other:uvcdat:cdat_conda:cdat_8_2_1
Differences
This shows you the differences between two versions of the page.
| Both sides previous revision Previous revision Next revision | Previous revision Next revision Both sides next revision | ||
|
other:uvcdat:cdat_conda:cdat_8_2_1 [2023/10/19 08:27] jypeter [Packages that have no dependency problems] Removed note about CMOR not supported by python 3 |
other:uvcdat:cdat_conda:cdat_8_2_1 [2024/03/07 16:39] jypeter [Extra packages list] Added eigen |
||
|---|---|---|---|
| Line 331: | Line 331: | ||
| </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'' | ||
| Line 354: | Line 354: | ||
| * [[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://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 | ||
| Line 359: | Line 361: | ||
| * 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 | ||
| Line 371: | Line 374: | ||
| * 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// | ||
| Line 409: | Line 416: | ||
| * [[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 | ||
| Line 416: | Line 425: | ||
| * 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 | ||
| Line 423: | Line 434: | ||
| * [[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. | ||
| Line 432: | Line 443: | ||
| * ''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://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 ==== | ||
other/uvcdat/cdat_conda/cdat_8_2_1.txt · Last modified: 2024/05/15 14:53 by jypeter
