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Repository: DRL/blobtools
Branch: master
Commit: 9426ca4a6dd0
Files: 41
Total size: 206.3 KB
Directory structure:
gitextract_36het1nj/
├── .gitignore
├── Dockerfile
├── LICENSE.md
├── MANIFEST.in
├── README.md
├── __init__.py
├── blobtools
├── data/
│ └── __init__.py
├── example/
│ ├── blast.out
│ ├── blobDB.json
│ ├── blobDB.table.txt
│ ├── catcolour.txt
│ ├── colours.txt
│ ├── diamond.out
│ ├── mapping_1.bam
│ ├── mapping_1.sorted.bam
│ ├── mapping_1.sorted.bam.bai
│ ├── mapping_2.bam
│ ├── mapping_2.sorted.bam
│ ├── mapping_2.sorted.bam.bai
│ └── refcov.txt
├── lib/
│ ├── BtCore.py
│ ├── BtIO.py
│ ├── BtLog.py
│ ├── BtPlot.py
│ ├── BtTax.py
│ ├── __init__.py
│ ├── bamfilter.py
│ ├── blobplot.py
│ ├── covplot.py
│ ├── create.py
│ ├── interface.py
│ ├── map2cov.py
│ ├── nodesdb.py
│ ├── seqfilter.py
│ ├── taxify.py
│ └── view.py
├── requirements.txt
├── setup.cfg
├── setup.py
└── test/
└── meta.json
================================================
FILE CONTENTS
================================================
================================================
FILE: .gitignore
================================================
!*.md
!*.py
!*install
!lib/*
!data/
!example/*
example/*.stats.txt
example/a*
example/test*
.DS_Store
!setup*
!requirements.txt
!blobtools
!MANIFEST.in
data/n*
samtools/
*.pyc
*.gz
*.fq
*.png
!blobplot.png
*.sam
================================================
FILE: Dockerfile
================================================
FROM continuumio/miniconda3
MAINTAINER Nick Waters <nickp60@gmail.com>
RUN conda install -c anaconda matplotlib docopt tqdm wget pyyaml git
RUN conda install -c bioconda pysam --update-deps
RUN git clone https://github.com/DRL/blobtools.git
WORKDIR blobtools
RUN ./blobtools -h
# RUN ./blobtools create -i example/assembly.fna -b example/mapping_1.bam -t example/blast.out -o example/test
RUN wget ftp://ftp.ncbi.nlm.nih.gov/pub/taxonomy/taxdump.tar.gz -P data/
RUN tar zxf data/taxdump.tar.gz -C data/ nodes.dmp names.dmp
RUN ./blobtools nodesdb --nodes data/nodes.dmp --names data/names.dmp
================================================
FILE: LICENSE.md
================================================
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PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
SUCH DAMAGES.
17. Interpretation of Sections 15 and 16.
If the disclaimer of warranty and limitation of liability provided
above cannot be given local legal effect according to their terms,
reviewing courts shall apply local law that most closely approximates
an absolute waiver of all civil liability in connection with the
Program, unless a warranty or assumption of liability accompanies a
copy of the Program in return for a fee.
END OF TERMS AND CONDITIONS
How to Apply These Terms to Your New Programs
If you develop a new program, and you want it to be of the greatest
possible use to the public, the best way to achieve this is to make it
free software which everyone can redistribute and change under these terms.
To do so, attach the following notices to the program. It is safest
to attach them to the start of each source file to most effectively
state the exclusion of warranty; and each file should have at least
the "copyright" line and a pointer to where the full notice is found.
{one line to give the program's name and a brief idea of what it does.}
Copyright (C) {year} {name of author}
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.
Also add information on how to contact you by electronic and paper mail.
If the program does terminal interaction, make it output a short
notice like this when it starts in an interactive mode:
{project} Copyright (C) {year} {fullname}
This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
This is free software, and you are welcome to redistribute it
under certain conditions; type `show c' for details.
The hypothetical commands `show w' and `show c' should show the appropriate
parts of the General Public License. Of course, your program's commands
might be different; for a GUI interface, you would use an "about box".
You should also get your employer (if you work as a programmer) or school,
if any, to sign a "copyright disclaimer" for the program, if necessary.
For more information on this, and how to apply and follow the GNU GPL, see
<http://www.gnu.org/licenses/>.
The GNU General Public License does not permit incorporating your program
into proprietary programs. If your program is a subroutine library, you
may consider it more useful to permit linking proprietary applications with
the library. If this is what you want to do, use the GNU Lesser General
Public License instead of this License. But first, please read
<http://www.gnu.org/philosophy/why-not-lgpl.html>.
================================================
FILE: MANIFEST.in
================================================
include README.md
include requirements.txt
================================================
FILE: README.md
================================================
BlobTools v1.1
===============================
A modular command-line solution for visualisation, quality control and taxonomic partitioning of genome datasets
- Discussions, questions and answers: [BlobTools GoogleGroup](https://groups.google.com/forum/#!forum/blobtools)
- Issues, bug reports and feature requests: [GitHub issues](https://github.com/DRL/blobtools/issues)
- Documentation: [blobtools.readme.io](https://blobtools.readme.io)
- Citation: [Laetsch DR and Blaxter ML, 2017](https://f1000research.com/articles/6-1287/v1)
Obtaining BlobTools
------------
- **Option A**: Download latest [release](https://github.com/DRL/blobtools/releases/latest)
- **Option B**: Clone repository
```
git clone https://github.com/DRL/blobtools.git
```
Enter directory
------------
```
cd blobtools
```
Install dependencies
------------
- Create [Conda](https://conda.io/en/latest/miniconda.html) environment and install dependencies
```
conda create -n blobtools
conda activate blobtools
conda install -c anaconda -c bioconda matplotlib docopt tqdm wget pyyaml git pysam
```
Download NCBI taxdump and create nodesdb
------------
```
wget ftp://ftp.ncbi.nlm.nih.gov/pub/taxonomy/taxdump.tar.gz -P data/
tar zxf data/taxdump.tar.gz -C data/ nodes.dmp names.dmp
./blobtools nodesdb --nodes data/nodes.dmp --names data/names.dmp
```
Create blobplot
------------
```
./blobtools create -i example/assembly.fna -b example/mapping_1.sorted.bam -t example/blast.out -o example/test && \
./blobtools view -i example/test.blobDB.json && \
./blobtools plot -i example/test.blobDB.json
```
Usage
-----
```
./blobtools --help
```
Docker
------
A docker container can be build using the following command:
```
docker build -t drl/blobtools .
```
This docker image can be run with sample data as follows:
```
docker run -v $PWD/example:/example/ -t drl/blobtools ./blobtools create -i /example/assembly.fna -b /example/mapping_1.sorted.bam -t /example/blast.out -o /example/test
```
================================================
FILE: __init__.py
================================================
================================================
FILE: blobtools
================================================
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
from lib.interface import main
if __name__ == '__main__':
main()
================================================
FILE: data/__init__.py
================================================
================================================
FILE: example/blast.out
================================================
contig_1 979556 200
contig_2 979556 500
contig_2 979556 1000
contig_2 979556 500
contig_2 979556 300
contig_3 979556 10000
contig_4 979556 1000
contig_5 6252 2000
contig_6 232323 2000
contig_6 6252 2000
contig_6 979556 2000
contig_6 232323 2000
contig_7 6252 2000
contig_8 6252 2000
contig_8 979556 2000
contig_9 6252 200
================================================
FILE: example/blobDB.json
================================================
{"tax_collision_random": false, "min_score": 0.0, "nodesDB_f": "/Users/dom/git/blobtools/data/nodesDB.txt", "title": "blobDB.json", "lineages": {"6252": {"superkingdom": "Eukaryota", "family": "Ascarididae", "order": "Ascaridida", "phylum": "Nematoda", "genus": "Ascaris", "species": "Ascaris lumbricoides"}, "232323": {"superkingdom": "Eukaryota", "family": "Hypsibiidae", "order": "Parachela", "phylum": "Tardigrada", "genus": "Hypsibius", "species": "Hypsibius dujardini"}, "979556": {"superkingdom": "Bacteria", "family": "Microbacteriaceae", "order": "Micrococcales", "phylum": "Actinobacteria", "genus": "Microbacterium", "species": "Microbacterium testaceum"}}, "taxrules": ["bestsum"], "hitLibs": {"tax0": {"fmt": "tax", "name": "tax0", "f": "/Users/dom/git/blobtools/example/blast.out"}}, "length": 18477, "version": "blobtools v1.0", "n_count": 0, "order_of_blobs": ["contig_1", "contig_2", "contig_3", "contig_4", "contig_5", "contig_6", "contig_7", "contig_8", "contig_9", "contig_10"], "seqs": 10, "min_diff": 0.0, "dict_of_blobs": {"contig_10": {"hits": {}, "name": "contig_10", "taxonomy": {"bestsum": {"superkingdom": {"score": 0.0, "tax": "no-hit", "c_index": 0}, "family": {"score": 0.0, "tax": "no-hit", "c_index": 0}, "order": {"score": 0.0, "tax": "no-hit", "c_index": 0}, "phylum": {"score": 0.0, "tax": "no-hit", "c_index": 0}, "genus": {"score": 0.0, "tax": "no-hit", "c_index": 0}, "species": {"score": 0.0, "tax": "no-hit", "c_index": 0}}}, "agct_count": 6273, "length": 6273, "gc": 0.3067, "n_count": 0, "covs": {"cov0": 310.634}, "read_cov": {"cov0": 8741}}, "contig_9": {"hits": {"tax0": [{"score": 200.0, "name": "contig_9", "taxId": "6252"}]}, "name": "contig_9", "taxonomy": {"bestsum": {"superkingdom": {"score": 200.0, "tax": "Eukaryota", "c_index": 0}, "family": {"score": 200.0, "tax": "Ascarididae", "c_index": 0}, "order": {"score": 200.0, "tax": "Ascaridida", "c_index": 0}, "phylum": {"score": 200.0, "tax": "Nematoda", "c_index": 0}, "genus": {"score": 200.0, "tax": "Ascaris", "c_index": 0}, "species": {"score": 200.0, "tax": "Ascaris lumbricoides", "c_index": 0}}}, "agct_count": 1599, "length": 1599, "gc": 0.2439, "n_count": 0, "covs": {"cov0": 74.757}, "read_cov": {"cov0": 554}}, "contig_8": {"hits": {"tax0": [{"score": 2000.0, "name": "contig_8", "taxId": "6252"}, {"score": 2000.0, "name": "contig_8", "taxId": "979556"}]}, "name": "contig_8", "taxonomy": {"bestsum": {"superkingdom": {"score": 2000.0, "tax": "unresolved", "c_index": 1}, "family": {"score": 2000.0, "tax": "unresolved", "c_index": 1}, "order": {"score": 2000.0, "tax": "unresolved", "c_index": 1}, "phylum": {"score": 2000.0, "tax": "unresolved", "c_index": 1}, "genus": {"score": 2000.0, "tax": "unresolved", "c_index": 1}, "species": {"score": 2000.0, "tax": "unresolved", "c_index": 1}}}, "agct_count": 2346, "length": 2346, "gc": 0.2801, "n_count": 0, "covs": {"cov0": 91.742}, "read_cov": {"cov0": 1008}}, "contig_1": {"hits": {"tax0": [{"score": 200.0, "name": "contig_1", "taxId": "979556"}]}, "name": "contig_1", "taxonomy": {"bestsum": {"superkingdom": {"score": 200.0, "tax": "Bacteria", "c_index": 0}, "family": {"score": 200.0, "tax": "Microbacteriaceae", "c_index": 0}, "order": {"score": 200.0, "tax": "Micrococcales", "c_index": 0}, "phylum": {"score": 200.0, "tax": "Actinobacteria", "c_index": 0}, "genus": {"score": 200.0, "tax": "Microbacterium", "c_index": 0}, "species": {"score": 200.0, "tax": "Microbacterium testaceum", "c_index": 0}}}, "agct_count": 756, "length": 756, "gc": 0.2606, "n_count": 0, "covs": {"cov0": 90.406}, "read_cov": {"cov0": 369}}, "contig_3": {"hits": {"tax0": [{"score": 10000.0, "name": "contig_3", "taxId": "979556"}]}, "name": "contig_3", "taxonomy": {"bestsum": {"superkingdom": {"score": 10000.0, "tax": "Bacteria", "c_index": 0}, "family": {"score": 10000.0, "tax": "Microbacteriaceae", "c_index": 0}, "order": {"score": 10000.0, "tax": "Micrococcales", "c_index": 0}, "phylum": {"score": 10000.0, "tax": "Actinobacteria", "c_index": 0}, "genus": {"score": 10000.0, "tax": "Microbacterium", "c_index": 0}, "species": {"score": 10000.0, "tax": "Microbacterium testaceum", "c_index": 0}}}, "agct_count": 602, "length": 602, "gc": 0.2342, "n_count": 0, "covs": {"cov0": 43.761}, "read_cov": {"cov0": 188}}, "contig_2": {"hits": {"tax0": [{"score": 500.0, "name": "contig_2", "taxId": "979556"}, {"score": 1000.0, "name": "contig_2", "taxId": "979556"}, {"score": 500.0, "name": "contig_2", "taxId": "979556"}, {"score": 300.0, "name": "contig_2", "taxId": "979556"}]}, "name": "contig_2", "taxonomy": {"bestsum": {"superkingdom": {"score": 2300.0, "tax": "Bacteria", "c_index": 0}, "family": {"score": 2300.0, "tax": "Microbacteriaceae", "c_index": 0}, "order": {"score": 2300.0, "tax": "Micrococcales", "c_index": 0}, "phylum": {"score": 2300.0, "tax": "Actinobacteria", "c_index": 0}, "genus": {"score": 2300.0, "tax": "Microbacterium", "c_index": 0}, "species": {"score": 2300.0, "tax": "Microbacterium testaceum", "c_index": 0}}}, "agct_count": 1060, "length": 1060, "gc": 0.2623, "n_count": 0, "covs": {"cov0": 168.409}, "read_cov": {"cov0": 844}}, "contig_5": {"hits": {"tax0": [{"score": 2000.0, "name": "contig_5", "taxId": "6252"}]}, "name": "contig_5", "taxonomy": {"bestsum": {"superkingdom": {"score": 2000.0, "tax": "Eukaryota", "c_index": 0}, "family": {"score": 2000.0, "tax": "Ascarididae", "c_index": 0}, "order": {"score": 2000.0, "tax": "Ascaridida", "c_index": 0}, "phylum": {"score": 2000.0, "tax": "Nematoda", "c_index": 0}, "genus": {"score": 2000.0, "tax": "Ascaris", "c_index": 0}, "species": {"score": 2000.0, "tax": "Ascaris lumbricoides", "c_index": 0}}}, "agct_count": 614, "length": 614, "gc": 0.329, "n_count": 0, "covs": {"cov0": 163.557}, "read_cov": {"cov0": 456}}, "contig_4": {"hits": {"tax0": [{"score": 1000.0, "name": "contig_4", "taxId": "979556"}]}, "name": "contig_4", "taxonomy": {"bestsum": {"superkingdom": {"score": 1000.0, "tax": "Bacteria", "c_index": 0}, "family": {"score": 1000.0, "tax": "Microbacteriaceae", "c_index": 0}, "order": {"score": 1000.0, "tax": "Micrococcales", "c_index": 0}, "phylum": {"score": 1000.0, "tax": "Actinobacteria", "c_index": 0}, "genus": {"score": 1000.0, "tax": "Microbacterium", "c_index": 0}, "species": {"score": 1000.0, "tax": "Microbacterium testaceum", "c_index": 0}}}, "agct_count": 951, "length": 951, "gc": 0.3155, "n_count": 0, "covs": {"cov0": 456.313}, "read_cov": {"cov0": 2096}}, "contig_7": {"hits": {"tax0": [{"score": 2000.0, "name": "contig_7", "taxId": "6252"}]}, "name": "contig_7", "taxonomy": {"bestsum": {"superkingdom": {"score": 2000.0, "tax": "Eukaryota", "c_index": 0}, "family": {"score": 2000.0, "tax": "Ascarididae", "c_index": 0}, "order": {"score": 2000.0, "tax": "Ascaridida", "c_index": 0}, "phylum": {"score": 2000.0, "tax": "Nematoda", "c_index": 0}, "genus": {"score": 2000.0, "tax": "Ascaris", "c_index": 0}, "species": {"score": 2000.0, "tax": "Ascaris lumbricoides", "c_index": 0}}}, "agct_count": 4060, "length": 4060, "gc": 0.2584, "n_count": 0, "covs": {"cov0": 52.312}, "read_cov": {"cov0": 1005}}, "contig_6": {"hits": {"tax0": [{"score": 2000.0, "name": "contig_6", "taxId": "232323"}, {"score": 2000.0, "name": "contig_6", "taxId": "6252"}, {"score": 2000.0, "name": "contig_6", "taxId": "979556"}, {"score": 2000.0, "name": "contig_6", "taxId": "232323"}]}, "name": "contig_6", "taxonomy": {"bestsum": {"superkingdom": {"score": 6000.0, "tax": "Eukaryota", "c_index": 1}, "family": {"score": 4000.0, "tax": "Hypsibiidae", "c_index": 2}, "order": {"score": 4000.0, "tax": "Parachela", "c_index": 2}, "phylum": {"score": 4000.0, "tax": "Tardigrada", "c_index": 2}, "genus": {"score": 4000.0, "tax": "Hypsibius", "c_index": 2}, "species": {"score": 4000.0, "tax": "Hypsibius dujardini", "c_index": 2}}}, "agct_count": 216, "length": 216, "gc": 0.1944, "n_count": 0, "covs": {"cov0": 25.88}, "read_cov": {"cov0": 52}}}, "assembly_f": "/Users/dom/git/blobtools/example/assembly.fna", "covLibs": {"cov0": {"reads_unmapped": 0, "mean_cov": 147.7771, "cov_sum": 1477.771, "name": "cov0", "f": "/Users/dom/git/blobtools/example/mapping_1.bam.cov", "fmt": "cov", "reads_total": 15313, "reads_mapped": 15313}}}
================================================
FILE: example/blobDB.table.txt
================================================
## blobtools v1.0
## assembly : /Users/dom/git/blobtools/example/assembly.fna
## coverage : cov0 - /Users/dom/git/blobtools/example/mapping_1.bam.cov
## taxonomy : tax0 - /Users/dom/git/blobtools/example/blast.out
## nodesDB : /Users/dom/git/blobtools/data/nodesDB.txt
## taxrule : bestsum
## min_score : 0.0
## min_diff : 0.0
## tax_collision_random : False
##
# name length GC N cov0 phylum.t.6 phylum.s.7 phylum.c.8
contig_1 756 0.2606 0 90.406 Actinobacteria 200.0 0
contig_2 1060 0.2623 0 168.409 Actinobacteria 2300.0 0
contig_3 602 0.2342 0 43.761 Actinobacteria 10000.0 0
contig_4 951 0.3155 0 456.313 Actinobacteria 1000.0 0
contig_5 614 0.329 0 163.557 Nematoda 2000.0 0
contig_6 216 0.1944 0 25.88 Tardigrada 4000.0 2
contig_7 4060 0.2584 0 52.312 Nematoda 2000.0 0
contig_8 2346 0.2801 0 91.742 unresolved 2000.0 1
contig_9 1599 0.2439 0 74.757 Nematoda 200.0 0
contig_10 6273 0.3067 0 310.634 no-hit 0.0 0
================================================
FILE: example/catcolour.txt
================================================
contig_1,A
contig_2,A
contig_3,A
contig_4,B
contig_5,B
contig_6,B
contig_7,B
contig_8,B
contig_9,C
contig_10,C
================================================
FILE: example/colours.txt
================================================
Nematoda,#48a365
Tardigrada,#48a365
Actinobacteria,#926eb3
other,#ffffff
================================================
FILE: example/diamond.out
================================================
contig_1 232323 200
contig_2 232323 500
contig_2 232323 1000
contig_2 232323 500
contig_2 979556 300
contig_3 979556 10000
contig_4 979556 1000
contig_5 6252 1000
contig_6 232323 1000
contig_6 6252 1000
contig_6 979556 1000
contig_6 232323 1000
contig_7 6252 1000
contig_9 6252 100
================================================
FILE: example/refcov.txt
================================================
cov0,15313,15300
bam0,15313,15300
cov1,37278,15300
bam1,37278,15300
covsum,52591,30600
================================================
FILE: lib/BtCore.py
================================================
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
File : BtCore.py
Author : Dominik R. Laetsch, dominik.laetsch at gmail dot com
"""
import lib.BtLog as BtLog
import lib.BtIO as BtIO
import lib.BtTax as BtTax
from os.path import abspath, isfile, basename, join
import re
from collections import defaultdict
import sys
from tqdm import tqdm
class BlobDb():
'''
class BlobDB holds all information parsed from files
'''
def __init__(self, title):
self.title = title
self.assembly_f = ''
self.dict_of_blobs = {}
self.order_of_blobs = [] # ordereddict
self.set_of_taxIds = set()
self.lineages = {}
self.length = 0
self.seqs = 0
self.n_count = 0
self.covLibs = {}
self.hitLibs = {}
self.nodesDB_f = ''
self.taxrules = []
self.version = ''
self.view_dir = ''
self.min_score = 0.0
self.min_diff = 0.0
self.tax_collision_random = False
def view(self, **kwargs):
# arguments
viewObjs = kwargs['viewObjs']
ranks = kwargs['ranks']
taxrule = kwargs['taxrule']
hits_flag = kwargs['hits_flag']
seqs = kwargs['seqs']
cov_libs = kwargs['cov_libs']
# Default sequences if no subset
if not (seqs):
seqs = self.order_of_blobs
# Default cov_libs if no subset
cov_lib_names = cov_libs
if not (cov_libs):
cov_lib_names = [covLib for covLib in self.covLibs]
tax_lib_names = [taxLib for taxLib in sorted(self.hitLibs)]
lineages = self.lineages
# setup
for viewObj in viewObjs:
#print("in view:", viewObj.name)
if viewObj.name == 'table':
viewObj.header = self.getTableHeader(taxrule, ranks, hits_flag, cov_lib_names)
if viewObj.name == 'concoct_cov':
viewObj.header = self.getConcoctCovHeader(cov_lib_names)
if viewObj.name == 'covlib':
viewObj.header = self.getCovHeader(cov_lib_names)
if viewObj.name == 'experimental':
viewObj.covs = {cov_lib: [] for cov_lib in cov_lib_names}
viewObj.covs["covsum"] = []
for taxrule in self.taxrules:
viewObj.tax[taxrule] = {rank: [] for rank in BtTax.RANKS}
# bodies
print("[+] Generating data for view")
with tqdm(total=len(seqs), desc="[%] ", ncols=200, unit_scale=True) as pbar:
for seq in seqs:
blob = self.dict_of_blobs[seq]
for viewObj in viewObjs:
if viewObj.name == 'table':
viewObj.body.append(self.getTableLine(blob, taxrule, ranks, hits_flag, cov_lib_names, tax_lib_names, lineages))
if viewObj.name == 'concoct_cov':
viewObj.body.append(self.getConcoctCovLine(blob, cov_lib_names))
if viewObj.name == 'experimental':
viewObj.names.append(blob['name'])
viewObj.gc.append(blob['gc'])
viewObj.length.append(blob['length'])
cov_sum = 0.0
for cov_lib in blob['covs']:
viewObj.covs[cov_lib].append(blob['covs'][cov_lib])
cov_sum += blob['covs'][cov_lib]
viewObj.covs['covsum'].append(cov_sum)
for taxrule in blob['taxonomy']:
for rank in blob['taxonomy'][taxrule]:
viewObj.tax[taxrule][rank].append(blob['taxonomy'][taxrule][rank]['tax'])
if viewObj.name == 'concoct_tax':
for rank in ranks:
if not rank in viewObj.body:
viewObj.body[rank] = []
viewObj.body[rank].append(self.getConcoctTaxLine(blob, rank, taxrule))
if viewObj.name == 'covlib':
viewObj.body.append(self.getCovLine(blob, cov_lib_names))
pbar.update()
for viewObj in viewObjs:
#print(viewObj.name)
viewObj.output()
def getCovHeader(self, cov_lib_names):
cov_lib_name = cov_lib_names[0]
header = '## %s\n' % (self.version)
if isinstance(self.covLibs[cov_lib_name], CovLibObj):
# CovLibObjs
header += "## Total Reads = %s\n" % (self.covLibs[cov_lib_name].reads_total)
header += "## Mapped Reads = %s\n" % (self.covLibs[cov_lib_name].reads_mapped)
header += "## Unmapped Reads = %s\n" % (self.covLibs[cov_lib_name].reads_total - self.covLibs[cov_lib_name].reads_mapped)
header += "## Source(s) : %s\n" % (self.covLibs[cov_lib_name].f)
else:
# CovLibObjs turned dicts
header += "## Total Reads = %s\n" % (self.covLibs[cov_lib_name]['reads_total'])
header += "## Mapped Reads = %s\n" % (self.covLibs[cov_lib_name]['reads_mapped'])
header += "## Unmapped Reads = %s\n" % (self.covLibs[cov_lib_name]['reads_total'] - self.covLibs[cov_lib_name]['reads_mapped'])
header += "## Source(s) : %s\n" % (self.covLibs[cov_lib_name]['f'])
header += "# %s\t%s\t%s\n" % ("contig_id", "read_cov", "base_cov")
return header
def getCovLine(self, blob, cov_lib_names):
if isinstance(blob, BlObj):
# BlobObj
return "%s\t%s\t%s\n" % (blob.name, blob.read_cov.get(cov_lib_names[0], 0), blob.covs.get(cov_lib_names[0], 0.0))
else:
# BlobObj turned dict
return "%s\t%s\t%s\n" % (blob['name'], blob['read_cov'].get(cov_lib_names[0], 0), blob['covs'].get(cov_lib_names[0], 0.0))
def getConcoctCovHeader(self, cov_lib_names):
return "contig\t%s\n" % "\t".join(cov_lib_names)
def getConcoctTaxLine(self, blob, rank, taxrule):
if taxrule in blob['taxonomy']:
return "%s,%s\n" % (blob['name'], blob['taxonomy'][taxrule][rank]['tax'])
def getConcoctCovLine(self, blob, cov_lib_names):
return "%s\t%s\n" % (blob['name'], "\t".join(map(str, [ blob['covs'][covLib] for covLib in cov_lib_names])))
def getTableHeader(self, taxrule, ranks, hits_flag, cov_lib_names):
header = []
header.append('## %s' % (self.version))
header.append("## assembly\t: %s" % self.assembly_f)
for libname in sorted(self.covLibs):
covLib = self.covLibs[libname]
header.append("## coverage\t %s - %s" % (covLib['name'], covLib["f"]))
if (self.hitLibs):
for libname in sorted(self.hitLibs):
hitLib = self.hitLibs[libname]
header.append("## taxonomy\t %s - %s" % (hitLib['name'], hitLib["f"]))
else:
header.append("## taxonomy\t: no taxonomy information found")
header.append("## nodesDB\t: %s" % self.nodesDB_f)
header.append("## taxrule\t: %s" % taxrule)
try:
header.append("## min_score\t: %s" % self.min_score)
header.append("## min_diff\t: %s" % self.min_diff)
header.append("## tax_collision_random\t: %s" % self.tax_collision_random)
except AttributeError():
header.append("## min_score\t: %s" % 0.0)
header.append("## min_diff\t: %s" % 0.0)
header.append("## tax_collision_random\t: %s" % False)
header.append("##")
main_header = []
main_header.append("# %s" % "\t".join(map(str, ["name", "length", "GC", "N"])))
col = 4
main_header.append("%s" % ("\t".join([cov_lib_name for cov_lib_name in cov_lib_names])))
col += len(cov_lib_names)
if (len(cov_lib_names)) > 1:
col += 1
main_header.append("%s" % "cov_sum")
for rank in ranks:
main_header.append("%s" % "\t".join([rank + ".t." + str(col + 1), rank + ".s." + str(col + 2), rank + ".c." + str(col + 3)]))
col += 3
if hits_flag:
main_header.append("%s" % rank + ".hits." + str(col + 1))
col += 1
header.append("\t".join(main_header))
return "\n".join(header)
def getTableLine(self, blob, taxrule, ranks, hits_flag, cov_lib_names, tax_lib_names, lineages):
sep = "\t"
line = ''
line += "\n%s" % sep.join(map(str, [ blob['name'], blob['length'], blob['gc'], blob['n_count'] ]))
line += sep
line += "%s" % sep.join(map(str, [ blob['covs'][covLib] for covLib in cov_lib_names]))
if len(cov_lib_names) > 1:
line += "%s%s" % (sep, sum([ blob['covs'][covLib] for covLib in cov_lib_names]))
for rank in ranks:
line += sep
tax, score, c_index = 'N/A', 'N/A', 'N/A'
if taxrule in blob['taxonomy']:
tax = blob['taxonomy'][taxrule][rank]['tax']
score = blob['taxonomy'][taxrule][rank]['score']
c_index = blob['taxonomy'][taxrule][rank]['c_index']
line += "%s" % sep.join(map(str, [tax, score, c_index ]))
if (hits_flag) and (taxrule in blob['taxonomy']):
line += sep
for tax_lib_name in tax_lib_names:
if tax_lib_name in blob['hits']:
line += "%s=" % tax_lib_name
sum_dict = {}
for hit in blob['hits'][tax_lib_name]:
tax_rank = lineages[hit['taxId']][rank]
sum_dict[tax_rank] = sum_dict.get(tax_rank, 0.0) + hit['score']
line += "%s" % "|".join([":".join(map(str, [tax_rank, sum_dict[tax_rank]])) for tax_rank in sorted(sum_dict, key=sum_dict.get, reverse=True)])
else:
line += "%s=no-hit:0.0" % (tax_lib_name)
line += ";"
return line
def dump(self):
dump = {'title' : self.title,
'assembly_f' : self.assembly_f,
'lineages' : self.lineages,
'order_of_blobs' : self.order_of_blobs,
'dict_of_blobs' : {name : blObj.__dict__ for name, blObj in self.dict_of_blobs.items()},
'length' : self.length,
'seqs' : self.seqs,
'n_count' : self.n_count,
'nodesDB_f' : self.nodesDB_f,
'covLibs' : {name : covLibObj.__dict__ for name, covLibObj in self.covLibs.items()},
'hitLibs' : {name : hitLibObj.__dict__ for name, hitLibObj in self.hitLibs.items()},
'taxrules' : self.taxrules,
'version' : self.version,
'min_score' : self.min_score,
'min_diff' : self.min_diff,
'tax_collision_random' : self.tax_collision_random
}
return dump
def load(self, BlobDb_f):
blobDict = BtIO.parseJson(BlobDb_f)
for k, v in blobDict.items():
setattr(self, k, v)
self.set_of_taxIds = blobDict['lineages'].keys()
def getPlotData(self, rank, min_length, hide_nohits, taxrule, c_index, catcolour_dict):
data_dict = {}
#read_cov_dict = {}
max_cov = 0.0
min_cov = 1000.0
cov_lib_dict = self.covLibs
cov_lib_names_l = self.covLibs.keys() # does not include cov_sum
if len(cov_lib_names_l) > 1:
# more than one cov_lib, cov_sum_lib has to be created
cov_lib_dict['covsum'] = CovLibObj('covsum', 'covsum', 'Sum of cov in %s' % basename(self.title)).__dict__ # ugly
cov_lib_dict['covsum']['reads_total'] = sum([self.covLibs[x]['reads_total'] for x in self.covLibs])
cov_lib_dict['covsum']['reads_mapped'] = sum([self.covLibs[x]['reads_mapped'] for x in self.covLibs])
cov_lib_dict['covsum']['cov_sum'] = sum([self.covLibs[x]['cov_sum'] for x in self.covLibs])
cov_lib_dict['covsum']['mean_cov'] = cov_lib_dict['covsum']['cov_sum']/self.seqs
for blob in self.dict_of_blobs.values():
name, gc, length, group = blob['name'], blob['gc'], blob['length'], ''
if (catcolour_dict): # annotation with categories specified in catcolour
group = str(catcolour_dict[name])
elif (c_index): # annotation with c_index instead of taxonomic group
if taxrule not in self.taxrules:
BtLog.error('11', taxrule, self.taxrules)
else:
group = str(blob['taxonomy'][taxrule][rank]['c_index'])
else: # annotation with taxonomic group
if not (taxrule) or taxrule not in self.taxrules:
BtLog.warn_d['9'] % (taxrule, self.taxrules)
if taxrule in blob['taxonomy']:
group = str(blob['taxonomy'][taxrule][rank]['tax'])
if not group in data_dict:
data_dict[group] = {
'name' : [],
'length' : [],
'gc' : [],
'covs' : {covLib : [] for covLib in cov_lib_dict.keys()}, # includes cov_sum if it exists
'reads_mapped' : {covLib : 0 for covLib in cov_lib_dict.keys()}, # includes cov_sum if it exists
'count' : 0,
'count_hidden' : 0,
'count_visible' : 0,
'span': 0,
'span_hidden' : 0,
'span_visible' : 0,
}
data_dict[group]['count'] = data_dict[group].get('count', 0) + 1
data_dict[group]['span'] = data_dict[group].get('span', 0) + int(length)
if ((hide_nohits) and group == 'no-hit') or length < min_length: # hidden
data_dict[group]['count_hidden'] = data_dict[group].get('count_hidden', 0) + 1
data_dict[group]['span_hidden'] = data_dict[group].get('span_hidden', 0) + int(length)
else: # visible
data_dict[group]['count_visible'] = data_dict[group].get('count_visible', 0) + 1
data_dict[group]['span_visible'] = data_dict[group].get('span_visible', 0) + int(length)
data_dict[group]['name'].append(name)
data_dict[group]['length'].append(length)
data_dict[group]['gc'].append(gc)
cov_sum = 0.0
reads_mapped_sum = 0
for cov_lib in sorted(cov_lib_names_l):
if cov_lib == 'covsum':
continue
cov = float(blob['covs'][cov_lib])
if cov < 0.1:
cov = 0.1
if cov < min_cov:
min_cov = cov
# increase max_cov
if cov > max_cov:
max_cov = cov
# add cov of blob to group
data_dict[group]['covs'][cov_lib].append(cov)
cov_sum += cov
# add readcov
if cov_lib in blob['read_cov']:
reads_mapped = blob['read_cov'][cov_lib]
data_dict[group]['reads_mapped'][cov_lib] += reads_mapped
reads_mapped_sum += reads_mapped
if len(cov_lib_names_l) > 1:
if cov_sum <= 0.1 * len(cov_lib_names_l): # puts no-cov contigs at 0.1
cov_sum = 0.1
data_dict[group]['covs']['covsum'].append(cov_sum)
if cov_sum > max_cov:
max_cov = cov_sum
if (reads_mapped_sum):
data_dict[group]['reads_mapped']['covsum'] += reads_mapped_sum
return data_dict, min_cov, max_cov, cov_lib_dict
def addCovLib(self, covLib):
self.covLibs[covLib.name] = covLib
for blObj in self.dict_of_blobs.values():
blObj.addCov(covLib.name, 0.0)
def parseFasta(self, fasta_f, fasta_type):
print(BtLog.status_d['1'] % ('FASTA', fasta_f))
self.assembly_f = abspath(fasta_f)
if (fasta_type):
# Set up CovLibObj for coverage in assembly header
self.covLibs[fasta_type] = CovLibObj(fasta_type, fasta_type, fasta_f)
for name, seq in BtIO.readFasta(fasta_f):
blObj = BlObj(name, seq)
if not blObj.name in self.dict_of_blobs:
self.seqs += 1
self.length += blObj.length
self.n_count += blObj.n_count
if (fasta_type):
cov = BtIO.parseCovFromHeader(fasta_type, blObj.name)
self.covLibs[fasta_type].cov_sum += cov
blObj.addCov(fasta_type, cov)
self.order_of_blobs.append(blObj.name)
self.dict_of_blobs[blObj.name] = blObj
else:
BtLog.error('5', blObj.name)
if self.seqs == 0 or self.length == 0:
BtLog.error('1')
def parseCoverage(self, **kwargs):
# arguments
covLibObjs = kwargs['covLibObjs']
estimate_cov = kwargs['estimate_cov']
for covLib in covLibObjs:
self.addCovLib(covLib)
print(BtLog.status_d['1'] % (covLib.name, covLib.f))
if covLib.fmt == 'bam':
base_cov_dict = {}
base_cov_dict, covLib.reads_total, covLib.reads_mapped, read_cov_dict = BtIO.parseBam(covLib.f, set(self.dict_of_blobs), estimate_cov)
if covLib.reads_total == 0:
print(BtLog.warn_d['4'] % covLib.f)
for name, base_cov in base_cov_dict.items():
cov = 0.0
if not self.dict_of_blobs[name].agct_count == 0:
cov = base_cov / self.dict_of_blobs[name].agct_count
covLib.cov_sum += cov
self.dict_of_blobs[name].addCov(covLib.name, cov)
self.dict_of_blobs[name].addReadCov(covLib.name, read_cov_dict[name])
# Create COV file for future use
out_f = BtIO.getOutFile(covLib.f, kwargs.get('prefix', None), None)
covView = ViewObj(name="covlib", out_f=out_f, suffix="cov", header="", body=[])
self.view(viewObjs=[covView], ranks=None, taxrule=None, hits_flag=None, seqs=None, cov_libs=[covLib.name], progressbar=False)
elif covLib.fmt == 'cas':
cov_dict, covLib.reads_total, covLib.reads_mapped, read_cov_dict = BtIO.parseCas(covLib.f, self.order_of_blobs)
if covLib.reads_total == 0:
print(BtLog.warn_d['4'] % covLib.f)
for name, cov in cov_dict.items():
covLib.cov_sum += cov
self.dict_of_blobs[name].addCov(covLib.name, cov)
self.dict_of_blobs[name].addReadCov(covLib.name, read_cov_dict[name])
out_f = BtIO.getOutFile(covLib.f, kwargs.get('prefix', None), None)
covView = ViewObj(name="covlib", out_f=out_f, suffix="cov", header="", body=[])
self.view(viewObjs=[covView], ranks=None, taxrule=None, hits_flag=None, seqs=None, cov_libs=[covLib.name], progressbar=False)
elif covLib.fmt == 'cov':
base_cov_dict, covLib.reads_total, covLib.reads_mapped, covLib.reads_unmapped, read_cov_dict = BtIO.parseCov(covLib.f, set(self.dict_of_blobs))
#cov_dict = BtIO.readCov(covLib.f, set(self.dict_of_blobs))
if not len(base_cov_dict) == self.seqs:
print(BtLog.warn_d['4'] % covLib.f)
for name, cov in base_cov_dict.items():
covLib.cov_sum += cov
self.dict_of_blobs[name].addCov(covLib.name, cov)
if name in read_cov_dict:
self.dict_of_blobs[name].addReadCov(covLib.name, read_cov_dict[name])
else:
pass
covLib.mean_cov = covLib.cov_sum/self.seqs
if covLib.cov_sum == 0.0:
print(BtLog.warn_d['6'] % (covLib.name))
self.covLibs[covLib.name] = covLib
def parseHits(self, hitLibs):
for hitLib in hitLibs:
self.hitLibs[hitLib.name] = hitLib
print(BtLog.status_d['1'] % (hitLib.name, hitLib.f))
# only accepts format 'seqID\ttaxID\tscore'
for hitDict in BtIO.readTax(hitLib.f, set(self.dict_of_blobs)):
if ";" in hitDict['taxId']:
hitDict['taxId'] = hitDict['taxId'].split(";")[0]
#print(BtLog.warn_d['5'] % (hitDict['name'], hitLib))
self.set_of_taxIds.add(hitDict['taxId'])
self.dict_of_blobs[hitDict['name']].addHits(hitLib.name, hitDict)
def computeTaxonomy(self, taxrules, nodesDB, min_score, min_bitscore_diff, tax_collision_random):
print(BtLog.status_d['6'] % ",".join(taxrules))
tree_lists = BtTax.getTreeList(self.set_of_taxIds, nodesDB)
self.lineages = BtTax.getLineages(tree_lists, nodesDB)
self.taxrules = taxrules
self.min_score = min_score
self.min_diff = min_bitscore_diff
self.tax_collision_random = tax_collision_random
with tqdm(total=self.seqs, desc="[%] ", ncols=200, unit_scale=True) as pbar:
for blObj in self.dict_of_blobs.values():
for taxrule in taxrules:
if (blObj.hits):
blObj.taxonomy[taxrule] = BtTax.taxRule(taxrule, blObj.hits, self.lineages, min_score, min_bitscore_diff, tax_collision_random)
else:
blObj.taxonomy[taxrule] = BtTax.noHit()
pbar.update()
self.set_of_taxIds = set()
def getBlobs(self):
for blObj in [self.dict_of_blobs[key] for key in self.order_of_blobs]:
yield blObj
class BlObj():
def __init__(self, name, seq):
self.name = name
self.length = len(seq)
self.n_count = seq.count('N')
self.agct_count = self.length - self.n_count
self.gc = round(self.calculateGC(seq), 4)
self.covs = {}
self.read_cov = {}
self.hits = {}
self.taxonomy = {}
def calculateGC(self, seq):
return float((seq.count('G') + seq.count('C') ) / self.agct_count \
if self.agct_count > 0 else 0.0)
def addCov(self, lib_name, cov):
self.covs[lib_name] = float("{0:.4f}".format(cov)) # changed to three decimal digits
def addReadCov(self, lib_name, read_cov):
self.read_cov[lib_name] = read_cov
def addHits(self, hitLibName, hitDict):
if not hitLibName in self.hits:
self.hits[hitLibName] = []
self.hits[hitLibName].append(hitDict)
class CovLibObj():
def __init__(self, name, fmt, f):
self.name = name
self.fmt = fmt
self.f = abspath(f) if isfile(f) else f # pass file/string/''
self.cov_sum = 0.0
self.reads_total = 0
self.reads_mapped = 0
self.reads_unmapped = 0
self.mean_cov = 0.0
class HitLibObj():
def __init__(self, name, fmt, f):
self.name = name
self.fmt = fmt
self.f = abspath(f) if isfile(f) else f # pass file/string/''
class ViewObj():
def __init__(self, name='', out_f='', suffix='', header='', body=''):
self.name = name
self.out_f = out_f
self.suffix = suffix
self.header = header
self.body = body
def output(self):
if isinstance(self.body, dict):
for category in self.body:
out_f = "%s.%s.%s" % (self.out_f, category, self.suffix)
print(BtLog.status_d['13'] % (out_f))
with open(out_f, "w") as fh:
fh.write(self.header + "".join(self.body[category]))
elif isinstance(self.body, list):
out_f = "%s.%s" % (self.out_f, self.suffix)
print(BtLog.status_d['13'] % (out_f))
with open(out_f, "w") as fh:
fh.write(self.header + "".join(self.body))
else:
sys.exit("[ERROR] - 001")
#class newBlobDb():
# def __init__(self, name='blobdb'):
# # meta
# self.title = title
# self.path = path
# self.version = version
# self.blob_count = 0
# self.tax_hit_count = 0
# self.sources = {}
# self.files = {}
# self.cov_lib = []
# self.tax_lib = []
# self.tax_rule = []
# self.reads_total = {}
# self.reads_mapped = {}
# self.ranks = []
#
# self.blobs_list = []
# self.blobs_dict = {}
#
# self.blob_id = []
# self.length = []
# self.gc = []
# self.n_count = []
# self.agct_count = []
#
# self.cov_base = {}
# self.cov_read = {}
# self.tax = {}
# self.tax_hit = {}
# self.tax_id = {}
#
# self.meta = {}
#
# def _set_meta(self):
# self.meta = {
# "title" : self.title,
# "version" : self.version,
# "count" : self.count,
# "sources" : self.sources,
# "files" : self.files,
# "cov_lib" : self.cov_lib,
# "tax_lib" : self.tax_lib,
# "tax_rule" : self.tax_rule,
# "reads_total" : self.reads_total,
# "reads_mapped" : self.reads_mapped,
# "tax_hit_count" : self.tax_hit_count
# }
#
# def _parse_meta(self, meta_f):
# pass
#
#
# def _set_files(self):
# self.files = {
# # primary
# 'meta' : "%s" % join(self.path, "meta"),
# 'blob_id' : "%s" % join(self.path, "blob_id"),
# 'length' : "%s" % join(self.path, "length"),
# 'gc' : "%s" % join(self.path, "gc"),
# 'n_count' : "%s" % join(self.path, "n_count"),
# 'agct_count' : "%s" % join(self.path, "agct_count"),
# 'tax_id' : "%s" % join(self.path, tax_id),
# # secondary
# 'cov_base' : {cov_lib : "%s" % join(self.path, "cov_base") for cov_lib in self.cov_lib},
# 'cov_read' : {cov_lib : "%s" % join(self.path, "cov_read") for cov_lib in self.cov_lib},
# 'tax' : {tax_rule : "%s" % join(self.path, "tax") for tax_rule in self.tax_rule},
# 'tax_hit' : {tax_lib : "%s" % join(self.path, "tax_hit") for tax_lib in self.tax_lib}
# }
#
# def _write_output(self):
# primary = ['meta', 'blob_id', 'length', 'gc', 'n_count', 'agct_count', 'tax_id']
# secondary = ['cov_base', 'cov_read', 'tax', 'tax_hit']
# directory = BtIO.create_dir(self.path)
# out_fs = []
# if (directory):
# out_fs = []
# strings = []
# for key in self.files:
# if key in primary:
# out_f.append(self.files[key])
# data.append(getattr(self, key))
# elif key in secondary:
# for key2 in self.files[key]:
# out_f.append(self.files[key][key2])
# data.append(getattr(self, key)[key2])
# else:
# pass
# with tarfile.open(out_f, "a:gz") as tar:
# for out_f, string in zip(out_fs, strings):
# with open(out_f, 'w') as fh:
# json.dump(string, fh, indent=1, separators=(',', ' : '))
# tar.add(out_f)
#
# def dump(self):
# self._set_files()
# self._set_meta()
# self._write_output()
#
# def load(self, blobdb):
# try:
# meta_f = blobdb.getmember('meta')
# except KeyError:
# pass
#
# def yield_blob(self, fields):
# Blob = collections.namedtuple('Blob', fields)
# for idx, blob_id in enumerate(self.blob_id):
# data = [x for x in getattr(self, fields)]
# blob = Blob()
#
# def get_idxs_by_ids(self, ids):
# # list of blob_ids
# if (ids) and isinstance(ids, list):
# return [int(self.blob_dict[id_]) for _id_ in ids]
#
# def get_data(self, *key, **kwargs):
# idxs = []
# # sort out which idxs
# if (kwargs['ids']): # delimited by list of ids
# idxs = self.get_idxs_by_ids(kwargs['ids'])
# elif (kwargs['idxs']): # delimited by list of idxs
# idxs = kwargs['idxs']
# else: # all
# idxs = range(self.count)
#
# # return data based on idxs
# if args[0] == "name":
# return [self.name[idx] for idx in idxs]
# elif args[0] == "length":
# return [self.length[idx] for idx in idxs]
# elif args[0] == "gc":
# return [self.gc[idx] for idx in idxs]
# elif args[0] == "n_count":
# return [self.n_count[idx] for idx in idxs]
# elif args[0] == "cov_base":
# if args[1] in self.cov_lib:
# return [self.cov_base[idx] for idx in idxs]
# elif args[0] == "cov_read":
# if args[1] in self.cov_lib:
# return [self.cov_read[idx] for idx in idxs]
# elif args[0] == "tax":
# if args[1] in self.tax_rule:
# if args[2] in self.ranks:
# return [self.tax[idx] for idx in idxs]
# elif args[0] == "tax_hit":
# if args[1] in self.tax_lib:
# return [self.tax_hit[idx] for idx in idxs]
# else:
# return None
#
# # Parse
# def parse_meta(self, meta_f):
# meta = BtIO.read_meta(meta_f)
# for key, value in meta.items():
# setattr(key, value)
# self.name = meta['name']
# self.covlib = meta['covlib']
# self.taxrule = meta['taxrule']
# self.taxlib = meta['taxlib']
# self.files = meta['files']
# self.blobs_count = meta['count']
# self.ranks = meta['ranks']
#
# def parse_data(self, key, exp_count):
# data = BtIO.read_json_list(self.files[key])
# if not len(data) == exp_count:
# # error
# pass
# else:
# setattr(self, key, data)
#
# def output(self):
# # meta
# meta = self.get_meta()
# meta_f = join(self.view_dir, "meta.json")
# BtIO.writeJson(meta, meta_f, indent=2)
# # gc
# gc_f = join(self.view_dir, "gc.json")
# print(BtLog.status_d['13'] % (gc_f))
# BtIO.writeJson(self.gc, gc_f, indent=1)
# # length
# length_f = join(self.view_dir, "length.json")
# print(BtLog.status_d['13'] % (length_f))
# BtIO.writeJson(self.length, length_f, indent=1)
# # names
# names_f = join(self.view_dir, "names.json")
# print(BtLog.status_d['13'] % (names_f))
# BtIO.writeJson(self.names, names_f, indent=1)
# # cov
# cov_d = join(self.view_dir, "covs")
# BtIO.create_dir(directory=cov_d)
# for cov_lib, cov in self.covs.items():
# cov_f = join(cov_d, "%s.json" % cov_lib)
# print(BtLog.status_d['13'] % (cov_f))
# BtIO.writeJson(cov, cov_f, indent=1)
# # tax
# taxrule_d = join(self.view_dir, "taxrule")
# BtIO.create_dir(directory=taxrule_d)
# for taxrule in self.tax:
# tax_d = join(taxrule_d, taxrule)
# BtIO.create_dir(directory=tax_d)
# for rank in self.tax[taxrule]:
# tax = self.tax[taxrule][rank]
# rank_f = join(tax_d, "%s.json" % rank)
# BtIO.writeJson(tax, rank_f, indent=1)
class ExperimentalViewObj():
def __init__(self, name='experimental', view_dir='',blobDb={},meta={}):
self.name = name
self.view_dir = re.sub(".blobDB", "", view_dir)
self.length = []
self.gc = []
self.n_count = []
self.names = []
self.tax = {}
self.covs = {}
self.read_covs = defaultdict(list)
self.tax_scores = {}
self.blobDb = blobDb
self.meta = meta
BtIO.create_dir(self.view_dir)
def _format_float(self,l,min_val=-float("inf")):
if min_val:
l = map(lambda x:max(x,min_val),l)
return map(lambda x:float("%.4f" % x),l)
def _remove_cov_suffix(self,id,meta):
rep_list = ['.bam','.bam.cov','.cas','.cas.cov','.cov','.sam','.sam.cov']
rep_list += list(map(lambda x: "%s." % x, self.view_dir.split('.')))
name = id
if id in meta:
name = re.sub("|".join(rep_list), "", basename(meta[id]['f']))
return name if name else id
def get_meta(self):
meta = self.meta
meta["id"] = self.view_dir
meta["name"] = self.view_dir
meta["records"] = len(self.names)
meta["record_type"] = "contigs"
meta["fields"] = [
{ "id":"length", "name":"Length", "type":"variable", "datatype":"integer", "range":[min(self.length),max(self.length)], "scale":"scaleLog", "preload":True },
{ "id":"gc", "name":"GC", "type":"variable", "datatype":"float", "range":self._format_float([min(self.gc),max(self.gc)]), "scale":"scaleLinear", "preload":True },
]
meta["plot"] = {
"x":"gc",
"z":"length"
}
cov_names = filter(lambda name: name != "covsum",self.covs)
for taxrule in self.tax:
self.tax_scores[taxrule] = defaultdict(lambda: {'score':[],'c_index':[]})
for _id in self.blobDb.order_of_blobs:
blob = self.blobDb.dict_of_blobs[_id]
self.read_covs['covsum'].append(0)
for cov_name in cov_names:
self.read_covs[cov_name].append(blob['read_cov'][cov_name])
self.read_covs['covsum'][-1] += blob['read_cov'][cov_name]
for taxrule in self.tax:
for rank in self.tax[taxrule]:
self.tax_scores[taxrule][rank]['score'].append(blob['taxonomy'][taxrule][rank]['score'])
self.tax_scores[taxrule][rank]['c_index'].append(blob['taxonomy'][taxrule][rank]['c_index'])
self.n_count.append(blob['length']-blob['agct_count'])
if max(self.n_count) > 0:
meta['fields'].append({ "id":"ncount", "name":"N count", "type":"variable", "datatype":"integer", "range":[max(0.1,min(self.n_count)),max(self.n_count)], "scale":"scaleLinear"})
if len(self.covs) > 0:
for cov in ['cov','read_cov']:
cov_libs = []
for cov_name in self.covs:
name = self._remove_cov_suffix(cov_name,self.blobDb.covLibs)
_id = "%s_%s" % (name,cov)
cov_lib_meta = {"id": _id, "name":name }
if cov_name == "cov0" and cov == "cov":
cov_lib_meta["preload"] = True
meta['plot']['y'] = _id
cov_libs.append(cov_lib_meta)
cov_meta = {"id":"%s" % cov, "name":"Coverage", "type":"variable", "datatype":"float", "scale":"scaleLog", "range":self._format_float([0.02,max(self.covs["covsum"])])}
if cov == 'read_cov':
cov_meta['name'] = "Read coverage"
cov_meta['datatype'] = "integer"
cov_meta['range'] = [0.2,max(self.read_covs["covsum"])]
cov_meta['children'] = sorted(cov_libs, key=lambda k: k['name'])
meta['fields'].append(cov_meta)
if len(self.tax) > 0:
tax_rules = []
for taxrule in self.tax:
taxrule_meta = {"id":taxrule, "name":taxrule, "children":[] }
for rank in self.tax[taxrule]:
_id = "%s_%s" % (taxrule,rank)
tax_rank_data = []
tax_rank_data.append({ "id":"%s_score" % _id, "name":"%s score" % _id, "type":"variable", "datatype":"float", "scale":"scaleLog", "range":[0.2,max(self.tax_scores[taxrule][rank]['score'])], "preload":False, "active":False })
tax_rank_data.append({ "id":"%s_cindex" % _id, "name":"%s c-index" % _id, "type":"variable", "datatype":"integer", "scale":"scaleLinear", "range":[0,max(self.tax_scores[taxrule][rank]['c_index'])], "preload":False, "active":False })
tax_rank_meta = { "id":_id, "name":_id, "data": tax_rank_data }
if rank == "phylum":
tax_rank_meta["preload"] = True
meta['plot']['cat'] = _id
taxrule_meta['children'].append(tax_rank_meta)
tax_rules.append(taxrule_meta)
tax_meta = {"id":"taxonomy", "name":"Taxonomy", "type":"category", "datatype":"string"}
tax_meta['children'] = sorted(tax_rules, key=lambda k: k['name'])
meta['fields'].append(tax_meta)
#for taxrule in self.tax:
# meta['datatypes'][taxrule] = {"name": taxrule, "type":"category"}
#for rank in BtTax.RANKS:
# meta['datatypes'][rank] = {"name": rank, "type":"category", "levels" : 7}
return meta
def _keyed_list(self,l):
d = {}
i = 0
o = []
for v in l:
if v not in d:
d[v] = i
i += 1
o.append(d[v])
return {'values':o,'keys':sorted(d, key=d.get)}
def output(self):
# meta
meta = self.get_meta()
meta_f = join(self.view_dir, "meta.json")
BtIO.writeJson(meta, meta_f)
# gc
gc_f = join(self.view_dir, "gc.json")
print(BtLog.status_d['13'] % (gc_f))
BtIO.writeJson({"values":self._format_float(self.gc)}, gc_f, indent=1)
# length
length_f = join(self.view_dir, "length.json")
print(BtLog.status_d['13'] % (length_f))
BtIO.writeJson({"values":self.length}, length_f, indent=1)
# Ns
if max(self.n_count) > 0:
n_f = join(self.view_dir, "ncount.json")
print(BtLog.status_d['13'] % (n_f))
BtIO.writeJson({"values":map(lambda x:max(x,0.2),self.n_count)}, n_f, indent=1)
# identifiers
ids_f = join(self.view_dir, "identifiers.json")
print(BtLog.status_d['13'] % (ids_f))
BtIO.writeJson(self.names, ids_f, indent=1)
# cov
for cov_name, cov in self.covs.items():
name = self._remove_cov_suffix(cov_name,self.blobDb.covLibs)
cov_f = join(self.view_dir, "%s_cov.json" % name)
print(BtLog.status_d['13'] % (cov_f))
BtIO.writeJson({"values":self._format_float(cov,0.02)}, cov_f, indent=1)
# read_cov
for cov_name, cov in self.read_covs.items():
name = self._remove_cov_suffix(cov_name,self.blobDb.covLibs)
cov_f = join(self.view_dir, "%s_read_cov.json" % name)
print(BtLog.status_d['13'] % (cov_f))
BtIO.writeJson({"values":map(lambda x:max(x,0.2),cov)}, cov_f, indent=1)
# tax
for taxrule in self.tax:
for rank in self.tax[taxrule]:
tax = self._keyed_list(self.tax[taxrule][rank])
rank_f = join(self.view_dir, "%s_%s.json" % (taxrule,rank))
BtIO.writeJson(tax, rank_f, indent=1)
score = self.tax_scores[taxrule][rank]['score']
score_f = join(self.view_dir, "%s_%s_score.json" % (taxrule,rank))
BtIO.writeJson({"values":map(lambda x:max(x,0.2),score)}, score_f, indent=1)
cindex = self.tax_scores[taxrule][rank]['c_index']
cindex_f = join(self.view_dir, "%s_%s_cindex.json" % (taxrule,rank))
BtIO.writeJson({"values":cindex}, cindex_f, indent=1)
if __name__ == '__main__':
pass
================================================
FILE: lib/BtIO.py
================================================
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
File : BtIO.py
Author : Dominik R. Laetsch, dominik.laetsch at gmail dot com
"""
from __future__ import division
import re
import subprocess
import os
import pysam
from collections import defaultdict
from os.path import basename, isfile, splitext, join, isdir
import shutil
import lib.BtLog as BtLog
import sys
from tqdm import tqdm
import yaml
# CONSTs
COMPLEMENT = {'A':'T','C':'G','G':'C','T':'A','N':'N'}
def create_dir(directory="", overwrite=True):
if directory:
if not isdir(directory):
os.makedirs(directory)
else:
if overwrite:
shutil.rmtree(directory) #removes all the subdirectories!
os.makedirs(directory)
return directory
else:
return None
def parseList(infile):
if not isfile(infile):
BtLog.error('0', infile)
with open(infile) as fh:
items = []
for l in fh:
items.append(l.rstrip("\n"))
return items
def parseReferenceCov(infile):
refcov_dict = {}
if infile:
if not isfile(infile):
BtLog.error('0', infile)
with open(infile) as fh:
for l in fh:
try:
cov_lib, reads_total_ref, reads_mapped_ref = l.split(",")
refcov_dict[cov_lib] = {'reads_total' : int(reads_total_ref),
'reads_mapped' : int(reads_mapped_ref)}
except:
BtLog.error('21', infile)
return refcov_dict
def parseCmdlist(temp):
_list = []
if temp:
if "," in temp:
_list = temp.split(",")
else:
_list.append(temp)
return _list
def parseCmdLabels(labels):
label_d = {}
name, groups = '', ''
if labels:
try:
for label in labels:
name, groups = str(label).split("=")
if "," in groups:
for group in groups.split(","):
label_d[group] = name
else:
label_d[groups] = name
except:
BtLog.error('17', labels)
return label_d
def parseCatColour(infile):
catcolour_dict = {}
if infile:
if not isfile(infile):
BtLog.error('0', infile)
with open(infile) as fh:
for l in fh:
try:
seq_name, category = l.rstrip("\n").split(",")
catcolour_dict[seq_name] = category
except:
BtLog.error('23', infile)
return catcolour_dict
def parseDict(infile, key, value):
items = {}
if infile:
if not isfile(infile):
BtLog.error('0', infile)
with open(infile) as fh:
items = {}
k_idx = int(key)
v_idx = int(value)
for l in fh:
temp = l.rstrip("\n").split()
items[temp[k_idx]] = temp[v_idx]
return items
def parseColours(infile):
items = {}
if infile:
if not isfile(infile):
BtLog.error('0', infile)
with open(infile) as fh:
for l in fh:
temp = l.rstrip("\n").split(",")
items[temp[0]] = temp[1]
return items
def parseSet(infile):
if not isfile(infile):
BtLog.error('0', infile)
with open(infile) as fh:
items = set()
for l in fh:
items.add(l.rstrip("\n").lstrip(">"))
return items
def parseFastaNameOrder(infile):
fasta_order = []
for name, seq in readFasta(infile):
fasta_order.append(name)
return fasta_order
def readFasta(infile):
if not isfile(infile):
BtLog.error('0', infile)
with open(infile) as fh:
header, seqs = '', []
for l in fh:
if l[0] == '>':
if header:
yield header, ''.join(seqs).upper()
header, seqs = l[1:-1].split()[0], [] # Header is split at first whitespace
else:
seqs.append(l[:-1])
yield header, ''.join(seqs).upper()
def runCmd(**kwargs):
command = kwargs['command']
cmd = command.split() # sanitation
p = subprocess.Popen(cmd,
stdout=subprocess.PIPE,
stderr=subprocess.STDOUT,
universal_newlines=True,
bufsize=-1) # buffersize of system
wait = kwargs.get('wait', False)
if wait :
p.wait()
if p.returncode == 0:
pass
else:
return iter(p.stdout.readline, b'')
def which(program):
def is_exe(fpath):
return os.path.isfile(fpath) and os.access(fpath, os.X_OK)
fpath, fname = os.path.split(program)
if fpath:
if is_exe(program):
return program
else:
for path in os.environ["PATH"].split(os.pathsep):
path = path.strip('"')
exe_file = os.path.join(path, program)
if is_exe(exe_file):
return exe_file
return None
def checkAlnIndex(aln):
try:
index_flag = aln.check_index()
except ValueError:
index_flag = False
return index_flag
def getAlnHeaderIntersection(aln, headers):
aln_set = set(aln.references)
headers_set = set(headers)
headers_aln_intersection = headers_set.intersection(aln_set)
return (len(headers_set), len(aln_set), len(headers_aln_intersection))
def estimate_read_lengths(aln, set_of_blobs):
_read_lengths = []
while len(_read_lengths) < 10000:
for header in set_of_blobs:
for read in aln.fetch(header):
_read_lengths.append(read.query_length)
return round(sum(_read_lengths)/len(_read_lengths), 4)
def checkBam(aln, set_of_blobs):
if not checkAlnIndex(aln):
print("[X] Please (sort and) index your BAM file")
sys.exit()
len_headers, len_aln, len_intersection = getAlnHeaderIntersection(aln, set_of_blobs)
if len_intersection == 0:
print("[X] Headers in FASTA and BAM don't seem to match")
sys.exit()
print("[+] -> %.2f (%s/%s) of sequences have reads aligned to them." % ((len_intersection / len_headers) * 100, len_intersection, len_headers))
reads_total = aln.mapped + aln.unmapped
print("[+] -> %.2f (%s/%s) of reads are mapped." % ((aln.mapped / reads_total) * 100, aln.mapped, reads_total))
return reads_total, aln.mapped
def parseBam(infile, set_of_blobs, estimate_cov):
# no_base_cov_flag [deprecated]
reads_total, reads_mapped = 0, 0
with pysam.AlignmentFile(infile) as aln:
reads_total, reads_mapped = checkBam(aln, set_of_blobs)
if estimate_cov:
base_cov_dict, read_cov_dict = estimate_coverage(aln, set_of_blobs)
else:
base_cov_dict, read_cov_dict = calculate_coverage(aln, reads_mapped, set_of_blobs)
return base_cov_dict, reads_total, reads_mapped, read_cov_dict
def estimate_coverage(aln, set_of_blobs):
base_cov_dict = {blob : 0.0 for blob in set_of_blobs}
read_cov_dict = {blob : 0 for blob in set_of_blobs}
est_read_length = estimate_read_lengths(aln, set_of_blobs)
with tqdm(total=len(set_of_blobs), desc="[%] ", ncols=200, unit_scale=True) as pbar:
for header in set_of_blobs:
read_count = aln.count(header, read_callback=check_mapped_read)
base_cov_dict[header] = read_count * est_read_length
read_cov_dict[header] += read_count
pbar.update()
return base_cov_dict, read_cov_dict
def check_mapped_read(read):
if read.is_unmapped or read.is_secondary or read.is_supplementary:
return False
return True
def calculate_coverage(aln, reads_mapped, set_of_blobs):
_base_cov_dict = {blob : [] for blob in set_of_blobs}
read_cov_dict = {blob : 0 for blob in set_of_blobs}
allowed_operations = set([0, 7, 8])
with tqdm(total=reads_mapped, desc="[%] ", ncols=200, unit_scale=True) as pbar:
for read in aln.fetch(until_eof=True):
if not check_mapped_read(read):
continue
for operation, length in read.cigartuples:
if operation in allowed_operations:
_base_cov_dict[read.reference_name].append(length)
read_cov_dict[read.reference_name] += 1
pbar.update()
base_cov_dict = {ref_name: sum(_base_cov) for ref_name, _base_cov in _base_cov_dict.items()}
return base_cov_dict, read_cov_dict
def write_read_pair_seqs(pair_count_by_type, seqs_by_type, out_fs_by_type):
for pair_type, pair_count in pair_count_by_type.items():
print(BtLog.status_d['23'] % (pair_type, pair_count))
if pair_count:
out_fs = out_fs_by_type[pair_type]
if len(set(out_fs)) == 1:
out_f = out_fs[0]
with open(out_f, 'w') as out_fh:
print(BtLog.status_d['24'] % out_f)
#out_fh.write("\n".join(seqs_by_type[pair_type]) + "\n")
out_fh.write("\n".join([pair for pair in seqs_by_type[pair_type]]) + "\n")
else:
out_f = out_fs[0]
with open(out_f, 'w') as out_fh:
print(BtLog.status_d['24'] % out_f)
out_fh.write("\n".join([pair for pair in seqs_by_type[pair_type][0::2]]) + "\n")
out_f = out_fs[1]
with open(out_f, 'w') as out_fh:
print(BtLog.status_d['24'] % out_f)
out_fh.write("\n".join([pair for pair in seqs_by_type[pair_type][1::2]]) + "\n")
def get_read_pair_fasta(read, read_format):
name = read.query_name
seq = read.get_forward_sequence()
if read_format == "fq":
qual = ''
if not read.is_reverse:
qual = read.qual
else:
qual = read.qual[::-1]
return "@{name}\n{seq}\n+\n{qual}".format(name=name, seq=seq, qual=qual)
else:
return ">{name}\n{seq}".format(name=name, seq=seq)
def init_read_pairs(outfile, include_unmapped, noninterleaved, include, exclude, read_format):
read_pair_types = []
if include or exclude:
read_pair_types = ['InUn', 'InIn', 'ExIn'] # strings have to be sorted alphabetically ('ExIn', not 'InEx')
else:
read_pair_types = ['InUn', 'InIn'] # strings have to be sorted alphabetically
if include_unmapped:
read_pair_types.append('UnUn')
pair_count_by_type = {read_pair_type : 0 for read_pair_type in read_pair_types}
# initialise read_pair tuples
# read_pair_seqs = {read_pair_type : tuple() for read_pair_type in read_pair_types}
read_pair_seqs = {read_pair_type : [] for read_pair_type in read_pair_types}
# initialise read_pair files
read_pair_out_fs = defaultdict(lambda: [])
if noninterleaved:
for read_pair_type in read_pair_types:
read_pair_out_fs[read_pair_type].append(getOutFile(outfile, None, read_pair_type + ".1." + read_format))
read_pair_out_fs[read_pair_type].append(getOutFile(outfile, None, read_pair_type + ".2." + read_format))
else:
for read_pair_type in read_pair_types:
read_pair_out_fs[read_pair_type].append(getOutFile(outfile, None, read_pair_type + "." + read_format))
return pair_count_by_type, read_pair_seqs, read_pair_out_fs
def print_bam(read_pair_out_fs, read_pair_type, read1, read2):
with open(read_pair_out_fs[read_pair_type] + ".txt", 'a') as fh:
fh.write("\t".join(read1) + "\n")
fh.write("\t".join(read2) + "\n")
def read_pair_generator(aln, region_string=None):
"""
Generate read pairs in a BAM file or within a region string.
Reads are added to read_dict until a pair is found.
"""
read_dict = defaultdict(lambda: [None, None])
for read in aln.fetch(until_eof=True):
if read.is_secondary or read.is_supplementary:
continue
qname = read.query_name
if qname not in read_dict:
if read.is_read1:
read_dict[qname][0] = read
else:
read_dict[qname][1] = read
else:
if read.is_read1:
yield read, read_dict[qname][1]
else:
yield read_dict[qname][0], read
del read_dict[qname]
def parseBamForFilter(infile, include_unmapped, noninterleaved, outfile, include, exclude, read_format):
'''
parse BAM to extract readpairs
'''
pair_count_by_type, seqs_by_type, out_fs_by_type = init_read_pairs(outfile, include_unmapped, noninterleaved, include, exclude, read_format)
if include:
sequence_to_type_dict = defaultdict(lambda: 'Ex')
for incl in include:
sequence_to_type_dict[incl] = 'In'
sequence_to_type_dict[None] = 'Un'
elif exclude:
sequence_to_type_dict = defaultdict(lambda: 'In')
for excl in exclude:
sequence_to_type_dict[excl] = 'Ex'
sequence_to_type_dict[None] = 'Un'
else:
sequence_to_type_dict = defaultdict(lambda: 'In')
sequence_to_type_dict[None] = 'Un'
seen_reads = 0
print(BtLog.status_d['26'] % infile)
with pysam.AlignmentFile(infile) as aln:
with tqdm(total=(aln.mapped + aln.unmapped) / 2, desc="[%] ", ncols=200, unit_scale=True) as pbar:
for read1, read2 in read_pair_generator(aln):
seen_reads += 2
read_pair_type = "".join(sorted([sequence_to_type_dict[read1.reference_name], sequence_to_type_dict[read2.reference_name]]))
if read_pair_type in seqs_by_type:
seqs_by_type[read_pair_type].append(get_read_pair_fasta(read1, read_format))
seqs_by_type[read_pair_type].append(get_read_pair_fasta(read2, read_format))
pair_count_by_type[read_pair_type] += 1
pbar.update()
write_read_pair_seqs(pair_count_by_type, seqs_by_type, out_fs_by_type)
# info log
info_string = []
info_string.append(('Total pairs', "{:,}".format(int(seen_reads / 2)), '{0:.1%}'.format(1.00)))
for read_pair_type, count in pair_count_by_type.items():
info_string.append((read_pair_type + ' pairs', "{:,}".format(count), '{0:.1%}'.format(count / int(seen_reads / 2))))
info_out_f = getOutFile(outfile, None, "info.txt")
with open(info_out_f, 'w') as info_fh:
print(BtLog.status_d['24'] % info_out_f)
info_fh.write(get_table(info_string))
return 1
def get_table(table):
col_width = [max(len(x) for x in col) for col in zip(*table)]
table_string = []
for line in table:
table_string.append('| %s | %s | %s |' % (line[0].rjust(col_width[0]), line[1].rjust(col_width[1]), line[2].rjust(col_width[2])))
return "\n".join(table_string) + "\n"
def parseCovFromHeader(fasta_type, header):
'''
Returns the coverage from the header of a FASTA
sequence depending on the assembly type
'''
ASSEMBLY_TYPES = [None, 'spades', 'velvet', 'platanus']
if not fasta_type in ASSEMBLY_TYPES:
BtLog.error('2', ",".join(ASSEMBLY_TYPES[1:]))
if fasta_type == 'spades':
spades_match_re = re.compile(r"_cov_(\d+\.*\d*)")
#cov = re.findall(r"_cov_(\d+\.*\d*)", header)
return float(spades_match_re.findall(header)[0])
elif fasta_type == 'velvet':
return float(header.split("_")[-1])
#elif fasta_type == 'abyss' or fasta_type == 'soap':
# temp = header.split(" ")
# return float(temp[2]/(temp[1]+1-75))
elif fasta_type == 'platanus':
temp = header.rstrip("\n").split("_")
if len(temp) >= 3:
return float(temp[2].replace("cov", "")) # scaffold/scaffoldBubble/contig
else:
return float(temp[1].replace("cov", "")) # gapClosed
else:
pass
def parseCov(infile, set_of_blobs):
if not isfile(infile):
BtLog.error('0', infile)
base_cov_dict = {}
cov_line_re = re.compile(r"^(\S+)\t(\d+\.*\d*)\t(\d+\.*\d*)")
reads_total = 0
reads_mapped = 0
reads_unmapped = 0
read_cov_dict = {}
with tqdm(total=len(set_of_blobs), desc="[%] ", ncols=200, unit_scale=True) as pbar:
with open(infile) as fh:
for line in fh:
if line.startswith('#'):
if line.startswith("## Total Reads"):
reads_total = int(line.split(" = ")[1])
elif line.startswith("## Mapped Reads"):
reads_mapped = int(line.split(" = ")[1])
elif line.startswith("## Unmapped Reads"):
reads_unmapped = int(line.split(" = ")[1])
else:
pass
else:
match = cov_line_re.search(line)
if match:
name, read_cov, base_cov = match.group(1), int(match.group(2)), float(match.group(3))
if name not in set_of_blobs:
print(BtLog.warn_d['2'] % (name))
else:
read_cov_dict[name] = read_cov
base_cov_dict[name] = base_cov
pbar.update()
#BtLog.progress(len(set_of_blobs), progress_unit, len(set_of_blobs))
return base_cov_dict, reads_total, reads_mapped, reads_unmapped, read_cov_dict
def checkCas(infile):
print(BtLog.status_d['12'])
if not isfile(infile):
BtLog.error('0', infile)
if not (which('clc_mapping_info')):
BtLog.error('20')
seqs_total_re = re.compile(r"\s+Contigs\s+(\d+)")
reads_total_re = re.compile(r"\s+Reads\s+(\d+)")
reads_mapping_re = re.compile(r"\s+Mapped reads\s+(\d+)\s+(\d+.\d+)\s+\%")
seqs_total, reads_total, reads_mapped = 0, 0, 0
output = ''
command = "clc_mapping_info -s " + infile
for line in runCmd(command=command):
output += line
seqs_total = int(seqs_total_re.search(output).group(1))
reads_mapped = int(reads_mapping_re.search(output).group(1))
reads_total = int(reads_total_re.search(output).group(1))
print(BtLog.status_d['11'] % ('{:,}'.format(reads_mapped), '{:,}'.format(reads_total), '{0:.1%}'.format(reads_mapped/reads_total)))
return seqs_total, reads_total, reads_mapped
def parseCas(infile, order_of_blobs):
if not isfile(infile):
BtLog.error('0', infile)
seqs_total, reads_total, reads_mapped = checkCas(infile)
progress_unit = int(len(order_of_blobs)/100)
cas_line_re = re.compile(r"\s+(\d+)\s+(\d+)\s+(\d+)\s+(\d+.\d{2})\s+(\d+)\s+(\d+.\d{2})")
command = "clc_mapping_info -n " + infile
cov_dict = {}
read_cov_dict = {}
seqs_parsed = 0
if (runCmd(command=command)):
for line in runCmd(command=command):
cas_line_match = cas_line_re.search(line)
if cas_line_match:
idx = int(cas_line_match.group(1)) - 1 # -1 because index of contig list starts with zero
try:
name = order_of_blobs[idx]
reads = int(cas_line_match.group(3))
cov = float(cas_line_match.group(6))
cov_dict[name] = cov
read_cov_dict[name] = reads
seqs_parsed += 1
except:
pass
BtLog.progress(seqs_parsed, progress_unit, seqs_total)
return cov_dict, reads_total, reads_mapped, read_cov_dict
def readTax(infile, set_of_blobs):
'''
If more fields need to be parsed:
- add as key-value pairs to hitDict
'''
if not isfile(infile):
BtLog.error('0', infile)
#hit_line_re = re.compile(r"^(\S+)\s+(\d+)[\;?\d+]*\s+(\d+\.*\d*)") # TEST TEST , if not split it afterwards
with open(infile) as fh:
for line in fh:
#match = hit_line_re.search(line)
#if match:
col = line.split()
try:
hitDict = {
'name' : col[0],
'taxId' : col[1], # string because if int, conversion is a nightmare ...
'score' : float(col[2])
}
except ValueError:
BtLog.error('46', infile, col[2])
if hitDict['name'] not in set_of_blobs:
#print(BtLog.warn_d['13'] % (hitDict['name'], infile))
BtLog.error('19', hitDict['name'], infile)
yield hitDict
#hitDict = {
# 'name' : match.group(1),
# 'taxId' : match.group(2), # string because if int, conversion is a nightmare ...
# 'score' : float(match.group(3))
# }
#if hitDict['name'] not in set_of_blobs:
# print(BtLog.warn_d['13'] % (hitDict['name'], infile))
# #BtLog.error('19', hitDict['name'], infile)
#if hitDict['taxId'] == 'N/A':
# BtLog.error('22', infile)
#yield hitDict
def getOutFile(base_file, prefix, suffix):
EXTENSIONS = ['.fasta', '.fa', '.fna', '.txt', '.cov', '.out', '.json']
out_f, extension = splitext(basename(base_file))
if not extension in EXTENSIONS:
out_f = '%s%s' % (out_f, extension)
if (prefix):
if prefix.endswith("/"):
out_f = "%s" % (join(prefix, out_f))
else:
out_f = "%s.%s" % (prefix, out_f)
if (suffix):
out_f = "%s.%s" % (out_f, suffix)
return out_f
def parseNodesDB(**kwargs):
'''
Parsing names.dmp and nodes.dmp into the 'nodes_db' dict of dicts that
gets JSON'ed into blobtools/data/nodes_db.json if this file
does not exist. Nodes_db.json is used if neither "--names" and "--nodes"
nor "--db" is specified. If all three are specified and "--db" does not
exist, then write 'nodes_db' to file specified by "--db". If all three
are specified and "--db" exists, error out.
'''
nodesDB = {}
names_f = kwargs['names']
nodes_f = kwargs['nodes']
nodesDB_f = kwargs['nodesDB']
nodesDB_default = kwargs['nodesDBdefault']
if (nodes_f and names_f):
if not isfile(names_f):
BtLog.error('0', names_f)
if not isfile(nodes_f):
BtLog.error('0', nodes_f)
if (nodesDB_f):
if isfile(nodesDB_f):
BtLog.error('47', nodesDB_f)
BtLog.status_d['27'] % (nodesDB_f, nodes_f, names_f)
else:
print(BtLog.status_d['3'] % (nodes_f, names_f))
try:
nodesDB = readNamesNodes(names_f, nodes_f)
except:
BtLog.error('3', nodes_f, names_f)
elif (nodesDB_f):
if not isfile(nodesDB_f):
BtLog.error('0', nodesDB_f)
print(BtLog.status_d['4'] % (nodesDB_f))
try:
nodesDB = readNodesDB(nodesDB_f)
except:
BtLog.error('27', nodesDB_f)
elif (nodesDB_default):
if not isfile(nodesDB_default):
BtLog.error('28')
print(BtLog.status_d['4'] % (nodesDB_default))
try:
nodesDB = readNodesDB(nodesDB_default)
except:
BtLog.error('27', nodesDB_default)
# Write nodesDB if names, nodes, nodesDB all given and nodesDB does not
# exist. Otherwise, write to nodesDB_default if it does not exist, unless
# nodesDB given, then do nothing with nodesDB_default.
if (nodes_f and names_f and nodesDB_f):
print(BtLog.status_d['28'] % nodesDB_f)
writeNodesDB(nodesDB, nodesDB_f)
elif (not nodesDB_f and not isfile(nodesDB_default)):
nodesDB_f = nodesDB_default
print(BtLog.status_d['5'] % nodesDB_f)
writeNodesDB(nodesDB, nodesDB_f)
return nodesDB, nodesDB_f
def readNamesNodes(names_f, nodes_f):
nodesDB = {}
nodes_count = 0
with open(nodes_f) as fh:
for line in fh:
nodes_col = line.split("\t")
node = {}
node_id = nodes_col[0]
node['parent'] = nodes_col[2]
node['rank'] = nodes_col[4]
nodesDB[node_id] = node
nodes_count += 1
with open(names_f) as fh:
for line in fh:
names_col = line.split("\t")
if names_col[6] == "scientific name":
nodesDB[names_col[0]]['name'] = names_col[2]
nodesDB['nodes_count'] = nodes_count
return nodesDB
def readNodesDB(nodesDB_f):
nodesDB = {}
with open(nodesDB_f) as fh:
nodes_count = int(fh.readline().lstrip("# nodes_count = ").rstrip("\n"))
with tqdm(total=nodes_count, desc="[%] ", ncols=200, unit_scale=True) as pbar:
for line in fh:
if line.startswith("#"):
pass
else:
node, rank, name, parent = line.rstrip("\n").split("\t")
nodesDB[node] = {'rank' : rank, 'name' : name, 'parent' : parent}
pbar.update()
nodesDB['nodes_count'] = nodes_count
return nodesDB
def writeNodesDB(nodesDB, nodesDB_f):
nodes_count = nodesDB['nodes_count']
with open(nodesDB_f, 'w') as fh:
fh.write("# nodes_count = %s\n" % nodes_count)
with tqdm(total=nodes_count, desc="[%] ", ncols=200, unit_scale=True) as pbar:
for node in nodesDB:
if not node == "nodes_count":
fh.write("%s\t%s\t%s\t%s\n" % (node, nodesDB[node]['rank'], nodesDB[node]['name'], nodesDB[node]['parent']))
pbar.update()
def byteify(input):
'''
http://stackoverflow.com/a/13105359
'''
if isinstance(input, dict):
return {byteify(key):byteify(value) for key, value in input.items}
elif isinstance(input, list):
return [byteify(element) for element in input]
#elif isinstance(input, unicode):
# return input.encode('utf-8')
else:
return input
def writeJsonGzip(obj, outfile):
import json
import gzip
with gzip.open(outfile, 'wb') as fh:
json.dump(obj, fh)
def writeJson(obj, outfile, indent=0, separators=(',', ': ')):
import json
with open(outfile, 'w') as fh:
#if (indent):
# json.dump(obj, fh, indent=indent, separators=separators)
#else:
# json.dump(obj, fh)
json.dump(obj, fh)
#json.dump(obj, fh, indent=4, separators=(',', ': ')) #
def parseJsonGzip(infile):
import json
import gzip
with gzip.open(infile, 'rb') as fh:
#obj = json.loads(fh.read().decode("ascii"))
obj = json.loads(fh.read())
#return byteify(obj)
return obj
def parseJson(infile):
'''http://artem.krylysov.com/blog/2015/09/29/benchmark-python-json-libraries/'''
if not isfile(infile):
BtLog.error('0', infile)
import time
start = time.time()
json_parser = ''
with open(infile, 'r') as fh:
print(BtLog.status_d['15'])
json_string = fh.read()
try:
import ujson as json # fastest
json_parser = 'ujson'
print(BtLog.status_d['16'] % json_parser)
except ImportError:
try:
import simplejson as json # fast
json_parser = 'simplejson'
except ImportError:
import json # default
json_parser = 'json'
print(BtLog.status_d['17'] % json_parser)
try:
#obj = json.loads(json_string.decode("ascii"))
obj = json.loads(json_string)
except ValueError:
BtLog.error('37', infile, "BlobDB")
#data = byteify(obj)
data = obj
print(BtLog.status_d['20'] % (time.time() - start))
return data
def readYaml(infile):
if not isfile(infile):
BtLog.error('0', infile)
with open(infile) as fh:
str = "".join(fh.readlines())
try:
data = yaml.safeload(str)
except yaml.YAMLError:
BtLog.error('37', infile, "yaml")
return data
if __name__ == "__main__":
pass
================================================
FILE: lib/BtLog.py
================================================
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
File : BtLog.py
Author : Dominik R. Laetsch, dominik.laetsch at gmail dot com
"""
from __future__ import division
import sys
def error(message, *argv):
if argv is None:
sys.exit(error_d[message])
else:
sys.exit(error_d[message] % (argv))
#exit(1) # change to exit with the actual ERROR number (different than 0)
error_d = {
'0': '[ERROR:0]\t: File %s does not exist.',
'1': '[ERROR:1]\t: Please provide coverage information.',
'2': '[ERROR:2]\t: Assembly type is not valid (%s).',
'3': '[ERROR:3]\t: names.dmp/nodes.dmp ("--names", "--nodes") could not be read. %s, %s',
'4': '[ERROR:4]\t: BlobDB.parseFasta() - no sequences found. Check FASTA file.',
'5': '[ERROR:5]\t: Sequence header %s is not unique.',
'6': '[ERROR:6]\t: BlobDB.readBam() - sequence header %s in %s was not in FASTA.',
'7': '[ERROR:7]\t: Please add "samtools" to you PATH variable.',
'8': '[ERROR:8]\t: Unsupported taxrule "%s".',
'9': '[ERROR:9]\t: Unsupported taxonomic rank "%s".',
'10': '[ERROR:10]\t: Unsupported output format "%s".',
'11': '[ERROR:11]\t: Taxrule "%s" was not computed for this BlobDb. Available taxrule(s) : %s.',
'12': '[ERROR:12]\t: Please provide an output file.',
'13': '[ERROR:13]\t: %s does not appear to be a comma-separated list or a file.',
'14': '[ERROR:14]\t: Unsupported sort order for plotting : %s. Must be either "span" or "count".',
'15': '[ERROR:15]\t: Unsupported histogram type for plotting : %s. Must be either "span" or "count".',
'16': '[ERROR:16]\t: Group "%s" was specified in multiple clusters.',
'17': '[ERROR:17]\t: Label could not be parsed from "%s".',
'18': '[ERROR:18]\t: Please provide a tax file in BLAST format.',
'19': '[ERROR:19]\t: Sequence %s in file %s is not part of the assembly.',
'20': '[ERROR:20]\t: Please add "clc_mapping_info" to your PATH variable.',
'21': '[ERROR:21]\t: Refcov file %s does not seem to have the right format.',
'23': '[ERROR:23]\t: Catcolour file %s does not seem to have the right format.',
'24': '[ERROR:24]\t: Catcolour file incompatible with c-index colouring.',
'25': '[ERROR:25]\t: COV file %s does not seem to have the right format.',
'26': '[ERROR:26]\t: TaxID must be integer.',
'27': '[ERROR:27]\t: nodesDB ("--db") %s could not be read.',
'28': '[ERROR:28]\t: Please specify "--names" and "--nodes", or "--db"',
'29': '[ERROR:29]\t: No mapping reads found in %s',
'30': '[ERROR:30]\t: The module docopt is not installed. Please install it to run blobtools\n\tpip install docopt',
'31': '[ERROR:31]\t: Please specify a read mapping file (BAM/SAM/CAS)',
'32': '[ERROR:32]\t: Choose either --cumulative or --multiplot',
'33': '[ERROR:33] : CovLib(s) not found. The available covlibs are: \n%s',
'34': '[ERROR:34] : Invalid plot type : %s',
'35': '[ERROR:35] : Directory %s could not be created',
'36': '[ERROR:36] : View %s could not be created',
'37': '[ERROR:37] : %s does not seem to be a valid %s file',
'38': '[ERROR:38] : %s is not an integer',
'39': '[ERROR:39] : Please specify a taxid file (mapping subjects to taxids)',
'40': '[ERROR:40] : CovLib \'%s\' not specified in refcov file',
'41': '[ERROR:41] : Please specify either a mapping file or a taxID.',
'42': '[ERROR:42] : SubjectID %s not found in mapping file %s.',
'43': '[ERROR:43] : %s could not be found.',
'44': '[ERROR:44] : Please specify integers for --map_col_sseqid and --map_col_taxid.',
'45': '[ERROR:45] : Both --min_score and --min_diff must be numbers.',
'46': '[ERROR:46] : Score in %s must be a float, not \'%s\'.',
'47': '[ERROR:47] : Cannot create new "--db" file from "--names", "--nodes", "--db" file exists. %s'
}
warn_d = {
'0': '[-] No tax files specified.',
'1': '[-] %s not in colour file %s ...',
'2': '[-] %s is not part of the assembly',
'3': '\n[-] Based on samtools flagstat: expected %s reads, %s reads were parsed',
'4': '[-] No coverage data found in %s',
'5': '[-] Hit for sequence %s in tax file %s has multiple taxIds, only first one is used.',
'6': '[-] Sum of coverage in cov lib %s is 0.0. Please ignore this warning if "--no_base_cov" was specified.',
'7': '[-] No taxonomy information found.',
'8': '[-] Duplicated sequences found :\n\t\t\t%s',
'9': '[-] Taxrule "%s" was not computed for this BlobDb. Available taxrule(s) : %s. Will proceed without taxonomic annotation ...',
'10': '[-] Line %s: sequence "%s" already has TaxID "%s". Skipped. (use --force to overwrite)',
'11': '\n[-] The BAM file appears to be truncated.',
'12': '[-] sseqid %s not found in ID-to-taxID mapping file %s.',
'13': '[-] Sequence %s in file %s is not part of the assembly.'
}
status_d = {
'0': '[+] Nothing to be done. %s',
'1': '[+] Parsing %s - %s',
'2': '[+] Done',
'3': '[+] Creating nodesDB from %s and %s',
'4': '[+] names.dmp/nodes.dmp not specified. Retrieving nodesDB from %s',
'5': '[+] Store nodesDB in default location %s',
'6': '[+] Computing taxonomy using taxrule(s) %s',
'7': '[+] Generating BlobDB and writing to file %s',
'8': '[+] Plotting %s',
'9': '[+] Reading BlobDB %s',
'10': '[+] \tChecking with \'samtools flagstat\'',
'11': '[+] \tMapping reads = %s, total reads = %s (mapping rate = %s)',
'12': '[+] \tChecking with \'clc_mapping_info\'',
'13': '[+] \tWriting %s',
'14': '[+] Preparing view(s) ...',
'15': '[+] \tLoading BlobDB into memory ...',
'16': '[+] \tDeserialising BlobDB (using \'%s\' module) (this may take a while) ...',
'17': '[+] \tDeserialising BlobDB (using \'%s\' module) (this may take a while, consider installing the \'ujson\' module) ...',
'18': '[+] Extracting data for plots ...',
'19': '[+] Writing output ...',
'20': '[+] \tFinished in %ss',
'22': '[+] Filtering %s ...',
'23': '[+] Filtered %s (pairs=%s) ...',
'24': '[+] Writing %s',
'25': '[+] Gzip\'ing %s',
'26': '[+] Reading %s',
'27': '[+] Creating nodesDB %s from %s and %s',
'28': '[+] Store nodesDB in %s',
}
info_d = {
'0': '[I]\t%s : sequences = %s, span = %s MB, N50 = %s nt'
}
if __name__ == "__main__":
pass
================================================
FILE: lib/BtPlot.py
================================================
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
File : BtPlot.py
Author : Dominik R. Laetsch, dominik.laetsch at gmail dot com
"""
from numpy import array, arange, logspace, mean, std
import math
import lib.BtLog as BtLog
import lib.BtTax as BtTax
import matplotlib as mat
from matplotlib import cm
from matplotlib.ticker import NullFormatter, MultipleLocator, AutoMinorLocator
from matplotlib.lines import Line2D
from matplotlib.colors import rgb2hex
mat.use('agg')
import matplotlib.pyplot as plt
from operator import itemgetter
#from itertools import izip
mat.rcParams.update({'font.size': 36})
mat.rcParams['xtick.major.pad'] = '8'
mat.rcParams['ytick.major.pad'] = '8'
mat.rcParams['lines.antialiased'] = True
LEGEND_FONTSIZE = 20
COLOURMAP = "tab10" # "Set1", "Paired", "Set2", "Spectral"
#GREY, BGGREY, WHITE, DGREY = '#d3d3d3', '#F0F0F5', '#ffffff', '#4d4d4d'
GREY, BGGREY, BGCOLOUR, WHITE, DGREY = '#d3d3d3', '#F0F0F5', '#F8F8F8', '#ffffff', '#4d4d4d'
nullfmt = NullFormatter()
def n50(list_of_lengths):
total_span = 0
sorted_list_of_lengths=sorted(list_of_lengths, reverse=True)
for contig_length in sorted_list_of_lengths:
total_span += contig_length
teoN50 = total_span/2.0
running_sum = 0
N50 = 0
for contig_length in sorted_list_of_lengths:
running_sum += contig_length
if teoN50 <= running_sum:
N50 = contig_length
break
return N50
def getSortedGroups(data_dict, sort_order, sort_first=()):
""" Returns list of sorted groups based on span or count. """
sorted_groups = []
visible_by_group = {}
if sort_order == 'span':
visible_by_group = {group: _dict['span_visible'] for group, _dict in data_dict.items()}
#sorted_groups = sorted(data_dict, key = lambda x : data_dict[x]['span_visible'] if data_dict[x]['span_visible'] > 0 else 0, reverse=True)
#sorted_groups = sorted(data_dict, key = lambda x : max(data_dict[x]['span_visible'], 0), reverse=True)
elif sort_order == 'count':
visible_by_group = {group: _dict['count_visible'] for group, _dict in data_dict.items()}
#sorted_groups = sorted(data_dict, key = lambda x : data_dict[x]['count_visible'] if data_dict[x]['count_visible'] > 0 else 0, reverse=True)
#sorted_groups = sorted(data_dict, key = lambda x : max(data_dict[x]['count_visible'], 0), reverse=True)
else:
pass
for group, visible in sorted(visible_by_group.items(), key=itemgetter(1), reverse=True):
if visible > 0:
sorted_groups.append(group)
# Now shuffle the stuff in sort_first to the front
for sf in reversed(sort_first):
try:
sorted_groups.remove(sf)
sorted_groups.insert(0, sf)
except ValueError:
#It wasn't in the list then. No probs.
pass
return sorted_groups
def generateColourDict(colour_groups, groups):
cmap = [rgb2hex(rgb) for rgb in cm.get_cmap(name=COLOURMAP).colors]
# remove green
del cmap[2]
# remove brown
del cmap[4]
colour_d = {}
idx_delay = 0
for idx, group in enumerate(groups):
if group in colour_groups:
#print(group,)
if group == 'no-hit' or group == 'None':
colour_d[group] = GREY
#print("GREY")
idx_delay -= 1
else:
colour_d[group] = cmap[idx+idx_delay]
#print(colour_d[group], idx+idx_delay)
return colour_d
def set_canvas():
left, width = 0.1, 0.60
bottom, height = 0.1, 0.60
bottom_h = left_h = left+width+0.02
rect_scatter = [left, bottom, width, height]
rect_histx = [left, bottom_h, width, 0.2]
rect_histy = [left_h, bottom, 0.2, height]
rect_legend = [left_h, bottom_h, 0.2, 0.2]
return rect_scatter, rect_histx, rect_histy, rect_legend
def set_format_scatterplot(axScatter, **kwargs):
min_x, max_x = None, None
min_y, max_y = None, None
if kwargs['plot'] == 'blobplot':
min_x, max_x = 0, 1
major_xticks = MultipleLocator(0.2)
minor_xticks = AutoMinorLocator(20)
min_y, max_y = kwargs['min_cov']*0.1, kwargs['max_cov']+100
axScatter.set_yscale('log')
axScatter.set_xscale('linear')
axScatter.xaxis.set_major_locator(major_xticks)
axScatter.xaxis.set_minor_locator(minor_xticks)
elif kwargs['plot'] == 'covplot':
min_x, max_x = kwargs['min_cov']*0.1, kwargs['max_cov']+100
min_y, max_y = kwargs['min_cov']*0.1, kwargs['max_cov']+100
axScatter.set_yscale('log')
axScatter.set_xscale('log')
else:
BtLog.error('34' % kwargs['plot'])
axScatter.set_xlim( (min_x, max_x) )
axScatter.set_ylim( (min_y, max_y) ) # This sets the max-Coverage so that all libraries + sum are at the same scale
axScatter.grid(True, which="major", lw=2., color=BGGREY, linestyle='-')
axScatter.set_axisbelow(True)
axScatter.xaxis.labelpad = 20
axScatter.yaxis.labelpad = 20
axScatter.yaxis.get_major_ticks()[0].label1.set_visible(False)
axScatter.tick_params(axis='both', which='both', direction='out')
return axScatter
def set_format_hist_x(axHistx, axScatter):
axHistx.set_xlim(axScatter.get_xlim())
axHistx.set_xscale(axScatter.get_xscale())
axHistx.grid(True, which="major", lw=2., color=BGGREY, linestyle='-')
axHistx.xaxis.set_major_locator(axScatter.xaxis.get_major_locator()) # no labels since redundant
axHistx.xaxis.set_minor_locator(axScatter.xaxis.get_minor_locator())
axHistx.xaxis.set_major_formatter(nullfmt) # no labels since redundant
axHistx.set_axisbelow(True)
axHistx.yaxis.labelpad = 20
axHistx.tick_params(axis='both', which='both', direction='out')
return axHistx
def set_format_hist_y(axHisty, axScatter):
axHisty.set_ylim(axScatter.get_ylim())
axHisty.set_yscale(axScatter.get_yscale())
axHisty.grid(True, which="major", lw=2., color=BGGREY, linestyle='-')
axHisty.yaxis.set_major_formatter(nullfmt) # no labels since redundant
axHisty.set_axisbelow(True)
axHisty.xaxis.labelpad = 20
axHisty.tick_params(axis='both', which='both', direction='out')
return axHisty
def get_ref_label(max_length, max_marker_size, fraction):
length = int(math.ceil(fraction * max_length / 100.0)) * 100
string = "%snt" % "{:,}".format(length)
markersize = length/max_length * max_marker_size
return length, string, markersize
def plot_ref_legend(axScatter, max_length, max_marker_size, ignore_contig_length):
if not (ignore_contig_length):
ref1_length, ref1_string, ref1_markersize = get_ref_label(max_length, max_marker_size, 0.05)
ref2_length, ref2_string, ref2_markersize = get_ref_label(max_length, max_marker_size, 0.1)
ref3_length, ref3_string, ref3_markersize = get_ref_label(max_length, max_marker_size, 0.25)
# markersize in scatter is in "points^2", markersize in Line2D is in "points" ... that's why we need math.sqrt()
ref_1 = (Line2D([0], [0], linewidth = 0.5, linestyle="none", marker="o", alpha=1, markersize=math.sqrt(ref1_markersize), markeredgecolor=WHITE, markerfacecolor=GREY))
ref_2 = (Line2D([0], [0], linewidth = 0.5, linestyle="none", marker="o", alpha=1, markersize=math.sqrt(ref2_markersize), markeredgecolor=WHITE, markerfacecolor=GREY))
ref_3 = (Line2D([0], [0], linewidth = 0.5, linestyle="none", marker="o", alpha=1, markersize=math.sqrt(ref3_markersize), markeredgecolor=WHITE, markerfacecolor=GREY))
axScatter.legend([ref_1,ref_2,ref_3], [ref1_string, ref2_string, ref3_string], numpoints=1, ncol = 3, loc = 8, fontsize=LEGEND_FONTSIZE, borderpad=1.2, labelspacing=1.8, handlelength=1, handletextpad=1)
def plot_legend(fig, axLegend, out_f, legend_flag, format, cumulative_flag):
if (legend_flag):
extent = axLegend.get_window_extent().transformed(fig.dpi_scale_trans.inverted())
legend_out_f = '%s.%s.%s' % (out_f, "legend", format)
print(BtLog.status_d['8'] % legend_out_f)
fig.savefig('%s' % legend_out_f, bbox_inches=extent, format=format)
fig.delaxes(axLegend)
return fig
def check_input(args):
rank = args['--rank']
c_index = args['--cindex']
multiplot = args['--multiplot']
sort_order = args['--sort']
sort_first = args['--sort_first']
taxrule = args['--taxrule']
hist_type = args['--hist']
catcolour_f = args['--catcolour']
cumulative_flag = args['--cumulative']
#Convert sort_first to a list
if sort_first:
args['--sort_first'] = sort_first.split(',')
else:
args['--sort_first'] = ()
if 'blobplot' in args or 'covplot' in args:
# Are ranks sane ?
if rank not in BtTax.RANKS:
BtLog.error('9', rank)
# is taxrule provided?
if taxrule not in BtTax.TAXRULES:
BtLog.error('8', taxrule)
# Are sort_order and hist_type sane?
if not sort_order in ['span', 'count']:
BtLog.error('14', sort_order)
if not hist_type in ['span', 'count']:
BtLog.error('15', hist_type)
if (catcolour_f) and (c_index):
BtLog.error('24')
if (cumulative_flag) and (multiplot):
BtLog.error('32')
return args
class PlotObj():
def __init__(self, data_dict, cov_lib_dict, cov_lib_selection, plot_type, sort_first=()):
self.labels = {'all'}
self.plot = plot_type # type of plot
self.group_labels = {}
self.cov_lib_dict = cov_lib_dict
self.cov_libs = self.subselect_cov_libs(cov_lib_dict, cov_lib_selection)
self.cov_libs_total_reads_dict = self.get_cov_libs_total_reads_dict(cov_lib_dict)
self.cov_libs_mapped_reads_dict = self.get_cov_libs_mapped_reads_dict(cov_lib_dict)
self.data_dict = data_dict
self.stats = {}
self.exclude_groups = []
self.version = None
self.colours = {}
self.group_order = []
self.plot_order = []
self.sort_first = sort_first
self.min_cov = 0.1
self.max_cov = 0.0
self.out_f = ''
self.no_title = ''
self.max_group_plot = 0
self.format = ''
self.legend_flag = ''
self.cumulative_flag = ''
self.dpi = 200
self.scatter_size = (35, 35)
self.readcov_size = (30, 10)
self.cov_y_dict = {}
self.xlabel = None
self.ylabel = None
self.refcov_dict = {}
def subselect_cov_libs(self, cov_lib_dict, cov_lib_selection):
selected_cov_libs = []
cov_lib_selection_error = 0
if (cov_lib_selection):
if cov_lib_selection == 'covsum':
selected_cov_libs.append('covsum')
elif "," in cov_lib_selection:
selected_cov_libs = cov_lib_selection.split(",")
if not set(selected_cov_libs).issubset(set(cov_lib_dict.keys())):
cov_lib_selection_error = 1
else:
selected_cov_libs.append(cov_lib_selection)
if not cov_lib_selection in cov_lib_dict:
cov_lib_selection_error = 1
else:
selected_cov_libs = cov_lib_dict.keys()
if cov_lib_selection_error:
covlib_string = []
for covlib in cov_lib_dict:
cov_lib_f = cov_lib_dict[covlib]['f']
if not cov_lib_f:
cov_lib_f = "sum of coverages from all covlibs"
covlib_string.append("\t\t%s : %s" % (covlib, cov_lib_f))
BtLog.error('33', "\n".join(covlib_string))
return selected_cov_libs
def get_cov_libs_total_reads_dict(self, cov_lib_dict):
return { x : cov_lib_dict[x]['reads_total'] for x in self.cov_libs}
def get_cov_libs_mapped_reads_dict(self, cov_lib_dict):
return { x : cov_lib_dict[x]['reads_mapped'] for x in self.cov_libs}
def get_stats_for_group(self, group):
stats = { 'name' : group,
'count_total' : "{:,}".format(self.stats[group]['count']),
'count_visible' : "{:,}".format(self.stats[group]['count_visible']),
'count_visible_perc' : '{0:.1%}'.format(self.stats[group]['count_visible']/self.stats[group]['count']) if self.stats[group]['count'] > 0 else '{0:.1%}'.format(0.0),
'span_visible' : "{:,}".format(self.stats[group]['span_visible']),
'span_total' : "{:,}".format(self.stats[group]['span']),
'span_visible_perc' : '{0:.1%}'.format(self.stats[group]['span_visible']/self.stats[group]['span']) if self.stats[group]['span'] > 0 else '{0:.1%}'.format(0.0),
'colour' : str(self.colours[group] if group in self.colours else None),
'n50' : "{:,}".format(self.stats[group]['n50']),
'gc_mean' : "{0:.2}".format(self.stats[group]['gc_mean']),
'gc_std' : "{0:.2}".format(self.stats[group]['gc_std']),
'cov_mean' : {cov_lib : "{0:0.1f}".format(cov_mean) for cov_lib, cov_mean in self.stats[group]['cov_mean'].items()},
'cov_std' : {cov_lib : "{0:0.1f}".format(cov_std) for cov_lib, cov_std in self.stats[group]['cov_std'].items()},
'reads_mapped' : {cov_lib : "{:,}".format(reads_mapped) for cov_lib, reads_mapped in self.stats[group]['reads_mapped'].items()},
'reads_mapped_perc' : {cov_lib : '{0:.1%}'.format(reads_mapped_perc) for cov_lib, reads_mapped_perc in self.stats[group]['reads_mapped_perc'].items()}
}
return stats
def write_stats(self, out_f):
stats = []
stats.append(self.get_stats_for_group('all'))
for group in self.plot_order: # group/label/other that has been plotted
stats.append(self.get_stats_for_group(group))
if not group in self.group_labels: # it is either a label or "other"
label = group
for g, labels in self.group_labels.items():
if label in labels:
stats.append(self.get_stats_for_group(g))
output = []
output.append('## %s' % self.version)
for cov_lib, cov_lib_dict in self.cov_lib_dict.items():
if cov_lib in self.cov_libs:
output.append("## %s=%s" % (cov_lib, cov_lib_dict['f']))
fields = ['name', 'colour', 'count_visible', 'count_visible_perc', 'span_visible','span_visible_perc', 'n50', 'gc_mean', 'gc_std']
header = [field for field in fields]
for cov_lib in sorted(self.cov_libs):
header.append('%s_mean' % cov_lib)
header.append('%s_std' % cov_lib)
header.append('%s_read_map' % cov_lib)
header.append('%s_read_map_p' % cov_lib)
output.append('# %s' % "\t".join(header))
for stat in stats:
line = []
for field in fields:
line.append(stat[field])
for cov_lib in sorted(self.cov_libs):
line.append(stat['cov_mean'][cov_lib])
line.append(stat['cov_std'][cov_lib])
line.append(stat['reads_mapped'][cov_lib])
line.append(stat['reads_mapped_perc'][cov_lib])
output.append("%s" % "\t".join(line))
out_f = "%s.stats.txt" % out_f
with open(out_f, 'w') as fh:
print(BtLog.status_d['24'] % ("%s" % out_f))
fh.write("\n".join(output))
def compute_stats(self):
stats = {}
for label in self.labels:
stats[label] = {
'name' : [],
'gc' : [],
'length': [],
'covs' : {cov_lib : [] for cov_lib in self.cov_libs},
'cov_mean' : {cov_lib : 0.0 for cov_lib in self.cov_libs},
'cov_std' : {cov_lib : 0.0 for cov_lib in self.cov_libs},
'reads_mapped' : {cov_lib : 0 for cov_lib in self.cov_libs},
'reads_mapped_perc' : {cov_lib: 0.0 for cov_lib in self.cov_libs},
'n50' : 0,
'gc_mean' : 0.0,
'gc_std' : 0.0,
'groups' : set(),
'count' : 0,
'span' : 0,
'count_visible' : 0,
'span_visible' : 0,
'count_hidden' : 0,
'span_hidden' : 0
}
for group, labels in self.group_labels.items():
for label in labels:
stats[label]['name'] = stats[label]['name'] + self.data_dict[group]['name']
stats[label]['groups'].add(group)
stats[label]['gc'] = stats[label]['gc'] + self.data_dict[group]['gc']
stats[label]['length'] = stats[label]['length'] + self.data_dict[group]['length']
stats[label]['count'] += self.data_dict[group]['count']
stats[label]['span'] += self.data_dict[group]['span']
stats[label]['count_visible'] += self.data_dict[group]['count_visible']
stats[label]['count_hidden'] += self.data_dict[group]['count_hidden']
stats[label]['span_visible'] += self.data_dict[group]['span_visible']
stats[label]['span_hidden'] += self.data_dict[group]['span_hidden']
for cov_lib in self.cov_libs:
stats[label]['covs'][cov_lib] = stats[label]['covs'][cov_lib] + self.data_dict[group]['covs'][cov_lib]
stats[label]['reads_mapped'][cov_lib] += self.data_dict[group]['reads_mapped'][cov_lib]
for label in stats:
stats[label]['gc_mean'] = mean(array(stats[label]['gc'])) if stats[label]['count_visible'] > 0.0 else 0.0
stats[label]['gc_std'] = std(array(stats[label]['gc'])) if stats[label]['count_visible'] > 0.0 else 0.0
stats[label]['n50'] = n50(stats[label]['length']) if stats[label]['count_visible'] > 0.0 else 0.0
for cov_lib in self.cov_libs:
stats[label]['cov_mean'][cov_lib] = mean(array(stats[label]['covs'][cov_lib])) if stats[label]['count_visible'] > 0.0 else 0.0
stats[label]['cov_std'][cov_lib] = std(array(stats[label]['covs'][cov_lib])) if stats[label]['count_visible'] > 0.0 else 0.0
if self.cov_libs_total_reads_dict[cov_lib]:
stats[label]['reads_mapped_perc'][cov_lib] = stats[label]['reads_mapped'][cov_lib]/self.cov_libs_total_reads_dict[cov_lib]
self.stats = stats
def relabel_and_colour(self, colour_dict, user_labels):
#print(user_labels)
groups = self.group_order[0:self.max_group_plot]
if (colour_dict):
groups_not_in_colour_dict = set(groups) - set(colour_dict.keys())
for _group in groups_not_in_colour_dict:
colour_dict[_group] = WHITE
else:
#print(groups)
colour_groups = list(set([_group if not (_group in user_labels) else user_labels[_group] for _group in groups]))
#print(colour_groups)
colour_dict = generateColourDict(colour_groups, groups)
#print(colour_dict)
for idx, group in enumerate(self.group_order):
if group in self.exclude_groups:
pass
elif group in user_labels:
#print(group, "in user_labels")
label = user_labels[group]
#print(label)
self.group_labels[group].add(label)
#print(self.group_labels[group])
self.group_labels[group].add(group)
#print(self.group_labels[group])
self.colours[label] = colour_dict[user_labels[group]]
if label not in self.plot_order:
self.plot_order.append(label)
elif group in colour_dict:
self.group_labels[group].add(group)
self.colours[group] = colour_dict[group]
self.plot_order.append(group)
elif idx > self.max_group_plot:
self.group_labels[group].add('other')
self.group_labels[group].add(group)
self.colours['other'] = WHITE
self.labels.add('other')
else:
self.group_labels[group].add('other')
self.group_labels[group].add(group)
self.colours['other'] = WHITE
self.labels.add('other')
self.group_labels[group].add('all')
if 'other' in self.labels:
if 'other' in self.sort_first:
#Slightly tricky. We need to insert 'other' after the location of
#the last thing before 'other' in self.sort_first that appears in
#self.plot_order, or else put it at the start.
ipos = 0
for l in reversed(self.sort_first[:self.sort_first.index('other')]):
try:
ipos = self.plot_order.index(l) + 1
break
except ValueError:
#keep looking
pass
self.plot_order.insert(ipos, 'other')
else:
# 'other' gets plotted last by default
self.plot_order.append('other')
def setupPlot(self, plot):
if plot == 'blobplot' or plot == 'covplot':
rect_scatter, rect_histx, rect_histy, rect_legend = set_canvas()
# Setting up plots and axes
fig = plt.figure(1, figsize=self.scatter_size, dpi=self.dpi)
try:
axScatter = plt.axes(rect_scatter, facecolor=BGCOLOUR)
except AttributeError:
axScatter = plt.axes(rect_scatter, axisbg=BGCOLOUR)
axScatter = set_format_scatterplot(axScatter, min_cov=self.min_cov, max_cov=self.max_cov, plot=plot)
axScatter.set_xlabel(self.xlabel)
axScatter.set_ylabel(self.ylabel)
try:
axHistx = plt.axes(rect_histx, facecolor=BGCOLOUR)
axHisty = plt.axes(rect_histy, facecolor=BGCOLOUR)
except AttributeError:
axHistx = plt.axes(rect_histx, axisbg=BGCOLOUR)
axHisty = plt.axes(rect_histy, axisbg=BGCOLOUR)
axHistx = set_format_hist_x(axHistx, axScatter)
axHisty = set_format_hist_y(axHisty, axScatter)
if self.hist_type == "span":
axHistx.set_ylabel("Span (kb)")
axHisty.set_xlabel("Span (kb)", rotation='horizontal')
else:
axHistx.set_ylabel("Count")
axHisty.set_xlabel("Count", rotation='horizontal')
for xtick in axHisty.get_xticklabels(): # rotate text for ticks in cov histogram
xtick.set_rotation(270)
try:
axLegend = plt.axes(rect_legend, facecolor=WHITE)
except AttributeError:
axLegend = plt.axes(rect_legend, axisbg=WHITE)
axLegend.xaxis.set_major_locator(plt.NullLocator())
axLegend.xaxis.set_major_formatter(nullfmt)
axLegend.yaxis.set_major_locator(plt.NullLocator())
axLegend.yaxis.set_major_formatter(nullfmt)
top_bins, right_bins = None, None
if plot == 'blobplot':
top_bins = arange(0, 1, 0.01)
if self.min_cov >= 1:
right_bins = logspace(0, (int(math.log(self.max_cov)) + 1), 200, base=10.0)
elif self.min_cov >= 0.1:
right_bins = logspace(-1, (int(math.log(self.max_cov)) + 1), 200, base=10.0)
else:
right_bins = logspace(-2, (int(math.log(self.max_cov)) + 1), 200, base=10.0)
elif plot == 'covplot':
if self.min_cov >= 1:
top_bins = logspace(0, (int(math.log(self.max_cov)) + 1), 200, base=10.0)
right_bins = logspace(0, (int(math.log(self.max_cov)) + 1), 200, base=10.0)
elif self.min_cov >= 0.1:
top_bins = logspace(-1, (int(math.log(self.max_cov)) + 1), 200, base=10.0)
right_bins = logspace(-1, (int(math.log(self.max_cov)) + 1), 200, base=10.0)
else:
top_bins = logspace(-2, (int(math.log(self.max_cov)) + 1), 200, base=10.0)
right_bins = logspace(-2, (int(math.log(self.max_cov)) + 1), 200, base=10.0)
else:
pass
return fig, axScatter, axHistx, axHisty, axLegend, top_bins, right_bins
elif plot == 'readcov':
main_columns = 2
if (self.refcov_dict):
main_columns += 2
group_columns = len(self.plot_order)
fig = plt.figure(1, figsize=self.readcov_size, dpi=self.dpi)
gs = mat.gridspec.GridSpec(1, 2, width_ratios=[main_columns, group_columns])
ax_main = plt.subplot(gs[0])
try:
ax_main.set_facecolor(WHITE)
except AttributeError:
ax_main.set_color(WHITE)
ax_main.set_ylim(0, 1.1)
ax_main.set_yticklabels(['{:.0f}%'.format(x*100) for x in ax_main.get_yticks()])
ax_main.grid(True, axis='y', which="major", lw=2., color=BGGREY, linestyle='--')
ax_group = plt.subplot(gs[1])
try:
ax_group.set_facecolor(WHITE)
except AttributeError:
ax_group.set_color(WHITE)
ax_group.set_ylim(0, 1.1)
ax_group.set_yticklabels(['{:.0f}%'.format(x*100) for x in ax_group.get_yticks()])
ax_group.grid(True, axis='y', which="major", lw=2., color=BGGREY, linestyle='--')
x_pos_main = arange(main_columns)
x_pos_group = arange(len(self.plot_order))
return fig, ax_main, ax_group, x_pos_main, x_pos_group
else:
return None
def plotBar(self, cov_lib, out_f):
fig, ax_main, ax_group, x_pos_main, x_pos_group = self.setupPlot('readcov')
ax_main_data = {'labels' : [], 'values' : [], 'colours' : [] }
ax_group_data = {'labels' : [], 'values' : [], 'colours' : [] }
reads_total = self.cov_libs_total_reads_dict[cov_lib]
reads_mapped = self.stats['all']['reads_mapped'][cov_lib]
reads_unmapped = reads_total - self.stats['all']['reads_mapped'][cov_lib]
ax_main_data['labels'].append('Unmapped (assembly)')
ax_main_data['values'].append(reads_unmapped/reads_total)
ax_main_data['colours'].append(DGREY)
ax_main_data['labels'].append('Mapped (assembly)')
ax_main_data['values'].append(reads_mapped/reads_total)
ax_main_data['colours'].append(DGREY)
if (self.refcov_dict):
if cov_lib in self.refcov_dict:
reads_total_ref = self.refcov_dict[cov_lib]['reads_total']
reads_mapped_ref = self.refcov_dict[cov_lib]['reads_mapped']
reads_unmapped_ref = reads_total_ref - reads_mapped_ref
ax_main_data['labels'].append('Unmapped (ref)')
ax_main_data['values'].append(reads_unmapped_ref/reads_total_ref)
ax_main_data['colours'].append(DGREY)
ax_main_data['labels'].append('Mapped (ref)')
ax_main_data['values'].append(reads_mapped_ref/reads_total_ref)
ax_main_data['colours'].append(DGREY)
else:
BtLog.error('40', cov_lib)
# mapped plotted groups
for group in self.plot_order:
ax_group_data['labels'].append(group)
ax_group_data['values'].append(self.stats[group]['reads_mapped_perc'][cov_lib])
ax_group_data['colours'].append(self.colours[group])
rect_group = ax_group.bar(x_pos_group, ax_group_data['values'], width = 0.5, tick_label=ax_group_data['labels'], align='center', color = ax_group_data['colours'])
for rect_g in rect_group:
height_g = float(rect_g.get_height())
ax_group.text(rect_g.get_x() + rect_g.get_width()/2., 0.005 + height_g, '{:.2f}%'.format(height_g*100), ha='center', va='bottom', fontsize=LEGEND_FONTSIZE)
rect_main = ax_main.bar(x_pos_main, ax_main_data['values'], width = 0.5, tick_label=ax_main_data['labels'], align='center', color = ax_main_data['colours'])
for rect_m in rect_main:
height_m = float(rect_m.get_height())
ax_main.text(rect_m.get_x() + rect_m.get_width()/2., 0.005 + height_m, '{:.2f}%'.format(height_m*100), ha='center', va='bottom', fontsize=LEGEND_FONTSIZE)
ax_main.set_xticklabels(ax_main_data['labels'], rotation=45, ha='center', fontsize=LEGEND_FONTSIZE)
ax_group.set_xticklabels(ax_group_data['labels'], rotation=45, ha='center', fontsize=LEGEND_FONTSIZE)
#figsuptitle = fig.suptitle(out_f, verticalalignment='top')
out_f = "%s.read_cov.%s" % (out_f, cov_lib)
print(BtLog.status_d['8'] % "%s.%s" % (out_f, self.format))
fig.tight_layout()
#fig.savefig("%s.%s" % (out_f, self.format), format=self.format, bbox_extra_artists=(figsuptitle,))
fig.savefig("%s.%s" % (out_f, self.format), format=self.format)
plt.close(fig)
def plotScatter(self, cov_lib, info_flag, out_f):
fig, axScatter, axHistx, axHisty, axLegend, top_bins, right_bins = self.setupPlot(self.plot)
# empty handles for big legend
legend_handles = []
legend_labels = []
# marker size scaled by biggest blob (size in points^2)
max_length = max(array(self.stats['all']['length'])) # length of biggest blob
max_marker_size = 12500 # marker size for biggest blob, i.e. area of 12500^2 pixel
for idx, group in enumerate(self.plot_order):
idx += 1
lw, alpha = 0.5, 0.8
if group == 'no-hit':
alpha = 0.5
group_length_array = array(self.stats[group]['length'])
if len(group_length_array) > 0 and group not in self.exclude_groups:
colour = self.colours[group]
group_x_array = ''
group_y_array = ''
if self.plot == 'blobplot':
group_x_array = array(self.stats[group]['gc'])
group_y_array = array(self.stats[group]['covs'][cov_lib])
elif self.plot == 'covplot':
group_x_array = array(self.stats[group]['covs'][cov_lib])
group_y_array = array([self.cov_y_dict.get(name, 0.02) for name in self.stats[group]['name']])
else:
BtLog.error('34', self.plot)
marker_size_array = []
if (self.ignore_contig_length): # no scaling
if group == "no-hit":
s = 20
else:
s = 100
marker_size_array = [s for length in group_length_array]
else: # scaling by max_length
marker_size_array = [(length/max_length)*max_marker_size for length in group_length_array]
# generate label for legend
group_span_in_mb = round(self.stats[group]['span_visible']/1000000, 2)
group_number_of_seqs = self.stats[group]['count_visible']
group_n50 = self.stats[group]['n50']
fmt_seqs = "{:,}".format(group_number_of_seqs)
fmt_span = "{:,}".format(group_span_in_mb)
fmt_n50 = "{:,}".format(group_n50)
label = "%s (%s;%sMB;%snt)" % (group, fmt_seqs, fmt_span, fmt_n50)
if (info_flag):
print(BtLog.info_d['0'] % (group, fmt_seqs, fmt_span, fmt_n50))
if group == "other":
legend_handles.append(Line2D([0], [0], linewidth = 0.5, linestyle="none", marker="o", alpha=1, markersize=24, markeredgecolor=DGREY, markerfacecolor=colour))
else:
legend_handles.append(Line2D([0], [0], linewidth = 0.5, linestyle="none", marker="o", alpha=1, markersize=24, markeredgecolor=WHITE, markerfacecolor=colour))
legend_labels.append(label)
weights_array = None
if self.hist_type == "span":
weights_array = group_length_array/1000
axHistx.hist(group_x_array, weights=weights_array, color = colour, bins = top_bins, histtype='step', lw = 3)
axHisty.hist(group_y_array, weights=weights_array, color = colour, bins = right_bins, histtype='step', orientation='horizontal', lw = 3)
if group == 'other':
axScatter.scatter(group_x_array, group_y_array, color = colour, s = marker_size_array, lw = lw, alpha=alpha, edgecolor=DGREY, label=label)
else:
axScatter.scatter(group_x_array, group_y_array, color = colour, s = marker_size_array, lw = lw, alpha=alpha, edgecolor=WHITE, label=label)
axLegend.axis('off')
if (self.multiplot):
fig_m, axScatter_m, axHistx_m, axHisty_m, axLegend_m, top_bins, right_bins = self.setupPlot(self.plot)
legend_handles_m = []
legend_labels_m = []
legend_handles_m.append(Line2D([0], [0], linewidth = 0.5, linestyle="none", marker="o", alpha=1, markersize=24, markeredgecolor=WHITE, markerfacecolor=colour))
legend_labels_m.append(label)
axHistx_m.hist(group_x_array, weights=weights_array, color = colour, bins = top_bins, histtype='step', lw = 3)
axHisty_m.hist(group_y_array, weights=weights_array, color = colour, bins = right_bins, histtype='step', orientation='horizontal', lw = 3)
if group == 'other':
axScatter_m.scatter(group_x_array, group_y_array, color = colour, s = marker_size_array, lw = lw, alpha=alpha, edgecolor=DGREY, label=label)
else:
axScatter_m.scatter(group_x_array, group_y_array, color = colour, s = marker_size_array, lw = lw, alpha=alpha, edgecolor=WHITE, label=label)
axLegend_m.axis('off')
axLegend_m.legend(legend_handles_m, legend_labels_m, loc=6, numpoints=1, fontsize=LEGEND_FONTSIZE, frameon=True)
plot_ref_legend(axScatter_m, max_length, max_marker_size, self.ignore_contig_length)
m_out_f = "%s.%s.%s.%s" % (out_f, cov_lib, idx, group.replace("/", "_").replace(" ", "_"))
fig_m = plot_legend(fig_m, axLegend_m, m_out_f, self.legend_flag, self.format, self.cumulative_flag)
print(BtLog.status_d['8'] % "%s.%s" % (m_out_f, self.format))
fig_m.savefig("%s.%s" % (m_out_f, self.format), format=self.format)
plt.close(fig_m)
elif (self.cumulative_flag):
axLegend.legend(legend_handles, legend_labels, loc=6, numpoints=1, fontsize=LEGEND_FONTSIZE, frameon=True)
plot_ref_legend(axScatter, max_length, max_marker_size, self.ignore_contig_length)
m_out_f = "%s.%s.%s.%s" % (out_f, cov_lib, idx, group.replace("/", "_").replace(" ", "_"))
fig.add_axes(axLegend)
fig = plot_legend(fig, axLegend, m_out_f, self.legend_flag, self.format, self.cumulative_flag)
if not (self.no_title):
fig.suptitle(out_f, fontsize=35, verticalalignment='top')
print(BtLog.status_d['8'] % "%s.%s" % (m_out_f, self.format))
fig.savefig("%s.%s" % (m_out_f, self.format), format=self.format)
else:
pass
plot_ref_legend(axScatter, max_length, max_marker_size, self.ignore_contig_length)
axLegend.legend(legend_handles, legend_labels, numpoints=1, fontsize=LEGEND_FONTSIZE, frameon=True, loc=6 )
out_f = "%s.%s" % (out_f, cov_lib)
fig.add_axes(axLegend)
fig = plot_legend(fig, axLegend, out_f, self.legend_flag, self.format, self.cumulative_flag)
if not (self.no_title):
fig.suptitle(out_f, fontsize=35, verticalalignment='top')
print(BtLog.status_d['8'] % "%s.%s" % (out_f, self.format))
fig.savefig("%s.%s" % (out_f, self.format), format=self.format)
plt.close(fig)
if __name__ == "__main__":
pass
================================================
FILE: lib/BtTax.py
================================================
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
File : BtTax.py
Author : Dominik R. Laetsch, dominik.laetsch at gmail dot com
"""
RANKS = ['species', 'genus', 'family', 'order', 'phylum', 'superkingdom']
TAXRULES = ['bestsum', 'bestsumorder'] # this should be re-named colour rules at one point
def noHit():
return {rank : {'tax' : 'no-hit', 'score' : 0.0, 'c_index' : 0} for rank in RANKS}
def getTreeList(taxIds, nodesDB):
known_tree_lists = {}
for taxId in taxIds:
if not taxId in known_tree_lists:
tree_list = []
nextTaxId = [taxId]
while nextTaxId:
thisTaxId = nextTaxId.pop(0)
if (not thisTaxId == '1') and (thisTaxId in nodesDB):
parent = nodesDB[thisTaxId]['parent']
nextTaxId.append(parent)
tree_list.append(thisTaxId)
else:
tree_list.append('1')
known_tree_lists[taxId] = tree_list
return known_tree_lists
def getLineages(tree_lists, nodesDB):
lineage = {}
for tree_list_id, tree_list in tree_lists.items():
lineage[tree_list_id] = {rank : 'undef' for rank in RANKS}
for taxId in tree_list:
node = nodesDB[taxId]
if node['rank'] in RANKS:
lineage[tree_list_id][node['rank']] = node['name']
# traverse ranks again so that undef is "higher_def_rank" + "-" + undef
def_rank = ''
for rank in reversed(list(RANKS)):
if not lineage[tree_list_id][rank] == 'undef':
def_rank = lineage[tree_list_id][rank]
else:
if (def_rank):
lineage[tree_list_id][rank] = def_rank + "-" + lineage[tree_list_id][rank]
return lineage
def taxRuleBestSum(taxDict, taxonomy, min_bitscore, min_bitscore_diff, tax_collision_random):
tempTax = { rank : {} for rank in RANKS }
for lib in sorted(taxDict):
for rank in RANKS:
for tax, score in sorted(taxDict[lib][rank].items()):
tempTax[rank][tax] = tempTax[rank].get(tax, 0.0) + score
for rank in tempTax:
for tax, score in sorted(tempTax[rank].items(), key=lambda x: x[1], reverse=True):
if taxonomy[rank]['tax'] == 'no-hit':
taxonomy[rank]['score'] = score
if score >= min_bitscore:
taxonomy[rank]['tax'] = tax
#taxonomy_assigned_in_hit_lib = lib
else:
if score == taxonomy[rank]['score']: # equal score in subsequent hit
if not tax_collision_random:
taxonomy[rank]['tax'] = 'unresolved'
elif (taxonomy[rank]['score'] - score) <= min_bitscore_diff:
taxonomy[rank]['tax'] = 'unresolved'
else:
pass
if not taxonomy[rank]['tax'] == tax:
taxonomy[rank]['c_index'] += 1
return taxonomy
def taxRuleBestSumOrder(taxDict, taxonomy, min_bitscore, min_bitscore_diff, tax_collision_random):
for rank in RANKS:
taxonomy_assigned_in_hit_lib = ''
for lib in sorted(taxDict):
for tax, score in sorted(taxDict[lib][rank].items(), key=lambda x: x[1], reverse=True):
if not taxonomy_assigned_in_hit_lib: # has not been taxonomically annotated yet
if taxonomy[rank]['tax'] == 'no-hit':
taxonomy[rank]['score'] = score
if score >= min_bitscore:
taxonomy[rank]['tax'] = tax
taxonomy_assigned_in_hit_lib = lib
elif taxonomy_assigned_in_hit_lib == lib:
if score == taxonomy[rank]['score']: # equal score in subsequent hit
if not tax_collision_random:
taxonomy[rank]['tax'] = 'unresolved'
elif (taxonomy[rank]['score'] - score) <= min_bitscore_diff:
taxonomy[rank]['tax'] = 'unresolved'
else:
pass
if not taxonomy[rank]['tax'] == tax:
taxonomy[rank]['c_index'] += 1
else:
pass
return taxonomy
def taxRule(taxrule, hits, lineages, min_score, min_bitscore_diff, tax_collision_random):
taxonomy = {rank: {'tax': 'no-hit', 'score': 0.0, 'c_index': 0 } for rank in RANKS }
taxDict = getTaxDict(hits, lineages) # here libs are separated
if taxrule == 'bestsum':
taxonomy = taxRuleBestSum(taxDict, taxonomy, min_score, min_bitscore_diff, tax_collision_random)
elif taxrule == 'bestsumorder':
taxonomy = taxRuleBestSumOrder(taxDict, taxonomy, min_score, min_bitscore_diff, tax_collision_random)
else:
pass
return taxonomy
def getTaxDict(hits, lineages):
taxDict = {}
for lib, hits in hits.items():
taxDict[lib] = {}
for hit in hits:
taxId = hit['taxId']
score = hit['score']
for rank in RANKS:
name = lineages[taxId][rank]
if not rank in taxDict[lib]:
taxDict[lib][rank] = {name : 0.0}
taxDict[lib][rank][name] = taxDict[lib][rank].get(name, 0.0) + score
return taxDict
if __name__ == "__main__":
pass
================================================
FILE: lib/__init__.py
================================================
================================================
FILE: lib/bamfilter.py
================================================
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""usage: blobtools bamfilter -b FILE [-i FILE] [-e FILE] [-U] [-n] [-o PREFIX] [-f FORMAT]
[-h|--help]
Options:
-h --help show this
-b, --bam FILE BAM file (sorted by name)
-i, --include FILE List of contigs whose reads are included
- writes FASTAs of pairs where at least
one read maps sequences in list
(InUn.fq, InIn.fq, ExIn.fq)
-e, --exclude FILE List of contigs whose reads are excluded (outputs reads that do not map to sequences in list)
- writes FASTAs of pairs where at least
one read does not maps to sequences in list
(InUn.fq, InIn.fq, ExIn.fq)
-U, --exclude_unmapped Exclude pairs where both reads are unmapped
-n, --noninterleaved Use if fw and rev reads should be in separate files
-f, --read_format FORMAT FASTQ = fq, FASTA = fa [default: fa]
-o, --out PREFIX Output prefix
"""
from __future__ import division
from docopt import docopt
import sys
import lib.BtLog as BtLog
import lib.BtIO as BtIO
def main():
args = docopt(__doc__)
#print(args)
bam_f = args['--bam']
include_f = args['--include']
exclude_f = args['--exclude']
out_prefix = args['--out']
read_format = args['--read_format']
if not read_format in set(['fq', 'fa']):
sys.exit("[X] Read format must be fq or fa!")
noninterleaved = args['--noninterleaved']
include_unmapped = True
if args['--exclude_unmapped']:
include_unmapped = False
out_f = BtIO.getOutFile(bam_f, out_prefix, None)
if include_f and exclude_f:
print(BtLog.error('43'))
elif include_f:
sequence_list = BtIO.parseList(include_f)
BtIO.parseBamForFilter(bam_f, include_unmapped, noninterleaved, out_f, sequence_list, None, read_format)
elif exclude_f:
sequence_list = BtIO.parseList(exclude_f)
BtIO.parseBamForFilter(bam_f, include_unmapped, noninterleaved, out_f, None, sequence_list, read_format)
else:
BtIO.parseBamForFilter(bam_f, include_unmapped, noninterleaved, out_f, None, None, read_format)
if __name__ == '__main__':
main()
================================================
FILE: lib/blobplot.py
================================================
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""usage: blobtools plot -i <BLOBDB>
[-p INT] [-l INT] [--cindex] [-n] [-s]
[-r RANK] [-x TAXRULE] [--label GROUPS...]
[--lib COVLIB] [-o PREFIX] [-m]
[--sort ORDER] [--sort_first LABELS] [--hist HIST] [--notitle] [--filelabel]
[--colours FILE] [--exclude FILE]
[--refcov FILE] [--catcolour FILE]
[--format FORMAT] [--noblobs] [--noreads] [--legend]
[--cumulative] [--multiplot]
[-h|--help]
Options:
-h --help show this
-i, --infile BLOBDB BlobDB file (created with "blobtools create")
--lib COVLIB Plot only certain covlib(s). Separated by ","
--notitle Do not add filename as title to plot
--filelabel Label axis based on filenames
-p, --plotgroups INT Number of (taxonomic) groups to plot, remaining
groups are placed in 'other' [default: 8]
-l, --length INT Minimum sequence length considered for plotting [default: 100]
--cindex Colour blobs by 'c index' [default: False]
-n, --nohit Hide sequences without taxonomic annotation [default: False]
-s, --noscale Do not scale sequences by length [default: False]
--legend Plot legend of blobplot in separate figure
-m, --multiplot Multi-plot. Print blobplot for each (taxonomic) group separately
--cumulative Print plot after addition of each (taxonomic) group
--sort <ORDER> Sort order for plotting [default: span]
span : plot with decreasing span
count : plot with decreasing count
--sort_first <L1,L2,...> Labels that should always be plotted first, regardless of sort order
("no-hit,other,undef" is often a useful setting)
--hist <HIST> Data for histograms [default: span]
span : span-weighted histograms
count : count histograms
-r, --rank <RANK> Taxonomic rank used for colouring of blobs [default: phylum]
(Supported: species, genus, family, order,
phylum, superkingdom)
-x, --taxrule <TAXRULE> Taxrule which has been used for computing taxonomy
(Supported: bestsum, bestsumorder) [default: bestsum]
--format FORMAT Figure format for plot (png, pdf, eps, jpeg,
ps, svg, svgz, tiff) [default: png]
--noblobs Omit blobplot [default: False]
--noreads Omit plot of reads mapping [default: False]
-o, --out PREFIX Output prefix
--label GROUPS... Relabel (taxonomic) groups, can be used several times.
e.g. "A=Actinobacteria,Proteobacteria"
--colours COLOURFILE File containing colours for (taxonomic) groups. This allows having more than 9 colours.
--exclude GROUPS Exclude these (taxonomic) groups (also works for 'other')
e.g. "Actinobacteria,Proteobacteria,other"
--refcov <FILE> File containing number of "total" and "mapped" reads
per coverage file. (e.g.: bam0,900,100). If provided, info
will be used in read coverage plot(s).
--catcolour <FILE> Colour plot based on categories from FILE
(format : "seq\tcategory").
"""
from docopt import docopt
from os.path import basename
import sys
import lib.BtLog as BtLog
import lib.BtIO as BtIO
import lib.BtCore as BtCore
import lib.BtPlot as BtPlot
import lib.interface as interface
def main():
args = docopt(__doc__)
args = BtPlot.check_input(args)
blobdb_f = args['--infile']
rank = args['--rank']
min_length = int(args['--length'])
max_group_plot = int(args['--plotgroups'])
colour_f = args['--colours']
if max_group_plot > 8 and not colour_f:
sys.exit("[X] '--plotgroups' must be less than 9 for using automatic colour assignation.")
hide_nohits = args['--nohit']
taxrule = args['--taxrule']
c_index = args['--cindex']
exclude_groups = args['--exclude']
labels = args['--label']
colour_f = args['--colours']
refcov_f = args['--refcov']
catcolour_f = args['--catcolour']
multiplot = args['--multiplot']
out_prefix = args['--out']
sort_order = args['--sort']
sort_first = args['--sort_first']
hist_type = args['--hist']
no_title = args['--notitle']
ignore_contig_length = args['--noscale']
format_plot = args['--format']
no_plot_blobs = args['--noblobs']
no_plot_reads = args['--noreads']
legend_flag = args['--legend']
cumulative_flag = args['--cumulative']
cov_lib_selection = args['--lib']
filelabel = args['--filelabel']
exclude_groups = BtIO.parseCmdlist(exclude_groups)
refcov_dict = BtIO.parseReferenceCov(refcov_f)
user_labels = BtIO.parseCmdLabels(labels)
catcolour_dict = BtIO.parseCatColour(catcolour_f)
colour_dict = BtIO.parseColours(colour_f)
# Load BlobDb
print(BtLog.status_d['9'] % blobdb_f)
blobDb = BtCore.BlobDb('blobplot')
blobDb.version = interface.__version__
blobDb.load(blobdb_f)
# Generate plot data
print(BtLog.status_d['18'])
data_dict, min_cov, max_cov, cov_lib_dict = blobDb.getPlotData(rank, min_length, hide_nohits, taxrule, c_index, catcolour_dict)
plotObj = BtPlot.PlotObj(data_dict, cov_lib_dict, cov_lib_selection, 'blobplot', sort_first)
plotObj.exclude_groups = exclude_groups
plotObj.version = blobDb.version
plotObj.format = format_plot
plotObj.max_cov = max_cov
plotObj.min_cov = min_cov
plotObj.no_title = no_title
plotObj.multiplot = multiplot
plotObj.hist_type = hist_type
plotObj.ignore_contig_length = ignore_contig_length
plotObj.max_group_plot = max_group_plot
plotObj.legend_flag = legend_flag
plotObj.cumulative_flag = cumulative_flag
# order by which to plot (should know about user label)
plotObj.group_order = BtPlot.getSortedGroups(data_dict, sort_order, sort_first)
# labels for each level of stats
plotObj.labels.update(plotObj.group_order)
# plotObj.group_labels is dict that contains labels for each group : all/other/user_label
if (user_labels):
for group, label in user_labels.items():
plotObj.labels.add(label)
plotObj.group_labels = {group : set() for group in plotObj.group_order}
plotObj.relabel_and_colour(colour_dict, user_labels)
plotObj.compute_stats()
plotObj.refcov_dict = refcov_dict
# Plotting
info_flag = 1
out_f = ''
for cov_lib in plotObj.cov_libs:
plotObj.ylabel = "Coverage"
plotObj.xlabel = "GC proportion"
if (filelabel):
plotObj.ylabel = basename(cov_lib_dict[cov_lib]['f'])
out_f = "%s.%s.%s.p%s.%s.%s" % (blobDb.title, taxrule, rank, max_group_plot, hist_type, min_length)
if catcolour_dict:
out_f = "%s.%s" % (out_f, "catcolour")
if ignore_contig_length:
out_f = "%s.%s" % (out_f, "noscale")
if c_index:
out_f = "%s.%s" % (out_f, "c_index")
if exclude_groups:
out_f = "%s.%s" % (out_f, "exclude_" + "_".join(exclude_groups))
if labels:
out_f = "%s.%s" % (out_f, "userlabel_" + "_".join(set([name for name in user_labels.values()])))
out_f = "%s.%s" % (out_f, "blobplot")
if (plotObj.cumulative_flag):
out_f = "%s.%s" % (out_f, "cumulative")
if (plotObj.multiplot):
out_f = "%s.%s" % (out_f, "multiplot")
out_f = BtIO.getOutFile(out_f, out_prefix, None)
if not (no_plot_blobs):
plotObj.plotScatter(cov_lib, info_flag, out_f)
info_flag = 0
if not (no_plot_reads) and (plotObj.cov_libs_total_reads_dict[cov_lib]):
# prevent plotting if --noreads or total_reads == 0
plotObj.plotBar(cov_lib, out_f)
plotObj.write_stats(out_f)
if __name__ == '__main__':
main()
================================================
FILE: lib/covplot.py
================================================
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""usage: blobtools covplot -i BLOBDB -c COV [--max FLOAT]
[--xlabel XLABEL] [--ylabel YLABEL]
[--lib COVLIB] [-o PREFIX] [-m]
[-p INT] [-l INT] [--cindex] [-n] [-s]
[-r RANK] [-x TAXRULE] [--label GROUPS...]
[--sort ORDER] [--sort_first LABELS]
[--hist HIST] [--notitle]
[--colours FILE] [--exclude FILE]
[--refcov FILE] [--catcolour FILE]
[--format FORMAT] [--noblobs] [--noreads] [--legend]
[--cumulative]
[-h|--help]
Options:
-h --help show this
-i, --infile BLOBDB BlobDB file
-c, --cov COV COV file to be used in y-axis
--xlabel XLABEL Label for x-axis
--ylabel YLABEL Label for y-axis
--max FLOAT Maximum values for x/y-axis [default: 1e10]
--lib COVLIB Plot only certain covlib(s). Separated by ","
--notitle Do not add filename as title to plot
-p, --plotgroups INT Number of (taxonomic) groups to plot, remaining
groups are placed in 'other' [default: 7]
-l, --length INT Minimum sequence length considered for plotting [default: 100]
--cindex Colour blobs by 'c index' [default: False]
-n, --nohit Hide sequences without taxonomic annotation [default: False]
-s, --noscale Do not scale sequences by length [default: False]
--legend Plot legend of blobplot in separate figure
-m, --multiplot Multi-plot. Print blobplot for each (taxonomic) group separately
--cumulative Print plot after addition of each (taxonomic) group
--sort <ORDER> Sort order for plotting [default: span]
span : plot with decreasing span
count : plot with decreasing count
--sort_first <L1,L2,...> Labels that should always be plotted first, regardless of sort order
("no-hit,other,undef" is often a useful setting)
--hist <HIST> Data for histograms [default: span]
span : span-weighted histograms
count : count histograms
-r, --rank <RANK> Taxonomic rank used for colouring of blobs [default: phylum]
(Supported: species, genus, family, order,
phylum, superkingdom)
-x, --taxrule <TAXRULE> Taxrule which has been used for computing taxonomy
(Supported: bestsum, bestsumorder) [default: bestsum]
--format FORMAT Figure format for plot (png, pdf, eps, jpeg,
ps, svg, svgz, tiff) [default: png]
--noblobs Omit blobplot [default: False]
--noreads Omit plot of reads mapping [default: False]
-o, --out PREFIX Output prefix
--label GROUPS... Relabel (taxonomic) groups, can be used several times.
e.g. "A=Actinobacteria,Proteobacteria"
--colours COLOURFILE File containing colours for (taxonomic) groups
--exclude GROUPS Exclude these (taxonomic) groups (also works for 'other')
e.g. "Actinobacteria,Proteobacteria,other"
--refcov <FILE> File containing number of "total" and "mapped" reads
per coverage file. (e.g.: bam0,900,100). If provided, info
will be used in read coverage plot(s).
--catcolour <FILE> Colour plot based on categories from FILE
(format : "seq,category").
"""
from __future__ import division
from docopt import docopt
from os.path import basename
import lib.interface as interface
import lib.BtLog as BtLog
import lib.BtIO as BtIO
import lib.BtCore as Bt
import lib.BtPlot as BtPlot
def main():
args = docopt(__doc__)
args = BtPlot.check_input(args)
blobdb_f = args['--infile']
cov_f = args['--cov']
rank = args['--rank']
min_length = int(args['--length'])
max_group_plot = int(args['--plotgroups'])
hide_nohits = args['--nohit']
taxrule = args['--taxrule']
c_index = args['--cindex']
exclude_groups = args['--exclude']
labels = args['--label']
colour_f = args['--colours']
refcov_f = args['--refcov']
catcolour_f = args['--catcolour']
sort_order = args['--sort']
sort_first = args['--sort_first']
multiplot = args['--multiplot']
out_prefix = args['--out']
sort_order = args['--sort']
hist_type = args['--hist']
no_title = args['--notitle']
ignore_contig_length = args['--noscale']
format_plot = args['--format']
no_plot_blobs = args['--noblobs']
#no_plot_reads = args['--noreads']
legend_flag = args['--legend']
cumulative_flag = args['--cumulative']
cov_lib_selection = args['--lib']
xlabel = args['--xlabel']
ylabel = args['--ylabel']
axis_max = float(args['--max'])
exclude_groups = BtIO.parseCmdlist(exclude_groups)
refcov_dict = BtIO.parseReferenceCov(refcov_f)
user_labels = BtIO.parseCmdLabels(labels)
catcolour_dict = BtIO.parseCatColour(catcolour_f)
colour_dict = BtIO.parseColours(colour_f)
# Load BlobDb
print(BtLog.status_d['9'] % blobdb_f)
blobDb = Bt.BlobDb('blobplot')
blobDb.version = interface.__version__
blobDb.load(blobdb_f)
# Generate plot data
print(BtLog.status_d['1'] % ('cov_y_axis', cov_f))
cov_y_dict, reads_total, reads_mapped, reads_unmapped, read_cov_dict = BtIO.parseCov(cov_f, set(blobDb.dict_of_blobs))
print(BtLog.status_d['18'])
data_dict, min_cov, max_cov, cov_lib_dict = blobDb.getPlotData(rank, min_length, hide_nohits, taxrule, c_index, catcolour_dict)
plotObj = BtPlot.PlotObj(data_dict, cov_lib_dict, cov_lib_selection, 'covplot', sort_first)
# set lowest coverage to 0.01
for contig in cov_y_dict:
if cov_y_dict[contig] < 0.1:
cov_y_dict[contig] = 0.1
plotObj.cov_y_dict = cov_y_dict
plotObj.exclude_groups = exclude_groups
plotObj.version = blobDb.version
plotObj.format = format_plot
plotObj.max_cov = axis_max
plotObj.no_title = no_title
plotObj.multiplot = multiplot
plotObj.hist_type = hist_type
plotObj.ignore_contig_length = ignore_contig_length
plotObj.max_group_plot = max_group_plot
plotObj.legend_flag = legend_flag
plotObj.cumulative_flag = cumulative_flag
# order by which to plot (should know about user label)
plotObj.group_order = BtPlot.getSortedGroups(data_dict, sort_order)
# labels for each level of stats
plotObj.labels.update(plotObj.group_order)
# plotObj.group_labels is dict that contains labels for each group : all/other/user_label
if (user_labels):
for group, label in user_labels.items():
plotObj.labels.add(label)
plotObj.group_labels = {group : set() for group in plotObj.group_order}
plotObj.relabel_and_colour(colour_dict, user_labels)
plotObj.compute_stats()
plotObj.refcov_dict = refcov_dict
# Plotting
info_flag = 1
out_f = ''
for cov_lib in plotObj.cov_libs:
plotObj.xlabel = basename(cov_lib_dict[cov_lib]['f'])
plotObj.ylabel = cov_f
if (ylabel):
plotObj.ylabel = ylabel
if (xlabel):
plotObj.xlabel = xlabel
out_f = "%s.%s.%s.p%s.%s.%s" % (blobDb.title, taxrule, rank, max_group_plot, hist_type, min_length)
if catcolour_dict:
out_f = "%s.%s" % (out_f, "catcolour")
if ignore_contig_length:
out_f = "%s.%s" % (out_f, "noscale")
if c_index:
out_f = "%s.%s" % (out_f, "c_index")
if exclude_groups:
out_f = "%s.%s" % (out_f, "exclude_" + "_".join(exclude_groups))
if labels:
out_f = "%s.%s" % (out_f, "userlabel_" + "_".join(set([name for name in user_labels.values()])))
out_f = "%s.%s" % (out_f, "covplot")
if (plotObj.cumulative_flag):
out_f = "%s.%s" % (out_f, "cumulative")
if (plotObj.multiplot):
out_f = "%s.%s" % (out_f, "multiplot")
out_f = BtIO.getOutFile(out_f, out_prefix, None)
if not (no_plot_blobs):
plotObj.plotScatter(cov_lib, info_flag, out_f)
info_flag = 0
plotObj.write_stats(out_f)
if __name__ == '__main__':
main()
================================================
FILE: lib/create.py
================================================
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""usage: blobtools create -i FASTA [-y FASTATYPE] [-o PREFIX] [--title TITLE]
[-b BAM...] [-C] [-a CAS...] [-c COV...]
[--nodes <NODES>] [--names <NAMES>] [--db <NODESDB>]
[-t HITS...] [-x TAXRULE...] [-m FLOAT] [-d FLOAT] [--tax_collision_random]
[-h|--help]
Options:
-h --help show this
-i, --infile FASTA FASTA file of assembly. Headers are split at whitespaces.
-y, --type FASTATYPE Assembly program used to create FASTA. If specified,
coverage will be parsed from FASTA header.
(Parsing supported for 'spades', 'velvet', 'platanus')
-t, --hitsfile HITS... Hits file in format (qseqid\\ttaxid\\tbitscore)
(e.g. BLAST output "--outfmt '6 qseqid staxids bitscore'")
Can be specified multiple times
-x, --taxrule <TAXRULE>... Taxrule determines how taxonomy of blobs
is computed (by default both are calculated)
"bestsum" : sum bitscore across all
hits for each taxonomic rank
"bestsumorder" : sum bitscore across all
hits for each taxonomic rank.
- If first <TAX> file supplies hits, bestsum is calculated.
- If no hit is found, the next <TAX> file is used.
-m, --min_score <FLOAT> Minimal score necessary to be considered for taxonomy calculaton, otherwise set to 'no-hit'
[default: 0.0]
-d, --min_diff <FLOAT> Minimal score difference between highest scoring
taxonomies (otherwise "unresolved") [default: 0.0]
--tax_collision_random Random allocation of taxonomy if highest scoring
taxonomies have equal scores (otherwise "unresolved") [default: False]
--nodes <NODES> NCBI nodes.dmp file. Not required if '--db'
--names <NAMES> NCBI names.dmp file. Not required if '--db'
--db <NODESDB> NodesDB file (default: $BLOBTOOLS/data/nodesDB.txt). If --nodes, --names and --db
are all given and NODESDB does not exist, create it from NODES and NAMES.
-b, --bam <BAM>... BAM file(s), can be specified multiple times
-a, --cas <CAS>... CAS file(s) (requires clc_mapping_info in $PATH), can be specified multiple times
-c, --cov <COV>... COV file(s), can be specified multiple times
-C, --calculate_cov Legacy coverage when getting coverage from BAM (does not apply to COV parsing).
New default is to estimate coverages which is faster,
-o, --out <PREFIX> BlobDB output prefix
--title TITLE Title of BlobDB [default: output prefix)
"""
from __future__ import division
from docopt import docopt
from os.path import join, dirname, abspath
import lib.BtCore as BtCore
import lib.BtLog as BtLog
import lib.BtIO as BtIO
import lib.interface as interface
def main():
#main_dir = dirname(__file__)
args = docopt(__doc__)
fasta_f = args['--infile']
fasta_type = args['--type']
bam_fs = args['--bam']
cov_fs = args['--cov']
cas_fs = args['--cas']
hit_fs = args['--hitsfile']
prefix = args['--out']
nodesDB_f = args['--db']
names_f = args['--names']
estimate_cov_flag = True if not args['--calculate_cov'] else False
nodes_f = args['--nodes']
taxrules = args['--taxrule']
try:
min_bitscore_diff = float(args['--min_diff'])
min_score = float(args['--min_score'])
except ValueError():
BtLog.error('45')
tax_collision_random = args['--tax_collision_random']
title = args['--title']
# outfile
out_f = BtIO.getOutFile("blobDB", prefix, "json")
if not (title):
title = out_f
# coverage
if not (fasta_type) and not bam_fs and not cov_fs and not cas_fs:
BtLog.error('1')
cov_libs = [BtCore.CovLibObj('bam' + str(idx), 'bam', lib_f) for idx, lib_f in enumerate(bam_fs)] + \
[BtCore.CovLibObj('cas' + str(idx), 'cas', lib_f) for idx, lib_f in enumerate(cas_fs)] + \
[BtCore.CovLibObj('cov' + str(idx), 'cov', lib_f) for idx, lib_f in enumerate(cov_fs)]
# taxonomy
hit_libs = [BtCore.HitLibObj('tax' + str(idx), 'tax', lib_f) for idx, lib_f in enumerate(hit_fs)]
# Create BlobDB object
blobDb = BtCore.BlobDb(title)
blobDb.version = interface.__version__
# Parse FASTA
blobDb.parseFasta(fasta_f, fasta_type)
# Parse nodesDB OR names.dmp, nodes.dmp
nodesDB_default = join(dirname(abspath(__file__)), "../data/nodesDB.txt")
nodesDB, nodesDB_f = BtIO.parseNodesDB(nodes=nodes_f, names=names_f, nodesDB=nodesDB_f, nodesDBdefault=nodesDB_default)
blobDb.nodesDB_f = nodesDB_f
# Parse similarity hits
if (hit_libs):
blobDb.parseHits(hit_libs)
if not taxrules:
if len(hit_libs) > 1:
taxrules = ['bestsum', 'bestsumorder']
else:
taxrules = ['bestsum']
blobDb.computeTaxonomy(taxrules, nodesDB, min_score, min_bitscore_diff, tax_collision_random)
else:
print(BtLog.warn_d['0'])
# Parse coverage
blobDb.parseCoverage(covLibObjs=cov_libs, estimate_cov=estimate_cov_flag, prefix=prefix)
# Generating BlobDB and writing to file
print(BtLog.status_d['7'] % out_f)
BtIO.writeJson(blobDb.dump(), out_f)
if __name__ == '__main__':
main()
================================================
FILE: lib/interface.py
================================================
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
blobtools
Usage:
./blobtools [<module>] [<args>...] [-h] [-v]
Modules:
create create a BlobDB
view generate tabular view, CONCOCT input or COV files from BlobDB
plot generate a BlobPlot from a BlobDB
covplot generate a CovPlot from a BlobDB and a COV file
map2cov generate a COV file from BAM file
taxify generate a BlobTools compatible HITS file (TSV)
bamfilter subset paired-end reads from a BAM file
seqfilter subset sequences in FASTA file based sequence IDs in list
nodesdb create nodesdb based on NCBI Taxdump's names.dmp and nodes.dmp
Options
-h, --help show this
-v, --version show version number
See 'blobtools <command> --help' for more information on a specific command.
Further documentation is available at https://blobtools.readme.io/
Examples:
# 1. Create a BlobDB
./blobtools create -i example/assembly.fna -b example/mapping_1.bam -t example/blast.out -o example/test
# 2. Generate a tabular view
./blobtools view -i example/test.blobDB.json
# 3. Generate a blobplot
./blobtools plot -i example/test.blobDB.json
"""
import sys
from docopt import docopt, DocoptExit
from timeit import default_timer as timer
__version__ = '1.1.1'
def main():
try:
start_time = timer()
try:
args = docopt(__doc__, version=__version__, options_first=True)
except DocoptExit:
print(__doc__)
else:
if args['<module>']:
if args['<module>'] == 'create':
import lib.create as create
create.main()
elif args['<module>'] == 'view':
import lib.view as view
view.main()
elif args['<module>'] == 'plot':
import lib.blobplot as plot
plot.main()
elif args['<module>'] == 'map2cov':
import lib.map2cov as map2cov
map2cov.main()
elif args['<module>'] == 'seqfilter':
import lib.seqfilter as seqfilter
seqfilter.main()
elif args['<module>'] == 'covplot':
import lib.covplot as covplot
covplot.main()
elif args['<module>'] == 'taxify':
import lib.taxify as taxify
taxify.main()
elif args['<module>'] == 'bamfilter':
import lib.bamfilter as bamfilter
bamfilter.main()
elif args['<module>'] == 'nodesdb':
import lib.nodesdb as nodesdb
nodesdb.main()
else:
sys.exit("%r is not a blobtools module. See 'blobtools -h'." % args['<module>'])
else:
print(__doc__)
except KeyboardInterrupt:
sys.stderr.write("\n[X] Interrupted by user after %i seconds!\n" % (timer() - start_time))
sys.exit(-1)
================================================
FILE: lib/map2cov.py
================================================
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""usage: blobtools map2cov -i FASTA [-b BAM...] [-a CAS...]
[-o PREFIX] [-c]
[-h|--help]
Options:
-h --help show this
-i, --infile FASTA FASTA file of assembly. Headers are split at whitespaces.
-b, --bam <BAM>... BAM file (requires pysam)
-a, --cas <CAS>... CAS file (requires clc_mapping_info in $PATH)
-o, --output <PREFIX> Output prefix
-c, --calculate_cov Legacy coverage, slower. New default is to estimate coverages
based on read lengths of first 10K reads.
"""
from __future__ import division
from docopt import docopt
import lib.BtLog as BtLog
import lib.BtCore as BtCore
import lib.interface as interface
def main():
args = docopt(__doc__)
fasta_f = args['--infile']
bam_fs = args['--bam']
cas_fs = args['--cas']
prefix = args['--output']
estimate_cov_flag = True if not args['--calculate_cov'] else False
# Make covLibs
cov_libs = [BtCore.CovLibObj('bam' + str(idx), 'bam', lib_f) for idx, lib_f in enumerate(bam_fs)] + \
[BtCore.CovLibObj('cas' + str(idx), 'cas', lib_f) for idx, lib_f in enumerate(cas_fs)]
if not (cov_libs):
BtLog.error('31')
blobDb = BtCore.BlobDb('cov')
blobDb.version = interface.__version__
blobDb.parseFasta(fasta_f, None)
bl
gitextract_36het1nj/
├── .gitignore
├── Dockerfile
├── LICENSE.md
├── MANIFEST.in
├── README.md
├── __init__.py
├── blobtools
├── data/
│ └── __init__.py
├── example/
│ ├── blast.out
│ ├── blobDB.json
│ ├── blobDB.table.txt
│ ├── catcolour.txt
│ ├── colours.txt
│ ├── diamond.out
│ ├── mapping_1.bam
│ ├── mapping_1.sorted.bam
│ ├── mapping_1.sorted.bam.bai
│ ├── mapping_2.bam
│ ├── mapping_2.sorted.bam
│ ├── mapping_2.sorted.bam.bai
│ └── refcov.txt
├── lib/
│ ├── BtCore.py
│ ├── BtIO.py
│ ├── BtLog.py
│ ├── BtPlot.py
│ ├── BtTax.py
│ ├── __init__.py
│ ├── bamfilter.py
│ ├── blobplot.py
│ ├── covplot.py
│ ├── create.py
│ ├── interface.py
│ ├── map2cov.py
│ ├── nodesdb.py
│ ├── seqfilter.py
│ ├── taxify.py
│ └── view.py
├── requirements.txt
├── setup.cfg
├── setup.py
└── test/
└── meta.json
SYMBOL INDEX (124 symbols across 15 files)
FILE: lib/BtCore.py
class BlobDb (line 18) | class BlobDb():
method __init__ (line 22) | def __init__(self, title):
method view (line 42) | def view(self, **kwargs):
method getCovHeader (line 108) | def getCovHeader(self, cov_lib_names):
method getCovLine (line 126) | def getCovLine(self, blob, cov_lib_names):
method getConcoctCovHeader (line 134) | def getConcoctCovHeader(self, cov_lib_names):
method getConcoctTaxLine (line 137) | def getConcoctTaxLine(self, blob, rank, taxrule):
method getConcoctCovLine (line 141) | def getConcoctCovLine(self, blob, cov_lib_names):
method getTableHeader (line 144) | def getTableHeader(self, taxrule, ranks, hits_flag, cov_lib_names):
method getTableLine (line 185) | def getTableLine(self, blob, taxrule, ranks, hits_flag, cov_lib_names,...
method dump (line 216) | def dump(self):
method load (line 236) | def load(self, BlobDb_f):
method getPlotData (line 242) | def getPlotData(self, rank, min_length, hide_nohits, taxrule, c_index,...
method addCovLib (line 327) | def addCovLib(self, covLib):
method parseFasta (line 332) | def parseFasta(self, fasta_f, fasta_type):
method parseCoverage (line 359) | def parseCoverage(self, **kwargs):
method parseHits (line 415) | def parseHits(self, hitLibs):
method computeTaxonomy (line 427) | def computeTaxonomy(self, taxrules, nodesDB, min_score, min_bitscore_d...
method getBlobs (line 446) | def getBlobs(self):
class BlObj (line 450) | class BlObj():
method __init__ (line 451) | def __init__(self, name, seq):
method calculateGC (line 462) | def calculateGC(self, seq):
method addCov (line 466) | def addCov(self, lib_name, cov):
method addReadCov (line 469) | def addReadCov(self, lib_name, read_cov):
method addHits (line 472) | def addHits(self, hitLibName, hitDict):
class CovLibObj (line 477) | class CovLibObj():
method __init__ (line 478) | def __init__(self, name, fmt, f):
class HitLibObj (line 488) | class HitLibObj():
method __init__ (line 489) | def __init__(self, name, fmt, f):
class ViewObj (line 495) | class ViewObj():
method __init__ (line 496) | def __init__(self, name='', out_f='', suffix='', header='', body=''):
method output (line 503) | def output(self):
class ExperimentalViewObj (line 725) | class ExperimentalViewObj():
method __init__ (line 726) | def __init__(self, name='experimental', view_dir='',blobDb={},meta={}):
method _format_float (line 741) | def _format_float(self,l,min_val=-float("inf")):
method _remove_cov_suffix (line 746) | def _remove_cov_suffix(self,id,meta):
method get_meta (line 754) | def get_meta(self):
method _keyed_list (line 826) | def _keyed_list(self,l):
method output (line 837) | def output(self):
FILE: lib/BtIO.py
function create_dir (line 24) | def create_dir(directory="", overwrite=True):
function parseList (line 36) | def parseList(infile):
function parseReferenceCov (line 45) | def parseReferenceCov(infile):
function parseCmdlist (line 60) | def parseCmdlist(temp):
function parseCmdLabels (line 69) | def parseCmdLabels(labels):
function parseCatColour (line 85) | def parseCatColour(infile):
function parseDict (line 99) | def parseDict(infile, key, value):
function parseColours (line 113) | def parseColours(infile):
function parseSet (line 124) | def parseSet(infile):
function parseFastaNameOrder (line 133) | def parseFastaNameOrder(infile):
function readFasta (line 139) | def readFasta(infile):
function runCmd (line 153) | def runCmd(**kwargs):
function which (line 169) | def which(program):
function checkAlnIndex (line 184) | def checkAlnIndex(aln):
function getAlnHeaderIntersection (line 191) | def getAlnHeaderIntersection(aln, headers):
function estimate_read_lengths (line 197) | def estimate_read_lengths(aln, set_of_blobs):
function checkBam (line 205) | def checkBam(aln, set_of_blobs):
function parseBam (line 218) | def parseBam(infile, set_of_blobs, estimate_cov):
function estimate_coverage (line 229) | def estimate_coverage(aln, set_of_blobs):
function check_mapped_read (line 241) | def check_mapped_read(read):
function calculate_coverage (line 246) | def calculate_coverage(aln, reads_mapped, set_of_blobs):
function write_read_pair_seqs (line 262) | def write_read_pair_seqs(pair_count_by_type, seqs_by_type, out_fs_by_type):
function get_read_pair_fasta (line 284) | def get_read_pair_fasta(read, read_format):
function init_read_pairs (line 298) | def init_read_pairs(outfile, include_unmapped, noninterleaved, include, ...
function print_bam (line 321) | def print_bam(read_pair_out_fs, read_pair_type, read1, read2):
function read_pair_generator (line 326) | def read_pair_generator(aln, region_string=None):
function parseBamForFilter (line 348) | def parseBamForFilter(infile, include_unmapped, noninterleaved, outfile,...
function get_table (line 393) | def get_table(table):
function parseCovFromHeader (line 401) | def parseCovFromHeader(fasta_type, header):
function parseCov (line 427) | def parseCov(infile, set_of_blobs):
function checkCas (line 465) | def checkCas(infile):
function parseCas (line 485) | def parseCas(infile, order_of_blobs):
function readTax (line 512) | def readTax(infile, set_of_blobs):
function getOutFile (line 549) | def getOutFile(base_file, prefix, suffix):
function parseNodesDB (line 563) | def parseNodesDB(**kwargs):
function readNamesNodes (line 623) | def readNamesNodes(names_f, nodes_f):
function readNodesDB (line 643) | def readNodesDB(nodesDB_f):
function writeNodesDB (line 658) | def writeNodesDB(nodesDB, nodesDB_f):
function byteify (line 668) | def byteify(input):
function writeJsonGzip (line 683) | def writeJsonGzip(obj, outfile):
function writeJson (line 689) | def writeJson(obj, outfile, indent=0, separators=(',', ': ')):
function parseJsonGzip (line 699) | def parseJsonGzip(infile):
function parseJson (line 708) | def parseJson(infile):
function readYaml (line 740) | def readYaml(infile):
FILE: lib/BtLog.py
function error (line 12) | def error(message, *argv):
FILE: lib/BtPlot.py
function n50 (line 34) | def n50(list_of_lengths):
function getSortedGroups (line 49) | def getSortedGroups(data_dict, sort_order, sort_first=()):
function generateColourDict (line 77) | def generateColourDict(colour_groups, groups):
function set_canvas (line 97) | def set_canvas():
function set_format_scatterplot (line 107) | def set_format_scatterplot(axScatter, **kwargs):
function set_format_hist_x (line 136) | def set_format_hist_x(axHistx, axScatter):
function set_format_hist_y (line 148) | def set_format_hist_y(axHisty, axScatter):
function get_ref_label (line 158) | def get_ref_label(max_length, max_marker_size, fraction):
function plot_ref_legend (line 164) | def plot_ref_legend(axScatter, max_length, max_marker_size, ignore_conti...
function plot_legend (line 175) | def plot_legend(fig, axLegend, out_f, legend_flag, format, cumulative_fl...
function check_input (line 184) | def check_input(args):
class PlotObj (line 219) | class PlotObj():
method __init__ (line 220) | def __init__(self, data_dict, cov_lib_dict, cov_lib_selection, plot_ty...
method subselect_cov_libs (line 253) | def subselect_cov_libs(self, cov_lib_dict, cov_lib_selection):
method get_cov_libs_total_reads_dict (line 279) | def get_cov_libs_total_reads_dict(self, cov_lib_dict):
method get_cov_libs_mapped_reads_dict (line 282) | def get_cov_libs_mapped_reads_dict(self, cov_lib_dict):
method get_stats_for_group (line 285) | def get_stats_for_group(self, group):
method write_stats (line 304) | def write_stats(self, out_f):
method compute_stats (line 342) | def compute_stats(self):
method relabel_and_colour (line 392) | def relabel_and_colour(self, colour_dict, user_labels):
method setupPlot (line 453) | def setupPlot(self, plot):
method plotBar (line 543) | def plotBar(self, cov_lib, out_f):
method plotScatter (line 594) | def plotScatter(self, cov_lib, info_flag, out_f):
FILE: lib/BtTax.py
function noHit (line 12) | def noHit():
function getTreeList (line 15) | def getTreeList(taxIds, nodesDB):
function getLineages (line 32) | def getLineages(tree_lists, nodesDB):
function taxRuleBestSum (line 50) | def taxRuleBestSum(taxDict, taxonomy, min_bitscore, min_bitscore_diff, t...
function taxRuleBestSumOrder (line 75) | def taxRuleBestSumOrder(taxDict, taxonomy, min_bitscore, min_bitscore_di...
function taxRule (line 100) | def taxRule(taxrule, hits, lineages, min_score, min_bitscore_diff, tax_c...
function getTaxDict (line 111) | def getTaxDict(hits, lineages):
FILE: lib/bamfilter.py
function main (line 32) | def main():
FILE: lib/blobplot.py
function main (line 72) | def main():
FILE: lib/covplot.py
function main (line 78) | def main():
FILE: lib/create.py
function main (line 56) | def main():
FILE: lib/interface.py
function main (line 49) | def main():
FILE: lib/map2cov.py
function main (line 25) | def main():
FILE: lib/nodesdb.py
function main (line 20) | def main():
FILE: lib/seqfilter.py
function main (line 23) | def main():
FILE: lib/taxify.py
function main (line 42) | def main():
FILE: lib/view.py
function main (line 38) | def main():
Condensed preview — 41 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (220K chars).
[
{
"path": ".gitignore",
"chars": 213,
"preview": "!*.md\n!*.py\n!*install\n!lib/*\n!data/\n!example/*\nexample/*.stats.txt\nexample/a*\nexample/test*\n.DS_Store\n!setup*\n!requireme"
},
{
"path": "Dockerfile",
"chars": 594,
"preview": "FROM continuumio/miniconda3\nMAINTAINER Nick Waters <nickp60@gmail.com>\nRUN conda install -c anaconda matplotlib docopt t"
},
{
"path": "LICENSE.md",
"chars": 35141,
"preview": " GNU GENERAL PUBLIC LICENSE\n Version 3, 29 June 2007\n\n Copyright (C) 2007 Free "
},
{
"path": "MANIFEST.in",
"chars": 42,
"preview": "include README.md\ninclude requirements.txt"
},
{
"path": "README.md",
"chars": 2104,
"preview": "BlobTools v1.1\n===============================\nA modular command-line solution for visualisation, quality control and ta"
},
{
"path": "__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "blobtools",
"chars": 117,
"preview": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\nfrom lib.interface import main\n\nif __name__ == '__main__':\n main()"
},
{
"path": "data/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "example/blast.out",
"chars": 396,
"preview": "contig_1 979556 200\ncontig_2 979556 500\ncontig_2 979556 1000\ncontig_2 979556 500\ncontig_2 979556 300"
},
{
"path": "example/blobDB.json",
"chars": 8197,
"preview": "{\"tax_collision_random\": false, \"min_score\": 0.0, \"nodesDB_f\": \"/Users/dom/git/blobtools/data/nodesDB.txt\", \"title\": \"bl"
},
{
"path": "example/blobDB.table.txt",
"chars": 918,
"preview": "## blobtools v1.0\n## assembly\t: /Users/dom/git/blobtools/example/assembly.fna\n## coverage\t: cov0 - /Users/dom/git/blobto"
},
{
"path": "example/catcolour.txt",
"chars": 111,
"preview": "contig_1,A\ncontig_2,A\ncontig_3,A\ncontig_4,B\ncontig_5,B\ncontig_6,B\ncontig_7,B\ncontig_8,B\ncontig_9,C\ncontig_10,C\n"
},
{
"path": "example/colours.txt",
"chars": 73,
"preview": "Nematoda,#48a365\nTardigrada,#48a365\nActinobacteria,#926eb3\nother,#ffffff\n"
},
{
"path": "example/diamond.out",
"chars": 346,
"preview": "contig_1 232323 200\ncontig_2 232323 500\ncontig_2 232323 1000\ncontig_2 232323 500\ncontig_2 979556 300"
},
{
"path": "example/refcov.txt",
"chars": 87,
"preview": "cov0,15313,15300\nbam0,15313,15300\ncov1,37278,15300\nbam1,37278,15300\ncovsum,52591,30600 "
},
{
"path": "lib/BtCore.py",
"chars": 40237,
"preview": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\n\"\"\"\nFile : BtCore.py\nAuthor : Dominik R. Laetsch, dominik.la"
},
{
"path": "lib/BtIO.py",
"chars": 28174,
"preview": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n\"\"\"\nFile : BtIO.py\nAuthor : Dominik R. Laetsch, dominik.laets"
},
{
"path": "lib/BtLog.py",
"chars": 6347,
"preview": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n\"\"\"\nFile : BtLog.py\nAuthor : Dominik R. Laetsch, dominik.laet"
},
{
"path": "lib/BtPlot.py",
"chars": 36845,
"preview": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\n\"\"\"\nFile : BtPlot.py\nAuthor : Dominik R. Laetsch, dominik.la"
},
{
"path": "lib/BtTax.py",
"chars": 5440,
"preview": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\n\"\"\"\nFile : BtTax.py\nAuthor : Dominik R. Laetsch, dominik.lae"
},
{
"path": "lib/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "lib/bamfilter.py",
"chars": 2483,
"preview": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\n\"\"\"usage: blobtools bamfilter -b FILE [-i FILE] [-e FILE] [-U] [-n] [-o"
},
{
"path": "lib/blobplot.py",
"chars": 8800,
"preview": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\n\"\"\"usage: blobtools plot -i <BLOBDB>\n [-p"
},
{
"path": "lib/covplot.py",
"chars": 9121,
"preview": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\n\"\"\"usage: blobtools covplot -i BLOBDB -c COV [--max FLOAT]\n "
},
{
"path": "lib/create.py",
"chars": 6174,
"preview": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\n\"\"\"usage: blobtools create -i FASTA [-y FASTATYPE] [-o PREFIX] [--ti"
},
{
"path": "lib/interface.py",
"chars": 3248,
"preview": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\n\"\"\"\nblobtools\n\n Usage:\n ./blobtools [<module>] [<args>...] [-h"
},
{
"path": "lib/map2cov.py",
"chars": 1644,
"preview": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\n\"\"\"usage: blobtools map2cov -i FASTA [-b BAM...] [-a CAS...]\n "
},
{
"path": "lib/nodesdb.py",
"chars": 860,
"preview": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\n\"\"\"usage: blobtools nodesdb --nodes <NODES> --names <NAMES>\n"
},
{
"path": "lib/seqfilter.py",
"chars": 2033,
"preview": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\n\"\"\"usage: blobtools seqfilter -i FASTA -l LIST [-o PREFIX] [-v]\n "
},
{
"path": "lib/taxify.py",
"chars": 4670,
"preview": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\n\"\"\"usage: blobtools taxify -f FILE [-a INT] [-b INT] [-c INT]\n "
},
{
"path": "lib/view.py",
"chars": 5130,
"preview": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\n\"\"\"usage: blobtools view -i <BLOBDB> [-x <TAXRULE>] [--rank <TAXRANK>"
},
{
"path": "requirements.txt",
"chars": 42,
"preview": "docopt\nmatplotlib\ntqdm\npysam\npyyaml>=4.2b1"
},
{
"path": "setup.cfg",
"chars": 64,
"preview": "[bdist_wheel]\nuniversal=1\n\n[metadata]\ndescription-file=README.md"
},
{
"path": "setup.py",
"chars": 1314,
"preview": "import pip\nfrom setuptools import setup, find_packages\n\n__version__ = '1.1'\n\n# Get the long description from the README "
},
{
"path": "test/meta.json",
"chars": 299,
"preview": "{\"id\": \"test\", \"name\": \"test\", \"records\": 10, \"record_type\": \"contigs\", \"fields\": [{\"id\": \"length\", \"name\": \"Length\", \"t"
}
]
// ... and 6 more files (download for full content)
About this extraction
This page contains the full source code of the DRL/blobtools GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 41 files (206.3 KB), approximately 55.2k tokens, and a symbol index with 124 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.