postprocessor.compiler.ExperimentCompiler

class postprocessor.compiler.ExperimentCompiler(CompilerParameters, exp_path: PosixPath)

Bases: Compiler

Attributes

ntraps

Get the number of traps in each position.

parameters

Methods

compile_dmetrics([stages])	Generate dataframe with dVol metrics without major cell picking.
compile_fluorescence([metrics, norm])	Get a single signal per.
compile_growth_metrics([min_nbuddings])	Filter mothers with n number of buddings and get their metrics.
compile_last_valid_tp()	Last valid timepoint per position.
compile_pertrap_metric([ranges, metric])	Get the number of cells per trap present during the given ranges.
fill_trapcount(srs[, fill_value])	Fill the last level of a MultiIndex in a pd.Series.
get_shared_ids(input_signals[, min_nbuddings])	Get the intersection id of multiple signals.
get_stages()	Use the metadata to give a prediction of the media being pumped at each time point.
guess_metrics([metrics])	First approach at autoselecting certain signals for automated analysis.
load_data(path)	Abstract function that must be reimplemented.
traploc_diffs(traplocs)	Obtain metrics for trap localisation.

add_column
compile_delta_traps
compile_ncells
compile_slice
compile_slice_end
compile_slices
compile_stages_dmetric
concat_signal
count_cells
get_filled_trapcounts
get_tp
run

__init__(CompilerParameters, exp_path: PosixPath)

Methods

__init__(CompilerParameters, exp_path)
add_column(df, new_values_d[, name])
compile_delta_traps()
compile_dmetrics([stages])	Generate dataframe with dVol metrics without major cell picking.
compile_fluorescence([metrics, norm])	Get a single signal per.
compile_growth_metrics([min_nbuddings])	Filter mothers with n number of buddings and get their metrics.
compile_last_valid_tp()	Last valid timepoint per position.
compile_ncells()
compile_pertrap_metric([ranges, metric])	Get the number of cells per trap present during the given ranges.
compile_slice([sigloc, tp, metrics, mode])
compile_slice_end(**kwargs)
compile_slices([nslices])
compile_stages_dmetric()
concat_signal([sigloc, mode])
count_cells([signal, mode])
fill_trapcount(srs[, fill_value])	Fill the last level of a MultiIndex in a pd.Series.
get_filled_trapcounts(signal, metric)
get_shared_ids(input_signals[, min_nbuddings])	Get the intersection id of multiple signals.
get_stages()	Use the metadata to give a prediction of the media being pumped at each time point.
get_tp([sigloc, tp, mode])
guess_metrics([metrics])	First approach at autoselecting certain signals for automated analysis.
load_data(path)	Abstract function that must be reimplemented.
run()
traploc_diffs(traplocs)	Obtain metrics for trap localisation.

Attributes

ntraps	Get the number of traps in each position.
parameters

compile_dmetrics(stages=None)

Generate dataframe with dVol metrics without major cell picking.

compile_fluorescence(metrics: Optional[Dict[str, Tuple[str]]] = None, norm: Optional[tuple] = None, **kwargs)

Get a single signal per.

compile_growth_metrics(min_nbuddings: int = 2)

Filter mothers with n number of buddings and get their metrics.

Select cells with at least two recorded buddings

compile_last_valid_tp() → Series

Last valid timepoint per position.

compile_pertrap_metric(ranges: Iterable[Iterable[int]] = [[0, -1]], metric: str = 'count')

Get the number of cells per trap present during the given ranges.

fill_trapcount(srs: Series, fill_value: Union[int, float] = 0) → Series

Fill the last level of a MultiIndex in a pd.Series.

Use self to get the max number of traps per position and use this information to add rows with empty values (with plottings of distributions in mind) Parameters ———- srs : pd.Series Series with a pd.MultiIndex index self : ExperimentSelf class with ‘ntraps’ information that returns a dictionary with position -> ntraps. fill_value : Union[int, float] Value used to fill new rows. Returns ——- pd.Series Series with no numbers skipped on the last level. Examples ——– FIXME: Add docs.

get_shared_ids(input_signals: Dict[str, DataFrame], min_nbuddings: Optional[int] = None)

Get the intersection id of multiple signals.

“buddings” must be one the keys in input_signals to use the argument min_nbuddings.

get_stages()

Use the metadata to give a prediction of the media being pumped at each time point. Works for traditional metadata (pre-fluigent).

Returns: —— A list of tuples where in each the first value is the active pump’s contents and the second its associated range of time points

guess_metrics(metrics: Optional[Dict[str, Tuple[str]]] = None)

First approach at autoselecting certain signals for automated analysis.

load_data(path: PosixPath)

Abstract function that must be reimplemented.

property ntraps: dict

Get the number of traps in each position.

Returns ——- dict str -> int Examples ——– FIXME: Add docs.

static traploc_diffs(traplocs: ndarray) → list

Obtain metrics for trap localisation.

Parameters ———- traplocs : ndarray (x,2) 2-dimensional array with the x,y coordinates of traps in each column Examples ——– FIXME: Add docs.