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other:uvcdat:cdat_conda:cdat_8_2_1 [2021/06/09 16:14] jypeter [Packages NOT supported in Python3] Added spanlib |
other:uvcdat:cdat_conda:cdat_8_2_1 [2023/12/19 12:42] jypeter [Extra packages list] Added rasterio and rioxarray |
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=== Pre-installation check with the dry-run option === | === Pre-installation check with the dry-run option === | ||
- | <code>/usr/bin/time mamba install -n cdatm19_nompi_py3 --dry-run -c conda-forge basemap basemap-data-hires cartopy cmocean cmor ferret_datasets gdal gdal geopandas glances gsw iris joblib netcdf4 palettable pandas pillow pyferret rpy2 scikit-image scikit-learn seaborn seawater shapely spyder statsmodels windspharm wrf-python xarray xlsxwriter | + | <code>/usr/bin/time mamba install -n cdatm19_nompi_py3 --dry-run -c conda-forge basemap basemap-data basemap-data-hires cartopy cmocean cmor ferret_datasets gdal gdal geopandas glances gsw iris joblib netcdf4 palettable pandas pillow pyferret rpy2 scikit-image scikit-learn seaborn seawater shapely spyder statsmodels windspharm wrf-python xarray xlsxwriter |
[... lots of information about what would be done followed by some information about how long it took ...] | [... lots of information about what would be done followed by some information about how long it took ...] | ||
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=== Actual installation === | === Actual installation === | ||
- | <code>/usr/bin/time mamba install -n cdatm19_nompi_py3 -c conda-forge basemap basemap-data-hires cartopy cmocean cmor ferret_datasets gdal gdal geopandas glances gsw iris joblib netcdf4 palettable pandas pillow pyferret rpy2 scikit-image scikit-learn seaborn seawater shapely spyder statsmodels windspharm wrf-python xarray xlsxwriter</code> | + | <code>/usr/bin/time mamba install -n cdatm19_nompi_py3 -c conda-forge basemap basemap-data basemap-data-hires cartopy cmocean cmor ferret_datasets gdal gdal geopandas glances gsw iris joblib netcdf4 palettable pandas pillow pyferret rpy2 scikit-image scikit-learn seaborn seawater shapely spyder statsmodels windspharm wrf-python xarray xlsxwriter</code> |
Resulting list of installed packages | Resulting list of installed packages | ||
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dreqPy version 01.00.29 [Version 01.00.29]</code> | dreqPy version 01.00.29 [Version 01.00.29]</code> | ||
- | * [[https://github.com/PBrockmann/ipython_ferretmagic|ipython_ferretmagic]]: IPython notebook extension for ferret | + | * ''ipython_ferretmagic'': more details in the [[#extra_packages_list|Extra packages list section]] |
- | * ''conda activate cdatm19_py3''\\ ''pip install ferretmagic'' | + | |
=== Packages with no dependency problems and were added (or updated) later === | === Packages with no dependency problems and were added (or updated) later === | ||
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* [[https://cmor.llnl.gov/|CMOR]]: CMOR (//Climate Model Output Rewriter//) is used to produce CF-compliant netCDF files | * [[https://cmor.llnl.gov/|CMOR]]: CMOR (//Climate Model Output Rewriter//) is used to produce CF-compliant netCDF files | ||
* ''conda install -n cdatm19_py3 -c conda-forge cmor'' | * ''conda install -n cdatm19_py3 -c conda-forge cmor'' | ||
- | * Get version number with: ''python -c 'from cmor import *; print( (CMOR_VERSION_MAJOR, CMOR_VERSION_MINOR, CMOR_VERSION_PATCH) )' '' | ||
- | * <wrap hi>Warning!</wrap> [[https://github.com/PCMDI/cmor/issues/449|CMOR currently requires Python 2.7]] | ||
- | |||
- | * [[http://www.ifremer.fr/vacumm/|vacumm]]: Validation, Analysis, Comparison - Utilities written in Python to validate and analyze Multi-Model outputs, and compare them to observations | ||
- | * ''conda install -n cdatm19_py3 -c conda-forge -c vacumm vacumm'' | ||
- | * <wrap hi>Warning!</wrap> [[https://github.com/VACUMM/vacumm/issues/15|standard install of vacumm does not work for Python3]] | ||
- | |||
- | * [[https://github.com/stefraynaud/spanlib|spanlib]]: Spectral Analysis Library | ||
- | * ''conda install -n cdatm19_py3 -c stefraynaud -c conda-forge spanlib'' | ||
- | * Test: ''python -c 'from spanlib.analyzer import Analyzer' '' | ||
* [[http://scitools.org.uk/cartopy/|cartopy]]: a Python package designed for geospatial data processing in order to produce maps and other geospatial data analyses | * [[http://scitools.org.uk/cartopy/|cartopy]]: a Python package designed for geospatial data processing in order to produce maps and other geospatial data analyses | ||
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* [[https://climaf.readthedocs.io/en/latest/|CliMAF]]: a Climate Model Assessment Framework | * [[https://climaf.readthedocs.io/en/latest/|CliMAF]]: a Climate Model Assessment Framework | ||
* [[other:uvcdat:cdat_conda:climaf_install|Installation notes]] | * [[other:uvcdat:cdat_conda:climaf_install|Installation notes]] | ||
- | |||
- | * [[https://jiffyclub.github.io/palettable/|Palettable]]: Color palettes for Python | ||
- | * ''conda install -n cdatm19_py3 -c conda-forge palettable'' | ||
==== TODO ==== | ==== TODO ==== | ||
//Add here packages that would be useful and have not been installed yet, or have some problems that prevent their installation// | //Add here packages that would be useful and have not been installed yet, or have some problems that prevent their installation// | ||
- | |||
- | * [[http://scitools.org.uk/iris/index.html|iris]]: A Python library for Meteorology and Climatology | ||
- | * ''conda install -n cdatxxx -c conda-forge iris'' | ||
* [[http://wrf-python.readthedocs.io/en/latest/|wrf-python]]: A collection of diagnostic and interpolation routines for use with output from the Weather Research and Forecasting (WRF-ARW) Model | * [[http://wrf-python.readthedocs.io/en/latest/|wrf-python]]: A collection of diagnostic and interpolation routines for use with output from the Weather Research and Forecasting (WRF-ARW) Model | ||
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* [[http://unidata.github.io/netcdf4-python/|netcdf4]]: a Python interface to the netCDF C library | * [[http://unidata.github.io/netcdf4-python/|netcdf4]]: a Python interface to the netCDF C library | ||
- | * [[https://github.com/PBrockmann/ipython_ferretmagic|ipython_ferretmagic]]: IPython notebook extension for ferret | ||
- | * [[https://github.com/conda-forge/basemap-data-hires-feedstock|basemap-data-hires]]: high resolution data for ''basemap'' | ||
* [[https://github.com/PCMDI/pcmdi_metrics|PCMDI metrics package]] (PMP): objectively compare results from climate models with observations using well-established statistical tests | * [[https://github.com/PCMDI/pcmdi_metrics|PCMDI metrics package]] (PMP): objectively compare results from climate models with observations using well-established statistical tests | ||
* [[https://xlsxwriter.readthedocs.io/|XlsxWriter]]: a Python module for creating Excel XLSX files | * [[https://xlsxwriter.readthedocs.io/|XlsxWriter]]: a Python module for creating Excel XLSX files | ||
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* [[https://earthsystemcog.org/projects/wip/CMIP6DataRequest|dreqPy]]: CMIP6 Data Request Python API | * [[https://earthsystemcog.org/projects/wip/CMIP6DataRequest|dreqPy]]: CMIP6 Data Request Python API | ||
* [[https://cmor.llnl.gov/|CMOR]]: CMOR (//Climate Model Output Rewriter//) is used to produce CF-compliant netCDF files | * [[https://cmor.llnl.gov/|CMOR]]: CMOR (//Climate Model Output Rewriter//) is used to produce CF-compliant netCDF files | ||
- | * [[https://github.com/pierre-rouanet/dtw|dtw]]: DTW (Dynamic Time Warping) python module | ||
* [[http://toblerity.org/shapely/project.html|shapely]]: a Python wrapper for GEOS for algebraic manipulation of geometry (manipulation and analysis of geometric objects in the Cartesian plane) | * [[http://toblerity.org/shapely/project.html|shapely]]: a Python wrapper for GEOS for algebraic manipulation of geometry (manipulation and analysis of geometric objects in the Cartesian plane) | ||
- | * [[http://scitools.org.uk/cartopy/|cartopy]]: a Python package designed for geospatial data processing in order to produce maps and other geospatial data analyses | ||
* [[https://ajdawson.github.io/windspharm/latest/|windspharm]]: spherical harmonic wind analysis in Python | * [[https://ajdawson.github.io/windspharm/latest/|windspharm]]: spherical harmonic wind analysis in Python | ||
* [[https://climaf.readthedocs.io/en/latest/|CliMAF]]: a Climate Model Assessment Framework | * [[https://climaf.readthedocs.io/en/latest/|CliMAF]]: a Climate Model Assessment Framework | ||
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</WRAP> | </WRAP> | ||
+ | * [[https://github.com/AutoViML/AutoViz|AutoViz]]: the One-Line Automatic Data Visualization Library. Automatically Visualize any dataset, any size with a single line of code | ||
+ | * [[https://matplotlib.org/basemap/|basemap]]: a library for plotting 2D data on maps in Python | ||
+ | * [[missing|basemap-data]] and [[https://github.com/conda-forge/basemap-data-hires-feedstock|basemap-data-hires]]: (high resolution) data for ''basemap'' | ||
+ | * [[http://scitools.org.uk/cartopy/|cartopy]]: a Python package designed for geospatial data processing in order to produce maps and other geospatial data analyses | ||
+ | * see also ''iris'' | ||
* [[https://github.com/Try2Code/cdo-bindings|python-cdo]]: Python scripting interface of [[https://code.mpimet.mpg.de/projects/cdo/wiki/Cdo#Documentation|cdo]] | * [[https://github.com/Try2Code/cdo-bindings|python-cdo]]: Python scripting interface of [[https://code.mpimet.mpg.de/projects/cdo/wiki/Cdo#Documentation|cdo]] | ||
* ''python-cdo'' will install the ''cdo'' package (providing the ''cdo ''executable) as a dependency | * ''python-cdo'' will install the ''cdo'' package (providing the ''cdo ''executable) as a dependency | ||
- | * see also [[https://code.mpimet.mpg.de/projects/cdo/wiki/Cdo%7Brbpy%7D#Python|Using CDO from python or ruby]] | + | * see also [[https://code.mpimet.mpg.de/projects/cdo/wiki/Cdo%7Brbpy%7D|Using CDO from python or ruby]] |
- | * [[http://matplotlib.org/cmocean/|cmocean]]: beautiful colormaps for oceanography | + | * [[https://cds.climate.copernicus.eu/api-how-to|cdsapi]]: The Climate Data Store (CDS) Application Program Interface (API) is a service providing programmatic access to CDS data |
+ | * CDS = Copernicus [[https://cds.climate.copernicus.eu/|Climate Data Store]] | ||
+ | * Example: [[https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-pressure-levels?tab=form|ERA5 hourly data on pressure levels from 1959 to present]] | ||
+ | * [[https://github.com/ecmwf/cfgrib|cfgrib]]: Python interface to map GRIB files to the Unidata's Common Data Model v4 following the CF Conventions | ||
+ | * see also ''eccodes'' and ''pygrib'' | ||
+ | * [[https://unidata.github.io/cftime/|cftime]]: Python library for decoding time units and variable values in a netCDF file conforming to the Climate and Forecasting (CF) netCDF conventions | ||
+ | * Used with ''xarray'' | ||
+ | * [[https://cf-xarray.readthedocs.io/|cf_xarray]]: provides an accessor (''DataArray.cf'' or ''Dataset.cf'') that allows you to interpret //Climate and Forecast// metadata convention attributes present on ''xarray'' objects | ||
+ | * Used with ''xarray'' | ||
+ | * [[https://clustergram.readthedocs.io/|clustergram]]: visualization and diagnostics for cluster analysis | ||
+ | * [[https://www.fabiocrameri.ch/colourmaps/|cmcrameri]]: Crameri's Scientific colour maps ''[color]'' | ||
+ | * the colormaps are also available in [[https://jiffyclub.github.io/palettable/scientific/|palettable.scientific]] | ||
+ | * [[http://matplotlib.org/cmocean/|cmocean]]: beautiful colormaps for oceanography ''[color]'' | ||
+ | * [[https://dash.plotly.com/|dash]] and [[https://github.com/plotly/jupyter-dash|jupyter-dash]]: the original low-code framework for rapidly building data apps in Python, R, Julia, and F# | ||
+ | * see also ''plotly'' | ||
+ | * [[https://github.com/man-group/dtale|D-Tale]] brings you an easy way to view & analyze Pandas data structures. It integrates seamlessly with ipython notebooks & python/ipython terminals. | ||
+ | * Install with: ''conda install dtale -c conda-forge'' | ||
+ | * [[https://dynamictimewarping.github.io/|python-dtw]]: implementation of Dynamic Time Warping-type (DTW) | ||
+ | * Note: older installations provided [[https://github.com/pierre-rouanet/dtw|dtw]]: DTW (Dynamic Time Warping) python module | ||
+ | * [[https://confluence.ecmwf.int/display/ECC/|ecCodes]]: ecCodes is a package developed by ECMWF which provides an application programming interface and a set of tools for decoding and encoding GRIB and BUFR messages | ||
+ | * need to install ''eccodes'' and ''python-eccodes'' ([[https://github.com/ecmwf/eccodes-python/issues/56|details]]) | ||
+ | * see also ''cfgrib'' and ''pygrib'' | ||
+ | * [[https://ajdawson.github.io/eofs/|eofs]]: a Python package for EOF analysis of spatial-temporal data | ||
+ | * [[https://docs.esmvaltool.org/en/latest/|ESMValTool]]: Earth System Model Evaluation Tool | ||
+ | * not installed yet (March 2022) because it required download of 302 Mb extra packages and a downgrade of gdal | ||
+ | * [[https://flox.readthedocs.io/|flox]]: fast & furious //GroupBy// reductions for dask.array | ||
+ | * See also | ||
+ | * [[https://xarray.dev/blog/flox|flox: Faster GroupBy reductions with Xarray]] | ||
+ | * [[https://flox.readthedocs.io/en/latest/implementation.html|Parallel Algorithms]] | ||
* [[http://ferret.pmel.noaa.gov/Ferret/documentation/pyferret|pyferret]] and ''ferret_datasets'': Ferret encapsulated in Python | * [[http://ferret.pmel.noaa.gov/Ferret/documentation/pyferret|pyferret]] and ''ferret_datasets'': Ferret encapsulated in Python | ||
+ | * [[https://github.com/PBrockmann/ipython_ferretmagic|ipython_ferretmagic]]: IPython notebook extension for ferret | ||
+ | * Install with: ''pip install ferretmagic'' | ||
* OSGeo/[[http://www.gdal.org/|gdal]]: Geospatial Data Abstraction Library. GDAL is a translator library for raster and vector geospatial data formats | * OSGeo/[[http://www.gdal.org/|gdal]]: Geospatial Data Abstraction Library. GDAL is a translator library for raster and vector geospatial data formats | ||
* [[https://pcjericks.github.io/py-gdalogr-cookbook/|Python GDAL/OGR Cookbook]] | * [[https://pcjericks.github.io/py-gdalogr-cookbook/|Python GDAL/OGR Cookbook]] | ||
* [[https://github.com/TEOS-10/GSW-python|gsw]]: Python implementation of the Thermodynamic Equation Of Seawater | * [[https://github.com/TEOS-10/GSW-python|gsw]]: Python implementation of the Thermodynamic Equation Of Seawater | ||
* see also //seawater// | * see also //seawater// | ||
+ | * [[https://noaa-orr-erd.github.io/gridded/index.html|gridded]]: a single API for accessing / working with gridded model results on multiple grid types | ||
+ | * Supports the [[http://cfconventions.org/|CF]], [[https://ugrid-conventions.github.io/ugrid-conventions/|UGRID]] and [[http://sgrid.github.io/sgrid/|SGRID]] conventions | ||
+ | * [[https://icclim.readthedocs.io/|icclim]]: icclim (Index Calculation for CLIMate) is a Python library to compute climate indices | ||
+ | * [[https://intake.readthedocs.io/|intake]]: a lightweight package for finding, investigating, loading and disseminating data | ||
+ | * [[https://intake-esm.readthedocs.io/|intake-esm]]: data cataloging utility built on top of intake, pandas, and xarray | ||
+ | * [[https://ipyleaflet.readthedocs.io/en/latest/|ipyleaflet]]: interactive maps in the Jupyter notebook | ||
+ | * [[https://ipywidgets.readthedocs.io/|ipywidgets]]: ipywidgets, also known as jupyter-widgets or simply widgets, are interactive HTML widgets for Jupyter notebooks and the IPython kernel | ||
+ | * [[https://scitools-iris.readthedocs.io/en/stable/|iris]]: a powerful, format-agnostic, community-driven Python package for analysing and visualising Earth science data | ||
+ | * see also ''cartopy'' | ||
+ | * install sample data with ''conda install -c conda-forge iris-sample-data'' | ||
* [[https://joblib.readthedocs.io/en/latest/|joblib]]: running Python functions as pipeline jobs | * [[https://joblib.readthedocs.io/en/latest/|joblib]]: running Python functions as pipeline jobs | ||
+ | * [[https://jupyterlab.readthedocs.io/|jupyterlab]]: the next-generation web-based user interface for Project Jupyter | ||
+ | * [[https://github.com/plotly/Kaleido|python-kaleido]]: a cross-platform library for generating static images (e.g. png, svg, pdf, etc.) for web-based visualization libraries | ||
+ | * [[https://unidata.github.io/MetPy/latest/|MetPy]]: a collection of tools in Python for reading, visualizing, and performing calculations with weather data | ||
+ | * [[https://mpltern.readthedocs.io/|mpltern]]: a Python plotting library based on Matplotlib specifically designed for ternary plots | ||
+ | * [[https://nc-time-axis.readthedocs.io/en/stable/|nc-time-axis]]: a package that enables making plots in matplotlib with axes made up of ''cftime.datetime'' dates with any calendar type | ||
+ | * [[https://gitlab.com/remikz/nccmp|nccmp]]: compare two NetCDF files bitwise, semantically or with a user defined tolerance (absolute or relative percentage) | ||
+ | * can probably also be done with [[https://code.mpimet.mpg.de/projects/cdo/embedded/index.html#x1-580002.1.3|cdo -v diffn file1.nc file2.nc]] | ||
* [[https://opencv.org/|OpenCV]]: OpenCV (Open Source Computer Vision Library) is an open-source library that includes several hundreds of computer vision algorithms. See also [[https://www.geeksforgeeks.org/opencv-python-tutorial/|OpenCV Python Tutorial]] and **scikit-image** | * [[https://opencv.org/|OpenCV]]: OpenCV (Open Source Computer Vision Library) is an open-source library that includes several hundreds of computer vision algorithms. See also [[https://www.geeksforgeeks.org/opencv-python-tutorial/|OpenCV Python Tutorial]] and **scikit-image** | ||
- | * [[https://jiffyclub.github.io/palettable/|Palettable]]: Color palettes for Python | + | * [[https://openpyxl.readthedocs.io/|openpyxl]]: a Python library to read/write Excel 2010 ''xlsx''/''xlsm'' files |
+ | * [[https://jiffyclub.github.io/palettable/|Palettable]]: Color palettes for Python ''[color]'' | ||
* [[http://pandas.pydata.org/|pandas]]: Python Data Analysis Library | * [[http://pandas.pydata.org/|pandas]]: Python Data Analysis Library | ||
* [[https://geopandas.org/|geopandas]]: an open source project to make working with geospatial data in python easier | * [[https://geopandas.org/|geopandas]]: an open source project to make working with geospatial data in python easier | ||
* [[https://peakutils.readthedocs.io/|PeakUtils]]: utilities related to the detection of peaks on 1D data | * [[https://peakutils.readthedocs.io/|PeakUtils]]: utilities related to the detection of peaks on 1D data | ||
* [[https://python-pillow.org/|pillow]]: the friendly PIL (//Python Imaging Library//) fork | * [[https://python-pillow.org/|pillow]]: the friendly PIL (//Python Imaging Library//) fork | ||
+ | * [[https://plotly.com/python/|plotly]]: Plotly's Python graphing library (sometimes referred to as //plotly.py//) makes interactive, publication-quality graphs | ||
+ | * see also ''python-kaleido'' and ''dash'' | ||
+ | * [[https://www.fatiando.org/pooch/|pooch]]: a friend to fetch your data files (makes it easy to download a file, without messing with ''requests'' and ''urllib'') | ||
* [[https://proplot.readthedocs.io/en/latest/|proplot]]: a lightweight **matplotlib wrapper** for making beautiful, publication-quality graphics | * [[https://proplot.readthedocs.io/en/latest/|proplot]]: a lightweight **matplotlib wrapper** for making beautiful, publication-quality graphics | ||
+ | * [[https://psyplot.github.io/|psyplot]]: Interactive Data Visualization from Python and GUIs | ||
+ | * [[https://github.com/jswhit/pygrib|pygrib]]: high-level interface to the ECWMF ECCODES C library for reading GRIB files | ||
+ | * see also ''eccodes'' and ''cfgrib'' | ||
+ | * [[https://pyleoclim-util.readthedocs.io/|pyleoclim]]: a Python package designed for the analysis of paleoclimate data | ||
+ | * **Wait** till it can be installed with ''conda'' ([[https://github.com/LinkedEarth/Pyleoclim_util/discussions/205|Why I'm not installing Pyleoclim yet]]) | ||
+ | * [[https://requests.readthedocs.io/|requests]]: is an elegant and simple HTTP library for Python, built for human beings | ||
+ | * See also ''pooch'' | ||
+ | * [[https://rasterio.readthedocs.io/|rasterio]]: access to geospatial raster data | ||
+ | * [[https://corteva.github.io/rioxarray/|rioxarray]]: ''rasterio'' xarray extension | ||
* [[https://rpy2.github.io/|rpy2]]: an interface to R running embedded in a Python process | * [[https://rpy2.github.io/|rpy2]]: an interface to R running embedded in a Python process | ||
- | * [[http://scikit-image.org/|scikit-image]]: image processing in Python | + | * [[http://scikit-image.org/|scikit-image]]: a collection of algorithms for image processing in Python |
- | * [[https://scikit-learn.org/stable/index.html|scikit-learn]]: Machine Learning in Python | + | * [[https://scikit-learn.org/|scikit-learn]]: an open source machine learning library that supports supervised and unsupervised learning. It also provides various tools for model fitting, data preprocessing, model selection and evaluation, and many other utilities. |
* [[https://seaborn.pydata.org/|seaborn]]: statistical data visualization | * [[https://seaborn.pydata.org/|seaborn]]: statistical data visualization | ||
* [[http://pythonhosted.org/seawater/|seawater]]: Python re-write of the CSIRO seawater toolbox | * [[http://pythonhosted.org/seawater/|seawater]]: Python re-write of the CSIRO seawater toolbox | ||
- | * see also //gsw// | + | * see also ''gsw'' |
- | * [[http://statsmodels.sourceforge.net/|statsmodels]]: a Python module that allows users to explore data, estimate statistical models, and perform statistical tests | + | * [[https://www.statsmodels.org/|statsmodels]]: a Python module that provides classes and functions for the estimation of many different statistical models, as well as for conducting statistical tests, and statistical data exploration. |
+ | * [[https://github.com/SatAgro/suntime|suntime]]: simple sunset and sunrise time calculation python library | ||
+ | * **Warning**: not available in conda, use ''pip install suntime'' | ||
+ | * [[https://github.com/fbdesignpro/sweetviz|Sweetviz]] is pandas based Python library that generates beautiful, high-density visualizations to kickstart EDA (Exploratory Data Analysis) with just two lines of code. | ||
+ | * [[https://www.tensorflow.org/|tensorflow-mkl]]: an end-to-end open source machine learning platform | ||
+ | * [[https://anaconda.org/anaconda/tensorflow-mkl|tensorflow-mkl]] will install the **CPU**-based (**not GPU**) version | ||
+ | * [[https://github.com/tqdm|tqdm]]: make your loops show a smart progress meter | ||
+ | * [[https://uxarray.readthedocs.io/|uxarray]]: provide xarray styled functionality for unstructured grid datasets following [[https://ugrid-conventions.github.io/ugrid-conventions/|UGRID Conventions]] | ||
+ | * [[https://docs.xarray.dev/en/stable/|xarray]]: Xarray makes working with labelled multi-dimensional arrays in Python simple, efficient, and fun! | ||
+ | * See also: ''flox'', ''xcdat'', ''rioxarray'', ... | ||
+ | * [[https://xcdat.readthedocs.io/|xcdat]]: Xarray extended with Climate Data Analysis Tools | ||
+ | * [[https://xclim.readthedocs.io/|xclim]]: an operational Python library for climate services, providing numerous climate-related indicator tools with an extensible framework for constructing custom climate indicators, statistical downscaling and bias adjustment of climate model simulations, as well as climate model ensemble analysis tools. | ||
+ | * [[https://xesmf.readthedocs.io/|xESMF]]: Universal Regridder for Geospatial Data | ||
+ | * [[https://xgrads.readthedocs.io/|xgrads]]: parse and read binary dataset described by a ''.ctl'' file commonly used by [[http://cola.gmu.edu/grads/|GrADS]] or [[http://www.opengrads.org/|openGrADS]] | ||
+ | * [[https://xlrd.readthedocs.io/|xlrd]]: a library for reading data and formatting information from Excel files in the historical .xls format | ||
* [[https://xoa.readthedocs.io/en/latest/|xoa]]: xarray-based ocean analysis library | * [[https://xoa.readthedocs.io/en/latest/|xoa]]: xarray-based ocean analysis library | ||
* ''xoa'' is the successor of [[http://www.ifremer.fr/vacumm/|vacumm]] (vacumm does **not** support Python3) | * ''xoa'' is the successor of [[http://www.ifremer.fr/vacumm/|vacumm]] (vacumm does **not** support Python3) | ||
+ | * [[https://xskillscore.readthedocs.io/|xskillscore]]: metrics for verifying forecasts | ||
+ | * [[https://docs.profiling.ydata.ai/|ydata-profiling]]: a leading package for data profiling, that automates and standardizes the generation of detailed reports, complete with statistics and visualizations. | ||
==== Removed packages ==== | ==== Removed packages ==== |