Density fig updates

nuc_morph_analysis/analyses/dataset_images_for_figures/figure_helper.py

@@ -98,7 +98,7 @@ def get_save_dir_and_fig_panel_str(figure, panel):
     return savedir, fig_panel_str
 
 
-def return_glasbey_on_dark(N=255, cell_id=None, cell_color=None):
+def return_glasbey_on_dark(N=255, cell_id=None, cell_color=None, from_list=False):
     """
     Publication
     The Glasbey LUT is based on the publication:
@@ -110,7 +110,7 @@ def return_glasbey_on_dark(N=255, cell_id=None, cell_color=None):
     cell_id: int, the cell id to change the color of
     cell_color: array, the RGB color to change the cell to
     """
-    from matplotlib.colors import LinearSegmentedColormap
+    from matplotlib.colors import LinearSegmentedColormap, ListedColormap
     from nuc_morph_analysis.analyses.dataset_images_for_figures.glasbey_on_dark import (
         glasbey_on_dark_func,
     )
@@ -136,7 +136,8 @@ def return_glasbey_on_dark(N=255, cell_id=None, cell_color=None):
     cmap_name = "glasbey_on_dark"
     rgb_array0_1 = [tuple(np.asarray(x) / 255) for x in glasbey_on_dark]
     cmap = LinearSegmentedColormap.from_list(cmap_name, rgb_array0_1, N=N + 1)
-
+    if from_list:
+        cmap = ListedColormap(rgb_array0_1, cmap_name, N=N)
     return np.asarray(rgb_array0_255), cmap, rgb_array0_255
 
 

nuc_morph_analysis/analyses/density/pseudocell_based_density_analysis.py → nuc_morph_analysis/analyses/density/confirm_no_corr_btn_density_and _nuclear_area.py

@@ -8,6 +8,7 @@
 from nuc_morph_analysis.lib.preprocessing import filter_data
 from pathlib import Path
 from sklearn.linear_model import LinearRegression
+from matplotlib.ticker import MaxNLocator
 
 #%%
 # set figure directory
@@ -21,52 +22,19 @@
 dfm = filter_data.all_timepoints_minimal_filtering(df)
 
 #%% # plot density over time for each colony  along colony time 
-x_col = "colony_time"
-column_val = 'label_img'
-feature_list = ['2d_area_nuc_cell_ratio','density','2d_area_nucleus','2d_area_pseudo_cell',
-                '2d_area_cyto','inv_cyto_density','2d_intensity_min_edge','2d_intensity_max_edge','2d_intensity_mean_edge'
-                ]
-for y_col in feature_list:
-    fig,ax = plt.subplots(figsize=(4,3))
-
-    for colony in ['small','medium','large']:
-
-        dfsub = dfm[dfm['colony']==colony].copy()
-        dfsub.dropna(subset=[y_col],inplace=True)
-
-        # create a pivot of the dataframe to get a 2d array of track_id x timepoint with each value being the density
-        pivot = dfsub.pivot(index=x_col, columns=column_val, values=y_col)
-        pivot.head()
-
-        mean = pivot.median(axis=1)
-        lower = pivot.quantile(0.05,axis=1)
-        upper = pivot.quantile(0.95,axis=1)
-
-        xscale_factor, xlabel, xunit, xlimit = get_plot_labels_for_metric(x_col)
-        x = mean.index * xscale_factor
-        yscale_factor, ylabel, yunit, ylimit = get_plot_labels_for_metric(y_col)
-        y = mean.values * yscale_factor
-        yl = lower.values * yscale_factor
-        yu = upper.values * yscale_factor
-
-        ax.plot(x, y, label=COLONY_LABELS[colony], color=COLONY_COLORS[colony])
-        ax.fill_between(x, yl, yu, alpha=0.2, color=COLONY_COLORS[colony],
-                        edgecolor='none')
-        ax.set_xlabel(f"{xlabel} {xunit}")
-        ax.set_ylabel(f"{ylabel} {yunit}\n(90% interpercentile range)")
+import matplotlib
+matplotlib.rcParams.update({'font.size': 8})
+matplotlib.rcParams.update({'axes.titlesize': 8})
+matplotlib.rcParams.update({'axes.labelsize': 8})
+matplotlib.rcParams.update({'xtick.labelsize': 8})
+matplotlib.rcParams.update({'ytick.labelsize': 8})
+matplotlib.rcParams.update({'legend.fontsize': 7})
+matplotlib.rcParams.update({'legend.title_fontsize': 8})
+matplotlib.rcParams.update({'figure.titlesize': 8})
+# make axis linewidth thinner
+matplotlib.rcParams.update({'axes.linewidth': 0.5})
 
 
-    ax.legend(loc="upper right", handletextpad=0.7, frameon=False)
-    plt.tight_layout()
-    for ext in ['.png','.pdf']:
-        save_and_show_plot(
-            f"{figdir}/{y_col}_vs_{x_col}_by_colony",
-            file_extension=ext,
-            dpi=300,
-            transparent=False,
-        )
-    plt.show()
-
 #%%
 # plot density as a function of nucleus size (and compare to old density metric)
 colony='medium'
@@ -88,6 +56,7 @@
     ax.set_xlabel(f"{xlabel} {xunit}")
     ax.set_ylabel(f"{ylabel} {yunit}")
 
+
     # add best fit line
     reg = LinearRegression().fit(x.reshape(-1,1), y)
     y_pred = reg.predict(x.reshape(-1,1))