# spacr.submodules

## Module Contents

class spacr.submodules.CellposeLazyDataset(image_files, label_files, settings, randomize=True, augment=False)

  Bases: `torch.utils.data.Dataset`

  An abstract class representing a `Dataset`.

  All datasets that represent a map from keys to data samples should subclass it. All subclasses should overwrite `__getitem__()`, supporting fetching a data sample for a given key. Subclasses could also optionally overwrite `__len__()`, which is expected to return the size of the dataset by many `Sampler` implementations and the default options of `DataLoader`. Subclasses could also optionally implement `__getitems__()`, for speedup batched samples loading. This method accepts list of indices of samples of batch and returns list of samples.

  Note

  `DataLoader` by default constructs an index sampler that yields integral indices. To make it work with a map-style dataset with non-integral indices/keys, a custom sampler must be provided.

  normalize
  percentiles
  target_size
  augment = False
  apply_augmentation(image, label, aug_idx)

spacr.submodules.train_cellpose(settings)

spacr.submodules.test_cellpose_model(settings)

spacr.submodules.apply_cellpose_model(settings)

spacr.submodules.plot_cellpose_batch(images, labels)

spacr.submodules.analyze_percent_positive(settings)

spacr.submodules.analyze_recruitment(settings)

  Analyze recruitment data by grouping the DataFrame by well coordinates and plotting controls and recruitment data.

  Parameters: settings (dict): settings.

  Returns: None

spacr.submodules.analyze_plaques(settings)

spacr.submodules.count_phenotypes(settings)

spacr.submodules.compare_reads_to_scores(reads_csv, scores_csv, empirical_dict={'r1': (90, 10), 'r2': (90, 10), 'r3': (80, 20), 'r4': (80, 20), 'r5': (70, 30), 'r6': (70, 30), 'r7': (60, 40), 'r8': (60, 40), 'r9': (50, 50), 'r10': (50, 50), 'r11': (40, 60), 'r12': (40, 60), 'r13': (30, 70), 'r14': (30, 70), 'r15': (20, 80), 'r16': (20, 80)}, pc_grna='TGGT1_220950_1', nc_grna='TGGT1_233460_4', y_columns=['class_1_fraction', 'TGGT1_220950_1_fraction', 'nc_fraction'], column='columnID', value='c3', plate=None, save_paths=None)

spacr.submodules.interperate_vision_model(settings={})

spacr.submodules.analyze_endodyogeny(settings)

spacr.submodules.analyze_class_proportion(settings)

spacr.submodules.generate_score_heatmap(settings)

spacr.submodules.post_regression_analysis(csv_file, grna_dict, grna_list, save=False)