[ Back to all versions ]

If you mostly want to use the CDAT 8.1 installed by JYP, just use the following steps and then read the What should I do now? section below

• Initialize conda with:

• Choose one of the installed environments and activate it with: conda activate env_name
  • python 2.7.x: conda activate cdatm_py2
  • python 3.x: conda activate cdatm_py3 (not available on ciclad, but could be installed)

• Type which python (or the wp alias) and make sure you get something like
  […]/miniconda3/envs/env_name/bin/python

You are ready if you have typed the conda activate command specified in the previous section!

• You may want to add the source line to your shell configuration file so that you don't have to remember it and type it each time you open a new terminal
  • Note: it's better to type the activate command in a terminal only when you want to use this specific version of python, rather than having the activate in your shell configuration file. This may have some side effects in some cases
• Have a look at the Extra package list at the bottom of this page to see what is available in this distribution
• If you have not done it yet, read (or at least have a look at the list of sections):
  • Working with Python
  • JYP's recommended steps for learning python

Read carefully the following sections of this page!

• 8.1 announcement and features summary (all versions)
• Full 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 package list at the bottom of this page to see what is available

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

Start python and paste the following lines

import numpy as np, vcs
id100 = np.identity(100)
x = vcs.init()
x.plot(id100)

If you get the kind of errors described in x.plot crashes with OpenGL2 error, the drivers on your server are probably not compatible with an interactive use of vcs… The workaround is to install mesalib and work in headless mode (e.g. without a display)…

Install mesalib with conda install -c cdat/label/v81 -c conda-forge mesalib vtk-cdat and try pasting again the few code lines above in python. If things work as expected, no canvas will be opened, but you'll be able to save the canvas to a file with

x.png('test_mesalib')
x.pdf('test_mesalib')

Note: using mesalib for headless mode work makes it possible to work without an X server, and with no DISPLAY variable defined

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 ferret_datasets 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]

• ipython_ferretmagic: IPython notebook extension for ferret
  • conda activate cdatm18_py2
pip install ferretmagic

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

• joblib: running Python functions as pipeline jobs
  • conda install -n cdatm18_py2 -c conda-forge joblib

• CliMAF: a Climate Model Assessment Framework
  • Installation notes

• Palettable: Color palettes for Python
  • conda install -n cdatm18_py2 -c conda-forge palettable

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 Python
• ipython_ferretmagic: IPython notebook extension for ferret
• 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
• joblib: running Python functions as pipeline jobs
• Palettable: Color palettes for Python

• 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

[ PMIP3 Wiki Home ] - [ Help! ] - [ Wiki syntax ]