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If you mostly want to use the CDAT 8.1 installed by JYP, just use the following steps and skip the next sections:

• Initialize conda:

• Activate the default environment: conda activate cdatm_py2
  • Check the Available environments for more options

• 8.1 announcement and features summary (all versions)
• Full Change log

We assume that Miniconda3 is already installed. Otherwise, follow the the Installing Miniconda steps (and the Post-Miniconda3 installation section) we followed when installing CDAT 8.0

The conda package itself can be updated (if need be) with
conda update -n base -c defaults conda

We first check that we have indeed access to a conda installation, and we assume that we have a write-access to the conda hierarchy, and a few Gb of disk space available

 >conda activate
(base) >conda env list | grep base
base                  *  /home/share/unix_files/cdat/miniconda3

(base) >df -h  /home/share/
Filesystem      Size  Used Avail Use% Mounted on
prolix3:/share  917G  171G  700G  20% /home/share

(base) >du -sh /home/share/unix_files/cdat/miniconda3
7.8G    /home/share/unix_files/cdat/miniconda3

We can also use conda env list and remove some obsolete versions with conda remove -n obsolete_name –all to get some space

$ conda create -n cdat-8.1_py2 -c cdat/label/v81 -c conda-forge python=2.7 cdat
# Generate the list of installed packages
$ conda list -n cdat-8.1_py2 > /home/scratch01/jypeter/cdat-8.1_py2_list_190307.txt

List of installed packages: cdat-8.1_py2_list_190307.txt

$ conda create -n cdat-8.1_py3 -c cdat/label/v81 -c conda-forge python=3.6 cdat

Notes:

• This page is about CDAT 8.1, but we have added a short nightly section here as a convenient shortcut. This should probably be moved to a stand-alone nightly page later
• The nightly version is not automatically updated, and may well be out-of-date
• Official nightly page

$ conda create -n cdat-nightly_py2 -c cdat/label/nightly -c conda-forge python=2.7 cdat

Not tested! Can this be updated with conda update -n cdat-nightly_py2 -c cdat/label/nightly -c conda-forge –all ???

$ conda create -n cdat-nightly_py3 -c cdat/label/nightly -c conda-forge python=3.6 cdat

Note: using hard links, cloning a full environment uses less disk space than making a real copy

$ conda create -n cdatm18_py2 --clone cdat-8.1_py2
$ du -sh cdat-8.1_py2 cdatm18_py2
1.9G    cdat-8.1_py2
448M    cdatm18_py2

conda create -n cdatm-nightly_py2 --clone cdat-nightly_py2

We create a cdatm symbolic link in the envs directory, that has a stable name but can be moved to point to the latest default CDAT. In that case, most users can just activate this cdatm version and always get the latest stable version

$ cd /home/share/unix_files/cdat/miniconda3/envs
$ ln -s cdatm18_py2 cdatm_py2

You should download the test data (174M of data…) and use it in the example scripts that you want to distribute, and scripts you write for reporting the errors you find (if any…)

$ conda activate cdatm18_py2

(cdatm18_py2) $ python -c 'import vcs; vcs.download_sample_data_files(); print("\nFinished downloading sample data to " + vcs.sample_data)'
[...]
Finished downloading sample data to /home/share/unix_files/cdat/miniconda3/envs/cdatm18_py2/share/cdat/sample_data

(cdatm18_py2) $ du -sh /home/share/unix_files/cdat/miniconda3/envs/cdatm18_py2/share/cdat/sample_data
174M    /home/share/unix_files/cdat/miniconda3/envs/cdatm18_py2/share/cdat/sample_data

After cloning, we are ready to install some extra packages that may be requested by LSCE users

• We first try to install together as many packages as possible that don't require other channels than conda-forge, and that don't request a downgrade of what is already installed
• We then install individual extra packages with conda install or pip install

# You can use the following to keep a trace of what will be installed
#$ conda install --dry-run -n cdatm18_py2 -c conda-forge pillow pandas statsmodels seaborn scikit-image seawater gsw netcdf4 pyferret basemap-data-hires xlsxwriter cmocean rpy2 gdal windspharm  > somewhere/extra_packages.txt

# Install...
$ conda install -n cdatm18_py2 -c conda-forge pillow pandas statsmodels seaborn scikit-image seawater gsw netcdf4 pyferret basemap-data-hires xlsxwriter cmocean rpy2 gdal windspharm
[...]

# Use a similar install line if you want to install the same packages in the python3 version

List of installed packages: lsce-extra_01_install_190304.txt

Packages installed with pip

• dreqPy: CMIP6 Data Request Python API
  • conda activate cdatm18_py2
pip install dreqPy
  • Update with: pip install --upgrade dreqPy
    • Get version number with:
      

      $ drq -v
      dreqPy version 01.00.29 [Version 01.00.29]

The following packages have no dependency problems and were installed (or updated) later

• CMOR: CMOR (Climate Model Output Rewriter) is used to produce CF-compliant netCDF files
  • conda install -n cdatm18_py2 -c conda-forge cmor
  • Get version number with: python -c 'from cmor import *; print( (CMOR_VERSION_MAJOR, CMOR_VERSION_MINOR, CMOR_VERSION_PATCH) )'
  • Warning! CMOR currently requires Python 2.7

• vacumm: Validation, Analysis, Comparison - Utilities written in Python to validate and analyze Multi-Model outputs, and compare them to observations
  • conda install -n cdatm18_py2 -c conda-forge -c vacumm vacumm
  • Warning! standard install of vacumm does not work for Python3

• spanlib: Spectral Analysis Library
  • conda install -n cdatm18_py2 -c stefraynaud -c conda-forge spanlib
  • Test: python -c 'from spanlib.analyzer import Analyzer'

• cartopy: a Python package designed for geospatial data processing in order to produce maps and other geospatial data analyses
  • conda install -n cdatm18_py2 -c conda-forge cartopy

• CliMAF: a Climate Model Assessment Framework
  • Installation notes

Add here packages that would be useful and have not been installed yet, or have some problems that prevent their installation

• iris: A Python library for Meteorology and Climatology
  • conda install -n cdatxxx -c conda-forge iris

• wrf-python: A collection of diagnostic and interpolation routines for use with output from the Weather Research and Forecasting (WRF-ARW) Model
  • conda install -n cdatm15 -c conda-forge wrf-python

• glances: a cross-platform monitoring tool (similar to top)
  • conda install -n cdatm15 -c conda-forge glances
  • glances@github

• NO such packages now!

Some packages change more often than others, and can be easily updated the following way:

• dreqPy
  • Update with: pip install --upgrade dreqPy
    • Get version number with: drq -v

Some packages may have files that can only be read by the person who installed CDAT and the LSCE extensions (eg pcmdi-metrics in 2.8.0 and cdp in 2.10)

We check if some of the installed files are missing read access for the group or other, and we manually change the permissions

 >find /home/share/unix_files/cdat/miniconda3/envs \! -perm /g+r,o+r -ls
# Everything OK!

• pillow: the friendly PIL (Python Imaging Library) fork
• pandas: Python Data Analysis Library
• statsmodels: a Python module that allows users to explore data, estimate statistical models, and perform statistical tests
• seaborn: statistical data visualization
• scikit-image: image processing in Python
• seawater: Python re-write of the CSIRO seawater toolbox
• gsw: Python implementation of the Thermodynamic Equation Of Seawater
• vacumm: Validation, Analysis, Comparison - Utilities written in Python to validate and analyze Multi-Model outputs, and compare them to observations
• netcdf4: a Python interface to the netCDF C library
• pyferret: Ferret encapsulated in
• basemap-data-hires: high resolution data for basemap
• PCMDI metrics package (PMP): objectively compare results from climate models with observations using well-established statistical tests
• XlsxWriter: a Python module for creating Excel XLSX files
  • Note: this is a dependency of dreqPy
• dreqPy: CMIP6 Data Request Python API
• CMOR: CMOR (Climate Model Output Rewriter) is used to produce CF-compliant netCDF files
• dtw: DTW (Dynamic Time Warping) python module
• shapely: a Python wrapper for GEOS for algebraic manipulation of geometry (manipulation and analysis of geometric objects in the Cartesian plane)
• cartopy: a Python package designed for geospatial data processing in order to produce maps and other geospatial data analyses
• rpy2: providing simple and robust access to R from within Python
• cmocean: beautiful colormaps for oceanography
• OSGeo/GDAL: Geospatial Data Abstraction Library. GDAL is a translator library for raster and vector geospatial data formats
  • Python GDAL/OGR Cookbook
• spanlib: Spectral Analysis Library
• windspharm: spherical harmonic wind analysis in Python
• CliMAF: a Climate Model Assessment Framework

• NO removed packages!

After following the steps above, we get the following environments. Use the conda env list command (same result as conda info --envs) to get the up-to-date list of available environments.

Use conda list -n env_name to get a detailed list of which packages/modules are installed, or check the conda Installation history section to get more details

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