Customizing yt: The Configuration and Plugin Files

yt features ways to customize it to your personal preferences in terms of how much output it displays, loading custom fields, loading custom colormaps, accessing test datasets regardless of where you are in the file system, etc. This customization is done through The Configuration and Plugin Files both of which exist in your $HOME/.config/yt directory.

The Configuration

The configuration is stored in simple text files (in the toml format). The files allow to set internal yt variables to custom default values to be used in future sessions. The configuration can either be stored globally or locally.

Global Configuration

If no local configuration file exists, yt will look for and recognize the file $HOME/.config/yt/yt.toml as a configuration file, containing several options that can be modified and adjusted to control runtime behavior. For example, a sample $HOME/.config/yt/yt.toml file could look like:

[yt]
log_level = 1
maximum_stored_datasets = 10000

This configuration file would set the logging threshold much lower, enabling much more voluminous output from yt. Additionally, it increases the number of datasets tracked between instantiations of yt. The configuration file can be managed using the yt config --global helper. It can list, add, modify and remove options from the configuration file, e.g.:

$ yt config -h
$ yt config list
$ yt config set yt log_level 1
$ yt config rm yt maximum_stored_datasets

Local Configuration

yt will look for a file named yt.toml in the current directory. If present, its options are loaded and the global configuration is not read. Local configuration files can contain the same options as the global one.

Local configuration files can either be edited manually, or alternatively they can be managed using yt config --local. It can list, add, modify and remove options, and display the path to the local configuration file, e.g.:

$ yt config -h
$ yt config list --local
$ yt config set --local yt log_level 1
$ yt config rm --local yt maximum_stored_datasets
$ yt config print-path --local

If no local configuration file is present, these commands will create an (empty) one in the current working directory.

Configuration Options At Runtime

In addition to setting parameters in the configuration file itself, you can set them at runtime.

Warning

Several parameters are only accessed when yt starts up: therefore, if you want to modify any configuration parameters at runtime, you should execute the appropriate commands at the very top of your script!

This involves importing the configuration object and then setting a given parameter to be equal to a specific string. Note that even for items that accept integers, floating points and other non-string types, you must set them to be a string or else the configuration object will consider them broken.

Here is an example script, where we adjust the logging at startup:

import yt

yt.set_log_level(1)

ds = yt.load("my_data0001")
ds.print_stats()

This has the same effect as setting log_level = 1 in the configuration file. Note that a log level of 1 means that all log messages are printed to stdout. To disable logging, set the log level to 50.

Available Configuration Options

The following external parameters are available. A number of parameters are used internally.

  • colored_logs (default: False): Should logs be colored?

  • default_colormap (default: arbre): What colormap should be used by default for yt-produced images?

  • plugin_filename (default my_plugins.py) The name of our plugin file.

  • log_level (default: 20): What is the threshold (0 to 50) for outputting log files?

  • test_data_dir (default: /does/not/exist): The default path the load() function searches for datasets when it cannot find a dataset in the current directory.

  • reconstruct_index (default: True): If true, grid edges for patch AMR datasets will be adjusted such that they fall as close as possible to an integer multiple of the local cell width. If you are working with a dataset with a large number of grids, setting this to False can speed up loading your dataset possibly at the cost of grid-aligned artifacts showing up in slice visualizations.

  • notebook_password (default: empty): If set, this will be fed to the IPython notebook created by yt notebook. Note that this should be an sha512 hash, not a plaintext password. Starting yt notebook with no setting will provide instructions for setting this.

  • requires_ds_strict (default: True): If true, answer tests wrapped with requires_ds() will raise YTUnidentifiedDataType rather than consuming it if required dataset is not present.

  • serialize (default: False): If true, perform automatic object serialization

  • sketchfab_api_key (default: empty): API key for https://sketchfab.com/ for uploading AMRSurface objects.

  • suppress_stream_logging (default: False): If true, execution mode will be quiet.

  • stdout_stream_logging (default: False): If true, logging is directed to stdout rather than stderr

  • skip_dataset_cache (default: False): If true, automatic caching of datasets is turned off.

  • supp_data_dir (default: /does/not/exist): The default path certain submodules of yt look in for supplemental data files.

Available per-field Configuration Options

It is possible to customize the default behaviour of plots using per-field configuration. The default options for plotting a given field can be specified in the configuration file in [plot.field_type.field_name] blocks. The available keys are

  • cmap (default: yt.default_colormap, see Available Configuration Options): the colormap to use for the field.

  • log (default: True): use a log scale (or symlog if linthresh is also set).

  • linthresh (default: None): if set to a float different than None and log is True, use a symlog normalization with the given linear threshold.

  • units (defaults to the units of the field): the units to use to represent the field.

  • path_length_units (default: cm): the unit of the integration length when doing e.g. projections. This always has the dimensions of a length. Note that this will only be used if units is also set for the field. The final units will then be units*path_length_units.

You can also set defaults for all fields of a given field type by omitting the field name, as illustrated below in the deposit block.

[plot.gas.density]
cmap = "plasma"
log = true
units = "mp/cm**3"

[plot.gas.velocity_divergence]
cmap = "bwr"  # use a diverging colormap
log = false   # and a linear scale

[plot.deposit]
path_length_units = "kpc"  # use kpc for deposition projections

Plugin Files

Plugin files are a means of creating custom fields, quantities, data objects, colormaps, and other code executable functions or classes to be used in future yt sessions without modifying the source code directly.

To enable a plugin file, call the function enable_plugins() at the top of your script.

Global system plugin file

yt will look for and recognize the file $HOME/.config/yt/my_plugins.py as a plugin file. It is possible to rename this file to $HOME/.config/yt/<plugin_filename>.py by defining plugin_filename in your yt.toml file, as mentioned above.

Note

You can tell that your system plugin file is being parsed by watching for a logging message when you import yt. Note that both the yt load and iyt command line entry points parse the plugin file.

Local project plugin file

Optionally, enable_plugins() can be passed an argument to specify a custom location for a plugin file. This can be useful to define project wise customizations. In that use case, any system-level plugin file will be ignored.

Plugin File Format

Plugin files should contain pure Python code. If accessing yt functions and classes they will not require the yt. prefix, because of how they are loaded.

For example, if one created a plugin file containing:

def _myfunc(field, data):
    return np.random.random(data["density"].shape)


add_field(
    "random",
    function=_myfunc,
    sampling_type="cell",
    dimensions="dimensionless",
    units="auto",
)

then all of my data objects would have access to the field random.

You can also define other convenience functions in your plugin file. For instance, you could define some variables or functions, and even import common modules:

import os

HOMEDIR = "/home/username/"
RUNDIR = "/scratch/runs/"


def load_run(fn):
    if not os.path.exists(RUNDIR + fn):
        return None
    return load(RUNDIR + fn)

In this case, we’ve written load_run to look in a specific directory to see if it can find an output with the given name. So now we can write scripts that use this function:

import yt

yt.enable_plugins()

my_run = yt.load_run("hotgasflow/DD0040/DD0040")

And because we have used yt.enable_plugins we have access to the load_run function defined in our plugin file.

Note

if your convenience function’s name colliding with an existing object within yt’s namespace, it will be ignored.

Note that using the plugins file implies that your script is no longer fully reproducible. If you share your script with someone else and use some of the functionality if your plugins file, you will also need to share your plugins file for someone else to re-run your script properly.

Adding Custom Colormaps

To add custom Colormaps to your plugin file, you must use the make_colormap() function to generate a colormap of your choice and then add it to the plugin file. You can see an example of this in Making and Viewing Custom Colormaps. Remember that you don’t need to prefix commands in your plugin file with yt., but you’ll only be able to access the colormaps when you load the yt.mods module, not simply yt.