Merge pull request #62 from didi-hou/plot_enhance

Plots enhancing

figures/MAM2EBRAINS/M2E_firing_rate.py

@@ -70,18 +70,18 @@ def plot_firing_rate_over_areas(M, data_path):
         matrix.append(binned_spikes)
 
     matrix = np.array(matrix)
-    normalized_matrix = (matrix - np.min(matrix)) / (np.max(matrix) - np.min(matrix))
+    # normalized_matrix = (matrix - np.min(matrix)) / (np.max(matrix) - np.min(matrix))
 
-    fig = pl.figure()
-    fig.suptitle('Normalized instantaneous firing rate over simulated areas', fontsize=16, x=0.45, y=0.95)
+    fig = pl.figure(figsize=(12, 5))
+    fig.suptitle('Instantaneous firing rate over simulated areas', fontsize=16, x=0.45, y=0.95)
     ax = pl.subplot()
 
     cmap = pl.get_cmap('YlOrBr')
 
     # masked_matrix = np.ma.masked_where(np.isnan(matrix), matrix)
     ax.patch.set_hatch('x')
-    im = ax.pcolormesh(normalized_matrix, cmap=cmap, edgecolors='None')
-    ax.set_xlim(0, normalized_matrix[0].size)
+    im = ax.pcolormesh(matrix, cmap=cmap, edgecolors='None', vmin=0)
+    ax.set_xlim(0, matrix[0].size)
 
     ax.set_xticks([i for i in np.arange(0, 100, 10)])
     ax.set_xticklabels([i for i in np.arange(500, 600, 10)])

figures/MAM2EBRAINS/M2E_visualize_interareal_connectivity.py

@@ -28,21 +28,21 @@ def visualize_interareal_connectivity(M):
     nrows = 1
     ncols = 2
     # width = 6.8556
-    width = 15
+    width = 12
     panel_wh_ratio = 0.7 * (1. + np.sqrt(5)) / 2.  # golden ratio
 
     height = width / panel_wh_ratio * float(nrows) / ncols
     # print(width, height)
     pl.rcParams['figure.figsize'] = (width, height)
 
     fig = pl.figure()
-    fig.suptitle('Interareal probabilistic connectivity for full-scale (left) and down-scaled (right) multi-area model', fontsize=16, y=1.05)
+    fig.suptitle('Area-level connectivity of the full-scale and down-scaled MAM expressed as relative indegrees for each target area', fontsize=15, x=0.5, y=1.05)
     axes = {}
 
     # gs1 = gridspec.GridSpec(2, 2)
     gs1 = gridspec.GridSpec(1, 2)
     # gs1.update(left=0.06, right=0.95, top=0.95, bottom=0.1, wspace=0.1, hspace=0.3)
-    gs1.update(left=0.06, right=0.95, top=0.95, bottom=0.1, wspace=0.3, hspace=0.3)
+    gs1.update(left=0.1, right=0.95, top=0.95, bottom=0.1, wspace=0.3, hspace=0.3)
 
     # axes['A'] = pl.subplot(gs1[:1, :1])
     # axes['B'] = pl.subplot(gs1[:1, 1:2])

figures/MAM2EBRAINS/M2E_visualize_time_ave_pop_rates.py

@@ -29,7 +29,7 @@ def plot_time_averaged_population_rates(M, data_path, area_list=None, **keywords
     # matrix = np.zeros((len(area_list), len(A.network.structure['V1'])))
     matrix = np.zeros((len(area_list), len(M.structure['V1'])))
 
-    fig = plt.figure(figsize=(12, 4))
+    fig = plt.figure(figsize=(12, 3))
     fig.suptitle('Time-averaged firing rate over simulated populations', fontsize=15, x=0.43)
     ax = fig.add_subplot(111)
 
@@ -51,20 +51,23 @@ def plot_time_averaged_population_rates(M, data_path, area_list=None, **keywords
                 rate = np.nan
             matrix[i][j] = rate
 
-    cm = plt.cm.jet
-    cm = cm.from_list('mycmap', [(0., 64./255., 192./255.),  # custom dark blue
-                                 (0., 128./255., 192./255.),  # custom light blue
-                                 'white',
-                                 (245./255., 157./255., 115./255.),  # custom light red
-                                 (192./255., 64./255., 0.)], N=256)  # custom dark red
+    # cm = plt.cm.jet
+    # cm = cm.from_list('mycmap', [(0., 64./255., 192./255.),  # custom dark blue
+    #                              (0., 128./255., 192./255.),  # custom light blue
+    #                              'white',
+    #                              (245./255., 157./255., 115./255.),  # custom light red
+    #                              (192./255., 64./255., 0.)], N=256)  # custom dark red
+    cm = plt.get_cmap('YlOrBr')
     cm.set_under('0.3')
-    cm.set_bad('k')
+    # cm.set_bad('k')
+    cm.set_bad('white')
 
     matrix = np.transpose(matrix)
     masked_matrix = np.ma.masked_where(np.isnan(matrix), matrix)
     ax.patch.set_hatch('x')
-    im = ax.pcolormesh(masked_matrix, cmap=cm, edgecolors='None', norm=LogNorm(
-        vmin=0.01, vmax=100.))
+    # im = ax.pcolormesh(masked_matrix, cmap=cm, edgecolors='None', norm=LogNorm(
+    #     vmin=0.01, vmax=100.))
+    im = ax.pcolormesh(masked_matrix, cmap=cm, edgecolors='None', vmin=0)
     ax.set_xlim(0, matrix[0].size)
 
     x_index = np.arange(4.5, 31.6, 5.0)
@@ -82,9 +85,17 @@ def plot_time_averaged_population_rates(M, data_path, area_list=None, **keywords
     ax.set_yticklabels(['6I', '6E', '5I', '5E', '4I', '4E', '2/3I', '2/3E'])
     ax.set_ylabel('Population', size=13)
     ax.set_xlabel('Area index', size=13)
-    t = FixedLocator([0.01, 0.1, 1., 10., 100.])
+    # t = FixedLocator([0.01, 0.1, 1., 10., 100.])
+    # t = FixedLocator([0, 10, 20, 30, 40, 50])
+
+    # Iterate over the data and add 'X' for masked cells
+    for i in range(masked_matrix.shape[0]):
+        for j in range(masked_matrix.shape[1]):
+            if masked_matrix.mask[i, j]:
+                ax.text(j + 0.5, i + 0.5, 'X', va='center', ha='center', color='black', fontsize=23)
 
-    plt.colorbar(im, ticks=t)
+    # plt.colorbar(im, ticks=t)
+    plt.colorbar(im)
 
     if 'output' in keywords:
         # plt.savefig(os.path.join(A.output_dir, '{}_rates.{}'.format(A.simulation.label,

multi-area-model.ipynb

@@ -537,7 +537,9 @@
   {
    "cell_type": "markdown",
    "id": "2714fd2b-df6e-45b9-a7f4-f240a8c65ecf",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "source": [
     "### 3.2. Normalized instantaneous firing rate over simulated areas <a class=\"anchor\" id=\"section_3_2\"></a>"
    ]
@@ -566,7 +568,7 @@
    "id": "b6746ea4-91bd-44f5-acb6-83df15b05480",
    "metadata": {},
    "source": [
-    "An overview of time-averaged firing rate over simulated populations encoded in colors with areas along x-axis and populations along y-axis."
+    "An overview of time-averaged firing rate over simulated populations encoded in colors with areas along x-axis and populations along y-axis. The cells of population 4E and 4I in area TH are labeled with X as area TH does not have layer 4."
    ]
   },
   {