cleaned up utils

utils.py

-# -*- coding: utf-8 -*-
-#
-#
-#  TheVirtualBrain-Scientific Package. This package holds all simulators, and
-# analysers necessary to run brain-simulations. You can use it stand alone or
-# in conjunction with TheVirtualBrain-Framework Package. See content of the
-# documentation-folder for more details. See also http://www.thevirtualbrain.org
-#
-# (c) 2012-2017, Baycrest Centre for Geriatric Care ("Baycrest") and others
-#
-# This program is free software: you can redistribute it and/or modify it under the
-# terms of the GNU General Public License as published by the Free Software Foundation,
-# either version 3 of the License, or (at your option) any later version.
-# This program is distributed in the hope that it will be useful, but WITHOUT ANY
-# WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A
-# PARTICULAR PURPOSE.  See the GNU General Public License for more details.
-# You should have received a copy of the GNU General Public License along with this
-# program.  If not, see <http://www.gnu.org/licenses/>.
-#
-#
-#   CITATION:
-# When using The Virtual Brain for scientific publications, please cite it as follows:
-#
-#   Paula Sanz Leon, Stuart A. Knock, M. Marmaduke Woodman, Lia Domide,
-#   Jochen Mersmann, Anthony R. McIntosh, Viktor Jirsa (2013)
-#       The Virtual Brain: a simulator of primate brain network dynamics.
-#   Frontiers in Neuroinformatics (7:10. doi: 10.3389/fninf.2013.00010)
-#
-#
-
-from pylab import *
 from tvb.simulator.lab import *
-import threading
-import numpy
-import time
-import sys
-from matplotlib.tri import Triangulation
-from matplotlib import pyplot as plt
-
-class SimThread(threading.Thread):
-    
-    def set_sim(self, sim, **kwds):
-        self.sim = sim
-        self.kwds = kwds
-        self.t = -1.0
-
-    def run(self):
-        "Convenience method to call the simulator with **kwds and collect output data."
-        tic = time.time()
-        ts, xs = [], []
-        
-        for _ in self.sim.monitors:
-            ts.append([])
-            xs.append([])
-            
-        for data in self.sim(**self.kwds):
-            for tl, xl, t_x in zip(ts, xs, data):
-                if t_x is not None:
-                    t, x = t_x
-                    if t > self.t:
-                        self.t = t
-                    tl.append(t)
-                    xl.append(x)
-                    
-        for i in range(len(ts)):
-            ts[i] = numpy.array(ts[i])
-            xs[i] = numpy.array(xs[i])
-            
-        self.results = list(zip(ts, xs))
-        self.wall_time = time.time() - tic
-        
-def formattime(eta):
-    m = 60
-    h = m*60
-    d = 24*h
-    msg = ''
-    if eta > d:
-        msg += '%d day(s), %d hour(s)' % (eta/d, ((eta/h) % 24))
-    elif eta > h:
-        msg += '%d hour(s), %d minute(s)' % (eta/h, ((eta/m) % 60))
-    else:
-        msg += '%d minute(s), %d seconds(s)' % (eta/m, eta%m)    
-    return msg
-
-def pbpct(p=1e2, eta=None, walltime=None):
-    i = int(p/2)
-    msg = '\r[%s%s] %d %%' % ('.'*i, ' '*(50 - i), p)
-    if eta:
-        msg += ', ETA: ' + formattime(eta)
-    elif walltime:
-        msg += ' Wall Time: ' + formattime(walltime)
-    sys.stdout.write(msg)
-    sys.stdout.flush()
-
-def run_sim_with_progress_bar(sim, simulation_length, polltime=1):
-    tic = time.time()
-    ar = SimThread()
-    ar.set_sim(sim, simulation_length=simulation_length)
-    ar.start()
-    while True:
-        prog = ar.t
-        pct = prog*1e2/simulation_length
-        toc = time.time() - tic
-        if pct > 0.0:
-            time_per_centile = toc / pct
-            centiles_left = 100 - pct
-            pbpct(pct, centiles_left * time_per_centile)
-        else:
-            pbpct(0.0)
-        time.sleep(polltime)
-        if hasattr(ar, 'wall_time'):
-            pbpct(100.0, walltime=ar.wall_time)
-            break 
-    return ar.results
-
-cortex = cortex.Cortex().from_file()
-
-def multiview(data, suptitle='', figsize=(15, 10), **kwds):
-
-    cs = cortex
-    vtx = cs.vertices
-    tri = cs.triangles
-    rm = cs.region_mapping
-    x, y, z = vtx.T
-    lh_tri = tri[(rm[tri] < 38).any(axis=1)]
-    lh_vtx = vtx[rm < 38]
-    lh_x, lh_y, lh_z = lh_vtx.T
-    lh_tx, lh_ty, lh_tz = lh_vtx[lh_tri].mean(axis=1).T
-    rh_tri = tri[(rm[tri] >= 38).any(axis=1)]
-    rh_vtx = vtx[rm < 38]
-    rh_x, rh_y, rh_z = rh_vtx.T
-    rh_tx, rh_ty, rh_tz = vtx[rh_tri].mean(axis=1).T
-    tx, ty, tz = vtx[tri].mean(axis=1).T
-
-    views = {
-        'lh-lateral': Triangulation(-x, z, lh_tri[argsort(lh_ty)[::-1]]),
-        'lh-medial': Triangulation(x, z, lh_tri[argsort(lh_ty)]),
-        'rh-medial': Triangulation(-x, z, rh_tri[argsort(rh_ty)[::-1]]),
-        'rh-lateral': Triangulation(x, z, rh_tri[argsort(rh_ty)]),
-        'both-superior': Triangulation(y, x, tri[argsort(tz)]),
-    }
-
-    def plotview(i, j, k, viewkey, z=None, zlim=None, zthresh=None, suptitle='', shaded=True, cmap=plt.cm.coolwarm, viewlabel=False):
-        v = views[viewkey]
-        ax = subplot(i, j, k)
-        if z is None:
-            z = rand(v.x.shape[0])
-        if not viewlabel:
-            axis('off')
-        kwargs = {'shading': 'gouraud'} if shaded else {'edgecolors': 'k', 'linewidth': 0.1}
-        if zthresh:
-            z = z.copy() * (abs(z) > zthresh)
-        tc = ax.tripcolor(v, z, cmap=cmap, **kwargs)
-        if zlim:
-            tc.set_clim(vmin=-zlim, vmax=zlim)
-        ax.set_aspect('equal')
-        if suptitle:
-            ax.set_title(suptitle, fontsize=24)
-        if viewlabel:
-            xlabel(viewkey)
-
-    figure(figsize=figsize)
-    plotview(2, 3, 1, 'lh-lateral', data, **kwds)
-    plotview(2, 3, 4, 'lh-medial', data, **kwds)
-    plotview(2, 3, 3, 'rh-lateral', data, **kwds)
-    plotview(2, 3, 6, 'rh-medial', data, **kwds)
-    plotview(1, 3, 2, 'both-superior', data, suptitle=suptitle, **kwds)
-    subplots_adjust(left=0.0, right=1.0, bottom=0.0, top=1.0, wspace=0, hspace=0)
+import numpy as np
+import matplotlib.pylab as plt
+
+
+def plot_connectivity(conn):
+    # Visualization
+    fig = plt.figure(figsize=(15, 7))
+    fig.suptitle('TVB SC', fontsize=20)
+
+    # Weights
+    plt.subplot(121)
+    plt.imshow(conn.weights, interpolation='nearest', aspect='equal', cmap='jet')
+    plt.xticks(range(0, conn.number_of_regions), conn.region_labels, fontsize=7, rotation=90)
+    plt.yticks(range(0, conn.number_of_regions), conn.region_labels, fontsize=7)
+    cb = plt.colorbar(shrink=0.2)
+    cb.set_label('Weights', fontsize=14)
+
+    # Tracts lengths
+    plt.subplot(122)
+    plt.imshow(conn.tract_lengths, interpolation='nearest', aspect='equal', cmap='jet')
+    plt.xticks(range(0, conn.number_of_regions), conn.region_labels, fontsize=7, rotation=90)
+    plt.yticks(range(0, conn.number_of_regions), conn.region_labels, fontsize=7)
+    cb = plt.colorbar(shrink=0.2)
+    cb.set_label('Tract lenghts', fontsize=14)
+
+    fig.tight_layout()
+
+    plt.show()