| Both sides previous revisionPrevious revisionNext revision | Previous revision |
| other:uvcdat:cdat_conda:cdat_8_2_1 [2023/12/13 14:49] – [Extra packages list] Added ydata-profiling jypeter | other:uvcdat:cdat_conda:cdat_8_2_1 [2024/07/02 12:44] (current) – [Extra packages list] Added nco jypeter |
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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 | * [[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'' | * [[missing|basemap-data]] and [[https://github.com/conda-forge/basemap-data-hires-feedstock|basemap-data-hires]]: (high resolution) data for ''basemap'' |
| * ''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|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]] |
| | * See also ''nco'' |
| * [[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 | * [[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]] | * CDS = Copernicus [[https://cds.climate.copernicus.eu/|Climate Data Store]] |
| * [[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# | * [[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'' | * see also ''plotly'' |
| | * [[https://docs.dask.org/|dask]]: a Python library for parallel and distributed computing |
| | * dask.[[https://distributed.dask.org/|distributed]]: lightweight library for distributed computing in Python |
| | * See also: flox |
| | * [[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) | * [[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 | * Note: older installations provided [[https://github.com/pierre-rouanet/dtw|dtw]]: DTW (Dynamic Time Warping) python module |
| * need to install ''eccodes'' and ''python-eccodes'' ([[https://github.com/ecmwf/eccodes-python/issues/56|details]]) | * need to install ''eccodes'' and ''python-eccodes'' ([[https://github.com/ecmwf/eccodes-python/issues/56|details]]) |
| * see also ''cfgrib'' and ''pygrib'' | * see also ''cfgrib'' and ''pygrib'' |
| | * [[https://eigen.tuxfamily.org/|eigen]]: a C++ template library for linear algebra: matrices, vectors, numerical solvers, and related algorithms |
| * [[https://ajdawson.github.io/eofs/|eofs]]: a Python package for EOF analysis of spatial-temporal data | * [[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 | * [[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 | * should install ''esmvaltool-python'' in order to reduce the size/number of dependencies, after checking that it does not require downgrading important packages |
| * [[https://flox.readthedocs.io/|flox]]: fast & furious //GroupBy// reductions for dask.array | * [[https://flox.readthedocs.io/|flox]]: fast & furious //GroupBy// reductions for dask.array |
| * See also | * See also |
| * 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://geopy.readthedocs.io/|GeoPy]]: a Python client for several popular geocoding web services |
| | * ''GeoPy'' makes it easy for Python developers to locate the coordinates of addresses, cities, countries, and landmarks across the globe using third-party geocoders and other data sources. |
| | * [[https://github.com/piskvorky/gensim?tab=readme-ov-file|gensim]]: a Python library for topic modelling, document indexing and similarity retrieval with large corpora |
| | * See also //scikit-learn// |
| * [[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 | * [[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 | * Supports the [[http://cfconventions.org/|CF]], [[https://ugrid-conventions.github.io/ugrid-conventions/|UGRID]] and [[http://sgrid.github.io/sgrid/|SGRID]] conventions |
| | * [[https://uber.github.io/h3-py/|h3-py]]: Uber’s H3 Hexagonal Hierarchical Geospatial Indexing System in Python |
| | * Additional reading: [[https://towardsdatascience.com/uber-h3-for-data-analysis-with-python-1e54acdcc908|Uber H3 for Data Analysis with Python]] |
| * [[https://icclim.readthedocs.io/|icclim]]: icclim (Index Calculation for CLIMate) is a Python library to compute climate indices | * [[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.readthedocs.io/|intake]]: a lightweight package for finding, investigating, loading and disseminating data |
| * [[https://gitlab.com/remikz/nccmp|nccmp]]: compare two NetCDF files bitwise, semantically or with a user defined tolerance (absolute or relative percentage) | * [[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]] | * 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://nco.sourceforge.net/|nco]]: The NCO toolkit manipulates and analyzes data stored in netCDF-accessible formats |
| | * Note: this only installs the [[https://nco.sourceforge.net/#Definition|nco executables]] (''ncks'', ''ncatted'', ...)! It is not really a Python package |
| | * See also ''cdo'' |
| * [[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://openpyxl.readthedocs.io/|openpyxl]]: a Python library to read/write Excel 2010 ''xlsx''/''xlsm'' files | * [[https://openpyxl.readthedocs.io/|openpyxl]]: a Python library to read/write Excel 2010 ''xlsx''/''xlsm'' files |
| * [[https://requests.readthedocs.io/|requests]]: is an elegant and simple HTTP library for Python, built for human beings | * [[https://requests.readthedocs.io/|requests]]: is an elegant and simple HTTP library for Python, built for human beings |
| * See also ''pooch'' | * 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]]: a collection of algorithms for image processing in Python | * [[http://scikit-image.org/|scikit-image]]: a collection of algorithms for image processing in Python |
| * see also ''gsw'' | * see also ''gsw'' |
| * [[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://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://streamlit.io/|streamlit]]: Streamlit turns data scripts into shareable web apps in minutes |
| * [[https://github.com/SatAgro/suntime|suntime]]: simple sunset and sunrise time calculation python library | * [[https://github.com/SatAgro/suntime|suntime]]: simple sunset and sunrise time calculation python library |
| * **Warning**: not available in conda, use ''pip install suntime'' | * **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://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://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://github.com/tqdm/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://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! | * [[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'', ... | * See also: ''flox'', ''xcdat'', ''rioxarray'', ... |
| * [[https://xcdat.readthedocs.io/|xcdat]]: Xarray extended with Climate Data Analysis Tools | * [[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://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. |
