Differences

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--- other:uvcdat:cdat_conda:cdat_2024_03 [2024/03/22 11:09] – [You want to replicate this distribution on your own server or a desktop/laptop] Improved jypeter
+++ other:uvcdat:cdat_conda:cdat_2024_03 [2026/10/19 09:51] (current) – [Extra packages list] Added conda-which warning jypeter
@@ Line 57: / Line 57: @@
 The notes at the beginning of each section will tell you if you can skip the section if you are just installing for you
-===== What's New in CDAT 8.2.1? =====
+===== What's New in JYP's 2024-03 distribution? =====
-  * [[https://github.com/CDAT/cdat/releases/tag/v8.2.1|8.2.1 announcement]] and features summary ([[https://github.com/UV-CDAT/uvcdat/releases|all versions]])
+  * This is the first distribution not //tied// to CDAT packages
-  * Full [[https://uvcdat.llnl.gov/changelog.html#8.2.1|Change log]]
-Note: this particular CDAT installation at LSCE (and on other machines/servers) provides **many extra (non-CDAT) packages**. You can jump directly to the [[#extra_packages_list|Extra package list]] at the bottom of this page, if you want to see what is available
+  * There are lots (and lots) of extra packages that all have their own history. You can always visit the [[#extra_packages_list|Extra package list]] on this page, to get news from individual packages
 ===== Installation with Miniconda3 =====
@@ Line 320: / Line 319: @@
 ===== Extra packages list =====
+<WRAP center round tip 60%>
+The extra packages below are (more or less) listed in package name alphabetical order. These are the extra packages that we explicitly installed, but there are many more packages installed (e.g. {{ :other:uvcdat:cdat_conda:cdatm19_nompi_py3_21-03-06.txt |}})!
+Initialize the environment and type ''conda list'' if you need an up-to-date list, including the packages' version and where they came from (conda-forge for most packages
+</WRAP>
+<note important>
+Do not forget to add the following packages to the future environments\\ \\
+  * [[https://anaconda.org/conda-forge/cxx-compiler/|cxx-compiler]]: a metapackage to obtain a C++ compiler
+  * [[https://f90nml.readthedocs.io/|f90nml]]: a Fortran namelist parser, generator, and editor
+  * [[https://flake8.pycqa.org/en/stable/|flake8]]: your Tool For Style Guide Enforcement
+  * [[https://loguru.readthedocs.io/|Loguru]]: a library which aims to bring enjoyable logging in Python
+  * [[https://pythonot.github.io/|pot]]: Python Optimal Transport\\ This open source Python library provides several solvers for optimization problems related to Optimal Transport for signal, image processing and machine learning
+  * [[https://pybind11.readthedocs.io/|pybind11]]: a lightweight header-only library that exposes C++ types in Python and vice versa
+  * [[https://docs.astral.sh/ruff/|ruff]]: An extremely fast Python linter and code formatter, written in Rust
+  * [[https://xrft.readthedocs.io/|xrft]]: Fourier transforms for xarray data
+  * [[https://zarr.readthedocs.io/|zarr]]: a file storage format for chunked, compressed, N-dimensional arrays based on an open-source specification
+</note>
 FIXME: need to clean the list below, and move the packages to the ordered list further down
@@ Line 327: / Line 347: @@
   * [[https://xlsxwriter.readthedocs.io/|XlsxWriter]]: a Python module for creating Excel XLSX files
     * Note: <wrap hi>this is a dependency of ''dreqPy''</wrap>
+    * See also ''pandas'', ''openpyxl'', ''xlrd''
   * [[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
   * [[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)
   * [[https://ajdawson.github.io/windspharm/latest/|windspharm]]: spherical harmonic wind analysis in Python
+  * [[https://docs.bokeh.org/|bokeh]]: a Python library for creating interactive visualizations for modern web browsers
+    * [[https://docs.bokeh.org/en/latest/docs/reference/sampledata.html|bokeh_sampledata]]: datasets that are used in examples and documentation
+    * see also ''holoviews''
   * [[https://climaf.readthedocs.io/en/latest/|CliMAF]]: a Climate Model Assessment Framework
-<WRAP center round tip 60%>
-The extra packages below are (more or less) listed in package name alphabetical order. These are the extra packages that we explicitly installed, but there are many more packages installed (e.g. {{ :other:uvcdat:cdat_conda:cdatm19_nompi_py3_21-03-06.txt |}})!
-Initialize the environment and type ''conda list'' if you need an up-to-date list, including the packages' version and where they came from (conda-forge for most packages
-</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
+    * 2026-09: <wrap hi>''basemap'' can't be installed with a recent version of ''numpy'' (''numpy >= 2.4'')</wrap>, see [[https://github.com/matplotlib/basemap/issues/655|Add numpy 2.5 compatibility]]
     * [[missing|basemap-data]] and [[https://github.com/conda-forge/basemap-data-hires-feedstock|basemap-data-hires]]: (high resolution) data for ''basemap''
+  * [[https://blosc.org/python-blosc2/getting_started/overview.html|Python-Blosc2]]: a high-performance compressed ''ndarray'' library with a flexible compute engine
+    * See also ''NumExpr''
   * [[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''
@@ Line 347: / Line 369: @@
     * ''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 ''nco'', ''pynco''
   * [[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''
+    * see also ''grib2io'', ''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''
@@ Line 360: / Line 383: @@
     * 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://conda.github.io/conda-pypi/|conda-pypi]]: provides better PyPI interoperability for the conda ecosystem
+  * [[https://github.com/kelvinou01/conda-which|conda-which]]: find the package owning a file
+    * <wrap hi>[[https://github.com/kelvinou01/conda-which/issues/7|conda-which 0.2.2 BREAKS conda > 26.7]]</wrap>
+    * See the [[https://github.com/conda/conda/issues/14026|Is there an easy way to find the package "owning" a file ?]] GH issue
+  * [[https://www.cvxpy.org/|cvxpy]]: an open source Python-embedded modeling language for convex optimization problems
   * [[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://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://dateutil.readthedocs.io/|python-dateutil]]: the ''dateutil'' module provides powerful extensions to the standard ''datetime'' module, available in Python
   * [[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''
+    * Install with: ''conda install -c conda-forge dtale''
   * [[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''
+    * see also ''grib2io'', ''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
@@ Line 379: / Line 411: @@
   * [[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''
+      * <wrap em>pip</wrap>: ''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
     * [[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
+  * [[https://radimrehurek.com/gensim/|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://geocat.ucar.edu/about/|GeoCAT]] (Geoscience Community Analysis Toolkit): create scalable data analysis and visualization tools for Earth System Science data. Some of the functionalities in the GeoCAT stack are inspired/reimplemented from NCL (NCAR Command Language)
-    * see also //seawater//
+    * [[https://geocat.ucar.edu/software/geocat_viz/|geocat-viz]]: a collection of Python plotting utility functions intended to make common Matplotlib and Cartopy routines easier for the user, as well as adding support for additional geoscientific plot types
+    * [[https://geocat.ucar.edu/software/geocat_comp/|geocat-comp]]: provides implementations of computational functions for operating on geosciences data
+    * [[https://geocat.ucar.edu/software/uxarray/|uxarray]]: address the geoscience community’s need for tools that enable standard data analysis techniques and visualization functionality to operate directly on unstructured grid data (i.e. without regridding to structured grids)
+    * ''geocat-datafiles'' and [[https://geocat-examples.readthedocs.io/en/latest/gallery/|Gallery]]
+  * [[https://docs.gpytorch.ai/|GPyTorch]]: a Gaussian process library implemented using PyTorch. GPyTorch is designed for creating scalable, flexible, and modular Gaussian process models with ease
+  * [[https://noaa-mdl.github.io/grib2io/|grib2io]]: a Python package that provides an interface to the NCEP GRIB2 C (g2c) library for the purpose of reading and writing WMO GRIdded Binary, Edition 2 (GRIB2) messages
+    * See also ''cfgrib'', ''eccodes'' and ''pygrib''
   * [[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://github.com/TEOS-10/GSW-python|gsw]]: Python implementation of the Thermodynamic Equation Of Seawater
+    * See also ''seawater''
+  * [[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://github.com/mapado/haversine|haversine]]: calculate the distance (in various units) between two points on Earth using their latitude and longitude
+    * See also ''[[https://scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.haversine_distances.html|haversine_distances]] @ scikit-learn''
+  * [[https://holoviews.org/|holoviews]]: an open-source Python library designed to make data analysis and visualization seamless and simple
   * [[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://intake-esgf.readthedocs.io/|intake-esgf]]: an ''intake'' and ''intake-esm'' inspired package under development in ESGF2
   * [[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
@@ Line 399: / Line 445: @@
     * 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://jupyterlab.readthedocs.io/|jupyterlab]]: the next-generation web-based user interface for Project Jupyter
+  * [[https://jupyter.org/|jupyter notebooks]]: Notebook Interface
+    * Installation: ''conda install -c conda-forge jupyter jupyterlab notebook nbconvert pandoc''
+    * Start local server with
+      * ''jupyter lab''
+      * or ''jupyter notebook''
+    * Convert notebook with [[https://nbconvert.readthedocs.io/en/latest/usage.html|nbconvert]]
+      * ''jupyter nbconvert --to html my_notebook.ipynb''
+      * ''jupyter nbconvert --to script my_notebook.ipynb''
+    * [[https://docs.jupyter.org/|Documentation]]
   * [[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
@@ Line 406: / Line 460: @@
   * [[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://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 ''pynco'', ''cdo''
+  * [[https://netgraph.readthedocs.io/|netgraph]]: publication-quality network visualisations in python
+    * FIXME: need a temporary version until [[https://github.com/paulbrodersen/netgraph/issues/106|"Community Node Layout" example broken with numpy>2.4.6]] is fixed
+      * ''conda create -n masa_tmp matplotlib networkx netgraph 'numpy<2.5.0' pandas openpyxl''
+  * [[https://networkx.org/|networkx]]: a Python package for the creation, manipulation, and study of the structure, dynamics, and functions of complex network
+  * [[https://pypi.org/project/nicopal/|nicopal]]: perceptually uniform and colour-vision-deficiency-friendly colour palettes for scientific data visualisation
+    * <wrap em>pip</wrap>: ''conda pypi install nicopal''
+  * [[https://numexpr.readthedocs.io/|NumExpr]]: supplies routines for the fast evaluation of array expressions elementwise by using a vector-based virtual machine
+    * See also ''Python-Blosc2''
   * [[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
+    * See also ''pandas'', ''XlsxWriter'', ''xlrd''
   * [[https://jiffyclub.github.io/palettable/|Palettable]]: Color palettes for Python ''[color]''
+  * [[https://python-graph-gallery.com/introduction-to-pypalettes/|PyPalettes]]: a python library that provides more than 2500 different palettes to use in your data visualization
+    * [[https://python-graph-gallery.com/color-palette-finder/|Python Color Palette Finder]]
   * [[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://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://platformdirs.readthedocs.io/|platformdirs]]: a Python library for determining platform-specific system directories (resolves the correct location for macOS, Windows, Linux/Unix, and Android)
   * [[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://plotnine.org/|plotnine]]: a data visualization package for Python based on the grammar of graphics, a coherent system for describing and building graphs
+    * similar to ''ggplot2'', a widely successful ''R'' package
+  * [[https://pola.rs/|polars]]: an open-source library for data manipulation, known for being one of the fastest data processing solutions on a single machine
+    * [[https://anaconda.org/channels/conda-forge/packages/polars/overview|polars @ conda-forge]]: official instructions only mention pip installation...
   * [[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
+  * //proplot//: a lightweight matplotlib wrapper for making beautiful, publication-quality graphics
+    * **Replaced by ''ultraplot''**
+  * [[https://psutil.readthedocs.io/|psutil]]: a cross-platform library for retrieving information on running processes and system utilization
   * [[https://psyplot.github.io/|psyplot]]: Interactive Data Visualization from Python and GUIs
+  * [[https://www.pygmt.org/latest/|PyGMT]]: a Python interface for the [[https://www.generic-mapping-tools.org/|Generic Mapping Tools]]
   * [[https://github.com/jswhit/pygrib|pygrib]]: high-level interface to the ECWMF ECCODES C library for reading GRIB files
-    * see also ''eccodes'' and ''cfgrib''
+    * see also ''grib2io'', ''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://www.pymoo.org/|pymoo]]: multi-objective Optimization in Python
+  * [[https://pynco.readthedocs.io/|pynco]]:Python bindings for NCO
+    * See also ''nco'', ''cdo'', ''python-cdo''
+  * [[https://github.com/ofajardo/pyreadr?tab=readme-ov-file#pyreadr|pyreadr]]: a python package to read and write R RData and Rds files into/from pandas dataframes
+  * [[https://pyresample.readthedocs.io/en/latest/|pyresample]]: a python package for resampling geospatial image data
+  * [[https://www.pytables.org/usersguide/|pytables]]: enable the end user to manipulate easily data tables and array objects in a hierarchical structure. Pythonic tool to **deal with (arbitrarily) large amounts of data** (typically bigger than available memory) in tables and arrays organized in a hierarchical and persistent disk storage structure
+  * [[https://docs.pytorch.org/|PyTorch]]: an optimized tensor library for deep learning using GPUs and CPU
+    * [[https://pytorch.org/get-started/locally/|Installation]] with ''conda'' is not detailed (understatement...)
+    * ''conda install -n my_env pytorch-cpu'' (or ''pytorch-gpu'' or ''pytorch-cpu'')
+      * This may trigger an [[https://github.com/pytorch/pytorch/issues/187188|acceptable downgrade of setuptools]]
+  * [[https://rasterio.readthedocs.io/|rasterio]]: access to geospatial raster data
+    * See ''rioxarray'' for //geospatial raster data// + ''xarray''
+  * [[https://regionmask.readthedocs.io/en/stable/|regionmask]]: create masks of geospatial regions
   * [[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://rich.readthedocs.io/|rich]]: a Python library for writing rich text (with color and style) to the terminal, and for displaying advanced content such as tables, markdown, and syntax highlighted code
-    * [[https://corteva.github.io/rioxarray/|rioxarray]]: ''rasterio'' xarray extension
+  * [[https://corteva.github.io/rioxarray/stable/|rioxarray]]: an extension of xarray that focuses on geospatial raster data
+    * [[https://geog-312.gishub.org/book/geospatial/rioxarray.html|rioxarray tutorial]]
   * [[https://rpy2.github.io/|rpy2]]: an interface to R running embedded in a Python process
+  * [[https://salib.readthedocs.io/|SALib]]: Python implementations of commonly used sensitivity analysis methods, including Sobol, Morris, and FAST methods. Useful in systems modeling to calculate the effects of model inputs or exogenous factors on outputs of interest
+  * [[https://scikit.bio/|scikit-bio]]: a community-driven Python library for bioinformatics, providing versatile data structures, algorithms and educational resources
   * [[http://scikit-image.org/|scikit-image]]: a collection of algorithms for image processing 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://github.com/scipy/scipy-stubs|scipy-stubs]]: official type stubs for SciPy
+    * It is kind of an extension of [[https://scipy.org/|scipy]]...
   * [[https://seaborn.pydata.org/|seaborn]]: statistical data visualization
   * [[http://pythonhosted.org/seawater/|seawater]]: Python re-write of the CSIRO seawater toolbox
@@ Line 435: / Line 529: @@
   * [[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
-    * **Warning**: not available in conda, use ''pip install suntime''
+    * <wrap em>pip</wrap>: ''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://github.com/tqdm/tqdm|tqdm]]: make your loops show a smart progress meter
+  * [[https://ultraplot.readthedocs.io/en/latest/|UltraPlot]]: a succinct matplotlib wrapper for making beautiful, publication-quality graphics
   * [[https://uxarray.readthedocs.io/|uxarray]]: provide xarray styled functionality for unstructured grid datasets following [[https://ugrid-conventions.github.io/ugrid-conventions/|UGRID Conventions]]
+  * [[https://amueller.github.io/word_cloud/|wordcloud]]: A little //word cloud// generator in Python
   * [[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'', ...
@@ Line 448: / Line 544: @@
   * [[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
+    * See also ''pandas'', ''XlsxWriter'', ''openpyxl''
-    * ''xoa'' is the successor of [[http://www.ifremer.fr/vacumm/|vacumm]] (vacumm does **not** support Python3)
+  * [[https://xoa.readthedocs.io/|xoa]]: xarray-based ocean analysis library
+    * ''xoa'' is the successor of [[https://umr-lops-vacumm.ifremer.fr/|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.