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XYmodel_metropolis_python/xymodel_mc.py

@@ -1,13 +1,5 @@
 import numpy as np
 
-import matplotlib.pylab as plt
-import matplotlib.animation as animation
-import matplotlib.patches as patches
-from matplotlib.collections import PatchCollection
-import matplotlib.cm as cm
-from matplotlib.colors import Normalize
-
-
 def cos(angle):
     """transforms angle from [0,1] to cos(2pi[0,1])"""
     return np.cos(2 * np.pi * angle)
@@ -138,56 +130,9 @@ def simulate(self, steps):
         self.get_Vdensity()
 
 
-def visualise(sim):
-    X = np.arange(sim.A.size).reshape(sim.A.shape) % sim.A.shape[0]
-    Y = (np.arange(sim.A.size).reshape(sim.A.shape) % sim.A.shape[1]).T
-
-    U = cos(sim.A)
-    V = sin(sim.A)
-
-    fig, ax = plt.subplots(1, 1, figsize=(15, 15))
-
-    rects = []
-
-    # create rectangles for vortex/antivortex determination
-    for i in range(sim.V.shape[0]):
-        for j in range(sim.V.shape[1]):
-            rect = patches.Rectangle(xy=(i, j), height=1, width=1)
-            rects.append(rect)
-    rects = PatchCollection(rects)
-
-    # Set colors for the rectangles
-    col = 'RdBu'
-    r_cmap = plt.get_cmap(col)
-    r_cmap_r = plt.get_cmap(col + "_r")  # eto kostil' =)
-    rects.set_cmap(r_cmap)
-    rects.set_clim(vmin=-1, vmax=1)
-
-    rects.set_animated(True)
-    rects.set_array(sim.V.flatten('F') / 2)
-    ax.add_collection(rects)
-
-    # create legend
-    legend_boxes = [patches.Patch(facecolor=r_cmap(0.7), label='Antiortex'),
-                    patches.Patch(facecolor=r_cmap_r(0.7), label='Vortex')]
-    ax.legend(handles=legend_boxes)
-
-    # build an initial quiver plot
-    q = ax.quiver(X, Y, U, V, pivot='tail', cmap=plt.cm.get_cmap('hsv'), units='inches', scale=4)
-    fig.colorbar(q, label='Angles (2 pi)')
-    ax.set_xlim(-1, sim.A.shape[0])
-    ax.set_ylim(-1, sim.A.shape[1])
-    q.set_UVC(U, V, C=sim.A)
-
-    plt.show()
-
-    return q, fig, rects
-
 if __name__=="__main__":
     sim = XYMetropolis((50,50),
                  beta=1,
                  J=5,
                  random_state=5,
-                 initial_state='hot')
-    sim.simulate(2000000)
-    visualise(sim)
+                 initial_state='hot')