Merge conflict

hnn_core/network.py

@@ -16,7 +16,7 @@
 from .cells_default import pyramidal, basket
 from .cell_response import CellResponse
 from .params import _long_name, _short_name
-from .viz import plot_cells, plot_cell_morphology
+from .viz import plot_cells, plot_cell_morphology, plot_connectivity_matrix
 from .externals.mne import _validate_type, _check_option
 
 
@@ -1030,6 +1030,7 @@ def add_connection(self, src_gids, target_gids, loc, receptor,
                 raise AssertionError(
                     'All target_gids must be of the same type')
         conn['target_type'] = target_type
+        conn['target_range'] = self.gid_ranges[_long_name(target_type)]
         conn['num_targets'] = len(target_set)
 
         if len(target_gids) != len(src_gids):
@@ -1048,6 +1049,7 @@ def add_connection(self, src_gids, target_gids, loc, receptor,
                 raise AssertionError('All src_gids must be of the same type')
             gid_pairs[src_gid] = target_src_pair
         conn['src_type'] = src_type
+        conn['src_range'] = self.gid_ranges[_long_name(src_type)]
         conn['num_srcs'] = len(src_gids)
 
         conn['gid_pairs'] = gid_pairs
@@ -1143,6 +1145,10 @@ class _Connectivity(dict):
         Number of unique source gids.
     num_targets : int
         Number of unique target gids.
+    src_range : range
+        Range of gids identified by src_type.
+    target_range : target_range
+        Range of gids identified by target_type.
     loc : str
         Location of synapse on target cell. Must be
         'proximal', 'distal', or 'soma'. Note that inhibitory synapses
@@ -1163,6 +1169,11 @@ class _Connectivity(dict):
     probability : float
         Probability of connection between any src-target pair.
         Defaults to 1.0 producing an all-to-all pattern.
+
+    Notes
+    -----
+    The len() of src_range or target_range may not match
+    num_srcs and num_targets for probability < 1.0.
     """
 
     def __repr__(self):
@@ -1230,6 +1241,22 @@ def drop(self, probability):
 
         self['probability'] = probability
 
+    def plot(self, ax=None, show=True):
+        """Plot connectivity matrix for instance of _Connectivity object.
+
+        Parameters
+        ----------
+        conn : Instance of _Connectivity object
+            The _Connectivity object
+
+        Returns
+        -------
+        fig : instance of matplotlib Figure
+            The matplotlib figure handle.
+        """
+
+        return plot_connectivity_matrix(self, ax=ax, show=show)
+
 
 class _NetworkDrive(dict):
     """A class for containing the parameters of external drives

hnn_core/viz.py

@@ -593,3 +593,43 @@ def plot_cell_morphology(axes=None, cell_types=None, show=True):
     plt.tight_layout()
     plt_show(show)
     return axes
+def plot_connectivity_matrix(conn, ax=None, show=True):
+    """Plot connectivity matrix for instance of _Connectivity object.
+
+    Parameters
+    ----------
+    conn : Instance of _Connectivity object
+        The _Connectivity object
+
+    Returns
+    -------
+    fig : instance of matplotlib Figure
+        The matplotlib figure handle.
+
+    """
+    import matplotlib.pyplot as plt
+    from.network import _Connectivity
+
+    if not isinstance(conn, _Connectivity):
+        raise TypeError('conn must be instance of _Connectivity')
+    if ax is None:
+        _, ax = plt.subplots(1, 1)
+
+    src_range = np.array(conn['src_range'])
+    target_range = np.array(conn['target_range'])
+    connectivity_matrix = np.zeros((len(src_range), len(target_range)))
+    for src_gid, target_src_pair in conn['gid_pairs'].items():
+        src_idx = np.where(src_range == src_gid)[0][0]
+        target_indeces = np.in1d(target_range, target_src_pair)
+        connectivity_matrix[src_idx, :] = target_indeces
+
+    ax.imshow(connectivity_matrix, cmap='Greys', interpolation='none')
+    ax.set_xlabel(f'target gids ({target_range[0]}-{target_range[-1]})')
+    ax.set_xticklabels(list())
+    ax.set_ylabel(f'source gids ({src_range[0]}-{src_range[-1]})')
+    ax.set_yticklabels(list())
+    ax.set_title(f"{conn['src_type']} -> {conn['target_type']} "
+                 f"({conn['loc']}, {conn['receptor']})")
+
+    plt_show(show)
+    return ax.get_figure()