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Repository: giswqs/python-geospatial
Branch: master
Commit: f788dac7f37a
Files: 13
Total size: 1.4 MB
Directory structure:
gitextract_oiqettza/
├── .gitignore
├── LICENSE
├── README.md
├── binder/
│ ├── apt.txt
│ ├── environment.yml
│ ├── postBuild
│ └── requirements.txt
└── examples/
├── arcgis-python-api.ipynb
├── lidar.ipynb
├── rasterio.ipynb
├── template.ipynb
├── tensorflow.ipynb
└── whitebox.ipynb
================================================
FILE CONTENTS
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================================================
FILE: .gitignore
================================================
# Byte-compiled / optimized / DLL files
__pycache__/
*.py[cod]
*$py.class
.idea/
.vscode/
# exclude files
dev/
examples/temp
examples/data
# tif files extra
*.tfw
*.aux.xml
# C extensions
*.so
# Distribution / packaging
.Python
env/
build/
develop-eggs/
dist/
downloads/
eggs/
.eggs/
lib/
lib64/
parts/
sdist/
var/
wheels/
*.egg-info/
.installed.cfg
*.egg
# PyInstaller
# Usually these files are written by a python script from a template
# before PyInstaller builds the exe, so as to inject date/other infos into it.
*.manifest
*.spec
# Installer logs
pip-log.txt
pip-delete-this-directory.txt
# Unit test / coverage reports
htmlcov/
.tox/
.coverage
.coverage.*
.cache
nosetests.xml
coverage.xml
*.cover
.hypothesis/
.pytest_cache/
# Translations
*.mo
*.pot
# Django stuff:
*.log
local_settings.py
# Flask stuff:
instance/
.webassets-cache
# Scrapy stuff:
.scrapy
# Sphinx documentation
docs/_build/
# PyBuilder
target/
# Jupyter Notebook
.ipynb_checkpoints
# pyenv
.python-version
# celery beat schedule file
celerybeat-schedule
# SageMath parsed files
*.sage.py
# dotenv
.env
# virtualenv
.venv
venv/
ENV/
# Spyder project settings
.spyderproject
.spyproject
# Rope project settings
.ropeproject
# mkdocs documentation
/site
# mypy
.mypy_cache/
================================================
FILE: LICENSE
================================================
MIT License
Copyright (c) 2018 Qiusheng Wu
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
================================================
FILE: README.md
================================================
# python-geospatial
A collection of Python packages for geospatial analysis with binder-ready notebook examples. Launch the interactive notebook tutorials with **mybinder.org** or **binder.pangeo.io** test all the pre-installed Python pakcages for geospatial analysis.
[](https://mybinder.org/v2/gh/giswqs/python-geospatial/master)
[](http://binder.pangeo.io/v2/gh/giswqs/python-geospatial/master)
[](https://opensource.org/licenses/MIT)
Author: Qiusheng Wu (https://wetlands.io)
## Installation
It is highly recommended that you use the [conda](https://conda.io/docs/index.html) package manager to install all the requirements. You can either install [Miniconda](https://conda.io/miniconda.html) or the (larger) [Anaconda](https://www.anaconda.com/download/) distribution. It is also recommended that you install [git](https://git-scm.com/downloads) so that you can clone this GitHub reposiotry to your computer.
Once conda and git are installed, the following commands will create a virtual Python environment named **pygeo** and install all the required packages:
```
git clone https://github.com/giswqs/python-geospatial.git
cd python-geospatial/binder/
conda env create -f environment.yml
source activate pygeo
ipython kernel install --user --name="pygeo"
```
## Tutorials
Launch the interactive notebook tutorials with **mybinder.org** or **binder.pangeo.io** now:
[](https://mybinder.org/v2/gh/giswqs/python-geospatial/master)
[](http://binder.pangeo.io/v2/gh/giswqs/python-geospatial/master)
## Python Packages
This list of Python packages is adapted from the Python list of [Awesome Geospatial](https://github.com/sacridini/Awesome-Geospatial#python). All the listed Python packages have been pre-installed in the binder environment.
### Geospatial Analysis
* [whitebox](https://github.com/giswqs/whitebox) :zap: - A Python package for advanced geospatial data analysis based on [WhiteboxTools](https://github.com/jblindsay/whitebox-tools).
* [lidar](https://github.com/giswqs/lidar) - lidar is a toolset for terrain and hydrological analysis using digital elevation models (DEMs).
* [pygis](https://github.com/giswqs/pygis) - pygis is a collection of Python snippets for geospatial analysis.
* [ArcGIS Python API](https://developers.arcgis.com/python/) - Esri's Python library for working with maps and geospatial data, powered by web GIS.
* [dask-rasterio](https://github.com/dymaxionlabs/dask-rasterio) - Read and write rasters in parallel using Rasterio and Dask.
* [earthengine-api](https://anaconda.org/conda-forge/earthengine-api) :zap: - The Earth Engine Python API allows developers to interact with Google Earth Engine.
* [EarthPy](https://github.com/earthlab/earthpy) - EarthPy is a python package that makes it easier to plot and work with spatial raster and vector data.
* [Fiona](http://toblerity.org/fiona/) :zap: - For making it easy to read/write geospatial data formats.
* [GDAL](https://anaconda.org/conda-forge/gdal) - The Geospatial Data Abstraction Library for reading and writing raster and vector geospatial data formats.
* [geeup](https://github.com/samapriya/geeup) - Simple CLI for Earth Engine Uploads.
* [geojson-area](https://github.com/scisco/area) - Calculate the area inside of any GeoJSON geometry. This is a port of Mapbox's geojson-area for Python.
* [geojsonio](https://github.com/jwass/geojsonio.py) - Open GeoJSON data on geojson.io from Python.
* [GeoPandas](https://github.com/geopandas/geopandas) - Python tools for geographic data.
* [GIPPY](https://github.com/gipit/gippy) - Geospatial Image Processing for Python.
* [gpdvega](https://github.com/iliatimofeev/gpdvega) - gpdvega is a bridge between GeoPandas and Altair that allows to seamlessly chart geospatial data.
* [mapboxgl-jupyter](https://github.com/mapbox/mapboxgl-jupyter) - Use Mapbox GL JS to visualize data in a Python Jupyter notebook.
* [networkx](http://networkx.github.io/) - To work with networks.
* [OSMnet](https://github.com/UDST/osmnet) - Tools for the extraction of OpenStreetMap street network data.
* [pandana](https://github.com/UDST/pandana) - Pandas Network Analysis - dataframes of network queries, quickly.
* [Peartree](https://github.com/kuanb/peartree) - Peartree: A library for converting transit data into a directed graph for network analysis.
* [pygdal](https://pypi.org/project/pygdal/) - Virtualenv and setuptools friendly version of standard GDAL python bindings.
* [pymap3d](https://github.com/scivision/pymap3d) - Python 3D coordinate conversions for geospace ecef enu eci.
* [Pyncf](https://github.com/karimbahgat/pyncf) - Pure Python NetCDF file reading and writing.
* [PyProj](https://github.com/jswhit/pyproj) - For conversions between projections.
* [PySAL](http://pysal.readthedocs.io/en/latest/) - For all your spatial econometrics needs.
* [PyShp](https://code.google.com/archive/p/pyshp/) - For reading and writing shapefiles.
* [rasterio](https://github.com/mapbox/rasterio) :zap: - rasterio employs GDAL under the hood for file I/O and raster formatting.
* [rasterstats](https://github.com/perrygeo/python-rasterstats/) - Python module for summarizing geospatial raster datasets based on vector geometries.
* [rio-cogeo](https://github.com/mapbox/rio-cogeo) - CloudOptimized GeoTIFF creation plugin for rasterio.
* [rio-color](https://github.com/mapbox/rio-color) - Color correction plugin for rasterio.
* [rio-hist](https://github.com/mapbox/rio-hist) - Histogram matching plugin for rasterio.
* [rio-tiler](https://github.com/mapbox/rio-tiler) - Get mercator tile from landsat, sentinel or other AWS hosted raster.
* [Rtree](http://toblerity.org/rtree/) - For efficiently querying spatial data.
* [sentinelhub](https://github.com/sentinel-hub/sentinelhub-py) - Download and process satellite imagery in Python scripts using Sentinel Hub services.
* [sentinelsat](https://github.com/sentinelsat/sentinelsat) - Search and download Copernicus Sentinel satellite images.
* [Shapely](https://pypi.python.org/pypi/Shapely) - Manipulation and analysis of geometric objects in the Cartesian plane.
* [ts-raster](https://github.com/adbeda/ts-raster) - ts-raster is a python package for analyzing time-series characteristics from raster data.
* [urbansim](https://github.com/UDST/urbansim) - New version of UrbanSim, a platform for modeling metropolitan real estate markets.
* [USGS API](https://github.com/kapadia/usgs) - USGS is a python module for interfacing with the US Geological Survey's API.
* [Verde](https://github.com/fatiando/verde) - Verde is a Python library for processing spatial data and interpolating it on regular grids.
* [xarray](http://xarray.pydata.org/en/stable/) - An open source project that aims to bring the labeled data power of pandas to the physical sciences.
### Mapping/Plotting
* [basemap](https://github.com/matplotlib/basemap) - Plot on map projections (with coastlines and political boundaries) using matplotlib.
* [bokeh](https://github.com/bokeh/bokeh) - Interactive Web Plotting for Python.
* [Cartopy](http://scitools.org.uk/cartopy/) - A library providing cartographic tools for python for plotting spatial data.
* [Descartes](https://pypi.python.org/pypi/descartes) - Plot geometries in matplotlib.
* [geoplot](https://github.com/ResidentMario/geoplot) - geoplot is a high-level Python geospatial plotting library.
* [geopy](https://github.com/geopy/geopy) - geopy is a Python 2 and 3 client for several popular geocoding web services.
* [folium](https://github.com/python-visualization/folium) - Python Data, Leaflet.js Maps.
* [matplotlib](http://matplotlib.org/) - Python 2D plotting library.
* [mplleaflet](https://github.com/jwass/mplleaflet) - mplleaflet converts a matplotlib plot into a webpage containing a pannable, zoomable Leaflet map.
* [pyWPS](http://pywps.org/) - An implementation of the Web Processing Service standard from the Open Geospatial Consortium.
* [pyCSW](http://pycsw.org/) - Fully implements the OpenGIS Catalogue Service Implementation Specification.
* [ipyleaflet](https://github.com/jupyter-widgets/ipyleaflet) - A Jupyter / Leaflet bridge enabling interactive maps in the Jupyter notebook.
* [here-map-widget-for-jupyter](https://github.com/heremaps/here-map-widget-for-jupyter) - Use HERE Maps API for JavaScript in your Jupyter Notebook.
### Deep Learning
* [label-maker](https://github.com/developmentseed/label-maker) - Data Preparation for Satellite Machine Learning.
* [label-maker-binder](https://github.com/giswqs/label-maker-binder/pulse) - Using label-maker in an interactive notebook on the cloud.
* [Keras](https://keras.io/) - Keras is a high-level neural networks API capable of running on top of TensorFlow, CNTK, or Theano.
* [TensorFlow](https://www.tensorflow.org/) - TensorFlow is an open source software library for numerical computation using data flow graphs.
### General Python
* [dask](https://github.com/dask/dask) - Dask is a flexible parallel computing library for analytics.
* [imageio](https://imageio.github.io/) - imageio provides an easy interface to read and write a wide range of image data.
* [Mahotas](https://github.com/luispedro/mahotas) - Mahotas is a library of fast computer vision algorithms operating over numpy arrays.
* [NumPy](http://www.numpy.org/) - NumPy is the fundamental package for scientific computing with Python.
* [Pandas](http://pandas.pydata.org/) - Open source library providing high-performance, easy-to-use data structures and data analysis tools.
* [scikit-image](http://scikit-image.org/) - Scikit-image is a collection of algorithms for image processing.
* [scikit-learn](https://github.com/scikit-learn/scikit-learn) - scikit-learn is a Python module for machine learning built on top of SciPy.
* [SciPy](https://github.com/scipy/scipy) - SciPy is open-source software for mathematics, science, and engineering.
* [Statsmodels](http://statsmodels.sourceforge.net/) - Python module that allows users to explore data, estimate statistical models, and perform statistical tests.
* [geojson-shave](https://github.com/ben-n93/geojson-shave) - A Python command-line tool for reducing the size of GeoJSON files.
## Cloud Computing Platforms
* [Google Earth Engine](https://earthengine.google.com/) :zap: - Planetary-scale geospatial analysis for everyone.
* [Pangeo](http://pangeo.io/) - A community platform for Big Data geoscience.
* [Geospatial Big Data Platform (GBDX)](https://platform.digitalglobe.com/gbdx/) - Cloud computing platform from Digital Globe.
* [Radiant Earth](https://www.radiant.earth/) - Open-source cloud computing infrastructure for geospatial analysis.
* [Radiant MLHub](https://www.mlhub.earth/) - Open Repository for Geospatial Training Data.
* [Sentinel Playground](https://www.sentinel-hub.com/) - Cloud platform for analysis of Sentinel-2A and B and so on.
* [Vane: Query Language](https://owm.io/vaneLanguage) - Creating Basemaps from different satellite images with online processing and computing.
## References
* [Awesome-Geospatial](https://github.com/sacridini/Awesome-Geospatial)
* [SpatialPython](https://github.com/SpatialPython/spatial_python)
* [python-geospatial-ecosystem](https://github.com/loicdtx/python-geospatial-ecosystem)
* [Automating-GIS-processes](https://github.com/Automating-GIS-processes/2018)
* [Geo-Python](https://github.com/geo-python/2018)
* [scipy2018-geospatial-data](https://github.com/geopandas/scipy2018-geospatial-data)
* [Geospatial_Data_with_Python](https://github.com/SocialDataSci/Geospatial_Data_with_Python)
* [Essential geospatial Python libraries](https://medium.com/@chrieke/essential-geospatial-python-libraries-5d82fcc38731)
* [Geo-spatial analysis with Python](https://medium.com/@lisa.mitford/geo-spatial-analysis-with-python-fdddd69eebea)
* [From Analysis Ready Data to Analysis Engines and Everything in between](https://medium.com/@samapriyaroy/from-analysis-ready-data-to-analysis-engines-and-everything-in-between-676d98792d2e)
================================================
FILE: binder/apt.txt
================================================
================================================
FILE: binder/environment.yml
================================================
name: pygeo
channels:
- conda-forge
- esri
dependencies:
- python=3.6
- arcgis
- earthengine-api
- gdal
- imageio
- matplotlib
- numpy
- scikit-image
- scikit-learn
- scipy
- pip:
- area
- bokeh
- cython
- dask-rasterio
- descartes
- descartes
- folium
- geeup
- geojsonio
- geopandas
- geoplot
- geopy
- gpdvega
- h5py
- lidar
- mahotas
- mapboxgl
- mplleaflet
- oauth2client
- pandana
- peartree
- plotly
- pygdal
- pygis
- pymap3d
- pyModis
- pysal
- pycsw
- pywps
- rasterio
- rasterstats
- requests
- rio-cogeo
- rio-color
- rio-hist
- rio-tiler
- rtree
- seaborn
- sentinelhub
- sentinelsat
- tifffile
- tsraster
- urbansim
- usgs
- verde
- whitebox
- xarray
- tensorflow
- keras
- "git+https://github.com/karimbahgat/pyncf.git"
# - gippy
# - "git+https://github.com/matplotlib/basemap.git"
================================================
FILE: binder/postBuild
================================================
#!/bin/bash
cd /srv/conda/lib/python3.6/site-packages/whitebox
wget http://www.uoguelph.ca/~hydrogeo/WhiteboxTools/WhiteboxTools_linux_amd64.tar.xz
tar -xvf WhiteboxTools_linux_amd64.tar.xz
mv WBT/whitebox_tools .
mkdir testdata
cd testdata
wget https://github.com/jblindsay/whitebox-tools/raw/master/testdata/DEM.tif
wget https://github.com/jblindsay/whitebox-tools/raw/master/testdata/DEM.dep
pip install cartopy
================================================
FILE: binder/requirements.txt
================================================
cartopy
# seaborn==0.8.1
================================================
FILE: examples/arcgis-python-api.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# ArcGIS API for Python\n",
"\n",
"## How to install ArcGIS API for Python\n",
"\n",
"* ArcGIS API for Python only for Python 3\n",
"* https://developers.arcgis.com/python/guide/install-and-set-up/#Using-the-API\n",
"* Create a Python virtual environment using Anaconda: `conda create -n py36 python=3.6`\n",
"* Activate the Python environment: `activate py36`\n",
"* Install arcgis to the Python environment: `conda install -c esri arcgis`"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from arcgis.gis import GIS"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a84dd4592bd1429b82a590af91e8de57",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"MapView(layout=Layout(height='400px', width='100%'))"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"my_gis = GIS()\n",
"my_gis.map()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<Item title:\"מיפוי שריפה חדש\" type:Feature Layer Collection owner:kkl_arcgis_10>,\n",
" <Item title:\"Intact Forest Landscapes (2000)\" type:Feature Layer Collection owner:GlobalForestWatch>,\n",
" <Item title:\"WRI_BeforeAndAfterPhotos\" type:Feature Layer Collection owner:FireFFSL>,\n",
" <Item title:\"Social Vulnerability 2010\" type:Feature Layer Collection owner:AtlasPublisher>,\n",
" <Item title:\"GIS_Export\" type:Feature Layer Collection owner:baszlera>]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from arcgis.gis import GIS\n",
"gis = GIS()\n",
"public_content = gis.content.search(\"Fire\", item_type=\"Feature Layer\", max_items=5)\n",
"public_content"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
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"<div class=\"item_container\" style=\"height: auto; overflow: hidden; border: 1px solid #cfcfcf; border-radius: 2px; background: #f6fafa; line-height: 1.21429em; padding: 10px;\">\n",
" <div class=\"item_left\" style=\"width: 210px; float: left;\">\n",
" <a href='https://www.arcgis.com/home/item.html?id=f867e79cccbd4ac1909e5883e323d0d7' target='_blank'>\n",
" <img src='https://www.arcgis.com/sharing/rest//content/items/f867e79cccbd4ac1909e5883e323d0d7/info/thumbnail/ago_downloaded.png' class=\"itemThumbnail\">\n",
" </a>\n",
" </div>\n",
"\n",
" <div class=\"item_right\" style=\"float: none; width: auto; overflow: hidden;\">\n",
" <a href='https://www.arcgis.com/home/item.html?id=f867e79cccbd4ac1909e5883e323d0d7' target='_blank'><b>מיפוי שריפה חדש</b>\n",
" </a>\n",
" <br/>שכבה פוליגונלית של היקף שריפה חיה איתה עובדים באופן שוטף ומעדכנים בקולקטור<img src='https://www.arcgis.com/home/js/jsapi/esri/css/images/item_type_icons/featureshosted16.png' style=\"vertical-align:middle;\">Feature Layer Collection by kkl_arcgis_10\n",
" <br/>Last Modified: November 15, 2018\n",
" <br/>0 comments, 21,729 views\n",
" </div>\n",
" </div>\n",
" "
],
"text/plain": [
"<Item title:\"מיפוי שריפה חדש\" type:Feature Layer Collection owner:kkl_arcgis_10>"
]
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},
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" <div class=\"item_left\" style=\"width: 210px; float: left;\">\n",
" <a href='https://www.arcgis.com/home/item.html?id=5a811508e1ab4ddbb79f7f0753d7d2d7' target='_blank'>\n",
" <img src='https://www.arcgis.com/sharing/rest//content/items/5a811508e1ab4ddbb79f7f0753d7d2d7/info/thumbnail/ago_downloaded.png' class=\"itemThumbnail\">\n",
" </a>\n",
" </div>\n",
"\n",
" <div class=\"item_right\" style=\"float: none; width: auto; overflow: hidden;\">\n",
" <a href='https://www.arcgis.com/home/item.html?id=5a811508e1ab4ddbb79f7f0753d7d2d7' target='_blank'><b>Intact Forest Landscapes (2000)</b>\n",
" </a>\n",
" <br/>Identifies the world’s last remaining unfragmented forest landscapes, large enough to retain all native biodiversity and showing no signs of human alteration as of the year 2013. This layer also shows the reduction in the extent of Intact Forest Landscapes from 2000 to 2013.<img src='https://www.arcgis.com/home/js/jsapi/esri/css/images/item_type_icons/featureshosted16.png' style=\"vertical-align:middle;\">Feature Layer Collection by GlobalForestWatch\n",
" <br/>Last Modified: October 29, 2018\n",
" <br/>0 comments, 2,863 views\n",
" </div>\n",
" </div>\n",
" "
],
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"<Item title:\"Intact Forest Landscapes (2000)\" type:Feature Layer Collection owner:GlobalForestWatch>"
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" <div class=\"item_left\" style=\"width: 210px; float: left;\">\n",
" <a href='https://www.arcgis.com/home/item.html?id=8c79338b862843afb99923fe8572c939' target='_blank'>\n",
" <img src='https://www.arcgis.com/sharing/rest//content/items/8c79338b862843afb99923fe8572c939/info/thumbnail/SkitzyAfter.jpg' class=\"itemThumbnail\">\n",
" </a>\n",
" </div>\n",
"\n",
" <div class=\"item_right\" style=\"float: none; width: auto; overflow: hidden;\">\n",
" <a href='https://www.arcgis.com/home/item.html?id=8c79338b862843afb99923fe8572c939' target='_blank'><b>WRI_BeforeAndAfterPhotos</b>\n",
" </a>\n",
" <br/>WRI Projects with Before and After Photos<img src='https://www.arcgis.com/home/js/jsapi/esri/css/images/item_type_icons/featureshosted16.png' style=\"vertical-align:middle;\">Feature Layer Collection by FireFFSL\n",
" <br/>Last Modified: January 24, 2018\n",
" <br/>0 comments, 9,409 views\n",
" </div>\n",
" </div>\n",
" "
],
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},
{
"data": {
"text/html": [
"<div class=\"item_container\" style=\"height: auto; overflow: hidden; border: 1px solid #cfcfcf; border-radius: 2px; background: #f6fafa; line-height: 1.21429em; padding: 10px;\">\n",
" <div class=\"item_left\" style=\"width: 210px; float: left;\">\n",
" <a href='https://www.arcgis.com/home/item.html?id=eee333118bd740268ba1cad0b78f83d6' target='_blank'>\n",
" <img src='https://www.arcgis.com/sharing/rest//content/items/eee333118bd740268ba1cad0b78f83d6/info/thumbnail/ago_downloaded2.png' class=\"itemThumbnail\">\n",
" </a>\n",
" </div>\n",
"\n",
" <div class=\"item_right\" style=\"float: none; width: auto; overflow: hidden;\">\n",
" <a href='https://www.arcgis.com/home/item.html?id=eee333118bd740268ba1cad0b78f83d6' target='_blank'><b>Social Vulnerability 2010</b>\n",
" </a>\n",
" <br/>This map shows a simple summary of social vulnerability of populations in the United States.<img src='https://www.arcgis.com/home/js/jsapi/esri/css/images/item_type_icons/featureshosted16.png' style=\"vertical-align:middle;\">Feature Layer Collection by AtlasPublisher\n",
" <br/>Last Modified: July 19, 2018\n",
" <br/>0 comments, 195,382 views\n",
" </div>\n",
" </div>\n",
" "
],
"text/plain": [
"<Item title:\"Social Vulnerability 2010\" type:Feature Layer Collection owner:AtlasPublisher>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<div class=\"item_container\" style=\"height: auto; overflow: hidden; border: 1px solid #cfcfcf; border-radius: 2px; background: #f6fafa; line-height: 1.21429em; padding: 10px;\">\n",
" <div class=\"item_left\" style=\"width: 210px; float: left;\">\n",
" <a href='https://www.arcgis.com/home/item.html?id=fa5be57a918845158064c94b18929382' target='_blank'>\n",
" <img src='https://www.arcgis.com/sharing/rest//content/items/fa5be57a918845158064c94b18929382/info/thumbnail/ago_downloaded.png' class=\"itemThumbnail\">\n",
" </a>\n",
" </div>\n",
"\n",
" <div class=\"item_right\" style=\"float: none; width: auto; overflow: hidden;\">\n",
" <a href='https://www.arcgis.com/home/item.html?id=fa5be57a918845158064c94b18929382' target='_blank'><b>GIS_Export</b>\n",
" </a>\n",
" <br/><img src='https://www.arcgis.com/home/js/jsapi/esri/css/images/item_type_icons/featureshosted16.png' style=\"vertical-align:middle;\">Feature Layer Collection by baszlera\n",
" <br/>Last Modified: April 26, 2018\n",
" <br/>0 comments, 518 views\n",
" </div>\n",
" </div>\n",
" "
],
"text/plain": [
"<Item title:\"GIS_Export\" type:Feature Layer Collection owner:baszlera>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from IPython.display import display\n",
"for item in public_content:\n",
" display(item)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c559b3634c3c4d9d87d064d72cef9a96",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"MapView(layout=Layout(height='400px', width='100%'))"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"map1 = gis.map('Collier County, FL')\n",
"map1"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"#get the first item\n",
"fire = public_content[0]\n",
"\n",
"#add to map\n",
"map1.add_layer(fire)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div class=\"item_container\" style=\"height: auto; overflow: hidden; border: 1px solid #cfcfcf; border-radius: 2px; background: #f6fafa; line-height: 1.21429em; padding: 10px;\">\n",
" <div class=\"item_left\" style=\"width: 210px; float: left;\">\n",
" <a href='https://www.arcgis.com/home/item.html?id=407c7fb33e784eca96a3bf345050c098' target='_blank'>\n",
" <img src='https://www.arcgis.com/sharing/rest//content/items/407c7fb33e784eca96a3bf345050c098/info/thumbnail/ago_downloaded.png' class=\"itemThumbnail\">\n",
" </a>\n",
" </div>\n",
"\n",
" <div class=\"item_right\" style=\"float: none; width: auto; overflow: hidden;\">\n",
" <a href='https://www.arcgis.com/home/item.html?id=407c7fb33e784eca96a3bf345050c098' target='_blank'><b>Landsat_Multispectral</b>\n",
" </a>\n",
" <br/><img src='https://www.arcgis.com/home/js/jsapi/esri/css/images/item_type_icons/imagery16.png' style=\"vertical-align:middle;\">Imagery Layer by Craig_ESRIAU\n",
" <br/>Last Modified: November 24, 2016\n",
" <br/>0 comments, 90 views\n",
" </div>\n",
" </div>\n",
" "
],
"text/plain": [
"<Item title:\"Landsat_Multispectral\" type:Imagery Layer owner:Craig_ESRIAU>"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from arcgis.gis import GIS\n",
"from arcgis.raster.functions import *\n",
"gis = GIS()\n",
"landsat_item = gis.content.search('\"Landsat Multispectral\"', 'Imagery Layer')[0]\n",
"landsat_item"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"landsat = landsat_item.layers[0]"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"def extract_stretch(bandids):\n",
" return stretch(extract_band(landsat, bandids),\n",
" stretch_type='PercentClip',\n",
" min_percent=2, \n",
" max_percent=2,\n",
" dra=True, \n",
" gamma=[0.8,0.8,0.8])"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"from arcgis.geocoding import geocode\n",
"area = geocode('Cambridge Gulf')[0]"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "7e3f856cf0d94d7aa7b8acbc5377219d",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"MapView(layout=Layout(height='400px', width='100%'), zoom=10.0)"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"m = gis.map(area, 10)\n",
"m"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"m.add_layer(extract_stretch([5, 4, 1]))"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "675b879bce9e4a46b93833f05e7f216e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"MapView(layout=Layout(height='400px', width='100%'), zoom=11.0)"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"m2 = gis.map(\"Guelb Er Richat\", 11)\n",
"m2"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"m2.add_layer(extract_stretch([6, 3, 1]))"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "6b4c1a055a304a0287f398fbf6742271",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"MapView(layout=Layout(height='400px', width='100%'), zoom=10.0)"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"m3 = gis.map(\"Gosses Bluff\", 10)\n",
"m3"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"m3.add_layer(extract_stretch([6, 3, 0]))"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d3bc43e9664b45d6b7ff4b35b8ec4811",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"MapView(layout=Layout(height='400px', width='100%'), zoom=11.0)"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"m4 = gis.map(\"Great Exumas, Bahamas\", 11)\n",
"m4"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"m4.add_layer(extract_stretch([5, 3, 0]))"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "54eee94768d04a51be26ec57e64b0d24",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"MapView(layout=Layout(height='400px', width='100%'), zoom=10.0)"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"m5 = gis.map(\"Mexico City\", 10)\n",
"m5"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"m5.add_layer(extract_stretch([6, 5, 3]))"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b4a07def91fc431b9556da364d1954c1",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"MapView(layout=Layout(height='400px', width='100%'), zoom=12.0)"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"m6 = gis.map(\"Ash Simasiyah, Saudi Arabia\", 12)\n",
"m6"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"m6.add_layer(extract_stretch([5, 4, 1]))"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "pygeo",
"language": "python",
"name": "pygeo"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.6"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
================================================
FILE: examples/lidar.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# A tutorial for the lidar Python package\n",
"\n",
"This notebook demonstrates the usage of the **lidar** Python package for terrain and hydrological analysis. It is useful for analyzing high-resolution topographic data, such as digital elevation models (DEMs) derived from Light Detection and Ranging (LiDAR) data.\n",
"\n",
"* GitHub repo: https://github.com/giswqs/lidar\n",
"* Documentation: https://lidar.readthedocs.io.\n",
"* PyPI: https://pypi.org/project/lidar/\n",
"* Binder: https://gishub.org/lidar-cloud\n",
"* Free software: [MIT license](https://opensource.org/licenses/MIT)\n",
"\n",
"This tutorial can be accessed in three ways:\n",
"\n",
"* HTML version: https://gishub.org/lidar-html\n",
"* Viewable Notebook: https://gishub.org/lidar-notebook\n",
"* Interactive Notebook: https://gishub.org/lidar-cloud\n",
"\n",
"**Launch this tutorial as an interactive Jupyter Notebook on the cloud - [MyBinder.org](https://gishub.org/lidar-cloud).**\n",
"\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Table of Content\n",
"\n",
"* [Installation](#Installation)\n",
"* [Getting data](#Getting-data)\n",
"* [Using lidar](#Using-lidar)\n",
"* [Displaying results](#Displaying-results)\n",
"* [lidar GUI](#lidar-GUI)\n",
"* [Citing lidar](#Citing-lidar)\n",
"* [Credits](#Credits)\n",
"* [Contact](#Contact)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Installation\n",
"\n",
"\n",
"The **lidar** Python package supports a variety of platforms, including Microsoft Windows, macOS, and Linux operating systems. Note that you will need to have **Python 3.x** installed. Python 2.x is not supported. The **lidar** Python package can be installed using the following command:\n",
"\n",
"`pip install lidar`\n",
"\n",
"If you have installed **lidar** Python package before and want to upgrade to the latest version, you can use the following command:\n",
"\n",
"`pip install lidar -U`\n",
"\n",
"If you encounter any installation issues, please check [Dependencies](https://github.com/giswqs/lidar#dependencies) on the **lidar** GitHub page and [Report Bugs](https://github.com/giswqs/lidar/issues)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Getting data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This section demonstrates two ways to get data into Binder so that you can test the **lidar** Python package on the cloud using your own data. \n",
"\n",
"* [Getting data from direct URLs](#Getting-data-from-direct-URLs) \n",
"* [Getting data from Google Drive](#Getting-data-from-Google-Drive)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Getting data from direct URLs\n",
"\n",
"If you have data hosted on your own HTTP server or GitHub, you should be able to get direct URLs. With a direct URL, users can automatically download the data when the URL is clicked. For example http://wetlands.io/file/data/lidar-dem.zip"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Import the following Python libraries and start getting data from direct URLs."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import pygis"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create a folder named *data* and set it as the working directory."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Working directory: /home/jovyan/examples/data\n"
]
}
],
"source": [
"root_dir = os.getcwd()\n",
"work_dir = os.path.join(root_dir, 'data')\n",
"if not os.path.exists(work_dir):\n",
" os.mkdir(work_dir)\n",
"print(\"Working directory: {}\".format(os.path.realpath(work_dir)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Replace the following URL with your own direct URL hosting the data you would like to use."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"url = \"https://github.com/giswqs/lidar/raw/master/examples/lidar-dem.zip\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Download data the from the above URL and unzip the file if needed."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Downloading lidar-dem.zip ...\n",
"Downloading done.\n",
"Unzipping lidar-dem.zip ...\n",
"Unzipping done.\n",
"Data directory: /home/jovyan/examples/data/lidar-dem\n"
]
}
],
"source": [
"pygis.download_from_url(url, out_dir=work_dir)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You have successfully downloaded data to Binder. Therefore, you can skip to [Using lidar](#Using-lidar) and start testing **lidar** with your own data. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Getting data from Google Drive\n",
"\n",
"Alternatively, you can upload data to [Google Drive](https://www.google.com/drive/) and then [share files publicly from Google Drive](https://support.google.com/drive/answer/2494822?co=GENIE.Platform%3DDesktop&hl=en). Once the file is shared publicly, you should be able to get a shareable URL. For example, https://drive.google.com/file/d/1c6v-ep5-klb2J32Nuu1rSyqAc8kEtmdh."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Replace the following URL with your own shareable URL from Google Drive.**"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"gfile_url = 'https://drive.google.com/file/d/1c6v-ep5-klb2J32Nuu1rSyqAc8kEtmdh'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Download the shared file from Google Drive.**"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Google Drive file id: 1c6v-ep5-klb2J32Nuu1rSyqAc8kEtmdh\n",
"Downloading 1c6v-ep5-klb2J32Nuu1rSyqAc8kEtmdh into /home/jovyan/examples/data/lidar-dem.zip... Done.\n",
"Unzipping...Done.\n"
]
}
],
"source": [
"pygis.download_from_gdrive(gfile_url, file_name='lidar-dem.zip', out_dir=work_dir)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You have successfully downloaded data from Google Drive to Binder. You can now continue to [Using lidar](#Using-lidar) and start testing **lidar** with your own data. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Using lidar"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here you can specify where your data are located. In this example, we will use [dem.tif](https://github.com/giswqs/lidar/blob/master/examples/lidar-dem/dem.tif), which has been downloaded to the *lidar-dem* folder."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Import the lidar package.**"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"import lidar"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**List data under the data folder.**"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['dsm.tif', 'sink.tif', 'dem.tif']\n"
]
}
],
"source": [
"data_dir = './data/lidar-dem/'\n",
"print(os.listdir(data_dir))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Create a temporary folder to save results.**"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"out_dir = os.path.join(os.getcwd(), \"temp\")\n",
"\n",
"if not os.path.exists(out_dir):\n",
" os.mkdir(out_dir)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this simple example, we smooth [dem.tif](https://github.com/giswqs/lidar/blob/master/examples/lidar-dem/dem.tif) using a median filter. Then we extract sinks (i.e., depressions) from the DEM. Finally, we delineate nested depression hierarchy using the [level-set algorithm](https://doi.org/10.1111/1752-1688.12689). "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Set parameters for the level-set algorithm.**"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"min_size = 1000 # minimum number of pixels as a depression\n",
"min_depth = 0.3 # minimum depth as a depression\n",
"interval = 0.3 # slicing interval for the level-set method\n",
"bool_shp = False # output shapefiles for each individual level"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Smooth the original DEM using a median filter.**"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Median filtering ...\n",
"Run time: 0.0258 seconds\n",
"Saving dem ...\n"
]
}
],
"source": [
"# extracting sinks based on user-defined minimum depression size\n",
"in_dem = os.path.join(data_dir, 'dem.tif')\n",
"out_dem = os.path.join(out_dir, \"median.tif\")\n",
"in_dem = lidar.MedianFilter(in_dem, kernel_size=3, out_file=out_dem)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Extract DEM sinks using a depression-filling algorithm.**"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Loading data ...\n",
"min = 379.70, max = 410.72, no_data = -3.402823e+38, cell_size = 1.0\n",
"Depression filling ...\n",
"Saving filled dem ...\n",
"Region grouping ...\n",
"Computing properties ...\n",
"Saving sink dem ...\n",
"Saving refined dem ...\n",
"Converting raster to vector ...\n",
"Total run time:\t\t\t 0.0723 s\n",
"\n"
]
}
],
"source": [
"sink = lidar.ExtractSinks(in_dem, min_size, out_dir)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Identify depression nested hierarchy using the level-set algorithm.**"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Reading data ...\n",
"rows, cols: (400, 400)\n",
"Pixel resolution: 1.0\n",
"Read data time: 0.0036 seconds\n",
"Data preparation time: 0.0737 seconds\n",
"Total number of regions: 1\n",
"Processing Region # 1 ...\n",
"=========== Run time statistics =========== \n",
"(rows, cols):\t\t\t (400, 400)\n",
"Pixel resolution:\t\t 1.0 m\n",
"Number of regions:\t\t 1\n",
"Data preparation time:\t\t 0.0737 s\n",
"Identify level time:\t\t 0.5304 s\n",
"Write image time:\t\t 0.0057 s\n",
"Polygonize time:\t\t 0.0130 s\n",
"Total run time:\t\t\t 0.6238 s\n"
]
}
],
"source": [
"dep_id, dep_level = lidar.DelineateDepressions(sink, min_size, min_depth, interval, out_dir, bool_shp)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Print the list of output files.**"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Results are saved in: /home/jovyan/examples/temp\n",
"['depression_id.tif', 'depressions.shp', 'regions.shx', 'region.tif', 'sink.tif', 'depressions.dbf', 'depression_level.tif', 'regions.shp', 'depressions_info.csv', 'regions.prj', 'depressions.prj', 'dem.tif', 'depth.tif', 'depressions.shx', 'regions.dbf', 'regions_info.csv', 'dem_filled.tif', 'median.tif', 'dem_diff.tif']\n"
]
}
],
"source": [
"print('Results are saved in: {}'.format(out_dir))\n",
"print(os.listdir(out_dir))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Displaying results\n",
"\n",
"This section demonstrates how to display images on Jupyter Notebook. Three Python packages are used here, including [matplotlib](https://matplotlib.org/), [imageio](https://imageio.readthedocs.io/en/stable/installation.html), and [tifffile](https://pypi.org/project/tifffile/). These three packages can be installed using the following command:\n",
"\n",
"`pip install matplotlib imageio tifffile`\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Import the libraries.**"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"# comment out the third line (%matplotlib inline) if you run the tutorial in other IDEs other than Jupyter Notebook\n",
"import matplotlib.pyplot as plt\n",
"import imageio\n",
"%matplotlib inline "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Display one single image.**"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"raster = imageio.imread(os.path.join(data_dir, 'dem.tif'))\n",
"plt.imshow(raster)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Read images as numpy arrays.**"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"smoothed = imageio.imread(os.path.join(out_dir, 'median.tif'))\n",
"sink = imageio.imread(os.path.join(out_dir, 'sink.tif'))\n",
"dep_id = imageio.imread(os.path.join(out_dir, 'depression_id.tif'))\n",
"dep_level = imageio.imread(os.path.join(out_dir, 'depression_level.tif'))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Display multiple images in one plot.**"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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
gitextract_oiqettza/
├── .gitignore
├── LICENSE
├── README.md
├── binder/
│ ├── apt.txt
│ ├── environment.yml
│ ├── postBuild
│ └── requirements.txt
└── examples/
├── arcgis-python-api.ipynb
├── lidar.ipynb
├── rasterio.ipynb
├── template.ipynb
├── tensorflow.ipynb
└── whitebox.ipynb
Condensed preview — 13 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (1,498K chars).
[
{
"path": ".gitignore",
"chars": 1274,
"preview": "# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n*$py.class\n.idea/\n.vscode/\n\n\n# exclude files\ndev/\nexample"
},
{
"path": "LICENSE",
"chars": 1068,
"preview": "MIT License\n\nCopyright (c) 2018 Qiusheng Wu\n\nPermission is hereby granted, free of charge, to any person obtaining a cop"
},
{
"path": "README.md",
"chars": 12173,
"preview": "# python-geospatial\n\nA collection of Python packages for geospatial analysis with binder-ready notebook examples. Launch"
},
{
"path": "binder/apt.txt",
"chars": 0,
"preview": ""
},
{
"path": "binder/environment.yml",
"chars": 1030,
"preview": "name: pygeo\nchannels:\n - conda-forge\n - esri\ndependencies:\n - python=3.6\n - arcgis\n - earthengine-api\n - gdal\n - "
},
{
"path": "binder/postBuild",
"chars": 414,
"preview": "#!/bin/bash\ncd /srv/conda/lib/python3.6/site-packages/whitebox\nwget http://www.uoguelph.ca/~hydrogeo/WhiteboxTools/White"
},
{
"path": "binder/requirements.txt",
"chars": 24,
"preview": "cartopy\n# seaborn==0.8.1"
},
{
"path": "examples/arcgis-python-api.ipynb",
"chars": 19791,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# ArcGIS API for Python\\n\",\n \"\\n"
},
{
"path": "examples/lidar.ipynb",
"chars": 303360,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# A tutorial for the lidar Python p"
},
{
"path": "examples/rasterio.ipynb",
"chars": 641572,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Advanced features in Rasterio\\n\","
},
{
"path": "examples/template.ipynb",
"chars": 79277,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# A tutorial for the template Pytho"
},
{
"path": "examples/tensorflow.ipynb",
"chars": 1154,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n "
},
{
"path": "examples/whitebox.ipynb",
"chars": 415668,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# A tutorial for the whitebox Pytho"
}
]
About this extraction
This page contains the full source code of the giswqs/python-geospatial GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 13 files (1.4 MB), approximately 939.9k tokens. 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.