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@JuliaKukulies
JuliaKukulies committed Sep 20, 2024
1 parent 84c347f commit 22314f7
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Showing 2 changed files with 41 additions and 38 deletions.
57 changes: 30 additions & 27 deletions tobac/feature_detection.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -627,9 +627,9 @@ def feature_detection_threshold(
# find the updated label, and overwrite all of label_ind indices with
# updated label
labels_2_alt = labels_2[label_z, y_val_alt, x_val_alt]
labels_2[
label_locs_v, label_locs_h1, label_locs_h2
] = labels_2_alt
labels_2[label_locs_v, label_locs_h1, label_locs_h2] = (
labels_2_alt
)
skip_list = np.append(skip_list, label_ind)
break

Expand Down Expand Up @@ -673,9 +673,9 @@ def feature_detection_threshold(
# find the updated label, and overwrite all of label_ind indices with
# updated label
labels_2_alt = labels_2[label_z, y_val_alt, label_x]
labels_2[
label_locs_v, label_locs_h1, label_locs_h2
] = labels_2_alt
labels_2[label_locs_v, label_locs_h1, label_locs_h2] = (
labels_2_alt
)
new_label_ind = labels_2_alt
skip_list = np.append(skip_list, label_ind)

Expand Down Expand Up @@ -717,9 +717,9 @@ def feature_detection_threshold(
# find the updated label, and overwrite all of label_ind indices with
# updated label
labels_2_alt = labels_2[label_z, label_y, x_val_alt]
labels_2[
label_locs_v, label_locs_h1, label_locs_h2
] = labels_2_alt
labels_2[label_locs_v, label_locs_h1, label_locs_h2] = (
labels_2_alt
)
new_label_ind = labels_2_alt
skip_list = np.append(skip_list, label_ind)

Expand Down Expand Up @@ -912,7 +912,7 @@ def feature_detection_multithreshold_timestep(
wavelength_filtering: tuple[float] = None,
strict_thresholding: bool = False,
statistic: Union[dict[str, Union[Callable, tuple[Callable, dict]]], None] = None,
statistics_unsmoothed: bool = False,
statistics_unsmoothed: bool = False,
) -> pd.DataFrame:
"""Find features in each timestep.
Expand Down Expand Up @@ -986,7 +986,7 @@ def feature_detection_multithreshold_timestep(
Dictionary with callable function(s) to apply over the region of each detected feature and the name of the statistics to appear in the feature ou tput dataframe. The functions should be the values and the names of the metric the keys (e.g. {'mean': np.mean})
statistics_unsmoothed: bool, optional
Default is False. If True, calculate the statistics on the raw data instead of the smoothed input data.
Default is False. If True, calculate the statistics on the raw data instead of the smoothed input data.
Returns
-------
Expand All @@ -1012,7 +1012,9 @@ def feature_detection_multithreshold_timestep(
# keep a copy of the unsmoothed data (that can be used for calculating stats)
if statistics_unsmoothed:
if not statistic:
raise ValueError('Please provide the input parameter statistic to determine what statistics to calculate.')
raise ValueError(
"Please provide the input parameter statistic to determine what statistics to calculate."
)
raw_data = data_i.core_data().copy()

track_data = gaussian_filter(
Expand Down Expand Up @@ -1129,22 +1131,23 @@ def feature_detection_multithreshold_timestep(
# the feature dataframe is updated by appending a column for each metric
if statistics_unsmoothed:
features_thresholds = get_statistics(
features_thresholds,
labels,
raw_data,
statistic=statistic,
index=np.unique(labels[labels > 0]),
id_column="idx", )
features_thresholds,
labels,
raw_data,
statistic=statistic,
index=np.unique(labels[labels > 0]),
id_column="idx",
)
else:
features_thresholds = get_statistics(
features_thresholds,
labels,
track_data,
statistic=statistic,
index=np.unique(labels[labels > 0]),
id_column="idx",
)
features_thresholds,
labels,
track_data,
statistic=statistic,
index=np.unique(labels[labels > 0]),
id_column="idx",
)

logging.debug(
"Finished feature detection for threshold "
+ str(i_threshold)
Expand Down Expand Up @@ -1177,7 +1180,7 @@ def feature_detection_multithreshold(
dz: Union[float, None] = None,
strict_thresholding: bool = False,
statistic: Union[dict[str, Union[Callable, tuple[Callable, dict]]], None] = None,
statistics_unsmoothed: bool = False
statistics_unsmoothed: bool = False,
) -> pd.DataFrame:
"""Perform feature detection based on contiguous regions.
Expand Down
22 changes: 11 additions & 11 deletions tobac/segmentation.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -824,15 +824,15 @@ def segmentation_timestep(
)

# edit value in buddy_features dataframe
buddy_features.hdim_1.values[
buddy_looper
] = pbc_utils.transfm_pbc_point(
float(buddy_feat.hdim_1), hdim1_min, hdim1_max
buddy_features.hdim_1.values[buddy_looper] = (
pbc_utils.transfm_pbc_point(
float(buddy_feat.hdim_1), hdim1_min, hdim1_max
)
)
buddy_features.hdim_2.values[
buddy_looper
] = pbc_utils.transfm_pbc_point(
float(buddy_feat.hdim_2), hdim2_min, hdim2_max
buddy_features.hdim_2.values[buddy_looper] = (
pbc_utils.transfm_pbc_point(
float(buddy_feat.hdim_2), hdim2_min, hdim2_max
)
)

buddy_looper = buddy_looper + 1
Expand Down Expand Up @@ -1010,9 +1010,9 @@ def segmentation_timestep(
segmentation_mask_3[z_val_o, y_val_o, x_val_o]
!= segmentation_mask_4.data[z_seg, y_seg, x_seg]
):
segmentation_mask_3[
z_val_o, y_val_o, x_val_o
] = segmentation_mask_4.data[z_seg, y_seg, x_seg]
segmentation_mask_3[z_val_o, y_val_o, x_val_o] = (
segmentation_mask_4.data[z_seg, y_seg, x_seg]
)
if not is_3D_seg:
segmentation_mask_3 = segmentation_mask_3[0]

Expand Down

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