metaclean3.binning

Module Contents

Functions

get_time_bin_unit(time_values[, time_bin, ...])

Determine a relative time bin unit.

adjust_time_bin(time_bin[, frac, reduce])

Reduce a time bin size by a fraction and return the new time bin size and scale.

data_to_flow(time_values[, min_bin, max_time, min_time])

Convert a dataset with timestamps into a flow dataframe.

_get_time_bin_while(flow_df, time_bin, reduce)

get_time_binned(time_values[, time_bin, time_step, ...])

Bin the time channel values.
metaclean3.binning.get_time_bin_unit(time_values: list | numpy.ndarray | pandas.Series, time_bin: str | int | float | None = None, bin_size_temp: int = 1000000, bin_no_max_temp: int = 10)

Determine a relative time bin unit.

Args:

time_values (list | numpy.ndarray | pandas.Series): Time channel values. time_bin (str | int | float | None, optional): The desired time bin

size, in seconds or milliseconds. Defaults to None.

bin_size_temp (int, optional): Default bin frequency.

Defaults to 1000000.

bin_no_max_temp (int, optional): Maximum bin size. Defaults to 10.

Returns:

str: Time bin unit.

metaclean3.binning.adjust_time_bin(time_bin: str, frac: float = 0.5, reduce: bool = True)

Reduce a time bin size by a fraction and return the new time bin size and scale.

Args:

time_bin (str): The initial time bin size. frac (int, optional): The fraction to reduce the time bin size by.

Defaults to 2.

Reduce (bool, optional): whether to reduce or increase time bin.

Defaults to True.

Returns:
tuple:

str: time_bin. str: time_unit.

metaclean3.binning.data_to_flow(time_values: numpy.ndarray | pandas.Series, min_bin: int = 2000, max_time: int = int(1000000000.0), min_time: int = 1000)

Convert a dataset with timestamps into a flow dataframe.

Args:

time_values (numpy.ndarray | pandas.Series): Time channel values. min_bin (int, optional): Minimum number of bins. Defaults to 2000. max_time (int, optional): Maximum time scale to use.

Defaults to 1e9 (1 second).

min_time (int, optional): Maximum time scale to use. Defaults to 1000.

Returns:
pandas.DataFrame: A flow dataframe with timestamped rows

and an ‘index’ column.

metaclean3.binning._get_time_bin_while(flow_df: pandas.DataFrame, time_bin: str, reduce: bool)
metaclean3.binning.get_time_binned(time_values: numpy.ndarray | pandas.Series, time_bin: str = '1S', time_step: float | None = None, min_bin_size: int = 2000, max_bin_size: int = 10000, min_events_per_bin: int = 50)

Bin the time channel values.

Args:

time_values (numpy.ndarray | pandas.Series): Time channel values. time_bin (str, optional): Time bin i.e. duration of each bin.

Defaults to ‘1S’.

time_step (float | None, optional): Time step parameter from FCS file

e.g. meta.get(‘$TIMESTEP’). Defaults to None.

min_bin_size (int, optional): Minimum number of bins. Defaults to 2000. max_bin_size (int, optional): Maximum number of bins. Defaults to 10000. min_events_per_bin (int, optional): Minimum number of events per bin.

Defaults to 50.

Returns:
numpy.ndarray: A 1D numpy array with the same length as time_values

containing integer bin labels.