CDAT 8.0 installation notes

UV-CDAT is now the “Community Data Analysis Tools” (CDAT)

Follow the instructions about the conda-based versions of UV-CDAT initialization for actually using an installed version of 8.0

What's New?

8.0 announcement and features summary (all versions)
Full Change log

Installation with Miniconda3

Installing Miniconda3

You don't need to be (and you should not be) root when you install Miniconda3. You just need enough disk space where you have write access
If you are installing Miniconda3 in a Linux environment on a Windows 10 computer using Windows Subsystem for Linux (WSL), pay special attention to the instructions on the WSL lines

The steps below are adapted from the older (but with lots of extra and possibly useful details) installing miniconda instructions

Get the Python 3.8 Linux 64-bit (bash installer) (unless there is a more recent version?)
- Find some temporary space on Linux (or Windows, if you are using WSL)
  - e.g. Linux at LSCE:
    cd /home/scratch01/<your_login>
  - e.g. WSL, assuming that there is a C:\Scratch\<your_login> directory:
    cd /mnt/c/Scratch/<your_login>
- Use wget to download the installer:
  wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh (90 Mb, 26 Feb 2021)

Execute the installer
- bash Miniconda3-latest-Linux-x86_64.sh
  - Accept the license
  - Note: at the end of the installation (next step), answer no to the following question, so that the installer does not change your existing shell initialization files!
    Do you wish the installer to initialize Miniconda3 by running conda init? [yes|no] ⇒ NO
  - Specify an explicit installation path outside of your home directory, with enough disk space (more than 3 Gb if you are going to install CDAT and some extra packages), preferably on a disk that is not backed up:
    - Installations by JYP:
      - Linux at LSCE: /home/share/unix_files/cdat/miniconda3, or another subdirectory of /home/share/unix_files/cdat/
      - Linux on ciclad: /data/jypmce/cdat/miniconda3, or another sub-directory of /data/jypmce/cdat/
    - WSL: installing to a directory that is not in /home/ does not work (e.g. /mnt/h/CDAT/miniconda3,assuming there is a H:\CDAT\ directory, does not work)
      You need to accept the installation in the default location: /home/<your_login>/miniconda3
  - The resulting miniconda3 directory size is 342M
```
 > du -sh miniconda3
342M    miniconda3

 > cd miniconda3

 > du -sh *
20M     bin
0       compiler_compat
4.0K    condabin
684K    conda-meta
0       envs
16K     etc
5.5M    include
4.0K    info
198M    lib
12K     LICENSE.txt
114M    pkgs
604K    share
4.0K    shell
0       ssl
0       x86_64-conda_cos6-linux-gnu
```

Initialize the newly installed conda environment:
- bash shell: source <installation_path>/miniconda3/etc/profile.d/conda.sh
- tcsh shell: source <installation_path>/miniconda3/etc/profile.d/conda.csh

Check if you can use the conda command, and use it to initialize the base environment

$ which conda
<installation_path>/miniconda3/condabin/conda
$ which python
/usr/bin/python
$ conda activate
(base) $ which python
<installation_path>/miniconda3/bin/python

Update the new installation
- ```
$ conda update --all
[...]
```
- During the update, the miniconda3 directory size goes from 432 Mb to 581 Mb. This directory will keep on growing, which is the reason why you should put it on a (preferably non backed up) disk where you have enough space

Make sure we have the latest conda (just in case we did not get it with the update)
conda update -n base -c defaults conda

Remove the installer:
rm Miniconda3-latest-Linux-x86_64.sh

Post-Miniconda3 installation

The idea is to remove the miniconda3 initialization lines that were automatically added at the end of .bashrc and put them (and other useful commands) in a special initialization file, that can be sourced only when we actually want to use conda and CDAT (in order to avoid potentiel side effects)

New style initialization

Note: this is the new conda activate some_version style

See the ~jypeter/.conda3_jyp.sh file below, and how to use it, in a bash shell. In a tcsh shell, see the ~jypeter/.conda3_jyp.csh further down. In both shell cases, if you are installing your own version of python, you need to use your own location of the initialization files in the source lines, and you can use another file name than conda3_jyp

$ which python
/usr/bin/python

$ cat ~jypeter/.conda3_jyp.sh
# Conda initialization by JYP, NEW style
#
# Use this for working with conda and CDAT centrally managed by JYP
#
# Execute this file in a BASH shell with
#     source path/this_file
# Then get the list of available python distributions with
#     conda env list
# Then activate a specific distribution with
#     conda activate version_name
#
# More details in:
#   https://wiki.lsce.ipsl.fr/pmip3/doku.php/other:python:starting#conda-based_versions_of_uv-cdat
#   https://wiki.lsce.ipsl.fr/pmip3/doku.php/other:uvcdat:conda_notes
#
# Jean-Yves Peterschmitt - LSCE - 11/2018

source /home/share/unix_files/cdat/miniconda3/etc/profile.d/conda.sh

# Use the alias below to easily determine where your python
# interpreter is located
alias wp="which python"

# Where are ALL the python interpreters in the search path
alias wpa="which -a python"

# The end

$ source ~jypeter/.conda3_jyp.sh

$ conda activate
(base) $ which python
/home/share/unix_files/cdat/miniconda3/bin/python

tcsh shell usage example

 >which python
/usr/bin/python

 >cat ~jypeter/.conda3_jyp.csh
# Conda initialization by JYP, NEW style
#
# Use this for working with conda and CDAT centrally managed by JYP
#
# Execute this file in a TCSH shell with
#     source path/this_file
# Then get the list of available python distributions with
#     conda env list
# Then activate a specific distribution with
#     conda activate version_name
#
# More details in:
#   https://wiki.lsce.ipsl.fr/pmip3/doku.php/other:python:starting#conda-based_versions_of_uv-cdat
#   https://wiki.lsce.ipsl.fr/pmip3/doku.php/other:uvcdat:conda_notes
#
# Jean-Yves Peterschmitt - LSCE - 11/2018

source /home/share/unix_files/cdat/miniconda3/etc/profile.d/conda.csh

# Use the alias below to easily determine where your python
# interpreter is located
alias wp "which python"

# The end

 >source  ~jypeter/.conda3_jyp.csh

 >conda activate

(base) >which python
/home/share/unix_files/cdat/miniconda3/bin/python

You probably don't want to type the source line each time you need to use your conda based python, so you can add a source ~jypeter/.conda3_jyp.sh line in your ~/.bashrc file, and source ~jypeter/.conda3_jyp.csh line in your ~/.cshrc file. Then, when you need a specific python environment, just type conda activate name_of_the_specific_environment

Old style initialization

The old style notes below are just for reference sake and you can skip them

Note: this is the old source activate some_version style

See the ~jypeter/.conda3old_jyp.sh file below, and how to use it, in a bash shell

bash-4.2$ which python
/usr/bin/python

$ cat .conda3old_jyp.sh
# Conda initialization by JYP, OLD style
#
# Use this for working with conda and CDAT centrally managed by JYP
#
# Execute this file in a BASH shell with
#     source path/this_file
# Then get the list of available python distributions with
#     conda env list
# Then activate a specific distribution with
#     source activate version_name
#
# More details in:
#   https://wiki.lsce.ipsl.fr/pmip3/doku.php/other:python:starting#conda-based_versions_of_uv-cdat
#   https://wiki.lsce.ipsl.fr/pmip3/doku.php/other:uvcdat:conda_notes
#
# Jean-Yves Peterschmitt - LSCE - 11/2018

export PATH="/home/share/unix_files/cdat/miniconda3/bin:$PATH"

# Use the alias below to easily determine where your python
# interpreter is located
alias wp="which python"

# Where are ALL the python interpreters in the search path
alias wpa="which -a python"

# The end

$ source ~jypeter/.conda3old_jyp.sh

$ wp
/home/share/unix_files/cdat/miniconda3/bin/python

Installing CDAT 8.0

Python 2.7 version

$ conda create -n cdat-8.0_py2 -c cdat/label/v80 -c conda-forge -c cdat python=2.7 cdat
# Generate the list of installed packages
$ conda list -n cdat-8.0_py2 > /home/scratch01/jypeter/cdat-8.0_py2_list_181108.txt

List of installed packages: cdat-8.0_py2_list_181108.txt

Disk space after 8.0 installation

$ du -sh /home/share/unix_files/cdat/miniconda3
4.3G    /home/share/unix_files/cdat/miniconda3
$ du -sh /home/share/unix_files/cdat/miniconda3/*
15M     /home/share/unix_files/cdat/miniconda3/bin
2.4M    /home/share/unix_files/cdat/miniconda3/compiler_compat
4.0K    /home/share/unix_files/cdat/miniconda3/conda-bld
3.7M    /home/share/unix_files/cdat/miniconda3/conda-meta
2.8G    /home/share/unix_files/cdat/miniconda3/envs
28K     /home/share/unix_files/cdat/miniconda3/etc
4.9M    /home/share/unix_files/cdat/miniconda3/include
156M    /home/share/unix_files/cdat/miniconda3/lib
8.0K    /home/share/unix_files/cdat/miniconda3/LICENSE.txt
1.4G    /home/share/unix_files/cdat/miniconda3/pkgs
1.3M    /home/share/unix_files/cdat/miniconda3/share
24K     /home/share/unix_files/cdat/miniconda3/ssl
12K     /home/share/unix_files/cdat/miniconda3/x86_64-conda_cos6-linux-gnu
$ du -sh /home/share/unix_files/cdat/miniconda3/envs/*
2.8G    /home/share/unix_files/cdat/miniconda3/envs/cdat-8.0_py2

Python 3.6 version

$ conda create -n cdat-8.0_py3 -c cdat/label/v80 -c conda-forge -c cdat python=3.6 cdat

Installing CDAT nightly

Notes:

This page is about CDAT 8.0, 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
Official nightly page

Python 2.7 version

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

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

Python 3.6 version

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

Cloning cdat to add specific packages for LSCE

This section is about the creation of the cdatm17 environment

Notes about actually using the cdatm17 conda-based python

Note: using hard links, cloning a full environment only adds an extra 582M of disk space

$ conda create -n cdatm17_py2 --clone cdat-8.0_py2
Source:      /home/share/unix_files/cdat/miniconda3/envs/cdat-8.0_py2
Destination: /home/share/unix_files/cdat/miniconda3/envs/cdatm17_py2
Packages: 226
Files: 3

[...]

$ du -sh /home/share/unix_files/cdat/miniconda3/envs/*
2.5G    /home/share/unix_files/cdat/miniconda3/envs/cdat-8.0_py2
392M    /home/share/unix_files/cdat/miniconda3/envs/cdatm17_py2
839M    /home/share/unix_files/cdat/miniconda3/envs/cdat-nightly_py2

cdat nightly case

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

Getting ready for a moving default CDAT

This step should probably be listed at the end, especially in a multi-user environment! If there is already a cdatm link, make sure that the new version is stable and working correctly before updating the cdatm link

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
$ conda env list
base                  *  /home/share/unix_files/cdat/miniconda3
cdat-8.0_py2             /home/share/unix_files/cdat/miniconda3/envs/cdat-8.0_py2
cdatm17_py2              /home/share/unix_files/cdat/miniconda3/envs/cdatm17_py2
$ ln -s cdatm17_py2 cdatm
$ conda env list
base                  *  /home/share/unix_files/cdat/miniconda3
cdat-8.0_py2             /home/share/unix_files/cdat/miniconda3/envs/cdat-8.0_py2
cdatm                    /home/share/unix_files/cdat/miniconda3/envs/cdatm
cdatm17_py2              /home/share/unix_files/cdat/miniconda3/envs/cdatm17_py2
$ source activate cdatm
(cdatm) bash-4.2$ conda env list
base                     /home/share/unix_files/cdat/miniconda3
cdat-8.0_py2             /home/share/unix_files/cdat/miniconda3/envs/cdat-8.0_py2
cdatm                 *  /home/share/unix_files/cdat/miniconda3/envs/cdatm
cdatm17_py2              /home/share/unix_files/cdat/miniconda3/envs/cdatm17_py2

Customizing UV-CDAT for LSCE

Downloading cdms2/vcs test data

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…)

$ source activate cdatm17_py2

(cdatm17_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/cdatm17_py2/share/uvcdat/sample_data

(cdatm17_py2) $ du -sh /home/share/unix_files/cdat/miniconda3/envs/cdatm17_py2/share/uvcdat/sample_data
174M    /home/share/unix_files/cdat/miniconda3/envs/cdatm17_py2/share/uvcdat/sample_data

Packages that have no dependency problems

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

# Keep a trace of what will be installed
$ conda install --dry-run -n cdatm17_py2 -c conda-forge pillow pandas statsmodels seaborn scikit-image seawater gsw netcdf4 pyferret basemap-data-hires xlsxwriter cmocean rpy2 gdal windspharm  > /home/scratch01/jypeter/lsce-extra_01_install_190304.txt

# Install...
$ conda install -n cdatm17_py2 -c conda-forge pillow pandas statsmodels seaborn scikit-image seawater gsw netcdf4 pyferret basemap-data-hires xlsxwriter cmocean rpy2 gdal windspharm
[...]

# Install extra packages in the nightly version (if you need 'nightly')
$ conda install -n cdatm-nightly_py2 -c conda-forge pillow pandas statsmodels seaborn scikit-image seawater gsw netcdf4 pyferret basemap-data-hires xlsxwriter cmocean rpy2 gdal windspharm

# Check the disk space after installation
$ du -sh /home/share/unix_files/cdat/miniconda3
7.5G    /home/share/unix_files/cdat/miniconda3

$ du -sh /home/share/unix_files/cdat/miniconda3/envs/*
2.5G    /home/share/unix_files/cdat/miniconda3/envs/cdat-8.0_py2
1.2G    /home/share/unix_files/cdat/miniconda3/envs/cdatm17_py2
938M    /home/share/unix_files/cdat/miniconda3/envs/cdatm-nightly_py2
450M    /home/share/unix_files/cdat/miniconda3/envs/cdat-nightly_py2

List of installed packages: lsce-extra_01_install_190304.txt

Packages installed with pip

dreqPy: CMIP6 Data Request Python API
- 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

vacumm: Validation, Analysis, Comparison - Utilities written in Python to validate and analyze Multi-Model outputs, and compare them to observations
- conda install -n cdatm17_py2 -c conda-forge -c vacumm vacumm

spanlib: Spectral Analysis Library
- conda install -n cdatm17_py2 -c stefraynaud -c conda-forge spanlib
- Test: python -c 'from spanlib.analyzer import Analyzer'

CliMAF: a Climate Model Assessment Framework
Installation notes

TODO

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 cdatxxx -c conda-forge wrf-python

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

Other packages

There is no warranty that the packages listed below will work correctly, because it was required to bypass the compatibility checks in order to install them…

NO such packages now!

Updating some packages

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

CMOR
- Update with: conda update -n cdatxxx -c conda-forge -c pcmdi -c uvcdat cmor
  - Get version number with: python -c 'from cmor import *; print CMOR_VERSION_MAJOR, CMOR_VERSION_MINOR, CMOR_VERSION_PATCH'

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

Installing CLIMAF

The CLIMAF installation is a special case, because we may want to use it with the Python installed for CDAT, but it is not installed using conda

We follow the steps for the installation of a non-standard server

Go to the appropriate installation directory (we choose the directory where we have also installed miniconda3):
- LSCE: cd /home/share/unix_files/cdat
- ciclad: /data/jypmce/cdat/
git clone https://github.com/senesis/climaf climaf
Add CLIMAF to the PYTHONPATH environment variable
- LSCE & tcsh: setenv PYTHONPATH ${PYTHONPATH}:/home/share/unix_files/cdat/climaf
- LSCE & bash: export PYTHONPATH=${PYTHONPATH}:/home/share/unix_files/cdat/climaf
- ciclad: same as above, using /data/jypmce/cdat/climaf
Test the installation
- make sure the Requirements are available:
  - LSCE: module load netcdf/4 nco cdo ncl ncview
  - ciclad: module load netcdf4 nco cdo ncl/6.3.0
  - optional: start an X server
  - ```
  cd climaf/testing
  ./test_install.sh
```

Once the tests are OK, add the required module load and environment variables definitions to the .basrc and .cshrc files. Note: maybe these lines should go to the .profile and .login files…(check Bash Startup Files)

LSCE: add to your .cshrc

# Prerequisites for using CLIMAF
module load netcdf/4 nco cdo ncl ncview

# CLIMAF environment variables
setenv CLIMAF /home/share/unix_files/cdat/climaf
setenv PYTHONPATH "${PYTHONPATH}:${CLIMAF}"
#setenv PYTHONPATH $CLIMAF
setenv PATH "${PATH}:${CLIMAF}/bin"
setenv CLIMAF_CACHE "/home/scratch01/${USER}/climaf_cache"
setenv TMPDIR $CLIMAF_CACHE
setenv CLIMAF_LOG_DIR $CLIMAF_CACHE

#conda activate cdatm-nightly_py2

ciclad: add to your .bashrc

# Prerequisites for using CLIMAF when not using the 
# default 'module load climaf'
module load netcdf4 nco cdo ncl/6.3.0

# CLIMAF environment variables
export CLIMAF=/data/jypmce/cdat/climaf
#export PYTHONPATH=$PYTHONPATH:$CLIMAF
export PYTHONPATH=$CLIMAF
export PATH=$PATH:$CLIMAF/bin
export CLIMAF_CACHE=/data/jypmce/climaf_cache
export TMPDIR=$CLIMAF_CACHE
export CLIMAF_LOG_DIR=$CLIMAF_CACHE

#conda activate cdatm-nightly_py2

Open a new shell and run the tests again. If there is an error that was not there, check what you have added to the shell init files
Test CLIMAF on ciclad

Cleaning up things

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!

Extra packages list

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 library providing cartographic tools for python
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

Removed packages

NO removed packages!

Environments summary

After following the steps above, we get the following environments. Use the conda info --envs or the conda env list command to get the up-to-date list of available environments

Environment name	Server	`conda list`
cdat-8.0_py2	LSCE
cdatm17_py2	LSCE
cdat-nightly_py2	LSCE ciclad
cdatm-nightly_py2	LSCE ciclad

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Table of Contents

CDAT 8.0 installation notes

What's New?

Installation with Miniconda3

Installing Miniconda3

Post-Miniconda3 installation

New style initialization

Old style initialization

Installing CDAT 8.0

Python 2.7 version

Python 3.6 version

Installing CDAT nightly

Python 2.7 version

Python 3.6 version

Cloning cdat to add specific packages for LSCE

cdat nightly case

Getting ready for a moving default CDAT

Customizing UV-CDAT for LSCE

Downloading cdms2/vcs test data

Packages that have no dependency problems

TODO

Other packages

Updating some packages

Installing CLIMAF

Cleaning up things

Extra packages list

Removed packages

Environments summary