src.original.DK_OGC_AmsterdamUMC.utils.data_processing.processors package

Submodules

src.original.DK_OGC_AmsterdamUMC.utils.data_processing.processors.AverageSignalsOfEqualXvals module

class src.original.DK_OGC_AmsterdamUMC.utils.data_processing.processors.AverageSignalsOfEqualXvals.AverageSignalsOfEqualXvals(**kwargs)[source]

Bases: Transform

apply_transform(subject)[source]

normalize signals Args:

signals: signals array to normalize xvals: xval array

Returns:

normalized_signals: normalized signals array

static average_signal_of_equal_xvals(signals, xvals)[source]

average the signal of equal xvals Args:

signals: signal matrix [signals X xvals] xvals: array of xvals

Returns:

averaged_signal_matrix: averaged signal matrix [signals X unique_xvals] unique_xval_arrays: unique xvals in averaged signal matrix

src.original.DK_OGC_AmsterdamUMC.utils.data_processing.processors.FlattenImageData module

class src.original.DK_OGC_AmsterdamUMC.utils.data_processing.processors.FlattenImageData.FlattenImageData(**kwargs)[source]

Bases: Transform

apply_transform(subject)[source]

flattens image data of signals image of subject Args:

signals: signals array to normalize xvals: xval array

Returns:

normalized_signals: normalized signals array

static flatten_image_data(signals)[source]

Flattens 4D array into 2D array Args:

signals: signals array to normalize

Returns:

normalized_signals: normalized signals array

src.original.DK_OGC_AmsterdamUMC.utils.data_processing.processors.NormalizeMaxSignal module

class src.original.DK_OGC_AmsterdamUMC.utils.data_processing.processors.NormalizeMaxSignal.NormalizeMaxSignal(**kwargs)[source]

Bases: Transform

apply_transform(subject)[source]

normalize signals Image of subject Args:

subject: Subject

Returns:

subject: Subject

static normalize_signals(signals)[source]

normalize signals Args:

signals: signals array to normalize

Returns:

normalized_signals: normalized signals array

src.original.DK_OGC_AmsterdamUMC.utils.data_processing.processors.NormalizeSignals module

class src.original.DK_OGC_AmsterdamUMC.utils.data_processing.processors.NormalizeSignals.NormalizeSignals(xval_threshold, **kwargs)[source]

Bases: Transform

apply_transform(subject)[source]

normalize xvals Image of subject Args:

subject: Subject

Returns:

subject: Subject

static normalize_signals(signals, xvals, xval_threshold)[source]

normalize signals Args:

signals: signals array to normalize xvals: xval array xval_threshold: threshold below which bvals are considered b0

Returns:

normalized_signals: normalized signals array

src.original.DK_OGC_AmsterdamUMC.utils.data_processing.processors.NormalizeXvals module

class src.original.DK_OGC_AmsterdamUMC.utils.data_processing.processors.NormalizeXvals.NormalizeXvals(normalization_factor, **kwargs)[source]

Bases: Transform

apply_transform(subject)[source]

normalize xvals Image of subject Args:

subject: Subject

Returns:

subject: Subject

static normalize_xvals(xvals, normalization_factor)[source]

normalize signals Args:

xvals: xvalue array normalization_factor: factor to multiply xvals with

Returns:

normalized_xvals: normalized signals array

src.original.DK_OGC_AmsterdamUMC.utils.data_processing.processors.SignalCuration module

class src.original.DK_OGC_AmsterdamUMC.utils.data_processing.processors.SignalCuration.SignalCuration(qmri_application, **kwargs)[source]

Bases: Transform

apply_transform(subject)[source]

curates signals Image of subject Args:

subject: Subject

Returns:

subject: Subject

static ivim_selection(signals, xvals)[source]

returns only those signals exhibiting ivim decay Args:

signals: signals for corresponding xvals xvals: xvals

Returns:

normalized_valid_signals: normalized_valid_signals that exhibit ivim-like decay masked_signals: normalized_signals where signals not exhibiting ivim-like decay are set to 0

src.original.DK_OGC_AmsterdamUMC.utils.data_processing.processors.SignalMask module

class src.original.DK_OGC_AmsterdamUMC.utils.data_processing.processors.SignalMask.SignalMask(**kwargs)[source]

Bases: Transform

apply_transform(subject)[source]

curates signals Image of subject Args:

subject: Subject

Returns:

subject: Subject

static signal_mask(signals)[source]

returns mask with nonzero element for signal vectors with nonzero entries Args:

signals: signals

Returns:

masked_signals: signals containing nonzero elements

src.original.DK_OGC_AmsterdamUMC.utils.data_processing.processors.SortSignalOnXval module

class src.original.DK_OGC_AmsterdamUMC.utils.data_processing.processors.SortSignalOnXval.SortSignalOnXval(**kwargs)[source]

Bases: Transform

apply_transform(subject)[source]

Sorts signals and xvals on ascending xvals Args:

subject: Subject

Returns:

subject: Subject

static sort_signals_on_xval_array(signals, xvals)[source]

Sorts signals and xvals on ascending xvals Args:

signals: signals to sort bval: bvalues to use for sorting

Returns:

sorted_signals: sorted signals sorted_bvals: sorted bvals

Module contents