How to manage configs

Note

This is a quick how-to on config management. See here for a long-form tutorial.

Show available configs

import spikewrap as sw

sw.show_configs("neuropixels+kilosort2_5")

The preprocessing options are: {
    "1": [
        "bandpass_filter",
        {
            "freq_max": 6000,
            "freq_min": 300
        }
    ],
    "2": [
        "common_reference",
        {
            "operator": "median",
            "reference": "global"
        }
    ]
}


The sorting options are: {
  "kilosort2_5": {
    "car": false,
    "freq_min": 150
  }
}

print(f"These are stored at:\n"
      f"{sw.get_configs_path()}")

These are stored at:
/home/runner/.spikewrap/configs

We can create and save our own configs, from the currently supported steps.

sw.show_supported_preprocessing_steps()

Currently supported preprocessing steps are:
['phase_shift', 'bandpass_filter', 'common_reference']

By default, these will be stored in the spikewrap configs folder (otherwise, pass the full filepath to where you want to save a .yaml file). This config can now be used by name in spikewrap processing functions.

config_dict = {
    "preprocessing": {
        "1": ["phase_shift", {}],
        "2": ["bandpass_filter", {"freq_min": 300, "freq_max": 6000}],
        "3": ["common_reference", {"operator": "median"}]
    },
    "sorting": {
        "kilosort2_5": {'car': False, 'freq_min': 150}}
}

sw.save_config_dict(config_dict, "my_config")

or load a config directly from .yaml

config_dict = sw.load_config_dict(
    sw.get_configs_path() / "neuropixels+kilosort2_5.yaml"
)

use spikewrap.save_config_dict() to save this to the spikewrap with a keyword name, to use by name in processing functions.