address PR comments and clean up code

nuc_morph_analysis/analyses/dataset_images_for_figures/figure_helper.py

@@ -109,6 +109,7 @@ def return_glasbey_on_dark(N=255, cell_id=None, cell_color=None, from_list=False
     N: int, the number of colors to return
     cell_id: int, the cell id to change the color of
     cell_color: array, the RGB color to change the cell to
+    from_list: bool, whether colormap should be returned from ListedColormap or not
     """
     from matplotlib.colors import LinearSegmentedColormap, ListedColormap
     from nuc_morph_analysis.analyses.dataset_images_for_figures.glasbey_on_dark import (

nuc_morph_analysis/analyses/density/confirm_no_corr_btn_density_and _nuclear_area.py → nuc_morph_analysis/analyses/density/extra_checks/check_for_size_dependence.py

@@ -1,4 +1,5 @@
 #%%
+# this code is not used in the final analysis, but was used to confirm that there is no correlation between density and nuclear area
 from nuc_morph_analysis.lib.visualization.reference_points import COLONY_COLORS, COLONY_LABELS
 import matplotlib.pyplot as plt
 import numpy as np
@@ -9,6 +10,8 @@
 from pathlib import Path
 from sklearn.linear_model import LinearRegression
 from matplotlib.ticker import MaxNLocator
+import matplotlib
+
 
 #%%
 # set figure directory
@@ -22,7 +25,6 @@
 dfm = filter_data.all_timepoints_minimal_filtering(df)
 
 #%% # plot density over time for each colony  along colony time 
-import matplotlib
 matplotlib.rcParams.update({'font.size': 8})
 matplotlib.rcParams.update({'axes.titlesize': 8})
 matplotlib.rcParams.update({'axes.labelsize': 8})
@@ -36,10 +38,10 @@
 
 
 #%%
-# plot density as a function of nucleus size (and compare to old density metric)
+# plot density as a function of nucleus size 
 colony='medium'
 x_col = '2d_area_nucleus'
-for yi,y_col in enumerate(['2d_area_nuc_cell_ratio','density','inv_cyto_density']):
+for yi,y_col in enumerate(['2d_area_nuc_cell_ratio']):
 
     dfsub = dfm[dfm['colony']==colony].copy()
     dfsub.dropna(subset=[y_col],inplace=True)

nuc_morph_analysis/analyses/density/figure_watershed_based_density_schematic.py

@@ -14,8 +14,7 @@
 
 from nuc_morph_analysis.analyses.dataset_images_for_figures.figure_helper import return_glasbey_on_dark
 
-
-from nuc_morph_analysis.analyses.density.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.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
 
 import matplotlib
@@ -64,7 +63,7 @@ def determine_colormaps(img,key,crop_exp):
 
 def run_validation_and_plot(TIMEPOINT=88,track=81463,colony='medium',RESOLUTION_LEVEL=1,plot_everything=False, testing=False):
     """
-    run an image through the watershed based pseudo cell segmentation and examine the outputs
+    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
 
     Parameters
@@ -121,7 +120,6 @@ def run_validation_and_plot(TIMEPOINT=88,track=81463,colony='medium',RESOLUTION_
     for full_crop, sizes in [('crop',(track_x,track_y,crop_w,crop_h)),('full',(0,0,mip.shape[1],mip.shape[0]))]:
         x1,y1,w,h = sizes
         crop_exp = np.index_exp[y1:y1+h,x1:x1+w]
-        # key_list = ['raw_image','mip_of_labeled_image','binarized_mip','distance','pseudo_cells_img','overlay','colorize']
         key_list = ['mip_of_labeled_image','distance','pseudo_cells_img','colorize']
 
 
@@ -201,29 +199,24 @@ def run_validation_and_plot(TIMEPOINT=88,track=81463,colony='medium',RESOLUTION_
                 ax.set_position(position)
                 # adjust position of colorbar
                 cbar_position = cbar.ax.get_position()
-                # cbar.ax.set_position([cbar_position.x0+0.05,cbar_position.y0,cbar_position.width,cbar_position.height]) # for left location
                 cbar.ax.set_position([cbar_position.x0,cbar_position.y0-0.15,cbar_position.width,cbar_position.height]) # for bottom location
 
         # now save the figure
         savedir = Path(__file__).parent / 'figures' / 'watershed_figure_illustration'
         savedir.mkdir(exist_ok=True,parents=True)
 
-        for ext in ['.png','.pdf']:
-            savename = f'{colony}_{track}_{TIMEPOINT}_{full_crop}_res{RESOLUTION_LEVEL}_{cmapstr}'
-            savepath = savedir / savename
-            print(f'Saved figure to {savepath}')
-            save_and_show_plot(str(savepath),
-                               file_extension=ext,
-                               figure=fig,
-                               transparent=True,
-                               keep_open=True,
-                               **{'dpi':600}
-                            )
+        ext = '.pdf'
+        savename = f'{colony}_{track}_{TIMEPOINT}_{full_crop}_res{RESOLUTION_LEVEL}_{cmapstr}'
+        savepath = savedir / savename
+        save_and_show_plot(str(savepath),
+                            file_extension=ext,
+                            figure=fig,
+                            transparent=True,
+                            keep_open=True,
+                            **{'dpi':600}
+                        )
 
 if __name__ == '__main__':
     # set the details
-    # dft0 = run_validation_and_plot(120,track=84619,colony='medium',RESOLUTION_LEVEL=1,plot_everything=True)
-    # dft1 = run_validation_and_plot(0,track=87172,colony='medium',RESOLUTION_LEVEL=1,plot_everything=False)
-    # dft1 = run_validation_and_plot(48,track=87099,colony='medium',RESOLUTION_LEVEL=1,plot_everything=False)
     dft1 = run_validation_and_plot(88,track=81463,colony='medium',RESOLUTION_LEVEL=1,plot_everything=False)