[MRG] Add function to visualize connectivity weights

examples/plot_connectivity.py

@@ -8,7 +8,7 @@
 
 # Author: Nick Tolley <nicholas_tolley@brown.edu>
 
-# sphinx_gallery_thumbnail_number = 5
+# sphinx_gallery_thumbnail_number = 1
 
 import os.path as op
 
@@ -38,9 +38,19 @@
 # Instantiating the network comes with a predefined set of connections that
 # reflect the canonical neocortical microcircuit. ``net.connectivity``
 # is a list of dictionaries which detail every cell-cell, and drive-cell
-# connection.
+# connection. The weights of these connections can be visualized with
+# :func:`~hnn_core.viz.plot_connectivity_weights` as well as
+# :func:`~hnn_core.viz.plot_cell_connectivity`
+from hnn_core.viz import plot_connectivity_matrix, plot_cell_connectivity
 print(len(net_erp.connectivity))
-print(net_erp.connectivity[0:2])
+
+conn_idx = 20
+print(net_erp.connectivity[conn_idx])
+plot_connectivity_matrix(net_erp, conn_idx)
+
+gid_idx = 11
+src_gid = net_erp.connectivity[conn_idx]['src_range'][gid_idx]
+fig, ax = plot_cell_connectivity(net_erp, conn_idx, src_gid)
 
 ###############################################################################
 # Data recorded during simulations are stored under
@@ -89,9 +99,7 @@
 # activity is visible as vertical lines where several cells fire simultaneously
 # We can additionally use the ``probability``. argument to create a sparse
 # connectivity pattern instead of all-to-all. Let's try creating the same
-# network with a 10% chance of cells connecting to each other. The resulting
-# connectivity pattern can also be visualized with
-# ``net.connectivity[idx].plot()``
+# network with a 10% chance of cells connecting to each other.
 probability = 0.1
 net_sparse = default_network(params, add_drives_from_params=True)
 net_sparse.clear_connectivity()
@@ -115,8 +123,10 @@
 dpl_sparse = simulate_dipole(net_sparse, n_trials=1)
 net_sparse.cell_response.plot_spikes_raster()
 
-net_sparse.connectivity[-2].plot()
-net_sparse.connectivity[-1].plot()
+# Get index of most recently added connection
+conn_idx = len(net_sparse.connectivity)
+plot_connectivity_matrix(net_sparse, conn_idx, show_weight=False)
+plot_connectivity_matrix(net_sparse, conn_idx - 1, show_weight=False)
 
 ###############################################################################
 # Using the sparse connectivity pattern produced a lot more spiking in

hnn_core/cell.py

@@ -25,6 +25,68 @@ def _get_cos_theta(p_secs, sec_name_apical):
     return cos_thetas
 
 
+def _calculate_gaussian(x_val, height, lamtha):
+    """Return height of gaussian at x_val.
+
+    Parameters
+    ----------
+    x_val : float
+        Value on x-axis to query height of gaussian curve.
+    height : float
+        Height of the gaussian curve at zero.
+    lamtha : float
+        Space constant.
+
+    Returns
+    -------
+    x_height : float
+        Height of gaussian at x_val.
+
+    Notes
+    -----
+    Gaussian curve is centered at zero and has a fixed peak height
+    such the _calculate_gaussian(0, lamtha) returns 1 for all lamtha.
+    """
+    x_height = height * np.exp(-(x_val**2) / (lamtha**2))
+
+    return x_height
+
+
+def _get_gaussian_connection(src_pos, target_pos, nc_dict):
+    """Calculate distance dependent connection properties.
+
+    Parameters
+    ----------
+    src_pos : float
+        Position of source cell.
+    target_pos : float
+        Position of target cell.
+    nc_dict : dict
+        Dictionary with keys: pos_src, A_weight, A_delay, lamtha
+        Defines the connection parameters
+
+    Returns
+    -------
+    weight : float
+        Weight of the synaptic connection.
+    delay : float
+        Delay of synaptic connection.
+
+    Notes
+    -----
+    Distance in xy plane is used for gaussian decay.
+    """
+    x_dist = target_pos[0] - src_pos[0]
+    y_dist = target_pos[1] - src_pos[1]
+    cell_dist = np.sqrt(x_dist**2 + y_dist**2)
+
+    weight = _calculate_gaussian(
+        cell_dist, nc_dict['A_weight'], nc_dict['lamtha'])
+    delay = nc_dict['A_delay'] / _calculate_gaussian(
+        cell_dist, 1, nc_dict['lamtha'])
+    return weight, delay
+
+
 class _ArtificialCell:
     """The ArtificialCell class for initializing a NEURON feed source.
 
@@ -477,24 +539,14 @@ def parconnect_from_src(self, gid_presyn, nc_dict, postsyn):
         from .network_builder import _PC
 
         nc = _PC.gid_connect(gid_presyn, postsyn)
-        # calculate distance between cell positions with pardistance()
-        d = self._pardistance(nc_dict['pos_src'])
-        # set props here
+
+        # set props here.
         nc.threshold = nc_dict['threshold']
-        nc.weight[0] = nc_dict['A_weight'] * \
-            np.exp(-(d**2) / (nc_dict['lamtha']**2))
-        nc.delay = nc_dict['A_delay'] / \
-            (np.exp(-(d**2) / (nc_dict['lamtha']**2)))
+        nc.weight[0], nc.delay = _get_gaussian_connection(
+            nc_dict['pos_src'], self.pos, nc_dict)
 
         return nc
 
-    # pardistance function requires pre position, since it is
-    # calculated on POST cell
-    def _pardistance(self, pos_pre):
-        dx = self.pos[0] - pos_pre[0]
-        dy = self.pos[1] - pos_pre[1]
-        return np.sqrt(dx**2 + dy**2)
-
     def plot_morphology(self, ax=None, cell_types=None, show=True):
         """Plot the cell morphology.
 

hnn_core/network.py

@@ -17,7 +17,7 @@
 from .cells_default import pyramidal, basket
 from .cell_response import CellResponse
 from .params import _long_name, _short_name
-from .viz import plot_cells, plot_connectivity_matrix
+from .viz import plot_cells
 from .externals.mne import _validate_type, _check_option
 
 
@@ -1268,25 +1268,6 @@ def __repr__(self):
 
         return entr
 
-    def plot(self, ax=None, show=True):
-        """Plot connectivity matrix for instance of _Connectivity object.
-
-        Parameters
-        ----------
-        ax : instance of matplotlib Axes3D | None
-            An axis object from matplotlib. If None,
-            a new figure is created.
-        show : bool
-            If True, show the figure.
-
-        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/tests/test_viz.py

@@ -7,6 +7,7 @@
 import hnn_core
 from hnn_core import read_params, default_network
 from hnn_core.viz import plot_cells, plot_dipole, plot_psd, plot_tfr_morlet
+from hnn_core.viz import plot_connectivity_matrix, plot_cell_connectivity
 from hnn_core.dipole import simulate_dipole
 
 matplotlib.use('agg')
@@ -24,6 +25,26 @@ def test_network_visualization():
     ax = net.cell_types['L2_pyramidal'].plot_morphology()
     assert len(ax.lines) == 8
 
+    conn_idx = 0
+    with pytest.raises(TypeError, match='net must be an instance of'):
+        plot_connectivity_matrix('blah', conn_idx, show_weight=False)
+
+    with pytest.raises(TypeError, match='conn_idx must be an instance of'):
+        plot_connectivity_matrix(net, 'blah', show_weight=False)
+
+    with pytest.raises(TypeError, match='show_weight must be an instance of'):
+        plot_connectivity_matrix(net, conn_idx, show_weight='blah')
+
+    src_gid = 5
+    with pytest.raises(TypeError, match='net must be an instance of'):
+        plot_cell_connectivity('blah', conn_idx, src_gid=src_gid)
+
+    with pytest.raises(TypeError, match='conn_idx must be an instance of'):
+        plot_cell_connectivity(net, 'blah', src_gid=src_gid)
+
+    with pytest.raises(TypeError, match='src_gid must be an instance of'):
+        plot_cell_connectivity(net, conn_idx, src_gid='blah')
+
 
 def test_dipole_visualization():
     """Test dipole visualisations."""