Finished fig_auditory_pathway.svg, pending feedback (all colors are temporary until color code is finalized).

python/save_latex_texts.py

@@ -9,22 +9,20 @@ plt.rc('text.latex', preamble=r'\usepackage{amsmath}')
 
 # Settings:
 fs_chart = 70
+fs_lines = 75
 fs_legend = 40
 show_figs = True
 grid_props = dict(left=0, right=1, top=1, bottom=0)
 
 fig_props = {
-    'figsize': (3, 3),
     'facecolor': 'none',
     'edgecolor': 'none',
     'frameon': False,
-    # 'rasterized': True,
     }
 
 ax_props = {
     'facecolor': 'none',
     'frame_on': False,
-    # 'rasterized': True,
     }
 
 text_props = {
@@ -33,40 +31,60 @@ text_props = {
     'y': 0.5,
     'ha': 'center',
     'va': 'center',
-    # 'rasterized': True,
     }
 
 # Targets:
 texts = {
-    'filt': (r'$x_{\text{filt}}$', fs_chart),
-    'env': (r'$x_{\text{env}}$', fs_chart),
-    'db': (r'$x_{\text{dB}}$', fs_chart),
-    'envdb': (r'$\begin{array}{c}x_{\text{env}}\\x_{\text{dB}}\end{array}$', fs_chart),
-    'adapt': (r'$x_{\text{adapt}}$', fs_chart),
-    'conv1': (r'$c_1$', fs_chart),
-    'conv2': (r'$c_2$', fs_chart),
-    'conv3': (r'$c_3$', fs_chart),
-    'bi1': (r'$b_{1, \Theta}$', fs_chart),
-    'bi2': (r'$b_{2, \Theta}$', fs_chart),
-    'bi3': (r'$b_{3, \Theta}$', fs_chart),
-    'feat1': (r'$f_{1, \Theta}$', fs_chart),
-    'feat2': (r'$f_{2, \Theta}$', fs_chart),
-    'feat3': (r'$f_{3, \Theta}$', fs_chart),
-    'BP': ('BP', fs_chart),
-    'LP': ('LP', fs_chart),
-    'HP': ('HP', fs_chart),
-    'tympanum': ('Tympanal\nMembrane', fs_legend),
-    'receptors': ('Receptor\nNeurons', fs_legend),
-    'interneurons': ('Local\nInterneurons', fs_legend),
-    'ascending': ('Ascending\nNeurons', fs_legend),
-    'threshold': ('Threshold\nNonlinearity', fs_legend),
-    'integration': ('Temporal\nAveraging', fs_legend),
-    'brain': ('Central\nBrain', fs_legend),
+    # CIRCUIT ELEMENTS:
+    # 'filt': (r'$x_{\text{filt}}$', fs_chart, (3, 3)),
+    # 'env': (r'$x_{\text{env}}$', fs_chart, (3, 3)),
+    # 'db': (r'$x_{\text{dB}}$', fs_chart, (3, 3)),
+    # 'envdb': (r'$\begin{array}{c}x_{\text{env}}\\x_{\text{dB}}\end{array}$', fs_chart, (3, 3)),
+    # 'adapt': (r'$x_{\text{adapt}}$', fs_chart, (3, 3)),
+    # 'conv1': (r'$c_1$', fs_chart, (3, 3)),
+    # 'conv2': (r'$c_2$', fs_chart, (3, 3)),
+    # 'conv3': (r'$c_3$', fs_chart, (3, 3)),
+    # 'bi1': (r'$b_{1, \Theta}$', fs_chart, (3, 3)),
+    # 'bi2': (r'$b_{2, \Theta}$', fs_chart, (3, 3)),
+    # 'bi3': (r'$b_{3, \Theta}$', fs_chart, (3, 3)),
+    # 'feat1': (r'$f_{1, \Theta}$', fs_chart, (3, 3)),
+    # 'feat2': (r'$f_{2, \Theta}$', fs_chart, (3, 3)),
+    # 'feat3': (r'$f_{3, \Theta}$', fs_chart, (3, 3)),
+    # 'out': (r'$\hat{y}$', fs_chart, (3, 3)),
+
+    # CIRCUIT OPERATIONS:
+    'rectlp': (r'$\lvert\cdot\lvert,h_{\text{LP}}$', fs_lines, (3, 3)),
+    'log': (r'$\text{log}$', fs_lines, (3, 3)),
+    'rectlplog': (r'$\begin{array}{c}||,h_{\text{LP}}\\\text{log}\end{array}$', fs_lines, (3, 3)),
+    'k1': (r'$k_1$', fs_lines, (3, 3)),
+    'k2': (r'$k_2$', fs_lines, (3, 3)),
+    'k3': (r'$k_3$', fs_lines, (3, 3)),
+    'bp': (r'$h_{\text{BP}}$', fs_lines, (3, 3)),
+    'lp': (r'$h_{\text{LP}}$', fs_lines, (3, 3)),
+    'hp': (r'$h_{\text{HP}}$', fs_lines, (3, 3)),
+    'theta1': (r'$\Theta_1$', fs_lines, (3, 3)),
+    'theta2': (r'$\Theta_2$', fs_lines, (3, 3)),
+    'theta3': (r'$\Theta_3$', fs_lines, (3, 3)),
+    'w1': (r'$\omega_1$', fs_lines, (3, 3)),
+    'w2': (r'$\omega_2$', fs_lines, (3, 3)),
+    'w3': (r'$\omega_3$', fs_lines, (3, 3)),
+    'thresh1': (r'$H(c_1-\Theta_1)$', fs_lines, (3, 3)),
+    'thresh2': (r'$H(c_2-\Theta_2)$', fs_lines, (3, 3)),
+    'thresh3': (r'$H(c_3-\Theta_3)$', fs_lines, (3, 3)),
+
+    # LEGEND ELEMENTS:
+    # 'tympanum': ('Tympanal\nMembrane', fs_legend, (3, 3)),
+    # 'receptors': ('Receptor\nNeuron\nPopulation', fs_legend, (3, 3)),
+    # 'interneurons': ('Local\nInterneuron\nPopulation', fs_legend, (3, 3)),
+    # 'ascending': ('Individual\nAscending\nNeurons', fs_legend, (3, 3)),
+    # 'threshold': ('Threshold\nNonlinearity', fs_legend, (3, 3)),
+    # 'integration': ('Temporal\nAveraging', fs_legend, (3, 3)),
+    # 'brain': ('Weighting\nand\nReadout', fs_legend, (3, 3)),
 }
 
 # Save each target string:
-for name, (text, fs) in texts.items():
-    fig, ax = plt.subplots(1, 1, gridspec_kw=grid_props, **fig_props)
+for name, (text, fs, size) in texts.items():
+    fig, ax = plt.subplots(figsize=size, gridspec_kw=grid_props, **fig_props)
     ax.set(**ax_props)
     ax.axis('off')
     ax.text(s=text, fontsize=fs, **text_props)