	.	364
chr22	31000549	31006906	.	605
chr22	31030276	31071819	.	285
chr22	31031677	31031888	.	763
chr22	31051447	31052860	.	481
chr22	31063305	31063416	.	1000
chr22	31064026	31064260	.	738
chr22	31089833	31100306	.	282
chr22	31090192	31090706	.	687
chr22	31116187	31116842	.	481
chr22	31140079	31140183	.	1000
chr22	31148996	31150385	.	391
chr22	31164674	31174492	.	295
chr22	31174234	31174440	.	739
chr22	31174334	31174452	.	964
chr22	31222730	31231098	.	289
chr22	31249831	31295983	.	291
chr22	31321674	31322177	.	513
chr22	31327016	31329035	.	396
chr22	31338535	31351926	.	305
chr22	31359375	31373925	.	458
chr22	31415587	31416765	.	397
chr22	31471912	31490026	.	426
chr22	31476595	31477481	.	1000
chr22	31479887	31482421	.	907
chr22	31484681	31485038	.	652
chr22	31497835	31504888	.	307
chr22	31509386	31546907	.	310
chr22	31556326	31556574	.	989
chr22	31565879	31576116	.	282
chr22	31607825	31609288	.	689
chr22	31620927	31632143	.	296
chr22	31633249	31659062	.	346
chr22	31659552	31690628	.	326
chr22	31662290	31662505	.	716
chr22	31665584	31667385	.	574
chr22	31668558	31668784	.	689
chr22	31668909	31669133	.	729
chr22	31680458	31680585	.	908
chr22	31688081	31688302	.	736
chr22	31709446	31710064	.	1000
chr22	31716283	31748626	.	526
chr22	31731030	31731135	.	1000
chr22	31733343	31733450	.	1000
chr22	31734585	31744649	.	981
chr22	31794250	31797232	.	593
chr22	31826838	31850982	.	289
chr22	31837644	31837765	.	945
chr22	31839095	31839391	.	672
chr22	31881216	31893380	.	328
chr22	31891841	31893064	.	491
chr22	31928178	31929197	.	401
chr22	31999304	32034845	.	367
chr22	32013865	32014008	.	880
chr22	32015552	32015660	.	1000
chr22	32042250	32042397	.	914
chr22	32042253	32042868	.	539
chr22	32043872	32043973	.	1000
chr22	32057123	32059174	.	848
chr22	32077721	32079965	.	384
chr22	32105792	32114116	.	303
chr22	32118732	32160250	.	358
chr22	32142844	32147338	.	734
chr22	32147936	32151582	.	670
chr22	32175384	32176627	.	385
chr22	32213872	32219027	.	302
chr22	32220991	32226807	.	299
chr22	32236153	32243042	.	310
chr22	32253139	32254198	.	415
chr22	32258326	32271196	.	319
chr22	32261854	32261968	.	992
chr22	32263153	32263560	.	651
chr22	32277891	32303383	.	285
chr22	32307963	32308163	.	756
chr22	32320115	32370525	.	344
chr22	32338806	32343652	.	735
chr22	32356391	32356502	.	1000
chr22	32711252	32742155	.	313
chr22	32750256	32751029	.	474
chr22	32790604	32791782	.	390
chr22	32807281	32808598	.	592
chr22	32870614	32871213	.	757
chr22	32871436	32871541	.	1000
chr22	32898557	32898696	.	845
chr22	32927854	32928680	.	880
chr22	33007715	33028950	.	300
chr22	33136906	33150298	.	274
chr22	33160304	33160804	.	515
chr22	33194233	33203204	.	297
chr22	33203701	33217102	.	297
chr22	33221753	33235799	.	302
chr22	33247738	33249086	.	380
chr22	33269727	33281483	.	280
chr22	33286852	33291977	.	300
chr22	33289876	33317190	.	278
chr22	33345639	33346793	.	402
chr22	33353902	33354538	.	478
chr22	33359386	33368660	.	296
chr22	33376932	33399196	.	313
chr22	33390507	33390614	.	1000
chr22	33390821	33391028	.	737
chr22	33411707	33468426	.	309
chr22	33518529	33518729	.	1000
chr22	33605107	34418634	.	289
chr22	34221739	34221871	.	939
chr22	34233643	34234198	.	677
chr22	34235264	34235490	.	827
chr22	34240186	34240602	.	697
chr22	34242621	34250761	.	743
chr22	34256502	34256936	.	693
chr22	34257137	34257457	.	683
chr22	34279325	34279532	.	737
chr22	34280120	34280416	.	962
chr22	34291182	34294692	.	847
chr22	34294963	34295071	.	1000
chr22	34295877	34295987	.	1000
chr22	34296337	34296439	.	1000
chr22	34297132	34297232	.	1000
chr22	34298520	34298621	.	1000
chr22	34316213	34316359	.	973
chr22	34317540	34317847	.	680
chr22	34318151	34318836	.	820
chr22	34382782	34383290	.	510
chr22	34412022	34412182	.	805
chr22	34450188	34460926	.	279
chr22	35165347	35166396	.	395
chr22	35236571	35236790	.	919
chr22	35237049	35238437	.	1000
chr22	35346387	35347388	.	406
chr22	35368387	35370217	.	385
chr22	35409408	35420149	.	279
chr22	35427168	35452729	.	282
chr22	35461176	35471450	.	280
chr22	35523491	35556239	.	281
chr22	35568100	35568702	.	496
chr22	35619229	35630164	.	305
chr22	35652389	35655601	.	713
chr22	35658134	35658438	.	659
chr22	35690888	35719166	.	324
chr22	35695348	35695518	.	905
chr22	35695881	35696185	.	915
chr22	35696329	35696773	.	716
chr22	35726298	35756818	.	314
chr22	35751724	35752003	.	647
chr22	35752458	35752596	.	906
chr22	35765222	35765928	.	459
chr22	35765234	35766428	.	394
chr22	35768042	35768546	.	744
chr22	35768476	35768577	.	1000
chr22	35771680	35775684	.	731
chr22	35777191	35777414	.	696
chr22	35785866	35834644	.	392
chr22	35794791	35798129	.	1000
chr22	35795128	35796884	.	1000
chr22	35797120	35797227	.	1000
chr22	35798826	35799043	.	818
chr22	35804481	35804600	.	1000
chr22	35808002	35808145	.	1000
chr22	35838272	35850507	.	301
chr22	35863525	35864419	.	503
chr22	35866533	35866754	.	736
chr22	35876294	35877466	.	385
chr22	35906452	35907768	.	391
chr22	35913486	35914649	.	407
chr22	35922335	35967725	.	292
chr22	35983728	35991867	.	292
chr22	35992650	36005763	.	300
chr22	36008659	36034624	.	307
chr22	36038249	36039255	.	412
chr22	36044401	36045203	.	512
chr22	36070555	36071631	.	498
chr22	36073670	36074268	.	472
chr22	36126739	36153068	.	328
chr22	36142407	36142618	.	726
chr22	36143302	36143405	.	1000
chr22	36144773	36145158	.	698
chr22	36145867	36146073	.	853
chr22	36162618	36163458	.	478
chr22	36175429	36184279	.	295
chr22	36210662	36217276	.	299
chr22	36222494	36244094	.	354
chr22	36230090	36233987	.	544
chr22	36235182	36236727	.	510
chr22	36238787	36239837	.	521
chr22	36251183	36257255	.	294
chr22	36260693	36267837	.	294
chr22	36274467	36284348	.	296
chr22	36292363	36292879	.	504
chr22	36296370	36297389	.	401
chr22	36299231	36300583	.	380
chr22	36305639	36322743	.	366
chr22	36327076	36335877	.	297
chr22	36344776	36436523	.	316
chr22	36448359	36448908	.	513
chr22	36461350	36462578	.	788
chr22	36506365	36506941	.	483
chr22	36525604	36526716	.	396
chr22	36539767	36540805	.	405
chr22	36542999	36544352	.	374
chr22	36552248	36553425	.	490
chr22	36573444	36573561	.	1000
chr22	36583601	36599365	.	312
chr22	36606046	36613027	.	294
chr22	36624794	36629980	.	303
chr22	36634533	36636541	.	576
chr22	36634997	36635114	.	1000
chr22	36635121	36635354	.	908
chr22	36635547	36635691	.	984
chr22	36635965	36636199	.	938
chr22	36654283	36656253	.	623
chr22	36671682	36793888	.	467
chr22	36717414	36734209	.	885
chr22	36750594	36772325	.	639
chr22	36775593	36775790	.	884
chr22	36779281	36779493	.	760
chr22	36780783	36783110	.	895
chr22	36783754	36784082	.	980
chr22	36791287	36791536	.	704
chr22	36805253	36805471	.	887
chr22	36805738	36807221	.	1000
chr22	36815295	36815955	.	455
chr22	36828028	36830035	.	393
chr22	36832286	36832517	.	712
chr22	36845262	36845768	.	511
chr22	36845953	36871172	.	353
chr22	36850628	36851983	.	1000
chr22	36853883	36854482	.	692
chr22	36855433	36856109	.	818
chr22	36866108	36866210	.	1000
chr22	36866301	36866414	.	1000
chr22	36877246	36878108	.	949
chr22	36878018	36878131	.	1000
chr22	36902893	36903209	.	936
chr22	36906545	36908579	.	398
chr22	36924183	36926478	.	885
chr22	36931280	36939678	.	295
chr22	36939757	36948527	.	304
chr22	36950069	36963914	.	323
chr22	36972587	36987046	.	300
chr22	36982117	36982239	.	938
chr22	36982167	36982378	.	726
chr22	37000220	37014616	.	324
chr22	37005212	37005427	.	969
chr22	37005671	37006190	.	698
chr22	37009987	37010194	.	737
chr22	37024321	37038743	.	319
chr22	37051072	37065324	.	278
chr22	37082778	37083320	.	488
chr22	37082819	37083820	.	406
chr22	37092543	37093622	.	410
chr22	37151325	37173660	.	289
chr22	37193528	37193757	.	682
chr22	37213819	37225459	.	303
chr22	37245098	37261604	.	281
chr22	37267552	37268189	.	453
chr22	37296139	37298199	.	603
chr22	37314205	37341734	.	282
chr22	37317827	37318393	.	530
chr22	37318227	37318396	.	910
chr22	37401741	37407402	.	301
chr22	37410136	37431624	.	339
chr22	37442226	37443465	.	386
chr22	37447713	37448383	.	474
chr22	37499837	37527204	.	279
chr22	37562246	37563649	.	394
chr22	37568409	37578606	.	301
chr22	37583311	37585772	.	628
chr22	37593444	37593627	.	768
chr22	37593514	37593716	.	751
chr22	37594947	37595486	.	750
chr22	37595664	37595972	.	678
chr22	37604392	37611570	.	300
chr22	37617089	37646538	.	282
chr22	37650635	37666126	.	280
chr22	37673840	37685252	.	298
chr22	37697914	37717681	.	310
chr22	37725557	37726569	.	403
chr22	37753447	37766078	.	300
chr22	37767822	37776647	.	298
chr22	37777746	37787557	.	299
chr22	37788593	37807679	.	295
chr22	37811857	37818634	.	297
chr22	37875816	37885354	.	295
chr22	37893419	37909292	.	303
chr22	37941218	37943259	.	634
chr22	37941615	37941849	.	938
chr22	37941983	37942099	.	978
chr22	37942177	37942952	.	816
chr22	37948244	37949307	.	414
chr22	37949028	37949130	.	1000
chr22	37954430	37968905	.	320
chr22	37997310	37997744	.	693
chr22	37997949	37998341	.	629
chr22	38002413	38006596	.	710
chr22	38014359	38042118	.	316
chr22	38019361	38019493	.	1000
chr22	38029304	38031019	.	563
chr22	38053326	38056059	.	434
chr22	38056575	38057651	.	404
chr22	38081802	38083448	.	849
chr22	38084212	38085406	.	387
chr22	38093105	38093387	.	946
chr22	38093441	38093679	.	958
chr22	38106164	38107434	.	387
chr22	38141343	38144070	.	526
chr22	38142328	38142815	.	1000
chr22	38142996	38143099	.	1000
chr22	38166759	38176433	.	304
chr22	38199501	38202065	.	579
chr22	38203454	38204311	.	518
chr22	38213507	38215259	.	568
chr22	38231492	38269420	.	328
chr22	38239956	38240322	.	938
chr22	38243808	38246918	.	781
chr22	38248451	38248995	.	516
chr22	38259325	38259427	.	1000
chr22	38259367	38259770	.	694
chr22	38276129	38402207	.	308
chr22	38290571	38290799	.	719
chr22	38290939	38291772	.	846
chr22	38292310	38292428	.	1000
chr22	38292575	38292710	.	1000
chr22	38294088	38294301	.	904
chr22	38294860	38295234	.	693
chr22	38309655	38309765	.	1000
chr22	38411822	38441097	.	312
chr22	38447846	38448885	.	397
chr22	38452014	38455034	.	758
chr22	38464122	38464781	.	479
chr22	38465822	38466325	.	513
chr22	38493058	38494295	.	500
chr22	38501646	38511638	.	298
chr22	38515065	38515720	.	469
chr22	38521967	38522971	.	405
chr22	38541547	38541751	.	783
chr22	38544177	38544834	.	469
chr22	38554077	38588110	.	341
chr22	38576528	38577910	.	1000
chr22	38578010	38578364	.	964
chr22	38588318	38756907	.	361
chr22	38624851	38625035	.	850
chr22	38636595	38637443	.	806
chr22	38776145	38804522	.	340
chr22	38811089	38829303	.	304
chr22	38839162	38840165	.	405
chr22	38856325	38857589	.	382
chr22	38870568	38870802	.	905
chr22	38872867	38914751	.	465
chr22	38877220	38877471	.	700
chr22	38882078	38882283	.	742
chr22	38899794	38904002	.	1000
chr22	38964844	38968008	.	726
chr22	39004996	39005652	.	457
chr22	39005090	39015171	.	283
chr22	39051668	39053281	.	1000
chr22	39077734	39078540	.	1000
chr22	39088597	39141542	.	395
chr22	39095824	39098129	.	1000
chr22	39099753	39103529	.	952
chr22	39104572	39104773	.	754
chr22	39147424	39155383	.	289
chr22	39161673	39200095	.	284
chr22	39161814	39162017	.	748
chr22	39210475	39236756	.	285
chr22	39255285	39256753	.	395
chr22	39258647	39262190	.	524
chr22	39263487	39266217	.	415
chr22	39266723	39270035	.	534
chr22	39342485	39344131	.	380
chr22	39359035	39368318	.	292
chr22	39377252	39378905	.	606
chr22	39377652	39378008	.	1000
chr22	39378135	39378431	.	856
chr22	39381369	39381480	.	1000
chr22	39393239	39393777	.	505
chr22	39405593	39420200	.	345
chr22	39410201	39410435	.	938
chr22	39413332	39415062	.	708
chr22	39427767	39428577	.	855
chr22	39435984	39438189	.	426
chr22	39448121	39448985	.	470
chr22	39480622	39481223	.	483
chr22	39483986	39484627	.	512
chr22	39493369	39493587	.	708
chr22	39494112	39494307	.	851
chr22	39527170	39527732	.	491
chr22	39532507	39532688	.	861
chr22	39535832	39551084	.	382
chr22	39541229	39541446	.	854
chr22	39541454	39541676	.	944
chr22	39541902	39542124	.	839
chr22	39542138	39542292	.	1000
chr22	39543747	39546535	.	586
chr22	39566744	39577693	.	793
chr22	39580388	39580588	.	756
chr22	39593987	39640326	.	319
chr22	39609602	39609745	.	935
chr22	39609633	39611279	.	911
chr22	39611749	39611936	.	922
chr22	39645228	39651251	.	303
chr22	39666357	39726316	.	390
chr22	39738755	39785227	.	294
chr22	39747505	39747752	.	708
chr22	39747827	39748250	.	669
chr22	39756772	39757287	.	671
chr22	39795056	39796716	.	646
chr22	39819038	39835155	.	367
chr22	39822636	39822888	.	698
chr22	39823838	39824598	.	756
chr22	39824754	39825511	.	831
chr22	39825797	39825897	.	1000
chr22	39825828	39826047	.	777
chr22	39837193	39855079	.	306
chr22	39841390	39841491	.	1000
chr22	39843844	39844015	.	764
chr22	39843855	39844076	.	701
chr22	39843906	39844443	.	520
chr22	39866075	39868109	.	398
chr22	39869947	39872071	.	396
chr22	39897505	39899510	.	394
chr22	39905835	39940529	.	433
chr22	39914891	39918144	.	1000
chr22	39918960	39919060	.	1000
chr22	39919224	39919481	.	748
chr22	39928154	39928872	.	964
chr22	40014016	40014249	.	707
chr22	40019808	40020326	.	503
chr22	40026756	40032267	.	301
chr22	40046566	40055579	.	299
chr22	40056424	40066274	.	299
chr22	40125156	40135158	.	281
chr22	40300716	40348180	.	281
chr22	40352699	40374280	.	280
chr22	40383148	40460543	.	331
chr22	40397914	40398203	.	873
chr22	40402443	40402552	.	1000
chr22	40416127	40416327	.	756
chr22	40420370	40420575	.	742
chr22	40424068	40424281	.	721
chr22	40438788	40442703	.	783
chr22	40480482	40480888	.	652
chr22	40497273	40507420	.	281
chr22	40497350	40498461	.	404
chr22	40514055	40521890	.	291
chr22	40562698	40695511	.	338
chr22	40571071	40577679	.	778
chr22	40587861	40591908	.	646
chr22	40605254	40605742	.	683
chr22	40621239	40621444	.	742
chr22	40701031	40701148	.	1000
chr22	40701403	40701557	.	830
chr22	40707910	40709077	.	386
chr22	40714999	40733272	.	346
chr22	40720492	40720630	.	963
chr22	40728376	40728558	.	771
chr22	40728395	40728649	.	663
chr22	40741164	40744626	.	561
chr22	40757021	40771858	.	312
chr22	40766554	40766737	.	938
chr22	40766737	40766976	.	954
chr22	40783594	40785361	.	388
chr22	40788053	40839040	.	325
chr22	40842571	40843573	.	405
chr22	40845847	40846059	.	723
chr22	40867538	40868552	.	403
chr22	40879822	40885749	.	294
chr22	40895022	40895667	.	462
chr22	40904912	40907873	.	419
chr22	40929503	40930214	.	512
chr22	40930679	40930782	.	1000
chr22	40930906	40931154	.	989
chr22	40937317	40937582	.	701
chr22	40947824	40957958	.	280
chr22	40973876	40985185	.	280
chr22	40982531	40984086	.	383
chr22	40988224	41012502	.	286
chr22	41023921	41025117	.	394
chr22	41032009	41033570	.	521
chr22	41032405	41032648	.	1000
chr22	41032985	41033111	.	976
chr22	41047571	41048844	.	552
chr22	41050764	41050982	.	708
chr22	41060580	41091330	.	305
chr22	41065012	41065231	.	706
chr22	41079287	41079402	.	985
chr22	41080764	41081321	.	732
chr22	41096170	41133855	.	300
chr22	41107563	41108124	.	478
chr22	41110106	41111152	.	902
chr22	41123045	41124079	.	496
chr22	41128703	41128905	.	751
chr22	41177720	41178800	.	417
chr22	41178693	41178809	.	978
chr22	41191979	41203401	.	280
chr22	41204733	41238221	.	297
chr22	41207644	41207755	.	1000
chr22	41213924	41214025	.	1000
chr22	41214703	41215850	.	770
chr22	41225247	41225378	.	886
chr22	41240077	41266905	.	409
chr22	41304346	41305495	.	389
chr22	41345822	41349413	.	671
chr22	41416275	41420041	.	838
chr22	41445038	41445730	.	499
chr22	41482102	41500159	.	520
chr22	41510413	41512311	.	390
chr22	41550558	41560305	.	299
chr22	41564267	41583039	.	313
chr22	41577907	41578052	.	871
chr22	41581125	41581356	.	880
chr22	41587948	41588171	.	696
chr22	41601141	41601951	.	1000
chr22	41608460	41624807	.	308
chr22	41628410	41672106	.	319
chr22	41633804	41633904	.	1000
chr22	41633854	41634057	.	748
chr22	41634100	41634676	.	673
chr22	41641877	41642083	.	739
chr22	41651117	41651325	.	734
chr22	41666673	41666798	.	982
chr22	41676160	41677020	.	480
chr22	41677094	41677684	.	476
chr22	41681009	41683403	.	719
chr22	41684178	41684815	.	637
chr22	41685793	41686055	.	678
chr22	41686474	41686809	.	1000
chr22	41687145	41687266	.	1000
chr22	41697365	41699568	.	409
chr22	41703833	41704922	.	422
chr22	41714581	41769304	.	322
chr22	41719581	41719783	.	751
chr22	41734716	41735049	.	687
chr22	41755681	41755904	.	871
chr22	41756185	41756748	.	739
chr22	41776329	41778603	.	929
chr22	41793500	41795481	.	381
chr22	41808772	41808971	.	955
chr22	41809413	41809873	.	1000
chr22	41809999	41810354	.	918
chr22	41810459	41810594	.	922
chr22	41826642	41849709	.	539
chr22	41858910	41890653	.	460
chr22	41862825	41866657	.	1000
chr22	41869802	41870028	.	758
chr22	41874573	41879677	.	563
chr22	41883962	41884853	.	513
chr22	41895419	41934216	.	362
chr22	41919650	41919767	.	1000
chr22	41920826	41920948	.	938
chr22	41920833	41921084	.	731
chr22	41939889	41940580	.	916
chr22	41953007	41954283	.	392
chr22	41956946	41957684	.	510
chr22	41970838	41971072	.	871
chr22	41972419	41974955	.	396
chr22	41985354	41985658	.	685
chr22	41986136	41986368	.	911
chr22	42015178	42019196	.	1000
chr22	42051295	42096838	.	312
chr22	42061101	42061632	.	510
chr22	42062818	42063249	.	1000
chr22	42063380	42063564	.	765
chr22	42063402	42063836	.	693
chr22	42077955	42078989	.	865
chr22	42084400	42085223	.	835
chr22	42093641	42093748	.	1000
chr22	42093645	42093848	.	786
chr22	42147119	42147277	.	862
chr22	42194818	42198054	.	613
chr22	42204315	42205964	.	380
chr22	42213648	42247901	.	431
chr22	42222190	42222409	.	741
chr22	42226902	42231180	.	939
chr22	42232319	42232630	.	949
chr22	42232738	42232868	.	951
chr22	42234920	42235107	.	839
chr22	42236336	42236549	.	721
chr22	42236990	42237227	.	665
chr22	42239359	42239583	.	694
chr22	42240297	42240818	.	696
chr22	42241588	42241689	.	1000
chr22	42242118	42242326	.	921
chr22	42242721	42242946	.	934
chr22	42243226	42243372	.	919
chr22	42252378	42253479	.	406
chr22	42257722	42259966	.	408
chr22	42261093	42263661	.	394
chr22	42270167	42280189	.	300
chr22	42284776	42305800	.	301
chr22	42329048	42330055	.	404
chr22	42329548	42330110	.	491
chr22	42332217	42348186	.	363
chr22	42357201	42375102	.	276
chr22	42371079	42377969	.	292
chr22	42393212	42394219	.	404
chr22	42452385	42481027	.	373
chr22	42461221	42461345	.	989
chr22	42461765	42461866	.	1000
chr22	42466160	42466622	.	729
chr22	42466728	42467374	.	920
chr22	42470293	42470509	.	713
chr22	42474303	42476737	.	921
chr22	42474703	42474811	.	1000
chr22	42475072	42476249	.	1000
chr22	42485751	42487501	.	991
chr22	42542842	42635829	.	295
chr22	42656137	42789282	.	306
chr22	42688486	42688741	.	722
chr22	42689360	42689659	.	745
chr22	42792610	42819305	.	290
chr22	42795828	42796064	.	733
chr22	42808410	42809208	.	748
chr22	42820312	42847283	.	286
chr22	42868566	42869771	.	385
chr22	42894888	42896058	.	532
chr22	42908584	42909018	.	693
chr22	42915319	42915528	.	769
chr22	42915644	42915857	.	904
chr22	42966353	42966968	.	476
chr22	42977780	42978100	.	1000
chr22	43003682	43029076	.	448
chr22	43008333	43012683	.	1000
chr22	43017284	43017525	.	657
chr22	43017376	43017535	.	809
chr22	43038792	43040919	.	472
chr22	43044598	43046360	.	636
chr22	43087604	43100707	.	317
chr22	43122725	43122857	.	880
chr22	43155208	43155376	.	1000
chr22	43179080	43219326	.	289
chr22	43252255	43254086	.	381
chr22	43263491	43263721	.	850
chr22	43264639	43265150	.	508
chr22	43266919	43267530	.	491
chr22	43273550	43274491	.	454
chr22	43276674	43279961	.	428
chr22	43282618	43284779	.	410
chr22	43288003	43288831	.	492
chr22	43290725	43292199	.	479
chr22	43295409	43295985	.	470
chr22	43296995	43297497	.	514
chr22	43299183	43299688	.	512
chr22	43300023	43300675	.	459
chr22	43301448	43302809	.	395
chr22	43310663	43314134	.	491
chr22	43318814	43318990	.	836
chr22	43321959	43322961	.	405
chr22	43327148	43327528	.	664
chr22	43330295	43330824	.	511
chr22	43333585	43333782	.	963
chr22	43334708	43335376	.	487
chr22	43335463	43335977	.	506
chr22	43341314	43344547	.	418
chr22	43347838	43348103	.	672
chr22	43354865	43357322	.	413
chr22	43360673	43361341	.	475
chr22	43368667	43369811	.	404
chr22	43373849	43374956	.	397
chr22	43377260	43416030	.	325
chr22	43380560	43380806	.	679
chr22	43386327	43386446	.	1000
chr22	43403895	43404013	.	1000
chr22	43438160	43438957	.	485
chr22	43439593	43440397	.	482
chr22	43446842	43448161	.	391
chr22	43484893	43485174	.	921
chr22	43491571	43491772	.	754
chr22	43513102	43535149	.	287
chr22	43536865	43733213	.	300
chr22	43582765	43583135	.	1000
chr22	43583789	43584033	.	1000
chr22	43779591	43801567	.	279
chr22	43820196	43827102	.	291
chr22	43829170	43839893	.	279
chr22	43871265	43882831	.	277
chr22	43879812	43881288	.	404
chr22	43928975	43940640	.	278
chr22	43933832	43940863	.	291
chr22	43957494	43977003	.	279
chr22	43994529	44006362	.	279
chr22	44022222	44028244	.	298
chr22	44131015	44141559	.	300
chr22	44142540	44152122	.	295
chr22	44163380	44173528	.	281
chr22	44169811	44171284	.	378
chr22	44185266	44192575	.	303
chr22	44198567	44266739	.	316
chr22	44208149	44209079	.	1000
chr22	44229486	44229603	.	971
chr22	44268223	44297204	.	291
chr22	44287666	44287910	.	715
chr22	44287844	44287947	.	1000
chr22	44314247	44335301	.	344
chr22	44319225	44319386	.	898
chr22	44319776	44320138	.	925
chr22	44320260	44320491	.	914
chr22	44320874	44321553	.	792
chr22	44321929	44322086	.	867
chr22	44340686	44342869	.	411
chr22	44350792	44351937	.	832
chr22	44383768	44397802	.	320
chr22	44425811	44431278	.	300
chr22	44457399	44513537	.	306
chr22	44494042	44494200	.	912
chr22	44503476	44503788	.	921
chr22	44504041	44504243	.	944
chr22	44526954	44596679	.	345
chr22	44540471	44546435	.	844
chr22	44568174	44568386	.	723
chr22	44648549	44768612	.	300
chr22	44785845	44793586	.	302
chr22	44798689	44808048	.	294
chr22	44816493	44822275	.	296
chr22	44816849	44827273	.	281
chr22	44830147	44838103	.	294
chr22	44846305	44846520	.	716
chr22	44887492	44888932	.	378
chr22	44892367	44894891	.	416
chr22	44933824	44934979	.	395
chr22	44961684	44980292	.	280
chr22	44981334	45025872	.	296
chr22	44992886	44993004	.	1000
chr22	44993010	44993110	.	1000
chr22	45013834	45014038	.	860
chr22	45015750	45015898	.	909
chr22	45016535	45016639	.	1000
chr22	45079505	45081539	.	398
chr22	45095161	45095817	.	505
chr22	45106322	45108337	.	396
chr22	45113625	45138809	.	314
chr22	45128587	45128833	.	679
chr22	45134100	45134301	.	754
chr22	45162280	45162403	.	932
chr22	45162948	45163215	.	697
chr22	45178221	45179719	.	385
chr22	45180420	45181520	.	392
chr22	45191778	45192344	.	723
chr22	45199022	45206039	.	295
chr22	45215340	45216704	.	372
chr22	45228495	45249686	.	301
chr22	45232469	45232710	.	689
chr22	45244676	45244901	.	726
chr22	45258911	45259914	.	413
chr22	45271131	45299423	.	292
chr22	45308713	45318080	.	299
chr22	45318921	45411379	.	389
chr22	45339109	45339214	.	1000
chr22	45397039	45397147	.	1000
chr22	45397395	45397501	.	1000
chr22	45403798	45403918	.	1000
chr22	45404051	45404438	.	857
chr22	45404478	45404676	.	959
chr22	45404849	45405819	.	823
chr22	45405961	45406409	.	1000
chr22	45419236	45433846	.	349
chr22	45419487	45419628	.	1000
chr22	45426996	45427612	.	767
chr22	45428150	45428775	.	795
chr22	45435961	45440970	.	301
chr22	45448148	45448410	.	678
chr22	45448854	45449006	.	941
chr22	45449388	45449693	.	1000
chr22	45466862	45467867	.	405
chr22	45467867	45470624	.	393
chr22	45541995	45641841	.	316
chr22	45558430	45558635	.	894
chr22	45558978	45560473	.	881
chr22	45560561	45560784	.	731
chr22	45663756	45665092	.	405
chr22	45664556	45664835	.	703
chr22	45686338	45686468	.	891
chr22	45694232	45695641	.	399
chr22	45700170	45720108	.	335
chr22	45704538	45706476	.	586
chr22	45713287	45714835	.	524
chr22	45726491	45727747	.	408
chr22	45727408	45727509	.	1000
chr22	45809604	45809705	.	1000
chr22	45865567	45866118	.	511
chr22	45888396	45956690	.	334
chr22	45897998	45898209	.	948
chr22	45898780	45899993	.	808
chr22	45900116	45901568	.	827
chr22	45910422	45910927	.	697
chr22	45912296	45912421	.	920
chr22	45913328	45913499	.	992
chr22	45917157	45917361	.	745
chr22	45921262	45921519	.	657
chr22	45934472	45934733	.	709
chr22	45974808	45990990	.	307
chr22	45992426	46003084	.	310
chr22	46024897	46026173	.	380
chr22	46027337	46027567	.	714
chr22	46035349	46037422	.	405
chr22	46062733	46092924	.	338
chr22	46067047	46069027	.	880
chr22	46079214	46079333	.	1000
chr22	46087797	46087982	.	930
chr22	46089722	46089934	.	723
chr22	46100878	46110893	.	299
chr22	46116443	46117484	.	404
chr22	46124984	46191701	.	307
chr22	46156003	46156273	.	865
chr22	46166550	46166685	.	1000
chr22	46203132	46203673	.	503
chr22	46209619	46219224	.	299
chr22	46225384	46246487	.	319
chr22	46256235	46256351	.	1000
chr22	46291222	46293661	.	393
chr22	46325775	46333159	.	301
chr22	46336163	46343350	.	292
chr22	46350484	46366735	.	302
chr22	46375226	46376020	.	486
chr22	46382865	46383866	.	406
chr22	46387626	46396403	.	294
chr22	46407822	46410882	.	493
chr22	46421990	46441523	.	320
chr22	46431359	46431527	.	961
chr22	46432048	46432261	.	867
chr22	46459880	46517995	.	311
chr22	46474222	46474427	.	818
chr22	46475017	46475325	.	703
chr22	46521167	46551705	.	275
chr22	46597192	46597305	.	1000
chr22	46668092	46702885	.	286
chr22	46731547	46732312	.	763
chr22	46732552	46732752	.	756
chr22	46732667	46732770	.	1000
chr22	46741093	46768625	.	314
chr22	46751649	46751875	.	896
chr22	46751915	46752096	.	732
chr22	46756234	46756443	.	731
chr22	46768865	46780947	.	331
chr22	46785715	46787943	.	392
chr22	46789694	46790810	.	389
chr22	46797966	46888125	.	299
chr22	46893505	46915963	.	341
chr22	46918312	46925208	.	295
chr22	46942789	46943812	.	401
chr22	46959599	46959817	.	815
chr22	46959938	46960157	.	741
chr22	46970979	46990811	.	305
chr22	47005159	47100482	.	300
chr22	47120587	47138699	.	302
chr22	47147951	47473713	.	306
chr22	47157462	47159623	.	944
chr22	47165500	47165703	.	748
chr22	47348738	47348940	.	751
chr22	47352776	47352995	.	706
chr22	47391395	47391783	.	654
chr22	47392162	47392418	.	659
chr22	47444067	47444317	.	671
chr22	47463708	47463809	.	1000
chr22	47482922	47492046	.	299
chr22	47497710	47499755	.	568
chr22	47503745	47513724	.	300
chr22	47519261	47520322	.	400
chr22	47523281	47537384	.	303
chr22	47541835	47564319	.	321
chr22	48073635	48074666	.	399
chr22	48143629	48145111	.	393
chr22	48155432	48164149	.	295
chr22	48168500	48178684	.	302
chr22	48197988	48214081	.	279
chr22	48232647	48233756	.	390
chr22	48440528	48441531	.	405
chr22	48456692	48486742	.	287
chr22	48601235	48615308	.	278
chr22	48617781	48625213	.	298
chr22	48650142	48655150	.	301
chr22	48657511	48662540	.	301
chr22	48725211	48732803	.	295
chr22	48743168	48750057	.	298
chr22	48763914	48767852	.	797
chr22	48792770	48794166	.	390
chr22	48818396	48826871	.	295
chr22	48871465	48872800	.	394
chr22	48881455	48965324	.	299
chr22	48967669	49123498	.	303
chr22	49023162	49023373	.	726
chr22	49029223	49029457	.	1000
chr22	49036033	49036150	.	971
chr22	49038472	49038715	.	685
chr22	49127834	49159718	.	291
chr22	49171118	49204757	.	287
chr22	49234591	49235898	.	399
chr22	49277378	49278497	.	388
chr22	49333820	49343344	.	298
chr22	49350390	49360810	.	280
chr22	49433488	49444980	.	277
chr22	49456636	49462001	.	301
chr22	49479676	49487960	.	298
chr22	49490559	49498690	.	297
chr22	49499320	49505311	.	298
chr22	49514770	49515281	.	508
chr22	49515359	49516056	.	474
chr22	49556730	49566946	.	281
chr22	49625848	49626357	.	509
chr22	49662255	49672892	.	283
chr22	49698702	49699847	.	403
chr22	49717004	49724575	.	288
chr22	49839643	49861996	.	279
chr22	49867864	49889021	.	278
chr22	49875027	49875232	.	742
chr22	49881682	49881943	.	709
chr22	49906310	49908249	.	506
chr22	49914192	49938949	.	424
chr22	49922026	49922129	.	1000
chr22	49928582	49931585	.	1000
chr22	49940692	49950680	.	300
chr22	49979717	49980924	.	398
chr22	50020237	50020754	.	504
chr22	50050406	50055922	.	296
chr22	50153017	50229266	.	311
chr22	50170109	50170344	.	703
chr22	50170263	50170364	.	1000
chr22	50201781	50202000	.	741
chr22	50247925	50248435	.	508
chr22	50250644	50251237	.	527
chr22	50273802	50274092	.	709
chr22	50290584	50325193	.	296
chr22	50327905	50368905	.	333
chr22	50340587	50340715	.	903
chr22	50353631	50354797	.	834
chr22	50355178	50357326	.	658
chr22	50436873	50438027	.	388
chr22	50446864	50447086	.	734
chr22	50590403	50590504	.	1000
chr22	50609870	50622694	.	297
chr22	50624101	50642785	.	309
chr22	50643012	50659816	.	297
chr22	50665429	50665644	.	716
chr22	50675082	50676275	.	394
chr22	50682716	50684719	.	701
chr22	50685821	50686782	.	480
chr22	50686896	50687414	.	518
chr22	50687296	50687431	.	922
chr22	50689137	50690616	.	483
chr22	50710863	50758741	.	326
chr22	50765746	50765863	.	971
chr22	50775374	50776843	.	390
chr22	50777027	50778848	.	382
chr22	50781062	50781620	.	647
chr22	50781848	50782025	.	876
chr22	50800035	50802202	.	399
chr22	50837822	50838373	.	511
chr22	50842036	50842228	.	903
chr22	50849340	50866179	.	280
chr22	50883182	50883382	.	756
chr22	50883190	50884175	.	488
chr22	50892206	50900217	.	292
chr22	50901979	50926563	.	314
chr22	50936619	50973523	.	304
chr22	50946454	50947103	.	1000
chr22	50954879	50955086	.	737
chr22	50963725	50963851	.	976
chr22	50964158	50964699	.	748
chr22	51020055	51021891	.	910
chr22	51066775	51067051	.	652
chr22	51079429	51081163	.	626
chr22	51079788	51080345	.	885
chr22	51080516	51080755	.	954
chr22	51111403	51112407	.	405
chr22	51113494	51113604	.	1000
chr22	51138607	51146485	.	289


================================================
FILE: bedtools/data/hg38.genome
================================================
chr22	50818468


================================================
FILE: bedtools/data/human_enhancers_chr22.bed
================================================
chr22	17679527	17679727
chr22	17739387	17739587
chr22	17745713	17745913
chr22	17915832	17916032
chr22	18546219	18546419
chr22	18647319	18647519
chr22	19163890	19164090
chr22	20232781	20232981
chr22	20778172	20778372
chr22	21369089	21369289
chr22	21387111	21387311
chr22	21469748	21469948
chr22	22292550	22292750
chr22	23652997	23653198
chr22	23863155	23863381
chr22	23875629	23875829
chr22	24360109	24360309
chr22	24632724	24632925
chr22	24659983	24660183
chr22	24803357	24803557
chr22	24960321	24960521
chr22	25784797	25784997
chr22	25800344	25800546
chr22	27283558	27283758
chr22	27290889	27291089
chr22	27298926	27299134
chr22	27506951	27507151
chr22	27530972	27531172
chr22	27530972	27531172
chr22	27606258	27606458
chr22	27613121	27613321
chr22	27693820	27694021
chr22	27709015	27709215
chr22	27831915	27832116
chr22	27832768	27832968
chr22	27914585	27914785
chr22	27933520	27933720
chr22	27937956	27938156
chr22	28035355	28035555
chr22	28172650	28172851
chr22	28445903	28446103
chr22	28644061	28644261
chr22	29489680	29489880
chr22	29492979	29493179
chr22	29511856	29512056
chr22	30592147	30592347
chr22	30744663	30744863
chr22	30938555	30938755
chr22	32226067	32226268
chr22	32272405	32272605
chr22	32294774	32294974
chr22	32927996	32928196
chr22	33018401	33018602
chr22	33271098	33271300
chr22	33276991	33277191
chr22	33279168	33279368
chr22	33353284	33353484
chr22	34367910	34368110
chr22	35746004	35746204
chr22	36311233	36311433
chr22	36334366	36334568
chr22	36367739	36367940
chr22	36413878	36414079
chr22	36417865	36418065
chr22	36432422	36432622
chr22	36452060	36452260
chr22	36461930	36462130
chr22	36553203	36553403
chr22	37194381	37194581
chr22	37584170	37584370
chr22	37608742	37608942
chr22	37760842	37761042
chr22	37905135	37905335
chr22	37942364	37942564
chr22	37948567	37948767
chr22	38029686	38029886
chr22	38137371	38137571
chr22	38213774	38213975
chr22	38364064	38364264
chr22	38538661	38538861
chr22	38539451	38539651
chr22	38582586	38582786
chr22	38875187	38875387
chr22	38875187	38875387
chr22	39728704	39728904
chr22	40342734	40342934
chr22	40394063	40394263
chr22	40706171	40706371
chr22	40874874	40875074
chr22	40881354	40881554
chr22	40905737	40905937
chr22	42750158	42750358
chr22	42761554	42761754
chr22	43355816	43356016
chr22	43660295	43660495
chr22	43688083	43688284
chr22	43698741	43698941
chr22	43709395	43709595
chr22	43726574	43726774
chr22	43727717	43727917
chr22	44766437	44766637
chr22	44867874	44868075
chr22	45034999	45035200
chr22	45034999	45035200
chr22	45520293	45520493
chr22	45850286	45850486
chr22	45856325	45856525
chr22	45865748	45865948
chr22	45869692	45869895
chr22	45874052	45874252
chr22	45878144	45878344
chr22	45900932	45901132
chr22	47560535	47560735
chr22	49263785	49263985
chr22	51080418	51080618


================================================
FILE: bedtools/data/ucscGenes_chr22.bed
================================================
chr22	10736170	10736283	ENST00000615943.1	0	-
chr22	10939387	10961338	ENST00000635667.1	0	-
chr22	11066417	11068174	ENST00000624155.2	0	+
chr22	11124336	11125705	ENST00000422332.1	0	+
chr22	11249808	11249959	ENST00000612732.1	0	-
chr22	11474743	11479643	ENST00000672261.1	0	+
chr22	11827522	11910358	ENST00000657566.1	0	+
chr22	11883601	11904000	ENST00000657645.1	0	+
chr22	12602465	12626642	ENST00000634617.1	0	+
chr22	15273854	15273961	ENST00000613107.1	0	+
chr22	15282556	15288670	ENST00000623391.1	0	-
chr22	15290717	15297196	ENST00000448473.1	0	+
chr22	15298377	15304556	ENST00000623199.1	0	-
chr22	15326036	15343065	ENST00000425869.1	0	-
chr22	15349466	15350043	ENST00000441221.1	0	+
chr22	15489989	15492564	ENST00000429435.1	0	+
chr22	15528158	15529139	ENST00000252835.5	0	+
chr22	15528191	15529139	ENST00000643195.1	0	+
chr22	15541435	15578006	ENST00000412729.1	0	+
chr22	15550903	15553838	ENST00000443989.1	0	-
chr22	15557576	15560694	ENST00000444162.1	0	-
chr22	15562016	15564276	ENST00000417863.1	0	-
chr22	15572088	15573265	ENST00000456143.1	0	-
chr22	15588360	15588478	ENST00000447704.1	0	+
chr22	15600907	15604882	ENST00000428118.1	0	-
chr22	15611758	15613096	ENST00000438441.1	0	-
chr22	15615401	15615578	ENST00000440999.1	0	-
chr22	15622600	15632051	ENST00000426025.1	0	-
chr22	15635179	15644330	ENST00000435410.1	0	-
chr22	15690025	15721631	ENST00000343518.11	0	+
chr22	15690077	15721631	ENST00000621704.4	0	+
chr22	15690245	15721522	ENST00000452800.1	0	+
chr22	15699360	15703403	ENST00000422014.1	0	-
chr22	15701458	15701565	ENST00000390914.1	0	+
chr22	15721485	15722608	ENST00000417657.1	0	+
chr22	15746629	15778297	ENST00000453395.5	0	+
chr22	15749155	15750825	ENST00000423297.1	0	-
chr22	15765686	15778297	ENST00000438574.1	0	+
chr22	15784958	15827434	ENST00000413768.5	0	+
chr22	15784962	15827708	ENST00000437781.5	0	+
chr22	15784967	15819165	ENST00000383038.7	0	+
chr22	15784976	15791387	ENST00000661115.1	0	+
chr22	15784982	15798258	ENST00000603308.2	0	+
chr22	15784991	15829984	ENST00000447898.5	0	+
chr22	15787699	15790573	ENST00000661156.1	0	+
chr22	15790708	15791814	ENST00000458623.1	0	+
chr22	15794900	15797909	ENST00000657965.1	0	+
chr22	15805262	15815897	ENST00000440946.5	0	-
chr22	15805811	15820884	ENST00000609679.1	0	-
chr22	15805846	15806503	ENST00000414726.1	0	-
chr22	15818492	15819134	ENST00000607933.1	0	+
chr22	15823196	15823890	ENST00000608286.1	0	+
chr22	15826565	15827187	ENST00000456786.2	0	+
chr22	15852989	15893942	ENST00000420638.2	0	+
chr22	15854194	15855243	ENST00000398242.2	0	-
chr22	15901790	15901911	ENST00000448070.1	0	+
chr22	15914720	15915800	ENST00000424770.1	0	-
chr22	16141766	16141872	ENST00000612550.1	0	+
chr22	16387693	16390887	ENST00000399687.3	0	+
chr22	16405329	16405417	ENST00000427602.1	0	+
chr22	16423942	16426031	ENST00000426076.1	0	+
chr22	16428946	16476870	ENST00000421957.1	0	-
chr22	16486984	16488322	ENST00000623630.1	0	+
chr22	16503303	16509475	ENST00000450340.1	0	+
chr22	16521058	16527237	ENST00000609641.1	0	+
chr22	16572026	16574637	ENST00000454360.1	0	+
chr22	16575362	16582707	ENST00000493696.2	0	-
chr22	16581076	16582602	ENST00000472972.1	0	-
chr22	16590750	16592810	ENST00000359963.4	0	-
chr22	16592894	16600110	ENST00000656324.1	0	+
chr22	16595088	16595485	ENST00000430910.1	0	-
chr22	16601886	16698742	ENST00000558085.6	0	+
chr22	16601910	16615111	ENST00000592918.5	0	+
chr22	16601910	16648830	ENST00000400593.6	0	+
chr22	16601947	16615111	ENST00000592107.5	0	+
chr22	16602043	16653809	ENST00000426585.5	0	+
chr22	16602049	16614333	ENST00000591299.1	0	+
chr22	16612581	16615104	ENST00000588548.1	0	+
chr22	16616169	16617114	ENST00000438850.1	0	+
chr22	16619614	16653692	ENST00000383140.7	0	+
chr22	16637019	16638980	ENST00000585784.1	0	+
chr22	16637039	16638740	ENST00000636884.1	0	+
chr22	16638578	16640737	ENST00000588424.1	0	+
chr22	16645694	16646581	ENST00000508033.1	0	-
chr22	16649929	16653805	ENST00000415121.1	0	+
chr22	16653891	16659628	ENST00000510265.2	0	-
chr22	16654065	16675540	ENST00000456726.5	0	-
chr22	16669463	16675452	ENST00000457060.1	0	-
chr22	16671089	16679493	ENST00000434304.2	0	-
chr22	16676015	16694648	ENST00000423580.2	0	-
chr22	16690102	16704477	ENST00000457911.1	0	-
chr22	16725071	16747761	ENST00000670988.1	0	+
chr22	16746627	16748549	ENST00000665507.1	0	-
chr22	16746868	16748645	ENST00000422917.1	0	-
chr22	16783411	16821699	ENST00000331428.5	0	-
chr22	16827473	16829335	ENST00000425038.1	0	+
chr22	16827550	16829209	ENST00000423928.2	0	+
chr22	16837521	16837666	ENST00000442797.1	0	+
chr22	16844398	16846357	ENST00000637740.1	0	+
chr22	16855803	16856162	ENST00000422608.1	0	+
chr22	16857618	16860718	ENST00000404435.1	0	-
chr22	16869477	16871126	ENST00000442403.1	0	+
chr22	16876217	16876421	ENST00000448961.1	0	-
chr22	16876551	16876902	ENST00000412238.1	0	-
chr22	16897444	16897546	ENST00000427470.1	0	+
chr22	16904218	16904696	ENST00000458718.2	0	-
chr22	16914234	16914981	ENST00000523309.2	0	-
chr22	16921442	16922173	ENST00000521969.2	0	-
chr22	16925951	16926444	ENST00000517943.2	0	-
chr22	16933520	16934003	ENST00000521497.2	0	-
chr22	16953894	16956808	ENST00000643267.1	0	+
chr22	16961935	17008222	ENST00000400588.5	0	-
chr22	16962458	17007999	ENST00000651146.1	0	-
chr22	16962459	17007999	ENST00000465611.1	0	-
chr22	16962732	17008114	ENST00000643316.1	0	-
chr22	16966099	16968383	ENST00000520505.5	0	-
chr22	16967300	17007999	ENST00000523144.1	0	-
chr22	17012403	17012932	ENST00000605217.1	0	+
chr22	17021397	17022570	ENST00000614184.1	0	-
chr22	17036569	17058792	ENST00000441006.5	0	+
chr22	17037211	17056558	ENST00000414401.5	0	+
chr22	17037238	17060825	ENST00000609932.5	0	+
chr22	17037285	17060003	ENST00000609596.1	0	+
chr22	17044872	17047426	ENST00000636648.1	0	+
chr22	17067820	17070675	ENST00000608373.1	0	+
chr22	17080700	17081456	ENST00000609791.1	0	+
chr22	17084953	17100469	ENST00000477874.1	0	+
chr22	17084958	17115693	ENST00000319363.10	0	+
chr22	17084958	17115694	ENST00000612619.1	0	+
chr22	17085056	17097509	ENST00000459971.1	0	+
chr22	17093611	17093939	ENST00000429106.1	0	-
chr22	17116296	17121360	ENST00000331437.4	0	-
chr22	17116298	17121367	ENST00000399875.1	0	-
chr22	17121559	17132102	ENST00000662102.1	0	+
chr22	17121585	17130443	ENST00000428078.4	0	+
chr22	17121591	17132102	ENST00000654656.1	0	+
chr22	17121594	17132104	ENST00000441544.2	0	+
chr22	17121613	17125043	ENST00000668330.1	0	+
chr22	17137510	17165287	ENST00000155674.9	0	-
chr22	17137519	17159277	ENST00000336737.8	0	-
chr22	17137521	17143647	ENST00000477157.5	0	-
chr22	17137578	17159255	ENST00000399852.3	0	-
chr22	17137581	17141843	ENST00000486462.1	0	-
chr22	17148447	17158986	ENST00000463033.1	0	-
chr22	17148448	17159168	ENST00000480451.1	0	-
chr22	17159383	17165439	ENST00000329743.3	0	+
chr22	17159398	17165445	ENST00000431923.1	0	+
chr22	17178789	17219435	ENST00000399837.8	0	-
chr22	17178789	17219585	ENST00000610390.4	0	-
chr22	17179303	17221989	ENST00000399839.5	0	-
chr22	17179305	17199783	ENST00000330232.8	0	-
chr22	17179305	17209889	ENST00000262607.3	0	-
chr22	17179316	17198789	ENST00000648668.1	0	-
chr22	17179321	17199596	ENST00000648061.1	0	-
chr22	17180989	17221848	ENST00000449907.7	0	-
chr22	17181086	17219434	ENST00000649540.1	0	-
chr22	17181382	17209504	ENST00000649915.1	0	-
chr22	17182640	17188727	ENST00000469063.1	0	-
chr22	17189783	17198791	ENST00000480276.1	0	-
chr22	17191594	17219434	ENST00000650635.1	0	-
chr22	17192935	17193308	ENST00000428401.1	0	+
chr22	17198856	17199595	ENST00000648343.1	0	-
chr22	17207156	17219435	ENST00000543038.1	0	-
chr22	17207205	17221854	ENST00000441548.1	0	-
chr22	17209605	17258235	ENST00000649310.1	0	-
chr22	17209605	17258235	ENST00000649746.1	0	-
chr22	17213515	17214155	ENST00000344033.5	0	+
chr22	17256858	17266733	ENST00000428828.1	0	-
chr22	17329033	17329232	ENST00000453421.1	0	+
chr22	17352880	17353177	ENST00000485262.3	0	-
chr22	17359948	17553332	ENST00000400585.6	0	+
chr22	17369365	17554145	ENST00000342247.9	0	+
chr22	17369887	17553332	ENST00000262608.12	0	+
chr22	17418696	17419828	ENST00000455617.1	0	+
chr22	17424387	17424775	ENST00000455251.1	0	-
chr22	17428272	17499458	ENST00000497534.1	0	+
chr22	17477092	17558149	ENST00000612582.1	0	+
chr22	17518675	17519029	ENST00000437347.1	0	-
chr22	17521513	17570078	ENST00000651475.1	0	-
chr22	17548998	17554133	ENST00000355219.3	0	+
chr22	17563449	17590881	ENST00000399813.1	0	+
chr22	17563463	17570256	ENST00000496051.1	0	+
chr22	17563497	17590995	ENST00000327451.11	0	+
chr22	17563588	17582208	ENST00000467228.1	0	+
chr22	17569700	17583484	ENST00000497401.1	0	+
chr22	17569706	17570393	ENST00000469889.1	0	+
chr22	17580156	17589192	ENST00000443935.5	0	-
chr22	17580313	17588018	ENST00000611039.1	0	-
chr22	17592135	17628749	ENST00000253413.10	0	-
chr22	17592168	17628747	ENST00000399796.6	0	-
chr22	17592408	17628749	ENST00000399798.6	0	-
chr22	17592668	17595292	ENST00000473248.1	0	-
chr22	17594538	17628733	ENST00000413576.1	0	-
chr22	17598000	17619528	ENST00000481365.5	0	-
chr22	17600679	17619528	ENST00000484653.5	0	-
chr22	17612613	17628748	ENST00000478963.5	0	-
chr22	17612975	17628749	ENST00000460085.1	0	-
chr22	17628854	17696209	ENST00000399781.5	0	+
chr22	17628915	17702428	ENST00000399782.5	0	+
chr22	17638583	17730855	ENST00000337612.9	0	+
chr22	17638583	17730855	ENST00000418951.6	0	+
chr22	17638583	17730855	ENST00000538149.5	0	+
chr22	17638583	17730855	ENST00000543133.5	0	+
chr22	17638583	17730855	ENST00000611738.4	0	+
chr22	17638583	17730855	ENST00000616863.4	0	+
chr22	17638583	17730855	ENST00000618481.4	0	+
chr22	17638740	17728699	ENST00000498133.5	0	+
chr22	17638756	17692395	ENST00000464649.1	0	+
chr22	17638756	17730855	ENST00000317582.10	0	+
chr22	17638771	17703733	ENST00000493680.5	0	+
chr22	17638810	17730622	ENST00000355028.4	0	+
chr22	17689073	17729140	ENST00000399777.1	0	+
chr22	17695737	17702363	ENST00000479296.1	0	+
chr22	17706709	17727670	ENST00000485631.1	0	+
chr22	17734137	17774016	ENST00000317361.11	0	-
chr22	17734141	17774042	ENST00000615414.4	0	-
chr22	17734141	17774495	ENST00000399767.5	0	-
chr22	17734141	17774495	ENST00000614949.4	0	-
chr22	17734141	17774495	ENST00000622694.4	0	-
chr22	17734170	17774492	ENST00000399774.7	0	-
chr22	17734174	17774489	ENST00000399765.5	0	-
chr22	17735221	17741194	ENST00000494097.5	0	-
chr22	17735498	17774412	ENST00000342111.9	0	-
chr22	17735551	17774412	ENST00000550946.5	0	-
chr22	17735551	17774665	ENST00000551952.5	0	-
chr22	17735551	17774665	ENST00000611040.1	0	-
chr22	17738057	17774495	ENST00000473439.5	0	-
chr22	17739447	17774770	ENST00000552886.1	0	-
chr22	17740030	17774495	ENST00000617586.1	0	-
chr22	17764179	17764259	ENST00000583102.1	0	-
chr22	17777321	17779481	ENST00000600723.1	0	+
chr22	17787648	18024561	ENST00000441493.7	0	-
chr22	17787648	18024561	ENST00000672019.1	0	-
chr22	17787651	17811497	ENST00000476405.1	0	-
chr22	17790539	17791667	ENST00000252134.11	0	-
chr22	17790836	17815669	ENST00000580469.1	0	-
chr22	17790945	17817489	ENST00000577821.5	0	-
chr22	17791263	17817425	ENST00000579997.5	0	-
chr22	17800721	17802035	ENST00000657505.1	0	+
chr22	17803553	17810736	ENST00000584751.1	0	-
chr22	17819021	17832000	ENST00000498573.5	0	-
chr22	17819035	17831927	ENST00000578984.1	0	-
chr22	17834327	17906835	ENST00000400561.6	0	-
chr22	17839591	18001501	ENST00000495076.5	0	-
chr22	17839950	17842482	ENST00000578905.1	0	-
chr22	17839950	17906812	ENST00000414725.6	0	-
chr22	17841674	17906827	ENST00000383094.7	0	-
chr22	17860162	17895288	ENST00000461307.5	0	-
chr22	17864614	17906874	ENST00000585038.1	0	-
chr22	17865923	17876703	ENST00000462645.1	0	-
chr22	17871929	17876872	ENST00000465886.1	0	-
chr22	17871933	17876739	ENST00000498345.1	0	-
chr22	17904727	18001180	ENST00000424046.1	0	-
chr22	17980867	17980961	ENST00000385046.1	0	-
chr22	17983204	17983619	ENST00000429618.1	0	-
chr22	18004269	18007308	ENST00000441167.1	0	-
chr22	18019213	18020200	ENST00000411920.1	0	+
chr22	18029384	18037968	ENST00000443243.1	0	+
chr22	18076526	18077797	ENST00000426483.1	0	-
chr22	18077185	18078884	ENST00000607927.1	0	-
chr22	18077919	18091019	ENST00000610387.4	0	+
chr22	18077922	18105396	ENST00000329627.11	0	+
chr22	18078007	18088460	ENST00000399744.7	0	+
chr22	18078014	18131138	ENST00000474897.5	0	+
chr22	18078054	18078775	ENST00000622035.1	0	-
chr22	18078376	18088119	ENST00000428061.2	0	+
chr22	18081465	18081675	ENST00000427227.2	0	-
chr22	18101611	18101888	ENST00000450866.1	0	+
chr22	18102456	18103649	ENST00000446487.1	0	-
chr22	18110330	18124268	ENST00000426208.5	0	+
chr22	18110686	18131154	ENST00000316027.10	0	+
chr22	18110758	18131154	ENST00000623543.1	0	-
chr22	18110808	18131732	ENST00000330423.8	0	+
chr22	18121106	18131117	ENST00000416740.1	0	+
chr22	18126706	18146554	ENST00000608634.1	0	+
chr22	18150169	18177397	ENST00000215794.8	0	+
chr22	18178037	18205915	ENST00000434390.1	0	-
chr22	18340144	18345899	ENST00000617623.1	0	+
chr22	18361819	18386597	ENST00000619172.5	0	-
chr22	18361829	18386526	ENST00000618517.4	0	-
chr22	18370584	18386480	ENST00000613790.4	0	-
chr22	18370665	18391105	ENST00000615974.1	0	-
chr22	18374105	18375689	ENST00000622906.1	0	+
chr22	18400406	18404206	ENST00000612861.1	0	-
chr22	18422243	18500594	ENST00000624459.2	0	+
chr22	18424976	18490146	ENST00000624001.2	0	+
chr22	18486998	18488582	ENST00000614584.1	0	-
chr22	18487125	18489905	ENST00000615181.4	0	+
chr22	18487868	18491247	ENST00000620909.1	0	-
chr22	18501793	18512187	ENST00000621762.1	0	-
chr22	18516343	18518161	ENST00000619998.1	0	-
chr22	18524619	18524935	ENST00000618466.1	0	+
chr22	18527156	18527626	ENST00000359859.4	0	+
chr22	18527801	18530571	ENST00000614395.4	0	+
chr22	18527801	18530573	ENST00000612978.5	0	+
chr22	18528946	18530222	ENST00000620199.4	0	+
chr22	18529041	18530178	ENST00000613577.4	0	+
chr22	18529065	18530204	ENST00000621725.4	0	+
chr22	18529408	18530573	ENST00000618859.1	0	+
chr22	18529809	18530459	ENST00000613597.1	0	+
chr22	18533645	18537791	ENST00000610959.4	0	-
chr22	18533655	18552527	ENST00000652586.1	0	-
chr22	18533656	18537865	ENST00000619742.4	0	-
chr22	18533656	18548353	ENST00000617243.4	0	-
chr22	18533670	18548828	ENST00000618794.4	0	-
chr22	18534004	18548798	ENST00000612579.4	0	-
chr22	18538022	18574700	ENST00000612500.4	0	-
chr22	18538055	18577968	ENST00000611748.1	0	-
chr22	18548010	18548770	ENST00000616531.1	0	-
chr22	18605354	18605497	ENST00000614296.1	0	+
chr22	18605801	18606102	ENST00000410698.2	0	-
chr22	18605814	18611919	ENST00000619918.1	0	-
chr22	18615580	18615900	ENST00000611762.1	0	+
chr22	18623338	18625289	ENST00000611186.1	0	-
chr22	18633983	18634682	ENST00000652395.1	0	+
chr22	18636444	18638405	ENST00000611168.1	0	-
chr22	18650022	18653617	ENST00000611403.1	0	-
chr22	18715814	18719341	ENST00000619065.1	0	+
chr22	18733913	18757894	ENST00000342888.3	0	+
chr22	18756347	18757906	ENST00000611876.1	0	+
chr22	18773664	18805479	ENST00000412448.5	0	-
chr22	18773824	18791223	ENST00000453783.2	0	-
chr22	18794413	18794804	ENST00000437537.1	0	-
chr22	18794530	18843399	ENST00000445651.1	0	-
chr22	18794597	18825001	ENST00000430306.1	0	-
chr22	18833693	18834438	ENST00000428850.2	0	+
chr22	18846810	18856684	ENST00000419447.5	0	+
chr22	18846931	18848320	ENST00000342005.5	0	+
chr22	18850117	18861049	ENST00000412938.5	0	+
chr22	18852875	18854933	ENST00000416498.1	0	+
chr22	18855620	18858640	ENST00000511549.1	0	-
chr22	18873542	18894407	ENST00000614596.2	0	-
chr22	18883501	18884682	ENST00000623570.1	0	-
chr22	18906027	18912088	ENST00000413981.5	0	+
chr22	18906237	18947732	ENST00000638240.1	0	+
chr22	18906319	18914238	ENST00000331444.11	0	+
chr22	18906324	18914238	ENST00000483718.5	0	+
chr22	18906350	18912082	ENST00000427407.5	0	+
chr22	18906352	18908269	ENST00000608842.1	0	+
chr22	18906354	18912087	ENST00000480608.5	0	+
chr22	18906780	18910389	ENST00000477156.1	0	+
chr22	18908662	18912078	ENST00000436645.1	0	+
chr22	18912776	18936553	ENST00000610940.4	0	-
chr22	18912780	18936295	ENST00000491604.5	0	-
chr22	18912780	18936296	ENST00000420436.5	0	-
chr22	18912781	18921574	ENST00000313755.9	0	-
chr22	18912781	18924002	ENST00000429300.5	0	-
chr22	18912781	18936142	ENST00000482858.5	0	-
chr22	18912781	18936451	ENST00000334029.6	0	-
chr22	18912781	18936553	ENST00000357068.10	0	-
chr22	18914529	18921554	ENST00000609229.1	0	-
chr22	18919555	18921539	ENST00000446371.1	0	-
chr22	18921380	18936142	ENST00000438924.5	0	-
chr22	18922245	18923310	ENST00000399694.1	0	-
chr22	18922371	18934828	ENST00000450579.1	0	-
chr22	18922879	18936266	ENST00000457083.1	0	-
chr22	18924724	18931110	ENST00000496625.1	0	-
chr22	18936410	18947741	ENST00000640084.1	0	+
chr22	18939445	18947692	ENST00000638287.1	0	+
chr22	18945122	18946971	ENST00000623496.1	0	+
chr22	18951933	18959512	ENST00000624224.1	0	-
chr22	18970513	18994628	ENST00000438934.5	0	+
chr22	18970524	19031242	ENST00000440005.6	0	+
chr22	18970582	18990883	ENST00000421572.1	0	+
chr22	18980694	18981325	ENST00000636418.1	0	-
chr22	18985835	18994501	ENST00000399539.4	0	+
chr22	18989918	19020248	ENST00000609630.2	0	+
chr22	18997137	18997595	ENST00000604539.1	0	-
chr22	18998273	19014786	ENST00000424407.1	0	+
chr22	19014352	19014506	ENST00000637415.1	0	+
chr22	19018042	19018916	ENST00000603208.1	0	-
chr22	19023055	19023550	ENST00000609839.1	0	+
chr22	19029523	19031242	ENST00000652053.1	0	+
chr22	19031563	19034564	ENST00000481698.1	0	-
chr22	19036281	19122454	ENST00000537045.5	0	-
chr22	19036281	19122454	ENST00000545799.5	0	-
chr22	19036285	19122407	ENST00000389262.8	0	-
chr22	19036285	19122412	ENST00000263196.12	0	-
chr22	19037836	19057053	ENST00000467659.1	0	-
chr22	19045255	19045367	ENST00000384012.1	0	+
chr22	19046161	19048375	ENST00000609958.1	0	-
chr22	19048378	19063277	ENST00000608548.1	0	-
chr22	19055800	19056512	ENST00000412461.2	0	+
chr22	19056993	19065904	ENST00000473832.1	0	-
chr22	19061040	19061843	ENST00000629517.1	0	-
chr22	19121528	19124503	ENST00000456035.1	0	+
chr22	19124308	19128449	ENST00000609936.1	0	-
chr22	19124878	19125646	ENST00000450724.1	0	+
chr22	19130278	19144651	ENST00000252137.11	0	-
chr22	19131307	19132622	ENST00000399635.4	0	+
chr22	19133936	19144684	ENST00000434568.5	0	-
chr22	19133967	19144633	ENST00000472073.1	0	-
chr22	19142849	19144659	ENST00000469466.1	0	-
chr22	19148575	19150283	ENST00000086933.2	0	-
chr22	19171394	19172839	ENST00000565162.2	0	+
chr22	19175580	19178736	ENST00000215882.10	0	-
chr22	19175592	19178521	ENST00000451283.5	0	-
chr22	19175609	19178739	ENST00000470922.5	0	-
chr22	19176666	19178480	ENST00000461267.1	0	-
chr22	19177989	19178635	ENST00000468824.1	0	-
chr22	19179472	19291685	ENST00000621271.4	0	-
chr22	19179472	19291719	ENST00000427926.6	0	-
chr22	19179475	19184831	ENST00000412649.5	0	-
chr22	19179475	19207676	ENST00000622493.4	0	-
chr22	19179774	19207674	ENST00000617926.4	0	-
chr22	19180732	19291661	ENST00000615606.4	0	-
chr22	19180732	19291661	ENST00000617103.4	0	-
chr22	19183475	19187728	ENST00000538828.1	0	-
chr22	19196362	19209327	ENST00000611723.1	0	-
chr22	19196401	19200083	ENST00000658236.1	0	+
chr22	19221572	19226320	ENST00000458188.2	0	-
chr22	19229844	19232821	ENST00000540896.1	0	-
chr22	19238442	19238739	ENST00000433141.1	0	-
chr22	19249872	19249966	ENST00000516131.1	0	+
chr22	19254033	19291716	ENST00000449918.1	0	-
chr22	19257519	19258806	ENST00000446618.1	0	+
chr22	19291968	19328901	ENST00000664918.1	0	+
chr22	19330697	19431696	ENST00000340170.8	0	-
chr22	19330697	19431733	ENST00000263208.5	0	-
chr22	19351367	19447691	ENST00000509549.5	0	-
chr22	19365765	19366044	ENST00000463821.3	0	+
chr22	19398060	19430263	ENST00000464189.5	0	-
chr22	19398067	19447450	ENST00000452818.1	0	-
chr22	19431901	19436075	ENST00000333130.3	0	+
chr22	19432578	19436067	ENST00000443660.5	0	+
chr22	19432938	19436010	ENST00000471259.1	0	+
chr22	19440885	19447692	ENST00000399568.5	0	-
chr22	19440907	19448232	ENST00000542103.5	0	-
chr22	19441776	19448232	ENST00000399562.8	0	-
chr22	19443149	19448232	ENST00000611555.4	0	-
chr22	19443370	19447697	ENST00000333059.5	0	-
chr22	19447892	19450105	ENST00000431090.1	0	+
chr22	19449910	19479193	ENST00000263202.15	0	-
chr22	19450620	19479172	ENST00000399523.5	0	-
chr22	19450620	19479172	ENST00000459854.5	0	-
chr22	19450624	19467716	ENST00000466373.1	0	-
chr22	19454178	19454605	ENST00000607934.1	0	+
chr22	19455679	19479202	ENST00000447868.5	0	-
chr22	19456502	19456962	ENST00000608816.1	0	+
chr22	19456879	19479185	ENST00000421968.6	0	-
chr22	19465201	19479083	ENST00000484101.5	0	-
chr22	19467402	19479187	ENST00000489406.1	0	-
chr22	19467872	19478931	ENST00000494054.1	0	-
chr22	19467894	19479144	ENST00000474226.1	0	-
chr22	19479456	19520612	ENST00000407835.6	0	+
chr22	19479458	19482766	ENST00000455750.6	0	+
chr22	19479737	19483873	ENST00000491520.5	0	+
chr22	19479825	19520611	ENST00000437685.6	0	+
chr22	19479900	19495986	ENST00000483431.5	0	+
chr22	19479909	19520601	ENST00000438587.6	0	+
chr22	19479915	19520605	ENST00000672837.1	0	+
chr22	19479919	19520608	ENST00000263201.6	0	+
chr22	19479937	19520578	ENST00000404724.7	0	+
chr22	19480149	19497444	ENST00000487669.5	0	+
chr22	19482750	19505370	ENST00000428937.1	0	+
chr22	19505053	19514784	ENST00000471470.1	0	+
chr22	19505412	19520601	ENST00000671972.1	0	+
chr22	19516433	19520594	ENST00000493724.1	0	+
chr22	19520571	19522769	ENST00000661478.1	0	-
chr22	19523023	19527545	ENST00000406028.1	0	-
chr22	19523026	19524400	ENST00000618236.1	0	-
chr22	19523026	19525383	ENST00000403084.1	0	-
chr22	19523051	19525423	ENST00000413119.2	0	-
chr22	19565202	19566839	ENST00000629028.1	0	-
chr22	19630749	19633574	ENST00000420012.1	0	-
chr22	19667022	19667555	ENST00000452326.1	0	+
chr22	19714502	19723319	ENST00000455784.7	0	+
chr22	19714503	19723274	ENST00000406395.5	0	+
chr22	19714528	19723319	ENST00000406172.6	0	+
chr22	19714674	19722331	ENST00000412544.5	0	+
chr22	19717219	19724216	ENST00000455843.5	0	+
chr22	19717269	19721720	ENST00000431124.5	0	+
chr22	19718158	19719055	ENST00000490204.1	0	+
chr22	19718431	19722629	ENST00000383045.7	0	+
chr22	19718457	19723319	ENST00000438754.6	0	+
chr22	19718468	19724772	ENST00000431044.5	0	+
chr22	19719434	19720833	ENST00000395109.6	0	+
chr22	19719529	19720213	ENST00000477238.1	0	+
chr22	19720187	19720874	ENST00000413258.1	0	+
chr22	19720341	19721869	ENST00000480423.5	0	+
chr22	19720553	19724224	ENST00000470814.1	0	+
chr22	19722944	19724771	ENST00000366425.3	0	+
chr22	19756702	19767334	ENST00000332710.8	0	+
chr22	19756702	19779546	ENST00000329705.11	0	+
chr22	19756702	19783593	ENST00000359500.7	0	+
chr22	19760820	19767332	ENST00000649276.1	0	+
chr22	19761196	19763702	ENST00000475303.1	0	+
chr22	19765078	19766327	ENST00000484336.1	0	+
chr22	19783222	19854874	ENST00000329517.11	0	-
chr22	19788414	19854843	ENST00000403325.5	0	-
chr22	19788417	19854939	ENST00000405009.5	0	-
chr22	19788463	19854806	ENST00000460402.5	0	-
chr22	19792293	19793094	ENST00000424559.2	0	+
chr22	19801998	19854816	ENST00000481086.1	0	-
chr22	19812294	19854843	ENST00000453108.1	0	-
chr22	19846137	19854896	ENST00000407472.5	0	-
chr22	19846145	19854863	ENST00000484072.5	0	-
chr22	19846145	19854874	ENST00000328554.9	0	-
chr22	19846145	19854886	ENST00000405640.1	0	-
chr22	19851988	19854816	ENST00000416337.1	0	-
chr22	19875516	19932553	ENST00000400518.5	0	-
chr22	19875517	19916366	ENST00000494454.5	0	-
chr22	19875517	19938179	ENST00000542719.6	0	-
chr22	19875518	19941807	ENST00000474308.5	0	-
chr22	19875521	19881369	ENST00000462330.5	0	-
chr22	19875521	19884504	ENST00000487165.5	0	-
chr22	19875521	19898041	ENST00000634537.1	0	-
chr22	19875521	19941803	ENST00000400519.6	0	-
chr22	19875521	19941818	ENST00000400521.7	0	-
chr22	19875521	19941820	ENST00000400525.6	0	-
chr22	19875523	19881347	ENST00000495655.2	0	-
chr22	19875640	19881164	ENST00000485358.5	0	-
chr22	19875691	19881154	ENST00000462843.2	0	-
chr22	19875760	19883567	ENST00000634471.1	0	-
chr22	19887288	19887970	ENST00000420617.1	0	+
chr22	19891437	19918177	ENST00000635155.1	0	-
chr22	19893901	19933526	ENST00000491939.6	0	-
chr22	19894257	19941804	ENST00000334363.14	0	-
chr22	19895015	19915789	ENST00000475995.3	0	-
chr22	19910934	19918221	ENST00000484672.5	0	-
chr22	19911257	19915837	ENST00000471835.1	0	-
chr22	19914899	19941808	ENST00000496729.2	0	-
chr22	19941606	19969975	ENST00000361682.10	0	+
chr22	19941732	19965645	ENST00000403184.5	0	+
chr22	19941772	19968958	ENST00000403710.5	0	+
chr22	19941787	19968998	ENST00000407537.5	0	+
chr22	19941792	19962876	ENST00000467943.5	0	+
chr22	19950941	19968589	ENST00000412786.5	0	+
chr22	19950978	19969016	ENST00000207636.9	0	+
chr22	19951502	19969016	ENST00000406520.7	0	+
chr22	19962569	19968998	ENST00000449653.5	0	+
chr22	19963344	19964300	ENST00000493893.1	0	+
chr22	19963698	19967694	ENST00000428707.1	0	+
chr22	19963752	19963834	ENST00000585066.1	0	+
chr22	19969895	20016823	ENST00000263207.8	0	-
chr22	19969897	19979416	ENST00000495096.5	0	-
chr22	19971227	19987093	ENST00000401994.5	0	-
chr22	19971227	19987093	ENST00000406522.5	0	-
chr22	19971227	19990794	ENST00000406259.1	0	-
chr22	19973161	19975762	ENST00000480792.1	0	-
chr22	19979996	19990792	ENST00000473551.5	0	-
chr22	19981151	19987190	ENST00000487793.5	0	-
chr22	19981381	19987155	ENST00000492625.5	0	-
chr22	19981544	19983753	ENST00000462319.1	0	-
chr22	19981990	20016745	ENST00000467828.1	0	-
chr22	20017013	20056204	ENST00000471707.5	0	+
chr22	20017013	20065358	ENST00000401886.5	0	+
chr22	20017030	20048180	ENST00000475446.5	0	+
chr22	20017030	20052584	ENST00000432198.5	0	+
chr22	20021068	20061656	ENST00000411907.5	0	+
chr22	20021068	20065925	ENST00000420290.6	0	+
chr22	20021068	20065925	ENST00000432883.5	0	+
chr22	20021068	20065925	ENST00000434570.6	0	+
chr22	20021068	20065925	ENST00000447208.6	0	+
chr22	20021068	20065925	ENST00000456048.5	0	+
chr22	20021071	20065926	ENST00000398042.6	0	+
chr22	20021088	20065862	ENST00000399807.7	0	+
chr22	20021101	20061576	ENST00000450664.5	0	+
chr22	20021106	20065264	ENST00000450019.5	0	+
chr22	20021109	20067164	ENST00000327374.9	0	+
chr22	20021121	20053542	ENST00000479679.5	0	+
chr22	20021126	20063421	ENST00000430807.5	0	+
chr22	20021138	20065364	ENST00000401833.5	0	+
chr22	20021154	20063411	ENST00000434168.5	0	+
chr22	20033138	20033220	ENST00000385288.3	0	+
chr22	20036758	20061802	ENST00000462579.5	0	+
chr22	20036758	20064907	ENST00000444651.5	0	+
chr22	20036759	20056241	ENST00000484373.5	0	+
chr22	20036759	20063383	ENST00000485715.2	0	+
chr22	20053434	20064959	ENST00000490583.5	0	+
chr22	20055942	20061227	ENST00000490121.1	0	+
chr22	20058029	20065251	ENST00000628442.1	0	-
chr22	20062979	20066077	ENST00000415503.5	0	-
chr22	20063010	20066434	ENST00000608610.5	0	-
chr22	20063010	20069162	ENST00000600937.5	0	-
chr22	20063010	20070261	ENST00000595864.5	0	-
chr22	20063010	20070454	ENST00000600617.5	0	-
chr22	20063010	20070500	ENST00000601746.5	0	-
chr22	20063010	20070522	ENST00000598339.5	0	-
chr22	20063010	20070569	ENST00000596334.5	0	-
chr22	20063033	20069165	ENST00000609644.5	0	-
chr22	20063033	20070543	ENST00000609191.1	0	-
chr22	20063572	20064929	ENST00000476940.1	0	+
chr22	20064551	20065705	ENST00000600090.1	0	-
chr22	20070715	20070965	ENST00000619837.1	0	+
chr22	20080231	20111875	ENST00000383024.6	0	+
chr22	20080240	20111872	ENST00000351989.8	0	+
chr22	20080568	20086339	ENST00000457069.1	0	+
chr22	20085745	20085833	ENST00000580330.1	0	+
chr22	20085819	20111872	ENST00000495826.5	0	+
chr22	20085821	20111872	ENST00000407755.1	0	+
chr22	20086057	20086142	ENST00000408439.3	0	+
chr22	20087035	20111877	ENST00000498171.5	0	+
chr22	20094711	20107036	ENST00000491892.1	0	+
chr22	20106181	20107575	ENST00000495351.1	0	+
chr22	20106566	20109184	ENST00000485802.1	0	+
chr22	20107571	20111261	ENST00000475941.1	0	+
chr22	20110820	20111875	ENST00000412713.1	0	-
chr22	20111874	20117226	ENST00000252136.12	0	-
chr22	20111874	20117392	ENST00000403707.7	0	-
chr22	20112300	20113178	ENST00000480339.1	0	-
chr22	20112300	20113507	ENST00000444845.5	0	-
chr22	20112303	20117245	ENST00000404751.7	0	-
chr22	20112327	20113798	ENST00000487668.5	0	-
chr22	20112337	20117222	ENST00000439169.2	0	-
chr22	20112498	20113378	ENST00000487378.1	0	-
chr22	20112567	20113808	ENST00000444256.1	0	-
chr22	20112891	20115812	ENST00000494820.5	0	-
chr22	20113117	20115688	ENST00000492988.5	0	-
chr22	20113485	20115439	ENST00000471040.5	0	-
chr22	20113649	20114868	ENST00000488335.1	0	-
chr22	20113703	20115392	ENST00000463710.1	0	-
chr22	20114685	20114751	ENST00000620368.1	0	-
chr22	20114766	20115622	ENST00000459644.1	0	-
chr22	20115336	20116550	ENST00000468917.1	0	-
chr22	20115348	20116100	ENST00000480460.1	0	-
chr22	20115576	20117183	ENST00000494641.1	0	-
chr22	20115609	20117203	ENST00000464535.1	0	-
chr22	20115937	20122377	ENST00000432879.5	0	+
chr22	20115937	20127181	ENST00000435265.5	0	+
chr22	20116103	20127355	ENST00000430524.6	0	+
chr22	20116241	20117239	ENST00000445045.1	0	-
chr22	20117423	20127355	ENST00000402752.5	0	+
chr22	20117523	20122684	ENST00000488484.1	0	+
chr22	20117547	20127181	ENST00000331821.7	0	+
chr22	20117556	20125488	ENST00000411892.5	0	+
chr22	20117652	20119529	ENST00000467920.1	0	+
chr22	20117781	20122421	ENST00000416427.5	0	+
chr22	20117874	20122421	ENST00000421656.5	0	+
chr22	20117933	20126627	ENST00000423859.5	0	+
chr22	20117952	20127035	ENST00000418705.2	0	+
chr22	20122606	20127353	ENST00000448394.2	0	+
chr22	20124138	20127181	ENST00000486575.1	0	+
chr22	20126401	20126526	ENST00000578179.1	0	+
chr22	20129455	20139909	ENST00000436518.5	0	+
chr22	20131806	20148007	ENST00000334554.11	0	+
chr22	20131947	20146450	ENST00000320602.11	0	+
chr22	20131947	20148006	ENST00000405930.3	0	+
chr22	20131994	20141532	ENST00000468112.5	0	+
chr22	20139936	20141326	ENST00000469212.5	0	+
chr22	20140404	20142987	ENST00000472497.1	0	+
chr22	20148415	20151055	ENST00000439765.4	0	-
chr22	20148585	20149903	ENST00000444532.2	0	-
chr22	20198728	20204918	ENST00000506039.1	0	-
chr22	20206396	20208524	ENST00000609602.1	0	+
chr22	20206576	20207183	ENST00000423736.1	0	+
chr22	20241414	20267689	ENST00000416372.5	0	-
chr22	20241414	20268318	ENST00000043402.8	0	-
chr22	20241416	20243684	ENST00000425986.1	0	-
chr22	20242596	20271747	ENST00000469601.1	0	-
chr22	20242848	20283246	ENST00000474642.1	0	-
chr22	20243019	20269365	ENST00000463936.1	0	-
chr22	20249133	20249211	ENST00000408112.3	0	-
chr22	20299759	20313216	ENST00000438948.1	0	+
chr22	20314237	20320060	ENST00000248879.8	0	-
chr22	20314282	20320009	ENST00000405465.3	0	-
chr22	20314393	20320080	ENST00000443409.1	0	-
chr22	20318118	20318749	ENST00000608275.1	0	-
chr22	20320738	20321203	ENST00000609632.1	0	+
chr22	20338804	20354372	ENST00000617303.4	0	+
chr22	20340995	20352255	ENST00000583722.5	0	+
chr22	20340996	20354047	ENST00000610338.1	0	+
chr22	20343202	20344365	ENST00000458154.1	0	-
chr22	20348757	20354387	ENST00000667786.1	0	-
chr22	20350577	20372696	ENST00000486536.6	0	-
chr22	20363620	20377251	ENST00000292729.12	0	-
chr22	20363620	20390758	ENST00000454608.3	0	-
chr22	20363624	20364317	ENST00000470202.1	0	-
chr22	20394114	20408461	ENST00000611540.4	0	+
chr22	20394127	20407407	ENST00000403682.7	0	+
chr22	20394150	20408455	ENST00000400451.7	0	+
chr22	20394160	20407681	ENST00000357502.5	0	+
chr22	20394187	20407680	ENST00000493734.5	0	+
chr22	20394188	20405493	ENST00000420626.1	0	+
chr22	20394402	20408455	ENST00000437275.5	0	+
chr22	20394628	20406968	ENST00000405993.2	0	+
chr22	20399510	20407393	ENST00000476678.1	0	+
chr22	20416796	20416899	ENST00000384613.1	0	-
chr22	20424814	20437825	ENST00000622235.4	0	-
chr22	20424814	20437826	ENST00000623402.1	0	-
chr22	20426086	20429926	ENST00000494535.1	0	-
chr22	20429240	20446620	ENST00000429594.1	0	-
chr22	20441518	20495795	ENST00000328879.9	0	-
chr22	20450121	20451824	ENST00000426448.1	0	+
chr22	20456217	20465486	ENST00000487090.1	0	-
chr22	20457789	20495792	ENST00000479601.5	0	-
chr22	20465061	20495802	ENST00000451553.1	0	-
chr22	20465179	20495798	ENST00000444967.5	0	-
chr22	20465264	20495795	ENST00000458248.5	0	-
chr22	20465316	20495795	ENST00000443285.5	0	-
chr22	20465391	20495817	ENST00000494929.5	0	-
chr22	20465541	20495823	ENST00000431430.1	0	-
chr22	20475202	20475311	ENST00000532431.1	0	-
chr22	20481157	20481268	ENST00000365046.1	0	+
chr22	20481934	20482213	ENST00000493791.3	0	-
chr22	20482814	20484105	ENST00000436075.1	0	-
chr22	20483627	20495844	ENST00000470335.1	0	-
chr22	20488530	20492053	ENST00000490556.1	0	-
chr22	20488984	20491557	ENST00000423364.1	0	-
chr22	20495912	20555135	ENST00000445987.5	0	+
chr22	20507540	20564493	ENST00000444094.5	0	+
chr22	20507581	20551748	ENST00000486656.5	0	+
chr22	20507581	20564500	ENST00000414658.5	0	+
chr22	20507584	20587619	ENST00000433831.5	0	+
chr22	20507593	20564606	ENST00000432052.5	0	+
chr22	20507601	20587632	ENST00000292733.11	0	+
chr22	20507609	20587619	ENST00000263205.11	0	+
chr22	20507611	20564466	ENST00000477824.5	0	+
chr22	20507617	20587617	ENST00000406969.5	0	+
chr22	20507640	20586867	ENST00000382974.6	0	+
chr22	20508047	20564509	ENST00000441501.5	0	+
chr22	20508056	20537183	ENST00000438962.1	0	+
chr22	20522069	20523870	ENST00000420225.1	0	-
chr22	20523642	20555148	ENST00000445189.5	0	+
chr22	20524274	20555091	ENST00000451058.5	0	+
chr22	20524286	20564624	ENST00000473028.5	0	+
chr22	20550986	20564597	ENST00000457322.5	0	+
chr22	20551006	20564598	ENST00000428629.5	0	+
chr22	20551240	20564636	ENST00000424287.5	0	+
chr22	20551289	20566536	ENST00000423862.5	0	+
chr22	20551312	20555077	ENST00000420849.5	0	+
chr22	20551406	20566490	ENST00000487550.5	0	+
chr22	20553096	20566526	ENST00000476767.5	0	+
chr22	20554238	20587617	ENST00000492381.5	0	+
chr22	20566464	20583227	ENST00000478831.5	0	+
chr22	20573677	20587617	ENST00000489651.5	0	+
chr22	20581900	20587632	ENST00000473244.5	0	+
chr22	20582093	20587617	ENST00000493216.1	0	+
chr22	20582929	20583749	ENST00000476187.1	0	+
chr22	20583337	20585803	ENST00000461076.1	0	+
chr22	20584945	20585423	ENST00000436496.2	0	+
chr22	20587495	20592218	ENST00000508880.1	0	-
chr22	20602594	20626134	ENST00000416717.6	0	-
chr22	20616229	20656914	ENST00000443839.1	0	-
chr22	20632618	20632751	ENST00000614590.1	0	+
chr22	20638360	20639135	ENST00000433066.1	0	-
chr22	20652729	20654750	ENST00000447821.1	0	+
chr22	20667835	20670984	ENST00000411545.2	0	-
chr22	20689928	20698938	ENST00000427789.3	0	+
chr22	20690030	20691317	ENST00000412250.3	0	+
chr22	20697961	20698934	ENST00000514371.1	0	-
chr22	20701113	20704606	ENST00000450925.6	0	+
chr22	20702006	20704490	ENST00000637163.1	0	+
chr22	20702311	20704340	ENST00000436618.5	0	+
chr22	20702676	20704602	ENST00000442222.5	0	+
chr22	20702840	20704505	ENST00000445836.5	0	+
chr22	20702920	20704606	ENST00000452055.5	0	+
chr22	20702972	20704254	ENST00000414022.5	0	+
chr22	20702974	20704119	ENST00000454833.5	0	+
chr22	20703051	20704606	ENST00000419950.5	0	+
chr22	20703094	20704234	ENST00000422715.1	0	+
chr22	20703715	20704558	ENST00000428752.1	0	+
chr22	20707690	20858811	ENST00000255882.11	0	-
chr22	20707692	20722009	ENST00000492581.5	0	-
chr22	20707702	20734667	ENST00000477245.5	0	-
chr22	20707703	20727357	ENST00000466772.5	0	-
chr22	20707718	20721418	ENST00000399213.2	0	-
chr22	20710751	20712993	ENST00000482030.5	0	-
chr22	20711301	20717844	ENST00000466394.1	0	-
chr22	20726315	20727293	ENST00000489966.1	0	-
chr22	20727568	20729471	ENST00000485123.1	0	-
chr22	20733735	20734495	ENST00000462210.1	0	-
chr22	20734476	20736866	ENST00000477368.5	0	-
chr22	20734476	20742917	ENST00000494113.1	0	-
chr22	20747483	20751680	ENST00000475414.1	0	-
chr22	20753145	20793374	ENST00000466162.5	0	-
chr22	20764614	20796158	ENST00000484220.1	0	-
chr22	20774112	20787720	ENST00000215727.10	0	+
chr22	20779180	20787232	ENST00000406799.1	0	+
chr22	20798477	20858812	ENST00000485963.5	0	-
chr22	20798939	20802018	ENST00000485950.1	0	-
chr22	20824346	20859417	ENST00000449120.1	0	-
chr22	20859006	20891214	ENST00000215730.12	0	+
chr22	20859080	20871041	ENST00000490458.1	0	+
chr22	20859482	20887819	ENST00000439214.1	0	+
chr22	20889205	20891214	ENST00000608856.1	0	-
chr22	20917406	20953747	ENST00000354336.8	0	+
chr22	20917433	20953747	ENST00000411769.1	0	+
chr22	20957091	20964679	ENST00000444039.1	0	+
chr22	20965107	20981360	ENST00000399167.6	0	+
chr22	20965162	20981358	ENST00000399163.6	0	+
chr22	20965166	20974814	ENST00000417515.5	0	+
chr22	20965171	20973645	ENST00000467926.5	0	+
chr22	20965171	20974096	ENST00000441376.6	0	+
chr22	20965176	20967036	ENST00000479523.5	0	+
chr22	20965176	20973619	ENST00000472575.5	0	+
chr22	20965267	20966798	ENST00000495869.1	0	+
chr22	20967158	20975778	ENST00000434714.5	0	+
chr22	20967247	20974096	ENST00000426113.5	0	+
chr22	20967248	20974297	ENST00000468124.5	0	+
chr22	20967254	20973678	ENST00000484206.5	0	+
chr22	20967254	20981360	ENST00000405089.5	0	+
chr22	20967262	20974267	ENST00000496097.1	0	+
chr22	20967723	20981360	ENST00000440238.3	0	+
chr22	20974512	20981348	ENST00000465606.5	0	+
chr22	20975691	20981356	ENST00000483107.5	0	+
chr22	20976872	20981355	ENST00000486003.1	0	+
chr22	20979461	20998121	ENST00000479606.5	0	+
chr22	20981360	20981755	ENST00000610278.1	0	-
chr22	20982268	20991685	ENST00000414985.5	0	+
chr22	20982296	20987162	ENST00000493460.2	0	+
chr22	20982296	20999032	ENST00000646124.2	0	+
chr22	20982322	20989675	ENST00000443265.5	0	+
chr22	20982330	20988113	ENST00000645935.1	0	+
chr22	20982465	20990474	ENST00000644435.1	0	+
chr22	20982493	20998659	ENST00000642151.1	0	+
chr22	20986078	20991679	ENST00000480895.1	0	+
chr22	20988030	20999016	ENST00000646506.1	0	+
chr22	20989648	20992848	ENST00000497716.5	0	+
chr22	20990814	20999016	ENST00000643578.1	0	+
chr22	20991491	20998972	ENST00000495142.6	0	+
chr22	20991735	20992532	ENST00000461510.1	0	+
chr22	20991780	20994300	ENST00000492480.1	0	+
chr22	20993347	20998821	ENST00000643710.1	0	+
chr22	20994226	20996112	ENST00000415354.6	0	+
chr22	20994476	20995999	ENST00000491432.5	0	+
chr22	20994585	20998924	ENST00000415817.2	0	+
chr22	20994660	20996712	ENST00000439171.5	0	+
chr22	20994905	20997541	ENST00000452988.5	0	+
chr22	20995088	20999032	ENST00000463909.1	0	+
chr22	20995787	20997769	ENST00000498649.1	0	+
chr22	20999103	21001742	ENST00000498406.1	0	-
chr22	20999103	21002118	ENST00000215742.9	0	-
chr22	20999772	21001795	ENST00000471073.1	0	-
chr22	20999776	21002196	ENST00000399133.2	0	-
chr22	20999801	21002106	ENST00000476667.5	0	-
chr22	21000229	21002117	ENST00000466670.1	0	-
chr22	21000271	21001518	ENST00000488975.1	0	-
chr22	21001291	21002118	ENST00000471723.1	0	-
chr22	21001885	21010125	ENST00000436079.1	0	+
chr22	21001921	21003272	ENST00000429962.1	0	+
chr22	21002207	21010342	ENST00000452284.1	0	+
chr22	21002244	21003264	ENST00000662660.1	0	+
chr22	21002894	21009453	ENST00000442739.1	0	-
chr22	21008192	21014287	ENST00000422086.5	0	-
chr22	21008636	21014292	ENST00000292748.7	0	-
chr22	21009807	21017942	ENST00000591411.5	0	+
chr22	21011383	21014217	ENST00000640124.1	0	-
chr22	21015026	21028830	ENST00000413302.6	0	+
chr22	21015152	21028008	ENST00000442475.5	0	+
chr22	21015169	21027198	ENST00000401443.5	0	+
chr22	21015174	21018791	ENST00000469722.2	0	+
chr22	21015182	21023152	ENST00000452228.5	0	+
chr22	21015186	21028008	ENST00000432930.5	0	+
chr22	21015189	21028013	ENST00000422210.5	0	+
chr22	21023348	21026597	ENST00000487342.1	0	+
chr22	21028717	21032549	ENST00000403586.5	0	-
chr22	21028717	21032561	ENST00000382932.3	0	-
chr22	21031353	21044117	ENST00000450652.2	0	+
chr22	21031567	21032840	ENST00000426145.1	0	-
chr22	21034175	21034272	ENST00000384843.1	0	-
chr22	21035242	21045012	ENST00000439119.5	0	-
chr22	21042342	21045017	ENST00000450626.1	0	-
chr22	21045959	21064168	ENST00000497328.5	0	+
chr22	21045980	21062276	ENST00000473769.1	0	+
chr22	21045986	21060528	ENST00000442047.2	0	+
chr22	21056529	21060397	ENST00000469766.1	0	+
chr22	21065461	21070097	ENST00000410028.3	0	+
chr22	21071081	21081018	ENST00000641915.1	0	-
chr22	21103015	21121784	ENST00000461808.5	0	+
chr22	21114489	21117524	ENST00000624908.1	0	+
chr22	21114606	21124451	ENST00000420508.1	0	-
chr22	21115928	21119758	ENST00000447763.1	0	+
chr22	21118589	21122285	ENST00000398241.3	0	+
chr22	21125659	21128063	ENST00000329949.3	0	-
chr22	21141623	21142371	ENST00000420708.1	0	-
chr22	21167757	21192156	ENST00000451257.5	0	+
chr22	21167828	21192149	ENST00000415376.5	0	+
chr22	21167852	21170852	ENST00000420583.5	0	+
chr22	21167852	21173530	ENST00000415704.5	0	+
chr22	21167884	21183322	ENST00000421576.5	0	+
chr22	21169812	21181056	ENST00000446717.5	0	+
chr22	21169864	21170655	ENST00000430463.1	0	+
chr22	21169945	21182974	ENST00000424410.1	0	+
chr22	21177891	21179875	ENST00000417463.1	0	-
chr22	21193359	21203755	ENST00000639507.1	0	-
chr22	21203453	21203631	ENST00000639755.1	0	-
chr22	21207972	21225554	ENST00000405188.8	0	-
chr22	21207973	21227637	ENST00000401924.5	0	-
chr22	21228759	21229138	ENST00000637734.1	0	-
chr22	21268160	21268903	ENST00000441743.1	0	+
chr22	21282880	21284161	ENST00000417732.1	0	+
chr22	21290759	21294586	ENST00000510112.1	0	-
chr22	21300989	21325042	ENST00000447720.5	0	-
chr22	21300990	21309881	ENST00000436681.5	0	-
chr22	21300996	21325042	ENST00000416615.5	0	-
chr22	21309820	21322854	ENST00000416405.5	0	-
chr22	21311739	21324937	ENST00000453273.5	0	-
chr22	21311739	21325042	ENST00000431488.5	0	-
chr22	21312919	21314904	ENST00000434730.1	0	+
chr22	21319269	21324967	ENST00000451574.5	0	-
chr22	21321947	21324967	ENST00000452952.1	0	-
chr22	21322140	21322935	ENST00000411581.1	0	-
chr22	21341594	21345258	ENST00000416640.2	0	+
chr22	21354562	21355763	ENST00000624642.1	0	+
chr22	21354612	21358067	ENST00000666255.1	0	+
chr22	21360600	21361299	ENST00000652479.1	0	-
chr22	21370063	21371945	ENST00000422450.1	0	+
chr22	21379381	21379701	ENST00000617743.1	0	-
chr22	21383373	21389478	ENST00000620804.1	0	+
chr22	21389190	21389491	ENST00000363187.1	0	+
chr22	21389795	21389938	ENST00000516211.1	0	-
chr22	21396272	21396590	ENST00000621315.1	0	-
chr22	21417370	21451463	ENST00000407464.7	0	+
chr22	21420635	21449434	ENST00000407598.2	0	+
chr22	21442459	21451463	ENST00000443632.2	0	+
chr22	21463962	21464279	ENST00000612964.1	0	+
chr22	21466422	21469935	ENST00000456303.6	0	+
chr22	21466449	21467023	ENST00000545656.2	0	+
chr22	21467139	21469935	ENST00000621561.4	0	+
chr22	21467261	21469933	ENST00000415764.5	0	+
chr22	21467318	21469925	ENST00000432134.7	0	+
chr22	21467390	21471269	ENST00000449424.5	0	+
chr22	21467391	21469933	ENST00000456153.5	0	+
chr22	21467464	21469925	ENST00000453397.5	0	+
chr22	21467619	21469267	ENST00000413877.5	0	+
chr22	21468097	21469925	ENST00000417708.5	0	+
chr22	21468287	21469919	ENST00000427147.5	0	+
chr22	21468746	21469935	ENST00000425458.5	0	+
chr22	21468755	21469717	ENST00000446867.5	0	+
chr22	21469147	21469935	ENST00000536718.2	0	+
chr22	21472999	21484134	ENST00000486209.5	0	-
chr22	21473008	21477143	ENST00000470274.5	0	-
chr22	21473008	21477217	ENST00000477296.5	0	-
chr22	21473010	21484134	ENST00000463608.5	0	-
chr22	21473022	21488382	ENST00000462560.6	0	-
chr22	21473034	21517533	ENST00000450651.5	0	-
chr22	21473058	21488358	ENST00000360806.9	0	-
chr22	21473075	21487907	ENST00000494740.5	0	-
chr22	21477379	21490611	ENST00000480319.5	0	-
chr22	21477379	21514255	ENST00000467443.5	0	-
chr22	21477412	21517520	ENST00000479693.1	0	-
chr22	21491975	21514223	ENST00000491562.5	0	-
chr22	21491975	21517481	ENST00000476537.1	0	-
chr22	21544896	21545039	ENST00000516334.1	0	+
chr22	21545343	21545644	ENST00000410420.1	0	-
chr22	21545356	21550831	ENST00000331505.5	0	-
chr22	21545677	21551461	ENST00000433039.1	0	-
chr22	21549446	21624034	ENST00000458578.6	0	+
chr22	21555137	21555457	ENST00000619894.1	0	+
chr22	21567546	21624034	ENST00000342192.8	0	+
chr22	21567546	21624034	ENST00000545681.2	0	+
chr22	21567729	21624034	ENST00000496722.1	0	+
chr22	21628088	21630012	ENST00000464015.5	0	-
chr22	21628088	21630031	ENST00000468686.5	0	-
chr22	21628088	21630064	ENST00000292778.10	0	-
chr22	21628088	21630064	ENST00000415762.6	0	-
chr22	21628113	21630038	ENST00000473985.1	0	-
chr22	21628121	21630014	ENST00000482998.1	0	-
chr22	21628166	21630035	ENST00000398873.4	0	-
chr22	21632715	21635802	ENST00000607942.5	0	+
chr22	21632717	21634209	ENST00000425975.1	0	+
chr22	21632759	21637329	ENST00000292779.4	0	+
chr22	21640843	21641284	ENST00000609038.1	0	+
chr22	21642301	21644299	ENST00000248958.5	0	+
chr22	21642353	21643292	ENST00000466935.1	0	+
chr22	21652269	21670237	ENST00000498589.1	0	+
chr22	21652980	21653058	ENST00000390813.1	0	+
chr22	21653303	21653385	ENST00000385018.1	0	+
chr22	21657810	21661021	ENST00000641967.1	0	+
chr22	21661241	21661295	ENST00000641933.1	0	-
chr22	21661475	21661593	ENST00000641242.1	0	-
chr22	21661858	21661929	ENST00000641337.1	0	-
chr22	21661933	21662363	ENST00000610143.1	0	+
chr22	21666008	21670283	ENST00000496819.1	0	+
chr22	21666008	21700015	ENST00000335025.12	0	+
chr22	21666011	21697907	ENST00000398831.8	0	+
chr22	21666021	21697866	ENST00000626352.2	0	+
chr22	21666022	21681390	ENST00000458567.5	0	+
chr22	21666022	21697912	ENST00000417788.5	0	+
chr22	21666028	21684878	ENST00000484439.5	0	+
chr22	21666039	21698276	ENST00000406385.1	0	+
chr22	21666078	21671025	ENST00000498109.1	0	+
chr22	21672329	21673609	ENST00000485930.1	0	+
chr22	21685672	21697901	ENST00000446951.1	0	+
chr22	21694660	21697902	ENST00000462188.1	0	+
chr22	21697535	21735794	ENST00000339468.8	0	-
chr22	21700864	21735761	ENST00000477675.1	0	-
chr22	21701128	21710849	ENST00000672036.1	0	-
chr22	21704081	21735755	ENST00000403503.1	0	-
chr22	21712760	21714013	ENST00000662004.1	0	+
chr22	21722894	21723182	ENST00000496490.3	0	-
chr22	21737447	21744108	ENST00000652589.1	0	-
chr22	21754499	21867629	ENST00000215832.10	0	-
chr22	21763983	21769428	ENST00000491588.1	0	-
chr22	21769039	21867680	ENST00000398822.7	0	-
chr22	21769203	21867440	ENST00000544786.1	0	-
chr22	21792433	21792524	ENST00000364115.1	0	+
chr22	21885281	21885673	ENST00000435222.1	0	-
chr22	21919424	21952848	ENST00000263212.10	0	-
chr22	21919426	21938584	ENST00000407142.5	0	-
chr22	21923124	21926062	ENST00000496143.5	0	-
chr22	21924875	21931248	ENST00000484588.1	0	-
chr22	21924875	21952831	ENST00000397495.8	0	-
chr22	21931265	21940657	ENST00000445205.1	0	-
chr22	21932713	21936929	ENST00000623652.1	0	-
chr22	21934023	21938576	ENST00000424647.1	0	-
chr22	21936961	21938531	ENST00000486259.1	0	-
chr22	21938268	21943372	ENST00000653280.1	0	+
chr22	21938292	21977632	ENST00000458178.2	0	+
chr22	21952019	21952821	ENST00000497072.1	0	-
chr22	21957024	21982816	ENST00000357179.9	0	-
chr22	21957026	21968740	ENST00000457270.5	0	-
chr22	21957026	21982790	ENST00000457179.5	0	-
chr22	21957030	21982750	ENST00000398793.6	0	-
chr22	21957030	21982790	ENST00000444502.5	0	-
chr22	21957031	21967650	ENST00000436282.1	0	-
chr22	21962127	21962895	ENST00000470338.1	0	-
chr22	21968618	21982780	ENST00000424393.5	0	-
chr22	21970334	21971632	ENST00000487485.1	0	-
chr22	21971894	21982766	ENST00000437929.5	0	-
chr22	21971896	21982813	ENST00000430142.5	0	-
chr22	21972218	21975796	ENST00000489581.1	0	-
chr22	21974375	21982766	ENST00000456075.5	0	-
chr22	21974378	21982787	ENST00000449704.5	0	-
chr22	21974418	21975969	ENST00000442653.1	0	-
chr22	21974432	21982793	ENST00000437103.1	0	-
chr22	21975645	21982773	ENST00000434517.1	0	-
chr22	21991098	22043858	ENST00000419303.5	0	-
chr22	21991099	22043934	ENST00000337471.8	0	-
chr22	21991235	21995428	ENST00000548391.1	0	-
chr22	22026075	22026593	ENST00000523272.1	0	+
chr22	22030933	22031472	ENST00000390282.2	0	+
chr22	22032744	22033194	ENST00000519525.1	0	+
chr22	22036343	22040065	ENST00000620649.1	0	+
chr22	22038642	22215269	ENST00000640991.1	0	+
chr22	22038642	22039126	ENST00000423821.2	0	+
chr22	22043617	22043942	ENST00000519446.1	0	+
chr22	22046429	22046591	ENST00000413294.1	0	+
chr22	22058432	22058892	ENST00000521832.1	0	+
chr22	22061076	22061538	ENST00000523818.1	0	+
chr22	22070224	22070707	ENST00000517313.1	0	+
chr22	22075365	22075666	ENST00000518581.1	0	+
chr22	22079769	22080263	ENST00000524184.1	0	+
chr22	22086560	22087110	ENST00000518003.1	0	+
chr22	22087923	22088085	ENST00000611529.1	0	+
chr22	22098699	22099212	ENST00000390283.2	0	+
chr22	22103218	22104857	ENST00000444224.1	0	+
chr22	22114843	22118005	ENST00000580133.2	0	-
chr22	22155581	22155942	ENST00000448601.1	0	+
chr22	22157381	22157631	ENST00000440562.1	0	+
chr22	22162198	22162681	ENST00000390284.2	0	+
chr22	22168288	22168634	ENST00000442890.1	0	+
chr22	22170982	22171556	ENST00000458366.1	0	-
chr22	22179227	22179477	ENST00000520218.1	0	+
chr22	22182581	22182885	ENST00000520445.1	0	+
chr22	22185940	22186619	ENST00000431230.1	0	+
chr22	22195798	22196276	ENST00000390285.4	0	+
chr22	22198559	22199011	ENST00000521342.1	0	+
chr22	22201662	22202161	ENST00000390286.3	0	+
chr22	22214793	22215270	ENST00000390287.2	0	+
chr22	22219646	22220133	ENST00000520107.1	0	+
chr22	22223186	22224566	ENST00000420768.1	0	-
chr22	22228718	22230469	ENST00000432623.1	0	-
chr22	22244779	22245505	ENST00000302273.2	0	+
chr22	22244785	22245515	ENST00000403807.4	0	+
chr22	22264600	22273020	ENST00000624350.1	0	+
chr22	22283927	22287220	ENST00000610778.1	0	-
chr22	22293732	22294794	ENST00000412149.1	0	+
chr22	22298123	22329122	ENST00000652112.1	0	+
chr22	22303223	22310401	ENST00000413293.1	0	+
chr22	22318726	22319226	ENST00000390289.2	0	+
chr22	22322471	22322969	ENST00000390290.3	0	+
chr22	22327299	22327814	ENST00000390291.2	0	+
chr22	22339572	22341214	ENST00000436538.1	0	+
chr22	22343186	22343732	ENST00000427632.2	0	+
chr22	22347806	22348164	ENST00000440829.1	0	+
chr22	22352939	22353433	ENST00000390293.1	0	+
chr22	22357738	22358260	ENST00000390294.2	0	+
chr22	22366209	22367770	ENST00000453999.1	0	+
chr22	22369613	22370087	ENST00000390295.3	0	+
chr22	22370828	22371120	ENST00000443400.1	0	+
chr22	22375985	22376505	ENST00000390296.2	0	+
chr22	22380765	22381347	ENST00000390297.3	0	+
chr22	22395017	22395489	ENST00000390298.2	0	+
chr22	22397031	22397524	ENST00000522485.1	0	+
chr22	22398848	22398999	ENST00000518405.1	0	+
chr22	22404206	22404721	ENST00000519790.1	0	+
chr22	22409765	22410282	ENST00000390299.2	0	+
chr22	22420325	22421310	ENST00000438037.1	0	+
chr22	22425820	22426314	ENST00000519982.1	0	+
chr22	22427539	22428035	ENST00000390300.2	0	+
chr22	22431957	22432465	ENST00000390301.3	0	+
chr22	22446685	22448336	ENST00000451224.1	0	+
chr22	22450201	22450652	ENST00000523314.1	0	+
chr22	22465658	22465986	ENST00000419501.1	0	+
chr22	22484420	22508742	ENST00000626650.3	0	-
chr22	22484857	22508733	ENST00000619852.2	0	-
chr22	22484872	22489468	ENST00000613655.1	0	-
chr22	22513735	22520270	ENST00000302097.3	0	-
chr22	22547700	22559265	ENST00000405655.8	0	-
chr22	22547700	22559340	ENST00000398743.6	0	-
chr22	22547700	22559361	ENST00000492657.5	0	-
chr22	22547701	22559143	ENST00000398741.5	0	-
chr22	22547701	22559289	ENST00000543184.5	0	-
chr22	22547766	22559288	ENST00000402697.5	0	-
chr22	22550073	22559279	ENST00000439106.5	0	-
chr22	22550182	22555926	ENST00000438888.5	0	-
chr22	22550220	22559083	ENST00000420709.5	0	-
chr22	22550245	22557721	ENST00000476336.5	0	-
chr22	22550298	22554179	ENST00000485532.1	0	-
chr22	22550753	22559267	ENST00000406503.1	0	-
chr22	22550818	22557592	ENST00000442481.5	0	-
chr22	22550851	22557754	ENST00000403441.1	0	-
chr22	22559342	22566602	ENST00000407120.1	0	+
chr22	22580013	22580296	ENST00000490007.2	0	+
chr22	22588154	22588688	ENST00000390302.3	0	+
chr22	22594527	22595056	ENST00000390303.3	0	+
chr22	22604444	22605165	ENST00000523713.1	0	+
chr22	22618571	22619163	ENST00000524362.1	0	+
chr22	22630064	22636153	ENST00000506079.2	0	+
chr22	22630545	22633849	ENST00000661466.1	0	-
chr22	22639176	22644473	ENST00000402027.1	0	-
chr22	22642250	22644085	ENST00000605258.1	0	-
chr22	22644474	22647903	ENST00000618722.4	0	+
chr22	22644617	22647087	ENST00000652219.1	0	+
chr22	22644639	22647898	ENST00000480559.6	0	+
chr22	22644655	22647898	ENST00000448514.2	0	+
chr22	22644657	22647072	ENST00000652249.1	0	+
chr22	22644668	22646879	ENST00000651213.1	0	+
chr22	22646081	22647903	ENST00000613850.4	0	+
chr22	22646309	22647898	ENST00000417145.2	0	+
chr22	22646777	22647878	ENST00000652467.1	0	+
chr22	22661298	22661542	ENST00000520084.1	0	+
chr22	22664472	22664907	ENST00000520681.1	0	+
chr22	22668287	22668806	ENST00000390304.2	0	+
chr22	22673151	22673602	ENST00000520756.1	0	+
chr22	22679096	22679345	ENST00000521893.1	0	+
chr22	22686725	22687271	ENST00000390305.2	0	+
chr22	22689830	22689978	ENST00000418731.1	0	+
chr22	22692777	22693312	ENST00000438185.1	0	-
chr22	22694438	22694952	ENST00000517477.1	0	+
chr22	22697788	22698407	ENST00000390306.2	0	+
chr22	22700759	22700970	ENST00000521183.1	0	+
chr22	22704264	22704822	ENST00000390307.2	0	+
chr22	22711688	22713203	ENST00000390308.2	0	+
chr22	22715289	22715419	ENST00000519296.1	0	+
chr22	22720622	22721145	ENST00000390309.2	0	+
chr22	22730724	22731090	ENST00000620749.1	0	-
chr22	22734606	22735089	ENST00000390310.3	0	+
chr22	22738943	22739187	ENST00000519099.1	0	+
chr22	22747382	22747921	ENST00000390311.3	0	+
chr22	22755553	22755797	ENST00000518356.1	0	+
chr22	22758699	22759218	ENST00000390312.2	0	+
chr22	22762293	22762516	ENST00000518475.1	0	+
chr22	22771823	22772582	ENST00000390313.3	0	+
chr22	22774528	22774691	ENST00000415642.1	0	+
chr22	22785642	22787268	ENST00000616239.1	0	-
chr22	22792490	22793007	ENST00000390314.2	0	+
chr22	22803075	22803360	ENST00000618197.1	0	+
chr22	22811746	22812281	ENST00000390315.3	0	+
chr22	22814670	22814926	ENST00000423259.1	0	+
chr22	22819009	22819756	ENST00000390316.2	0	+
chr22	22822657	22823289	ENST00000620395.2	0	+
chr22	22822775	22822871	ENST00000385101.1	0	+
chr22	22834864	22835117	ENST00000458673.1	0	+
chr22	22838601	22838872	ENST00000522952.1	0	+
chr22	22850374	22850624	ENST00000523937.1	0	+
chr22	22856761	22857038	ENST00000522922.1	0	+
chr22	22858024	22858268	ENST00000432300.1	0	+
chr22	22871506	22872035	ENST00000390318.2	0	+
chr22	22873211	22873440	ENST00000519345.1	0	+
chr22	22880705	22881396	ENST00000390319.2	0	+
chr22	22886266	22886379	ENST00000577998.1	0	+
chr22	22887779	22896107	ENST00000532223.2	0	+
chr22	22887815	22896111	ENST00000526893.6	0	+
chr22	22887828	22895825	ENST00000531372.1	0	+
chr22	22893691	22893818	ENST00000390320.2	0	+
chr22	22895374	22895834	ENST00000390321.2	0	+
chr22	22899480	22899655	ENST00000390322.2	0	+
chr22	22900975	22901437	ENST00000390323.2	0	+
chr22	22904849	22905025	ENST00000390324.2	0	+
chr22	22906341	22906803	ENST00000390325.2	0	+
chr22	22910573	22910606	ENST00000390326.2	0	+
chr22	22910827	22911075	ENST00000517690.1	0	+
chr22	22914236	22914308	ENST00000390327.2	0	+
chr22	22915634	22915927	ENST00000519670.1	0	+
chr22	22918131	22918201	ENST00000390328.2	0	+
chr22	22919534	22919851	ENST00000410078.2	0	+
chr22	22921389	22921435	ENST00000390330.2	0	+
chr22	22922593	22923034	ENST00000390331.3	0	+
chr22	22988262	22988581	ENST00000415639.1	0	+
chr22	22990689	23008356	ENST00000420944.1	0	+
chr22	23059414	23141990	ENST00000216036.9	0	-
chr22	23070518	23125032	ENST00000615612.2	0	+
chr22	23070573	23095716	ENST00000492538.1	0	+
chr22	23122377	23123342	ENST00000479571.1	0	+
chr22	23124226	23134074	ENST00000421213.1	0	-
chr22	23131176	23138914	ENST00000439064.1	0	-
chr22	23132729	23133005	ENST00000635920.1	0	-
chr22	23135992	23140300	ENST00000452757.5	0	-
chr22	23136197	23145021	ENST00000406876.1	0	-
chr22	23137804	23137864	ENST00000459276.2	0	-
chr22	23145325	23164350	ENST00000263116.6	0	+
chr22	23145365	23161564	ENST00000341989.8	0	+
chr22	23150112	23156311	ENST00000420895.1	0	+
chr22	23179703	23261428	ENST00000479188.5	0	+
chr22	23179917	23180172	ENST00000412037.2	0	-
chr22	23180364	23315950	ENST00000359540.7	0	+
chr22	23180508	23318037	ENST00000305877.13	0	+
chr22	23198252	23253833	ENST00000463770.5	0	+
chr22	23198331	23253833	ENST00000487679.1	0	+
chr22	23251031	23261393	ENST00000480973.1	0	+
chr22	23253959	23285114	ENST00000427791.1	0	+
chr22	23261781	23262071	ENST00000467969.3	0	-
chr22	23262766	23265005	ENST00000426721.2	0	+
chr22	23283208	23291131	ENST00000487968.5	0	+
chr22	23283787	23287173	ENST00000466076.1	0	+
chr22	23289614	23310433	ENST00000419722.6	0	+
chr22	23295040	23302863	ENST00000471452.1	0	+
chr22	23306153	23310433	ENST00000478978.5	0	+
chr22	23306744	23313999	ENST00000475025.5	0	+
chr22	23309473	23318034	ENST00000436990.2	0	+
chr22	23312387	23314695	ENST00000458056.2	0	+
chr22	23322542	23322927	ENST00000440602.1	0	-
chr22	23326615	23328493	ENST00000450776.1	0	+
chr22	23359602	23377014	ENST00000400418.7	0	-
chr22	23366301	23376896	ENST00000422151.6	0	-
chr22	23370094	23380752	ENST00000431237.5	0	-
chr22	23370094	23387731	ENST00000415114.1	0	-
chr22	23373647	23377071	ENST00000424029.1	0	-
chr22	23390605	23402567	ENST00000255890.8	0	-
chr22	23390605	23402726	ENST00000433168.5	0	-
chr22	23391376	23402572	ENST00000420968.5	0	-
chr22	23392552	23394792	ENST00000512987.2	0	+
chr22	23392584	23399608	ENST00000456279.1	0	-
chr22	23397937	23402157	ENST00000418264.1	0	-
chr22	23433563	23435071	ENST00000449711.1	0	+
chr22	23434170	23434731	ENST00000428415.1	0	+
chr22	23446597	23460788	ENST00000454268.1	0	+
chr22	23458378	23460833	ENST00000457193.1	0	+
chr22	23460348	23460511	ENST00000426024.1	0	+
chr22	23462085	23486980	ENST00000320372.9	0	-
chr22	23510153	23512662	ENST00000653461.1	0	+
chr22	23512493	23513602	ENST00000432249.2	0	-
chr22	23536880	23539480	ENST00000629869.1	0	+
chr22	23536898	23547167	ENST00000626825.1	0	+
chr22	23543862	23547797	ENST00000627127.1	0	+
chr22	23567063	23573122	ENST00000454863.4	0	+
chr22	23567063	23573507	ENST00000458318.2	0	+
chr22	23573124	23580239	ENST00000249053.3	0	-
chr22	23573124	23580290	ENST00000330377.3	0	-
chr22	23573368	23580302	ENST00000438703.1	0	-
chr22	23580879	23583859	ENST00000608615.1	0	-
chr22	23607040	23608407	ENST00000614827.1	0	+
chr22	23608451	23632321	ENST00000317749.9	0	-
chr22	23610328	23610538	ENST00000622821.1	0	+
chr22	23613286	23632023	ENST00000614553.1	0	-
chr22	23630617	23638941	ENST00000390329.3	0	-
chr22	23638486	23652886	ENST00000430357.4	0	-
chr22	23638486	23693151	ENST00000455485.5	0	-
chr22	23638486	23717321	ENST00000451837.5	0	-
chr22	23638486	23717321	ENST00000452737.5	0	-
chr22	23638486	23717356	ENST00000422506.5	0	-
chr22	23638486	23717356	ENST00000445682.2	0	-
chr22	23638491	23640762	ENST00000430707.2	0	-
chr22	23638677	23640425	ENST00000425948.2	0	-
chr22	23649212	23653334	ENST00000428790.1	0	-
chr22	23652859	23690293	ENST00000423913.5	0	-
chr22	23653168	23717347	ENST00000421064.5	0	-
chr22	23653169	23686914	ENST00000434893.1	0	-
chr22	23653183	23700843	ENST00000651432.1	0	-
chr22	23658093	23693185	ENST00000417194.5	0	-
chr22	23659868	23717295	ENST00000435868.1	0	-
chr22	23659875	23690491	ENST00000438858.1	0	-
chr22	23678609	23690297	ENST00000458554.5	0	-
chr22	23683724	23687980	ENST00000451919.1	0	-
chr22	23683743	23687819	ENST00000437862.1	0	-
chr22	23688135	23699176	ENST00000441897.5	0	+
chr22	23688141	23699176	ENST00000401461.5	0	+
chr22	23690860	23698651	ENST00000612432.4	0	+
chr22	23690860	23699171	ENST00000615003.4	0	+
chr22	23690860	23699176	ENST00000290691.9	0	+
chr22	23691305	23698978	ENST00000423392.5	0	+
chr22	23691305	23698978	ENST00000460003.4	0	+
chr22	23691305	23699176	ENST00000467354.2	0	+
chr22	23696483	23699176	ENST00000452208.1	0	+
chr22	23698069	23698544	ENST00000460167.1	0	+
chr22	23700788	23717267	ENST00000432595.1	0	-
chr22	23709840	23717327	ENST00000444220.2	0	-
chr22	23714185	23717356	ENST00000437294.1	0	-
chr22	23738681	23751112	ENST00000341976.5	0	-
chr22	23752742	23754409	ENST00000398465.3	0	-
chr22	23752742	23754425	ENST00000248948.4	0	-
chr22	23763020	23764363	ENST00000498542.5	0	+
chr22	23763020	23765861	ENST00000477921.1	0	+
chr22	23763053	23765861	ENST00000402217.7	0	+
chr22	23763206	23765861	ENST00000305199.9	0	+
chr22	23763222	23765861	ENST00000382821.3	0	+
chr22	23765833	23767876	ENST00000520222.1	0	-
chr22	23765833	23767942	ENST00000401675.7	0	-
chr22	23765833	23768443	ENST00000484558.2	0	-
chr22	23765873	23766464	ENST00000523865.1	0	-
chr22	23765934	23767920	ENST00000517886.1	0	-
chr22	23768225	23784316	ENST00000465385.5	0	+
chr22	23768642	23781007	ENST00000477567.5	0	+
chr22	23768659	23780666	ENST00000489582.5	0	+
chr22	23772845	23779697	ENST00000465730.1	0	+
chr22	23772845	23780484	ENST00000428253.1	0	+
chr22	23772848	23784316	ENST00000215743.8	0	+
chr22	23772848	23784316	ENST00000437086.5	0	+
chr22	23777640	23781100	ENST00000460352.1	0	+
chr22	23780879	23783441	ENST00000434318.1	0	+
chr22	23780940	23782315	ENST00000493132.1	0	+
chr22	23780956	23781751	ENST00000492464.1	0	+
chr22	23781263	23782628	ENST00000488363.1	0	+
chr22	23781813	23782483	ENST00000480185.1	0	+
chr22	23786930	23834540	ENST00000407422.8	0	+
chr22	23786945	23838008	ENST00000644036.1	0	+
chr22	23786962	23834505	ENST00000344921.11	0	+
chr22	23786977	23802275	ENST00000646421.1	0	+
chr22	23786979	23802060	ENST00000491967.2	0	+
chr22	23786982	23834505	ENST00000647057.1	0	+
chr22	23787000	23816927	ENST00000644619.1	0	+
chr22	23787010	23834501	ENST00000263121.12	0	+
chr22	23787016	23803401	ENST00000407082.4	0	+
chr22	23787026	23816833	ENST00000417137.6	0	+
chr22	23787181	23834270	ENST00000646723.1	0	+
chr22	23787201	23804383	ENST00000643421.1	0	+
chr22	23787257	23826248	ENST00000646911.1	0	+
chr22	23791800	23834358	ENST00000644462.1	0	+
chr22	23791810	23803322	ENST00000634926.1	0	+
chr22	23791810	23803322	ENST00000635578.1	0	+
chr22	23801298	23803629	ENST00000642727.1	0	+
chr22	23802000	23818669	ENST00000644467.1	0	+
chr22	23802546	23818580	ENST00000642275.1	0	+
chr22	23814990	23833587	ENST00000477836.2	0	+
chr22	23823107	23834353	ENST00000645799.1	0	+
chr22	23834502	23836514	ENST00000464023.1	0	-
chr22	23834502	23839006	ENST00000404056.1	0	-
chr22	23834502	23839006	ENST00000406855.7	0	-
chr22	23834502	23839128	ENST00000290730.11	0	-
chr22	23836464	23837684	ENST00000493596.5	0	-
chr22	23836733	23837698	ENST00000464034.5	0	-
chr22	23836733	23838203	ENST00000488272.5	0	-
chr22	23836733	23839004	ENST00000318109.11	0	-
chr22	23836733	23839012	ENST00000476077.1	0	-
chr22	23836766	23839004	ENST00000464110.1	0	-
chr22	23856426	23857039	ENST00000609825.1	0	-
chr22	23856702	23886309	ENST00000345044.10	0	+
chr22	23856862	23870182	ENST00000403208.7	0	+
chr22	23856871	23885536	ENST00000398356.6	0	+
chr22	23856933	23877328	ENST00000405847.5	0	+
chr22	23856945	23882952	ENST00000467660.5	0	+
chr22	23856950	23882951	ENST00000405286.6	0	+
chr22	23856956	23884933	ENST00000255830.7	0	+
chr22	23856956	23885551	ENST00000489322.5	0	+
chr22	23856961	23866806	ENST00000440493.5	0	+
chr22	23856999	23875211	ENST00000473740.5	0	+
chr22	23856999	23886112	ENST00000611880.4	0	+
chr22	23857335	23862325	ENST00000492516.1	0	+
chr22	23857416	23877287	ENST00000423972.5	0	+
chr22	23857470	23875189	ENST00000418102.5	0	+
chr22	23857874	23868796	ENST00000491948.5	0	+
chr22	23857894	23877172	ENST00000436643.5	0	+
chr22	23857918	23885553	ENST00000405340.6	0	+
chr22	23857960	23885536	ENST00000461809.5	0	+
chr22	23857962	23884867	ENST00000316185.8	0	+
chr22	23862187	23895223	ENST00000433835.3	0	+
chr22	23865247	23873277	ENST00000624923.1	0	-
chr22	23875159	23882952	ENST00000482576.1	0	+
chr22	23877857	23884784	ENST00000472526.1	0	+
chr22	23879492	23879790	ENST00000491172.3	0	-
chr22	23880408	23883307	ENST00000473357.1	0	+
chr22	23882959	23885536	ENST00000486907.1	0	+
chr22	23894382	23895223	ENST00000215754.8	0	+
chr22	23894425	23898930	ENST00000406213.1	0	-
chr22	23894449	23895227	ENST00000465752.1	0	+
chr22	23894697	23895227	ENST00000498385.1	0	+
chr22	23901431	23907068	ENST00000609510.1	0	-
chr22	23926899	23929574	ENST00000439492.1	0	+
chr22	23939997	23942798	ENST00000443377.1	0	+
chr22	23949917	23954042	ENST00000440099.1	0	+
chr22	23957413	23961186	ENST00000290765.8	0	-
chr22	23957618	23961153	ENST00000404172.3	0	-
chr22	23963779	23964374	ENST00000423307.1	0	+
chr22	23966887	23972556	ENST00000215770.6	0	+
chr22	23969210	23969873	ENST00000609736.1	0	+
chr22	23971364	23974584	ENST00000398344.8	0	-
chr22	23971364	23979828	ENST00000350608.7	0	-
chr22	23971369	23973183	ENST00000428792.1	0	-
chr22	23971377	23980469	ENST00000404092.5	0	-
chr22	23973080	23974491	ENST00000444947.2	0	-
chr22	23973103	23974459	ENST00000403754.7	0	-
chr22	23973103	23974459	ENST00000430101.2	0	-
chr22	23976871	23991421	ENST00000437021.2	0	-
chr22	23976903	23977585	ENST00000506097.1	0	-
chr22	23980057	23983710	ENST00000402588.6	0	+
chr22	23980057	23983915	ENST00000634759.1	0	+
chr22	23980122	23983911	ENST00000621118.4	0	+
chr22	23998400	24005361	ENST00000611600.4	0	-
chr22	23998400	24005409	ENST00000621179.5	0	-
chr22	23998489	24005453	ENST00000612717.4	0	-
chr22	24000149	24005361	ENST00000617532.4	0	-
chr22	24006304	24008258	ENST00000440539.1	0	-
chr22	24011191	24056304	ENST00000454754.5	0	+
chr22	24011303	24178628	ENST00000263119.10	0	+
chr22	24011314	24178628	ENST00000617531.4	0	+
chr22	24011347	24178628	ENST00000405822.6	0	+
chr22	24011415	24056318	ENST00000445422.5	0	+
chr22	24011417	24178628	ENST00000398319.6	0	+
chr22	24055067	24056525	ENST00000474981.1	0	+
chr22	24067004	24071183	ENST00000484593.1	0	+
chr22	24087587	24092013	ENST00000496016.1	0	+
chr22	24098112	24119606	ENST00000467937.1	0	+
chr22	24101688	24103354	ENST00000444093.1	0	+
chr22	24155837	24178627	ENST00000337989.11	0	+
chr22	24156532	24164489	ENST00000495121.1	0	+
chr22	24167222	24173398	ENST00000485008.1	0	+
chr22	24175778	24177538	ENST00000459824.1	0	+
chr22	24181486	24189106	ENST00000358321.4	0	+
chr22	24182576	24189106	ENST00000463101.1	0	+
chr22	24219653	24245078	ENST00000263112.11	0	-
chr22	24219653	24245142	ENST00000327365.8	0	-
chr22	24219659	24226112	ENST00000425408.5	0	-
chr22	24219659	24245059	ENST00000398292.3	0	-
chr22	24232972	24245082	ENST00000424217.1	0	-
chr22	24241088	24245108	ENST00000623756.1	0	-
chr22	24248073	24249466	ENST00000402176.3	0	-
chr22	24251827	24265525	ENST00000414583.6	0	+
chr22	24252004	24253289	ENST00000441984.1	0	+
chr22	24256633	24261900	ENST00000419222.1	0	+
chr22	24259928	24263764	ENST00000444246.1	0	-
chr22	24269345	24270134	ENST00000396982.3	0	-
chr22	24270816	24417739	ENST00000437398.5	0	+
chr22	24270821	24330401	ENST00000421374.5	0	+
chr22	24270830	24417738	ENST00000314328.14	0	+
chr22	24270836	24417206	ENST00000651059.1	0	+
chr22	24270882	24324304	ENST00000416735.5	0	+
chr22	24270882	24414773	ENST00000541492.1	0	+
chr22	24270897	24442356	ENST00000358654.2	0	+
chr22	24304395	24321688	ENST00000440893.1	0	+
chr22	24412159	24414752	ENST00000472799.1	0	+
chr22	24417878	24433192	ENST00000467385.5	0	+
chr22	24422487	24433208	ENST00000486351.1	0	+
chr22	24423596	24433667	ENST00000424232.5	0	+
chr22	24423596	24442356	ENST00000611543.4	0	+
chr22	24423630	24433729	ENST00000486108.1	0	+
chr22	24424397	24433423	ENST00000464977.5	0	+
chr22	24424408	24441345	ENST00000444262.6	0	+
chr22	24427561	24433654	ENST00000496258.1	0	+
chr22	24427598	24442357	ENST00000337539.12	0	+
chr22	24427626	24441173	ENST00000496497.1	0	+
chr22	24429205	24494800	ENST00000326341.8	0	-
chr22	24432118	24433492	ENST00000472248.5	0	+
chr22	24432118	24442356	ENST00000610595.4	0	+
chr22	24432118	24442356	ENST00000618076.3	0	+
chr22	24432166	24433597	ENST00000436735.1	0	+
chr22	24432839	24433634	ENST00000439591.1	0	+
chr22	24438269	24494800	ENST00000650766.1	0	-
chr22	24438275	24495074	ENST00000427813.6	0	-
chr22	24438279	24494815	ENST00000412790.2	0	-
chr22	24494106	24526668	ENST00000415388.5	0	+
chr22	24495241	24503738	ENST00000382760.2	0	+
chr22	24495331	24528390	ENST00000326010.10	0	+
chr22	24516507	24518386	ENST00000432032.1	0	+
chr22	24519800	24528390	ENST00000498140.1	0	+
chr22	24520173	24523700	ENST00000486043.1	0	+
chr22	24540422	24555935	ENST00000621833.4	0	-
chr22	24540437	24555182	ENST00000407471.7	0	-
chr22	24540437	24555316	ENST00000435822.5	0	-
chr22	24540441	24555171	ENST00000398245.8	0	-
chr22	24540531	24548032	ENST00000490922.1	0	-
chr22	24542397	24555935	ENST00000404664.7	0	-
chr22	24542686	24555089	ENST00000447813.6	0	-
chr22	24542755	24555125	ENST00000402766.5	0	-
chr22	24542829	24544083	ENST00000493099.1	0	-
chr22	24544044	24547390	ENST00000480272.1	0	-
chr22	24546916	24555859	ENST00000407973.2	0	-
chr22	24547715	24555124	ENST00000468170.1	0	-
chr22	24547772	24548961	ENST00000490810.1	0	-
chr22	24555957	24558086	ENST00000468770.1	0	+
chr22	24555998	24574971	ENST00000215829.8	0	+
chr22	24556006	24582052	ENST00000402849.5	0	+
chr22	24556006	24628987	ENST00000652248.1	0	+
chr22	24556008	24609980	ENST00000404603.5	0	+
chr22	24556031	24609150	ENST00000439775.1	0	+
chr22	24583749	24628968	ENST00000651180.1	0	+
chr22	24583749	24629005	ENST00000248923.9	0	+
chr22	24585619	24593066	ENST00000446942.5	0	-
chr22	24585634	24593073	ENST00000318753.13	0	-
chr22	24585634	24593073	ENST00000491910.5	0	-
chr22	24585648	24593064	ENST00000404045.6	0	-
chr22	24585661	24593079	ENST00000460524.5	0	-
chr22	24588681	24592285	ENST00000495297.5	0	-
chr22	24589164	24593034	ENST00000465334.1	0	-
chr22	24589405	24593208	ENST00000464490.1	0	-
chr22	24594810	24611171	ENST00000456869.5	0	+
chr22	24594813	24615107	ENST00000411974.5	0	+
chr22	24598053	24599398	ENST00000438893.2	0	+
chr22	24603146	24628134	ENST00000412658.5	0	+
chr22	24603183	24614814	ENST00000445029.5	0	+
chr22	24603192	24628996	ENST00000400382.6	0	+
chr22	24603197	24620468	ENST00000419133.5	0	+
chr22	24603212	24611192	ENST00000438643.6	0	+
chr22	24603212	24615127	ENST00000452551.5	0	+
chr22	24603214	24614851	ENST00000412898.5	0	+
chr22	24603215	24629001	ENST00000400380.5	0	+
chr22	24603216	24614904	ENST00000455483.5	0	+
chr22	24603234	24615071	ENST00000430289.5	0	+
chr22	24607620	24615125	ENST00000447416.5	0	+
chr22	24607638	24615078	ENST00000432867.5	0	+
chr22	24607658	24615093	ENST00000451366.5	0	+
chr22	24607663	24615125	ENST00000428855.5	0	+
chr22	24609461	24628996	ENST00000425895.5	0	+
chr22	24610365	24615972	ENST00000474191.6	0	+
chr22	24614861	24620833	ENST00000487766.1	0	+
chr22	24620407	24628976	ENST00000466310.5	0	+
chr22	24627179	24628996	ENST00000401885.5	0	+
chr22	24627251	24628975	ENST00000404532.5	0	+
chr22	24627254	24629005	ENST00000403838.5	0	+
chr22	24627407	24628996	ENST00000404223.5	0	+
chr22	24627443	24628839	ENST00000404920.1	0	+
chr22	24627875	24628976	ENST00000490087.1	0	+
chr22	24632914	24647760	ENST00000447261.6	0	+
chr22	24632953	24650012	ENST00000621462.4	0	+
chr22	24633271	24633443	ENST00000639500.1	0	+
chr22	24644790	24650832	ENST00000382243.5	0	+
chr22	24645170	24651657	ENST00000639904.1	0	-
chr22	24647698	24650003	ENST00000611425.1	0	+
chr22	24648245	24653356	ENST00000614808.1	0	+
chr22	24657480	24658765	ENST00000436994.1	0	-
chr22	24686167	24686679	ENST00000430731.1	0	+
chr22	24686922	24688087	ENST00000409926.2	0	+
chr22	24691121	24696003	ENST00000633827.1	0	-
chr22	24703347	24703503	ENST00000456260.1	0	+
chr22	24712291	24712520	ENST00000442565.1	0	+
chr22	24719033	24762523	ENST00000616349.4	0	-
chr22	24719033	24774716	ENST00000332271.9	0	-
chr22	24719249	24774477	ENST00000527701.5	0	-
chr22	24719249	24774477	ENST00000533313.5	0	-
chr22	24719471	24774720	ENST00000532537.2	0	-
chr22	24806168	24926844	ENST00000610372.4	0	+
chr22	24806268	24927578	ENST00000400358.8	0	+
chr22	24806306	24814192	ENST00000470591.1	0	+
chr22	24806318	24925137	ENST00000400359.4	0	+
chr22	24843459	24925137	ENST00000473458.5	0	+
chr22	24844740	24924593	ENST00000480523.1	0	+
chr22	24901146	24901214	ENST00000362913.1	0	-
chr22	24934953	24946694	ENST00000640159.1	0	-
chr22	24935109	24946695	ENST00000407886.1	0	-
chr22	24935240	24939535	ENST00000423535.1	0	-
chr22	24952729	25194457	ENST00000406486.8	0	+
chr22	25009684	25015072	ENST00000623888.1	0	-
chr22	25022593	25024652	ENST00000624101.1	0	-
chr22	25027973	25197444	ENST00000358431.7	0	+
chr22	25040580	25186451	ENST00000494730.2	0	+
chr22	25041312	25049689	ENST00000461374.1	0	+
chr22	25048966	25051966	ENST00000623239.1	0	-
chr22	25052121	25065241	ENST00000623980.1	0	-
chr22	25069968	25197448	ENST00000401395.1	0	+
chr22	25102432	25112692	ENST00000366110.4	0	-
chr22	25111841	25197399	ENST00000637069.1	0	+
chr22	25179308	25179554	ENST00000604037.1	0	-
chr22	25199857	25207359	ENST00000215855.7	0	+
chr22	25199872	25207267	ENST00000404334.1	0	+
chr22	25212084	25213826	ENST00000454253.2	0	+
chr22	25212104	25213777	ENST00000651679.1	0	+
chr22	25212563	25231870	ENST00000651629.1	0	+
chr22	25219638	25231869	ENST00000398215.3	0	+
chr22	25251522	25252602	ENST00000623422.1	0	-
chr22	25279528	25282291	ENST00000411511.5	0	-
chr22	25281285	25282674	ENST00000432495.1	0	-
chr22	25282612	25320086	ENST00000650352.1	0	+
chr22	25282942	25300791	ENST00000649082.1	0	+
chr22	25318255	25320080	ENST00000412472.2	0	+
chr22	25327251	25339975	ENST00000433457.2	0	+
chr22	25349542	25350322	ENST00000434827.1	0	-
chr22	25351417	25362557	ENST00000467672.5	0	-
chr22	25351420	25362557	ENST00000610821.4	0	-
chr22	25351420	25381577	ENST00000402859.6	0	-
chr22	25351456	25405377	ENST00000444995.7	0	-
chr22	25351456	25405377	ENST00000650168.1	0	-
chr22	25351461	25360189	ENST00000402785.2	0	-
chr22	25351611	25405274	ENST00000650500.2	0	-
chr22	25351787	25360225	ENST00000474163.1	0	-
chr22	25354756	25357806	ENST00000484509.1	0	-
chr22	25360081	25381531	ENST00000468442.1	0	-
chr22	25434323	25435070	ENST00000609330.1	0	-
chr22	25436311	25436915	ENST00000609964.1	0	+
chr22	25437305	25437823	ENST00000603243.1	0	-
chr22	25448104	25461678	ENST00000382734.9	0	+
chr22	25455645	25455711	ENST00000615588.1	0	+
chr22	25455711	25459681	ENST00000415709.5	0	+
chr22	25455717	25511628	ENST00000354451.6	0	+
chr22	25455795	25520854	ENST00000509460.2	0	+
chr22	25459975	25461668	ENST00000609084.1	0	+
chr22	25476217	25479971	ENST00000609475.1	0	+
chr22	25518034	25520853	ENST00000609513.1	0	+
chr22	25561116	25564462	ENST00000412773.1	0	-
chr22	25561116	25564715	ENST00000668059.1	0	-
chr22	25561123	25564150	ENST00000422876.1	0	-
chr22	25562640	25564181	ENST00000666865.1	0	-
chr22	25563100	25564259	ENST00000453811.1	0	-
chr22	25563100	25564719	ENST00000661676.1	0	-
chr22	25564674	25729294	ENST00000324198.11	0	+
chr22	25564911	25723018	ENST00000619906.4	0	+
chr22	25565106	25684568	ENST00000455558.2	0	+
chr22	25580240	25581382	ENST00000413494.1	0	-
chr22	25647260	25649232	ENST00000418510.1	0	+
chr22	25715040	25715169	ENST00000410653.1	0	-
chr22	25742143	26027766	ENST00000536101.5	0	+
chr22	25742152	26031041	ENST00000335473.11	0	+
chr22	25742233	26030644	ENST00000407587.6	0	+
chr22	25756317	25756669	ENST00000624255.1	0	-
chr22	25761033	26030586	ENST00000539302.5	0	+
chr22	25770026	26027766	ENST00000418374.6	0	+
chr22	25876853	25901042	ENST00000453457.7	0	-
chr22	25883395	25901050	ENST00000600211.5	0	-
chr22	25892225	25901033	ENST00000597284.5	0	-
chr22	25892362	25898998	ENST00000595102.5	0	-
chr22	25892397	25895595	ENST00000593715.5	0	-
chr22	25892397	25900622	ENST00000594585.5	0	-
chr22	25892397	25901010	ENST00000609157.5	0	-
chr22	25892399	25896847	ENST00000597614.6	0	-
chr22	25892399	25901067	ENST00000599080.5	0	-
chr22	25892404	25900630	ENST00000597548.5	0	-
chr22	25892413	25895643	ENST00000596813.1	0	-
chr22	25892436	25896744	ENST00000609820.5	0	-
chr22	25892436	25896756	ENST00000608921.5	0	-
chr22	25892436	25898994	ENST00000608507.5	0	-
chr22	25892436	25901016	ENST00000608115.5	0	-
chr22	25892436	25901036	ENST00000609570.5	0	-
chr22	25892436	25901067	ENST00000609275.5	0	-
chr22	25892436	25903246	ENST00000609889.5	0	-
chr22	25892436	25903247	ENST00000608257.5	0	-
chr22	25892437	25897596	ENST00000609809.1	0	-
chr22	25892443	25900473	ENST00000609823.1	0	-
chr22	25892444	25900537	ENST00000607895.1	0	-
chr22	25892464	25898916	ENST00000594542.1	0	-
chr22	25894693	25899820	ENST00000600269.1	0	-
chr22	25894849	25899827	ENST00000599792.5	0	-
chr22	25895141	25898752	ENST00000594856.1	0	-
chr22	25897463	25898633	ENST00000600903.1	0	-
chr22	25898932	25899827	ENST00000595093.1	0	-
chr22	25901177	25903764	ENST00000534908.5	0	+
chr22	25901177	25903797	ENST00000536204.1	0	+
chr22	25953534	25965071	ENST00000539544.1	0	+
chr22	25955314	26030645	ENST00000543971.1	0	+
chr22	25959073	25983526	ENST00000436423.1	0	-
chr22	25963982	25964273	ENST00000411124.1	0	-
chr22	26006388	26006532	ENST00000440996.1	0	-
chr22	26027048	26030644	ENST00000540454.1	0	+
chr22	26161833	26164303	ENST00000420391.1	0	+
chr22	26161904	26168753	ENST00000652418.1	0	-
chr22	26161909	26169083	ENST00000650717.1	0	-
chr22	26161911	26169341	ENST00000434510.2	0	-
chr22	26169461	26383596	ENST00000248933.11	0	+
chr22	26169473	26380471	ENST00000404234.7	0	+
chr22	26169473	26383597	ENST00000529632.6	0	+
chr22	26169513	26383596	ENST00000360929.7	0	+
chr22	26169574	26380373	ENST00000629590.2	0	+
chr22	26169657	26380334	ENST00000343706.8	0	+
chr22	26241330	26254092	ENST00000414989.2	0	-
chr22	26292250	26380468	ENST00000402979.1	0	+
chr22	26292250	26380468	ENST00000403121.5	0	+
chr22	26347788	26351456	ENST00000483291.1	0	+
chr22	26364748	26377720	ENST00000494013.1	0	+
chr22	26390666	26390776	ENST00000516458.1	0	-
chr22	26422070	26423193	ENST00000450463.1	0	-
chr22	26429259	26445015	ENST00000215906.6	0	+
chr22	26443422	26464015	ENST00000519774.5	0	-
chr22	26444226	26464192	ENST00000493455.6	0	-
chr22	26451479	26452892	ENST00000491142.1	0	-
chr22	26451479	26483812	ENST00000398145.6	0	-
chr22	26452082	26479448	ENST00000429411.5	0	-
chr22	26452082	26479546	ENST00000464362.5	0	-
chr22	26452082	26479681	ENST00000402105.7	0	-
chr22	26452082	26483795	ENST00000336873.9	0	-
chr22	26452082	26483836	ENST00000439453.5	0	-
chr22	26452082	26483837	ENST00000466781.5	0	-
chr22	26453111	26479488	ENST00000496385.5	0	-
chr22	26453645	26472950	ENST00000485842.5	0	-
chr22	26464238	26483783	ENST00000422379.2	0	-
chr22	26465520	26477011	ENST00000459918.1	0	-
chr22	26475369	26479411	ENST00000481910.1	0	-
chr22	26476990	26483836	ENST00000483631.1	0	-
chr22	26481834	26483836	ENST00000479064.5	0	-
chr22	26481857	26483828	ENST00000473782.1	0	-
chr22	26483876	26494658	ENST00000215917.11	0	+
chr22	26487981	26491070	ENST00000477945.1	0	+
chr22	26490772	26491832	ENST00000471799.1	0	+
chr22	26491224	26510707	ENST00000619735.4	0	-
chr22	26491239	26512473	ENST00000407690.6	0	-
chr22	26491926	26494619	ENST00000492137.1	0	-
chr22	26491926	26512377	ENST00000407431.5	0	-
chr22	26491926	26512496	ENST00000407148.5	0	-
chr22	26491985	26512499	ENST00000405938.5	0	-
chr22	26498702	26499391	ENST00000481357.1	0	-
chr22	26499413	26506332	ENST00000450493.1	0	-
chr22	26499536	26506105	ENST00000496523.5	0	-
chr22	26502035	26506712	ENST00000493698.1	0	-
chr22	26506672	26512505	ENST00000479489.5	0	-
chr22	26506737	26512478	ENST00000472918.5	0	-
chr22	26506773	26512460	ENST00000455080.5	0	-
chr22	26506873	26512478	ENST00000418876.5	0	-
chr22	26506877	26512496	ENST00000420242.5	0	-
chr22	26510144	26512489	ENST00000440258.1	0	-
chr22	26511688	26512495	ENST00000464449.1	0	-
chr22	26512536	26514568	ENST00000566814.1	0	+
chr22	26512554	26513912	ENST00000565764.1	0	+
chr22	26521995	26590132	ENST00000338754.9	0	-
chr22	26525745	26565368	ENST00000398110.6	0	-
chr22	26525745	26596717	ENST00000403880.5	0	-
chr22	26535894	26540871	ENST00000445720.1	0	-
chr22	26541205	26590125	ENST00000442495.5	0	-
chr22	26541270	26550653	ENST00000454778.5	0	-
chr22	26541438	26590125	ENST00000440953.5	0	-
chr22	26541484	26565726	ENST00000453117.1	0	-
chr22	26541489	26590125	ENST00000450022.1	0	-
chr22	26555211	26555323	ENST00000408833.1	0	-
chr22	26560525	26561088	ENST00000395823.2	0	+
chr22	26597027	26611453	ENST00000668614.1	0	+
chr22	26599277	26618027	ENST00000647684.1	0	-
chr22	26601948	26608329	ENST00000647569.1	0	-
chr22	26621962	26630669	ENST00000354760.4	0	+
chr22	26621982	26630641	ENST00000466315.1	0	+
chr22	26644888	26645336	ENST00000425548.1	0	-
chr22	26646410	26666225	ENST00000668051.1	0	+
chr22	26646420	26666225	ENST00000421867.6	0	+
chr22	26646438	26647382	ENST00000656640.1	0	+
chr22	26646451	26669655	ENST00000643270.1	0	+
chr22	26646470	26664333	ENST00000669142.1	0	+
chr22	26646473	26650519	ENST00000643002.1	0	+
chr22	26646639	26665639	ENST00000418918.5	0	+
chr22	26646814	26665190	ENST00000660931.1	0	+
chr22	26647210	26665737	ENST00000440347.5	0	+
chr22	26647223	26665909	ENST00000450203.5	0	+
chr22	26647227	26665809	ENST00000430483.5	0	+
chr22	26657220	26666549	ENST00000436238.6	0	+
chr22	26657225	26665872	ENST00000439738.5	0	+
chr22	26657255	26665769	ENST00000422403.5	0	+
chr22	26657266	26665815	ENST00000425476.5	0	+
chr22	26657279	26665918	ENST00000455640.5	0	+
chr22	26657279	26665949	ENST00000451141.1	0	+
chr22	26657283	26666023	ENST00000452429.5	0	+
chr22	26657457	26666180	ENST00000413665.5	0	+
chr22	26657467	26676475	ENST00000616469.4	0	+
chr22	26657519	26676475	ENST00000613780.4	0	+
chr22	26657519	26676475	ENST00000616213.4	0	+
chr22	26657519	26676475	ENST00000620145.4	0	+
chr22	26657520	26657655	ENST00000617016.1	0	+
chr22	26657587	26665214	ENST00000670887.1	0	+
chr22	26657587	26665852	ENST00000458302.5	0	+
chr22	26657588	26666038	ENST00000419237.1	0	+
chr22	26657610	26665679	ENST00000449717.1	0	+
chr22	26657613	26665793	ENST00000453023.1	0	+
chr22	26665360	26670599	ENST00000665574.1	0	+
chr22	26667692	26672654	ENST00000382641.1	0	-
chr22	26671401	26671550	ENST00000618919.1	0	+
chr22	26672163	26672248	ENST00000613562.1	0	+
chr22	26672746	26719282	ENST00000651474.1	0	+
chr22	26672766	26734455	ENST00000444388.5	0	+
chr22	26672776	26776848	ENST00000450963.5	0	+
chr22	26672790	26780100	ENST00000449017.6	0	+
chr22	26672804	26778635	ENST00000435162.5	0	+
chr22	26672804	26780207	ENST00000437071.5	0	+
chr22	26672813	26776882	ENST00000440816.5	0	+
chr22	26672855	26718392	ENST00000421253.1	0	+
chr22	26673253	26673630	ENST00000613656.1	0	+
chr22	26677661	26743965	ENST00000653953.1	0	+
chr22	26693692	26719686	ENST00000668496.1	0	+
chr22	26717251	26742838	ENST00000446336.1	0	+
chr22	26717368	26722172	ENST00000417083.1	0	+
chr22	26735073	26738240	ENST00000662474.1	0	+
chr22	26775522	26780100	ENST00000664301.1	0	+
chr22	26779186	26780893	ENST00000669899.1	0	+
chr22	26858633	26865786	ENST00000434868.1	0	+
chr22	26858667	26870212	ENST00000653531.1	0	+
chr22	26860525	26886182	ENST00000671424.1	0	+
chr22	26860547	26885691	ENST00000658323.1	0	+
chr22	26860552	26881803	ENST00000664041.1	0	+
chr22	26860552	26968763	ENST00000670559.1	0	+
chr22	26860567	26881431	ENST00000664057.1	0	+
chr22	26886908	26887548	ENST00000422915.1	0	-
chr22	26903291	26908113	ENST00000447149.5	0	+
chr22	26903339	26920935	ENST00000438113.2	0	+
chr22	27019368	27024550	ENST00000623027.1	0	+
chr22	27035886	27035991	ENST00000363573.1	0	+
chr22	27040196	27041494	ENST00000623814.1	0	+
chr22	27044000	27045315	ENST00000624792.1	0	+
chr22	27048143	27060518	ENST00000453934.1	0	-
chr22	27057512	27061247	ENST00000658755.1	0	-
chr22	27102765	27107019	ENST00000623947.1	0	+
chr22	27143477	27159878	ENST00000430468.1	0	+
chr22	27204210	27205126	ENST00000427360.1	0	+
chr22	27221348	27224727	ENST00000444114.5	0	-
chr22	27223294	27224651	ENST00000660027.1	0	-
chr22	27223296	27224657	ENST00000418271.1	0	-
chr22	27223296	27225134	ENST00000659538.1	0	-
chr22	27223298	27224478	ENST00000650737.1	0	-
chr22	27276239	27286349	ENST00000449126.1	0	+
chr22	27307482	27318538	ENST00000454387.1	0	-
chr22	27307776	27312015	ENST00000662296.1	0	+
chr22	27307801	27317456	ENST00000657960.1	0	+
chr22	27307801	27318501	ENST00000665304.1	0	+
chr22	27310650	27317456	ENST00000426467.1	0	+
chr22	27331633	27357627	ENST00000668323.1	0	+
chr22	27331694	27357622	ENST00000669277.1	0	+
chr22	27331786	27379265	ENST00000413244.1	0	+
chr22	27414461	27415205	ENST00000625043.1	0	-
chr22	27414492	27419713	ENST00000623572.1	0	-
chr22	27436291	27441774	ENST00000654206.1	0	+
chr22	27457246	27460060	ENST00000440477.1	0	-
chr22	27563838	27573573	ENST00000415655.1	0	-
chr22	27676558	27714970	ENST00000440255.1	0	-
chr22	27707579	27709931	ENST00000422833.1	0	+
chr22	27716479	27721677	ENST00000417957.1	0	+
chr22	27748276	27801756	ENST00000302326.5	0	-
chr22	27750063	27796896	ENST00000424656.1	0	-
chr22	27750677	27791883	ENST00000497225.1	0	-
chr22	27851668	27919256	ENST00000335272.10	0	-
chr22	27851669	27919255	ENST00000320996.14	0	-
chr22	27851669	27919306	ENST00000455418.7	0	-
chr22	27853578	27919199	ENST00000634017.1	0	-
chr22	27854905	27898073	ENST00000477861.1	0	-
chr22	27858430	27860615	ENST00000493007.1	0	-
chr22	27860195	27920134	ENST00000415296.5	0	-
chr22	27873774	27919214	ENST00000436663.1	0	-
chr22	27894160	27896702	ENST00000465179.1	0	-
chr22	27896899	27919256	ENST00000460566.5	0	-
chr22	27910447	27919244	ENST00000471825.1	0	-
chr22	27919375	27993292	ENST00000430853.5	0	+
chr22	27919383	27919545	ENST00000614690.1	0	+
chr22	27919387	27924916	ENST00000662329.1	0	+
chr22	27919387	28002595	ENST00000417497.6	0	+
chr22	27919387	28002612	ENST00000434221.6	0	+
chr22	27919391	28002588	ENST00000424161.6	0	+
chr22	27919400	27986392	ENST00000419253.1	0	+
chr22	27919420	28002679	ENST00000454741.5	0	+
chr22	27919422	28002588	ENST00000453632.6	0	+
chr22	27919422	28002640	ENST00000665376.1	0	+
chr22	27919422	28002641	ENST00000659843.1	0	+
chr22	27919425	27924963	ENST00000437713.6	0	+
chr22	27919428	28002588	ENST00000662682.1	0	+
chr22	27919432	28002588	ENST00000655332.1	0	+
chr22	27919436	27922570	ENST00000436996.1	0	+
chr22	27919437	28002636	ENST00000665505.1	0	+
chr22	27919439	27986379	ENST00000454996.6	0	+
chr22	27919456	27924963	ENST00000428818.5	0	+
chr22	27919456	28002640	ENST00000435348.6	0	+
chr22	27919465	28002666	ENST00000664946.1	0	+
chr22	27919467	27923011	ENST00000417381.6	0	+
chr22	27919469	27973089	ENST00000654553.1	0	+
chr22	27919471	27922087	ENST00000428858.1	0	+
chr22	27919483	27924950	ENST00000670661.1	0	+
chr22	27919483	28002588	ENST00000669227.1	0	+
chr22	27919490	27999476	ENST00000452612.5	0	+
chr22	27919497	27924714	ENST00000670585.1	0	+
chr22	27919498	27986392	ENST00000662424.1	0	+
chr22	27919517	27986229	ENST00000425112.1	0	+
chr22	27920524	27920612	ENST00000582434.1	0	-
chr22	27920525	27920611	ENST00000636243.1	0	+
chr22	27922021	27922178	ENST00000611117.1	0	+
chr22	27940017	27986176	ENST00000654619.1	0	+
chr22	27978013	28306645	ENST00000612946.4	0	-
chr22	27978013	28679865	ENST00000397906.6	0	-
chr22	27983598	27996239	ENST00000431039.1	0	-
chr22	27983751	27990076	ENST00000480563.1	0	-
chr22	27991469	28002588	ENST00000655196.1	0	+
chr22	27992468	28008581	ENST00000428584.5	0	+
chr22	27992611	27996424	ENST00000469670.1	0	-
chr22	27992927	27997423	ENST00000540665.5	0	+
chr22	27993112	28000723	ENST00000433317.1	0	+
chr22	27994473	27994654	ENST00000615579.1	0	+
chr22	27994559	28002602	ENST00000665529.1	0	+
chr22	27997410	28002588	ENST00000663167.1	0	+
chr22	27999394	28002612	ENST00000654312.1	0	+
chr22	28002483	28002595	ENST00000619378.1	0	+
chr22	28028814	28094135	ENST00000442232.1	0	-
chr22	28056152	28056453	ENST00000466709.3	0	-
chr22	28232755	28232823	ENST00000364339.1	0	-
chr22	28296213	28443073	ENST00000490475.1	0	-
chr22	28303753	28303850	ENST00000362927.1	0	+
chr22	28459868	28459948	ENST00000619803.1	0	+
chr22	28629427	28679812	ENST00000468807.1	0	-
chr22	28642978	28643270	ENST00000579233.2	0	-
chr22	28687742	28711931	ENST00000434810.5	0	-
chr22	28687742	28711955	ENST00000456369.5	0	-
chr22	28687742	28741820	ENST00000404276.6	0	-
chr22	28687742	28741834	ENST00000348295.7	0	-
chr22	28687742	28742422	ENST00000416671.5	0	-
chr22	28687743	28712495	ENST00000648295.1	0	-
chr22	28687748	28689410	ENST00000472807.1	0	-
chr22	28687750	28741806	ENST00000649563.1	0	-
chr22	28687761	28741800	ENST00000405598.5	0	-
chr22	28687762	28741838	ENST00000382580.6	0	-
chr22	28687765	28741806	ENST00000650281.1	0	-
chr22	28687886	28734721	ENST00000417588.5	0	-
chr22	28687886	28734721	ENST00000433728.5	0	-
chr22	28687886	28734721	ENST00000448511.5	0	-
chr22	28687896	28734721	ENST00000402731.5	0	-
chr22	28687896	28734721	ENST00000403642.5	0	-
chr22	28696900	28734727	ENST00000447421.5	0	-
chr22	28696901	28741806	ENST00000433028.6	0	-
chr22	28696922	28712146	ENST00000464581.5	0	-
chr22	28696943	28712257	ENST00000491919.5	0	-
chr22	28696967	28741811	ENST00000425190.6	0	-
chr22	28699763	28725248	ENST00000439346.5	0	-
chr22	28711908	28741783	ENST00000439200.5	0	-
chr22	28721599	28741834	ENST00000382565.5	0	-
chr22	28724964	28730501	ENST00000454252.1	0	-
chr22	28725040	28741820	ENST00000650233.1	0	-
chr22	28725082	28741802	ENST00000398017.3	0	-
chr22	28742038	28757510	ENST00000216027.8	0	+
chr22	28742041	28757510	ENST00000450178.5	0	+
chr22	28742054	28757515	ENST00000398941.6	0	+
chr22	28742060	28757213	ENST00000420442.5	0	+
chr22	28742095	28745867	ENST00000651322.1	0	+
chr22	28742150	28745543	ENST00000485599.5	0	+
chr22	28742678	28757210	ENST00000483861.5	0	+
chr22	28742700	28757510	ENST00000495977.1	0	+
chr22	28772673	28789301	ENST00000421503.6	0	+
chr22	28772673	28789301	ENST00000448492.6	0	+
chr22	28772694	28789295	ENST00000249064.9	0	+
chr22	28772703	28786177	ENST00000453543.5	0	+
chr22	28772727	28781103	ENST00000444523.1	0	+
chr22	28773240	28786093	ENST00000432510.1	0	+
chr22	28794554	28800597	ENST00000216037.10	0	-
chr22	28794556	28800558	ENST00000403532.7	0	-
chr22	28794558	28797045	ENST00000484256.1	0	-
chr22	28794559	28800572	ENST00000611155.4	0	-
chr22	28794588	28800133	ENST00000405219.7	0	-
chr22	28795174	28800524	ENST00000344347.5	0	-
chr22	28798905	28800569	ENST00000482720.1	0	-
chr22	28800682	28848559	ENST00000458080.1	0	+
chr22	28800703	28839033	ENST00000418292.1	0	+
chr22	28800708	28801224	ENST00000585003.1	0	+
chr22	28801891	28822215	ENST00000451486.5	0	+
chr22	28814913	28815662	ENST00000607919.1	0	+
chr22	28818330	28822215	ENST00000422972.1	0	+
chr22	28835892	28836137	ENST00000447099.1	0	-
chr22	28883571	29057488	ENST00000544604.7	0	+
chr22	28917388	29053685	ENST00000402174.5	0	+
chr22	28987051	29053965	ENST00000406323.3	0	+
chr22	28992720	29018620	ENST00000412798.1	0	+
chr22	29024998	29031476	ENST00000325660.3	0	-
chr22	29058671	29061844	ENST00000216071.4	0	-
chr22	29073077	29146820	ENST00000400335.8	0	+
chr22	29073117	29168333	ENST00000327813.9	0	+
chr22	29073130	29142112	ENST00000407188.5	0	+
chr22	29099040	29111683	ENST00000456740.1	0	-
chr22	29121440	29138771	ENST00000453585.1	0	+
chr22	29125196	29138642	ENST00000474001.1	0	+
chr22	29134013	29134120	ENST00000364001.1	0	+
chr22	29142029	29154769	ENST00000479755.1	0	+
chr22	29191696	29191808	ENST00000516143.1	0	-
chr22	29205895	29259597	ENST00000334018.11	0	+
chr22	29205905	29233428	ENST00000429226.5	0	+
chr22	29205911	29259597	ENST00000404755.7	0	+
chr22	29205911	29259597	ENST00000404820.7	0	+
chr22	29205922	29231615	ENST00000430127.1	0	+
chr22	29205958	29232345	ENST00000435427.5	0	+
chr22	29215576	29233667	ENST00000484039.5	0	+
chr22	29231017	29243489	ENST00000433143.1	0	+
chr22	29231681	29233862	ENST00000473933.1	0	+
chr22	29232335	29255612	ENST00000488820.6	0	+
chr22	29233345	29254805	ENST00000487477.1	0	+
chr22	29259871	29267981	ENST00000216085.12	0	-
chr22	29259903	29267945	ENST00000413137.6	0	-
chr22	29260498	29264401	ENST00000496342.1	0	-
chr22	29260811	29268209	ENST00000414672.5	0	-
chr22	29260888	29262037	ENST00000433125.1	0	+
chr22	29263956	29267728	ENST00000406335.1	0	-
chr22	29265034	29267960	ENST00000493894.1	0	-
chr22	29265445	29267921	ENST00000488106.1	0	-
chr22	29268008	29278041	ENST00000444626.5	0	+
chr22	29268008	29300522	ENST00000332050.10	0	+
chr22	29268008	29300524	ENST00000629659.2	0	+
chr22	29268017	29300525	ENST00000397938.6	0	+
chr22	29268252	29282424	ENST00000436425.5	0	+
chr22	29268252	29282456	ENST00000447973.5	0	+
chr22	29268272	29274453	ENST00000485037.5	0	+
chr22	29268272	29300336	ENST00000406548.5	0	+
chr22	29268281	29288687	ENST00000437155.6	0	+
chr22	29268289	29287106	ENST00000415761.5	0	+
chr22	29268290	29300525	ENST00000331029.11	0	+
chr22	29268312	29291019	ENST00000483415.5	0	+
chr22	29268315	29300343	ENST00000414183.6	0	+
chr22	29268317	29290684	ENST00000333395.10	0	+
chr22	29268325	29288679	ENST00000455726.5	0	+
chr22	29268325	29300336	ENST00000332035.10	0	+
chr22	29269222	29273792	ENST00000493426.1	0	+
chr22	29275628	29300525	ENST00000479135.5	0	+
chr22	29290829	29300525	ENST00000469669.5	0	+
chr22	29291928	29300525	ENST00000483629.5	0	+
chr22	29292158	29300525	ENST00000360091.3	0	+
chr22	29295525	29300336	ENST00000610553.1	0	+
chr22	29306581	29312785	ENST00000616432.4	0	+
chr22	29306617	29308520	ENST00000416823.1	0	+
chr22	29306649	29308535	ENST00000428622.1	0	+
chr22	29307022	29312785	ENST00000406549.7	0	+
chr22	29307040	29312094	ENST00000610653.4	0	+
chr22	29307051	29312785	ENST00000621062.4	0	+
chr22	29307057	29307799	ENST00000487341.1	0	+
chr22	29307057	29312785	ENST00000611648.2	0	+
chr22	29307057	29312785	ENST00000618518.3	0	+
chr22	29310278	29311107	ENST00000491016.1	0	+
chr22	29312932	29315692	ENST00000401450.3	0	-
chr22	29312932	29315697	ENST00000216101.7	0	-
chr22	29313502	29319679	ENST00000608559.1	0	-
chr22	29313534	29314306	ENST00000474590.1	0	-
chr22	29316743	29317220	ENST00000608014.1	0	-
chr22	29327679	29332321	ENST00000482818.1	0	-
chr22	29327681	29388583	ENST00000357586.6	0	-
chr22	29327681	29388583	ENST00000432560.6	0	-
chr22	29327864	29367275	ENST00000405198.5	0	-
chr22	29327864	29388583	ENST00000317368.11	0	-
chr22	29328737	29367275	ENST00000402502.5	0	-
chr22	29328820	29423179	ENST00000415447.5	0	-
chr22	29331456	29340837	ENST00000472057.1	0	-
chr22	29333157	29333267	ENST00000625572.1	0	-
chr22	29333162	29333258	ENST00000459538.1	0	-
chr22	29341562	29370659	ENST00000421126.5	0	-
chr22	29349272	29351747	ENST00000415756.1	0	-
chr22	29359672	29388529	ENST00000473606.1	0	-
chr22	29387908	29417757	ENST00000650462.1	0	+
chr22	29406802	29410767	ENST00000649455.1	0	+
chr22	29419154	29423193	ENST00000439655.1	0	-
chr22	29432943	29435005	ENST00000616792.1	0	+
chr22	29436533	29478175	ENST00000461286.4	0	-
chr22	29438421	29478136	ENST00000248980.9	0	-
chr22	29438582	29442455	ENST00000354373.2	0	+
chr22	29440766	29478138	ENST00000661328.1	0	-
chr22	29440766	29478868	ENST00000658349.1	0	-
chr22	29440791	29478602	ENST00000657516.1	0	-
chr22	29441339	29441678	ENST00000539579.2	0	-
chr22	29470264	29478156	ENST00000669415.1	0	-
chr22	29471940	29478070	ENST00000419368.1	0	-
chr22	29480217	29491390	ENST00000310624.7	0	+
chr22	29480219	29481113	ENST00000634116.1	0	-
chr22	29505878	29553782	ENST00000490103.5	0	-
chr22	29508167	29553655	ENST00000397871.5	0	-
chr22	29508167	29553655	ENST00000397872.5	0	-
chr22	29508326	29554254	ENST00000442555.5	0	-
chr22	29508328	29511442	ENST00000472164.1	0	-
chr22	29508330	29549157	ENST00000358079.8	0	-
chr22	29508357	29553766	ENST00000397873.6	0	-
chr22	29513888	29535390	ENST00000411969.1	0	-
chr22	29519005	29536738	ENST00000484924.5	0	-
chr22	29525837	29542888	ENST00000443089.5	0	-
chr22	29528382	29539469	ENST00000492707.5	0	-
chr22	29531552	29536738	ENST00000488052.1	0	-
chr22	29534537	29549144	ENST00000475187.5	0	-
chr22	29536666	29553655	ENST00000440771.5	0	-
chr22	29539337	29545144	ENST00000455450.5	0	-
chr22	29539443	29553680	ENST00000428374.5	0	-
chr22	29542953	29553741	ENST00000414902.5	0	-
chr22	29543502	29555216	ENST00000418021.1	0	-
chr22	29554807	29581113	ENST00000216121.12	0	-
chr22	29560807	29581327	ENST00000437094.5	0	-
chr22	29561169	29581074	ENST00000415100.5	0	-
chr22	29561629	29581093	ENST00000455496.1	0	-
chr22	29569865	29581100	ENST00000496944.1	0	-
chr22	29579756	29581026	ENST00000494966.1	0	-
chr22	29603555	29698594	ENST00000413209.6	0	+
chr22	29603567	29694945	ENST00000672805.1	0	+
chr22	29603587	29684162	ENST00000403435.5	0	+
chr22	29603602	29697525	ENST00000672461.1	0	+
chr22	29603616	29698594	ENST00000361452.8	0	+
chr22	29603632	29683915	ENST00000403999.7	0	+
chr22	29603632	29698598	ENST00000338641.9	0	+
chr22	29603632	29698598	ENST00000672896.1	0	+
chr22	29603726	29683915	ENST00000334961.11	0	+
chr22	29603746	29694811	ENST00000353887.8	0	+
chr22	29603948	29694945	ENST00000432151.5	0	+
chr22	29603998	29694811	ENST00000361676.8	0	+
chr22	29603998	29694811	ENST00000397789.3	0	+
chr22	29604085	29695058	ENST00000673312.1	0	+
chr22	29608899	29609501	ENST00000437636.1	0	+
chr22	29654656	29695059	ENST00000361166.9	0	+
chr22	29705255	29719859	ENST00000416352.1	0	-
chr22	29711812	29719748	ENST00000451180.1	0	-
chr22	29711819	29719714	ENST00000420180.1	0	-
chr22	29720002	29731833	ENST00000216144.4	0	+
chr22	29730955	29767007	ENST00000344318.4	0	-
chr22	29746166	29767011	ENST00000489010.1	0	-
chr22	29758757	29758859	ENST00000364106.1	0	+
chr22	29767090	29768096	ENST00000668996.1	0	-
chr22	29767368	29770413	ENST00000330029.6	0	+
chr22	29767373	29770027	ENST00000401406.3	0	+
chr22	29788608	29838304	ENST00000542393.5	0	-
chr22	29788610	29804824	ENST00000487486.5	0	-
chr22	29788610	29838274	ENST00000307790.8	0	-
chr22	29788612	29813542	ENST00000472433.5	0	-
chr22	29788615	29834550	ENST00000458594.5	0	-
chr22	29788641	29838276	ENST00000397771.6	0	-
chr22	29793576	29802102	ENST00000411532.1	0	-
chr22	29793586	29799302	ENST00000483380.1	0	-
chr22	29806213	29816740	ENST00000478812.5	0	-
chr22	29806835	29810288	ENST00000464287.1	0	-
chr22	29813470	29838272	ENST00000449900.6	0	-
chr22	29814656	29834550	ENST00000431535.5	0	-
chr22	29815205	29838274	ENST00000495967.5	0	-
chr22	29821891	29832342	ENST00000412689.5	0	-
chr22	29822141	29838228	ENST00000465667.6	0	-
chr22	29822351	29825358	ENST00000463203.1	0	-
chr22	29825086	29838274	ENST00000495681.5	0	-
chr22	29825102	29832580	ENST00000462454.1	0	-
chr22	29825107	29838261	ENST00000453160.5	0	-
chr22	29825160	29838282	ENST00000460313.1	0	-
chr22	29825213	29826131	ENST00000477074.1	0	-
chr22	29825693	29838274	ENST00000454854.5	0	-
chr22	29834085	29838274	ENST00000492821.1	0	-
chr22	29883154	30027880	ENST00000401950.6	0	+
chr22	29883168	30030866	ENST00000333027.7	0	+
chr22	29883178	29978968	ENST00000495098.5	0	+
chr22	29883196	29991628	ENST00000445401.5	0	+
chr22	29883197	30027858	ENST00000323630.9	0	+
chr22	29883212	30026495	ENST00000351488.7	0	+
chr22	29883268	29991669	ENST00000415511.1	0	+
chr22	29899586	29899693	ENST00000384724.1	0	-
chr22	29971050	30026037	ENST00000406629.1	0	+
chr22	29978086	29979037	ENST00000480776.1	0	+
chr22	29978949	30028236	ENST00000624945.1	0	-
chr22	30003894	30007975	ENST00000487907.1	0	+
chr22	30007048	30007113	ENST00000621576.1	0	+
chr22	30008741	30080230	ENST00000429350.5	0	-
chr22	30017682	30019651	ENST00000492087.1	0	+
chr22	30018631	30080480	ENST00000453743.6	0	-
chr22	30022462	30025963	ENST00000491251.1	0	+
chr22	30046275	30047193	ENST00000453616.1	0	-
chr22	30047624	30080438	ENST00000432568.1	0	-
chr22	30080173	30177075	ENST00000336726.10	0	+
chr22	30080471	30122033	ENST00000450612.5	0	+
chr22	30081010	30177073	ENST00000403975.1	0	+
chr22	30093957	30104577	ENST00000491605.1	0	+
chr22	30105093	30105408	ENST00000423857.1	0	-
chr22	30136672	30137681	ENST00000451219.1	0	-
chr22	30182817	30207195	ENST00000432360.2	0	-
chr22	30239193	30240538	ENST00000447565.1	0	+
chr22	30240452	30246759	ENST00000249075.4	0	-
chr22	30243532	30246739	ENST00000403987.3	0	-
chr22	30246204	30246998	ENST00000608354.1	0	+
chr22	30262828	30266851	ENST00000215781.3	0	-
chr22	30263867	30265818	ENST00000403389.1	0	-
chr22	30264074	30266839	ENST00000403463.1	0	-
chr22	30275214	30276951	ENST00000623328.1	0	-
chr22	30285116	30287390	ENST00000459785.1	0	-
chr22	30285116	30289627	ENST00000407689.7	0	-
chr22	30285145	30289607	ENST00000404953.7	0	-
chr22	30285237	30295843	ENST00000330168.9	0	-
chr22	30285237	30299476	ENST00000434987.5	0	-
chr22	30285244	30299482	ENST00000434291.5	0	-
chr22	30285246	30299482	ENST00000418047.5	0	-
chr22	30285246	30299482	ENST00000447976.1	0	-
chr22	30285496	30289569	ENST00000440839.5	0	-
chr22	30285831	30289622	ENST00000421236.5	0	-
chr22	30286273	30289531	ENST00000497605.6	0	-
chr22	30286346	30289505	ENST00000415484.5	0	-
chr22	30286346	30289540	ENST00000425691.5	0	-
chr22	30286541	30289531	ENST00000463795.5	0	-
chr22	30286671	30289546	ENST00000471480.6	0	-
chr22	30287084	30289179	ENST00000492159.5	0	-
chr22	30287166	30289496	ENST00000440704.5	0	-
chr22	30287197	30287950	ENST00000464854.1	0	-
chr22	30287438	30289546	ENST00000498572.1	0	-
chr22	30291989	30327046	ENST00000215790.11	0	-
chr22	30291990	30297480	ENST00000467596.5	0	-
chr22	30291990	30299796	ENST00000462073.5	0	-
chr22	30291992	30326901	ENST00000403477.7	0	-
chr22	30292007	30307890	ENST00000403362.5	0	-
chr22	30292009	30318835	ENST00000433426.5	0	-
chr22	30293398	30294849	ENST00000466666.5	0	-
chr22	30293920	30326947	ENST00000437122.1	0	-
chr22	30294088	30305631	ENST00000393906.2	0	-
chr22	30300470	30326901	ENST00000490449.1	0	-
chr22	30331987	30356894	ENST00000215793.13	0	-
chr22	30337800	30356884	ENST00000411423.1	0	-
chr22	30337816	30341850	ENST00000444440.1	0	-
chr22	30338839	30340471	ENST00000485618.1	0	-
chr22	30338960	30340779	ENST00000498259.1	0	-
chr22	30342319	30356919	ENST00000447376.1	0	-
chr22	30342546	30345154	ENST00000471037.1	0	-
chr22	30344909	30346489	ENST00000471342.1	0	-
chr22	30346220	30356891	ENST00000463818.1	0	-
chr22	30352544	30356894	ENST00000496189.1	0	-
chr22	30356634	30367096	ENST00000399824.6	0	+
chr22	30356634	30376829	ENST00000405659.5	0	+
chr22	30356637	30376827	ENST00000338306.7	0	+
chr22	30357000	30370494	ENST00000445005.5	0	+
chr22	30357006	30370508	ENST00000430839.5	0	+
chr22	30357113	30378658	ENST00000475975.5	0	+
chr22	30368810	30379780	ENST00000332468.5	0	-
chr22	30373691	30376827	ENST00000482413.1	0	+
chr22	30374323	30374878	ENST00000619645.1	0	-
chr22	30377819	30379780	ENST00000630264.1	0	-
chr22	30377819	30379780	ENST00000631046.2	0	-
chr22	30377845	30379774	ENST00000421022.1	0	-
chr22	30378820	30387411	ENST00000382363.8	0	-
chr22	30378982	30387125	ENST00000215798.10	0	-
chr22	30379589	30384834	ENST00000463319.1	0	-
chr22	30384369	30386886	ENST00000473077.1	0	-
chr22	30385906	30421771	ENST00000431544.2	0	-
chr22	30396940	30415865	ENST00000428195.5	0	+
chr22	30396991	30423838	ENST00000617837.4	0	+
chr22	30396994	30423840	ENST00000619483.4	0	+
chr22	30397017	30409136	ENST00000416523.5	0	+
chr22	30397017	30409249	ENST00000437022.5	0	+
chr22	30397017	30409477	ENST00000452649.1	0	+
chr22	30397017	30425303	ENST00000615189.5	0	+
chr22	30397036	30416975	ENST00000405717.7	0	+
chr22	30397044	30422601	ENST00000402592.7	0	+
chr22	30397050	30409282	ENST00000459728.5	0	+
chr22	30397537	30407542	ENST00000429917.5	0	+
chr22	30398576	30407477	ENST00000415072.1	0	+
chr22	30399683	30400467	ENST00000484486.5	0	+
chr22	30399683	30400467	ENST00000485482.5	0	+
chr22	30399683	30416673	ENST00000464335.5	0	+
chr22	30409021	30416007	ENST00000483116.5	0	+
chr22	30409114	30423838	ENST00000620251.1	0	+
chr22	30409254	30428990	ENST00000454552.5	0	+
chr22	30409261	30428987	ENST00000439838.5	0	+
chr22	30409262	30428990	ENST00000439023.3	0	+
chr22	30420511	30420912	ENST00000608677.1	0	+
chr22	30421044	30421148	ENST00000410983.1	0	+
chr22	30421205	30421536	ENST00000608952.1	0	-
chr22	30425622	30427151	ENST00000629597.2	0	+
chr22	30425767	30429054	ENST00000266263.10	0	+
chr22	30425774	30427192	ENST00000614920.1	0	+
chr22	30425775	30429053	ENST00000355143.8	0	+
chr22	30425779	30428643	ENST00000412752.5	0	+
chr22	30425816	30428951	ENST00000407550.3	0	+
chr22	30435543	30436247	ENST00000607893.1	0	+
chr22	30435543	30436244	ENST00000366413.5	0	+
chr22	30439836	30440318	ENST00000426397.1	0	+
chr22	30447958	30472017	ENST00000403066.5	0	-
chr22	30459228	30472049	ENST00000215812.8	0	-
chr22	30459229	30470585	ENST00000540910.5	0	-
chr22	30459505	30471986	ENST00000401751.5	0	-
chr22	30459505	30471986	ENST00000402286.5	0	-
chr22	30459505	30471986	ENST00000434642.5	0	-
chr22	30461669	30470577	ENST00000435069.1	0	-
chr22	30475363	30492616	ENST00000610936.4	0	+
chr22	30481310	30481911	ENST00000453285.1	0	-
chr22	30483138	30486366	ENST00000628773.1	0	+
chr22	30483353	30492768	ENST00000610156.1	0	+
chr22	30483412	30492788	ENST00000610737.1	0	+
chr22	30488901	30505695	ENST00000255858.12	0	-
chr22	30488912	30505711	ENST00000320982.3	0	-
chr22	30488912	30505711	ENST00000321205.9	0	-
chr22	30489395	30505650	ENST00000381982.3	0	-
chr22	30491801	30492804	ENST00000442126.1	0	+
chr22	30522798	30546682	ENST00000402034.6	0	-
chr22	30524645	30529163	ENST00000437871.1	0	-
chr22	30542535	30544305	ENST00000448467.1	0	+
chr22	30554634	30560776	ENST00000406955.5	0	-
chr22	30554634	30565133	ENST00000402321.5	0	-
chr22	30554634	30574578	ENST00000402369.5	0	-
chr22	30554634	30574665	ENST00000406361.6	0	-
chr22	30554636	30557586	ENST00000338911.6	0	-
chr22	30554636	30564889	ENST00000401975.5	0	-
chr22	30555387	30566161	ENST00000441967.5	0	-
chr22	30555431	30574511	ENST00000431313.5	0	-
chr22	30555497	30560776	ENST00000452827.5	0	-
chr22	30555626	30560759	ENST00000437282.5	0	-
chr22	30555713	30572864	ENST00000416358.5	0	-
chr22	30555736	30560773	ENST00000427899.5	0	-
chr22	30555777	30564914	ENST00000423299.5	0	-
chr22	30555793	30572830	ENST00000443136.5	0	-
chr22	30555889	30572868	ENST00000423371.5	0	-
chr22	30555916	30564901	ENST00000428682.5	0	-
chr22	30555923	30557730	ENST00000453479.1	0	-
chr22	30555928	30572834	ENST00000426220.5	0	-
chr22	30555937	30560759	ENST00000447224.5	0	-
chr22	30555969	30572869	ENST00000411821.5	0	-
chr22	30555997	30572858	ENST00000445645.5	0	-
chr22	30556009	30572869	ENST00000448604.1	0	-
chr22	30576624	30591910	ENST00000354694.12	0	-
chr22	30576627	30607083	ENST00000402281.5	0	-
chr22	30576629	30579923	ENST00000441668.5	0	-
chr22	30576631	30606988	ENST00000405677.5	0	-
chr22	30576635	30580074	ENST00000488719.1	0	-
chr22	30576664	30591880	ENST00000402284.7	0	-
chr22	30576834	30591858	ENST00000335214.8	0	-
chr22	30581333	30591866	ENST00000406208.7	0	-
chr22	30584226	30588023	ENST00000477762.5	0	-
chr22	30585223	30591878	ENST00000433575.5	0	-
chr22	30586997	30591890	ENST00000466614.1	0	-
chr22	30598308	30606687	ENST00000432130.1	0	+
chr22	30603777	30606869	ENST00000467368.1	0	-
chr22	30603779	30607058	ENST00000492986.1	0	-
chr22	30607002	30611114	ENST00000423350.1	0	+
chr22	30607082	30627046	ENST00000450638.5	0	+
chr22	30607151	30627005	ENST00000405742.7	0	+
chr22	30607173	30627271	ENST00000215838.8	0	+
chr22	30607203	30627060	ENST00000407817.3	0	+
chr22	30615691	30617717	ENST00000471659.1	0	+
chr22	30615715	30623296	ENST00000493542.1	0	+
chr22	30635780	30647893	ENST00000343605.5	0	+
chr22	30636322	30663299	ENST00000406566.1	0	+
chr22	30636522	30669016	ENST00000451479.1	0	+
chr22	30652050	30663506	ENST00000430175.5	0	-
chr22	30652050	30667843	ENST00000404885.5	0	-
chr22	30652050	30667860	ENST00000407308.1	0	-
chr22	30652095	30667887	ENST00000461301.1	0	-
chr22	30653876	30654814	ENST00000510957.1	0	-
chr22	30654145	30667809	ENST00000377087.3	0	-
chr22	30654183	30663457	ENST00000412865.1	0	-
chr22	30661444	30667855	ENST00000403268.1	0	-
chr22	30662030	30667890	ENST00000334679.3	0	-
chr22	30663514	30667885	ENST00000342474.4	0	-
chr22	30663970	30667353	ENST00000442752.1	0	-
chr22	30664960	30674134	ENST00000673554.1	0	+
chr22	30693781	30906554	ENST00000438716.3	0	+
chr22	30694075	30907632	ENST00000407373.5	0	+
chr22	30694205	30907824	ENST00000403222.7	0	+
chr22	30694873	30907813	ENST00000332585.11	0	+
chr22	30694888	30906406	ENST00000446658.6	0	+
chr22	30731556	30731641	ENST00000580767.3	0	+
chr22	30764252	30870613	ENST00000477355.5	0	+
chr22	30764252	30887491	ENST00000445781.5	0	+
chr22	30764252	30906772	ENST00000401475.5	0	+
chr22	30764335	30844687	ENST00000464341.1	0	+
chr22	30770659	30872523	ENST00000496367.1	0	+
chr22	30803085	30889635	ENST00000424224.5	0	+
chr22	30822515	30907824	ENST00000437268.6	0	+
chr22	30881680	30889617	ENST00000454145.5	0	+
chr22	30881680	30893135	ENST00000453621.5	0	+
chr22	30881680	30907623	ENST00000431368.5	0	+
chr22	30881680	30907824	ENST00000535268.5	0	+
chr22	30887425	30907643	ENST00000452656.5	0	+
chr22	30890948	30894261	ENST00000496575.1	0	+
chr22	30893462	30894463	ENST00000433183.2	0	+
chr22	30902218	30902781	ENST00000424380.1	0	+
chr22	30922307	30926550	ENST00000432624.2	0	+
chr22	30922313	30926586	ENST00000668983.1	0	+
chr22	30922344	30926654	ENST00000422995.2	0	+
chr22	30922369	30925709	ENST00000655707.1	0	+
chr22	30922409	30925192	ENST00000609557.1	0	+
chr22	30922448	30932449	ENST00000441558.1	0	+
chr22	30925129	30968774	ENST00000397641.8	0	-
chr22	30926610	30928228	ENST00000429468.5	0	-
chr22	30926610	30932894	ENST00000445980.5	0	-
chr22	30926610	30968201	ENST00000215862.8	0	-
chr22	30936577	30942251	ENST00000469915.1	0	-
chr22	30941899	30958884	ENST00000476152.2	0	-
chr22	30969244	30971483	ENST00000646021.1	0	+
chr22	30969268	30978843	ENST00000646496.1	0	+
chr22	30969269	30979395	ENST00000644773.1	0	+
chr22	30969271	30978578	ENST00000569384.2	0	+
chr22	30969277	30978831	ENST00000643920.1	0	+
chr22	30969294	30976022	ENST00000602971.2	0	+
chr22	30969471	30978848	ENST00000519077.4	0	+
chr22	30969852	30975997	ENST00000643280.1	0	+
chr22	30969931	30974103	ENST00000643877.1	0	+
chr22	30969958	30978824	ENST00000643071.1	0	+
chr22	30970093	30976058	ENST00000540687.6	0	+
chr22	30970181	30978847	ENST00000644027.1	0	+
chr22	30970301	30978819	ENST00000647354.1	0	+
chr22	30970689	30978823	ENST00000643553.1	0	+
chr22	30970757	30976039	ENST00000569149.2	0	+
chr22	30971004	30971149	ENST00000610654.1	0	+
chr22	30971063	30976114	ENST00000563812.2	0	+
chr22	30971196	30973372	ENST00000566220.2	0	+
chr22	30971295	30971382	ENST00000614234.1	0	+
chr22	30971366	30976063	ENST00000521091.6	0	+
chr22	30971438	30978722	ENST00000643077.1	0	+
chr22	30972262	30976063	ENST00000602393.1	0	+
chr22	30972856	30973093	ENST00000616187.1	0	+
chr22	30973416	30973597	ENST00000619464.1	0	+
chr22	30976514	30978848	ENST00000602847.1	0	-
chr22	30977515	30977858	ENST00000602955.1	0	-
chr22	31051629	31068327	ENST00000623789.1	0	+
chr22	31059988	31060285	ENST00000488974.3	0	+
chr22	31064104	31088025	ENST00000432777.5	0	+
chr22	31064117	31088024	ENST00000422839.5	0	+
chr22	31081317	31088574	ENST00000426927.5	0	+
chr22	31081317	31088591	ENST00000482444.5	0	+
chr22	31081317	31088605	ENST00000440425.5	0	+
chr22	31081317	31088724	ENST00000475548.5	0	+
chr22	31081318	31088253	ENST00000497697.5	0	+
chr22	31081318	31104623	ENST00000358743.5	0	+
chr22	31081318	31104624	ENST00000333137.11	0	+
chr22	31081318	31104624	ENST00000347557.6	0	+
chr22	31081319	31104623	ENST00000489337.5	0	+
chr22	31082155	31083565	ENST00000609017.1	0	-
chr22	31082484	31088880	ENST00000438223.5	0	+
chr22	31082859	31104623	ENST00000619644.4	0	+
chr22	31084995	31104623	ENST00000612341.4	0	+
chr22	31085093	31089785	ENST00000416786.5	0	+
chr22	31087877	31089725	ENST00000431481.1	0	+
chr22	31088101	31101783	ENST00000460658.5	0	+
chr22	31089655	31091027	ENST00000466272.1	0	+
chr22	31092467	31099173	ENST00000455608.5	0	+
chr22	31093357	31104623	ENST00000404574.5	0	+
chr22	31093367	31104623	ENST00000493335.5	0	+
chr22	31095559	31104624	ENST00000624247.1	0	+
chr22	31099661	31104623	ENST00000472911.1	0	+
chr22	31101875	31104757	ENST00000504335.1	0	+
chr22	31104771	31120069	ENST00000402395.5	0	-
chr22	31104776	31107533	ENST00000490967.5	0	-
chr22	31104776	31107568	ENST00000400299.6	0	-
chr22	31104778	31107568	ENST00000611680.1	0	-
chr22	31104925	31107528	ENST00000465536.5	0	-
chr22	31105000	31107553	ENST00000491958.5	0	-
chr22	31105012	31105764	ENST00000495533.1	0	-
chr22	31105024	31107541	ENST00000460642.5	0	-
chr22	31105113	31107466	ENST00000469262.1	0	-
chr22	31105438	31107556	ENST00000465447.1	0	-
chr22	31113016	31113396	ENST00000420779.2	0	+
chr22	31122730	31124881	ENST00000463528.5	0	+
chr22	31122922	31134696	ENST00000412277.6	0	+
chr22	31122922	31134696	ENST00000620191.4	0	+
chr22	31122965	31125373	ENST00000412985.5	0	+
chr22	31122965	31134697	ENST00000331075.10	0	+
chr22	31122974	31134696	ENST00000461241.1	0	+
chr22	31122983	31127037	ENST00000420017.5	0	+
chr22	31122993	31134696	ENST00000400294.6	0	+
chr22	31122993	31134696	ENST00000404390.7	0	+
chr22	31122993	31134696	ENST00000405300.5	0	+
chr22	31127759	31134636	ENST00000402238.5	0	+
chr22	31127788	31134636	ENST00000404453.5	0	+
chr22	31127801	31134636	ENST00000401755.1	0	+
chr22	31134806	31140508	ENST00000215885.4	0	-
chr22	31155965	31156647	ENST00000397595.2	0	-
chr22	31160061	31160119	ENST00000583386.1	0	-
chr22	31160181	31207019	ENST00000326132.11	0	+
chr22	31160182	31204571	ENST00000494514.5	0	+
chr22	31160207	31207013	ENST00000471384.5	0	+
chr22	31160210	31207013	ENST00000426256.6	0	+
chr22	31160210	31207013	ENST00000518626.5	0	+
chr22	31160242	31197045	ENST00000468921.5	0	+
chr22	31160242	31207012	ENST00000266252.8	0	+
chr22	31205263	31205616	ENST00000526089.1	0	-
chr22	31212238	31259046	ENST00000462625.1	0	+
chr22	31212273	31277638	ENST00000406516.5	0	+
chr22	31212297	31280080	ENST00000331728.9	0	+
chr22	31222744	31222853	ENST00000362825.1	0	-
chr22	31230070	31230172	ENST00000365149.1	0	-
chr22	31248357	31258484	ENST00000425203.1	0	+
chr22	31248401	31279734	ENST00000333611.8	0	+
chr22	31248486	31277638	ENST00000340552.4	0	+
chr22	31258958	31266132	ENST00000465937.1	0	+
chr22	31272470	31275665	ENST00000482270.5	0	+
chr22	31272673	31278586	ENST00000467301.1	0	+
chr22	31281593	31290754	ENST00000480654.5	0	-
chr22	31281599	31292488	ENST00000215912.10	0	-
chr22	31281612	31292534	ENST00000441972.5	0	-
chr22	31281678	31290726	ENST00000493034.1	0	-
chr22	31289037	31292479	ENST00000402249.7	0	-
chr22	31289640	31292488	ENST00000443175.1	0	-
chr22	31292498	31338021	ENST00000440456.5	0	+
chr22	31305403	31305512	ENST00000391240.1	0	+
chr22	31325803	31346346	ENST00000266269.10	0	-
chr22	31325808	31346232	ENST00000351933.8	0	-
chr22	31325808	31346232	ENST00000405309.7	0	-
chr22	31335348	31338021	ENST00000451161.1	0	+
chr22	31335417	31343285	ENST00000494109.1	0	-
chr22	31340676	31343591	ENST00000465287.1	0	-
chr22	31340676	31345776	ENST00000215919.3	0	-
chr22	31346776	31348719	ENST00000504184.3	0	+
chr22	31346885	31347406	ENST00000614284.1	0	+
chr22	31388732	31388837	ENST00000383888.1	0	-
chr22	31399603	31434452	ENST00000331457.9	0	+
chr22	31399609	31423319	ENST00000416465.5	0	+
chr22	31399616	31423346	ENST00000433341.5	0	+
chr22	31399632	31411060	ENST00000486584.1	0	+
chr22	31426512	31434155	ENST00000469673.1	0	+
chr22	31426740	31528740	ENST00000548143.1	0	+
chr22	31436976	31439907	ENST00000441289.1	0	-
chr22	31439358	31489824	ENST00000344710.9	0	-
chr22	31439364	31451260	ENST00000495101.5	0	-
chr22	31439366	31489587	ENST00000397525.5	0	-
chr22	31439366	31489842	ENST00000330125.10	0	-
chr22	31439372	31488745	ENST00000397523.5	0	-
chr22	31441802	31449383	ENST00000445424.5	0	-
chr22	31442034	31443419	ENST00000475437.1	0	-
chr22	31444437	31449490	ENST00000487671.1	0	-
chr22	31444631	31455208	ENST00000418321.1	0	-
chr22	31452880	31464304	ENST00000655902.1	0	+
chr22	31454250	31464056	ENST00000464523.1	0	+
chr22	31461663	31464204	ENST00000483736.1	0	+
chr22	31463699	31489242	ENST00000423097.5	0	-
chr22	31468281	31489937	ENST00000397518.5	0	-
chr22	31468281	31496108	ENST00000397520.1	0	-
chr22	31479382	31479489	ENST00000362378.1	0	-
chr22	31488687	31529067	ENST00000465646.5	0	+
chr22	31490149	31491022	ENST00000665497.1	0	+
chr22	31496138	31618551	ENST00000540643.5	0	+
chr22	31496274	31618551	ENST00000432498.5	0	+
chr22	31496386	31618446	ENST00000443011.5	0	+
chr22	31496463	31528866	ENST00000465437.5	0	+
chr22	31496518	31531105	ENST00000411518.5	0	+
chr22	31496535	31618588	ENST00000400288.7	0	+
chr22	31496537	31618548	ENST00000400289.5	0	+
chr22	31496557	31528835	ENST00000444859.5	0	+
chr22	31496582	31618548	ENST00000382162.7	0	+
chr22	31508254	31613453	ENST00000524296.5	0	+
chr22	31508284	31530634	ENST00000629688.1	0	+
chr22	31521198	31521592	ENST00000444403.1	0	-
chr22	31528744	31561333	ENST00000450787.1	0	+
chr22	31528819	31550677	ENST00000486708.1	0	+
chr22	31538351	31538589	ENST00000416920.1	0	-
chr22	31559482	31559939	ENST00000445477.1	0	+
chr22	31572762	31583934	ENST00000476010.5	0	+
chr22	31572976	31583877	ENST00000484806.1	0	+
chr22	31573173	31618547	ENST00000452250.5	0	+
chr22	31577947	31585126	ENST00000478887.1	0	+
chr22	31580087	31583969	ENST00000467357.1	0	+
chr22	31580271	31614860	ENST00000417682.6	0	+
chr22	31583905	31587912	ENST00000425671.1	0	+
chr22	31604313	31606726	ENST00000488883.5	0	+
chr22	31604568	31611769	ENST00000466991.5	0	+
chr22	31607650	31611801	ENST00000491973.1	0	+
chr22	31611765	31612442	ENST00000463436.1	0	+
chr22	31612772	31613715	ENST00000495107.1	0	+
chr22	31613834	31618548	ENST00000474741.5	0	+
chr22	31614166	31618548	ENST00000476577.5	0	+
chr22	31614370	31618547	ENST00000464333.5	0	+
chr22	31614385	31618548	ENST00000357852.3	0	+
chr22	31618490	31628221	ENST00000382151.6	0	-
chr22	31618490	31662215	ENST00000460723.5	0	-
chr22	31618510	31662221	ENST00000437808.5	0	-
chr22	31618514	31630824	ENST00000266095.9	0	-
chr22	31618558	31630862	ENST00000473770.5	0	-
chr22	31618613	31630831	ENST00000397500.5	0	-
chr22	31618976	31662180	ENST00000435900.5	0	-
chr22	31619102	31662432	ENST00000439502.6	0	-
chr22	31619443	31662203	ENST00000474017.5	0	-
chr22	31620552	31625786	ENST00000478893.5	0	-
chr22	31620577	31638398	ENST00000491342.1	0	-
chr22	31621053	31628982	ENST00000422020.5	0	-
chr22	31621466	31621531	ENST00000613748.1	0	-
chr22	31621861	31626294	ENST00000442379.1	0	-
chr22	31623683	31638405	ENST00000431201.5	0	-
chr22	31623683	31638445	ENST00000429683.1	0	-
chr22	31625821	31630006	ENST00000486675.5	0	-
chr22	31625877	31630799	ENST00000479851.1	0	-
chr22	31676255	31712745	ENST00000330495.8	0	-
chr22	31681346	31750132	ENST00000327423.11	0	-
chr22	31681349	31703718	ENST00000432485.1	0	-
chr22	31685041	31713845	ENST00000431684.1	0	-
chr22	31704443	31712347	ENST00000492705.1	0	-
chr22	31716726	31750072	ENST00000461722.1	0	-
chr22	31725543	31750140	ENST00000412743.1	0	-
chr22	31753866	31906992	ENST00000535622.6	0	+
chr22	31753897	31837279	ENST00000645693.1	0	+
chr22	31753897	31837324	ENST00000642974.1	0	+
chr22	31753899	31907005	ENST00000645711.1	0	+
chr22	31753911	31906989	ENST00000644331.1	0	+
chr22	31753931	31907001	ENST00000645560.1	0	+
chr22	31753943	31784788	ENST00000437411.6	0	+
chr22	31753954	31769772	ENST00000643948.1	0	+
chr22	31753954	31906989	ENST00000645755.1	0	+
chr22	31753954	31906998	ENST00000433147.2	0	+
chr22	31753957	31907015	ENST00000400246.7	0	+
chr22	31753963	31815973	ENST00000646755.1	0	+
chr22	31753963	31906970	ENST00000644162.1	0	+
chr22	31753967	31755511	ENST00000469969.2	0	+
chr22	31753970	31906989	ENST00000382112.8	0	+
chr22	31753973	31815971	ENST00000647438.1	0	+
chr22	31753973	31907014	ENST00000400248.7	0	+
chr22	31753974	31906976	ENST00000642771.1	0	+
chr22	31753976	31906994	ENST00000646515.1	0	+
chr22	31753978	31778137	ENST00000456178.6	0	+
chr22	31753981	31906997	ENST00000643751.2	0	+
chr22	31753994	31906975	ENST00000646969.1	0	+
chr22	31754000	31908033	ENST00000642696.1	0	+
chr22	31754013	31906222	ENST00000647343.1	0	+
chr22	31754014	31906975	ENST00000642684.1	0	+
chr22	31754018	31815991	ENST00000400242.8	0	+
chr22	31754018	31907015	ENST00000651528.1	0	+
chr22	31754027	31784504	ENST00000645015.1	0	+
chr22	31754042	31906980	ENST00000646998.1	0	+
chr22	31754053	31834257	ENST00000643166.1	0	+
chr22	31754058	31906989	ENST00000645494.1	0	+
chr22	31754067	31837279	ENST00000645564.1	0	+
chr22	31754067	31906992	ENST00000646465.1	0	+
chr22	31754108	31906768	ENST00000643395.1	0	+
chr22	31754132	31907009	ENST00000400249.7	0	+
chr22	31754861	31819040	ENST00000645967.1	0	+
chr22	31754861	31906898	ENST00000382111.6	0	+
chr22	31754898	31956243	ENST00000646701.1	0	+
chr22	31754914	31906730	ENST00000645407.1	0	+
chr22	31757201	31757498	ENST00000488827.3	0	+
chr22	31758577	31784511	ENST00000458532.2	0	+
chr22	31760643	31792768	ENST00000645785.1	0	+
chr22	31765045	31859069	ENST00000642551.1	0	+
chr22	31766638	31802738	ENST00000469974.6	0	+
chr22	31778025	31784504	ENST00000642605.1	0	+
chr22	31792686	31804219	ENST00000473802.1	0	+
chr22	31804135	31805109	ENST00000643075.1	0	+
chr22	31804841	31880237	ENST00000645893.1	0	+
chr22	31813915	31823167	ENST00000647289.1	0	+
chr22	31816378	31817491	ENST00000429025.1	0	-
chr22	31819065	31906994	ENST00000448753.6	0	+
chr22	31833848	31838721	ENST00000462414.1	0	+
chr22	31833865	31843662	ENST00000471914.5	0	+
chr22	31836847	31843659	ENST00000490731.1	0	+
chr22	31837237	31837298	ENST00000517100.1	0	+
chr22	31844744	31906989	ENST00000646135.1	0	+
chr22	31846938	31875443	ENST00000494060.1	0	+
chr22	31852906	31906338	ENST00000646830.1	0	+
chr22	31852906	31906965	ENST00000643097.1	0	+
chr22	31852915	31906798	ENST00000644690.1	0	+
chr22	31853247	31906975	ENST00000643021.1	0	+
chr22	31861363	31906342	ENST00000642956.1	0	+
chr22	31861370	31906966	ENST00000645547.1	0	+
chr22	31873826	31906975	ENST00000479261.2	0	+
chr22	31873867	31906975	ENST00000646383.1	0	+
chr22	31875738	31907587	ENST00000642212.1	0	+
chr22	31875750	31894902	ENST00000642915.1	0	+
chr22	31890343	31891434	ENST00000454534.1	0	-
chr22	31897634	31907012	ENST00000497340.1	0	+
chr22	31926213	31928407	ENST00000654264.1	0	-
chr22	31932713	31938524	ENST00000670472.1	0	-
chr22	31932729	31938396	ENST00000667186.1	0	-
chr22	31933520	31945350	ENST00000623735.1	0	-
chr22	31934010	31938417	ENST00000641789.1	0	-
chr22	31934039	31945518	ENST00000486651.1	0	-
chr22	31934347	31938454	ENST00000484682.1	0	-
chr22	31944521	31956482	ENST00000443669.5	0	+
chr22	31944521	31957603	ENST00000397492.1	0	+
chr22	31944534	31957603	ENST00000248975.6	0	+
chr22	31944746	31956604	ENST00000479649.1	0	+
chr22	31944965	31957603	ENST00000471374.1	0	+
chr22	31947421	31956576	ENST00000420430.1	0	+
chr22	31963356	31970400	ENST00000430449.1	0	+
chr22	31970822	32006886	ENST00000416995.2	0	+
chr22	31970827	32037995	ENST00000658411.1	0	+
chr22	31970860	31973475	ENST00000436395.1	0	+
chr22	31981146	31981427	ENST00000464862.3	0	-
chr22	32039489	32039896	ENST00000423610.1	0	+
chr22	32043260	32113029	ENST00000266088.9	0	+
chr22	32059169	32113002	ENST00000543737.2	0	+
chr22	32083050	32086326	ENST00000486394.1	0	+
chr22	32084848	32102207	ENST00000477969.1	0	+
chr22	32121976	32133469	ENST00000432373.1	0	+
chr22	32129688	32130612	ENST00000423412.1	0	+
chr22	32132316	32132507	ENST00000448124.1	0	+
chr22	32134027	32134133	ENST00000416196.1	0	+
chr22	32141266	32143272	ENST00000436294.1	0	+
chr22	32149005	32159303	ENST00000382097.4	0	-
chr22	32149005	32159322	ENST00000467813.1	0	-
chr22	32149179	32149926	ENST00000490640.1	0	-
chr22	32159453	32160392	ENST00000426354.1	0	-
chr22	32188399	32188487	ENST00000618747.1	0	-
chr22	32190434	32202451	ENST00000248983.8	0	-
chr22	32190437	32202571	ENST00000626996.2	0	-
chr22	32190437	32203477	ENST00000400237.2	0	-
chr22	32190541	32205001	ENST00000652607.1	0	-
chr22	32190703	32193401	ENST00000489846.1	0	-
chr22	32190705	32202511	ENST00000628378.1	0	-
chr22	32199918	32200234	ENST00000523725.1	0	+
chr22	32205114	32278382	ENST00000452181.2	0	+
chr22	32205164	32273615	ENST00000434942.2	0	+
chr22	32218475	32255341	ENST00000266086.5	0	-
chr22	32269380	32273110	ENST00000397477.2	0	+
chr22	32284682	32285131	ENST00000608926.1	0	+
chr22	32327063	32343062	ENST00000667569.1	0	-
chr22	32327167	32334260	ENST00000668765.1	0	-
chr22	32327170	32343105	ENST00000446543.1	0	-
chr22	32354884	32361161	ENST00000397468.5	0	+
chr22	32356675	32356988	ENST00000605398.1	0	-
chr22	32357866	32361161	ENST00000249007.4	0	+
chr22	32359885	32371076	ENST00000400234.6	0	-
chr22	32359887	32371002	ENST00000382084.9	0	-
chr22	32359905	32366557	ENST00000658273.1	0	-
chr22	32359907	32370165	ENST00000659039.1	0	-
chr22	32359917	32382106	ENST00000655996.1	0	-
chr22	32360003	32360436	ENST00000621921.2	0	-
chr22	32360085	32368959	ENST00000657380.1	0	-
chr22	32360629	32364426	ENST00000657810.1	0	-
chr22	32360651	32363117	ENST00000461833.1	0	-
chr22	32362800	32370999	ENST00000382086.6	0	-
chr22	32362971	32363059	ENST00000617561.1	0	+
chr22	32362973	32382052	ENST00000577714.1	0	-
chr22	32368496	32368567	ENST00000617624.1	0	+
chr22	32376663	32384343	ENST00000420171.1	0	+
chr22	32376681	32377135	ENST00000430579.1	0	+
chr22	32383785	32385631	ENST00000444848.1	0	+
chr22	32386667	32386868	ENST00000453679.1	0	+
chr22	32387581	32412247	ENST00000216038.6	0	-
chr22	32397947	32399985	ENST00000498434.1	0	-
chr22	32398041	32407526	ENST00000476619.5	0	-
chr22	32398047	32407409	ENST00000485373.5	0	-
chr22	32401561	32412241	ENST00000487704.5	0	-
chr22	32407878	32412248	ENST00000463455.1	0	-
chr22	32413846	32457386	ENST00000300399.7	0	-
chr22	32413846	32457386	ENST00000534972.3	0	-
chr22	32413846	32464484	ENST00000397452.5	0	-
chr22	32453333	32464449	ENST00000397450.2	0	-
chr22	32474675	32498829	ENST00000420700.5	0	+
chr22	32474797	32498829	ENST00000425028.5	0	+
chr22	32474810	32498829	ENST00000266087.12	0	+
chr22	32475014	32498829	ENST00000492535.1	0	+
chr22	32475236	32498608	ENST00000452138.3	0	+
chr22	32475256	32498829	ENST00000397426.5	0	+
chr22	32475592	32483983	ENST00000444207.1	0	+
chr22	32478034	32479231	ENST00000465418.1	0	+
chr22	32487287	32491113	ENST00000484607.1	0	+
chr22	32512551	33058372	ENST00000358763.6	0	-
chr22	32513575	32529071	ENST00000483062.5	0	-
chr22	32513674	32519686	ENST00000467095.5	0	-
chr22	32513691	32541716	ENST00000332840.9	0	-
chr22	32514438	32529082	ENST00000461446.1	0	-
chr22	32518051	32530202	ENST00000459990.5	0	-
chr22	32518192	32530188	ENST00000468922.1	0	-
chr22	32527671	32541716	ENST00000619146.1	0	-
chr22	32583299	32584204	ENST00000428201.1	0	+
chr22	32629605	32869100	ENST00000462268.1	0	-
chr22	32635101	32635198	ENST00000516081.1	0	-
chr22	32783176	32784982	ENST00000444982.1	0	+
chr22	32801700	32863043	ENST00000266085.6	0	+
chr22	32864574	32869058	ENST00000467824.1	0	-
chr22	32930994	32947579	ENST00000472027.1	0	-
chr22	32980644	33057858	ENST00000441821.5	0	-
chr22	32980672	33014529	ENST00000412575.1	0	-
chr22	33014979	33015461	ENST00000446798.1	0	+
chr22	33104329	33145861	ENST00000653890.1	0	+
chr22	33105382	33106059	ENST00000447396.1	0	-
chr22	33108528	33116294	ENST00000452505.1	0	+
chr22	33108533	33145740	ENST00000670696.1	0	+
chr22	33115857	33145859	ENST00000659583.1	0	+
chr22	33137139	33137338	ENST00000421145.1	0	+
chr22	33162225	33384227	ENST00000608642.5	0	-
chr22	33164062	33166439	ENST00000434741.1	0	+
chr22	33166004	33384227	ENST00000610186.5	0	-
chr22	33166386	33221751	ENST00000421232.1	0	-
chr22	33166733	33384227	ENST00000609799.5	0	-
chr22	33272860	33920421	ENST00000354992.6	0	-
chr22	33273075	33920428	ENST00000397394.6	0	-
chr22	33273088	33920414	ENST00000402320.5	0	-
chr22	33320864	33322813	ENST00000453924.1	0	+
chr22	33337768	33572253	ENST00000421768.1	0	-
chr22	33436581	33436669	ENST00000580680.1	0	-
chr22	33562912	33565019	ENST00000476315.1	0	-
chr22	33650366	33861624	ENST00000430220.6	0	-
chr22	33650367	33922841	ENST00000413114.5	0	-
chr22	33650374	33657216	ENST00000462606.1	0	-
chr22	33650439	33922596	ENST00000494763.1	0	-
chr22	33650448	33889228	ENST00000434071.5	0	-
chr22	33650594	33873405	ENST00000432776.5	0	-
chr22	33650631	33873387	ENST00000423375.1	0	-
chr22	33704785	33704920	ENST00000517198.2	0	-
chr22	33704785	33704922	ENST00000630737.1	0	-
chr22	33723831	33724912	ENST00000445391.1	0	-
chr22	33725006	33750723	ENST00000448275.6	0	+
chr22	33725007	33750729	ENST00000664675.1	0	+
chr22	33725013	33737775	ENST00000453336.5	0	+
chr22	33725026	33750843	ENST00000424291.5	0	+
chr22	33725032	33750742	ENST00000663088.1	0	+
chr22	33725039	33750742	ENST00000438159.5	0	+
chr22	33725044	33750811	ENST00000661135.1	0	+
chr22	33725071	33744192	ENST00000450974.1	0	+
chr22	33744008	33750657	ENST00000445892.5	0	+
chr22	33744128	33750683	ENST00000416275.1	0	+
chr22	33922421	33922766	ENST00000610170.1	0	+
chr22	34017421	34206653	ENST00000655904.1	0	+
chr22	34017425	34207090	ENST00000665701.1	0	+
chr22	34017425	34218794	ENST00000652942.1	0	+
chr22	34017425	34218794	ENST00000664480.1	0	+
chr22	34017431	34218790	ENST00000669802.1	0	+
chr22	34017431	34218794	ENST00000656244.1	0	+
chr22	34017431	34218794	ENST00000667570.1	0	+
chr22	34017431	34218796	ENST00000655455.1	0	+
chr22	34017431	34218796	ENST00000656434.1	0	+
chr22	34017431	34218796	ENST00000658589.1	0	+
chr22	34017431	34218796	ENST00000658699.1	0	+
chr22	34017431	34218796	ENST00000664743.1	0	+
chr22	34017437	34218792	ENST00000654009.1	0	+
chr22	34017437	34218794	ENST00000664028.1	0	+
chr22	34017437	34218796	ENST00000661903.1	0	+
chr22	34017450	34175779	ENST00000658014.1	0	+
chr22	34017450	34218796	ENST00000663820.1	0	+
chr22	34017450	34218796	ENST00000665392.1	0	+
chr22	34017450	34218796	ENST00000669488.1	0	+
chr22	34017451	34218796	ENST00000658055.1	0	+
chr22	34017469	34206631	ENST00000670742.1	0	+
chr22	34017469	34206637	ENST00000665458.1	0	+
chr22	34017472	34175779	ENST00000668175.1	0	+
chr22	34017472	34175780	ENST00000668901.1	0	+
chr22	34017472	34188425	ENST00000412218.1	0	+
chr22	34017472	34218794	ENST00000671554.1	0	+
chr22	34017472	34218796	ENST00000653369.1	0	+
chr22	34017472	34218796	ENST00000659067.1	0	+
chr22	34017472	34218796	ENST00000661800.1	0	+
chr22	34017472	34218796	ENST00000670329.1	0	+
chr22	34017477	34218796	ENST00000657115.1	0	+
chr22	34191193	34210564	ENST00000661218.1	0	-
chr22	34207858	34209534	ENST00000658408.1	0	-
chr22	34208140	34209262	ENST00000450365.1	0	-
chr22	34589084	34591278	ENST00000457251.1	0	-
chr22	34641178	34651862	ENST00000624442.1	0	+
chr22	34703125	34704885	ENST00000423712.1	0	-
chr22	34711907	34724919	ENST00000440858.1	0	-
chr22	34756675	35002862	ENST00000668433.1	0	-
chr22	34873404	34893847	ENST00000665218.1	0	-
chr22	35066135	35087387	ENST00000308700.6	0	+
chr22	35066157	35087387	ENST00000404699.7	0	+
chr22	35119823	35231056	ENST00000423311.1	0	-
chr22	35122660	35127419	ENST00000610215.1	0	+
chr22	35164303	35165347	ENST00000414048.1	0	+
chr22	35194698	35194942	ENST00000458450.1	0	-
chr22	35249771	35249833	ENST00000459160.1	0	+
chr22	35257451	35265570	ENST00000420166.5	0	+
chr22	35257486	35295807	ENST00000418170.5	0	+
chr22	35257488	35258588	ENST00000466438.1	0	+
chr22	35257492	35295807	ENST00000216106.6	0	+
chr22	35257495	35265567	ENST00000455359.5	0	+
chr22	35261853	35264052	ENST00000498325.5	0	+
chr22	35262762	35264663	ENST00000464480.1	0	+
chr22	35286881	35293028	ENST00000498212.1	0	+
chr22	35298837	35299541	ENST00000609073.1	0	-
chr22	35299274	35323630	ENST00000608749.5	0	+
chr22	35299274	35347992	ENST00000447733.5	0	+
chr22	35299375	35323603	ENST00000608674.5	0	+
chr22	35299799	35333492	ENST00000456128.5	0	+
chr22	35299803	35347992	ENST00000411850.5	0	+
chr22	35299803	35347992	ENST00000425375.5	0	+
chr22	35299855	35347973	ENST00000404284.6	0	+
chr22	35299865	35318316	ENST00000465529.1	0	+
chr22	35299868	35347972	ENST00000382034.9	0	+
chr22	35299870	35322612	ENST00000487670.1	0	+
chr22	35299873	35327338	ENST00000439512.5	0	+
chr22	35299883	35347981	ENST00000424387.5	0	+
chr22	35299889	35323810	ENST00000395736.7	0	+
chr22	35299893	35323874	ENST00000449508.1	0	+
chr22	35299893	35347973	ENST00000449058.7	0	+
chr22	35311061	35323819	ENST00000443206.2	0	+
chr22	35323339	35323914	ENST00000497448.1	0	+
chr22	35323867	35338788	ENST00000491987.1	0	+
chr22	35335639	35335758	ENST00000579518.1	0	+
chr22	35336720	35336799	ENST00000620069.1	0	-
chr22	35344185	35347983	ENST00000492723.1	0	+
chr22	35372173	35372621	ENST00000632196.1	0	+
chr22	35376421	35377261	ENST00000631502.1	0	+
chr22	35380360	35387101	ENST00000412893.5	0	+
chr22	35381092	35386908	ENST00000481190.1	0	+
chr22	35381095	35394207	ENST00000216117.9	0	+
chr22	35387039	35390293	ENST00000494998.1	0	+
chr22	35400133	35403257	ENST00000451351.5	0	+
chr22	35400136	35424501	ENST00000382011.9	0	+
chr22	35400139	35425431	ENST00000216122.9	0	+
chr22	35400141	35410008	ENST00000417343.1	0	+
chr22	35400169	35406725	ENST00000416905.1	0	+
chr22	35400430	35410872	ENST00000444778.1	0	+
chr22	35410358	35415908	ENST00000464908.5	0	+
chr22	35410408	35424501	ENST00000493076.5	0	+
chr22	35411243	35415965	ENST00000493569.5	0	+
chr22	35411438	35416672	ENST00000465557.1	0	+
chr22	35501860	35503586	ENST00000417397.2	0	-
chr22	35526931	35527415	ENST00000610000.1	0	+
chr22	35540830	35553999	ENST00000216127.5	0	+
chr22	35606763	35615713	ENST00000401702.5	0	-
chr22	35606763	35617329	ENST00000397326.7	0	-
chr22	35606763	35617508	ENST00000397328.5	0	-
chr22	35606763	35623354	ENST00000359787.5	0	-
chr22	35607271	35622561	ENST00000406324.5	0	-
chr22	35607332	35623401	ENST00000443033.5	0	-
chr22	35607374	35617329	ENST00000442617.1	0	-
chr22	35610883	35617586	ENST00000451685.5	0	-
chr22	35610883	35637951	ENST00000447607.5	0	-
chr22	35610969	35622524	ENST00000419229.1	0	-
chr22	35626987	35635134	ENST00000472240.1	0	-
chr22	35648445	35668404	ENST00000409652.5	0	+
chr22	35685939	35689373	ENST00000417782.1	0	+
chr22	35703802	35704176	ENST00000415961.2	0	+
chr22	35717871	35729483	ENST00000249044.2	0	+
chr22	35738735	35840587	ENST00000405409.6	0	-
chr22	35738735	36028824	ENST00000438146.7	0	-
chr22	35743601	35840218	ENST00000414461.6	0	-
chr22	35743601	35840407	ENST00000449924.6	0	-
chr22	35743932	35840407	ENST00000262829.11	0	-
chr22	35743957	35810004	ENST00000397303.6	0	-
chr22	35743982	35938897	ENST00000359369.8	0	-
chr22	35744086	35748632	ENST00000463509.1	0	-
chr22	35744147	35840284	ENST00000416721.6	0	-
chr22	35744163	35778032	ENST00000495377.6	0	-
chr22	35760009	35824373	ENST00000473487.6	0	-
chr22	35768267	35961666	ENST00000408983.2	0	-
chr22	35777655	35840403	ENST00000491982.1	0	-
chr22	35778027	35840413	ENST00000397305.3	0	-
chr22	35897269	35898404	ENST00000436210.1	0	-
chr22	35992320	36000469	ENST00000625157.1	0	+
chr22	36066532	36070110	ENST00000623420.1	0	+
chr22	36070999	36085573	ENST00000624264.1	0	+
chr22	36091147	36093352	ENST00000623490.1	0	+
chr22	36137429	36139350	ENST00000624395.1	0	-
chr22	36140329	36160929	ENST00000349314.6	0	-
chr22	36140329	36160929	ENST00000397289.6	0	-
chr22	36140329	36160929	ENST00000397293.6	0	-
chr22	36140329	36160929	ENST00000422426.5	0	-
chr22	36140329	36160929	ENST00000432700.5	0	-
chr22	36140329	36166177	ENST00000361710.6	0	-
chr22	36140329	36166177	ENST00000397287.6	0	-
chr22	36141026	36149126	ENST00000424878.3	0	-
chr22	36141527	36160755	ENST00000487355.5	0	-
chr22	36141702	36156936	ENST00000487423.5	0	-
chr22	36141715	36160754	ENST00000426939.6	0	-
chr22	36141862	36156949	ENST00000534251.1	0	-
chr22	36145482	36160754	ENST00000531195.1	0	-
chr22	36145488	36160755	ENST00000487783.5	0	-
chr22	36145506	36156885	ENST00000525184.1	0	-
chr22	36145543	36156938	ENST00000485453.1	0	-
chr22	36146258	36160755	ENST00000530895.5	0	-
chr22	36149074	36160755	ENST00000472303.1	0	-
chr22	36154448	36160783	ENST00000531095.1	0	-
chr22	36155719	36156886	ENST00000528740.1	0	-
chr22	36159229	36160755	ENST00000533061.1	0	-
chr22	36172421	36172779	ENST00000425366.1	0	+
chr22	36172933	36173948	ENST00000437339.1	0	+
chr22	36174260	36174822	ENST00000414147.1	0	+
chr22	36175166	36175854	ENST00000454926.1	0	+
chr22	36175856	36176526	ENST00000439552.1	0	+
chr22	36178553	36179385	ENST00000426108.1	0	+
chr22	36189123	36204806	ENST00000613247.4	0	-
chr22	36189128	36204833	ENST00000352371.5	0	-
chr22	36189128	36204840	ENST00000616056.4	0	-
chr22	36190796	36204806	ENST00000332987.5	0	-
chr22	36191258	36204808	ENST00000449084.2	0	-
chr22	36191864	36201877	ENST00000493203.6	0	-
chr22	36191905	36204806	ENST00000457630.6	0	-
chr22	36195325	36204806	ENST00000419360.5	0	-
chr22	36197454	36204743	ENST00000397275.6	0	-
chr22	36197455	36204743	ENST00000328429.8	0	-
chr22	36199334	36204806	ENST00000436763.1	0	-
chr22	36201583	36204715	ENST00000480236.2	0	-
chr22	36226208	36239527	ENST00000358502.10	0	-
chr22	36226209	36239954	ENST00000249066.10	0	-
chr22	36227341	36239527	ENST00000451256.6	0	-
chr22	36228031	36239227	ENST00000529194.5	0	-
chr22	36228194	36239561	ENST00000454728.5	0	-
chr22	36236884	36239239	ENST00000476579.1	0	-
chr22	36236926	36239582	ENST00000489186.1	0	-
chr22	36236932	36239673	ENST00000473549.1	0	-
chr22	36236963	36239570	ENST00000484830.1	0	-
chr22	36253009	36267525	ENST00000397278.7	0	+
chr22	36253070	36267530	ENST00000422706.5	0	+
chr22	36253070	36267530	ENST00000426053.5	0	+
chr22	36253077	36267521	ENST00000319136.8	0	+
chr22	36253130	36261673	ENST00000422471.5	0	+
chr22	36253132	36257560	ENST00000475519.5	0	+
chr22	36253132	36261675	ENST00000438034.5	0	+
chr22	36253132	36261699	ENST00000439680.5	0	+
chr22	36253132	36265315	ENST00000427990.5	0	+
chr22	36253140	36267083	ENST00000397279.8	0	+
chr22	36253160	36261644	ENST00000433768.5	0	+
chr22	36253163	36261633	ENST00000431184.1	0	+
chr22	36281276	36388067	ENST00000216181.10	0	-
chr22	36284161	36286044	ENST00000475726.5	0	-
chr22	36284213	36286021	ENST00000486218.1	0	-
chr22	36286846	36286907	ENST00000622122.1	0	-
chr22	36295511	36297323	ENST00000459960.1	0	-
chr22	36300909	36301714	ENST00000495928.1	0	-
chr22	36302307	36304025	ENST00000473022.1	0	-
chr22	36316512	36319835	ENST00000477189.1	0	-
chr22	36320240	36321922	ENST00000472210.5	0	-
chr22	36320273	36328952	ENST00000463027.1	0	-
chr22	36325032	36365112	ENST00000401701.1	0	-
chr22	36335420	36343530	ENST00000663925.1	0	+
chr22	36348927	36365111	ENST00000456729.1	0	-
chr22	36388625	36396517	ENST00000424761.1	0	+
chr22	36421272	36421644	ENST00000413110.1	0	-
chr22	36440879	36440970	ENST00000516416.1	0	-
chr22	36445394	36454944	ENST00000442579.1	0	-
chr22	36467045	36481634	ENST00000416967.1	0	-
chr22	36467045	36481640	ENST00000216185.7	0	-
chr22	36467548	36481632	ENST00000487725.1	0	-
chr22	36467590	36481340	ENST00000403313.5	0	-
chr22	36467883	36481596	ENST00000411915.1	0	-
chr22	36487189	36507101	ENST00000397224.8	0	-
chr22	36488241	36507022	ENST00000216187.10	0	-
chr22	36488598	36498397	ENST00000366463.6	0	-
chr22	36489986	36506530	ENST00000397223.4	0	-
chr22	36506163	36507043	ENST00000423980.1	0	-
chr22	36510854	36529166	ENST00000216190.13	0	-
chr22	36510855	36511641	ENST00000478547.1	0	-
chr22	36510867	36512929	ENST00000462641.1	0	-
chr22	36510898	36528897	ENST00000405442.5	0	-
chr22	36511576	36516931	ENST00000426531.5	0	-
chr22	36511700	36519501	ENST00000458572.1	0	-
chr22	36516062	36516923	ENST00000462794.1	0	-
chr22	36518812	36529166	ENST00000455547.5	0	-
chr22	36520632	36529077	ENST00000457241.5	0	-
chr22	36523114	36529184	ENST00000432675.4	0	-
chr22	36523915	36528942	ENST00000402116.2	0	-
chr22	36524694	36529173	ENST00000496875.1	0	-
chr22	36536655	36537604	ENST00000339367.4	0	-
chr22	36552164	36552700	ENST00000433970.1	0	+
chr22	36560869	36562915	ENST00000562756.1	0	-
chr22	36563920	36703558	ENST00000300105.6	0	-
chr22	36584488	36585454	ENST00000480002.1	0	-
chr22	36703917	36721472	ENST00000430281.1	0	+
chr22	36754085	36755731	ENST00000656882.1	0	-
chr22	36758201	36776153	ENST00000340630.9	0	-
chr22	36758210	36760907	ENST00000495987.5	0	-
chr22	36758210	36761102	ENST00000474616.5	0	-
chr22	36758210	36766454	ENST00000495555.6	0	-
chr22	36758210	36767819	ENST00000415653.5	0	-
chr22	36758210	36776119	ENST00000433985.7	0	-
chr22	36758210	36776125	ENST00000471809.5	0	-
chr22	36762932	36776103	ENST00000417951.6	0	-
chr22	36763918	36776125	ENST00000430701.5	0	-
chr22	36763995	36768554	ENST00000440696.2	0	-
chr22	36766477	36769022	ENST00000667675.1	0	+
chr22	36767062	36775699	ENST00000465023.1	0	-
chr22	36772402	36776256	ENST00000476548.1	0	-
chr22	36800683	36819479	ENST00000216200.9	0	-
chr22	36800700	36817001	ENST00000406910.6	0	-
chr22	36800702	36817035	ENST00000417718.7	0	-
chr22	36800766	36815391	ENST00000404171.1	0	-
chr22	36813449	36817035	ENST00000467935.1	0	-
chr22	36813645	36817514	ENST00000443735.1	0	-
chr22	36816325	36819736	ENST00000417792.1	0	+
chr22	36847371	36870441	ENST00000619915.1	0	-
chr22	36858020	36870380	ENST00000431290.1	0	-
chr22	36860987	36875832	ENST00000447071.5	0	+
chr22	36860987	36878017	ENST00000397147.7	0	+
chr22	36861005	36878015	ENST00000248899.11	0	+
chr22	36862302	36878010	ENST00000650827.1	0	+
chr22	36862523	36878010	ENST00000651053.1	0	+
chr22	36863090	36878014	ENST00000650698.1	0	+
chr22	36872136	36877764	ENST00000415063.2	0	+
chr22	36913627	36940439	ENST00000403662.8	0	+
chr22	36913632	36940433	ENST00000262825.9	0	+
chr22	36922133	36938611	ENST00000406230.5	0	+
chr22	36922611	36930744	ENST00000421539.1	0	+
chr22	36950528	36953723	ENST00000447905.1	0	-
chr22	36965247	36968172	ENST00000400221.1	0	+
chr22	36991119	37002256	ENST00000442538.5	0	-
chr22	36991124	37007798	ENST00000402860.7	0	-
chr22	36991124	37007841	ENST00000405091.6	0	-
chr22	36991124	37007851	ENST00000381821.2	0	-
chr22	37010858	37019467	ENST00000403892.7	0	-
chr22	37010858	37020183	ENST00000622841.1	0	-
chr22	37010864	37019447	ENST00000249042.8	0	-
chr22	37013224	37013319	ENST00000516603.1	0	+
chr22	37018360	37019640	ENST00000438203.1	0	-
chr22	37019634	37029801	ENST00000401419.7	0	+
chr22	37019695	37029794	ENST00000404802.7	0	+
chr22	37019767	37029819	ENST00000341116.7	0	+
chr22	37019767	37029822	ENST00000429360.5	0	+
chr22	37019771	37029796	ENST00000404393.5	0	+
chr22	37019797	37029812	ENST00000485587.1	0	+
chr22	37023036	37029821	ENST00000397225.2	0	+
chr22	37024192	37025081	ENST00000628507.1	0	+
chr22	37033123	37035513	ENST00000666567.1	0	+
chr22	37051735	37063389	ENST00000610767.4	0	+
chr22	37051738	37063390	ENST00000402077.7	0	+
chr22	37051738	37063390	ENST00000403888.7	0	+
chr22	37051851	37059338	ENST00000421900.5	0	+
chr22	37051875	37063374	ENST00000456470.1	0	+
chr22	37051904	37059516	ENST00000483389.5	0	+
chr22	37052169	37053195	ENST00000431531.1	0	+
chr22	37053064	37061564	ENST00000478231.5	0	+
chr22	37055032	37055347	ENST00000364208.1	0	-
chr22	37057397	37061408	ENST00000462640.1	0	+
chr22	37065435	37103653	ENST00000346753.8	0	-
chr22	37065435	37103653	ENST00000381792.6	0	-
chr22	37065435	37109409	ENST00000406725.6	0	-
chr22	37065435	37109713	ENST00000406856.7	0	-
chr22	37080067	37082847	ENST00000414203.2	0	+
chr22	37082849	37084825	ENST00000429068.1	0	-
chr22	37083939	37103656	ENST00000442782.6	0	-
chr22	37096069	37103518	ENST00000423761.1	0	-
chr22	37125842	37149916	ENST00000216223.10	0	-
chr22	37128521	37136408	ENST00000483573.1	0	-
chr22	37136364	37175054	ENST00000453962.5	0	-
chr22	37137684	37175054	ENST00000429622.5	0	-
chr22	37139071	37175054	ENST00000461607.5	0	-
chr22	37139142	37143621	ENST00000440958.1	0	-
chr22	37142449	37175054	ENST00000445595.1	0	-
chr22	37166756	37182850	ENST00000419128.1	0	+
chr22	37180165	37188247	ENST00000337843.7	0	-
chr22	37180166	37188286	ENST00000397110.6	0	-
chr22	37180166	37198301	ENST00000470655.5	0	-
chr22	37180170	37185948	ENST00000493023.1	0	-
chr22	37182173	37188236	ENST00000434784.1	0	-
chr22	37190552	37197748	ENST00000467564.5	0	-
chr22	37190552	37199385	ENST00000497071.1	0	-
chr22	37204236	37212477	ENST00000610913.2	0	-
chr22	37206479	37210965	ENST00000617123.1	0	-
chr22	37215737	37215891	ENST00000436709.1	0	-
chr22	37225269	37244269	ENST00000249071.11	0	-
chr22	37225851	37244448	ENST00000406508.5	0	-
chr22	37225965	37231503	ENST00000481215.1	0	-
chr22	37226672	37244432	ENST00000405484.5	0	-
chr22	37226752	37244441	ENST00000441619.5	0	-
chr22	37232403	37244278	ENST00000469532.1	0	-
chr22	37240804	37244248	ENST00000401529.3	0	-
chr22	37282026	37297682	ENST00000457992.5	0	+
chr22	37282474	37300173	ENST00000402997.5	0	+
chr22	37282486	37300271	ENST00000405206.3	0	+
chr22	37282507	37315341	ENST00000248901.11	0	+
chr22	37282514	37298402	ENST00000469886.5	0	+
chr22	37282524	37315341	ENST00000462927.5	0	+
chr22	37282526	37296603	ENST00000480510.5	0	+
chr22	37282530	37302019	ENST00000439667.1	0	+
chr22	37292143	37294692	ENST00000467664.1	0	+
chr22	37300881	37303295	ENST00000447919.1	0	+
chr22	37309967	37313638	ENST00000446506.1	0	+
chr22	37339582	37385283	ENST00000430883.5	0	-
chr22	37340643	37427445	ENST00000452946.1	0	-
chr22	37352189	37354839	ENST00000445088.1	0	-
chr22	37352189	37355002	ENST00000666674.1	0	-
chr22	37367959	37427479	ENST00000402918.7	0	-
chr22	37367960	37427470	ENST00000613079.4	0	-
chr22	37371683	37372858	ENST00000609322.1	0	+
chr22	37375704	37418953	ENST00000414347.5	0	-
chr22	37375704	37420491	ENST00000424973.5	0	-
chr22	37375730	37420445	ENST00000435824.5	0	-
chr22	37375762	37427003	ENST00000451509.5	0	-
chr22	37375762	37427470	ENST00000415408.1	0	-
chr22	37469062	37486384	ENST00000356998.8	0	-
chr22	37469062	37486387	ENST00000416983.7	0	-
chr22	37469067	37472724	ENST00000489515.1	0	-
chr22	37469512	37474564	ENST00000454291.1	0	-
chr22	37474511	37486379	ENST00000438891.5	0	-
chr22	37474562	37481371	ENST00000436341.5	0	-
chr22	37476895	37486387	ENST00000430411.1	0	-
chr22	37479411	37484629	ENST00000442496.1	0	-
chr22	37480210	37481067	ENST00000466943.1	0	-
chr22	37480278	37486393	ENST00000424765.2	0	-
chr22	37490361	37496780	ENST00000488141.5	0	-
chr22	37490361	37519415	ENST00000251973.10	0	-
chr22	37490361	37519542	ENST00000403299.5	0	-
chr22	37490362	37494576	ENST00000467812.1	0	-
chr22	37490363	37509149	ENST00000406271.7	0	-
chr22	37492760	37512241	ENST00000437756.5	0	-
chr22	37495849	37497235	ENST00000486118.1	0	-
chr22	37495900	37504080	ENST00000433485.1	0	-
chr22	37507881	37510565	ENST00000476871.5	0	-
chr22	37508526	37514805	ENST00000494166.1	0	-
chr22	37550025	37551735	ENST00000428838.1	0	+
chr22	37560479	37566559	ENST00000430687.1	0	+
chr22	37560479	37569405	ENST00000249014.5	0	+
chr22	37563653	37566530	ENST00000415670.1	0	+
chr22	37564405	37566495	ENST00000434728.1	0	+
chr22	37570247	37580087	ENST00000215886.6	0	-
chr22	37571848	37582616	ENST00000416480.1	0	-
chr22	37578668	37578922	ENST00000615981.1	0	+
chr22	37608724	37632711	ENST00000381756.9	0	+
chr22	37608829	37618146	ENST00000489772.5	0	+
chr22	37608833	37620044	ENST00000405147.7	0	+
chr22	37608833	37633564	ENST00000343632.9	0	+
chr22	37608834	37621631	ENST00000429218.5	0	+
chr22	37608844	37633564	ENST00000325180.12	0	+
chr22	37608934	37620332	ENST00000439161.5	0	+
chr22	37608947	37623564	ENST00000449944.5	0	+
chr22	37608959	37620275	ENST00000411501.5	0	+
chr22	37608976	37620816	ENST00000453208.5	0	+
chr22	37609039	37618546	ENST00000447515.5	0	+
chr22	37609039	37633563	ENST00000406772.5	0	+
chr22	37609059	37618515	ENST00000431745.5	0	+
chr22	37609065	37614274	ENST00000484804.5	0	+
chr22	37610141	37614195	ENST00000488672.1	0	+
chr22	37612535	37620267	ENST00000326597.6	0	+
chr22	37613969	37621632	ENST00000413251.5	0	+
chr22	37614132	37621632	ENST00000423024.1	0	+
chr22	37618402	37620249	ENST00000491295.1	0	+
chr22	37620853	37623916	ENST00000481613.1	0	+
chr22	37623828	37627162	ENST00000475445.1	0	+
chr22	37629461	37630299	ENST00000463672.1	0	+
chr22	37630667	37632677	ENST00000460957.1	0	+
chr22	37634653	37656042	ENST00000417536.5	0	+
chr22	37639668	37656119	ENST00000649765.1	0	+
chr22	37639692	37666932	ENST00000451997.6	0	+
chr22	37639694	37650614	ENST00000644149.1	0	+
chr22	37640385	37656041	ENST00000469947.5	0	+
chr22	37641381	37647291	ENST00000495174.5	0	+
chr22	37641831	37658377	ENST00000456099.1	0	-
chr22	37643454	37647506	ENST00000471650.5	0	+
chr22	37643713	37645055	ENST00000459646.1	0	+
chr22	37646767	37650778	ENST00000466097.1	0	+
chr22	37646817	37655684	ENST00000643339.1	0	+
chr22	37647512	37656038	ENST00000649107.1	0	+
chr22	37658722	37666932	ENST00000215904.7	0	+
chr22	37664910	37666932	ENST00000403251.1	0	+
chr22	37669463	37669739	ENST00000468873.3	0	-
chr22	37675635	37679802	ENST00000215909.10	0	+
chr22	37675651	37677228	ENST00000464120.1	0	+
chr22	37675656	37679802	ENST00000425542.5	0	+
chr22	37675681	37677596	ENST00000480207.5	0	+
chr22	37675687	37679640	ENST00000454173.1	0	+
chr22	37676124	37679802	ENST00000489315.5	0	+
chr22	37676998	37679802	ENST00000472321.1	0	+
chr22	37681672	37693475	ENST00000493862.5	0	+
chr22	37686322	37693476	ENST00000438329.5	0	+
chr22	37686342	37688299	ENST00000474032.1	0	+
chr22	37686342	37693474	ENST00000359114.9	0	+
chr22	37686345	37690900	ENST00000468597.5	0	+
chr22	37686359	37693471	ENST00000611699.1	0	+
chr22	37686413	37726503	ENST00000455236.4	0	+
chr22	37687784	37691465	ENST00000484650.1	0	+
chr22	37697047	37776556	ENST00000644935.1	0	+
chr22	37697620	37726570	ENST00000492485.5	0	+
chr22	37697620	37774048	ENST00000344404.10	0	+
chr22	37746232	37774140	ENST00000403663.6	0	+
chr22	37746237	37760346	ENST00000407319.7	0	+
chr22	37746269	37758135	ENST00000428075.5	0	+
chr22	37746321	37757963	ENST00000413051.2	0	+
chr22	37750996	37757982	ENST00000418339.5	0	+
chr22	37751384	37755574	ENST00000452519.5	0	+
chr22	37751397	37758047	ENST00000417857.1	0	+
chr22	37754881	37755510	ENST00000331103.5	0	+
chr22	37805092	37807436	ENST00000340857.3	0	+
chr22	37807904	37817176	ENST00000323205.10	0	+
chr22	37807911	37816899	ENST00000248924.10	0	+
chr22	37807915	37813300	ENST00000445195.5	0	+
chr22	37807922	37813485	ENST00000415371.1	0	+
chr22	37807933	37813076	ENST00000478203.1	0	+
chr22	37807937	37815208	ENST00000426858.1	0	+
chr22	37808013	37813609	ENST00000451984.1	0	+
chr22	37823381	37825485	ENST00000249041.3	0	+
chr22	37830854	37844334	ENST00000215941.9	0	-
chr22	37830855	37834728	ENST00000498417.5	0	-
chr22	37831824	37843882	ENST00000406423.5	0	-
chr22	37831859	37849327	ENST00000609454.5	0	-
chr22	37831934	37844321	ENST00000411961.6	0	-
chr22	37831969	37844006	ENST00000458278.6	0	-
chr22	37831978	37844170	ENST00000407117.6	0	-
chr22	37832675	37840234	ENST00000464849.5	0	-
chr22	37832992	37844276	ENST00000413497.5	0	-
chr22	37833008	37843882	ENST00000424350.5	0	-
chr22	37833040	37844323	ENST00000434930.1	0	-
chr22	37844271	37844371	ENST00000385210.1	0	-
chr22	37847677	37847774	ENST00000384963.1	0	-
chr22	37848178	37849318	ENST00000609706.1	0	-
chr22	37848867	37889407	ENST00000412331.6	0	+
chr22	37849371	37888782	ENST00000624234.3	0	+
chr22	37849410	37870273	ENST00000439997.5	0	+
chr22	37849416	37871284	ENST00000476955.5	0	+
chr22	37849416	37888479	ENST00000381683.10	0	+
chr22	37849418	37889407	ENST00000652021.1	0	+
chr22	37849419	37863033	ENST00000414316.5	0	+
chr22	37849419	37870189	ENST00000436452.6	0	+
chr22	37849420	37886868	ENST00000477256.5	0	+
chr22	37849480	37888630	ENST00000406934.5	0	+
chr22	37849619	37863288	ENST00000451427.1	0	+
chr22	37858564	37871290	ENST00000464790.1	0	+
chr22	37870164	37888631	ENST00000482600.5	0	+
chr22	37876147	37895563	ENST00000623726.1	0	+
chr22	37878060	37888631	ENST00000450376.1	0	+
chr22	37886703	37888631	ENST00000460638.1	0	+
chr22	37891346	37891448	ENST00000384498.1	0	+
chr22	37905656	37922286	ENST00000445494.5	0	+
chr22	37906296	37942822	ENST00000215957.10	0	+
chr22	37906881	37922036	ENST00000489812.1	0	+
chr22	37925846	37937153	ENST00000454685.5	0	+
chr22	37926562	37942009	ENST00000402631.1	0	+
chr22	37932594	37941187	ENST00000424008.1	0	+
chr22	37943049	37944898	ENST00000624344.1	0	+
chr22	37943049	37953601	ENST00000403305.6	0	-
chr22	37943049	37953669	ENST00000619227.4	0	-
chr22	37943525	37953596	ENST00000249079.6	0	-
chr22	37944124	37953669	ENST00000403026.5	0	-
chr22	37944512	37953601	ENST00000418863.5	0	-
chr22	37945160	37953545	ENST00000422191.1	0	-
chr22	37950964	37951778	ENST00000609976.1	0	+
chr22	37952606	37972298	ENST00000484894.5	0	+
chr22	37953696	37967376	ENST00000460648.5	0	+
chr22	37953696	38041106	ENST00000407936.5	0	+
chr22	37953698	37967293	ENST00000488684.5	0	+
chr22	37953698	37967694	ENST00000492213.5	0	+
chr22	37953698	37967798	ENST00000483713.5	0	+
chr22	37953698	37969312	ENST00000442738.7	0	+
chr22	37953698	37988334	ENST00000443002.5	0	+
chr22	37953710	37967560	ENST00000606538.5	0	+
chr22	37953710	38025768	ENST00000405557.5	0	+
chr22	37953726	37967801	ENST00000470701.1	0	+
chr22	37953787	37972272	ENST00000651769.1	0	+
chr22	37967562	37967624	ENST00000611508.1	0	+
chr22	37970685	37978031	ENST00000651746.1	0	-
chr22	37970685	37983414	ENST00000446929.5	0	-
chr22	37972293	37984555	ENST00000396884.7	0	-
chr22	37972299	37987422	ENST00000360880.6	0	-
chr22	37977922	37984346	ENST00000427770.1	0	-
chr22	37978081	37984408	ENST00000470555.1	0	-
chr22	37982997	37984543	ENST00000652356.1	0	-
chr22	37986192	38026685	ENST00000333418.4	0	+
chr22	37986198	38026637	ENST00000427034.1	0	+
chr22	37988793	37988853	ENST00000578108.1	0	+
chr22	38032164	38034228	ENST00000410099.1	0	-
chr22	38040303	38041915	ENST00000608713.1	0	+
chr22	38043229	38043920	ENST00000609265.1	0	+
chr22	38056310	38059306	ENST00000445628.5	0	+
chr22	38057179	38073940	ENST00000445483.1	0	-
chr22	38057199	38075701	ENST00000404072.7	0	+
chr22	38057227	38072608	ENST00000424694.5	0	+
chr22	38057254	38075693	ENST00000484021.5	0	+
chr22	38057394	38068114	ENST00000437453.5	0	+
chr22	38057403	38058158	ENST00000476157.5	0	+
chr22	38057403	38070891	ENST00000426258.5	0	+
chr22	38057403	38070891	ENST00000468288.5	0	+
chr22	38057403	38075701	ENST00000356976.8	0	+
chr22	38057406	38065226	ENST00000469819.5	0	+
chr22	38057482	38068085	ENST00000466374.1	0	+
chr22	38057652	38069085	ENST00000435166.1	0	+
chr22	38057655	38071728	ENST00000432756.6	0	+
chr22	38065112	38072007	ENST00000472724.5	0	+
chr22	38067476	38072534	ENST00000494434.1	0	+
chr22	38078133	38083142	ENST00000320521.9	0	-
chr22	38078133	38084093	ENST00000469516.5	0	-
chr22	38081954	38084093	ENST00000427592.1	0	-
chr22	38084899	38110684	ENST00000381669.8	0	-
chr22	38084902	38110645	ENST00000332536.9	0	-
chr22	38085164	38088938	ENST00000428572.1	0	-
chr22	38090126	38091559	ENST00000609162.1	0	+
chr22	38111494	38181826	ENST00000663895.1	0	-
chr22	38111494	38181830	ENST00000332509.8	0	-
chr22	38111495	38181754	ENST00000335539.7	0	-
chr22	38111499	38181829	ENST00000402064.5	0	-
chr22	38111539	38181820	ENST00000668499.1	0	-
chr22	38111581	38181835	ENST00000664587.1	0	-
chr22	38111606	38181825	ENST00000655142.1	0	-
chr22	38111753	38214778	ENST00000660610.1	0	-
chr22	38111826	38181830	ENST00000665987.1	0	-
chr22	38111830	38181800	ENST00000667521.1	0	-
chr22	38111871	38112855	ENST00000463287.1	0	-
chr22	38111872	38181820	ENST00000673413.1	0	-
chr22	38111875	38168077	ENST00000671093.1	0	-
chr22	38111882	38181707	ENST00000668949.1	0	-
chr22	38112167	38181842	ENST00000436218.6	0	-
chr22	38113537	38121040	ENST00000496409.1	0	-
chr22	38115547	38168076	ENST00000427114.6	0	-
chr22	38115555	38126442	ENST00000454670.1	0	-
chr22	38116093	38135063	ENST00000448094.5	0	-
chr22	38119016	38181868	ENST00000668208.1	0	-
chr22	38120758	38126755	ENST00000491986.1	0	-
chr22	38123203	38127228	ENST00000490473.1	0	-
chr22	38126370	38181814	ENST00000471636.5	0	-
chr22	38126995	38132877	ENST00000452794.5	0	-
chr22	38128160	38129506	ENST00000480154.1	0	-
chr22	38129517	38140033	ENST00000427453.5	0	-
chr22	38129540	38143214	ENST00000452542.5	0	-
chr22	38130215	38150612	ENST00000624072.1	0	+
chr22	38134987	38143494	ENST00000498338.1	0	-
chr22	38140064	38181780	ENST00000430886.5	0	-
chr22	38140133	38168077	ENST00000479641.5	0	-
chr22	38143228	38163672	ENST00000445591.5	0	-
chr22	38143236	38168090	ENST00000452972.1	0	-
chr22	38145492	38181808	ENST00000447598.6	0	-
chr22	38145591	38181828	ENST00000435484.5	0	-
chr22	38145605	38192100	ENST00000417303.6	0	-
chr22	38145623	38180404	ENST00000455341.2	0	-
chr22	38147367	38181829	ENST00000420435.5	0	-
chr22	38147378	38181783	ENST00000426674.1	0	-
chr22	38169274	38205690	ENST00000594306.1	0	-
chr22	38200766	38204050	ENST00000624676.1	0	+
chr22	38201931	38216503	ENST00000538320.5	0	+
chr22	38201931	38216503	ENST00000538999.1	0	+
chr22	38201993	38216507	ENST00000338483.7	0	+
chr22	38201995	38214750	ENST00000441709.1	0	+
chr22	38202795	38214760	ENST00000417948.5	0	+
chr22	38203019	38216503	ENST00000426621.6	0	+
chr22	38213534	38216505	ENST00000407965.1	0	+
chr22	38219290	38273010	ENST00000361906.8	0	-
chr22	38219311	38224103	ENST00000633056.1	0	-
chr22	38219311	38273010	ENST00000436674.5	0	-
chr22	38219323	38272664	ENST00000361684.8	0	-
chr22	38220974	38222932	ENST00000504337.1	0	-
chr22	38221144	38246092	ENST00000633438.1	0	-
chr22	38225519	38239502	ENST00000464059.5	0	-
chr22	38225577	38246738	ENST00000403210.2	0	-
chr22	38226379	38231318	ENST00000488844.1	0	-
chr22	38226598	38231456	ENST00000474416.1	0	-
chr22	38230717	38273005	ENST00000457534.5	0	-
chr22	38231319	38232248	ENST00000420172.1	0	+
chr22	38231330	38272938	ENST00000411679.2	0	-
chr22	38231719	38231993	ENST00000478699.2	0	+
chr22	38246715	38273005	ENST00000466117.1	0	-
chr22	38281027	38281289	ENST00000622344.1	0	-
chr22	38290690	38317433	ENST00000396832.6	0	-
chr22	38290690	38318084	ENST00000359867.7	0	-
chr22	38291917	38398522	ENST00000400206.7	0	-
chr22	38291929	38294527	ENST00000366216.6	0	-
chr22	38292313	38298852	ENST00000431632.5	0	-
chr22	38292908	38294459	ENST00000495232.5	0	-
chr22	38293249	38317439	ENST00000403904.5	0	-
chr22	38293259	38300763	ENST00000431611.5	0	-
chr22	38293902	38294534	ENST00000494610.1	0	-
chr22	38296204	38317411	ENST00000413574.6	0	-
chr22	38296205	38317439	ENST00000405675.7	0	-
chr22	38296625	38300065	ENST00000498529.5	0	-
chr22	38297601	38300025	ENST00000442216.1	0	-
chr22	38298028	38303009	ENST00000451964.5	0	-
chr22	38298579	38302990	ENST00000612795.1	0	-
chr22	38299955	38316973	ENST00000430335.1	0	-
chr22	38335761	38398916	ENST00000428294.5	0	-
chr22	38336336	38344434	ENST00000428606.1	0	-
chr22	38344531	38398873	ENST00000407491.6	0	-
chr22	38344623	38398873	ENST00000443042.5	0	-
chr22	38350376	38398860	ENST00000457665.5	0	-
chr22	38360398	38397445	ENST00000381645.7	0	-
chr22	38361139	38398929	ENST00000443658.5	0	-
chr22	38361380	38384096	ENST00000381646.6	0	-
chr22	38368897	38398921	ENST00000455209.5	0	-
chr22	38380150	38398920	ENST00000454577.1	0	-
chr22	38387917	38388857	ENST00000454123.1	0	-
chr22	38416564	38423933	ENST00000652019.1	0	-
chr22	38424287	38427336	ENST00000433230.1	0	+
chr22	38426326	38455199	ENST00000303592.3	0	-
chr22	38434306	38436686	ENST00000662373.1	0	+
chr22	38468077	38481635	ENST00000409006.3	0	+
chr22	38468095	38483447	ENST00000216014.9	0	+
chr22	38474328	38482922	ENST00000471268.1	0	+
chr22	38483437	38506311	ENST00000403230.2	0	-
chr22	38483439	38506337	ENST00000396821.7	0	-
chr22	38485676	38488961	ENST00000431312.2	0	-
chr22	38485680	38506294	ENST00000216019.11	0	-
chr22	38485934	38506237	ENST00000640332.1	0	-
chr22	38485934	38506237	ENST00000640668.1	0	-
chr22	38488025	38493771	ENST00000475004.6	0	-
chr22	38490312	38507660	ENST00000432525.5	0	-
chr22	38493360	38495046	ENST00000497196.5	0	-
chr22	38494309	38495046	ENST00000477112.1	0	-
chr22	38498552	38505901	ENST00000467279.1	0	-
chr22	38505164	38506306	ENST00000479734.1	0	-
chr22	38518947	38570183	ENST00000216024.7	0	-
chr22	38518948	38568409	ENST00000428462.6	0	-
chr22	38538294	38570131	ENST00000439567.5	0	-
chr22	38545767	38570184	ENST00000616615.4	0	-
chr22	38549497	38570184	ENST00000478820.1	0	-
chr22	38552670	38568409	ENST00000415483.1	0	-
chr22	38569283	38570204	ENST00000464842.1	0	-
chr22	38574930	38575431	ENST00000446147.2	0	-
chr22	38578119	38656629	ENST00000535113.5	0	-
chr22	38578119	38656629	ENST00000540952.6	0	-
chr22	38582168	38586145	ENST00000543828.1	0	-
chr22	38582168	38656303	ENST00000355830.11	0	-
chr22	38591433	38656303	ENST00000544346.5	0	-
chr22	38597206	38636551	ENST00000541689.6	0	-
chr22	38620215	38629002	ENST00000543295.1	0	-
chr22	38636545	38653853	ENST00000608409.5	0	-
chr22	38636556	38656278	ENST00000424706.5	0	-
chr22	38638771	38656281	ENST00000484582.1	0	-
chr22	38649944	38656362	ENST00000490082.1	0	-
chr22	38656635	38668467	ENST00000475924.5	0	+
chr22	38656635	38671235	ENST00000485501.5	0	+
chr22	38656639	38670989	ENST00000411557.5	0	+
chr22	38656650	38673846	ENST00000396811.6	0	+
chr22	38656654	38673854	ENST00000216029.7	0	+
chr22	38656670	38671309	ENST00000492537.5	0	+
chr22	38656675	38671136	ENST00000492576.5	0	+
chr22	38656675	38671188	ENST00000416285.5	0	+
chr22	38656687	38673413	ENST00000489847.1	0	+
chr22	38665594	38673844	ENST00000619293.1	0	+
chr22	38666507	38668750	ENST00000423346.1	0	-
chr22	38667584	38681770	ENST00000431924.2	0	-
chr22	38667627	38670354	ENST00000653554.1	0	-
chr22	38667861	38670303	ENST00000422408.2	0	-
chr22	38667895	38670945	ENST00000467118.1	0	+
chr22	38670397	38681812	ENST00000666365.1	0	-
chr22	38670467	38681820	ENST00000444381.1	0	-
chr22	38674874	38681847	ENST00000656432.1	0	-
chr22	38675813	38681787	ENST00000412067.1	0	-
chr22	38675875	38677800	ENST00000666731.1	0	+
chr22	38681956	38685421	ENST00000216034.6	0	+
chr22	38682171	38684160	ENST00000492561.1	0	+
chr22	38685542	38700667	ENST00000216039.9	0	-
chr22	38687717	38699970	ENST00000545590.1	0	-
chr22	38687923	38689323	ENST00000482442.1	0	-
chr22	38689073	38701030	ENST00000427389.5	0	-
chr22	38689295	38700940	ENST00000412832.1	0	-
chr22	38699800	38701556	ENST00000417712.1	0	-
chr22	38699802	38700930	ENST00000456626.1	0	-
chr22	38700160	38701032	ENST00000462610.1	0	-
chr22	38700275	38701418	ENST00000490169.1	0	-
chr22	38700281	38700942	ENST00000493939.1	0	-
chr22	38705741	38716867	ENST00000484657.5	0	+
chr22	38705943	38733587	ENST00000216044.10	0	+
chr22	38705948	38721822	ENST00000418601.1	0	+
chr22	38706117	38716240	ENST00000461428.1	0	+
chr22	38706510	38716669	ENST00000488787.5	0	+
chr22	38706519	38715962	ENST00000470836.1	0	+
chr22	38722742	38723505	ENST00000418695.1	0	-
chr22	38725175	38726428	ENST00000460605.1	0	+
chr22	38725806	38726388	ENST00000475959.1	0	+
chr22	38726305	38738299	ENST00000458073.5	0	+
chr22	38728255	38732239	ENST00000462332.1	0	+
chr22	38730612	38736184	ENST00000484971.1	0	+
chr22	38734323	38736175	ENST00000487538.1	0	+
chr22	38734324	38738295	ENST00000489527.1	0	+
chr22	38734724	38738765	ENST00000420118.1	0	+
chr22	38734724	38755528	ENST00000405510.5	0	-
chr22	38734729	38738990	ENST00000418803.1	0	+
chr22	38734735	38755999	ENST00000405018.5	0	-
chr22	38735908	38794143	ENST00000406622.5	0	-
chr22	38736609	38736792	ENST00000609428.1	0	-
chr22	38737759	38739423	ENST00000455125.1	0	-
chr22	38738237	38739247	ENST00000470642.1	0	-
chr22	38738925	38742438	ENST00000477262.5	0	-
chr22	38739002	38749041	ENST00000416406.1	0	+
chr22	38739340	38742481	ENST00000469086.5	0	-
chr22	38740291	38742578	ENST00000464202.1	0	-
chr22	38742624	38743115	ENST00000609212.1	0	+
chr22	38743494	38743910	ENST00000607991.1	0	+
chr22	38745074	38750314	ENST00000430185.5	0	-
chr22	38745686	38756000	ENST00000438058.5	0	-
chr22	38748712	38754993	ENST00000456894.5	0	-
chr22	38749765	38751441	ENST00000480307.1	0	-
chr22	38750224	38754951	ENST00000420859.5	0	-
chr22	38750887	38754982	ENST00000452294.5	0	-
chr22	38750897	38754943	ENST00000494273.1	0	-
chr22	38750991	38755634	ENST00000433561.5	0	-
chr22	38751006	38754775	ENST00000417332.5	0	-
chr22	38751019	38755429	ENST00000439339.1	0	-
chr22	38778507	38794143	ENST00000216068.9	0	-
chr22	38778852	38794148	ENST00000486019.1	0	-
chr22	38780717	38794198	ENST00000406199.3	0	-
chr22	38785304	38794132	ENST00000475512.2	0	-
chr22	38818451	38844028	ENST00000333039.3	0	-
chr22	38822608	38843858	ENST00000640136.1	0	-
chr22	38861421	38872216	ENST00000407418.8	0	-
chr22	38864228	38872216	ENST00000216083.6	0	-
chr22	38869571	38872249	ENST00000469420.1	0	-
chr22	38886836	38903794	ENST00000656940.1	0	+
chr22	38921226	38924708	ENST00000450216.1	0	+
chr22	38952740	38953416	ENST00000495988.1	0	+
chr22	38952750	38963183	ENST00000402255.5	0	+
chr22	38957521	38959686	ENST00000488758.1	0	+
chr22	38957521	38992778	ENST00000618553.1	0	+
chr22	38957608	38963184	ENST00000249116.7	0	+
chr22	38982346	38992546	ENST00000407298.7	0	+
chr22	38982346	38992779	ENST00000333467.4	0	+
chr22	38982398	38992804	ENST00000402182.7	0	+
chr22	38982405	38992761	ENST00000335760.9	0	+
chr22	38991558	38998209	ENST00000513758.2	0	-
chr22	39014256	39020352	ENST00000361441.5	0	+
chr22	39014261	39018926	ENST00000428892.1	0	+
chr22	39014362	39033276	ENST00000381568.9	0	+
chr22	39021112	39033261	ENST00000427494.6	0	+
chr22	39021126	39033277	ENST00000216099.13	0	+
chr22	39021501	39032316	ENST00000622217.1	0	+
chr22	39040603	39045192	ENST00000491387.1	0	+
chr22	39040856	39044423	ENST00000381565.2	0	+
chr22	39040863	39055972	ENST00000308521.10	0	+
chr22	39045183	39052287	ENST00000476513.1	0	+
chr22	39070413	39070671	ENST00000558766.1	0	+
chr22	39077066	39079700	ENST00000463934.1	0	+
chr22	39077078	39081687	ENST00000480000.5	0	+
chr22	39077083	39083884	ENST00000461827.5	0	+
chr22	39077274	39087743	ENST00000407997.4	0	+
chr22	39078511	39081194	ENST00000486357.1	0	+
chr22	39079661	39082679	ENST00000494150.1	0	+
chr22	39080634	39083214	ENST00000481958.1	0	+
chr22	39091585	39092110	ENST00000560933.5	0	+
chr22	39091633	39092855	ENST00000424436.1	0	+
chr22	39097223	39102558	ENST00000421988.6	0	+
chr22	39097223	39102571	ENST00000613677.4	0	+
chr22	39097223	39104067	ENST00000348946.8	0	+
chr22	39097270	39100536	ENST00000474235.1	0	+
chr22	39097274	39103998	ENST00000401756.5	0	+
chr22	39097284	39104067	ENST00000442487.7	0	+
chr22	39100278	39102558	ENST00000613996.1	0	+
chr22	39120166	39149832	ENST00000475962.5	0	-
chr22	39120292	39120566	ENST00000414780.1	0	-
chr22	39130771	39152680	ENST00000216133.10	0	-
chr22	39133089	39136760	ENST00000624656.1	0	+
chr22	39133583	39152444	ENST00000401405.7	0	-
chr22	39133690	39134779	ENST00000490741.1	0	-
chr22	39134568	39152552	ENST00000434260.1	0	-
chr22	39141119	39152540	ENST00000477827.1	0	-
chr22	39147117	39152674	ENST00000482294.1	0	-
chr22	39155524	39155945	ENST00000441207.1	0	+
chr22	39223358	39244982	ENST00000331163.11	0	-
chr22	39224934	39240909	ENST00000381551.8	0	-
chr22	39230158	39243016	ENST00000455790.5	0	-
chr22	39230183	39241130	ENST00000440375.1	0	-
chr22	39242187	39279897	ENST00000641859.1	0	+
chr22	39242224	39251814	ENST00000641466.1	0	+
chr22	39264986	39279897	ENST00000642075.1	0	+
chr22	39292987	39295563	ENST00000434516.1	0	+
chr22	39294141	39295589	ENST00000641533.1	0	+
chr22	39312881	39318614	ENST00000401609.5	0	-
chr22	39312881	39319623	ENST00000216146.9	0	-
chr22	39312883	39313705	ENST00000464182.5	0	-
chr22	39312883	39313729	ENST00000473638.5	0	-
chr22	39312883	39320389	ENST00000465618.5	0	-
chr22	39312886	39314965	ENST00000467105.1	0	-
chr22	39313706	39319623	ENST00000402527.5	0	-
chr22	39313818	39313911	ENST00000386745.1	0	-
chr22	39313930	39315486	ENST00000481985.5	0	-
chr22	39314149	39315429	ENST00000471290.1	0	-
chr22	39314699	39319111	ENST00000453303.5	0	-
chr22	39315212	39315307	ENST00000386747.1	0	-
chr22	39315361	39317068	ENST00000484358.1	0	-
chr22	39315368	39319595	ENST00000427905.5	0	-
chr22	39315551	39319623	ENST00000420536.1	0	-
chr22	39317050	39319649	ENST00000484615.5	0	-
chr22	39317395	39319592	ENST00000460589.5	0	-
chr22	39317854	39319623	ENST00000461967.1	0	-
chr22	39318111	39319577	ENST00000498462.1	0	-
chr22	39318399	39319625	ENST00000459859.1	0	-
chr22	39319049	39319113	ENST00000583861.1	0	-
chr22	39349924	39378389	ENST00000318801.8	0	+
chr22	39349948	39376033	ENST00000216155.11	0	+
chr22	39349986	39377911	ENST00000415332.1	0	+
chr22	39349990	39377075	ENST00000406293.7	0	+
chr22	39349990	39385575	ENST00000328933.10	0	+
chr22	39350005	39376210	ENST00000489206.1	0	+
chr22	39364169	39378381	ENST00000381535.4	0	+
chr22	39379609	39380015	ENST00000623458.1	0	+
chr22	39399777	39416849	ENST00000473613.5	0	+
chr22	39399779	39431882	ENST00000216160.11	0	+
chr22	39399783	39436823	ENST00000331454.3	0	+
chr22	39414729	39416796	ENST00000461775.1	0	+
chr22	39416743	39417816	ENST00000490819.1	0	+
chr22	39421734	39428134	ENST00000473491.1	0	+
chr22	39432430	39437060	ENST00000488859.1	0	+
chr22	39457011	39492194	ENST00000341184.7	0	+
chr22	39472787	39487830	ENST00000429402.1	0	+
chr22	39475806	39476822	ENST00000432239.1	0	-
chr22	39477512	39487588	ENST00000418314.1	0	+
chr22	39499431	39504302	ENST00000464629.5	0	+
chr22	39500099	39515708	ENST00000402881.5	0	+
chr22	39500346	39504454	ENST00000467866.1	0	+
chr22	39501730	39502385	ENST00000465564.1	0	+
chr22	39502210	39503862	ENST00000489792.1	0	+
chr22	39502251	39504534	ENST00000478342.5	0	+
chr22	39502255	39504485	ENST00000481746.1	0	+
chr22	39502289	39518132	ENST00000325301.7	0	+
chr22	39502301	39516380	ENST00000433117.6	0	+
chr22	39502354	39504711	ENST00000494219.1	0	+
chr22	39502376	39516178	ENST00000404569.5	0	+
chr22	39502394	39511422	ENST00000434364.5	0	+
chr22	39502396	39513866	ENST00000428069.1	0	+
chr22	39503456	39504510	ENST00000479514.1	0	+
chr22	39504230	39504443	ENST00000637854.1	0	+
chr22	39519694	39522683	ENST00000404241.6	0	+
chr22	39520563	39522683	ENST00000337304.2	0	+
chr22	39520563	39522685	ENST00000396680.2	0	+
chr22	39529092	39532748	ENST00000334678.8	0	-
chr22	39529096	39532093	ENST00000488602.1	0	-
chr22	39529155	39532761	ENST00000420879.5	0	-
chr22	39529213	39532738	ENST00000459956.1	0	-
chr22	39529445	39532732	ENST00000491966.1	0	-
chr22	39531728	39532760	ENST00000472291.1	0	-
chr22	39570752	39684528	ENST00000407673.5	0	+
chr22	39570752	39685211	ENST00000401624.5	0	+
chr22	39570752	39689730	ENST00000404898.5	0	+
chr22	39570752	39689735	ENST00000402142.4	0	+
chr22	39642796	39643512	ENST00000471970.1	0	+
chr22	39743043	39893760	ENST00000325157.7	0	-
chr22	39750126	39751293	ENST00000458183.1	0	+
chr22	39874254	39874515	ENST00000459176.1	0	-
chr22	39875288	39876108	ENST00000435169.1	0	+
chr22	39893214	39893864	ENST00000490326.1	0	-
chr22	39901083	39973721	ENST00000344138.9	0	+
chr22	39926590	39960175	ENST00000420971.5	0	+
chr22	39926602	39966109	ENST00000478445.1	0	+
chr22	39926648	39948481	ENST00000461082.1	0	+
chr22	39946816	39971105	ENST00000407075.3	0	+
chr22	39960396	39964718	ENST00000424496.2	0	+
chr22	39966049	39968696	ENST00000481263.1	0	+
chr22	39969374	39971187	ENST00000460449.1	0	+
chr22	39994953	40043534	ENST00000333407.11	0	+
chr22	39995361	39999125	ENST00000488874.1	0	+
chr22	40009687	40030031	ENST00000473717.1	0	+
chr22	40043067	40044530	ENST00000569912.1	0	-
chr22	40044816	40156182	ENST00000441751.5	0	+
chr22	40044816	40335807	ENST00000301923.13	0	+
chr22	40045473	40323360	ENST00000402203.5	0	+
chr22	40106324	40107066	ENST00000449200.1	0	-
chr22	40177924	40246362	ENST00000489500.1	0	+
chr22	40177924	40335808	ENST00000454349.7	0	+
chr22	40177941	40335808	ENST00000335727.13	0	+
chr22	40265002	40321799	ENST00000446273.1	0	+
chr22	40276839	40300498	ENST00000497559.1	0	+
chr22	40300855	40301495	ENST00000467658.1	0	+
chr22	40346499	40361313	ENST00000623632.3	0	+
chr22	40346499	40369367	ENST00000623063.3	0	+
chr22	40346502	40366996	ENST00000342312.9	0	+
chr22	40346513	40387476	ENST00000636714.1	0	+
chr22	40346517	40350323	ENST00000466863.1	0	+
chr22	40346517	40352055	ENST00000624503.1	0	+
chr22	40346523	40387489	ENST00000637669.1	0	+
chr22	40346527	40366614	ENST00000216194.11	0	+
chr22	40346528	40359302	ENST00000477111.2	0	+
chr22	40346528	40361566	ENST00000623978.3	0	+
chr22	40346528	40364362	ENST00000636265.1	0	+
chr22	40346528	40410054	ENST00000639722.1	0	+
chr22	40346529	40361577	ENST00000623287.3	0	+
chr22	40346531	40360431	ENST00000624474.1	0	+
chr22	40346554	40363014	ENST00000637666.1	0	+
chr22	40347303	40358969	ENST00000636124.1	0	+
chr22	40348638	40358912	ENST00000638161.1	0	+
chr22	40353823	40366489	ENST00000636433.1	0	+
chr22	40354258	40366517	ENST00000625194.3	0	+
chr22	40358967	40363053	ENST00000480775.2	0	+
chr22	40364557	40366556	ENST00000623387.1	0	+
chr22	40364961	40368038	ENST00000423176.6	0	+
chr22	40364961	40368105	ENST00000624027.1	0	+
chr22	40365030	40390463	ENST00000498234.2	0	+
chr22	40370590	40406223	ENST00000457767.5	0	+
chr22	40370593	40410289	ENST00000248929.14	0	+
chr22	40370638	40410108	ENST00000485962.5	0	+
chr22	40372663	40388290	ENST00000414261.1	0	-
chr22	40372731	40388217	ENST00000607915.5	0	-
chr22	40372736	40388217	ENST00000608572.1	0	-
chr22	40404112	40410103	ENST00000478085.5	0	+
chr22	40406112	40408409	ENST00000480830.5	0	+
chr22	40406437	40408429	ENST00000481408.5	0	+
chr22	40407250	40407701	ENST00000481028.1	0	+
chr22	40407276	40409116	ENST00000470518.5	0	+
chr22	40407338	40408687	ENST00000467915.1	0	+
chr22	40408312	40409811	ENST00000427834.5	0	+
chr22	40408387	40410047	ENST00000469719.5	0	+
chr22	40408626	40409785	ENST00000462457.1	0	+
chr22	40408793	40410118	ENST00000417424.1	0	+
chr22	40410280	40636685	ENST00000651595.1	0	-
chr22	40410281	40436310	ENST00000614754.4	0	-
chr22	40410281	40436310	ENST00000620651.4	0	-
chr22	40410281	40636685	ENST00000396617.7	0	-
chr22	40410288	40463440	ENST00000407029.7	0	-
chr22	40410288	40533827	ENST00000652095.2	0	-
chr22	40410288	40636719	ENST00000355630.9	0	-
chr22	40410289	40636691	ENST00000402042.6	0	-
chr22	40411143	40552311	ENST00000618417.1	0	-
chr22	40411389	40463227	ENST00000618196.6	0	-
chr22	40411838	40417734	ENST00000477468.1	0	-
chr22	40415002	40415445	ENST00000609279.1	0	+
chr22	40429350	40463418	ENST00000402630.5	0	-
chr22	40429640	40533822	ENST00000651694.1	0	-
chr22	40431404	40533811	ENST00000422851.1	0	-
chr22	40433135	40433641	ENST00000442557.1	0	+
chr22	40463227	40636687	ENST00000463769.6	0	-
chr22	40519127	40636649	ENST00000650658.1	0	-
chr22	40519584	40636702	ENST00000466278.1	0	-
chr22	40521799	40526707	ENST00000417123.1	0	+
chr22	40569285	40569895	ENST00000312310.3	0	+
chr22	40586699	40588003	ENST00000438116.2	0	-
chr22	40587142	40636698	ENST00000651158.1	0	-
chr22	40673483	40674451	ENST00000442317.2	0	-
chr22	40678749	40682814	ENST00000249016.4	0	+
chr22	40679272	40682083	ENST00000381433.2	0	+
chr22	40679272	40682083	ENST00000498400.1	0	+
chr22	40680106	40681412	ENST00000465662.1	0	+
chr22	40769629	40819330	ENST00000263255.10	0	-
chr22	40769629	40819338	ENST00000491545.5	0	-
chr22	40769629	40819346	ENST00000435456.7	0	-
chr22	40769629	40819399	ENST00000544408.5	0	-
chr22	40770087	40780194	ENST00000402844.7	0	-
chr22	40770488	40819350	ENST00000447566.5	0	-
chr22	40770894	40819326	ENST00000420970.5	0	-
chr22	40773936	40819316	ENST00000430221.5	0	-
chr22	40777059	40819367	ENST00000427084.5	0	-
chr22	40777227	40819399	ENST00000458600.5	0	-
chr22	40777298	40819380	ENST00000443810.5	0	-
chr22	40777337	40819389	ENST00000412879.5	0	-
chr22	40777359	40819332	ENST00000426396.5	0	-
chr22	40779033	40819337	ENST00000434193.5	0	-
chr22	40779061	40780255	ENST00000478550.1	0	-
chr22	40779077	40819346	ENST00000449676.5	0	-
chr22	40788776	40789267	ENST00000424746.1	0	-
chr22	40792531	40819375	ENST00000434185.1	0	-
chr22	40798038	40798285	ENST00000441314.1	0	+
chr22	40813882	40813958	ENST00000584704.1	0	-
chr22	40824534	40856639	ENST00000216218.8	0	-
chr22	40826420	40856565	ENST00000620312.4	0	-
chr22	40827149	40835905	ENST00000480048.5	0	-
chr22	40827220	40856616	ENST00000455824.1	0	-
chr22	40830874	40844890	ENST00000413424.5	0	-
chr22	40832651	40854598	ENST00000411695.1	0	-
chr22	40835392	40835949	ENST00000495652.1	0	-
chr22	40857076	40870343	ENST00000417688.5	0	+
chr22	40857096	40862770	ENST00000482652.1	0	+
chr22	40857119	40860781	ENST00000614001.1	0	+
chr22	40857130	40927505	ENST00000428799.1	0	+
chr22	40857147	40932815	ENST00000357137.9	0	+
chr22	40859548	40862113	ENST00000307221.5	0	-
chr22	40862265	40881924	ENST00000465258.1	0	+
chr22	40917110	40917381	ENST00000616394.1	0	+
chr22	40917742	40917846	ENST00000363813.1	0	+
chr22	40951346	40964411	ENST00000476110.1	0	+
chr22	40951377	40973309	ENST00000216225.9	0	+
chr22	40966200	40972662	ENST00000467617.1	0	+
chr22	41045496	41045591	ENST00000410766.1	0	+
chr22	41064051	41064633	ENST00000418783.1	0	+
chr22	41065553	41065646	ENST00000362645.1	0	-
chr22	41074179	41075239	ENST00000423293.1	0	-
chr22	41092512	41092566	ENST00000408233.1	0	+
chr22	41092591	41180077	ENST00000263253.9	0	+
chr22	41108024	41117212	ENST00000637592.1	0	+
chr22	41117770	41126308	ENST00000634787.1	0	+
chr22	41125851	41127725	ENST00000634860.1	0	+
chr22	41131532	41146809	ENST00000634690.1	0	+
chr22	41141047	41149153	ENST00000634728.1	0	+
chr22	41146552	41147890	ENST00000635538.1	0	+
chr22	41148818	41149891	ENST00000634422.1	0	+
chr22	41157199	41158604	ENST00000635691.1	0	+
chr22	41158467	41160419	ENST00000635552.1	0	+
chr22	41162726	41169329	ENST00000635584.1	0	+
chr22	41168216	41168316	ENST00000517050.1	0	+
chr22	41169189	41183144	ENST00000415054.1	0	-
chr22	41174590	41197456	ENST00000420537.1	0	-
chr22	41176058	41176915	ENST00000635083.1	0	+
chr22	41189726	41190188	ENST00000452297.1	0	-
chr22	41205281	41227810	ENST00000481902.5	0	+
chr22	41205301	41217173	ENST00000489136.5	0	+
chr22	41205308	41230654	ENST00000466589.5	0	+
chr22	41205311	41210008	ENST00000453659.1	0	+
chr22	41205311	41231271	ENST00000216237.10	0	+
chr22	41205320	41231270	ENST00000479978.5	0	+
chr22	41205323	41231271	ENST00000452106.5	0	+
chr22	41207591	41217910	ENST00000658453.1	0	-
chr22	41208551	41217646	ENST00000441316.2	0	-
chr22	41209100	41228500	ENST00000657161.1	0	-
chr22	41209681	41217623	ENST00000451176.1	0	-
chr22	41209695	41221367	ENST00000449635.5	0	+
chr22	41209695	41224165	ENST00000450939.1	0	+
chr22	41229509	41239621	ENST00000417999.5	0	-
chr22	41229512	41240931	ENST00000216241.14	0	-
chr22	41235139	41240894	ENST00000455425.1	0	-
chr22	41245610	41285965	ENST00000405486.5	0	-
chr22	41245610	41286251	ENST00000356244.7	0	-
chr22	41245614	41282514	ENST00000455915.6	0	-
chr22	41252991	41253050	ENST00000613185.1	0	-
chr22	41256020	41274660	ENST00000446258.5	0	-
chr22	41264743	41281621	ENST00000452543.5	0	-
chr22	41268096	41281638	ENST00000418067.1	0	-
chr22	41280932	41285905	ENST00000422838.1	0	-
chr22	41301524	41360147	ENST00000352645.5	0	+
chr22	41301714	41336843	ENST00000486331.1	0	+
chr22	41367332	41399324	ENST00000406644.7	0	+
chr22	41377176	41377283	ENST00000362995.1	0	-
chr22	41381941	41399326	ENST00000266304.9	0	+
chr22	41382934	41394274	ENST00000413942.1	0	+
chr22	41430933	41431375	ENST00000609612.1	0	+
chr22	41433493	41446801	ENST00000327492.4	0	-
chr22	41437041	41446785	ENST00000434408.1	0	-
chr22	41459716	41468692	ENST00000216252.4	0	-
chr22	41460302	41468625	ENST00000459687.5	0	-
chr22	41460431	41468499	ENST00000491254.1	0	-
chr22	41469116	41528974	ENST00000216254.9	0	+
chr22	41469129	41528989	ENST00000396512.3	0	+
chr22	41469159	41511966	ENST00000471094.1	0	+
chr22	41507837	41512287	ENST00000482208.1	0	+
chr22	41508014	41516123	ENST00000478010.1	0	+
chr22	41515512	41517452	ENST00000481310.1	0	+
chr22	41515765	41521590	ENST00000466237.1	0	+
chr22	41525798	41544470	ENST00000355209.9	0	-
chr22	41525803	41544489	ENST00000396504.6	0	-
chr22	41528841	41544462	ENST00000337566.9	0	-
chr22	41528842	41544455	ENST00000431534.5	0	-
chr22	41528864	41544427	ENST00000432789.1	0	-
chr22	41529211	41544606	ENST00000407461.5	0	-
chr22	41532118	41544460	ENST00000442616.1	0	-
chr22	41532624	41544386	ENST00000420561.1	0	-
chr22	41539142	41544472	ENST00000483837.1	0	-
chr22	41551709	41560905	ENST00000664614.1	0	-
chr22	41561009	41576666	ENST00000306149.12	0	+
chr22	41572016	41577741	ENST00000460790.1	0	+
chr22	41576899	41589840	ENST00000216259.8	0	-
chr22	41576902	41580689	ENST00000463617.5	0	-
chr22	41576902	41585402	ENST00000482178.5	0	-
chr22	41576930	41589871	ENST00000472620.5	0	-
chr22	41576936	41589818	ENST00000485648.5	0	-
chr22	41583982	41589796	ENST00000466645.5	0	-
chr22	41584005	41589825	ENST00000478337.1	0	-
chr22	41584019	41589847	ENST00000414636.1	0	-
chr22	41586105	41589829	ENST00000493389.1	0	-
chr22	41598027	41621043	ENST00000263256.7	0	-
chr22	41600892	41602523	ENST00000468151.1	0	-
chr22	41602000	41604225	ENST00000463886.1	0	-
chr22	41602974	41604087	ENST00000482612.1	0	-
chr22	41621118	41664039	ENST00000360079.7	0	+
chr22	41621162	41664048	ENST00000428575.6	0	+
chr22	41621284	41628318	ENST00000464116.2	0	+
chr22	41621294	41664048	ENST00000402580.7	0	+
chr22	41621349	41664039	ENST00000359308.8	0	+
chr22	41621460	41664039	ENST00000405878.5	0	+
chr22	41621982	41664034	ENST00000405506.2	0	+
chr22	41648088	41648190	ENST00000363462.1	0	-
chr22	41656952	41661699	ENST00000478914.1	0	+
chr22	41673932	41688867	ENST00000401959.6	0	-
chr22	41673933	41676323	ENST00000648161.1	0	-
chr22	41674614	41688895	ENST00000648674.1	0	-
chr22	41674706	41682622	ENST00000215956.10	0	-
chr22	41674788	41688804	ENST00000463675.6	0	-
chr22	41674816	41688825	ENST00000649479.1	0	-
chr22	41674816	41690462	ENST00000648350.1	0	-
chr22	41674924	41690504	ENST00000402458.1	0	-
chr22	41675030	41688799	ENST00000469028.2	0	-
chr22	41675032	41690504	ENST00000649722.1	0	-
chr22	41675151	41682456	ENST00000488571.2	0	-
chr22	41676744	41677125	ENST00000647971.1	0	-
chr22	41679710	41679817	ENST00000384726.1	0	+
chr22	41687741	41688847	ENST00000469522.1	0	-
chr22	41688876	41697774	ENST00000664954.1	0	+
chr22	41688936	41697886	ENST00000472110.3	0	+
chr22	41688938	41698136	ENST00000656592.1	0	+
chr22	41688983	41698127	ENST00000660688.1	0	+
chr22	41690542	41698136	ENST00000668666.1	0	+
chr22	41690627	41698136	ENST00000654032.1	0	+
chr22	41699502	41799454	ENST00000401548.8	0	+
chr22	41699513	41767608	ENST00000540833.1	0	+
chr22	41709224	41709489	ENST00000448085.1	0	-
chr22	41722042	41722131	ENST00000516794.1	0	-
chr22	41731055	41799414	ENST00000651135.1	0	+
chr22	41747311	41747413	ENST00000384086.1	0	+
chr22	41752459	41763249	ENST00000492484.5	0	+
chr22	41752503	41770809	ENST00000482055.5	0	+
chr22	41752516	41770706	ENST00000476614.1	0	+
chr22	41753582	41758478	ENST00000460702.1	0	+
chr22	41774179	41781737	ENST00000462246.5	0	+
chr22	41776046	41781363	ENST00000473736.5	0	+
chr22	41776048	41781824	ENST00000462450.5	0	+
chr22	41776110	41784360	ENST00000498456.1	0	+
chr22	41781350	41799340	ENST00000487535.5	0	+
chr22	41781398	41799305	ENST00000476893.5	0	+
chr22	41781445	41795773	ENST00000484966.1	0	+
chr22	41781734	41799456	ENST00000403492.5	0	+
chr22	41795421	41799455	ENST00000423900.1	0	+
chr22	41800678	41825893	ENST00000402061.7	0	+
chr22	41800678	41826299	ENST00000255784.5	0	+
chr22	41831214	41834665	ENST00000333487.2	0	-
chr22	41833078	41907307	ENST00000612482.4	0	+
chr22	41833104	41907305	ENST00000361204.9	0	+
chr22	41833163	41907306	ENST00000424354.5	0	+
chr22	41873688	41877396	ENST00000462539.1	0	+
chr22	41875430	41877366	ENST00000464119.1	0	+
chr22	41885595	41907306	ENST00000491541.1	0	+
chr22	41897067	41899188	ENST00000463741.1	0	+
chr22	41898675	41904973	ENST00000435061.1	0	+
chr22	41900082	41903003	ENST00000490262.1	0	+
chr22	41900943	41901012	ENST00000385197.1	0	+
chr22	41909553	41914667	ENST00000621082.1	0	-
chr22	41911292	41914566	ENST00000457093.1	0	-
chr22	41922022	41926818	ENST00000291232.4	0	-
chr22	41923221	41923297	ENST00000582688.1	0	-
chr22	41938736	41940219	ENST00000472374.6	0	-
chr22	41938736	41946688	ENST00000404067.5	0	-
chr22	41938736	41946693	ENST00000402338.5	0	-
chr22	41938736	41947152	ENST00000215980.10	0	-
chr22	41938748	41947136	ENST00000407253.7	0	-
chr22	41939171	41946729	ENST00000402420.1	0	-
chr22	41944920	41947151	ENST00000396437.3	0	-
chr22	41946307	41947133	ENST00000460824.1	0	-
chr22	41952173	41958933	ENST00000381348.4	0	+
chr22	41958215	41958540	ENST00000669957.1	0	+
chr22	41969474	41998221	ENST00000644076.1	0	+
chr22	41976271	41989589	ENST00000449288.5	0	+
chr22	41976809	41998221	ENST00000396426.7	0	+
chr22	41976844	41997728	ENST00000406029.5	0	+
chr22	41976933	41998221	ENST00000396425.7	0	+
chr22	41977049	41998066	ENST00000396417.2	0	+
chr22	41981303	41982217	ENST00000424613.1	0	-
chr22	41985641	41987689	ENST00000460267.1	0	+
chr22	41998724	42032769	ENST00000412113.5	0	+
chr22	41998724	42032929	ENST00000329620.9	0	+
chr22	41998771	42058456	ENST00000436265.5	0	+
chr22	41998787	42027040	ENST00000475341.5	0	+
chr22	41998787	42028475	ENST00000328823.13	0	+
chr22	41998792	42003463	ENST00000461730.1	0	+
chr22	41998792	42026794	ENST00000445185.1	0	+
chr22	42000445	42001991	ENST00000652665.1	0	-
chr22	42001068	42001966	ENST00000450250.1	0	-
chr22	42019712	42032650	ENST00000543212.5	0	+
chr22	42027162	42032769	ENST00000487176.1	0	+
chr22	42030545	42032931	ENST00000470812.1	0	+
chr22	42058333	42070812	ENST00000396398.8	0	-
chr22	42059795	42070830	ENST00000403363.5	0	-
chr22	42059801	42070842	ENST00000402937.1	0	-
chr22	42070948	42071488	ENST00000662963.1	0	+
chr22	42074247	42079438	ENST00000321753.8	0	+
chr22	42074356	42077618	ENST00000419475.1	0	+
chr22	42076057	42076161	ENST00000458891.1	0	-
chr22	42079690	42083353	ENST00000422252.1	0	+
chr22	42079699	42082225	ENST00000484235.1	0	+
chr22	42079699	42084284	ENST00000331479.4	0	+
chr22	42085525	42090772	ENST00000498737.8	0	-
chr22	42085526	42090884	ENST00000617763.1	0	-
chr22	42086115	42087335	ENST00000470753.1	0	-
chr22	42089629	42090028	ENST00000602718.1	0	-
chr22	42090930	42137742	ENST00000439129.5	0	+
chr22	42090955	42125349	ENST00000417327.5	0	+
chr22	42090966	42091596	ENST00000608974.5	0	+
chr22	42090966	42124959	ENST00000536447.6	0	+
chr22	42091203	42124959	ENST00000595777.5	0	+
chr22	42091351	42124959	ENST00000600968.5	0	+
chr22	42091401	42125002	ENST00000434834.5	0	+
chr22	42107764	42108953	ENST00000396387.2	0	-
chr22	42118255	42124899	ENST00000610250.5	0	+
chr22	42118325	42124899	ENST00000547929.6	0	+
chr22	42118346	42124562	ENST00000617396.4	0	+
chr22	42118346	42124899	ENST00000609499.5	0	+
chr22	42118498	42124899	ENST00000609833.5	0	+
chr22	42121196	42124884	ENST00000608643.5	0	+
chr22	42123733	42125028	ENST00000451451.1	0	+
chr22	42126498	42129909	ENST00000360124.9	0	-
chr22	42126498	42130810	ENST00000645361.2	0	-
chr22	42126500	42130881	ENST00000389970.7	0	-
chr22	42126534	42130813	ENST00000488442.1	0	-
chr22	42126555	42130865	ENST00000359033.4	0	-
chr22	42132030	42135239	ENST00000617009.4	0	+
chr22	42132030	42137050	ENST00000621190.1	0	+
chr22	42132542	42132998	ENST00000417586.1	0	+
chr22	42136432	42139927	ENST00000428786.1	0	-
chr22	42138059	42139726	ENST00000626627.1	0	+
chr22	42140202	42141924	ENST00000435101.1	0	-
chr22	42140202	42144549	ENST00000358097.8	0	-
chr22	42140202	42144577	ENST00000433992.2	0	-
chr22	42140204	42144554	ENST00000614967.4	0	-
chr22	42140204	42144577	ENST00000612115.1	0	-
chr22	42140216	42149455	ENST00000651010.1	0	-
chr22	42141241	42144549	ENST00000610593.4	0	-
chr22	42142736	42143999	ENST00000424775.1	0	-
chr22	42143283	42144549	ENST00000435688.1	0	-
chr22	42149885	42155001	ENST00000433633.1	0	-
chr22	42160012	42215442	ENST00000359486.7	0	-
chr22	42161055	42215440	ENST00000335626.8	0	-
chr22	42161097	42210208	ENST00000404876.1	0	-
chr22	42214846	42343616	ENST00000515426.1	0	-
chr22	42269702	42279493	ENST00000609564.2	0	+
chr22	42269743	42279534	ENST00000332965.4	0	+
chr22	42269767	42275033	ENST00000446578.1	0	+
chr22	42269784	42277001	ENST00000415205.2	0	+
chr22	42276354	42277052	ENST00000420096.2	0	+
chr22	42364389	42369284	ENST00000653912.1	0	-
chr22	42364399	42369168	ENST00000432473.1	0	-
chr22	42364401	42369208	ENST00000412060.1	0	-
chr22	42364402	42369249	ENST00000669665.1	0	-
chr22	42364524	42369236	ENST00000424852.1	0	-
chr22	42380409	42432395	ENST00000329021.9	0	-
chr22	42410276	42432362	ENST00000355469.4	0	-
chr22	42438022	42446195	ENST00000428765.1	0	+
chr22	42500567	42512544	ENST00000652221.1	0	+
chr22	42500578	42512559	ENST00000455578.5	0	+
chr22	42500589	42512560	ENST00000359906.6	0	+
chr22	42500670	42512237	ENST00000642172.1	0	+
chr22	42501595	42502653	ENST00000434218.1	0	+
chr22	42508343	42519796	ENST00000323013.7	0	-
chr22	42514676	42519794	ENST00000416699.5	0	-
chr22	42515200	42519796	ENST00000483303.1	0	-
chr22	42553616	42558412	ENST00000447870.6	0	+
chr22	42553858	42574381	ENST00000477564.5	0	+
chr22	42553861	42574381	ENST00000340239.8	0	+
chr22	42553884	42571664	ENST00000534080.5	0	+
chr22	42553917	42573997	ENST00000416156.6	0	+
chr22	42553934	42574381	ENST00000527167.5	0	+
chr22	42553955	42574382	ENST00000327678.10	0	+
chr22	42554014	42574165	ENST00000407614.8	0	+
chr22	42554020	42574055	ENST00000335879.5	0	+
chr22	42554908	42555966	ENST00000472589.1	0	+
chr22	42555222	42582038	ENST00000357802.6	0	-
chr22	42565047	42565330	ENST00000365188.1	0	-
chr22	42569128	42582009	ENST00000437211.5	0	-
chr22	42569146	42569250	ENST00000516104.1	0	-
chr22	42573243	42582011	ENST00000458605.5	0	-
chr22	42574732	42582001	ENST00000566851.5	0	-
chr22	42577097	42581997	ENST00000421116.1	0	-
chr22	42583720	42614869	ENST00000445215.5	0	-
chr22	42583720	42614883	ENST00000252115.10	0	-
chr22	42583720	42614931	ENST00000348657.6	0	-
chr22	42583721	42614911	ENST00000617178.4	0	-
chr22	42583721	42614962	ENST00000451060.6	0	-
chr22	42584983	42614857	ENST00000339677.10	0	-
chr22	42585886	42603012	ENST00000491021.1	0	-
chr22	42597707	42614860	ENST00000454057.1	0	-
chr22	42602767	42614877	ENST00000463133.1	0	-
chr22	42615243	42615393	ENST00000362512.1	0	+
chr22	42615243	42615907	ENST00000602478.1	0	+
chr22	42617839	42644509	ENST00000361740.8	0	-
chr22	42617839	42649392	ENST00000352397.10	0	-
chr22	42618841	42646977	ENST00000407623.7	0	-
chr22	42618852	42636890	ENST00000407332.5	0	-
chr22	42618874	42633348	ENST00000470741.1	0	-
chr22	42619743	42646960	ENST00000402438.5	0	-
chr22	42627640	42647070	ENST00000438270.1	0	-
chr22	42631040	42646679	ENST00000466276.1	0	-
chr22	42639802	42640601	ENST00000505920.1	0	-
chr22	42692120	42694986	ENST00000401850.5	0	-
chr22	42692120	42720870	ENST00000642412.2	0	-
chr22	42692121	42695633	ENST00000249005.3	0	-
chr22	42692121	42720819	ENST00000381278.4	0	-
chr22	42693970	42720829	ENST00000483026.2	0	-
chr22	42695170	42721298	ENST00000465765.2	0	-
chr22	42718245	42720862	ENST00000647439.1	0	-
chr22	42762859	42762959	ENST00000363578.1	0	-
chr22	42776405	42777296	ENST00000426545.1	0	-
chr22	42784461	42787100	ENST00000441462.1	0	+
chr22	42791813	42794313	ENST00000616842.1	0	-
chr22	42796501	42857273	ENST00000263245.10	0	-
chr22	42797413	42857239	ENST00000437119.6	0	-
chr22	42807073	42831652	ENST00000453516.5	0	-
chr22	42823655	42857268	ENST00000454099.5	0	-
chr22	42835411	42876207	ENST00000507586.1	0	-
chr22	42835412	42858106	ENST00000435208.1	0	-
chr22	42847233	42857294	ENST00000493606.1	0	-
chr22	42853402	42853799	ENST00000423154.1	0	-
chr22	42869767	42947041	ENST00000337959.8	0	-
chr22	42869772	43015149	ENST00000263246.8	0	-
chr22	42869774	42959881	ENST00000407585.5	0	-
chr22	42870929	42947041	ENST00000403744.7	0	-
chr22	42871127	42959878	ENST00000402229.5	0	-
chr22	42876136	42912157	ENST00000634914.1	0	-
chr22	42890934	43015135	ENST00000453643.5	0	-
chr22	42890943	42945873	ENST00000418133.5	0	-
chr22	42890943	43002440	ENST00000422336.5	0	-
chr22	42893456	43015098	ENST00000445706.5	0	-
chr22	42893573	42959899	ENST00000453079.1	0	-
chr22	43038584	43052366	ENST00000443063.1	0	+
chr22	43039515	43089391	ENST00000266254.12	0	-
chr22	43039643	43089338	ENST00000439248.5	0	-
chr22	43039668	43089419	ENST00000440761.1	0	-
chr22	43039775	43075586	ENST00000331018.8	0	-
chr22	43092166	43092524	ENST00000446663.1	0	+
chr22	43110749	43129712	ENST00000216115.3	0	+
chr22	43132208	43143398	ENST00000290429.11	0	-
chr22	43132879	43143364	ENST00000327555.5	0	-
chr22	43140829	43142964	ENST00000608052.1	0	-
chr22	43141055	43143280	ENST00000464244.1	0	-
chr22	43151546	43163241	ENST00000583777.5	0	+
chr22	43151558	43163242	ENST00000337554.8	0	+
chr22	43151572	43159672	ENST00000472378.1	0	+
chr22	43151898	43161190	ENST00000428336.5	0	+
chr22	43151930	43163210	ENST00000329563.8	0	+
chr22	43151934	43163241	ENST00000396265.4	0	+
chr22	43166621	43187134	ENST00000216129.7	0	-
chr22	43166622	43170274	ENST00000484711.1	0	-
chr22	43167602	43171750	ENST00000494035.1	0	-
chr22	43174292	43175678	ENST00000484118.1	0	-
chr22	43197279	43343372	ENST00000360835.9	0	-
chr22	43203991	43343283	ENST00000615096.4	0	-
chr22	43212673	43213661	ENST00000420269.1	0	+
chr22	43231755	43262799	ENST00000449304.5	0	-
chr22	43232140	43239010	ENST00000419643.1	0	+
chr22	43237575	43343323	ENST00000290460.7	0	-
chr22	43270131	43272539	ENST00000477991.1	0	-
chr22	43270136	43272514	ENST00000470433.1	0	-
chr22	43275954	43283830	ENST00000458463.1	0	+
chr22	43316836	43320065	ENST00000461595.5	0	-
chr22	43316957	43320065	ENST00000480833.1	0	-
chr22	43400330	43409681	ENST00000450750.1	0	+
chr22	43411195	43507848	ENST00000417669.6	0	+
chr22	43412012	43435167	ENST00000334209.9	0	+
chr22	43412013	43507848	ENST00000443721.2	0	+
chr22	43416454	43425209	ENST00000447567.1	0	+
chr22	43416615	43419200	ENST00000465861.1	0	+
chr22	43416754	43425225	ENST00000480239.1	0	+
chr22	43507823	43513727	ENST00000667077.1	0	-
chr22	43516106	43537117	ENST00000431327.4	0	+
chr22	43516249	43560063	ENST00000656483.1	0	+
chr22	43528743	43570143	ENST00000461800.5	0	-
chr22	43528743	43812190	ENST00000396231.6	0	-
chr22	43528777	43533505	ENST00000483324.5	0	-
chr22	43528777	43812305	ENST00000262726.12	0	-
chr22	43528779	43532061	ENST00000477145.1	0	-
chr22	43551845	43672270	ENST00000468552.1	0	-
chr22	43683663	43812337	ENST00000404038.5	0	-
chr22	43716670	43812305	ENST00000356087.8	0	-
chr22	43759198	43759910	ENST00000419730.1	0	-
chr22	43765304	43812253	ENST00000476600.5	0	-
chr22	43782209	43812274	ENST00000485142.1	0	-
chr22	43802543	43802772	ENST00000476875.1	0	+
chr22	43812455	43817394	ENST00000563715.1	0	+
chr22	43812492	43813955	ENST00000564696.1	0	+
chr22	43824508	43862513	ENST00000330884.9	0	-
chr22	43824509	43862406	ENST00000432404.5	0	-
chr22	43824509	43862470	ENST00000422525.1	0	-
chr22	43828805	43838913	ENST00000475131.1	0	-
chr22	43879677	43892010	ENST00000597664.5	0	-
chr22	43879678	43892013	ENST00000216177.8	0	-
chr22	43879678	43892013	ENST00000593866.5	0	-
chr22	43879875	43892013	ENST00000381198.6	0	-
chr22	43884212	43891995	ENST00000438734.1	0	-
chr22	43923791	43964488	ENST00000406117.6	0	+
chr22	43923802	43947061	ENST00000423180.2	0	+
chr22	43923804	43947582	ENST00000216180.8	0	+
chr22	43924113	43933087	ENST00000478713.1	0	+
chr22	43933006	43939393	ENST00000497129.1	0	+
chr22	43955441	43973322	ENST00000493161.1	0	+
chr22	43955441	43996529	ENST00000350028.5	0	+
chr22	43957026	43957348	ENST00000430869.2	0	+
chr22	43974594	43996529	ENST00000474323.5	0	+
chr22	43986343	43996529	ENST00000494795.1	0	+
chr22	43990327	44010531	ENST00000465768.1	0	+
chr22	43995390	43996376	ENST00000493621.1	0	+
chr22	43999210	44168923	ENST00000406477.7	0	+
chr22	44024276	44172949	ENST00000338758.11	0	+
chr22	44024301	44119934	ENST00000402876.3	0	+
chr22	44026303	44169221	ENST00000619710.4	0	+
chr22	44031364	44132965	ENST00000444029.5	0	+
chr22	44069060	44169184	ENST00000404989.1	0	+
chr22	44086683	44100089	ENST00000477795.1	0	+
chr22	44102778	44116232	ENST00000623440.1	0	+
chr22	44136463	44139124	ENST00000495824.1	0	+
chr22	44139364	44153626	ENST00000624919.1	0	+
chr22	44162733	44168243	ENST00000484345.1	0	+
chr22	44168178	44168927	ENST00000477438.1	0	+
chr22	44172955	44208468	ENST00000422871.5	0	+
chr22	44172983	44187172	ENST00000453888.7	0	+
chr22	44180924	44208469	ENST00000444313.8	0	+
chr22	44181021	44183408	ENST00000416291.5	0	+
chr22	44181335	44206622	ENST00000475485.6	0	+
chr22	44181397	44183419	ENST00000417767.1	0	+
chr22	44181399	44206635	ENST00000356909.7	0	+
chr22	44181738	44198717	ENST00000415224.5	0	+
chr22	44181749	44186114	ENST00000471836.1	0	+
chr22	44181860	44184261	ENST00000466375.2	0	+
chr22	44183030	44187739	ENST00000623969.1	0	-
chr22	44185525	44207155	ENST00000468564.6	0	+
chr22	44196097	44219533	ENST00000472551.1	0	+
chr22	44210441	44229512	ENST00000624215.1	0	-
chr22	44243664	44312951	ENST00000381176.5	0	-
chr22	44365550	44366284	ENST00000406912.1	0	+
chr22	44413484	44414814	ENST00000434327.1	0	-
chr22	44443326	44444788	ENST00000415702.1	0	+
chr22	44483494	44483717	ENST00000423587.1	0	-
chr22	44486294	44486914	ENST00000624619.1	0	-
chr22	44492582	44498233	ENST00000341255.4	0	-
chr22	44566964	44568247	ENST00000435622.1	0	+
chr22	44569294	44572453	ENST00000657777.1	0	+
chr22	44569338	44572210	ENST00000334566.9	0	+
chr22	44569351	44572341	ENST00000603530.5	0	+
chr22	44569351	44572341	ENST00000605505.1	0	+
chr22	44570050	44571028	ENST00000460077.1	0	+
chr22	44571042	44572453	ENST00000666755.1	0	+
chr22	44606938	44625419	ENST00000443783.1	0	-
chr22	44651834	44652379	ENST00000454525.1	0	+
chr22	44668546	44737681	ENST00000611394.4	0	+
chr22	44668546	44737681	ENST00000617066.4	0	+
chr22	44668712	44736909	ENST00000432186.5	0	+
chr22	44676807	44737681	ENST00000624862.3	0	+
chr22	44676980	44736874	ENST00000492475.5	0	+
chr22	44677056	44737680	ENST00000006251.11	0	+
chr22	44677084	44729408	ENST00000459857.5	0	+
chr22	44677084	44737681	ENST00000403581.5	0	+
chr22	44677133	44730932	ENST00000492289.5	0	+
chr22	44702203	44737681	ENST00000336985.11	0	+
chr22	44702217	44732317	ENST00000403696.5	0	+
chr22	44702217	44736786	ENST00000457960.5	0	+
chr22	44702218	44737441	ENST00000431834.5	0	+
chr22	44702232	44862671	ENST00000361473.9	0	+
chr22	44702474	44862706	ENST00000352766.11	0	+
chr22	44702485	44737662	ENST00000432916.5	0	+
chr22	44702519	44737215	ENST00000477331.5	0	+
chr22	44725245	44809801	ENST00000495250.2	0	+
chr22	44725260	44862671	ENST00000515632.2	0	+
chr22	44730803	44737632	ENST00000455389.3	0	+
chr22	44730883	44737681	ENST00000495017.5	0	+
chr22	44734107	44737681	ENST00000475850.1	0	+
chr22	44752557	44862784	ENST00000389774.6	0	+
chr22	44752573	44850963	ENST00000460809.5	0	+
chr22	44752585	44809098	ENST00000396119.6	0	+
chr22	44752585	44825591	ENST00000447333.5	0	+
chr22	44752602	44862788	ENST00000336963.8	0	+
chr22	44752605	44862784	ENST00000356099.10	0	+
chr22	44752704	44814718	ENST00000412433.5	0	+
chr22	44765228	44786508	ENST00000469872.1	0	+
chr22	44786456	44862784	ENST00000389772.8	0	+
chr22	44786551	44808809	ENST00000495219.1	0	+
chr22	44801819	44825551	ENST00000498310.1	0	+
chr22	44859660	44862463	ENST00000469342.1	0	+
chr22	44881161	45008993	ENST00000396103.7	0	-
chr22	44881161	45008993	ENST00000403565.5	0	-
chr22	44881161	45010005	ENST00000313237.10	0	-
chr22	44881164	45009649	ENST00000629843.2	0	-
chr22	44889772	45009999	ENST00000414269.1	0	-
chr22	44901546	44902471	ENST00000431036.1	0	+
chr22	44901591	44902430	ENST00000456225.1	0	+
chr22	44913915	45008986	ENST00000420689.1	0	-
chr22	44916320	45009321	ENST00000490679.5	0	-
chr22	44916454	44969083	ENST00000460507.5	0	-
chr22	44916594	45009592	ENST00000474327.5	0	-
chr22	44968987	44971470	ENST00000462631.1	0	-
chr22	44989121	45009547	ENST00000495348.1	0	-
chr22	45000564	45003619	ENST00000424580.1	0	-
chr22	45008152	45009612	ENST00000491522.1	0	-
chr22	45133019	45163781	ENST00000426282.6	0	-
chr22	45133700	45139757	ENST00000665704.1	0	-
chr22	45133705	45152623	ENST00000609284.2	0	-
chr22	45134289	45163659	ENST00000432502.1	0	-
chr22	45155538	45156011	ENST00000609432.1	0	-
chr22	45163924	45188017	ENST00000347635.9	0	+
chr22	45163985	45184830	ENST00000407019.6	0	+
chr22	45163986	45171667	ENST00000424634.5	0	+
chr22	45163996	45171639	ENST00000417702.5	0	+
chr22	45163996	45172206	ENST00000497960.5	0	+
chr22	45163998	45171019	ENST00000461437.5	0	+
chr22	45163999	45176006	ENST00000434760.5	0	+
chr22	45163999	45178314	ENST00000430547.5	0	+
chr22	45164281	45171746	ENST00000484186.5	0	+
chr22	45164607	45168207	ENST00000445721.5	0	+
chr22	45164634	45184504	ENST00000493456.5	0	+
chr22	45164645	45184830	ENST00000396096.6	0	+
chr22	45164665	45171143	ENST00000461194.5	0	+
chr22	45164679	45168190	ENST00000432340.1	0	+
chr22	45168165	45178900	ENST00000419387.5	0	+
chr22	45168170	45183515	ENST00000422489.1	0	+
chr22	45171049	45178430	ENST00000486184.1	0	+
chr22	45175817	45178467	ENST00000491860.1	0	+
chr22	45177698	45185624	ENST00000469163.1	0	+
chr22	45178989	45179310	ENST00000610217.1	0	+
chr22	45190337	45240769	ENST00000336156.9	0	-
chr22	45195827	45205849	ENST00000423262.5	0	-
chr22	45195827	45212488	ENST00000251993.11	0	-
chr22	45195827	45226903	ENST00000391627.6	0	-
chr22	45196037	45212457	ENST00000488038.5	0	-
chr22	45196701	45198033	ENST00000483374.1	0	-
chr22	45196748	45200209	ENST00000498418.5	0	-
chr22	45196963	45202684	ENST00000474515.5	0	-
chr22	45197105	45202715	ENST00000493003.1	0	-
chr22	45200953	45201019	ENST00000408671.3	0	-
chr22	45203130	45212081	ENST00000440039.5	0	-
chr22	45205220	45236277	ENST00000414854.5	0	-
chr22	45205222	45213499	ENST00000424508.5	0	-
chr22	45205259	45235413	ENST00000486640.5	0	-
chr22	45205883	45240742	ENST00000417906.1	0	-
chr22	45211333	45212445	ENST00000496226.1	0	-
chr22	45211334	45212417	ENST00000492273.1	0	-
chr22	45262272	45263585	ENST00000609206.2	0	-
chr22	45275598	45276085	ENST00000445867.1	0	+
chr22	45284948	45295874	ENST00000216211.9	0	+
chr22	45284990	45295822	ENST00000396082.2	0	+
chr22	45308967	45310173	ENST00000491671.1	0	+
chr22	45308981	45341955	ENST00000216214.7	0	+
chr22	45309040	45310100	ENST00000481447.1	0	+
chr22	45309933	45341955	ENST00000441876.7	0	+
chr22	45310123	45329412	ENST00000405673.5	0	+
chr22	45310136	45328241	ENST00000477714.1	0	+
chr22	45318419	45327960	ENST00000427777.1	0	+
chr22	45318479	45322426	ENST00000452238.5	0	+
chr22	45318790	45322383	ENST00000424557.1	0	+
chr22	45329147	45330719	ENST00000476478.1	0	+
chr22	45329634	45332691	ENST00000459849.1	0	+
chr22	45329643	45332655	ENST00000476754.1	0	+
chr22	45330456	45336410	ENST00000487732.5	0	+
chr22	45335024	45340761	ENST00000483102.5	0	+
chr22	45335215	45340846	ENST00000462361.1	0	+
chr22	45336394	45340882	ENST00000479180.1	0	+
chr22	45344062	45413599	ENST00000357450.9	0	-
chr22	45344457	45413619	ENST00000404354.3	0	-
chr22	45413692	45432509	ENST00000614167.2	0	+
chr22	45414322	45422506	ENST00000621287.1	0	+
chr22	45418006	45431016	ENST00000466226.1	0	+
chr22	45435863	45448743	ENST00000454439.1	0	-
chr22	45502237	45531324	ENST00000411478.5	0	+
chr22	45502276	45525678	ENST00000445110.5	0	+
chr22	45502831	45533095	ENST00000455233.5	0	+
chr22	45502882	45563352	ENST00000402984.7	0	+
chr22	45502882	45563362	ENST00000262722.11	0	+
chr22	45502882	45601135	ENST00000327858.11	0	+
chr22	45502969	45565701	ENST00000442170.6	0	+
chr22	45502975	45558711	ENST00000340923.9	0	+
chr22	45503045	45525563	ENST00000439835.1	0	+
chr22	45517344	45518786	ENST00000450975.1	0	+
chr22	45518581	45531324	ENST00000454279.5	0	+
chr22	45518782	45533898	ENST00000451475.5	0	+
chr22	45525674	45542214	ENST00000437711.1	0	+
chr22	45532892	45541279	ENST00000460538.5	0	+
chr22	45533150	45535584	ENST00000465578.1	0	+
chr22	45533780	45535431	ENST00000484531.1	0	+
chr22	45541228	45551129	ENST00000476366.1	0	+
chr22	45577038	45578434	ENST00000460300.2	0	+
chr22	45604431	45605621	ENST00000422971.1	0	-
chr22	45624530	45624632	ENST00000516176.1	0	-
chr22	45657018	45680130	ENST00000623075.1	0	+
chr22	45671797	45845304	ENST00000381061.8	0	+
chr22	45671833	45845307	ENST00000252934.10	0	+
chr22	45672063	45740541	ENST00000640901.1	0	+
chr22	45672187	45718464	ENST00000498009.5	0	+
chr22	45689465	45700322	ENST00000470722.1	0	+
chr22	45703972	45738839	ENST00000476998.5	0	+
chr22	45718422	45740800	ENST00000483549.5	0	+
chr22	45729440	45823277	ENST00000435026.5	0	+
chr22	45738473	45843671	ENST00000493643.3	0	+
chr22	45738738	45811794	ENST00000451241.2	0	+
chr22	45740509	45744817	ENST00000464482.1	0	+
chr22	45760523	45760598	ENST00000585261.1	0	+
chr22	45792417	45793636	ENST00000438230.1	0	-
chr22	45793595	45843797	ENST00000402380.3	0	+
chr22	45875931	45879247	ENST00000623717.1	0	+
chr22	45875998	45887748	ENST00000451118.1	0	-
chr22	45876127	45891438	ENST00000650444.1	0	-
chr22	45920365	45977162	ENST00000339464.9	0	-
chr22	45922110	45972715	ENST00000409496.7	0	-
chr22	45922110	45976022	ENST00000410089.5	0	-
chr22	45927481	45976805	ENST00000410058.1	0	-
chr22	45931295	45975909	ENST00000428540.1	0	-
chr22	46006615	46010777	ENST00000624893.1	0	+
chr22	46013605	46015498	ENST00000608290.1	0	+
chr22	46039906	46044064	ENST00000609737.1	0	-
chr22	46040265	46044610	ENST00000421538.1	0	-
chr22	46041008	46044853	ENST00000444890.1	0	-
chr22	46049477	46054144	ENST00000649288.1	0	-
chr22	46053092	46053560	ENST00000608644.1	0	+
chr22	46053704	46057210	ENST00000416202.5	0	+
chr22	46053868	46106023	ENST00000381051.6	0	+
chr22	46054459	46057210	ENST00000451166.5	0	+
chr22	46054914	46057210	ENST00000445441.1	0	+
chr22	46055739	46058160	ENST00000439423.1	0	-
chr22	46067355	46069891	ENST00000412643.1	0	-
chr22	46080311	46098558	ENST00000443490.5	0	+
chr22	46085996	46111475	ENST00000435439.5	0	+
chr22	46086002	46113928	ENST00000360737.4	0	+
chr22	46091043	46091126	ENST00000579667.1	0	+
chr22	46112748	46112822	ENST00000362116.3	0	+
chr22	46113565	46113657	ENST00000581461.1	0	+
chr22	46113685	46113768	ENST00000385140.1	0	+
chr22	46150520	46198410	ENST00000440343.5	0	+
chr22	46150551	46198591	ENST00000415785.5	0	+
chr22	46150595	46243756	ENST00000262735.9	0	+
chr22	46150605	46215364	ENST00000420804.5	0	+
chr22	46150827	46151970	ENST00000460086.1	0	+
chr22	46151883	46243755	ENST00000407236.5	0	+
chr22	46151885	46156593	ENST00000496865.1	0	+
chr22	46154996	46156552	ENST00000624793.1	0	+
chr22	46163302	46165347	ENST00000624818.1	0	+
chr22	46171283	46198410	ENST00000484619.1	0	+
chr22	46171331	46198410	ENST00000481567.5	0	+
chr22	46176747	46235403	ENST00000402126.1	0	+
chr22	46176765	46220657	ENST00000493286.1	0	+
chr22	46244010	46248285	ENST00000404583.5	0	-
chr22	46244012	46250293	ENST00000314567.8	0	-
chr22	46244020	46250289	ENST00000381037.4	0	-
chr22	46244050	46248285	ENST00000404744.1	0	-
chr22	46244622	46250294	ENST00000475605.1	0	-
chr22	46244908	46250311	ENST00000474256.1	0	-
chr22	46255662	46263343	ENST00000253255.7	0	-
chr22	46267960	46275285	ENST00000417709.5	0	+
chr22	46268007	46268927	ENST00000475467.1	0	+
chr22	46268007	46280242	ENST00000421359.5	0	+
chr22	46268007	46294008	ENST00000381031.8	0	+
chr22	46268039	46269192	ENST00000629918.1	0	+
chr22	46272371	46284032	ENST00000422713.1	0	+
chr22	46288509	46292933	ENST00000451998.1	0	+
chr22	46295142	46296660	ENST00000623117.1	0	-
chr22	46295694	46296000	ENST00000617229.1	0	-
chr22	46296869	46330810	ENST00000454366.2	0	+
chr22	46316036	46327316	ENST00000466510.5	0	+
chr22	46316036	46329130	ENST00000479645.1	0	+
chr22	46328922	46330152	ENST00000491863.1	0	+
chr22	46330874	46333699	ENST00000476901.1	0	+
chr22	46331446	46352330	ENST00000486620.5	0	+
chr22	46335400	46357335	ENST00000441818.5	0	+
chr22	46335424	46357337	ENST00000381021.7	0	+
chr22	46335424	46357337	ENST00000453630.5	0	+
chr22	46335424	46357337	ENST00000456595.5	0	+
chr22	46335424	46357337	ENST00000457572.5	0	+
chr22	46335688	46338211	ENST00000493556.2	0	+
chr22	46335713	46354319	ENST00000645026.1	0	+
chr22	46335713	46357340	ENST00000645190.1	0	+
chr22	46335715	46357311	ENST00000647301.1	0	+
chr22	46335724	46350392	ENST00000642562.1	0	+
chr22	46335744	46357184	ENST00000381019.3	0	+
chr22	46335892	46356071	ENST00000642923.1	0	+
chr22	46335894	46350420	ENST00000496831.5	0	+
chr22	46335921	46352911	ENST00000465378.6	0	+
chr22	46335950	46356979	ENST00000644006.1	0	+
chr22	46335957	46352329	ENST00000485175.5	0	+
chr22	46336144	46357016	ENST00000643137.1	0	+
chr22	46350344	46352841	ENST00000463785.1	0	+
chr22	46351649	46352312	ENST00000479648.1	0	+
chr22	46352828	46357337	ENST00000491612.1	0	+
chr22	46353516	46357339	ENST00000485559.1	0	+
chr22	46354709	46357338	ENST00000470831.1	0	+
chr22	46360833	46537170	ENST00000262738.7	0	-
chr22	46361173	46365380	ENST00000473624.1	0	-
chr22	46372912	46377624	ENST00000468025.1	0	-
chr22	46462779	46535294	ENST00000454637.1	0	-
chr22	46481328	46481760	ENST00000447383.1	0	-
chr22	46530307	46534200	ENST00000497509.1	0	-
chr22	46541494	46548196	ENST00000426112.1	0	+
chr22	46576011	46637936	ENST00000447351.5	0	+
chr22	46577128	46626899	ENST00000431155.1	0	+
chr22	46620385	46679785	ENST00000406902.6	0	+
chr22	46626760	46679790	ENST00000361034.7	0	+
chr22	46626842	46637958	ENST00000490378.1	0	+
chr22	46663106	46671167	ENST00000456069.1	0	+
chr22	46674607	46679683	ENST00000408031.1	0	+
chr22	46684409	46738252	ENST00000216264.13	0	-
chr22	46686982	46738207	ENST00000443629.5	0	-
chr22	46690054	46693812	ENST00000471929.1	0	-
chr22	46735158	46738090	ENST00000460254.1	0	-
chr22	46761893	46762563	ENST00000564152.1	0	-
chr22	46762616	47175699	ENST00000380995.5	0	+
chr22	46762649	47175693	ENST00000337137.9	0	+
chr22	46762650	46793675	ENST00000441936.5	0	+
chr22	46762658	46793671	ENST00000486163.5	0	+
chr22	46762671	47174083	ENST00000407381.7	0	+
chr22	46762680	47174086	ENST00000355704.7	0	+
chr22	46762682	47174087	ENST00000394449.6	0	+
chr22	46762686	46793616	ENST00000496139.5	0	+
chr22	46763265	46793775	ENST00000472791.5	0	+
chr22	46763741	47174092	ENST00000441162.5	0	+
chr22	46773926	47174092	ENST00000406733.1	0	+
chr22	46914091	46916042	ENST00000640274.1	0	-
chr22	46915327	46915908	ENST00000625149.1	0	-
chr22	47115837	47117215	ENST00000639527.1	0	-
chr22	47115837	47117217	ENST00000623531.1	0	-
chr22	47259365	47261007	ENST00000624605.1	0	+
chr22	47345568	47373541	ENST00000434707.1	0	+
chr22	47461298	47487111	ENST00000405369.3	0	-
chr22	47463466	47487088	ENST00000481625.1	0	-
chr22	47612230	47630511	ENST00000669762.1	0	-
chr22	47621042	47631569	ENST00000380990.5	0	-
chr22	47622377	47630551	ENST00000660125.1	0	-
chr22	47625229	47629902	ENST00000609786.5	0	-
chr22	47625234	47629902	ENST00000610069.1	0	-
chr22	47630826	47635565	ENST00000652442.1	0	+
chr22	47631193	47691494	ENST00000650947.2	0	+
chr22	47631255	47638150	ENST00000426452.2	0	+
chr22	47631405	47976455	ENST00000664761.1	0	+
chr22	47631504	47691484	ENST00000665418.1	0	+
chr22	47631654	47739115	ENST00000651071.1	0	+
chr22	47631658	47729028	ENST00000652499.1	0	+
chr22	47631658	47929010	ENST00000651536.1	0	+
chr22	47631658	47929020	ENST00000650683.1	0	+
chr22	47631662	47799280	ENST00000652072.1	0	+
chr22	47631662	47855601	ENST00000652656.1	0	+
chr22	47631669	47976347	ENST00000651445.1	0	+
chr22	47631673	47707583	ENST00000650834.1	0	+
chr22	47631673	47855600	ENST00000423737.5	0	+
chr22	47631677	47691501	ENST00000651381.1	0	+
chr22	47631677	47929010	ENST00000650660.1	0	+
chr22	47631678	47863015	ENST00000651056.1	0	+
chr22	47631679	47841255	ENST00000652611.1	0	+
chr22	47631689	47863750	ENST00000652399.1	0	+
chr22	47631693	47643298	ENST00000651394.1	0	+
chr22	47631696	47689670	ENST00000650749.1	0	+
chr22	47631696	47739105	ENST00000651458.1	0	+
chr22	47631696	47778979	ENST00000651311.1	0	+
chr22	47631696	47864662	ENST00000652123.1	0	+
chr22	47631700	47643918	ENST00000651884.1	0	+
chr22	47631701	47643918	ENST00000652597.1	0	+
chr22	47631702	47752718	ENST00000652702.2	0	+
chr22	47631702	47856478	ENST00000652505.1	0	+
chr22	47631707	47689670	ENST00000650895.1	0	+
chr22	47631707	47729030	ENST00000652334.1	0	+
chr22	47631709	47863018	ENST00000652030.2	0	+
chr22	47631710	47704856	ENST00000651754.1	0	+
chr22	47631710	47778987	ENST00000651985.1	0	+
chr22	47631710	47823939	ENST00000650972.1	0	+
chr22	47631710	47947902	ENST00000651124.1	0	+
chr22	47631710	48013818	ENST00000652228.1	0	+
chr22	47631710	48023004	ENST00000651403.1	0	+
chr22	47631713	47642797	ENST00000652206.2	0	+
chr22	47631713	47643939	ENST00000651308.1	0	+
chr22	47631714	47831077	ENST00000651637.1	0	+
chr22	47631715	47778987	ENST00000650924.1	0	+
chr22	47631721	47687333	ENST00000651104.1	0	+
chr22	47631726	47739105	ENST00000652168.1	0	+
chr22	47631726	47947838	ENST00000651003.1	0	+
chr22	47631727	47862423	ENST00000651293.1	0	+
chr22	47631727	47863775	ENST00000651948.2	0	+
chr22	47631732	47739107	ENST00000652690.1	0	+
chr22	47631732	47823943	ENST00000651662.1	0	+
chr22	47631732	47847606	ENST00000651517.1	0	+
chr22	47631732	47855445	ENST00000650787.1	0	+
chr22	47631732	47947782	ENST00000651939.1	0	+
chr22	47631744	47739120	ENST00000652750.1	0	+
chr22	47631744	47864167	ENST00000651638.1	0	+
chr22	47631744	47864167	ENST00000670853.1	0	+
chr22	47631746	47637215	ENST00000651037.1	0	+
chr22	47631750	47941077	ENST00000652080.1	0	+
chr22	47631770	47863015	ENST00000651294.1	0	+
chr22	47631775	47844141	ENST00000652164.1	0	+
chr22	47631786	47662817	ENST00000651625.1	0	+
chr22	47631805	47638099	ENST00000651924.2	0	+
chr22	47631991	47638225	ENST00000670886.1	0	+
chr22	47631991	47691012	ENST00000651938.2	0	+
chr22	47631991	47729032	ENST00000650775.1	0	+
chr22	47631991	47864492	ENST00000651538.1	0	+
chr22	47635180	47691012	ENST00000652675.1	0	+
chr22	47662558	47663603	ENST00000413815.2	0	-
chr22	47686199	47730421	ENST00000651983.1	0	+
chr22	47686240	47690847	ENST00000438810.1	0	+
chr22	47686602	48015839	ENST00000651033.1	0	+
chr22	47686878	47928994	ENST00000651584.1	0	+
chr22	47699141	47739102	ENST00000650884.1	0	+
chr22	47699193	47730785	ENST00000651270.1	0	+
chr22	47860609	47863018	ENST00000449941.2	0	+
chr22	47916727	47926264	ENST00000623760.1	0	+
chr22	48044709	48048549	ENST00000457518.1	0	-
chr22	48052645	48058275	ENST00000624219.1	0	+
chr22	48139460	48141001	ENST00000414483.1	0	-
chr22	48141541	48143229	ENST00000446364.1	0	+
chr22	48254902	48259116	ENST00000648313.1	0	-
chr22	48274363	48274415	ENST00000578320.1	0	+
chr22	48403461	48415571	ENST00000648629.1	0	+
chr22	48448889	48451217	ENST00000453866.1	0	+
chr22	48474134	48489267	ENST00000667577.1	0	-
chr22	48474155	48488425	ENST00000666116.1	0	-
chr22	48474155	48489324	ENST00000663363.1	0	-
chr22	48474223	48488866	ENST00000659917.1	0	-
chr22	48475084	48487743	ENST00000662490.1	0	-
chr22	48475488	48489180	ENST00000669310.1	0	-
chr22	48489459	48750823	ENST00000402357.5	0	+
chr22	48489591	48850912	ENST00000336769.9	0	+
chr22	48538899	48547387	ENST00000626321.1	0	-
chr22	48538901	48547342	ENST00000630584.1	0	-
chr22	48543320	48547348	ENST00000625694.1	0	-
chr22	48576305	48751932	ENST00000358295.9	0	+
chr22	48576452	48750488	ENST00000473898.1	0	+
chr22	48693246	48750408	ENST00000406880.1	0	+
chr22	48780294	48780353	ENST00000580946.1	0	+
chr22	48866769	48898386	ENST00000380981.3	0	+
chr22	48892712	48897220	ENST00000660776.1	0	+
chr22	49047483	49052413	ENST00000657875.1	0	-
chr22	49047490	49052549	ENST00000667851.1	0	-
chr22	49047650	49052069	ENST00000665699.1	0	-
chr22	49047782	49052403	ENST00000669789.1	0	-
chr22	49166420	49188062	ENST00000647629.1	0	-
chr22	49213910	49214247	ENST00000433284.1	0	-
chr22	49264200	49266080	ENST00000623446.1	0	+
chr22	49372923	49377119	ENST00000649077.1	0	+
chr22	49414523	49657504	ENST00000414287.5	0	-
chr22	49415775	49425437	ENST00000498829.1	0	-
chr22	49436223	49657457	ENST00000416411.1	0	-
chr22	49500567	49501585	ENST00000625038.1	0	+
chr22	49534061	49534926	ENST00000623197.1	0	+
chr22	49543392	49543466	ENST00000581025.1	0	-
chr22	49548680	49556473	ENST00000391625.2	0	+
chr22	49572263	49575426	ENST00000393264.2	0	-
chr22	49612656	49615716	ENST00000420902.1	0	-
chr22	49619641	49624920	ENST00000343999.1	0	-
chr22	49619643	49657542	ENST00000400023.5	0	-
chr22	49620470	49657430	ENST00000405854.5	0	-
chr22	49624166	49626894	ENST00000444628.2	0	-
chr22	49662311	49664968	ENST00000662299.1	0	-
chr22	49718052	49724506	ENST00000447372.1	0	-
chr22	49738676	49739003	ENST00000411210.1	0	+
chr22	49757626	49758980	ENST00000398869.2	0	-
chr22	49773282	49823213	ENST00000438393.5	0	-
chr22	49773282	49824804	ENST00000216267.12	0	-
chr22	49773282	49825900	ENST00000404034.5	0	-
chr22	49773282	49827512	ENST00000404760.5	0	-
chr22	49773292	49824804	ENST00000457780.3	0	-
chr22	49775260	49776115	ENST00000479985.1	0	-
chr22	49805451	49807208	ENST00000624882.1	0	+
chr22	49817247	49817712	ENST00000444011.1	0	+
chr22	49818211	49823301	ENST00000459821.1	0	-
chr22	49820832	49823205	ENST00000494833.1	0	-
chr22	49832448	49838901	ENST00000666898.1	0	-
chr22	49832615	49837786	ENST00000565177.1	0	-
chr22	49833127	49838901	ENST00000661712.1	0	-
chr22	49845928	49846090	ENST00000422315.1	0	+
chr22	49846455	49846720	ENST00000621807.1	0	+
chr22	49851424	49853364	ENST00000624617.1	0	+
chr22	49853843	49890080	ENST00000216268.6	0	+
chr22	49900228	49918438	ENST00000330817.11	0	-
chr22	49902227	49904576	ENST00000610245.1	0	+
chr22	49903649	49907918	ENST00000486602.1	0	-
chr22	49903960	49910088	ENST00000492791.1	0	-
chr22	49918166	49922707	ENST00000450207.5	0	+
chr22	49918634	49927540	ENST00000404488.7	0	+
chr22	49918695	49927528	ENST00000328268.8	0	+
chr22	49918732	49926505	ENST00000483652.5	0	+
chr22	49918732	49927534	ENST00000403427.3	0	+
chr22	49918732	49927534	ENST00000407217.7	0	+
chr22	49918769	49923400	ENST00000482956.5	0	+
chr22	49921288	49922033	ENST00000498354.5	0	+
chr22	49921306	49923264	ENST00000462253.5	0	+
chr22	49921583	49925129	ENST00000444954.1	0	+
chr22	49923209	49927528	ENST00000487969.1	0	+
chr22	49933197	49934074	ENST00000623759.1	0	-
chr22	49960767	49961646	ENST00000467480.1	0	+
chr22	49960771	49964072	ENST00000360612.5	0	+
chr22	49962865	49962939	ENST00000617625.1	0	+
chr22	49994512	50008692	ENST00000341280.5	0	-
chr22	49994512	50012659	ENST00000389983.6	0	-
chr22	50015122	50054626	ENST00000266182.10	0	-
chr22	50015126	50056935	ENST00000433387.1	0	-
chr22	50047167	50050218	ENST00000477219.1	0	-
chr22	50059390	50085354	ENST00000395876.6	0	-
chr22	50059390	50085902	ENST00000311597.9	0	-
chr22	50061460	50072815	ENST00000483836.1	0	-
chr22	50070538	50085384	ENST00000442311.1	0	-
chr22	50070541	50074684	ENST00000470008.1	0	-
chr22	50089878	50161618	ENST00000545383.5	0	+
chr22	50090005	50161687	ENST00000262794.10	0	+
chr22	50090044	50108375	ENST00000419054.5	0	+
chr22	50090053	50108375	ENST00000395854.6	0	+
chr22	50090064	50161690	ENST00000395858.7	0	+
chr22	50090255	50108184	ENST00000475190.1	0	+
chr22	50090255	50161686	ENST00000540615.5	0	+
chr22	50092744	50096437	ENST00000623740.1	0	+
chr22	50114457	50142186	ENST00000434497.1	0	+
chr22	50146964	50161677	ENST00000395852.5	0	+
chr22	50149388	50159257	ENST00000354853.2	0	+
chr22	50170730	50180292	ENST00000159647.9	0	+
chr22	50170730	50180295	ENST00000395842.3	0	+
chr22	50170760	50179598	ENST00000402472.2	0	+
chr22	50185914	50199564	ENST00000380909.8	0	+
chr22	50185916	50199598	ENST00000303434.8	0	+
chr22	50185917	50199595	ENST00000463233.1	0	+
chr22	50190597	50199475	ENST00000395827.5	0	+
chr22	50191723	50192402	ENST00000607943.1	0	-
chr22	50193013	50199569	ENST00000395829.1	0	+
chr22	50196892	50198711	ENST00000472677.1	0	+
chr22	50199089	50200837	ENST00000608025.2	0	-
chr22	50200978	50217615	ENST00000611222.1	0	+
chr22	50201010	50217616	ENST00000380903.7	0	+
chr22	50205584	50206062	ENST00000610050.1	0	-
chr22	50206441	50217616	ENST00000492092.1	0	+
chr22	50208460	50209542	ENST00000608016.1	0	-
chr22	50217688	50244971	ENST00000439308.6	0	-
chr22	50217688	50244992	ENST00000498611.5	0	-
chr22	50217693	50220416	ENST00000425018.1	0	-
chr22	50217693	50226189	ENST00000491449.5	0	-
chr22	50217693	50245023	ENST00000248846.10	0	-
chr22	50221759	50224592	ENST00000489511.5	0	-
chr22	50222986	50226191	ENST00000473946.1	0	-
chr22	50226734	50240360	ENST00000434349.1	0	-
chr22	50245182	50251261	ENST00000415993.5	0	-
chr22	50245182	50251265	ENST00000216271.10	0	-
chr22	50245185	50251405	ENST00000626012.2	0	-
chr22	50245404	50251079	ENST00000429374.5	0	-
chr22	50245441	50251032	ENST00000454936.5	0	-
chr22	50245441	50251240	ENST00000448072.5	0	-
chr22	50245441	50251242	ENST00000483222.5	0	-
chr22	50245441	50251366	ENST00000498366.5	0	-
chr22	50245449	50249208	ENST00000477814.5	0	-
chr22	50245449	50261143	ENST00000497036.5	0	-
chr22	50245506	50251032	ENST00000349505.4	0	-
chr22	50245790	50247948	ENST00000497952.5	0	-
chr22	50245978	50248372	ENST00000496235.5	0	-
chr22	50246328	50249040	ENST00000471375.5	0	-
chr22	50247137	50248455	ENST00000475965.1	0	-
chr22	50247695	50249075	ENST00000476310.5	0	-
chr22	50247907	50249203	ENST00000470378.1	0	-
chr22	50248694	50250890	ENST00000496909.5	0	-
chr22	50249286	50250255	ENST00000488270.1	0	-
chr22	50249858	50251228	ENST00000489424.5	0	-
chr22	50249867	50251385	ENST00000470965.1	0	-
chr22	50250118	50251405	ENST00000497483.1	0	-
chr22	50250256	50250972	ENST00000482213.1	0	-
chr22	50252900	50261683	ENST00000215659.13	0	-
chr22	50252902	50261308	ENST00000395780.5	0	-
chr22	50252902	50261545	ENST00000488504.5	0	-
chr22	50252902	50261716	ENST00000622558.4	0	-
chr22	50252919	50261708	ENST00000467891.5	0	-
chr22	50253780	50257107	ENST00000496942.5	0	-
chr22	50253787	50261231	ENST00000482969.5	0	-
chr22	50253852	50256153	ENST00000497738.1	0	-
chr22	50257562	50261169	ENST00000492218.1	0	-
chr22	50257688	50261543	ENST00000395778.3	0	-
chr22	50263712	50270380	ENST00000330651.11	0	-
chr22	50263714	50270393	ENST00000395764.5	0	-
chr22	50263738	50270393	ENST00000417877.1	0	-
chr22	50267137	50270767	ENST00000495277.1	0	-
chr22	50274978	50278840	ENST00000479818.5	0	-
chr22	50274978	50282562	ENST00000479701.5	0	-
chr22	50274978	50283068	ENST00000610984.4	0	-
chr22	50274978	50307627	ENST00000449103.5	0	-
chr22	50274978	50307646	ENST00000359337.9	0	-
chr22	50274984	50281934	ENST00000463165.1	0	-
chr22	50277634	50280978	ENST00000614805.1	0	-
chr22	50277852	50289561	ENST00000411680.1	0	-
chr22	50281126	50282006	ENST00000492578.1	0	-
chr22	50281573	50282880	ENST00000427829.1	0	-
chr22	50283337	50286253	ENST00000496720.1	0	-
chr22	50284166	50285843	ENST00000434732.1	0	-
chr22	50287986	50301462	ENST00000432455.5	0	-
chr22	50290099	50307603	ENST00000425954.1	0	-
chr22	50309029	50327012	ENST00000413817.8	0	-
chr22	50311965	50326977	ENST00000495607.5	0	-
chr22	50314202	50318967	ENST00000471942.6	0	-
chr22	50314630	50316008	ENST00000453835.1	0	+
chr22	50314821	50319095	ENST00000433760.1	0	-
chr22	50316034	50317025	ENST00000623684.1	0	+
chr22	50316653	50318970	ENST00000460087.1	0	-
chr22	50343303	50445090	ENST00000359139.7	0	+
chr22	50343322	50444374	ENST00000395741.7	0	+
chr22	50343326	50445090	ENST00000612753.5	0	+
chr22	50343330	50444374	ENST00000395744.7	0	+
chr22	50372071	50444391	ENST00000216061.9	0	+
chr22	50419441	50444390	ENST00000401672.7	0	+
chr22	50423604	50444371	ENST00000427222.2	0	+
chr22	50439752	50441074	ENST00000473283.1	0	+
chr22	50443086	50444378	ENST00000470046.1	0	+
chr22	50444999	50475035	ENST00000380817.8	0	-
chr22	50446805	50474942	ENST00000348911.10	0	-
chr22	50446809	50455377	ENST00000418590.3	0	-
chr22	50446825	50455989	ENST00000470434.1	0	-
chr22	50447082	50447609	ENST00000473724.1	0	-
chr22	50456588	50457347	ENST00000476293.1	0	-
chr22	50466195	50467355	ENST00000477234.1	0	-
chr22	50466430	50474849	ENST00000399627.3	0	-
chr22	50481542	50486437	ENST00000395737.2	0	+
chr22	50481555	50486440	ENST00000395738.2	0	+
chr22	50486783	50490036	ENST00000395733.7	0	+
chr22	50486858	50490648	ENST00000216075.11	0	+
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================================================
FILE: bedtools/index.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Manipulating files with bedtoools\n",
    "### Bioinformatics Coffee Hour - May 18, 2020\n",
    "\n",
    "### Genomic Intervals\n",
    "Many types of bioinformatics data are stored as genomic intervals, that is ranges (potentially discontinuous) along a sequence. Of course this includes things like gene models, but also the output of peak callers, enhancer predictions, and many other things. This genomic interval information is typically stored in either GFF or bed files. Today we are going to focus on bed files. \n",
    "\n",
    "Bed files use a 0-based start (first base of a chromosome is 0), and a 1-based end. This means the interval from the first base to the hundredth base of a chromosome would be represented as start = 0, end = 100 in a bed file. Another way to think about this is that bed files are 0-based, half-open (the start is included in the interval but not the end). Note that GFF files use a different convention, just to make things more confusing. \n",
    "\n",
    "Bed files are just tab-delimited files with a fixed set of fields: \n",
    "\n",
    "|col|field|meaning|\n",
    "|:---|:---|:--|\n",
    "|1|seqname|chr or scaffold id interval is on|\n",
    "|2|start|start position (0-based) of interval|\n",
    "|3|end|end position (1-based) of interval|\n",
    "|4|name|name of interval|\n",
    "|5|score|score, e.g. for peak calls or other intervals with scores; can be .|\n",
    "|6|strand|strand interval is on for strand-specific intervals|\n",
    "|7|thickStart|graphical parameter for genome browser display|\n",
    "|8|thickEnd|graphical parameter for genome browser display|\n",
    "|9|itermRgb|graphical parameter for genome browser display|\n",
    "|10|blockCount|for multi-exon genes and similar|\n",
    "|11|blockSizes|for multi-exon genes and similar|\n",
    "|12|blockStarts| for multi-exon genes and similar|\n",
    "\n",
    "Only the first three fields are required, but you cannot skip fields (so if you want to include strand, you need to also include name and score in that order). This means that you can manipulate bed files with standard Unix tools (such as awk that we introduced a few weeks ago). However, many operations on intervals can get quite complicated, and so there are specialized tools for dealing with this specific kind of data. One of the most popular and useful is bedtools, which will be the focus of our demo today.\n",
    "\n",
    "First, we'll just look at an example bed file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "head data/human_enhancers_chr22.bed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Finding the closest gene to something\n",
    "\n",
    "One very common task in interval manipulation is trying to find the closest interval from one file to intervals in another file, e.g. you have a list of putative ehnancers (as a bed file) and you want to know the closest gene to each one. In bedtools, this can be done very simply with the `closest` option:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bedtools closest -a data/human_enhancers_chr22.bed -b data/ucscGenes_chr22.bed | head"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note we pipe the output to head with the `|` operator so that we only see the first few lines of the output.\n",
    "\n",
    "Many bedtools commands for comparing things use `-a` and `-b` to indicate the two files to compare. Although the exact meaning varies, usually this can be read as \"for each element in a, compare to all elements in b.\" So in this case, we get the closest gene to each enhancer. We could also do this in reverse, to get the closest enhancer for each gene."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bedtools closest -a data/ucscGenes_chr22.bed -b data/human_enhancers_chr22.bed | head"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that each element in the `-a` file only appears once, but elements in `-b` can appear many times. \n",
    "\n",
    "We might want to list not just the closest element, but how far away it is. We can do this using the `-d` option to bedtools closest, like so:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bedtools closest -a data/human_enhancers_chr22.bed -b data/ucscGenes_chr22.bed -d | head"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, the last column is the distance between the element in the A file and the element in the B file. Notice that some of these distances are 0, which means that the closest element in B overlaps the element in A. We might want to find the closest non-overlapping element, instead of the closest element. We can do this with the `-io` option:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bedtools closest -a data/human_enhancers_chr22.bed -b data/ucscGenes_chr22.bed -d -io | head"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Like most bedtools tools, `closest` has a number of additional options to control filtering, reporting, and what is considered closest (e.g., requiring the same or different strand, or looking only at upstream or downstream features). You can see all the options with `-h` to display the help:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bedtools closest -h"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As an exercise, look through the help display and think about how you would find the closest **upstream** enhancer to each gene."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Finding overlaps\n",
    "\n",
    "Another common task is finding overlaps between two sets of intervals. For example, we might want to know which of our enhancers identified in neural crest cells are shared with enhancers identified in some other cell type. In your directory you should see another bed file, H1-hESC-H3K27Ac_chr22.bed, which has H3K27Ac peaks from human embryonic stem cells. We'll use bedtools to get the overlaps between this file and our human enhancers file.\n",
    "\n",
    "Intersect can be a little tricky to sort out the options, so we'll use this figure as reference:\n",
    "\n",
    "<div style='height: 400px; overflow: hidden'><img src='https://raw.githubusercontent.com/arq5x/bedtools2/v2.29.2/docs/content/images/tool-glyphs/intersect-glyph.png'></div>",
    "\n",
    "\n",
    "Like `closest`, `bedtools intersect` uses a `-a` and a `-b` file. Each feature in A is compared to all features in B to identify overlaps. The simplest option is:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bedtools intersect -a data/human_enhancers_chr22.bed -b data/H1-hESC-H3K27Ac_chr22.bed | head"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that this only lists the features in `A` that have an overlap in `B`, and it doesn't tell us what they overlap. If we wanted to list all features in `A` and indicate what they overlap, we could use the `-wao` option:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bedtools intersect -a data/human_enhancers_chr22.bed -b data/H1-hESC-H3K27Ac_chr22.bed -wao | head"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This output is a little complicated, so let's walk through it. The first 5 columns are the elements in A, and would look the same as `head data/human_enhancers_chr22.bed`. Next we have the element in B that overlaps, or `.` if no elements overlap. This is also a five-column bed file, ending with a score that is between 0 and 1000. Finally, we have an additional column that is the number of base pairs that overlap. Note that in this case, most of the elements in B are much bigger than the elements in A, so the overlap is typically the length of the element in A."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's say now we want to split our human_enhancers file into two separate files, one for those that overlap a H1-hESC-H3K27Ac and one for those that don't. We could do this using `awk` or `grep` on the output we just produced, but we can also do this with bedtools itself. There is an option, `-wa` that writes the original entry in A if it overlaps any entry in B, and other option `-v` that only reports elements in A with no overlap in B:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bedtools intersect -a data/human_enhancers_chr22.bed -b data/H1-hESC-H3K27Ac_chr22.bed -wa -v | head"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bedtools intersect -a data/human_enhancers_chr22.bed -b data/H1-hESC-H3K27Ac_chr22.bed -wa | head"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bedtools intersect has **lots** of options, and usually there is a way to do anything that involves comparing to sets of intervals and finding overlaps with it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Computing promoter intervals \n",
    "\n",
    "So far we've looked at examples of comparing two different files, but some bedtools options work with a single bed file. An example is the `flank` command, which creates a new interval adjacent to existing intervals. We can use this to, for example, compute promoter intervals for each gene, where we define promoters as the 2kb upstream of the start site:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bedtools flank -i data/ucscGenes_chr22.bed -g data/hg38.genome -l 2000 -r 0 -s | head"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One of the powerful features of bedtools is that you can combine different tools with the Unix `|`, to allow the output of one tool to be the input to another. We can use this to identify all the enhancers that fall within promoter regions of genes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bedtools flank -i data/ucscGenes_chr22.bed -g data/hg38.genome -l 2000 -r 0 -s | bedtools intersect -a data/human_enhancers_chr22.bed -b - -u | head"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Quick hits\n",
    "\n",
    "There are many other possibilities for what bedtools can do. We'll end with just a couple of more examples of how to solve common problems with bedtools."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Make a bed file of intergenic regions from a bed file of genes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bedtools complement -i data/ucscGenes_chr22.bed -g data/hg38.genome | head"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Randomly shuffle the location of enhancers, avoiding placing them in genes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bedtools shuffle -i data/human_enhancers_chr22.bed -g data/hg38.genome -excl data/ucscGenes_chr22.bed | head"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Compute a single metric to measure similarity between two sets of intervals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bedtools flank -i data/ucscGenes_chr22.bed -g data/hg38.genome -l 2000 -r 0 -s | bedtools sort -i - | bedtools jaccard -a data/H1-hESC-H3K27Ac_chr22.bed -b -"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bedtools random -n 743 -l 2000 -g data/hg38.genome | bedtools sort -i - | bedtools jaccard -a data/H1-hESC-H3K27Ac_chr22.bed -b -"
   ]
  }
 ],
 "metadata": {
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   "codemirror_mode": "shell",
   "file_extension": ".sh",
   "mimetype": "text/x-sh",
   "name": "bash"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}


================================================
FILE: differential-expression-analysis/binder/Dockerfile
================================================
FROM rocker/binder:3.6.2

ARG NB_USER
ARG NB_UID

USER root

# install dependencies
RUN install2.r statmod \
  && Rscript -e 'BiocManager::install("edgeR", update=FALSE)' \
  && rm -rf /tmp/downloaded_files

# copy files
COPY ./differential-expression-analysis/ ${HOME}

RUN chown -R ${NB_USER} ${HOME}

USER ${NB_USER}


================================================
FILE: differential-expression-analysis/data/dme_elev_samples.tab
================================================
sample population temp
SRR1576457 maine low
SRR1576458 maine low
SRR1576459 maine low
SRR1576460 maine high
SRR1576461 maine high
SRR1576462 maine high
SRR1576463 panama low
SRR1576464 panama low
SRR1576465 panama low
SRR1576514 panama high
SRR1576515 panama high
SRR1576516 panama high


================================================
FILE: differential-expression-analysis/index.Rmd
================================================
---
title: 'Differential Expression Analysis with limma-voom'
output:
  html_document: default
---

# I. Preliminaries
This tutorial consists of a workflow demostration for differential expression analyses using RNA-seq data using limma voom. While there are now many published methods for tackling specific steps, as well as full-blown pipelines, limma voom has  been show to be one of the top performers with respect to controlling the false discovery rate. In our daylong differential expression workshop, we also cover DE testing with sleuth, another top performer with respect to FDR control, and which is designed explicitly to handle psueduo-alignment based estimates of abundance derived from kallisto. Our limma voom workflow will use a gene-level count matrix derived from estimates of RNA abundance in each samples. These estimates have been generated with RSEM, using bowtie2 read alignments. Our limma voom workflow will use a gene-level count matrix derived from estimates of RNA abundance in each samples. These estimates have been generated with RSEM, using bowtie2 read alignments. These alignments were to transcripts derived from an annotated reference genome.

Specific topics covered today include:

* Importing and pre-processing a count matrix
* Using a sample table to define factors used in DE analysis
* Analysis of single-factor designs
* Analysis of 2-factor designs


## Sample data
Our sample data comprises 12 paired-end RNA-seq libraries for whole body samples of *Drosophila melanogaster* from two geographic regions (Panama and Maine), with two temperature treatments ("low" ane "high") for each region, featuring three biological replicates for each region x treatment combination. Previously, these data were used to look for parallel gene expression patterns between high and low latitude populations (Zhao et al, 2015, *PLoS Genetics*)

## Loading required R libraries
First, load all the R libraries that will be used for today's analyses:
```{r, echo=TRUE}
library(edgeR)
library(limma)
```

## Data management
1. Load and view the table that associates sample IDs and treatments (dme_elev_samples.tab):
```{r, echo=TRUE}
s2c<-read.table("data/dme_elev_samples.tab",header = TRUE, stringsAsFactors=FALSE)
s2c
```

2. Open RSEM matrix
```{r,echo=TRUE}
rsem_gene_data<-read.table("data/dme_elevgrad_rsem_bt2_gene_counts.matrix.bz2",header=TRUE,row.names=1)
```
## Pre-processing and filtering
### Handling non-integer RSEM estimates
3. Round the expression matrix
```{r,echo=TRUE}
rnaseqMatrix=round(rsem_gene_data)
```
### Filtering out lowly expressed genes
4. Create a boolean variable that classifies samples according to whether CPM>=1:
```{r,echo=TRUE}
filter=rowSums(cpm(rnaseqMatrix)>=1)>=6
```
5. Apply the filter to the expression matrix:
```{r,echo=TRUE}
cpm_filtered_matrix=rnaseqMatrix[filter,]
```
This operation filters out ~5000 genes, which reduces our multiple testing burden as well (although limma might not atually try and conduct tests on a subset of these).

## Creating a Digital Gene Expression list object 
To run limma, we need to transform the expression matrix into a DGElist ("digital gene expression list") which is an object class that comes from edgeR

6. Create the DGE object and normalized expression matrix:
```{r,echo=TRUE}
DGE<-DGEList(cpm_filtered_matrix)
```
## Normalization using TMM method

7. Calculate normalization factors and do MDS plot:
```{r,echo=TRUE}
DGE<-calcNormFactors(DGE,method =c("TMM"))
```
## Quick and dirty outlier check

8. Generate an MDS plot
```{r,echo=TRUE}
tempvals<-s2c$temp
plotMDS(DGE,top=500,col=ifelse(tempvals=="low","blue","red"),gene.selection="common")
```

## The vanilla one factor, two treatment DE analysis
A fundamental step of DE analysis is to construct a design matrix that will be used to fit the linear model. In our simple vanilla 2-condition, example we can simply use the temp variable in the s2c table to create a matrix. which represents binary 0/1 encodings of the temp conditions.

9. Create design matrix:
```{r,echo=TRUE}
design_temp=model.matrix(~temp, data=s2c)
design_temp
```
After creating the design matrix object, the standard approach is to next run limma voom on the DGE object, e.g.:
```{r,echo=TRUE,eval=FALSE}
v <- voom(DGE, design=design_temp, plot=TRUE)
```
**However** ... while this works fine under an ideal scenario, it becomes a problem if there is variation in sample quality, or more generally, there is some indication that a subset of samples appear as outliers via MDS, PCA, etc. Particularly for RNA-seq experiments where researchers may only have a few repicates per sample, discarding outlier samples is not feasible because it may lead to few if any biological replciates for some subset of treatments. 

A better solution to this problem is to apply weights to samples such that outlier samples are downweighted during differential expression calcuations. Limma voom does this by calculating "empirical quality weights" for each sample. 

10. Run limma voom with sample quality weights:
```{r,echo=TRUE}
vwts <- voomWithQualityWeights(DGE, design=design_temp,normalize.method="none", plot=TRUE)
```

**Note:** we have already applied TMM normalization, thus can set the normalization argument to none. This above command will also generate a plot with two panels showing the mean-variance relationship fit on the left, and a barplot of weights assigned to individual samples.

11. Then, run the linear model fitting procedure 1st step:
```{r,echo=TRUE}
fit=lmFit(vwts,design_temp)
```
12. Then apply the empirical bayes procedure:
```{r,echo=TRUE}
fit=eBayes(fit,robust=TRUE)
```
We use the robust=TRUE setting to leverage the quality weights such that the analysis is robust to outliers.

One can then get a quick and dirty summary of how many genes are differentially expressed, setting the FDR threshold,where the "fdr" and "BH" methods are synonymous for Benjamini Hochberg adjusted p-values.

13. Get summary table:
```{r,echo=TRUE}
summary(decideTests(fit,adjust.method="fdr",p.value = 0.05))
```
One piece of important info is the factor relative to which logfold change is being calculated, i.e. low will be the numerator for logfold change calculations.

14. Explore the data by extracting the top 10 DE genes (sorted by p-value):
```{r,echo=TRUE}
topTable(fit, adjust="BH",resort.by="P")
```
The full table will be useful for many purposes, such as creating custom MA or volcano plots with color-coding and symbols to meet your needs.
15. Create a table of all genes (significant and not)
```{r,echo=TRUE}
all_genes<-topTable(fit, adjust="BH",coef="templow", p.value=1, number=Inf ,resort.by="P")
```
coeff = the coefficient or contrast you want to extract  
number = the max number of genes to list  
adjust = the P value adjustment method  
resort.by determines what criteria with which to sort the table  

## Analysis of a 2-factor design
Extending limma to analyze more complex designs is relatively straightforward. A key part is to specify the design matrix properly. For the 2-factor design, one would do this as follows:
16. Construct the design matrix to incorporate temperate and population effects
```{r,echo=TRUE}
population <- factor(s2c$population)
temperature <- factor(s2c$temp, levels=c("low","high"))
design_2factor<- model.matrix(~population+temperature)
design_2factor
```
Then, you would proceed with DE analysis in a similar fashion as with the single factor experiment described above.

    



================================================
FILE: enrichment-analysis/binder/Dockerfile
================================================
FROM rocker/binder:3.6.3

USER root

# install dependencies
RUN install2.r statmod \
  && Rscript -e 'BiocManager::install("edgeR", update=FALSE)' \
  && rm -rf /tmp/downloaded_files

# copy files
COPY --chown=rstudio:rstudio ./differential-expression-analysis/data ${HOME}/data/
COPY --chown=rstudio:rstudio ./enrichment-analysis/data ${HOME}/data/
COPY --chown=rstudio:rstudio ./enrichment-analysis/index.Rmd ${HOME}

USER rstudio


================================================
FILE: enrichment-analysis/data/dme_mapk_ensembl_geneids.txt
================================================
geneid
FBgn0261434
FBgn0015402
FBgn0243512
FBgn0024326
FBgn0014380
FBgn0003205
FBgn0004595
FBgn0011217
FBgn0024329
FBgn0014006
FBgn0262582
FBgn0046689
FBgn0003867
FBgn0039015
FBgn0003118
FBgn0267339
FBgn0015765
FBgn0000206
FBgn0001139
FBgn0039421
FBgn0039532
FBgn0086361
FBgn0001297
FBgn0003720
FBgn0003870
FBgn0010333
FBgn0010909
FBgn0016794
FBgn0014388
FBgn0262733
FBgn0003984
FBgn0014011
FBgn0026252
FBgn0015296
FBgn0004390
FBgn0004569
FBgn0003410
FBgn0259794
FBgn0052179
FBgn0003997
FBgn0003256
FBgn0264959
FBgn0003733
FBgn0011656
FBgn0033483
FBgn0001291
FBgn0004907
FBgn0004638
FBgn0013725
FBgn0015295
FBgn0010213
FBgn0021818
FBgn0023214
FBgn0086358
FBgn0003731
FBgn0016641
FBgn0265193
FBgn0004650
FBgn0000382
FBgn0003079
FBgn0000179
FBgn0003053
FBgn0005390
FBgn0029944
FBgn0265464
FBgn0030018
FBgn0010269
FBgn0002576
FBgn0003366
FBgn0010303
FBgn0261524
FBgn0003969
FBgn0030941
FBgn0026323
FBgn0263933
FBgn0000097
FBgn0000490
FBgn0283531
FBgn0003716
FBgn0000308
FBgn0004177
FBgn0044323
FBgn0003502
FBgn0020497
FBgn0001137
FBgn0003209
FBgn0003751
FBgn0032210
FBgn0015399
FBgn0024846
FBgn0001965
FBgn0000153
FBgn0005672


================================================
FILE: enrichment-analysis/data/heatshockprotein_ensembl_gene_ids.txt
================================================
geneid
FBgn0010247
FBgn0010173
FBgn0024330
FBgn0037878
FBgn0013275
FBgn0013276
FBgn0013277
FBgn0051354
FBgn0013278
FBgn0013279
FBgn0026207
FBgn0001217
FBgn0038145
FBgn0004237
FBgn0038274
FBgn0266599
FBgn0038578
FBgn0020238
FBgn0263738
FBgn0263757
FBgn0038609
FBgn0038722
FBgn0028396
FBgn0044812
FBgn0038838
FBgn0044809
FBgn0044810
FBgn0039004
FBgn0024509
FBgn0039120
FBgn0039125
FBgn0001230
FBgn0039301
FBgn0039562
FBgn0035110
FBgn0035243
FBgn0011573
FBgn0001233
FBgn0263106
FBgn0035852
FBgn0001229
FBgn0001223
FBgn0001225
FBgn0001227
FBgn0001224
FBgn0001226
FBgn0036263
FBgn0086708
FBgn0001216
FBgn0026418
FBgn0263606
FBgn0004401
FBgn0266580
FBgn0033247
FBgn0033264
FBgn0013756
FBgn0033737
FBgn0028683
FBgn0001220
FBgn0034091
FBgn0034118
FBgn0001222
FBgn0034310
FBgn0040273
FBgn0034646
FBgn0034707
FBgn0034722
FBgn0034795
FBgn0010660
FBgn0011296
FBgn0013548
FBgn0002174
FBgn0034939
FBgn0023529
FBgn0003371
FBgn0029676
FBgn0260484
FBgn0013987
FBgn0028982
FBgn0011661
FBgn0266421
FBgn0001218
FBgn0061200
FBgn0010602
FBgn0011244
FBgn0031701
FBgn0031728
FBgn0027868
FBgn0021761
FBgn0262647
FBgn0032474
FBgn0032525
FBgn0086691
FBgn0032586
FBgn0044811
FBgn0053117
FBgn0032906


================================================
FILE: enrichment-analysis/index.Rmd
================================================
---
title: 'Enrichment analysis for bulk RNA-seq with Camera and Roast'
output:
  html_document: default
---

# I. Preliminaries
This tutorial builds on a workflow presented in our last office hours tutorial on conducting bulk RNA-seq analyses with limma voom. In that tutorial, we analyzed gene-level expression estimates for an experiment looking at parallel climate adaptation in *Drosophila melanogaster*. Today, we introduce methods available in the limma voom R package for conducting enrichment tests, specifically evaluating the extent of DE in a set of features (isoforms or genes), that typically correspond to a pathway of interest. 

These methods have been developed specifically for expression analyses, including RNA-seq, in order to explicitly account for the correlation among expression levels across assayed features, i.e. isoforms or genes. CAMERA performs competitive tests, that compare the extent of DE in a feature set compared to a background set. ROAST performs focused tests on feature sets, to test the hypothesis that there is a significant level of DE observed in features within the set. We will specifically use mROAST that performs multiple single-set tests while adjusting for multiple comparisons.  These methods are more appropriate for RNA-seq experiments than GSEA, because GSEA calculates P-values by permuting sample ids. When there are few samples, as is the case in most bulk RNA-seq experiments, sample permutations lead to elevated false discovery rates.

Specific topics covered today include:

* Quick review of steps for obtaining DE results with limma voom
* Running CAMERA on limma voom results for MAPK pathway genes involved in stress response and heat shock proteins
* Running mROAST on limma voom DE results on two gene sets: MAPK pathway genes and heat shock proteins


## Sample data
Our sample data comprises 12 paired-end RNA-seq libraries for whole body samples of *Drosophila melanogaster* from two geographic regions (Panama and Maine), with two temperature treatments ("low" and "high") for each region, featuring three biological replicates for each region x treatment combination. Previously, these data were used to look for parallel gene expression patterns between high and low latitude populations (Zhao et al, 2015, *PLoS Genetics*)

## Loading required R libraries
First, load all the R libraries that will be used for today's analyses:
```{r, echo=TRUE}
library(edgeR)
library(limma)
library(statmod)
```

## Data management
1. Load and view the table that associates sample IDs and treatments (dme_elev_samples.tab):
```{r, echo=TRUE}
s2c<-read.table("data/dme_elev_samples.tab",header = TRUE, stringsAsFactors=FALSE)
s2c
```

2. Open RSEM matrix
```{r,echo=TRUE}
rsem_gene_data<-read.table("data/dme_elevgrad_rsem_bt2_gene_counts.matrix.bz2",header=TRUE,row.names=1)
```
## Pre-processing and filtering
### Handling non-integer RSEM estimates
3. Round the expression matrix
```{r,echo=TRUE}
rnaseqMatrix=round(rsem_gene_data)
```
### Filtering out lowly expressed genes
4. Create a boolean variable that classifies samples according to whether CPM>=1:
```{r,echo=TRUE}
filter=rowSums(cpm(rnaseqMatrix)>=1)>=6
```
5. Apply the filter to the expression matrix:
```{r,echo=TRUE}
cpm_filtered_matrix=rnaseqMatrix[filter,]
```

## Creating a Digital Gene Expression list object 
To run limma, we need to transform the expression matrix into a DGElist ("digital gene expression list") which is an object class that comes from edgeR

6. Create the DGE object and normalized expression matrix:
```{r,echo=TRUE}
DGE<-DGEList(cpm_filtered_matrix)
```
## Normalization using TMM method

7. Calculate normalization factors and do MDS plot:
```{r,echo=TRUE}
DGE<-calcNormFactors(DGE,method =c("TMM"))
```

## Analysis of a 2-factor design
Extending limma to analyze more complex designs is relatively straightforward. A key part is to specify the design matrix properly. For the 2-factor design, one would do this as follows:
8. Construct the design matrix to incorporate temperate and population effects
```{r,echo=TRUE}
population <- factor(s2c$population)
temperature <- factor(s2c$temp, levels=c("low","high"))
design_2factor<- model.matrix(~population+temperature)
design_2factor
```
9. Run limma voom with sample quality weights:
```{r,echo=TRUE}
vwts <- voomWithQualityWeights(DGE, design=design_2factor,normalize.method="none", plot=TRUE)
```
10. Then, run the linear model fitting procedure 1st step:
```{r,echo=TRUE}
fit=lmFit(vwts,design_2factor)
```
11. Then apply the empirical bayes procedure:
```{r,echo=TRUE}
fit=eBayes(fit,robust=TRUE)
```
12. Get summary table of all tests, including NS results:
```{r,echo=TRUE}
all_genes<-topTable(fit, adjust="BH",coef="temperaturehigh", p.value=1, number=Inf ,resort.by="P")
```
II. Running Camera

13. load table of KEGG-classfied MAPK pathway genes
```{r}
mapk<-read.table("data/dme_mapk_ensembl_geneids.txt",header=TRUE)
head(mapk)
```

14. create vector of row (gene) indices for the heat shock protein genes:
```{r,echo=TRUE}
mapk_indices<-ids2indices(mapk,row.names(cpm_filtered_matrix))
```
CameraPR runs camera on precomputed test statistics such at the t-statistics in the limma fit object, so is directly applicable to our limma pipeline.

15. run Camera on the precomputed limma fit object
It is important to use the correct column of the design matrix. 
```{r,echo=TRUE}
colnames(design_2factor)
```
As we can see, column 3 is for the temperature factor that we are interested in
```{r,echo=TRUE}
temp_camera_pr<-cameraPR(fit$t[,3],mapk_indices)
temp_camera_pr
```

**Note:** The current default for inter.gene.cor is 0.01, because in the words of the authors, with this value "camera will rank biologically interpretable sets more highly. This gives a useful compromise between strict error rate control and interpretable gene set rankings." This is particularly applicable to relative rankings when one supplies several sets for testing. As it turns out, one can't change this setting in cameraPR. If one wants the rigorous error rate control that results from estimating the inter-gene correlation, and if one isn't worried about relative rankings of different sets, one could call camera, and set inter.gene.cor to NULL, which makes it estimate that correlation. With camera, you need to be careful to specify the relevant expression matrix. The raw input is not necessarily appropriate in this case, as DE testing is done on TMM normalized, quality-weighted data, which we can access via the vmwts object:

16. Run Camera with inter-gene correlation estimation
```{r,echo=TRUE}
temp_camera_wcorest<-camera(vwts$E,mapk_indices,design_2factor,contrast="temperaturehigh",inter.gene.cor=NA)
temp_camera_wcorest
```
When camera is asked to do estimation, it returns the estimated correlation in the output. The estimated correlation coefficient is substantially larger than the default used with cameraPR, leading to a more conservative P-value, and a non-significant result.

With either cameraPR, or camera, if you provide multiple gene sets, an FDR value will also be provided.

17. Load the heat shock proteins table
```{r,echo=TRUE}
hsps<-read.table("data/heatshockprotein_ensembl_gene_ids.txt",header=TRUE)
```

18. Create heat shock protein indices
```{r,ech=TRUE}
hsps_indices<-ids2indices(hsps,row.names(cpm_filtered_matrix))
```
19. Run Camera with inter-gene correlation estimation for both gene sets
```{r,echo=TRUE}
temp_camera_wcorest_2sets<-camera(vwts$E,index=list(mapk=mapk_indices$geneid,heatshock=hsps_indices$geneid),design_2factor,contrast="temperaturehigh",inter.gene.cor=NA)
temp_camera_wcorest_2sets
```

Our results demonstrate a potential pitfall of using default behavior of an enrichment tool, by assuming a weak correlation among pathway genes!



**NOTE:** it is entirely possible to run camera on expression data or differential expression test results derived from another pipeline. For example, one could supply an expression matrix derived from kallisto estimates, and a design matrix, and use camera. Perhaps more ideal, to take advantage of the advantages sleuth offers for kallisto-derived estimates, one could supply test statistics obtained from sleuth and run cameraPR.

III. Running targeted enrichment analyses with mRoast
20. Run mRoast on heat shock protein and MAPK pathway gene sets
```{r,echo=TRUE}
roast_results<-mroast(vwts$E,index=list(mapk = mapk_indices$geneid, heatshock = hsps_indices$geneid),design=design_2factor,contrast="temperaturehigh",adjust.method="BH",set.statistic="mean50")
roast_results
```
Roast provides P-values and adjusted P-values (FDR) for the direction with the strongest signal, as well as the significance of the combination of both up and down regulation relative to the factor-level that the coefficient in the design describes, in our case "temperaturehigh". There are a number of arguments one can change, but perhaps the most important is "set.statistic" which defines the summary statistic on which significant testing is performed. The right option depends in part on prior knowledge of the extent of DE in the gene set. "mean.50" performs well under a variety of conditions, representing a balance between power and false discovery and will detect as few as 25% differentially expressed genes. When it is expected that only a small fraction of genes in the set will be differentially expressed, "msq" is thought to perform better. "Mean" should only be used when a majority of genes are expected to be differentially expressed.For out data set, choosing between these two statistics only has minimal effect on P-values, and resulting test-specific FDR estimates.

In the big picture, Camera competitive tests show there is not significant enrichment of DE signals in the two pathways relative ot overall patterns of DE. However, there are so many differentially expressed genes in this experiment, that this may obscure interesting biology! In contrast, Roast detects DE in both pathways. Perhaps more informative would be an analysis of a larger set of pathway gene sets, and examining how the fractions of DE genes differ amongst pathways to highlight pathways with particularly strong signal.



================================================
FILE: ggplot-basics/binder/Dockerfile
================================================
FROM rocker/binder:3.6.3

ARG NB_USER

USER root
RUN install2.r dslabs \
  && rm -rf /tmp/downloaded_packages
USER ${NB_USER}

COPY --chown=${NB_USER} ./ggplot-basics/ ${HOME}


================================================
FILE: ggplot-basics/index.Rmd
================================================
---
title: "ggplot basics"
subtitle: "Bioinformatics Coffee Hour"
date: "August 25, 2020"
author: "Tim Sackton"
output: html_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

The goal of this workshop is to teach the grammar of graphics in R, with a focus on **ggplot2**. The consistent grammar implemented in ggplot2 is advantageous both because it is easily extendible - that is you can both produce simple plots, but then develop them into complex publication-ready figures. In addition to the basic ggplot2 R package, many extensions for different types of data have been written using the same standardized grammar.

ggplot2 is part of the tidyverse package, and to make it easier to load our dataset and manipulate it prior to plotting, we will load the entire tidyverse package.

```{r, echo=FALSE}
library(tidyverse)
library(dslabs)
```

# Basic grammar of ggplot

Today we will be introducing the basics of ggplot, using a variety of datasets from the 'dslabs' package, which includes data that has already been cleaned and tidied, and is appropriate for various plotting tasks.

There are three key components that make up every ggplot:
1. **data**
2. **aesthetic mappings** (which variables in your data map to which visual properties)
3. **geometric object (geom) function** (a layer describing how to render each observation)

There are other optional components that control the visualization of the plots, but for now, we will focus on getting these three key elements down. The basic formula for these options is:

`ggplot(data=<dataset>, aes(<mappings>)) + <geom_function>()`

Let's make a basic plot using this grammar. You can see that this is really not much more complicated than the base R *plot* function.

We'll use a dataset from the website [Spurious Correlations](https://www.tylervigen.com/spurious-correlations), which is amusing to browse and demonstrates the limitations of inference from correlations along.

```{r, echo=FALSE}
data(divorce_margarine)
ggplot(data=divorce_margarine, aes(x=divorce_rate_maine, y=margarine_consumption_per_capita)) +
  geom_point()
```
Let's break down what this is actually doing a little bit, to try to get an inuititive understanding for how the 'grammer of graphics' works.

The initial ggplot plot command sets up a coordinate axis. We can just run this, without the geom_point() command, to see what happens:

```{r fig.show='asis'}
ggplot(divorce_margarine, aes(x=divorce_rate_maine, y=margarine_consumption_per_capita))
```

Note that this just sets up the coordinate system: no points are plotted. The plotting happens only when we *add* geom_point() to the plot. So each ggplot is set up as layers: first, a coordinate system, and then one or more overlays plotting data on that coordinate system. 

This has a few implications for how to think about plots. First, it means that you can layer multiple plots on single coordinate system -- we'll see how to do this in a minute. Second, it means that you can save a coordinate system as an object, and then add different plots to it.

We'll do both these things now.

```{r echo=TRUE}
spurCor <- ggplot(divorce_margarine, aes(x=divorce_rate_maine, y=margarine_consumption_per_capita))
spurCor + geom_point()
spurCor + geom_point() + geom_smooth()
```
Note, this adds a y ~ x line to our plot, on top of the scatterplot.

If we take our spurCor coordinate system, we can also just add a line, without the scatterplot, by adding geom_smooth(). We'll also add some options here, instead of using the defaults. In this case, we are using a linear model (method="lm"), with a simple y = x formula. 

```{r echo=FALSE}
spurCor + geom_smooth(method = "lm", formula = y ~ x)
```

Again, remember the basic framework is:
1. **data**
2. **aesthetic mappings** (which variables in your data map to which visual properties)
3. **geometric object (geom) function** (a layer describing how to render each observation)

In this case, we've stored the data and aesthetic mappings/coordinate system in the spurCor object.

As an aside: ggplot includes a huge number of options for controlling how plots look - colors, background, axis, labels, titles, legends, and more. In addition, these options can be wrapped in themes that apply a consistent look to all your graphs. While we will see a few of these in operation in the next few examples, in the interest of time we won't be able to cover all the possible options for controlling the look of graphics today. However, you can look at some [other](http://r-statistics.co/Complete-Ggplot2-Tutorial-Part2-Customizing-Theme-With-R-Code.html) [tutorials](http://zevross.com/blog/2014/08/04/beautiful-plotting-in-r-a-ggplot2-cheatsheet-3/).

Next, let's look at a slightly more complicated dataset and see some basic customization options.

We'll use another dataset available from the dslabs package, the gapminder package.

```{r}
data(gapminder)
ggplot(gapminder, aes(x=life_expectancy, y=gdp/population)) + geom_point()
```
A few things to note. First of all, we can define new variables in the aes statement, using syntax like the tidyverse mutate() command. This can be very useful. Second, though, this plot doesn't look so good. There are lots of points, they are overplotted on top of each other, just plain black is boring, and there is a lot more data in our dataset we might want to know about. 

Let's do this again, but introduce some customization options.

```{r}
gapminder %>% filter(year == 2000, !is.na(gdp)) %>% # let's just look at one year
  mutate(gdpPerCap = gdp/population) %>%
  ggplot(aes(x = life_expectancy, y=gdpPerCap, color=continent)) + geom_point()
```

We've added a few things here. First, we did some filtering and rearranging with tidyverse before sending the data to ggplot. Second, we added a color = <variable> to the aes() code, which tells ggplot to color each point based on the value of that variable.

Let's try these two changes. First, we'll keep the same plot as above, but make all the points red. Second, we'll look at Europe by year, and color the points by GDP 

```{r}
gapminder %>% filter(year==2000, !is.na(gdp)) %>%
  mutate(gdpPerCap = gdp/population) %>%
  ggplot(aes(x = life_expectancy, y=gdpPerCap)) + geom_point(color="red")

gapminder %>% filter(continent == "Europe", !is.na(gdp)) %>%
  mutate(gdpPerCap = gdp/population) %>%
  ggplot(aes(y = life_expectancy, x=year, color=gdpPerCap)) + geom_point()
```
Note that we put the color option in geom_point() if we wanted everything to be the same color. This sets the color for that layer only. This means we can do things like this:

```{r}
gapminder %>% filter(year==2000, !is.na(gdp)) %>%
  mutate(gdpPerCap = gdp/population) %>%
  ggplot(aes(x = life_expectancy, y=log10(gdpPerCap), color=continent)) +
  geom_point(color="black", alpha = 0.2) +
  geom_smooth(method="lm", formula = y ~ x, se = FALSE)
```

In this case, this may not be a particularly useful graph, but note two things:
 * The update to the aesthetics (changing color to "black") in a geom statement override the default aes we call in the ggplot command
 * This doesn't persist to the next layer, which goes back to using the default aesthetics
 
This means we can change certain parts of the aesthetic on a layer by layer basis.
 
Another way to change the aesthetic is by using themes, which replace a bunch of aesthetics. Here are few examples; there are tons more available in other packages, e.g. the ggthemes package, to help you customize your plots. You can also make your own, but that is beyond the scope of this tutorial.

I will also add a command to scale the y axis to be log10 scale.
 
```{r}
gdp_v_le <- gapminder %>% filter(year==2000, !is.na(gdp)) %>%
  mutate(gdpPerCap = gdp/population) %>%
  ggplot(aes(x = life_expectancy, y=gdpPerCap, color=continent)) +
  geom_point(color="black", alpha = 0.2) +
  geom_smooth(method="lm", formula = y ~ x, se = FALSE) +
  scale_y_log10()
gdp_v_le + theme_dark()
gdp_v_le + theme_classic()
gdp_v_le + theme_minimal()
```

So far, we've only looked at plots involving two dimensional continous data, such as scatter and line plots. Let's now look at some additional types of plots.

## 1-d plots

The simplest kind of one-dimensional plots are those that summarize the distribution of a single continuous variable, such as histograms and density plots. These are very useful, so let's look at how to make them with ggplot.

```{r}
gapminder %>% ggplot(aes(life_expectancy)) + geom_histogram(binwidth=2)
```

```{r}
le_dist <- gapminder %>% mutate(decade = as.factor(floor(year/10))) %>% 
  ggplot(aes(life_expectancy, color=decade))
le_dist + geom_histogram(binwidth = 3)
le_dist + geom_freqpoly(binwidth = 3)
le_dist + geom_density()
```
## Categorical data

Sometimes we want to plot categorical data, either bar plots with counts of each discrete value, or boxplots or similar that summarize the distribution of a continuous variable by category. 

For example, we might want to look at a summary of the plot we just made.

```{r}
gapminder %>% mutate(decade = as.factor(floor(year/10))) %>% 
  ggplot(aes(x=decade, y=life_expectancy)) + geom_boxplot()
gapminder %>% mutate(decade = as.factor(floor(year/10))) %>% 
  ggplot(aes(x=decade, y=life_expectancy, fill=continent)) + geom_boxplot()
gapminder %>% mutate(decade = as.factor(floor(year/10))) %>% 
  ggplot(aes(x=continent, y=life_expectancy)) + geom_boxplot(aes(color=decade))
```

Notice that the 'color' argument here in aes() functions like a group_by argument, and groups points by default; fill does the same thing but colors the inside of the boxplot instead of the outside. Note also that we can put the aes() argument either in the ggplot call or the geom. For this plot, it doesn't make a difference, but if we wanted to include multiple geoms in one plot, it could. 

Finally, let's end with something a bit more complicated:

```{r}
gapminder %>% mutate(decade = as.factor(floor(year/10)),
                      log_gdpPerCap = log10(gdp/population)) %>%
  filter(!is.na(log_gdpPerCap)) %>%
  ggplot(aes(x=log_gdpPerCap, y=life_expectancy)) + 
  geom_point(alpha=0.2, color="gray50") +
  geom_smooth(color="red", method="loess", formula = y~x) +
  facet_grid(cols=vars(decade), rows=vars(continent))

```

## Review

We've covered a lot of material here, so let's just touch on the basics.

A plot in ggplot is built up from a dataset, a set of aesthetics establishing a coordinate system and axis, and geoms mapping data to the aesthetics. 

For many simple plots, the default options for the geom call work fine, as we've seen. There are geoms for almost any way of plotting data you could imagine.

Grouping is available as well, which lets you naturally summarize subsets of your data. While we didn't talk about it today, facet_grid and facet_wrap can help you create grids of plots each summarizing a different subset of the data. 


================================================
FILE: intro-to-awk/binder/environment.yml
================================================
dependencies:
  - bash_kernel


================================================
FILE: intro-to-awk/data/Homo_sapiens.GRCh38.subset.gff3
================================================
##gff-version 3
##sequence-region   1 1 248956422
1	Ensembl	chromosome	1	248956422	.	.	.	ID=chromosome:1
1	.	biological_region	10469	11240	1.3e+03	.	.	external_name=oe %3D 0.79
1	.	biological_region	10650	10657	0.999	+	.	logic_name=eponine
1	.	biological_region	10655	10657	0.999	-	.	logic_name=eponine
1	.	biological_region	10678	10687	0.999	+	.	logic_name=eponine
1	.	biological_region	10681	10688	0.999	-	.	logic_name=eponine
1	.	biological_region	10707	10716	0.999	+	.	logic_name=eponine
1	.	biological_region	10708	10718	0.999	-	.	logic_name=eponine
1	.	biological_region	10735	10747	0.999	-	.	logic_name=eponine
1	.	biological_region	10737	10744	0.999	+	.	logic_name=eponine
1	.	biological_region	10766	10773	0.999	+	.	logic_name=eponine
1	.	biological_region	10770	10779	0.999	-	.	logic_name=eponine
1	.	biological_region	10796	10801	0.999	+	.	logic_name=eponine
1	.	biological_region	10810	10819	0.999	-	.	logic_name=eponine
1	.	biological_region	10870	10872	0.999	+	.	logic_name=eponine
1	.	biological_region	10889	10893	0.999	-	.	logic_name=eponine
1	havana	pseudogene	11869	14409	.	+	.	ID=gene:ENSG00000223972
1	havana	lnc_RNA	11869	14409	.	+	.	ID=transcript:ENST00000456328
1	havana	exon	11869	12227	.	+	.	Parent=transcript:ENST00000456328
1	havana	exon	12613	12721	.	+	.	Parent=transcript:ENST00000456328
1	havana	exon	13221	14409	.	+	.	Parent=transcript:ENST00000456328
1	havana	pseudogenic_transcript	12010	13670	.	+	.	ID=transcript:ENST00000450305
1	havana	exon	12010	12057	.	+	.	Parent=transcript:ENST00000450305
1	havana	exon	12179	12227	.	+	.	Parent=transcript:ENST00000450305
1	havana	exon	12613	12697	.	+	.	Parent=transcript:ENST00000450305
1	havana	exon	12975	13052	.	+	.	Parent=transcript:ENST00000450305
1	havana	exon	13221	13374	.	+	.	Parent=transcript:ENST00000450305
1	havana	exon	13453	13670	.	+	.	Parent=transcript:ENST00000450305
1	havana	pseudogene	14404	29570	.	-	.	ID=gene:ENSG00000227232
1	havana	pseudogenic_transcript	14404	29570	.	-	.	ID=transcript:ENST00000488147
1	havana	exon	14404	14501	.	-	.	Parent=transcript:ENST00000488147
1	havana	exon	15005	15038	.	-	.	Parent=transcript:ENST00000488147
1	havana	exon	15796	15947	.	-	.	Parent=transcript:ENST00000488147
1	havana	exon	16607	16765	.	-	.	Parent=transcript:ENST00000488147
1	havana	exon	16858	17055	.	-	.	Parent=transcript:ENST00000488147
1	havana	exon	17233	17368	.	-	.	Parent=transcript:ENST00000488147
1	havana	exon	17606	17742	.	-	.	Parent=transcript:ENST00000488147
1	havana	exon	17915	18061	.	-	.	Parent=transcript:ENST00000488147
1	havana	exon	18268	18366	.	-	.	Parent=transcript:ENST00000488147
1	havana	exon	24738	24891	.	-	.	Parent=transcript:ENST00000488147
1	havana	exon	29534	29570	.	-	.	Parent=transcript:ENST00000488147
1	.	biological_region	15796	16060	0.999	-	.	external_name=rank %3D 1
1	mirbase	ncRNA_gene	17369	17436	.	-	.	ID=gene:ENSG00000278267
1	mirbase	miRNA	17369	17436	.	-	.	ID=transcript:ENST00000619216
1	mirbase	exon	17369	17436	.	-	.	Parent=transcript:ENST00000619216
1	.	biological_region	28736	29810	1.01e+03	.	.	external_name=oe %3D 0.88
1	.	biological_region	29116	29118	0.999	+	.	logic_name=eponine
1	.	biological_region	29127	29206	1	+	.	external_name=rank %3D 1
1	.	biological_region	29321	29395	1	-	.	external_name=rank %3D 1
1	.	biological_region	29394	29396	0.999	-	.	logic_name=eponine
1	.	biological_region	29448	29451	0.999	+	.	logic_name=eponine
1	havana	ncRNA_gene	29554	31109	.	+	.	ID=gene:ENSG00000243485
1	havana	lnc_RNA	29554	31097	.	+	.	ID=transcript:ENST00000473358
1	havana	exon	29554	30039	.	+	.	Parent=transcript:ENST00000473358
1	havana	exon	30564	30667	.	+	.	Parent=transcript:ENST00000473358
1	havana	exon	30976	31097	.	+	.	Parent=transcript:ENST00000473358
1	havana	lnc_RNA	30267	31109	.	+	.	ID=transcript:ENST00000469289
1	havana	exon	30267	30667	.	+	.	Parent=transcript:ENST00000469289
1	havana	exon	30976	31109	.	+	.	Parent=transcript:ENST00000469289
1	.	biological_region	29583	29584	0.999	-	.	logic_name=eponine
1	mirbase	ncRNA_gene	30366	30503	.	+	.	ID=gene:ENSG00000284332
1	mirbase	miRNA	30366	30503	.	+	.	ID=transcript:ENST00000607096
1	mirbase	exon	30366	30503	.	+	.	Parent=transcript:ENST00000607096
1	havana	ncRNA_gene	34554	36081	.	-	.	ID=gene:ENSG00000237613
1	havana	lnc_RNA	34554	36081	.	-	.	ID=transcript:ENST00000417324
1	havana	exon	34554	35174	.	-	.	Parent=transcript:ENST00000417324
1	havana	exon	35277	35481	.	-	.	Parent=transcript:ENST00000417324
1	havana	exon	35721	36081	.	-	.	Parent=transcript:ENST00000417324
1	havana	lnc_RNA	35245	36073	.	-	.	ID=transcript:ENST00000461467
1	havana	exon	35245	35481	.	-	.	Parent=transcript:ENST00000461467
1	havana	exon	35721	36073	.	-	.	Parent=transcript:ENST00000461467
1	.	biological_region	35904	36086	0.879	-	.	external_name=rank %3D 1
1	havana	pseudogene	52473	53312	.	+	.	ID=gene:ENSG00000268020
1	havana	pseudogenic_transcript	52473	53312	.	+	.	ID=transcript:ENST00000606857
1	havana	exon	52473	53312	.	+	.	Parent=transcript:ENST00000606857
1	havana	pseudogene	57598	64116	.	+	.	ID=gene:ENSG00000240361
1	havana	lnc_RNA	57598	64116	.	+	.	ID=transcript:ENST00000642116
1	havana	exon	57598	57653	.	+	.	Parent=transcript:ENST00000642116
1	havana	exon	58700	58856	.	+	.	Parent=transcript:ENST00000642116
1	havana	exon	62916	64116	.	+	.	Parent=transcript:ENST00000642116
1	havana	pseudogenic_transcript	62949	63887	.	+	.	ID=transcript:ENST00000492842
1	havana	exon	62949	63887	.	+	.	Parent=transcript:ENST00000492842
1	ensembl_havana	gene	65419	71585	.	+	.	ID=gene:ENSG00000186092
1	havana	mRNA	65419	71585	.	+	.	ID=transcript:ENST00000641515
1	havana	exon	65419	65433	.	+	.	Parent=transcript:ENST00000641515
1	havana	five_prime_UTR	65419	65433	.	+	.	Parent=transcript:ENST00000641515
1	havana	exon	65520	65573	.	+	.	Parent=transcript:ENST00000641515
1	havana	five_prime_UTR	65520	65573	.	+	.	Parent=transcript:ENST00000641515
1	havana	five_prime_UTR	69037	69090	.	+	.	Parent=transcript:ENST00000641515
1	havana	exon	69037	71585	.	+	.	Parent=transcript:ENST00000641515
1	havana	CDS	69091	70008	.	+	0	ID=CDS:ENSP00000493376
1	havana	three_prime_UTR	70009	71585	.	+	.	Parent=transcript:ENST00000641515
1	ensembl	mRNA	69055	70108	.	+	.	ID=transcript:ENST00000335137
1	ensembl	five_prime_UTR	69055	69090	.	+	.	Parent=transcript:ENST00000335137
1	ensembl	exon	69055	70108	.	+	.	Parent=transcript:ENST00000335137
1	ensembl	CDS	69091	70008	.	+	0	ID=CDS:ENSP00000334393
1	ensembl	three_prime_UTR	70009	70108	.	+	.	Parent=transcript:ENST00000335137
1	ensembl_havana	ncRNA_gene	89295	133723	.	-	.	ID=gene:ENSG00000238009
1	havana	lnc_RNA	89295	120932	.	-	.	ID=transcript:ENST00000466430
1	havana	exon	89295	91629	.	-	.	Parent=transcript:ENST00000466430


================================================
FILE: intro-to-awk/data/Homo_sapiens_ucscGenes.subset.bed
================================================
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chr1	6685209	6693642	uc001aod.3
chr1	6694227	6761966	uc001aof.2
chr1	6845383	7829766	uc001aoi.3
chr1	7831328	7841492	uc001aol.3
chr1	7844713	7905237	uc001aoo.3
chr1	7907671	7913565	uc001aos.3
chr1	7975930	8003225	uc001aot.3
chr1	7990338	7990408	uc021ofs.1
chr1	8021713	8045342	uc001aox.4
chr1	8071778	8086393	uc001aoz.3
chr1	8384389	8404227	uc001apb.3
chr1	8412463	8877699	uc001apf.3
chr1	8440651	8441235	uc001apg.1
chr1	8921058	8939151	uc001apj.2
chr1	8938893	8939943	uc021oft.1
chr1	9005892	9034503	uc031plc.1
chr1	9063358	9086404	uc009vmo.1
chr1	9097004	9129887	uc001apo.3
chr1	9164475	9189229	uc001apq.1
chr1	9186983	9189250	uc001aps.3
chr1	9208345	9242451	uc009vmq.3
chr1	9294862	9331394	uc001apt.3
chr1	9352940	9429590	uc010oae.2
chr1	9497727	9497837	uc021ofx.1
chr1	9599527	9642831	uc001apw.3
chr1	9648931	9674935	uc021ofy.1
chr1	9711789	9789172	uc001aqb.4
chr1	9712667	9714644	uc001aqc.4
chr1	9740902	9747627	uc021oga.1
chr1	9789078	9884550	uc001aqh.3
chr1	9908333	9970316	uc001aql.1
chr1	9989775	10002840	uc001aqm.3
chr1	10003485	10045556	uc001aqp.3
chr1	10057254	10076078	uc001aqq.3
chr1	10093040	10241296	uc001aqs.4
chr1	10270763	10441661	uc001aqw.4
chr1	10459084	10480201	uc001arc.3
chr1	10490158	10512060	uc021ogd.1
chr1	10520602	10532613	uc001arj.3
chr1	10535002	10690815	uc001arn.3
chr1	10696665	10856733	uc001aro.4
chr1	10744633	10744736	uc021ogh.1
chr1	11006529	11042094	uc010oao.2
chr1	11072678	11085549	uc001art.3
chr1	11086579	11107296	uc001aru.3
chr1	11114648	11120091	uc001arz.1
chr1	11126675	11159938	uc001asa.3
chr1	11166587	11322608	uc001asd.3
chr1	11203954	11209595	uc031plf.1
chr1	11249345	11256038	uc001ase.4
chr1	11333254	11348491	uc001asg.3
chr1	11539294	11597640	uc001ash.4
chr1	11708417	11714888	uc001asj.3
chr1	11714913	11723384	uc001asm.3
chr1	11724149	11734409	uc001aso.3
chr1	11734536	11751678	uc009vnc.3
chr1	11751780	11780336	uc001asr.1
chr1	11782186	11785914	uc001ass.2
chr1	11796141	11810828	uc001asv.3
chr1	11824461	11826573	uc001asy.1
chr1	11832138	11849642	uc001asz.3
chr1	11845786	11866160	uc001atc.2
chr1	11866152	11903201	uc001ate.5
chr1	11905766	11907840	uc001ati.3
chr1	11917520	11918992	uc001atj.3
chr1	11979644	11986485	uc001atk.3
chr1	11994723	12035599	uc001atm.3
chr1	12040237	12073572	uc009vni.3
chr1	12079298	12092106	uc001ato.2
chr1	12123433	12204264	uc001atq.3
chr1	12227059	12269277	uc001att.3
chr1	12251761	12251839	uc021ogi.1
chr1	12290112	12572098	uc001atv.3
chr1	12567299	12567451	uc001atz.1
chr1	12627938	12677820	uc001auc.3
chr1	12704565	12727097	uc001auf.3
chr1	12776117	12788726	uc009vnn.1
chr1	12806162	12821102	uc001auh.3
chr1	12834983	12838048	uc001aui.3
chr1	12851545	12856777	uc001auj.2
chr1	12884467	12891264	uc001auk.2
chr1	12907235	12908237	uc009vno.2
chr1	12916940	12921764	uc001aum.1
chr1	12939032	12946025	uc001aun.2
chr1	12952726	12958094	uc001auo.3
chr1	12976449	12980568	uc001aup.3
chr1	12998301	13007406	uc001auq.2
chr1	13035542	13038381	uc009vnq.1
chr1	13182959	13184326	uc010obg.2
chr1	13328195	13331692	uc001aut.1
chr1	13359818	13369057	uc001auu.1
chr1	13386646	13390765	uc001auv.3
chr1	13421175	13428191	uc001auw.1
chr1	13447413	13452656	uc010obi.1
chr1	13474052	13477569	uc009vnu.1
chr1	13495253	13498259	uc001aux.3
chr1	13516065	13526943	uc009vnv.1
chr1	13607430	13611550	uc001auy.2
chr1	13629937	13635299	uc001auz.4
chr1	13641972	13648988	uc001ava.1
chr1	13694888	13698405	uc009vny.1
chr1	13716087	13719064	uc009vnz.1
chr1	13736906	13747803	uc009voa.1
chr1	13801444	13840242	uc001avb.3
chr1	13910251	13944452	uc001avd.3
chr1	14031349	14151574	uc001avi.3
chr1	14925212	15444544	uc001avm.4
chr1	15438310	15478960	uc009voh.3
chr1	15480228	15546974	uc001avx.3
chr1	15573767	15724622	uc001awb.2
chr1	15653175	15670372	uc001awc.1
chr1	15736390	15756839	uc001awh.2
chr1	15764937	15773153	uc001awi.1
chr1	15783222	15798586	uc001awk.3
chr1	15802595	15817895	uc001awl.3
chr1	15817895	15850940	uc001awn.4
chr1	15853351	15898228	uc001aws.3
chr1	15898193	15911605	uc001awv.2
chr1	15943952	15987552	uc001awx.2
chr1	15986363	15988217	uc010obn.2
chr1	15992765	15995537	uc001awz.3
chr1	16010826	16061264	uc010obo.2
chr1	16062808	16067884	uc001axb.1
chr1	16068986	16074292	uc001axc.4
chr1	16085254	16113084	uc001axe.1
chr1	16133656	16134194	uc009vol.1
chr1	16160709	16174642	uc001axj.2
chr1	16174358	16266950	uc001axk.1
chr1	16268363	16302627	uc001axl.4
chr1	16317618	16317647	uc001axm.1
chr1	16330730	16333184	uc001axn.3
chr1	16340522	16345285	uc001axo.2
chr1	16348485	16360545	uc001axu.3
chr1	16384263	16400127	uc001axz.4
chr1	16450831	16482582	uc001aya.2
chr1	16524598	16539104	uc001ayc.1
chr1	16558181	16563659	uc001ayd.3
chr1	16576558	16678948	uc001ayg.3
chr1	16693524	16724643	uc001aym.5
chr1	16725137	16763919	uc001ayn.3
chr1	16767166	16786584	uc001ayq.3
chr1	16793930	16819196	uc001ayt.2
chr1	16847079	16847153	uc021ogp.1
chr1	16858892	16858966	uc021ogq.1
chr1	16860349	16862144	uc021ogr.1
chr1	16862254	16864669	uc001ayv.2
chr1	16872433	16872504	uc021ogs.1
chr1	16874159	16874232	uc021ogt.1
chr1	16875408	16875482	uc021ogu.1
chr1	16888921	16940100	uc001ayw.4
chr1	16944756	16959841	uc001azf.3
chr1	16972068	16976915	uc010och.2
chr1	17004765	17004836	uc021ogw.1
chr1	17006500	17006573	uc021ogx.1
chr1	17007749	17007823	uc021ogy.1
chr1	17017712	17046652	uc001azn.1
chr1	17052060	17052133	uc021ogz.1
chr1	17053779	17053850	uc021oha.1
chr1	17081128	17090975	uc010ock.3
chr1	17180899	17180971	uc021ohb.1
chr1	17185443	17185516	uc021ohc.1
chr1	17186692	17186765	uc021ohd.1
chr1	17188415	17188486	uc021ohe.1
chr1	17197439	17200574	uc021ohf.2
chr1	17201957	17202031	uc021ohg.1
chr1	17215040	17216161	uc001azs.1
chr1	17216171	17216245	uc021ohh.1
chr1	17222645	17222720	uc021ohi.1
chr1	17248444	17299474	uc001azt.2
chr1	17300998	17307173	uc001azw.3
chr1	17312452	17338423	uc001baa.2
chr1	17345224	17380665	uc001bae.3
chr1	17393255	17445948	uc001baf.3
chr1	17531620	17572501	uc001bah.1
chr1	17566189	17572501	uc009vpb.1
chr1	17575592	17610727	uc001bai.3
chr1	17581660	17581778	uc021ohk.1
chr1	17634689	17690495	uc001baj.2
chr1	17698740	17728195	uc001bak.1
chr1	17733250	17765059	uc001bal.3
chr1	17866329	18024370	uc001ban.3
chr1	18081807	18153558	uc001bat.3
chr1	18434239	18704977	uc001bau.2
chr1	18701063	18702174	uc001baw.1
chr1	18807423	18812480	uc001bax.3
chr1	18957499	19062632	uc001bay.3
chr1	19166092	19186155	uc001bba.1
chr1	19197923	19229293	uc001bbc.3
chr1	19209695	19209769	uc021ohm.2
chr1	19230773	19282826	uc001bbd.2
chr1	19400999	19536746	uc001bbi.3
chr1	19542157	19578053	uc001bbo.4
chr1	19578074	19586622	uc001bbs.3
chr1	19592475	19600568	uc021ohn.1
chr1	19609056	19615280	uc001bbv.1
chr1	19619740	19622230	uc021ohp.1
chr1	19629201	19638640	uc001bbw.3
chr1	19638739	19655794	uc001bby.3
chr1	19665266	19812066	uc021ohr.1
chr1	19673334	19675427	uc001bcf.2
chr1	19750877	19751182	uc021ohs.1
chr1	19923470	19956315	uc021ohu.1
chr1	19934300	19935138	uc021ohx.2
chr1	19969722	19984949	uc001bcj.2
chr1	19991779	20006055	uc001bcl.3
chr1	20008705	20126410	uc001bcn.3
chr1	20140521	20141771	uc001bcr.3
chr1	20208887	20239437	uc001bcs.4
chr1	20246799	20250110	uc001bct.1
chr1	20301923	20306932	uc010odb.2
chr1	20396700	20418394	uc001bcy.3
chr1	20439142	20446059	uc001bcz.4
chr1	20465822	20476879	uc009vpp.1
chr1	20490483	20501687	uc009vpq.1
chr1	20512577	20519942	uc001bdb.3
chr1	20617411	20681387	uc009vps.2
chr1	20686293	20755287	uc001bdf.2
chr1	20808883	20812728	uc001bdh.3
chr1	20825940	20834674	uc001bdi.4
chr1	20878931	20881513	uc001bdj.3
chr1	20915443	20945400	uc001bdk.3
chr1	20959947	20978004	uc001bdm.3
chr1	20978259	20988037	uc001bdo.1
chr1	20990506	21044317	uc001bdr.4
chr1	21046224	21059133	uc009vpy.1
chr1	21069170	21113181	uc001bdw.1
chr1	21132784	21503381	uc001bef.3
chr1	21543739	21616766	uc001bei.2
chr1	21602542	21604868	uc001ben.1
chr1	21619782	21626362	uc001beo.2
chr1	21749600	21754300	uc001bep.1
chr1	21761832	21762609	uc001beq.1
chr1	21766582	21811393	uc001ber.4
chr1	21835857	21904905	uc001bet.3
chr1	21922707	21978348	uc001bew.3
chr1	22004791	22109688	uc001bfb.3
chr1	22138757	22151714	uc001bfg.1
chr1	22148736	22263750	uc001bfj.3
chr1	22303417	22315847	uc001bfk.3
chr1	22351706	22357715	uc001bfm.4
chr1	22379119	22419436	uc001bfr.3
chr1	22443797	22469519	uc001bfs.4
chr1	22778343	22857650	uc001bfu.2
chr1	22890003	22930087	uc001bfx.1
chr1	22963117	22966175	uc001bfy.3
chr1	22970117	22974603	uc001bga.4
chr1	22979681	22988029	uc001bgd.3
chr1	23037330	23241823	uc001bge.3
chr1	23046009	23046091	uc021oib.1
chr1	23189651	23189719	uc021oic.1
chr1	23243782	23247347	uc001bgg.1
chr1	23337326	23342343	uc001bgh.1
chr1	23345940	23410184	uc001bgj.2
chr1	23370797	23370865	uc021oid.1
chr1	23410515	23495517	uc010odv.1
chr1	23490445	23490546	uc021oie.1
chr1	23518387	23521222	uc001bgn.3
chr1	23636275	23670853	uc001bgp.4
chr1	23685940	23694879	uc001bgt.3
chr1	23695463	23698330	uc001bgw.3
chr1	23707554	23751261	uc021oig.1
chr1	23755055	23810750	uc001bha.2
chr1	23801092	23803135	uc001bhd.4
chr1	23832919	23857712	uc001bhe.2
chr1	23853364	23855542	uc001bhf.1
chr1	23884420	23886285	uc001bhh.4
chr1	23907984	23967056	uc001bhi.3
chr1	24018268	24022915	uc001bhk.3
chr1	24069855	24088549	uc001bho.3
chr1	24086871	24104787	uc001bhp.2
chr1	24104875	24114722	uc001bhq.3
chr1	24117645	24122029	uc001bht.3
chr1	24122088	24126060	uc009vqo.1
chr1	24128366	24151949	uc001bib.3
chr1	24171571	24194859	uc001bie.3
chr1	24200459	24239817	uc001bif.3
chr1	24255559	24255637	uc021oik.1
chr1	24286300	24289949	uc001big.3
chr1	24295572	24306953	uc021oir.1
chr1	24320925	24320957	uc021oit.1
chr1	24382530	24438665	uc001bin.4
chr1	24446260	24469775	uc001biq.2
chr1	24480646	24513765	uc001bis.3
chr1	24526729	24538180	uc010oei.1
chr1	24578722	24578758	uc021oiu.1
chr1	24579767	24579803	uc021oiv.1
chr1	24645811	24690970	uc021oiw.1
chr1	24683488	24740262	uc001bjc.3
chr1	24742244	24799473	uc001bjh.3
chr1	24822822	24828850	uc021oiz.1
chr1	24829386	24863510	uc001bjj.3
chr1	24882566	24935818	uc001bjk.2
chr1	24969593	24999772	uc001bjm.3
chr1	25071759	25170815	uc001bjo.2
chr1	25226001	25256770	uc001bjq.3
chr1	25548766	25559013	uc001bjt.1
chr1	25568739	25573985	uc001bjw.3
chr1	25598980	25656936	uc001bjz.3
chr1	25629228	25631643	uc001bkd.1
chr1	25664788	25688852	uc001bke.3
chr1	25688739	25747363	uc001bkf.3
chr1	25757387	25826698	uc001bkk.3
chr1	25870075	25895377	uc001bkl.4
chr1	25943958	26111258	uc001bkm.2
chr1	26126666	26144713	uc021ojl.1
chr1	26146396	26159433	uc001bkq.4
chr1	26146444	26150097	uc010oeu.1
chr1	26160496	26185848	uc001bkw.1
chr1	26187974	26197744	uc001bkx.3
chr1	26210676	26232993	uc010oev.2
chr1	26286257	26324648	uc001bld.4
chr1	26348270	26362954	uc001blf.3
chr1	26364513	26372604	uc001blg.1
chr1	26377795	26394125	uc001bli.2
chr1	26438267	26452039	uc009vsb.3
chr1	26485510	26489119	uc001blk.3
chr1	26496387	26497364	uc001bll.4
chr1	26503980	26516375	uc001blm.4
chr1	26517118	26529033	uc010oez.2
chr1	26551810	26556331	uc001blq.3
chr1	26560692	26605299	uc001bls.1
chr1	26606212	26608013	uc001blu.3
chr1	26608772	26633195	uc001blw.3
chr1	26644410	26647014	uc001bmc.3
chr1	26648349	26680621	uc001bmd.4
chr1	26688124	26699266	uc009vsj.1
chr1	26737268	26756219	uc001bmj.3
chr1	26758772	26797795	uc001bmk.3
chr1	26789254	26794028	uc001bmo.1
chr1	26798901	26803133	uc001bmp.4
chr1	26872342	26901520	uc001bms.1
chr1	26881032	26881084	uc021ojp.1
chr1	27022521	27108601	uc001bmv.1
chr1	27114453	27124894	uc001bmz.3
chr1	27145536	27392034	uc021ojq.1
chr1	27153200	27182211	uc001bnb.3
chr1	27189632	27190947	uc001bnc.1
chr1	27189786	27190449	uc010ofi.1
chr1	27205872	27216869	uc001bnd.1
chr1	27216978	27226962	uc001bne.3
chr1	27237974	27240567	uc001bnf.3
chr1	27248212	27273362	uc001bng.2
chr1	27276046	27286901	uc001bni.2
chr1	27320194	27327377	uc001bnj.4
chr1	27331510	27339333	uc010ofj.2
chr1	27425299	27481621	uc001bnm.4
chr1	27533765	27533868	uc021ojs.1
chr1	27561006	27635124	uc009vst.3
chr1	27648635	27662891	uc001bnr.4
chr1	27650364	27653016	uc021oju.1
chr1	27668482	27680423	uc001bnw.2
chr1	27681669	27693337	uc001bny.1
chr1	27695600	27701315	uc001boa.3
chr1	27705595	27709805	uc001boc.3
chr1	27719147	27722317	uc001bod.4
chr1	27730733	27816678	uc001bof.2
chr1	27860755	27930143	uc009vsy.3
chr1	27938800	27961727	uc001bom.3
chr1	27992571	27998724	uc001bon.1
chr1	28052489	28089423	uc001bor.3
chr1	28099693	28150963	uc001bou.4
chr1	28157251	28178183	uc001bov.2
chr1	28160911	28161077	uc001boy.1
chr1	28199054	28213193	uc001bpc.4
chr1	28218048	28241236	uc001bpe.1
chr1	28261503	28285663	uc001bpg.3
chr1	28286503	28294604	uc001bph.1
chr1	28296854	28415148	uc001bpi.2
chr1	28473676	28503455	uc001bpl.3
chr1	28526789	28559542	uc001bpn.3
chr1	28562601	28564616	uc001bpq.3
chr1	28567542	28567564	uc021okb.1
chr1	28585962	28609002	uc001bps.3
chr1	28655512	28662478	uc009vtg.3
chr1	28764660	28826881	uc001bpy.3
chr1	28832454	28837404	uc001bqd.3
chr1	28844744	28865708	uc001bqf.2
chr1	28879528	28905057	uc001bqi.3
chr1	28905049	28908383	uc001bqo.3
chr1	28905254	28905334	uc001bqq.1
chr1	28919006	28921088	uc001bqv.3
chr1	28929608	28969604	uc001bqy.3
chr1	28975111	28975246	uc009vtj.1
chr1	28995239	29042115	uc001bra.3
chr1	29016176	29016306	uc021oke.1
chr1	29063132	29096287	uc021okf.1
chr1	29138653	29190208	uc001brf.1
chr1	29213602	29446558	uc001brm.2
chr1	29445936	29450421	uc001brn.2
chr1	29474249	29508637	uc001bro.3
chr1	29519384	29557454	uc001brq.1
chr1	29563027	29653325	uc001bru.3
chr1	30486798	30510456	uc001bry.3
chr1	31184123	31196432	uc001brz.3
chr1	31191618	31199593	uc001bsb.1
chr1	31205314	31230683	uc001bsc.2
chr1	31212002	31212079	uc021okj.1
chr1	31342312	31381480	uc001bse.2
chr1	31404352	31538564	uc001bsh.1
chr1	31408535	31408623	uc021okk.1
chr1	31421964	31422052	uc021okl.1
chr1	31441009	31441084	uc001bsl.1
chr1	31652591	31712734	uc010ogd.2
chr1	31732414	31742513	uc009vtt.3
chr1	31732414	31769644	uc001bso.3
chr1	31769841	31837780	uc001bsp.1
chr1	31838099	31845923	uc001bss.1
chr1	31886659	31907527	uc010ogh.2
chr1	31971838	31974167	uc021okn.1
chr1	31984035	31989846	uc001bsy.1
chr1	32042085	32053287	uc001bta.3
chr1	32083300	32092919	uc009vtx.2
chr1	32095462	32110838	uc001bth.2
chr1	32117847	32169768	uc001btk.1
chr1	32192717	32229648	uc001btn.3
chr1	32224260	32224336	uc021okr.1
chr1	32256024	32281580	uc001bts.1
chr1	32372021	32403988	uc001bty.2
chr1	32479294	32509482	uc001bub.4
chr1	32538502	32568467	uc010ogv.2
chr1	32573643	32642168	uc001bug.3
chr1	32645344	32663886	uc001bui.3
chr1	32666201	32670991	uc001bul.1
chr1	32671235	32674288	uc001bum.2
chr1	32674694	32681797	uc001bun.2
chr1	32681797	32687926	uc001buq.4
chr1	32687958	32697205	uc009vuc.3
chr1	32697260	32707311	uc001buv.4
chr1	32712817	32714461	uc001buw.3
chr1	32739711	32751766	uc001buy.3
chr1	32757707	32799224	uc001bvb.1
chr1	32799429	32801840	uc001bvd.4
chr1	32826870	32827844	uc021oku.1
chr1	32827861	32829924	uc001bvf.3
chr1	32830704	32860062	uc010ohg.2
chr1	32930657	32953459	uc001bvl.4
chr1	33004771	33071542	uc001bvn.3
chr1	33087306	33116185	uc001bvp.3
chr1	33116748	33151812	uc001bvr.3
chr1	33145506	33168361	uc001bvt.2
chr1	33207511	33240571	uc001bvu.1
chr1	33240839	33283633	uc001bvy.1
chr1	33283118	33324480	uc001bwc.4
chr1	33327868	33338082	uc001bwg.3
chr1	33352097	33360247	uc001bwh.3
chr1	33360195	33366953	uc001bwi.1
chr1	33402049	33430286	uc010oho.2
chr1	33452675	33498070	uc001bwn.3
chr1	33476825	33502512	uc001bwp.2
chr1	33546713	33585995	uc001bwr.3
chr1	33607471	33608831	uc001bxa.1
chr1	33611002	33647671	uc001bxb.3
chr1	33722173	33766320	uc001bxc.1
chr1	33772366	33786699	uc031plq.1
chr1	33789223	33841194	uc001bxg.1
chr1	33797993	33798093	uc021okw.1
chr1	33802166	33802465	uc021okx.1
chr1	33938231	33961995	uc001bxj.4
chr1	33979608	34631443	uc001bxn.1
chr1	34326075	34330392	uc001bxp.3
chr1	34334556	34351059	uc001bxr.3
chr1	34642552	34684731	uc001bxt.3
chr1	35220647	35224113	uc001bxu.4
chr1	35225341	35229325	uc001bxv.1
chr1	35244163	35244247	uc021ola.1
chr1	35247910	35251967	uc001bxy.3
chr1	35258598	35261348	uc001bya.3
chr1	35315962	35325417	uc001byb.3
chr1	35331036	35370984	uc001byc.3
chr1	35441299	35444307	uc021olf.1
chr1	35447126	35450948	uc001bye.3
chr1	35451766	35497569	uc001byh.3
chr1	35544971	35581455	uc001bym.3
chr1	35641980	35646083	uc001byq.3
chr1	35649200	35658743	uc001bys.3
chr1	35734567	35887545	uc001byt.3
chr1	35833677	35835012	uc031plr.1
chr1	35899090	36023037	uc001byx.3
chr1	36023392	36032380	uc001bza.3
chr1	36038970	36060927	uc010ohy.2
chr1	36065142	36107445	uc001bzf.2
chr1	36179476	36184790	uc001bzh.1
chr1	36197712	36235551	uc001bzi.3
chr1	36273827	36323490	uc001bzj.2
chr1	36348809	36389899	uc001bzl.3
chr1	36391432	36395210	uc001bzm.1
chr1	36396682	36522063	uc001bzp.3
chr1	36549675	36553876	uc001bzr.3
chr1	36554452	36559533	uc001bzt.3
chr1	36560843	36565850	uc001bzv.2
chr1	36602169	36615115	uc031pls.1
chr1	36621802	36646441	uc001bzz.3
chr1	36690016	36770957	uc001cae.4
chr1	36771993	36787379	uc010oia.1
chr1	36787631	36789755	uc001caj.1
chr1	36805224	36851485	uc001cak.1
chr1	36859030	36863493	uc001cao.1
chr1	36883506	36916086	uc001caq.3
chr1	36921361	36930040	uc001cas.2
chr1	36931643	36948915	uc001cax.2
chr1	37261127	37499844	uc001caz.2
chr1	37627163	37627235	uc021oll.1
chr1	37920479	37940044	uc021olm.1
chr1	37940118	37949978	uc001cbb.4
chr1	37955560	37980420	uc001cbe.2
chr1	37966535	37966595	uc031pma.1
chr1	38000049	38019945	uc001cbi.4
chr1	38022519	38032458	uc001cbj.3
chr1	38032412	38061586	uc001cbk.3
chr1	38076950	38100595	uc001cbm.2
chr1	38147241	38156192	uc001cbp.3
chr1	38147242	38149864	uc031pmb.1
chr1	38158072	38175391	uc001cbs.4
chr1	38181645	38230824	uc009vvi.3
chr1	38259773	38267278	uc001cby.2
chr1	38268613	38273865	uc001cca.1
chr1	38273472	38275126	uc001ccd.2
chr1	38275238	38325292	uc001cce.1
chr1	38326368	38412729	uc001ccg.1
chr1	38349908	38349989	uc021oln.1
chr1	38422651	38455761	uc001cci.3
chr1	38462441	38471187	uc001cck.3
chr1	38478383	38490497	uc001ccn.4
chr1	38509522	38512450	uc001ccp.1
chr1	38554902	38555001	uc021olo.1
chr1	38674705	38680439	uc021olp.1
chr1	39303868	39325495	uc001ccq.3
chr1	39328161	39339050	uc001ccs.3
chr1	39340222	39341770	uc021olr.1
chr1	39351478	39407456	uc001ccu.1
chr1	39456915	39471737	uc001ccw.3
chr1	39491966	39500308	uc001ccy.3
chr1	39549838	39952810	uc031pmc.1
chr1	39619835	39619968	uc021olu.1
chr1	39648409	39648505	uc021olv.1
chr1	39875175	39882154	uc009vvt.1
chr1	39957317	39995541	uc001cdi.3
chr1	39970194	39970267	uc021olx.1
chr1	39987951	40025370	uc001cdk.3
chr1	40026484	40042521	uc001cdl.2
chr1	40033045	40033182	uc001cdo.1
chr1	40089102	40105348	uc001cdp.3
chr1	40124792	40137710	uc001cdq.1
chr1	40144644	40157089	uc001cdr.3
chr1	40204516	40229586	uc001cdw.3
chr1	40223902	40254533	uc001cdz.1
chr1	40235196	40237020	uc001ceb.1
chr1	40306705	40349177	uc021olz.1
chr1	40361095	40367687	uc001cer.2
chr1	40420783	40435628	uc001cev.3
chr1	40506254	40538321	uc001cey.4
chr1	40538381	40563142	uc001cfb.2
chr1	40627040	40706593	uc001cfc.4
chr1	40713572	40717365	uc001cfe.2
chr1	40723721	40759856	uc001cfg.4
chr1	40766162	40782981	uc001cfh.1
chr1	40839377	40888998	uc001cfj.3
chr1	40916336	40929390	uc001cfn.2
chr1	40943301	40962015	uc001cfo.3
chr1	40974432	40982214	uc001cfp.3
chr1	40997232	41013841	uc001cft.2
chr1	41086351	41131324	uc001cfu.1
chr1	41154751	41157933	uc010ojl.1
chr1	41157241	41237275	uc009vwd.3
chr1	41220026	41220118	uc001cgf.2
chr1	41222955	41223044	uc001cgg.3
chr1	41249683	41306124	uc001cgh.2
chr1	41326727	41328018	uc001cgj.3
chr1	41347313	41347427	uc021omc.1
chr1	41445006	41478235	uc001cgk.4
chr1	41480261	41509562	uc021omd.1
chr1	41481268	41487427	uc001cgm.2
chr1	41492870	41707815	uc001cgs.3
chr1	41932607	41932699	uc021ome.1
chr1	41944445	41949874	uc009vwh.3
chr1	41944445	41950344	uc001cgx.3
chr1	41972035	42384496	uc001cha.4
chr1	42619091	42621495	uc001chc.1
chr1	42628361	42630395	uc001chd.1
chr1	42642209	42800903	uc001chf.3
chr1	42846467	42889900	uc001chi.2
chr1	42896000	42921938	uc001chj.3
chr1	42922172	42926086	uc001chl.3
chr1	43000559	43120335	uc009vwk.1
chr1	43124047	43142429	uc001chq.3
chr1	43144356	43147330	uc001chr.3
chr1	43148065	43168020	uc001chs.3
chr1	43198763	43205925	uc001cht.1
chr1	43212005	43232755	uc001chx.4
chr1	43232915	43241413	uc001cia.4
chr1	43253660	43263901	uc021omg.1
chr1	43272722	43283059	uc001cib.2
chr1	43291248	43310660	uc001cie.1
chr1	43312243	43318146	uc021omh.1
chr1	43323292	43354460	uc001cij.1
chr1	43391045	43424847	uc001cik.2
chr1	43424719	43449029	uc001cil.3
chr1	43489219	43489308	uc021omj.1
chr1	43585818	43611958	uc009vwn.1
chr1	43613593	43622067	uc009vwo.3
chr1	43629844	43638241	uc010ojx.2
chr1	43638000	43720029	uc021omk.1
chr1	43735664	43739673	uc021oml.1
chr1	43747556	43751250	uc001cit.4
chr1	43766565	43788781	uc001ciu.3
chr1	43803474	43820135	uc001ciw.3
chr1	43824625	43828873	uc001cix.3
chr1	43829067	43833409	uc031pmf.1
chr1	43849578	43855483	uc001cje.2
chr1	43855555	43919918	uc001cjk.3
chr1	43916673	43919660	uc001cjo.3
chr1	43996546	44089343	uc001cjr.3
chr1	44115796	44171189	uc001cjx.3
chr1	44165355	44173012	uc001cjy.4
chr1	44173203	44396837	uc001cjz.4
chr1	44182137	44182227	uc021oms.1
chr1	44398991	44402912	uc001ckt.3
chr1	44412477	44433694	uc001ckx.3
chr1	44435652	44439043	uc001ckz.3
chr1	44440601	44443972	uc001cld.3
chr1	44445607	44456843	uc010okl.2
chr1	44457279	44462198	uc001clj.3
chr1	44462154	44483012	uc001cll.4
chr1	44584521	44600809	uc001clp.3
chr1	44679124	44686351	uc001clq.1
chr1	44686741	44820939	uc001clt.3
chr1	44870959	45117396	uc001clv.1
chr1	45011164	45011224	uc031pmh.1
chr1	45119500	45140099	uc001cmc.3
chr1	45140393	45191263	uc001cmf.2
chr1	45205489	45233438	uc001cmg.4
chr1	45241245	45244412	uc001cmi.3
chr1	45241536	45241610	uc001cmj.1
chr1	45242163	45242261	uc001cmk.1
chr1	45243513	45243584	uc009vxi.3
chr1	45244061	45244130	uc001cml.3
chr1	45249256	45253426	uc001cmm.3
chr1	45266035	45271667	uc001cmn.3
chr1	45271585	45272957	uc001cmp.3
chr1	45274153	45279801	uc010ole.1
chr1	45287937	45308616	uc010olf.2
chr1	45316193	45452394	uc001cmt.3
chr1	45468219	45477027	uc009vxk.3
chr1	45477804	45481341	uc001cna.2
chr1	45482075	45672250	uc001cnd.2
chr1	45769581	45771291	uc010olk.2
chr1	45792544	45794346	uc001cne.3
chr1	45794913	45806142	uc009vxp.3
chr1	45805341	45809650	uc009vxq.3
chr1	45809554	45956840	uc001cns.1
chr1	45959597	45965751	uc001cnw.3
chr1	45965855	45976739	uc009vxv.3
chr1	45976706	45988562	uc021omw.1
chr1	46016454	46035723	uc001coe.3
chr1	46049659	46084578	uc001coi.2
chr1	46085715	46089731	uc010olt.2
chr1	46092975	46152302	uc001coq.3
chr1	46111451	46112357	uc010olu.1
chr1	46153846	46160108	uc001cor.1
chr1	46164406	46216485	uc001cou.3
chr1	46269284	46501796	uc001cov.3
chr1	46505811	46598380	uc001cpb.4
chr1	46640748	46651634	uc001cpd.3
chr1	46654352	46685977	uc001cpg.3
chr1	46669005	46686928	uc010oma.2
chr1	46713366	46744145	uc009vye.2
chr1	46744071	46769038	uc001cpn.3
chr1	46769379	46782447	uc001cpp.3
chr1	46805848	46830824	uc001cpr.2
chr1	46859938	46879520	uc001cpu.2
chr1	46899498	46911374	uc021ona.1
chr1	46972667	46979886	uc001cpx.3
chr1	47004367	47035927	uc021onb.1
chr1	47011315	47015678	uc001cpy.2
chr1	47011315	47016887	uc009vyh.1
chr1	47023078	47069966	uc001cqb.4
chr1	47073386	47080805	uc001cqe.4
chr1	47100710	47134099	uc001cqh.4
chr1	47137496	47139256	uc001cqj.3
chr1	47139707	47157769	uc021ond.1
chr1	47140830	47184736	uc001cqk.4
chr1	47264669	47285021	uc001cqn.4
chr1	47308766	47366147	uc031pmm.1
chr1	47394845	47407156	uc001cqp.4
chr1	47489239	47516423	uc001cqt.3
chr1	47533159	47583992	uc001cqu.1
chr1	47603106	47614526	uc001cqv.1
chr1	47644921	47646011	uc031pmn.1
chr1	47649260	47655771	uc001cqw.3
chr1	47681962	47689770	uc009vyq.2
chr1	47681962	47695443	uc001cqx.2
chr1	47691628	47691655	uc021onf.1
chr1	47715810	47779819	uc001crd.1
chr1	47799468	47844511	uc001cri.3
chr1	47859449	47861215	uc001crj.1
chr1	47881743	47883724	uc001crk.3
chr1	47897806	47900313	uc001crl.3
chr1	47901688	47906363	uc001crm.3
chr1	48226199	48462562	uc021ong.1
chr1	48567386	48648100	uc010omr.1
chr1	48688356	48714316	uc001crn.2
chr1	48761043	48937876	uc001crr.2
chr1	48998526	50489626	uc001cru.2
chr1	49193539	49242547	uc001crx.4
chr1	50574593	50667540	uc001csb.2
chr1	50883222	50889119	uc010onb.2
chr1	50906934	51425936	uc001cse.1
chr1	51048075	51048183	uc021onh.1
chr1	51435641	51440309	uc001csg.3
chr1	51567905	51613754	uc001csh.3
chr1	51701944	51739119	uc001csi.4
chr1	51752929	51810785	uc010onf.2
chr1	51819934	51984995	uc001csq.1
chr1	52004296	52004401	uc021oni.1
chr1	52082545	52254891	uc001csu.3
chr1	52254865	52344609	uc001ctc.4
chr1	52373627	52456436	uc001cth.3
chr1	52461412	52461712	uc021onk.1
chr1	52485803	52521843	uc001cti.4
chr1	52497776	52499472	uc001ctj.1
chr1	52521856	52556388	uc001ctk.3
chr1	52607765	52812358	uc001cto.4
chr1	52816264	52831877	uc001ctq.2
chr1	52838500	52870143	uc001ctu.3
chr1	52870218	52883992	uc001ctv.4
chr1	52870218	52883992	uc001ctw.4
chr1	52888947	53018762	uc001cty.2
chr1	53068042	53074723	uc001cue.3
chr1	53099065	53122737	uc001cuf.3
chr1	53152013	53164038	uc001cui.2
chr1	53192130	53293013	uc001cuj.3
chr1	53308182	53360247	uc001cuk.2
chr1	53361581	53387591	uc001cup.4
chr1	53392900	53517289	uc001cur.2
chr1	53527723	53551174	uc010onr.2
chr1	53552854	53608289	uc001cuy.3
chr1	53580247	53584281	uc001cva.1
chr1	53662100	53679869	uc001cvb.4
chr1	53679771	53686289	uc001cvd.3
chr1	53692563	53704282	uc001cvf.2
chr1	53704281	53708455	uc001cvg.3
chr1	53708040	53793821	uc001cvi.2
chr1	53793904	53802889	uc001cvn.1
chr1	53904042	53905693	uc009vzj.3
chr1	53925071	53933158	uc001cvq.1
chr1	53971905	54199877	uc001cvr.1
chr1	54231133	54304225	uc001cvs.3
chr1	54317391	54355487	uc001cvu.3
chr1	54359860	54376759	uc001cwb.3
chr1	54387233	54411288	uc001cwh.3
chr1	54411998	54433841	uc001cwj.2
chr1	54472970	54483859	uc001cwm.2
chr1	54497348	54519111	uc001cwp.3
chr1	54519273	54565416	uc001cwt.1
chr1	54519751	54519827	uc021ons.1
chr1	54604667	54618679	uc001cwv.2
chr1	54638026	54665746	uc009vzo.3
chr1	54665839	54684056	uc001cxa.4
chr1	54691103	54872068	uc001cxe.4
chr1	55013806	55076005	uc001cxl.2
chr1	55074849	55089200	uc001cxn.3
chr1	55107426	55175939	uc010ooe.1
chr1	55181494	55208328	uc001cxx.4
chr1	55222570	55230226	uc001cxy.3
chr1	55246751	55266941	uc009vzt.1
chr1	55271735	55307937	uc001cyb.4
chr1	55315299	55352921	uc001cyc.1
chr1	55352654	55353883	uc021onu.1
chr1	55423541	55423614	uc021onv.1
chr1	55446464	55457966	uc001cyd.3
chr1	55464616	55474465	uc001cye.3
chr1	55505148	55530526	uc001cyf.2
chr1	55532031	55681039	uc021onw.1
chr1	55681080	55683128	uc021onx.1
chr1	55691313	55691396	uc021ony.1
chr1	55842198	55842525	uc021onz.1
chr1	55950543	55950645	uc021ooa.1
chr1	56046709	56200675	uc001cyi.1
chr1	56960418	57045257	uc001cyj.2
chr1	57110989	57181008	uc001cyk.4
chr1	57184476	57285369	uc001cym.4
chr1	57289353	57292593	uc001cyn.3
chr1	57320442	57383894	uc001cyo.2
chr1	57394882	57431688	uc001cyp.3
chr1	57463578	58716211	uc001cys.1
chr1	58326214	58328786	uc001cyu.1
chr1	58933598	58934677	uc001cyw.1
chr1	58946390	59012446	uc001cyy.3
chr1	59041094	59043166	uc001cyz.4
chr1	59120410	59165747	uc009wab.2
chr1	59246462	59249785	uc001cze.3
chr1	59250822	59365384	uc010oop.1
chr1	59597607	59612479	uc010ooq.2
chr1	59762624	60228402	uc009wac.3
chr1	60198898	60198968	uc021oob.1
chr1	60238466	60254501	uc001czn.3
chr1	60280532	60342050	uc001czo.3
chr1	60358979	60392423	uc001czq.3
chr1	60454823	60539442	uc001czs.2
chr1	61125302	61291256	uc001czt.1
chr1	61405915	61436448	uc001czu.3
chr1	61547533	61928460	uc010oos.2
chr1	62119913	62121800	uc031pmt.1
chr1	62146718	62191095	uc001czz.1
chr1	62208148	62629591	uc001dab.3
chr1	62318170	62318274	uc031pmu.1
chr1	62660473	62678001	uc001dae.4
chr1	62701836	62785083	uc001dah.4
chr1	62901974	62917475	uc001dak.2
chr1	62920396	63154039	uc001daq.4
chr1	63063157	63071976	uc001das.2
chr1	63249776	63330941	uc001dau.3
chr1	63624753	63782901	uc001daw.2
chr1	63704613	63704845	uc021ood.1
chr1	63788729	63790797	uc001dax.2
chr1	63799429	63799491	uc021ooe.1
chr1	63833260	63904233	uc021oof.1
chr1	63906440	63988944	uc001dbb.2
chr1	63989012	64038364	uc001dbf.3
chr1	64014650	64016307	uc001dbg.1
chr1	64088886	64125916	uc010ooz.2
chr1	64239689	64647179	uc001dbj.3
chr1	64262024	64262128	uc021oog.1
chr1	64571005	64636980	uc001dbl.3
chr1	64669489	64710027	uc001dbn.1
chr1	64936475	65158741	uc001dbo.1
chr1	65045529	65045604	uc021ooh.1
chr1	65210777	65298914	uc001dbs.2
chr1	65298905	65432187	uc001dbu.1
chr1	65445259	65468159	uc001dbw.3
chr1	65488650	65488757	uc021ooj.1
chr1	65523437	65523525	uc021ook.1
chr1	65524116	65524191	uc001dbx.3
chr1	65613231	65697828	uc001dby.3
chr1	65775217	65881552	uc001dce.2
chr1	65886130	66103176	uc001dci.3
chr1	65886130	65901690	uc001dcf.3
chr1	66258855	66840262	uc001dco.3
chr1	66560143	66560229	uc021oom.1
chr1	66999824	67210768	uc001dcr.3
chr1	67094122	67094200	uc021oon.1
chr1	67132271	67142710	uc010ope.1
chr1	67218139	67244730	uc001dcv.3
chr1	67263423	67266942	uc001dcw.3
chr1	67278571	67390570	uc001dcx.3
chr1	67390577	67454302	uc001dde.2
chr1	67465014	67520080	uc001ddk.2
chr1	67557858	67600654	uc001ddm.2
chr1	67632168	67725650	uc001ddo.3
chr1	67661822	67661926	uc021ooo.1
chr1	67773046	67862583	uc001ddu.3
chr1	67873492	67896123	uc001ddv.3
chr1	68150859	68154021	uc001ddz.2
chr1	68167148	68299155	uc001dea.2
chr1	68238275	68238336	uc021oop.1
chr1	68297970	68668670	uc001deb.2
chr1	68511644	68516481	uc001ded.3
chr1	68564141	68698284	uc001dee.3
chr1	68649200	68649293	uc021oos.1
chr1	68649301	68649321	uc021oot.1
chr1	68894506	68915642	uc001dei.1
chr1	68939834	68962799	uc001dem.4
chr1	68962358	69004310	uc001den.3
chr1	70225857	70589171	uc001dep.3
chr1	70385004	70386000	uc009wbh.2
chr1	70610484	70671361	uc001der.2
chr1	70671364	70717701	uc001des.3
chr1	70724684	70820417	uc001dex.4
chr1	70820492	70833705	uc001dfa.3
chr1	70876900	70905534	uc001dfd.3
chr1	71172135	71252151	uc001dff.3
chr1	71418114	71513491	uc001dfo.3
chr1	71512188	71532865	uc001dfr.3
chr1	71528973	71546972	uc001dft.3
chr1	71533313	71533399	uc010oqr.1
chr1	71547006	71703406	uc001dfu.2
chr1	71868624	72748405	uc001dfw.3
chr1	72259914	72302695	uc031pmw.1
chr1	73771852	73804560	uc001dfx.3
chr1	74491701	74663871	uc001dfy.4
chr1	74663895	75010116	uc001dge.2
chr1	75033794	75139422	uc001dgg.3
chr1	75043113	75091782	uc001dgh.3
chr1	75171171	75199092	uc001dgj.3
chr1	75198835	75232360	uc001dgn.3
chr1	75595658	75598261	uc001dgp.1
chr1	75600566	75627218	uc031pmx.1
chr1	75672074	76076799	uc001dgu.3
chr1	76103850	76188721	uc001dgv.3
chr1	76190042	76229355	uc009wbp.3
chr1	76251878	76260775	uc001dgy.2
chr1	76252756	76252834	uc001dgz.2
chr1	76253573	76253657	uc009wbu.1
chr1	76255161	76255232	uc009wbv.1
chr1	76262555	76378923	uc001dhd.2
chr1	76384557	76398116	uc001dhe.2
chr1	76540388	77096669	uc001dhh.2
chr1	77333185	77529737	uc001dhi.3
chr1	77554666	77685132	uc001dhk.3
chr1	77747661	78025654	uc001dhn.3
chr1	78030189	78148343	uc001dhq.3
chr1	78161673	78225564	uc001dht.4
chr1	78245308	78345225	uc010ork.3
chr1	78354199	78409578	uc001dic.4
chr1	78413590	78444777	uc001dii.3
chr1	78470635	78482995	uc001dij.3
chr1	78511588	78603112	uc001dik.3
chr1	78560490	78560599	uc021oov.1
chr1	78695282	78759574	uc001dil.1
chr1	78956727	79006386	uc001din.3
chr1	79086087	79111830	uc010oro.2
chr1	79115476	79129763	uc001dip.4
chr1	79152744	79152828	uc021oow.1
chr1	79355448	79472495	uc001diq.4
chr1	82266081	82458107	uc001diu.3
chr1	82312940	82313045	uc021oox.1
chr1	83439565	83451891	uc001dix.4
chr1	83911736	83920454	uc001diy.3
chr1	84041470	84326679	uc001diz.4
chr1	84259583	84259634	uc021ooy.1
chr1	84259597	84379059	uc031pmy.1
chr1	84267442	84326229	uc001dja.1
chr1	84335056	84464833	uc001djc.3
chr1	84379036	84379062	uc021ooz.1
chr1	84609951	84704181	uc001djl.3
chr1	84743003	84743140	uc021opa.1
chr1	84764048	84816481	uc001djr.3
chr1	84810360	84816481	uc001djs.3
chr1	84830640	84863576	uc009wcg.3
chr1	84864214	84880691	uc001djt.1
chr1	84944919	84964033	uc001djv.4
chr1	84964005	84972262	uc001djw.4
chr1	84972012	85014647	uc021opb.1
chr1	85018803	85040163	uc001dka.2
chr1	85093912	85097429	uc010ory.1
chr1	85109389	85156240	uc001dkj.3
chr1	85279085	85358896	uc009wcj.1
chr1	85391265	85462796	uc001dkm.3
chr1	85483764	85514223	uc001dkp.3
chr1	85527992	85598821	uc001dkt.3
chr1	85599476	85599556	uc021opc.1
chr1	85623355	85666728	uc009wcm.3
chr1	85715636	85725355	uc001dkv.3
chr1	85731459	85742587	uc021opd.1
chr1	85742040	85865646	uc001dla.2
chr1	85784167	85930889	uc001dlb.3
chr1	86046443	86049648	uc001dle.3
chr1	86115105	86174116	uc001dlh.3
chr1	86194915	86622154	uc001dlj.3
chr1	86815776	86862025	uc001dll.2
chr1	86889768	86922240	uc001dlr.4
chr1	86934394	86965974	uc001dlt.3
chr1	87012758	87046432	uc009wcs.3
chr1	87099958	87121059	uc010osh.2
chr1	87170252	87213867	uc001dly.3
chr1	87328127	87380107	uc021opi.1
chr1	87380334	87575681	uc010osk.2
chr1	87597679	87598718	uc021opj.1
chr1	87777576	87777676	uc021opk.1
chr1	87794150	87814607	uc001dmi.3
chr1	87819209	87837338	uc001dmk.3
chr1	87918922	87919056	uc021opl.1
chr1	88943510	88943810	uc021opm.1
chr1	89004735	89150887	uc021opn.1
chr1	89149921	89301938	uc001dmn.3
chr1	89318320	89357301	uc001dmo.4
chr1	89401455	89458643	uc001dmp.2
chr1	89445138	89458643	uc001dms.3
chr1	89472359	89488549	uc001dmt.3
chr1	89517986	89531043	uc001dmx.2
chr1	89573309	89591799	uc001dmz.1
chr1	89597433	89641723	uc001dna.2
chr1	89646830	89664633	uc001dnb.3
chr1	89724633	89738544	uc001dnd.3
chr1	89754940	89756045	uc031pna.1
chr1	89829435	89853719	uc001dnf.2
chr1	89873237	89890493	uc009wcy.1
chr1	89990396	90063420	uc001dni.3
chr1	90090407	90098453	uc001dnk.1
chr1	90098643	90185094	uc001dnl.4
chr1	90287479	90401989	uc001dnn.3
chr1	90453271	90453503	uc021opr.1
chr1	90458823	90460525	uc001dno.3
chr1	90460677	90494094	uc001dnq.2
chr1	91177578	91182794	uc001dns.3
chr1	91295103	91317175	uc001dnu.2
chr1	91380856	91487812	uc001dnw.3
chr1	91726322	91870426	uc001doa.4
chr1	91966403	91991321	uc001dof.3
chr1	92145899	92351836	uc001doh.3
chr1	92295329	92295626	uc021ops.1
chr1	92414927	92479985	uc010osz.2
chr1	92495532	92529093	uc001don.2
chr1	92545861	92613401	uc001doo.3
chr1	92632608	92650280	uc010otd.2
chr1	92683572	92711367	uc001doq.3
chr1	92711954	92764566	uc001dor.3
chr1	92764521	92853732	uc001dot.2
chr1	92940317	92951628	uc001dov.4
chr1	92974252	93257961	uc001dox.3
chr1	93297593	93307481	uc001doz.3
chr1	93302845	93302940	uc001dpe.2
chr1	93303574	93303627	uc021opt.1
chr1	93307716	93427079	uc001dpg.3
chr1	93544791	93604638	uc009wdj.3
chr1	93615298	93646246	uc001dpn.3
chr1	93646280	93744287	uc021opx.1
chr1	93775665	93811368	uc001dpt.2
chr1	93811477	93828148	uc001dpu.3
chr1	93913687	94020218	uc010otk.2
chr1	93981833	93981906	uc021opz.1
chr1	94027342	94146926	uc001dpz.4
chr1	94057524	94065587	uc009wdn.3
chr1	94219111	94240930	uc001dqd.1
chr1	94312387	94312467	uc021oqa.1
chr1	94313128	94313213	uc021oqb.1
chr1	94317792	94319887	uc001dqe.1
chr1	94335013	94344762	uc001dqf.3
chr1	94352589	94375012	uc001dqg.1
chr1	94458393	94586705	uc001dqh.3
chr1	94634462	94703307	uc001dqj.4
chr1	94883932	94984219	uc001dqn.4
chr1	94994731	95007413	uc001dqr.3
chr1	95123088	95285834	uc001dqu.3
chr1	95285897	95360803	uc001dqv.5
chr1	95362506	95392735	uc001dqz.4
chr1	95393583	95428826	uc021oqc.2
chr1	95448278	95538507	uc001dra.2
chr1	95550007	95550119	uc021oqd.1
chr1	95582893	95663161	uc001drb.3
chr1	95628774	95699538	uc001dre.1
chr1	95699710	95712781	uc009wdu.3
chr1	95940292	95944912	uc021oqf.1
chr1	95970531	95970615	uc021oqg.1
chr1	95975895	95981020	uc001drl.3
chr1	97161411	97161723	uc021oqh.1
chr1	97187174	97280605	uc001drq.3
chr1	97543299	98386615	uc001drv.3
chr1	97561478	97788511	uc031pne.1
chr1	98453555	98515249	uc001drx.2
chr1	98510798	98510907	uc021oqj.1
chr1	98676266	98738214	uc031pnf.1
chr1	99127235	99226056	uc010ouc.2
chr1	99207463	99207540	uc021oql.1
chr1	99355800	99470449	uc001dsb.3
chr1	99469831	99614408	uc001dsd.1
chr1	99729847	99775138	uc001dse.3
chr1	100111430	100160097	uc001dsg.3
chr1	100174258	100231349	uc001dsh.1
chr1	100178485	100178513	uc021oqn.1
chr1	100178485	100178513	uc021oqm.1
chr1	100315639	100389579	uc001dsi.1
chr1	100434000	100435404	uc001dso.2
chr1	100435991	100492534	uc001dsr.2
chr1	100503788	100548929	uc001dst.3
chr1	100549101	100598511	uc001dsu.3
chr1	100598705	100616054	uc001dsv.3
chr1	100614003	100643829	uc001dsx.2
chr1	100652477	100715409	uc001dta.3
chr1	100731713	100758325	uc001dtd.3
chr1	100746796	100746864	uc021oqp.1
chr1	100818022	100965021	uc001dtf.2
chr1	101003727	101007583	uc001dth.3
chr1	101092605	101112560	uc021oqr.1
chr1	101185195	101204601	uc001dti.3
chr1	101337927	101360735	uc001dtk.2
chr1	101361631	101447311	uc001dto.2
chr1	101455179	101491362	uc001dtt.2
chr1	101491408	101552819	uc001dua.3
chr1	101702304	101707076	uc001dud.2
chr1	101746453	101746573	uc021oqu.1
chr1	101806424	101806451	uc031pni.1
chr1	102268126	102462790	uc001dug.2
chr1	102337566	102360299	uc021oqv.1
chr1	103342022	103574052	uc001dul.3
chr1	104068577	104097859	uc010oun.2
chr1	104095895	104122149	uc001duq.3
chr1	104159998	104168400	uc001dut.3
chr1	104198301	104207173	uc001duv.3
chr1	104230039	104238912	uc001duw.1
chr1	104257374	104262492	uc010our.1
chr1	104292439	104301311	uc001duz.3
chr1	104615644	104619693	uc021oqw.1
chr1	106144773	106161557	uc001dva.3
chr1	107599266	107601916	uc010ous.2
chr1	107682628	108024475	uc001dvh.4
chr1	107937775	108024475	uc001dvi.3
chr1	107937775	108024475	uc009wem.3
chr1	108113781	108507545	uc001dvk.1
chr1	108496274	108496345	uc021oqx.1
chr1	108507064	108537229	uc031pnj.1
chr1	108677343	108742980	uc001dvn.5
chr1	108765962	108786703	uc009weo.2
chr1	108803819	108816311	uc001dvp.2
chr1	108918459	108953434	uc001dvq.3
chr1	108963310	108975804	uc001dvr.2
chr1	108992903	109013260	uc009wep.3
chr1	109102970	109181949	uc010ouy.2
chr1	109190909	109203744	uc001dvt.4
chr1	109234931	109244422	uc001dvv.4
chr1	109255555	109285367	uc001dvx.3
chr1	109289284	109352148	uc001dvy.3
chr1	109358519	109399726	uc001dwa.4
chr1	109399838	109401146	uc001dwc.3
chr1	109419602	109473044	uc010ovc.2
chr1	109472129	109506121	uc001dwf.2
chr1	109512837	109584850	uc001dwl.3
chr1	109606997	109618624	uc001dwm.1
chr1	109633402	109639554	uc001dwn.3
chr1	109642814	109643234	uc001dwo.1
chr1	109648572	109656479	uc009wew.3
chr1	109656584	109749403	uc021orb.1
chr1	109756514	109780804	uc001dwu.2
chr1	109792640	109818378	uc001dxa.4
chr1	109822175	109825790	uc001dxd.3
chr1	109834986	109849663	uc001dxk.1
chr1	109852187	109940563	uc001dxm.2
chr1	109941652	109969070	uc001dxn.3
chr1	109965120	109966952	uc021orf.1
chr1	110009099	110024764	uc001dxp.3
chr1	110026560	110035420	uc001dxr.3
chr1	110036700	110043063	uc010ovo.2
chr1	110049445	110052336	uc001dxx.4
chr1	110082493	110088455	uc001dxy.2
chr1	110091185	110138454	uc001dxz.3
chr1	110141514	110141589	uc010ovq.1
chr1	110145888	110155705	uc001dya.3
chr1	110162434	110174677	uc009wfh.2
chr1	110198697	110204331	uc001dyf.3
chr1	110230417	110236367	uc001dyk.3
chr1	110254863	110260890	uc001dyn.3
chr1	110276553	110283660	uc001dyo.2
chr1	110292701	110306644	uc001dyq.2
chr1	110453232	110473616	uc001dyw.4
chr1	110527386	110566364	uc001dyx.3
chr1	110577222	110597424	uc001dza.2
chr1	110602996	110613322	uc001dzb.3
chr1	110603303	110603368	uc021orl.1
chr1	110655061	110656569	uc001dzc.3
chr1	110693131	110744823	uc009wfq.3
chr1	110753335	110776674	uc001dzh.3
chr1	110815105	110815229	uc021orm.1
chr1	110828998	110881793	uc001dzj.3
chr1	110881944	110889303	uc001dzl.1
chr1	110887099	110888734	uc001dzn.1
chr1	110905472	110933704	uc001dzo.2
chr1	110943876	110950546	uc001dzr.3
chr1	110950430	110958896	uc031pnm.1
chr1	110993787	110999976	uc001dzs.3
chr1	111023387	111033891	uc009wfu.1
chr1	111059838	111061797	uc001dzt.1
chr1	111145775	111148975	uc009wfw.3
chr1	111214309	111217655	uc001dzv.1
chr1	111413820	111442558	uc001dzw.3
chr1	111489811	111506566	uc001eaa.3
chr1	111659953	111682838	uc001ead.4
chr1	111682248	111727724	uc001eah.1
chr1	111728590	111747160	uc001eal.2
chr1	111770280	111786062	uc001eam.3
chr1	111823145	111828730	uc009wgb.3
chr1	111833473	111863188	uc001eas.4
chr1	111889194	111895639	uc001eaw.2
chr1	111927140	111932473	uc021orp.1
chr1	111956936	111970399	uc001eba.3
chr1	111982511	111991830	uc001ebb.3
chr1	111991742	112004525	uc001ebc.3
chr1	112016603	112021134	uc001ebe.3
chr1	112025969	112046743	uc001ebf.3
chr1	112032938	112033045	uc021orr.1
chr1	112141628	112150940	uc001ebj.2
chr1	112162404	112256101	uc001ebl.3
chr1	112256829	112259310	uc001ebn.1
chr1	112264685	112282046	uc001ebo.2
chr1	112282462	112290420	uc001ebq.1
chr1	112287934	112298131	uc001ebr.3
chr1	112298189	112310199	uc001ebs.3
chr1	112318453	112531777	uc001ebu.1
chr1	112533189	112541463	uc001ebw.4
chr1	112913625	112913729	uc021oru.1
chr1	112938799	113003786	uc001ebx.3
chr1	112938880	112941439	uc001eby.1
chr1	113004391	113004455	uc021orv.1
chr1	113051369	113063910	uc001ecb.3
chr1	113066140	113162040	uc001ecd.3
chr1	113162074	113214241	uc001ecj.1
chr1	113217047	113243368	uc001eck.3
chr1	113243748	113249678	uc001ecp.1
chr1	113252615	113257950	uc001ect.1
chr1	113263188	113269856	uc001ecu.3
chr1	113362795	113393265	uc001ecw.1
chr1	113392521	113420491	uc021orw.1
chr1	113454469	113498975	uc001ecy.3
chr1	113465971	113467295	uc001eda.1
chr1	113499070	113511603	uc001edd.3
chr1	113554308	113615724	uc001ede.1
chr1	113615830	113667342	uc001edf.1
chr1	113739403	113748875	uc001edg.2
chr1	113933474	114228545	uc001edk.3
chr1	114239823	114301777	uc009wgp.1
chr1	114304453	114355070	uc001edq.3
chr1	114356432	114414375	uc001eds.3
chr1	114399256	114443859	uc001edv.2
chr1	114419435	114430169	uc001edw.3
chr1	114437370	114447525	uc001eeb.3
chr1	114447914	114456708	uc001eeg.3
chr1	114466622	114471880	uc001eek.3
chr1	114471995	114520491	uc001eem.3
chr1	114522029	114524875	uc001eer.1
chr1	114631913	114695062	uc021orz.1
chr1	114935398	115053781	uc001eew.3
chr1	115110180	115124265	uc001efa.3
chr1	115125468	115126223	uc021osb.1
chr1	115127195	115212732	uc001efd.1
chr1	115215719	115238239	uc001efe.2
chr1	115247084	115259515	uc009wgu.3
chr1	115259533	115300671	uc001efi.3
chr1	115312104	115323308	uc001efp.4
chr1	115397454	115537990	uc001efr.3
chr1	115572444	115576930	uc001efs.2
chr1	115590632	115632121	uc001eft.3
chr1	115828536	115880857	uc001efu.1
chr1	116184573	116240845	uc001efv.1
chr1	116242625	116311426	uc001efx.4
chr1	116378998	116383747	uc001efy.3
chr1	116461996	116468529	uc001efz.3
chr1	116519118	116612675	uc001egb.4
chr1	116654375	116677861	uc001egc.1
chr1	116821227	116821443	uc021osg.1
chr1	116915794	116947396	uc001ege.3
chr1	116947335	116961244	uc001egj.3
chr1	116956387	116956491	uc021osh.1
chr1	116995157	116995249	uc021osi.1
chr1	117057155	117113715	uc001egm.3
chr1	117117019	117209578	uc031pnr.1
chr1	117134723	117134827	uc021osj.1
chr1	117214370	117214449	uc021osk.1
chr1	117297085	117311851	uc001egu.4
chr1	117452688	117532972	uc001egv.1
chr1	117504968	117505093	uc021osl.1
chr1	117544371	117579173	uc010oxb.2
chr1	117602948	117645491	uc001egy.3
chr1	117637264	117637350	uc021osm.1
chr1	117653676	117664411	uc001egz.2
chr1	117686208	117753582	uc001ehb.3
chr1	117785831	117786130	uc021osp.1
chr1	117910084	118068320	uc001ehd.1
chr1	118148603	118171011	uc001ehe.3
chr1	118406106	118472302	uc001ehf.3
chr1	118472371	118503049	uc010oxe.1
chr1	118496287	118727848	uc001ehk.2
chr1	119425665	119532179	uc001ehl.1
chr1	119573838	119683295	uc001ehn.3
chr1	119683047	119726446	uc009whk.2
chr1	119911401	119936751	uc001ehr.1
chr1	119957742	119965662	uc001eht.3
chr1	120049825	120057681	uc001ehv.1
chr1	120106502	120115199	uc021osu.1
chr1	120140324	120141914	uc021osv.1
chr1	120161999	120190390	uc001ehy.1
chr1	120254418	120286849	uc001ehz.3
chr1	120290618	120311555	uc001eid.3
chr1	120336640	120354203	uc001eif.3
chr1	120377387	120387503	uc010oxk.3
chr1	120436155	120439147	uc001eij.3
chr1	120454175	120612317	uc001eik.3
chr1	120839004	120855681	uc009whp.3
chr1	120906033	120914842	uc001eio.2
chr1	120906460	120906507	uc021osz.1
chr1	120926127	120935944	uc001eip.3
chr1	121107151	121129827	uc031pnu.1
chr1	121306423	121313686	uc009wht.1
chr1	142618798	143257763	uc001eiw.1
chr1	142660105	142660135	uc001eiy.2
chr1	142672190	142672219	uc001eiz.2
chr1	142688238	142688268	uc021ota.1
chr1	142689205	142689235	uc021otb.1
chr1	142689523	142689553	uc021otc.1
chr1	142697420	142713605	uc031pnv.1
chr1	142803223	142888797	uc001ejb.3
chr1	142803530	142826645	uc001ejc.4
chr1	142851394	142851424	uc001eje.3
chr1	142853227	142855999	uc009whu.1
chr1	143168647	143168675	uc001ejg.1
chr1	143184707	143184737	uc001ejh.1
chr1	143185980	143186010	uc001ejj.3
chr1	143286955	143286985	uc021otd.1
chr1	143289062	143289092	uc001ejk.3
chr1	143402283	143402313	uc001ejl.1
chr1	143403553	143403583	uc002zkn.1
chr1	143419286	143419314	uc021ote.1
chr1	143424332	143467651	uc001ejn.1
chr1	143431367	143431397	uc001ejo.2
chr1	143647638	143744587	uc001ejp.3
chr1	143687129	143714180	uc001ejq.3
chr1	143690027	143690101	uc021otf.1
chr1	143702303	143702331	uc031pnw.1
chr1	143767143	143767881	uc001ejt.3
chr1	143879831	143879905	uc021otg.1
chr1	143896451	143913143	uc002qvn.1
chr1	143915747	144094477	uc010oxm.1
chr1	144146810	146467744	uc021ott.2
chr1	144171303	144172451	uc021otn.1
chr1	144209457	144212180	uc021otx.1
chr1	144275887	144290006	uc001ekq.1
chr1	144300511	144340773	uc001ekt.4
chr1	144301610	144301684	uc021oty.1
chr1	144308613	144308687	uc021otz.1
chr1	144340773	144341077	uc021oua.1
chr1	144363461	144364246	uc001ekw.3
chr1	144456162	144470244	uc021oub.1
chr1	144480744	144521009	uc001ela.4
chr1	144481839	144481913	uc021ouc.1
chr1	144488842	144488916	uc021oud.1
chr1	144514872	144519720	uc001elb.4
chr1	144610814	144612727	uc001elf.4
chr1	144832199	144833038	uc001els.1
chr1	144833046	144833912	uc001elt.1
chr1	144833912	144834808	uc001elu.1
chr1	144839435	144839507	uc021oug.1
chr1	144851423	144995033	uc021ouh.1
chr1	144944671	144944750	uc031pnz.1
chr1	145004780	145005287	uc001emi.4
chr1	145013718	145018395	uc001emj.3
chr1	145096406	145116997	uc031poa.1
chr1	145209110	145285912	uc001emn.4
chr1	145372087	145373798	uc001eng.1
chr1	145373798	145374694	uc001enh.1
chr1	145379319	145379391	uc021our.1
chr1	145385728	145385804	uc021ous.1
chr1	145395521	145395594	uc021out.1
chr1	145396880	145396952	uc021ouu.1
chr1	145397863	145397935	uc021ouv.1
chr1	145399232	145399304	uc021ouw.1
chr1	145413190	145417545	uc001eni.2
chr1	145438461	145442628	uc001enn.4
chr1	145456235	145470387	uc001enp.1
chr1	145470507	145475647	uc001enq.1
chr1	145477084	145499091	uc001enr.3
chr1	145500506	145501669	uc021ouz.1
chr1	145507556	145513535	uc001ent.2
chr1	145509751	145515899	uc009wiv.3
chr1	145514173	145515899	uc001enx.4
chr1	145516164	145523732	uc001eny.2
chr1	145524989	145543868	uc001eoa.3
chr1	145549208	145568526	uc001eob.1
chr1	145575987	145586546	uc001eoc.1
chr1	145586492	145589435	uc001eoe.3
chr1	145592604	145610884	uc001eoh.3
chr1	145611035	145688776	uc001eoj.3
chr1	145695797	145715565	uc001eol.1
chr1	145727665	145764206	uc001eoo.2
chr1	145764594	145827103	uc001eot.2
chr1	145883867	145924049	uc001eou.4
chr1	145924387	145934507	uc010ozf.2
chr1	145963303	145963375	uc021ova.1
chr1	145979033	145979107	uc021ovb.1
chr1	146032541	146057734	uc010ozg.2
chr1	146124160	146124283	uc021ovd.1
chr1	146215990	146217136	uc021ove.1
chr1	146476759	146476831	uc021ovh.1
chr1	146490894	146514599	uc001epd.3
chr1	146544772	146544844	uc021ovi.1
chr1	146550177	146550249	uc021ovj.1
chr1	146571065	146585928	uc031poj.1
chr1	146626684	146644129	uc001epe.3
chr1	146649429	146651528	uc001epg.1
chr1	146657837	146697230	uc001epi.2
chr1	146714290	146767447	uc001epm.5
chr1	146853913	146989699	uc021ovk.1
chr1	147013181	147098015	uc001epq.3
chr1	147119167	147142634	uc001epr.2
chr1	147228331	147232714	uc001eps.1
chr1	147374945	147381395	uc001epu.2
chr1	147400505	147465755	uc001epv.4
chr1	147466093	147484331	uc001epz.1
chr1	147505037	147505109	uc021ovn.1
chr1	147520766	147520840	uc021ovo.1
chr1	147574322	147599571	uc010ozx.2
chr1	147665992	147666115	uc021ovp.1
chr1	147680370	147691166	uc001eqh.1
chr1	147719028	147719102	uc021ovq.1
chr1	147737381	147737453	uc021ovr.1
chr1	147751385	147763966	uc001eqi.1
chr1	147753470	147753542	uc021ovs.1
chr1	147774844	147774916	uc021ovt.1
chr1	147781029	147781101	uc021ovu.1
chr1	147800936	147801008	uc021ovv.1
chr1	147801123	147816764	uc001eqk.1
chr1	147806602	147806678	uc031pom.1
chr1	147825688	147825760	uc021ovw.1
chr1	147835126	147931980	uc001eql.2
chr1	147874654	147893482	uc010paa.1
chr1	147877536	147877610	uc021ovx.1
chr1	147925822	147930669	uc009wka.2
chr1	147954634	147955419	uc001eqp.3
chr1	148000804	148000878	uc021ovy.1
chr1	148201751	148202536	uc010pag.2
chr1	148248114	148248188	uc021owi.1
chr1	148250248	148346791	uc021owp.2
chr1	148556107	148556133	uc010pay.2
chr1	148560846	148596267	uc001esc.2
chr1	148598313	148598387	uc021oxb.1
chr1	148644010	148644795	uc010paz.2
chr1	148739441	148758311	uc010pba.1
chr1	148760355	148760429	uc021oxc.1
chr1	148806014	148806799	uc010pbb.2
chr1	148876039	148902123	uc009wkv.1
chr1	148930404	148953054	uc009wkw.1
chr1	149079363	149079435	uc021oxd.1
chr1	149155827	149155899	uc021oxe.1
chr1	149161627	149161699	uc021oxf.1
chr1	149186124	149186196	uc021oxg.1
chr1	149211947	149212021	uc021oxh.1
chr1	149230569	149230643	uc021oxi.1
chr1	149230715	149232553	uc010pbe.1
chr1	149278784	149278857	uc021oxj.1
chr1	149279475	149291742	uc010pbf.2
chr1	149294665	149294736	uc021oxk.1
chr1	149298554	149298627	uc021oxl.1
chr1	149326271	149326345	uc021oxm.1
chr1	149334271	149334340	uc021oxn.1
chr1	149343080	149343151	uc021oxo.1
chr1	149369293	149378297	uc010pbh.2
chr1	149398714	149398761	uc010pbi.2
chr1	149400063	149400109	uc021oxp.1
chr1	149553002	149553787	uc001esj.3
chr1	149576261	149616786	uc001esk.5
chr1	149577754	149582605	uc009wle.2
chr1	149608608	149608682	uc021oxq.1
chr1	149615616	149615690	uc021oxr.1
chr1	149627308	149651107	uc001eso.1
chr1	149664354	149664427	uc021oxs.1
chr1	149670056	149670130	uc021oxt.1
chr1	149672904	149672976	uc031poq.1
chr1	149680209	149680280	uc021oxu.1
chr1	149684087	149684161	uc021oxv.1
chr1	149711797	149711871	uc021oxw.1
chr1	149719801	149719870	uc021oxx.1
chr1	149728270	149728341	uc021oxy.1
chr1	149754244	149783928	uc010pbj.2
chr1	149754249	149764074	uc001esp.4
chr1	149763335	149765367	uc001esq.1
chr1	149784779	149785236	uc010pbl.2
chr1	149804220	149804616	uc001ess.3
chr1	149812258	149812765	uc001esv.3
chr1	149813784	149814318	uc001esw.3
chr1	149821758	149822340	uc021oxz.1
chr1	149822627	149823161	uc001esx.3
chr1	149824180	149824687	uc001esy.3
chr1	149832329	149832725	uc001etb.3
chr1	149856009	149858232	uc001etc.3
chr1	149858524	149858961	uc001etd.3
chr1	149859018	149859466	uc001ete.3
chr1	149871154	149872348	uc001etf.3
chr1	149874871	149889434	uc001etg.3
chr1	149895208	149900144	uc001etk.2
chr1	149900542	149908791	uc001etl.4
chr1	149912231	149982686	uc001etn.3
chr1	150017381	150017452	uc021oyb.1
chr1	150039341	150117505	uc001etp.3
chr1	150122169	150131825	uc001ett.3
chr1	150190716	150208504	uc001etw.3
chr1	150230217	150237480	uc001etx.3
chr1	150237798	150241609	uc001ety.2
chr1	150245182	150253335	uc001eud.3
chr1	150255228	150259501	uc001euj.3
chr1	150266268	150280819	uc001euk.3
chr1	150293927	150325704	uc001eum.4
chr1	150336989	150449041	uc009wlr.3
chr1	150459839	150480085	uc001euq.4
chr1	150480486	150486265	uc001eus.3
chr1	150488232	150490508	uc031pos.1
chr1	150521897	150533412	uc001eux.3
chr1	150524404	150524490	uc021oye.1
chr1	150547026	150552214	uc001euz.3
chr1	150594598	150602098	uc001eve.3
chr1	150618700	150669672	uc001evj.2
chr1	150670534	150693364	uc001evk.2
chr1	150702671	150738433	uc001evn.3
chr1	150768683	150780917	uc001evp.2
chr1	150782180	150849244	uc001evr.2
chr1	150785066	150785174	uc021oyg.1
chr1	150898814	150937220	uc001evu.2
chr1	150937648	150947479	uc001evz.3
chr1	150954498	150968114	uc001ewa.2
chr1	150969300	150980854	uc010pcn.2
chr1	150980972	151008189	uc001ewh.1
chr1	150995221	150995328	uc021oyh.1
chr1	151009028	151020076	uc001ewl.2
chr1	151020258	151023871	uc001ewn.3
chr1	151023446	151032125	uc001ewp.3
chr1	151032150	151040973	uc001ewq.3
chr1	151043079	151091007	uc001ewr.2
chr1	151104162	151119140	uc001ewv.3
chr1	151129104	151132225	uc001ewx.2
chr1	151132223	151138370	uc001ewy.3
chr1	151138497	151142773	uc001ewz.3
chr1	151142462	151148547	uc001exc.4
chr1	151148775	151162689	uc001exe.2
chr1	151171020	151222007	uc001exj.3
chr1	151227196	151239954	uc001exl.3
chr1	151252499	151254405	uc001exo.3
chr1	151254790	151264381	uc001exq.3
chr1	151264272	151300191	uc001exu.3
chr1	151265261	151265284	uc031pov.1
chr1	151265461	151265487	uc031pow.1
chr1	151266582	151266606	uc031pox.1
chr1	151271387	151271414	uc031poy.1
chr1	151271499	151271523	uc031poz.1
chr1	151273139	151273440	uc021oyp.1
chr1	151274383	151274409	uc031ppa.1
chr1	151278732	151278766	uc031ppb.1
chr1	151313115	151319769	uc001exw.1
chr1	151336777	151345210	uc010pcy.3
chr1	151372040	151374412	uc001eyc.1
chr1	151375199	151431941	uc001eyd.2
chr1	151483861	151511167	uc009wmw.3
chr1	151512780	151556059	uc001eyl.3
chr1	151518271	151518367	uc021oyr.1
chr1	151584661	151671559	uc001eyn.1
chr1	151670318	151670850	uc001eyq.1
chr1	151672533	151689290	uc001eys.2
chr1	151694012	151702082	uc010pdj.1
chr1	151732122	151736040	uc001eyv.3
chr1	151739130	151743806	uc010pdm.2
chr1	151745954	151763010	uc001eza.4
chr1	151763317	151766878	uc001eze.3
chr1	151772764	151777882	uc001ezf.1
chr1	151778546	151804348	uc001ezh.3
chr1	151810338	151813033	uc010pdq.1
chr1	151810944	151816641	uc010pdr.2
chr1	151819576	151826173	uc021oyw.1
chr1	151843342	151882361	uc001ezj.2
chr1	151955385	151966714	uc001ezl.3
chr1	151967006	152015250	uc001ezm.1
chr1	152004981	152009511	uc001ezn.3
chr1	152056619	152061540	uc001ezo.1
chr1	152078792	152087930	uc001ezp.3
chr1	152126070	152131704	uc001ezs.1
chr1	152184551	152196672	uc001ezt.2
chr1	152274650	152297679	uc001ezu.1
chr1	152285934	152339168	uc001ezv.3
chr1	152321212	152332482	uc001ezw.4
chr1	152381718	152386750	uc001ezx.2
chr1	152483319	152484653	uc001ezy.3
chr1	152486977	152488481	uc001ezz.3
chr1	152538174	152539229	uc001faa.4
chr1	152551859	152552980	uc001fab.3
chr1	152573137	152573562	uc001fac.2
chr1	152586286	152586574	uc010pds.2
chr1	152595309	152595579	uc010pdt.2
chr1	152635886	152637135	uc001fag.4
chr1	152647790	152649049	uc001fah.4
chr1	152658598	152659876	uc001fai.3
chr1	152670839	152671918	uc001faj.3
chr1	152681522	152681910	uc001fak.2
chr1	152691997	152692905	uc010pdu.2
chr1	152730505	152734529	uc001fal.1
chr1	152748847	152749445	uc010pdv.2
chr1	152758752	152760901	uc001fan.3
chr1	152769226	152770657	uc009wnp.3
chr1	152777310	152779107	uc001fap.1
chr1	152784446	152785585	uc001faq.3
chr1	152799948	152800573	uc010pdw.2
chr1	152815329	152816459	uc001fas.4
chr1	152850797	152857523	uc001fat.3
chr1	152881038	152884362	uc001fau.3
chr1	152943127	152945069	uc001fav.1
chr1	152956563	152958290	uc001faw.3
chr1	152974222	152976332	uc001faz.4
chr1	153003678	153005376	uc001fba.3
chr1	153012200	153013594	uc001fbb.2
chr1	153028595	153029988	uc001fbd.3
chr1	153042717	153044084	uc001fbg.3
chr1	153065610	153067001	uc001fbh.3
chr1	153084612	153085989	uc001fbi.3
chr1	153112593	153113969	uc001fbj.3
chr1	153122057	153123427	uc009wod.2
chr1	153175905	153177601	uc001fbl.4
chr1	153190059	153191793	uc021ozw.1
chr1	153195064	153195185	uc021ozx.1
chr1	153232178	153234600	uc001fbm.3
chr1	153270337	153283194	uc001fbn.1
chr1	153302596	153321022	uc001fbo.3
chr1	153330329	153333503	uc001fbq.3
chr1	153346183	153348075	uc001fbr.1
chr1	153362507	153363664	uc001fbs.3
chr1	153388999	153395701	uc001fbt.1
chr1	153409470	153412503	uc010pdx.2
chr1	153430219	153433137	uc001fbv.1
chr1	153507075	153508717	uc001fbw.1
chr1	153509622	153514241	uc001fbx.3
chr1	153516094	153518282	uc001fbz.3
chr1	153518229	153524245	uc009wog.1
chr1	153519808	153521734	uc001fca.1
chr1	153533584	153538306	uc001fcb.3
chr1	153579366	153585514	uc001fcd.1
chr1	153586731	153588808	uc001fce.3
chr1	153591275	153600117	uc001fch.3
chr1	153600872	153604513	uc001fck.1
chr1	153606457	153618782	uc001fcn.2
chr1	153631129	153634328	uc001fcq.4
chr1	153634263	153643504	uc001fcr.4
chr1	153643725	153643797	uc021paa.1
chr1	153651163	153666468	uc001fcs.4
chr1	153700566	153746555	uc001fct.3
chr1	153747767	153752633	uc001fcz.3
chr1	153777202	153895451	uc001fdb.4
chr1	153901976	153919154	uc001fdd.1
chr1	153920147	153931132	uc021pab.1
chr1	153931574	153940660	uc031ppi.1
chr1	153940674	153946840	uc001fdr.3
chr1	153946744	153950451	uc001fds.3
chr1	153954092	153958853	uc001fdt.2
chr1	153963238	153964631	uc001fdv.3
chr1	153965167	154127592	uc001fdw.3
chr1	154134288	154164611	uc001fec.2
chr1	154166140	154166219	uc021pac.1
chr1	154171561	154178841	uc001fee.2
chr1	154179182	154193273	uc001fei.2
chr1	154193324	154243329	uc001fep.4
chr1	154232202	154232338	uc021pae.1
chr1	154245038	154248355	uc001fes.3
chr1	154293591	154297801	uc001feu.3
chr1	154300275	154323780	uc001fex.3
chr1	154377668	154441926	uc001fez.2
chr1	154451953	154474526	uc001ffb.3
chr1	154474694	154520623	uc009wow.3
chr1	154521050	154531120	uc001fff.1
chr1	154540256	154552353	uc001ffg.3
chr1	154554533	154580724	uc001ffh.3
chr1	154669941	154842754	uc021pah.1
chr1	154897207	154909484	uc001ffq.3
chr1	154916558	154928567	uc001ffr.3
chr1	154929501	154934258	uc001fft.3
chr1	154934773	154943223	uc001ffw.3
chr1	154947117	154951725	uc001fgb.3
chr1	154948168	154948259	uc021pai.1
chr1	154955769	154965587	uc001fgf.2
chr1	154966061	154966791	uc001fgi.3
chr1	154975105	154991001	uc010peq.3
chr1	154979449	154979509	uc021pal.1
chr1	154991002	155006257	uc001fgm.3
chr1	155006281	155023406	uc001fgn.2
chr1	155017667	155036467	uc021pan.2
chr1	155023747	155035252	uc001fgr.2
chr1	155051347	155060014	uc001fhf.3
chr1	155100348	155107386	uc001fhh.3
chr1	155108287	155111334	uc001fhj.4
chr1	155112366	155112883	uc001fhm.3
chr1	155141883	155145804	uc001fho.3
chr1	155146262	155157447	uc001fhs.2
chr1	155155666	155157427	uc021pao.2
chr1	155158299	155162706	uc031ppv.1
chr1	155161986	155162007	uc021par.1
chr1	155164967	155165063	uc021pas.1
chr1	155165378	155177772	uc001fix.3
chr1	155178489	155183624	uc001fjb.3
chr1	155183615	155188809	uc001fjd.3
chr1	155203712	155204236	uc021pav.1
chr1	155204238	155214653	uc001fjl.3
chr1	155216995	155225274	uc001fjm.3
chr1	155225769	155232176	uc001fjs.3
chr1	155232658	155243281	uc001fjw.3
chr1	155247217	155259639	uc001fjz.2
chr1	155259083	155271225	uc001fkb.4
chr1	155278538	155290457	uc001fkc.2
chr1	155290250	155293938	uc001fki.3
chr1	155290639	155300909	uc001fkj.2
chr1	155305051	155532324	uc001fkt.3
chr1	155402970	155404053	uc010pgd.2
chr1	155403092	155403473	uc010pgc.1
chr1	155531771	155533735	uc010pge.2
chr1	155531863	155533735	uc001fkv.3
chr1	155579960	155584758	uc001fky.4
chr1	155596545	155618335	uc001flf.3
chr1	155658881	155708800	uc001flr.3
chr1	155715558	155718322	uc010pgo.2
chr1	155716586	155720673	uc031pqb.1
chr1	155719509	155826972	uc001fly.1
chr1	155829259	155854990	uc001fmg.3
chr1	155867598	155880706	uc031pqc.1
chr1	155882835	155904188	uc001fmi.1
chr1	155889699	155889833	uc001fmk.1
chr1	155895748	155895877	uc001fmn.1
chr1	155911479	155912625	uc010pgs.2
chr1	155916629	155948336	uc001fmt.2
chr1	155978838	155990758	uc001fmx.3
chr1	156005091	156023516	uc001fna.3
chr1	156024516	156028605	uc001fnb.3
chr1	156030965	156040295	uc001fnc.3
chr1	156041803	156051789	uc001fnd.4
chr1	156084460	156109880	uc001fni.3
chr1	156104903	156107058	uc009wrp.3
chr1	156123321	156147542	uc009wrq.3
chr1	156163729	156182587	uc001fnp.3
chr1	156182778	156213123	uc021pbb.1
chr1	156211950	156213123	uc001fnt.3
chr1	156213111	156217908	uc010phh.2
chr1	156219014	156252620	uc001foc.4
chr1	156254069	156262234	uc009wrw.3
chr1	156262477	156265480	uc001foh.4
chr1	156268414	156269428	uc001fok.3
chr1	156278751	156308206	uc001fol.2
chr1	156307104	156316785	uc001foo.3
chr1	156338979	156355013	uc010pho.3
chr1	156374048	156377645	uc001fot.1
chr1	156374054	156399340	uc001fou.1
chr1	156390132	156390221	uc001foz.1
chr1	156433512	156460391	uc001fpb.4
chr1	156453889	156454002	uc021pbe.1
chr1	156495196	156542396	uc001fpf.3
chr1	156549518	156556562	uc021pbf.1
chr1	156561557	156564091	uc001fph.3
chr1	156564099	156571279	uc001fpl.3
chr1	156589085	156595517	uc001fpn.1
chr1	156589085	156594259	uc031pqq.1
chr1	156611456	156613427	uc021pbg.1
chr1	156611739	156629324	uc001fpp.3
chr1	156638555	156647189	uc001fpq.3
chr1	156669399	156675459	uc001fpr.3
chr1	156692412	156697705	uc001fps.1
chr1	156698262	156706752	uc001fpu.3
chr1	156707093	156710923	uc001fpx.1
chr1	156711898	156721543	uc001fpy.4
chr1	156737273	156770609	uc001fqa.3
chr1	156776034	156786640	uc009wsh.2
chr1	156810664	156828712	uc010pht.2
chr1	156830670	156851642	uc001fqh.1
chr1	156863522	156886226	uc001fqj.1
chr1	156890423	156902880	uc001fqm.2
chr1	156904631	157015162	uc001fqn.3
chr1	156905922	156906036	uc021pbj.1
chr1	157061834	157069600	uc001fqq.2
chr1	157094458	157108383	uc001fqr.2
chr1	157098153	157098463	uc001fqs.3
chr1	157483166	157522310	uc009wsm.3
chr1	157543538	157567870	uc001fqw.3
chr1	157647977	157670647	uc001frb.3
chr1	157715522	157746922	uc001fre.2
chr1	157764193	157789940	uc001frg.3
chr1	157800703	157811634	uc001frk.4
chr1	157895763	157918861	uc001frl.1
chr1	157963062	158065844	uc001frn.4
chr1	158066315	158069836	uc001frp.2
chr1	158101833	158110430	uc001frq.3
chr1	158149736	158156216	uc001frr.3
chr1	158169170	158173667	uc001frs.1
chr1	158223926	158228058	uc001frt.3
chr1	158259562	158264564	uc001fru.3
chr1	158297739	158301321	uc001frx.3
chr1	158323485	158327343	uc001fse.3
chr1	158368311	158369256	uc010pih.2
chr1	158389717	158390656	uc010pii.2
chr1	158435351	158436293	uc010pij.2
chr1	158444244	158464676	uc001fso.1
chr1	158449667	158450675	uc010pik.2
chr1	158516917	158517895	uc010pil.2
chr1	158532440	158533394	uc010pim.2
chr1	158548708	158549689	uc010pin.2
chr1	158576228	158577170	uc010pio.2
chr1	158580495	158656506	uc001fst.1
chr1	158669467	158670442	uc001fsu.1
chr1	158686957	158687905	uc021pbn.1
chr1	158724605	158725637	uc001fsw.1
chr1	158735533	158736472	uc010piq.2
chr1	158746471	158747425	uc010pir.2
chr1	158801167	158819270	uc001fsz.1
chr1	158901336	158946849	uc001ftb.3
chr1	158979681	159024945	uc010pis.2
chr1	159032274	159046647	uc001ftj.1
chr1	159111400	159111474	uc021pbo.1
chr1	159141376	159172932	uc001ftk.2
chr1	159165774	159172212	uc001ftm.2
chr1	159175048	159176290	uc001ftp.4
chr1	159259505	159278014	uc001ftq.3
chr1	159283459	159284449	uc010piu.2
chr1	159315958	159438858	uc001fts.4
chr1	159409511	159410600	uc010piv.2
chr1	159504867	159505797	uc010piw.2
chr1	159557615	159558661	uc001ftv.3
chr1	159682078	159684379	uc001ftw.3
chr1	159750758	159752333	uc001ftz.1
chr1	159772172	159786047	uc001fud.4
chr1	159796478	159807282	uc001fue.4
chr1	159824105	159832447	uc001fuh.3
chr1	159842153	159869906	uc001fui.3
chr1	159887896	159894341	uc031pqu.1
chr1	159896828	159915386	uc001fur.2
chr1	159921281	159924044	uc001fus.3
chr1	159931013	159948876	uc001fuu.3
chr1	159997461	160001783	uc001fuv.1
chr1	160007256	160040051	uc001fuw.2
chr1	160051359	160059212	uc001fuy.1
chr1	160061128	160068618	uc009wtf.3
chr1	160085519	160113374	uc001fvc.3
chr1	160121351	160156767	uc001fve.4
chr1	160160284	160171676	uc010pja.2
chr1	160171988	160178659	uc001fvj.1
chr1	160175124	160185162	uc001fvk.3
chr1	160185504	160232350	uc010pjb.1
chr1	160258376	160313354	uc001fvv.4
chr1	160287054	160288260	uc001fvw.3
chr1	160295893	160296006	uc021pbr.1
chr1	160313062	160328742	uc001fvx.3
chr1	160336860	160342638	uc001fwa.2
chr1	160370363	160398468	uc001fwc.2
chr1	160454819	160493052	uc001fwe.2
chr1	160510883	160549306	uc001fwh.4
chr1	160579890	160617081	uc001fwl.4
chr1	160650211	160681641	uc001fwo.2
chr1	160709076	160724601	uc001fwq.3
chr1	160765863	160798045	uc001fwu.4
chr1	160799949	160832692	uc009wtq.3
chr1	160846329	160854960	uc001fxc.3
chr1	160914815	160924589	uc001fxd.3
chr1	160965000	160991133	uc009wtt.3
chr1	161009040	161014727	uc031pqv.1
chr1	161016731	161039760	uc001fxl.3
chr1	161040780	161059385	uc001fxo.2
chr1	161068180	161070138	uc001fxr.3
chr1	161070345	161087866	uc001fxu.3
chr1	161087861	161090984	uc001fxv.2
chr1	161090768	161102256	uc001fxz.3
chr1	161123533	161128646	uc001fyd.4
chr1	161129253	161135516	uc010pkd.2
chr1	161136180	161141010	uc001fyg.2
chr1	161141099	161147758	uc001fys.2
chr1	161159537	161168845	uc001fyt.4
chr1	161171936	161184184	uc001fyw.3
chr1	161185086	161189038	uc001fyz.1
chr1	161192082	161193418	uc001fzc.1
chr1	161195832	161200407	uc001fzd.3
chr1	161196975	161197051	uc031pqw.1
chr1	161199455	161208000	uc001fzp.3
chr1	161228516	161255240	uc001gad.3
chr1	161274524	161279762	uc001gaf.4
chr1	161284165	161334535	uc001gag.3
chr1	161334520	161337673	uc001gal.4
chr1	161369489	161369562	uc021pbx.1
chr1	161390660	161390733	uc021pby.1
chr1	161391882	161391954	uc021pbz.1
chr1	161397866	161397940	uc021pca.1
chr1	161409960	161410032	uc021pcb.1
chr1	161410614	161410686	uc021pcc.1
chr1	161411322	161411405	uc021pcd.1
chr1	161413093	161413164	uc021pce.1
chr1	161417017	161417089	uc021pcf.1
chr1	161417374	161417446	uc021pcg.1
chr1	161418032	161418104	uc021pch.1
chr1	161418740	161418823	uc021pci.1
chr1	161420466	161420537	uc021pcj.1
chr1	161424397	161424469	uc021pck.1
chr1	161424755	161424827	uc021pcl.1
chr1	161425413	161425485	uc021pcm.1
chr1	161426121	161426204	uc021pcn.1
chr1	161427897	161427968	uc021pco.1
chr1	161431808	161431880	uc021pcp.1
chr1	161432165	161432237	uc021pcq.1
chr1	161432823	161432895	uc021pcr.1
chr1	161433531	161433614	uc021pcs.1
chr1	161435257	161435328	uc021pct.1
chr1	161439188	161439260	uc021pcu.1
chr1	161439546	161439618	uc021pcv.1
chr1	161440204	161440276	uc021pcw.1
chr1	161440912	161440995	uc021pcx.1
chr1	161450355	161450426	uc021pcy.1
chr1	161475204	161489360	uc001gan.3
chr1	161492934	161493006	uc021pdb.1
chr1	161493636	161493707	uc021pdc.1
chr1	161494035	161496687	uc001gaq.3
chr1	161500131	161500214	uc021pdd.1
chr1	161500902	161500974	uc021pde.1
chr1	161501914	161501986	uc021pdf.1
chr1	161510030	161510104	uc021pdg.1
chr1	161511550	161519818	uc001gar.3
chr1	161551128	161571010	uc021pdi.2
chr1	161574587	161574659	uc021pdk.1
chr1	161575848	161578341	uc010pkp.1
chr1	161581735	161581819	uc021pdl.1
chr1	161582507	161582579	uc021pdm.1
chr1	161591464	161591538	uc021pdn.1
chr1	161592987	161601252	uc009wul.3
chr1	161632904	161648444	uc001gaz.2
chr1	161653494	161655042	uc001gbc.3
chr1	161676761	161684142	uc001gbe.3
chr1	161692442	161697933	uc001gbi.3
chr1	161719580	161726952	uc001gbo.3
chr1	161736033	161933860	uc001gbs.3
chr1	161952981	161993644	uc001gbu.3
chr1	162039580	162339813	uc001gbv.2
chr1	162126896	162126972	uc021pdp.1
chr1	162308432	162308532	uc021pdq.1
chr1	162312335	162312430	uc021pdr.1
chr1	162343514	162346644	uc001gbx.2
chr1	162348695	162356608	uc010pkt.1
chr1	162365055	162381928	uc001gbz.1
chr1	162467594	162499419	uc001gcc.2
chr1	162531295	162569633	uc001gce.4
chr1	162602227	162750247	uc001gcg.3
chr1	162747518	162747805	uc021pds.1
chr1	162751900	162756362	uc001gch.1
chr1	162760495	162782608	uc001gci.3
chr1	162824086	162838605	uc001gck.2
chr1	163038395	163046592	uc001gcl.4
chr1	163112088	163172963	uc021pdt.1
chr1	163291722	163325553	uc001gcr.1
chr1	163438285	163438395	uc021pdv.1
chr1	163479273	163479385	uc021pdw.1
chr1	164528596	164821060	uc001gct.3
chr1	164595272	164651720	uc001gcu.1
chr1	164738352	164743878	uc021pdx.1
chr1	165171103	165325478	uc001gcz.2
chr1	165370158	165414592	uc001gda.3
chr1	165446078	165551341	uc001gdc.2
chr1	165513477	165533185	uc001gde.2
chr1	165566149	165566222	uc021peb.1
chr1	165600109	165625372	uc001gdf.3
chr1	165631448	165667900	uc001gdh.1
chr1	165667986	165679199	uc001gdi.3
chr1	165693527	165738159	uc001gdj.5
chr1	165738165	165744685	uc001gdo.3
chr1	165796731	165880855	uc001gdp.3
chr1	166011480	166011587	uc021ped.1
chr1	166026689	166135958	uc021pee.1
chr1	166039068	166135958	uc021pef.1
chr1	166123979	166124035	uc021peg.1
chr1	166573152	166594473	uc010pld.2
chr1	166808723	166823709	uc001gdt.1
chr1	166825748	166845654	uc001gdw.3
chr1	166882440	166944561	uc001gdx.2
chr1	166958518	166991447	uc001gdy.1
chr1	167022081	167059868	uc001gea.1
chr1	167064070	167098402	uc001geb.1
chr1	167144598	167165042	uc021pei.1
chr1	167190065	167396582	uc001gee.3
chr1	167399876	167487847	uc001gei.4
chr1	167510250	167523056	uc001gel.3
chr1	167599473	167675486	uc001gem.3
chr1	167683961	167684033	uc021pej.1
chr1	167684724	167684796	uc021pek.1
chr1	167691186	167761156	uc001geo.3
chr1	167778624	167883453	uc001ger.3
chr1	167885912	167906307	uc001get.3
chr1	167905796	168045083	uc001gex.3
chr1	168048779	168106811	uc009wvo.4
chr1	168148170	168171351	uc001gfg.3
chr1	168195254	168212088	uc001gfi.4
chr1	168214818	168216668	uc010plo.2
chr1	168218461	168220378	uc001gfk.2
chr1	168250277	168283664	uc001gfl.3
chr1	168344761	168344859	uc021pel.1
chr1	168369426	168391894	uc021pem.1
chr1	168510002	168513235	uc001gfn.4
chr1	168545710	168551315	uc001gfo.2
chr1	168664694	168698442	uc001gfp.3
chr1	168756178	168762126	uc001gfq.3
chr1	169075946	169101960	uc001gfr.1
chr1	169101768	169337186	uc001gfu.3
chr1	169337193	169365780	uc001gfy.3
chr1	169364113	169396670	uc001gfz.1
chr1	169433148	169455208	uc001gge.4
chr1	169481191	169555769	uc001ggg.1
chr1	169558087	169599377	uc001ggi.4
chr1	169659805	169680843	uc001ggk.3
chr1	169691780	169703220	uc001ggm.4
chr1	169761669	169764061	uc001ggn.4
chr1	169764549	169822229	uc001ggq.3
chr1	169822214	169863100	uc001ggs.3
chr1	169828896	169829193	uc021peo.1
chr1	169890469	170043879	uc001ggv.3
chr1	170115187	170136923	uc009wvv.1
chr1	170120518	170120603	uc021per.1
chr1	170120518	170120603	uc021peq.1
chr1	170240545	170253349	uc001ggx.3
chr1	170429593	170489887	uc001ggy.1
chr1	170501262	170522974	uc001gha.2
chr1	170633312	170708541	uc001ghf.3
chr1	170904611	171033906	uc010plz.2
chr1	171060017	171086959	uc001ghh.3
chr1	171070868	171070947	uc021pes.1
chr1	171070879	171070939	uc031prg.1
chr1	171106878	171130702	uc001ghj.1
chr1	171154387	171181822	uc001ghk.1
chr1	171217662	171255113	uc001ghl.3
chr1	171223044	171223161	uc021pet.1
chr1	171283485	171311223	uc001gho.3
chr1	171308034	171310463	uc010pmf.2
chr1	171454665	171562650	uc010pmg.2
chr1	171604556	171621773	uc001ghu.3
chr1	171669295	171711379	uc001ghx.2
chr1	171750760	171766856	uc001ghz.3
chr1	171810617	172381857	uc001gie.4
chr1	171964791	171964994	uc021peu.1
chr1	172106018	172113975	uc021pev.2
chr1	172107947	172108028	uc021pew.1
chr1	172157538	172157607	uc021pex.1
chr1	172389827	172437969	uc001gik.3
chr1	172410596	172413230	uc001gio.3
chr1	172502259	172580973	uc001giq.4
chr1	172628184	172636012	uc001gis.3
chr1	173010359	173020103	uc001giu.2
chr1	173152869	173176471	uc001giw.3
chr1	173204198	173446294	uc001gix.1
chr1	173387087	173430501	uc010pmp.1
chr1	173446485	173457946	uc001giy.1
chr1	173469603	173572233	uc001giz.2
chr1	173577474	173639001	uc001gja.1
chr1	173604660	173606272	uc021pez.1
chr1	173684079	173755840	uc001gjc.3
chr1	173768687	173793270	uc001gje.4
chr1	173793796	173827682	uc001gjh.2
chr1	173832385	173833079	uc021pfa.1
chr1	173833038	173837125	uc001gjk.3
chr1	173833312	173833355	uc009wwi.1
chr1	173834487	173834568	uc009wwk.1
chr1	173834770	173834824	uc009wwl.3
chr1	173835105	173835166	uc001gjn.1
chr1	173835448	173835509	uc009wwm.1
chr1	173836016	173836076	uc009wwo.1
chr1	173836811	173836883	uc001gjo.1
chr1	173837492	173855774	uc009wwp.1
chr1	173867684	173867712	uc021pfb.1
chr1	173872941	173886516	uc001gjt.3
chr1	173900351	173962210	uc001gju.4
chr1	174128551	174927327	uc001gjx.3
chr1	174417211	174418683	uc001gka.1
chr1	174417553	174418334	uc010pmu.1
chr1	174968891	174981163	uc001gkj.1
chr1	174982093	174992591	uc001gkk.3
chr1	175036993	175117202	uc001gkl.1
chr1	175126122	175161949	uc001gkn.3
chr1	175291934	175712752	uc009wwu.1
chr1	175526123	175533005	uc001gkr.1
chr1	175913966	176176370	uc001gku.1
chr1	175937532	175937676	uc001gkx.1
chr1	176432306	176811970	uc001gkz.3
chr1	176830202	177134024	uc001glc.3
chr1	176998498	176998581	uc021pfc.1
chr1	177140632	177251558	uc001glf.3
chr1	177897488	177939050	uc001gli.1
chr1	178060642	178062735	uc001gln.1
chr1	178062863	178448648	uc001glq.3
chr1	178482211	178491789	uc001glt.2
chr1	178511930	178518024	uc001glx.1
chr1	178530047	178530164	uc021pff.1
chr1	178646883	178646969	uc021pfg.1
chr1	178678037	178678110	uc021pfh.1
chr1	178694299	178889237	uc001glz.3
chr1	178818669	178840215	uc001gma.3
chr1	178995073	179045702	uc001gmc.3
chr1	179051111	179065129	uc001gmd.3
chr1	179068461	179112224	uc001gmi.4
chr1	179170962	179170982	uc021pfi.1
chr1	179262848	179327814	uc001gml.3
chr1	179334854	179523870	uc001gmo.3
chr1	179519673	179545084	uc001gmq.4
chr1	179556206	179556240	uc021pfk.1
chr1	179556492	179556554	uc001gmt.3
chr1	179556689	179556738	uc010pnm.1
chr1	179556805	179556836	uc001gmu.3
chr1	179556860	179556891	uc001gmv.3
chr1	179557007	179557111	uc001gmw.3
chr1	179557405	179557438	uc001gmx.3
chr1	179557454	179557483	uc021pfl.1
chr1	179557496	179557540	uc001gmz.1
chr1	179557562	179557602	uc001gna.3
chr1	179557656	179557734	uc001gnb.3
chr1	179557814	179557850	uc001gnc.1
chr1	179558162	179558192	uc010pnn.1
chr1	179558664	179558701	uc001gnd.3
chr1	179558711	179558760	uc001gne.3
chr1	179558972	179559006	uc010pno.2
chr1	179560756	179660407	uc010pnp.2
chr1	179712297	179785333	uc001gnj.3
chr1	179809101	179846941	uc001gnk.3
chr1	179828164	179846941	uc001gnn.3
chr1	179851176	179889212	uc001gnp.2
chr1	179923907	180084015	uc001gnt.3
chr1	180123967	180167169	uc001gnz.3
chr1	180167143	180169859	uc001god.4
chr1	180184275	180184348	uc021pfo.1
chr1	180199432	180244188	uc001goe.2
chr1	180238797	180243816	uc001gof.2
chr1	180257351	180472022	uc001gog.3
chr1	180318292	180318386	uc021pfp.1
chr1	180407448	180407525	uc021pfq.1
chr1	180528109	180535654	uc001goh.1
chr1	180601145	180859415	uc001goi.3
chr1	180882312	180915239	uc001gok.2
chr1	180942175	180992046	uc021pfr.1
chr1	181002560	181031074	uc001goq.2
chr1	181057637	181059979	uc001got.4
chr1	181143619	181151342	uc009wxq.3
chr1	181205523	181207740	uc031pri.1
chr1	181382578	181382716	uc001gov.3
chr1	181452685	181775921	uc009wxt.3
chr1	181456006	181456111	uc021pfs.1
chr1	181740701	181740780	uc021pft.1
chr1	182023704	182030847	uc001goz.3
chr1	182350838	182360539	uc001gpa.2
chr1	182367251	182369751	uc001gpe.3
chr1	182376755	182383948	uc009wxv.1
chr1	182419255	182529732	uc009wxw.3
chr1	182542768	182558394	uc009wxz.2
chr1	182567757	182573548	uc001gpl.4
chr1	182584274	182585764	uc021pfy.1
chr1	182615791	182642067	uc001gpm.1
chr1	182758583	182799519	uc009wyb.3
chr1	182808438	182857117	uc001gpr.3
chr1	182868999	182922553	uc001gpu.3
chr1	182992594	183114727	uc001gpy.4
chr1	183155173	183214262	uc001gqa.2
chr1	183217371	183387634	uc001gqc.2
chr1	183430010	183441117	uc001gqe.3
chr1	183441505	183523328	uc001gqf.3
chr1	183524696	183560056	uc001gqk.4
chr1	183595327	183605076	uc021pgb.2
chr1	183605207	183897666	uc001gqm.3
chr1	183615410	183622448	uc001gqn.3
chr1	183904965	184006863	uc001gqr.3
chr1	184020810	184043344	uc001gqt.4
chr1	184356149	184598155	uc001gqv.1
chr1	184659624	184724041	uc010pok.2
chr1	184727265	184730133	uc001gqz.1
chr1	184760158	184943718	uc001gra.4
chr1	185014550	185071740	uc001grc.1
chr1	185087217	185126116	uc001grf.4
chr1	185126191	185260913	uc001grg.4
chr1	185220508	185220534	uc021pgd.1
chr1	185265521	185286461	uc001grl.3
chr1	185292978	185304171	uc001grn.4
chr1	185405427	185405453	uc021pge.1
chr1	185527511	185597620	uc001gro.3
chr1	185576315	185591914	uc001grp.1
chr1	185703682	186160085	uc001grq.1
chr1	186029866	186446655	uc021pgf.1
chr1	186265404	186283688	uc001gru.4
chr1	186280785	186344457	uc001grv.3
chr1	186348898	186390503	uc021pgj.1
chr1	186369703	186370587	uc001gry.3
chr1	186412697	186430240	uc001gsa.4
chr1	186640943	186649559	uc001gsb.3
chr1	186798031	186958113	uc001gsc.3
chr1	187610357	187612988	uc001gsd.3
chr1	190066796	190446759	uc001gse.1
chr1	190447389	190450524	uc001gsf.3
chr1	190594019	190770788	uc021pgm.2
chr1	192127591	192154945	uc001gsg.3
chr1	192286121	192336414	uc001gsh.3
chr1	192544856	192549159	uc001gsi.1
chr1	192605267	192629440	uc001gsk.3
chr1	192778168	192781407	uc001gsl.3
chr1	192844815	192845115	uc021pgn.1
chr1	192981495	193028523	uc001gsm.3
chr1	193026410	193026545	uc021pgo.1
chr1	193027466	193029189	uc001gsr.1
chr1	193028551	193055115	uc001gss.3
chr1	193065594	193074608	uc001gsz.2
chr1	193091087	193223942	uc001gtb.3
chr1	193105632	193105713	uc021pgq.1
chr1	193147859	193155743	uc001gtc.4
chr1	196194912	196577499	uc001gtd.1
chr1	196551542	196551611	uc021pgs.1
chr1	196621007	196716634	uc001gtj.4
chr1	196743929	196763203	uc001gtl.3
chr1	196912933	196928356	uc001gtq.1
chr1	196946666	196978803	uc001gts.4
chr1	197008320	197036397	uc001gtt.1
chr1	197053256	197115824	uc001gtu.3
chr1	197122813	197169672	uc001gtx.1
chr1	197237333	197447585	uc001gtz.3
chr1	197473878	197744623	uc021pgu.1
chr1	197871681	197876497	uc001guh.3
chr1	197886516	197899273	uc001guk.1
chr1	198126107	198291548	uc001gun.4
chr1	198492351	198510075	uc001gup.3
chr1	198608097	198726605	uc001gur.2
chr1	198777131	198906558	uc021pgz.1
chr1	198828001	198828111	uc001gux.1
chr1	198828172	198828282	uc001guy.3
chr1	198975168	198990166	uc001gva.4
chr1	199996729	200146550	uc001gvb.4
chr1	200023188	200023293	uc021pha.1
chr1	200311671	200342920	uc001gvd.1
chr1	200375419	200379166	uc001gve.3
chr1	200380927	200444641	uc010ppi.1
chr1	200520624	200589862	uc010ppk.1
chr1	200613164	200639126	uc009wzk.3
chr1	200708685	200829831	uc001gvk.3
chr1	200842082	200843306	uc001gvn.2
chr1	200860626	200884864	uc001gvo.4
chr1	200898071	200935796	uc001gvp.1
chr1	200938513	200992828	uc001gvs.2
chr1	200993076	200997920	uc001gvu.3
chr1	201008639	201081694	uc001gvv.3
chr1	201083080	201084500	uc031prl.1
chr1	201103899	201123632	uc001gvy.3
chr1	201159952	201198080	uc001gwc.3
chr1	201252579	201302121	uc001gwd.3
chr1	201328135	201346828	uc001gwf.4
chr1	201349965	201368669	uc001gwm.3
chr1	201372894	201390874	uc021phd.1
chr1	201434606	201438299	uc031prm.1
chr1	201434606	201438299	uc001gwq.4
chr1	201452657	201475967	uc021phh.1
chr1	201489031	201489720	uc010ppt.3
chr1	201604354	201606516	uc001gwt.1
chr1	201617449	201796102	uc001gwu.3
chr1	201688635	201688755	uc031prn.1
chr1	201777738	201777830	uc021phj.1
chr1	201798287	201853422	uc001gwz.3
chr1	201857804	201861715	uc001gxa.3
chr1	201865583	201915716	uc021phl.1
chr1	201924618	201939789	uc001gxc.3
chr1	201951765	201975275	uc001gxd.3
chr1	201979689	201986315	uc001gxh.4
chr1	202092028	202098634	uc001gxj.3
chr1	202102531	202113871	uc001gxk.2
chr1	202116140	202130716	uc010ppx.2
chr1	202152694	202156267	uc009xaa.2
chr1	202156130	202158577	uc001gxs.3
chr1	202163117	202288889	uc001gxu.3
chr1	202300784	202311094	uc001gxx.4
chr1	202317829	202557697	uc001gya.2
chr1	202379235	202379335	uc021phn.1
chr1	202559724	202679551	uc010pqb.2
chr1	202696531	202777549	uc001gyf.3
chr1	202780073	202781041	uc031prp.1
chr1	202794328	202795421	uc021php.1
chr1	202827080	202830736	uc001gyj.3
chr1	202830881	202844369	uc001gyk.1
chr1	202847409	202858385	uc001gyl.3
chr1	202860229	202896371	uc001gyo.1
chr1	202909959	202927524	uc001gyq.5
chr1	202931000	202936404	uc001gyt.2
chr1	202955579	202976393	uc021phr.1
chr1	202976533	202993197	uc001gyu.1
chr1	203003611	203047864	uc009xaj.3
chr1	203052256	203055166	uc001gzd.4
chr1	203096835	203136533	uc001gzf.1
chr1	203097405	203136533	uc009xak.1
chr1	203136938	203144942	uc001gzh.1
chr1	203148058	203155922	uc001gzi.2
chr1	203185206	203198860	uc001gzn.2
chr1	203267885	203274453	uc009xao.2
chr1	203274663	203278729	uc001gzq.3
chr1	203309751	203320289	uc001gzr.3
chr1	203444882	203460479	uc001gzt.3
chr1	203463270	203478077	uc001gzu.1
chr1	203595914	203713209	uc001gzw.3
chr1	203696748	203696784	uc010pqk.1
chr1	203698708	203698833	uc001gzy.1
chr1	203699704	203700979	uc031prr.1
chr1	203734283	203745480	uc001haa.3
chr1	203764750	203823256	uc001hac.3
chr1	203766650	203769590	uc021phs.1
chr1	203830739	203840280	uc001hai.3
chr1	203968378	203968471	uc021pht.1
chr1	204001574	204010392	uc010pqo.1
chr1	204042245	204096871	uc001ham.3
chr1	204100189	204121307	uc001hao.4
chr1	204110535	204112137	uc001hap.3
chr1	204123943	204135465	uc001haq.2
chr1	204159468	204165619	uc001har.3
chr1	204167287	204183220	uc001has.1
chr1	204187978	204329057	uc001hau.4
chr1	204337557	204338847	uc010pqu.1
chr1	204372491	204380944	uc001hav.4
chr1	204379010	204380945	uc031prt.1
chr1	204391757	204459474	uc001haw.3
chr1	204475654	204475727	uc021phv.1
chr1	204476157	204476230	uc021phw.1
chr1	204485506	204527248	uc001hba.3
chr1	204586302	204654597	uc001hbf.1
chr1	204595902	204598840	uc031pru.1
chr1	204676447	204676558	uc021phz.1
chr1	204797781	204991950	uc001hbj.3
chr1	204981852	204991950	uc001hbo.2
chr1	204989426	204991950	uc001hbp.1
chr1	205012339	205047171	uc001hbr.3
chr1	205052256	205053588	uc001hbt.3
chr1	205055269	205091150	uc001hbu.2
chr1	205111630	205180727	uc001hbw.3
chr1	205197037	205242471	uc021pia.1
chr1	205271190	205290883	uc001hce.3
chr1	205305192	205326218	uc031prx.1
chr1	205342379	205356568	uc001hch.1
chr1	205350505	205391214	uc001hcj.2
chr1	205417429	205417526	uc010prh.1
chr1	205425185	205438152	uc001hco.2
chr1	205443270	205443343	uc021pib.1
chr1	205473683	205495965	uc010pri.2
chr1	205473683	205501921	uc001hcr.3
chr1	205523400	205525763	uc001hcu.2
chr1	205538111	205572046	uc001hcv.4
chr1	205564168	205564275	uc021pic.1
chr1	205626980	205649630	uc001hda.1
chr1	205681946	205719372	uc001hdb.3
chr1	205737113	205744610	uc001hde.4
chr1	205758220	205782161	uc001hdh.1
chr1	205765004	205765744	uc001hdi.1
chr1	205797149	205819276	uc001hdj.3
chr1	205831206	205865215	uc001hdk.1
chr1	205882176	205912588	uc001hdp.3
chr1	206138910	206155074	uc001hdr.4
chr1	206224282	206231482	uc001hds.2
chr1	206238881	206288236	uc001hdt.2
chr1	206317458	206332104	uc001hdu.3
chr1	206516199	206637783	uc001hdy.3
chr1	206643585	206670223	uc001hdz.2
chr1	206680878	206762616	uc001hed.3
chr1	206764973	206785904	uc001heh.2
chr1	206808880	206822542	uc001hej.3
chr1	206858364	206907630	uc001hem.2
chr1	206940947	206945839	uc001hen.1
chr1	206972214	207016326	uc001heo.3
chr1	207039153	207042568	uc001her.3
chr1	207070787	207077484	uc001heu.2
chr1	207076630	207095378	uc001hey.3
chr1	207101866	207119811	uc001hez.3
chr1	207131311	207143970	uc001hfa.4
chr1	207178153	207178230	uc021pih.1
chr1	207191865	207206101	uc001hfd.2
chr1	207217193	207224422	uc001hfe.1
chr1	207226619	207251162	uc001hfg.3
chr1	207262211	207273337	uc001hfj.3
chr1	207277510	207277617	uc001hfn.1
chr1	207277606	207318317	uc001hfo.3
chr1	207494816	207534311	uc001hfr.4
chr1	207627644	207663240	uc001hfv.3
chr1	207669472	207815110	uc001hfx.3
chr1	207818457	207897036	uc001hga.4
chr1	207925382	207968861	uc001hgj.3
chr1	207975196	207975868	uc021pik.2
chr1	207991723	207995941	uc010psi.2
chr1	208039465	208042417	uc001hgu.2
chr1	208059882	208084683	uc001hgw.1
chr1	208195587	208417665	uc001hgz.3
chr1	209602167	209605892	uc009xcn.3
chr1	209757044	209787284	uc001hhd.3
chr1	209788217	209825820	uc001hhh.3
chr1	209796788	209796855	uc021pil.1
chr1	209848669	209849735	uc001hhi.4
chr1	209878135	209908295	uc001hhk.3
chr1	209929393	209955668	uc001hho.3
chr1	209955661	209957890	uc001hhp.1
chr1	209958967	209979520	uc001hhq.2
chr1	210001311	210030910	uc001hhr.2
chr1	210111518	210337633	uc001hhs.5
chr1	210404803	210407466	uc001hhx.4
chr1	210406194	210416440	uc001hhy.3
chr1	210501595	210849638	uc009xcx.3
chr1	210851656	211307457	uc001hib.2
chr1	211432707	211489725	uc010psw.2
chr1	211500147	211548286	uc001hii.3
chr1	211556096	211605877	uc001hil.4
chr1	211649863	211666259	uc001hin.2
chr1	211748380	211752099	uc001hio.1
chr1	211836113	211848972	uc001hir.2
chr1	211916798	212004114	uc001hiv.3
chr1	212113740	212209002	uc001hiw.2
chr1	212208918	212278187	uc009xdc.3
chr1	212250954	212251027	uc021pis.1
chr1	212317864	212318301	uc010ptc.1
chr1	212458878	212535205	uc001hjb.3
chr1	212526159	212526292	uc009xdf.1
chr1	212537815	212588267	uc010pte.2
chr1	212606228	212619721	uc001hjd.3
chr1	212781969	212794119	uc021pit.1
chr1	212797625	212800113	uc010pth.1
chr1	212797788	212800120	uc001hjk.3
chr1	212859758	212873327	uc001hjl.2
chr1	212899494	212965139	uc001hjn.3
chr1	212965169	212990167	uc001hjo.2
chr1	213003484	213020991	uc001hjq.3
chr1	213029945	213031480	uc001hjs.5
chr1	213031596	213072705	uc001hjt.3
chr1	213123886	213164927	uc001hjw.3
chr1	213165523	213189168	uc001hjz.3
chr1	213224587	213446808	uc010ptr.2
chr1	213992977	214139607	uc031psa.1
chr1	214098091	214099996	uc031psb.1
chr1	214161277	214214847	uc001hkg.2
chr1	214454564	214510477	uc021pix.1
chr1	214522038	214725024	uc001hkk.2
chr1	214655881	214656819	uc010ptz.1
chr1	214776531	214837914	uc001hkm.3
chr1	215256559	215410436	uc001hkr.4
chr1	215740734	215795149	uc001hks.3
chr1	215796235	216596738	uc001hku.1
chr1	216676587	216896814	uc001hkw.2
chr1	216825011	216825068	uc021pjb.1
chr1	217603833	217804409	uc001hlf.1
chr1	217804694	218040484	uc001hlh.1
chr1	218066241	218094146	uc031psc.1
chr1	218458628	218511325	uc001hlj.3
chr1	218517537	218519020	uc031psd.1
chr1	218518675	218617961	uc001hln.3
chr1	219254316	219347130	uc001hlp.3
chr1	219347191	219386207	uc001hlq.4
chr1	220046618	220292777	uc021pjd.1
chr1	220087605	220101993	uc001hlw.3
chr1	220141941	220220000	uc001hly.1
chr1	220230823	220263191	uc001hma.3
chr1	220267454	220321383	uc001hmc.3
chr1	220291194	220291304	uc010pui.2
chr1	220291498	220291583	uc010puj.2
chr1	220291547	220291569	uc021pje.1
chr1	220321609	220445843	uc010puk.1
chr1	220373883	220374018	uc010pul.2
chr1	220439520	220441057	uc001hmj.2
chr1	220701567	220837799	uc001hmn.4
chr1	220863627	220872499	uc001hmp.1
chr1	220921675	220957596	uc001hmq.3
chr1	220960038	220987741	uc001hms.3
chr1	220999117	220999235	uc021pjg.1
chr1	221002596	221005770	uc001hmu.3
chr1	221052742	221058400	uc001hmv.4
chr1	221503269	221509638	uc001hmw.1
chr1	221874763	221915516	uc001hmy.2
chr1	222638346	222638419	uc021pjh.1
chr1	222646009	222646043	uc010puo.1
chr1	222646722	222646767	uc001hna.1
chr1	222646839	222646870	uc001hnb.3
chr1	222647751	222647786	uc010pup.1
chr1	222648391	222648423	uc021pji.1
chr1	222648536	222648596	uc001hnd.3
chr1	222648680	222648717	uc021pjj.1
chr1	222649002	222649064	uc010puq.2
chr1	222649161	222649251	uc021pjk.1
chr1	222650319	222650381	uc001hnf.3
chr1	222650411	222650443	uc001hng.2
chr1	222695601	222721444	uc001hnh.1
chr1	222731243	222763275	uc009xdz.2
chr1	222791443	222841351	uc001hnl.3
chr1	222841354	222885864	uc001hnn.3
chr1	222885905	222906106	uc001hnq.1
chr1	222900275	222946483	uc001hnr.1
chr1	222906202	222908533	uc001hns.1
chr1	222910557	222924002	uc001hnt.3
chr1	222988430	223179337	uc001hnu.2
chr1	223282747	223316624	uc001hnw.2
chr1	223394160	223537544	uc001hny.4
chr1	223566714	223568812	uc001hoa.2
chr1	223714971	223853436	uc009xee.2
chr1	223900118	223963720	uc001hob.4
chr1	223967594	224033674	uc010pvb.2
chr1	224051997	224052306	uc021pjm.1
chr1	224139721	224139941	uc001hof.1
chr1	224189608	224197818	uc001hog.1
chr1	224294983	224295243	uc021pjn.1
chr1	224301788	224349749	uc001hoh.3
chr1	224370909	224381142	uc001hoj.3
chr1	224415035	224517891	uc001hok.3
chr1	224444705	224444843	uc021pjo.1
chr1	224544512	224566223	uc001hom.2
chr1	224572844	224622001	uc001hop.4
chr1	224585928	224586013	uc021pjq.1
chr1	224586541	224586601	uc021pjr.1
chr1	224804178	224928249	uc001hos.1
chr1	225117355	225586996	uc001how.2
chr1	225589203	225615815	uc001hoy.3
chr1	225674533	225840845	uc001hpc.1
chr1	225888305	225939945	uc001hpe.1
chr1	225965514	225978168	uc001hpg.3
chr1	225997796	226033262	uc001hpk.3
chr1	226033232	226070420	uc001hpm.2
chr1	226073981	226076846	uc001hpo.3
chr1	226107576	226112040	uc001hpq.4
chr1	226124297	226129083	uc001hpt.2
chr1	226170402	226187066	uc001hpu.4
chr1	226250407	226259703	uc001hpw.3
chr1	226271655	226277994	uc001hpx.3
chr1	226332379	226374423	uc001hpy.3
chr1	226374532	226385086	uc031psl.1
chr1	226411382	226413513	uc010pvm.2
chr1	226418849	226497204	uc001hqa.3
chr1	226548391	226595801	uc001hqd.4
chr1	226723318	226730469	uc001hqe.2
chr1	226736500	226796915	uc021pjw.1
chr1	226819390	226926876	uc010pvo.2
chr1	227058272	227083804	uc009xeo.1
chr1	227127937	227175246	uc001hqn.1
chr1	227177565	227505826	uc001hqr.3
chr1	227581291	227618723	uc001hqv.3
chr1	227751219	227850164	uc021pjy.1
chr1	227884732	227885408	uc021pjz.1
chr1	227918889	227923112	uc001hrb.3
chr1	227922696	227968932	uc001hrf.2
chr1	228003417	228034171	uc001hrh.3
chr1	228109164	228135676	uc001hri.2
chr1	228129290	228129384	uc031psm.1
chr1	228155293	228155325	uc001hrj.3
chr1	228155355	228155417	uc001hrk.3
chr1	228156485	228156575	uc021pka.1
chr1	228156672	228156704	uc021pkb.1
chr1	228157022	228157062	uc010pvt.1
chr1	228157172	228157203	uc010pvu.2
chr1	228160064	228160098	uc001hrn.2
chr1	228160168	228160213	uc021pkc.1
chr1	228194722	228248972	uc001hrq.2
chr1	228270360	228286913	uc001hrr.3
chr1	228284963	228285042	uc021pkd.1
chr1	228288427	228291022	uc001hrx.3
chr1	228294379	228297013	uc001hrz.4
chr1	228327928	228336655	uc021pkf.1
chr1	228337414	228347527	uc001hsk.3
chr1	228353428	228369958	uc001hsl.4
chr1	228391206	228401365	uc031psn.1
chr1	228395830	228566575	uc001hsq.2
chr1	228581376	228594517	uc001hss.3
chr1	228595635	228604583	uc001hsv.3
chr1	228612545	228613026	uc001hsx.1
chr1	228645064	228645560	uc001hsy.3
chr1	228645807	228646259	uc001hsz.3
chr1	228649774	228649853	uc021pkh.1
chr1	228651845	228651891	uc021pki.1
chr1	228673888	228674821	uc001hta.3
chr1	228675067	228683889	uc001htb.3
chr1	228780393	228788159	uc001htd.2
chr1	228870823	228882416	uc001htf.3
chr1	229050379	229052954	uc001htg.2
chr1	229406808	229441640	uc001hth.4
chr1	229440128	229441250	uc001htk.4
chr1	229456751	229478688	uc001htl.4
chr1	229566992	229569843	uc001htm.3
chr1	229577043	229644088	uc001htn.3
chr1	229589676	229591617	uc001hto.1
chr1	229652328	229694442	uc001htp.4
chr1	229728865	229761794	uc001htq.3
chr1	229761980	229795946	uc001hts.1
chr1	230202955	230417875	uc010pwa.1
chr1	230457391	230561674	uc031psq.1
chr1	230778201	230829731	uc001htw.3
chr1	230838271	230850336	uc001hty.4
chr1	230883129	230937749	uc001htz.1
chr1	230972864	231004302	uc001hub.3
chr1	231010591	231014761	uc001hue.1
chr1	231041986	231114618	uc001huf.4
chr1	231114822	231136479	uc001huh.3
chr1	231154703	231175995	uc001hui.2
chr1	231298673	231357314	uc009xfn.1
chr1	231319844	231323373	uc031pss.1
chr1	231359508	231376924	uc001hul.3
chr1	231376918	231413719	uc001hup.4
chr1	231468481	231473578	uc001huq.3
chr1	231473681	231490769	uc001hur.4
chr1	231499496	231560790	uc001huv.2
chr1	231611509	231612271	uc021pkl.1
chr1	231664398	231702269	uc001huw.3
chr1	231727037	231747836	uc021pkm.2
chr1	231762560	232177019	uc010pxh.2
chr1	231950371	231954263	uc001hvd.3
chr1	232533711	232651243	uc001hvg.3
chr1	232940637	232946092	uc001hvh.2
chr1	233086369	233114219	uc001hvj.1
chr1	233119881	233431459	uc001hvl.2
chr1	233463513	233520894	uc001hvt.4
chr1	233584371	233584527	uc021pko.1
chr1	233749749	233808258	uc010pxo.1
chr1	233759897	233759965	uc021pkp.1
chr1	234040678	234460262	uc001hvy.1
chr1	234348351	234350834	uc001hvz.1
chr1	234442212	234442285	uc021pkq.1
chr1	234509428	234519795	uc001hwc.3
chr1	234527058	234614849	uc001hwd.3
chr1	234663636	234667525	uc001hwe.3
chr1	234740014	234745271	uc001hwg.3
chr1	234765056	234770526	uc021pkr.1
chr1	234782034	234796670	uc001hwh.3
chr1	234859788	234867390	uc001hwj.2
chr1	235093089	235099746	uc001hwk.4
chr1	235272657	235292256	uc001hwl.3
chr1	235291117	235291252	uc001hwm.1
chr1	235294497	235324772	uc001hwn.3
chr1	235330209	235491532	uc001hwq.3
chr1	235353348	235353431	uc021pkt.1
chr1	235491752	235507844	uc001hwv.3
chr1	235530727	235612280	uc001hxa.1
chr1	235610504	235667781	uc001hxc.3
chr1	235710984	235814054	uc001hxh.4
chr1	235712454	235714587	uc021pku.1
chr1	235824330	236030227	uc001hxj.3
chr1	236016299	236016360	uc021pkv.1
chr1	236139131	236228481	uc001hxo.3
chr1	236227659	236229779	uc001hxp.1
chr1	236305831	236372209	uc001hxq.3
chr1	236378421	236445339	uc001hxt.3
chr1	236557679	236648008	uc001hxu.1
chr1	236686368	236687808	uc001hxx.3
chr1	236686738	236716279	uc001hxy.2
chr1	236712304	236767841	uc001hyd.2
chr1	236849769	236927558	uc001hyf.2
chr1	236958580	237067281	uc001hyi.4
chr1	236973802	236992568	uc009xgj.1
chr1	237167402	237167718	uc001hyk.2
chr1	237205701	237997288	uc001hyl.1
chr1	237284106	237284409	uc021pkw.1
chr1	238025474	238091619	uc010pyc.2
chr1	238041163	238054222	uc001hym.3
chr1	238105861	238105933	uc021pla.1
chr1	238106955	238107030	uc021plb.1
chr1	238643683	238649317	uc001hyn.3
chr1	239792372	240072717	uc001hyp.3
chr1	240170823	240176560	uc021pld.1
chr1	240255184	240638489	uc010pyd.2
chr1	240317950	240318072	uc021ple.1
chr1	240652872	240775462	uc001hys.3
chr1	240938816	241520478	uc001hyv.2
chr1	241295571	241295646	uc021plg.1
chr1	241660856	241683085	uc001hyx.3
chr1	241695433	241758949	uc009xgp.3
chr1	241756451	241803701	uc001hza.3
chr1	241792166	241799232	uc001hzd.3
chr1	241815579	241965434	uc001hzg.2
chr1	242011492	242053241	uc001hzh.3
chr1	242158791	242162385	uc001hzk.2
chr1	242251688	242687998	uc001hzn.2
chr1	243218732	243218763	uc021plm.1
chr1	243219238	243219328	uc021pln.1
chr1	243219615	243265046	uc001hzq.2
chr1	243287729	243418708	uc021plo.1
chr1	243351942	243352032	uc021plr.1
chr1	243419306	243663393	uc001hzw.3
chr1	243509477	243509557	uc021plt.1
chr1	243663020	244006584	uc001iab.2
chr1	243729624	243729741	uc021plv.1
chr1	244080703	244210619	uc001iac.3
chr1	244212240	244220780	uc031psv.1
chr1	244515936	244552388	uc001iah.4
chr1	244571793	244615436	uc001iaj.3
chr1	244624672	244803662	uc001iam.3
chr1	244816351	244872334	uc001iao.3
chr1	244998638	245008359	uc001iar.3
chr1	245003939	245010243	uc001iav.3
chr1	245013601	245027827	uc001iaz.1
chr1	245133170	245251148	uc001ibc.2
chr1	245318286	245866428	uc001ibf.1
chr1	245533635	245533719	uc021plw.1
chr1	245912641	246670644	uc001ibl.3
chr1	246679340	246687589	uc021plx.1
chr1	246703862	246729565	uc001ibn.3
chr1	246729638	246831884	uc001ibp.3
chr1	246887377	246931440	uc001ibr.3
chr1	246939314	246955685	uc001ibs.1
chr1	246970416	246970487	uc021ply.1
chr1	247002401	247094726	uc001ibv.2
chr1	247148624	247171395	uc009xgu.3
chr1	247197939	247242115	uc001icd.2
chr1	247263263	247267674	uc001ice.2
chr1	247273461	247275719	uc001icg.1
chr1	247319202	247335318	uc001icj.1
chr1	247337903	247338870	uc001icl.1
chr1	247365268	247365362	uc021pma.1
chr1	247419373	247420447	uc010pyu.2
chr1	247463621	247495045	uc001ico.3
chr1	247579457	247612406	uc001icr.3
chr1	247614330	247615284	uc010pyx.2
chr1	247654369	247655711	uc001icz.2
chr1	247681594	247683942	uc001idc.3
chr1	247693433	247697141	uc009xgy.3
chr1	247712346	247739848	uc001idf.3
chr1	247751661	247752615	uc010pyy.2
chr1	247768887	247769817	uc010pyz.2
chr1	247835419	247836343	uc001idi.1
chr1	247875130	247876057	uc001idj.1
chr1	247920763	247921708	uc010pza.2
chr1	247978101	247979031	uc001idm.1
chr1	248004229	248005198	uc001idn.1
chr1	248020500	248043438	uc001ido.3
chr1	248058888	248059833	uc010pzb.2
chr1	248084319	248085258	uc010pzc.2
chr1	248100492	248264224	uc001ids.3
chr1	248112159	248113098	uc001idt.1
chr1	248128633	248129641	uc010pzd.2
chr1	248153568	248154493	uc001idv.1
chr1	248185249	248186188	uc031psy.1
chr1	248201473	248202607	uc001idw.3
chr1	248223983	248224922	uc001idx.1
chr1	248285437	248286082	uc001idy.1
chr1	248308449	248309388	uc010pze.2
chr1	248343287	248344331	uc010pzf.2
chr1	248366369	248367308	uc010pzg.2
chr1	248402230	248403166	uc010pzh.2
chr1	248436153	248437116	uc010pzi.2
chr1	248457917	248458880	uc010pzj.2
chr1	248486931	248487870	uc010pzk.2
chr1	248512076	248513015	uc010pzl.2
chr1	248524882	248525929	uc001ieh.1
chr1	248550909	248551836	uc001iei.1
chr1	248569295	248570405	uc010pzm.2
chr1	248616098	248617073	uc001iek.1
chr1	248636651	248637608	uc001iel.1
chr1	248651889	248652837	uc001iem.1
chr1	248684947	248685898	uc001ien.1
chr1	248721844	248722792	uc001ieo.2
chr1	248737101	248738058	uc001iep.1
chr1	248756130	248757069	uc010pzn.2
chr1	248789478	248790429	uc001ier.1
chr1	248801587	248802559	uc001ies.1
chr1	248813231	248814185	uc010pzo.2
chr1	248844669	248845605	uc001ieu.1
chr1	248881947	248885507	uc031psz.1
chr1	248902716	248903151	uc009xhb.3
chr1	249104650	249120154	uc001iew.1
chr1	249120575	249120642	uc021pmd.1
chr1	249132529	249143714	uc001iex.3
chr1	249144202	249153315	uc001ifc.2
chr1	249168053	249168159	uc021pmf.1
chr1	249168446	249168518	uc021pmg.1
chr1	249200441	249213345	uc001ifh.3


================================================
FILE: intro-to-awk/data/enhancers.csv
================================================
chr1,1015066,1015266,HSE897,9.5706324138
chr1,1590473,1590673,HSE853,17.3206898329
chr1,2120861,2121064,HSE86,66.0424471614
chr1,6336418,6336624,HSE394,14.8892086906
chr1,7404594,7404794,HSE315,24.228051023
chr1,11941325,11941525,HSE322,17.5192630328
chr1,15055555,15055755,HSE962,11.7065788965
chr1,15478024,15478224,HSE354,17.5771586196
chr1,16065307,16065508,HSE264,23.9092446094
chr1,23244249,23244449,HSE434,17.1331422162
chr1,24621426,24621626,HSE638,11.9517576968
chr1,24710189,24710389,HSE537,14.6283754801
chr1,26216838,26217042,HSE237,39.9988740347
chr1,28931703,28931903,HSE150,37.3158468645
chr1,29139636,29139836,HSE696,9.3933913885
chr1,29301011,29301211,HSE837,15.2711109524
chr1,30644209,30644410,HSE452,13.1331424716
chr1,37323436,37323636,HSE144,24.6337166989
chr1,39114092,39114292,HSE642,13.5250552809
chr1,40887095,40887295,HSE715,14.4526764769
chr1,43259581,43259781,HSE285,23.3546182652
chr1,48180470,48180671,HSE581,14.0513005602
chr1,51911123,51911323,HSE728,10.9727627129
chr1,58868480,58868680,HSE744,10.9393507729
chr1,61907580,61907780,HSE219,24.9065022738
chr1,62649216,62649416,HSE160,31.6287882019
chr1,64297272,64297472,HSE542,16.032234097
chr1,71842002,71842202,HSE525,13.177872984
chr1,76529679,76529879,HSE217,28.2285409421
chr1,77034610,77034811,HSE117,36.8686336226
chr1,77861083,77861283,HSE95,33.1051379887
chr1,81139876,81140076,HSE648,14.2447596506
chr1,84078697,84078897,HSE229,19.6626071807
chr1,85040012,85040217,HSE554,18.2435809591
chr1,85294189,85294389,HSE649,12.4198070103
chr1,86952858,86953058,HSE227,20.0246313683
chr1,87198821,87199021,HSE396,14.181885534
chr1,87568855,87569055,HSE187,27.2483801299
chr1,87718281,87718481,HSE761,10.7346086388
chr1,90228438,90228639,HSE725,10.8919859879
chr1,90567852,90568052,HSE750,11.3840777324
chr1,101544928,101545128,HSE344,15.9279633229
chr1,105180209,105180409,HSE319,15.9878097581
chr1,112056258,112056458,HSE234,20.4981560995
chr1,113265359,113265559,HSE675,15.7764663556
chr1,120009313,120009513,HSE287,17.3492427166
chr1,120096931,120097131,HSE391,16.3648875858
chr1,145474303,145474503,HSE568,15.9686715881
chr1,146037814,146038014,HSE867,8.3915764216
chr1,149294554,149294754,HSE758,13.8367903871
chr1,149298520,149298734,HSE549,19.9633772792
chr1,161893534,161893734,HSE511,14.2318525715
chr1,165509248,165509448,HSE683,13.7673640003
chr1,167774457,167774657,HSE42,47.5183173
chr1,170555465,170555665,HSE348,25.4137901578
chr1,171264616,171264816,HSE405,18.0971419626
chr1,182113250,182113450,HSE687,17.1842942374
chr1,182635108,182635309,HSE247,28.4610896172
chr1,183573285,183573486,HSE44,36.1099998528
chr1,183681289,183681489,HSE77,46.3313570709
chr1,190550474,190550674,HSE461,24.1417645601
chr1,192030096,192030296,HSE839,8.898236823
chr1,192602518,192602718,HSE747,12.1928710846
chr1,193387547,193387747,HSE251,22.6738118926
chr1,201469917,201470118,HSE286,21.167260695
chr1,205411096,205411296,HSE399,15.4863010561
chr1,207682168,207682368,HSE389,14.4420873863
chr1,208751067,208751267,HSE250,28.3356979117
chr1,211556044,211556244,HSE913,10.1071053671
chr1,215227649,215227849,HSE643,12.2289936805
chr1,222946396,222946596,HSE136,28.3288830309
chr1,224716721,224716921,HSE669,11.1939548835
chr1,224767352,224767552,HSE659,12.7072133153
chr1,229007236,229007436,HSE259,25.6576825275
chr1,231831560,231831760,HSE103,34.1933527171
chr1,234707634,234707834,HSE458,16.2547381587
chr1,240192775,240192976,HSE192,23.6438756614
chr1,240751207,240751408,HSE41,39.1665180557
chr1,242513807,242514007,HSE29,41.8646049022
chr1,243428176,243428376,HSE909,8.9768456445
chr1,245362206,245362406,HSE514,20.8510241208
chr1,245748622,245748822,HSE400,15.8809014188
chr1,245751245,245751445,HSE614,11.9977672181
chr10,1796728,1796928,HSE887,8.8759918247
chr10,2188344,2188544,HSE210,17.5664225697
chr10,2435771,2435971,HSE331,14.4636659658
chr10,3441348,3441548,HSE787,8.9210657436
chr10,4012461,4012661,HSE959,9.8681000165
chr10,4659880,4660080,HSE109,39.294649348
chr10,5734710,5734910,HSE80,39.253086776
chr10,5918486,5918686,HSE902,13.4688692863
chr10,11575356,11575556,HSE741,13.0848348743
chr10,13005187,13005387,HSE721,10.6222160126
chr10,14195913,14196113,HSE544,13.1977478994
chr10,14227600,14227810,HSE569,12.7906533757
chr10,14364637,14364837,HSE882,8.5664694124
chr10,17825468,17825668,HSE300,17.0051022824
chr10,22748482,22748683,HSE737,14.6364554664
chr10,25811340,25811541,HSE58,31.2099871039
chr10,30602601,30602801,HSE23,49.0758646578
chr10,31080176,31080376,HSE918,9.2503197227
chr10,33270658,33270858,HSE949,11.4436365263
chr10,33294606,33294809,HSE995,12.672352369
chr10,38534930,38535130,HSE430,15.2638491526
chr10,44435726,44435926,HSE655,14.2956522267
chr10,44729078,44729278,HSE392,17.4461418303
chr10,46168148,46168348,HSE152,37.019176366
chr10,47640802,47641002,HSE244,20.7338411549
chr10,52710256,52710456,HSE489,16.0012334733
chr10,64400190,64400390,HSE143,23.5037143277
chr10,71081718,71081918,HSE723,10.7476600785
chr10,71470586,71470786,HSE37,54.1450393752
chr10,71536580,71536780,HSE664,12.2052671593
chr10,72471766,72471966,HSE783,17.3113965123
chr10,73599983,73600183,HSE403,21.5414372314
chr10,75126686,75126888,HSE71,29.3819259713
chr10,79473783,79473983,HSE43,45.6987326408
chr10,83728971,83729171,HSE212,18.9337565568
chr10,87807719,87807919,HSE859,8.6435515636
chr10,87872095,87872295,HSE481,12.756492704
chr10,88160082,88160282,HSE845,11.0856156511
chr10,88600426,88600626,HSE424,15.9686715881
chr10,90683407,90683607,HSE476,14.8937152174
chr10,90686708,90686908,HSE479,15.6187716761
chr10,93044375,93044575,HSE777,9.7964029914
chr10,94521429,94521629,HSE151,31.5694945963
chr10,97443603,97443803,HSE419,12.9499575441
chr10,97620593,97620793,HSE886,12.518441498
chr10,97667629,97667829,HSE201,34.3491300983
chr10,99223587,99223788,HSE446,23.975955174
chr10,104629190,104629462,HSE784,14.8155161441
chr10,105527665,105527865,HSE467,20.6230257395
chr10,106258325,106258525,HSE665,10.9578861725
chr10,107102583,107102783,HSE216,19.2077151804
chr10,108788448,108788648,HSE699,10.646721976
chr10,108792229,108792429,HSE432,17.1288876915
chr10,109186134,109186334,HSE960,11.8900697742
chr10,115258601,115258801,HSE108,28.6223256015
chr10,120523320,120523520,HSE531,21.3827794751
chr10,121452806,121453006,HSE989,8.0812359893
chr10,123179381,123179581,HSE732,9.5557622755
chr10,124108496,124108696,HSE927,8.9214956882
chr10,129573104,129573305,HSE54,44.0543064465
chr11,353431,353635,HSE551,15.321918756
chr11,3181559,3181759,HSE793,14.7046892301
chr11,6947739,6947942,HSE92,27.9827053442
chr11,13157034,13157234,HSE241,21.0632306147
chr11,15139410,15139611,HSE456,12.2189043207
chr11,15910292,15910492,HSE724,13.7294254547
chr11,16795802,16796002,HSE799,12.802186876
chr11,17550965,17551165,HSE639,14.1086468033
chr11,17943304,17943504,HSE891,11.4856542942
chr11,21656183,21656383,HSE700,14.9043957142
chr11,21662095,21662295,HSE440,17.2950629632
chr11,21673343,21673543,HSE894,8.2797517913
chr11,23447019,23447219,HSE6,75.8807514012
chr11,24085949,24086149,HSE492,15.300706048
chr11,24275024,24275224,HSE651,11.4443945585
chr11,25505910,25506110,HSE186,21.8909002434
chr11,31021450,31021650,HSE877,9.1198497163
chr11,31706283,31706483,HSE74,40.630635226
chr11,33868837,33869038,HSE857,11.3116139483
chr11,35595495,35595695,HSE183,19.9118859606
chr11,38009596,38009796,HSE907,9.9148121335
chr11,39564123,39564323,HSE936,9.1935010973
chr11,41468242,41468442,HSE36,44.9449175171
chr11,42922737,42922937,HSE200,32.2091247664
chr11,44407611,44407811,HSE657,10.3471228352
chr11,44747886,44748086,HSE475,15.1102404858
chr11,44986217,44986417,HSE619,10.668936584
chr11,45047769,45047969,HSE851,10.1383014015
chr11,45111276,45111476,HSE866,10.220733306
chr11,45369052,45369252,HSE627,12.2239177674
chr11,48016992,48017192,HSE767,10.1950143759
chr11,49411924,49412124,HSE512,18.574232402
chr11,63859735,63859935,HSE770,14.6837739902
chr11,63865941,63866141,HSE184,35.4114322006
chr11,65244823,65245023,HSE265,23.3441037512
chr11,67784892,67785092,HSE824,9.8386696158
chr11,73249610,73249812,HSE762,9.9839312996
chr11,73694258,73694459,HSE176,22.3483349613
chr11,76842280,76842482,HSE919,11.0703469831
chr11,78306590,78306790,HSE113,25.2078122498
chr11,79767266,79767466,HSE69,30.2148994861
chr11,81284367,81284567,HSE695,9.8185385502
chr11,92228348,92228548,HSE539,20.2344657972
chr11,100340586,100340786,HSE388,15.4919091437
chr11,104561099,104561299,HSE812,15.176333042
chr11,108070756,108070956,HSE809,9.4991448266
chr11,111156211,111156411,HSE714,10.0243534212
chr11,115147624,115147824,HSE134,28.6194026789
chr11,115846389,115846603,HSE722,16.8886385104
chr11,117592848,117593048,HSE705,13.7637540477
chr11,120106662,120106862,HSE937,10.9173422723
chr11,123178915,123179115,HSE672,11.3022531999
chr11,124816687,124816887,HSE601,14.6082914089
chr11,125805553,125805756,HSE945,20.5577441884
chr11,126357947,126358147,HSE493,15.3602555927
chr11,126976167,126976367,HSE968,9.1843932734
chr11,128052898,128053098,HSE911,12.5881181856
chr11,129137803,129138003,HSE311,18.8031509882
chr11,131335724,131335924,HSE517,23.7420612884
chr11,131510396,131510597,HSE406,27.563239098
chr11,131526809,131527010,HSE398,14.0540782683
chr11,132029024,132029225,HSE232,18.1785539558
chr11,132863261,132863461,HSE792,9.4066623256
chr12,1619367,1619567,HSE271,19.5724460831
chr12,2393824,2394024,HSE153,38.5409899219
chr12,4310999,4311199,HSE612,13.3477965884
chr12,4715838,4716038,HSE124,22.969040262
chr12,5977983,5978183,HSE981,8.9923072646
chr12,6649669,6649869,HSE666,18.8465977854
chr12,8983842,8984042,HSE206,21.6055505061
chr12,10253851,10254051,HSE850,13.368224383
chr12,12009176,12009379,HSE996,8.2025875972
chr12,14088837,14089037,HSE361,16.6852824277
chr12,15118302,15118502,HSE158,36.7485762947
chr12,15186700,15186902,HSE472,17.2685676671
chr12,15283754,15283954,HSE862,11.6244209976
chr12,16064197,16064397,HSE796,18.7455509923
chr12,23364365,23364565,HSE716,12.767223137
chr12,29608107,29608307,HSE84,29.4815974061
chr12,30672107,30672307,HSE459,16.6113321667
chr12,31079386,31079586,HSE928,15.5521683508
chr12,31233235,31233435,HSE817,14.0452336182
chr12,32029945,32030149,HSE3,134.4896822737
chr12,32418004,32418204,HSE443,14.9171957071
chr12,32715083,32715283,HSE566,11.7700863315
chr12,41442900,41443100,HSE448,15.8907964207
chr12,44547984,44548184,HSE595,19.5258881951
chr12,47299063,47299263,HSE710,17.9511083583
chr12,51318352,51318553,HSE278,38.5126987285
chr12,51714654,51714854,HSE105,36.1187300648
chr12,60334522,60334722,HSE296,18.4243312773
chr12,64388551,64388751,HSE411,16.4173206854
chr12,64960555,64960755,HSE893,11.4399085223
chr12,65100062,65100262,HSE677,12.0906755698
chr12,67180805,67181005,HSE302,18.0232362712
chr12,67820036,67820236,HSE693,28.0050105025
chr12,67824724,67824924,HSE562,19.9790499282
chr12,70590594,70590798,HSE938,10.4065399522
chr12,72665258,72665458,HSE870,9.4908568562
chr12,72843735,72843935,HSE352,14.6912721831
chr12,72903763,72903963,HSE622,12.4776159762
chr12,76258547,76258747,HSE366,15.4992304899
chr12,81139497,81139697,HSE263,19.1724365769
chr12,83358298,83358498,HSE956,10.9748220911
chr12,88826891,88827091,HSE470,14.4491790975
chr12,93693111,93693311,HSE337,17.5773205057
chr12,93793026,93793226,HSE231,36.7866454534
chr12,93964730,93964930,HSE701,14.9318726929
chr12,94680437,94680638,HSE800,10.1294106105
chr12,95448207,95448407,HSE85,41.328100082
chr12,98833530,98833731,HSE572,21.9773288843
chr12,102047388,102047588,HSE177,36.6536035992
chr12,105121890,105122090,HSE733,12.9059778339
chr12,106581316,106581516,HSE974,11.9119885347
chr12,108780634,108780834,HSE195,21.1967998006
chr12,110145174,110145374,HSE326,19.9633772792
chr12,110147281,110147481,HSE269,19.4570397688
chr12,116307853,116308063,HSE340,22.419418574
chr12,117846190,117846392,HSE393,15.5403001234
chr12,117850284,117850484,HSE557,16.5566891443
chr12,123543660,123543860,HSE861,10.4781563732
chr12,128314695,128314895,HSE528,13.3821736799
chr12,128455126,128455326,HSE906,9.3701763362
chr12,131041357,131041557,HSE827,8.9137558728
chr12,131261218,131261419,HSE39,40.7349907933
chr12,131596799,131596999,HSE61,38.7314139499
chr12,131622505,131622705,HSE735,9.6402688286
chr12,133175461,133175661,HSE560,17.0748830696
chr13,21347729,21347929,HSE450,18.700409108
chr13,24148647,24148847,HSE923,12.1565857536
chr13,29459319,29459519,HSE4,83.6939521234
chr13,29464970,29465170,HSE235,21.5042630726
chr13,33536518,33536718,HSE484,11.9616526488
chr13,36045055,36045255,HSE466,15.1339680818
chr13,37089667,37089867,HSE53,34.1508321281
chr13,38195831,38196031,HSE63,29.4908349793
chr13,47090817,47091017,HSE240,20.9194834678
chr13,47169753,47169953,HSE795,10.6129192636
chr13,48685195,48685395,HSE383,17.4161328951
chr13,52195812,52196012,HSE804,33.9458114683
chr13,52928037,52928237,HSE971,10.2210243551
chr13,58387875,58388075,HSE898,9.7709738074
chr13,63964002,63964202,HSE155,21.8968073472
chr13,63982654,63982855,HSE755,10.6894623919
chr13,66918963,66919163,HSE942,8.137382847
chr13,78382044,78382244,HSE709,13.2425052878
chr13,79968085,79968285,HSE207,19.0520301367
chr13,81969573,81969773,HSE464,17.1316537183
chr13,88325009,88325225,HSE169,22.404724465
chr13,94551722,94551922,HSE47,39.9300964523
chr13,96720972,96721172,HSE242,17.6289298545
chr13,99193496,99193696,HSE775,11.8286452332
chr13,102857806,102858006,HSE175,26.1349566103
chr13,103659347,103659547,HSE879,9.4672756235
chr13,106860061,106860261,HSE523,15.2219187287
chr13,107025740,107025940,HSE545,22.9167462321
chr13,107144094,107144294,HSE826,10.0932827937
chr13,111223922,111224122,HSE55,37.6150776113
chr14,21439340,21439545,HSE697,14.9749639521
chr14,24505425,24505632,HSE598,21.3172605404
chr14,25814644,25814844,HSE884,8.4198607932
chr14,26543395,26543598,HSE239,19.7243319678
chr14,31281660,31281860,HSE64,42.0539122341
chr14,33531812,33532012,HSE374,13.6641312978
chr14,59129293,59129493,HSE803,12.357054467
chr14,59386085,59386285,HSE932,9.6556661977
chr14,61083031,61083231,HSE780,12.1130184536
chr14,65539073,65539273,HSE324,18.8187496116
chr14,76804206,76804406,HSE129,28.6611225834
chr14,81636859,81637059,HSE65,73.8674420954
chr14,81790696,81790896,HSE209,42.4669581462
chr14,91091974,91092174,HSE480,17.813206233
chr14,91716580,91716780,HSE469,17.2797598596
chr14,93949158,93949358,HSE746,9.1621125053
chr14,95071723,95071923,HSE924,8.9302006066
chr14,102783279,102783479,HSE816,24.4849615987
chr14,105147964,105148164,HSE939,11.1238681848
chr15,27524028,27524228,HSE431,15.0234209239
chr15,28817788,28817988,HSE191,21.3327480149
chr15,29247425,29247625,HSE279,18.2157833949
chr15,30182592,30182792,HSE840,10.1016943223
chr15,31792516,31792716,HSE429,15.2456002355
chr15,33752266,33752466,HSE806,7.7973232785
chr15,33958947,33959147,HSE650,11.2642323912
chr15,39474262,39474462,HSE703,12.786143498
chr15,52554008,52554208,HSE832,10.0325076389
chr15,54967526,54967726,HSE230,24.1378839612
chr15,55510355,55510555,HSE339,18.1931930527
chr15,61269359,61269560,HSE1,89.2659435972
chr15,61979890,61980090,HSE922,8.1022018494
chr15,62752114,62752314,HSE611,14.5613676786
chr15,62917755,62917955,HSE305,28.6662298913
chr15,68723591,68723791,HSE462,23.2304619822
chr15,69830362,69830562,HSE757,10.8328824475
chr15,72203345,72203545,HSE147,31.2986371967
chr15,74076783,74076983,HSE771,12.0826546194
chr15,74596170,74596370,HSE993,12.6758156277
chr15,74641836,74642036,HSE221,25.8164928998
chr15,75523071,75523271,HSE874,9.5089830845
chr15,80478573,80478773,HSE211,20.487080964
chr15,80828906,80829107,HSE277,17.6534731911
chr15,81616460,81616661,HSE101,37.8792330626
chr15,81861640,81861840,HSE987,12.411938064
chr15,81868202,81868403,HSE587,14.0269438152
chr15,82201705,82201905,HSE376,27.8796005059
chr15,86374626,86374826,HSE588,14.9950787059
chr15,88418444,88418645,HSE580,12.4183759864
chr15,88670015,88670215,HSE521,11.1843930949
chr15,92692436,92692637,HSE26,72.0593711439
chr15,98775365,98775565,HSE381,14.9440294085
chr15,102501629,102501829,HSE592,17.8647492194
chr16,626697,626897,HSE208,23.3415017382
chr16,970811,971011,HSE408,18.879805744
chr16,3137379,3137579,HSE656,13.1191220261
chr16,11114848,11115048,HSE998,11.8718870193
chr16,11173789,11173989,HSE128,39.6118207734
chr16,19756698,19756898,HSE349,18.1890890859
chr16,22776292,22776492,HSE631,10.2281468082
chr16,23037379,23037579,HSE661,11.5338091882
chr16,24508521,24508721,HSE731,10.9292976321
chr16,27323049,27323272,HSE62,34.6319360162
chr16,30204374,30204574,HSE426,34.9445834473
chr16,31885117,31885317,HSE805,17.0726534038
chr16,48565689,48565889,HSE914,14.4461848582
chr16,52484957,52485157,HSE999,9.9367824367
chr16,56581581,56581781,HSE386,14.3620121795
chr16,59736519,59736719,HSE342,19.0406902327
chr16,66545956,66546156,HSE905,9.2931748668
chr16,68484705,68484905,HSE218,20.7512770405
chr16,68507748,68507957,HSE548,18.0413570871
chr16,73322142,73322343,HSE702,12.5680470461
chr16,78355884,78356084,HSE303,20.3741349926
chr16,89307265,89307465,HSE18,70.9382862202
chr16,89679724,89679939,HSE578,17.1619789211
chr16,89988292,89988493,HSE347,20.0128724894
chr17,999331,999532,HSE482,15.5034626446
chr17,5579439,5579642,HSE48,43.9178228518
chr17,6111243,6111443,HSE198,30.4698391934
chr17,8279924,8280124,HSE45,65.9896646322
chr17,8759325,8759525,HSE422,15.0545562099
chr17,9340019,9340219,HSE948,9.8219471135
chr17,11166409,11166610,HSE122,22.963059202
chr17,12830908,12831108,HSE145,27.0976894901
chr17,13298874,13299074,HSE156,34.5395597863
chr17,17002215,17002415,HSE654,16.2936251743
chr17,17302370,17302570,HSE372,29.9282249338
chr17,21030152,21030352,HSE903,14.116644535
chr17,21367528,21367728,HSE833,9.7516907593
chr17,30088186,30088386,HSE678,13.3589311568
chr17,33774498,33774698,HSE818,9.4607428819
chr17,34541456,34541656,HSE553,11.9696962671
chr17,42385237,42385437,HSE149,30.3729032338
chr17,43419034,43419235,HSE437,27.2450803684
chr17,45681996,45682196,HSE438,15.031012053
chr17,49008988,49009188,HSE538,15.8789561003
chr17,49433023,49433224,HSE11,58.5889197596
chr17,49687897,49688097,HSE413,16.1529684506
chr17,49807832,49808032,HSE2,84.3204244342
chr17,50624462,50624662,HSE20,42.9975194309
chr17,50767466,50767666,HSE258,24.1676282047
chr17,53412056,53412256,HSE243,26.149501739
chr17,53828393,53828601,HSE617,14.5154098498
chr17,55785272,55785472,HSE892,10.3430074886
chr17,57998741,57998941,HSE978,10.1861279752
chr17,59327373,59327573,HSE774,9.3428992552
chr17,59589126,59589326,HSE529,18.5934605421
chr17,62254441,62254641,HSE133,33.7747197413
chr17,64370576,64370776,HSE964,7.825535696
chr17,72113540,72113740,HSE607,21.5911019025
chr17,72967433,72967633,HSE377,16.2493907656
chr17,73966346,73966547,HSE881,9.5338664732
chr17,75125788,75125988,HSE407,21.4349393235
chr17,75279445,75279646,HSE527,11.5655867334
chr17,79050003,79050203,HSE445,15.299617834
chr17,80189664,80189864,HSE957,8.575676987
chr18,2847905,2848105,HSE680,16.1823388257
chr18,3297314,3297514,HSE51,55.4969408846
chr18,4107882,4108082,HSE756,10.6509843928
chr18,4455211,4455413,HSE173,26.658560462
chr18,5960317,5960517,HSE404,17.5337872123
chr18,6610590,6610790,HSE5,67.4765594962
chr18,6878970,6879170,HSE21,114.4591803017
chr18,6941554,6941754,HSE925,8.8831899962
chr18,8067140,8067340,HSE515,15.8060479397
chr18,8982773,8982973,HSE674,10.8336659886
chr18,8991089,8991289,HSE871,9.2212572719
chr18,11092726,11092926,HSE603,14.5685707705
chr18,20949254,20949454,HSE940,10.1089249342
chr18,22006557,22006757,HSE575,21.5810391792
chr18,24341571,24341772,HSE865,12.220860758
chr18,30186665,30186865,HSE96,29.1942230497
chr18,33597390,33597591,HSE935,14.2979081707
chr18,38116709,38116909,HSE94,27.5166517092
chr18,38614509,38614709,HSE516,22.9528532124
chr18,41456364,41456564,HSE483,22.2977384345
chr18,43802028,43802228,HSE508,12.8490635247
chr18,46386532,46386733,HSE676,14.0371357841
chr18,47441756,47441956,HSE282,17.402175324
chr18,48344045,48344245,HSE640,26.5533837237
chr18,57865414,57865614,HSE637,12.1799986999
chr18,60810573,60810773,HSE712,10.7941794086
chr18,66106367,66106567,HSE78,34.0539230971
chr18,67067626,67067826,HSE748,20.3756153543
chr18,67206189,67206389,HSE707,14.8194102675
chr18,77862401,77862601,HSE778,8.9773592552
chr19,1855466,1855666,HSE615,21.0205633353
chr19,7864445,7864654,HSE420,22.2334775436
chr19,7936518,7936718,HSE1000,9.890269251
chr19,14134468,14134668,HSE552,19.247739132
chr19,16568459,16568659,HSE690,12.8248560895
chr19,22053766,22053966,HSE325,15.1569835215
chr19,28284760,28284962,HSE35,44.2571129827
chr19,28609278,28609478,HSE262,25.334253518
chr19,28660492,28660692,HSE644,23.3221045263
chr19,30811887,30812087,HSE120,27.7702458019
chr19,35800455,35800659,HSE498,13.5519561083
chr19,36792015,36792215,HSE953,7.5220582555
chr19,39122462,39122662,HSE582,17.8131880246
chr19,39167150,39167350,HSE395,15.4118441749
chr19,42905988,42906189,HSE934,12.7825653114
chr19,43059635,43059838,HSE535,11.755421727
chr19,44711537,44711737,HSE798,18.193542685
chr19,50175593,50175793,HSE781,13.7950661987
chr19,51289242,51289442,HSE917,9.3858171773
chr19,51900838,51901038,HSE453,18.6017463436
chr19,53039209,53039409,HSE463,19.5962420571
chr2,4564065,4564265,HSE689,11.0832062757
chr2,9136176,9136376,HSE166,25.108740724
chr2,11761490,11761690,HSE955,9.1404770745
chr2,14359777,14359977,HSE729,9.1222131108
chr2,15208167,15208367,HSE751,11.003441743
chr2,17699479,17699679,HSE759,12.4462128012
chr2,17721672,17721872,HSE742,14.1970571736
chr2,20024086,20024286,HSE83,28.7067393754
chr2,23725997,23726197,HSE449,24.6822465458
chr2,25752130,25752330,HSE346,16.6877757619
chr2,29434845,29435045,HSE841,9.2654187933
chr2,29840944,29841144,HSE785,11.1516309255
chr2,33308933,33309133,HSE810,10.6180361689
chr2,40274443,40274643,HSE543,16.5775325601
chr2,40277040,40277240,HSE901,17.7522146515
chr2,43020786,43020986,HSE536,13.7021711702
chr2,43525771,43525971,HSE140,23.6931498342
chr2,45907824,45908024,HSE790,12.332179446
chr2,49482822,49483023,HSE764,11.6109483119
chr2,49776850,49777050,HSE382,20.4425087695
chr2,49829759,49829959,HSE559,13.8791198577
chr2,50199950,50200150,HSE306,21.1830037687
chr2,50522255,50522455,HSE294,20.4771502209
chr2,50573272,50573472,HSE520,11.3849059039
chr2,53710769,53710969,HSE673,11.4462899835
chr2,54846090,54846291,HSE652,12.3643734532
chr2,55381344,55381548,HSE333,16.5384641969
chr2,55649064,55649265,HSE161,26.4627203219
chr2,65290575,65290775,HSE860,14.2637154595
chr2,65615621,65615821,HSE811,10.640516431
chr2,68546802,68547002,HSE585,12.806795592
chr2,72975882,72976082,HSE506,11.8250162965
chr2,74234353,74234554,HSE447,19.3111885875
chr2,76692583,76692783,HSE308,24.5495576527
chr2,84914260,84914460,HSE273,18.7931730309
chr2,85289844,85290044,HSE490,17.6255300394
chr2,85378807,85379007,HSE442,18.4954416724
chr2,87051461,87051665,HSE139,28.4199186304
chr2,87064810,87065010,HSE708,10.458886264
chr2,87067122,87067449,HSE379,30.0280259634
chr2,87069310,87069510,HSE885,16.1725172352
chr2,88316314,88316515,HSE214,34.3179189629
chr2,88414573,88414773,HSE220,18.6297613359
chr2,89034794,89034994,HSE641,10.882267071
chr2,91750589,91750877,HSE327,20.2352669039
chr2,91762380,91762580,HSE468,12.6786942321
chr2,97019268,97019468,HSE786,8.3601047373
chr2,98758986,98759186,HSE679,16.1171102076
chr2,100697871,100698071,HSE706,13.479967802
chr2,100699949,100700149,HSE163,38.236433871
chr2,104094111,104094311,HSE59,33.1831421211
chr2,106095656,106095858,HSE390,15.6220351252
chr2,106554244,106554444,HSE497,18.2333367245
chr2,109968022,109968222,HSE763,10.4860988331
chr2,110515113,110515315,HSE19,48.1747064465
chr2,110806206,110806406,HSE977,8.2999349047
chr2,111400651,111400851,HSE808,9.0427754883
chr2,112020360,112020560,HSE444,14.0773788267
chr2,112221956,112222156,HSE720,15.6782160603
chr2,114395544,114395744,HSE67,33.2899481068
chr2,119068189,119068389,HSE104,27.8409422337
chr2,119071047,119071247,HSE25,58.9652879287
chr2,121088717,121088917,HSE726,14.2115751447
chr2,122336997,122337197,HSE249,20.8047512788
chr2,124279519,124279719,HSE495,11.9685617057
chr2,124730178,124730378,HSE30,48.1797064921
chr2,131094775,131094975,HSE988,12.5951506631
chr2,131484996,131485196,HSE76,33.2819734412
chr2,133300942,133301142,HSE997,7.8669044953
chr2,134090771,134090971,HSE180,24.5244184665
chr2,137008268,137008468,HSE307,16.6788740279
chr2,137015528,137015728,HSE947,7.5774875118
chr2,142934541,142934741,HSE345,21.8622427277
chr2,150036853,150037053,HSE807,12.9999546517
chr2,165778793,165778993,HSE358,17.0239953486
chr2,177391858,177392058,HSE260,19.1403997668
chr2,182327537,182327737,HSE681,17.5160573028
chr2,182393251,182393451,HSE40,38.4626716079
chr2,194117851,194118051,HSE321,18.0633419546
chr2,202563650,202563850,HSE626,12.3289416007
chr2,202741843,202742043,HSE359,20.9626551138
chr2,207294294,207294494,HSE670,11.1162813392
chr2,210363875,210364075,HSE734,12.3968615154
chr2,216306316,216306516,HSE868,14.1928417965
chr2,217522315,217522515,HSE293,35.0627763697
chr2,218641534,218641734,HSE82,37.7257060365
chr2,225243534,225243734,HSE416,20.099420911
chr2,229706577,229706777,HSE943,10.1343101996
chr2,231797519,231797723,HSE719,12.2696761634
chr2,232478862,232479063,HSE856,8.5861916221
chr2,236011384,236011584,HSE496,11.7277379839
chr2,238512362,238512563,HSE608,13.5849351928
chr2,240078429,240078629,HSE174,24.7169603423
chr2,241308943,241309143,HSE590,12.5097816417
chr2,241406758,241406958,HSE727,15.8620577497
chr20,1791874,1792074,HSE487,14.0309392186
chr20,1864025,1864225,HSE950,9.0977150918
chr20,1871245,1871445,HSE16,56.7490572952
chr20,2012791,2012991,HSE87,31.037092624
chr20,3629754,3629958,HSE215,20.9130441286
chr20,3660554,3660754,HSE193,24.974343769
chr20,4063903,4064104,HSE111,56.1553976804
chr20,14705232,14705432,HSE33,47.5410662718
chr20,16719908,16720108,HSE270,16.3543605271
chr20,32774062,32774262,HSE309,18.1246253801
chr20,34463418,34463619,HSE367,15.2757490918
chr20,34681486,34681692,HSE753,9.3728031841
chr20,34961484,34961684,HSE355,27.5542266393
chr20,36875508,36875708,HSE12,88.6429013544
chr20,39492043,39492243,HSE616,13.836833653
chr20,39504403,39504603,HSE791,11.0391746143
chr20,39522366,39522566,HSE522,15.4476576681
chr20,41278899,41279099,HSE130,24.1052112576
chr20,41281047,41281248,HSE138,25.1054094538
chr20,43678567,43678767,HSE118,27.5802502403
chr20,47354941,47355141,HSE829,10.0084428
chr20,48196544,48196744,HSE13,62.2017999936
chr20,48391843,48392070,HSE343,17.2886280147
chr20,48451672,48451872,HSE658,15.2435487162
chr20,57999712,57999912,HSE593,11.851459255
chr20,58010717,58010917,HSE888,8.4178547638
chr20,58028998,58029198,HSE589,11.8594187455
chr20,62892549,62892749,HSE22,49.3467939523
chr21,18183390,18183590,HSE846,12.0921264014
chr21,25470304,25470504,HSE848,9.043990116
chr21,29314093,29314293,HSE540,11.1333587451
chr21,29522195,29522395,HSE90,42.5669364456
chr21,30279211,30279411,HSE802,8.9817364292
chr21,31599287,31599487,HSE255,25.8534564105
chr21,34305184,34305384,HSE75,34.8701267573
chr21,40357115,40357316,HSE931,14.2521728213
chr21,40369917,40370117,HSE596,13.6512982833
chr21,42458559,42458759,HSE941,7.986093663
chr21,42461230,42461430,HSE46,32.6233218231
chr21,45610526,45610726,HSE373,19.1339980285
chr22,18647319,18647519,HSE600,15.4170005372
chr22,21387111,21387311,HSE465,16.7936897957
chr22,25800344,25800546,HSE855,15.133649847
chr22,27530972,27531172,HSE91,32.8296818218
chr22,27613121,27613321,HSE878,9.9026827262
chr22,30938555,30938755,HSE991,12.3953908195
chr22,37608742,37608942,HSE199,25.5079108823
chr22,38213774,38213975,HSE336,29.8698643259
chr22,38875187,38875387,HSE31,75.0968010157
chr22,45034999,45035200,HSE944,11.0058314418
chr22,47560535,47560735,HSE820,9.6159416626
chr22,49263785,49263985,HSE123,27.4756435262
chr3,1033708,1033908,HSE547,11.8167271684
chr3,4648618,4648819,HSE594,13.1354478948
chr3,6170925,6171125,HSE274,19.6422205175
chr3,9861617,9861817,HSE561,16.1351899583
chr3,20581162,20581362,HSE88,84.6550501758
chr3,22766006,22766206,HSE926,7.4746803691
chr3,25039886,25040088,HSE745,12.5677664568
chr3,32452491,32452691,HSE958,8.8781271039
chr3,34337067,34337267,HSE238,23.4517631822
chr3,44106796,44106996,HSE645,14.6078435944
chr3,45454944,45455144,HSE213,19.0172183728
chr3,50468482,50468682,HSE70,32.3982070336
chr3,50553584,50553784,HSE409,31.7674822371
chr3,50759518,50759718,HSE556,12.7286106999
chr3,51312927,51313129,HSE179,22.7291813917
chr3,54024155,54024359,HSE606,12.4118581011
chr3,55583333,55583544,HSE895,20.4730234134
chr3,57432272,57432472,HSE170,23.9015532829
chr3,57731978,57732178,HSE979,13.0317416952
chr3,58143711,58143912,HSE488,21.4279391783
chr3,59117232,59117432,HSE310,21.6692936097
chr3,59537533,59537733,HSE831,10.4335147498
chr3,60313490,60313690,HSE797,11.4508642018
chr3,60739661,60739861,HSE222,37.1649368806
chr3,61899965,61900165,HSE972,9.9582764633
chr3,62859461,62859661,HSE671,12.0850192866
chr3,62874394,62874594,HSE454,12.2791253714
chr3,63748080,63748280,HSE387,14.5767368616
chr3,74103231,74103431,HSE633,15.5075880796
chr3,85652655,85652855,HSE203,18.8184809601
chr3,97829189,97829389,HSE776,9.1102578423
chr3,103166013,103166213,HSE896,9.9523912683
chr3,105602785,105602985,HSE433,40.5785957197
chr3,107148262,107148462,HSE261,18.4043131499
chr3,109288204,109288404,HSE356,14.9310679943
chr3,111197315,111197518,HSE370,15.859503117
chr3,112219616,112219816,HSE368,15.0704271372
chr3,123405019,123405220,HSE115,27.2314354098
chr3,124880427,124880628,HSE579,14.8608441809
chr3,127740739,127740949,HSE314,26.0837216172
chr3,128256027,128256227,HSE630,13.9854481485
chr3,132829089,132829289,HSE474,15.9443427668
chr3,133123857,133124057,HSE471,13.3464634396
chr3,136537878,136538084,HSE505,14.6728277902
chr3,136915437,136915640,HSE584,13.4830219375
chr3,136922639,136922839,HSE304,16.1133399857
chr3,143048781,143048981,HSE663,12.6197529914
chr3,145155998,145156198,HSE963,7.937403129
chr3,150102651,150102851,HSE441,27.9319328699
chr3,152314041,152314241,HSE863,9.389594265
chr3,152446287,152446487,HSE202,21.6487393195
chr3,152497432,152497632,HSE782,15.2232552726
chr3,153501047,153501247,HSE973,8.9058148252
chr3,156106412,156106612,HSE298,17.4725292847
chr3,156467111,156467311,HSE984,12.8714197176
chr3,159583162,159583362,HSE245,24.3670741479
chr3,167647788,167647988,HSE162,23.142026511
chr3,167754605,167754805,HSE157,25.5019260862
chr3,168309609,168309809,HSE985,8.3878224111
chr3,170459966,170460166,HSE299,23.2572944826
chr3,171083365,171083565,HSE329,19.6170760846
chr3,171622769,171622969,HSE141,35.5269780554
chr3,173303292,173303492,HSE635,12.8762248581
chr3,173340053,173340253,HSE910,13.8353115715
chr3,175722586,175722786,HSE154,26.1510590855
chr3,177471354,177471554,HSE332,24.5244184665
chr3,184084866,184085066,HSE428,17.9640764471
chr3,192869895,192870095,HSE246,22.5875292012
chr3,194592300,194592501,HSE362,36.8838467218
chr3,196346498,196346699,HSE280,19.4679975855
chr3,197199171,197199371,HSE876,13.6753444396
chr4,1209278,1209519,HSE564,13.9004104602
chr4,1682431,1682632,HSE233,22.8144061287
chr4,2673293,2673493,HSE435,15.7039657822
chr4,5205616,5205843,HSE875,9.1366703212
chr4,5774976,5775176,HSE248,21.1824928836
chr4,7153027,7153227,HSE983,9.7283332501
chr4,7158073,7158273,HSE164,31.8768175016
chr4,7987860,7988060,HSE685,14.8308227861
chr4,8317712,8317912,HSE912,11.2750910314
chr4,10298688,10298888,HSE7,56.3001544011
chr4,10307533,10307733,HSE478,12.8133414228
chr4,10410626,10410826,HSE197,21.3709305662
chr4,16325714,16325914,HSE351,19.7714522278
chr4,16843354,16843554,HSE872,11.1479071197
chr4,17189915,17190115,HSE794,12.4374877889
chr4,17399671,17399871,HSE698,12.9259482624
chr4,18421836,18422036,HSE418,18.6650288814
chr4,20654001,20654201,HSE842,9.8868805638
chr4,35935735,35935935,HSE864,11.6965490821
chr4,37141687,37141887,HSE384,14.5996824189
chr4,37189108,37189308,HSE188,35.9761823699
chr4,38192503,38192704,HSE513,20.6670228424
chr4,41059770,41059970,HSE550,14.2940364222
chr4,41146900,41147100,HSE920,10.9173925032
chr4,42598985,42599185,HSE834,12.2213134666
chr4,43551753,43551953,HSE613,10.9664468122
chr4,52918093,52918293,HSE813,10.8199181309
chr4,67296659,67296859,HSE330,17.1184863176
chr4,68783414,68783614,HSE194,27.8865779207
chr4,73531199,73531399,HSE485,16.0641880211
chr4,79442149,79442349,HSE254,31.3053467034
chr4,96027806,96028006,HSE60,38.0420908281
chr4,98417455,98417655,HSE773,12.7094671878
chr4,102826820,102827020,HSE954,9.8953082642
chr4,108956348,108956548,HSE196,28.1854857987
chr4,109035842,109036042,HSE574,15.4815826792
chr4,109331833,109332033,HSE880,8.5800439974
chr4,112855557,112855758,HSE873,8.9177752517
chr4,113045048,113045248,HSE718,12.9338359083
chr4,115673675,115673875,HSE908,9.0213984225
chr4,126033815,126034015,HSE266,27.8144026005
chr4,126926133,126926333,HSE624,13.8939469933
chr4,134018135,134018335,HSE591,13.1426859966
chr4,135498284,135498484,HSE289,16.9047451361
chr4,139144837,139145037,HSE858,11.0683425726
chr4,142822079,142822279,HSE291,16.8719939446
chr4,143883214,143883420,HSE125,23.7564028153
chr4,155022297,155022497,HSE486,24.4008504204
chr4,155876015,155876219,HSE567,14.3792053309
chr4,156523530,156523730,HSE335,17.9101352191
chr4,163926681,163926881,HSE583,13.7123935
chr4,165404029,165404229,HSE965,10.3169505717
chr4,171168842,171169042,HSE500,12.3708601884
chr4,176356648,176356848,HSE586,12.5175850781
chr4,181756449,181756649,HSE116,25.0123586931
chr4,185517517,185517717,HSE768,10.7243756024
chr4,186037200,186037400,HSE849,9.15728802
chr4,187112549,187112749,HSE73,29.6147089784
chr4,188809131,188809331,HSE127,26.3736706202
chr5,9015815,9016015,HSE754,10.9860464939
chr5,9140785,9140986,HSE34,39.8342153912
chr5,9284766,9284966,HSE159,44.8345695381
chr5,9423793,9423993,HSE397,23.9014518508
chr5,9544694,9544894,HSE976,18.4787378901
chr5,13572514,13572715,HSE970,7.981996643
chr5,14844977,14845178,HSE623,11.8533435499
chr5,26843843,26844043,HSE609,10.9929164023
chr5,35374800,35375000,HSE992,11.2155834064
chr5,36059728,36059928,HSE743,9.7015183684
chr5,38011944,38012144,HSE182,23.9400859933
chr5,39421419,39421619,HSE825,14.5866459668
chr5,39533959,39534159,HSE760,9.9360929783
chr5,43066506,43066706,HSE288,64.294192312
chr5,50917138,50917340,HSE313,22.7435698695
chr5,51008092,51008292,HSE766,10.2876577874
chr5,58130163,58130363,HSE686,14.7084178919
chr5,68296916,68297116,HSE57,43.9761647554
chr5,70529394,70529594,HSE292,16.3167607327
chr5,73557824,73558024,HSE283,26.1537486394
chr5,81951100,81951300,HSE838,11.1124928906
chr5,82305319,82305520,HSE256,21.9987593589
chr5,93459233,93459433,HSE79,88.077239222
chr5,96180256,96180457,HSE112,28.97133338
chr5,98859253,98859453,HSE457,16.1598314579
chr5,102729064,102729264,HSE89,35.4121997783
chr5,107225179,107225379,HSE752,12.8723311418
chr5,108006706,108006906,HSE167,20.5736395644
chr5,112562162,112562362,HSE146,28.4736858728
chr5,115731439,115731639,HSE662,14.441740722
chr5,115870785,115870985,HSE889,11.1682472441
chr5,121403180,121403380,HSE738,10.4404525204
chr5,123709637,123709837,HSE385,34.5371219842
chr5,124534387,124534587,HSE189,41.4113583783
chr5,126015956,126016156,HSE350,48.9260098909
chr5,130651056,130651256,HSE353,19.9406119279
chr5,131054387,131054588,HSE275,16.8672736168
chr5,138872592,138872792,HSE97,33.7294648775
chr5,139113346,139113546,HSE499,24.5461507056
chr5,139503491,139503691,HSE135,22.5832471238
chr5,140365894,140366094,HSE477,22.7804027056
chr5,152496425,152496625,HSE297,23.9517433229
chr5,153157431,153157632,HSE994,7.2712465887
chr5,154985156,154985356,HSE27,47.3698229496
chr5,156798061,156798261,HSE421,18.0953061141
chr5,159614141,159614341,HSE504,16.644367563
chr5,169678151,169678351,HSE205,23.5872306039
chr5,169969996,169970196,HSE252,26.7061548422
chr5,170556363,170556585,HSE81,53.3489093817
chr5,170880119,170880319,HSE533,21.0213665516
chr5,176794222,176794423,HSE599,23.2425155735
chr5,177712082,177712283,HSE921,9.6878527385
chr6,1720956,1721156,HSE961,13.9546110024
chr6,2908962,2909162,HSE900,9.4385437272
chr6,3624882,3625082,HSE49,38.6547390654
chr6,6477697,6477897,HSE425,14.0308308131
chr6,6927996,6928198,HSE573,13.2644125542
chr6,7261536,7261736,HSE899,20.57594766
chr6,8857485,8857685,HSE226,19.7424552164
chr6,11764188,11764388,HSE423,13.6453117051
chr6,12657164,12657364,HSE541,24.5587040398
chr6,13069178,13069378,HSE148,25.4358868167
chr6,13071666,13071866,HSE328,21.7306804565
chr6,13440803,13441003,HSE854,11.7878221637
chr6,13605303,13605503,HSE439,16.0336294076
chr6,15357986,15358186,HSE772,12.1002596677
chr6,15953729,15953929,HSE223,22.2692616975
chr6,20879847,20880047,HSE789,11.3511396601
chr6,22074016,22074216,HSE830,17.8828299952
chr6,22223835,22224035,HSE847,11.3189534245
chr6,22754318,22754518,HSE765,10.933963726
chr6,24191674,24191874,HSE17,64.4494432065
chr6,24355724,24355924,HSE822,12.2330307591
chr6,24684045,24684245,HSE68,40.6121039818
chr6,24908692,24908892,HSE52,36.3357402285
chr6,24981608,24981808,HSE460,14.7455299562
chr6,26866614,26866814,HSE316,17.4817827982
chr6,26987817,26988017,HSE301,17.0996119934
chr6,28953271,28953471,HSE532,18.2243327433
chr6,30163047,30163247,HSE668,9.9208283821
chr6,32862428,32862628,HSE883,10.9304672886
chr6,33048550,33048750,HSE100,28.3569655356
chr6,37190063,37190263,HSE225,25.1598854782
chr6,37752532,37752732,HSE190,21.4887428904
chr6,37839872,37840072,HSE692,15.9827010242
chr6,42104428,42104628,HSE107,67.5217188687
chr6,42125896,42126096,HSE836,11.8802206566
chr6,48152098,48152298,HSE317,16.53498743
chr6,48440896,48441096,HSE410,20.2304692131
chr6,48531624,48531824,HSE14,56.1553976804
chr6,48595126,48595327,HSE8,86.3398854446
chr6,57207153,57207353,HSE779,15.3463418546
chr6,58265076,58265285,HSE632,13.3681109557
chr6,68599806,68600006,HSE526,12.8237395122
chr6,71584109,71584310,HSE114,27.0742020553
chr6,77915769,77915969,HSE236,16.9378739354
chr6,87169783,87169984,HSE546,11.573392587
chr6,89894075,89894277,HSE647,13.1942619616
chr6,90904900,90905100,HSE491,25.8304194594
chr6,92350596,92350796,HSE653,17.364572389
chr6,101840815,101841015,HSE131,28.2436702832
chr6,105240470,105240670,HSE604,18.8779663264
chr6,107221861,107222061,HSE844,13.1640641167
chr6,107611759,107611959,HSE904,13.5023269104
chr6,112619369,112619569,HSE369,17.0366032561
chr6,114999140,114999341,HSE821,10.3959314865
chr6,116138273,116138476,HSE730,13.0350910622
chr6,116621676,116621877,HSE929,9.2537035432
chr6,131428060,131428260,HSE284,18.8889226922
chr6,132596060,132596260,HSE990,8.2742348935
chr6,138104942,138105142,HSE253,23.0468563465
chr6,140757614,140757815,HSE967,10.1788738588
chr6,143968529,143968729,HSE852,8.5873299746
chr6,147322278,147322478,HSE268,19.7936739006
chr6,151695336,151695536,HSE519,19.0160524829
chr6,157265666,157265866,HSE119,24.7735526841
chr6,161392326,161392526,HSE334,18.7149756358
chr6,161740337,161740537,HSE713,9.6675137739
chr6,162838242,162838442,HSE132,26.1781329147
chr6,166188920,166189120,HSE417,14.5239466546
chr6,167892584,167892784,HSE814,8.7865263368
chr6,169077927,169078127,HSE110,27.1403888015
chr6,170794092,170794292,HSE524,13.6109134035
chr7,4198833,4199033,HSE801,11.1292518601
chr7,4949917,4950117,HSE172,21.4927855963
chr7,13461438,13461638,HSE228,17.5121934963
chr7,22122623,22122823,HSE930,12.3048991783
chr7,23062178,23062378,HSE509,25.1202244572
chr7,23246200,23246400,HSE257,22.9981606665
chr7,25559731,25559931,HSE966,8.5262161829
chr7,36664999,36665199,HSE605,16.0994530522
chr7,37372863,37373063,HSE204,19.2684485089
chr7,38492364,38492564,HSE597,12.8932215725
chr7,40616066,40616266,HSE380,17.182635963
chr7,47064486,47064686,HSE577,13.0570408987
chr7,47084856,47085056,HSE576,16.6838216652
chr7,48031471,48031671,HSE740,11.7452011534
chr7,48748205,48748405,HSE98,30.5044348782
chr7,66050120,66050320,HSE276,21.6755649273
chr7,66213153,66213353,HSE534,13.7182502443
chr7,66223024,66223224,HSE364,16.185690078
chr7,74867660,74867860,HSE618,16.9538639948
chr7,77015807,77016008,HSE415,18.6613812869
chr7,78390011,78390211,HSE602,16.2431492916
chr7,100434184,100434384,HSE869,13.0394840372
chr7,101616503,101616703,HSE951,9.2324134965
chr7,102153402,102153602,HSE975,9.0245761817
chr7,110397521,110397721,HSE916,12.3054838817
chr7,110400563,110400763,HSE338,16.3230063175
chr7,116607477,116607678,HSE621,18.3522646727
chr7,118072236,118072438,HSE694,11.1158534833
chr7,125587404,125587604,HSE455,17.556648341
chr7,145100201,145100414,HSE401,15.6280338065
chr7,145839272,145839472,HSE171,20.1680033829
chr7,145844066,145844266,HSE9,54.1909479525
chr7,147449071,147449271,HSE704,10.2463146144
chr7,148178409,148178609,HSE835,10.6172870294
chr7,151235436,151235636,HSE365,18.0341069228
chr8,2377506,2377706,HSE181,20.7659351902
chr8,2511126,2511326,HSE320,15.27069181
chr8,7266669,7266869,HSE819,8.435349452
chr8,13356648,13356848,HSE281,18.4464980795
chr8,14336890,14337104,HSE66,46.656249178
chr8,23032626,23032826,HSE412,26.2529950816
chr8,26601555,26601755,HSE890,11.220125934
chr8,26628311,26628511,HSE788,10.9551178433
chr8,28009163,28009363,HSE510,15.0235522925
chr8,30385906,30386106,HSE629,17.1134130395
chr8,34312488,34312688,HSE178,30.2724339491
chr8,49930877,49931077,HSE507,18.1489727778
chr8,50149648,50149848,HSE518,13.7760589052
chr8,52875070,52875270,HSE501,15.5123958666
chr8,53660034,53660234,HSE93,37.3464806395
chr8,54605619,54605820,HSE684,14.2519866729
chr8,62892804,62893004,HSE121,31.87775364
chr8,73254670,73254870,HSE185,23.4900882124
chr8,73412628,73412828,HSE38,37.3035911391
chr8,75862050,75862250,HSE620,13.858825618
chr8,88877867,88878069,HSE563,12.7713866408
chr8,92115315,92115515,HSE530,14.8449505644
chr8,92248603,92248804,HSE691,15.9124555229
chr8,93852146,93852346,HSE290,32.7317983798
chr8,95213282,95213482,HSE494,13.4997136969
chr8,98387996,98388197,HSE628,12.1439641326
chr8,105236101,105236301,HSE969,9.7479394792
chr8,115779521,115779721,HSE688,12.8255728825
chr8,122167781,122167981,HSE371,17.391031161
chr8,125610826,125611026,HSE165,35.3095256409
chr8,131652848,131653048,HSE363,19.456186422
chr8,134045654,134045854,HSE168,31.8082809938
chr8,134829150,134829350,HSE646,10.9003096576
chr8,144094801,144095001,HSE717,15.3147108573
chr8,144343458,144343659,HSE72,32.0323018157
chr8,145987786,145988059,HSE451,17.6753394576
chr9,7510288,7510488,HSE312,33.7426439062
chr9,10593086,10593286,HSE414,16.6788740279
chr9,11458448,11458648,HSE843,9.0784819552
chr9,12117105,12117305,HSE142,27.950209127
chr9,14993242,14993442,HSE634,14.4731722458
chr9,16301282,16301482,HSE102,50.9103305769
chr9,17765448,17765655,HSE739,18.2392565514
chr9,20825252,20825452,HSE946,12.4789871155
chr9,33402443,33402643,HSE555,16.1608716083
chr9,33523607,33523818,HSE224,19.2005783714
chr9,34170779,34170979,HSE341,24.6371580044
chr9,41824106,41824306,HSE769,10.5181109381
chr9,42019392,42019592,HSE28,41.4263636489
chr9,44245346,44245546,HSE32,37.5468627247
chr9,46661510,46661710,HSE378,18.5997971629
chr9,67872401,67872601,HSE272,19.7212214429
chr9,69651218,69651418,HSE15,56.5573849515
chr9,69654841,69655041,HSE50,50.1850231356
chr9,69723884,69724084,HSE126,32.6894006406
chr9,70077265,70077469,HSE357,19.5503409497
chr9,70090005,70090205,HSE56,47.863549167
chr9,72158059,72158265,HSE736,10.8002448051
chr9,72749929,72750129,HSE667,9.6151470352
chr9,81641307,81641507,HSE565,12.5343408216
chr9,85896509,85896709,HSE360,15.976780491
chr9,87992926,87993126,HSE682,10.0728691654
chr9,91288879,91289079,HSE570,20.1264215633
chr9,93232164,93232364,HSE267,16.2689597218
chr9,97610366,97610566,HSE571,14.362464282
chr9,97933380,97933580,HSE660,11.3259473431
chr9,100000625,100000826,HSE473,16.0316421967
chr9,100937326,100937526,HSE502,14.7363484454
chr9,112871691,112871891,HSE427,20.2200008236
chr9,116605468,116605668,HSE24,44.0402200911
chr9,118163144,118163344,HSE10,116.5676393698
chr9,136738864,136739065,HSE828,21.7259174217
chr9,136826692,136826892,HSE823,8.8396928196
chr9,138884709,138884909,HSE749,21.506795832
chr9,140320110,140320310,HSE558,17.6159627193
chrX,191631,191831,HSE375,22.299626784
chrX,197891,198101,HSE318,51.9353250981
chrX,199983,200184,HSE711,18.1602756116
chrX,9247138,9247338,HSE915,8.9889411466
chrX,9294553,9294753,HSE636,15.8583274764
chrX,13467472,13467672,HSE625,13.3123255447
chrX,22410208,22410409,HSE982,9.6853480348
chrX,29231637,29231837,HSE610,12.7761366615
chrX,30489623,30489824,HSE137,24.5548014378
chrX,31825117,31825317,HSE99,25.9398284883
chrX,36439734,36439934,HSE323,16.5354248431
chrX,36467884,36468084,HSE402,16.6156581564
chrX,40432956,40433156,HSE933,11.0828028555
chrX,40810144,40810344,HSE106,48.7539914758
chrX,49953599,49953799,HSE295,25.7542688302
chrX,57163623,57163823,HSE503,22.7827962324
chrX,114733851,114734051,HSE986,7.9927176453
chrX,124903682,124903882,HSE436,12.6648421132
chrX,128211486,128211686,HSE815,14.3611432709
chrX,128270781,128270981,HSE952,10.1358588237
chrX,130964620,130964822,HSE980,11.9103854587


================================================
FILE: intro-to-awk/data/example.fastq
================================================
@ERR117184_2.14044282
CTGGTGCTCAGTCATTTTGCTAGATTGTAGCTCACCATTGCCTCTCTGCCTGCATTGTGCACTCCAATCCCTCAGCAGCTACAAAAACACTTTGTCAACTCCAGATCGG
+
IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII
@ERR117185_2.23793537
TTGTTCAATTTTTTTAGTAGTTTTAGAGTAATTAATGAGCTTTGAGGTCACTTAACAATAAAAGCATATTTTAAAGTAAAGGTTGCTGATCAACAGTCTAATTTTCATA
+
IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII
@ERR117166_2.4649205
ACTTAACAATAAAAGCATATTTTAAAGTAAAGGTT
+
IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII
@ERR117185_2.949669
ATGCTTTTATTGTTAAGTGACCTCAAAGCTCATTAATTACTCTAAAACTACTAAAAAAATTGAACAAAAAAGTATTTCCAGTAATCAAGATTTTCTTCATGCCCTAGAT
+
IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII
@ERR117163_2.2292219
AAATCGTAAGTGAAGCAACAGAATTCAGAACTAAA
+
IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII
@ERR117165_2.36526615
AACGCTAAGGTTATATTAAAATGTTTCTTATGTTG
+
IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII
@ERR117174_2.23007391
GCAAGTGGTTGGACTCAAGGAAGTTATAAATACCT
+
IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII
@ERR117175_2.11345437
GCAAGTGGTTGGACTCAAGGAAGTTATAAATACCT
+
IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII
@ERR117176_2.30285510
GCAAGTGGTTGGACTCAAGGAAGTTATAAATACCT
+
IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII
@ERR117177_2.33680511
GCAAGTGGTTGGACTCAAGGAAGTTATAAATACCT
+
IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII


================================================
FILE: intro-to-awk/data/hg38.genome
================================================
chrom	size
1	248956422
2	242193529
3	198295559
4	190214555
5	181538259
6	170805979
7	159345973
X	156040895
8	145138636
9	138394717
11	135086622
10	133797422
12	133275309
13	114364328
14	107043718
15	101991189
16	90338345
17	83257441
18	80373285
20	64444167
19	58617616
Y	57227415
22	50818468
21	46709983
15_KI270905v1_alt	5161414
6_GL000256v2_alt	4929269
6_GL000254v2_alt	4827813
6_GL000251v2_alt	4795265
6_GL000253v2_alt	4677643
6_GL000250v2_alt	4672374
6_GL000255v2_alt	4606388
6_GL000252v2_alt	4604811
17_KI270857v1_alt	2877074
16_KI270853v1_alt	2659700
16_KI270728v1_random	1872759
17_GL000258v2_alt	1821992
5_GL339449v2_alt	1612928
14_KI270847v1_alt	1511111
17_KI270908v1_alt	1423190
14_KI270846v1_alt	1351393
5_KI270897v1_alt	1144418
7_KI270803v1_alt	1111570
19_GL949749v2_alt	1091841
19_KI270938v1_alt	1066800
19_GL949750v2_alt	1066390
19_GL949748v2_alt	1064304
19_GL949751v2_alt	1002683
19_GL949746v1_alt	987716
19_GL949752v1_alt	987100
8_KI270821v1_alt	985506
1_KI270763v1_alt	911658
6_KI270801v1_alt	870480
19_GL949753v2_alt	796479
19_GL949747v2_alt	729520
8_KI270822v1_alt	624492
4_GL000257v2_alt	586476
12_KI270904v1_alt	572349
4_KI270925v1_alt	555799
15_KI270852v1_alt	478999
15_KI270727v1_random	448248
9_KI270823v1_alt	439082
15_KI270850v1_alt	430880
1_KI270759v1_alt	425601
12_GL877876v1_alt	408271
Un_KI270442v1	392061
17_KI270862v1_alt	391357
15_GL383555v2_alt	388773
19_GL383573v1_alt	385657
4_KI270896v1_alt	378547
4_GL383528v1_alt	376187
17_GL383563v3_alt	375691
8_KI270810v1_alt	374415
1_GL383520v2_alt	366580
1_KI270762v1_alt	354444
15_KI270848v1_alt	327382
17_KI270909v1_alt	325800
14_KI270844v1_alt	322166
8_KI270900v1_alt	318687
10_GL383546v1_alt	309802
13_KI270838v1_alt	306913
8_KI270816v1_alt	305841
22_KI270879v1_alt	304135
8_KI270813v1_alt	300230
11_KI270831v1_alt	296895
15_GL383554v1_alt	296527
8_KI270811v1_alt	292436
18_GL383567v1_alt	289831
X_KI270880v1_alt	284869
8_KI270812v1_alt	282736
19_KI270921v1_alt	282224
17_KI270729v1_random	280839
17_JH159146v1_alt	278131
X_KI270913v1_alt	274009
6_KI270798v1_alt	271782
7_KI270808v1_alt	271455
22_KI270876v1_alt	263666
15_KI270851v1_alt	263054
22_KI270875v1_alt	259914
1_KI270766v1_alt	256271
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================================================
FILE: intro-to-awk/index.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Manipulating files with AWK\n",
    "### Bioinformatics Coffee Hour - April 14, 2020\n",
    "\n",
    "### What is awk?\n",
    "Invented in the 1970's, [awk](https://en.wikipedia.org/wiki/AWK) is a scripting language included in most Unix-like operating systems. It specializes in one-liner programs and manipulating text files.\n",
    "\n",
    "In many cases, if you're parsing information from a text file (such as a [BED](https://en.wikipedia.org/wiki/BED_(file_format)) file, [FASTA](https://en.wikipedia.org/wiki/FASTA) file, etc.), you could write a Python script...or you could do it with awk in a single line!\n",
    "\n",
    "### Syntax\n",
    "awk scripts are organized as:\n",
    "\n",
    "`awk 'pattern { action; other action }' file`\n",
    "\n",
    "Meaning that every time that the pattern is true, awk will execute the action in the brackets.\n",
    "If no pattern is specified, the action will be taken for every line in the input file, e.g. the following command prints every line:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "awk '{print}' data/hg38.genome | head"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The two most important patterns are `BEGIN` and `END`, which tell the action to take place before any lines are read and after the last line."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "awk 'BEGIN{sum=0} {sum+=1} END {print sum}' data/hg38.genome"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " The above line sets a variable at the start of the script, adds 1 to it every line, then prints its value at the end."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If a variable hasn't been initialized, it is treated as 0 in numeric expressions, and an empty string in string expressions&mdash;awk will not print an error!\n",
    "So the following awk script also prints the number of lines in the file data/hg38.genome:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "awk '{sum+=1} END {print sum}' data/hg38.genome"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Input and output\n",
    "Input to awk is split into **records** and **fields**.\n",
    "- By default, **records** are separated by newline character, i.e # of records = # of lines in input file\n",
    "- Each record is subdivided into **fields**, i.e. columns, as determined by the field separator (see below)\n",
    "\n",
    "There are several important built-in variable in awk.\n",
    "The fields (columns) of each record are referred to by `$number`, so the first column would be `$1`, second would be `$2`, etc. `$0` refers to the entire record.\n",
    "\n",
    "So to print the second column of each line in the file, we'd use:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "awk '{print $2}' data/hg38.genome | head"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And if we wanted to print the second then the first:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "awk '{print $2,$1}' data/hg38.genome | head"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that when the different fields are separated with commas in the `print` statement, they are joined by the output field separator (the **OFS** variable, described below), which is by default a space.\n",
    "If the comma is omitted between fields (e.g., `awk '{print $2 $1}'`, they are concatenated without a separator.\n",
    "\n",
    "We can also print strings using using quotation marks:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "awk '{print \"First column:\" $1}' data/hg38.genome | head"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Which for every line of the file will print the text \"First column:\" followed by the value in the first field."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "awk has several other built-in variables that are very useful for parsing text, including:\n",
    "\n",
    "|  |   |\n",
    "---|---|\n",
    "| **FS** | field separator (default: white space) |\n",
    "| **OFS** |  output field separator, i.e. what character separates fields when printing|\n",
    "| **RS** | record separator, i.e. what character records are split on (default: new line) |\n",
    "| **ORS** | output record separator |\n",
    "| **NR** | number of records in input (# lines by default) |\n",
    "\n",
    "Assigning to a field causes the entire record ($0) to be recomputed using **OFS**. We can use this to convert between file formats, e.g. make a comma-separated text file into a tab-separated file. First, let's look at the first few lines on our comma-separated file using `head`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "head data/enhancers.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's convert the file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "awk 'BEGIN{FS=\",\" ; OFS=\"\\t\"} {$1 = $1; print $0}' data/enhancers.csv | head"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conditionals and pattern matching\n",
    "Like other programming languages, awk allows conditional matching with if/else statements.\n",
    "\n",
    "`awk '{if(condition) action; else other_action}'`\n",
    "\n",
    "awk uses the following conditional operators:\n",
    "\n",
    "| | |\n",
    "|-|-|\n",
    "|==|equal to|\n",
    "|!=|not equal to|\n",
    "|>|greater than|\n",
    "|>=|greater than or equal to|\n",
    "|<|less than|\n",
    "|<=|less than or equal to|\n",
    "|&&|AND|\n",
    "| \\|\\| |OR|\n",
    "| ! | NOT |\n",
    "\n",
    "In addition, awk also supports string matching using regular expressions, using the following expressions:\n",
    "\n",
    "| | |\n",
    "|-|-|\n",
    "|\\~|matches|\n",
    "|!~|does not match|\n",
    "\n",
    "For string matching, the pattern being matched must be enclosed by slashes, like so:\n",
    "\n",
    "`awk '{if($1 ~ /pattern/) print}'`\n",
    "\n",
    "Note that if an action isn't specified, the default action is `{print}`, so the previous awk command is equivalent to the following, which specifies only a pattern expression:\n",
    "\n",
    "`awk '$1 ~ /pattern/'`\n",
    "\n",
    "---\n",
    "### Example uses:\n",
    "\n",
    "- Count number of sequences in a FASTQ file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "awk 'END{print NR/4}' data/example.fastq"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note**: this is technically safer than using grep, as you don't have to worry about accidentally counting the quality line.\n",
    "\n",
    "- Only print annotations on a specific scaffold (chr1) that fall between 1Mb and 2Mb from a BED annotation file. First let's look at the first few lines of the file using `head`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "head data/Homo_sapiens_ucscGenes.subset.bed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now subset the file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "awk 'BEGIN{FS=\"\\t\";OFS=\"\\t\"} {if($1 == \"chr1\" && $2 >=1000000 && $2 <= 2000000) print}' data/Homo_sapiens_ucscGenes.subset.bed | head"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note**: when we specify that we only want annotations from chr1, we're using exact match (`== \"chr1\"`) and not pattern match (`~ /chr1/`)...why is this??"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Only print lines of GFF annotation file that match the string \"exon\" in their third column. Again, let's look at the first few lines of the file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "head data/Homo_sapiens.GRCh38.subset.gff3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then subset the file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "awk 'BEGIN{FS=\"\\t\"} {if($3 ~ /exon/) print $0}' data/Homo_sapiens.GRCh38.subset.gff3 | head"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Convert from GFF (genome feature file) to BED file "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "grep -v '^#' data/Homo_sapiens.GRCh38.subset.gff3 | awk 'BEGIN{FS=\"\\t\"; OFS=\"\\t\"} {print $1,$4-1,$5}' | head"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note**: remember that BED and GFF files have different coordinate systems, i.e. BED start coordinate is 0 based, half-open, GFF is 1-based inclusive! Also, we are first using grep to skip the header lines in the GFF file.\n",
    "\n",
    "Alternatively, you could do the whole thing with only awk, no grep required:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "awk 'BEGIN{FS=\"\\t\"; OFS=\"\\t\"} !/^#/ {print $1,$4-1,$5}' data/Homo_sapiens.GRCh38.subset.gff3 | head"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With this command, `!/^#/` is a pattern (like `BEGIN` or `END`) that tell awk to execute the print statement when the start of the line does not match a `#`. Use whichever makes the most sense to you!\n",
    "\n",
    "### Practice\n",
    "Using awk:\n",
    "\n",
    "* Pull out only the CDS annotations (i.e. has \"CDS\" in the 3rd column) from the GFF file data/Homo_sapiens.GRCh38.subset.gff3 and output them in BED format"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Try it*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "awk '...' data/Homo_sapiens.GRCh38.subset.gff3 | head"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Solution*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "awk 'BEGIN{FS=\"\\t\"; OFS=\"\\t\"} {if($3 ~ /CDS/) print $1,$4-1,$5}' data/Homo_sapiens.GRCh38.subset.gff3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Calculate the average length of gene annotations from the file data/Homo_sapiens_ucscGenes.subset.bed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Try it*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "awk '...' data/Homo_sapiens_ucscGenes.subset.bed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Solution*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "awk 'BEGIN{FS=\"\\t\"; sum=0} {len=$3-$2; sum=sum+len} END{print sum/NR}' data/Homo_sapiens_ucscGenes.subset.bed"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Bash",
   "language": "bash",
   "name": "bash"
  },
  "language_info": {
   "codemirror_mode": "shell",
   "file_extension": ".sh",
   "mimetype": "text/x-sh",
   "name": "bash"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}


================================================
FILE: intro-to-r/binder/Dockerfile
================================================
FROM rocker/binder:3.6.3

COPY --chown=rstudio:rstudio ./intro-to-r ${HOME}


================================================
FILE: intro-to-r/data/mbta_bus.tsv
================================================
route	type	cost.per.pax	ridership	pax.per.trip
1	Key	0.63	13306	416
4	Commuter	4.38	459	21
5	Community	2.99	156	5
7	Local	1.52	3893	177
8	Local	2.02	3844	85
9	Local	1.21	5980	187
10	Local	1.75	3184	398
11	Local	1.84	3312	237
14	Local	2.66	1285	22
15	Key	1.11	6227	111
16	Local	0.99	5100	2550
17	Local	0.71	3461	115
18	Local	2.71	630	16
19	Local	1.07	3591	211
21	Local	0.57	4696	90
22	Key	1.05	8151	170
23	Key	1	11687	899
24	Local	1.3	1725	21
26	Local	0.8	1960	41
27	Local	1.13	929	22
28	Key	0.8	13550	387
29	Local	1.71	2242	90
30	Local	1.17	2230	60
31	Local	1.07	4506	90
32	Key	1.02	10130	103
33	Local	1.67	1206	33
34	Local	2.43	5959	60
35	Local	1.75	2368	103
36	Local	1.47	3245	54
37	Local	2.1	1481	53
38	Local	2.29	1074	21
39	Key	0.72	15018	406
40	Local	1.54	1360	22
41	Local	1.84	2336	10
42	Local	0.8	2995	73
43	Local	1.93	1885	209
44	Local	1.28	3440	63
45	Local	1.53	3326	47
47	Local	1.41	4539	60
50	Local	1.74	1226	19
51	Local	1.95	1956	29
52	Local	4.97	747	22
55	Local	2.34	956	7
57	Key	0.86	12081	263
59	Local	2.71	1363	17
60	Local	2.99	1418	709
62	Local	2.53	1609	34
64	Local	2.33	1826	37
65	Local	1.43	2250	30
66	Key	0.79	13630	147
67	Local	2.98	666	4
68	Local	2.23	491	8
69	Local	0.82	3131	10
70	Local	1.96	7021	195
71	Key	1.35	5488	157
72	Local	3.05	808	31
73	Key	1.75	5834	46
74	Local	2.34	1144	29
75	Local	1.88	556	4
76	Local	3.67	1156	17
77	Key	1.95	7253	165
78	Local	3.6	1550	20
79	Local	3.05	1054	12
80	Local	2.35	1932	23
83	Local	1.49	2257	9
84	Commuter	2	356	9
85	Local	1.82	599	33
86	Local	1.07	5844	76
87	Local	1.17	3612	82
88	Local	0.89	3883	75
89	Local	1.01	3801	29
90	Local	1.75	1225	7
91	Local	1.01	1840	22
92	Local	2.68	1251	13
93	Local	1.03	4632	39
94	Local	2.02	1429	33
95	Local	2.2	1737	25
96	Local	2.09	1850	19
97	Local	2.12	841	4
99	Local	2.45	1452	12
100	Local	2.31	953	5
101	Local	1.04	4878	49
104	Local	0.9	4008	43
105	Local	1.91	983	11
106	Local	1.52	2682	47
108	Local	1.17	3166	15
109	Local	1.04	3697	127
110	Local	1.51	2885	40
111	Key	1.06	11695	38
112	Local	2.98	1367	13
114	Local	0.83	483	1
116	Key	0.88	5077	26
117	Key	0.73	4584	38
119	Local	2.16	1101	10
120	Local	1.18	3071	20
121	Commuter	1.1	443	1
131	Local	3.12	633	5
132	Local	2.49	796	3
134	Local	2.21	2186	18
136	Local	3.18	1076	16
137	Local	2.91	1028	11
170	Commuter	5.56	87	22
171	Commuter	3.23	38	0
210	Local	3.06	807	6
211	Local	2.72	890	3
212	Local	2.62	268	3
214	Local	1.35	1371	6
215	Local	2.3	1668	8
216	Local	2.65	1204	13
217	Local	7.19	223	8
220	Local	3.92	1512	6
221	Local	3.55	105	1
222	Local	2.99	1605	7
225	Local	1.86	2859	15
230	Local	3.3	1532	12
236	Local	3.56	597	2
238	Local	2.15	1894	7
240	Local	2.68	2840	13
245	Local	4.2	551	28
325	Express	7.17	289	8
326	Express	3.87	471	8
350	Local	3.36	1849	21
351	Commuter	6.33	246	11
352	Express	3.14	382	11
354	Express	5.16	764	40
411	Local	1.91	1181	39
424	Commuter	2.35	225	3
426	Local	2.14	2006	334
428	Commuter	3.5	171	7
429	Local	3.11	1559	16
430	Local	2.37	1228	26
431	Local	1.74	141	2
434	Express	2.17	75	1
435	Local	4.22	767	11
436	Local	4.44	790	790
439	Commuter	9.76	88	5
441	Local	1.86	1442	8
442	Local	1.8	2112	45
448	Commuter	4.47	162	9
449	Commuter	4.71	181	7
450	Local	2.6	1549	20
451	Local	6.34	323	25
455	Local	1.47	1797	20
456	Local	3.38	259	3
459	Local	2.75	1085	16
465	Local	4.9	348	87
468	Local	5.89	30	3
501	Express	2.68	1639	18
502	Express	1.34	1180	16
503	Express	3.45	347	9
504	Express	3.23	1452	19
505	Express	3.95	1042	15
553	Local	3.23	789	34
554	Local	4.95	618	14
556	Commuter	3.66	490	4
558	Commuter	4.11	407	7
714	Local	2.55	228	4
716	Local	4.19	106	1
201.202	Local	2.26	1582	22
712.713	Local	2.72	1597	27
CT1	Local	0.94	2261	33
CT2	Local	1.8	2580	31
CT3	Local	5.11	708	17


================================================
FILE: intro-to-r/index.Rmd
================================================
---
title: "Intro to R"
subtitle: "Bioinformatics Coffee Hour"
date: "Augus 4, 2020"
author: "Danielle Khost"
output: html_document
---

##Setting up our working directory and saving objects vs files

R is a functional programming language, which means that most of what one does is apply functions to objects.

We will begin with a very brief introduction to R objects and how functions work, and then focus on getting data into R, manipulating that data in R, plotting, and analysis.

First, we need to make sure we are working out of the correct directory. You can see the working directory above the console in RStudio, or use the `getwd()` function to see the current working directory. If you are not currently working in a good location to read and save files, you can use `setwd()` to change the working directory to the location of your choice.

```{r}
getwd()
```


## Vectors

Let's start by creating one of the simplest R objects, a vector of numbers.
```{r}

```

v1 is an *object* which we created by using the ```<-``` operator (less followed by a dash).

v1 now contains the output of the *function* ```c(1,2,3,4,5)``` (c, for **c**ombined, combines elements into a vector

Let's display the contents of v1:
```{r}

```

We can also just type the name of an object (in this case ```v1```) to display it.

Let's make a few more objects:
```{r}

```
With this last variable, we had to use ```""``` to specify that we want to create a character vector. Otherwise it thinks we are looking for variables to combine called a,b,c and d.

```{r, eval=FALSE}

```


We might want to get a list of all the objects we've created at this point. In R, the function ls() returns a character vector with the names of all the objects in a specified environment (by default, the set of user-defined objects and functions).

```{r}
ls()
```


## Getting Help

R has very good built-in documentation that describes what functions do.

To get help about a particular function, use ? followed by the function name, like so:

?ls

If you are not exactly sure of the function name, you can perform a search:

??mean

We can manipulate objects like so:
```{r}

```

Note that the value of ```x``` is not modified here. We did not save the output to a new object, so it is printed to the screen.

If we want to update the value of ```x```, we need to use our assignment operator:
```{r}

```

R handles vector math for us automatically:
```{r}

```

> **Vector exercises:**
>
>1. Create a new vector,  (called nums), that contains any 5 numbers you like
>
>2. There are a number of functions that operate on vectors, including length(), max(), min(), and mean(), all of which do exactly what they sound like they do. So for example length(nums) should return 5, because nums is a 5-element vector. Use these functions to get the minimum, maximum, and mean value for the nums vector you created.


```{r}


```


## Object Types

All objects have a type. Object types are a complex topic and we are only going to scratch the surface today. To slightly simplify, all data objects in R are either atomic vectors (contain only a single type of data), or data structures that combine atomic vectors in various ways. We'll walk through a few examples. First, let's consider a couple of the vectors that we've already made.

```{r}

```

*Numeric* and *character* data types are two of the most common we'll encounter, and are just what they sound like. Another useful type is *logical* data, as in TRUE/FALSE. We can create a logical vector directly like so:
```{r}

```

Or we can use logical tests.
```{r}

```

Note that the logical test here (>2) is applied independently to each element in the vector. We'll come back to this when we talk about data subsets.

> **Object type exercises:**
>
>1. Construct a logical vector with the same number of elements as your nums vector, that is TRUE if the corresponding element in the nums vector is less than the mean of the nums vector, and FALSE otherwise. Call this vector big_nums


```{r}


```


## Subsetting and Logical Vectors

We can also extract portions of vectors that we've created using ```[]``` following the vector, with the positions we wish to extract. For example, we can extract portions of the nums vector:

```{r}

```

Now it can be useful to specify which positions you would like to include, but often it is more useful to create new objects based on some sort of rule. That is where logical vectors come in, and really how you will mostly be using logical vectors going forward.

For example, in the last exercise, we constructed a logical vector that was the same length as our nums vector that was ```TRUE``` with if the corresponding element in the nums vector is less than the mean of the nums vector, and ```FALSE``` otherwise.

Let's take a look at both of these vectors again.

```{r}

```

Let's say we actually want to extract all of the numbers that are bigger than the mean (so all of the ```TRUE```) values. All we have to do is put the logical vector in brackets.

```{r}

```

We don't necessarily have to define this vector ahead of time, but can put the rule in the brackets as well.

```{r}

```

> **Vector Subsetting Data exercises:**
>
> 1. Subset nums for all numbers that are larger than the minimum.
>
> Hints -- logical tests in R - greater than, less than, equal to, not equal to
>
>  > '> , < , == , !='

```{r}


```


## Data Frames

So far we've been talking about atomic vectors, which only contain a single data type (every element is logical, or character, or numeric). However, data sets will usually have multiple different data types: numeric for continunous data, character for categorical data and sample labels. Depending on how underlying types are combined, we can have four different "higher-level" data types in R:

**Dimensions** | **Homogeneous** | **Heterogeneous**
---------------|-----------------|-------------------
1-D            | atomic vector   | list
2-D            | matrix          | data frame / tibble

We'll focus on data frames for today, but lists and matrices can also be very powerful. A data frame is a collection of vectors, which can be (but don't have to be) different types, but all have to have the same length. Let's make a couple of toy data frames.

One way to do this is with the data.frame() function.

```{r}

```

str() gives lots of information about the data type of the constituent parts of a data frame
```{r}

```

We can use the function head() to look at part of a a dataframe (or any R object). This can be very useful if you have a very large or long dataframe. You also can control how many lines of the dataframe you view with ```n=#```, although the default is 6.

```{r}

```

The summary() function can also be very useful to get a snapshot of the data in your dataframe.

```{r}

```



###Reading files into R

So far we have been working with small objects we created by hand. A more common way to create dataframes is by reading from a file. There are a few functions to do this in R: read.table() and read.csv() make this easy.

```{r}

```


> **File reading exercise**
>
>1. Try using head() to see the first 20 rows and summary() to see some information about the data.


```{r}

```

Again, summary() is a good way to see some basic information about a dataframe

```{r}

```

The view function can be useful for viewing dataframes in a spread-sheet like format.

```{r, eval=FALSE}

```

### Subsetting and Manipulating Dataframes

We learned about how to subset vectors earlier, now let's look at ways to get subsets of data from two-dimensional data. R uses ```[]``` to index rows and columns, like so:

```{r, eval=FALSE}

```

The first number refers to rows, and the second to columns in the case for 2-dimensional objects.

Notice that when you are extracting data from a single column, the data are returned as a vector of the same type as the column. However, if you extract more than one column, the data are returend as a data frame.

```{r, eval=FALSE}

```


Often we want to select a set of rows from a data frame matching some criteria. We can use the subset() function for this. Let's start by just getting the data for key bus routes.

```{r}

```

Sometimes we might want something more complicated. Let's say we want to get data for all "local" bus routes with at least 1000 daily riders.

```{r}


```

> **Subsetting Data exercises:**
>
> 1. Subset bus when ridership is greater than 2500.
>
> 2. Subset bus when bus routes are NOT classified as Key.
>
> 3. Subset bus using the conditions of both #1 and #2, save as bus_2500_notKey
>
> Hints -- logical tests in R
>
>  > '> , < , == , !='
>
>  > '& is and -->> z <- x & y TRUE only when both x and y are true'
>
>  > '| is or -->> z <- x | y TRUE if either x or y is true'
>

```{r}

```


We can add and manipulate the data frame as well, but we will save that for the next session, and use some packages specifically built for that purpose.

## Writing Data

We've added several variables now, and we might want to write our updated dataset to a file. We do this with the write.table() function, which writes out a data.frame.

```{r}

```

But where did R put the file on our computer? R does all file operations without a full path in a working directory. RStudio has a preference to set the default working directory, which is typically something like /Users/Danielle/R.

To see the current working directory, use:
```{r}
getwd()
```

We can change the working directory with ```setwd()```.


>**File operators exercises:**
>
>1. Write a copy of bus_2500_notKey, but **be sure to give it a different name than the original!** Otherwise you will overwrite your original data.
>

```{r}

```


================================================
FILE: intro-to-r/instructor_copy.Rmd
================================================
---
title: "Intro to R"
subtitle: "Bioinformatics Coffee Hour"
date: "Augus 4, 2020"
author: "Danielle Khost"
output: html_document
---

##Setting up our working directory and saving objects vs files

R is a functional programming language, which means that most of what one does is apply functions to objects.

We will begin with a very brief introduction to R objects and how functions work, and then focus on getting data into R, manipulating that data in R, plotting, and analysis.

First, we need to make sure we are working out of the correct directory. You can see the working directory above the console in RStudio, or use the `getwd()` function to see the current working directory. If you are not currently working in a good location to read and save files, you can use `setwd()` to change the working directory to the location of your choice.

```{r}
getwd()
```


## Vectors

Let's start by creating one of the simplest R objects, a vector of numbers.
```{r}
v1<-c(1,2,3,4,5)
```

v1 is an *object* which we created by using the ```<-``` operator (less followed by a dash).

v1 now contains the output of the *function* ```c(1,2,3,4,5)``` (c, for **c**ombined, combines elements into a vector

Let's display the contents of v1:
```{r}
print(v1)
```

We can also just type the name of an object (in this case ```v1```) to display it.

Let's make a few more objects:
```{r}
x<-10
x

y<-11
y

some_letters<-c("a", "b", "c", "d")
```
With this last variable, we had to use ```""``` to specify that we want to create a character vector. Otherwise it thinks we are looking for variables to combine called a,b,c and d.

```{r, eval=FALSE}
some_letters <- c(a,b,c,d)
```


We might want to get a list of all the objects we've created at this point. In R, the function ls() returns a character vector with the names of all the objects in a specified environment (by default, the set of user-defined objects and functions).

```{r}
ls()
```


## Getting Help

R has very good built-in documentation that describes what functions do.

To get help about a particular function, use ? followed by the function name, like so:

?ls

If you are not exactly sure of the function name, you can perform a search:

??mean

We can manipulate objects like so:
```{r}
x+5
x*2
x
```

Note that the value of ```x``` is not modified here. We did not save the output to a new object, so it is printed to the screen.

If we want to update the value of ```x```, we need to use our assignment operator:
```{r}
x<-x+5
x
```

R handles vector math for us automatically:
```{r}
v1*x
v2<-v1*x
v2
```

> **Vector exercises:**
>
>1. Create a new vector,  (called nums), that contains any 5 numbers you like
>
>2. There are a number of functions that operate on vectors, including length(), max(), min(), and mean(), all of which do exactly what they sound like they do. So for example length(nums) should return 5, because nums is a 5-element vector. Use these functions to get the minimum, maximum, and mean value for the nums vector you created.


```{r}
#1
nums <- c(33, 22, 41, 54, 91)

#2
length(nums)
min(nums)
max(nums)
mean(nums)

```


## Object Types

All objects have a type. Object types are a complex topic and we are only going to scratch the surface today. To slightly simplify, all data objects in R are either atomic vectors (contain only a single type of data), or data structures that combine atomic vectors in various ways. We'll walk through a few examples. First, let's consider a couple of the vectors that we've already made.

```{r}
nums <- c(33, 22, 41, 54, 91)
class(nums)
class(some_letters)
```

*Numeric* and *character* data types are two of the most common we'll encounter, and are just what they sound like. Another useful type is *logical* data, as in TRUE/FALSE. We can create a logical vector directly like so:
```{r}
logic1<-c(TRUE, TRUE, FALSE, FALSE)
logic1
class(logic1)
```

Or we can use logical tests.
```{r}
logic2<-v1>2
logic2
```

Note that the logical test here (>2) is applied independently to each element in the vector. We'll come back to this when we talk about data subsets.

> **Object type exercises:**
>
>1. Construct a logical vector with the same number of elements as your nums vector, that is TRUE if the corresponding element in the nums vector is less than the mean of the nums vector, and FALSE otherwise. Call this vector big_nums


```{r}

#1
big_nums <- nums>mean(nums)

```


## Subsetting and Logical Vectors

We can also extract portions of vectors that we've created using ```[]``` following the vector, with the positions we wish to extract. For example, we can extract portions of the nums vector:

```{r}
nums[1]
nums[1:5]
nums[c(1,4,5)]
```

Now it can be useful to specify which positions you would like to include, but often it is more useful to create new objects based on some sort of rule. That is where logical vectors come in, and really how you will mostly be using logical vectors going forward.

For example, in the last exercise, we constructed a logical vector that was the same length as our nums vector that was ```TRUE``` with if the corresponding element in the nums vector is less than the mean of the nums vector, and ```FALSE``` otherwise.

Let's take a look at both of these vectors again.

```{r}
nums
big_nums
```

Let's say we actually want to extract all of the numbers that are bigger than the mean (so all of the ```TRUE```) values. All we have to do is put the logical vector in brackets.

```{r}
nums[big_nums]
```

We don't necessarily have to define this vector ahead of time, but can put the rule in the brackets as well.

```{r}
nums[nums>mean(nums)]
```

> **Vector Subsetting Data exercises:**
>
> 1. Subset nums for all numbers that are larger than the minimum.
>
> Hints -- logical tests in R - greater than, less than, equal to, not equal to
>
>  > '> , < , == , !='

```{r}
#1
nums[nums>min(nums)]
```


## Data Frames

So far we've been talking about atomic vectors, which only contain a single data type (every element is logical, or character, or numeric). However, data sets will usually have multiple different data types: numeric for continunous data, character for categorical data and sample labels. Depending on how underlying types are combined, we can have four different "higher-level" data types in R:

**Dimensions** | **Homogeneous** | **Heterogeneous**
---------------|-----------------|-------------------
1-D            | atomic vector   | list
2-D            | matrix          | data frame / tibble

We'll focus on data frames for today, but lists and matrices can also be very powerful. A data frame is a collection of vectors, which can be (but don't have to be) different types, but all have to have the same length. Let's make a couple of toy data frames.

One way to do this is with the data.frame() function.

```{r}
df1<-data.frame(label=c("rep1", "rep2", "rep3", "rep4"), data=c(23, 34, 15, 19))
df1
class(df1)
```

str() gives lots of information about the data type of the consituent parts of a data frame
```{r}
str(df1)
```

We can use the function head() to look at part of a a dataframe (or any R object). This can be very useful if you have a very large or long dataframe. You also can control how many lines of the dataframe you view with ```n=#```, although the default is 6.

```{r}
head(df1)
head(df1, n=2)
```

The summary() function can also be very useful to get a snapshot of the data in your dataframe.

```{r}
summary(df1)
```



###Reading files into R

So far we have been working with small objects we created by hand. A more common way to create dataframes is by reading from a file. There are a few functions to do this in R: read.table() and read.csv() make this easy.

```{r}
bus<-read.table(file="/home/rstudio/data/mbta_bus.tsv", sep="\t", header=TRUE)
```


> **File reading exercise**
>
>1. Try using head() to see the first 20 rows and summary() to see some information about the data.


```{r}
#2
head(bus, n=20)
```

Again, summary() is a good way to see some basic information about a dataframe

```{r}
summary(bus)
```

The view function can be useful for viewing dataframes in a spread-sheet like format.

```{r, eval=FALSE}
View(bus)
```

### Subsetting and Manipulating Dataframes

We learned about how to subset vectors earlier, now let's look at ways to get subsets of data from two-dimensional data. R uses ```[]``` to index rows and columns, like so:

```{r, eval=FALSE}
bus[1,1]
bus[1,]
bus[1:5,]
bus[1:5,4]
bus[c(1,3,5),c(2,3)]
```

The first number refers to rows, and the second to columns in the case for 2-dimensional objects.

Notice that when you are extracting data from a single column, the data are returned as a vector of the same type as the column. However, if you extract more than one column, the data are returend as a data frame.

```{r, eval=FALSE}
str(bus[1,1])
str(bus[1:5,3])
str(bus[1:3,1:3])
```


Often we want to select a set of rows from a data frame matching some criteria. We can use the subset() function for this. Let's start by just getting the data for key bus routes.

```{r}
key.bus<-subset(bus,type=="Key")
View(key.bus)
```

Sometimes we might want something more complicated. Let's say we want to get data for all "local" bus routes with at least 1000 daily riders.

```{r}
local.bus<-subset(bus, type=="Local" & ridership > 1000)
View(local.bus)
```

> **Subsetting Data exercises:**
>
> 1. Subset bus when ridership is greater than 2500.
>
> 2. Subset bus when bus routes are NOT classified as Key.
>
> 3. Subset bus using the conditions of both #1 and #2, save as bus_2500_notKey
>
> Hints -- logical tests in R
>
>  > '> , < , == , !='
>
>  > '& is and -->> z <- x & y TRUE only when both x and y are true'
>
>  > '| is or -->> z <- x | y TRUE if either x or y is true'
>

```{r}
#1
subset(bus,ridership>2500)
#2
subset(bus,type!="Key")
#3
bus_2500_notKey <- subset(bus, ridership>2500 & type != "Key")
```


We can add and manipulate the data frame as well, but we will save that for the next session, and use some packages specifically built for that purpose.

## Writing Data

We've added several variables now, and we might want to write our updated dataset to a file. We do this with the write.table() function, which writes out a data.frame.

```{r}
write.table(bus, file="mbta_bus_data.tsv", quote=F, sep="\t", row.names=F, col.names=T)
```

But where did R put the file on our computer? R does all file operations without a full path in a working directory. RStudio has a preference to set the default working directory, which is typically something like /Users/Danielle/R.

To see the current working directory, use:
```{r}
getwd()
```

We can change the working directory with ```setwd()```.


>**File operators exercises:**
>
>1. Write a copy of bus_2500_notKey, but **be sure to give it a different name than the original!** Otherwise you will overwrite your original data.
>

```{r}
write.table(bus_2500_notKey,file="bus_2500_notKey.tsv",quote=F,sep="\t",row.names=F,col.names=F)
```


================================================
FILE: intro_awk_spring2021/.ipynb_checkpoints/index-checkpoint.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Manipulating files with AWK\n",
    "### Bioinformatics Coffee Hour - April 14, 2020\n",
    "\n",
    "### What is awk?\n",
    "Invented in the 1970's, [awk](https://en.wikipedia.org/wiki/AWK) is a scripting language included in most Unix-like operating systems. It specializes in one-liner programs and manipulating text files.\n",
    "\n",
    "In many cases, if you're parsing information from a text file (such as a [BED](https://en.wikipedia.org/wiki/BED_(file_format)) file, [FASTA](https://en.wikipedia.org/wiki/FASTA) file, etc.), you could write a Python script...or you could do it with awk in a single line!\n",
    "\n",
    "### Syntax\n",
    "awk scripts are organized as:\n",
    "\n",
    "`awk 'pattern { action; other action }' file`\n",
    "\n",
    "Meaning that every time that the pattern is true, awk will execute the action in the brackets.\n",
    "If no pattern is specified, the action will be taken for every line in the input file, e.g. the following command prints every line:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "awk '{print}' data/hg38.genome | head"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The two most important patterns are `BEGIN` and `END`, which tell the action to take place before any lines are read and after the last line."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "awk 'BEGIN{sum=0} {sum+=1} END {print sum}' data/hg38.genome"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " The above line sets a variable at the start of the script, adds 1 to it every line, then prints its value at the end."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If a variable hasn't been initialized, it is treated as 0 in numeric expressions, and an empty string in string expressions&mdash;awk will not print an error!\n",
    "So the following awk script also prints the number of lines in the file data/hg38.genome:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "awk '{sum+=1} END {print sum}' data/hg38.genome"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Input and output\n",
    "Input to awk is split into **records** and **fields**.\n",
    "- By default, **records** are separated by newline character, i.e # of records = # of lines in input file\n",
    "- Each record is subdivided into **fields**, i.e. columns, as determined by the field separator (see below)\n",
    "\n",
    "There are several important built-in variable in awk.\n",
    "The fields (columns) of each record are referred to by `$number`, so the first column would be `$1`, second would be `$2`, etc. `$0` refers to the entire record.\n",
    "\n",
    "So to print the second column of each line in the file, we'd use:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "awk '{print $2}' data/hg38.genome | head"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And if we wanted to print the second then the first:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "awk '{print $2,$1}' data/hg38.genome | head"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that when the different fields are separated with commas in the `print` statement, they are joined by the output field separator (the **OFS** variable, described below), which is by default a space.\n",
    "If the comma is omitted between fields (e.g., `awk '{print $2 $1}'`, they are concatenated without a separator.\n",
    "\n",
    "We can also print strings using using quotation marks:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "awk '{print \"First column:\" $1}' data/hg38.genome | head"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Which for every line of the file will print the text \"First column:\" followed by the value in the first field."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "awk has several other built-in variables that are very useful for parsing text, including:\n",
    "\n",
    "|  |   |\n",
    "---|---|\n",
    "| **FS** | field separator (default: white space) |\n",
    "| **OFS** |  output field separator, i.e. what character separates fields when printing|\n",
    "| **RS** | record separator, i.e. what character records are split on (default: new line) |\n",
    "| **ORS** | output record separator |\n",
    "| **NR** | number of records in input (# lines by default) |\n",
    "\n",
    "Assigning to a field causes the entire record ($0) to be recomputed using **OFS**. We can use this to convert between file formats, e.g. make a comma-separated text file into a tab-separated file. First, let's look at the first few lines on our comma-separated file using `head`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "head data/enhancers.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's convert the file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "awk 'BEGIN{FS=\",\" ; OFS=\"\\t\"} {$1 = $1; print $0}' data/enhancers.csv | head"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conditionals and pattern matching\n",
    "Like other programming languages, awk allows conditional matching with if/else statements.\n",
    "\n",
    "`awk '{if(condition) action; else other_action}'`\n",
    "\n",
    "awk uses the following conditional operators:\n",
    "\n",
    "| | |\n",
    "|-|-|\n",
    "|==|equal to|\n",
    "|!=|not equal to|\n",
    "|>|greater than|\n",
    "|>=|greater than or equal to|\n",
    "|<|less than|\n",
    "|<=|less than or equal to|\n",
    "|&&|AND|\n",
    "| \\|\\| |OR|\n",
    "| ! | NOT |\n",
    "\n",
    "In addition, awk also supports string matching using regular expressions, using the following expressions:\n",
    "\n",
    "| | |\n",
    "|-|-|\n",
    "|\\~|matches|\n",
    "|!~|does not match|\n",
    "\n",
    "For string matching, the pattern being matched must be enclosed by slashes, like so:\n",
    "\n",
    "`awk '{if($1 ~ /pattern/) print}'`\n",
    "\n",
    "Note that if an action isn't specified, the default action is `{print}`, so the previous awk command is equivalent to the following, which specifies only a pattern expression:\n",
    "\n",
    "`awk '$1 ~ /pattern/'`\n",
    "\n",
    "---\n",
    "### Example uses:\n",
    "\n",
    "- Count number of sequences in a FASTQ file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "awk 'END{print NR/4}' data/example.fastq"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note**: this is technically safer than using grep, as you don't have to worry about accidentally counting the quality line.\n",
    "\n",
    "- Only print annotations on a specific scaffold (chr1) that fall between 1Mb and 2Mb from a BED annotation file. First let's look at the first few lines of the file using `head`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "head data/Homo_sapiens_ucscGenes.subset.bed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now subset the file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "awk 'BEGIN{FS=\"\\t\";OFS=\"\\t\"} {if($1 == \"chr1\" && $2 >=1000000 && $2 <= 2000000) print}' data/Homo_sapiens_ucscGenes.subset.bed | head"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note**: when we specify that we only want annotations from chr1, we're using exact match (`== \"chr1\"`) and not pattern match (`~ /chr1/`)...why is this??"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Only print lines of GFF annotation file that match the string \"exon\" in their third column. Again, let's look at the first few lines of the file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "head data/Homo_sapiens.GRCh38.subset.gff3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then subset the file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "awk 'BEGIN{FS=\"\\t\"} {if($3 ~ /exon/) print $0}' data/Homo_sapiens.GRCh38.subset.gff3 | head"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Convert from GFF (genome feature file) to BED file "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "grep -v '^#' data/Homo_sapiens.GRCh38.subset.gff3 | awk 'BEGIN{FS=\"\\t\"; OFS=\"\\t\"} {print $1,$4-1,$5}' | head"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note**: remember that BED and GFF files have different coordinate systems, i.e. BED start coordinate is 0 based, half-open, GFF is 1-based inclusive! Also, we are first using grep to skip the header lines in the GFF file.\n",
    "\n",
    "Alternatively, you could do the whole thing with only awk, no grep required:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "awk 'BEGIN{FS=\"\\t\"; OFS=\"\\t\"} !/^#/ {print $1,$4-1,$5}' data/Homo_sapiens.GRCh38.subset.gff3 | head"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With this command, `!/^#/` is a pattern (like `BEGIN` or `END`) that tell awk to execute the print statement when the start of the line does not match a `#`. Use whichever makes the most sense to you!\n",
    "\n",
    "### Practice\n",
    "Using awk:\n",
    "\n",
    "* Pull out only the CDS annotations (i.e. has \"CDS\" in the 3rd column) from the GFF file data/Homo_sapiens.GRCh38.subset.gff3 and output them in BED format"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Try it*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "awk '...' data/Homo_sapiens.GRCh38.subset.gff3 | head"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Solution*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "awk 'BEGIN{FS=\"\\t\"; OFS=\"\\t\"} {if($3 ~ /CDS/) print $1,$4-1,$5}' data/Homo_sapiens.GRCh38.subset.gff3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Calculate the average length of gene annotations from the file data/Homo_sapiens_ucscGenes.subset.bed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Try it*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "awk '...' data/Homo_sapiens_ucscGenes.subset.bed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Solution*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "awk 'BEGIN{FS=\"\\t\"; sum=0} {len=$3-$2; sum=sum+len} END{print sum/NR}' data/Homo_sapiens_ucscGenes.subset.bed"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}


================================================
FILE: intro_awk_spring2021/binder/.ipynb_checkpoints/environment-checkpoint.yml
================================================
dependencies:
  - bash_kernel


================================================
FILE: intro_awk_spring2021/binder/environment.yml
================================================
dependencies:
  - bash_kernel


================================================
FILE: intro_awk_spring2021/data/.ipynb_checkpoints/Homo_sapiens.GRCh38.subset-checkpoint.gff3
================================================
##gff-version 3
##sequence-region   1 1 248956422
1	Ensembl	chromosome	1	248956422	.	.	.	ID=chromosome:1
1	.	biological_region	10469	11240	1.3e+03	.	.	external_name=oe %3D 0.79
1	.	biological_region	10650	10657	0.999	+	.	logic_name=eponine
1	.	biological_region	10655	10657	0.999	-	.	logic_name=eponine
1	.	biological_region	10678	10687	0.999	+	.	logic_name=eponine
1	.	biological_region	10681	10688	0.999	-	.	logic_name=eponine
1	.	biological_region	10707	10716	0.999	+	.	logic_name=eponine
1	.	biological_region	10708	10718	0.999	-	.	logic_name=eponine
1	.	biological_region	10735	10747	0.999	-	.	logic_name=eponine
1	.	biological_region	10737	10744	0.999	+	.	logic_name=eponine
1	.	biological_region	10766	10773	0.999	+	.	logic_name=eponine
1	.	biological_region	10770	10779	0.999	-	.	logic_name=eponine
1	.	biological_region	10796	10801	0.999	+	.	logic_name=eponine
1	.	biological_region	10810	10819	0.999	-	.	logic_name=eponine
1	.	biological_region	10870	10872	0.999	+	.	logic_name=eponine
1	.	biological_region	10889	10893	0.999	-	.	logic_name=eponine
1	havana	pseudogene	11869	14409	.	+	.	ID=gene:ENSG00000223972
1	havana	lnc_RNA	11869	14409	.	+	.	ID=transcript:ENST00000456328
1	havana	exon	11869	12227	.	+	.	Parent=transcript:ENST00000456328
1	havana	exon	12613	12721	.	+	.	Parent=transcript:ENST00000456328
1	havana	exon	13221	14409	.	+	.	Parent=transcript:ENST00000456328
1	havana	pseudogenic_transcript	12010	13670	.	+	.	ID=transcript:ENST00000450305
1	havana	exon	12010	12057	.	+	.	Parent=transcript:ENST00000450305
1	havana	exon	12179	12227	.	+	.	Parent=transcript:ENST00000450305
1	havana	exon	12613	12697	.	+	.	Parent=transcript:ENST00000450305
1	havana	exon	12975	13052	.	+	.	Parent=transcript:ENST00000450305
1	havana	exon	13221	13374	.	+	.	Parent=transcript:ENST00000450305
1	havana	exon	13453	13670	.	+	.	Parent=transcript:ENST00000450305
1	havana	pseudogene	14404	29570	.	-	.	ID=gene:ENSG00000227232
1	havana	pseudogenic_transcript	14404	29570	.	-	.	ID=transcript:ENST00000488147
1	havana	exon	14404	14501	.	-	.	Parent=transcript:ENST00000488147
1	havana	exon	15005	15038	.	-	.	Parent=transcript:ENST00000488147
1	havana	exon	15796	15947	.	-	.	Parent=transcript:ENST00000488147
1	havana	exon	16607	16765	.	-	.	Parent=transcript:ENST00000488147
1	havana	exon	16858	17055	.	-	.	Parent=transcript:ENST00000488147
1	havana	exon	17233	17368	.	-	.	Parent=transcript:ENST00000488147
1	havana	exon	17606	17742	.	-	.	Parent=transcript:ENST00000488147
1	havana	exon	17915	18061	.	-	.	Parent=transcript:ENST00000488147
1	havana	exon	18268	18366	.	-	.	Parent=transcript:ENST00000488147
1	havana	exon	24738	24891	.	-	.	Parent=transcript:ENST00000488147
1	havana	exon	29534	29570	.	-	.	Parent=transcript:ENST00000488147
1	.	biological_region	15796	16060	0.999	-	.	external_name=rank %3D 1
1	mirbase	ncRNA_gene	17369	17436	.	-	.	ID=gene:ENSG00000278267
1	mirbase	miRNA	17369	17436	.	-	.	ID=transcript:ENST00000619216
1	mirbase	exon	17369	17436	.	-	.	Parent=transcript:ENST00000619216
1	.	biological_region	28736	29810	1.01e+03	.	.	external_name=oe %3D 0.88
1	.	biological_region	29116	29118	0.999	+	.	logic_name=eponine
1	.	biological_region	29127	29206	1	+	.	external_name=rank %3D 1
1	.	biological_region	29321	29395	1	-	.	external_name=rank %3D 1
1	.	biological_region	29394	29396	0.999	-	.	logic_name=eponine
1	.	biological_region	29448	29451	0.999	+	.	logic_name=eponine
1	havana	ncRNA_gene	29554	31109	.	+	.	ID=gene:ENSG00000243485
1	havana	lnc_RNA	29554	31097	.	+	.	ID=transcript:ENST00000473358
1	havana	exon	29554	30039	.	+	.	Parent=transcript:ENST00000473358
1	havana	exon	30564	30667	.	+	.	Parent=transcript:ENST00000473358
1	havana	exon	30976	31097	.	+	.	Parent=transcript:ENST00000473358
1	havana	lnc_RNA	30267	31109	.	+	.	ID=transcript:ENST00000469289
1	havana	exon	30267	30667	.	+	.	Parent=transcript:ENST00000469289
1	havana	exon	30976	31109	.	+	.	Parent=transcript:ENST00000469289
1	.	biological_region	29583	29584	0.999	-	.	logic_name=eponine
1	mirbase	ncRNA_gene	30366	30503	.	+	.	ID=gene:ENSG00000284332
1	mirbase	miRNA	30366	30503	.	+	.	ID=transcript:ENST00000607096
1	mirbase	exon	30366	30503	.	+	.	Parent=transcript:ENST00000607096
1	havana	ncRNA_gene	34554	36081	.	-	.	ID=gene:ENSG00000237613
1	havana	lnc_RNA	34554	36081	.	-	.	ID=transcript:ENST00000417324
1	havana	exon	34554	35174	.	-	.	Parent=transcript:ENST00000417324
1	havana	exon	35277	35481	.	-	.	Parent=transcript:ENST00000417324
1	havana	exon	35721	36081	.	-	.	Parent=transcript:ENST00000417324
1	havana	lnc_RNA	35245	36073	.	-	.	ID=transcript:ENST00000461467
1	havana	exon	35245	35481	.	-	.	Parent=transcript:ENST00000461467
1	havana	exon	35721	36073	.	-	.	Parent=transcript:ENST00000461467
1	.	biological_region	35904	36086	0.879	-	.	external_name=rank %3D 1
1	havana	pseudogene	52473	53312	.	+	.	ID=gene:ENSG00000268020
1	havana	pseudogenic_transcript	52473	53312	.	+	.	ID=transcript:ENST00000606857
1	havana	exon	52473	53312	.	+	.	Parent=transcript:ENST00000606857
1	havana	pseudogene	57598	64116	.	+	.	ID=gene:ENSG00000240361
1	havana	lnc_RNA	57598	64116	.	+	.	ID=transcript:ENST00000642116
1	havana	exon	57598	57653	.	+	.	Parent=transcript:ENST00000642116
1	havana	exon	58700	58856	.	+	.	Parent=transcript:ENST00000642116
1	havana	exon	62916	64116	.	+	.	Parent=transcript:ENST00000642116
1	havana	pseudogenic_transcript	62949	63887	.	+	.	ID=transcript:ENST00000492842
1	havana	exon	62949	63887	.	+	.	Parent=transcript:ENST00000492842
1	ensembl_havana	gene	65419	71585	.	+	.	ID=gene:ENSG00000186092
1	havana	mRNA	65419	71585	.	+	.	ID=transcript:ENST00000641515
1	havana	exon	65419	65433	.	+	.	Parent=transcript:ENST00000641515
1	havana	five_prime_UTR	65419	65433	.	+	.	Parent=transcript:ENST00000641515
1	havana	exon	65520	65573	.	+	.	Parent=transcript:ENST00000641515
1	havana	five_prime_UTR	65520	65573	.	+	.	Parent=transcript:ENST00000641515
1	havana	five_prime_UTR	69037	69090	.	+	.	Parent=transcript:ENST00000641515
1	havana	exon	69037	71585	.	+	.	Parent=transcript:ENST00000641515
1	havana	CDS	69091	70008	.	+	0	ID=CDS:ENSP00000493376
1	havana	three_prime_UTR	70009	71585	.	+	.	Parent=transcript:ENST00000641515
1	ensembl	mRNA	69055	70108	.	+	.	ID=transcript:ENST00000335137
1	ensembl	five_prime_UTR	69055	69090	.	+	.	Parent=transcript:ENST00000335137
1	ensembl	exon	69055	70108	.	+	.	Parent=transcript:ENST00000335137
1	ensembl	CDS	69091	70008	.	+	0	ID=CDS:ENSP00000334393
1	ensembl	three_prime_UTR	70009	70108	.	+	.	Parent=transcript:ENST00000335137
1	ensembl_havana	ncRNA_gene	89295	133723	.	-	.	ID=gene:ENSG00000238009
1	havana	lnc_RNA	89295	120932	.	-	.	ID=transcript:ENST00000466430
1	havana	exon	89295	91629	.	-	.	Parent=transcript:ENST00000466430


================================================
FILE: intro_awk_spring2021/data/.ipynb_checkpoints/Homo_sapiens_ucscGenes.subset-checkpoint.bed
================================================
chr1	11873	14409	uc010nxq.1
chr1	14361	19759	uc009viu.3
chr1	14406	29370	uc009viw.2
chr1	34610	36081	uc001aak.3
chr1	69090	70008	uc001aal.1
chr1	134772	140566	uc021oeg.2
chr1	321083	321115	uc001aaq.2
chr1	321145	321207	uc001aar.2
chr1	322036	326938	uc009vjk.2
chr1	327545	328439	uc021oei.1
chr1	367658	368597	uc010nxu.2
chr1	420205	421839	uc001aax.1
chr1	566092	566115	uc021oej.1
chr1	566134	566155	uc021oek.1
chr1	566239	566263	uc021oel.1
chr1	568843	568913	uc001abb.3
chr1	621095	622034	uc010nxv.2
chr1	661138	670994	uc009vjm.3
chr1	668417	668479	uc001abi.2
chr1	668509	668541	uc001abj.3
chr1	671823	671885	uc010nxw.2
chr1	671915	671947	uc001abl.3
chr1	674239	679736	uc001abm.2
chr1	700244	714068	uc001abo.3
chr1	761585	762902	uc010nxx.2
chr1	762970	794826	uc001abp.2
chr1	803450	812182	uc001abt.4
chr1	846814	850328	uc001abu.1
chr1	852952	854817	uc010nxy.1
chr1	861120	879961	uc001abw.1
chr1	879582	894679	uc001abz.4
chr1	895966	901099	uc001aca.2
chr1	901876	910484	uc001acd.3
chr1	910578	917473	uc001ach.2
chr1	934341	935552	uc001aci.2
chr1	948846	949919	uc001acj.4
chr1	955502	991499	uc001ack.2
chr1	995116	1001833	uc001acl.1
chr1	1007125	1009687	uc021oen.1
chr1	1017197	1051736	uc001acu.2
chr1	1072396	1079434	uc001acv.3
chr1	1102483	1102578	uc001acw.2
chr1	1103242	1103332	uc010nye.1
chr1	1103295	1103317	uc031pkr.1
chr1	1104384	1104467	uc010nyf.1
chr1	1104434	1104456	uc031pks.1
chr1	1108435	1114935	uc001acx.1
chr1	1109285	1133313	uc001acy.2
chr1	1138887	1142089	uc001add.3
chr1	1146705	1149548	uc001ade.3
chr1	1152287	1167447	uc001adh.4
chr1	1167628	1170420	uc001adk.3
chr1	1177825	1182102	uc001adl.2
chr1	1189291	1209234	uc001ado.3
chr1	1215815	1227409	uc001adt.1
chr1	1227763	1243269	uc001aeb.2
chr1	1243993	1247057	uc001aed.3
chr1	1246964	1260067	uc001aee.2
chr1	1260142	1264276	uc001aeo.3
chr1	1266725	1269844	uc010nyk.2
chr1	1270657	1284492	uc001aer.4
chr1	1288070	1293915	uc001aew.3
chr1	1309109	1310562	uc009vkb.1
chr1	1321090	1334718	uc001afi.2
chr1	1334909	1337426	uc001afm.3
chr1	1337275	1342693	uc001afo.4
chr1	1353799	1356824	uc010nyo.2
chr1	1361507	1363167	uc010nyp.2
chr1	1370902	1378262	uc001afs.3
chr1	1385068	1405538	uc001aft.2
chr1	1407163	1431582	uc001afv.3
chr1	1447522	1470067	uc001afz.2
chr1	1470157	1475740	uc009vkf.3
chr1	1477052	1510262	uc001agd.3
chr1	1510354	1510644	uc021oer.1
chr1	1533387	1535476	uc021oes.1
chr1	1535818	1543166	uc001agf.1
chr1	1550794	1565990	uc001agg.3
chr1	1567559	1570030	uc001agp.3
chr1	1571099	1655775	uc001agv.1
chr1	1586822	1590469	uc001ahc.1
chr1	1592938	1624243	uc001ahg.4
chr1	1631377	1633247	uc001ahi.1
chr1	1656053	1663343	uc001ahx.2
chr1	1658823	1677438	uc001aia.2
chr1	1682670	1711508	uc001aie.3
chr1	1716724	1822526	uc001aif.3
chr1	1846265	1848733	uc001aih.1
chr1	1849028	1850740	uc001aij.2
chr1	1853395	1858842	uc001aik.3
chr1	1884751	1935276	uc001aim.1
chr1	1944651	1946969	uc001aio.1
chr1	1950767	1962192	uc001aip.2
chr1	1981908	2116834	uc001aiq.3
chr1	2112574	2114663	uc001aiu.1
chr1	2115898	2126214	uc031pkt.1
chr1	2121236	2123179	uc001aiz.2
chr1	2160133	2241652	uc001aja.4
chr1	2252695	2322993	uc001ajb.1
chr1	2281852	2284100	uc001ajc.3
chr1	2309493	2322993	uc010nyy.2
chr1	2323213	2336885	uc001aje.2
chr1	2336240	2344010	uc001ajg.3
chr1	2407753	2436964	uc001aji.1
chr1	2439974	2458035	uc001ajm.1
chr1	2460183	2461684	uc001ajn.3
chr1	2481358	2484284	uc001ajo.2
chr1	2486162	2488450	uc021oev.1
chr1	2487804	2495188	uc001ajt.1
chr1	2487804	2495267	uc001ajr.3
chr1	2518188	2522908	uc001ajv.2
chr1	2522080	2564481	uc001ajy.2
chr1	2572806	2706230	uc021oey.1
chr1	2938045	2939467	uc001ajz.3
chr1	2976180	2980350	uc001aka.3
chr1	2980635	2984289	uc010nzg.1
chr1	2985741	3355185	uc001akf.3
chr1	3044538	3044599	uc021oez.1
chr1	3371146	3397677	uc001akg.4
chr1	3404505	3528059	uc001akl.3
chr1	3477259	3477354	uc021ofa.1
chr1	3541555	3546694	uc001akm.3
chr1	3547330	3566671	uc001ako.3
chr1	3569128	3652765	uc001akp.3
chr1	3652547	3663937	uc009vlm.3
chr1	3668964	3688209	uc001akv.2
chr1	3689351	3692546	uc001akw.4
chr1	3696783	3713068	uc001akx.1
chr1	3728644	3773797	uc001aky.2
chr1	3773844	3801993	uc001alc.3
chr1	3805696	3816857	uc001alf.3
chr1	3816967	3832011	uc001alg.3
chr1	4000671	4012643	uc001ali.2
chr1	4472110	4484744	uc001alj.2
chr1	4715104	4843851	uc001aln.3
chr1	4847557	4852183	uc001alo.4
chr1	5621768	5728315	uc001alp.1
chr1	5624130	5624203	uc021ofm.1
chr1	5922731	5922801	uc021ofn.1
chr1	5922869	6052533	uc001alq.2
chr1	6105980	6161253	uc001aly.2
chr1	6161846	6240194	uc001amb.2
chr1	6245079	6259679	uc001amd.3
chr1	6266188	6281359	uc001amg.3
chr1	6281252	6296044	uc001amk.3
chr1	6297870	6299502	uc001amm.3
chr1	6304251	6305638	uc009vly.2
chr1	6307405	6321035	uc001amp.2
chr1	6324331	6453826	uc001amt.3
chr1	6475293	6479979	uc001amx.3
chr1	6484847	6521004	uc001amy.3
chr1	6489893	6489956	uc021ofo.1
chr1	6521213	6526255	uc001anh.3
chr1	6526151	6545529	uc010nzr.1
chr1	6581406	6614658	uc001ans.3
chr1	6615337	6639817	uc001ant.3
chr1	6640062	6649340	uc001anx.3
chr1	6650783	6662929	uc001aoa.3
chr1	6673755	6684093	uc001aob.4
chr1	6685209	6693642	uc001aod.3
chr1	6694227	6761966	uc001aof.2
chr1	6845383	7829766	uc001aoi.3
chr1	7831328	7841492	uc001aol.3
chr1	7844713	7905237	uc001aoo.3
chr1	7907671	7913565	uc001aos.3
chr1	7975930	8003225	uc001aot.3
chr1	7990338	7990408	uc021ofs.1
chr1	8021713	8045342	uc001aox.4
chr1	8071778	8086393	uc001aoz.3
chr1	8384389	8404227	uc001apb.3
chr1	8412463	8877699	uc001apf.3
chr1	8440651	8441235	uc001apg.1
chr1	8921058	8939151	uc001apj.2
chr1	8938893	8939943	uc021oft.1
chr1	9005892	9034503	uc031plc.1
chr1	9063358	9086404	uc009vmo.1
chr1	9097004	9129887	uc001apo.3
chr1	9164475	9189229	uc001apq.1
chr1	9186983	9189250	uc001aps.3
chr1	9208345	9242451	uc009vmq.3
chr1	9294862	9331394	uc001apt.3
chr1	9352940	9429590	uc010oae.2
chr1	9497727	9497837	uc021ofx.1
chr1	9599527	9642831	uc001apw.3
chr1	9648931	9674935	uc021ofy.1
chr1	9711789	9789172	uc001aqb.4
chr1	9712667	9714644	uc001aqc.4
chr1	9740902	9747627	uc021oga.1
chr1	9789078	9884550	uc001aqh.3
chr1	9908333	9970316	uc001aql.1
chr1	9989775	10002840	uc001aqm.3
chr1	10003485	10045556	uc001aqp.3
chr1	10057254	10076078	uc001aqq.3
chr1	10093040	10241296	uc001aqs.4
chr1	10270763	10441661	uc001aqw.4
chr1	10459084	10480201	uc001arc.3
chr1	10490158	10512060	uc021ogd.1
chr1	10520602	10532613	uc001arj.3
chr1	10535002	10690815	uc001arn.3
chr1	10696665	10856733	uc001aro.4
chr1	10744633	10744736	uc021ogh.1
chr1	11006529	11042094	uc010oao.2
chr1	11072678	11085549	uc001art.3
chr1	11086579	11107296	uc001aru.3
chr1	11114648	11120091	uc001arz.1
chr1	11126675	11159938	uc001asa.3
chr1	11166587	11322608	uc001asd.3
chr1	11203954	11209595	uc031plf.1
chr1	11249345	11256038	uc001ase.4
chr1	11333254	11348491	uc001asg.3
chr1	11539294	11597640	uc001ash.4
chr1	11708417	11714888	uc001asj.3
chr1	11714913	11723384	uc001asm.3
chr1	11724149	11734409	uc001aso.3
chr1	11734536	11751678	uc009vnc.3
chr1	11751780	11780336	uc001asr.1
chr1	11782186	11785914	uc001ass.2
chr1	11796141	11810828	uc001asv.3
chr1	11824461	11826573	uc001asy.1
chr1	11832138	11849642	uc001asz.3
chr1	11845786	11866160	uc001atc.2
chr1	11866152	11903201	uc001ate.5
chr1	11905766	11907840	uc001ati.3
chr1	11917520	11918992	uc001atj.3
chr1	11979644	11986485	uc001atk.3
chr1	11994723	12035599	uc001atm.3
chr1	12040237	12073572	uc009vni.3
chr1	12079298	12092106	uc001ato.2
chr1	12123433	12204264	uc001atq.3
chr1	12227059	12269277	uc001att.3
chr1	12251761	12251839	uc021ogi.1
chr1	12290112	12572098	uc001atv.3
chr1	12567299	12567451	uc001atz.1
chr1	12627938	12677820	uc001auc.3
chr1	12704565	12727097	uc001auf.3
chr1	12776117	12788726	uc009vnn.1
chr1	12806162	12821102	uc001auh.3
chr1	12834983	12838048	uc001aui.3
chr1	12851545	12856777	uc001auj.2
chr1	12884467	12891264	uc001auk.2
chr1	12907235	12908237	uc009vno.2
chr1	12916940	12921764	uc001aum.1
chr1	12939032	12946025	uc001aun.2
chr1	12952726	12958094	uc001auo.3
chr1	12976449	12980568	uc001aup.3
chr1	12998301	13007406	uc001auq.2
chr1	13035542	13038381	uc009vnq.1
chr1	13182959	13184326	uc010obg.2
chr1	13328195	13331692	uc001aut.1
chr1	13359818	13369057	uc001auu.1
chr1	13386646	13390765	uc001auv.3
chr1	13421175	13428191	uc001auw.1
chr1	13447413	13452656	uc010obi.1
chr1	13474052	13477569	uc009vnu.1
chr1	13495253	13498259	uc001aux.3
chr1	13516065	13526943	uc009vnv.1
chr1	13607430	13611550	uc001auy.2
chr1	13629937	13635299	uc001auz.4
chr1	13641972	13648988	uc001ava.1
chr1	13694888	13698405	uc009vny.1
chr1	13716087	13719064	uc009vnz.1
chr1	13736906	13747803	uc009voa.1
chr1	13801444	13840242	uc001avb.3
chr1	13910251	13944452	uc001avd.3
chr1	14031349	14151574	uc001avi.3
chr1	14925212	15444544	uc001avm.4
chr1	15438310	15478960	uc009voh.3
chr1	15480228	15546974	uc001avx.3
chr1	15573767	15724622	uc001awb.2
chr1	15653175	15670372	uc001awc.1
chr1	15736390	15756839	uc001awh.2
chr1	15764937	15773153	uc001awi.1
chr1	15783222	15798586	uc001awk.3
chr1	15802595	15817895	uc001awl.3
chr1	15817895	15850940	uc001awn.4
chr1	15853351	15898228	uc001aws.3
chr1	15898193	15911605	uc001awv.2
chr1	15943952	15987552	uc001awx.2
chr1	15986363	15988217	uc010obn.2
chr1	15992765	15995537	uc001awz.3
chr1	16010826	16061264	uc010obo.2
chr1	16062808	16067884	uc001axb.1
chr1	16068986	16074292	uc001axc.4
chr1	16085254	16113084	uc001axe.1
chr1	16133656	16134194	uc009vol.1
chr1	16160709	16174642	uc001axj.2
chr1	16174358	16266950	uc001axk.1
chr1	16268363	16302627	uc001axl.4
chr1	16317618	16317647	uc001axm.1
chr1	16330730	16333184	uc001axn.3
chr1	16340522	16345285	uc001axo.2
chr1	16348485	16360545	uc001axu.3
chr1	16384263	16400127	uc001axz.4
chr1	16450831	16482582	uc001aya.2
chr1	16524598	16539104	uc001ayc.1
chr1	16558181	16563659	uc001ayd.3
chr1	16576558	16678948	uc001ayg.3
chr1	16693524	16724643	uc001aym.5
chr1	16725137	16763919	uc001ayn.3
chr1	16767166	16786584	uc001ayq.3
chr1	16793930	16819196	uc001ayt.2
chr1	16847079	16847153	uc021ogp.1
chr1	16858892	16858966	uc021ogq.1
chr1	16860349	16862144	uc021ogr.1
chr1	16862254	16864669	uc001ayv.2
chr1	16872433	16872504	uc021ogs.1
chr1	16874159	16874232	uc021ogt.1
chr1	16875408	16875482	uc021ogu.1
chr1	16888921	16940100	uc001ayw.4
chr1	16944756	16959841	uc001azf.3
chr1	16972068	16976915	uc010och.2
chr1	17004765	17004836	uc021ogw.1
chr1	17006500	17006573	uc021ogx.1
chr1	17007749	17007823	uc021ogy.1
chr1	17017712	17046652	uc001azn.1
chr1	17052060	17052133	uc021ogz.1
chr1	17053779	17053850	uc021oha.1
chr1	17081128	17090975	uc010ock.3
chr1	17180899	17180971	uc021ohb.1
chr1	17185443	17185516	uc021ohc.1
chr1	17186692	17186765	uc021ohd.1
chr1	17188415	17188486	uc021ohe.1
chr1	17197439	17200574	uc021ohf.2
chr1	17201957	17202031	uc021ohg.1
chr1	17215040	17216161	uc001azs.1
chr1	17216171	17216245	uc021ohh.1
chr1	17222645	17222720	uc021ohi.1
chr1	17248444	17299474	uc001azt.2
chr1	17300998	17307173	uc001azw.3
chr1	17312452	17338423	uc001baa.2
chr1	17345224	17380665	uc001bae.3
chr1	17393255	17445948	uc001baf.3
chr1	17531620	17572501	uc001bah.1
chr1	17566189	17572501	uc009vpb.1
chr1	17575592	17610727	uc001bai.3
chr1	17581660	17581778	uc021ohk.1
chr1	17634689	17690495	uc001baj.2
chr1	17698740	17728195	uc001bak.1
chr1	17733250	17765059	uc001bal.3
chr1	17866329	18024370	uc001ban.3
chr1	18081807	18153558	uc001bat.3
chr1	18434239	18704977	uc001bau.2
chr1	18701063	18702174	uc001baw.1
chr1	18807423	18812480	uc001bax.3
chr1	18957499	19062632	uc001bay.3
chr1	19166092	19186155	uc001bba.1
chr1	19197923	19229293	uc001bbc.3
chr1	19209695	19209769	uc021ohm.2
chr1	19230773	19282826	uc001bbd.2
chr1	19400999	19536746	uc001bbi.3
chr1	19542157	19578053	uc001bbo.4
chr1	19578074	19586622	uc001bbs.3
chr1	19592475	19600568	uc021ohn.1
chr1	19609056	19615280	uc001bbv.1
chr1	19619740	19622230	uc021ohp.1
chr1	19629201	19638640	uc001bbw.3
chr1	19638739	19655794	uc001bby.3
chr1	19665266	19812066	uc021ohr.1
chr1	19673334	19675427	uc001bcf.2
chr1	19750877	19751182	uc021ohs.1
chr1	19923470	19956315	uc021ohu.1
chr1	19934300	19935138	uc021ohx.2
chr1	19969722	19984949	uc001bcj.2
chr1	19991779	20006055	uc001bcl.3
chr1	20008705	20126410	uc001bcn.3
chr1	20140521	20141771	uc001bcr.3
chr1	20208887	20239437	uc001bcs.4
chr1	20246799	20250110	uc001bct.1
chr1	20301923	20306932	uc010odb.2
chr1	20396700	20418394	uc001bcy.3
chr1	20439142	20446059	uc001bcz.4
chr1	20465822	20476879	uc009vpp.1
chr1	20490483	20501687	uc009vpq.1
chr1	20512577	20519942	uc001bdb.3
chr1	20617411	20681387	uc009vps.2
chr1	20686293	20755287	uc001bdf.2
chr1	20808883	20812728	uc001bdh.3
chr1	20825940	20834674	uc001bdi.4
chr1	20878931	20881513	uc001bdj.3
chr1	20915443	20945400	uc001bdk.3
chr1	20959947	20978004	uc001bdm.3
chr1	20978259	20988037	uc001bdo.1
chr1	20990506	21044317	uc001bdr.4
chr1	21046224	21059133	uc009vpy.1
chr1	21069170	21113181	uc001bdw.1
chr1	21132784	21503381	uc001bef.3
chr1	21543739	21616766	uc001bei.2
chr1	21602542	21604868	uc001ben.1
chr1	21619782	21626362	uc001beo.2
chr1	21749600	21754300	uc001bep.1
chr1	21761832	21762609	uc001beq.1
chr1	21766582	21811393	uc001ber.4
chr1	21835857	21904905	uc001bet.3
chr1	21922707	21978348	uc001bew.3
chr1	22004791	22109688	uc001bfb.3
chr1	22138757	22151714	uc001bfg.1
chr1	22148736	22263750	uc001bfj.3
chr1	22303417	22315847	uc001bfk.3
chr1	22351706	22357715	uc001bfm.4
chr1	22379119	22419436	uc001bfr.3
chr1	22443797	22469519	uc001bfs.4
chr1	22778343	22857650	uc001bfu.2
chr1	22890003	22930087	uc001bfx.1
chr1	22963117	22966175	uc001bfy.3
chr1	22970117	22974603	uc001bga.4
chr1	22979681	22988029	uc001bgd.3
chr1	23037330	23241823	uc001bge.3
chr1	23046009	23046091	uc021oib.1
chr1	23189651	23189719	uc021oic.1
chr1	23243782	23247347	uc001bgg.1
chr1	23337326	23342343	uc001bgh.1
chr1	23345940	23410184	uc001bgj.2
chr1	23370797	23370865	uc021oid.1
chr1	23410515	23495517	uc010odv.1
chr1	23490445	23490546	uc021oie.1
chr1	23518387	23521222	uc001bgn.3
chr1	23636275	23670853	uc001bgp.4
chr1	23685940	23694879	uc001bgt.3
chr1	23695463	23698330	uc001bgw.3
chr1	23707554	23751261	uc021oig.1
chr1	23755055	23810750	uc001bha.2
chr1	23801092	23803135	uc001bhd.4
chr1	23832919	23857712	uc001bhe.2
chr1	23853364	23855542	uc001bhf.1
chr1	23884420	23886285	uc001bhh.4
chr1	23907984	23967056	uc001bhi.3
chr1	24018268	24022915	uc001bhk.3
chr1	24069855	24088549	uc001bho.3
chr1	24086871	24104787	uc001bhp.2
chr1	24104875	24114722	uc001bhq.3
chr1	24117645	24122029	uc001bht.3
chr1	24122088	24126060	uc009vqo.1
chr1	24128366	24151949	uc001bib.3
chr1	24171571	24194859	uc001bie.3
chr1	24200459	24239817	uc001bif.3
chr1	24255559	24255637	uc021oik.1
chr1	24286300	24289949	uc001big.3
chr1	24295572	24306953	uc021oir.1
chr1	24320925	24320957	uc021oit.1
chr1	24382530	24438665	uc001bin.4
chr1	24446260	24469775	uc001biq.2
chr1	24480646	24513765	uc001bis.3
chr1	24526729	24538180	uc010oei.1
chr1	24578722	24578758	uc021oiu.1
chr1	24579767	24579803	uc021oiv.1
chr1	24645811	24690970	uc021oiw.1
chr1	24683488	24740262	uc001bjc.3
chr1	24742244	24799473	uc001bjh.3
chr1	24822822	24828850	uc021oiz.1
chr1	24829386	24863510	uc001bjj.3
chr1	24882566	24935818	uc001bjk.2
chr1	24969593	24999772	uc001bjm.3
chr1	25071759	25170815	uc001bjo.2
chr1	25226001	25256770	uc001bjq.3
chr1	25548766	25559013	uc001bjt.1
chr1	25568739	25573985	uc001bjw.3
chr1	25598980	25656936	uc001bjz.3
chr1	25629228	25631643	uc001bkd.1
chr1	25664788	25688852	uc001bke.3
chr1	25688739	25747363	uc001bkf.3
chr1	25757387	25826698	uc001bkk.3
chr1	25870075	25895377	uc001bkl.4
chr1	25943958	26111258	uc001bkm.2
chr1	26126666	26144713	uc021ojl.1
chr1	26146396	26159433	uc001bkq.4
chr1	26146444	26150097	uc010oeu.1
chr1	26160496	26185848	uc001bkw.1
chr1	26187974	26197744	uc001bkx.3
chr1	26210676	26232993	uc010oev.2
chr1	26286257	26324648	uc001bld.4
chr1	26348270	26362954	uc001blf.3
chr1	26364513	26372604	uc001blg.1
chr1	26377795	26394125	uc001bli.2
chr1	26438267	26452039	uc009vsb.3
chr1	26485510	26489119	uc001blk.3
chr1	26496387	26497364	uc001bll.4
chr1	26503980	26516375	uc001blm.4
chr1	26517118	26529033	uc010oez.2
chr1	26551810	26556331	uc001blq.3
chr1	26560692	26605299	uc001bls.1
chr1	26606212	26608013	uc001blu.3
chr1	26608772	26633195	uc001blw.3
chr1	26644410	26647014	uc001bmc.3
chr1	26648349	26680621	uc001bmd.4
chr1	26688124	26699266	uc009vsj.1
chr1	26737268	26756219	uc001bmj.3
chr1	26758772	26797795	uc001bmk.3
chr1	26789254	26794028	uc001bmo.1
chr1	26798901	26803133	uc001bmp.4
chr1	26872342	26901520	uc001bms.1
chr1	26881032	26881084	uc021ojp.1
chr1	27022521	27108601	uc001bmv.1
chr1	27114453	27124894	uc001bmz.3
chr1	27145536	27392034	uc021ojq.1
chr1	27153200	27182211	uc001bnb.3
chr1	27189632	27190947	uc001bnc.1
chr1	27189786	27190449	uc010ofi.1
chr1	27205872	27216869	uc001bnd.1
chr1	27216978	27226962	uc001bne.3
chr1	27237974	27240567	uc001bnf.3
chr1	27248212	27273362	uc001bng.2
chr1	27276046	27286901	uc001bni.2
chr1	27320194	27327377	uc001bnj.4
chr1	27331510	27339333	uc010ofj.2
chr1	27425299	27481621	uc001bnm.4
chr1	27533765	27533868	uc021ojs.1
chr1	27561006	27635124	uc009vst.3
chr1	27648635	27662891	uc001bnr.4
chr1	27650364	27653016	uc021oju.1
chr1	27668482	27680423	uc001bnw.2
chr1	27681669	27693337	uc001bny.1
chr1	27695600	27701315	uc001boa.3
chr1	27705595	27709805	uc001boc.3
chr1	27719147	27722317	uc001bod.4
chr1	27730733	27816678	uc001bof.2
chr1	27860755	27930143	uc009vsy.3
chr1	27938800	27961727	uc001bom.3
chr1	27992571	27998724	uc001bon.1
chr1	28052489	28089423	uc001bor.3
chr1	28099693	28150963	uc001bou.4
chr1	28157251	28178183	uc001bov.2
chr1	28160911	28161077	uc001boy.1
chr1	28199054	28213193	uc001bpc.4
chr1	28218048	28241236	uc001bpe.1
chr1	28261503	28285663	uc001bpg.3
chr1	28286503	28294604	uc001bph.1
chr1	28296854	28415148	uc001bpi.2
chr1	28473676	28503455	uc001bpl.3
chr1	28526789	28559542	uc001bpn.3
chr1	28562601	28564616	uc001bpq.3
chr1	28567542	28567564	uc021okb.1
chr1	28585962	28609002	uc001bps.3
chr1	28655512	28662478	uc009vtg.3
chr1	28764660	28826881	uc001bpy.3
chr1	28832454	28837404	uc001bqd.3
chr1	28844744	28865708	uc001bqf.2
chr1	28879528	28905057	uc001bqi.3
chr1	28905049	28908383	uc001bqo.3
chr1	28905254	28905334	uc001bqq.1
chr1	28919006	28921088	uc001bqv.3
chr1	28929608	28969604	uc001bqy.3
chr1	28975111	28975246	uc009vtj.1
chr1	28995239	29042115	uc001bra.3
chr1	29016176	29016306	uc021oke.1
chr1	29063132	29096287	uc021okf.1
chr1	29138653	29190208	uc001brf.1
chr1	29213602	29446558	uc001brm.2
chr1	29445936	29450421	uc001brn.2
chr1	29474249	29508637	uc001bro.3
chr1	29519384	29557454	uc001brq.1
chr1	29563027	29653325	uc001bru.3
chr1	30486798	30510456	uc001bry.3
chr1	31184123	31196432	uc001brz.3
chr1	31191618	31199593	uc001bsb.1
chr1	31205314	31230683	uc001bsc.2
chr1	31212002	31212079	uc021okj.1
chr1	31342312	31381480	uc001bse.2
chr1	31404352	31538564	uc001bsh.1
chr1	31408535	31408623	uc021okk.1
chr1	31421964	31422052	uc021okl.1
chr1	31441009	31441084	uc001bsl.1
chr1	31652591	31712734	uc010ogd.2
chr1	31732414	31742513	uc009vtt.3
chr1	31732414	31769644	uc001bso.3
chr1	31769841	31837780	uc001bsp.1
chr1	31838099	31845923	uc001bss.1
chr1	31886659	31907527	uc010ogh.2
chr1	31971838	31974167	uc021okn.1
chr1	31984035	31989846	uc001bsy.1
chr1	32042085	32053287	uc001bta.3
chr1	32083300	32092919	uc009vtx.2
chr1	32095462	32110838	uc001bth.2
chr1	32117847	32169768	uc001btk.1
chr1	32192717	32229648	uc001btn.3
chr1	32224260	32224336	uc021okr.1
chr1	32256024	32281580	uc001bts.1
chr1	32372021	32403988	uc001bty.2
chr1	32479294	32509482	uc001bub.4
chr1	32538502	32568467	uc010ogv.2
chr1	32573643	32642168	uc001bug.3
chr1	32645344	32663886	uc001bui.3
chr1	32666201	32670991	uc001bul.1
chr1	32671235	32674288	uc001bum.2
chr1	32674694	32681797	uc001bun.2
chr1	32681797	32687926	uc001buq.4
chr1	32687958	32697205	uc009vuc.3
chr1	32697260	32707311	uc001buv.4
chr1	32712817	32714461	uc001buw.3
chr1	32739711	32751766	uc001buy.3
chr1	32757707	32799224	uc001bvb.1
chr1	32799429	32801840	uc001bvd.4
chr1	32826870	32827844	uc021oku.1
chr1	32827861	32829924	uc001bvf.3
chr1	32830704	32860062	uc010ohg.2
chr1	32930657	32953459	uc001bvl.4
chr1	33004771	33071542	uc001bvn.3
chr1	33087306	33116185	uc001bvp.3
chr1	33116748	33151812	uc001bvr.3
chr1	33145506	33168361	uc001bvt.2
chr1	33207511	33240571	uc001bvu.1
chr1	33240839	33283633	uc001bvy.1
chr1	33283118	33324480	uc001bwc.4
chr1	33327868	33338082	uc001bwg.3
chr1	33352097	33360247	uc001bwh.3
chr1	33360195	33366953	uc001bwi.1
chr1	33402049	33430286	uc010oho.2
chr1	33452675	33498070	uc001bwn.3
chr1	33476825	33502512	uc001bwp.2
chr1	33546713	33585995	uc001bwr.3
chr1	33607471	33608831	uc001bxa.1
chr1	33611002	33647671	uc001bxb.3
chr1	33722173	33766320	uc001bxc.1
chr1	33772366	33786699	uc031plq.1
chr1	33789223	33841194	uc001bxg.1
chr1	33797993	33798093	uc021okw.1
chr1	33802166	33802465	uc021okx.1
chr1	33938231	33961995	uc001bxj.4
chr1	33979608	34631443	uc001bxn.1
chr1	34326075	34330392	uc001bxp.3
chr1	34334556	34351059	uc001bxr.3
chr1	34642552	34684731	uc001bxt.3
chr1	35220647	35224113	uc001bxu.4
chr1	35225341	35229325	uc001bxv.1
chr1	35244163	35244247	uc021ola.1
chr1	35247910	35251967	uc001bxy.3
chr1	35258598	35261348	uc001bya.3
chr1	35315962	35325417	uc001byb.3
chr1	35331036	35370984	uc001byc.3
chr1	35441299	35444307	uc021olf.1
chr1	35447126	35450948	uc001bye.3
chr1	35451766	35497569	uc001byh.3
chr1	35544971	35581455	uc001bym.3
chr1	35641980	35646083	uc001byq.3
chr1	35649200	35658743	uc001bys.3
chr1	35734567	35887545	uc001byt.3
chr1	35833677	35835012	uc031plr.1
chr1	35899090	36023037	uc001byx.3
chr1	36023392	36032380	uc001bza.3
chr1	36038970	36060927	uc010ohy.2
chr1	36065142	36107445	uc001bzf.2
chr1	36179476	36184790	uc001bzh.1
chr1	36197712	36235551	uc001bzi.3
chr1	36273827	36323490	uc001bzj.2
chr1	36348809	36389899	uc001bzl.3
chr1	36391432	36395210	uc001bzm.1
chr1	36396682	36522063	uc001bzp.3
chr1	36549675	36553876	uc001bzr.3
chr1	36554452	36559533	uc001bzt.3
chr1	36560843	36565850	uc001bzv.2
chr1	36602169	36615115	uc031pls.1
chr1	36621802	36646441	uc001bzz.3
chr1	36690016	36770957	uc001cae.4
chr1	36771993	36787379	uc010oia.1
chr1	36787631	36789755	uc001caj.1
chr1	36805224	36851485	uc001cak.1
chr1	36859030	36863493	uc001cao.1
chr1	36883506	36916086	uc001caq.3
chr1	36921361	36930040	uc001cas.2
chr1	36931643	36948915	uc001cax.2
chr1	37261127	37499844	uc001caz.2
chr1	37627163	37627235	uc021oll.1
chr1	37920479	37940044	uc021olm.1
chr1	37940118	37949978	uc001cbb.4
chr1	37955560	37980420	uc001cbe.2
chr1	37966535	37966595	uc031pma.1
chr1	38000049	38019945	uc001cbi.4
chr1	38022519	38032458	uc001cbj.3
chr1	38032412	38061586	uc001cbk.3
chr1	38076950	38100595	uc001cbm.2
chr1	38147241	38156192	uc001cbp.3
chr1	38147242	38149864	uc031pmb.1
chr1	38158072	38175391	uc001cbs.4
chr1	38181645	38230824	uc009vvi.3
chr1	38259773	38267278	uc001cby.2
chr1	38268613	38273865	uc001cca.1
chr1	38273472	38275126	uc001ccd.2
chr1	38275238	38325292	uc001cce.1
chr1	38326368	38412729	uc001ccg.1
chr1	38349908	38349989	uc021oln.1
chr1	38422651	38455761	uc001cci.3
chr1	38462441	38471187	uc001cck.3
chr1	38478383	38490497	uc001ccn.4
chr1	38509522	38512450	uc001ccp.1
chr1	38554902	38555001	uc021olo.1
chr1	38674705	38680439	uc021olp.1
chr1	39303868	39325495	uc001ccq.3
chr1	39328161	39339050	uc001ccs.3
chr1	39340222	39341770	uc021olr.1
chr1	39351478	39407456	uc001ccu.1
chr1	39456915	39471737	uc001ccw.3
chr1	39491966	39500308	uc001ccy.3
chr1	39549838	39952810	uc031pmc.1
chr1	39619835	39619968	uc021olu.1
chr1	39648409	39648505	uc021olv.1
chr1	39875175	39882154	uc009vvt.1
chr1	39957317	39995541	uc001cdi.3
chr1	39970194	39970267	uc021olx.1
chr1	39987951	40025370	uc001cdk.3
chr1	40026484	40042521	uc001cdl.2
chr1	40033045	40033182	uc001cdo.1
chr1	40089102	40105348	uc001cdp.3
chr1	40124792	40137710	uc001cdq.1
chr1	40144644	40157089	uc001cdr.3
chr1	40204516	40229586	uc001cdw.3
chr1	40223902	40254533	uc001cdz.1
chr1	40235196	40237020	uc001ceb.1
chr1	40306705	40349177	uc021olz.1
chr1	40361095	40367687	uc001cer.2
chr1	40420783	40435628	uc001cev.3
chr1	40506254	40538321	uc001cey.4
chr1	40538381	40563142	uc001cfb.2
chr1	40627040	40706593	uc001cfc.4
chr1	40713572	40717365	uc001cfe.2
chr1	40723721	40759856	uc001cfg.4
chr1	40766162	40782981	uc001cfh.1
chr1	40839377	40888998	uc001cfj.3
chr1	40916336	40929390	uc001cfn.2
chr1	40943301	40962015	uc001cfo.3
chr1	40974432	40982214	uc001cfp.3
chr1	40997232	41013841	uc001cft.2
chr1	41086351	41131324	uc001cfu.1
chr1	41154751	41157933	uc010ojl.1
chr1	41157241	41237275	uc009vwd.3
chr1	41220026	41220118	uc001cgf.2
chr1	41222955	41223044	uc001cgg.3
chr1	41249683	41306124	uc001cgh.2
chr1	41326727	41328018	uc001cgj.3
chr1	41347313	41347427	uc021omc.1
chr1	41445006	41478235	uc001cgk.4
chr1	41480261	41509562	uc021omd.1
chr1	41481268	41487427	uc001cgm.2
chr1	41492870	41707815	uc001cgs.3
chr1	41932607	41932699	uc021ome.1
chr1	41944445	41949874	uc009vwh.3
chr1	41944445	41950344	uc001cgx.3
chr1	41972035	42384496	uc001cha.4
chr1	42619091	42621495	uc001chc.1
chr1	42628361	42630395	uc001chd.1
chr1	42642209	42800903	uc001chf.3
chr1	42846467	42889900	uc001chi.2
chr1	42896000	42921938	uc001chj.3
chr1	42922172	42926086	uc001chl.3
chr1	43000559	43120335	uc009vwk.1
chr1	43124047	43142429	uc001chq.3
chr1	43144356	43147330	uc001chr.3
chr1	43148065	43168020	uc001chs.3
chr1	43198763	43205925	uc001cht.1
chr1	43212005	43232755	uc001chx.4
chr1	43232915	43241413	uc001cia.4
chr1	43253660	43263901	uc021omg.1
chr1	43272722	43283059	uc001cib.2
chr1	43291248	43310660	uc001cie.1
chr1	43312243	43318146	uc021omh.1
chr1	43323292	43354460	uc001cij.1
chr1	43391045	43424847	uc001cik.2
chr1	43424719	43449029	uc001cil.3
chr1	43489219	43489308	uc021omj.1
chr1	43585818	43611958	uc009vwn.1
chr1	43613593	43622067	uc009vwo.3
chr1	43629844	43638241	uc010ojx.2
chr1	43638000	43720029	uc021omk.1
chr1	43735664	43739673	uc021oml.1
chr1	43747556	43751250	uc001cit.4
chr1	43766565	43788781	uc001ciu.3
chr1	43803474	43820135	uc001ciw.3
chr1	43824625	43828873	uc001cix.3
chr1	43829067	43833409	uc031pmf.1
chr1	43849578	43855483	uc001cje.2
chr1	43855555	43919918	uc001cjk.3
chr1	43916673	43919660	uc001cjo.3
chr1	43996546	44089343	uc001cjr.3
chr1	44115796	44171189	uc001cjx.3
chr1	44165355	44173012	uc001cjy.4
chr1	44173203	44396837	uc001cjz.4
chr1	44182137	44182227	uc021oms.1
chr1	44398991	44402912	uc001ckt.3
chr1	44412477	44433694	uc001ckx.3
chr1	44435652	44439043	uc001ckz.3
chr1	44440601	44443972	uc001cld.3
chr1	44445607	44456843	uc010okl.2
chr1	44457279	44462198	uc001clj.3
chr1	44462154	44483012	uc001cll.4
chr1	44584521	44600809	uc001clp.3
chr1	44679124	44686351	uc001clq.1
chr1	44686741	44820939	uc001clt.3
chr1	44870959	45117396	uc001clv.1
chr1	45011164	45011224	uc031pmh.1
chr1	45119500	45140099	uc001cmc.3
chr1	45140393	45191263	uc001cmf.2
chr1	45205489	45233438	uc001cmg.4
chr1	45241245	45244412	uc001cmi.3
chr1	45241536	45241610	uc001cmj.1
chr1	45242163	45242261	uc001cmk.1
chr1	45243513	45243584	uc009vxi.3
chr1	45244061	45244130	uc001cml.3
chr1	45249256	45253426	uc001cmm.3
chr1	45266035	45271667	uc001cmn.3
chr1	45271585	45272957	uc001cmp.3
chr1	45274153	45279801	uc010ole.1
chr1	45287937	45308616	uc010olf.2
chr1	45316193	45452394	uc001cmt.3
chr1	45468219	45477027	uc009vxk.3
chr1	45477804	45481341	uc001cna.2
chr1	45482075	45672250	uc001cnd.2
chr1	45769581	45771291	uc010olk.2
chr1	45792544	45794346	uc001cne.3
chr1	45794913	45806142	uc009vxp.3
chr1	45805341	45809650	uc009vxq.3
chr1	45809554	45956840	uc001cns.1
chr1	45959597	45965751	uc001cnw.3
chr1	45965855	45976739	uc009vxv.3
chr1	45976706	45988562	uc021omw.1
chr1	46016454	46035723	uc001coe.3
chr1	46049659	46084578	uc001coi.2
chr1	46085715	46089731	uc010olt.2
chr1	46092975	46152302	uc001coq.3
chr1	46111451	46112357	uc010olu.1
chr1	46153846	46160108	uc001cor.1
chr1	46164406	46216485	uc001cou.3
chr1	46269284	46501796	uc001cov.3
chr1	46505811	46598380	uc001cpb.4
chr1	46640748	46651634	uc001cpd.3
chr1	46654352	46685977	uc001cpg.3
chr1	46669005	46686928	uc010oma.2
chr1	46713366	46744145	uc009vye.2
chr1	46744071	46769038	uc001cpn.3
chr1	46769379	46782447	uc001cpp.3
chr1	46805848	46830824	uc001cpr.2
chr1	46859938	46879520	uc001cpu.2
chr1	46899498	46911374	uc021ona.1
chr1	46972667	46979886	uc001cpx.3
chr1	47004367	47035927	uc021onb.1
chr1	47011315	47015678	uc001cpy.2
chr1	47011315	47016887	uc009vyh.1
chr1	47023078	47069966	uc001cqb.4
chr1	47073386	47080805	uc001cqe.4
chr1	47100710	47134099	uc001cqh.4
chr1	47137496	47139256	uc001cqj.3
chr1	47139707	47157769	uc021ond.1
chr1	47140830	47184736	uc001cqk.4
chr1	47264669	47285021	uc001cqn.4
chr1	47308766	47366147	uc031pmm.1
chr1	47394845	47407156	uc001cqp.4
chr1	47489239	47516423	uc001cqt.3
chr1	47533159	47583992	uc001cqu.1
chr1	47603106	47614526	uc001cqv.1
chr1	47644921	47646011	uc031pmn.1
chr1	47649260	47655771	uc001cqw.3
chr1	47681962	47689770	uc009vyq.2
chr1	47681962	47695443	uc001cqx.2
chr1	47691628	47691655	uc021onf.1
chr1	47715810	47779819	uc001crd.1
chr1	47799468	47844511	uc001cri.3
chr1	47859449	47861215	uc001crj.1
chr1	47881743	47883724	uc001crk.3
chr1	47897806	47900313	uc001crl.3
chr1	47901688	47906363	uc001crm.3
chr1	48226199	48462562	uc021ong.1
chr1	48567386	48648100	uc010omr.1
chr1	48688356	48714316	uc001crn.2
chr1	48761043	48937876	uc001crr.2
chr1	48998526	50489626	uc001cru.2
chr1	49193539	49242547	uc001crx.4
chr1	50574593	50667540	uc001csb.2
chr1	50883222	50889119	uc010onb.2
chr1	50906934	51425936	uc001cse.1
chr1	51048075	51048183	uc021onh.1
chr1	51435641	51440309	uc001csg.3
chr1	51567905	51613754	uc001csh.3
chr1	51701944	51739119	uc001csi.4
chr1	51752929	51810785	uc010onf.2
chr1	51819934	51984995	uc001csq.1
chr1	52004296	52004401	uc021oni.1
chr1	52082545	52254891	uc001csu.3
chr1	52254865	52344609	uc001ctc.4
chr1	52373627	52456436	uc001cth.3
chr1	52461412	52461712	uc021onk.1
chr1	52485803	52521843	uc001cti.4
chr1	52497776	52499472	uc001ctj.1
chr1	52521856	52556388	uc001ctk.3
chr1	52607765	52812358	uc001cto.4
chr1	52816264	52831877	uc001ctq.2
chr1	52838500	52870143	uc001ctu.3
chr1	52870218	52883992	uc001ctv.4
chr1	52870218	52883992	uc001ctw.4
chr1	52888947	53018762	uc001cty.2
chr1	53068042	53074723	uc001cue.3
chr1	53099065	53122737	uc001cuf.3
chr1	53152013	53164038	uc001cui.2
chr1	53192130	53293013	uc001cuj.3
chr1	53308182	53360247	uc001cuk.2
chr1	53361581	53387591	uc001cup.4
chr1	53392900	53517289	uc001cur.2
chr1	53527723	53551174	uc010onr.2
chr1	53552854	53608289	uc001cuy.3
chr1	53580247	53584281	uc001cva.1
chr1	53662100	53679869	uc001cvb.4
chr1	53679771	53686289	uc001cvd.3
chr1	53692563	53704282	uc001cvf.2
chr1	53704281	53708455	uc001cvg.3
chr1	53708040	53793821	uc001cvi.2
chr1	53793904	53802889	uc001cvn.1
chr1	53904042	53905693	uc009vzj.3
chr1	53925071	53933158	uc001cvq.1
chr1	53971905	54199877	uc001cvr.1
chr1	54231133	54304225	uc001cvs.3
chr1	54317391	54355487	uc001cvu.3
chr1	54359860	54376759	uc001cwb.3
chr1	54387233	54411288	uc001cwh.3
chr1	54411998	54433841	uc001cwj.2
chr1	54472970	54483859	uc001cwm.2
chr1	54497348	54519111	uc001cwp.3
chr1	54519273	54565416	uc001cwt.1
chr1	54519751	54519827	uc021ons.1
chr1	54604667	54618679	uc001cwv.2
chr1	54638026	54665746	uc009vzo.3
chr1	54665839	54684056	uc001cxa.4
chr1	54691103	54872068	uc001cxe.4
chr1	55013806	55076005	uc001cxl.2
chr1	55074849	55089200	uc001cxn.3
chr1	55107426	55175939	uc010ooe.1
chr1	55181494	55208328	uc001cxx.4
chr1	55222570	55230226	uc001cxy.3
chr1	55246751	55266941	uc009vzt.1
chr1	55271735	55307937	uc001cyb.4
chr1	55315299	55352921	uc001cyc.1
chr1	55352654	55353883	uc021onu.1
chr1	55423541	55423614	uc021onv.1
chr1	55446464	55457966	uc001cyd.3
chr1	55464616	55474465	uc001cye.3
chr1	55505148	55530526	uc001cyf.2
chr1	55532031	55681039	uc021onw.1
chr1	55681080	55683128	uc021onx.1
chr1	55691313	55691396	uc021ony.1
chr1	55842198	55842525	uc021onz.1
chr1	55950543	55950645	uc021ooa.1
chr1	56046709	56200675	uc001cyi.1
chr1	56960418	57045257	uc001cyj.2
chr1	57110989	57181008	uc001cyk.4
chr1	57184476	57285369	uc001cym.4
chr1	57289353	57292593	uc001cyn.3
chr1	57320442	57383894	uc001cyo.2
chr1	57394882	57431688	uc001cyp.3
chr1	57463578	58716211	uc001cys.1
chr1	58326214	58328786	uc001cyu.1
chr1	58933598	58934677	uc001cyw.1
chr1	58946390	59012446	uc001cyy.3
chr1	59041094	59043166	uc001cyz.4
chr1	59120410	59165747	uc009wab.2
chr1	59246462	59249785	uc001cze.3
chr1	59250822	59365384	uc010oop.1
chr1	59597607	59612479	uc010ooq.2
chr1	59762624	60228402	uc009wac.3
chr1	60198898	60198968	uc021oob.1
chr1	60238466	60254501	uc001czn.3
chr1	60280532	60342050	uc001czo.3
chr1	60358979	60392423	uc001czq.3
chr1	60454823	60539442	uc001czs.2
chr1	61125302	61291256	uc001czt.1
chr1	61405915	61436448	uc001czu.3
chr1	61547533	61928460	uc010oos.2
chr1	62119913	62121800	uc031pmt.1
chr1	62146718	62191095	uc001czz.1
chr1	62208148	62629591	uc001dab.3
chr1	62318170	62318274	uc031pmu.1
chr1	62660473	62678001	uc001dae.4
chr1	62701836	62785083	uc001dah.4
chr1	62901974	62917475	uc001dak.2
chr1	62920396	63154039	uc001daq.4
chr1	63063157	63071976	uc001das.2
chr1	63249776	63330941	uc001dau.3
chr1	63624753	63782901	uc001daw.2
chr1	63704613	63704845	uc021ood.1
chr1	63788729	63790797	uc001dax.2
chr1	63799429	63799491	uc021ooe.1
chr1	63833260	63904233	uc021oof.1
chr1	63906440	63988944	uc001dbb.2
chr1	63989012	64038364	uc001dbf.3
chr1	64014650	64016307	uc001dbg.1
chr1	64088886	64125916	uc010ooz.2
chr1	64239689	64647179	uc001dbj.3
chr1	64262024	64262128	uc021oog.1
chr1	64571005	64636980	uc001dbl.3
chr1	64669489	64710027	uc001dbn.1
chr1	64936475	65158741	uc001dbo.1
chr1	65045529	65045604	uc021ooh.1
chr1	65210777	65298914	uc001dbs.2
chr1	65298905	65432187	uc001dbu.1
chr1	65445259	65468159	uc001dbw.3
chr1	65488650	65488757	uc021ooj.1
chr1	65523437	65523525	uc021ook.1
chr1	65524116	65524191	uc001dbx.3
chr1	65613231	65697828	uc001dby.3
chr1	65775217	65881552	uc001dce.2
chr1	65886130	66103176	uc001dci.3
chr1	65886130	65901690	uc001dcf.3
chr1	66258855	66840262	uc001dco.3
chr1	66560143	66560229	uc021oom.1
chr1	66999824	67210768	uc001dcr.3
chr1	67094122	67094200	uc021oon.1
chr1	67132271	67142710	uc010ope.1
chr1	67218139	67244730	uc001dcv.3
chr1	67263423	67266942	uc001dcw.3
chr1	67278571	67390570	uc001dcx.3
chr1	67390577	67454302	uc001dde.2
chr1	67465014	67520080	uc001ddk.2
chr1	67557858	67600654	uc001ddm.2
chr1	67632168	67725650	uc001ddo.3
chr1	67661822	67661926	uc021ooo.1
chr1	67773046	67862583	uc001ddu.3
chr1	67873492	67896123	uc001ddv.3
chr1	68150859	68154021	uc001ddz.2
chr1	68167148	68299155	uc001dea.2
chr1	68238275	68238336	uc021oop.1
chr1	68297970	68668670	uc001deb.2
chr1	68511644	68516481	uc001ded.3
chr1	68564141	68698284	uc001dee.3
chr1	68649200	68649293	uc021oos.1
chr1	68649301	68649321	uc021oot.1
chr1	68894506	68915642	uc001dei.1
chr1	68939834	68962799	uc001dem.4
chr1	68962358	69004310	uc001den.3
chr1	70225857	70589171	uc001dep.3
chr1	70385004	70386000	uc009wbh.2
chr1	70610484	70671361	uc001der.2
chr1	70671364	70717701	uc001des.3
chr1	70724684	70820417	uc001dex.4
chr1	70820492	70833705	uc001dfa.3
chr1	70876900	70905534	uc001dfd.3
chr1	71172135	71252151	uc001dff.3
chr1	71418114	71513491	uc001dfo.3
chr1	71512188	71532865	uc001dfr.3
chr1	71528973	71546972	uc001dft.3
chr1	71533313	71533399	uc010oqr.1
chr1	71547006	71703406	uc001dfu.2
chr1	71868624	72748405	uc001dfw.3
chr1	72259914	72302695	uc031pmw.1
chr1	73771852	73804560	uc001dfx.3
chr1	74491701	74663871	uc001dfy.4
chr1	74663895	75010116	uc001dge.2
chr1	75033794	75139422	uc001dgg.3
chr1	75043113	75091782	uc001dgh.3
chr1	75171171	75199092	uc001dgj.3
chr1	75198835	75232360	uc001dgn.3
chr1	75595658	75598261	uc001dgp.1
chr1	75600566	75627218	uc031pmx.1
chr1	75672074	76076799	uc001dgu.3
chr1	76103850	76188721	uc001dgv.3
chr1	76190042	76229355	uc009wbp.3
chr1	76251878	76260775	uc001dgy.2
chr1	76252756	76252834	uc001dgz.2
chr1	76253573	76253657	uc009wbu.1
chr1	76255161	76255232	uc009wbv.1
chr1	76262555	76378923	uc001dhd.2
chr1	76384557	76398116	uc001dhe.2
chr1	76540388	77096669	uc001dhh.2
chr1	77333185	77529737	uc001dhi.3
chr1	77554666	77685132	uc001dhk.3
chr1	77747661	78025654	uc001dhn.3
chr1	78030189	78148343	uc001dhq.3
chr1	78161673	78225564	uc001dht.4
chr1	78245308	78345225	uc010ork.3
chr1	78354199	78409578	uc001dic.4
chr1	78413590	78444777	uc001dii.3
chr1	78470635	78482995	uc001dij.3
chr1	78511588	78603112	uc001dik.3
chr1	78560490	78560599	uc021oov.1
chr1	78695282	78759574	uc001dil.1
chr1	78956727	79006386	uc001din.3
chr1	79086087	79111830	uc010oro.2
chr1	79115476	79129763	uc001dip.4
chr1	79152744	79152828	uc021oow.1
chr1	79355448	79472495	uc001diq.4
chr1	82266081	82458107	uc001diu.3
chr1	82312940	82313045	uc021oox.1
chr1	83439565	83451891	uc001dix.4
chr1	83911736	83920454	uc001diy.3
chr1	84041470	84326679	uc001diz.4
chr1	84259583	84259634	uc021ooy.1
chr1	84259597	84379059	uc031pmy.1
chr1	84267442	84326229	uc001dja.1
chr1	84335056	84464833	uc001djc.3
chr1	84379036	84379062	uc021ooz.1
chr1	84609951	84704181	uc001djl.3
chr1	84743003	84743140	uc021opa.1
chr1	84764048	84816481	uc001djr.3
chr1	84810360	84816481	uc001djs.3
chr1	84830640	84863576	uc009wcg.3
chr1	84864214	84880691	uc001djt.1
chr1	84944919	84964033	uc001djv.4
chr1	84964005	84972262	uc001djw.4
chr1	84972012	85014647	uc021opb.1
chr1	85018803	85040163	uc001dka.2
chr1	85093912	85097429	uc010ory.1
chr1	85109389	85156240	uc001dkj.3
chr1	85279085	85358896	uc009wcj.1
chr1	85391265	85462796	uc001dkm.3
chr1	85483764	85514223	uc001dkp.3
chr1	85527992	85598821	uc001dkt.3
chr1	85599476	85599556	uc021opc.1
chr1	85623355	85666728	uc009wcm.3
chr1	85715636	85725355	uc001dkv.3
chr1	85731459	85742587	uc021opd.1
chr1	85742040	85865646	uc001dla.2
chr1	85784167	85930889	uc001dlb.3
chr1	86046443	86049648	uc001dle.3
chr1	86115105	86174116	uc001dlh.3
chr1	86194915	86622154	uc001dlj.3
chr1	86815776	86862025	uc001dll.2
chr1	86889768	86922240	uc001dlr.4
chr1	86934394	86965974	uc001dlt.3
chr1	87012758	87046432	uc009wcs.3
chr1	87099958	87121059	uc010osh.2
chr1	87170252	87213867	uc001dly.3
chr1	87328127	87380107	uc021opi.1
chr1	87380334	87575681	uc010osk.2
chr1	87597679	87598718	uc021opj.1
chr1	87777576	87777676	uc021opk.1
chr1	87794150	87814607	uc001dmi.3
chr1	87819209	87837338	uc001dmk.3
chr1	87918922	87919056	uc021opl.1
chr1	88943510	88943810	uc021opm.1
chr1	89004735	89150887	uc021opn.1
chr1	89149921	89301938	uc001dmn.3
chr1	89318320	89357301	uc001dmo.4
chr1	89401455	89458643	uc001dmp.2
chr1	89445138	89458643	uc001dms.3
chr1	89472359	89488549	uc001dmt.3
chr1	89517986	89531043	uc001dmx.2
chr1	89573309	89591799	uc001dmz.1
chr1	89597433	89641723	uc001dna.2
chr1	89646830	89664633	uc001dnb.3
chr1	89724633	89738544	uc001dnd.3
chr1	89754940	89756045	uc031pna.1
chr1	89829435	89853719	uc001dnf.2
chr1	89873237	89890493	uc009wcy.1
chr1	89990396	90063420	uc001dni.3
chr1	90090407	90098453	uc001dnk.1
chr1	90098643	90185094	uc001dnl.4
chr1	90287479	90401989	uc001dnn.3
chr1	90453271	90453503	uc021opr.1
chr1	90458823	90460525	uc001dno.3
chr1	90460677	90494094	uc001dnq.2
chr1	91177578	91182794	uc001dns.3
chr1	91295103	91317175	uc001dnu.2
chr1	91380856	91487812	uc001dnw.3
chr1	91726322	91870426	uc001doa.4
chr1	91966403	91991321	uc001dof.3
chr1	92145899	92351836	uc001doh.3
chr1	92295329	92295626	uc021ops.1
chr1	92414927	92479985	uc010osz.2
chr1	92495532	92529093	uc001don.2
chr1	92545861	92613401	uc001doo.3
chr1	92632608	92650280	uc010otd.2
chr1	92683572	92711367	uc001doq.3
chr1	92711954	92764566	uc001dor.3
chr1	92764521	92853732	uc001dot.2
chr1	92940317	92951628	uc001dov.4
chr1	92974252	93257961	uc001dox.3
chr1	93297593	93307481	uc001doz.3
chr1	93302845	93302940	uc001dpe.2
chr1	93303574	93303627	uc021opt.1
chr1	93307716	93427079	uc001dpg.3
chr1	93544791	93604638	uc009wdj.3
chr1	93615298	93646246	uc001dpn.3
chr1	93646280	93744287	uc021opx.1
chr1	93775665	93811368	uc001dpt.2
chr1	93811477	93828148	uc001dpu.3
chr1	93913687	94020218	uc010otk.2
chr1	93981833	93981906	uc021opz.1
chr1	94027342	94146926	uc001dpz.4
chr1	94057524	94065587	uc009wdn.3
chr1	94219111	94240930	uc001dqd.1
chr1	94312387	94312467	uc021oqa.1
chr1	94313128	94313213	uc021oqb.1
chr1	94317792	94319887	uc001dqe.1
chr1	94335013	94344762	uc001dqf.3
chr1	94352589	94375012	uc001dqg.1
chr1	94458393	94586705	uc001dqh.3
chr1	94634462	94703307	uc001dqj.4
chr1	94883932	94984219	uc001dqn.4
chr1	94994731	95007413	uc001dqr.3
chr1	95123088	95285834	uc001dqu.3
chr1	95285897	95360803	uc001dqv.5
chr1	95362506	95392735	uc001dqz.4
chr1	95393583	95428826	uc021oqc.2
chr1	95448278	95538507	uc001dra.2
chr1	95550007	95550119	uc021oqd.1
chr1	95582893	95663161	uc001drb.3
chr1	95628774	95699538	uc001dre.1
chr1	95699710	95712781	uc009wdu.3
chr1	95940292	95944912	uc021oqf.1
chr1	95970531	95970615	uc021oqg.1
chr1	95975895	95981020	uc001drl.3
chr1	97161411	97161723	uc021oqh.1
chr1	97187174	97280605	uc001drq.3
chr1	97543299	98386615	uc001drv.3
chr1	97561478	97788511	uc031pne.1
chr1	98453555	98515249	uc001drx.2
chr1	98510798	98510907	uc021oqj.1
chr1	98676266	98738214	uc031pnf.1
chr1	99127235	99226056	uc010ouc.2
chr1	99207463	99207540	uc021oql.1
chr1	99355800	99470449	uc001dsb.3
chr1	99469831	99614408	uc001dsd.1
chr1	99729847	99775138	uc001dse.3
chr1	100111430	100160097	uc001dsg.3
chr1	100174258	100231349	uc001dsh.1
chr1	100178485	100178513	uc021oqn.1
chr1	100178485	100178513	uc021oqm.1
chr1	100315639	100389579	uc001dsi.1
chr1	100434000	100435404	uc001dso.2
chr1	100435991	100492534	uc001dsr.2
chr1	100503788	100548929	uc001dst.3
chr1	100549101	100598511	uc001dsu.3
chr1	100598705	100616054	uc001dsv.3
chr1	100614003	100643829	uc001dsx.2
chr1	100652477	100715409	uc001dta.3
chr1	100731713	100758325	uc001dtd.3
chr1	100746796	100746864	uc021oqp.1
chr1	100818022	100965021	uc001dtf.2
chr1	101003727	101007583	uc001dth.3
chr1	101092605	101112560	uc021oqr.1
chr1	101185195	101204601	uc001dti.3
chr1	101337927	101360735	uc001dtk.2
chr1	101361631	101447311	uc001dto.2
chr1	101455179	101491362	uc001dtt.2
chr1	101491408	101552819	uc001dua.3
chr1	101702304	101707076	uc001dud.2
chr1	101746453	101746573	uc021oqu.1
chr1	101806424	101806451	uc031pni.1
chr1	102268126	102462790	uc001dug.2
chr1	102337566	102360299	uc021oqv.1
chr1	103342022	103574052	uc001dul.3
chr1	104068577	104097859	uc010oun.2
chr1	104095895	104122149	uc001duq.3
chr1	104159998	104168400	uc001dut.3
chr1	104198301	104207173	uc001duv.3
chr1	104230039	104238912	uc001duw.1
chr1	104257374	104262492	uc010our.1
chr1	104292439	104301311	uc001duz.3
chr1	104615644	104619693	uc021oqw.1
chr1	106144773	106161557	uc001dva.3
chr1	107599266	107601916	uc010ous.2
chr1	107682628	108024475	uc001dvh.4
chr1	107937775	108024475	uc001dvi.3
chr1	107937775	108024475	uc009wem.3
chr1	108113781	108507545	uc001dvk.1
chr1	108496274	108496345	uc021oqx.1
chr1	108507064	108537229	uc031pnj.1
chr1	108677343	108742980	uc001dvn.5
chr1	108765962	108786703	uc009weo.2
chr1	108803819	108816311	uc001dvp.2
chr1	108918459	108953434	uc001dvq.3
chr1	108963310	108975804	uc001dvr.2
chr1	108992903	109013260	uc009wep.3
chr1	109102970	109181949	uc010ouy.2
chr1	109190909	109203744	uc001dvt.4
chr1	109234931	109244422	uc001dvv.4
chr1	109255555	109285367	uc001dvx.3
chr1	109289284	109352148	uc001dvy.3
chr1	109358519	109399726	uc001dwa.4
chr1	109399838	109401146	uc001dwc.3
chr1	109419602	109473044	uc010ovc.2
chr1	109472129	109506121	uc001dwf.2
chr1	109512837	109584850	uc001dwl.3
chr1	109606997	109618624	uc001dwm.1
chr1	109633402	109639554	uc001dwn.3
chr1	109642814	109643234	uc001dwo.1
chr1	109648572	109656479	uc009wew.3
chr1	109656584	109749403	uc021orb.1
chr1	109756514	109780804	uc001dwu.2
chr1	109792640	109818378	uc001dxa.4
chr1	109822175	109825790	uc001dxd.3
chr1	109834986	109849663	uc001dxk.1
chr1	109852187	109940563	uc001dxm.2
chr1	109941652	109969070	uc001dxn.3
chr1	109965120	109966952	uc021orf.1
chr1	110009099	110024764	uc001dxp.3
chr1	110026560	110035420	uc001dxr.3
chr1	110036700	110043063	uc010ovo.2
chr1	110049445	110052336	uc001dxx.4
chr1	110082493	110088455	uc001dxy.2
chr1	110091185	110138454	uc001dxz.3
chr1	110141514	110141589	uc010ovq.1
chr1	110145888	110155705	uc001dya.3
chr1	110162434	110174677	uc009wfh.2
chr1	110198697	110204331	uc001dyf.3
chr1	110230417	110236367	uc001dyk.3
chr1	110254863	110260890	uc001dyn.3
chr1	110276553	110283660	uc001dyo.2
chr1	110292701	110306644	uc001dyq.2
chr1	110453232	110473616	uc001dyw.4
chr1	110527386	110566364	uc001dyx.3
chr1	110577222	110597424	uc001dza.2
chr1	110602996	110613322	uc001dzb.3
chr1	110603303	110603368	uc021orl.1
chr1	110655061	110656569	uc001dzc.3
chr1	110693131	110744823	uc009wfq.3
chr1	110753335	110776674	uc001dzh.3
chr1	110815105	110815229	uc021orm.1
chr1	110828998	110881793	uc001dzj.3
chr1	110881944	110889303	uc001dzl.1
chr1	110887099	110888734	uc001dzn.1
chr1	110905472	110933704	uc001dzo.2
chr1	110943876	110950546	uc001dzr.3
chr1	110950430	110958896	uc031pnm.1
chr1	110993787	110999976	uc001dzs.3
chr1	111023387	111033891	uc009wfu.1
chr1	111059838	111061797	uc001dzt.1
chr1	111145775	111148975	uc009wfw.3
chr1	111214309	111217655	uc001dzv.1
chr1	111413820	111442558	uc001dzw.3
chr1	111489811	111506566	uc001eaa.3
chr1	111659953	111682838	uc001ead.4
chr1	111682248	111727724	uc001eah.1
chr1	111728590	111747160	uc001eal.2
chr1	111770280	111786062	uc001eam.3
chr1	111823145	111828730	uc009wgb.3
chr1	111833473	111863188	uc001eas.4
chr1	111889194	111895639	uc001eaw.2
chr1	111927140	111932473	uc021orp.1
chr1	111956936	111970399	uc001eba.3
chr1	111982511	111991830	uc001ebb.3
chr1	111991742	112004525	uc001ebc.3
chr1	112016603	112021134	uc001ebe.3
chr1	112025969	112046743	uc001ebf.3
chr1	112032938	112033045	uc021orr.1
chr1	112141628	112150940	uc001ebj.2
chr1	112162404	112256101	uc001ebl.3
chr1	112256829	112259310	uc001ebn.1
chr1	112264685	112282046	uc001ebo.2
chr1	112282462	112290420	uc001ebq.1
chr1	112287934	112298131	uc001ebr.3
chr1	112298189	112310199	uc001ebs.3
chr1	112318453	112531777	uc001ebu.1
chr1	112533189	112541463	uc001ebw.4
chr1	112913625	112913729	uc021oru.1
chr1	112938799	113003786	uc001ebx.3
chr1	112938880	112941439	uc001eby.1
chr1	113004391	113004455	uc021orv.1
chr1	113051369	113063910	uc001ecb.3
chr1	113066140	113162040	uc001ecd.3
chr1	113162074	113214241	uc001ecj.1
chr1	113217047	113243368	uc001eck.3
chr1	113243748	113249678	uc001ecp.1
chr1	113252615	113257950	uc001ect.1
chr1	113263188	113269856	uc001ecu.3
chr1	113362795	113393265	uc001ecw.1
chr1	113392521	113420491	uc021orw.1
chr1	113454469	113498975	uc001ecy.3
chr1	113465971	113467295	uc001eda.1
chr1	113499070	113511603	uc001edd.3
chr1	113554308	113615724	uc001ede.1
chr1	113615830	113667342	uc001edf.1
chr1	113739403	113748875	uc001edg.2
chr1	113933474	114228545	uc001edk.3
chr1	114239823	114301777	uc009wgp.1
chr1	114304453	114355070	uc001edq.3
chr1	114356432	114414375	uc001eds.3
chr1	114399256	114443859	uc001edv.2
chr1	114419435	114430169	uc001edw.3
chr1	114437370	114447525	uc001eeb.3
chr1	114447914	114456708	uc001eeg.3
chr1	114466622	114471880	uc001eek.3
chr1	114471995	114520491	uc001eem.3
chr1	114522029	114524875	uc001eer.1
chr1	114631913	114695062	uc021orz.1
chr1	114935398	115053781	uc001eew.3
chr1	115110180	115124265	uc001efa.3
chr1	115125468	115126223	uc021osb.1
chr1	115127195	115212732	uc001efd.1
chr1	115215719	115238239	uc001efe.2
chr1	115247084	115259515	uc009wgu.3
chr1	115259533	115300671	uc001efi.3
chr1	115312104	115323308	uc001efp.4
chr1	115397454	115537990	uc001efr.3
chr1	115572444	115576930	uc001efs.2
chr1	115590632	115632121	uc001eft.3
chr1	115828536	115880857	uc001efu.1
chr1	116184573	116240845	uc001efv.1
chr1	116242625	116311426	uc001efx.4
chr1	116378998	116383747	uc001efy.3
chr1	116461996	116468529	uc001efz.3
chr1	116519118	116612675	uc001egb.4
chr1	116654375	116677861	uc001egc.1
chr1	116821227	116821443	uc021osg.1
chr1	116915794	116947396	uc001ege.3
chr1	116947335	116961244	uc001egj.3
chr1	116956387	116956491	uc021osh.1
chr1	116995157	116995249	uc021osi.1
chr1	117057155	117113715	uc001egm.3
chr1	117117019	117209578	uc031pnr.1
chr1	117134723	117134827	uc021osj.1
chr1	117214370	117214449	uc021osk.1
chr1	117297085	117311851	uc001egu.4
chr1	117452688	117532972	uc001egv.1
chr1	117504968	117505093	uc021osl.1
chr1	117544371	117579173	uc010oxb.2
chr1	117602948	117645491	uc001egy.3
chr1	117637264	117637350	uc021osm.1
chr1	117653676	117664411	uc001egz.2
chr1	117686208	117753582	uc001ehb.3
chr1	117785831	117786130	uc021osp.1
chr1	117910084	118068320	uc001ehd.1
chr1	118148603	118171011	uc001ehe.3
chr1	118406106	118472302	uc001ehf.3
chr1	118472371	118503049	uc010oxe.1
chr1	118496287	118727848	uc001ehk.2
chr1	119425665	119532179	uc001ehl.1
chr1	119573838	119683295	uc001ehn.3
chr1	119683047	119726446	uc009whk.2
chr1	119911401	119936751	uc001ehr.1
chr1	119957742	119965662	uc001eht.3
chr1	120049825	120057681	uc001ehv.1
chr1	120106502	120115199	uc021osu.1
chr1	120140324	120141914	uc021osv.1
chr1	120161999	120190390	uc001ehy.1
chr1	120254418	120286849	uc001ehz.3
chr1	120290618	120311555	uc001eid.3
chr1	120336640	120354203	uc001eif.3
chr1	120377387	120387503	uc010oxk.3
chr1	120436155	120439147	uc001eij.3
chr1	120454175	120612317	uc001eik.3
chr1	120839004	120855681	uc009whp.3
chr1	120906033	120914842	uc001eio.2
chr1	120906460	120906507	uc021osz.1
chr1	120926127	120935944	uc001eip.3
chr1	121107151	121129827	uc031pnu.1
chr1	121306423	121313686	uc009wht.1
chr1	142618798	143257763	uc001eiw.1
chr1	142660105	142660135	uc001eiy.2
chr1	142672190	142672219	uc001eiz.2
chr1	142688238	142688268	uc021ota.1
chr1	142689205	142689235	uc021otb.1
chr1	142689523	142689553	uc021otc.1
chr1	142697420	142713605	uc031pnv.1
chr1	142803223	142888797	uc001ejb.3
chr1	142803530	142826645	uc001ejc.4
chr1	142851394	142851424	uc001eje.3
chr1	142853227	142855999	uc009whu.1
chr1	143168647	143168675	uc001ejg.1
chr1	143184707	143184737	uc001ejh.1
chr1	143185980	143186010	uc001ejj.3
chr1	143286955	143286985	uc021otd.1
chr1	143289062	143289092	uc001ejk.3
chr1	143402283	143402313	uc001ejl.1
chr1	143403553	143403583	uc002zkn.1
chr1	143419286	143419314	uc021ote.1
chr1	143424332	143467651	uc001ejn.1
chr1	143431367	143431397	uc001ejo.2
chr1	143647638	143744587	uc001ejp.3
chr1	143687129	143714180	uc001ejq.3
chr1	143690027	143690101	uc021otf.1
chr1	143702303	143702331	uc031pnw.1
chr1	143767143	143767881	uc001ejt.3
chr1	143879831	143879905	uc021otg.1
chr1	143896451	143913143	uc002qvn.1
chr1	143915747	144094477	uc010oxm.1
chr1	144146810	146467744	uc021ott.2
chr1	144171303	144172451	uc021otn.1
chr1	144209457	144212180	uc021otx.1
chr1	144275887	144290006	uc001ekq.1
chr1	144300511	144340773	uc001ekt.4
chr1	144301610	144301684	uc021oty.1
chr1	144308613	144308687	uc021otz.1
chr1	144340773	144341077	uc021oua.1
chr1	144363461	144364246	uc001ekw.3
chr1	144456162	144470244	uc021oub.1
chr1	144480744	144521009	uc001ela.4
chr1	144481839	144481913	uc021ouc.1
chr1	144488842	144488916	uc021oud.1
chr1	144514872	144519720	uc001elb.4
chr1	144610814	144612727	uc001elf.4
chr1	144832199	144833038	uc001els.1
chr1	144833046	144833912	uc001elt.1
chr1	144833912	144834808	uc001elu.1
chr1	144839435	144839507	uc021oug.1
chr1	144851423	144995033	uc021ouh.1
chr1	144944671	144944750	uc031pnz.1
chr1	145004780	145005287	uc001emi.4
chr1	145013718	145018395	uc001emj.3
chr1	145096406	145116997	uc031poa.1
chr1	145209110	145285912	uc001emn.4
chr1	145372087	145373798	uc001eng.1
chr1	145373798	145374694	uc001enh.1
chr1	145379319	145379391	uc021our.1
chr1	145385728	145385804	uc021ous.1
chr1	145395521	145395594	uc021out.1
chr1	145396880	145396952	uc021ouu.1
chr1	145397863	145397935	uc021ouv.1
chr1	145399232	145399304	uc021ouw.1
chr1	145413190	145417545	uc001eni.2
chr1	145438461	145442628	uc001enn.4
chr1	145456235	145470387	uc001enp.1
chr1	145470507	145475647	uc001enq.1
chr1	145477084	145499091	uc001enr.3
chr1	145500506	145501669	uc021ouz.1
chr1	145507556	145513535	uc001ent.2
chr1	145509751	145515899	uc009wiv.3
chr1	145514173	145515899	uc001enx.4
chr1	145516164	145523732	uc001eny.2
chr1	145524989	145543868	uc001eoa.3
chr1	145549208	145568526	uc001eob.1
chr1	145575987	145586546	uc001eoc.1
chr1	145586492	145589435	uc001eoe.3
chr1	145592604	145610884	uc001eoh.3
chr1	145611035	145688776	uc001eoj.3
chr1	145695797	145715565	uc001eol.1
chr1	145727665	145764206	uc001eoo.2
chr1	145764594	145827103	uc001eot.2
chr1	145883867	145924049	uc001eou.4
chr1	145924387	145934507	uc010ozf.2
chr1	145963303	145963375	uc021ova.1
chr1	145979033	145979107	uc021ovb.1
chr1	146032541	146057734	uc010ozg.2
chr1	146124160	146124283	uc021ovd.1
chr1	146215990	146217136	uc021ove.1
chr1	146476759	146476831	uc021ovh.1
chr1	146490894	146514599	uc001epd.3
chr1	146544772	146544844	uc021ovi.1
chr1	146550177	146550249	uc021ovj.1
chr1	146571065	146585928	uc031poj.1
chr1	146626684	146644129	uc001epe.3
chr1	146649429	146651528	uc001epg.1
chr1	146657837	146697230	uc001epi.2
chr1	146714290	146767447	uc001epm.5
chr1	146853913	146989699	uc021ovk.1
chr1	147013181	147098015	uc001epq.3
chr1	147119167	147142634	uc001epr.2
chr1	147228331	147232714	uc001eps.1
chr1	147374945	147381395	uc001epu.2
chr1	147400505	147465755	uc001epv.4
chr1	147466093	147484331	uc001epz.1
chr1	147505037	147505109	uc021ovn.1
chr1	147520766	147520840	uc021ovo.1
chr1	147574322	147599571	uc010ozx.2
chr1	147665992	147666115	uc021ovp.1
chr1	147680370	147691166	uc001eqh.1
chr1	147719028	147719102	uc021ovq.1
chr1	147737381	147737453	uc021ovr.1
chr1	147751385	147763966	uc001eqi.1
chr1	147753470	147753542	uc021ovs.1
chr1	147774844	147774916	uc021ovt.1
chr1	147781029	147781101	uc021ovu.1
chr1	147800936	147801008	uc021ovv.1
chr1	147801123	147816764	uc001eqk.1
chr1	147806602	147806678	uc031pom.1
chr1	147825688	147825760	uc021ovw.1
chr1	147835126	147931980	uc001eql.2
chr1	147874654	147893482	uc010paa.1
chr1	147877536	147877610	uc021ovx.1
chr1	147925822	147930669	uc009wka.2
chr1	147954634	147955419	uc001eqp.3
chr1	148000804	148000878	uc021ovy.1
chr1	148201751	148202536	uc010pag.2
chr1	148248114	148248188	uc021owi.1
chr1	148250248	148346791	uc021owp.2
chr1	148556107	148556133	uc010pay.2
chr1	148560846	148596267	uc001esc.2
chr1	148598313	148598387	uc021oxb.1
chr1	148644010	148644795	uc010paz.2
chr1	148739441	148758311	uc010pba.1
chr1	148760355	148760429	uc021oxc.1
chr1	148806014	148806799	uc010pbb.2
chr1	148876039	148902123	uc009wkv.1
chr1	148930404	148953054	uc009wkw.1
chr1	149079363	149079435	uc021oxd.1
chr1	149155827	149155899	uc021oxe.1
chr1	149161627	149161699	uc021oxf.1
chr1	149186124	149186196	uc021oxg.1
chr1	149211947	149212021	uc021oxh.1
chr1	149230569	149230643	uc021oxi.1
chr1	149230715	149232553	uc010pbe.1
chr1	149278784	149278857	uc021oxj.1
chr1	149279475	149291742	uc010pbf.2
chr1	149294665	149294736	uc021oxk.1
chr1	149298554	149298627	uc021oxl.1
chr1	149326271	149326345	uc021oxm.1
chr1	149334271	149334340	uc021oxn.1
chr1	149343080	149343151	uc021oxo.1
chr1	149369293	149378297	uc010pbh.2
chr1	149398714	149398761	uc010pbi.2
chr1	149400063	149400109	uc021oxp.1
chr1	149553002	149553787	uc001esj.3
chr1	149576261	149616786	uc001esk.5
chr1	149577754	149582605	uc009wle.2
chr1	149608608	149608682	uc021oxq.1
chr1	149615616	149615690	uc021oxr.1
chr1	149627308	149651107	uc001eso.1
chr1	149664354	149664427	uc021oxs.1
chr1	149670056	149670130	uc021oxt.1
chr1	149672904	149672976	uc031poq.1
chr1	149680209	149680280	uc021oxu.1
chr1	149684087	149684161	uc021oxv.1
chr1	149711797	149711871	uc021oxw.1
chr1	149719801	149719870	uc021oxx.1
chr1	149728270	149728341	uc021oxy.1
chr1	149754244	149783928	uc010pbj.2
chr1	149754249	149764074	uc001esp.4
chr1	149763335	149765367	uc001esq.1
chr1	149784779	149785236	uc010pbl.2
chr1	149804220	149804616	uc001ess.3
chr1	149812258	149812765	uc001esv.3
chr1	149813784	149814318	uc001esw.3
chr1	149821758	149822340	uc021oxz.1
chr1	149822627	149823161	uc001esx.3
chr1	149824180	149824687	uc001esy.3
chr1	149832329	149832725	uc001etb.3
chr1	149856009	149858232	uc001etc.3
chr1	149858524	149858961	uc001etd.3
chr1	149859018	149859466	uc001ete.3
chr1	149871154	149872348	uc001etf.3
chr1	149874871	149889434	uc001etg.3
chr1	149895208	149900144	uc001etk.2
chr1	149900542	149908791	uc001etl.4
chr1	149912231	149982686	uc001etn.3
chr1	150017381	150017452	uc021oyb.1
chr1	150039341	150117505	uc001etp.3
chr1	150122169	150131825	uc001ett.3
chr1	150190716	150208504	uc001etw.3
chr1	150230217	150237480	uc001etx.3
chr1	150237798	150241609	uc001ety.2
chr1	150245182	150253335	uc001eud.3
chr1	150255228	150259501	uc001euj.3
chr1	150266268	150280819	uc001euk.3
chr1	150293927	150325704	uc001eum.4
chr1	150336989	150449041	uc009wlr.3
chr1	150459839	150480085	uc001euq.4
chr1	150480486	150486265	uc001eus.3
chr1	150488232	150490508	uc031pos.1
chr1	150521897	150533412	uc001eux.3
chr1	150524404	150524490	uc021oye.1
chr1	150547026	150552214	uc001euz.3
chr1	150594598	150602098	uc001eve.3
chr1	150618700	150669672	uc001evj.2
chr1	150670534	150693364	uc001evk.2
chr1	150702671	150738433	uc001evn.3
chr1	150768683	150780917	uc001evp.2
chr1	150782180	150849244	uc001evr.2
chr1	150785066	150785174	uc021oyg.1
chr1	150898814	150937220	uc001evu.2
chr1	150937648	150947479	uc001evz.3
chr1	150954498	150968114	uc001ewa.2
chr1	150969300	150980854	uc010pcn.2
chr1	150980972	151008189	uc001ewh.1
chr1	150995221	150995328	uc021oyh.1
chr1	151009028	151020076	uc001ewl.2
chr1	151020258	151023871	uc001ewn.3
chr1	151023446	151032125	uc001ewp.3
chr1	151032150	151040973	uc001ewq.3
chr1	151043079	151091007	uc001ewr.2
chr1	151104162	151119140	uc001ewv.3
chr1	151129104	151132225	uc001ewx.2
chr1	151132223	151138370	uc001ewy.3
chr1	151138497	151142773	uc001ewz.3
chr1	151142462	151148547	uc001exc.4
chr1	151148775	151162689	uc001exe.2
chr1	151171020	151222007	uc001exj.3
chr1	151227196	151239954	uc001exl.3
chr1	151252499	151254405	uc001exo.3
chr1	151254790	151264381	uc001exq.3
chr1	151264272	151300191	uc001exu.3
chr1	151265261	151265284	uc031pov.1
chr1	151265461	151265487	uc031pow.1
chr1	151266582	151266606	uc031pox.1
chr1	151271387	151271414	uc031poy.1
chr1	151271499	151271523	uc031poz.1
chr1	151273139	151273440	uc021oyp.1
chr1	151274383	151274409	uc031ppa.1
chr1	151278732	151278766	uc031ppb.1
chr1	151313115	151319769	uc001exw.1
chr1	151336777	151345210	uc010pcy.3
chr1	151372040	151374412	uc001eyc.1
chr1	151375199	151431941	uc001eyd.2
chr1	151483861	151511167	uc009wmw.3
chr1	151512780	151556059	uc001eyl.3
chr1	151518271	151518367	uc021oyr.1
chr1	151584661	151671559	uc001eyn.1
chr1	151670318	151670850	uc001eyq.1
chr1	151672533	151689290	uc001eys.2
chr1	151694012	151702082	uc010pdj.1
chr1	151732122	151736040	uc001eyv.3
chr1	151739130	151743806	uc010pdm.2
chr1	151745954	151763010	uc001eza.4
chr1	151763317	151766878	uc001eze.3
chr1	151772764	151777882	uc001ezf.1
chr1	151778546	151804348	uc001ezh.3
chr1	151810338	151813033	uc010pdq.1
chr1	151810944	151816641	uc010pdr.2
chr1	151819576	151826173	uc021oyw.1
chr1	151843342	151882361	uc001ezj.2
chr1	151955385	151966714	uc001ezl.3
chr1	151967006	152015250	uc001ezm.1
chr1	152004981	152009511	uc001ezn.3
chr1	152056619	152061540	uc001ezo.1
chr1	152078792	152087930	uc001ezp.3
chr1	152126070	152131704	uc001ezs.1
chr1	152184551	152196672	uc001ezt.2
chr1	152274650	152297679	uc001ezu.1
chr1	152285934	152339168	uc001ezv.3
chr1	152321212	152332482	uc001ezw.4
chr1	152381718	152386750	uc001ezx.2
chr1	152483319	152484653	uc001ezy.3
chr1	152486977	152488481	uc001ezz.3
chr1	152538174	152539229	uc001faa.4
chr1	152551859	152552980	uc001fab.3
chr1	152573137	152573562	uc001fac.2
chr1	152586286	152586574	uc010pds.2
chr1	152595309	152595579	uc010pdt.2
chr1	152635886	152637135	uc001fag.4
chr1	152647790	152649049	uc001fah.4
chr1	152658598	152659876	uc001fai.3
chr1	152670839	152671918	uc001faj.3
chr1	152681522	152681910	uc001fak.2
chr1	152691997	152692905	uc010pdu.2
chr1	152730505	152734529	uc001fal.1
chr1	152748847	152749445	uc010pdv.2
chr1	152758752	152760901	uc001fan.3
chr1	152769226	152770657	uc009wnp.3
chr1	152777310	152779107	uc001fap.1
chr1	152784446	152785585	uc001faq.3
chr1	152799948	152800573	uc010pdw.2
chr1	152815329	152816459	uc001fas.4
chr1	152850797	152857523	uc001fat.3
chr1	152881038	152884362	uc001fau.3
chr1	152943127	152945069	uc001fav.1
chr1	152956563	152958290	uc001faw.3
chr1	152974222	152976332	uc001faz.4
chr1	153003678	153005376	uc001fba.3
chr1	153012200	153013594	uc001fbb.2
chr1	153028595	153029988	uc001fbd.3
chr1	153042717	153044084	uc001fbg.3
chr1	153065610	153067001	uc001fbh.3
chr1	153084612	153085989	uc001fbi.3
chr1	153112593	153113969	uc001fbj.3
chr1	153122057	153123427	uc009wod.2
chr1	153175905	153177601	uc001fbl.4
chr1	153190059	153191793	uc021ozw.1
chr1	153195064	153195185	uc021ozx.1
chr1	153232178	153234600	uc001fbm.3
chr1	153270337	153283194	uc001fbn.1
chr1	153302596	153321022	uc001fbo.3
chr1	153330329	153333503	uc001fbq.3
chr1	153346183	153348075	uc001fbr.1
chr1	153362507	153363664	uc001fbs.3
chr1	153388999	153395701	uc001fbt.1
chr1	153409470	153412503	uc010pdx.2
chr1	153430219	153433137	uc001fbv.1
chr1	153507075	153508717	uc001fbw.1
chr1	153509622	153514241	uc001fbx.3
chr1	153516094	153518282	uc001fbz.3
chr1	153518229	153524245	uc009wog.1
chr1	153519808	153521734	uc001fca.1
chr1	153533584	153538306	uc001fcb.3
chr1	153579366	153585514	uc001fcd.1
chr1	153586731	153588808	uc001fce.3
chr1	153591275	153600117	uc001fch.3
chr1	153600872	153604513	uc001fck.1
chr1	153606457	153618782	uc001fcn.2
chr1	153631129	153634328	uc001fcq.4
chr1	153634263	153643504	uc001fcr.4
chr1	153643725	153643797	uc021paa.1
chr1	153651163	153666468	uc001fcs.4
chr1	153700566	153746555	uc001fct.3
chr1	153747767	153752633	uc001fcz.3
chr1	153777202	153895451	uc001fdb.4
chr1	153901976	153919154	uc001fdd.1
chr1	153920147	153931132	uc021pab.1
chr1	153931574	153940660	uc031ppi.1
chr1	153940674	153946840	uc001fdr.3
chr1	153946744	153950451	uc001fds.3
chr1	153954092	153958853	uc001fdt.2
chr1	153963238	153964631	uc001fdv.3
chr1	153965167	154127592	uc001fdw.3
chr1	154134288	154164611	uc001fec.2
chr1	154166140	154166219	uc021pac.1
chr1	154171561	154178841	uc001fee.2
chr1	154179182	154193273	uc001fei.2
chr1	154193324	154243329	uc001fep.4
chr1	154232202	154232338	uc021pae.1
chr1	154245038	154248355	uc001fes.3
chr1	154293591	154297801	uc001feu.3
chr1	154300275	154323780	uc001fex.3
chr1	154377668	154441926	uc001fez.2
chr1	154451953	154474526	uc001ffb.3
chr1	154474694	154520623	uc009wow.3
chr1	154521050	154531120	uc001fff.1
chr1	154540256	154552353	uc001ffg.3
chr1	154554533	154580724	uc001ffh.3
chr1	154669941	154842754	uc021pah.1
chr1	154897207	154909484	uc001ffq.3
chr1	154916558	154928567	uc001ffr.3
chr1	154929501	154934258	uc001fft.3
chr1	154934773	154943223	uc001ffw.3
chr1	154947117	154951725	uc001fgb.3
chr1	154948168	154948259	uc021pai.1
chr1	154955769	154965587	uc001fgf.2
chr1	154966061	154966791	uc001fgi.3
chr1	154975105	154991001	uc010peq.3
chr1	154979449	154979509	uc021pal.1
chr1	154991002	155006257	uc001fgm.3
chr1	155006281	155023406	uc001fgn.2
chr1	155017667	155036467	uc021pan.2
chr1	155023747	155035252	uc001fgr.2
chr1	155051347	155060014	uc001fhf.3
chr1	155100348	155107386	uc001fhh.3
chr1	155108287	155111334	uc001fhj.4
chr1	155112366	155112883	uc001fhm.3
chr1	155141883	155145804	uc001fho.3
chr1	155146262	155157447	uc001fhs.2
chr1	155155666	155157427	uc021pao.2
chr1	155158299	155162706	uc031ppv.1
chr1	155161986	155162007	uc021par.1
chr1	155164967	155165063	uc021pas.1
chr1	155165378	155177772	uc001fix.3
chr1	155178489	155183624	uc001fjb.3
chr1	155183615	155188809	uc001fjd.3
chr1	155203712	155204236	uc021pav.1
chr1	155204238	155214653	uc001fjl.3
chr1	155216995	155225274	uc001fjm.3
chr1	155225769	155232176	uc001fjs.3
chr1	155232658	155243281	uc001fjw.3
chr1	155247217	155259639	uc001fjz.2
chr1	155259083	155271225	uc001fkb.4
chr1	155278538	155290457	uc001fkc.2
chr1	155290250	155293938	uc001fki.3
chr1	155290639	155300909	uc001fkj.2
chr1	155305051	155532324	uc001fkt.3
chr1	155402970	155404053	uc010pgd.2
chr1	155403092	155403473	uc010pgc.1
chr1	155531771	155533735	uc010pge.2
chr1	155531863	155533735	uc001fkv.3
chr1	155579960	155584758	uc001fky.4
chr1	155596545	155618335	uc001flf.3
chr1	155658881	155708800	uc001flr.3
chr1	155715558	155718322	uc010pgo.2
chr1	155716586	155720673	uc031pqb.1
chr1	155719509	155826972	uc001fly.1
chr1	155829259	155854990	uc001fmg.3
chr1	155867598	155880706	uc031pqc.1
chr1	155882835	155904188	uc001fmi.1
chr1	155889699	155889833	uc001fmk.1
chr1	155895748	155895877	uc001fmn.1
chr1	155911479	155912625	uc010pgs.2
chr1	155916629	155948336	uc001fmt.2
chr1	155978838	155990758	uc001fmx.3
chr1	156005091	156023516	uc001fna.3
chr1	156024516	156028605	uc001fnb.3
chr1	156030965	156040295	uc001fnc.3
chr1	156041803	156051789	uc001fnd.4
chr1	156084460	156109880	uc001fni.3
chr1	156104903	156107058	uc009wrp.3
chr1	156123321	156147542	uc009wrq.3
chr1	156163729	156182587	uc001fnp.3
chr1	156182778	156213123	uc021pbb.1
chr1	156211950	156213123	uc001fnt.3
chr1	156213111	156217908	uc010phh.2
chr1	156219014	156252620	uc001foc.4
chr1	156254069	156262234	uc009wrw.3
chr1	156262477	156265480	uc001foh.4
chr1	156268414	156269428	uc001fok.3
chr1	156278751	156308206	uc001fol.2
chr1	156307104	156316785	uc001foo.3
chr1	156338979	156355013	uc010pho.3
chr1	156374048	156377645	uc001fot.1
chr1	156374054	156399340	uc001fou.1
chr1	156390132	156390221	uc001foz.1
chr1	156433512	156460391	uc001fpb.4
chr1	156453889	156454002	uc021pbe.1
chr1	156495196	156542396	uc001fpf.3
chr1	156549518	156556562	uc021pbf.1
chr1	156561557	156564091	uc001fph.3
chr1	156564099	156571279	uc001fpl.3
chr1	156589085	156595517	uc001fpn.1
chr1	156589085	156594259	uc031pqq.1
chr1	156611456	156613427	uc021pbg.1
chr1	156611739	156629324	uc001fpp.3
chr1	156638555	156647189	uc001fpq.3
chr1	156669399	156675459	uc001fpr.3
chr1	156692412	156697705	uc001fps.1
chr1	156698262	156706752	uc001fpu.3
chr1	156707093	156710923	uc001fpx.1
chr1	156711898	156721543	uc001fpy.4
chr1	156737273	156770609	uc001fqa.3
chr1	156776034	156786640	uc009wsh.2
chr1	156810664	156828712	uc010pht.2
chr1	156830670	156851642	uc001fqh.1
chr1	156863522	156886226	uc001fqj.1
chr1	156890423	156902880	uc001fqm.2
chr1	156904631	157015162	uc001fqn.3
chr1	156905922	156906036	uc021pbj.1
chr1	157061834	157069600	uc001fqq.2
chr1	157094458	157108383	uc001fqr.2
chr1	157098153	157098463	uc001fqs.3
chr1	157483166	157522310	uc009wsm.3
chr1	157543538	157567870	uc001fqw.3
chr1	157647977	157670647	uc001frb.3
chr1	157715522	157746922	uc001fre.2
chr1	157764193	157789940	uc001frg.3
chr1	157800703	157811634	uc001frk.4
chr1	157895763	157918861	uc001frl.1
chr1	157963062	158065844	uc001frn.4
chr1	158066315	158069836	uc001frp.2
chr1	158101833	158110430	uc001frq.3
chr1	158149736	158156216	uc001frr.3
chr1	158169170	158173667	uc001frs.1
chr1	158223926	158228058	uc001frt.3
chr1	158259562	158264564	uc001fru.3
chr1	158297739	158301321	uc001frx.3
chr1	158323485	158327343	uc001fse.3
chr1	158368311	158369256	uc010pih.2
chr1	158389717	158390656	uc010pii.2
chr1	158435351	158436293	uc010pij.2
chr1	158444244	158464676	uc001fso.1
chr1	158449667	158450675	uc010pik.2
chr1	158516917	158517895	uc010pil.2
chr1	158532440	158533394	uc010pim.2
chr1	158548708	158549689	uc010pin.2
chr1	158576228	158577170	uc010pio.2
chr1	158580495	158656506	uc001fst.1
chr1	158669467	158670442	uc001fsu.1
chr1	158686957	158687905	uc021pbn.1
chr1	158724605	158725637	uc001fsw.1
chr1	158735533	158736472	uc010piq.2
chr1	158746471	158747425	uc010pir.2
chr1	158801167	158819270	uc001fsz.1
chr1	158901336	158946849	uc001ftb.3
chr1	158979681	159024945	uc010pis.2
chr1	159032274	159046647	uc001ftj.1
chr1	159111400	159111474	uc021pbo.1
chr1	159141376	159172932	uc001ftk.2
chr1	159165774	159172212	uc001ftm.2
chr1	159175048	159176290	uc001ftp.4
chr1	159259505	159278014	uc001ftq.3
chr1	159283459	159284449	uc010piu.2
chr1	159315958	159438858	uc001fts.4
chr1	159409511	159410600	uc010piv.2
chr1	159504867	159505797	uc010piw.2
chr1	159557615	159558661	uc001ftv.3
chr1	159682078	159684379	uc001ftw.3
chr1	159750758	159752333	uc001ftz.1
chr1	159772172	159786047	uc001fud.4
chr1	159796478	159807282	uc001fue.4
chr1	159824105	159832447	uc001fuh.3
chr1	159842153	159869906	uc001fui.3
chr1	159887896	159894341	uc031pqu.1
chr1	159896828	159915386	uc001fur.2
chr1	159921281	159924044	uc001fus.3
chr1	159931013	159948876	uc001fuu.3
chr1	159997461	160001783	uc001fuv.1
chr1	160007256	160040051	uc001fuw.2
chr1	160051359	160059212	uc001fuy.1
chr1	160061128	160068618	uc009wtf.3
chr1	160085519	160113374	uc001fvc.3
chr1	160121351	160156767	uc001fve.4
chr1	160160284	160171676	uc010pja.2
chr1	160171988	160178659	uc001fvj.1
chr1	160175124	160185162	uc001fvk.3
chr1	160185504	160232350	uc010pjb.1
chr1	160258376	160313354	uc001fvv.4
chr1	160287054	160288260	uc001fvw.3
chr1	160295893	160296006	uc021pbr.1
chr1	160313062	160328742	uc001fvx.3
chr1	160336860	160342638	uc001fwa.2
chr1	160370363	160398468	uc001fwc.2
chr1	160454819	160493052	uc001fwe.2
chr1	160510883	160549306	uc001fwh.4
chr1	160579890	160617081	uc001fwl.4
chr1	160650211	160681641	uc001fwo.2
chr1	160709076	160724601	uc001fwq.3
chr1	160765863	160798045	uc001fwu.4
chr1	160799949	160832692	uc009wtq.3
chr1	160846329	160854960	uc001fxc.3
chr1	160914815	160924589	uc001fxd.3
chr1	160965000	160991133	uc009wtt.3
chr1	161009040	161014727	uc031pqv.1
chr1	161016731	161039760	uc001fxl.3
chr1	161040780	161059385	uc001fxo.2
chr1	161068180	161070138	uc001fxr.3
chr1	161070345	161087866	uc001fxu.3
chr1	161087861	161090984	uc001fxv.2
chr1	161090768	161102256	uc001fxz.3
chr1	161123533	161128646	uc001fyd.4
chr1	161129253	161135516	uc010pkd.2
chr1	161136180	161141010	uc001fyg.2
chr1	161141099	161147758	uc001fys.2
chr1	161159537	161168845	uc001fyt.4
chr1	161171936	161184184	uc001fyw.3
chr1	161185086	161189038	uc001fyz.1
chr1	161192082	161193418	uc001fzc.1
chr1	161195832	161200407	uc001fzd.3
chr1	161196975	161197051	uc031pqw.1
chr1	161199455	161208000	uc001fzp.3
chr1	161228516	161255240	uc001gad.3
chr1	161274524	161279762	uc001gaf.4
chr1	161284165	161334535	uc001gag.3
chr1	161334520	161337673	uc001gal.4
chr1	161369489	161369562	uc021pbx.1
chr1	161390660	161390733	uc021pby.1
chr1	161391882	161391954	uc021pbz.1
chr1	161397866	161397940	uc021pca.1
chr1	161409960	161410032	uc021pcb.1
chr1	161410614	161410686	uc021pcc.1
chr1	161411322	161411405	uc021pcd.1
chr1	161413093	161413164	uc021pce.1
chr1	161417017	161417089	uc021pcf.1
chr1	161417374	161417446	uc021pcg.1
chr1	161418032	161418104	uc021pch.1
chr1	161418740	161418823	uc021pci.1
chr1	161420466	161420537	uc021pcj.1
chr1	161424397	161424469	uc021pck.1
chr1	161424755	161424827	uc021pcl.1
chr1	161425413	161425485	uc021pcm.1
chr1	161426121	161426204	uc021pcn.1
chr1	161427897	161427968	uc021pco.1
chr1	161431808	161431880	uc021pcp.1
chr1	161432165	161432237	uc021pcq.1
chr1	161432823	161432895	uc021pcr.1
chr1	161433531	161433614	uc021pcs.1
chr1	161435257	161435328	uc021pct.1
chr1	161439188	161439260	uc021pcu.1
chr1	161439546	161439618	uc021pcv.1
chr1	161440204	161440276	uc021pcw.1
chr1	161440912	161440995	uc021pcx.1
chr1	161450355	161450426	uc021pcy.1
chr1	161475204	161489360	uc001gan.3
chr1	161492934	161493006	uc021pdb.1
chr1	161493636	161493707	uc021pdc.1
chr1	161494035	161496687	uc001gaq.3
chr1	161500131	161500214	uc021pdd.1
chr1	161500902	161500974	uc021pde.1
chr1	161501914	161501986	uc021pdf.1
chr1	161510030	161510104	uc021pdg.1
chr1	161511550	161519818	uc001gar.3
chr1	161551128	161571010	uc021pdi.2
chr1	161574587	161574659	uc021pdk.1
chr1	161575848	161578341	uc010pkp.1
chr1	161581735	161581819	uc021pdl.1
chr1	161582507	161582579	uc021pdm.1
chr1	161591464	161591538	uc021pdn.1
chr1	161592987	161601252	uc009wul.3
chr1	161632904	161648444	uc001gaz.2
chr1	161653494	161655042	uc001gbc.3
chr1	161676761	161684142	uc001gbe.3
chr1	161692442	161697933	uc001gbi.3
chr1	161719580	161726952	uc001gbo.3
chr1	161736033	161933860	uc001gbs.3
chr1	161952981	161993644	uc001gbu.3
chr1	162039580	162339813	uc001gbv.2
chr1	162126896	162126972	uc021pdp.1
chr1	162308432	162308532	uc021pdq.1
chr1	162312335	162312430	uc021pdr.1
chr1	162343514	162346644	uc001gbx.2
chr1	162348695	162356608	uc010pkt.1
chr1	162365055	162381928	uc001gbz.1
chr1	162467594	162499419	uc001gcc.2
chr1	162531295	162569633	uc001gce.4
chr1	162602227	162750247	uc001gcg.3
chr1	162747518	162747805	uc021pds.1
chr1	162751900	162756362	uc001gch.1
chr1	162760495	162782608	uc001gci.3
chr1	162824086	162838605	uc001gck.2
chr1	163038395	163046592	uc001gcl.4
chr1	163112088	163172963	uc021pdt.1
chr1	163291722	163325553	uc001gcr.1
chr1	163438285	163438395	uc021pdv.1
chr1	163479273	163479385	uc021pdw.1
chr1	164528596	164821060	uc001gct.3
chr1	164595272	164651720	uc001gcu.1
chr1	164738352	164743878	uc021pdx.1
chr1	165171103	165325478	uc001gcz.2
chr1	165370158	165414592	uc001gda.3
chr1	165446078	165551341	uc001gdc.2
chr1	165513477	165533185	uc001gde.2
chr1	165566149	165566222	uc021peb.1
chr1	165600109	165625372	uc001gdf.3
chr1	165631448	165667900	uc001gdh.1
chr1	165667986	165679199	uc001gdi.3
chr1	165693527	165738159	uc001gdj.5
chr1	165738165	165744685	uc001gdo.3
chr1	165796731	165880855	uc001gdp.3
chr1	166011480	166011587	uc021ped.1
chr1	166026689	166135958	uc021pee.1
chr1	166039068	166135958	uc021pef.1
chr1	166123979	166124035	uc021peg.1
chr1	166573152	166594473	uc010pld.2
chr1	166808723	166823709	uc001gdt.1
chr1	166825748	166845654	uc001gdw.3
chr1	166882440	166944561	uc001gdx.2
chr1	166958518	166991447	uc001gdy.1
chr1	167022081	167059868	uc001gea.1
chr1	167064070	167098402	uc001geb.1
chr1	167144598	167165042	uc021pei.1
chr1	167190065	167396582	uc001gee.3
chr1	167399876	167487847	uc001gei.4
chr1	167510250	167523056	uc001gel.3
chr1	167599473	167675486	uc001gem.3
chr1	167683961	167684033	uc021pej.1
chr1	167684724	167684796	uc021pek.1
chr1	167691186	167761156	uc001geo.3
chr1	167778624	167883453	uc001ger.3
chr1	167885912	167906307	uc001get.3
chr1	167905796	168045083	uc001gex.3
chr1	168048779	168106811	uc009wvo.4
chr1	168148170	168171351	uc001gfg.3
chr1	168195254	168212088	uc001gfi.4
chr1	168214818	168216668	uc010plo.2
chr1	168218461	168220378	uc001gfk.2
chr1	168250277	168283664	uc001gfl.3
chr1	168344761	168344859	uc021pel.1
chr1	168369426	168391894	uc021pem.1
chr1	168510002	168513235	uc001gfn.4
chr1	168545710	168551315	uc001gfo.2
chr1	168664694	168698442	uc001gfp.3
chr1	168756178	168762126	uc001gfq.3
chr1	169075946	169101960	uc001gfr.1
chr1	169101768	169337186	uc001gfu.3
chr1	169337193	169365780	uc001gfy.3
chr1	169364113	169396670	uc001gfz.1
chr1	169433148	169455208	uc001gge.4
chr1	169481191	169555769	uc001ggg.1
chr1	169558087	169599377	uc001ggi.4
chr1	169659805	169680843	uc001ggk.3
chr1	169691780	169703220	uc001ggm.4
chr1	169761669	169764061	uc001ggn.4
chr1	169764549	169822229	uc001ggq.3
chr1	169822214	169863100	uc001ggs.3
chr1	169828896	169829193	uc021peo.1
chr1	169890469	170043879	uc001ggv.3
chr1	170115187	170136923	uc009wvv.1
chr1	170120518	170120603	uc021per.1
chr1	170120518	170120603	uc021peq.1
chr1	170240545	170253349	uc001ggx.3
chr1	170429593	170489887	uc001ggy.1
chr1	170501262	170522974	uc001gha.2
chr1	170633312	170708541	uc001ghf.3
chr1	170904611	171033906	uc010plz.2
chr1	171060017	171086959	uc001ghh.3
chr1	171070868	171070947	uc021pes.1
chr1	171070879	171070939	uc031prg.1
chr1	171106878	171130702	uc001ghj.1
chr1	171154387	171181822	uc001ghk.1
chr1	171217662	171255113	uc001ghl.3
chr1	171223044	171223161	uc021pet.1
chr1	171283485	171311223	uc001gho.3
chr1	171308034	171310463	uc010pmf.2
chr1	171454665	171562650	uc010pmg.2
chr1	171604556	171621773	uc001ghu.3
chr1	171669295	171711379	uc001ghx.2
chr1	171750760	171766856	uc001ghz.3
chr1	171810617	172381857	uc001gie.4
chr1	171964791	171964994	uc021peu.1
chr1	172106018	172113975	uc021pev.2
chr1	172107947	172108028	uc021pew.1
chr1	172157538	172157607	uc021pex.1
chr1	172389827	172437969	uc001gik.3
chr1	172410596	172413230	uc001gio.3
chr1	172502259	172580973	uc001giq.4
chr1	172628184	172636012	uc001gis.3
chr1	173010359	173020103	uc001giu.2
chr1	173152869	173176471	uc001giw.3
chr1	173204198	173446294	uc001gix.1
chr1	173387087	173430501	uc010pmp.1
chr1	173446485	173457946	uc001giy.1
chr1	173469603	173572233	uc001giz.2
chr1	173577474	173639001	uc001gja.1
chr1	173604660	173606272	uc021pez.1
chr1	173684079	173755840	uc001gjc.3
chr1	173768687	173793270	uc001gje.4
chr1	173793796	173827682	uc001gjh.2
chr1	173832385	173833079	uc021pfa.1
chr1	173833038	173837125	uc001gjk.3
chr1	173833312	173833355	uc009wwi.1
chr1	173834487	173834568	uc009wwk.1
chr1	173834770	173834824	uc009wwl.3
chr1	173835105	173835166	uc001gjn.1
chr1	173835448	173835509	uc009wwm.1
chr1	173836016	173836076	uc009wwo.1
chr1	173836811	173836883	uc001gjo.1
chr1	173837492	173855774	uc009wwp.1
chr1	173867684	173867712	uc021pfb.1
chr1	173872941	173886516	uc001gjt.3
chr1	173900351	173962210	uc001gju.4
chr1	174128551	174927327	uc001gjx.3
chr1	174417211	174418683	uc001gka.1
chr1	174417553	174418334	uc010pmu.1
chr1	174968891	174981163	uc001gkj.1
chr1	174982093	174992591	uc001gkk.3
chr1	175036993	175117202	uc001gkl.1
chr1	175126122	175161949	uc001gkn.3
chr1	175291934	175712752	uc009wwu.1
chr1	175526123	175533005	uc001gkr.1
chr1	175913966	176176370	uc001gku.1
chr1	175937532	175937676	uc001gkx.1
chr1	176432306	176811970	uc001gkz.3
chr1	176830202	177134024	uc001glc.3
chr1	176998498	176998581	uc021pfc.1
chr1	177140632	177251558	uc001glf.3
chr1	177897488	177939050	uc001gli.1
chr1	178060642	178062735	uc001gln.1
chr1	178062863	178448648	uc001glq.3
chr1	178482211	178491789	uc001glt.2
chr1	178511930	178518024	uc001glx.1
chr1	178530047	178530164	uc021pff.1
chr1	178646883	178646969	uc021pfg.1
chr1	178678037	178678110	uc021pfh.1
chr1	178694299	178889237	uc001glz.3
chr1	178818669	178840215	uc001gma.3
chr1	178995073	179045702	uc001gmc.3
chr1	179051111	179065129	uc001gmd.3
chr1	179068461	179112224	uc001gmi.4
chr1	179170962	179170982	uc021pfi.1
chr1	179262848	179327814	uc001gml.3
chr1	179334854	179523870	uc001gmo.3
chr1	179519673	179545084	uc001gmq.4
chr1	179556206	179556240	uc021pfk.1
chr1	179556492	179556554	uc001gmt.3
chr1	179556689	179556738	uc010pnm.1
chr1	179556805	179556836	uc001gmu.3
chr1	179556860	179556891	uc001gmv.3
chr1	179557007	179557111	uc001gmw.3
chr1	179557405	179557438	uc001gmx.3
chr1	179557454	179557483	uc021pfl.1
chr1	179557496	179557540	uc001gmz.1
chr1	179557562	179557602	uc001gna.3
chr1	179557656	179557734	uc001gnb.3
chr1	179557814	179557850	uc001gnc.1
chr1	179558162	179558192	uc010pnn.1
chr1	179558664	179558701	uc001gnd.3
chr1	179558711	179558760	uc001gne.3
chr1	179558972	179559006	uc010pno.2
chr1	179560756	179660407	uc010pnp.2
chr1	179712297	179785333	uc001gnj.3
chr1	179809101	179846941	uc001gnk.3
chr1	179828164	179846941	uc001gnn.3
chr1	179851176	179889212	uc001gnp.2
chr1	179923907	180084015	uc001gnt.3
chr1	180123967	180167169	uc001gnz.3
chr1	180167143	180169859	uc001god.4
chr1	180184275	180184348	uc021pfo.1
chr1	180199432	180244188	uc001goe.2
chr1	180238797	180243816	uc001gof.2
chr1	180257351	180472022	uc001gog.3
chr1	180318292	180318386	uc021pfp.1
chr1	180407448	180407525	uc021pfq.1
chr1	180528109	180535654	uc001goh.1
chr1	180601145	180859415	uc001goi.3
chr1	180882312	180915239	uc001gok.2
chr1	180942175	180992046	uc021pfr.1
chr1	181002560	181031074	uc001goq.2
chr1	181057637	181059979	uc001got.4
chr1	181143619	181151342	uc009wxq.3
chr1	181205523	181207740	uc031pri.1
chr1	181382578	181382716	uc001gov.3
chr1	181452685	181775921	uc009wxt.3
chr1	181456006	181456111	uc021pfs.1
chr1	181740701	181740780	uc021pft.1
chr1	182023704	182030847	uc001goz.3
chr1	182350838	182360539	uc001gpa.2
chr1	182367251	182369751	uc001gpe.3
chr1	182376755	182383948	uc009wxv.1
chr1	182419255	182529732	uc009wxw.3
chr1	182542768	182558394	uc009wxz.2
chr1	182567757	182573548	uc001gpl.4
chr1	182584274	182585764	uc021pfy.1
chr1	182615791	182642067	uc001gpm.1
chr1	182758583	182799519	uc009wyb.3
chr1	182808438	182857117	uc001gpr.3
chr1	182868999	182922553	uc001gpu.3
chr1	182992594	183114727	uc001gpy.4
chr1	183155173	183214262	uc001gqa.2
chr1	183217371	183387634	uc001gqc.2
chr1	183430010	183441117	uc001gqe.3
chr1	183441505	183523328	uc001gqf.3
chr1	183524696	183560056	uc001gqk.4
chr1	183595327	183605076	uc021pgb.2
chr1	183605207	183897666	uc001gqm.3
chr1	183615410	183622448	uc001gqn.3
chr1	183904965	184006863	uc001gqr.3
chr1	184020810	184043344	uc001gqt.4
chr1	184356149	184598155	uc001gqv.1
chr1	184659624	184724041	uc010pok.2
chr1	184727265	184730133	uc001gqz.1
chr1	184760158	184943718	uc001gra.4
chr1	185014550	185071740	uc001grc.1
chr1	185087217	185126116	uc001grf.4
chr1	185126191	185260913	uc001grg.4
chr1	185220508	185220534	uc021pgd.1
chr1	185265521	185286461	uc001grl.3
chr1	185292978	185304171	uc001grn.4
chr1	185405427	185405453	uc021pge.1
chr1	185527511	185597620	uc001gro.3
chr1	185576315	185591914	uc001grp.1
chr1	185703682	186160085	uc001grq.1
chr1	186029866	186446655	uc021pgf.1
chr1	186265404	186283688	uc001gru.4
chr1	186280785	186344457	uc001grv.3
chr1	186348898	186390503	uc021pgj.1
chr1	186369703	186370587	uc001gry.3
chr1	186412697	186430240	uc001gsa.4
chr1	186640943	186649559	uc001gsb.3
chr1	186798031	186958113	uc001gsc.3
chr1	187610357	187612988	uc001gsd.3
chr1	190066796	190446759	uc001gse.1
chr1	190447389	190450524	uc001gsf.3
chr1	190594019	190770788	uc021pgm.2
chr1	192127591	192154945	uc001gsg.3
chr1	192286121	192336414	uc001gsh.3
chr1	192544856	192549159	uc001gsi.1
chr1	192605267	192629440	uc001gsk.3
chr1	192778168	192781407	uc001gsl.3
chr1	192844815	192845115	uc021pgn.1
chr1	192981495	193028523	uc001gsm.3
chr1	193026410	193026545	uc021pgo.1
chr1	193027466	193029189	uc001gsr.1
chr1	193028551	193055115	uc001gss.3
chr1	193065594	193074608	uc001gsz.2
chr1	193091087	193223942	uc001gtb.3
chr1	193105632	193105713	uc021pgq.1
chr1	193147859	193155743	uc001gtc.4
chr1	196194912	196577499	uc001gtd.1
chr1	196551542	196551611	uc021pgs.1
chr1	196621007	196716634	uc001gtj.4
chr1	196743929	196763203	uc001gtl.3
chr1	196912933	196928356	uc001gtq.1
chr1	196946666	196978803	uc001gts.4
chr1	197008320	197036397	uc001gtt.1
chr1	197053256	197115824	uc001gtu.3
chr1	197122813	197169672	uc001gtx.1
chr1	197237333	197447585	uc001gtz.3
chr1	197473878	197744623	uc021pgu.1
chr1	197871681	197876497	uc001guh.3
chr1	197886516	197899273	uc001guk.1
chr1	198126107	198291548	uc001gun.4
chr1	198492351	198510075	uc001gup.3
chr1	198608097	198726605	uc001gur.2
chr1	198777131	198906558	uc021pgz.1
chr1	198828001	198828111	uc001gux.1
chr1	198828172	198828282	uc001guy.3
chr1	198975168	198990166	uc001gva.4
chr1	199996729	200146550	uc001gvb.4
chr1	200023188	200023293	uc021pha.1
chr1	200311671	200342920	uc001gvd.1
chr1	200375419	200379166	uc001gve.3
chr1	200380927	200444641	uc010ppi.1
chr1	200520624	200589862	uc010ppk.1
chr1	200613164	200639126	uc009wzk.3
chr1	200708685	200829831	uc001gvk.3
chr1	200842082	200843306	uc001gvn.2
chr1	200860626	200884864	uc001gvo.4
chr1	200898071	200935796	uc001gvp.1
chr1	200938513	200992828	uc001gvs.2
chr1	200993076	200997920	uc001gvu.3
chr1	201008639	201081694	uc001gvv.3
chr1	201083080	201084500	uc031prl.1
chr1	201103899	201123632	uc001gvy.3
chr1	201159952	201198080	uc001gwc.3
chr1	201252579	201302121	uc001gwd.3
chr1	201328135	201346828	uc001gwf.4
chr1	201349965	201368669	uc001gwm.3
chr1	201372894	201390874	uc021phd.1
chr1	201434606	201438299	uc031prm.1
chr1	201434606	201438299	uc001gwq.4
chr1	201452657	201475967	uc021phh.1
chr1	201489031	201489720	uc010ppt.3
chr1	201604354	201606516	uc001gwt.1
chr1	201617449	201796102	uc001gwu.3
chr1	201688635	201688755	uc031prn.1
chr1	201777738	201777830	uc021phj.1
chr1	201798287	201853422	uc001gwz.3
chr1	201857804	201861715	uc001gxa.3
chr1	201865583	201915716	uc021phl.1
chr1	201924618	201939789	uc001gxc.3
chr1	201951765	201975275	uc001gxd.3
chr1	201979689	201986315	uc001gxh.4
chr1	202092028	202098634	uc001gxj.3
chr1	202102531	202113871	uc001gxk.2
chr1	202116140	202130716	uc010ppx.2
chr1	202152694	202156267	uc009xaa.2
chr1	202156130	202158577	uc001gxs.3
chr1	202163117	202288889	uc001gxu.3
chr1	202300784	202311094	uc001gxx.4
chr1	202317829	202557697	uc001gya.2
chr1	202379235	202379335	uc021phn.1
chr1	202559724	202679551	uc010pqb.2
chr1	202696531	202777549	uc001gyf.3
chr1	202780073	202781041	uc031prp.1
chr1	202794328	202795421	uc021php.1
chr1	202827080	202830736	uc001gyj.3
chr1	202830881	202844369	uc001gyk.1
chr1	202847409	202858385	uc001gyl.3
chr1	202860229	202896371	uc001gyo.1
chr1	202909959	202927524	uc001gyq.5
chr1	202931000	202936404	uc001gyt.2
chr1	202955579	202976393	uc021phr.1
chr1	202976533	202993197	uc001gyu.1
chr1	203003611	203047864	uc009xaj.3
chr1	203052256	203055166	uc001gzd.4
chr1	203096835	203136533	uc001gzf.1
chr1	203097405	203136533	uc009xak.1
chr1	203136938	203144942	uc001gzh.1
chr1	203148058	203155922	uc001gzi.2
chr1	203185206	203198860	uc001gzn.2
chr1	203267885	203274453	uc009xao.2
chr1	203274663	203278729	uc001gzq.3
chr1	203309751	203320289	uc001gzr.3
chr1	203444882	203460479	uc001gzt.3
chr1	203463270	203478077	uc001gzu.1
chr1	203595914	203713209	uc001gzw.3
chr1	203696748	203696784	uc010pqk.1
chr1	203698708	203698833	uc001gzy.1
chr1	203699704	203700979	uc031prr.1
chr1	203734283	203745480	uc001haa.3
chr1	203764750	203823256	uc001hac.3
chr1	203766650	203769590	uc021phs.1
chr1	203830739	203840280	uc001hai.3
chr1	203968378	203968471	uc021pht.1
chr1	204001574	204010392	uc010pqo.1
chr1	204042245	204096871	uc001ham.3
chr1	204100189	204121307	uc001hao.4
chr1	204110535	204112137	uc001hap.3
chr1	204123943	204135465	uc001haq.2
chr1	204159468	204165619	uc001har.3
chr1	204167287	204183220	uc001has.1
chr1	204187978	204329057	uc001hau.4
chr1	204337557	204338847	uc010pqu.1
chr1	204372491	204380944	uc001hav.4
chr1	204379010	204380945	uc031prt.1
chr1	204391757	204459474	uc001haw.3
chr1	204475654	204475727	uc021phv.1
chr1	204476157	204476230	uc021phw.1
chr1	204485506	204527248	uc001hba.3
chr1	204586302	204654597	uc001hbf.1
chr1	204595902	204598840	uc031pru.1
chr1	204676447	204676558	uc021phz.1
chr1	204797781	204991950	uc001hbj.3
chr1	204981852	204991950	uc001hbo.2
chr1	204989426	204991950	uc001hbp.1
chr1	205012339	205047171	uc001hbr.3
chr1	205052256	205053588	uc001hbt.3
chr1	205055269	205091150	uc001hbu.2
chr1	205111630	205180727	uc001hbw.3
chr1	205197037	205242471	uc021pia.1
chr1	205271190	205290883	uc001hce.3
chr1	205305192	205326218	uc031prx.1
chr1	205342379	205356568	uc001hch.1
chr1	205350505	205391214	uc001hcj.2
chr1	205417429	205417526	uc010prh.1
chr1	205425185	205438152	uc001hco.2
chr1	205443270	205443343	uc021pib.1
chr1	205473683	205495965	uc010pri.2
chr1	205473683	205501921	uc001hcr.3
chr1	205523400	205525763	uc001hcu.2
chr1	205538111	205572046	uc001hcv.4
chr1	205564168	205564275	uc021pic.1
chr1	205626980	205649630	uc001hda.1
chr1	205681946	205719372	uc001hdb.3
chr1	205737113	205744610	uc001hde.4
chr1	205758220	205782161	uc001hdh.1
chr1	205765004	205765744	uc001hdi.1
chr1	205797149	205819276	uc001hdj.3
chr1	205831206	205865215	uc001hdk.1
chr1	205882176	205912588	uc001hdp.3
chr1	206138910	206155074	uc001hdr.4
chr1	206224282	206231482	uc001hds.2
chr1	206238881	206288236	uc001hdt.2
chr1	206317458	206332104	uc001hdu.3
chr1	206516199	206637783	uc001hdy.3
chr1	206643585	206670223	uc001hdz.2
chr1	206680878	206762616	uc001hed.3
chr1	206764973	206785904	uc001heh.2
chr1	206808880	206822542	uc001hej.3
chr1	206858364	206907630	uc001hem.2
chr1	206940947	206945839	uc001hen.1
chr1	206972214	207016326	uc001heo.3
chr1	207039153	207042568	uc001her.3
chr1	207070787	207077484	uc001heu.2
chr1	207076630	207095378	uc001hey.3
chr1	207101866	207119811	uc001hez.3
chr1	207131311	207143970	uc001hfa.4
chr1	207178153	207178230	uc021pih.1
chr1	207191865	207206101	uc001hfd.2
chr1	207217193	207224422	uc001hfe.1
chr1	207226619	207251162	uc001hfg.3
chr1	207262211	207273337	uc001hfj.3
chr1	207277510	207277617	uc001hfn.1
chr1	207277606	207318317	uc001hfo.3
chr1	207494816	207534311	uc001hfr.4
chr1	207627644	207663240	uc001hfv.3
chr1	207669472	207815110	uc001hfx.3
chr1	207818457	207897036	uc001hga.4
chr1	207925382	207968861	uc001hgj.3
chr1	207975196	207975868	uc021pik.2
chr1	207991723	207995941	uc010psi.2
chr1	208039465	208042417	uc001hgu.2
chr1	208059882	208084683	uc001hgw.1
chr1	208195587	208417665	uc001hgz.3
chr1	209602167	209605892	uc009xcn.3
chr1	209757044	209787284	uc001hhd.3
chr1	209788217	209825820	uc001hhh.3
chr1	209796788	209796855	uc021pil.1
chr1	209848669	209849735	uc001hhi.4
chr1	209878135	209908295	uc001hhk.3
chr1	209929393	209955668	uc001hho.3
chr1	209955661	209957890	uc001hhp.1
chr1	209958967	209979520	uc001hhq.2
chr1	210001311	210030910	uc001hhr.2
chr1	210111518	210337633	uc001hhs.5
chr1	210404803	210407466	uc001hhx.4
chr1	210406194	210416440	uc001hhy.3
chr1	210501595	210849638	uc009xcx.3
chr1	210851656	211307457	uc001hib.2
chr1	211432707	211489725	uc010psw.2
chr1	211500147	211548286	uc001hii.3
chr1	211556096	211605877	uc001hil.4
chr1	211649863	211666259	uc001hin.2
chr1	211748380	211752099	uc001hio.1
chr1	211836113	211848972	uc001hir.2
chr1	211916798	212004114	uc001hiv.3
chr1	212113740	212209002	uc001hiw.2
chr1	212208918	212278187	uc009xdc.3
chr1	212250954	212251027	uc021pis.1
chr1	212317864	212318301	uc010ptc.1
chr1	212458878	212535205	uc001hjb.3
chr1	212526159	212526292	uc009xdf.1
chr1	212537815	212588267	uc010pte.2
chr1	212606228	212619721	uc001hjd.3
chr1	212781969	212794119	uc021pit.1
chr1	212797625	212800113	uc010pth.1
chr1	212797788	212800120	uc001hjk.3
chr1	212859758	212873327	uc001hjl.2
chr1	212899494	212965139	uc001hjn.3
chr1	212965169	212990167	uc001hjo.2
chr1	213003484	213020991	uc001hjq.3
chr1	213029945	213031480	uc001hjs.5
chr1	213031596	213072705	uc001hjt.3
chr1	213123886	213164927	uc001hjw.3
chr1	213165523	213189168	uc001hjz.3
chr1	213224587	213446808	uc010ptr.2
chr1	213992977	214139607	uc031psa.1
chr1	214098091	214099996	uc031psb.1
chr1	214161277	214214847	uc001hkg.2
chr1	214454564	214510477	uc021pix.1
chr1	214522038	214725024	uc001hkk.2
chr1	214655881	214656819	uc010ptz.1
chr1	214776531	214837914	uc001hkm.3
chr1	215256559	215410436	uc001hkr.4
chr1	215740734	215795149	uc001hks.3
chr1	215796235	216596738	uc001hku.1
chr1	216676587	216896814	uc001hkw.2
chr1	216825011	216825068	uc021pjb.1
chr1	217603833	217804409	uc001hlf.1
chr1	217804694	218040484	uc001hlh.1
chr1	218066241	218094146	uc031psc.1
chr1	218458628	218511325	uc001hlj.3
chr1	218517537	218519020	uc031psd.1
chr1	218518675	218617961	uc001hln.3
chr1	219254316	219347130	uc001hlp.3
chr1	219347191	219386207	uc001hlq.4
chr1	220046618	220292777	uc021pjd.1
chr1	220087605	220101993	uc001hlw.3
chr1	220141941	220220000	uc001hly.1
chr1	220230823	220263191	uc001hma.3
chr1	220267454	220321383	uc001hmc.3
chr1	220291194	220291304	uc010pui.2
chr1	220291498	220291583	uc010puj.2
chr1	220291547	220291569	uc021pje.1
chr1	220321609	220445843	uc010puk.1
chr1	220373883	220374018	uc010pul.2
chr1	220439520	220441057	uc001hmj.2
chr1	220701567	220837799	uc001hmn.4
chr1	220863627	220872499	uc001hmp.1
chr1	220921675	220957596	uc001hmq.3
chr1	220960038	220987741	uc001hms.3
chr1	220999117	220999235	uc021pjg.1
chr1	221002596	221005770	uc001hmu.3
chr1	221052742	221058400	uc001hmv.4
chr1	221503269	221509638	uc001hmw.1
chr1	221874763	221915516	uc001hmy.2
chr1	222638346	222638419	uc021pjh.1
chr1	222646009	222646043	uc010puo.1
chr1	222646722	222646767	uc001hna.1
chr1	222646839	222646870	uc001hnb.3
chr1	222647751	222647786	uc010pup.1
chr1	222648391	222648423	uc021pji.1
chr1	222648536	222648596	uc001hnd.3
chr1	222648680	222648717	uc021pjj.1
chr1	222649002	222649064	uc010puq.2
chr1	222649161	222649251	uc021pjk.1
chr1	222650319	222650381	uc001hnf.3
chr1	222650411	222650443	uc001hng.2
chr1	222695601	222721444	uc001hnh.1
chr1	222731243	222763275	uc009xdz.2
chr1	222791443	222841351	uc001hnl.3
chr1	222841354	222885864	uc001hnn.3
chr1	222885905	222906106	uc001hnq.1
chr1	222900275	222946483	uc001hnr.1
chr1	222906202	222908533	uc001hns.1
chr1	222910557	222924002	uc001hnt.3
chr1	222988430	223179337	uc001hnu.2
chr1	223282747	223316624	uc001hnw.2
chr1	223394160	223537544	uc001hny.4
chr1	223566714	223568812	uc001hoa.2
chr1	223714971	223853436	uc009xee.2
chr1	223900118	223963720	uc001hob.4
chr1	223967594	224033674	uc010pvb.2
chr1	224051997	224052306	uc021pjm.1
chr1	224139721	224139941	uc001hof.1
chr1	224189608	224197818	uc001hog.1
chr1	224294983	224295243	uc021pjn.1
chr1	224301788	224349749	uc001hoh.3
chr1	224370909	224381142	uc001hoj.3
chr1	224415035	224517891	uc001hok.3
chr1	224444705	224444843	uc021pjo.1
chr1	224544512	224566223	uc001hom.2
chr1	224572844	224622001	uc001hop.4
chr1	224585928	224586013	uc021pjq.1
chr1	224586541	224586601	uc021pjr.1
chr1	224804178	224928249	uc001hos.1
chr1	225117355	225586996	uc001how.2
chr1	225589203	225615815	uc001hoy.3
chr1	225674533	225840845	uc001hpc.1
chr1	225888305	225939945	uc001hpe.1
chr1	225965514	225978168	uc001hpg.3
chr1	225997796	226033262	uc001hpk.3
chr1	226033232	226070420	uc001hpm.2
chr1	226073981	226076846	uc001hpo.3
chr1	226107576	226112040	uc001hpq.4
chr1	226124297	226129083	uc001hpt.2
chr1	226170402	226187066	uc001hpu.4
chr1	226250407	226259703	uc001hpw.3
chr1	226271655	226277994	uc001hpx.3
chr1	226332379	226374423	uc001hpy.3
chr1	226374532	226385086	uc031psl.1
chr1	226411382	226413513	uc010pvm.2
chr1	226418849	226497204	uc001hqa.3
chr1	226548391	226595801	uc001hqd.4
chr1	226723318	226730469	uc001hqe.2
chr1	226736500	226796915	uc021pjw.1
chr1	226819390	226926876	uc010pvo.2
chr1	227058272	227083804	uc009xeo.1
chr1	227127937	227175246	uc001hqn.1
chr1	227177565	227505826	uc001hqr.3
chr1	227581291	227618723	uc001hqv.3
chr1	227751219	227850164	uc021pjy.1
chr1	227884732	227885408	uc021pjz.1
chr1	227918889	227923112	uc001hrb.3
chr1	227922696	227968932	uc001hrf.2
chr1	228003417	228034171	uc001hrh.3
chr1	228109164	228135676	uc001hri.2
chr1	228129290	228129384	uc031psm.1
chr1	228155293	228155325	uc001hrj.3
chr1	228155355	228155417	uc001hrk.3
chr1	228156485	228156575	uc021pka.1
chr1	228156672	228156704	uc021pkb.1
chr1	228157022	228157062	uc010pvt.1
chr1	228157172	228157203	uc010pvu.2
chr1	228160064	228160098	uc001hrn.2
chr1	228160168	228160213	uc021pkc.1
chr1	228194722	228248972	uc001hrq.2
chr1	228270360	228286913	uc001hrr.3
chr1	228284963	228285042	uc021pkd.1
chr1	228288427	228291022	uc001hrx.3
chr1	228294379	228297013	uc001hrz.4
chr1	228327928	228336655	uc021pkf.1
chr1	228337414	228347527	uc001hsk.3
chr1	228353428	228369958	uc001hsl.4
chr1	228391206	228401365	uc031psn.1
chr1	228395830	228566575	uc001hsq.2
chr1	228581376	228594517	uc001hss.3
chr1	228595635	228604583	uc001hsv.3
chr1	228612545	228613026	uc001hsx.1
chr1	228645064	228645560	uc001hsy.3
chr1	228645807	228646259	uc001hsz.3
chr1	228649774	228649853	uc021pkh.1
chr1	228651845	228651891	uc021pki.1
chr1	228673888	228674821	uc001hta.3
chr1	228675067	228683889	uc001htb.3
chr1	228780393	228788159	uc001htd.2
chr1	228870823	228882416	uc001htf.3
chr1	229050379	229052954	uc001htg.2
chr1	229406808	229441640	uc001hth.4
chr1	229440128	229441250	uc001htk.4
chr1	229456751	229478688	uc001htl.4
chr1	229566992	229569843	uc001htm.3
chr1	229577043	229644088	uc001htn.3
chr1	229589676	229591617	uc001hto.1
chr1	229652328	229694442	uc001htp.4
chr1	229728865	229761794	uc001htq.3
chr1	229761980	229795946	uc001hts.1
chr1	230202955	230417875	uc010pwa.1
chr1	230457391	230561674	uc031psq.1
chr1	230778201	230829731	uc001htw.3
chr1	230838271	230850336	uc001hty.4
chr1	230883129	230937749	uc001htz.1
chr1	230972864	231004302	uc001hub.3
chr1	231010591	231014761	uc001hue.1
chr1	231041986	231114618	uc001huf.4
chr1	231114822	231136479	uc001huh.3
chr1	231154703	231175995	uc001hui.2
chr1	231298673	231357314	uc009xfn.1
chr1	231319844	231323373	uc031pss.1
chr1	231359508	231376924	uc001hul.3
chr1	231376918	231413719	uc001hup.4
chr1	231468481	231473578	uc001huq.3
chr1	231473681	231490769	uc001hur.4
chr1	231499496	231560790	uc001huv.2
chr1	231611509	231612271	uc021pkl.1
chr1	231664398	231702269	uc001huw.3
chr1	231727037	231747836	uc021pkm.2
chr1	231762560	232177019	uc010pxh.2
chr1	231950371	231954263	uc001hvd.3
chr1	232533711	232651243	uc001hvg.3
chr1	232940637	232946092	uc001hvh.2
chr1	233086369	233114219	uc001hvj.1
chr1	233119881	233431459	uc001hvl.2
chr1	233463513	233520894	uc001hvt.4
chr1	233584371	233584527	uc021pko.1
chr1	233749749	233808258	uc010pxo.1
chr1	233759897	233759965	uc021pkp.1
chr1	234040678	234460262	uc001hvy.1
chr1	234348351	234350834	uc001hvz.1
chr1	234442212	234442285	uc021pkq.1
chr1	234509428	234519795	uc001hwc.3
chr1	234527058	234614849	uc001hwd.3
chr1	234663636	234667525	uc001hwe.3
chr1	234740014	234745271	uc001hwg.3
chr1	234765056	234770526	uc021pkr.1
chr1	234782034	234796670	uc001hwh.3
chr1	234859788	234867390	uc001hwj.2
chr1	235093089	235099746	uc001hwk.4
chr1	235272657	235292256	uc001hwl.3
chr1	235291117	235291252	uc001hwm.1
chr1	235294497	235324772	uc001hwn.3
chr1	235330209	235491532	uc001hwq.3
chr1	235353348	235353431	uc021pkt.1
chr1	235491752	235507844	uc001hwv.3
chr1	235530727	235612280	uc001hxa.1
chr1	235610504	235667781	uc001hxc.3
chr1	235710984	235814054	uc001hxh.4
chr1	235712454	235714587	uc021pku.1
chr1	235824330	236030227	uc001hxj.3
chr1	236016299	236016360	uc021pkv.1
chr1	236139131	236228481	uc001hxo.3
chr1	236227659	236229779	uc001hxp.1
chr1	236305831	236372209	uc001hxq.3
chr1	236378421	236445339	uc001hxt.3
chr1	236557679	236648008	uc001hxu.1
chr1	236686368	236687808	uc001hxx.3
chr1	236686738	236716279	uc001hxy.2
chr1	236712304	236767841	uc001hyd.2
chr1	236849769	236927558	uc001hyf.2
chr1	236958580	237067281	uc001hyi.4
chr1	236973802	236992568	uc009xgj.1
chr1	237167402	237167718	uc001hyk.2
chr1	237205701	237997288	uc001hyl.1
chr1	237284106	237284409	uc021pkw.1
chr1	238025474	238091619	uc010pyc.2
chr1	238041163	238054222	uc001hym.3
chr1	238105861	238105933	uc021pla.1
chr1	238106955	238107030	uc021plb.1
chr1	238643683	238649317	uc001hyn.3
chr1	239792372	240072717	uc001hyp.3
chr1	240170823	240176560	uc021pld.1
chr1	240255184	240638489	uc010pyd.2
chr1	240317950	240318072	uc021ple.1
chr1	240652872	240775462	uc001hys.3
chr1	240938816	241520478	uc001hyv.2
chr1	241295571	241295646	uc021plg.1
chr1	241660856	241683085	uc001hyx.3
chr1	241695433	241758949	uc009xgp.3
chr1	241756451	241803701	uc001hza.3
chr1	241792166	241799232	uc001hzd.3
chr1	241815579	241965434	uc001hzg.2
chr1	242011492	242053241	uc001hzh.3
chr1	242158791	242162385	uc001hzk.2
chr1	242251688	242687998	uc001hzn.2
chr1	243218732	243218763	uc021plm.1
chr1	243219238	243219328	uc021pln.1
chr1	243219615	243265046	uc001hzq.2
chr1	243287729	243418708	uc021plo.1
chr1	243351942	243352032	uc021plr.1
chr1	243419306	243663393	uc001hzw.3
chr1	243509477	243509557	uc021plt.1
chr1	243663020	244006584	uc001iab.2
chr1	243729624	243729741	uc021plv.1
chr1	244080703	244210619	uc001iac.3
chr1	244212240	244220780	uc031psv.1
chr1	244515936	244552388	uc001iah.4
chr1	244571793	244615436	uc001iaj.3
chr1	244624672	244803662	uc001iam.3
chr1	244816351	244872334	uc001iao.3
chr1	244998638	245008359	uc001iar.3
chr1	245003939	245010243	uc001iav.3
chr1	245013601	245027827	uc001iaz.1
chr1	245133170	245251148	uc001ibc.2
chr1	245318286	245866428	uc001ibf.1
chr1	245533635	245533719	uc021plw.1
chr1	245912641	246670644	uc001ibl.3
chr1	246679340	246687589	uc021plx.1
chr1	246703862	246729565	uc001ibn.3
chr1	246729638	246831884	uc001ibp.3
chr1	246887377	246931440	uc001ibr.3
chr1	246939314	246955685	uc001ibs.1
chr1	246970416	246970487	uc021ply.1
chr1	247002401	247094726	uc001ibv.2
chr1	247148624	247171395	uc009xgu.3
chr1	247197939	247242115	uc001icd.2
chr1	247263263	247267674	uc001ice.2
chr1	247273461	247275719	uc001icg.1
chr1	247319202	247335318	uc001icj.1
chr1	247337903	247338870	uc001icl.1
chr1	247365268	247365362	uc021pma.1
chr1	247419373	247420447	uc010pyu.2
chr1	247463621	247495045	uc001ico.3
chr1	247579457	247612406	uc001icr.3
chr1	247614330	247615284	uc010pyx.2
chr1	247654369	247655711	uc001icz.2
chr1	247681594	247683942	uc001idc.3
chr1	247693433	247697141	uc009xgy.3
chr1	247712346	247739848	uc001idf.3
chr1	247751661	247752615	uc010pyy.2
chr1	247768887	247769817	uc010pyz.2
chr1	247835419	247836343	uc001idi.1
chr1	247875130	247876057	uc001idj.1
chr1	247920763	247921708	uc010pza.2
chr1	247978101	247979031	uc001idm.1
chr1	248004229	248005198	uc001idn.1
chr1	248020500	248043438	uc001ido.3
chr1	248058888	248059833	uc010pzb.2
chr1	248084319	248085258	uc010pzc.2
chr1	248100492	248264224	uc001ids.3
chr1	248112159	248113098	uc001idt.1
chr1	248128633	248129641	uc010pzd.2
chr1	248153568	248154493	uc001idv.1
chr1	248185249	248186188	uc031psy.1
chr1	248201473	248202607	uc001idw.3
chr1	248223983	248224922	uc001idx.1
chr1	248285437	248286082	uc001idy.1
chr1	248308449	248309388	uc010pze.2
chr1	248343287	248344331	uc010pzf.2
chr1	248366369	248367308	uc010pzg.2
chr1	248402230	248403166	uc010pzh.2
chr1	248436153	248437116	uc010pzi.2
chr1	248457917	248458880	uc010pzj.2
chr1	248486931	248487870	uc010pzk.2
chr1	248512076	248513015	uc010pzl.2
chr1	248524882	248525929	uc001ieh.1
chr1	248550909	248551836	uc001iei.1
chr1	248569295	248570405	uc010pzm.2
chr1	248616098	248617073	uc001iek.1
chr1	248636651	248637608	uc001iel.1
chr1	248651889	248652837	uc001iem.1
chr1	248684947	248685898	uc001ien.1
chr1	248721844	248722792	uc001ieo.2
chr1	248737101	248738058	uc001iep.1
chr1	248756130	248757069	uc010pzn.2
chr1	248789478	248790429	uc001ier.1
chr1	248801587	248802559	uc001ies.1
chr1	248813231	248814185	uc010pzo.2
chr1	248844669	248845605	uc001ieu.1
chr1	248881947	248885507	uc031psz.1
chr1	248902716	248903151	uc009xhb.3
chr1	249104650	249120154	uc001iew.1
chr1	249120575	249120642	uc021pmd.1
chr1	249132529	249143714	uc001iex.3
chr1	249144202	249153315	uc001ifc.2
chr1	249168053	249168159	uc021pmf.1
chr1	249168446	249168518	uc021pmg.1
chr1	249200441	249213345	uc001ifh.3


================================================
FILE: intro_awk_spring2021/data/.ipynb_checkpoints/example-checkpoint.fastq
================================================
@ERR117184_2.14044282
CTGGTGCTCAGTCATTTTGCTAGATTGTAGCTCACCATTGCCTCTCTGCCTGCATTGTGCACTCCAATCCCTCAGCAGCTACAAAAACACTTTGTCAACTCCAGATCGG
+
IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII
@ERR117185_2.23793537
TTGTTCAATTTTTTTAGTAGTTTTAGAGTAATTAATGAGCTTTGAGGTCACTTAACAATAAAAGCATATTTTAAAGTAAAGGTTGCTGATCAACAGTCTAATTTTCATA
+
IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII
@ERR117166_2.4649205
ACTTAACAATAAAAGCATATTTTAAAGTAAAGGTT
+
IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII
@ERR117185_2.949669
ATGCTTTTATTGTTAAGTGACCTCAAAGCTCATTAATTACTCTAAAACTACTAAAAAAATTGAACAAAAAAGTATTTCCAGTAATCAAGATTTTCTTCATGCCCTAGAT
+
IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII
@ERR117163_2.2292219
AAATCGTAAGTGAAGCAACAGAATTCAGAACTAAA
+
IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII
@ERR117165_2.36526615
AACGCTAAGGTTATATTAAAATGTTTCTTATGTTG
+
IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII
@ERR117174_2.23007391
GCAAGTGGTTGGACTCAAGGAAGTTATAAATACCT
+
IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII
@ERR117175_2.11345437
GCAAGTGGTTGGACTCAAGGAAGTTATAAATACCT
+
IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII
@ERR117176_2.30285510
GCAAGTGGTTGGACTCAAGGAAGTTATAAATACCT
+
IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII
@ERR117177_2.33680511
GCAAGTGGTTGGACTCAAGGAAGTTATAAATACCT
+
IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII


================================================
FILE: intro_awk_spring2021/data/.ipynb_checkpoints/hg38-checkpoint.genome
================================================
chrom	size
1	248956422
2	242193529
3	198295559
4	190214555
5	181538259
6	170805979
7	159345973
X	156040895
8	145138636
9	138394717
11	135086622
10	133797422
12	133275309
13	114364328
14	107043718
15	101991189
16	90338345
17	83257441
18	80373285
20	64444167
19	58617616
Y	57227415
22	50818468
21	46709983
15_KI270905v1_alt	5161414
6_GL000256v2_alt	4929269
6_GL000254v2_alt	4827813
6_GL000251v2_alt	4795265
6_GL000253v2_alt	4677643
6_GL000250v2_alt	4672374
6_GL000255v2_alt	4606388
6_GL000252v2_alt	4604811
17_KI270857v1_alt	2877074
16_KI270853v1_alt	2659700
16_KI270728v1_random	1872759
17_GL000258v2_alt	1821992
5_GL339449v2_alt	1612928
14_KI270847v1_alt	1511111
17_KI270908v1_alt	1423190
14_KI270846v1_alt	1351393
5_KI270897v1_alt	1144418
7_KI270803v1_alt	1111570
19_GL949749v2_alt	1091841
19_KI270938v1_alt	1066800
19_GL949750v2_alt	1066390
19_GL949748v2_alt	1064304
19_GL949751v2_alt	1002683
19_GL949746v1_alt	987716
19_GL949752v1_alt	987100
8_KI270821v1_alt	985506
1_KI270763v1_alt	911658
6_KI270801v1_alt	870480
19_GL949753v2_alt	796479
19_GL949747v2_alt	729520
8_KI270822v1_alt	624492
4_GL000257v2_alt	586476
12_KI270904v1_alt	572349
4_KI270925v1_alt	555799
15_KI270852v1_alt	478999
15_KI270727v1_random	448248
9_KI270823v1_alt	439082
15_KI270850v1_alt	430880
1_KI270759v1_alt	425601
12_GL877876v1_alt	408271
Un_KI270442v1	392061
17_KI270862v1_alt	391357
15_GL383555v2_alt	388773
19_GL383573v1_alt	385657
4_KI270896v1_alt	378547
4_GL383528v1_alt	376187
17_GL383563v3_alt	375691
8_KI270810v1_alt	374415
1_GL383520v2_alt	366580
1_KI270762v1_alt	354444
15_KI270848v1_alt	327382
17_KI270909v1_alt	325800
14_KI270844v1_alt	322166
8_KI270900v1_alt	318687
10_GL383546v1_alt	309802
13_KI270838v1_alt	306913
8_KI270816v1_alt	305841
22_KI270879v1_alt	304135
8_KI270813v1_alt	300230
11_KI270831v1_alt	296895
15_GL383554v1_alt	296527
8_KI270811v1_alt	292436
18_GL383567v1_alt	289831
X_KI270880v1_alt	284869
8_KI270812v1_alt	282736
19_KI270921v1_alt	282224
17_KI270729v1_random	280839
17_JH159146v1_alt	278131
X_KI270913v1_alt	274009
6_KI270798v1_alt	271782
7_KI270808v1_alt	271455
22_KI270876v1_alt	263666
15_KI270851v1_alt	263054
22_KI270875v1_alt	259914
1_KI270766v1_alt	256271
19_KI270882v1_alt	248807
3_KI270778v1_alt	248252
15_KI270849v1_alt	244917
4_KI270786v1_alt	244096
12_KI270835v1_alt	238139
17_KI270858v1_alt	235827
19_KI270867v1_alt	233762
16_KI270855v1_alt	232857
8_KI270926v1_alt	229282
5_GL949742v1_alt	226852
3_KI270780v1_alt	224108
17_GL383565v1_alt	223995
2_KI270774v1_alt	223625
4_KI270790v1_alt	220246
11_KI270927v1_alt	218612
19_KI270932v1_alt	215732
11_KI270903v1_alt	214625
2_KI270894v1_alt	214158
14_GL000225v1_random	211173
Un_KI270743v1	210658
11_KI270832v1_alt	210133
7_KI270805v1_alt	209988
4_GL000008v2_random	209709
7_KI270809v1_alt	209586
19_KI270887v1_alt	209512
4_KI270789v1_alt	205944
3_KI270779v1_alt	205312
19_KI270914v1_alt	205194
19_KI270886v1_alt	204239
11_KI270829v1_alt	204059
14_GL000009v2_random	201709
21_GL383579v2_alt	201197
11_JH159136v1_alt	200998
19_KI270930v1_alt	200773
Un_KI270747v1	198735
18_GL383571v1_alt	198278
19_KI270920v1_alt	198005
6_KI270797v1_alt	197536
3_KI270935v1_alt	197351
17_KI270861v1_alt	196688
15_KI270906v1_alt	196384
5_KI270791v1_alt	195710
14_KI270722v1_random	194050
16_GL383556v1_alt	192462
13_KI270840v1_alt	191684
14_GL000194v1_random	191469
11_JH159137v1_alt	191409
19_KI270917v1_alt	190932
7_KI270899v1_alt	190869
19_KI270923v1_alt	189352
10_KI270825v1_alt	188315
19_GL383576v1_alt	188024
19_KI270922v1_alt	187935
Un_KI270742v1	186739
22_KI270878v1_alt	186262
19_KI270929v1_alt	186203
11_KI270826v1_alt	186169
6_KB021644v2_alt	185823
17_GL000205v2_random	185591
1_KI270765v1_alt	185285
19_KI270916v1_alt	184516
19_KI270890v1_alt	184499
3_KI270784v1_alt	184404
12_GL383551v1_alt	184319
20_KI270870v1_alt	183433
Un_GL000195v1	182896
1_GL383518v1_alt	182439
22_KI270736v1_random	181920
10_KI270824v1_alt	181496
14_KI270845v1_alt	180703
3_GL383526v1_alt	180671
13_KI270839v1_alt	180306
22_KI270733v1_random	179772
Un_GL000224v1	179693
10_GL383545v1_alt	179254
Un_GL000219v1	179198
5_KI270792v1_alt	179043
17_KI270860v1_alt	178921
19_GL000209v2_alt	177381
11_KI270830v1_alt	177092
9_KI270719v1_random	176845
Un_GL000216v2	176608
22_KI270928v1_alt	176103
1_KI270712v1_random	176043
6_KI270800v1_alt	175808
1_KI270706v1_random	175055
2_KI270776v1_alt	174166
18_KI270912v1_alt	174061
3_KI270777v1_alt	173649
5_GL383531v1_alt	173459
3_JH636055v2_alt	173151
14_KI270725v1_random	172810
5_KI270796v1_alt	172708
9_GL383541v1_alt	171286
19_KI270885v1_alt	171027
19_KI270919v1_alt	170701
19_KI270889v1_alt	170698
19_KI270891v1_alt	170680
19_KI270915v1_alt	170665
19_KI270933v1_alt	170537
19_KI270883v1_alt	170399
19_GL383575v2_alt	170222
19_KI270931v1_alt	170148
12_GL383550v2_alt	169178
13_KI270841v1_alt	169134
Un_KI270744v1	168472
18_KI270863v1_alt	167999
18_GL383569v1_alt	167950
12_GL877875v1_alt	167313
21_KI270874v1_alt	166743
3_KI270924v1_alt	166540
1_KI270761v1_alt	165834
3_KI270937v1_alt	165607
22_KI270734v1_random	165050
18_GL383570v1_alt	164789
5_KI270794v1_alt	164558
4_GL383527v1_alt	164536
Un_GL000213v1	164239
3_KI270936v1_alt	164170
3_KI270934v1_alt	163458
9_GL383539v1_alt	162988
3_KI270895v1_alt	162896
22_GL383582v2_alt	162811
3_KI270782v1_alt	162429
1_KI270892v1_alt	162212
Un_GL000220v1	161802
2_KI270767v1_alt	161578
2_KI270715v1_random	161471
2_KI270893v1_alt	161218
Un_GL000218v1	161147
18_GL383572v1_alt	159547
8_KI270817v1_alt	158983
4_KI270788v1_alt	158965
Un_KI270749v1	158759
7_KI270806v1_alt	158166
7_KI270804v1_alt	157952
18_KI270911v1_alt	157710
Un_KI270741v1	157432
17_KI270910v1_alt	157099
19_KI270884v1_alt	157053
19_GL383574v1_alt	155864
19_KI270888v1_alt	155532
3_GL000221v1_random	155397
11_GL383547v1_alt	154407
2_KI270716v1_random	153799
12_GL383553v2_alt	152874
6_KI270799v1_alt	152148
22_KI270731v1_random	150754
Un_KI270751v1	150742
Un_KI270750v1	148850
8_KI270818v1_alt	145606
X_KI270881v1_alt	144206
21_KI270873v1_alt	143900
2_GL383521v1_alt	143390
8_KI270814v1_alt	141812
12_GL383552v1_alt	138655
Un_KI270519v1	138126
2_KI270775v1_alt	138019
17_KI270907v1_alt	137721
Un_GL000214v1	137718
8_KI270901v1_alt	136959
2_KI270770v1_alt	136240
16_KI270854v1_alt	134193
8_KI270819v1_alt	133535
17_GL383564v2_alt	133151
2_KI270772v1_alt	133041
8_KI270815v1_alt	132244
5_KI270795v1_alt	131892
5_KI270898v1_alt	130957
20_GL383577v2_alt	128386
1_KI270708v1_random	127682
7_KI270807v1_alt	126434
5_KI270793v1_alt	126136
6_GL383533v1_alt	124736
2_GL383522v1_alt	123821
19_KI270918v1_alt	123111
12_GL383549v1_alt	120804
2_KI270769v1_alt	120616
4_KI270785v1_alt	119912
12_KI270834v1_alt	119498
7_GL383534v2_alt	119183
20_KI270869v1_alt	118774
21_GL383581v2_alt	116689
3_KI270781v1_alt	113034
17_KI270730v1_random	112551
Un_KI270438v1	112505
4_KI270787v1_alt	111943
18_KI270864v1_alt	111737
2_KI270771v1_alt	110395
1_GL383519v1_alt	110268
2_KI270768v1_alt	110099
1_KI270760v1_alt	109528
3_KI270783v1_alt	109187
17_KI270859v1_alt	108763
11_KI270902v1_alt	106711
18_GL383568v1_alt	104552
22_KI270737v1_random	103838
13_KI270843v1_alt	103832
22_KI270877v1_alt	101331
5_GL383530v1_alt	101241
11_KI270721v1_random	100316
22_KI270738v1_random	99375
22_GL383583v2_alt	96924
2_GL582966v2_alt	96131
Un_KI270748v1	93321
Un_KI270435v1	92983
5_GL000208v1_random	92689
Un_KI270538v1	91309
17_GL383566v1_alt	90219
16_GL383557v1_alt	89672
17_JH159148v1_alt	88070
5_GL383532v1_alt	82728
21_KI270872v1_alt	82692
Un_KI270756v1	79590
6_KI270758v1_alt	76752
12_KI270833v1_alt	76061
6_KI270802v1_alt	75005
21_GL383580v2_alt	74653
22_KB663609v1_alt	74013
22_KI270739v1_random	73985
9_GL383540v1_alt	71551
Un_KI270757v1	71251
2_KI270773v1_alt	70887
17_JH159147v1_alt	70345
11_KI270827v1_alt	67707
1_KI270709v1_random	66860
Un_KI270746v1	66486
16_KI270856v1_alt	63982
21_GL383578v2_alt	63917
Un_KI270753v1	62944
19_KI270868v1_alt	61734
9_GL383542v1_alt	60032
20_KI270871v1_alt	58661
12_KI270836v1_alt	56134
19_KI270865v1_alt	52969
1_KI270764v1_alt	50258
Un_KI270589v1	44474
14_KI270726v1_random	43739
19_KI270866v1_alt	43156
22_KI270735v1_random	42811
1_KI270711v1_random	42210
Un_KI270745v1	41891
1_KI270714v1_random	41717
22_KI270732v1_random	41543
1_KI270713v1_random	40745
Un_KI270754v1	40191
1_KI270710v1_random	40176
12_KI270837v1_alt	40090
9_KI270717v1_random	40062
14_KI270724v1_random	39555
9_KI270720v1_random	39050
14_KI270723v1_random	38115
9_KI270718v1_random	38054
Un_KI270317v1	37690
13_KI270842v1_alt	37287
Y_KI270740v1_random	37240
Un_KI270755v1	36723
8_KI270820v1_alt	36640
1_KI270707v1_random	32032
Un_KI270579v1	31033
Un_KI270752v1	27745
Un_KI270512v1	22689
Un_KI270322v1	21476
M	16569
Un_GL000226v1	15008
Un_KI270311v1	12399
Un_KI270366v1	8320
Un_KI270511v1	8127
Un_KI270448v1	7992
Un_KI270521v1	7642
Un_KI270581v1	7046
Un_KI270582v1	6504
Un_KI270515v1	6361
Un_KI270588v1	6158
Un_KI270591v1	5796
Un_KI270522v1	5674
Un_KI270507v1	5353
Un_KI270590v1	4685
Un_KI270584v1	4513
Un_KI270320v1	4416
Un_KI270382v1	4215
Un_KI270468v1	4055
Un_KI270467v1	3920
Un_KI270362v1	3530
Un_KI270517v1	3253
Un_KI270593v1	3041
Un_KI270528v1	2983
Un_KI270587v1	2969
Un_KI270364v1	2855
Un_KI270371v1	2805
Un_KI270333v1	2699
Un_KI270374v1	2656
Un_KI270411v1	2646
Un_KI270414v1	2489
Un_KI270510v1	2415
Un_KI270390v1	2387
Un_KI270375v1	2378
Un_KI270420v1	2321
Un_KI270509v1	2318
Un_KI270315v1	2276
Un_KI270302v1	2274
Un_KI270518v1	2186
Un_KI270530v1	2168
Un_KI270304v1	2165
Un_KI270418v1	2145
Un_KI270424v1	2140
Un_KI270417v1	2043
Un_KI270508v1	1951
Un_KI270303v1	1942
Un_KI270381v1	1930
Un_KI270529v1	1899
Un_KI270425v1	1884
Un_KI270396v1	1880
Un_KI270363v1	1803
Un_KI270386v1	1788
Un_KI270465v1	1774
Un_KI270383v1	1750
Un_KI270384v1	1658
Un_KI270330v1	1652
Un_KI270372v1	1650
Un_KI270548v1	1599
Un_KI270580v1	1553
Un_KI270387v1	1537
Un_KI270391v1	1484
Un_KI270305v1	1472
Un_KI270373v1	1451
Un_KI270422v1	1445
Un_KI270316v1	1444
Un_KI270338v1	1428
Un_KI270340v1	1428
Un_KI270583v1	1400
Un_KI270334v1	1368
Un_KI270429v1	1361
Un_KI270393v1	1308
Un_KI270516v1	1300
Un_KI270389v1	1298
Un_KI270466v1	1233
Un_KI270388v1	1216
Un_KI270544v1	1202
Un_KI270310v1	1201
Un_KI270412v1	1179
Un_KI270395v1	1143
Un_KI270376v1	1136
Un_KI270337v1	1121
Un_KI270335v1	1048
Un_KI270378v1	1048
Un_KI270379v1	1045
Un_KI270329v1	1040
Un_KI270419v1	1029
Un_KI270336v1	1026
Un_KI270312v1	998
Un_KI270539v1	993
Un_KI270385v1	990
Un_KI270423v1	981
Un_KI270392v1	971
Un_KI270394v1	970


================================================
FILE: intro_awk_spring2021/data/Homo_sapiens.GRCh38.subset.gff3
================================================
##gff-version 3
##sequence-region   1 1 248956422
1	Ensembl	chromosome	1	248956422	.	.	.	ID=chromosome:1
1	.	biological_region	10469	11240	1.3e+03	.	.	external_name=oe %3D 0.79
1	.	biological_region	10650	10657	0.999	+	.	logic_name=eponine
1	.	biological_region	10655	10657	0.999	-	.	logic_name=eponine
1	.	biological_region	10678	10687	0.999	+	.	logic_name=eponine
1	.	biological_region	10681	10688	0.999	-	.	logic_name=eponine
1	.	biological_region	10707	10716	0.999	+	.	logic_name=eponine
1	.	biological_region	10708	10718	0.999	-	.	logic_name=eponine
1	.	biological_region	10735	10747	0.999	-	.	logic_name=eponine
1	.	biological_region	10737	10744	0.999	+	.	logic_name=eponine
1	.	biological_region	10766	10773	0.999	+	.	logic_name=eponine
1	.	biological_region	10770	10779	0.999	-	.	logic_name=eponine
1	.	biological_region	10796	10801	0.999	+	.	logic_name=eponine
1	.	biological_region	10810	10819	0.999	-	.	logic_name=eponine
1	.	biological_region	10870	10872	0.999	+	.	logic_name=eponine
1	.	biological_region	10889	10893	0.999	-	.	logic_name=eponine
1	havana	pseudogene	11869	14409	.	+	.	ID=gene:ENSG00000223972
1	havana	lnc_RNA	11869	14409	.	+	.	ID=transcript:ENST00000456328
1	havana	exon	11869	12227	.	+	.	Parent=transcript:ENST00000456328
1	havana	exon	12613	12721	.	+	.	Parent=transcript:ENST00000456328
1	havana	exon	13221	14409	.	+	.	Parent=transcript:ENST00000456328
1	havana	pseudogenic_transcript	12010	13670	.	+	.	ID=transcript:ENST00000450305
1	havana	exon	12010	12057	.	+	.	Parent=transcript:ENST00000450305
1	havana	exon	12179	12227	.	+	.	Parent=transcript:ENST00000450305
1	havana	exon	12613	12697	.	+	.	Parent=transcript:ENST00000450305
1	havana	exon	12975	13052	.	+	.	Parent=transcript:ENST00000450305
1	havana	exon	13221	13374	.	+	.	Parent=transcript:ENST00000450305
1	havana	exon	13453	13670	.	+	.	Parent=transcript:ENST00000450305
1	havana	pseudogene	14404	29570	.	-	.	ID=gene:ENSG00000227232
1	havana	pseudogenic_transcript	14404	29570	.	-	.	ID=transcript:ENST00000488147
1	havana	exon	14404	14501	.	-	.	Parent=transcript:ENST00000488147
1	havana	exon	15005	15038	.	-	.	Parent=transcript:ENST00000488147
1	havana	exon	15796	15947	.	-	.	Parent=transcript:ENST00000488147
1	havana	exon	16607	16765	.	-	.	Parent=transcript:ENST00000488147
1	havana	exon	16858	17055	.	-	.	Parent=transcript:ENST00000488147
1	havana	exon	17233	17368	.	-	.	Parent=transcript:ENST00000488147
1	havana	exon	17606	17742	.	-	.	Parent=transcript:ENST00000488147
1	havana	exon	17915	18061	.	-	.	Parent=transcript:ENST00000488147
1	havana	exon	18268	18366	.	-	.	Parent=transcript:ENST00000488147
1	havana	exon	24738	24891	.	-	.	Parent=transcript:ENST00000488147
1	havana	exon	29534	29570	.	-	.	Parent=transcript:ENST00000488147
1	.	biological_region	15796	16060	0.999	-	.	external_name=rank %3D 1
1	mirbase	ncRNA_gene	17369	17436	.	-	.	ID=gene:ENSG00000278267
1	mirbase	miRNA	17369	17436	.	-	.	ID=transcript:ENST00000619216
1	mirbase	exon	17369	17436	.	-	.	Parent=transcript:ENST00000619216
1	.	biological_region	28736	29810	1.01e+03	.	.	external_name=oe %3D 0.88
1	.	biological_region	29116	29118	0.999	+	.	logic_name=eponine
1	.	biological_region	29127	29206	1	+	.	external_name=rank %3D 1
1	.	biological_region	29321	29395	1	-	.	external_name=rank %3D 1
1	.	biological_region	29394	29396	0.999	-	.	logic_name=eponine
1	.	biological_region	29448	29451	0.999	+	.	logic_name=eponine
1	havana	ncRNA_gene	29554	31109	.	+	.	ID=gene:ENSG00000243485
1	havana	lnc_RNA	29554	31097	.	+	.	ID=transcript:ENST00000473358
1	havana	exon	29554	30039	.	+	.	Parent=transcript:ENST00000473358
1	havana	exon	30564	30667	.	+	.	Parent=transcript:ENST00000473358
1	havana	exon	30976	31097	.	+	.	Parent=transcript:ENST00000473358
1	havana	lnc_RNA	30267	31109	.	+	.	ID=transcript:ENST00000469289
1	havana	exon	30267	30667	.	+	.	Parent=transcript:ENST00000469289
1	havana	exon	30976	31109	.	+	.	Parent=transcript:ENST00000469289
1	.	biological_region	29583	29584	0.999	-	.	logic_name=eponine
1	mirbase	ncRNA_gene	30366	30503	.	+	.	ID=gene:ENSG00000284332
1	mirbase	miRNA	30366	30503	.	+	.	ID=transcript:ENST00000607096
1	mirbase	exon	30366	30503	.	+	.	Parent=transcript:ENST00000607096
1	havana	ncRNA_gene	34554	36081	.	-	.	ID=gene:ENSG00000237613
1	havana	lnc_RNA	34554	36081	.	-	.	ID=transcript:ENST00000417324
1	havana	exon	34554	35174	.	-	.	Parent=transcript:ENST00000417324
1	havana	exon	35277	35481	.	-	.	Parent=transcript:ENST00000417324
1	havana	exon	35721	36081	.	-	.	Parent=transcript:ENST00000417324
1	havana	lnc_RNA	35245	36073	.	-	.	ID=transcript:ENST00000461467
1	havana	exon	35245	35481	.	-	.	Parent=transcript:ENST00000461467
1	havana	exon	35721	36073	.	-	.	Parent=transcript:ENST00000461467
1	.	biological_region	35904	36086	0.879	-	.	external_name=rank %3D 1
1	havana	pseudogene	52473	53312	.	+	.	ID=gene:ENSG00000268020
1	havana	pseudogenic_transcript	52473	53312	.	+	.	ID=transcript:ENST00000606857
1	havana	exon	52473	53312	.	+	.	Parent=transcript:ENST00000606857
1	havana	pseudogene	57598	64116	.	+	.	ID=gene:ENSG00000240361
1	havana	lnc_RNA	57598	64116	.	+	.	ID=transcript:ENST00000642116
1	havana	exon	57598	57653	.	+	.	Parent=transcript:ENST00000642116
1	havana	exon	58700	58856	.	+	.	Parent=transcript:ENST00000642116
1	havana	exon	62916	64116	.	+	.	Parent=transcript:ENST00000642116
1	havana	pseudogenic_transcript	62949	63887	.	+	.	ID=transcript:ENST00000492842
1	havana	exon	62949	63887	.	+	.	Parent=transcript:ENST00000492842
1	ensembl_havana	gene	65419	71585	.	+	.	ID=gene:ENSG00000186092
1	havana	mRNA	65419	71585	.	+	.	ID=transcript:ENST00000641515
1	havana	exon	65419	65433	.	+	.	Parent=transcript:ENST00000641515
1	havana	five_prime_UTR	65419	65433	.	+	.	Parent=transcript:ENST00000641515
1	havana	exon	65520	65573	.	+	.	Parent=transcript:ENST00000641515
1	havana	five_prime_UTR	65520	65573	.	+	.	Parent=transcript:ENST00000641515
1	havana	five_prime_UTR	69037	69090	.	+	.	Parent=transcript:ENST00000641515
1	havana	exon	69037	71585	.	+	.	Parent=transcript:ENST00000641515
1	havana	CDS	69091	70008	.	+	0	ID=CDS:ENSP00000493376
1	havana	three_prime_UTR	70009	71585	.	+	.	Parent=transcript:ENST00000641515
1	ensembl	mRNA	69055	70108	.	+	.	ID=transcript:ENST00000335137
1	ensembl	five_prime_UTR	69055	69090	.	+	.	Parent=transcript:ENST00000335137
1	ensembl	exon	69055	70108	.	+	.	Parent=transcript:ENST00000335137
1	ensembl	CDS	69091	70008	.	+	0	ID=CDS:ENSP00000334393
1	ensembl	three_prime_UTR	70009	70108	.	+	.	Parent=transcript:ENST00000335137
1	ensembl_havana	ncRNA_gene	89295	133723	.	-	.	ID=gene:ENSG00000238009
1	havana	lnc_RNA	89295	120932	.	-	.	ID=transcript:ENST00000466430
1	havana	exon	89295	91629	.	-	.	Parent=transcript:ENST00000466430


================================================
FILE: intro_awk_spring2021/data/Homo_sapiens_ucscGenes.subset.bed
================================================
chr1	11873	14409	uc010nxq.1
chr1	14361	19759	uc009viu.3
chr1	14406	29370	uc009viw.2
chr1	34610	36081	uc001aak.3
chr1	69090	70008	uc001aal.1
chr1	134772	140566	uc021oeg.2
chr1	321083	321115	uc001aaq.2
chr1	321145	321207	uc001aar.2
chr1	322036	326938	uc009vjk.2
chr1	327545	328439	uc021oei.1
chr1	367658	368597	uc010nxu.2
chr1	420205	421839	uc001aax.1
chr1	566092	566115	uc021oej.1
chr1	566134	566155	uc021oek.1
chr1	566239	566263	uc021oel.1
chr1	568843	568913	uc001abb.3
chr1	621095	622034	uc010nxv.2
chr1	661138	670994	uc009vjm.3
chr1	668417	668479	uc001abi.2
chr1	668509	668541	uc001abj.3
chr1	671823	671885	uc010nxw.2
chr1	671915	671947	uc001abl.3
chr1	674239	679736	uc001abm.2
chr1	700244	714068	uc001abo.3
chr1	761585	762902	uc010nxx.2
chr1	762970	794826	uc001abp.2
chr1	803450	812182	uc001abt.4
chr1	846814	850328	uc001abu.1
chr1	852952	854817	uc010nxy.1
chr1	861120	879961	uc001abw.1
chr1	879582	894679	uc001abz.4
chr1	895966	901099	uc001aca.2
chr1	901876	910484	uc001acd.3
chr1	910578	917473	uc001ach.2
chr1	934341	935552	uc001aci.2
chr1	948846	949919	uc001acj.4
chr1	955502	991499	uc001ack.2
chr1	995116	1001833	uc001acl.1
chr1	1007125	1009687	uc021oen.1
chr1	1017197	1051736	uc001acu.2
chr1	1072396	1079434	uc001acv.3
chr1	1102483	1102578	uc001acw.2
chr1	1103242	1103332	uc010nye.1
chr1	1103295	1103317	uc031pkr.1
chr1	1104384	1104467	uc010nyf.1
chr1	1104434	1104456	uc031pks.1
chr1	1108435	1114935	uc001acx.1
chr1	1109285	1133313	uc001acy.2
chr1	1138887	1142089	uc001add.3
chr1	1146705	1149548	uc001ade.3
chr1	1152287	1167447	uc001adh.4
chr1	1167628	1170420	uc001adk.3
chr1	1177825	1182102	uc001adl.2
chr1	1189291	1209234	uc001ado.3
chr1	1215815	1227409	uc001adt.1
chr1	1227763	1243269	uc001aeb.2
chr1	1243993	1247057	uc001aed.3
chr1	1246964	1260067	uc001aee.2
chr1	1260142	1264276	uc001aeo.3
chr1	1266725	1269844	uc010nyk.2
chr1	1270657	1284492	uc001aer.4
chr1	1288070	1293915	uc001aew.3
chr1	1309109	1310562	uc009vkb.1
chr1	1321090	1334718	uc001afi.2
chr1	1334909	1337426	uc001afm.3
chr1	1337275	1342693	uc001afo.4
chr1	1353799	1356824	uc010nyo.2
chr1	1361507	1363167	uc010nyp.2
chr1	1370902	1378262	uc001afs.3
chr1	1385068	1405538	uc001aft.2
chr1	1407163	1431582	uc001afv.3
chr1	1447522	1470067	uc001afz.2
chr1	1470157	1475740	uc009vkf.3
chr1	1477052	1510262	uc001agd.3
chr1	1510354	1510644	uc021oer.1
chr1	1533387	1535476	uc021oes.1
chr1	1535818	1543166	uc001agf.1
chr1	1550794	1565990	uc001agg.3
chr1	1567559	1570030	uc001agp.3
chr1	1571099	1655775	uc001agv.1
chr1	1586822	1590469	uc001ahc.1
chr1	1592938	1624243	uc001ahg.4
chr1	1631377	1633247	uc001ahi.1
chr1	1656053	1663343	uc001ahx.2
chr1	1658823	1677438	uc001aia.2
chr1	1682670	1711508	uc001aie.3
chr1	1716724	1822526	uc001aif.3
chr1	1846265	1848733	uc001aih.1
chr1	1849028	1850740	uc001aij.2
chr1	1853395	1858842	uc001aik.3
chr1	1884751	1935276	uc001aim.1
chr1	1944651	1946969	uc001aio.1
chr1	1950767	1962192	uc001aip.2
chr1	1981908	2116834	uc001aiq.3
chr1	2112574	2114663	uc001aiu.1
chr1	2115898	2126214	uc031pkt.1
chr1	2121236	2123179	uc001aiz.2
chr1	2160133	2241652	uc001aja.4
chr1	2252695	2322993	uc001ajb.1
chr1	2281852	2284100	uc001ajc.3
chr1	2309493	2322993	uc010nyy.2
chr1	2323213	2336885	uc001aje.2
chr1	2336240	2344010	uc001ajg.3
chr1	2407753	2436964	uc001aji.1
chr1	2439974	2458035	uc001ajm.1
chr1	2460183	2461684	uc001ajn.3
chr1	2481358	2484284	uc001ajo.2
chr1	2486162	2488450	uc021oev.1
chr1	2487804	2495188	uc001ajt.1
chr1	2487804	2495267	uc001ajr.3
chr1	2518188	2522908	uc001ajv.2
chr1	2522080	2564481	uc001ajy.2
chr1	2572806	2706230	uc021oey.1
chr1	2938045	2939467	uc001ajz.3
chr1	2976180	2980350	uc001aka.3
chr1	2980635	2984289	uc010nzg.1
chr1	2985741	3355185	uc001akf.3
chr1	3044538	3044599	uc021oez.1
chr1	3371146	3397677	uc001akg.4
chr1	3404505	3528059	uc001akl.3
chr1	3477259	3477354	uc021ofa.1
chr1	3541555	3546694	uc001akm.3
chr1	3547330	3566671	uc001ako.3
chr1	3569128	3652765	uc001akp.3
chr1	3652547	3663937	uc009vlm.3
chr1	3668964	3688209	uc001akv.2
chr1	3689351	3692546	uc001akw.4
chr1	3696783	3713068	uc001akx.1
chr1	3728644	3773797	uc001aky.2
chr1	3773844	3801993	uc001alc.3
chr1	3805696	3816857	uc001alf.3
chr1	3816967	3832011	uc001alg.3
chr1	4000671	4012643	uc001ali.2
chr1	4472110	4484744	uc001alj.2
chr1	4715104	4843851	uc001aln.3
chr1	4847557	4852183	uc001alo.4
chr1	5621768	5728315	uc001alp.1
chr1	5624130	5624203	uc021ofm.1
chr1	5922731	5922801	uc021ofn.1
chr1	5922869	6052533	uc001alq.2
chr1	6105980	6161253	uc001aly.2
chr1	6161846	6240194	uc001amb.2
chr1	6245079	6259679	uc001amd.3
chr1	6266188	6281359	uc001amg.3
chr1	6281252	6296044	uc001amk.3
chr1	6297870	6299502	uc001amm.3
chr1	6304251	6305638	uc009vly.2
chr1	6307405	6321035	uc001amp.2
chr1	6324331	6453826	uc001amt.3
chr1	6475293	6479979	uc001amx.3
chr1	6484847	6521004	uc001amy.3
chr1	6489893	6489956	uc021ofo.1
chr1	6521213	6526255	uc001anh.3
chr1	6526151	6545529	uc010nzr.1
chr1	6581406	6614658	uc001ans.3
chr1	6615337	6639817	uc001ant.3
chr1	6640062	6649340	uc001anx.3
chr1	6650783	6662929	uc001aoa.3
chr1	6673755	6684093	uc001aob.4
chr1	6685209	6693642	uc001aod.3
chr1	6694227	6761966	uc001aof.2
chr1	6845383	7829766	uc001aoi.3
chr1	7831328	7841492	uc001aol.3
chr1	7844713	7905237	uc001aoo.3
chr1	7907671	7913565	uc001aos.3
chr1	7975930	8003225	uc001aot.3
chr1	7990338	7990408	uc021ofs.1
chr1	8021713	8045342	uc001aox.4
chr1	8071778	8086393	uc001aoz.3
chr1	8384389	8404227	uc001apb.3
chr1	8412463	8877699	uc001apf.3
chr1	8440651	8441235	uc001apg.1
chr1	8921058	8939151	uc001apj.2
chr1	8938893	8939943	uc021oft.1
chr1	9005892	9034503	uc031plc.1
chr1	9063358	9086404	uc009vmo.1
chr1	9097004	9129887	uc001apo.3
chr1	9164475	9189229	uc001apq.1
chr1	9186983	9189250	uc001aps.3
chr1	9208345	9242451	uc009vmq.3
chr1	9294862	9331394	uc001apt.3
chr1	9352940	9429590	uc010oae.2
chr1	9497727	9497837	uc021ofx.1
chr1	9599527	9642831	uc001apw.3
chr1	9648931	9674935	uc021ofy.1
chr1	9711789	9789172	uc001aqb.4
chr1	9712667	9714644	uc001aqc.4
chr1	9740902	9747627	uc021oga.1
chr1	9789078	9884550	uc001aqh.3
chr1	9908333	9970316	uc001aql.1
chr1	9989775	10002840	uc001aqm.3
chr1	10003485	10045556	uc001aqp.3
chr1	10057254	10076078	uc001aqq.3
chr1	10093040	10241296	uc001aqs.4
chr1	10270763	10441661	uc001aqw.4
chr1	10459084	10480201	uc001arc.3
chr1	10490158	10512060	uc021ogd.1
chr1	10520602	10532613	uc001arj.3
chr1	10535002	10690815	uc001arn.3
chr1	10696665	10856733	uc001aro.4
chr1	10744633	10744736	uc021ogh.1
chr1	11006529	11042094	uc010oao.2
chr1	11072678	11085549	uc001art.3
chr1	11086579	11107296	uc001aru.3
chr1	11114648	11120091	uc001arz.1
chr1	11126675	11159938	uc001asa.3
chr1	11166587	11322608	uc001asd.3
chr1	11203954	11209595	uc031plf.1
chr1	11249345	11256038	uc001ase.4
chr1	11333254	11348491	uc001asg.3
chr1	11539294	11597640	uc001ash.4
chr1	11708417	11714888	uc001asj.3
chr1	11714913	11723384	uc001asm.3
chr1	11724149	11734409	uc001aso.3
chr1	11734536	11751678	uc009vnc.3
chr1	11751780	11780336	uc001asr.1
chr1	11782186	11785914	uc001ass.2
chr1	11796141	11810828	uc001asv.3
chr1	11824461	11826573	uc001asy.1
chr1	11832138	11849642	uc001asz.3
chr1	11845786	11866160	uc001atc.2
chr1	11866152	11903201	uc001ate.5
chr1	11905766	11907840	uc001ati.3
chr1	11917520	11918992	uc001atj.3
chr1	11979644	11986485	uc001atk.3
chr1	11994723	12035599	uc001atm.3
chr1	12040237	12073572	uc009vni.3
chr1	12079298	12092106	uc001ato.2
chr1	12123433	12204264	uc001atq.3
chr1	12227059	12269277	uc001att.3
chr1	12251761	12251839	uc021ogi.1
chr1	12290112	12572098	uc001atv.3
chr1	12567299	12567451	uc001atz.1
chr1	12627938	12677820	uc001auc.3
chr1	12704565	12727097	uc001auf.3
chr1	12776117	12788726	uc009vnn.1
chr1	12806162	12821102	uc001auh.3
chr1	12834983	12838048	uc001aui.3
chr1	12851545	12856777	uc001auj.2
chr1	12884467	12891264	uc001auk.2
chr1	12907235	12908237	uc009vno.2
chr1	12916940	12921764	uc001aum.1
chr1	12939032	12946025	uc001aun.2
chr1	12952726	12958094	uc001auo.3
chr1	12976449	12980568	uc001aup.3
chr1	12998301	13007406	uc001auq.2
chr1	13035542	13038381	uc009vnq.1
chr1	13182959	13184326	uc010obg.2
chr1	13328195	13331692	uc001aut.1
chr1	13359818	13369057	uc001auu.1
chr1	13386646	13390765	uc001auv.3
chr1	13421175	13428191	uc001auw.1
chr1	13447413	13452656	uc010obi.1
chr1	13474052	13477569	uc009vnu.1
chr1	13495253	13498259	uc001aux.3
chr1	13516065	13526943	uc009vnv.1
chr1	13607430	13611550	uc001auy.2
chr1	13629937	13635299	uc001auz.4
chr1	13641972	13648988	uc001ava.1
chr1	13694888	13698405	uc009vny.1
chr1	13716087	13719064	uc009vnz.1
chr1	13736906	13747803	uc009voa.1
chr1	13801444	13840242	uc001avb.3
chr1	13910251	13944452	uc001avd.3
chr1	14031349	14151574	uc001avi.3
chr1	14925212	15444544	uc001avm.4
chr1	15438310	15478960	uc009voh.3
chr1	15480228	15546974	uc001avx.3
chr1	15573767	15724622	uc001awb.2
chr1	15653175	15670372	uc001awc.1
chr1	15736390	15756839	uc001awh.2
chr1	15764937	15773153	uc001awi.1
chr1	15783222	15798586	uc001awk.3
chr1	15802595	15817895	uc001awl.3
chr1	15817895	15850940	uc001awn.4
chr1	15853351	15898228	uc001aws.3
chr1	15898193	15911605	uc001awv.2
chr1	15943952	15987552	uc001awx.2
chr1	15986363	15988217	uc010obn.2
chr1	15992765	15995537	uc001awz.3
chr1	16010826	16061264	uc010obo.2
chr1	16062808	16067884	uc001axb.1
chr1	16068986	16074292	uc001axc.4
chr1	16085254	16113084	uc001axe.1
chr1	16133656	16134194	uc009vol.1
chr1	16160709	16174642	uc001axj.2
chr1	16174358	16266950	uc001axk.1
chr1	16268363	16302627	uc001axl.4
chr1	16317618	16317647	uc001axm.1
chr1	16330730	16333184	uc001axn.3
chr1	16340522	16345285	uc001axo.2
chr1	16348485	16360545	uc001axu.3
chr1	16384263	16400127	uc001axz.4
chr1	16450831	16482582	uc001aya.2
chr1	16524598	16539104	uc001ayc.1
chr1	16558181	16563659	uc001ayd.3
chr1	16576558	16678948	uc001ayg.3
chr1	16693524	16724643	uc001aym.5
chr1	16725137	16763919	uc001ayn.3
chr1	16767166	16786584	uc001ayq.3
chr1	16793930	16819196	uc001ayt.2
chr1	16847079	16847153	uc021ogp.1
chr1	16858892	16858966	uc021ogq.1
chr1	16860349	16862144	uc021ogr.1
chr1	16862254	16864669	uc001ayv.2
chr1	16872433	16872504	uc021ogs.1
chr1	16874159	16874232	uc021ogt.1
chr1	16875408	16875482	uc021ogu.1
chr1	16888921	16940100	uc001ayw.4
chr1	16944756	16959841	uc001azf.3
chr1	16972068	16976915	uc010och.2
chr1	17004765	17004836	uc021ogw.1
chr1	17006500	17006573	uc021ogx.1
chr1	17007749	17007823	uc021ogy.1
chr1	17017712	17046652	uc001azn.1
chr1	17052060	17052133	uc021ogz.1
chr1	17053779	17053850	uc021oha.1
chr1	17081128	17090975	uc010ock.3
chr1	17180899	17180971	uc021ohb.1
chr1	17185443	17185516	uc021ohc.1
chr1	17186692	17186765	uc021ohd.1
chr1	17188415	17188486	uc021ohe.1
chr1	17197439	17200574	uc021ohf.2
chr1	17201957	17202031	uc021ohg.1
chr1	17215040	17216161	uc001azs.1
chr1	17216171	17216245	uc021ohh.1
chr1	17222645	17222720	uc021ohi.1
chr1	17248444	17299474	uc001azt.2
chr1	17300998	17307173	uc001azw.3
chr1	17312452	17338423	uc001baa.2
chr1	17345224	17380665	uc001bae.3
chr1	17393255	17445948	uc001baf.3
chr1	17531620	17572501	uc001bah.1
chr1	17566189	17572501	uc009vpb.1
chr1	17575592	17610727	uc001bai.3
chr1	17581660	17581778	uc021ohk.1
chr1	17634689	17690495	uc001baj.2
chr1	17698740	17728195	uc001bak.1
chr1	17733250	17765059	uc001bal.3
chr1	17866329	18024370	uc001ban.3
chr1	18081807	18153558	uc001bat.3
chr1	18434239	18704977	uc001bau.2
chr1	18701063	18702174	uc001baw.1
chr1	18807423	18812480	uc001bax.3
chr1	18957499	19062632	uc001bay.3
chr1	19166092	19186155	uc001bba.1
chr1	19197923	19229293	uc001bbc.3
chr1	19209695	19209769	uc021ohm.2
chr1	19230773	19282826	uc001bbd.2
chr1	19400999	19536746	uc001bbi.3
chr1	19542157	19578053	uc001bbo.4
chr1	19578074	19586622	uc001bbs.3
chr1	19592475	19600568	uc021ohn.1
chr1	19609056	19615280	uc001bbv.1
chr1	19619740	19622230	uc021ohp.1
chr1	19629201	19638640	uc001bbw.3
chr1	19638739	19655794	uc001bby.3
chr1	19665266	19812066	uc021ohr.1
chr1	19673334	19675427	uc001bcf.2
chr1	19750877	19751182	uc021ohs.1
chr1	19923470	19956315	uc021ohu.1
chr1	19934300	19935138	uc021ohx.2
chr1	19969722	19984949	uc001bcj.2
chr1	19991779	20006055	uc001bcl.3
chr1	20008705	20126410	uc001bcn.3
chr1	20140521	20141771	uc001bcr.3
chr1	20208887	20239437	uc001bcs.4
chr1	20246799	20250110	uc001bct.1
chr1	20301923	20306932	uc010odb.2
chr1	20396700	20418394	uc001bcy.3
chr1	20439142	20446059	uc001bcz.4
chr1	20465822	20476879	uc009vpp.1
chr1	20490483	20501687	uc009vpq.1
chr1	20512577	20519942	uc001bdb.3
chr1	20617411	20681387	uc009vps.2
chr1	20686293	20755287	uc001bdf.2
chr1	20808883	20812728	uc001bdh.3
chr1	20825940	20834674	uc001bdi.4
chr1	20878931	20881513	uc001bdj.3
chr1	20915443	20945400	uc001bdk.3
chr1	20959947	20978004	uc001bdm.3
chr1	20978259	20988037	uc001bdo.1
chr1	20990506	21044317	uc001bdr.4
chr1	21046224	21059133	uc009vpy.1
chr1	21069170	21113181	uc001bdw.1
chr1	21132784	21503381	uc001bef.3
chr1	21543739	21616766	uc001bei.2
chr1	21602542	21604868	uc001ben.1
chr1	21619782	21626362	uc001beo.2
chr1	21749600	21754300	uc001bep.1
chr1	21761832	21762609	uc001beq.1
chr1	21766582	21811393	uc001ber.4
chr1	21835857	21904905	uc001bet.3
chr1	21922707	21978348	uc001bew.3
chr1	22004791	22109688	uc001bfb.3
chr1	22138757	22151714	uc001bfg.1
chr1	22148736	22263750	uc001bfj.3
chr1	22303417	22315847	uc001bfk.3
chr1	22351706	22357715	uc001bfm.4
chr1	22379119	22419436	uc001bfr.3
chr1	22443797	22469519	uc001bfs.4
chr1	22778343	22857650	uc001bfu.2
chr1	22890003	22930087	uc001bfx.1
chr1	22963117	22966175	uc001bfy.3
chr1	22970117	22974603	uc001bga.4
chr1	22979681	22988029	uc001bgd.3
chr1	23037330	23241823	uc001bge.3
chr1	23046009	23046091	uc021oib.1
chr1	23189651	23189719	uc021oic.1
chr1	23243782	23247347	uc001bgg.1
chr1	23337326	23342343	uc001bgh.1
chr1	23345940	23410184	uc001bgj.2
chr1	23370797	23370865	uc021oid.1
chr1	23410515	23495517	uc010odv.1
chr1	23490445	23490546	uc021oie.1
chr1	23518387	23521222	uc001bgn.3
chr1	23636275	23670853	uc001bgp.4
chr1	23685940	23694879	uc001bgt.3
chr1	23695463	23698330	uc001bgw.3
chr1	23707554	23751261	uc021oig.1
chr1	23755055	23810750	uc001bha.2
chr1	23801092	23803135	uc001bhd.4
chr1	23832919	23857712	uc001bhe.2
chr1	23853364	23855542	uc001bhf.1
chr1	23884420	23886285	uc001bhh.4
chr1	23907984	23967056	uc001bhi.3
chr1	24018268	24022915	uc001bhk.3
chr1	24069855	24088549	uc001bho.3
chr1	24086871	24104787	uc001bhp.2
chr1	24104875	24114722	uc001bhq.3
chr1	24117645	24122029	uc001bht.3
chr1	24122088	24126060	uc009vqo.1
chr1	24128366	24151949	uc001bib.3
chr1	24171571	24194859	uc001bie.3
chr1	24200459	24239817	uc001bif.3
chr1	24255559	24255637	uc021oik.1
chr1	24286300	24289949	uc001big.3
chr1	24295572	24306953	uc021oir.1
chr1	24320925	24320957	uc021oit.1
chr1	24382530	24438665	uc001bin.4
chr1	24446260	24469775	uc001biq.2
chr1	24480646	24513765	uc001bis.3
chr1	24526729	24538180	uc010oei.1
chr1	24578722	24578758	uc021oiu.1
chr1	24579767	24579803	uc021oiv.1
chr1	24645811	24690970	uc021oiw.1
chr1	24683488	24740262	uc001bjc.3
chr1	24742244	24799473	uc001bjh.3
chr1	24822822	24828850	uc021oiz.1
chr1	24829386	24863510	uc001bjj.3
chr1	24882566	24935818	uc001bjk.2
chr1	24969593	24999772	uc001bjm.3
chr1	25071759	25170815	uc001bjo.2
chr1	25226001	25256770	uc001bjq.3
chr1	25548766	25559013	uc001bjt.1
chr1	25568739	25573985	uc001bjw.3
chr1	25598980	25656936	uc001bjz.3
chr1	25629228	25631643	uc001bkd.1
chr1	25664788	25688852	uc001bke.3
chr1	25688739	25747363	uc001bkf.3
chr1	25757387	25826698	uc001bkk.3
chr1	25870075	25895377	uc001bkl.4
chr1	25943958	26111258	uc001bkm.2
chr1	26126666	26144713	uc021ojl.1
chr1	26146396	26159433	uc001bkq.4
chr1	26146444	26150097	uc010oeu.1
chr1	26160496	26185848	uc001bkw.1
chr1	26187974	26197744	uc001bkx.3
chr1	26210676	26232993	uc010oev.2
chr1	26286257	26324648	uc001bld.4
chr1	26348270	26362954	uc001blf.3
chr1	26364513	26372604	uc001blg.1
chr1	26377795	26394125	uc001bli.2
chr1	26438267	26452039	uc009vsb.3
chr1	26485510	26489119	uc001blk.3
chr1	26496387	26497364	uc001bll.4
chr1	26503980	26516375	uc001blm.4
chr1	26517118	26529033	uc010oez.2
chr1	26551810	26556331	uc001blq.3
chr1	26560692	26605299	uc001bls.1
chr1	26606212	26608013	uc001blu.3
chr1	26608772	26633195	uc001blw.3
chr1	26644410	26647014	uc001bmc.3
chr1	26648349	26680621	uc001bmd.4
chr1	26688124	26699266	uc009vsj.1
chr1	26737268	26756219	uc001bmj.3
chr1	26758772	26797795	uc001bmk.3
chr1	26789254	26794028	uc001bmo.1
chr1	26798901	26803133	uc001bmp.4
chr1	26872342	26901520	uc001bms.1
chr1	26881032	26881084	uc021ojp.1
chr1	27022521	27108601	uc001bmv.1
chr1	27114453	27124894	uc001bmz.3
chr1	27145536	27392034	uc021ojq.1
chr1	27153200	27182211	uc001bnb.3
chr1	27189632	27190947	uc001bnc.1
chr1	27189786	27190449	uc010ofi.1
chr1	27205872	27216869	uc001bnd.1
chr1	27216978	27226962	uc001bne.3
chr1	27237974	27240567	uc001bnf.3
chr1	27248212	27273362	uc001bng.2
chr1	27276046	27286901	uc001bni.2
chr1	27320194	27327377	uc001bnj.4
chr1	27331510	27339333	uc010ofj.2
chr1	27425299	27481621	uc001bnm.4
chr1	27533765	27533868	uc021ojs.1
chr1	27561006	27635124	uc009vst.3
chr1	27648635	27662891	uc001bnr.4
chr1	27650364	27653016	uc021oju.1
chr1	27668482	27680423	uc001bnw.2
chr1	27681669	27693337	uc001bny.1
chr1	27695600	27701315	uc001boa.3
chr1	27705595	27709805	uc001boc.3
chr1	27719147	27722317	uc001bod.4
chr1	27730733	27816678	uc001bof.2
chr1	27860755	27930143	uc009vsy.3
chr1	27938800	27961727	uc001bom.3
chr1	27992571	27998724	uc001bon.1
chr1	28052489	28089423	uc001bor.3
chr1	28099693	28150963	uc001bou.4
chr1	28157251	28178183	uc001bov.2
chr1	28160911	28161077	uc001boy.1
chr1	28199054	28213193	uc001bpc.4
chr1	28218048	28241236	uc001bpe.1
chr1	28261503	28285663	uc001bpg.3
chr1	28286503	28294604	uc001bph.1
chr1	28296854	28415148	uc001bpi.2
chr1	28473676	28503455	uc001bpl.3
chr1	28526789	28559542	uc001bpn.3
chr1	28562601	28564616	uc001bpq.3
chr1	28567542	28567564	uc021okb.1
chr1	28585962	28609002	uc001bps.3
chr1	28655512	28662478	uc009vtg.3
chr1	28764660	28826881	uc001bpy.3
chr1	28832454	28837404	uc001bqd.3
chr1	28844744	28865708	uc001bqf.2
chr1	28879528	28905057	uc001bqi.3
chr1	28905049	28908383	uc001bqo.3
chr1	28905254	28905334	uc001bqq.1
chr1	28919006	28921088	uc001bqv.3
chr1	28929608	28969604	uc001bqy.3
chr1	28975111	28975246	uc009vtj.1
chr1	28995239	29042115	uc001bra.3
chr1	29016176	29016306	uc021oke.1
chr1	29063132	29096287	uc021okf.1
chr1	29138653	29190208	uc001brf.1
chr1	29213602	29446558	uc001brm.2
chr1	29445936	29450421	uc001brn.2
chr1	29474249	29508637	uc001bro.3
chr1	29519384	29557454	uc001brq.1
chr1	29563027	29653325	uc001bru.3
chr1	30486798	30510456	uc001bry.3
chr1	31184123	31196432	uc001brz.3
chr1	31191618	31199593	uc001bsb.1
chr1	31205314	31230683	uc001bsc.2
chr1	31212002	31212079	uc021okj.1
chr1	31342312	31381480	uc001bse.2
chr1	31404352	31538564	uc001bsh.1
chr1	31408535	31408623	uc021okk.1
chr1	31421964	31422052	uc021okl.1
chr1	31441009	31441084	uc001bsl.1
chr1	31652591	31712734	uc010ogd.2
chr1	31732414	31742513	uc009vtt.3
chr1	31732414	31769644	uc001bso.3
chr1	31769841	31837780	uc001bsp.1
chr1	31838099	31845923	uc001bss.1
chr1	31886659	31907527	uc010ogh.2
chr1	31971838	31974167	uc021okn.1
chr1	31984035	31989846	uc001bsy.1
chr1	32042085	32053287	uc001bta.3
chr1	32083300	32092919	uc009vtx.2
chr1	32095462	32110838	uc001bth.2
chr1	32117847	32169768	uc001btk.1
chr1	32192717	32229648	uc001btn.3
chr1	32224260	32224336	uc021okr.1
chr1	32256024	32281580	uc001bts.1
chr1	32372021	32403988	uc001bty.2
chr1	32479294	32509482	uc001bub.4
chr1	32538502	32568467	uc010ogv.2
chr1	32573643	32642168	uc001bug.3
chr1	32645344	32663886	uc001bui.3
chr1	32666201	32670991	uc001bul.1
chr1	32671235	32674288	uc001bum.2
chr1	32674694	32681797	uc001bun.2
chr1	32681797	32687926	uc001buq.4
chr1	32687958	32697205	uc009vuc.3
chr1	32697260	32707311	uc001buv.4
chr1	32712817	32714461	uc001buw.3
chr1	32739711	32751766	uc001buy.3
chr1	32757707	32799224	uc001bvb.1
chr1	32799429	32801840	uc001bvd.4
chr1	32826870	32827844	uc021oku.1
chr1	32827861	32829924	uc001bvf.3
chr1	32830704	32860062	uc010ohg.2
chr1	32930657	32953459	uc001bvl.4
chr1	33004771	33071542	uc001bvn.3
chr1	33087306	33116185	uc001bvp.3
chr1	33116748	33151812	uc001bvr.3
chr1	33145506	33168361	uc001bvt.2
chr1	33207511	33240571	uc001bvu.1
chr1	33240839	33283633	uc001bvy.1
chr1	33283118	33324480	uc001bwc.4
chr1	33327868	33338082	uc001bwg.3
chr1	33352097	33360247	uc001bwh.3
chr1	33360195	33366953	uc001bwi.1
chr1	33402049	33430286	uc010oho.2
chr1	33452675	33498070	uc001bwn.3
chr1	33476825	33502512	uc001bwp.2
chr1	33546713	33585995	uc001bwr.3
chr1	33607471	33608831	uc001bxa.1
chr1	33611002	33647671	uc001bxb.3
chr1	33722173	33766320	uc001bxc.1
chr1	33772366	33786699	uc031plq.1
chr1	33789223	33841194	uc001bxg.1
chr1	33797993	33798093	uc021okw.1
chr1	33802166	33802465	uc021okx.1
chr1	33938231	33961995	uc001bxj.4
chr1	33979608	34631443	uc001bxn.1
chr1	34326075	34330392	uc001bxp.3
chr1	34334556	34351059	uc001bxr.3
chr1	34642552	34684731	uc001bxt.3
chr1	35220647	35224113	uc001bxu.4
chr1	35225341	35229325	uc001bxv.1
chr1	35244163	35244247	uc021ola.1
chr1	35247910	35251967	uc001bxy.3
chr1	35258598	35261348	uc001bya.3
chr1	35315962	35325417	uc001byb.3
chr1	35331036	35370984	uc001byc.3
chr1	35441299	35444307	uc021olf.1
chr1	35447126	35450948	uc001bye.3
chr1	35451766	35497569	uc001byh.3
chr1	35544971	35581455	uc001bym.3
chr1	35641980	35646083	uc001byq.3
chr1	35649200	35658743	uc001bys.3
chr1	35734567	35887545	uc001byt.3
chr1	35833677	35835012	uc031plr.1
chr1	35899090	36023037	uc001byx.3
chr1	36023392	36032380	uc001bza.3
chr1	36038970	36060927	uc010ohy.2
chr1	36065142	36107445	uc001bzf.2
chr1	36179476	36184790	uc001bzh.1
chr1	36197712	36235551	uc001bzi.3
chr1	36273827	36323490	uc001bzj.2
chr1	36348809	36389899	uc001bzl.3
chr1	36391432	36395210	uc001bzm.1
chr1	36396682	36522063	uc001bzp.3
chr1	36549675	36553876	uc001bzr.3
chr1	36554452	36559533	uc001bzt.3
chr1	36560843	36565850	uc001bzv.2
chr1	36602169	36615115	uc031pls.1
chr1	36621802	36646441	uc001bzz.3
chr1	36690016	36770957	uc001cae.4
chr1	36771993	36787379	uc010oia.1
chr1	36787631	36789755	uc001caj.1
chr1	36805224	36851485	uc001cak.1
chr1	36859030	36863493	uc001cao.1
chr1	36883506	36916086	uc001caq.3
chr1	36921361	36930040	uc001cas.2
chr1	36931643	36948915	uc001cax.2
chr1	37261127	37499844	uc001caz.2
chr1	37627163	37627235	uc021oll.1
chr1	37920479	37940044	uc021olm.1
chr1	37940118	37949978	uc001cbb.4
chr1	37955560	37980420	uc001cbe.2
chr1	37966535	37966595	uc031pma.1
chr1	38000049	38019945	uc001cbi.4
chr1	38022519	38032458	uc001cbj.3
chr1	38032412	38061586	uc001cbk.3
chr1	38076950	38100595	uc001cbm.2
chr1	38147241	38156192	uc001cbp.3
chr1	38147242	38149864	uc031pmb.1
chr1	38158072	38175391	uc001cbs.4
chr1	38181645	38230824	uc009vvi.3
chr1	38259773	38267278	uc001cby.2
chr1	38268613	38273865	uc001cca.1
chr1	38273472	38275126	uc001ccd.2
chr1	38275238	38325292	uc001cce.1
chr1	38326368	38412729	uc001ccg.1
chr1	38349908	38349989	uc021oln.1
chr1	38422651	38455761	uc001cci.3
chr1	38462441	38471187	uc001cck.3
chr1	38478383	38490497	uc001ccn.4
chr1	38509522	38512450	uc001ccp.1
chr1	38554902	38555001	uc021olo.1
chr1	38674705	38680439	uc021olp.1
chr1	39303868	39325495	uc001ccq.3
chr1	39328161	39339050	uc001ccs.3
chr1	39340222	39341770	uc021olr.1
chr1	39351478	39407456	uc001ccu.1
chr1	39456915	39471737	uc001ccw.3
chr1	39491966	39500308	uc001ccy.3
chr1	39549838	39952810	uc031pmc.1
chr1	39619835	39619968	uc021olu.1
chr1	39648409	39648505	uc021olv.1
chr1	39875175	39882154	uc009vvt.1
chr1	39957317	39995541	uc001cdi.3
chr1	39970194	39970267	uc021olx.1
chr1	39987951	40025370	uc001cdk.3
chr1	40026484	40042521	uc001cdl.2
chr1	40033045	40033182	uc001cdo.1
chr1	40089102	40105348	uc001cdp.3
chr1	40124792	40137710	uc001cdq.1
chr1	40144644	40157089	uc001cdr.3
chr1	40204516	40229586	uc001cdw.3
chr1	40223902	40254533	uc001cdz.1
chr1	40235196	40237020	uc001ceb.1
chr1	40306705	40349177	uc021olz.1
chr1	40361095	40367687	uc001cer.2
chr1	40420783	40435628	uc001cev.3
chr1	40506254	40538321	uc001cey.4
chr1	40538381	40563142	uc001cfb.2
chr1	40627040	40706593	uc001cfc.4
chr1	40713572	40717365	uc001cfe.2
chr1	40723721	40759856	uc001cfg.4
chr1	40766162	40782981	uc001cfh.1
chr1	40839377	40888998	uc001cfj.3
chr1	40916336	40929390	uc001cfn.2
chr1	40943301	40962015	uc001cfo.3
chr1	40974432	40982214	uc001cfp.3
chr1	40997232	41013841	uc001cft.2
chr1	41086351	41131324	uc001cfu.1
chr1	41154751	41157933	uc010ojl.1
chr1	41157241	41237275	uc009vwd.3
chr1	41220026	41220118	uc001cgf.2
chr1	41222955	41223044	uc001cgg.3
chr1	41249683	41306124	uc001cgh.2
chr1	41326727	41328018	uc001cgj.3
chr1	41347313	41347427	uc021omc.1
chr1	41445006	41478235	uc001cgk.4
chr1	41480261	41509562	uc021omd.1
chr1	41481268	41487427	uc001cgm.2
chr1	41492870	41707815	uc001cgs.3
chr1	41932607	41932699	uc021ome.1
chr1	41944445	41949874	uc009vwh.3
chr1	41944445	41950344	uc001cgx.3
chr1	41972035	42384496	uc001cha.4
chr1	42619091	42621495	uc001chc.1
chr1	42628361	42630395	uc001chd.1
chr1	42642209	42800903	uc001chf.3
chr1	42846467	42889900	uc001chi.2
chr1	42896000	42921938	uc001chj.3
chr1	42922172	42926086	uc001chl.3
chr1	43000559	43120335	uc009vwk.1
chr1	43124047	43142429	uc001chq.3
chr1	43144356	43147330	uc001chr.3
chr1	43148065	43168020	uc001chs.3
chr1	43198763	43205925	uc001cht.1
chr1	43212005	43232755	uc001chx.4
chr1	43232915	43241413	uc001cia.4
chr1	43253660	43263901	uc021omg.1
chr1	43272722	43283059	uc001cib.2
chr1	43291248	43310660	uc001cie.1
chr1	43312243	43318146	uc021omh.1
chr1	43323292	43354460	uc001cij.1
chr1	43391045	43424847	uc001cik.2
chr1	43424719	43449029	uc001cil.3
chr1	43489219	43489308	uc021omj.1
chr1	43585818	43611958	uc009vwn.1
chr1	43613593	43622067	uc009vwo.3
chr1	43629844	43638241	uc010ojx.2
chr1	43638000	43720029	uc021omk.1
chr1	43735664	43739673	uc021oml.1
chr1	43747556	43751250	uc001cit.4
chr1	43766565	43788781	uc001ciu.3
chr1	43803474	43820135	uc001ciw.3
chr1	43824625	43828873	uc001cix.3
chr1	43829067	43833409	uc031pmf.1
chr1	43849578	43855483	uc001cje.2
chr1	43855555	43919918	uc001cjk.3
chr1	43916673	43919660	uc001cjo.3
chr1	43996546	44089343	uc001cjr.3
chr1	44115796	44171189	uc001cjx.3
chr1	44165355	44173012	uc001cjy.4
chr1	44173203	44396837	uc001cjz.4
chr1	44182137	44182227	uc021oms.1
chr1	44398991	44402912	uc001ckt.3
chr1	44412477	44433694	uc001ckx.3
chr1	44435652	44439043	uc001ckz.3
chr1	44440601	44443972	uc001cld.3
chr1	44445607	44456843	uc010okl.2
chr1	44457279	44462198	uc001clj.3
chr1	44462154	44483012	uc001cll.4
chr1	44584521	44600809	uc001clp.3
chr1	44679124	44686351	uc001clq.1
chr1	44686741	44820939	uc001clt.3
chr1	44870959	45117396	uc001clv.1
chr1	45011164	45011224	uc031pmh.1
chr1	45119500	45140099	uc001cmc.3
chr1	45140393	45191263	uc001cmf.2
chr1	45205489	45233438	uc001cmg.4
chr1	45241245	45244412	uc001cmi.3
chr1	45241536	45241610	uc001cmj.1
chr1	45242163	45242261	uc001cmk.1
chr1	45243513	45243584	uc009vxi.3
chr1	45244061	45244130	uc001cml.3
chr1	45249256	45253426	uc001cmm.3
chr1	45266035	45271667	uc001cmn.3
chr1	45271585	45272957	uc001cmp.3
chr1	45274153	45279801	uc010ole.1
chr1	45287937	45308616	uc010olf.2
chr1	45316193	45452394	uc001cmt.3
chr1	45468219	45477027	uc009vxk.3
chr1	45477804	45481341	uc001cna.2
chr1	45482075	45672250	uc001cnd.2
chr1	45769581	45771291	uc010olk.2
chr1	45792544	45794346	uc001cne.3
chr1	45794913	45806142	uc009vxp.3
chr1	45805341	45809650	uc009vxq.3
chr1	45809554	45956840	uc001cns.1
chr1	45959597	45965751	uc001cnw.3
chr1	45965855	45976739	uc009vxv.3
chr1	45976706	45988562	uc021omw.1
chr1	46016454	46035723	uc001coe.3
chr1	46049659	46084578	uc001coi.2
chr1	46085715	46089731	uc010olt.2
chr1	46092975	46152302	uc001coq.3
chr1	46111451	46112357	uc010olu.1
chr1	46153846	46160108	uc001cor.1
chr1	46164406	46216485	uc001cou.3
chr1	46269284	46501796	uc001cov.3
chr1	46505811	46598380	uc001cpb.4
chr1	46640748	46651634	uc001cpd.3
chr1	46654352	46685977	uc001cpg.3
chr1	46669005	46686928	uc010oma.2
chr1	46713366	46744145	uc009vye.2
chr1	46744071	46769038	uc001cpn.3
chr1	46769379	46782447	uc001cpp.3
chr1	46805848	46830824	uc001cpr.2
chr1	46859938	46879520	uc001cpu.2
chr1	46899498	46911374	uc021ona.1
chr1	46972667	46979886	uc001cpx.3
chr1	47004367	47035927	uc021onb.1
chr1	47011315	47015678	uc001cpy.2
chr1	47011315	47016887	uc009vyh.1
chr1	47023078	47069966	uc001cqb.4
chr1	47073386	47080805	uc001cqe.4
chr1	47100710	47134099	uc001cqh.4
chr1	47137496	47139256	uc001cqj.3
chr1	47139707	47157769	uc021ond.1
chr1	47140830	47184736	uc001cqk.4
chr1	47264669	47285021	uc001cqn.4
chr1	47308766	47366147	uc031pmm.1
chr1	47394845	47407156	uc001cqp.4
chr1	47489239	47516423	uc001cqt.3
chr1	47533159	47583992	uc001cqu.1
chr1	47603106	47614526	uc001cqv.1
chr1	47644921	47646011	uc031pmn.1
chr1	47649260	47655771	uc001cqw.3
chr1	47681962	47689770	uc009vyq.2
chr1	47681962	47695443	uc001cqx.2
chr1	47691628	47691655	uc021onf.1
chr1	47715810	47779819	uc001crd.1
chr1	47799468	47844511	uc001cri.3
chr1	47859449	47861215	uc001crj.1
chr1	47881743	47883724	uc001crk.3
chr1	47897806	47900313	uc001crl.3
chr1	47901688	47906363	uc001crm.3
chr1	48226199	48462562	uc021ong.1
chr1	48567386	48648100	uc010omr.1
chr1	48688356	48714316	uc001crn.2
chr1	48761043	48937876	uc001crr.2
chr1	48998526	50489626	uc001cru.2
chr1	49193539	49242547	uc001crx.4
chr1	50574593	50667540	uc001csb.2
chr1	50883222	50889119	uc010onb.2
chr1	50906934	51425936	uc001cse.1
chr1	51048075	51048183	uc021onh.1
chr1	51435641	51440309	uc001csg.3
chr1	51567905	51613754	uc001csh.3
chr1	51701944	51739119	uc001csi.4
chr1	51752929	51810785	uc010onf.2
chr1	51819934	51984995	uc001csq.1
chr1	52004296	52004401	uc021oni.1
chr1	52082545	52254891	uc001csu.3
chr1	52254865	52344609	uc001ctc.4
chr1	52373627	52456436	uc001cth.3
chr1	52461412	52461712	uc021onk.1
chr1	52485803	52521843	uc001cti.4
chr1	52497776	52499472	uc001ctj.1
chr1	52521856	52556388	uc001ctk.3
chr1	52607765	52812358	uc001cto.4
chr1	52816264	52831877	uc001ctq.2
chr1	52838500	52870143	uc001ctu.3
chr1	52870218	52883992	uc001ctv.4
chr1	52870218	52883992	uc001ctw.4
chr1	52888947	53018762	uc001cty.2
chr1	53068042	53074723	uc001cue.3
chr1	53099065	53122737	uc001cuf.3
chr1	53152013	53164038	uc001cui.2
chr1	53192130	53293013	uc001cuj.3
chr1	53308182	53360247	uc001cuk.2
chr1	53361581	53387591	uc001cup.4
chr1	53392900	53517289	uc001cur.2
chr1	53527723	53551174	uc010onr.2
chr1	53552854	53608289	uc001cuy.3
chr1	53580247	53584281	uc001cva.1
chr1	53662100	53679869	uc001cvb.4
chr1	53679771	53686289	uc001cvd.3
chr1	53692563	53704282	uc001cvf.2
chr1	53704281	53708455	uc001cvg.3
chr1	53708040	53793821	uc001cvi.2
chr1	53793904	53802889	uc001cvn.1
chr1	53904042	53905693	uc009vzj.3
chr1	53925071	53933158	uc001cvq.1
chr1	53971905	54199877	uc001cvr.1
chr1	54231133	54304225	uc001cvs.3
chr1	54317391	54355487	uc001cvu.3
chr1	54359860	54376759	uc001cwb.3
chr1	54387233	54411288	uc001cwh.3
chr1	54411998	54433841	uc001cwj.2
chr1	54472970	54483859	uc001cwm.2
chr1	54497348	54519111	uc001cwp.3
chr1	54519273	54565416	uc001cwt.1
chr1	54519751	54519827	uc021ons.1
chr1	54604667	54618679	uc001cwv.2
chr1	54638026	54665746	uc009vzo.3
chr1	54665839	54684056	uc001cxa.4
chr1	54691103	54872068	uc001cxe.4
chr1	55013806	55076005	uc001cxl.2
chr1	55074849	55089200	uc001cxn.3
chr1	55107426	55175939	uc010ooe.1
chr1	55181494	55208328	uc001cxx.4
chr1	55222570	55230226	uc001cxy.3
chr1	55246751	55266941	uc009vzt.1
chr1	55271735	55307937	uc001cyb.4
chr1	55315299	55352921	uc001cyc.1
chr1	55352654	55353883	uc021onu.1
chr1	55423541	55423614	uc021onv.1
chr1	55446464	55457966	uc001cyd.3
chr1	55464616	55474465	uc001cye.3
chr1	55505148	55530526	uc001cyf.2
chr1	55532031	55681039	uc021onw.1
chr1	55681080	55683128	uc021onx.1
chr1	55691313	55691396	uc021ony.1
chr1	55842198	55842525	uc021onz.1
chr1	55950543	55950645	uc021ooa.1
chr1	56046709	56200675	uc001cyi.1
chr1	56960418	57045257	uc001cyj.2
chr1	57110989	57181008	uc001cyk.4
chr1	57184476	57285369	uc001cym.4
chr1	57289353	57292593	uc001cyn.3
chr1	57320442	57383894	uc001cyo.2
chr1	57394882	57431688	uc001cyp.3
chr1	57463578	58716211	uc001cys.1
chr1	58326214	58328786	uc001cyu.1
chr1	58933598	58934677	uc001cyw.1
chr1	58946390	59012446	uc001cyy.3
chr1	59041094	59043166	uc001cyz.4
chr1	59120410	59165747	uc009wab.2
chr1	59246462	59249785	uc001cze.3
chr1	59250822	59365384	uc010oop.1
chr1	59597607	59612479	uc010ooq.2
chr1	59762624	60228402	uc009wac.3
chr1	60198898	60198968	uc021oob.1
chr1	60238466	60254501	uc001czn.3
chr1	60280532	60342050	uc001czo.3
chr1	60358979	60392423	uc001czq.3
chr1	60454823	60539442	uc001czs.2
chr1	61125302	61291256	uc001czt.1
chr1	61405915	61436448	uc001czu.3
chr1	61547533	61928460	uc010oos.2
chr1	62119913	62121800	uc031pmt.1
chr1	62146718	62191095	uc001czz.1
chr1	62208148	62629591	uc001dab.3
chr1	62318170	62318274	uc031pmu.1
chr1	62660473	62678001	uc001dae.4
chr1	62701836	62785083	uc001dah.4
chr1	62901974	62917475	uc001dak.2
chr1	62920396	63154039	uc001daq.4
chr1	63063157	63071976	uc001das.2
chr1	63249776	63330941	uc001dau.3
chr1	63624753	63782901	uc001daw.2
chr1	63704613	63704845	uc021ood.1
chr1	63788729	63790797	uc001dax.2
chr1	63799429	63799491	uc021ooe.1
chr1	63833260	63904233	uc021oof.1
chr1	63906440	63988944	uc001dbb.2
chr1	63989012	64038364	uc001dbf.3
chr1	64014650	64016307	uc001dbg.1
chr1	64088886	64125916	uc010ooz.2
chr1	64239689	64647179	uc001dbj.3
chr1	64262024	64262128	uc021oog.1
chr1	64571005	64636980	uc001dbl.3
chr1	64669489	64710027	uc001dbn.1
chr1	64936475	65158741	uc001dbo.1
chr1	65045529	65045604	uc021ooh.1
chr1	65210777	65298914	uc001dbs.2
chr1	65298905	65432187	uc001dbu.1
chr1	65445259	65468159	uc001dbw.3
chr1	65488650	65488757	uc021ooj.1
chr1	65523437	65523525	uc021ook.1
chr1	65524116	65524191	uc001dbx.3
chr1	65613231	65697828	uc001dby.3
chr1	65775217	65881552	uc001dce.2
chr1	65886130	66103176	uc001dci.3
chr1	65886130	65901690	uc001dcf.3
chr1	66258855	66840262	uc001dco.3
chr1	66560143	66560229	uc021oom.1
chr1	66999824	67210768	uc001dcr.3
chr1	67094122	67094200	uc021oon.1
chr1	67132271	67142710	uc010ope.1
chr1	67218139	67244730	uc001dcv.3
chr1	67263423	67266942	uc001dcw.3
chr1	67278571	67390570	uc001dcx.3
chr1	67390577	67454302	uc001dde.2
chr1	67465014	67520080	uc001ddk.2
chr1	67557858	67600654	uc001ddm.2
chr1	67632168	67725650	uc001ddo.3
chr1	67661822	67661926	uc021ooo.1
chr1	67773046	67862583	uc001ddu.3
chr1	67873492	67896123	uc001ddv.3
chr1	68150859	68154021	uc001ddz.2
chr1	68167148	68299155	uc001dea.2
chr1	68238275	68238336	uc021oop.1
chr1	68297970	68668670	uc001deb.2
chr1	68511644	68516481	uc001ded.3
chr1	68564141	68698284	uc001dee.3
chr1	68649200	68649293	uc021oos.1
chr1	68649301	68649321	uc021oot.1
chr1	68894506	68915642	uc001dei.1
chr1	68939834	68962799	uc001dem.4
chr1	68962358	69004310	uc001den.3
chr1	70225857	70589171	uc001dep.3
chr1	70385004	70386000	uc009wbh.2
chr1	70610484	70671361	uc001der.2
chr1	70671364	70717701	uc001des.3
chr1	70724684	70820417	uc001dex.4
chr1	70820492	70833705	uc001dfa.3
chr1	70876900	70905534	uc001dfd.3
chr1	71172135	71252151	uc001dff.3
chr1	71418114	71513491	uc001dfo.3
chr1	71512188	71532865	uc001dfr.3
chr1	71528973	71546972	uc001dft.3
chr1	71533313	71533399	uc010oqr.1
chr1	71547006	71703406	uc001dfu.2
chr1	71868624	72748405	uc001dfw.3
chr1	72259914	72302695	uc031pmw.1
chr1	73771852	73804560	uc001dfx.3
chr1	74491701	74663871	uc001dfy.4
chr1	74663895	75010116	uc001dge.2
chr1	75033794	75139422	uc001dgg.3
chr1	75043113	75091782	uc001dgh.3
chr1	75171171	75199092	uc001dgj.3
chr1	75198835	75232360	uc001dgn.3
chr1	75595658	75598261	uc001dgp.1
chr1	75600566	75627218	uc031pmx.1
chr1	75672074	76076799	uc001dgu.3
chr1	76103850	76188721	uc001dgv.3
chr1	76190042	76229355	uc009wbp.3
chr1	76251878	76260775	uc001dgy.2
chr1	76252756	76252834	uc001dgz.2
chr1	76253573	76253657	uc009wbu.1
chr1	76255161	76255232	uc009wbv.1
chr1	76262555	76378923	uc001dhd.2
chr1	76384557	76398116	uc001dhe.2
chr1	76540388	77096669	uc001dhh.2
chr1	77333185	77529737	uc001dhi.3
chr1	77554666	77685132	uc001dhk.3
chr1	77747661	78025654	uc001dhn.3
chr1	78030189	78148343	uc001dhq.3
chr1	78161673	78225564	uc001dht.4
chr1	78245308	78345225	uc010ork.3
chr1	78354199	78409578	uc001dic.4
chr1	78413590	78444777	uc001dii.3
chr1	78470635	78482995	uc001dij.3
chr1	78511588	78603112	uc001dik.3
chr1	78560490	78560599	uc021oov.1
chr1	78695282	78759574	uc001dil.1
chr1	78956727	79006386	uc001din.3
chr1	79086087	79111830	uc010oro.2
chr1	79115476	79129763	uc001dip.4
chr1	79152744	79152828	uc021oow.1
chr1	79355448	79472495	uc001diq.4
chr1	82266081	82458107	uc001diu.3
chr1	82312940	82313045	uc021oox.1
chr1	83439565	83451891	uc001dix.4
chr1	83911736	83920454	uc001diy.3
chr1	84041470	84326679	uc001diz.4
chr1	84259583	84259634	uc021ooy.1
chr1	84259597	84379059	uc031pmy.1
chr1	84267442	84326229	uc001dja.1
chr1	84335056	84464833	uc001djc.3
chr1	84379036	84379062	uc021ooz.1
chr1	84609951	84704181	uc001djl.3
chr1	84743003	84743140	uc021opa.1
chr1	84764048	84816481	uc001djr.3
chr1	84810360	84816481	uc001djs.3
chr1	84830640	84863576	uc009wcg.3
chr1	84864214	84880691	uc001djt.1
chr1	84944919	84964033	uc001djv.4
chr1	84964005	84972262	uc001djw.4
chr1	84972012	85014647	uc021opb.1
chr1	85018803	85040163	uc001dka.2
chr1	85093912	85097429	uc010ory.1
chr1	85109389	85156240	uc001dkj.3
chr1	85279085	85358896	uc009wcj.1
chr1	85391265	85462796	uc001dkm.3
chr1	85483764	85514223	uc001dkp.3
chr1	85527992	85598821	uc001dkt.3
chr1	85599476	85599556	uc021opc.1
chr1	85623355	85666728	uc009wcm.3
chr1	85715636	85725355	uc001dkv.3
chr1	85731459	85742587	uc021opd.1
chr1	85742040	85865646	uc001dla.2
chr1	85784167	85930889	uc001dlb.3
chr1	86046443	86049648	uc001dle.3
chr1	86115105	86174116	uc001dlh.3
chr1	86194915	86622154	uc001dlj.3
chr1	86815776	86862025	uc001dll.2
chr1	86889768	86922240	uc001dlr.4
chr1	86934394	86965974	uc001dlt.3
chr1	87012758	87046432	uc009wcs.3
chr1	87099958	87121059	uc010osh.2
chr1	87170252	87213867	uc001dly.3
chr1	87328127	87380107	uc021opi.1
chr1	87380334	87575681	uc010osk.2
chr1	87597679	87598718	uc021opj.1
chr1	87777576	87777676	uc021opk.1
chr1	87794150	87814607	uc001dmi.3
chr1	87819209	87837338	uc001dmk.3
chr1	87918922	87919056	uc021opl.1
chr1	88943510	88943810	uc021opm.1
chr1	89004735	89150887	uc021opn.1
chr1	89149921	89301938	uc001dmn.3
chr1	89318320	89357301	uc001dmo.4
chr1	89401455	89458643	uc001dmp.2
chr1	89445138	89458643	uc001dms.3
chr1	89472359	89488549	uc001dmt.3
chr1	89517986	89531043	uc001dmx.2
chr1	89573309	89591799	uc001dmz.1
chr1	89597433	89641723	uc001dna.2
chr1	89646830	89664633	uc001dnb.3
chr1	89724633	89738544	uc001dnd.3
chr1	89754940	89756045	uc031pna.1
chr1	89829435	89853719	uc001dnf.2
chr1	89873237	89890493	uc009wcy.1
chr1	89990396	90063420	uc001dni.3
chr1	90090407	90098453	uc001dnk.1
chr1	90098643	90185094	uc001dnl.4
chr1	90287479	90401989	uc001dnn.3
chr1	90453271	90453503	uc021opr.1
chr1	90458823	90460525	uc001dno.3
chr1	90460677	90494094	uc001dnq.2
chr1	91177578	91182794	uc001dns.3
chr1	91295103	91317175	uc001dnu.2
chr1	91380856	91487812	uc001dnw.3
chr1	91726322	91870426	uc001doa.4
chr1	91966403	91991321	uc001dof.3
chr1	92145899	92351836	uc001doh.3
chr1	92295329	92295626	uc021ops.1
chr1	92414927	92479985	uc010osz.2
chr1	92495532	92529093	uc001don.2
chr1	92545861	92613401	uc001doo.3
chr1	92632608	92650280	uc010otd.2
chr1	92683572	92711367	uc001doq.3
chr1	92711954	92764566	uc001dor.3
chr1	92764521	92853732	uc001dot.2
chr1	92940317	92951628	uc001dov.4
chr1	92974252	93257961	uc001dox.3
chr1	93297593	93307481	uc001doz.3
chr1	93302845	93302940	uc001dpe.2
chr1	93303574	93303627	uc021opt.1
chr1	93307716	93427079	uc001dpg.3
chr1	93544791	93604638	uc009wdj.3
chr1	93615298	93646246	uc001dpn.3
chr1	93646280	93744287	uc021opx.1
chr1	93775665	93811368	uc001dpt.2
chr1	93811477	93828148	uc001dpu.3
chr1	93913687	94020218	uc010otk.2
chr1	93981833	93981906	uc021opz.1
chr1	94027342	94146926	uc001dpz.4
chr1	94057524	94065587	uc009wdn.3
chr1	94219111	94240930	uc001dqd.1
chr1	94312387	94312467	uc021oqa.1
chr1	94313128	94313213	uc021oqb.1
chr1	94317792	94319887	uc001dqe.1
chr1	94335013	94344762	uc001dqf.3
chr1	94352589	94375012	uc001dqg.1
chr1	94458393	94586705	uc001dqh.3
chr1	94634462	94703307	uc001dqj.4
chr1	94883932	94984219	uc001dqn.4
chr1	94994731	95007413	uc001dqr.3
chr1	95123088	95285834	uc001dqu.3
chr1	95285897	95360803	uc001dqv.5
chr1	95362506	95392735	uc001dqz.4
chr1	95393583	95428826	uc021oqc.2
chr1	95448278	95538507	uc001dra.2
chr1	95550007	95550119	uc021oqd.1
chr1	95582893	95663161	uc001drb.3
chr1	95628774	95699538	uc001dre.1
chr1	95699710	95712781	uc009wdu.3
chr1	95940292	95944912	uc021oqf.1
chr1	95970531	95970615	uc021oqg.1
chr1	95975895	95981020	uc001drl.3
chr1	97161411	97161723	uc021oqh.1
chr1	97187174	97280605	uc001drq.3
chr1	97543299	98386615	uc001drv.3
chr1	97561478	97788511	uc031pne.1
chr1	98453555	98515249	uc001drx.2
chr1	98510798	98510907	uc021oqj.1
chr1	98676266	98738214	uc031pnf.1
chr1	99127235	99226056	uc010ouc.2
chr1	99207463	99207540	uc021oql.1
chr1	99355800	99470449	uc001dsb.3
chr1	99469831	99614408	uc001dsd.1
chr1	99729847	99775138	uc001dse.3
chr1	100111430	100160097	uc001dsg.3
chr1	100174258	100231349	uc001dsh.1
chr1	100178485	100178513	uc021oqn.1
chr1	100178485	100178513	uc021oqm.1
chr1	100315639	100389579	uc001dsi.1
chr1	100434000	100435404	uc001dso.2
chr1	100435991	100492534	uc001dsr.2
chr1	100503788	100548929	uc001dst.3
chr1	100549101	100598511	uc001dsu.3
chr1	100598705	100616054	uc001dsv.3
chr1	100614003	100643829	uc001dsx.2
chr1	100652477	100715409	uc001dta.3
chr1	100731713	100758325	uc001dtd.3
chr1	100746796	100746864	uc021oqp.1
chr1	100818022	100965021	uc001dtf.2
chr1	101003727	101007583	uc001dth.3
chr1	101092605	101112560	uc021oqr.1
chr1	101185195	101204601	uc001dti.3
chr1	101337927	101360735	uc001dtk.2
chr1	101361631	101447311	uc001dto.2
chr1	101455179	101491362	uc001dtt.2
chr1	101491408	101552819	uc001dua.3
chr1	101702304	101707076	uc001dud.2
chr1	101746453	101746573	uc021oqu.1
chr1	101806424	101806451	uc031pni.1
chr1	102268126	102462790	uc001dug.2
chr1	102337566	102360299	uc021oqv.1
chr1	103342022	103574052	uc001dul.3
chr1	104068577	104097859	uc010oun.2
chr1	104095895	104122149	uc001duq.3
chr1	104159998	104168400	uc001dut.3
chr1	104198301	104207173	uc001duv.3
chr1	104230039	104238912	uc001duw.1
chr1	104257374	104262492	uc010our.1
chr1	104292439	104301311	uc001duz.3
chr1	104615644	104619693	uc021oqw.1
chr1	106144773	106161557	uc001dva.3
chr1	107599266	107601916	uc010ous.2
chr1	107682628	108024475	uc001dvh.4
chr1	107937775	108024475	uc001dvi.3
chr1	107937775	108024475	uc009wem.3
chr1	108113781	108507545	uc001dvk.1
chr1	108496274	108496345	uc021oqx.1
chr1	108507064	108537229	uc031pnj.1
chr1	108677343	108742980	uc001dvn.5
chr1	108765962	108786703	uc009weo.2
chr1	108803819	108816311	uc001dvp.2
chr1	108918459	108953434	uc001dvq.3
chr1	108963310	108975804	uc001dvr.2
chr1	108992903	109013260	uc009wep.3
chr1	109102970	109181949	uc010ouy.2
chr1	109190909	109203744	uc001dvt.4
chr1	109234931	109244422	uc001dvv.4
chr1	109255555	109285367	uc001dvx.3
chr1	109289284	109352148	uc001dvy.3
chr1	109358519	109399726	uc001dwa.4
chr1	109399838	109401146	uc001dwc.3
chr1	109419602	109473044	uc010ovc.2
chr1	109472129	109506121	uc001dwf.2
chr1	109512837	109584850	uc001dwl.3
chr1	109606997	109618624	uc001dwm.1
chr1	109633402	109639554	uc001dwn.3
chr1	109642814	109643234	uc001dwo.1
chr1	109648572	109656479	uc009wew.3
chr1	109656584	109749403	uc021orb.1
chr1	109756514	109780804	uc001dwu.2
chr1	109792640	109818378	uc001dxa.4
chr1	109822175	109825790	uc001dxd.3
chr1	109834986	109849663	uc001dxk.1
chr1	109852187	109940563	uc001dxm.2
chr1	109941652	109969070	uc001dxn.3
chr1	109965120	109966952	uc021orf.1
chr1	110009099	110024764	uc001dxp.3
chr1	110026560	110035420	uc001dxr.3
chr1	110036700	110043063	uc010ovo.2
chr1	110049445	110052336	uc001dxx.4
chr1	110082493	110088455	uc001dxy.2
chr1	110091185	110138454	uc001dxz.3
chr1	110141514	110141589	uc010ovq.1
chr1	110145888	110155705	uc001dya.3
chr1	110162434	110174677	uc009wfh.2
chr1	110198697	110204331	uc001dyf.3
chr1	110230417	110236367	uc001dyk.3
chr1	110254863	110260890	uc001dyn.3
chr1	110276553	110283660	uc001dyo.2
chr1	110292701	110306644	uc001dyq.2
chr1	110453232	110473616	uc001dyw.4
chr1	110527386	110566364	uc001dyx.3
chr1	110577222	110597424	uc001dza.2
chr1	110602996	110613322	uc001dzb.3
chr1	110603303	110603368	uc021orl.1
chr1	110655061	110656569	uc001dzc.3
chr1	110693131	110744823	uc009wfq.3
chr1	110753335	110776674	uc001dzh.3
chr1	110815105	110815229	uc021orm.1
chr1	110828998	110881793	uc001dzj.3
chr1	110881944	110889303	uc001dzl.1
chr1	110887099	110888734	uc001dzn.1
chr1	110905472	110933704	uc001dzo.2
chr1	110943876	110950546	uc001dzr.3
chr1	110950430	110958896	uc031pnm.1
chr1	110993787	110999976	uc001dzs.3
chr1	111023387	111033891	uc009wfu.1
chr1	111059838	111061797	uc001dzt.1
chr1	111145775	111148975	uc009wfw.3
chr1	111214309	111217655	uc001dzv.1
chr1	111413820	111442558	uc001dzw.3
chr1	111489811	111506566	uc001eaa.3
chr1	111659953	111682838	uc001ead.4
chr1	111682248	111727724	uc001eah.1
chr1	111728590	111747160	uc001eal.2
chr1	111770280	111786062	uc001eam.3
chr1	111823145	111828730	uc009wgb.3
chr1	111833473	111863188	uc001eas.4
chr1	111889194	111895639	uc001eaw.2
chr1	111927140	111932473	uc021orp.1
chr1	111956936	111970399	uc001eba.3
chr1	111982511	111991830	uc001ebb.3
chr1	111991742	112004525	uc001ebc.3
chr1	112016603	112021134	uc001ebe.3
chr1	112025969	112046743	uc001ebf.3
chr1	112032938	112033045	uc021orr.1
chr1	112141628	112150940	uc001ebj.2
chr1	112162404	112256101	uc001ebl.3
chr1	112256829	112259310	uc001ebn.1
chr1	112264685	112282046	uc001ebo.2
chr1	112282462	112290420	uc001ebq.1
chr1	112287934	112298131	uc001ebr.3
chr1	112298189	112310199	uc001ebs.3
chr1	112318453	112531777	uc001ebu.1
chr1	112533189	112541463	uc001ebw.4
chr1	112913625	112913729	uc021oru.1
chr1	112938799	113003786	uc001ebx.3
chr1	112938880	112941439	uc001eby.1
chr1	113004391	113004455	uc021orv.1
chr1	113051369	113063910	uc001ecb.3
chr1	113066140	113162040	uc001ecd.3
chr1	113162074	113214241	uc001ecj.1
chr1	113217047	113243368	uc001eck.3
chr1	113243748	113249678	uc001ecp.1
chr1	113252615	113257950	uc001ect.1
chr1	113263188	113269856	uc001ecu.3
chr1	113362795	113393265	uc001ecw.1
chr1	113392521	113420491	uc021orw.1
chr1	113454469	113498975	uc001ecy.3
chr1	113465971	113467295	uc001eda.1
chr1	113499070	113511603	uc001edd.3
chr1	113554308	113615724	uc001ede.1
chr1	113615830	113667342	uc001edf.1
chr1	113739403	113748875	uc001edg.2
chr1	113933474	114228545	uc001edk.3
chr1	114239823	114301777	uc009wgp.1
chr1	114304453	114355070	uc001edq.3
chr1	114356432	114414375	uc001eds.3
chr1	114399256	114443859	uc001edv.2
chr1	114419435	114430169	uc001edw.3
chr1	114437370	114447525	uc001eeb.3
chr1	114447914	114456708	uc001eeg.3
chr1	114466622	114471880	uc001eek.3
chr1	114471995	114520491	uc001eem.3
chr1	114522029	114524875	uc001eer.1
chr1	114631913	114695062	uc021orz.1
chr1	114935398	115053781	uc001eew.3
chr1	115110180	115124265	uc001efa.3
chr1	115125468	115126223	uc021osb.1
chr1	115127195	115212732	uc001efd.1
chr1	115215719	115238239	uc001efe.2
chr1	115247084	115259515	uc009wgu.3
chr1	115259533	115300671	uc001efi.3
chr1	115312104	115323308	uc001efp.4
chr1	115397454	115537990	uc001efr.3
chr1	115572444	115576930	uc001efs.2
chr1	115590632	115632121	uc001eft.3
chr1	115828536	115880857	uc001efu.1
chr1	116184573	116240845	uc001efv.1
chr1	116242625	116311426	uc001efx.4
chr1	116378998	116383747	uc001efy.3
chr1	116461996	116468529	uc001efz.3
chr1	116519118	116612675	uc001egb.4
chr1	116654375	116677861	uc001egc.1
chr1	116821227	116821443	uc021osg.1
chr1	116915794	116947396	uc001ege.3
chr1	116947335	116961244	uc001egj.3
chr1	116956387	116956491	uc021osh.1
chr1	116995157	116995249	uc021osi.1
chr1	117057155	117113715	uc001egm.3
chr1	117117019	117209578	uc031pnr.1
chr1	117134723	117134827	uc021osj.1
chr1	117214370	117214449	uc021osk.1
chr1	117297085	117311851	uc001egu.4
chr1	117452688	117532972	uc001egv.1
chr1	117504968	117505093	uc021osl.1
chr1	117544371	117579173	uc010oxb.2
chr1	117602948	117645491	uc001egy.3
chr1	117637264	117637350	uc021osm.1
chr1	117653676	117664411	uc001egz.2
chr1	117686208	117753582	uc001ehb.3
chr1	117785831	117786130	uc021osp.1
chr1	117910084	118068320	uc001ehd.1
chr1	118148603	118171011	uc001ehe.3
chr1	118406106	118472302	uc001ehf.3
chr1	118472371	118503049	uc010oxe.1
chr1	118496287	118727848	uc001ehk.2
chr1	119425665	119532179	uc001ehl.1
chr1	119573838	119683295	uc001ehn.3
chr1	119683047	119726446	uc009whk.2
chr1	119911401	119936751	uc001ehr.1
chr1	119957742	119965662	uc001eht.3
chr1	120049825	120057681	uc001ehv.1
chr1	120106502	120115199	uc021osu.1
chr1	120140324	120141914	uc021osv.1
chr1	120161999	120190390	uc001ehy.1
chr1	120254418	120286849	uc001ehz.3
chr1	120290618	120311555	uc001eid.3
chr1	120336640	120354203	uc001eif.3
chr1	120377387	120387503	uc010oxk.3
chr1	120436155	120439147	uc001eij.3
chr1	120454175	120612317	uc001eik.3
chr1	120839004	120855681	uc009whp.3
chr1	120906033	120914842	uc001eio.2
chr1	120906460	120906507	uc021osz.1
chr1	120926127	120935944	uc001eip.3
chr1	121107151	121129827	uc031pnu.1
chr1	121306423	121313686	uc009wht.1
chr1	142618798	143257763	uc001eiw.1
chr1	142660105	142660135	uc001eiy.2
chr1	142672190	142672219	uc001eiz.2
chr1	142688238	142688268	uc021ota.1
chr1	142689205	142689235	uc021otb.1
chr1	142689523	142689553	uc021otc.1
chr1	142697420	142713605	uc031pnv.1
chr1	142803223	142888797	uc001ejb.3
chr1	142803530	142826645	uc001ejc.4
chr1	142851394	142851424	uc001eje.3
chr1	142853227	142855999	uc009whu.1
chr1	143168647	143168675	uc001ejg.1
chr1	143184707	143184737	uc001ejh.1
chr1	143185980	143186010	uc001ejj.3
chr1	143286955	143286985	uc021otd.1
chr1	143289062	143289092	uc001ejk.3
chr1	143402283	143402313	uc001ejl.1
chr1	143403553	143403583	uc002zkn.1
chr1	143419286	143419314	uc021ote.1
chr1	143424332	143467651	uc001ejn.1
chr1	143431367	143431397	uc001ejo.2
chr1	143647638	143744587	uc001ejp.3
chr1	143687129	143714180	uc001ejq.3
chr1	143690027	143690101	uc021otf.1
chr1	143702303	143702331	uc031pnw.1
chr1	143767143	143767881	uc001ejt.3
chr1	143879831	143879905	uc021otg.1
chr1	143896451	143913143	uc002qvn.1
chr1	143915747	144094477	uc010oxm.1
chr1	144146810	146467744	uc021ott.2
chr1	144171303	144172451	uc021otn.1
chr1	144209457	144212180	uc021otx.1
chr1	144275887	144290006	uc001ekq.1
chr1	144300511	144340773	uc001ekt.4
chr1	144301610	144301684	uc021oty.1
chr1	144308613	144308687	uc021otz.1
chr1	144340773	144341077	uc021oua.1
chr1	144363461	144364246	uc001ekw.3
chr1	144456162	144470244	uc021oub.1
chr1	144480744	144521009	uc001ela.4
chr1	144481839	144481913	uc021ouc.1
chr1	144488842	144488916	uc021oud.1
chr1	144514872	144519720	uc001elb.4
chr1	144610814	144612727	uc001elf.4
chr1	144832199	144833038	uc001els.1
chr1	144833046	144833912	uc001elt.1
chr1	144833912	144834808	uc001elu.1
chr1	144839435	144839507	uc021oug.1
chr1	144851423	144995033	uc021ouh.1
chr1	144944671	144944750	uc031pnz.1
chr1	145004780	145005287	uc001emi.4
chr1	145013718	145018395	uc001emj.3
chr1	145096406	145116997	uc031poa.1
chr1	145209110	145285912	uc001emn.4
chr1	145372087	145373798	uc001eng.1
chr1	145373798	145374694	uc001enh.1
chr1	145379319	145379391	uc021our.1
chr1	145385728	145385804	uc021ous.1
chr1	145395521	145395594	uc021out.1
chr1	145396880	145396952	uc021ouu.1
chr1	145397863	145397935	uc021ouv.1
chr1	145399232	145399304	uc021ouw.1
chr1	145413190	145417545	uc001eni.2
chr1	145438461	145442628	uc001enn.4
chr1	145456235	145470387	uc001enp.1
chr1	145470507	145475647	uc001enq.1
chr1	145477084	145499091	uc001enr.3
chr1	145500506	145501669	uc021ouz.1
chr1	145507556	145513535	uc001ent.2
chr1	145509751	145515899	uc009wiv.3
chr1	145514173	145515899	uc001enx.4
chr1	145516164	145523732	uc001eny.2
chr1	145524989	145543868	uc001eoa.3
chr1	145549208	145568526	uc001eob.1
chr1	145575987	145586546	uc001eoc.1
chr1	145586492	145589435	uc001eoe.3
chr1	145592604	145610884	uc001eoh.3
chr1	145611035	145688776	uc001eoj.3
chr1	145695797	145715565	uc001eol.1
chr1	145727665	145764206	uc001eoo.2
chr1	145764594	145827103	uc001eot.2
chr1	145883867	145924049	uc001eou.4
chr1	145924387	145934507	uc010ozf.2
chr1	145963303	145963375	uc021ova.1
chr1	145979033	145979107	uc021ovb.1
chr1	146032541	146057734	uc010ozg.2
chr1	146124160	146124283	uc021ovd.1
chr1	146215990	146217136	uc021ove.1
chr1	146476759	146476831	uc021ovh.1
chr1	146490894	146514599	uc001epd.3
chr1	146544772	146544844	uc021ovi.1
chr1	146550177	146550249	uc021ovj.1
chr1	146571065	146585928	uc031poj.1
chr1	146626684	146644129	uc001epe.3
chr1	146649429	146651528	uc001epg.1
chr1	146657837	146697230	uc001epi.2
chr1	146714290	146767447	uc001epm.5
chr1	146853913	146989699	uc021ovk.1
chr1	147013181	147098015	uc001epq.3
chr1	147119167	147142634	uc001epr.2
chr1	147228331	147232714	uc001eps.1
chr1	147374945	147381395	uc001epu.2
chr1	147400505	147465755	uc001epv.4
chr1	147466093	147484331	uc001epz.1
chr1	147505037	147505109	uc021ovn.1
chr1	147520766	147520840	uc021ovo.1
chr1	147574322	147599571	uc010ozx.2
chr1	147665992	147666115	uc021ovp.1
chr1	147680370	147691166	uc001eqh.1
chr1	147719028	147719102	uc021ovq.1
chr1	147737381	147737453	uc021ovr.1
chr1	147751385	147763966	uc001eqi.1
chr1	147753470	147753542	uc021ovs.1
chr1	147774844	147774916	uc021ovt.1
chr1	147781029	147781101	uc021ovu.1
chr1	147800936	147801008	uc021ovv.1
chr1	147801123	147816764	uc001eqk.1
chr1	147806602	147806678	uc031pom.1
chr1	147825688	147825760	uc021ovw.1
chr1	147835126	147931980	uc001eql.2
chr1	147874654	147893482	uc010paa.1
chr1	147877536	147877610	uc021ovx.1
chr1	147925822	147930669	uc009wka.2
chr1	147954634	147955419	uc001eqp.3
chr1	148000804	148000878	uc021ovy.1
chr1	148201751	148202536	uc010pag.2
chr1	148248114	148248188	uc021owi.1
chr1	148250248	148346791	uc021owp.2
chr1	148556107	148556133	uc010pay.2
chr1	148560846	148596267	uc001esc.2
chr1	148598313	148598387	uc021oxb.1
chr1	148644010	148644795	uc010paz.2
chr1	148739441	148758311	uc010pba.1
chr1	148760355	148760429	uc021oxc.1
chr1	148806014	148806799	uc010pbb.2
chr1	148876039	148902123	uc009wkv.1
chr1	148930404	148953054	uc009wkw.1
chr1	149079363	149079435	uc021oxd.1
chr1	149155827	149155899	uc021oxe.1
chr1	149161627	149161699	uc021oxf.1
chr1	149186124	149186196	uc021oxg.1
chr1	149211947	149212021	uc021oxh.1
chr1	149230569	149230643	uc021oxi.1
chr1	149230715	149232553	uc010pbe.1
chr1	149278784	149278857	uc021oxj.1
chr1	149279475	149291742	uc010pbf.2
chr1	149294665	149294736	uc021oxk.1
chr1	149298554	149298627	uc021oxl.1
chr1	149326271	149326345	uc021oxm.1
chr1	149334271	149334340	uc021oxn.1
chr1	149343080	149343151	uc021oxo.1
chr1	149369293	149378297	uc010pbh.2
chr1	149398714	149398761	uc010pbi.2
chr1	149400063	149400109	uc021oxp.1
chr1	149553002	149553787	uc001esj.3
chr1	149576261	149616786	uc001esk.5
chr1	149577754	149582605	uc009wle.2
chr1	149608608	149608682	uc021oxq.1
chr1	149615616	149615690	uc021oxr.1
chr1	149627308	149651107	uc001eso.1
chr1	149664354	149664427	uc021oxs.1
chr1	149670056	149670130	uc021oxt.1
chr1	149672904	149672976	uc031poq.1
chr1	149680209	149680280	uc021oxu.1
chr1	149684087	149684161	uc021oxv.1
chr1	149711797	149711871	uc021oxw.1
chr1	149719801	149719870	uc021oxx.1
chr1	149728270	149728341	uc021oxy.1
chr1	149754244	149783928	uc010pbj.2
chr1	149754249	149764074	uc001esp.4
chr1	149763335	149765367	uc001esq.1
chr1	149784779	149785236	uc010pbl.2
chr1	149804220	149804616	uc001ess.3
chr1	149812258	149812765	uc001esv.3
chr1	149813784	149814318	uc001esw.3
chr1	149821758	149822340	uc021oxz.1
chr1	149822627	149823161	uc001esx.3
chr1	149824180	149824687	uc001esy.3
chr1	149832329	149832725	uc001etb.3
chr1	149856009	149858232	uc001etc.3
chr1	149858524	149858961	uc001etd.3
chr1	149859018	149859466	uc001ete.3
chr1	149871154	149872348	uc001etf.3
chr1	149874871	149889434	uc001etg.3
chr1	149895208	149900144	uc001etk.2
chr1	149900542	149908791	uc001etl.4
chr1	149912231	149982686	uc001etn.3
chr1	150017381	150017452	uc021oyb.1
chr1	150039341	150117505	uc001etp.3
chr1	150122169	150131825	uc001ett.3
chr1	150190716	150208504	uc001etw.3
chr1	150230217	150237480	uc001etx.3
chr1	150237798	150241609	uc001ety.2
chr1	150245182	150253335	uc001eud.3
chr1	150255228	150259501	uc001euj.3
chr1	150266268	150280819	uc001euk.3
chr1	150293927	150325704	uc001eum.4
chr1	150336989	150449041	uc009wlr.3
chr1	150459839	150480085	uc001euq.4
chr1	150480486	150486265	uc001eus.3
chr1	150488232	150490508	uc031pos.1
chr1	150521897	150533412	uc001eux.3
chr1	150524404	150524490	uc021oye.1
chr1	150547026	150552214	uc001euz.3
chr1	150594598	150602098	uc001eve.3
chr1	150618700	150669672	uc001evj.2
chr1	150670534	150693364	uc001evk.2
chr1	150702671	150738433	uc001evn.3
chr1	150768683	150780917	uc001evp.2
chr1	150782180	150849244	uc001evr.2
chr1	150785066	150785174	uc021oyg.1
chr1	150898814	150937220	uc001evu.2
chr1	150937648	150947479	uc001evz.3
chr1	150954498	150968114	uc001ewa.2
chr1	150969300	150980854	uc010pcn.2
chr1	150980972	151008189	uc001ewh.1
chr1	150995221	150995328	uc021oyh.1
chr1	151009028	151020076	uc001ewl.2
chr1	151020258	151023871	uc001ewn.3
chr1	151023446	151032125	uc001ewp.3
chr1	151032150	151040973	uc001ewq.3
chr1	151043079	151091007	uc001ewr.2
chr1	151104162	151119140	uc001ewv.3
chr1	151129104	151132225	uc001ewx.2
chr1	151132223	151138370	uc001ewy.3
chr1	151138497	151142773	uc001ewz.3
chr1	151142462	151148547	uc001exc.4
chr1	151148775	151162689	uc001exe.2
chr1	151171020	151222007	uc001exj.3
chr1	151227196	151239954	uc001exl.3
chr1	151252499	151254405	uc001exo.3
chr1	151254790	151264381	uc001exq.3
chr1	151264272	151300191	uc001exu.3
chr1	151265261	151265284	uc031pov.1
chr1	151265461	151265487	uc031pow.1
chr1	151266582	151266606	uc031pox.1
chr1	151271387	151271414	uc031poy.1
chr1	151271499	151271523	uc031poz.1
chr1	151273139	151273440	uc021oyp.1
chr1	151274383	151274409	uc031ppa.1
chr1	151278732	151278766	uc031ppb.1
chr1	151313115	151319769	uc001exw.1
chr1	151336777	151345210	uc010pcy.3
chr1	151372040	151374412	uc001eyc.1
chr1	151375199	151431941	uc001eyd.2
chr1	151483861	151511167	uc009wmw.3
chr1	151512780	151556059	uc001eyl.3
chr1	151518271	151518367	uc021oyr.1
chr1	151584661	151671559	uc001eyn.1
chr1	151670318	151670850	uc001eyq.1
chr1	151672533	151689290	uc001eys.2
chr1	151694012	151702082	uc010pdj.1
chr1	151732122	151736040	uc001eyv.3
chr1	151739130	151743806	uc010pdm.2
chr1	151745954	151763010	uc001eza.4
chr1	151763317	151766878	uc001eze.3
chr1	151772764	151777882	uc001ezf.1
chr1	151778546	151804348	uc001ezh.3
chr1	151810338	151813033	uc010pdq.1
chr1	151810944	151816641	uc010pdr.2
chr1	151819576	151826173	uc021oyw.1
chr1	151843342	151882361	uc001ezj.2
chr1	151955385	151966714	uc001ezl.3
chr1	151967006	152015250	uc001ezm.1
chr1	152004981	152009511	uc001ezn.3
chr1	152056619	152061540	uc001ezo.1
chr1	152078792	152087930	uc001ezp.3
chr1	152126070	152131704	uc001ezs.1
chr1	152184551	152196672	uc001ezt.2
chr1	152274650	152297679	uc001ezu.1
chr1	152285934	152339168	uc001ezv.3
chr1	152321212	152332482	uc001ezw.4
chr1	152381718	152386750	uc001ezx.2
chr1	152483319	152484653	uc001ezy.3
chr1	152486977	152488481	uc001ezz.3
chr1	152538174	152539229	uc001faa.4
chr1	152551859	152552980	uc001fab.3
chr1	152573137	152573562	uc001fac.2
chr1	152586286	152586574	uc010pds.2
chr1	152595309	152595579	uc010pdt.2
chr1	152635886	152637135	uc001fag.4
chr1	152647790	152649049	uc001fah.4
chr1	152658598	152659876	uc001fai.3
chr1	152670839	152671918	uc001faj.3
chr1	152681522	152681910	uc001fak.2
chr1	152691997	152692905	uc010pdu.2
chr1	152730505	152734529	uc001fal.1
chr1	152748847	152749445	uc010pdv.2
chr1	152758752	152760901	uc001fan.3
chr1	152769226	152770657	uc009wnp.3
chr1	152777310	152779107	uc001fap.1
chr1	152784446	152785585	uc001faq.3
chr1	152799948	152800573	uc010pdw.2
chr1	152815329	152816459	uc001fas.4
chr1	152850797	152857523	uc001fat.3
chr1	152881038	152884362	uc001fau.3
chr1	152943127	152945069	uc001fav.1
chr1	152956563	152958290	uc001faw.3
chr1	152974222	152976332	uc001faz.4
chr1	153003678	153005376	uc001fba.3
chr1	153012200	153013594	uc001fbb.2
chr1	153028595	153029988	uc001fbd.3
chr1	153042717	153044084	uc001fbg.3
chr1	153065610	153067001	uc001fbh.3
chr1	153084612	153085989	uc001fbi.3
chr1	153112593	153113969	uc001fbj.3
chr1	153122057	153123427	uc009wod.2
chr1	153175905	153177601	uc001fbl.4
chr1	153190059	153191793	uc021ozw.1
chr1	153195064	153195185	uc021ozx.1
chr1	153232178	153234600	uc001fbm.3
chr1	153270337	153283194	uc001fbn.1
chr1	153302596	153321022	uc001fbo.3
chr1	153330329	153333503	uc001fbq.3
chr1	153346183	153348075	uc001fbr.1
chr1	153362507	153363664	uc001fbs.3
chr1	153388999	153395701	uc001fbt.1
chr1	153409470	153412503	uc010pdx.2
chr1	153430219	153433137	uc001fbv.1
chr1	153507075	153508717	uc001fbw.1
chr1	153509622	153514241	uc001fbx.3
chr1	153516094	153518282	uc001fbz.3
chr1	153518229	153524245	uc009wog.1
chr1	153519808	153521734	uc001fca.1
chr1	153533584	153538306	uc001fcb.3
chr1	153579366	153585514	uc001fcd.1
chr1	153586731	153588808	uc001fce.3
chr1	153591275	153600117	uc001fch.3
chr1	153600872	153604513	uc001fck.1
chr1	153606457	153618782	uc001fcn.2
chr1	153631129	153634328	uc001fcq.4
chr1	153634263	153643504	uc001fcr.4
chr1	153643725	153643797	uc021paa.1
chr1	153651163	153666468	uc001fcs.4
chr1	153700566	153746555	uc001fct.3
chr1	153747767	153752633	uc001fcz.3
chr1	153777202	153895451	uc001fdb.4
chr1	153901976	153919154	uc001fdd.1
chr1	153920147	153931132	uc021pab.1
chr1	153931574	153940660	uc031ppi.1
chr1	153940674	153946840	uc001fdr.3
chr1	153946744	153950451	uc001fds.3
chr1	153954092	153958853	uc001fdt.2
chr1	153963238	153964631	uc001fdv.3
chr1	153965167	154127592	uc001fdw.3
chr1	154134288	154164611	uc001fec.2
chr1	154166140	154166219	uc021pac.1
chr1	154171561	154178841	uc001fee.2
chr1	154179182	154193273	uc001fei.2
chr1	154193324	154243329	uc001fep.4
chr1	154232202	154232338	uc021pae.1
chr1	154245038	154248355	uc001fes.3
chr1	154293591	154297801	uc001feu.3
chr1	154300275	154323780	uc001fex.3
chr1	154377668	154441926	uc001fez.2
chr1	154451953	154474526	uc001ffb.3
chr1	154474694	154520623	uc009wow.3
chr1	154521050	154531120	uc001fff.1
chr1	154540256	154552353	uc001ffg.3
chr1	154554533	154580724	uc001ffh.3
chr1	154669941	154842754	uc021pah.1
chr1	154897207	154909484	uc001ffq.3
chr1	154916558	154928567	uc001ffr.3
chr1	154929501	154934258	uc001fft.3
chr1	154934773	154943223	uc001ffw.3
chr1	154947117	154951725	uc001fgb.3
chr1	154948168	154948259	uc021pai.1
chr1	154955769	154965587	uc001fgf.2
chr1	154966061	154966791	uc001fgi.3
chr1	154975105	154991001	uc010peq.3
chr1	154979449	154979509	uc021pal.1
chr1	154991002	155006257	uc001fgm.3
chr1	155006281	155023406	uc001fgn.2
chr1	155017667	155036467	uc021pan.2
chr1	155023747	155035252	uc001fgr.2
chr1	155051347	155060014	uc001fhf.3
chr1	155100348	155107386	uc001fhh.3
chr1	155108287	155111334	uc001fhj.4
chr1	155112366	155112883	uc001fhm.3
chr1	155141883	155145804	uc001fho.3
chr1	155146262	155157447	uc001fhs.2
chr1	155155666	155157427	uc021pao.2
chr1	155158299	155162706	uc031ppv.1
chr1	155161986	155162007	uc021par.1
chr1	155164967	155165063	uc021pas.1
chr1	155165378	155177772	uc001fix.3
chr1	155178489	155183624	uc001fjb.3
chr1	155183615	155188809	uc001fjd.3
chr1	155203712	155204236	uc021pav.1
chr1	155204238	155214653	uc001fjl.3
chr1	155216995	155225274	uc001fjm.3
chr1	155225769	155232176	uc001fjs.3
chr1	155232658	155243281	uc001fjw.3
chr1	155247217	155259639	uc001fjz.2
chr1	155259083	155271225	uc001fkb.4
chr1	155278538	155290457	uc001fkc.2
chr1	155290250	155293938	uc001fki.3
chr1	155290639	155300909	uc001fkj.2
chr1	155305051	155532324	uc001fkt.3
chr1	155402970	155404053	uc010pgd.2
chr1	155403092	155403473	uc010pgc.1
chr1	155531771	155533735	uc010pge.2
chr1	155531863	155533735	uc001fkv.3
chr1	155579960	155584758	uc001fky.4
chr1	155596545	155618335	uc001flf.3
chr1	155658881	155708800	uc001flr.3
chr1	155715558	155718322	uc010pgo.2
chr1	155716586	155720673	uc031pqb.1
chr1	155719509	155826972	uc001fly.1
chr1	155829259	155854990	uc001fmg.3
chr1	155867598	155880706	uc031pqc.1
chr1	155882835	155904188	uc001fmi.1
chr1	155889699	155889833	uc001fmk.1
chr1	155895748	155895877	uc001fmn.1
chr1	155911479	155912625	uc010pgs.2
chr1	155916629	155948336	uc001fmt.2
chr1	155978838	155990758	uc001fmx.3
chr1	156005091	156023516	uc001fna.3
chr1	156024516	156028605	uc001fnb.3
chr1	156030965	156040295	uc001fnc.3
chr1	156041803	156051789	uc001fnd.4
chr1	156084460	156109880	uc001fni.3
chr1	156104903	156107058	uc009wrp.3
chr1	156123321	156147542	uc009wrq.3
chr1	156163729	156182587	uc001fnp.3
chr1	156182778	156213123	uc021pbb.1
chr1	156211950	156213123	uc001fnt.3
chr1	156213111	156217908	uc010phh.2
chr1	156219014	156252620	uc001foc.4
chr1	156254069	156262234	uc009wrw.3
chr1	156262477	156265480	uc001foh.4
chr1	156268414	156269428	uc001fok.3
chr1	156278751	156308206	uc001fol.2
chr1	156307104	156316785	uc001foo.3
chr1	156338979	156355013	uc010pho.3
chr1	156374048	156377645	uc001fot.1
chr1	156374054	156399340	uc001fou.1
chr1	156390132	156390221	uc001foz.1
chr1	156433512	156460391	uc001fpb.4
chr1	156453889	156454002	uc021pbe.1
chr1	156495196	156542396	uc001fpf.3
chr1	156549518	156556562	uc021pbf.1
chr1	156561557	156564091	uc001fph.3
chr1	156564099	156571279	uc001fpl.3
chr1	156589085	156595517	uc001fpn.1
chr1	156589085	156594259	uc031pqq.1
chr1	156611456	156613427	uc021pbg.1
chr1	156611739	156629324	uc001fpp.3
chr1	156638555	156647189	uc001fpq.3
chr1	156669399	156675459	uc001fpr.3
chr1	156692412	156697705	uc001fps.1
chr1	156698262	156706752	uc001fpu.3
chr1	156707093	156710923	uc001fpx.1
chr1	156711898	156721543	uc001fpy.4
chr1	156737273	156770609	uc001fqa.3
chr1	156776034	156786640	uc009wsh.2
chr1	156810664	156828712	uc010pht.2
chr1	156830670	156851642	uc001fqh.1
chr1	156863522	156886226	uc001fqj.1
chr1	156890423	156902880	uc001fqm.2
chr1	156904631	157015162	uc001fqn.3
chr1	156905922	156906036	uc021pbj.1
chr1	157061834	157069600	uc001fqq.2
chr1	157094458	157108383	uc001fqr.2
chr1	157098153	157098463	uc001fqs.3
chr1	157483166	157522310	uc009wsm.3
chr1	157543538	157567870	uc001fqw.3
chr1	157647977	157670647	uc001frb.3
chr1	157715522	157746922	uc001fre.2
chr1	157764193	157789940	uc001frg.3
chr1	157800703	157811634	uc001frk.4
chr1	157895763	157918861	uc001frl.1
chr1	157963062	158065844	uc001frn.4
chr1	158066315	158069836	uc001frp.2
chr1	158101833	158110430	uc001frq.3
chr1	158149736	158156216	uc001frr.3
chr1	158169170	158173667	uc001frs.1
chr1	158223926	158228058	uc001frt.3
chr1	158259562	158264564	uc001fru.3
chr1	158297739	158301321	uc001frx.3
chr1	158323485	158327343	uc001fse.3
chr1	158368311	158369256	uc010pih.2
chr1	158389717	158390656	uc010pii.2
chr1	158435351	158436293	uc010pij.2
chr1	158444244	158464676	uc001fso.1
chr1	158449667	158450675	uc010pik.2
chr1	158516917	158517895	uc010pil.2
chr1	158532440	158533394	uc010pim.2
chr1	158548708	158549689	uc010pin.2
chr1	158576228	158577170	uc010pio.2
chr1	158580495	158656506	uc001fst.1
chr1	158669467	158670442	uc001fsu.1
chr1	158686957	158687905	uc021pbn.1
chr1	158724605	158725637	uc001fsw.1
chr1	158735533	158736472	uc010piq.2
chr1	158746471	158747425	uc010pir.2
chr1	158801167	158819270	uc001fsz.1
chr1	158901336	158946849	uc001ftb.3
chr1	158979681	159024945	uc010pis.2
chr1	159032274	159046647	uc001ftj.1
chr1	159111400	159111474	uc021pbo.1
chr1	159141376	159172932	uc001ftk.2
chr1	159165774	159172212	uc001ftm.2
chr1	159175048	159176290	uc001ftp.4
chr1	159259505	159278014	uc001ftq.3
chr1	159283459	159284449	uc010piu.2
chr1	159315958	159438858	uc001fts.4
chr1	159409511	159410600	uc010piv.2
chr1	159504867	159505797	uc010piw.2
chr1	159557615	159558661	uc001ftv.3
chr1	159682078	159684379	uc001ftw.3
chr1	159750758	159752333	uc001ftz.1
chr1	159772172	159786047	uc001fud.4
chr1	159796478	159807282	uc001fue.4
chr1	159824105	159832447	uc001fuh.3
chr1	159842153	159869906	uc001fui.3
chr1	159887896	159894341	uc031pqu.1
chr1	159896828	159915386	uc001fur.2
chr1	159921281	159924044	uc001fus.3
chr1	159931013	159948876	uc001fuu.3
chr1	159997461	160001783	uc001fuv.1
chr1	160007256	160040051	uc001fuw.2
chr1	160051359	160059212	uc001fuy.1
chr1	160061128	160068618	uc009wtf.3
chr1	160085519	160113374	uc001fvc.3
chr1	160121351	160156767	uc001fve.4
chr1	160160284	160171676	uc010pja.2
chr1	160171988	160178659	uc001fvj.1
chr1	160175124	160185162	uc001fvk.3
chr1	160185504	160232350	uc010pjb.1
chr1	160258376	160313354	uc001fvv.4
chr1	160287054	160288260	uc001fvw.3
chr1	160295893	160296006	uc021pbr.1
chr1	160313062	160328742	uc001fvx.3
chr1	160336860	160342638	uc001fwa.2
chr1	160370363	160398468	uc001fwc.2
chr1	160454819	160493052	uc001fwe.2
chr1	160510883	160549306	uc001fwh.4
chr1	160579890	160617081	uc001fwl.4
chr1	160650211	160681641	uc001fwo.2
chr1	160709076	160724601	uc001fwq.3
chr1	160765863	160798045	uc001fwu.4
chr1	160799949	160832692	uc009wtq.3
chr1	160846329	160854960	uc001fxc.3
chr1	160914815	160924589	uc001fxd.3
chr1	160965000	160991133	uc009wtt.3
chr1	161009040	161014727	uc031pqv.1
chr1	161016731	161039760	uc001fxl.3
chr1	161040780	161059385	uc001fxo.2
chr1	161068180	161070138	uc001fxr.3
chr1	161070345	161087866	uc001fxu.3
chr1	161087861	161090984	uc001fxv.2
chr1	161090768	161102256	uc001fxz.3
chr1	161123533	161128646	uc001fyd.4
chr1	161129253	161135516	uc010pkd.2
chr1	161136180	161141010	uc001fyg.2
chr1	161141099	161147758	uc001fys.2
chr1	161159537	161168845	uc001fyt.4
chr1	161171936	161184184	uc001fyw.3
chr1	161185086	161189038	uc001fyz.1
chr1	161192082	161193418	uc001fzc.1
chr1	161195832	161200407	uc001fzd.3
chr1	161196975	161197051	uc031pqw.1
chr1	161199455	161208000	uc001fzp.3
chr1	161228516	161255240	uc001gad.3
chr1	161274524	161279762	uc001gaf.4
chr1	161284165	161334535	uc001gag.3
chr1	161334520	161337673	uc001gal.4
chr1	161369489	161369562	uc021pbx.1
chr1	161390660	161390733	uc021pby.1
chr1	161391882	161391954	uc021pbz.1
chr1	161397866	161397940	uc021pca.1
chr1	161409960	161410032	uc021pcb.1
chr1	161410614	161410686	uc021pcc.1
chr1	161411322	161411405	uc021pcd.1
chr1	161413093	161413164	uc021pce.1
chr1	161417017	161417089	uc021pcf.1
chr1	161417374	161417446	uc021pcg.1
chr1	161418032	161418104	uc021pch.1
chr1	161418740	161418823	uc021pci.1
chr1	161420466	161420537	uc021pcj.1
chr1	161424397	161424469	uc021pck.1
chr1	161424755	161424827	uc021pcl.1
chr1	161425413	161425485	uc021pcm.1
chr1	161426121	161426204	uc021pcn.1
chr1	161427897	161427968	uc021pco.1
chr1	161431808	161431880	uc021pcp.1
chr1	161432165	161432237	uc021pcq.1
chr1	161432823	161432895	uc021pcr.1
chr1	161433531	161433614	uc021pcs.1
chr1	161435257	161435328	uc021pct.1
chr1	161439188	161439260	uc021pcu.1
chr1	161439546	161439618	uc021pcv.1
chr1	161440204	161440276	uc021pcw.1
chr1	161440912	161440995	uc021pcx.1
chr1	161450355	161450426	uc021pcy.1
chr1	161475204	161489360	uc001gan.3
chr1	161492934	161493006	uc021pdb.1
chr1	161493636	161493707	uc021pdc.1
chr1	161494035	161496687	uc001gaq.3
chr1	161500131	161500214	uc021pdd.1
chr1	161500902	161500974	uc021pde.1
chr1	161501914	161501986	uc021pdf.1
chr1	161510030	161510104	uc021pdg.1
chr1	161511550	161519818	uc001gar.3
chr1	161551128	161571010	uc021pdi.2
chr1	161574587	161574659	uc021pdk.1
chr1	161575848	161578341	uc010pkp.1
chr1	161581735	161581819	uc021pdl.1
chr1	161582507	161582579	uc021pdm.1
chr1	161591464	161591538	uc021pdn.1
chr1	161592987	161601252	uc009wul.3
chr1	161632904	161648444	uc001gaz.2
chr1	161653494	161655042	uc001gbc.3
chr1	161676761	161684142	uc001gbe.3
chr1	161692442	161697933	uc001gbi.3
chr1	161719580	161726952	uc001gbo.3
chr1	161736033	161933860	uc001gbs.3
chr1	161952981	161993644	uc001gbu.3
chr1	162039580	162339813	uc001gbv.2
chr1	162126896	162126972	uc021pdp.1
chr1	162308432	162308532	uc021pdq.1
chr1	162312335	162312430	uc021pdr.1
chr1	162343514	162346644	uc001gbx.2
chr1	162348695	162356608	uc010pkt.1
chr1	162365055	162381928	uc001gbz.1
chr1	162467594	162499419	uc001gcc.2
chr1	162531295	162569633	uc001gce.4
chr1	162602227	162750247	uc001gcg.3
chr1	162747518	162747805	uc021pds.1
chr1	162751900	162756362	uc001gch.1
chr1	162760495	162782608	uc001gci.3
chr1	162824086	162838605	uc001gck.2
chr1	163038395	163046592	uc001gcl.4
chr1	163112088	163172963	uc021pdt.1
chr1	163291722	163325553	uc001gcr.1
chr1	163438285	163438395	uc021pdv.1
chr1	163479273	163479385	uc021pdw.1
chr1	164528596	164821060	uc001gct.3
chr1	164595272	164651720	uc001gcu.1
chr1	164738352	164743878	uc021pdx.1
chr1	165171103	165325478	uc001gcz.2
chr1	165370158	165414592	uc001gda.3
chr1	165446078	165551341	uc001gdc.2
chr1	165513477	165533185	uc001gde.2
chr1	165566149	165566222	uc021peb.1
chr1	165600109	165625372	uc001gdf.3
chr1	165631448	165667900	uc001gdh.1
chr1	165667986	165679199	uc001gdi.3
chr1	165693527	165738159	uc001gdj.5
chr1	165738165	165744685	uc001gdo.3
chr1	165796731	165880855	uc001gdp.3
chr1	166011480	166011587	uc021ped.1
chr1	166026689	166135958	uc021pee.1
chr1	166039068	166135958	uc021pef.1
chr1	166123979	166124035	uc021peg.1
chr1	166573152	166594473	uc010pld.2
chr1	166808723	166823709	uc001gdt.1
chr1	166825748	166845654	uc001gdw.3
chr1	166882440	166944561	uc001gdx.2
chr1	166958518	166991447	uc001gdy.1
chr1	167022081	167059868	uc001gea.1
chr1	167064070	167098402	uc001geb.1
chr1	167144598	167165042	uc021pei.1
chr1	167190065	167396582	uc001gee.3
chr1	167399876	167487847	uc001gei.4
chr1	167510250	167523056	uc001gel.3
chr1	167599473	167675486	uc001gem.3
chr1	167683961	167684033	uc021pej.1
chr1	167684724	167684796	uc021pek.1
chr1	167691186	167761156	uc001geo.3
chr1	167778624	167883453	uc001ger.3
chr1	167885912	167906307	uc001get.3
chr1	167905796	168045083	uc001gex.3
chr1	168048779	168106811	uc009wvo.4
chr1	168148170	168171351	uc001gfg.3
chr1	168195254	168212088	uc001gfi.4
chr1	168214818	168216668	uc010plo.2
chr1	168218461	168220378	uc001gfk.2
chr1	168250277	168283664	uc001gfl.3
chr1	168344761	168344859	uc021pel.1
chr1	168369426	168391894	uc021pem.1
chr1	168510002	168513235	uc001gfn.4
chr1	168545710	168551315	uc001gfo.2
chr1	168664694	168698442	uc001gfp.3
chr1	168756178	168762126	uc001gfq.3
chr1	169075946	169101960	uc001gfr.1
chr1	169101768	169337186	uc001gfu.3
chr1	169337193	169365780	uc001gfy.3
chr1	169364113	169396670	uc001gfz.1
chr1	169433148	169455208	uc001gge.4
chr1	169481191	169555769	uc001ggg.1
chr1	169558087	169599377	uc001ggi.4
chr1	169659805	169680843	uc001ggk.3
chr1	169691780	169703220	uc001ggm.4
chr1	169761669	169764061	uc001ggn.4
chr1	169764549	169822229	uc001ggq.3
chr1	169822214	169863100	uc001ggs.3
chr1	169828896	169829193	uc021peo.1
chr1	169890469	170043879	uc001ggv.3
chr1	170115187	170136923	uc009wvv.1
chr1	170120518	170120603	uc021per.1
chr1	170120518	170120603	uc021peq.1
chr1	170240545	170253349	uc001ggx.3
chr1	170429593	170489887	uc001ggy.1
chr1	170501262	170522974	uc001gha.2
chr1	170633312	170708541	uc001ghf.3
chr1	170904611	171033906	uc010plz.2
chr1	171060017	171086959	uc001ghh.3
chr1	171070868	171070947	uc021pes.1
chr1	171070879	171070939	uc031prg.1
chr1	171106878	171130702	uc001ghj.1
chr1	171154387	171181822	uc001ghk.1
chr1	171217662	171255113	uc001ghl.3
chr1	171223044	171223161	uc021pet.1
chr1	171283485	171311223	uc001gho.3
chr1	171308034	171310463	uc010pmf.2
chr1	171454665	171562650	uc010pmg.2
chr1	171604556	171621773	uc001ghu.3
chr1	171669295	171711379	uc001ghx.2
chr1	171750760	171766856	uc001ghz.3
chr1	171810617	172381857	uc001gie.4
chr1	171964791	171964994	uc021peu.1
chr1	172106018	172113975	uc021pev.2
chr1	172107947	172108028	uc021pew.1
chr1	172157538	172157607	uc021pex.1
chr1	172389827	172437969	uc001gik.3
chr1	172410596	172413230	uc001gio.3
chr1	172502259	172580973	uc001giq.4
chr1	172628184	172636012	uc001gis.3
chr1	173010359	173020103	uc001giu.2
chr1	173152869	173176471	uc001giw.3
chr1	173204198	173446294	uc001gix.1
chr1	173387087	173430501	uc010pmp.1
chr1	173446485	173457946	uc001giy.1
chr1	173469603	173572233	uc001giz.2
chr1	173577474	173639001	uc001gja.1
chr1	173604660	173606272	uc021pez.1
chr1	173684079	173755840	uc001gjc.3
chr1	173768687	173793270	uc001gje.4
chr1	173793796	173827682	uc001gjh.2
chr1	173832385	173833079	uc021pfa.1
chr1	173833038	173837125	uc001gjk.3
chr1	173833312	173833355	uc009wwi.1
chr1	173834487	173834568	uc009wwk.1
chr1	173834770	173834824	uc009wwl.3
chr1	173835105	173835166	uc001gjn.1
chr1	173835448	173835509	uc009wwm.1
chr1	173836016	173836076	uc009wwo.1
chr1	173836811	173836883	uc001gjo.1
chr1	173837492	173855774	uc009wwp.1
chr1	173867684	173867712	uc021pfb.1
chr1	173872941	173886516	uc001gjt.3
chr1	173900351	173962210	uc001gju.4
chr1	174128551	174927327	uc001gjx.3
chr1	174417211	174418683	uc001gka.1
chr1	174417553	174418334	uc010pmu.1
chr1	174968891	174981163	uc001gkj.1
chr1	174982093	174992591	uc001gkk.3
chr1	175036993	175117202	uc001gkl.1
chr1	175126122	175161949	uc001gkn.3
chr1	175291934	175712752	uc009wwu.1
chr1	175526123	175533005	uc001gkr.1
chr1	175913966	176176370	uc001gku.1
chr1	175937532	175937676	uc001gkx.1
chr1	176432306	176811970	uc001gkz.3
chr1	176830202	177134024	uc001glc.3
chr1	176998498	176998581	uc021pfc.1
chr1	177140632	177251558	uc001glf.3
chr1	177897488	177939050	uc001gli.1
chr1	178060642	178062735	uc001gln.1
chr1	178062863	178448648	uc001glq.3
chr1	178482211	178491789	uc001glt.2
chr1	178511930	178518024	uc001glx.1
chr1	178530047	178530164	uc021pff.1
chr1	178646883	178646969	uc021pfg.1
chr1	178678037	178678110	uc021pfh.1
chr1	178694299	178889237	uc001glz.3
chr1	178818669	178840215	uc001gma.3
chr1	178995073	179045702	uc001gmc.3
chr1	179051111	179065129	uc001gmd.3
chr1	179068461	179112224	uc001gmi.4
chr1	179170962	179170982	uc021pfi.1
chr1	179262848	179327814	uc001gml.3
chr1	179334854	179523870	uc001gmo.3
chr1	179519673	179545084	uc001gmq.4
chr1	179556206	179556240	uc021pfk.1
chr1	179556492	179556554	uc001gmt.3
chr1	179556689	179556738	uc010pnm.1
chr1	179556805	179556836	uc001gmu.3
chr1	179556860	179556891	uc001gmv.3
chr1	179557007	179557111	uc001gmw.3
chr1	179557405	179557438	uc001gmx.3
chr1	179557454	179557483	uc021pfl.1
chr1	179557496	179557540	uc001gmz.1
chr1	179557562	179557602	uc001gna.3
chr1	179557656	179557734	uc001gnb.3
chr1	179557814	179557850	uc001gnc.1
chr1	179558162	179558192	uc010pnn.1
chr1	179558664	179558701	uc001gnd.3
chr1	179558711	179558760	uc001gne.3
chr1	179558972	179559006	uc010pno.2
chr1	179560756	179660407	uc010pnp.2
chr1	179712297	179785333	uc001gnj.3
chr1	179809101	179846941	uc001gnk.3
chr1	179828164	179846941	uc001gnn.3
chr1	179851176	179889212	uc001gnp.2
chr1	179923907	180084015	uc001gnt.3
chr1	180123967	180167169	uc001gnz.3
chr1	180167143	180169859	uc001god.4
chr1	180184275	180184348	uc021pfo.1
chr1	180199432	180244188	uc001goe.2
chr1	180238797	180243816	uc001gof.2
chr1	180257351	180472022	uc001gog.3
chr1	180318292	180318386	uc021pfp.1
chr1	180407448	180407525	uc021pfq.1
chr1	180528109	180535654	uc001goh.1
chr1	180601145	180859415	uc001goi.3
chr1	180882312	180915239	uc001gok.2
chr1	180942175	180992046	uc021pfr.1
chr1	181002560	181031074	uc001goq.2
chr1	181057637	181059979	uc001got.4
chr1	181143619	181151342	uc009wxq.3
chr1	181205523	181207740	uc031pri.1
chr1	181382578	181382716	uc001gov.3
chr1	181452685	181775921	uc009wxt.3
chr1	181456006	181456111	uc021pfs.1
chr1	181740701	181740780	uc021pft.1
chr1	182023704	182030847	uc001goz.3
chr1	182350838	182360539	uc001gpa.2
chr1	182367251	182369751	uc001gpe.3
chr1	182376755	182383948	uc009wxv.1
chr1	182419255	182529732	uc009wxw.3
chr1	182542768	182558394	uc009wxz.2
chr1	182567757	182573548	uc001gpl.4
chr1	182584274	182585764	uc021pfy.1
chr1	182615791	182642067	uc001gpm.1
chr1	182758583	182799519	uc009wyb.3
chr1	182808438	182857117	uc001gpr.3
chr1	182868999	182922553	uc001gpu.3
chr1	182992594	183114727	uc001gpy.4
chr1	183155173	183214262	uc001gqa.2
chr1	183217371	183387634	uc001gqc.2
chr1	183430010	183441117	uc001gqe.3
chr1	183441505	183523328	uc001gqf.3
chr1	183524696	183560056	uc001gqk.4
chr1	183595327	183605076	uc021pgb.2
chr1	183605207	183897666	uc001gqm.3
chr1	183615410	183622448	uc001gqn.3
chr1	183904965	184006863	uc001gqr.3
chr1	184020810	184043344	uc001gqt.4
chr1	184356149	184598155	uc001gqv.1
chr1	184659624	184724041	uc010pok.2
chr1	184727265	184730133	uc001gqz.1
chr1	184760158	184943718	uc001gra.4
chr1	185014550	185071740	uc001grc.1
chr1	185087217	185126116	uc001grf.4
chr1	185126191	185260913	uc001grg.4
chr1	185220508	185220534	uc021pgd.1
chr1	185265521	185286461	uc001grl.3
chr1	185292978	185304171	uc001grn.4
chr1	185405427	185405453	uc021pge.1
chr1	185527511	185597620	uc001gro.3
chr1	185576315	185591914	uc001grp.1
chr1	185703682	186160085	uc001grq.1
chr1	186029866	186446655	uc021pgf.1
chr1	186265404	186283688	uc001gru.4
chr1	186280785	186344457	uc001grv.3
chr1	186348898	186390503	uc021pgj.1
chr1	186369703	186370587	uc001gry.3
chr1	186412697	186430240	uc001gsa.4
chr1	186640943	186649559	uc001gsb.3
chr1	186798031	186958113	uc001gsc.3
chr1	187610357	187612988	uc001gsd.3
chr1	190066796	190446759	uc001gse.1
chr1	190447389	190450524	uc001gsf.3
chr1	190594019	190770788	uc021pgm.2
chr1	192127591	192154945	uc001gsg.3
chr1	192286121	192336414	uc001gsh.3
chr1	192544856	192549159	uc001gsi.1
chr1	192605267	192629440	uc001gsk.3
chr1	192778168	192781407	uc001gsl.3
chr1	192844815	192845115	uc021pgn.1
chr1	192981495	193028523	uc001gsm.3
chr1	193026410	193026545	uc021pgo.1
chr1	193027466	193029189	uc001gsr.1
chr1	193028551	193055115	uc001gss.3
chr1	193065594	193074608	uc001gsz.2
chr1	193091087	193223942	uc001gtb.3
chr1	193105632	193105713	uc021pgq.1
chr1	193147859	193155743	uc001gtc.4
chr1	196194912	196577499	uc001gtd.1
chr1	196551542	196551611	uc021pgs.1
chr1	196621007	196716634	uc001gtj.4
chr1	196743929	196763203	uc001gtl.3
chr1	196912933	196928356	uc001gtq.1
chr1	196946666	196978803	uc001gts.4
chr1	197008320	197036397	uc001gtt.1
chr1	197053256	197115824	uc001gtu.3
chr1	197122813	197169672	uc001gtx.1
chr1	197237333	197447585	uc001gtz.3
chr1	197473878	197744623	uc021pgu.1
chr1	197871681	197876497	uc001guh.3
chr1	197886516	197899273	uc001guk.1
chr1	198126107	198291548	uc001gun.4
chr1	198492351	198510075	uc001gup.3
chr1	198608097	198726605	uc001gur.2
chr1	198777131	198906558	uc021pgz.1
chr1	198828001	198828111	uc001gux.1
chr1	198828172	198828282	uc001guy.3
chr1	198975168	198990166	uc001gva.4
chr1	199996729	200146550	uc001gvb.4
chr1	200023188	200023293	uc021pha.1
chr1	200311671	200342920	uc001gvd.1
chr1	200375419	200379166	uc001gve.3
chr1	200380927	200444641	uc010ppi.1
chr1	200520624	200589862	uc010ppk.1
chr1	200613164	200639126	uc009wzk.3
chr1	200708685	200829831	uc001gvk.3
chr1	200842082	200843306	uc001gvn.2
chr1	200860626	200884864	uc001gvo.4
chr1	200898071	200935796	uc001gvp.1
chr1	200938513	200992828	uc001gvs.2
chr1	200993076	200997920	uc001gvu.3
chr1	201008639	201081694	uc001gvv.3
chr1	201083080	201084500	uc031prl.1
chr1	201103899	201123632	uc001gvy.3
chr1	201159952	201198080	uc001gwc.3
chr1	201252579	201302121	uc001gwd.3
chr1	201328135	201346828	uc001gwf.4
chr1	201349965	201368669	uc001gwm.3
chr1	201372894	201390874	uc021phd.1
chr1	201434606	201438299	uc031prm.1
chr1	201434606	201438299	uc001gwq.4
chr1	201452657	201475967	uc021phh.1
chr1	201489031	201489720	uc010ppt.3
chr1	201604354	201606516	uc001gwt.1
chr1	201617449	201796102	uc001gwu.3
chr1	201688635	201688755	uc031prn.1
chr1	201777738	201777830	uc021phj.1
chr1	201798287	201853422	uc001gwz.3
chr1	201857804	201861715	uc001gxa.3
chr1	201865583	201915716	uc021phl.1
chr1	201924618	201939789	uc001gxc.3
chr1	201951765	201975275	uc001gxd.3
chr1	201979689	201986315	uc001gxh.4
chr1	202092028	202098634	uc001gxj.3
chr1	202102531	202113871	uc001gxk.2
chr1	202116140	202130716	uc010ppx.2
chr1	202152694	202156267	uc009xaa.2
chr1	202156130	202158577	uc001gxs.3
chr1	202163117	202288889	uc001gxu.3
chr1	202300784	202311094	uc001gxx.4
chr1	202317829	202557697	uc001gya.2
chr1	202379235	202379335	uc021phn.1
chr1	202559724	202679551	uc010pqb.2
chr1	202696531	202777549	uc001gyf.3
chr1	202780073	202781041	uc031prp.1
chr1	202794328	202795421	uc021php.1
chr1	202827080	202830736	uc001gyj.3
chr1	202830881	202844369	uc001gyk.1
chr1	202847409	202858385	uc001gyl.3
chr1	202860229	202896371	uc001gyo.1
chr1	202909959	202927524	uc001gyq.5
chr1	202931000	202936404	uc001gyt.2
chr1	202955579	202976393	uc021phr.1
chr1	202976533	202993197	uc001gyu.1
chr1	203003611	203047864	uc009xaj.3
chr1	203052256	203055166	uc001gzd.4
chr1	203096835	203136533	uc001gzf.1
chr1	203097405	203136533	uc009xak.1
chr1	203136938	203144942	uc001gzh.1
chr1	203148058	203155922	uc001gzi.2
chr1	203185206	203198860	uc001gzn.2
chr1	203267885	203274453	uc009xao.2
chr1	203274663	203278729	uc001gzq.3
chr1	203309751	203320289	uc001gzr.3
chr1	203444882	203460479	uc001gzt.3
chr1	203463270	203478077	uc001gzu.1
chr1	203595914	203713209	uc001gzw.3
chr1	203696748	203696784	uc010pqk.1
chr1	203698708	203698833	uc001gzy.1
chr1	203699704	203700979	uc031prr.1
chr1	203734283	203745480	uc001haa.3
chr1	203764750	203823256	uc001hac.3
chr1	203766650	203769590	uc021phs.1
chr1	203830739	203840280	uc001hai.3
chr1	203968378	203968471	uc021pht.1
chr1	204001574	204010392	uc010pqo.1
chr1	204042245	204096871	uc001ham.3
chr1	204100189	204121307	uc001hao.4
chr1	204110535	204112137	uc001hap.3
chr1	204123943	204135465	uc001haq.2
chr1	204159468	204165619	uc001har.3
chr1	204167287	204183220	uc001has.1
chr1	204187978	204329057	uc001hau.4
chr1	204337557	204338847	uc010pqu.1
chr1	204372491	204380944	uc001hav.4
chr1	204379010	204380945	uc031prt.1
chr1	204391757	204459474	uc001haw.3
chr1	204475654	204475727	uc021phv.1
chr1	204476157	204476230	uc021phw.1
chr1	204485506	204527248	uc001hba.3
chr1	204586302	204654597	uc001hbf.1
chr1	204595902	204598840	uc031pru.1
chr1	204676447	204676558	uc021phz.1
chr1	204797781	204991950	uc001hbj.3
chr1	204981852	204991950	uc001hbo.2
chr1	204989426	204991950	uc001hbp.1
chr1	205012339	205047171	uc001hbr.3
chr1	205052256	205053588	uc001hbt.3
chr1	205055269	205091150	uc001hbu.2
chr1	205111630	205180727	uc001hbw.3
chr1	205197037	205242471	uc021pia.1
chr1	205271190	205290883	uc001hce.3
chr1	205305192	205326218	uc031prx.1
chr1	205342379	205356568	uc001hch.1
chr1	205350505	205391214	uc001hcj.2
chr1	205417429	205417526	uc010prh.1
chr1	205425185	205438152	uc001hco.2
chr1	205443270	205443343	uc021pib.1
chr1	205473683	205495965	uc010pri.2
chr1	205473683	205501921	uc001hcr.3
chr1	205523400	205525763	uc001hcu.2
chr1	205538111	205572046	uc001hcv.4
chr1	205564168	205564275	uc021pic.1
chr1	205626980	205649630	uc001hda.1
chr1	205681946	205719372	uc001hdb.3
chr1	205737113	205744610	uc001hde.4
chr1	205758220	205782161	uc001hdh.1
chr1	205765004	205765744	uc001hdi.1
chr1	205797149	205819276	uc001hdj.3
chr1	205831206	205865215	uc001hdk.1
chr1	205882176	205912588	uc001hdp.3
chr1	206138910	206155074	uc001hdr.4
chr1	206224282	206231482	uc001hds.2
chr1	206238881	206288236	uc001hdt.2
chr1	206317458	206332104	uc001hdu.3
chr1	206516199	206637783	uc001hdy.3
chr1	206643585	206670223	uc001hdz.2
chr1	206680878	206762616	uc001hed.3
chr1	206764973	206785904	uc001heh.2
chr1	206808880	206822542	uc001hej.3
chr1	206858364	206907630	uc001hem.2
chr1	206940947	206945839	uc001hen.1
chr1	206972214	207016326	uc001heo.3
chr1	207039153	207042568	uc001her.3
chr1	207070787	207077484	uc001heu.2
chr1	207076630	207095378	uc001hey.3
chr1	207101866	207119811	uc001hez.3
chr1	207131311	207143970	uc001hfa.4
chr1	207178153	207178230	uc021pih.1
chr1	207191865	207206101	uc001hfd.2
chr1	207217193	207224422	uc001hfe.1
chr1	207226619	207251162	uc001hfg.3
chr1	207262211	207273337	uc001hfj.3
chr1	207277510	207277617	uc001hfn.1
chr1	207277606	207318317	uc001hfo.3
chr1	207494816	207534311	uc001hfr.4
chr1	207627644	207663240	uc001hfv.3
chr1	207669472	207815110	uc001hfx.3
chr1	207818457	207897036	uc001hga.4
chr1	207925382	207968861	uc001hgj.3
chr1	207975196	207975868	uc021pik.2
chr1	207991723	207995941	uc010psi.2
chr1	208039465	208042417	uc001hgu.2
chr1	208059882	208084683	uc001hgw.1
chr1	208195587	208417665	uc001hgz.3
chr1	209602167	209605892	uc009xcn.3
chr1	209757044	209787284	uc001hhd.3
chr1	209788217	209825820	uc001hhh.3
chr1	209796788	209796855	uc021pil.1
chr1	209848669	209849735	uc001hhi.4
chr1	209878135	209908295	uc001hhk.3
chr1	209929393	209955668	uc001hho.3
chr1	209955661	209957890	uc001hhp.1
chr1	209958967	209979520	uc001hhq.2
chr1	210001311	210030910	uc001hhr.2
chr1	210111518	210337633	uc001hhs.5
chr1	210404803	210407466	uc001hhx.4
chr1	210406194	210416440	uc001hhy.3
chr1	210501595	210849638	uc009xcx.3
chr1	210851656	211307457	uc001hib.2
chr1	211432707	211489725	uc010psw.2
chr1	211500147	211548286	uc001hii.3
chr1	211556096	211605877	uc001hil.4
chr1	211649863	211666259	uc001hin.2
chr1	211748380	211752099	uc001hio.1
chr1	211836113	211848972	uc001hir.2
chr1	211916798	212004114	uc001hiv.3
chr1	212113740	212209002	uc001hiw.2
chr1	212208918	212278187	uc009xdc.3
chr1	212250954	212251027	uc021pis.1
chr1	212317864	212318301	uc010ptc.1
chr1	212458878	212535205	uc001hjb.3
chr1	212526159	212526292	uc009xdf.1
chr1	212537815	212588267	uc010pte.2
chr1	212606228	212619721	uc001hjd.3
chr1	212781969	212794119	uc021pit.1
chr1	212797625	212800113	uc010pth.1
chr1	212797788	212800120	uc001hjk.3
chr1	212859758	212873327	uc001hjl.2
chr1	212899494	212965139	uc001hjn.3
chr1	212965169	212990167	uc001hjo.2
chr1	213003484	213020991	uc001hjq.3
chr1	213029945	213031480	uc001hjs.5
chr1	213031596	213072705	uc001hjt.3
chr1	213123886	213164927	uc001hjw.3
chr1	213165523	213189168	uc001hjz.3
chr1	213224587	213446808	uc010ptr.2
chr1	213992977	214139607	uc031psa.1
chr1	214098091	214099996	uc031psb.1
chr1	214161277	214214847	uc001hkg.2
chr1	214454564	214510477	uc021pix.1
chr1	214522038	214725024	uc001hkk.2
chr1	214655881	214656819	uc010ptz.1
chr1	214776531	214837914	uc001hkm.3
chr1	215256559	215410436	uc001hkr.4
chr1	215740734	215795149	uc001hks.3
chr1	215796235	216596738	uc001hku.1
chr1	216676587	216896814	uc001hkw.2
chr1	216825011	216825068	uc021pjb.1
chr1	217603833	217804409	uc001hlf.1
chr1	217804694	218040484	uc001hlh.1
chr1	218066241	218094146	uc031psc.1
chr1	218458628	218511325	uc001hlj.3
chr1	218517537	218519020	uc031psd.1
chr1	218518675	218617961	uc001hln.3
chr1	219254316	219347130	uc001hlp.3
chr1	219347191	219386207	uc001hlq.4
chr1	220046618	220292777	uc021pjd.1
chr1	220087605	220101993	uc001hlw.3
chr1	220141941	220220000	uc001hly.1
chr1	220230823	220263191	uc001hma.3
chr1	220267454	220321383	uc001hmc.3
chr1	220291194	220291304	uc010pui.2
chr1	220291498	220291583	uc010puj.2
chr1	220291547	220291569	uc021pje.1
chr1	220321609	220445843	uc010puk.1
chr1	220373883	220374018	uc010pul.2
chr1	220439520	220441057	uc001hmj.2
chr1	220701567	220837799	uc001hmn.4
chr1	220863627	220872499	uc001hmp.1
chr1	220921675	220957596	uc001hmq.3
chr1	220960038	220987741	uc001hms.3
chr1	220999117	220999235	uc021pjg.1
chr1	221002596	221005770	uc001hmu.3
chr1	221052742	221058400	uc001hmv.4
chr1	221503269	221509638	uc001hmw.1
chr1	221874763	221915516	uc001hmy.2
chr1	222638346	222638419	uc021pjh.1
chr1	222646009	222646043	uc010puo.1
chr1	222646722	222646767	uc001hna.1
chr1	222646839	222646870	uc001hnb.3
chr1	222647751	222647786	uc010pup.1
chr1	222648391	222648423	uc021pji.1
chr1	222648536	222648596	uc001hnd.3
chr1	222648680	222648717	uc021pjj.1
chr1	222649002	222649064	uc010puq.2
chr1	222649161	222649251	uc021pjk.1
chr1	222650319	222650381	uc001hnf.3
chr1	222650411	222650443	uc001hng.2
chr1	222695601	222721444	uc001hnh.1
chr1	222731243	222763275	uc009xdz.2
chr1	222791443	222841351	uc001hnl.3
chr1	222841354	222885864	uc001hnn.3
chr1	222885905	222906106	uc001hnq.1
chr1	222900275	222946483	uc001hnr.1
chr1	222906202	222908533	uc001hns.1
chr1	222910557	222924002	uc001hnt.3
chr1	222988430	223179337	uc001hnu.2
chr1	223282747	223316624	uc001hnw.2
chr1	223394160	223537544	uc001hny.4
chr1	223566714	223568812	uc001hoa.2
chr1	223714971	223853436	uc009xee.2
chr1	223900118	223963720	uc001hob.4
chr1	223967594	224033674	uc010pvb.2
chr1	224051997	224052306	uc021pjm.1
chr1	224139721	224139941	uc001hof.1
chr1	224189608	224197818	uc001hog.1
chr1	224294983	224295243	uc021pjn.1
chr1	224301788	224349749	uc001hoh.3
chr1	224370909	224381142	uc001hoj.3
chr1	224415035	224517891	uc001hok.3
chr1	224444705	224444843	uc021pjo.1
chr1	224544512	224566223	uc001hom.2
chr1	224572844	224622001	uc001hop.4
chr1	224585928	224586013	uc021pjq.1
chr1	224586541	224586601	uc021pjr.1
chr1	224804178	224928249	uc001hos.1
chr1	225117355	225586996	uc001how.2
chr1	225589203	225615815	uc001hoy.3
chr1	225674533	225840845	uc001hpc.1
chr1	225888305	225939945	uc001hpe.1
chr1	225965514	225978168	uc001hpg.3
chr1	225997796	226033262	uc001hpk.3
chr1	226033232	226070420	uc001hpm.2
chr1	226073981	226076846	uc001hpo.3
chr1	226107576	226112040	uc001hpq.4
chr1	226124297	226129083	uc001hpt.2
chr1	226170402	226187066	uc001hpu.4
chr1	226250407	226259703	uc001hpw.3
chr1	226271655	226277994	uc001hpx.3
chr1	226332379	226374423	uc001hpy.3
chr1	226374532	226385086	uc031psl.1
chr1	226411382	226413513	uc010pvm.2
chr1	226418849	226497204	uc001hqa.3
chr1	226548391	226595801	uc001hqd.4
chr1	226723318	226730469	uc001hqe.2
chr1	226736500	226796915	uc021pjw.1
chr1	226819390	226926876	uc010pvo.2
chr1	227058272	227083804	uc009xeo.1
chr1	227127937	227175246	uc001hqn.1
chr1	227177565	227505826	uc001hqr.3
chr1	227581291	227618723	uc001hqv.3
chr1	227751219	227850164	uc021pjy.1
chr1	227884732	227885408	uc021pjz.1
chr1	227918889	227923112	uc001hrb.3
chr1	227922696	227968932	uc001hrf.2
chr1	228003417	228034171	uc001hrh.3
chr1	228109164	228135676	uc001hri.2
chr1	228129290	228129384	uc031psm.1
chr1	228155293	228155325	uc001hrj.3
chr1	228155355	228155417	uc001hrk.3
chr1	228156485	228156575	uc021pka.1
chr1	228156672	228156704	uc021pkb.1
chr1	228157022	228157062	uc010pvt.1
chr1	228157172	228157203	uc010pvu.2
chr1	228160064	228160098	uc001hrn.2
chr1	228160168	228160213	uc021pkc.1
chr1	228194722	228248972	uc001hrq.2
chr1	228270360	228286913	uc001hrr.3
chr1	228284963	228285042	uc021pkd.1
chr1	228288427	228291022	uc001hrx.3
chr1	228294379	228297013	uc001hrz.4
chr1	228327928	228336655	uc021pkf.1
chr1	228337414	228347527	uc001hsk.3
chr1	228353428	228369958	uc001hsl.4
chr1	228391206	228401365	uc031psn.1
chr1	228395830	228566575	uc001hsq.2
chr1	228581376	228594517	uc001hss.3
chr1	228595635	228604583	uc001hsv.3
chr1	228612545	228613026	uc001hsx.1
chr1	228645064	228645560	uc001hsy.3
chr1	228645807	228646259	uc001hsz.3
chr1	228649774	228649853	uc021pkh.1
chr1	228651845	228651891	uc021pki.1
chr1	228673888	228674821	uc001hta.3
chr1	228675067	228683889	uc001htb.3
chr1	228780393	228788159	uc001htd.2
chr1	228870823	228882416	uc001htf.3
chr1	229050379	229052954	uc001htg.2
chr1	229406808	229441640	uc001hth.4
chr1	229440128	229441250	uc001htk.4
chr1	229456751	229478688	uc001htl.4
chr1	229566992	229569843	uc001htm.3
chr1	229577043	229644088	uc001htn.3
chr1	229589676	229591617	uc001hto.1
chr1	229652328	229694442	uc001htp.4
chr1	229728865	229761794	uc001htq.3
chr1	229761980	229795946	uc001hts.1
chr1	230202955	230417875	uc010pwa.1
chr1	230457391	230561674	uc031psq.1
chr1	230778201	230829731	uc001htw.3
chr1	230838271	230850336	uc001hty.4
chr1	230883129	230937749	uc001htz.1
chr1	230972864	231004302	uc001hub.3
chr1	231010591	231014761	uc001hue.1
chr1	231041986	231114618	uc001huf.4
chr1	231114822	231136479	uc001huh.3
chr1	231154703	231175995	uc001hui.2
chr1	231298673	231357314	uc009xfn.1
chr1	231319844	231323373	uc031pss.1
chr1	231359508	231376924	uc001hul.3
chr1	231376918	231413719	uc001hup.4
chr1	231468481	231473578	uc001huq.3
chr1	231473681	231490769	uc001hur.4
chr1	231499496	231560790	uc001huv.2
chr1	231611509	231612271	uc021pkl.1
chr1	231664398	231702269	uc001huw.3
chr1	231727037	231747836	uc021pkm.2
chr1	231762560	232177019	uc010pxh.2
chr1	231950371	231954263	uc001hvd.3
chr1	232533711	232651243	uc001hvg.3
chr1	232940637	232946092	uc001hvh.2
chr1	233086369	233114219	uc001hvj.1
chr1	233119881	233431459	uc001hvl.2
chr1	233463513	233520894	uc001hvt.4
chr1	233584371	233584527	uc021pko.1
chr1	233749749	233808258	uc010pxo.1
chr1	233759897	233759965	uc021pkp.1
chr1	234040678	234460262	uc001hvy.1
chr1	234348351	234350834	uc001hvz.1
chr1	234442212	234442285	uc021pkq.1
chr1	234509428	234519795	uc001hwc.3
chr1	234527058	234614849	uc001hwd.3
chr1	234663636	234667525	uc001hwe.3
chr1	234740014	234745271	uc001hwg.3
chr1	234765056	234770526	uc021pkr.1
chr1	234782034	234796670	uc001hwh.3
chr1	234859788	234867390	uc001hwj.2
chr1	235093089	235099746	uc001hwk.4
chr1	235272657	235292256	uc001hwl.3
chr1	235291117	235291252	uc001hwm.1
chr1	235294497	235324772	uc001hwn.3
chr1	235330209	235491532	uc001hwq.3
chr1	235353348	235353431	uc021pkt.1
chr1	235491752	235507844	uc001hwv.3
chr1	235530727	235612280	uc001hxa.1
chr1	235610504	235667781	uc001hxc.3
chr1	235710984	235814054	uc001hxh.4
chr1	235712454	235714587	uc021pku.1
chr1	235824330	236030227	uc001hxj.3
chr1	236016299	236016360	uc021pkv.1
chr1	236139131	236228481	uc001hxo.3
chr1	236227659	236229779	uc001hxp.1
chr1	236305831	236372209	uc001hxq.3
chr1	236378421	236445339	uc001hxt.3
chr1	236557679	236648008	uc001hxu.1
chr1	236686368	236687808	uc001hxx.3
chr1	236686738	236716279	uc001hxy.2
chr1	236712304	236767841	uc001hyd.2
chr1	236849769	236927558	uc001hyf.2
chr1	236958580	237067281	uc001hyi.4
chr1	236973802	236992568	uc009xgj.1
chr1	237167402	237167718	uc001hyk.2
chr1	237205701	237997288	uc001hyl.1
chr1	237284106	237284409	uc021pkw.1
chr1	238025474	238091619	uc010pyc.2
chr1	238041163	238054222	uc001hym.3
chr1	238105861	238105933	uc021pla.1
chr1	238106955	238107030	uc021plb.1
chr1	238643683	238649317	uc001hyn.3
chr1	239792372	240072717	uc001hyp.3
chr1	240170823	240176560	uc021pld.1
chr1	240255184	240638489	uc010pyd.2
chr1	240317950	240318072	uc021ple.1
chr1	240652872	240775462	uc001hys.3
chr1	240938816	241520478	uc001hyv.2
chr1	241295571	241295646	uc021plg.1
chr1	241660856	241683085	uc001hyx.3
chr1	241695433	241758949	uc009xgp.3
chr1	241756451	241803701	uc001hza.3
chr1	241792166	241799232	uc001hzd.3
chr1	241815579	241965434	uc001hzg.2
chr1	242011492	242053241	uc001hzh.3
chr1	242158791	242162385	uc001hzk.2
chr1	242251688	242687998	uc001hzn.2
chr1	243218732	243218763	uc021plm.1
chr1	243219238	243219328	uc021pln.1
chr1	243219615	243265046	uc001hzq.2
chr1	243287729	243418708	uc021plo.1
chr1	243351942	243352032	uc021plr.1
chr1	243419306	243663393	uc001hzw.3
chr1	243509477	243509557	uc021plt.1
chr1	243663020	244006584	uc001iab.2
chr1	243729624	243729741	uc021plv.1
chr1	244080703	244210619	uc001iac.3
chr1	244212240	244220780	uc031psv.1
chr1	244515936	244552388	uc001iah.4
chr1	244571793	244615436	uc001iaj.3
chr1	244624672	244803662	uc001iam.3
chr1	244816351	244872334	uc001iao.3
chr1	244998638	245008359	uc001iar.3
chr1	245003939	245010243	uc001iav.3
chr1	245013601	245027827	uc001iaz.1
chr1	245133170	245251148	uc001ibc.2
chr1	245318286	245866428	uc001ibf.1
chr1	245533635	245533719	uc021plw.1
chr1	245912641	246670644	uc001ibl.3
chr1	246679340	246687589	uc021plx.1
chr1	246703862	246729565	uc001ibn.3
chr1	246729638	246831884	uc001ibp.3
chr1	246887377	246931440	uc001ibr.3
chr1	246939314	246955685	uc001ibs.1
chr1	246970416	246970487	uc021ply.1
chr1	247002401	247094726	uc001ibv.2
chr1	247148624	247171395	uc009xgu.3
chr1	247197939	247242115	uc001icd.2
chr1	247263263	247267674	uc001ice.2
chr1	247273461	247275719	uc001icg.1
chr1	247319202	247335318	uc001icj.1
chr1	247337903	247338870	uc001icl.1
chr1	247365268	247365362	uc021pma.1
chr1	247419373	247420447	uc010pyu.2
chr1	247463621	247495045	uc001ico.3
chr1	247579457	247612406	uc001icr.3
chr1	247614330	247615284	uc010pyx.2
chr1	247654369	247655711	uc001icz.2
chr1	247681594	247683942	uc001idc.3
chr1	247693433	247697141	uc009xgy.3
chr1	247712346	247739848	uc001idf.3
chr1	247751661	247752615	uc010pyy.2
chr1	247768887	247769817	uc010pyz.2
chr1	247835419	247836343	uc001idi.1
chr1	247875130	247876057	uc001idj.1
chr1	247920763	247921708	uc010pza.2
chr1	247978101	247979031	uc001idm.1
chr1	248004229	248005198	uc001idn.1
chr1	248020500	248043438	uc001ido.3
chr1	248058888	248059833	uc010pzb.2
chr1	248084319	248085258	uc010pzc.2
chr1	248100492	248264224	uc001ids.3
chr1	248112159	248113098	uc001idt.1
chr1	248128633	248129641	uc010pzd.2
chr1	248153568	248154493	uc001idv.1
chr1	248185249	248186188	uc031psy.1
chr1	248201473	248202607	uc001idw.3
chr1	248223983	248224922	uc001idx.1
chr1	248285437	248286082	uc001idy.1
chr1	248308449	248309388	uc010pze.2
chr1	248343287	248344331	uc010pzf.2
chr1	248366369	248367308	uc010pzg.2
chr1	248402230	248403166	uc010pzh.2
chr1	248436153	248437116	uc010pzi.2
chr1	248457917	248458880	uc010pzj.2
chr1	248486931	248487870	uc010pzk.2
chr1	248512076	248513015	uc010pzl.2
chr1	248524882	248525929	uc001ieh.1
chr1	248550909	248551836	uc001iei.1
chr1	248569295	248570405	uc010pzm.2
chr1	248616098	248617073	uc001iek.1
chr1	248636651	248637608	uc001iel.1
chr1	248651889	248652837	uc001iem.1
chr1	248684947	248685898	uc001ien.1
chr1	248721844	248722792	uc001ieo.2
chr1	248737101	248738058	uc001iep.1
chr1	248756130	248757069	uc010pzn.2
chr1	248789478	248790429	uc001ier.1
chr1	248801587	248802559	uc001ies.1
chr1	248813231	248814185	uc010pzo.2
chr1	248844669	248845605	uc001ieu.1
chr1	248881947	248885507	uc031psz.1
chr1	248902716	248903151	uc009xhb.3
chr1	249104650	249120154	uc001iew.1
chr1	249120575	249120642	uc021pmd.1
chr1	249132529	249143714	uc001iex.3
chr1	249144202	249153315	uc001ifc.2
chr1	249168053	249168159	uc021pmf.1
chr1	249168446	249168518	uc021pmg.1
chr1	249200441	249213345	uc001ifh.3


================================================
FILE: intro_awk_spring2021/data/dmel-subset-chromosomes.fasta
================================================
>X
GAATTCGTCAGAAATGAGCTAAACAAATTTAAATCATTAAATGCGAGCGGCGAATCCGGAAACAGCAACTTCAAACCAGT
>2L
CGACAATGCACGACAGAGGAAGCAGAACAGATATTTAGATTGCCTCTCATTTTCTCTCCCATATTATAGGGAGAAATATG
>2R
CTCAAGATACCTTCTACAGATTATTTAAAGCTAGTGCACAACAACAATAAATTGACTAAGTTATGTCATTTTAAGCGGTC
>3L
TAGGGAGAAATATGATCGCGTATGCGAGAGTAGTGCCAACATATTGTGCTCTTTGATTTTTTGGCAACCCAAAATGGTGG
>3R
ACGGGACCGAGTATAGTACCAGTACGCGACCAGTACGGGAGCAGTACGGAACCAATACGGGTCCAGTACGGGTCCAGTAC


================================================
FILE: intro_awk_spring2021/data/enhancers.csv
================================================
chr1,1015066,1015266,HSE897,9.5706324138
chr1,1590473,1590673,HSE853,17.3206898329
chr1,2120861,2121064,HSE86,66.0424471614
chr1,6336418,6336624,HSE394,14.8892086906
chr1,7404594,7404794,HSE315,24.228051023
chr1,11941325,11941525,HSE322,17.5192630328
chr1,15055555,15055755,HSE962,11.7065788965
chr1,15478024,15478224,HSE354,17.5771586196
chr1,16065307,16065508,HSE264,23.9092446094
chr1,23244249,23244449,HSE434,17.1331422162
chr1,24621426,24621626,HSE638,11.9517576968
chr1,24710189,24710389,HSE537,14.6283754801
chr1,26216838,26217042,HSE237,39.9988740347
chr1,28931703,28931903,HSE150,37.3158468645
chr1,29139636,29139836,HSE696,9.3933913885
chr1,29301011,29301211,HSE837,15.2711109524
chr1,30644209,30644410,HSE452,13.1331424716
chr1,37323436,37323636,HSE144,24.6337166989
chr1,39114092,39114292,HSE642,13.5250552809
chr1,40887095,40887295,HSE715,14.4526764769
chr1,43259581,43259781,HSE285,23.3546182652
chr1,48180470,48180671,HSE581,14.0513005602
chr1,51911123,51911323,HSE728,10.9727627129
chr1,58868480,58868680,HSE744,10.9393507729
chr1,61907580,61907780,HSE219,24.9065022738
chr1,62649216,62649416,HSE160,31.6287882019
chr1,64297272,64297472,HSE542,16.032234097
chr1,71842002,71842202,HSE525,13.177872984
chr1,76529679,76529879,HSE217,28.2285409421
chr1,77034610,77034811,HSE117,36.8686336226
chr1,77861083,77861283,HSE95,33.1051379887
chr1,81139876,81140076,HSE648,14.2447596506
chr1,84078697,84078897,HSE229,19.6626071807
chr1,85040012,85040217,HSE554,18.2435809591
chr1,85294189,85294389,HSE649,12.4198070103
chr1,86952858,86953058,HSE227,20.0246313683
chr1,87198821,87199021,HSE396,14.181885534
chr1,87568855,87569055,HSE187,27.2483801299
chr1,87718281,87718481,HSE761,10.7346086388
chr1,90228438,90228639,HSE725,10.8919859879
chr1,90567852,90568052,HSE750,11.3840777324
chr1,101544928,101545128,HSE344,15.9279633229
chr1,105180209,105180409,HSE319,15.9878097581
chr1,112056258,112056458,HSE234,20.4981560995
chr1,113265359,113265559,HSE675,15.7764663556
chr1,120009313,120009513,HSE287,17.3492427166
chr1,120096931,120097131,HSE391,16.3648875858
chr1,145474303,145474503,HSE568,15.9686715881
chr1,146037814,146038014,HSE867,8.3915764216
chr1,149294554,149294754,HSE758,13.8367903871
chr1,149298520,149298734,HSE549,19.9633772792
chr1,161893534,161893734,HSE511,14.2318525715
chr1,165509248,165509448,HSE683,13.7673640003
chr1,167774457,167774657,HSE42,47.5183173
chr1,170555465,170555665,HSE348,25.4137901578
chr1,171264616,171264816,HSE405,18.0971419626
chr1,182113250,182113450,HSE687,17.1842942374
chr1,182635108,182635309,HSE247,28.4610896172
chr1,183573285,183573486,HSE44,36.1099998528
chr1,183681289,183681489,HSE77,46.3313570709
chr1,190550474,190550674,HSE461,24.1417645601
chr1,192030096,192030296,HSE839,8.898236823
chr1,192602518,192602718,HSE747,12.1928710846
chr1,193387547,193387747,HSE251,22.6738118926
chr1,201469917,201470118,HSE286,21.167260695
chr1,205411096,205411296,HSE399,15.4863010561
chr1,207682168,207682368,HSE389,14.4420873863
chr1,208751067,208751267,HSE250,28.3356979117
chr1,211556044,211556244,HSE913,10.1071053671
chr1,215227649,215227849,HSE643,12.2289936805
chr1,222946396,222946596,HSE136,28.3288830309
chr1,224716721,224716921,HSE669,11.1939548835
chr1,224767352,224767552,HSE659,12.7072133153
chr1,229007236,229007436,HSE259,25.6576825275
chr1,231831560,231831760,HSE103,34.1933527171
chr1,234707634,234707834,HSE458,16.2547381587
chr1,240192775,240192976,HSE192,23.6438756614
chr1,240751207,240751408,HSE41,39.1665180557
chr1,242513807,242514007,HSE29,41.8646049022
chr1,243428176,243428376,HSE909,8.9768456445
chr1,245362206,245362406,HSE514,20.8510241208
chr1,245748622,245748822,HSE400,15.8809014188
chr1,245751245,245751445,HSE614,11.9977672181
chr10,1796728,1796928,HSE887,8.8759918247
chr10,2188344,2188544,HSE210,17.5664225697
chr10,2435771,2435971,HSE331,14.4636659658
chr10,3441348,3441548,HSE787,8.9210657436
chr10,4012461,4012661,HSE959,9.8681000165
chr10,4659880,4660080,HSE109,39.294649348
chr10,5734710,5734910,HSE80,39.253086776
chr10,5918486,5918686,HSE902,13.4688692863
chr10,11575356,11575556,HSE741,13.0848348743
chr10,13005187,13005387,HSE721,10.6222160126
chr10,14195913,14196113,HSE544,13.1977478994
chr10,14227600,14227810,HSE569,12.7906533757
chr10,14364637,14364837,HSE882,8.5664694124
chr10,17825468,17825668,HSE300,17.0051022824
chr10,22748482,22748683,HSE737,14.6364554664
chr10,25811340,25811541,HSE58,31.2099871039
chr10,30602601,30602801,HSE23,49.0758646578
chr10,31080176,31080376,HSE918,9.2503197227
chr10,33270658,33270858,HSE949,11.4436365263
chr10,33294606,33294809,HSE995,12.672352369
chr10,38534930,38535130,HSE430,15.2638491526
chr10,44435726,44435926,HSE655,14.2956522267
chr10,44729078,44729278,HSE392,17.4461418303
chr10,46168148,46168348,HSE152,37.019176366
chr10,47640802,47641002,HSE244,20.7338411549
chr10,52710256,52710456,HSE489,16.0012334733
chr10,64400190,64400390,HSE143,23.5037143277
chr10,71081718,71081918,HSE723,10.7476600785
chr10,71470586,71470786,HSE37,54.1450393752
chr10,71536580,71536780,HSE664,12.2052671593
chr10,72471766,72471966,HSE783,17.3113965123
chr10,73599983,73600183,HSE403,21.5414372314
chr10,75126686,75126888,HSE71,29.3819259713
chr10,79473783,79473983,HSE43,45.6987326408
chr10,83728971,83729171,HSE212,18.9337565568
chr10,87807719,87807919,HSE859,8.6435515636
chr10,87872095,87872295,HSE481,12.756492704
chr10,88160082,88160282,HSE845,11.0856156511
chr10,88600426,88600626,HSE424,15.9686715881
chr10,90683407,90683607,HSE476,14.8937152174
chr10,90686708,90686908,HSE479,15.6187716761
chr10,93044375,93044575,HSE777,9.7964029914
chr10,94521429,94521629,HSE151,31.5694945963
chr10,97443603,97443803,HSE419,12.9499575441
chr10,97620593,97620793,HSE886,12.518441498
chr10,97667629,97667829,HSE201,34.3491300983
chr10,99223587,99223788,HSE446,23.975955174
chr10,104629190,104629462,HSE784,14.8155161441
chr10,105527665,105527865,HSE467,20.6230257395
chr10,106258325,106258525,HSE665,10.9578861725
chr10,107102583,107102783,HSE216,19.2077151804
chr10,108788448,108788648,HSE699,10.646721976
chr10,108792229,108792429,HSE432,17.1288876915
chr10,109186134,109186334,HSE960,11.8900697742
chr10,115258601,115258801,HSE108,28.6223256015
chr10,120523320,120523520,HSE531,21.3827794751
chr10,121452806,121453006,HSE989,8.0812359893
chr10,123179381,123179581,HSE732,9.5557622755
chr10,124108496,124108696,HSE927,8.9214956882
chr10,129573104,129573305,HSE54,44.0543064465
chr11,353431,353635,HSE551,15.321918756
chr11,3181559,3181759,HSE793,14.7046892301
chr11,6947739,6947942,HSE92,27.9827053442
chr11,13157034,13157234,HSE241,21.0632306147
chr11,15139410,15139611,HSE456,12.2189043207
chr11,15910292,15910492,HSE724,13.7294254547
chr11,16795802,16796002,HSE799,12.802186876
chr11,17550965,17551165,HSE639,14.1086468033
chr11,17943304,17943504,HSE891,11.4856542942
chr11,21656183,21656383,HSE700,14.9043957142
chr11,21662095,21662295,HSE440,17.2950629632
chr11,21673343,21673543,HSE894,8.2797517913
chr11,23447019,23447219,HSE6,75.8807514012
chr11,24085949,24086149,HSE492,15.300706048
chr11,24275024,24275224,HSE651,11.4443945585
chr11,25505910,25506110,HSE186,21.8909002434
chr11,31021450,31021650,HSE877,9.1198497163
chr11,31706283,31706483,HSE74,40.630635226
chr11,33868837,33869038,HSE857,11.3116139483
chr11,35595495,35595695,HSE183,19.9118859606
chr11,38009596,38009796,HSE907,9.9148121335
chr11,39564123,39564323,HSE936,9.1935010973
chr11,41468242,41468442,HSE36,44.9449175171
chr11,42922737,42922937,HSE200,32.2091247664
chr11,44407611,44407811,HSE657,10.3471228352
chr11,44747886,44748086,HSE475,15.1102404858
chr11,44986217,44986417,HSE619,10.668936584
chr11,45047769,45047969,HSE851,10.1383014015
chr11,45111276,45111476,HSE866,10.220733306
chr11,45369052,45369252,HSE627,12.2239177674
chr11,48016992,48017192,HSE767,10.1950143759
chr11,49411924,49412124,HSE512,18.574232402
chr11,63859735,63859935,HSE770,14.6837739902
chr11,63865941,63866141,HSE184,35.4114322006
chr11,65244823,65245023,HSE265,23.3441037512
chr11,67784892,67785092,HSE824,9.8386696158
chr11,73249610,73249812,HSE762,9.9839312996
chr11,73694258,73694459,HSE176,22.3483349613
chr11,76842280,76842482,HSE919,11.0703469831
chr11,78306590,78306790,HSE113,25.2078122498
chr11,79767266,79767466,HSE69,30.2148994861
chr11,81284367,81284567,HSE695,9.8185385502
chr11,92228348,92228548,HSE539,20.2344657972
chr11,100340586,100340786,HSE388,15.4919091437
chr11,104561099,104561299,HSE812,15.176333042
chr11,108070756,108070956,HSE809,9.4991448266
chr11,111156211,111156411,HSE714,10.0243534212
chr11,115147624,115147824,HSE134,28.6194026789
chr11,115846389,115846603,HSE722,16.8886385104
chr11,117592848,117593048,HSE705,13.7637540477
chr11,120106662,120106862,HSE937,10.9173422723
chr11,123178915,123179115,HSE672,11.3022531999
chr11,124816687,124816887,HSE601,14.6082914089
chr11,125805553,125805756,HSE945,20.5577441884
chr11,126357947,126358147,HSE493,15.3602555927
chr11,126976167,126976367,HSE968,9.1843932734
chr11,128052898,128053098,HSE911,12.5881181856
chr11,129137803,129138003,HSE311,18.8031509882
chr11,131335724,131335924,HSE517,23.7420612884
chr11,131510396,131510597,HSE406,27.563239098
chr11,131526809,131527010,HSE398,14.0540782683
chr11,132029024,132029225,HSE232,18.1785539558
chr11,132863261,132863461,HSE792,9.4066623256
chr12,1619367,1619567,HSE271,19.5724460831
chr12,2393824,2394024,HSE153,38.5409899219
chr12,4310999,4311199,HSE612,13.3477965884
chr12,4715838,4716038,HSE124,22.969040262
chr12,5977983,5978183,HSE981,8.9923072646
chr12,6649669,6649869,HSE666,18.8465977854
chr12,8983842,8984042,HSE206,21.6055505061
chr12,10253851,10254051,HSE850,13.368224383
chr12,12009176,12009379,HSE996,8.2025875972
chr12,14088837,14089037,HSE361,16.6852824277
chr12,15118302,15118502,HSE158,36.7485762947
chr12,15186700,15186902,HSE472,17.2685676671
chr12,15283754,15283954,HSE862,11.6244209976
chr12,16064197,16064397,HSE796,18.7455509923
chr12,23364365,23364565,HSE716,12.767223137
chr12,29608107,29608307,HSE84,29.4815974061
chr12,30672107,30672307,HSE459,16.6113321667
chr12,31079386,31079586,HSE928,15.5521683508
chr12,31233235,31233435,HSE817,14.0452336182
chr12,32029945,32030149,HSE3,134.4896822737
chr12,32418004,32418204,HSE443,14.9171957071
chr12,32715083,32715283,HSE566,11.7700863315
chr12,41442900,41443100,HSE448,15.8907964207
chr12,44547984,44548184,HSE595,19.5258881951
chr12,47299063,47299263,HSE710,17.9511083583
chr12,51318352,51318553,HSE278,38.5126987285
chr12,51714654,51714854,HSE105,36.1187300648
chr12,60334522,60334722,HSE296,18.4243312773
chr12,64388551,64388751,HSE411,16.4173206854
chr12,64960555,64960755,HSE893,11.4399085223
chr12,65100062,65100262,HSE677,12.0906755698
chr12,67180805,67181005,HSE302,18.0232362712
chr12,67820036,67820236,HSE693,28.0050105025
chr12,67824724,67824924,HSE562,19.9790499282
chr12,70590594,70590798,HSE938,10.4065399522
chr12,72665258,72665458,HSE870,9.4908568562
chr12,72843735,72843935,HSE352,14.6912721831
chr12,72903763,72903963,HSE622,12.4776159762
chr12,76258547,76258747,HSE366,15.4992304899
chr12,81139497,81139697,HSE263,19.1724365769
chr12,83358298,83358498,HSE956,10.9748220911
chr12,88826891,88827091,HSE470,14.4491790975
chr12,93693111,93693311,HSE337,17.5773205057
chr12,93793026,93793226,HSE231,36.7866454534
chr12,93964730,93964930,HSE701,14.9318726929
chr12,94680437,94680638,HSE800,10.1294106105
chr12,95448207,95448407,HSE85,41.328100082
chr12,98833530,98833731,HSE572,21.9773288843
chr12,102047388,102047588,HSE177,36.6536035992
chr12,105121890,105122090,HSE733,12.9059778339
chr12,106581316,106581516,HSE974,11.9119885347
chr12,108780634,108780834,HSE195,21.1967998006
chr12,110145174,110145374,HSE326,19.9633772792
chr12,110147281,110147481,HSE269,19.4570397688
chr12,116307853,116308063,HSE340,22.419418574
chr12,117846190,117846392,HSE393,15.5403001234
chr12,117850284,117850484,HSE557,16.5566891443
chr12,123543660,123543860,HSE861,10.4781563732
chr12,128314695,128314895,HSE528,13.3821736799
chr12,128455126,128455326,HSE906,9.3701763362
chr12,131041357,131041557,HSE827,8.9137558728
chr12,131261218,131261419,HSE39,40.7349907933
chr12,131596799,131596999,HSE61,38.7314139499
chr12,131622505,131622705,HSE735,9.6402688286
chr12,133175461,133175661,HSE560,17.0748830696
chr13,21347729,21347929,HSE450,18.700409108
chr13,24148647,24148847,HSE923,12.1565857536
chr13,29459319,29459519,HSE4,83.6939521234
chr13,29464970,29465170,HSE235,21.5042630726
chr13,33536518,33536718,HSE484,11.9616526488
chr13,36045055,36045255,HSE466,15.1339680818
chr13,37089667,37089867,HSE53,34.1508321281
chr13,38195831,38196031,HSE63,29.4908349793
chr13,47090817,47091017,HSE240,20.9194834678
chr13,47169753,47169953,HSE795,10.6129192636
chr13,48685195,48685395,HSE383,17.4161328951
chr13,52195812,52196012,HSE804,33.9458114683
chr13,52928037,52928237,HSE971,10.2210243551
chr13,58387875,58388075,HSE898,9.7709738074
chr13,63964002,63964202,HSE155,21.8968073472
chr13,63982654,63982855,HSE755,10.6894623919
chr13,66918963,66919163,HSE942,8.137382847
chr13,78382044,78382244,HSE709,13.2425052878
chr13,79968085,79968285,HSE207,19.0520301367
chr13,81969573,81969773,HSE464,17.1316537183
chr13,88325009,88325225,HSE169,22.404724465
chr13,94551722,94551922,HSE47,39.9300964523
chr13,96720972,96721172,HSE242,17.6289298545
chr13,99193496,99193696,HSE775,11.8286452332
chr13,102857806,102858006,HSE175,26.1349566103
chr13,103659347,103659547,HSE879,9.4672756235
chr13,106860061,106860261,HSE523,15.2219187287
chr13,107025740,107025940,HSE545,22.9167462321
chr13,107144094,107144294,HSE826,10.0932827937
chr13,111223922,111224122,HSE55,37.6150776113
chr14,21439340,21439545,HSE697,14.9749639521
chr14,24505425,24505632,HSE598,21.3172605404
chr14,25814644,25814844,HSE884,8.4198607932
chr14,26543395,26543598,HSE239,19.7243319678
chr14,31281660,31281860,HSE64,42.0539122341
chr14,33531812,33532012,HSE374,13.6641312978
chr14,59129293,59129493,HSE803,12.357054467
chr14,59386085,59386285,HSE932,9.6556661977
chr14,61083031,61083231,HSE780,12.1130184536
chr14,65539073,65539273,HSE324,18.8187496116
chr14,76804206,76804406,HSE129,28.6611225834
chr14,81636859,81637059,HSE65,73.8674420954
chr14,81790696,81790896,HSE209,42.4669581462
chr14,91091974,91092174,HSE480,17.813206233
chr14,91716580,91716780,HSE469,17.2797598596
chr14,93949158,93949358,HSE746,9.1621125053
chr14,95071723,95071923,HSE924,8.9302006066
chr14,102783279,102783479,HSE816,24.4849615987
chr14,105147964,105148164,HSE939,11.1238681848
chr15,27524028,27524228,HSE431,15.0234209239
chr15,28817788,28817988,HSE191,21.3327480149
chr15,29247425,29247625,HSE279,18.2157833949
chr15,30182592,30182792,HSE840,10.1016943223
chr15,31792516,31792716,HSE429,15.2456002355
chr15,33752266,33752466,HSE806,7.7973232785
chr15,33958947,33959147,HSE650,11.2642323912
chr15,39474262,39474462,HSE703,12.786143498
chr15,52554008,52554208,HSE832,10.0325076389
chr15,54967526,54967726,HSE230,24.1378839612
chr15,55510355,55510555,HSE339,18.1931930527
chr15,61269359,61269560,HSE1,89.2659435972
chr15,61979890,61980090,HSE922,8.1022018494
chr15,62752114,62752314,HSE611,14.5613676786
chr15,62917755,62917955,HSE305,28.6662298913
chr15,68723591,68723791,HSE462,23.2304619822
chr15,69830362,69830562,HSE757,10.8328824475
chr15,72203345,72203545,HSE147,31.2986371967
chr15,74076783,74076983,HSE771,12.0826546194
chr15,74596170,74596370,HSE993,12.6758156277
chr15,74641836,74642036,HSE221,25.8164928998
chr15,75523071,75523271,HSE874,9.5089830845
chr15,80478573,80478773,HSE211,20.487080964
chr15,80828906,80829107,HSE277,17.6534731911
chr15,81616460,81616661,HSE101,37.8792330626
chr15,81861640,81861840,HSE987,12.411938064
chr15,81868202,81868403,HSE587,14.0269438152
chr15,82201705,82201905,HSE376,27.8796005059
chr15,86374626,86374826,HSE588,14.9950787059
chr15,88418444,88418645,HSE580,12.4183759864
chr15,88670015,88670215,HSE521,11.1843930949
chr15,92692436,92692637,HSE26,72.0593711439
chr15,98775365,98775565,HSE381,14.9440294085
chr15,102501629,102501829,HSE592,17.8647492194
chr16,626697,626897,HSE208,23.3415017382
chr16,970811,971011,HSE408,18.879805744
chr16,3137379,3137579,HSE656,13.1191220261
chr16,11114848,11115048,HSE998,11.8718870193
chr16,11173789,11173989,HSE128,39.6118207734
chr16,19756698,19756898,HSE349,18.1890890859
chr16,22776292,22776492,HSE631,10.2281468082
chr16,23037379,23037579,HSE661,11.5338091882
chr16,24508521,24508721,HSE731,10.9292976321
chr16,27323049,27323272,HSE62,34.6319360162
chr16,30204374,30204574,HSE426,34.9445834473
chr16,31885117,31885317,HSE805,17.0726534038
chr16,48565689,48565889,HSE914,14.4461848582
chr16,52484957,52485157,HSE999,9.9367824367
chr16,56581581,56581781,HSE386,14.3620121795
chr16,59736519,59736719,HSE342,19.0406902327
chr16,66545956,66546156,HSE905,9.2931748668
chr16,68484705,68484905,HSE218,20.7512770405
chr16,68507748,68507957,HSE548,18.0413570871
chr16,73322142,73322343,HSE702,12.5680470461
chr16,78355884,78356084,HSE303,20.3741349926
chr16,89307265,89307465,HSE18,70.9382862202
chr16,89679724,89679939,HSE578,17.1619789211
chr16,89988292,89988493,HSE347,20.0128724894
chr17,999331,999532,HSE482,15.5034626446
chr17,5579439,5579642,HSE48,43.9178228518
chr17,6111243,6111443,HSE198,30.4698391934
chr17,8279924,8280124,HSE45,65.9896646322
chr17,8759325,8759525,HSE422,15.0545562099
chr17,9340019,9340219,HSE948,9.8219471135
chr17,11166409,11166610,HSE122,22.963059202
chr17,12830908,12831108,HSE145,27.0976894901
chr17,13298874,13299074,HSE156,34.5395597863
chr17,17002215,17002415,HSE654,16.2936251743
chr17,17302370,17302570,HSE372,29.9282249338
chr17,21030152,21030352,HSE903,14.116644535
chr17,21367528,21367728,HSE833,9.7516907593
chr17,30088186,30088386,HSE678,13.3589311568
chr17,33774498,33774698,HSE818,9.4607428819
chr17,34541456,34541656,HSE553,11.9696962671
chr17,42385237,42385437,HSE149,30.3729032338
chr17,43419034,43419235,HSE437,27.2450803684
chr17,45681996,45682196,HSE438,15.031012053
chr17,49008988,49009188,HSE538,15.8789561003
chr17,49433023,49433224,HSE11,58.5889197596
chr17,49687897,49688097,HSE413,16.1529684506
chr17,49807832,49808032,HSE2,84.3204244342
chr17,50624462,50624662,HSE20,42.9975194309
chr17,50767466,50767666,HSE258,24.1676282047
chr17,53412056,53412256,HSE243,26.149501739
chr17,53828393,53828601,HSE617,14.5154098498
chr17,55785272,55785472,HSE892,10.3430074886
chr17,57998741,57998941,HSE978,10.1861279752
chr17,59327373,59327573,HSE774,9.3428992552
chr17,59589126,59589326,HSE529,18.5934605421
chr17,62254441,62254641,HSE133,33.7747197413
chr17,64370576,64370776,HSE964,7.825535696
chr17,72113540,72113740,HSE607,21.5911019025
chr17,72967433,72967633,HSE377,16.2493907656
chr17,73966346,73966547,HSE881,9.5338664732
chr17,75125788,75125988,HSE407,21.4349393235
chr17,75279445,75279646,HSE527,11.5655867334
chr17,79050003,79050203,HSE445,15.299617834
chr17,80189664,80189864,HSE957,8.575676987
chr18,2847905,2848105,HSE680,16.1823388257
chr18,3297314,3297514,HSE51,55.4969408846
chr18,4107882,4108082,HSE756,10.6509843928
chr18,4455211,4455413,HSE173,26.658560462
chr18,5960317,5960517,HSE404,17.5337872123
chr18,6610590,6610790,HSE5,67.4765594962
chr18,6878970,6879170,HSE21,114.4591803017
chr18,6941554,6941754,HSE925,8.8831899962
chr18,8067140,8067340,HSE515,15.8060479397
chr18,8982773,8982973,HSE674,10.8336659886
chr18,8991089,8991289,HSE871,9.2212572719
chr18,11092726,11092926,HSE603,14.5685707705
chr18,20949254,20949454,HSE940,10.1089249342
chr18,22006557,22006757,HSE575,21.5810391792
chr18,24341571,24341772,HSE865,12.220860758
chr18,30186665,30186865,HSE96,29.1942230497
chr18,33597390,33597591,HSE935,14.2979081707
chr18,38116709,38116909,HSE94,27.5166517092
chr18,38614509,38614709,HSE516,22.9528532124
chr18,41456364,41456564,HSE483,22.2977384345
chr18,43802028,43802228,HSE508,12.8490635247
chr18,46386532,46386733,HSE676,14.0371357841
chr18,47441756,47441956,HSE282,17.402175324
chr18,48344045,48344245,HSE640,26.5533837237
chr18,57865414,57865614,HSE637,12.1799986999
chr18,60810573,60810773,HSE712,10.7941794086
chr18,66106367,66106567,HSE78,34.0539230971
chr18,67067626,67067826,HSE748,20.3756153543
chr18,67206189,67206389,HSE707,14.8194102675
chr18,77862401,77862601,HSE778,8.9773592552
chr19,1855466,1855666,HSE615,21.0205633353
chr19,7864445,7864654,HSE420,22.2334775436
chr19,7936518,7936718,HSE1000,9.890269251
chr19,14134468,14134668,HSE552,19.247739132
chr19,16568459,16568659,HSE690,12.8248560895
chr19,22053766,22053966,HSE325,15.1569835215
chr19,28284760,28284962,HSE35,44.2571129827
chr19,28609278,28609478,HSE262,25.334253518
chr19,28660492,28660692,HSE644,23.3221045263
chr19,30811887,30812087,HSE120,27.7702458019
chr19,35800455,35800659,HSE498,13.5519561083
chr19,36792015,36792215,HSE953,7.5220582555
chr19,39122462,39122662,HSE582,17.8131880246
chr19,39167150,39167350,HSE395,15.4118441749
chr19,42905988,42906189,HSE934,12.7825653114
chr19,43059635,43059838,HSE535,11.755421727
chr19,44711537,44711737,HSE798,18.193542685
chr19,50175593,50175793,HSE781,13.7950661987
chr19,51289242,51289442,HSE917,9.3858171773
chr19,51900838,51901038,HSE453,18.6017463436
chr19,53039209,53039409,HSE463,19.5962420571
chr2,4564065,4564265,HSE689,11.0832062757
chr2,9136176,9136376,HSE166,25.108740724
chr2,11761490,11761690,HSE955,9.1404770745
chr2,14359777,14359977,HSE729,9.1222131108
chr2,15208167,15208367,HSE751,11.003441743
chr2,17699479,17699679,HSE759,12.4462128012
chr2,17721672,17721872,HSE742,14.1970571736
chr2,20024086,20024286,HSE83,28.7067393754
chr2,23725997,23726197,HSE449,24.6822465458
chr2,25752130,25752330,HSE346,16.6877757619
chr2,29434845,29435045,HSE841,9.2654187933
chr2,29840944,29841144,HSE785,11.1516309255
chr2,33308933,33309133,HSE810,10.6180361689
chr2,40274443,40274643,HSE543,16.5775325601
chr2,40277040,40277240,HSE901,17.7522146515
chr2,43020786,43020986,HSE536,13.7021711702
chr2,43525771,43525971,HSE140,23.6931498342
chr2,45907824,45908024,HSE790,12.332179446
chr2,49482822,49483023,HSE764,11.6109483119
chr2,49776850,49777050,HSE382,20.4425087695
chr2,49829759,49829959,HSE559,13.8791198577
chr2,50199950,50200150,HSE306,21.1830037687
chr2,50522255,50522455,HSE294,20.4771502209
chr2,50573272,50573472,HSE520,11.3849059039
chr2,53710769,53710969,HSE673,11.4462899835
chr2,54846090,54846291,HSE652,12.3643734532
chr2,55381344,55381548,HSE333,16.5384641969
chr2,55649064,55649265,HSE161,26.4627203219
chr2,65290575,65290775,HSE860,14.2637154595
chr2,65615621,65615821,HSE811,10.640516431
chr2,68546802,68547002,HSE585,12.806795592
chr2,72975882,72976082,HSE506,11.8250162965
chr2,74234353,74234554,HSE447,19.3111885875
chr2,76692583,76692783,HSE308,24.5495576527
chr2,84914260,84914460,HSE273,18.7931730309
chr2,85289844,85290044,HSE490,17.6255300394
chr2,85378807,85379007,HSE442,18.4954416724
chr2,87051461,87051665,HSE139,28.4199186304
chr2,87064810,87065010,HSE708,10.458886264
chr2,87067122,87067449,HSE379,30.0280259634
chr2,87069310,87069510,HSE885,16.1725172352
chr2,88316314,88316515,HSE214,34.3179189629
chr2,88414573,88414773,HSE220,18.6297613359
chr2,89034794,89034994,HSE641,10.882267071
chr2,91750589,91750877,HSE327,20.2352669039
chr2,91762380,91762580,HSE468,12.6786942321
chr2,97019268,97019468,HSE786,8.3601047373
chr2,98758986,98759186,HSE679,16.1171102076
chr2,100697871,100698071,HSE706,13.479967802
chr2,100699949,100700149,HSE163,38.236433871
chr2,104094111,104094311,HSE59,33.1831421211
chr2,106095656,106095858,HSE390,15.6220351252
chr2,106554244,106554444,HSE497,18.2333367245
chr2,109968022,109968222,HSE763,10.4860988331
chr2,110515113,110515315,HSE19,48.1747064465
chr2,110806206,110806406,HSE977,8.2999349047
chr2,111400651,111400851,HSE808,9.0427754883
chr2,112020360,112020560,HSE444,14.0773788267
chr2,112221956,112222156,HSE720,15.6782160603
chr2,114395544,114395744,HSE67,33.2899481068
chr2,119068189,119068389,HSE104,27.8409422337
chr2,119071047,119071247,HSE25,58.9652879287
chr2,121088717,121088917,HSE726,14.2115751447
chr2,122336997,122337197,HSE249,20.8047512788
chr2,124279519,124279719,HSE495,11.9685617057
chr2,124730178,124730378,HSE30,48.1797064921
chr2,131094775,131094975,HSE988,12.5951506631
chr2,131484996,131485196,HSE76,33.2819734412
chr2,133300942,133301142,HSE997,7.8669044953
chr2,134090771,134090971,HSE180,24.5244184665
chr2,137008268,137008468,HSE307,16.6788740279
chr2,137015528,137015728,HSE947,7.5774875118
chr2,142934541,142934741,HSE345,21.8622427277
chr2,150036853,150037053,HSE807,12.9999546517
chr2,165778793,165778993,HSE358,17.0239953486
chr2,177391858,177392058,HSE260,19.1403997668
chr2,182327537,182327737,HSE681,17.5160573028
chr2,182393251,182393451,HSE40,38.4626716079
chr2,194117851,194118051,HSE321,18.0633419546
chr2,202563650,202563850,HSE626,12.3289416007
chr2,202741843,202742043,HSE359,20.9626551138
chr2,207294294,207294494,HSE670,11.1162813392
chr2,210363875,210364075,HSE734,12.3968615154
chr2,216306316,216306516,HSE868,14.1928417965
chr2,217522315,217522515,HSE293,35.0627763697
chr2,218641534,218641734,HSE82,37.7257060365
chr2,225243534,225243734,HSE416,20.099420911
chr2,229706577,229706777,HSE943,10.1343101996
chr2,231797519,231797723,HSE719,12.2696761634
chr2,232478862,232479063,HSE856,8.5861916221
chr2,236011384,236011584,HSE496,11.7277379839
chr2,238512362,238512563,HSE608,13.5849351928
chr2,240078429,240078629,HSE174,24.7169603423
chr2,241308943,241309143,HSE590,12.5097816417
chr2,241406758,241406958,HSE727,15.8620577497
chr20,1791874,1792074,HSE487,14.0309392186
chr20,1864025,1864225,HSE950,9.0977150918
chr20,1871245,1871445,HSE16,56.7490572952
chr20,2012791,2012991,HSE87,31.037092624
chr20,3629754,3629958,HSE215,20.9130441286
chr20,3660554,3660754,HSE193,24.974343769
chr20,4063903,4064104,HSE111,56.1553976804
chr20,14705232,14705432,HSE33,47.5410662718
chr20,16719908,16720108,HSE270,16.3543605271
chr20,32774062,32774262,HSE309,18.1246253801
chr20,34463418,34463619,HSE367,15.2757490918
chr20,34681486,34681692,HSE753,9.3728031841
chr20,34961484,34961684,HSE355,27.5542266393
chr20,36875508,36875708,HSE12,88.6429013544
chr20,39492043,39492243,HSE616,13.836833653
chr20,39504403,39504603,HSE791,11.0391746143
chr20,39522366,39522566,HSE522,15.4476576681
chr20,41278899,41279099,HSE130,24.1052112576
chr20,41281047,41281248,HSE138,25.1054094538
chr20,43678567,43678767,HSE118,27.5802502403
chr20,47354941,47355141,HSE829,10.0084428
chr20,48196544,48196744,HSE13,62.2017999936
chr20,48391843,48392070,HSE343,17.2886280147
chr20,48451672,48451872,HSE658,15.2435487162
chr20,57999712,57999912,HSE593,11.851459255
chr20,58010717,58010917,HSE888,8.4178547638
chr20,58028998,58029198,HSE589,11.8594187455
chr20,62892549,62892749,HSE22,49.3467939523
chr21,18183390,18183590,HSE846,12.0921264014
chr21,25470304,25470504,HSE848,9.043990116
chr21,29314093,29314293,HSE540,11.1333587451
chr21,29522195,29522395,HSE90,42.5669364456
chr21,30279211,30279411,HSE802,8.9817364292
chr21,31599287,31599487,HSE255,25.8534564105
chr21,34305184,34305384,HSE75,34.8701267573
chr21,40357115,40357316,HSE931,14.2521728213
chr21,40369917,40370117,HSE596,13.6512982833
chr21,42458559,42458759,HSE941,7.986093663
chr21,42461230,42461430,HSE46,32.6233218231
chr21,45610526,45610726,HSE373,19.1339980285
chr22,18647319,18647519,HSE600,15.4170005372
chr22,21387111,21387311,HSE465,16.7936897957
chr22,25800344,25800546,HSE855,15.133649847
chr22,27530972,27531172,HSE91,32.8296818218
chr22,27613121,27613321,HSE878,9.9026827262
chr22,30938555,30938755,HSE991,12.3953908195
chr22,37608742,37608942,HSE199,25.5079108823
chr22,38213774,38213975,HSE336,29.8698643259
chr22,38875187,38875387,HSE31,75.0968010157
chr22,45034999,45035200,HSE944,11.0058314418
chr22,47560535,47560735,HSE820,9.6159416626
chr22,49263785,49263985,HSE123,27.4756435262
chr3,1033708,1033908,HSE547,11.8167271684
chr3,4648618,4648819,HSE594,13.1354478948
chr3,6170925,6171125,HSE274,19.6422205175
chr3,9861617,9861817,HSE561,16.1351899583
chr3,20581162,20581362,HSE88,84.6550501758
chr3,22766006,22766206,HSE926,7.4746803691
chr3,25039886,25040088,HSE745,12.5677664568
chr3,32452491,32452691,HSE958,8.8781271039
chr3,34337067,34337267,HSE238,23.4517631822
chr3,44106796,44106996,HSE645,14.6078435944
chr3,45454944,45455144,HSE213,19.0172183728
chr3,50468482,50468682,HSE70,32.3982070336
chr3,50553584,50553784,HSE409,31.7674822371
chr3,50759518,50759718,HSE556,12.7286106999
chr3,51312927,51313129,HSE179,22.7291813917
chr3,54024155,54024359,HSE606,12.4118581011
chr3,55583333,55583544,HSE895,20.4730234134
chr3,57432272,57432472,HSE170,23.9015532829
chr3,57731978,57732178,HSE979,13.0317416952
chr3,58143711,58143912,HSE488,21.4279391783
chr3,59117232,59117432,HSE310,21.6692936097
chr3,59537533,59537733,HSE831,10.4335147498
chr3,60313490,60313690,HSE797,11.4508642018
chr3,60739661,60739861,HSE222,37.1649368806
chr3,61899965,61900165,HSE972,9.9582764633
chr3,62859461,62859661,HSE671,12.0850192866
chr3,62874394,62874594,HSE454,12.2791253714
chr3,63748080,63748280,HSE387,14.5767368616
chr3,74103231,74103431,HSE633,15.5075880796
chr3,85652655,85652855,HSE203,18.8184809601
chr3,97829189,97829389,HSE776,9.1102578423
chr3,103166013,103166213,HSE896,9.9523912683
chr3,105602785,105602985,HSE433,40.5785957197
chr3,107148262,107148462,HSE261,18.4043131499
chr3,109288204,109288404,HSE356,14.9310679943
chr3,111197315,111197518,HSE370,15.859503117
chr3,112219616,112219816,HSE368,15.0704271372
chr3,123405019,123405220,HSE115,27.2314354098
chr3,124880427,124880628,HSE579,14.8608441809
chr3,127740739,127740949,HSE314,26.0837216172
chr3,128256027,128256227,HSE630,13.9854481485
chr3,132829089,132829289,HSE474,15.9443427668
chr3,133123857,133124057,HSE471,13.3464634396
chr3,136537878,136538084,HSE505,14.6728277902
chr3,136915437,136915640,HSE584,13.4830219375
chr3,136922639,136922839,HSE304,16.1133399857
chr3,143048781,143048981,HSE663,12.6197529914
chr3,145155998,145156198,HSE963,7.937403129
chr3,150102651,150102851,HSE441,27.9319328699
chr3,152314041,152314241,HSE863,9.389594265
chr3,152446287,152446487,HSE202,21.6487393195
chr3,152497432,152497632,HSE782,15.2232552726
chr3,153501047,153501247,HSE973,8.9058148252
chr3,156106412,156106612,HSE298,17.4725292847
chr3,156467111,156467311,HSE984,12.8714197176
chr3,159583162,159583362,HSE245,24.3670741479
chr3,167647788,167647988,HSE162,23.142026511
chr3,167754605,167754805,HSE157,25.5019260862
chr3,168309609,168309809,HSE985,8.3878224111
chr3,170459966,170460166,HSE299,23.2572944826
chr3,171083365,171083565,HSE329,19.6170760846
chr3,171622769,171622969,HSE141,35.5269780554
chr3,173303292,173303492,HSE635,12.8762248581
chr3,173340053,173340253,HSE910,13.8353115715
chr3,175722586,175722786,HSE154,26.1510590855
chr3,177471354,177471554,HSE332,24.5244184665
chr3,184084866,184085066,HSE428,17.9640764471
chr3,192869895,192870095,HSE246,22.5875292012
chr3,194592300,194592501,HSE362,36.8838467218
chr3,196346498,196346699,HSE280,19.4679975855
chr3,197199171,197199371,HSE876,13.6753444396
chr4,1209278,1209519,HSE564,13.9004104602
chr4,1682431,1682632,HSE233,22.8144061287
chr4,2673293,2673493,HSE435,15.7039657822
chr4,5205616,5205843,HSE875,9.1366703212
chr4,5774976,5775176,HSE248,21.1824928836
chr4,7153027,7153227,HSE983,9.7283332501
chr4,7158073,7158273,HSE164,31.8768175016
chr4,7987860,7988060,HSE685,14.8308227861
chr4,8317712,8317912,HSE912,11.2750910314
chr4,10298688,10298888,HSE7,56.3001544011
chr4,10307533,10307733,HSE478,12.8133414228
chr4,10410626,10410826,HSE197,21.3709305662
chr4,16325714,16325914,HSE351,19.7714522278
chr4,16843354,16843554,HSE872,11.1479071197
chr4,17189915,17190115,HSE794,12.4374877889
chr4,17399671,17399871,HSE698,12.9259482624
chr4,18421836,18422036,HSE418,18.6650288814
chr4,20654001,20654201,HSE842,9.8868805638
chr4,35935735,35935935,HSE864,11.6965490821
chr4,37141687,37141887,HSE384,14.5996824189
chr4,37189108,37189308,HSE188,35.9761823699
chr4,38192503,38192704,HSE513,20.6670228424
chr4,41059770,41059970,HSE550,14.2940364222
chr4,41146900,41147100,HSE920,10.9173925032
chr4,42598985,42599185,HSE834,12.2213134666
chr4,43551753,43551953,HSE613,10.9664468122
chr4,52918093,52918293,HSE813,10.8199181309
chr4,67296659,67296859,HSE330,17.1184863176
chr4,68783414,68783614,HSE194,27.8865779207
chr4,73531199,73531399,HSE485,16.0641880211
chr4,79442149,79442349,HSE254,31.3053467034
chr4,96027806,96028006,HSE60,38.0420908281
chr4,98417455,98417655,HSE773,12.7094671878
chr4,102826820,102827020,HSE954,9.8953082642
chr4,108956348,108956548,HSE196,28.1854857987
chr4,109035842,109036042,HSE574,15.4815826792
chr4,109331833,109332033,HSE880,8.5800439974
chr4,112855557,112855758,HSE873,8.9177752517
chr4,113045048,113045248,HSE718,12.9338359083
chr4,115673675,115673875,HSE908,9.0213984225
chr4,126033815,126034015,HSE266,27.8144026005
chr4,126926133,126926333,HSE624,13.8939469933
chr4,134018135,134018335,HSE591,13.1426859966
chr4,135498284,135498484,HSE289,16.9047451361
chr4,139144837,139145037,HSE858,11.0683425726
chr4,142822079,142822279,HSE291,16.8719939446
chr4,143883214,143883420,HSE125,23.7564028153
chr4,155022297,155022497,HSE486,24.4008504204
chr4,155876015,155876219,HSE567,14.3792053309
chr4,156523530,156523730,HSE335,17.9101352191
chr4,163926681,163926881,HSE583,13.7123935
chr4,165404029,165404229,HSE965,10.3169505717
chr4,171168842,171169042,HSE500,12.3708601884
chr4,176356648,176356848,HSE586,12.5175850781
chr4,181756449,181756649,HSE116,25.0123586931
chr4,185517517,185517717,HSE768,10.7243756024
chr4,186037200,186037400,HSE849,9.15728802
chr4,187112549,187112749,HSE73,29.6147089784
chr4,188809131,188809331,HSE127,26.3736706202
chr5,9015815,9016015,HSE754,10.9860464939
chr5,9140785,9140986,HSE34,39.8342153912
chr5,9284766,9284966,HSE159,44.8345695381
chr5,9423793,9423993,HSE397,23.9014518508
chr5,9544694,9544894,HSE976,18.4787378901
chr5,13572514,13572715,HSE970,7.981996643
chr5,14844977,14845178,HSE623,11.8533435499
chr5,26843843,26844043,HSE609,10.9929164023
chr5,35374800,35375000,HSE992,11.2155834064
chr5,36059728,36059928,HSE743,9.7015183684
chr5,38011944,38012144,HSE182,23.9400859933
chr5,39421419,39421619,HSE825,14.5866459668
chr5,39533959,39534159,HSE760,9.9360929783
chr5,43066506,43066706,HSE288,64.294192312
chr5,50917138,50917340,HSE313,22.7435698695
chr5,51008092,51008292,HSE766,10.2876577874
chr5,58130163,58130363,HSE686,14.7084178919
chr5,68296916,68297116,HSE57,43.9761647554
chr5,70529394,70529594,HSE292,16.3167607327
chr5,73557824,73558024,HSE283,26.1537486394
chr5,81951100,81951300,HSE838,11.1124928906
chr5,82305319,82305520,HSE256,21.9987593589
chr5,93459233,93459433,HSE79,88.077239222
chr5,96180256,96180457,HSE112,28.97133338
chr5,98859253,98859453,HSE457,16.1598314579
chr5,102729064,102729264,HSE89,35.4121997783
chr5,107225179,107225379,HSE752,12.8723311418
chr5,108006706,108006906,HSE167,20.5736395644
chr5,112562162,112562362,HSE146,28.4736858728
chr5,115731439,115731639,HSE662,14.441740722
chr5,115870785,115870985,HSE889,11.1682472441
chr5,121403180,121403380,HSE738,10.4404525204
chr5,123709637,123709837,HSE385,34.5371219842
chr5,124534387,124534587,HSE189,41.4113583783
chr5,126015956,126016156,HSE350,48.9260098909
chr5,130651056,130651256,HSE353,19.9406119279
chr5,131054387,131054588,HSE275,16.8672736168
chr5,138872592,138872792,HSE97,33.7294648775
chr5,139113346,139113546,HSE499,24.5461507056
chr5,139503491,139503691,HSE135,22.5832471238
chr5,140365894,140366094,HSE477,22.7804027056
chr5,152496425,152496625,HSE297,23.9517433229
chr5,153157431,153157632,HSE994,7.2712465887
chr5,154985156,154985356,HSE27,47.3698229496
chr5,156798061,156798261,HSE421,18.0953061141
chr5,159614141,159614341,HSE504,16.644367563
chr5,169678151,169678351,HSE205,23.5872306039
chr5,169969996,169970196,HSE252,26.7061548422
chr5,170556363,170556585,HSE81,53.3489093817
chr5,170880119,170880319,HSE533,21.0213665516
chr5,176794222,176794423,HSE599,23.2425155735
chr5,177712082,177712283,HSE921,9.6878527385
chr6,1720956,1721156,HSE961,13.9546110024
chr6,2908962,2909162,HSE900,9.4385437272
chr6,3624882,3625082,HSE49,38.6547390654
chr6,6477697,6477897,HSE425,14.0308308131
chr6,6927996,6928198,HSE573,13.2644125542
chr6,7261536,7261736,HSE899,20.57594766
chr6,8857485,8857685,HSE226,19.7424552164
chr6,11764188,11764388,HSE423,13.6453117051
chr6,12657164,12657364,HSE541,24.5587040398
chr6,13069178,13069378,HSE148,25.4358868167
chr6,13071666,13071866,HSE328,21.7306804565
chr6,13440803,13441003,HSE854,11.7878221637
chr6,13605303,13605503,HSE439,16.0336294076
chr6,15357986,15358186,HSE772,12.1002596677
chr6,15953729,15953929,HSE223,22.2692616975
chr6,20879847,20880047,HSE789,11.3511396601
chr6,22074016,22074216,HSE830,17.8828299952
chr6,22223835,22224035,HSE847,11.3189534245
chr6,22754318,22754518,HSE765,10.933963726
chr6,24191674,24191874,HSE17,64.4494432065
chr6,24355724,24355924,HSE822,12.2330307591
chr6,24684045,24684245,HSE68,40.6121039818
chr6,24908692,24908892,HSE52,36.3357402285
chr6,24981608,24981808,HSE460,14.7455299562
chr6,26866614,26866814,HSE316,17.4817827982
chr6,26987817,26988017,HSE301,17.0996119934
chr6,28953271,28953471,HSE532,18.2243327433
chr6,30163047,30163247,HSE668,9.9208283821
chr6,32862428,32862628,HSE883,10.9304672886
chr6,33048550,33048750,HSE100,28.3569655356
chr6,37190063,37190263,HSE225,25.1598854782
chr6,37752532,37752732,HSE190,21.4887428904
chr6,37839872,37840072,HSE692,15.9827010242
chr6,42104428,42104628,HSE107,67.5217188687
chr6,42125896,42126096,HSE836,11.8802206566
chr6,48152098,48152298,HSE317,16.53498743
chr6,48440896,48441096,HSE410,20.2304692131
chr6,48531624,48531824,HSE14,56.1553976804
chr6,48595126,48595327,HSE8,86.3398854446
chr6,57207153,57207353,HSE779,15.3463418546
chr6,58265076,58265285,HSE632,13.3681109557
chr6,68599806,68600006,HSE526,12.8237395122
chr6,71584109,71584310,HSE114,27.0742020553
chr6,77915769,77915969,HSE236,16.9378739354
chr6,87169783,87169984,HSE546,11.573392587
chr6,89894075,89894277,HSE647,13.1942619616
chr6,90904900,90905100,HSE491,25.8304194594
chr6,92350596,92350796,HSE653,17.364572389
chr6,101840815,101841015,HSE131,28.2436702832
chr6,105240470,105240670,HSE604,18.8779663264
chr6,107221861,107222061,HSE844,13.1640641167
chr6,107611759,107611959,HSE904,13.5023269104
chr6,112619369,112619569,HSE369,17.0366032561
chr6,114999140,114999341,HSE821,10.3959314865
chr6,116138273,116138476,HSE730,13.0350910622
chr6,116621676,116621877,HSE929,9.2537035432
chr6,131428060,131428260,HSE284,18.8889226922
chr6,132596060,132596260,HSE990,8.2742348935
chr6,138104942,138105142,HSE253,23.0468563465
chr6,140757614,140757815,HSE967,10.1788738588
chr6,143968529,143968729,HSE852,8.5873299746
chr6,147322278,147322478,HSE268,19.7936739006
chr6,151695336,151695536,HSE519,19.0160524829
chr6,157265666,157265866,HSE119,24.7735526841
chr6,161392326,161392526,HSE334,18.7149756358
chr6,161740337,161740537,HSE713,9.6675137739
chr6,162838242,162838442,HSE132,26.1781329147
chr6,166188920,166189120,HSE417,14.5239466546
chr6,167892584,167892784,HSE814,8.7865263368
chr6,169077927,169078127,HSE110,27.1403888015
chr6,170794092,170794292,HSE524,13.6109134035
chr7,4198833,4199033,HSE801,11.1292518601
chr7,4949917,4950117,HSE172,21.4927855963
chr7,13461438,13461638,HSE228,17.5121934963
chr7,22122623,22122823,HSE930,12.3048991783
chr7,23062178,23062378,HSE509,25.1202244572
chr7,23246200,23246400,HSE257,22.9981606665
chr7,25559731,25559931,HSE966,8.5262161829
chr7,36664999,36665199,HSE605,16.0994530522
chr7,37372863,37373063,HSE204,19.2684485089
chr7,38492364,38492564,HSE597,12.8932215725
chr7,40616066,40616266,HSE380,17.182635963
chr7,47064486,47064686,HSE577,13.0570408987
chr7,47084856,47085056,HSE576,16.6838216652
chr7,48031471,48031671,HSE740,11.7452011534
chr7,48748205,48748405,HSE98,30.5044348782
chr7,66050120,66050320,HSE276,21.6755649273
chr7,66213153,66213353,HSE534,13.7182502443
chr7,66223024,66223224,HSE364,16.185690078
chr7,74867660,74867860,HSE618,16.9538639948
chr7,77015807,77016008,HSE415,18.6613812869
chr7,78390011,78390211,HSE602,16.2431492916
chr7,100434184,100434384,HSE869,13.0394840372
chr7,101616503,101616703,HSE951,9.2324134965
chr7,102153402,102153602,HSE975,9.0245761817
chr7,110397521,110397721,HSE916,12.3054838817
chr7,110400563,110400763,HSE338,16.3230063175
chr7,116607477,116607678,HSE621,18.3522646727
chr7,118072236,118072438,HSE694,11.1158534833
chr7,125587404,125587604,HSE455,17.556648341
chr7,145100201,145100414,HSE401,15.6280338065
chr7,145839272,145839472,HSE171,20.1680033829
chr7,145844066,145844266,HSE9,54.1909479525
chr7,147449071,147449271,HSE704,10.2463146144
chr7,148178409,148178609,HSE835,10.6172870294
chr7,151235436,151235636,HSE365,18.0341069228
chr8,2377506,2377706,HSE181,20.7659351902
chr8,2511126,2511326,HSE320,15.27069181
chr8,7266669,7266869,HSE819,8.435349452
chr8,13356648,13356848,HSE281,18.4464980795
chr8,14336890,14337104,HSE66,46.656249178
chr8,23032626,23032826,HSE412,26.2529950816
chr8,26601555,26601755,HSE890,11.220125934
chr8,26628311,26628511,HSE788,10.9551178433
chr8,28009163,28009363,HSE510,15.0235522925
chr8,30385906,30386106,HSE629,17.1134130395
chr8,34312488,34312688,HSE178,30.2724339491
chr8,49930877,49931077,HSE507,18.1489727778
chr8,50149648,50149848,HSE518,13.7760589052
chr8,52875070,52875270,HSE501,15.5123958666
chr8,53660034,53660234,HSE93,37.3464806395
chr8,54605619,54605820,HSE684,14.2519866729
chr8,62892804,62893004,HSE121,31.87775364
chr8,73254670,73254870,HSE185,23.4900882124
chr8,73412628,73412828,HSE38,37.3035911391
chr8,75862050,75862250,HSE620,13.858825618
chr8,88877867,88878069,HSE563,12.7713866408
chr8,92115315,92115515,HSE530,14.8449505644
chr8,92248603,92248804,HSE691,15.9124555229
chr8,93852146,93852346,HSE290,32.7317983798
chr8,95213282,95213482,HSE494,13.4997136969
chr8,98387996,98388197,HSE628,12.1439641326
chr8,105236101,105236301,HSE969,9.7479394792
chr8,115779521,115779721,HSE688,12.8255728825
chr8,122167781,122167981,HSE371,17.391031161
chr8,125610826,125611026,HSE165,35.3095256409
chr8,131652848,131653048,HSE363,19.456186422
chr8,134045654,134045854,HSE168,31.8082809938
chr8,134829150,134829350,HSE646,10.9003096576
chr8,144094801,144095001,HSE717,15.3147108573
chr8,144343458,144343659,HSE72,32.0323018157
chr8,145987786,145988059,HSE451,17.6753394576
chr9,7510288,7510488,HSE312,33.7426439062
chr9,10593086,10593286,HSE414,16.6788740279
chr9,11458448,11458648,HSE843,9.0784819552
chr9,12117105,12117305,HSE142,27.950209127
chr9,14993242,14993442,HSE634,14.4731722458
chr9,16301282,16301482,HSE102,50.9103305769
chr9,17765448,17765655,HSE739,18.2392565514
chr9,20825252,20825452,HSE946,12.4789871155
chr9,33402443,33402643,HSE555,16.1608716083
chr9,33523607,33523818,HSE224,19.2005783714
chr9,34170779,34170979,HSE341,24.6371580044
chr9,41824106,41824306,HSE769,10.5181109381
chr9,42019392,42019592,HSE28,41.4263636489
chr9,44245346,44245546,HSE32,37.5468627247
chr9,46661510,46661710,HSE378,18.5997971629
chr9,67872401,67872601,HSE272,19.7212214429
chr9,69651218,69651418,HSE15,56.5573849515
chr9,69654841,69655041,HSE50,50.1850231356
chr9,69723884,69724084,HSE126,32.6894006406
chr9,70077265,70077469,HSE357,19.5503409497
chr9,70090005,70090205,HSE56,47.863549167
chr9,72158059,72158265,HSE736,10.8002448051
chr9,72749929,72750129,HSE667,9.6151470352
chr9,81641307,81641507,HSE565,12.5343408216
chr9,85896509,85896709,HSE360,15.976780491
chr9,87992926,87993126,HSE682,10.0728691654
chr9,91288879,91289079,HSE570,20.1264215633
chr9,93232164,93232364,HSE267,16.2689597218
chr9,97610366,97610566,HSE571,14.362464282
chr9,97933380,97933580,HSE660,11.3259473431
chr9,100000625,100000826,HSE473,16.0316421967
chr9,100937326,100937526,HSE502,14.7363484454
chr9,112871691,112871891,HSE427,20.2200008236
chr9,116605468,116605668,HSE24,44.0402200911
chr9,118163144,118163344,HSE10,116.5676393698
chr9,136738864,136739065,HSE828,21.7259174217
chr9,136826692,136826892,HSE823,8.8396928196
chr9,138884709,138884909,HSE749,21.506795832
chr9,140320110,140320310,HSE558,17.6159627193
chrX,191631,191831,HSE375,22.299626784
chrX,197891,198101,HSE318,51.9353250981
chrX,199983,200184,HSE711,18.1602756116
chrX,9247138,9247338,HSE915,8.9889411466
chrX,9294553,9294753,HSE636,15.8583274764
chrX,13467472,13467672,HSE625,13.3123255447
chrX,22410208,22410409,HSE982,9.6853480348
chrX,29231637,29231837,HSE610,12.7761366615
chrX,30489623,30489824,HSE137,24.5548014378
chrX,31825117,31825317,HSE99,25.9398284883
chrX,36439734,36439934,HSE323,16.5354248431
chrX,36467884,36468084,HSE402,16.6156581564
chrX,40432956,40433156,HSE933,11.0828028555
chrX,40810144,40810344,HSE106,48.7539914758
chrX,49953599,49953799,HSE295,25.7542688302
chrX,57163623,57163823,HSE503,22.7827962324
chrX,114733851,114734051,HSE986,7.9927176453
chrX,124903682,124903882,HSE436,12.6648421132
chrX,128211486,128211686,HSE815,14.3611432709
chrX,128270781,128270981,HSE952,10.1358588237
chrX,130964620,130964822,HSE980,11.9103854587


================================================
FILE: intro_awk_spring2021/data/example.fastq
================================================
@ERR117184_2.14044282
CTGGTGCTCAGTCATTTTGCTAGATTGTAGCTCACCATTGCCTCTCTGCCTGCATTGTGCACTCCAATCCCTCAGCAGCTACAAAAACACTTTGTCAACTCCAGATCGG
+
IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII
@ERR117185_2.23793537
TTGTTCAATTTTTTTAGTAGTTTTAGAGTAATTAATGAGCTTTGAGGTCACTTAACAATAAAAGCATATTTTAAAGTAAAGGTTGCTGATCAACAGTCTAATTTTCATA
+
IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII
@ERR117166_2.4649205
ACTTAACAATAAAAGCATATTTTAAAGTAAAGGTT
+
IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII
@ERR117185_2.949669
ATGCTTTTATTGTTAAGTGACCTCAAAGCTCATTAATTACTCTAAAACTACTAAAAAAATTGAACAAAAAAGTATTTCCAGTAATCAAGATTTTCTTCATGCCCTAGAT
+
IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII
@ERR117163_2.2292219
AAATCGTAAGTGAAGCAACAGAATTCAGAACTAAA
+
IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII
@ERR117165_2.36526615
AACGCTAAGGTTATATTAAAATGTTTCTTATGTTG
+
IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII
@ERR117174_2.23007391
GCAAGTGGTTGGACTCAAGGAAGTTATAAATACCT
+
IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII
@ERR117175_2.11345437
GCAAGTGGTTGGACTCAAGGAAGTTATAAATACCT
+
IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII
@ERR117176_2.30285510
GCAAGTGGTTGGACTCAAGGAAGTTATAAATACCT
+
IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII
@ERR117177_2.33680511
GCAAGTGGTTGGACTCAAGGAAGTTATAAATACCT
+
IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII


================================================
FILE: intro_awk_spring2021/data/hg38.genome
================================================
chrom	size
1	248956422
2	242193529
3	198295559
4	190214555
5	181538259
6	170805979
7	159345973
X	156040895
8	145138636
9	138394717
11	135086622
10	133797422
12	133275309
13	114364328
14	107043718
15	101991189
16	90338345
17	83257441
18	80373285
20	64444167
19	58617616
Y	57227415
22	50818468
21	46709983
15_KI270905v1_alt	5161414
6_GL000256v2_alt	4929269
6_GL000254v2_alt	4827813
6_GL000251v2_alt	4795265
6_GL000253v2_alt	4677643
6_GL000250v2_alt	4672374
6_GL000255v2_alt	4606388
6_GL000252v2_alt	4604811
17_KI270857v1_alt	2877074
16_KI270853v1_alt	2659700
16_KI270728v1_random	1872759
17_GL000258v2_alt	1821992
5_GL339449v2_alt	1612928
14_KI270847v1_alt	1511111
17_KI270908v1_alt	1423190
14_KI270846v1_alt	1351393
5_KI270897v1_alt	1144418
7_KI270803v1_alt	1111570
19_GL949749v2_alt	1091841
19_KI270938v1_alt	1066800
19_GL949750v2_alt	1066390
19_GL949748v2_alt	1064304
19_GL949751v2_alt	1002683
19_GL949746v1_alt	987716
19_GL949752v1_alt	987100
8_KI270821v1_alt	985506
1_KI270763v1_alt	911658
6_KI270801v1_alt	870480
19_GL949753v2_alt	796479
19_GL949747v2_alt	729520
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9_KI270823v1_alt	439082
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4_GL000008v2_random	209709
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3_KI270784v1_alt	184404
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20_KI270870v1_alt	183433
Un_GL000195v1	182896
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22_KI270736v1_random	181920
10_KI270824v1_alt	181496
14_KI270845v1_alt	180703
3_GL383526v1_alt	180671
13_KI270839v1_alt	180306
22_KI270733v1_random	179772
Un_GL000224v1	179693
10_GL383545v1_alt	179254
Un_GL000219v1	179198
5_KI270792v1_alt	179043
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19_GL000209v2_alt	177381
11_KI270830v1_alt	177092
9_KI270719v1_random	176845
Un_GL000216v2	176608
22_KI270928v1_alt	176103
1_KI270712v1_random	176043
6_KI270800v1_alt	175808
1_KI270706v1_random	175055
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14_KI270725v1_random	172810
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19_KI270919v1_alt	170701
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19_KI270891v1_alt	170680
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19_GL383575v2_alt	170222
19_KI270931v1_alt	170148
12_GL383550v2_alt	169178
13_KI270841v1_alt	169134
Un_KI270744v1	168472
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22_KI270734v1_random	165050
18_GL383570v1_alt	164789
5_KI270794v1_alt	164558
4_GL383527v1_alt	164536
Un_GL000213v1	164239
3_KI270936v1_alt	164170
3_KI270934v1_alt	163458
9_GL383539v1_alt	162988
3_KI270895v1_alt	162896
22_GL383582v2_alt	162811
3_KI270782v1_alt	162429
1_KI270892v1_alt	162212
Un_GL000220v1	161802
2_KI270767v1_alt	161578
2_KI270715v1_random	161471
2_KI270893v1_alt	161218
Un_GL000218v1	161147
18_GL383572v1_alt	159547
8_KI270817v1_alt	158983
4_KI270788v1_alt	158965
Un_KI270749v1	158759
7_KI270806v1_alt	158166
7_KI270804v1_alt	157952
18_KI270911v1_alt	157710
Un_KI270741v1	157432
17_KI270910v1_alt	157099
19_KI270884v1_alt	157053
19_GL383574v1_alt	155864
19_KI270888v1_alt	155532
3_GL000221v1_random	155397
11_GL383547v1_alt	154407
2_KI270716v1_random	153799
12_GL383553v2_alt	152874
6_KI270799v1_alt	152148
22_KI270731v1_random	150754
Un_KI270751v1	150742
Un_KI270750v1	148850
8_KI270818v1_alt	145606
X_KI270881v1_alt	144206
21_KI270873v1_alt	143900
2_GL383521v1_alt	143390
8_KI270814v1_alt	141812
12_GL383552v1_alt	138655
Un_KI270519v1	138126
2_KI270775v1_alt	138019
17_KI270907v1_alt	137721
Un_GL000214v1	137718
8_KI270901v1_alt	136959
2_KI270770v1_alt	136240
16_KI270854v1_alt	134193
8_KI270819v1_alt	133535
17_GL383564v2_alt	133151
2_KI270772v1_alt	133041
8_KI270815v1_alt	132244
5_KI270795v1_alt	131892
5_KI270898v1_alt	130957
20_GL383577v2_alt	128386
1_KI270708v1_random	127682
7_KI270807v1_alt	126434
5_KI270793v1_alt	126136
6_GL383533v1_alt	124736
2_GL383522v1_alt	123821
19_KI270918v1_alt	123111
12_GL383549v1_alt	120804
2_KI270769v1_alt	120616
4_KI270785v1_alt	119912
12_KI270834v1_alt	119498
7_GL383534v2_alt	119183
20_KI270869v1_alt	118774
21_GL383581v2_alt	116689
3_KI270781v1_alt	113034
17_KI270730v1_random	112551
Un_KI270438v1	112505
4_KI270787v1_alt	111943
18_KI270864v1_alt	111737
2_KI270771v1_alt	110395
1_GL383519v1_alt	110268
2_KI270768v1_alt	110099
1_KI270760v1_alt	109528
3_KI270783v1_alt	109187
17_KI270859v1_alt	108763
11_KI270902v1_alt	106711
18_GL383568v1_alt	104552
22_KI270737v1_random	103838
13_KI270843v1_alt	103832
22_KI270877v1_alt	101331
5_GL383530v1_alt	101241
11_KI270721v1_random	100316
22_KI270738v1_random	99375
22_GL383583v2_alt	96924
2_GL582966v2_alt	96131
Un_KI270748v1	93321
Un_KI270435v1	92983
5_GL000208v1_random	92689
Un_KI270538v1	91309
17_GL383566v1_alt	90219
16_GL383557v1_alt	89672
17_JH159148v1_alt	88070
5_GL383532v1_alt	82728
21_KI270872v1_alt	82692
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6_KI270758v1_alt	76752
12_KI270833v1_alt	76061
6_KI270802v1_alt	75005
21_GL383580v2_alt	74653
22_KB663609v1_alt	74013
22_KI270739v1_random	73985
9_GL383540v1_alt	71551
Un_KI270757v1	71251
2_KI270773v1_alt	70887
17_JH159147v1_alt	70345
11_KI270827v1_alt	67707
1_KI270709v1_random	66860
Un_KI270746v1	66486
16_KI270856v1_alt	63982
21_GL383578v2_alt	63917
Un_KI270753v1	62944
19_KI270868v1_alt	61734
9_GL383542v1_alt	60032
20_KI270871v1_alt	58661
12_KI270836v1_alt	56134
19_KI270865v1_alt	52969
1_KI270764v1_alt	50258
Un_KI270589v1	44474
14_KI270726v1_random	43739
19_KI270866v1_alt	43156
22_KI270735v1_random	42811
1_KI270711v1_random	42210
Un_KI270745v1	41891
1_KI270714v1_random	41717
22_KI270732v1_random	41543
1_KI270713v1_random	40745
Un_KI270754v1	40191
1_KI270710v1_random	40176
12_KI270837v1_alt	40090
9_KI270717v1_random	40062
14_KI270724v1_random	39555
9_KI270720v1_random	39050
14_KI270723v1_random	38115
9_KI270718v1_random	38054
Un_KI270317v1	37690
13_KI270842v1_alt	37287
Y_KI270740v1_random	37240
Un_KI270755v1	36723
8_KI270820v1_alt	36640
1_KI270707v1_random	32032
Un_KI270579v1	31033
Un_KI270752v1	27745
Un_KI270512v1	22689
Un_KI270322v1	21476
M	16569
Un_GL000226v1	15008
Un_KI270311v1	12399
Un_KI270366v1	8320
Un_KI270511v1	8127
Un_KI270448v1	7992
Un_KI270521v1	7642
Un_KI270581v1	7046
Un_KI270582v1	6504
Un_KI270515v1	6361
Un_KI270588v1	6158
Un_KI270591v1	5796
Un_KI270522v1	5674
Un_KI270507v1	5353
Un_KI270590v1	4685
Un_KI270584v1	4513
Un_KI270320v1	4416
Un_KI270382v1	4215
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Un_KI270467v1	3920
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Un_KI270517v1	3253
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Un_KI270528v1	2983
Un_KI270587v1	2969
Un_KI270364v1	2855
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Un_KI270390v1	2387
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Un_KI270315v1	2276
Un_KI270302v1	2274
Un_KI270518v1	2186
Un_KI270530v1	2168
Un_KI270304v1	2165
Un_KI270418v1	2145
Un_KI270424v1	2140
Un_KI270417v1	2043
Un_KI270508v1	1951
Un_KI270303v1	1942
Un_KI270381v1	1930
Un_KI270529v1	1899
Un_KI270425v1	1884
Un_KI270396v1	1880
Un_KI270363v1	1803
Un_KI270386v1	1788
Un_KI270465v1	1774
Un_KI270383v1	1750
Un_KI270384v1	1658
Un_KI270330v1	1652
Un_KI270372v1	1650
Un_KI270548v1	1599
Un_KI270580v1	1553
Un_KI270387v1	1537
Un_KI270391v1	1484
Un_KI270305v1	1472
Un_KI270373v1	1451
Un_KI270422v1	1445
Un_KI270316v1	1444
Un_KI270338v1	1428
Un_KI270340v1	1428
Un_KI270583v1	1400
Un_KI270334v1	1368
Un_KI270429v1	1361
Un_KI270393v1	1308
Un_KI270516v1	1300
Un_KI270389v1	1298
Un_KI270466v1	1233
Un_KI270388v1	1216
Un_KI270544v1	1202
Un_KI270310v1	1201
Un_KI270412v1	1179
Un_KI270395v1	1143
Un_KI270376v1	1136
Un_KI270337v1	1121
Un_KI270335v1	1048
Un_KI270378v1	1048
Un_KI270379v1	1045
Un_KI270329v1	1040
Un_KI270419v1	1029
Un_KI270336v1	1026
Un_KI270312v1	998
Un_KI270539v1	993
Un_KI270385v1	990
Un_KI270423v1	981
Un_KI270392v1	971
Un_KI270394v1	970


================================================
FILE: intro_awk_spring2021/data/sample.txt
================================================
my dog is brown


================================================
FILE: intro_awk_spring2021/index.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Manipulating files with AWK and grep\n",
    "### Bioinformatics Coffee Hour - April 20, 2021\n",
    "### Author: Danielle Khost\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## Manipulating files with grep\n",
    "--------\n",
    "In this lesson, we will explore how we can use basic command line tools to parse, subset, and rearrange common file types you will come across in bioinformatics.\n",
    "\n",
    "**grep** is a powerful command-line search tools that is included as part of most Unix-like systems. It is one of the most useful tools in bioinformatics! At the most basic level, grep searches for a string of characters that match a pattern and will print lines containing a match. Basic syntax is:  \n",
    "\n",
    "`grep 'pattern' file_to_search`  \n",
    "\n",
    "By default, grep will match any part of the string, so for example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "grep 'dog' data/sample.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This will match the line, as will:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "grep 'do' data/sample.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is important to be mindful of partial matching, as you can end up selecting lines that you do not intend! This is just the most basic use of grep however, and there are a huge number of ways to modify its behavior. Here are just a few useful examples:\n",
    "\n",
    "`grep -w` matches *entire words*.\n",
    "- So in the above example:  \n",
    "`grep -w 'dog' data/sample.txt` would match the string, but `grep -w 'do' data/sample.txt` would not.\n",
    "\n",
    "`grep -i` allows case-insensitive matches\n",
    "- In the above example, `grep -i 'DOG'` would still match the line\n",
    "\n",
    "`grep -v` *inverts*, returning lines that *do not* match the pattern.\n",
    "\n",
    "`grep -o` returns only the matching words, not the entire line.\n",
    "\n",
    "`grep -c` counts the number of lines that match the pattern.\n",
    "- Equivalent to `grep 'pattern' file | wc -l`\n",
    "\n",
    "Print lines before/after a match:\n",
    "- `grep -A [n]` returns matching line and *n* lines after match\n",
    "- `grep -B [n]` returns matching line and *n* lines before match\n",
    "- `grep -C [n]` returns matching line and *n* lines before *and* after match\n",
    "\n",
    "---\n",
    "\n",
    "The real strength of grep is that we can combine these different arguments together. For instance, let's say we have a single-line fasta file and we want to quickly pull out the X chromosome:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "grep -w -A 1 '>X' data/dmel-subset-chromosomes.fasta"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This code will match line that start with \">X\" (using the -w flag to match whole word only) and will also print the line *after* the match as well using the `-A 1` flag (i.e. the sequence line of our fasta file; note this will not work if fasta file is multi-line!).\n",
    "\n",
    "There are many other functions of grep! When in doubt, remember you can check the help page using `man grep`...or just by using google :)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pattern matching with regular expressions\n",
    "Regular expressions, aka \"regex\", are patterns that describe sets of strings. In other words, they allow you to match complex patterns with grep, not just exact matches. Regex is extremely powerful, but can also get (very) complicated, so we'll just stick to a few basic uses.\n",
    "\n",
    "Regex has certain meta-characters that are reserved for special uses:\n",
    "\n",
    "```\n",
    "^: matches pattern at start of string\n",
    "$: matches pattern at end of string\n",
    ".: matches any character except new lines\n",
    "[]: matches any of enclose characters\n",
    "[^]: matches any characters *except* ones enclosed (note: is different from ^)\n",
    "\\: \"escapes\" meta-characters, allows literal matching\n",
    "```\n",
    "\n",
    "Note that if we want to match any of these special characters literally, we would need to use a \"\\\\\" to escape it first. So if we wanted to literally match a period (\".\") character we would run:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "grep '\\.' data/Homo_sapiens.GRCh38.subset.gff3 | head"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One example of how regex can come in handy is using the ^ special character to quickly count how many sequences are in a FASTA file, which we would do as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "grep -c '^>' data/dmel-subset-chromosomes.fasta"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This command matches lines in the FASTA file that start with a \">\" character, i.e. the header lines, and uses the -c argument to count how many matches!\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What is awk?\n",
    "Invented in the 1970's, [awk](https://en.wikipedia.org/wiki/AWK) is a scripting language included in most Unix-like operating systems. It specializes in one-liner programs and manipulating text files.\n",
    "\n",
    "In many cases, if you're parsing information from a text file (such as a [BED](https://en.wikipedia.org/wiki/BED_(file_format)) file, [FASTA](https://en.wikipedia.org/wiki/FASTA) file, etc.), you could write a Python script...or you could do it with awk in a single line!\n",
    "\n",
    "### Syntax\n",
    "awk scripts are organized as:\n",
    "\n",
    "`awk 'pattern { action; other action }' file`\n",
    "\n",
    "Meaning that every time that the pattern is true, awk will execute the action in the brackets.\n",
    "If no pattern is specified, the action will be taken for every line in the input file, e.g. the following command prints every line:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "awk '{print}' data/hg38.genome | head"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The two most important patterns are `BEGIN` and `END`, which tell the action to take place before any lines are read and after the last line."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "awk 'BEGIN{sum=0} {sum+=1} END {print sum}' data/hg38.genome"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " The above line sets a variable at the start of the script, adds 1 to it every line, then prints its value at the end."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If a variable hasn't been initialized, it is treated as 0 in numeric expressions, and an empty string in string expressions&mdash;awk will not print an error!\n",
    "So the following awk script also prints the number of lines in the file data/hg38.genome:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "awk '{sum+=1} END {print sum}' data/hg38.genome"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Input and output\n",
    "Input to awk is split into **records** and **fields**.\n",
    "- By default, **records** are separated by newline character, i.e # of records = # of lines in input file\n",
    "- Each record is subdivided into **fields**, i.e. columns, as determined by the field separator (see below)\n",
    "\n",
    "There are several important built-in variable in awk.\n",
    "The fields (columns) of each record are referred to by `$number`, so the first column would be `$1`, second would be `$2`, etc. `$0` refers to the entire record.\n",
    "\n",
    "So to print the second column of each line in the file, we'd use:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "awk '{print $2}' data/hg38.genome | head"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And if we wanted to print the second then the first:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "awk '{print $2,$1}' data/hg38.genome | head"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that when the different fields are separated with commas in the `print` statement, they are joined by the output field separator (the **OFS** variable, described below), which is by default a space.\n",
    "If the comma is omitted between fields (e.g., `awk '{print $2 $1}'`, they are concatenated without a separator.\n",
    "\n",
    "We can also print strings using using quotation marks:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "awk '{print \"First column:\" $1}' data/hg38.genome | head"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Which for every line of the file will print the text \"First column:\" followed by the value in the first field."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "awk has several other built-in variables that are very useful for parsing text, including:\n",
    "\n",
    "|  |   |\n",
    "---|---|\n",
    "| **FS** | field separator (default: white space) |\n",
    "| **OFS** |  output field separator, i.e. what character separates fields when printing|\n",
    "| **RS** | record separator, i.e. what character records are split on (default: new line) |\n",
    "| **ORS** | output record separator |\n",
    "| **NR** | number of records in input (# lines by default) |\n",
    "\n",
    "Assigning to a field causes the entire record ($0) to be recomputed using **OFS**. We can use this to convert between file formats, e.g. make a comma-separated text file into a tab-separated file. First, let's look at the first few lines on our comma-separated file using `head`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "head data/enhancers.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's convert the file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "awk 'BEGIN{FS=\",\" ; OFS=\"\\t\"} {$1 = $1; print $0}' data/enhancers.csv | head"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conditionals and pattern matching\n",
    "Like other programming languages, awk allows conditional matching with if/else statements.\n",
    "\n",
    "`awk '{if(condition) action; else other_action}'`\n",
    "\n",
    "awk uses the following conditional operators:\n",
    "\n",
    "| | |\n",
    "|-|-|\n",
    "|==|equal to|\n",
    "|!=|not equal to|\n",
    "|>|greater than|\n",
    "|>=|greater than or equal to|\n",
    "|<|less than|\n",
    "|<=|less than or equal to|\n",
    "|&&|AND|\n",
    "| \\|\\| |OR|\n",
    "| ! | NOT |\n",
    "\n",
    "In addition, awk also supports string matching using regular expressions, using the following expressions:\n",
    "\n",
    "| | |\n",
    "|-|-|\n",
    "|\\~|matches|\n",
    "|!~|does not match|\n",
    "\n",
    "For string matching, the pattern being matched must be enclosed by slashes, like so:\n",
    "\n",
    "`awk '{if($1 ~ /pattern/) print}'`\n",
    "\n",
    "Note that if an action isn't specified, the default action is `{print}`, so the previous awk command is equivalent to the following, which specifies only a pattern expression:\n",
    "\n",
    "`awk '$1 ~ /pattern/'`\n",
    "\n",
    "---\n",
    "### Example uses:\n",
    "\n",
    "- Count number of sequences in a FASTQ file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "awk 'END{print NR/4}' data/example.fastq"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note**: this is technically safer than using grep, as you don't have to worry about accidentally counting the quality line.\n",
    "\n",
    "- Only print annotations on a specific scaffold (chr1) that fall between 1Mb and 2Mb from a BED annotation file (a common file type that lists genomic coordinates of certain features). First let's look at the first few lines of the file using `head`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "head data/Homo_sapiens_ucscGenes.subset.bed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now subset the file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "awk 'BEGIN{FS=\"\\t\";OFS=\"\\t\"} {if($1 == \"chr1\" && $2 >=1000000 && $2 <= 2000000) print}' data/Homo_sapiens_ucscGenes.subset.bed | head"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note**: when we specify that we only want annotations from chr1, we're using exact match (`== \"chr1\"`) and not pattern match (`~ /chr1/`)...why is this??"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Another common type of annotation file is a Genome Feature File (GFF), which also lists coordinates. Let's look at the first few lines of the file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "head data/Homo_sapiens.GRCh38.subset.gff3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's subset the file and only print lines that match the string \"exon\" in their third column:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "awk 'BEGIN{FS=\"\\t\"} {if($3 ~ /exon/) print $0}' data/Homo_sapiens.GRCh38.subset.gff3 | head"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Convert from GFF (genome feature file) to BED file "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "grep -v '^#' data/Homo_sapiens.GRCh38.subset.gff3 | awk 'BEGIN{FS=\"\\t\"; OFS=\"\\t\"} {print $1,$4-1,$5}' | head"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note**: Annoyingly, BED and GFF files have different coordinate systems, i.e. BED start coordinate is 0 based, half-open, GFF is 1-based inclusive! Also, we are first using grep to skip the header lines in the GFF file.\n",
    "\n",
    "Alternatively, you could do the whole thing with only awk, no grep required:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "awk 'BEGIN{FS=\"\\t\"; OFS=\"\\t\"} !/^#/ {print $1,$4-1,$5}' data/Homo_sapiens.GRCh38.subset.gff3 | head"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With this command, `!/^#/` is a pattern (like `BEGIN` or `END`) that tell awk to execute the print statement when the start of the line does not match a `#`. Use whichever makes the most sense to you!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Practice\n",
    "Using awk:\n",
    "\n",
    "* Pull out only the CDS annotations (i.e. has \"CDS\" in the 3rd column) from the GFF file data/Homo_sapiens.GRCh38.subset.gff3 and output them in BED format"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Try it*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "awk '...' data/Homo_sapiens.GRCh38.subset.gff3 | head"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Solution*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "awk 'BEGIN{FS=\"\\t\"; OFS=\"\\t\"} {if($3 ~ /CDS/) print $1,$4-1,$5}' data/Homo_sapiens.GRCh38.subset.gff3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Calculate the average length of gene annotations from the file data/Homo_sapiens_ucscGenes.subset.bed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Try it*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "awk '...' data/Homo_sapiens_ucscGenes.subset.bed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Solution*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "awk 'BEGIN{FS=\"\\t\"; sum=0} {len=$3-$2; sum=sum+len} END{print sum/NR}' data/Homo_sapiens_ucscGenes.subset.bed"
   ]
  }
 ],
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================================================
FILE: intro_data_science/part_1/binder/requirements.txt
================================================
pandas==1.2.0
matplotlib==3.3.3
numpy==1.19.5

================================================
FILE: intro_data_science/part_1/ds_part_1.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# A Brief Introduction to NumPy\n",
    "### \"...the fundamental package for scientific computing with Python.\" - numpy.org\n",
    "\n",
    "In this notebook, we will cover the basics of NumPy, a package that is the basis for many other libraries in the data science ecosystem. Let's get started."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from IPython.display import Image\n",
    "import time\n",
    "from sys import getsizeof"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. NumPy Arrays\n",
    "The array data structure is the backbone of the NumPy library. They can be single-dimensional (vectors), two-dimensional (matrices), or multi-dimensional for more complex tasks.\n",
    "\n",
    "In many ways, they are similar to Python lists."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "a\n['b' 'c']\na\nb\nc\nd\ne\nf\n"
     ]
    }
   ],
   "source": [
    "a = ['a', 'b', 'c', 'd', 'e', 'f']\n",
    "b = np.array(['a', 'b', 'c', 'd', 'e', 'f'])\n",
    "\n",
    "# Accessible by index\n",
    "print(b[0])\n",
    "\n",
    "# Sliceable\n",
    "print(b[1:3])\n",
    "\n",
    "# Iterable\n",
    "for letter in b:\n",
    "    print(letter)"
   ]
  },
  {
   "source": [
    "So why use NumPy arrays at all? One word: performance! Generally speaking, Python lists take up more space and require more computation than NumPy arrays. Let's take a look at the size differences."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "8697456\n8000096\n"
     ]
    }
   ],
   "source": [
    "n_elements = 1_000_000\n",
    "# Create using list comprehension\n",
    "python_list = [x for x in range(n_elements)]\n",
    "print(getsizeof(python_list))\n",
    "\n",
    "# Create with existing python list\n",
    "np_arr = np.array(python_list)\n",
    "print(getsizeof(np_arr))"
   ]
  },
  {
   "source": [
    "Now let's look at the speed differences."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "0.07268199999999991\n0.004588999999999954\n"
     ]
    }
   ],
   "source": [
    "start = time.process_time()\n",
    "# Add 100 to every element in the Python list\n",
    "python_list_mod = [x + 100 for x in python_list]\n",
    "python_time = time.process_time() - start\n",
    "print(python_time)\n",
    "\n",
    "# Add 100 to every element in the Numpy array\n",
    "start = time.process_time()\n",
    "np_arr_mod = np_arr + 100\n",
    "np_time = time.process_time() - start\n",
    "print(np_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "execution_count": 7
    }
   ],
   "source": [
    "Image('python_memory1.png')"
   ]
  },
  {
   "source": [
    "If NumPy arrays are more efficient computationally and in regards to space, why not use them all the time? There are some constraints, most notably, all of their items must be of the same type. [NumPy Array](https://numpy.org/doc/stable/reference/generated/numpy.array.html#numpy.array)"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[1, 'a', 0.222, 'hello from inside the list!']\n['1' 'a' '0.222' 'hello from inside the list!']\n"
     ]
    }
   ],
   "source": [
    "python_list = [1, 'a', 0.222, 'hello from inside the list!']\n",
    "np_arr = np.array(python_list)\n",
    "print(python_list)\n",
    "print(np_arr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1.1 Creating\n",
    "NumPy arrays are created with existing data (standard python lists or lists of lists) or by using a collection of built-in methods."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.1.1 Existing Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### .array()\n",
    "Use python lists (or lists of lists) as input."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4, 5])"
      ]
     },
     "metadata": {},
     "execution_count": 2
    }
   ],
   "source": [
    "std_list = [1, 2, 3, 4, 5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([[1, 2, 3],\n",
       "       [4, 5, 6],\n",
       "       [7, 8, 9]])"
      ]
     },
     "metadata": {},
     "execution_count": 3
    }
   ],
   "source": [
    "std_matrix = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: the .array() method is a convenience function for constructing objects of the class ndarray. While it is possible to call .ndarray() directly, it is specifically regarded as an anti-pattern by the NumPy documentation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.1.2 Fixed Values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### .zeros(), .ones()\n",
    "Return a new array of given shape and type, filled with zeros or ones."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([0., 0., 0.])"
      ]
     },
     "metadata": {},
     "execution_count": 4
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])"
      ]
     },
     "metadata": {},
     "execution_count": 5
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([[0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0.]])"
      ]
     },
     "metadata": {},
     "execution_count": 8
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: the numbers have periods after them to indicate that these are floating point numbers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### .full()\n",
    "Return a new array of given shape and type, filled with fill_value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([[72, 72, 72, 72],\n",
       "       [72, 72, 72, 72],\n",
       "       [72, 72, 72, 72],\n",
       "       [72, 72, 72, 72]])"
      ]
     },
     "metadata": {},
     "execution_count": 10
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.1.3 Range"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### .arange()\n",
    "Return evenly spaced values within a given interval. Notice that the output is inclusive of the first number parameter and exclusive of the second."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 2, 4, 6, 8])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  5, 10, 15])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### .linspace()\n",
    "Return evenly spaced numbers over a specified interval. Notice that the output is inclusive of both the first and second number parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.,  5., 10., 15., 20.])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.        , 0.05263158, 0.10526316, 0.15789474, 0.21052632,\n",
       "       0.26315789, 0.31578947, 0.36842105, 0.42105263, 0.47368421,\n",
       "       0.52631579, 0.57894737, 0.63157895, 0.68421053, 0.73684211,\n",
       "       0.78947368, 0.84210526, 0.89473684, 0.94736842, 1.        ])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: the main difference between .linspace() and .arange() is that with .linspace() you have precise control over the end value, whereas with .arange() you can specify the increments explicitly."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### .logspace()\n",
    "Return numbers spaced evenly on a log scale."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 100.        ,  215.443469  ,  464.15888336, 1000.        ])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4.        , 5.0396842 , 6.34960421, 8.        ])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.1.4 Random"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### .rand()\n",
    "Create an array of the given shape and populate it with random samples from a uniform distribution over 0,1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7125801811390514"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.91155984, 0.98323076, 0.60797164, 0.48538736])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.56443505, 0.13692564, 0.4367128 ],\n",
       "       [0.66786468, 0.86782966, 0.88696338],\n",
       "       [0.05537221, 0.54016645, 0.9654299 ],\n",
       "       [0.19105141, 0.63037385, 0.51600478],\n",
       "       [0.53737417, 0.76494641, 0.61205375],\n",
       "       [0.09621612, 0.37848038, 0.60698676],\n",
       "       [0.47772402, 0.61733058, 0.75352393]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### .randn()\n",
    "Return a sample (or samples) from the “standard normal” distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.36910179629879897"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.08320502, -0.69825957,  0.80139519,  0.53540454,  0.78953503])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: .rand() is from a uniform distribution, whereas .randn() is from the standard **normal** distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.33667905,  0.46529544,  1.51069451,  0.69167808,  1.60270943],\n",
       "       [ 1.62614154, -0.10463841, -0.999014  , -0.70942201, -0.15136812],\n",
       "       [-0.70328834, -0.48982338,  2.03118893,  2.32867839,  2.08885297]])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### .randint()\n",
    "Return random integers from low (inclusive) to high (exclusive)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 5, 5, 6, 1, 2, 1])"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[9, 3],\n",
       "       [6, 8],\n",
       "       [7, 6]])"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.2 Attributes and Methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2, 0, 8, 7, 8, 4, 6],\n",
       "       [3, 5, 6, 6, 7, 6, 2]])"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### .shape\n",
    "Tuple of array dimensions. Note: this is an attribute NOT a method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 7)"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### .reshape()\n",
    "Gives a new shape to an array without changing its data. Note: this happens 'in place' (does not return new values)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2, 0],\n",
       "       [8, 7],\n",
       "       [8, 4],\n",
       "       [6, 3],\n",
       "       [5, 6],\n",
       "       [6, 7],\n",
       "       [6, 2]])"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "cannot reshape array of size 14 into shape (12,4)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-85-17f9948b247d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0marr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m12\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m: cannot reshape array of size 14 into shape (12,4)"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### .newaxis\n",
    "Alternate syntax."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[2, 0, 8, 7, 8, 4, 6],\n",
       "        [3, 5, 6, 6, 7, 6, 2]]])"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### .dtype\n",
    "The type of data in the array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int64')"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### .astype()\n",
    "Casts values to a specified type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[9, 5, 7, 3, 6, 8, 9],\n",
       "       [9, 6, 1, 1, 4, 2, 3]], dtype=int8)"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[9.+0.j, 5.+0.j, 7.+0.j, 3.+0.j, 6.+0.j, 8.+0.j, 9.+0.j],\n",
       "       [9.+0.j, 6.+0.j, 1.+0.j, 1.+0.j, 4.+0.j, 2.+0.j, 3.+0.j]])"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: in mathematics i is used to denote imaginary numbers, but in Python (and many other languages) j is used because i tends to indicate the current value in a system. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ True,  True,  True,  True],\n",
       "       [ True,  True,  True,  True],\n",
       "       [ True,  True,  True,  True],\n",
       "       [ True,  True,  True,  True]])"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.3 Indexing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.3.1 One-dimensional"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  2,  4,  6,  8, 10, 12, 14, 16, 18, 20])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the value at index 5 (the sixth element)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 4, 6, 8])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get a slice of the array from index 1 (inclusive) to index 5 (exclusive)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 8, 10, 12, 14, 16, 18, 20])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get a slice of the array from index 4 to the end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the last element in the array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reverse the array"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.3.2 Two-dimensional"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3],\n",
       "       [ 4,  5,  6,  7],\n",
       "       [ 8,  9, 10, 11]])"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3])"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the first row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the second element of the second row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Alternative syntax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 2, 3],\n",
       "       [4, 5, 6, 7]])"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get first and second rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 4])"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get first element of both first and second rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0],\n",
       "       [4]])"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Maintain shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.3.3 Fancy Indexing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[4, 6, 14, 16]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 4,  6, 14, 16])"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 4,  6],\n",
       "       [14, 16]])"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Output of fancy indexing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 7])"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 3])"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Notice if second row value is not provided, NumPy compensates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.4 Selection\n",
    "We will see this syntax mirrored in the Pandas library."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False, False,  True,  True])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 4])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "       17, 18, 19])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([12, 13, 14, 15, 16, 17, 18, 19])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Operations\n",
    "One of the most powerful features of NumPy arrays is that operations are vectorized."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.1 Arithmetic\n",
    "Arithmetic operations work on NumPy arrays."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1.1 One-dimensional"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "arr_1d = np.arange(0,11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15])"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add five to each item"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([-12, -11, -10,  -9,  -8,  -7,  -6,  -5,  -4,  -3,  -2])"
      ]
     },
     "metadata": {},
     "execution_count": 6
    }
   ],
   "source": [
    "# Subtract 12 from each item"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  2,  4,  6,  8, 10, 12, 14, 16, 18, 20])"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Multiply each item by 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.  , 0.25, 0.5 , 0.75, 1.  , 1.25, 1.5 , 1.75, 2.  , 2.25, 2.5 ])"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Divide each item by 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2])"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Floor division on each item"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([   0,    1,    8,   27,   64,  125,  216,  343,  512,  729, 1000])"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Raise each item to the third power"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1.2 Two-dimensional"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3,  4],\n",
       "       [ 5,  6,  7,  8,  9],\n",
       "       [10, 11, 12, 13, 14]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr_2d = np.arange(15).reshape((3,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  2,  4,  6,  8],\n",
       "       [10, 12, 14, 16, 18],\n",
       "       [20, 22, 24, 26, 28]])"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Multiply each item by 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  0,   1,   4,   9,  16],\n",
       "       [ 25,  36,  49,  64,  81],\n",
       "       [100, 121, 144, 169, 196]])"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Raise each item to the power of 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1.3 Multiple values\n",
    "These operations work with multiple values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4, 5])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr_mult = np.array([1,2,3,4,5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  2,  6, 12, 20],\n",
       "       [ 5, 12, 21, 32, 45],\n",
       "       [10, 22, 36, 52, 70]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr_2d * arr_mult"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "arr_mult_2 = np.array([1,2,3])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.2 Ufuncs\n",
    "Universal functions. For more information, visit: https://docs.scipy.org/doc/numpy/reference/ufuncs.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10])"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.arange(1,11)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### .sum()\n",
    "Sum of array elements over a given axis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "70"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### .sqrt()\n",
    "Return the non-negative square-root of an array, element-wise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.0"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.        , 1.41421356, 1.73205081, 2.        , 2.23606798,\n",
       "       2.44948974, 2.64575131, 2.82842712, 3.        , 3.16227766])"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(arr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### .power()\n",
    "First array elements raised to powers from second array, element-wise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.power(3,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  1,   4,   9,  16,  25,  36,  49,  64,  81, 100])"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.power(arr, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### .min(), .max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 5, 4, 0, 3, 5, 5, 9, 7, 7])"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.random.randint(0,10, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.min(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.max(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.3 Broadcasting\n",
    "The term broadcasting describes how numpy treats arrays with different shapes during arithmetic operations. Subject to certain constraints, the smaller array is “broadcast” across the larger array so that they have compatible shapes. (NumPy documentation) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3,  4],\n",
       "       [ 5,  6,  7,  8,  9],\n",
       "       [10, 11, 12, 13, 14],\n",
       "       [15, 16, 17, 18, 19]])"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr_one = np.arange(0,20).reshape(4,5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[10, 11, 12, 13, 14],\n",
       "       [15, 16, 17, 18, 19],\n",
       "       [20, 21, 22, 23, 24],\n",
       "       [25, 26, 27, 28, 29]])"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr_one + 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[10, 11, 12, 13, 14],\n",
       "       [15, 16, 17, 18, 19],\n",
       "       [20, 21, 22, 23, 24],\n",
       "       [25, 26, 27, 28, 29]])"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr_one + np.array([10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "operands could not be broadcast together with shapes (4,5) (2,) ",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-97-066ab7061aca>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0marr_one\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m20\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m: operands could not be broadcast together with shapes (4,5) (2,) "
     ]
    }
   ],
   "source": [
    "arr_one + np.array([10, 20])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "arr_two = np.array([10,20,30,40,50])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[10, 21, 32, 43, 54],\n",
       "       [15, 26, 37, 48, 59],\n",
       "       [20, 31, 42, 53, 64],\n",
       "       [25, 36, 47, 58, 69]])"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr_one + arr_two"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4, 5)"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr_one.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5,)"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr_two.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[10],\n",
       "       [20],\n",
       "       [30],\n",
       "       [40],\n",
       "       [50]])"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr_two.reshape(5,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[10, 21, 32, 43, 54],\n",
       "       [15, 26, 37, 48, 59],\n",
       "       [20, 31, 42, 53, 64],\n",
       "       [25, 36, 47, 58, 69]])"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr_one + arr_two"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Array comparison begins with the trailing dimensions and subsequently works its way foward. Two array dimensions are compatible when:\n",
    "- they are equal, or\n",
    "- one of them is 1\n",
    "(NumPy documentation)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://jakevdp.github.io/PythonDataScienceHandbook/02.05-computation-on-arrays-broadcasting.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot sin and cos on the same graph, using matplotlib\n",
    "# Compute the x and y coordinates for points on sine and cosine curves \n",
    "\n",
    "# Set up a subplot grid that has height 2 and width 1, \n",
    "# and set the first such subplot as active. \n",
    "\n",
    "# Make the first plot \n",
    "   \n",
    "# Set the second subplot as active, and make the second plot. \n",
    "\n",
    "# Ensure tight layout\n",
    "   \n",
    "# Show the figure. "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.2-final"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}

================================================
FILE: intro_data_science/part_1/ds_part_1_complete.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# A Brief Introduction to NumPy\n",
    "### \"...the fundamental package for scientific computing with Python.\" - numpy.org\n",
    "\n",
    "In this notebook, we will cover the basics of NumPy, a package that is the basis for many other libraries in the data science ecosystem. Let's get started."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from IPython.display import Image\n",
    "import time\n",
    "from sys import getsizeof\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. NumPy Arrays\n",
    "The array data structure is the backbone of the NumPy library. They can be single-dimensional (vectors), two-dimensional (matrices), or multi-dimensional for more complex tasks.\n",
    "\n",
    "In many ways, they are similar to Python lists."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "a\n['b' 'c']\na\nb\nc\nd\ne\nf\n"
     ]
    }
   ],
   "source": [
    "a = ['a', 'b', 'c', 'd', 'e', 'f']\n",
    "b = np.array(['a', 'b', 'c', 'd', 'e', 'f'])\n",
    "\n",
    "# Accessible by index\n",
    "print(b[0])\n",
    "\n",
    "# Sliceable\n",
    "print(b[1:3])\n",
    "\n",
    "# Iterable\n",
    "for letter in b:\n",
    "    print(letter)"
   ]
  },
  {
   "source": [
    "So why use NumPy arrays at all? One word: performance! Generally speaking, Python lists take up more space and require more computation than NumPy arrays. Let's take a look at the size differences."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "8697456\n8000096\n"
     ]
    }
   ],
   "source": [
    "n_elements = 1_000_000\n",
    "# Create using list comprehension\n",
    "python_list = [x for x in range(n_elements)]\n",
    "print(getsizeof(python_list))\n",
    "\n",
    "# Create with existing python list\n",
    "np_arr = np.array(python_list)\n",
    "print(getsizeof(np_arr))"
   ]
  },
  {
   "source": [
    "Now let's look at the speed differences."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "0.07268199999999991\n0.004588999999999954\n"
     ]
    }
   ],
   "source": [
    "start = time.process_time()\n",
    "# Add 100 to every element in the Python list\n",
    "python_list_mod = [x + 100 for x in python_list]\n",
    "python_time = time.process_time() - start\n",
    "print(python_time)\n",
    "\n",
    "# Add 100 to every element in the Numpy array\n",
    "start = time.process_time()\n",
    "np_arr_mod = np_arr + 100\n",
    "np_time = time.process_time() - start\n",
    "print(np_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "execution_count": 7
    }
   ],
   "source": [
    "Image('python_memory1.png')"
   ]
  },
  {
   "source": [
    "If NumPy arrays are more efficient computationally and in regards to space, why not use them all the time? There are some constraints, most notably, all of their items must be of the same type. [NumPy Array](https://numpy.org/doc/stable/reference/generated/numpy.array.html#numpy.array)"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[1, 'a', 0.222, 'hello from inside the list!']\n['1' 'a' '0.222' 'hello from inside the list!']\n"
     ]
    }
   ],
   "source": [
    "python_list = [1, 'a', 0.222, 'hello from inside the list!']\n",
    "np_arr = np.array(python_list)\n",
    "print(python_list)\n",
    "print(np_arr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1.1 Creating\n",
    "NumPy arrays are created with existing data (standard python lists or lists of lists) or by using a collection of built-in methods."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.1.1 Existing Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### .array()\n",
    "Use python lists (or lists of lists) as input."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4, 5])"
      ]
     },
     "metadata": {},
     "execution_count": 2
    }
   ],
   "source": [
    "std_list = [1, 2, 3, 4, 5]\n",
    "np.array(std_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([[1, 2, 3],\n",
       "       [4, 5, 6],\n",
       "       [7, 8, 9]])"
      ]
     },
     "metadata": {},
     "execution_count": 3
    }
   ],
   "source": [
    "std_matrix = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]\n",
    "np.array(std_matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: the .array() method is a convenience function for constructing objects of the class ndarray. While it is possible to call .ndarray() directly, it is specifically regarded as an anti-pattern by the NumPy documentation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.1.2 Fixed Values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### .zeros(), .ones()\n",
    "Return a new array of given shape and type, filled with zeros or ones."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([0., 0., 0.])"
      ]
     },
     "metadata": {},
     "execution_count": 4
    }
   ],
   "source": [
    "np.zeros(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])"
      ]
     },
     "metadata": {},
     "execution_count": 5
    }
   ],
   "source": [
    "np.ones(17)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([[0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0.]])"
      ]
     },
     "metadata": {},
     "execution_count": 8
    }
   ],
   "source": [
    "# Notice the shape represented as a tuple\n",
    "np.zeros((5,5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: the numbers have periods after them to indicate that these are floating point numbers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### .full()\n",
    "Return a new array of given shape and type, filled with fill_value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([[72, 72, 72, 72],\n",
       "       [72, 72, 72, 72],\n",
       "       [72, 72, 72, 72],\n",
       "       [72, 72, 72, 72]])"
      ]
     },
     "metadata": {},
     "execution_count": 10
    }
   ],
   "source": [
    "np.full((4,4), 72)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.1.3 Range"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### .arange()\n",
    "Return evenly spaced values within a given interval. Notice that the output is inclusive of the first number parameter and exclusive of the second."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(0,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 2, 4, 6, 8])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(0,10,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  5, 10, 15])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(0,20,5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### .linspace()\n",
    "Return evenly spaced numbers over a specified interval. Notice that the output is inclusive of both the first and second number parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.,  5., 10., 15., 20.])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.linspace(0,20,5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.        , 0.05263158, 0.10526316, 0.15789474, 0.21052632,\n",
       "       0.26315789, 0.31578947, 0.36842105, 0.42105263, 0.47368421,\n",
       "       0.52631579, 0.57894737, 0.63157895, 0.68421053, 0.73684211,\n",
       "       0.78947368, 0.84210526, 0.89473684, 0.94736842, 1.        ])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.linspace(0,1, 20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: the main difference between .linspace() and .arange() is that with .linspace() you have precise control over the end value, whereas with .arange() you can specify the increments explicitly."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### .logspace()\n",
    "Return numbers spaced evenly on a log scale."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 100.        ,  215.443469  ,  464.15888336, 1000.        ])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.logspace(2.0, 3.0, num=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4.        , 5.0396842 , 6.34960421, 8.        ])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.logspace(2.0, 3.0, num=4, base=2.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.1.4 Random"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### .rand()\n",
    "Create an array of the given shape and populate it with random samples from a uniform distribution over 0,1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7125801811390514"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.rand()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.91155984, 0.98323076, 0.60797164, 0.48538736])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.rand(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.56443505, 0.13692564, 0.4367128 ],\n",
       "       [0.66786468, 0.86782966, 0.88696338],\n",
       "       [0.05537221, 0.54016645, 0.9654299 ],\n",
       "       [0.19105141, 0.63037385, 0.51600478],\n",
       "       [0.53737417, 0.76494641, 0.61205375],\n",
       "       [0.09621612, 0.37848038, 0.60698676],\n",
       "       [0.47772402, 0.61733058, 0.75352393]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.rand(7,3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### .randn()\n",
    "Return a sample (or samples) from the “standard normal” distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.36910179629879897"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.randn()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.08320502, -0.69825957,  0.80139519,  0.53540454,  0.78953503])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.randn(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: .rand() is from a uniform distribution, whereas .randn() is from the standard **normal** distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.33667905,  0.46529544,  1.51069451,  0.69167808,  1.60270943],\n",
       "       [ 1.62614154, -0.10463841, -0.999014  , -0.70942201, -0.15136812],\n",
       "       [-0.70328834, -0.48982338,  2.03118893,  2.32867839,  2.08885297]])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.randn(3, 5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### .randint()\n",
    "Return random integers from low (inclusive) to high (exclusive)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.randint(0,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 5, 5, 6, 1, 2, 1])"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.randint(0,10,size=7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[9, 3],\n",
       "       [6, 8],\n",
       "       [7, 6]])"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.randint(0,10,size=(3,2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.2 Attributes and Methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2, 0, 8, 7, 8, 4, 6],\n",
       "       [3, 5, 6, 6, 7, 6, 2]])"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.random.randint(0,10,size=(2,7))\n",
    "arr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### .shape\n",
    "Tuple of array dimensions. Note: this is an attribute NOT a method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 7)"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### .reshape()\n",
    "Gives a new shape to an array without changing its data. Note: this happens 'in place' (does not return new values)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2, 0],\n",
       "       [8, 7],\n",
       "       [8, 4],\n",
       "       [6, 3],\n",
       "       [5, 6],\n",
       "       [6, 7],\n",
       "       [6, 2]])"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.reshape(7,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "cannot reshape array of size 14 into shape (12,4)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-85-17f9948b247d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0marr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m12\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m: cannot reshape array of size 14 into shape (12,4)"
     ]
    }
   ],
   "source": [
    "arr.reshape(12,4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### .newaxis\n",
    "Alternate syntax."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[2, 0, 8, 7, 8, 4, 6],\n",
       "        [3, 5, 6, 6, 7, 6, 2]]])"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr[np.newaxis, :]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### .dtype\n",
    "The type of data in the array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int64')"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### .astype()\n",
    "Casts values to a specified type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[9, 5, 7, 3, 6, 8, 9],\n",
       "       [9, 6, 1, 1, 4, 2, 3]], dtype=int8)"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.astype('int8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[9.+0.j, 5.+0.j, 7.+0.j, 3.+0.j, 6.+0.j, 8.+0.j, 9.+0.j],\n",
       "       [9.+0.j, 6.+0.j, 1.+0.j, 1.+0.j, 4.+0.j, 2.+0.j, 3.+0.j]])"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Complex numbers are the combination of a real and an imaginary number.\n",
    "arr.astype('complex')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: in mathematics i is used to denote imaginary numbers, but in Python (and many other languages) j is used because i tends to indicate the current value in a system. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "arr2 = np.full((4,4), 199)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ True,  True,  True,  True],\n",
       "       [ True,  True,  True,  True],\n",
       "       [ True,  True,  True,  True],\n",
       "       [ True,  True,  True,  True]])"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr2.astype('bool')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.3 Indexing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.3.1 One-dimensional"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  2,  4,  6,  8, 10, 12, 14, 16, 18, 20])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr_1d = np.arange(0,21, 2)\n",
    "arr_1d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the value at index 5 (the sixth element)\n",
    "arr_1d[5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 4, 6, 8])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get a slice of the array from index 1 (inclusive) to index 5 (exclusive)\n",
    "arr_1d[1:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 8, 10, 12, 14, 16, 18, 20])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get a slice of the array from index 4 to the end\n",
    "arr_1d[4:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the last element in the array\n",
    "arr_1d[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'arr_1d' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-c2747bf9783f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Reverse the array\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0marr_1d\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'arr_1d' is not defined"
     ]
    }
   ],
   "source": [
    "# Reverse the array\n",
    "arr_1d[::-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.3.2 Two-dimensional"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3],\n",
       "       [ 4,  5,  6,  7],\n",
       "       [ 8,  9, 10, 11]])"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr_2d = np.arange(12).reshape((3, 4))\n",
    "arr_2d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3])"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the first row\n",
    "arr_2d[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the second element of the second row\n",
    "arr_2d[1][1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Alternative syntax\n",
    "arr_2d[1,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 2, 3],\n",
       "       [4, 5, 6, 7]])"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get first and second rows\n",
    "arr_2d[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 4])"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get first element of both first and second rows\n",
    "arr_2d[:2,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0],\n",
       "       [4]])"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Maintain shape\n",
    "arr_2d[:2,0:1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.3.3 Fancy Indexing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[4, 6, 14, 16]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[arr_1d[2], arr_1d[3], arr_1d[7], arr_1d[8]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 4,  6, 14, 16])"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr_1d[[2,3,7,8]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 4,  6],\n",
       "       [14, 16]])"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Output of fancy indexing\n",
    "ind = np.array([[2, 3],\n",
    "                [7, 8]])\n",
    "arr_1d[ind]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 7])"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "row = [0, 1]\n",
    "col = [2, 3]\n",
    "arr_2d[(row, col)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 3])"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Notice if second row value is not provided, NumPy compensates\n",
    "row = [0]\n",
    "col = [2, 3]\n",
    "arr_2d[(row, col)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.4 Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.arange(1,5)\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False, False,  True,  True])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bool_arr = np.array([False, False, True, True])\n",
    "bool_arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 4])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr[bool_arr]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "       17, 18, 19])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr_long = np.arange(0,20)\n",
    "arr_long"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([12, 13, 14, 15, 16, 17, 18, 19])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bool_arr_long = arr_long > 11\n",
    "arr_long[bool_arr_long]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr_long[arr_long < 5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "## TODO add exercise"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Operations\n",
    "One of the most powerful features of NumPy arrays is that operations are vectorized."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.1 Arithmetic\n",
    "Arithmetic operations work on NumPy arrays."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1.1 One-dimensional"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10])"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr_1d = np.arange(0,11)\n",
    "arr_1d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15])"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr_1d + 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-12, -11, -10,  -9,  -8,  -7,  -6,  -5,  -4,  -3,  -2])"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr_1d - 12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  2,  4,  6,  8, 10, 12, 14, 16, 18, 20])"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr_1d * 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.  , 0.25, 0.5 , 0.75, 1.  , 1.25, 1.5 , 1.75, 2.  , 2.25, 2.5 ])"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr_1d / 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2])"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr_1d // 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([   0,    1,    8,   27,   64,  125,  216,  343,  512,  729, 1000])"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr_1d ** 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1.2 Two-dimensional"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3,  4],\n",
       "       [ 5,  6,  7,  8,  9],\n",
       "       [10, 11, 12, 13, 14]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr_2d = np.arange(15).reshape((3,5))\n",
    "arr_2d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  2,  4,  6,  8],\n",
       "       [10, 12, 14, 16, 18],\n",
       "       [20, 22, 24, 26, 28]])"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr_2d * 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  0,   1,   4,   9,  16],\n",
       "       [ 25,  36,  49,  64,  81],\n",
       "       [100, 121, 144, 169, 196]])"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr_2d ** 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1.3 Multiple values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4, 5])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr_mult = np.array([1,2,3,4,5])\n",
    "arr_mult"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  2,  6, 12, 20],\n",
       "       [ 5, 12, 21, 32, 45],\n",
       "       [10, 22, 36, 52, 70]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr_2d * arr_mult"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "arr_mult_2 = np.array([1,2,3])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.2 Ufuncs\n",
    "Universal functions. For more information, visit: https://docs.scipy.org/doc/numpy/reference/ufuncs.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10])"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.arange(1,11)\n",
    "arr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### .sum()\n",
    "Sum of array elements over a given axis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "70"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(arr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### .sqrt()\n",
    "Return the non-negative square-root of an array, element-wise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.0"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.        , 1.41421356, 1.73205081, 2.        , 2.23606798,\n",
       "       2.44948974, 2.64575131, 2.82842712, 3.        , 3.16227766])"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(arr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### .power()\n",
    "First array elements raised to powers from second array, element-wise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.power(3,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  1,   4,   9,  16,  25,  36,  49,  64,  81, 100])"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.power(arr, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### .min(), .max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 5, 4, 0, 3, 5, 5, 9, 7, 7])"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.random.randint(0,10, 10)\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.min(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.max(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.3 Broadcasting\n",
    "The term broadcasting describes how numpy treats arrays with different shapes during arithmetic operations. Subject to certain constraints, the smaller array is “broadcast” across the larger array so that they have compatible shapes. (NumPy documentation) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3,  4],\n",
       "       [ 5,  6,  7,  8,  9],\n",
       "       [10, 11, 12, 13, 14],\n",
       "       [15, 16, 17, 18, 19]])"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr_one = np.arange(0,20).reshape(4,5)\n",
    "arr_one"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[10, 11, 12, 13, 14],\n",
       "       [15, 16, 17, 18, 19],\n",
       "       [20, 21, 22, 23, 24],\n",
       "       [25, 26, 27, 28, 29]])"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr_one + 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[10, 11, 12, 13, 14],\n",
       "       [15, 16, 17, 18, 19],\n",
       "       [20, 21, 22, 23, 24],\n",
       "       [25, 26, 27, 28, 29]])"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr_one + np.array([10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "operands could not be broadcast together with shapes (4,5) (2,) ",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-97-066ab7061aca>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0marr_one\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m20\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m: operands could not be broadcast together with shapes (4,5) (2,) "
     ]
    }
   ],
   "source": [
    "arr_one + np.array([10, 20])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "arr_two = np.array([10,20,30,40,50])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[10, 21, 32, 43, 54],\n",
       "       [15, 26, 37, 48, 59],\n",
       "       [20, 31, 42, 53, 64],\n",
       "       [25, 36, 47, 58, 69]])"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr_one + arr_two"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4, 5)"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr_one.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5,)"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr_two.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[10],\n",
       "       [20],\n",
       "       [30],\n",
       "       [40],\n",
       "       [50]])"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr_two.reshape(5,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[10, 21, 32, 43, 54],\n",
       "       [15, 26, 37, 48, 59],\n",
       "       [20, 31, 42, 53, 64],\n",
       "       [25, 36, 47, 58, 69]])"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr_one + arr_two"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Array comparison begins with the trailing dimensions and subsequently works its way foward. Two array dimensions are compatible when:\n",
    "- they are equal, or\n",
    "- one of them is 1\n",
    "(NumPy documentation)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://jakevdp.github.io/PythonDataScienceHandbook/02.05-computation-on-arrays-broadcasting.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# Plot sin and cos on the same graph, using matplotlib\n",
    "# Compute the x and y coordinates for points on sine and cosine curves \n",
    "x = np.arange(0, 20 * np.pi, 0.2) \n",
    "\n",
    "y_sin = np.sin(x) \n",
    "y_cos = np.cos(x)  \n",
    "   \n",
    "# Set up a subplot grid that has height 2 and width 1, \n",
    "# and set the first such subplot as active. \n",
    "plt.subplot(2, 1, 1)\n",
    "   \n",
    "# Make the first plot \n",
    "plt.plot(x, y_sin) \n",
    "plt.title('Sine')  \n",
    "   \n",
    "# Set the second subplot as active, and make the second plot. \n",
    "plt.subplot(2, 1, 2) \n",
    "plt.plot(x, y_cos) \n",
    "plt.title('Cosine')  \n",
    "\n",
    "# Ensure tight layout\n",
    "plt.tight_layout()\n",
    "   \n",
    "# Show the figure. \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.2-final"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}

================================================
FILE: intro_data_science/part_2/binder/requirements.txt
================================================
pandas==1.2.0
matplotlib==3.3.3
numpy==1.19.5

================================================
FILE: intro_data_science/part_2/ds_part_2.ipynb
================================================
{
 "metadata": {
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.2-final"
  },
  "orig_nbformat": 2,
  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3",
   "language": "python"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2,
 "cells": [
  {
   "source": [
    "# A Brief Introduction to Matplotlib\n",
    "\n",
    "In this notebook, we will cover the basics of Matplotlib, a powerful, prolific library \n",
    "for organizing and visualizing data of all kinds. If you work in science, chances are you'll\n",
    "find a use case for this. Enjoy!"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "source": [
    "Now, let's make our first plot - a line graph! We're going to visualize data related to CO2 emissions."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use numpy to generate range of years to cover\n",
    "\n",
    "# Emissions data for China\n",
    "china_emissions = [8500.543, 9388.199, 9633.899, 9796.527, 9820.36, 9716.468, 9704.479, 9838.754, 10064.686]\n",
    "\n",
    "# Emissions data for USA\n",
    "us_emissions = [5700.108, 5572.585, 5371.777, 5522.908, 5572.106, 5422.966, 5306.662, 5270.749, 5416.278]\n",
    "\n",
    "# Emissions data for India\n",
    "india_emissions = [1700.027, 1811.961, 1979.047, 1994.101, 2199.4, 2298.17, 2371.752, 2456.954, 2654.101]\n",
    "\n",
    "# Emissions data for Russia\n",
    "russia_emissions = [1613.523, 1613.523, 1679.385, 1618.434, 1617.678, 1622.498, 1617.653, 1647.041, 1710.688]\n",
    "\n",
    "# Add style to the x ticks\n",
    "\n",
    "# Add labels\n",
    "\n",
    "# Add a legend\n",
    "\n",
    "# Ensure a tight, uncluttered layout\n",
    "\n",
    "# Save the figure as png, jpg, and svg\n",
    "\n",
    "# Annotate with source\n",
    "\n",
    "# Set the plot style\n",
    "\n",
    "# Show the figure"
   ]
  },
  {
   "source": [
    "Looks pretty good! But it's hard to see the relationship between Russia and the other countries. Let's transform this into a bar graph."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Simple bar graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Stacked bar graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Grouped bar graph"
   ]
  },
  {
   "source": [
    "Great! Now let's explore this data as a percentage of the total global emissions."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Total global emissions for those years\n",
    "world_emissions = [33066.651, 34357.366, 34919.289, 35207.886, 35505.827, 35462.747, 35675.099, 36153.262, 36572.754]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot multiple years on the same figure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}

================================================
FILE: intro_data_science/part_2/ds_part_2_complete.ipynb
================================================
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   "source": [
    "# A Brief Introduction to Matplotlib\n",
    "\n",
    "In this notebook, we will cover the basics of Matplotlib, a powerful, prolific library \n",
    "for organizing and visualizing data of all kinds. If you work in science, chances are you'll\n",
    "find a use case for this. Enjoy!"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "source": [
    "Now, let's make our first plot - a line graph! We're going to visualize data related to CO2 emissions."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
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EBwcDsGjRIrp160ZERARBQUH88MMPZGRkEBoaipOTE3Xr1uXMmTMsX76c8ePHo9FoCA0NpW3btkyePBmA2rVrs2/fPkJDQ/niiy9K8RURQogHXG4umitX8gcN4yUx8Z5W63D1ajEXKkpTmR0zEhkZSXR0NB06dFCnOTk50apVKw4dOsTw4cMJDw/H1dWVFi1aqG1CQkJwcXHh0KFDBAUFER4eTsuWLXFyclLbdOzYkVmzZhEZGUnVqlU5fPgwI0eONHn8jh07smLFipJ/okII8aBJSjIfNC5exObyZTQ6XbE/pIQR8+bMmcOFCxcK/D4LCQnhww8/pG3btqVcmakyG0aib27/8/HxMZnu4+NDVFQUADExMXh5eZlsx9NoNHh7exMTE6O2qVixYr51GOdVrVqV6Ohos49jXEdB7veIf9ZyxEBrqROk1pJgLXWC1FpSbq9Vk5OD/fXrOFy9ql7s89zWpqaWWC055cuTVbEiWZUqqZfsSpXIqFaN3Pt4TYOCgoqxytL3ww8/sGzZMiIiInB1daVBgwa89tprhS73xx9/lEJ1hSuzYcQa3M+b17gZqayzljpBai0J1lInSK3FRlEgLQ1NfDw2sbFEHzpEpcxMk94NzdWraPT6knl4Bwf0gYHoq1a9dW28BAaCqytg+PLSAsb9Msr0a1rCPvnkExYvXszChQvp2LEj9vb27Nq1i19++QVnZ2dLl1ckZTaM+Pn5ARAbG0uVKlXU6bGxsfj6+gLg6+tLfHw8iqKovSOKohAXF2fSJjY21mTdxvvGNn5+fmbbGOcLUWwyMtDExOB44QI2OTmQmwt6vWFQXm4u6HSGbep6veG+cZpOZ+javtM043qM04xt8k67ud47TsuzXK20NJx8fFA8PAwXd3fTa09PMN6+OR07O0u/yiKvrCw0CQlo4uMNASMhAU1cnHrfOM8mLu5Wu6wsdXHXEihJ7+dnPmxUrYri7w82Zf94nB6eniW6/hu37RVaYLsbN5gzZw7Lli2jV69e6vRu3brRrVs35syZQ3Z2NqNGjWLr1q1UrlyZ0NBQmjRpAkCDBg1YunQpjz/+OHPmzOH06dM4ODiYbbto0SJWrVpFXFwclSpVYvr06fTs2bNYnm+ZDSOBgYH4+fkRFhZG06ZNAcjMzOTgwYN88MEHAAQHB5Oamkp4eLg6biQ8PJy0tDT1fnBwMO+99x6ZmZk4OjoCEBYWRoUKFQgMDASgefPmhIWFMWHCBPXxw8LCTMaiCFEgvR5NYiKa69exiYlBc/06mpgYbG6/jo5Gk5wMQH0Ll1xU7vewjOLsbD64GMPK7cHmtrY4O4PsQmmeTocmKelWkLgZJmzMhAtNfDw28fFoUlJKvUzF0bHAsKEPCAAzx5kQ9+bw4cNkZmby5JNPFthm27ZtfPPNNyxfvpyZM2fy+uuvs2vXrrtuW61aNbZt24afnx8///wzo0aNonnz5vj7+9/387BoGElNTeX8+fMA6PV6rly5wvHjxylXrhxVqlRhzJgxLFy4kKCgIGrWrMmCBQtwcXFhwIABgGGvl06dOjFp0iQWL14MwKRJk+jSpYvaXTdgwADmzZvH2LFjmTx5MmfPnmXx4sVMmTJF7U0ZPXo03bt3Z9GiRfTo0YMtW7awb98+tm/fXvoviig7MjPRREerAcPkOjr61rzo6AJ3N3wYadLT0aSnw82xXXdL0WpNAgp3CDZ5bzvExaGxszMEGRsb00ueaYrxV/cd2qj3S5KiQGrqrdBQUMDIEy40iYkltnnkbukrVCi4d8PPTwJlKUlISMDLywuttuCv85CQEHXP1YEDBxIaGnpPbfv06aPe7tevHwsXLuTIkSP06NHjPp+FhcPIsWPHTLp45syZw5w5cxg8eDChoaFMnDiRjIwMXn/9dZKSknj00Uf56aef1GOMAKxcuZIpU6bQv39/wNA1NX/+fHW+h4cHGzZsYPLkybRv3x5PT0/GjRvH+PHj1TYtWrTgyy+/ZObMmcyePZtq1arx5ZdfyjFGHkSKYviDbgwT0dHmezGuX0dz44alq30oaXJz0cTHQ3z8XS3XoARqUcwFlNvu52uTt12eayXPvIY3bmB34waa7OwSqPr+KY6OKN7eKOXKkVyuHM716uXv3cizh6KwnPLlyxMfH09ubm6BgcQ47AEMe6VmZmYW2P5ObdesWcOyZcu4dOkSYDhWWPxdfk4LYtEw0rZt23xHS81Lo9Ewbdo0pk2bVmAbT0/PQnfBrVevHtu2bbtjm969e6vHMxFW6Oboftvk5II3l9wMIJqcHIuVqWi1KL6+ZNnZYe/sDLa2YGuLotWqt9FqUW5em51mY2O4Nk7L2ybvMnmnGZe507Q8yxhrunrlCpU9PNDcuIEmOdlwXcBtjLfLyC/34qBRlFvjaApqcw/rtb33ku6aYmOD4uVluJQvj+Llhd7b2+S+Ou3m/bybys49xAND76SoYzpKWvPmzdUxHiX5HXbp0iUmTpzIxo0bCQ4OxtbWljZt2hTb+svsmBEh8snNxebSJWzOnbt1OX/ecH35Mg1L4NgFRaW4u6P380Px9UXv729yrfj7o795rZQrBzY2VjPyPyUigty7qdO46aGg8JJnGgWFmzyDJ0V+irs7emO4yHPRG8OFMWgYLx4eVjEgVNwbDw8Ppk2bxuTJk7G1taVDhw7Y2dnx66+/sm/fPpNjbN2P9PR09dAZAN9++22+07PcDwkjomzR6dBcvoytMWTkDRyRkaU6NkOxtTWEibwBw88Pxc/P9NrX1/BLUhh+Tbu5obi5oRTe2rzMzDuHl7zBJc/93KQk7LRaw95BimK46PWmF0Ux9NzknWemnUa55+rviro5pHz5WwEjT6DQ5+29uLnZBHv7UqlNWI+XX34ZPz8/FixYwMiRI3F1daVx48a89tpr7Nmzp1geo06dOowfP54nnngCGxsbBg0aVKw7eWiSkpJK51MnTFjLL+MSqVOvR3P1Kjbnz2N7ey/HxYslvh1dcXMz9FQU0Huh9mKUL2/YbFECHur//xJS7LXeHlLMhRswhJs7tVMUQ7gx3tfpuBATQ+Cjj1rFnkPW8h4oyTpv3LiBh4dHiaz7YVPQayk9I6Jk6PVooqLUkGGbN3BcuFAiXfE55ctjU6lSvs0kJoHDx0c9aJIQd6TR3Bp7cwdF+TV3e5tsW1vZvVWIPCSMiHunKIY9UvIEDTV0XLiAJiOj2B9S7+uLvkYN9NWro69RA53xdvXqRFy7ZhW/4IQQQpiSMCLuTFHQxMbmG79hawwcJXAOCr23txow9DVqGELHzfu438thuIQQQpRlEkaEWTZ//YXjggU0DgtDm5ZW7OvXlytn0sOhho5q1aCED7MshBCibJEwIkzYnD6N4+zZ2G3ceN/rUtzdDZtRzIQOpVy5YqhWCCHEg0DCiABAc/EijvPmYbdu3V0dtEpxc0NfvbphM8ptoUPx8irzewoIIYSwPAkjDznN9es4fPQR9l9/XeCRSRVn5/wDRo2Bw8dHAocQQoj7ImHkIaVJSMDh44+xX7GiwL1edA0bcv6FF/AdNkwChxBCiBIjYeRhk5yMQ2goDsuWqaezv52uVi0y33qL3J49uXHuHL4SRIQQQpQgOWHBwyIjA/ulS3Fr3BjHOXPMBhF9QADpy5eTeuAAub17y/kshBDCCnh6enL+/HmTaXPmzGHkyJHq/Y8++oiGDRtSqVIl6taty/Dhw/OtZ/Xq1Xh6evLTTz+VeM23k56RB112NvbffIPDggXYREWZbaL38yPr9dfJHjpUznshhBAPmO+++45169axceNGqlWrRnR0tNkz2a9Zs4Zy5cqxdu1a+vXrV6o1Shh5UOl02P3wA45z5mATGWm2ib5cObImTSJ7xAg50ZsQQtwFT8+SPVdNUtKNYlvXsWPH6NChA9WqVQPAz8+P559/3qTNpUuX+P333/n666954YUXiI6Oxs/Pr9hqKIz0wz9oFAXtpk24tm6N8+jRZoOI4upK5tSppPz1F9kTJkgQEUKIB1izZs1Yu3YtS5Ys4dixY+h0unxt1q5dS5MmTejduze1a9fm+++/L9UaJYw8KBQF7e7duLRvj8vQodj+91/+Jo6OZL38Mil//03WtGkgZ6EUQogH3sCBA5k/fz67d++mR48e1KxZk8WLF5u0Wbt2LQMGDABgwIABrF27tlRrlDDyALA9cACX7t1x6d8f7V9/5ZuvaLVkvfgiKUePkjljhuFgZEIIIR4Itra25Nx2nKjc3Fy02lsjMZ5++mk2btxIZGQkixYtYtasWezevRuAP/74g8jISPr37w8YwsjJkyc5fvx4qT0HGTNixWz++gvHWbOw27nT7HxFoyFn4EAy33gDpWrV0i1OCCEeYMU5puN+Va5cmUuXLlG7dm11WmRkJDVq1MjX1s7Ojj59+rB48WJOnjxJx44dWbNmDYqi0LZtW5O2a9asoWHDhiVeP0gYsUpFOX9MTq9eZL75Jvo6dUqxMiGEEKWtX79+LFiwgLp161KhQgX27t3L9u3b+d///gcYdtn19vamVatWuLi4sHv3bv777z+aNWtGZmYmGzZsYPHixXTp0kVd56ZNm5g/fz4zZsww6WEpKRJGrEhRzh+T06kTmdOno2/cuHSLE0IIYRFTpkxh9uzZdO3alRs3blC1alVWrFhB3bp1AXBzc2PhwoWMHDkSvV5PlSpV+Oijj2jZsiU//vgjTk5ODB48GDs7O3Wdzz77LLNnz2bXrl107dq1xJ+DhBEroImKMpw/ZtWqAs8fk9uyJZlvv42uVatSrk4IIYQlOTk5MWPGDGbMmGF2fq9evejVq5fZef3791fHity+zgsXLhRrnXciYaQM0yQk4LB4seH8MZmZZtvoGjUi8+23ye3YUc4fI4QQwipJGCmLkpNxWL7ccP6YlBSzTXS1a5P55pvk9uolIUQIIYRVkzBSlmRkYL9yJQ6LFmGTkGC2iT4wkMw33iDn6afB1raUCxRCCCGKn4SRssB4/pgPP8Tm+nWzTfT+/obzxzz3nJw/RgghxANFwogl6XTYff89jnPn3vn8Ma++SvaLL8ph24UQQjyQJIxYgqLguWcPrs8+i+3p0+abuLmRNW4cWWPHgrt7KRcohBBClB4JI6VMExuL81NP4WHmsO1gOH9M9siRZL3yCkr58qVbnBBCCGEBEkZKmeLtbX66nR3Zw4aR9dprKBUqlHJVQgghhOXIifJKm0ZD1vTp6l3FxobswYNJOXyYzAULJIgIIYQoFXPmzGHkyJEAXL58mUqVKqHT6SxSi4QRC8jt2JGUJk3I6d2b1IMHyQgNlRPZCSGEuCcNGjTg119/va91VKlShatXr2JroUNGyGYaS9BoOPPJJ9SsV8/SlQghhChBv5z9hWWHl3E99Tr+rv6Maz6O7jW7W7qsMkfCiIUocqwQIYSwWk0/b3rXy0SlRjE9bDrTw6YX2vboS0fvev2rV6/mm2++oVmzZnzzzTd4eHjw0Ucf8cQTTwBw8eJFxo4dy/Hjx2nWrBlBQUHqspGRkTRq1Ii4uDi0Wi3ffvstS5Ys4dq1a3h5efHKK68wfPjwu66pqGQzjRBCCPGA+PPPPwkKCuL8+fNMnDiRl19+GUVRAHjppZdo3Lgx586d4/XXX2fNmjUFrsfHx4d169Zx+fJlli1bxptvvslfBewFWhwkjAghhBAPiCpVqjBs2DBsbW0ZPHgw169fJyYmhsuXL3P06FHeeustHBwcaN26NV27di1wPV26dKFatWpoNBratGlD+/btOXjwYInVLWFECCGEeED4+fmpt51vHrU7LS2N69ev4+npiYuLizq/SpUqBa5n586ddOrUiapVqxIQEMDOnTuJj48vsbplzIgQQghxl4oypuOXs78wc99MMnMz1WmOWkemt51e6oNY/fz8SEpKIi0tTQ0kV65cQWPmrO9ZWVkMHTqUTz/9lO7du2NnZ8czzzxTovVJz4gQQghRArrX7M70ttOp4FoBDRoquFawSBABCAgIoEmTJsyZM4fs7GwOHjzI9u3bzbbNzs4mKysLLy8vtFotO3fuJCwsrETrK9NhRKfTMXPmTBo2bIifnx8NGzZk5syZ5Obmqm0URWHOnDnUqVMHf39/evTowalTp0zWk5SUxMiRIwkICCAgIICRI0eSlJRk0ubEiRN0794df39/HnnkEebNm6cO+hFCCCHuRfea3dk6eCtHXjrC1sFbLbpb78qVKzly5AjVqlVj3rx5DBo0yGw7Nzc35s2bx/DhwwkMDOSHH36gW7duJVpbmd5Ms3jxYlauXEloaCh169blxIkTjBkzBnt7e6ZMmQLAxx9/zLJly1i2bBlBQUHMnz+fvn37cvjwYdzc3AAYMWIEV65cYf369QBMmDCBUaNGsW7dOgCSk5Pp27cvrVq1Ys+ePURERDBu3DicnZ15+eWXLfPkhRBCiCL4559/1NtDhgwxmZf3h3fVqlXZtm2b2XUEBgaatH3ppZd46aWXirXOOynTYSQ8PJyuXbuqiSwwMJBu3bpx5MgRwNArEhoayiuvvELv3r0BCA0NJSgoiPXr1zN8+HBOnz7Nrl272L59O8HBwQAsWrSIbt26ERERQVBQED/88AMZGRmEhobi5ORE3bp1OXPmDMuXL2f8+PFmt6kJIYQQoniU6c00ISEh7N+/nzNnzgDw33//sW/fPvUALpGRkURHR9OhQwd1GScnJ1q1asWhQ4cAQ6BxdXWlRYsWJut1cXExadOyZUucnJzUNh07diQqKorIyMgSf55CCCHEw6xM94y88sorpKam0qJFC2xtbcnNzWXy5MmMGDECgOjoaMBwcJa8fHx8iIqKAiAmJgYvLy+T3g2NRoO3tzcxMTFqm4oVK+Zbh3Fe1QLOGxMREXFfz+9+ly8t1lInSK0lwVrqBKm1pFhLrfdTZ96jkYrSV6bDyE8//cTatWtZuXIlderU4Z9//uGNN94gICCAoUOHWrq8+3rzGjcRlXXWUidIrSXBWuoEqbWkWEut1lKnMK9Mh5F33nmH8ePH079/fwDq1avH5cuXWbRoEUOHDlUP7hIbG2ty8JbY2Fh8fX0B8PX1JT4+HkVR1N4RRVGIi4szaRMbG2vy2Mb7xjZCCCGEKBllesxIenp6vtMZ29raotfrAcOAVj8/P5P9nzMzMzl48KA6RiQ4OJjU1FTCw8PVNuHh4aSlpZm0OXjwIJmZtw5MExYWRoUKFQgMDCyx5yeEEEKIMh5GunbtyuLFi9mxYweRkZFs3ryZZcuW8eSTTwKGsR9jxozh448/ZtOmTZw8eZKxY8fi4uLCgAEDAKhduzadOnVi0qRJhIeHEx4ezqRJk+jSpYvapTdgwACcnJwYO3YsJ0+eZNOmTSxevJixY8fKnjRCCCFECSvTm2nmz5/PrFmzeO2114iLi8PPz49hw4apxxgBmDhxIhkZGbz++uskJSXx6KOP8tNPP6nHGAHDgV6mTJmibu7p1q0b8+fPV+d7eHiwYcMGJk+eTPv27fH09GTcuHGMHz++9J6sEEII8ZAq02HEzc2NuXPnMnfu3ALbaDQapk2bxrRp0wps4+npyYoVK+74WPXq1SvwYDBCCCGEMGxJ6NevX7Gfq6ZMhxEhhBBC3FmDBg2IjY3F1tYWFxcXOnbsyIcffoirq2uxP5bxSObFrUyPGRFCCCFE4dauXcvVq1fZt28fx48fZ9GiRZYu6a5Iz4gQQghxlxZ7Li7R9b+S9Mo9Lefn50fHjh35559/2LdvH6NGjeLkyZPq/AYNGrB06VIef/xxjhw5wmuvvca5c+dwdHTkqaeeYvbs2WRmZjJhwgR27tyJTqejRo0arFu3Dl9fX3r06MHAgQMZOnQoFy5cYMKECfz7779oNBq1R8bT0/Ou65aeESGEEOIBcfXqVXbt2kW1atUKbfvGG28wevRoLl++zLFjx+jbty8Aa9as4caNG5w4cYILFy6waNEiHB0d8y2vKAqvvvoq//33H+Hh4Vy5cuWOYzzvRMKIEEIIYeWGDBlC5cqVqVevHt7e3nfcqcNIq9Vy/vx54uPjcXV1pXnz5ur0xMREzp8/j62tLY0bN8bd3T3f8tWrV6d9+/Y4ODjg7e3NuHHj+P333++pfgkjQgghhJVbvXo1V65cYcuWLURERJCQkFDoMp988gnnzp2jefPmtG/fnu3btwMwaNAgOnTowIsvvkidOnV45513yMnJybd8TEwML7zwAo888ghVqlRh1KhRRXpcc2TMiBBCCHGX7nVMR0lr06YNzzzzDNOnT2fy5Mmkp6er83Q6HfHx8er9GjVq8MUXX6DX69m8eTPDhg3j/PnzuLi48MYbb/DGG28QGRnJ008/Tc2aNfOdE+6DDz5Ao9Fw4MABypUrx5YtW0yOA3Y3pGdECCGEeICMGTOGX3/9FTs7O7KystixYwc5OTl8+OGHZGVlqe3WrVtHXFwcNjY2eHh4AGBjY8PevXs5ceIEOp0Od3d3tFotNjb540JqaiouLi64u7tz7do1li5des81SxgRQgghHiDe3t4MGjSI+fPns2DBAiZMmMAjjzyCi4sLFStWVNvt3r2bkJAQKlWqxBtvvMEXX3yBk5MTMTExDBs2jCpVqhAcHEzr1q0ZNGhQvseZOnUqf//9NwEBATz99NPqqVruhSYpKUm556XFPbOW011bS50gtZYEa6kTpNaSYi21lmSdN27cUHsOxP0p6LWUnhEhhBBCWJSEESGEEEJYlIQRIYQQQliUhBEhhBBCWJSEESGEEEJYlIQRIYQQ4g60Wi1paWkoiux8eq8URSEtLQ2t1vyxVuUIrEIIIcQduLi4kJWVRXJysqVLsWqOjo44ODiYnSdhRAghhCiEg4NDgV+k4v7JZhohhBBCWJSEESGEEEJYlIQRIYQQQliUhBEhhBBCWJSEESGEEEJYlIQRIYQQQliUhBEhhBBCWJSEESGEEEJYlIQRIYQQQliUhBEhhBBCWJSEESGEEEJYlIQRIYQQQliUhBEhhBBCWJSEESGEEEJYlIQRIYQQQliUhBEhhBBCWFSRwsipU6d46aWXaNu2LX369OGbb75BUZR87b7//nvKly9f7EUKIYQQ4sFVaBg5d+4cTzzxBJs3b8bW1paIiAgmTJhAt27diI6OLo0ahRBCCPEAKzSMzJw5ExcXFw4cOMCvv/7KiRMn+PTTTzl58iRPPPEEERERpVGnEEIIIR5QhYaRw4cPM3LkSKpXr65OGzhwILt27cLGxoauXbty5MiREi1SCCGEEA+uQsNIYmIivr6++abXqlWL//3vf1SsWJHevXuze/fuEilQCCGEEA+2QsNIQEAAJ06cMDvP19eXrVu30qBBAwYPHszPP/9c3PVx/fp1Ro8eTY0aNfDz86NFixbs379fna8oCnPmzKFOnTr4+/vTo0cPTp06ZbKOpKQkRo4cSUBAAAEBAYwcOZKkpCSTNidOnKB79+74+/vzyCOPMG/ePLODdIUQQghRvAoNI23atGHjxo3k5uaane/u7s7PP/9Mx44d2bZtW7EWl5SURJcuXVAUhe+//55Dhw4xf/58fHx81DYff/wxy5YtY968eezZswcfHx/69u1LSkqK2mbEiBEcP36c9evXs379eo4fP86oUaPU+cnJyfTt2xdfX1/27NnD3LlzWbp0KZ988kmxPh8hhBBC5KctrMEzzzxDTEwMx44do3nz5mbbODg4sHr1at58803+/fffYituyZIl+Pv789lnn6nTqlatqt5WFIXQ0FBeeeUVevfuDUBoaChBQUGsX7+e4cOHc/r0aXbt2sX27dsJDg4GYNGiRXTr1o2IiAiCgoL44YcfyMjIIDQ0FCcnJ+rWrcuZM2dYvnw548ePR6PRFNtzEkIIIYSpQntGmjRpwqpVqwoMIuqKbGyYO3cuW7ZsKbbitm7dyqOPPsrw4cOpWbMmbdq0YcWKFermk8jISKKjo+nQoYO6jJOTE61ateLQoUMAhIeH4+rqSosWLdQ2ISEhuLi4mLRp2bIlTk5OapuOHTsSFRVFZGRksT0fIYQQQuRXaM+IJV28eJEvvviCsWPH8sorr/DPP/8wdepUAEaOHKke5yTvZhvj/aioKABiYmLw8vIy6d3QaDR4e3sTExOjtqlYsWK+dRjn5e2Nyet+d2u2lt2iraVOkFpLgrXUCVJrSbGWWu+nzqCgoGKsRNytIoeRGzdu8PXXX7N9+3bOnDlDSkoKrq6u1K5dm27dujFs2DA8PDyKtTi9Xk+TJk149913AWjUqBHnz59n5cqVjBw5slgf617cz5vXuImorLOWOkFqLQnWUidIrSXFWmq1ljqFeUUKI4cPH2bYsGFERUXh4OBAzZo1qVWrFikpKRw7dow//viDzz77jK+//rrQzTl3w8/Pj9q1a5tMq1WrFleuXFHnA8TGxlKlShW1TWxsrLo7sq+vL/Hx8SiKovaOKIpCXFycSZvY2FiTxzHeN7dbsxBCCCGKT6FjRq5cucKAAQPQ6XSEhoZy6dIl9u/fz7Zt29i/fz+XLl0iNDSU3NxcnnrqKTUoFIeQkBDOnj1rMu3s2bNq8AgMDMTPz4+wsDB1fmZmJgcPHlTHiAQHB5Oamkp4eLjaJjw8nLS0NJM2Bw8eJDMzU20TFhZGhQoVCAwMLLbnI4QQQoj8Cg0jCxcuBGDHjh0MGjQIe3t7k/n29vYMGjSIHTt2oCgKixYtKrbixo4dy+HDh1mwYAHnz5/n559/ZsWKFYwYMQIwjP0YM2YMH3/8MZs2beLkyZOMHTsWFxcXBgwYAEDt2rXp1KkTkyZNIjw8nPDwcCZNmkSXLl3ULr0BAwbg5OTE2LFjOXnyJJs2bWLx4sWMHTtW9qQRQgghSlihm2l27drFsGHDChzEaVS1alWGDh3Kxo0bi6s2mjZtyurVq/nggw/48MMPqVy5Mm+++aYaRgAmTpxIRkYGr7/+OklJSTz66KP89NNPuLm5qW1WrlzJlClT6N+/PwDdunVj/vz56nwPDw82bNjA5MmTad++PZ6enowbN47x48cX23PJKzFRw969HiQm2lK+vEL58goeHgq2tiXycEIIIUSZVmgYiY6OplatWkVaWe3atYv9TL5dunShS5cuBc7XaDRMmzaNadOmFdjG09OTFStW3PFx6tWrV+wHbSvI8eM2vPaa6UArjUbB09MQTLy8FMqVU9SgYpimzzetfHkFB4dSKVkIIYQoMYWGEXd39yIHjOjoaNzd3e+7qAddQkL+rWOKoiExUUNiIpw7V/R1ubrmDS76fGHF9GKY7+oKsvVJCCFEWVFoGAkODua7775j/PjxONzhZ3hmZibfffedepRTUbCEhOJLAqmpGlJTNVy+DFC07Tx2dncOK+XKGXpnypdXSElxoFIlcHYutpJFGaUokJVluGRna8jMhCtX7PHxAQ8PCbBCiJJTaBh5+eWX6d69OwMGDGD58uUmu9AaXblyhbFjx3LhwgWWLVtWIoU+SPz89LRseYOsLBcSEjQkJNiQnFx6f+lzcjRER2soWodXAwC8vPRUrqxQpYqeypUNF8NtwzRvbwWbQodDC3NycyEz0xAAjGEgK0ujTjOdZ9rG3HV2NmRmasxem29/6zHyawiAvb2Cj4+Ct7eCr6/+5rWCt7ceX1/jPMNtLy8FO7vSfQ2FENat0DASEhLCnDlzePPNN2natCktW7akfv36uLq6kpqayokTJzhw4AB6vZ6ZM2cSEhJSGnVbtSefzKV2bdMD9OTkGAa2GsKJhvh4TZ77Nur0vJfERA06XemEmPh4G+Lj4e+/zfe+ODgoVKpkCCe3gsqtwFKpkp48R9t/IOl0EBenITZWQ2ysDTEx5m/HxWlISWlMbq4tmZmg15f9LofsbA1Xr2q4ehWK0gNXvrweHx/l5uVWePHxuTXdGGpcXUu8fCFEGVekg56NGjWKRo0a8eGHH7J371727dt3awVaLW3btmXy5Mm0atWqxAp90NnZga+v4Q92Uen1kJxMgWHl1sV0fmZm8X/5ZWVpOH/elvPnC27j42PsVVFMeleqVDHc9/ZWytymgKwsiInREBdnCBSGUGFzM1hoiIkxhIuYGEOAVJSiPoEHuxvJ8J6D06cLb+vsbOhxMRdUbu99KVdO9joT4kFU5MPBh4SE8OOPP5KRkcG5c+dITU3F1dWV6tWr4ywDCizCxgY8PcHTU0/16kVfLj2dO4aVvJdr13TExtoXSw+M4UvchmPHzM93dDT0oBh7U24PLBUr6nF0vL8aFAVSUzHbc2EIFTY3Q4ZhemluPisL7OwMe2g5OBiudbocUlLsSU8vudchPV3DpUsaLl0qPKDZ2BiCi3Fz0a3eFwWdzpvatbWUK2fYM8147ews410eVDqdoUc5NlZDbu6DHfAfdHd9ojwnJyfq169fErWIUuLsbPg1WrmysRdGV2DbiIgIqlUL4vp1DVeu2HDlig2XL9tw5Yomz+3i+dLOzNRw7pwt584V/NPX1/dW78rt41eiox2JirJVezFu30Ri7MXIyChb30w2NqYBwN7eEMzMXTs4mJtmXD7vOhQcHW9dOzgYlzGdl3cZBwfyjfsxnu8jLQ319TT2Ct0e4oybqBIS7qaH6O7o9ZqbPVRw8uTt75OqZpextzcNJ8bL7aHl9mtPTxn7UtoUBVJSIC7u1vvM8L6yMbkdF2e4HR+vUTdzfvmlEw0aWPgJiHtWaBi5fv06PXv2pHfv3kyfPr3AdjNnzmTTpk388ssveHt7F2uRwrK0Wm72VOgoKLjcuIEaVgwXjRpUrlyx4do1TbGMjYiJsSEmxoajR83NtXxILlfO/KYG01/xemJjz/HII9VxcDC8vmX9l7uLC7i4KFStWnBwNcrNhfh4jcnmLeMXSd7NWsZ55gfOFp/s7FsB5m65uhYlvOhNQo6np4K7e/5g97DKzDQE2fj4/KHi9oARG3vv74eEhDJ9EnpRiEL/9z777DMSExOZOHHiHdtNnDiRr7/+ms8++4y33nqr2AoU1sHDAzw89NSrpzc7PzcXoqJMA0re3pXLl21ITS1738i2tvk3C+QdiJl3TIO3t6H3oShycnQP7MBNrRb8/BT8/BTA/PvBSFEM455u783K2/uS9/aNG6X7HjHuOn+3p9yysTEcVbmgAJOR4Y+/vz329obxYnZ2ys3rW7ft7UGrNb1trr29vYJWi3q/pENQbq5hM68xSBh7MfIHDMO8lJTS+T9LTJRuLGtWaBj53//+R9++fU0Or26Om5sb/fv3Z9u2bRJGRD5aLVSpolClivneFUW51btSUGCJiiqe7n8Hh1s9FMaBkXl7MfIGjfLlZZflkqTR3AqyNWoU3j4rK/8eS8YvwQsXUtDpPEhK0pCUZDyIYMn3vJij1986iKF5lUvssW1tb4UWY4DRag2hxTDt1u38Qcg03NjaKly+XJ3MTJdS2Qx3Lzw9DZ9XO7uiD/4XZU+hYeTChQuMHDmySCurW7cu33zzzX0XJR4+Gs2twbj165v/NZ2TA9euFTx2JT09h4oVtWq4yLtZxLinkre3Hnf3sr9ZRJjn4ACVKilUqpS/1yUi4qLJ7vJgCLkZGajh5PbrGzfyT887zxp2u76dTqchI8PwvKE46i/dc044OZnuEm48ho3xtvG+j4/hmDbG3siIiHigfKnWKopPoWFEo9Gg19+5q9VIr9fLWW5FibGzg8BAhcBA870rxsGWQhhpNLcGbFeseHe/nI27zhsDiiGk2BQYbPK2K4ubHC1Fq721qdPY62guZBgDhouLpSsWllBoGAkICODIkSMMHz680JUdPXqUgICAYilMCCEs6dau8wpQ+J5neWVnY7bXxXh97Voirq7lycnh5kVT6O3sbA25uaa3s7Pzty8NxoHat3or8ocK43wPD9nUKQpXaBjp0qULn332GRMmTLjj2XvPnDnD+vXrGT16dLEWKIQQ1sbeHvUL2ZyIiKsEBRX/8ZkUhTyBBXJzNSa3DeHF9La58GNob2iXkRFFgwa+atgoX152eRbFr0jnplm9ejU9e/Zk1qxZ9OnTB6321mK5ubn8/PPPTJ8+HTc3N8aPH1+iBQshhDBPo7k1ENVwLMq8YejeBnhGRCQQFORVHOUJUaBCw4iXlxc//PADzz77LCNHjmTChAnUrFlTPTfN2bNnyczMpEKFCqxduxYvL3nTCiGEEKLoinSUmMaNG3PgwAG++uortm/fzn///UdKSgpubm40bNiQbt268fzzz+Ph4VHS9QohhBDiAVPkQ9a5u7szceLEQg9+JoQQQghxN2SMsxBCCCEsSsKIEEIIISxKwogQQgghLErCiBBCCCEsSsKIEEIIISxKwogQQgghLErCiBBCCCEsSsKIEEIIISxKwogQQgghLErCiBBCCCEsSsKIEEIIISxKwogQQgghLErCiBBCCCEsSsKIEEIIISxKwogQQgghLErCiBBCCCEsSsKIEEIIISxKwogQQgghLErCiBBCCCEsSsKIEEIIISxKwogQQgghLMqqwsjChQvx9PTk9ddfV6cpisKcOXOoU6cO/v7+9OjRg1OnTpksl5SUxMiRIwkICCAgIICRI0eSlJRk0ubEiRN0794df39/HnnkEebNm4eiKKXxtIQQQoiHmtWEkcOHD/P1119Tr149k+kff/wxy5YtY968eezZswcfHx/69u1LSkqK2mbEiBEcP36c9evXs379eo4fP86oUaPU+cnJyfTt2xdfX1/27NnD3LlzWbp0KZ988kmpPT8hhBDiYWUVYeTGjRu89NJLfPLJJ3h6eqrTFUUhNDSUV155hd69e1O3bl1CQ0NJTU1l/fr1AJw+fZpdu3axePFigoODCQ4OZtGiRezYsYOIiAgAfvjhBzIyMggNDaVu3br07t2biRMnsnz5cukdEUIIIUqYVYQRY9h47LHHTKZHRkYSHR1Nhw4d1GlOTk60atWKQ4cOARAeHo6rqystWrRQ24SEhODi4mLSpmXLljg5OaltOnbsSFRUFJGRkSX51IQQQoiHntbSBRRm1apVnD9/nhUrVuSbFx0dDYCPj4/JdB8fH6KiogCIiYnBy8sLjUajztdoNHh7exMTE6O2qVixYr51GOdVrVrVbG3GnpV7db/LlxZrqROk1pJgLXWC1FpSrKXW+6kzKCioGCsRd6tMh5GIiAg++OADtm/fjp2dnaXLyed+3rwRERFW8ea3ljpBai0J1lInSK0lxVpqtZY6hXllejNNeHg48fHxhISE4OXlhZeXF7///jsrV67Ey8uL8uXLAxAbG2uyXGxsLL6+vgD4+voSHx9vMvZDURTi4uJM2phbh3GeEEIIIUpOmQ4jPXr04MCBA+zbt0+9NGnShP79+7Nv3z5q1qyJn58fYWFh6jKZmZkcPHhQHSMSHBxMamoq4eHhapvw8HDS0tJM2hw8eJDMzEy1TVhYGBUqVCAwMLCUnq0QQgjxcCrTm2k8PT1N9p4BcHZ2ply5ctStWxeAMWPGsHDhQoKCgqhZsyYLFizAxcWFAQMGAFC7dm06derEpEmTWLx4MQCTJk2iS5cuapfegAEDmDdvHmPHjmXy5MmcPXuWxYsXM2XKFJOxJkIIIYQofmU6jBTFxIkTycjI4PXXXycpKYlHH32Un376CTc3N7XNypUrmTJlCv379wegW7duzJ8/X53v4eHBhg0bmDx5Mu3bt8fT05Nx48Yxfvz4Un8+QgghxMPG6sLI1q1bTe5rNBqmTZvGtGnTClzG09PT7N44edWrV49t27YVS41CCCGEKLoyPWZECCGEEA8+CSNCCCGEsCgJI0IIIYSwKAkjQgghhLAoCSNCCCGEsCgJI0IIIYSwKAkjQgghhLAoCSNCCCGEsCgJI0IIIYSwKAkjQgghhLAoCSNCCCGEsCgJI0IIIYSwKAkjQgghhLAoCSNCCCGEsCgJI0IIIYSwKAkjQgghhLAoCSNCCCGEsCgJI0IIIYSwKAkjQgghhLAoCSNCCCGEsCgJI0IIIYSwKAkjQgghhLAoCSNCCCGEsCgJI0IIIYSwKK2lCxBCCCHu1S9nf2HZ4WVcT72O/5/+jGs+ju41u1u6LHGXJIwIIYQo87Jys4hOi+Z66nX1+vDVwxyNPope0QMQlRrFzH0zASSQWBkJI0IIISxKr+iJz4g3BI1UQ9CISotSb19Pu05CRkKR1pWZm8myw8skjFgZCSNCCCFKVGp2qtqjEZUadSt0pF1Xp+fqc4vt8a6nXi+2dYnSIWFECCHEPcvR5RCTFqMGi7ybUYy9GqnZqaVak7+rf6k+nrh/EkaEEELk88vZX/jk8CdcT72O1yEvOlfvTCX3Svk2o8Slx6GglHg9NhobfJx98HPxw9/VH39XfxLSE9hxfgc5+hy1naPWkXHNx5V4PaJ4SRgRQogHSK4+l/ScdNJy0kjPTic9N91wPztNnZ6Rk2GYb2yXk65e0nLSiE2LJSkrSV1nfEY8a06sKdG63R3c8Xfxx8/VEDYquFRQb/u7+uPt7I2djV2+5UKqhNzam8ZV9qaxVhJGhBCilJjbDbVz9c7mQ8LN8JCem056dvodw0Pe5bN0WZZ+mvnY2dgZgoWLPxVc84QMF0PQ8HPxw8Xe5Z7W3b1md7rX7E5ERARBQUHFXLkoLRJGhBCiEIqikKnLJDMnk4zcDDJyMgzXuRmk56STmZtpMi3vbeMyl29c5nzSeXWTRlRqFNPDpjM9bLqFn93983byVnsw8m5GMQaOck7lsNHIMTZFwSSMCCGsmrG3ISo1Cr/DfgxvNJxWVVqZhIKihIX0nPR804xtM3MzS2VcRFnnpHVieOPhJr0avi6+2NvaW7o0YeUkjAghLC5Hl0NqTiqp2fkvadlppGankpKTkm96VEoUsRmx6nqi06KZe2CuBZ+J5WnQ4GLngrO9M85aZ5ztDBcXexfDtZ0LTnZOhjY37xvbGO8fvnaY5X8uN9nk46h15K22b8l4DFEiJIwIIfIp6iG2FUUhS5eVP0DkpJGSnWI+XOSk5ZtWFsc5lCZXO1eTgJA3PDhrnXG2d75jeMh721HriEajua966njXwcvZSwaGilIjYUQIocrIyWDdyXWE/hmq7i4ZlRrFu7++y+rjq3GxdzENEjmpxXqwqrLM3tYeZ60zjnaOOGmdDBc7J/V2YdP/jfmXNSfWkK3LVtfpYOvA223fpntQ2fuSl4GhojRJGBHiIaEoCklZSUSlRBGVGqUeCVO9nXLdZHfOvHSKjlPxp0q34Ptgo7GhqkfVW6EgTzgwGyDMzHfUOppMt7Wxva+aOlbrSJBXkPQ2CGGGhBEhHhA6vY7Y9FjToJEneESlRpGZm2npMs2y0djgau+Kq52r4frmxcXeRb3tZueWb96x68f4/Ojn+cY2TG87vUx+yUtvgxDmSRgRwkpk5WapPRl5ezSiUqOISokiJi2GXKX0N5lobbQmIcHVzhU3e7d8YcJc2DBenLRO9zTOoYl/E/xd/aW3QQgrV6bDyMKFC9m8eTNnz57F3t6eZs2a8e6771K3bl21jaIozJ07l1WrVpGUlMSjjz7KggULeOSRR9Q2SUlJTJkyhe3btwPQtWtX5s+fj6enp9rmxIkTvP766xw9epRy5crx/PPPM2XKlPseCCZEUaVkpZgGjJubToy34zPiS7wGrcYQLG5k3TDZldXOxo4BjwzgscDH8oUKe1t7i35OpLdBCOtXpsPI/v37efHFF2natCmKojB79mz69OnDoUOHKFeuHAAff/wxy5YtY9myZQQFBTF//nz69u3L4cOHcXNzA2DEiBFcuXKF9evXAzBhwgRGjRrFunXrAEhOTqZv3760atWKPXv2EBERwbhx43B2dubll1+2zJMXRZZvz49m4+hasys6RYeiKOgVPYqimNzXo0evN1yr0/JcTNpjZvm8bSlk+TzTj14/yo5zO7iRdQPn/c5Uca+CDh3XU66TmlPyJxNz1DpSwbXCrYub4drf1XBkTB9nH2xtbE1fU+ltEEKUME1SUpLVHMknNTWVgIAAVq9eTbdu3VAUhTp16vDSSy8xefJkADIyMggKCmLGjBkMHz6c06dP06JFC7Zv305ISAgABw8epFu3bhw+fJigoCC++OIL3nvvPc6cOYOTkxMAH374IV9++SUnT54skV991vIrrizUmZ6TTlx6HLHpsSbXcelx/Bf3HxeSLsgBqW7ydPDE383fNHAYw4ZbBTwdPO/q/VwW/v+LSmotGdZSq7XUKcwr0z0jt0tNTUWv16ubVyIjI4mOjqZDhw5qGycnJ1q1asWhQ4cYPnw44eHhuLq60qJFC7VNSEgILi4uHDp0iKCgIMLDw2nZsqUaRAA6duzIrFmziIyMpGrVqmbriYiIuK/nc7/Ll5aSqFNRFNJy00jKTiIxO5GkrCQSshMM97MSScpOUudl6srmoMvSpkFDeYfyeDt44+3ojY+jj+HawXDt7eCNo9Yx/4K5QBLEJcURR9xdP661vE9Bai0p1lLr/dQpQcayrCqMvPHGGzRo0IDg4GAAoqOjAfDx8TFp5+PjQ1RUFAAxMTF4eXmZ/BrUaDR4e3sTExOjtqlYsWK+dRjnFRRG7ufNay0p/m7r1Ct6kjKT8vVg5L0dlx5HXEacyfEWhOE4Fv4u/uqmE5NeDdcK+Lr6mj1raUmylvcpSK0lxVpqtZY6hXlWE0befPNN/vjjD7Zv346t7f3t7y8Kd/s4jDGPjiG4UnCBwSI2LZa4jDgS0hMsskeHObYaWzQajXpto7G5dcGmwHkajQYbbLCxMbQzTjPbltvWe4e2v1/63Wwvj5eTF2v6raG8U3k5mZgQ4qFkFWFk2rRp/PTTT2zevNmkl8LPzw+A2NhYqlSpok6PjY3F19cXAF9fX+Lj41EURe0dURSFuLg4kzaxsbfOb2Fch3HegyRXn0tKdgopWSmkZKeQnJVMclYyKVkpJGcbbp+MPcnR60fRK3rAcATOd357x2I129nY4e3sbXLxcfLB28Wbi0kXWXtirUkvi6OtI9MfK3vHmfjl7C/M3DfT5FgfjlpHJoVMwtvZ24KVCSGEZZX5MDJ16lQ2bNjA5s2bqVWrlsm8wMBA/Pz8CAsLo2nTpgBkZmZy8OBBPvjgAwCCg4NJTU0lPDxcHTcSHh5OWlqaej84OJj33nuPzMxMHB0N29zDwsKoUKECgYGBpfVUiyxHl2MIETfDQ2p2qtlQYRI4sg3303LSLF2+ylHriI+zT/6Qkfe+sw/uDu53HHRZy6uWVez5YazJGmoVQojSVKbDyOTJk1m3bh3ffvstnp6e6hgRFxcXXF1d0Wg0jBkzhoULFxIUFETNmjVZsGABLi4uDBgwAIDatWvTqVMnJk2axOLFiwGYNGkSXbp0UbcvDhgwgHnz5jF27FgmT57M2bNnWbx4cYkcZyTv6c59w30ZVG8QjfwbFRge1JCRfWteWT2KppGrvashTDgZwoS3S/6Q4e3sjYudS7G8vtZ0nAlrqlWIskZRFHRZOnIzc8lNzyU3M5ec9BzDtU2OpcsT96FMh5GVK1cC0Lt3b5PpU6dOZdq0aQBMnDiRjIwMXn/9dfWgZz/99JN6jBHjeqZMmUL//v0B6NatG/Pnz1fne3h4sGHDBiZPnkz79u3x9PRk3LhxjB8/vlifz+3d9DHpMSw5vKRYH6MkadBQo3yNWyEjT++F8baXsxdOWqfCV1YEep2e3PRccjJyDH94Mm7ezjDcVudlGP4gxcbEkl4lHa2jFjtnO7SOWrROWsO18bZTnmlOWmy0MkZDiPuly9Hd+lxmmP+s5mbeNs1cO+PFGDLMTCtoL/7mi5tD09J93qL4WNVxRqxdjzU9iEqNsmgNGjSG83zYu+Hu4I6bg+Ha3d4dVwdX3O3duXzjMtvObVPP2gqm4zD0Or35PyjmgsPNP0K3Bwd1OePtvH+sbi6vy9aV+Otho7XJF1DyXRd1mqMWrfNt4ee2EGRjawg/1tIzUhbqVBRF/QIyd1tRDBPOnj1LzZo1Db1tNzvczN0uC0dVNve6KnoFfa7ecMnRo9fdvM5zX8lV0OXoDNe5ulv3dcqtdrl60/Xk3rauPPOUXKXAecbbSXFJONo65v+MZt76/Otz9RZ6JW9pOq8pj416zNJliHskYaQUPfr5oygoVDxdkSbbmtzzeox7cNhqbLG1sVVv22hssLWxLfR2USRnJRObHktubi4OOgc88MA229YQErJKPiQ8qGzsDOFH0SiFnwW2kO/MIn2p3uc6cnW5t+rM88Wv3s4bDG5OV+8XEBpQCliXmfWWuLzhxNxtDWbvF3S7oHm3rzc3Jxf0GELEzS9+RS9/iu9Ho/cb0X5ie0uXIe5Rmd5M86Dxd/UnKjUK+wx7vK56ldjj6G7+y+H+tqG6467ezr35T9wffY6e7BzDnj/3+/8jikHeIARyJF8rYAz0xp5HO2c7tE5a7FxL9xg8onhJGClF45qPY+a+mZYuw3poUP/QaJ3yjANxvnnfyQ6t881rJy1JN5JwdXRFl3FzgFuewW25GbnoMnXqbeMmI/nuEeL+aWw0tzZR3v5ZzbOp0vhZzXspdNrN9RinFTTOy1qOEivMkzBSioy7cK7/d72FK7l/ef84mISF224bA8Ptf1DU2875//gYl7V1sC3R86goioIuW2cSVPKGF5Nr4zby2+flGVxndt5t0yT83IdCxoEUZVNRWWejtcHGzsZwffOi0Wpu3b9tnsnFzHK2WlvD8sZ5tmbWcds8jVaDrZ0tGlvD48YmxVKlehXTgeE3P7+2jrbYOdsZlisDY3GE9ZIwUsq61+xOh7c78O/j/xJQJcDS5RTq0pVLVK9d3aQXwtbx7kJCWaXRaNA6aNE6lM7HIG/4ORdxjuo1qt+hceHrKvwB738d58+dp3qN6kUbV0Hh4yryLlvU8RpFfa/dTRg1N46lwHErhY2VuW1Tz53GyhinXYi8QM1aNU0CgcZGUyY/V9oILdWCqlm6DPGAkzBiAY6ejnjU8cA3qOwf3fWGyw3KB5W3dBkPhLzhx97THqfyxbMLdElyvOGIi6+Lpcsodmo4KmyEbwlxSHbA0dPMSQ2FeEjJQRaEEEIIYVESRoQQQghhURJGhBBCCGFREkaEEEIIYVESRoQQQghhURJGhBBCCGFRcm4aIYQQQliU9IwIIYQQwqIkjAghhBDCoiSMCCGEEMKiJIwIIYQQwqIkjAghhBDCoiSMCCGEEMKiJIyUQUU6Pby4K/KalgxreV2tpc7c3FxLl1BkSUlJZGRkWLqMu2It74OHkYSRMiQzM9Pk2ho+OGW9RuMfd+N1Wa43IyMDnU5HUlISAHq93rIF3cG1a9dISUlBo9GU6dcU4MyZM6xZs4bs7GxLl3JHERERTJs2jStXrli6lEKdOnWKFi1asHPnTkuXUqjExETOnz/P2bNn0Wg0li5HFEDCSBlx6tQpXnzxRbp3785zzz3H9u3bLV1Sgc6fP8/u3bsByvSXUUREBG+++SZPP/00U6dO5fjx42W23tOnTzNy5EiefPJJnnrqKcLDw7GxKZsfz8jISOrVq0evXr3KfCD5559/aNGiBQkJCdjb2wNlM5CeOHGCzp07o9frSUxMBMpmnQDHjx+na9euJCYmsmrVKjU8l0UnT56kd+/eDBkyhObNm/PJJ59YuiRRgLL51+4hc+7cObp06YKfnx/NmzfHx8eHwYMH884773D16lVLl2fi7NmztG/fnoEDB7Jx40agbAaSkydP0rlzZ9LT03F3dycyMpLXXnuNa9eulblfR8Za/f396dSpExUqVGD69OmkpaVZujSzMjMzqVatGvHx8XTr1k0NJGXNv//+S5cuXZg4cSLjx4/PN7+s9DzFx8czYsQIBg4cyEcffUSDBg0AyuSX/D///EOXLl146aWX+Pjjjzl27BhxcXFA2Xk9jc6ePUuvXr3o1KkTS5YsYcaMGXzwwQdER0cDZTfsPay0li5AwNq1a2nSpAkLFy5Up7Vv354xY8aQmZnJ1KlT8fb2tmCFBrGxsUybNo0WLVrg4+PDtGnT0Ol09OvXTw0kZeFLKSoqilGjRjF06FDef/99AH777TcmT55MZGQkFStWtHCFt0RGRjJs2DBGjBjB22+/DcDq1avZvXs3Wq2WhIQEypcvb+Eqb9Hr9Wg0Gtzd3Zk9ezZTpkyhZ8+e7Ny5Ezs7O86cOUOtWrUsXSanT5/m8ccf54UXXuC9995DURS+/vprzpw5g52dHX369KFp06aWLhOAy5cv4+LiwnvvvYder2f06NFcuXKF//77j+HDh9OvXz/q1atn6TL5+++/6dChA6+88grTp08HYOHChcybN4/PP/+8TPXkKYrC559/TuvWrXnnnXcAqFChAnv37iU5OZmEhARq1Kih9pYJyys7756HWHx8PHZ2doDhj71Op+Ppp5/myy+/5Msvv2TNmjWA5ZN8QkICNjY2jB49mokTJ9K1a1emT5/OTz/9BJSdHpJjx47h5+fHkCFD1HratWuHk5MTJ0+eBCz/WhqdPXuWJk2aMGLECJNp4eHhdOzYkQ4dOrBq1SqgbNRsY2NDrVq1qFixIr6+vixbtoyMjAy6d+/OSy+9xNKlS0lJSbF0mZw5cwadTkdgYCCxsbF0796d77//nj///JPw8HA6derEhg0bAMu/rsnJyaSnp6vBPjExkYEDB/Laa6/x3XffsWTJEq5du2bRGjMzM1m5ciUvv/wyb7/9Nnq9Hr1ez4ABAzhx4gQRERGA5V9LI71ez8WLF3Fzc1OnrV27lrCwMIYOHUq7du2YNGkSkZGRFqxS5CU9I2VAvXr1WLNmDREREQQFBZGbm4uiKPTu3ZsPPviAmTNn0rlzZ2rXrm3ROmvXrs3MmTMJCgoCYOTIkSiKov5KMvaQ6HQ6bG1tLVanv78//fr1U3+h63Q6bGxs0Gq16uj/vD04er3eYr/qOnbsSI0aNahQoQIAn332GaGhocydOxd/f39OnjzJpEmTqFmzJq1bt7ZIjbfLzc0lLi6OQ4cOMWTIEH744QfatWvHn3/+yS+//IKbm5vFe8l69uzJxx9/zMSJE1myZAktWrRg1qxZVKpUieTkZD788EPGjx9P/fr11fezpXh4eHDx4kW2bt2Kv78/b731FlWqVAGgfv36DB48mCeeeIIBAwZYrEZHR0feeust/P39AcPnR6PRMGDAAJYuXcrWrVt55ZVXykTPKICtrS0hISHMnj0bT09PEhMT+fHHH1m5ciXBwcGcPXuWQYMG0ahRI0aOHGnpcgXSM2IxOp1Ovd2tWzdatmzJlClTiIyMRKvVqnt/9OzZEy8vL/WXhyXkrTXvH+46deowevRounTpYtJD8uabb7Jp06ZSr9P4mjVt2pRnnnkGMAQNW1tbNBoNnp6eJr/c5s6dy59//mmRIJL3Na1atSoAOTk5JCUlsX79ep5//nm6du3KmDFjCAgI4I8//ij1Go3y1qooClqtlkcffVSdNnPmTGxsbAgMDOTdd98lOTnZYl9KOp1O/T8eOnQoS5cuxc3NjZdffpkqVapgY2ODp6cngwcPxt7e3mKfq7yvaaNGjejfvz+TJk1SB4aD4b3brl07QkJCLPr/n5OTA6AGETCEEb1eT40aNXjxxRdZs2YNFy9etFCFt+R9XUePHs0bb7xBTk4OV65cYdy4cfTu3Rt/f3/atm1L27Zt2bdvX5kb6/KwkjBSyoyDvWxtbdUvz4oVK/LMM8+Qnp7OtGnTuHDhgrrZply5cuovTUvWmvdDnlft2rXVQPLuu+/Su3dvVqxYQWBgYKnXqdVq1T+cRnmDRt4vyJkzZzJv3jy02tLtHLzTa2pnZ8eUKVNo06aN+v+dnJyMn58fderUKdU6C6rV+BrWqFGDv/76ixEjRvDbb7/x448/snbtWiIjI002j1miVr1erz7+s88+y3fffUf9+vWBW5sR7Ozs8Pf3x9fX12J15v3/Hzp0KG3atCEpKYkTJ04At967Wq2WgICAUq0zb612dnZmP//G+lq2bMm1a9fUYGeJL3dzf1ednJx47bXXmDdvHg4ODvj4+ACmu/nXrFmzTI11eZjJ/0IpOn36NEFBQbzwwguA4Y+M8dgHAwYMYMiQISQlJTFo0CB27drFgQMHWLRoEfHx8TRu3NiitZr78jT+Ya9duzYvvvgiGo2Gv//+m3379tGoUSOL1GnuD6eiKCiKwo0bN3BycmL58uUsXbqUsLCwUn1di/KaGhm/9D///HNu3LhBkyZNSq1OKLxWHx8fvvjiC/766y/WrVtH48aNqVOnDhs2bGDJkiWl2jNirta8gaRWrVo4OTkBt17XNWvWYGdnp/ZKWapOY3hu3rw5Y8eOJSQkhOeee45PP/2U77//nvfff5+jR4/So0ePUquzoFoLeq927dqVNm3a8MEHH5CZmVnqX+7m/q7efvC42rVr89lnn/Hff/9x9epV5s6dy9GjR9UeVGF5mqSkpLIx4ugBFxUVxdChQ1EUhYiICLp06cKKFSsAyM7OVkd17927l2+//Zaff/6ZwMBAbGxsWLFiRal9uRdWq7nxIDqdjrfffpsvv/ySPXv2ULdu3TJZ58CBAzl8+DBZWVls2bKlVL/g77bWf/75h9WrV7N27Vo2bdpEw4YNy0Stubm5am/SvHnz6NatW6nWdje1mntd//rrL1avXs3333/Pli1b1N1oLVln3s//xYsX+eabb/j+++/x8PDAzc2NefPmlZn//9tfU+N4q2XLlvHdd9+xadMmvLy8ylytBw8e5KOPPmL37t3UrVuXrKwsvvzyS4u+d4UpCSOlQFEUvv32W7Zt28b48eOJiYlhwoQJdO3aVf3gZGVl4eDgoC4TERGBi4sLDg4OpfrhLkqteb+QwHCclLFjxzJ//vxSC033Uufzzz/Pnj172LZtW6nuKnm3tV68eJEff/yRnTt3smDBAnUTQ1mpNTMzE0dHx1KrqSD38rquXbuWHTt2sHTp0lJ7XYtSZ95AAoYvWXd3d3Q6He7u7qVSZ1Frvf1zBYajByckJFCpUqUyVWtOTo66yTshIYE//vgDNzc3atasqQ4aF2WDhJFSEh8fz/79++nduzc6nY7NmzczYcIEOnfuzMqVKwHDB0er1Vp8RHpRar39F1JaWhouLi5lus49e/ZQo0aNUh3Pcq+1Xrt2DUdHR4scY6QotZr7QrKEu31dL1++jLOzc6kG/KLWmZubqw62tqS7fU0tuedUUf+uGgOJKLskjJSivB/arKwstm/fzssvv2zywVm3bh2tWrVSd+2zlKLWGhISQmBgoMX+IBWlzjVr1tChQwf8/PxKvb68ilrrY489Vqq/MM150N6ra9asoV27dhY94N2D9ppKraI4SRgpIZcvX+bkyZNER0fTuXNn3N3dcXZ2NjmmRWZmJjt27ODll1+ma9eu+Pj4sHz5co4fP16qHxprqdVa6pRapVZrqVNqFWWFhJES8O+//9KvXz/8/PyIjIzE1dWV/v37M2LECAIDA9VDams0GrKysti6dSsvvvginp6ebNiwoVT38LCWWq2lTqlVarWWOqVWUZbIrr3FLCkpifHjxzNo0CA2bdrEpUuXePbZZwkPD+eNN97g/PnzJru+OTg48Ouvv+Li4sK2bdtK9QNjLbVaS51Sq9RqLXVKraKskTBSzFJSUoiPj6ddu3aUK1cOMByR9LnnniMpKYnZs2cTHR2tbuPcvHkzYWFhbNmypdQPbGUttVpLnVKr1GotdUqtoqyRMFLMbG1tcXJy4urVq8Cto/09++yz6kml9uzZo7Zv06YNO3bssEhyt5ZaraVOqVVqtZY6pVZR1siYkRIwePBgLl26pB4AKO9ucM899xzR0dH873//s/jJxKypVmupU2qVWq2lTqlVlCXSM3KfUlNTSUpKIjExUZ32ySefkJaWxgsvvEB6errJ8Tg6duyIXq8nOzu71D8w1lKrtdQptUqt1lKn1CrKOgkj9+G///5jyJAhPPnkkzRr1owvvviC9PR0vLy8+Pzzzzl37hz9+/fn1KlT6qnrjx07ZpET31lLrdZSp9QqtVpLnVKrsAaymeYenT59mu7duzNo0CBatGjB8ePHWbRoEVu2bKFly5YAnDx5khEjRpCWloanpyf+/v4cOHCAbdu2leohvq2lVmupU2qVWq2lTqlVWAsJI/cgMTGRF198kZo1azJ//nx1ev/+/fH392fZsmUm2y1XrlzJ1atXcXR0pF+/fgQFBUmtVlqn1Cq1WkudUquwJpY/uYQVysnJISkpid69ewO3ztNQvXp1oqOjAcOpyo3TR4wYIbU+IHVKrVKrtdQptQprImNG7oGvry8rVqygdevWgOE02gD+/v4mg6psbW2Ji4tT71tie6a11GotdYLUWlKspVZrqROkVmE9JIzco5o1awKGD4zxjJDZ2dkmH5IFCxawYMECsrKyACw2yttaarWWOkFqLSnWUqu11AlSq7AOspnmPuU9BDGgnlZ91qxZLFiwgN9++w0HBwdLlJaPtdRqLXWC1FpSrKVWa6kTpFZRtknPSDEwdidqtVoqV67MkiVLWLJkCWFhYTRs2NDC1ZmyllqtpU6QWkuKtdRqLXWC1CrKLukZKQbGFG9jY8O3336Lu7s727dvL5OHIraWWq2lTpBaS4q11GotdYLUKsou6RkpRh06dABgx44dNGnSxMLV3Jm11GotdYLUWlKspVZrqROkVlH2yHFGillaWhouLi6WLqNIrKVWa6kTpNaSYi21WkudILWKskXCiBBCCCEsSjbTCCGEEMKiJIwIIYQQwqIkjAghhBDCoiSMCCGEEMKiJIwIIYQQwqIkjAjxAOvZsyfVqlUzObeHUWpqKvXr16d169bk5uZaoDohhDCQMCLEA2zx4sVkZGTw5ptv5ps3e/Zsrl27xpIlS9RzfwghhCVIGBHiAVajRg0mT57M999/T1hYmDr977//5rPPPmPEiBE8+uijJVqDTqdTz7AqhBDmSBgR4gE3ceJE6taty6RJk8jIyECv1/Pqq6/i7+/P22+/zdmzZ3n++eepVq0afn5+tG3blo0bN5qsIzExkbfffptWrVpRuXJlKlWqRI8ePThw4IBJu8jISDw9PVm0aBErVqygadOm+Pr6Eh4eXppPWQhhZaRvVogHnJ2dHYsXL6Zr167Mnz+fSpUqceTIEb777juuXbtG586d8fPzY+LEibi4uLBlyxaGDRvGZ599xsCBAwG4ePEiGzdupG/fvlStWpUbN27wzTff0KdPH/bs2UP9+vVNHnPdunWkpaXx/PPP4+rqir+/vyWeuhDCSsjh4IV4SEyePJmvv/4aJycn2rdvz//93//Rt29foqKiCAsLw8nJSW3bt29fTp8+zYkTJ9BoNGRlZWFnZ6eeSRUgKSmJ5s2b07VrV5YuXQoYekYaNWqEi4sLR44ckRAihCgS2UwjxEPinXfewcvLC0VRmD9/PomJifz666/06dOH9PR04uPj1UvHjh25du0aZ8+eBcDBwUENIpmZmSQkJKDT6WjatCl//fVXvsfq0aOHBBEhRJHJZhohHhLu7u7UrFmTmJgY/P39OXLkCIqiMHfuXObOnWt2mdjYWIKCgtDr9Xz88cd8/fXXREZGmrQJDAzMt1zVqlVL4ikIIR5QEkaEeEjp9XoAxo4dS+fOnc22qVu3LgALFy5k5syZDB48mOnTp1O+fHlsbW1ZuHAhFy5cyLdc3k0+QghRGAkjQjykjL0XWq2Wxx9//I5tf/75Z9q0aUNoaKjJ9Dlz5pRQdUKIh4mMGRHiIeXj40Pbtm1ZtWoV165dyzc/71FbbW1tURTTse6HDh2SXXaFEMVCekaEeIgtXLiQLl260Lp1a4YNG0a1atWIjY3lzz//5PTp0xw7dgyAbt26MXfuXEaNGkWrVq04d+4cX3/9NXXq1CE1NdXCz0IIYe0kjAjxEAsKCiIsLIx58+axdu1a4uPj8fb2pn79+rz11ltqu1dffZWMjAx++OEHNm7cyCOPPMKXX37Jjz/+yP79+y34DIQQD4J72kxz9epVBg8eTNOmTWncuDFTp04lOzsbgLZt23L8+HEAcnNzqVSpEuvWrVOXbdeuHX/99RerV6/m9ddfBwzbnR955BHatGlD06ZNefbZZ/nvv/8KrWPOnDnq8Q3ySkpKYuXKler9yMhIfvjhh3t5qmZdv36dvn37qvcHDBjA1atXi7Rsjx491F+bTz31FElJScVWV0nI+//05ZdfsmbNmmJd55gxY/Id7RNg37596gG3StKiRYv4/vvvS/xxyoqtW7dy+PBhk2mBgYEsX76c//77j9jYWE6dOsUPP/zAgAED1Db29va8//77nDx5kuvXrxMWFkanTp0IDQ3ln3/+MVlXUlISkyZNKrXnJISwfncdRhRF4bnnnqNHjx4cPXqUI0eOkJaWxowZMwAICQlRtyP/+++/1KhRQ72flpbGhQsXaNCgQb71jh07lv3793P06FH69etHr169zJ5ptChu3LjBF198od6/dOkS69evv6d1mbNr1y46dOgAQEZGBgkJCVSqVOmu1/PDDz/g6elZbHWVtBdeeIHBgwdbuoxitXv3bvX/UgghhGXcdRjZu3cvDg4OPPvss4BhYNvs2bP59ttvSU9PJzg4WA0fhw4d4oUXXlB/OR05coTGjRtja2t7x8fo168f7du3L1Jvxn///UePHj1o1KgRn376KQDvv/8+Fy5coE2bNrz99tu8//77HDx4kDZt2rBs2TJWr17N4MGD6dGjB02bNlWPsZCWlsbTTz9N69atadmyJT/99JPZx9y9ezdPPPEEAPv376dNmzYAHD16lM6dO9O6dWs6dOhASkoKGRkZvPDCCwQHBzNkyBAyMzPV9TRo0ID4+HgAnnnmGdq1a0dISAhff/212mbXrl089thjtG7dml69epmtZ8mSJbRv355WrVoxe/bsOz6X9957jxYtWtCqVSumT59udn3ffvstjz76KB06dODQoUPqdGNP1JkzZ0y+wCMjI2nVqhUAf/31F927d6ddu3b069eP69evm32MvH799Vcef/xxHn30UbZv337Htnq9ntdee43mzZvTp08fnnrqKbVn5bfffqNt27a0atWKcePGkZWVxa5duxg2bJi6fN4el+TkZHJycvD29i60RiGEECXnrseMnDp1isaNG5tMc3d3p3Llypw/f54WLVowc+ZMAMLDw5k6dSrr168nJSWF8PBwgoODi/Q4jRo1IiIiAoBZs2bRpEkTunfvnq9dREQEmzdvJjU1lWbNmvHiiy/y7rvvcurUKXVb9r59+/jkk0/UzUWrV6/m6NGjHDx4ECcnJzp06ECXLl24dOkS/v7+arf9jRs38j2+TqcjIiKCOnXqALBz50569OhBdnY2w4cP56uvvqJp06YkJyfj5OTEp59+ipOTE+Hh4fz777+0a9fO7PNdtmwZ5cqVIyMjgw4dOtCrVy/0ej0TJ05k69atVK1alcTExHzL7dmzh3PnzrFnzx4URWHw4MH8/vvvxMXF5XsuCQkJbNmyhcOHD6PRaMxuIrp+/Tpz587l119/xd3dnZ49e9KwYUOTNrVq1SI7O5uLFy9StWpVNmzYQN++fcnJyWHKlCl89913eHt789NPPzFjxgyWLVt2x//rS5cusWfPHi5cuEDPnj3vuJvp5s2buXTpEocOHSI2Npbg4GCeffZZMjMzGTt2LBs3bqRmzZqMGjWKL774gpEjR/LKK6+QlpaGi4sLGzZsoF+/foAhBD322GN3rE0IIUTJK/ZdewMCAsjJySE6OpozZ84QFBRE06ZNOXLkCOHh4YSEhBRpPXl3I3zrrbfMBhGAzp074+DggJeXFz4+PsTExBRp/Y8//jjly5fHycmJJ598koMHD1KvXj3CwsJ49913OXDgAB4eHvke/88//6RZs2bqeg4dOkTLli2JiIjA39+fpk2bAoaAptVqOXDgAE8//TQA9evXp169embr+fTTT2ndujWdOnXi6tWrnDt3jsOHD9OqVSv1eBDlypXLt9yePXvYs2cPbdu25bHHHuPMmTOcO3f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     },
     "metadata": {}
    }
   ],
   "source": [
    "# Use numpy to generate range of years to cover\n",
    "years = np.arange(2010, 2019)\n",
    "\n",
    "# Emissions data for China\n",
    "china_emissions = [8500.543, 9388.199, 9633.899, 9796.527, 9820.36, 9716.468, 9704.479, 9838.754, 10064.686]\n",
    "plt.plot(years, china_emissions, label='China', color='red')\n",
    "\n",
    "# Emissions data for USA\n",
    "us_emissions = [5700.108, 5572.585, 5371.777, 5522.908, 5572.106, 5422.966, 5306.662, 5270.749, 5416.278]\n",
    "plt.plot(years, us_emissions, label='USA', color='blue')\n",
    "\n",
    "# Emissions data for India\n",
    "india_emissions = [1700.027, 1811.961, 1979.047, 1994.101, 2199.4, 2298.17, 2371.752, 2456.954, 2654.101]\n",
    "plt.plot(years, india_emissions, marker=\"o\", label='India', color='#228B22')\n",
    "\n",
    "russia_emissions = [1613.523, 1613.523, 1679.385, 1618.434, 1617.678, 1622.498, 1617.653, 1647.041, 1710.688]\n",
    "plt.plot(years, russia_emissions, label='Russia', color='purple')\n",
    "\n",
    "# Add style to the x ticks\n",
    "plt.xticks(ticks=years, labels=years, rotation=45)\n",
    "\n",
    "# Change scaling for y axis\n",
    "# plt.axis([2010, 2018, 0, 30])\n",
    "\n",
    "# Add labels\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('CO2')\n",
    "plt.title('CO2 Emissions')\n",
    "\n",
    "# Add a legend\n",
    "plt.legend(bbox_to_anchor=(1, 1), prop={'size': 12})\n",
    "\n",
    "# Ensure a tight, uncluttered layout\n",
    "# plt.tight_layout()\n",
    "\n",
    "# Save the figure\n",
    "plt.savefig('./out/co2_plot1.png')\n",
    "plt.savefig('./out/co2_plot2.jpg')\n",
    "plt.savefig('./out/co2_plot3.svg')\n",
    "\n",
    "# Annotate with source\n",
    "# Placement of annotation is fraction of the axes coordinate system\n",
    "# Placement of text is an offset in points\n",
    "plt.annotate('OWID: https://cdiac.ess-dive.lbl.gov/', (0,0), (-65,-65), fontsize=10, \n",
    "             xycoords='axes fraction', textcoords='offset points')\n",
    "\n",
    "# Set the plot style\n",
    "plt.style.use('fivethirtyeight')\n",
    "\n",
    "# Show the figure\n",
    "plt.show()"
   ]
  },
  {
   "source": [
    "Looks pretty good! But it's hard to see the relationship between Russia and the other countries. Let's transform this into a bar graph."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'CO2 Emissions - 2010')"
      ]
     },
     "metadata": {},
     "execution_count": 229
    },
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {}
    }
   ],
   "source": [
    "emissions_arr = [china_emissions[0], us_emissions[0], india_emissions[0], russia_emissions[0]]\n",
    "countries = ['China', 'USA', 'India', 'Russia']\n",
    "# plt.bar(countries, emissions_arr)\n",
    "plt.barh(countries, emissions_arr)\n",
    "plt.title('CO2 Emissions - 2010')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'CO2 Emissions by Year')"
      ]
     },
     "metadata": {},
     "execution_count": 230
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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     },
     "metadata": {}
    }
   ],
   "source": [
    "# Stacked bar chart\n",
    "plt.bar(years, china_emissions)\n",
    "plt.bar(years, us_emissions)\n",
    "plt.bar(years, india_emissions)\n",
    "plt.bar(years, russia_emissions)\n",
    "plt.legend(['China', 'USA', 'India', 'Russia'], bbox_to_anchor=(1, 1))\n",
    "plt.title('CO2 Emissions by Year')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'CO2 Emissions by Year')"
      ]
     },
     "metadata": {},
     "execution_count": 231
    },
    {
     "output_type": "display_data",
     "data": {
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sFuuoxM08YfXw4cPWNRRFNm7cSEpKCp6envj6+hY7pugZGcOHD+fMmTMMGTLE7rdO16tXj0GDBpGWlsbMmTNL/Pf75ZdfbNZwuLu7A9iMWAF8+umnJS7UFfkz0hoOAa48Q+DSpUvExMTQs2dPfHx8aNu2LbVr1+bEiRPs3buXgwcPFpsrnzRpEgUFBcybN49HHnmE7t27Wx9t/u233/LFF19QrVo1Zs+ebb27o0hMTAzLly/nL3/5Cx06dGD+/PnF8nJ3d7/ubbR/dK3bYjt27HhLt9requeff55ffvmFTp064e7uzl133cWBAwf49NNPueeee3jmmWeA/7s19cEHH8TLy4v69euTm5vLtm3bOHXqFBEREcUWr/7RlClTSE5O5t133yU1NZXu3btz+vRpPvroI06dOsXEiRPp0KHDLbfl/PnzBAYG0qRJE9q2bUvjxo25ePEiu3fvJi0tjY4dO9KtW7cbPp+/vz8vvvgi27dvx9vbm8OHD/Pxxx/j4ODA/PnzS5xq6NixI23atCE1NZUqVapY+8/eZs2axZEjR4iJiWHt2rV06dIFV1dXjh8/zg8//MCXX37JjBkzrE98HTZsmHXqq1+/fri5ufH999+zfft2goODWb9+vSF5ilQmKjjEatKkSQQHB7Ns2TJ2797NmjVrOHfuHHXr1qVVq1aEhobyt7/9zeYYk8nEK6+8Qv/+/XnzzTf54osv2LNnDyaTiYYNGzJixAhGjRpV4qK+zMxM4MqzCt54440Sc+ratetNFxyLFy8udd+oUaPKtOAYP348mzdv5ttvv7U+LbRBgwaEhYURHh5uvROkbdu2PPfcc+zevZukpCROnTrFPffcQ7NmzXjttddu6BkTZrOZbdu2ERcXR2JiIosWLaJGjRq0adOGkSNH3vQj4v/I0dGRqVOnsmvXLr788ku2bNmCg4MDHh4eTJ8+naFDh97UaEOHDh2YMGECM2bMsD7108/Pj5dffvmaUz9DhgxhwoQJPProo4Z9l0zt2rXZtGkTK1euZN26dWzatImCggKcnZ3x8PBgypQpBAcHW+NbtWrFxx9/zPTp09m2bRuXL1+mVatWrFy5kjp16qjgEEFf3iYilczYsWNZsWIF77///g09zlxEKgYVHCJSaWRlZdGuXTtcXV355ptvSnyOh4hUTJpSEZEKb926dRw+fJj169dz/vx5XnjhBRUbIpWMRjhEpMILCgpiz549NGjQgNDQUMaMGVPeKYnITVLBISIiIobTmKSIiIgYTgWHiIiIGE4Fh4iIiBhOBUcZysjIKO8UKiX1281Tn90a9dutUb/JjVDBISIiIoZTwSEiIiKGU8EhIiIihlPBISIiIobTo81FROSOcunSJfLz88s7jT+datWq4ejoWPr+GznJF198wfz58/nuu+/Iyspi4cKFNl8ZbrFYiI6O5p133iE3N5f27dsTGxtLixYtrDG5ublMnDiRrVu3AvD4448za9YszGazNebAgQNMmDCBb775hrp16/Lss88yceJETCaTNWbjxo289tpr/Pjjj9x777289NJL9OnT54Y7RERE7lyXLl3i7NmzmM1mm88OMV5+fj6///47NWrUKHH/DU2p5Ofn07JlS6Kjo3FwcCi2Py4ujoULFxITE8OOHTtwdnYmODiYs2fPWmOGDx9OamoqCQkJJCQkkJqaysiRI637z5w5Q3BwMC4uLuzYsYPo6Gjmz5/PggULrDEpKSn84x//4IknnmDXrl088cQTPPvss3z11Vc33CEiInLnys/PV7FRTmrWrElBQUGp+29ohOOxxx7jscceAyA8PNxmn8ViIT4+nrFjx9KvXz8A4uPj8fT0JCEhgaFDh3Lw4EG2b9/O1q1b8fHxAWDu3LkEBgaSkZGBp6cn69at4/z588THx+Pg4EDLli05dOgQixYtIiIiApPJRHx8PN26dSMyMhIALy8vdu3aRXx8PG+99dbN946IiNxxVGyUj+v1+20vGs3MzOT48eP06NHDus3BwYEuXbqwb98+4MrIRK1atfD19bXGdOrUCUdHR5uYzp0724yg+Pv7k5WVRWZmJgBffvmlzXWKYorOISIi12d++5dr/hExwm0XHMePHwfA2dnZZruzszPZ2dkAZGdn4+TkZFP9mEwm6tWrZxNT0jmK9hVd61rXERERkYrpT3GXSkV67G5FyqUyUb/dPPXZrflz9FvNa+69lT4oq37z9PQsk+tUVGazmXfeece6hOFWY8rDbRccrq6uAJw4cYLGjRtbt584cQIXFxcAXFxcyMnJwWKxWEc5LBYLJ0+etIk5ceKEzbmLXhfFuLq6lhhTtL80FeUNWrReRW6O+u3mqc9uzZ+m33Zfe9rkZvugMvRbWU8V5Q5teEvHZWdn8/rrr7Nt2zZ+/fVXnJyc8Pb2JjQ01LqW8noOHjxocwdoRXHbUyoeHh64urqSlJRk3VZQUEBycrJ1zYaPjw95eXmkpKRYY1JSUsjPz7eJSU5OtlnhmpSURP369fHw8ACgY8eONtcpirl6bYiIiEhllJmZySOPPMKOHTuYMmUKX3zxBR999BGPPfYYzz333A2fx9XVtdRbU8vTDRUceXl5pKamkpqaSmFhIT///DOpqakcO3YMk8lEWFgYcXFxJCYmkp6eTnh4OI6OjoSEhABX7ibp2bMn48aNIyUlhZSUFMaNG0dAQIC1Kg4JCcHBwYHw8HDS09NJTExk3rx5hIeHW0dFRo0axc6dO5k7dy6HDh1izpw57Nq1i7CwMIO6R0Tk1mhhptysojswk5KSCA4OxtPTEy8vL0JDQ9m9e7c17tSpUzzzzDM0aNCABx54gLVr19qcx2w2s3HjRuBKEVP0un///tSvXx9fX1+bX94vX75MREQEbdq0wc3NjXbt2hEXF0dhYaFd23dDUyrffvutzcO1Zs6cycyZM/nb3/5GfHw8Y8aM4fz580yYMMH64K/169dTu3Zt6zHLli1j4sSJDBw4EIDAwEBmzZpl3V+nTh02bNhAZGQkfn5+mM1mRo8eTUREhDXG19eX5cuXM336dF577TXuvfdeli9fTocOHW67I+TP4Xr/0d/qMKg9VOTcRMRYp06dYvv27bz00kvUqlWr2P6rp0hmzZrFlClTmDJlCitXriQiIoIuXbrYLGv4o+nTpzN16lRef/11Zs+ezT/+8Q/S0tKoVasWhYWF1K9fnxUrVuDk5MQ333zDmDFjqFu3Lk8//bTd2nhDBUe3bt3Izc0tdb/JZCIqKoqoqKhSY8xmM0uXLr3mdby9vdmyZcs1Y/r161fhFsKIiIjcjiNHjmCxWGjWrNl1YwcNGsSgQYMAePHFF1m8eDF79uyxbitJeHg4gYGBAEyePJn333+ftLQ0OnfuTPXq1XnxxRetsR4eHnz33Xd8+OGHZV9wiMifW0UefblWbhoVksrCYrHccKy3t7f179WqVcPJyanYDRXXOqZ+/foANscsX76cd999l2PHjlFQUMDFixevOWJyK/RtsSIiIuXs/vvvx2QycejQoevGVq9e3ea1yWS6bsFy9TFX3y0KsH79eqKionjqqaf48MMP2bVrF8OGDePChQs324xr0giH2F1F/m1YRKQiqlu3Lv7+/rz55puMHDmy2DqO3Nxcw251TU5Opn379oSGhlq3/fjjj3a/jkY4REREKoDY2FgsFgt+fn589NFHZGRkcOjQId566y0eeughw67btGlTUlNT+eSTTzh8+DCzZs1iz549dr+ORjhEREQqgCZNmvD555/z+uuvM2XKFLKysrjnnnto1aoV8+bNM+y6Q4cOJS0tjeHDh2OxWOjbty+jR49m1apVdr2OCg4REbnjVZapXDc3N2bPns3s2bNL3F/SHaNpaWmlxnh4eJR4zNXb/vKXv7BgwQIWLFhgEzNp0qQbzvtGaEpFREREDKcRjkpKCzNFRKQy0QiHiIiIGE4Fh4iIiBhOBYeIiIgYTgWHiIiIGE4Fh4iIiBhOBYeIiIgYTgWHiIiIGE4Fh4iIiBhOD/4SEZE7Xq1nupfp9fLe+eym4oOCgmjZsmWxR5qvXr2aiRMn8ssvv3Du3DliY2PZsGEDv/76K46Ojnh6ejJixAhCQkJsjrtw4QItWrSgoKCA9PR06tSpc7tNum0qOERERCqBcePGsW/fPqKjo2nRogW5ubl89dVXnDp1qljs5s2b8fDw4O677yYhIYFhw4aVQ8a2VHCIiIhUAlu2bGH69Ok8/vjjwJUvZnvggQdKjF25ciWDBg2iTp06LF68uEIUHFrDISIiUgm4urqyfft2Tp8+fc24n376id27dzNw4ED69OlDRkZGsW+ULQ8qOERERCqBefPm8fXXX3P//ffz8MMPM2HCBJKSkorFrV69Gj8/P+rVq4ejoyO9e/fm3XffLYeMbangEBERqQS6du3K/v37SUxMJDg4mB9++IHg4GDGjh1rjSksLGT16tUMGjTIuu2vf/0rH3zwAQUFBeWQ9f9RwSEiIlLOateuXeJUyenTp7n77rutr6tXr06XLl0YN24cGzZs4MUXX2TFihVkZmYCsGPHDn7++WdGjBiBk5MTTk5OhISEcPr0aRITE8usPSXRolEREZFy5unpySeffILFYsFkMlm3f/fddzRt2rTU47y8vADIz88HriwW7dOnD1FRUTZxS5YsYeXKlTz55JMGZH9jVHCIiIiUs2HDhvHmm28yceJEnn76ae666y7+3//7f3z44YesWbMGuPKsjpCQENq2bUvdunU5ePAg06ZNo1mzZnh5eXHy5Em2bNnCihUraNmypc35hwwZwqOPPsqPP/7IvffeWx5N1JSKiIhIeWvSpAn//ve/OXz4MAMGDMDf35/169ezYsUKHn30UQD8/f1Zu3YtAwYMwMfHh/Hjx9O5c2fWr19P1apVef/996lRowb+/v7Fzt++fXsaNmzIypUry7ppVhrhEBGRO97NPvmzPLRr147169eXuv+5557jueeeK3V/REQEERERJe4zmUz85z//ue0cb4cKjuswv/1LqftyhzYsw0xEREQqL02piIiIiOFUcIiIiIjhVHCIiIiI4VRwiIiIiOFUcIiIiIjh7FJwXL58menTp9OmTRtcXV1p06YN06dP59KlS9YYi8XCzJkzad68OW5ubgQFBfH999/bnCc3N5fQ0FDc3d1xd3cnNDSU3Nxcm5gDBw7Qq1cv3NzcaNGiBTExMVgsFns0Q0RERAxil4Jj3rx5LFu2jJiYGFJSUoiOjubNN99kzpw51pi4uDgWLlxITEwMO3bswNnZmeDgYM6ePWuNGT58OKmpqSQkJJCQkEBqaiojR4607j9z5gzBwcG4uLiwY8cOoqOjmT9/PgsWLLBHM0RERMQgdnkOR0pKCo8//jiBgYEAeHh4EBgYyNdffw1cGd2Ij49n7Nix9OvXD4D4+Hg8PT1JSEhg6NChHDx4kO3bt7N161Z8fHwAmDt3LoGBgWRkZODp6cm6des4f/488fHxODg40LJlSw4dOsSiRYuIiIiwef68iIiIVBx2GeHo1KkTu3fv5tChQwD897//ZdeuXdbHsWZmZnL8+HF69OhhPcbBwYEuXbqwb98+4ErRUqtWLXx9fW3O6+joaBPTuXNnHBwcrDH+/v5kZWVZvylPRETkz2rQoEGEhYVZXwcFBTFhwoRyzOj/2GWEY+zYseTl5eHr60vVqlW5dOkSkZGRDB8+HIDjx48D4OzsbHOcs7MzWVlZAGRnZ+Pk5GQzSmEymahXrx7Z2dnWmAYNGhQ7R9G+Jk2a2KM5IiJyh8nf8XiZXs+xx9abPiYsLIzffvuNtWvX2i2PVatWUa1axXiouF2yWL9+Pe+//z7Lli2jefPmpKWl8fzzz+Pu7s7TTz9tj0vcloyMjNs4uqZdz3t7uVyt9Lzse51bYf/c1G83n5t921I5+03vtdJU5J/Ra/P09CyT69wp6tatW94pWNml4Jg8eTIREREMHDgQAG9vb44dO8bcuXN5+umncXV1BeDEiRM0btzYetyJEydwcXEBwMXFhZycHCwWi3WUw2KxcPLkSZuYEydO2Fy76HVRTElu6w26u/TvUrnZ8xatRbGLa+QF5fxDaefc1G9X3Exudu0zqLT9pvdaKSryz6gA/zfa0b17d9544w3OnTtHUFAQsbGx1Kx5pWA8d+4c48ePJzExkZo1azJq1Khi5wkKCqJly5bMnj0bgLVr17J48WIyMjK466676Nq1KzNnziw2e2AEu6zhOHfuHFWrVrXZVrVqVQoLC4Eri0hdXV1JSkqy7i8oKCA5Odm6ZsPHx4e8vDxSUlKsMSkpKeTn59vEJCcnU1BQYI1JSkqifv36eHh42KMpIiIiFUJycjLff/89H330EW+//TabNm1i8eLF1v0vv/wyn332Ge+++y4bN24kNTWVPXv2XPOcFy5cICoqit27d7N27VpycnIYNmyY0U0B7FRwPP7448ybN49t27aRmZnJxx9/zMKFC+nduzdwZS1GWFgYcXFxJCYmkp6eTnh4OI6OjoSEhADg5eVFz549GTduHCkpKaSkpDBu3DgCAgKslXNISAgODg6Eh4eTnp5OYmIi8+bNIzw8XHeoiIjIHaV27drMnTsXLy8vevToQf/+/fn8888ByMvLY+XKlbz66qv4+/vTsmVLFi5ceN3PwiFDhvDYY4/RpEkT2rdvz5w5c0hOTuaXX6496mUPdplSmTVrFjNmzGD8+PGcPHkSV1dXnnnmGSZOnGiNGTNmDOfPn2fChAnk5ubSvn171q9fT+3ata0xy5YtY+LEidapmcDAQGbNmmXdX6dOHTZs2EBkZCR+fn6YzWZGjx5NRESEPZohIiJSYXh5ednMHri5ufHVV18B8OOPP3LhwgXrYyQAatWqhbe39zXPuX//fmJiYkhLSyM3N9f64Myff/6Zhg0bGtCK/2OXgqN27dpER0cTHR1daozJZCIqKoqoqKhSY8xmM0uXLr3mtby9vdmyZcst5yoiIlIZVK9e3ea1yWS6rSdr5+fnM3DgQLp3786SJUtwdnYmJyeHwMBALly4cLvpXpe+S0VERKSSuffee6levTpffvmldVt+fj7p6emlHpORkUFOTg4vv/wyXbt2pVmzZsVuxDBSxbg5V0RERG5YrVq1GDJkCK+88gr16tXDzc2NWbNmWW/WKEmjRo2oUaMGb775JiNGjODgwYO89tprZZazCg4REZFKaNq0aeTn5/P3v/8dBwcHQkNDOXfuXKnx9erVIz4+nqlTp7Js2TK8vb2ZMWOGdd2k0VRwiIjIHe9WnvxZ1uLj40v8e5E/roN0dHRkyZIl1zzn5s2bbV4PGDCAAQMG2Gz747eyG0VrOERERMRwKjhERETEcCo4RERExHAqOERERMRwKjhERETEcCo4RERExHAqOERERMRwKjhERETEcCo4RERExHAqOERERISgoCAmTJhg2Pn1aHMREbnjvbzimTK93rRn37npY8LCwlizZg0AVatWpX79+jz22GNMnjwZs9ls5wyLW7VqFdWqGVcWqOAQERGpILp3786SJUu4dOkSBw8eJCIigtOnT/PWW28Zfu26desaen5NqYiIiFQQNWrUwNXVlYYNG9KjRw+Cg4PZsWMHcGUEZNCgQTbxM2fOpHPnztbXBw4coG/fvjRu3JiGDRvStWtXdu7cCcDFixeZOHEizZs3x8XFBW9vb1555RXrsX+cUlm7di1+fn40atSIpk2b8swzz/Drr7/ects0wiEiIlIBHT16lE8//ZTq1avf8DEjRoygVatWfPrpp1SrVo0DBw5w1113AbB48WI2b97MW2+9hbu7O7/++isZGRmlnuvChQtERUXRrFkzcnJymDJlCsOGDWPLli231B4VHCIiIhXE9u3badiwIZcvX6agoACAGTNm3PDxx44dIyIigmbNmgFw33332ey7//776dKlCyaTicaNG+Pr61vquYYMGWL9e5MmTZgzZw4+Pj788ssvNGzY8GabpikVERGRiqJLly7s2rWLTz/9lNDQUB577DFGjRp1w8eHh4fzr3/9iz59+hAbG8uhQ4es+5566inS0tJo3749kZGRbNu2jcLCwlLPtX//fv72t7/RqlUrGjVqhJ+fHwA///zzLbVNBYeIiEgFUbNmTe677z68vb2ZNWsW586dY9asWQBUqVIFi8ViE3/p0iWb11FRUezbt4+goCBSUlLo2rUrK1euBODBBx8kNTWVKVOmUFhYSFhYGP379y+x6MjPz2fgwIHUrFmTJUuWsGPHDhISEoArUy23QgWHiIhIBTVp0iTi4uLIysqiXr16/O9//7PZn5aWVuyY+++/n1GjRvHBBx8wZMgQa8EBULt2bfr168ecOXP44IMP2LlzJ0eOHCl2joyMDHJycnj55Zfp2rUrzZo148SJE7fVFhUcIiIiFVS3bt3w8vIiNjaWhx9+mNTUVFauXMmRI0eIi4tj79691tjz588TGRnJrl27yMzM5KuvvmLv3r14eXkBsGDBAhISEjh48CBHjhxh3bp13H333TRo0KDYdRs1akSNGjV48803OXr0KNu2beO11167rbao4BAREanAIiIiWLlyJZ6enkyaNInp06fTvXt3fvrpJ4YPH26Nq1q1Krm5uYSHh9OxY0f+/ve/07FjR+ui09q1a/PGG2/g7+/PI488QlpaGuvWraNmzZrFrlmvXj3i4+PZvHkzvr6+xMTE3NTi1ZKYcnNzLdcP+/Myv/1Lqftyh97cKt2MjAw8PT1vNyXg2nnBzedmT/bOTf12xc3kZs8+g8rbb3qvlawi/4zertOnT1OnTp3yTuNP61r9rxEOERERMZwKDhERETGcCg4RERExnAoOERERMZwKDhERETGcCg4REbmj/PFpnFI2rtfvKjhEROSO4ejoSG5uroqOcnDu3DnrN9OWRN8WKyIid4xq1apRu3Ztzpw5U96p/OlUq1aNGjVqlL6/DHMRERExXLVq1fTwrwrIblMq//vf/xg1ahT3338/rq6u+Pr6snv3but+i8XCzJkzad68OW5ubgQFBfH999/bnCM3N5fQ0FDc3d1xd3cnNDSU3Nxcm5gDBw7Qq1cv3NzcaNGiBTExMRo6ExERqeDsUnDk5uYSEBCAxWLhgw8+YN++fcyaNQtnZ2drTFxcHAsXLiQmJoYdO3bg7OxMcHAwZ8+etcYMHz6c1NRUEhISSEhIIDU1lZEjR1r3nzlzhuDgYFxcXNixYwfR0dHMnz+fBQsW2KMZIiIiYhC7TKm88cYbuLm5sWTJEuu2Jk2aWP9usViIj49n7Nix9OvXD4D4+Hg8PT1JSEhg6NChHDx4kO3bt7N161Z8fHwAmDt3LoGBgdbn9K9bt47z588THx+Pg4MDLVu25NChQyxatIiIiAhMJpM9miMiIiJ2ZpcRjs2bN9O+fXuGDh1K06ZNeeihh1i6dKl1qiMzM5Pjx4/To0cP6zEODg506dKFffv2AZCSkkKtWrXw9fW1xnTq1AlHR0ebmM6dO+Pg4GCN8ff3Jysri8zMTHs0RURERAxglxGOo0eP8tZbbxEeHs7YsWNJS0tj0qRJAISGhnL8+HEAmymWotdZWVkAZGdn4+TkZDNKYTKZqFevHtnZ2daYBg0aFDtH0b6rR1WulpGRcRutK/61vbdz3tvL5Wql52Xf69wK++emfrv53OzblsrZb3qvlaYi/4xeW0X5Vlq5eXYpOAoLC2nbti1TpkwB4IEHHuDIkSMsW7aM0NBQe1zittzWG3R36V/jfLPntetXOF8jLyjnH0o756Z+u+JmcrP714VX0n7Te60UFflnVO5YdplScXV1xcvLy2Zbs2bN+Pnnn637AU6cOGETc+LECVxcXABwcXEhJyfH5o4Ti8XCyZMnbWJKOkfRPhEREamY7FJwdOrUiR9++MFm2w8//EDjxo0B8PDwwNXVlaSkJOv+goICkpOTrWs2fHx8yMvLIyUlxRqTkpJCfn6+TUxycjIFBQXWmKSkJOrXr4+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     },
     "metadata": {}
    }
   ],
   "source": [
    "# Grouped bar chart\n",
    "width = 0.2\n",
    "plt.bar(years - (width * 2), china_emissions, width=width)\n",
    "plt.bar(years - width, us_emissions, width=width)\n",
    "plt.bar(years, india_emissions, width=width)\n",
    "plt.bar(years + width, russia_emissions, width=width)\n",
    "plt.legend(['China', 'USA', 'India', 'Russia'], bbox_to_anchor=(1, 1))\n",
    "plt.title('CO2 Emissions by Year')"
   ]
  },
  {
   "source": [
    "Great! Now let's explore this data as a percentage of the total global emissions."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Text(-65, -65, 'OWID: https://cdiac.ess-dive.lbl.gov/')"
      ]
     },
     "metadata": {},
     "execution_count": 232
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "# Total global emissions for those years\n",
    "world_emissions = [33066.651, 34357.366, 34919.289, 35207.886, 35505.827, 35462.747, 35675.099, 36153.262, 36572.754]\n",
    "\n",
    "year_idx = 0\n",
    "\n",
    "# Get percentage of total emissions contributed by China for 2010\n",
    "china_prct = (china_emissions[year_idx] / world_emissions[year_idx]) * 100\n",
    "us_prct = (us_emissions[year_idx] / world_emissions[year_idx]) * 100\n",
    "india_prct = (india_emissions[year_idx] / world_emissions[year_idx]) * 100\n",
    "russia_prct = (russia_emissions[year_idx] / world_emissions[year_idx]) * 100\n",
    "world_prct = 100\n",
    "other_prct = (world_prct - (china_prct + us_prct + india_prct + russia_prct)) / world_prct * 100\n",
    "\n",
    "percentages = [china_prct, us_prct, india_prct, russia_prct, other_prct]\n",
    "\n",
    "plt.pie(\n",
    "    x=percentages, \n",
    "    shadow=True, \n",
    "    labels=['China', 'USA', 'India', 'Russia', 'Rest of World'], \n",
    "    autopct='%1.1f%%',\n",
    "    # explode=[0,0.1,0.2,0.3,0.1],\n",
    "    # startangle=90\n",
    "    )\n",
    "\n",
    "plt.title('CO2 Emissions')\n",
    "plt.annotate('OWID: https://cdiac.ess-dive.lbl.gov/', (0,0), (-65,-65), fontsize=10, \n",
    "             xycoords='axes fraction', textcoords='offset points')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "year_idx = 0\n",
    "fig, axes = plt.subplots(nrows=4, ncols=2)\n",
    "for row in axes:\n",
    "    for col in row:\n",
    "        china_prct = (china_emissions[year_idx] / world_emissions[year_idx]) * 100\n",
    "        us_prct = (us_emissions[year_idx] / world_emissions[year_idx]) * 100\n",
    "        india_prct = (india_emissions[year_idx] / world_emissions[year_idx]) * 100\n",
    "        russia_prct = (russia_emissions[year_idx] / world_emissions[year_idx]) * 100\n",
    "        world_prct = 100\n",
    "        other_prct = (world_prct - (china_prct + us_prct + india_prct + russia_prct)) / world_prct * 100\n",
    "        percentages = [china_prct, us_prct, india_prct, russia_prct, other_prct]\n",
    "\n",
    "        col.pie(\n",
    "            x=percentages, \n",
    "            shadow=True, \n",
    "            labels=['China', 'USA', 'India', 'Russia', 'Rest of World'], \n",
    "            autopct='%1.1f%%',\n",
    "            # explode=[0,0.1,0.2,0.3,0.1],\n",
    "            # startangle=90\n",
    "        )\n",
    "        col.set_xlabel(f'Year: {years[year_idx]}')\n",
    "        year_idx = year_idx + 1\n",
    "\n",
    "fig.set_size_inches(10, 15)\n",
    "fig.suptitle('Top CO2 Emitters by Percentage')\n",
    "fig.tight_layout()\n",
    "fig.show()"
   ]
  }
 ]
}

================================================
FILE: intro_data_science/part_3/binder/requirements.txt
================================================
pandas==1.2.0
matplotlib==3.3.3
numpy==1.19.5
openpyxl==3.0.6

================================================
FILE: intro_data_science/part_3/data/complete/eur_data_final.csv
================================================
,country,unemp_rate,gdp,median_income,total_pop
0,Austria,6.0,356237.6,23071,8401940
1,Belgium,7.8,424660.3,21335,11000638
2,Bulgaria,7.6,48128.6,6742,7364570
3,Croatia,13.1,46639.5,8985,4284889
4,Cyprus,13.0,18490.2,16173,840407
5,Czechia,4.0,176370.1,12478,10436560
6,Denmark,6.2,282089.9,21355,5560628
7,Estonia,6.8,21682.6,11867,1294455
8,Finland,8.8,216073.0,19997,5375276
9,France,10.1,2228568.0,20621,64933400
10,Germany,4.1,3159750.0,21152,80219695
11,Greece,23.6,176487.9,9048,10816286
12,Hungary,5.1,113903.8,8267,9937628
13,Iceland,3.0,18646.1,22193,315556
14,Ireland,8.4,273238.2,18286,4574888
15,Italy,11.7,1689824.0,16237,59433744
16,Latvia,9.6,25037.7,9257,2070371
17,Lithuania,7.9,38849.4,9364,3043429
18,Luxembourg,6.3,53303.0,28663,512353
19,Malta,4.7,10344.1,17264,417432
20,Netherlands,6.0,708337.0,21189,16655799
21,Norway,4.7,335747.5,27670,4979954
22,Poland,6.2,426547.5,10865,38044565
23,Portugal,11.2,186480.5,10805,10562178
24,Romania,5.9,170393.6,4724,20121641
25,Slovakia,9.7,81226.1,10466,5397036
26,Slovenia,8.0,40357.2,15250,2050189
27,Spain,19.6,1118743.0,15347,46815910
28,Sweden,7.0,463147.5,20955,9482855
29,Switzerland,5.0,605753.7,27692,7954662
30,Turkey,10.9,780224.9,6501,7954662
31,United Kingdom,4.8,2403382.6,17296,63182180


================================================
FILE: intro_data_science/part_3/data/gdp_2016.csv
================================================
country,gdp
Cyprus,18490.2
Latvia,25037.7
Lithuania,38849.4
Luxembourg,53303
Hungary,113903.8
Malta,10344.1
Netherlands,708337
Austria,356237.6
Poland,426547.5
Portugal,186480.5
Romania,170393.6
Slovenia,40357.2
Slovakia,81226.1
Finland,216073
Sweden,463147.5
United Kingdom,2403382.6
Iceland,18646.1
Norway,335747.5
Switzerland,605753.7
Turkey,780224.9
Belgium,424660.3
Bulgaria,48128.6
Czechia,176370.1
Denmark,282089.9
Germany,3159750
Estonia,21682.6
Ireland,273238.2
Greece,176487.9
Spain,1118743
France,2228568
Croatia,46639.5
Italy,1689824


================================================
FILE: intro_data_science/part_3/data/out/eur_data_sorted.csv
================================================
,country,unemp_rate,gdp,total_pop
0,Austria,6.0,356237.6,8401940
1,Belgium,7.8,424660.3,11000638
2,Bulgaria,7.6,48128.6,7364570
13,Croatia,13.1,46639.5,4284889
4,Cyprus,13.0,18490.2,840407
5,Czechia,4.0,176370.1,10436560
7,Denmark,6.2,282089.9,5560628
8,Estonia,6.8,21682.6,1294455
11,Finland,8.8,216073.0,5375276
12,France,10.1,2228568.0,64933400
6,Germany,4.1,3159750.0,80219695
9,Greece,23.6,176487.9,10816286
14,Hungary,5.1,113903.8,9937628
16,Iceland,3.0,18646.1,315556
15,Ireland,8.4,273238.2,4574888
17,Italy,11.7,1689824.0,59433744
20,Latvia,9.6,25037.7,2070371
18,Lithuania,7.9,38849.4,3043429
19,Luxembourg,6.3,53303.0,512353
21,Malta,4.7,10344.1,417432
22,Netherlands,6.0,708337.0,16655799
23,Norway,4.7,335747.5,4979954
24,Poland,6.2,426547.5,38044565
25,Portugal,11.2,186480.5,10562178
26,Romania,5.9,170393.6,20121641
29,Slovakia,9.7,81226.1,5397036
28,Slovenia,8.0,40357.2,2050189
10,Spain,19.6,1118743.0,46815910
27,Sweden,7.0,463147.5,9482855
3,Switzerland,5.0,605753.7,7954662
30,Turkey,10.9,780224.9,7954662
31,United Kingdom,4.8,2403382.6,63182180


================================================
FILE: intro_data_science/part_3/data/out/eur_data_sorted.html
================================================
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>country</th>
      <th>unemp_rate</th>
      <th>gdp</th>
      <th>total_pop</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>Austria</td>
      <td>6.0</td>
      <td>356237.6</td>
      <td>8401940</td>
    </tr>
    <tr>
      <th>1</th>
      <td>Belgium</td>
      <td>7.8</td>
      <td>424660.3</td>
      <td>11000638</td>
    </tr>
    <tr>
      <th>2</th>
      <td>Bulgaria</td>
      <td>7.6</td>
      <td>48128.6</td>
      <td>7364570</td>
    </tr>
    <tr>
      <th>13</th>
      <td>Croatia</td>
      <td>13.1</td>
      <td>46639.5</td>
      <td>4284889</td>
    </tr>
    <tr>
      <th>4</th>
      <td>Cyprus</td>
      <td>13.0</td>
      <td>18490.2</td>
      <td>840407</td>
    </tr>
    <tr>
      <th>5</th>
      <td>Czechia</td>
      <td>4.0</td>
      <td>176370.1</td>
      <td>10436560</td>
    </tr>
    <tr>
      <th>7</th>
      <td>Denmark</td>
      <td>6.2</td>
      <td>282089.9</td>
      <td>5560628</td>
    </tr>
    <tr>
      <th>8</th>
      <td>Estonia</td>
      <td>6.8</td>
      <td>21682.6</td>
      <td>1294455</td>
    </tr>
    <tr>
      <th>11</th>
      <td>Finland</td>
      <td>8.8</td>
      <td>216073.0</td>
      <td>5375276</td>
    </tr>
    <tr>
      <th>12</th>
      <td>France</td>
      <td>10.1</td>
      <td>2228568.0</td>
      <td>64933400</td>
    </tr>
    <tr>
      <th>6</th>
      <td>Germany</td>
      <td>4.1</td>
      <td>3159750.0</td>
      <td>80219695</td>
    </tr>
    <tr>
      <th>9</th>
      <td>Greece</td>
      <td>23.6</td>
      <td>176487.9</td>
      <td>10816286</td>
    </tr>
    <tr>
      <th>14</th>
      <td>Hungary</td>
      <td>5.1</td>
      <td>113903.8</td>
      <td>9937628</td>
    </tr>
    <tr>
      <th>16</th>
      <td>Iceland</td>
      <td>3.0</td>
      <td>18646.1</td>
      <td>315556</td>
    </tr>
    <tr>
      <th>15</th>
      <td>Ireland</td>
      <td>8.4</td>
      <td>273238.2</td>
      <td>4574888</td>
    </tr>
    <tr>
      <th>17</th>
      <td>Italy</td>
      <td>11.7</td>
      <td>1689824.0</td>
      <td>59433744</td>
    </tr>
    <tr>
      <th>20</th>
      <td>Latvia</td>
      <td>9.6</td>
      <td>25037.7</td>
      <td>2070371</td>
    </tr>
    <tr>
      <th>18</th>
      <td>Lithuania</td>
      <td>7.9</td>
      <td>38849.4</td>
      <td>3043429</td>
    </tr>
    <tr>
      <th>19</th>
      <td>Luxembourg</td>
      <td>6.3</td>
      <td>53303.0</td>
      <td>512353</td>
    </tr>
    <tr>
      <th>21</th>
      <td>Malta</td>
      <td>4.7</td>
      <td>10344.1</td>
      <td>417432</td>
    </tr>
    <tr>
      <th>22</th>
      <td>Netherlands</td>
      <td>6.0</td>
      <td>708337.0</td>
      <td>16655799</td>
    </tr>
    <tr>
      <th>23</th>
      <td>Norway</td>
      <td>4.7</td>
      <td>335747.5</td>
      <td>4979954</td>
    </tr>
    <tr>
      <th>24</th>
      <td>Poland</td>
      <td>6.2</td>
      <td>426547.5</td>
      <td>38044565</td>
    </tr>
    <tr>
      <th>25</th>
      <td>Portugal</td>
      <td>11.2</td>
      <td>186480.5</td>
      <td>10562178</td>
    </tr>
    <tr>
      <th>26</th>
      <td>Romania</td>
      <td>5.9</td>
      <td>170393.6</td>
      <td>20121641</td>
    </tr>
    <tr>
      <th>29</th>
      <td>Slovakia</td>
      <td>9.7</td>
      <td>81226.1</td>
      <td>5397036</td>
    </tr>
    <tr>
      <th>28</th>
      <td>Slovenia</td>
      <td>8.0</td>
      <td>40357.2</td>
      <td>2050189</td>
    </tr>
    <tr>
      <th>10</th>
      <td>Spain</td>
      <td>19.6</td>
      <td>1118743.0</td>
      <td>46815910</td>
    </tr>
    <tr>
      <th>27</th>
      <td>Sweden</td>
      <td>7.0</td>
      <td>463147.5</td>
      <td>9482855</td>
    </tr>
    <tr>
      <th>3</th>
      <td>Switzerland</td>
      <td>5.0</td>
      <td>605753.7</td>
      <td>7954662</td>
    </tr>
    <tr>
      <th>30</th>
      <td>Turkey</td>
      <td>10.9</td>
      <td>780224.9</td>
      <td>7954662</td>
    </tr>
    <tr>
      <th>31</th>
      <td>United Kingdom</td>
      <td>4.8</td>
      <td>2403382.6</td>
      <td>63182180</td>
    </tr>
  </tbody>
</table>

================================================
FILE: intro_data_science/part_3/data/out/eur_data_sorted.json
================================================
{"country":{"0":"Austria","1":"Belgium","2":"Bulgaria","13":"Croatia","4":"Cyprus","5":"Czechia","7":"Denmark","8":"Estonia","11":"Finland","12":"France","6":"Germany","9":"Greece","14":"Hungary","16":"Iceland","15":"Ireland","17":"Italy","20":"Latvia","18":"Lithuania","19":"Luxembourg","21":"Malta","22":"Netherlands","23":"Norway","24":"Poland","25":"Portugal","26":"Romania","29":"Slovakia","28":"Slovenia","10":"Spain","27":"Sweden","3":"Switzerland","30":"Turkey","31":"United Kingdom"},"unemp_rate":{"0":6.0,"1":7.8,"2":7.6,"13":13.1,"4":13.0,"5":4.0,"7":6.2,"8":6.8,"11":8.8,"12":10.1,"6":4.1,"9":23.6,"14":5.1,"16":3.0,"15":8.4,"17":11.7,"20":9.6,"18":7.9,"19":6.3,"21":4.7,"22":6.0,"23":4.7,"24":6.2,"25":11.2,"26":5.9,"29":9.7,"28":8.0,"10":19.6,"27":7.0,"3":5.0,"30":10.9,"31":4.8},"gdp":{"0":356237.6,"1":424660.3,"2":48128.6,"13":46639.5,"4":18490.2,"5":176370.1,"7":282089.9,"8":21682.6,"11":216073.0,"12":2228568.0,"6":3159750.0,"9":176487.9,"14":113903.8,"16":18646.1,"15":273238.2,"17":1689824.0,"20":25037.7,"18":38849.4,"19":53303.0,"21":10344.1,"22":708337.0,"23":335747.5,"24":426547.5,"25":186480.5,"26":170393.6,"29":81226.1,"28":40357.2,"10":1118743.0,"27":463147.5,"3":605753.7,"30":780224.9,"31":2403382.6000000001},"total_pop":{"0":8401940,"1":11000638,"2":7364570,"13":4284889,"4":840407,"5":10436560,"7":5560628,"8":1294455,"11":5375276,"12":64933400,"6":80219695,"9":10816286,"14":9937628,"16":315556,"15":4574888,"17":59433744,"20":2070371,"18":3043429,"19":512353,"21":417432,"22":16655799,"23":4979954,"24":38044565,"25":10562178,"26":20121641,"29":5397036,"28":2050189,"10":46815910,"27":9482855,"3":7954662,"30":7954662,"31":63182180}}

================================================
FILE: intro_data_science/part_3/data/unemployment_2016.csv
================================================
country,unemp_rate
Austria,6
Belgium,7.8
Bulgaria,7.6
Switzerland,5
Cyprus,13
Czechia,4
Germany,4.1
Denmark,6.2
Estonia,6.8
Greece,23.6
Spain,19.6
Finland,8.8
France,10.1
Croatia,13.1
Hungary,5.1
Ireland,8.4
Iceland,3
Italy,11.7
Lithuania,7.9
Luxembourg,6.3
Latvia,9.6
Malta,4.7
Netherlands,6
Norway,4.7
Poland,6.2
Portugal,11.2
Romania,5.9
Sweden,7
Slovenia,8
Slovakia,9.7
Turkey,10.9
United Kingdom,4.8


================================================
FILE: intro_data_science/part_3/ds_part_3.ipynb
================================================
{
 "metadata": {
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.2-final"
  },
  "orig_nbformat": 2,
  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3",
   "language": "python"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2,
 "cells": [
  {
   "source": [
    "# A Brief Introduction to Pandas\n",
    "### Part 1\n",
    "\n",
    "In this notebook, we will cover the basics of Pandas, a dynamic, powerful Python library that is ubiquitous in the field of Data Science. It has robust I/O functionality and makes working with data a lot simpler than doing so in Excel. Let's get stared."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "source": [
    "## 1.1 DataFrames\n",
    "Let's read in some existing data using Pandas convenient read_csv method."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "unemployment_df = pd.read_csv('./data/unemployment_2016.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "metadata": {},
     "execution_count": 95
    }
   ],
   "source": [
    "# Check the type\n",
    "# https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use .info() and .describe to inspect dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select a single column\n",
    "# Using bracket notation\n",
    "\n",
    "# Using dot notation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Access by numeric index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Check the type\n",
    "# https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "error",
     "ename": "NameError",
     "evalue": "name 'countries' is not defined",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-4-014f3ce25eb4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Series objects do not have the same methods as dataframes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mcountries\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mcountries\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdescribe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'countries' is not defined"
     ]
    }
   ],
   "source": [
    "# Series objects do not have the same methods as dataframes\n",
    "countries.info()\n",
    "countries.describe()"
   ]
  },
  {
   "source": [
    "## 1.2 Vectorized Operations\n",
    "In Pandas, operations work on each value in most data structures, such as dataframes and series."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add a string to the countries series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "output_type": "error",
     "ename": "TypeError",
     "evalue": "can only concatenate str (not \"int\") to str",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m~/Library/Python/3.8/lib/python/site-packages/pandas/core/ops/array_ops.py\u001b[0m in \u001b[0;36m_na_arithmetic_op\u001b[0;34m(left, right, op, is_cmp)\u001b[0m\n\u001b[1;32m    141\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 142\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mexpressions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mleft\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mright\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    143\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Library/Python/3.8/lib/python/site-packages/pandas/core/computation/expressions.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(op, a, b, use_numexpr)\u001b[0m\n\u001b[1;32m    234\u001b[0m             \u001b[0;31m# error: \"None\" not callable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 235\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0m_evaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mop_str\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# type: ignore[misc]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    236\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0m_evaluate_standard\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mop_str\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Library/Python/3.8/lib/python/site-packages/pandas/core/computation/expressions.py\u001b[0m in \u001b[0;36m_evaluate_standard\u001b[0;34m(op, op_str, a, b)\u001b[0m\n\u001b[1;32m     68\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merrstate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"ignore\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 69\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     70\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: can only concatenate str (not \"int\") to str",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-103-84e40da47f8f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Add a number to the countries series\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mcountries\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m12\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/Library/Python/3.8/lib/python/site-packages/pandas/core/ops/common.py\u001b[0m in \u001b[0;36mnew_method\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m     63\u001b[0m         \u001b[0mother\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mitem_from_zerodim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     64\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 65\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     66\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     67\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mnew_method\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Library/Python/3.8/lib/python/site-packages/pandas/core/arraylike.py\u001b[0m in \u001b[0;36m__add__\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m     87\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0munpack_zerodim_and_defer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"__add__\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     88\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__add__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 89\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_arith_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moperator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     90\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     91\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0munpack_zerodim_and_defer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"__radd__\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Library/Python/3.8/lib/python/site-packages/pandas/core/series.py\u001b[0m in \u001b[0;36m_arith_method\u001b[0;34m(self, other, op)\u001b[0m\n\u001b[1;32m   4957\u001b[0m         \u001b[0mlvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mextract_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mextract_numpy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4958\u001b[0m         \u001b[0mrvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mextract_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mextract_numpy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4959\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marithmetic_op\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4960\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4961\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_construct_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mres_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Library/Python/3.8/lib/python/site-packages/pandas/core/ops/array_ops.py\u001b[0m in \u001b[0;36marithmetic_op\u001b[0;34m(left, right, op)\u001b[0m\n\u001b[1;32m    187\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    188\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merrstate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"ignore\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 189\u001b[0;31m             \u001b[0mres_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_na_arithmetic_op\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    190\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    191\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mres_values\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Library/Python/3.8/lib/python/site-packages/pandas/core/ops/array_ops.py\u001b[0m in \u001b[0;36m_na_arithmetic_op\u001b[0;34m(left, right, op, is_cmp)\u001b[0m\n\u001b[1;32m    147\u001b[0m             \u001b[0;31m#  will handle complex numbers incorrectly, see GH#32047\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    148\u001b[0m             \u001b[0;32mraise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 149\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_masked_arith_op\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mright\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    150\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    151\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mis_cmp\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mis_scalar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNotImplemented\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Library/Python/3.8/lib/python/site-packages/pandas/core/ops/array_ops.py\u001b[0m in \u001b[0;36m_masked_arith_op\u001b[0;34m(x, y, op)\u001b[0m\n\u001b[1;32m    109\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0many\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    110\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merrstate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"ignore\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 111\u001b[0;31m                 \u001b[0mresult\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxrav\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    112\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    113\u001b[0m     \u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmaybe_upcast_putmask\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m~\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnan\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: can only concatenate str (not \"int\") to str"
     ]
    }
   ],
   "source": [
    "# Add a number to the countries series\n",
    "countries + 12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select the unemployment rates as a series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Sort the dataframe by rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use .head() to get the top five values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use .tail() to get the  five values"
   ]
  },
  {
   "source": [
    "### Exercise - What is the average unemployment of the seven countries with the highest unemployment in europe?"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Sort the values of the unemployment_df - descending or ascending?\n",
    "\n",
    "# Get the first seven rows in the returned series\n",
    "\n",
    "# Get the average of those rows"
   ]
  },
  {
   "source": [
    "### Extra - explore these methods:\n",
    ".min(), .max(), .sum(), .unique(), .nunique(), .count(), .duplicated()"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# "
   ]
  },
  {
   "source": [
    "## 1.3 Merging DataFrames\n",
    "Data can come from a variety of different sources and it can be helpful to put them all in one place."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "gdp_filepath = './data/gdp_2016.csv'\n",
    "# Read in data using this filepath"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Inspect the GDP data"
   ]
  },
  {
   "source": [
    "Let's create a new dataframe by merging two existing dataframes. This is the one we'll use for the rest of section 2."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Merge unemployment df with gdp df\n",
    "# https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.merge.html"
   ]
  },
  {
   "source": [
    "What other data can we include? Are there other file types that we can work with?"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read in misc data from excel\n",
    "misc_filepath = './data/misc_data.xlsx'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# .read_excel() gets the first sheet in the excel file. Instead, get the \"Income\" sheet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get \"Population\" sheet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get \"Population\" sheet with proper rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Do we need all of these columns?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select total population column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Merge totial population df with eur_data df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select population with country names, so pandas can perform merge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Perform merge and sort values by country name"
   ]
  },
  {
   "source": [
    "It looks like there's an issue with our indexes. What is an index, anyway?"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "## 1.4 Indexes\n",
    "https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Index.html"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Our data is indexed by integer values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Indexes are immutable. And for good reason..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dataframes can also be indexed by labels, rather than numbers.\n",
    "# Use the .set_index() method to set the index for our dataframe"
   ]
  },
  {
   "source": [
    "## 1.5 Exporting Data\n",
    "Pandas has robust I/O functionality."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Helper function for generating a filepath\n",
    "def get_filepath(filename, extension):\n",
    "    return './data/out/' + filename + '.' + extension"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Write df to csv using helper function"
   ]
  },
  {
   "source": [
    "### Exercise - Write this dataframe to Excel,  JSON, and HTML"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Excel (.xlsx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "# JSON (.json)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "# HTML (.html)"
   ]
  },
  {
   "source": [
    "## 2.1 Working with DataFrames\n",
    "Let's work with an existing dataframe that includes a lot of the same data from the previous sections."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read in the complete version of the europe data, using the first column as the index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Inspect the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the average of all the columns"
   ]
  },
  {
   "source": [
    "## 2.2 Boolean Indexing\n",
    "First of all, what is the boolean data type? It is data type that represents one of two possible values. For example, True False, On Off, etc."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select all rows with the country name equal to Austria"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now do the same with bracket notation for the country of Greece"
   ]
  },
  {
   "source": [
    "Queries can also be generated using partial matches."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get all countries with the letter l in their name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Notice that there is one country missing: Luxembourg. Why is that?"
   ]
  },
  {
   "source": [
    "## 2.3 Multiple queries\n",
    "So far, we've looked at queries with a single condition. Let's look at multiple query conditions."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create bool series for countries with unemployment less than 7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Combine with this l_names series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# In set theory, this is intersection. i.e. 'and'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# In set theory, this is union. i.e. 'or'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# In set theory, this is complement. i.e. 'not'"
   ]
  },
  {
   "source": [
    "### Exercise - What countries have an unemployment rate greater than 9% and a GDP less than 280000?"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "### Exercise - What countries have a median income between 10,000 and 20,000?"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a bool series from the df that combines these two conditions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Alternative, using .between()"
   ]
  },
  {
   "source": [
    "## 3.1 Selection\n",
    "Using .loc(), .iloc()"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data from the eur_data_final df, represented as a python dictionary\n",
    "countries_dict = {\n",
    "    15: {'country': 'Italy', \n",
    "         'unemp_rate': 11.7, \n",
    "         'gdp': 1689824.0, \n",
    "         'median_income': 16237, \n",
    "         'total_pop': 59433744\n",
    "        }\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# With vanilla python, how do we get the word 'Italy' from a dictionary?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# How do we do this with a dataframe?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We can also get multiple columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Or an entire row/entry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Or multiple rows and columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We can also use python's index slicing syntax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select by column value (Pandas is smart!)"
   ]
  },
  {
   "source": [
    "### Exercise - What countries have a higher unemployment rate than Slovenia and have a lowercase 't' in their name?"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select slovenia unemployment values\n",
    "\n",
    "# Generate comparison query\n",
    "\n",
    "# Generate 'contains' query\n",
    "\n",
    "# Make selection using queries"
   ]
  },
  {
   "source": [
    "### Exercise - Generate a correlation matrix for the various columns and plot them using matplotlib"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select columns\n",
    "\n",
    "# Make subplot and figure\n",
    "\n",
    "# Generate correlation matrix\n",
    "\n",
    "# Generate matplotlib plot\n",
    "\n",
    "# Add colorbar to figure\n",
    "\n",
    "# Set tick labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}

================================================
FILE: intro_data_science/part_3/ds_part_3_complete.ipynb
================================================
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 "cells": [
  {
   "source": [
    "# A Brief Introduction to Pandas\n",
    "### Part 1\n",
    "\n",
    "In this notebook, we will cover the basics of Pandas, a dynamic, powerful Python library that is ubiquitous in the field of Data Science. It has robust I/O functionality and makes working with data a lot simpler than doing so in Excel. Let's get stared."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "source": [
    "## 1.1 DataFrames\n",
    "Let's read in some existing data using Pandas convenient read_csv method."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "unemployment_df = pd.read_csv('./data/unemployment_2016.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "metadata": {},
     "execution_count": 3
    }
   ],
   "source": [
    "# Check the type\n",
    "# https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html\n",
    "type(unemployment_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 32 entries, 0 to 31\nData columns (total 2 columns):\n #   Column      Non-Null Count  Dtype  \n---  ------      --------------  -----  \n 0   country     32 non-null     object \n 1   unemp_rate  32 non-null     float64\ndtypes: float64(1), object(1)\nmemory usage: 640.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "unemployment_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "       unemp_rate\n",
       "count   32.000000\n",
       "mean     8.337500\n",
       "std      4.393378\n",
       "min      3.000000\n",
       "25%      5.700000\n",
       "50%      7.300000\n",
       "75%      9.800000\n",
       "max     23.600000"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>unemp_rate</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>32.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>8.337500</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>4.393378</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>3.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>5.700000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>7.300000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>9.800000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>23.600000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 5
    }
   ],
   "source": [
    "unemployment_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select a single column\n",
    "# Using bracket notation\n",
    "countries = unemployment_df['country']\n",
    "# Using dot notation\n",
    "countries = unemployment_df.country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "error",
     "ename": "KeyError",
     "evalue": "0",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m~/Library/Python/3.8/lib/python/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3079\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3080\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3081\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 0",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-7-736c59be97bf>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Access by numeric index\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0munemployment_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/Library/Python/3.8/lib/python/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3022\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3023\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3024\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3025\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3026\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Library/Python/3.8/lib/python/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3080\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3081\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3082\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3083\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3084\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mtolerance\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 0"
     ]
    }
   ],
   "source": [
    "# Access by numeric index\n",
    "unemployment_df[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "metadata": {},
     "execution_count": 8
    }
   ],
   "source": [
    "# Check the type\n",
    "# https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html\n",
    "type(countries)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "output_type": "error",
     "ename": "AttributeError",
     "evalue": "'Series' object has no attribute 'info'",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-9-014f3ce25eb4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Series objects do not have the same methods as dataframes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mcountries\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mcountries\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdescribe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Library/Python/3.8/lib/python/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   5458\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_can_hold_identifiers_and_holds_name\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5459\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5460\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5461\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5462\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'Series' object has no attribute 'info'"
     ]
    }
   ],
   "source": [
    "# Series objects do not have the same methods as dataframes\n",
    "countries.info()\n",
    "countries.describe()"
   ]
  },
  {
   "source": [
    "## 1.2 Vectorized Operations\n",
    "In Pandas, operations work on each value in most data structures, such as dataframes and series."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0            Austria is in Europe.\n",
       "1            Belgium is in Europe.\n",
       "2           Bulgaria is in Europe.\n",
       "3        Switzerland is in Europe.\n",
       "4             Cyprus is in Europe.\n",
       "5            Czechia is in Europe.\n",
       "6            Germany is in Europe.\n",
       "7            Denmark is in Europe.\n",
       "8            Estonia is in Europe.\n",
       "9             Greece is in Europe.\n",
       "10             Spain is in Europe.\n",
       "11           Finland is in Europe.\n",
       "12            France is in Europe.\n",
       "13           Croatia is in Europe.\n",
       "14           Hungary is in Europe.\n",
       "15           Ireland is in Europe.\n",
       "16           Iceland is in Europe.\n",
       "17             Italy is in Europe.\n",
       "18         Lithuania is in Europe.\n",
       "19        Luxembourg is in Europe.\n",
       "20            Latvia is in Europe.\n",
       "21             Malta is in Europe.\n",
       "22       Netherlands is in Europe.\n",
       "23            Norway is in Europe.\n",
       "24            Poland is in Europe.\n",
       "25          Portugal is in Europe.\n",
       "26           Romania is in Europe.\n",
       "27            Sweden is in Europe.\n",
       "28          Slovenia is in Europe.\n",
       "29          Slovakia is in Europe.\n",
       "30            Turkey is in Europe.\n",
       "31    United Kingdom is in Europe.\n",
       "Name: country, dtype: object"
      ]
     },
     "metadata": {},
     "execution_count": 10
    }
   ],
   "source": [
    "# Add a string to the countries series\n",
    "countries + \" is in Europe.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "output_type": "error",
     "ename": "TypeError",
     "evalue": "can only concatenate str (not \"int\") to str",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m~/Library/Python/3.8/lib/python/site-packages/pandas/core/ops/array_ops.py\u001b[0m in \u001b[0;36m_na_arithmetic_op\u001b[0;34m(left, right, op, is_cmp)\u001b[0m\n\u001b[1;32m    141\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 142\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mexpressions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mleft\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mright\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    143\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Library/Python/3.8/lib/python/site-packages/pandas/core/computation/expressions.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(op, a, b, use_numexpr)\u001b[0m\n\u001b[1;32m    234\u001b[0m             \u001b[0;31m# error: \"None\" not callable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 235\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0m_evaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mop_str\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# type: ignore[misc]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    236\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0m_evaluate_standard\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mop_str\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Library/Python/3.8/lib/python/site-packages/pandas/core/computation/expressions.py\u001b[0m in \u001b[0;36m_evaluate_standard\u001b[0;34m(op, op_str, a, b)\u001b[0m\n\u001b[1;32m     68\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merrstate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"ignore\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 69\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     70\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: can only concatenate str (not \"int\") to str",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-11-84e40da47f8f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Add a number to the countries series\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mcountries\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m12\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/Library/Python/3.8/lib/python/site-packages/pandas/core/ops/common.py\u001b[0m in \u001b[0;36mnew_method\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m     63\u001b[0m         \u001b[0mother\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mitem_from_zerodim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     64\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 65\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     66\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     67\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mnew_method\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Library/Python/3.8/lib/python/site-packages/pandas/core/arraylike.py\u001b[0m in \u001b[0;36m__add__\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m     87\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0munpack_zerodim_and_defer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"__add__\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     88\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__add__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 89\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_arith_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moperator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     90\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     91\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0munpack_zerodim_and_defer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"__radd__\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Library/Python/3.8/lib/python/site-packages/pandas/core/series.py\u001b[0m in \u001b[0;36m_arith_method\u001b[0;34m(self, other, op)\u001b[0m\n\u001b[1;32m   4957\u001b[0m         \u001b[0mlvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mextract_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mextract_numpy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4958\u001b[0m         \u001b[0mrvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mextract_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mextract_numpy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4959\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marithmetic_op\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4960\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4961\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_construct_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mres_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Library/Python/3.8/lib/python/site-packages/pandas/core/ops/array_ops.py\u001b[0m in \u001b[0;36marithmetic_op\u001b[0;34m(left, right, op)\u001b[0m\n\u001b[1;32m    187\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    188\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merrstate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"ignore\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 189\u001b[0;31m             \u001b[0mres_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_na_arithmetic_op\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    190\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    191\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mres_values\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Library/Python/3.8/lib/python/site-packages/pandas/core/ops/array_ops.py\u001b[0m in \u001b[0;36m_na_arithmetic_op\u001b[0;34m(left, right, op, is_cmp)\u001b[0m\n\u001b[1;32m    147\u001b[0m             \u001b[0;31m#  will handle complex numbers incorrectly, see GH#32047\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    148\u001b[0m             \u001b[0;32mraise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 149\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_masked_arith_op\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mright\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    150\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    151\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mis_cmp\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mis_scalar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNotImplemented\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Library/Python/3.8/lib/python/site-packages/pandas/core/ops/array_ops.py\u001b[0m in \u001b[0;36m_masked_arith_op\u001b[0;34m(x, y, op)\u001b[0m\n\u001b[1;32m    109\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0many\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    110\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merrstate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"ignore\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 111\u001b[0;31m                 \u001b[0mresult\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxrav\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    112\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    113\u001b[0m     \u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmaybe_upcast_putmask\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m~\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnan\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: can only concatenate str (not \"int\") to str"
     ]
    }
   ],
   "source": [
    "# Add a number to the countries series\n",
    "countries + 12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "0      6.0\n1      7.8\n2      7.6\n3      5.0\n4     13.0\n5      4.0\n6      4.1\n7      6.2\n8      6.8\n9     23.6\n10    19.6\n11     8.8\n12    10.1\n13    13.1\n14     5.1\n15     8.4\n16     3.0\n17    11.7\n18     7.9\n19     6.3\n20     9.6\n21     4.7\n22     6.0\n23     4.7\n24     6.2\n25    11.2\n26     5.9\n27     7.0\n28     8.0\n29     9.7\n30    10.9\n31     4.8\nName: unemp_rate, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Select the unemployment rates as a series\n",
    "unemployment_rates = unemployment_df['unemp_rate']\n",
    "print(unemployment_rates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Sort the dataframe by rate\n",
    "unemployment_sorted_asc = unemployment_df.sort_values('unemp_rate', ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "    country  unemp_rate\n",
       "16  Iceland         3.0\n",
       "5   Czechia         4.0\n",
       "6   Germany         4.1\n",
       "23   Norway         4.7\n",
       "21    Malta         4.7"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>country</th>\n      <th>unemp_rate</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>16</th>\n      <td>Iceland</td>\n      <td>3.0</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Czechia</td>\n      <td>4.0</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Germany</td>\n      <td>4.1</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>Norway</td>\n      <td>4.7</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>Malta</td>\n      <td>4.7</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 14
    }
   ],
   "source": [
    "# Use .head() to get the top five values\n",
    "unemployment_sorted_asc.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "    country  unemp_rate\n",
       "17    Italy        11.7\n",
       "4    Cyprus        13.0\n",
       "13  Croatia        13.1\n",
       "10    Spain        19.6\n",
       "9    Greece        23.6"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>country</th>\n      <th>unemp_rate</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>17</th>\n      <td>Italy</td>\n      <td>11.7</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Cyprus</td>\n      <td>13.0</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>Croatia</td>\n      <td>13.1</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>Spain</td>\n      <td>19.6</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>Greece</td>\n      <td>23.6</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 15
    }
   ],
   "source": [
    "# Use .tail() to get the  five values\n",
    "unemployment_sorted_asc.tail()"
   ]
  },
  {
   "source": [
    "### Exercise - What is the average unemployment of the seven countries with the highest unemployment in europe?"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Sort the values of the unemployment_df - descending or ascending?\n",
    "unemployment_sorted_desc = unemployment_df.sort_values('unemp_rate', ascending=False)\n",
    "# Get the first seven rows in the returned series\n",
    "highest_unemployment = unemployment_sorted_desc.head(7)\n",
    "# Get the average of those rows\n",
    "highest_unemployment_avg = highest_unemployment['unemp_rate'].mean()"
   ]
  },
  {
   "source": [
    "### Extra - explore these methods:\n",
    ".min(), .max(), .sum(), .unique(), .nunique(), .count(), .duplicated()"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "        country  unemp_rate\n",
       "0       Austria         6.0\n",
       "7       Denmark         6.2\n",
       "21        Malta         4.7\n",
       "22  Netherlands         6.0\n",
       "23       Norway         4.7\n",
       "24       Poland         6.2"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>country</th>\n      <th>unemp_rate</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Austria</td>\n      <td>6.0</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>Denmark</td>\n      <td>6.2</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>Malta</td>\n      <td>4.7</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>Netherlands</td>\n      <td>6.0</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>Norway</td>\n      <td>4.7</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>Poland</td>\n      <td>6.2</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 17
    }
   ],
   "source": [
    "unemployment_rates.min()\n",
    "unemployment_rates.max()\n",
    "unemployment_rates.sum()\n",
    "unemployment_rates.unique()\n",
    "unemployment_rates.nunique()\n",
    "unemployment_rates.count()\n",
    "dups = unemployment_rates.duplicated()\n",
    "dups_no_keep = unemployment_rates.duplicated(keep=False)\n",
    "unemployment_df[dups_no_keep]"
   ]
  },
  {
   "source": [
    "## 1.3 Merging DataFrames\n",
    "Data can come from a variety of different sources and it can be helpful to put them all in one place."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "gdp_filepath = './data/gdp_2016.csv'\n",
    "# Read in data using this filepath\n",
    "gdp_df = pd.read_csv(gdp_filepath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 32 entries, 0 to 31\nData columns (total 2 columns):\n #   Column   Non-Null Count  Dtype  \n---  ------   --------------  -----  \n 0   country  32 non-null     object \n 1   gdp      32 non-null     float64\ndtypes: float64(1), object(1)\nmemory usage: 640.0+ bytes\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "      country       gdp\n",
       "0      Cyprus   18490.2\n",
       "1      Latvia   25037.7\n",
       "2   Lithuania   38849.4\n",
       "3  Luxembourg   53303.0\n",
       "4     Hungary  113903.8"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>country</th>\n      <th>gdp</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Cyprus</td>\n      <td>18490.2</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Latvia</td>\n      <td>25037.7</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Lithuania</td>\n      <td>38849.4</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Luxembourg</td>\n      <td>53303.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Hungary</td>\n      <td>113903.8</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 19
    }
   ],
   "source": [
    "# Inspect the GDP data\n",
    "gdp_df.info()\n",
    "gdp_df.describe()\n",
    "gdp_df.head()"
   ]
  },
  {
   "source": [
    "Let's create a new dataframe by merging two existing dataframes. This is the one we'll use for the rest of section 2."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "       country  unemp_rate       gdp\n",
       "0      Austria         6.0  356237.6\n",
       "1      Belgium         7.8  424660.3\n",
       "2     Bulgaria         7.6   48128.6\n",
       "3  Switzerland         5.0  605753.7\n",
       "4       Cyprus        13.0   18490.2"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>country</th>\n      <th>unemp_rate</th>\n      <th>gdp</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Austria</td>\n      <td>6.0</td>\n      <td>356237.6</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Belgium</td>\n      <td>7.8</td>\n      <td>424660.3</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Bulgaria</td>\n      <td>7.6</td>\n      <td>48128.6</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Switzerland</td>\n      <td>5.0</td>\n      <td>605753.7</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Cyprus</td>\n      <td>13.0</td>\n      <td>18490.2</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 20
    }
   ],
   "source": [
    "# Merge unemployment df with gdp df\n",
    "# https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.merge.html\n",
    "eur_data = pd.merge(unemployment_df, gdp_df)\n",
    "eur_data.head()"
   ]
  },
  {
   "source": [
    "What other data can we include? Are there other file types that we can work with?"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "       country  avg_hrs_worked\n",
       "0      Austria            36.5\n",
       "1      Belgium            37.0\n",
       "2     Bulgaria            40.8\n",
       "3  Switzerland            34.7\n",
       "4       Cyprus            39.2"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>country</th>\n      <th>avg_hrs_worked</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Austria</td>\n      <td>36.5</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Belgium</td>\n      <td>37.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Bulgaria</td>\n      <td>40.8</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Switzerland</td>\n      <td>34.7</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Cyprus</td>\n      <td>39.2</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 21
    }
   ],
   "source": [
    "# Read in misc data from excel\n",
    "misc_filepath = './data/misc_data.xlsx'\n",
    "misc_data = pd.read_excel(misc_filepath)\n",
    "misc_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "    country  median_income\n",
       "0   Belgium          21335\n",
       "1  Bulgaria           6742\n",
       "2   Czechia          12478\n",
       "3   Denmark          21355\n",
       "4   Germany          21152"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>country</th>\n      <th>median_income</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Belgium</td>\n      <td>21335</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Bulgaria</td>\n      <td>6742</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Czechia</td>\n      <td>12478</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Denmark</td>\n      <td>21355</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Germany</td>\n      <td>21152</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 22
    }
   ],
   "source": [
    "# .read_excel() gets the first sheet in the excel file. Instead, get the \"Income\" sheet\n",
    "income_df = pd.read_excel('./data/misc_data.xlsx', \"Income\")\n",
    "income_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                                          Unnamed: 0 Unnamed: 1  \\\n",
       "0  This is an overview of the population data for...        NaN   \n",
       "1                                                NaN        NaN   \n",
       "2                                            country  total_pop   \n",
       "3                                            Belgium   11000638   \n",
       "4                                           Bulgaria    7364570   \n",
       "\n",
       "         Unnamed: 2  \n",
       "0               NaN  \n",
       "1               NaN  \n",
       "2  prct_yng_adt_pop  \n",
       "3         18.485146  \n",
       "4         18.432577  "
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Unnamed: 0</th>\n      <th>Unnamed: 1</th>\n      <th>Unnamed: 2</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>This is an overview of the population data for...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>country</td>\n      <td>total_pop</td>\n      <td>prct_yng_adt_pop</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Belgium</td>\n      <td>11000638</td>\n      <td>18.485146</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Bulgaria</td>\n      <td>7364570</td>\n      <td>18.432577</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 23
    }
   ],
   "source": [
    "pop_df = pd.read_excel('./data/misc_data.xlsx', 'Population')\n",
    "pop_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "    country  total_pop  prct_yng_adt_pop\n",
       "0   Belgium   11000638         18.485146\n",
       "1  Bulgaria    7364570         18.432577\n",
       "2   Estonia    1294455         19.688363\n",
       "3   Ireland    4574888         20.601335\n",
       "4    Greece   10816286         17.604416"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>country</th>\n      <th>total_pop</th>\n      <th>prct_yng_adt_pop</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Belgium</td>\n      <td>11000638</td>\n      <td>18.485146</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Bulgaria</td>\n      <td>7364570</td>\n      <td>18.432577</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Estonia</td>\n      <td>1294455</td>\n      <td>19.688363</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Ireland</td>\n      <td>4574888</td>\n      <td>20.601335</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Greece</td>\n      <td>10816286</td>\n      <td>17.604416</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 24
    }
   ],
   "source": [
    "pop_df = pd.read_excel('./data/misc_data.xlsx', 'Population', skiprows=3)\n",
    "pop_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "['country', 'total_pop', 'prct_yng_adt_pop']"
      ]
     },
     "metadata": {},
     "execution_count": 25
    }
   ],
   "source": [
    "# Do we need all of these columns?\n",
    "pop_df_col_names = pop_df.columns\n",
    "list(pop_df_col_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select total population column\n",
    "total_pop = pop_df['total_pop']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "output_type": "error",
     "ename": "MergeError",
     "evalue": "No common columns to perform merge on. Merge options: left_on=None, right_on=None, left_index=False, right_index=False",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mMergeError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-27-1871d17e024e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0meur_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmerge\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0meur_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtotal_pop\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/Library/Python/3.8/lib/python/site-packages/pandas/core/reshape/merge.py\u001b[0m in \u001b[0;36mmerge\u001b[0;34m(left, right, how, on, left_on, right_on, left_index, right_index, sort, suffixes, copy, indicator, validate)\u001b[0m\n\u001b[1;32m     72\u001b[0m     \u001b[0mvalidate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     73\u001b[0m ) -> \"DataFrame\":\n\u001b[0;32m---> 74\u001b[0;31m     op = _MergeOperation(\n\u001b[0m\u001b[1;32m     75\u001b[0m         \u001b[0mleft\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     76\u001b[0m         \u001b[0mright\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Library/Python/3.8/lib/python/site-packages/pandas/core/reshape/merge.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, left, right, how, on, left_on, right_on, axis, left_index, right_index, sort, suffixes, copy, indicator, validate)\u001b[0m\n\u001b[1;32m    648\u001b[0m             \u001b[0mwarnings\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwarn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mUserWarning\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    649\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 650\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_specification\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    651\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    652\u001b[0m         \u001b[0mcross_col\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Library/Python/3.8/lib/python/site-packages/pandas/core/reshape/merge.py\u001b[0m in \u001b[0;36m_validate_specification\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1281\u001b[0m                 \u001b[0mcommon_cols\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mleft_cols\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mintersection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mright_cols\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1282\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcommon_cols\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1283\u001b[0;31m                     raise MergeError(\n\u001b[0m\u001b[1;32m   1284\u001b[0m                         \u001b[0;34m\"No common columns to perform merge on. \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1285\u001b[0m                         \u001b[0;34mf\"Merge options: left_on={self.left_on}, \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mMergeError\u001b[0m: No common columns to perform merge on. Merge options: left_on=None, right_on=None, left_index=False, right_index=False"
     ]
    }
   ],
   "source": [
    "eur_data = pd.merge(eur_data, total_pop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select population with country names, so pandas can perform merge\n",
    "total_pop_with_countries = pop_df[['country', 'total_pop']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "     country  unemp_rate       gdp  total_pop\n",
       "0    Austria         6.0  356237.6    8401940\n",
       "1    Belgium         7.8  424660.3   11000638\n",
       "2   Bulgaria         7.6   48128.6    7364570\n",
       "13   Croatia        13.1   46639.5    4284889\n",
       "4     Cyprus        13.0   18490.2     840407"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>country</th>\n      <th>unemp_rate</th>\n      <th>gdp</th>\n      <th>total_pop</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Austria</td>\n      <td>6.0</td>\n      <td>356237.6</td>\n      <td>8401940</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Belgium</td>\n      <td>7.8</td>\n      <td>424660.3</td>\n      <td>11000638</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Bulgaria</td>\n      <td>7.6</td>\n      <td>48128.6</td>\n      <td>7364570</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>Croatia</td>\n      <td>13.1</td>\n      <td>46639.5</td>\n      <td>4284889</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Cyprus</td>\n      <td>13.0</td>\n      <td>18490.2</td>\n      <td>840407</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 29
    }
   ],
   "source": [
    "# Perform merge and sort values by country name\n",
    "eur_data = pd.merge(eur_data, total_pop_with_countries)\n",
    "eur_data_sorted = eur_data.sort_values('country')\n",
    "eur_data_sorted.head()"
   ]
  },
  {
   "source": [
    "It looks like there's an issue with our indexes. What is an index, anyway?"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "## 1.4 Indexes\n",
    "https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Index.html"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "pandas.core.indexes.numeric.Int64Index"
      ]
     },
     "metadata": {},
     "execution_count": 30
    }
   ],
   "source": [
    "# Our data is indexed by integer values\n",
    "eur_index = eur_data.index\n",
    "type(eur_index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "output_type": "error",
     "ename": "TypeError",
     "evalue": "Index does not support mutable operations",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-31-80a77fa7e4a2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Indexes are immutable. And for good reason...\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0meur_index\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m12\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/Library/Python/3.8/lib/python/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36m__setitem__\u001b[0;34m(self, key, value)\u001b[0m\n\u001b[1;32m   4275\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mfinal\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4276\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__setitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4277\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Index does not support mutable operations\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4278\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4279\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__getitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: Index does not support mutable operations"
     ]
    }
   ],
   "source": [
    "# Indexes are immutable. And for good reason...\n",
    "eur_index[0] = 12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "          unemp_rate       gdp  total_pop\n",
       "country                                  \n",
       "Austria          6.0  356237.6    8401940\n",
       "Belgium          7.8  424660.3   11000638\n",
       "Bulgaria         7.6   48128.6    7364570\n",
       "Croatia         13.1   46639.5    4284889\n",
       "Cyprus          13.0   18490.2     840407"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>unemp_rate</th>\n      <th>gdp</th>\n      <th>total_pop</th>\n    </tr>\n    <tr>\n      <th>country</th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Austria</th>\n      <td>6.0</td>\n      <td>356237.6</td>\n      <td>8401940</td>\n    </tr>\n    <tr>\n      <th>Belgium</th>\n      <td>7.8</td>\n      <td>424660.3</td>\n      <td>11000638</td>\n    </tr>\n    <tr>\n      <th>Bulgaria</th>\n      <td>7.6</td>\n      <td>48128.6</td>\n      <td>7364570</td>\n    </tr>\n    <tr>\n      <th>Croatia</th>\n      <td>13.1</td>\n      <td>46639.5</td>\n      <td>4284889</td>\n    </tr>\n    <tr>\n      <th>Cyprus</th>\n      <td>13.0</td>\n      <td>18490.2</td>\n      <td>840407</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 32
    }
   ],
   "source": [
    "# Dataframes can also be indexed by labels, rather than numbers.\n",
    "# Use the .set_index() method to set the index for our dataframe\n",
    "eur_data_country_index = eur_data_sorted.set_index('country')\n",
    "eur_data_country_index.head()"
   ]
  },
  {
   "source": [
    "## 1.5 Exporting Data\n",
    "Pandas has robust I/O functionality."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Helper function for generating a filepath\n",
    "def get_filepath(filename, extension):\n",
    "    return './data/out/' + filename + '.' + extension"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "filepath = get_filepath('eur_data_sorted', 'csv')\n",
    "eur_data_sorted.to_csv(filepath)"
   ]
  },
  {
   "source": [
    "### Exercise - Write this dataframe to Excel,  JSON, and HTML"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Excel (.xlsx)\n",
    "eur_data_sorted.to_excel(get_filepath('eur_data_sorted', 'xlsx'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# JSON (.json)\n",
    "eur_data_sorted.to_json(get_filepath('eur_data_sorted', 'json'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# HTML (.html)\n",
    "eur_data_sorted.to_html(get_filepath('eur_data_sorted', 'html'))"
   ]
  },
  {
   "source": [
    "## 2.1 Working with DataFrames\n",
    "Let's work with an existing dataframe that includes a lot of the same data from the previous sections."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read in the complete version of the europe data, using the first column as the index\n",
    "eur_data_final = pd.read_csv('./data/complete/eur_data_final.csv', index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "       unemp_rate           gdp  median_income     total_pop\n",
       "count   32.000000  3.200000e+01      32.000000  3.200000e+01\n",
       "mean     8.337500  5.218333e+05   15972.343750  1.637612e+07\n",
       "std      4.393378  7.781849e+05    6640.636617  2.198819e+07\n",
       "min      3.000000  1.034410e+04    4724.000000  3.155560e+05\n",
       "25%      5.700000  4.775632e+04   10190.500000  3.974524e+06\n",
       "50%      7.300000  2.012768e+05   16205.000000  7.954662e+06\n",
       "75%      9.800000  4.987990e+05   21161.250000  1.241443e+07\n",
       "max     23.600000  3.159750e+06   28663.000000  8.021970e+07"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>unemp_rate</th>\n      <th>gdp</th>\n      <th>median_income</th>\n      <th>total_pop</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>32.000000</td>\n      <td>3.200000e+01</td>\n      <td>32.000000</td>\n      <td>3.200000e+01</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>8.337500</td>\n      <td>5.218333e+05</td>\n      <td>15972.343750</td>\n      <td>1.637612e+07</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>4.393378</td>\n      <td>7.781849e+05</td>\n      <td>6640.636617</td>\n      <td>2.198819e+07</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>3.000000</td>\n      <td>1.034410e+04</td>\n      <td>4724.000000</td>\n      <td>3.155560e+05</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>5.700000</td>\n      <td>4.775632e+04</td>\n      <td>10190.500000</td>\n      <td>3.974524e+06</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>7.300000</td>\n      <td>2.012768e+05</td>\n      <td>16205.000000</td>\n      <td>7.954662e+06</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>9.800000</td>\n      <td>4.987990e+05</td>\n      <td>21161.250000</td>\n      <td>1.241443e+07</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>23.600000</td>\n      <td>3.159750e+06</td>\n      <td>28663.000000</td>\n      <td>8.021970e+07</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 39
    }
   ],
   "source": [
    "# Inspect the data\n",
    "eur_data_final.head()\n",
    "eur_data_final.tail()\n",
    "eur_data_final.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "unemp_rate       8.337500e+00\n",
       "gdp              5.218333e+05\n",
       "median_income    1.597234e+04\n",
       "total_pop        1.637612e+07\n",
       "dtype: float64"
      ]
     },
     "metadata": {},
     "execution_count": 40
    }
   ],
   "source": [
    "eur_data_final.mean()"
   ]
  },
  {
   "source": [
    "## 2.2 Boolean Indexing\n",
    "First of all, what is the boolean data type? It is data type that represents one of two possible values. For example, True False, On Off, etc."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "   country  unemp_rate       gdp  median_income  total_pop\n",
       "0  Austria         6.0  356237.6          23071    8401940"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>country</th>\n      <th>unemp_rate</th>\n      <th>gdp</th>\n      <th>median_income</th>\n      <th>total_pop</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Austria</td>\n      <td>6.0</td>\n      <td>356237.6</td>\n      <td>23071</td>\n      <td>8401940</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 41
    }
   ],
   "source": [
    "# Select all rows with the country name equal to Austria\n",
    "eur_data_final.country\n",
    "austria_bool = eur_data_final.country == 'Austria'\n",
    "eur_data_final[austria_bool]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "   country  unemp_rate       gdp  median_income  total_pop\n",
       "11  Greece        23.6  176487.9           9048   10816286"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>country</th>\n      <th>unemp_rate</th>\n      <th>gdp</th>\n      <th>median_income</th>\n      <th>total_pop</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>11</th>\n      <td>Greece</td>\n      <td>23.6</td>\n      <td>176487.9</td>\n      <td>9048</td>\n      <td>10816286</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 42
    }
   ],
   "source": [
    "# Now do the same with bracket notation for the country of Greece\n",
    "greece_bool = eur_data_final['country'] == 'Greece'\n",
    "eur_data_final[greece_bool]"
   ]
  },
  {
   "source": [
    "Queries can also be generated using partial matches."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "        country  unemp_rate        gdp  median_income  total_pop\n",
       "1       Belgium         7.8   424660.3          21335   11000638\n",
       "2      Bulgaria         7.6    48128.6           6742    7364570\n",
       "8       Finland         8.8   216073.0          19997    5375276\n",
       "13      Iceland         3.0    18646.1          22193     315556\n",
       "14      Ireland         8.4   273238.2          18286    4574888\n",
       "15        Italy        11.7  1689824.0          16237   59433744\n",
       "19        Malta         4.7    10344.1          17264     417432\n",
       "20  Netherlands         6.0   708337.0          21189   16655799\n",
       "22       Poland         6.2   426547.5          10865   38044565\n",
       "23     Portugal        11.2   186480.5          10805   10562178\n",
       "25     Slovakia         9.7    81226.1          10466    5397036\n",
       "26     Slovenia         8.0    40357.2          15250    2050189\n",
       "29  Switzerland         5.0   605753.7          27692    7954662"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>country</th>\n      <th>unemp_rate</th>\n      <th>gdp</th>\n      <th>median_income</th>\n      <th>total_pop</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>Belgium</td>\n      <td>7.8</td>\n      <td>424660.3</td>\n      <td>21335</td>\n      <td>11000638</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Bulgaria</td>\n      <td>7.6</td>\n      <td>48128.6</td>\n      <td>6742</td>\n      <td>7364570</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>Finland</td>\n      <td>8.8</td>\n      <td>216073.0</td>\n      <td>19997</td>\n      <td>5375276</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>Iceland</td>\n      <td>3.0</td>\n      <td>18646.1</td>\n      <td>22193</td>\n      <td>315556</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>Ireland</td>\n      <td>8.4</td>\n      <td>273238.2</td>\n      <td>18286</td>\n      <td>4574888</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>Italy</td>\n      <td>11.7</td>\n      <td>1689824.0</td>\n      <td>16237</td>\n      <td>59433744</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>Malta</td>\n      <td>4.7</td>\n      <td>10344.1</td>\n      <td>17264</td>\n      <td>417432</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>Netherlands</td>\n      <td>6.0</td>\n      <td>708337.0</td>\n      <td>21189</td>\n      <td>16655799</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>Poland</td>\n      <td>6.2</td>\n      <td>426547.5</td>\n      <td>10865</td>\n      <td>38044565</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>Portugal</td>\n      <td>11.2</td>\n      <td>186480.5</td>\n      <td>10805</td>\n      <td>10562178</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>Slovakia</td>\n      <td>9.7</td>\n      <td>81226.1</td>\n      <td>10466</td>\n      <td>5397036</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>Slovenia</td>\n      <td>8.0</td>\n      <td>40357.2</td>\n      <td>15250</td>\n      <td>2050189</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>Switzerland</td>\n      <td>5.0</td>\n      <td>605753.7</td>\n      <td>27692</td>\n      <td>7954662</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 43
    }
   ],
   "source": [
    "# Get all countries with the letter l in their name\n",
    "l_names = eur_data_final['country'].str.contains('l')\n",
    "eur_data_final[l_names]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "        country  unemp_rate        gdp  median_income  total_pop\n",
       "1       Belgium         7.8   424660.3          21335   11000638\n",
       "2      Bulgaria         7.6    48128.6           6742    7364570\n",
       "8       Finland         8.8   216073.0          19997    5375276\n",
       "13      Iceland         3.0    18646.1          22193     315556\n",
       "14      Ireland         8.4   273238.2          18286    4574888\n",
       "15        Italy        11.7  1689824.0          16237   59433744\n",
       "16       Latvia         9.6    25037.7           9257    2070371\n",
       "17    Lithuania         7.9    38849.4           9364    3043429\n",
       "18   Luxembourg         6.3    53303.0          28663     512353\n",
       "19        Malta         4.7    10344.1          17264     417432\n",
       "20  Netherlands         6.0   708337.0          21189   16655799\n",
       "22       Poland         6.2   426547.5          10865   38044565\n",
       "23     Portugal        11.2   186480.5          10805   10562178\n",
       "25     Slovakia         9.7    81226.1          10466    5397036\n",
       "26     Slovenia         8.0    40357.2          15250    2050189\n",
       "29  Switzerland         5.0   605753.7          27692    7954662"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>country</th>\n      <th>unemp_rate</th>\n      <th>gdp</th>\n      <th>median_income</th>\n      <th>total_pop</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>Belgium</td>\n      <td>7.8</td>\n      <td>424660.3</td>\n      <td>21335</td>\n      <td>11000638</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Bulgaria</td>\n      <td>7.6</td>\n      <td>48128.6</td>\n      <td>6742</td>\n      <td>7364570</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>Finland</td>\n      <td>8.8</td>\n      <td>216073.0</td>\n      <td>19997</td>\n      <td>5375276</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>Iceland</td>\n      <td>3.0</td>\n      <td>18646.1</td>\n      <td>22193</td>\n      <td>315556</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>Ireland</td>\n      <td>8.4</td>\n      <td>273238.2</td>\n      <td>18286</td>\n      <td>4574888</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>Italy</td>\n      <td>11.7</td>\n      <td>1689824.0</td>\n      <td>16237</td>\n      <td>59433744</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>Latvia</td>\n      <td>9.6</td>\n      <td>25037.7</td>\n      <td>9257</td>\n      <td>2070371</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>Lithuania</td>\n      <td>7.9</td>\n      <td>38849.4</td>\n      <td>9364</td>\n      <td>3043429</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>Luxembourg</td>\n      <td>6.3</td>\n      <td>53303.0</td>\n      <td>28663</td>\n      <td>512353</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>Malta</td>\n      <td>4.7</td>\n      <td>10344.1</td>\n      <td>17264</td>\n      <td>417432</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>Netherlands</td>\n      <td>6.0</td>\n      <td>708337.0</td>\n      <td>21189</td>\n      <td>16655799</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>Poland</td>\n      <td>6.2</td>\n      <td>426547.5</td>\n      <td>10865</td>\n      <td>38044565</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>Portugal</td>\n      <td>11.2</td>\n      <td>186480.5</td>\n      <td>10805</td>\n      <td>10562178</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>Slovakia</td>\n      <td>9.7</td>\n      <td>81226.1</td>\n      <td>10466</td>\n      <td>5397036</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>Slovenia</td>\n      <td>8.0</td>\n      <td>40357.2</td>\n      <td>15250</td>\n      <td>2050189</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>Switzerland</td>\n      <td>5.0</td>\n      <td>605753.7</td>\n      <td>27692</td>\n      <td>7954662</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 44
    }
   ],
   "source": [
    "# Notice that there is one country missing: Luxembourg. Why is that?\n",
    "l_names_insensitive = eur_data_final.country.str.contains('l', case=False)\n",
    "eur_data_final[l_names_insensitive]"
   ]
  },
  {
   "source": [
    "## 2.3 Multiple queries\n",
    "So far, we've looked at queries with a single condition. Let's look at multiple query conditions."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create bool series for countries with unemployment less than 7\n",
    "low_unemployment = eur_data_final.unemp_rate < 7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "output_type": "error",
     "ename": "ValueError",
     "evalue": "The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-46-57600958909a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Combine with this l_names series\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0meur_data_final\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0ml_names_insensitive\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mlow_unemployment\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/Library/Python/3.8/lib/python/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m__nonzero__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1437\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mfinal\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1438\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__nonzero__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1439\u001b[0;31m         raise ValueError(\n\u001b[0m\u001b[1;32m   1440\u001b[0m             \u001b[0;34mf\"The truth value of a {type(self).__name__} is ambiguous. \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1441\u001b[0m             \u001b[0;34m\"Use a.empty, a.bool(), a.item(), a.any() or a.all().\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all()."
     ]
    }
   ],
   "source": [
    "# Combine with this l_names series\n",
    "eur_data_final[l_names_insensitive and low_unemployment]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "        country  unemp_rate       gdp  median_income  total_pop\n",
       "13      Iceland         3.0   18646.1          22193     315556\n",
       "18   Luxembourg         6.3   53303.0          28663     512353\n",
       "19        Malta         4.7   10344.1          17264     417432\n",
       "20  Netherlands         6.0  708337.0          21189   16655799\n",
       "22       Poland         6.2  426547.5          10865   38044565\n",
       "29  Switzerland         5.0  605753.7          27692    7954662"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>country</th>\n      <th>unemp_rate</th>\n      <th>gdp</th>\n      <th>median_income</th>\n      <th>total_pop</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>13</th>\n      <td>Iceland</td>\n      <td>3.0</td>\n      <td>18646.1</td>\n      <td>22193</td>\n      <td>315556</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>Luxembourg</td>\n      <td>6.3</td>\n      <td>53303.0</td>\n      <td>28663</td>\n      <td>512353</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>Malta</td>\n      <td>4.7</td>\n      <td>10344.1</td>\n      <td>17264</td>\n      <td>417432</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>Netherlands</td>\n      <td>6.0</td>\n      <td>708337.0</td>\n      <td>21189</td>\n      <td>16655799</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>Poland</td>\n      <td>6.2</td>\n      <td>426547.5</td>\n      <td>10865</td>\n      <td>38044565</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>Switzerland</td>\n      <td>5.0</td>\n      <td>605753.7</td>\n      <td>27692</td>\n      <td>7954662</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 47
    }
   ],
   "source": [
    "# In set theory, this is intersection. i.e. 'and'\n",
    "eur_data_final[l_names_insensitive & low_unemployment]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "           country  unemp_rate        gdp  median_income  total_pop\n",
       "0          Austria         6.0   356237.6          23071    8401940\n",
       "1          Belgium         7.8   424660.3          21335   11000638\n",
       "2         Bulgaria         7.6    48128.6           6742    7364570\n",
       "5          Czechia         4.0   176370.1          12478   10436560\n",
       "6          Denmark         6.2   282089.9          21355    5560628\n",
       "7          Estonia         6.8    21682.6          11867    1294455\n",
       "8          Finland         8.8   216073.0          19997    5375276\n",
       "10         Germany         4.1  3159750.0          21152   80219695\n",
       "12         Hungary         5.1   113903.8           8267    9937628\n",
       "13         Iceland         3.0    18646.1          22193     315556\n",
       "14         Ireland         8.4   273238.2          18286    4574888\n",
       "15           Italy        11.7  1689824.0          16237   59433744\n",
       "18      Luxembourg         6.3    53303.0          28663     512353\n",
       "19           Malta         4.7    10344.1          17264     417432\n",
       "20     Netherlands         6.0   708337.0          21189   16655799\n",
       "21          Norway         4.7   335747.5          27670    4979954\n",
       "22          Poland         6.2   426547.5          10865   38044565\n",
       "23        Portugal        11.2   186480.5          10805   10562178\n",
       "24         Romania         5.9   170393.6           4724   20121641\n",
       "25        Slovakia         9.7    81226.1          10466    5397036\n",
       "26        Slovenia         8.0    40357.2          15250    2050189\n",
       "29     Switzerland         5.0   605753.7          27692    7954662\n",
       "31  United Kingdom         4.8  2403382.6          17296   63182180"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>country</th>\n      <th>unemp_rate</th>\n      <th>gdp</th>\n      <th>median_income</th>\n      <th>total_pop</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Austria</td>\n      <td>6.0</td>\n      <td>356237.6</td>\n      <td>23071</td>\n      <td>8401940</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Belgium</td>\n      <td>7.8</td>\n      <td>424660.3</td>\n      <td>21335</td>\n      <td>11000638</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Bulgaria</td>\n      <td>7.6</td>\n      <td>48128.6</td>\n      <td>6742</td>\n      <td>7364570</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Czechia</td>\n      <td>4.0</td>\n      <td>176370.1</td>\n      <td>12478</td>\n      <td>10436560</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Denmark</td>\n      <td>6.2</td>\n      <td>282089.9</td>\n      <td>21355</td>\n      <td>5560628</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>Estonia</td>\n      <td>6.8</td>\n      <td>21682.6</td>\n      <td>11867</td>\n      <td>1294455</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>Finland</td>\n      <td>8.8</td>\n      <td>216073.0</td>\n      <td>19997</td>\n      <td>5375276</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>Germany</td>\n      <td>4.1</td>\n      <td>3159750.0</td>\n      <td>21152</td>\n      <td>80219695</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>Hungary</td>\n      <td>5.1</td>\n      <td>113903.8</td>\n      <td>8267</td>\n      <td>9937628</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>Iceland</td>\n      <td>3.0</td>\n      <td>18646.1</td>\n      <td>22193</td>\n      <td>315556</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>Ireland</td>\n      <td>8.4</td>\n      <td>273238.2</td>\n      <td>18286</td>\n      <td>4574888</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>Italy</td>\n      <td>11.7</td>\n      <td>1689824.0</td>\n      <td>16237</td>\n      <td>59433744</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>Luxembourg</td>\n      <td>6.3</td>\n      <td>53303.0</td>\n      <td>28663</td>\n      <td>512353</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>Malta</td>\n      <td>4.7</td>\n      <td>10344.1</td>\n      <td>17264</td>\n      <td>417432</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>Netherlands</td>\n      <td>6.0</td>\n      <td>708337.0</td>\n      <td>21189</td>\n      <td>16655799</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>Norway</td>\n      <td>4.7</td>\n      <td>335747.5</td>\n      <td>27670</td>\n      <td>4979954</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>Poland</td>\n      <td>6.2</td>\n      <td>426547.5</td>\n      <td>10865</td>\n      <td>38044565</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>Portugal</td>\n      <td>11.2</td>\n      <td>186480.5</td>\n      <td>10805</td>\n      <td>10562178</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>Romania</td>\n      <td>5.9</td>\n      <td>170393.6</td>\n      <td>4724</td>\n      <td>20121641</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>Slovakia</td>\n      <td>9.7</td>\n      <td>81226.1</td>\n      <td>10466</td>\n      <td>5397036</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>Slovenia</td>\n      <td>8.0</td>\n      <td>40357.2</td>\n      <td>15250</td>\n      <td>2050189</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>Switzerland</td>\n      <td>5.0</td>\n      <td>605753.7</td>\n      <td>27692</td>\n      <td>7954662</td>\n    </tr>\n    <tr>\n      <th>31</th>\n      <td>United Kingdom</td>\n      <td>4.8</td>\n      <td>2403382.6</td>\n      <td>17296</td>\n      <td>63182180</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 48
    }
   ],
   "source": [
    "# In set theory, this is union. i.e. 'or'\n",
    "eur_data_final[l_names | low_unemployment]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "      country  unemp_rate        gdp  median_income  total_pop\n",
       "1     Belgium         7.8   424660.3          21335   11000638\n",
       "2    Bulgaria         7.6    48128.6           6742    7364570\n",
       "3     Croatia        13.1    46639.5           8985    4284889\n",
       "4      Cyprus        13.0    18490.2          16173     840407\n",
       "8     Finland         8.8   216073.0          19997    5375276\n",
       "9      France        10.1  2228568.0          20621   64933400\n",
       "11     Greece        23.6   176487.9           9048   10816286\n",
       "14    Ireland         8.4   273238.2          18286    4574888\n",
       "15      Italy        11.7  1689824.0          16237   59433744\n",
       "16     Latvia         9.6    25037.7           9257    2070371\n",
       "17  Lithuania         7.9    38849.4           9364    3043429\n",
       "23   Portugal        11.2   186480.5          10805   10562178\n",
       "25   Slovakia         9.7    81226.1          10466    5397036\n",
       "26   Slovenia         8.0    40357.2          15250    2050189\n",
       "27      Spain        19.6  1118743.0          15347   46815910\n",
       "28     Sweden         7.0   463147.5          20955    9482855\n",
       "30     Turkey        10.9   780224.9           6501    7954662"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>country</th>\n      <th>unemp_rate</th>\n      <th>gdp</th>\n      <th>median_income</th>\n      <th>total_pop</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>Belgium</td>\n      <td>7.8</td>\n      <td>424660.3</td>\n      <td>21335</td>\n      <td>11000638</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Bulgaria</td>\n      <td>7.6</td>\n      <td>48128.6</td>\n      <td>6742</td>\n      <td>7364570</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Croatia</td>\n      <td>13.1</td>\n      <td>46639.5</td>\n      <td>8985</td>\n      <td>4284889</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Cyprus</td>\n      <td>13.0</td>\n      <td>18490.2</td>\n      <td>16173</td>\n      <td>840407</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>Finland</td>\n      <td>8.8</td>\n      <td>216073.0</td>\n      <td>19997</td>\n      <td>5375276</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>France</td>\n      <td>10.1</td>\n      <td>2228568.0</td>\n      <td>20621</td>\n      <td>64933400</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>Greece</td>\n      <td>23.6</td>\n      <td>176487.9</td>\n      <td>9048</td>\n      <td>10816286</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>Ireland</td>\n      <td>8.4</td>\n      <td>273238.2</td>\n      <td>18286</td>\n      <td>4574888</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>Italy</td>\n      <td>11.7</td>\n      <td>1689824.0</td>\n      <td>16237</td>\n      <td>59433744</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>Latvia</td>\n      <td>9.6</td>\n      <td>25037.7</td>\n      <td>9257</td>\n      <td>2070371</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>Lithuania</td>\n      <td>7.9</td>\n      <td>38849.4</td>\n      <td>9364</td>\n      <td>3043429</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>Portugal</td>\n      <td>11.2</td>\n      <td>186480.5</td>\n      <td>10805</td>\n      <td>10562178</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>Slovakia</td>\n      <td>9.7</td>\n      <td>81226.1</td>\n      <td>10466</td>\n      <td>5397036</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>Slovenia</td>\n      <td>8.0</td>\n      <td>40357.2</td>\n      <td>15250</td>\n      <td>2050189</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>Spain</td>\n      <td>19.6</td>\n      <td>1118743.0</td>\n      <td>15347</td>\n      <td>46815910</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>Sweden</td>\n      <td>7.0</td>\n      <td>463147.5</td>\n      <td>20955</td>\n      <td>9482855</td>\n    </tr>\n    <tr>\n      <th>30</th>\n      <td>Turkey</td>\n      <td>10.9</td>\n      <td>780224.9</td>\n      <td>6501</td>\n      <td>7954662</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 49
    }
   ],
   "source": [
    "# In set theory, this is complement. i.e. 'not'\n",
    "eur_data_final[~low_unemployment]"
   ]
  },
  {
   "source": [
    "### Exercise - What countries have an unemployment rate greater than 9% and a GDP less than 280000?"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "   country  unemp_rate        gdp  median_income  total_pop\n",
       "9   France        10.1  2228568.0          20621   64933400\n",
       "15   Italy        11.7  1689824.0          16237   59433744\n",
       "27   Spain        19.6  1118743.0          15347   46815910\n",
       "30  Turkey        10.9   780224.9           6501    7954662"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>country</th>\n      <th>unemp_rate</th>\n      <th>gdp</th>\n      <th>median_income</th>\n      <th>total_pop</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>9</th>\n      <td>France</td>\n      <td>10.1</td>\n      <td>2228568.0</td>\n      <td>20621</td>\n      <td>64933400</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>Italy</td>\n      <td>11.7</td>\n      <td>1689824.0</td>\n      <td>16237</td>\n      <td>59433744</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>Spain</td>\n      <td>19.6</td>\n      <td>1118743.0</td>\n      <td>15347</td>\n      <td>46815910</td>\n    </tr>\n    <tr>\n      <th>30</th>\n      <td>Turkey</td>\n      <td>10.9</td>\n      <td>780224.9</td>\n      <td>6501</td>\n      <td>7954662</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 62
    }
   ],
   "source": [
    "unemp_gt_9 = eur_data_final.unemp_rate > 9\n",
    "gdp_lt_280 = eur_data_final.gdp > 280_000\n",
    "final = eur_data_final[unemp_gt_9 & gdp_lt_280]\n",
    "final"
   ]
  },
  {
   "source": [
    "### Exercise - What countries have a median income between 10,000 and 20,000?"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "           country  unemp_rate        gdp  median_income  total_pop\n",
       "4           Cyprus        13.0    18490.2          16173     840407\n",
       "5          Czechia         4.0   176370.1          12478   10436560\n",
       "7          Estonia         6.8    21682.6          11867    1294455\n",
       "8          Finland         8.8   216073.0          19997    5375276\n",
       "14         Ireland         8.4   273238.2          18286    4574888\n",
       "15           Italy        11.7  1689824.0          16237   59433744\n",
       "19           Malta         4.7    10344.1          17264     417432\n",
       "22          Poland         6.2   426547.5          10865   38044565\n",
       "23        Portugal        11.2   186480.5          10805   10562178\n",
       "25        Slovakia         9.7    81226.1          10466    5397036\n",
       "26        Slovenia         8.0    40357.2          15250    2050189\n",
       "27           Spain        19.6  1118743.0          15347   46815910\n",
       "31  United Kingdom         4.8  2403382.6          17296   63182180"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>country</th>\n      <th>unemp_rate</th>\n      <th>gdp</th>\n      <th>median_income</th>\n      <th>total_pop</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>4</th>\n      <td>Cyprus</td>\n      <td>13.0</td>\n      <td>18490.2</td>\n      <td>16173</td>\n      <td>840407</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Czechia</td>\n      <td>4.0</td>\n      <td>176370.1</td>\n      <td>12478</td>\n      <td>10436560</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>Estonia</td>\n      <td>6.8</td>\n      <td>21682.6</td>\n      <td>11867</td>\n      <td>1294455</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>Finland</td>\n      <td>8.8</td>\n      <td>216073.0</td>\n      <td>19997</td>\n      <td>5375276</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>Ireland</td>\n      <td>8.4</td>\n      <td>273238.2</td>\n      <td>18286</td>\n      <td>4574888</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>Italy</td>\n      <td>11.7</td>\n      <td>1689824.0</td>\n      <td>16237</td>\n      <td>59433744</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>Malta</td>\n      <td>4.7</td>\n      <td>10344.1</td>\n      <td>17264</td>\n      <td>417432</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>Poland</td>\n      <td>6.2</td>\n      <td>426547.5</td>\n      <td>10865</td>\n      <td>38044565</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>Portugal</td>\n      <td>11.2</td>\n      <td>186480.5</td>\n      <td>10805</td>\n      <td>10562178</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>Slovakia</td>\n      <td>9.7</td>\n      <td>81226.1</td>\n      <td>10466</td>\n      <td>5397036</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>Slovenia</td>\n      <td>8.0</td>\n      <td>40357.2</td>\n      <td>15250</td>\n      <td>2050189</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>Spain</td>\n      <td>19.6</td>\n      <td>1118743.0</td>\n      <td>15347</td>\n      <td>46815910</td>\n    </tr>\n    <tr>\n      <th>31</th>\n      <td>United Kingdom</td>\n      <td>4.8</td>\n      <td>2403382.6</td>\n      <td>17296</td>\n      <td>63182180</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 63
    }
   ],
   "source": [
    "# Create a bool series from the df that combines these two conditions\n",
    "income_query = (eur_data_final['median_income'] > 10_000) & (eur_data_final['median_income'].mean() < 20_000)\n",
    "eur_data_final[income_query]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "           country  unemp_rate        gdp  median_income  total_pop\n",
       "4           Cyprus        13.0    18490.2          16173     840407\n",
       "5          Czechia         4.0   176370.1          12478   10436560\n",
       "7          Estonia         6.8    21682.6          11867    1294455\n",
       "8          Finland         8.8   216073.0          19997    5375276\n",
       "14         Ireland         8.4   273238.2          18286    4574888\n",
       "15           Italy        11.7  1689824.0          16237   59433744\n",
       "19           Malta         4.7    10344.1          17264     417432\n",
       "22          Poland         6.2   426547.5          10865   38044565\n",
       "23        Portugal        11.2   186480.5          10805   10562178\n",
       "25        Slovakia         9.7    81226.1          10466    5397036\n",
       "26        Slovenia         8.0    40357.2          15250    2050189\n",
       "27           Spain        19.6  1118743.0          15347   46815910\n",
       "31  United Kingdom         4.8  2403382.6          17296   63182180"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>country</th>\n      <th>unemp_rate</th>\n      <th>gdp</th>\n      <th>median_income</th>\n      <th>total_pop</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>4</th>\n      <td>Cyprus</td>\n      <td>13.0</td>\n      <td>18490.2</td>\n      <td>16173</td>\n      <td>840407</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Czechia</td>\n      <td>4.0</td>\n      <td>176370.1</td>\n      <td>12478</td>\n      <td>10436560</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>Estonia</td>\n      <td>6.8</td>\n      <td>21682.6</td>\n      <td>11867</td>\n      <td>1294455</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>Finland</td>\n      <td>8.8</td>\n      <td>216073.0</td>\n      <td>19997</td>\n      <td>5375276</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>Ireland</td>\n      <td>8.4</td>\n      <td>273238.2</td>\n      <td>18286</td>\n      <td>4574888</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>Italy</td>\n      <td>11.7</td>\n      <td>1689824.0</td>\n      <td>16237</td>\n      <td>59433744</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>Malta</td>\n      <td>4.7</td>\n      <td>10344.1</td>\n      <td>17264</td>\n      <td>417432</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>Poland</td>\n      <td>6.2</td>\n      <td>426547.5</td>\n      <td>10865</td>\n      <td>38044565</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>Portugal</td>\n      <td>11.2</td>\n      <td>186480.5</td>\n      <td>10805</td>\n      <td>10562178</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>Slovakia</td>\n      <td>9.7</td>\n      <td>81226.1</td>\n      <td>10466</td>\n      <td>5397036</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>Slovenia</td>\n      <td>8.0</td>\n      <td>40357.2</td>\n      <td>15250</td>\n      <td>2050189</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>Spain</td>\n      <td>19.6</td>\n      <td>1118743.0</td>\n      <td>15347</td>\n      <td>46815910</td>\n    </tr>\n    <tr>\n      <th>31</th>\n      <td>United Kingdom</td>\n      <td>4.8</td>\n      <td>2403382.6</td>\n      <td>17296</td>\n      <td>63182180</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 51
    }
   ],
   "source": [
    "# Alternative, using .between()\n",
    "income_query_alt = eur_data_final.median_income.between(10_000, 20_000, inclusive=True)\n",
    "eur_data_final[income_query_alt]"
   ]
  }
 ]
}

================================================
FILE: intro_data_science/part_4/binder/requirements.txt
================================================
pandas==1.2.0
matplotlib==3.3.3
numpy==1.19.5
scipy==1.6.0


================================================
FILE: intro_data_science/part_4/data/complete/eur_data_final.csv
================================================
,country,unemp_rate,gdp,median_income,total_pop
0,Austria,6.0,356237.6,23071,8401940
1,Belgium,7.8,424660.3,21335,11000638
2,Bulgaria,7.6,48128.6,6742,7364570
3,Croatia,13.1,46639.5,8985,4284889
4,Cyprus,13.0,18490.2,16173,840407
5,Czechia,4.0,176370.1,12478,10436560
6,Denmark,6.2,282089.9,21355,5560628
7,Estonia,6.8,21682.6,11867,1294455
8,Finland,8.8,216073.0,19997,5375276
9,France,10.1,2228568.0,20621,64933400
10,Germany,4.1,3159750.0,21152,80219695
11,Greece,23.6,176487.9,9048,10816286
12,Hungary,5.1,113903.8,8267,9937628
13,Iceland,3.0,18646.1,22193,315556
14,Ireland,8.4,273238.2,18286,4574888
15,Italy,11.7,1689824.0,16237,59433744
16,Latvia,9.6,25037.7,9257,2070371
17,Lithuania,7.9,38849.4,9364,3043429
18,Luxembourg,6.3,53303.0,28663,512353
19,Malta,4.7,10344.1,17264,417432
20,Netherlands,6.0,708337.0,21189,16655799
21,Norway,4.7,335747.5,27670,4979954
22,Poland,6.2,426547.5,10865,38044565
23,Portugal,11.2,186480.5,10805,10562178
24,Romania,5.9,170393.6,4724,20121641
25,Slovakia,9.7,81226.1,10466,5397036
26,Slovenia,8.0,40357.2,15250,2050189
27,Spain,19.6,1118743.0,15347,46815910
28,Sweden,7.0,463147.5,20955,9482855
29,Switzerland,5.0,605753.7,27692,7954662
30,Turkey,10.9,780224.9,6501,7954662
31,United Kingdom,4.8,2403382.6,17296,63182180


================================================
FILE: intro_data_science/part_4/data/complete/un_world_data.csv
================================================
country,Region,Surface area (km2),Population in thousands (2017),"Population density (per km2, 2017)","Sex ratio (m per 100 f, 2017)",GDP: Gross domestic product (million current US$),"GDP growth rate (annual %, const. 2005 prices)",GDP per capita (current US$),Economy: Agriculture (% of GVA),Economy: Industry (% of GVA),Economy: Services and other activity (% of GVA),Employment: Agriculture (% of employed),Employment: Industry (% of employed),Employment: Services (% of employed),Unemployment (% of labour force),Labour force participation (female/male pop. %),Agricultural production index (2004-2006=100),Food production index (2004-2006=100),International trade: Exports (million US$),International trade: Imports (million US$),International trade: Balance (million US$),"Balance of payments, current account (million US$)",Population growth rate (average annual %),Urban population (% of total population),Urban population growth rate (average annual %),"Fertility rate, total (live births per woman)","Life expectancy at birth (females/males, years)","Population age distribution (0-14 / 60+ years, %)",International migrant stock (000/% of total pop.),Refugees and others of concern to UNHCR (in thousands),Infant mortality rate (per 1000 live births,Health: Total expenditure (% of GDP),Health: Physicians (per 1000 pop.),Education: Government expenditure (% of GDP),Education: Primary gross enrol. ratio (f/m per 100 pop.),Education: Secondary gross enrol. ratio (f/m per 100 pop.),Education: Tertiary gross enrol. ratio (f/m per 100 pop.),Seats held by women in national parliaments %,Mobile-cellular subscriptions (per 100 inhabitants),Mobile-cellular subscriptions (per 100 inhabitants),Individuals using the Internet (per 100 inhabitants),Threatened species (number),Forested area (% of land area),CO2 emission estimates (million tons/tons per capita),"Energy production, primary (Petajoules)",Energy supply per capita (Gigajoules),"Pop. using improved drinking water (urban/rural, %)","Pop. using improved sanitation facilities (urban/rural, %)",Net Official Development Assist. received (% of GNI)
Afghanistan,SouthernAsia,652864,35530,54.4,106.3,20270,-2.4,623.2,23.3,23.3,53.3,61.6,10.0,28.5,8.6,19.3/83.6,125,125,1458,3568,-2110,-5121,3.2,26.7,4.0,5.3,63.5/61.0,43.2/4.1,382.4/1.2,1513.1,68.6,8.2,0.3,3.3,91.1/131.6,39.7/70.7,3.7/13.3,27.7,61.6,8.3,42,2.1,9.8/0.3,63,5,78.2/47.0,45.1/27.0,21.43,-99
Albania,SouthernEurope,28748,2930,106.9,101.9,11541,2.6,3984.2,22.4,26.0,51.7,41.4,18.3,40.3,15.8,40.2/61.0,134,134,1962,4669,-2707,-1222,-0.1,57.4,2.2,1.7,79.9/75.6,17.4/19.0,57.6/2.0,8.8,14.6,5.9,1.3,3.5,111.7/115.5,92.5/98.8,68.1/48.7,22.9,106.4,63.3,130,28.2,5.7/2.0,84,36,94.9/95.2,95.5/90.2,2.96,-99
Armenia,WesternAsia,29743,2930,102.9,88.8,10529,3.0,3489.1,19.0,28.3,52.8,35.0,15.7,49.3,16.6,55.3/74.2,135,135,1776,3230,-1455,-279,0.3,62.7,-0.1,1.6,77.0/70.6,20.0/16.9,191.2/6.3,19.3,13.2,4.5,2.8,2.8,98.5/98.5,89.0/88.1,46.9/41.6,9.9,115.2,58.2,114,11.7,5.5/1.8,48,46,100.0/100.0,96.2/78.2,3.17,-99
Azerbaijan,WesternAsia,86600,9828,118.9,99.3,53049,0.7,5438.7,6.7,49.9,43.4,36.7,14.2,49.1,5.2,62.0/68.8,131,136,9143,8532,611,-222,1.3,54.6,1.6,2.1,74.6/68.6,23.3/10.1,264.2/2.7,623.3,31.4,6.0,3.4,2.6,105.6/107.4,-99,27.5/23.6,16.8,111.3,77.0,97,13.5,37.5/3.9,2459,61,94.7/77.8,91.6/86.6,0.14,-99
Belize,CentralAmerica,22966,375,16.4,99.2,1721,1.2,4789.4,14.6,18.5,66.9,16.1,15.9,67.9,11.5,56.7/83.8,97,97,246,952,-706,-175,2.2,44.0,1.9,2.6,72.7/67.2,31.4/6.2,53.9/15.0,3.1,14.3,5.8,...,6.4,110.4/115.8,81.8/79.8,28.7/17.9,9.4,48.9,41.6,117,60.1,0.5/1.4,9,36,98.9/100.0,93.5/88.2,1.68,-99
Benin,WesternAfrica,114763,11176,99.1,99.5,8476,5.2,779.1,23.2,24.9,51.9,43.2,10.2,46.6,1.0,70.0/73.4,152,158,410,2630,-2220,-885,2.8,44.0,3.7,5.2,61.4/58.5,42.7/5.0,245.4/2.3,0.8,67.7,4.6,0.1,4.4,123.7/134.2,46.8/66.7,8.4/22.4,7.2,85.6,6.8,88,38.7,6.3/0.6,96,17,85.2/72.1,35.6/7.3,5.09,-99
Bhutan,SouthernAsia,38394,808,21.2,113.1,2074,5.2,2677.1,17.2,43.9,38.8,56.6,9.7,33.7,2.4,59.4/73.5,98,98,616,1688,-1072,-579,1.6,38.6,3.7,2.2,68.9/68.6,26.5/7.3,51.1/6.6,-99,30.5,3.6,0.3,7.4,96.9/96.6,87.1/81.4,9.2/12.6,8.5,87.1,39.8,71,72.0,1.0/1.3,75,82,100.0/100.0,77.9/33.1,5.26,-99
Bolivia,SouthAmerica,1098581,11052,10.2,100.2,32998,4.8,3076.8,12.6,31.0,56.5,29.5,21.1,49.4,3.8,64.1/82.6,142,143,6969,8374,-1405,-1854,1.6,68.5,2.3,3.0,70.2/65.3,31.6/9.5,143.0/1.3,0.8,42.9,6.3,0.5,7.3,95.7/98.5,85.7/87.1,.../...,53.1,92.2,45.1,231,50.8,20.4/1.9,962,32,96.7/75.6,60.8/27.5,2.38,-99
Brazil,SouthAmerica,8515767,209288,25.0,96.6,1772591,-3.8,8528.3,5.2,22.7,72.0,15.2,21.5,63.2,12.4,56.0/78.2,135,136,185235,137552,47683,-58882,0.9,85.7,1.2,1.8,78.4/71.0,21.7/12.6,713.6/0.3,41.1,15.8,8.3,1.9,6.0,113.8/116.8,102.2/97.2,59.3/42.4,10.7,126.6,59.1,990,59.2,529.8/2.6,10948,61,100.0/87.0,88.0/51.5,0.06,-99
Burkina Faso,WesternAfrica,272967,19193,70.2,99.5,11065,4.1,611.1,34.5,21.8,43.7,80.0,4.9,15.1,2.9,76.5/90.5,131,134,2019,3699,-1680,-998,3.0,29.9,5.9,5.6,59.3/58.0,45.2/3.9,704.7/3.9,32.7,64.8,5.0,~0.0,4.1,86.1/89.9,32.2/35.1,3.8/7.3,11.0,80.6,11.4,31,19.8,2.8/0.1,123,9,97.5/75.8,50.4/6.7,9.11,-99
Burundi,EasternAfrica,27830,10864,423.1,96.9,2735,-4.1,244.6,36.3,13.9,49.8,91.1,2.6,6.3,1.7,84.6/82.7,109,122,123,625,-502,-375,3.0,12.1,5.7,6.0,58.0/54.2,45.0/4.4,286.8/2.6,103.2,77.8,7.5,...,5.4,124.5/123.1,40.5/44.5,2.4/7.7,36.4,46.2,4.9,61,10.6,0.4/~0.0,56,6,91.1/73.8,43.8/48.6,11.91,-99
Cambodia,South-easternAsia,181035,16005,90.7,95.3,18050,7.0,1158.7,28.2,29.4,42.3,42.4,19.6,38.0,0.3,75.6/87.0,175,177,13204,15313,-2109,-1657,1.6,20.7,2.6,2.7,69.6/65.5,31.3/7.1,74.0/0.5,0.3,29.9,5.7,0.2,1.9,116.2/117.1,.../...,11.8/14.3,20.3,133.0,19.0,255,54.3,6.7/0.4,178,17,100.0/69.1,88.1/30.5,3.97,-99
Cameroon,MiddleAfrica,475650,24054,50.9,100.2,28416,5.8,1217.3,22.7,28.3,49.1,61.8,8.7,29.5,4.6,71.1/81.2,155,160,2130,4899,-2768,-1173,2.7,54.4,3.6,5.0,57.7/55.1,42.7/4.8,382.0/1.6,544.9,67.5,4.1,...,3.0,110.7/123.5,53.5/62.6,15.2/19.7,31.1,71.8,20.7,775,40.3,7.0/0.3,408,14,94.8/52.7,61.8/26.8,2.31,-99
Chile,SouthAmerica,756102,18055,24.3,98.2,240796,2.3,13416.2,3.9,32.8,63.3,9.6,22.9,67.5,6.8,50.7/74.7,111,111,59884,58804,1080,-4761,0.9,89.5,1.1,1.8,81.3/76.2,20.3/16.0,469.4/2.6,3.7,7.4,7.8,...,4.9,100.0/103.3,101.3/100.0,94.4/83.0,15.8,129.5,64.3,197,23.4,82.6/4.7,540,85,99.7/93.3,100.0/90.9,0.02,-99
China,EasternAsia,9600000,1409517,150.1,106.3,11158457,6.9,8109.1,9.2,41.1,49.7,27.0,23.9,49.1,4.6,63.0/77.7,132,133,2118981,1588696,530285,330602,0.5,55.6,3.0,1.6,77.2/74.2,17.7/16.2,978.0/0.1,301.7,11.6,5.5,1.5,-99,104.3/104.0,95.6/93.2,47.3/39.9,23.7,93.2,50.3,1080,22.0,10291.9/7.5,101394,87,97.5/93.0,86.6/63.7,0.00,-99
Colombia,SouthAmerica,1141748,49066,44.2,96.8,292080,3.1,6056.1,6.8,34.0,59.2,13.5,16.6,69.9,10.5,58.0/79.8,114,115,31045,44831,-13786,-18922,1.0,76.4,1.7,1.9,77.4/70.2,23.5/11.6,133.1/0.3,7127.0,17.9,7.2,...,4.5,111.6/115.4,101.5/94.8,59.9/51.5,18.7,115.7,55.9,835,52.8,84.1/1.8,5380,31,96.8/73.8,85.2/67.9,0.47,-99
Congo,MiddleAfrica,342000,5261,15.4,100.1,8493,1.2,1838.1,4.7,70.0,25.3,40.7,25.8,33.5,11.5,67.1/72.6,106,106,2540,9793,-7253,...,2.6,65.4,3.2,4.9,64.1/61.0,42.3/5.2,393.0/8.5,54.9,46.5,5.2,...,...,114.8/107.0,50.6/58.4,8.3/11.1,7.4,111.7,7.6,134,65.4,3.1/0.7,648,24,95.8/40.0,20.0/5.6,1.05,-99
Costa Rica,CentralAmerica,51100,4906,96.1,100.1,52958,3.7,11015.0,5.1,21.5,73.4,11.6,19.1,69.3,8.6,46.9/76.5,127,129,9908,15322,-5414,-2493,1.1,76.8,2.7,1.9,81.7/76.7,21.6/13.6,421.7/8.8,10.1,9.3,9.3,1.2,7.2,109.5/110.1,125.6/120.7,60.9/46.6,35.1,150.7,59.8,340,53.4,7.8/1.6,110,44,99.6/91.9,95.2/92.3,0.22,-99
Cote D'Ivoire,WesternAfrica,322463,24295,76.4,102.7,32076,9.5,1412.9,23.7,28.3,48.1,56.0,5.7,38.4,9.3,52.6/80.9,122,123,10661,8380,2281,-633,2.5,54.2,3.7,5.1,53.2/50.4,42.4/4.8,2175.4/9.6,1023.0,71.6,5.7,...,5.0,88.0/99.2,36.6/51.0,7.3/11.0,11.5,119.3,21.0,249,32.7,11.0/0.5,540,26,93.1/68.8,32.8/10.3,2.22,-99
Dominican Republic,Caribbean,48671,10767,222.8,99.2,67103,7.0,6373.6,6.6,28.0,65.4,13.1,16.9,70.0,14.4,52.3/78.6,136,138,3747,22725,-18978,-1335,1.2,79.0,2.6,2.5,76.4/70.2,29.3/10.2,415.6/3.9,1.3,25.1,4.4,1.5,...,98.7/108.1,81.7/74.1,65.0/35.4,26.8,82.6,51.9,184,40.4,21.5/2.1,28,28,85.4/81.9,86.2/75.7,0.43,-99
Ecuador,SouthAmerica,257217,16625,66.9,99.9,100177,0.2,6205.1,10.1,34.1,55.8,25.7,19.2,55.1,5.8,49.1/79.9,118,119,16798,16189,609,-2201,1.6,63.7,1.9,2.6,78.4/72.8,28.4/10.5,387.5/2.4,133.1,21.1,9.2,1.7,5.0,107.9/107.4,109.2/105.3,45.3/34.6,41.6,79.4,48.9,2358,50.8,43.9/2.8,1247,42,93.4/75.5,87.0/80.7,0.31,-99
Egypt,NorthernAfrica,1002000,97553,98.0,102.3,315917,4.2,3452.3,11.2,36.3,52.5,25.4,25.3,49.3,11.5,23.1/76.2,120,122,22507,58053,-35545,-16786,2.2,43.1,1.7,3.4,73.0/68.7,33.5/7.9,491.6/0.5,256.5,18.9,5.6,0.8,...,103.8/104.1,85.9/86.3,35.6/36.9,14.9,111.0,35.9,156,0.1,201.9/2.2,3509,36,100.0/99.0,96.8/93.1,0.78,-99
El Salvador,CentralAmerica,21041,6378,307.8,88.5,25850,2.5,4219.4,10.8,25.8,63.3,18.6,20.2,61.2,6.4,49.5/79.5,111,117,5335,9855,-4519,-920,0.5,66.7,1.4,2.2,77.1/67.9,27.4/11.6,42.0/0.7,4.7,17.0,6.8,...,3.5,106.8/111.4,79.7/79.2,30.5/27.7,32.1,145.3,26.9,86,13.0,6.3/1.0,86,28,97.5/86.5,82.4/60.0,0.36,-99
Georgia,WesternAsia,69700,3912,56.3,91.4,13965,2.8,3491.4,9.0,24.1,66.9,44.7,11.2,44.1,11.4,57.9/79.0,85,86,2114,7236,-5122,-1775,-1.4,53.6,-0.1,2.0,77.0/68.5,19.2/20.8,168.8/4.2,274.1,11.2,7.4,4.8,2.0,118.2/115.5,103.8/103.6,47.8/39.2,16.0,129.0,45.2,120,40.6,9.0/2.2,61,47,100.0/100.0,95.2/75.9,3.30,-99
Ghana,WesternAfrica,238537,28834,126.7,99.3,37156,3.9,1355.6,19.0,26.9,54.1,41.9,14.2,43.8,5.9,75.7/78.8,144,144,7221,11939,-4718,-2809,2.4,54.0,3.4,4.2,62.6/60.7,38.5/5.3,399.5/1.5,27.3,46.5,3.6,...,6.2,108.7/107.3,61.1/63.1,13.2/19.1,12.7,129.7,23.5,238,40.9,14.5/0.6,410,14,92.6/84.0,20.2/8.6,4.81,-99
Guam,Micronesia,549,164,304.1,102.6,-99,-99,-99,-99,-99,-99,0.2,14.0,85.7,10.7,55.2/68.7,88,88,-99,-99,-99,-99,0.3,94.5,1.4,2.4,81.5/76.4,24.7/14.0,76.1/44.8,-99,9.6,-99,-99,-99,-99,-99,-99,-99,...,73.1,99,46.3,-99,-99,-99,99.5/99.5,89.8/89.8,-99,-99
Guatemala,CentralAmerica,108889,16914,157.8,96.9,63794,4.1,3903.5,10.8,27.2,62.0,32.0,18.5,49.4,2.4,41.7/84.0,158,158,10572,16987,-6415,-96,2.1,51.6,3.4,3.2,75.6/69.2,35.1/6.9,76.4/0.5,1.8,26.9,6.2,...,3.0,99.8/103.8,63.3/67.9,23.5/20.2,12.7,111.5,27.1,290,33.4,18.3/1.1,327,32,98.4/86.8,77.5/49.3,0.66,-99
Haiti,Caribbean,27750,10981,398.4,97.8,8501,1.7,793.7,16.7,38.2,45.1,46.9,12.6,40.6,12.9,62.0/71.7,163,166,984,3316,-2332,-723,1.4,58.6,3.8,3.1,64.4/60.2,33.0/7.3,39.5/0.4,1.9,46.9,7.6,-99,-99,-99,-99,-99,2.6,69.9,12.2,205,3.5,2.9/0.3,136,16,64.9/47.6,33.6/19.2,11.73,-99
Honduras,CentralAmerica,112492,9265,82.8,99.4,20365,3.6,2521.9,13.0,25.1,61.9,29.4,21.6,49.0,5.6,47.6/84.5,122,119,3657,8448,-4791,-1291,1.8,54.7,3.1,2.6,75.4/70.4,31.6/7.0,28.1/0.3,176.2,27.8,8.7,...,5.9,110.1/111.3,77.0/64.8,25.4/18.8,25.8,95.5,20.4,301,42.1,9.5/1.2,105,28,97.4/83.8,86.7/77.7,2.86,-99
India,SouthernAsia,3287263,1339180,450.4,107.6,2116239,7.6,1614.2,17.0,29.7,53.2,44.3,24.5,31.2,3.4,27.0/79.1,143,142,260327,356705,-96378,-22457,1.2,32.7,2.4,2.4,69.1/66.2,27.8/9.4,5241.0/0.4,211.1,41.3,4.7,0.7,3.8,115.1/102.8,74.5/73.5,26.7/27.0,11.8,78.8,26.0,1052,23.7,2238.4/1.7,23103,27,97.1/92.6,62.6/28.5,0.16,-99
Indonesia,South-easternAsia,1910931,263991,145.7,101.4,861934,4.8,3346.5,14.0,41.3,44.7,31.4,22.4,46.2,5.8,51.0/83.7,139,140,144490,135653,8837,-17697,1.2,53.7,2.7,2.4,70.7/66.6,27.4/8.6,328.8/0.1,13.8,25.0,2.8,0.2,3.6,104.4/107.2,86.0/85.7,25.7/22.9,19.8,132.4,22.0,1281,50.6,464.2/1.8,19481,35,94.2/79.5,72.3/47.5,0.00,-99
Iraq,WesternAsia,435052,38275,88.1,102.5,164234,-2.4,4509.0,4.6,58.0,37.4,20.4,21.0,58.6,16.1,15.3/69.9,123,123,27341,45831,-18490,4121,3.2,69.5,3.0,4.6,71.4/67.0,40.4/5.0,353.9/1.0,4736.2,32.1,5.5,0.9,-99,.../...,.../...,.../...,25.3,93.8,17.2,72,1.9,168.4/4.8,6744,58,93.8/70.1,86.4/83.8,0.88,-99
Israel,WesternAsia,22072,8322,384.5,98.7,299413,2.5,37129.4,1.3,21.2,77.5,1.0,17.9,81.0,5.9,58.5/69.0,112,112,60571,65803,-5232,13642,1.6,92.1,1.4,3.0,83.7/80.0,27.9/16.1,2011.7/24.9,44.7,3.4,7.8,3.6,5.8,105.1/104.4,103.0/102.0,75.5/54.6,27.5,133.5,78.9,174,7.5,64.6/8.1,313,119,100.0/100.0,100.0/100.0,-99,-99
Jordan,WesternAsia,89318,9702,109.3,102.6,37517,2.4,4940.1,4.0,27.7,68.4,2.0,17.8,80.2,13.4,14.5/64.5,134,135,7509,19207,-11698,-3332,4.9,83.7,3.8,3.6,75.5/72.2,35.5/5.7,3112.0/41.0,721.4,17.1,7.5,2.6,-99,97.6/97.1,84.8/80.2,47.3/42.5,15.4,179.4,53.4,113,1.1,26.5/3.6,7,47,97.8/92.3,98.6/98.9,5.80,-99
Kenya,EasternAfrica,591958,49700,87.3,98.8,63399,5.6,1376.7,32.0,19.0,49.0,61.9,8.6,29.5,10.8,62.4/72.4,126,126,5688,14109,-8420,-6339,2.7,25.6,4.3,4.1,67.8/63.0,40.5/4.3,1084.4/2.4,582.4,39.4,5.7,0.2,5.3,108.7/109.3,.../...,.../...,19.4,80.7,45.6,480,7.7,14.3/0.3,750,20,81.6/56.8,31.2/29.7,3.93,-99
Kyrgyzstan,CentralAsia,199949,6045,31.5,98.4,6572,3.5,1106.4,15.4,25.9,58.7,29.2,21.0,49.8,7.7,49.7/77.5,108,110,1423,3844,-2421,-721,1.6,35.7,1.6,3.1,74.3/66.4,31.8/7.6,204.4/3.4,8.2,19.6,6.5,1.9,5.5,106.8/107.9,92.8/91.4,53.3/40.8,19.2,132.8,30.2,44,3.4,9.6/1.7,80,27,96.7/86.2,89.1/95.6,12.16,-99
Lao People's Democratic Republic,South-easternAsia,236800,6858,29.7,99.5,12585,7.6,1850.2,23.6,32.8,43.6,78.3,4.0,17.7,1.5,77.9/77.8,193,186,2066,4513,-2447,-2264,1.3,38.6,4.9,2.9,66.8/63.9,32.9/6.3,22.2/0.3,-99,47.3,1.9,0.2,3.3,109.1/113.5,59.3/64.1,16.5/17.3,27.5,53.1,18.2,209,80.5,2.0/0.3,72,12,85.6/69.4,94.5/56.0,4.03,-99
Lebanon,WesternAsia,10452,6082,594.6,100.6,50149,1.5,8571.4,3.2,19.6,77.2,8.2,22.4,69.4,7.0,23.8/70.6,95,95,3402,20409,-17007,-8146,6.0,87.8,3.2,1.7,80.9/77.3,23.1/12.0,1997.8/34.1,1054.2,9.2,6.4,2.4,2.6,88.3/96.6,61.0/61.5,45.7/39.5,3.1,87.1,74.0,87,13.4,24.1/4.3,7,55,99.0/99.0,80.7/80.7,2.04,-99
Lesotho,SouthernAfrica,30355,2233,73.6,94.4,2008,2.8,940.6,7.7,31.3,61.0,39.7,20.0,40.3,27.5,59.7/74.6,101,100,648,1727,-1079,-168,1.3,27.3,3.0,3.3,54.7/50.1,35.4/6.7,6.6/0.3,~0.0,59.8,10.6,...,...,104.0/107.0,62.0/45.7,11.7/8.0,25.0,105.5,16.1,18,1.6,2.5/1.2,31,28,94.6/77.0,37.3/27.6,4.11,-99
Liberia,WesternAfrica,111369,4732,49.1,101.8,2053,0.3,455.9,70.8,11.4,17.9,45.3,11.7,43.0,4.1,57.9/64.1,104,121,641,727,-86,-860,2.6,49.7,3.4,4.8,61.6/59.8,41.8/4.9,113.8/2.5,22.1,59.0,10.0,...,2.8,89.1/98.5,32.5/41.9,9.0/14.2,12.3,81.1,5.9,172,43.7,0.9/0.2,73,19,88.6/62.6,28.0/5.9,61.73,-99
Madagascar,EasternAfrica,587295,25571,44.0,99.5,9739,3.1,401.8,24.9,18.4,56.7,74.2,9.4,16.5,2.3,83.8/89.0,119,120,2256,2965,-709,-622,2.7,35.1,4.7,4.4,66.0/63.0,41.0/4.8,32.1/0.1,0.1,36.8,3.0,0.1,2.1,148.9/148.9,38.1/38.8,4.6/5.0,19.2,46.0,4.2,1324,21.5,3.1/0.1,129,7,81.6/35.3,18.0/8.7,7.05,-99
Malawi,EasternAfrica,118484,18622,197.5,98.2,6420,3.0,372.9,26.1,16.6,57.3,69.9,4.8,25.4,6.8,81.3/81.0,149,150,875,1649,-774,-710,2.9,16.3,3.8,4.9,63.1/58.2,44.0/4.3,215.2/1.2,25.7,66.5,11.4,...,5.6,147.0/144.0,41.0/45.8,0.6/1.0,16.7,35.3,9.3,176,33.6,1.3/0.1,89,6,95.7/89.1,47.3/39.8,16.53,-99
Mali,WesternAfrica,1240192,18542,15.2,100.2,13100,7.6,744.3,39.9,19.6,40.5,56.7,14.7,28.6,8.1,50.5/82.3,154,157,3030,2510,520,-676,2.9,39.9,5.1,6.4,56.9/55.6,47.7/4.0,363.1/2.1,90.8,78.5,7.0,...,3.7,72.1/79.3,36.8/45.6,4.1/9.6,8.8,139.6,10.3,42,3.9,1.4/0.1,55,4,96.5/64.1,37.5/16.1,9.45,-99
Mauritania,WesternAfrica,1030700,4420,4.3,101.6,5023,1.2,1235.0,20.9,41.7,37.4,40.3,9.5,50.2,12.0,29.3/65.5,125,125,1623,2174,-551,-1066,2.9,59.9,3.5,4.9,64.1/61.2,39.9/5.0,138.2/3.4,69.0,68.0,3.8,...,2.9,105.0/100.0,29.5/31.7,3.6/7.1,25.2,89.3,15.2,86,0.2,2.7/0.7,31,13,58.4/57.1,57.5/13.8,5.02,-99
Mexico,CentralAmerica,1964375,129163,66.4,99.2,1140724,2.5,8980.9,3.6,36.0,60.4,13.4,25.2,61.3,4.1,45.5/79.5,120,120,373883,387064,-13181,-33216,1.4,79.2,1.6,2.3,78.9/74.0,26.7/10.1,1193.2/0.9,5.9,18.8,6.3,2.1,5.3,103.2/103.6,93.5/87.7,30.0/29.9,42.6,85.3,57.4,1162,34.0,480.3/3.8,8514,62,97.2/92.1,88.0/74.5,0.03,-99
Moldova,EasternEurope,33846,4051,123.3,92.2,6475,-0.7,1591.4,13.4,20.8,65.8,28.8,30.9,40.3,5.0,39.1/46.5,109,109,2045,4020,-1975,-415,-0.1,45.0,-0.7,1.3,75.2/66.7,15.7/17.6,142.9/3.5,5.5,14.3,10.3,2.5,7.5,91.9/92.9,86.5/85.8,47.4/35.3,22.8,108.0,49.8,35,12.3,4.9/1.2,15,23,96.9/81.4,87.8/67.1,4.49,-99
Mongolia,EasternAsia,1564116,3076,2.0,97.9,11758,2.3,3973.4,14.8,34.1,51.1,28.4,20.7,51.0,6.3,56.8/69.3,147,149,4917,3358,1559,-948,1.9,72.0,2.8,2.8,72.7/64.5,29.7/6.6,17.6/0.6,~0.0,22.8,4.7,2.9,4.6,100.0/101.8,92.4/90.5,79.7/57.7,17.1,105.0,21.4,41,8.1,20.8/7.2,677,84,66.4/59.2,66.4/42.6,2.21,-99
Mozambique,EasternAfrica,799380,29669,37.7,95.5,14806,6.6,529.2,24.6,21.0,54.4,75.0,4.1,21.0,24.1,82.2/75.6,137,137,3352,5295,-1943,-5833,2.9,32.2,3.3,5.4,58.1/54.0,44.8/4.8,222.9/0.8,24.0,67.3,7.0,0.1,6.5,101.2/110.4,31.1/33.8,5.4/7.4,39.6,74.2,9.0,309,48.5,8.4/0.3,779,20,80.6/37.0,42.4/10.1,12.54,-99
Myanmar (Burma),South-easternAsia,676577,53371,81.7,95.5,62601,7.3,1161.5,26.7,34.5,38.7,23.6,14.6,61.7,0.8,74.7/80.8,130,128,11673,15696,-4023,-3921,0.9,34.1,2.5,2.3,68.3/63.7,26.8/9.4,73.3/0.1,1392.1,45.0,2.3,0.6,-99,98.3/101.0,52.0/50.6,14.9/12.1,10.2,76.7,21.8,321,45.3,21.6/0.4,1078,15,92.7/74.4,84.3/77.1,2.23,-99
Namibia,SouthernAfrica,824116,2534,3.1,94.7,11491,5.3,4673.6,6.6,30.6,62.8,29.1,14.6,56.3,24.9,56.4/64.3,88,89,4816,6721,-1905,-1700,2.2,46.7,4.2,3.6,64.3/59.1,36.7/5.5,93.9/3.8,4.8,36.4,-99,...,...,109.5/113.3,.../...,.../...,41.3,102.1,22.3,115,8.5,3.8/1.6,19,31,98.2/84.6,54.5/16.8,1.24,-99
Nepal,SouthernAsia,147181,29305,204.4,94.3,20658,2.7,724.5,31.8,14.9,53.3,72.6,10.9,16.5,3.0,79.7/86.7,139,139,703,5249,-4547,2447,1.2,18.6,3.2,2.3,70.4/67.4,30.9/8.8,518.3/1.8,30.7,32.8,5.8,...,3.7,141.2/130.7,72.2/67.1,15.1/14.8,29.6,96.8,17.6,104,25.4,8.0/0.3,451,19,90.9/91.8,56.0/43.5,5.73,-99
Nicaragua,CentralAmerica,130373,6218,51.7,97.2,12693,4.9,2086.9,18.8,26.8,54.4,24.1,14.5,61.4,6.1,49.6/80.6,130,130,2225,5927,-3701,-1045,1.2,58.8,2.0,2.3,77.5/71.4,29.0/8.4,40.3/0.7,0.6,20.0,9.0,0.9,...,.../...,.../...,.../...,45.7,116.1,19.7,144,25.9,4.9/0.8,91,26,99.3/69.4,76.5/55.7,3.68,-99
Nigeria,WesternAfrica,923768,190886,209.6,102.7,494583,2.7,2714.5,20.9,20.4,58.8,27.9,14.7,57.4,5.4,48.5/64.3,116,117,104084,36533,67551,-15763,2.7,47.8,4.7,5.7,52.6/51.2,44.0/4.5,1199.1/0.7,2437.5,76.3,3.7,...,-99,92.8/94.5,53.5/57.8,8.3/11.8,5.6,82.2,47.4,361,8.1,96.3/0.6,10851,32,80.8/57.3,32.8/25.4,0.52,-99
Pakistan,SouthernAsia,796095,197016,255.6,105.6,266458,5.5,1410.4,25.5,19.0,55.5,42.1,19.8,38.1,5.9,24.8/82.5,135,138,20534,46998,-26464,-1603,2.1,38.8,2.8,3.7,66.8/65.0,34.8/6.7,3629.0/1.9,2739.4,69.8,2.6,0.8,2.6,85.2/99.7,39.2/49.5,9.2/10.6,20.6,66.9,18.0,140,2.0,166.3/0.9,2202,17,93.9/89.9,83.1/51.1,1.32,-99
Palestine,WesternAsia,6020,4921,817.4,102.9,12677,3.5,2715.5,4.8,23.4,71.9,8.7,29.8,61.5,24.2,18.4/69.7,90,90,929,5058,-4128,-1713,2.7,75.3,2.8,4.2,74.8/71.1,39.6/4.6,255.5/5.5,~0.0,20.0,-99,-99,1.3,94.3/94.4,87.0/79.2,54.4/34.5,-99,77.6,57.4,31,1.5,2.8/0.6,9,15,50.7/81.5,93.0/90.2,17.52,-99
Panama,CentralAmerica,75320,4099,55.1,100.4,52132,5.8,13268.1,2.8,27.1,70.0,14.5,19.6,65.9,6.2,50.5/80.4,117,118,636,11697,-11061,-3377,1.7,66.6,2.1,2.6,80.5/74.3,27.4/11.4,184.7/4.7,21.5,15.2,8.0,1.6,3.2,100.9/103.5,78.3/72.9,46.5/31.2,18.3,174.2,51.2,383,62.3,8.8/2.3,31,35,97.7/88.6,83.5/58.0,0.02,-99
Paraguay,SouthAmerica,406752,6811,17.1,102.9,27714,3.1,4174.4,19.0,29.5,51.6,19.6,19.5,61.0,5.5,58.3/84.7,160,166,8494,9753,-1259,-462,1.3,59.7,2.1,2.6,74.9/70.7,29.4/9.4,156.5/2.4,0.2,28.8,9.8,1.3,5.0,104.3/107.6,79.1/74.2,.../...,13.8,105.4,44.4,59,39.4,5.7/0.9,325,38,100.0/94.9,95.5/78.4,0.22,-99
Peru,SouthAmerica,1285216,32166,25.1,99.8,190428,3.3,6069.1,7.6,33.3,59.2,24.4,17.3,58.3,5.3,66.1/82.8,145,148,36040,36185,-145,-9210,1.3,78.6,1.7,2.5,76.8/71.5,27.4/10.4,90.9/0.3,2.5,18.6,5.5,1.1,4.0,101.7/101.7,95.8/95.7,.../...,27.7,109.9,40.9,685,57.9,61.7/2.0,1020,30,91.4/69.2,82.5/53.2,0.18,-99
Philippines,South-easternAsia,300000,104918,351.9,101.3,292449,5.9,2904.2,10.5,31.3,58.2,27.7,16.3,56.1,5.9,50.8/78.9,122,120,56313,85909,-29596,7694,1.6,44.4,1.3,3.0,72.1/65.4,31.7/7.6,211.9/0.2,240.2,22.2,4.7,...,...,116.9/116.8,92.7/84.4,40.3/31.4,29.5,118.1,40.7,783,26.2,105.7/1.1,991,19,93.7/90.3,77.9/70.8,0.15,-99
Puerto Rico,Caribbean,8868,3663,413.0,92.6,102906,-~0.0,27939.0,0.8,50.0,49.1,2.0,17.5,80.5,12.8,34.4/51.7,110,111,-99,-99,-99,-99,-0.2,93.6,-0.2,1.5,83.2/75.2,17.9/20.4,275.0/7.5,-99,6.3,-99,-99,6.4,90.4/89.1,83.9/78.8,98.8/70.1,-99,87.1,79.5,126,55.5,-99,1,16,.../...,99.3/99.3,-99,-99
Rwanda,EasternAfrica,26338,12208,494.9,96.2,8096,6.9,697.3,34.6,15.1,50.3,75.0,7.2,17.8,2.4,86.1/83.3,139,139,622,1778,-1157,-1099,2.5,28.8,6.4,4.2,67.1/63.1,40.1/4.9,441.5/3.8,155.1,44.0,7.5,...,3.6,133.4/131.7,38.2/35.2,6.9/9.1,61.3,70.5,18.0,62,19.2,0.8/0.1,84,8,86.6/71.9,58.5/62.9,13.67,-99
Saint Vincent and the Grenadines,Caribbean,389,110,281.8,101.7,738,1.6,6739.2,7.5,17.2,75.3,22.1,16.1,61.8,19.1,56.4/76.9,115,115,47,335,-288,-210,~0.0,50.6,0.7,2.0,74.9/70.7,23.8/11.7,4.6/4.2,-99,16.5,8.6,...,...,103.2/105.8,105.0/107.9,-99,13.0,103.6,51.8,58,69.2,0.2/1.9,0,29,95.1/95.1,.../...,1.80,-99
Samoa,Polynesia,2842,196,69.4,106.6,774,2.8,4006.0,9.3,24.2,66.6,5.3,14.8,79.8,6.5,23.3/58.2,107,107,56,350,-294,-44,0.8,19.1,-0.2,4.2,77.4/71.1,36.6/8.5,4.9/2.6,~0.0,18.0,7.2,...,...,106.7/106.7,89.5/80.7,.../...,10.0,58.5,25.4,93,60.4,0.2/1.0,2,24,97.5/99.3,93.3/91.1,12.75,-99
Senegal,WesternAfrica,196712,15851,82.3,96.6,13633,6.5,901.1,15.5,24.1,60.4,51.4,20.7,27.9,9.3,45.3/70.5,126,127,2640,5478,-2838,-1348,3.0,43.7,3.6,5.0,67.5/63.8,42.9/4.7,263.2/1.7,17.6,43.9,4.7,...,7.4,86.9/77.6,49.1/50.2,7.8/12.9,42.7,100.0,21.7,123,43.2,8.9/0.6,77,11,92.9/67.3,65.4/33.8,6.49,-99
Sierra Leone,WesternAfrica,72300,7557,104.7,98.1,4483,-20.3,694.8,51.4,17.6,31.1,68.0,6.5,25.5,3.1,65.1/68.8,168,168,466,958,-492,-1317,2.3,39.9,2.7,4.8,50.7/49.6,42.1/4.2,91.2/1.4,0.8,94.4,11.1,...,2.7,128.2/127.0,40.1/46.5,.../...,12.4,89.5,2.5,177,41.3,1.3/0.2,53,11,84.9/47.8,22.8/6.9,21.56,-99
Solomon Islands,Melanesia,28896,611,21.8,103.4,1075,3.2,1841.6,28.2,15.6,56.3,48.1,2.4,49.5,31.4,61.1/73.5,118,118,437,454,-17,-36,2.1,22.3,4.2,4.1,71.1/68.3,38.8/5.4,2.6/0.4,~0.0,30.0,5.1,0.2,...,113.6/115.0,47.0/49.8,-99,2.0,72.7,10.0,245,78.3,0.2/0.4,3,11,93.2/77.2,81.4/15.0,16.47,-99
Somalia,EasternAfrica,637657,14742,23.5,99.3,1559,2.7,144.5,60.2,7.4,32.5,72.0,4.8,23.2,6.6,33.3/75.9,112,112,925,530,394,-99,2.9,39.6,4.1,6.6,56.5/53.3,46.4/4.4,25.3/0.2,1168.4,79.5,-99,~0.0,-99,.../...,.../...,-99,24.2,52.5,1.8,175,10.3,0.6/0.1,129,12,69.6/8.8,52.0/6.3,22.85,-99
South Africa,SouthernAfrica,1221037,56717,46.8,96.4,314571,1.3,5773.0,2.4,28.9,68.7,6.1,26.2,67.7,26.0,46.4/61.1,125,126,74111,74744,-633,-13644,1.4,64.8,1.6,2.6,63.0/56.1,29.0/8.4,3142.5/5.8,1201.9,36.5,8.8,0.8,6.0,97.3/102.2,111.5/88.0,23.3/15.7,42.2,159.3,51.9,581,7.6,489.8/9.1,7102,122,99.6/81.4,69.6/60.5,0.47,-99
South Sudan,EasternAfrica,658841,12576,20.6,100.4,13167,2.0,1067.0,4.6,58.0,37.4,-99,-99,-99,...,.../...,-99,-99,2389,750,1640,-935,3.3,18.8,5.1,5.2,56.0/54.1,41.7/5.1,824.1/6.7,2231.2,77.7,2.7,-99,1.8,53.1/74.9,6.6/12.3,-99,28.5,23.9,17.9,49,-99,1.5/0.1,337,2,66.7/56.9,16.4/4.5,21.07,-99
Suriname,SouthAmerica,163820,563,3.6,100.7,4879,-2.7,8985.3,11.4,27.4,61.1,3.4,22.3,74.3,9.9,40.5/68.6,152,152,1437,1244,193,-808,1.0,66.0,0.8,2.5,74.2/67.8,26.4/10.4,46.8/8.6,~0.0,17.4,5.7,...,-99,121.4/124.2,90.9/71.7,.../...,25.5,180.7,42.8,83,98.3,2.0/3.7,44,62,98.1/88.4,88.4/61.4,0.32,-99
Tajikistan,CentralAsia,142600,8921,63.7,100.9,7853,4.2,925.9,25.0,28.0,47.1,57.2,13.4,29.4,10.8,59.6/77.9,155,172,899,3030,-2132,-472,2.2,26.8,2.6,3.5,73.5/67.7,35.3/5.8,275.1/3.2,21.8,38.9,6.9,1.7,5.2,101.2/99.8,83.1/92.5,24.0/33.6,19.0,98.6,19.0,45,3.0,5.2/0.6,76,14,93.1/66.7,93.8/95.5,4.55,-99
Tanzania,EasternAfrica,947303,57310,64.7,97.8,45628,7.0,877.3,31.1,26.1,42.9,66.9,6.4,26.7,2.7,73.9/83.2,176,180,4742,7876,-3134,-3312,3.1,31.6,5.4,5.2,64.8/60.8,44.9/4.7,261.2/0.5,402.1,44.0,5.6,~0.0,3.5,82.9/80.5,30.8/33.7,2.5/4.9,36.4,75.9,5.4,1082,52.4,11.6/0.2,935,20,77.2/45.5,31.3/8.3,5.85,-99
Thailand,South-easternAsia,513120,69038,135.1,95.2,395168,2.8,5814.8,9.1,35.7,55.1,34.0,22.7,43.3,0.6,62.7/79.8,129,126,213927,195666,18260,32149,0.4,50.4,3.0,1.5,78.4/70.8,17.3/16.9,3913.3/5.8,554.1,11.2,4.1,...,4.1,99.2/106.1,125.3/132.6,57.3/40.5,4.9,125.8,39.3,611,32.0,316.2/4.7,3338,83,97.6/98.0,89.9/96.1,0.02,-99
The Democratic Republic of the Congo,MiddleAfrica,2344858,81340,35.9,99.6,37569,7.0,486.2,19.9,44.2,35.9,65.3,5.9,28.9,3.6,70.4/71.8,104,104,5103,5906,-803,-1546,3.3,42.5,4.0,6.4,59.5/56.7,46.3/4.7,545.7/0.7,2163.3,73.2,4.3,...,2.2,101.8/112.0,33.3/53.6,4.2/9.1,8.9,53.0,3.8,349,67.4,4.7/0.1,1179,16,81.1/31.2,28.5/28.7,8.01,-99
Timor-Leste,South-easternAsia,14919,1296,87.2,103.2,2873,4.3,2425.4,5.2,79.9,14.9,50.8,2.0,47.2,4.3,26.9/55.6,111,117,94,647,-553,238,2.2,32.8,3.8,5.9,69.5/66.1,43.6/5.4,10.8/0.9,~0.0,43.9,1.5,0.1,7.8,136.2/137.5,79.6/74.1,.../...,38.5,117.4,13.4,24,46.9,0.5/0.4,143,7,95.2/60.5,69.0/26.8,8.89,-99
Togo,WesternAfrica,56785,7798,143.4,99.4,4086,5.5,559.4,45.7,19.7,34.6,62.5,8.7,28.7,6.8,81.0/80.7,140,143,715,1716,-1001,-461,2.6,40.0,3.8,4.7,59.8/58.3,41.6/4.6,276.8/3.8,13.8,55.7,5.2,...,5.2,118.5/125.1,.../...,6.4/14.9,17.6,65.0,7.1,80,3.8,2.6/0.4,111,20,91.4/44.2,24.7/2.9,5.51,-99
Turkey,WesternAsia,783562,80745,104.9,97.0,717888,4.0,9125.8,8.6,26.4,65.0,19.6,27.5,52.9,10.8,30.4/71.4,120,122,142606,198602,-55996,-32278,1.6,73.4,2.0,2.1,78.1/71.5,25.0/12.0,2964.9/3.8,3006.3,12.6,5.4,1.7,4.8,102.1/102.8,101.1/103.8,88.3/101.0,14.9,96.0,53.7,388,15.1,346.0/4.5,1303,65,100.0/100.0,98.3/85.5,0.30,-99
Uganda,EasternAfrica,241550,42863,214.5,99.0,25282,5.4,647.7,25.6,21.5,52.9,72.0,7.4,20.5,2.4,82.3/87.7,93,90,2755,3750,-996,-2353,3.4,16.1,5.4,5.9,60.7/56.5,47.7/3.3,749.5/1.9,727.1,60.2,7.2,...,2.2,110.9/108.9,22.1/24.3,4.2/5.4,34.3,50.4,19.2,196,11.0,5.2/0.1,409,12,95.5/75.8,28.5/17.3,6.35,-99
Ukraine,EasternEurope,603500,44223,76.3,86.0,90615,-9.9,2021.6,14.0,26.3,59.7,15.7,24.6,59.7,8.8,52.3/67.5,137,137,36369,39184,-2815,-189,-0.5,69.7,-0.3,1.5,76.0/66.1,15.5/23.2,4834.9/10.8,1644.8,8.8,7.1,3.0,5.9,105.1/102.8,98.2/100.2,88.4/76.5,12.3,144.0,49.3,102,16.6,227.3/5.1,3203,98,95.5/97.8,97.4/92.6,1.63,-99
United States,NorthernAmerica,9833517,324460,35.5,98.0,18036648,2.6,56053.8,1.0,19.7,79.3,1.5,17.2,81.3,4.9,55.7/68.1,111,113,1453167,2249661,-796494,-462961,0.7,81.6,1.0,1.9,81.2/76.5,18.9/21.5,46627.1/14.5,616.5,6.0,17.1,2.6,5.4,100.0/100.3,98.5/96.7,99.6/72.8,19.1,117.6,74.6,1513,33.9,5254.3/16.2,83887,289,99.4/98.2,100.0/100.0,-99,-99
Vanuatu,Melanesia,12189,276,22.7,102.4,737,-1.0,2783.0,26.7,8.4,64.9,61.4,6.8,31.8,5.3,61.7/80.5,123,122,50,416,-366,-82,2.3,26.1,3.4,3.4,73.6/69.4,35.9/6.7,3.2/1.2,~0.0,24.3,5.0,0.2,5.5,118.7/120.6,56.4/53.4,.../...,0.0,66.2,22.4,137,36.1,0.2/0.6,1,12,98.9/92.9,65.1/55.4,12.32,-99
Vietnam,South-easternAsia,330967,95541,308.1,98.0,193241,6.7,2067.9,18.9,37.0,44.2,41.8,22.9,35.2,2.2,73.9/83.3,136,134,176632,174111,2520,906,1.1,33.6,3.0,2.0,80.3/70.7,23.1/11.1,72.8/0.1,11.0,19.3,7.1,1.2,5.7,108.4/109.3,-99,28.9/28.8,26.7,130.6,52.7,616,47.2,166.9/1.8,2977,30,99.1/96.9,94.4/69.7,1.73,-99
Virgin Islands,Caribbean,347,105,299.7,91.1,-99,-99,-99,-99,-99,-99,10.9,24.0,65.2,9.0,52.5/70.7,109,109,-99,-99,-99,-99,-0.2,95.3,0.3,2.3,81.5/76.7,20.1/25.3,56.7/53.4,-99,9.3,-99,-99,-99,-99,-99,-99,-99,...,54.8,58,50.6,-99,-99,-99,100.0/100.0,96.4/96.4,-99,-99
Yemen,WesternAsia,527968,28250,53.5,102.1,29688,-28.1,1106.4,14.7,36.9,48.4,32.9,17.9,49.2,16.1,26.2/73.7,136,137,570,6861,-6291,-3026,2.6,34.6,4.0,4.4,65.6/62.8,39.9/4.6,344.1/1.3,3371.4,47.2,5.6,0.3,...,88.9/105.7,39.5/57.4,6.1/13.7,0.0,68.0,25.1,298,1.0,22.7/0.9,668,12,72.0/46.5,92.5/34.1,2.99,-99
Zambia,EasternAfrica,752612,17094,23.0,98.5,21255,2.9,1311.1,8.2,32.3,59.5,54.8,9.9,35.3,7.4,69.9/80.9,179,180,6505,7442,-937,-768,3.0,40.9,4.3,5.2,61.9/57.5,44.8/3.7,127.9/0.8,55.3,53.8,5.0,0.2,...,104.0/103.3,-99,3.4/4.5,18.0,74.5,21.0,88,65.6,4.5/0.3,374,26,85.6/51.3,55.6/35.7,3.96,-99
Zimbabwe,EasternAfrica,390757,16530,42.7,95.0,13893,1.1,890.4,13.0,30.5,56.5,67.5,7.3,25.2,5.0,78.0/87.5,99,98,2832,5212,-2379,-1521,2.3,32.4,2.3,4.0,59.0/56.1,41.2/4.2,398.9/2.6,308.6,46.5,6.0,0.1,8.4,99.1/100.8,47.1/48.1,8.0/8.9,32.6,84.8,16.4,89,37.2,12.0/0.8,482,30,97.0/67.3,49.3/30.8,6.00,-99


================================================
FILE: intro_data_science/part_4/ds_part_4.ipynb
================================================
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
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 },
 "nbformat": 4,
 "nbformat_minor": 2,
 "cells": [
  {
   "source": [
    "# A Brief Introduction to Pandas\n",
    "### Part 2"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "## 3.1 Selection\n",
    "Using .loc(), .iloc()\n",
    "https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.loc.html"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read in the complete version of the europe data, using the first column as the index\n",
    "eur_data_final = pd.read_csv('./data/complete/eur_data_final.csv', index_col=0)\n",
    "eur_data_final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data from the eur_data_final df, represented as a python dictionary\n",
    "countries_dict = {\n",
    "    15: {\n",
    "        'country': 'Italy', \n",
    "         'unemp_rate': 11.7, \n",
    "         'gdp': 1689824, \n",
    "         'median_income': 16237, \n",
    "         'total_pop': 59433744\n",
    "        }\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# With vanilla python, how do we get the word 'Italy' from a dictionary?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# How do we do this with a dataframe?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We can also get multiple columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Or an entire row/entry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Or multiple rows and columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We can also use python's index slicing syntax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select by column value (Pandas is smart!)\n",
    "# DF must be indexed by country name"
   ]
  },
  {
   "source": [
    "### Exercise - Use .loc() to create a new dataframe with all countries from Cypress to France (alphabetically) with gdp and total_pop columns."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "source": [
    "### Exercise - What countries have a higher unemployment rate than Slovenia and have a lowercase 't' in their name?"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select slovenia unemployment values\n",
    "\n",
    "# Generate comparison query\n",
    "\n",
    "# Generate 'contains' query\n",
    "\n",
    "# Make selection using queries\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Explore .iloc()\n",
    "# https://stackoverflow.com/questions/31593201/how-are-iloc-and-loc-different\n"
   ]
  },
  {
   "source": [
    "## 4.1 MultiIndexes (hierarchical indexes)\n",
    "Pandas also supports multindexes, which allow users to index by multiple values or groups of values.\n",
    "https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.MultiIndex.html"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "parent_array = ['bar', 'bar', 'baz', 'baz', 'foo', 'foo', 'qux', 'qux']\n",
    "child_array = ['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two']\n",
    "# Add arrays to one array"
   ]
  },
  {
   "source": [
    "We want a multidimensional array of random numbers. Numpy for the win!"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set the seed for number generation\n",
    "np.random.seed(42)\n",
    "# Create multidimensional array of pseudorandom numbers with shape 8,4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert md array to dataframe\n",
    "multi_df = pd.DataFrame(md_array)\n",
    "\n",
    "# https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.RangeIndex.html\n",
    "multi_df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add multindex\n",
    "multi_df = pd.DataFrame(md_array, index=arrays)\n",
    "multi_df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Update column names of multi_df\n",
    "col_names = ['var1', 'var2', 'var3', 'var4']\n",
    "multi_df.columns = col_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select all columns and rows for 'bar'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select all rows for index foo and column var1\n",
    "# Using loc, fancy indexing\n",
    "\n",
    "# # Using loc, bracket notation\n",
    "\n",
    "# # Using loc, dot notation\n",
    "\n",
    "# # Without loc\n"
   ]
  },
  {
   "source": [
    "## 4.2 Groupby\n",
    "From the documentation: \"A groupby operation involves some combination of splitting the object, applying a function, and combining the results.\"\n",
    "https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.groupby.html\n",
    "\n",
    "This is tied closely to the split-apply-combine strategy: https://pandas.pydata.org/pandas-docs/stable/user_guide/groupby.html. This was approach outlined by Hadley Wickcham in this paper: https://www.jstatsoft.org/article/view/v040i01.\n"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read in the UN world data\n",
    "un_data = pd.read_csv('./data/complete/un_world_data.csv')"
   ]
  },
  {
   "source": [
    "This data is wide! Let's get rid of some of the columns."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This data is wide! Let's get rid of some of the columns.\n",
    "columns_to_keep = [\n",
    "    'country',\n",
    "    'Region',\n",
    "    'Surface area (km2)',\n",
    "    'GDP: Gross domestic product (million current US$)', \n",
    "    'Population in thousands (2017)', \n",
    "    'Population density (per km2, 2017)'\n",
    "    ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Rename the columns \n",
    "columns = {\n",
    "    \"Surface area (km2)\": \"\", \n",
    "    \"GDP: Gross domestic product (million current US$)\": \"\",\n",
    "    \"Population in thousands (2017)\": \"\",\n",
    "    \"Population density (per km2, 2017)\": ''\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Group the data by region\n",
    "un_region = un_data.groupby('Region', as_index=False)\n",
    "# The groupby object is iterable"
   ]
  },
  {
   "source": [
    "Note: generally speaking, you want to avoid iteration with Pandas. It's best to leverage the power of vectorized operations. If you find yourself looping through a dataframe or a series, you might be doing unecessary work. https://towardsdatascience.com/you-dont-always-have-to-loop-through-rows-in-pandas-22a970b347ac"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select Caribbean group\n",
    "carib = un_region.get_group('Caribbean')\n",
    "\n",
    "# Get the head and tail"
   ]
  },
  {
   "source": [
    "### Exercise - Get the average surface area of all the countries in CentralAmerica"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "source": [
    "We can do vectorized operations on each group object."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# .min(), .max(), .mean(), .count()"
   ]
  },
  {
   "source": [
    "Let's take a look at aggregating the data using the .agg() method: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.agg.html"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Get the mean of each column for each region"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get mean, median, and sum"
   ]
  },
  {
   "source": [
    "We can also pass in other functions or define our own. These are referred to as higher-order functions, i.e. functions that take in other functions as arguments. \n",
    "\n"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Example of higher-order function\n",
    "def do_calculation(val, func):\n",
    "    return func(val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Example of filtering"
   ]
  },
  {
   "source": [
    "Let's use the statistics module from scipy to calculate some new values: https://docs.scipy.org/doc/scipy/reference/stats.html"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# stats.tsem(), stats.tstd(), stats.skew()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use a lambda function\n",
    "# https://www.geeksforgeeks.org/python-lambda-anonymous-functions-filter-map-reduce/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the max of each column in each region"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select a single column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get sum and mean of surface area, mean of population for each region"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define custom column names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# .filter()\n",
    "# https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.core.groupby.DataFrameGroupBy.filter.html\n",
    "# https://pandas.pydata.org/pandas-docs/stable/reference/groupby.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filter by population"
   ]
  },
  {
   "source": [
    "### Exercise - Get standard deviation of gdp for each region using np.std for all regions with a population density over 100"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "source": [
    "### Exercise - Generate corr plot for the UN data"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select columns\n",
    "\n",
    "# Make subplot and figure\n",
    "\n",
    "# Generate correlation matrix\n",
    "\n",
    "# Generate matplotlib plot\n",
    "\n",
    "# Add colorbar to figure\n",
    "\n",
    "# Set tick labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}

================================================
FILE: intro_data_science/part_4/ds_part_4_complete.ipynb
================================================
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
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 },
 "nbformat": 4,
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 "cells": [
  {
   "source": [
    "# A Brief Introduction to Pandas\n",
    "### Part 2"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "## 3.1 Selection\n",
    "Using .loc(), .iloc()\n",
    "https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.loc.html"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "    country  unemp_rate       gdp  median_income  total_pop\n",
       "0   Austria         6.0  356237.6          23071    8401940\n",
       "1   Belgium         7.8  424660.3          21335   11000638\n",
       "2  Bulgaria         7.6   48128.6           6742    7364570\n",
       "3   Croatia        13.1   46639.5           8985    4284889\n",
       "4    Cyprus        13.0   18490.2          16173     840407"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>country</th>\n      <th>unemp_rate</th>\n      <th>gdp</th>\n      <th>median_income</th>\n      <th>total_pop</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Austria</td>\n      <td>6.0</td>\n      <td>356237.6</td>\n      <td>23071</td>\n      <td>8401940</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Belgium</td>\n      <td>7.8</td>\n      <td>424660.3</td>\n      <td>21335</td>\n      <td>11000638</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Bulgaria</td>\n      <td>7.6</td>\n      <td>48128.6</td>\n      <td>6742</td>\n      <td>7364570</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Croatia</td>\n      <td>13.1</td>\n      <td>46639.5</td>\n      <td>8985</td>\n      <td>4284889</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Cyprus</td>\n      <td>13.0</td>\n      <td>18490.2</td>\n      <td>16173</td>\n      <td>840407</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 201
    }
   ],
   "source": [
    "# Read in the complete version of the europe data, using the first column as the index\n",
    "eur_data_final = pd.read_csv('./data/complete/eur_data_final.csv', index_col=0)\n",
    "eur_data_final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data from the eur_data_final df, represented as a python dictionary\n",
    "countries_dict = {\n",
    "    15: {\n",
    "        'country': 'Italy', \n",
    "         'unemp_rate': 11.7, \n",
    "         'gdp': 1689824, \n",
    "         'median_income': 16237, \n",
    "         'total_pop': 59433744\n",
    "        }\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "'Italy'"
      ]
     },
     "metadata": {},
     "execution_count": 133
    }
   ],
   "source": [
    "# With vanilla python, how do we get the word 'Italy' from a dictionary?\n",
    "countries_dict[15]['country']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "'Italy'"
      ]
     },
     "metadata": {},
     "execution_count": 134
    }
   ],
   "source": [
    "# How do we do this with a dataframe?\n",
    "eur_data_final.loc[15, 'country']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "country              Italy\n",
       "unemp_rate            11.7\n",
       "gdp              1689824.0\n",
       "median_income        16237\n",
       "Name: 15, dtype: object"
      ]
     },
     "metadata": {},
     "execution_count": 136
    }
   ],
   "source": [
    "# We can also get multiple columns\n",
    "eur_data_final.loc[15, ['country', 'gdp']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "country           Hungary\n",
       "unemp_rate            5.1\n",
       "gdp              113903.8\n",
       "median_income        8267\n",
       "total_pop         9937628\n",
       "Name: 12, dtype: object"
      ]
     },
     "metadata": {},
     "execution_count": 9
    }
   ],
   "source": [
    "# Or an entire row/entry\n",
    "eur_data_final.loc[12]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "    country        gdp\n",
       "9    France  2228568.0\n",
       "10  Germany  3159750.0"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>country</th>\n      <th>gdp</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>9</th>\n      <td>France</td>\n      <td>2228568.0</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>Germany</td>\n      <td>3159750.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 10
    }
   ],
   "source": [
    "# Or multiple rows and columns\n",
    "eur_data_final.loc[[9,10], ['country','gdp']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "    country  unemp_rate        gdp  median_income\n",
       "9    France        10.1  2228568.0          20621\n",
       "10  Germany         4.1  3159750.0          21152\n",
       "11   Greece        23.6   176487.9           9048\n",
       "12  Hungary         5.1   113903.8           8267\n",
       "13  Iceland         3.0    18646.1          22193\n",
       "14  Ireland         8.4   273238.2          18286"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>country</th>\n      <th>unemp_rate</th>\n      <th>gdp</th>\n      <th>median_income</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>9</th>\n      <td>France</td>\n      <td>10.1</td>\n      <td>2228568.0</td>\n      <td>20621</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>Germany</td>\n      <td>4.1</td>\n      <td>3159750.0</td>\n      <td>21152</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>Greece</td>\n      <td>23.6</td>\n      <td>176487.9</td>\n      <td>9048</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>Hungary</td>\n      <td>5.1</td>\n      <td>113903.8</td>\n      <td>8267</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>Iceland</td>\n      <td>3.0</td>\n      <td>18646.1</td>\n      <td>22193</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>Ireland</td>\n      <td>8.4</td>\n      <td>273238.2</td>\n      <td>18286</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 137
    }
   ],
   "source": [
    "# We can also use python's index slicing syntax\n",
    "eur_data_final.loc[9:14, 'country':'median_income']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "unemp_rate             8.0\n",
       "gdp                40357.2\n",
       "median_income      15250.0\n",
       "total_pop        2050189.0\n",
       "Name: Slovenia, dtype: float64"
      ]
     },
     "metadata": {},
     "execution_count": 11
    }
   ],
   "source": [
    "# Select by column value (Pandas is smart!)\n",
    "# DF must be indexed by country name\n",
    "eur_data_country_index = eur_data_final.sort_values('country').set_index('country')\n",
    "eur_data_country_index.loc['Slovenia']"
   ]
  },
  {
   "source": [
    "### Exercise - Use .loc() to create a new dataframe with all countries from Cypress to France (alphabetically) with gdp and total_pop columns."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "               gdp  total_pop\n",
       "country                      \n",
       "Cyprus     18490.2     840407\n",
       "Czechia   176370.1   10436560\n",
       "Denmark   282089.9    5560628\n",
       "Estonia    21682.6    1294455\n",
       "Finland   216073.0    5375276\n",
       "France   2228568.0   64933400"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>gdp</th>\n      <th>total_pop</th>\n    </tr>\n    <tr>\n      <th>country</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Cyprus</th>\n      <td>18490.2</td>\n      <td>840407</td>\n    </tr>\n    <tr>\n      <th>Czechia</th>\n      <td>176370.1</td>\n      <td>10436560</td>\n    </tr>\n    <tr>\n      <th>Denmark</th>\n      <td>282089.9</td>\n      <td>5560628</td>\n    </tr>\n    <tr>\n      <th>Estonia</th>\n      <td>21682.6</td>\n      <td>1294455</td>\n    </tr>\n    <tr>\n      <th>Finland</th>\n      <td>216073.0</td>\n      <td>5375276</td>\n    </tr>\n    <tr>\n      <th>France</th>\n      <td>2228568.0</td>\n      <td>64933400</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 144
    }
   ],
   "source": [
    "eur_data_country_index.loc['Cypress': 'France', ['gdp', 'total_pop']]"
   ]
  },
  {
   "source": [
    "### Exercise - What countries have a higher unemployment rate than Slovenia and have a lowercase 't' in their name?"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "     country  unemp_rate        gdp  median_income  total_pop\n",
       "3    Croatia        13.1    46639.5           8985    4284889\n",
       "15     Italy        11.7  1689824.0          16237   59433744\n",
       "16    Latvia         9.6    25037.7           9257    2070371\n",
       "23  Portugal        11.2   186480.5          10805   10562178"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>country</th>\n      <th>unemp_rate</th>\n      <th>gdp</th>\n      <th>median_income</th>\n      <th>total_pop</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>3</th>\n      <td>Croatia</td>\n      <td>13.1</td>\n      <td>46639.5</td>\n      <td>8985</td>\n      <td>4284889</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>Italy</td>\n      <td>11.7</td>\n      <td>1689824.0</td>\n      <td>16237</td>\n      <td>59433744</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>Latvia</td>\n      <td>9.6</td>\n      <td>25037.7</td>\n      <td>9257</td>\n      <td>2070371</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>Portugal</td>\n      <td>11.2</td>\n      <td>186480.5</td>\n      <td>10805</td>\n      <td>10562178</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 20
    }
   ],
   "source": [
    "# Select slovenia unemployment values\n",
    "slovenia_unemployment = eur_data_country_index.loc['Slovenia', 'unemp_rate']\n",
    "# Generate comparison query\n",
    "gt_slov = eur_data_final.unemp_rate > slovenia_unemployment\n",
    "# Generate 'contains' query\n",
    "t_names = eur_data_final.country.str.contains('t')\n",
    "# Make selection using queries\n",
    "eur_data_final[gt_slov & t_names]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "country           Austria\n",
       "unemp_rate            6.0\n",
       "gdp              356237.6\n",
       "median_income       23071\n",
       "total_pop         8401940\n",
       "Name: 0, dtype: object"
      ]
     },
     "metadata": {},
     "execution_count": 14
    }
   ],
   "source": [
    "# Explore .iloc()\n",
    "# https://stackoverflow.com/questions/31593201/how-are-iloc-and-loc-different\n",
    "eur_data_final.iloc[0]"
   ]
  },
  {
   "source": [
    "## 4.1 MultiIndexes (hierarchical indexes)\n",
    "Pandas also supports multindexes, which allow users to index by multiple values or groups of values.\n",
    "https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.MultiIndex.html"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "parent_array = ['bar', 'bar', 'baz', 'baz', 'foo', 'foo', 'qux', 'qux']\n",
    "child_array = ['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two']\n",
    "arrays = [parent_array, child_array]"
   ]
  },
  {
   "source": [
    "We want a multidimensional array of random numbers. Numpy for the win!"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([[6, 3, 7, 4],\n",
       "       [6, 9, 2, 6],\n",
       "       [7, 4, 3, 7],\n",
       "       [7, 2, 5, 4],\n",
       "       [1, 7, 5, 1],\n",
       "       [4, 0, 9, 5],\n",
       "       [8, 0, 9, 2],\n",
       "       [6, 3, 8, 2]])"
      ]
     },
     "metadata": {},
     "execution_count": 7
    }
   ],
   "source": [
    "# Set the seed for number generation\n",
    "np.random.seed(42)\n",
    "# Create multidimensional array with shape 8,4\n",
    "md_array = np.random.randint(10,size=(8,4))\n",
    "md_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=8, step=1)"
      ]
     },
     "metadata": {},
     "execution_count": 10
    }
   ],
   "source": [
    "# Convert md array to dataframe\n",
    "multi_df = pd.DataFrame(md_array)\n",
    "\n",
    "# https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.RangeIndex.html\n",
    "multi_df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "MultiIndex([('bar', 'one'),\n",
       "            ('bar', 'two'),\n",
       "            ('baz', 'one'),\n",
       "            ('baz', 'two'),\n",
       "            ('foo', 'one'),\n",
       "            ('foo', 'two'),\n",
       "            ('qux', 'one'),\n",
       "            ('qux', 'two')],\n",
       "           )"
      ]
     },
     "metadata": {},
     "execution_count": 12
    }
   ],
   "source": [
    "# Add multindex\n",
    "multi_df = pd.DataFrame(md_array, index=arrays)\n",
    "multi_df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Update column names of multi_df\n",
    "multi_df.columns = ['var1', 'var2', 'var3', 'var4']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "     var1  var2  var3  var4\n",
       "one     6     3     7     4\n",
       "two     6     9     2     6"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>var1</th>\n      <th>var2</th>\n      <th>var3</th>\n      <th>var4</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>one</th>\n      <td>6</td>\n      <td>3</td>\n      <td>7</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>two</th>\n      <td>6</td>\n      <td>9</td>\n      <td>2</td>\n      <td>6</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 18
    }
   ],
   "source": [
    "# Select all columns and rows for 'bar'\n",
    "multi_df.loc['bar']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "one    1\n",
       "two    4\n",
       "Name: var1, dtype: int64"
      ]
     },
     "metadata": {},
     "execution_count": 34
    }
   ],
   "source": [
    "# Select all rows for index foo and column var1\n",
    "# Using loc, fancy indexing\n",
    "multi_df.loc['foo', 'var1']\n",
    "# # Using loc, bracket notation\n",
    "# multi_df.loc['foo']['var1']\n",
    "# # Using loc, dot notation\n",
    "# multi_df.loc['foo'].var1\n",
    "# # Without loc\n",
    "# multi_df.var1.foo.one"
   ]
  },
  {
   "source": [
    "## 4.2 Groupby\n",
    "From the documentation: \"A groupby operation involves some combination of splitting the object, applying a function, and combining the results.\"\n",
    "https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.groupby.html\n",
    "\n",
    "This is tied closely to the split-apply-combine strategy: https://pandas.pydata.org/pandas-docs/stable/user_guide/groupby.html. This was approach outlined by Hadley Wickcham in this paper: https://www.jstatsoft.org/article/view/v040i01.\n"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read in the UN world data\n",
    "un_data = pd.read_csv('./data/complete/un_world_data.csv')"
   ]
  },
  {
   "source": [
    "This data is wide! Let's get rid of some of the columns."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This data is wide! Let's get rid of some of the columns.\n",
    "columns_to_keep = [\n",
    "    'country',\n",
    "    'Region',\n",
    "    'Surface area (km2)',\n",
    "    'GDP: Gross domestic product (million current US$)', \n",
    "    'Population in thousands (2017)', \n",
    "    'Population density (per km2, 2017)'\n",
    "    ]\n",
    "un_data = un_data[columns_to_keep]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Rename the columns \n",
    "columns = {\n",
    "    \"Surface area (km2)\": \"surface_area\", \n",
    "    \"GDP: Gross domestic product (million current US$)\": \"gdp\",\n",
    "    \"Population in thousands (2017)\": 'population',\n",
    "    \"Population density (per km2, 2017)\": 'population_density'\n",
    "    }\n",
    "\n",
    "un_data = un_data.rename(columns=columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Group the data by region\n",
    "un_region = un_data.groupby('Region', as_index=False)\n",
    "# The groupby object is iterable\n",
    "for region, region_df in un_region:\n",
    "    print(region)\n",
    "    print(region_df)"
   ]
  },
  {
   "source": [
    "Note: generally speaking, you want to avoid iteration with Pandas. It's best to leverage the power of vectorized operations. If you find yourself looping through a dataframe or a series, you might be doing unecessary work. https://towardsdatascience.com/you-dont-always-have-to-loop-through-rows-in-pandas-22a970b347ac"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                             country     Region  surface_area     gdp  \\\n",
       "19                Dominican Republic  Caribbean         48671   67103   \n",
       "27                             Haiti  Caribbean         27750    8501   \n",
       "59                       Puerto Rico  Caribbean          8868  102906   \n",
       "61  Saint Vincent and the Grenadines  Caribbean           389     738   \n",
       "82                    Virgin Islands  Caribbean           347     -99   \n",
       "\n",
       "    population  population_density  \n",
       "19       10767               222.8  \n",
       "27       10981               398.4  \n",
       "59        3663               413.0  \n",
       "61         110               281.8  \n",
       "82         105               299.7  "
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>country</th>\n      <th>Region</th>\n      <th>surface_area</th>\n      <th>gdp</th>\n      <th>population</th>\n      <th>population_density</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>19</th>\n      <td>Dominican Republic</td>\n      <td>Caribbean</td>\n      <td>48671</td>\n      <td>67103</td>\n      <td>10767</td>\n      <td>222.8</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>Haiti</td>\n      <td>Caribbean</td>\n      <td>27750</td>\n      <td>8501</td>\n      <td>10981</td>\n      <td>398.4</td>\n    </tr>\n    <tr>\n      <th>59</th>\n      <td>Puerto Rico</td>\n      <td>Caribbean</td>\n      <td>8868</td>\n      <td>102906</td>\n      <td>3663</td>\n      <td>413.0</td>\n    </tr>\n    <tr>\n      <th>61</th>\n      <td>Saint Vincent and the Grenadines</td>\n      <td>Caribbean</td>\n      <td>389</td>\n      <td>738</td>\n      <td>110</td>\n      <td>281.8</td>\n    </tr>\n    <tr>\n      <th>82</th>\n      <td>Virgin Islands</td>\n      <td>Caribbean</td>\n      <td>347</td>\n      <td>-99</td>\n      <td>105</td>\n      <td>299.7</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 203
    }
   ],
   "source": [
    "# Select Caribbean group\n",
    "carib = un_region.get_group('Caribbean')\n",
    "\n",
    "# Get the head and tail\n",
    "carib.head()\n",
    "carib.tail()"
   ]
  },
  {
   "source": [
    "### Exercise - Get the average surface area of all the countries in CentralAmerica"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "310819.5"
      ]
     },
     "metadata": {},
     "execution_count": 46
    }
   ],
   "source": [
    "central_america = un_region.get_group('CentralAmerica')\n",
    "central_america['surface_area'].mean()"
   ]
  },
  {
   "source": [
    "We can do vectorized operations on each group object."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "               Region  surface_area           gdp     population  \\\n",
       "0           Caribbean  1.720500e+04  3.582980e+04    5125.200000   \n",
       "1      CentralAmerica  3.108195e+05  1.712796e+05   22164.750000   \n",
       "2         CentralAsia  1.712745e+05  7.212500e+03    7483.000000   \n",
       "3       EasternAfrica  4.816671e+05  1.883158e+04   25645.750000   \n",
       "4         EasternAsia  5.582058e+06  5.585108e+06  706296.500000   \n",
       "5       EasternEurope  3.186730e+05  4.854500e+04   24137.000000   \n",
       "6           Melanesia  2.054250e+04  9.060000e+02     443.500000   \n",
       "7          Micronesia  5.490000e+02 -9.900000e+01     164.000000   \n",
       "8        MiddleAfrica  1.054169e+06  2.482600e+04   36885.000000   \n",
       "9      NorthernAfrica  1.002000e+06  3.159170e+05   97553.000000   \n",
       "10    NorthernAmerica  9.833517e+06  1.803665e+07  324460.000000   \n",
       "11          Polynesia  2.842000e+03  7.740000e+02     196.000000   \n",
       "12  South-easternAsia  5.205436e+05  2.298626e+05   76377.250000   \n",
       "13       SouthAmerica  1.703150e+06  3.327079e+05   42953.250000   \n",
       "14     SouthernAfrica  6.918360e+05  1.093567e+05   20494.666667   \n",
       "15       SouthernAsia  9.843594e+05  4.851398e+05  320367.800000   \n",
       "16     SouthernEurope  2.874800e+04  1.154100e+04    2930.000000   \n",
       "17      WesternAfrica  4.164142e+05  5.688491e+04   30298.545455   \n",
       "18        WesternAsia  2.060487e+05  1.389109e+05   19296.700000   \n",
       "\n",
       "    population_density  \n",
       "0           323.140000  \n",
       "1           104.262500  \n",
       "2            47.600000  \n",
       "3           139.458333  \n",
       "4            76.050000  \n",
       "5            99.800000  \n",
       "6            22.250000  \n",
       "7           304.100000  \n",
       "8            34.066667  \n",
       "9            98.000000  \n",
       "10           35.500000  \n",
       "11           69.400000  \n",
       "12          153.762500  \n",
       "13           27.050000  \n",
       "14           41.166667  \n",
       "15          197.200000  \n",
       "16          106.900000  \n",
       "17           89.181818  \n",
       "18          243.040000  "
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Region</th>\n      <th>surface_area</th>\n      <th>gdp</th>\n      <th>population</th>\n      <th>population_density</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Caribbean</td>\n      <td>1.720500e+04</td>\n      <td>3.582980e+04</td>\n      <td>5125.200000</td>\n      <td>323.140000</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>CentralAmerica</td>\n      <td>3.108195e+05</td>\n      <td>1.712796e+05</td>\n      <td>22164.750000</td>\n      <td>104.262500</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>CentralAsia</td>\n      <td>1.712745e+05</td>\n      <td>7.212500e+03</td>\n      <td>7483.000000</td>\n      <td>47.600000</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>EasternAfrica</td>\n      <td>4.816671e+05</td>\n      <td>1.883158e+04</td>\n      <td>25645.750000</td>\n      <td>139.458333</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>EasternAsia</td>\n      <td>5.582058e+06</td>\n      <td>5.585108e+06</td>\n      <td>706296.500000</td>\n      <td>76.050000</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>EasternEurope</td>\n      <td>3.186730e+05</td>\n      <td>4.854500e+04</td>\n      <td>24137.000000</td>\n      <td>99.800000</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Melanesia</td>\n      <td>2.054250e+04</td>\n      <td>9.060000e+02</td>\n      <td>443.500000</td>\n      <td>22.250000</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>Micronesia</td>\n      <td>5.490000e+02</td>\n      <td>-9.900000e+01</td>\n      <td>164.000000</td>\n      <td>304.100000</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>MiddleAfrica</td>\n      <td>1.054169e+06</td>\n      <td>2.482600e+04</td>\n      <td>36885.000000</td>\n      <td>34.066667</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>NorthernAfrica</td>\n      <td>1.002000e+06</td>\n      <td>3.159170e+05</td>\n      <td>97553.000000</td>\n      <td>98.000000</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>NorthernAmerica</td>\n      <td>9.833517e+06</td>\n      <td>1.803665e+07</td>\n      <td>324460.000000</td>\n      <td>35.500000</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>Polynesia</td>\n      <td>2.842000e+03</td>\n      <td>7.740000e+02</td>\n      <td>196.000000</td>\n      <td>69.400000</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>South-easternAsia</td>\n      <td>5.205436e+05</td>\n      <td>2.298626e+05</td>\n      <td>76377.250000</td>\n      <td>153.762500</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>SouthAmerica</td>\n      <td>1.703150e+06</td>\n      <td>3.327079e+05</td>\n      <td>42953.250000</td>\n      <td>27.050000</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>SouthernAfrica</td>\n      <td>6.918360e+05</td>\n      <td>1.093567e+05</td>\n      <td>20494.666667</td>\n      <td>41.166667</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>SouthernAsia</td>\n      <td>9.843594e+05</td>\n      <td>4.851398e+05</td>\n      <td>320367.800000</td>\n      <td>197.200000</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>SouthernEurope</td>\n      <td>2.874800e+04</td>\n      <td>1.154100e+04</td>\n      <td>2930.000000</td>\n      <td>106.900000</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>WesternAfrica</td>\n      <td>4.164142e+05</td>\n      <td>5.688491e+04</td>\n      <td>30298.545455</td>\n      <td>89.181818</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>WesternAsia</td>\n      <td>2.060487e+05</td>\n      <td>1.389109e+05</td>\n      <td>19296.700000</td>\n      <td>243.040000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 76
    }
   ],
   "source": [
    "# .min(), .max(), .mean(), .count()\n",
    "un_region.min()\n",
    "un_region.max()\n",
    "un_region.mean()\n",
    "un_region.count()"
   ]
  },
  {
   "source": [
    "Let's take a look at aggregating the data using the .agg() method: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.agg.html"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                   surface_area           gdp     population  \\\n",
       "                           mean          mean           mean   \n",
       "Region                                                         \n",
       "Caribbean          1.720500e+04  3.582980e+04    5125.200000   \n",
       "CentralAmerica     3.108195e+05  1.712796e+05   22164.750000   \n",
       "CentralAsia        1.712745e+05  7.212500e+03    7483.000000   \n",
       "EasternAfrica      4.816671e+05  1.883158e+04   25645.750000   \n",
       "EasternAsia        5.582058e+06  5.585108e+06  706296.500000   \n",
       "EasternEurope      3.186730e+05  4.854500e+04   24137.000000   \n",
       "Melanesia          2.054250e+04  9.060000e+02     443.500000   \n",
       "Micronesia         5.490000e+02 -9.900000e+01     164.000000   \n",
       "MiddleAfrica       1.054169e+06  2.482600e+04   36885.000000   \n",
       "NorthernAfrica     1.002000e+06  3.159170e+05   97553.000000   \n",
       "NorthernAmerica    9.833517e+06  1.803665e+07  324460.000000   \n",
       "Polynesia          2.842000e+03  7.740000e+02     196.000000   \n",
       "South-easternAsia  5.205436e+05  2.298626e+05   76377.250000   \n",
       "SouthAmerica       1.703150e+06  3.327079e+05   42953.250000   \n",
       "SouthernAfrica     6.918360e+05  1.093567e+05   20494.666667   \n",
       "SouthernAsia       9.843594e+05  4.851398e+05  320367.800000   \n",
       "SouthernEurope     2.874800e+04  1.154100e+04    2930.000000   \n",
       "WesternAfrica      4.164142e+05  5.688491e+04   30298.545455   \n",
       "WesternAsia        2.060487e+05  1.389109e+05   19296.700000   \n",
       "\n",
       "                  population_density  \n",
       "                                mean  \n",
       "Region                                \n",
       "Caribbean                 323.140000  \n",
       "CentralAmerica            104.262500  \n",
       "CentralAsia                47.600000  \n",
       "EasternAfrica             139.458333  \n",
       "EasternAsia                76.050000  \n",
       "EasternEurope              99.800000  \n",
       "Melanesia                  22.250000  \n",
       "Micronesia                304.100000  \n",
       "MiddleAfrica               34.066667  \n",
       "NorthernAfrica             98.000000  \n",
       "NorthernAmerica            35.500000  \n",
       "Polynesia                  69.400000  \n",
       "South-easternAsia         153.762500  \n",
       "SouthAmerica               27.050000  \n",
       "SouthernAfrica             41.166667  \n",
       "SouthernAsia              197.200000  \n",
       "SouthernEurope            106.900000  \n",
       "WesternAfrica              89.181818  \n",
       "WesternAsia               243.040000  "
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead tr th {\n        text-align: left;\n    }\n\n    .dataframe thead tr:last-of-type th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr>\n      <th></th>\n      <th>surface_area</th>\n      <th>gdp</th>\n      <th>population</th>\n      <th>population_density</th>\n    </tr>\n    <tr>\n      <th></th>\n      <th>mean</th>\n      <th>mean</th>\n      <th>mean</th>\n      <th>mean</th>\n    </tr>\n    <tr>\n      <th>Region</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Caribbean</th>\n      <td>1.720500e+04</td>\n      <td>3.582980e+04</td>\n      <td>5125.200000</td>\n      <td>323.140000</td>\n    </tr>\n    <tr>\n      <th>CentralAmerica</th>\n      <td>3.108195e+05</td>\n      <td>1.712796e+05</td>\n      <td>22164.750000</td>\n      <td>104.262500</td>\n    </tr>\n    <tr>\n      <th>CentralAsia</th>\n      <td>1.712745e+05</td>\n      <td>7.212500e+03</td>\n      <td>7483.000000</td>\n      <td>47.600000</td>\n    </tr>\n    <tr>\n      <th>EasternAfrica</th>\n      <td>4.816671e+05</td>\n      <td>1.883158e+04</td>\n      <td>25645.750000</td>\n      <td>139.458333</td>\n    </tr>\n    <tr>\n      <th>EasternAsia</th>\n      <td>5.582058e+06</td>\n      <td>5.585108e+06</td>\n      <td>706296.500000</td>\n      <td>76.050000</td>\n    </tr>\n    <tr>\n      <th>EasternEurope</th>\n      <td>3.186730e+05</td>\n      <td>4.854500e+04</td>\n      <td>24137.000000</td>\n      <td>99.800000</td>\n    </tr>\n    <tr>\n      <th>Melanesia</th>\n      <td>2.054250e+04</td>\n      <td>9.060000e+02</td>\n      <td>443.500000</td>\n      <td>22.250000</td>\n    </tr>\n    <tr>\n      <th>Micronesia</th>\n      <td>5.490000e+02</td>\n      <td>-9.900000e+01</td>\n      <td>164.000000</td>\n      <td>304.100000</td>\n    </tr>\n    <tr>\n      <th>MiddleAfrica</th>\n      <td>1.054169e+06</td>\n      <td>2.482600e+04</td>\n      <td>36885.000000</td>\n      <td>34.066667</td>\n    </tr>\n    <tr>\n      <th>NorthernAfrica</th>\n      <td>1.002000e+06</td>\n      <td>3.159170e+05</td>\n      <td>97553.000000</td>\n      <td>98.000000</td>\n    </tr>\n    <tr>\n      <th>NorthernAmerica</th>\n      <td>9.833517e+06</td>\n      <td>1.803665e+07</td>\n      <td>324460.000000</td>\n      <td>35.500000</td>\n    </tr>\n    <tr>\n      <th>Polynesia</th>\n      <td>2.842000e+03</td>\n      <td>7.740000e+02</td>\n      <td>196.000000</td>\n      <td>69.400000</td>\n    </tr>\n    <tr>\n      <th>South-easternAsia</th>\n      <td>5.205436e+05</td>\n      <td>2.298626e+05</td>\n      <td>76377.250000</td>\n      <td>153.762500</td>\n    </tr>\n    <tr>\n      <th>SouthAmerica</th>\n      <td>1.703150e+06</td>\n      <td>3.327079e+05</td>\n      <td>42953.250000</td>\n      <td>27.050000</td>\n    </tr>\n    <tr>\n      <th>SouthernAfrica</th>\n      <td>6.918360e+05</td>\n      <td>1.093567e+05</td>\n      <td>20494.666667</td>\n      <td>41.166667</td>\n    </tr>\n    <tr>\n      <th>SouthernAsia</th>\n      <td>9.843594e+05</td>\n      <td>4.851398e+05</td>\n      <td>320367.800000</td>\n      <td>197.200000</td>\n    </tr>\n    <tr>\n      <th>SouthernEurope</th>\n      <td>2.874800e+04</td>\n      <td>1.154100e+04</td>\n      <td>2930.000000</td>\n      <td>106.900000</td>\n    </tr>\n    <tr>\n      <th>WesternAfrica</th>\n      <td>4.164142e+05</td>\n      <td>5.688491e+04</td>\n      <td>30298.545455</td>\n      <td>89.181818</td>\n    </tr>\n    <tr>\n      <th>WesternAsia</th>\n      <td>2.060487e+05</td>\n      <td>1.389109e+05</td>\n      <td>19296.700000</td>\n      <td>243.040000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 85
    }
   ],
   "source": [
    "\n",
    "# Get the mean of each column for each region\n",
    "un_region.agg(['mean'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                   surface_area                                gdp  \\\n",
       "                           mean     median       sum          mean   \n",
       "Region                                                               \n",
       "Caribbean          1.720500e+04     8868.0     86025  3.582980e+04   \n",
       "CentralAmerica     3.108195e+05    92104.5   2486556  1.712796e+05   \n",
       "CentralAsia        1.712745e+05   171274.5    342549  7.212500e+03   \n",
       "EasternAfrica      4.816671e+05   589626.5   5780005  1.883158e+04   \n",
       "EasternAsia        5.582058e+06  5582058.0  11164116  5.585108e+06   \n",
       "EasternEurope      3.186730e+05   318673.0    637346  4.854500e+04   \n",
       "Melanesia          2.054250e+04    20542.5     41085  9.060000e+02   \n",
       "Micronesia         5.490000e+02      549.0       549 -9.900000e+01   \n",
       "MiddleAfrica       1.054169e+06   475650.0   3162508  2.482600e+04   \n",
       "NorthernAfrica     1.002000e+06  1002000.0   1002000  3.159170e+05   \n",
       "NorthernAmerica    9.833517e+06  9833517.0   9833517  1.803665e+07   \n",
       "Polynesia          2.842000e+03     2842.0      2842  7.740000e+02   \n",
       "South-easternAsia  5.205436e+05   315483.5   4164349  2.298626e+05   \n",
       "SouthAmerica       1.703150e+06   927341.5  13625203  3.327079e+05   \n",
       "SouthernAfrica     6.918360e+05   824116.0   2075508  1.093567e+05   \n",
       "SouthernAsia       9.843594e+05   652864.0   4921797  4.851398e+05   \n",
       "SouthernEurope     2.874800e+04    28748.0     28748  1.154100e+04   \n",
       "WesternAfrica      4.164142e+05   238537.0   4580556  5.688491e+04   \n",
       "WesternAsia        2.060487e+05    78150.0   2060487  1.389109e+05   \n",
       "\n",
       "                                            population                     \\\n",
       "                       median       sum           mean    median      sum   \n",
       "Region                                                                      \n",
       "Caribbean              8501.0    179149    5125.200000    3663.0    25626   \n",
       "CentralAmerica        38991.0   1370237   22164.750000    6298.0   177318   \n",
       "CentralAsia            7212.5     14425    7483.000000    7483.0    14966   \n",
       "EasternAfrica         13530.0    225979   25645.750000   17858.0   307749   \n",
       "EasternAsia         5585107.5  11170215  706296.500000  706296.5  1412593   \n",
       "EasternEurope         48545.0     97090   24137.000000   24137.0    48274   \n",
       "Melanesia               906.0      1812     443.500000     443.5      887   \n",
       "Micronesia              -99.0       -99     164.000000     164.0      164   \n",
       "MiddleAfrica          28416.0     74478   36885.000000   24054.0   110655   \n",
       "NorthernAfrica       315917.0    315917   97553.000000   97553.0    97553   \n",
       "NorthernAmerica    18036648.0  18036648  324460.000000  324460.0   324460   \n",
       "Polynesia               774.0       774     196.000000     196.0      196   \n",
       "South-easternAsia    127921.0   1838901   76377.250000   61204.5   611018   \n",
       "SouthAmerica         145302.5   2661663   42953.250000   17340.0   343626   \n",
       "SouthernAfrica        11491.0    328070   20494.666667    2534.0    61484   \n",
       "SouthernAsia          20658.0   2425699  320367.800000   35530.0  1601839   \n",
       "SouthernEurope        11541.0     11541    2930.000000    2930.0     2930   \n",
       "WesternAfrica         11065.0    625734   30298.545455   15851.0   333284   \n",
       "WesternAsia           43833.0   1389109   19296.700000    9012.0   192967   \n",
       "\n",
       "                  population_density                  \n",
       "                                mean  median     sum  \n",
       "Region                                                \n",
       "Caribbean                 323.140000  299.70  1615.7  \n",
       "CentralAmerica            104.262500   74.60   834.1  \n",
       "CentralAsia                47.600000   47.60    95.2  \n",
       "EasternAfrica             139.458333   54.35  1673.5  \n",
       "EasternAsia                76.050000   76.05   152.1  \n",
       "EasternEurope              99.800000   99.80   199.6  \n",
       "Melanesia                  22.250000   22.25    44.5  \n",
       "Micronesia                304.100000  304.10   304.1  \n",
       "MiddleAfrica               34.066667   35.90   102.2  \n",
       "NorthernAfrica             98.000000   98.00    98.0  \n",
       "NorthernAmerica            35.500000   35.50    35.5  \n",
       "Polynesia                  69.400000   69.40    69.4  \n",
       "South-easternAsia         153.762500  112.90  1230.1  \n",
       "SouthAmerica               27.050000   24.65   216.4  \n",
       "SouthernAfrica             41.166667   46.80   123.5  \n",
       "SouthernAsia              197.200000  204.40   986.0  \n",
       "SouthernEurope            106.900000  106.90   106.9  \n",
       "WesternAfrica              89.181818   82.30   981.0  \n",
       "WesternAsia               243.040000  107.10  2430.4  "
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead tr th {\n        text-align: left;\n    }\n\n    .dataframe thead tr:last-of-type th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr>\n      <th></th>\n      <th colspan=\"3\" halign=\"left\">surface_area</th>\n      <th colspan=\"3\" halign=\"left\">gdp</th>\n      <th colspan=\"3\" halign=\"left\">population</th>\n      <th colspan=\"3\" halign=\"left\">population_density</th>\n    </tr>\n    <tr>\n      <th></th>\n      <th>mean</th>\n      <th>median</th>\n      <th>sum</th>\n      <th>mean</th>\n      <th>median</th>\n      <th>sum</th>\n      <th>mean</th>\n      <th>median</th>\n      <th>sum</th>\n      <th>mean</th>\n      <th>median</th>\n      <th>sum</th>\n    </tr>\n    <tr>\n      <th>Region</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Caribbean</th>\n      <td>1.720500e+04</td>\n      <td>8868.0</td>\n      <td>86025</td>\n      <td>3.582980e+04</td>\n      <td>8501.0</td>\n      <td>179149</td>\n      <td>5125.200000</td>\n      <td>3663.0</td>\n      <td>25626</td>\n      <td>323.140000</td>\n      <td>299.70</td>\n      <td>1615.7</td>\n    </tr>\n    <tr>\n      <th>CentralAmerica</th>\n      <td>3.108195e+05</td>\n      <td>92104.5</td>\n      <td>2486556</td>\n      <td>1.712796e+05</td>\n      <td>38991.0</td>\n      <td>1370237</td>\n      <td>22164.750000</td>\n      <td>6298.0</td>\n      <td>177318</td>\n      <td>104.262500</td>\n      <td>74.60</td>\n      <td>834.1</td>\n    </tr>\n    <tr>\n      <th>CentralAsia</th>\n      <td>1.712745e+05</td>\n      <td>171274.5</td>\n      <td>342549</td>\n      <td>7.212500e+03</td>\n      <td>7212.5</td>\n      <td>14425</td>\n      <td>7483.000000</td>\n      <td>7483.0</td>\n      <td>14966</td>\n      <td>47.600000</td>\n      <td>47.60</td>\n      <td>95.2</td>\n    </tr>\n    <tr>\n      <th>EasternAfrica</th>\n      <td>4.816671e+05</td>\n      <td>589626.5</td>\n      <td>5780005</td>\n      <td>1.883158e+04</td>\n      <td>13530.0</td>\n      <td>225979</td>\n      <td>25645.750000</td>\n      <td>17858.0</td>\n      <td>307749</td>\n      <td>139.458333</td>\n      <td>54.35</td>\n      <td>1673.5</td>\n    </tr>\n    <tr>\n      <th>EasternAsia</th>\n      <td>5.582058e+06</td>\n      <td>5582058.0</td>\n      <td>11164116</td>\n      <td>5.585108e+06</td>\n      <td>5585107.5</td>\n      <td>11170215</td>\n      <td>706296.500000</td>\n      <td>706296.5</td>\n      <td>1412593</td>\n      <td>76.050000</td>\n      <td>76.05</td>\n      <td>152.1</td>\n    </tr>\n    <tr>\n      <th>EasternEurope</th>\n      <td>3.186730e+05</td>\n      <td>318673.0</td>\n      <td>637346</td>\n      <td>4.854500e+04</td>\n      <td>48545.0</td>\n      <td>97090</td>\n      <td>24137.000000</td>\n      <td>24137.0</td>\n      <td>48274</td>\n      <td>99.800000</td>\n      <td>99.80</td>\n      <td>199.6</td>\n    </tr>\n    <tr>\n      <th>Melanesia</th>\n      <td>2.054250e+04</td>\n      <td>20542.5</td>\n      <td>41085</td>\n      <td>9.060000e+02</td>\n      <td>906.0</td>\n      <td>1812</td>\n      <td>443.500000</td>\n      <td>443.5</td>\n      <td>887</td>\n      <td>22.250000</td>\n      <td>22.25</td>\n      <td>44.5</td>\n    </tr>\n    <tr>\n      <th>Micronesia</th>\n      <td>5.490000e+02</td>\n      <td>549.0</td>\n      <td>549</td>\n      <td>-9.900000e+01</td>\n      <td>-99.0</td>\n      <td>-99</td>\n      <td>164.000000</td>\n      <td>164.0</td>\n      <td>164</td>\n      <td>304.100000</td>\n      <td>304.10</td>\n      <td>304.1</td>\n    </tr>\n    <tr>\n      <th>MiddleAfrica</th>\n      <td>1.054169e+06</td>\n      <td>475650.0</td>\n      <td>3162508</td>\n      <td>2.482600e+04</td>\n      <td>28416.0</td>\n      <td>74478</td>\n      <td>36885.000000</td>\n      <td>24054.0</td>\n      <td>110655</td>\n      <td>34.066667</td>\n      <td>35.90</td>\n      <td>102.2</td>\n    </tr>\n    <tr>\n      <th>NorthernAfrica</th>\n      <td>1.002000e+06</td>\n      <td>1002000.0</td>\n      <td>1002000</td>\n      <td>3.159170e+05</td>\n      <td>315917.0</td>\n      <td>315917</td>\n      <td>97553.000000</td>\n      <td>97553.0</td>\n      <td>97553</td>\n      <td>98.000000</td>\n      <td>98.00</td>\n      <td>98.0</td>\n    </tr>\n    <tr>\n      <th>NorthernAmerica</th>\n      <td>9.833517e+06</td>\n      <td>9833517.0</td>\n      <td>9833517</td>\n      <td>1.803665e+07</td>\n      <td>18036648.0</td>\n      <td>18036648</td>\n      <td>324460.000000</td>\n      <td>324460.0</td>\n      <td>324460</td>\n      <td>35.500000</td>\n      <td>35.50</td>\n      <td>35.5</td>\n    </tr>\n    <tr>\n      <th>Polynesia</th>\n      <td>2.842000e+03</td>\n      <td>2842.0</td>\n      <td>2842</td>\n      <td>7.740000e+02</td>\n      <td>774.0</td>\n      <td>774</td>\n      <td>196.000000</td>\n      <td>196.0</td>\n      <td>196</td>\n      <td>69.400000</td>\n      <td>69.40</td>\n      <td>69.4</td>\n    </tr>\n    <tr>\n      <th>South-easternAsia</th>\n      <td>5.205436e+05</td>\n      <td>315483.5</td>\n      <td>4164349</td>\n      <td>2.298626e+05</td>\n      <td>127921.0</td>\n      <td>1838901</td>\n      <td>76377.250000</td>\n      <td>61204.5</td>\n      <td>611018</td>\n      <td>153.762500</td>\n      <td>112.90</td>\n      <td>1230.1</td>\n    </tr>\n    <tr>\n      <th>SouthAmerica</th>\n      <td>1.703150e+06</td>\n      <td>927341.5</td>\n      <td>13625203</td>\n      <td>3.327079e+05</td>\n      <td>145302.5</td>\n      <td>2661663</td>\n      <td>42953.250000</td>\n      <td>17340.0</td>\n      <td>343626</td>\n      <td>27.050000</td>\n      <td>24.65</td>\n      <td>216.4</td>\n    </tr>\n    <tr>\n      <th>SouthernAfrica</th>\n      <td>6.918360e+05</td>\n      <td>824116.0</td>\n      <td>2075508</td>\n      <td>1.093567e+05</td>\n      <td>11491.0</td>\n      <td>328070</td>\n      <td>20494.666667</td>\n      <td>2534.0</td>\n      <td>61484</td>\n      <td>41.166667</td>\n      <td>46.80</td>\n      <td>123.5</td>\n    </tr>\n    <tr>\n      <th>SouthernAsia</th>\n      <td>9.843594e+05</td>\n      <td>652864.0</td>\n      <td>4921797</td>\n      <td>4.851398e+05</td>\n      <td>20658.0</td>\n      <td>2425699</td>\n      <td>320367.800000</td>\n      <td>35530.0</td>\n      <td>1601839</td>\n      <td>197.200000</td>\n      <td>204.40</td>\n      <td>986.0</td>\n    </tr>\n    <tr>\n      <th>SouthernEurope</th>\n      <td>2.874800e+04</td>\n      <td>28748.0</td>\n      <td>28748</td>\n      <td>1.154100e+04</td>\n      <td>11541.0</td>\n      <td>11541</td>\n      <td>2930.000000</td>\n      <td>2930.0</td>\n      <td>2930</td>\n      <td>106.900000</td>\n      <td>106.90</td>\n      <td>106.9</td>\n    </tr>\n    <tr>\n      <th>WesternAfrica</th>\n      <td>4.164142e+05</td>\n      <td>238537.0</td>\n      <td>4580556</td>\n      <td>5.688491e+04</td>\n      <td>11065.0</td>\n      <td>625734</td>\n      <td>30298.545455</td>\n      <td>15851.0</td>\n      <td>333284</td>\n      <td>89.181818</td>\n      <td>82.30</td>\n      <td>981.0</td>\n    </tr>\n    <tr>\n      <th>WesternAsia</th>\n      <td>2.060487e+05</td>\n      <td>78150.0</td>\n      <td>2060487</td>\n      <td>1.389109e+05</td>\n      <td>43833.0</td>\n      <td>1389109</td>\n      <td>19296.700000</td>\n      <td>9012.0</td>\n      <td>192967</td>\n      <td>243.040000</td>\n      <td>107.10</td>\n      <td>2430.4</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 86
    }
   ],
   "source": [
    "# Get mean, median, and sum\n",
    "un_region.agg(['mean', 'median', 'sum'])"
   ]
  },
  {
   "source": [
    "We can also pass in other functions or define our own. These are referred to as higher-order functions, i.e. functions that take in other functions as arguments. \n",
    "\n"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "metadata": {},
     "execution_count": 101
    }
   ],
   "source": [
    "# Example of higher-order function\n",
    "def add_two(val):\n",
    "    return val + 2\n",
    "\n",
    "def do_calculation(val, func):\n",
    "    return func(val)\n",
    "\n",
    "def subtract_nine(val):\n",
    "    return val - 9\n",
    "\n",
    "do_calculation(12, add_two)\n",
    "do_calculation(15, subtract_nine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "[1, 2, 6, 11]"
      ]
     },
     "metadata": {},
     "execution_count": 96
    }
   ],
   "source": [
    "# Example of filtering\n",
    "def is_nine(val):\n",
    "    return val is not 9\n",
    "\n",
    "values = [1, 2, 6, 9, 9, 11]\n",
    "res = list(filter(is_nine, values))"
   ]
  },
  {
   "source": [
    "Let's use the statistics module from scipy to calculate some new values: https://docs.scipy.org/doc/scipy/reference/stats.html"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                   surface_area                                   gdp  \\\n",
       "                           tsem          tstd      skew          tsem   \n",
       "Region                                                                  \n",
       "Caribbean          9.321432e+03  2.084335e+04  0.685299  2.091220e+04   \n",
       "CentralAmerica     2.366682e+05  6.693987e+05  2.250712  1.387055e+05   \n",
       "CentralAsia        2.867450e+04  4.055187e+04  0.000000  6.405000e+02   \n",
       "EasternAfrica      9.050907e+04  3.135326e+05 -0.263652  5.318552e+03   \n",
       "EasternAsia        4.017942e+06  5.682228e+06  0.000000  5.573349e+06   \n",
       "EasternEurope      2.848270e+05  4.028062e+05  0.000000  4.207000e+04   \n",
       "Melanesia          8.353500e+03  1.181363e+04  0.000000  1.690000e+02   \n",
       "Micronesia                  NaN           NaN  0.000000           NaN   \n",
       "MiddleAfrica       6.464966e+05  1.119765e+06  0.695791  8.583308e+03   \n",
       "NorthernAfrica              NaN           NaN  0.000000           NaN   \n",
       "NorthernAmerica             NaN           NaN  0.000000           NaN   \n",
       "Polynesia                   NaN           NaN  0.000000           NaN   \n",
       "South-easternAsia  2.110175e+05  5.968477e+05  1.783458  1.038262e+05   \n",
       "SouthAmerica       9.847489e+05  2.785290e+06  2.162869  2.090794e+05   \n",
       "SouthernAfrica     3.500259e+05  6.062627e+05 -0.381757  1.026437e+05   \n",
       "SouthernAsia       5.934819e+05  1.327066e+06  1.284080  4.107010e+05   \n",
       "SouthernEurope              NaN           NaN  0.000000           NaN   \n",
       "WesternAfrica      1.298416e+05  4.306358e+05  0.995896  4.390537e+04   \n",
       "WesternAsia        8.687679e+04  2.747285e+05  1.157696  7.048297e+04   \n",
       "\n",
       "                                              population                 \\\n",
       "                           tstd      skew           tsem           tstd   \n",
       "Region                                                                    \n",
       "Caribbean          4.676111e+04  0.607884    2435.292270    5445.479061   \n",
       "CentralAmerica     3.923185e+05  2.253862   15379.223415   43499.012664   \n",
       "CentralAsia        9.058038e+02  0.000000    1438.000000    2033.639103   \n",
       "EasternAfrica      1.842400e+04  1.434065    4595.949529   15920.836188   \n",
       "EasternAsia        7.881906e+06  0.000000  703220.500000  994503.968439   \n",
       "EasternEurope      5.949596e+04  0.000000   20086.000000   28405.893614   \n",
       "Melanesia          2.390021e+02  0.000000     167.500000     236.880772   \n",
       "Micronesia                  NaN  0.000000            NaN            NaN   \n",
       "MiddleAfrica       1.486673e+04 -0.417756   22879.972909   39629.275555   \n",
       "NorthernAfrica              NaN  0.000000            NaN            NaN   \n",
       "NorthernAmerica             NaN  0.000000            NaN            NaN   \n",
       "Polynesia                   NaN  0.000000            NaN            NaN   \n",
       "South-easternAsia  2.936648e+05  1.333305   30201.355172   85422.332172   \n",
       "SouthAmerica       5.913659e+05  2.125620   24368.804859   68925.388662   \n",
       "SouthernAfrica     1.777841e+05  0.704844   18111.375103   31369.821873   \n",
       "SouthernAsia       9.183553e+05  1.449172  257017.757855  574709.177989   \n",
       "SouthernEurope              NaN  0.000000            NaN            NaN   \n",
       "WesternAfrica      1.456176e+05  2.814527   16239.477886   53860.254939   \n",
       "WesternAsia        2.228867e+05  1.993615    7749.004611   24504.504171   \n",
       "\n",
       "                            population_density                        \n",
       "                       skew               tsem        tstd      skew  \n",
       "Region                                                                \n",
       "Caribbean          0.215651          36.100853   80.723962  0.030881  \n",
       "CentralAmerica     2.214198          32.501153   91.927144  1.489869  \n",
       "CentralAsia        0.000000          16.100000   22.768838  0.000000  \n",
       "EasternAfrica      0.939269          47.199301  163.503175  1.306365  \n",
       "EasternAsia        0.000000          74.050000  104.722514  0.000000  \n",
       "EasternEurope      0.000000          23.500000   33.234019  0.000000  \n",
       "Melanesia          0.000000           0.450000    0.636396  0.000000  \n",
       "Micronesia         0.000000                NaN         NaN  0.000000  \n",
       "MiddleAfrica       0.532459          10.288883   17.820868 -0.186994  \n",
       "NorthernAfrica     0.000000                NaN         NaN  0.000000  \n",
       "NorthernAmerica    0.000000                NaN         NaN  0.000000  \n",
       "Polynesia          0.000000                NaN         NaN  0.000000  \n",
       "South-easternAsia  1.382405          40.638603  114.943327  0.866714  \n",
       "SouthAmerica       2.057515           7.109526   20.108776  0.932068  \n",
       "SouthernAfrica     0.707034          20.545586   35.585999 -0.283531  \n",
       "SouthernAsia       1.436007          77.116950  172.438743  0.439830  \n",
       "SouthernEurope     0.000000                NaN         NaN  0.000000  \n",
       "WesternAfrica      2.732611          17.592038   58.346188  0.460914  \n",
       "WesternAsia        1.776751          84.348314  266.732788  1.290031  "
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead tr th {\n        text-align: left;\n    }\n\n    .dataframe thead tr:last-of-type th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr>\n      <th></th>\n      <th colspan=\"3\" halign=\"left\">surface_area</th>\n      <th colspan=\"3\" halign=\"left\">gdp</th>\n      <th colspan=\"3\" halign=\"left\">population</th>\n      <th colspan=\"3\" halign=\"left\">population_density</th>\n    </tr>\n    <tr>\n      <th></th>\n      <th>tsem</th>\n      <th>tstd</th>\n      <th>skew</th>\n      <th>tsem</th>\n      <th>tstd</th>\n      <th>skew</th>\n      <th>tsem</th>\n      <th>tstd</th>\n      <th>skew</th>\n      <th>tsem</th>\n      <th>tstd</th>\n      <th>skew</th>\n    </tr>\n    <tr>\n      <th>Region</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Caribbean</th>\n      <td>9.321432e+03</td>\n      <td>2.084335e+04</td>\n      <td>0.685299</td>\n      <td>2.091220e+04</td>\n      <td>4.676111e+04</td>\n      <td>0.607884</td>\n      <td>2435.292270</td>\n      <td>5445.479061</td>\n      <td>0.215651</td>\n      <td>36.100853</td>\n      <td>80.723962</td>\n      <td>0.030881</td>\n    </tr>\n    <tr>\n      <th>CentralAmerica</th>\n      <td>2.366682e+05</td>\n      <td>6.693987e+05</td>\n      <td>2.250712</td>\n      <td>1.387055e+05</td>\n      <td>3.923185e+05</td>\n      <td>2.253862</td>\n      <td>15379.223415</td>\n      <td>43499.012664</td>\n      <td>2.214198</td>\n      <td>32.501153</td>\n      <td>91.927144</td>\n      <td>1.489869</td>\n    </tr>\n    <tr>\n      <th>CentralAsia</th>\n      <td>2.867450e+04</td>\n      <td>4.055187e+04</td>\n      <td>0.000000</td>\n      <td>6.405000e+02</td>\n      <td>9.058038e+02</td>\n      <td>0.000000</td>\n      <td>1438.000000</td>\n      <td>2033.639103</td>\n      <td>0.000000</td>\n      <td>16.100000</td>\n      <td>22.768838</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>EasternAfrica</th>\n      <td>9.050907e+04</td>\n      <td>3.135326e+05</td>\n      <td>-0.263652</td>\n      <td>5.318552e+03</td>\n      <td>1.842400e+04</td>\n      <td>1.434065</td>\n      <td>4595.949529</td>\n      <td>15920.836188</td>\n      <td>0.939269</td>\n      <td>47.199301</td>\n      <td>163.503175</td>\n      <td>1.306365</td>\n    </tr>\n    <tr>\n      <th>EasternAsia</th>\n      <td>4.017942e+06</td>\n      <td>5.682228e+06</td>\n      <td>0.000000</td>\n      <td>5.573349e+06</td>\n      <td>7.881906e+06</td>\n      <td>0.000000</td>\n      <td>703220.500000</td>\n      <td>994503.968439</td>\n      <td>0.000000</td>\n      <td>74.050000</td>\n      <td>104.722514</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>EasternEurope</th>\n      <td>2.848270e+05</td>\n      <td>4.028062e+05</td>\n      <td>0.000000</td>\n      <td>4.207000e+04</td>\n      <td>5.949596e+04</td>\n      <td>0.000000</td>\n      <td>20086.000000</td>\n      <td>28405.893614</td>\n      <td>0.000000</td>\n      <td>23.500000</td>\n      <td>33.234019</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>Melanesia</th>\n      <td>8.353500e+03</td>\n      <td>1.181363e+04</td>\n      <td>0.000000</td>\n      <td>1.690000e+02</td>\n      <td>2.390021e+02</td>\n      <td>0.000000</td>\n      <td>167.500000</td>\n      <td>236.880772</td>\n      <td>0.000000</td>\n      <td>0.450000</td>\n      <td>0.636396</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>Micronesia</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>MiddleAfrica</th>\n      <td>6.464966e+05</td>\n      <td>1.119765e+06</td>\n      <td>0.695791</td>\n      <td>8.583308e+03</td>\n      <td>1.486673e+04</td>\n      <td>-0.417756</td>\n      <td>22879.972909</td>\n      <td>39629.275555</td>\n      <td>0.532459</td>\n      <td>10.288883</td>\n      <td>17.820868</td>\n      <td>-0.186994</td>\n    </tr>\n    <tr>\n      <th>NorthernAfrica</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>NorthernAmerica</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>Polynesia</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>South-easternAsia</th>\n      <td>2.110175e+05</td>\n      <td>5.968477e+05</td>\n      <td>1.783458</td>\n      <td>1.038262e+05</td>\n      <td>2.936648e+05</td>\n      <td>1.333305</td>\n      <td>30201.355172</td>\n      <td>85422.332172</td>\n      <td>1.382405</td>\n      <td>40.638603</td>\n      <td>114.943327</td>\n      <td>0.866714</td>\n    </tr>\n    <tr>\n      <th>SouthAmerica</th>\n      <td>9.847489e+05</td>\n      <td>2.785290e+06</td>\n      <td>2.162869</td>\n      <td>2.090794e+05</td>\n      <td>5.913659e+05</td>\n      <td>2.125620</td>\n      <td>24368.804859</td>\n      <td>68925.388662</td>\n      <td>2.057515</td>\n      <td>7.109526</td>\n      <td>20.108776</td>\n      <td>0.932068</td>\n    </tr>\n    <tr>\n      <th>SouthernAfrica</th>\n      <td>3.500259e+05</td>\n      <td>6.062627e+05</td>\n      <td>-0.381757</td>\n      <td>1.026437e+05</td>\n      <td>1.777841e+05</td>\n      <td>0.704844</td>\n      <td>18111.375103</td>\n      <td>31369.821873</td>\n      <td>0.707034</td>\n      <td>20.545586</td>\n      <td>35.585999</td>\n      <td>-0.283531</td>\n    </tr>\n    <tr>\n      <th>SouthernAsia</th>\n      <td>5.934819e+05</td>\n      <td>1.327066e+06</td>\n      <td>1.284080</td>\n      <td>4.107010e+05</td>\n      <td>9.183553e+05</td>\n      <td>1.449172</td>\n      <td>257017.757855</td>\n      <td>574709.177989</td>\n      <td>1.436007</td>\n      <td>77.116950</td>\n      <td>172.438743</td>\n      <td>0.439830</td>\n    </tr>\n    <tr>\n      <th>SouthernEurope</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>WesternAfrica</th>\n      <td>1.298416e+05</td>\n      <td>4.306358e+05</td>\n      <td>0.995896</td>\n      <td>4.390537e+04</td>\n      <td>1.456176e+05</td>\n      <td>2.814527</td>\n      <td>16239.477886</td>\n      <td>53860.254939</td>\n      <td>2.732611</td>\n      <td>17.592038</td>\n      <td>58.346188</td>\n      <td>0.460914</td>\n    </tr>\n    <tr>\n      <th>WesternAsia</th>\n      <td>8.687679e+04</td>\n      <td>2.747285e+05</td>\n      <td>1.157696</td>\n      <td>7.048297e+04</td>\n      <td>2.228867e+05</td>\n      <td>1.993615</td>\n      <td>7749.004611</td>\n      <td>24504.504171</td>\n      <td>1.776751</td>\n      <td>84.348314</td>\n      <td>266.732788</td>\n      <td>1.290031</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 100
    }
   ],
   "source": [
    "# stats.tsem(), stats.tstd(), stats.skew()\n",
    "un_region.agg([stats.tsem, stats.tstd, stats.skew])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use a lambda function\n",
    "# https://www.geeksforgeeks.org/python-lambda-anonymous-functions-filter-map-reduce/\n",
    "\n",
    "get_max = lambda x : x.max()\n",
    "def get_max_alt(x):\n",
    "    return x.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                                                country surface_area  \\\n",
       "                                            get_max_alt  get_max_alt   \n",
       "Region                                                                 \n",
       "Caribbean                                Virgin Islands        48671   \n",
       "CentralAmerica                                   Panama      1964375   \n",
       "CentralAsia                                  Tajikistan       199949   \n",
       "EasternAfrica                                  Zimbabwe       947303   \n",
       "EasternAsia                                    Mongolia      9600000   \n",
       "EasternEurope                                   Ukraine       603500   \n",
       "Melanesia                                       Vanuatu        28896   \n",
       "Micronesia                                         Guam          549   \n",
       "MiddleAfrica       The Democratic Republic of the Congo      2344858   \n",
       "NorthernAfrica                                    Egypt      1002000   \n",
       "NorthernAmerica                           United States      9833517   \n",
       "Polynesia                                         Samoa         2842   \n",
       "South-easternAsia                               Vietnam      1910931   \n",
       "SouthAmerica                                   Suriname      8515767   \n",
       "SouthernAfrica                             South Africa      1221037   \n",
       "SouthernAsia                                   Pakistan      3287263   \n",
       "SouthernEurope                                  Albania        28748   \n",
       "WesternAfrica                                      Togo      1240192   \n",
       "WesternAsia                                       Yemen       783562   \n",
       "\n",
       "                          gdp  population population_density  \n",
       "                  get_max_alt get_max_alt        get_max_alt  \n",
       "Region                                                        \n",
       "Caribbean              102906       10981              413.0  \n",
       "CentralAmerica        1140724      129163              307.8  \n",
       "CentralAsia              7853        8921               63.7  \n",
       "EasternAfrica           63399       57310              494.9  \n",
       "EasternAsia          11158457     1409517              150.1  \n",
       "EasternEurope           90615       44223              123.3  \n",
       "Melanesia                1075         611               22.7  \n",
       "Micronesia                -99         164              304.1  \n",
       "MiddleAfrica            37569       81340               50.9  \n",
       "NorthernAfrica         315917       97553               98.0  \n",
       "NorthernAmerica      18036648      324460               35.5  \n",
       "Polynesia                 774         196               69.4  \n",
       "South-easternAsia      861934      263991              351.9  \n",
       "SouthAmerica          1772591      209288               66.9  \n",
       "SouthernAfrica         314571       56717               73.6  \n",
       "SouthernAsia          2116239     1339180              450.4  \n",
       "SouthernEurope          11541        2930              106.9  \n",
       "WesternAfrica          494583      190886              209.6  \n",
       "WesternAsia            717888       80745              817.4  "
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead tr th {\n        text-align: left;\n    }\n\n    .dataframe thead tr:last-of-type th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr>\n      <th></th>\n      <th>country</th>\n      <th>surface_area</th>\n      <th>gdp</th>\n      <th>population</th>\n      <th>population_density</th>\n    </tr>\n    <tr>\n      <th></th>\n      <th>get_max_alt</th>\n      <th>get_max_alt</th>\n      <th>get_max_alt</th>\n      <th>get_max_alt</th>\n      <th>get_max_alt</th>\n    </tr>\n    <tr>\n      <th>Region</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Caribbean</th>\n      <td>Virgin Islands</td>\n      <td>48671</td>\n      <td>102906</td>\n      <td>10981</td>\n      <td>413.0</td>\n    </tr>\n    <tr>\n      <th>CentralAmerica</th>\n      <td>Panama</td>\n      <td>1964375</td>\n      <td>1140724</td>\n      <td>129163</td>\n      <td>307.8</td>\n    </tr>\n    <tr>\n      <th>CentralAsia</th>\n      <td>Tajikistan</td>\n      <td>199949</td>\n      <td>7853</td>\n      <td>8921</td>\n      <td>63.7</td>\n    </tr>\n    <tr>\n      <th>EasternAfrica</th>\n      <td>Zimbabwe</td>\n      <td>947303</td>\n      <td>63399</td>\n      <td>57310</td>\n      <td>494.9</td>\n    </tr>\n    <tr>\n      <th>EasternAsia</th>\n      <td>Mongolia</td>\n      <td>9600000</td>\n      <td>11158457</td>\n      <td>1409517</td>\n      <td>150.1</td>\n    </tr>\n    <tr>\n      <th>EasternEurope</th>\n      <td>Ukraine</td>\n      <td>603500</td>\n      <td>90615</td>\n      <td>44223</td>\n      <td>123.3</td>\n    </tr>\n    <tr>\n      <th>Melanesia</th>\n      <td>Vanuatu</td>\n      <td>28896</td>\n      <td>1075</td>\n      <td>611</td>\n      <td>22.7</td>\n    </tr>\n    <tr>\n      <th>Micronesia</th>\n      <td>Guam</td>\n      <td>549</td>\n      <td>-99</td>\n      <td>164</td>\n      <td>304.1</td>\n    </tr>\n    <tr>\n      <th>MiddleAfrica</th>\n      <td>The Democratic Republic of the Congo</td>\n      <td>2344858</td>\n      <td>37569</td>\n      <td>81340</td>\n      <td>50.9</td>\n    </tr>\n    <tr>\n      <th>NorthernAfrica</th>\n      <td>Egypt</td>\n      <td>1002000</td>\n      <td>315917</td>\n      <td>97553</td>\n      <td>98.0</td>\n    </tr>\n    <tr>\n      <th>NorthernAmerica</th>\n      <td>United States</td>\n      <td>9833517</td>\n      <td>18036648</td>\n      <td>324460</td>\n      <td>35.5</td>\n    </tr>\n    <tr>\n      <th>Polynesia</th>\n      <td>Samoa</td>\n      <td>2842</td>\n      <td>774</td>\n      <td>196</td>\n      <td>69.4</td>\n    </tr>\n    <tr>\n      <th>South-easternAsia</th>\n      <td>Vietnam</td>\n      <td>1910931</td>\n      <td>861934</td>\n      <td>263991</td>\n      <td>351.9</td>\n    </tr>\n    <tr>\n      <th>SouthAmerica</th>\n      <td>Suriname</td>\n      <td>8515767</td>\n      <td>1772591</td>\n      <td>209288</td>\n      <td>66.9</td>\n    </tr>\n    <tr>\n      <th>SouthernAfrica</th>\n      <td>South Africa</td>\n      <td>1221037</td>\n      <td>314571</td>\n      <td>56717</td>\n      <td>73.6</td>\n    </tr>\n    <tr>\n      <th>SouthernAsia</th>\n      <td>Pakistan</td>\n      <td>3287263</td>\n      <td>2116239</td>\n      <td>1339180</td>\n      <td>450.4</td>\n    </tr>\n    <tr>\n      <th>SouthernEurope</th>\n      <td>Albania</td>\n      <td>28748</td>\n      <td>11541</td>\n      <td>2930</td>\n      <td>106.9</td>\n    </tr>\n    <tr>\n      <th>WesternAfrica</th>\n      <td>Togo</td>\n      <td>1240192</td>\n      <td>494583</td>\n      <td>190886</td>\n      <td>209.6</td>\n    </tr>\n    <tr>\n      <th>WesternAsia</th>\n      <td>Yemen</td>\n      <td>783562</td>\n      <td>717888</td>\n      <td>80745</td>\n      <td>817.4</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 107
    }
   ],
   "source": [
    "# Get the max of each column in each region\n",
    "un_region.agg([get_max_alt])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                   get_max_alt\n",
       "Region                        \n",
       "Caribbean                48671\n",
       "CentralAmerica         1964375\n",
       "CentralAsia             199949\n",
       "EasternAfrica           947303\n",
       "EasternAsia            9600000\n",
       "EasternEurope           603500\n",
       "Melanesia                28896\n",
       "Micronesia                 549\n",
       "MiddleAfrica           2344858\n",
       "NorthernAfrica         1002000\n",
       "NorthernAmerica        9833517\n",
       "Polynesia                 2842\n",
       "South-easternAsia      1910931\n",
       "SouthAmerica           8515767\n",
       "SouthernAfrica         1221037\n",
       "SouthernAsia           3287263\n",
       "SouthernEurope           28748\n",
       "WesternAfrica          1240192\n",
       "WesternAsia             783562"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>get_max_alt</th>\n    </tr>\n    <tr>\n      <th>Region</th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Caribbean</th>\n      <td>48671</td>\n    </tr>\n    <tr>\n      <th>CentralAmerica</th>\n      <td>1964375</td>\n    </tr>\n    <tr>\n      <th>CentralAsia</th>\n      <td>199949</td>\n    </tr>\n    <tr>\n      <th>EasternAfrica</th>\n      <td>947303</td>\n    </tr>\n    <tr>\n      <th>EasternAsia</th>\n      <td>9600000</td>\n    </tr>\n    <tr>\n      <th>EasternEurope</th>\n      <td>603500</td>\n    </tr>\n    <tr>\n      <th>Melanesia</th>\n      <td>28896</td>\n    </tr>\n    <tr>\n      <th>Micronesia</th>\n      <td>549</td>\n    </tr>\n    <tr>\n      <th>MiddleAfrica</th>\n      <td>2344858</td>\n    </tr>\n    <tr>\n      <th>NorthernAfrica</th>\n      <td>1002000</td>\n    </tr>\n    <tr>\n      <th>NorthernAmerica</th>\n      <td>9833517</td>\n    </tr>\n    <tr>\n      <th>Polynesia</th>\n      <td>2842</td>\n    </tr>\n    <tr>\n      <th>South-easternAsia</th>\n      <td>1910931</td>\n    </tr>\n    <tr>\n      <th>SouthAmerica</th>\n      <td>8515767</td>\n    </tr>\n    <tr>\n      <th>SouthernAfrica</th>\n      <td>1221037</td>\n    </tr>\n    <tr>\n      <th>SouthernAsia</th>\n      <td>3287263</td>\n    </tr>\n    <tr>\n      <th>SouthernEurope</th>\n      <td>28748</td>\n    </tr>\n    <tr>\n      <th>WesternAfrica</th>\n      <td>1240192</td>\n    </tr>\n    <tr>\n      <th>WesternAsia</th>\n      <td>783562</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 127
    }
   ],
   "source": [
    "# Select a single column\n",
    "un_region['surface_area'].agg([get_max_alt])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "               Region surface_area                   population\n",
       "                               sum          mean           mean\n",
       "0           Caribbean        86025  1.720500e+04    5125.200000\n",
       "1      CentralAmerica      2486556  3.108195e+05   22164.750000\n",
       "2         CentralAsia       342549  1.712745e+05    7483.000000\n",
       "3       EasternAfrica      5780005  4.816671e+05   25645.750000\n",
       "4         EasternAsia     11164116  5.582058e+06  706296.500000\n",
       "5       EasternEurope       637346  3.186730e+05   24137.000000\n",
       "6           Melanesia        41085  2.054250e+04     443.500000\n",
       "7          Micronesia          549  5.490000e+02     164.000000\n",
       "8        MiddleAfrica      3162508  1.054169e+06   36885.000000\n",
       "9      NorthernAfrica      1002000  1.002000e+06   97553.000000\n",
       "10    NorthernAmerica      9833517  9.833517e+06  324460.000000\n",
       "11          Polynesia         2842  2.842000e+03     196.000000\n",
       "12  South-easternAsia      4164349  5.205436e+05   76377.250000\n",
       "13       SouthAmerica     13625203  1.703150e+06   42953.250000\n",
       "14     SouthernAfrica      2075508  6.918360e+05   20494.666667\n",
       "15       SouthernAsia      4921797  9.843594e+05  320367.800000\n",
       "16     SouthernEurope        28748  2.874800e+04    2930.000000\n",
       "17      WesternAfrica      4580556  4.164142e+05   30298.545455\n",
       "18        WesternAsia      2060487  2.060487e+05   19296.700000"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead tr th {\n        text-align: left;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr>\n      <th></th>\n      <th>Region</th>\n      <th colspan=\"2\" halign=\"left\">surface_area</th>\n      <th>population</th>\n    </tr>\n    <tr>\n      <th></th>\n      <th></th>\n      <th>sum</th>\n      <th>mean</th>\n      <th>mean</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Caribbean</td>\n      <td>86025</td>\n      <td>1.720500e+04</td>\n      <td>5125.200000</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>CentralAmerica</td>\n      <td>2486556</td>\n      <td>3.108195e+05</td>\n      <td>22164.750000</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>CentralAsia</td>\n      <td>342549</td>\n      <td>1.712745e+05</td>\n      <td>7483.000000</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>EasternAfrica</td>\n      <td>5780005</td>\n      <td>4.816671e+05</td>\n      <td>25645.750000</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>EasternAsia</td>\n      <td>11164116</td>\n      <td>5.582058e+06</td>\n      <td>706296.500000</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>EasternEurope</td>\n      <td>637346</td>\n      <td>3.186730e+05</td>\n      <td>24137.000000</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Melanesia</td>\n      <td>41085</td>\n      <td>2.054250e+04</td>\n      <td>443.500000</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>Micronesia</td>\n      <td>549</td>\n      <td>5.490000e+02</td>\n      <td>164.000000</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>MiddleAfrica</td>\n      <td>3162508</td>\n      <td>1.054169e+06</td>\n      <td>36885.000000</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>NorthernAfrica</td>\n      <td>1002000</td>\n      <td>1.002000e+06</td>\n      <td>97553.000000</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>NorthernAmerica</td>\n      <td>9833517</td>\n      <td>9.833517e+06</td>\n      <td>324460.000000</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>Polynesia</td>\n      <td>2842</td>\n      <td>2.842000e+03</td>\n      <td>196.000000</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>South-easternAsia</td>\n      <td>4164349</td>\n      <td>5.205436e+05</td>\n      <td>76377.250000</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>SouthAmerica</td>\n      <td>13625203</td>\n      <td>1.703150e+06</td>\n      <td>42953.250000</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>SouthernAfrica</td>\n      <td>2075508</td>\n      <td>6.918360e+05</td>\n      <td>20494.666667</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>SouthernAsia</td>\n      <td>4921797</td>\n      <td>9.843594e+05</td>\n      <td>320367.800000</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>SouthernEurope</td>\n      <td>28748</td>\n      <td>2.874800e+04</td>\n      <td>2930.000000</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>WesternAfrica</td>\n      <td>4580556</td>\n      <td>4.164142e+05</td>\n      <td>30298.545455</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>WesternAsia</td>\n      <td>2060487</td>\n      <td>2.060487e+05</td>\n      <td>19296.700000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 159
    }
   ],
   "source": [
    "# Get sum and mean of surface area, mean of population for each region\n",
    "un_region.agg({'surface_area':['sum', 'mean'], 'population': 'mean'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "               Region  sum_surface_area  mean_population\n",
       "0           Caribbean             86025      5125.200000\n",
       "1      CentralAmerica           2486556     22164.750000\n",
       "2         CentralAsia            342549      7483.000000\n",
       "3       EasternAfrica           5780005     25645.750000\n",
       "4         EasternAsia          11164116    706296.500000\n",
       "5       EasternEurope            637346     24137.000000\n",
       "6           Melanesia             41085       443.500000\n",
       "7          Micronesia               549       164.000000\n",
       "8        MiddleAfrica           3162508     36885.000000\n",
       "9      NorthernAfrica           1002000     97553.000000\n",
       "10    NorthernAmerica           9833517    324460.000000\n",
       "11          Polynesia              2842       196.000000\n",
       "12  South-easternAsia           4164349     76377.250000\n",
       "13       SouthAmerica          13625203     42953.250000\n",
       "14     SouthernAfrica           2075508     20494.666667\n",
       "15       SouthernAsia           4921797    320367.800000\n",
       "16     SouthernEurope             28748      2930.000000\n",
       "17      WesternAfrica           4580556     30298.545455\n",
       "18        WesternAsia           2060487     19296.700000"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Region</th>\n      <th>sum_surface_area</th>\n      <th>mean_population</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Caribbean</td>\n      <td>86025</td>\n      <td>5125.200000</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>CentralAmerica</td>\n      <td>2486556</td>\n      <td>22164.750000</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>CentralAsia</td>\n      <td>342549</td>\n      <td>7483.000000</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>EasternAfrica</td>\n      <td>5780005</td>\n      <td>25645.750000</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>EasternAsia</td>\n      <td>11164116</td>\n      <td>706296.500000</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>EasternEurope</td>\n      <td>637346</td>\n      <td>24137.000000</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Melanesia</td>\n      <td>41085</td>\n      <td>443.500000</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>Micronesia</td>\n      <td>549</td>\n      <td>164.000000</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>MiddleAfrica</td>\n      <td>3162508</td>\n      <td>36885.000000</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>NorthernAfrica</td>\n      <td>1002000</td>\n      <td>97553.000000</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>NorthernAmerica</td>\n      <td>9833517</td>\n      <td>324460.000000</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>Polynesia</td>\n      <td>2842</td>\n      <td>196.000000</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>South-easternAsia</td>\n      <td>4164349</td>\n      <td>76377.250000</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>SouthAmerica</td>\n      <td>13625203</td>\n      <td>42953.250000</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>SouthernAfrica</td>\n      <td>2075508</td>\n      <td>20494.666667</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>SouthernAsia</td>\n      <td>4921797</td>\n      <td>320367.800000</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>SouthernEurope</td>\n      <td>28748</td>\n      <td>2930.000000</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>WesternAfrica</td>\n      <td>4580556</td>\n      <td>30298.545455</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>WesternAsia</td>\n      <td>2060487</td>\n      <td>19296.700000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 160
    }
   ],
   "source": [
    "# Define custom column names\n",
    "un_region.agg(sum_surface_area=('surface_area','sum'), mean_population=('population', 'mean'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [],
   "source": [
    "def inspect(df):\n",
    "    print(df['population_density'] > 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                             country          Region  surface_area     gdp  \\\n",
       "1                            Albania  SouthernEurope         28748   11541   \n",
       "19                Dominican Republic       Caribbean         48671   67103   \n",
       "25                              Guam      Micronesia           549     -99   \n",
       "27                             Haiti       Caribbean         27750    8501   \n",
       "59                       Puerto Rico       Caribbean          8868  102906   \n",
       "61  Saint Vincent and the Grenadines       Caribbean           389     738   \n",
       "82                    Virgin Islands       Caribbean           347     -99   \n",
       "\n",
       "    population  population_density  \n",
       "1         2930               106.9  \n",
       "19       10767               222.8  \n",
       "25         164               304.1  \n",
       "27       10981               398.4  \n",
       "59        3663               413.0  \n",
       "61         110               281.8  \n",
       "82         105               299.7  "
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>country</th>\n      <th>Region</th>\n      <th>surface_area</th>\n      <th>gdp</th>\n      <th>population</th>\n      <th>population_density</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>Albania</td>\n      <td>SouthernEurope</td>\n      <td>28748</td>\n      <td>11541</td>\n      <td>2930</td>\n      <td>106.9</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>Dominican Republic</td>\n      <td>Caribbean</td>\n      <td>48671</td>\n      <td>67103</td>\n      <td>10767</td>\n      <td>222.8</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>Guam</td>\n      <td>Micronesia</td>\n      <td>549</td>\n      <td>-99</td>\n      <td>164</td>\n      <td>304.1</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>Haiti</td>\n      <td>Caribbean</td>\n      <td>27750</td>\n      <td>8501</td>\n      <td>10981</td>\n      <td>398.4</td>\n    </tr>\n    <tr>\n      <th>59</th>\n      <td>Puerto Rico</td>\n      <td>Caribbean</td>\n      <td>8868</td>\n      <td>102906</td>\n      <td>3663</td>\n      <td>413.0</td>\n    </tr>\n    <tr>\n      <th>61</th>\n      <td>Saint Vincent and the Grenadines</td>\n      <td>Caribbean</td>\n      <td>389</td>\n      <td>738</td>\n      <td>110</td>\n      <td>281.8</td>\n    </tr>\n    <tr>\n      <th>82</th>\n      <td>Virgin Islands</td>\n      <td>Caribbean</td>\n      <td>347</td>\n      <td>-99</td>\n      <td>105</td>\n      <td>299.7</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 170
    }
   ],
   "source": [
    "# .filter()\n",
    "# https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.core.groupby.DataFrameGroupBy.filter.html\n",
    "# https://pandas.pydata.org/pandas-docs/stable/reference/groupby.html\n",
    "un_region.filter(lambda x: (x['population_density'] > 100).all())\n",
    "# un_region.filter(inspect)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "          country           Region  surface_area       gdp  population  \\\n",
       "10        Burundi    EasternAfrica         27830      2735       10864   \n",
       "21          Egypt   NorthernAfrica       1002000    315917       97553   \n",
       "34          Kenya    EasternAfrica        591958     63399       49700   \n",
       "40     Madagascar    EasternAfrica        587295      9739       25571   \n",
       "41         Malawi    EasternAfrica        118484      6420       18622   \n",
       "47     Mozambique    EasternAfrica        799380     14806       29669   \n",
       "60         Rwanda    EasternAfrica         26338      8096       12208   \n",
       "66        Somalia    EasternAfrica        637657      1559       14742   \n",
       "68    South Sudan    EasternAfrica        658841     13167       12576   \n",
       "71       Tanzania    EasternAfrica        947303     45628       57310   \n",
       "77         Uganda    EasternAfrica        241550     25282       42863   \n",
       "79  United States  NorthernAmerica       9833517  18036648      324460   \n",
       "84         Zambia    EasternAfrica        752612     21255       17094   \n",
       "85       Zimbabwe    EasternAfrica        390757     13893       16530   \n",
       "\n",
       "    population_density  \n",
       "10               423.1  \n",
       "21                98.0  \n",
       "34                87.3  \n",
       "40                44.0  \n",
       "41               197.5  \n",
       "47                37.7  \n",
       "60               494.9  \n",
       "66                23.5  \n",
       "68                20.6  \n",
       "71                64.7  \n",
       "77               214.5  \n",
       "79                35.5  \n",
       "84                23.0  \n",
       "85                42.7  "
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>country</th>\n      <th>Region</th>\n      <th>surface_area</th>\n      <th>gdp</th>\n      <th>population</th>\n      <th>population_density</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>10</th>\n      <td>Burundi</td>\n      <td>EasternAfrica</td>\n      <td>27830</td>\n      <td>2735</td>\n      <td>10864</td>\n      <td>423.1</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>Egypt</td>\n      <td>NorthernAfrica</td>\n      <td>1002000</td>\n      <td>315917</td>\n      <td>97553</td>\n      <td>98.0</td>\n    </tr>\n    <tr>\n      <th>34</th>\n      <td>Kenya</td>\n      <td>EasternAfrica</td>\n      <td>591958</td>\n      <td>63399</td>\n      <td>49700</td>\n      <td>87.3</td>\n    </tr>\n    <tr>\n      <th>40</th>\n      <td>Madagascar</td>\n      <td>EasternAfrica</td>\n      <td>587295</td>\n      <td>9739</td>\n      <td>25571</td>\n      <td>44.0</td>\n    </tr>\n    <tr>\n      <th>41</th>\n      <td>Malawi</td>\n      <td>EasternAfrica</td>\n      <td>118484</td>\n      <td>6420</td>\n      <td>18622</td>\n      <td>197.5</td>\n    </tr>\n    <tr>\n      <th>47</th>\n      <td>Mozambique</td>\n      <td>EasternAfrica</td>\n      <td>799380</td>\n      <td>14806</td>\n      <td>29669</td>\n      <td>37.7</td>\n    </tr>\n    <tr>\n      <th>60</th>\n      <td>Rwanda</td>\n      <td>EasternAfrica</td>\n      <td>26338</td>\n      <td>8096</td>\n      <td>12208</td>\n      <td>494.9</td>\n    </tr>\n    <tr>\n      <th>66</th>\n      <td>Somalia</td>\n      <td>EasternAfrica</td>\n      <td>637657</td>\n      <td>1559</td>\n      <td>14742</td>\n      <td>23.5</td>\n    </tr>\n    <tr>\n      <th>68</th>\n      <td>South Sudan</td>\n      <td>EasternAfrica</td>\n      <td>658841</td>\n      <td>13167</td>\n      <td>12576</td>\n      <td>20.6</td>\n    </tr>\n    <tr>\n      <th>71</th>\n      <td>Tanzania</td>\n      <td>EasternAfrica</td>\n      <td>947303</td>\n      <td>45628</td>\n      <td>57310</td>\n      <td>64.7</td>\n    </tr>\n    <tr>\n      <th>77</th>\n      <td>Uganda</td>\n      <td>EasternAfrica</td>\n      <td>241550</td>\n      <td>25282</td>\n      <td>42863</td>\n      <td>214.5</td>\n    </tr>\n    <tr>\n      <th>79</th>\n      <td>United States</td>\n      <td>NorthernAmerica</td>\n      <td>9833517</td>\n      <td>18036648</td>\n      <td>324460</td>\n      <td>35.5</td>\n    </tr>\n    <tr>\n      <th>84</th>\n      <td>Zambia</td>\n      <td>EasternAfrica</td>\n      <td>752612</td>\n      <td>21255</td>\n      <td>17094</td>\n      <td>23.0</td>\n    </tr>\n    <tr>\n      <th>85</th>\n      <td>Zimbabwe</td>\n      <td>EasternAfrica</td>\n      <td>390757</td>\n      <td>13893</td>\n      <td>16530</td>\n      <td>42.7</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 97
    }
   ],
   "source": [
    "un_region.filter(lambda x: (x.population > 10000).all())"
   ]
  },
  {
   "source": [
    "### Exercise - Get standard deviation of gdp for each region using np.std for all regions with a population density over 100"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "gdp    27227.285714\n",
       "dtype: float64"
      ]
     },
     "metadata": {},
     "execution_count": 196
    }
   ],
   "source": [
    "un_region.filter(lambda x: (x.population_density > 100).all()).agg({'gdp': np.mean})"
   ]
  },
  {
   "source": [
    "### Exercise - Generate corr plot for the UN data"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# Select columns\n",
    "cols = un_data.columns\n",
    "\n",
    "# Make subplot and figure\n",
    "fig, ax = plt.subplots(1, 1)\n",
    "\n",
    "# Generate correlation matrix\n",
    "corr_data = un_data.corr()\n",
    "\n",
    "# Generate matplotlib plot\n",
    "cax = ax.matshow(corr_data) \n",
    "\n",
    "# Add colorbar to figure\n",
    "fig.colorbar(cax)\n",
    "\n",
    "# Set tick labels\n",
    "ax.set_xticklabels(cols)\n",
    "ax.set_yticklabels(cols)\n",
    "\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}

================================================
FILE: samtools/binder/environment.yml
================================================
channels:
  - conda-forge
  - bioconda
  - defaults
dependencies:
  - bash_kernel
  - samtools


================================================
FILE: samtools/data/subset.bed
================================================
1	1	100
2	1	100
3	1	100


================================================
FILE: samtools/index.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## Intro to SAM/BAM format\n",
    "SAM (Sequence Alignment/Map) format is one of the most common file formats produced by many different pieces of alignment software, both for long and short read sequence data. It is a tab delineated text file, with 11 mandatory fields (listed below), plus header lines denoted with an `@` at the start of the line.\n",
    "\n",
    "SAM files are human readable, but can be quite large. An alternate format is the Binary Alignment/Map (BAM) file, which is binary compressed and not human readable, but is more compact and easy to work with. Most pipelines will use BAM format over SAM.\n",
    "\n",
    "\n",
    "## SAMtools\n",
    "[SAMtools](http://www.htslib.org/doc/samtools.html) is a suite of programs that are extremely useful for processing mapped reads and for downstream analysis. It has a ton of functions (which you can check out on the manual page), but we will go through several of the most common uses.\n",
    "\n",
    "### General pipeline\n",
    "Once you've obtained your mapped reads in BAM/SAM format (from BWA, bowtie, minimap, etc.), there are several steps needed before starting downstream analysis.\n",
    "\n",
    "*Filter*: generally this means removing unmapped reads from your file, which we will discuss below.\n",
    "\n",
    "*Sort*: sort the mapped reads by contig/scaffold and by coordinates. This can be done using `samtools sort`:\n",
    "\n",
    "`samtools sort -o data/file.sorted.bam data/file.bam`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Index*: creates a searchable index of your sorted BAM file, which is required for some programs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "samtools index data/file.sorted.bam"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One of the most useful tools for the first processing of mapped reads is `samtools view`, which allows you to view the contents of a BAM/SAM file in SAM format:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "samtools view data/file.sorted.bam | head"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "| **Column**|  **Description** |\n",
    "|-----|---|\n",
    "|   1  | Read name  |\n",
    "|   2  | Bitwise flag  |\n",
    "|   3  |  Reference name |\n",
    "|   4  |  Leftmost mapping position |\n",
    "|   5  |  MAPQ quality score |\n",
    "|   6  |  CIGAR string  |\n",
    "|   7  |  Name of 2nd read in pair |\n",
    "|   8  |  Position of 2nd read in pair |\n",
    "|   9  |  Length of mapping segment |\n",
    "|   10  |  Sequence of segment  |\n",
    "|   11  |  Phred33 quality score at each position  |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### Viewing specific regions\n",
    "By default `samtools view` prints all alignments, but you can specify a specific chromosome or subregion to only print alignments in that window:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "samtools view data/file.sorted.bam 1 | head\n",
    "samtools view data/file.sorted.bam 1:100-200 | head"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you have numerous regions of interest, you can format them as a BED file and pass that to `samtools view`. This can be slow with large BAM files, as it does not does not use the index.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat data/subset.bed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "samtools view -h -o data/file.subset.sam -L data/subset.bed data/file.sorted.bam"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `-o` option specifies an output file, rather than printing to screen. The `-h` option is important to remember, as it adds a header to the output. This is important, as many programs require a header to parse BAM/SAM files!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Converting between formats\n",
    "By default `samtools view` outputs in SAM format, so converting from BAM --> SAM is as easy as running `samtools view -h -o outfile.sam file.bam`.\n",
    "\n",
    "For SAM --> BAM, include the `-b` option:\n",
    "`samtools view -b -h -o outfile.bam file.sam`\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SAM flags\n",
    "The second column in a BAM/SAM file is the *bitwise flag*. The flag value is an integer, which is the sum of a series of decimal values that give information about how a read is mapped\n",
    "\n",
    "| **Integer**|  **Description** |\n",
    "|-----|---|\n",
    "|   1  | read is paired  |\n",
    "|   2  | read mapped in proper pair  |\n",
    "|   4  |  read unmapped |\n",
    "|   8  |  mate is unmapped |\n",
    "|   16  |  read on reverse strand |\n",
    "|   32  |  mate on reverse strand  |\n",
    "|   64  |  first read in pair |\n",
    "|   128  |  second read in pair |\n",
    "|   256  |  not primary alignment |\n",
    "|   512  |  alignment fails quality checks  |\n",
    "|   1024  |  PCR or optical duplicate  |\n",
    "|   2048  |  supplementary alignment |\n",
    "\n",
    "So e.g., for a paired-end mapping data set, a flag = **99** (1+2+32+64) means the read is mapped along with its mate (1 and 2) and in the proper orientation (32 and 64)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Filtering reads\n",
    "Probably the most important flag to remember is **4**, which means the read is **unmapped**. Unmapped reads are most often filtered out. You can filter reads containing a given flag using the `-f` (only take reads that match given flags) and `-F` (only take reads that do **NOT** match given flag) options in `samtools view`.\n",
    "\n",
    "So to remove unmapped reads, you would run:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "samtools view -F 4 data/file.sorted.bam | head"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This removes any read that contains the 4 flag (e.g. 77, 141, etc.). You can filter on any other criteria using flags as well, e.g. only gets reads that map in proper pair:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "samtools view -f 2 data/file.sorted.bam | head"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(Note this uses `-f`, not `-F`!)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Downstream analysis\n",
    "\n",
    "### Calculating coverage\n",
    "One of the most common things you will want to know about your mapped reads is their coverage and depth, as this can impact your confidence in the assembly, the validity of your SNP calls, etc. There are many approaches you can take to calculate depth, several of which you can do with SAMtools.    \n",
    "\n",
    "`samtools coverage`: for each contig/scaffold in the BAM/SAM file, outputs several useful summary stats as a table:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "samtools coverage data/file.sorted.bam"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Like with `samtools view`, can also specify coordinates:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "samtools coverage data/file.sorted.bam -r 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As a quick way to visualize coverage, you can use the `-m` option create a histogram of coverage over a contig:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "samtools coverage -m data/file.sorted.bam -r 1:1-1000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You might also want to look at per-base coverage rather than the average. For this you can use `samtools depth`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "samtools depth -a data/file.sorted.bam -r 1:1-1000 | head"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This outputs a three column list, where the 1st column is the contig name, the 2nd is the position, and the 3rd is the depth over that base. This list is convenient for importing to programs like R, where you can plot e.g. a histogram showing the distribution of per-base depth, or distribution of depth over a contig."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stats\n",
    "Another useful function built into SAMtools is `samtools stats`, which gives some quick summary statistics about your mapping reads. The amount of information it generates is somewhat overkill in most cases, so we will just look at the summary:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "samtools stats data/file.sorted.bam | grep ^SN | cut -f 2-"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Other tricks\n",
    "\n",
    "### BAM to FASTQ/A\n",
    "If you want to extract the sequence info from the reads you can use `samtools fastq` or `samtools fasta`:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "samtools fastq data/file.sorted.bam | head"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also output the pairs to different files.\n",
    "\n",
    "### Merge BAM files\n",
    "You can combine multiple sorted BAM/SAM files, which can be useful if you have done multiple rounds of mapping:\n",
    "\n",
    "`samtools merge file.bam file2.bam ...`\n",
    "\n",
    "Unless otherwise specified, the headers will also be merged.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
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 "nbformat": 4,
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}


================================================
FILE: samtools/introduction.md
================================================

## Intro to SAM/BAM format
SAM (Sequence Alignment/Map) format is one of the most common file formats produced by many different pieces of alignment software, both for long and short read sequence data. It is a tab delineated text file, with 11 mandatory fields (listed below), plus header lines denoted with an `@` at the start of the line.

SAM files are human readable, but can be quite large. An alternate format is the Binary Alignment/Map (BAM) file, which is binary compressed and not human readable, but is more compact and easy to work with. Most pipelines will use BAM format over SAM.


## SAMtools
[SAMtools](http://www.htslib.org/doc/samtools.html) is a suite of programs that are extremely useful for processing mapped reads and for downstream analysis. It has a ton of functions (which you can check out on the manual page), but we will go through several of the most common uses.

### General pipeline
Once you've obtained your mapped reads in BAM/SAM format (from BWA, bowtie, minimap, etc.), there are several steps needed before starting downstream analysis.

*Filter*: generally this means removing unmapped reads from your file, which we will discuss below.

*Sort*: sort the mapped reads by contig/scaffold and by coordinates. This can be done using `samtools sort`:

`samtools sort -o file.sorted.bam file.bam`

*Index*: creates a searchable index of your sorted BAM file, which is required for some programs.

`samtools index file.sorted.bam`
 ___
One of the most useful tools for the first processing of mapped reads is `samtools view`, which allows you to view the contents of a BAM/SAM file in SAM format:

`samtools view data/file.sorted.bam | head`

| **Column**|  **Description** |
|-----|---|
|   1  | Read name  |
|   2  | Bitwise flag  |
|   3  |  Reference name |
|   4  |  Leftmost mapping position |
|   5  |  MAPQ quality score |
|   6  |  CIGAR string  |
|   7  |  Name of 2nd read in pair |
|   8  |  Position of 2nd read in pair |
|   9  |  Length of mapping segment |
|   10  |  Sequence of segment  |
|   11  |  Phred33 quality score at each position  |


### Viewing specific regions
By default `samtools view` prints all alignments, but you can specify a specific chromosome or subregion to only print alignments in that window:

`samtools view data/file.sorted.bam 1 | head`  
`samtools view data/file.sorted.bam 1:1-1000 | head`

If you have numerous regions of interest, you can format them as a BED file and pass that to `samtools view`. This can be slow with large BAM files, as it does not does not use the index.

`samtools view -h -o file.subset.bam -L subset.bed file.bam`

The `-o` option specifies an output file, rather than printing to screen. The `-h` option is important to remember, as it adds a header to the output. This is important, as many programs require a header to parse BAM/SAM files!

### Converting between formats
By default `samtools view` outputs in SAM format, so converting from BAM --> SAM is as easy as running `samtools view -h -o outfile.sam file.bam`.

For SAM --> BAM, include the `-b` option:

`samtools view -b -h -o outfile.bam file.sam`

### SAM flags
The second column in a BAM/SAM file is the *bitwise flag*. The flag value is an integer, which is the sum of a series of decimal values that give information about how a read is mapped

| **Integer**|  **Description** |
|-----|---|
|   1  | read is paired  |
|   2  | read mapped in proper pair  |
|   4  |  read unmapped |
|   8  |  mate is unmapped |
|   16  |  read on reverse strand |
|   32  |  mate on reverse strand  |
|   64  |  first read in pair |
|   128  |  second read in pair |
|   256  |  not primary alignment |
|   512  |  alignment fails quality checks  |
|   1024  |  PCR or optical duplicate  |
|   2048  |  supplementary alignment |

So e.g., for a paired-end mapping data set, a flag = **99** (1+2+32+64) means the read is mapped along with its mate (1 and 2) and in the proper orientation (32 and 64).

### Filtering reads
Probably the most important flag to remember is **4**, which means the read is **unmapped**. Unmapped reads are most often filtered out. You can filter reads containing a given flag using the `-f` (only take reads that match given flags) and `-F` (only take reads that do **NOT** match given flag) options in `samtools view`.

So to remove unmapped reads, you would run:

`samtools view -F 4 -h file.sorted.bam | head`

This removes any read that contains the 4 flag (e.g. 77, 141, etc.). You can filter on any other criteria using flags as well, e.g. only gets reads that map in proper pair:

`samtools view -f 2 -h file.sorted.bam`

(Note this uses `-f`, not `-F`!)

## Downstream analysis

### Calculating coverage
One of the most common things you will want to know about your mapped reads is their coverage and depth, as this can impact your confidence in the assembly, the validity of your SNP calls, etc. There are many approaches you can take to calculate depth, several of which you can do with SAMtools.    

`samtools coverage`: for each contig/scaffold in the BAM/SAM file, outputs several useful summary stats as a table:
`samtools coverage file.sorted.bam`

Like with `samtools view`, can also specify coordinates:

`samtools coverage file.sorted.bam -r 1`

As a quick way to visualize coverage, you can use the `-m` option create a histogram of coverage over a contig:

`samtools coverage -m ERR1013163.sorted.bam -r 1:1-1000`

You might also want to look at per-base coverage rather than the average. For this you can use `samtools depth`:

`samtools depth -a file.sorted.bam -r 1:1-1000 | head`

This outputs a three column list, where the 1st column is the contig name, the 2nd is the position, and the 3rd is the depth over that base. This list is convenient for importing to programs like R, where you can plot e.g. a histogram showing the distribution of per-base depth, or distribution of depth over a contig.

### Stats/flagstats
Another useful function built into SAMtools is `samtools stats`, which gives some quick summary statistics about your mapping reads. The amount of information it generates is somewhat overkill in most cases, so we will just look at the summary:

`samtools stats ERR1013163.sorted.subset.bam | grep ^SN | cut -f 2-`
## Other tricks

### BAM to FASTQ/A
If you want to extract the sequence info from the reads you can use `samtools fastq` or `samtools fasta`:

`samtools fastq file.bam | head`

You can also output the pairs to different files.

### Merge BAM files
You can combine multiple sorted BAM/SAM files, which can be useful if you have done multiple rounds of mapping:

`samtools merge file.bam file2.bam ...`

Unless otherwise specified, the headers will also be merged.


================================================
FILE: singularity/images/singularity_container.drawio
================================================
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================================================
FILE: singularity/images/singularity_image.drawio
================================================
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FILE: singularity/images/trinity.drawio
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FILE: singularity/part1.md
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class: middle, center

# Singularity
## Finding & Using Containers
### FAS Bioinformatics Workshop
#### April 27, 2021
<img src="https://raw.githubusercontent.com/hpcng/singularity-userdocs/v3.6.0/logo.png" style="width:35%">
#### Your Host: Nathan Weeks

---

# Roadmap

### Part 1 (this presentation)

* What are (Singularity) containers?
* Finding container images
* Using Singularity on Cannon

### Part 2 (next week)

* Building Singularity container images
* Common pitfalls
* Useful tips & tricks

---

### Prerequisites (to follow along with the exercises)...

1. Log into Cannon (either via [SSH](https://docs.rc.fas.harvard.edu/kb/terminal-access/), or a web browser using the [FASRC VDI portal](https://docs.rc.fas.harvard.edu/kb/virtual-desktop/) ([FASRC VPN](https://docs.rc.fas.harvard.edu/kb/vpn-setup/) connection required)

2. Launch an [interactive job](https://docs.rc.fas.harvard.edu/kb/running-jobs/#Interactive_jobs_and_salloc) on a compute node:
```
      salloc -p test,shared -t 2:00:00 --mem=4g
```
    *NOTE:* the `singularity` *command is not available on Cannon login nodes*


---

# What is a *container*?

A set of one or more processes (*running programs*) that share (at least<!-- <sup>1</sup> -->) a different root file system ("/"; usually provided by a *container image*) than processes running outside of the container on the same host operating system kernel (*typically Linux*).

---

# What is Singularity?
* An open-source container platform for Linux
  - https://github.com/hpcng/singularity
  - [Old beta for macOS](https://sylabs.io/singularity-desktop-macos/) (don't bother)
* Started in 2015 by Greg Kurtzer from Lawrence Berkeley National Laboratory; now commercially developed/supported by [Sylabs](https://sylabs.io/)
* Provides software tooling for both building *container images* and creating *containers* in which commands are run
* Used:
  - Primarily for HPC clusters
    - Cannon: [FASRC VDI portal](https://docs.rc.fas.harvard.edu/kb/virtual-desktop/) (Open OnDemand) to run JupyterLab, RStudio Server, and other interactive apps
  - Workflow management systems, such as [Snakemake](https://snakemake.readthedocs.io/en/stable/snakefiles/deployment.html#running-jobs-in-containers), [NextFlow](https://www.nextflow.io/docs/latest/singularity.html), and [Galaxy](https://docs.galaxyproject.org/en/master/admin/special_topics/mulled_containers.html)
  - [Open Science Grid](https://support.opensciencegrid.org/support/solutions/articles/12000024676-docker-and-singularity-containers)

---

# Singularity Image

* A Singularity *image* is a file containing a (compressed, read-only [SquashFS](https://en.wikipedia.org/wiki/SquashFS)) file system, usually containing:
  * A base (Linux) operating system (e.g., Ubuntu, CentOS, Alpine)
  * Target software (e.g., NCBI BLAST+) and all software dependencies
* Typical file extension: *.sif* ([Singularity Image Format]()); older (pre Singularity 3.x) may use ".simg"
* Since it's a *file*, can be copied to other hosts (e.g., `scp`), or archived/shared (e.g., alongside code and research data)
![singularity image](images/singularity_image.jpeg)

---

# Singularity Container

* A Singularity *container* runs user processes with a software environment reflecting the (read-only) file system within the Singularity image file, plus select (generally-writable) directories from the host that are *bind-mounted*  onto this file system.
  * On Cannon, this includes **/n/** (network) file systems, like **/n/holyscratch01** and **/n/home* **

<img src="images/singularity_container.jpeg">

---

# Why use containers?

<img src="images/installing_software.gif" style="width: 100%">

---

# Why use containers?
* Save time
  - (Re)creating a complex software environment on a different host can be difficult
* Portability
  - A container image can run on another (Linux) host, or...
  - Easily shared with other users on the same system (e.g., on Cannon with other members of the same lab)
* Reproducibility
  - Guarantees software environment (including exact versions) recorded & reproduced faithfully

#### SEE ALSO
* [Introduction to Singularity - Use Cases](https://sylabs.io/guides/3.7/user-guide/introduction.html#use-cases)

---

## Example: [Trinity](https://trinityrnaseq.github.io/) de novo transcriptome assembler

* Difficult to install; many software dependencies
  - Bespoke environment modules for a couple older versions exist on Cannon (see `module-query trinityrnaseq`)
* "Official" *container image* encapsulates Trinity & dependencies (see the [Dockerfile](https://github.com/trinityrnaseq/trinityrnaseq/blob/v2.12.0/Docker/Dockerfile) container image "recipe")
  - Can be used on Cannon

<div style="text-align: center"><img src="images/trinity.svg" style="width: 55%"></div>

---

## Running a command in a Singularity container

[singularity exec](https://sylabs.io/guides/3.7/user-guide/quick_start.html#executing-commands) is the most common mechanism to run a command in singularity container, both for batch and interactive jobs:

```
singularity exec [...options...] singularity_image.sif command [...command arguments...]
```

e.g.:

```
$ image=/n/singularity_images/informatics/braker2/braker2_2.1.6.sif 
$ singularity exec ${image} which braker.pl
/usr/local/bin/braker.pl
$ singularity exec ${image} braker.pl --version
braker.pl version 2.1.6
```

*NOTE: It is good practice to use the `singularity exec --cleanenv` option; this will be discussed in more detail in part 2*

---

## Running a command in a Singularity container
[singularity shell](https://sylabs.io/guides/3.7/user-guide/quick_start.html#shell) is used to get a "shell" in the container (almost like logging into a virtual machine) for interactive exploration and (short) interactive work:

```
$ singularity shell /n/singularity_images/informatics/maker/maker:3.01.03-repbase.sif
Singularity> type maker
maker is /usr/local/bin/maker
Singularity> cat /etc/os-release
PRETTY_NAME="Debian GNU/Linux 8 (jessie)"
NAME="Debian GNU/Linux"
VERSION_ID="8"
VERSION="8 (jessie)"
ID=debian
HOME_URL="http://www.debian.org/"
SUPPORT_URL="http://www.debian.org/support"
BUG_REPORT_URL="https://bugs.debian.org/"
```

*The shell prompt changes to `Singularity>` to indicate the shell is in a container*

---

## Your Turn

1. Start a shell in an NCBI BLAST container using the `singularity shell` command:

    ```
    singularity shell /n/singularity_images/informatics/ncbi-blast/ncbi-blast:2.10.0.sif
    ```

2. Answer the following questions:
    1. What is the base operating system of the container image?
       (*hint*: `cat /etc/os-release`)
    2. Where is the location of the `blastn` executable?
       (*hint*: `type blastn` or `which blastn` or `command -v blastn`)

---

## Finding Container Images - Container Registries

* Container images are typically hosted in a *container registry*
  - Hosting service for  container *repositories*, analogous to git repositories
* Many container registries have bad (or no) search interfaces, and may not be your first stop when looking for container images
  - Notable possible exception: NVIDIA GPU Accelerated Container Registry (NGC) for GPU-accelerated container images
 
---

## Singularity container registries

1. (*Update: now defunct?*) Singularity Hub (https://singularity-hub.org/)
  - The first Singularity container registry
  - Requires(d) exessive privileges to your GitHub account to be able to search / use
2. Sylabs Cloud Library (https://cloud.sylabs.io/library)
  - [singularity search](https://sylabs.io/guides/3.7/user-guide/cli/singularity_search.html) command can be used to search---but is not too useful (except for custom-built images)

---

### Container Registries - Docker / OCI (Open Container Image)

Singularity can build SIF images from Docker / OCI images in container other registries.
The most popular:

* DockerHub
  - https://hub.docker.com/
  - The original and most popular (public) container registry
  - [Image "pull" limits](https://www.docker.com/increase-rate-limits)
    - 100 anonymous image pulls per 6 hours per public IP address
    - 200 anonymous images pulls per (free) Docker account (see [singularity remote login](https://sylabs.io/guides/3.7/user-guide/cli/singularity_remote_login.html) to authenticate)
* Quay
  - https://quay.io/search
      - *Caveat: looks like search will required RedHat SSO on July 1, 2021...*

---


## Other OCI Container Registries

* GitHub Container Registry
  - https://docs.github.com/en/packages/guides/about-github-container-registry
  - Newish, but looks promising for continuous integration / automated builds of GitHub-hosted projects
* GitLab Container Registry
  - https://docs.gitlab.com/ee/user/packages/container_registry/
  - More mature than GitHub Container Registry; images hosted per-project / repository
* NVIDIA GPU Accelerated Container Registry (NGC)
  - https://ngc.nvidia.com/
  - singularity `--nv` option to use host GPU in container
    (see Singularity [GPU Support (NVIDIA CUDA & AMD ROCm](https://sylabs.io/guides/3.7/user-guide/gpu.html))

---

## Other OCI Container Registries

Mainly for paying customers:
* Azure Container Registry
  - https://azure.microsoft.com/en-us/services/container-registry/
  - Hosts some "native" Singularity images
* Amazon Elastic Container Registry (ECR)
  - https://gallery.ecr.aws/
* Oracle Container Registry
  - https://container-registry.oracle.com/

---

# BioContainers

* [Bioconda](https://bioconda.github.io/) is a bioinformatics-focused channel of software packages for the [conda](https://docs.rc.fas.harvard.edu/kb/python/) package manager
  - See earlier [conda tutorial](https://github.com/harvardinformatics/bioinformatics-coffee-hour/blob/master/taste-of-conda/index.ipynb)
* [BioContainers](https://biocontainers.pro/) provides container images for (mostly) [Bioconda](https://bioconda.github.io/) packages (including dependencies)
* Can search for BioContainers images:
  - [BioContainers registry](https://biocontainers.pro/registry)
  - [Bioconda Package Index](https://bioconda.github.io/conda-package_index.html)
    - Click on a package name > "container" link > tag > Fetch Tag (download icon)

---

## Generating a Singularity image from a Docker image

Many container registries assume [Docker](https://docker.com), and suggest syntax like:

```
docker pull registry/user/repository:tag
```

To adapt to Singularity, replace with:

```
singularity pull docker://registry/user/repository:tag
```

### Example

```
singularity pull --disable-cache docker://quay.io/biocontainers/samtools:1.12--h9aed4be_1
```

* *The `--disable-cache` option prevents image layers from being cached in ${HOME}/.singularity/cache*

---
## Finding container images for bioinformatics

Prefer "official" container images provided by the project; e.g.
* Check docs, or for existence of Dockerfile in git repo
* Examples:
  - [Trinity](https://github.com/trinityrnaseq/trinityrnaseq/wiki/Trinity-in-Docker#running-trinity-using-singularity)
```
curl -O https://data.broadinstitute.org/Trinity/TRINITY_SINGULARITY/\
trinityrnaseq.v2.12.0.simg
```
      - FAS Informatics [Best Practices for De Novo Transcriptome Assembly with Trinity](https://informatics.fas.harvard.edu/best-practices-for-de-novo-transcriptome-assembly-with-trinity.html) illustrates optimized use on Cannon
  - [QIIME 2](https://docs.qiime2.org/2021.2/install/virtual/docker/)
      - `singularity pull --disable-cache docker://quay.io/qiime2/core:2021.2`
  - [MultiQC](https://github.com/ewels/MultiQC)
      - https://github.com/ewels/MultiQC
          - Links to: https://hub.docker.com/r/ewels/multiqc
          - `singularity pull --disable-cache docker://ewels/multiqc:1.10.1`
* Existence of Dockerfile doesn't mean an image is avialable in a container registry (like Docker Hub)
  - e.g., [Augustus](https://github.com/Gaius-Augustus/Augustus) provides a Dockerfile, but needs to be built

---

# CernVM-FS (CVMFS)

* https://cernvm.cern.ch/fs/
* Singularity images of Biocontainers (maintained by the Galaxy Project) available at:

```
/cvmfs/singularity.galaxyproject.org/FIRST_LETTER/SECOND_LETTER/\
PACKAGE_NAME:VERSION--CONDA_BUILD
```

ex.

```
$ singularity exec --cleanenv \
  /cvmfs/singularity.galaxyproject.org/b/l/blast:2.11.0--pl526he19e7b1_0 \
  blastn -version
WARNING: Skipping mount /var/singularity/mnt/session/etc/resolv.conf [files]: /etc/resolv.conf doesn't exist in container
blastn: 2.11.0+
 Package: blast 2.11.0, build Mar 12 2021 10:19:58
```

* `/etc/resolv.conf` warning is a known issue:
  https://github.com/bioconda/bioconda-recipes/issues/11583
* https://docs.rc.fas.harvard.edu/kb/singularity-on-the-cluster/#BioContainers

---

# CVMFS Caveats
* Delay (up to a minute or so) when auto-mounting `/cvmfs/singularity.galaxyproject.org` on a given compute node
* Further delay when fetching a container images

## Recommendations
* Don't use for a [large number of jobs](https://docs.rc.fas.harvard.edu/kb/submitting-large-numbers-of-jobs/)
* Copy frequently-used containers to [high-performance shared storage](https://docs.rc.fas.harvard.edu/kb/cluster-storage/#Networked_High-performance_Shared_Scratch_Storage)

---

## Exercise - Biocontainers & CVMFS

1. Choose a Biocontainers image, using your favorite interface:
    * [Bioconda package index](https://bioconda.github.io/conda-package_index.html)
    * [BioContainers Registry](https://biocontainers.pro/registry)

  `ls /cvmfs/singularity.galaxyproject.org/FIRST_LETTER/SECOND_LETTER/`

2. Find the container image in CVMFS, and execute a command in the container

```
singularity exec --cleanenv \
  /cvmfs/singularity.galaxyproject.org/b/l/blast:2.11.0--pl526he19e7b1_0 \
  blastn -version
```

3. Copy & paste the `singularity exec` command into the chat

---

# Miscellanea - Pipelines of Containers

Processes in a Singularity container behave the same as processes on the host outside of a container w.r.t. I/O (including stdin/stdout/stderr).
As such, they can be used with (unix) pipes:

```
singularity pull --disable-cache docker://quay.io/biocontainers/samtools:1.12--h9aed4be_1
singularity pull --disable-cache docker://quay.io/biocontainers/bwa-mem2:2.2.1--h9a82719_1
singularity exec --cleanenv bwa-mem2_2.2.1--h9a82719_1.sif bwa-mem2 index reference.fa.gz 
singularity exec --cleanenv bwa-mem2_2.2.1--h9a82719_1.sif \
                            bwa-mem2 mem reference.fa.gz left.fastq.gz right.fastq.gz |
  singularity exec --cleanenv samtools_1.12--h9aed4be_1.sif \
                              samtools sort --output-fmt bam > output.bam
```

### SEE ALSO

   For a more detailed tutorial on Unix pipes, see [harvardinformatics/bioinformatics-coffee-hour/unix-pipes](https://github.com/harvardinformatics/bioinformatics-coffee-hour/blob/master/unix-pipes/index.ipynb)

--- 

## Links to other useful resources

* Singularity User Guide
  - https://sylabs.io/guides/latest/user-guide/
* Software Carpentries - Introduction to Singularity (alpha)
  - https://carpentries-incubator.github.io/singularity-introduction
* Creating and running software containers with Singularity (originally NIH)
  - https://singulari)ty-tutorial.github.io/
* Singularity Containers for Bioinformatics (Pawsey Supercomputing Centre)
  - https://pawseysc.github.io/containers-bioinformatics-workshop/
* BioContainers Registry: searching for bioinformatics tools, packages and containers
  - https://dx.doi.org/10.1021/acs.jproteome.0c00904


================================================
FILE: snakemake/LICENSE
================================================
BSD 3-Clause License

Copyright (c) 2019, C. Titus Brown
All rights reserved.

Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:

* Redistributions of source code must retain the above copyright notice, this
  list of conditions and the following disclaimer.

* Redistributions in binary form must reproduce the above copyright notice,
  this list of conditions and the following disclaimer in the documentation
  and/or other materials provided with the distribution.

* Neither the name of the copyright holder nor the names of its
  contributors may be used to endorse or promote products derived from
  this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.


================================================
FILE: snakemake/README.md
================================================
# 2020-snakemake-Harvard-informatics-coffee-hour-demo

Intro to workflows for efficient automated data analysis, using snakemake.

See [the tutorial](tutorial.md) for more information!



================================================
FILE: snakemake/Snakefile
================================================
#my first Snakefile


================================================
FILE: snakemake/binder/README.md
================================================
# binder directory

Configuration files for [mybinder](https://mybinder.org).


================================================
FILE: snakemake/binder/environment.yml
================================================
channels:
  - conda-forge
  - bioconda
  - defaults
dependencies:
  - bash_kernel
  - snakemake>=5.2, <5.11
  - fastqc
  - multiqc


================================================
FILE: snakemake/data/.gitignore
================================================
*
!.gitignore


================================================
FILE: snakemake/index.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Intro to workflows for data analysis, using snakemake\n",
    "### Authors: Meghan Correa and Ming Tang\n",
    "### Bioinformatics Coffee Hour - May 26, 2020\n",
    "\n",
    "Modified from https://github.com/ctb/2019-snakemake-ucdavis\n",
    "\n",
    "No license; the below content is under CC0. (Do with it what you will, and I hope it's useful!)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction and thoughts\n",
    "\n",
    "Hi! I'm Meghan Correa, I a member of the Software Operations team at FAS informatics. I support data analysis for the Bauer core among other things.\n",
    "\n",
    "This 25min teaser meant to show you how to get started using snakemake.\n",
    "\n",
    "Note, the only way you'll really learn to do all of this is by applying it to your own research and spending time on it :)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Why use a workflow management tool?\n",
    "\n",
    "* Dependency management\n",
    "* Reentrancy - start back up where you left off\n",
    "* Reusable\n",
    "* Documented\n",
    "* Portable\n",
    "\n",
    "There are many to choose from:\n",
    "https://github.com/pditommaso/awesome-pipeline\n",
    "\n",
    "FAS Informatics choose to use snakemake for the post sequencing pipeline and other workflows mainly because it is written in python and is popular in the bioinformatics community."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tasks we are going to do\n",
    "\n",
    "Inside the `data` folder, there are 4 fastq.gz files. We will use `Snakemake` do `fastqc` on each fastq files and compile multiple html files into a single one using `multiQC`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Software we're going to use\n",
    "\n",
    "We're going to be using [conda](https://conda.io/en/latest/) and [snakemake](https://snakemake.readthedocs.io/en/stable/), a well as packages from [bioconda](https://bioconda.github.io). If you wanted to run all of this on your own computer, you'll need to follow the bioconda install instructions.\n",
    "\n",
    "We'll be implementing a short read quality check pipeline, using [fastqc](https://www.bioinformatics.babraham.ac.uk/projects/fastqc/) and [multiqc](https://multiqc.info/). No worries if you don't know what any of this means, it's not super critical to the snakemake side of things :)\n",
    "\n",
    "You can see the full set of installed software requirements for python in `environment.yml` file and installs from apt repository in `apt.txt` in the binder folder of the repository.\n",
    "\n",
    "**Note we have installed all the required tools in the binder container**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Download some data\n",
    "\n",
    "Execute the below cell to fetch some data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "curl -L https://github.com/ctb/2019-snakemake-ucdavis/raw/9db09bc0b6a3469f8a0d4996d4b2995bf36e5d27/data/0Hour_001_1.fq.gz > data/0Hour_001_1.fq.gz\n",
    "curl -L https://github.com/ctb/2019-snakemake-ucdavis/raw/9db09bc0b6a3469f8a0d4996d4b2995bf36e5d27/data/6Hour_001_1.fq.gz > data/6Hour_001_1.fq.gz\n",
    "curl -L https://github.com/ctb/2019-snakemake-ucdavis/raw/9db09bc0b6a3469f8a0d4996d4b2995bf36e5d27/data/0Hour_001_2.fq.gz > data/0Hour_001_2.fq.gz\n",
    "curl -L https://github.com/ctb/2019-snakemake-ucdavis/raw/9db09bc0b6a3469f8a0d4996d4b2995bf36e5d27/data/6Hour_001_2.fq.gz > data/6Hour_001_2.fq.gz"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Running snakemake!\n",
    "\n",
    "### Getting started - your first Snakefile\n",
    "\n",
    "Open the `Snakemake` file with a text editor and paste the below into it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rule fastqc_a_file:\n",
    "  shell:\n",
    "    \"fastqc data/0Hour_001_1.fq.gz\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(I suggest copy/pasting this into the Snakefile.)\n",
    "\n",
    "remember to click `File` --> `Save File`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "snakemake"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and you should see:\n",
    "```\n",
    "Building DAG of jobs...\n",
    "Using shell: /bin/bash\n",
    "Provided cores: 1\n",
    "Rules claiming more threads will be scaled down.\n",
    "...\n",
    "Approx 95% complete for 0Hour_001_1.fq.gz\n",
    "Analysis complete for 0Hour_001_1.fq.gz\n",
    "[Wed Feb 27 13:09:51 2019]\n",
    "Finished job 0.\n",
    "1 of 1 steps (100%) done\n",
    "Complete log: /home/jovyan/.snakemake/log/2019-02-27T130941.260352.snakemake.log\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and there will be two new files,\n",
    "\n",
    "Points to make:\n",
    "* the snakemake configuration file is by default called `Snakefile`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Updating the Snakefile to track inputs and outputs\n",
    "\n",
    "At the moment this is basically just a shell script with extra syntax... what's the point?\n",
    "\n",
    "Well, shell scripts will rerun the command every time you run the file, even if there's no reason to do so because the file hasn't changed but with snakemake, you can annotate the rule with input and output files!\n",
    "\n",
    "**Digression:** This is particularly important for large or long workflows, where you're dealing with 10s to 100s of files that may take hours to days to process! It can be hard to figure out which files to rerun, but (spoiler alert) snakemake can really help you do this!\n",
    "\n",
    "Change your snakefile to look like this:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rule fastqc_a_file:\n",
    "  input:\n",
    "    \"data/0Hour_001_1.fq.gz\"\n",
    "  output:\n",
    "    \"data/0Hour_001_1_fastqc.html\",\n",
    "    \"data/0Hour_001_1_fastqc.zip\"\n",
    "  shell:\n",
    "    \"fastqc data/0Hour_001_1.fq.gz\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "here, we've annotated the rule with the required\n",
    "**input** file, as well as the expected **output** files.\n",
    "\n",
    "Question: how do we know what the output files are?\n",
    "\n",
    "Now run:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "snakemake"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and you should see:\n",
    "```\n",
    "Building DAG of jobs...\n",
    "Nothing to be done.\n",
    "Complete log: /home/jovyan/.snakemake/log/2019-02-27T132031.813143.snakemake.log\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What happened??\n",
    "\n",
    "snakemake looked at the file, saw that the output files existed, and figured out that it didn't need to do anything!\n",
    "\n",
    "### Forcibly re-running things\n",
    "\n",
    "You can tell snakemake to run all the rules no matter what with `-F`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "snakemake -F"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also remove an output file and it will automatically re-run:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rm data/*.html\n",
    "snakemake"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "note that you don't need to remove *all* the output files to rerun a command - just remove *one* of them.\n",
    "\n",
    "You can *also* update the timestamp on an *input* file, and snakemake will figure out that the output file is older than the input file, and rerun things."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "touch data/*.fq.gz\n",
    "snakemake"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another usefule snakemake option is the --dryrun option, this will show you what snakemake would do but won't actually run any of the rules.  I highly recommend running this to check that was will be run is what you expect."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "snakemake --dryrun"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Multiple rules\n",
    "\n",
    "Let's add a rule to run fastqc on a second file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rule fastqc_a_file:\n",
    "  input:\n",
    "    \"data/0Hour_001_1.fq.gz\"\n",
    "  output:\n",
    "    \"data/0Hour_001_1_fastqc.html\",\n",
    "    \"data/0Hour_001_1_fastqc.zip\"\n",
    "  shell:\n",
    "    \"fastqc data/0Hour_001_1.fq.gz\"\n",
    "\n",
    "rule fastqc_a_file2:\n",
    "  input:\n",
    "    \"data/6Hour_001_1.fq.gz\"\n",
    "  output:\n",
    "    \"data/6Hour_001_1_fastqc.html\",\n",
    "    \"data/6Hour_001_1_fastqc.zip\"\n",
    "  shell:\n",
    "    \"fastqc data/6Hour_001_1.fq.gz\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "snakemake"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, if you run this, the Right Thing won't happen: snakemake will do nothing.  Why?\n",
    "\n",
    "Well, snakemake only runs the *first* rule in a Snakefile, by default. You can give a rule name on the command line, if you like, **or** you can tell snakemake what output file(s) you want. "
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "snakemake data/0Hour_001_1_fastqc.html data/6Hour_001_1_fastqc.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But the conventional way to do this in snakemake is to add an \"all\" rule as the first rule, let's try it. \n",
    "\n",
    "\n",
    "### A first refactoring: adding a better default rule\n",
    "\n",
    "Let's start refactoring (cleaning up) this Snakefile.\n",
    "\n",
    "First, let's add a rule at the top:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rule all:\n",
    "  input:\n",
    "    \"data/0Hour_001_1_fastqc.html\",\n",
    "    \"data/6Hour_001_1_fastqc.html\"\n",
    "\n",
    "rule fastqc_a_file:\n",
    "  input:\n",
    "    \"data/0Hour_001_1.fq.gz\"\n",
    "  output:\n",
    "    \"data/0Hour_001_1_fastqc.html\",\n",
    "    \"data/0Hour_001_1_fastqc.zip\"\n",
    "  shell:\n",
    "    \"fastqc data/0Hour_001_1.fq.gz\"\n",
    "\n",
    "rule fastqc_a_file2:\n",
    "  input:\n",
    "    \"data/6Hour_001_1.fq.gz\"\n",
    "  output:\n",
    "    \"data/6Hour_001_1_fastqc.html\",\n",
    "    \"data/6Hour_001_1_fastqc.zip\"\n",
    "  shell:\n",
    "    \"fastqc data/6Hour_001_1.fq.gz\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "snakemake"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is a blank rule that gathers together all of the various files you want produced, and says \"hey, snakemake, I depend on all of these files for my input - make them for me!\"\n",
    "\n",
    "Let's run snakemake again and now you should see the second fastqc command run, with the appropriate output files!\n",
    "\n",
    "Note that snakemake only runs the second rule, because it looks at the output files and sees that the first file you wanted, `0Hour_001_1_fastqc.html` already exists!\n",
    "\n",
    "This is a blank rule that gathers together all of the various files you want produced, and says \"hey, snakemake, I depend on all of these files for my input - make them for me!\"\n",
    "\n",
    "### A second refactoring: doing a bit of templating\n",
    "\n",
    "There's a lot of repetition in each of these rules. Let's collapse it down a little bit by replacing the filename in the fastqc command with a magic variable, `{input}`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rule all:\n",
    "  input:\n",
    "    \"data/0Hour_001_1_fastqc.html\",\n",
    "    \"data/6Hour_001_1_fastqc.html\"\n",
    "\n",
    "rule fastqc_a_file:\n",
    "  input:\n",
    "    \"data/0Hour_001_1.fq.gz\"\n",
    "  output:\n",
    "    \"data/0Hour_001_1_fastqc.html\",\n",
    "    \"data/0Hour_001_1_fastqc.zip\"\n",
    "  shell:\n",
    "    \"fastqc {input}\"\n",
    "\n",
    "rule fastqc_a_file2:\n",
    "  input:\n",
    "    \"data/6Hour_001_1.fq.gz\"\n",
    "  output:\n",
    "    \"data/6Hour_001_1_fastqc.html\",\n",
    "    \"data/6Hour_001_1_fastqc.zip\"\n",
    "  shell:\n",
    "    \"fastqc {input}\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This all works as before, but now the rule is a bit more generic and will work with any input file. Sort of.\n",
    "\n",
    "### Refactoring 3: templating output files, too\n",
    "\n",
    "The output filenames ALSO depend on the input file names in some way - specifically, fastqc replace part of the filename with `_fastqc.html` and `_fastqc.zip` to make its two output files.\n",
    "\n",
    "Let's rewrite the rule using some snakemake pattern matching:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rule all:\n",
    "  input:\n",
    "    \"data/0Hour_001_1_fastqc.html\",\n",
    "    \"data/6Hour_001_1_fastqc.html\"\n",
    "\n",
    "rule fastqc_a_file:\n",
    "  input:\n",
    "    \"{filename}.fq.gz\"\n",
    "  output:\n",
    "    \"{filename}_fastqc.html\",\n",
    "    \"{filename}_fastqc.zip\"\n",
    "  shell:\n",
    "    \"fastqc {input}\"\n",
    "\n",
    "rule fastqc_a_file2:\n",
    "  input:\n",
    "    \"{filename}.fq.gz\"\n",
    "  output:\n",
    "    \"{filename}_fastqc.html\",\n",
    "    \"{filename}_fastqc.zip\"\n",
    "  shell:\n",
    "    \"fastqc {input}\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What we've done here is tell snakemake that anytime we say we *want* a file that ends with `_fastqc.html`, it should look for a file that ends in `.fq.gz` and then run `fastqc` on it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you look at the rule above, they are now identical rules! \n",
    "\n",
    "Let's remove one, to get a trimmer, leaner, and above all *functional* snakefile:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rule all:\n",
    "  input:\n",
    "    \"data/0Hour_001_1_fastqc.html\",\n",
    "    \"data/6Hour_001_1_fastqc.html\"\n",
    "\n",
    "rule fastqc_a_file:\n",
    "  input:\n",
    "    \"{filename}.fq.gz\"\n",
    "  output:\n",
    "    \"{filename}_fastqc.html\",\n",
    "    \"{filename}_fastqc.zip\"\n",
    "  shell:\n",
    "    \"fastqc {input}\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the variable name in input and output does not have to be \"filename\", it can be anything as long as you're consistant.  So let's run snakemake again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rm data/*.html\n",
    "snakemake"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Building out the workflow\n",
    "\n",
    "So, we've gotten fastqc sorted out. What's next?\n",
    "\n",
    "Let's add in a new rule - multiqc, to summarize our fastqc results.\n",
    "\n",
    "multiqc takes a directory name under which there are one or more fastqc reports, and then summarizes them.\n",
    "\n",
    "Running it on the command line,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "multiqc data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "you can see that it creates two outputs, `multiqc_report.html` and the directory `multiqc_data/` which contains a bunch of files. Let's create a snakemake rule for this; add:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rule all:\n",
    "  input:\n",
    "    \"data/0Hour_001_1_fastqc.html\",\n",
    "    \"data/6Hour_001_1_fastqc.html\"\n",
    "\n",
    "rule fastqc_a_file:\n",
    "  input:\n",
    "    \"{filename}.fq.gz\"\n",
    "  output:\n",
    "    \"{filename}_fastqc.html\",\n",
    "    \"{filename}_fastqc.zip\"\n",
    "  shell:\n",
    "    \"fastqc {input}\"\n",
    "\n",
    "rule run_multiqc:\n",
    "  input:\n",
    "    \"data/0Hour_001_1_fastqc.html\",\n",
    "    \"data/6Hour_001_1_fastqc.html\",\n",
    "  output:\n",
    "    \"multiqc_report.html\",\n",
    "    directory(\"multiqc_data\")\n",
    "  shell:\n",
    "    \"multiqc data/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "snakemake"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This ...doesn't really do what we want, for a few reasons.\n",
    "\n",
    "First of all, the output of run_multiqc is not specified in the all rule so snakemake doesn't look for a rule to create this. \n",
    "\n",
    "Second of all, `multiqc_report.html` already exists, so snakemake won't run the rule. \n",
    "\n",
    "Let's fix the first two things first:\n",
    "\n",
    "* add `multiqc_report.html` to the inputs for the first all.\n",
    "* then remove `multiqc_report.html` and re-run snakemake.\n",
    "\n",
    "Your snakefile should look like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rule all:\n",
    "  input:\n",
    "    \"data/0Hour_001_1_fastqc.html\",\n",
    "    \"data/6Hour_001_1_fastqc.html\",\n",
    "    \"multiqc_report.html\"\n",
    "\n",
    "rule fastqc_a_file:\n",
    "  input:\n",
    "    \"{filename}.fq.gz\"\n",
    "  output:\n",
    "    \"{filename}_fastqc.html\",\n",
    "    \"{filename}_fastqc.zip\"\n",
    "  shell:\n",
    "    \"fastqc {input}\"\n",
    "\n",
    "rule run_multiqc:\n",
    "  input:\n",
    "    \"data/0Hour_001_1_fastqc.html\",\n",
    "    \"data/6Hour_001_1_fastqc.html\",\n",
    "  output:\n",
    "    \"multiqc_report.html\",\n",
    "    directory(\"multiqc_data\")\n",
    "  shell:\n",
    "    \"multiqc data/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rm multiqc_report.html\n",
    "snakemake"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Yay, that seems to work!\n",
    "\n",
    "Points to make:\n",
    "\n",
    "* other than the first rule, rules can be in any order\n",
    "* the rule name doesn't really matter, it's mostly for debugging. It just needs to be \"boring\" (text, underscores, etc. only)\n",
    "\n",
    "\n",
    "But providing input files explicitly to the multiqc rule is not great because those are the same files we have in the all rule and to add new files we'd now have to add them to two places. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can fix the first issue by using **variables**. \n",
    "\n",
    "To use variables, let's make a Python list at the very top, containing all of our expected output files from fastqc:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fastqc_output = [\"data/0Hour_001_1_fastqc.html\", \"data/6Hour_001_1_fastqc.html\",\n",
    "  \"data/0Hour_001_2_fastqc.html\", \"data/6Hour_001_2_fastqc.html\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and modify the `all` and `multiqc` rules to contain this list. The final snakefile looks like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fastqc_output = [\"data/0Hour_001_1_fastqc.html\", \"data/6Hour_001_1_fastqc.html\",\n",
    "  \"data/0Hour_001_2_fastqc.html\", \"data/6Hour_001_2_fastqc.html\"]\n",
    "\n",
    "rule all:\n",
    "  input:\n",
    "    fastqc_output,\n",
    "    \"multiqc_report.html\"\n",
    "\n",
    "rule fastqc_a_file:\n",
    "  input:\n",
    "    \"{filename}.fq.gz\"\n",
    "  output:\n",
    "    \"{filename}_fastqc.html\",\n",
    "    \"{filename}_fastqc.zip\"\n",
    "  shell:\n",
    "    \"fastqc {input}\"\n",
    "\n",
    "rule run_multiqc:\n",
    "  input:\n",
    "    fastqc_output\n",
    "  output:\n",
    "    \"multiqc_report.html\",\n",
    "    directory(\"multiqc_data\")\n",
    "  shell:\n",
    "    \"multiqc data/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "snakemake"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Refactoring this to make it slightly more concise --\n",
    "\n",
    "We've got one more redundancy in this file - the `fastqc_output` is listed in the `all` rule, but you don't need it there! Why?\n",
    "\n",
    "Well, `multiqc_report.html` is already in the all rule, and the multiqc rule depends on `fastqc_output`, so `fastqc_output` already needs to be created to satisfy the all rule, so... specifying it in the all rule is redundant! And you can remove it!\n",
    "\n",
    "(It's not a big deal and I usually leave it in. But I wanted to talk about dependencies!)\n",
    "\n",
    "The Snakefile now looks like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fastqc_output = [\"data/0Hour_001_1_fastqc.html\", \"data/6Hour_001_1_fastqc.html\",\n",
    "  \"data/0Hour_001_2_fastqc.html\", \"data/6Hour_001_2_fastqc.html\"]\n",
    "\n",
    "rule all:\n",
    "  input:\n",
    "    \"multiqc_report.html\"\n",
    "\n",
    "rule fastqc_a_file:\n",
    "  input:\n",
    "    \"{filename}.fq.gz\"\n",
    "  output:\n",
    "    \"{filename}_fastqc.html\",\n",
    "    \"{filename}_fastqc.zip\"\n",
    "  shell:\n",
    "    \"fastqc {input}\"\n",
    "\n",
    "rule run_multiqc:\n",
    "  input:\n",
    "    fastqc_output\n",
    "  output:\n",
    "    \"multiqc_report.html\",\n",
    "    directory(\"multiqc_data\")\n",
    "  shell:\n",
    "    \"multiqc data/\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and we can rerun it from scratch by doing:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rm data/*.html multiqc_report.html\n",
    "snakemake"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Recap\n",
    "\n",
    "So, we've put all this work into making this snakefile with its input rules and its output rules... and there are a lot of advantages to our current approach already! Let's list a few of them --\n",
    "\n",
    "* we've completely automated our analysis!\n",
    "* we can easily add new data files into fastqc and multiqc!\n",
    "* we can rerun things easily, and (even better) by default only things that *need* to be run will be run.\n",
    "* the snakefile is actually pretty reusable - we could drop this into a new project, and, with little effort, run all of these things on new data!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## More advanced snakemake"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Running things in parallel\n",
    "\n",
    "You can use `snakemake --cores 4` to run four jobs in parallel. If you are running on a cluster then --cores becomes the number of jobs that can be submitted at a time.  \n",
    "\n",
    "### Specifying software required for a rule\n",
    "\n",
    "You can specify software on a per-rule basis! This is really helpful when you have incompatible software requirements for different rules, or want to run on a cluster, or just want to pin your snakemake workflow to a specific version.\n",
    "\n",
    "For example, if you create a file `env_fastqc.yml` with the following content,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "channels:\n",
    "  - bioconda\n",
    "  - defaults\n",
    "  - conda-forge\n",
    "dependencies:\n",
    "  - fastqc==0.11.8"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and then change the fastqc rule to look like this:"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "rule fastqc_a_file:\n",
    "  input:\n",
    "    \"{filename}.fq.gz\"\n",
    "  output:\n",
    "    \"{filename}_fastqc.html\",\n",
    "    \"{filename}_fastqc.zip\"\n",
    "  conda:\n",
    "    \"env_fastqc.yml\"\n",
    "  shell:\n",
    "    \"fastqc {input}\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "you can now run snakemake like so,"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "snakemake --use-conda"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and for that rule, snakemake will install just that software, with the specified version.\n",
    "\n",
    "This aids in reproducibility, in addition to the practical advantages of isolating software installs from each other.\n",
    "\n",
    "(You can also do this with docker and singularity containers, too!)\n",
    "\n",
    "### Running on a cluster\n",
    "\n",
    "You can specify a cluster submit command:\n",
    "\n",
    "snakemake --cluster sbatch -j 32\n",
    "\n",
    "You can also specify per rule cluster config in a file (example below):\n",
    "\n",
    "snakemake --cluster sbatch --cluster-config cluster-config.json -j 32 \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "{\n",
    "   \"default\":{\n",
    "      \"time\":\"12:00:00\",\n",
    "      \"nodes\":1,\n",
    "      \"cores\":24,\n",
    "      \"mem\":100,\n",
    "      \"partition\":\"shared\",\n",
    "      \"job-name\":\"{rule}\",\n",
    "      \"output\":\"log/{rule}-%j.out\",\n",
    "      \"error\":\"log/{rule}-%j.err\"\n",
    "   },\n",
    "   \"fastqc\":{\n",
    "      \"time\":\"4-12:00:00\",\n",
    "      \"mem\":32000\n",
    "   },\n",
    "   \"multiqc\":{\n",
    "      \"mem\":64000\n",
    "   }\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or you can use snakemake profiles and bundle cluster config files in a directory:\n",
    "\n",
    "snakemake --profile slurm -j 32\n",
    "\n",
    "https://snakemake.readthedocs.io/en/v5.18.1/executing/cli.html#profiles"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adding in some Python...\n",
    "\n",
    "You can add in some Python to load in the input files, like so:"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "import glob, sys\n",
    "fastqc_input = glob.glob('data/?Hour_00?_?.fq.gz')\n",
    "\n",
    "fastqc_output = []\n",
    "for filename in fastqc_input:\n",
    "  new_filename = filename.split('.')[0] + '_fastqc.html'\n",
    "  fastqc_output.append(new_filename)\n",
    "\n",
    "rule all:\n",
    "  input:\n",
    "    \"multiqc_report.html\"\n",
    "\n",
    "rule clean:\n",
    "  shell:\n",
    "    \"rm -f {fastqc_output} multiqc_report.html\"\n",
    "\n",
    "rule fastqc_a_file:\n",
    "  input:\n",
    "    \"{arglebarf}.fq.gz\"\n",
    "  output:\n",
    "    \"{arglebarf}_fastqc.html\",\n",
    "    \"{arglebarf}_fastqc.zip\"\n",
    "  conda:\n",
    "    \"env_fastqc.yml\"\n",
    "  shell:\n",
    "    \"fastqc {input}\"\n",
    "\n",
    "\n",
    "rule run_multiqc:\n",
    "  input:\n",
    "    fastqc_output\n",
    "  output:\n",
    "    \"multiqc_report.html\",\n",
    "    directory(\"multiqc_data\")\n",
    "  shell:\n",
    "    \"multiqc data/\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Go forth and workflow!"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Bash",
   "language": "bash",
   "name": "bash"
  },
  "language_info": {
   "codemirror_mode": "shell",
   "file_extension": ".sh",
   "mimetype": "text/x-sh",
   "name": "bash"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}


================================================
FILE: taste-of-conda/binder/environment.yml
================================================
dependencies:
  - bash_kernel


================================================
FILE: taste-of-conda/index.ipynb
================================================
{"metadata":{"kernelspec":{"display_name":"Bash","language":"bash","name":"bash"},"language_info":{"codemirror_mode":"shell","file_extension":".sh","mimetype":"text/x-sh","name":"bash"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# A Taste of Conda\n## Bioinformatics Coffee Hour - April 28, 2020\n#### Your Host: Nathan Weeks","metadata":{"slideshow":{"slide_type":"slide"}}},{"cell_type":"markdown","source":"## Problems\n#### How do I install scientific software on _INSERT LINUX SERVER OR CLUSTER NAME HERE_ as an unprivileged user?\n##### (and how do I get the same software on my workstation?)\n#### How do I create a reproducible software environment for data analysis?","metadata":{"slideshow":{"slide_type":"slide"}}},{"cell_type":"markdown","source":"### Possible solutions\n* [Environment modules](https://docs.rc.fas.harvard.edu/kb/modules-intro/)\n  - (+) Maintained by FAS RC staff\n  - (+) Easy to use\n  - (-) New / updated software requests: submit ticket (wait...)\n  - (-) Not reproducible outside of Cannon (mostly)\n* language-specific (e.g., [pip](https://pip.pypa.io/) (Python))\n* [Singularity containers](https://docs.rc.fas.harvard.edu/kb/singularity-on-the-cluster/) (future topic...)","metadata":{"slideshow":{"slide_type":"slide"}}},{"cell_type":"markdown","source":"### Conda is...\n> Package, dependency and environment management for any language--Python, R, Ruby, Lua, Scala, Java, JavaScript, C/ C++, FORTRAN, and more. (source: https://docs.conda.io)","metadata":{"slideshow":{"slide_type":"slide"}}},{"cell_type":"markdown","source":"### Where can I get conda... for my workstation?\n* [Anaconda Python](https://www.anaconda.com/distribution/)\n  - 100s of bundled scientific packages\n  - Anaconda Navigator (GUI for launching apps & installing packages)\n![Anaconda Navigator](https://docs.anaconda.com/_images/nav-defaults.png)","metadata":{"slideshow":{"slide_type":"slide"}}},{"cell_type":"markdown","source":"* [Miniconda](https://docs.conda.io/en/latest/miniconda.html)\n  - Minimal python+conda environment\n  - Can be installed in your home directory on a Linux cluster (not necessary on Cannon...)","metadata":{"slideshow":{"slide_type":"fragment"}}},{"cell_type":"markdown","source":"#### Cannon\nFind latest version with `module-query Anaconda3`, or searching the [FAS RC Application Portal](https://portal.rc.fas.harvard.edu/p3/build-reports/Anaconda3)\n```\n$ module load Anaconda3/2019.10\n```\n\n*For this lesson, we'll use the conda bundled with this Jupyter Lab Binder image*","metadata":{"slideshow":{"slide_type":"subslide"}}},{"cell_type":"markdown","source":"## Channel\n* Package set maintained by an organization\n* Main ones a computational biologist will use:\n","metadata":{"slideshow":{"slide_type":"slide"}}},{"cell_type":"markdown","source":"#### 1. [defaults](https://anaconda.org/anaconda/repo)\n  - Maintained by Anaconda, Inc.","metadata":{"slideshow":{"slide_type":"fragment"}}},{"cell_type":"markdown","source":"#### 2. [conda-forge](https://conda-forge.org/)\n  - Large community-led package collection\n  - *Well-curated, up-to-date*","metadata":{"slideshow":{"slide_type":"fragment"}}},{"cell_type":"markdown","source":"#### 3. [bioconda](https://bioconda.github.io)\n  - Specializes in bioinformatics software","metadata":{"slideshow":{"slide_type":"fragment"}}},{"cell_type":"markdown","source":"## Where to find Conda packages\n- [anaconda.org](https://anaconda.org/)\n- [Bioconda recipe index](https://bioconda.github.io/conda-recipe_index.html)","metadata":{"slideshow":{"slide_type":"slide"}}},{"cell_type":"markdown","source":"## An interactive walkthrough...","metadata":{"slideshow":{"slide_type":"slide"}}},{"cell_type":"markdown","source":"---","metadata":{"slideshow":{"slide_type":"slide"}}},{"cell_type":"markdown","source":"#### Getting help\nInvoke `conda` with the `-h` or `--help` option to display a list of subcommands.\n- Displays usage for subcommands (e.g., `conda list -h`)","metadata":{}},{"cell_type":"code","source":"conda --help","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Setting conda channels for bioinformatics\n\nbioconda packages may have dependencies on packages in the *conda-forge* and *defaults* channels.\nWe can specify channels to search as command-line arguments for conda operations, e.g.:\n\n`conda search -c conda-forge -c bioconda bwa`\n\nHowever, it is convenient to configure a default list of channels.\nPer [the bioconda documentation](https://bioconda.github.io/user/install.html#set-up-channels):","metadata":{}},{"cell_type":"code","source":"conda config --add channels defaults\nconda config --add channels bioconda\nconda config --add channels conda-forge","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"---\n*Pro tip:* Set `channel_priority` to `strict` to [speed up conda package searches](https://docs.conda.io/projects/conda/en/latest/user-guide/concepts/conda-performance.html#set-strict-channel-priority)","metadata":{}},{"cell_type":"code","source":"conda config --set channel_priority strict","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"*Note:* strict channel priority will be the [default in conda 5.0](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-channels.html#strict-channel-priority)","metadata":{}},{"cell_type":"markdown","source":"We can verify the list of channels:","metadata":{}},{"cell_type":"code","source":"conda info","metadata":{"scrolled":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#### Searching for packages with conda","metadata":{}},{"cell_type":"markdown","source":"Use `conda search` to search for packages by name.\n\nE.g., to search for the package `bwa` (exact match)","metadata":{}},{"cell_type":"code","source":"conda search bwa","metadata":{"scrolled":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"The `*` character can be used as a wildcard.\n\nE.g., to search for all packages *beginning* with the string \"bwa\":","metadata":{}},{"cell_type":"code","source":"conda search 'bwa*'","metadata":{"scrolled":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Environments","metadata":{}},{"cell_type":"markdown","source":"Conda packages can be installed into separate *environments* (directories containing separate sets of conda packages).\n\nconda is installed into a \"base\" environment.","metadata":{}},{"cell_type":"code","source":"conda env list","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#### Listing packages installed in an environment\nWe can see what packages are already installed in our current environment using `conda list`","metadata":{}},{"cell_type":"code","source":"conda list","metadata":{"scrolled":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"*Never install packages into the base environment*. Always create a new environment.\n- base environment is read-only on Cannon","metadata":{}},{"cell_type":"markdown","source":"#### Creating a new environment","metadata":{}},{"cell_type":"code","source":"conda create -h","metadata":{"scrolled":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"The `-n/--name ENVIRONMENT` option creates a \"named\" conda environment in your \"envs\" directory (by default `${HOME}/.conda/envs`).\n\nLet's create an environment called *bwa*:","metadata":{}},{"cell_type":"code","source":"conda create -y -n bwa","metadata":{"scrolled":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Verify the new environment was created:","metadata":{}},{"cell_type":"code","source":"conda env list","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"---","metadata":{}},{"cell_type":"markdown","source":"*Pro tip (Jupyter notebooks & conda via environment module)*: activate the _base_ environment before issuing any subsequent `conda activate` / `conda deactivate` commands ([gory details...](https://github.com/conda/conda/issues/7980))","metadata":{}},{"cell_type":"code","source":"source activate","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"*Note: currently slightly-incompatible with Jupyter notebooks; will cause subsequent commands to display error exit status `: 1`*","metadata":{}},{"cell_type":"markdown","source":"---","metadata":{}},{"cell_type":"markdown","source":"Before we activate the new *bwa* environment, let's check our PATH environment variable (list of directories your shell searches for commands)","metadata":{}},{"cell_type":"code","source":"echo ${PATH}","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Activate the new \"bwa\" environment.\nSubsequent \nNotice that the `/srv/conda/envs/bwa/bin` directory was prepended to your PATH.\nIn addition, in an interactive shell, the shell prompt would be prefixed with `(bwa)`.","metadata":{}},{"cell_type":"code","source":"conda activate bwa\necho ${PATH}","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"conda install -y bwa","metadata":{"scrolled":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"In addition to named environments, we can also create an environment in an arbitrary directory using the `-p PATH` option. This can be useful for installing software in a shared directory that is accessible by your lab/group.\n\nSuppose we want to install samtools at `/srv/shiny-server/sample-apps/samtools` (*Note: this directory is just for illustration*).\nFurthermore, suppose we need an old version of samtools (0.1.19).\nWe'll select the version using the `=` operator.\n\n","metadata":{}},{"cell_type":"code","source":"conda create -y -p /srv/shiny-server/sample-apps/samtools samtools=0.1.19 ","metadata":{"scrolled":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Note the directory structure in `/srv/shiny-server/sample-apps/samtools`:","metadata":{}},{"cell_type":"code","source":"ls /srv/shiny-server/sample-apps/samtools","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"`conda list -p` treats that directory as a conda environment, and lists installed packages:","metadata":{}},{"cell_type":"code","source":"conda list -p /srv/shiny-server/sample-apps/samtools","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Nested (aka \"stacked\") environments","metadata":{}},{"cell_type":"markdown","source":"Normally, when a conda environment is activated, it replaces the previous environment.\nWe can use the `--stack` option to instead nest the environment so that we have access to packages in both.","metadata":{}},{"cell_type":"code","source":"conda activate --stack /srv/shiny-server/sample-apps/samtools","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"`conda env list` shows only the most recently-activated directory (on the top of the environment \"stack\"):","metadata":{}},{"cell_type":"code","source":"conda env list","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"But both `samtools` and `bwa` are in our PATH:","metadata":{}},{"cell_type":"code","source":"type samtools; type bwa","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"To deactivate the current environment, use `conda deactivate`.\n\nAfter this is executed, we're back to the *bwa* environment (only).\nIf executed a second time, we would be back to the *base* environment.","metadata":{}},{"cell_type":"code","source":"conda deactivate\nconda env list","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Sharing environments","metadata":{}},{"cell_type":"markdown","source":"OK, we used *bwa* to cure COVID-19. Way to go!\n\nNow for the important part (in academia): publish our work.\n\nWe'll use `conda env export` to export our current conda environment (*bwa*) to a [YAML](https://en.wikipedia.org/wiki/YAML) file that will record and can be used to recreate our (conda) environment:","metadata":{}},{"cell_type":"code","source":"conda env export","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Redirect this output to a file to save it (let's call the file `environment.yaml`)","metadata":{}},{"cell_type":"code","source":"conda env export > environment.yml\ncat environment.yml","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"To show that this suffices to recreate the *bwa* environment, we'll delete our current bwa environment... ","metadata":{}},{"cell_type":"code","source":"conda deactivate\nconda env remove -n bwa\nconda env list","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"...and recreate the environment using `conda env create`:","metadata":{}},{"cell_type":"code","source":"conda env create -f environment.yml\nconda env list","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"And there's our *bwa* environment:","metadata":{}},{"cell_type":"code","source":"conda list -n bwa","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Your turn!\nSearch for a package at [bioconda.io]([Bioconda recipe index](https://bioconda.github.io/conda-recipe_index.html)), [Anaconda.org](https://anaconda.org), or using `conda search`, and try to install it into a new environment:","metadata":{}},{"cell_type":"code","source":"conda create -y -n myenv PACKAGE1 [PACKAGE2...]","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"conda activate myenv","metadata":{},"execution_count":null,"outputs":[]}]}


================================================
FILE: tidyverse/part1/binder/Dockerfile
================================================
FROM rocker/binder:3.6.2

ARG NB_USER
ARG NB_UID

USER root

COPY ./tidyverse/part1/ ${HOME}

RUN chown -R ${NB_USER} ${HOME}

USER ${NB_USER}


================================================
FILE: tidyverse/part1/index.Rmd
================================================
---
title: "Tidyverse Tibbles and Bits"
subtitle: "Bioinformatics Coffee Hour"
date: "Mar 2, 2021"
author: "Danielle Khost; Brian Arnold"
output: html_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

# Introduction

One of the reasons R is so useful is because many people have built extensions for R in the form of R packages that you can install. These packages may do broad statistical analyses or analyze specific data types (e.g. comparative phylogenetics).

This lesson will introduce some data manipulation tools within the [tidyverse](https://www.tidyverse.org), which contains several R packages. These packages include:
* tidyr to convert long vs. wide form objects (not covered here)
* dplyr to transform and filter data objects, as well as to summarize their contents

For an overview of how to manipulate data with tidyverse, this [cheat sheet](https://rstudio.com/wp-content/uploads/2015/02/data-wrangling-cheatsheet.pdf) is particularly helpful.

To install new packages you have never used before in R, use the command `install.packages()`:

```{r}
# NOTE that tidyverse is already installed, this is just to illustrate installation
# install.packages("tidyverse")
```

For packages that are already installed, we need to load them every session (i.e. if you restart R). It's good practice to load all packages at the top of your R script. More generally, the [tidyverse style guide](http://style.tidyverse.org) offers some good advice on coding style to help make your code easier to read and write.

```{r}
library(tidyverse)
```

# Data tables: wide format vs long format

When using tidyverse, you will usually want your data to be "tidy". Tidy data is data where:

1. Every column is variable.
2. Every row is an observation.
3. Every cell is a single value.

This is also referred to as **"long format"** (in contrast to "wide format"). Most of the functions in tidyverse assume that your data is in long format; if it isn't, you will want to pre-process it before doing further analysis!

Let's load in some data to use:

```{r}
housing<-read.csv("https://raw.githubusercontent.com/datasets/house-prices-us/master/data/cities-month.csv", stringsAsFactors=F, strip.white = T)
housing=housing[c(1:(length(housing)-3),length(housing))]

View(housing)
```

### Tibbles

The tidyverse uses its own version of the data frame (from base R) that is similar but has several properties that make it superior. This object is a **tibble**. Let's make a data frame called 'df1' and change it into a tibble to see what it looks like.

```{r}
df1<-data.frame(label=c("rep1", "rep2", "rep3", "rep4"), data=c(23, 34, 15, 19))

tbdf1 <- as_tibble(df1)
tbdf1

class(tbdf1)
```

You see here that just printing the tibble to screen displays the data types in each of the columns and the dimensions. Although not apparent with this small dataset, another very handy feature of tibbles is that by default they will only print out the first 10 rows and as many columns as fit in your window. Many packages will accept tibbles instead of data frames with no problems, but if you come across an older package that requires a data frame, it is easy to revert with the `as.data.frame()` function.

#### Side Note: %>%

One important piece of syntax is the %>% operator, which acts like a Unix pipe in R. This means you can pass the output of one command to another in linear fashion, as opposed to having to use either nested operations or temporary objects. This makes your code much easier to read!

---

If we look at the dataset we just downloaded, we can see that it is not in proper long, tidy format.

```{r, echo = TRUE}
View(housing)
```

Let's polish it using some tidyr functions so that it is easier to work with.

## Going from wide to long: pivot_longer()
The pivot_longer() function takes a tibble (or data frame) and lengthens it, i.e. increases the number of rows and decreases number of columns:

```{r, echo = TRUE}
housing_clean <- housing %>% as_tibble %>%
  pivot_longer(cols=c(-Date, -National.US), names_to="location", values_to="local_index")

View(housing_clean)
```

The **cols** argument describes what columns need to be reshaped, i.e. which need to be converted into columns. For us, we want to keep the US national average and the date as columns. We give it a list using the **c** ombine function, and by putting an **-** in front of the column names, we tell it to reshape every column *except* Date and National.US

The **names_to** argument gives a name for the new variable (i.e. column) that stores the data that formerly was in the column names. In our case, we make a variable called "location" that holds all the city names.

The **values_to** argument gives a name for the new variable (i.e. column) that stores the data that was in the cell values, which for us was the local index values in each city.


## Turning a column into multiple columns: separate()
The separate() function separates a single character column into several, splitting on a given regular expression. In the following command we run the function twice, separating two different columns:

```{r, echo = TRUE}
housing_clean2 <- housing_clean %>%
  separate(Date, into=c("year", "month"), extra="drop", remove=F) %>%
  separate(location, into=c("state", "city"),extra="merge")

View(housing_clean2)
```

The first argument is the name of the column in the tibble that we are separating ("location" for the first command, "Date" for the second).

The **into** argument gives the names (as a character vector) of the columns that we are separating into, i.e. what the names of the new columns will be. Note that the number of elements in the vector determine how many columns the target column will be split into (in this case, two).

The **extra** argument tells the function what to do with the "pieces" that are leftover after splitting. "Merge" tells the function to add the leftover pieces back to the final column; "drop" discards them.

To better understand this, we should look at how separate() splits a character column. You might be wondering how separate() "knows" to split our columns on a period ("."). This is because by default, separate() splits on every non-alphanumeric character (e.g. space, period, dash, etc.) given a character vector. This is controlled by the **sep** argument, which we did not included in our code so separate() just uses the default (remember to get the whole list of arguments for a function, you can type ?functionname, e.g. ?separate).

Finally, the **remove** argument determines whether the original column is retained or not (defaults to TRUE). So for our data, the original "location" column is discarded and we keep only the split columns, while the original "Date" column is retained along with its children.

Now let's put it all together. We could do things one function at a time, as above, but it is much cleaner and easier to read if we create our final, cleaned dataset in a single pipeline:

```{r, echo = TRUE}
housing_clean <- housing %>% as_tibble %>%
  pivot_longer(cols=c(-Date, -National.US), names_to="location", values_to="local_index") %>%
  separate(Date, c("year", "month"), extra="drop", remove=F) %>%
  separate(location, c("state", "city"),extra="merge")

View(housing_clean)
```

We can observe the differences between housing and housing_clean to see what's being done here, and a more in-depth description of what these functions do can be found in the cheat sheet.

## Subsetting and Manipulating Data with dplyr

Dplyr is a package included in tidyverse, and is useful for manipulating our data. Each action gets its own (verb) function -- so for example filtering data by rows is done with the filter() function. All of these functions have a very similar syntax to tidyr functions.

arrange(), rename(), select(), mutate() are used to sort data, rename columns, and update/create columns

We'll use the housing dataset to look at how these functions work.

arrange() sorts by one or more columns, with subsequent columns used to break ties in earlier columns. E.g.,
```{r}
housing_clean %>% arrange(year, month)
housing_clean %>% arrange(city, year)
housing_clean %>% arrange(month,state)
housing_clean %>% arrange(desc(year))
```

rename() renames a column:

```{r}
housing_clean %>% arrange(year, month, state, city) %>%
  rename(national_index = National.US)
```

select() selects columns to keep. Note that we can simultaneously rename and reorder columns:

```{r}
housing_clean %>% arrange(year, month, state, city) %>%
  select(year, month, city, state, local_index, national_index = National.US)
```

distinct() can be used to identify all unique values in your call set:

```{r}
housing_clean %>%
  select(state,city) %>%
  distinct
```

mutate() is an especially useful function that can do a variety of things, usually in combination with other functions. It can create new columns or update existing ones, while keeping everthing else unchanged. For instance, say we want to change our "month" column from the integer value to the three letter abbreviation:

```{r}
housing_clean %>% arrange(year, month, state, city) %>%
  select(year, month, city, state, local_index, national_index = National.US) %>%
  mutate(month = month.abb[as.integer(month)])
```

R has several built-in constants, one of which is this "month.abb" function, which has the three letter abbreviations of the months in the Gregorian calendar. What this mutate() call is doing is passing the values of the month column as integers to the month.abb function, which then automatically converts them to their appropriate abbreviations.

Let's look at some other things mutate() can do. We can include several mutate calls at once:

```{r}
housing_clean %>% arrange(year, month, state, city) %>%
  select(year, month, city, state, local_index, national_index = National.US) %>%
  mutate(month = month.abb[as.integer(month)], city=sub(".", "_", city, fixed=TRUE), rel_index=local_index/national_index)
```

This command uses mutate() three times:
1) The first changes the months, as above

2) In the second mutate() call, we are pairing mutate() with the sub() function, which substitutes characters in a string; we are telling substitute to replace all "." characters with a "_" character for the "city" character vector (i.e. our city column)

3) In the third mutate() call, we are using mutate to create an entirely new column called "rel_index", whose value is local_index / national_index


Now that we've gone through all the dplyr functions, let's update our nice clean housing dataset.

```{r}
housing_clean <- housing_clean %>% arrange(year, month, state, city) %>%
  select(year, month, city, state, local_index, national_index = National.US) %>%
  mutate(month = month.abb[as.integer(month)], city=sub(".", "_", city, fixed=TRUE), rel_index=local_index/national_index)
```


## Filtering functions

Sometimes we need to filter datasets, and we can do this by selecting rows that meet logical conditions. We set these up with logical tests.

The filter() function can be used to select rows meeting these conditions:

```{r}
housing_clean %>%
  filter(city == "Boston")
```

We can give filter many logical tests, all separated by commas. These are linked by logical and (all must be true to select that row):

```{r}
housing_clean %>%
  filter(city=="Boston", month=="Aug")
```

If we want to use other booleans, just put them in one statement, e.g.:

```{r}
housing_clean %>%
  filter(city=="Boston", month == "Aug" | month == "Jan")
```

Note that we can combine filters and the other statements to get useful subsets, for example let's say we want to see three years when the relative index in Boston was the highest (as of Jan):

```{r}
housing_clean %>%
  filter(city=="Boston", month=="Jan") %>%
  arrange(desc(rel_index)) %>%
  select(year,rel_index) %>%
  head(n=3)
```

Finally, many times you want to filter out missing data (typically indicated by `NA` in R), prior to conducting any analyses. To do this, we an use the `is.na()` fuction, which will return a vector with TRUE if NA is present, and FALSE otherwise. For example, let's identify all rows that are missing the local_index in housing_clean.

```{r}
housing_clean %>%
  filter(is.na(local_index))
```

To get the inverse for this (or any logical statement), simply add a `!` to the front.

```{r}
housing_clean %>%
  filter(!is.na(local_index))
```


================================================
FILE: tidyverse/part1_winter2021/binder/Dockerfile
================================================
FROM rocker/binder:3.6.2

ARG NB_USER
ARG NB_UID

USER root

COPY ./tidyverse/part1/ ${HOME}

RUN chown -R ${NB_USER} ${HOME}

USER ${NB_USER}


================================================
FILE: tidyverse/part1_winter2021/index.Rmd
================================================
---
title: "Tidyverse Tibbles and Bits"
subtitle: "Bioinformatics Coffee Hour"
date: "Mar 2, 2021"
author: "Danielle Khost; Brian Arnold"
output: html_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

# Introduction

One of the reasons R is so useful is because many people have built extensions for R in the form of R packages that you can install. These packages may do broad statistical analyses or analyze specific data types (e.g. comparative phylogenetics).

This lesson will introduce some data manipulation tools within the [tidyverse](https://www.tidyverse.org), which contains several R packages. These packages include:
* tidyr to convert long vs. wide form objects (not covered here)
* dplyr to transform and filter data objects, as well as to summarize their contents

For an overview of how to manipulate data with tidyverse, this [cheat sheet](https://rstudio.com/wp-content/uploads/2015/02/data-wrangling-cheatsheet.pdf) is particularly helpful.

To install new packages you have never used before in R, use the command `install.packages()`:

```{r}
# NOTE that tidyverse is already installed, this is just to illustrate installation
# install.packages("tidyverse")
```

For packages that are already installed, we need to load them every session (i.e. if you restart R). It's good practice to load all packages at the top of your R script. More generally, the [tidyverse style guide](http://style.tidyverse.org) offers some good advice on coding style to help make your code easier to read and write.

```{r}
library(tidyverse)
```

# Data tables: wide format vs long format

When using tidyverse, you will usually want your data to be "tidy". Tidy data is data where:

1. Every column is variable.
2. Every row is an observation.
3. Every cell is a single value.

This is also referred to as **"long format"** (in contrast to "wide format"). Most of the functions in tidyverse assume that your data is in long format; if it isn't, you will want to pre-process it before doing further analysis!

Let's load in some data to use:

```{r}
housing<-read.csv("https://raw.githubusercontent.com/datasets/house-prices-us/master/data/cities-month.csv", stringsAsFactors=F, strip.white = T)
housing=housing[c(1:(length(housing)-3),length(housing))]

View(housing)
```

### Tibbles

The tidyverse uses its own version of the data frame (from base R) that is similar but has several properties that make it superior. This object is a **tibble**. Let's make a data frame called 'df1' and change it into a tibble to see what it looks like.

```{r}
df1<-data.frame(label=c("rep1", "rep2", "rep3", "rep4"), data=c(23, 34, 15, 19))

tbdf1 <- as_tibble(df1)
tbdf1

class(tbdf1)
```

You see here that just printing the tibble to screen displays the data types in each of the columns and the dimensions. Although not apparent with this small dataset, another very handy feature of tibbles is that by default they will only print out the first 10 rows and as many columns as fit in your window. Many packages will accept tibbles instead of data frames with no problems, but if you come across an older package that requires a data frame, it is easy to revert with the `as.data.frame()` function.

#### Side Note: %>%

One important piece of syntax is the %>% operator, which acts like a Unix pipe in R. This means you can pass the output of one command to another in linear fashion, as opposed to having to use either nested operations or temporary objects. This makes your code much easier to read!

---

If we look at the dataset we just downloaded, we can see that it is not in proper long, tidy format.

```{r, echo = TRUE}
View(housing)
```

Let's polish it using some tidyr functions so that it is easier to work with.

## Going from wide to long: pivot_longer()
The pivot_longer() function takes a tibble (or data frame) and lengthens it, i.e. increases the number of rows and decreases number of columns:

```{r, echo = TRUE}
housing_clean <- housing %>% as_tibble %>%
  pivot_longer(cols=c(-Date, -National.US), names_to="location", values_to="local_index")

View(housing_clean)
```

The **cols** argument describes what columns need to be reshaped, i.e. which need to be converted into columns. For us, we want to keep the US national average and the date as columns. We give it a list using the **c** ombine function, and by putting an **-** in front of the column names, we tell it to reshape every column *except* Date and National.US

The **names_to** argument gives a name for the new variable (i.e. column) that stores the data that formerly was in the column names. In our case, we make a variable called "location" that holds all the city names.

The **values_to** argument gives a name for the new variable (i.e. column) that stores the data that was in the cell values, which for us was the local index values in each city.


## Turning a column into multiple columns: separate()
The separate() function separates a single character column into several, splitting on a given regular expression. In the following command we run the function twice, separating two different columns:

```{r, echo = TRUE}
housing_clean2 <- housing_clean %>%
  separate(Date, into=c("year", "month"), extra="drop", remove=F) %>%
  separate(location, into=c("state", "city"),extra="merge")

View(housing_clean2)
```

The first argument is the name of the column in the tibble that we are separating ("location" for the first command, "Date" for the second).

The **into** argument gives the names (as a character vector) of the columns that we are separating into, i.e. what the names of the new columns will be. Note that the number of elements in the vector determine how many columns the target column will be split into (in this case, two).

The **extra** argument tells the function what to do with the "pieces" that are leftover after splitting. "Merge" tells the function to add the leftover pieces back to the final column; "drop" discards them.

To better understand this, we should look at how separate() splits a character column. You might be wondering how separate() "knows" to split our columns on a period ("."). This is because by default, separate() splits on every non-alphanumeric character (e.g. space, period, dash, etc.) given a character vector. This is controlled by the **sep** argument, which we did not included in our code so separate() just uses the default (remember to get the whole list of arguments for a function, you can type ?functionname, e.g. ?separate).

Finally, the **remove** argument determines whether the original column is retained or not (defaults to TRUE). So for our data, the original "location" column is discarded and we keep only the split columns, while the original "Date" column is retained along with its children.

Now let's put it all together. We could do things one function at a time, as above, but it is much cleaner and easier to read if we create our final, cleaned dataset in a single pipeline:

```{r, echo = TRUE}
housing_clean <- housing %>% as_tibble %>%
  pivot_longer(cols=c(-Date, -National.US), names_to="location", values_to="local_index") %>%
  separate(Date, c("year", "month"), extra="drop", remove=F) %>%
  separate(location, c("state", "city"),extra="merge")

View(housing_clean)
```

We can observe the differences between housing and housing_clean to see what's being done here, and a more in-depth description of what these functions do can be found in the cheat sheet.

## Subsetting and Manipulating Data with dplyr

Dplyr is a package included in tidyverse, and is useful for manipulating our data. Each action gets its own (verb) function -- so for example filtering data by rows is done with the filter() function. All of these functions have a very similar syntax to tidyr functions.

arrange(), rename(), select(), mutate() are used to sort data, rename columns, and update/create columns

We'll use the housing dataset to look at how these functions work.

arrange() sorts by one or more columns, with subsequent columns used to break ties in earlier columns. E.g.,
```{r}
housing_clean %>% arrange(year, month)
housing_clean %>% arrange(city, year)
housing_clean %>% arrange(month,state)
housing_clean %>% arrange(desc(year))
```

rename() renames a column:

```{r}
housing_clean %>% arrange(year, month, state, city) %>%
  rename(national_index = National.US)
```

select() selects columns to keep. Note that we can simultaneously rename and reorder columns:

```{r}
housing_clean %>% arrange(year, month, state, city) %>%
  select(year, month, city, state, local_index, national_index = National.US)
```

distinct() can be used to identify all unique values in your call set:

```{r}
housing_clean %>%
  select(state,city) %>%
  distinct
```

mutate() is an especially useful function that can do a variety of things, usually in combination with other functions. It can create new columns or update existing ones, while keeping everthing else unchanged. For instance, say we want to change our "month" column from the integer value to the three letter abbreviation:

```{r}
housing_clean %>% arrange(year, month, state, city) %>%
  select(year, month, city, state, local_index, national_index = National.US) %>%
  mutate(month = month.abb[as.integer(month)])
```

R has several built-in constants, one of which is this "month.abb" function, which has the three letter abbreviations of the months in the Gregorian calendar. What this mutate() call is doing is passing the values of the month column as integers to the month.abb function, which then automatically converts them to their appropriate abbreviations.

Let's look at some other things mutate() can do. We can include several mutate calls at once:

```{r}
housing_clean %>% arrange(year, month, state, city) %>%
  select(year, month, city, state, local_index, national_index = National.US) %>%
  mutate(month = month.abb[as.integer(month)], city=sub(".", "_", city, fixed=TRUE), rel_index=local_index/national_index)
```

This command uses mutate() three times:
1) The first changes the months, as above

2) In the second mutate() call, we are pairing mutate() with the sub() function, which substitutes characters in a string; we are telling substitute to replace all "." characters with a "_" character for the "city" character vector (i.e. our city column)

3) In the third mutate() call, we are using mutate to create an entirely new column called "rel_index", whose value is local_index / national_index


Now that we've gone through all the dplyr functions, let's update our nice clean housing dataset.

```{r}
housing_clean <- housing_clean %>% arrange(year, month, state, city) %>%
  select(year, month, city, state, local_index, national_index = National.US) %>%
  mutate(month = month.abb[as.integer(month)], city=sub(".", "_", city, fixed=TRUE), rel_index=local_index/national_index)
```


## Filtering functions

Sometimes we need to filter datasets, and we can do this by selecting rows that meet logical conditions. We set these up with logical tests.

The filter() function can be used to select rows meeting these conditions:

```{r}
housing_clean %>%
  filter(city == "Boston")
```

We can give filter many logical tests, all separated by commas. These are linked by logical and (all must be true to select that row):

```{r}
housing_clean %>%
  filter(city=="Boston", month=="Aug")
```

If we want to use other booleans, just put them in one statement, e.g.:

```{r}
housing_clean %>%
  filter(city=="Boston", month == "Aug" | month == "Jan")
```

Note that we can combine filters and the other statements to get useful subsets, for example let's say we want to see three years when the relative index in Boston was the highest (as of Jan):

```{r}
housing_clean %>%
  filter(city=="Boston", month=="Jan") %>%
  arrange(desc(rel_index)) %>%
  select(year,rel_index) %>%
  head(n=3)
```

Finally, many times you want to filter out missing data (typically indicated by `NA` in R), prior to conducting any analyses. To do this, we an use the `is.na()` fuction, which will return a vector with TRUE if NA is present, and FALSE otherwise. For example, let's identify all rows that are missing the local_index in housing_clean.

```{r}
housing_clean %>%
  filter(is.na(local_index))
```

To get the inverse for this (or any logical statement), simply add a `!` to the front.

```{r}
housing_clean %>%
  filter(!is.na(local_index))
```


================================================
FILE: tidyverse/part2/binder/Dockerfile
================================================
FROM rocker/binder:3.6.2

ARG NB_USER
ARG NB_UID

USER root

COPY ./tidyverse/part2/ ${HOME}

RUN chown -R ${NB_USER} ${HOME}

USER ${NB_USER}


================================================
FILE: tidyverse/part2/index.Rmd
================================================
---
title: "Tidyverse Tibbles and Bits"
subtitle: "Bioinformatics Coffee Hour"
date: "May 11 2020"
author: "Brian Arnold"
output: html_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

### Making data tidy with tidyr

The goal of tidyr is to convert between 'wide' data and 'long' data. Long data is tidy data: each row is an observation, each column is a variable. Wide data has many columns for the same variable, one for each level of a classification variable. For example, we will use some data on M&Ms to illustrate the differences between these two different formats and why you should care about knowing how to convert between them.

```{r}
library(tidyverse)
mms_wide <-read.table("http://www.randomservices.org/random/data/MM.txt", header=TRUE,stringsAsFactors=F)
mms_wide$BagID = seq(1:length(mms_wide$Red))
```

mms_wide is in wide format: there are many columns for the various colors. This data would be more tidy if instead we had 1.) a column representing color as a variable that could take on one of the 6 values and 2.) a corresponding column that contained the count data for each of these colors.

We can use the pivot_longer() function to convert from wide format to long format:
```{r}
mms_long <- mms_wide %>% 
  as_tibble %>% 
  pivot_longer(cols=c(-Weight, -BagID), names_to="color", values_to="count") %>% 
  arrange(BagID,color)
```

We need to select which columns to use for this operation. One way to do this would be to list all the columns. An alternative way (that we used here) would be to list all the columns you DO NOT want to use by preceding their names with "-".

However, say we inherited our data table in long format but would like it in wide format, we may also convert our data from long back to (the original) wide format using pivot_wider():
```{r}
mms_wide2 <- mms_long %>%
  pivot_wider(names_from="color", values_from="count")
```

While it may sound pointless at first to transform our data table in a way that makes it have either more columns or rows (at least it did to me!), it actually is extremely useful for analyzing features of these data using relatively little code.

To illustrate, let's use the 'summarize' function from dplyr. Say we wanted to know the total number of M&Ms in the dataset. In wide format, we'd have to go across all the columns, one for each color. In long format, these data are now in a single column named 'count':
```{r}
mms_long %>% summarize(total_mms=sum(count))
```

However, it gets even more interesting when we also use the group_by() function (also from dplyr), which is able to communicate to the summarize function to tell it how to summarize the data. For instance, group_by is able to analyze our data as a function of the "color" variable we created when we converted to long format. Here, we can see how many observations there are per color.
```{r}
mms_long %>%
  group_by(color) %>%
  summarize( mms_per_color=sum(count) )
```

Note 1: the summarize function outputs a new tibble, which contains a new column we created (named 'mms_per_color') that contains the summed count data. You can continue to do calculations on this tibble (for example, calculate the variance of colors), but you cannot call your original tibble again within the same set of pipes.

Note 2: we could have also done this by hand with the data in wide format, summing all the values underneath a column that corresponds to 'Red', 'Blue', etc... but it would be much more tedious and involve more code. Using less code is preferable because it can get difficult to read and understand code that is unecessarily long, especially if it involves many complex procedures. Writing clean code can also help the author understand their own code faster when they come back to it after not having thought about it for months (or however long it takes reviews to come back from a journal!).

Instead of summing over all bags of M&Ms for a each color, we can use the mean() function within summarize() to get the mean number of M&Ms for a each color.
```{r}
mms_long %>%
  group_by(color) %>%
  summarize( mean_mms_per_color=mean(count) )
```

Let's instead combine the group_by function with mutate. Here, mutate() creates a new column for the current tibble, instead of the summarize() function above which creates an entirely new tibble. Let's compute the fraction of each color in each bag and store this in a new column:
```{r}
mms_long %>%
  group_by(BagID) %>%
  mutate( percent = 100*(count/sum(count)) )
```

Note there is some subtle stuff going on here. The group_by function communicates to the mutate function. Here we use mutate on a grouped tibble (by BagID), so operations within the mutate function like sum() work **within the grouping**. So sum(count) computes the group-wise sum, which means it is easy to get frequencies or percents.

To more easily visualize these data (all percentages for a particular BagID on the same line/row), lets convert it to wide format just for printing to screen:
```{r}
mms_long %>%
  group_by(BagID) %>%
  mutate(percent = 100*(count/sum(count))) %>%
  pivot_wider(names_from=color, values_from=percent)
```

We can then use this insight to explore the data. For example, let's get the 5 bags of M&Ms with the highest percentage of red candies, conditioning on a weight of at least 47g:
```{r}
mms_long %>%
  group_by(BagID) %>%
  mutate(percent = 100*(count/sum(count))) %>%
  select(-count) %>%
  filter(color == "Red", Weight > 47) %>%
  arrange(desc(percent)) %>%
  head(n=5)
```

## Bonus material

Let's do a problem to help commit these concepts to memory (and to get used to thinking of which functions we need based on how a problem/question is phrased). We will reload the housing dataset from before, and processes it in a few ways that I'll skip for the moment:

```{r}
housing<-read.csv("https://raw.githubusercontent.com/datasets/house-prices-us/master/data/cities-month.csv", stringsAsFactors=F, strip.white = T)
housing=housing[c(1:(length(housing)-3),length(housing))]
 
housing_clean <- housing %>% 
  as_tibble %>% 
  pivot_longer(cols=c(-Date, -National.US), names_to="location", values_to="local_index") %>% 
  separate(location, c("state", "city"),extra="merge") %>% 
  separate(Date, c("year", "month"), extra="drop", remove=F) %>% 
  select(year, month, city, state, local_index, national_index=National.US) %>% 
  arrange(year, month, state, city) %>% 
  mutate(year = as.integer(year), month = month.abb[as.integer(month)], city = sub(".", "_", city, fixed=TRUE), rel_index = local_index/national_index)
```


### Exercises with summarize and group_by


### Problem

Using the housing_clean dataset, find the three cities with the highest relative index in February, averaged across the years. Get rid of missing data for this analysis too!

1.) This question is asking us to analyze **cities**, so we should probably group by that variable.
2.) It only wants us to consider February, so we should probably filter on that. While we're doing that, we might as well filter on missing data too using !is.na().
3.) We want to study the *averaged* relative index for each city, so we can probably put that variable into the summarize function with mean(). 
4.) It also asks us for the cities with the 3 highest mean relative indices, so lets just arrange our results by the mean relative index and peek at the first 3


```{r, echo=TRUE}
housing_clean %>% 
  filter(month=="Feb", !is.na(rel_index)) %>% 
  group_by(city) %>% 
  summarize(mean_rel_index = mean(rel_index)) %>% 
  arrange(desc(mean_rel_index)) %>% 
  head(n=3)
```

Let's understand how this works more by breaking it :). Let's switch around the functions to see why their order is important. Let's use the filter function second, after grouping by city:
```{r, echo=TRUE}
housing_clean %>% 
  group_by(city) %>% 
  filter(month=="Feb", !is.na(rel_index)) %>% 
  summarize(mean_rel_index = mean(rel_index)) %>% 
  arrange(desc(mean_rel_index)) %>% 
  head(n=3)
```

We get the same results! How about if we move the filter statement down again, making it the third operation:
```{r, echo=TRUE}
housing_clean %>% 
  group_by(city) %>% 
  summarize(mean_rel_index = mean(rel_index)) %>% 
  filter(month=="Feb", !is.na(rel_index)) %>% 
  arrange(desc(mean_rel_index)) %>% 
  head(n=3)
```
This breaks because at the point summarize() is used, we've created a new tibble with two columns: city and mean_rel_index, which is an average across all months and all years for each city (because we grouped by city). Since we had not filtered on the particular month of February by that part of the code, the mean is now calculated for all data so these numbers will be different than before (and not what the question asked for!). Then, when we try to filter on the month of February, our tibble at that point no onger has a column corresponding to months!

This breaking emphasizes that we cannot just selecting a bunch of operations that we want to do and expect R to figure everything out. Because of the use of pipes and the flow of information, the sequence of operations matters here. We must use logic and our understanding of these functions (that summarize produces a new tibble) to figure out what order of operations is needed to get what we want.

Try running each line of the code, starting with the first 2 lines, then the first 3 lines etc to see exactly where the code breaks. This will give us an idea of how to fix our code!



================================================
FILE: tidyverse/part2_winter2021/binder/Dockerfile
================================================
FROM rocker/binder:3.6.2

ARG NB_USER
ARG NB_UID

USER root

COPY ./tidyverse/part2_winter2021/ ${HOME}

RUN chown -R ${NB_USER} ${HOME}

USER ${NB_USER}


================================================
FILE: tidyverse/part2_winter2021/mask-use-by-county.csv
================================================
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17023,0.127,0.089,0.107,0.226,0.451
17025,0.062,0.115,0.145,0.306,0.372
17027,0.102,0.063,0.083,0.166,0.587
17029,0.112,0.055,0.201,0.221,0.411
17031,0.023,0.021,0.072,0.162,0.722
17033,0.146,0.196,0.047,0.219,0.392
17035,0.095,0.075,0.194,0.212,0.424
17037,0.007,0.012,0.034,0.191,0.756
17039,0.035,0.106,0.073,0.28,0.507
17041,0.048,0.061,0.105,0.191,0.594
17043,0.01,0.01,0.056,0.163,0.76
17045,0.174,0.09,0.131,0.208,0.398
17047,0.086,0.148,0.233,0.306,0.228
17049,0.072,0.072,0.214,0.232,0.41
17051,0.069,0.076,0.156,0.229,0.47
17053,0.011,0.082,0.167,0.172,0.567
17055,0.099,0.166,0.119,0.211,0.405
17057,0.016,0.03,0.311,0.199,0.444
17059,0.201,0.06,0.128,0.238,0.373
17061,0.136,0.029,0.109,0.217,0.508
17063,0.056,0.056,0.105,0.183,0.599
17065,0.138,0.179,0.1,0.21,0.373
17067,0.147,0.154,0.125,0.163,0.411
17069,0.163,0.072,0.235,0.191,0.34
17071,0.096,0.044,0.101,0.155,0.604
17073,0.067,0.072,0.061,0.203,0.597
17075,0.097,0.168,0.069,0.242,0.424
17077,0.066,0.078,0.151,0.209,0.496
17079,0.099,0.135,0.131,0.254,0.38
17081,0.122,0.126,0.08,0.279,0.393
17083,0.074,0.079,0.124,0.185,0.537
17085,0.034,0.104,0.22,0.178,0.465
17087,0.124,0.05,0.189,0.141,0.496
17089,0.013,0.026,0.048,0.165,0.748
17091,0.044,0.084,0.096,0.249,0.527
17093,0.001,0.009,0.065,0.136,0.788
17095,0.035,0.031,0.194,0.246,0.494
17097,0.02,0.018,0.046,0.146,0.769
17099,0.018,0.046,0.113,0.267,0.556
17101,0.197,0.157,0.105,0.281,0.26
17103,0.013,0.054,0.058,0.224,0.65
17105,0.03,0.147,0.08,0.234,0.51
17107,0.046,0.045,0.23,0.128,0.551
17109,0.041,0.091,0.097,0.182,0.59
17111,0.03,0.012,0.066,0.176,0.716
17113,0.085,0.062,0.091,0.201,0.561
17115,0.016,0.062,0.088,0.286,0.549
17117,0.015,0.05,0.147,0.227,0.561
17119,0.035,0.045,0.083,0.189,0.648
17121,0.1,0.093,0.101,0.302,0.404
17123,0.048,0.083,0.133,0.198,0.538
17125,0.238,0.031,0.292,0.126,0.313
17127,0.097,0.043,0.232,0.256,0.373
17129,0.102,0.047,0.138,0.172,0.54
17131,0.039,0.066,0.11,0.215,0.571
17133,0.003,0.04,0.112,0.174,0.67
17135,0.018,0.05,0.091,0.316,0.525
17137,0.05,0.062,0.177,0.178,0.533
17139,0.081,0.073,0.213,0.243,0.39
17141,0.053,0.045,0.129,0.184,0.589
17143,0.039,0.036,0.096,0.251,0.577
17145,0.055,0.092,0.104,0.358,0.392
17147,0.003,0.052,0.06,0.198,0.688
17149,0.138,0.079,0.218,0.205,0.36
17151,0.104,0.054,0.213,0.214,0.415
17153,0.117,0.064,0.189,0.225,0.405
17155,0.021,0.025,0.168,0.251,0.535
17157,0.008,0.032,0.117,0.391,0.452
17159,0.111,0.151,0.107,0.3,0.331
17161,0.078,0.025,0.066,0.235,0.596
17163,0.032,0.027,0.037,0.138,0.767
17165,0.117,0.106,0.111,0.204,0.462
17167,0.017,0.03,0.155,0.233,0.564
17169,0.132,0.1,0.152,0.198,0.417
17171,0.113,0.057,0.148,0.162,0.52
17173,0.07,0.047,0.168,0.245,0.469
17175,0.013,0.101,0.058,0.155,0.672
17177,0.036,0.059,0.041,0.222,0.644
17179,0.044,0.04,0.141,0.215,0.56
17181,0.1,0.078,0.162,0.195,0.466
17183,0.023,0.119,0.088,0.266,0.504
17185,0.078,0.113,0.271,0.325,0.212
17187,0.042,0.02,0.141,0.276,0.522
17189,0.118,0.046,0.053,0.327,0.456
17191,0.086,0.142,0.128,0.3,0.345
17193,0.19,0.144,0.095,0.239,0.332
17195,0.025,0.066,0.042,0.218,0.649
17197,0.041,0.024,0.062,0.134,0.739
17199,0.095,0.122,0.136,0.183,0.464
17201,0.031,0.03,0.089,0.193,0.657
17203,0.062,0.067,0.112,0.242,0.517
18001,0.136,0.047,0.16,0.348,0.309
18003,0.07,0.14,0.099,0.228,0.464
18005,0.138,0.084,0.085,0.286,0.406
18007,0.082,0.126,0.112,0.183,0.496
18009,0.093,0.152,0.106,0.203,0.447
18011,0.052,0.085,0.142,0.308,0.413
18013,0.055,0.061,0.041,0.184,0.66
18015,0.059,0.104,0.17,0.22,0.447
18017,0.037,0.192,0.283,0.206,0.283
18019,0.068,0.06,0.141,0.228,0.501
18021,0.122,0.073,0.112,0.226,0.466
18023,0.088,0.033,0.132,0.285,0.462
18025,0.067,0.089,0.118,0.261,0.465
18027,0.049,0.061,0.13,0.214,0.546
18029,0.083,0.1,0.168,0.277,0.371
18031,0.175,0.083,0.101,0.204,0.437
18033,0.109,0.074,0.118,0.149,0.55
18035,0.095,0.109,0.124,0.197,0.475
18037,0.01,0.105,0.086,0.25,0.55
18039,0.03,0.097,0.094,0.252,0.528
18041,0.161,0.041,0.048,0.237,0.514
18043,0.053,0.086,0.154,0.233,0.474
18045,0.077,0.089,0.147,0.212,0.474
18047,0.07,0.082,0.104,0.199,0.545
18049,0.142,0.114,0.128,0.217,0.399
18051,0.09,0.1,0.204,0.255,0.351
18053,0.053,0.108,0.133,0.311,0.393
18055,0.099,0.027,0.081,0.235,0.558
18057,0.07,0.051,0.049,0.234,0.596
18059,0.068,0.04,0.166,0.306,0.42
18061,0.094,0.084,0.205,0.258,0.359
18063,0.054,0.052,0.113,0.257,0.523
18065,0.183,0.148,0.125,0.111,0.434
18067,0.008,0.083,0.183,0.301,0.426
18069,0.15,0.182,0.053,0.199,0.416
18071,0.097,0.098,0.105,0.388,0.312
18073,0.186,0.1,0.065,0.328,0.321
18075,0.141,0.139,0.157,0.235,0.328
18077,0.083,0.219,0.218,0.225,0.255
18079,0.095,0.105,0.172,0.308,0.319
18081,0.057,0.094,0.1,0.192,0.557
18083,0.158,0.125,0.121,0.259,0.338
18085,0.073,0.147,0.089,0.195,0.496
18087,0.059,0.082,0.171,0.236,0.452
18089,0.043,0.057,0.088,0.219,0.592
18091,0.084,0.075,0.168,0.206,0.466
18093,0.092,0.117,0.109,0.191,0.491
18095,0.062,0.084,0.15,0.145,0.559
18097,0.051,0.053,0.116,0.232,0.548
18099,0.12,0.103,0.159,0.229,0.389
18101,0.041,0.064,0.123,0.223,0.55
18103,0.054,0.125,0.253,0.226,0.343
18105,0.037,0.037,0.094,0.242,0.591
18107,0.07,0.116,0.168,0.156,0.49
18109,0.06,0.131,0.136,0.188,0.485
18111,0.102,0.153,0.092,0.338,0.316
18113,0.057,0.11,0.135,0.197,0.5
18115,0.06,0.078,0.18,0.223,0.459
18117,0.096,0.086,0.073,0.315,0.43
18119,0.094,0.038,0.087,0.282,0.5
18121,0.116,0.111,0.174,0.195,0.405
18123,0.01,0.109,0.087,0.252,0.542
18125,0.052,0.089,0.163,0.2,0.495
18127,0.05,0.1,0.108,0.231,0.512
18129,0.132,0.056,0.115,0.226,0.472
18131,0.135,0.219,0.171,0.253,0.223
18133,0.075,0.162,0.204,0.194,0.366
18135,0.128,0.13,0.228,0.179,0.336
18137,0.054,0.074,0.15,0.294,0.429
18139,0.133,0.118,0.063,0.279,0.408
18141,0.031,0.04,0.102,0.228,0.598
18143,0.036,0.073,0.168,0.263,0.46
18145,0.16,0.057,0.177,0.193,0.413
18147,0.017,0.134,0.104,0.216,0.529
18149,0.177,0.151,0.088,0.219,0.365
18151,0.04,0.089,0.17,0.169,0.532
18153,0.127,0.071,0.077,0.207,0.517
18155,0.089,0.125,0.209,0.234,0.344
18157,0.093,0.083,0.102,0.204,0.518
18159,0.033,0.082,0.089,0.444,0.352
18161,0.095,0.053,0.106,0.178,0.569
18163,0.07,0.039,0.115,0.234,0.542
18165,0.113,0.105,0.134,0.223,0.426
18167,0.118,0.064,0.102,0.25,0.466
18169,0.07,0.11,0.237,0.202,0.381
18171,0.079,0.115,0.092,0.223,0.492
18173,0.045,0.038,0.117,0.252,0.548
18175,0.097,0.074,0.062,0.356,0.411
18177,0.162,0.09,0.138,0.261,0.348
18179,0.205,0.05,0.077,0.337,0.332
18181,0.056,0.177,0.194,0.189,0.384
18183,0.061,0.186,0.132,0.16,0.46
19001,0.073,0.128,0.128,0.307,0.364
19003,0.148,0.11,0.15,0.311,0.281
19005,0.138,0.049,0.132,0.212,0.469
19007,0.044,0.11,0.239,0.193,0.413
19009,0.224,0.073,0.194,0.187,0.322
19011,0.098,0.067,0.13,0.2,0.504
19013,0.032,0.053,0.18,0.289,0.447
19015,0.09,0.08,0.1,0.197,0.533
19017,0.048,0.062,0.197,0.268,0.424
19019,0.021,0.124,0.087,0.394,0.373
19021,0.183,0.062,0.26,0.207,0.288
19023,0.052,0.094,0.208,0.23,0.417
19025,0.212,0.117,0.213,0.153,0.304
19027,0.153,0.11,0.241,0.139,0.358
19029,0.341,0.07,0.124,0.238,0.227
19031,0.026,0.096,0.142,0.355,0.381
19033,0.061,0.365,0.146,0.213,0.215
19035,0.183,0.105,0.221,0.182,0.309
19037,0.075,0.136,0.197,0.236,0.356
19039,0.078,0.167,0.143,0.194,0.418
19041,0.224,0.035,0.112,0.189,0.441
19043,0.123,0.084,0.137,0.284,0.371
19045,0.092,0.13,0.079,0.269,0.43
19047,0.089,0.045,0.141,0.064,0.66
19049,0.036,0.102,0.118,0.26,0.485
19051,0.038,0.101,0.249,0.201,0.411
19053,0.248,0.132,0.258,0.068,0.294
19055,0.035,0.137,0.137,0.408,0.283
19057,0.135,0.081,0.109,0.122,0.553
19059,0.276,0.027,0.091,0.173,0.433
19061,0.037,0.155,0.189,0.189,0.43
19063,0.241,0.083,0.067,0.181,0.428
19065,0.054,0.075,0.128,0.349,0.394
19067,0.063,0.271,0.148,0.25,0.268
19069,0.088,0.278,0.187,0.186,0.261
19071,0.048,0.061,0.071,0.209,0.611
19073,0.116,0.141,0.235,0.153,0.354
19075,0.038,0.042,0.203,0.238,0.48
19077,0.162,0.073,0.143,0.283,0.34
19079,0.143,0.118,0.129,0.153,0.457
19081,0.08,0.384,0.146,0.174,0.216
19083,0.074,0.075,0.195,0.214,0.441
19085,0.118,0.044,0.094,0.178,0.565
19087,0.123,0.142,0.091,0.183,0.461
19089,0.077,0.151,0.176,0.183,0.413
19091,0.243,0.268,0.147,0.102,0.24
19093,0.153,0.094,0.246,0.128,0.38
19095,0.027,0.149,0.135,0.239,0.45
19097,0.032,0.096,0.229,0.232,0.411
19099,0.102,0.105,0.233,0.212,0.347
19101,0.074,0.116,0.221,0.191,0.398
19103,0.014,0.044,0.069,0.2,0.673
19105,0.04,0.105,0.165,0.336,0.355
19107,0.035,0.126,0.287,0.28,0.272
19109,0.135,0.318,0.101,0.173,0.272
19111,0.17,0.146,0.131,0.161,0.392
19113,0.039,0.081,0.115,0.201,0.563
19115,0.097,0.058,0.15,0.214,0.482
19117,0.032,0.204,0.196,0.185,0.384
19119,0.078,0.173,0.157,0.247,0.344
19121,0.01,0.102,0.136,0.268,0.484
19123,0.069,0.067,0.294,0.283,0.287
19125,0.053,0.049,0.209,0.26,0.429
19127,0.077,0.064,0.153,0.226,0.48
19129,0.064,0.084,0.091,0.281,0.481
19131,0.046,0.282,0.149,0.26,0.263
19133,0.078,0.069,0.087,0.127,0.64
19135,0.034,0.078,0.256,0.247,0.385
19137,0.218,0.034,0.084,0.378,0.286
19139,0.12,0.022,0.221,0.273,0.364
19141,0.173,0.054,0.267,0.187,0.319
19143,0.195,0.051,0.233,0.199,0.322
19145,0.088,0.053,0.068,0.31,0.481
19147,0.199,0.116,0.088,0.179,0.419
19149,0.125,0.145,0.166,0.162,0.402
19151,0.214,0.124,0.162,0.165,0.335
19153,0.044,0.089,0.136,0.259,0.473
19155,0.115,0.083,0.147,0.264,0.39
19157,0.121,0.145,0.187,0.164,0.383
19159,0.144,0.138,0.281,0.162,0.275
19161,0.18,0.071,0.286,0.159,0.303
19163,0.075,0.027,0.126,0.277,0.494
19165,0.099,0.059,0.182,0.145,0.516
19167,0.12,0.06,0.285,0.2,0.335
19169,0.045,0.075,0.071,0.235,0.574
19171,0.066,0.053,0.209,0.181,0.491
19173,0.09,0.151,0.185,0.282,0.292
19175,0.03,0.125,0.172,0.21,0.462
19177,0.107,0.128,0.175,0.218,0.372
19179,0.034,0.082,0.293,0.221,0.37
19181,0.055,0.182,0.181,0.245,0.337
19183,0.035,0.102,0.123,0.151,0.589
19185,0.138,0.148,0.252,0.12,0.342
19187,0.209,0.167,0.161,0.101,0.362
19189,0.07,0.329,0.176,0.197,0.228
19191,0.122,0.044,0.151,0.177,0.505
19193,0.113,0.165,0.137,0.16,0.425
19195,0.072,0.271,0.175,0.231,0.251
19197,0.172,0.299,0.173,0.125,0.232
20001,0.153,0.212,0.126,0.167,0.341
20003,0.189,0.063,0.137,0.267,0.344
20005,0.137,0.122,0.169,0.256,0.316
20007,0.069,0.147,0.118,0.329,0.336
20009,0.107,0.131,0.12,0.211,0.43
20011,0.167,0.146,0.077,0.12,0.49
20013,0.089,0.149,0.183,0.197,0.382
20015,0.053,0.133,0.122,0.222,0.47
20017,0.151,0.082,0.212,0.225,0.331
20019,0.128,0.137,0.185,0.214,0.337
20021,0.131,0.121,0.164,0.174,0.41
20023,0.065,0.27,0.174,0.235,0.256
20025,0.163,0.13,0.126,0.232,0.348
20027,0.081,0.069,0.115,0.229,0.507
20029,0.154,0.077,0.118,0.217,0.434
20031,0.085,0.119,0.165,0.404,0.227
20033,0.112,0.144,0.128,0.271,0.346
20035,0.075,0.093,0.28,0.131,0.421
20037,0.108,0.143,0.118,0.139,0.492
20039,0.163,0.118,0.211,0.235,0.273
20041,0.106,0.118,0.148,0.243,0.385
20043,0.137,0.193,0.184,0.214,0.272
20045,0.006,0.009,0.024,0.227,0.734
20047,0.052,0.086,0.135,0.221,0.506
20049,0.061,0.05,0.295,0.282,0.312
20051,0.154,0.154,0.224,0.148,0.32
20053,0.174,0.133,0.133,0.187,0.373
20055,0.166,0.089,0.163,0.239,0.343
20057,0.143,0.084,0.143,0.232,0.399
20059,0.063,0.06,0.159,0.354,0.364
20061,0.05,0.068,0.113,0.27,0.499
20063,0.24,0.089,0.234,0.151,0.287
20065,0.218,0.156,0.247,0.129,0.25
20067,0.14,0.108,0.15,0.231,0.37
20069,0.158,0.092,0.146,0.24,0.364
20071,0.135,0.131,0.181,0.229,0.323
20073,0.034,0.079,0.256,0.302,0.33
20075,0.131,0.095,0.189,0.217,0.368
20077,0.079,0.118,0.21,0.193,0.4
20079,0.075,0.058,0.121,0.323,0.423
20081,0.153,0.107,0.137,0.239,0.364
20083,0.126,0.075,0.163,0.225,0.412
20085,0.01,0.116,0.161,0.136,0.577
20087,0.026,0.069,0.09,0.182,0.634
20089,0.171,0.142,0.087,0.259,0.34
20091,0.029,0.019,0.075,0.224,0.654
20093,0.155,0.098,0.172,0.229,0.346
20095,0.081,0.212,0.114,0.128,0.464
20097,0.062,0.107,0.128,0.233,0.469
20099,0.168,0.123,0.091,0.147,0.471
20101,0.197,0.075,0.197,0.204,0.326
20103,0.087,0.135,0.092,0.142,0.544
20105,0.171,0.132,0.16,0.163,0.375
20107,0.14,0.053,0.121,0.285,0.4
20109,0.206,0.131,0.194,0.189,0.28
20111,0.113,0.111,0.133,0.3,0.344
20113,0.12,0.065,0.106,0.313,0.396
20115,0.101,0.068,0.209,0.279,0.343
20117,0.048,0.049,0.233,0.263,0.407
20119,0.164,0.123,0.115,0.235,0.362
20121,0.071,0.044,0.095,0.25,0.54
20123,0.173,0.116,0.144,0.18,0.388
20125,0.164,0.132,0.102,0.195,0.407
20127,0.11,0.094,0.148,0.244,0.404
20129,0.12,0.113,0.144,0.218,0.405
20131,0.035,0.094,0.203,0.236,0.432
20133,0.133,0.204,0.12,0.127,0.416
20135,0.15,0.083,0.199,0.198,0.371
20137,0.161,0.163,0.177,0.225,0.275
20139,0.018,0.09,0.088,0.24,0.564
20141,0.174,0.233,0.163,0.136,0.293
20143,0.131,0.117,0.164,0.191,0.396
20145,0.053,0.096,0.137,0.217,0.497
20147,0.15,0.244,0.126,0.19,0.29
20149,0.012,0.057,0.145,0.236,0.55
20151,0.02,0.183,0.101,0.309,0.387
20153,0.128,0.174,0.209,0.22,0.268
20155,0.135,0.077,0.143,0.191,0.454
20157,0.183,0.073,0.076,0.295,0.373
20159,0.153,0.086,0.11,0.241,0.41
20161,0.03,0.05,0.124,0.261,0.535
20163,0.179,0.22,0.218,0.123,0.26
20165,0.11,0.122,0.182,0.183,0.403
20167,0.16,0.174,0.174,0.157,0.335
20169,0.126,0.133,0.175,0.209,0.357
20171,0.197,0.09,0.19,0.21,0.313
20173,0.06,0.055,0.126,0.26,0.499
20175,0.138,0.14,0.092,0.226,0.403
20177,0.009,0.078,0.095,0.184,0.633
20179,0.231,0.112,0.236,0.154,0.267
20181,0.1,0.27,0.159,0.24,0.231
20183,0.142,0.257,0.088,0.195,0.319
20185,0.041,0.125,0.109,0.252,0.472
20187,0.127,0.098,0.172,0.216,0.388
20189,0.122,0.132,0.112,0.225,0.408
20191,0.104,0.039,0.115,0.298,0.444
20193,0.188,0.157,0.208,0.184,0.263
20195,0.203,0.115,0.256,0.137,0.291
20197,0.011,0.077,0.12,0.201,0.591
20199,0.128,0.21,0.154,0.246,0.262
20201,0.148,0.075,0.111,0.234,0.432
20203,0.173,0.112,0.184,0.219,0.313
20205,0.125,0.148,0.145,0.184,0.398
20207,0.092,0.175,0.171,0.247,0.314
20209,0.025,0.056,0.076,0.178,0.665
21001,0.076,0.115,0.167,0.243,0.399
21003,0.101,0.072,0.097,0.211,0.519
21005,0.062,0.01,0.044,0.483,0.401
21007,0.107,0.033,0.198,0.257,0.404
21009,0.053,0.093,0.09,0.176,0.588
21011,0.076,0.058,0.084,0.339,0.443
21013,0.146,0.094,0.12,0.199,0.44
21015,0.08,0.087,0.081,0.232,0.52
21017,0.024,0.037,0.042,0.129,0.769
21019,0.084,0.125,0.202,0.235,0.354
21021,0.092,0.037,0.112,0.161,0.598
21023,0.056,0.249,0.065,0.238,0.392
21025,0.114,0.081,0.126,0.241,0.438
21027,0.023,0.146,0.177,0.138,0.517
21029,0.037,0.091,0.156,0.181,0.535
21031,0.066,0.097,0.133,0.17,0.535
21033,0.059,0.067,0.26,0.177,0.438
21035,0.066,0.084,0.248,0.17,0.432
21037,0.073,0.085,0.134,0.278,0.43
21039,0.104,0.045,0.188,0.236,0.428
21041,0.155,0.171,0.12,0.208,0.346
21043,0.069,0.102,0.122,0.324,0.383
21045,0.164,0.076,0.161,0.154,0.444
21047,0.06,0.118,0.178,0.279,0.365
21049,0.083,0.093,0.11,0.233,0.48
21051,0.153,0.082,0.165,0.16,0.44
21053,0.096,0.115,0.248,0.165,0.375
21055,0.121,0.117,0.29,0.153,0.319
21057,0.067,0.106,0.224,0.229,0.374
21059,0.055,0.057,0.122,0.236,0.531
21061,0.055,0.057,0.086,0.179,0.624
21063,0.117,0.1,0.051,0.289,0.443
21065,0.082,0.151,0.09,0.275,0.402
21067,0.031,0.051,0.066,0.202,0.65
21069,0.068,0.069,0.075,0.287,0.502
21071,0.131,0.066,0.129,0.134,0.54
21073,0.088,0.036,0.142,0.214,0.521
21075,0.1,0.102,0.202,0.217,0.38
21077,0.076,0.048,0.198,0.211,0.466
21079,0.077,0.043,0.063,0.276,0.54
21081,0.047,0.056,0.183,0.106,0.608
21083,0.103,0.069,0.266,0.222,0.34
21085,0.043,0.042,0.134,0.154,0.627
21087,0.063,0.076,0.127,0.247,0.488
21089,0.092,0.101,0.236,0.228,0.342
21091,0.019,0.08,0.121,0.235,0.546
21093,0.057,0.062,0.106,0.217,0.558
21095,0.076,0.093,0.098,0.126,0.606
21097,0.073,0.044,0.031,0.047,0.804
21099,0.053,0.034,0.118,0.185,0.61
21101,0.072,0.061,0.136,0.229,0.501
21103,0.093,0.082,0.174,0.254,0.398
21105,0.097,0.065,0.221,0.202,0.415
21107,0.04,0.067,0.174,0.217,0.502
21109,0.183,0.068,0.092,0.235,0.421
21111,0.036,0.048,0.083,0.23,0.602
21113,0.022,0.036,0.104,0.21,0.628
21115,0.102,0.053,0.106,0.127,0.612
21117,0.052,0.056,0.146,0.245,0.5
21119,0.157,0.073,0.13,0.142,0.497
21121,0.187,0.073,0.103,0.165,0.471
21123,0.074,0.053,0.09,0.213,0.57
21125,0.201,0.047,0.083,0.152,0.517
21127,0.155,0.091,0.151,0.125,0.479
21129,0.129,0.069,0.129,0.273,0.399
21131,0.094,0.103,0.142,0.174,0.487
21133,0.078,0.05,0.127,0.122,0.624
21135,0.057,0.081,0.21,0.296,0.357
21137,0.172,0.054,0.093,0.172,0.509
21139,0.08,0.1,0.29,0.191,0.339
21141,0.099,0.1,0.08,0.198,0.523
21143,0.052,0.11,0.316,0.138,0.384
21145,0.085,0.058,0.232,0.255,0.37
21147,0.096,0.116,0.143,0.203,0.441
21149,0.081,0.105,0.107,0.249,0.459
21151,0.088,0.094,0.114,0.233,0.471
21153,0.107,0.078,0.111,0.164,0.54
21155,0.046,0.082,0.074,0.144,0.654
21157,0.061,0.083,0.28,0.18,0.395
21159,0.108,0.055,0.096,0.104,0.637
21161,0.056,0.119,0.061,0.284,0.479
21163,0.018,0.154,0.115,0.154,0.559
21165,0.161,0.088,0.067,0.215,0.468
21167,0.047,0.012,0.178,0.195,0.567
21169,0.048,0.109,0.134,0.222,0.487
21171,0.082,0.134,0.185,0.15,0.448
21173,0.03,0.041,0.085,0.392,0.452
21175,0.133,0.116,0.071,0.182,0.498
21177,0.035,0.154,0.123,0.2,0.488
21179,0.033,0.106,0.068,0.244,0.548
21181,0.023,0.025,0.09,0.271,0.591
21183,0.064,0.145,0.109,0.19,0.492
21185,0.025,0.039,0.098,0.278,0.56
21187,0.091,0.039,0.114,0.198,0.558
21189,0.121,0.058,0.157,0.282,0.382
21191,0.035,0.121,0.103,0.129,0.613
21193,0.123,0.093,0.111,0.171,0.502
21195,0.096,0.088,0.123,0.148,0.545
21197,0.112,0.099,0.102,0.232,0.455
21199,0.13,0.123,0.096,0.164,0.487
21201,0.048,0.123,0.039,0.204,0.586
21203,0.215,0.079,0.075,0.157,0.474
21205,0.159,0.097,0.047,0.231,0.467
21207,0.096,0.138,0.195,0.206,0.365
21209,0.04,0.044,0.076,0.081,0.759
21211,0.092,0.107,0.054,0.261,0.487
21213,0.137,0.066,0.162,0.208,0.426
21215,0.032,0.058,0.047,0.41,0.453
21217,0.073,0.073,0.111,0.21,0.533
21219,0.058,0.147,0.135,0.266,0.394
21221,0.066,0.053,0.131,0.152,0.597
21223,0.134,0.172,0.135,0.217,0.342
21225,0.211,0.079,0.229,0.172,0.309
21227,0.076,0.067,0.063,0.185,0.609
21229,0.035,0.066,0.116,0.221,0.562
21231,0.102,0.151,0.16,0.169,0.417
21233,0.088,0.084,0.167,0.21,0.451
21235,0.176,0.062,0.113,0.174,0.476
21237,0.14,0.095,0.114,0.203,0.448
21239,0.042,0.017,0.049,0.193,0.698
22001,0.122,0.111,0.138,0.265,0.362
22003,0.196,0.07,0.118,0.2,0.416
22005,0.049,0.092,0.142,0.148,0.569
22007,0.079,0.034,0.213,0.206,0.468
22009,0.13,0.092,0.073,0.246,0.459
22011,0.086,0.069,0.169,0.191,0.486
22013,0.078,0.052,0.17,0.291,0.409
22015,0.068,0.096,0.144,0.225,0.468
22017,0.058,0.079,0.158,0.209,0.497
22019,0.102,0.139,0.077,0.212,0.471
22021,0.102,0.061,0.098,0.241,0.498
22023,0.089,0.127,0.079,0.201,0.505
22025,0.11,0.056,0.086,0.147,0.6
22027,0.146,0.068,0.04,0.273,0.473
22029,0.11,0.104,0.112,0.095,0.579
22031,0.035,0.075,0.132,0.162,0.596
22033,0.052,0.031,0.09,0.183,0.642
22035,0.022,0.046,0.121,0.137,0.674
22037,0.067,0.053,0.095,0.366,0.42
22039,0.215,0.077,0.111,0.211,0.387
22041,0.123,0.054,0.102,0.156,0.565
22043,0.086,0.041,0.109,0.229,0.535
22045,0.112,0.084,0.085,0.253,0.467
22047,0.061,0.021,0.084,0.19,0.644
22049,0.116,0.083,0.127,0.29,0.383
22051,0.044,0.031,0.066,0.174,0.685
22053,0.249,0.111,0.09,0.196,0.354
22055,0.062,0.083,0.105,0.217,0.533
22057,0.078,0.039,0.159,0.151,0.573
22059,0.082,0.035,0.089,0.223,0.571
22061,0.102,0.08,0.111,0.29,0.417
22063,0.06,0.043,0.144,0.29,0.463
22065,0.034,0.068,0.109,0.172,0.617
22067,0.039,0.105,0.124,0.166,0.566
22069,0.096,0.063,0.113,0.167,0.561
22071,0.019,0.034,0.087,0.206,0.654
22073,0.076,0.098,0.121,0.229,0.476
22075,0.065,0.035,0.165,0.222,0.513
22077,0.044,0.082,0.068,0.198,0.608
22079,0.106,0.046,0.087,0.236,0.524
22081,0.068,0.081,0.135,0.189,0.527
22083,0.066,0.08,0.126,0.194,0.534
22085,0.098,0.08,0.113,0.145,0.564
22087,0.035,0.075,0.136,0.23,0.524
22089,0.093,0.048,0.027,0.165,0.667
22091,0.064,0.126,0.138,0.237,0.434
22093,0.034,0.099,0.202,0.061,0.603
22095,0.096,0.059,0.19,0.079,0.577
22097,0.064,0.105,0.117,0.199,0.515
22099,0.069,0.11,0.096,0.178,0.548
22101,0.066,0.02,0.179,0.28,0.454
22103,0.031,0.073,0.074,0.211,0.611
22105,0.066,0.072,0.103,0.193,0.566
22107,0.081,0.064,0.097,0.134,0.625
22109,0.068,0.052,0.14,0.221,0.519
22111,0.071,0.142,0.106,0.244,0.436
22113,0.076,0.088,0.115,0.271,0.449
22115,0.092,0.066,0.145,0.244,0.453
22117,0.103,0.046,0.148,0.242,0.461
22119,0.134,0.044,0.063,0.265,0.494
22121,0.044,0.04,0.07,0.197,0.649
22123,0.022,0.063,0.124,0.114,0.677
22125,0.072,0.13,0.037,0.265,0.496
22127,0.086,0.106,0.13,0.236,0.443
23001,0.083,0.099,0.041,0.199,0.577
23003,0.143,0.047,0.12,0.243,0.447
23005,0.05,0.053,0.046,0.182,0.669
23007,0.006,0.063,0.191,0.161,0.579
23009,0.034,0.052,0.118,0.202,0.594
23011,0.016,0.074,0.125,0.245,0.54
23013,0.007,0.01,0.111,0.248,0.625
23015,0.031,0.014,0.065,0.204,0.688
23017,0.069,0.065,0.064,0.213,0.589
23019,0.038,0.071,0.085,0.189,0.617
23021,0.062,0.146,0.123,0.189,0.479
23023,0.061,0.026,0.033,0.216,0.664
23025,0.035,0.095,0.142,0.17,0.558
23027,0.023,0.06,0.15,0.182,0.585
23029,0.026,0.032,0.126,0.204,0.612
23031,0.076,0.036,0.061,0.173,0.655
24001,0.007,0.01,0.035,0.213,0.736
24003,0.018,0.022,0.067,0.154,0.739
24005,0.013,0.024,0.043,0.142,0.777
24009,0.049,0.049,0.071,0.11,0.72
24011,0.009,0.019,0.033,0.168,0.77
24013,0.014,0.008,0.062,0.17,0.746
24015,0.009,0.018,0.054,0.182,0.738
24017,0.035,0.034,0.107,0.157,0.667
24019,0.02,0.006,0.011,0.119,0.844
24021,0.028,0.036,0.062,0.113,0.762
24023,0.077,0.008,0.04,0.305,0.571
24025,0.008,0.009,0.037,0.168,0.778
24027,0.024,0.015,0.067,0.17,0.723
24029,0.005,0.014,0.06,0.207,0.714
24031,0.025,0.029,0.041,0.128,0.777
24033,0.087,0.034,0.064,0.094,0.72
24035,0.002,0.042,0.041,0.309,0.605
24037,0.04,0.009,0.036,0.152,0.763
24039,0.004,0.01,0.047,0.128,0.812
24041,0.012,0.012,0.006,0.127,0.843
24043,0.031,0.047,0.085,0.194,0.642
24045,0.006,0.011,0.063,0.116,0.804
24047,0.003,0.004,0.042,0.11,0.842
24510,0.024,0.032,0.063,0.148,0.733
25001,0.019,0.007,0.026,0.109,0.839
25003,0.03,0.004,0.049,0.082,0.834
25005,0.016,0.02,0.056,0.161,0.747
25007,0.006,0.01,0.037,0.16,0.786
25009,0.023,0.01,0.047,0.131,0.788
25011,0.043,0.011,0.028,0.156,0.762
25013,0.013,0.017,0.055,0.13,0.786
25015,0.011,0.006,0.017,0.109,0.857
25017,0.023,0.011,0.041,0.127,0.798
25019,0.015,0.003,0.034,0.156,0.792
25021,0.02,0.015,0.035,0.125,0.805
25023,0.014,0.017,0.045,0.112,0.812
25025,0.02,0.007,0.049,0.136,0.788
25027,0.02,0.027,0.043,0.142,0.768
26001,0.065,0.046,0.133,0.18,0.576
26003,0.025,0.034,0.118,0.229,0.595
26005,0.05,0.035,0.133,0.283,0.5
26007,0.025,0.04,0.144,0.18,0.612
26009,0.027,0.042,0.088,0.218,0.625
26011,0.052,0.136,0.076,0.252,0.484
26013,0.012,0.063,0.109,0.257,0.558
26015,0.043,0.121,0.121,0.211,0.504
26017,0.014,0.055,0.103,0.202,0.627
26019,0.01,0.049,0.062,0.164,0.715
26021,0.039,0.037,0.122,0.247,0.555
26023,0.072,0.091,0.205,0.234,0.399
26025,0.063,0.121,0.078,0.178,0.559
26027,0.046,0.076,0.123,0.274,0.48
26029,0.027,0.057,0.085,0.205,0.626
26031,0.027,0.052,0.086,0.252,0.584
26033,0.03,0.022,0.068,0.287,0.593
26035,0.05,0.026,0.102,0.308,0.514
26037,0.033,0.056,0.046,0.263,0.603
26039,0.043,0.05,0.061,0.181,0.666
26041,0.058,0.023,0.17,0.217,0.532
26043,0.043,0.065,0.127,0.249,0.516
26045,0.047,0.039,0.147,0.247,0.52
26047,0.025,0.062,0.075,0.219,0.618
26049,0.032,0.043,0.104,0.288,0.533
26051,0.025,0.024,0.109,0.338,0.503
26053,0.039,0.064,0.139,0.257,0.501
26055,0.004,0.041,0.082,0.2,0.672
26057,0.057,0.074,0.105,0.169,0.596
26059,0.135,0.075,0.119,0.247,0.424
26061,0.008,0.078,0.129,0.281,0.505
26063,0.08,0.072,0.032,0.271,0.544
26065,0.062,0.03,0.076,0.237,0.595
26067,0.035,0.203,0.139,0.229,0.394
26069,0.1,0.084,0.099,0.206,0.511
26071,0.046,0.068,0.126,0.227,0.533
26073,0.096,0.057,0.112,0.191,0.544
26075,0.11,0.08,0.133,0.166,0.511
26077,0.02,0.059,0.062,0.167,0.693
26079,0.018,0.037,0.07,0.228,0.646
26081,0.019,0.061,0.063,0.215,0.641
26083,0.006,0.071,0.129,0.277,0.518
26085,0.052,0.122,0.073,0.204,0.548
26087,0.036,0.105,0.104,0.133,0.622
26089,0.003,0.038,0.092,0.203,0.663
26091,0.018,0.027,0.083,0.26,0.612
26093,0.042,0.021,0.052,0.198,0.687
26095,0.028,0.03,0.088,0.237,0.617
26097,0.032,0.04,0.073,0.256,0.599
26099,0.034,0.043,0.09,0.145,0.688
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37139,0.067,0.021,0.11,0.238,0.563
37141,0.05,0.035,0.142,0.169,0.603
37143,0.036,0.014,0.154,0.299,0.498
37145,0.01,0.008,0.07,0.166,0.746
37147,0.034,0.065,0.085,0.204,0.612
37149,0.018,0.115,0.105,0.263,0.499
37151,0.066,0.078,0.105,0.138,0.613
37153,0.05,0.039,0.145,0.153,0.612
37155,0.062,0.031,0.116,0.165,0.626
37157,0.125,0.094,0.124,0.111,0.546
37159,0.086,0.065,0.131,0.175,0.542
37161,0.045,0.17,0.098,0.156,0.532
37163,0.006,0.005,0.084,0.197,0.708
37165,0.137,0.029,0.178,0.199,0.458
37167,0.029,0.083,0.097,0.217,0.574
37169,0.015,0.034,0.116,0.247,0.588
37171,0.019,0.041,0.1,0.382,0.458
37173,0.079,0.061,0.086,0.247,0.528
37175,0.025,0.013,0.047,0.224,0.691
37177,0.054,0.016,0.103,0.301,0.527
37179,0.06,0.045,0.067,0.213,0.615
37181,0.042,0.031,0.137,0.159,0.631
37183,0.015,0.02,0.07,0.168,0.727
37185,0.027,0.024,0.105,0.158,0.686
37187,0.113,0.053,0.057,0.301,0.476
37189,0.062,0.01,0.072,0.363,0.493
37191,0.017,0.018,0.09,0.132,0.744
37193,0.046,0.071,0.164,0.208,0.511
37195,0.013,0.067,0.081,0.248,0.59
37197,0.073,0.027,0.08,0.199,0.621
37199,0.028,0.019,0.063,0.224,0.665
38001,0.219,0.166,0.189,0.233,0.193
38003,0.111,0.079,0.137,0.248,0.425
38005,0.289,0.194,0.118,0.195,0.204
38007,0.2,0.201,0.137,0.244,0.218
38009,0.206,0.119,0.142,0.259,0.274
38011,0.188,0.141,0.146,0.262,0.263
38013,0.191,0.147,0.151,0.258,0.253
38015,0.172,0.233,0.091,0.297,0.207
38017,0.09,0.113,0.142,0.233,0.423
38019,0.146,0.272,0.165,0.222,0.195
38021,0.151,0.077,0.227,0.277,0.267
38023,0.186,0.168,0.163,0.255,0.227
38025,0.195,0.189,0.125,0.252,0.239
38027,0.269,0.159,0.117,0.209,0.246
38029,0.16,0.221,0.094,0.302,0.222
38031,0.265,0.081,0.081,0.27,0.304
38033,0.187,0.206,0.149,0.256,0.203
38035,0.097,0.122,0.222,0.253,0.305
38037,0.177,0.233,0.125,0.275,0.19
38039,0.162,0.054,0.197,0.217,0.37
38041,0.218,0.207,0.15,0.232,0.193
38043,0.193,0.206,0.074,0.301,0.226
38045,0.115,0.07,0.193,0.307,0.315
38047,0.172,0.156,0.104,0.295,0.272
38049,0.206,0.132,0.124,0.263,0.275
38051,0.169,0.128,0.136,0.275,0.291
38053,0.19,0.182,0.142,0.253,0.232
38055,0.189,0.185,0.107,0.273,0.246
38057,0.189,0.197,0.111,0.266,0.237
38059,0.172,0.235,0.096,0.293,0.205
38061,0.192,0.16,0.134,0.258,0.256
38063,0.133,0.183,0.193,0.218,0.274
38065,0.181,0.217,0.102,0.28,0.221
38067,0.08,0.216,0.202,0.256,0.247
38069,0.253,0.136,0.12,0.246,0.246
38071,0.242,0.25,0.138,0.175,0.195
38073,0.073,0.114,0.223,0.236,0.354
38075,0.193,0.13,0.141,0.262,0.274
38077,0.155,0.174,0.189,0.116,0.366
38079,0.248,0.154,0.135,0.235,0.228
38081,0.081,0.163,0.249,0.251,0.257
38083,0.203,0.186,0.091,0.285,0.235
38085,0.157,0.228,0.108,0.295,0.211
38087,0.217,0.179,0.142,0.246,0.215
38089,0.207,0.205,0.129,0.24,0.219
38091,0.172,0.061,0.302,0.182,0.283
38093,0.202,0.053,0.076,0.314,0.354
38095,0.263,0.232,0.127,0.198,0.18
38097,0.144,0.115,0.205,0.233,0.303
38099,0.087,0.181,0.208,0.253,0.271
38101,0.195,0.138,0.127,0.263,0.276
38103,0.254,0.163,0.083,0.275,0.226
38105,0.186,0.182,0.155,0.254,0.223
39001,0.074,0.116,0.149,0.222,0.439
39003,0.085,0.104,0.114,0.382,0.314
39005,0.068,0.035,0.268,0.19,0.438
39007,0.144,0.008,0.099,0.257,0.492
39009,0.071,0.115,0.164,0.236,0.413
39011,0.131,0.089,0.153,0.342,0.285
39013,0.082,0.086,0.118,0.25,0.464
39015,0.177,0.177,0.101,0.128,0.417
39017,0.047,0.072,0.106,0.244,0.531
39019,0.147,0.261,0.052,0.15,0.39
39021,0.084,0.051,0.274,0.269,0.321
39023,0.066,0.027,0.19,0.247,0.471
39025,0.082,0.069,0.074,0.192,0.584
39027,0.171,0.198,0.071,0.105,0.455
39029,0.098,0.08,0.115,0.156,0.551
39031,0.077,0.194,0.294,0.178,0.258
39033,0.101,0.107,0.126,0.184,0.482
39035,0.042,0.052,0.084,0.213,0.609
39037,0.216,0.084,0.15,0.28,0.271
39039,0.052,0.114,0.152,0.221,0.462
39041,0.018,0.081,0.084,0.177,0.64
39043,0.078,0.022,0.091,0.179,0.63
39045,0.1,0.095,0.135,0.142,0.527
39047,0.157,0.055,0.328,0.158,0.302
39049,0.042,0.054,0.09,0.198,0.617
39051,0.029,0.046,0.318,0.272,0.335
39053,0.074,0.082,0.203,0.178,0.463
39055,0.03,0.057,0.064,0.218,0.631
39057,0.041,0.082,0.095,0.264,0.519
39059,0.101,0.162,0.137,0.181,0.419
39061,0.069,0.056,0.102,0.224,0.549
39063,0.076,0.127,0.122,0.302,0.373
39065,0.149,0.121,0.134,0.227,0.368
39067,0.107,0.141,0.153,0.233,0.365
39069,0.038,0.111,0.189,0.197,0.465
39071,0.157,0.144,0.068,0.162,0.468
39073,0.169,0.137,0.21,0.104,0.38
39075,0.05,0.087,0.134,0.271,0.457
39077,0.191,0.064,0.204,0.172,0.369
39079,0.166,0.141,0.247,0.105,0.341
39081,0.076,0.142,0.152,0.209,0.42
39083,0.138,0.1,0.065,0.17,0.526
39085,0.067,0.039,0.104,0.246,0.544
39087,0.089,0.097,0.185,0.23,0.399
39089,0.056,0.102,0.176,0.127,0.539
39091,0.136,0.117,0.314,0.248,0.186
39093,0.089,0.097,0.099,0.207,0.508
39095,0.078,0.056,0.111,0.189,0.566
39097,0.037,0.087,0.167,0.262,0.447
39099,0.046,0.099,0.099,0.231,0.525
39101,0.084,0.18,0.115,0.159,0.462
39103,0.068,0.077,0.125,0.227,0.503
39105,0.072,0.137,0.182,0.177,0.432
39107,0.102,0.105,0.245,0.314,0.233
39109,0.077,0.07,0.19,0.227,0.436
39111,0.04,0.144,0.139,0.291,0.386
39113,0.041,0.065,0.065,0.222,0.607
39115,0.142,0.114,0.153,0.259,0.332
39117,0.059,0.094,0.075,0.31,0.462
39119,0.136,0.145,0.121,0.182,0.416
39121,0.109,0.176,0.139,0.219,0.357
39123,0.052,0.035,0.084,0.219,0.609
39125,0.095,0.061,0.24,0.314,0.29
39127,0.138,0.149,0.214,0.19,0.309
39129,0.067,0.087,0.092,0.244,0.51
39131,0.186,0.124,0.124,0.158,0.409
39133,0.028,0.078,0.126,0.213,0.555
39135,0.045,0.146,0.186,0.248,0.375
39137,0.041,0.118,0.253,0.307,0.28
39139,0.066,0.08,0.213,0.221,0.419
39141,0.181,0.103,0.101,0.168,0.447
39143,0.139,0.074,0.109,0.209,0.469
39145,0.121,0.096,0.242,0.156,0.387
39147,0.119,0.195,0.243,0.164,0.279
39149,0.182,0.119,0.216,0.283,0.2
39151,0.065,0.11,0.102,0.198,0.525
39153,0.048,0.056,0.125,0.207,0.565
39155,0.054,0.059,0.121,0.23,0.535
39157,0.126,0.204,0.116,0.215,0.339
39159,0.175,0.018,0.099,0.245,0.463
39161,0.069,0.084,0.223,0.412,0.212
39163,0.09,0.165,0.218,0.147,0.38
39165,0.062,0.044,0.085,0.162,0.647
39167,0.072,0.121,0.142,0.29,0.375
39169,0.064,0.082,0.144,0.245,0.466
39171,0.035,0.133,0.199,0.208,0.425
39173,0.029,0.061,0.086,0.252,0.572
39175,0.1,0.201,0.138,0.135,0.426
40001,0.105,0.068,0.258,0.213,0.356
40003,0.203,0.096,0.188,0.25,0.262
40005,0.076,0.184,0.111,0.284,0.345
40007,0.153,0.154,0.085,0.223,0.384
40009,0.088,0.265,0.187,0.121,0.339
40011,0.029,0.081,0.244,0.32,0.326
40013,0.094,0.087,0.125,0.237,0.457
40015,0.013,0.111,0.194,0.239,0.444
40017,0.043,0.073,0.103,0.309,0.472
40019,0.128,0.169,0.138,0.213,0.351
40021,0.099,0.121,0.212,0.24,0.327
40023,0.064,0.113,0.119,0.251,0.454
40025,0.14,0.089,0.153,0.26,0.358
40027,0.046,0.101,0.079,0.25,0.524
40029,0.075,0.12,0.105,0.307,0.394
40031,0.054,0.179,0.127,0.226,0.413
40033,0.06,0.117,0.102,0.173,0.548
40035,0.247,0.113,0.236,0.112,0.292
40037,0.154,0.074,0.145,0.212,0.415
40039,0.006,0.274,0.413,0.13,0.177
40041,0.188,0.187,0.202,0.107,0.316
40043,0.066,0.185,0.286,0.219,0.244
40045,0.132,0.22,0.135,0.207,0.306
40047,0.233,0.103,0.127,0.28,0.258
40049,0.075,0.087,0.157,0.249,0.433
40051,0.047,0.098,0.185,0.249,0.422
40053,0.205,0.136,0.145,0.255,0.257
40055,0.073,0.19,0.148,0.225,0.365
40057,0.105,0.158,0.16,0.239,0.338
40059,0.158,0.189,0.118,0.257,0.278
40061,0.146,0.09,0.203,0.258,0.303
40063,0.089,0.037,0.146,0.213,0.514
40065,0.021,0.127,0.148,0.291,0.411
40067,0.084,0.108,0.163,0.14,0.505
40069,0.088,0.137,0.157,0.244,0.374
40071,0.099,0.12,0.135,0.225,0.419
40073,0.046,0.093,0.05,0.192,0.619
40075,0.025,0.19,0.131,0.239,0.415
40077,0.159,0.136,0.184,0.277,0.245
40079,0.142,0.152,0.156,0.196,0.354
40081,0.135,0.103,0.204,0.222,0.337
40083,0.03,0.027,0.114,0.31,0.519
40085,0.188,0.08,0.103,0.195,0.435
40087,0.092,0.099,0.068,0.197,0.545
40089,0.131,0.141,0.117,0.187,0.423
40091,0.085,0.057,0.265,0.221,0.373
40093,0.169,0.072,0.212,0.285,0.261
40095,0.13,0.074,0.114,0.256,0.425
40097,0.173,0.175,0.213,0.135,0.303
40099,0.042,0.154,0.247,0.218,0.34
40101,0.063,0.088,0.262,0.198,0.388
40103,0.069,0.085,0.1,0.253,0.493
40105,0.148,0.121,0.164,0.181,0.386
40107,0.112,0.035,0.241,0.193,0.419
40109,0.096,0.064,0.097,0.225,0.518
40111,0.078,0.052,0.204,0.276,0.39
40113,0.077,0.103,0.23,0.218,0.372
40115,0.154,0.13,0.229,0.151,0.337
40117,0.178,0.085,0.159,0.213,0.365
40119,0.083,0.068,0.129,0.207,0.514
40121,0.117,0.112,0.153,0.36,0.258
40123,0.029,0.033,0.102,0.235,0.601
40125,0.042,0.052,0.278,0.236,0.392
40127,0.077,0.152,0.122,0.261,0.388
40129,0.109,0.248,0.192,0.091,0.36
40131,0.097,0.105,0.164,0.201,0.433
40133,0.095,0.034,0.225,0.169,0.478
40135,0.106,0.11,0.209,0.212,0.362
40137,0.041,0.088,0.21,0.286,0.375
40139,0.123,0.12,0.102,0.225,0.43
40141,0.042,0.142,0.11,0.225,0.48
40143,0.074,0.061,0.141,0.243,0.482
40145,0.032,0.112,0.175,0.306,0.375
40147,0.118,0.183,0.162,0.192,0.345
40149,0.024,0.256,0.243,0.164,0.313
40151,0.165,0.116,0.166,0.281,0.272
40153,0.146,0.232,0.145,0.247,0.229
41001,0.051,0.189,0.179,0.189,0.392
41003,0.007,0.007,0.059,0.156,0.77
41005,0.016,0.024,0.071,0.168,0.721
41007,0.052,0.027,0.051,0.073,0.797
41009,0.048,0.015,0.158,0.174,0.604
41011,0.128,0.061,0.136,0.099,0.577
41013,0.019,0.075,0.027,0.246,0.633
41015,0.025,0.058,0.1,0.162,0.655
41017,0.01,0.061,0.014,0.25,0.666
41019,0.082,0.052,0.116,0.239,0.511
41021,0.008,0.003,0.049,0.332,0.607
41023,0.014,0.062,0.132,0.12,0.671
41025,0.032,0.099,0.114,0.157,0.598
41027,0.03,0.071,0.127,0.162,0.61
41029,0.044,0.077,0.075,0.138,0.665
41031,0.018,0.062,0.012,0.25,0.657
41033,0.028,0.056,0.072,0.192,0.652
41035,0.097,0.031,0.062,0.227,0.583
41037,0.05,0.017,0.127,0.247,0.558
41039,0.031,0.036,0.077,0.137,0.719
41041,0.001,0.012,0.047,0.131,0.81
41043,0.057,0.025,0.096,0.159,0.664
41045,0.071,0.206,0.213,0.206,0.305
41047,0.029,0.013,0.053,0.196,0.71
41049,0.026,0.004,0.083,0.197,0.69
41051,0.013,0.016,0.054,0.178,0.738
41053,0.013,0.022,0.078,0.242,0.645
41055,0.008,0.014,0.046,0.271,0.66
41057,0.125,0.04,0.092,0.163,0.581
41059,0.043,0.01,0.081,0.155,0.71
41061,0.034,0.088,0.086,0.189,0.603
41063,0.041,0.083,0.146,0.229,0.501
41065,0.019,0.034,0.069,0.197,0.681
41067,0.006,0.009,0.059,0.169,0.756
41069,0.023,0.07,0.049,0.232,0.626
41071,0.021,0.035,0.072,0.143,0.729
42001,0.039,0.034,0.083,0.238,0.606
42003,0.017,0.022,0.035,0.165,0.761
42005,0.027,0.074,0.159,0.173,0.568
42007,0.015,0.023,0.1,0.137,0.725
42009,0.014,0.111,0.1,0.205,0.57
42011,0.036,0.012,0.06,0.174,0.719
42013,0.041,0.132,0.083,0.249,0.495
42015,0.042,0.017,0.051,0.194,0.697
42017,0.014,0.018,0.032,0.131,0.805
42019,0.035,0.062,0.062,0.194,0.647
42021,0.026,0.062,0.153,0.228,0.53
42023,0.147,0.014,0.048,0.142,0.649
42025,0.013,0.062,0.015,0.145,0.764
42027,0.017,0.013,0.076,0.101,0.792
42029,0.006,0.022,0.038,0.113,0.82
42031,0.057,0.03,0.152,0.155,0.605
42033,0.051,0.055,0.176,0.206,0.512
42035,0.014,0.024,0.077,0.19,0.696
42037,0.02,0.046,0.05,0.163,0.721
42039,0.024,0.016,0.136,0.266,0.558
42041,0.031,0.032,0.183,0.192,0.562
42043,0.032,0.023,0.258,0.147,0.539
42045,0.01,0.01,0.044,0.123,0.814
42047,0.055,0.016,0.084,0.15,0.695
42049,0.016,0.025,0.057,0.136,0.766
42051,0.053,0.064,0.144,0.161,0.578
42053,0.016,0.008,0.158,0.196,0.622
42055,0.023,0.04,0.082,0.248,0.607
42057,0.013,0.145,0.053,0.23,0.559
42059,0.104,0.11,0.187,0.124,0.475
42061,0.07,0.06,0.155,0.232,0.483
42063,0.015,0.047,0.066,0.22,0.652
42065,0.083,0.046,0.142,0.197,0.531
42067,0.065,0.068,0.093,0.214,0.56
42069,0.007,0.006,0.039,0.106,0.843
42071,0.037,0.013,0.054,0.172,0.724
42073,0.034,0.07,0.093,0.301,0.503
42075,0.026,0.008,0.062,0.178,0.725
42077,0.014,0.004,0.053,0.103,0.826
42079,0.016,0.024,0.045,0.136,0.779
42081,0.062,0.057,0.06,0.207,0.614
42083,0.012,0.004,0.03,0.134,0.82
42085,0.028,0.044,0.094,0.215,0.62
42087,0.084,0.049,0.129,0.171,0.567
42089,0.019,0.047,0.052,0.061,0.822
42091,0.016,0.019,0.044,0.16,0.761
42093,0.042,0.022,0.079,0.143,0.714
42095,0.015,0.012,0.034,0.118,0.822
42097,0.064,0.013,0.078,0.197,0.649
42099,0.035,0.017,0.1,0.199,0.649
42101,0.023,0.012,0.051,0.12,0.794
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53041,0.033,0.112,0.128,0.111,0.616
53043,0.017,0.086,0.023,0.108,0.765
53045,0.011,0.024,0.091,0.152,0.721
53047,0.042,0.066,0.075,0.199,0.617
53049,0.082,0.016,0.128,0.063,0.712
53051,0.027,0.122,0.052,0.212,0.587
53053,0.028,0.057,0.103,0.206,0.606
53055,0.015,0.004,0.021,0.229,0.73
53057,0.035,0.097,0.037,0.177,0.653
53059,0.019,0.048,0.092,0.08,0.76
53061,0.017,0.014,0.056,0.191,0.721
53063,0.033,0.057,0.08,0.151,0.679
53065,0.05,0.039,0.069,0.257,0.585
53067,0.022,0.051,0.109,0.132,0.685
53069,0.045,0.057,0.079,0.161,0.658
53071,0.083,0.022,0.061,0.193,0.641
53073,0.042,0.028,0.061,0.122,0.747
53075,0.002,0.009,0.049,0.31,0.629
53077,0.012,0.035,0.069,0.143,0.74
54001,0.092,0.031,0.071,0.287,0.519
54003,0.065,0.049,0.196,0.166,0.524
54005,0.097,0.138,0.093,0.197,0.475
54007,0.102,0.131,0.167,0.202,0.398
54009,0.067,0.11,0.195,0.192,0.436
54011,0.092,0.09,0.154,0.186,0.476
54013,0.085,0.07,0.227,0.21,0.408
54015,0.098,0.097,0.162,0.152,0.491
54017,0.187,0.011,0.075,0.307,0.42
54019,0.14,0.088,0.226,0.137,0.409
54021,0.141,0.076,0.144,0.236,0.403
54023,0.1,0.232,0.067,0.193,0.407
54025,0.07,0.03,0.113,0.233,0.554
54027,0.021,0.035,0.088,0.236,0.619
54029,0.072,0.128,0.151,0.19,0.459
54031,0.039,0.152,0.103,0.23,0.476
54033,0.132,0.021,0.071,0.319,0.457
54035,0.111,0.055,0.097,0.216,0.522
54037,0.041,0.038,0.2,0.169,0.552
54039,0.145,0.039,0.064,0.147,0.605
54041,0.161,0.029,0.068,0.288,0.454
54043,0.091,0.077,0.164,0.167,0.501
54045,0.082,0.11,0.116,0.247,0.445
54047,0.07,0.095,0.189,0.226,0.42
54049,0.069,0.06,0.111,0.281,0.479
54051,0.075,0.095,0.077,0.256,0.497
54053,0.081,0.095,0.195,0.149,0.481
54055,0.076,0.152,0.174,0.103,0.495
54057,0.033,0.016,0.042,0.234,0.674
54059,0.084,0.093,0.093,0.172,0.558
54061,0.046,0.077,0.139,0.221,0.517
54063,0.048,0.063,0.116,0.14,0.634
54065,0.025,0.085,0.169,0.258,0.463
54067,0.06,0.088,0.166,0.143,0.542
54069,0.108,0.068,0.092,0.243,0.49
54071,0.06,0.142,0.128,0.214,0.456
54073,0.088,0.108,0.17,0.287,0.346
54075,0.075,0.03,0.17,0.227,0.499
54077,0.057,0.028,0.087,0.247,0.58
54079,0.087,0.048,0.088,0.184,0.593
54081,0.087,0.083,0.257,0.148,0.426
54083,0.098,0.05,0.14,0.265,0.447
54085,0.141,0.059,0.183,0.262,0.356
54087,0.082,0.024,0.191,0.21,0.493
54089,0.053,0.104,0.166,0.111,0.567
54091,0.072,0.032,0.092,0.288,0.517
54093,0.108,0.087,0.048,0.292,0.465
54095,0.083,0.056,0.137,0.272,0.452
54097,0.131,0.039,0.077,0.278,0.475
54099,0.111,0.119,0.186,0.162,0.423
54101,0.065,0.083,0.163,0.211,0.479
54103,0.051,0.067,0.081,0.279,0.521
54105,0.112,0.059,0.219,0.222,0.389
54107,0.094,0.099,0.149,0.277,0.381
54109,0.073,0.098,0.176,0.245,0.408
55001,0.163,0.189,0.227,0.213,0.208
55003,0.078,0.038,0.118,0.363,0.403
55005,0.138,0.163,0.07,0.259,0.37
55007,0.067,0.039,0.107,0.392,0.395
55009,0.069,0.053,0.184,0.208,0.486
55011,0.039,0.106,0.172,0.269,0.414
55013,0.097,0.163,0.082,0.236,0.422
55015,0.064,0.1,0.141,0.33,0.365
55017,0.059,0.098,0.184,0.246,0.414
55019,0.162,0.149,0.158,0.146,0.384
55021,0.035,0.073,0.082,0.222,0.589
55023,0.202,0.033,0.11,0.158,0.497
55025,0.017,0.02,0.092,0.23,0.641
55027,0.11,0.138,0.166,0.2,0.386
55029,0.021,0.03,0.059,0.264,0.627
55031,0.044,0.042,0.092,0.327,0.495
55033,0.062,0.076,0.179,0.266,0.417
55035,0.042,0.076,0.203,0.255,0.424
55037,0.053,0.073,0.143,0.238,0.494
55039,0.121,0.155,0.158,0.244,0.322
55041,0.085,0.053,0.162,0.215,0.484
55043,0.103,0.102,0.18,0.166,0.449
55045,0.092,0.084,0.119,0.269,0.436
55047,0.092,0.122,0.184,0.117,0.486
55049,0.065,0.077,0.092,0.333,0.433
55051,0.04,0.052,0.142,0.248,0.518
55053,0.027,0.1,0.14,0.329,0.404
55055,0.114,0.093,0.096,0.179,0.518
55057,0.113,0.124,0.18,0.232,0.352
55059,0.077,0.083,0.097,0.155,0.589
55061,0.033,0.035,0.112,0.208,0.612
55063,0.064,0.072,0.076,0.289,0.498
55065,0.056,0.098,0.197,0.253,0.396
55067,0.085,0.102,0.142,0.239,0.432
55069,0.101,0.105,0.132,0.204,0.459
55071,0.175,0.089,0.259,0.133,0.345
55073,0.123,0.136,0.165,0.236,0.34
55075,0.093,0.104,0.066,0.258,0.479
55077,0.174,0.191,0.2,0.163,0.271
55078,0.042,0.103,0.31,0.246,0.3
55079,0.053,0.067,0.119,0.264,0.497
55081,0.073,0.03,0.154,0.284,0.46
55083,0.07,0.082,0.186,0.215,0.447
55085,0.057,0.05,0.155,0.186,0.552
55087,0.085,0.124,0.135,0.302,0.353
55089,0.076,0.113,0.091,0.3,0.42
55091,0.071,0.056,0.198,0.233,0.443
55093,0.063,0.094,0.144,0.25,0.449
55095,0.081,0.113,0.154,0.253,0.399
55097,0.157,0.159,0.215,0.188,0.28
55099,0.059,0.081,0.079,0.15,0.631
55101,0.061,0.067,0.167,0.193,0.512
55103,0.312,0.074,0.055,0.116,0.443
55105,0.023,0.139,0.096,0.277,0.464
55107,0.095,0.135,0.106,0.168,0.496
55109,0.101,0.102,0.085,0.342,0.37
55111,0.063,0.163,0.113,0.193,0.468
55113,0.058,0.126,0.133,0.256,0.427
55115,0.05,0.121,0.355,0.188,0.286
55117,0.128,0.114,0.174,0.199,0.385
55119,0.201,0.165,0.09,0.12,0.424
55121,0.02,0.126,0.085,0.381,0.387
55123,0.157,0.052,0.093,0.244,0.454
55125,0.051,0.047,0.138,0.231,0.533
55127,0.116,0.146,0.137,0.191,0.41
55129,0.082,0.168,0.117,0.243,0.39
55131,0.031,0.142,0.22,0.298,0.309
55133,0.071,0.165,0.145,0.258,0.361
55135,0.076,0.069,0.206,0.143,0.506
55137,0.109,0.147,0.202,0.186,0.356
55139,0.074,0.194,0.126,0.156,0.45
55141,0.131,0.177,0.215,0.181,0.296
56001,0.136,0.1,0.151,0.181,0.432
56003,0.182,0.187,0.12,0.257,0.253
56005,0.216,0.164,0.131,0.201,0.289
56007,0.044,0.137,0.087,0.278,0.454
56009,0.138,0.113,0.121,0.293,0.335
56011,0.145,0.115,0.143,0.192,0.404
56013,0.16,0.062,0.06,0.294,0.424
56015,0.201,0.169,0.111,0.223,0.296
56017,0.208,0.093,0.068,0.307,0.324
56019,0.183,0.208,0.103,0.216,0.29
56021,0.143,0.127,0.1,0.221,0.409
56023,0.131,0.183,0.128,0.235,0.324
56025,0.1,0.084,0.094,0.325,0.398
56027,0.169,0.191,0.177,0.222,0.241
56029,0.189,0.153,0.191,0.205,0.262
56031,0.149,0.149,0.123,0.16,0.418
56033,0.17,0.251,0.099,0.203,0.278
56035,0.223,0.111,0.061,0.231,0.374
56037,0.061,0.295,0.23,0.146,0.268
56039,0.095,0.157,0.16,0.247,0.34
56041,0.098,0.278,0.154,0.207,0.264
56043,0.204,0.155,0.069,0.285,0.287
56045,0.142,0.129,0.148,0.207,0.374


================================================
FILE: tidyverse/part2_winter2021/tidyverse_part2_winter2021.Rmd
================================================
---
title: "Tidyverse Tibbles and Bits"
subtitle: "Bioinformatics Coffee Hour"
date: "Mar 9, 2021"
author: "Brian Arnold; Danielle Khost"
output: html_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```


This is the second session covering tidyverse commands. We will show you how to use some new functions, but we will also use some of the functions we previously used last week which will be good for extra practice. If you missed the previous session, that should not be a problem.

From last time, we looked at this [cheat sheet](https://rstudio.com/wp-content/uploads/2015/02/data-wrangling-cheatsheet.pdf) to occasionally guide us.

It's typical to load all the libraries you need at the top of your code.
```{r}
library(tidyverse)
```

------
For this week, rather than use an example dataset, we are going to use several publically available datasets from the NYT github repository on the COVID-19 pandemic. This is because "wild-caught" datasets are often much messier than example datasets, so it will help us become more familiar with how you would actually go about processing data. We are going to keep our analysis purely descriptive, as it would be irresponsible to make any conclusive claims after fiddling around with R for only an hour!


## Dataset 1: mask useage

Let's load in the first data set on mask usage in the US, which is in the form of a comma separated list. This data is from a NYT survey where they asked the question: "How often do you wear a mask in public when you expect to be within six feet of another person?" (As a note, this survey was done several months ago, so these numbers are not the most up-to-date.) 

```{r}
<<<<<<< HEAD
mask_use_wide <- read_delim(file="mask-use-by-county.csv", delim=",")
=======
mask_use_wide <- read_delim(file="https://raw.githubusercontent.com/nytimes/covid-19-data/bde13b021e99c6b4a63fb66a6144e889cc635e31/mask-use/mask-use-by-county.csv", delim=",")
>>>>>>> bfb375bc22239322d6e6089bdac1ac5be52372c1
mask_use_wide
```


It looks like for all the counties are represented with a 5-digit FIPS code, which is not exactly ideal because we don't know what these numbers mean. We'll get the names in a moment by combining these data with another file that also has these FIPS codes AND county names. We can thus match these data tables based on shared FIPS codes to combine them.


## Dataset 2: US case counts

In addition to the mask data, let's also load in data on COVID-19 case counts across the US, also supplied by the NYT, and combine it with our mask use data. Unlike the previous data that we loaded in from our local file system, these data we will directly download because they are so large. This will also show how you can specify web addresses instead of file names for read_delim(). This file is also comma separated, so we will let read_delim() know this.

```{r, include=FALSE}
cases <- read_delim(file="https://github.com/nytimes/covid-19-data/raw/master/us-counties.csv", delim=",") %>%
  arrange(fips)
```

<<<<<<< HEAD
This is a lot of data. For ~3000 US counties, there's an estimate of cumulative COVID-19 cases and deaths for each day since last March (it's now March again!!!). For now, let's just look at the start of this month. 
=======
This is a lot of data. For ~3000 US counties, there's an estimate of cumulative COVID-19 cases and deaths for each day since last March (it's now March again!!!). For now, let's just look at the start of this month.
>>>>>>> bfb375bc22239322d6e6089bdac1ac5be52372c1

```{r, include=FALSE}
cases_latest <- cases %>% 
  filter(date == "2021-03-01") %>%
  arrange(fips)
```

As a refresher, we are using the filter() function to select only the rows that match the string "2021-03-01" in the date column, then using the arrange() function to sort the counties by FIPS code.

-----

## Dataset 3: census data

Let's load in some additional data on population size by county, which are obtained from www.census.gov.

```{r, include=FALSE}
pop_sizes <- read_delim(file="co-est2019-annres.csv.xz", delim=",")
```

When I'm working with larger files and I want to make sure they looks like I expect them to, instead of opening up the file and scrolling through all the lines, checking each by eye, I typically use various commands to just get an idea of what it looks like before proceeding. Oftentimes, this is sufficient; we don't need to check every line.

These are just a few commands I might use to look at the first and last few lines, how many rows there are overall, and also get an idea of how much missing data there might be:
```{r}
head(pop_sizes) 
nrow(pop_sizes) 
sum(!is.na(pop_sizes$'2019')) 
tail(pop_sizes)
```

Ok so these data are a little messy and need to be cleaned up.

From these commands, we can see that the county names are all preceded with a ".", something we'll deal with in a moment. The number of rows (i.e. the number of counties) in the table is very similar to what we expect (3,142 according to wikipedia), but maybe larger by 7 rows or so. We can see that the entire US was included in this table, which is not a county so we should remove that.

Looking at the bottom of the table, we see some footnotes were left in. These are also contributing to the extra number of rows and should absolutely be removed.

Let's remove these rows at the beginning and end with the **slice()** function, which pulls out rows from a data frame/tibble based on their ordinal position. It keeps all the columns, in contrast to the select() function that we discussed last week.

```{r, include=FALSE}
pop_sizes <- pop_sizes %>% dplyr::slice(2:3143) 
```

With this command, we are slicing out the middle of the data frame to remove the row with data from the whole US, as well as the footnotes at the bottom.

The 'Geographic Area' column has two pieces of info in it: county and state. We should split this column into two columns so that we can separately access the info. For instance, later on we will do an analysis not by county but by state, summing up across a state's counties, and this requires having this information in it's own separate column. To separate this info into 2 seperate columns, we will use the separate() function:

```{r, include=FALSE}
pop_sizes <- pop_sizes %>% separate(col = 'Geographic Area',
                                    into = c("County", "State"),
                                    sep = ", ")
```

Note that unlike last time we are specifying what we want to separate on with the "sep=" argument, which is a comma followed by a white space.


Let's get rid of extra characters in the new 'County' column. We don't need the "." that precedes each name, and it's pointless that each individual county name is followed by "County"... we know they're counties based on the name of the column. We can use the mutate() function to add new rows with new names, but if the name of the new row we want is the same as an existing one, it just replaces it!

```{r, include=FALSE}
pop_sizes <- pop_sizes %>% 
  mutate(County = str_remove(string = County,pattern = ".")) %>%
  mutate(County = str_remove(string = County,pattern = " County"))
```

We are combining the mutate() function with **str_remove()** which, as the name suggests, will remove a given string that matches a pattern from a character column, the County column in this case.


This table includes population size data for quite a few years. Let's just get the most recent population estimate, selecting the 2019 column with the select() function, which as mentioned above pulls out specific columns:

```{r, include=FALSE}
pop_sizes <- pop_sizes %>%
  select(County, State, "2019")
```


As we did above for the data sets we used last week, we can also combine all of these individual commands into one compact command that might look something like this:
```{r, include=FALSE}
pop_sizes <- read_delim(file="co-est2019-annres.csv.xz", delim=",") %>% 
  slice(2:3143) %>%
  separate(col = 'Geographic Area', into = c("County", "State"), sep = ", ") %>%
  mutate(County = str_remove(string = County,pattern = ".")) %>%
  mutate(County = str_remove(string = County,pattern = " County")) %>%
  select(County, State, "2019")
```


We can take a quick peek at these population size data, now that they're cleaned, using the summary() function, which shows there's a ton of variability in county sizes!
```{r}
summary(pop_sizes)
```



## Merging data sets with inner_join()

Let's combine these data on population sizes with our previous data on case counts and mask usage, as it may reveal interesting dynamics that vary by county size.

We will first use the join command on the case count data and the county population size data, joining by BOTH county and state. There are many different types of joining functions and it can get fairly confusing, so today we will focus just on **inner_join**. 

```{r, include=FALSE}
cases_popsize <- inner_join(x=cases_latest, y=pop_sizes, 
                                  by=c("county"="County", "state"="State"))
```

The "x=" and "y=" arguments specify which datasets we want to merge. Inner_join() returns all rows in dataset x that has matching values in dataset y, and all the columns in both x and y.

The "by=" arugment tells what variables the function should merge by, as a character vector. In other words we tell the function what columns we want to match: it compares the values in the columns in x and y, and if they match it merges them. We are matching the "county" coumn in cases_latest with the "County" column in pop_sizes, and the "state" and "State" columns in the same.

Note that because the column names (i.e. variable names) are slightly different between datasets, we have to specify which we are matching using an "="; if they had the same names, we could just list them. 


## More merging 

Now let's merge this table that has case counts and population size with the mask data! For now, let's not add in all the mask data. Let's only incorporate the the proportion of people who are "ALWAYS" masked for each county.

```{r, include=FALSE}
cases_popsize_masked <- inner_join(x=cases_popsize, 
                            y=select(mask_use_wide, c(COUNTYFP, ALWAYS)),
                            by=c("fips" = "COUNTYFP"))
```

Note that we are nesting the select() function within our inner_join() call, allowing us to subset the mask useage dataset without making an entirely new data frame.


Looking at this data table, for each county we now have cases, deaths, population size, and the proportion of people who are always masked.

You may also notice the population size data had a column entitled "2019" for size estimates during that year. Let's make this more informative and change it to "pop_size", and rename the ALWAYS column so that we know it's referring to always masked.

```{r, include=FALSE}
cases_popsize_masked <- cases_popsize_masked %>%
  rename("pop_size" = "2019", "AlwaysMasked" = ALWAYS)
```


## Visualizing data

Let's quickly probe these data just to get an idea of what they look like. The main goal here is to teach you how to use R/tidyverse commands to quickly and easily explore your data. Again, due to the context of these data, let's keep any results as fairly descriptive observations. We can't say anything conclusive without more complicated analyses that we'll leave to the public health officials.

Let's assume these case count data are accurately measuring the number of people who are getting coronavirus infections (which is obviously an underestimate, as many cases can be asymptomatic!). Assuming this, what fraction of a county's population is testing positive, and how does this fraction vary with population size? Let's use the mutate() function to add a new column with number of cases normalized by population size:

```{r}
cases_popsize_masked <- cases_popsize_masked %>%
  mutate(FracPos = cases/pop_size)
```


Do counties with large populations have a higher proportion of case counts? Let's use a simple plotting function to plot population size on the x axis and proportion of cases on y axis. Since we will look at the proportion of cases by dividing the data in the case counts column by the data in the population size column, let's just make a new column called 'FracPos' that contains this information

We can make a quick plot of these data using a base R (i.e. not tidyverse) plotting function:
```{r}
cases_popsize_masked <- cases_popsize_masked %>%
  mutate(FracPos = cases/pop_size)

plot(x=cases_popsize_masked$pop_size,
     y=cases_popsize_masked$FracPos,
     log="x",
     ylab="Fraction of cases",
     xlab="pop size")
# DON'T RUN CORRELATION ANALYSES WITH HETEROSCEDASTICITY
```

Looks interesting. Counties with larger population sizes may look like they have higher proportion of cases, but it's a little complicated because there's a lot of variability for counties with smaller population sizes. 


One thing we might be interested in are those outliers with very high rates of positive cases. What counties are these, and in what states? We can easily check this with the following:

```{r}
cases_popsize_masked %>%
  filter(FracPos > 0.08) %>%
  arrange(desc(FracPos))
```


Let's add another column for death rate, and visualize the relationship between the fraction of positive cases and the death rate:
```{r}
cases_popsize_masked <- cases_popsize_masked %>%
  mutate(FracDeaths = deaths/cases)

plot(x=cases_popsize_masked$FracPos,
     y=cases_popsize_masked$FracDeaths,
     log="",
     xlab="FracCases",
     ylab="FracDeaths")
```


What are these counties that have been severely impacted by deaths? Is this just noise from counties with small sizes and/or small case counts?

```{r}
cases_popsize_masked %>%
  filter(FracDeaths > 0.06) %>%
  arrange(desc(FracDeaths))
```


# Using group_by() and summarize() functions to evaluate data

The use of the group_by() and summarize() functions allows us to quickly get extremely informative information from our data. 

We'll show how they're useful in many ways, but first we need to do a little more reshaping of our data.

Let's go back to our original mask use data table, not the combined one. What if we wanted to quickly get the overall mean values of ALWAYS, ... , NEVER across all the counties? We could use the original data in wide format, and calculate the mean for each of these columns. We could also use the simple summary() function which outputs a table:

```{r}
mean(mask_use_wide$NEVER)
mean(mask_use_wide$RARELY)
mean(mask_use_wide$SOMETIMES)
mean(mask_use_wide$FREQUENTLY)
mean(mask_use_wide$ALWAYS)
summary(mask_use_wide)
```

You can do it with way, but it is kind of clunky, and it wouldn't be practical if your dataset has too many variables. It is easier to first convert the data in long format:

```{r, include=FALSE}
mask_use_long <- mask_use_wide %>%
  pivot_longer(cols = -COUNTYFP, names_to = "MaskUseResponse", values_to = "MaskUseProportion") %>%
  rename("fips" = COUNTYFP) %>%
  arrange(fips)
```

As a reminder, the "cols=" argument specifies which columns we are reshaping into rows (all *except* the "COUNTYFP" column), the "names_to=" argument specifies the name of the column that will hold the names of the reshaped columns, and the "values_to=" argument gives the name of the column that will hold the values of the reshaped columns.

We can now use the **group_by()** function, which takes our existing dataset and converts it to a grouped dataset based on a given variable, and combine it with the **summarize()** function (different from summary used above!) to get mean for each group:

```{r}
mask_use_long %>% 
  group_by(MaskUseResponse) %>% 
  summarize(mean(MaskUseProportion))
```


## More data analysis and visualization

Let's explore another example of group_by() and summarize() using our tibble that has all our combined information.

Instead of focusing on counties as our unit of analysis, we can use group_by() to easily switch to a state level analysis by telling the summarize() function to perform analyses for each state, combining all the rows that have the same value under the "state" column. When we combine the group_by() function with summarize(), we can easily do some pretty powerful analyses that would take a lot of effort using other programs.

```{r}
cases_popsize_masked %>%
  group_by(state) %>%
  summarize(MeanMasked = mean(AlwaysMasked)) %>%
  arrange(desc(MeanMasked))
```


This is potentially a bad analysis because we are calculating a mean for a state based on it's counties, and some of these counties may be represented by fewer people. So if we truly wanted the average behavior of a state, we should weight each county by its population size. Why should a county with a size of 400 contribute equally to the mean as a county of size 40,000?

The following code calculates a custom mean value, where each county is weighted by its population size:

```{r}
cases_popsize_masked %>%
  group_by(state) %>%
  summarize(WeightedMeanMasked = sum(AlwaysMasked*pop_size)/sum(pop_size)) %>%
  arrange(desc(WeightedMeanMasked))
```


Did this weighting by county size actually make a difference? Let's store these analyses as 'x' and 'y' and compare them:
```{r}
x <- cases_popsize_masked %>%
  group_by(state) %>%
  summarize(MeanMasked = mean(AlwaysMasked)) %>%
  arrange(state)
y <- cases_popsize_masked %>%
  group_by(state) %>%
  summarize(WeightedMeanMasked = sum(AlwaysMasked*pop_size)/sum(pop_size)) %>%
  arrange(state)
hist(x$MeanMasked - y$WeightedMeanMasked)
```

It looks like on average, AlwaysMasked estimates that don't take pop_size into account are systematically lower than those that do. When we don't take county size into account, we effectively give more weight to smaller counties. 


# Summary:
## New tidyverse functions used today:
- slice() to select specific rows by index number
- inner_join() to merge datasets based on given variable(s)
- mutate() with str_remove to get rid of characters or words we don't want, there are many similar functions in the stringr package, which has its own cheat sheet
- group_by() to categorize our data by the values in a particular column (here, by state)
- summarize() to calculate simple but very informative statistics, such as mean and variance


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) 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padding-right: 8px;
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margin-bottom: 10px;
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margin: 0;
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.pagedtable th {
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border: none;
border-bottom: 2px solid #dddddd;
min-width: 45px;
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.pagedtable td {
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.pagedtable .even {
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.pagedtable a {
-webkit-touch-callout: none;
-webkit-user-select: none;
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cursor: pointer;
padding: 0 5px 0 5px;
float: right;
border: 0;
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color: #999;
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a.pagedtable-index-nav-disabled:hover {
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a.pagedtable-index-current:hover {
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border: 0;
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color: #333;
font-size: 9px;
padding: 0 0 0 0;
cursor: default;
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color: #333;
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padding-bottom: 5px;
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color: #999;
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text-overflow: ellipsis;
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text-overflow: ellipsis;
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font-weight: 400;
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        max: positiveIntOrNull(columns.max),
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      character: 8,
      height: 15,
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        end: end,
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      };
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      return rowStart;
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        }

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          // width in characters
          chars: maxChars,
          // width for the inner html columns
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        };
      });
    };

    me.getWidth = function() {
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      widthOuter = widthOuter + me.padding * paddingColChars * measurer.measures.character;

      if (me.hasMoreLeftColumns()) {
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      if (me.hasMoreRightColumns()) {
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    me.setVisibleColumns = function(columnNumber, newVisibleColumns, paddingCount) {
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      me.padding = paddingCount;

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    me.incColumnNumber = function(increment) {
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    me.setColumnNumber = function(newNumber) {
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  var measurer = new Measurer(data, options);
  var columns = new Columns(data, source.columns, options);

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  var header = null;
  var footer = null;
  var tbody = null;

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      "border-bottom: " + columnNavigationWidthPX + "px solid transparent;" +
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    var header = document.createElement("th");
    header.appendChild(arrow);
    header.setAttribute("style",
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      "width: " + columnNavigationWidthPX + "px;");

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      header.appendChild(renderColumnNavigation(columns.visible, false));

    if (typeof(clear) == "undefined" || clear) clearHeader();
    var thead = pagedTable.querySelectorAll("thead")[0];
    thead.appendChild(fragment);
  };

  me.animateColumns = function(backwards) {
    var thead = pagedTable.querySelectorAll("thead")[0];

    var headerOld = thead.querySelectorAll("tr")[0];
    var tbodyOld = table.querySelectorAll("tbody")[0];

    me.fitColumns(backwards);

    renderHeader(false);

    header.style.opacity = "0";
    header.style.transform = backwards ? "translateX(-30px)" : "translateX(30px)";
    header.style.transition = "transform 200ms linear, opacity 200ms";
    header.style.transitionDelay = "0";

    renderBody(false);

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      headerOld.style.transform = "translateX(0px)";
      headerOld.style.opacity = "1";
      headerOld.style.transition = "transform 100ms linear, opacity 100ms";
      headerOld.setAttribute("class", "pagedtable-remove-head");
      if (headerOld.style.transitionEnd) {
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          var headerOldByClass = thead.querySelector(".pagedtable-remove-head");
          if (headerOldByClass) thead.removeChild(headerOldByClass);
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      else {
        thead.removeChild(headerOld);
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    if (tbodyOld) table.removeChild(tbodyOld);

    tbody.style.opacity = "0";
    tbody.style.transition = "transform 200ms linear, opacity 200ms";
    tbody.style.transitionDelay = "0ms";

    // force relayout
    window.getComputedStyle(header).opacity;
    window.getComputedStyle(tbody).opacity;

    if (headerOld) {
      headerOld.style.transform = backwards ? "translateX(20px)" : "translateX(-30px)";
      headerOld.style.opacity = "0";
    }

    header.style.transform = "translateX(0px)";
    header.style.opacity = "1";

    tbody.style.opacity = "1";
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  me.onChange = function(callback) {
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    var pageData = data.slice(page.getRowStart(), page.getRowEnd());

    pageData.forEach(function(dataRow, idxRow) {
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        if (dataCell === "NA") htmlCell.setAttribute("class", "pagedtable-na-cell");
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      for (var idx = 0; idx < columns.getPaddingCount(); idx++) {
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    for (var idxPadding = 0; idxPadding < page.getPaddingRows(); idxPadding++) {
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    var pageEnd = page.getRowEnd();
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      infoText = infoText + " | " + (columns.number + 1) + "-" +
        (Math.min(columns.number + columns.visible, columns.total)) +
        " of " + totalColumnsLabel + " columns";
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    return infoText;
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  var clearFooter = function() {
    footer = pagedTable.querySelectorAll("div.pagedtable-footer")[0];
    footer.innerHTML = "";

    return footer;
  };

  var createPageLink = function(idxPage) {
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    pageLinkClass = idxPage === page.number ? "pagedtable-index pagedtable-index-current" : "pagedtable-index";
    pageLink.setAttribute("class", pageLinkClass);
    pageLink.setAttribute("data-page-index", idxPage);
    pageLink.onclick = function() {
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      renderBody();
      renderFooter();

      triggerOnChange();
    };

    pageLink.appendChild(document.createTextNode(idxPage + 1));

    return pageLink;
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    var next = document.createElement("a");
    next.appendChild(document.createTextNode("Next"));
    next.onclick = function() {
      page.setPageNumber(page.number + 1);
      renderBody();
      renderFooter();

      triggerOnChange();
    };
    if (data.length > page.rows) footer.appendChild(next);

    var pageNumbers = document.createElement("div");
    pageNumbers.setAttribute("class", "pagedtable-indexes");

    var pageRange = page.getVisiblePageRange();

    if (pageRange.first) {
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      pageNumbers.appendChild(pageLink);

      var pageSeparator = document.createElement("div");
      pageSeparator.setAttribute("class", "pagedtable-index-separator-left");
      pageSeparator.appendChild(document.createTextNode("..."))
      pageNumbers.appendChild(pageSeparator);
    }

    for (var idxPage = pageRange.start; idxPage < pageRange.end; idxPage++) {
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      pageNumbers.appendChild(pageLink);
    }

    if (pageRange.last) {
      var pageSeparator = document.createElement("div");
      pageSeparator.setAttribute("class", "pagedtable-index-separator-right");
      pageSeparator.appendChild(document.createTextNode("..."))
      pageNumbers.appendChild(pageSeparator);

      var pageLink = createPageLink(page.total - 1);
      pageNumbers.appendChild(pageLink);
    }

    if (data.length > page.rows) footer.appendChild(pageNumbers);

    var previous = document.createElement("a");
    previous.appendChild(document.createTextNode("Previous"));
    previous.onclick = function() {
      page.setPageNumber(page.number - 1);
      renderBody();
      renderFooter();

      triggerOnChange();
    };
    if (data.length > page.rows) footer.appendChild(previous);

    var infoLabel = document.createElement("div");
    infoLabel.setAttribute("class", "pagedtable-info");
    infoLabel.setAttribute("title", getLabelInfo());
    infoLabel.appendChild(document.createTextNode(getLabelInfo()));
    footer.appendChild(infoLabel);

    var enabledClass = "pagedtable-index-nav";
    var disabledClass = "pagedtable-index-nav pagedtable-index-nav-disabled";
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    measuresTable.style.position = "absolute";
    measuresTable.style.whiteSpace = "nowrap";
    measuresTable.style.height = "auto";
    measuresTable.style.width = "auto";

    var measuresRow = document.createElement("tr");
    measuresTable.appendChild(measuresRow);

    measuresCell = document.createElement("td");
    var sampleString = "ABCDEFGHIJ0123456789";
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    measuresRow.appendChild(measuresCell);

    tableDiv.appendChild(measuresTable);
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    var pagedTableClass = data.length > 0 ?
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    tableDiv.setAttribute("class", pagedTableClass);

    renderMeasures();
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    columns.calculateWidths(measurer.measures);

    table = document.createElement("table");
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    table.setAttribute("class", "table table-condensed");
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    table.appendChild(document.createElement("thead"));

    var footerDiv = document.createElement("div");
    footerDiv.setAttribute("class", "pagedtable-footer");
    tableDiv.appendChild(footerDiv);

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    me.render();

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    if (tableDiv.clientWidth <= 0) {
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  var registerWidths = function() {
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  var parsePadding = function(value) {
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  me.fixedHeight = function() {
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  me.fitRows = function() {
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      return;

    measurer.calculate(measuresCell);

    var rows = options.rows.min !== null ? options.rows.min : 0;
    var headerHeight = header !== null && header.offsetHeight > 0 ? header.offsetHeight : 0;
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    rows = options.rows.min !== null ? Math.max(options.rows.min, rows) : rows;

    page.setRows(rows);
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  // The goal of this function is to add as many columns as possible
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    var columnNumber = columns.number;
    var paddingCount = 0;

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    var columnHistory = [];

    var lastTableHeight = 0;
    var backwards = startBackwards;

    var tableDivStyle = window.getComputedStyle(tableDiv, null);
    var tableDivPadding = parsePadding(tableDivStyle.paddingLeft) +
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    var currentWidth = 0;

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        break;
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      columnHistory.push({
        columnNumber: columnNumber,
        visibleColumns: visibleColumns,
        paddingCount: paddingCount
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      if (columns.max !== null &&
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        break;
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        if (!startBackwards && columnNumber > 0) {
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          backwards = false;
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      // when moving backwards try fitting left columns first
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      if (columnNumber + visibleColumns < columns.total) {
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    var lastRenderableColumn = {
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        visibleColumns: visibleColumns,
        paddingCount: paddingCount
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    if (columnHistory.length > 0) {
      lastRenderableColumn = columnHistory[columnHistory.length - 1];
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    columns.setVisibleColumns(
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    if (pagedTable.offsetWidth > 0) {
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    registerWidths();
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  me.fit = function(startBackwards) {
    me.fitRows();
    me.fitColumns(startBackwards);
  }

  me.render = function() {
    me.fitColumns(false);

    // render header/footer to measure height accurately
    renderHeader();
    renderFooter();

    me.fitRows();
    renderBody();

    // re-render footer to match new rows
    renderFooter();
  }

  var resizeLastWidth = -1;
  var resizeLastHeight = -1;
  var resizeNewWidth = -1;
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  me.resize = function(newWidth, newHeight) {

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      if (
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        resizeLastHeight = resizeNewHeight;

        setTimeout(resizeDelayed, 200);
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        resizeLastWidth = -1;
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    resizeNewWidth = newWidth;
    resizeNewHeight = newHeight;

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};

var PagedTableDoc;
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  window.addEventListener("resize", PagedTableDoc.resizeAll);

  return PagedTableDoc;
})(PagedTableDoc || (PagedTableDoc = {}));

window.onload = function() {
  PagedTableDoc.initAll();
};
</script>
<script>

/**
 * jQuery Plugin: Sticky Tabs
 *
 * @author Aidan Lister <aidan@php.net>
 * adapted by Ruben Arslan to activate parent tabs too
 * http://www.aidanlister.com/2014/03/persisting-the-tab-state-in-bootstrap/
 */
(function($) {
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window.buildTabsets = function(tocID) {

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};

</script>
<script>
window.initializeCodeFolding = function(show) {

  // handlers for show-all and hide all
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    $(this).before(div);
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}
</script>
<script>
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<h1 class="title toc-ignore">R Notebook</h1>

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<div id="new-fxns-slice-inner_join-summarize-group_by" class="section level1">
<h1>NEW FXNS: slice(), inner_join(), summarize(), group_by()</h1>
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<p>This is the second session covering tidyverse commands. We will show you how to use some new functions, but we will also use some of the functions we previously used last week which will be good for extra practice. If you missed the previous session, that should not be a problem.</p>
<p>From last time, we looked at this <a href="https://rstudio.com/wp-content/uploads/2015/02/data-wrangling-cheatsheet.pdf">cheat sheet</a> to occasionally guide us.</p>
<p>It’s typical to load all the libraries you need at the top of your code.</p>
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<pre class="r"><code>library(tidyverse)</code></pre>
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<hr />
<p>For this week, rather than use an example dataset, we are going to use several publically available datasets from the NYT github repository on the COVID-19 pandemic. This is because “wild-caught” datasets are often much messier than example datasets, so it will help us become more familiar with how you would actually go about processing data. We are going to keep our analysis purely descriptive, as it would be irresponsible to make any conclusive claims after fiddling around with R for only an hour!</p>
<div id="dataset-1-mask-useage" class="section level2">
<h2>Dataset 1: mask useage</h2>
<p>Let’s load in the first data set on mask usage in the US, which is in the form of a comma separated list. This data is from a NYT survey where they asked the question: “How often do you wear a mask in public when you expect to be within six feet of another person?” (As a note, this survey was done several months ago, so these numbers are not the most up-to-date.)</p>
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<pre class="r"><code>mask_use_wide &lt;- read_delim(file=&quot;~/Documents/GitHub/bioinformatics-coffee-hour/tidyverse/part2_winter2021/mask-use-by-county.csv&quot;, delim=&quot;,&quot;)</code></pre>
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<!-- rnb-output-begin eyJkYXRhIjoiUGFyc2VkIHdpdGggY29sdW1uIHNwZWNpZmljYXRpb246XG5jb2xzKFxuICBDT1VOVFlGUCA9IFx1MDAxYlszMW1jb2xfY2hhcmFjdGVyKClcdTAwMWJbMzltLFxuICBORVZFUiA9IFx1MDAxYlszMm1jb2xfZG91YmxlKClcdTAwMWJbMzltLFxuICBSQVJFTFkgPSBcdTAwMWJbMzJtY29sX2RvdWJsZSgpXHUwMDFiWzM5bSxcbiAgU09NRVRJTUVTID0gXHUwMDFiWzMybWNvbF9kb3VibGUoKVx1MDAxYlszOW0sXG4gIEZSRVFVRU5UTFkgPSBcdTAwMWJbMzJtY29sX2RvdWJsZSgpXHUwMDFiWzM5bSxcbiAgQUxXQVlTID0gXHUwMDFiWzMybWNvbF9kb3VibGUoKVx1MDAxYlszOW1cbilcbiJ9 -->
<pre><code>Parsed with column specification:
cols(
  COUNTYFP = col_character(),
  NEVER = col_double(),
  RARELY = col_double(),
  SOMETIMES = col_double(),
  FREQUENTLY = col_double(),
  ALWAYS = col_double()
)</code></pre>
<!-- rnb-output-end -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxubWFza191c2VcbmBgYCJ9 -->
<pre class="r"><code>mask_use</code></pre>
<!-- rnb-source-end -->
<!-- rnb-frame-begin {"metadata":{"classes":["spec_tbl_df","tbl_df","tbl","data.frame"],"ncol":6,"nrow":3142},"rdf":"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  </script>
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<p>It looks like for all the counties are represented with a 5-digit FIPS code, which is not exactly ideal because we don’t know what these numbers mean. We’ll get the names in a moment by combining these data with another file that also has these FIPS codes AND county names. We can thus match these data tables based on shared FIPS codes to combine them.</p>
</div>
<div id="dataset-2-us-case-counts" class="section level2">
<h2>Dataset 2: US case counts</h2>
<p>In addition to the mask data, let’s also load in data on COVID-19 case counts across the US, also supplied by the NYT, and combine it with our mask use data. Unlike the previous data that we loaded in from our local file system, these data we will directly download because they are so large. This will also show how you can specify web addresses instead of file names for read_delim(). This file is also comma separated, so we will let read_delim() know this.</p>
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<p>This is a lot of data. For ~3000 US counties, there’s an estimate of cumulative COVID-19 cases and deaths for each day since late January (it’s now March again!!!). For now, let’s just look at the start of this month.</p>
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<p>As a refresher, we are using the filter() function to select only the rows that match the string “2021-03-01” in the date column, then using the arrange() function to sort the counties by FIPS code.</p>
<hr />
</div>
<div id="dataset-3-census-data" class="section level2">
<h2>Dataset 3: census data</h2>
<p>Let’s load in some additional data on population size by county, which are obtained from www.census.gov.</p>
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<p>When I’m working with larger files and I want to make sure they looks like I expect them to, instead of opening up the file and scrolling through all the lines, checking each by eye, I typically use various commands to just get an idea of what it looks like before proceeding. Oftentimes, this is sufficient; we don’t need to check every line.</p>
<p>These are just a few commands I might use to look at the first and last few lines, how many rows there are overall, and also get an idea of how much missing data there might be:</p>
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<pre class="r"><code>head(pop_sizes) </code></pre>
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<pre class="r"><code>nrow(pop_sizes) </code></pre>
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<pre><code>[1] 3149</code></pre>
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<pre class="r"><code>sum(!is.na(pop_sizes$'2019')) # how many counties have size estimates for 2019? it's even closer to the 3142 we expect!</code></pre>
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<pre><code>[1] 3143</code></pre>
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<pre class="r"><code>tail(pop_sizes)</code></pre>
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<p>Ok so these data are a little messy and need to be cleaned up.</p>
<p>From these commands, we can see that the entire US was included in this table, which is not a county. We should remove that. Also, county names are all preceded with a “.”, something we’ll deal with in a moment. The number of rows in the table is very similar to what we expect (3,142 according to wikipedia), but maybe larger by 7 rows or so. The row containing the entire US contributes to this excess of rows beyond our expectation.</p>
<p>Looking at the bottom of the table, we see some footnotes were left in. These are probably contributing to the extra number of rows and should absolutely be removed.</p>
<p>Let’s remove these rows at the beginning and end with the <strong>slice()</strong> function, which pulls out rows from a data frame/tibble based on their ordinal position. It keeps all the columns, in contrast to the select() function that we discussed last week.</p>
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<p>With this command, we are slicing out the middle of the data frame to remove the row with data from the whole US, as well as the footnotes at the bottom.</p>
<p>The ‘Geographic Area’ column has two pieces of info in it: county and state. We should split this column into two columns so that we can separately access the info. For instance, later on we will do an analysis not by county but by state, summing up across a state’s counties, and this requires having this information in it’s own separate column. To separate this info into 2 seperate columns, we will use the separate() function:</p>
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<p>Note that unlike last time we are specifying what we want to separate on with the “sep=” argument, which is a comma followed by a white space.</p>
<p>Let’s get rid of extra characters in the new ‘County’ column. We don’t need the “.” that precedes each name, and it’s pointless that each individual county name is followed by “County”… we know they’re counties based on the name of the column. We can use the mutate() function to add new rows with new names, but if the name of the new row we want is the same as an existing one, it just replaces it!</p>
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<p>We are combining the mutate() function with <strong>str_remove()</strong> which, as the name suggests, will remove a given pattern from a character column, the County column in this case.</p>
<p>This table includes population size data for quite a few years. Let’s just get the most recent population estimate, selecting the 2019 column with the select() function, which as mentioned above pulls out specific columns:</p>
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<p>As we did above for the data sets we used last week, we can also combine all of these individual commands into one compact command that might look something like this:</p>
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<p>We can take a quick peek at these population size data, now that they’re cleaned, using the summary() function, which shows there’s a ton of variability in county sizes!</p>
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<pre class="r"><code>summary(pop_sizes)</code></pre>
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<pre><code>    County             State                2019         
 Length:3142        Length:3142        Min.   :      86  
 Class :character   Class :character   1st Qu.:   10902  
 Mode  :character   Mode  :character   Median :   25726  
                                       Mean   :  104468  
                                       3rd Qu.:   68073  
                                       Max.   :10039107  </code></pre>
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</div>
<div id="merging-data-sets-with-inner_join" class="section level2">
<h2>Merging data sets with inner_join()</h2>
<p>Let’s combine these data on population sizes with our previous data on case counts and mask usage, as it may reveal interesting dynamics that vary by county size.</p>
<p>We will first use the join command on the case count data and the county population size data, joining by BOTH county and state. There are many different types of joining functions and it can get fairly confusing, so today we will focus just on <strong>inner_join</strong>.</p>
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<p>The “x=” and “y=” arguments specify which datasets we want to merge. Inner_join() returns all rows in dataset x that has matching values in dataset y, and all the columns in both x and y.</p>
<p>The “by=” arugment tells what variables the function should merge by, as a character vector. In other words we tell the function what columns we want to match: it compares the values in the columns in x and y, and if they match it merges them. We are matching the “county” coumn in cases_latest with the “County” column in pop_sizes, and the “state” and “State” columns in the same.</p>
<p>Note that because the column names (i.e. variable names) are slightly different between datasets, we have to specify which we are matching using an “=”; if they had the same names, we could just list them.</p>
</div>
<div id="more-merging" class="section level2">
<h2>More merging</h2>
<p>Now let’s merge this table that has case counts and population size with the mask data! For now, let’s not add in all the mask data. Let’s only incorporate the the proportion of people who are “ALWAYS” masked for each county.</p>
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<p>Note that we are nesting the select() function within our inner_join() call, allowing us to subset the mask useage dataset without making an entirely new data frame.</p>
<p>Looking at this data table, for each county we now have cases, deaths, population size, and the proportion of people who are always masked.</p>
<p>You may also notice the population size data had a column entitled “2019” for size estimates during that year. Let’s make this more informative and change it to “pop_size”, and rename the ALWAYS column so that we know it’s referring to always masked.</p>
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<div id="visualizing-data" class="section level2">
<h2>Visualizing data</h2>
<p>Let’s quickly probe these data just to get an idea of what they look like. The main goal here is to teach you how to use R/tidyverse commands to quickly and easily explore your data. Again, due to the context of these data, let’s keep any results as fairly descriptive observations. We can’t say anything conclusive without more complicated analyses that we’ll leave to the public health officials.</p>
<p>Let’s assume these case count data are accurately measuring the number of people who are getting coronavirus infections (which is obviously an underestimate, as many cases can be asymptomatic!). Assuming this, what fraction of a county’s population is testing positive, and how does this fraction vary with population size? Let’s use the mutate() function to add a new column with number of cases normalized by population size:</p>
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<pre class="r"><code>cases_popsize_masked &lt;- cases_popsize_masked %&gt;%
  mutate(FracPos = cases/pop_size)</code></pre>
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<p>Do counties with large populations have a higher proportion of case counts? Let’s use a simple plotting function to plot population size on the x axis and proportion of cases on y axis. Since we will look at the proportion of cases by dividing the data in the case counts column by the data in the population size column, let’s just make a new column called ‘FracPos’ that contains this information</p>
<p>We can make a quick plot of these data using a base R (i.e. not tidyverse) plotting function:</p>
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<pre class="r"><code>cases_popsize_masked &lt;- cases_popsize_masked %&gt;%
  mutate(FracPos = cases/pop_size)
plot(x=cases_popsize_masked$pop_size,
     y=cases_popsize_masked$FracPos,
     log=&quot;x&quot;,
     ylab=&quot;Fraction of cases&quot;,
     xlab=&quot;pop size&quot;)</code></pre>
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4C3VatW9XE4xyBAgAABAgQIECBAgAABAgQIECDQrAVmaNZX7+IJECBAgAABAgQIECBAgAABAgQIECBQxQICvFXceZpOgAABAgQIECBAgAABAgQIECBAgEDzFhDgbd797+oJECBAgAABAgQIECBAgAABAgQIEKhiAQHeKu48TSdAgAABAgQIECBAgAABAgQIECBAoHkLCPA27/539QQIECBAgAABAgQIECBAgAABAgQIVLGAAG8Vd56mEyBAgAABAgQIECBAgAABAgQIECDQvAUEeJt3/7t6AgQIECBAgAABAgQIECBAgAABAgSqWECAt4o7T9MJECBAgAABAgQIECBAgAABAgQIEGjeAgK8zbv/XT0BAgQIECBAgAABAgQIECBAgAABAlUsIMBbxZ2n6QQIECBAgEDjCdx3332x6667xsILLxwzzTRTtGvXLpZddtk44ogjYvDgwY3XEGciQIAAAQIECBAgQIDAeAICvONhWCRAgAABAgQI/Fjgww8/jPXXXz823njjuOaaa+K9996L77//Pr766qsYNGhQ9O7dOzp37hxHHnlk/PDDDz/e3XsCBAgQIECAAAECBAg0qIAAb4PyOjgBAgQIECBQzQLvvPNOrLzyyvHvf/875plnnjjllFPitddei9GjR8fIkSPjsccei7333jvGjh0bp556avz6178W5K3mDtd2AgQIECBAgAABAlUoIMBbhZ2myQQIECBAgEDDC3z33Xex1VZbxQcffBDdu3cvgd0cpbvUUktFq1atom3btrHmmmvGxRdfHI8++mi0b98++vbtG7169Wr4xjkDAQIECBAgQIAAAQIE/n8BAV63AgECBAgQIEBgIgLnnXdeScGw9NJLx1133RVzzz33RGr976rVV1897rzzzmjZsmWcddZZ8frrr0+yrg0ECBAgQIAAAQIECBCoTwEB3vrUdCwCBAgQIEBguhE455xzyrVkwDYnVJtSWW211Uq6hjFjxsQFF1wwpeq2EyBAgAABAgQIECBAoF4EBHjrhdFBCBAgQIAAgelJIEfgZv7djh07lsnVantte+yxR6l6zz331HYX9QgQIECAAAECBAgQIDBVAgK8U8VnZwIECBAgQGB6FHj77bfLZXXp0iVatGhR60tcYYUVSt0MDisECBAgQIAAAQIECBBoDAEB3sZQdg4CBAgQIECgqgRGjRpV2tumTZs6tTsnX8s8vN9//31kqgaFAAECBAgQIECAAAECDS0gwNvQwo5PgAABAgQIVJ1ApmbIUteRuO+9914J7M4777wl0Ft1F67BBAgQIECAAAECBAhUnYAAb9V1mQYTIECAAAECDS2QqRbatm0bL774Yrz77ru1Pt0dd9xR6nbr1q3W+6hIgAABAgQIECBAgACBqREQ4J0aPfsSIECAAAEC06VA69atY9ttt41x48bFMcccU6trHDFiRJx66qml7i677FKrfVQiQIAAAQIECBAgQIDA1AoI8E6toP0JECBAgACB6VLg2GOPjZlnnjn69OkTZ5111mSv8dtvv40ddtghhg0bFquttlr06NFjsvVtJECAAAECBAgQIECAQH0JCPDWl6TjECBAgAABAtOVwCKLLBKXXnppuaaePXvG7rvvHkOHDv3JNT7yyCOxxhprxL333hvt27eP66+//id1rCBAgAABAgQIECBAgEBDCbRsqAM7LgECBAgQIECg2gV22mmnGDt2bOy9995x1VVXxTXXXBNdu3aNxRdfPEaNGlVy9FYmYvvFL34Rd955Zyy88MLVftnaT4AAAQIECBAgQIBAFQkI8FZRZ2kqAQIECBAg0PgCmU939dVXj7/+9a9xyy23xNNPP13+Ki2Zf/75Y7/99osc5TvLLLNUVnslQIAAAQIECBAgQIBAowgI8DYKs5MQIECAAAEC1Syw2GKLxdVXXx0XXXRRPPPMM/HBBx9EmzZtItM4LL/88tGiRYtqvjxtJ0CAAAECBAgQIECgigUEeKu48zSdAAECBAgQaFyBnHStW7dujXtSZyNAgAABAgQIECBAgMBkBEyyNhkcmwgQIECAAAECBAgQIECAAAECBAgQINCUBQR4m3LvaBsBAgQIECBAgAABAgQIECBAgAABAgQmIyDAOxkcmwgQIECAAAECBAgQIECAAAECBAgQINCUBQR4m3LvaBsBAgQIECBAgAABAgQIECBAgAABAgQmIyDAOxkcmwgQIECAAAECBAgQIECAAAECBAgQINCUBQR4m3LvaBsBAgQIECBAgAABAgQIECBAgAABAgQmIyDAOxkcmwgQIECAAAECBAgQIECAAAECBAgQINCUBQR4m3LvaBsBAgQIECBAgAABAgQIECBAgAABAgQmIyDAOxkcmwgQIECAAAECBAgQIECAAAECBAgQINCUBQR4m3LvaBsBAgQIECBAgAABAgQIECBAgAABAgQmIyDAOxkcmwgQIECAAAECBAgQIECAAAECBAgQINCUBQR4m3LvaBsBAgQIECBAgAABAgQIECBAgAABAgQmIyDAOxkcmwgQIECAAAECBAgQIECAAAECBAgQINCUBQR4m3LvaBsBAgQIECBAgAABAgQIECBAgAABAgQmIyDAOxkcmwgQIECAAAECBAgQIECAAAECBAgQINCUBQR4m3LvaBsBAgQIECBAgAABAgQIECBAgAABAgQmIyDAOxkcmwgQIECAAAECBAgQIECAAAECBAgQINCUBQR4m3LvaBsBAgQIECBAgAABAgQIECBAgAABAgQmIyDAOxkcmwgQIECAAAECBAgQIECAAAECBAgQINCUBQR4m3LvaBsBAgQIECBAgAABAgQIECBAgAABAgQmIyDAOxkcmwgQIECAAAECBAgQIECAAAECBAgQINCUBQR4m3LvaBsBAgQIECBAgAABAgQIECBAgAABAgQmI9ByMttsagSBYcOGxfDhw+Obb74pf23atInZZ589Zptttph77rkj3ysECBAgQIAAAQIECBAgQIAAAQIECBCYmIAA78RUGnDdyJEj46qrroo+ffrEoEGDIt9PqrRs2TKWXXbZWHXVVWOLLbaIzTbbLFq0aDGp6tYTIECAAAECBAgQIECAAAECBAgQINDMBKRoaKQO//jjj+OAAw6IBRZYIA488MDo37//ZIO72awxY8bE888/HxdccEEJ8C633HLRr1+/Rmqx0xAgQIAAAQIECBAgQIAAAQIECBAg0NQFjOBthB76/PPPY8MNN4yBAwfWnC1H4nbs2DEWWmihaN++fcw888zRunXrEtQdNWpUjBgxIoYOHRpDhgyJ7777ruyXI3632mqrOOOMM+Lggw+uOZYFAgQIECBAgAABAgQIECBAgAABAgSap4AAbwP3+9dffx2bb755TXB35ZVXjp49e8b6669fArtTOv33338fTz31VEnrcPnll0e+P+SQQ2LJJZcsKRumtL/tBAgQIECAAAECBAgQIECAAAECBAhMvwJSNDRw3954440lHUOeZscdd4wBAwaU1xy1W5vSqlWrWHPNNePCCy+M22+/PfJ9liOPPDLGjh1bm0OoQ4AAAQIECBAgQIAAAQIECBAgQIDAdCogwNvAHfvEE0+UM2T+3JxcbYYZfj55TrJ2+umnl+Nluod33nmngVvv8AQIECBAgAABAgQIECBAgAABAgQINGWBnx9tbMpX1YTa9vjjj5fWbLnlljWjb6emedtuu23N7m+88UbNsgUCBAgQIECAAAECBAgQIECAAAECBJqfgABvA/f5+++/X87QqVOnejnT3HPPXRMo/vbbb+vlmA5CgAABAgQIECBAgAABAgQIECBAgEB1CgjwNnC/Lb744uUM/fv3r5czZcqHnGgtyworrFAvx3QQAgQIECBAgAABAgQIECBAgAABAgSqU0CAt4H7rWvXruUMN9xwQzz88MNTdbYvvvgiDj300HKMueaaKxZddNGpOp6dCRAgQIAAAQIECBAgQIAAAQIECBCobgEB3gbuv169epWUCqNGjYqtt946Lrzwwhg9enSdz/rCCy/ERhttFPma5Q9/+EOdj2EHAgQIECBAgAABAgQIECBAgAABAgSmL4GW09flNL2ryRQNJ598chx++OHx5ZdflsBsLq+99trRpUuXMgq3Q4cOMfPMM0ebNm1izJgxkcHgESNGxNChQ2Pw4MHxyCOPxKBBg2ouLgO9J5xwQs17CwQIECBAgAABAgQIECBAgAABAgQINE8BAd5G6PfDDjsscnK0Aw44IHJitJEjR8Zdd91V/up6+k022ST69OkTM8xg8HVd7dQnQIAAAQIECBAgQIAAAQIECBAgML0JiBI2Uo/uueeeMWTIkDjqqKNivvnmq9NZW7duXdI79O3bN+65557I/LsKAQIECBAgQIAAAQIECBAgQIAAAQIEjOBtxHugffv2cdJJJ5W/d999NwYMGBBvvvlmSceQ6RtyZG+rVq2ibdu2Mdtss0Wmd+jcuXMsv/zyZV0jNtWpCBAgQIAAAQIECBAgQIAAAQIECBCoAgEB3mnUSYssskjkn0KAAAECBAgQIECAAAECBAgQIECAAIGfKyBFw8+Vsx8BAgQIECBAgAABAgQIECBAgAABAgSmsYARvNO4AyZ1+g8//DAGDx4cX3/9dXTp0qXOeXsndVzrCRAgQIAAAQIECBAgQIAAAQIECBCYfgSM4G3kvvzuu+/i9ddfj+HDh0/0zAMHDowtttgi5p9//ujevXtsuumm0bFjx1hggQXizDPPjHHjxk10PysJECBAgAABAgQIECBAgAABAgQIEGh+AgK8jdTnn3/+eey7774x55xzxtJLLx3zzjtvdOvWLc4///yaFmRwN4O6/fr1q1lXWRg2bFgceuihsdFGG8VHH31UWe2VAAECBAgQIECAAAECBAgQIECAAIFmLCBFQyN0/rfffhtbbrllPP744zVn++GHH+Kxxx4rf7lyv/32i5133jm++OKLUmeWWWaJZZddNjp06BAvv/xyvP3222X07gMPPBC77rpr3H///TXHskCAAAECBAgQIECAAAECBAgQIECAQPMUMIK3gfs9A7k77LBDTXB3jjnmiHXWWSe6du0arVq1Kmc/6KCD4qSTTopBgwaV9xnoHTp0aAwYMCDuuOOOkou3f//+ZeRvVsgg79VXX13q+g8BAgQIECBAgAABAgQIECBAgAABAs1XQIC3gfv+2muvjb59+5az7LbbbiVw++CDD8YzzzxTlldcccUYM2ZMHHPMMaXOZpttFn369Im55pprgpatuuqq8cQTT5TcvLmhV69eE2z3hgABAgQIECBAgAABAgQIECBAgACB5icgwNvAfZ7B2izLL798XHrppdG2bduaM2b6hXvvvTcyHUOlnHHGGZXFn7xm/t6zzjqrrP/ggw/iP//5z0/qWEGAAAECBAgQIECAAAECBAgQIECAQPMREOBt4L7O/LlZdtppp2jZ8qcpj9u3bx+//e1vS5127drFUkstVZYn9Z911123ZlPl2DUrLBAgQIAAAQIECBAgQIAAAQIECBAg0KwEBHgbuLs//fTTcoYFF1xwkmdaaaWVyrZZZ501WrRoMcl6uSEDwm3atCl1chSvQoAAAQIECBAgQIAAAQIECBAgQIBA8xUQ4G3gvl9kkUXKGQYPHjzJM+Wo3Y4dO8bHH38cX3755STr5Yb33nsvRo0aVeosvvjik61rIwECBAgQIECAAAECBAgQIECAAAEC07fAT3MGTN/X2+hXt/TSS8drr71W8u8eeuihE+TgrTSme/fuMWzYsBK4bdWqVWX1RF//53/+p2Z9586da5YbciEDypkOYty4cVN9mm+++Waqj+EABAgQIECAAAECBAgQIECAAAECBAj8r4AAbwPfCbvvvnvcfvvtMXTo0Nh6663jqquuigUWWGCiZ62kXpjoxv+uzODu2WefXTZvsMEGMfvss0+qar2u32uvveK6666r12NKL1GvnA5GgAABAgQIECBAgAABAgQIECDQTAUEeBu443v06BHrrLNOPPTQQ/Hvf/87llxyyVhvvfXi2GOPja5du07x7Jm24Zprrokbbrghnn766VI/R/mOP5J3igeZygobbrhhvPXWWzF27NipPFKUkcDffvttzDbbbFN9LAcgQIAAAQIECBAgQIAAAQIECBAg0NwFBHgb4Q64/vrrY9ttt43HH388MkXBXXfdFQcddFCtzvzII4/EYYcdVlO3ZcuWcdZZZ8UyyyxTs66hF/bcc8/Iv/ooK664Yjz//PPRrl27+jicYxAgQIAAAQIECBAgQIAAAQIECBBo1gImWWuE7u/QoUM8+OCDccwxx0SnTp3KGZdbbrlanXmxxRarqZepHXIU8AEHHFCzzgIBAgQIECBAgAABAgQIECBAgAABAs1XQIC3kfo+0yqccMIJMWTIkBgwYEBk0Lc2ZYklloi//OUv8dhjj8W7774b3bp1q81u6hAgQIAAAQIECBAgQIAAAQIECBAg0AwEpGho5E5u0aJFrLrqqrU+a06kdtxxx9W6vooECBAgQIAAAQIECBAgQIAAAQIECDQfASN4m09fu1ICBAgQIECAAAECBAgQIECAAAECBKYzAQHe6axDXQ4BAgQIECBAgAABAgQIECBAgAABAs1HQIqGKurr77//Pj7++OOaFi+44II1yxYIECBAgAABAgQIECBAgAABAgQIEGh+AgK8VdTnL730Uqy00ko1LR43blzNsgUCBAgQIECAAAECBAgQIECAAAECBJqfgBQNza/PXTEBAgQIECBAgAABAgQIECBAgAABAtOJgADvdNKRLoMAAQIECBAgQIAAAQIECBAgQIAAgeYnIEVDFfX58ssvHx999FEVtVhTCRAgQIAAAQIECBAgQIAAAQIECBBoSAEB3obUredjt2zZMjp06FDPR3U4AgQIECBAgAABAgQIECBAgAABAgSqVUCKhmrtOe0mQIAAAQIECBAgQIAAAQIECBAgQKDZCxjBO41vgWHDhsXw4cPjm2++KX9t2rSJ2WefPWabbbaYe+65I98rBAgQIECAAAECBAgQIECAAAECBAgQmJiAAO/EVBpw3ciRI+Oqq66KPn36xKBBgyLfT6pkSoZll102Vl111dhiiy1is802ixYtWkyquvUECBAgQIAAAQIECBAgQIAAAQIECDQzASkaGqnDP/744zjggANigQUWiAMPPDD69+8/2eBuNmvMmDHx/PPPxwUXXFACvMstt1z069evkVrsNAQIECBAgAABAgQIECBAgAABAgQINHUBI3gboYc+//zz2HDDDWPgwIE1Z8uRuB07doyFFloo2rdvHzPPPHO0bt26BHVHjRoVI0aMiKFDh8aQIUPiu+++K/vliN+tttoqzjjjjDj44INrjmWBAAECBAgQIECAAAECBAgQIECAAIHmKSDA28D9/vXXX8fmm29eE9xdeeWVo2fPnrH++uuXwO6UTv/999/HU089VdI6XH755ZHvDznkkFhyySVLyoYp7W87AQIECBAgQIAAAQIECBAgQIAAAQLTr4AUDQ3ctzfeeGNJx5Cn2XHHHWPAgAHlNUft1qa0atUq1lxzzbjwwgvj9ttvj3yf5cgjj4yxY8fW5hDqECBAgAABAgQIECBAgAABAgQIECAwnQoYwdvAHfvEE0+UM2T+3JxcbYYZfn5MPSdZO/300+OPf/xjGRH8zjvvxOKLL97AV+DwBAgQIECAwPQs8M0338Rdd90V999/f0kPNW7cuOjUqVN52mjLLbeMtm3bTs+X79oIECBAgAABAgQIVL2AAG8Dd+Hjjz9ezpBfkCqjb6fmlNtuu20J8OYx3njjDQHeqcG0LwECBAgQaOYCmf7pqKOOio8++ugnEpdeemlJJ3X88cfHH/7wh59st4IAAQIECBAgQIAAgaYhIMDbwP3w/vvvlzPkSJj6KHPPPXcJFGcu3m+//bY+DukYBAgQIECAQDMTyFG6+++/f1xwwQXlyldZZZWSQqpz586RE8G+9tprccMNN0Q+ibTffvuVFFOXXXbZVD2J1MyIXS4BAgQIECBAgACBRhP4+fkCGq2J1X2iSgqF/v3718uF5BetDO5mWWGFFerlmA5CgAABAgQINC+BE044oQR3Z5lllrjmmmviySefLJO4brzxxrHRRhvFQQcdFPkU0k033RTt2rWLK6+8soz0bV5KrpYAAQIECBAgQIBAdQgI8DZwP3Xt2rWcIUfBPPzww1N1ti+++CIOPfTQcoy55porFl100ak6np0JECBAgACB5ifw+uuvRwZ4Z5xxxrjttttil112mSTCdtttF/369StPD/Xu3TtefPHFSda1gQABAgQIECBAgACBaSMgwNvA7r169SpfikaNGhVbb711XHjhhTF69Og6n/WFF14oI2ryNYtceHUmtAMBAgQIECDwX4EM1I4ZMyZ+//vfl88WU0Lp1q1byf8/duzYOPXUU6dU3XYCBAgQIECAAAECBBpZQA7eBgbPFA0nn3xyHH744fHll1+WwGwur7322tGlS5cyCrdDhw4x88wzR5s2bcoXrgwGjxgxosxkPXjw4HjkkUdi0KBBNS3NRydz5I1CgAABAgQIEKiLQObevfPOO8sumYahtuX//b//F6effnoZzZvB4ZYtfYSsrZ16BAgQIECAAAECBBpawKfzhhb+7/EPO+ywyMnRDjjggDIx2siRI+Ouu+4qf3U9/SabbBJ9+vQxyUld4dQnQIAAAQIE4pNPPil/7du3j6WXXrrWIgsttFAsvPDCMWTIkMgJZBdZZJFa76siAQIECBAgQIAAAQINKyBFQ8P61hx9zz33LF+KjjrqqJhvvvlq1tdmoXXr1iW9Q9++feOee+6JzL+rECBAgAABAgTqKvD555+XXX7OZ4nKPpVj1PXc6hMgQIAAAQIECBAg0DACRvA2jOtEj5qjZU466aTy9+6778aAAQPizTffLOkYMn1Djuxt1apVtG3bNmabbbbI9A6dO3eO5Zdfvqyb6EGtJECAAAECBAjUUiA/i2T5+OOPa7nH/1X76KOPypvKMf5viyUCBAgQIECAAAECBKalgADvNNLPRxs93jiN8J2WAAECBAg0U4EchZvpFt5777145plnYqWVVqqVxCuvvBIffvhhzDvvvDH//PPXah+VCBAgQIAAAQIECBBoHAEpGhrH2VkIECBAgAABAk1CYNttty3tOO2002rdnkrdX//61+YBqLWaigQIECBAgAABAgQaR0CAt3GcnYUAAQIECBAg0CQEDj/88JhlllnipptuiksvvXSKbbruuuviyiuvjJwT4Mgjj5xifRUIECBAgAABAgQIEGhcAQHexvV2NgIECBAgQIDANBXo2LFjnHvuuaUN++yzT/z1r3+NUaNG/aRNo0ePjlNOOSV23XXXsu3MM8+UXuonSlYQIECAAAECBAgQmPYCcvBO+z7QAgIECBAgQIBAowrsscceMXz48MjRvMcff3xcdNFFkekXlllmmZKC4dVXX43bb789Pvjgg2jRokWceOKJsf/++zdqG52MAAECBAgQIECAAIHaCQjw1s5JLQIECBAgQIDAdCXQs2fPMslavj777LNx/vnn/+T6lltuuTjjjDNigw02+Mk2KwgQIECAAAECBAgQaBoCArxNox+0ggABAgQIECDQ6ALdu3ePZ555Jl544YW4//7747333otx48ZFp06dYv311y8B4EZvlBMSIECAAAECBAgQIFAnAQHeOnGpTIAAAQIECBCY/gS6dOkS+acQIECAAAECBAgQIFB9AiZZq74+02ICBAgQIECAAAECBAgQIECAAAECBAgUAQFeNwIBAgQIECBAgAABAgQIECBAgAABAgSqVECAt0o7TrMJECBAgAABAgQIECBAgAABAgQIECAgwOseIECAAAECBAgQIECAAAECBAgQIECAQJUKCPBWacdpNgECBAgQIECAAAECBAgQIECAAAECBAR43QMECBAgQIAAAQIECBAgQIAAAQIECBCoUgEB3irtOM0mQIAAAQIECBAgQIAAAQIECBAgQICAAK97gAABAgQIECBAgAABAgQIECBAgAABAlUqIMBbpR2n2QQIECBAgAABAgQIECBAgAABAgQIEBDgdQ8QIECAAAECBAgQIECAAAECBAgQIECgSgVaVmm7NZsAAQIECBAgUK8Co0aNijvuuCPuueeeeOeddyLfd+zYMbp37x7bb799dOrUqV7P52AECBAgQIAAAQIECBCoDwEB3vpQdAwCBAgQIECgqgVuvvnm6NmzZwwdOvQn15FB3169esUBBxwQp5xySrRu3fondawgQIAAAQIECBAgQIDAtBKQomFayTsvAQIECBAg0CQEjj322DJCN4O7K664Ypx99tnx8MMPx5NPPhnXXXdd7LjjjjFmzJg466yzYp111okvv/yySbRbIwgQIECAAAECBAgQIJACRvC6DwgQIECAAIFmK3DZZZfFcccdF61atYq///3vsd9++0WLFi1qPFZZZZUS4P3Tn/4U22yzTQwYMCB22GGHksZh/Ho1O1ggQIAAAQIECBAgQIBAIwsYwdvI4E5HgAABAgQINA2Bzz77rKRlyNZccsklsf/++08Q3B2/lV26dCmjejt06BD//Oc/49prrx1/s2UCBAgQIECAAAECBAhMMwEB3mlG78QECBAgQIDAtBS48MILS7qFzTbbLHbbbbcpNiUnWevdu3epd+qpp06xvgoECBAgQIAAAQIECBBoDAEB3sZQdg4CBAgQIECgyQnk5GlZcvK02paddtop5pxzzhg4cGC8++67td1NPQIECBAgQIAAAQIECDSYgABvg9E6MAECBAgQINCUBV5//fXSvDXXXLPWzWzZsmWsuuqqpf5rr71W6/1UJECAAAECBAgQIECAQEMJCPA2lKzjEiBAgAABAk1WYOzYsTFixIiYYYYZYrbZZqtTO+eYY45S/4svvqjTfioTIECAAAECBAgQIECgIQQEeBtC1TEJECBAgACBJi2Qgd155pknMtD70Ucf1amtw4YNK/XnnXfeOu2nMgECBAgQIECAAAECBBpCQIC3IVQdkwABAgQIEGjyAl27di1tvO+++2rd1hz1O2DAgJhxxhmjS5cutd5PRQIECBAgQIAAAQIECDSUgABvQ8k6LgECBAgQINCkBbbffvvSvtNOOy3GjBlTq7aeccYZMXr06Fh//fVjrrnmqtU+KhEgQIAAAQIECBAgQKAhBQR4G1LXsQkQIECAAIEmK7DrrrvGEkssEa+88kr88Y9/nGI7//Wvf8Upp5xS6h177LFTrK8CAQIECBAgQIAAAQIEGkNAgLcxlJ2DAAECBAgQaHICrVq1ij59+kSbNm3ivPPOix122GGi+XhzdO8555wTm2++eXz//ffRq1evWH311Zvc9WgQAQIECBAgQIAAAQLNU6Bl87xsV02AAAECBAgQiFhllVXitttuK8HdG2+8Mfr27RubbLJJya+bgd+33347+vXrF++//37hOvjgg+Okk05CR4AAAQIECBAgQIAAgSYjIMDbZLpCQwgQIECAAIFpIZAB3eeeey6OPPLIuOWWW0rAN4O+45dlllkm/va3v8VWW201/mrLBAgQIECAAAECBAgQmOYCArzTvAs0gAABAgQIEJjWAosvvnjcdNNN8eGHH8Y///nPeOedd2LUqFHRsWPH6N69e6y44orTuonOT4AAAQIECBAgQIAAgYkKCPBOlMVKAgQIECBAoDkKZEB3jz32aI6X7poJECBAgAABAgQIEKhSAZOsVWnHaTYBAgQIECBAgAABAgQIECBAgAABAgQEeN0DBAgQIECAAAECBAgQIECAAAECBAgQqFIBAd4q7TjNJkCAAAECBAgQIECAAAECBAgQIECAgACve4AAAQIECBAgQIAAAQIECBAgQIAAAQJVKiDAW6Udp9kECBAgQIAAAQIECBAgQIAAAQIECBAQ4HUPECBAgAABAgQIECBAgAABAgQIECBAoEoFBHirtOM0mwABAgQIECBAgAABAgQIECBAgAABAgK87gECBAgQIECAAAECBAgQIECAAAECBAhUqYAAb5V2nGYTIECAAAECBAgQIECAAAECBAgQIEBAgNc9QIAAAQIECBAgQIAAAQIECBAgQIAAgSoVEOCt0o7TbAIECBAgQIAAAQIECBAgQIAAAQIECAjwugcIECBAgAABAgQIECBAgAABAgQIECBQpQICvFXacZpNgAABAgQIECBAgAABAgQIECBAgAABAV73AAECBAgQIECAAAECBAgQIECAAAECBKpUoGWVtluzG1Hgb3/7W5x55pkxbty4qT7r559/Xo5ReZ3qAzoAAQIECBAgQIAAAQIECBAgQIAAgWYsIMDbjDu/tpf+3nvvxSeffFLb6rWqN3r06FrVU4kAAQIECBAgQIAAAQIECBAgQIAAgUkLCPBO2saW/1/gvPPOixNPPDHGjh071SbrrbdeDBw4MDp06DDVx3IAAgQIECBAgAABAgQIECBAgAABAs1dQIC3ud8Btbz+ueaaq5Y1J1+tZUu33OSFbCVAgAABAgQIECBAgAABAgQIECBQewGTrNXeSk0CBAgQIECAAAECBAgQIECAAAECBAg0KQEB3ibVHRpDgAABAgQIEKhega+++iref//9+O6776r3IrScAAECBAgQIECAQJUJCPBWWYdpLgECBAgQIECgKQkMHjw49t9//1hwwQWjXbt20alTp2jTpk0su+yycfzxx8eXX37ZlJqrLQQIECBAgAABAgSmOwEB3umuS10QAQIECBAgQKDhBcaNGxcnnHBCdO7cOc4///z44IMPom3btiXQ27p16xg0aFD89a9/jcUWWyz69u3b8A1yBgIECBAgQIAAAQLNVECAt5l2vMsmQIAAAQIECEyNwL777ht/+ctfYuzYsfH73/8+XnzxxRg5cmQMHTo0MlXDAw88EOuvv34MHz48evToEVdfffXUnM6+BAgQIECAAAECBAhMQkCAdxIwVhMgQIAAAQIEKgKffPJJPPXUU/Hggw/Gq6++Gj/88ENlU7N8vfDCC+Piiy+OWWedNe6+++7I98stt1yNRcuWLUtwN4O8J598cgkC77PPPiUIXFPJAgECBAgQIECAAAEC9SLQsl6O8qOD5CN7Y8aMiVatWk2wJb8Q5ZeAhx56KOacc87YZpttYuutt44WLVpMUM8bAgQIECBAgMC0FsjPM9dff32cffbZJbib7ytlnnnmid/85jdx9NFHx/zzz19ZPdHX0aNHl88+b7zxRslH26FDh1h55ZVj+eWXn2j9pr5yxIgRccwxx5RmXnbZZbHRRhtNtsm9evUqE6+dd955cfjhh8d999032fo2EiBAgAABAgQIECBQN4F6DfAOGzasjOC46qqryuv4H/hzBMcmm2wywYiXfFRvt912iyuvvLJurVabAAECBAgQINCAAp999lkJ4P773/8uZ8mRqr/85S/LiNUhQ4bE22+/HRmwzM8wl19+eWy//fY/aU2mKzjttNNKgDiXf1yWWmqpMglZBoqrqdx2223x6aefRrdu3YpRbdp+0kknxTXXXBP3339/vPPOO7HooovWZjd1CBAgQIAAAQIECBCohUC9BXi/++67EsAdOHBgOe1bb71Vc/rMxbbjjjtOENytbMxgcI5g6dmzZ2WVVwIECBAgQIDANBPIYOw666xTJgnr2LFjSTGQn2PatGlT06ZXXnmlTCB28803xw477BA5SneXXXap2T548ODYcsst47XXXivrunbtGquvvnrMPvvsZTRrBjpff/31sm+/fv1KuoOZZpqpZv+mvFAZgTv+9U6pvXPMMUdsvvnmcd1115Ugb+bsVQgQIECAAAECBAgQqB+BesvBe9RRR0UluJspF2aY4f8OfcEFF0SOhMnSpUuXeOyxx8rEGyuuuGJZd8QRR8TLL79clv2HAAECBAgQIDAtBfbbb78S3M0Ru88++2zsscceEwR3s22dO3eOm266KXr37h2ZumHvvfeOTMGQJfP1brDBBiW4mz9iP/nkk/HMM8/EOeecEyeeeGJcccUV8d5778Ull1wSbdu2jfyxO/evlpIjmLP86le/qlOTK/Ur+9dpZ5UJECBAgAABAgQIEJikwP9FYSdZZcobcqKRc889t1TMoG1+GcqZlSsl89dVSuZqW3PNNWsm3phtttnKyN4M+ioECBAgQIAAgWkp8Pzzz8e1114bM888c9x5552RI3gnVw477LDYc889Y9SoUTV5aTNAnEHM/Lzz+OOPxyqrrPKTQ8w444zxu9/9Lh599NFo165dZNqqHN1aDaUywVxeQ11KpX5l/7rsqy4BAgQIECBAgAABApMWqJcAb6ZjyBQNWY488shYYYUVas6Yjx9mnrosmWtu/G050dp2221Xtj333HPl1X8IECBAgAABAtNKIEfT5ojcDNIutthitWrGySefHC1btozbb789HnnkkbjllltKrt4c4Zu5eydX8smmnMQty5///OfJVW0y2xZccMHSlvyMV5dSqV/Zvy77qkuAAAECBAgQIECAwKQF6iXAm3nosuTIjA033HCCs91zzz017zfddNOa5crCIossUhZzxIxCgAABAgQIEJiWAg8++GA5feUH6Nq0Zb755iujdb///vv4xz/+UXbJlAtTGv1bOfbuu+8eCy20UOQP5pnKoamX9dZbrzQxA9i1LTnC+a677irVK/vXdl/1CBAgQIAAAQIECBCYvEC9BHgzj1yWBRZYIHISjfHL+AHejTfeePxNZTm/DGUZM2ZMefUfAgQIECBAgMC0Evjggw/KqX/xi1/UqQlLLrlkqf/SSy+V14n9qD2pA+a8BZXPSE899dSkqjWZ9Rn8ztzBOTlcJSA+pcadeuqpJTfxyiuvXPIXT6m+7QQIECBAgAABAgQI1F6gXgK8lUft/vOf/0xw5m+//bY8qpgrc+bptddee4Lt+aYyu3SOXFEIECBAgAABAtNSIFMtZKnrD8+VH6xHjhxZ9u/UqVN5re1/KvU/+uij2u4yzeq1b98+/vSnP5Xz77DDDlEJak+qQZnT+Pjjjy8T8OakdAoBAgQIECBAgAABAvUrUC8B3iWWWKK0Kh+/e+CBB2paeMMNN5RJR3LFuuuuWyYsqdn434V33nmn5KvLdYsuuuj4mywTIECAAAECBBpdYOGFFy7nHDhwYJ3OPWjQoFI/J4/NMmLEiPJa2/9U6lf2r+1+06reUUcdFZtvvnkZlbvGGmtE5iEePnz4BM0ZPHhwmUhul112ibFjx5Y6E/uxf4KdvCFAgAABAgQIECBAoM4C9RLgXXbZZWOZZZYpJ88ZoXPW6Ryt0bNnz5oG7brrrjXLudC/f//YYIMNojLiJfPPKQQIECBAgACBaSlQSZVwzTXX1LoZr776ajz77LMlbcGKK65Y9nvyySdrvX9WHDBgQKlf19QQdTpJPVbOtBK33npr7LXXXvH111/H0UcfHfPOO2/86le/im7dusXiiy8eeS2XXXZZzDTTTHHuuefWjPqtx2Y4FAECBAgQIECAAAEC/xWolwBvixYt4i9/+UsBzXy8W2+9deRojc8//7ys6969e2y77bY14Ouss07kaI+33367rNtqq60iZ5FWCBAgQIAAAQLTUmDPPfcsAckM8D7xxBNTbEqOTD3ooINi3Lhxsccee0SPHj3KPhdffHEZtTrFA/y3Qo7+zXNlOqv88btaSgZuL7300njooYcicw7nZLsvv/xyPPbYY+Uz3pxzzhnpmQHw/fffv1ouSzsJECBAgAABAgQIVJ1AvQR486p33HHHuOKKK6JVq1YTIOTI3ttvv718WapsmH322SuLscUWW0RdRsnU7GiBAAECBAgQIFDPAossskgceuihJTj761//Op555plJniGfQsonlzI9VY5e/fOf/1wCvDl6NQOdp5xyyiT3rWz47rvvYp999inn22+//WLWWWetbKqa10y7cPfdd5cUDZna4tFHH43XX389cm6GHMG72GKLVc21aCgBAgQIECBAgACBahT435lE6qnlmWZho402iocffjjefffdknc3Z0vOx/jGLzlaN0e65MQcO+2000+2j1/XMgECBAgQIECgMQVOOOGEMnFYv379Yq211iqjT/fee+/o3LlzacZXX30Vue3EE08so29nmWWWkq4gg7xZMh3BZpttVgK++ZTTkUceOdHPOp999ln5HJTpGXIuggwQV3PJ4HSmaFAIECBAgAABAgQIEGhcgRb/DbSOa9xTOltzFsjchM8//3wZ4XTbbbc1ZwrXToAAAQJNWGDMmDFlJO8//vGPmlQLM888cxlhm4HZysennGj2xhtvjBVWWGGCqzn//PPjwAMPLPsut9xyse+++8bqq68eOYnaBx98EPfee29knS+++CLmm2++uP/++wVHJxD0hgABAgQIECBAoDkI5Jxd+WT/1VdfHb/97W+bwyU3yDXW6wjeBmmhgxIgQIAAAQIEGlmgZcuWcfbZZ5cUDOecc04Zsfvhhx/Gt99+W9JRZbA2n0LKFA0/Tk+VTc10CzkqN3PPvvTSS3HAAQdM9Ao22WSTyHy9Cy644ES3W0mAAAECBAgQIECAAIEpCTRogPeTTz6JN998s/yNHDmyjGTJBr311luxwAILlMlEptRA2wkQIECAAAEC00ogR99mADZLBne//vrrmHvuuSNTL0ypZPD2tddei5tvvjn69u1b8tKOGDEiOnToEJnC6je/+U2ZdHZKx7GdAAECBAgQIECAAAECkxNokADv9ddfX/LNDRkypObc+WUoH1XMcvrpp8ctt9xSRrUcffTREx35UrOjBQIECBAgQIBAExDIFA35V5cy00wzxc4771z+6rKfugQIECBAgAABAgQIEKitwISzn9V2r0nUe+edd6Jbt27lkcXxg7s/rp4TsOXo3uOOOy5yhuocEaMQIECAAAECBAgQIECAAAECBAgQIECAQN0E6i3Am5OR7LDDDvHYY4+VFrRr1y7y0cQNNtjgJy3q1KlTzbqchTrz0ykECBAgQIAAAQIECBAgQIAAAQIECBAgUDeBegvw5mjcp59+upx9r732ihyle88998SOO+74kxZddNFF8eSTT0bHjh3LtpwpL3P1KgQIECBAgACBxhQYPHhw9O7dO3L23vxhepdddolTTjklXn311cZshnMRIECAAAECBAgQIEDgZwvUSw7eHL2beXWzbLzxxmUykhlmmHzseJVVVokHHnggcvKSH374IS655JI49dRTf/aF2JEAAQIECBAgUFuBjz/+OHr27BnXXXddjBs37ie7HXXUUbHNNtvE3//+9xj/yaOfVLSCAAECBAgQIECAAAEC01hg8lHYWjYuZ4geNWpUqX3GGWfElIK7lcN27tw5tt566/L2jTfeqKz2SoAAAQIECBBoMIGXX345Vl555bj22mujdevWsfvuu8eVV14Z9957b1xzzTWx9957R9u2bePWW28t9Z555pkGa4sDEyBAgAABAgQIECBAYGoF6iXA+8ILL5R2ZN7dZZZZpk5tyhG8Wd5+++067acyAQIECBAgQKCuAjnJ6+abbx5Dhw6N7t27lxRRV1xxRey2227lKaRM0XDxxRdHpm7IlA050neLLbaI999/v66nUp8AAQIECBAgQIAAAQKNIlAvAd7vvvuuNHammWaq9ejdytWNHDmyLM4666yVVV4JECBAgAABAg0i0KtXrxgyZEisueaacf/998eCCy440fN06NAh7rrrrhL0zSDvIYccMtF6VhIgQIAAAQIECBAgQGBaC9RLgHf55Zcv1/HZZ5+VETF1uahnn322VP/Vr35Vl93UJUCAAAECBAjUSWDYsGGRo3XzB+mrrrqqvE7uADPOOGOpnz9C33zzzfH6669PrrptBAgQIECAAAECBAgQmCYC9RLgzeBsfgnKctxxx9X6QjLX3UMPPVTqC/DWmk1FAgQIECBA4GcI3HnnnWVi1y233DIWW2yxWh1hvvnmix122KHUveOOO2q1j0oECBAgQIAAAQIECBBoTIF6CfC2adOmzDSdDb/00kujd+/eMXbs2Mlex4MPPhh77rlnqTPLLLOU/HaT3cFGAgQIECBAgMBUCOTkalky925dSqX+oEGD6rKbugQIECBAgAABAgQIEGgUgZb1dZbzzz8/Hnvssfjwww/jiCOOiJtuuim23nrr+Oijj8opxo0bFzkZ2/PPPx/33HNP2V4598knn1zrkTSVfbwSIECAAAECBOoiMHz48FJ9nnnmqctuUalf2b9OO6tMgAABAgQIECBAgACBBhaotwDv3HPPHVdeeWUZyfvVV1/F008/Xf4q7c8vRSussELlbc20OCRlAABAAElEQVTrZpttFgcddFDNewsECBAgQIAAgYYQaN++fTlsTppWl1KpX9m/LvuqS4AAAQIECBAgQIAAgYYWqJcUDZVGbrjhhmUCkl133TVatGhRWT3R18xplwHhnKF6SnUnegArCRAgQIAAAQJ1EKhMCvuvf/2rDntFVOpX9q/TzioTIECAAAECBAgQIECggQXqbQRvpZ3zzz9/mZm6Z8+e8cQTT8Sbb75Z/j755JNYdNFFY8kllyx/W221Vcw222yV3bwSIECAAAECBBpUYIsttoiZZpqppIrKfLy//OUvp3i+d999N26++eaYYYYZokePHlOsrwIBAgQIECBAgAABAgQaW6DeA7yVC+jSpUvkn0KAAAECBAgQaAoCmWJhv/32i7PPPjt23nnnMndAu3btJtm0UaNGxU477RSjR4+OPfbYIxZZZJFJ1rWBAAECBAgQIECAAAEC00qgXlM0TKuLcF4CBAgQIECAQG0Ejj/++OjcuXO89NJLsdZaa8XAgQMnutsbb7wRa6+9dgwYMKA8gdS7d++J1rOSAAECBAgQIECAAAEC01qgQUbwjhs3LsaMGROtWrWa4PpeffXVuPvuu+Ohhx6KOeecs0zItvXWW8vBO4GSNwQIECBAgEBDCWR6qH79+sXmm29egrz5tNHGG28c6623Xsw777zx6aefxsMPP1w+r+RnmcUXX7zUn2eeeRqqSY5LgAABAgQIECBAgACBqRKo1wDvsGHD4sILLyw5ePN1o402qmncAw88EJtsskn88MMPNeuuvvrq2G233cpkazUrLRAgQIAAAQIEGlAgUy30798/jj322Dj33HNLTt577rlngjO2bNmypHM48cQTY6655ppgW23f3H///dGnT5+SCiI/I80888xlNPBmm20W++yzT3Tq1Km2h1KPAAECBAgQIECAAAECkxSotwDvd999VwK4lUcd33rrrZqTDh06NHbccccJgruVjVdddVXkrNQ5KZtCgAABAgQIEGgMgRzJe+aZZ8bRRx9dRui++OKLZfTu3HPPXSZf23LLLcuI3p/Tlg8//DB23XXX+Ne//jXB7t9++20MHz48nn322ciUD0cddVQcc8wxnmSaQMkbAgQIECBAgAABAgTqKlBvAd78klIJ7rZo0aLMNl1pzAUXXBCfffZZeZuPQv7jH/+InLjkiCOOiOeee6685uORtZnNunJMrwQIECBAgACBqRXIgG4+TVRfJX/g7t69e+SI3UzrcMghh0SPHj3KyN0M8D7//PNx+eWXx7XXXht/+ctf4pVXXinL+dlJIUCAAAECBAgQIECAwM8RqJcAb6ZdyEccs6y44opxySWXxAorrFDTnuuvv75m+bLLLqvZlmkb8jHJESNGlMcXBXhrmCwQIECAAAECVSaQP15vtdVWJbi7zjrrxC233DJBeodM0bD++uuXv7322iu23XbbyM9IyyyzTAn2Vtnlai4BAgQIECBAgAABAk1EYIb6aEeOVskUDVmOPPLImgBuvn/99dfj7bffzsVYaqmlJtiWE61tt912ZVuO5FUIECBAgAABAtUqcPbZZ5cRufmDdd++fScI7v74mnJStwwAzzDDDHHyySfHkCFDflzFewIECBAgQIAAAQIECNRKoF4CvPl4YZYZZ5wxNtxwwwlOPP6kJZtuuukE2/JNjuDNko8sKgQIECBAgACBahQYN25cnHPOOaXpGeht27btFC8jg7y77LJL+ZH8oosummJ9FQgQIECAAAECBAgQIDAxgXpJ0fDee++VYy+wwAIxxxxzTHCe8QO8mWf3x+X7778vq8aMGfPjTd4TIECAAIFmJfDNN9/EvffeG4888kh5zL9Nmzblh9D8gXS11VYzGVcTvhteeuml+OCDD2LhhRcuKRhq29RM1XD11VfH3XffHSeddFJtd1OPAAECBAgQIECAAAECNQL1EuBdcMEFywH/85//1Bw4F3IykfySmiW/pK699tplefz/vPbaa+XtQgstNP5qywQIECBAoNkI5OjPzGV/3HHHxaeffvqT6z7hhBOia9eu8fe//z3WWmutn2y3YtoLvPPOO6UR489BUJtW5dwFWSrprGqzjzoECBAgQIAAAQIECBAYX6BeArxLLLFEOWZOLpITp22wwQbl/Q033BC5Lsu6664bObnI+CW/DN1+++1l1aKLLjr+pmaznLNsDx8+PHLUVv5lIHz22WeP2WabLXJm73yvECBAgMD0KzB69OjYeeedSz7WvMpVV101evToEYsvvnj5N/TFF18sE3E9++yz5d/SDAT//ve/n35BqvTK8t/wLLPMMkudrqDy2Sh/FFcIECBAgAABAgQIECDwcwTqJcC77LLLlhmgX3311fjd735XctB99dVX0bNnz5o27brrrjXLudC/f//47W9/G5UUDbvvvvsE26fXNyNHjoyrrroq+vTpE4MGDYp8P6nSsmXLSNv8sr/FFlvEZptt5vHcSWFZT4AAgSoV2G+//UpwNycevfzyy2Prrbee4Ery388TTzwxjj/++DjllFPiD3/4Q8w333yx1VZbTVDPm2kr0LFjx9KAykje2ramUr+yf233a6x6Obq8RYsWjXU65yFAgAABAgQIECBA4GcI1Mska/nB/y9/+Us5febjzS+nOWnI559/XtZ17949tt1225rmrbPOOrHGGmvUPI6YX1K7dOlSs316XPj444/jgAMOiMxTfOCBB5YA9+SCu2mQeYlz8rkLLrigBHiXW2656Nev3/TI45oIECDQLAXuu+++uOyyy2LWWWeNBx988CfB3QpKPs1x8sknR+/evSMDbvvss0/kD6lK0xFYeeWVo3Xr1vHUU0/FRx99VOuG3XnnnaVuU0m98eabb8bhhx8ev/rVr8p9OdNMM0Wm0cof5TM/tEKAAAECBAgQIECAQNMTqJcRvHlZO+64Y5kFOr90Vkbl5vplllmmpGHILwiVkikIKiVHpl5zzTWVt9Plawa6N9xwwxg4cGDN9WVQPEfr5Jem9u3bl/QV+cUwg7qZ1mLEiBExdOjQGDJkSHHNHXPEbwbDzzjjjDj44INrjmWBAAECBKpTIHPrZsnRucsvv/wUL+Kwww6LDAg++uijcdFFF03wpMwUd1ahQQXatm1b/o2+6aab4thjjy0/zk7phF9++WWcfvrppVqm6ZiW5YcffohevXrFWWedVT6LVNqSn1fy80g+eZR/mXIrX5vqiONKu70SIECAAAECBAgQaE4C9RbgTbRMs7DRRhvFww8/HO+++275EpAjWmaYYcKBwjlaN0cg7bDDDrHTTjv9ZPv01AFff/11bL755jXB3fTI1BXrr79+CexO6VozWJ6jgTKtQz66m+8POeSQWHLJJUvKhintbzsBAgQINE2BnJj0iSeeKD/wZdqF2pb8NyQDvLfeeqsAb23RGqleBupzboELL7wwVlpppdh7770neebMubv99ttHPuHTrVu38llhkpUbeEMGdzPv81133RWZHipzPOdnuvy8lj/Q5wRweV0ZjM6R5vlZJu/B5jp/QgN3h8MTIECAAAECBAgQqLNAi/8GWsfVeS871Fogg7J77bVXqZ+jnHPUy48D3rU92N13312+gGWQN3PzvvDCCz/7WLU9Z33Xy9nCM+1EfpG87bbb6vvwjkeAAIGqEchA2XrrrReZtiiXa1u++OKLyHy9ORlnjgBVmpbAxRdfXDMJXgbujzvuuJh33nknaGQGRw866KDy73iHDh3i6aefjk6dOk1QpzHf5MjwfDoo23nHHXfEaqutNtHT56Sw22yzTfkhP1M4PPPMMyUtxUQrW0mAAAECBAgQIECgFgI550g+2X/11VeXtGC12EWViQhMOLR2IhWsmjqBHJ2VJfPn5ijcnxvczWPkJGuVRzkz3UNlYpbcphAgQIBAdQl89tlnpcHzzDNPnRo+xxxzlFGWmcon0/ooTUsgU1XlCN5WrVqVNA2Zez9H6O62227xm9/8JpZYYonIuQnyR9p8GiefepqWwd3XX389zj777HJPZfqPSQV3U3muueaKvn37lvRbmTbq3HPPbVr4WkOAAAECBAgQIECgmQo0iQBvTkaSXxSmx/L444+Xy9pyyy3Ll72pvcbxJ6t74403pvZw9idAgACBaSQw99xzlzN/8skndWpBjuDNwG6O4M3H6ZWmJ5ApDl588cUywWz+sPvYY4+VEQmZn/ett96KHLWb6Ryee+65WGqppabpBeRErnk/ZTqJVVdddYptadeuXZx55pml3j/+8Y8p1leBAAECBAgQIECAAIGGF6j3b4YffPBB3HLLLZG5BUePHh1jx46d4Coyz1v+5ZeJ/JL6/vvvlxyExxxzTJmxeYLK08GbvL4s9TU6JwMCOSoo0zRk/j6FAAECBKpT4Je//GV5qiPzrH/zzTcxyyyz1OpCKukc8skQpekK5CSzN998c5k0Nfv4ww8/jDZt2sRiiy0WK6ywwlQ90VOfV53pn7LsueeetT7sxhtvXCZZyyeJcgTwtA5S17rhKhIgQIAAAQIECBCYTgXqNcB74IEHllm9M/io/K/A4osvXh7D7N+/f+y7775TzZIpHyq++QVRIUCAAIHqFMh8p2uuuWaZrOr888+PQw89tFYXUhk9+etf/7pW9VWatgI50nqDDTaYto2YxNlzGoacFLdFixZlQrVJVPvJ6kr9DFrnBGwCvD8hsoIAAQIECBAgQIBAowrUW4qGK6+8suRiqwQfa3sVM844Y6y++uqTzflW22M1xXpdu3YtzbrhhhtKnr2paWOOeK4EADIPntmrp0bTvgQIEJj2An/+859LI/7617+WHwOn1KLTTjutPO6fj/hnGgCFwNQI5BNV+bktP4vl00F1KTkaOcuoUaPqsluj1s2nyXr37l0mM8yR0/PPP3+stNJK0bNnz3j22WcbtS1ORoAAAQIECBAgQKAhBeolwJtpGPLDcqXkJCKXXXZZ9OvXr+aR05xF+tZbb41LLrmkfCmdeeaZS/V11123pGjYZJNNKrtPV6+9evUqX5ryC9DWW29dJl7J1BV1LTkZy0YbbVQTAMiZuRUCBAgQqG6BDTfcMH73u9/F119/Hfnv4e233z7RC8p/Q4488sj405/+VB7tv/jii6Nt27YTrWslgdoKZA7nHEmeabPee++92u5W6uXI3SwZNG2KJUe651NURxxxRGRak0wnkSOOM7B71llnlUDvjjvuWNKFNcX2axMBAgQIECBAgACBugjUS4qGoUOHxvDhw8t5M/CYj5pWylprrRX33Xdf+fJaeZw0v8zuvPPOscUWW8QDDzwQ1113Xey0006VXaar1/xycfLJJ8fhhx8eX375ZaRPLq+99trlccgchZsjsTLgnaNh8ktWfpHP2dHTdfDgwfHII49MMAldBnpPOOGE6crJxRAgQKC5Cpx33nnl//NzAq78d3LllVcurzni8Lvvvis/7OVTIMOGDSuTqmX9nLhTIVAfAt26dSu5gu+444446KCDanXIDJbmJHL5I0OXLl1qtU9jVsp8wldccUU55VZbbRV77LFHaWd+zspJ7vKHlJxcLv939fzzz5cnrOabb77GbKJzESBAgAABAgQIEKhXgXoJ8L7xxhs1jcoRRuOXzC+YAd5//etf468uAc77778/1lhjjTjkkENi0003jTnmmGOCOtPLm8MOOyxycrQDDjigTIw2cuTIuOuuu8pfXa8xRzr36dOnyUzOUtf2q0+AAAECEwrMNNNMJdCUP/zl0y5PP/10+ZuwVpQRh2effXb5d/PH27wn8HMFdtlllxLgPfXUU0sgNHMGT6nkxLhZtttuu2jduvWUqjfq9vxRPYO77dq1KwMINt988wnO37Fjx8jBBxnM7tGjRwnw5g8rjz76aPkBZYLK3hAgQIAAAQIECBCoEoF6SdGQo0yz5AzgCy+88ASXnrNIZ3n55Zcjc72NX1ZbbbVYdtll4+OPPy5fbsffNr0t52iSIUOGxFFHHRV1HSWSX54yvUPfvn3jnnvuicy/qxAgQIDA9COQk1blj4A54dVtt91WfvjcYYcdYrfddovMzztgwIB46qmnBHenny5vMleSQc6cCyFHiGeKrW+//XaybTvjjDPi2muvLU8eHXvssZOt29gbM81EPuGU/3vKEfE/Du6O356FFloocqDBIossUv73demll46/2TIBAgQIECBAgACBqhKolxG8GdjNMuecc/7k4pdccsmyLtMO5EjfSsC3UjFHLL300ksxcODAyqrp9rV9+/Zx0kknlb/8Ep9f2N98883yaG6mb8iRvTnJST7ymCNoMr1D586dY/nll5drcbq9K1wYAQIE/k8g/z3NgFv+KQQaSyBTZWVqkH/+858l2JsjxfPz2fglg6dHH310XHPNNWV1zrXw4x/1x68/LZYzRVh+3vztb38bG2+88RSbkE9XZa7ebbbZprzuu+++U9xHBQIECBAgQIAAAQJNUaBeArxLL710ubacrXjcuHFl5ETlYn/xi1+U97k+87X9OMCbI3izNIcAb8UkX3PESP4pBAgQIECAAIFpKZCB2kxRkLmd87PaOuusUz6j5A/M+RRR5q197rnnyme8zGObwd2coKyplZzcN8s+++xT66blE1L5A3wOQsgn0pZYYola76siAQIECBAgQIAAgaYiUK8B3u+//75MCDb+qI8cjZQzLH/wwQfxzDPP/OQLwWOPPVYsKpO0NRWYhm5HTpzzc/LWffbZZzWPTy644IIN3UzHJ0CAAAECBJqBwFJLLVUm9DvrrLPKZLn5pFH+VUp+ntt2221LnuicILYxS35WzDRVlaeecnLaVVZZpYy87dSpU01TKinDcjRybcsMM8wQXbt2jXvvvVeAt7Zo6hEgQIAAAQIECDQ5gXrJwTv77LPXjEY9+OCD48MPP5zgQldYYYXyPmcr/vzzz2u2jR07Nu6+++7yPmcLn97LzTffXCaTyy8mM888cxnNnPkVH3/88Vpfes4EnV9mxv9CU+udVSRAgAABAgQITEIgg7iZhmHo0KFlJO/tt99e5kjI0b2ffvppXHXVVdGYwd0cNZz5gbt16xannXZayU+dk/ZmDuD8vJmfHTN39VdffVXmecj0DDPOOGPkKOO6lEyNlSWPoxAgQIAAAQIECBCoRoF6CfDmhWe+tiwvvPBCCVzmB+5KyaBklvfffz+22GKLyC8MDz30UBkJkl8YslRSNZQ309l/vv7669h9991j++23LyNEKqksXnvttbj66quje/fu0bNnz5qRudPZ5bscAgQIECBQVQL51FFOIJYjQTNPa/4om8HEXXfdtUx2WlUX8zMam5OULbfccmWC15x4ba211ioGP+NQP3uXW265JdZcc80yX0HHjh3jiCOOiFtvvTUeeOCBuOKKKyInIcxy3nnnlckHc8LeeeaZpwR6s//qUnIS3Cx1nQS3LudQlwABAgQIECBAgEBDCrT4b27ccfV1ggxi5uiOLAsssEAJ6OZyjtTNPL35aN3ESk4oltvmnXfeiW2u+nV//OMf43/+539qrmPWWWct15qPPo7PnxPS5SOCkxsdk/nx7rrrrnKs8fetOXgTX1hxxRXj+eefLxMI5UzxCgECBAjUn0A+QZOjL/Ox83zSI58YUWovkP+u/u1vf4sTTjhhsj+65g+zOdmYp2lqb1uXmjkJbeYBznRW++23X5x++umRo4t/XF599dXy4/nLL78cK620UrnfMw/vBRdcELWdMO2jjz4qn1lnmmmmMko5P6MpBAgQIECAAAECjSeQgyjys3UOgMzJcpWfJ1BvI3jz9Dl7cY7kzQkqxk+5kF80c2bmDGD+uOSomAsvvHC6De7miOZzzz23XHZ+0b7jjjtixIgR8fbbb5d0FfnIYaa4yJITfOQXmnfeeae89x8CBAgQIDAlgXwsPQNgnTt3LjnvV1111TLyNEcj5ijM/IFx9OjRUzqM7f8VyCeOjjrqqEjTnETsvvvui0ru+wwmnnrqqeXzyiOPPFKMX3/9dW71LJCDAnKStAzu5g/kOUJ3YsHdPG1O3Jt9kZ87c56Hdu3aldZkP3377be1alkG8/Oc+QO64G6tyFQiQIAAAQIECBBoggL1OoK3cn05AiZH5P44oJtfkjIin+kZMjVD5ubNkRn5pXR6LTmC5KKLLoqWLVuWLx85I/WPS4642nTTTUu+u9yWI3j79+8/0ZFXRvD+WM97AgQINF+BQYMGlcfo80fDLHPOOWf84he/KAGr/Hf4yy+/LOvzKZr8gfHH/y6Xjf5TBHLkbq9evSKfKrrxxhtj4403nqjMF198EZm24P777y/W+VSKwOBEqX7WyrxPe/ToUQYKZFA9R9ZOqeTnynXXXbfc/zmq+qWXXip91KdPn/L5a1L750iRHDGSn9FynwwYKwQIECBAgAABAo0rYARv/XjX6wjeSpMyd9vEvkRmHrucFCNz8OaMyOecc850HdxNj/xykmXnnXeOiQV3c1vmlssRKGuvvXa+LSN4N99888jcvQoBAgQIEJiYQD6WnjlKM7jbpUuXkhv2k08+iSeffDKefvrp8kNqBsvyR9TM+b7GGmvE4MGDJ3aoZr9u2LBhJS1Dfn7JCWEnFdxNqDnmmKMEy9M8g+hnnHFGs/erT4D8jJglBwDUJribdfPpp5zLISfyzc+ZlSD9BhtsEPm/kx+X/OHj8MMPj5zoNsvf//53wd0fI3lPgAABAgQIECBQVQINEuDNx+IefvjhiUIMHDgwTjnllPJlc6IVprOVlcc3u3btOtkryy8j99xzT5ktOis+++yzZfTJDz/8MNn9bCRAgACB5ieQj6/nKMdM+bPddtuViag22WSTmHHGGWswclTiVlttVYK9+aNhPkWzzTbblEmoaipZKAKXXHJJfPPNNyWfazpOrowZM6Y8ZVP5UbZ3797lyST/Xk9OrfbbXnnllVK5W7dutd/pvzUzL3KW/HE8J2Kbf/75y2fRDPyussoq8fvf/z4OOuigyP8t5A/rmdYkU4idddZZMf7EwOUg/kOAAAECBAgQIECgygTqNcCbX3oOO+ywklogv0ROrGTgMvPb5WNwObLiP//5z8SqTTfrKnkPJ5U/bvwLzXzEd9555//H3pmASVFdb/8mX2ISExM17vuKRtyiUQEXRECjcceoiEYUl6iIWxSNG4KIJO5xQUTFfQEVUYGIihuuUaPiGlfcUKJxj9ms7/zOP6dzp6a6u3qmZuieOed5Zrq76t5bt96qrq5673vfo15yLJ8yZUo49NBD4yL+3hFwBBwBR8ARUM971LiQV0wz/853vlMWFX5/UKVi3cAg6/jx48uW7awr+L0l9tlnn7IQQKrjm4+ffu/evTXnAIU///xztQcguSwzk7gX8mg5Ah999JFWZtZXLbHwwgtrcepvsMEGarmAhy/2GSjaL7nkEj0+HGuO5ZZbbqlqdxS/Ho6AI+AIOAKOgCPgCDgCjkCjI1AYwYvyZZttttGpip999lngBhuf3XTECcTuvvvugLLV1Brpsh3hMw/URN59XGSRRcK0adPCoosuqvVIXHfWWWfpe//nCDgCjoAj4AiAgJG0I0eOrEjuGlqQXMOGDdOPVtfW+WtQmwtwWG+99TLhmDNnjtooDR06VO9vSF7HAOyaa66p5VGLvv/++6oQxd4BqwCPliFg9z9gXkuAP2H1IYixXsC2BL9k8iGQcHDixIkBSw6S/1abXVXL9r2sI+AIOAKOgCPgCDgCjoAjMC8RKIzghYTkBppARXHEEUeEb3/72832jQci1EZbbLGFrnv77bd12lyzgh1kgRG8JPowVUq1XVt55ZVVyYuil0AVfeWVV1ar5usdAUfAEXAEOgEC/JY8/fTT4Qc/+EGoZicQw7HDDjuop+lDDz0Uvvrqq3hVp3+PtRSRNdvGBrDxNl5ppZUCg9PgD1mIUpQ49dRTw+233662APfcc48mvvvXv/6l6/xfbQjgbUxgs1BLcFwIEvjG8d3vfldnjO2///5Kyvfr1y8ziW1cx987Ao6AI+AIOAKOgCPgCDgCjYbAt4roMIpdvMyIrl27qpcsWYyzAoXqgAEDNOnYiSeeGFAfzZw5UzNWk5W6owXJ1a699lq1ouD9FVdckevBolu3bgFSGG/Fr7/+WqeNon7mfXsHD1mQ8kVs+4033tDuO7nQ3kfRt+cIOAIdBYF33nlHd2XFFVfMHEgtt5+oeLER4LfkvffeC9T3+D8EUOC+/PLLig22F3GccMIJ4amnngqrrbaa3q/E1gE2KwlPV8j2xx57TJPZPfDAA+H0008P3Od41IYA9z3MXuKPAW4GMqrFTTfdFF599dWwzDLLhI022qhacV/vCDgCjoAj4Ag4Ao6AI+AIdDgECiF4n3nmmUBGYgKPs3LkbowemapPOeUUzUQ9a9YsVcR0RIKXZB54DUOSMh0Q72FUVEwLHDx4cAxJs/c77bRTuPDCCzWTNOQq02tJCNLegacg3sBFhk2lLLJNb8sRcAQcgc6AgCVSa0lSL6tjbXQGvPLs48Ybb6wE76233qq+xlYHm4ALLrhAf3tvvPHGEJO7JK1jgJpkdgzKEhDoDIiS8Ivka/i7LrDAAtacv+ZAgBlem2yySXjwwQfDvvvuG66//vqK9z4MHB988MHaMoQ695cejoAj4Ag4Ao6AI+AIOAKOQGdDoBCCl0QvxIILLhi6d++eG0MeMCE/IXhfeOGF3PUareDFF1+s2c5JboMvH/6HJPyoRvCynwceeKD6KzK1kMQtRahoa8UPFc0vf/nLkCRJrVWblefh680338w1CNCssi9wBBwBR8ARUJUiJBbqUawFzM6nGjQff/xxQP2LfdISSyxRrXinWs/Mossvv1w9Ww866KASkQvhS7LUHXfcMeC7G8fw4cMDNgzbb7+93v/Yuk033VT9eu+77z5NlrrbbrvZKn/NicCll16qStwJEyaEL774Qv1zIc/Tceedd4a9995bZ0mRB2K//fZLF/HPjoAj4Ag4Ao6AI+AIOAKOQKdAoBCCF4sGoiXq0h/96Eda99NPP9XXjvgPzz6mbTLVEHKXhxWmg+aNgQMHhp/97GeqUGHaZ3sHfd1zzz0L2ezZZ5+tBG9LzpVCOuCNOAKOgCPQ4Aj88Ic/VMXoww8/HG6++Wa1PcqzSzfccENAwYtCcr755stTpdOU6d27d2m2DRYB+OliacFvN5H2OsYXHw9e1Lv476aD8hC8DOY6wZtGp/rnLl26hDvuuEOJ9SlTpgTyGWy77bZ63nP+v/XWW2oHBr4EYgFUvMcdd1xp4IN7F5TU1LN7TWYPzZ49WwfLsXPIIo2r985LOAKOgCPgCDgCjoAj4Ag4AvWHQCEE73LLLad7RuIXpsqtsMIKufcUXzsi7XmXu4EGKUiSj/PPP1/VQX/6059CrYQ2mbrvv/9+TbaGovb5559vkD33bjoCjoAj4AgUjcABBxwQIHjxh8UKiBk0lWLu3Llqi0QZ6no0RwDSlqRp9957rxKJELhmJwQZSIAjyl1+zwkGLbPuX6y81dfC/q8mBHr06BGeeOKJMHToULVpQM3LXxwLLbSQEujYOUDkpmPs2LEBQnjzzTdX4peZVHFAJO+zzz5hyJAhmQn24rL+3hFwBBwBR8ARcAQcAUfAEahnBAoheMlYjCIT+wCSiowZMybXPkNSWpbk9NTHXA00YKHYq68l3f/Vr34V+PNwBBwBR8AR6LwI8DuANywDhvi6T5o0KUB2ZcUHH3wQtttuO02shqIRhapHcwRIlIbqFssFrKNQOqPiJbi3GT16tJLq2CXxWw65W85qyQZxIRc9Wo4AOR1IVPu73/1OFb0kwgNbLEY23HDDgEXY0Ucfrcp0xAUks1133XUDg+qvvfZauO6668Kjjz5ayiPA8SBZHves1KU9VL/ct/Idoq6HI+AIOAKOgCPgCDgCjoAj0IgIFELwcgPet29fTSKG3+zKK6+siUXw+SsXeO7269cvfPnllzpVFAWShyPgCDgCjoAj4AhURwCC6pZbblGlKbM7UJHicY5f+sILL6wNoDbFlgELAZSkK664YjMFZPUtda4S3L9Amp977rmBBKPvvvuuAoBClOC+Bj/eESNGBGbWlAvU1QQKUY/WI4AimpwEcXD+H3XUUbro5JNPDr/97W+bWI8wo2zkyJG6npwP2JNgncF3gkCUcM8994Rjjz1WlcJ4J2OD5SSvwuP/HAFHwBFwBBwBR8ARcAQaDIFvSOKs1mfOkp3mgYisx//4xz8UApQUTAPlYQkLh/nnn1+Tu7z99tuBpBg33XRTKWkXyhim4Hl0fATWW2+9gC0HD8g8nHk4Ao6AI+AItBwBkqbtuuuu4aGHHtJGIH4XXXRR/X2F4LWf+F69euk098UWW6zlG+tkNcEOBSgJ2H7wgx+Eq6++OmwuU/3Nz7UcHJDp3Pvgt//qq68GfPg9ikWA3A9gzDmOkvrwww9vsgHIW3IXcL+x1VZbKdGLz/Inn3wSSJyHStuCRHnkOkApzL0rs8vyJi60NvzVEXAEHAFHwBFwBBwBR6DlCOy11156r33VVVcVlv+p5b1p3JrfLKrr3EgzxQ2VBIFyAjUFyUW6d+8e1llnnUCGY0jfiRMnlh46Wcb0Oo/qCPAQAkFuf9VreAlHwBFwBByBjowASaJQl6JKRJ2IwhSCEVuG73znO+rPy2AaSkUnd2s7E77xjW/olH+sGj7//HMdlKxG7kIKozSF3N15553LkrsQlM8884weu7/85S+qLq2td5279CWXXKLkLpYjaXIXZK655hold021vv7664fTTjtNQTNVryHId+byyy8PDEBz74py28MRcAQcAUfAEXAEHAFHwBFoNAQKI3jZcRQQZJzGF61aLL/88vpASpZkFEce1RHgYRA7DPurXsNLOAKOgCPgCHR0BCAiUfFOnTpVbY/mzJmjJC8WSLfffrvOmOjoGLTl/pFsDQXvFVdcoYPUX331VebmIIHxgEUhik3GmWee2awcM5iwtPrxj3+sA9/YAmDjsPjiiysxPHv27GZ1fMH/IYCyltlf48aN0z+WZpG7LB8/fjwvAeuGBRZYQN8PGjRIjyP3qSjf45hvvvlKBLDVjdf7e0fAEXAEHAFHwBFwBBwBR6DeESjEgzfeSRQQJLRAkcKDJQksUBNh3cA0xVVWWUX/mCpHEgwPR8ARcAQcAUfAESgGAQZMIQs9ikOga9euOiCNvzHK0WnTpoVf//rXoWfPnmGRRRbRexwU0sxi4n4HlS+qaab7W0C277vvviX/V1SjJJcl6RezclCOjh07Nlx55ZXhoosu0gFzq9uZX/HNhXDFyoukaOnAX5p7ya233rq0iiR4+FIzo2ynnXYqLUfR3q1bN03uC1mM+j2OPn36aKJCckQwSEIiNw9HwBFwBBwBR8ARcAQcAUegURAonOC1HV911VXDEUccYR/91RFwBBwBR8ARcAQcgYZEADsprDD2339/Tch1/PHHZ+7HxhtvrOrS1VdfvbQeeyUSyd57771K/p500kkBNWls9/Diiy9qMjysBfbZZx+1eDjkkENKbfAGEhiV9uuvvx7+/ve/h6WWWiqgAO7Ro0eHnAn10UcfqTL97rvvVhwgXMF3wQUXDJdeeqkue/LJJ0v2X+eff37JogSSFwIXAj0OCHniww8/jBfrewhhS7IH6e4EbzOIfIEj4Ag4Ao6AI+AIOAKOQB0j0GYEbx3vc8N2DR9jVCUejoAj4Ag4Ao6AI9C+CPz0pz8Njz/+uCp4Ueg+99xzShRCGvL7jOcus5PSARkMubvMMsuoenS11VZLFwkQwiRxQxVMrgKsBzbaaCNNFAb5S64CZkVlBT6z+MvuvvvuWasbchk2GKhysVNYcsklw7nnnht22WWXgB0JAf4QwCNGjAijRo1S9TN1sNGwXBCof9OBwpowoje93upYG+n1/tkRcAQcAUfAEXAEHAFHwBGoVwSc4K3XI5PRr29961s+9TYDF1/kCDgCjoAj4Ai0BwIQjBCPsSVApe3iqQs5iXXGzTffHLLI3bg+CuGXXnpJ/XuHDh0aDjvssDBgwABN8oaX7Lbbbhsgmr/3ve+F1157Ldx2221qXdC/f39NpIe9Q0cgJyHFIXdR1GK3gFo5Dgj1GTNmBGaL3XfffaFXr15qb4HNAnhh2wCZO3fu3LDoootqVWwyHnnkET0Wa621VtycvsdKDHsxYrnlltNX/+cIOAKOgCPgCDgCjoAj4Ag0CgKe3axRjpT30xFwBBwBR8ARcAQaCoHrrrsu/POf/wy77bZb2GCDDXL1HQsHyFx8fVGtkrxtr732UkL32muvVTXv4MGDw1lnnaVkMHYF888/v/oDH3XUUbm2Uc+FSICG3QKk+MSJE5uRu/S9X79+ugskslt//fXDhRdeqJ9POOGEkCRJgOjlFfwtKIO1BTYPWT7VkydPVqxpjyR4Ho6AI+AIOAKOgCPgCDgCjkAjIeAEbyMdLe+rI+AIOAKOgCPgCDQMApC0BAna8ga+sX379tXi+Pdi10DytSxbAUhQkrfdeeedgSRiqIVRtDZy3HTTTUqKQ+Kuu+66mbvCPuOxi2XGKaecEvbcc8/QpUuXgGJ65syZanNBxZEjR6q11RNPPBFOPvlkbQsCPR2ffvpp+O1vf6uLscjwcAQcAUfAEXAEHAFHwBFwBBoNASd45/ERe/fdd8OsWbN0KiIefUwfJIMzChb85DwcAUfAEXAEHAFHoDER4LecgHysJez3H6XpGWecUbUqqlQjLocNG1a1fD0XgKAltttuu7LdxKLi8ssvVzsKCN4hQ4aELbfcUstTn7qQ5B988IEmodt8880DFg0HHnigqnvjhj/++OOw4447qtUFhDLksYcj4Ag4Ao6AI+AIOAKOgCPQaAi4B287H7HPPvtMlThkyobY5XO5wHMXnzgSreC7RxZvSzBSro4vdwQcAUfAEXAEHIH6QMD8cC15V95eMdBLbLbZZhU9dfHrfeCBBwKDxah555tvPlXwQmwutthieTdXV+Xee+897c8KK6xQsV8QuCRVGzRokFo6fP/739fyqJnx7MWnFzXz66+/rsvXW2+9gIWDxd/+9je1gCBR21tvvRWWWGKJMGnSpMC9l4cj4Ag4Ao6AI+AIOAKOgCPQaAj4XWw7HTGSfQwfPjxcddVVFUnduDv//ve/w1NPPaV/Y8aMCWuuuWY4/fTTwy9+8Yu4mL93BBwBR8ARcAQcgTpEgGRdzzzzjA7orr322rl7aMpflKdZcdddd4Vjjz02YD2QFXj2XnbZZWpjkLW+npfhJ0yguK0WJFT7yU9+ojYWEN0Es6H4s4D0xgf5ySef1ORp+OtCvEOC49NLMJA+YcKEsOyyy1o1f3UEHAFHwBFwBBwBR8ARcAQaCgEneNvhcKESQWny7LPPlraGEnfJJZfUhw0yPDPdEP88SF2mZuIHh6LkzTffDGR2JlD8br/99ppdG08+D0fAEXAEHAFHwBGoXwSwDbj99tsDs3b22GOPXB2dM2eOEpIUtmRiVhFCEhXqaaedpovw5f35z38eVlppJb13wKuX+qhYUbDiZ9uzZ0+r3hCv7AsBeb3VVltV7TPK3Pvvv18tGqZPnx423XRTxQMVL++32GKLACE+duxYff3rX/+qbXLPBYE+cOBATYLnM6SqQu0FHAFHwBFwBBwBR8ARcATqGIGaCN6jjz46TJs2LXTv3l1vlOt4v+qma1988YUqbo3cJYv2kUceGXr37h0gdqsFCVYee+wxtXXAb47PRxxxhPr5Ydng4Qg4Ao6AI+AIOAL1iUD//v3D8ccfH6ZMmRKmTp0att5666od5V7L4tvf/ra91VfsBCB3sRE49dRTVbkKUWmBWpj7NO41SEDGjB88aSF7GyXo80UXXaT2C8ccc0wuywRmSZl377XXXhuWWWaZJrtLm/xBkH/44YcBywzuwbC1qIeYO3euks8kiaNv9L9Xr16uKK6Hg+N9cAQcAUfAEXAEHAFHoEEQqOnO9rXXXlMV6YMPPthk97hZJjEFf7z3+B8CN954Y3j44Yd1we67765J1HjNQ+5SiYc7kqdcfPHF6g1nD3tMzfz666//tyF/5wg4Ao6AI+AIOAJ1hQAKW0t+hoIXG4FXXnlFSV9IWHxyF1544YB9w2GHHaYJvq6++uoS8Wi2A+zUn/70pzBs2DC1F7j11lvD0KFDdeaP7TAzgB599FH9eP3116sylUFmtlurB7C1OS9eUe2uttpq4eWXXw6jR4/O1YVDDz1ULR1QPKfJ3bgBVLocE5LX1QO5+5e//EVV2vj/cpy4t2NAYO+999YZXn369ClrwxHvl793BBwBR8ARcAQcAUfAEXAEalLwopAgsBxABWHT2XioePrpp3UdClOP/yHw0EMP6Qce3pg62ZoHChS7ZNPmIRBFMIlDVl555f9tzN85Ao6AI+AIOAKOQF0hcNRRR6n/63XXXaeWAHQuPUDLfZXN9OHearfddguU/8Mf/hB22GEH3R+IYu69ULVmzeCBGKYd7jewOWBgGFXr888/r23tueeedYVLuc6gTj7vvPNU7XziiScGPHmZuZQV3HMOHjxY/XN/9KMf5SaEs9pq72W33HJLwCsZEv673/2uzuwi1wL+wCTPw2bj7rvvDt26dVM8DjrooPbuom/PEXAEHAFHwBFwBBwBR6CBEKhJwbvgggvqruHvZiqRBtrXedJVmzK43XbbqRq3tZ2I/fhQt3g4Ao6AI+AIOAKOQP0iAGFLwrMVVlhBiV0jd/HeR7kJoRkHJC6D5gsttJASfBC1ELf4y5IwDII3HW+88UYwa4fjjjtOV1PWljGbqJEC7+JzzjlHu4ytFTOZILzJTQAhigoaG4c11lhDLcPAkn1slEFvPIF33XVX3ReId44fXs0k0h05cmSYOHFiePvtt9XSCxHFwQcfrJYVjXQMva+OgCPgCDgCjoAj4Ag4Au2LQNOniirbxoLhjjvu0FLcfPPHNLe///3vpZq//e1vVW1RWpDzDWqULEVKzup1W4wbdKKozMxkf8amAdVKjHvdAuAdcwQcAUfAEXAEOjkCKFAh8bAH2GSTTdSuicFyfschgH/6058q4YdPLDZOqG5Rc0LsHnLIIaoAhugjaZgNthuk+PTvsssugeRh2267rap/bR0J2Aj8eBstsF3g3gnlKrOhbEZUej8gea+66qpAsrVaA1uxSy+9NNx2221KGn/22WdKuqOaRUXN4HzR8fnnnwdIXY4n98wQulnxwx/+UJPqdunSJfz6178O+++/f3jnnXfUloMkvT169NBBg6y6vswRcAQcAUfAEXAEHAFHoPMhUBPByw3m+PHj9QaTm2CyM6eDRGAtCR56OiLBi5rkz3/+s/rwHnjggS2BpkkdHnDMBoMHQg9HwBFwBBwBR8ARqF8EnnjiCbVLQGWKCpfBcoL7qE8//VR9eM1fn+WUWX/99TXnwYABAwJJw8aOHcsqLf/HP/5Rp/Rj0zRp0qQwefJktW6A/KWs2WdRfumll9bPH3zwQRNrLdY1Quy4446hb9++eu+J7/ALL7ygpDfexdwDQWyjhMXWoNZAGY2P8SeffNKk6quvvhr4u+aaa8JGG22kr0Uqg7HdwPIMop9EeZUCgh+1Mipv7v3w542DpMck3dt8883jxf7eEXAEHAFHwBFwBBwBR6ATIlCTRQOJK0jywUOERz4EeEgjbrjhhnDfffflq1Sm1Mcffxzw8iNIyrLiiiuWKemLHQFHwBFwBBwBR6AeEIDQw3ZhyJAhJXKXfi2wwAJKwMbkLsuXWmqpEvEHiYu6FMUmgXUDqlwIvX322SdAeuLfii0Dfq20GQckMttmeUz8xmXq/f33v/99VTHjSYtFAwpYkv4iMujfv3+LyF2sKxAtQO4iLgDH9957T9uGRCbfAccBO7INN9wwPPXUU4XBZHYZkLWVjskzzzyjJDZ9Qe1LcKxRg2PXhecwSXx79eoVTjjhBD3OhXXSG3IEHAFHwBFwBBwBR8ARaDgEalLwsnf4xd1///2BqYUoEL766qvAFDemFRJYOEA+1hqVsh7X2lY9leehi+Rq4ESiFDJC81CGN14tgQr4gAMOUDUw9Xgw8XAEHAFHwBFwBByB+kbArK0GDRqUu6MQl1gUPPLIIwGVJuQtVgQQfJtttln4xz/+oaQvA+4777yz3ptlNQ4BSDDN3+P/EMAPGdIULJl1hiUGBCpkLjYaYNu1a9eA9QX2GJC/3L9xH9aS+9sY93/+859K0nMP2Lt373hVk/f0g/XYbnD88RumD2+++ab68a6yyipq73HWWWeFYcOGqc0DSXyHDx/epB3/4Ag4Ao6AI+AIOAKOgCPQeRComeA1aCB6+SMgei3wQLPltqwzvzKtj+lzqEVQikDM8r5nz56q5EGFi48xUzd52OAhAzKYaZsoVZiaB6E+a9asEox4H48YMaL02d84Ao6AI+AIOAKOQP0hwG85JB1+qquuumruDs4///zhJz/5iXrvQvZxb7X66quHF198MUAUY0uQJyAGibbwks2z/Xorw32YJam75JJLwtZbb63qZ0tkF/cXZTXiBbB/8skn9b7r7LPPjovU/N6sMrCYSCu348b22msvPW/Y/s0336yiAIQQELzcc0Pwct+ICpiZYhxf7gu32morTUgXt+XvHQFHwBFwBBwBR8ARcAQ6BwItJnhjeL7zne9oYg+WQVJ6NEXgN7/5TSA5GkoQEqowZZJsyfzVGkzNxBcOpYaHI+AIOAKOgCPgCNQvAl9++aV2DsK21sCagPjiiy/0lcFhyN0jjzxSVbzVBtOxhsKfF3sGn/WjEOr9E7POsDUgid3aa68dZs+erSv5vM4662gSMzx4Z86cqR7H+N8SEMKjRo1q1X0uRD8B8V8upk2bFh588EEd/MdT2WZ8WR1rw+pzXwjRe8opp6hVw4wZM2yVvzoCjoAj4Ag4Ao6AI+AIdCIECmEJyeiMRxx/6ezOnQjLiruKLQPKCzImV3soSzcEgc7UPPCdOnVqq6cIptv3z46AI+AIOAKOgCNQPAKLLrqoesTOnTs3GNmbdysodwnz391777119s8777wTtthii/DSSy/p+qx/JMSlPIE1FP3wCGHKlCkKA8pYMITcxQKB/BLPPvtsuPrqq8Oll14a7r33Xk0oTHJc87+FaGd5awJylllbkLXPP/98ZlPXX3+9Lj/88MNV+c0HcjCg3kb1m5V/ASEBgwjkesBCzcMRcAQcAUfAEXAEHAFHoPMhUIiCNws2bka5eeUBhJtSPM14wODGFnuCWqYqZrXfiMvY/5EjR+ofD2546/3lL3/RG32mDaLs5eb9Bz/4gd7UY++A5x6KEpZ5OAKOgCPgCDgCjkB9IYAilMRZf/zjH5Uw/M9//qPJ0yAQ8Xft1q2bqkEhF3fZZZdcncfvFZsmBoS5FyD+3//7f2HixImhT58+6uOK+pSp/DvuuGNYaaWV1N6JZGD4yqI+JVD9HnTQQfre/4WAMpeAxP3b3/4WdtpppwChairZGCNsFMaMGaN2WoYhPr0oZlsT2CmMGzdO2z7vvPOaNcW9IWG5LXhPf//1r3+Fvn37Zt4Pco/IvTUiAPqIKMDDEXAEHAFHwBFwBBwBR6CTISDZlQsN8Y9NJMlDIlYNiUBZ9k8eTBKxKCh0295Y/SPw05/+VM8JeSCt/856Dx0BR8ARcAQcgQoISJKr5Ec/+lHZex2ZgZMIKafrue+RJFsVWvvfqu23317rHHbYYf9b+N93n3/+ebL//vsnQviW3a6Qk8kVV1zRrG5nX7D88suXMOO9qHJzQSL2DVpP/Hhzla9U6LnnntNjJwP6yQMPPNCsqJ1PIpTQdSKSKJ1jMojQrLwtkES82kfxE7ZF/uoIOAKOgCPgCDgCjkBDILDnnnvqfcxVV13VEP2t104WYtFgnDjT29Zaa61w0kknqZLElme9PvPMM+rbe9xxx2Wt9mWOgCPgCDgCjoAj0EAIYB0ghFXAQxQFKorD9g652dJZMbwWEVgrPPbYYzr1/eWXXw5ff/21Nssr6ln8cJmBQ3KrK6+8UpNxPf3002HChAmaCA0Mpk+frr6t3PfghWttlOsfM33MOxdbp3TgzTt27FidJYXvKopeZvuQDAyFMGpPlKq/+tWv0lXb/LMQ2IrDzjvvHFZbbTWduUXf9thjj3DTTTdV3fe27qDZXbCdY489Vm0N8mxzueWW02JCzla9v63WHnhgqcC5gZqX70sceCYTzOp6/PHH9fhyjqEGJ8luuRDiX1f5jK9yCPlyR8ARcAQcAUfAEXAEOjYChVk0YMHQv39/tRwAMqwGeNAgCzR+YSRfw4OWv1tvvbWU1OL000/XMvPiQaRjH1rfO0fAEXAEHAFHoG0RgKyE2DznnHPUNiDemigR9T6AQV8jyOL1Rb2H/LrgggvCzTffrD6qkIzcg0CkMQV/8ODBmug07/Ygh0lmyvR5vFljshibKe51CPxa8VSl7LbbbtukeewTuAdiuny/fv3UzxWLhcsuu0zvf2ib+6M4IMiHDh2q7X3jG98IomAI2ASUiy5duuiAern17b38nnvuCfvtt194/fXXm2z6r3/9a3jhhRfCddddp0nNxos/sMzmaVKmvT5svPHGao/F9iBX8wTJcc3ygntdCPyNNtooT9WyZU477TS9H8YeYuutt1Y7hgEDBmjiN0jot99+W8+zhx56SEnxzTffXM+dsg3KCs41ojNaoOmO+z9HwBFwBBwBR8ARcAQ6OwLy4FJIHHPMMSqpFjwTedBJXnnllbLtYuMgDzclGwdRKySiVChb3ld0HATcoqHjHEvfE0fAEejcCLz//vvJJptsUvrtZ2r5pptumohHaSLkZWk5lk1ChrYJWKIKTX784x+XtiXEaCKka8Ir9yP80a+8070++OAD3QerK2rIZMMNN0zE3zQRkrrUJuu/+c1vJpJ0q+p+CbmpfbK+WNtM95fB7USSsCZCPJYsFyRZViKevlXbracCQvIn3/rWtxQfmcmViMI4wVpAFNCJzO5KZAAgkcF+Xc/+VbIaaMv9En/b0jEUEjXXpkaMGKF1FlpoIX295ZZbctWrVkgGR5Lf/e53CffAdk6kX7H44P66mrXH3XffrW2IZ3NCux6OgCPgCDgCjoAj4Ag0EgJu0VDM0UKZ0uqQaWYJN6HcmP7sZz9LROGQq018wuxmlocBj46PgBO8Hf8Y+x46Ao5Ax0cAf9DVV19df8OXXXbZ5Nprr21GQjHQK6rE0u+8JP8qFJiLLrqoROT27t07kSRmiagtdRvch4g1QrLNNtuUtv/73/++4vYl6VYitgJafplllkkgLRmQjkOsJ5oQvaIa1tX//ve/42LN3p9xxhnaLkTxoYcemknqcR8lVgaJ2Cs0q1/PC0RlmuAny/3ciSeemEiSuczuckxE4avlIN0lyWxmubZcCPlp5P/666+fcMwrBbkijLjm/pZ9LJqcZlAB0QPiCMhxzkHbJsR4tRAFe2lARWbFVSteF+v5vogqWr9jfI8nTZqUzJkzpy765p1wBBwBR8ARcAQcgfZHwAneYjAvhOCdNWuW3vRy4/vwww/X1DNusKknUxhrqueFGxMBJ3gb87h5rx0BR8ARiBHYbbfd9Ld7nXXWSVDyVgrIK37n55tvvuT555+vVDT3ugcffLBEgpHorFIwgIzaFmKvEjknvrHaz3XXXbfiPskUei3HPtEuilTeoyQWL95k3LhxzchuiERIUP4+/fRTHQhH/UsiNLFt0H414kwmCFOUyOy/+MpWOgyldXbukEiuvYOBCZKr0V/+xOYi85zgGJ1wwgklVTXvF110Ua3THsT0JZdcotuC9K+ULO/dd99NunfvrmUhh22Ao71xzbs9EgSKb3SyyCKLaJ/tk1iGXQAAQABJREFUOPDK93OLLbao+Tki77a9nCPgCDgCjoAj4AjULwJO8BZzbL5BM3Jj1arAV40EGnIjqslN5CEud3tDhgwJf/jDH4IoIzSZRO6KXrAhESAJzFNPPRV23HHHINMcG3IfvNOOgCPgCMwrBPjJlunY6mWPr6kQh5rIyhJs8drWgS/tBhtsEEj2JYRtLn/dAw88UBODkXyLZFutDbZPP0jUip9ptRg1alQgYRm+tySExQ83DrxO8WclwRX7JAreeHXpPYmv8N1NB+2JcrW0eOWVV1Yv3dirVcjwQKI1PFzx6O0Icd999wX8YUXFHUhCR76FakHiupVWWimQFIxkcLyvFrQ9depU9ffFB3eppZYKm222WRCLkGbHMt0WHrrnn3++eiaDf1YI6RvEikOP/1tvvaXJAkW9HYR0DCeffHLg2OHnjO/xSy+9lNVE4ctIAjd69GhtF4zxNxZRhH7v3njjjXDbbbeFMWPGaDK2FVZYIcyYMSPwWq8hxLj6Hht+MgOgdB0hPwfn0pdffqmYDx8+PAipXq+74v1yBBwBR8ARcAQcgYIRIHkx+S3IQSFkb8Gtd6LmiuCJmV4lkCX4k9UaqCKoi2LGo+Mj4Arejn+MfQ8dAUegbRB48sknEyE2mynf+A21P0kcleT1Fm1pL2VgVrd39NFH524Cla8pWKtNi6/W6GOPPabbR0krhFC14roeKykhXbUefqXpEAJa1x1//PHpVU0+Y69gWPfp00fff+9739N+MMUctWXXrl11Od7DQkqW6ptf8QMPPFBa1uhvOAfAQ4j2mnbFrDtQd1cKIcMT7DcM8/SrEJqJiAzKNvH44483sdTAU9nOg3Rb8WeU2RxfZqV99NFHpTqSzK/sttpiBbYmWWrXuK8yYK5ex22x/aLalASCiZDyehxR/aPATweqab5/Mlii5fA+9nAEHAFHwBFwBByBzoGAK3iLOc7flJvEVgej8ARKotdee62m9p544gktL1PLaqrnhR0BR8ARcAQcgc6CwOTJk1VhKoRVWHrppVVVeNdddwV+Q8UnNBx++OGqLEXVh7q1nFKxCLzEWkCbQY2bNxZbbDFVWwrRGlDLtiamTZum1X/5y18GIVdzNSWepqF///5a1urHFVE/Ervssku8uMn766+/XmccsRDFrnj+KtYoRIV0DosvvniQpGmq0D3kkEMCClCxIyjdFwnJpe1RrqOE+CzrrnDO1RJW3upn1b3hhhsCCmgU6wsuuGAYOHBgEE/aIKKCIHYQYdVVVw0oWTmuKMTFLqJJM0LOqsp39uzZqnzleAlZG9imEIlaVuw1gvgB63tU2yh977jjjiBkfRAP54CyV5IGqtKYmWYHHHBAk2209Qf2GZWzEMtB/KSD+POqWhq1Md/5Rx99VGdDCQnc1l1pVfvsh9hJhF69eun3H7V8OlDPn3rqqarw5/t10kknBUmKly7mnx0BR8ARcAQcAUfAEXAEyiFQBE+MMkfa1z+UPXkDZQYKF+qSSdij4yPgCt6Of4x9Dx0BR6BYBMTWpuTzitK0nGqVZE19+/bV39Tlllsu4XNbBF6z/G7/9a9/ran5/fffX+u1NqnqoEGDtB28bmuJG2+8UesJMdysmvnofvHFF83WsYDlpkAkARv7jzrXlKgypaxZvV133bW0PZmWru/BrlpCtmYN1fGCLbfcUverkrdxVvc5dmDIscwKGbwoeSxzzuOdmw6SuV166aWl78ZRRx1VKoJKfIklltBt7LvvvgkK7jioy3lAH/gj6Ruvv/jFL3RbMnCiHrxix6HLxUaizZXxcf860nsZPFEMF1544dzXDJvdhyevR+MggCc314KDDz5Y/ZTx58aXXOxxEmageDgCjoAj4Ag4AuUQcAVvOWRqW16IghdlDgoHQqbbBbnh1veV/qGoQCmDwgVljdWvVMfXOQKOgCPgCDgCnQ0BeVhWb0ohSNVzs5xqVZJAqfpw0003DfzGnnjiiW0Clfns//Of/6ypfdS7hFg11FQvXdi2a/1Iry/32bZr/YjLcR9CCPkaLy69nzRpkioQUZ5yPAi8f60tq1+qIG9Qm3KsJk6cWFKMonpGndhRQkhU3RXOt1oCz1XC6sd1UUTvvffeeixQ2uIzayrbuJzYKAQhb4MQSoFz4cwzzwwzZ87UIqeffrqqcHv27Knez+njQ10UwsOGDVPf4E8++UTrod5FLYzXLWpSsQ0IkgxOFdoo5z1qR4A8HQQ5N2SAI1cDQ4cODairUdajpvaofwTMm10I3XDhhReGe+65Jwipq99PrpX4w5N/AyW3hyPgCDgCjoAj4Ai0DQKFELx0jQcZe3giEUS3bt10mhXJSkiawNQ5S1xx2GGH6dQ6Ei4QJElxiwaFwv85Ao6AI+AIOAIlBO6///7AVHPxm9Xf2dKKMm/4HWaQFUKL1w8//LBJSQZVSWZ0zTXXBIgXCDEjKZsUrPDBEjnVagNh5a1+hU1UXGVEm91DVCwcrTQ7AKsfrdKp+HwmAVtWkOCLgHgUH97AwLZ46SqJwXKm8qeDYyYK1yDj7krykojWrAHSZRv1c/fu3bXr2ITUEhCpRI8ePZpV47zFzgIyXXxYm61PLyDRmuF6yimn6P2meNdqsTPOOKMsoW4J1EjgRiI1ErcRfIdIxoftg3jFakLDvMSkNuD/miBgliziD95keaUP4pUcSCzHd4frn0d9I0DSZAYWsQwi4SLfQ6xVzEKI5z4sOG699Vb9Xs+aNau+d8h75wg4Ao6AI+AINCoCtQl+K5cm2Zo8wOhULMGjyas8bDb5bOvlBj4RNU7lhn1th0HALRo6zKH0HXEEHIF2QIBp5/xeHnvssTVtTfw6td6VV16p9Ui8JoOvyfe///1mv8VMT8deKa+lg6getY099tgjd5+EONU68pCf/OMf/8hdL6ugeOhqW2uuuWbClOC8IWSk1sOqIR3i6arrfv3rX6dX6WfxDNX1lhzqzjvvTEjExbER9WgiqrRm9T777LNECEwtQ7laLSWaNViHC9577z212hKyNMHWIE8IyaOYiOI8kQGHZlVEdavrRWHbbF25BaLA1ePAvaYMYGh9kqnVEi+88ILWw4LDozgEWmrpctBBB+nxEDVoZmewquH6Jj7XmnxyjTXW0IR84t2bPPfcc5l1fGHxCIhyt2S3J97jiSjwMzeCnR+WG1wLSY5Yq8VPZqO+0BFwBBwBR6DDIOAWDcUcysIUvPKDHeTBSEdrhcTjY5NIT3tkdH706NGqjjDlb5MK/sERcAQcAUfAEejkCLz44ouKQFZSokrQWHnqk1xKyI8gBKPOqCFZlHjHanKqtddeOzA9HXulrl276m9ypXZZt88++wTUqCQdQ2FcLcTvVFWvlBPPVZ1OX61OpfUkaiIhFiow8b6tVLS0DuUYSkCSUZGsKh30C+sE1KNMK06HkNK6iP0m6AMJrwgsI4RMDDvssIMqSUkOtfvuu2sfTb3Yp08f3Xet0IH+YbHA1Hu5JQ0kvRPCt+LecT5iq0CAk+EZV/rzn/+sH8Esb4hXrioDudc0zFHh1hIkbeMcYB84Zz2KQUAGlbQh8bGuqUErb/XjyhMmTNCZgCQ1xGqD5JPMGEQ1Onz4cJ0ViNoeiw2PtkOA770Q8Wq3h3UNSQolt0rmBpn1wEwIVPtvvPGGquYzC/pCR8ARcAQcAUfAEWgxAoUSvPSCB0Sy+nLDxc07N/yistEHHbzQsG+QhGpBEo6EY445ptUPei3ec6/oCDgCjoAj4AjUOQLmDYovaC1hnqVYDuAhCtGx0047BWwNIEMgRq+99togyU6VKIVMmzt3rloK4KVYKSSBW8AjE+slPGWNUMuqAzEKCXPvvfeqzYQkT8oqVtMy/FZHjhypdSAXzHe1XCNMEx44cKCuZip+FmG0+uqrB1GfqV0FeJmdhLVp0/dfe+21wD5BHoniU60aIIyxvpg8ebL68mIrwD0Qx848ZmmzowaE2kYbbRTAhsEDpmunA8IU2wQsHbANgQwH73RgFwJuDPxLUq706oqfDWv7zlQs7CvbDQEGP4hq15V0h6y81bf1fL8kgWHJxkNmD6pHsqh21YMcsQnnj6h71S6u2qCDteuvtSOARzK/J9jeYIdSLbh2M4jGQIok2wySDLFaFV/vCDgCjoAj4Ag4ArUgUIwQ2FtxBPIh4BYN+XDyUo6AI+AIgIAQqDqltZbp6tQja7ncCySS5EtfhZBlcdkQAk4tHKgjfrJlp9laA5QXwljbFjJFs6aTJZ3lhJB4yfjx45MuXbpoGZm1kzzyyCNWvZBXUd1q21gkCHGbfPTRR03aFaIvkeQ+JQyEFGqyPv0BuyibQgxu2GLIYLQW+/3vf6/bwuZBlKGlfXrsscd0PVYFV111VSLEcyKEZyIkRiLerokQw1pWVKnpzXWozxxvUTXrvnIOySBAIipxtf5gCr2o90rrOA5Mry8XZiOCxUUtYdsX4ki3VatFgyhAtR6WJeLrm2AtIERUsvjiiyeSCFgtNlprL1LL/nSUskL8Ka5bb7117l2SQSOtwzEQVXapngxM6XKsOC644ILS8vQbGWxI1llnHS274YYbttoWJt2+f/4/BMRbVzGWhJ41QSLe5FpPBhlrqueFHQFHwBFwBDouAm7RUMyxZVqdhyPQbgg4wdtuUPuGHAFHoAMgAGEIYSbT2mvaG1FSaj3q9u7dO5dXragnE7tGn3322VW3B/Fy9NFHKwnGdviDeMFn1z7zCiGKB2/RwfaPPPLI0rbYNvuN/zCkDsSv9UMSZuXy+4fAO+CAAxI8Za0u5LSoSUufWS72DFX36ayzztI6eAV3huB4jBkzRgcIDLv4lfNAbD2qQkFuBupJIraqZa0Avp8Qwxw3/KaNUBZ1oRWp+tqvX78mxzjuu71faaWVEvNhrtpgwQXEsiARSxTFEB9jUZkXvIW2aQ6vVUhzMMwzUAX5b9chBm4sGLARixVt55JLLrHFZV8ZdOB4sV1JBF22nK9oOQJbbbWV4ivWCzU1Yj7u+CV7OAKOgCPgCDgCIOAEbzHngRO8xeDoreREwG7ad9xxx5w1vJgj4Ag4Ap0XARSkkFbia5i8+eabuYAgARikhiUBQw2XNyZNmqR1IdnyBsmpSK5D4hzbJmRb3759k8suu6yJAi9vm7WUE2/dBHVgTOiy/xC+Yj2RiD1ELc1pWRIHiQ1DQiIw2orxRFX46quvVmzzj3/8Y6k/tRCVFRttoJUkXEPRfO655yao9FDH5g0b1IA8yhuoOTlGYhWhVVCs83mzzTbLdf6Z4p063KeIzUQiliVa95133lE1+lprraVtcp5BsLZXiK1KQkJDU+PTR/sjIdzpp59eVXHfXn0ttx0SpdFn9iEryaHVg5RlQIqyqP9jFTeDTixHqZ03+O5RxxPn5UWstnLdunVTfLkG1xIQ7hwXFMAejoAj4Ag4Ao4ACDjBW8x54ARvMTh6KzkRcII3J1BezBFwBByB/yJgNzyQV6j4KsVbb71VsgXgARqCUrxyK1Vpsg5CBWKUuhB04m+bS/lqjaACFr9f+6ivEMBYHIg3pt68/eY3v0luuumm5PPPP29SrrUfUPhJDgBVfkI4iL9ja5vU+mDywQcfKIlmhAYKUSwosHWIA5sIyELDkH1thIDMRGX9+uuvz/Pp7GAontN6DuZRakritlJ5I1459uLJq21IUsCE87JciFerluOc5x6lXKBQPvzww7Usqm7O67YOzjFJRKfbZPCEgRcsL8TTWW0w6DN/qKMhgus5bDo//UVlf/PNNyezZ89WOxfsXbA2YfCE9UsuuWTCcY3D7FMqEcRxeXuP5Qxtsg2PYhHgPARbrue1hORg0XoM5ng4Ao6AI+AIOAIgYM87PH94tBwBJ3hbjp3XbAECTvC2ADSv4gg4Ap0aAaY421TjddddtyxRMWXKFCVGeOA2i4a8SlwIzEMPPbQ0lZo27O+HP/yhKnTnzJlT03FAtYl3qbWTfl1ooYUS/DkhzholPv744yb7BBGJUlkSyiYbb7xxgh8x+4nqGl/KWsj19sYAEpQ+rrrqqk2OESpLiJt77rmnvbtU2h439+CIBy6DA+XOEdTZRuSmPZYh+U31uv766yvxHxPyEMkoCdkG28Jrl0GCaiFJA7X8L37xi2pFW7Uecte+M5DUWE+k46677kqwAKEc5Ci2IKimsUKArC8i8I/GeoNp9XxfIdFbOjiDktfsGmzf0q8oeLP2FRUuZRnEqiUgxKmXxx6klna9bJKccsopii22NrWE+SMz08HDEXAEHAFHwBEAASd4izkPnOAtBkdvJScCTvDmBMqLOQKOgCMQIfDKK68kq6++uj5MQx5uuummqhSF/MKH1qaPQ2RAkFiSoq5du0atZL+FJDPlHPWN8Nphhx1KiYpYjg/t3Xffnd1Iauntt9+eoHKkHoQOHsKS7V5VwaeeempCsjLW8QcJ3FLCKLXZdvkIaYvtgJEUth+8otzFLsKSr7VLh1qwEYiV+JhDVEMUxjYb7A/WAJUSorVg07mrYD3AuU4/OPc5bzivpk+froRjPHhA0qYsdTsevCR8s2OEdQhqVwZMTGXNOpajYs4TDLiYzzTJvNoiUOOacvf8888vuwmODcSrDSzYftorXtQtJdFQZdo1x9qzVyxjUOSCRa1BHa5bm2++ebLsssvqdWWNNdbQJI+Vri8MNLH9PCR83CfIR+pdfPHF8WJ/XwACzz33nH5HGUjJS7wzEMnxwE/ZkxYWcBC8CUfAEXAEOggCTvAWcyCd4C0GR28lJwJO8OYEyos5Ao6AI5BCAGKDqa2mSjSyxV55YMan0mwSIGrxC01bJsTNonI0IgmSDIKF9iCwTH2KEhfSkuW098ADD8RNNHsPuQkBRPm99tpLp2A3KyQLIJ4sGRa+7La9rLL1uuyNN97Q/WDaOER5UbYQbbm/+Cwbic+09/vuuy/5z3/+U9rk+++/n4waNSoxQo3BhHlFxEDorrLKKnou2XkevzKIwNT+cgpfdgoSFEIxTchzLkMw0t5pp51W2v88byC+qYf3b1tE//79tf1KyRVR6MYDO3ZMUV/vtNNOJdsK+gkZWwmjeB+4fuy///4lzJdaaqkEBfHJkvDsqKOOSjbZZJOS1zbkOQrf9gg7D2rxc6Zf2EGAAee9R/EI7L777opvjx49MgdZ4i1yvTTFfZ5EnnFdf+8IOAKOgCPQsRFwgreY4+sEbzE4eis5EXCCNydQXswRcAQcgTIIoHbFvxL/Qjxef/e736mqMZ5+TlUUchAbKGezAsXj0ksvrWUOOuggJfkgy6iD5UAckK9YOLCOB/Q4+VFcDqIQ1TDlaLNaoFREGUx599yqhlbr16M4NWU1XsGVSHU8UE39OmTIkNZvvIUtcF5D9JLID1sEEudhkzBu3LiaFaRYbEAQkiTvq6++KtltTJ06tabeQRhzzkJ4Fh18LxkgwXM3y6qA7cW2LSiSGVwYPXq09mnAgAHaJfaPftoADt/fPIFXNvs2//zz67UjixieNWuWWpJQDp9viLu2DiPVsaHIG5D7nO8owcthmbctL5eNAPY+KP85F9Zbb72yhP9tt91WInextck6r7K34EsdAUfAEXAEOgMCTvAWc5Sd4C0GR28lJwJO8OYEyos5Ao6AI9BKBCCBeehGJZs1fZYp8KwneRsP2ySNMvIvS6ULeYtKizojRozI7B1KVtavvPLKzRKQZVaQhZbkqkuXLuWK+PKCEID84/jgS5onnnrqKbUywM7g5ZdfzlOlocrYIMiMGTNq6je2CeAI6Vx0oKimbewVygUDMJThu2uWBS+99JIug5SP4/777y8p6iHZKgXraZdZAo888kiloqrqNnVsr169KpYtYiUKXPqGF2+WHUfWNlBmUwfVsUfbIcC1ges3WDMwsdlmm6mFEIOPRxxxRMknmvXMFLFztu165C07Ao6AI+AINBoCTvAWc8S+KT+2Ho6AI+AIOAKOgCPQwRCQadphq622CqKw0ldRbzbZw2uuuUY/i4dnEFVjEE/TIOrgIDdYQQiRJmX5IA/uQRS+ulw8aJutZ4GQyrpcpoQH8QXNLJNeKKRjWHLJJYOQBEGUgenVHfLz3//+93DFFVeEXXbZJUjivCCEeJAkbUEUoUEsLtpkn0V1HSZMmBCErA2SLCvXNujbwIEDgwwAaH9zVWqgQpx3hNgd1NRr+y6JfUFN9fIUfvfdd7WY+ARnFhfSXY+j+AYH8ckNYqWh5VZccUV9fe+995rUE4uNIOpeXSaq7Sbr0h9OOOEEXUR5IY/Tq5t8FouLwDVErGGCEOT616RAwR/EeiJI8sggStwglhFBBpwqbkGI8sC1jbDrln7wf4UjIIka9bolM0qCKMaDDCoEIdeDWAoFsWLQ67oMNIbzzjsviAdv6ZwtvCPeoCPgCDgCjoAj0MkRcIK3k58AvvuOgCPgCDgCHRcByRwf1l57bSVwxa9TH7j/9Kc/BUikZ599NshU8CCqPSVO3nzzTSUZx44dWxYQUTzqw7mofcOHH37YrNzTTz+ty8Tbtdm6cgsgjnv27KmrrX65sh1h+S233BJE7abEKQQd+wxhKInxgkw/V2JNEtwF8cItdHchXcTuQI+xqCBzty0em1pW/Jlz12mUgpCfhKhDc3dZ9BX6naFC1kBI7obKFOQ7SYjFQmaJG264QZeLT24Qi5VSGfFJ1vcQbOkQu5Sw+OKL63eewZysYICFcxHCVmwasoo0WybJ+cLgwYN1ufWrWaGCFojNQrj66quDJG0MMlNAB6Qk+WSz1sVDWIlEBrc43xk0EYVxs3K+oFgEOC5iCaIDilzXxEIoHH300UFUvIFrxzvvvBPEJiSIV3SxG/bWHAFHwBFwBBwBR6CEwLdK7wp6g/oHhYhMFQsyhUpVH9wMV4ttt9028OfhCDgCjoAj4Ag4AsUgAAEzc+ZMJWxQ2/EAzp8FJNKFF16o6lxIINRWWQSRlefhXKaAqyILpeCPf/xjW6Wv4g2qr6i1agnIJ8Lq11K3kcqC/dChQ7HHUlIdkg6lpCS1C7Nnz1Z128UXXxwmT54cUGpCjKCOKyLEpkObWX311Wtqzspb/Zoq13nhnXfeWQlABjkeffTRqqpVdmf8+PFBvKOD+I4qWV70LpoSl+OfFQwEEOJH3GS1lc9S/qKml6nxQXyugyRWDGussUaTunww5Xjv3r1zq++pJwkYVSn7+OOP87FNY7XVVguSnDFIUsZw1113BfEfDmIHoN8l8QwO4gUcpk2bFubMmaP9YCYBBKNH+yEgNj+B75WHI+AIOAKOgCPgCLQ/AoUSvNxEMWIrGbtr3hNJ2uIEb82oeQVHwBFwBByBzo6AJMpSgrYcDjxwo3w7/PDDlZy65557AmpdSUCkRA6qqn333TdIcrRyTTRZjr0AIT6dTZbzAcJ37ty5StRK8qVm68stMGI3TRiXK9+Iy7GvgNxFsXzmmWcGSVwWUCVaQMxtLgppFIfisRrEBzlst912AcU1x7C1EW+rtW3Ni/qQ4pCwEHtM02d/ll122SAJm5Tga8n+MbAgHqE6nRzMUTlD3JYLyFG+LwT3u22hRkRxjzKX7yjWB2n1qZGXyy+/fJNuXn755foZwjUrTBmM0haVK8Q9CmQsOwhTjMeq4Kx20susvPUrvb7ozwyIQGaffPLJgX3mesZfHGuuuWYYNWqUP1fEoPh7R8ARcAQcAUfAEejwCBRm0cD0Nh5cWkLudniUfQcdAUfAEXAEHIGCEEA9CAmICg+SFXUeXqCSOEuVn+U2g3+lJIdSu4aPP/5Y7RnwsTz22GNzk7sfffSREk/4bxqxE2/PSGL8L2sJKw8x0xEDMh1iEJISGwaUheUISUhHVIj43zIbqigFImQogb1GLWHlrX4tdYsqCx5YjHTv3j2ceOKJAZXzmDFjwvHHHx8kGVmQBK6qdq5le5z7qNDxc4XoRKG8wQYb6CAInsNxMCNNkhIq2cr7QYMGhT322CMuUth7zgtJ3qbtMSjDuROHkf3MmLNgMECSFQa+lwcccIAt1lcIXZSvl1xyiX6ePn16QK0Pccy5BlHNoA1T7Ila7+OtvNXXRtr4H6IQzgEGhiZOnKgewxC+nBNYz/DnswLb+CB4846AI+AIOAKOgCNQdwgUouBFPcTNrsV6662n00GZZkYSiHIPMVae11r84OJ6/t4RcAQcAUfAEegMCEBIkSQJgjAmoFCEQlThS8kfybquu+46VTeWwwVSuE+fPuH222/X5Fkkx8kTTPFm2/hbmiIwrkdiN/wX//CHPwTsB6opHCE8TzrpJFVk0h7en5BOEG0kIDN7gHgbjfie44HvMWQkyt1qwXRzyKpu3bqpnyikZt6kdeXaxm8WAhDLDsjMvIQtPs4E58u8iFNOOSXwx7mCPQjTvzkv+Az5bD7GKHlRbSI2qBRTp04NF110kSqBTY3OfepCCy2khCGE75FHHqnYL7zwwjrdH1sEK8vx4zvYlgGxSxK+Z555JuDHjPUZdisECflQsGKJALH94IMPBr53NlhjFg3Yr3BvbgkROfZ40vbr1099dtkniFASq9E+ZDmBSrmWsPL4Srd3kGCO/fFwBBwBR8ARcAQcAUfAERAE5Aa51SEJGzDZ1T956EtE3dDqNr2BjomAPIzoeSL+aR1zB32vHAFHwBFoAwSEVE0ki7xeP2VKdSIqvUSIlURIp0SInUSSdCVCOiVLLrmklhGSNBH1Z8WeiJellhViS+tXLCwrxd8yEQsFrSPEcGZxIZAS8YzVMpJBPbOMLaT/okLWsnYPEb8K6ZbsueeeiSRzsyoN+8pvHvt26aWX1rQPouLVeuLFW1O9coXBk37suuuu5Yo0Wf7kk08mnG/8iXK8ybr2+HDuuedqf9m+KJkTzq90SHKxZMSIEYkMdGhZUaqmi+jnv/3tb4n41moZMOD84vvCn9VlOdviNf5jvXi9JmIFkNl2UQtFiZvIYEuyzTbbJBx78cPWfvC9E/9mPQacQ/RNSO5ErFUSGUTRz0J867XA+iKKfl0uJGgyfPhw3V9R/De5RxdLCm2H9vjeComsdehH3pBBC60zbty4vFW8nCPgCDgCjoAj4Ag4Ak0QsHtUEZM0We4fakOgEIJXvP305o4bxDvvvLO2HnjpToWAE7yd6nD7zjoCjkBBCIgqUX9nxdc2EeVd2VbFBzORaexaVmbRJGLFULYsK0T5p2UhdyoRwpB7Ms1by4ovbMU2xW4hEbWplh08eHAiU8mblRdVYRMiTVSHyZQpU5JHHnkkkSnXyX777ZeIolfbEMViLgK62UbaYQED2qLOTQYOHJjIlPdEpvonu+++eyLq2+SDDz4o9UCsK3RfZs2aVVqW540omrWeKE7zFK9aBsLWcIWwFA9TJe6zKjJ4Lypf3b4oSrOKtOkyBi1EdarEpCjTq25LFK/aV0hM8ehtUv6TTz5JxP5D10OWigdyIsrfZP3119dlMZlr7yWBVyK2DAkDIXyv2jJESavnjm27lle+a6Lsb0LuclxpQ2wTkieeeCIRVb9+FmuQZrsBNjLzTtfbgALnq1gvNCubXiBJGbWe2LUkYiWRXu2fHQFHwBFwBBwBR8ARyIWAE7y5YKpaqBCCd/z48XqDhxrC1btVMe/UBZzg7dSH33feEXAEWoAA5CokDkq9cuSuTNdOUPDJ1H79PTaCCIJMvEKT5557LnPLkDjizat1IMbE2zehLUhZfs8lwVdy9NFHl9rlGg4hVC1kWn+JSBS/zESmvCsRettttyWS1EpJO+tjjx49EtSV6UAxLFYN2jcIp88++yxdZJ5+RmFgimnbl/hVvFJVWYr62ohSSZxVU5/BnjZRr7YmIEshnk3tGfeT95C9kOuE2H0kp556arLAAgvotnv27Jmgkm3vEIsP3b5YJuTeNPvI/qSJTL4bLOc8mj17th4X7llZtthiiyVsQ+wdErELSSArWc4fZDjncluG+AsnnCvWFwZzUMjzXYeEFr/ckpIXJTHH0JS97APq3DhQ9Nv5hprZVMtib5GIZ21ctPT+9ddf1zZpzxT4ksysGVFO22LnkEhCupKan35zXWAQwp8BSpD6G0fAEXAEHAFHwBGoAQEneGsAq0LRQgheVD92Myy+bhU256s6OwJO8Hb2M8D33xFwBGpFgCnb/Maiak0HhAukrJFVvDJ129SK9tsMKQRplxWQMihQrQ2rk35lWn+WGjerTZZBDpuaON0Wn5kKL/6fFclDSF3bF/GhLbepdl8OYW37BAmNlQDT95naftlll+kUe1u/9dZbl5Si4n9bU1+NsESd2tK44447Eqbp0x8I/5///OdK0EHoWx/tlSn68XnAzTY2IBacb+wDlggHHnhgcvDBB6vKFZVokcF2UKvTL/HZzd00/aAOBKfFvffeq8tQszJoYKpTvhPsR7x/1IHMXmeddUrYUA4Vb1sEim4jd/fee++ygxji35xsvvnm2qfll19eidrTTjut1EeU4ww4MBjEIAoYoFQ2DBdZZJHkz3/+c8VdMEKdARjU/7QheTQS8RzW/b/llluSVVZZpbRN1qf/GPCQpMsVt+MrHQFHwBFwBBwBR8ARSCPgBG8akZZ9LoTgZdPmy4ea18MRKIeAE7zlkPHljoAj4AhkI2DT+yWZUrMCv/rVr5Rkgbg77rjjVH1phSCCIGBQ8EJS8R4yuFwwfR9LBQhiiB3Ui5BisSqYZb1791YSE2VqnsBrFyIXstLUwqgmUYrmCZSl9B1FqSSOylOlTcsYQYiKEjK3XEAs4oVM383eQhJZlSvebDl+sxBz1H/xxRebrc+zAMKZc4M2IOjfeeedUjWU2MOGDSspNinDH2pxPIOx2rCgLGpiSTqmZaxs/MqxxdO1iEAsQNtLLbVUzc3hKU1dU6vaAwNKVwQJDCxAYleyfZgxY4a2wflPW5yvbaEgRzlN+3yPqwXnvg2YoOolUBfbOUY7WX9sA4VutYDApT6KX7BDpZvVni2DzMVqhbYhlyUhoJYHW0myWG1zvt4RaFME+K5LcsZEkkOqch87FkncqT7obfFdbtOd8cYdAUfAEegECNj9mnvwtu5gF0bwnnPOOXpjx8343LlzW9crr91hEXCCt8MeWt8xR8ARaCMEIFshVdLq2YsvvliXQ3xmkb877LCDrkdRh4rTiL6bb765Yk8hFlFmxkpOiF0jzozgIUFaOeuHchtgKjz1a7UcMKsGfHrnZUA8ggXYQIhVCxSaZnXAfoOhEY/V6lqCMdSkLQmUqcsss4zijSqzUnz00UeJKUIhQF955RUtjq8qpHRW4jH2BxzwqoUA5TMDCfS7tcFgA+1xz1BrGJlupLj1jX2yhwe8jSvF119/reQyfTCfXvx4iwwsGGgflW0ev1u2zT5xLPiz8wiy6sILL0xQiqOwtQEZvjO15MVAcW/7a/v51FNPJWBlxx8xBwMF11xzTeZgC97GnBNYSdSybduevzoC1RDAzgebEJIOosAn6WCcAJLfSZKQ2qAm53T6j2tCa2ZFVOujr3cEHAFHwBGoHQG7R3OCt3bs4hqFEbw0SpIHfkS5Abzgggt0Wp0nXYjh9vdO8Po54Ag4Ao5AfgQgmiBXIE3+9a9/lSpiq2DEVTklIkQMv8k33HCD1uN3mc+QQOXUt2xjyy231HIoVFH8xiQuD9dXXnll0qVLFy0DeQkxlDdMgVjOS7hcOxCU9P2ss84qV6RdlptdBqrovHHGGWdo3039iqIsbQuQbgt8IJLZZ6bctyRs4B1lLZYHeWLQoEG6TewCUFib4pp+cA5us802ybhx4zRJGSpfGzSASGaKP2UoS/Ld1gRWCrSDb2ytYapnktyheqUdzmWsF4xsz+OFjFKduiNHjtRXiN4iwxInck7VEhC59IvvYVagVGZ9rZYmplrGCiIOu46QkJHrUbXACobtQ7TH16xq9Xy9I1AJAQbXsBKyBJ6cY/EfntEMZpq9CuW4nnH9ZKCN3yk8qS3hIHWZleDhCDgCjoAjUB8IOMFbzHEohOAlS/eAAQP0r5ynG6Rvpb/Ro0cXs0feSl0j4ARvXR8e75wj4AjUIQKWMOnll18u9Q4FEw+oG264YWlZ+o2RczZtHlLXPDSxD8gK85Zl+jUqynIBQbnbbrtpH0hKlZUkLasu9g/0+/nnn89aXXYZU22pRxKseRmWgKoWghp7A4hQFGU2nR51ZRa+EGJMb7d7KZTULQ0j06sptuP2mW4PzhChplzlM8Rtlrfqq6++qgnaKMMMLlSuVh+CtaUBDmaPQJ/SAWGDwgPC//LLL08effRRJR85r9g+gx+QkYgM+Ayezz77rL7nHMwTffv21fIkPOPY8VduYCRPe+ky22+/vbafhWu6bPzZCGcsWbIC2wT2mYGaWsKOHUS9BZYeHHvwi+09bH3WKxiZirqlgxNZ7c7rZainOd+wsdl3331V2T516tSKHuLzus8dZfsMPvAMyXkNccsgGQMjWP8w2GaDZ6znj++4KfizMMBO0BIVon73cAQcAUfAEZj3CDjBW8wx+Jb8ELY6RCERZLpW2XaEAC67zlbIw6K99VdHwBFwBBwBR8AR+C8Cm266aRDSJggRFERxpEsloZe+ik9mJk6idgqScCrIdO0gRK+WEYIq7LzzzkHsEQL1e/bs2aSuEMjhvPPOC6IYDrfeemuQAbkm6+MPoi4NotIMQvoEsYcIo0aNCjJQGxfJfC/EW5AH7yDq0CBT+zPLZC2kPEH9eRVCOgaZ5h+EYAiiFsvdDUlwFtZee+0gCjLFV2Y7hccffzwIgRqE6NW2JNFW4JjJtPYgxKi2LT6rQewOqm5H1NxBkoCFp59+Osi0/SBkRxDfZt2GkHNhq622qtqGFVhhhRWCkCN6jDh/6JdMeQ4yTT+I5YcVK72utNJK4a677tJ9kOn8QdTKWkdsA8Jaa62l55AkydNzUJSnASzyBOfgtttuq/eWnJNCrGk18ZwNQvZr/9LtCMEc6D8h5Glg34WYDEIMhQ8//DBMmzZN13EuUY7jKD7VQZLOBXmoCEsssYSut3/i4alv2UcwFfsxbaeoc1AGRbR92q4lxOZDi5e7t2Z/wI/vOOerDOpUbV5I2SB+0loO3C2EwEQIEjh24JsnuM6IAjxwnos1jB7HPPXqtYzMYAgy8KXfzaw+ct5wTu61115h+vTpQQYS9DzhvOM7IAMFeh5m1fVl1RGQ5IBBfKGDDNbouSQDYPr9jWvyHNq/f3/9jWS5qM2DDDLERZq85/zk2iCDlEGU9EG8eYNYpTQp4x8cAUfAEXAEHIGGRKAInhiVBtPzWvM3r6ddFoGDt1EdAVfwVsfISzgCjoAjECOA76zcYKgqUYgqXbXddtvpssmTJ8dFS+8t+Rqj4XHgV0hbKNDSccwxx+i6WMGXLpP+LCSg1kFBlUfdaFYLTE+vJVZeeWXdjhDPqiTFf/i6665L2H6eaeO1bKtcWTxPwQ4lWa1halB8SZn1BNbmrUyb8Z+QQgn7WS1Qp6JoLtcObWLzgIK4lujVq1epP9b2a6+9ltmEkC8JU6Pj/pd7T1v0F9uEPIHiFi9XLEpQSppinPaFVEuE0Ek4n7CTEBK2SR9IzGchgxpN1mX1j77JIEXpXCIxIOVQJWNvYWq/vH23bVd67devn27DLFQqlY3XYb1A3yqp2fEgpQzHMo9NAm1RHuVj/D1GJcxy/JlrCc5z6nHeN3Lgs22exgsuuGCChzgKe66jqEfNDoB95TzlNf0ngyTJySefrGryRsZiXvSdc5dzEkz5zap0rbdyKM65buAfXS3Mpx5bEw9HwBFwBByBeYuAK3iLwb8Qi4ZiuuKtdAYEnODtDEfZ99ERcASKRgBfTB5yIWxIqoT/KZ+zEn2dffbZug5yjyn0cYwdO1bXZZG4RlaYpUNcr9J7kq3RF0ixajFz5kwtS7IxI6ur1YEAo33ze+V9/IdFBIPEeKy2ZUD0QeJAHtRK9ImKVfsMIWqBzQXEpShUlUCDNIr9jq1c1uu7777bxEJhk002URIJn2XsLOx8ASeIj/R5kNWmLTMbD5tmD3maFQw8GAGM760RXODDH9s2coxp1XbMunXrluS1byDBG/WwR+AV2wZRmjbzFMamId7Geuutl+BTjXWIDQ5QH/KHfkIek5gJ+wq8Za1vu+++e0KSQVHN6zJyS4jaWt+vuOKKWTC0eJlZLeATWkuAH/2tZO0wZ86cUpI4rCDKkfyc05Bb4ALGovpt0pXDDjtMt4Wfcy1B0kf62KNHj1qq1VVZ9sGuORC7JCJMB7YV8eACCfMgxbke8Wo2KWDBOfn222+nm/DPFRDASgHsuBbxvSwXfJcpx3XIbIY476sF11/qYZnj4Qh0JgQYLGEglBwBfGf4reUeJK8VT2fCyve1/RBwgrcYrJ3gLQZHbyUnAk7w5gTKizkCjoAjECHATTdqQh5GIVSN4CWLuAWJqWSasJahXFaiqyFDhuh6EiGlA4Ua9fL66Vp9U1bK9HlbVPHVkriRLKrSQzuNyDT5UrIx+gahSKIvFIqQcTLVvrS/eMamCRTaF/sCJT3xs0U5TKIdCNKWhPkay9Tz3NU5dvQdsrNacrU8jUJc2m8pnsDliHU7X9g25fIcV9o2ohZvS+pCUqUDD1yUiawXm5CSPyafxRIiEesDXSe2EYmpzVF5m580iY7yEPI8hEIS0i5/EOU8kOKLi7+r2I2UsGA9ZKklADz00ENLCQONaLYka2eeeWaTXeIcsfPfBjpQCaO2JrETbUN2Fhl4hEKs4m87e/bshO/v3XffrftFQqgsfPDOpi9idVFVEUob5lsK8Ths2LAE72i2w0ADgwGoxWkPcpfvRTrMlxeldC0htm3aLirlRgyuGzYwAPmRFWJdUlLwkgyQ4wiWad9hMLdzUqxaEup55EOAaz2YQjxVCq7HlKM8My24hkHOlxvYsLb4flOP75OHI9BZEJg4caLeE3Dup//4TeK6LfY+nQUO3886QsAJ3mIOhhO8xeDoreREwB5KISc8HAFHwBFwBPIjwA23qWXtphySjSnoXFu5MWc5yt0schfVKWpXyjz22GPNNmxqzFoJiFpvyN588021m6Afm222WRInj4s7hTqZfaEc+waZDQGZDvFWLSWPg0iByIQYhMAi2Rb103+0B4EJscZD/kUXXZSg+ILwQsFKv1C0QGTGYYQXCs9qgcKSByXbB/oAwYl6GvKN4JiQIIx9RU0GoV0tIKlpC1VbJRU0BCflULXxmqXaTm+LhEOU5c+m7YNpOkwhjBUIhC7ljYhFTWxE6qeffqpT/o3UBw8jf8GyWkByWmI6S7pm/YtfOa+vvPJKbe6ZZ55RwtJUv9SH0ESBa3U4Jth7WKBkRXVp6yGHIOYgjlAj8/n1jGRvVr+lrwxSsE0jB237vIIh5LJtV7yDE4hE1qH+zRMot8Vru7Rfcfv2HgUqBHdWoOanHAMElabHp+vafjWq/RqKcPZbvKybWFbE+3nUUUdpGRTyqHtR4lMnSw3Kert210qWx9vsbO/t2lVtQA67HrDnvCNMOX3fffdVhIzvPb8FfMdrOb8rNuorHYE6RQD7HfH21+8K3xcGyA855JCEAU8G/fl9tsFQfm+5t/JwBNoTgVqfJ9qzb420rTYjeHnAYiomU+nwmkP5wMUDhUC5h7lGAs772jIEnOBtGW5eyxFwBBwBEEAB+vvf/75E9HCTbn8QtBBu5fxS8YGkLNfhrIdZU5nl8S6Mj4Z5sKand8dl0u/ZBr799Ae1lSSGUpUt9hIQJxArtl88gKPYrBQQKEyBps6uu+6amLcin1HNHXvssUr4QipCiBvpCkGOesu2lX7lwX9v8XiFqCR4NdK4nG8jKmJIznRbRjjackgzU8HaMl4h3MpZTrCf9B1MYoIyCxsGBLAtYB/YNn+Q65B+KEWxWID4NI9WzhtTfNIPvF552GNbkIsWENKsh3yhHd5jucGgAu+NUOT4WkCkW7/NcoM61awuIB5pk2MIuT9hwoTkwAMPTCTpUiLJwPRhFZuFtDIaVTH1+OO+k/tRpsyjyrXlvEL6ct7Ql3i5JF1KJLFh6WEXf96iA9LKzlm2zfHZcMMNdb/M0oPl4Mb5a+Qu51aWurdS/zhOkihPyUcIdvDke8Ixq9QWhIBt1wj0StthHfYX7AvnHudbIwbnFtiXU45iMYIvM98ts10BR7uWZO03Aw+UZ7Dg/fffb0RY2rXPnHtceziXsn6v4s7w28PxsoE3+/7feOONcbFm7yWxpdZjEMjDEejoCJh9CQOKY8aMyRy84ree3wb77ckSA3R0nHz/5h0CTvAWg33hBC836zz02MMTF4isP24uqz2wFbOL3ko9IeAEbz0dDe+LI+AINDIC5rXLAzADqGmSK943vHd5WIZgmDFjRryq9N4SM5E8KB2QgBCAkHsQh2atAJFBmzwwZKlr0+3EnyE5IOvMEiB9r2DLIbTzBFPP43sP+oTSlGRcv/nNb/SewwhFHuxjeweItSuuuCKBhMHPEWIRSwdrD/WdkTIQo+wz/UUNE1sfgNGSSy6p61BgWjmIZdRiKILNssD2FzKPWS2Q3DEBiZrY1Ju2/1dddZW2jSI2TzAtn+0YlkzVt+3aK9YJPNAZ4Y7VBev69OmjZDjv8aK1MF/cww8/XAfwWQ8pbw+PNu1/8ODBVkVtASwJEphyLlJviy22UKK5VDD1xvoPOVtLkAiL9sHfptrzudIfRKYlZLOkapTnPCg6IOrN6xj1sR13+gvu+CgzIBMTvfQFcpcp6O0ZELtsm/PZlOfltg85YANF2ME0ath3gWtKVkD8ggkDDXGYpUC55Jf23c+yw4jbqZf3/KZUszloy77mnVXC94lrHIMKvLdZBdUUiBwHjiPHxcMR6MgIoGbnXGeAKU+eBVP6cj23QeCOjI/vW30g4ARvMcehUIKXByMUKVxA8v6hSvDoPAg4wdt5jrXvqSPgCLQ9AhBv/N5CmA0YMEATQpnaCUKRREGxmrXSlGlLjIQKzbxssQxAwZpWOEL2MB0WH122j/dvSwObAZSEWAJAGqKWNAIFQrKSwjC9TSMny92D4Et7uUy/jglAypK4KitQuxphiQWBPejgMWrkL1MZuSll2rwRdTxEWR8gRC1QhdpyIy9I4GTBsYNANjIU8i+ensz0burjPZsnaI9pl7ZNXjl2ENqo3cyyw9azXchvUycy9Zlzi8+oYAnUrZRHHWdECu8hiq0dykNOs31I2pgwtTLxK6pftpsOVKeUw0IjT6AMh5CME6dRH6Uu59b555+fMDDCOW3HL+5H/B6FclsRcdY/zlcIWwYJ6F983sR94T0DOXgaVwoU5uw/30cw5RgzwMF+VLLzqNQm65gZQB9QdJ977rnNvpMcZ5TURoyyX19++WW1Zut2vZ0b5QbNIP3BAyzi4LvMcmYvZoVddziv6zVIKsh+2LWM/eFaxbWe6x6/K+0V9swwffr0qpu03yJmjdq1iAG3csHvij2zMnDm4Qh0ZATMVikr/0LWfnOvY/chDH57OALtgYATvMWgXBjBiyomnlLJKCo3lah5+eFkWh2JMfCEs+le3DTw5xeOYg5mI7RiN2vuwdsIR8v76Ag4Ao2AAN6PRkjYwzher7GvJ6ReninWRjwxdZwp+qb8pF2mdkMMxlnjWU6ZSg/SLcFw3Lhxen/AzV7eMEWz9QnVLvcXkKooIvGsZV38F9sklFN7MR3b7lsuvvjiUncg21Bbxu3F71Ff4qtrAUZ2nLgfev7550sWCOlpkBB1+ADTHipXi1pvfuPkfHHf0u9NUcvrrbfemhiRzG+2eflC/N90001J3759tV933nlnyWLAcLB22D9IP0vAhzLVfDEZiIi9jM3yAkIpbeHFMaSvlVTcEE4QarHHbrx/kD0o2NPEFMpEI+Qoj1IJEp/7Vz5nTbO349CaV7x9aZ/vpA2kWHv4QUOqM9gB7txDk7TL+glZXy44N7MU2oYFAxHgyHGpNVDs4+Ect4VnNQp81N/xQAHE8ty5c2vdRF2VtwSF6eNjnbRBDp5t4gATMMJ/Oyv4/rAepXi9BWT2vvvuW1LX00+uV2Y7Yceea0J7JWAyH/BK573hCDHN9ceuQTyTlgu+A/ad+slPfpI5Vb1cXV+ejQA2MPi58v3nt49XrmEzyswYym7Fl7YFAsw+4jeYe8JacizYfRiWNR6OQHsgUOs9bnv0qRG3URjBy4ip/fhzIaj04w8ZHD+QcpP72WefNSJ+3ucaEXCCt0bAvLgj4Ag4AjkQwOMUBWBsO8BvMgQmM2XMXqBaU0xvNVUT9XlYhthByWqBzYApN+13f7/99rPVhbySUI22s+wisjaAssz6wivT3y1QavGgGZPVcVl7j1LXrCesrr0aMQM26eB+h4RuphqDJMnyx0VVybZi0ppjwzLqpANlpyUZMvLZyNa0cjBd1z7zkE37psT52c9+ptORscmC2ICYRXkM+YwlAGW5J3vhhRdKhDhJoxiUZR1/1idI4LSFAOuxayDMXgH1Nx6ZdkzZZ9tvts0517t3b20bEp4HUM5XCHAISdrEUxOyjUGK0047TQliiHuwt2nxlENBako+Psd/WGBAoKbD+gXRjFqaOiiO2ypM/RnbXlTbFnjYFPT0PkBWxeTrxhtvrOfjvffeq1NxUe8aKc++QS6aEr3adtPrsVbjHIpxtfdce/B1bGnb6W3Ny8+GF+dYVnC9Y78ZOLDAM9Y8rNMDFVbGCJOs77uVmRevPJeh9mafUGnz/eQaYAFhz3lkvy9c65577jlb3WavWH7YTAeuwdXCBoTYD64ZZskT18N2A2sNytC2eSjHZfx9fgS4H7DBOzDN+sNaJj6f8rfuJYtAwLzs+Z2tJbiv5HhiO+XhCLQHAk7wFoNyIQQvN3M2/Y4bv7zTKVEb2A9BfJNUzK55K/WIgBO89XhUvE+OgCPQkRCAIOPGvKVTpMePH1/6beY3GgUXykYeDmL1LgnCUBjaA3i5hEQtwZaszmwbX9dqARGKOtHuJ3iFpEQBi9LO+gxBBjGDutnKQpTawz7LUMtmkQKoP0nEQ5kXX3yxWZdeFzsC1kF+QvSkg2NhCctiZSgPvdQr9wBlSlcjhS+88EItD3lRLSDjaBviEksC3lebnmketCgz8SI2/1qsA1BvZyWFo13+WAfeTEOHGKMOqiFLvmeJ5yZOnKjnE3VQCxP4N5vC2s4na5dXU+XFy+L3kMjXX3+9toPdRrwOWxI7dhDeRuJzTPGUxVrACHBsR6jLudNWYbPdUBzWEkbWx6pw6huRDm5gUC4YJLBBiNb6CnO+Q7hB6LLNatYR5fpUr8sZsOE8IIFkVqBQZz3fKwtmC7CsknLUVKN5LVas7bZ+Nf91ZipA2JULxDiWgI5rQ62+6+XarbScazS4oj7EyqdccF22awjl+eN6zMAag1cIkSDuTaEPGc8giEfLEbjrrrv0txasmYEBzih2SbTIdZ9ZQHbt5Tfa8W451q2pad79dh+Rty34HY4t904ejkB7IOAEbzEoF0LwcjNgP6YPP/xwTT0zv7w8029qatgLF4YAI+jcwDNtsLV/NvrPNDYPR8ARcAQcgfpCAHLSFLx77LFHYoNy9hvPKw/NTMXkt4Ew9SwPeC0llUnehiIUJRCELHYQbItp89Xa5KGSspUUuhCEkJY8sMRkMGSFqVTwOKWdgQMHZh4UIzaYMp8OHlypi3oSZRtesFg7WDzwwAO6nkHwdJjvatbgOB7ItAvxQuBTC2nKoHq5BFDWvtkjYE9h06x58K4Utj0wggjFtxVSim3Sj1r/OH8gzEnqQl2IdwgA3kNIGkEE4WX4sw7yBYVxmuxFZU3CtaFDhzYhc8ADj8HYlsT6ynnBQ4Mlv6M+CsVKdgb4QKcDpSzHFLWz9TtdJs/nBRdcUPc/Ts6Xp96gQdNeC1MAAEAASURBVIO0HipQC44X+wd2WGZUC0hlw6jW+/Vqbc+r9VyzUGHecccdikE59Wwt/eP4mk0DgyzpYBt2Ds+ZM0fPCVO2cz3MClTYNkCSNUiUVac9loEd10fsGKpdH+gPVg72u1BtwKio/jM7xb7PDHRcLj7qXGMZIGMgi4E7I27XXXddHXyw50urZ698XyDmszy/i+pvZ2iH74D9lkKil5v6z6CADZwxwBTPBuoMONXDPnLPwvnPPUwtgXKfevweezgC7YGAE7zFoFwIwYtSgwsAN9hZDyiVumrTDbMeeirV83Xth0CcoMdukFr7ir+jhyPgCDgCjkB9IWCZllFn4QPLwxgJVEnAhloH8jJLoWoP06ghawnIEfOtLPe7ggK10vRc83CN62MTgHKTZTHxiyWBkZVGlJonKmSyEYrsbzrIK0B76WnbEH9mORD3gfeQlFgKmLoP0jUdRvpkWVVBskK+QF6wHcISXvXq1avidHhLkmTTzfM+3JnCNPYF5mEe1TBqLMMvva8MDOCZCflvXsOUgXAxchXy3ghtCDSOk1lX0K5h8eSTTyrOto14myStwz8Xspj13E9YufgVFW6s/LXy8TKOOb7TsQUFbUCImy0GpCiDHUbM2jawueDYZh239DGOP5uijXMfSwhUsChq8bIl+RaJ4Bh0SEfWg48pL2tJ2mW+ptzbNXJAth922GElyxA7Lrzixzx69GglI1u6j5AinHecL5xz6ecbU7szqGNk8HbbbVf6nsbb5btr17l6y0Fx0EEH6fcnz2wJzldsUmywAcIu61yN972o9wxsGIkeH2t7zyAH34eYaIQAZoYoKnfOB5JBYgnj0XoEzAon6zct3Trnv/n7N/p1J71vjfCZ2Th8TyBqs+7fyu3DLbfcovWYfePhCLQHAln3Oe2x3Y62jUIIXrIbc+Fgilytgb8edXkA8KhPBHjI4eGGEdjW/tkDEv6NHo6AI+AIOAL1hYARlWl1I0odrv8obbOCh2d+yyH38gakodklQKgdffTROoUTUpkp35b1mXb5YxvpsPsP1mOvwCsP+jzkmzcr6lAUtEYyGvm4xhpraHnLIcD+2XR3rAbSYR6VJJOxgKQz6wG2DRlEu9zT2O8dy3mw4jX9cEt9lrP/WcF+sJ4BdMgsSCX2y4hoCOssf2WbWmlkpimnsCUACwgrkp1BKkIYo2hDqctnS1Zm06Eh+NNTn2O1Lf2zPx72UaaassvITFtf7hUiDeWjkWaQOVhaUB7LB1S/YGD1za4AAj0mf209Sl+C5Gu2LH6F2DWLBM43U2dTxiwe2EcIO8OQdXwvwMfOIZaxj9OnT29CLGUdS1uG1QX12GY8+MAy+2OfOB6xCpx9Yj3Hz8LwjafVo2xEtc19G/Yj+L1i7QE5R3DO0T5KXtSYjRh4SsbfLwYX+F4wmGHfc7DiOLdGLYuVnB0jZqDxvWEQC5sM1KumGmVbEL0MiqUDz2RmKVKG68DrYm/RHoE/NQQN3yVeIXmywixsOG/KBecX51L8XWB/7A/cGZRp62BgB8IWspBrLNYqXPe5bqNm92gfBFB6c+y5zjPLI0/wO2WDbH6s8iBWbBm737Hf9TytW/LUerOUydN3L9OYCDjBW8xxK4Tgtel2XOxrnXphI4AoODw6PgI2raveFAwdH3nfQ0fAEXAEKiOAzYBN3+b3nPc8/KcVsiR0SqvZ8FKlTl77HVSPRhpCnGaRlJBbMVlD+/i3WlDHiE7IPyNiKMd0bZZB0jHNkIDoiUkhyEHKGsmMBRGJvCAxUJLGShdUX5A5bMOm1uP9ax61KPjs4dWS9lCfafNG6LEtVLVxmKcuRGJWmNqSuuX+IBsh1lE5Q/5NmjRJE+5ZefaZ5Xgiojq15dVewQ+S0TCGTMHLz0hwzguzXYrbis+heLm955xCNQ1RZm2zjsR4pvLacssttZ8x0Q6pQBI1a6fSa+ydCsZpEpjkcGCGyjcmqykHOY6a0doHX2wdYmIOZTVKWyNYrSxEPcQttlamuE4fV5u5Rh3OJ/YZEvuGG27Q5GiQ7KZyNk9UyDfKc15CcLFt88rm+BKoFU2tbf2JXzl/hwwZoopjiE/WtYb8TO9Xe33mO2XHjGcIBiDiAHfKGCGO6hNbjZYGsxryfG8QuZDki2sP6ndeGbSywR3WxzMDIINJJMixjYn8lvbT6nGNXGeddUrnb3wOgAnnmZ2bvHIOcs3jvM8KCCEb0GDQhft3EgTatc++75xfJGLz6PgIMHOB8wrFdC2B/RH18LD2aF8EUN6DPb9ZeexJ+J2hPIPDds/Tvj32rXVGBJzgLeaoF0Lw8pDFRYA/bh7zBgodu2nw0aG8qDV2OSd4G/v4ee8dAUegYyIAGRGr0VCLWjIq9hi/V2bcmKoSAi4mBGwqX97BO5KvcM+ACquSnykqS4gRu8eAQCMxFkou+mjL7dUILiNtIQ3jgEizskai8RkFoO3P0ksvrWUgey1M5YuKzeLnP/+5lsNiCoLGfCIhneKgXYhS2y5YE+yDEZZMHU7H4MGDS3UgqUaOHKkkLkTuiBEjSgS5tVvuFcKXREVGckIaQiLasYTINRKKNuLzwNqEaMVLl2NlJDmWHSiMIaGN5LHy5V65eceuAtVzlhrQts1DJW0YWW7YcE6mSVX2A+Wvqc+pxz7F1gkQtOX6lO4H7XHeWHmOHduFjEPQgHctREW6npW3V2xL0qIHHpRjUhzCPCsgJE0ZzuCBWQBY2/Er99Fnn312yWeZ7wuWDxBz+JOiQgUfI0Uh+exe7JFHHsnafN0uY7DGrgeQp0ZUZnUY724TkfAdrVQ2q368DOU7AyRYikDI893nvCCxJNcoU7rFxyV+T4JKFLT0AfI/PbBAWQZQ+I7HFgNxH6q9Z3/5Xtt2+Q4x4IadAjMH7DvFehTFbIdrE9cFSF72MR2TJ08uXTcY2GNQy4I2aQvlPc9+tl0Gyjw6NgLM+OB4M8hUSxhpyLnk0b4I8P22gVMGWcsp9inH74n9XtSi+G3fPfKtdUQEnOAt5qgWQvDSFXvQ4YIfJ4Ao102ySFsiF24s0iPw5er58sZGwB4q8pIAjb233ntHwBFwBOobAYgk1DhGdtr0fBKeZQXkqil6IT0tmK7M73+eQV7IMpvCbwQTA770g+nkTPOHvIUIQeWGlQOKTCMQyr2SeAsSMlbMohKOAxIkTUZCcMwQ4s7C1I02jRR1MmUg9J544gktxhRx+gExasnmIHmN/KQvcaDmXf6/iePAD6IEqyLaoL8QPxC+kCN4YkL22H6yXdSZ6aAOD8xGijIFE1IQUgeFHaQsbRjJRTuob3mwM7UxZUzhjPrO2uLhzjx8aYPp5xw3I/KxMYiDqf4M1LMN/mLCGDsFiHzaiffLHiAh47MITNTbWWFku+Fjqkgwjo/tmWeeWapO/ylvRBykaLx/9Jn1saLY2ofwteNqy3jlnIAoY/q6Yc1nPHUh0SkDDhDDBOQu5zbLjaRmvfW/1Nn/vkElb/dMtl2OMcQi6uP4vtvWY8tQTm1FP22arn3fW6NsTfe3PT6bsprvTh7CluuHHYu2Jh4h/jk3OS4bbLCBvkJCWzI7rg9mLcLxYhCCWQRpOxcGmOy6mBdTSBkGn2iXc5Xrgg1YWRt8hpA2gpx+8p2x70F6MIJZC1YW4jkddi6Z1cl5551X+g7ZNTFdxz93DAR4huNc4/eglkA9Tj0GIjzaHwF+G+xeit8vjgMDNFyjGMBCWW0zH/hN5J7MwxFoTwSc4C0G7cIIXqZ52YMBF28uIEwTIiELig9uPjDiJ5MyD4B2c0lZlDwenQMBe1hxgrdzHG/fS0fAEahPBFBGmtqV3+H0H7/nkJgEJAADt1mqM9rAp50p77RBYqJqgYctZSE2GNw179x0H/gMETxq1Cjtg01ttynFVt6IQn5fuNfAazQmEvHWRYXCVGsUjabutfr0HRKZYF+N5MOrdPjw4SUlS0xyGFGX9gUGC+sPnqBxVnosMGyblkwMT1dINh6sskhEK8+Ua+pnhSWD4pjFPptga8Ql7TCtnzCPWW6kIckgfrgPi8lR2278ihLQVLJ4vGaFKbtsSjv3ehBFF1xwQWnfIbghoIwkmDBhgjbVo0ePUhm2yxTwrDBrAspAksVEn/mJsg4SG/xjlaIlh7L9gsxH7cpnjgnH3PbRjqOVRZSQRURzLnD+cF5yDCDqUQ8b4cYxtnsfa4tXI8/YDknSIMo4/whmxnFu8RBudbJmusVKY8qh7K4UHAsbaOB42/Yq1amXdXy3jRivxe8VBTPY8H2cV4FPqYlaOIe4nsYKc44DPs5GvjAAMXPmzNzdZTCDfeRcyxoMihviXDUcTz/99MSSR6bPL8uRguovHWyD7XHNiolk89BmNoNHx0XgwAMP1OPPjJhaws5Tfss95g0CDIIzUBbzMHyX4z9mmTDzx8MRaG8EnOAtBvHCCF66Q7ITbrbji4S958bX3sevjHKjCvHoHAjYQ44TvJ3jePteOgKOQP0hwLRw+63G8sDUqRBbKGjj32uUtF27di39frMOWwGzV4p/zyFO0968WXuPwpF6EAdmE4CSEZILhRnKXcgGCGVrHxLYiAhblvVKHyA5IU9MpZpVzpZZGUg21HVGUICLEXCQpCeffHKTXbFs7q9nJEyC5I3JWogdyCXUozHhCpZgzvRt6w+/kZA09hmS0whp+oifbFYw5ZU66amvpmbmeKOShkSiHOpYBt/BKZ3MzshnK2cKSD6vt956Wp/kZVmBbQPljCTnPcfOlIssN+sLSzyFGMBIOMrbH1ilVYVsMyZ4wYQkVhZ2XGyqOlhyPtEm+xwrizkeZ511VkmcwHlJWF+tH7yCI4Q53xWwhHxjGqsp0Xv16lWqh+KbgFy27xlt2AM155yd9/E2eE/7aaLdvo+xIlk3IP9IlEc9iGX7Hpti1MqkX03RTR0e9hslUEKzr5APEKIISxi0QVUPcV0uUM5Sj3NvXoWR/Xx/svzGrV/sl32XIWHLqbGtPK98h+1aRfK3PGHXAc5fvn/gw3fF1Py0YTMZwDgdzBKgDrMN4oB4Zzkq5HjgJS7j79sXAWYI7LfffpockmsQv90cW2YBlJumX62HDFBwnPEbryXMIgA/WI95iwA+vPz+cc/DoC+/YQyAcj3Icx83b3vvW++oCDjBW8yRLZTgpUtMATMSj4t/uT8eIFAn+EWkmAPZKK3YueEEb6McMe+nI+AINBoCqMWYdof6FKsAiKhHH31UH7hN7clvMw/nRn5BplrgEZv+7eaBkDZNdQaRQJmYjIQIRL1YLSBvqQs5xysPFTFJF9dH7WtTiK1P3D9AJBgRhiIt9rjlARbSx3xX6TuEH/tov0G0xTKmEqNGiok42w6v3bt3b6acxY6AdWynXEDiMFspJhTjdmPc4uXxe6bbQ/hwn4SaDsKTv6xpsUZ+oY6NiRWzRrD+WvIlI6xtmj/kIGXoF0pSOzaoU2PlpPXvnHPOydx1y8lgRLYpBa0e27Mwz2L8UW29HR+rz8NnOkxha3U49gTEPsvwGuUYGalm5Ywotc9GYPE5nkkWJ/aLFbumOGbQwwIbD/NsNvIbVRvnZOx5ynlgSmGwo39890yBy7mMstZwNwIYMtimvqOOTkesSDYlPN/DX/7yl/rgTl8hnNkegZKd/TV8p0yZkm6ybj/b4AF4G5FvxxLc+H6XU50xYEPZPNenIgGYIdYvZsXC9jmuDJjh5VtOact33gYZ0lYvWX1DAU/bKOBrCRtAw7rCBpmweOFaDE60ybWW738c2L+wDkyzrBjsO28DOXFdf99+CHAcbRCN45X1x3WAQV2si2oJBk34zeR3Iy9JTOJBtsd3gHsUD0fAEfj/7N37r27dWdf/f8TExGiMRn9Rf0BRQYmIUSBRE0FTPAFRqBxsS4SqBdEUD1UOAlIEBLEYxIKlBCi1VUCQWmgQJWqQVhPwgCFq9Ofn+7xuv+/F9Yxn3qe1772ftfaeV7LWnPec43iNMcfhMz7jGrsGVg3sAO+qkfv9vjnAKxkYuezsOHjDINP2R4wfAxariCZ3P/dzP3e/FO++HrUGmrztAO+jLsY98bsGdg08QA3oV4FdgUTrhA7zLXZm5gYwQrnDnp1iq/f0D9ydwCFQYL4PyDShPCeTgTnBsmP+YrQVn50/pEOoHDpG5Ck3wDwAdkxS4FcAHDcmppMdB7h2eJJ3/NiRtMUeFQ/AlTtsqHMCrGEOA2iONZcZAvcBJaV5vZoMAziBhaTtrUDcgPYZv+fCYGaAKK9Yo8ZegXrcqCO9K14s1RjJ2WKOoYXtlTtXutoSTMrpzqJ/7ELPMQblCfN0mtFQXmwAf9qnfdrBP9uk3EvnzCt9BorOem7Lb4CxMAhgig3B6vxMV/dA1epP+QnsB/wDXtNb4WSHOfcWT9SnQHILKtknBTIHAGNxi9e3lLARm6kU4yPb3YEtQBdu2ba2KOO+ssiv68q+Ll/rFWht3FUaA/TWbfkz7Id2n8mA8qaOAil913NHwSd+4icebHfP9Pc+oHu+exr3mLDrAkPp7qr+qrNbuxgtsnAHLF0B1jW91XvmFq4RZmHE4TBHbUY7BXxfzLl4Z/dBoh2rDkt7h0X2vmvt7Pqd9H6/Pn0NsPFe+2qRyAKWHQX6Lt+AsrFNv++CaZBTh51upfiNb3zjoY5YKJvM7y23Fv7sRlGn9Hu77BrYNbBrYEsDO8C7pZXrnz0VgPf6ZOw+XhQN7ADvi1LSez53DewamBowAbINHSvTYidQB3AJXLrFNmlbxgP4bL22DdukzqQf0AnIMrnyB+zJbmK2UbELpwDGct8VwIXhGUjUc1eLuAFjW1t6Z9i2BfIjnHPMIaxM+Qlk4w8gQTAh/e6QMs+ADjF7vVv/egcESpwPYDGaW0Aj1vA5iaU2bd6ufoCRWGwm1gAUgGtgn+3ldFb6sD0NbGN5YseVVnpv2/9v+22/7eBna7t+oCg2r/IF+AvfBJ9gWmU6YuoTqxcLcAL4mXYAErzzne+8O9Ar5qQy2VqoB3aK03tX+pkmPsrvegVkkgDLWN7c+W6S2KyAp0DKycyVL3mhb3rHpp12eYsXOIgJvX57P/MzP3NIN3ctIDioL3/C3wLkMgnB3Wd+5mce3PMHNAkUA9B6/xVf8RVl53AF8pbGb/qmbzo8C5wBqnnG3xaDN1vHpa8rnatP2oEWX3rH1ElbpS18/NAP/dBZEPEVCX4NfrQlXB4sTCjbKRYBLFaVV/U3MBfLmj/f7LMQ30ULJL63FgacSSKdP/IjP3JYUOsb0Rds1anCOHcIdfbAMcKvkUwztCinHWyMXl2RRqZMSovn8nSK+R2Qd6ptvCadu9vrNKAdt91eWWnblesx0VfU9rBVf41o2wLzhYGtviWY940/5ne55XZ/tmtg18CLrYEd4L1N+e8A7230uIdyoQYaPO4M3gsVtjvbNbBr4FFrAMjHVEJM0ibO82pLdQdN3SeztvpmC9R24FiYMywssLndHFALgAp8ixWbnyb0ADJpnYDg/G27ZcByedo6lKdwXQNTuV/BrunOvVPpucP4LA0YwAQ4XJ7e/OY3H575B+zmB0AKzGJmYYKA3v3BP/gHDyALBmBMUEDoJYfEiaNBqLJdBZCDRTqZq+Jsy740f/EXf/EhjZ5jJgZ0A4Q9A8IDKaeNVcA5sNV74OMqgbeYeCsIz74s0PejP/qjD/4xeoUDwGHyAIBcGoTboXncHPuzULGyCyuvWLZA+IB/k3sgs0UNLFandrO5LHxMMt8A8E552MJbGOo2u7IA2UAxW/FjtqqD1Y1jaY2d2Hugq0WMwDP5UK8CsHO3XsWz5pm+sI9zm1mMvulMO1ho4WZr/MMutncAfAChPPnt+2uL/1//639dVK+QzEzQmW3/qzmT0nTuqq6qIxPkf0VEr+EPZRTbvPwdM8Wg7QtgpG+ibZD/LYB8zRYGukUKB0wC1wOJV3fHfmvvWxxxBa6pM+rtqtsPfvCDd+3XusAm/GwlnwJTucs8zTG72NxsCTuo9KItS7QRgPIY6LPeaOexhW3PPybaLG2PtnddPDnmZ39+Ww3YQavc9CFbJjTW2OwQyYTJsYM8Vz/9Vt4dDChObZ86YjeN61yA0vecqjuFuV93DewaeHE10NjamGiX+2tgB3jvr7vd5z00sAO891Da7mXXwK6BR6kBgFlMOZMfoBImmknUe97znpcANtlE9R7Qd60ADbJhmr3UrTACBsVz7M+WYhMwzCtugCnCD+z1DBiNUdgWcuAJN8DH2DzcnToBPnYid4C9U26bIP7G3/gb79I9t7hjBwXeAi07bCngy1ZnoAuTUSvoKX5/wKPXve51L33kIx/ZUt3mM2XIL/DxP/2n/3RwQw/KMMDYe0A6Zu0EDqUjQBKQOgV4yA+/2ehsKyzGbwAwMDeRvz/2x/7YnX74pROAtfv+AF+TNdzzrkA+dfId73jHnR/vAAUxI6tr+bEwkAkFemghwxkL3PTbPXuiDmfC5vIc6Momb2yzdAJ0Jw30i6urcsbgxFiMSe2duoJJa9HCcyCVxQR/saELY17FH3N4PncPlFfn5/Otreef/MmffHADKJEPAG0AV2A5oE65+AOqKLdA9f/zf/7PXT2ecc17JiOmrWrgYXUt0LgFAWAmsB3o67ouNsxw572yefvb3353EF517LW8ZuoACJrpAN/3MXu6zIIAVOmGzquD//Jf/suj2WBT3IJF+kwn/GI2YjpeInTHL3M4FikApuqDMtc+rGLBI1umAOUp2cc+d3Ba5nSYUblGMu3wluUAycLIZI3vx6LV1sJGbrta8JJ/38Mur40G2nlgrHGp2EGh3Cx8Xivaube+9a1H2xhtqB0ZzEPssmtg18CugVMaaNy3A7yntHT+3VUAr9Vhkxl/GDEJ2zo9v+9V57DL86+BHeB9/st4z+GugV0D/08D2KMmTcCmY2YLTPqZSYiZiEV3jWBeigNwd8yGXiYRuAM0mMS5B4SxBWu7u9/+ABOxCW3fJrF7gNEAJoy6ABTbnxOAVQCJPE8wKjeugZ2BxMA3W++3tikHzkhbrMaV9YuxFIAnrUCuX//rf/0hP1hwQKvyByR617vedWCpsbP77ne/+1X2OmdaT92zJStcAJ4TqctPcc1r6ZvP3GPFrYemZXPYAVoEsAJI5R4jNqDac3+lw3tpcQBdYEzsxWwtFn/1DWAL4Il12HtXoG7s234DrIo/t8Z9b3rTm+4Yt7Z0A3hjXebu1HXVj/q8Pjvmv/RIK9AK2xfQ/7M/+7MHRtkxf3Tge+i9urYyF6XjB3/wB+/ccKs+ASssCKi3lY131W3hkMwD0JH6nSmHQD1+gPXTDIR8x/aOlV0aLbZknoD96p6LF+DZwgYdEPWy75fbgHr3FgDmwklhdQWsAvaehmgrtCOYstrGY9vIzS+Ur/oLMKXDFsUAsscOa+obSn/H2LsAdgBueVYuytPCnG+pRQft2haDetVN7c63f/u3371qlwGzLFsChBe/NE9pYeFcGfje+bcw1GLLDGfrXl8RGxpjeUuAz/JPJ6fA8fz+5E/+5B2Y/sM//MM93q/PUAP63L4Xi0aXSguHzIlsLURcEo420cI180Hs7LoaX2z165eEt7vZNbBr4MXTwA7w3qbMrwJ4TTwbBJkYJrZo9Py+V9sVd3n+NbADvM9/Ge853DWwa+D/2YLVHwJXYmKe0kusSRMsfeqlkv1FYIeBkW3JAN3sH9rKbJIeUAE8MhEL+ACCYRNOADBgAVA3mb+ALuECgeVtizE2QblpNmHmJzALwJPNWeEBO4GzX/iFX3iwsztBR8BWtky/9Vu/dQZ3uAd6tZV9axwizmPbul8V2IUPAGqZuJj5Fj+wEFN5Amqez7KY6cTW69A3h7sBH7ntADBX7gMTAYAEOFs4/MQmLgvCBC7mxnWCmkwCJBYLqifcAd46hM9vDNnMX4hfOc1wt+5neHQiPPkDPkgr0wYYwvlV91a2KR2q50BkZQw8Bb6pDw40U4cxmGdchXffqwWKth73bWR6QZhY2afi83056CgWJPDOoUarn8DpmU6LDvMAPnabv+/7vu8OhAfkAZf7TmNVawOE430A/0d91Efd6dY4OzBwxkffFqMan3lXHVGvHah4K7FlXLuxfhfi9C1ZdLGIABRSHysDYLTyZkYD4BloCqRUBwKI5RuDNyBduAgpMaVnPsSjjePGNyKcFSAFfLH5Xbn53o4JMF1Yyl7YCXDZ89WvMrRgUx2xgOXgM3agAaT8KPtLALdsc2+Zeigd89rigPpxSmL5+t6BdceEHWffjDS3MHXM7f786WlAn6oMLDRcK+0c2c0oXKu53f2ugV0Dt9LADvDeRpNXAby2RZrY+Zs29hwe0/P7Xg1ydnn+NdAEou2Ez3+O9xzuGtg18CJqoAn3yjY9pYuYmJeYagDcBe6a0G39OdQplhbQN0AIyBOokJ1QLNhAjMLivm3CgCg2SwOGgSZtQS9PARyZBsB+3JIYxMA+glEb8624u5YmwGKgDqbYMQEeYkQHGjrwCvvsEpDkWJinniuH7NqWZmCIsVAgmefA1wlqcZP7WJ8A1ACmWNf8GR8Bk7kvTCYFAJv99o691i0JUOdGulwnsGgL/wc+8IEDyOddfwD67OBO995LP3BoPsdytEDBFIT61TdQeK7KU9lgh9rGPdml3ssv8xfMNPgNHAus3MrbfGaLO0BKXYrtXf0Rlu9hmkzxbP0rP4BN+mVSIjfebbGSxYHlyF0H3bkPWKeTwNfCOnWlgxkPNjTxvWWXNeBW2BZd+i6FyxQGcZhX8WCp2rK92iJmf7k6x0/mMuQHUMe/fJ+zA8vvOQHY1jYIV91SHsp6fg8Y0vJV2tcr3QDLqyO9l+b5PXgunGN2dB1CyY36eG4RzkKX+q2sjwGdMb3nogmdAD/FI+8fftlGsDmTBYnqWumf19oKpmUuEWZDWoiw8HLsm9EOlm969N2fEkxrh7CVNodTWoz0rWmH9Rve952pn/tW/FMafbrvOuTSQuC10vhgXSS8Npzd/a6BXQO7Bu6rgR3gva/mXunvKoD3lV73X7sGrtfADvBer7Pdx66BXQOPSwMm8CbvJtzHzCZs5ej9L59CbSKNnXhKsLyAQE261yumZJN972zbNrHHRPObKQEsOvdsWiYAGH7X8PxuAu8eQLu1/dOWzN5jnrnHDl7FYrF3GK4AhISdS6agAMnASrYnHVZWmK7Aw3MCaKZ/IN01+j8X7tZ7YEZgsjIJ6JPW/pi6AM4Bg5Wt58C/QDxAWqYBMCmTzCuU7xm2MCeod2wX1D/9p//0EB9waZZhadu6lp/eYYgCdNiAzQRH7y65yucpf5ixQDDApPACtwDftuhfK+p6YIXw6A0LFftzfjcx1tRVixFrXiygWMjwHAC5goere7+5WRnIH/dxH3cXtnoJ/GaTcj7fCiub1uoxe8ME0B9Tkh+APR2tdq3VI0DpVrg9YxpgFXU04BGQ9yVf8iWHMDIBsbq/9DeAtPoH2F/NDmC8YqmvwG4LAMpn2gKXh9/yW37LwbyJ9qg6I+1A7A5yPNZeYBLTqzSdsgE+86ddEq9x7JbYnu797/pdv+tVr2d6qmu+C+WExS4t/GIe963IyzWmDtSR6qjFom962VY6lrA20FX9j4XO3dZOiFcl/OUHvid5X8tGevuTfmD0bM+3wtqfPV0NtMiqbVNulwqGu7bR97AD9JdqbXe3a2DXwK01sAO8t9HoDvDeRo97KBdqYAd4L1TU7mzXwK6BR6uBttcCIK4REysTLBOtJmfsNmLPfcd3fMcBBJtbjwEEASBf93Vfdzi5OgDRxLsJOYCLCQPsVn4ABzH11kk+cDrgq8m7K0DA9nhbxbfEtk4mFri1xbzw3/e+973KOTAnW6FbINP0EOgdOAS0OCcOTJMO5h6eVJQDO6oOoQHSYM7a1g0kx17rMDHxAe3kzbbuDrvCGq0spYX+uPUXO5ZuJ7g+WXXYzekqf+sVCHdMmOzg/hyYuIbZb/loezsTCZ4HIuVmXufCgueBhdPN1r16AFAMgFVPj9mtPpbXnjtYb8YN2JoAI6b8BMsB7EwCAAO30gYwZAJgBb5Xt32L6/N+C38e1tVCEEAvgI/b6jqAvy3ynvv+MO47dK9wL70qi8BDfnznWL6rBBa306oD6BzmdR+huxaOTtVVbOKZF8x434Vn2lJndVhsqI54jtXPDbGIkmkEdq29z474mm62k70vj+v7rd/CD1y3FX4Vbawwfder/O///b9fwe4GzH/wgx88OGPKgz91sva7cpK/n/u5n1uDO/pbe7kC4cKef4B/h1NeKxYS7EjRprGDDERmNsPC3jVpvDbe3f11Gqj8r1kcML5QR86Z7LguJbvrXQO7BnYNXKeBHeC9Tl/HXN8E4DUwNqjwd+3Kn4MIME/WQ0aOJXh//rg1sAO8j7v89tTvGtg1cF4D3/3d332YLJkIXyuBsoDDX/ErfsUrJuZzko6RCThpy/a3fMu3HKICJjJ9ACCb7tmzJX/lr/yVw/NAJGDYKpia0y+3AIpj4qCjmI4BKm3Pt215S0w+S6P4joXPf2AcxuqxQ5WKw0FI0g4wA2ifkw+/vGXaQU8B6KWDX+YdKo+pj3k/wc5OuzcOCuzrYCUgKRbcukW+sOiisgTKTsHaDDDnHkMQ+Fcc3vkDxgL6pw3FmMHFA3wKAFBmmW9YgdjAZ3WJzv/4H//jr6gTwgNYAmbL01wYmCxW9QdQSacdflV6jl2B9PcVLPDCtY0fgJa5hA7KspV5gqoYuscAXqDiNAeQ7eHpv/iOXZWVcpzyPd/zPYd0xsT2jQIcCwNr1lZ751/kpnfHrms5cme7NvBcOaaH3Pm2sKenaLf4wyAmTBL4fR+7nvxn6sECyTHBMK1NAuxWf9jNDuw8lmfP2epluzbJDEzmLXreNXM4157UXV4AxKtgr8YkXhnK3DI3MvOgDLDHM9HSO98eMzraUs+Y17hGtD/f8A3fcDCD4ttkrsQVGIstDKje5fnVABNP6o0+YS4uHsux+pKJpLmj55j7/fmugV0DuwaelgZ2gPc2mr0JwDsPWbPt6RrJHtd6euw1YexuH48GdoD38ZTVntJdA7sG7qeB7OA5of4aYfbAxCygwz0buoC/CfB5DljEZnUokd/AuikxcrwLQAiANNH33B9mHBAPkIrNh+24BSxru7Fxp21Hti0xgDHChOWK5cVNcQJujok0BpIBB//Mn/kzLwGqbdVmgzaAQ9gxQ4FwFoTXrcDskE6bxACtYyJ98hlbLl24Alod9hQANt/NcvF8/d1hVDG4bYcmwJ4JHk4G4gy/eyAR4AkYw5RGzGjv1QWs3hle/rpiSwKJV9vAve8K/AW6/qk/9afu6kN56oqlF5DcM/6VT3kLjCvcrauyZnf0nNsWBgB09xVM6tLgGwmQVe+nYGFzN/Wbvxin2fLtufoxQf2e09GpcvV9Tlu3wN4JmgP6ML9/8Rd/8RU60pYQ4OyxQwQxvIHa7KOWHt9V5WWcjWHpG+k90LCFHN/eN3/zNx8OPGQ+IX0AGhE4sGJbZLkWHOS+RZJTCy4xzOkRc1tdkdbYxO6xZ9V98wx2QjPDEQAMpAeMaqfkXdkdY5Z2eBv73NfIl33Zlx3SlZ3j1W+mb1Zm8M///M8fgGp1hC1kIHptWmWi3QWwRZRRHypHZbfLroFLNPA//+f/vNuFw1b1KZDXt92OF31Kde+SeHY3uwZ2DewauLUGdoD3Nhp9TQFejJYGylbTd3n+NbADvM9/Ge853DXwomsAkAMQATzYhn2pAKOa7ANtmDpocjaZfS2Mcgucbev4elhpBxfFzrGQis0YgzMQqzjXq365U+Z7B3zECAReTvALYBKYwqQE9/JwTj70oQ+9gh1ZPF3l9S/9pb/0EqYtwLznAGQAsMO6JktVmphOOCbyP8FygCNGrIOCtgDf4rvkajzDdjBbmNwDnAEzlQOdsYmLlTjD+5zP+ZxXbN+e7+Y9cMiBaAF3gW62SAO1AMITvJ9+6c6WcKzRGLfeA/ewuOlsC9QujOqY3wABADtTAauf0pa/wLd+n7umqxWMPVae63NgeuxMcQEsW2xYba3G9gb0AuAsVpQ+wIh7W+SB3D2fV3XN95cJC2lRnwHmq174U36+vfUAPnkG5iWf//mffxdfdRkjUxj02aKIa/cWLGIEF/f8vgHn2cgWDj1Z6Dhn/gPoyLxFdcaugWuEzsXXYseWX23cKXBcfgJDlUfzBuH291t/62893AOnW0Q4ZVIi9yt7eSt985kwxXnsIEz1KEB7xo+FzR/7ywnbuLH2tT1z8Sw3sX5jnvd8v+4aOKUB/UHfiT5y6xBBB/O1oKatYGd9l10DuwZ2DbyWGtgB3tto/2qAF7PGBGb+dfCAwYv7+e7YvUNiWrHnz6muuzz/GtgB3ue/jPcc7hrYNfDSS5/0SZ90mNAfY3qtOgJytA0bAISFkwCQYnLpLwG1gCX3GIXsIgLWACG2JAcUtE05wAFQFYgJkAV2YpN9zMd8zN1WYXELU9gmhe9/2fSSe2Cid+77E6dwANPFCbQoDoDSpQJ8ZJ8TeAig+6zP+qwD4ObQpwTbSL4nq6+02Hr+6Z/+6QezFblfrxjSgCZ+AEUOexMmViDWbmGtV1vnAx7psvvcTaDbs5iWtqSzVekZdiXGcxI46x22rvKPUekZv/IDJA7cyv4ykI9uP/dzP/cQ9rRjrMxmegL7mEggzDcA04Tx8R//8Qf/WOLA+Q60q5+WDgfzsRs8gVssSUCV94Vf3VB+2U31fv0TN6YY4PAUqHctg/dnf/Zn7765Nc5+A6mVpbBtsccU9U7dwZDFSs6tsWwmJ/p+vKss3P/oj/5oxfmq6wriA5kn2O2+ugj0nTL1p25YJIpF/Qmf8Al3acS+9/1Ki3Lou2PLNpCz/LgG+ABBfa+Bh73Tdhib56eDEv2uTVjNTMx0b90zw8b/MfMM2qDAVu4ATfKlTSodW1cLP4HbvZ+AtjakxbGtdGUjW3tyjfQNWkw5JnYY9L34fj/88uJUB1xqqwnmcCxi7X6mXNYwW/RTVrvsGrhGA0wPNabwjVjo1fb5NjLN47l2sZ0C14S/u901sGtg18CtNbADvLfR6NUAb4O1BlS3umIq7fL8a6CJ47p97fnP+Z7DXQO7Bl4kDWDHmOQDVUy0zkl2L/WpGJ0YeZk8wrbzPMCJ/VWSPVOTNuBUoAKgBwAITNjqo4GZmGa2AxfmljtAIfAlBiOmrImgQ8AwFYFiUzCXY5CaQF67nXuGde4eaIINa+xA17aSn5P0BSCWfyBXIKn8B1Zu6cIzk2WMRNtY5/Z67wL+ZhgBZMBM4HJCbzMOfmyZn4vlmH0T5BJfk3WgHgGKK3PlRB8kxrX6lL1VcUmDdAciA2ixcLGXvTeoxuCSlgm8OpXdOQmlF7DWb+kJ/Mw8BhvPmUTIT1cL+RYAEvUv/7mZVzqYh5Llb70yiZHtU+kDjAdmBkzOcLtXZvTi96d+6qfe3fsNWAuA9XsFE5m/OCcTcKRvZYTdK86+1dLCtmvgKVMpPcfEs/DhNyZebGS/6ceY/C/+xb/4ivDYjg6sL5yu4gUuW0DxjP5rAzC5A+rVKWUFCE+XdHutBHRjUq/C3nWgtLRIW21GbOPSW/qnTuVj1SN3ns/vrXiF/c/+2T87mIGpLcg2eW5OXYHR1VcmIk4JkySlVRpbFLL74Vf9ql91V77MTJxiTj6JPfdT6dvfvRgaYELRtz77gr4lbYkdIXMR9cXQyp7LXQO7Bh6qBnaA9zYlczXAK9omBHUST3rFNtjlxdDADvC+GOW853LXwK6Blw7Ai/4RyGQ3y2o3lo5s599i2/EHGMB8s03b7+yuAomIyRvwRfjAElu0A6yO9cuAJIzAuY3d9v2P/diPfQXDcPUfkALUY4dybinHNmYDNNMPJo7//t//+0Ma7/MPgGxSCogFqJicYh/ZNg/svo9gyAF+6Cq23Jve9KaDXrFps0M4852Nz/kMAGq7NHbgtHlKf9PdvKebKZM5iRE42bzTn3vApYPTOhwPg3MKYJI7IPx73vOewz0wyaR92tblJhCaHgBdxJZ7z5UvMDcAuHRgI7Kt3O95DTSwwNDBPralb+mSv7anA5pLd+GtQGxAGpAMo/WYYNHGbgWAdsBc30rhCydzBNjbyq46TR+5C6BnWqP67F0AnXvlhcV8TjCjCxdLLsDPM3EKszQUh7xmjkFaAJUOO/N+zVNhawPmYkDPz135A47H5J75xeRNei/N6sg1YiFIOlaWsjCayE1GPkY96dDGyiO2q8UHuyL8nuWmvkwQHus8CeQCUG/pxCLWJaYnOmBty3yIXRZsnGME/62/9bcObbZ2jC3hWe7Fj0XtmwnUL63rtbognF12DdxXAxZAtZX/6B/9o4ONe4s5W+OR+4a/+3v6GlBedp0YS77lLW85LL7ZYVWb+fRTsMewa+Dpa6BxwbUHoD79lD2uGO4F8Fq5xm7ob7KE2ADs+bErZpJJLeYNRsMuL44GdoD3xSnrPae7Bh6zBgA4mJJvfetbD6fYG1hfOyECAmbL08QeUOYgMX2mg6B+za/5Na8CHLB3MSQx3gIsArt+2S/7ZQf3AJGk7c3APfKv//W/PoBnmHgBcIEKgGTvY/f5bcIXgJM7LFBbiGNlThAqN64AKgcfBcJ4po0/dZhS6d66AqkxWWcc6z1QB/h77WEwJkTCwu4kbc8HLAJmAgnLK0BZevr9UR/1Ua9I11/+y3/5AHKXd7qcgNNMd0xnTD22fntnmzgBLk17tjEmMXoDgDD/+GPfeIr31aMALOzEGIrFtV6BTuq2OmoRwXtM0MJa3R/7LRz5AkJzA7jtgKzVD2AUAByzme5iJcfAzY96GRBOH+qFMlGnfQ/cl9/8AP8A4MxR9GxeLQ6UPwAhwG4Ct7llb/a9733vIYxA8d65+i5+4Ad+4Gx7sAXsAcMdaIhN6jsRTvoQtvqWvWNMe1J9mGnwzaozrpNxrQ6aIE3AfL5fw2J/eMYvDmFm3gCAwE/fgfbsGhFO+QEmE8+Ur7ikE0u1b8chi6R6wY32CNu+xYd2gP2H//Af7soZIx/zuXSy202kf9YtYLkFCHVrLoZpa5lq2RLpbQGDu7nI5FBJ7VFt9SwjaWGKjp3fFpN+02/6TQfmfSZttuKbz5rsdqjhfLff7xrYNfD8awA4D1vp8MvZxri3oG5B7tox0fOvuT2Hj1ED9Xk7wPtkpXcvgHeN0up4DU5bSlc3++9dAzSwA7x7Pdg1sGvgoWrARB7AcIwpZyDNTmXbiC/Nh23K0+Zd/WVXzMKApLm1GGgR+JbbwIoYaoFgtlL/4T/8h+8AjtwfuwJ9ACZzuzC3ANBp7oA5CM9NLgLD6GeCa0AaIAzdXQpcrLoDgrZdGzCGpccEAbMPGMJMIzihPuDK4TDTpu0a3vq7g9WwnEmMxz/9p//0HUNSPtuuDlAFzKQ/AGYMR8CNP0BdNna5i4kbyJRf4G/l1rNf+St/5V0SsUUDm4COv/23//ZDvLbnWwxXBwrTIgGmKhug8g/kUkZr+OLJT3F2LZ1+Yx4Hqud+rRP5m1duA8XlrwO+ALC2/U6397lnAsG3kHmQc2FY0AgkzC0wrXvXFdyc7+5zTwfSt2WLV91Yw1Tm6jRzG3Q236f7+QwZ4ou+6Ite4c57Cz1TLBBkE9n7vpEYxCZKW+HPuLoHQPddYRP3nbcotMXEnWnZus+kh/Gf/GBQF1/Xyg5r3uJaz10z04GdLW/K8Rd/8RfvdhOq+0kHLtppaJGqug743zpoai5G0BEgpTYMKxfoyyawdEgjkxgJQNkCl3cWK7COtScWGDDEA/j5owNtrvutdBTmvCLTSL+w2ZjeZdfAroEXSwPMwjRW0c5YoLSgpD0xHppnB+gz28HyYmlpz+3zpIEd4L1Nad4E4GWny6DI37Svdpsk7qE8TxrYAd7nqTT3vOwaeH40oO+a7EogCvCUCSFAaMCBQTbgyKFU1wjwGODAvEEH9Zi42+lCAugM6FcxoBevv8AGQB/2WCzb/GOSYeW94x3vuNvmzF8ASuGsVwCZQ1RXke7abboAOABCgDBAWQu8ASKr32t+ZzvWhOXDJ3b2/ORP/uRLmU4AOl0qAezMM1iIln/6X/Uwf7dN3DOMXTZj3ccC9R6b0LMZVumbYbkPZANwSYM/LNqY1lilQKW2owcor+H0m53UwuzZsSuWpl1TM0+rW2GZNGZ7Vln7HcMzsIw/AKZvJmBRfQyY7oDBNfz197k6ucWKLAxpmUAhoBUI1gF33PlOM8uQv3NXgOa5dG2FAbTtO/iZn/mZuzLdctszOlPWMx+927rG5Acq+i6J7blYqupmCzD8WiiIde4wQc/Us9i0a/jqL5aordsWsdotMN1V164lclikWfMYCK09WxcUZrkrvym1Ew4WLG1MpiTtaPDtYGJzw1RJ+srdvPI/y9w9XZZfYQDLsa2Tf/Nv/s0d+I2lm9mX3rti11vsKByLUsLCIp4LedOPe9/VO9/5zrsFJeWo3dtl18CugRdHA4gEjfe0x+vunTThjAd9ibbFwnQLdL3fr7sGHpMGdoD3NqV1E4B3TYoJSvbd1ne2h9p2eOkK9up///24NRBQ0Ba7x52bPfW7BnYNPA8aMPlnEsEAGbMO420LEHCoFxYed4BIW/jvI9hkwrDtN2EH1zNbw1cB4kxWcQCjtARi8AvcyEYmO5Ce+QOGYYPOg9xsGQ70BCCfEsxc4QDPYmdmXuCYP+CvRV/bvQE4JigONLLFG4txStu1AVengHPAL3vEWIABRIDGUwIYAdRP9maAizwVTtvYMX2n23RoCyTgzm86j4EZiNbv3J+6YkMD2mY6gP5AH+xpwG3+lTHwLiYl3QGkJthqEkgvsYnlZU0PAC+AylZ5DGmA00yDhYzKWvwWCQi2uN+zrlmoILaFBkgDFdNn6e+afvs9r8o98Gs+7z59lNZ00fvAQ/Uze7UxK6d919yXRmkqzN6du6rHAfjqySwHQLh24ZIwgbXKa4Kyx+IOfGSPtXaAWQNmXuwqOOav5+nHYlCsdHrpQMTcbV3piv9ZfurWMbDhUCmWf8DQylAc6n5laIFIW+tQsnYxcFN8FtamOEBuppM+EsBoIHjxAfkvMa2DLVu5Fr40aLN8bxOQFV7MObsm/Pbe/IbpF/ObuQviW7/1Ww91QjkGxKin6zwIK1n7OOtUaXHV7gN+d9k1cGsNGDd88zd/82Ehk713/cCpvvjW8e/hvVoDDiX13Vt8t5vrlFjsb2yqb99l18Bj1cAO8N6m5G4K8JqEWlU3cDNo3RJspQYsVr01Sru8OBrYAd4Xp6z3nO4aeCwacDCOfgkLdouJNfNhEp4pAYDYtcI/YAhwMk+vzsajwc2W2K5d3wl4mGCb54ADDFzustfqObDANmMgCkCoMABg0gGMmgembcXtPX+AGSxJ9/p5oDHAMRuz+f3qr/7qV9jQLM55BWjSBQnce/vb314Qr7gCJgMRZxju6RG7FrgzRZrmQXKrv/V3kyNjl8o3kIhbesWMcQ+IyqzBFhizBRCv8fmtHG3lpsdknmmgfgUUZWJCOgKXYtaKD7AUeKT8LbS3rb645QfYNMWW8t6//vWvvwO3sXh//Md//GCH2oQ/RmfAHD9MQ4jnx37sxw5hABuZjii8S67y/23f9m138W75AbD1nN7VZYB2ICDgK9MagbeYqLMcbJfvXWGduk5GNmZ/5ij4sf2f2Yzp5lRY17wTz7FwLQL1DU+Wq/pg0Xzm91icMZqVVfWHW+3ALFuLRXPxBJssfXMvjS0AzPq03qu/xQlQbhGi9J0rF+kC6kpP4G1+lScgKvmSL/mSu3oSKA4Iv1SYXBC270j7sbXIJywLgNxpMyzI/Z7f83teVbewpdn67bDJdmFwmz6Ul4XFbLKvYL/ywD5mRmIyr9nX3g9WurRUd3enNMB++tz10Lfl6tvzzf7UT/3UqSD2d09BA8aGmXjZMgG0FaWxmDIzPnGQ7i67Bh6jBnaA9zaldjOA1+TKoHd2DlsNjEM8phuThnlgwW2ytYfyUDWwA7wPtWT2dO0aeDE1AICI7fed3/mdFykBCAzsACJcaxvRdmp9IMBiykc+8pEDGAs42doBk1mBCewcA4LqY7HbsMowj0/ZMsVcs437mNCRMAESAEGTiOJwlQ723wDMARmeYyoD4tiJxRACQmKoNXHB7MVi49+kZLLkpAUz7vM///Pv4gKaMAngoDq2QmcagLKVBV1ivcz35+7PgWPirQzUl2wTz3DlA1sxsH6+cw8MnkChZ/rEWNcmdYHKmIcAJkAlIC1AOSBTfm19Lx2xuoWpfNZt7zMtAOC3ve1tB5MN7PnOd91PsK9nW1db19WJDoC5FGgsrMAuv2OH9y7gYZqqSFfqR++5n2xUwB+ZB3VhNQX6Ff4lV/nDVv/ar/3aVxykpb4AAi8J41I36pX6w9xDfizkVCc8U/dbVNAWWLzBfj0HXp9rK4pP3cFWXyXmr/JygBn3vtlz4A+9cYuJCyRmxkJbM8tGnOYCgMsPfOADF7GSS68rFr0DMdfypadrDh7yvfV9nVr06ptpYUoaxO0QQGZK5rennjB9oe2onRO2dnIC5jM/3GHDzzmUtpAuqwv3WVxcy3T//WJpwI4jO1os3Fh4sKBau2HxxEKW/sQOGf1d/bTvaD/w6NnWFQux2gQmaa6R+kG7PXbZNfAYNbADvLcptZsBvE5GboBiFdqkzMEoqxiwaLh+5+/8nXfuTQx3eTE0sAO8L0Y577ncNfBYNPCe97zn0BeZnF8jDUKwC68RgKe+EoN1lRZA9aGTPccd8MHEHxiiD23yVb87r9heDgUjwJrAncCLua0bSJNfQN0Wa832wNzMK6AQq3CCzt77zezCMXEoVCYpYqMCoFf53M/93EO8JqGYi+vhdtkdztQEUFfYK5ht8RnTLnC19B4DWIA1K9NQvhye5ArEwTydunCPXWuMEwAn3dmzDZgBxLagECsPIMT8QzpRxupC79d4/MYEtCNKfieYXVlPP1vP5vtT94A4oB6dznrDT/XplP9L3tE31nf2pPNj23wHhXlGn1M6vMs7ZTrLMzamd74VOnX/hV/4hYdt9+472A4IdwmgXbnxe+oPMxZ4suqdLicIKT99f6UPcD8BXsC5gw/X+HLvGzi3OMGveNb0CEM6M2tRHMIDmBJMVlu3Mw1h4YGf2j+LOKckG5LavSnNGYBKKwhrgau0zGvfkO/LwYQOlly/kWlj2ndxrcifOC2sHJPJtJUmOwjWXQwfftmcDPZt5aSuFrYDIwnzFHQZOx6Yj817Clxmx732yyF4u+waOKcBYxx94Gwf53dlp8fcTVR46rSFBG7VY4fF7vJsNPA5n/M5B71vLbadSsFXfdVXHfztC0CntLS/e8gaaGyxLyo9WSndBOC1KtghISZrJhznxCQy5onOYx38nfO/v3+cGtgB3sdZbnuqdw08rxoIaLjWbhlWpb4Lk/QayZ4p27SrYLfFTAUEYXEBA5gGsGAqPuBGgACAyDPAgQE9+/bTvm32VAE7WHdYO9wHlLgH0mF7BBrol1dpkse9ren5d4gQsYOnrePcAM5WkwlrmCaPgUb8MMEwhY1Pz+V3i9HMbXr46Z/+6bvDw+ZhePwDXwgGXaAtfTRm4ebav9VWJ/9/9GU7vxjIAdbiYOvWs0xmBPYEDjsdO3MQgcLCmmmjS6DVuj29NAuzMHo2AT+2VnvPluqphYEOSSucrSvwsnxsvb/vsxjK079t6B1I6Ln0T8FO9zzQNFuznmGDFlbpxcT92I/92Fc9n+ZOpu6Ap9iXJtnVncLs6tvzboKnx8Biz//hP/yHd/oDQIp7+i2EHoJzAABAAElEQVTcW137Vo+Fpw7GorbIhb3HrTRpi2a9nGF4H2AEiN8SwG3u1vbAPAHorGwweplXkBZtAtMHUyctzBQ/N0BwZt5m+gDotWPcAsevldqPYzsLMaxLh7gsxJya82BXq5/yiczC7zzM0q4Gz4DGmaw5l+ZAnK1FsXN+9/cvjgaYz2Ejuvrqe1Vn9LW1id75hiw0HBP21rnzfa0LGcf87M+fTAMtduovrhG70JTVfs7NNVrb3T4kDewA721K4yYAr0lMHQjWxKViy1ETDza2dnn+NbADvM9/Ge853DXwmDTw2Z/92Yf+y4EWgERbhOfW2GN5acvyamrhmPueY3bpLx1UtGVHEcjLjMEEmrifgIffQMNAsdi6xeH63//7f79jJTZ5+4//8T++wu5mwBi2GjMO4jTx+9CHPnQXFKCjySDAJ0ACa3ZKbMqASKzDc8I+ZmMH7EiTR8zlt7zlLXdsW88BPxgt4k5MXk1Y6YUJCSzjFby0vZGNQWzX8lB8x64TIMrNOb/0BpAGkOUH2J4At9jj7N2x62TEAt2ECTjOfYzlU+lZ60l+j10dIuXd6g8QitnVc1t2A0I9q+64B6wGapa2tSyKPxbsG9/4xsMCRs+3rswRNGbwHtC9iolsfifDeDJ/vY8pm9trrkBS9U8dL3/X+J9u0+d8du5+gpjn3G69L04gIqb4ynrtPVvLWHoro7cwjdMd3qguzPYJCG9hCcOemRTthwOamErh14LIlhwzZVJ88zrB+/lcPbQjcLLYgVrVx2nSYysN85kFK4tm8sb8yRTznMkOnmlwD+j/1E/91ENa1FnmQ/7cn/tzBybwW9/61oMeahudep9k0kb/c6noN2or1oPaLg3j1u4wQH/iJ37i0H4zyaMf2+W104A6YgFE3bRAYpGqXbXGF557P/sX3/Yx6WBYC0C7PH0NtEDZ2O3SGO0iUrb67l12DTxGDewA721K7SYAbw2K1b1rpS2PbFnt8vxroMnavrr4/Jf1nsNdAw9dA7bIti3eoLg/IA7gC7Nsy2SBfH3P93zPwT3w8FrJhICtz4ktxwCUALDSAsQMTPMMwwwrbO6CwX5bJSbeJ33SJx3sejZBK9x5BcYxqfCmN73pkCfbppNsuwK5gKrZ2l8BibYfY8wGFAtzC8QubHmYwM0l4BmwF4CABScP7G8mk+3pHVZxQBSQLCZg+p86OHZ/SZpiMhYGoHEr3xYQAtNyC+iPyWwM5TlADGDSJE/5YFFhoHqvnmzV28LsCuRc09a7roFP5TMgFJhJ/vbf/tuHOLGlpak6kvvCufaqTk+m8iX+AWTrgTMAvPRWmubhYZeEe8rNDIsOAuTyM7/Nnm1dA+TWd0Dsc7Z8z7Fw1zD9tnBlC3+H83kW4zO71ivQu7W4UdjS/+53v/vQlqwAb27Sf79ry3wPq2C2Z76Be35/3+/7fS9Z9FkPByw834725du//dtf+sZv/MbDAojyZ6okswnqu12Fdhrwt9U2rmnpd3UdYDyFGZ6+27lwwfwKtvGa79LbVR+RjWrPLLQltZvXkGP4BSYLa/YhhfksrxYEMP/X70B9UQcB/bs8ew3U/+tf10WAFuoyQ+L7UYeV4dq+lvKIXOr7Lk9fAx0saqfCNcIsjHahnUvX+N3d7hp4CBrYAd7blMJNAN62CxlgXSuYOhojW9x2ef41sAO8z38Z7zncNfAYNGBiGstLHwTcAB5ixs5JPCbjFqM3e7mYfdcK4FicABDbew3GJxAX6DfTxz3wbaYF0Ov51gKprfneMc0QQATgNHgSvncBse79Oa3dlXvbhbMD5xmTFNLpnv+5nZgJiYBB7+cft2z7Ytiugnm7BW4Fyk7gBKswhiZAz0RTPOxwJlihxX0sPd4DVC9hRZaOwjx1DQDKjUk0/a3bt7P7GuPQgX2Zo8gvYASY5rc0bAGhgMfAofx1lZZv+7ZvO5iHUE8Dv3o/r7EKPRMXUN49HTNrAYQPTFZvSeO26lXXGe4t7o8B8UyGWGxIAqmL82mkJ3Mos04AIvsGi9t1y4THfD/v53c/n6/3K4C2vl9/s+tsNwIQZ75jIqb2J9Mh8/167zuZIHf1nB6O5ZOftc6xLTslUzTCoMdr8ufQPe2yRbjZVlc2zGpkQkf9ZsLlnDAzUfsybdv+nb/zdw760xYBVbe+xVVn2iQLgID0FpWqP6vZiMC2tZ04l94WWixivRaC7QyQL+/K3PheXzTbdOVz7Tbz1yI/z1OcbDjr/9TZFbDtoNB10YVtcmW5NZagG4zs6vLs+58nvT2kvBgXKA86n2O+U2lULo3tHKa3y66Bx6iBHeC9TandBOA1GKqTt/30GsEu4leB7vL8a2AHeJ//Mt5zuGvgoWsAiBV4yBauexPUbEkCLO1MiV2KxZq9WXmzFblJ7A/8wA9cnV2gmS28DeBdARyYmWw8Jtx9wRd8wV1auZuApm3EARyTAcb2pTwBPAI9/sSf+BMHEwhN/oS19Vd4se+4YY6hg54APFh2Cbu+HXLGLVZgwOMEWU1Upj/+szM302GxF+vOM1tJgXcxC4FCv//3//67dANH5sFrsZZmeO6VFfB+AlWrm1O/qysraNWEN7+BOOpSz+hgllnAbm6wYtlAzr0r3Qc2zefX3GNHVpb5C5w6BqZ98id/8qFYf+/v/b2H9LDVaNIYsxGDmJjsrzZp5adyKr70JpwWHHo3r+liPjt3T9e+DduO23J8zs993mOsZ6N29e/EcmDi+hwAuT4797u6c87d1vtV77mh/xUkV/aVS+66MgHSu8y/AIqZU5vfHXD1v/23//YqgBfZo3qBPQ5Mnt+c9+R7v/d7D/qxiGDngrYVUGl3wpoXcQGf0k8ARml2DXSez9zX9mnHmWo5JnYE1IYp04S5ieLtmyiO9OQ3Zndx+a1dqL8AzkwWNbbrlGx2z3Z/vj92n110APSzFosrLQ5op77ma77mVSYt5CcQn070p7s8Gw287W1vO3xfWOyr+A6Ux0qqwqhvJ8RkmE//teErI3i62e9vp4Fs9StHY8Fz0g6frfMdzvnd3+8aeCga2AHe25TETQBeK88NsAw6LhW2/WJavFar0JemdXd3Gw3sAO9t9LiHsmtg18D9NGDSXr/TNrbsozJjMG0HYrsEkAAjk5iituheMvDO37wCpmJzmnABIB02inHHvi8wJKDU++zIAmhsl0xiU2KlxvTAluWnfpktSAIMxdDxjjkD4Gmgn2frXwziwAtgJWZcwsZmW89zw8anbdPCAu6YDAIO/ZYezFQCFPBsgo2BJunc9nVCJ4GpE2zD6rW99Fu/9VsPOothXD4AOw7iSpxA37uu4lSO/T53TaerO5Pqv/pX/+ohHOw6rFzmI3IH0MemoqfCADxPID23p67piG67P+V+vqv/te1zK15pw8rDSg2kVEdjyE4buOy1zrAxxfsN3HMP4Kle0Enb1Mu/+j0XAfJ/zVW9n2EU9jVhnHKrrVAft5i26uS0ASwcoHr59PvaMjqVlmf5LmCTPn1nMx/y6FDK0gOEdc+MCHvTbF/7Dcx873vfe+dOWL7B3vtmbEUOWCq8eQUwW7gJJAV6aFMcjjnLffrZqgPaGTsJ2Phm69vCiu8RyzZQ2feRnVJthkMuhRtorQ5kTqUy9gzJRVs4Ga1MeZAPv0x6mba53U9h7kIcf/fv/t35+Ox9beT73ve+s25v7SBw2S4K+SbMYtADprYy1U9YTGTXVf4s4hw7uO6S9OlTHDiq7yvOS/y9iG5ajNg6vNz4R3lo31fJ7MfXf/3Xr68OvzPpspvd2FTPzR9a/KptNEZdD6osQgsuFvCVqzHbDsCnmf36GDWwA7y3KbWbALySEhtJAwOstfXylNgy1WqggeSTdPyn4tnfPSwNNMHcbfA+rHLZU7Nr4EXRQIMHB/EkJqJN2IFS84Adk8mYkFiysUT1Wyac9xWD9UC2CXLqQ+cfIK8trm9+85sP72xNTrCNY1MBH4AWgNzCAM4AoW35C3DEfGV/mAA0bNmW74Dv/M4rYGX208LEzORG/x/Qha1KZ54DMJMOVAKKmWQGDAccAUps1+43/4AXzEHg92TPboFt3K/Pp31KeZxhcH/sT36kPXByugMezTRmRxQIZELNbYehTX/zPvBsPjt1r/4Bu4Fdp9zd8p0FhfrrwpUO4AGmZc9cuZvAe+YzmIdY3U5/7ufYcX137nfA3Dl36/tjwODq7j6/Y13dx++T+vH9boGbTxru9D9Nenjud3YfAUQEeFp7aqFjsn8tpvhOfVuz7H3j7D4HoAr7WLtY2KVLm2IRInvhQFwmSgKkcnfs6nt22BQWY8KG9mwvpBcjOTM2wpr1CLjLNrBDN72TR99sdTS9icsBmAlbwtxrwy9dLNQP8QP8OTfXKp5bXYFHyqV5m74TuLTVpskre8b1De0QmGnBntZOWxyzU4CO9WlEP2YRoLmiPPennljQm2U2w32R71uQ2GKt292jnVA2q9mkFouzvz51WJ9uQRijf5dnowGH37bQbsHaQioCgJ1TFjt8A3YQ+C70z76fXXYNPGYNNEf7+3//7z/mbLzmab8ZwIthM7fZ2YboFGkTHhNO9mAM9JwIaSKpc6mjtuq/y4uhgSaMO8D7YpT3nstdAw9JAyaDJuVAkNXmoUlzbAnAq4G0vsvEpkl924NN9p908BEYaGLPNATWE5NFWI1AAoMcLKhpa9R9oHA21myhx/7KlqN+FfAaOIktZ9txvzF3jtmkNNmuX8bQA3r4PQHlyjNbrSYXtiNnixRI27t1O3Lgl/QIN1Zucd7nKq+2JsZom2EAXzGyjTF6nokEv+c4pPeugBl+sjG6mkuYIBogp7HPTINnTbxm2Pe9N7mrDC8JQ/zH8neJf26UD7beFlA20xJQlx7WeKe+pr9L03FLd/JkbHrLMB9LWFsgXGmvXen3uUWK3M0r8DaJza4dw+6c3wY/gRbiAXJihNe+TmB33s+4ulfnVje2NAN57WjIfjT36iFTDIBbbf2v+3W/7qXP/dzPfelDH/pQyb671hYG0FpcI9n2FR5Gu791Yam0ufoW7Jaob/HMroNEm54ZC2zXc6IPixEMkHvW0gKndgF7uHqiDDC52R23w8UYP1Cb3r339z/+x/84JPn7v//7X1E2U2eAKrsMJtivzOj6oz/6o+8WD/jRRv/ET/zEs1bDM40Pk/P973//Sw7pPGY+YSaoOv/BD35wPr67b8FlrT8twmLHr2IXEH23iLO+338/PQ381E/91N1i+vxO5r1x4xag//RStYe8a+DpaGAHeG+j15sBvJJj0LIOtGYDtHVvoje3xN4mW3soD1UDO8D7UEtmT9eugedfA20XnlvNZ66xIkwgt/qqnmEqmmw9qWTT9pJJ/Yyr7bHsUppsXQqYcWeLchPsGWb3b3jDG+7ybmu+CXZg3coMyezCV3/1Vx+8Y581sQzYBNpMYZeRHifzLb3KF9AaQ6wtpr07dWW3mGzZQuUvUNnY5Ou+7uvu0ngKlJnxAZCa+KaL+f7YfWMh/tlxnSDnMT89PxWPctzSX367FgamNIC+510vzX/uT10n03HLHTAPkxdws/X+1DP5vdRfeVaP2SE8Fe5r9e7SenCpu2vy0Rbsa/zM9kVdnvXm4z7u415iAxbbvTCZXiG+Y88sqBDA5FxcyX3fSb/XxZSen7rS1dY3YZcGkzFzIUnbcolgxxUnYNxOB3OVgOverddATc/5822ks95lMqd0fOd3fuehnZUPjNVjTF6My3YM+J5eC/Zqu0CUc+A39rJdGasA9lcb8hYCp81sADF7z9w5XHJtq+xacGDdZI3SD2ZjO1e0D1sg/Zqex/TbLiJM2vltVdf0zRapjm3Zz+zHMbvHMcAxeafeYlp/y7d8yytU5bBG35f29Rho/AoP+4+nogGLIsZJ2gDjUONY4x82zY+1GU8lIXuguwaeogZ2gPc2yr0pwCtJP/7jP/6qDrpOaV51Wlv2gW6TrT2Uh6qBHeB9qCWzp2vXwPOvARMe/RBm6il5z3vecxg4m0Dqq2JMmdDaunsLCYAyaF8FKxejl71czDrxAkcw0zCb5CHQAIgAQGRLkp1Ti6YrU+8zPuMz7uzzrnH1e9rUnH31vMfCM7Fn3iFG2tzmCSAPxDAZBKivMllZws5eqx0+RB5nnOfubdeWpgCxWLerPwACkIA7oNJqr3d17/cEFlcgygSLzcn7AFJbcc1nAZXz2Xpfftfn8zddAIICYua7Y/fVq633AD7sZgBBh1JNd+KZaZ/3092x+9X9qvNj/tbnyqTDq9Z3z+p35gLW+NZvc32/9VtZB6xtvb/Ps+qPOp6e12/zXLif93mfd/hmW3TiXrjatMzEqDPJardZeXOPIW5S16LXjNd7u/4y+zHfdZ9dcYCVXRcOaYwhjOEf+1A+heeAuHOCNVf4zEcA3Np50HPhvf71r3/pb/yNv3Foty79zjB2Z7spLYDd6r9xsjYNmKZN1Y5q32tb9UmA69dC2ikiDfSgbzkHLjHBkM6qx74DfRyTAVO00bMeWqDq8NPpzj3zFB0spX+cu11Wt4/pt3JvkZTefB/Y0erhZNrbjTNtzJdHiy78GRccE8AgN/rPf/7P//nBbMiW6QZAeixtbf8uuwZ2DewaeJoa2AHe22j35gBvybK9CaOGbS6riQZGBkJf/uVffjhkJRtLud+vL4YGdoD3xSjnPZe7Bu6jARNZ9vyAZyboJh8mhMwlNCE2mfyu7/qulxw6AXC0dd1WWywgtmpPAbAO4zKpMSm8RhzKwx821q2kg4Z+4Ad+4BVB2gnTVmVxnvqz9ZcNxFXYwJzMO8ABEGKyoKYf9laLByDjXjlsbc3PnesEb4TnAKWAX+/Fi5FlHACgwPoFsBSGd5m/cLALUKN3wEkT1SaXnncg3DHwjBtlO+MoPNfA2ICU+a57IE1swA+/fECSOjWB3kAWdiGJw3+OgcqFeek13Z9zP9PDbWBW/o7lv/fr9ZQ+Vrd+z8MBt96feubbnozKU257J32AHnVh1ofeP8Tr/P620ncKSF/dA6u/9mu/9u7bWN8/ye/AXWGcqzfez7oC5GNPtvqHgSkcYKzn7rUHybqoAoytvQagFs5Wfi75NpiGScxBqqftNgBgCZut3HOCHZtubGkvDPVw7vKgE20Ycov2eCvtngGdAXS9B/KuIK0Fo3NtibRMG77n8nHr95OFrV5eagN42uIG7h47HI65IToC8tZHAjePCYCYnXl+nofDupmbqH039mFqZALo7o2TyrM+cTW5ZLxSP2ystCX01k4Z33T1Th21k4YNayajqq/YvX2rW+Htz3YN7BrYNXALDewA7y20+NJLTw3gvU3y9lCeNw3sAO/zVqJ7fnYNPLkGgLdN5ppQrFeTc/YS2Xlc383fgDfba7fE4RTcAoavEbbv+NN+3Upi4n7VV33VXZAOpSovGL627gKlbcmbwEpu2OoFfv+hP/SHDtv1MI4tqAqzg1Zy62q7K3MDds/YKmubZ4ws74UXCJI/foAKx8AfunH4GjZf7ESAMVuMTVQLa72y1ehgHc/FEQMOMNNp9mwOHot7hhdQbHD4O37H77jT43Rzyb1t57GrP+3TPu0ldoSnv/QFjEq2dD39XHK/Vb6X+ONmMihXP9/wDd9wqB/r84f8GzgRgJG+pZfNSADxfdMOVMV6u9b/MUB6pu3aMM+5n2xoYHFgzDl/l74/lqdT/tl5bPt37loM8Rv43jcca1Y5Jkwm5M/1D/yBP9CrA+g631177zCvVZiJKD3CC3S/1CxOdbBdBvLHHmo2vZmkqc07ll6LgoFsmR+wIMm9haqVdYr5zL67Nl346rs2yO4MIDKzB0zNAN8ArOqz/qH3qw5u/XuC1PqmS6W8y7c+65jExnYopoVKgDo/W7tBCsMWdW7o4zEL4lMAun7sFKCqnvh+5NtuihVo/4qv+IrDO/3UMTCdyRFtat+FsNY/fbiFmcdgSpEOmKCagPhjrg972ncNvIga2AHe25T6DvDeRo97KBdqYAd4L1TU7mzXwAuiARNmLFwTCxNoB3zYdmgiayutrfu26M6JB+YYRiq2y8/93M8dGCdf+ZVfebdt3KR7i6VlAhALdGtr4zGVt3V4tZ14zP0lz2MTm9ASjBl5BEIxJWHib5JfvgEp1wBKMQRj4Zbvwluv7PNOlqCJIVt8TZawc+e20dW/34BYTN2YwiastmsL144eBxTNyaT8sN0orQGcrpOVnN3eCfLGos2PuLPraAHg3e9+953evANKHAO0rgX8SocrMQlPFzGEcwNc+u7v/u4Dww8bPXddz+kzd+eugQJb7ir/rXcP9VnbkOnHdwDwqj5fkuZZL1b36zb79f3W72PfTgzPLT9P+mx+J08a1ur/VNi3Ykj3DVhQIg5ijFUoPekusNX275lObT5QD2AzQcXpxvfVgpT7LQF85icwVl9xiah3+XXVfhF9lvxhFVs0tFh3rM79yT/5Jw+2VO1gFIa6JE+BvBbZLhWmDgI8Z7rmPRM3GJxPS6aZDaaELhE7IaoP0vov/sW/2PSGVcqdbz37wsYD/OiDj4k2uG/0tWQ3H0vfpc+ZrJBXCwsrYLsVhv613Sx2v6zCFJXwfO920cwxD10DxgPU1V/zM4vf/thVtuOWOaaHLMx3GHNgfPcNyq8FBfqsHj3kPOxp2zWwa+CXNLADvL+kiye5exAA73/5L//lJfaudnn+NbADvM9/Ge853DVwqQaAN01QbMM8dgCY7baTiYVRsiUmeh0UBhDFeFrFZMCkR3wBkaub+ftf/at/dZggmTRIx60EgB2YwgxAQBx2lnxgkkqniSs2jolKp60HcHrfn8ncj/3Yjx3AsLYT985VXMBWgIetxNg/7FN6hw0GtIhxC7jY0h3WENMNM1z3dAPIONePT+AV24rfv/AX/sLBrmthrixpJiy8C8gBCgd081u9MCENxJWOyTR0EBygGnA8QXPhVgbuMeUAgC042GpuslvaJkgLoMJq6nA3wETp4r60AIDEvcXyPbUlvTjvew3Muq//h+APMzmJ6f1apStA8lnGr771TV4b76zX+Q04147M3QK9d51A3Hx+6j4/2qV2JuTedwhQ/YRP+ITDd9QOjNp97iwuZU7Bb6YeiO81swdT/8UHMA445W81eSAMJ8uXlnTJFMKlkh/gEVulSeCZXRNf9EVfdIijNLbQU7zzqh3TR8VwlocJvBX+enWoZuEA5SwCWvyyQIahqV8rrcCu//yf//MaxE1+swlcOqY+jgWu7Ztb/fnVD2zJRz7ykUPYFnAT/RA/7OCfkuYWFn1PiT5fm0x/QNF3vvOdL5mDPgQJ9M8e/SVpspBNP8w5rEL3+kj9c2Wmjujf5oKZdsa5A49NHHg4TbfIk3YvoFeese9XM1iPLZ97encN3EcDFlUt0phLGJuaS9gRAkB917vedZ8gn4mfHeC9jZpvDvAakJmMvvnNbz5MJA1k5p9tnFbqDeic6GugYlD0lpe3iezy/GugQRh7TrvsGtg18GJrAHupydsppgXwjjsAnMG7STLm7jH5rM/6rIN7W1dXASK35ZYdX0yWY+KE6YBXDNFbS6BV25wBrsSkTH5NxP7dv/t3h2e2rHrWH2AT0KH/DFj4vu/7voNb/9juC2TMT2HlyJZr74CUgZp+S9cxAWpOoLOwmZM4JQBt5jNyL63SrTxN1MoDwEPaE2yv/LgGbjMnoexiILHxyIQHN7ZrM/GQP3EAnpzG7tCYAKLeuwKgAvwLx1bsyfBdD6JjIzgdCrNyFF/AFBMJHe4XADPjdc99z84x9HL3UK4YmE8zLb4/9TaGe3FNgKJnt7zOsprfxi3jENZWXbx1HIXXYohvrbrau1tdJ+CyFaa2Q30XP2b/1kIE8NQ3HDBlIYQJF+HNb8Xv+X0Kiy1R40s7JCyUYULOdNC3fgco6v05abJZGBbPhI1d3ILbLMOYpLn3uzZrK6+5Ywpn7piY6dKGcMf/N37jN85Xr7hnDxzgzK2xtgW5W4v2vzIApllUPCba0wD46p60HQOGzR+9p9ckW87axVPSQajHwHI2ni1qMJkhjvknP+obm9CvlRiXSJM++5J6WTotkvvm5MH9lgDlMXhnO8a9xRbfgr75sUn2vn17TCnZYQTQJpjNACzjPzrVjkz73I8tr3t6dw1cqwG7T04tNPouzDduSVq5No3H3NfnysMu99fATQFeDe59B907wHv/QnxMPneA9zGV1p7WXQNPTwMG44Gn73//+49GZABiMGKS/wu/8At3wNkXf/EXH/UDLA7E3Zq0eRaAg0kKPDIBTEyIvuALvuAOBHEa9TWTrsK55DrZtmwuAg5Mhk3AgJvSsrJmATQYN9ktbkC02hbGZJ2gAjAEmIrJy3buFsgDYD23PTR7oLHylA/m6zGxlRdjmrvGCPIFSJZPzydI4jfGK910+Itn/QmrbcgxdYHH6kcsxckaK478r1dxm1irCxagA26wqUuXtGTDOf/ptvAD1KU9pjgWaqBXdoLz78pvOul54fV7vSrnwlzfPenvQLWturEVdoB09iBXNwFeAKz13bP4/VrFeyxv2p0tkOmY+/X5ubqxuu/302SLS9NWfVmfrYdHGvdro2Lzl9b1qk0KILym3mMrzfZ1DVfdxXA61bZPkwTndN+3I555qJjfk1HdYlbth/yVNu3W7Iu0nenNhBeAif3roDftuXaPzXMsaW2iha3sVDNjdB/RN1sMsyuDuQBtsL5JP8TkDIZw6VUeFj/ZO04AbOwO88MdN8g9+Tm2OKscuNXmtpuHDXb+MLyPCWCzPpPd3lUsEM12QH2ziIBopN9qd4jyZRLitRAHm8nn2odfkpbY73R1ToyN6P9cH38unNfyfQfxKXP18Ziox8hm9KqMnRXwIonFGAx4i+/OXLAY85jL/UUquyfJKxvntbXOo3Dmhh0d+ga7PpguabFH3/KBD3zgSaK7ud/mMzvA+2SqvRnAG0OlSnXp1UDnN//m33ywBfRkWdl9PwYN7ADvYyilPY27Bp6+Bj74wQ+enbhJRVvgTcgIcFL/wsbaKckmqwH+lti6Oyd9Jncm/AG/4vAM8+UUy3cr7HPPTDwMura27dd3Yn3NLf8TBPyMz/iMQxRAaO6BEIHl8jWlA4EKd+tqopSdY0y5c/Ipn/Iph3jZhIzBSld0DgQ1AePGBN9224Ae4Gts7GxvmnhM8GMrffOZwesEZDq8B7ub/MiP/MirypD/z/zMz3zpY162xcuuawDLDHe9n2CNd/LFvqV7+XGIXH7KX2AmZpR89/7UVfrpbrpxGFXgz3zuXj0oHes7v42ntp4/zWcAn5VhW3y/+3f/7meenuJe9drzW1wvqUNb8QB3W4TYev80n8Voe5pxFHYLI/0GSmr3MOkCuLnxvXaoYW67AqWvAXTtcgByWZgJ9CosV2ExUePbnaCySfAxe7JY+vz2bbtX9toHbfQEO2dc3csjUy9+a2ctHgVG5oZusIJrBy0otpsASMsdthVmcn62roBii2YW/7w3iafzawTQuILTa1yVX9fe6zv1Q/Pb0Icp48xYKINTko3sDh9l91j42pFjkj37LTMFWMH1jeYfWwu+AHW2okv3awHy/vRP//Qhn0yXXCuNE56WWY5r0/M03WMbt6joe79EWiC3MP0iiEUOu43SU9+nqzYCq975Frs8fxowr1DOxolf//VffzSDFnpalNdPPKT6sAO8R4vtqhc3AXitEs2O3tZE24isALcy+iVf8iUHW0cmfiZaBjgqIQbSLi+OBnaA98Up6z2nuwZOaSDbccCqU5K5BXZUSVsZTYZPCbu2+hg2V48JNoMJPHBwAqi2EWNbbtmhLSwgo8kj27WnzEvkvqvJ5GSXGnC3hXf2o9LuzzMHzAAuAk3+8T/+x4fgsJi5wYTTr7rHSJviEJLC0v9ilAJUgJ0Bk+wWZi4i+5czjPWeeQRh2ub88z//83fhF8+xKwCAWQzvTUr/7//9v4egMQvmVmvvAZzcAF+y1+nZHIgCQcrDnJQ34Z/poLvc9lx4Tep7Nq8Awrb+YrL1DkhN2iba8/tct4BgZUS3c7HhPmE/Kz9Yz8rpWcX3EOKxQ2DWCWnKhuZDSN9DSAOQVdsqLdrxRLsZoxhQWrv2pGkGlBKMpFivM0yLg1PYOo3JJJ1b7DbzmBnGuXtb/YHIn/iJn3jwF7h4zh/3+pt2nrAzD6hOT9O/MAE4bA5jzlpcmQspFiVrTy2kJtzrH5QFsIttRjsgmC4Qhj55XdjSN0nTqXZyps29dhMgbREPm1d5z0WNaYKntHVlH1kY+mC2cfVTfh8zGwTM6pDJb/qmbyqYu2u27F2PmTDIMdaxfEq/Lf/PUgCXvgMA+DXjCaxtadaXbdXfZ5mHZxFXZDIM9kvFmLExFjMmz7Ng6dam+W6QGCwMAfPmopb2cbdN/HzVBOPpyn7r0MU1t+YwjdvsZngosgO8tymJmwC8GcbXmBhYTGllHtNoiu2hNbidTDvf7/fPpwZ2gPf5LNc9V7sGrtVAA/WYucf8s6+mb8EWJQYxfls8PCVNzDMdYHIHzGVWAKhmOz32Feaqya3BDnt/5yZXTCjY9tsipbT407ZhW5nQTjFJNehmbsEEuwkrgJptRfFmasHhM21rbaINaCbSVVwYaITuPJOvv/bX/trhHrgwhU38/GFnTWn7Mv+Bolh+GMHsALORaUvXFBNKYLg/k2t5En6LucXlCrwGeEjvNOfgufd0L/8kgNlzDNYkW72Fix0bUykWrQl525oxtAITDF6B0d7n39VkeP5e7+Wt/GwxCNWXtkrzK/w1jsJkz5fpCOOkWGy9O3U9Ft4pP0/6rjp3Kpy5EJI7dW/qdLX99lrkpbSduq7pPOW2dxPEXfN1X6AS+LQF5BXnQ7iuiyMzTfSw6sJ3o30AonLLjMCU1bY2N1N/zQ9mPPO+NmQ+O3XfIV0WpACBb3l5UQwgbGGo9kK7pz0CAllIcp0M3dqEY/EA2v7rf/2vhzCaaB9zO5+nW3FhOHu36rNnE2jF/reA98M//MOHs0/svuj7bGFKW2URsrZ+xnvsXjkwMTPtMzJzYPfD6sd3z5SD8Eub7+qNb3zjgehjMfJX/+pfffCXLgFNtdezTnRffxgzFRNxy04s4K7FRraHkY2m6KulV1+7xdDWJgOb3/+yiSg6Im3p900+a8me/BZQfSwtmM7yqH99EcTZPfJrzHKNtOvFIvdjEwskvnGALDMTaz0vP3ZvtSjsu3BA8Cr8x5LXVqj7uzwfGmhOdW5348wtU2nabf31Vhs73T6r+x3gvY2mbwLwth1Io6vDnIK567lJ6yomYgYxBlcGgrs8/xrYAd7nv4z3HO4auEQDmVoAeJ6SzBDE0DRA1aeYLJ4S4CR32Q3MrqpnW3+2pU620xq2CWlmDPjXdwnT9tsJ9rJXaEuqFfRjJhiAy2xPJsAGYWLhxnjjv8E68JXdvNJtYO6gmoAJIGy2IoGICfB1MtlW5lTbX6W5xdjimFegauy3yqPtjg0quQdSmdCbgGyxiYwV2oYcIAiEBR5g8U7QxjZCB8AEWAAcAgqAP4EA4pUWjFcHqQUyAJQCj4UdcD7z5V46tsCU1d38Xdrns617/R3TDhiFtpNiHW25u8+z+4CT94nnEj+rneQm0/m9VF+5f5rXWdaArACdpxnntWHP9uRSvxZFMktzqZ+n4W4Cs/JRXiweWOzRrlkEm/OGU+kI/ORma4fDKb/rOyDYLP/1vfowQebua4e5B05ajNJXmBw7tDHWrffaduzVGba8W6iyOKlN1jdNkw/0kqmbSxi/2v3iLI0zvnmPrVu7WlvqvXYYKLp+u94Bst/+9rffAUnsnNaPKI/6nRkPvWSDfD7vXjwW5mIRWki0+LklgNt5kKe6DZhPLMQqg3QFeN5iZgKexc+sT+LgOQux2Skufa4AZf2bPNIrsP5ZSgepyo9FwXNCJ+kJ+/ghizk+dradWOq/BfhTIP+xvGRmxgLRNZJdUgvXj0HsTvINssk866h7Cx4Wt2f9ZEasBRQAWWOfrbwy25JNbGEZH+3y+DXQuMvOj2sk0sm5g5KvCfNJ3O4A75No75f83gTgNWDT6BgUrNI2XB1mNqWmmxqva1fjZhj7/ePRwA7wPp6y2lO6a+BpasCA34TTnwndMXnnO9956F/YFiXZlMXsPSXZev0jf+SP3E3arWw7qfzDH/7wS//rf/2vwwTdhGPaSDQRXwX7t9PJTZYdVLYOrk2CAxEniGAiL94m8A3WMbzaBmqbrQklICymFWZO4KnJMVBEuE3oA046lIUpCmGbFJAPfehDB6ayZwEUgNQpgNi2t5YuVwNFEyLs4SaQntvmJw3+Ml8Ro1G6gKznhL/SHtjLL/bSBG1nerijL9fyMt/Pe/XJJH0db2RvbLp90numIxqMPmlYj8V/APqp9F7CBF79Nzldn9/y9wS45jd6yzguDavv+FL359wxjZb9vXNun9Z7wN8l3wMgLXBupkUb2SLOfH7J/WpvskWFtS5qe5glYN7mrW9966FNW+s0gG0C1TN+h5qtuxpiFU538x6bdYrDY+Z78TNnsO4WsHBV2izc2cWSuZpZf82vbLFl1kFctaszjmwSaz/ZZtT268MsKh6ri3Ra3zjDWu9nWtZ387c+2c4HO2g8tyBox4kdnfpkesVeDcSbft3re1eQX78xwd+p52wJ6wuJfnYCu/LHv0M7J+O6cnjWoCngrUVAiwTTHNHMl3usTguv9MJm80MViyAWxtfvULrp2RijHTmX5MFYit9rD4ayWMxfZlwuieu1cqNsG29Ks3qvXhgf9R17bkGlQ+baAYW5f8mZEb7/Dr59LKD3a1UejyXe5h+niCpbeXnDG95w+DbueyjnVphP8qwxxH7I2pNo8aWXbgLwNgnFZlrFlgENkT9G5Fdhu827S+z+rX73349PAzvA+/jKbE/xroGnpYFs0WZyYCserKfYQ8DeAMctIDb/tqaZdAJ0TF7dA2WPCbZpNmwBsm3XzL1JiH7KZPfUSdU/+7M/ewdempjrG2OyWsQUBqC6g7BMzAN5sU+9bxusiYwJX4CHSXCM4Mm6AvCyed/2YfdMNwQMZCfUJGFldRgINpkVdyCbCTiWFDFZwA6eIIDfxGm8/PlbzTN5D2SlL3k00cOeIjGWMeowzOihcK69AmwAGga3QBAMuVVMZorDVT3C8J1xmeC3RXY+P3e/2m8+5359fwk40lblWQZrOM/i99YkfY33IZoaKN2X6HrNz7P4XfqeRVyXxKEtmSZVLvHDzboLoDbJO+2vHQO1R57F+HRVNr5lZmTWcvK79mwrLdrsgM/5HlCYyQPP6XnOQ4CNMWlnWu0WyYbtXFCqDZEWgLr2HYN32r8t/umP6QSTVgtnc6Fvgo35O3XVN6zAeLoC7iaZJyqs0k2/zAURfWSgZmHkvj62312ZFKIzpg/ssOi5q/wqWwC49qowbStnD78dB8BIzN1zB8ZZLFBfmPigv9muKCtgcbboy/d6bdzAPIN+IXAYCPa93/u9dwzl/NkGH8AqT+wcP2uhm8YA6qY0APwSJjkscpcX44NTC+T5ey2uzIOoc3RpLMbshf7eWEFf2zctn8rjErE4Izy7mq6RxleA0Icsxl0tumuDkOQaQ5Zu32A2vo0JMKMdMEgvDq29VOwu4kc8j0ksCADsAdQAb/pSnyzabY3/HlPeniSt2l7laSHrGmGDnT877h+C7ADvbUrhJgCvyZvKoQFfT2w1Oa+j19ivgm3Er0nSiyi23dqqa9D1/pdt4TBbYQCqQ19tOT4P+tkB3uehFPc87Bq4jQYMVA3y9RGnVmsDEmNlnTLrgM3T5LxJpQHMKdHWss3WhBfoaRII2MWklT4TlZUBO8PU9zXIbmLJrECCSayvE57JSTYSTYw9Ayq3fTUgD1iLkcUf0PacmQnu+tMfs//Kn2cmOFOAvQEMJgsrA04ePuMzPuNwSF35KWx9PhCkvt3zOagE6toe2yQ0fybmJusmc/JIp3Rvq6ZJChAoHeTn3BV4QX9bNhbLL6Za4XzlV37lwb3fdP+2t71tExzKfddAuOpIz7eup1hxua8u93u//lLdvbUufAtrmJXn+vzUb/U1VuApd6fe9c0EunHLhvG6rf9UGM/iXem8b1xAQocF8q+Nb8eDxZYmorVzFpZsw+c2xmggx7n4A4lXdwBB/cb6XptIzE3qJwCSaxsnvEBFZV6apW+rPq3xvxa/ta/qaKC1NMy0ssdOtM+xfJnIqZ+MObtV9sJc5yQWDcune6aJYg/W3/3Nv/k3D3Fa/MyshPpOsDCZFdFvKm9tvx0s5oVbtvDt+mED/lIJKAWKtjgKGD512JpFyRY61Yn7mBG4NH3TncVPfVF9cnrtqhznIoTnGN6n+r0Z/rO+t927emQc1fdfOowhtA+Z++AWE94c+JR0pgCg91Kh2xYtVvb9pWE8K3ctrPkmzpVtwFyLSerIqbq95sGYtXHfXERY3T2U377NP//n//zdYn3fxrzq17XxtUMPJe3PIh0dmHbpYklp6pyTdv/1/LW67gDvbTR/E4CX/ZY+MCyjVZqUooGvUsWyzfNFEEasrSBicDV4THfrVUMFELXa6fCdFTx/jPraAd7HWGp7mncNPD0NANy0fQb4BmZbEzj2ZmsvuQPobbWHQML6m5ivJmsrA2Lm5l3vetcdY2Ztg+fvFSCdYbhve7RJZcwIE8RYs9m5mozZGT4QBMgxGV7e//L/fzvrdOveZBkDeoYHNDRJZ17BJL6D0zz3e4qtsMIBXNAPYBygOwGBGafJN1uRnjWhaAIHyE2U5wwD2xh45TTn3LsGDmD2EuUZo3vG2z39YK9hbVQXeucKLPIO42uVDtvhBkMqACQG2JzUC4u+6HcFmYByxjvsPa7v5EkaZ5qe9/uYxc9LPms7npf8lI/7gNn5ve8VozZADzBFTLp9Oyvb1lZzbdYEvYvXt+pQoGNlY/s3Rj6bnCsAVhjzShcAv2yJz3fH7o3ZAZS1O9oN21kxEoG/00zNsTBu8XwuCklLAPmpsGtnuclUQXbetbf6J6Cw97XPx8IDCgNxCfMBue+aP/qoLQfuJdldlqYtADd3t7rW57ZTFNuPDX/sZf3hsXFBYwf5US+ftugDZ5wAd2VkEbiySbfKHXucLh+qYKG2sIJpOQXD8pgppvLou1p3URWGcQqdqHPVxd4du2K3C9sun4csjVOMp1ZA/Fi6p+kpY61rJVMQ15q8uDaeJ3XvW+1wOGX/KZ/yKS8Zu/uOtc1ATWP0+jqHzFnEe5Gk8wSw5C8Vem2R5aEsfuwA76Wld9rdTQBeUTTZMVBb7SFlC9HEd65IWclv5dhg4HkWjbWPrgFiHdk1V4MmQO9jlh3gfcylt6d918DT0QBWRuCYSayVaIejGcRNRkvAonZTf/K6173uJQuHJpGxsbwD9AEr3Z/adgQobnIKWMWYbRD5hV/4hYetkKWLTcZT7JL8ZU8+JiqmygQVpUn+THzYg/MbMNHkXb6YGmiC5P38C0zwLFaKiX59qUmhCRZ2RwAAO8OrlF55nmIR0m4bW1XzP+PvHpONTUi/TZyJybDfdIrBO09h954eDEInKN2W4fWAKPownlD+DdoLuzS4preZVvaI5+AeO5tbbrDH3DPRkGD4VA/YwkxMIGdc0mHwqQzWrdLeXcLuneHd9760bvmf4PrW+1s+2wLkbhn+sw4rgHCLzfms0/K8xFddbcfCVr7m9731/pJn7VSYbmsfayN6BzSuvQA2ahNizgGDSnPuAU7mK5mW0fbNvih363WGI4+Y/c0BAr/5qY9xP/2sOyDW8Pud/s59j/rCQCTMPXbo5av46SQbn4U12+rSh50bSFwauq7tzwqqZZ4IKP+0pZ0/Lej1fZdWz+VlAhvMNHhf/6usn6YAqOq79SsWKJXJFOOOTEcojwlsfvjDHz4wntlR1bc6wGztd2dYz+K+vtzOoCkWYarTrnYnMSdijBTAHaCtDVYWW4LFqYz0t6d2VfHLRIv67btiTuUhS4S3aW7lXHoB4dVn3/S1UrtsN91DFjbG5VObfqxeSL9FrEyYWQh5kcRucPVc2/2Rj3zkoqwb79JrZ3lc5OkpO9oB3tso+GYAL+ZUjYxOc64gsAnTO+wiWzfe/7I5gmmDScf0vApQu0Y0PfgIdU62YegENUTACltUbD0xoDSxXQdXBmBzAvrYdLYDvI+txPb07hp4Nhpge1Fb2GS1ttLVoM7kFPjI7mGT0OnGPZYXMBMryfZFz44tirHhqz3VFpsIxgjGzOKvHSdNdD2z0r0uYKadJibZw+tEWyzaWBLCmLZqxdlE3+6XJs36BuZ7OnCNP3/S22S1Z9KPqcRWX9vtSjP3x2wP5/ZYfuTL5JFu9En6Kv1Sq/2AAttXpQPY8f3f//0HXZpMYTOfEhNW6eZX2tueXZ66mvhiZZgYBkh7pz4Io0l4YAvAPP0A0WNPN9Hnt4Np2LZLstMp7UBqkwmT1E//9E8/pLH0nLuWjtWdcNsSjc18DXtwDesWv5V9urtPeOpX5ce//B07lOo+4V/ip/jV8UvcX+NmLhZt+QukmO+eRjpm+M/7PSAQIwvDt7I9l2eAiIWsCdxd6lfYwEg7RsRZnXYf2DT7IqAS8Kw0iceioLbLgpIdCr3bumIaM9njnUU819rpLfczT94bO6/zgfytoOqsi425udV+Zzajhc9Aa+/NObJDXjtfHKeu4sgkA7AckNYCJ39zETE2pTbwaYu+eAX3gT/KLVKS9NGXeoRV3MHf7HkGvKoTT0MAuZWPBce5KLnGZ5dJZWfMAOQ6xYTFYDy1IL2Gf6vfxjSx7TuIVdgAp/RpPMHcBsG8tJjfmE4dz7921hhkFf26uqzs9PfmxKspDWUGh6g9eBZM7DWd1/4OmJzmri4JozpLH9eYWlDftUHqv51JD1WcFaEcpXXWqWPptcDROPAhM92Ppf9JntcOa1cmoXIrTDhcfcqxedKWv6f9bAd4b6PhmwG8ktPqk0ZGA53oxE4Nfkw0Tp0WWjiP8aoBbcWaXkz0saM6wOZcntDndeRW/ucgzqrnY5QGM8D9XXYN7Bp4nBrAePyFX/iFO1D0lrkwKDHod6CLthLDc2W0iM9KPbAXIGsCyV0grfdYvNpcg5hVtKuYOd53YFhuvuZrvubwHMhHmoQ3oTAZWcVEUlgmJIlFO89M7F0DcoEHJmtJE+of/MEfPGwZLR6ToCY9/LsHpLk3aTWoD4jwbP1jc1CYW0KfBswG9lNnW27XZ+XLwTkkhnVpXbdkTv/iwvo9x3QFbgSIytcELGLlAco7/bcFVOACRk8TRCxs8nVf93V3+mlAOw/j2DokadVnv6VN/8WcSGXXu65028SyZ135OfYuN+u1ifH6/Hn5PYG0x5anuQX+saX9WafXd2wM3OR7xm98a1HL97uCm3Q824D8BRJml9PzY8Ck7259Jx5tcexdYC2Zi0nCFPdaR7eYg8cWeAIZ5xhe3Nialy4iASQtgJb3rSvgcm2T2lXBvf4JkO4eazJge4a1ts2lfbrZum/B0FyFWJjLnTA62b1dH+aLx8QhSswyOaCNSYXZ/7Mxim0IRHaI9ylTD9mvLx127EwBOn7e533eXdmWd2aLhJtdbP2KsYZ5lzqa2JFiAdViov4PcO9wVPXSgvU5ce6AtFlQWgHKLb/qKuCWn74H82c7nZQz8Bw40gKU8UJ2l7fCexrPmD+RPuOdKWzve25Hr/L0/Vg09uzUH+bylqgH8p1f/bq2xSF+FhfqY+mAbeOHLsZG2hjpbmH60jQ3xqSLdTx7Kox2NqlTD1kiADSeuyStETWM7V4ksXDSmNwcZ+swat+OcXptuzbwIckO8N6mNG4K8Fp91JmoVAYZU9hIqdLVILtiPW0dvjb9PuZ7wEP51RnNgcq1+TK4aIBoQvskYV0b963c7wDvrTS5h7Nr4NlqwKr4Z3/2Z99tf9KuaY/syvjyL//yVx3A8mxT9+rY7IaQxm962dbsKraHescW5ARbuYtdGeOjxUkr3PJrwrDaCc4OPeAiiVURs9eEtT7wrW99a87uFgDbdmnSuAIFAAgirSvTwoTq67/+61+yRZNphyZN5wb6bRXGir5GAkM7yCG7X/RpgnzscAvjgzkR4f7UH10DNObCMdDadloDU+XQBCW9AksI8MJECThvQg7kF1f9p3sggokU8x6lw6T5TW9600tA/uwN967r3EEDtOj51vUW4J+8ffd3f/fJeLbifuzPVkDtMecnsOEx5+FWaW/B7Fh4tZfzvYUcbLz5rHsgXsBcz1ynztuqm13Y6a6FKYsoMTVbHMzdtBms3fF8PXAMIDjjzO/WFVOPHV8Lg+94xzs285V96xWU3gqvZ3Z8BEqWzljDuUlX2j99hOfYo2vaK6e1L9oqn7kYB2CzKySQvvabTgl7xuI0lphi0dW7rUMMLephpgEHC7f8APY8r/8sTPrlZrb55dU7/Sb9a8Ptniw8pgHYxLXzJh30zpWekHYs9K66Wd3J+yngNgbuZDiX/mPXGNDiev3rX/+q8QB/6qLdLJXp1hjoWPhP+lzfK22A7sTY0TP6Yq7QjpwWeZg6Ajaxi9zCrDzOenBqLGMcom5V3ysDO0qMAa9lw5ZmgKtxInDR96+Owza0IcgHSA63lurb2raci2fu9AL4H7NfPMMxzszMVIv18/1DuVcOjaPgSJeKMboxhLHiKWb8peE9JndY3C3k+R4sIPkW7ACws3HuttJ/PjQsaQd4b1PbbgrwliQfZAen9MxVZ2pypDGyKqdzsjr7PEur8yb7BjBPKpOpMBlITxrus/K/A7zPStN7PLsGbqMBnb/tqRNwMakyIW4CYRBhQnrKNtZtUnN5KE0kMUdWMTmTZlv3V2liwQQBwRTl1oQpptc/+Af/YPV2N2EBEvz0T//0wU+T0VgoJiNARH+Zksje59xaV5zipfdr+skmtgZypwTbS/hW+LGxXbGrvuu7vuuQ/i2/ANHYQQ22DaSbmAD7t8SYIAZPrAFxr3+xktbn87dyzR4bO7/eNTG0VToJTHaKu3Q3SSgsE9AWASYIYEtfac3tvH7pl35pURyA4N7Nb6FnT3KNYS6MAOxbx3Ft+kxyq9NbftdJ9pabh/qMbmc9kM5L6uOan636fQoIWv0/xt9PWi9rA8v7lg5n2bQ4lftzV33VtP09+7L8embxShvdBLh8VQ+q+8KbYmy/sn4L11V4bb+ez0/dF/cpN6feSaP32pFZ/2oH3/KWt7z0G37Dbzi4sYCEeTvDq533LD/zfffppt0k4mpxjJkiC4iVr3lhgO9cKMOkzXSOcMXNXBEAdJpSKM4O/DSvmm2Ow9QsgmrvHa7Kvf6i/F9Sb4DJLWIW38d//Me/9Ef/6B89PC+s3pnPMjvIlq6+32KidNQnIuOY+66iT1TPlXPmClY362997TStc87OpoVf6QTKt3ixhnnr3xZgxamMktiUdGicE9vbwstkYLdQQF/Gnc0X1bGtA9wL39XOLweF6b+xpy8BYC1yGB84qMzYxe4pZzeomxaTK+Otq++5Re6Zjie57xu69sCzFqnUU2lV97d2GJQ2+S5/2Ofq4kMVu53lSZ25VjDx+b0vyH9tfA/JvfqvbregN+uwNsec5H3ve99DSvJdWnaA904VT3RzM4B3ZUE9UaqeI8+t/N/KxrDOsQ9167Twh666OuzdRMNDL6k9fbsG/p8GsulkAmwFuJO4vbUyjuXQarEJy60HvfctBywiQLTJwWq3i91X7eg6abCbxHODIpNEwvSDZyZ2ncad/cKZtmzuYqNgRPETiMq+X2K7oHf+Mi1gYG9CI83f8i3f8tIv/+W//M4NdtE1Iq/CNnk/JfLAGjm1mwAAQABJREFUnYFzk/TS5QoYYNogPQgLq8k7h8FMmeAG0wUO75nSRHOCNDMu5TR/n7o3SbeljJuAoMJti7W4K0tlACjYymPxxLbut+sEgSbgaxtui7Wxu7kPUJhhuF/jnYDE6nb+DrSez7bAjvn+tbjHZrs0T69F+h5SnNXXh5SmJ02LyeK6Ff7aMH2jmV6Zfo+BnAEh0+16D8BYgbrVTb+1gfN791yfNttqz6rnMYK1M8wFrGYRuC3tXZsLSLv2Emjau9Jxy6st8sVZuo+FD4D78Ic/vJkebauFtLUdK6xA70wQ9dzvQEXAnuf6xkDWWIb6vNo67SmwdM4n63PTlXTMuQ8wEFgdoKoPYL5BfBbG9KvMHfnNrq68ArUAdABFfRlw+/0vA7P1x9wGpK79qG3i3vcHsN4SBJzsEGOYSscUZgmFod5fKnbo8BPgjgl7TlrExJgGphpfeKbdljfsZyYoMJpvIRjL6oy/dhtFdDIOiC1tjDR1YsG4vJWOduh4TpfTfW7uc8WQNYY41x4rG2Cvw6vUM0xk7N0WRtTFt7/97fdJwqafz/mczznoQJ2/VNQB+vG90b2Fa7+B+trln/iJnziA3cYswHdj0eo2ANTi/kOW6gUW6rXS2RfXAubXxvOQ3QPv7VKwU8QB0NrXSSZ5iGnfAd7blMpNAF4VyGBKR+Gwkoe8GnQbtV0eSoOZTla/3Oe2Sx1Tk1lMrccmO8D72EpsT++LrIGYF7bTmQAdEwNvwJ6BJbcmiw9BAh4wHOagJkA6u4DSCrhu26BB/JQmSYEQtvGvwl6g/AdWYi01scXWneIQstgq/Jz6Y3rhGvmhH/qhQ3in7KrpowNJxa1PwboyWcVImpN14WBR0F9b+jB9pxRWQIIJqAkcEwvA9Pqs8tlkPVAl8KArdkF+VreFcewKBKUDTJ7pRtoKcz7fup8Au/eBDLk1qVOm/QZMXMvQy2/XdNfvh3ztOzBRDJx5yOl9Gmmrrl4a9rXuLw33tXRXW9e47j5p6fu+j99zfrT7W988YCfWmzCYNcAAdFBl6bHoA2jcMg1hmzZQEDCd+/oO4Z37lvNzLv3r+8nqX9/5HfDnXtsKzFvdpQ/906yTAbWr+1O/YwmvboRry30CQOQmYJ7+ksxhYOyuC4P6Sf6kWdlkc5WubfWfwjRAwFXAfjY7HQAqHMxg7MVj0sIvxnUmMoCSCVBaWpSvA/dieq/9Ye5tg49BpyymqG/SpBwulRYUYyZeAlwBJ8VDN6fMffgmmPO4hPl6Lr2ZnmC6i7SbBmCrbmg3VkA54ByhIGk3UqzpCezn5torvQf8KUe7fuzIApSyibyaIDEGXsX4KfMmvrOVKLC6v/Q3AJZ+lAX70ueEOazyYpGDsK/K/vXaxqy/50F3B48P9B8SifqrXCbZ4JLkVt9PffOXhLO7ebYa2AHe2+j7JgAv24I+QH9YLLv8kgbadmSifwtpZZqu2yJ7i3CfVRhNBHYG77PS+B7ProH7aQCo1wLVpYc62uqmbZqD9PvFfhtfJivZJAROfvu3f/thAZL5AunEPsZsmNs4gbmr2GY62T0YnbFTuAVwv/9lAHxua2XWogPTgKfibgLr2jZWE+sm24Azk9MJKmB7XSMYJ/K29jkmJdi90pGpiQb9JhQT1ObW5CrQ0sQyhqo8rUwaW8HEqV2fevJs/Qtg8LzT5N2XFpM/97GuVv+X/m5SKD5MZOVlG+0K3p4LD4hh0iVNgSCxxc75Pfb+HAB0zN+p5+nvlJtbvFPHA/pvEd5jDaOJ9X3Srz7l71mVW/G91ldt3ZoG5JD12fzdQtB8tnWfXn2nbQPfcrf1TDsRYK095AbbLYbclh/PpG0FhY65fdrPtddTV+faqdrIS9N1SV2tjQRWJQ4iKw6gqEVLi6/ZDlUnphkDwDo76DMv+e9qrLFKgGDlOIHXDqjDBF3BReEAjCv3wHoLwZNN3CK2bfykQ9KAwcfAp9yY+6wSIB3TeX0/fxszyLsya1y2gtzT/byf5YzVjYFaOQlzlisb/o1TZhjX3BsvCle56nMbz3RY1gT4hWtx3TckHXNhwKF4wqmtFc6TiHFLYzLjmRVEbReWcYw6W3tioWFLjPGkDwt8HRNtub/kGaa1MI2N7A44JoDPxkgWtVdbz8Z69GVRyPegfeOOeTIL4I9J2mFloeZS6XwEC9DGs7s8Hg3sAO9tyuomAG+njmuUbDHY5Zc0kI1AA60nXeVjpynA2CTrMcoO8D7GUtvT/CJqAPtCm27yfamYqBhImphhSjwEkY4G9fIDnDIgd29CPFlPmA/HDglj/25O5OUTI8nulRnGnCyJY/7xIy0BmwDdLVvqf/bP/tmDP2GZdJ6ztZeeTTIaDANyiV0fGFQxiWZ6TGBsfeuZbZt2mzBLwY7dN718QF2TXW5MeGe50hUAJKZ3E2tu+aObmHKn9OId8x/8xbqIJVrajl0DAWy/xdqNVZV7LLsv+7IvO9iRtuX6HACdv/XqOwgYX9+JY4vNtpWHc6DLGvZD/q0/D2x4yOm8ZdqeBjh/y/Q9q7As4l26C2GmSdsXcDKf3/JeO2RR6r7xBEILJ9Zo6bskTG7OfefCPsfILc5z13N1Eksx8PJcWNLevIVbfZYrYDAATHzabIzX3nOjb41dirEKZNH31Caf6gP0WwAcpgvs4Cid0gN01G/afj7jA7Ct4E2HjPKv/0os8NXv668xMGe/6uDT4nRVT1fmX4ucMWfFXX6P2bPURwbkzl1E0hWzdR66WnrXK/awdLW7xlzwEuksAn4z2VE+zSOFt9ZpbOpVr5fENd0EPur/HVgrzuq7Q9QSh+PV5zt7YEqgfDuE2Jh9EunQc/XU4atTjF3VT8A3syEk3Vks2jqMFjmg8Y0zDG4h6gsTIvRlvOnQ32lD1jjUYnzxqsvGaudkqzyN5eZ47lwYr9X72NJ2k23lYytdFhHo0Fh6l8elgR3gvU153QTgBVzWYaysodsk8/GGYvLegMSE1+T5GIBwKpdWOFvFpGu2mB6j7ADvYyy1Pc0vogayI3vtCbtO0dZGfcd3fMeDURsWju2BTdDqr7oC/diXPSV/7+/9vUO+gHbYtwGLhWHy0hbUns3r6h6TaE4wi9tENCZwtm1dL2GIZDMSU0M/8+GXTWUE+EqL5wG2MTs8N7FpEj7TvN7HeLH11EFnTVxXd7bdmqiT6sPqZv1twtKkZYKGp0CBwmADM7u4Tcp6t3VtQruCMJhO58CSrfDOPVvj2QLbJzh+LryH+r5tzY15Hmo693S9cuHpRdHHJW3J2k4f0402KlM/0404/LVgNd+5r+1Zn1/y+5L0nwpH32XuYKFqXXw6BwBrG+s7xDH7i2vSBTw3T3TAaWHMtMS8PtYeap+nDh1+OqXFRmlcTSnpW7Ezp47ka+3HsP4C+QrbGELc/iarVxzCA2IeE4ApN3a7TmlHJpBuayww3bKVKozyfonZPwzfyd7lX14BrBO8Bl5iGhsfcOMPMH9MgGwWgY03MLExsoHpxgUJRmm7p3xTpVvYzAkwtwDI6bk6WR8uDOxV79TLd7/73Yc0HTvEtTjPXTusdGtMa5ePtAHdp1R2mZuY79x3lsE1dnPXMNbfdp4xATO/K2OI+f1Jq7Hd1oH2a3jztwMVfQN9e8JRR7Dp/8k/+SfT6YO5V5daBAB4n5N2DGijG4ee87O/fzga2AHe25TFTQBenV0sIB3V1vaX2yT3cYZiIKMR7U+jgw1k9dlKnM5LR48JZQBgFZstY9tebbFo1Tn/VjEvmew/RG3tAO9DLJU9TbsGXq2BX/trf+2hzfq3//bfvvrliSexTw3+H6KwSWeA32B/DqKxNwx0tclTbHdrQMxeG7FFzknFQE8TqbabYrXZKopNahvpBLvcN0jfYpGYODnAR1uPsYTx04QXYw4b95iwCWvyKT/vete7DnZzM0eAZcRGsLIUtgk1ANiuELYNm2RJL7YXppTJg0mXg3FKk8mM/kk/X3+EpbsFdJigZwsxt1PXPZtXk7km++fc5o+eJgulCVnv9bfyCFTwbIZrIs+kRZOH/FxyNeGqbMQZOym/M57J7q38c/eQr9WLY2lU33p3DhhTtup87vfrL40Jn0QXMfqeJIz5PT9JONf4nXVny999vknhCBfrdl1U2Yrj2mfqsDZ8BR/71o99270vvrk93rMWRnp/7Lrau9a2HXPbc0y24qebuZD53ve+94n0pH3I3JE+xq6RAEXlh2U6QUPsWguDb3jDGw5b4KVR2ix4zX6y9JaHc1d9baLvy715KdMJ+gc2cwFb5iDS5LuZO3Jmfdw6KBbIJFzjgCntYJWvY9JC7Zapq5iu2LUWZI8JExa1sdweMwkx/QNe04WrBe6tHUP5Yb+1A8TUdYeyrWLhPlvAM2z3ys34oXwYr9DL1O3qRzxA+gmaK6/Gn+wCd2Aq01j3FSxYcauXW/NnwKH3wMEp6rTnWLVbkjmKa01pbYW1PjPuZFJh9pvaDuMqDPmtfKxh9JvJNWOlqX+LLe1W6Ll8PkRQFEZS/TceXdnw8imPHeinLhqH7/L4NLADvLcps5sAvJKiQY9haoXQpOlHf/RHX2FX6TZJfpyh2BoyBxM1ptdeTbht0XissgO8j7Xk9nS/aBoIAL2WIXBsoPxQ9GdgyBTBbHtNVNfJsoU0wC3gtEnrsROzHcQiPOCuk5YT2/4wPwJQTNAxXdqCZwtiYlLTlk3u0zt7aaUNYIuhZJLFLpyJz1d+5Ve+4hT3mDdtUZPXbOrFUgF2TrHQ2KRaeKttP6wP+TP5a5BtkVLaXKcu73M/AROTQVvxLgnH5HFOdNn72/Ln5OCYxPQzt+OaQAG7Zxq2wpgAROWRO5O7Juo9S5/9fozXmefHmP6HnObXsn68lnFfUibsUE5QCBOwhZ9z3ynAyG4N8ZxzO9Nyi/G58MR/LN4WTJ6m/ldgu2/YQpz0+f2DP/iDh+af6Zqpgye5ZyKH6QJhANHM/ywUPkmY/AK3sFUtppr7bB2EBSBuXsSuPn+10frB+t+ttNCH+pb5AmmfTNLZTzYOKC7v2jVzait4JB2A3Sr65nbZWCD40i/90lecr6IvZot2gnxMSV0ilbl8+56YeTgn2LzVU+zcBACrv02HxiIISAhM2MvA8+oefVtQToCrwprfhTxbMF6Zy+pNY09ugMyve93rDvE2tinca67VC+ncEnn5/9i701/7kqp+/Pf3lxgf6IMvCZoYSUQQERSEqEGQQWLTghAwICjzIFODzMikLd3MMsnYCAoN3c0MjUwNKM08iSAONA8gxGj8/M5r4/uwbn32Pmefe8+506cqubf2rl3j2vtUrfWutVYZW3sgLk1u6TYExkLOILLJu8sALKeBfpAAsI11lHkUPlO1rX1jz33uc5cb3TZA6vODtLmLMjZv8rv2uwVY05zmGxs2ku/Ld1g3snbRl17n7ijQAd7t0HYrAK8Fis+miy+++DxTFxOjRYtgu+rvpGp7bYfMP6rF7hLzqE01LkxatMMI2ac9dID3tL/B3v8LhQIBIVtt1nXjD9B1Ehks1iUxj7ceEXAzJ1mraPzYqIywH0HHM2vcmHsdWhTqkid+bx2AGbBWevuHAQ1Qyr/Y/e9//6UQSjhq/bU7sCYaLW1ducf4AnwFfu2k22ytjDrTSOmEcYGZJLPHMcCBRhZGC/Osf8oFdCHMEtoybs/Qig/+miZ9zp9+pG750X/VOpn3QmgTaD47nGWdT1DjzOGk1V1HfNtrm+AKpHE9RhfprGto7lSNvaoJM1VO2ZP4F9rnkMST2Mfj6lNoc1ztX4jtBsxtx95qvrbP2/tt/Q65K8o6oW8B+tr2Dnu/rr/WjHV5xvpgDgfQ0ZT0HJh57bXXDnO2uZRGbcC5sfI1TdmxTT0H0t1i4ZNX3vilreX8jrLm1XTX0uMSqn3WakY6zDTzc82LLlwnkUOlx6ImeWzwOXTtE5/4xDlWPNYOVjHt75v2d9YU6661yVzPz2k0IGlPJqhDG1wDjQUApufWZ6b3YwGo2s69vvXWdYRv0DgrSD9WX9IqndpN3eQZi/Ots1BJoElrHNZmgHvVuE0efm1p2cqn/y2gjf/Sd8/92XwmEwMcxXXj3WYy14QsXfPN83WP3ujfHiqWPkzF8b87BY4D1vWpdQEAWJXu9zEW9MVzmssnMeCNYtVkw6XdvK99hlHErQY/2MqetIB/c1hf/Y7Q35/fMgC/+iw+af3v/VlPgQ7wrqfRnBxbAXhNGPmBHTSOwDmn02chD41nwridTzubTmXFWNiptEvKpPSyyy4794EPfGDjhewk0yeMw9Qu6knue+9bp8CFRIH4FptiiMdoYbOPCSOGfM7J0GN17CqNQBLzQ4JozPoJXbRiae6MrV/Gc8UVV0x2yxytHLNFgdlrwD5CjoNpmP3RPokmz1g70pjeVQ3gNEowrcKaa0Kxzb8qNBOQALoBZB/5yEemiiGOxhaBPi4LtKufhIAAp1P9kw4EJwjETDPaR9GskCfuLMbqSRvRtpCHME4TJsB6yiVv7sV1vL4z7g8qbZK39iE+jT0D8NM0e/WrX73vfUdgmPoOUq/YO10HJtf8p+HaN5sTvE9Df3fdx4AKu26n1/8j4XwbdBibL8bq9W4zZ7QA31h+WooULFJmLE+bVufDzC1tHveeBRCl6DK1QcYsGbgRAGasrjYNPbJBy8SfVmU0QbNhCPgM3eqGVVtXveemKPN/TZ+65o+YViwz67Ql79hvLDTOMzGgj6VG1SBV3ro11WaA1zy/293udh7gyL1SC1bb+Hv0ox+93AhOeXEAVxqmNnv5xc17zsbhvgV3cYN/UnbK+qfmt7ELvKpri/XO+oV3AbIaR/pCo3EqcDNR+/7Zz352Kut56dmkVx5YBlR3bc1mtbMqAAXJs/LbDGiBYBqytV/ttXWbawm8S8sP1Lw2WlgezQ1+B8rT9BwL+DbP/S5q8F6lO5B3LMR91ZSP3rEyR5nG0kv/8T34tnXBN51DgPFIJzXQZsaXszhDe1Zt5rgeTj8FOsC7nXe4FYCX+j8h6zB/hzG92A4pei1HQYEO8B4FlXsbnQKHpwBfbYQLfw68mBMAh5hJmi7HGfiS038m+/FbG7Ndpmpjpm40cZkHvnThQgHDyLdeBK1VAK+8xgwcIzBGu4smS9WeDT30CQMTYIFmCM1aQuxYIMSo3x8h0eZgDQRfmsM0T+UheDs0zrXxJBC0AtqnjwQo2jPopV9JV5agG2Ci1aYNCMH3LtM++f0x7SNQRKhNehXqkxagIffcMtS6kl5jYHIOdfVdhoY1z9h1+h8tNeW0V013W2HSNwwon9vGWLvr0sbosq7MSX0eYOak9u8g/ZoLeB2k7rYMYKOaVLfPj+K+boqkvVvf+tbDvJP7w8YB7g5bz1j5VYdcjuWf25eD/E4DoGbu0X5tL3XSqnPmhucAsXbuHOt3m1Y3y9pnDvOk2To1j6VP3NZUq4a2nlo+fdfu2DdTyypnjbK+ZjMQeEeJJflq3dJyn9+ffgE+PUt/KQVVf7ueRVMx+dTDqqTO7erkL73O/eaumkd5f9ZUmsW0iGPinmfSAyIDicdCXDepf8z3LT/+5Ge08UdL2PrpN5d2xuK6YSkvHoFGtoPPbAbHL3LKel/qr8FGMq1ZWrHAUryO+Yd1Tt2Ifsc73rGkK3/8cwIwPxsArSUXANgGqb6hOTCZhrmD3xzyxf1F3Cml/wBHmwPAZXwZq6M8s0bPCbSLfTt4HPxOG3yjse7yDhJoK2trTCkJX+wb835PmkJD+u+3r/8UxuaG8JwUIXroFDhqCnSAdzsU3wrAu52u9FouBAp0gPdCeMt9jGeFAvxbYQ5pbbbAojFi1jHkGHpMIQYaw/uxj33syElAgAHO3vzmN9+nJaQ/GNUIaHFjMKeDfN8Zf2smWssSkuRh4hcBlpC+zryNxoFy/viIHQu0bgKctb7h2vw0L6KhHIEfwEzTiIZyDgRLm4lppgCpq3AfM0WgcxWEAaTypywAOQesyedbYJKK5vJUEDcCX8oeJGZmGSEeQOCd0zCKFtVB6hwr43vJuxx7fpi0ABSHqeOoynrXAXOOqs0LsZ18E9mA2AUN1H3W32XmnV3Qb06d5l1rUHUxwc96gPtK/1h54IkdfpmD1uYCvDa45vQpefTN3DkGHtJI5Y4gecXAvCm3CZ7TZq0bginrW44rA2nWAAFA5x5oGldHzNpTrsYB2qqvdM8zZmsJkE492YT0PPRNPkCgAHwDIobGaUs7NmbxNlnH80z+NuBrst4lHy1i/oFrYB2kfPLYXE7wrmm41rURzdLnlBFb1wDbaMdNBM3ibK7KPwZKpzzgHd/m3u8i/AhejSbwWHspW2M+j/XPOAHSc0P8/bPOaYNN92gia8smiHFSEAiflj7YAOEHtw2A44y/PRitzZv7AMNTCmUB5PEywNsKVOdw3dTFcjk8zdi3knzHGaOz9+w3knMY5vSHyxB8p+9mk3c+p+6ep1NgHQU6wLuOQvOed4B3Hp16ri1RoAO8WyJkr6ZT4AgowH2Bk68x2zQ8nEQNSAQYMhWtwlUYclqiRx0IaJlb9ANzCrAkLNQ+EgDnHDSS/jP5UoZw3JoaJg9hQZs51IwW65SvvZQR00YJzYCJYyGmkkxc5wSCbNWootFT/drRcIsQ7H3qa/ogjmZQFUirWSygAkCeMjSKc60sGulr0iKou4+Pu4BZyXOQGIByww03DCR50IMetGxPXUwwa50t6GP8FYAJPWqZ9rqto30eTe82fc6973OMJlOA/Jw6e54fbZx0OhyeDtzGZA04jfRkWbitfvtNjv1Wa/1+z/e5z32WbQKLbEK15aKdWsvu8jr+UIGRtZ3MbW1/rPMCcA2YBoCr83mto14DdyvwC4QTYt0DpEywXqX9Wkfc5Fgz4hYhzwFW0oG7NviymZnnNHszvwPRW4DKuoEHiIsmfVFXCyyqz2Yx65hslPIhCwSsa6z3Cji8733vO2gL2wwO8Kiv2Zi1NnLHV8er7+13oV1gap3/AfABkbkXigl9xixWr2+d9qs2meMHZPXcmPEl0ejUtz/4gz84d8011wxaxHidD37wg8tN4tSN13Ot3k0Cnkw5fZ0Kr33ta5fuSdJeYvyEtlfxUtymhD9rD2sba5OLEvXb7KrWTTVvXC4A13PYa+tqgqZxNgsA/mMawbXO47rOobO+7U1DwOsxl2Gb1tXzdwpsQoEO8G5Crem8GwG8doMs+v7sbPXQKbApBQLCjJm7bFpXz98p0CmwewrYzaeFFMZ7XUxomatRsY3eEyQi0GFkaZw4eTnBYRwBF/WdkLqJOV2EWiZ+YwGzr94ACbRh54T4w42A2jLSBFFggb/4TiPQEsKYMxIoaR+hdXXvkIN09CkALk2ed77znUtTYMKd53y+c9Hg2l8Ec0KfgJbRIPacsMbnWfKnnoydlksVTKsgrP2U2ySmlYwGtQwtJtrNtJLS5wjOOdW85icw0xxLWvrtvgUQkgcwk+8qaXPiOYDxnHp6nsODk52G+2kIxItW31za0GzMCfRzy1xI+cYAOuOvrhlYgtxiYRUwRRfzm/eyyhKhzllT9Uifmn8CrJrba3nAq/mxplmT2mCuZb5f87lutc7Nm9K5qMicS/M1m5U5GDP15zDXtt5198aZOT95gW3AWu8k72XKOibtix2Qpo6Mxdq8bu7Puj32XvSN9nPWZbTLIWreNbcRNlFTFnhatZ6tuVdeeeWwZseSxCYiwDljnYq1/biFyydtokXWRyC0g2KVs36OWVnxU9vSNO6e7nGPe1SSDde0voHIQHUb3aySoimqbW0Zy7rg0DuuJbjOUIbbBbzOnJBNZhu9c0LOJ/DNewdtOwDl9oDcuGvikqNaMNGIz0bInLaPOs/nP//5gZ42CDYNQG3vYhPfzZu20fN3CoxRoAO8Y1TZPG0jgDe+aPzo7Zz10CmwKQU6wLspxXr+ToHjp4ANPaZy5v6pP1or/LlFwAQ+7jrQion5KyEJGD0WnJyt3wEcf/7nf/48xn6snLRoanzjG98YzUJDqArITDDXBfSMS4Ro2Fa/b8ozydRnArBgzc1hPGPvwJgc+EZrtz6ntRshJC43gMOhRbR2A6ISyAjh1Tw3mizerQNQ1B+6uGbOK45QzPw0ArB013znufYXbTHpTmue0oAlAEdATdnEtLxoAOU+fYxgn/Rtx6k/YxirP3nGnm2aFm0w5QAmla6b1rXNfm3ads8/PXfumjaZk7fdjnpbQGjbbaQ+8+XUZkzynIT4l3/5l5fAXfpjDrOZlvtdxHVtCFBZ3TGY1/m+bcGrunbVfsV3vbUHWBgfuMmTg9OswdG4zTOxjblYtQDFsvH3mMc8Zp9WLT5B/qlvNHOWzUXgXDZca1v1OgBc5skAf3ysOjSK/1fglbUKzWint2uMOoCw1jrrr/GhH2CUv92Xvexl5/im1S5NRweasW7iQx7Iya9uDdlgttZ+6EMfGg6DyroLYMdDpA/xTwsotxFNO7QFwa2v3CyEprTsUz60oEUu1A1cz6wn7ZkKwFgAbXiAaDPnO1KuuloABAf4TXuJ0Unb2pCGznOCDW3rvfJTfNxYPeF35mqp+g5oLqe/ynnX6PTkJz/5nLMTsuZOfZN4H1rS6jrJgXKD34/x1N/zuj77HjIvBLBfV6Y/7xTYFgU6wLsdSu4E4HUIisXO30k1XdgO+Xotm1KgA7ybUqzn7xQ4XgoQPgC3YYhpA9BwYapImHnpwucgASPPCSMRDKqp/y5GEZ9pND9WCQUELv0DEEQApJ26LmCQCR1AjFVWK/XAmDnawQ996EOH/jgA5w//8A+Ha1pENWRshJFWK5dwZ31VhguFaIvpZzSD8z5ohRkDQDKCi3fIVDMCYvK2MSHTO6xuGcJ8BVhXhkAQ00/3DmmJtpB7gnFAXIJthHZ9F5gPt23Xe8IkgKEFkyLEiyOQ1HKbXKs7/dqkXJt3G3XUOrddn7rVGdrVttrrFjRon/f74wNtO+0PTvtND2PblNb1N2vuXVVe3pp/Vd72WS2X64BSNoO4IYgmXlt23f273/3uYW72L75w61gAk0zoM6+vq68+B/rR/BSy6RiT8OSzoQz0y2F1NIATuIzIOJNfHBrUNC573ve+9507yDsHNj7u/7Rh03ZiwHa0lVtAN3nELH+sTfrGl67AVYM+2gzAV8QKyJgBasBUzy+++OJBhq60AUAmsOSRD9huEzdawhl/+K/IXdKtc4D3xz72sQOgec973nMfQB8fw94r66DU5Z0BNKtLJMD3PRaavWiEp8ED5BsJbWi5zgkf/ehHh7a0v0mw+eA9oS8+ZG7A0wTIzhgTq8tGCBcHtGBtJNPCxzPG1cncdubm8x1wM+GgvbHDecfq8Q1yNaF/+kY7v3VJYuPfuPDqcwOtd2Vs0vTQKXDUFIiMMUdGO+q+nab2dgLw1t2xuRPVaSJa7+vBKRBGo7toODgNe8lOgaOkQHySYfimTovWHydaR8PK71t+AOIq4PWw4+AHWDtzDk4DpsobM9NbLExm14VXvOIVQ5l1eQll0X7BFE+5cyCARLiTn8ZtDhtpD/Fgsqi/6hPLT8tkTGvERipT6jEBV9lN/2g2AYodmiYA8VNHNI+qBivtEHlDY3l/7dd+bVkmZdv4iiuuGOqv2uHRHA5oTbBkRvm9731vEK7qCd9tfWP3gID0eex50qLRnPujjCstD9pufnsHLd/Lbf476TQ73TQDdmXePsnvst28sq7e/va3X/oIrX2PWx4g4boNvFquvQYqmtf54K3th17ZKG3LrZtHAwAqZ6PxZ3/2Z4d1orooSJ34jdRnU/OrC3dMzOTzfNOYFjjNS9YycYcRIDJ12ZympOSwu4DINhcBiW2IPMNCyKFbQGvlrGE0cWlMx4c9wNCGsXW10iDt1jh9k49rjfpM3Qn66VlAVH28613vuswPgOWuioundbyBTfJo5XrHAH68G/A07cdVl81BvMjYpjdfv1VzPAe2ps9T8VVXXTW04zvYNMRFVCyV5paPyyvay4BrPBRQfIp/m1vvJvkA276VfOehNY113/wYhmLjg1LBGF9jEx9A9pWvfGXoRtyP2CiZo8XLRUV4LO64eugUOGoKdIB3OxTvAO926DhZi4XHTugu/iYbPcEPwhB1gPcEv6TetU6B/6MA08MwnBjHdQw0DQJCib9ogjBn3EUAqhJa9GuOpcgznvGMYSx82ukf4X4Vw0sYixsFAOe6QBsntCIQ08wFetP4QBfaNvF/SWgKoBtXB4TEGt773vcu6yNo0vJZF2Lumn7Q4sq18QY0TVp9Lo12kJhgWkNOBI/GS6v9SUgRYmaZ+sdiayHQ1jOmq6FJm5fQk3zRCJdHGsE/fUm5CHm5F6NbDtXzPh12lEPxar5Nrn07m+Q/zrwBKBKnL0CIXPf46EHK9n1caO8gIOFpHnedk3Y5jnwr8TW6rq2Y86fcGAgEEJzTf3N15n7rh7YdXJY+cJewq/lwHSCZPiSOdUrux2Lr8kte8pLBosRzm9df+tKXhvFkHGK+ZQXrdgBgbojaEO1iWrDr6Enx6SCAe+iQfhhDQtwgAGetwcBAoGzWTWO0+e0ANdcZo2s8CH7A+us9S/OnLN4jIaBvngf8zfOxmHVR8gPwx8BxQCI6A5Kvu+66c9dee+1Qhsb5JkE9vnXfZ+tPd5N6jjqvjXouR/J+0Qs/hj+tvAwrmvCK+oifBP6Gvr7Bu9zlLgOoy91Ifvd+DzYygPDRXqfhjV5TwbO4CLHBs4lG9FSdPb1TYFMKdIB3U4qN5+8A7zhdtpaayTaT8TbjrXXyCCvqAO8RErs31SlwSApUs3vM4ZzAv6t5jhApplGyi+DwB/XPFQiAuQETY3rOBK8N8jEN4lZA/QQq2jc0f2jpTAWaH/JXAdh9+4eBd1CaEFM4jH0bMOYBQjDw6wJBtdXijHAf4bztS3sfugAAE4DnfP7J6xTqqqWb8sBspoGEzByU4hmhNDRP3oPG1cXEWB3Rgq3CESHHoXf8HjrIhV/jaJeP1bFJ2i7X9rYfVTBvn626T7mY/K7K25+d/1vtNNkNTebOR2eB/gCcrCVj46GtyQUP0+ysIWP5apq12GGSNEb5Zo8WrDyZB11X0/6UNz9mXUmauAJNNb1eV34AyJT1ouZxXf38zqm3lm81Geuzds4dq1v5bJrWsu06hD8BsjFzb+lRtUgddqYefADNThuFgrhqt+oL1wfOqvn7v//7c6973esGa52pbz3p1sj44a/9bTdR+XAOyEsjFz/SbnIqD/Cr7wn4/PWvf30Yg3cG4OZyq6UdHoR/ZJYyNcRKKu8FT9H68E1+ACvN3owt/Ec96M4ZBXjC1JcxewcpN3XeQdqpMX/A6jCm0xIA3tlMtzFAcziuvbhV8c05uC60Nz6gOZ43PJ6zC8YOykO7aLl7x2984xuHcpkXzBXqr2C4a4fgxlpMG9nkOC007f08OxToAO923mUHeLdDx8laCPLMkLKIie2aYvgO+zfZ6Al+0AHeE/xyetc6BQoFMPoAoghVT3jCE8rT6UuCqnkuQBvhZReBoKAdAs3cQFOkChZcMFRGl6ZE1Wap87ZrQp6DV8bcJACGI1jmgBbmlJhmWlWAbwBj2qNtE00MzHsbALwRIPlRG2uzlmn976XvhFWCqQNEvIuAfq2Al/xi2mICITannxubQ2powd7udrfbt6bVsrkmrAEsosmd9DaOUJd0/RvrGzNZYPMXvvCFQRD1LlImMVq3wnqeTcVj9UzlPQvpm9LnLIz5qMcQIfyo2+3tbQZIV01HtFs3Vx2EvnyVj81n6pLusK4E1i7SA+TV9loNUWbU5kLATF3TapltXmsv69G6erlvABjWfMA+Wprr1h8HdLUyU61n6to64nBQIQeBtnnr5iTrGu4LgGvJxwVBNmi5TuLbln/ZbFqGF7JxGOseZQGtU75ZW6uZ1K8cecjaHFcLZMIpGgOoo6UczeH023cM4Kx155nDZ2li+tbUHQ1O1lgAQpuffL9Ohdr/jB+t0YfvV26W8BY2ofPOtcVv87Of/eyBtvIKtKCz3svDFRTLJ5veSddv4xtz/9D2EVCaw1X5Cz4tIeck+E38wz/8w9Bt3w++Kr+PvL/E6JVNCprn6+gTv9k2YXxjNrjrhgD+/KY3velwyG14dW1pYwrAPy307f083RToAO923l8HeLdDx5W12JGrPgYxFBdq6ADvhfrm+7hPGwU++clPDsx5hGBM/NxQQbNdaVbERYO2VrlaaPtMoAnTLAboMm2LlmqeGTc/uPzkErxp52CyPQeatodZaIfmDiFIPkLmFBMOBI92Ff++Y+Ctwz20FcEHUz81Tgx8+i1OP5Wt2jAA25vf/Ob78srfAgTKA4PTdq3bdQCIaIXkOWHC+87Ykh6hdZMDf4BkVfBQF1qFpjZPU38Ez6opl2+QpoyxAChoxIQ2YlpSuU9d24rTp7a+Ka23mm9Xfapt9OvNwMBOr5NJL6DcFChyEt9ZNvUy5wLQohXqIKT0udXgNCe083E7x/C/u2oDpy2ftg4aaz++eLOpEW3itm/a4O6BX9Gx9lb1eyz/WBoA1Fru2bo51HvIOpa8c+iT95b2lX3961/fshnnaJbSum3pkHVJORZEQFKblxl/vo/U38bpK9A5dX3kIx8Z2qeNzK1WBciZ57///e9fannPcfWUwQAftZ9Nb371Weus+70Bemkw2wxWXl9zEC162ACPxmra4sNfesZrrR5z7ZD8YgfFyc/XcH5D9flJvAasx8eyzRkB4JvvBK8E9Pbt2Fxvvwe/8Sk+sB1vtIRzmK1ygPD6fYTeeDb9mOPurG2n33cKbJMCHeDdDjU7wLsdOq6tBRgR4dZEntNU1xY8Yxk6wHvGXmgfzpmlQLRKor1SzfbXDbpqfTAP31XICcGEibkhB6dhlMcYXX4FmfWP+R8DKnqOKearbCwwm42gSGOC5jPNXX7tAMbK5TkwdMqvMWBZO7RbIjzTGqIVSziifUNY+8u//Mt9pnw0eQMiAgloKQnGE22XCIZh7gnFKZO0GjP5dGo6jeT03XNmhATUtr5a1nX6X9NrPdIJPQGfI+zU/BGsHZInxDSz5qnXt7nNbQb60QgD6hNMI0TLx1VD2qvlTuN1NmEO0vcABrVsqy0YU9uap1+fTMDzrL0XWok5iHLu2OrvfG6ZsXxj89BYvnVpFRDLtXmdBp2y/LML1bQeKLeu3jwPUElbM2nrYmv0QTWWrYE2MD/84Q8PQGo0S8XWm3ow67p+TD0nL/F9T/OQiX/oNpU/NMjzrO3r3qE18aEPfeh5dJv6hpi/0zxNO2J9C8jLVVJA5ppn7PogPnnV4z3bZHZdD13zDaFXvquxNvEkgOA5Qd3qyIGpwMYxulj/8Sa+p/oecihbvg9luRRZFeoGgPajcVzLAINzmDs+gsuB0xK41kBT2rqC375736nvkEJYG+rGj7U4h9+2+dr7uDED0Lfv3MY/HtMfFx49dAqcFAp0gHc7b6IDvNuh46xaCJlhNjBIQN/TEjjuZ7J92D9MrcWsH7J2Wt587+eFSoFoX8TtABBpjuYAhrwKY8DSXQX+7swnhLkAmavaMufGzI3wKDjcLIAWsHRdoFkbIYaZ51hwcEg1B9XH+kdLw6nNq7Ql4oKCoItRz8nGtZ72mhYGDddK/2hvAGjlp3FFcwg4PGbS2dbp4DZ5H/CAByw1TyoY/PznP38QwuLKIeWNMYJd0sZia0I2BAKeEwIdpFbzq8v6SUi0QXpY0JGwOgZu1jb79Y++2xaM73TZ/3vu9NgdPYBgNsKOgsbmA/5B27ZigdCmz7kHxo2BYuY6Ps3VwSSd9YfrtHWve91rsDCobdT5ql4nz9jvdCwtckjaSvm5sc02/ta57FEmwDILCsGGZupa1YZnWUtrPtfWvARujVLfuji0Dn2iAWmdiYVHtKMvv/zywd9+6gxdcq8uh5RZ55PWximjzzaasy6xCGrXxFvf+tbn1YNPeO5znzsAbfyqqj91tm25j2m9a5qr1UKHduZYWZvZdRPWPZluVbB5qw2bs+1Ba9K1U98ZvsI3wRrJJnTlLcKP8M87J9QNAr9/vwXapcBQriCiAYsnsMF8mkK0am32UxjwnaJl3ItMjaW+17nnYagrChqA/x46BU4DBTrAu5231AHe7dBxdi05kdYCuYnJ8+wGdpDRSZ/6u80/O8w9dAp0CpxcCmDUw6QHrLzkkkv2dZgbhyc+8YmDORlNXdqS1cwOcMp9wK6CPgb4pBGxCuQl0ETA4qs1vnAdLGJuw3jPDS94wQuGMqvmMfXzrwgY1TeHuhBOCHOteeJYu8DfgJoOdwNIR1Cqc7E8VYivwliEXQetuCYkVF+BgFJ1jdVb26jXXFXo/ytf+cplWZsBAgHv4osvHgTPlCFMAzCigZt0sT45MIZvvqTTNiF4RwivY4smUQRLwpH2UnZMUzjPNhljyhxFfBjt2130D72rMLmLNqbqPKnvaKq/Pf18vvCw7zAA2Sa0DZi3SZmxvJlXxp7VtE3GWOev2k/AFa26gFW3v/3th3mMFl9bf+5pjlo/9EVfY5FR+7bumvn83/zN36zd3Eqbtb46L2ifNYXn0bq03lv3oolscxhwFRC31tVet+2hT3zD5pAwZfg0bjf/ks7vKCWU9Ed65lcar5G/sqH79Kc/fbl2Ak5ZhcTSMv3TrzrupIvru3WffBRYuAx42MMetlybPG/dQEnzTqORaQO61Q6WZ+zPegokTOArWD7fWNb9gLp4JJvzcZMgH95uDPSztkdjXl2+l7SP13DtN+oQLpvJwPxswHgOtBT4h67vXT1jrqjSfzyOb7/6iU27YzHt3tN4EFiszri/4HLK2PCgq4JvOjTIu8VrzQnZfIk7iDllep5OgeOkQAd4t0P9DvBuh46zawFARIOMIHsa/AYBdSzU/C0e9i8MELCjh06BToGTTQGaExhLwBpmn0DDFJF2aUwEw3iOxQScXYcvfvGLSx9xNHlp0VRNY0ApLZwcikLrJYKjvgUQveaaa2Z3VZ2EJzT5zne+M7vcphkJiuga4d8132xf+cpXBu3fgNQ0dzxrQdR73OMeS6DUcy5yCAbKM6/lLkK6vwi9rglPgPoIE3zCEUIA+jXQDpYfcN0GNKrmhjSf0Uv+rAO3ve1th2L/8R//se/0d3nyV/3w2kBIuhjIkD7WdNdT6W2+Offp75y8NU/GW9Pa6+RJ3D4/rvuT1p/jokNv98e/xV3RIgCR+rOpuKu2Tkq9gCwhgGLAuG9961vnMq+mr/yRugaYCmMuBZI38SZzljUl5VrQMuljcTbUAL18vcpTQU0bqe08gvfPWMfqrGk0WoV6aBrA1oGlNV+uaUXf+973Pve+971vud6nff5agVzJK161mWA9VF/Kyz9nTQEIZwM3ALiyLV2tfemLMT3iEY84F5cGSZ8TA9OdEaCf3nk245VlXRM/+TZSBRuz9dsAONoktQlswyD9ZKUTjWIbtLGyUW9ruQS4Da+Iv8q6H3/MynhnU4HbrMq7uI7f39BAnx1We9lll51qlwIBeNHQOzPWVYoJaMa9VugQ9x82MeaEbAJx+dJDp8BpoEAHeLfzljrAux06blSLk2Qf/ehHD38X2mmV3QfvRp9Kz9wpcKwU+OpXv7oECAM2YjSjbUNA4tsUgw5EjZAXZhQzO+ZjlhYp37QWcpq/TqumoQroO0gA8kaTV9sESEIJ4TlaJ9IJHMaU4BAPQhtGu/W5C6CkOTEVwjgTJncVHJ4SWor5bxsL1hTPW6GI5jAgtwpPtb5c066JMCeNhrLA36B7QtVYoJ1N8EJj9FoXWgGZWSeBvGr6pE/eYbTbktaWT3qN6/uu6flma9pBr6vQf9A6tlFuDj220U6vY/cg51mh8UG+yVWA30n5rW36fuqG2dQY+BGNWxr1mwf5xIzWadpMefM7SxMm9nl2mDgape08u67OOjZ5bQBz5TNWzvqQ/rf9Dpif52296rOGBXQcq18aoHmd9vU3v/nNc+16mvqUt2HdHg46ZxObpnFr5eg9coP0rGc9a6BJ+l+1YfV3aq1Kv6bi0CtrWuqvgHXc4EXm4ooqgWXRVN3S1QNMdQ3otSEcn7csa8b4NNroNoKVsQHx4he/eLgOmGyzOQGv5TA4wH1cCCjn/dNgtsHxzGc+cygP0NR26rHxfJoD9wrGmk2cuWdU5FtxFobyNsXWBRrWAfh3aUm3rh/9eafAJhToAO8m1JrOe2CA1+EktHbG/qqJhclrLE+bFgf1013tT84CBcJshPk4C2PqY+gUOMsUYC6PoSRURPvAvT/MOBMwv+sw4NJp7sRSwVqB0RRsaEUISB01Jujb/Bo7WGMdjWmR8Ml7k5vcZClQqlu/CQn8wrUHTdCckIcQIwBDL7roon0CtmeETAB2PdU52jatNsu6fm76PJrHBEKHzYwFmrwVFIiwa+yf+tSnllrKTFsdhOLdRNMnGkcBgb1HGkEEiWjtqNuazedia2aZOR3IfN111w3vzwFn0r1rmjv8S+pjBPiMqb77XEd4zf1YbHw0UrjdIEi3eSLw1vSxtPq8Xht7fDfW9H599EDnJu+tv5+jfz/raD7n91zrqOtITR+7jhuXsWcnMa3Sol6v6qvvfywvYJhZfoCftg4bkJlv86zSy1pLPsuzdbHNuKk8bf/iSkd+G621HMApGsnSld0UYK71pY6kTc0XAEPrWvLV2CYtEDOAt2ct7ZI/B4+5X/Wt2uzOQamhewXzW5rZGGnTtCG9WrGkH4nHxvu7v/u7SxdUXFrIa22uwXouHUCdusZiaz+rn+r2Qp3W3TbYuFcHXiLgcw67s4GM/3Imw6rxtH3gxkDgPiPv5K//+q+HtNP479JLLx1oFFcczmJYF7juyMZZ3lv8Xa8qi2dFT++rh06B00KBDvBu500dGOBtJ+HD3nPo3sPZp0DAgA7wnv133Ud4diiAKa/CDAY+DGed++UB0Ap8qkUodLjGFVdcsTTNxODTesGoS3/e8553jsliBJxf+IVfWPqlOwgVMcTcSDioZd1hItEeaw/XIThH+yFjpDVBEBRiVkyImzKx44eOX8A73elOgw9eoDDtlrnmcjfccMOgvRS66AdByfugAUPo4Z4hQpznBCsCXvpMwM7z+G1jQpqNWGakNHKSf53AR8vpPe95z/K1BCSI6WDqaWN9CPAMAPY83xBh5xnPeMZSk9emAS2iKXAjQp9OoIW6APttm+19pWP7rN6jYQSwmt6vTx6A2N/J/HeSAzNPOs2qT+6T3td2Tqkg3qq+K7fp+zAnOQiUqxybjTbvahvt3J1DxWqeg16vm1/N1XzSr6of0Dnn3fLzvqqesWfWBBu01jKbtGN5ojHcPmNNFNAxa1Sbx320RwM0Jq3eh5/wjJZmtSyqdQI+WT5Js6ayznFdQWb3fPhXzdyAxbWuXOsHn7gJNsptUPvW4uc3z7isSDnfrAPMHAT+pS996ZzN2mpVgxdCX/nzfeuHzVsuH/h0xssB0Wv/0DUapzZMq1uKtC02Zpu+Aavxl6GptmPpysorfZhjMZSxnqTY9xnXJsbCem1diO9o+fN90nxeFVjOhf+mTd1Dp8BpoUAHeLfzpjrAux069lpmUqADvDMJ1bN1CpwwCsRkDpPZ/hEgaIcCNGsAQspLsArjz1cbAHYsECxixslEsNUWHStz2DRgcsZDGOJrto4Do/zCF77wXMwrCYKvetWrlmB0BHxaSAQYQpsyhMwKiqeNxIBaAvqqAMCVHy24TWhdMKQuMbAgh8hFeyY0j4YULWYCGT95ytCkcthH/BCnPsCAU6tpX0sjlGs//iLVrx6hamMRzAik+s0klF9jgmPeaerXr9BNnd/+9rcHf/Sttlfyt7F3wGyTRlbGko2CVTRv6zmK+wit22grNNtGXb2O8+exTpPjo0mAo129A3NODhya00bmzDl52zwBYZLe3id9kzjzGrAvboGYvasj833V5j3KuSLtA/xoJE6NK1Y9eW4DlXVMBUXzrI3TRpuee/QBeoVOSRdXNwDuzcnZ9Kz55ly371J7q+b4G9/4xufRI3TIRidwM0Bw/e5cx6Q/fbMuW/e4xMgmrWfZLM3BYyxm4g/Zuqx+/nof8pCHDAffpR282Q9+8IN9bIjNg9DbO1V/7p3Jgtdb930BJbm4cpCd8skf4Dj3GVcb1+f4hwDUrMPkfcMb3rCvz6fpxmZ2xov/WRWyge393vnOd16Ww3dNBe5IWGppA3+bsxqm8l8I6WjgAEDfI210iiiAbz7PezhZFOgA73bex/+nmsUkMCsshN69xQ7prLybZlr4RtxbLLibFuv5TxkFFovz3kL7YG+hwbu3MDc+Zb3v3e0UuHApsABm917ykpfsLYSGvYUm0d6CidxbMP17C+BzbwEs7i1M+UaJI+/igK3h2UJrd29x8MdoviQuGK69hbbQ3sJn2N5Cq2Vv4eM3j3YSLxjlvQXIubcQ3PYWgOswtrGGFgdi7i0OJdu78sor9z1GgwV4sLcQkpbp6lq4pRjSF9q0ewuT1b0FgLG3EHj2Fkzm3uL0673F4Wx7C0B577Wvfe3eQmBals3F4lCUvQUjurdwi7C3ADH3FuDnUMdCwN/Tl4WG9N5CsNxbaGkM9S9MR4f34f08+9nPTjV7C+Fub6FhPNwvhCabusP1QqjcWwC5ewsN5L2FQLfMvwBB9t7ylrfsLbR89xZC4d4CZN9bMFzD+11o4ewtNHz2FlpkA73kWxwAN5T3zhaa2HsLzenh3S0EkuHbUN9CY2VvcYjM3kKTe9lO7csysVwsNKr3FoLd3kKgLanjl76x66+/fni4AJX3FgfG7C1OXh/PXFIX5o57i02IvYWW9966/pRiB75cCLfDNzCnAu92IZTMybovzwJw2Pc+9z1ccXPQ9lZUubVHfjvmhR46BU4DBaZ+S1kX5o5hAQTuLXzGD9nNT9aqxSboMKcvNDCHObXO3XPrPUy+hWblsO7U9e4g9S1A3b0FuDisaZ/73OeGNa3OwdbVhUn/MC+7tg7Jb21bgI57Cz+us5u1bi/cAM7OXzPq50JbtCatvNbHheXNQKPM33VcClvXFpvAS55hAUAP62pb8cLNw8A71e9p4TZp4FPwW9Y8fIFAtlpsqu49+clP3lsAuXuLDdBhLW7rbO+t2wvfxPuSfVN4GjE6LyyQBp5gX6b/uzE237V1B8+TMSdv3qN73w76LDZ083hoB/+Dt1hs/O8ttIj3YA2CuuECC/cSAy/k3S8UDfYWVlB7C83XvYVLrmU9p+kCD+abfOMb3zh0e6HlvOf37LsQPMeTepdvfetbBzoszkHYW2zo7C20nfcWYPdAb3zOQpFgb7FBv4ffwpup87nPfe7A0/iuFtZie76ZCzksXFXsLQDd5VxaaeEbWwDnA58Z+tfn/froKbA49HNvoUCyt7AKGGSPo+/BGWlxMZH00ClwZBToGrxHRureUKfAVikQE01atpuE+OOjdRlfvOvKL4DDQfuAJulCyFiX/cDPaa8ulvKldskjH/nIybr4j6v+95Tj44xmgEBrl5ZrNGkWQtmgyTpWIZcOOXWa9tJVV101+AimXcOkkbuDhdA09E077R9tN1rEU+FNb3rTeWXaOnLPFLIejsdVBc2fMU2olMkYaQZJ09eqCZ18YvXQPOGzb8r/8qq2al31uqVPXIOgCdPUmrdee4cL4Wp4/qhHPepcNX+s+fRpIYwu61kIAsvrmm9X1+vMnXfV7kmsN+a6J7FvvU/nz0+b0CRzyCZlTmreqTkirhNYG9BY3aT/C9BhqY23Sbld5TUnLjbPluucdmii0iZt5/GMe25fUr6lY/y4OgNAcOjnujoXwOJkHod70ZlwRisAAEAASURBVIxsLWK4VOBuIP3QhnVcLI05PU3w2vZUO9GQTflaZpNrrg2i9RprGXTgAktI+w6QVS+rm6zP7rk/ePe7333uM5/5zLl3vvOdy8PS0gf82ViIxRJLpPZ9KFtpZKyLjd59afKMlUu7Ke891LAAKJc0l5cPYPXQTqdpzJ+/dOv4aQ74yWgjhyb4Ohq9ed/S0fZ1r3vdMFSxNN/guu+KG8RoPZ9mOh2m7ywAFyD48vdqjuJmZbFRMFiW3eEOd1j6MDev4cN7OH4KdA3e7byDjVw0bKfJXstBKQAc4Tsxfwet5zjLdYD3OKnf2+4UODgFcjAWH22bhJgecl+wSUh7mwLKm7QRkBWoGUEJQzjGGMfcMcw4JrwNyRPfvYQzfnSnQhiZ1DkVc7/A3QFhKL5/5eXPdyzENYa8C43jwT9eTsEm/AEZAKIAaiad6gKiVcGw7Qv6TAFtEeQwyWhJgF5o7A5CXwRigspiZ37JbLf1u29B27E8NU17zEUj7DjARAAmp081/66vI7Tuup1N6596b5vW0/MfDszs9NtPv/ii3PXvBjh0kPkggNppem8AsfhF31W/bZBVEAq4aCPWxiUXApu8T3njKzT9BXwFIJUWVwbmMfxHNug8s5Zl7Xbveqz92gb/swLXS2mzAp1jwLRNUBvC9Tuy/jiEdqy91CuOK4W4E6rPpq654uDDNq6xuJRYWPXsq2thDbV0b5G1u/YPDfEMXHpkc915CNpMfu2otw0VGLMuB0BWFm9mE5XPXsBuxhA64H9udatbLdM9zxqdvFxUueZT2rez0CI+d/e7331J38rnxD+yA23D2/gmTnvAZ/q9okP9ht3b2MFP5t3YtI8rEzTjRuzhD3/44GrEb5F7MRsh97nPfYbD/U47bbbR/4X130BbtOGOIcoYte6FpeA5QC+aoy//5j0cLwUiF53mwxSPl4I/ar0DvCfhLczsw8c+9rFhEjIR+TuNoQO8p/Gt9T53CvzY1yqmfpMQLZlV2rFj9UXAIEDtKgBgzaUOZFuYcC4ZaILR3e52t+GANLv9BI/Mu+Iw2tVXL+E2QAJhJQIMJnwsONV6k0NwCJMPfvCDh37e+973XgoEVXM17ei7fhIQa6CBJJ0gxn8j8DyHuzgALsB0HSt/gQDbgN40gSKw1nx8F6NBG/g4y8nPNb9rYwot22cHuSe06msrLB2krqkyBK9NNKTVs8v+TPWzp+8HEzs9jo8eFXTKnJe0FvjxnmoasK31EX5U7zKA1VR7m/6ubXbxUZs1MfXe6EY3Gg4bXZicD6DOWL3r+pK6jjqOBjYf6AL/+g6D0w/+Y2klpk+AOmsSGpinF66KlgCWg1aTr92QClDjOUAy3w4gW+Dv3bP63bgPzfgjrYeKWfsEYHHaVGc2CAMo5pnYvJ/NyqTTtkybLage4O5Zz3rWcJZArGTS99SBfvEHrP48BwbzZZ/1kVWTAPDjSzfl58bWdnQPX6VcQF5+dtvgENzUTau2Htwq3QYuwIwVTPIl/u3f/u2BV8m9GPCYsdV046t09e6B0ja05cs7dI0X88y1DeuzEHy7+CBjom2NrvhHALoD2WgsB4CUx3vpYT0FKDagl+9pnZKIDYYoe1BIGQOC17fYc2yLAh3g3Q4lTydKuJ2xn7paOsB76l5Z73CnwJmhAFARw7TwC7bRmCKsEhI2CdGGfdrTnrZJsdl5mW8RHvQvDB1t1tYNgzHnj5C08Im2PMzsXe9617K9gH5MJIWPf/zjQzkCqbZqcAgGZl69EXpoGbgn7ESoS7vrYofILHz5DpowzDHVibH9xje+UZsdrh2YVgWqtL+uDWWA1v4i1NYyhPWp8MMf/nB5cvRY2dQzty/JT0B12Ew2DpPexuoNeD3VxlR6W5d7tK2C51geaZvUOVVH1WSbyrOL9FXvaRfttXVug3Ztnf3+x3PZcdIic18LdF588cXLuTb9sxEWE/2kTcXH/c20c0JAvTqf2+hs++/QH4HlQcCe4/796WNLT/Ne2/es00A9gnlAwzZf7q2P3Bm14fGPf/x5dafMLW5xi+FZ2x9gMcAzspG5MvwGNwYBA/XLAaOpz71g81hatRyxlo5p8KZs2wf3+gEkCqgrb9bYL37xi0NboVPqmYp/6Zd+adnPfE/AUfUbq01lVkHM9cc2WlMvl0l+TzanAyB7Vt8PXgC9/H33u98d+ukfYDHa9crYgAhQr83U8VM/9VPnfSNpv42rpq9nWY+TT71+BzR0F+eznAOMe1Y1xdE5rosWZxMs+3vaL3wjvtfQYiy2EbDwrXvah3pk/efmDB1pwM8JC1/Tg2a6MnjpHo6PAh3g3Q7tO8C7HToeSS1hYjL5H0mjW24kgjj/QD10CnQKnB4KxDSOBtbi0JFZHWdSl/mKeeMmgS9aZQlmuwhAR/UTWtvw1a9+ddDmoU1B6IjW2dVXXz1kDQhMuyLhSU960lAfjZYEgpE2Wq1n85/0CIGuW4GH6wFaxU6L9rz9AwRE6MozPm4DEBCYp8LXvva1YVyE2ZRt400BBqBNexq39r2/Fshp2zrMPbBcqFpebX2EXGaqSa90T9ph49ArAMNh6zvO8mdhDMdJv972+fMVmlQgbYxGFSDL84BJuT+LMaDN3NnO55uMdRUouUk9q/IGcKx5aO7W+znXLF1o0ZJpFgdcnqOdOnfOGdOurW2ah2360YCMpuvicNjBtV3yLQ6FHbRjWcRIs85HNkmeObE1e3FI6bC+P+5xjzuPDuiFV7A5XkF+dQcoFv/ET/zEec/TPh4CUNuOO+WTr8Y0qAHAtKkFG8wve9nL9gHQQF8h7i+qT3+a5OrjZiEutlI/6yCbyIuDu84bb/LMiQGW0fRGOxrWY3XWccYCCS0WB80O/T8r/wD4gG3WTr4HPAq+iZuYSy65ZOlz+ayMd5fj4MbSN2jDB58/N8SajeVBD8dHgQ7wbof2HeDdDh2PpBaLNMf6+TuSRrfcSJioDvBumbC9uk6BHVMAAxotg5g4rmoS8FYF1mc84xmrsp/3jEkkJm1TYPi8ilYkBHgc03StxZKPZosQYJSGEADYIWLMNfWXcIlWQlwlVG0TGmnyBey48Y1vPNxL80fwAYwLtApudrOb7XsuT3z5xXRZ/1Kf57SpopU8VLTiXzXFjLYMAYrbiircEVYj4FftHu0R2MUOqEmwXrU+d1cBq4Rl30toq74q3LnPXwUDtM3vL03pPB+LAe8BYdvnU+20+YBNhK823X3rc3AsT0/78TvstOi06N/Aj74BwHbm1koTa8G6eU3+qXmt1rWr62wopn5joY1Z5+g8O2jMnQM/9MoDw+POwdwP2M66Qvv1+uuvH5ag5DFnW8fQsm0/8z4AVbCJDVCb2lTwjmJpoy4A0lTetq16z50R/givkL7X51PX1t2x72Qqv3TjrmVybWObX15asfLFLzE6ALikAYVpC9cDVJVH89BuVdv12dj3ANiteVzz70s+DG9Tn4fH4YO2h06BKQrkkGb+uTcJNKl9b9yS9XB8FOgA73Zo3wHe7dCx1zKTAh3gnUmonq1T4ARS4NOf/vRSUAJqjpla6jatlhxqEkYdMEarZk6I1qo6ApbOKbdpnggyDh5ZFWImT9voU5/61MAErhJwHEjynve8Z9BSwjASlBIIq9Ji7hoz0ggy73jHO5L1HEFQOjrE3Df5VsV81QkAYuD1qoPe6knO8SNIe9qhM9oAHr/iFa8YAGMHUoxp2aQvyiUQXpM+NwZc57shSDuwZ6xsK7SO5ZEGHG/f02HBkIDgbZubCOpt2X7fwc7+DZzdb8BaaW5s56LDvvOsSwepJ6buY2VX9bNu2tayNFQpnyRwrVSft9cB65JuU9EmHLP/pInN9XgA7pD0y/zt8J24+bBGSree0tyzgVrXtFpXvQbiVHBW2+E18Ck17zavgd814Cm4r0IvG5VjbQHRbRZn7coGawVN885yMKD7rEny2YB2HgAf+gF5aTCH/3jgAx+47NZNbnKToR98wQoBy8f6VtPSP2m1bzVPvfYO00fpv/mbv3kOyIZvQSdprJJqmVss3HX83d/93Tl+g22q6qvzA7gO+/znP78cQ7+4cCkQ1yub+ml2hoRvDe/Zw/FRoAO826F9B3i3Q8dey0wKdIB3JqF6tk6BE0oBglbcCRA0CK5Oo37Na15z7ulPf/q5W97ylkuGnL89h3PlBGamiuvC17/+9aWG6mWXXbYu+6Gex4UE0DKHiI1VGHCVwBPBEiPopGeCBc3V+NiL0EjYiZBSffUSRiKwELoIVrknPCcAzwnNBLUPf/jD+w4zIdhHazZlmS/mVHDl0DxCnzyAWprXBOAE40l5cQTj+MtDF9oQ3gOz1qq9VMulHdo3gvHW5znMrqatu6bZHJpO5U29LViQ/A6UCX3Txzw7TEwjLNrZh6mnlz27oN6F9m6nfoNTdAAyVUBoVb6pZ9tKryDTtupMPSwSaJYGVEv6pnE737fl1b/NOa6t3/2qdwzE5iPUplrt67p+pc+0Qsc2z6q59BOf+MRhXVEGb1H7SAP1xS9+8T5gMWuxesfo31rPPOQhDxkshi666KJ9dVtPsyFb2zzItbUbEN0GoKZD0Ob8JrQ71R9uneKuiGup73znO+dZ83hX6vA+4/aAZnRCLLVYT7VAfXi/FsCNz1P1cjPA5QhN25ZGYxrp1tNaXzYualrqGXuP9Rlt7FUb2hljj88uBfwGfBN17pgz2rh28Lvp4fgo0AHe7dC+A7zboWOvZSYFOsA7k1A9W6fACaaAgz74iA1j3ca0bviAJbQITgomJMnnNOYpMJXWa/zd8g33P//zPzunQg41oRkSn3VtoxGC4hrBOFrfwMwd48qhnjhNSKn+4qogRGMl2jjqDECqfcKqND78BMKze3+ELLSJQEyQFd773vfu00oiwDFtTT5lCcd/9Ed/NPjlC4AamkegivC4zq9jhPP0i/D1ve99b6nl6zvgfzjuK9r8ykUITx1TMTPRFmAmGI71MXUCpbmKuO1tb7ukXb7DVWDFVB+Sri9+A3OF8ZTrcQd0z+I3cJjf0mmnR+sbdWo8QOTDuHEZmzun2to0PRuYc8pVP8m1T2PA3Vh9WVvGno2l0ejl5ze+NK2DWadq/rqOSo+rH+tD5mlWJfzot24laj2rrut4x/Jlo8Aaw59nu16ljM3X6q8efxCNamvo3N9TaFnHbhMdbxBN3rFDah2olr6ElnUTmosnzyuIHhpGw7p+91lvU6eNZuH5z3/+UA8+IM/EbX4awvzP8pUfGtb8ru9whzssNzCM15kHV1111Tmb1Cy+bOamrO95ndutoYP935mkgMOSfTOUMaKVP2egDldTzjzdw/FRoAO826F9B3i3Q8cD18LklcmMRYrjeppan/3sZwf/TGFoDlz5CSzYAd4T+FJ6lzoFDkiBz33uc+f41nXKM/NC2pL8zQL52sC/XTQzAJT3ute9Bs1Qmr+EEABrmHpmeWN1tHVu494cHICT8ELQaIHlgK3pH8FlLHCNII/6orVMWKtj4Rcs9cTdQbRSCJAxc0Uf+eJvrvqrI0AKOR3bYSXMFqvgdKc73WkJsMsLkKShFCEIuJwDVgJgp181jlDrnbWCWs2X62j/ur/22muHfuVZ4giV7r3/pE/FDssR/uRP/mRtXoJ9xpiDZIC88ek81cZYehW0846SrwrVSRuLK7g+9rynddC3fwNn6xsI8Oa9PuABDzjHLU+1/NjV+44Fxqb11/6uKsuNQQX22ryA0zpn5nmd75M2Fptja1+y9sirDjQMDzFWXpo1sAWs1esdBOz50pe+tPRtnLU/9bV91Ye6sZt8Y3H85cdSiQZszYc2AUjrIajAS/m4scphrbXNjNnGYugLhHXIq3KA2KwznrOCyrOsgcMCWv61gDzrIRrNP/3TP7200Erf9Zl1Vu7zXgL24yXyTPzlL3950BzOGF7+8pcvwdmab+61b66uv/re8miGpt3ImPivuYcBF7L0yzNCAcoSvi/g/9yQ368Nhx6OjwId4N0O7TvAux06zq6Fv6UXvOAFg9P6LNpTixxGw2LF19Db3va2JXMyu7ETmDGLbz9k7QS+nN6lToEdUwAgTCN1as6LZgZA7ijD1772tSVYqm8ECMyenXxCV+0vLd0cttb2Ub+ri4oIQtX8Echa64sQlLxATCFAsLlfqPUSuAirEdIIeBH8AjwS1vhMbgOhM1qvAR20kfbTN/cxta3AcZ6viwmcAIH0p+avIIH+VLcV8gW4Thmaxvzr1Xfx0Ic+dPBdaD1NPjHBLkL7z/3czw3Dr+B4NJHE7ZhrPa5pHuf95FkVNJO2SUyD28E20STepOxh8uInpjTKDlNvL3u2wMn+Pg/2PsfmEml1w2subTPfjs2dc+vYdj5z8qq1+yDtmYNtTgb0rHVkPbNZx49/nXcBizQ2P/7xjw8+W/narGVzbc77nd/5nX1uibIe8isvHzCYT9+UqXH8wcsTDVvPV2kA2wS99NJLzz3rWc8aNr9rv5WND37vlq/ft771rUPb1nLAc9xDtLTWpoNTWekYlz+uodTJDy16pO+0d7kpyBpTLYgy/gqkt31MPYnvd7/7DZvU/+///b9lG57RUhbnXbn2TvF53Fe5z+Zs3FVJm/OHf0i/shHODUe+lTHexthspvumtAEsbwMfv/w2oxH+R91cUjziEY9YHtDXlun3p48C3Ir5BvCC3/rWt9YOIH6m8Xtjv5e1FfQMW6NAB3i3Q8oO8G6HjmtroZXFL9I6UHfVwsccNcL+2gZPaIYO8J7QF9O71SlwhBRgpUAjhKYLzV++55hHTblIOIqucbEALLzRjW50ngBCoKwCPG3Q173udYP5f+0bLdlo49S5nCBBExfQSwunPnOoWoBabRBqzPPxk0eI4zdXesBJdIugWOsau+aH7Nvf/nbt5iAkpi1l9CGC5VgdSSOUEpqAq0mbG1d/hznERVl1xaRuqq5o5OY54ZaJK1NTPqCTLjauaE+LI4SGdhFsCYrRQKrld3FNmyTC9le/+tXhXfze7/3evn7vot1aJy3tnJhe04/yOgL7UbZ5obc1R+v+rNDIvDDnGzMHZD4YG/uqZ2P5N02ra8mmZY8r/zqaAPmAjAH7xLEQ2bTP5CRunFihKGtjz0FkgFOWKtUixmSaNRdIYy3J/M/H/lQAlmbz7p/+6Z+GNTfAuja9I+6N2u/JOpbDT7OWbDI+60583NKyjeVSrIJyqFjWyPx+qyZufOzyqa+fNu6e8pSnDLSKj1z91H99w5fUUN09ZXw2Xb0v7qWqi6mMTX3XXHPNcs1SjpsqyjrJkzh91hd95DvZO8zzNra+26Tm9isAbvKkL/gyWrs5/LUeRlvH5vp973vf0BZQPMoC+Ls//uM/HnXvkbZ848DsuYcBt+32+5NDAd9Kfku+qakNAfkc5ut79lviv7eH46VAB3i3Q/8O8G6HjitrIYTmJPksJCYSJjG0eWgvEeYJfA5uwazQHGMq0wq2JqF1J76v7MwxP+wA7zG/gN58p0CnwFoKOGzh/e9//+Dj7frrrz/3gQ98YBAYmMBWH7oECAIabZtojZjjab4STCJsZd4fi+WLtm6EJYImzRf5gd8RWCM8txqv8llTaCLFN/Iv/uIvLjU2HUYGVK8hALKyj3zkIwdfuWP9k2bchDtCscPfosGkzZRJ33IP1M4YIkTmWS0H+PzKV76yzzQ3+aZi6wgmsNK3AhARMAngWUO5gvit3/qtob/RTJ6qf126/rdjSpnaj6TFVJe2ED/TNJvr95J8Zzn2HiqAcpbHeiGNrf6WL6RxbzJWv/s57mU2qfMk5fXbBqYGUNQ3m5Tx5bppX22gAtlszrXrirrMI9wCfPKTnxyWtPjbZeUhxEUDTdJVgXav+mwuC3GL1PbXxkHSXGdNGZvrk29VHIA3a4DfEJkP0BTfuVlfAKTqIg8mAKuk0U4MEGyjXBqQPT7v0webew6D5YNYPUlPzI1IdWdAxvQsbo3yG7fuZm1N2YPE4XPWlUWDbBIEAM+ZCTSZV4XI3PgW7g4D9nl/3jP/vdxz2fx+98I9IkvZrE824b///e+vqr4/OwUUYGnHist35rcKX+FCzrkQ3j/XcNFK941zsdbD8VOgA7zbeQcd4N0OHSdrsUhUjS0LO0Hz3/7t3ybL1Ad2HYELfFxm8TFZ2cU+jaEDvKfxrfU+dwqcDQoAEml3fPCDHxw9yXpqlIRX8y5g1Y7/5Zdffo5FRSugMCukXZsDPggWEQaZkTIDfNnLXjbM6dVcPgIPTYPWhUMEvQiZidN2zBeBqbSeAnoSbpmmRUOIiWQ93O4tb3nLsv80WZk/qrOtnzaVPtNYpjlU+93mTZ82jQmNrbCccY/RudYfsDYasnmWdPc5TZkJbHwVJl8bR5hdpXGkTMDrlA8tWgHYug1cl28MrEj5Hs8z3b3Q6BR3KhfauM/KeM3HQIXMC5lf5oyvnRPnlDmuPHFnMLfPmd/beTv9b0FK6471sXVbAQDNnO6QLgdrhtY0R1dZBUUTluWOkDndXM6Uf+4mbfqcmEUO8BhwCFiVPve913zWDq4WxPpEWSghdHvJS14y1J97IBVXgEDb9GdVjN9ow4Mf/OChLAAMKFzLZ52raWPX5i1g8iZWMsYOrAfs593E6tVBvbRxs7kcHqvte+5p4uoXy6mA5Da6ufWYCs7D+cmf/MmhHO3xHk4/BWxcOFS44ift9+qbo03fw8mgQAd4t/MeOsC7HTpO1pLF14Rih5UG1EFDPUTH7uRh6jpoHw5brgO8h6VgL98p0CmwCQUIO3ywRhulMnd275/znOfsO4xsrG6AsHK0d2sgZNDIxBzSJrIhVwM3AsrRJG2DU6srCBkGlCYPU8IAtbW/ua5AYQ6GACDH7P+2t73tsjmaUNEKwugmADvTNzGLEvFc1w/pSzQgcj8Vb2LOGk0qQv0ll1wy9Guq3lXpEZYJm3wuEu6iLb2qXJ4BIeYKtDT0ImSnfGLAfq4JkO4PM67U1eMOCp/2b2AuIHhaxxk3APo/dy7Z9ljrplyte9P+BDitdcy9zvoyll+92Qitz4HG1ueAwfXZ3Gs+86P9mbUvcavBG3BPe3UuH+vbVPuART48HdZqjo/mqPwtvQNe2kzUl7hqSt0sa4S4JeBaISHfFQ1U7g1SJq4LuIOShmfhDovViDWqtgEwjwuD1CuOC6ZHP/rRA38DNI7slnbE1lVAcvyX1meHubYp7aC4yh9xbeTALPXa9F0X0E5eylFi3xhXHKsC/o1mcN7LlVdeuSr7Bf3MN05JgLa3b+2lL33psClyUonie+IXm1UBpTt8NmUMv9OWbz+pY7hQ+tUB3u286Q7wboeOk7XE+T//RduYRKrvJAL6aQthEmis9dApcNYoQGOfaSKNEEwx/6lzHPyfNTqclPF85CMfWQKXmHzaroQhB6ZUQYe2UOunro6B5gzhDLC6SiOolnFtU067DhwZC9w/xJRQvlV/BBQuBiq4O5afYNq6YyDYAFKUjf9C41WeEFVBFv0JMDpWf9IcHvThD394MCtNWhsTlOfU1ZbL/VjZVkhO3l3GtJ+5qRjrj3aN0ybCr/7qr658h/ISIGlXxzx3k35Ptb9JHT3v6t9Zp8/Jp08OUdzlu5oCFf3GV22+jfVJfqDCJnPXcf3Wow07No6xtGxM5lnGWOkX7d7kEce9keupA7gArhVUBtYxuY/bpLhmSL1obOM0VjAB6jyn3cv6JqH1wWs9bGluozQao9kgtl5W8DdtJ77//e+/z8ol6QeJuV8Q8JTKA6sdCMsyyJrtz5gCyMoDzAdcBdS/4oorBk1im6ZZ53O46pi7P3TJRnHb59Anm7Vow8I0tEl+IJp0Ck7tMxrY7TfDeigbxWmDC4560Nzb3/725XfyV3/1V3mNk3G0dmOiz//8VKBpjbeqgHJoqRwlgR5+RAHnCXFnMbXp4Zu9+uqrO7k6BQ5MgQ7wHph0+wp2gHcfObZ/EzMjO6HbCN/85jeXAuRpNCnoAO82voJex0mjABCX8BHGN4yuGMNKKFxlGnbSxnMW+gPcDcMO0OWa4X//93+XQyMYOcE6ggVtFC4cpgLTT++TyeXcQEtAGRoOqwLhhSAS4bh+P1XArelT1wSoly60KWi8VIE2/eciiJZPhLypelalE3CtP+qvLohqGeaQ9Z6wjt7cRET4ArJXIKDmb68f//jHLw+naZ/t+r41Cz5Iew6xUU48d8wHaWeTMujvu9+kTM978gHQs/6OAIIVvJsab8CiqedJD/CV+23G5o5VgOC6ttZt6E2V33SOyToD0AKGTtWbsVSaraKzdSA+cWud6qmbrJ6N8U/SMxZuExKsB57VNVM/bDraaI/rIqb+mb+5Hkjgr155bgSsY1mrpflzMCbFHNf8wNJYjA/3rJ2rxs0HfqxFQrPUXePQUXsZZ9YK+WjfCvG7r7/cDngGTGOZopy+3PKWtxzSa/31GrAK/I4c9rGPfSzkGGI+S6vGsTpZCNnMDS1qfa5DAyB+xuJAOJq9Y+8T35CD01IX7e5rr7323MUXXzz0P3UC2JMnmzoOWwMyrgpA6vAfeED1kZ3bwHy/9gUdjaP1U8zNhI2FCz14R/ldedf8ITsjgrY0C+X6e4Z5VH77QqddH/98CnSAdz6tVuXsAO8q6mzhWZjQOTuOc5qzc5vdT6fKnrYQxqJr8J62N9f7O0UBGnlhyDGIGGJ+y5jZY8rD3GOImLL1sHsKEMYiEDh0ZMwMkcYODRYHkCQvIYtbhbHAFQNhg8Dg0Kx1IdYWtG6iNbuuDEHT9+SgtIMK9hGIxBhu4ycY0zCVRismgqR7wC8fw2PCWK2LkET4qWXD7CdftIZyX2Pl0dq6RbiOiWmbJ0BDTWe+StAdK+P31WqAtbSzDvs90nQK85j6I0y6Bzzn5PY8P82xd8WfYTY62rHUQ5HaZ/3+R0BPp8M0Haa0uE4bzcwXdR7YRf+Z4de587BtrOvvpm0x19enqXUg7QFu2r5XkLV9tsl97XP6EXlHPQBIgQWENvFU1q4AuPJYIwBLr3rVq4Z+erePecxjhvXUGPhy/fKXvzxow8r/4he/eDjk2vXY94wu+AnBIW5j65Oy9Y9GKuutrEM5KKzmGbs2prh4iOapPgPQaC2zIlEu76JagQCyaQ/nXZjz0QX/oZw6ApBHq9YmcIIxxpoI4BqQs4LirT/esTGsSqPx29Iva5D3bOw5NE494Z3H6jQG6/nYeTYvf/nLBzplnMbThv/6r/8aeHV1o5NN+AocRzu80ljfgfsUBrjfcEjbhRJYheV3ZjMkBxrW8cMn0DHfoE2YHjoFNqVAeHRyQg8Hp0AHeA9Ou1klc/o5wXEbgelDFrtV2mbbaGsXdXSAdxdU7XUeFwUIC2FCaRuEga79Ae5hksOUb2uzp7bRr/dTwOEq5kn+51pwl3BIIyXzaBtj4gHxYz7Oc/AHRvf1r3/9/kb/747PW35svW9/U/lGC5dEQDNtl2gB6SehLya0+kD4rAJIHUuES2mEp2gfudcvwjQBGdDdmmTmm671zbnON14BRaB5hPU5daR/6/ICk6sVS96NckxEW40s2sNChOR6eBs6KEfrh+b33D60fcz42/Tjvp/Ssj7ufl3o7Z/U7+Wo3ouNlwrgHbTdmNEftPyccpvOYXPqlMc8xcd7ALW55XaZbwzobIG5TdsPkLdJOWtYfKkqxzRciI92c77QbsqZ22m2PvCBD1yu8wH7zIXZmJQvmp7W07Fx8wdbQUR+RwNgtWPJmgtUzbOHP/zhKw8a8xsIsP2UpzxlGI9/XEFlXVKXuQK4WNPSRo3VZSMV2DYVHPatDG3nhFjVsGji19cmdsYZ3782i5VL3tpu5jJr/xgda972Wp9Tvn02dl9pgH7A9ATuqOJChc9V5fFJbQCGe2bjBU/YhsofjPVBmrGy6DrrmqrGF9n9Lne5yz7LsJZu7rkm8468U/xUD50Cm1CgA7ybUGs6bwd4p2mzlSdhRDCHNMAOE5ymGsCYZtZpDFkkugbvaXx7vc+VAoDDCGW///u/PwoI1vzM5jGFBNoxILjmPSnXNGZoeTBla8O///u/nzMm4LXx06aw48oE/7gDUBGt+VarASBIIPCMQHfRRRcNB7AQBOpBJZ7zAdeO2zuvJn0AVww+AYNAyfwxgiwGN6dz1z6susZI0yxJcB0ABCgtxFTzTW9606Cdq68EsQg9FYjgB5rrkFZ4igDEdYgNh9Qh9udAlqqNS2BjuklQAqbERDX5xbWNVtMWzSNUy6u/EWrdexe1rmrqx/+g98PsM3n0/5//+Z+Hg1jyjnyneQ4ER7vq+5FGDi0oebgleOpTn7rMLy1ra+o4TFxpkfd3mPoOUvawgMxB2rwQyhzX+7wQaHvUY9wUiNpV/5i/1/ntoO2s2pi7wx3usG++O2gb5vFd/AaMP2tY+uZwsVjXeOaP5mDcypjPbaDnENGUqzHz+gCvNb1eW8df9rKXDfSJy6b63Hhpt7J2QcdV7aWcMsCw9D/pbWytqmuRg9Di5z9ALJC5pU1bj+c0hT/zmc+EfZiMc/AYLVTh05/+9LB+4x3q2S4vfOELl+u6jeas6x/96EeHA9vaPljTAenGXNfA5EMT/MtVV1117o53vONAb2b+GRuwdewbJjcmPb9ZZeqaffnllw98WDZ70J7vXG1rFw3xLUDZHPinDkoabXB4bvgT6yit9bi/uPOd7zxsLIszRjzhWQZ5KSmgo/cTfqulWXsfTX+/4R46BTahQAd4N6HWdN4O8E7TZitPLJYWF5MjoZQwXQX4uY04oT2LvboI4KcxdID3NL613ucxCrzoRS8aftf8bM89QDGHLvJxd1IDX8EW2DDK5ht/BDAaJoA12g+Z1/I8MQackHJcB1NgzvWFEFiZbkJJNEsx5PIZT0D69F8cgYNm0Fgwj0fYqeVyTfCYu6HHXBRIThM3giiAE6AbzSSAaL6x+B4ksGgPEEtQi4ZS+pCYAJUySRuLI6x4Zvyt1ieBx4alcfFn134fY3VumlaBXWWNKSHmo9Ij4Kd+75CmV6UBLSTrZvKkvpSn5VufRVspaQHBcz8Va7ua4OYdJj/3LYTP3Pd4vzlzp0enR/8Gzv8GAmidVNoEABvrXzuXjuWpaXVTsqZnPeD2yiayZzRixeZVYCbf7imT/AEBkz4WW+OAq8Ba6zkwUz68WdX8reviWD2bpBnn2LppDaeNHLoBFQHJADX1V/rIg78Cbl933XXDQWZ1c/phD3tYlszR2OFn1SrI5mvW0/CleDwukaxrNJzru0YPNI8lUcZftZalraNb3tEb3vCGcx/60IeWfVC28pY5oBb/lo3l9EcbrLTSh8T4JkCkjffwcnnWxgD9CvLis8IT6mMOSbaBrF3vgo9n4Z3vfOeSDmfZHUH40Oc///mj39RYoo0X9EP/uFYZy9fTOgVaCnSAt6XIwe47wHswum1U6hnPeMa+BciCysciv1D8P9FQ4nqBmRb/i1deeeVg1kv7yqLfmorYERszH96oU8eUuQO8x0T43uzWKZBDq2huzg1ARUwpBp4p/0kKGFs+1ipjjmkHmlUBIwyzGAhJEHBaMi0UAkGYb4dhOPH5qAOzQ0w8xj/BfKk/0pl5Av4iPEkzToJeBKwqBDznOc9JNftiAoQTqvl2pc3BzQHXDObxCizvK1Ru5OGrrwKCaF/v0w/gZgKhNuniqk3kPYX+NU97rR3AL0aqatbKF+HGda2rXrf1tffRRvM90aTJN0MoR69HPOIRA6gdzd3E6Ffrsl4kBJzPc78hGro1vfaR2aWD9cbeaeqYG6+qI5r5+lNpt6rM3HY3zQfgqL/fTcu3+bdZV1t3vz8f1DtOmpz0d21Dzdxd16LQqwWXkn5WY3ONzbZVm4xjYx9bW8byTaXxhT5342uqjk3T8R8267jYUTYbj9IDTKKHZ9GErWvC3O86dVjLcwBb7ethaacuPEP82Ka9tKF+WsT4yqk+kxtpU1pDaZTiaYC7NHKBvukjwLoN3/3udwfQOO2NxdZPikQBX8fy1LS0V9OmrpPXulh/r3gCoDPfrWNlX/e61y2H4jugJDG1tmrDZireqpW7HfRnY9o7yGZxNHltprzrXe8a2gFiph9oWoOD8zxzSG0C2R3d0AwIfBZDLKGqdnc7TvIDwBtPe+973/vcH/3RH52LH+NKr7Zcv+8UaCnQAd6WIge77wDvwei2cSkASGU6soBsGvPzeRJMoDcmwP8V6ADvQSnXy500CoRB3/T3mNOI2xOMj3N8DveKX1oCNC0ZmqUJmDeCRRWuneQ8Fj71qU8tGWjaETE3HMu7izSaHubVaH5ogyaHNK4b+JaL+wGmmZjSbJhF8HBIR4BP4OS2GXcCyN3udrehT+rn244WS6w7CDIElQCWNFrqwW4RcgOc0qa59NJLh/I0TqpW09gaY8wJEVrky/vFmDNtHSu7Ki2CYcD0+MhlVhtzSsKVA118F8aVMQArLrvssn1t6oPA9UXalZ+WeYJ39/73v39wqZE8YhpZVROoPvNd8kmctuszwrXvYErIrnltvka7hRa1Ddr6vF6PtVWfz2mv5h+7fsITnjDZ/lj+nnayQNbjeh8H+fYoKuT3fhT9fvvb3z5sigLfsiF0mHYztx6mjl2WJS8EIEk70eo1fm6ebGDlWY2jyVrTNrnO2lfLrJu/at5tXsdPrEOe1JtNtPTHRmUANhY5aZtlFYAua1rSxQEbkxa6uvdd597vgvbw3N+H30P7XbnXF3Vz52RNqnkCMnrusFC8Fg1aCj7pn2+BRm97mGieJ44Fi++DC62E73znO4N7Jfk8o1z05je/efk7Ci1Tj5gLBQei4vPyPdXvAthcrWDQ1NkHwFquseoY1Wfc3pXr0DMyMbql77UP8lX/xxkPjVDzQGiHdw1P52wamwHK6oNDZdOOA9zwPuFtKFzEqg4Phc+LhjX+HiheQ/LizWq4x+KMHf1epz1dy5ym6/xewp+2faeolndR31+u8Ul8I/fQKTCHAh3gnUOl9Xk6wLueRlvLYaHiWqEukpkAV8UWP8JoDonZWoeOoaIO8B4D0XuTW6cAEy2/WUzlpiFAKs39kxKiOYnxnQKe40888xdBCEM8FrhnCJDN3PEoA2HGu9G/MKS0cqTx5RrNXW4aCFMJgNH6TpmYBSAEWAYETv7DxNFWIVRcc801y6oAv4Qq/U0/9SnjycnFESik02Zp14a73vWuQxnaVtmISD1iQKDDLwjAEWikV60omkZ8CNOwqWVdE5jGwJ3M7xG2qkmfbyLAr3WQlpG6IjzqSw7U8e7SZhXapRHc+Ax85jOfOWiJsHJBTwd7pIw4Wj7pi7R8uzVfez0m7LZ56n0EWe8mmkGeRyhyHYG2lptzHdrIG9BhTrmTmie/p5Pav9PcL99YNBpP8zim+s48u/6Wp/JJz2//oL+7WvecOgAY2RysZbd57d1W/7DWDtYzmX/iskebSZtqHx3HaGnjMBtxYyB65rTM41P1bzOdCb8Qdzv6F4DO2sU3vPZskmZM5vm6Keh5npmDrEWUZaRnHWvHG2A46zC/surM+vCqV71q6dZharwARc9Sd8rW9c2BaOGV5OX+iiZvgOypuqV7z2hhozra3Hn31kQB35JNXAeM0mIVrr322n3ttu3oY4BpzwClNmb5z823nvEk/ou/+ItBezbjSV9SN74hvEnSVsXA9VXBO1DeWQLZMAdIrwIb5c9v+l//9V+H/gaorvxBNqdr+1MAbzZazANnMYQHrJsGxol/5tYj79C8gIezUe9gvpxH4bnfn02FHjoF1lGgA7zrKDTveQd459Fp67kAI695zWuGHU9MA5MGCx8z3/vd736DBp1J0m6oRfWshAAA/ZC1s/JGL8xxYJoxtRhY2q+bhAhic320blL3QfLSYiDMYHppQo6Fr3/968N4CUfMtJhqYtris22szD/+4z8OZQiFY1oYY2W2lZZ5htaxwDxPf5k2iglEFdyVJyZ9NGUSzFPy+6Mxso3wL//yLwNYh95VkxZAqF9pbyxGSwIMNz31ue/wV37lVwbzOJo/EYBrnmpaWxnvmqdeA0locQc08r2PgcXKVOCuAsZAGWaztLoF2tXG7XuL0KC8Mr6RABg2NNOXaN9UVxR5ti6u7y/fIVrpQ+1nW0+ADOmtkNrmzX00kNJf6TTGc1/7chCwNiBB2puKvfuD1D9VX0/vWr4n7Rvw+7VBF1cwU/0D+tQ5K0DUVP7jSg8gHbAxce1P1jQ+UQNI1ecBMGva2LX5LOBl+xxdM1+1zzIPArFYUNTntb6MpT4fuwZQAxzr+zHHteMAgAo58wCYx1+9OrOmme/qnN22Z6PMoalJt9aQqxwaKq1upCVPjYGd+BnymXTymgAkrfnqtW8NndJGnoWOWaNtOmfzPHnaeOx7aPO4rxrfQFYhh8dZ88OHPe95z1sC1Wi4inbex5//+Z/v2+A27oxDuzRXxdK4ZErf8vv0XSUtbdFG1t9aT/vtreORgdXqxWewoHHtm0pbQG8b1ax58H4tHdHEu2x5C7+1sRCgvHU5wO2DNvX/LIZYjLWKBGhn3H638IwaKCsEaA/v491PyRi1bL++sCnQAd7tvP8O8G6Hjr2WmRQIk2rC76FT4DRTIIw7ZnduACoGqIkmxdyyu8oXbVIbTFPBbjxGLnmYEBLkAHs0XacCMz7lmHAdZYh2KAGCK4BonOSd8XNeA5A7Ql59RptG//3d6la3qkU2vvbumS4CkNWHaaZBInC/QOCVTuhAbya4yZs+JA7oSth+0IMedJ7gknwRaPhQu+iii5Zj8dx3+NCHPnQQ3pKfoBwNLmkAyjxDFxsbTO34na1Cm+8izHzyt7GNDUJAxlmfez85KAZYwL1GrZ/2FA2tWsahO+hEyzhuEupz1wEAXAPPaXTXMSV/3n3u58YtGEFYrWm0pPMt1vHke1zVjvdThd9VefPMeKOtnrSjjnMQzlG3u832Kui0zXpPe135jR7nOPyOaNIJVeMufcqc5978H8AxwFLytXHdpGqf7eJ+09928gdIMR/Tqpzqmzkzz1rgTHrmo3XgYuoQA8Ks96xOanr6Ji1zaU2reaeuAbc//OEPz7361a8+r27r3FS5OekZa+JYT9E4nSo/tk4l79Oe9rRzXEdEazbpc2NzZL7TOZq6qdf8zg3CW97yluU6jV9g/ZI5KxuXvnshm8avfOUrh3trd+rDnzlDIpuTfk8OfG01tN/0pjedszHNNQH3XQFgUs8PfvCDwTqqfefA1pxVkbxi4HMUl/AU0bauGzC+h3XBORbqQ8sAvGmHZjfgtQZ83j0W7hTafiqTb8M1fsK3iDaUGfBE8UOrjy3wfBiFj9q/k3odGcH3kgDkRis845jVn2/Uc+7CBN+o+7ghST097hRoKZD5ZVtKNW39F8p9B3gvlDd9QsbZAd4T8iJ6Nw5NgWiDAp7mBgsWJmdKQ2BuPdvMFxPHv/3bvx2qxawD8VgP0CoFTAZcq77HaIwai4PGpgLND3ke8IAHTGXZSfp///d/LwUg46u+6whAVesa0x8BEgAsP6GLEBIhGfMPAKjl5nb8hhtuGDRcKtiIJv4IGoDPaLMSLNA/4bWvfe2QT94IhBWIoJUkALH5GeZ6IQAmQYTPP2VpsTB1TbvAQ64sEqoGC22XananDLok+B5aoKeWlx+9KqBShaf04bAxevA7TGCtNJlqi1AWzfO0HYGSj/xoqeRZG9Ocu/jii5cAhud5J21e9943zSYaaPX7G8vb07qW7Gn4BlgHVLPtbfW5birNrbP+5muZVaBnzbet67F5Xd2ZW6basREULc42z2/8xm8srTTqfGYzr4a4I2rLu69uAOpz2q9Z883jcSujv+bT+EmtZeq1zamxb8BcOAYk17Jj1zZOq79/YNnUux0r36bRGg2o3z4LkEkb2DobVwLy1c3cttyq+1rHuneOr6jazgEarf/t+4rWY237SU960vL1639cQAAt0TCazSkTDUtrscPKgHLr+ohPuP766wdeIaDxuvdhk1XgJ7t+r+lHG+sPDXxWU9w4jYHA1uR1Ae+mbnxau7FZXV+19dCsrv00r/FLnH7aGAKc534sBnbaKMCjOYhXHr/nsxi4ZohiivEKDjE2ZrRrAx/JOQci3wbeOXz2USt8tP3r9yebAh3g3c776QDvdujYa5lJgQ7wziRUz3biKYChxqhimPklWxf4eI2W40namQz4RAAgWFYBBANHkwW45TogsLECbaUx95sKtI3kiebvVL5dpH/ta18bTPe0X8fklGiBFjL3Aas0dZStf7RYavjMZz4zaGgCUgGsfPvVwDVBfPipB5ARDU+nYFeB2HsATNeAKW6FtirIqJspnKBsTujWFl9otHFc81cbM0r3NHUqWP2JT3xiCcgS5ghX8uUvJ1lj9GnWSI9wGsEUcI5xXycMpk6/BSctVyA4z5hWx61G0tbFBDZ9GwNdCf3R1vK+bVRUNwYOlIkwrJ2MqbZJiBMA9nmH9fk2r/VtCqTYZju7qCvfxS7q7nX++Dd5XLSIhuZU+xU4mcrTpgc8aNNPw31Aw9rXw/520aOdYwBQbYh/8mqWXvuR67E5Ns8S+92qj9ZjNgnzTLzqd932Vf4xIJH2Z+pMfb4XIKm/OcCaNXOsbvUCJPn8dz0FvHuGL3nEIx4x5Mt6xfw8fdtGDAin5UuTMX1hlZON8VVtsPqgoZ08+Z7QilXP7W53u4Gn8k3oP1pyncX1X+UpUn4stqEJSIkv4uTRBgA862Pa1kblo5I/MT7lc5/73NBn7yHrMCDQxuqm67mNmnUh7ies++lbQOkczjdVB9/96btNdq4y3Nf5y++KSywbu56hLTB96vfku6QMAXg+ayGWSN4rxQ10QmuazjU45C6uOWz8UwhIsBmPjr7fbYcvfOELAz/vW7OBZYPEb3zdd7DtfvT6Dk+BDvAenoZq6ADvdujYa5lJgQ7wziRUz3YqKPD4xz9+YFgIZA55mgoY+2i9APYCyk3lP8r0CkBivjDyfKHyNxbwV7o/wFtCBKkAX0mvsd175TDTxxGAuK1mpvGNAQoEJSCfwzVoLgNF9T2Cg2tuAQCjtLfHhFp50Ozqq68eNGpiik/IAaIK0dqiffXtb397H5BHyGgD9w0RltSv//UeiEswz+aBPMwyhUc/+tHDGHynrVBCKKqaUzSxpwRn5nXGHLrV9rWH0Y+f3fgppF2UscrT/kU7Ouk0YiIIJ81Yc33QmKBKIz19YW4qOLSnpcm6Nvx2bXJEiFyXf+p5xhXheSyfb2eusD5WfiqtfXdT+TZNzybQpuXOUv6p389ZGuNpHEs1rz5o/7f9bgHl8TVeQaWx/tEubQMwLfMxbcUIxbV86g2IWZ9NXafOqeeHSb/pTW964Pk8gOOm7RuPNT3zv/LvXrhKMreiT+ataK229QMup9Z6ec3l4ZNqH9UNNOamJ/N81rfQWL3W6Mc97nHLPNIEWpDpCx4t7zJpbczNgY30mh4XEuFD8swGL5/CAr/40o2z5kufUyZxXTPbtSTrovHd5z73Geq1IS3geeOKIXUlxmPV31d4ruoua6ik/KPpHSCxnkkQfi+uTEqRfZfcO9jsTx9aKyT3vpNou6MHjePWLYwxt+u0DWLA/lkLXHqFXmIauQ68Jd84T4KCQd4deQd/W0M0rvGq2wp+J3jO8FS1f7kG4Od731a7vZ7dUSBr2UlShNrdaHdXcwd4d0fbXvMIBTrAO0KUnnRqKYBpDaCFmWAyz8TNbrKDyQB9mJ4wwrRUMCQnJeh/mHqMOyAwB3Gkj0DAyvzKI0TzI6dcJ3+NYxJPe/K4gjHy6xfGM0wfISS+bKN9kj7yjxxfokzQUkbcCnsYfDSsgo98oSutaL5fE2KO/KEPfWgw8ZOX6SYBjpBz3XXXJesyZgq4ioGt/SPgxPccjVrPaEbVPBHECP7e45/+6Z+e41fQ4T3r2okglm9avVWLm49AaerkYzj1pc3aD9c0f/1OBCaoed5q0cZ/chWik3dV/N73vncQtuQJ8B3CBgBvy0eIyzdDwM77bPMe5p6vYeUr+NDW592Fhu2zeu/7CYBQ03Md34y+9aStitvveVXe/uz8DYxOk8PTpJ1rK01bUCW/1eSpgNiq30Xybzve5PeTvmY9qr93z+r6Ye6yRgS0BhLHqmLuGOrcXcukHzXtMNepb+w9sqTYZE4FXjo0NevAVL8CZkezOu6wstmsHFcXYsCP/Na01vdv3l80p/FzaZPlCQuZ3KdNa027biVP4tvc5jZLdwJcKglVe5i7AoE1UMq0MR4CgNZ+822+rC95D56nr/gbfE5AN2sebcw5PoGzlqQumrT5ZtOWsymyVuMtvrrQLo4GN1dUH/zgBwdQEEBorOErlEdffbUJYoN7LEQDu7UoiFZ7VUYYKy+Nm4uMpaVdvedCgwZrxgP0pRla3XPgLfDKrIGURQ8bC2ct0MJd9Y17fwA6bsPaQJsXbXw32wiA5biCMKdRwPA7Bsy/+c1vHizYMqfqc3s43jb60OvYPgU6wLsdmnaAdzt07LXMpEAHeGcSqmc7VRSwsz8FYIXZw3zkYImTMrhoqepj/LmO9Y1Gqzz5Y7KPocKUM9sfCxh6eTC6NGmPOwAR039C1mWXXTbc0ybgPiOBQBHhkLAVgDdzlzowsYD9//zP/0yxIX7f+963TwAlgLXvPJrPtCEAo+rj7iI+nWmy0lwhhBLe9ZW/ughAGUMb0zBRJ80WAbBNq7zmi1YOf7RMCXNf87iOsNam555wGO0gaTS+jYG2EmBXGqA3Y9IOWlU/wPL4PmLO6Bub6g96e0cVKJCW/qyKCWbRPNGnGgh4EeRTRwQU9/XkdYIEQCD5thEHSFZ3fGFuo96xOtBaekvjVkAeK9vTfjz3XYi0mGte3X5bh6VVC+Kuqi/zwbq5a1Ud7bPU2aZv815/wzuwesjvtLYBQAPGcQXlgLnQmQl55uGAfWPla10HvQ6QctDybbmARJnTx2jNVYSDNFeNCaCXjczaBpr6ftAXEGSNyvOs7zmc07wewDx54i+Un+KE/A7QXagayfIL1XcuICsAIgDQ+iikHkC9zdhqfROLKGt0+iKmrZqNdr5uBWs8ILPmcx1eAS+R7yJ91Xd+j+VjKq+OtM+qBb8SILOtl1UXWsf1lueAZoEv3Zqf26oKqtdn6IJOQFL+cNtNR+B/fLzq/6Me9aglDwk4vO997zu01X4zNu0B8epHA+DxuoAGtW/tNS1f2vJxW+KwWlZc+l3XbFZCCfHnaxwnSaEj/TtsnLMhzD1oYBOa727viduyqUCjF32VO2xw2GM2I/wuuGQbCzYPooRjTljVv7HyPe3oKdAB3u3QvAO826Fjr2UmBQKSWNx76BQ4SxRgcg8wBQrSMiEw0KakaXASmYrvfve7S40/QhChcUx7FFBrXDFlxKBF4Jr6Hdupz2Et/JedlBBfasYQIRngSmACBBKIIjQQiD772c8utXzroRv8sK0KVRgmnNcQk0iCbbSA+MclCEQwa4WM3Huuv9GEkU5YzVjcEygx3DEDTdnE3Awk8CnMbNK7IqDe7GY3W763KuQRlANGpp6pOMLiy1/+8qXp6u1vf/vhULcI1ilbfTMDsaU7lCzfV/3m3vjGNy61qmm8tkJ76hyL0/ecJJ7xi23OtGXucpe7DGk5/K59vov7AB67qHtVnVPfScrU7yBpbZzfTJve77cLDB+HJqp3eFreb9y+bBPk3fY3DLBat6my7jdnfMzSMz8C7OrveO745+bbNg22XV+09Np6Ax7HGicbBtnUy5wL9GzL2rD0Hnz71mwhFibWW1ZaOTRXWZZawMexNUN+Juzc+7zqVa9amvQDvAIkp/2nP/3pQ1vbq3EwAABAAElEQVTA1KSJ1QEkdu0gVIBq5TNq3rxXG5vhDeKKC38ANM/YgazZdGbBQwNy6veOT3D4LhA4vEp4IaBn/a6t4aFz7duqa+6m9CF5WssWAF3aTR7v2HsA6uo3bfdoAOOD6ub9QNjmX1ynpb4/+7M/G/h17yG0S5toh0+pwK52lX3Xu961r+ZYuOGvzlrg79mYve/23IhVY3VOhXKVB12Vf9UzbsPU5bdro35d8J3Kj8ft4WRToAO823k/HeDdDh17LTMp0AHemYTq2ToFdkyBHKRFOAxDTAgAnAmEFT5pw9xijto/JmrxLZvuAg0xcPICRZnpn5SAGcX0t+Oo94RAwCuNgMxXiZPPuFeFqg1EKGhDXFdE6HKfk5i1ASTll49f3JiTVqErgp680mnN0mqJIJt+qp/meN6hvB/72McGrR0C4pRgrHz6lrpqDKht6Uigi7aQvL6LaKYR3OKnMJo58sR1B3948uofjd4I4lXwTppy6EUjOGCy8XG3UGlU+5vrMR/HNIfbcusAltR3kDiHvaVshHH3FaTRp00F5NQpDkhe0za9JuC239SqOrbR5qr6+7Pz5+DTQJM6L+y6v1VTc9dtHbb+zDvmL9YO1mTm+gHu6tww1ZZ5kbak58kf8K5dt2odabumjV2HNxh7dtC0AGfmt02/DZuw69odGxsgcJNvQ/74tL3RjW40uBGI5qL2zYt5T+4Bg9kYtDk8x/1E7WcOX2O1Qzt01RjrPFvraMtEa7dNr/f3ute9Bl+pU2teQPJaptZrDebfFAieTf2a16FwgL18k/VZvfZuKAbQKKb5u4o3UQ74izeKn9e4S6DlTis67in0j8WZemuQh4uM9CGbZ1Xr1zUXW8lTY9q5NtrRT3p4mbSRjXybTm3byXOa44DcXDbMDdm0sMFwmEA28Rsw3+Xsh3X12ZSItQNLux5OLgU6wLudd9MB3u3QsdcykwJhOKc0/2ZW07N1CnQKHJICWURprtJ6CPOFcWViFl93NBTufve7n7vkkkv2+RwLs+v5Yx/72HO0IzG7ERQwtnOZr0MOZbI4n3i0UwhPBADAMy3eCC0E65gsSue2gbDilOAIGOiQgzsCahKqV4UWmGv9yN1www1L8zJ0bPPT+uXWIsKDPExzuXQIkyqNtnjeAzNHGjt8jzEpBNTTxqlgsLzGEz/A7tGFYFqF3ypApn4xADLmofEbyNw0wELNS/iNIJ90pnS02XOfg1CYhkojSNMEynOC5BjQTHut+p3UTj0Vu2oBqSsgAqE8wpZY+1XraJWwnD5NxQGw2+dpO+lVqyppYzFaJN07bL+RPOvx8YGeU7+T/k6O5p2MzTtzaW/uj9n1VJmp+WAOeDdV51i6vlx66aWD9mLmC5uE7dyt7FSfUu+v//qvD/OGeT6ulcxxdT5J3m3G7Vx/mLrb99rOfeExtBENyoO0h5Y0MkPzsTqy6UbTk9wizxhYGbAXbxBXCmP1jaUBrW2E+w6Mx6bsWL5VadbJ8CxtvpZ+vvukadOa3lrXpI4An7mv8VSZmse7wlNKQ7ep75d7ElZlCQBYB5WxbsKr4n8AqvwwB9RNXrENcW2gpWADOIoG0vFNwGa0pV0bHtAzmvDhM1is1YAf8T36syn+ile84hyAMJqrsaZ66lOfWosN19mA/spXvnLes9OeEDcdNNbnjM/met7D97///UMN/6CawPHbTPu3h5NLgcim/ZC1w72jDvAejn699IYU6ADvhgTr2TsFdkSBnA7sQAIB0xXNUoyYPwAGALBq8UUwSJ42xsADDNeZxg2N7ugfgJNfvbZvq+4JZpXplxeQyHVChBwaHxEoaZtMBVq7ygeMvd/97ndeVkJKgOP0KxqvETaSXuNKf+B0va/56jVN6voO67N6DQjAVBlzPWE6eWiY0Z4QCELSCW8OVFnVj2wC0NSNYKAs/8aCwy/c8zes7bTHR+IqDTL1VoEx12EQKxCQOvnVpZkegVx61qUpwRzt1tHP95M2VsVjfZrKb3xTQvtUmZ5+NOBip/N+Oue3d5R0OY42j3J8aStrTu6nYiBr/OpzdXPNNdcMcxLNSht+MVMeo1sA2qxBgL+xdlI2/i/bfNaBVXN26tSeua2dD2t90RJUxlrXAr/6YiMUOJh6DxsDCtGwbWus3tBi7FnKG6NN0FV5lcev8O/qOmVTb0ujsY0lm8HhU1IuMW1WLo4qkL2Kx0i5Gs/5Buu7q2VXXdexAERpL68LzhlwSC1/wN4Vd1A28wPGv+hFLxqqAM66jrJC24/QGQ+ctf/973//vuZZpCmnb2OB8oPnLx3RZI2mswPlzlqwQR7tZsocU2PEJzuY2ffvjwLCYYP60JxiySbBoXfKAfh7OLkUCP/eAd7DvaMO8B6Ofr30hhSIIN01eDckXM/eKbBlCuSgDSZtNeRAAoxQ/cPgMiMNc0UY4ZMuzDOgj6kocPU4A0Y/gg7hysEP/KPRJjZWpvzrwDjmf3yn0c6gDRrhLJo1gNBVIeBohGBaVWOBADJm1ljpri982ALfM3/W5/oWQaWm55ogzeUEzZekEcRooYmNhfY2n3M0iwU+zaowmHKJ+Q10uE38B1btVf2JuWkEPnRIiCClLpsMb3vb25baN4T4CvDSqOYqI8JlBN28j/RHTBMr/vQCNnvfq2hDyAVa056qgmat1/UYLVZp8a1qs627AirxFdzmae8DxrTp/X7/nNXpsR16jH3/c2i7bm6bU8dR57E21M2fg7Rv/jG3jc0D2fybqtd6yoKkgpvJe8c73nGwQIlmoA1Jc3e1yMg8Rltyqq30K4dBqT9acdnkTD2Ze9OHxFnbMtcm/aCxuZ12ZOZwa0sOQVOnTdJogtY2fGNZF6RbKzO+mm/uNXBBYP2ijLkWQJq1bKoedKMljV/IGmdz3CG0zmeIhZTyaJs1rPIiAMpKbxvE2Visc77vM+lT/dHfrMPqBIpaS/Pexso5sI027xT9pONl6jql/7Rs802O1dumWfNo2jp0Vagb3d4xzfOxwNLMb6O2X+vOhrp+Pu1pT1tq2arLeQpvetObhvXedx8e8T73uc/QFJ/L6sIv1oAnkq5fbbC5EG1uAD2wlxIAiyQh/Frrxqyt57TeO8AsiiJo5MA6FoH8TXMvBoANb+h75HpjGyEbSDmQcG6dFBH00++zh5NLgQ7wbufddIB3O3TstcykQBa8DvDOJFjP1imwIwoErKxMEg3LaEEAx5wM7ORnJxPHtD3l4kdLmZjqAeuOMzAVA1hi4u585zsPfvNqfwCXBEDP80fYYl7nj4ATATfPx2Lms6vMzAKCR4jgV28qVMGvtkXQJ2yhuz+0DbNc8+Wa4GXHmzBBm6TNSxs5YGLKiAmaTIEdNoY+yge0r/nWXQOiYxJJq8Z9LeN7AgpLC13q81zn/bmn2SsQwvKc8EbYFcdvsbEyyZSWfA69EdJWDsjxHC0w+/mm5aPxlrKHBaZolNW+pN7EUwJqhP7km4ppOufZqnaSZ1cxcKd9z7tqa1f1Ai8reLKNdirgtI36puoIOLQOfJoqv+v0ud/z3H7MmZun6qJFOfZe2o0av6exeTL1VgAuaTX22/zhD3+4NLNvv63295rnzMQzPlqkLXCq3JVXXmmqWs6jTI4F617tw9R12/faRjR/6/zL/HpqrqptcA90//vff9kH32PbVvIHzPM84JjvhCslG7HZNASoBShVNgeBzf3WUw/gcRPwUVvRRowP3rjy0CfvhjsnYFbeFxolxH8uvsJmt3Up6ylNR2uzdZbPXm3ht+p8HjqJ0YXFSf0dzR1/rcd1/M/TqKw8UP1NPPGJTxw0pWtZvwV9dTicjdD0u37HXFfVDWTlraE5P8A9s3g80/e+972QahnTspUHfcQ2euvaLKO28+3Ig6b3vOc9z/HvD1gdA7zlATICkh1wx2Ko8ls2EGKJ5fA49XrHNplryEZJXBEYB2WHVe8CL+QbQKfqeqLWexau0Q+N63eEjvXPxsfVV1+9teGGl8v8N7dioLN+xY3H3HI939FSoAO826F3B3i3Q8dey0wKdIB3JqF6tk6BHVMgpvLAqISYygP8xoKDCiIQX3/99cssAe74STuuwHcujRkMHGGe+R7zugQCAw0oz2n21sO+CDwCQQBTHuYUA08TJKB3FVq1NeWGQnpleGm4tEHfaDtEyK7tEugISPdYnPxLIyiCj35F2Au4I009wpe//OVBiGGuSTDMYW8BETKuxISP1Cetji/3EeQIoQS2Ke02QB/tF+XQOadI51TxKWGIAAmMDxCbvomZAApO+q7prgli8UFMmKKNmzzq4yOPMCkNiCdUIZBWFi0tgiPtJu8o5RNHMM/9NuIpOqyru34D6/L25/sFzCl6MGGPlttYngowjT2fSqu/qak8h003J+W3mTnksHVuUj5z4iZljjtvnWPTF/NIrsfilAmtx/LUNJp8bZ1t2Xwf4myO1jra/Or03PwFHAQSugdsmfMzz9c6XJurx+bVNp/7qjmb534b1tLc135lHuOOiSVKAN6MJ+CnsnVdiXVL6mzjulahT2gVcK+uq7Vs8tW0Ta7r2FwDMsMvqAffE+DXWpKNbs/4o3cGgXWo9sNYwi/9zM/8zACCenesRmg52lBOH+u4Q9s8G4vRgeulWq7m801k7ZWuX9w0CEA5PFDNP3WtXHg9YHVA+fi5zwZA+83bjODzNvXiD/CcDlGtwdqbzQXfauYy4Ho0eQGq6Kcu31fOAWjrcXZB+rFqk8YmBgWFFkQOz8K6zSZNQtww0PzF21WNeX1ywG2+ARvLdZPE7+pCCDTVL7/88oFndT4CpQb8ou/c/LDNQNkB3fFwmwTvRjmHEvdwcinQAd7tvJsO8G6Hjr2WmRToAO9MQvVsnQI7pgBBMcw500iBhgMGCGP2/7N3bz/fdus9//8Lu/4CW4Id/GhixxaJkFQyp2BPGkVjQ1BNSXRDLGo1VU1VkZqmqpitLqY+DVU6W9KqKObUGSEoW7ohsfX8ntel79vxDOP8Lq77ez/36hzJdY3ze55jeYzl8TmOcYydo4Xh+6/+1b/6Y5/TfmBz7JN2NENpfCjX+oc5sdGkSdORS9oYGAc3JE9mzPeYecdB2WmVnsvHpFHaGIyYYeA4DeadazMpHlMDORthQPglJm6Wq3wBmIHS80I03wGdTBTs4hU/H1Dosrbq0Pudz7RD74GdOQyiY6ddbDYZeOHRKIZ8HuErLW3g1umOIQt/BKY68lg48SdwUHrTp+kV40bryDeMN1c6HeWc8XbP6Omvi4vWMMo9gfb1+/pbvR3fnGDCGubo960M+VH8R76Pjo9M89Fp3TIWGu9Heb/t2slH9Xof3gO3XGgYILj22YCkS7QoTGO8/eulOM/9Zj1IU3CmAUgHcDX3O6IOcBNGHZvHCWXXeYWmW3PzXG8ujY315Mcsy+6ZINWFWO0lrMeAlxk2kHO+m88AF39zDYj2+bP8xb0VvC58fhq9tWvvL/kuB+UAlNPmLyHk/F0a5o7ZHjuaEzzqU9fmIf0iIPe55lIqF1McnTAiAEg72fdZxqk5TLtcH0zT1gkYewhxrK2lHahqfZ3au33Pt390URl7t+u4LAwf/T79kZC7vRhNcfunS87YCGBldoGGL6BeOwFiv+u7vuv/OdlVeoTuCYEJIjKt0NF+c0ma4dHKyZ/V2W/Wt7Tt675seC3f2/4b+B6QT0h7iyPIr3/+1E/91C1RzjCviQInwPsYwp8A72PoeKZyIwXaIJ8mGm4k2BnspMArpACNBxtpm9Hv/d7vfWKy/HZUb3WBvxjGL33pSx/7zL6ZeJ+0toLjkm2027wBezEwNExi7oGCaUCqc45ZAuX2F3AIIOSAmI4eVq/CYTZossQY0fBdHY2FGN7iYTAwRZgl7zCspe939Sg8RoNdXFo3wGiXU0yGsXDTx0zYHNGkYG+OZm8MS+EwtznpCgsIABxMZk14NpXTAsJMrw6QmtYLACJ6l9clH30ApLs6aYuVod+Fk75whc2ennJqv4BwADuN3rSPMHLagiZO7V766AXYvsZ0X6rbpW8x6uU3w3q3ez/DrM/qpt63xosGazrr7xjU3q+/ez99c0P0nO/X57Wvr99f5+83uWyvky7lXf/t9+v0V026WRYCEc5pBu8J1YAB97avdeVlTbbMcs3ntSzGcOtD4YCjAFTrgHeOmHNpNHoHdHK6ZgUaS/8awFpeq89ea3Po+q3fAC626v2WP2f/0PdbfAJizrq51v+W+Pdok0eTo3SP1jDvV3DVqZbAYhqzNHOtadKmWZ3m9ZqXdpZeZTFn0sYVrnfFMd4IpIGb3jGbRUhoPzBPrRSe7/1f/It/8cNv/uZvPqSnMtCEXNd88Wmwagt7gtI1/9cXCE4yX9H36c+1Qj2Lx5/rT2tW/ZPGuHpJSxpzDRZW3W9x9j7SADTbB9zj7GXb22kLpzjYfO102Kyndde+Yjp2jgOt2wM2Lma48/nlKMCkmrYw7lxYfMmx3dydI7TKT/dmU+AEeB/TPifA+xg6nqncSIET4L2RUGewkwKfEAU6WmkDnZYQzUzOJv+Dj24YDsSz4Z3aqBVx3tbdu1ftAz9t8IB7jlOyx+b3LN9//+///cN5aRxNGEcCp+uoobho4DIzpgZoK88LSdKEEs6xVBpHnmmLzE0+7QI2x3zzBzCeTE3vb/GBFMBk9t4Kj+GbjE/v+bQapkYDIDOmdDKO//k//+dJghfP2TksTbRpYwzo3bm0yTDZLrATN00sbUODhv27ozKX13P8WSeMtuOTGESaOgEFygXUZ35DHtoGAHGk/SXNv/SX/tKHXUCESW9c+HYU7znln3HQZ2cmYoaJIZ7vXgZwm/Sbab7q59eV76uu13PTX8fGSZ//M3deomeCuMCYXVgah1/5yleetFjRtDl7F/aR76wVzfmXxmdzFC3VVRA3y8Pm6//8n//zaf4CBnJ//+///RdrgtMQ3E7LX713msHSBxruNHYDDs03BIBpRM4y9ZyGrbBMDuxM+KygqbBTuEoj1GVWBIbTHi3apTFZftMP4GeS554xM9uEhqg074kvvFMztKY9WyO6oLS1P+H5LK/ndQ6fZfEdcJ9ZjMpkDct2rFMp3Gd/0UxH9J/5WHeBkkwZJAhI43GG2z0rj31fDmi9lnHGU8YErLNu9SttwxyCOMrk5Jj+XBq0easbmtmfScefsdsYse6Ke6sLkM1u9a3xhGPayVp8SVhpT8xkh3FJu5m2L8FwigbAbJewpVjwnHLcU+b3LSzQVt/Rj8xXLlfeuS9/+csvwtkj2hOf7s2mwAnwPqZ9ToD3MXQ8U7mRAifAeyOhzmAnBT5BCrChOxkFm3YaJYGDNlEYhG49XouW7dWY7vX7o3+zt4upwATQ1uAwEsrpCOLqXKrgG60Q2lDTpe3h+9GfjT67YkffgcuO5an/pBngmQM0xwwCcyagk/3EmDngYqCq/GLMYrIww5VjMvQxcpjpbnGmXSzsZLz8xojvXMxp4dWlzdaOdqURo11dYtK1CQckkK+LaX7yJ3/yiRFi9gBwSqOCdjjtnM997nNP3zBn0aO6HvmAlBixaLQLGxM6gRTa3phOGt+tTcUNOKIpkrmJvj3CR+PKLT00vLXOu/zvjas/Yn6mEGOX7tv4zhHxFVR60+sx541LgNqbXo9PonzsnwLUzJW7/AJZjAlzaQKawjYX5Pd++veOp+K2jmbWxdpwq0kY5Sl+6ZnTlMW8mWYkMIkWZWEAyt/wDd/w4nfvn+srB9Cy+J3i6Hf+vTSadbMGTuBZPed8WB584yFN2fk+4DAQ0LfaPoB9LSPhNfqVzhQA9+7ItxZbC2aa8g7ctdbNvUbp0AC3L2l9tB6yTVofKRzfusM8gmdlnXl5Z74mdG+tnnHniaD5fn229gBg9aeV5ui3utUsxZpevwN1/XZiie9+g2zsEvYShrfmzLrR4g0sFU+5uCmAZ67pVucCLuncE2dNm7D++77v+54A6kBpfeDSvCFP+x514dJmJ3Q+3WMp4DK8hAPozkTcN350qs/JNfs2YzHFDGPPibXTvfkUiOdwafTpnk+BE+B9Pu3OmM+gQEz0aaLhGcQ7o5wUeIUUcEMwhiJwz4bJH7MLNJ9oku4cbYeO2LGp9kk4WrbKBpzLpYnsWOHqYgTF+TN/5s987PO3fuu3PqXlG7CXFgnTBG6dXrVwMKG3aHBiAKYm8b//9//+ia6YWkda5UUrhxYWJqLjmd53g7P4fvvDCAXWBuT2LWbDBSZpMdMeoYUTUCy+srfZ1cZs6K4upngeSVbO8pq3Thd3mmkQLqZdXkBv7lOf+tRTGp/5zGeKdtVPaCBNDD7m1V/aZZUJQxoNepev7OzvTSDcNxpSHQuuIG55Lt4cAzHsfUPL6ti7W3xpTvofxVnBqBlO/LWuNOYmozzDX3o2htzu3dhdw96b5gSE1rRe5e+VHq8yr9eddprxL1OOBEa7NC61uf77tgDP5rpd/XpnHM051/tZ98Y/QRihHU2xvlsjS2fnT+Gd7wFopbnGcSydpjEgyVqaMKxwHflnYsK7ytH3R/pdWvmINJvrHpHWEe3S0lzzsGfZAcLCOQ0DACpO9DSPzEvVei8cwWQgPRDpJ37iJ14Icq0l8wQP0wilnd93gIV3+lNurvO+yTdhsou7fuiHfuim9YamqPsAmiPcG9BYZyoAQEnTuzL9yI/8yFMRaEImXGrcHF0e210E7ROsg0xWzTXEejzbS96t/8wiEeIqA+1x9wZUHsIaDnjtnX0ON9cV9bnV5ELt4D6FR7hMlLEtTBgNrKZdbo9hjOsHQFz2eqejWao+v+bX/Jr5+nx+EAX0XybG5j61PsXXp5nSoGRxureDAifA+5h2OgHex9DxTOVGCpwA742EOoOdFHhNFGhjTGsngO6oKEDEtE0wqhzQyI3S7PKxf0jTiC04YKmLzDAW9xy1W/OmgUs7DyM0GZHAWNqjq+uyLRu+9SK4CeA5EsjZDNrQT40UDCOzDWy0AQMxIJMJlLbfNFdcBIJpV5bJ/NiECmPT6ZKRXCYupEHjBaPPTTA5G4zC+IuJamP74z/+4x/a7MbgaYOp0eN5ApOOyK4uxg2jIo8YPvQuH8ypS0O0oY31ESPNdt1Xv/rVpyxiWGkt3eLYk4w5VY5pW1d8Gtwdz4se06chFDAy3/dM60odv+d7vufFJXmY8Emv6Fsc7dYN8Ou3whz5jm5yafPog2njzTjovLbz/L57nlpxu++X3t1bj0tpvSnf9Fl9f95y/6aU7SzH8SmJR9OGZtcKtl7LY2qqGhvGPDCPsE9coOWck6+ld+17WopPk8Mv/puCLfEbo2mmete8fC39a99X4cicc60Vc+1aw15Ke4ZFwzUszVXrnPl21oUgLkB8jXPrb2sY+7fCz7ZSJmXx11o20yS8aP2b7/Uhx7ppsbamAIlrF2EDJzUhbd7iNzd3QZuj/NIT1zNnD9VpkeJVvi7nEgY423H/wvGjtf0PZ9/hfaCt9XleTJawk0Cca78307Qnsteh/epSsjSUaSEXzlplL/RjP/ZjT+8mPQqTH9jPlnDgjfFZ2YVzCRb323/7b39Kzzjg9BPfM/0B8L7FZcLL/vMRLvNT9jz3OGCw8hO6n+7VUcB+lHkuprWyC/7Zj8yYnMDuq6P5q0q5OeLU4H05Cp8A78vR74x9JwVOgPdOgp3BTwp8whTATHTE3nH2I9thLpMIuMMIYkZoNdzCVAO3bOT/zt/5O08bMpoctPppWwBjAzh3VXdU1YY5DY/C0DCWLgZOmOncmiuOP0xXznuMSUworU7HCXeMXvGFp7ViQ9ntzjSc0zRxPHwCheKVfmlgeCZTqDzzKCLmemq3iDfLtDLBmLZsC9OSFZ62TUyR36UXUyU9oPB0mBBhM3fhuT+g6AQAYtr6vvO1BwYxIQCN5VucfiG9GOrV9AdNpdaSXb7AkAnW7ML0ztE9bYERAwz2fucDX3f1ph21ez/T0GYx/L0H9PacT+NptnXv33b/0rwwQZ5H1PNI6BDo8Yg8bkljbe9b4lwKMwGrS+He92/GvjmYRiozLNfoYc42FxIsvUxfNN9di78CYc3b5kWXBQXuXStz3807HXnv3SN95bWOANwIcaXdGlI+yrCucX37JP1f8kt+yVP5ElDOOZkpho7Z31Mma3sO2K+ejesu0bKG5NLYlEdtzVxArrYCdjq902Vlha1s1lqnY1bn1FJh8gmi//yf//NP66x+X1rK+h3f8R0fSyKhNRuyhLy15aVxgo72eu0P5GuvpvzRoLJY09g1ziRV7/nW8/Lz21gJ3Gan//u///uf6GueBqhzNGaFNUb5Tlzd4tLGniepbol3FKYL577u677uKMj2fRrJTkWd7qTASYHrFDgB3us0uiXECfDeQqUzzMMoEFN+mmh4GEnPhE4KPJwCANZ5XJPmIkbH0UaaEcZvgI0NPk3IbMJhLtipm9qiGF+bc38279eYQUwu+7XA5tU5yiodWkAcLWPMDY3hjrdjuFbJ/QQ71Y8phC4z6ajnNH8QsygvGly0T2kGxNyZyzIX8M/+2T97MmExmXP0wFzRTEkLZQWYlDn3H//jf7wKEgbwrEel/+gf/aMl8yHgXZmBvh1DRu+0T6f5Be9p62hXF7JkEzAmjE/r9NJFN/Lqj2CA/V9lSBPHt9Jl6/YWR3tIvJjO1azG1LoWDqNYu1QWfvXoHQ2obC7qIzsgdaVtce/1o7d4jZXSoGWdJtIsQ7aMC/cm+vpM43cVNLxJ5UVzJkUeWabG36U09c0A5vY7hXd0vOfVD5RZ36+/o33v19+9v+aL1/i6Ftb35rpbwr6qMMq8zqG35qU/0PrvqP2MR4jpxMYqmJthPF8Dbwt/qS2BZHPc0IDkrEmtX11qelRXc/u0PVm+R/6ujxDK3tP+0pZO6/zMa7XXj9a//tf/+hfjQNgJtvrtO3MXhH+7uXuuxTOv5z47WbOjg/SYC+jky5r+L/zCL3xsybL3KQyw1jrit3VSH0o4Pud869C3f/u3P2nCBvCyFdqexL5hd+JAmN1pJ+8rwy2+fs+Ek1NDlYsGbnHVv+drfkJe7TnB3dLN9iyiXbo0lIkge7eE5DSKWwu7u0AaX/ziF5/K1tzrNNg15xLZxo77JZwcI/TfAebX0up7p6z0/3ucfTOa3gsM35PHGfakwLtEgRPgfUxrngDvY+h4pnIjBWJ4ToD3RoKdwU4KvCYKAFdps06gdm7+MbFsq9mkB1z8sl/2yz783u/93hdHOzGzmDjaHsb8jrmW5u/5Pb/n6XgVLYlf9+t+3Qtmw4ViHWWMDG4uFgfouNOArIwYjm4XF3faunPUMM0RcxJbd8XDZNA6jRmkdTLtvqlvwLA4QEOMAxvGMTmrXb5s0AmPBtIvLK3RHABUGAxf9nP9jgFmmqA51Ht/GDWaZzlge992fheG/cpf+Su3ZgJmnC984QtPydIyU2ZaMZUbg4cx7NIyWjur65hkcQAo2SNcw87fbF8qR4IBNoxzLs+ojH3/0pe+9PS546aY7sBd5QzAVw40rG2lM59jCkv/mg/0AlQAOFbwZ44b+dJISjMa0/k3/sbfeDITUTnlxaQJOk/m+1oZXvX3eTT8ZfKadJaO31Mg8jJpz7joGUi/tskM96qeOwIt/cCQ8ppt3bvX4RsPTkq8KeW5RoO171wL73tzzi1hpzCvvnMUDygHgJoXe+3CmufTOpzf9ckEb97rL+aDgD/pM23j2wqKznQuPe/qbr2eQjfxs6l6lJZj/8wdAMCncPNoTrAmA+CAfASi0p0nKWZ9so3KLNIt4zRQXN6thwnE7ukf5vhOHh3Ve74neJ3ur/21v/Zi/VHuo7k6el8bY4Bsp0cAsOU76WsOsW4w88M0BC3qHb3QwJpDgOuYOlMC08RGaU9fX0sQqR/aO/zsz/7sC9MW0dU8HcCqX3cip7SULaGW/kII34kmacwxVTs6zYO2bNOWDp+W9XT2VrWz79fs+NOYnfnNtJWRsN4JoHudfUtzO03jWxzhQHOLcp3upMBJgesUOAHe6zS6JcQJ8N5CpTPMwygQOHECvA8j6ZnQSYFXSgHH8ACgtEQxeWyasfPmOClHM8EmGmiAsYthONpkt+HGIAfWAepofebYuA0MZrpgal7QzI3xkBYmBrgoLYwyzZDJVGGmaYSy41re+UBO9cCUlCaGIwbGu93GnMZuWlouv+F+22/7bS/Spw00XUCnfIG/XLbyaMEEYqtnIGDlEScwQTvQ3pn18x2NAIhpw1S/Sz5GHC0B35gqWmMA3NoN4wpomC77sEBtdvk6PokJnfaQZ5yOoMYg3qKB04VCyk8jKucYdXRJqKDv1D8ysRA4XtipfTjBecddaVjHXKuzOgYIH9EvUFvet2o/0c4LkA6g2KXf0dbC7sK8qe8u1eu5ZW6c3RP/k6Ddc8p1Tx1mWCZK5u/1mRAqgGX9tvut3zc2dt/vfUco9sj07s3/KPyuH5g7j96jMwHQEWgnHyCYeXGXZ3MnUAfA1hy1C7t7Z80DvM9LQXfhvDuidwDaUbz5PqBwAorzO0DYekTwZH0l8FvnO7ZJCTdXTdPW23msfwJ1QFYO0CtPNttn3tPeLO1Oc3zCybSICYPRYa6Hl4RGxuxujnLCZILPsxxO30zTQvYDvs88/bbvAcCmlTvTWJ/F/c2/+Tc/CfMIZwmc5/il1e3C2iMt7V3baxc2c+1lEqKiGc3i5qraW3kIUZ188gyE7/4BbfKf/tN/+phwQp8CRF+ibXWcZQOI6jPMZwX4Fm71jcndvQDKgxaF1xenWRPfOe/Qv3B8wld7VnWd7UJzXR3vdV20Zj92tN8pzbmXOy9Yiyqnf1LgOgVOgPc6jW4JcQK8t1DpDPMwCpwA78NIeSZ0UuC1UwA4hjHCsNBCyXZqm2zgK0aPZiXNi5VBAEwGADqiOB2tzbQgMUC5LlOTByYxcLTvfOYXVs2QyjR9GsK0fNNkihEqzM5uGm2meTQRIPfzP//zT0xUmlMY5unUpTSBIQGSMbW0OXMYFcf6JjNW3Ol3nHG+W58nMwt8iKETDgjhKON0TCH4FpMmDq1qDBqXnWCgfpecaf9Ll6cBZaVJc0q6/laTC7MM6l/boQHAJTfNSwCKpYsxzgU4x8xli3Ft19qpm8TF7zg/baTAd+n7Swun30d+Qom+K++RlnllRL/sIhaPf639Z9hHPaPTClw8Ku2Zzj0g1Iw3nz+Jcs785rO2uwQCrv1txr3n+ZZ0zJ+f/egyGQKmI5Dqnjzf1bCN+efWrzlAOs81H3BLe14qn5MJQM3Gz5re0ZiwDiSUupT+Ld9WoA4YvXMJqqTZyYhZXuAlF7CZX90qyxTwOT2ToNB3bUFIW9h869Ol9saHZJ7CHmVn17a08q13QEFgb+/41c3JCy4B7QxjXM66+6aehM/rmLW3mA54bs9DsD5B75n++myNBTg7gQV472SLcJXD/QPRnFBzdU5wMStBC739wJqPU1Df8A3f8OI0VN/1N3WzHssnNwFraabdbb+UwkBhp5/gtvQB/Ork8joX4DG/0J5BGG2/uzvCu0xx8F0IfI9zkqv+T2jsNNnOqUtKAvqrfnu6kwInBW6jwAnw3kana6FOgPcahc7vD6XACfA+lJxnYicFXisFvu3bvu2J2bGZ5SaYxTTDtF/X5V8Yv7R8bcY7Ngqw/N//+39/rD4YDGGYfuBs5iewAgD+8pe//LE480eAZWAZ3xFLl57MdGIcVh+TQmsD4EdzdjKTtETTfHLxh7gAYz4wYHWT8aORwrncS3gaJtMdXSKG+aOh7LZgjoaK+EcMmG8YEvTzHAAQo6i8HEYpgFM4oHcMit/oxrxC8b3zB9B0qczqtCPal2/hp49h/vznP/+kRYyZ1LZsFk/tWfTtwj2aSeLHEAP91RszKT6gP4BVOb2nUR54X97ipNH7L/7Fv/jQ5S4uEPqNv/E3vqhTGtcxw+LO59LKlyYwOeax9zTDMMD95it/Wm3K+bnPfe5j5kNm2Ec9B0I8Kr3XnY5xyCzHc8pxaaw8J72jOIEn8/uf+BN/4kUfne9fxXPj5FWk/TrTbN42vnflSIh4azsLZ5yu4/te7dvKMs1ySLvy7trDvAokm3FKZ/qBnuyHA+2cWCHcPAJtj+ouv3UOn4LC9dtahh3NaU3unLWz+MIQnPWbT1h4zZyQcLRSp3MRakflfW8ta+6fecz5GNA2w0SjtV28b12VFq3jBLr8hLLaZJ4GYXseEFm64pp3K2smONb89Dtgc/1vXshWvQHLtbX+khb1rM+s93w2HhoTvaeVzB6v350oKq+dT2D+L//lv3y6iHCeEnKHAKD1Uv9FD3s863j7xejZ/snJqyPHZESCfvllWqu6rL76UDY4ck4eJXxY911HceZ7tJj7XH3lW77lW57a3r4JONV4JTxA59OdFDgpcDsFToD3dlpdCnkCvJeoc357OAVOgPfhJD0TPCnwUhSgTQoscQTTUUjHVNkpA/pdc2zn2mD/uT/35z78X//rf71gUqSxui4f+c7v/M6nS73Ei6FMgzFbqsV11LBjr44QpmkDLO2WZMflaIkeuTbz8nPphyOltD2AfJimGETfMaG0WeYFc97PP8yGo4RAxbRYHRsUBsPAdxx/dQDMNv40V/xm6mENz1RFDEQaLsKsGs7Sd0xwlj+tJIylNsnsBSBzaj5NRj/NNHkAI5gryLGnrN1Wxh6z6ujnBPCLgyGLmZWmfMs7sMP7S380JKuLS6uAyI48i+M4Oh8YHFDq2Ky+632gMiEC58jzzCvNWVo+9a35ffesnwLWfdMHK8MurHfC746B78Jj0mvv3XfvYv6Pvk9Q4SjMu/B+joe3rc7GnL9X1Q7NLa8q/TVdIM00pbJ+/yR+r+NiN78Y780/lemWvtPaJI5TKDSl1/y6YKt084vL/I3TG0fapFPwoozWpFvKVj7Tn0DtfL97vjWscqvjpfDo24mU1gB+87D8mVxyMiNgz7vAPc/ougKfs9wYfuttDuA554IZtmenYMSZF+fRvJ7lKuz0rQnKUxsSJgEFM/tTWHuA1iTvrAmN7wmozr6XQJoN1/qqcgbUosG6pqJtpilob/+7f/fvnuaQe/tJ84N6EZY6maPc1s17nLJHA8LjngmLf+fv/J1Pv+0XCE2tla2DQOlOeNEKjlbKNU/pzLK4HK1yaxfCX/kQRhPK0OQlSCsP+d/i2P4VBw1/+qd/+pYoL8IAjwHSsw7RYPr2p19dTFy9SOR8OClwUuCQAifAe0iauz6cAO9d5DoDvywFToD3ZSl4xj8p8DgK/MN/+A+3t2K3UcVYACGPXFqptCmzG2jTvGP4AiLZiqWBKo8AOpoOftPoWV2apOKkFUdbhsZmDIY8bfqBkz/xEz/xIRt2f/kv/+WPAWeYp5VBr575HTfFjFYmdQTgAoYdzwRk5zAZ4mZ/N9uAwM+dw+jGmCmPm73Fx0TLm6ZLYABmn9ZwZQOC/5t/82/+n2RpoBYmn6YXzVCaxWz9xUyhnzbtd+GBvMB6oPPOscMMqIjpdRkcTR4Aj2cayWjmKGLaNi7cUTYgfeA+wBgN00BSDuGli/lnB5hWM63pI02dwFW2G9mFVocEBGiXNnVHe4Hb1ZNf+XoHCFYH2uIxirOfxGB+7dd+7VM6yk7bt754CZgojyM/eh59916ZABqV7VLY536rTxZfW6ygft8+CX/Sf+YHLGkeme9fxfNRGbT3D/3QD30MOJr5v0x/SNtvprd7vtQ2jW3lOKrDLs313don1u9+39J/d/FexTv1Jly6hf63ljs6/4E/8AdezCET7ASOdQJlVyfCR/PF2g4rbc0xzfu7dOY79bxW/mvfZ3qXngnWHEv/4CMB2rz8dI0zLwpt/WhvICzhrKP6a73XdPye7UcLuPlX/jRAv/jFLz5dkpWgrrUE8JdJHunIi5DwlvkCvYB2E7AvLRq8nP1Gba+M9iD9ntrK2ieb+Wv99Bd27//H//gfT5rJ63eaoKuztxJOmtb0o7RLqz7rtzVjXe/U1f4sjWoXBt7j/tW/+lcvxoI8ANXKlZtjhaCcoLk2ir7NUfVT9LT2W4et/e5MuNZu4hK8tL6bO9cTYMoE5NePmbgA0jMTYd+VuYidxnR1mb7+OzW7o3c+YY0+6uQSAP10JwVOCjyPAifA+zy6rbFOgHelyPn7lVLgBHhfKXnPxE8K3EwBNy23OcWgsFsHHLNBBd4FNtiMA912zgUW0vB9MoCYwtWlrQQgzcbppz71qY8BVzu7aWmF0CqW1zR/IB8b9NKuPtMH1MZYYiQwbvKxCQeYAoUnAw7M5YC00qH1s14gU92AmMI4OlieGJ5LF3BklqHwOx+zSvulck/GyPHc7/7u735iMAHZfu/SmO+0IUCUJhLHdAG7v8KoA3D2msNYT+Zxpu+Z1m7MJ7MIAeGYyTS3aB9xvgVuA/m7FEabBJJLE3MfDfLL1+8Y7N7pg9XLUU3AxKrBW1jHSgHOOSC2NGdfKCz/EgCz6380joDCE7DAlEZDpkXQPQZ+5rU+X9NWW8Pf+jtGe4IA7BJm31g6aanfmuabFK765e/KdnS8OPBhF2cCOrvvhDOX4u/iXHoHqNr1sV0cQqEJru3C9O6or9dHC/cI/9LYWsex/NbxfksZaEMKN9u7dBICrenQ0CRcY0olrco1TOmxYZ4pF2G0s0uynmuTVxq7Nkh4tJaj3wAwF2423/Y+f+17hF0JiMxjgaaFP/Jp2c7y+Q2kXPuH9nOEf7ppN5WQeNKo+ASkabjOeb85U7oz/8pZO9GU9ayNCSfN4cKo/y5e8QvDL1xrVGEyx8ScAZfGq+/GYvMGMAKAPdMqjXt8a+ZOMJ4QmUmL+nLpMvnCjry9USYc+rbz6xe0hIHJwgA+73HA0pn2TigPiL92OobgnuauvWbja6bbs72Pfam9DpCVoN9ppvpIY4CgZXX2Amu7lm6+PgS0vuSs0c2/6kWwzRwDc1/20VMr3Jq/2/9eSv/8dlLgpMD/pcAJ8P5fWrzM0wnwvgz1zrh3U+AEeO8m2RnhpMDDKcCOnw2ujbWjhrsNKVCTSYM2wja5q6Od4TvTBHNDD5y0Af6+7/u+F0BfG3HH4wIllWMCw7REV+eIvjyyp4tRXJ3LwjBgmCRH52ipKtNnP7p8aF6+oTw7l2ZGIB6tFho7mRqg+bFeyKEeyoXpjQ5+Y7ouuX/+z//5x2glDsYt5svv/jCf0tYWANpLjJByoLnwgHcmBcShybwDnGmFyke7XHPMd0zm0lFJx48xcoCRtKmkB1AP3JUuENV7GrnTqVP0pUWLSYoGAEd2cAG/BA8x4dHlET6QDpOLcaR5rI7SrZ4Yukv0PioDICXmc9qABERo+/raD//wDz9dDpMgZeZ9lPY976vHLXGiO5rQggwM4h8BY7eke0sYGtnX2nf2rxX8uqWeBCa3lOXeMNHt3njPCZ+QZxd30gAgRGh1KVw0vLf8hc/f5fGod+XRWJrz+MxjHoePDvNdY9g81POMTxAEAL0GkBEMcpnvaT0D+lrvZpqXnivjGkY9aT+y8RmotwsbXZy+aQ4vrWm/vHd8Wqg0IycQNb9fekYzQkBAlstEzcmrXXHxzW9o0V6CNmVAbPOJcAG6AaeB8tMuv7WsMpmHO82B5jNc7fk1X/M1T23jsi3xopG46u6dMqBr/an0py+e9p0CB+OuUyMzrGdrLHv51kK/mfEgRAUY0vB1D8GMI33gtnLPtjXH7rRPAb6B4aUTYImOqwOq1/dLHyD/hS984UXft8fhuiyOeaB7nL1RZVGPLmBd00AHexFji5mMAHFx7dWmk4ayO5EjvDDWBIKXHegtrjV72sm2T7B22afpg5QHKicfHfGf9nKEynPN0SfmRbezbNbp+pl9FeH4zrlkL9rTLOaYyGKj2HjRFwhOpZdAe5fO+e6kwPtOgRPgfUwPOAHex9DxTOVGCpwA742EOoOdFHgFFMB4AG3b/NN6oF1ztIlWhMBLWj823ABh9k/ZLrOBldYlpgmTZ/MdY2xz7514tBhj3NmhW52NeowBMwA26rSmbnVpc7aZ3wGd0uoCOGkrFyYIgEvjNIYKEIe5oXUCnOxoLgYweiof+hw5GqVpgkzQajIi85nmXgyzNGkKKQNGFeMKyE7T6Fb7c5WNLTl5YZIuuS5GQUNABl97T3vJGJap1Qi452gKdTxzZeoAFNP+76x3z9km9PuIyS7sPX7gzKU4LkdZtVcBzzF7l+LuvrlRnMvkCBt99QVCCXG05y7ufIexj5Gc7689zz46w1YG76aQZoZ59HPjcZfupW9r+NoCQHJpDlrTLJ70AHuAGQB/Ap41n7fhNy1v9dTOa30rf31gBQIJWtgNL1y+/r/aHu3bJX+XP3ALyJFZnkvx+zbBLf0+YV/f+YHVvVPHORfN9z0f+RNYXMM4Wm/uD/xLaKDfJaRRFqZuqr+yWFMCGaXZqQ9rYmsA2nRZKOCqdqoMs79GE2lyjrbvwvXukp+AbYYJlAVeuhir0x20Y6091+YI+wRCLPSZc8vMwzNegMALuBdN1zB+M2sEtEMDdac1uYbLbnwninzXbwHSXPOr7+bfda6Q7q1azeVtLWzfxHyT9wC81REg1ubF5esj+oZn6xygmzasvQXH1FTlnvHmMzMLK1jI7us88fFP/+k/fUqvMa/NOfm0D2PmICc+00roDAT+uZ/7uT49gdD12coBSL3FMb9QnKNL0PS3+pdTXtccUzlrW8pjXR9d8rcCs04kVZ78VRsZeJ9gDa2vOQKGxs+ltdz+46//9b/+seTs85wGON1JgfedAifA+5gecAK8j6HjmcqNFDgB3hsJdQY7KfBACmAydgx8G1sgHy2P1dl00pw9Aj5oKLYBLi3MKeYFgGWTG7Ob5k5MtGPENt0xgZgZIAt7bpkSyJyBeQMjEyPIju8tbh4LBc7FrK5x1TMtoS5lAYRztKSmlnH1nL5NfaYkMBw7TeH/9t/+2wu7vsDhNCMxSOrpUhJMBC1cx2+zQRcwuJa53zRClGWn2VyYnR+jTOv5yDn2GsMSQ4JZkh+mtMvtALnaPfABDQDAv/bX/tqnsDTHpoYSrZb6xQQzgOnqjvYxbr7TQp5Axg7Ame2xe54giWPWHfU0LmKI64v8NJzWtGZ5129+K3d0mN/VVx9c4wOEshuc5t6Mh3a79GaYo+fG2tF371dm+FLYR39rTqgvHKU/224No63m92tpzfi1BWCO5v0KFs6wnqcm4vqttNb3b/pvAhjz6a6/m4u4WwQij6rnbD9CneYf6d8Lwh2VSR6NeWuLi6MAq60Bc90ojVuAaeMYSFQcoBpTP/VP5a8OTBqUD1vyHAFe609pXPI///nPP9mkfWTfkxb6zMunrIFdeqk8gXCegdPN1ZfK6ttuPkL/6DPjS9dvGqbW/kDyXV0BqITXxRF2rjeAwJn20bN9jrRKx3in4U1D2LH8Tl+Ib/3IMRPlHTu+Oxfw7EQNTVUg8+znszz6Oy3llSb6H9NMq1AUkL0Cg04ulSYQnUtDfa73mZZCLxqos42Lz9dPXZDGNIjflb2xaY8yL8FbaUB7fLbbbq8pTifLKANccvoDgH2mqVzmsspU+dOmXdNLcD0Ba+vR1Ei275COPhqYv6az/u7CXfH0d8JDQn9zKXMYCbx9p8WuX5lfq4s9rv0J01JH+9U1z/P3SYF3iQInwPuY1jwB3sfQ8UzlRgpYKC1sv+k3/aYbY5zBTgq8fxT4mZ/5mafjakAtYwbAaKMK/Lq0kd5RCpgWs0DTKLDom77pmz78vb/3977QIjEugYw52i+YK+/7swF2jNQCfASGdMROXoBldm5XgNQm3IZ33YyXj/K6HCyGMGYq25K3XIwBtJBe4LSLxy45GjMTwFlNCrDby+xDoJS0ARDo2OVkMUwYRGWcZh0wd+IA7GLSaN/SjNo5WjXo4C/btbtwwOm0nGPmduHWdwH+GJ0jF5jrErkc5io7lMqGeXXbtbphhmujwFL0crwZPfRFwDIGEUMDVJtmDGhSc7R7pRejnRY1gDTmsn6sDMJe+hNnXv4irOOg2rS6KA8G2jd9JrCndJUhJrl3l/xV8LELK099oTrE5O3CVm/fzAnz9y7863zXBYW3lAGglRZ/Apxb4u3CRMf5rT6UNnjA3gzzss/6hjmAgOaeuu/yXfvALTRRb/Fm/au3POZR5l2e8sgm5/xOyLQ7jj/DvG3PK32Ndettl1Va12jT7sZXNAXMAPPW9REtZhtcoo21r/UNQEajsHlcvDQuPVuXzH2X0tt9W+u6C7N7Z+4GOpmbaXEH6JrLVwBa37En0U+ak0uT8Iigt99HvnL+ql/1qw7D7QBkcaw9lacyej/tAQPm7CVm3my4Ng/kd+lW4fR9wDvBs3a2/5q2yZ3GyTFNIZ50V0dYXdkA8q0L6hRoTat2Ao2VYfrMQHDVd34DGAdA0sydJlrsIRO0ijNNSBGwz/XXd22sf6sLjfm13yk3GvuzL6vN2VC2tjv5BFz/r//1vz7Zl4+uwgcgp3G90qpy/t2/+3fXTy9+qyd7xMpq7LZH8dsYniY8vJOvE2erS8PWnmPSgDZ27jf8ht/wlA9Q/hZHqN1+U97mldUpP2H52qf9bj4Q15/+kWb/ms75+6TAu0qBE+B9TMueAO9j6HimciMFToD3RkKdwd5LCrhdGZhmU9omb/VtpF10cYubFzgBH11SIb0JXgLsaFi04cToYxIaq44RApYD7AIkAaKznDG/3sXEYJJpvbhQq+9rffzG2DrCp4w21TPs1OZwBE76NtFMS1xyjnZKW3jluHaRhrRow06GBsPhKC1wkOZL2lfKx1zF6mze1SO6oCnmEEMy66RcgPA0ldd0+u0CEmGvAdrASuFo2nTEszR2fhqjAMtL4WtHbT2dPoMmt4IZyrb+Aby5CaSyP8llGoEd3smwAVUmAxVzKe2AMKBCAPrMc4ad7+czJnsVOhQPiDcFAMXTro2N3h35aW0TDMx6HIV/zvtd3e9Jp757Kc4OZJjhAQS3lIO2U1qMBELMYlwDI2c+63Pjs/eBJhjlwHnChnlcuLAv45u/2J12xDiA17zxMmleirsDH9GvOh7F1bb6K3/X/25p+6O0d+9vmR+MnV19duk95906NglxGofNycC8BADAOgK1o7ysD/pQaQjXHLGLo271aWt3ec6w1r3AnWi2avNXj8LN+Pc+P5Le0sp+KS3o9hFAy9YWQtC1jADs9hi+TcHpGvbSb/1+9908TvOWMonvxiWtz5X+jdfKvUtr9+53/I7f8bRW+Ze5I20D6JaPtco+LVuwhFiNT+sb4G41OWJMzr40n/VRJqYSltMMneUCYlrXA6znt/n8jR9dwpizX5kmWNDAngNwC6yk+WxvEUAtHfN1YwWYywTEtfVAeOMusHeC45WFX7rA4SOXDWDt1gmixmJAbfMYBQZlRte5/8sklzXdXQudFBNWGrnWsEvlKSwhR9q5nXjYXRpMID9pLk+Cb23BMQUDzM8klflmBxSX7+mfFHjXKHACvI9p0RPgfQwdz1RupEAbulOD90aCncHeGwo4nhkggTkBgNFIoI1iQ4xhCAyzEd9pJUxiActimtjR5X70R3/0acNro706R0sxaxigFlgamTacXMfiM48AxLE5BdLEJK3Mk++3/LHN6Ega7ZJVkwdoMzfntJ2kCVRxcc6Ro41c3plbOAo737uw7ageGAeA8wp4zvieAdHos2MaMbbq2oZ+jTt/a3d1+OW//JfP1//PsyOaMRcYSeYgjpy+UP0cuzxywGd560NHDhAC6C3vwkd3zAlNIKY39N80NX0HZGBIo5GwHM1x/RCYCiCglVx6mFPPNJ8n0+ldzFhhAzECTHq/+sba59lBbgAAQABJREFUDrgtXMdGla93tNLY42y89n76MZnznWfvKxuNpm6PPwo/4xdvvnsdzyvtX7YMxjJBSH3hFlrckmftSoOvPk9jm8v+aIBZ32e6+s4EWeY3z4Cc2g9T3pFeIMw8qrvGm793+c7vv+W3/JYXYOzLtj+gO4CotNLkn3k+6pnQJeHTUZqPauuj9Nf3hGXelS+QpzCAGZdj3aJ1Ko5+SyA55wZ0rR/7Dhwyl+pL3ic0mxrTgbfFqzz5tVW/7/XlPct4LT7aNC52YQksG1t9d5lX65V35mOCwNx3fud3XgUfxZMvTdDAQO/mGFE2IGY0K/98tErY1zs+EI8zb7MvfGlcCy8d6wwwrjab6XlmXsfFsNJbv83f8gI2eyc9Gq4/8iM/8iKO+hkngD5hCAQCCWc69n9OCXgHYG4v5Hf92fO65s3+I69OynRxrb5BoDDTmPl6tsbXhzJXEk2BlsxC2PMAotUXWEu4TxBuf2I/2hhY7eHWR+pT85LWvvHtDeqXwOeck1VreeXPdXKKJjOnbzXO0iS29xa/+le+6HaLqQRavtKwrlSeneavNUM4JwU6HaGfrQ7wTKlAWPuM1QzHGv78fVLgXaFA/Kc143TPp8AJ8D6fdmfMZ1DgBHifQbQzyjtPATZM0/LBcLN5t3M20jGoNsuYhCPn1mubwwAN4dI0kdfOYVjEsdHFCEzNgTbWNppdemKj7Dm7g9OeJ+DDxhVIjNkDpE1Grc2z/NY/gDGt4hZ6dvHaZNOUyAyAeBhsF3LQJAFgMzkQM+U7pqS4uzqv72hRi6cu7FJiUIB8mIFMMaxxjn7T6KPF023agIN7HHBdWWgpXXMYzUB4ZWfvTf9wSQp7iuiyasNeSlN68p4aLZfCT/BWPH8uxpuui+kCmQrHx5RzMb60p7nKIUzrB2AaaJIAY6bjGaiMkY8eK4ilL65x9McYvPlt1YgCuHGYzQkOibMeBZ7prM+02jj9Lc2jNUy/r4ERhTvyARRACODCru67eMbq1PI8AlR2cZ/7zpw2bS3PdLSD0wRz3tDPY9Zn2Gkr03vx+OL6A+J1uiFQYcYP0Kn+K2Ayw87nwgf6zm/PfT6iuzmRJrJx7hj5ru/ekiewBrjU/H4UB50AQ9fyWduPINLccy2efIVJc22WA1A6fz/iedWiu5amdWRnG3XGW+vI/m4uG+31rYS1M77n+uOcqwuj7wJ7Zj6AIsKzIy1Ka6m5ijBOuPYZpbkCiU4QHc03re8BfdKg8Vkf7VKwtGqZnZgOcDaB28pw5JuraJMmrJXv3EPsxiWhIWd9+PSnP/2i30jLCYEc4WGnB9b8q/9Xv/rVgj/Vs3CT/s1Hcx7Sdru1yRjTFvZV9Wnx2YDmnHSSx7d/+7c/mezwPPPy28kf9fZnXWfOYA1TOfmEOgGOaboKb/5LMJ9NXGumOQVQqw6E2faEykcgnS3j+qi+2ImuF4Q6eMh+L3MSR655GuC6c134i07TdeFh8696A5rVx0XArXmz701NZmCqONHRnoOLPik5zDzX5zTF530B610MzJXpv/oKE1wucWtM0YZene+NqVneNdz5+6TAu0SB+L4T4H25Vj0B3pej3xn7TgrEoJ8avHcS7gz+TlOgW5hpd9psXnOFB55OLZkZL0ZugsA0IzBjmAqMxupshmMOJhhJi9h7gBnXRj8QzjuMhDCOr8b0YCgAzZgQ2rarxiOtPUCXiya+9mu/9kkbFEPX0U4b3JiStC3khdn4U3/qT22ZqMqPgbKRVpZ7gFmAhDQwDY9ytEakubPRdykPF36IR2PyFkdzFwMVDS75aIPBxTgAGVdX3pjqW1zaQJWX/x/+w3/4WNRAC8c///gf/+MvyhnDiMnRV8SlJcZhYqtHwIGjwPp9DB0NbUB2jLl+wwWcZhe6dNKc8Tumrm/62/qub/zsHMdUxqDNMNeepQ9wZUNyp212Lf6l7/p7gItwjgHnABYTIJnppJXlXXSd3x/xvGpZzzTlCViIqV6/9Rvj/ksHYNv76bswaAWu9LFANmAYIVqAyox773N9NxBhjb8DLNcw8/c1oHUNy7Y4wd202TrD9KyctP+sHTTDjvpB4Xd+8/ru23w300YXdsfn992zOJndmX1xF3a+e874m/E9m5etO0cgqTDAIcJFaxOBplMJhK0EkYR4bLTOdPXnKai1BjV/FW6OM3OCdLIpOueg+ph4E1z123xk3dY2M45vtcM9c4zxR2C6psVmKY3E5tjSt3Y7Nl9e3meWYNp7NQcp66wLmpmrpzmKOV7MxfYa5Sm+eR8tE2Cv7e99LtAt267oP4/ba59ZHmUHggO5PaMDUwpTQzdgftZX2N2f/mR9rfy0YPWbOcbRLtf+KIDTyZ3SnX2n92jFnmthqov5HwCsDeXnu/WeYMuFX34HQDsxdY9jl1j8hKdMRRgXlxzbw/qnP2bCjlya88yk7FzztRNQHME9MPzWeUm59e/19Jt+4FvptDeu3yj/NdeaZG8SbVfQNo1rAv+cNU/elAl2rlN3hMm3nPrapXG+OynwNlHgBHgf01onwPsYOp6p3EiBE+C9kVBnsPeGAjQg2vADvm5xmBzgro3hbvOJafINA7luvgMAaW3tXGWZjIfjgNJzDJPrOBqNzFxaNhgamjvXtNikv5attKaffTTakaujPUw7DEMGLMaEYzhs+mnwpOk7N9RrGvM3DemAcWk8yv3gD/7gE/2U8R7nkjp0B97c49imAxRgyh1VXJlRDP9k4DHJQOjVpaE6NbmZy/gjf+SPPGlaAvUxfLS4Vs09eaDndJlyUK+pTdN7oC+QQ51959Ko9i6tJ7b1JhCgLoFCTHpw8g50i+GK0ZaWP/WmZQ34pO3TDdrdel646QMZaDx5F0NozEwzADN8z11KKM7aHoV5tI+G2kt5HVWt/pj92f6X8q2Ol8L0bQLLvVv9ad7BHEGwc1SWAA+gDc2yQIWZprgJl3oPmEkI1js+AcMKCM3vntWhOXD9tvtd2c07O1oBpHfxHvmuMgCfAhHW9KeAzPhwZDqboGvY1/XbKQBzZevLBEAfXSbzU/MCIC4amjPSFCzPxg0w3fF6DthiznAhJOElszyzbzulEFAkvLXOaZA59uezvPTxaaLoUj9Me5VpkADIW4FcdTVfXkpfeRor1T967Pzsp/pmbec7jp6zHtffOu2hHPYLExwPpMyedjSiAWod4rdGy6M1qjLReMwFhNpXJeQzt+e0YfkUXxmbd6ZASjh7LaDdnOfQ0D4DPdFAeepL0tS3omN55OsvCSOVqXCZCOjSVuGtkavQqnSmj8ei1e/dBIUzyyCf6C/M3Of5ds0RrItnvqu8TkwBNldHWcFeoXmRTf1LrlMV6D/pUpzmeUA1Ldt+V//6Tr/5+m7jgiDBer+6LokVngAlIPVP/+k//VRX9Lrm2mt0vwHzS6trn2MPkcuUjzXryKXkoN6nOynwrlPgBHgf08InwPsYOp6p3EiBE+C9kVBnsPeGAo5x2Vju7HBdIkIbbdoyq0sjAUOwOlpS8sOk7ADlmF4amRw7wDboNsqAMC6TDDTBctmfjIHC0AJ62WiljZn2przlcauTpziAknvdz/7sz77QZEnr4ygN2iBp+LGjdqQZfRT/0nuMDnqjYzaML4XvW2ANsPaSw5AAQh3DBXrnaPfEXGFStGXa0YAK7ZNWDBoDWKejje09JkT61y4AFDZQBrOlT8zyALiFSUszBjrwU7+gLY6plw7TJVx9MoBXGkd/4rr0JxA2gOAoPA3XqdUlv7SId3GYCoimvhO00KDUBpMB78bwwgb+AGZomAYCAAZsaJl+2OX3Kt6tJgzWPAKP1vfXflenS+GmIEA5jLtsT67x6kvom/NMa5LWZXZ013g7QOqWsq3pPPf3BFaupTHLOssIhF/jBmDQWC9svrCEdrQ557vSAPY39qNlQFBhdv6l8VPfnvF27+b3+TzBzJmPuXLSpTgBhP2+x98JB2Z8AI219KsfrTcffPDBC9orF42/gECnAqx7K7A40+oZLZQZ8Dfbc9a1sNPf0RA9Aqpm2DRK1zELeFz7QWUwfriEvdfKM/ObzwBroPRKC2u+cIQsucBK69AKdgeOzfl9CkaYmaIpvKPLLE/PmYboeDub+QBB8Y2hxgGtyeLwd+mbg6yR9g+0TwGPzELNeMbcdDRwCcVnfQoPFO0ECeHWdAF5tIa5AMaA1DSSrZ8TZC5ta9NqdkJ9uwSvvGirFwe4eY9zWZq4BJqAytoe7cwxTDqonz1f/VU/XLW5d3laCzqJQJji93TVjYmmVZM9DdjGgzLWr6PrNJky021fIo58c8yRtUdpzPRt9euv7VV2ihfN39PGcGad7DmOXO2u3U53UuBdp8AJ8D6mhU+A9zF0PFO5kQInwHsjoc5g7w0FbBxtLIF59ziba/F2R+wwML5hnHaOJq7vGJB/8A/+wceCpNVDE4FWZ4zE1L4AsoiPeU3bocuoMHc7h8kQx989YDZNTHEwp89xaWNhMhxR3l1WwZQAJlI+aAZkfLTDOEif5sstDkOK9sp9pLmBEaT9NLWn5MHOI7t7MfTXtHQ++9nPPjFD8gIC59AFsOB9jA760IjWb5SLCZDsQst7/cP46ScrOOMmcRfa1d8CG2hnxXBhCjF5MU2lHfDXkWplisEqTExxv/m0ZDpKOd9jFvV1TBnwbC3rDDufAWppKXue35R9/p7PAZO03oHLMXkzzCOetT+mMnpdSlPYT3/6/9is/Lqv+7qttuyl+Effmj9234HeAWe7773D2NNAI7DYtV/hpo+pp/kLaJjvMf2BEvP97lkfoiFJ+7XvjYN+3+IHNNwS9lWG0RYECbTqgJnG9a35qYM1YAeCefcyfbh56lpZ0sI8CkfYclQn6+QKgjavOCUS6AJECZiSD+EDx1TM2vfkdZTfpbJId52zq9MuvQRgheHvwMPZz2qn2V8DPLugKk1YpzBm2j2v9Op9p3OMDY4AE1jZ904XGa+cOTyhB7DV/oFg2xzZXFjc6bc2VAfzmLkpPsL8iV7qOumGXtaW+mQAZ+sKrWt7pOwhzzzVedJxfvOsTNllb+46uuzV2kDInHDbOiWNHQCOTgF50dU4FZ6AdQo8gJhoar3yfdbdb3/GOmH7TqBMi7Vw955USru+01u01O0HAi9LN5+ZA/cj3OqcOKndmS+Y4DlhrHSjf30coJxTn/LWXwrrnX3O6tp/+46Oxvl0AG3v/dE2b72fYTynLS6dad6scPbJ5jnplIY2bB5gn/fIpaH/uc997ijI+f6kwDtDgRPgfUxTngDvY+h4pnIjBdqY2cic7qTASYEPnzaNNoU0Ye5xGAfxMFM7FzPq6OjqaKdmvkAaGLy/8Bf+woc0I/z218bT8w587hhjgGCbUFqbOze1ZY40KXbxHCVWBlorz3XszsUMYN6BPrTXHCFV9xg6zMC0Wfzc/HbxaIbFEGIUuKMLSjBlMclAt53DHE9gBH0wy4EWtaOLUi45mrkA7bSFgaYxIOJpq9ICmk27kqWLaS8MP2YIIDMZ0wADzCd7wVwgLbA4DZ3ohEHbHbWXhzDTTEP515b9Lsz6vu9HfoDE7nt9CZ0IDBKYCBs4lC1N71zytmocqRvmN5qUz7VyYqYxnY4Kr6ByaRz5yk3QMMf+DGsO8huwWVvM7899rg/MOeXWtHbAAYCDyQmCIn3tKC31NJayxbwLV9l8W9PSNuat7H0KUzvOY867dOc7plLm75d51jZoYv6lUU9TEHBXn1zTBhYFmKzf7vmtvwKHJ2BSfOYIgODT1u5Recwhae42NqVDqOLiM+CcMdA8dpROefOrH0GWeXYdU8KYKwlxdt8A/sA+dZzpev4v/+W/PM1TgKfmJW1QWH1GPWgupik40zAvA6qMVYAkGs0xvva5GfeeZ5fu7fKfaQQwtsYlFFOfGe7oWfrM/wRQEurmAFhzLKljl1O5UEqaBDrzlMPMR/h5isH+xd6iMPJOk7+9R3Ot9XQV5Khb83IXzXYixZqyHu+Xj7pp79lHah/9UDv3W/j6QABytFj91oLmoUxerSdzEoI71UHLuctI7cvMQ82FygJQT2isLHMvYC+WKZG1LP1uXHXiqvfX/C5LAz5PBzAHdLOfa79F43kHLs84R8/MNDXP6FNOfWizhPD1Cb49HKA0h27zAsXmBmF/+Id/uGBPJ7ScWJrteaScoE6NWX3PekDjlpDCt6nRbd5KQ/xFZr/4kEDB3p1LcGjfdsnFN98DlF9K7/x2UuBNpsAJ8D6mdU6A9zF0PFO5kQItVCfAeyPBzmDvPAUAqzafNDHvcf/oH/2jp3hAop1jp1S6OxMOwmPImGEIsBB2/cN0H91kamMrfIwX5tFmeQcAym9q0sY0e3/N0RSVD5DkZRyN5ximtZ6YCFoy91zG9pyy2NDHUMQwYNoc53fZHHCCxljgIiZ0d+keLSZ1wKTpNzG+lSnGoXpiuKYD8NNay4xA4fIdfw98XgFWRygBKcx0YMBp9QEXihvjDKCWD4ZLe/vDiDneLKx+S2sOAOI3oJlJhi5aKb1rfky2cIFCxZlMr3eZSfAMWI7JLTyNNkeJ/8k/+SdPx097f+QD1gJNhNG2wAbPUwuYkCXwdJeWvoCp9209Sj6ZR99d7obumOA06XZp7jRnvaO9NoGUGRdg0hgJ/PEdsz3BmxmnZ3Wfde69uqU5mCZe3271J3ClDZmWCQC4lkbglbpNZl88/aXvR+mIU5+urzZ2j+J4j9aThoVlWue5dCAw6cTEHM+eCcLq7wDLQCpzvHEHCOldZdn59Wf13gEquzi3vKv/EDglfMz2avQQpjoUfo7vo3wCArUPjcId3cUFrLD/HTB4lJ6x2LgD5gDLEkQ5GZEAjK3UacpnzoO7tJv7d9+8q1+hhzVA3fVzz+tcJbz2TAt3l+bR2m78c3P+d7x+l8bMt1M8XUK27g0mXecpnXkBpzzMceyh6l+zfZV3CoHqA+I4UWReBnTOuQ3QaUwE6gXCV5fKr6+lSdu3/NIjNAuARlsmEpgYyk6qtjCXr+MI0HfJAdTlFUAb0Ey4PJ1+VT8D3iq7smWqaF5MWtmnD4C+xeQCe8jF07d2p5pmuXq2P6pPAYYJTNaLVAv7sr59JOF2Y6Ly5pvj9IWd02a7tiZYsm8lJF1PcRAG2qscOeDwpfFtvmx9McfvnL248hPOq19l+OxGs7j46GvekPbOLnHhTv+kwLtCgRPgfUxLngDvY+h4pnIjBU6A90ZCncHeGwrQGrHpszk/Yt53xHCUXLxVC6SwjrZhnmwOL9nuclyP5ojjbzFDAWXzkpTSzceMYOKUofA2yTvH5mjMjfA7+2S7eN4FMrjd+hGOVoljhphVNgQxCTFQj0j/KA2AfMwjGlz7O7oQBFMtLsZvx1iqCxBCmHmMlwYZzZKf+7mfe2HnThhMuXkZCBej6z1NFUAaxkIfwlyu4KNw6x/6dpnIrp1p7pZOoCbGefZ9tAoUnOnHEM13nq8BJ8KgReGAepyy0AQtPX008IYGkveTJoU78pUZuD6ZUoAsB5yY8bIVaAwB56IJgMs4bCzmz7j3PNMa0nZpuF1Lz1huPJfP0THqFeiIvsW7llfhbvHRJa3iSV8CAcx37RRAuKYp7oy3fve779Ujf63nLu7u3a7+wAemVSawtYu7vqssl056pPW/xqVt7OgycyDmvVtoIT+CoTWte3+jwQQS61vGsgsypVfdLqU9aZlGpPDamzPO/KYtXzuu6QEPp6atcGhj/mMPmgkfpnG41mX9KpuptG+PNCP1wVnG8gY4Bvz07ppvzZvAsfIA4Sb4KQ3AaSB5aSaM8Ls5prmlMHxH09O4RsNJ07lWJ5ATh+YpwV/CFTbfc+v8ZszQPBUmoYE5mGCKc6FloKw5KkC9vnA01886uNzTugGwNJ60ZydOrEEJZGYcz8o2x3SCvwngOSWRIyDJdjh6z3VVevY311zmIYRn07nyThpKAx3nHNbFtr4xayQ+Aat+XDuh2Q/8wA9cBCjFz2VHN+GWtC45wDfAubZRhvmnbv/4H//jS0k8+5t+ArS2hnVHgrwB05dMaemnAPRZzt0zGpoT5/7jqLD6gb0JTV/rqT0a8J8pEvM6oTca+dMP1zHMNrQyoHtj0rwl3SPXyTh1P91JgfeBAifA+5hWPgHex9DxTOVGCpwA742EOoO9NxSwsQTu2vhduwgsogCEYjJcmnbkuogNM4GZPtrE0oAqPQwFLae0Z2hwHmk20C6YWjs0Pz/44IMnu2m0pWzMY9zUr8udbG7nDeNH5c/eG1BAnd9Wx3ZaDCXNEmALLdwjwPfITi+N1+wx7rQ+aP/GfO+YGUxRTJ3johjwCVh0+UphAs3SxqJBAhDXRzD+Lp+hDeO4YkCAo5vZtTvSHsfUTtBQmQkTxNWHpgZe9d3V5953GGcgDL+byvWpabePVhIXYETbJq2qe/MTPiYUA7zGN3aMg0DmjvDSpkuTdo2z+50JgCNgawWeAlx2afXuiJnv+yN95b6Wn+8/+qM/+gK8Uyf9C4MdsyyMuSs6XEuzOsw5zLt5JLcwj/LNZbeWKwGIvIGM0Qn4OB2A60gDc5bbOjMBwPnt0vMRUHYpTt8CdK1BM2/12ZkK0RaO/s/j56WV35xQuzkdYBylGVy4a/6R0JCQ5wd/8Adf2AKvvQhvdo4ga9UcDVwNlAUkBSQqV2nWV9eyAqcSNpXnBKzMScbDjEfYIc5857k8jvqIsrf+C99FV55pyM700rY2VxEWOjZO6KB9hWudK0559xtdgOrlR+v/3/7bf/u0N+n4f2Hz0aq5u3d8msRcADzAzT5BnspD2OnEwnrSIQ1adt+lW1uUNpvyqyOUMedYE9e2tl6iPaE7bd3dPott1/KRb2AlkHvurwjmWweUh8as8mbWQd6ZOOk7f5rLWMu+/g4oBurXbvZ5u3Kz11xbyUcd2N8V3j4ljV7vrZWv0gVMB/4bUwTX64kwWtbZM1bmbPd6NmfYbxAEOVXm5NAjHU3z1lvtCOhlusK+2J6t8aMs9oLm7iOXsEKbP9fcxVHa5/uTAm8qBU6A9zEtcwK8j6HjmcqNFDgB3hsJdQZ7ryhAA8CGz2Y5+1xHBADIBT7RKrnmspcnfeOPVhbNTxofNqOOyvvmD8Od1gFN0Zg1QANTEj/zMz/zZMaATb1v+ZZveQFQFq50Vl+92ObFyGS7Upqr9sqsC0A6kNnz2+rQLE0bNhhXJop2DPMRbDyiUbTcaV0DUtGWRtx0NEAmIBKowEwH8D6QpXYBLO+0ljHDwgDlMW+Fn1pEM9/53KV/GGCMjLgBwzOcZ7eRz/KWz/QBwH/7b//tF4xtWsno4/mbv/mbn4DmCRTP+PM5kEFfT8iw9qnANCBUtqgxtjRzPv3RUdHSU78JzMcg9331MXEAjHkJS2FooKWBp490qQ4GerYZTbu07cQFav7JP/knX5Sp9KamICbT8W3pYr6BdMCEmM/i8H2PRvP9q34GrsgDmDZp3BhY89/RcA0DaAnAWb/5PdtOvVewRhigxpwXS2e2Se8e5QP/6gulCVSqXYBo5g/fmGrIAdlmuxf3Vp/2XadBijPpDxAJEOr7JV951z4mPWvObOOpLSk9WqvZqCREutbW6twJEuZm0GHOWdLU99eyzLKbg6azttLmm0DsDC8t4BxNPAIb8/Wun7Ru1b+l0akG80tz2Uy7OXu+Y87iC1/4wpM5HcK0+Q2I5ft8Z63hdn16huuZoGe2Q0C+4/eFiX60RhOk9G31hc0u+LV5UVwgtcvQckwN1d9n2tYjl1BZOwGos8z6rnz92ZdwmXT5yle+8vTbnibt/5muuaC26j3hwZE7AsiLm2+fBXyfrrXbnCMckDFBG2EerV7CzehGyFn/Lt3p21MBdb//+7//KT1pJUyc+a7P+rd0AI20wgktGu/6pnIAEwlzmceafRiNuzivdJ0KYpbIN+kemSco/Mv4NKXlQat19lE0JbzSxlPAbY4Qx7gWT1t/Es76ccl0irL400/1iwnwK59xEECtblOb/JMo/5nHSYHXSYET4H0M9U+A9zF0PFO5kQInwHsjoc5g7xUFMC7ZxbRxB2ztHCYnJoMWw5G92zUuTdoJbLTBzMfQOu65go+OnV7TXnQs/V//63/9oUuyXFxGM4ImFYaWRg0QGUiWc/FKGkI23L/7d//uJ0ZCGi7r+sxnPvOURmUDarzNrpvf1fMWx2yEumNYpnatuAFXmLTp0mACxmAYsveK+eUAtNEzBhNAujptJRztInmnKYPBvsU56il+QNh6ASAtpkuaqZhbN58DL3LATmkm1KhOfefTwgEwY6ymhox4GE91DrxIwICBnto70VbYGG9CDcC7dPwFBjBfEbgCmAFeAeQxnQEUwnYZUMyvsk3tqzSk5QfUwlzLZwJMmG/CllkvYHQA2LStqv3LXzq1deWPFv0GlOzmhTVe4Ve/cd578S6BS5P5Lk6+uMpX+0Trvl/zZ90mKLGLV3uYW2h+mrNmOHnvwKzAlxkWzedveWubqfU2v++eAZMJXGpX4cwZhZceAEsZtDHwStv3fecbSx3xXb9/zdd8zVPcaOE7cGsHPvom/+zFr2ld+l07NufLr/El3gS6nDDwbpbpUtqXvgXyzTDyChwM1DLnNYdMIBvYRaPUmjbT2D3P+uy+dyJC/uZuIPEMN+sbveb3nue4pFnbmOk7gWl2Pnu3+tW797NPV472IoX5/z7SJtb2CSrtCezlrVFz/rB2T+Ct8LR2E0KY85oXpW/e5WgeB4KX7+rLD4AImDT3zO9zTayNA7yVS1jjZh3r3k+6+u1USuPxqXAf/bM32pkNqhyEbkx8NG+YBzoNQsO0UykA8J1QoLooi7Yl6LcmtN/zHWhI65bG+LTN3zxhjO4Ew+rAZAH78tLRztLO2edVj8qx+tqZUsCRIxRVdjS2l3sVLjMg5kjO6TWmEtY1RxsYu13kat+sPvrPq3bAcVrtTJEAw+29CK/se8ytTofY38x53npvjNkrTu1tawuzD6c7KfA+UeAEeB/T2ifA+xg6nqncSIET4L2RUGew944CNCGyk2kzSjMRA09T1nE4DHngjc044OkeZ4MP/MOAAV4BZjRGgEfzuPqaJsaL6QhAAcYJCEBTwpFB2kUrKLzG3/3GoGFKqs/KTPgNXLzVZMUuj1f1jpaMo4i0LzHTjnMC+FYwVv5AGHUBeE6Q+1LZaOOmrbkya25jlx6655QHYwUYwABxjgMKpw9Jj+kGv/19/dd//RPdMfrZmyyttFu7FTxgGON4i6MpNfuweMwfYHjY0FuBN8wrDRt9PGBPGQk5ADQY2sCAAE5H8C+5ae9TWgFw+luMYMCqsAQOTF+sTDfggf2/NOsAxDRxAT/R8sivrICc8jwK670jxDT01qPEASSX4mamQJgAmkvhd9/0nUvA0i7O7h1tsIDy3ffdO3SdAIv5qXrP97u4t7wrrcK6OKkTE8aZ8WFs1s8Kl39pjirMrX4AN+B1thUAMDBqAmSFCeQBstS/E75cyxsotbtwaBePAIYz/tbvhDxpuRsT5juXLiXUWcP3u/Hnt7Zmw3LOEYXb+TR6p0BkhjF/ESBFo/nNqQUubVLflL+TBeJ26ae6sk0c8CdPGro5QlTxmRIAkFUfoK4xmymG8pd2YXq389e5UJiA4gl0anOaxmmgAoOav8SRH189AFiedzTx/ugvYO7o+3wPBAf2Oh0SuCs+cxe0cSuP+VQdfWPnnzAtIZs+A2BsbrQ+tUYdjfnSVRZg8ZyvPBOO6ZPGcwJGApGpNX4NxJz1lAaggXaqfdI63gKRi5PgkeZrQKq5Q70SkgP50ta0jglnXxf4XVqrryzriZP6J1+9pyDHeKdxbW1FF6BwZk3Q8a/8lb8yoz89K7c87A2B0dFbG2lv+8drrstfu8TvWvh7vyuD8ugjX/3oQtqc+tPgdzLIWF4dYTaa6guvwikXAcPuNBHA1mkbe4zp7JWcPFjXfOU0TggZVtMTM/75fFLgXaXACfA+pmVPgPcxdDxTuZECJ8B7I6HOYO8FBTC7juVi3jAQGAGgQwzKutHHyADd3mZ7tLNhaRx90zd90xPzbaMLjGAmAiM+NVRmnNf1zO5ltk7XdvEbAwXMnI62hm82LPc4zIJ4+sZ0XTjHplsuO5YxmN5jIgOsMG1p3sVsByIBqXO0c+Sp72UXjoaMd/5uZTYA/pe0dKUFjECbHOZHmW4BJi7ZbvYtEKpyT5+GIKBmvts9syeM+Zphu0QGgEDwoC12QI46TPBB+spEy5r5AEB2Gn3aI0Z6V471XeUBLJWGMAGHM7y5xPFdYCZbfoAKWvprH9bXCJeA8OID7+4p08xzBVPnt/k8tQbLC7AW6BXAdTQPzrQAbBPAZOZitstaJlp1AIE0PAEhnFMTM91P+tl4Jfial10pA6GO9vn/PtLwulSm6ChM9JvgNBBp0l3fA3qWpv4CiDN+awffhCtMPtNAyhVthQdkpb1YuNUn5NkdXZYHYdAurzWN+dtYWwHgP/yH//CTYCa71oUHgJW++ZBLC795VRuk9fcU4KN/gBiAqrwIg8yNgTIBspP2gD8urcHy5xOq0vRk2gEQFWhbmIDbAEHvAVoBo3wnFQDQ5V3c5hzjN6GUb80N5r7CFIcPFJ79ZH675dnc5uQEl61TdJ51AMQDrPSRykBoYU6SxwSn/a6douscz7NMa18SLuC1fISXTnW0TgfWmhfZi7+2Xs08PdO4N05of1dWwLu5xDrKPMOqBW1vc7RvkxatXGs5EFo/ICQEpgMHCRqvOWno12nBr2X2WxnM87e4xgYFgFudPm2cKL8561W4bBFrg1ucPUEnYuy3H+1oK08hj/HG7I3+3dhDe3PLkVIGM11Mj1ivhUkI8OiynumdFHgbKHACvI9ppRPgfQwdz1RupMAJ8N5IqDPYO00BG7q0JXcbce8wPRY6wB3G2EVdlzRt32mCvebKAYUCPTDPNJBowdCOoSXW8WPtRtsKKMBlOoHd43tcYCtAYLpsdGbnjnZ12j+ruY7s1QFh0vzFAHNsvCkrJpLDVMSMzKOumMYYZczwLQ4TC6yRPu1mwErABUYfeA9IygFMOq6Ixi7OiaGn2YvBnMAUkFS5VkeTuWOw8vYn39KOqfdeuAmQFv7IR0PHK41F9mLRPUcQwTwJQAlYgsGd6QDDMNa03Wj4+J25CeHQF9Au7RW08T0AYaZJGzmtzkmbGQZosXOTGRUeEAMYCOyL9tpttWla+oRQvh/lXbhbfW0OEMDkzjR3dZ9pzkuvjDHfxN/RsXjAe469aW3lD4gC9G0sFfaSn/bhpTC3fJNnwD2bm2vZ1Ue/CaC6liaQiTZepmEKXx5r//S9MW7NSWBUfoG4pbP60jVOzXkBpRN0pTU3AfgZX33144C8vnkHEEEb40bf3GnHFX71j/qNOhFy5NLkrq5HF5YGXAIDOZrO5QnMaS5BR2mZAwnWCpNf3yv/eYFZ60thV5/9bJea5tJAXsP1O41NtKhPGbNTaCescRMg3MkD8ybTFOomDOGL0z+0Tatr+fC1YxdCzfe7Z/UoH9/Xtp9xAvMIpQjYnCzRfgHsU/t1rsGloaz6Tn3efQSEiWnYEpbnpFU8fY+QhIDJmCC4mOMA0J9bT36UxvTVedXgLP6jffOokz8EReZVJzwA6dp9t24e5V87H4HSR/Fab4/G0lG8W9+7hK7+fO1SN3uLNPNvua/i1jIUjhAnIaSTPQBaAuCcZ/uFTl2g6asyX1Gep39S4G2nwAnwPqYFT4D3MXQ8U7mRAifAeyOhzmDvLAWAaTE4QDUarGzP0n5wZB4TM20BAnffZAfsYm8M2An0pF2aBuibXO5by8YcQgwiu25HminsHAeOAYC5jixixu9xgC4MIq296dIyBKBwGH7haCPu3KrFNk0tpNWi/wVsMSkwGRRp0iiShzAYmksOSBZ46agn9zf/5t98io9hXjVTMJwBUY4ylr4xIM9s+AbEeOdPmafD9KW51BojHHAokwrsDALcV/Cnti1tvneToZ/fPAMAaFztnDqywzeByjV+v3fgxq48gJk0Tot75Ku/vAFN64WNtNCLt17a1Pt8oBYN+2vAU+Ffxtc23AcffPDi2Pe19GiYrQ5QvsYLqJjvaUFytMijN23KjrjPsK/rGbj+HNprf5rA+iFt5uq3q4f+B+AzvhLK7MLd8s5YTaMS4FscdkmbF2mgEjxVpmx60lCcgDmh2epWYFLZS6e8jnxj3jzzpS996WmtYuecpi9zNcUB8O0cMxTNo/I0vwHsikfYVv2a+wi2AtQL11hP21Ve2RcXxpoBIGq+8q65igmg1QHtSruwtIMBoNkwdwlXaUSryjHjeg7k1w7KwQElA6cCwYXVjkB7ZQ1sm+mZ+42txp7LuoCqtbF+ol/sQHuAaQJBx+6jI3NVAZTT3IO5WN72VQlAgW60aAnE0yqfawZzKOhhnsxMkf2LdKrnvAdhamMTYuSAn62dhMDM0+gDBBQ0eLNnPeMU9032mRxAC+1wryPEEPdV2o7N3q98mHtZ1zlzH4FEa6Z5fYLy99ZpF96+15xXGQjVjpx9kTVcWAJVv093UuCkwJ4CJ8C7p8u9b0+A916KvYfhMds2LKT5L/vXZtAG+XQnBd43CrD1GIhAo+ASEOqIeMwY7d03zakLwCwNBpvX/pTbLcA0Rd9mRzsz7aaOcl+qD+Ao5hRIyy4hmrBXeI/7q3/1rz7Fo+E1nfIkHACUEAxIn4bozgFr1+OngCzgXeWszWhLrUzKqtVIm0m+O4cJTuOLtmwaxdnb1J9Xl5YxGhdeGOWO7mw1Mg+RZpDyYs67MV34ABcg8Q5Yte4A2QM7Gle0gQtf2aMHP3DbmAXgA8w6/u47LbfVAZsDHYShScU2svoBTKVTeX2naVd5/F7/MIRMQmSXOSAuIEL4qZ3M3iCwyHvmIKa2cZpvax79rhwBi+jWml2YfGEChXrH7zj5fHftWVoB/WvYgKn1PVBydZj46FJb149mfMKN5iYCKRqK8/urfq5s5QMMmvNo2pe+1xaF3floDgQMcKCFm0t7v3hTcKHPAwX7pj3lHQ17v/rAMwJJQJlj6uUrnPayNgTmTfoD/Wi01W86iZAQqPjoE+hfPfgBqWv5Atm04zQNID1lA9LKe46TtU40PafwDrhp7FbWNXy/0Tdgm4DT+9o3cNE7cydfW9N29ldZjTtgJgeUbByWFrvRNM61MdB2zoXC+AOE7px42cot7JEPtM0Oc2kBZ6uPeOY2mq8AU/PMnA+P0mVzGjirjs1B6M2pU/G0D618vwHLnLmzNkDrAFnam8IFInciynghKOKYwhAGvQKHnz589I9dXd+0nf5BKNLa6r35NpfgCB0mfVxGKqxy7dxXv/rVp+/WkbfJoRWaq++9YGTtsBu/j6TBd3/3d78A17VBp2MIxRNee6/t597iUWXI7BUTJbdoZ9tbtYYTMJ3upMBJgT0FToB3T5d7354A770Uew/DO+ZjoXzk3xEg8R6S96zye0QBRwSNI8fGVjBtR4Y2kZi3yXzuwn6S74Brk2kEGjoKiBGywQ+U4F+6HOSTLPNz8gJAaC8CrlsdIFgcGmEuUvGMKV61Vy+lFyiDQV1dIAJQI4BUW+wcwUBgwQSQlKk/2uJAF2YGaFXRjHTc1fuOrfoNDC0OIJV2sqPtf+gP/aGnNg90wczSpsoJK54LylZnHfCt28Z9d1EPQHwCUcIANifQ4Jm2ouPBlSsf814f7F2+/jmPkHsPsJiac94BqmiyqRcQGADAAZ+nLUnaijmaT/OoMHCLTU/taHzQ7iL4MA+sl5EBY2kpBlYJlzCosmvLAI3e8SdwZbzRcGt8AvNpFK91Fi/AbKblGTB1BOyuYedvTHaA/nx/z7NyRwPxjgBjZZymPoyvLtOZ+U2AOAa77+ZV747oULhL/uyTl8Jd+uYkx9qPASxpeAICgUkzr8a1dNGLEIRGf3XR12hDToBV2IBGzwFawFiavPVx4F3lnXlmxmVqN+r7wEpzpDjozaxK8S/5ymqsN1b1HydWxEGP6bRvc4zvngORj/JQd3WZY9tYAfoYD6sGqXDGCkFBIKr6mPsAxFNwM/PMXqk5MzBSvMyGzLDoOfukb+arnDnYuzmmZ/yeAfGZ0+jdTtOXRm9tuM6p4s2yADtdUBeoZ3zVf7THBM7Kc+cbswSLs33seWhqA1NLkyZmpzUqS6A4IWeO1nP9Vl20ze4EgjUi7WNxExrt7MgSlmYDGa0/+5HpoADh6gSkNG/3W/tyAFAX33qPpgmLnj6Of7/wC7/wFEY/f9tcQHy2528pv37T+mQdf9Uu7fpdvwS8Eiqbm16Fa74+Osmzy5NWsz6T8GIX5nx3UuB9p8AJ8D6mB5wA72Po+E6nAljCeDLO/7J/MfuY19OdFHifKEDKDzTBoEwtkGs0CJhZL/C6Fu9VfQfUBRZieACDq8Mgd6zVhnYCeGvYN/l3oAVtkVsdpg4gCIBAh7S0gIK3OBpLaIY5Fn/n0hwC8sS0TwDVM828GPtv/Og4L01Y6WKU/faMmQZAXgPlMMC0ovTBgEPx5x9gA+i72uyLMafVNx0NQOUDTiXswNRXn9KW9zWwQ1hAhaPBtAuNtRhU36JDaU4fQBsQ0Hvrk2dMXDYWATzToac8gVNf/vKXnz51OV5atgCjVVO6PPiZlfBsbnDRXGAXugSozTie0zoGUAaIFCaGk03i4u/q79KjCXhOULW0JqDWu0t+YFH+DDtBtvl+ApXz/S3PADd9UhveoklIO1Af2wECu/rfUoZHh6ltlbM6Ae27PEp++h4zADNvdpkBWffWw/hc53BA3KV20ZZTYOUkwRy3PQcgV05lI7Q4KqNj+IQkjR+2V82fhEaBgvoW8xW7PlY+z/EDxtmoDvizX51rtbl9at8f5TPH9VGY9T3zOPqyehmvaZUzO6Btnfgxv5kjjHFzHFo1v0rPHDKPxmfSR3qOiAc+C3skOPHN+KApzC643+ZA7Q0sc1EV0JgZjeZJYRJG1X+ZNnJ6YF7qqD8DVj/zmc+8SNcpCPGb9zybd2f/MrkSYKj7ru+gGeEywG+6bLID7XdOP5/tKe3dWtNYICwDgHfaw/x46XSVdUF9zFNvm2t+WU8RXapHppQIXj9Jp18ap7TV7VPccfEqnT5sTFnbjcFbHQA8occUTt4a/wx3UuB9oMAJ8D6mlU+A9zF0PFO5kQIWfhueE+C9kWBnsHeGAh1FpMlyj+uyFiDc63a0F9NQdNR0Hv/elY0GhfGO6ZsXxOzCvonvYibv3YwHDLNNzBYcGmAGrt3i7Ag/0E54IOyR0w4YsAlyKCvNpphaaWDoMeO5mNkAFEBxAKH4ABb9lCAv7dpAAUwuBk7eQGgXDrGr6KgsTayjCwAT6qnbdAHZaUVNbTenRhzhVYcu0sIsA5AnWMm2IfBHOEftaQ3RwkrTboIG+qAjxTT6CBwCddM4CogFjANPaoe0uIEeax/O5qM4tLqKM4FR5aUJrU8EIinvc/4Cfy7FlQcNcIx5WkaXwvsG1MAkB/oQ4NDCzm7gjP+ydZhp3fs8234Xl1bjDkiODmkFait9YNXg3KXZO/040xfeTTMKhbnFn33yKLw5lvag7/ptY7C+CribdkGFW2kDSDD/0Gic88SaJ8BqdZl+mWErd/l0QRXhVwI//SUwrLKWxhwTvVt9F1Qya3NL2DXu/G0s0/A0t8z3ntESYNppmvX7/A1cdnkYYDdnbhSm+aLw0QW4u9a9MKuPpgDONXwAM43aaxqIU5Ba+kBV835jNbC477Wl38rLHjFzS/O9b/W7bFZHA7531VnYlS60njkKIu37hSNUElfa+mUC7PoofxU4PCX0i/8IA5k/AkBLzx/t252rjS6BcOYCZnOsd7M+pc3f9Udz5rSnvMs/jWAmKt42x6wBQQ2aMNNxzQmfEAY932VnL6NfWBPudTSLxT3S+r43vTP8SYF3jQInwPuYFj0B3sfQ8UzlRgq00TsB3hsJdgZ7ZyjQrddAqnsczQQbQkz/63YdvQUEsUdKqxEQcYkJ7SZszPvb5DCS6A5gudelEQvc5bS5tDDQmOlVmxUDyhxHwKsjv2hKi4k5DBp7AEw2LzGkOX1jd1SV5iYQYwUkHd2f2mUBeswD0KgFZAO+0saigcfmITMMyg+8wVzf4zC34tLami6BB600aWLs/WHWgchpUq23TmOqpbf+rUw4kAQIlgbuGn7+Br5GixgvYLcwAOEujgEcT40+mtLCoLcLnDwHVHhGR6YYAr28Uy/axeXnnb+0HvvNl5Yj7ECgmQatvcCLW4BG6WRuQ7riqBOQzm/fuDbW3vlWHn73R4sNCEcb7wgQKWz+reEKf80PFLoWzncCgAQbtApXxy5oQHbaVbt0E7iw8eh7ddppA+/iv8w77UeYEXgNHAOmzD4hfb+N38xrZNanUwSVubIQCK3u53/+5z+kxVqY/MZjv/mTXgRF6xgsrPnD3EIQxKyLtSMbqIXhByj2DkjYRVW9ywcqdwFY7/IBa4Q0AZzeG+O1nX6ddnEXMRU337zfs7Af/KJdVza2vY+WwMpAm3VMF/+SbxzNOaMxp+zr3Le2ld9p6d6ad21WeAK6HKGdspqfAuvQYSfIzdaq8OY/bgLq5qictmgduESLxhI+YdXGLS2+PprdY319rokzXKdNbtXotAY6AZJAyNpq7NTWym5OJ4ykxXnJWb/TwN+ZiLgU93V9Q8ef+qmf+vDv/b2/92T+KeEBGq9mWWYZrZkJcpmGOWoPcazt5oJLYWbab+KzNUNfeI5mdkLFVeD9JtbzLNNJgddBgfahb8u8+TpodEueJ8B7C5XOMA+jwAnwPoyUZ0JvGQWyzcrG3j0OSGcziRF9XQ7QiGmO+VyZNMyiY9IdtZ/lxFxhkDAJzBRg9n//7//9Txo4P/7jP/5Gb/RjhrNNOet16TlQMTAUU5NtRbQD9NLkxvQCHcrHN2ATEAGwFxM+6U1D94/9sT/2wl6mcriERxhAAa3ancaSd4CVmVbPmHmg3wRogD/KkQvkxbSux2cLs/MTCgBBZjzAtfwdPQ+oUS+uS5cwQ6vDHFZufWr2SeC1o860ftGco8kqPEDT+gMYQv+0sx37xdgLA8jJAcq8A0Rp/44aA9G8A7jTKs4MQprtlQ0glnADXTHLK1NXPYuj/TItMYH7AAZAfEDbBIVWDUDHlV3MBjyXNnuRUyuT5jaXfX2ALZe2uThoW7lW3xHlynQEkM04qwbh/Hbt2dhIOFbYjo6jwQTi+p5fGfn+VpMnbl+PztroB37gBz7Wn0qHLx/9k7mqSe/ZDjN8z5VB35rjq/eF4wfy6/dp1vMnmDbDr8/KqN0DNmnV63Mz3xnH2KEJyUwLR3iyCgxopQemzbi7Z3UKOJ3fjzQyuyhrhpVGQqiVRuYKNlsDSWqHXf0C9aVNocDcY+6YwoHolObqTAddnLgw5qQhjCPgXPN1d0lY12Yd1mdAY227fvNbPZuP/AaOGq+3ONrFxqo0mHgg2LNGRDvlJuQgZCQkkL75Nu1MYyBHIOC9Pp32qfBA+eloa3ofHYxvDjg6x0MChuIm+EzjWxr+ojsBTG1r7WPyZl6QBfQFQs8+lsCcCQd7EMIqAgp1TmB6ywWplRGYXbyvfnRJGufdd3zHdzyVVZ+7BXhPQKg+lwTgTxm85n/aie3rdexrmylEQlt0AAITntLstRY2fuwZdhcHA+QJto3f0iPMMX6sPUenf14zWQ6zt5dSD3/mlFudfYR66+/XBAS3pnmGOynwrlHgBHgf06InwPsYOp6p3EiBE+C9kVBnsHeOAgFG99g0Q4SO6gJVXoez+Wb/L2aMbwEGFrLjOW1D0nwBlk1HAzRGfKbRMwYoTdcZ7014TpNnp/l3VL7JJGNspgNoA91iiKIBZhzgS1P3i1/84sdshLoFHmg5zRGIh4HFaNHKBaBoi9IDKNDApGENHKNFmkYW5gIjnLZcvrjKBRgBygaQVn5Masz3PTaJMcdpENJEzREGTFADkIRxoh0TaArMWV23rNen0hoDyOxcoMLax1qLXDCHYVd/4GoOUOYdDUJOeYEJMajRevWjZ9qxAJN58U/p8wFra3zAvneOMjNxMgGTNazf+ujXf/3XfywdZQTcpIlN8xsgLby+pg+4rM8zkC8QBQiWNuIur9q/bxj6tO7qX317WX+OEW0X0CZdQGYgDLMc2hC4laBAGDTwboJJxgzzFQC/gHRhhemCqsxseB+o7/ll/gAZGHoa35eAvvJwuVr9s3f6lTFemdQD4BhIWbh8bUuL/Vr/meF7nj4amgs++EiDlfa2b9pa2jsBVHHlW5vRyj9y5tZL6ZQeky00QTk2VQMXG2+EFvPYfvHQQTn0jcL2jU9AwZRFoHLf2Pnl1J1A0ntzDtNEhdFGxkzzR+8v+U5nmMsDNWfYhCqZrXkqwA3/uvDL+Ox0SPRJOAnoRwtjJ5CbgGd1CSfT5lU+Yy3aC9/cSGgmTWM1oWKCbPH014QH4hGgeG9NE88z4UHlR08gbkC07/708eb73vHlSwDucsVoN7/PZ+Z0Zh2UZ+cyK6Usq+vSQ3MeLdedQ/9Pf/rTT+VGawKhN9mZk9K+RS9rhvlR37hktmLS1lxLgLmjL7M4c44yL5hva3/pWO/tfd4mlwBpPZl0qQ7xAMbY6U4KnBTYU+AEePd0ufftCfDeS7Ez/EtRIKblNNHwUmQ8I7+FFAC42MxiJHdHHmniuPRjddkK5H/SDqjXRjatGZt54xeDj1FzWzbzC2mceZ92AlBpbu6BE7S22DVlzxLgEqMAEFtBxU+6vmt+tEuUDw1udTHGl5h0Wh+0nQClgJNAcc/oK0+gOjuoq2O7FrAmzA68mRqt0TYfw+ZWbHY3vSM0QHP5Y6ynhu2ar9/ZyaW1c4+TZ4AdRjDQoY2csnhP4zktImDSeoxTf+xisI7VZ3rgSLM4oJVGFbAKuN0FMkADF5/sNHiBBsrFNuV0X/nKV57MNuj3gOuAmnmUHEhYPS6BW9INzK6NAB7ZEE5zU1qYQj5QPDDD2NIfAq4meIhB15cw0uimv8mjcsVg075DO0AxAOMbP7L93LfKlA+8AFr0+2X8XTr1Z/UDOmcqQD1dZrSCz8oJPAcAJ4xRJn2NdjTQoLo0rmaZtT8QqvGnPaYNa9rdlUk82vbKQMOSJnVtP9Ncn9WFUIKGYYKONYzf+kFl3X33Tr2MvcBtYGM0Oopz73u0BlAFZukPtO2Bc7TwpEcz3Vyw9t2ZFzqmBXoN0EDnGbdnJho8SystP3OUMhUmmnXaoHHYfFM4vvYyptDQb3EzyfJjP/ZjHwPftS/BASEaTd4p3ACeJtigMdtphPIyHuWv79CKJrzzzbzdGkcQOIE18431X1x9ddV+1TePnLAJN6zJ2cM1xnIBr63jhICz3xcugRjhoDJHx2mrucskCewCixP6mR+jAx8NrDtOq3Q5Wd+Vz6Wc9guNDQJQba2/mePr6+IYr/pSewqnYuoj2tIz4FUZ9AdCmylEVI76UfWdvr4QkLwDcIHY0Ud58DMumwPcoZdxkUBJH5k0m/m8Kc9olGCD+ZdOHM3yEabY39Vm+rA9nnXKvoiN6i4ZnfE8+1Y8e0b7m/a+aGl/aB0VRj+xPr8tznqk3NbT9WTIrg76eSZDCEhOd1LgpMCeAvEFp4mGPX1ufXsCvLdS6gz3EAqcAO9DyHgm8osUsPnEWGAkMdFujn+TnY2xTaEj9ZwNL02hycQAaTA53/Vd3/UEusWg3Hpk85H1B4YpL8DKMU3PR38YrJgbmo40VwJx0u5bLyXBrDveGig5NTwfWY/npgWITbvsW7/1W68mA5CtvY60No8ScbQxwIRG5gpuFllt6yMAAEAASURBVI+gYAIDGCP9yl95ayOMvI2StsCQz2PSAbUreFkeR37AwXO0yTFEtbOyVa4AmvqIsmMc58VGyoMeAUG07mj+CUsjDqjhmSbYdMAU2su+7f7Qi4YzkDfgI8AnwOQSkz5t8AJLqkuAzk4LrPLp+8Z/Y0T5im8+C7T1vsvBxFW+AF7founv+32/78Pv+Z7veQE66hfS8wfgxYQKfwsoKdzuL+3FxvkuzLV3wLpZ5114bUI7NQ3kwij7DqjtOx9Q5NQDUyXRc36fz+hoXmusaZPGu3Diz/6jTWh7ckCpAM+ZZvH4k07X6N7YL62ANb9pGwOPSgMYE8hV+Ft8mpGAZvNs4Z0oYaaifgQEA7QE6uxoWNjKRjjjRActydk+hC8BoZeYtSnoq476b+8JN7WpMqWZWvn5ykPzEniUfcy+W1/FBcJ2fDyhj7JO17xY3J2vXNZi61t0WE2QdBkf8CzzFp478l+ema+RTzb2A5JXcy7FOfIJ6Npfa7PoCHik2Tw109H1yMxAJyL08wRNygdUz/3ZP/tnn/oPoVnP1h/0D9QjoAO4RqNJy/oUQVvOkf/ARqCZS+LQyxxu7gK2A9OrF8DY+iVdfQwIv3Pm2Nkn2YjdOeYGGvuXTlmZK9jLb72Y9eqZcLZ1ZJfXm/Ku0wAuHN1p385yJrjWntrlmiNcQw+0353EmfHbZ5rz2LJ/G5x+QNigjr/iV/yKj2mqr+XXf9sT2Du13qzhzt8nBU4K/N+7IC7tGU46XafACfBep9EZ4oEUaAN6avA+kKjvYVI2+22Y2lTnk5JjOtIUeJPI0zFpQEGbw8qNYY8Z7l2AHWbmk3a0ggKep9aKsmHmaTYBuGjJAN4mIIUJiznH7AZeYL53juZI8WkavkmuS2wwpZjXo36FDjGINODuddm4pWF2xAAAd2sTmoRpGwYAAippMwZ4ovt60ZpyBUzRGrvHdRR5mjK4Jz4BTABCfXz6+oC+BZCcjqYRMxXCYjAxgR33xbBPJh6wp40AxI6xz/QxV9mwrb/5bix+6lOfegr7u37X73q6DMp7oHNHj2d5embrVjjAAxfdAypW25XFI7xZwctZToDRPG6O8QX8YCQDdwM6xDP+gJMcEx/zm+/mxMpUPoCqnp/jY9wdi1cXQppArTWtmU9lX8Osv4H2890EaOb7+SyMdtRPJgjoPSDKHMOcg/FAO8+cWnxHzbXlOv/2fQpUvAOWTQC2cNNXV1rILumbmtUzzPqsv5lnJs0CiWkaAxbTApth1nT6Hd30BwBWzngpDAEGLXB9q3erbx4xL639CjA8x6oLx4rb2tVFU8bt5z//+YrwwjdniTPTJgC4Vj/9ufqVp7wAhdMe9g7Q67I68TpNoEAEdKUFZCRoAlyqO4FU4zswEd2a84vHn+Y//DZX77QcCd2KZz4CZnZCYXd64wXRDh6s2bRaL/U3bcasz86ZM9FeW9A4TOtXGYH3ucA72pdM1wQE07hN8DAFIQSDBPGtcdIz1jInUbrm+XVfN/uFeMadOjI745vf0r/kzFFTqATQ/LZv+7YnUxNOFE3tf3PBpTm/fNSbkFFZaISb/4Cg9kRvgzMHoqd+fkmredYlrf5r+xvrbycLUmaY6eyeM4PiMsS3xTHBlQ16gL+6Tq145knwIgmvrfnz+9tSz7OcJwU+SQqcGryPofYJ8D6GjmcqN1LgBHhvJNQZbEsBIEaXV9icYiZoigA4aOr80nHk39HBN3EzNTXCML0u7pm3PGOsaFgFIGB02W79pF0MQECUsmaLN0Brlomt3fUINUa3o2w0mS45QIg2vcccwqX0HvmNNlGAA2YWo6peGF1MXbYmlR+DiPm716UF5qjwzmGaAhgIyGjcdEmXo+PT0S6srQAUgX+FoY2srPriPS5QFeDxMu4nf/Inn4AvoOq04dyYBr4ZAzSZaQEG5DgaD8Dk0hwDyHIuf2nMaKMYTIC48kq7P1qetJAw6JkWAQoVPyCSJu2R0/b6hDiBNy6qKQ++MNMBcGg7FqZ8+v0cf6eFzV6ptCbQ6Xeac4ARAMxKe2Hq52tZ5ntzgVMG2gfIu477Ne78HWA2383n8gGcBZ6Z/wgHfutv/a3/T/mq00yjZ/1gJ+CoTWjZr2BYvycgVHqX8irMc339CDjZxXylY+5JGGGcJ1TwPVMtnmd5tfvU2NQvp9MPtaF4E+wsn/JuPPgdSOEZ4Ft8bUIgBTyf6y8wKAEU0KY0CQsJaIHfBCATjC0MO9Ta2280r1zaBtDYkejqbwynKQsQz2yL+Oow11d0CMjX1+bcaN6sjYGA1jTaw/q3vPsmX3NUwrbKvfrCEwo6wq9OzEcY/+Yu889P//RPv6CLuGiYiQACVACRtrrXOXkSkwx4Ap4Zd/JY56SZdpfeoR9njTHnikfIkdkIQLA2MY8YX+bz2f8IaFbHbEJ9CF30gSNn7wEsJozTRvqa9tUWzbX6gXIB3m9xwPTMQIi3/plrjLUj4eotebxNYTqNoc63On1W2xHetMcBDgPQAd3mJgLWwFptdmv/NfYSQj5HuHFrHR4dzly0CiSNmcZN/cweZL2T4dFlOdM7KfAuUKC169TgfbnWPAHel6PfGftOCpwA750EO4N/jAKBI5gJx853mhZAmzRKAIzrUe+PJfgafmQzro0fJhHTiwFcwZIAGvWg6fRJOht2ZYy5pn2FucIwBxCt5cGQzo2t46G1xSWwjEYm0DgGWr0x1WiCCX4THGBxB4jVjurNtvBzGESMk3QwtEcuUBZdOk6pb2OAAaArI0UzLZBztW3neLH8aJ/cU94Ytz/4B//gUTHvfp/9SqBR9viiab4+R2uoy8Bk0vHSv/W3/taLPGnUXWoj6aEVwCuwghAoOqWNIxwQZ6WpjLxzRLdxYR7KAYQqMx+gUz7CYH69N3+VV33ee+Nqxt89GxsB3r4ry3TmxMAugKAw8uviospdGgQLtM6AQgCmwPAJECbkCXzdlWvWY37XTrVJ8WladhR9hu3Z9zTngUK1g7YunzSxzY3AIpfGAStoukoHuHhNwOeCnPKUrnSMFe/8BjJOcLKwr8IHEpgDAznKgxCTEMP46N2Rjxa+aeOEMX5bc6ZDl+jou3YhYOsSIO+Mddq5EwD23p85GagXwKl89ZeARMCiUw3CA11ppKfVWzqXfJqFczwwmbK2Z+OWoET/7aRJIH3p0+KdQG4gqrVpugmet86VxpFfWkffG2/rd/RfbdLG2M6w6kagdwkQnXXoGWAWoEoTN7McR3M37etA+2mLdQovjVlCRWuLNldOAllH7NPcrew0kQndmTsIiO/bnLMr75E/222G0T+kR5P3VmcuNheiPVorG9AYoDzn6VvTe1Q4mvnMVZijadITLiiX9exVufoGrel7XPPBBx+dAiDoXsdabczH76mXUzuEP9Yafd4pHWvl6gLt55q6hnlTfzshQbg/5zhrtRMll4Qqb2p9znKdFHhdFGgdPAHel2uB/5+9u3vVbyvrx3//wD8g6iSKLDoJIuhEouzAKIwKFDSoKGLnA5VQO1IJImyXdqKSRflUm9wlbMsU3TsRy8zN7kHL0pS9SbNga9FJJ0ZQp5/ffE17r++1xmfOec95r3utda+1xgX3mnPNOcaYY1zjYc7xvt7jGh3gvZj+euyNGugA70aF9eBnGrD03EejD8p9y/ixYLPUEWh6KmLipAwmvBiHc8tisUksn8cOCFiD9XOVQm/5WDdxjmTHa4BmlqrmnmPuixt2liWQdSlvwpsommTlOVNH4IOJ9xSYn3Su6gho4s/RJBnDyMc7owP/zxcxJKzxbRufsNnMJmUO0NWCH+6H4dtu+AbUDWN47WQb+yRgxrH9QWfSqM0DceXbJM+SR5PvdglpwD9gWF1irczqIYYR7xt9DMvfZjh1Wb5xBACKyZT3Utv+MKa5s7ApCj2ZmGbJubBAk1YCZiYtOvPcbP5UgTWT3hb8SLx6NGkM+IJ9WH13ugfkwPDGiMyS/hp/6Vx+sHFN2E1QhQ34m3h0WN0UAO60efUDEJ0D5tM2Ux/SC2M6rgbyjHrUHsL+1L+AtxVoAtJY3h2QuILcARi1oSXBkATieW6ANoxkepS+6/oIoxXXJBVgDfPaGDjlcgSrv3XBk/Kprzw3gLd79XwKNFFPiZe06lFbYgRMmGwQmbqsvi2Vs8Z1DqirfkWNb9w3RDcJD3Q1AQMAxxiQe9JQL1lmr38EIGcgpMtWL8qVPiGv+nQLimrTfAdXyWZeAYG9T7EKM5YkT7mvbQOBSRi8ykjEi2Eg8XIEDltVwagWAN296NV5rS/XsZoT3xF4D+A2hmir3nk1Tg3rXFvQ9oBw0Y3rytCOhWMBZv4wdqdd2XRRGtJsx0z5CkDLP3EECKc8dJjVI9JwTd+ueXPdqhH9NcC/a+1PGN9BU8AtUNp3Hn/i8ovNjgHNmFBFXUiXDrcKH9PiHvsdtjUfwgNXY3hr9ZT/9RfM/mMK3as79brFwCsPMaLE6Ccd44B3t3HauNMaqVKWetSGrMarxIVs7MfdxU0WY2DrfuQml6fnvWvgKjXQAd7jaLsDvMfRY09lpQYyke4+eFcqrAc700AmGPw9rhFsUxNFE5ytG5asSf+QMAF5qu9TfmmBJdhnwJK//Mu/PDf5wQzwYQzovUrBns0HeWUPATlNjN3zcY+lY0MkAKNdwNtl6pZFtpNz5bDhTQBgABDQ1GRauphklmXLQ8AWoMF1smwuU/cmsMoN7JsSunKfnuqESNgwpqcmFCbM6QMtQP7II4+MaYq/bwIpbsAcoPaxhaFAW9JX91ntgQNhyWBgthJDEObolFiWD5QL8EGv9Qf8AqKHHVjv5Rz7dsqfqOe5nnBzII78c62gzirAG2ALgIglGZATuA+cCkCddAOSYrPlPM92TVkCDOe6Z2QszbWpI7Cv+jBOXxXWZlQR+pyK7xq2eZjKCaOeTe7zf47p5/l/6hjwEgBEUtdAXcI4IB5AEFC7JEBtYS3xNtY4B2KTOoZhpr7nPe8Zw9U8Gdew7KbaEZ1j36WPiQccVOcpZwt0Kpu82GAuwHh9XnseXegPYTOKLxwwOuN33BVkKTvQtuaZm5QAnu0z8r/0lvqDcNjHAe2sEEgdx1UMXeX9l3TXHuWvHb+yCVxY6urJRmOpO8+PMSZ9gOG0GhS9b7gliLFL20ye0hf977s1q4f8z0gRkN//4mdFgf/bNg8Uaw2cWH3Ctj9lrT5yvVcZuzLOa+stQLvUzrXBtJUcge4AcUav2q+1heTTeybGi/j45utduw1oXvNOBzbTYlzX9zDIGX6mwooHaLYqRZsBMjIWLbVDhoaAsgzf0gAkbpUYoxi0rlOUJUCo8cqY5luPIYYBDfM19xkJp4zpF8l/xqG4WlibVtz/0L/3iHJ4B+j/3mX6RlvnxjMuMrhK0S6wvzM+aGP5rgMQS5dBv8t+DVj9pH8bH8wdsPC3Avb7n9JDdA1crQY6wHscfXeA9zh67Kms1EAHeFcqqgc7p4Es4wZiZBJ5LsDMPz46fTBigZyCZAJjSf5aUd7E+7d/+7e10S4cDtOI7vxadqqPSDrNxDnh2iPmH9B3SjLxBwLFT2auAYwjdqOOmwdg8lUJQA3I5qP5svVuQg9cAOBOTbgwNOkWO7SKCZPrJohzErAhOk44dRg/fMADRoapyQF/eNmEBuA4BdYnzYscwzhUHh94rR8+fcYkOCCFjW2mJEuRAT1LgpGMjQ28M9kHWnp2GP+Ajj//8z8f3SoAhYBTjBCMHfvAw7DlpIeFZvJl0gtYAwCrYyAFxqMw7Q/YQcTLPZN+xpUw0FyvjMuEyxGIU0GqXK9HE+/oM9cBgr//+78/gkjABtczGU8Y4BZ3E/ysTj2jneCLV0HFpFOP7X0gtvbOsMOgFLBJmYFIRD2EVY4dmhUSQL8lEVZ/Uw4guzHW8+WbjoFbafPJY6uDVm/ChbmaOECvtlzu0XEAFv8Lw7AXH6iuBWxk2MOwDFs5aWfsxS4G/tT8McboH8JiZDrSJSCghlP/dMiAFANO8uuoPOIY/wOIS4uhgTsa7SRs4NafN+NLyiBO3mHOgYrY5rX95rnJK+Z+jQ8gz3sorjXokIE0/YiO8q6QDqZ/6inMcdenfjGauKcdWCUEgAzINhUHozb1Ej0nHEa1dpj/AZSAb+KdliXyue+oPJVpXdswtmcAa+PLFmHAqAB0faZzBgJjTcTYGJ1qwxVwFsbYyJ2LcIyTld2fctRnqEfjkfbN0JBVScLoC7VtaXPeBfy6Aq+ETzuhH8x8fVQ71mYCDibv+44xVOwzau5L5yL3GSJixGFEy3jWpqm9xyDhHX9MokKMfL5v1orvg6zG0M+01aV2pX4zTnkPVgH4alvCpD3n3W1fii7TGtD2ESDqOFf7mr6MCR1DzXQq/WrXwOlqoAO8x6mbDvAeR489lZUa6ADvSkX1YOc0EP+jWy37YckADq9bsG58iGHvbZUsc93nmmJrukvhAWr5cKy+T2sc1/lu9JFvwgIorODO1BJ28U3epW3ynLR9kIal5NlVLBUNkPPEJTNvbJ5mOXgFHOTVhJ1f4immbM3roedx1zG1tBwgIA/A8CpAdtczQar3cp6llK1O3bfUtfqExkq0PJLPRgAetlb0ACiWj8sUfpozIVQuE3tgUGWnyo96MNGZEiw0cTHUtsgHP/jBMZ4lsxcVbMPKzA3DUl1wtYIFFb3Ka/vT1i3zx8DOPWCkslWAzr0Aha5nmXXi5Jg2ACAK4JV7jib7cUGA1Rfhm9d9oAgArwJuNb7zgE8VvLHRZRtuzf/ARgA4PRorsuweCNeOgek3wAZL0qUPNFkS4JFwXHdEwsgMYOT5Lchb804XqcPoXXuVduq7hncu/xVIzH3P1l79rx6t5oirj6zcCKgpTPLqPOBbBYeTrmPqu9ZdDGkVdAkgHONEDB3RT65LU/uNYAS79oIXvCCXzo6Y8DWvwtFZ9OZ/P+xeIGQ1FrQMZ+GAf3WTVQAgYUSccp9gLNeP2j7DhUBWAbiX/ASE5gs0YowMc14egLaMn/Fdyje76wDk6p5IujFaVd17x+V54tUfhuySALPT1tp+sBQv9/iUrmMIsI7RNMxRdQlQzTO049bQlrTq0btb3rWLxFUu9QmgjK5qHM8MyCks3eu/U2IcTL37vmB4i+sQLP61kvHMs7YQBdamvzZcAAz6WpOPuPzgm/dYEsMMdvZayYa56gvICHR2jj3PoKoN+D/9rfZ9fUD7rQKwThqYvTEUMNR1uV8DAH/vLDr2M+boz76zjd8x5rtnnJozHNyfcr/SNXA6Gsj4uG813+nk+DRz0gHe06yXW5urDvDe2qq91ILZzMFHi0nsFuFnUbws4d0S99hhs8ze5HKr+FhTjqv+8A14xIfgHKBWy2KyIJ+JB3yZkiwbrr4zscHEBehNSQCM6g94Ktyh14AEdTdkQCM/zkCqMIjkz+Swuqw49HltvJQfq6X9MOd30QQJOARwIFyQZDKN/TclWMgmW35TzGBxsHJM1uYYbvSAOXtZwHabb2xp/jDr5J/e9RuM0SnAQRkAfFiFAQOF3cI2Dui3BJa3eV36X56mQCplya8aQ3KtHoGmFfSq9+bOjXXpf8IwomBLOrc0NiCH//N842s23gN8BeQMeKUcJONQ8hT2IjYi9wPSBAQHKPO/H0ARgAnAtwwXoFdBr4TLUX9jUMlzXAdSt7445SmsWfmPqxP9dkm0E2nyI0v0N//rT1X0GT6YK1CRPOaIXYxJH9AUSIEdqD1VsDvh22MMGunLDz/88Mie5k4gYekNmOn/1Fnu6bsBa3Jt7mhssZwaiC8M41wkGxyFVQfMIcA7Rrjqo7auJIibjLlxuY6pyVfqXlnqag1M5AA+bdj87ygeEBz7GEikfVd91bBrzzHi4jsWKEmvAOrq+1la3uOtZOyMIXrqmannqXsxCDCq7ZPUXQXn98Vp7/suSr+Zyo9r3Khhmm6VbFRnHNrnL1hbz/PTF5eel7LTN5/G4nJfswYklW5chFQAf+l5l3HP+0371x7Wrgzy7o3xG/N1jTCOPTMYyrGsvYta0W/ybRBmeRum/g9cTDvVV8JsBz4D4EmAaCtd0pdyTV3FTUxN1woB92LQkvba+qzp3PZzOmEQoCvGWO+XqXplpIlB3Ldr69rmtuupl+/ma6ADvMepww7wHkePPZWVGugA70pF9WDnNBBwDxCxRbJ5FYbpKUiWt2GHbZH44gTqXaVkmaaPSsDXnO8/S5p9vAtn4lJBiLr0U96lYWIBvMkEEEASENVy5Ckx2TQxosM1YPNUGnPX5CMMMMAiECqTlsQxCQpYoXwYn8cUZcpyR8zBdtO0TMgxQOkobWLJbUUm0Opxn3i+/gI4wZAFmgA45up8X3oXvW/yAqDlJsNEeG7SJ48BGrW/+tNeuKFo3VNM5Q2ILS4W3rFEGWzMFkCr5q0yOfWHMKkrC6eGX3OuT9X4AZ4CNPtfPXue9Ey+c44RGFbcY489NqogoC1AlgBdxdP+6hJzfR7g5B7GaL2XfAOO54DSsPOFrexGemMk0ianDBTKEuYxEFIYzxHPmDIn+pBnYQWSsGcrM7XGBeoFsBBPOeSpMtIYU8LapoMHHnhgkQEcvdSj8UWbYZxwHaM+9zFknVdd+T/vFOeAfSCpshvDUl9Jw9H1MEv5+YzkHSuMsQVQ4JlTwKSx2jJqfnDT9xjtWkCQK4y0r+SFrsOyT7tKHhwt/c84WPM9dd72K3ngdiV5rm3JNe5NMPSB8Vx+OE/7mUo/1wIeA5+nJHWTiWkAlsRfOmpXYZ5zVbBPYqABtl1UnhhWw/DVzBexfkYfDBprQcT2+c8MgKJ3o/Zv1c0+SX+hH3qI8XIunr6RzUaB6TFGWHXi3pJkAy/j7lpgdSm9Q+/ZQ0J5jb1bJAaWapRp4wPzMNarGx/PYjxiiOIXvEpcIgBrbSQ4JwxgMe5JL36btRdjMHGMkYp+A8DrO3mvAKnbepJ27afcqnS5XwMBwn2f1vfO/SHvjd+O+Q5YYzSaSqNf6xq4Lg3kPdoZvBergQ7wXkx/PfZGDXSAd6PCevBRA2HXmYhsERvW+CD1wjgFyaZFb3vb21Znx4e3MgBnrlq4C/DsfID7uDTBAAYBm/lv8+EZf2Am0fwSioPB42iCD1DJhz12m+tZeox1lyXkJrgJN1VWSwHF3cLMnEqnvZblx1gkLUhRw8qbD2Z5ALQAI44pQN18mGNBcpVAz0BWjCrPbX8Yg/zHtkwNE6cADfzr3jYxoTSxjz5M9k2CUz/aXYAewAzm4pwA8wNKbvFJOJdee10fbsEo+Q5D1dJWGxS5ZqzSrvSzgGMpYwDS9LfK1K2AcdJ99NFHzybdaQuYiQFJ9NGHHnpofC5wLCslbAKJ+RUw0fL5+EE3BugH+mBA3eSvHtNXXav5NOHX31I3NU49BzICA5cEmCkOIDSSyUHrYiD3HQHY4ikz41TcS9DNnNhcMPkTfkoAVFYxZLxM+LVH9Zrn0D2XKADZuFmo6QDSapsCurbAnP+Tlyx/llYA3gpov/71rz8rX607z5C2+PV5NS85l7Y+yWCmjdQxK0xYjN2AqnM+RY13acNJW1vN8+nGs+jAe1Gd69/GQKBT4jgKl36T64xl3mFc3rgm3eQpYeoxflvlaer9FF/mFbBMujWdeh7GomX3Vh+4V1e80KEJrj4G+PftY08B30Kp+1PzsxlQj4FinzBCKLOxWbmdc6m1T+hEWPpgdM3Yot88MwDMrViZU98T1w0axBDum2iLMKopNxbylBijA3gLRy++q2I4d81PXwmQzmga44R3jfalX3gfVnZ82Lr1HWP8jnst+fE+kL6xg9T+X/uyVSER7mUYk2vecq8f/58G1EcMjNyTrBHf1cY19eRd3qVr4KZoIN9w1z1W3xR9zeWzA7xzmunXL0UDHeC9FLXe+kQtUfMBarK2z3odZfjAzHLtUwG44sMM2ILxtU+AWGHfHZNZuO+5uW8CEGZrPvLzMd4eLYsGNAIUfFjy9wcoSjisD0ujLTd2zeTDxDgABJZHGL15fnsM+LmGkdnGnfsfoCc/mH9TE8SpeAEutvqEnkqrvWbSk+Xe0d3UEVBWl8IDueIjF7gb1gz/zVOgRPvcm/Z/2KUmMFhRleEbkMzkFXOJ/oQDpEwJtwLC0NVlSdquSTg3EybE8XvKv6rdyOVBv4gEeHQ9S+iBTQHjMr65r6xhfAKAXDPpDzM5E3ksq7DBTLzlw8RdP4xPZxtGZbd58bQfgIo06b1KQC3Ag3gBhS0PxfIMSK3cwHc+dcNGl15+GV/EDxjpHsA0LLH6XOBWfORihKpDLDP+hAMEzo2Zxt4AhGFOi7cEmMXYJU/0viRhqKYeckxZHaOXKTcOWLnA64h+vTQm6OtTrgOwWeszw/oX3vP9bDJoFUANN3UeMLTeS7mASPzU5n/tNoY+4bU5gLvzbGY4x4ZNmWMgrM+bOvcuZRwB7mbMEw6gTG8Z+7RzLM7oIHn1XorblyxBB0L75hA/xpCET9jk07HVjTaMCctgF4Y1trP2mnQCeDNq2kBRntUxYZiuY/tUuV1jnDglSTtaMzEPaxkgH9cLdLZPGL+UPUAiwCvuC+jWGADQxUyWtnFfeP3dipbrFu1AfvYZr9p8po1MsbyffPLJs3Jqr4DyuuIBEGts943j2djrue+9yaUC/biXX8bQ/M8Ylk2LXWtdsmBsu27sjmif3AIlDUeGVGWQz/SF3GdE7HK/BuIGZ+tKxPjrZSTs0jVwUzTQAd7j1FQHeI+jx57KSg10gHelonqw+zSQCSLGy9SEv41gJ3ofjoCFfMy2Ya76f5PNgBuWzWdX8Kl8KGOAJx/FawDhqXQues0S+UwMMG34J/ShCUwyYcUwAlxjHOaDvfpKBuhkeW4+5OsR08RSwX2TVfczWQsD5aJlEz86xiRZK1i1JkD0cllty8TVBLVOtIAFmYgBKkxY3/ve954tmQQsANUCYmj7gI3bJnwOa0MAyim/1OoHgCQMEDV+IS0nryCe8wCgQE5GicuSGEq04YBEWa5rsmxsSL3ZgOiR//O5mr4SY8krXvGKsx3uK4jGFYr6TnhHIIrNa5yboOfDOf3IdaCSTc2ch8keoEt+MNotpXcf6MR4UCV+RwFl+ujTTz99xugHGgPqAt5Ko/2pQ64C9KMsLcZY5J87jDEARBX1FhA1/aFNN//rP8Yf41RlcVfd6Vf8ki6JuEnTODfH9GeAlCdlz3gYMNdYpx4qoy1ptkfvCUC8n/pJGglXGacAzlYsxw5IiFHZ1sG+PBh7AKIA9qTj2crVxtU2//Vf//UexnhlDAbc1J7iYxy4JB1ta0mysWjKq/zY4/4HCD700EOjj/Tcj6sI/wvXutjJs7xH47pEfdT2HIASEzXCH3XapbQBhxGglvpMHtqj9F3TByLY/AEk3WP40b6d80MbP7H+Z9xVB94F+rHl9wyp7vkBMk9JMo4wLOyTtAeuTLKB4BrfuJilyq6PRQCY+nf0Hf04aq9WTmXMTZzrOmYs5QJpixjflYe+qmBnZiygv/p+q+Gcf/aznx3HQul4j1TxzvQdhlme7wzvE++MjJupJ/EZGav4lnVd3DYPARrdrz/1xYAYdupt/FapOjr0PK40bGS3RawUpO+l1Sxb0uthuwauQgP5Tl1jKLyK/NzUZ3SA96bW3A3Ndwd4b2jFnUC2ffxl8mgitgTyAU5Mrk3u40/yBIowZoF/skxUscbkr/0g5gs1zF0fwZbNXacA1AIQmGib1JqMWtKtLgIA0LeJdyvAG0CkD02TwEzELFdc6+Ygu7VjDB5TAgxsBffC7Jrb4OyieaSzAHYV5K0TJOcmsMCSCtq5Dkhp/fheNE9r4vMvDdQH0JmUmuzvA+/XpFvDBEjASpoToEgAJiy/tF8scqCw9htAFVigfV6mBHBWNya0JshAG/3JWIUtGLAUANTWOcOKuNnI0LLzMMnVPQZj2waACQBX8bAMtdX4T3Qtv/THHF1nwFGPDGr+l8+6vDa6UrdZwo6dZizDfgSct/nJ8xy1W4AVP62RL3zhC2djvLERcOi5wmMjM3ppT2HuJj39RFibjaXOc689Yv7pu1W/VhJUBnjyk6PxN6Bc4nkXPTOxJDws6IRLG1Ne4zoByoRZ7n6AbHnNO67NtzYqn627AeEs2yfKgKWujURv4jAeAGCAShmr2/QDRntO7nHhUHe3z/Up1nHu1SPDinLLi/d3fHQK4zlLxrGaz+gyeQxYp1zGmJrnfYYauo9u5KOCuQwrrjGoVkY08CtxPIuOjS+1T4sHlE4fqux6fYmrgAiGsPApVwCubEgl3bnvlrjXiC6qMTXpX9cx45FxY59E1wwJ8S/arg6YSoNhhe7C4K1htHHfCUBxPrKx009teXpcLfiG2iIxvrWuHeLywQqLNcQHfYeBJu+cuTzoW63ow2mzU+/eGDH1jYgVSVnVod70C2xkK1a4YskKKka4LtMaOJT1XVny0yn3q10Dp6eBDvAep046wHscPfZUVmqgA7wrFdWDTWrABDkfi1g4AALACFYOn25cMWTpo4/JrSyJyYdewkUsr8rEAR5gxQJ1TVzk3c9E8zLBXZN3rA/LqTGJMHQxYUwu28mCiZXldi17K3mVxhPDpi1rxMZT4lkGPjWRaNMAGgVQBdAdUwKYYGptkTCtTNguQ7KMnp6AJNo1xhbgDCNprh6EB6ys0esx8w0gxwaV17SJHPVZYCPg76ICQPYM4OG+DeCwz9Nukpepozbgo3LNxkCH5B/wlucGmMn/mTAD9rgwqGCfMAGMAmpn/AsTUhj6MA4GHAwYZVwJmJR2nucuHU224xdbOOnUSXurA75eA6xqp/z/EfVjEzzjsDYZkF2Zp5jX4nC/Ev+knp3yA8ESv+ZdnwAUYlbmuv5hDAPwZIOs3MtRusIFHMTSBEzFcMg4AigCxKTOgLLA1NSZtAAWWOJYpcbO2i+B1Hleu0Eow5Y2LK3KVgXyh5HH5QVDCdDKOy5uSYzTLXiu3dS+pw1YldCO42GCM3Dw/Zz8pd2pJ2NaQMfcb4/qO6CTZ3l/YB0Dl5VBuYSJCw/1wae4dNI+lWtKtI36PKBWddlAx9xAeIdW0FicfeNxNp7MShrGiSr5frDCprKAA756BvA5+ko+9S8gLjcDrqXd5uhdVw25gM3EdYzRQXi+ZadEmwzwjc2nvrVf7OlTkPg/xzjcJ4w5yg3MDpt+jQsFLHHxqv/ofc+6ivv6GQMYIxPGqvHaNwJ3G9WIrR9nLF7r752van3Gr24Qx7iW94Hxf60YM+nQO3mr5H3KaNoabjO26EO+P+gkdZu5X7u5XFzPaDtdpjWQb0Hvgi2SvUuM0126Bm6KBjrAe5ya6gDvcfTYU1mpgbzkWfq7dA0cogGbdGXS6CN16gfoCKvpkGdcRRxLDU2EK5iRsmCkmSiaDFyGmAxkgptntkdMP6y5VvjJBdoAWrGHbEy0dYKpXAFr9k0yTBLi+41e2s3E2vxt/T/uJ/aBhW26mfBfxq7P2SBDnWjrUz6HMcziXkA4LKhMfrf6amvLtvV/bKmAYIAnrE9LQDG2M+bLo0nhRf3sffjDHx77vAn0GjHRBPSFSSkfwCdACYCo+rk1gdYnjymez22G5+bHcBN95Vo9VpCwXs95gDxpZIk4Vmt8kKZvCe9ZiUf/8ReKVWi5b8DEhKlH6WhjWFj7xFhRjVOY8d7z3E+E2ZW09y01NTYCV6fYxvKtHNLKMuMsRQW6cRdTRVr6g/CA0ZQfgG0sYzTMkn1htI2AvslvjsYfOos/5VyfOgbEcc8Y04Ih8phN7SoTFFBsXBWPQS3Cj6K8aaMYpGnPAJ6APNoD/QC754wVdC9t/onDIq7tZaosuSb9APkB8dJ/MPKqBLSOn2njGB0E0JEmQ0IA9cQ1Dtc2CTQn2pE42ljaf/JV62vft2XALWNW+gaGbsRy/6yw8W4Oaz3L02tbyfMd+f2mA8B58qcfA7bTXiv7Mu5ZkkbCtIaA5Esf1H6Fp0MCjPc/N0OnIIzR8rPkIqPmM+8GbZrO9i3RN5bGXcaWjWrrMy/jnDuUaoBIneaoHQB+09ZjBGBcYLBcEqzksMEZT6rEVZH7W8SYJ2/G163ieyfl8g6udSavMTI++OCDZytMGNniA74a6LPagTGpukrZmqfbHh6Rhc69e7ZIvg/nfNFvSauH7Rq4Kg10gPc4mu4A73H02FNZqYF80O37CF+ZXA92RzXgQ9/EybJlmzqYIJt8Y3UAaHxo3iTB8OCbz2YZW8HSreXEjgogYLKMdeHZQEQgDaDQbu4+KIF19HwZYqm1iZ3n+PifAiSeeuqpcQfx5GVuk6yaP+nwBwz4Aj6YeGGRmBDWZbeJE8BnLZsm8QJazbGtEm7rESAV9iTgYgoYqmmGhWcSCbjHADNZzmSyhr2Mc0Cc+gE+ATemdMy/aRinJnMYSYdKNrtas1O7Z9Dn84aNd+Qx+WzBfGwyTHZlEOaYzH8T+zzbEeCFOZol/+qqAlTCZJJc402de48CCOIiobYb4SvIiFUWv7VAuixzN+GuaQOagFjAL2wtgFjckGC56yfGBP237qJO1+oeE2sKMFTvAb62tE2sxYCIGJ9hd/LrKj9Yk2Gbzhn1jKl0DKCU5wBlAdMYjeghvoejD30JC9fy5/YeMLkCs4lTjxkj2g2J6IpgdPI3XOPQOSOIawwQDJraesJ4v3lPpK3GdzhdtGzdLz/l/N+kJZ20jxjQ8owctZOAoHSLiY7J7X7AoQCvNgKqAlQQDmAdUFRbks9sbOe+ejFWA5utYFFHeb7NmDwzAIf8AJT0X/5EtWFL8LUr8ehEvXjGnIR1rDzRfctOB/imzcmL/KfNBOBOHueOecfKVwwv2ukjg29tY03Skfe8B6XFGFXrUVn4qw0ArK8HDFN+cbzHT0VSVuDzPlHW6M87ep94rwtPF8c29O579tx9Bu6M38Zfq5O4nzA+qre6kSwjB6Be3uNmxreW/jwlDAYZQ7TH1uAfg67v4C1inKZH7XGr+B4Ji1waxhB1rT8ps/6esSnPwKrXxv28a43peRdp//TVZV4Dvmnp0mqFNcZWKVl9kLH72N+o8zntd7oGLq6BDvBeXIdS6ADvcfTYU1mpgQ7wrlRUD9Y1cAka4BoiH+cm+nMTYR/xlgfnA37fBkSHZtXmHQFnPMv4YFmjSXh1YQEwwpJZEqARJlMFCKRZf8r+9re//VwyAeD4slsrlv5LF0hXl92ujS8cI8TUJDVMMeljkK2RsOQwPMIunGL9rklrSxjgU8CJqY2ealqA1oBBQCxGmkMkfuWApGskGxiFXaoNzImJqAmn3772NpdGvc6vr3oEFIbxXdtjdFevzZ0LG1BYGcLyNbkHdjFw1bhAB+Wo15yLBxTEJmQQy/06Kc+1ejRZbN0CuI89BnSv9QmgwkLn8gDwx3AEiBUe8LVV1LW46p4RwXnA2YB/WONLkjTsXg84kYaxR7skSZeetZnKrDNZDkMv4wvgFwNY2YDajAiVeYplSceeg802J8YAdShcfgFea/3RPX+zDFW1HsLmnEu/XgeMpg3FB2p9hucrh9UdASiTpw9+8INjUvRFR+IZ+9KnWx/W2eTU+MqAqA9IizHP2MSX7FKbkz7/zPE5Le7Uagn6c49OshrG+DwnWN/CW+4cNwkpW+IAwACx3CoEiBWn/vghBlhnXFE+xkJgF1dRmL9ArH19XDrAsZq2OsJU9Q6sriCwoqvPavnN+3PKsJbytEd1qH8ymAC3DzEq0zFDGLBaOW2USI+MsGm/dD31jpMf+Q2rU9mNadnMq82vd3v6p/43F66Nd9n/W02S8UD554yxdIWIoJyMGPoNw0RdRcWAIA39hnGEQTT9Q7sGjLZiEyJpAkS2iHFLvEPGYs+JUTfvIGnN/fTjtAfl4PokgLjrU316S1nuStj0lbXuFjLOMbh06Rq4SRroAO9xaqsDvMfRY09lpQY6wLtSUT1Y18AlaCCT8Re96EXnAJm5R2UJKJ9qlyUYWSbxrS9FEwaTV8uN97EW+HaNbziTB0ubgUGAL+A0UCdL2aWbpd3KhCVsEmKy4nyfYJTFH6pJ51oBgJnIA5riFkJeLGe1DBlQRJK2eyb/a+SZYbMn4ekwbDk+qS9bsJM8l4+4NcKgYFm2OO1S+jXxhQFGiG/Zp7pYEmxNwIv6tZmSePsmPBhIwl3UzYW8hVnLDUkFijBvs7QVAIKV2oJ8lnxngo99CzzA4ApQAFhNOwL6AQzCTpT/Q35Zmu55dAeYqyCwNIFY2HbaaWWwYkMu+VjW/vVNZZoDQubqkn9vz5afALX6NwkLExuwChBFv+Abmt/K+IcMMBz3D3Rv0x/pGwP+4i/+YgR9AXSW8WPk5flAXWNE4j4wGFRayYY4yhnXHHEz0IZVZwxLVY9tvQH4AEHSrSA+9ycZM9cCXvHhC2SKPjyPTsNQDrNbHQEqAyYD+MMcDcNc/rMCIuNXysiIIO244wAkBlzWH4GVGLoBe1LutHl1EVDYERtyToCD4oc9qg7nBHNZWH0lG2zGgMnoRkd5bvJUjwGrsmEe0N19YDCwvQW9gP70zXWGcIA8LjiysRjgJucpe32ea8rVGlFSvqy2YbzdJ/oBtvZUe6ML/mJj8JhLC7s8foprPnNuDPE+CDCtzWoDWKr6rD6D6RpglL6iG2kANt/4xjeOm14CMC0zD4gu7FI7mMvzZVzXP7JiQj/fJ4D5vJvjS5XvcG1hqj7oQlvTj9oVJ3nWE8OeB8JpU1sECC+evnuIMOB5P0kDSOw7wM87ATvfOxTYX7+5hM2P0QNjf2rV1iH5uQtx6CrtxNg/t0Gl916MIcburZsH3wVd9jKetgY6wHuc+ukA73H02FNZqYEO8K5UVA/WNXBkDWBN+cAGNLUsoLlHmVhkMt9O4OfiHHrdByvWJBYlIBSYs2+y6VmAo4A8JqlYpXOCLZIJuklkBPOXboCPQOE5kR9gjbAV8JgLn+uApvgOzCQHOBPwJNeknWsm9s7nWNZJO8cALQGrt4JoSWftUdsAhJioW3K5VkxulZeRYZ8oA92pk7o8NWUFeiyJOvYsYI5l9s5f//rXL0UZ9R3A6CITUICG5wEB4rLABDggjklYrVtlrRuCVeBLfuiBfO5znzsDCtJWTPy4TrBcOMCJZ/upI2UHJGCmAqGwvLWThBGHT9BsfGTSrq1j6CVMlogDYzDAiDCY2wGGvd9rmcZA5U+A0fg1LbdmT4FugB15BCDH1UcY1i3ABaAElmcynPwHDAeUEpNk9wB0YecC2rSP1H/i5gigxLBTB/RKh+3mRpiJAQgDXAGGW8H+C/sy6QNZgYG17nMvR2Nc3CHIq+vyu298ZgQQFriCvZtx0DhGYsxr/TW2m9cBd9SHdgw8kCa9ABaqBBSvG6lxRcT4lnabMs0d6ReQ1Oq4Psd5DJcBAgPYtuH8z/Al3eg4hiL9K+OK+94p+gzfxxjartV8Wo5OtIV6nX6lA5SPn2T3w+TlZoAw8rieFRdJg27aPuy9MOXWRF3EcDQHAo4PG/4wqKXOPcvSf+CycuYd7zrD0ZxxkEuAtG3AtfemFScMqA899NCZr1jpGHPyvZ+yLR2Vo+avDculwVY3Sin7ZRwBzfJo7FAPa+Txxx8f4xhL64oHRj7gt/aG8c74Yp+DusnfVPri0Zu+uGXFTgw9W1Yttc83zqff0YMyPTCA9/qKsYRxOnXIKGmDRcxf7n22sM3b597l/70z0t8ZTnzfMLgYV415VozEaGWMbo2ed1l3vew3RwMd4D1OXXWA9zh67Kms1EA++LoP3pUK68G6Bo6kgSwXXuu7NI/NRg02BjlFiU9WIFPdrXour8ANEyIMo/gQBS5nUyBAkEl43a3a5BqoEiDIRBQAvUYAAFm67aOci4i6/Nt9rMI6uQX+Jj8mz2sEcJcJlcn1ZUs2eAHkbBHggXyqrznBqLR8OmBCygUgBGrZbT1ppA6n0grbMwA+IGrN5DIfmMCLQyXszbQZTFhAwCPD0m+TL/kHrnGFAmS0aZVrFUgCgmPTuo4NHOMFA02dXLu/9Zc+IB5wNxLWMSOLvPg5ByZkc8v4fU0c/Q6IKS1LtucECCSMMq2VgJjYYCTllidCh9JkZNCPqv4YbHxzhJ0qHPDMWBgXNOK4ro/GL6b/tTUASNiKYZ+6B4SNoQdbrZXq1zgs2ycGtl0EwzSAnzxKE6geFmaYqAGatRfgPOZdBYacx58tYFBZ2v4AhA3T3nMYWALmagPywX2G8cx941+MCfIbtp+w7ocBC7yO0QQIXAX4njJNAZOAK33cWCg/gC/jAjAYCBTAsYLDNX3nxmwsQfpPPjyTrvYBY/H/KTy2KYNFfO5yi1ABYkz8pO8YZqp6o7e64kJbca0Kg0N1z/IjP/IjI5vVs6NTbTjjP6OSegVqW7Kf61YUtLrMZlnqYkn0lTyLsatuKieecYmuAw5hmrZGYAzsAM/6zhygDEiOcQVbmFG1Gq6M+0B77FxuIviQj9GGTowN0teujcPq95SA3eg5AD0DwBYJ67fdlHBLGjUsgwm9GRfXiBUw2pT2cNENT7VTBr68M+Sj/hg2gZBzbNM1+e1hzmvA2JR5dNV1PQeuZ4XB+dj9v66B09dAvr+9I7ocroEO8B6uux7zAA3kxdQB3gOU16N0DVxAAwE0TNC3iMmhj0eT1FMUjCP5WwuEKgM/v+KYQEdMnvNhkY9loI/JbthermNcrZ0YAcUyabYceWnzPyBHZfTFPx9WWgsaJM/1mE2A5DEMv3r/2Ocm7p61tV0AEwIctswnQEuWfkoboGBCDHwJc8V1zLiAceqjAjK1nAFXA0zs8xOcuNnMZwq8S5h9x2xcBUBT3soGxrZM/SrP3I+LAG0mdQu84l4EWMjFw5/92Z+duQtIGnQTtqa2i9UL4OPChB4BpdwOpIytKwr/Syu6q4YdgIx7QMuWIY5lrZ/QNaPFlHC1AsiUhnztE/Waes+ye0ww8fmdJsln2o08MEq1PivDzhTXL/0yPl4DpmIE51nSjz5c+9CHPnTGng4gGOBZ2CrVnYLnAQQxkP3yrACZ6hXgSeLfEhAmbABcjMsplySYs4DoAHh5Fr2EOe4aPZowqbeAzoBq9zAGSYBIz6rGsmo8CkCVbzngXTVYceMQwOe1r33tmO7WP3HvMPWdSAdY1BU0VIb6A2LW/LfP5xM54YGLAevprI7RQE6+nYVVH8DzCg5HB/ScMSbL79tnii8dY0E1FnDtYHwPo9y4EQHEYkSmvrQ5Bs0YqdLmhZkTYH1AacaSJbFKImArg1TEeywM/jVjovdjgOn0TwagpdUxGLEx6M3pMPk5hWPGj7mxbi6P+cZY+y6aSyfXtcmMqb5nqgEoYXJktA6zVj6OKQxJDJVWfTCMMgBPjVfHfOZdTUsd+140Fulf3vkMkr5rGVi63u9qy7gd5c4Y2QHei9VnB3gvpr8ee6MG8kE89eG+MakevGuga2CDBjIx3QKESj7+KQGUpyYmNybNGENr3Dkk/1k+D8ho5a//+q/HJf2ZNEkfeAKQ46NwCxsl7EOssJZ9lecCovg09Zy5H6B5aZmyNMJc3OdjNs+96BF7Tn4xbbcIdp14AKcqruf9AACzeVZ8fgoHyAKwBdgFimWyClwB6nDvge0FABU/wIbnxRdofebcOVBTnABfU+GA01gyNouyNBVIxM9vfJhmkxPpAE2mBGDqGeo/IE7qUTygCmaltoMZW++5X3/aKJYnPQGp3MPGnJO0uRZoDXAhvn7VsiHD4rVctJUwQzF15yRLm9UZdtfcZFT7ChCKpRYBlNMDMAh4hYEZPWhT6r4V7UYYoKBl5gHjXIu/QufK3vr7DlCbZeuYwgE5xdEGpySbN7Z1Vo1F4gN+tSOTdoB+8hYf1eo+fpcBKHOCEcivZVxRSNvP5F9e4vM5rE+GsTBt9RNtUbvJRmXAZ0Ab0A+zuBqfAibrg1Xf0khe9VPA4CHCt2/KgfEekb8ArsqmLeqrlpy3etZ2WxabvhlWv/BpX9JSJi46LGtmRMCIjHEBwzaMy7wPo1/xGGxiVHFdPVSjEx/Jroe1nbh0FUAZs1LbkB6QH3s/4doj8DSgu3GjAuzRVY55769duaP9BzxPH9cO5IGboSUAMc90jM9z8TDo0/5qmPZcmxde3WsDpyYMLoA1+kj7bA1J+/Ic44K+fizh3iHjhk3MMp4kfYZTqzRi8AEKzjGwE6cfuwa6BroGrkMDHeA9jtY7wHscPfZUVmogE/gO8K5UWA/WNXAkDQB/TJ62AF0ejY0hHgDr1ASrTt4sk90iQFrxABRzYiJrsov10rIV5+K01+MfNBPl9j5gIJMuE3VswoBHmLtAy4ApAAlLsOvE16QZkBlwEOi1NNlvn3+R/zGX6BD4sXbS73k2tRIP46RKPuowvZYmzQDBsC4B39wvqEdpzv3sdr9FAn5Osay0HcBkliFPPRM4FlcC7mPPTwnwDhM1dTyVlmsYuCbpQH5sPYxe7QN4F0CtdXsQYBLoNiWMG9J++umnz27/x3/8x5iX6BNQ1UqA6+rWIWECfmElLgkALWUGqmITv//97x/9UAI+wiyVP0BX2/8CWtEDlyvRmyX/rWhL8c3KdQIBgogDKMNmTvx//Md/bKOfbQBVl7XHnYF4wJIpCfCMxQr4boFdzHPgJDCGLgOc0wsWXBVMFs9aM84xdCkzgBEY3fbNgGip2+q+hC6BWBVElW9tTF+Lnhy5hACOA8yBedwXpE6VZYtf7lrWnDM8eA7gij60gYCzxpwwnhl1ArYxBFQDhfxwIQMMjT9kaeq7QH/tPXqvZavnDH3craQN0meNow1GsGvDXJUG1q5xrgV2kz5/pFXiRin3Aczaunbtmzlp5r7jVJtPmsYX+aGHpTE14XMMS96zSTbLsuneWvHstAfg91pJOY0JpyKYx8bzqXo0VmJda0trJCx57faY4hsjwLx24bsCIG/lQsZz1xnqwwA/5vN7Wl0DXQNdA8fQQOYCncF7MW12gPdi+uuxN2qgA7wbFdaDdw0cSQPvfe97xwki365bJBOuLZO0LelfJKwJskkLQG6rZDnoFkbulmf88z//85g34FILskgHIzDgLuAiwGyYscqFHWe5awsmYp7VZb7C+l3EZ+yWsiVslmJjNa0VTDJ5rX5cseNcA0ZUIG0uTezZbKyHnQiEB94BAwE6AEbAaYDgNcBYngVAx2gETrQglUk8VwfRt6PJMz0AkNKm6n3nWFutYMaGjex5cQVQ61W9B7w3UZ9y1REfsu0y7WyO5WN5SgIAh5kqjDTkNyzO6sIkacRXMLZjK2nzAMF9op0HnG715X96YLyY6jv0EMAu5RAHU7oy0/gVBQa6Z8k544Axo7I9w5QEjkxJwODW7UlcNKjDKclSd+w6Is8BKKfK6xqXGjZXagW4L8/A1tatSRt23//S9yzlJgDh6qMXuG6lAuAbeF11NZfvXNeOtRng3jEEOz4gYQBm/QHD1YZU+Z70fAZIugHEGgsC+iZvjtLQF6uRjE/cGgagjK1s3A6D132GpyzHZwhIvoQDikcY3TwjRoWkTY82NUufl3bLXpd+HUMAxIxT6iP9PM9NutpZwOfkwdF7I+Of9wx9Wd0w1Z9qPOcATeljl5PovuptvLHwJ5u6SgeYvla4nREHiH4KAmRInRjrvXcYbzL+pD68k/dtHqm+45Yl/tSPWUbtUDuJkThtBDDtHciI1qVroGuga+CUNdAB3uPUTgd4j6PHnspKDeSDvDN4VyqsB+saOJIGAEoBCquPyaXkscBMTE1wWrBrKd5V3fv4xz9+0GQQM9fkhz4uSzBqPKP6MqzPCrDExUEFbZxn0og9ZnK3DBvAAABAAElEQVRsAh9AqbKIAKIBMkw810zeax4ueh5/oQDOCnLMpWvyrmzYf3UTu7AIsczXiuXW9AsI5JZgSiwvzzLstTtK5+MyAFjStZS6+nFVBszL6hrEBB7I7l4m147AgFbC5KI7wE7asvABJbnbwLzj4sN1dVzFszFB3XuicU1Av/JB34w7rQRoskyf8JmYJeLZWBFDs5UAYlOuXp566qkxLwDiKvob1wzAP3XNjyeglU4tXwfS+yYAgGK7YoUCNZeE24BaH1miHPZ0vYddiskM9A5gGUA93ySuTxkXsLXpt/1mAca6PtW/LbeXHv1XFxdxicD1BiOEPo1lh6kPSFpyaRBgaAvINqU/wJ981zZp3ODXthoXhKm/gHzyzE0Gn7FAT9eB4/St3VTXBFPP33rN+Jexr+Yn54BUG3W1om2l72MAGy9aQFV9h1mNDQ8Aq/3ZOcZzjAgAPkCyfl9dVtCbdq2NA9ABsnGhIJ9YvPEJHAMXEK6VbOj3vOc978zIknLWY5jryRcGdcQKidYIVeNi7u8DXJVbHLrRNowhflveL2HzS2cLW1W9iQPI3yLybPyxGZyVMVPGsC3pCavulVt+sMNr39NWXOd+AivWue+kpQ3htFPh9JXLFu2N4dSqj2r0uuzn9vS7BroGugYuooF8g3cG70W0eO9eB3gvpr8ee6MGMplqJ0sbk+nBuwZurQYAEQAQk36TRMskTRjb3dEPUQBGVCYllbk3lRZGaZa7mrifogBPgCjAlHbX76X8WnpPD1PgzFK8Lfcw9zxjykct9o57wNk6aUz6dUmlcJhUJuaZaGOIPTD4fQ2ziD/NNQBr0j/W0aQaQCWPjgExptLH2IxfY2yyKmFDTS2Rr+Ha84B4JvRzAoyRP4w5YOKcAC/CYBUWG7VKNk2TlnJ85jOfqbfPnbsX5q3wfpXx+tGPfnS8xsAQUBEIkrBhRodBixUY0LcC1fQoDj204JUMZck3oIbfyGpI0IbE9QxLr8NWBLJjq7qHvVqFoSHLgIF8+hwQwc+5JerixV2EMYTv4GqUcD8/5bccXDs6RAAX0VXSrEflBsQFpMk941qMCvpWroflW/OiXNFN2o9l8YmD/VqFjgP4AKurfOITnxjj1WX99f7SeYxzjBZV6P5Vr3rVyAAHGhonbL7F0DAF7Mhf2hKjQhWgujoEZnn3qEcMUkzouJzw7Zb+Gh20R8YDaf/P//xPTf7sHADuXYTRShfAbv2rBVgTgV90z6ADGwnROaOAlQNLoLj3qHhTLloYhcJUF6bdfFR/ApwGJG7LmP/TH/J/PYbRXPNYDTspnyMDjbiA/NSbuuXH3SZ6jBMZ72P8iBFRHvQhzOUYMADfaYeY2OLHKKE/LPnJ9l6VF+2eRAfVJ/p4Y+FPNmeVTvrNQvCzW8ZhZZDHOl6dBWhOtKUHhndh3i3RvzSwm7esLqlJG3PT7yuAnjDymQ1ePT/jCdC91nfCA1qTR8Bxl66BroGuga6B+zXQAd77dXLIlQ7wHqK1HudgDXSA92DV9Yi3XAMmAPF1l0lKPQJJTLIxXA4Vk9o8wyR/ajmwtE0cs8wPG2huon5oPo4ZL7vMA6zWCB0EGJxifq1JY00YTDb1B0xqJT4O647pCQN4F8/kMhPM2g7quUkwMKayBJPOVR2BugHZANZAUuAO4FpZTLDDVpZ3k+GWCRbG69TEeKkcWVpeQc+p8Nn8y6Sf6wbASfJgObk8cu0gf/pZu5QVCBvgRJg1E3Qs1FpXzvk2xZ4HkvmfP2w+WAP6tOGzYZsyxRc2dh95+OGHxzzJF2b0nHCFkXS1e5u66fdYja6H+eqcboAqQETgKF08M2xkFuEyQThsxwqOJf2wyQHLQO4wDKWFnQvU4+qFT2JpJJ77xhl5zTL4PHPfMW4PuBSgT/qpflDzDEfPqf87DwM64AvDWutiIJslAhi175qOZfARIJv+KF2gG5CoCsDbPXra4hpGHYiHKRrBiq5+nt1vf8BCgG0r2UTQxpFTy/vb8MYyRifjTfqBPp9NDtvn1v/VM7/NDAKAS4B/bXM1rHPgcQsIxkgj7hbBSpfm1MaT6U9xk2NSGfGOxfpO3oDQlr5zmeBaZcjSyyPDRnDanrHNWAfs40NYHbUS/9KYz1WAuNJWN1OStuOdYDUNNwpYqmH3x3VKwFtjKYOZNLVxon0yNMXgMrfRlzFJPMYCkjxPrQQYA0z8CfgsnS3Gae8TcbTdfcLQUo2hGOWMUuor7xRpGW8Dmu9LM/eNUeLqY3NijMs7Wr2nLlq9MpiFHc9A0aVroGuga6BrYFoDHeCd1svWqx3g3aqxHv5CGugA74XU1yPfUg1YMhnGH2AAWIkB47qd022Ik0mZSdeWCVOrMuBN3UTHRB0AZmJpYhrw0+TGMuYphmmb5nX+b5MogIGJ7T6gD6gHJFA2QMKhzME15cUAC7vL0tEqWaY7tQQJW0j+8gMmYNJhC6WNpC0IE5ZnTf+qzwEN+8AmbDbgG6Yvdhk2GX/QNqEKM+2yAF76wHatYIBz9aPdRNeW22LXthLmu3Ct+4E2bP7X1sKAT/rtsQKFQElAYvXdqZ8CSoBwAAp5BrDV/suFwD4BMGTDvzYP/pcP7jaqWIrunranLHHhUONjx6pP/jIrqMo4FOAMiy4sZf3NaoDafmt6zvVljMu1AGg2fQtDmmuK6BDz0GQBOzrjGNANW1N79LwAQQCasGQB00AaLG7GE76Xs3w++Y3/YKxJYypA39jsvrQtlZ+SbNC1b9m6+mYgYVyzyZd0f/RHf3RMEuCb+gQWY/BigLpuPAQ4xgeweMDvKhigAd8ZSVpAu4Y1doX5KS39xaqSAOKO2L4Zm4SZ+qnXuJnQ3oChxmusZnlXfwGMtXGrVuTTOzCbHoqzZcwGKsvLlE9o4LZ7jBHKpM6AiwwcMRowglawWX7FwYitbVi7AbjuE2zwjHXS+dSnPnUWxdjo2tT4I1A2A6xjhvDRe65Xo6V+m74QVyzSiusBcfjKbSXvSO2KxLCjTa2VvMfodsvqkrgxUAdLkjzRAXchXLZU0W+tWgj7GMi/xpghDW0gbbUa2Wr6OWewC3ib94u+xZDFYBXQVz69I7e+4/Kcfuwa6BroGrgLGugA73FquQO8x9FjT2WlBjrAu1JRPdid0UBlgfz4j//4CBZMFd4EMpMF/egirFrgI/ZLZdGZgOQHnDFBWzshmsrvVV4Dssg7YMDS0ynd8GVq6bJwJvNYnJctWQbfumnI0tnsBJ98AEoDlMljy5gCCMs/gBirLRPKljGU9K76yP8gJhow2gTZRBcoAASsQGraWXvkH3SLBFBb8ntY0wMCWKoMHEt+ADXYu9rQVLsRP+1GfrcA6p7VlrH67pQH4BMgRX8MkMZHaw2nXQdESHqANa5G1oq+zE2GPAEzgWZA3AAg6qy6umBEyj36CYDk+cYhLkji+1i6abcBlIRjIMoYAtiwFN91OgfK8JcJ5AKsu16fA5CZYqZjJWpj2HqemXxhRFrKHwAVYDYHLAGPw8b0XD/6Vf7Kzsy9pWMYfAnDcLTUjjE7heUaol3yDpxWPwFfk2ba6gc+8IERjOXT1T0gJfB5ToBlGSP4h61CjwFpGTr4d6ZXoJh+AtwGUBmHPCtAOGZjygywEkf7FEa/YnBQrrD6XU/9OteupY2FCgTHlldvQHLvJasbUt4KoorrB0zVX9b4WDUuiNNuhKddeYYyAbfjVxprtzJ7W9BQG44epFuZyNr6PsHeFC8uLhhqgLAkYxmAvgqwMT6xxfUDPPNLHINErrfllM6v/uqvjnG06wouhmnuWEUfoHe/uKlhbAjgaZzcJ75Vat6w3tcI8D5GsSkf30lDvcaouxROeAB2ViNx67FGYhjQD9eIdpLVJKmLelRfAN/U9Zo071IY32K+23zXeC9ZFWG8WtPH75Keelm7Bu6CBjrAe5xa7gDvcfTYU1mpgQ7wrlRUD3ZnNJCl4QCPffKlL31pBDZMHrC6LiomHJbTm3xYum1y/+STT07687zosy47PoZmgAEggqXWv/RLv3Tv1a9+9ehrN0AQIGftJnMXzTPWYIA6vk0zwcOAU4eYdhFstYyPymGZbCvZ8Ck+kQHAASrqpmVtvOv6X3tNmTDXgENAHUvX5RfAjZWYyTDgI6DhvjxnQy/1uTZOTRPwCIhKndR77Xk2JJNPQOxaieFBPEBuBYS0R0uHgUpheguHKWo5NnASuxnw2gJdGPctOLg2T204IA6wNHWAnYjBB0QLIJt7jowTgCL6wwo0KU+/E894kvDSTd2kzQOvWwDU7vPiqEs+yAPIcF0RAcQBo/KsPGPqCDwFGM4JvYoHVKtAr7JoDxjPxg9L24GZQOvnDYAxo5f8VfBSOoBmu9QDB/cxTAF2YY8a+7E6CSZpBQ8xcz23ltc1qzk8E9s1vljnyum6MUZ4bazqxAqROQOf8Pl5ftoB9noAdLoLeJywOapjLg38X/Ofc/WT88TRHxjC+M2t/QTLPxuWVf0AjfcZ6TJeMmRU0eY9N8xezN20gzxjyg2L8TzgNoMCY08tI7Z9K/wXM6hU5q52Ht15P2lzcYPwxMBmrhJQ2HMAhWHDCyOedhodOnqXVwFmp71ixcZVQXSgXQDWCTa1Z0gn7N2kBYhXZ37YxDHc5H6OjDYBg/m0Fl59MggtibL4BvJs4HXyNBVHXxRurbGNMUMetLEWtJ9KP649jMVbxDstRhPGet9o3OfsGxO2POM2hVUv7coI9ZqfcXWprd0mXfSydA10DXxZAx3gPU5L6ADvcfTYU1mpgUz2TWy7dA3cdQ2YDPmYNVmdYw+2Oop/PBOJyshpw93F/wFOmH+ZINQjkAzIdNVAqCW+ASwAFSY1Af58yJjIPvbYY2fMJXme8yccoKOylvIxtJaddJXtIq4EAH1hg009P6w5ZW/BmKnwQDLggfAYjxEgYAWxcn3NUT0AnPm5BrpqRxiKQKLaptYw2PK8sOfkk6sD7Lz433Wt/rhG4Y5lCthQXmymMGpb36553qFHPkdNpCvrruatBZiBYBWg8z+fviT+XWPYULf6pfQAlHyNT0mYs+oAgy55MUYCqWII8ywGDuCeMdMYyP9pzS/gco79hRmGcerH0AMQEzd9lB/iOZcF8hGDBJBOm14DsrblVX9xsaBOA1jJh/RtesenatizwFQbktUyzi3lb5/l//hdx/TXvrIEX3rAP+0dczI6yHPUO1A7DNe4w2jDyR/mLdcaYadKI20g6QHucg68UW79Qd22bSwuPgKa07e4+khAIe/AufYEVAuw2frUtjGXtNyPaE95hratrO9+97tHlwmO/o87BHEZ14CSlbmJ2Z5xDkM5oG3K7BhDY71mLIsf7hhupR19C6s/PDO44Gglq3qSLj1WJr7wdBQjklUCDLmY34B4aQNfGd+SBiNODDP1eVaNpO6B9BjegHAgtrbFyJFySUM/ijFFvri6mXK9YnWN8VFc7cI7ck4YWBLO2LB2vAe4itdu8Dn1HKsKhDVf2SppIxfZK2HrM29ieEa0jG/6um8e/sJ9C3HPkm8H9WAV0NxqjJtY9p7nroGugXkNZE4z5cJuPla/02qgA7ytRvr/l6qBDvBeqnp74jdMAybEPmAtT9siNhERz9LoUxQsI8uJLSEHcIaldlV5tSzzkYG5BvgB0PGdiE16XYLFE/BAvWViDUgIkOW6//2m9MWVB1AKE6yyNwET4ppwn5LEXyU21xqf0XVCV/1etmUCUAX4wKITlm/OMM/oAssOwMDPKXB0n2DNVd/T0qg/dZL/t2ySE1+e4lYgOixIrG5tYwurrIJS+8q19T5QDHAIVAPsmXDrywFfMTjDcFQmegHaAnYAb1h9KXM2fcJWzUaIwJ45sYxbmgFg+GT1P1ZnWIoYtf/0T/80mUQ20hPHr10yDxhKOu7zd0wAB0ArP+CSewBiKxow+Sw3t6rBGB2WJ2BRm7mI8AUcg03yrH/Xtua8As4AtYRdAnj1kcqwBFCKxw1DlvsrKxcObf8AjDOC6VPiYJpiujqvfRSz3DUgTfwbRx+eF2OEMPUXIBeIU4Wv09wTPgacuLfRLlwHRNNd6hK4PGUUSTm12baMgEf1rWwViKu+tmue63nqCNhPPLuybIU1pgcITVz6CMMXwMz4k/aWMI7Gd/0pLFjXGIADHFedaYOJa1zABvb/FPPUOzHfDYlT25pr8sxQOAXu5rlWYMRFSNKpR2Vn7Kk6t5ImYYzZwFb9SZ9nXAiwTEdz/QoIXQ1tSU9cbaQFtZPfHAGH4mhX+8S3gno21qWe98Vx3waRngGw7DKvgY997GNnDHbuGMIqb2NYDRJ3NcauqX7exun/dw10DdxsDXSA9zj11wHe4+ixp7JSAx3gXamoHuxOaCBLX5cm61OKyEQuGwtNhbnqaxhHLK5TQJnJEjbT3OTtqvN6Hc8DSgAdKhCZSSpmW5Y0W3rdCpYgoEL4V77yledu03uWDW+ZjJ5L5BL+yWT8d3/3d1elbpIX1h6Qw5L1ysI0uQOQZHk7cKYCTnQDbKqAuWvAyamNhJIp+QuwhH0I2AO6YspjUlq2r/2mrqYAraRVj9h2AS7EDbgjjLK5xh3CmuW7wJKwAS1hv0rBkJNXdVL960Yf9YiVGpYmUFQc9wFZwKQlf7G/8Ru/MYYNE11/AThFh0BVzN45wY6ueXGOTYc9Xpmk6g+wWSW+j9V7dcfRpud/YBuASxxgozbYttWa9tI5EDbjgXarnWmL9Aj4aF1ZxAe3fABY9f0IABvIDRCRDn0DCTFD4/c3z1IvddOtpNEejTWelf4RdqI+BdB3j8/MKQF2VWOAsMoIWHfObUGV+ITFNk67EQ6oTzC1Y2jAWmXwCvBcVzQAb7FRxTVGAJOmBHtYGLqJhAlOjwBY7Fxgq6P/c7/dbMz4Ii3GjjBl/e9nPBevAsl5njEiZZDXxKlHwO3USh1GPrpJWOCoMTTA+hQgrL1g3KqzhBNfW6azOTZ08puj8QjQy0DFZQh2ufbKIDTHqOWGJYat5DlH7cvqmilDoPoMg1z4jAf6Xsv8ZgCpwHLy66hs4vs+WSNhibcuL5bicmvhGYwyXaY14D2ed4TvoX2iTeS9w/DYpWuga+B2a6ADvMep3w7wHkePPZWVGugA70pF9WB3QgNhwpisbpEwjdb6oNuS9iFhTeoqG80k15JLTB3MC6BKJnOYjHeZiWECarJpiW8YgUClRx99dNRRCx58+tOfPtuAB8g7xXYJQHXV7ifm2gpgFsik3te6HpEWFlbaiaOJP4ADSy/gkusmfFmaDNwGjldAA/gBuI0vV+1xapd77N+AYVwUzIGtwMMKwAB8lkT7ruCzvAZ4UH+AkJQHmAeYsNFUBeySvmtAT+UG2B3iEiBpHXIMMJJ60VYxo/kttdz6i1/84r1HBrZ86iOswA996ENn4JW4WLFLQv/CAdIidbl/mL251x6NoeIDdPWh5DdHfYQep0DmtBOAG30DegFW/L8CIxgVal6SZj0CkS1h3yJYidLQvrUP7SbtZCod6Qsfg47+AvwDKEXv7oedWvPnPEAtwHGNyEveUTUtzw3YiZ05JzFKJC7gFGjtf2BtRL+LYQa7lQuBxAFERrQ5unJPWeK2AzP18ccfH/sRAN594LJVBHOiDwqnXcSQFP3+9E//9H3RsDqtkhCnAsoCZhUFAJPxQBirNLhp2CfcE8QAgdUaUDzlmAK1vBO0c8/RH8P2NU7F1zUdLgngTHyg9Ny4txT/0HuAf2MzVxSYvtjec8vv9QcMX/nUPrC+0+6Ay0RcbmEC/AKqpyTjg/a8Rt73vveNz2V0NMbtE22AYcL7RP10mdZAVhMA6JeY4jU2dw7aAOPV1Duyhu3nXQNdAzdbAx3gPU79dYD3OHrsqazUQAd4VyqqB7sTGohvxMruW1PwAD6WUV+3AHksh/YBjiGGZdYCuEA+oHSYWXxAdvnykk4AJt1lSa8lrAAzTDeTWxNG94G7UxNNIExAgSmm13XoOX4SAR5bJeDV93zP95wDVekASPjCF77wXth3gLm5JfueC0yNywAAHTZYRLv9+v8DbNYAXpnwy4cfsGoKGMGijksC4dQrtpt64r+3uupIWjkCvSqzUtksP3YfgCGdq5YYHuSBn9U5Ub64ZRAWI7MyDE3olySrGSooV8HFKWC2pgfU91zAOcAOIK/vaIsB8Gr4nKsvuhV2yj+o5wbcZbDAOsNOZkxhVLBZWdwJeD525BqXG57P76Q4WONrJEBH/Pdic2fslTegl80Hjb+AEOChd0Rtc4BgoCmAFIgov8DOdsxOfiprWF79ALwAOudAySnRHsK8Szxs1QB2gPMIn6vCZPWCvATg1EcJv8XATsC7+qiAdtLPEegJcFsS+slqAAYBbUT/kkbL8gQiBqyWtrJViU914GpYqtrFWgF0eq5xw7cAtnrKon4AylwMAG2Nf3kn+J4GItOJ8MakpMX9wZKkr3J1caoS/70MoTHQ0auycsFTxSqovAfrGJIwAfSxjddKxl7tFjA9J4xzAdl/9md/di5Yvz5oIK5VWhct+5STd7XxvEvXQNfA7dVAB3iPU7cd4D2OHnsqKzXQAd6ViurB7oQG4idwKxM3wIdlj9ctcRcBbNvHILXsPX4Ht0yAr7uMl/l8wAKfsgF4M7HPEShOx1PMXfnKBlb7ALTLLEObdoASQOxWyTJyfoix5gAV7bLn6IaLAxtlLQlQVzhxKkBpsy3X2qXuS2mZvOfZjthdGIwAP2VWT2Eiug+EwlYDvANmEhczlG9oRo8aPnEAvWHEugags6HQVQsQLPnQPte4AMlmSUBTIGLaNeB+DkTEeANaAWiylN2zw2xk9FgjqWebPoXpidW4JAF+Wr+lAGN1G9AodQOEdJ3fWeAvtmcFUFPHGJa//du/PWkESH6y4V67CVjut0cMVm0qhrIs7wf4Lhk6UifJ29QRqDm1uRUAsQ3PNVCYqnM+qbPZXhs3/yuHcU9921jP9QrahYWt/TEIph0lfnsEslsiry+uFfUXf+iMR5bjx3WBd6vxR/+tYVo3Aow8MQBkTMGKnTIWzOVLPpRHPAJMTNtvy+l/9c99RlZH8JMdIwU2tzBhuE49k29p4dXBVJ1Pxbnqa1y00IeyVMOWfqkt0AG9VcmGiYymLQifFRVvfvOba5TFcyuTAth7ng04nxj8Hnuubx3t1qaIAdz1s7Ws1MUH3+KbGZf53N4iYabrY126BroGbq8GOsB7nLrtAO9x9NhTWamBDvCuVFQPdic0gPlhAgPAmvNd1yrC8mFxgB5TDMI2/GX+D3Aw8THBaf1Fzj334YcfHvOP3ddOwubi3IXr//mf/3m25BiIBCzEnptbvhqdBDi0zP9UBEChjQL9twjAQlsygcc+zPtCWibtPvzi4xlA4bofYGepLQGBhQsbUJ4CrmmPW6RlJSYP7RFQDQAg+WDVz3Mtz1S/2HQBlmo6wF8bMU5tupf4l3kElslP3FO0S9Onnm0ciysAcfkwzaR+CqRW56lnbT6SsVEaa9tR/PgCBZPmEuOL0STgHJ+yEaBvC7yru1ouwK+xL/WFkRowKNccGRBa5j1wyDPSlgHGcwac5CnHAJ95BuA87MaEqUfGw4TNkXEBEKWPYcUGKGZ8a/3BA9oBgYnrqB8BlJ0DIlvgP3523a9xtWUMWNcDilmZwF2NazEIMcpE/3Hvo7/z28vfMVa5uq7AtTJw77BVMIMZXDy/5tcYlDy6jintfddKjLTaeFzMHLJyIUYsACaJ31njFFdHmLwYugDnqfGA8Uo+A4QzLkwJH7wBTpdA4Km4V3kt3wkV9M/zwwJt2cfeAVk1YPyIcN9BN9rUEps/4evR+PTyl7/8XFuQVv0ZB/ThOQNWTe+un+f9qd9tkfgDx5bv0jXQNXB7NZDvZfOfLodroAO8h+uuxzxAA5l0+Wjt0jXQNXDvnqXoJgv6xBJIRVc+isMmOoUP3WwWZWnpWqmTMIzeLv9PA/ydBljgn3OfvP3tbx/bjmXpAOJTEXWcDdP2MShrngP0AIECvGEmV6Y6cEl/sQTbcuSAj3w7zwmQCkNPvIBh2egl/8/Fba8/8MADYzovfelLR7DKcurUGeCEP9CwdsX9wAc+cFZHS7oADihTPm4BvhiE1ynY03QWII3u9xkc5DfhxbW0PWxOLFl1EQEwxo8qxms1cvHdLb6fOsaC5n8YWA/gw3ptWWBYk2k3aRctyy/PBtxkCbYVEQFnAhJ7bsDFMGyFAXqr4+QNsFeXb8c9B2ZrgFPuBoC8NvzKUv/EzxFIhKm6z29r2PGJt2TY4Ss54dL+/Q8Ar2KzN+3ZPe2uAtLq2/UAh0nP5m3ZiCp50O+zWaRw6qKCpMoI8HV8YmBCZuPIrEgBFssL3eU5jlwpYKm28qlPfWoMl7o2VhzSZ7RJvkG5j6iGI88G7FptUtutfGg/GJ3CeL66/fjHPz7+L79bJf7YA3wF4NQP1go2efTGeADI/+AHP3iPP2ztOu48hDGOqS/tjXHW2HTV/r2XypVx0Fjaik3t4sMc8KcckQDu8dnt3ZGwv/mbv5lgm4/0YzzU9/VnvpsZW7iRqP1lc8J3LEL6/FY/xWkPa4yMd0ylvbhdA7dKA+nrHeC9WLV2gPdi+uuxN2qgA7wbFdaD33oNYB2FvWejsimWECVYih9wC9Pputm78mQHbZPFd77znf5dLcA48QB0pyCWgwJDsKD4ljSprJt2XWUeTUzpBmiAhRXgqeYB2GVTqrDjsAFPTQJ+7NuQLPnGKMR2VHYuChyf85znnAP96ALQ5Be9ALwARsIDM+YkS3RtrEaqK4i5OFPX4/8aYLJGAmDOMeqm0njuc587lucigMRUuluvpX8DuMK8jL/SubT0pfiIVSd+VhvENQvW3R/+4R+OG4PF/QHWXQXwAggbF/PNQI8B8pKuI6CFn9ksiQfE5FnCY0ynrcizcZM/Uyx58TEnw/zEXgVI6lf827oPzKngHvZt/EQL5wdAi2hf4tkQCKCWPupafuK/+MUvPnPbAYAKgMowUv0wJ90cuTQI8Jz0/A94MnYEgP/EJz5xpi/Pq4xo8bixAFJGlBFL1L0f+7Efy+UxnGsx2AQsc626puBDOe8n97Cuazuo8QCg6kFeA8inP2YMiN6kMedbHCPYswDjGTNcu4hoK9wWhF2tXvSDd7zjHaObFH6QMV/DRNSG4/NV24iuapvZlx/AqnaHIR6wkm7CYl4a12raAddbkFqe8lOPxhWbGcYAkXvKCrSfArTrc67iHPNfvuZc8DCAppzaPiMM5jcXDOIxQGA8p18BtLtcvwa4tFA/a9+fcqwvhdhwqi5Frl+zPQddA7dDAx3gPU49doD3OHrsqazUQCZrncG7UmE92J3QAOZPNukwWQeCYMfyN2bSXllflpNmGed1KydLW+tyyDV5MsH0kb/EulyTzlwY4IFlmTaf4ZsRKGXCZ2OPyuizGZKlsJkEylP9AbWq/7+55x37ejbAkReuCSxdx+jC2LVLeMAF+b5qEBBr0hJs/jP5ewTG2RioNTjQc4CwfWxzEzggjfJmcyUgHcCkCnYf4MfEvgIoYV1iAs5JWKUYtSS+ZbEAt0iWB68B1elAfjF7A0CueRbAiC4Ycq5TskEdMIXhKZNsbFBtsS53Nia99a1vPWub8o8RGZan/9sfUMvy84xn/Jtmabq2DTgNI1Zc14w5QALfEMDdpKnun3nmmbFdMAzkuiMgEgjkekAz14G8n//850cVa0/x4Qv8yrJuLlCqhOlqPMmGVtIJiBx3IPX57blyAwm5ZQiwabyPEUK/+cxnPlMfO55zz1Pz36brf8aObNzof33FOyTAaY3jXQO8tHKA6G/i0zOgnoRJHR+YwPj0pZpWPZ8C4ut958BMY3NYp56ZMLWMcz51vTOVTTwsfD9pujblwmAszIY/3E7wuS295Ks9AqpbFyBxETCX76ksYCVKG6hZJZu3YTb/7d/+bb113zmfyMYa+mCosBrC+w9LneEY2Am4NVaHLeyZjCjAcUz0GMpcB9inDdz3sCu4IM/yUY0n7WP5Es6YJKzyx7jjfz/1x2hb3xdtOv3/q9NANorUT7zP1wiDoLpk7OnSNdA1cLs10AHe49RvB3iPo8eeykoNdIB3paJ6sDunAeCGpakmKZmc1CMGlOWAaz+Kr0KBmfxP+dZcer7NpZRt6+ZyS2nmHrZTWGFVfzk3iQWgAnACJLhmImypsV3hfWDEJ6L6oPerFoyklmGVMjialG8BES6af0DGElhnot1ugGLZc/LMj6GNqVrB3MxSeWw4TCtx5nYjjyEEmBcBLuf63GZTXABIN4BungOsXitAvH3PqWnFX3a7zBpLD8OSfoA7+k82FxMfsCKvwJfrFKCQfIR9DLCOUcd1QCWwyK+OW9ir7qd/c8VieTMWboA84fkqBewDooDZuQfg1P7jAkZafsDfAKnRCx3H3QbAN4Ah1rB+GyZm0nAEBjP2VKOEPLonDWMsAN//lc0KpAfWy7tl4ljnAVOBan4VJPN8Bpk8G8MVQBzQELudD1z3pas/B0itoLGycktB30nLEZjVgtm5n/qoY2Fl/mLYJj16f9Ob3jSqNKC+8idv2iEQPrpUr9pvjCR55r6jMQKIXX0ZT8UJs5sha0oAe+pX3Oq3OQzkqWX9U+m4BtTWvrVDxgRH/8e45L2srWgH+jE/wNryHMva+0O+9JO2rU7lAZCcNvTII4+cCwKUZEiQnnbl/dRu8sYQEKOIcEsGP8bKgO/y98TgJiOMYQ+WF0BxDIhcXoQRfi5jV/BP+vG+VT5Yu3E/kX5FD4w63iFh519BlvsjVmhAe8u4oY73CSN8Vg9g0XfpGugauN0a6ADvceq3A7zH0WNPZaUGOsC7UlE92J3VAH9ub3nLW+49+OCDI9gFjLQxSgWATkU5YV1uAcnkPUu/AQjHFD4HA2wASEwOMZZMxrmRMIEPqJFwQJYptxgmjgCagE7SvmoBEAAzMJAApIAwbiRa1thl54svrABXgBX5wNY04bK5TDbRMrH+iZ/4iXPABr0nLnCBz2nsX+ChJeypD4A6wCFsdfU2JQE8LMuvggnq+QCKVizbdw9QFdAlvnGxUetS9TZu/R9wJB3lXSPxgcpwQzA21WVAYmnlRzfaIiYiYEf79LtO5hnAXv4wLSPyYzzCsAtYJIw6tiES5mDe81N+pI1vwPW0iZTfUXrYuSb1fDTqe3SgHwacxHJs/Tda4p5nSgeAqC1F+DUFSPJxPGVkEC4uGWzYR/julBajRiQgMDZsJMviuSTRvlKejBv5P0fXGTSwMl173eteN/YZ58pvvI9rg/ib5GYi/UQ4eQxTEfDKSCKscVhbq2GFxxLGtAdMJl/0iUVsDM9YaGyJewxAbMIClwkAOiAakBdAbsm0+sGoru3Bc/MLMKxejAf6oOckfPqDsaTqEAip3vjVVW8MIfp5ygfQTX+WvxgEgOr7hM9yKxCSVvKao+vA+K0sVu/pAKTGyQqgtnnieuIFL3jBqCdGh1qWhHXNSpfUkfwB57XBsL9dU5+PNABx0nDUTgKKa6tL+cKATn9qGew1zcs8D4Oez9alvNY8aCd8otNHmPn1fj8/DQ0YhzOe689zRgSrIdKXGJ7WtoPTKGXPRddA18AhGugA7yFauz9OB3jv10m/cokayEdjd9FwiUruSXcNXJEG+LI0mTKBX/vxbaKepfthSR0ju5afyouJ+Rve8IbJybLnWNobBhmm4b58W1Jocg3seOqpp46R1RuVBp+iAXowooCUrQD9gMBYiOqgZd8CLfkcjd6Fyc9ED8iUzYWwxtyb84EMNHQfKJSl/fITYApg1kpYiTVf8mzZvrSAYvvaAdcU8SMan5vtc9r/gdTSB2pjFoep5xp2NrAaY5MLkegY6BWXE0Dv6xQT79TpFFuczmzS5Bf9ve997zurnznfqcqkHQEuGXm4e8C8r0as1Bmfx8QmVhXQMuYAxOkwrhXo1a8F/8cE9vwBOosbFjqwCJjpF1cUcZ3huZEsH87S/ABM0lLf6jXgnGMA0vivBXQzdtS2kXIAgSsDV3zuFkiWsHPd0gqgN2noJ2HM5Vo96pPZfEv68bUsjP9b4x2ANmWQN0Y0RihM0lo/4vsfmEMYNvJcoKa+AYQPW9e9uhFcwk4d6Ux/T5tL+VMXGK1Loi/GX7D+xu3K2972ttE44ej/gM/A1LlVAXPP0FZjwMBMb90rGHuUP+0WqF19UE+ly6inrac/Ri8AMID2lJGypmMzMnGwkFu91XA5l16MENVYkvuXfcSij6FjiZVc8wFQTxnr9X5+ehow3qcta2cM2AyojKLGlLpaSJut74bTK03PUddA18CxNNAB3uNosgO8x9FjT2WlBjrAu1JRPVjXwA3QAAZXgIm1DNcsJz2mkQeDL+wkzNIlMYE3CQzYCLDYJ9jU4lRgZ1+c23AfszUMGn5J9wnwPIDNFCAIMMTMUkcmc8Djlj2bd0RcKUw9MxvwmAT+13/91xgku6dnqbmLgBQsRXVnEtmyNwE3AeQAinMGB8BR3Hn4+FwrQGsgGRA77ZNrDUzQVrDmwoiXX7/WJ2cb5yr+x86UF+1g33JnIF+Ay339cCnvAFXAKJAt4KrwzrFXK8szulKPXD74nx63iiX44lbfyvE1G1ZvWIWV1YixLF7LBMW6tHLAvbhR0Dewk/kmdn3Ljy6wWSNYteIDIFuji3adtLU/53xbB9RULkaHNs+J46i9TjGwPR+gG1ZujVPPje+tL1zs+vS3hM04nP/jxsH/2j+2vL4LqAY+Y1p/4QtfiBrOHblWEA9DdU4wd6MHmxmqjylxPZsd0vFWJi+/9BXwZkwEVClTvQ4k3wIge+c+M7inwZzmMsUYt0+wgDP+tOz3pbjp+4xQ1yHctKhPbSRs9ql80IFNR4XVx6b8V0/F69euVwPaffyOq7v2553LnRdXOF26BroG7oYGOsB7nHruAO9x9HhwKj7QsML+7u/+bmQzmCB76bGeL7FfDn7gNUfM5P2Y4M41F6k/vmvgxmjAhzI/kUBOH86W2GMmXkQsdQciAGSkOyeenV3PTfL3MZbm0pm6ns1obAyzTwIOAgFMKAAV+wRDERhi8tgCkvvi3uT72QzPZkJrBcOQXr/7u797bZRz4YDo4i/50VQf2VwHQII1igUrHhCOAIjDctQ2gclTAoQL+IF1Z9m3MljujfUW/67Sxuzbslma5wUkEp8Lg33vdWxWYf2AFtct+i0wUH4Aq8aMdsKNbQdcCyNLHa4BnubKFpa2506J5/tmwqjlxxhgLg+f/OQnx3yu6dNtujEEPDT4UI0A0fR7P0Au4IgeAH4Rbg1SXzlqM8of8DdjjusE2z3AqzjAvz/5kz8ZvwEZnOLCxr20zZe97GV55HjEwsz3FMC2tqvKlpWGssUAog7DfAfAYn+GrVrzFObtuYeWf4CG+pp8MbRwMRDW6tI4DCiVP3mPMSh645OTaxwMbNe2AosZO6pxQVv53Oc+d8YyzuRRv9zXl91P/+WuYatggfObm40jU05HhlHs6FpvW9NfG95Y6JlWR2wRehPPWHtdEpBZPsL8js4Ydq2w4dvbfeM8Y1yXm6UBmzzqC1YyWE1jrDpVt2Q3S7M9t10DN08DeUf7TupyuAY6wHu47g6KaZnJm9/85nFJXJZ7+jCZ+rFa+wj20WvCc5EJ00GZvYRImZB0gPcSlNuT7BqY0QBQ0kQp7Lp2vAE8AEoOFS4RkiZgD+CGKWVyjRX5e7/3e+OO9cKY1FuafUyJ31aTvX0SEArQEPBkDdiMeSn/7XLbfc+7yfezTBJQtVa84wA9JtsBktbGFY4fSXo2aV8SvlzzPknbA1ABl8LQc12btxHXkmijGJxzjEZLx7XhQyS+XeUNq2+fcDGR8gD6TkEwRL2zky8gIbCFv2X+ZCsrk6uDfcDZvjLF17EJ/xYBqMkjxudW+dCHPjTG5fOzfmvF3YG2wUesDeU8Iwz1sIajG2ztuBfJJnsxRnA3Uf3YBuDE6KwC5JReBVwrMz1hMaqzGSSfrHxWynvGw+Qp/dg35ZRva6SCWocAyUOE3j3TuNqyiufSM4lLPmOEUS5lp8t9rgeSrk3vANXGHXGkoX0mT3mGo7TjVzjx546YvNJV/3MM/7m4ua5OuKnhQ5iBVV6vUqaY52ueD8SnTz/n1yXG3to+1aG2UetUm9262et1lac/t2uga6BroGtgWgMd4J3Wy9arHeDdqrEDw2NKYIztA3XrB0t7buIxt2TuwGxdebRMyE8Z4LWTMKAIIGaZpQm3D2STri5dAzdNA5azVh+MmFaMRjbcscFFluAbb/x/KDiDOROwoR278j+/o5cBkGaX5XYJfltXgG55sUERyRJs7Ll9ks1w1oTdl9ZNuZ9J9dwmKHPlCJvWru1bRR2F8VaZeFPpMCBgjmbDqbQzR6AZBu6WpdXe0xiqNs7ilgM784nBT+gSuAGsUU7A3TPD0ulWsimcPAGbLDueEuUOyxKYFIDL9VMQIBU/13UsqfrmhgCb/xgS0M+H/hbhEkOejGlbBQM4QGy7HByjO2Br2K5cBjBmVR04r+MbcDD31anVEvExbZMyG6q5zyd5FW3OdRtF5pvRBpFTApytfnoZI5LHPNtRX14ydMQNjbDeA4cIIDDPxKbd5+sVCBsjm3jVPUZc+dCxfr4k3lk2ZJMGRjESRfwEu4YtC+Su7zrXAcBr3nfpw8aamyjZUNL7bosY97Rb9bqvLoXdF2bLs9uwYX4zZIQprt9411hpcirjZJvv/n/XQNdA10DXwHoNdIB3va6WQnaAd0k7R7pnctxOikwWTGItfQUc+ID0UW15yote9KJxSaSJQD5kfIz6+diyActNlVMGeAG7JvZTYAHdm2jxQ5nlYTe1Dnq+744G+KzM5N+yZcvRWzEx4xsxQIJd7A8V/lAtrzWZNnHHGDPO8W9q86J9E/VDn5vJPP+ESwKkEtb4K6wlwPp2C+hMpfG8YbMcYQ8BLafSu+prDFTK6V2Djez9gukH6LcUvRX6UV762irAKHG3MH/rM7DJxdd+llw1YAlyqSAsZiUg4yMf+cjIlrtMsMG7ggEwmwB5fn76G/cnMQgGrI67COGAob/2a782bigDULb5W/xyAuf0lWwAx9/pqQlgjtHzkYFtTefY1McULhfoif/VLRLXDpbVHyLcI3iub4C2n8tTxoDUdY6+y5y3TFxL47Vh97jnSLu2YsK4y4DhXgxOybPvQNd9j/gudI6xKM6UaGuMEZW5Lo5fgGn9w/PbfoFxz2VC8imODcwOkRj4YhhS5jnWq02WgOSel6NrEe7L0ne46vD/lGh7MSgpP5Z1yg7srWzZ+LhGMMhKDnmc02ueF7/th/h2ThrXeYxrEePVFnn66adHXdLrlGCMm4wzKugD2hD3JfS0z8XHVHpbrmV83RKnh+0a6BroGugaOG0NdID3OPXTAd7j6HE2FRPB7FDso9OkDdPN8uU1YpJtWacPpoAY0qkfwmvSOZUwpwrwmiQEhDchAlCZ5GAGmGQB4jNpUId8QHbpGjh1DWTjIG123y7ENl/JxHzNxmOnVPYAAWuApmwOBDh7/vOfP/Zry7OXBMsrADiW4E0TKxL27VDPHUDrUiEgyL620+qDmwTjpeXIh0r1vQhY897EkGW0AFjYaTvL5bkM4H/1KsRzvu7rvu7sfSAP8uedEaBK2b/xG79xZGxiv3mnMG685S1vuRe2ed4n9eg9w7csCWvwIq5TrkIfl/EMulKndBN9rHlODAvA80OFWwjP9b3l3d9+q2Hh2iwr9cYIH/BeXWMaGku5Y6gGev0vbi5+67d+a8xeDEz6WQRIrb1w38Agw+9zniU8oJmhak64AqhgLV/S8QEsHe0PqcAkCjs2biLcyzmQ9BDRB6QDbE47l+aLX/zie9z48C8t7bi7EdYmS3Gj0G54xvCUsR3AjXHLqEBHDCPKEB3zY8wwQnfAxne96133FSHtg+FJPaa/7ttAMr6U51jU9z3oxC5oLxkr7fOxVrR/dfTyl7/8XBTvCa5Z3MtP24+hwzX1oL12Zu051fV/uga6BroGugYWNNAB3gXlbLjVAd4NyjokqI/QfABZctayJ7akCdQNyAuMvEhaW557zLCnCPDaqMFSSPUE/JljHgDaw4Y0QekMgmO2jJ7WsTWAfROgYA3w6flhsGHhLIEIx87rRdOLqwVM5H1iabS+DtDEnKOjfQYbPgDFwby8aYJNmneQ/L/61a8egRLACv+hxr4s6QZK1h3qs3nNlqX3xsW1et2nS0u26xLulKMesTUxSq9CAHeWBXv+t33bt43uG2o/8U7mBzLvObuABzQL0EE/jz322L1XvepV44qdl7zkJSMb+B/+4R/OFSFteovuzyVww/8JE5Nxas0yen691Yu2dxEjjDrEajcuSA9oxa0NUPdbv/Vbx+XqaX9hXed/x8RLXBtLhmEaMBJwaQOrfM9xIUAAiWlfMbbVtHMuP0vGk4STdyvIMFSNYd/0Td90NhbUMBiynp28/87v/M5Bradudvnv//7vo1/rpJnn5ahfM6BnbFWmKcHcBXLPpaOMWM7c82gr0sdInpIweLMZKJa28HS+5N7npjN46SL9yXi5ZiWNMTVtsbod4cZGe6U3/s25L/n85z8/zkekCzjXf/RDYdQJokuXroGuga6BroGugX0a6ADvPg2tu98B3nV6OjgUy7ePHB+v+5YPr3kINob0/K5qUrsmX2vDZOJ7Sj54+UZOHQF7lwRzIT70fMR26Ro4VQ08NOwGr13zt7tWgFVAPvG2MH3Wpn9Z4TC35BlYuW/yagOeukEL1tmSAMfDRrtpbMosCcfoA+wGsKWr9hcAha/3GK+wZIUDbq0V7ERxrFw5hhiTpYltbakwZqd6fuCBB46+Wd9SfrnmiUsGbkyW3ucAybAFA55vcbWgH4ZxbYfxuyjqPfrGFlxikducK4CoTdGOIQy93H8EoE9/AdIC34Fe6kkfi//XhNFGjbvALoLVq1/lPkZr/P26BgRjcMn9HH03YpeG8QrMDOvU+ZS7Lj5505el8573vOecOmwAx484P8d88mIoK8cLX/jCs+dns7NzEVf8o85ikOEHlwBouVnhb/infuqn7nkvISvoIxi7CY+hvyRARCxgrFAuf/R/gHJcMOgnymuszvjVpseHrjAA40h8qye/uV6PYdPfVB+8yqJuMqYYv/icnpNnhpUSMQZUl02MHwBiOmQsXDKkaGchRFR9zz2zX+8a6BroGuga6BroAO9x2kAHeI+jx9lUwgw9dMlbmzBwwseV37F3om+fdRn/nxrAS5/ADz9LG9cIppUJFJDIcuEuXQOnqIEABls3BYvBYwo8OMVyyhNQ1wZuxkWMuX3yMz/zM2fj6NLYzBVAAG8gEyDkpgjWVBiGL33pS8+AKhsNAVsAP4Cchx9+eFw6XkEhS3PJl770pbOl8twL7BMgS4C2m74haFtWvqW1L5v8LIEjiQcQzvtfPMvL14p6EQegfZflqaeeOgMAtds3velN9/7lX/5lBNf5++Zvmb/atF2g37FFP8K4xczmU5ff51YAXwFe1Zsf9zhAyYh4cTuRMO0xDF/MSQBsRLkSFus3LlBce/TRR8dgQE0AZPpfwktrqS8aO+OWInF8Fx0qMSoBoK0emPNvy4AYkBvIetGxFUAr/xjxc8IfsHwxusQdRFatzLlfEE548eb8Cc8979SuM1rE7Q6DA9Z23VPCShYgOmYuXfper+zb6IqLmjWbV+qraY+tT+tT003PT9dA10DXQNfA9WugA7zHqYMO8B5Hj7OpxG8j32PHEB9jWdbHr+JNk1MDeIEWPmTt+LxFvvd7v3eMd9N8lW4pYw97szUAiNK2pzbQWipZlvRzZXCTBKMubDugz9SSW0CMJcFZPko/fnyous54g3X3/ve/f1z2y6+g+5ivGFA3SbKsmDufsEgB23EV0JZFOwnDi8ErE/8/+qM/GnUA4ODbdI4h/eEPf/iM6Qzcum2CTaktLIFlbZn/+I//eIwDgKS/NSAHpqpNuDwLQ/GuCzZh3ceAXtofYyu/r9cpgDH5AvQGqPU/wxMXCMaQjCeuZ6zS1xjjuOxwXV+tm2ECPuPzORMf4ZKW+FiVdUzDrhQmKw+0P0xMLPIYJ6xGAiLbyE7YfFfK50VFu9XepYsJatzA2uVWghsdgG5AeRs9LrGz1+Ylrne8v5YEACxfdIZF7L3h/6lyux/GKiPZbRBGyzBrlVt745KJASV14rrNntt6oSP3tnz3qntxuETq0jXQNdA10DXQNbCkgXznVCP3Uvh+b1oDHeCd1svRrgZkORazxK61Ppb8wkA4WmavIKFTA3h9tNPlVp9zYXPZSKVL18ApaiAb3mxZGq4c2ViFb72bJkCEGNUw1yyt5WaAaxt9Nb4D9Xnuc/T7LBHOuFqPQI8HH3zwDOy8SfrIJjgm7srERcM+AYoDjIT/uZ/7ubPgmJMBbOjwl3/5l+8xMNI39wl1EyfLpwMOnyVww0+s1KATbWvJNUNbTKzKCvZhxj3xxBNtsLP/seLSbwHzAKYuX9bA448/fs8+BsBO/ZJ/Y64LXvOa19zj7/W6RV0FWAXQA7TChKxjivOAu/pUNniMz2Wbu1bhh1ocKwmIvQC4K0k/TdrAOUA4n83aXdix3KvkecIKV/93jU7jtkafPoYoV9xZJY/16HlWT2zpT0v5ojfpv+51r1sKNrqkAGgKC7y1kiHnNaLv64C7wq9hrNb4p3wO5Md4BthWUJeRgFsSxrpWjIHCCrNlfMd6pl9M3i5dA10DXQNdA10DSxroAO+Sdtbf6wDvel0dFPJlL3vZ+HHjg3oNe2fpIZbLBjC21O8myqkBvJlUbd3IxrJIH63VP9lNrI+e59urgRgv3vjGN24qJLaZto19eBPFsujsVq8c7Y9fz+rvETOXmwJLdG06BljDqMMIvIlGtNRZZWkBWuaYtwmfI0CIzgBoGM8Rk/7qcqDVq/D8n9Y4iXvTj1jdyqttbJUYFb7/+79/TAOo5wOWsRZoglHNFQFgKkv4gXOYq13mNcB3LMONdzh2NSaqfotJe12AL9cGNmrVVjAjGfYZR4BpfgDNgKvAsmz2xX0DQwBWbgUSjWVpE3/wB39wThk2T+NGx7OMaTa/qsK/bli+3gU///M/P+YtbH6Am77OJc9XfMVXjOkci4iQfBhzAMbc5jA4MQRh9yv31AqLxDvkyM8vXTAC7BN+igPyRkfAeS4L5I1hMHoSTvjbKgwTxhr+dJfGbu5J6Nc3/Fb5qq/6qjEu1niXroGuga6BroGugTkNdIB3TjPbrneAd5u+Noe2EVqWvpkAc9WQJXJbErN8NjsE+8iKj8QtaZxC2FMDeO2+TJ9blpzRY/y9mRzdJjEhA2aYHGIKLW2icZvKfRvL8r73vW9s29hkc34Q23LbGMWE15jFx+VNFswhDF2bIWLhYvJq00uT2Jtc3jbv1ScoIGyt0JUx0e9jH/vYuWjaEV+kr3zlK+/ZKApow3+npVQ3vb2cK2jzD//s9KEvbZVnP/vZY9wvfOEL937lV37lHKM3eq5H7n/4wuwyrQEuCxgS4ku06i7nwNJf/MVfXG3UmH7SYVcZjBj2AxwmT/UYNjxwGij8yU9+cmwjDPgRvtOzusBy+SkftdivnuM3Ncb7rglQaTzgooWLBJtSWjpfvykZuG4yY1z/omMse+D3PgGkxwha6ybn6sj9CrjvS/M23+czmW6+/du/fXMxv+ZrvmaMa8O9Ll0DXQNdA10DXQNzGugA75xmtl1/1vDC7nKJGhjYO7uBCbYbNrXZDR/+u2Fn5fF88Pm4Gz7mdwPzYjdshLMblqvtBmbHbvhI3w0f0TBTvQAAQABJREFU2bvB99VuYKHsBoB4Nyyx3j399NNnuRwmgLuB7XP2fz85XAPDhH2MPAA/uwHsXZ3Q3/zN34xhE391xBMNOEwyxzY1LFfcDUzxc7kclsHuhuXtu4F5sxsmPefu9X9OVwODn8PdwNzcfe5zn9sN7hZ2w7L6xcwO4P5uAOt2AwC6+8mf/MndYJBaDH/qN4dlx2M5Tj2fl5U/75WB5Tgm732zVowFkWHTtN2w7Dv/7gYgaTew/sbf2cU7cDIwasexb2C67QbG7W5w/7Gq1MbSAdTYDSDbbnCVsXvooYd2w2ZruwFk2w1L2HfS874fAJDd4JphHGMHBv2qtO9iIGPTsGpmN6wuGIs/AKTj/wN7d2ybg4FqvOc9Nvhi3X3iE5/YDT6Tx2+rq9LXwIwd63fwCbuTj8HH625g1+4GQHo3sOp38izM4Pd/zNvgo3c3rBwYs6ctDCzf3bCB7u7Tn/70WRkHA8puYPzeV4TBELcb2Le7gQm+Gwwsu4EpeS6Mb5qBdb8b3K3sBmPNbjBMn7vvn8FP7/huGFi2k8+4L8KJXvj6r//63cDg3g3G6d1AgNgNTNzFnA7g+Rj+He94x9ifB5cau4FJPepwYOrvBlcFO++QLl/WgDGKGLOGaebqtjJs0rYb2Npj//RO6tI10DXQNdA10DXQNXDJGtiGB/fQh2pg+Ig883E2VOlozT7k+H3f933jss5D83Hd8U6NwTsA52NdWK64lqmBccN/nCWWWCM3XSxXj/89bdJybiyy7/zO7zzbAdl1m6FYFtvl5mgA2xK7S1vF2Jpjr2Jf8p2qni0P7/V8c+p4Lqd8DOcds3bJOgZflmuLiynZ5csaiG/cRx55ZLVKsokn1wFdLq4BfqS1S75trTKZExtnhTWIDXKK8sUvfvGezSCNzemn9WiDNP6t58ZsZbLiZonBW8uNJcwNCFaq59rU7bHHHhv99dZwN/nc6qP4vPa+WxLth592Oj/WJshLz7sN9wYQfdTXQHBYXZx3vetdYxzjZ5euga6BroGuga6BJQ10Bu+Sdtbf6y4a1uvqwiGBJlwrfPVXf/XkB339uK/nPlhtfLDVT+yFM3wJCZwawKuIlknStyXHU8sgqxosgzRZF94k6aYLv35pa3zXWaJfhTsRm7x87dd+7RiOD05LULvcHA2YvGZJMF+Vb33rW+8N7LDROGGiZsOsLAV25Guvy83XAJ+56dtr/b9n88j4TGSY7PJlDcTvOgPIGl+S3CwMjLWxDrhL6XIxDfBTbBzjcmANwMRvajZc5O/4VIXBmAsVZQP2/sIv/MK48doaV14f/ehHx/b1Ld/yLadavCvPl++VvO/4/DUO1o3cuDvjVgYwbnwcVqtceR5v6gP5kKaz7/qu71o0PKR8NmPj1kYcPpK7dA10DXQNdA10DSxpoAO8S9pZf68DvOt1ddSQwzKncaOf1772tePu5j44AWx8wr7iFa8Y/Ub6IOIz0gYct0VOEeDlJzm7Xdv5es6XpA1xAgabuJuY3WQZlu6fsV1s1rIkNnABDvpQX7OJyVJa/d7Va+BP//RPx4141N/cz2TYuNTl9mggAKNJdgU5pkr4V3/1V2fjQQB/rL8uX9YA499zn/vcsf/YfX5pkyi+y/Ous2lhl4tr4CUvecmo+8Fd0OrEMNCNdza4O3XJCorBjcfqrOZ75DWvec3qOHch4OCW48xoqf75lMfoDmM31zCa9xn174K+1pbRtzFmOf0Nbj8Wded943taWO8fbPMuXQNdA10DXQNdA0sa6ADvknbW3+sA73pd9ZBH0EAmvXa5PyV58sknz5YmY68NPpPvmSRY6mmzE5PKLF0Gfgy+/U4p+wflZfABOH582xBmjXBHkQnS3//936+J0sOckAYwwmyGZdIFrLfc0iY7DEpPPPHECeW0Z+VYGrChkgm2H3CSS5pWTLwtBR98wI/huAESfvAP3wa98/9j5X7DN3zDqB+bVjGM1Y0oucJ4wxvecC8M6MG/6mrXP3deuXsUEKNDu8pkKRqjLKYm1u/gO3kp6LXfs6GhfmfFVru54VTmHn744TH8FvdSU+nc1mtWGgFwveuqGwwMfJvjMux32a4Bq0GycbTVbNxitIJhzwimPWufgy/3Nkj/v2uga6BroGuga+A+DXSA9z6VHHTh/xNreAl3uSYNDJPD3bDj7+5///d/x5+N1mxuZBMOG2b4/zaJDbuGJXK7AeDdvf/97z+pon3+85/fDWDXblj2OJuvgQm0G5a474ZJ/myYm3DDhn82V9H9baq0dvOLYfno7o1vfOPuwQcf3A3LSm9CUXseuwburAb0bxtQffaznx11MAAdu8G39m4A9sdNnwY/oOOmRDbBIcOH1bjBk3fS4Gt23BBsvNH/nGnAxnU2+hqW/Z9d876ma5s0RbwrhuXiu6/8yq/MpX48UAM2q6PHYaXN+L20JZlv/uZvHtv/4LJh3HBsS9yrDmszU+/VwbXEbgBwdz/8wz98XxZshukdPLB2dwP7dPfOd75z0wax9yV4wy/odzZWe/zxx3fqWFsZjC+7gUwwbmQ3AI27gU26G9yqjN/WazdIvOFqudTsG/t+6Id+6Kwv2mzYhq42QbQx50AGGJ//7Gc/e6wXG0p36RroGuga6BroGtinAZvD+na2uaw5SZfDNPCsw6L1WIdqwATQB/ngz283sKnOTQjbNJ/1rGft7ObrA9XOyz/wAz+weufaNq3+/34N+ED14frxj398BJ/VD6BjYA7tBhbIbvDRu3vOc56zP6EbEMKO2iaKgy+11eCuYtGByeXA4rgBpexZ7Bq42xoA6L773e/efcd3fMduYDCO74/B7c/Or8rgw3MEQ+w8b8x74QtfuHvggQdqkH7+fxoAHn3kIx/ZDSs8doOP4vGd8d///d/jXcbZ5z//+bthg7vdwG7rOjuSBqJfQPpWSRxGzVOXX//1Xx8BSt+Igyuk8V37gz/4g/9/e3cCb9d0Ngx8BZFESQQRWiFES02hpFWlaqgxES2KDkqrhlfNU6P9RBGiWjOlLdoaWoqmJYJqa6gGNYs5poaaGxFDRLjffvb37fOe3Nx7z733nHvPucl//X6nd5+91157nf/W7LOfs/azUjYSPL9eZ3mI0+9///uUpdJJWZ7ZfHvcDC2o5Z577kn77rtvyvLGz0MQ3+WynOIpeyIhXXDBBSmCjUptBLK0MylL8ZXGjRuX34jHcryKkqXDyP8NzCZFzH+sKNb7S4AAAQIECHS9gBG8XW+cHyFGSGX5dvNfJMpH+XTk8HETPn78+JTlauvIbg1Vt5FH8DYUVBd3Jnt0O2V5n/Mv4TFSqL0le+Q1D3jH6PIYEaMQIND4AvFUQpaSJQ8eZY+A56N6Y9R+vGJEW5ZyJWVpZ/IPEgGRP/zhD/kI38b/ZI3RwwhARjA9Rl4qtRfIJmtKiy22WP5EU3x/ih+/21uGDBmSP6USQdGhPeTJm7gmZ5Nfpiz3fYsfM0Yln3HGGfmPCS1WWABWTpgwIWUT3aZZs2blwdv4PrPJJpvkI72zVCr5jzDhmOWNzUf0Tpo0KcX3T6W2AjFq98EHH8z/P5alQ8nPRdyrxL+HCgECBAgQ6IiAEbwd0Wq9bvu/Jbfehi0VBOKRsRjVE6MvihJffpZffvn8y1A8Kt+vX78UN95z5szJv7DGDWOWzy/FI7RZ7sx8txhRGiOrYpRHPMqnEOisQJZDLd81RvF2pBT1i/07sq+6BAjUR2DzzTdPMdotRlRFapyWRrzFI/DZLOkpmzwnxY260n6BYpRo+/dQsyMC8f0onmaK71Dx9MiWW27Zrt3jO1ORgqgnjeCMYGU8mnjTTTflnzc+Q1xzs7zp+YjUCGTGCN4FtUSar0iTEsHdLJ9u/p24PJ1Z/Ley1VZbpR/84Ae5YwR347tzNmlkh55YWlB9O/K541oRgXPB846oqUuAAAECBLpOQIC362zzluOx2BhxWwR3I/fhYYcdluIRpwjsVioRUIuRVfHIXuREjPeHHnponu8qUjYoBDojEDeKUYr/LtvbRjahRl612L+9+6lHgEB9BVZZZZV0zTXX5I93R3qBbJKhPEXQcsstlzbccMP8R8gIpCkEGlEgcn7G9erEE09sd4A36kaJ0es9LSAa/18cPXp0/mrE81HPPkVQN0Z1R3qGc889t9WuxI9W2SS5ebA3m0g0/wErnl5SCBAgQIAAAQLzq4AUDV18ZiMo+53vfCc/SuRUi9y7nb3RuP766/PJySLIGyMUHnjggU631cUfu9XmpWholaZbN8SkI5FbOB53jVFOMRFTe0rx6MTYsWPTcccd155d1CFAgAABAlUJxFNNq666anrttdfy9AU//vGP22wv8klHIDACpU899VT6xCc+0WZ9G3uGwG233ZY23XTT/Am4+JEqUndUKs8880yKicBiUrqY2DjyaCsECBAgQIBAYwkUcQaTrFV3XhbcZ7yqc2v33jGZVZSYpCtG4XY2uBttxIjdmDQiSoxkiZxyCoHOCCy66KKlHx4OPPDAfPbjSu3ExEyXX355nkpkr732qlTddgIECBAgUBOBSIMRX/jjkfCYz2DPPfdMr7766jxtx2Rq8ZRTBHej/OIXvxDcnUep56649tpr887HJJDtCe5G5Xh6IXKLR77YGCihECBAgAABAgTmVwEB3i4+s3fccUd+hFGjRuU51Ko9XMyoXJQnn3yyWPSXQIcFfvSjH6V4PDseXYyb5RjV21q566670le/+tV8BMyRRx6Z5wJsra71BAgQIECg1gJbb711+t3vfpcH9n7zm9+kodmkaZFbdcyYMfnj95GKISZViwnIYiK2888/P8/BWut+aK9+Ak888UR+8I022qhDnSjqF/t3aGeVCRAgQIAAAQI9RECAt4tPVEyOESVuOmpRll566VKgOHKQKQQ6KxApGmLCpcUXXzxdeumlafjw4fnfmHU6SlNTU4qcuwcddFA+O3U8Ghs305Ueje1sf+xHgAABAgTaEthll13ySQJ33HHHfALaGNE5fvz4dNJJJ6Wrr746TzsUk7DFj5KRo1WZvwRihHaUAQMGdOiDFfWL/Tu0s8oECBAgQIAAgR4iYJK1Lj5Rw4YNy3PlTp48uSY3G5HyIXLwRllvvfW6uPean98FYnKlGGUeE9g8/vjjKXLfRImbofgBoRjVG4/FxuSAP/nJT6pKMzK/e/p8BAgQINC1Aquttlr+42SkaLj55pvTv//97/zpkhVWWCFtttlmNftBvWs/hdY7I1Dkz33xxRc7tHtRv9i/QzurTIAAAQIECBDoIQICvF18otZff/08wHvFFVekyFsak0N0tsTIysMPPzzfPWYHXnnllTvblP0IlAQiP3RMtBaPvMbjr/EjQoxy6dWrV36jvP3226fI07vGGmuU9rFAgAABAgTqKRDBuq9//ev17IJjd7PAiBEj0lVXXZVuvPHGtPvuu7f76FE/SuyvECBAgAABAgTmVwEB3i4+s5EbLiZXmzVrVho9enQ65ZRT8kBvTHLVkfLAAw+kffbZJw8Wx3777bdfR3avqu4jjzySJkyYkD+yX1VD2c4vv/xy3sScOXOqbcr+NRSIfIXf/e5381c0GwHemH28o/+d1rBLmiJAgAABAgQIlARiHor4Xh0/Ro8dO7ZdAx1uuummdM8996QYGLHFFluU2rJAgAABAgQIEJjfBAR4u/iMRoqGyA0XE1NF0CwCs7EcI3nXXXfd/Mvp4MGD82Ba3759UwQ+Ixj81ltvpWnTpqWpU6em2267LR9hWXR1q622SieccELxtsv/Hn300WnixIk1Pc5LL71U0/Y0VluBIl9dbVvVGgECBAgQIECgcwLxnXqPPfZIv/71r1PkY45JYpdYYolWG4v0HTGJbJRjjjkmxfdshQABAgQIECAwvwoI8HbDmT3iiCNSTI52wAEH5HlNZ86cma677rr81dHDb7PNNumyyy7r1jyoJ554YopUEx999FFHuztP/fjcMRp55MiR82yzggABAgQIECBAgEBrAqeddlo+d8C9996bNtpoo3TRRRe1mHohvm9+73vfy58ci5G7Bx98cGtNWk+AAAECBAgQmC8EBHi76TRG/t0Iap5xxhn5l9EiVUF7Dt+nT58Ugd299967LoHRGGkcr1qUt99+Ow/wLrnkkrVoThsECBAgQIAAAQILiMDAgQNTpF0YNWpU/nTbZz/72RQTxm6yySZ5GoZ4Qiwm33v00UdzkXjq7corr0yRikohQIAAAQIECMzPAr7tdOPZHTRoUBo3blz+eu6559Kdd96ZnnrqqTwdQ6RviJG9vXv3Tosvvnjq379/ikfRYmKr4cOH5+u6sasORYAAAQIECBAgQKDhBIYOHZomT56cxo8fn84888z8+3R8py4vkf7sRz/6Udp///3TwgsvXL7JMgECBAgQIEBgvhQQ4K3TaY0vp/FSCBAgQIAAAQIECBBov0AMhogUYj/84Q/TX//613zE7vTp09Oyyy6b1ltvvXxEr8Bu+z3VJECAAAECBHq+gABvzz+HPgEBAgQIECBAgACBBU6gX79+efoyczsscKfeByZAgAABAgSaCSzU7L23BAgQIECAAAECBAgQIECAAAECBAgQINBDBIzg7SEnKrr5wQcfpFdeeaXU4xVWWKG0bIEAAQIECBAgQIAAAQIECBAgQIAAgQVPQIC3B53zhx56KG2wwQalHjc1NZWWLRAgQIAAAQIECBAgQIAAAQIECBAgsOAJSNGw4J1zn5gAAQIECBAgQIAAAQIECBAgQIAAgflEQIB3PjmRPgYBAgQIECBAgAABAgQIECBAgAABAguegBQNPeicDx8+PL388ss9qMe6SoAAAQIECBAgQIAAAQIECBAgQIBAVwoI8Halbo3bXmSRRdLgwYNr3KrmCBAgQIAAAQIECBAgQIAAAQIECBDoqQJSNPTUM6ffBAgQIECAAAECBAgQIECAAAECBAgs8AICvAv8fwIACBAgQIAAAQIECBAgQIAAAQIECBDoqQJSNNT5zP3nP/9J//3vf9O7776bv/r27ZsGDBiQ+vfvn5ZeeukU7xUCBAgQIECAAAECBAgQIECAAAECBAi0JCDA25JKF66bOXNm+u1vf5suu+yyNGXKlBTvWyuRc3fttddOn/vc59LIkSPTdtttl3r16tVadesJECBAgAABAgQIECBAgAABAgQIEFjABAR4u+mEv/LKK+n4449Pl1xySZtB3fLuzJkzJ91///356/zzz09rrbVWGj9+fNp+++3Lq/XI5ZNPPjldcMEFPbLvlTr92muv5aOyI0AvIF9Jy3YCBAgQ6E6B+G4RJa5RCgECBAgQaBSBpqamFNeogQMHpmWXXbZRuqUfBAh0g0A82a5UL+DbffWGFVuYPn16+vKXv5wefvjhUt0I/C2//PJpxRVXTIMGDUr9+vVLffr0yS9qs2bNSm+99VaaNm1aev7559P777+f7xcjfnfYYYf0s5/9LB1yyCGltnrSwtChQ/Puvvrqqyle83P54IMP5ueP57MRIECAQA8WmD17dg/uva4TIECAwPwqEOkL46UQILBgCUSMbKWVVlqwPnSNP22v7Jeyphq3qbkygXfeeScP7k6ePDlfO2LEiHTYYYelLbbYIg/sllVtcTGChHfffXee1uHiiy9ORdBw4sSJecqGFndq8JVPP/106XM0eFc71b0xY8akCRMm5EH4COwrBAgQIECgUQQi5VN89fvzn/+cFl544Ubpln4QIECAwAIucMstt6RTTz01v8eNAU0KAQILlsDiiy+eVlhhhQXrQ9f40xrBW2PQ5s1deeWVqQju7rbbbnnu3YUWWqh5tVbf9+7dO33hC1/IX6NHj0477rhjHhz9wQ9+kLbZZpvUkbZaPUg3bxg2bFg3H7F7DxePFUVZZ511emwQvnvFHI0AAQIEuksgvjd8+OGH+fVJgLe71B2HAAECBCoJzJgxI68SE46vvvrqlarbToAAAQLNBNofaWy2o7ftE/jnP/+ZV4xgX0yuVk1ANiZZ++lPf5q3F+kenn322fZ1Qi0CBAgQIECAAAECBAgQIECAAAECBOZLAQHeLj6td9xxR36EUaNGpRiNW23ZaaedSk08+eSTpWULBAgQIECAAAECBAgQIECAAAECBAgseAICvF18zl944YX8CEOGDKnJkZZeeulSoPi9996rSZsaIUCAAAECBAgQIECAAAECBAgQIECgZwoI8HbxeSvyzRZ5eKs9XKR8KCZaW2+99aptzv4ECBAgQIAAAQIECBAgQIAAAQIECPRgAQHeLj5566+/fn6EK664It16661VHe3NN99Mhx9+eN7GUkstlVZeeeWq2rMzAQIECBAgQIAAAQIECBAgQIAAAQI9W0CAt4vP35gxY/KUCrNmzUqjR49OF1xwQZo9e3aHj/rAAw+krbbaKsXfKPvtt1+H27ADAQIECBAgQIAAAQIECBAgQIAAAQLzl8Ai89fHabxPEykaTjrppHTkkUemGTNm5IHZWN50003Tuuuum4/CHTx4cOrXr1/q27dvmjNnTopg8FtvvZWmTZuWpk6dmm677bY0ZcqU0oeLQO8JJ5xQem+BAAECBAgQIECAAAECBAgQIECAAIEFU0CAtxvO+xFHHJFicrQDDjggxcRoM2fOTNddd13+6ujht9lmm3TZZZelhRYy+LqjduoTIECAAAECBAgQIECAAAECBAgQmN8ERAm76Yzutdde6fnnn0/HHHNMWm655Tp01D59+uTpHa699to0adKkFPl3FQIECBAgQIAAAQIECBAgQIAAAQIECBjB243/DQwaNCiNGzcufz333HPpzjvvTE899VSejiHSN8TI3t69e6fFF1889e/fP0V6hzXWWCMNHz48X9eNXXUoAgQIECBAgAABAgQIECBAgAABAgR6gIAAb51O0tChQ1O8FAIECBAgQIAAAQIECBAgQIAAAQIECHRWQIqGzsrZjwABAgQIECBAgAABAgQIECBAgAABAnUWEOCt8wlweAIECBAgQIAAAQIECBAgQIAAAQIECHRWQIC3s3L2I0CAAAECBAgQIECAAAECBAgQIECAQJ0FBHjrfAIcngABAgQIECBAgAABAgQIECBAgAABAp0VEODtrJz9CBAgQIAAAQIECBAgQIAAAQIECBAgUGeBRep8fIcnMN8J9O/fP/9Mxd/57gP6QAQIECDQYwUGDBiQZs+enRZayG/8PfYk6jgBAgTmQ4Hi3imuUwoBAgQIdFygV1NWOr6bPQgQaE1gxowZ6R//+Efadttt3UC3hmQ9AQIECNRF4OGHH04fffRRGj58eF2O76AECBAgQKAlgQhLTJo0KX3+859PAwcObKmKdQQIECDQhoAAbxs4NhEgQIAAAQIECBAgQIAAAQIECBAgQKCRBTyf18hnR98IECBAgAABAgQIECBAgAABAgQIECDQhoAAbxs4NhEgQIAAAQIECBAgQIAAAQIECBAgQKCRBQR4G/ns6BsBAgQIECBAgAABAgQIECBAgAABAgTaEBDgbQPHJgIECBAgQIAAAQIECBAgQIAAAQIECDSygABvI58dfSNAgAABAgQIECBAgAABAgQIECBAgEAbAgK8beDYRIAAAQIECBAgQIAAAQIECBAgQIAAgUYWEOBt5LOjbwQIECBAgAABAgQIECBAgAABAgQIEGhDQIC3DRybCBAgQIAAAQIECBAgQIAAAQIECBAg0MgCAryNfHb0jQABAgQIECBAgAABAgQIECBAgAABAm0ICPC2gWMTAQIECBAgQIAAAQIECBAgQIAAAQIEGllAgLeRz46+ESBAgAABAgQIECBAgAABAgQIECBAoA0BAd42cGwiQIAAAQIECBAgQIAAAQIECBAgQIBAIwsI8Dby2dE3AgQIECBAgAABAgQIECBAgAABAgQItCEgwNsGjk0ECBAgQIAAAQIECBAgQIAAAQIECBBoZAEB3kY+O/pGgAABAgQIECBAgAABAgQIECBAgACBNgQEeNvAsYkAAQIECBAgQIAAAQIECBAgQIAAAQKNLCDA28hnR98IECBAgAABAgQIECBAgAABAgQIECDQhoAAbxs4NhEgQIAAAQIECBAgQIAAAQIECBAgQKCRBQR4G/ns6BsBAgQIECBAgAABAgQIECBAgAABAgTaEBDgbQPHJgIECBAgQIAAAQIECBAgQIAAAQIECDSygABvI58dfSNAgAABAgQIECBAgAABAgQIECBAgEAbAgK8beDYRIAAAQIECBAgQIAAAQIECBAgQIAAgUYWEOBt5LOjbwQIECBAgAABAgQIECBAgAABAgQIEGhDQIC3DRybCBAgQIAAAQIECBAgQIAAAQIECBAg0MgCAryNfHb0jQABAgQIECBAgAABAgQIECBAgAABAm0ICPC2gWNTzxH45z//mRZeeOG0zDLL9JxOt9DTDz/8MF144YVpyy23TCuttFIaOHBg2nrrrdPxxx+f7rnnnhb2aHnVG2+8kX74wx+mbbbZJg0bNiwttthiac0110xf/epX0+mnn55mz57d8o7WEiBAgEDNBeaXa9TTTz+d9t5777Teeuul/v37pzXWWCN/f/HFF6d33nmnKrfzzjsvLbfccvlr6tSpVbVlZwIECBBov8C2226bevXqlc4555z279RgNWt1DxUf6957703HHnts2m677dLgwYPz16abbprfW82YMaPBPrnuECBA4H8FejVl5X/fWiLQ8wSmT5+eNtxww/Tkk0+mpZdeOr3++us970NkPX7xxRfzgOyUKVNa7P8iiyyS4ib6m9/8Zovbi5VnnXVWGjt2bHrzzTeLVfP8XW211dL555+fvvSlL82zzQoCBAgQqJ3A/HKN+ulPf5qOOeaY9MEHH7SI8/nPfz5NnDgx/2GyxQptrHz00UfT+uuvn2bNmpXXeuyxx9Lqq6/exh42ESBAgEAtBOLHtQMOOCBv6uyzz07f//73a9Fst7ZRq3uo6HQYHH744a1e6wYNGpTfj22//fbd+hkdjAABAu0RWKQ9ldQh0KgCb731Vj7CNYK7PbnE54hfiYvg7rrrrptGjRqVPvGJT6Tbbrst/fGPf0zvvfde2mOPPVL8clx8EWv+mS+77LJ08MEHl1bHL/KbbLJJ+vjHP55iRNQ111yT4kb6iSeeyI8Xo4JjBJZCgAABArUXmF+uUfHj4pFHHpkD9e3bN+26664pArrTpk1L1113XXrwwQfT5MmTU4xw+stf/pKPdmqvZjxR8o1vfKMU3G3vfuoRIECAQHUCl19+eTrwwAOra6TOe9fqHirGvH37299Ol1xySf6JFl100fyJyrgfmzlzZn4vFte51157Le222275k5UxYEYhQIBAQwnECF6FQE8UyB55bfr0pz8dI9BLr2wEb0/8KE3ZL8Wlz/C1r32t6f3335/rc9x+++1NAwYMyOtkI3mbsl+q59oeb5555pmmJZZYIq/Tu3fvpiwoPE+d7Ea6KfsiVzpWFkhuinUKAQIECNRWYH65Rr366qtN/fr1y68bcR265ZZb5oKKa0h2s1u6rvzP//zPXNsrvckCx6V9i+t5NoK30m62EyBAgEAnBd59992m+Lc3S28317+/2ejVTrZYv91qcQ8Vvc9S5JUsPvWpTzW98MILc32oLAVEblZcp7IBNHNt94YAAQKNICAHb0OF23WmPQKR5++QQw5JG2+8cYrHOButrLXWWvmjpeUjadvq43//+990wQUX5FVWXHHF/Jfj+NW4vMRnvfTSS/NVc+bMSb/4xS/KN+fLv//97/NfmONNjLTacccd56mTBX7zHLwjRozItz3wwAPpvvvum6eeFQQIECDQOYFGvkZFap5IfRCvp556ql0f8IwzzsifIInKp5xySj5Kt3zHuK7E0yPFdeW3v/1t6VpUXq+l5SxYnH72s5/lm7IfKFuqYh0BAgQI1FAgngwcPnx4OvXUU1PkrW2kUq97qGxgTTr66KNzipi3JJ5Eiacoy8tCCy2UTj755JQFf/PVd9xxRz6at7yOZQIECNRbQIC33mfA8TskcNddd6W11147nXnmmemjjz5KcbGNnIAxMUujlEh/EK///Oc/7erSH/7wh/T222/ndffbb7/UPLhbNDJy5MhUPAoUAd7meRBvvfXWomraZ599SsvNF2Iyup122qm0WoC3RGGBAAECVQk0+jUqJuAsrlGRGqE95aKLLsqrRQB2r732anGXuBYfdthh+ba4nkWQt1KJPPGRdiiu5TGZaKQpKkpM9qMQIECAQO0EspFleYq3mH+j+IEvfuxrpBQNxfWpu++hrr/++tIcLnEtiwE3LZW4h9pzzz3TJz/5yfS5z30un/+lpXrWESBAoF4CArz1knfcTglErr9nn3023zfyyt58881p3LhxKSYgi9LRm8IYwfqjH/0oRfB0yJAhadVVV00777xzOvHEE9PLL7+ct9nV/xP5nIqy1VZbFYst/t1yyy3z9S+99FL6+9//PledCHLHZHMRBG7ti0mxw/LLL18spuwRpNKyBQIECBDovEAtr1HxI1483bH33nunDTbYIMXELvE0R0yAM2HChM53sgN7Pvfcc6VrYeTXbe0HyGhyiy22KF2DY0RvpbL//vvnOXyXWmqpFEHkjl6/K7VvOwECBAj8r0D8mBYTqkWgN8p3v/vddO+99+ajeYtaHfl3uBGuUbW6h4rBNkWJAG5bZcyYMXlgN0vDlL7whS+0VdU2AgQIdLvA/4uKdfthHZBA5wWyPLv5SKG4ye3fv3+nGoovOePHj0/HHXfcPCNhn3766XT11Vens846K/3yl79Mo0eP7tQx2rvTnXfemVeNEVDrrLNOm7vFI1VFiQnZygPCv/71r4tNFf/GhDhFqXTMop6/BAgQIFBZoBbXqEg/FBOP3X///XMd8PXXX0/xWOi5556bT3T285//PA0cOHCuOrV8U1yfos2Y/LOtEgHo+PEwRl4VE4a2Vj8CwJFWKEp8hvjBViFAgACBrheIe4djjz22quBko12jqr2HikB3lBggM2zYsK4/CY5AgACBLhIQ4O0iWM12jUA2AVmeI2nxxRev6gAxI2o8jhMlmzwm7b777vnN66xZs9Ldd9+dj46KWVIjj+1pp52WDj300KqO19bOU6dOzTdHrqfIZdhWKR+Z+/jjj7dVtdVtMRNs+eiqz372s63WtYEAAQIE2i9Qi2tUpNvZdtttS3lvY/RupDAYOnRoHji94YYb8jQLV1xxRYoAbPxgl01+1v5OdqBm8Rhv7LLyyitX3DOuURHgjetM/G0pcPv888/njwlHYxHEDjOFAAECBLpWIIKgcc2I1ALVlEa6RtXiHuq9995LRTvFD5kxEChG6Ea+4njFNW399ddPG220UX7NCkuFAAECjSggwNuIZ0WfWhWI/LvVlhg1VAR3Y0RsPJYTuZTKS1zMd9lll5TNHp7+z//5P/lIqZZuVMv36cxyTMZTTHAwePDgik3ECKmixORsnSknnHBCeuWVV/JdI2jQnpv2zhzHPgQIEFjQBKq9RsUkmgcccEApuBuT4EQ+wPKbyfghMtIbxFMbESz98Y9/nP8Q2RXWb731VqnZzlyjml8346Y58u7OmDEjT4t0zjnnlNq3QIAAAQJdJxDpF6oN7jbSNapW91BxHY1rU5R4AidST8R1qnjKpDgjEfA9++yz86c7f/Ob36QVVlih2OQvAQIEGkZAgLdhToWOdIfAu+++m4466qj8UDFy98orr5wnuBsbv/jFL6ZLLrkkHzUVXyBiZtV4X5RnnnkmnXTSScXbuf4WAdt77rknz50418bsTYy0KmYNj5vcokR/KpXyOvFZOloiIBABgyiR3iJSUCgECBAg0BgC559/fnrkkUfyznznO99JRxxxxDwd69u3b7r44ovzUbyRfzCCpDGxZkyWU5QzzjijxTQJkXe+KPGIbkvpHb71rW+lyLcbpdbXqLj+xA+oEWiI69GSSy5ZdMdfAgQIEGhwgVpcoxrtHipG5xYlrq877LBDiidlYn6XNdZYI79PjKdZHn300RQB7r/97W9pvfXWSw8//HBDTfJdfAZ/CRBYsAUEeBfs87/Affprr702n9QlPnjcxH7qU59q1SByVK255pr5zfbvfve7dOGFF5YmmIn0DfG+rRKT07RUJyZDKwK8zb9UtNVebOvTp0+pSkcDvPHZv/e975X2P/300/MRVKUVFggQIECgrgKRW7coMdlnWyVG9saTJjHa6PLLL0/HH398qfqNN96Y36CWVrSwcM0117SwNqURI0aUAry1vEbdd999+RMxcdCDDz44bb755i0e30oCBAgQaEyBWlyjGu0eqvw6FwNfIogbAdxLL700D/AWZyJyy0dKv/gb+fAPOeSQeUb5FnX9JUCAQL0EJJCpl7zj1kXgySefLB23PTOfFvlpY1Tus88+W9q3VgvlOXfjC0WlUl4nfmVub4mRUjvttFP+pSX2ibQTMTpMIUCAAIHGEIjrTIxsihKpc2LCsrZKcX2KOuW5ctvap6PbanWNihyHkW83gtExIurkk0/uaFfUJ0CAAIE6CjTaNapW16fZs2eXVOM+K+ZEiTzDca0qL2uttVb6+9//XnryJPLgxxMpCgECBBpJwAjeRjob+tLlAuU3wfHo6zHHHNPmMcvzD8a+q622Wl4/cliVB1vLG4lRtvEl6Ktf/WqeAqJ8W/Pl8sniIq9ipVJep72T6kR+xuOOO67U9NixY+d6X9pggQABAgTqJhB5AIsbzZigrCP5/cqvbfEBJk6cmJqamub5LOPGjUtxDYgS6RrihrV5Kc/3W6tr1JFHHpliYtC4IY9RUR35gbJ5/7wnQIAAge4XqNU1qtHuocqvc6F6yimnpCWWWKJF4GWWWSaNGTMmT90XFSLAG2n9FAIECDSKgABvo5wJ/egWgWKW1DhYPCLUkfL000/PVX3hhRee633xJnILRomb5NbqFHXLv0CUB5OL7c3/lteJHLptlQhAR17GyNUYJfoSj1btu+++be1mGwECBAjUQaD8+vT++++nF198sd29aH59Kg/SljdSfk2K/ILl78vrFcvl15ny60+xvfnf8jrFvpMmTcqvPVH38MMPz0cnv/nmm813LQW3Y0O0U9SJm+/oq0KAAAEC9ROo5TWqtWtPPe6hyu/FQrdSwLb8CdDIy6sQIECgkQR8Y26ks6EvXS7wsY99rHSMGNUas6W2t5Rf0Nu7T6V6MWlazDIeo7WmTZtWqfpcdSKXb2slbo533nnn9Je//CWvEp87ZoMdOXJka7tYT4AAAQJ1FCi/PkX6hcgT395S/qhqe/dpT71hw4aVqnXkGhUB2eL6+uc//7nUxvjx41O8KpXy9BO33HJLKSdwpf1sJ0CAAIGuEWi0a1St7qHKn5aJa2n5+5YkV1111dLq8qB3aaUFAgQI1FFAgLeO+A7d/QIxqdrNN9+cHzhuILfddtvu70SzI0aOpwjwRlA2RhUPGjSoWY3/fVv+GG5MhNNSicT/W265ZXrwwQfzzZHHMSZYW3/99Vuqbh0BAgQINIBA80k/v//979e9V+U5CCvdyEZ+3XiEN8raa68tDUPdz54OECBAoHYCjXqNqvYeKn6MjHull156Kc8TH0/ElAdxmwtOnz69tGrgwIGlZQsECBBoBAEB3kY4C/rQbQLlX05iRu9KAd77778/f2x06NChadlll03Fo0O17HDkoiqCzpHLKSZDa63cfvvtpU2xX/MSj7RutdVWpeBu5Fe8/vrr05AhQ5pX9Z4AAQIEGkggftxbcskl89QEDz/8cH6j2dbI3HfffTfFNSGuTyuuuGKK0Uy1LsOHD0+RVz5SRlSaTObuu+/O60Ufyq9PcZ2NvIWVyjXXXJOKx13322+/0j4rrbRSpV1tJ0CAAIEuFmjEa1St7qGinQkTJuSCkydPbjPAWz7YJiZEVQgQINBIAgs1Umf0hUBXC2y44YalQ5x33nlp5syZpffNF2LW77gxjX0iHcJjjz3WvEpN3kcqhaJccsklxeI8f//973/ns7rGhg022CANHjx4njr7779/iqB0lBjhG7PACu7Ow2QFAQIEGlKguEbF9efss89us48XXHBB2mabbdLqq6+e9txzzzbrdnZj5L+NY0SZMmVK6frSUnu//e1vS6vL0wHtsMMO6YQTTqj4WmeddUr7H3LIIaX6EcBWCBAgQKD+Ao12jarVPdTuu+9ewo2Udi1NUlpUKOY2ifejR48uVvtLgACBhhAQ4G2I06AT3SUQv9Dusssu+eHikZ7DDjssffjhhy0e/qijjkqvvPJKvi1SHpQ/qtriDv9/5Z/+9Kd03XXXpR/84AdtVSttW3fddUvpEyJXYcww3rzEzf7ee++dj+iKbUcffXTzKumvf/1rnmc3NsSjRtHWUkstNU89KwgQIECgMQVi9u5i8pmxY8emRx55pMWOPvHEE2ncuHGlbQcddFBpua2FXXfdNb8+xTUqRv22p8S1pygxSWcx+VmxLv7GkyLFTW88ObLddtuVb7ZMgAABAvOBQFdfo+p1DxU/RBZPi8T17JhjjmnxbMW1849//GO+La6h8dSkQoAAgYYSyH6hUgj0eIEsIX5T9n+spuwx0IqfJRsJ27TYYovl9WOfbPK0pr///e9NM2bMaJozZ07TQw891JT9klvaHnWzR08rtltNhexxoKYs/UN+zGz286YTTzyx6ZlnnmmaPXt2U/YIblM2o2upP9mv501ZUHquw2WPzzatttpqpTpZKoqm7bffvl2vs846a662vCFAgACB2gp05Bp14IEHlv4t79u3b9Opp57a9OSTTzZ99NFHTVnuv6aLLrqoKfsRr1QnG8FU28620Fo2ird0vOxHyabsB8Wm7IfHphdeeKHpzDPPbMpSSeTb4/qV3QC30ELlVbvttlvpGI8//njlHdQgQIAAgaoFfvWrX5X+7T3nnHMqttdo16hq76GKD3zjjTc2ZT+wliyypyLz+8Msv3xT3DtmP6rOtT3qKwQIEGg0gXgEQSHQ4wU6cvMcHzZuTrNfXksX8Qj0RoA1m/l7rnVx0zpp0qRu8bniiiuashlq5zl+9K14ZUn/m7KJ2Obpzw033FCqU9Rt79/vfve787RnBQECBAjUTqAj16i33367aZ999pnn3/RFF110nnXZ0yVNs2bNql1HW2npjTfeaMpGKs11/CKoW36tOf3001tpofJqAd7KRmoQIECg1gIdDfA24jWqmnuocs+//e1vTVkKvLmudc2vvfFDZjXXuvLjWSZAgECtBaRoyO5MlAVPYPPNN8/zCcbjpsUMqNn/uVI2gjfHiEdk99hjj/zx2CL/YFcrfe1rX0t33nlnnl+3eEQ3ZiWPkn25SJGTMLa3NFlNa4/xdnWftU+AAAECtRXIfuhLkV/3pptuSmuvvXYpZUP2REfpQMOGDUvZSN6U/QCZT4JW2tBFC5HuJ44Vj60WqX+K61McMvo5ceLE/DrVRV3QLAECBAg0gEAjXqOquYcqJ91ss83yXPORW3eJJZbIN5VfezfeeOP82hz3ZAoBAgQaUaBXRIwbsWP6RKA7BSIfb0wg884776RVVlklfxUX9u7sR3GsmB39gQceSDGxWvQnS7+QBgwYUGz2lwABAgQWEIFshG7KUhakmLk7ZjGPa0I2Ijhlo4jqJvDss8/mN8H9+vVLWUqgFDOJ17M/dYNwYAIECCzgAo12jarVPVSESLL0SOnBBx9M/fv3z691cf1VCBAg0MgCAryNfHb0jQABAgQIECBAgAABAgQIECBAgAABAm0I1G/4RxudsokAAQIECBAgQIAAAQIECBAgQIAAAQIEKgsI8FY2UoMAAQIECBAgQIAAAQIECBAgQIAAAQINKSDA25CnRacIECBAgAABAgQIECBAgAABAgQIECBQWUCAt7KRGgQIECBAgAABAgQIECBAgAABAgQIEGhIAQHehjwtOkWAAAECBAgQIECAAAECBAgQIECAAIHKAgK8lY3UIECAAAECBAgQIECAAAECBAgQIECAQEMKCPA25GnRKQIECBAgQIAAAQIECBAgQIAAAQIECFQWEOCtbKQGAQIECBAgQIAAAQIECBAgQIAAAQIEGlJAgLchT4tOESBAgAABAgQIECBAgAABAgQIECBAoLKAAG9lIzUIECBAgAABAgQIECBAgAABAgQIECDQkAICvA15WnSKAAECBAgQIECAAAECBAgQIECAAAEClQUEeCsbqUGAAAECBAgQIECAAAECBAgQIECAAIGGFBDgbcjTolMECBAgQIAAAQIECBAgQIAAAQIECBCoLCDAW9lIDQIECBAgQIAAAQIECBAgQIAAAQIECDSkgABvQ54WnSJAgAABAgQIECBAgAABAgQIECBAgEBlAQHeykZqECBAgAABAgQIECBAgAABAgQIECBAoCEFBHgb8rToFAECBAgQIECAAAECBAgQIECAAAECBCoLCPBWNlKDAAECBAgQIECAAAECBAgQIECAAAECDSkgwNuQp0WnCBAgQIAAAQIECBAgQIAAAQIECBAgUFlAgLeykRoECBAgQIAAAQIECBAgQIAAAQIECBBoSAEB3oY8LTpFgAABAgQIECBAgAABAgQIECBAgACBygICvJWN1CBAgAABAgQIECBAgAABAgQIECBAgEBDCgjwNuRp0SkCBAgQIECAAAECBAgQIECAAAECBAhUFhDgrWykBgECBAgQIECAAAECBAgQIECAAAECBBpSQIC3IU+LThEgQIAAAQIECBAgQIAAAQIECBAgQKCygABvZSM1CBAgQIAAAQIECBAgQIAAAQIECBAg0JACArwNeVp0igABAgQIECBAgAABAgQIECBAgAABApUFBHgrG6lBgAABAgQIECBAgAABAgQIECBAgACBhhQQ4G3I06JTBAgQIECAAAECBAgQIECAAAECBAgQqCwgwFvZSA0CBAgQIECAAAECBAgQIECAAAECBAg0pIAAb0OeFp0iQIAAAQIECBAgQIAAAQIECBAgQIBAZQEB3spGahAgQIAAAQIECBAgQIAAAQIECBAgQKAhBQR4G/K06BQBAgQIECBAgAABAgQIECBAgAABAgQqCwjwVjZSgwABAgQIECBAgAABAgQIECBAgAABAg0pIMDbkKdFpwgQIECAAAECBAgQIECAAAECBAgQIFBZQIC3spEaBAgQIECAAAECBAgQIECAAAECBAgQaEgBAd6GPC06RYAAAQIECBAgQIAAAQIECBAgQIAAgcoCAryVjdQgQIAAAQIECBAgQIAAAQIECBAgQIBAQwoI8DbkadEpAgQIECBAgAABAgQIECBAgAABAgQIVBYQ4K1spAYBAgQIECBAgAABAgQIECBAgAABAgQaUkCAtyFPi04RIECAAAECBAgQIECAAAECBAgQIECgsoAAb2UjNQgQIECAAAECBAgQIECAAAECBAgQINCQAgK8DXladIoAAQIECBAgQIAAAQIECBAgQIAAAQKVBQR4KxupQYAAAQIECBAgQIAAAQIECBAgQIAAgYYUEOBtyNOiUwQIECBAgAABAgQIECBAgAABAgQIEKgsIMBb2UgNAgQIECBAgAABAgQIECBAgAABAgQINKSAAG9DnhadIkCAAAECBAgQIECAAAECBAgQIECAQGUBAd7KRmoQIECAAAECBAgQIECAAAECBAgQIECgIQUEeBvytOgUAQIECBAgQIAAAQIECBAgQIAAAQIEKgsI8FY2UoMAAQIECBAgQIAAAQIECBAgQIAAAQINKSDA25CnRacIECBAgAABAgQIECBAgAABAgQIECBQWUCAt7KRGgQIECBAgAABAgQIECBAgAABAgQIEGhIAQHehjwtOkWAAAECBAgQIECg4wIjRoxIyy23XFpttdU6vrM9CBAgQIAAAQIEeqTAIj2y1zpNgAABAgQIECBAgMA8Aq+//np65ZVX0vvvvz/PNisIECBAgAABAgTmTwEjeOfP8+pTESBAgAABAgQIECBAgAABAgQIECCwAAgYwbsAnGQfkQABAgQIECBAYMEQOO+889K7776bevfuvWB8YJ+SAAECBAgQIEAg9WrKCgcCBAgQIECAAAECBAgQIECAAAECBAgQ6HkCUjT0vHOmxwQIECBAgAABAgQIECBAgAABAgQIEMgFpGjwHwIBAgQIECBAgEBDCdxxxx3pkksuyft07LHHpuWWWy79+te/Trfeemv65z//macgGDZsWNphhx3Svvvum5ZYYomK/Y+0BZG+4K677kqPPfZYPhHZpz/96bTOOuukzTbbLO20006ttjFmzJg0ffr09JnPfCbts88+6dFHH02/+93vUvTzoYceyvsX7Rx00EFpww03bLWd9m748MMP01VXXZWuuOKKNHXq1PTCCy+kFVdcMa2xxhr569vf/nYaMmRIi8398Ic/TG+88UZabLHF0mmnnVaqE8tPPvlk6X17FuIY0V5L5YEHHsj7GH8ffPDB1KdPn7Tuuuvmr7333js3aWk/6wgQIECAAAECBGovIEVD7U21SIAAAQIECBAgUIXAhRdemCJIGOW+++5Lp5xySh7sbKnJQYMGpeuuuy599rOfbWlzvu7KK69MRxxxRJo2bVqrdUaNGpV+8YtftBiYjEBnBFl33nnnPIgbdWfMmNFiW9/5znfydhZeeOEWt1da+frrr6cvfelL6ZFHHmm16pJLLpnOPffc9PWvf32eOiuvvHJ67rnnUtSJoHRRIoh9yy23FG/b9TcCtvfff/9cdT/66KM0fvz4dNxxx6UPPvhgrm3Fmzgnv/zlL9Po0aOLVf4SIECAAAECBAh0oYARvF2Iq2kCBAgQIECAAIHqBL71rW/lwc4BAwbko3W/8IUv5MHWP/zhD3nA8rXXXktbbLFFuv322/PRo82Pdu2116Zdd921tDpG2G666ab5iNgYeTpx4sT04osvpqi31lpr5QHlGC3bUolg89Zbb53ee++9/Jgx6vfjH/94Pqr45z//eZo5c2a66KKL0vvvv58uvfTSlppoc92cOXPyUclFcDc+awSTl1122TRlypR0ww035KOH33zzzfSNb3wjfexjH2t3EHXjjTfOg74tdSCC0X379k3xN0YmF4HbOH7zEv25/vrr89X9+vVLu+++e+4+a9asdPfdd6cJEyakOCc77rhjPoL40EMPbd6E9wQIECBAgAABArUWiEnWFAIECBAgQIAAAQKNIvCrX/0qJgEuvbJRqU1ZWoS5upelMWjKRuWW6my00UZzbY832WjYpsGDB+d1evfu3ZSlKZinzn//+9+mLGhZaicLTM5TZ4UVVihtj35lKRuaspGsc9V7+OGHm7LAcKnejTfeONf29rzJ0k+U9j/wwANb3CUbzVyqk6WomKfO0KFD8+3ZCN55tlVa8b3vfa/U9uabb940e/bsuXbJgr+l7cOHD2/KUj7MtT3eZGk0mrKAdF4vC0A3ZcHzeepYQYAAAQIECBAgUFsBk6zVOmKuPQIECBAgQIAAgZoKRP7dyJdbXhZaaKH0k5/8JM+hG+sjN+/NN99cXiUdfPDBea7dWBn5cVsaTTpw4MB09dVXp8jpGyVGoMao3tZKpDo46aSTUq9eveaqEqN/Tz/99NK6448/vrTc3oV//OMfpaqt5QSOzxEjjGNUb4zkjdHCtSgnn3xynlYh2lp99dVzkywoXmo6chgfddRR+fsYuRtpLz75yU+WthcLX/ziF0v5k99555109NFHF5v8JUCAAAECBAgQ6CIBAd4ugtUsAQIECBAgQIBA9QLZSNIUQcOWSgRZyycB+/Of/zxXtUmTJuXvYxK2mCittRKBzBNOOKG0+bLLListN1/48Y9/3HxV6f1XvvKVtOaaa+bvJ0+enCKfbhUhCf0AAA78SURBVEdKpHsoyvnnn99i8DZSKTzzzDN54DomnYvJzaotkZahcFxmmWXyAHfk8C0vkcKiyGEcaTM+9alPlW+ea3mrrbYqOUTb2UjgubZ7Q4AAAQIECBAgUFsBAd7aemqNAAECBAgQIECghgJZGoI2W4vcskWJgGdRIg9sln4hfxt5d5deeuliU4t/R44cWVr/+OOPl5bLFyLouckmm5Svmms5As5Zqoh8XUxGFnmBO1IiMFqMDP7973+fj04eO3ZsimBxlpKi1FRnJ3ArNVC2cNttt6W99torZQ8J5sHiP/3pT2mVVVYpq/H/FrN0DKV1LeXmLW38/wvFpHfR72effbb5Zu8JECBAgAABAgRqKCDAW0NMTREgQIAAAQIECNRWYKWVVmqzweWXXz4tuuiieZ2XXnqpVPexxx4rLWd5aUvLrS3EKN8iCBzBzAh4Ni+V+hL1y+uU96d5Wy29HzRoUDr33HPzyc5ie/QjUj1E0DhSMsSEZpdcckl66623Wtq9w+sikB2ToRVpHi6++OJSgLp5Y0899VRpVZb7OGV5idt8XXXVVaX65fuWVlogQIAAAQIECBComcAiNWtJQwQIECBAgAABAgRqLBD5ZtsqMeI1gp8vvPBCeuONN0pVy0ecDhkypLS+rYUIWkYbkTs2/ka6gvJSqS9Rd7nllivtUt6f0soKC/vvv3+eY/fwww9PTzzxRKl2jEaOUb3xijQN2SRsKfLmdnY076uvvpq22267NH369PwYkXoiAsitlalTp5Y2xejojpSnn366I9XVJUCAAAECBAgQ6KCAAG8HwVQnQIAAAQIECBDoPoGY3KtSKXLdlueNXWqppUq7xWRk7SnlAdn+/fvPs0tH+hI7l/dnnsbaWLH99tuneN19993p+uuvTzfccEP617/+lSLtQ5RZs2alU089Nd17770pcuMutthibbQ276b4HKNGjSqlTvjmN7+Zjj322Hkrlq352Mc+Vnp33HHHlUY7l1a2sdCelA5t7G4TAQIECBAgQIBABQEB3gpANhMgQIAAAQIECNRP4N///nebB4/RpBHwjFI+evaTn/xkab/nnnuutNzawgcffJD+85//5JsHDBhQSvtQXr9SX6JueZ3y/pS3097lyGMbrwioxgjev/zlLykmLYvJ5CKFxN/+9rd83ejRo9vbZB4k/vrXv54Hj2OnyCl84YUXVtw/JlW7+eab83rRp2233bbiPioQIECAAAECBAh0j4AcvN3j7CgECBAgQIAAAQKdEChPtdDS7lOmTCmtHjFiRGl51VVXLU1YFnVayqlbqpwtRJ1ihGzs21KZNm1aeu+991raVFpX3p8NNtigtL49CzGy9v7770833XTTPNVjRPKuu+6aJkyYkKdmKCrE6N6OlEMOOSTFRGpR4nP+8Y9/bDGY3bzNCPAW5b777isWW/0bn+Ouu+5Kr7zySkX7VhuxgQABAgQIECBAoF0CArztYlKJAAECBAgQIECgHgIXXHBBm0HVSFVQlK985SvFYurXr18aPnx4/j6CxFdffXVpW0sLJ510Uml1eTulldlCjBSO/rRWIqXCbbfdlm9ed91108orr9xa1RbXR38/85nPpJEjR7Y5kdo222xT2j+Czu0tp59+ejr77LPz6hEwnjhxYrtTLWy44Yalw5x33nlp5syZpffNFyIIHiN8Y58YxVw+4V3zut4TIECAAAECBAhULyDAW72hFggQIECAAAECBLpI4OWXX07jxo1rsfUYiTpp0qR8W4xGLQ98xsqf/exnpf3GjBmTXnzxxdL78oXIY1sEgPv06dPmZGOnnHJKaimo+vbbb6cjjjii1Oz3v//90nJ7F3beeee8aqSLaO0zR4UIzBYlAsntKddcc02pfxH8jjQP5aNyK7Xxuc99Lu2yyy55tUhlcdhhh6UPP/ywxd2OOuqofORubNxyyy3TGmus0WI9KwkQIECAAAECBGoj0Ct7XK2pNk1phQABAgQIECBAgED1ApETdu+9956roW9961t5gHLNNdfMJwe79NJL0wknnFBKqxBpDb785S/PtU+8iaDkVVddla9fZpll0mmnnZY222yztPzyy6dHH300RTsxCrj4SnzmmWemgw46aK52hgwZkl544YXSukGDBqVzzjknbbHFFqlv375p8uTJ6dBDD83TPESljTfeOB/J26tXr9I+7VmYOnVqWm+99VIEi6OEQbQb+YQXXnjhvA+/+tWv8hQNc+bMSYsvvnh64okn0sc//vFS8zFqOHIOxwRv06dPz9c/+OCD6fOf/3xpJPQ+++yT9z1SQsRo29YCtbFzTPZWjESOwPbqq6+eisnmYvK0E088MR91HJOwhefJJ5+c5wmOfWPyt1tuuSWVp86I9QoBAgQIECBAgEBtBQR4a+upNQIECBAgQIAAgSoFygO8W2+9dR4sLXLfLrLIIimCm0Xp3bt3njZhr732KlbN9feNN95I++67b2mEbrGxeTsLLbRQGjt2bDr22GOLKqW/RYB3iSWWyCc9++tf/5pviwBu7FceII1AaowsjiBwZ0qMJt5xxx1LgetoI44RAd4Y2VuUCLpGPt511lmnWJX/bSnA+5vf/Cbtueeec9Vr75s4RvkkbjGxW1iXTyYXDtG/5uclRgk3H1Xd3uOqR4AAAQIECBAg0H4BKRrab6UmAQIECBAgQIBANwuMGjUq3X777XkgMwKJ5UHEIvjbWnA3urr00kvnI3gvv/zyNGzYsNLEa0U7EZiMY9x6660tBnfLP26kb4hJzSI9Qf/+/fNRv0Vwd9lll03HH398igBoZ4O7cazoS+TyjSBvMQI4Jn8rgruR03a33XZL//rXv+YJ7pb3tauWN99883ykcgTNBw4cmB8mRj+Xe+6xxx7pkUceEdztqpOgXQIECBAgQIBAMwEjeJuBeEuAAAECBAgQIFBfgfIRvJEK4YADDsg7FKNxIx1CBFo//elPpxVWWKHDHY3JwaZMmZLn0f3EJz6R56GtFJAtRvBGiofXXnstP2YEXR966KH09NNP5ykMInVE9KuWJVIsPP/88/lo2RhxHOkbIrVEI5XIxxue77zzTlpllVXyV4x0VggQIECAAAECBLpPYJHuO5QjESBAgAABAgQIEOi8QIzGHTlyZOcbyPaM4GOkUYhXNSXSJsQEZ+2d5Kwzx4oRsvHqymN0pl/l+0T+3/IcwOXbLBMgQIAAAQIECHSPgBQN3ePsKAQIECBAgAABAgQIECBAgAABAgQIEKi5gABvzUk1SIAAAQIECBAgQIAAAQIECBAgQIAAge4REODtHmdHIUCAAAECBAgQIECAAAECBAgQIECAQM0FBHhrTqpBAgQIECBAgAABAgQIECBAgAABAgQIdI+ASda6x9lRCBAgQIAAAQIE2inwpS99KV166aV57REjRrRzr66rdtZZZ6V333039enTp+sOomUCBAgQIECAAAECnRTo1ZSVTu5rNwIECBAgQIAAAQIECBAgQIAAAQIECBCoo4AUDXXEd2gCBAgQIECAAAECBAgQIECAAAECBAhUIyDAW42efQkQIECAAAECBAgQIECAAAECBAgQIFBHAQHeOuI7NAECBAgQIECAAAECBAgQIECAAAECBKoREOCtRs++BAgQIECAAAECBAgQIECAAAECBAgQqKOAAG8d8R2aAAECBAgQIECAAAECBAgQIECAAAEC1QgI8FajZ18CBAgQIECAAAECBAgQIECAAAECBAjUUUCAt474Dk2AAAECBAgQIECAAAECBAgQIECAAIFqBAR4q9GzLwECBAgQIECAAAECBAgQIECAAAECBOooIMBbR3yHJkCAAAECBAgQIECAAAECBAgQIECAQDUCArzV6NmXAAECBAgQIECAAAECBAgQIECAAAECdRQQ4K0jvkMTIECAAAECBAgQIECAAAECBAgQIECgGgEB3mr07EuAAAECBAgQIECAAAECBAgQIECAAIE6Cgjw1hHfoQkQIECAAAECBAgQIECAAAECBAgQIFCNgABvNXr2JUCAAAECBAgQIECAAAECBAgQIECAQB0FBHjriO/QBAgQIECAAAECBAgQIECAAAECBAgQqEZAgLcaPfsSIECAAAECBAgQIECAAAECBAgQIECgjgICvHXEd2gCBAgQIECAAAECBAgQIECAAAECBAhUIyDAW42efQkQIECAAAECBAgQIECAAAECBAgQIFBHAQHeOuI7NAECBAgQIECAAAECBAgQIECAAAECBKoREOCtRs++BAgQIECAAAECBAgQIECAAAECBAgQqKOAAG8d8R2aAAECBAgQIECAAAECBAgQIECAAAEC1QgI8FajZ18CBAgQIECAAAECBAgQIECAAAECBAjUUUCAt474Dk2AAAECBAgQIECAAAECBAgQIECAAIFqBAR4q9GzLwECBAgQIECAAAECBAgQIECAAAECBOooIMBbR3yHJkCAAAECBAgQIECAAAECBAgQIECAQDUCArzV6NmXAAECBAgQIECAAAECBAgQIECAAAECdRQQ4K0jvkMTIECAAAECBAgQIECAAAECBAgQIECgGgEB3mr07EuAAAECBAgQIECAAAECBAgQIECAAIE6Cgjw1hHfoQkQIECAAAECBAgQIECAAAECBAgQIFCNgABvNXr2JUCAAAECBAgQIECAAAECBAgQIECAQB0FBHjriO/QBAgQIECAAAECBAgQIECAAAECBAgQqEZAgLcaPfsSIECAAAECBAgQIECAAAECBAgQIECgjgICvHXEd2gCBAgQIECAAAECBAgQIECAAAECBAhUIyDAW42efQkQIECAAAECBAgQIECAAAECBAgQIFBHAQHeOuI7NAECBAgQIECAAAECBAgQIECAAAECBKoREOCtRs++BAgQIECAAAECBAgQIECAAAECBAgQqKOAAG8d8R2aAAECBAgQIECAAAECBAgQIECAAAEC1QgI8FajZ18CBAgQIECAAAECBAgQIECAAAECBAjUUUCAt474Dk2AAAECBAgQIECAAAECBAgQIECAAIFqBAR4q9GzLwECBAgQIECAAAECBAgQIECAAAECBOooIMBbR3yHJkCAAAECBAgQIECAAAECBAgQIECAQDUC/xfSPLnOmRxg3wAAAABJRU5ErkJggg==" /></p>
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<pre class="r"><code># DON'T RUN CORRELATION ANALYSES WITH HETEROSCEDASTICITY</code></pre>
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<p>Looks interesting. Counties with larger population sizes may look like they have higher proportion of cases, but it’s a little complicated because there’s a lot of variability for counties with smaller population sizes.</p>
<p>One thing we might be interested in are those outliers with very high rates of positive cases. What counties are theses, and in what states? We can very easily check this with the following:</p>
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<pre class="r"><code>cases_popsize_masked %&gt;%
  filter(FracPos &gt; 0.08) %&gt;%
  arrange(desc(FracPos))</code></pre>
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<!-- rnb-frame-begin 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Carolina","4":"37023","5":"9227","6":"138","7":"90485","8":"0.540","9":"0.1019727"},{"1":"2021-03-01","2":"Vilas","3":"Wisconsin","4":"55125","5":"2263","6":"38","7":"22195","8":"0.533","9":"0.1019599"},{"1":"2021-03-01","2":"Clark","3":"Wisconsin","4":"55019","5":"3543","6":"61","7":"34774","8":"0.384","9":"0.1018865"},{"1":"2021-03-01","2":"Nantucket","3":"Massachusetts","4":"25019","5":"1161","6":"2","7":"11399","8":"0.792","9":"0.1018510"},{"1":"2021-03-01","2":"Kearney","3":"Nebraska","4":"31099","5":"661","6":"5","7":"6495","8":"0.399","9":"0.1017706"},{"1":"2021-03-01","2":"Meigs","3":"Tennessee","4":"47121","5":"1264","6":"21","7":"12422","8":"0.453","9":"0.1017550"},{"1":"2021-03-01","2":"Dodge","3":"Georgia","4":"13091","5":"2096","6":"98","7":"20605","8":"0.443","9":"0.1017229"},{"1":"2021-03-01","2":"Marion","3":"Tennessee","4":"47115","5":"2940","6":"44","7":"28907","8":"0.381","9":"0.1017055"},{"1":"2021-03-01","2":"Delaware","3":"Oklahoma","4":"40041","5":"4372","6":"62","7":"43009","8":"0.316","9":"0.1016531"},{"1":"2021-03-01","2":"Kingman","3":"Kansas","4":"20095","5":"727","6":"11","7":"7152","8":"0.464","9":"0.1016499"},{"1":"2021-03-01","2":"Logan","3":"Nebraska","4":"31113","5":"76","6":"0","7":"748","8":"0.304","9":"0.1016043"},{"1":"2021-03-01","2":"Nolan","3":"Texas","4":"48353","5":"1495","6":"50","7":"14714","8":"0.544","9":"0.1016039"},{"1":"2021-03-01","2":"Uinta","3":"Wyoming","4":"56041","5":"2055","6":"12","7":"20226","8":"0.264","9":"0.1016019"}],"options":{"columns":{"min":{},"max":[10],"total":[9]},"rows":{"min":[10],"max":[10],"total":[1967]},"pages":{}}}
  </script>
</div>
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<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
<p>Let’s add another column for death rate, and visualize the relationship between the fraction of positive cases and the death rate:</p>
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuY2FzZXNfcG9wc2l6ZV9tYXNrZWQgPC0gY2FzZXNfcG9wc2l6ZV9tYXNrZWQgJT4lXG4gIG11dGF0ZShGcmFjRGVhdGhzID0gZGVhdGhzL2Nhc2VzKVxucGxvdCh4PWNhc2VzX3BvcHNpemVfbWFza2VkJEZyYWNQb3MsXG4gICAgIHk9Y2FzZXNfcG9wc2l6ZV9tYXNrZWQkRnJhY0RlYXRocyxcbiAgICAgbG9nPVwiXCIsXG4gICAgIHhsYWI9XCJGcmFjQ2FzZXNcIixcbiAgICAgeWxhYj1cIkZyYWNEZWF0aHNcIilcbmBgYCJ9 -->
<pre class="r"><code>cases_popsize_masked &lt;- cases_popsize_masked %&gt;%
  mutate(FracDeaths = deaths/cases)
plot(x=cases_popsize_masked$FracPos,
     y=cases_popsize_masked$FracDeaths,
     log=&quot;&quot;,
     xlab=&quot;FracCases&quot;,
     ylab=&quot;FracDeaths&quot;)</code></pre>
<!-- rnb-source-end -->
<!-- rnb-plot-begin eyJjb25kaXRpb25zIjpbXSwiaGVpZ2h0Ijo0MzIuNjMyOSwic2l6ZV9iZWhhdmlvciI6MCwid2lkdGgiOjcwMH0= -->
<p><img 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" /></p>
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<p>What are these counties that have been severely impacted by deaths? Is this just noise from counties with small sizes and/or small case counts?</p>
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<pre class="r"><code>cases_popsize_masked %&gt;%
  filter(FracDeaths &gt; 0.06) %&gt;%
  arrange(desc(FracDeaths))</code></pre>
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<div id="using-group_by-and-summarize-functions-to-evaluate-data" class="section level1">
<h1>Using group_by() and summarize() functions to evaluate data</h1>
<p>The use of the group_by() and summarize() functions allows us to quickly get extremely informative information from our data.</p>
<p>We’ll show how they’re useful in many ways, but first we need to do a little more reshaping of our data.</p>
<p>Let’s go back to our original mask use data table, not the combined one. What if we wanted to quickly get the overall mean values of ALWAYS, … , NEVER across all the counties? We could use the original data in wide format, and calculate the mean for each of these columns. We could also use the simple summary() function which outputs a table:</p>
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<pre class="r"><code>mean(mask_use_wide$NEVER)</code></pre>
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<pre><code>[1] 0.07993953</code></pre>
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<pre class="r"><code>mean(mask_use_wide$RARELY)</code></pre>
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<pre><code>[1] 0.08291852</code></pre>
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<pre class="r"><code>mean(mask_use_wide$SOMETIMES)</code></pre>
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<pre><code>[1] 0.121318</code></pre>
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<pre class="r"><code>mean(mask_use_wide$FREQUENTLY)</code></pre>
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<pre><code>[1] 0.2077247</code></pre>
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<pre class="r"><code>mean(mask_use_wide$ALWAYS)</code></pre>
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<pre><code>[1] 0.5080936</code></pre>
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<pre class="r"><code>summary(mask_use_wide)</code></pre>
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<pre><code>   COUNTYFP             NEVER             RARELY          SOMETIMES        FREQUENTLY    
 Length:3142        Min.   :0.00000   Min.   :0.00000   Min.   :0.0010   Min.   :0.0290  
 Class :character   1st Qu.:0.03400   1st Qu.:0.04000   1st Qu.:0.0790   1st Qu.:0.1640  
 Mode  :character   Median :0.06800   Median :0.07300   Median :0.1150   Median :0.2040  
                    Mean   :0.07994   Mean   :0.08292   Mean   :0.1213   Mean   :0.2077  
                    3rd Qu.:0.11300   3rd Qu.:0.11500   3rd Qu.:0.1560   3rd Qu.:0.2470  
                    Max.   :0.43200   Max.   :0.38400   Max.   :0.4220   Max.   :0.5490  
     ALWAYS      
 Min.   :0.1150  
 1st Qu.:0.3932  
 Median :0.4970  
 Mean   :0.5081  
 3rd Qu.:0.6138  
 Max.   :0.8890  </code></pre>
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<p>You can do it with way, but it is kind of clunky, and it wouldn’t be practical if your dataset has too many variables. It is easier to first convert the data in long format:</p>
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<p>As a reminder, the “cols=” argument specifies which columns we are reshaping into rows (all <em>except</em> the “COUNTYFP” column), the “names_to=” argument specifies the name of the column that will hold the names of the reshaped columns, and the “values_to=” argument gives the name of the column that will hold the values of the reshaped columns.</p>
<p>We can now use the <strong>group_by()</strong> function, which takes our existing dataset and converts it to a grouped dataset based on a given variable, and combine it with the <strong>summarize()</strong> function (different from summary used above!) to get mean for each group:</p>
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<pre class="r"><code>mask_use_long %&gt;% 
  group_by(MaskUseResponse) %&gt;% 
  summarize(mean(MaskUseProportion))</code></pre>
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<div id="more-data-analysis-and-visualization" class="section level2">
<h2>More data analysis and visualization</h2>
<p>Let’s explore another example of group_by() and summarize() using our tibble that has all our combined information.</p>
<p>Instead of focusing on counties as our unit of analysis, we can use group_by() to easily switch to a state level analysis by telling the summarize() function to perform analyses for each state, combining all the rows that have the same value under the “state” column. When we combine the group_by() function with summarize(), we can easily do some pretty powerful analyses that would take a lot of effort using other programs.</p>
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<pre class="r"><code>cases_popsize_masked %&gt;%
  group_by(state) %&gt;%
  summarize(MeanMasked = mean(AlwaysMasked)) %&gt;%
  arrange(desc(MeanMasked))</code></pre>
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{"columns":[{"label":["state"],"name":[1],"type":["chr"],"align":["left"]},{"label":["MeanMasked"],"name":[2],"type":["dbl"],"align":["right"]}],"data":[{"1":"Delaware","2":"0.8176667"},{"1":"Hawaii","2":"0.8150000"},{"1":"Rhode Island","2":"0.8020000"},{"1":"Massachusetts","2":"0.7972857"},{"1":"Connecticut","2":"0.7795000"},{"1":"New York","2":"0.7715789"},{"1":"New Jersey","2":"0.7484286"},{"1":"District of Columbia","2":"0.7430000"},{"1":"Maryland","2":"0.7427500"},{"1":"California","2":"0.7146379"},{"1":"Pennsylvania","2":"0.6719701"},{"1":"New Mexico","2":"0.6700000"},{"1":"Washington","2":"0.6698718"},{"1":"Vermont","2":"0.6600000"},{"1":"Arizona","2":"0.6550000"},{"1":"Oregon","2":"0.6404167"},{"1":"Virginia","2":"0.6378271"},{"1":"Nevada","2":"0.6286471"},{"1":"New Hampshire","2":"0.6263000"},{"1":"North Carolina","2":"0.6033100"},{"1":"Texas","2":"0.6025591"},{"1":"Maine","2":"0.5923750"},{"1":"Florida","2":"0.5825522"},{"1":"Michigan","2":"0.5748072"},{"1":"Colorado","2":"0.5474375"},{"1":"South Carolina","2":"0.5328261"},{"1":"Mississippi","2":"0.5268780"},{"1":"Georgia","2":"0.5123145"},{"1":"Illinois","2":"0.5100784"},{"1":"Kentucky","2":"0.4914000"},{"1":"West Virginia","2":"0.4824182"},{"1":"Alabama","2":"0.4753134"},{"1":"Arkansas","2":"0.4628667"},{"1":"Indiana","2":"0.4502174"},{"1":"Alaska","2":"0.4443333"},{"1":"Ohio","2":"0.4388523"},{"1":"Wisconsin","2":"0.4335000"},{"1":"Tennessee","2":"0.4271895"},{"1":"Utah","2":"0.4197586"},{"1":"Iowa","2":"0.3962020"},{"1":"Kansas","2":"0.3955905"},{"1":"Idaho","2":"0.3868409"},{"1":"Oklahoma","2":"0.3861039"},{"1":"Missouri","2":"0.3765565"},{"1":"Minnesota","2":"0.3755287"},{"1":"South Dakota","2":"0.3609394"},{"1":"Nebraska","2":"0.3514516"},{"1":"Wyoming","2":"0.3364348"},{"1":"Montana","2":"0.3175357"},{"1":"North Dakota","2":"0.2569231"}],"options":{"columns":{"min":{},"max":[10],"total":[2]},"rows":{"min":[10],"max":[10],"total":[50]},"pages":{}}}
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<p>This is potentially a bad analysis because we are calculating a mean for a state based on it’s counties, and some of these counties may be represented by fewer people. So if we truly wanted the average behavior of a state, we should weight each county by its population size. Why should a county with a size of 400 contribute equally to the mean as a county of size 40,000?</p>
<p>The following code calculates a custom mean value, where each county is weighted by its population size:</p>
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<pre class="r"><code>cases_popsize_masked %&gt;%
  group_by(state) %&gt;%
  summarize(WeightedMeanMasked = sum(AlwaysMasked*pop_size)/sum(pop_size)) %&gt;%
  arrange(desc(WeightedMeanMasked))</code></pre>
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{"columns":[{"label":["state"],"name":[1],"type":["chr"],"align":["left"]},{"label":["WeightedMeanMasked"],"name":[2],"type":["dbl"],"align":["right"]}],"data":[{"1":"Delaware","2":"0.8074789"},{"1":"Hawaii","2":"0.8001153"},{"1":"Massachusetts","2":"0.7917454"},{"1":"Connecticut","2":"0.7799761"},{"1":"Rhode Island","2":"0.7765693"},{"1":"New York","2":"0.7764355"},{"1":"California","2":"0.7663693"},{"1":"Maryland","2":"0.7467422"},{"1":"New Jersey","2":"0.7453277"},{"1":"District of Columbia","2":"0.7430000"},{"1":"Pennsylvania","2":"0.7291974"},{"1":"Arizona","2":"0.7199469"},{"1":"Texas","2":"0.7172061"},{"1":"Nevada","2":"0.7151433"},{"1":"New Mexico","2":"0.7067253"},{"1":"Oregon","2":"0.7001751"},{"1":"Washington","2":"0.6992988"},{"1":"Florida","2":"0.6854983"},{"1":"Illinois","2":"0.6779168"},{"1":"Virginia","2":"0.6769196"},{"1":"Vermont","2":"0.6711151"},{"1":"Colorado","2":"0.6460138"},{"1":"North Carolina","2":"0.6443466"},{"1":"New Hampshire","2":"0.6366984"},{"1":"Michigan","2":"0.6342676"},{"1":"Maine","2":"0.6108901"},{"1":"Georgia","2":"0.5646205"},{"1":"South Carolina","2":"0.5614682"},{"1":"Utah","2":"0.5322512"},{"1":"Kentucky","2":"0.5318694"},{"1":"Ohio","2":"0.5275387"},{"1":"Mississippi","2":"0.5272208"},{"1":"Kansas","2":"0.5263340"},{"1":"Tennessee","2":"0.5107912"},{"1":"Alaska","2":"0.5098718"},{"1":"Arkansas","2":"0.5088815"},{"1":"Alabama","2":"0.5082705"},{"1":"Missouri","2":"0.5082544"},{"1":"Indiana","2":"0.5005169"},{"1":"West Virginia","2":"0.4995554"},{"1":"Minnesota","2":"0.4744907"},{"1":"Wisconsin","2":"0.4558316"},{"1":"Iowa","2":"0.4494081"},{"1":"Oklahoma","2":"0.4443248"},{"1":"Nebraska","2":"0.4255619"},{"1":"Idaho","2":"0.4118755"},{"1":"South Dakota","2":"0.3680496"},{"1":"Montana","2":"0.3613755"},{"1":"Wyoming","2":"0.3529925"},{"1":"North Dakota","2":"0.2960054"}],"options":{"columns":{"min":{},"max":[10],"total":[2]},"rows":{"min":[10],"max":[10],"total":[50]},"pages":{}}}
  </script>
</div>
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<p>Did this weighting by county size actually make a difference? Let’s store these analyses as ‘x’ and ‘y’ and compare them:</p>
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<pre class="r"><code>x &lt;- cases_popsize_masked %&gt;%
  group_by(state) %&gt;%
  summarize(MeanMasked = mean(AlwaysMasked)) %&gt;%
  arrange(state)
y &lt;- cases_popsize_masked %&gt;%
  group_by(state) %&gt;%
  summarize(WeightedMeanMasked = sum(AlwaysMasked*pop_size)/sum(pop_size)) %&gt;%
  arrange(state)
hist(x$MeanMasked - y$WeightedMeanMasked)</code></pre>
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<p><img 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" /></p>
<!-- rnb-plot-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
<p>It looks like on average, AlwaysMasked estimates that don’t take pop_size into account are systematically lower than those that do. When we don’t take county size into account, we effectively give more weight to smaller counties.</p>
</div>
</div>
<div id="summary" class="section level1">
<h1>Summary:</h1>
<div id="new-tidyverse-functions-used-today" class="section level2">
<h2>New tidyverse functions used today:</h2>
<ul>
<li>slice() to select specific rows by index number</li>
<li>inner_join() to merge datasets based on given variable(s)</li>
<li>mutate() with str_remove to get rid of characters or words we don’t want, there are many similar functions in the stringr package, which has its own cheat sheet</li>
<li>group_by() to categorize our data by the values in a particular column (here, by state)</li>
<li>summarize() to calculate simple but very informative statistics, such as mean and variance</li>
</ul>
<!-- rnb-text-end -->
</div>
</div>

<div id="rmd-source-code">---
title: "R Notebook"
output: html_notebook
---

# NEW FXNS: slice(), inner_join(), summarize(), group_by()

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```


This is the second session covering tidyverse commands. We will show you how to use some new functions, but we will also use some of the functions we previously used last week which will be good for extra practice. If you missed the previous session, that should not be a problem.

From last time, we looked at this [cheat sheet](https://rstudio.com/wp-content/uploads/2015/02/data-wrangling-cheatsheet.pdf) to occasionally guide us.

It's typical to load all the libraries you need at the top of your code.
```{r}
library(tidyverse)
```

------
For this week, rather than use an example dataset, we are going to use several publically available datasets from the NYT github repository on the COVID-19 pandemic. This is because "wild-caught" datasets are often much messier than example datasets, so it will help us become more familiar with how you would actually go about processing data. We are going to keep our analysis purely descriptive, as it would be irresponsible to make any conclusive claims after fiddling around with R for only an hour!


## Dataset 1: mask useage

Let's load in the first data set on mask usage in the US, which is in the form of a comma separated list. This data is from a NYT survey where they asked the question: "How often do you wear a mask in public when you expect to be within six feet of another person?" (As a note, this survey was done several months ago, so these numbers are not the most up-to-date.) 

```{r}
mask_use_wide <- read_delim(file="~/Documents/GitHub/bioinformatics-coffee-hour/tidyverse/part2_winter2021/mask-use-by-county.csv", delim=",")
mask_use
```


It looks like for all the counties are represented with a 5-digit FIPS code, which is not exactly ideal because we don't know what these numbers mean. We'll get the names in a moment by combining these data with another file that also has these FIPS codes AND county names. We can thus match these data tables based on shared FIPS codes to combine them.


## Dataset 2: US case counts

In addition to the mask data, let's also load in data on COVID-19 case counts across the US, also supplied by the NYT, and combine it with our mask use data. Unlike the previous data that we loaded in from our local file system, these data we will directly download because they are so large. This will also show how you can specify web addresses instead of file names for read_delim(). This file is also comma separated, so we will let read_delim() know this.

```{r, include=FALSE}
cases <- read_delim(file="https://github.com/nytimes/covid-19-data/raw/master/us-counties.csv", delim=",") %>%
  arrange(fips)
```

This is a lot of data. For ~3000 US counties, there's an estimate of cumulative COVID-19 cases and deaths for each day since late January (it's now March again!!!). For now, let's just look at the start of this month. 

```{r, include=FALSE}
cases_latest <- cases %>% 
  filter(date == "2021-03-01") %>%
  arrange(fips)
```

As a refresher, we are using the filter() function to select only the rows that match the string "2021-03-01" in the date column, then using the arrange() function to sort the counties by FIPS code.

-----

## Dataset 3: census data

Let's load in some additional data on population size by county, which are obtained from www.census.gov.

```{r, include=FALSE}
pop_sizes <- read_delim(file="~/Documents/GitHub/bioinformatics-coffee-hour/tidyverse/part2_winter2021/co-est2019-annres.csv.xz", delim=",")
```

When I'm working with larger files and I want to make sure they looks like I expect them to, instead of opening up the file and scrolling through all the lines, checking each by eye, I typically use various commands to just get an idea of what it looks like before proceeding. Oftentimes, this is sufficient; we don't need to check every line.

These are just a few commands I might use to look at the first and last few lines, how many rows there are overall, and also get an idea of how much missing data there might be:
```{r}
head(pop_sizes) 
nrow(pop_sizes) 
sum(!is.na(pop_sizes$'2019')) # how many counties have size estimates for 2019? it's even closer to the 3142 we expect!
tail(pop_sizes)
```

Ok so these data are a little messy and need to be cleaned up.

From these commands, we can see that the entire US was included in this table, which is not a county. We should remove that. Also, county names are all preceded with a ".", something we'll deal with in a moment. The number of rows in the table is very similar to what we expect (3,142 according to wikipedia), but maybe larger by 7 rows or so. The row containing the entire US contributes to this excess of rows beyond our expectation. 

Looking at the bottom of the table, we see some footnotes were left in. These are probably contributing to the extra number of rows and should absolutely be removed.

Let's remove these rows at the beginning and end with the **slice()** function, which pulls out rows from a data frame/tibble based on their ordinal position. It keeps all the columns, in contrast to the select() function that we discussed last week.

```{r, include=FALSE}
pop_sizes <- pop_sizes %>% dplyr::slice(2:3143) 
```

With this command, we are slicing out the middle of the data frame to remove the row with data from the whole US, as well as the footnotes at the bottom.

The 'Geographic Area' column has two pieces of info in it: county and state. We should split this column into two columns so that we can separately access the info. For instance, later on we will do an analysis not by county but by state, summing up across a state's counties, and this requires having this information in it's own separate column. To separate this info into 2 seperate columns, we will use the separate() function:

```{r, include=FALSE}
pop_sizes <- pop_sizes %>% separate(col = 'Geographic Area',
                                    into = c("County", "State"),
                                    sep = ", ")
```

Note that unlike last time we are specifying what we want to separate on with the "sep=" argument, which is a comma followed by a white space.


Let's get rid of extra characters in the new 'County' column. We don't need the "." that precedes each name, and it's pointless that each individual county name is followed by "County"... we know they're counties based on the name of the column. We can use the mutate() function to add new rows with new names, but if the name of the new row we want is the same as an existing one, it just replaces it!

```{r, include=FALSE}
pop_sizes <- pop_sizes %>% 
  mutate(County = str_remove(string = County,pattern = ".")) %>%
  mutate(County = str_remove(string = County,pattern = " County"))
```

We are combining the mutate() function with **str_remove()** which, as the name suggests, will remove a given pattern from a character column, the County column in this case.


This table includes population size data for quite a few years. Let's just get the most recent population estimate, selecting the 2019 column with the select() function, which as mentioned above pulls out specific columns:

```{r, include=FALSE}
pop_sizes <- pop_sizes %>%
  select(County, State, "2019")
```


As we did above for the data sets we used last week, we can also combine all of these individual commands into one compact command that might look something like this:
```{r, include=FALSE}
pop_sizes <- read_delim(file="~/Documents/GitHub/bioinformatics-coffee-hour/tidyverse/part2_winter2021/co-est2019-annres.csv.xz", delim=",") %>% 
  slice(2:3143) %>%
  separate(col = 'Geographic Area', into = c("County", "State"), sep = ", ") %>%
  mutate(County = str_remove(string = County,pattern = ".")) %>%
  mutate(County = str_remove(string = County,pattern = " County")) %>%
  select(County, State, "2019")
```


We can take a quick peek at these population size data, now that they're cleaned, using the summary() function, which shows there's a ton of variability in county sizes!
```{r}
summary(pop_sizes)
```



## Merging data sets with inner_join()

Let's combine these data on population sizes with our previous data on case counts and mask usage, as it may reveal interesting dynamics that vary by county size.

We will first use the join command on the case count data and the county population size data, joining by BOTH county and state. There are many different types of joining functions and it can get fairly confusing, so today we will focus just on **inner_join**. 

```{r, include=FALSE}
cases_popsize <- inner_join(x=cases_latest, y=pop_sizes, 
                                  by=c("county"="County", "state"="State"))
```

The "x=" and "y=" arguments specify which datasets we want to merge. Inner_join() returns all rows in dataset x that has matching values in dataset y, and all the columns in both x and y.

The "by=" arugment tells what variables the function should merge by, as a character vector. In other words we tell the function what columns we want to match: it compares the values in the columns in x and y, and if they match it merges them. We are matching the "county" coumn in cases_latest with the "County" column in pop_sizes, and the "state" and "State" columns in the same.

Note that because the column names (i.e. variable names) are slightly different between datasets, we have to specify which we are matching using an "="; if they had the same names, we could just list them. 


## More merging 

Now let's merge this table that has case counts and population size with the mask data! For now, let's not add in all the mask data. Let's only incorporate the the proportion of people who are "ALWAYS" masked for each county.

```{r, include=FALSE}
cases_popsize_masked <- inner_join(x=cases_popsize, 
                            y=select(mask_use_wide, c(COUNTYFP, ALWAYS)),
                            by=c("fips" = "COUNTYFP"))
```

Note that we are nesting the select() function within our inner_join() call, allowing us to subset the mask useage dataset without making an entirely new data frame.


Looking at this data table, for each county we now have cases, deaths, population size, and the proportion of people who are always masked.

You may also notice the population size data had a column entitled "2019" for size estimates during that year. Let's make this more informative and change it to "pop_size", and rename the ALWAYS column so that we know it's referring to always masked.

```{r, include=FALSE}
cases_popsize_masked <- cases_popsize_masked %>%
  rename("pop_size" = "2019", "AlwaysMasked" = ALWAYS)
```


## Visualizing data

Let's quickly probe these data just to get an idea of what they look like. The main goal here is to teach you how to use R/tidyverse commands to quickly and easily explore your data. Again, due to the context of these data, let's keep any results as fairly descriptive observations. We can't say anything conclusive without more complicated analyses that we'll leave to the public health officials.

Let's assume these case count data are accurately measuring the number of people who are getting coronavirus infections (which is obviously an underestimate, as many cases can be asymptomatic!). Assuming this, what fraction of a county's population is testing positive, and how does this fraction vary with population size? Let's use the mutate() function to add a new column with number of cases normalized by population size:

```{r}
cases_popsize_masked <- cases_popsize_masked %>%
  mutate(FracPos = cases/pop_size)
```


Do counties with large populations have a higher proportion of case counts? Let's use a simple plotting function to plot population size on the x axis and proportion of cases on y axis. Since we will look at the proportion of cases by dividing the data in the case counts column by the data in the population size column, let's just make a new column called 'FracPos' that contains this information

We can make a quick plot of these data using a base R (i.e. not tidyverse) plotting function:
```{r}
cases_popsize_masked <- cases_popsize_masked %>%
  mutate(FracPos = cases/pop_size)
plot(x=cases_popsize_masked$pop_size,
     y=cases_popsize_masked$FracPos,
     log="x",
     ylab="Fraction of cases",
     xlab="pop size")
# DON'T RUN CORRELATION ANALYSES WITH HETEROSCEDASTICITY
```

Looks interesting. Counties with larger population sizes may look like they have higher proportion of cases, but it's a little complicated because there's a lot of variability for counties with smaller population sizes. 


One thing we might be interested in are those outliers with very high rates of positive cases. What counties are theses, and in what states? We can very easily check this with the following:

```{r}
cases_popsize_masked %>%
  filter(FracPos > 0.08) %>%
  arrange(desc(FracPos))
```


Let's add another column for death rate, and visualize the relationship between the fraction of positive cases and the death rate:
```{r}
cases_popsize_masked <- cases_popsize_masked %>%
  mutate(FracDeaths = deaths/cases)
plot(x=cases_popsize_masked$FracPos,
     y=cases_popsize_masked$FracDeaths,
     log="",
     xlab="FracCases",
     ylab="FracDeaths")
```


What are these counties that have been severely impacted by deaths? Is this just noise from counties with small sizes and/or small case counts?

```{r}
cases_popsize_masked %>%
  filter(FracDeaths > 0.06) %>%
  arrange(desc(FracDeaths))
```


# Using group_by() and summarize() functions to evaluate data

The use of the group_by() and summarize() functions allows us to quickly get extremely informative information from our data. 

We'll show how they're useful in many ways, but first we need to do a little more reshaping of our data.

Let's go back to our original mask use data table, not the combined one. What if we wanted to quickly get the overall mean values of ALWAYS, ... , NEVER across all the counties? We could use the original data in wide format, and calculate the mean for each of these columns. We could also use the simple summary() function which outputs a table:

```{r}
mean(mask_use_wide$NEVER)
mean(mask_use_wide$RARELY)
mean(mask_use_wide$SOMETIMES)
mean(mask_use_wide$FREQUENTLY)
mean(mask_use_wide$ALWAYS)
summary(mask_use_wide)
```

You can do it with way, but it is kind of clunky, and it wouldn't be practical if your dataset has too many variables. It is easier to first convert the data in long format:

```{r, include=FALSE}
mask_use_long <- mask_use %>%
  pivot_longer(cols = -COUNTYFP, names_to = "MaskUseResponse", values_to = "MaskUseProportion") %>%
  rename("fips" = COUNTYFP) %>%
  arrange(fips)
```

As a reminder, the "cols=" argument specifies which columns we are reshaping into rows (all *except* the "COUNTYFP" column), the "names_to=" argument specifies the name of the column that will hold the names of the reshaped columns, and the "values_to=" argument gives the name of the column that will hold the values of the reshaped columns.

We can now use the **group_by()** function, which takes our existing dataset and converts it to a grouped dataset based on a given variable, and combine it with the **summarize()** function (different from summary used above!) to get mean for each group:

```{r}
mask_use_long %>% 
  group_by(MaskUseResponse) %>% 
  summarize(mean(MaskUseProportion))
```


## More data analysis and visualization

Let's explore another example of group_by() and summarize() using our tibble that has all our combined information.

Instead of focusing on counties as our unit of analysis, we can use group_by() to easily switch to a state level analysis by telling the summarize() function to perform analyses for each state, combining all the rows that have the same value under the "state" column. When we combine the group_by() function with summarize(), we can easily do some pretty powerful analyses that would take a lot of effort using other programs.

```{r}
cases_popsize_masked %>%
  group_by(state) %>%
  summarize(MeanMasked = mean(AlwaysMasked)) %>%
  arrange(desc(MeanMasked))
```


This is potentially a bad analysis because we are calculating a mean for a state based on it's counties, and some of these counties may be represented by fewer people. So if we truly wanted the average behavior of a state, we should weight each county by its population size. Why should a county with a size of 400 contribute equally to the mean as a county of size 40,000?

The following code calculates a custom mean value, where each county is weighted by its population size:

```{r}
cases_popsize_masked %>%
  group_by(state) %>%
  summarize(WeightedMeanMasked = sum(AlwaysMasked*pop_size)/sum(pop_size)) %>%
  arrange(desc(WeightedMeanMasked))
```


Did this weighting by county size actually make a difference? Let's store these analyses as 'x' and 'y' and compare them:
```{r}
x <- cases_popsize_masked %>%
  group_by(state) %>%
  summarize(MeanMasked = mean(AlwaysMasked)) %>%
  arrange(state)
y <- cases_popsize_masked %>%
  group_by(state) %>%
  summarize(WeightedMeanMasked = sum(AlwaysMasked*pop_size)/sum(pop_size)) %>%
  arrange(state)
hist(x$MeanMasked - y$WeightedMeanMasked)
```

It looks like on average, AlwaysMasked estimates that don't take pop_size into account are systematically lower than those that do. When we don't take county size into account, we effectively give more weight to smaller counties. 


# Summary:
## New tidyverse functions used today:
- slice() to select specific rows by index number
- inner_join() to merge datasets based on given variable(s)
- mutate() with str_remove to get rid of characters or words we don't want, there are many similar functions in the stringr package, which has its own cheat sheet
- group_by() to categorize our data by the values in a particular column (here, by state)
- summarize() to calculate simple but very informative statistics, such as mean and variance
</div>



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================================================
FILE: tidyverse_covid_data/part1/binder/Dockerfile
================================================
FROM rocker/binder:3.6.3

COPY --chown=rstudio:rstudio ./tidyverse_covid_data/part1/ ${HOME}


================================================
FILE: tidyverse_covid_data/part1/tidyverse_1.R
================================================
# always make sure you're in the correct working directory for loading/saving files!
setwd("./")

# previous workshop: basic R
# today's workshop: tidyverse, a collection of packages
# see cheat sheet here: https://rstudio.com/wp-content/uploads/2015/02/data-wrangling-cheatsheet.pdf


# At the top of your code, always load required packages, install if necessary

#install.packages("tidyverse")
library(tidyverse)

# within "tidyverse", the data table used is called a "tibble"


# load data using read_delim() function, which crates a tibble.
mask_use <- read_delim(file="https://github.com/nytimes/covid-19-data/raw/bde13b021e99c6b4a63fb66a6144e889cc635e31/mask-use/mask-use-by-county.csv", delim=",")
mask_use


# oftentimes it's useful to perform various sanity checks on your data to make sure they look like you expect
sum(mask_use[1,2:6])
sum(mask_use[2,2:6])
sum(mask_use[3,2:6])

# Data tables: wide format vs long format
# long format is tidy, where every column is a variable, every row is an observation

# mask data looks like it's in WIDE format, let's convert it to LONG format using pivot_longer(), see cheat sheet!
mask_use_long <- pivot_longer(data = mask_use, 
                              cols = -COUNTYFP, 
                              names_to = "MaskUseResponse", 
                              values_to = "MaskUseProportion")

# stringing functions together: pivot_longer() to convert to long format -> rename() to change column name -> arrange() to sort based on column
# option 1: do each operation on a separate line
mask_use_long <- pivot_longer(data = mask_use, cols = -COUNTYFP, names_to = "MaskUseResponse", values_to = "MaskUseProportion")
mask_use_long2 <- rename(mask_use_long, "fips" = COUNTYFP)
mask_use_long3 <- arrange(mask_use_long2, "fips")

# option 2: do all operations on a single line (not preferred method)
mask_use_long <- pivot_longer(data = arrange(rename(mask_use, "fips" = COUNTYFP), "fips"), cols = -"fips", names_to = "MaskUseResponse", values_to = "MaskUseProportion")

# option 3: the tidyverse way using the %>% pipe operator
mask_use_long <- mask_use %>%
  pivot_longer(cols = -COUNTYFP, names_to = "MaskUseResponse", values_to = "MaskUseProportion") %>%
  rename("fips" = COUNTYFP) %>%
  arrange(fips)

# Let's load in case count data, also from NYT github repository
cases <- read_delim(file="https://github.com/nytimes/covid-19-data/raw/ccc8c7988a089fed287a9005e5335d8716d8db57/us-counties.csv", delim=",") %>%
  arrange(fips)

# too much data! let's look at a particular date, August 3rd, by selecting rows using the filter() function
cases_latest <- cases %>% 
  filter(date == "2020-08-03") %>%
  arrange(fips)

# let's combine mask data with case count data! type '?join' in console to see options
# the different functions depend on how we want to treat missing data... do we have missing data? or "NA"s?
cases_latest %>% filter(is.na(fips)) %>%
  nrow
mask_use_long %>% filter(is.na(fips)) %>%
  nrow

cases_NAs <- cases_latest %>% 
  filter(is.na(fips)) %>%
  arrange()

# NYC is missing FIPS number 36061! let's fix this particular cell using mutate() function, which add new columns or modifies existing ones (as we do here)
cases_latest <- cases_latest %>%
    mutate(fips = if_else(county == "New York City", true = "36061", false = fips))

# Now that NYC FIPS data is entered, let's combine the tables with inner_join():
cases_masks <- inner_join(x=cases_latest, y=mask_use_long, by="fips")


# data under "date" column all the same, not informative, and we don't need FIPS codes
# we can remove columns with the select() function
cases_masks <- cases_masks %>% dplyr::select(-date, -fips)

# simple commands to get an idea of what the data look like!

# Which counties use masks most frequently?
cases_masks %>% filter(MaskUseResponse == "ALWAYS") %>%
  arrange(desc(MaskUseProportion), county) %>%
  head(n=20)

# Which counties use masks least frequently?
cases_masks %>% filter(MaskUseResponse == "NEVER") %>%
  arrange(desc(MaskUseProportion), county) %>%
  head(n=10)

# For the counties with the most cases, how frequently are people ALWAYS wearing masks?
cases_masks %>% filter(MaskUseResponse == "ALWAYS") %>%
  arrange(desc(cases), county) %>%
  head(n=10)

# Summary
# Key tidyverse functions used today:
# - read_delim() to read files in and convert them to a tibble, a special kind of data table
# - pivot_longer()/pivot_wider() to convert data between long and wide format
# - rename() to change the names of particular columns
# - arrange() to sort the tibble according to values in particular columns
# - filter() to select particular rows that meet specific conditions
# - mutate(), with if_else() to modify particular cells in a table!
# - select() to select specific columns to keep


================================================
FILE: tidyverse_covid_data/part1/tidyverse_1.Rmd
================================================
---
title: "Untitled"
output: html_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

```{r}
setwd("./")
```

The previous workshop gave a basic introduction to data structures in R. Today we will talk about the 'tidyverse' package, which is a collection of packages that contain many useful functions with intuitive names that are used to clean and process data sets. You don't need to use the tidyverse paclages to necessarily analyze your data in R, but it has rapidly become the industry standard.

Tidyverse has many functions, especially when you see them all in a [cheat sheet](https://rstudio.com/wp-content/uploads/2015/02/data-wrangling-cheatsheet.pdf). Don't worry about necessarily memorizing them all. We'll go through several of the more important ones, and as you analyze data yourselves, you can quickly find what functions may be useful based on how they're categorized here. Alternatively, google searches that include 'tidyverse' in the search may also be fast and informative. 

The Tidyverse package is already installed in this environment we're working in, but when you use R studio on your own computers, you'll need to install these libraries (if they aren't already installed), and then load them using the install.packages() and library() functions, respectivey:
```{r}
#install.packages("tidyverse")
library(tidyverse)
```

Many of the tidyverse functions operate on a data table. At the end of the previous workshop, we introduced the 'data frame', which is essentially a table with rows and columns, and the columns have informative names. When we use the functions within tidyverse, we will instead use a 'tibble', which is a table that is very similar to a data frame but has a few differences that I won't discuss in detail. Just know that when you see the word "tibble", that's basically a data table.

For these next two workshops on tidyverse, we will grab data from different sources. Downloading, cleaning, and combining a data sets are very common tasks for all sorts of scientists, and these skills will be valuable for you regardless of what kind of data you want to analyze.

Let's load in the first data set on mask usage in the US, recently provided by the New York Times. Let's take a peek at the raw file to get an idea of what it looks like using the head command on terminal. As you can see, it looks like a bunch of information separated by commas. Let's read in these data with the read_delim() function and let R know that our data are separated, or delimited, by commas.
```{r}
mask_use <- read_delim(file="https://github.com/nytimes/covid-19-data/raw/bde13b021e99c6b4a63fb66a6144e889cc635e31/mask-use/mask-use-by-county.csv", delim=",")
mask_use
```

From the NYT github repo re. these data, they asked the question: "How often do you wear a mask in public when you expect to be within six feet of another person?"

It looks like for all the counties are represented with a 5-digit FIPS code, which is not exactly ideal because we don't know what these numbers mean. We'll get the names in a moment by combining these data with another file that also has these FIPS codes AND county names. We can thus match these data tables based on shared FIPS codes to combine them.

When I download other peoples data, I typically like to do many sanity checks on them to make sure they make sense. Even if you trust your source, it's always good to verify. One question that immediately came to my mind is whether these values sum to 1 for each row, as it seems like they should. We can quick check this for a few rows using the bracket notation to access particular parts of the table
```{r}
sum(mask_use[1,2:6])
sum(mask_use[2,2:6])
sum(mask_use[3,2:6])
```

# Data tables: wide format vs long format

Oftentimes, it is ideal to make our data "tidy". Tidy data is data where:

1. Every column is variable.
2. Every row is an observation.
3. Every cell is a single value.

When we look at this mask use data, is each column it's own variable? It doesn't seem like it. The most basic unit of analysis here is a person within a county. This person was asked a question, and they replied with one of five responses. So, we could define a single categorical variable "MaskUseResponse" that takes on one of these five values measuring approximate frequency. This could be a new column, entitled "MaskUseResponse", and in the rows beneath would be one of these five responses. Then, we could have another column that, for each of these responses, has the value of how many people wore masks that frequently. Here we are gathering the data across five columns into two columns. This used to be done with a function called gather(), which you may encounter frequently, but we'll use its newer version: pivot_longer(). (SEE CHEAT SHEET!)

Also, see [here](https://en.wikipedia.org/wiki/Wide_and_narrow_data) for another simple example wide vs long format.

```{r}
mask_use_long <- pivot_longer(data = mask_use, 
                              cols = -COUNTYFP, 
                              names_to = "MaskUseResponse", 
                              values_to = "MaskUseProportion")
```

As you can see, the tibble is much longer and each county is represented by 5 rows, once for each value of the MaskUseResponse categorical variable. If we wanted to go from this long format back to wide format, we could use the spread() function, or its newer version pivot_wider(), to spread out these two columns into the 5 original columns, but we will not do this today.

I'd also like to note that long format is not more correct or better than wide format. It completely depends on what analyses you're doing and both may be acceptable. If you load in your data set, you don't NEED to convert it to long format, but in the next workshop session I'll show you how converting to long format can potentially allow you to do some powerful stuff.


Let's say right after we convert these data to long format, we want to polish it by renaming one of the columns, "COUNTYFP", to "fips", and arrange the table such that it is sorted by this column. To do this, we will use the rename() and arrange() functions, respectively. In R, we can easily do multiple operations in a row, but like all problems in coding, there are multiple, technically correct ways of doing something:
```{r, include=FALSE}
# do each operation on a separate line
mask_use_long <- pivot_longer(data = mask_use, cols = -COUNTYFP, names_to = "MaskUseResponse", values_to = "MaskUseProportion")
mask_use_long <- rename(mask_use_long, "fips" = COUNTYFP)
mask_use_long <- arrange(mask_use_long, "fips")

# most dense: do all operations on a single line
mask_use_long <- pivot_longer(data = arrange(rename(mask_use, "fips" = COUNTYFP), "fips"), cols = -"fips", names_to = "MaskUseResponse", values_to = "MaskUseProportion")
```

When we want to do multiple operations in a row, it is generally considered better to string together multiple commands using the pipe operator in tidyverse, which is specified by %>%. This piping together of commands allows us to do many complicated things while keeping our code very easy to read.
```{r, include=FALSE}
mask_use_long <- mask_use %>%
  pivot_longer(cols = -COUNTYFP, names_to = "MaskUseResponse", values_to = "MaskUseProportion") %>%
  rename("fips" = COUNTYFP) %>%
  arrange(fips)
```

In addition to the mask data, let's also load in data on covid19 case counts across the US, also supplied by the NYT, and combine it with our mask use data. Unlike the previous data that we loaded in from our local file system, these data we will directly download because they are so large. This will also show how you can specify web addresses instead of file names for read_delim(). This file is also comma separated, so we will let read_delim() know this.
```{r, include=FALSE}
cases <- read_delim(file="https://github.com/nytimes/covid-19-data/raw/ccc8c7988a089fed287a9005e5335d8716d8db57/us-counties.csv", delim=",") %>%
  arrange(fips)
```

This is a lot of data. For some 3 thousand counties, there's an estimate of cumulative covid19 cases and deaths for each day since late January (it's now early August). For now, let's just look at the most recent date: 2020-08-03 in the date column. We can use the filter() function to filter our data table by row
```{r, include=FALSE}
cases_latest <- cases %>% 
  filter(date == "2020-08-03") %>%
  arrange(fips)
```

You'll notice that these data have both a county name and a county FIPS number, whereas the mask data only had an FIPS number. We can combine these two tables in order to get an actual county name and state for the mask data. 

There are several flavors of join functions (type ?join) that combine data sets based on values in a column. Here, we want to match 2 tables, 'cases_latest' and 'mask_use_long', based on FIPS number, such that rows in either table that have a matching FIPS number are combined. Which join function we choose depends on how we want to deal with missing data. 

For instance, what if one table doesn't have an FIPS code? If we use inner_join(), we are more conservative and the joined/combined table doesn't include any rows where either 'cases_latest' OR 'mask_use_long' had missing data. If we use left_join(), we include all the rows in the 'left' table (specified as x), and if there isn't a FIPS code in the 'right' table (specified as y), then we just fill in the gaps with NAs as needed. The other join functions follow a similar logic and you can see them in the help page.

Since the outcome of using a join function depends on missing data, let's take a quick peek at how complete our data are.
```{r}
cases_latest %>% filter(is.na(fips)) %>%
  nrow
mask_use_long %>% filter(is.na(fips)) %>%
  nrow
```

Ok so it looks like there are 29 rows in the case count data that don't have FIPS codes... do we care about them? Let's take a peek:
```{r}
cases_NAs <- cases_latest %>% 
  filter(is.na(fips)) %>%
  arrange()
```

Oof, the FIPS number for New York City, perhaps one of the most important counties given the context, is missing in the case count data, yet it appears to be present in the mask use data (using a FIPS code of 36061 for Manhattan). In cases like this, we can modify the 'fips' column in the case count data to include the number 36061. Specifically, we can use the mutate() function, which allows us to change columns, and supply it with an if_else() function so that it ONLY changes values in the fips column (from NA to 36061) if the value for county in the same row is "New York City":

```{r}
cases_latest <- cases_latest %>%
    mutate(fips = if_else(county == "New York City", true = "36061", false = fips))
```

Now that we've modified our data table to incorporate New York City, let's use inner_join() so that we know each county has BOTH case counts and mask data.
```{r, include=FALSE}
cases_masks <- inner_join(x=cases_latest, y=mask_use_long, by="fips")
```

Another thing you will notice is that there are only one cases and deaths value for each county, but these get replicated 5 times because we have 5 mask use responses for each county. This kind of looks awkward, but it's not necessarily bad to have it this way as long as we analyze our data appropriately and take this into account as we do below. Again, depending on the analysis, we could also just keep these data in wide format so that the data don't get duplicated.

Since these data are all from the same date, we don't need this column anymore as it's not informative. Also, we can get rid of the county FIPS code column since we only used that information to combine the cases data and the mask data. Now that we've done that, we don't need that information either

```{r, include=FALSE}
cases_masks <- cases_masks %>% dplyr::select(-date, -fips)
```

Let's use some basic tidyverse functions to superficially explore these data, just scratching the surface.

Which counties use masks most frequently?
```{r}
cases_masks %>% filter(MaskUseResponse == "ALWAYS") %>%
  arrange(desc(MaskUseProportion), county) %>%
  head(n=20)
```

Which counties use masks least frequently?
```{r}
cases_masks %>% filter(MaskUseResponse == "NEVER") %>%
  arrange(desc(MaskUseProportion), county) %>%
  head(n=10)
```

For the counties with the most cases, how frequently are people ALWAYS wearing masks?
```{r}
cases_masks %>% filter(MaskUseResponse == "ALWAYS") %>%
  arrange(desc(cases), county) %>%
  head(n=10)
```

# Summary
Key tidyverse functions used today:
- read_delim() to read files in and convert them to a tibble, a special kind of data table
- pivot_longer()/pivot_wider() to convert data between long and wide format
- rename() to change the names of particular columns
- arrange() to sort the tibble according to values in particular columns
- filter() to select particular rows that meet specific conditions
- mutate(), with if_else() to modify particular cells in a table!
 - select() to select specific columns to keep

This is just taking quick peeks at these data. In the next session, we'll review and learn some more tidyverse tools to manipulate data, but we'll also learn some easy-to-use functions that allow us to analyze these data in more complex ways, using relatively little code!










END OF WORKSHOP, PERSONAL NOTES:

What is mask use behavior like in places with many cases? Let's first quickly peek at the distributions for cases and mask-wearing
```{r}
cases_masks %>% 
  ggplot(aes(cases)) + geom_histogram()
cases_masks %>% filter(MaskUseResponse == "ALWAYS") %>% 
  ggplot(aes(MaskUseProportion)) +
  geom_histogram()
```

```{r}
mask_wearers <- cases_masks %>% filter(MaskUseResponse == "ALWAYS") %>%
  arrange(desc(MaskUseProportion), county) %>%
  head(n=50)
mean(mask_wearers$cases)
live_free_or_die <- cases_masks %>% filter(MaskUseResponse == "NEVER") %>%
  arrange(desc(MaskUseProportion), county) %>%
  head(n=50)
mean(live_free_or_die$cases)
```




================================================
FILE: tidyverse_covid_data/part2/binder/Dockerfile
================================================
FROM rocker/binder:3.6.3

COPY --chown=rstudio:rstudio ./tidyverse_covid_data/part2/ ${HOME}


================================================
FILE: tidyverse_covid_data/part2/mask-use-by-county.csv
================================================
COUNTYFP,NEVER,RARELY,SOMETIMES,FREQUENTLY,ALWAYS
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01007,0.02,0.034,0.096,0.278,0.572
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01011,0.031,0.04,0.144,0.286,0.5
01013,0.102,0.053,0.257,0.137,0.451
01015,0.152,0.108,0.13,0.167,0.442
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01037,0.055,0.131,0.167,0.263,0.384
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02100,0.055,0.05,0.123,0.419,0.352
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02110,0.051,0.067,0.133,0.396,0.353
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17155,0.021,0.025,0.168,0.251,0.535
17157,0.008,0.032,0.117,0.391,0.452
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18003,0.07,0.14,0.099,0.228,0.464
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18009,0.093,0.152,0.106,0.203,0.447
18011,0.052,0.085,0.142,0.308,0.413
18013,0.055,0.061,0.041,0.184,0.66
18015,0.059,0.104,0.17,0.22,0.447
18017,0.037,0.192,0.283,0.206,0.283
18019,0.068,0.06,0.141,0.228,0.501
18021,0.122,0.073,0.112,0.226,0.466
18023,0.088,0.033,0.132,0.285,0.462
18025,0.067,0.089,0.118,0.261,0.465
18027,0.049,0.061,0.13,0.214,0.546
18029,0.083,0.1,0.168,0.277,0.371
18031,0.175,0.083,0.101,0.204,0.437
18033,0.109,0.074,0.118,0.149,0.55
18035,0.095,0.109,0.124,0.197,0.475
18037,0.01,0.105,0.086,0.25,0.55
18039,0.03,0.097,0.094,0.252,0.528
18041,0.161,0.041,0.048,0.237,0.514
18043,0.053,0.086,0.154,0.233,0.474
18045,0.077,0.089,0.147,0.212,0.474
18047,0.07,0.082,0.104,0.199,0.545
18049,0.142,0.114,0.128,0.217,0.399
18051,0.09,0.1,0.204,0.255,0.351
18053,0.053,0.108,0.133,0.311,0.393
18055,0.099,0.027,0.081,0.235,0.558
18057,0.07,0.051,0.049,0.234,0.596
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18061,0.094,0.084,0.205,0.258,0.359
18063,0.054,0.052,0.113,0.257,0.523
18065,0.183,0.148,0.125,0.111,0.434
18067,0.008,0.083,0.183,0.301,0.426
18069,0.15,0.182,0.053,0.199,0.416
18071,0.097,0.098,0.105,0.388,0.312
18073,0.186,0.1,0.065,0.328,0.321
18075,0.141,0.139,0.157,0.235,0.328
18077,0.083,0.219,0.218,0.225,0.255
18079,0.095,0.105,0.172,0.308,0.319
18081,0.057,0.094,0.1,0.192,0.557
18083,0.158,0.125,0.121,0.259,0.338
18085,0.073,0.147,0.089,0.195,0.496
18087,0.059,0.082,0.171,0.236,0.452
18089,0.043,0.057,0.088,0.219,0.592
18091,0.084,0.075,0.168,0.206,0.466
18093,0.092,0.117,0.109,0.191,0.491
18095,0.062,0.084,0.15,0.145,0.559
18097,0.051,0.053,0.116,0.232,0.548
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18101,0.041,0.064,0.123,0.223,0.55
18103,0.054,0.125,0.253,0.226,0.343
18105,0.037,0.037,0.094,0.242,0.591
18107,0.07,0.116,0.168,0.156,0.49
18109,0.06,0.131,0.136,0.188,0.485
18111,0.102,0.153,0.092,0.338,0.316
18113,0.057,0.11,0.135,0.197,0.5
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18119,0.094,0.038,0.087,0.282,0.5
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18133,0.075,0.162,0.204,0.194,0.366
18135,0.128,0.13,0.228,0.179,0.336
18137,0.054,0.074,0.15,0.294,0.429
18139,0.133,0.118,0.063,0.279,0.408
18141,0.031,0.04,0.102,0.228,0.598
18143,0.036,0.073,0.168,0.263,0.46
18145,0.16,0.057,0.177,0.193,0.413
18147,0.017,0.134,0.104,0.216,0.529
18149,0.177,0.151,0.088,0.219,0.365
18151,0.04,0.089,0.17,0.169,0.532
18153,0.127,0.071,0.077,0.207,0.517
18155,0.089,0.125,0.209,0.234,0.344
18157,0.093,0.083,0.102,0.204,0.518
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18175,0.097,0.074,0.062,0.356,0.411
18177,0.162,0.09,0.138,0.261,0.348
18179,0.205,0.05,0.077,0.337,0.332
18181,0.056,0.177,0.194,0.189,0.384
18183,0.061,0.186,0.132,0.16,0.46
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19003,0.148,0.11,0.15,0.311,0.281
19005,0.138,0.049,0.132,0.212,0.469
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19017,0.048,0.062,0.197,0.268,0.424
19019,0.021,0.124,0.087,0.394,0.373
19021,0.183,0.062,0.26,0.207,0.288
19023,0.052,0.094,0.208,0.23,0.417
19025,0.212,0.117,0.213,0.153,0.304
19027,0.153,0.11,0.241,0.139,0.358
19029,0.341,0.07,0.124,0.238,0.227
19031,0.026,0.096,0.142,0.355,0.381
19033,0.061,0.365,0.146,0.213,0.215
19035,0.183,0.105,0.221,0.182,0.309
19037,0.075,0.136,0.197,0.236,0.356
19039,0.078,0.167,0.143,0.194,0.418
19041,0.224,0.035,0.112,0.189,0.441
19043,0.123,0.084,0.137,0.284,0.371
19045,0.092,0.13,0.079,0.269,0.43
19047,0.089,0.045,0.141,0.064,0.66
19049,0.036,0.102,0.118,0.26,0.485
19051,0.038,0.101,0.249,0.201,0.411
19053,0.248,0.132,0.258,0.068,0.294
19055,0.035,0.137,0.137,0.408,0.283
19057,0.135,0.081,0.109,0.122,0.553
19059,0.276,0.027,0.091,0.173,0.433
19061,0.037,0.155,0.189,0.189,0.43
19063,0.241,0.083,0.067,0.181,0.428
19065,0.054,0.075,0.128,0.349,0.394
19067,0.063,0.271,0.148,0.25,0.268
19069,0.088,0.278,0.187,0.186,0.261
19071,0.048,0.061,0.071,0.209,0.611
19073,0.116,0.141,0.235,0.153,0.354
19075,0.038,0.042,0.203,0.238,0.48
19077,0.162,0.073,0.143,0.283,0.34
19079,0.143,0.118,0.129,0.153,0.457
19081,0.08,0.384,0.146,0.174,0.216
19083,0.074,0.075,0.195,0.214,0.441
19085,0.118,0.044,0.094,0.178,0.565
19087,0.123,0.142,0.091,0.183,0.461
19089,0.077,0.151,0.176,0.183,0.413
19091,0.243,0.268,0.147,0.102,0.24
19093,0.153,0.094,0.246,0.128,0.38
19095,0.027,0.149,0.135,0.239,0.45
19097,0.032,0.096,0.229,0.232,0.411
19099,0.102,0.105,0.233,0.212,0.347
19101,0.074,0.116,0.221,0.191,0.398
19103,0.014,0.044,0.069,0.2,0.673
19105,0.04,0.105,0.165,0.336,0.355
19107,0.035,0.126,0.287,0.28,0.272
19109,0.135,0.318,0.101,0.173,0.272
19111,0.17,0.146,0.131,0.161,0.392
19113,0.039,0.081,0.115,0.201,0.563
19115,0.097,0.058,0.15,0.214,0.482
19117,0.032,0.204,0.196,0.185,0.384
19119,0.078,0.173,0.157,0.247,0.344
19121,0.01,0.102,0.136,0.268,0.484
19123,0.069,0.067,0.294,0.283,0.287
19125,0.053,0.049,0.209,0.26,0.429
19127,0.077,0.064,0.153,0.226,0.48
19129,0.064,0.084,0.091,0.281,0.481
19131,0.046,0.282,0.149,0.26,0.263
19133,0.078,0.069,0.087,0.127,0.64
19135,0.034,0.078,0.256,0.247,0.385
19137,0.218,0.034,0.084,0.378,0.286
19139,0.12,0.022,0.221,0.273,0.364
19141,0.173,0.054,0.267,0.187,0.319
19143,0.195,0.051,0.233,0.199,0.322
19145,0.088,0.053,0.068,0.31,0.481
19147,0.199,0.116,0.088,0.179,0.419
19149,0.125,0.145,0.166,0.162,0.402
19151,0.214,0.124,0.162,0.165,0.335
19153,0.044,0.089,0.136,0.259,0.473
19155,0.115,0.083,0.147,0.264,0.39
19157,0.121,0.145,0.187,0.164,0.383
19159,0.144,0.138,0.281,0.162,0.275
19161,0.18,0.071,0.286,0.159,0.303
19163,0.075,0.027,0.126,0.277,0.494
19165,0.099,0.059,0.182,0.145,0.516
19167,0.12,0.06,0.285,0.2,0.335
19169,0.045,0.075,0.071,0.235,0.574
19171,0.066,0.053,0.209,0.181,0.491
19173,0.09,0.151,0.185,0.282,0.292
19175,0.03,0.125,0.172,0.21,0.462
19177,0.107,0.128,0.175,0.218,0.372
19179,0.034,0.082,0.293,0.221,0.37
19181,0.055,0.182,0.181,0.245,0.337
19183,0.035,0.102,0.123,0.151,0.589
19185,0.138,0.148,0.252,0.12,0.342
19187,0.209,0.167,0.161,0.101,0.362
19189,0.07,0.329,0.176,0.197,0.228
19191,0.122,0.044,0.151,0.177,0.505
19193,0.113,0.165,0.137,0.16,0.425
19195,0.072,0.271,0.175,0.231,0.251
19197,0.172,0.299,0.173,0.125,0.232
20001,0.153,0.212,0.126,0.167,0.341
20003,0.189,0.063,0.137,0.267,0.344
20005,0.137,0.122,0.169,0.256,0.316
20007,0.069,0.147,0.118,0.329,0.336
20009,0.107,0.131,0.12,0.211,0.43
20011,0.167,0.146,0.077,0.12,0.49
20013,0.089,0.149,0.183,0.197,0.382
20015,0.053,0.133,0.122,0.222,0.47
20017,0.151,0.082,0.212,0.225,0.331
20019,0.128,0.137,0.185,0.214,0.337
20021,0.131,0.121,0.164,0.174,0.41
20023,0.065,0.27,0.174,0.235,0.256
20025,0.163,0.13,0.126,0.232,0.348
20027,0.081,0.069,0.115,0.229,0.507
20029,0.154,0.077,0.118,0.217,0.434
20031,0.085,0.119,0.165,0.404,0.227
20033,0.112,0.144,0.128,0.271,0.346
20035,0.075,0.093,0.28,0.131,0.421
20037,0.108,0.143,0.118,0.139,0.492
20039,0.163,0.118,0.211,0.235,0.273
20041,0.106,0.118,0.148,0.243,0.385
20043,0.137,0.193,0.184,0.214,0.272
20045,0.006,0.009,0.024,0.227,0.734
20047,0.052,0.086,0.135,0.221,0.506
20049,0.061,0.05,0.295,0.282,0.312
20051,0.154,0.154,0.224,0.148,0.32
20053,0.174,0.133,0.133,0.187,0.373
20055,0.166,0.089,0.163,0.239,0.343
20057,0.143,0.084,0.143,0.232,0.399
20059,0.063,0.06,0.159,0.354,0.364
20061,0.05,0.068,0.113,0.27,0.499
20063,0.24,0.089,0.234,0.151,0.287
20065,0.218,0.156,0.247,0.129,0.25
20067,0.14,0.108,0.15,0.231,0.37
20069,0.158,0.092,0.146,0.24,0.364
20071,0.135,0.131,0.181,0.229,0.323
20073,0.034,0.079,0.256,0.302,0.33
20075,0.131,0.095,0.189,0.217,0.368
20077,0.079,0.118,0.21,0.193,0.4
20079,0.075,0.058,0.121,0.323,0.423
20081,0.153,0.107,0.137,0.239,0.364
20083,0.126,0.075,0.163,0.225,0.412
20085,0.01,0.116,0.161,0.136,0.577
20087,0.026,0.069,0.09,0.182,0.634
20089,0.171,0.142,0.087,0.259,0.34
20091,0.029,0.019,0.075,0.224,0.654
20093,0.155,0.098,0.172,0.229,0.346
20095,0.081,0.212,0.114,0.128,0.464
20097,0.062,0.107,0.128,0.233,0.469
20099,0.168,0.123,0.091,0.147,0.471
20101,0.197,0.075,0.197,0.204,0.326
20103,0.087,0.135,0.092,0.142,0.544
20105,0.171,0.132,0.16,0.163,0.375
20107,0.14,0.053,0.121,0.285,0.4
20109,0.206,0.131,0.194,0.189,0.28
20111,0.113,0.111,0.133,0.3,0.344
20113,0.12,0.065,0.106,0.313,0.396
20115,0.101,0.068,0.209,0.279,0.343
20117,0.048,0.049,0.233,0.263,0.407
20119,0.164,0.123,0.115,0.235,0.362
20121,0.071,0.044,0.095,0.25,0.54
20123,0.173,0.116,0.144,0.18,0.388
20125,0.164,0.132,0.102,0.195,0.407
20127,0.11,0.094,0.148,0.244,0.404
20129,0.12,0.113,0.144,0.218,0.405
20131,0.035,0.094,0.203,0.236,0.432
20133,0.133,0.204,0.12,0.127,0.416
20135,0.15,0.083,0.199,0.198,0.371
20137,0.161,0.163,0.177,0.225,0.275
20139,0.018,0.09,0.088,0.24,0.564
20141,0.174,0.233,0.163,0.136,0.293
20143,0.131,0.117,0.164,0.191,0.396
20145,0.053,0.096,0.137,0.217,0.497
20147,0.15,0.244,0.126,0.19,0.29
20149,0.012,0.057,0.145,0.236,0.55
20151,0.02,0.183,0.101,0.309,0.387
20153,0.128,0.174,0.209,0.22,0.268
20155,0.135,0.077,0.143,0.191,0.454
20157,0.183,0.073,0.076,0.295,0.373
20159,0.153,0.086,0.11,0.241,0.41
20161,0.03,0.05,0.124,0.261,0.535
20163,0.179,0.22,0.218,0.123,0.26
20165,0.11,0.122,0.182,0.183,0.403
20167,0.16,0.174,0.174,0.157,0.335
20169,0.126,0.133,0.175,0.209,0.357
20171,0.197,0.09,0.19,0.21,0.313
20173,0.06,0.055,0.126,0.26,0.499
20175,0.138,0.14,0.092,0.226,0.403
20177,0.009,0.078,0.095,0.184,0.633
20179,0.231,0.112,0.236,0.154,0.267
20181,0.1,0.27,0.159,0.24,0.231
20183,0.142,0.257,0.088,0.195,0.319
20185,0.041,0.125,0.109,0.252,0.472
20187,0.127,0.098,0.172,0.216,0.388
20189,0.122,0.132,0.112,0.225,0.408
20191,0.104,0.039,0.115,0.298,0.444
20193,0.188,0.157,0.208,0.184,0.263
20195,0.203,0.115,0.256,0.137,0.291
20197,0.011,0.077,0.12,0.201,0.591
20199,0.128,0.21,0.154,0.246,0.262
20201,0.148,0.075,0.111,0.234,0.432
20203,0.173,0.112,0.184,0.219,0.313
20205,0.125,0.148,0.145,0.184,0.398
20207,0.092,0.175,0.171,0.247,0.314
20209,0.025,0.056,0.076,0.178,0.665
21001,0.076,0.115,0.167,0.243,0.399
21003,0.101,0.072,0.097,0.211,0.519
21005,0.062,0.01,0.044,0.483,0.401
21007,0.107,0.033,0.198,0.257,0.404
21009,0.053,0.093,0.09,0.176,0.588
21011,0.076,0.058,0.084,0.339,0.443
21013,0.146,0.094,0.12,0.199,0.44
21015,0.08,0.087,0.081,0.232,0.52
21017,0.024,0.037,0.042,0.129,0.769
21019,0.084,0.125,0.202,0.235,0.354
21021,0.092,0.037,0.112,0.161,0.598
21023,0.056,0.249,0.065,0.238,0.392
21025,0.114,0.081,0.126,0.241,0.438
21027,0.023,0.146,0.177,0.138,0.517
21029,0.037,0.091,0.156,0.181,0.535
21031,0.066,0.097,0.133,0.17,0.535
21033,0.059,0.067,0.26,0.177,0.438
21035,0.066,0.084,0.248,0.17,0.432
21037,0.073,0.085,0.134,0.278,0.43
21039,0.104,0.045,0.188,0.236,0.428
21041,0.155,0.171,0.12,0.208,0.346
21043,0.069,0.102,0.122,0.324,0.383
21045,0.164,0.076,0.161,0.154,0.444
21047,0.06,0.118,0.178,0.279,0.365
21049,0.083,0.093,0.11,0.233,0.48
21051,0.153,0.082,0.165,0.16,0.44
21053,0.096,0.115,0.248,0.165,0.375
21055,0.121,0.117,0.29,0.153,0.319
21057,0.067,0.106,0.224,0.229,0.374
21059,0.055,0.057,0.122,0.236,0.531
21061,0.055,0.057,0.086,0.179,0.624
21063,0.117,0.1,0.051,0.289,0.443
21065,0.082,0.151,0.09,0.275,0.402
21067,0.031,0.051,0.066,0.202,0.65
21069,0.068,0.069,0.075,0.287,0.502
21071,0.131,0.066,0.129,0.134,0.54
21073,0.088,0.036,0.142,0.214,0.521
21075,0.1,0.102,0.202,0.217,0.38
21077,0.076,0.048,0.198,0.211,0.466
21079,0.077,0.043,0.063,0.276,0.54
21081,0.047,0.056,0.183,0.106,0.608
21083,0.103,0.069,0.266,0.222,0.34
21085,0.043,0.042,0.134,0.154,0.627
21087,0.063,0.076,0.127,0.247,0.488
21089,0.092,0.101,0.236,0.228,0.342
21091,0.019,0.08,0.121,0.235,0.546
21093,0.057,0.062,0.106,0.217,0.558
21095,0.076,0.093,0.098,0.126,0.606
21097,0.073,0.044,0.031,0.047,0.804
21099,0.053,0.034,0.118,0.185,0.61
21101,0.072,0.061,0.136,0.229,0.501
21103,0.093,0.082,0.174,0.254,0.398
21105,0.097,0.065,0.221,0.202,0.415
21107,0.04,0.067,0.174,0.217,0.502
21109,0.183,0.068,0.092,0.235,0.421
21111,0.036,0.048,0.083,0.23,0.602
21113,0.022,0.036,0.104,0.21,0.628
21115,0.102,0.053,0.106,0.127,0.612
21117,0.052,0.056,0.146,0.245,0.5
21119,0.157,0.073,0.13,0.142,0.497
21121,0.187,0.073,0.103,0.165,0.471
21123,0.074,0.053,0.09,0.213,0.57
21125,0.201,0.047,0.083,0.152,0.517
21127,0.155,0.091,0.151,0.125,0.479
21129,0.129,0.069,0.129,0.273,0.399
21131,0.094,0.103,0.142,0.174,0.487
21133,0.078,0.05,0.127,0.122,0.624
21135,0.057,0.081,0.21,0.296,0.357
21137,0.172,0.054,0.093,0.172,0.509
21139,0.08,0.1,0.29,0.191,0.339
21141,0.099,0.1,0.08,0.198,0.523
21143,0.052,0.11,0.316,0.138,0.384
21145,0.085,0.058,0.232,0.255,0.37
21147,0.096,0.116,0.143,0.203,0.441
21149,0.081,0.105,0.107,0.249,0.459
21151,0.088,0.094,0.114,0.233,0.471
21153,0.107,0.078,0.111,0.164,0.54
21155,0.046,0.082,0.074,0.144,0.654
21157,0.061,0.083,0.28,0.18,0.395
21159,0.108,0.055,0.096,0.104,0.637
21161,0.056,0.119,0.061,0.284,0.479
21163,0.018,0.154,0.115,0.154,0.559
21165,0.161,0.088,0.067,0.215,0.468
21167,0.047,0.012,0.178,0.195,0.567
21169,0.048,0.109,0.134,0.222,0.487
21171,0.082,0.134,0.185,0.15,0.448
21173,0.03,0.041,0.085,0.392,0.452
21175,0.133,0.116,0.071,0.182,0.498
21177,0.035,0.154,0.123,0.2,0.488
21179,0.033,0.106,0.068,0.244,0.548
21181,0.023,0.025,0.09,0.271,0.591
21183,0.064,0.145,0.109,0.19,0.492
21185,0.025,0.039,0.098,0.278,0.56
21187,0.091,0.039,0.114,0.198,0.558
21189,0.121,0.058,0.157,0.282,0.382
21191,0.035,0.121,0.103,0.129,0.613
21193,0.123,0.093,0.111,0.171,0.502
21195,0.096,0.088,0.123,0.148,0.545
21197,0.112,0.099,0.102,0.232,0.455
21199,0.13,0.123,0.096,0.164,0.487
21201,0.048,0.123,0.039,0.204,0.586
21203,0.215,0.079,0.075,0.157,0.474
21205,0.159,0.097,0.047,0.231,0.467
21207,0.096,0.138,0.195,0.206,0.365
21209,0.04,0.044,0.076,0.081,0.759
21211,0.092,0.107,0.054,0.261,0.487
21213,0.137,0.066,0.162,0.208,0.426
21215,0.032,0.058,0.047,0.41,0.453
21217,0.073,0.073,0.111,0.21,0.533
21219,0.058,0.147,0.135,0.266,0.394
21221,0.066,0.053,0.131,0.152,0.597
21223,0.134,0.172,0.135,0.217,0.342
21225,0.211,0.079,0.229,0.172,0.309
21227,0.076,0.067,0.063,0.185,0.609
21229,0.035,0.066,0.116,0.221,0.562
21231,0.102,0.151,0.16,0.169,0.417
21233,0.088,0.084,0.167,0.21,0.451
21235,0.176,0.062,0.113,0.174,0.476
21237,0.14,0.095,0.114,0.203,0.448
21239,0.042,0.017,0.049,0.193,0.698
22001,0.122,0.111,0.138,0.265,0.362
22003,0.196,0.07,0.118,0.2,0.416
22005,0.049,0.092,0.142,0.148,0.569
22007,0.079,0.034,0.213,0.206,0.468
22009,0.13,0.092,0.073,0.246,0.459
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22013,0.078,0.052,0.17,0.291,0.409
22015,0.068,0.096,0.144,0.225,0.468
22017,0.058,0.079,0.158,0.209,0.497
22019,0.102,0.139,0.077,0.212,0.471
22021,0.102,0.061,0.098,0.241,0.498
22023,0.089,0.127,0.079,0.201,0.505
22025,0.11,0.056,0.086,0.147,0.6
22027,0.146,0.068,0.04,0.273,0.473
22029,0.11,0.104,0.112,0.095,0.579
22031,0.035,0.075,0.132,0.162,0.596
22033,0.052,0.031,0.09,0.183,0.642
22035,0.022,0.046,0.121,0.137,0.674
22037,0.067,0.053,0.095,0.366,0.42
22039,0.215,0.077,0.111,0.211,0.387
22041,0.123,0.054,0.102,0.156,0.565
22043,0.086,0.041,0.109,0.229,0.535
22045,0.112,0.084,0.085,0.253,0.467
22047,0.061,0.021,0.084,0.19,0.644
22049,0.116,0.083,0.127,0.29,0.383
22051,0.044,0.031,0.066,0.174,0.685
22053,0.249,0.111,0.09,0.196,0.354
22055,0.062,0.083,0.105,0.217,0.533
22057,0.078,0.039,0.159,0.151,0.573
22059,0.082,0.035,0.089,0.223,0.571
22061,0.102,0.08,0.111,0.29,0.417
22063,0.06,0.043,0.144,0.29,0.463
22065,0.034,0.068,0.109,0.172,0.617
22067,0.039,0.105,0.124,0.166,0.566
22069,0.096,0.063,0.113,0.167,0.561
22071,0.019,0.034,0.087,0.206,0.654
22073,0.076,0.098,0.121,0.229,0.476
22075,0.065,0.035,0.165,0.222,0.513
22077,0.044,0.082,0.068,0.198,0.608
22079,0.106,0.046,0.087,0.236,0.524
22081,0.068,0.081,0.135,0.189,0.527
22083,0.066,0.08,0.126,0.194,0.534
22085,0.098,0.08,0.113,0.145,0.564
22087,0.035,0.075,0.136,0.23,0.524
22089,0.093,0.048,0.027,0.165,0.667
22091,0.064,0.126,0.138,0.237,0.434
22093,0.034,0.099,0.202,0.061,0.603
22095,0.096,0.059,0.19,0.079,0.577
22097,0.064,0.105,0.117,0.199,0.515
22099,0.069,0.11,0.096,0.178,0.548
22101,0.066,0.02,0.179,0.28,0.454
22103,0.031,0.073,0.074,0.211,0.611
22105,0.066,0.072,0.103,0.193,0.566
22107,0.081,0.064,0.097,0.134,0.625
22109,0.068,0.052,0.14,0.221,0.519
22111,0.071,0.142,0.106,0.244,0.436
22113,0.076,0.088,0.115,0.271,0.449
22115,0.092,0.066,0.145,0.244,0.453
22117,0.103,0.046,0.148,0.242,0.461
22119,0.134,0.044,0.063,0.265,0.494
22121,0.044,0.04,0.07,0.197,0.649
22123,0.022,0.063,0.124,0.114,0.677
22125,0.072,0.13,0.037,0.265,0.496
22127,0.086,0.106,0.13,0.236,0.443
23001,0.083,0.099,0.041,0.199,0.577
23003,0.143,0.047,0.12,0.243,0.447
23005,0.05,0.053,0.046,0.182,0.669
23007,0.006,0.063,0.191,0.161,0.579
23009,0.034,0.052,0.118,0.202,0.594
23011,0.016,0.074,0.125,0.245,0.54
23013,0.007,0.01,0.111,0.248,0.625
23015,0.031,0.014,0.065,0.204,0.688
23017,0.069,0.065,0.064,0.213,0.589
23019,0.038,0.071,0.085,0.189,0.617
23021,0.062,0.146,0.123,0.189,0.479
23023,0.061,0.026,0.033,0.216,0.664
23025,0.035,0.095,0.142,0.17,0.558
23027,0.023,0.06,0.15,0.182,0.585
23029,0.026,0.032,0.126,0.204,0.612
23031,0.076,0.036,0.061,0.173,0.655
24001,0.007,0.01,0.035,0.213,0.736
24003,0.018,0.022,0.067,0.154,0.739
24005,0.013,0.024,0.043,0.142,0.777
24009,0.049,0.049,0.071,0.11,0.72
24011,0.009,0.019,0.033,0.168,0.77
24013,0.014,0.008,0.062,0.17,0.746
24015,0.009,0.018,0.054,0.182,0.738
24017,0.035,0.034,0.107,0.157,0.667
24019,0.02,0.006,0.011,0.119,0.844
24021,0.028,0.036,0.062,0.113,0.762
24023,0.077,0.008,0.04,0.305,0.571
24025,0.008,0.009,0.037,0.168,0.778
24027,0.024,0.015,0.067,0.17,0.723
24029,0.005,0.014,0.06,0.207,0.714
24031,0.025,0.029,0.041,0.128,0.777
24033,0.087,0.034,0.064,0.094,0.72
24035,0.002,0.042,0.041,0.309,0.605
24037,0.04,0.009,0.036,0.152,0.763
24039,0.004,0.01,0.047,0.128,0.812
24041,0.012,0.012,0.006,0.127,0.843
24043,0.031,0.047,0.085,0.194,0.642
24045,0.006,0.011,0.063,0.116,0.804
24047,0.003,0.004,0.042,0.11,0.842
24510,0.024,0.032,0.063,0.148,0.733
25001,0.019,0.007,0.026,0.109,0.839
25003,0.03,0.004,0.049,0.082,0.834
25005,0.016,0.02,0.056,0.161,0.747
25007,0.006,0.01,0.037,0.16,0.786
25009,0.023,0.01,0.047,0.131,0.788
25011,0.043,0.011,0.028,0.156,0.762
25013,0.013,0.017,0.055,0.13,0.786
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25017,0.023,0.011,0.041,0.127,0.798
25019,0.015,0.003,0.034,0.156,0.792
25021,0.02,0.015,0.035,0.125,0.805
25023,0.014,0.017,0.045,0.112,0.812
25025,0.02,0.007,0.049,0.136,0.788
25027,0.02,0.027,0.043,0.142,0.768
26001,0.065,0.046,0.133,0.18,0.576
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26013,0.012,0.063,0.109,0.257,0.558
26015,0.043,0.121,0.121,0.211,0.504
26017,0.014,0.055,0.103,0.202,0.627
26019,0.01,0.049,0.062,0.164,0.715
26021,0.039,0.037,0.122,0.247,0.555
26023,0.072,0.091,0.205,0.234,0.399
26025,0.063,0.121,0.078,0.178,0.559
26027,0.046,0.076,0.123,0.274,0.48
26029,0.027,0.057,0.085,0.205,0.626
26031,0.027,0.052,0.086,0.252,0.584
26033,0.03,0.022,0.068,0.287,0.593
26035,0.05,0.026,0.102,0.308,0.514
26037,0.033,0.056,0.046,0.263,0.603
26039,0.043,0.05,0.061,0.181,0.666
26041,0.058,0.023,0.17,0.217,0.532
26043,0.043,0.065,0.127,0.249,0.516
26045,0.047,0.039,0.147,0.247,0.52
26047,0.025,0.062,0.075,0.219,0.618
26049,0.032,0.043,0.104,0.288,0.533
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37091,0.02,0.007,0.06,0.217,0.696
37093,0.009,0.047,0.092,0.236,0.616
37095,0.133,0.03,0.08,0.259,0.498
37097,0.08,0.051,0.106,0.219,0.544
37099,0.077,0.065,0.067,0.176,0.616
37101,0.032,0.022,0.113,0.151,0.682
37103,0.064,0.051,0.208,0.233,0.445
37105,0.066,0.102,0.081,0.115,0.636
37107,0.109,0.041,0.112,0.228,0.51
37109,0.029,0.09,0.084,0.234,0.563
37111,0.075,0.114,0.105,0.143,0.563
37113,0.153,0.039,0.085,0.182,0.541
37115,0.016,0.034,0.08,0.178,0.692
37117,0.084,0.089,0.071,0.15,0.605
37119,0.029,0.037,0.075,0.166,0.694
37121,0.096,0.035,0.146,0.154,0.569
37123,0.027,0.056,0.078,0.184,0.655
37125,0.039,0.105,0.041,0.237,0.578
37127,0.044,0.032,0.038,0.212,0.673
37129,0.019,0.032,0.032,0.25,0.667
37131,0.017,0.003,0.067,0.201,0.712
37133,0.039,0.039,0.159,0.254,0.509
37135,0.01,0.02,0.028,0.142,0.801
37137,0.036,0.057,0.181,0.243,0.482
37139,0.067,0.021,0.11,0.238,0.563
37141,0.05,0.035,0.142,0.169,0.603
37143,0.036,0.014,0.154,0.299,0.498
37145,0.01,0.008,0.07,0.166,0.746
37147,0.034,0.065,0.085,0.204,0.612
37149,0.018,0.115,0.105,0.263,0.499
37151,0.066,0.078,0.105,0.138,0.613
37153,0.05,0.039,0.145,0.153,0.612
37155,0.062,0.031,0.116,0.165,0.626
37157,0.125,0.094,0.124,0.111,0.546
37159,0.086,0.065,0.131,0.175,0.542
37161,0.045,0.17,0.098,0.156,0.532
37163,0.006,0.005,0.084,0.197,0.708
37165,0.137,0.029,0.178,0.199,0.458
37167,0.029,0.083,0.097,0.217,0.574
37169,0.015,0.034,0.116,0.247,0.588
37171,0.019,0.041,0.1,0.382,0.458
37173,0.079,0.061,0.086,0.247,0.528
37175,0.025,0.013,0.047,0.224,0.691
37177,0.054,0.016,0.103,0.301,0.527
37179,0.06,0.045,0.067,0.213,0.615
37181,0.042,0.031,0.137,0.159,0.631
37183,0.015,0.02,0.07,0.168,0.727
37185,0.027,0.024,0.105,0.158,0.686
37187,0.113,0.053,0.057,0.301,0.476
37189,0.062,0.01,0.072,0.363,0.493
37191,0.017,0.018,0.09,0.132,0.744
37193,0.046,0.071,0.164,0.208,0.511
37195,0.013,0.067,0.081,0.248,0.59
37197,0.073,0.027,0.08,0.199,0.621
37199,0.028,0.019,0.063,0.224,0.665
38001,0.219,0.166,0.189,0.233,0.193
38003,0.111,0.079,0.137,0.248,0.425
38005,0.289,0.194,0.118,0.195,0.204
38007,0.2,0.201,0.137,0.244,0.218
38009,0.206,0.119,0.142,0.259,0.274
38011,0.188,0.141,0.146,0.262,0.263
38013,0.191,0.147,0.151,0.258,0.253
38015,0.172,0.233,0.091,0.297,0.207
38017,0.09,0.113,0.142,0.233,0.423
38019,0.146,0.272,0.165,0.222,0.195
38021,0.151,0.077,0.227,0.277,0.267
38023,0.186,0.168,0.163,0.255,0.227
38025,0.195,0.189,0.125,0.252,0.239
38027,0.269,0.159,0.117,0.209,0.246
38029,0.16,0.221,0.094,0.302,0.222
38031,0.265,0.081,0.081,0.27,0.304
38033,0.187,0.206,0.149,0.256,0.203
38035,0.097,0.122,0.222,0.253,0.305
38037,0.177,0.233,0.125,0.275,0.19
38039,0.162,0.054,0.197,0.217,0.37
38041,0.218,0.207,0.15,0.232,0.193
38043,0.193,0.206,0.074,0.301,0.226
38045,0.115,0.07,0.193,0.307,0.315
38047,0.172,0.156,0.104,0.295,0.272
38049,0.206,0.132,0.124,0.263,0.275
38051,0.169,0.128,0.136,0.275,0.291
38053,0.19,0.182,0.142,0.253,0.232
38055,0.189,0.185,0.107,0.273,0.246
38057,0.189,0.197,0.111,0.266,0.237
38059,0.172,0.235,0.096,0.293,0.205
38061,0.192,0.16,0.134,0.258,0.256
38063,0.133,0.183,0.193,0.218,0.274
38065,0.181,0.217,0.102,0.28,0.221
38067,0.08,0.216,0.202,0.256,0.247
38069,0.253,0.136,0.12,0.246,0.246
38071,0.242,0.25,0.138,0.175,0.195
38073,0.073,0.114,0.223,0.236,0.354
38075,0.193,0.13,0.141,0.262,0.274
38077,0.155,0.174,0.189,0.116,0.366
38079,0.248,0.154,0.135,0.235,0.228
38081,0.081,0.163,0.249,0.251,0.257
38083,0.203,0.186,0.091,0.285,0.235
38085,0.157,0.228,0.108,0.295,0.211
38087,0.217,0.179,0.142,0.246,0.215
38089,0.207,0.205,0.129,0.24,0.219
38091,0.172,0.061,0.302,0.182,0.283
38093,0.202,0.053,0.076,0.314,0.354
38095,0.263,0.232,0.127,0.198,0.18
38097,0.144,0.115,0.205,0.233,0.303
38099,0.087,0.181,0.208,0.253,0.271
38101,0.195,0.138,0.127,0.263,0.276
38103,0.254,0.163,0.083,0.275,0.226
38105,0.186,0.182,0.155,0.254,0.223
39001,0.074,0.116,0.149,0.222,0.439
39003,0.085,0.104,0.114,0.382,0.314
39005,0.068,0.035,0.268,0.19,0.438
39007,0.144,0.008,0.099,0.257,0.492
39009,0.071,0.115,0.164,0.236,0.413
39011,0.131,0.089,0.153,0.342,0.285
39013,0.082,0.086,0.118,0.25,0.464
39015,0.177,0.177,0.101,0.128,0.417
39017,0.047,0.072,0.106,0.244,0.531
39019,0.147,0.261,0.052,0.15,0.39
39021,0.084,0.051,0.274,0.269,0.321
39023,0.066,0.027,0.19,0.247,0.471
39025,0.082,0.069,0.074,0.192,0.584
39027,0.171,0.198,0.071,0.105,0.455
39029,0.098,0.08,0.115,0.156,0.551
39031,0.077,0.194,0.294,0.178,0.258
39033,0.101,0.107,0.126,0.184,0.482
39035,0.042,0.052,0.084,0.213,0.609
39037,0.216,0.084,0.15,0.28,0.271
39039,0.052,0.114,0.152,0.221,0.462
39041,0.018,0.081,0.084,0.177,0.64
39043,0.078,0.022,0.091,0.179,0.63
39045,0.1,0.095,0.135,0.142,0.527
39047,0.157,0.055,0.328,0.158,0.302
39049,0.042,0.054,0.09,0.198,0.617
39051,0.029,0.046,0.318,0.272,0.335
39053,0.074,0.082,0.203,0.178,0.463
39055,0.03,0.057,0.064,0.218,0.631
39057,0.041,0.082,0.095,0.264,0.519
39059,0.101,0.162,0.137,0.181,0.419
39061,0.069,0.056,0.102,0.224,0.549
39063,0.076,0.127,0.122,0.302,0.373
39065,0.149,0.121,0.134,0.227,0.368
39067,0.107,0.141,0.153,0.233,0.365
39069,0.038,0.111,0.189,0.197,0.465
39071,0.157,0.144,0.068,0.162,0.468
39073,0.169,0.137,0.21,0.104,0.38
39075,0.05,0.087,0.134,0.271,0.457
39077,0.191,0.064,0.204,0.172,0.369
39079,0.166,0.141,0.247,0.105,0.341
39081,0.076,0.142,0.152,0.209,0.42
39083,0.138,0.1,0.065,0.17,0.526
39085,0.067,0.039,0.104,0.246,0.544
39087,0.089,0.097,0.185,0.23,0.399
39089,0.056,0.102,0.176,0.127,0.539
39091,0.136,0.117,0.314,0.248,0.186
39093,0.089,0.097,0.099,0.207,0.508
39095,0.078,0.056,0.111,0.189,0.566
39097,0.037,0.087,0.167,0.262,0.447
39099,0.046,0.099,0.099,0.231,0.525
39101,0.084,0.18,0.115,0.159,0.462
39103,0.068,0.077,0.125,0.227,0.503
39105,0.072,0.137,0.182,0.177,0.432
39107,0.102,0.105,0.245,0.314,0.233
39109,0.077,0.07,0.19,0.227,0.436
39111,0.04,0.144,0.139,0.291,0.386
39113,0.041,0.065,0.065,0.222,0.607
39115,0.142,0.114,0.153,0.259,0.332
39117,0.059,0.094,0.075,0.31,0.462
39119,0.136,0.145,0.121,0.182,0.416
39121,0.109,0.176,0.139,0.219,0.357
39123,0.052,0.035,0.084,0.219,0.609
39125,0.095,0.061,0.24,0.314,0.29
39127,0.138,0.149,0.214,0.19,0.309
39129,0.067,0.087,0.092,0.244,0.51
39131,0.186,0.124,0.124,0.158,0.409
39133,0.028,0.078,0.126,0.213,0.555
39135,0.045,0.146,0.186,0.248,0.375
39137,0.041,0.118,0.253,0.307,0.28
39139,0.066,0.08,0.213,0.221,0.419
39141,0.181,0.103,0.101,0.168,0.447
39143,0.139,0.074,0.109,0.209,0.469
39145,0.121,0.096,0.242,0.156,0.387
39147,0.119,0.195,0.243,0.164,0.279
39149,0.182,0.119,0.216,0.283,0.2
39151,0.065,0.11,0.102,0.198,0.525
39153,0.048,0.056,0.125,0.207,0.565
39155,0.054,0.059,0.121,0.23,0.535
39157,0.126,0.204,0.116,0.215,0.339
39159,0.175,0.018,0.099,0.245,0.463
39161,0.069,0.084,0.223,0.412,0.212
39163,0.09,0.165,0.218,0.147,0.38
39165,0.062,0.044,0.085,0.162,0.647
39167,0.072,0.121,0.142,0.29,0.375
39169,0.064,0.082,0.144,0.245,0.466
39171,0.035,0.133,0.199,0.208,0.425
39173,0.029,0.061,0.086,0.252,0.572
39175,0.1,0.201,0.138,0.135,0.426
40001,0.105,0.068,0.258,0.213,0.356
40003,0.203,0.096,0.188,0.25,0.262
40005,0.076,0.184,0.111,0.284,0.345
40007,0.153,0.154,0.085,0.223,0.384
40009,0.088,0.265,0.187,0.121,0.339
40011,0.029,0.081,0.244,0.32,0.326
40013,0.094,0.087,0.125,0.237,0.457
40015,0.013,0.111,0.194,0.239,0.444
40017,0.043,0.073,0.103,0.309,0.472
40019,0.128,0.169,0.138,0.213,0.351
40021,0.099,0.121,0.212,0.24,0.327
40023,0.064,0.113,0.119,0.251,0.454
40025,0.14,0.089,0.153,0.26,0.358
40027,0.046,0.101,0.079,0.25,0.524
40029,0.075,0.12,0.105,0.307,0.394
40031,0.054,0.179,0.127,0.226,0.413
40033,0.06,0.117,0.102,0.173,0.548
40035,0.247,0.113,0.236,0.112,0.292
40037,0.154,0.074,0.145,0.212,0.415
40039,0.006,0.274,0.413,0.13,0.177
40041,0.188,0.187,0.202,0.107,0.316
40043,0.066,0.185,0.286,0.219,0.244
40045,0.132,0.22,0.135,0.207,0.306
40047,0.233,0.103,0.127,0.28,0.258
40049,0.075,0.087,0.157,0.249,0.433
40051,0.047,0.098,0.185,0.249,0.422
40053,0.205,0.136,0.145,0.255,0.257
40055,0.073,0.19,0.148,0.225,0.365
40057,0.105,0.158,0.16,0.239,0.338
40059,0.158,0.189,0.118,0.257,0.278
40061,0.146,0.09,0.203,0.258,0.303
40063,0.089,0.037,0.146,0.213,0.514
40065,0.021,0.127,0.148,0.291,0.411
40067,0.084,0.108,0.163,0.14,0.505
40069,0.088,0.137,0.157,0.244,0.374
40071,0.099,0.12,0.135,0.225,0.419
40073,0.046,0.093,0.05,0.192,0.619
40075,0.025,0.19,0.131,0.239,0.415
40077,0.159,0.136,0.184,0.277,0.245
40079,0.142,0.152,0.156,0.196,0.354
40081,0.135,0.103,0.204,0.222,0.337
40083,0.03,0.027,0.114,0.31,0.519
40085,0.188,0.08,0.103,0.195,0.435
40087,0.092,0.099,0.068,0.197,0.545
40089,0.131,0.141,0.117,0.187,0.423
40091,0.085,0.057,0.265,0.221,0.373
40093,0.169,0.072,0.212,0.285,0.261
40095,0.13,0.074,0.114,0.256,0.425
40097,0.173,0.175,0.213,0.135,0.303
40099,0.042,0.154,0.247,0.218,0.34
40101,0.063,0.088,0.262,0.198,0.388
40103,0.069,0.085,0.1,0.253,0.493
40105,0.148,0.121,0.164,0.181,0.386
40107,0.112,0.035,0.241,0.193,0.419
40109,0.096,0.064,0.097,0.225,0.518
40111,0.078,0.052,0.204,0.276,0.39
40113,0.077,0.103,0.23,0.218,0.372
40115,0.154,0.13,0.229,0.151,0.337
40117,0.178,0.085,0.159,0.213,0.365
40119,0.083,0.068,0.129,0.207,0.514
40121,0.117,0.112,0.153,0.36,0.258
40123,0.029,0.033,0.102,0.235,0.601
40125,0.042,0.052,0.278,0.236,0.392
40127,0.077,0.152,0.122,0.261,0.388
40129,0.109,0.248,0.192,0.091,0.36
40131,0.097,0.105,0.164,0.201,0.433
40133,0.095,0.034,0.225,0.169,0.478
40135,0.106,0.11,0.209,0.212,0.362
40137,0.041,0.088,0.21,0.286,0.375
40139,0.123,0.12,0.102,0.225,0.43
40141,0.042,0.142,0.11,0.225,0.48
40143,0.074,0.061,0.141,0.243,0.482
40145,0.032,0.112,0.175,0.306,0.375
40147,0.118,0.183,0.162,0.192,0.345
40149,0.024,0.256,0.243,0.164,0.313
40151,0.165,0.116,0.166,0.281,0.272
40153,0.146,0.232,0.145,0.247,0.229
41001,0.051,0.189,0.179,0.189,0.392
41003,0.007,0.007,0.059,0.156,0.77
41005,0.016,0.024,0.071,0.168,0.721
41007,0.052,0.027,0.051,0.073,0.797
41009,0.048,0.015,0.158,0.174,0.604
41011,0.128,0.061,0.136,0.099,0.577
41013,0.019,0.075,0.027,0.246,0.633
41015,0.025,0.058,0.1,0.162,0.655
41017,0.01,0.061,0.014,0.25,0.666
41019,0.082,0.052,0.116,0.239,0.511
41021,0.008,0.003,0.049,0.332,0.607
41023,0.014,0.062,0.132,0.12,0.671
41025,0.032,0.099,0.114,0.157,0.598
41027,0.03,0.071,0.127,0.162,0.61
41029,0.044,0.077,0.075,0.138,0.665
41031,0.018,0.062,0.012,0.25,0.657
41033,0.028,0.056,0.072,0.192,0.652
41035,0.097,0.031,0.062,0.227,0.583
41037,0.05,0.017,0.127,0.247,0.558
41039,0.031,0.036,0.077,0.137,0.719
41041,0.001,0.012,0.047,0.131,0.81
41043,0.057,0.025,0.096,0.159,0.664
41045,0.071,0.206,0.213,0.206,0.305
41047,0.029,0.013,0.053,0.196,0.71
41049,0.026,0.004,0.083,0.197,0.69
41051,0.013,0.016,0.054,0.178,0.738
41053,0.013,0.022,0.078,0.242,0.645
41055,0.008,0.014,0.046,0.271,0.66
41057,0.125,0.04,0.092,0.163,0.581
41059,0.043,0.01,0.081,0.155,0.71
41061,0.034,0.088,0.086,0.189,0.603
41063,0.041,0.083,0.146,0.229,0.501
41065,0.019,0.034,0.069,0.197,0.681
41067,0.006,0.009,0.059,0.169,0.756
41069,0.023,0.07,0.049,0.232,0.626
41071,0.021,0.035,0.072,0.143,0.729
42001,0.039,0.034,0.083,0.238,0.606
42003,0.017,0.022,0.035,0.165,0.761
42005,0.027,0.074,0.159,0.173,0.568
42007,0.015,0.023,0.1,0.137,0.725
42009,0.014,0.111,0.1,0.205,0.57
42011,0.036,0.012,0.06,0.174,0.719
42013,0.041,0.132,0.083,0.249,0.495
42015,0.042,0.017,0.051,0.194,0.697
42017,0.014,0.018,0.032,0.131,0.805
42019,0.035,0.062,0.062,0.194,0.647
42021,0.026,0.062,0.153,0.228,0.53
42023,0.147,0.014,0.048,0.142,0.649
42025,0.013,0.062,0.015,0.145,0.764
42027,0.017,0.013,0.076,0.101,0.792
42029,0.006,0.022,0.038,0.113,0.82
42031,0.057,0.03,0.152,0.155,0.605
42033,0.051,0.055,0.176,0.206,0.512
42035,0.014,0.024,0.077,0.19,0.696
42037,0.02,0.046,0.05,0.163,0.721
42039,0.024,0.016,0.136,0.266,0.558
42041,0.031,0.032,0.183,0.192,0.562
42043,0.032,0.023,0.258,0.147,0.539
42045,0.01,0.01,0.044,0.123,0.814
42047,0.055,0.016,0.084,0.15,0.695
42049,0.016,0.025,0.057,0.136,0.766
42051,0.053,0.064,0.144,0.161,0.578
42053,0.016,0.008,0.158,0.196,0.622
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51790,0.027,0.039,0.057,0.158,0.718
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51810,0.017,0.041,0.062,0.202,0.678
51820,0.027,0.028,0.049,0.186,0.71
51830,0.005,0.009,0.001,0.249,0.737
51840,0.025,0.056,0.032,0.277,0.611
53001,0.077,0.051,0.072,0.076,0.724
53003,0.014,0.029,0.115,0.32,0.522
53005,0.084,0.011,0.064,0.08,0.761
53007,0.062,0.048,0.029,0.297,0.564
53009,0.001,0,0.028,0.225,0.746
53011,0.02,0.021,0.048,0.17,0.741
53013,0.08,0.031,0.079,0.223,0.586
53015,0.041,0.02,0.098,0.216,0.625
53017,0.067,0.047,0.042,0.271,0.573
53019,0.082,0.066,0.082,0.204,0.565
53021,0.106,0.013,0.059,0.069,0.753
53023,0.031,0.029,0.085,0.292,0.563
53025,0.032,0.052,0.108,0.146,0.661
53027,0.03,0.009,0.087,0.213,0.662
53029,0.015,0.009,0.066,0.15,0.759
53031,0.011,0,0.064,0.171,0.754
53033,0.016,0.02,0.05,0.191,0.724
53035,0.024,0.003,0.051,0.136,0.786
53037,0.032,0.084,0.033,0.179,0.672
53039,0.038,0.029,0.113,0.288,0.532
53041,0.033,0.112,0.128,0.111,0.616
53043,0.017,0.086,0.023,0.108,0.765
53045,0.011,0.024,0.091,0.152,0.721
53047,0.042,0.066,0.075,0.199,0.617
53049,0.082,0.016,0.128,0.063,0.712
53051,0.027,0.122,0.052,0.212,0.587
53053,0.028,0.057,0.103,0.206,0.606
53055,0.015,0.004,0.021,0.229,0.73
53057,0.035,0.097,0.037,0.177,0.653
53059,0.019,0.048,0.092,0.08,0.76
53061,0.017,0.014,0.056,0.191,0.721
53063,0.033,0.057,0.08,0.151,0.679
53065,0.05,0.039,0.069,0.257,0.585
53067,0.022,0.051,0.109,0.132,0.685
53069,0.045,0.057,0.079,0.161,0.658
53071,0.083,0.022,0.061,0.193,0.641
53073,0.042,0.028,0.061,0.122,0.747
53075,0.002,0.009,0.049,0.31,0.629
53077,0.012,0.035,0.069,0.143,0.74
54001,0.092,0.031,0.071,0.287,0.519
54003,0.065,0.049,0.196,0.166,0.524
54005,0.097,0.138,0.093,0.197,0.475
54007,0.102,0.131,0.167,0.202,0.398
54009,0.067,0.11,0.195,0.192,0.436
54011,0.092,0.09,0.154,0.186,0.476
54013,0.085,0.07,0.227,0.21,0.408
54015,0.098,0.097,0.162,0.152,0.491
54017,0.187,0.011,0.075,0.307,0.42
54019,0.14,0.088,0.226,0.137,0.409
54021,0.141,0.076,0.144,0.236,0.403
54023,0.1,0.232,0.067,0.193,0.407
54025,0.07,0.03,0.113,0.233,0.554
54027,0.021,0.035,0.088,0.236,0.619
54029,0.072,0.128,0.151,0.19,0.459
54031,0.039,0.152,0.103,0.23,0.476
54033,0.132,0.021,0.071,0.319,0.457
54035,0.111,0.055,0.097,0.216,0.522
54037,0.041,0.038,0.2,0.169,0.552
54039,0.145,0.039,0.064,0.147,0.605
54041,0.161,0.029,0.068,0.288,0.454
54043,0.091,0.077,0.164,0.167,0.501
54045,0.082,0.11,0.116,0.247,0.445
54047,0.07,0.095,0.189,0.226,0.42
54049,0.069,0.06,0.111,0.281,0.479
54051,0.075,0.095,0.077,0.256,0.497
54053,0.081,0.095,0.195,0.149,0.481
54055,0.076,0.152,0.174,0.103,0.495
54057,0.033,0.016,0.042,0.234,0.674
54059,0.084,0.093,0.093,0.172,0.558
54061,0.046,0.077,0.139,0.221,0.517
54063,0.048,0.063,0.116,0.14,0.634
54065,0.025,0.085,0.169,0.258,0.463
54067,0.06,0.088,0.166,0.143,0.542
54069,0.108,0.068,0.092,0.243,0.49
54071,0.06,0.142,0.128,0.214,0.456
54073,0.088,0.108,0.17,0.287,0.346
54075,0.075,0.03,0.17,0.227,0.499
54077,0.057,0.028,0.087,0.247,0.58
54079,0.087,0.048,0.088,0.184,0.593
54081,0.087,0.083,0.257,0.148,0.426
54083,0.098,0.05,0.14,0.265,0.447
54085,0.141,0.059,0.183,0.262,0.356
54087,0.082,0.024,0.191,0.21,0.493
54089,0.053,0.104,0.166,0.111,0.567
54091,0.072,0.032,0.092,0.288,0.517
54093,0.108,0.087,0.048,0.292,0.465
54095,0.083,0.056,0.137,0.272,0.452
54097,0.131,0.039,0.077,0.278,0.475
54099,0.111,0.119,0.186,0.162,0.423
54101,0.065,0.083,0.163,0.211,0.479
54103,0.051,0.067,0.081,0.279,0.521
54105,0.112,0.059,0.219,0.222,0.389
54107,0.094,0.099,0.149,0.277,0.381
54109,0.073,0.098,0.176,0.245,0.408
55001,0.163,0.189,0.227,0.213,0.208
55003,0.078,0.038,0.118,0.363,0.403
55005,0.138,0.163,0.07,0.259,0.37
55007,0.067,0.039,0.107,0.392,0.395
55009,0.069,0.053,0.184,0.208,0.486
55011,0.039,0.106,0.172,0.269,0.414
55013,0.097,0.163,0.082,0.236,0.422
55015,0.064,0.1,0.141,0.33,0.365
55017,0.059,0.098,0.184,0.246,0.414
55019,0.162,0.149,0.158,0.146,0.384
55021,0.035,0.073,0.082,0.222,0.589
55023,0.202,0.033,0.11,0.158,0.497
55025,0.017,0.02,0.092,0.23,0.641
55027,0.11,0.138,0.166,0.2,0.386
55029,0.021,0.03,0.059,0.264,0.627
55031,0.044,0.042,0.092,0.327,0.495
55033,0.062,0.076,0.179,0.266,0.417
55035,0.042,0.076,0.203,0.255,0.424
55037,0.053,0.073,0.143,0.238,0.494
55039,0.121,0.155,0.158,0.244,0.322
55041,0.085,0.053,0.162,0.215,0.484
55043,0.103,0.102,0.18,0.166,0.449
55045,0.092,0.084,0.119,0.269,0.436
55047,0.092,0.122,0.184,0.117,0.486
55049,0.065,0.077,0.092,0.333,0.433
55051,0.04,0.052,0.142,0.248,0.518
55053,0.027,0.1,0.14,0.329,0.404
55055,0.114,0.093,0.096,0.179,0.518
55057,0.113,0.124,0.18,0.232,0.352
55059,0.077,0.083,0.097,0.155,0.589
55061,0.033,0.035,0.112,0.208,0.612
55063,0.064,0.072,0.076,0.289,0.498
55065,0.056,0.098,0.197,0.253,0.396
55067,0.085,0.102,0.142,0.239,0.432
55069,0.101,0.105,0.132,0.204,0.459
55071,0.175,0.089,0.259,0.133,0.345
55073,0.123,0.136,0.165,0.236,0.34
55075,0.093,0.104,0.066,0.258,0.479
55077,0.174,0.191,0.2,0.163,0.271
55078,0.042,0.103,0.31,0.246,0.3
55079,0.053,0.067,0.119,0.264,0.497
55081,0.073,0.03,0.154,0.284,0.46
55083,0.07,0.082,0.186,0.215,0.447
55085,0.057,0.05,0.155,0.186,0.552
55087,0.085,0.124,0.135,0.302,0.353
55089,0.076,0.113,0.091,0.3,0.42
55091,0.071,0.056,0.198,0.233,0.443
55093,0.063,0.094,0.144,0.25,0.449
55095,0.081,0.113,0.154,0.253,0.399
55097,0.157,0.159,0.215,0.188,0.28
55099,0.059,0.081,0.079,0.15,0.631
55101,0.061,0.067,0.167,0.193,0.512
55103,0.312,0.074,0.055,0.116,0.443
55105,0.023,0.139,0.096,0.277,0.464
55107,0.095,0.135,0.106,0.168,0.496
55109,0.101,0.102,0.085,0.342,0.37
55111,0.063,0.163,0.113,0.193,0.468
55113,0.058,0.126,0.133,0.256,0.427
55115,0.05,0.121,0.355,0.188,0.286
55117,0.128,0.114,0.174,0.199,0.385
55119,0.201,0.165,0.09,0.12,0.424
55121,0.02,0.126,0.085,0.381,0.387
55123,0.157,0.052,0.093,0.244,0.454
55125,0.051,0.047,0.138,0.231,0.533
55127,0.116,0.146,0.137,0.191,0.41
55129,0.082,0.168,0.117,0.243,0.39
55131,0.031,0.142,0.22,0.298,0.309
55133,0.071,0.165,0.145,0.258,0.361
55135,0.076,0.069,0.206,0.143,0.506
55137,0.109,0.147,0.202,0.186,0.356
55139,0.074,0.194,0.126,0.156,0.45
55141,0.131,0.177,0.215,0.181,0.296
56001,0.136,0.1,0.151,0.181,0.432
56003,0.182,0.187,0.12,0.257,0.253
56005,0.216,0.164,0.131,0.201,0.289
56007,0.044,0.137,0.087,0.278,0.454
56009,0.138,0.113,0.121,0.293,0.335
56011,0.145,0.115,0.143,0.192,0.404
56013,0.16,0.062,0.06,0.294,0.424
56015,0.201,0.169,0.111,0.223,0.296
56017,0.208,0.093,0.068,0.307,0.324
56019,0.183,0.208,0.103,0.216,0.29
56021,0.143,0.127,0.1,0.221,0.409
56023,0.131,0.183,0.128,0.235,0.324
56025,0.1,0.084,0.094,0.325,0.398
56027,0.169,0.191,0.177,0.222,0.241
56029,0.189,0.153,0.191,0.205,0.262
56031,0.149,0.149,0.123,0.16,0.418
56033,0.17,0.251,0.099,0.203,0.278
56035,0.223,0.111,0.061,0.231,0.374
56037,0.061,0.295,0.23,0.146,0.268
56039,0.095,0.157,0.16,0.247,0.34
56041,0.098,0.278,0.154,0.207,0.264
56043,0.204,0.155,0.069,0.285,0.287
56045,0.142,0.129,0.148,0.207,0.374


================================================
FILE: tidyverse_covid_data/part2/tidyverse_2.R
================================================

# This is the second session covering tidyverse commands.

# From last time, we looked at this cheat sheet: https://rstudio.com/wp-content/uploads/2015/02/data-wrangling-cheatsheet.pdf 

# Let's quickly reload the two data sets we used last time and clean them up. 
# load mask data
mask_use_wide <- read_delim(file="mask-use-by-county.csv", delim=",")
mask_use_long <- mask_use_wide %>%
  pivot_longer(cols = -COUNTYFP, names_to = "MaskUseResponse", values_to = "MaskUseProportion") %>%
  rename("fips" = COUNTYFP) %>%
  arrange(fips)
# load case count data, filter based on most recent date
cases <- read_delim(file="https://github.com/nytimes/covid-19-data/raw/master/us-counties.csv", delim=",")
cases_latest <- cases %>% filter(date == "2020-08-03") %>%
  dplyr::select(-date) %>%
  arrange(fips)

# These data from last time are all at the county level, which vary considerably by size.

# Let's load in some additional data on population size by county, obtained from www.census.gov.
pop_sizes <- read_delim(file="co-est2019-annres.csv.xz", delim=",")

# Always double check file to make sure it looks alright!
head(pop_sizes) 
nrow(pop_sizes) 
sum(!is.na(pop_sizes$'2019')) # how many counties have size estimates for 2019? it's even closer to the 3142 we expect!
tail(pop_sizes)

# These data are a little messy and need to be cleaned up! Let's do this with tidyverse commands.

# Let's remove rows that we don't need using the slice() function
pop_sizes <- pop_sizes %>% dplyr::slice(2:3143) # this also gets rid of the notes at the bottom

# The 'Geographic Area' column has two pieces of info in it: county and state. Let's split this into 2 separate columnds using the separate() function. 
pop_sizes <- pop_sizes %>% separate(col = 'Geographic Area',
                                    into = c("County", "State"),
                                    sep = ", ")

# Let's get rid of extra characters in the new 'County' column.  We can use the mutate() function along with another function called str_remove(). 
pop_sizes <- pop_sizes %>% 
  mutate(County = str_remove(string = County,pattern = ".")) %>%
  mutate(County = str_remove(string = County,pattern = " County"))

# Let's use only the most recent estimate of county population size using the select() function.
pop_sizes <- pop_sizes %>%
  dplyr::select(County, State, "2019")

# OPTIONAL: we can combine all of our cleaning/filtering commands into one chain of commands to send to others: 
pop_sizes <- read_delim(file="co-est2019-annres.csv", delim=",") %>% 
  dplyr::slice(2:3143) %>%
  separate(col = 'Geographic Area', into = c("County", "State"), sep = ", ") %>%
  mutate(County = str_remove(string = County,pattern = ".")) %>%
  mutate(County = str_remove(string = County,pattern = " County")) %>%
  dplyr::select(County, State, "2019")

# The summary() function gives us a quick peek at these data. NOTE: summary() is very different from the summarize() function that we'll cover below. 
summary(pop_sizes)



# Combining our previous data on case counts with county size
cases_popsize <- inner_join(x=cases_latest, y=pop_sizes, 
                                  by=c("county"="County", "state"="State"))

# Combining these data with mask usage data
cases_popsize_masked <- inner_join(x=cases_popsize, 
                            y=dplyr::select(mask_use_wide, c(COUNTYFP, ALWAYS)),
                            by=c("fips" = "COUNTYFP"))

# Renaming a coupe of columns to something more informative: 
cases_popsize_masked <- cases_popsize_masked %>%
  rename("pop_size" = "2019", "AlwaysMasked" = ALWAYS)

##
# Visualizing data and finding outliers
##

# What is the relationship between the fraction of positive cases and the population size of the county? Do counties with large populations have a higher proportion of case counts? 
# We will make our first plot the the "base" R function plot()
cases_popsize_masked <- cases_popsize_masked %>%
  mutate(FracPos = cases/pop_size)

plot(x=cases_popsize_masked$pop_size,
     y=cases_popsize_masked$FracPos,
     log="x",
     ylab="Fraction of cases",
     xlab="pop size")
# DON'T RUN CORRELATION ANALYSES WITH HETEROSCEDASTICITY


# What are those outliers with high rates of positive cases? What counties/states are these?
cases_popsize_masked %>%
  filter(FracPos > 0.08) %>%
  arrange(desc(FracPos))


# Let's add another column for death rate, and visualize the relationship between the fraction of positive cases and the death rate:
cases_popsize_masked <- cases_popsize_masked %>%
  mutate(FracDeaths = deaths/cases)

plot(x=cases_popsize_masked$FracPos,
     y=cases_popsize_masked$FracDeaths,
     log="",
     xlab="FracCases",
     ylab="FracDeaths")

# What are these counties that have been severely impacted by deaths rates? Is this just noise from small population sizes? 
cases_popsize_masked %>%
  filter(FracDeaths > 0.15) %>%
  arrange(desc(FracDeaths))


##
# group_by() and summarize() functions are extremely easy and powerful
##

# The use of the group_by() and summarize() functions allows us to quickly get extremely informative information from our data. 

# One way to get the mean values for the different mask use frequency categories:
mean(mask_use_wide$NEVER)
mean(mask_use_wide$RARELY)
mean(mask_use_wide$SOMETIMES)
mean(mask_use_wide$FREQUENTLY)
mean(mask_use_wide$ALWAYS)

summary(mask_use_wide)

# Alternatively, we can convert our data into long format (which we did above) and use the group_by() and summarize() functions to quickly do useful analyses:
mask_use_long %>% 
  group_by(MaskUseResponse) %>% 
  summarize(mean(MaskUseProportion))

# Looking at the cheat sheet, you will see summarize can take a variety of functions.


# Instead of analyzing data by county, we can easily switch to a state-level analysis using group_by() and sumamrize()
cases_popsize_masked %>%
  group_by(state) %>%
  summarize(MeanMasked = mean(AlwaysMasked)) %>%
  arrange(desc(MeanMasked))


# Calculating the mean value for a state using counties of very different sizes is bad; mean() treats each county equally, but in reality they vary tremendously in size
# Let's compute a custom weighted mean
cases_popsize_masked %>%
  group_by(state) %>%
  summarize(WeightedMeanMasked = sum(AlwaysMasked*pop_size)/sum(pop_size)) %>%
  arrange(desc(WeightedMeanMasked))


# Did this weighting by county size actually make a difference? Let's store these analyses as 'x' and 'y' and compare them:
x <- cases_popsize_masked %>%
  group_by(state) %>%
  summarize(MeanMasked = mean(AlwaysMasked)) %>%
  arrange(state)

y <- cases_popsize_masked %>%
  group_by(state) %>%
  summarize(WeightedMeanMasked = sum(AlwaysMasked*pop_size)/sum(pop_size)) %>%
  arrange(state)

hist(x$MeanMasked - y$WeightedMeanMasked)

# The histogram suggests smaller counties have, on average, lower values for "AlwaysMasked"? Ploe the variables to verify:
plot(x=cases_popsize_masked$pop_size,
     y=cases_popsize_masked$AlwaysMasked,
     log="x",
     xlab="pop size",
     ylab="AlwaysMasked")

# Summary:
## New tidyverse functions used today:
- slice() to select specific rows by index number
- separate() to take a column with multiple pieces of information and split it into multiple columns
- mutate() with str_remove to get rid of characters or words we don't want, there are many similar functions in the stringr package, which has its own cheat sheet
- select() to
- group_by() to categorize our data by the values in a particular column (here, by state)
- summarize() to calculate simple but very informative statistics, such as mean and variance



================================================
FILE: tidyverse_covid_data/part2/tidyverse_2.Rmd
================================================
---
title: "Untitled"
output: html_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```


This is the second session covering tidyverse commands. We will show you how to use some new functions, but we will also use some of the functions we previously used last week which will be good for extra practice. If you missed the previous session, that should not be a problem.

From last time, we looked at this [cheat sheet](https://rstudio.com/wp-content/uploads/2015/02/data-wrangling-cheatsheet.pdf) to occasionally guide us.

It's typical to load all the libraries you need at the top of your code.
```{r}
library(tidyverse)
```

Let's quickly reload the two data sets we used last time and clean them up. As a reminder, we converted the mask use data from wide format into long format, but let's keep the data in both versions today.
```{r, include=FALSE}
# load mask data
mask_use_wide <- read_delim(file="mask-use-by-county.csv", delim=",")
mask_use_long <- mask_use_wide %>%
  pivot_longer(cols = -COUNTYFP, names_to = "MaskUseResponse", values_to = "MaskUseProportion") %>%
  rename("fips" = COUNTYFP) %>%
  arrange(fips)

# load case count data, filter based on most recent date
cases <- read_delim(file="https://github.com/nytimes/covid-19-data/raw/master/us-counties.csv", delim=",")
cases_latest <- cases %>% filter(date == "2020-08-03") %>%
  dplyr::select(-date) %>%
  arrange(fips)
```

These data from last time are all at the county level, which vary considerably by size. So for instance if we wanted to know the fraction of positive cases in a state, or if we wanted to know what mask-wearing behavior was in a particular state, we should really take this variability in county size into account. It's also useful to see which counties have been hit particularly hard, since any positive cases may be expected if the county is quite large.

Let's load in some additional data on population size by county, which I obtained from www.census.gov.
```{r, include=FALSE}
pop_sizes <- read_delim(file="co-est2019-annres.csv.xz", delim=",")
```

When I'm working with larger files and I want to make sure they looks like I expect them to, instead of opening up the file and scrolling through all the lines, checking each by eye, I typically use various commands to just get an idea of what it looks like before proceeding. Oftentimes, this is sufficient; we don't need to check every line.

These are just a few commands I might use to look at the first and last few lines, how many rows there are overall, and also get an idea of how much missing data there might be:
```{r}
head(pop_sizes) 
nrow(pop_sizes) 
sum(!is.na(pop_sizes$'2019')) # how many counties have size estimates for 2019? it's even closer to the 3142 we expect!
tail(pop_sizes)
```

Ok so these data are a little messy and need to be cleaned up.

From these commands, we can see that the entire US was included in this table, which is not a county. We should remove that. Also, county names are all preceded with a ".", something we'll deal with in a moment. The number of rows in the table is very similar to what we expect (3,142 according to wikipedia), but maybe larger by 7 rows or so. The row containing the entire US contributes to this excess of rows beyond our expectation. 

Looking at the number of entries for the year 2019, they're extremely close to what we expect.

Looking at the bottom of the table, we see some footnotes were left in. These are probably contributing to the extra number of rows and should absolutely be removed.

Let's remove these rows at the beginning and end with the slice() function (unlike the select() function that removes columns).
```{r, include=FALSE}
pop_sizes <- pop_sizes %>% dplyr::slice(2:3143) # this also gets rid of the notes at the bottom
```

As you can see in the cheat sheet, there are many ways to select rows, even at random! We won't do this, but this could be useful if you wanted to set up an analysis on a smaller subset of your data to make sure everything runs correctly but quickly. Then, after everything is set up, run your analyses on the entire data, which could take a while with huge data sets.

The 'Geographic Area' column has two pieces of info in it: county and state. We should split this column into two columns so that we can separately access the info. For instance, later on we will do an analysis not by county but by state, summing up across a state's counties, and this requires having this information in it's own separate column. To separate this info into 2 seperate columns, we will use the separate() function:
```{r, include=FALSE}
pop_sizes <- pop_sizes %>% separate(col = 'Geographic Area',
                                    into = c("County", "State"),
                                    sep = ", ")
```
This illustrates how even though we split our data up by commas when we loaded it in above, we can further split columns based on specific characters. Above, we asked the read_delim() function to split by commas, so you would think this column/field would also get separated, but when you look at the raw data file you can see that data from each column is contained within quotes, and this info within quotes is separated by commas.

And just so you know, there's a related function called unite() that does just the opposite: combining columns into a single column.

Let's get rid of extra characters in the new 'County' column. We don't need the "." that precedes each name, and it's pointless that each individual county name is followed by "County"... we know they're counties based on the name of the column. We can use the mutate() function to add new rows with new names, but if the name of the new row we want is the same as an existing one, it just replaces it!

```{r, include=FALSE}
pop_sizes <- pop_sizes %>% 
  mutate(County = str_remove(string = County,pattern = ".")) %>%
  mutate(County = str_remove(string = County,pattern = " County"))
```

This table includes population size data for quite a few years. Let's just get the most recent population estimate, selecting the 2019 column with the select() function:
```{r, include=FALSE}
pop_sizes <- pop_sizes %>%
  dplyr::select(County, State, "2019")
```

As we did above for the data sets we used last week, we can also combine all of these individual commands into one compact command that might look something like this:
```{r, include=FALSE}
pop_sizes <- read_delim(file="co-est2019-annres.csv", delim=",") %>% 
  dplyr::slice(2:3143) %>%
  separate(col = 'Geographic Area', into = c("County", "State"), sep = ", ") %>%
  mutate(County = str_remove(string = County,pattern = ".")) %>%
  mutate(County = str_remove(string = County,pattern = " County")) %>%
  dplyr::select(County, State, "2019")
```

We can take a quick peek at these population size data, now that they're cleaned, using the summary() function, which shows there's a ton of variability in county sizes!
```{r}
summary(pop_sizes)
```


Let's combine these data on population sizes with our previous data on case counts and mask usage, as it may reveal interesting dynamics that vary by county size.

We will first use the join command on the case count data and the county population size data, joining by BOTH county and state. Why can't we just join by county?
```{r, include=FALSE}
cases_popsize <- inner_join(x=cases_latest, y=pop_sizes, 
                                  by=c("county"="County", "state"="State"))
```

Using this table with case counts and population size, let's add in the mask data. For now, let's not add in all the mask data. Let's only incorporate the the proportion of people who are "ALWAYS" masked for each county
```{r, include=FALSE}
cases_popsize_masked <- inner_join(x=cases_popsize, 
                            y=dplyr::select(mask_use_wide, c(COUNTYFP, ALWAYS)),
                            by=c("fips" = "COUNTYFP"))
```

Looking at this data table, for each county we now have cases, deaths, population size, and the proportion of people who are always masked.

You may also notice the population size data had a column entitled "2019" for size estimates during that year. Let's make this more informative and change it to "pop_size", and rename the ALWAYS column so that we know it's referring to always masked.
```{r, include=FALSE}
cases_popsize_masked <- cases_popsize_masked %>%
  rename("pop_size" = "2019", "AlwaysMasked" = ALWAYS)
```


# Visualizing data and finding outliers

Let's quickly probe these data just to get an idea of what they look like. The main goal here is to teach you how to use R/tidyverse commands to quickly and easily explore your data. Again, due to the context of these data, let's keep any results as descriptive observations. We can't say anything conclusive without more complicated analyses that we'll leave to the public health officials.

Let's assume these case count data are accurately measuring the number of people who are getting coronavirus infections (which is a horrible assumption because TONS of younger people may be completely asymptomatic and never get tested). Assuming this, what fraction of a county's population is testing positive, and how does this fraction vary with population size? 

```{r}
cases_popsize_masked <- cases_popsize_masked %>%
  mutate(FracPos = cases/pop_size)
```

Do counties with large populations have a higher proportion of case counts? Let's use a simple plotting function to plot population size on the x axis and proportion of cases on y axis. Since we will look at the proportion of cases by dividing the data in the case counts column by the data in the population size column, let's just make a new column called 'FracPos' that contains this information

We can make a quick plot of these data using a base R (i.e. not tidyverse) plotting function:
```{r}
cases_popsize_masked <- cases_popsize_masked %>%
  mutate(FracPos = cases/pop_size)

plot(x=cases_popsize_masked$pop_size,
     y=cases_popsize_masked$FracPos,
     log="x",
     ylab="Fraction of cases",
     xlab="pop size")
# DON'T RUN CORRELATION ANALYSES WITH HETEROSCEDASTICITY
```
Looks interesting. Counties with larger population sizes may look like they have higher proportion of cases, but it's a little complicated because there's a lot of variability for counties with smaller population sizes. 

One thing that might catch our eye are those outliers with very high rates of positive cases? What counties are theses, and in what states? We can very easily check this with the following:
```{r}
cases_popsize_masked %>%
  filter(FracPos > 0.08) %>%
  arrange(desc(FracPos))
```


Let's add another column for death rate, and visualize the relationship between the fraction of positive cases and the death rate:
```{r}
cases_popsize_masked <- cases_popsize_masked %>%
  mutate(FracDeaths = deaths/cases)

plot(x=cases_popsize_masked$FracPos,
     y=cases_popsize_masked$FracDeaths,
     log="",
     xlab="FracCases",
     ylab="FracDeaths")
```

What are these counties that have been severely impacted by deaths? Is this something to be concerned about, or is this just noise from counties with small sizes and/or small case counts?

```{r}
cases_popsize_masked %>%
  filter(FracDeaths > 0.15) %>%
  arrange(desc(FracDeaths))
```



# group_by() and summarize() functions are extremely easy and powerful

The use of the group_by() and summarize() functions allows us to quickly get extremely informative information from our data. 

We'll show how they're useful in many ways, but I'd like to start with showing the importance of changing your data from long format to wide format.

Let's go back to our original mask use data table, not the combined one. What if we wanted to quickly get the overall mean values of ALWAYS, ... , NEVER across all the counties? We could use the original data in wide format, and calculate the mean for each of these columns. We could also use the simple summary() function which outputs a table:
```{r}
mean(mask_use_wide$NEVER)
mean(mask_use_wide$RARELY)
mean(mask_use_wide$SOMETIMES)
mean(mask_use_wide$FREQUENTLY)
mean(mask_use_wide$ALWAYS)

summary(mask_use_wide)
```

There's nothing wrong with doing it this way, but it's not very elegant and would get more difficult if our data were more complicated. Also, the summary function outputs a table that is not the easiest to analyze.

Another way of calculating these values is to convert our mask data into long format where these mask-wearing frequency categories (NEVER, ... , ALWAYS) are different values of a categorical variable (MaskUseResponse), and we tell R that we want to do an analysis separately for each of these different categorical variable values, where here this analysis is just calculating the mean. In other words, for all the rows with values of NEVER for MaskUseResponse, calculate a mean separately, and repeat for all the other possible MaskUseResponse values. We tell R to treat these different categorical variable values separately using group_by(), and the summarize() function can then use these groupings to perform analyses, here calculating the mean.


```{r}
mask_use_long %>% 
  group_by(MaskUseResponse) %>% 
  summarize(mean(MaskUseProportion))
```
As you can see, this also outputs a tibble, which we can use for other downstream analyses.

Looking at the cheat sheet, you will see summarize can take a variety of functions.

Let's explore another example of group_by() and summarize() using our tibble that has all our combined information.

Instead of focusing on counties as our unit of analysis, we can use group_by() to easily switch to a state level analysis by telling the summarize() function to perform analyses for each state, combining all the rows that have the same value under the "state" column. When we combine the group_by() function with summarize(), we can easily do some pretty powerful analyses that would take a lot of effort using other programs.
```{r}
cases_popsize_masked %>%
  group_by(state) %>%
  summarize(MeanMasked = mean(AlwaysMasked)) %>%
  arrange(desc(MeanMasked))
```

You can comment out the line containing the group_by() statement to see it's effect.


Note that this creates a new tibble, and that a new column is created called "MeanMasked" that calculates the mean as a function of whatever variable was put into group_by(). So what is happening here is because we gave "state" to group_by(), R goes through all the rows of AlwaysMasked and if they have the same value for "state", from different counties from the same state, it uses all that data for the state to calculate a mean. 

I'd like to point out that this is potentially a bad analysis because we are calculating a mean for a state based on it's counties, and some of these counties may be represented by fewer people. So if we truly wanted the average behavior of a state, we should weight each county by its population size. Why should a county with a size of 400 contribute equally to the mean as a county of size 40,000?

The following code calculates a custom mean value, where each county is weighted by its population size:
```{r}
cases_popsize_masked %>%
  group_by(state) %>%
  summarize(WeightedMeanMasked = sum(AlwaysMasked*pop_size)/sum(pop_size)) %>%
  arrange(desc(WeightedMeanMasked))
```

See the element-wise vector math figure provided in the github repository for a graphical explanation of what's being done here:

Did this weighting by county size actually make a difference? Let's store these analyses as 'x' and 'y' and compare them:
```{r}
x <- cases_popsize_masked %>%
  group_by(state) %>%
  summarize(MeanMasked = mean(AlwaysMasked)) %>%
  arrange(state)

y <- cases_popsize_masked %>%
  group_by(state) %>%
  summarize(WeightedMeanMasked = sum(AlwaysMasked*pop_size)/sum(pop_size)) %>%
  arrange(state)

hist(x$MeanMasked - y$WeightedMeanMasked)
```

It looks like on average, AlwaysMasked estimates that don't take pop_size into account are systematically lower than those that do. When we don't take county size into account, we effectively give more weight to smaller counties. Thus, if by doing this we produce lower estimates of AlwaysMasked by state, this suggests that smaller counties in general have lower values for AlwaysMasked. Is this true?

```{r}
plot(x=cases_popsize_masked$pop_size,
     y=cases_popsize_masked$AlwaysMasked,
     log="x",
     xlab="pop size",
     ylab="AlwaysMasked")
```

# Summary:
## New tidyverse functions used today:
- slice() to select specific rows by index number
- separate() to take a column with multiple pieces of information and split it into multiple columns
- mutate() with str_remove to get rid of characters or words we don't want, there are many similar functions in the stringr package, which has its own cheat sheet
- select() to
- group_by() to categorize our data by the values in a particular column (here, by state)
- summarize() to calculate simple but very informative statistics, such as mean and variance



================================================
FILE: unix-pipes/binder/environment.yml
================================================
channels:
  - conda-forge
  - bioconda
  - defaults
dependencies:
  - bash_kernel
  - bwa
  - fastp
  - samtools
  - time


================================================
FILE: unix-pipes/index.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Unix Pipes\n",
    "### Bioinformatics Coffee Hour - June 16, 2020\n",
    "#### Your Host: Nathan Weeks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## What this lesson will cover\n",
    "* Unix pipes in shell scripts to improve efficiency of simple bioinformatics workflows\n",
    "* _Example:_ Short-read (FASTQ) alignment to BAM "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Software Utilities Used"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "#### curl\n",
    "    https://curl.haxx.se/\n",
    "command line tool and library for transferring data with URLs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "#### gzip\n",
    "    https://www.gnu.org/software/gzip/\n",
    "popular data compression program"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### fastp\n",
    "    https://github.com/OpenGene/fastp\n",
    "all-in-one FASTQ preprocessor (QC/adapters/trimming/filtering/splitting/merging...)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "#### bwa\n",
    "    http://bio-bwa.sourceforge.net/\n",
    "software package for mapping low-divergent sequences against a large reference genome"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "#### samtools\n",
    "    https://github.com/samtools/samtools\n",
    "tools... for manipulating [Reading/writing/editing/indexing/viewing] next-generation sequencing data [SAM/BAM/CRAM format]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Motivation\n",
    "Bioinformatics workflows are usually *big*...\n",
    "  - big input files\n",
    "  - many workflow **stages**\n",
    "    + each workflow stage usually takes as input a file(s) from the previous stage, and produces output file(s) for the next stage\n",
    "    + each stage may use a different software tool"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Example: 1/2 of a variant-calling pipeline\n",
    "<img src='https://github.com/harvardinformatics/shortRead_mapping_variantCalling/raw/3965d337e41b6d5fc8b873d7d485fb4bf8528bee/docs/workflowSchematic_fastq2bam.png'>\n",
    "\n",
    "*source: Brian Arnold, https://github.com/harvardinformatics/shortRead_mapping_variantCalling/ *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Definition\n",
    "<dl>\n",
    "    <dt>file</dt>\n",
    "    <dd>An object that can be written to, or read from, or both.</dd>\n",
    "</dl>\n",
    "\n",
    "*source: POSIX.1-2018*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### Unix processes start with 3 open \"files\" (connected to a terminal in an interactive shell)\n",
    "<dl>\n",
    "    <dt>standard input (\"stdin\")</dt>\n",
    "    <dd>An input stream usually intended to be used for primary data input.</dd>\n",
    "    <dt>standard output (\"stdout\")<dt>\n",
    "    <dd>An output stream usually intended to be used for primary data output.</dd>\n",
    "    <dt>standard error (\"stderr\")<dt>\n",
    "    <dd>An output stream usually intended to be used for diagnostic messages.<dd>\n",
    "</dl>\n",
    "\n",
    "![process](images/process.svg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<dl>\n",
    "    <dt>filter</dt>\n",
    "    <dd>A command whose operation consists of reading data from standard input or a list of input files and writing data to standard output.\n",
    "        Typically, its function is to perform some transformation on the data stream.</dd>\n",
    "</dl>\n",
    "\n",
    "source: POSIX.1-2018_\n",
    "\n",
    "_Many unix programs act as filters_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "## Example: grep\n",
    "\n",
    "When given only a pattern argument (no file arguments), grep operates as a _filter_, reading input from stdin, and writing output to stdout (_enter CTRL-D to indicate end of input_)\n",
    "\n",
    "```\n",
    "$ grep PATTERN\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "### Redirect stdout to a file\n",
    "\n",
    "To write grep's output to a file (instead of a terminal), use the shell redirection operator (`>`)\n",
    "\n",
    "```\n",
    "$ grep PATTERN > matched.txt\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Introducing Pipes\n",
    "Pipes are a form of [Interprocess Communication](https://en.wikipedia.org/wiki/Inter-process_communication) with the following properties:\n",
    "* One-way commmunication channel between two _processes_\n",
    "  - **process**: a running program\n",
    "* Data written (sequentially) by one process (usually to its stdout) is read (sequentially) by another (usually from its stdin)\n",
    "* Operating system kernel blocks writer (process in a \"sleep\" state) until data is read by reader\n",
    "  + Similarly, reader blocks until writer writes data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Pipes in Programming Languages \n",
    "* The (Unix/Linux/POSIX) shell command language (understood by bash, zsh, sh, and others) has a construct called a pipe (`|` operator)\n",
    "* Many other programming languages allow use of pipes in some fashion:\n",
    "  - AWK: `command | getline [var]` to read, `print ... > command` to \n",
    "  - Python: [subprocess.Popen()](https://docs.python.org/3/library/subprocess.html#replacing-shell-pipeline)\n",
    "  - R: `pipe()`\n",
    "    + Not to be confused with the pipe `%>%` operator from the magrittr package"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### (shell) Pipeline\n",
    "* A sequence of commands separated by the `|` (pipe) operator\n",
    "* standard output of the command on the left of the `|` is connected to the standard input of the command to the right of the `|`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Why use pipes?\n",
    "\n",
    "1. I/O efficiency\n",
    "  - Data flows between processes without being written to disk\n",
    "2. Concurrency\n",
    "  - Processes in a pipeline execute concurrently\n",
    "    - e.g., utilizing multicore processors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Example: Sequential Laundry\n",
    "![laundry1](images/laundry1.gif)\n",
    "*source: https://cs.stanford.edu/people/eroberts/courses/soco/projects/risc/pipelining/index.html*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Example: Pipelined Laundry\n",
    "![laundry2](images/laundry2.gif)\n",
    "*source: https://cs.stanford.edu/people/eroberts/courses/soco/projects/risc/pipelining/index.html*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "---\n",
    "## Examples\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### Example 1: download FASTA file and compress on-the-fly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mkdir -p input\n",
    "GENOME_FASTA_URL=https://github.com/harvardinformatics/shortRead_mapping_variantCalling/raw/b120a823c23b1eaf1cfb95ea5d5ca0ce26a50c32/data/genome/Tgut_subseg_renamed.fa\n",
    "curl -sL ${GENOME_FASTA_URL} | gzip -v -9 > input/Tgut_subseg_renamed.fa.gz\n",
    "ls -lh input/Tgut_subseg_renamed.fa.gz"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Explanation: \n",
    "* `curl` downloads the FASTA file from the specified URL and writes it to its stdout.\n",
    "* `gzip`, when not given a file argument, reads (uncompressed) data from stdin and writes compressed data to stdout.\n",
    "  - The `gzip -v` option prints the compression ratio to stderr_, the `-9` option specififies maximal compression (==more run time, but better compression)\n",
    "* The shell output redirection operator (`>`) writes the stdout of `gzip` to a file (Tgut_subseg_renamed.fa.gz)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Example 2: Print the length of each sequence in the compressed FASTA file\n",
    "Our FASTA file has one sequence per line. [AWK](https://en.wikipedia.org/wiki/AWK) can generate a tab-separated list of sequence ID and sequence length (\n",
    "\n",
    "_Problem:_ AWK cannot read a gzip file directly.\n",
    "\n",
    "_Solution:_ Combine `gzip` options`-d` (decompress) and `-c` (write to stdout), and pipe to awk's stdin:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "gzip -dc input/Tgut_subseg_renamed.fa.gz |\n",
    "  awk '/^>/ { if (len) print len; len = 0; printf(\"%s\\t\", substr($1,2)) }\n",
    "       !/^>/ { len+=length }\n",
    "       END { printf(\"%i\\n\", len) }'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fetch sequence reads to align:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "(cd input && curl -LO 'https://github.com/harvardinformatics/shortRead_mapping_variantCalling/raw/5530f1991da66af82d0213bc3492da8a431a1a92/data/fastq/ERR1013163_[1-2].fastq.gz')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, index the genome file so `bwa` can align reads to it:\n",
    "\n",
    "_Note: `bwa` can index (gzip-)compressed reference sequences_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bwa index input/Tgut_subseg_renamed.fa.gz"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sequence alignment workflow (sequential)\n",
    "\n",
    "* Output of each stage written as file(s)\n",
    "* Command in each stage must be run to completion before next stage is executed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "set -o xtrace\n",
    "mkdir -p orig\n",
    "time (\n",
    "cd orig\n",
    "ln -sf ../input\n",
    "# trim reads, generate HTML report\n",
    "fastp --in1 input/ERR1013163_1.fastq.gz --in2 input/ERR1013163_2.fastq.gz --out1 ERR1013163_1.fastq-trimmed.gz --out2 ERR1013163_2.fastq-trimmed.gz\n",
    "# align reads\n",
    "bwa mem input/Tgut_subseg_renamed.fa.gz ERR1013163_1.fastq-trimmed.gz ERR1013163_2.fastq-trimmed.gz > ERR1013163.sam\n",
    "# sort alignments by read name\n",
    "samtools sort -n --output-fmt bam -o ERR1013163.bam ERR1013163.sam \n",
    "# fill in mate coordinates and insert size in BAM records\n",
    "samtools fixmate -m ERR1013163.bam ERR1013163-fixmate.bam\n",
    "# sort by alignment coordinate\n",
    "samtools sort -o ERR1013163-fixmate-sort.bam ERR1013163-fixmate.bam\n",
    "# mark duplicate alignments\n",
    "samtools markdup -f markdup.stats --output-fmt-option level=9 ERR1013163-fixmate-sort.bam out.bam\n",
    "# index resulting bam file \n",
    "samtools index out.bam\n",
    "samtools stats out.bam > out.bam.stats.txt\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check files created :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ls -lh orig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A special \"file\": /dev/stdin\n",
    "\n",
    "* Some utilities either accept an optional file argument (e.g., gzip, awk, most other unix utilities), reading from stdin if no input file is specified\n",
    "* Some utilities accept \"-\" in place of either an input or output file argument to mean stdin (or stdout) respectively\n",
    "* Other utilities require an actual file argument; e.g. `bwa mem`:\n",
    "```\n",
    "      bwa mem [options] db.prefix reads.fq [mates.fq]\n",
    "```\n",
    "  - **reads.fq** is required. However, `bwa mem` reads **reads.fq** sequentially, and can conceptually use a pipe.\n",
    "  \n",
    "```\n",
    "    fastp reads.fq | bwa mem db.prefix /dev/stdin\n",
    "```\n",
    "  \n",
    "**Note: this works only if the utility reads (or writes) the file sequentially**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sequence alignment workflow (pipeline)\n",
    "\n",
    "* Process in each stage writes output directly to input of another process(es)\n",
    "* Processes execute concurrently"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mkdir -p ex-pipe\n",
    "time (\n",
    "cd ex-pipe\n",
    "ln -s ../input\n",
    "# fastp supports interleaved (https://github.com/OpenGene/fastp#output-to-stdout) output\n",
    "fastp --in1 input/ERR1013163_1.fastq.gz --in2 input/ERR1013163_2.fastq.gz --stdout 2>/dev/null |\n",
    "  # bwa mem requires a FASTQ file operand <in1.fq> (will not take reads from stdin).\n",
    "  # /dev/stdin is a special file that represents a process's standard input\n",
    "  bwa mem input/Tgut_subseg_renamed.fa.gz /dev/stdin 2> /dev/null |\n",
    "    # sort input SAM records by read name (\"-n\"), write uncompressed BAM ((compression) level=0) to stdout\n",
    "    # (otherwise, samtools will detect input format & use same output format)\n",
    "    samtools sort -n --output-fmt bam --output-fmt-option level=0 |\n",
    "      # Usage: samtools fixmate <in.nameSrt.bam> <out.nameSrt.bam>\n",
    "      # samtools allows \"-\" to be used in place of \n",
    "      samtools fixmate -m - - |\n",
    "        # samtools sort reads from stdin and writes to stdout by default\n",
    "        samtools sort |\n",
    "          samtools markdup -f markdup.stats --output-fmt-option level=9 - - |\n",
    "            # samtools index requires an input bam file argument; use /dev/stdin\n",
    "            tee out.bam >(samtools index /dev/stdin out.bam.bai) |\n",
    "              # samtools stats reads from stdin and writes to stdout by default\n",
    "              samtools stats > out.bam.stats.txt\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Much less data written to / read from disk:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ls -lh ex-pipe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a closer look at the last stages of the pipeline.\n",
    "\n",
    "Two additional tools are being introduced: the [tee](https://en.wikipedia.org/wiki/Tee_(command)) command, and [process substitution](https://en.wikipedia.org/wiki/Process_substitution) ( `>(...)` )\n",
    "\n",
    "### tee\n",
    "\n",
    "`tee` duplicates its stdin to one or more files in addition to stdout:\n",
    "\n",
    "![tee](https://upload.wikimedia.org/wikipedia/commons/2/24/Tee.svg)\n",
    "\n",
    "*source: Wikipedia*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```\n",
    "... |\n",
    "  samtools markdup -f markdup.stats --output-fmt-option level=9 - - |\n",
    "    tee out.bam >(samtools index /dev/stdin out.bam.bai) |\n",
    "      samtools stats > out.bam.stats.txt\n",
    "```\n",
    "![pipe](images/pipe.svg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The End!\n",
    "## Questions, comments...?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Bonus material\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### pipefail\n",
    "By convention, processes return an exit status of 0 to indicate success, and > 0 to indicate failure.\n",
    "\n",
    "By default, the exit status of a pipeline is the exit status of the last command in the pipeline:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "samtools sort foo/bar.sam | head\n",
    "echo \"exit status: $?\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is inconvenient when trying to determine if a (Slurm) job script ran successfully, or if it's desired to terminate the script if any command fails (e.g., using `set -o errexit`).\n",
    "\n",
    "The `pipefail` option causes the exit status of a pipeline to be the exit status of the last command in the pipeline to have a non-zero exit status:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "set -o pipefail\n",
    "samtools sort foo/bar.sam | head\n",
    "echo \"exit status: $?\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Other Terms\n",
    "\n",
    "### Regular File\n",
    "> A file that is a _**randomly accessible**_ sequence of bytes.\n",
    "```\n",
    "$ ls -lh ERR1013163_1.fastq.gz\n",
    "-rw-r--r-- 1 jovyan root 3.8M Jun 10 16:00 ERR1013163_1.fastq.gz\n",
    "\n",
    "### FIFO (aka \"named pipe\")\n",
    "**F**irst **I**n **F**irst **O**ut\n",
    "\n",
    "> A type of file with the property that data written to such a file is read on a first-in-first-out basis.\n",
    "\n",
    "_In other words, data are written / read **sequentially**_\n",
    "\n",
    "### FIFO Example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "set -o xtrace\n",
    "mkfifo test.fifo\n",
    "printf 'line1\\nline2\\nline3\\n' > test.fifo &\n",
    "sleep 5\n",
    "jobs\n",
    "sleep 5\n",
    "cat test.fifo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Process Substitution\n",
    "`command1 >(command2)` is loosely analogous to:\n",
    "```\n",
    "mkfifo tmp.fifo\n",
    "command1 > tmp.fifo &\n",
    "command2 < tmp.fifo\n",
    "rm tmp.fifo\n",
    "```"
   ]
  }
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