From 050dfa4c4017bbda48bfd1b985ac42cc9bd441ad Mon Sep 17 00:00:00 2001 From: kapoorlab Date: Sat, 13 Jul 2024 14:14:59 +0000 Subject: [PATCH] remove unnecesayry npy files --- src/napatrackmater/Trackvector.py | 196 ++---------------------------- src/napatrackmater/_version.py | 4 +- 2 files changed, 9 insertions(+), 191 deletions(-) diff --git a/src/napatrackmater/Trackvector.py b/src/napatrackmater/Trackvector.py index 81e3b77..9356d6b 100644 --- a/src/napatrackmater/Trackvector.py +++ b/src/napatrackmater/Trackvector.py @@ -522,7 +522,6 @@ def plot_mitosis_times(self, full_dataframe, save_path=""): ] dividing_counts = subset.groupby("t").size() / 2 - times = dividing_counts.index counts = dividing_counts.values data = {"Time": times, "Count": counts} @@ -558,6 +557,7 @@ def plot_mitosis_times(self, full_dataframe, save_path=""): ) all_split_data = [] + for split_id in tqdm(self.split_cell_ids, desc="Cell split IDs"): spot_properties = self.unique_spot_properties[split_id] track_id = spot_properties[self.trackid_key] @@ -1184,7 +1184,6 @@ def simple_unsupervised_clustering( distance_vectors="shape", ): - csv_file_name_original = csv_file_name analysis_track_ids = [] shape_dynamic_covariance_matrix = [] position_matrix = [] @@ -1296,98 +1295,6 @@ def simple_unsupervised_clustering( distance_vectors=distance_vectors, ) - silhouette_file_name = os.path.join( - csv_file_name_original - + "shape_dynamic" - + f"_silhouette_{metric}_{cluster_threshold_shape_dynamic}.npy" - ) - np.save(silhouette_file_name, shape_dynamic_silhouette) - - wcss_file_name = os.path.join( - csv_file_name_original - + "shape_dynamic" - + f"_wcss_{metric}_{cluster_threshold_shape_dynamic}.npy" - ) - np.save(wcss_file_name, shape_dynamic_wcss_value) - - silhouette_file_name = os.path.join( - csv_file_name_original - + "dynamic" - + f"_silhouette_{metric}_{cluster_threshold_dynamic}.npy" - ) - np.save(silhouette_file_name, dynamic_silhouette) - - wcss_file_name = os.path.join( - csv_file_name_original - + "dynamic" - + f"_wcss_{metric}_{cluster_threshold_dynamic}.npy" - ) - np.save(wcss_file_name, dynamic_wcss_value) - - silhouette_file_name = os.path.join( - csv_file_name_original - + "shape" - + f"_silhouette_{metric}_{cluster_threshold_shape}.npy" - ) - np.save(silhouette_file_name, shape_silhouette) - - wcss_file_name = os.path.join( - csv_file_name_original - + "shape" - + f"_wcss_{metric}_{cluster_threshold_shape}.npy" - ) - np.save(wcss_file_name, shape_wcss_value) - - cluster_distance_map_shape_dynamic_file_name = os.path.join( - csv_file_name_original - + "shape_dynamic" - + "_cluster_distance_map_shape_dynamic.npy" - ) - np.save( - cluster_distance_map_shape_dynamic_file_name, - cluster_distance_map_shape_dynamic, - ) - - cluster_distance_map_shape_file_name = os.path.join( - csv_file_name_original + "shape" + "_cluster_distance_map_shape.npy" - ) - np.save(cluster_distance_map_shape_file_name, cluster_distance_map_shape) - - cluster_distance_map_dynamic_file_name = os.path.join( - csv_file_name_original + "dynamic" + "_cluster_distance_map_dynamic.npy" - ) - np.save(cluster_distance_map_dynamic_file_name, cluster_distance_map_dynamic) - - cluster_eucledian_distance_map_shape_dynamic_file_name = os.path.join( - csv_file_name_original - + "shape_dynamic" - + "_cluster_eucledian_distance_map_shape_dynamic.npy" - ) - np.save( - cluster_eucledian_distance_map_shape_dynamic_file_name, - cluster_eucledian_distance_map_shape_dynamic, - ) - - cluster_eucledian_distance_map_shape_file_name = os.path.join( - csv_file_name_original - + "shape" - + "_cluster_eucledian_distance_map_shape.npy" - ) - np.save( - cluster_eucledian_distance_map_shape_file_name, - cluster_eucledian_distance_map_shape, - ) - - cluster_eucledian_distance_map_dynamic_file_name = os.path.join( - csv_file_name_original - + "dynamic" - + "_cluster_eucledian_distance_map_dynamic.npy" - ) - np.save( - cluster_eucledian_distance_map_dynamic_file_name, - cluster_eucledian_distance_map_dynamic, - ) - def unsupervised_clustering( full_dataframe, @@ -1402,7 +1309,6 @@ def unsupervised_clustering( distance_vectors="shape", ): - csv_file_name_original = csv_file_name analysis_track_ids = [] shape_dynamic_covariance_matrix = [] shape_covariance_matrix = [] @@ -1501,98 +1407,6 @@ def unsupervised_clustering( distance_vectors=distance_vectors, ) - silhouette_file_name = os.path.join( - csv_file_name_original - + "shape_dynamic" - + f"_silhouette_{metric}_{cluster_threshold_shape_dynamic}.npy" - ) - np.save(silhouette_file_name, shape_dynamic_silhouette) - - wcss_file_name = os.path.join( - csv_file_name_original - + "shape_dynamic" - + f"_wcss_{metric}_{cluster_threshold_shape_dynamic}.npy" - ) - np.save(wcss_file_name, shape_dynamic_wcss_value) - - silhouette_file_name = os.path.join( - csv_file_name_original - + "dynamic" - + f"_silhouette_{metric}_{cluster_threshold_dynamic}.npy" - ) - np.save(silhouette_file_name, dynamic_silhouette) - - wcss_file_name = os.path.join( - csv_file_name_original - + "dynamic" - + f"_wcss_{metric}_{cluster_threshold_dynamic}.npy" - ) - np.save(wcss_file_name, dynamic_wcss_value) - - silhouette_file_name = os.path.join( - csv_file_name_original - + "shape" - + f"_silhouette_{metric}_{cluster_threshold_shape}.npy" - ) - np.save(silhouette_file_name, shape_silhouette) - - wcss_file_name = os.path.join( - csv_file_name_original - + "shape" - + f"_wcss_{metric}_{cluster_threshold_shape}.npy" - ) - np.save(wcss_file_name, shape_wcss_value) - - cluster_distance_map_shape_dynamic_file_name = os.path.join( - csv_file_name_original - + "shape_dynamic" - + "_cluster_distance_map_shape_dynamic.npy" - ) - np.save( - cluster_distance_map_shape_dynamic_file_name, - cluster_distance_map_shape_dynamic, - ) - - cluster_distance_map_shape_file_name = os.path.join( - csv_file_name_original + "shape" + "_cluster_distance_map_shape.npy" - ) - np.save(cluster_distance_map_shape_file_name, cluster_distance_map_shape) - - cluster_distance_map_dynamic_file_name = os.path.join( - csv_file_name_original + "dynamic" + "_cluster_distance_map_dynamic.npy" - ) - np.save(cluster_distance_map_dynamic_file_name, cluster_distance_map_dynamic) - - cluster_eucledian_distance_map_shape_dynamic_file_name = os.path.join( - csv_file_name_original - + "shape_dynamic" - + "_cluster_eucledian_distance_map_shape_dynamic.npy" - ) - np.save( - cluster_eucledian_distance_map_shape_dynamic_file_name, - cluster_eucledian_distance_map_shape_dynamic, - ) - - cluster_eucledian_distance_map_dynamic_file_name = os.path.join( - csv_file_name_original - + "dynamic" - + "_cluster_eucledian_distance_map_dynamic.npy" - ) - np.save( - cluster_eucledian_distance_map_dynamic_file_name, - cluster_eucledian_distance_map_dynamic, - ) - - cluster_eucledian_distance_map_shape_file_name = os.path.join( - csv_file_name_original - + "shape" - + "_cluster_eucledian_distance_map_shape.npy" - ) - np.save( - cluster_eucledian_distance_map_shape_file_name, - cluster_eucledian_distance_map_shape, - ) - def convert_tracks_to_arrays( analysis_vectors, @@ -2856,8 +2670,6 @@ def populate_zero_gen_tracklets( feature, ) - print(f"Good zero_gens, {good_zero_gens}") - def generic_polynomial_fits( track_array, @@ -3662,3 +3474,9 @@ def angular_plot(global_shape_dynamic_dataframe, column="Radial_Angle_Z", time_p plt.show() clear_output(wait=True) + + +def normalize_list(lst): + mean_val = np.mean(lst) + std_val = np.std(lst) + return [(x - mean_val) / std_val for x in lst] diff --git a/src/napatrackmater/_version.py b/src/napatrackmater/_version.py index d8b0454..b52969e 100644 --- a/src/napatrackmater/_version.py +++ b/src/napatrackmater/_version.py @@ -1,2 +1,2 @@ -__version__ = version = "5.3.4" -__version_tuple__ = version_tuple = (5, 3, 4) +__version__ = version = "5.3.5" +__version_tuple__ = version_tuple = (5, 3, 5)