Installation & LMR setup

Get the LMR code

The source code for this project can be found on Github at:

You can either clone directly from the public repository:

$ git clone

Or download the source tarball:

$ curl -OL

Installing Python 3

After retrieving the LMR code, you’ll need to set up a Python 3.6+ installation to run it. The LMR codebase utilizes many packages typical of the scientific Python community. For ease of use, we recommended using a Python distribution such as Anaconda/Miniconda to create your working environment.

  • Anaconda (recommended) provides many of the required packages and many more useful packages pre-installed. This option requires a sizeable chunk of disk space.
  • Miniconda is a barebones installation with no additional packages.

Installing required packages


Due to package dependencies of our regridding facility, we do not currently support LMR code on Windows OS. It may be possible to omit regridding packages in the code and run reconstructions on Windows, but we have not tested this use case. We welcome user input and contributions towards using the LMR framework on Windows.

The Anaconda/Miniconda distributions come with a built in package manager conda, that makes it easy to install/update/remove Python packages. It also allows for the creation of encapsulated environments so you can keep package versions specific to projects!

If you’re using Anaconda/Conda package management, we strongly recommend using our provided environment file to setup the required packages. Under the LMR source directory you’ll find a .yml file misc/lmr_pyenv.yml. In a terminal, change to the LMR code directory and use the following command to setup your new Python environment:

conda env create -f ./misc/lmr_pyenv.yml

This creates the new python environment, separate from your main installation, based on the packages listed in the .yml file. The new environment (named lmr_py3 by default) is located under your Anaconda installation directory in the ‘envs’ folder.

Using a Python environment

In order to use this environment, you can use the activate command provided by conda (recommended), or you can manually prepend the environment directory to your path environment variable.


The activate script provided by conda works by default with bash for versions before conda v4.4. It can be edited to work with other modern shells, but may not work with csh or related shells.

To activate the LMR environment (i.e., set it as the current python target) use the following:

$ source activate lmr_py3

Available option for conda >=4.4:

$ conda activate lmr_py3

After this you should see small indicator in your terminal with the name of the environment, e.g.,:

(lmr_py3) [user@machine] $

This indicates that you are using the LMR python environment (lmr_py3) in the current session and that any scripts you run in this session use that environment by default.

To return/exit to your default Python environment use:

$ source deactivate

You should see the environment prefix disappear, indicating you’re back in your original Python environment.


if an executable (like ipython) is not in the LMR python environment, but your path can still find the executable in your original installation you might not notice you’re using an older/different version.

Retrieving LMR data

Before running an experiment, you’ll have to download some of the source data for proxies, models, and instrumental analyses.

Download this tar file, LMR_data.tar.gz, and move it to a directory where you will unpack it; here we will call that directory /home/disk/foo/LMR_data. This directory must be readable from wherever you plan to perform the experiment. Extract files using:

$ tar -xvf LMR_data.tar.gz

giving you something that looks like this in the /home/disk/foo/LMR_data directory

data/  LMR_data.tar.gz  PSM/ ye_precalc_files/

This is the default directory structure, which allows the LMR framework to easily look for data sources in known locations. However, non-standard data directories can still be specified in the LMR configuration.

Default folder description

The bulk of the required data exists under the data/ directory

    |-> analyses/
        |-> analysis_exp_folder
            |- ....
    |-> model/
        |-> model_exp_folder
            |- ....
    |-> proxies/
        |- proxy_db_file.pckl
        |- ....

The analyses folder holds observational analysis experiments used for calibrating of LMR’s statistical proxy system models (PSMs). (E.g., NOAA MLOST, NASA GISTEMP, 20th Century Reanalysis, etc.). The model folder is where climate model simulations used for creating a prior are stored. (E.g., various CMIP5 simulations). And finally, the proxies folder is where the proxy databases (pandas dataframes created using are stored.

The directory PSM/ holds precalibrated statistical PSM files created by Anytime proxy databases are updated, or adjustments to statistical calibration are made, the files in this folder should be updated.

The directory ye_precalc_files/ holds precalculated estimated observations based on the current config.yml.

Again, if necessary, the path to most of these files can be directly specified in the configuration file, but we recommend using the default directory structure.