mth5.processing

Submodules

`RUN_SUMMARY_LIST`
`RUN_SUMMARY_COLUMNS`
`RUN_SUMMARY_DTYPE`
`ADDED_KERNEL_DATASET_DTYPE`
`ADDED_KERNEL_DATASET_COLUMNS`
`KERNEL_DATASET_DTYPE`
`KERNEL_DATASET_COLUMNS`
`MINI_SUMMARY_COLUMNS`

Class to contain a run-summary table from one or more mth5s.

mth5.processing.MINI_SUMMARY_COLUMNS = ['survey', 'station', 'run', 'start', 'end', 'duration'][source]

class mth5.processing.RunSummary(input_dict: dict | None | None = None, df: pandas.DataFrame | None | None = None)[source]

Class to contain a run-summary table from one or more mth5s.

WIP: For the full MMT case this may need modification to a channel based summary.

clone()[source]: 2022-10-20: Cloning may be causing issues with extra instances of open h5 files …

from_mth5s(mth5_list) → list[source]: Iterates over mth5s in list and creates one big dataframe summarizing the runs

property mini_summary: pandas.DataFrame: Shows the dataframe with only a few columns for readbility.

property print_mini_summary: str: Calls minisummary through logger so it is formatted.

drop_no_data_rows() → bool[source]: Drops rows marked has_data = False and resets the index of self.df.

set_sample_rate(sample_rate: float, inplace: bool = False)[source]

Set the sample rate so that the run summary represents all runs for a single sample rate.

Parameters:

Returns:

DESCRIPTION.

Return type:

TYPE