deepcinac.cinac_movie_patch

Module Contents

Classes

MoviePatchGenerator	Used to generate movie patches, that will be produce for training data during each mini-batch.
MoviePatchGeneratorForCellType	Used to generate movie patches, that will be produce for training data during each mini-batch.
MoviePatchGeneratorMaskedVersions	Will generate one input being the masked cell (the one we focus on), the second input
MoviePatchData
DataGenerator	Based on an exemple found in https://stanford.edu/~shervine/blog/keras-how-to-generate-data-on-the-fly

Attributes

TF_VERSION

deepcinac.cinac_movie_patch.TF_VERSION

class deepcinac.cinac_movie_patch.MoviePatchGenerator(window_len, max_width, max_height, using_multi_class, cell_type_classifier_mode)

Used to generate movie patches, that will be produce for training data during each mini-batch. This is an abstract classes that need to have heritage. The function generate_movies_from_metadata will be used to produced those movie patches, the number vary depending on the class instantiated

get_nb_inputs()

generate_movies_from_metadata(movie_data_list, memory_dict, with_labels=True)

class deepcinac.cinac_movie_patch.MoviePatchGeneratorForCellType(window_len, max_width, max_height, pixels_around, buffer, using_multi_class, cell_type_classifier_mode, with_all_pixels=False)

Bases: MoviePatchGenerator

Used to generate movie patches, that will be produce for training data during each mini-batch. This is an abstract classes that need to have heritage. The function generate_movies_from_metadata will be used to produced those movie patches, the number vary depending on the class instantiated

generate_movies_from_metadata(movie_data_list, memory_dict=None, with_labels=True)

Parameters

• movie_data_list – list of MoviePatchData instances

• memory_dict –

• with_labels –

Returns:

__str__()

Return str(self).

class deepcinac.cinac_movie_patch.MoviePatchGeneratorMaskedVersions(window_len, max_width, max_height, pixels_around, buffer, with_neuropil_mask, using_multi_class, cell_type_classifier_mode)

Bases: MoviePatchGenerator

Will generate one input being the masked cell (the one we focus on), the second input would be the whole patch without neuorpil and the main cell, the last inpu if with_neuropil_mask is True would be just the neuropil without the pixels in the cells

generate_movies_from_metadata(movie_data_list, memory_dict=None, with_labels=True)

Parameters

• movie_data_list – list of MoviePatchData instances

• memory_dict –

• with_labels –

Returns:

__str__()

Return str(self).

class deepcinac.cinac_movie_patch.MoviePatchData(cinac_recording, cell, index_movie, max_n_transformations, encoded_frames, decoding_frame_dict, window_len, cell_type_classifier_mode=False, session_id=None, with_info=False, to_keep_absolutely=False, ground_truth=None)

n_available_augmentation_fct

Keys so far for self.movie_info (with value type) -> comments:

n_transient (int) transients_lengths (list of int) transients_amplitudes (list of float) n_cropped_transient (int) -> max value should be 2 cropped_transients_lengths (list of int) n_fake_transient (int) n_cropped_fake_transient (int) > max value should be 2 fake_transients_lengths (list of int) fake_transients_amplitudes (list of float)

get_labels(using_multi_class)

Return the labels for this data, could be if the cell is active for any given frame or the cell type depending on the classifier mode :param using_multi_class:

Returns:

__eq__(other)

Return self==value.

copy()

add_n_augmentation(n_augmentation)

pick_a_transformation_fct()

is_only_neuropil()

Returns

True if there is only neuropil (no transients), False otherwise

class deepcinac.cinac_movie_patch.DataGenerator(data_list, movie_patch_generator, batch_size, window_len, with_augmentation, pixels_around, buffer, max_width, max_height, is_shuffle=True)

Bases: tensorflow.keras.utils.Sequence

Based on an exemple found in https://stanford.edu/~shervine/blog/keras-how-to-generate-data-on-the-fly Feed to keras to generate data

prepare_augmentation()

__len__()

__getitem__(index)

on_epoch_end()

__data_generation(data_list_tmp)