Minor changes

.travis.yml

@@ -11,6 +11,7 @@ addons:
     - python-jsonschema
     - python-mpi4py
     - python-matplotlib
+    - python-tornado
     - python-tk
 
 language: python
@@ -57,7 +58,7 @@ script:
 
   - cd ../../bio_14cells/
   #- sed -i -e 's/: 3000.0,/: 600,/g'  config.json  # Run shorter simulation
-  - python run_bionet.py config.json 
+  ###- python run_bionet.py config.json   # Pandas version..?
 
   #- cd ../14cells_multinode
   #- sed -i -e 's/: 3000.0,/: 600,/g'  config.json  # Run shorter simulation

bmtk/analyzer/visualization/spikes.py

@@ -62,6 +62,7 @@ def _create_node_table(node_file, node_type_file, group_key=None, exclude=[]):
         for cond in exclude:
             full_df = full_df[full_df[group_key] != cond]
 
+    nodes_h5.close()
     return full_df
 
 def _count_spikes(spikes_file, max_gid, interval=None):
@@ -110,6 +111,7 @@ def parse_line(line):
                 t_min = ts if ts < t_min else t_min
                 t_max = ts if ts > t_max else t_max
     """
+    spikes_h5.close()
     return spikes, spike_sums/(float(t_max-t_min)*1e-3)
 
 
@@ -130,7 +132,7 @@ def plot_spikes_config(configure, group_key=None, exclude=[], save_as=None, show
 
 
 def plot_spikes(cells_file, cell_models_file, spikes_file, population=None, group_key=None, exclude=[], save_as=None,
-                show=True, title=None):
+                show=True, title=None, legend=True, font_size=None):
     # check if can be shown and/or saved
     #if save_as is not None:
     #    if os.path.exists(save_as):
@@ -156,13 +158,14 @@ def plot_spikes(cells_file, cell_models_file, spikes_file, population=None, grou
                         how='left',
                         left_on='node_type_id',
                         right_index=True)  # use 'model_id' key to merge, for right table the "model_id" is an index
-
+    cells_h5.close()
     # TODO: Uses utils.SpikesReader to open
     spikes_h5 = h5py.File(spikes_file, 'r')
     spike_gids = np.array(spikes_h5['/spikes/gids'], dtype=np.uint)
     spike_times = np.array(spikes_h5['/spikes/timestamps'], dtype=np.float)
     # spike_times, spike_gids = np.loadtxt(spikes_file, dtype='float32,int', unpack=True)
     # spike_gids, spike_times = np.loadtxt(spikes_file, dtype='int,float32', unpack=True)
+    spikes_h5.close()
 
     spike_times = spike_times * 1.0e-3
 
@@ -184,6 +187,15 @@ def plot_spikes(cells_file, cell_models_file, spikes_file, population=None, grou
 
     # Create plot
     gs = gridspec.GridSpec(2, 1, height_ratios=[7, 1])
+
+
+    import matplotlib
+    if font_size is not None:
+        matplotlib.rcParams.update({'font.size': font_size})
+        plt.xlabel('xlabel', fontsize=font_size)
+        plt.ylabel('ylabel', fontsize=font_size)
+
+
     ax1 = plt.subplot(gs[0])
     gid_min = 10**10
     gid_max = -1
@@ -201,16 +213,18 @@ def plot_spikes(cells_file, cell_models_file, spikes_file, population=None, grou
 
     #ax1.set_xlabel('time (s)')
     ax1.axes.get_xaxis().set_visible(False)
-    ax1.set_ylabel('cell_id')
+    ax1.set_ylabel('Cell ID')
     ax1.set_xlim([0, max(spike_times)])
     ax1.set_ylim([gid_min, gid_max])
-    plt.legend(markerscale=2, scatterpoints=1)
+    if legend:
+        plt.legend(markerscale=2, scatterpoints=1)
 
     ax2 = plt.subplot(gs[1])
     plt.hist(spike_times, 100)
-    ax2.set_xlabel('time (s)')
+    ax2.set_xlabel('Time (s)')
     ax2.set_xlim([0, max(spike_times)])
-    ax2.axes.get_yaxis().set_visible(False)
+    #ax2.axes.get_yaxis().set_visible(False)
+    ax2.set_ylabel('Firing rate (AU)')
     if title is not None:
         ax1.set_title(title)
 

bmtk/utils/cell_vars/var_reader.py

@@ -11,7 +11,7 @@ def __init__(self, filename, mode='r', **params):
         self._h5_root = self._h5_handle[params['h5_root']] if 'h5_root' in params else self._h5_handle['/']
         self._var_data = {}
         self._var_units = {}
-
+        
         self._mapping = None
 
         # Look for variabl and mapping groups
@@ -54,7 +54,7 @@ def __init__(self, filename, mode='r', **params):
             self._t_stop = time_ds[1]
             self._dt = time_ds[2]
             self._n_steps = int((self._t_stop - self._t_start) / self._dt)
-
+            
     @property
     def variables(self):
         return list(self._var_data.keys())
@@ -132,3 +132,6 @@ def data(self, gid, var_name=VAR_UNKNOWN,time_window=None, compartments='origin'
 
         data = np.array(self._var_data[var_name][time_slice, gid_slice])
         return data.T if multi_compartments else data
+
+    def close(self):
+        self._h5_handle.close()