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 File and
The Plugin File both of which exist in your
The configuration is a simple text file setting internal yt variables to custom default values to be used in future sessions.
yt will look for and recognize the file
$HOME/.yt/config as a configuration
file, containing several options that can be modified and adjusted to control
runtime behavior. For example, a sample
$HOME/.yt/config file could look
[yt] loglevel = 1 maximumstoreddatasets = 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.
In addition to setting parameters in the configuration file itself, you can set them at runtime.
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.funcs.mylog.setLevel(1) ds = yt.load("my_data0001") ds.print_stats()
This has the same effect as setting
loglevel = 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.
The following external parameters are available. A number of parameters are used internally.
'False'): Should logs be colored?
'arbre'): What colormap should be used by default for yt-produced images?
'True'): Do we want to load the plugin file?
'my_plugins.py') The name of our plugin file.
'False'): Should we output to a log file in the filesystem?
'20'): What is the threshold (0 to 50) for outputting log files?
'/does/not/exist'): The default path the
load()function searches for datasets when it cannot find a dataset in the current directory.
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 notebookwith no setting will provide instructions for setting this.
'False'): If true, perform automatic object serialization
sketchfab_api_key(default: empty): API key for https://sketchfab.com/ for uploading AMRSurface objects.
'False'): If true, execution mode will be quiet.
'False'): If true, logging is directed to stdout rather than stderr
'False'): If true, automatic caching of datasets is turned off.
The plugin file is a means of creating custom fields, quantities, data objects, colormaps, and other code classes and objects to be used in future yt sessions without modifying the source code directly.
To force the plugin file to be parsed, call the function
enable_plugins() at the top of your script.
You can tell that your plugins file is being parsed by watching for a logging
message when you import yt. Note that both the
yt load and
command line entry points parse the plugin file, so the
file will be parsed if you enter yt that way.
yt will look for and recognize the file
$HOME/.yt/my_plugins.py as a plugin
file, which should contain python code. If accessing yt functions and classes
they will not require the
yt. prefix, because of how they are loaded.
For example, if I created a plugin file containing:
def _myfunc(field, data): return np.random.random(data["density"].shape) add_field("random", function=_myfunc, units='auto')
then all of my data objects would have access to the field
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 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.
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