PyTrajPlot

PyTrajPlot is a Python-based tool to visualize trajectories calculated with LAGRANTO based on ECMWF's IFS-HRES (on an European or global domain) or the COSMO model (on a limited domain centered over Switzerland).

Installation

PyTrajPlot is hosted on GitHub. For the available releases, see Releases.

Create a local copy of the repository and continue installation from the root of the pytrajplot directory.

git clone https://github.com/MeteoSwiss-APN/pytrajplot.git
cd pytrajplot

Prerequisite

Having Conda installed is a prerequisite for the further installation process. To install an appropriate Miniconda version, use

tools/setup_miniconda.sh

or install the latest version manually from the miniconda webpage. Then follow the instructions here below to set up a conda environment and test multiple use-cases.

Create environment

Create an environment and install the package dependencies with the script setup_env.sh provided in the tools directory. Check available options with

tools/setup_env.sh -h

We distinguish pinned installations based on exported (reproducible) environments, saved in requirements/environment.yml, and free installations, where the installation is based on top-level dependencies listed in requirements/requirements.txt. A pinned installation in an conda environment with the default name pytrajplot is done with

tools/setup_env.sh

Add the option -n <package_env_name> to create an environment with a custom name.

If you start developing a new version, you might want to do an unpinned installation with option -u and export the environment with option -e:

tools/setup_env.sh -u -e

Note: The flag -m can be used to use mamba as solver instead of the built-in conda solver. However since conda version 23, the mamba solver is the default solver in conda an no speed up is achieved by this option, thus we no longer recommend its use.

Install Package

Activate the newly created environment with (replace pytrajplot by your custom name <package_env_name> if you have used the -n <package_env_name> option).

conda activate pytrajplot

The package itself is installed with pip. As all dependencies are already installed by conda and should not be modified by pip, use the --no-deps flag.

pip install --no-deps .

For development, install the package in editable mode:

pip install --editable --no-deps .

Warning: Make sure you use the right pip, i.e. the one from the installed conda environment (which pip should point to something like path/to/miniconda/envs/<package_env_name>/bin/pip).

Once your package is installed, run the basic tests by typing:

pytest

A more comprehensive set of tests can be exectuted by running the script

tests/test_pytrajplot.sh

If developing, make sure to update the requirements file and export your environment after installation every time you add new imports while developing.

---

Test results

If no errors occur, the above test script save plots such as these here below in their respective folders in the local directory.

Usage

Activate the conda environment (you might have chosen a different name for the environment than the default name pytrajplot):

conda activate pytrajplot

To get a list of all available commands, just type:

pytrajplot --help

The possible options are as follows:

Usage: pytrajplot [OPTIONS] INPUT_DIR OUTPUT_DIR

Options:
  --start-prefix TEXT             Prefix for the start files. Default: startf_
  --traj-prefix TEXT              Prefix for the start files. Default:
                                  tra_geom_

  --info-name TEXT                Prefix for the plot info files. Default:
                                  plot_info

  --separator TEXT                Separator str between origin of trajectory
                                  and side trajectory index. Default: ~

  --language [en|english|de|ger|german|Deutsch]
                                  Choose language. Default: en
  --domain [ch|europe|centraleurope|alps|dynamic|dynamic_zoom]
                                  Choose domains for map plots. Default:
                                  centraleurope, europe, dynamic

  --datatype [eps|jpeg|jpg|pdf|pgf|png|ps|raw|rgba|svg|svgz|tif|tiff]
                                  Choose data type(s) of final result.
                                  Default: pdf

  -V, --version                   Print version and exit.
  --help                          Show this message and exit.

The only mandatory arguments are INPUT_DIR and OUTPUT_DIR. The input directory specifies the path to the source files. In the input directory, there should be at exactly one plot_info file, and for each trajectory file one corresponding start file.

File Nomenclature

Should the prefixes of the file names deviate from the default values (tra_geom_, startf_, plot_info), it is possible to specify the prefix of the start and trajectory files, as well as the name of the plot_info file.

The relevant part in the filename of the trajectory/start files, is the key. In general, the key looks like: XXX-YYYF/B. It has to satisfy the following conditions:

1. Keys must match between start/trajectory file

   traj_prefix+key <---> start_prefix+key
   

2. Keys must end with F / B to determine the trajectories direction (forward/backward)

3. XXX refers to the start of the computation of trajectories (w.r.t the model base time, which is specified in the corresponding plot_info file)

4. YYY refers to the end-time of the trajectory computation (w.r.t to the model base time.

5. XXX and YYY are seperated by a dash

6. The difference of XXX and YYY equals the trajectory length (in hours).

Information in the header and footer of the output plots, is partially generated from the information in the key.

Examples

Backward Trajectories; 33 h in the past from model base time until model base time.

startf_033-000B/tra_geom_033-000B

Forwart trajectories; 48h to the future from model base time.

startf_000-048F/tra_geom_000-048F

Code Overview

This part is a small step-by-step guide, how an exemplary pytrajplot command runs through the code with references to the corresponding (Python) scripts and functions.

Example

pytrajplot tests/test_hres/4_altitudes/ plots
--- Parsing Input Files
--- Assembling Ouput
--- Done.

0. cli.py

Before the input files get parsed, the user inputs need to be parsed using the function interpret_options.

1. Parsing Input Files: parse_data.py

In the next step the check_input_dir function from the data parser script is initialised.

Procedure

1. iterate through the directory and read the start & plot_info files. simultaneously collect all present keys Remark: The start file is parsed using the read_startf function and the plot_info file is parsed using the PLOT_INFO class.
2. for each found key, parse corresponding trajectory file using the read_trajectory function.

There is a number of different helper-functions involved in the parsing of these files. The code is well commented and the docstrings should provide further information on the use of each function, see here.

Ultimately, the parsing-pipeline returns two dictionaries. The main dictionary, containing all information, is the trajectory_dict. Each key contains a pandas dataframe, with the combined information of the corresponding start/trajectory file. The second dictionary contains the relevant information of the plot_info file, which corresponds to all start/trajecotry files.

3. Assembling Ouput: generate_pdf.py

Once all the data from one directory is in this usable dictionary format, the plotting pipeline is initialised. The first part of the generate_pdf script iterates through this dictionary, retrieves the dataframes and "parses" them. For each trajectory origin, the plotting pipeline is called and one plot generated. Usually, there are several trajectories/origins per dataframe.

Fun Fact: @MeteoSwiss approximately 2800 trajectory plots are generated each day for the IFS-HRES (over Europe and globally) and COSMO-1E models.

Procedure

1. iterate through trajectory_dict
2. retrieve df for current key
3. iterate through dataframe
4. for each origin, present in current dataframe, fill a new dictionary (plot_dict) with plot-specific information.
5. initialise pipeline with the plot_dict by calling assemble_pdf 5.1 create output directory (if it doesn't exist) 5.2 add altitude plot to figure 5.3 add footer to figure 5.4 add header to figure 5.5 add map figure
6. save figure
7. repeat steps 3.-6. until all plots for the current dataframes/domains/datatypes have been generated
8. repeat steps 2.-7. until all figures for all start/trajectory files have been generated
9. return -- done

Remark

Again, this procedure outlines the inner workings of the plotting scripts. For greater insight, it is recommended to read the scripts and pay special attention to the comments and docstrings. All plotting-scripts are located here.

Credits

This package was created with Cookiecutter and the MeteoSwiss blueprint for the CSCS systems.