code/plots/delay_filtering.py

@@ -8,7 +8,7 @@ import matplotlib as mplt
 import matplotlib.pyplot as plt
 
 from ..util import DelayType
-plt.style.use("./code/plots/pnas_onecolumn.mplstyle")
+plt.style.use("code/plots/pnas_onecolumn.mplstyle")
 
 from .figure_style import subfig_labelsize, subfig_labelweight, despine
 

code/plots/lif_results.py

@@ -7,7 +7,7 @@ import matplotlib.image as mpimg
 
 from .figure_style import subfig_labelsize, subfig_labelweight, despine
 
-plt.style.use("./code/plots/pnas_onecolumn.mplstyle")
+plt.style.use("code/plots/pnas_onecolumn.mplstyle")
 
 def plot_info_errorbar(axis, pop_size, info, label="", ls="-", color="tab:blue"):
     info_mean = np.mean(info, axis=1)

code/plots/property_correlations.py

@@ -107,7 +107,7 @@ def plot_baseline_properties(axes, df, markersize, correctionFactor):
     twinAx.set_ylim([0, 1.5])
     twinAx.patch.set_visible(False)
     despine(twinAx, ["top","right", "bottom", "left"], True)
-    
+
     ax = axes[0]
     ax.set_xticks(np.arange(0, 1.1, 0.2))
     ax.set_xlim([0, 1.])
@@ -117,8 +117,9 @@ def plot_baseline_properties(axes, df, markersize, correctionFactor):
     ax.set_ylim([0, 60])
     ax.set_yticks([0, 20, 40])
     ax.set_yticks([0, 10, 20, 30, 40], minor=True)
-    ax.yaxis.set_label_coords(-0.28, 0.5)
+    ax.yaxis.set_label_coords(-0.15, 0.5)
 
+    # firing rate
     y_values = df.firing_rate.values
     remove_bounds(xPositions.copy(), y_values.copy())
     ax = axes[1]
@@ -135,14 +136,9 @@ def plot_baseline_properties(axes, df, markersize, correctionFactor):
     ax.set_xticks(np.arange(0.0, 1.01, 0.2))
     ax.set_xticks(np.arange(0.0, 1.0, 0.1), minor=True)
     ax.set_xticklabels([])
-    ax.yaxis.set_label_coords(-0.35, 0.5)
-
-    box_axis = axes[7]
-    box_axis.boxplot(y_values, positions=[0.6], widths=[0.2], showfliers=False,
-                     boxprops=boxprops, whiskerprops=whiskerprops, capprops=capprops)
-    box_axis.set_xlim([0.35, 1])
-    box_axis.set_ylim(ax.get_ylim())
+    ax.yaxis.set_label_coords(-0.15, 0.5)
 
+    # CV of the interspike interval
     y_values = df.cv.values
     ax = axes[2]
     plotMeanProperty(ax, posBins, xPositions, y_values)
@@ -156,14 +152,9 @@ def plot_baseline_properties(axes, df, markersize, correctionFactor):
     ax.set_xticks(np.arange(0.0, 1.01, 0.2))
     ax.set_xticks(np.arange(0.0, 1.0, 0.1), minor=True)
     ax.set_xticklabels([])
-    ax.yaxis.set_label_coords(-0.28, 0.5)
-
-    box_axis = axes[8]
-    box_axis.boxplot(y_values, positions=[0.6], widths=[0.2], showfliers=False,
-                     boxprops=boxprops, whiskerprops=whiskerprops, capprops=capprops)
-    box_axis.set_xlim([0.35, 1])
-    box_axis.set_ylim(ax.get_ylim())
+    ax.yaxis.set_label_coords(-0.15, 0.5)
 
+    # burst fraction
     y_values = df.burst_fraction.values
     ax = axes[3]
     remove_bounds(xPositions.copy(), y_values.copy(), label="burst fraction")
@@ -178,21 +169,16 @@ def plot_baseline_properties(axes, df, markersize, correctionFactor):
     ax.set_xticks(np.arange(0.0, 1.01, 0.2))
     ax.set_xticks(np.arange(0.0, 1.0, 0.1), minor=True)
     ax.set_xticklabels([])
-    ax.yaxis.set_label_coords(-0.35, 0.5)
-    
-    box_axis = axes[9]
-    box_axis.boxplot(y_values, positions=[0.6], widths=[0.2], showfliers=False,
-                     boxprops=boxprops, whiskerprops=whiskerprops, capprops=capprops)
-    box_axis.set_xlim([0.35, 1])
-    box_axis.set_ylim(ax.get_ylim())
+    ax.yaxis.set_label_coords(-0.15, 0.5)
 
+    # Vector strength
     y_values = df.vector_strength.values
     ax = axes[4]
     plotMeanProperty(ax, posBins, xPositions, y_values)
     plotLinregress(ax, xPositions, y_values, bonferroni_factor=correctionFactor, feature="vector strength")
     ax.scatter(xPositions, y_values, color="tab:blue", s=markersize)
     ax.set_ylabel('vector strength', fontsize=7)
-    ax.yaxis.set_label_coords(-0.28, 0.5)
+    ax.yaxis.set_label_coords(-0.15, 0.5)
     ax.set_ylim(0.6, 1.0)
     ax.set_yticks(np.arange(0.6, 1.01, 0.2))
     ax.set_yticks(np.arange(0.6, 1.01, 0.1), minor=True)
@@ -200,44 +186,14 @@ def plot_baseline_properties(axes, df, markersize, correctionFactor):
     ax.set_xticks(np.arange(0.0, 1.01, 0.2))
     ax.set_xticks(np.arange(0.0, 1.0, 0.1), minor=True)
     ax.set_xlabel("receptor position [rel.]", fontsize=7)
-    ax.xaxis.set_label_coords(0.5, -0.45)
-
-    box_axis = axes[10]
-    box_axis.boxplot(y_values, positions=[0.6], widths=[0.2], showfliers=False,
-                     boxprops=boxprops, whiskerprops=whiskerprops, capprops=capprops)
-    box_axis.set_xlim([0.35, 1])
-    box_axis.set_ylim(ax.get_ylim())
-
-    absolute_positions = df.receptor_pos_absolute.values
-    delay_times = df.phase_time.values * 1000
-    ax = axes[5]
-    ax.scatter(absolute_positions, delay_times, color="tab:blue", s=markersize)
-    m, n = plotLinregress(ax, absolute_positions, delay_times, bonferroni_factor=correctionFactor, feature="phase")
-    plotMeanProperty(ax, np.linspace(0, 120, 12), absolute_positions, delay_times)
-
-    print(f"Inverse slope of delay and position regression: {1./m} mm/ms")
-    ax.set_xlim([0, 140])
-    ax.set_xticks(range(0, 141, 40), minor=False)
-    ax.set_xticks(range(0, 141, 20), minor=True)
-    ax.set_ylim([0, 3])
-    ax.set_yticks(range(0, 4, 1))
-    ax.set_ylabel("delay [ms]", fontsize=7)
-    ax.set_xlabel("receptor position [mm]", fontsize=7)
-    ax.yaxis.set_major_formatter(FormatStrFormatter('%.1f'))
-    ax.yaxis.set_label_coords(-0.35, 0.5)
-    ax.xaxis.set_label_coords(0.5, -0.425)
-
-    box_axis = axes[11]
-    box_axis.boxplot( delay_times[delay_times >= 0], positions=[0.6], widths=[0.2], showfliers=False,
-                     boxprops=boxprops, whiskerprops=whiskerprops, capprops=capprops)
-    box_axis.set_xlim([0.35, 1])
-    box_axis.set_ylim(ax.get_ylim())
+    ax.xaxis.set_label_coords(0.5, -0.35)
 
 
 def plot_driven_properties(axes, df, markersize, correctionFactor):
     xPositions = df.receptor_pos_relative.values
     posBins = np.linspace(0, 1, 10)
 
+    # response modulation
     y_values = df.response_modulation.values
     ax = axes[0]
     ax.scatter(xPositions, y_values, color="tab:blue", s=markersize)
@@ -250,13 +206,9 @@ def plot_driven_properties(axes, df, markersize, correctionFactor):
     ax.set_yticks(np.arange(0, 301, 100))
     ax.set_yticks(np.arange(0, 300, 50), minor=True)
     ax.set_ylabel("modulation [Hz]", fontsize=7)
-    ax.yaxis.set_label_coords(-0.35, 0.5)
-    box_axis = axes[6]
-    box_axis.boxplot(y_values, positions=[0.6], widths=[0.2], showfliers=False,
-                     boxprops=boxprops, whiskerprops=whiskerprops, capprops=capprops)
-    box_axis.set_xlim([0.35, 1])
-    box_axis.set_ylim(ax.get_ylim())
+    ax.yaxis.set_label_coords(-0.15, 0.5)
 
+    # response variability
     y_values = df.response_variability.values
     ax = axes[1]
     ax.scatter(xPositions, y_values, color="tab:blue", s=markersize)
@@ -269,13 +221,9 @@ def plot_driven_properties(axes, df, markersize, correctionFactor):
     ax.set_yticks(np.arange(0, 301, 100))
     ax.set_yticks(np.arange(0, 300, 50), minor=True)
     ax.set_ylabel("variability [Hz]", fontsize=7)
-    ax.yaxis.set_label_coords(-0.28, 0.5)
-    box_axis = axes[7]
-    box_axis.boxplot(y_values, positions=[0.6], widths=[0.2], showfliers=False,
-                     boxprops=boxprops, whiskerprops=whiskerprops, capprops=capprops)
-    box_axis.set_xlim([0.35, 1])
-    box_axis.set_ylim(ax.get_ylim())
+    ax.yaxis.set_label_coords(-0.15, 0.5)
 
+    # lower and upper cutoff
     ax = axes[2]
     ax.scatter(xPositions, df.gain_cutoff_lower.values, color="tab:blue", s=markersize)
     plotLinregress(ax, xPositions, df.gain_cutoff_lower.values, correctionFactor, color="tab:blue", label="lower", feature="lower cutoff")
@@ -290,38 +238,11 @@ def plot_driven_properties(axes, df, markersize, correctionFactor):
     ax.set_yticks(np.arange(0, 301, 100))
     ax.set_yticks(np.arange(0, 300, 50), minor=True)
     ax.set_ylabel("cutoff [Hz]", fontsize=7)
-    ax.yaxis.set_label_coords(-0.35, 0.5)
-    box_axis = axes[8]
-    box_axis.boxplot(df.gain_cutoff_lower.values, boxprops=boxprops, whiskerprops=whiskerprops,
-                     capprops=capprops, positions=[0.45], widths=[0.2], showfliers=False)
-    box_axis.boxplot(df.gain_cutoff_upper.values, positions=[0.75], widths=[0.2],
-                     boxprops={"color": "tab:red","linewidth": 0.75}, 
-                     whiskerprops={"color": "tab:red","linewidth": 0.75}, 
-                     capprops={"color": "tab:red","linewidth": 0.75}, showfliers=False)
-    box_axis.set_xlim([0.30, 1])
-    box_axis.set_ylim(ax.get_ylim())
-
-    ax = axes[3]
-    y_values = df.gain_pass_band.values
-    ax.scatter(xPositions, y_values , color="tab:blue", s=markersize)
-    plotLinregress(ax, xPositions, y_values, correctionFactor, color="tab:red",feature="width")
-    plotMeanProperty(ax, posBins, xPositions, y_values)
-    ax.set_xticks(np.arange(0, 1.1, 0.2))
-    ax.set_xticks(np.arange(0, 1.01, 0.1), minor=True)
-    ax.set_xticklabels([])
-    ax.set_ylim([0, 200])
-    ax.set_yticks(np.arange(0, 201, 100))
-    ax.set_yticks(np.arange(0, 201, 50), minor=True)
-    ax.set_ylabel("width [Hz]", fontsize=7)
-    ax.yaxis.set_label_coords(-0.28, 0.5)
-    box_axis = axes[9]
-    box_axis.boxplot(y_values, positions=[0.6], widths=[0.2], showfliers=False, boxprops=boxprops, 
-                    whiskerprops=whiskerprops, capprops=capprops,)
-    box_axis.set_xlim([0.35, 1])
-    box_axis.set_ylim(ax.get_ylim())
+    ax.yaxis.set_label_coords(-0.15, 0.5)
 
+    # maximum gain
     y_values = df.gain_maximum.values / 1000   # convert ot kHz/mV
-    ax = axes[4]
+    ax = axes[3]
     ax.scatter(xPositions, y_values, color="tab:blue", s=markersize)
     plotLinregress(ax, xPositions, y_values, correctionFactor, color="tab:red", feature="gain")
     plotMeanProperty(ax, posBins, xPositions, y_values)
@@ -332,17 +253,12 @@ def plot_driven_properties(axes, df, markersize, correctionFactor):
     ax.set_yticks(np.arange(0, 4.1, 2))
     ax.set_yticks(np.arange(0, 4.1, 1), minor=True)
     ax.set_ylabel("gain [kHz/mV]", fontsize=7)
-    ax.yaxis.set_label_coords(-0.35, 0.5)
+    ax.yaxis.set_label_coords(-0.15, 0.5)
     ax.yaxis.set_major_formatter(FormatStrFormatter('%.1f'))
-    box_axis = axes[10]
-    box_axis.boxplot(y_values, positions=[0.6], widths=[0.2], showfliers=False,
-                     boxprops=boxprops, whiskerprops=whiskerprops, capprops=capprops)
-    box_axis.set_xlim([0.35, 1])
-    box_axis.set_ylim(ax.get_ylim())
 
+    # mututal information
     y_values = df.mi.values
-
-    ax = axes[5]
+    ax = axes[4]
     ax.scatter(xPositions, y_values, color="tab:blue", s=markersize)
     plotLinregress(ax, xPositions, y_values, correctionFactor, color="tab:red", feature="mutual info.")
     plotMeanProperty(ax, posBins, xPositions, y_values)
@@ -353,52 +269,37 @@ def plot_driven_properties(axes, df, markersize, correctionFactor):
     ax.set_yticks(np.arange(0, 601, 100), minor=True)
     ax.set_ylabel("mutual info. [bit/s]", fontsize=7)
     ax.set_xlabel("receptor position [rel.]", fontsize=7)
-    ax.yaxis.set_label_coords(-0.28, 0.5)
+    ax.yaxis.set_label_coords(-0.15, 0.5)
     ax.yaxis.set_major_formatter(FormatStrFormatter('%i'))
-    ax.xaxis.set_label_coords(0.5, -0.425)
-    box_axis = axes[11]
-    box_axis.boxplot(y_values, positions=[0.6], widths=[0.2], showfliers=False, 
-                     boxprops=boxprops, whiskerprops=whiskerprops, capprops=capprops)
-    box_axis.set_xlim([0.35, 1])
-    box_axis.set_ylim(ax.get_ylim())
+    ax.xaxis.set_label_coords(0.5, -0.35)
 
 
 def layout_figure():
-    driven_axes = list(range(12))
-    baseline_axes = list(range(12))
+    baseline_subfig_labels = ["A", "B", "C", "D", "E"]
+    driven_subfig_labels = ["F", "G", "H", "I", "J"]
+    subplots_per_col = max([len(baseline_subfig_labels), len(driven_subfig_labels)])
 
-    fig = plt.figure(figsize=(5.7, 5.0))
-    subfigs = fig.subfigures(1, 2, wspace=0.07)
-    gr = gridspec.GridSpec(25, 2, width_ratios=[3, 1], wspace=0.0)
+    driven_axes = list(range(len(baseline_subfig_labels)))
+    baseline_axes = list(range(len(driven_subfig_labels)))
 
-    baseline_subfig_labels = ["A", "B", "C", "D", "E", "F"]
-    driven_subfig_labels = ["G", "H", "I", "J", "K", "L"]
-    subplots_per_col = max([len(baseline_subfig_labels), len(driven_subfig_labels)])
+    fig = plt.figure(figsize=(5.7, 5.5))
+    subfigs = fig.subfigures(1, 2, wspace=0.05)
+    gr = gridspec.GridSpec(subplots_per_col, 1, hspace=0.5)
 
     for i in range(subplots_per_col):
-        if i < subplots_per_col-1:
-            rowstart = i * subplots_per_col // 2 + i
-            rowend = rowstart + subplots_per_col // 2
-        else:
-            rowstart = i * subplots_per_col // 2 + i + 1
-            rowend = rowstart + subplots_per_col // 2
         baseline_label = baseline_subfig_labels[i]
         driven_label = driven_subfig_labels[i]
-        baseline_axes[i] = subfigs[0].add_subplot(gr[rowstart:rowend, 0])
-        baseline_axes[subplots_per_col + i] = subfigs[0].add_subplot(gr[rowstart:rowend, 1])
-        driven_axes[i] = subfigs[1].add_subplot(gr[rowstart:rowend, 0])
-        driven_axes[subplots_per_col + i] = subfigs[1].add_subplot(gr[rowstart:rowend, 1])
+        baseline_axes[i] = subfigs[0].add_subplot(gr[i, 0])
+        driven_axes[i] = subfigs[1].add_subplot(gr[i, 0])
 
-        baseline_axes[i].text(-0.5, 1.0, baseline_label, fontsize=subfig_labelsize, fontweight=subfig_labelweight, 
+        baseline_axes[i].text(-0.225, 1.05, baseline_label, fontsize=subfig_labelsize, fontweight=subfig_labelweight, 
                               transform=baseline_axes[i].transAxes, ha="center", va="bottom")
-        driven_axes[i].text(-0.55, 1.0, driven_label, fontsize=subfig_labelsize, fontweight=subfig_labelweight, 
+        driven_axes[i].text(-0.225, 1.05, driven_label, fontsize=subfig_labelsize, fontweight=subfig_labelweight, 
                               transform=driven_axes[i].transAxes, ha="center", va="bottom")
         despine(baseline_axes[i], ["top", "right"], False)
         despine(driven_axes[i], ["top", "right"], False)
-        baseline_axes[i + subplots_per_col].axis("off")
-        driven_axes[i + subplots_per_col].axis("off")
 
-    pic_ax = subfigs[0].add_axes((0.325, 0.88, 0.5, 0.15))
+    pic_ax = subfigs[0].add_axes((0.095, 0.88, 1.0, 0.155))
     if os.path.exists("figures/fishsketch.png"):
         img = mpimg.imread("figures/fishsketch.png")
         pic_ax.imshow(img)
@@ -412,6 +313,7 @@ def layout_figure():
 def plot_position_correlations(args):
     if not os.path.exists(args.driven_frame) or not os.path.exists(args.baseline_frame):
         raise ValueError(f"Results data frame(s) not found! ({args.driven_frame} or {args.baseline_frame})")
+
     driven_df = pd.read_csv(args.driven_frame, sep=";", index_col=0)
     baseline_df = pd.read_csv(args.baseline_frame, sep=";", index_col=0)
     markersize = 0.5
@@ -422,7 +324,7 @@ def plot_position_correlations(args):
     print("Driven response:")
     plot_driven_properties(driven_axes, driven_df, markersize, correctionFactor=5)
 
-    fig.subplots_adjust(left=0.3, right=0.995, top=0.95, hspace=0.5, wspace=0.7, bottom=0.025 )
+    fig.subplots_adjust(left=0.2, right=0.975, top=0.95, bottom=0.075)
     if args.nosave:
         plt.show()
     else: