Suggested edits to address reviews of Chris' density branch

nuc_morph_analysis/analyses/density/extra_checks/visually_validate_watershed_psuedo_cell_seg_workflow.py

@@ -20,7 +20,6 @@ def get_contours_from_pair_of_2d_seg_image(nuc_mip,cell_mip,dft=None):
                 #ask if the label_img is in the dataframe
                 if label_img not in dft['label_img'].values:
                     continue
-            # color = np.float64(rgb_array0_255[label_img % len(rgb_array0_255)])
             color = np.float64(cmapper(label_img))
 
             # get the nucleus boundary
@@ -96,8 +95,6 @@ def plot_colorized_image_with_contours(img_dict,dft,feature,cmapstr,colony='test
         cmap = cm.get_cmap(cmapstr)
         if categorical:
             cimg = np.round(cimg).astype('uint16')
-
-        # rgb = np.take(np.uint16(cmaparr*255),cimg.astype('uint16'),axis=0)
 
         # create the figure
         fig, axlist = plt.subplots(1, 1, figsize=(6, 4))
@@ -245,6 +242,4 @@ def run_validation_and_plot(TIMEPOINT=48,colony='medium',RESOLUTION_LEVEL=1,plot
 if __name__ == '__main__':
     # set the details
     dft_test = run_validation_and_plot(testing=True,plot_everything=False)
-    dft0 = run_validation_and_plot(testing=False,plot_everything=False)
-    # dft0 = run_validation_and_plot(247,colony='small',RESOLUTION_LEVEL=1,plot_everything=True)
-    # dft0 = run_validation_and_plot(110,colony='medium',RESOLUTION_LEVEL=1,plot_everything=True)
+    dft0 = run_validation_and_plot(testing=False,plot_everything=False)

nuc_morph_analysis/analyses/density/figure_watershed_based_density_schematic.py

@@ -17,6 +17,8 @@
 from nuc_morph_analysis.analyses.density.extra_checks.visually_validate_watershed_psuedo_cell_seg_workflow import get_contours_from_pair_of_2d_seg_image, draw_contours_on_image
 from nuc_morph_analysis.analyses.dataset_images_for_figures.figure_helper import INTENSITIES_DICT
 
+from nuc_morph_analysis.lib.visualization.example_tracks import EXAMPLE_TRACKS
+
 import matplotlib
 matplotlib.rcParams['pdf.fonttype'] = 42
 matplotlib.rcParams['ps.fonttype'] = 42
@@ -61,7 +63,7 @@ def determine_colormaps(img,key,crop_exp):
     return cmap, vmin, vmax
 
 
-def run_validation_and_plot(TIMEPOINT=88,track=81463,colony='medium',RESOLUTION_LEVEL=1,plot_everything=False, testing=False):
+def run_validation_and_plot(TIMEPOINT=88,track=EXAMPLE_TRACKS["pseudocell_density_example"],colony='medium',RESOLUTION_LEVEL=1,plot_everything=False, testing=False):
     """
     run an image through the watershed based pseudo cell segmentation and examine the outputs (as full fov and crop within that fov)
     optionally, run a test image through the same pipeline

nuc_morph_analysis/analyses/height/figure_3_s4_workflow.py

@@ -1,15 +1,16 @@
 # %%
 from nuc_morph_analysis.analyses.colony_context.colony_context_analysis import plot_radial_profile
 from nuc_morph_analysis.lib.preprocessing.global_dataset_filtering import load_dataset_with_features
-from nuc_morph_analysis.lib.preprocessing import load_data, filter_data, global_dataset_filtering
+from nuc_morph_analysis.lib.preprocessing import load_data, filter_data
 from nuc_morph_analysis.lib.preprocessing.load_data import get_dataset_pixel_size
 from nuc_morph_analysis.analyses.height import plot
-from nuc_morph_analysis.analyses.height.plot_crowding import plot_density_schematic
 from nuc_morph_analysis.analyses.height.toymodel import toymodel
 from nuc_morph_analysis.analyses.height.centroid import (
     get_centroid,
     get_neighbor_centroids,
 )
+from nuc_morph_analysis.analyses.density import figure_watershed_based_density_schematic
+from nuc_morph_analysis.analyses.neighbor_of_X import figure_mitotic_filtering_examples
 from nuc_morph_analysis.lib.preprocessing.load_data import get_dataset_pixel_size
 from pathlib import Path
 import matplotlib.pyplot as plt
@@ -67,13 +68,12 @@
 
 # plot density schematic
 pix_size = get_dataset_pixel_size("medium")
-plot_density_schematic(
-    df_timepoint, track_centroid, neighbor_centroids, frame_centroids, pix_size, figdir,
-)
 
 # plot colony-averaged density over aligned colony time
-plot.density_colony_time_alignment(df_all, pixel_size, interval, time_axis="colony_time",
-                                   use_old_density=False)
+plot.density_colony_time_alignment(df_all, pixel_size, interval, time_axis="colony_time")
+
+figure_watershed_based_density_schematic.run_validation_and_plot() #SuppFigS4 panel C
+figure_mitotic_filtering_examples.run_validation_and_plot() #SuppFigS4 panel D, this code takes ~6 min to run
 
 # %%
 # Run and plot toy model

nuc_morph_analysis/analyses/height/plot.py

@@ -96,7 +96,7 @@ def height_colony_time_alignment(
     )
 
 
-def calculate_mean_density(df, scale, use_old_density=False):
+def calculate_mean_density(df, scale):
     """
     Calculate the mean height for a given index_sequence (i.e. timepoint) and the standard deviation of the mean.
 
@@ -106,8 +106,6 @@ def calculate_mean_density(df, scale, use_old_density=False):
         DataFrame containing the data.
     pixel_size : float
         Pixel size in microns.
-    use_old_density : bool
-        Whether to use the old density calculation method ('density') or the new method ('2d_area_nuc_cell_ratio')
 
     Returns
     -------
@@ -118,7 +116,7 @@ def calculate_mean_density(df, scale, use_old_density=False):
     """
     mean = []
     standard_dev = []
-    feature_col = "density" if use_old_density else "2d_area_nuc_cell_ratio"
+    feature_col = "2d_area_nuc_cell_ratio"
     for _, df_frame in df.groupby("index_sequence"):
         density = df_frame[feature_col].values * scale
         mean.append(np.nanmean(density))
@@ -134,7 +132,6 @@ def density_colony_time_alignment(
     show_legend=False,
     error="percentile",
     figdir="height/figures",
-    use_old_density=False,
 ):
     """
     Plot the mean nuclear height across the colony over time for each colony. This is done in real time and colony time.
@@ -159,17 +156,14 @@ def density_colony_time_alignment(
     error: str
         "std" or percentile
 
-    use_old_density: bool
-        Whether to use the old density calculation method ('density') or the new method ('2d_area_nuc_cell_ratio')
-
     Returns
     -------
     Plot of mean nuclear height across the colony over time for each colony.
     """
     plt.close()
     fig, ax = plt.subplots(1, 1, figsize=(5, 4))
 
-    feature_col = "density" if use_old_density else "2d_area_nuc_cell_ratio"
+    feature_col = "2d_area_nuc_cell_ratio"
     scale, label, units, _ = get_plot_labels_for_metric(feature_col)
 
     for colony, df_colony in df.groupby("colony"):
@@ -212,7 +206,6 @@ def density_colony_time_alignment(
             time, mean_density, linewidth=1.2, color=color, label=COLONY_LABELS[colony], zorder=20
         )
 
-    # ax.set_ylim(0.0005, 0.0065)
     ax.set_ylabel(f"Average Density \n Across Colony {units}")
     ax.set_xlabel(x_label)
     if show_legend is True: