Some plot updates

Author: constantinpape

scripts/figures/plot_fig2.py

@@ -5,7 +5,7 @@
 import pandas as pd
 import matplotlib.pyplot as plt
 
-from util import literature_reference_values
+from util import literature_reference_values, SYNAPSE_DIR_ROOT
 
 png_dpi = 300
 
@@ -66,10 +66,10 @@ def fig_02c(save_path, plot=False, all_versions=False):
         # Plot
         plt.figure(figsize=(8, 5))
 
-        main_label_size = 20
+        main_label_size = 22
         sub_label_size = 16
-        main_tick_size = 12
-        legendsize = 16
+        main_tick_size = 16
+        legendsize = 18
 
         plt.scatter(x - offset, precision, label="Precision", marker="o", s=80)
         plt.scatter(x,         recall, label="Recall", marker="^", s=80)
@@ -100,7 +100,7 @@ def fig_02c(save_path, plot=False, all_versions=False):
 # Load the synapse counts for all IHCs from the relevant tables.
 def _load_ribbon_synapse_counts():
     ihc_version = "ihc_counts_v4c"
-    synapse_dir = f"/mnt/vast-nhr/projects/nim00007/data/moser/cochlea-lightsheet/predictions/synapses/{ihc_version}"
+    synapse_dir = os.path.join(SYNAPSE_DIR_ROOT, ihc_version)
     tables = [entry.path for entry in os.scandir(synapse_dir) if "ihc_count_M_LR" in entry.name]
     syn_counts = []
     for tab in tables:
@@ -112,8 +112,8 @@ def _load_ribbon_synapse_counts():
 def fig_02d_01(save_path, plot=False, all_versions=False, plot_average_ribbon_synapses=False):
     """Box plot showing the counts for SGN and IHC per (mouse) cochlea in comparison to literature values.
     """
-    main_tick_size = 16
-    main_label_size = 24
+    main_tick_size = 20
+    main_label_size = 26
 
     rows = 1
     columns = 3 if plot_average_ribbon_synapses else 2
@@ -217,7 +217,7 @@ def fig_02d_02(save_path, filter_zeros=True, plot=False):
     """
     cochleae = ["M_LR_000226_L", "M_LR_000226_R", "M_LR_000227_L", "M_LR_000227_R"]
     ihc_version = "ihc_counts_v4b"
-    synapse_dir = f"/mnt/vast-nhr/projects/nim00007/data/moser/cochlea-lightsheet/predictions/synapses/{ihc_version}"
+    synapse_dir = os.path.join(SYNAPSE_DIR_ROOT, ihc_version)
 
     max_dist = 3
     bins = 10

scripts/figures/plot_fig3.py

@@ -3,23 +3,29 @@
 import imageio.v3 as imageio
 from glob import glob
 
+import matplotlib
 import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
 from matplotlib import cm, colors
 
-from util import sliding_runlength_sum, frequency_mapping
+from util import sliding_runlength_sum, frequency_mapping, SYNAPSE_DIR_ROOT
 
 INPUT_ROOT = "/home/pape/Work/my_projects/flamingo-tools/scripts/M_LR_000227_R/scale3/frequency_mapping"
 
 png_dpi = 300
 
 
-def fig_03a(save_path):
-    import napari
-
-    path = os.path.join(INPUT_ROOT, "frequencies_IHC_v4c.tif")
-    vol = imageio.imread(path)
+def _plot_colormap(vol, title, plot, save_path):
+    # before creating the figure:
+    matplotlib.rcParams.update({
+        "font.size": 14,          # base font size
+        "axes.titlesize": 18,     # for plt.title / ax.set_title
+        "figure.titlesize": 18,   # for fig.suptitle (if you use it)
+        "xtick.labelsize": 14,
+        "ytick.labelsize": 14,
+        "legend.fontsize": 14,
+    })
 
     # Create the colormap
     fig, ax = plt.subplots(figsize=(6, 1.3))
@@ -32,43 +38,37 @@ def fig_03a(save_path):
 
     cb = plt.colorbar(cm.ScalarMappable(norm=norm, cmap=cmap), cax=ax, orientation="horizontal")
     cb.set_label("Frequency [kHz]")
-    plt.title("Tonotopic Mapping: IHCs")
+    plt.title(title)
     plt.tight_layout()
-    out_path = os.path.join(save_path)
-    plt.savefig(out_path)
-
-    # Show the image in napari for rendering.
-    v = napari.Viewer()
-    v.add_image(vol, colormap="viridis")
-    napari.run()
-
+    if plot:
+        plt.show()
+    plt.savefig(save_path)
+    plt.close()
 
-def fig_03b(save_path):
-    import napari
 
-    path = os.path.join(INPUT_ROOT, "frequencies_SGN_v2.tif")
+def fig_03a(save_path, plot, plot_napari):
+    path = os.path.join(INPUT_ROOT, "frequencies_IHC_v4c.tif")
     vol = imageio.imread(path)
+    _plot_colormap(vol, title="Tonotopic Mapping: IHCs", plot=plot, save_path=save_path)
 
-    # Create the colormap
-    fig, ax = plt.subplots(figsize=(6, 1.3))
-    fig.subplots_adjust(bottom=0.5)
+    # Show the image in napari for rendering.
+    if plot_napari:
+        import napari
+        v = napari.Viewer()
+        v.add_image(vol, colormap="viridis")
+        napari.run()
 
-    freq_min = np.min(np.nonzero(vol))
-    freq_max = vol.max()
-    norm = colors.Normalize(vmin=freq_min, vmax=freq_max, clip=True)
-    cmap = plt.get_cmap("viridis")
 
-    cb = plt.colorbar(cm.ScalarMappable(norm=norm, cmap=cmap), cax=ax, orientation="horizontal")
-    cb.set_label("Frequency [kHz]")
-    plt.title("Tonotopic Mapping: SGNs")
-    plt.tight_layout()
-    out_path = os.path.join(save_path)
-    plt.savefig(out_path)
+def fig_03b(save_path, plot, plot_napari):
+    path = os.path.join(INPUT_ROOT, "frequencies_SGN_v2.tif")
+    vol = imageio.imread(path)
+    _plot_colormap(vol, title="Tonotopic Mapping: SGNs", plot=plot, save_path=save_path)
 
     # Show the image in napari for rendering.
-    v = napari.Viewer()
-    v.add_image(vol, colormap="viridis")
-    napari.run()
+    if plot_napari:
+        import napari
+        v = napari.Viewer()
+        v.add_image(vol, colormap="viridis")
 
 
 def fig_03c_rl(save_path, plot=False):
@@ -102,7 +102,9 @@ def fig_03c_rl(save_path, plot=False):
 
 
 def fig_03c_octave(save_path, plot=False):
-    tables = glob("./ihc_counts/ihc_count_M_LR*.tsv")
+    # TODO update this table
+    ihc_version = "ihc_counts_v4c"
+    tables = glob(os.path.join(SYNAPSE_DIR_ROOT, ihc_version, "ihc_count_M_LR*.tsv"))
 
     result = {"cochlea": [], "octave_band": [], "value": []}
     for tab_path in tables:
@@ -152,14 +154,14 @@ def main():
     os.makedirs(args.figure_dir, exist_ok=True)
 
     # Panel A: Tonotopic mapping of IHCs (rendering in napari)
-    # fig_03a(save_path=os.path.join(args.figure_dir, "fig_03a.png"))
+    fig_03a(save_path=os.path.join(args.figure_dir, "fig_03a.png"), plot=args.plot, plot_napari=False)
 
     # Panel B: Tonotopic mapping of SGNs (rendering in napari)
-    # fig_03b(save_path=os.path.join(args.figure_dir, "fig_03b.png"))
+    fig_03b(save_path=os.path.join(args.figure_dir, "fig_03b.png"), plot=args.plot, plot_napari=False)
 
     # Panel C: Spatial distribution of synapses across the cochlea.
     # We have two options: running sum over the runlength or per octave band
-    fig_03c_rl(save_path=os.path.join(args.figure_dir, "fig_03c_runlength.png"), plot=args.plot)
+    # fig_03c_rl(save_path=os.path.join(args.figure_dir, "fig_03c_runlength.png"), plot=args.plot)
     fig_03c_octave(save_path=os.path.join(args.figure_dir, "fig_03c_octave.png"), plot=args.plot)
 
     # TODO: Panel D: Spatial distribution of SGN sub-types.

scripts/figures/util.py

@@ -1,6 +1,10 @@
 import pandas as pd
 import numpy as np
 
+# Directory with synapse measurement tables
+SYNAPSE_DIR_ROOT = "/mnt/vast-nhr/projects/nim00007/data/moser/cochlea-lightsheet/predictions/synapses"
+# SYNAPSE_DIR_ROOT = "./synapses"
+
 
 # Define the animal specific octave bands.
 def _get_mapping(animal):