`pygieons` documentation¶

What is `pygieons`?¶

The name (you would anyway ask 😉): Python GIS and Earch Observation Networks

As described elsewhere, this website is largely generated automatically by using a Python tool called pygieons. This tool auto-generates content regarding the listed packages and adds them to the website either as tables or interactive Network visualizations. pygieons is currently focused on parsing information about Python GIS and Earth Observation libraries, but it is a rather generic tool, with modest modifications it would be possible to parse and visualize information from any collection of libraries available on PyPi.

pygieons is largely based on pandas which is used for data manipulation, while pyvis library is used to visualize the connections between libraries as an interactive network of links. In addition, under the hood libraries such as requests, beautifulsoup4 and pypistats are used to extract relevant information about the listed libraries from the PyPi web pages.

Below, we demonstrate some basic functionalities that can be used to auto-generate content using pygieons.

Basic usage¶

Initializing the tool¶

To generate content with pygieons, you need to first initialize the tool with Ecosystem() object which accepts a parameter plot_type that can be used to define which category of libraries we want to plot, and parameter log which can be used to disable logging to the screen. Below is the slightly modified output from help(Ecosystem) command showing information about how the tool can be initialized:

class Ecosystem(builtins.object)
 |  Ecosystem(plot_type='all', keep_all=False, log=True)
 |        
 |      Parameters
 |      ==========
 |      
 |      plot_type : str
 |          This can be used to specify if you want to plot all packages in same figure, or only vector or raster.
 |          Possible values are: "all", "vector", "raster", "generic", "vector+generic", "raster+generic".
 |      keep_all : bool
 |          If True, also packages that are not available from PyPi or which are not maintained will be kept.
 |      log: bool
 |          If True, will print messages during process.

Let’s initialize the tool and configure it for plotting raster libraries.

from pygieons import Ecosystem

# Initialize the tool 
e = Ecosystem(plot_type="raster", log=True)

Generating tables¶

To generate a table showing various information about the libraries, you can use method prepare_table(), which autogenerates the table contents based on the packages listed in ecosystem_connections.py and ecosystem_pkgs.py files. The tool will print out information about the progress if log was specified as True:

# Generate the content
tbl = e.prepare_table()

Find out the number of monthly downloads from PyPi for the libraries ..

Extract project details ..

You can visualize the table by calling .show() of the returned Table object:

tbl.show()

**Analysis / modelling** (count=9)
Name	Homepage	Info	License	PyPi downloads (monthly)	Conda-forge version	Conda-forge downloads	Conda-forge latest release
gstools	🏠	ⓘ		1,668
pykrige	🏠	ⓘ		17,754
pysheds	🏠	ⓘ		925
pyspatialml	🏠	ⓘ	NA	78	NA	NA	NA
rasterstats	🏠	ⓘ		61,113
richdem	🏠	ⓘ		2,187
scikit-learn	🏠	ⓘ		31,705,557
spyndex	🏠	ⓘ		287
xarray-spatial	🏠	ⓘ		1,843

**Core / data structures** (count=8)
Name	Homepage	Info	PyPi downloads (monthly)
affine	🏠	ⓘ	669,468
iris	🏠	ⓘ	948
rasterio	🏠	ⓘ	744,408
rio-cogeo	🏠	ⓘ	13,656
rioxarray	🏠	ⓘ	38,468
sarpy	🏠	ⓘ	911
sarsen	🏠	ⓘ	40
xarray	🏠	ⓘ	1,626,657

**Data extraction / processing** (count=22)
Name	Homepage	Info	License	PyPi downloads (monthly)	Conda-forge version	Conda-forge downloads	Conda-forge latest release
earthengine-api	🏠	ⓘ		32,896
easystac	🏠	ⓘ		87
eemont	🏠	ⓘ		598
eoreader	🏠	ⓘ		434
lidar	🏠	ⓘ		279
odc-stac	🏠	ⓘ		849
planetary-computer	🏠	ⓘ		2,874
pymap3d	🏠	ⓘ		43,146
pyrosar	🏠	ⓘ		478
pystac	🏠	ⓘ		31,727
pystac-client	🏠	ⓘ		9,996
radiant-mlhub	🏠	ⓘ		6,902
rio-hist	🏠	ⓘ	NA	168	NA	NA	NA
rio-mucho	🏠	ⓘ		8,470
rio-tiler	🏠	ⓘ		8,373
salem	🏠	ⓘ		4,972
satpy	🏠	ⓘ		3,841
sentinelsat	🏠	ⓘ		12,338
stackstac	🏠	ⓘ		1,142
verde	🏠	ⓘ		729
xarray-sentinel	🏠	ⓘ		70
xyzservices	🏠	ⓘ		164,884

**Visualization** (count=4)
Name	Homepage	Info	License	PyPi downloads (monthly)	Conda-forge version	Conda-forge downloads	Conda-forge latest release
contextily	🏠	ⓘ		64,948
pyvista-xarray	🏠	ⓘ	NA	362	NA	NA	NA
rio-color	🏠	ⓘ		8,260
xarray_leaflet	🏠	ⓘ		848

Generating Network visualization¶

To generate an interactive network visualization showing the connections between libraries, you can use method prepare_net(), which autogenerates the Network based on the packages listed in ecosystem_connections.py file. The tool will print out information about the progress if log was specified as True:

# Generate the content
net = e.prepare_net()

You can visualize the network by calling .show() of the returned Network object

net.show()

In addition, you can also save the Network visualization as a HTML file to your own computer by calling .save() method:

net.save("test.html")

How to contribute?

Python OS Ecosystem for GIS and Earth Observation