-
Notifications
You must be signed in to change notification settings - Fork 29
/
scale_animation.py
297 lines (232 loc) · 8.04 KB
/
scale_animation.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
import numpy as np
import networkx as nx
import math
import re
from scaled_embedding2coords import omega2coords
from write_cifs import PBC3DF, PBC3DF_sym
import matplotlib
matplotlib.use('Agg')
from matplotlib import pyplot as plt
from matplotlib import animation
from matplotlib.lines import Line2D
def nn(string):
return re.sub('[^a-zA-Z]','', string)
def nl(string):
return re.sub('[^0-9]','', string)
def roundup(x):
return int(math.ceil(x / 10.0)) * 10.0
pi = np.pi
def scaling_callback_animation(callbackresults, alpha, Bstar_inv, ncra, ncca, num_vertices, num_edges, TG, template, g, cfiles):
frames = []
frames_append = frames.append
for l in callbackresults:
X, fX = l
uc_params = X[0:6]
rc_a, rc_b, rc_c, rc_alpha, rc_beta, rc_gamma = uc_params
rc_covars = np.array(X[6:])
rc_covars = rc_covars.reshape(ncra,ncca)
rc_Alpha = np.r_[alpha[0:num_edges-num_vertices+1,:], rc_covars]
rc_omega_plus = np.dot(Bstar_inv, rc_Alpha)
rc_coords = omega2coords(np.array([0.0,0.0,0.0]), TG, rc_omega_plus, uc_params, num_vertices, template, g, cfiles)
ax_rc = rc_a
ay_rc = 0.0
az_rc = 0.0
bx_rc = rc_b * np.cos(rc_gamma * pi/180.0)
by_rc = rc_b * np.sin(rc_gamma * pi/180.0)
bz_rc = 0.0
cx_rc = rc_c * np.cos(rc_beta * pi/180.0)
cy_rc = (rc_c * rc_b * np.cos(rc_alpha * pi/180.0) - bx_rc * cx_rc) / by_rc
cz_rc = (rc_c ** 2.0 - cx_rc ** 2.0 - cy_rc ** 2.0) ** 0.5
rc_unit_cell = np.asarray([[ax_rc,ay_rc,az_rc],[bx_rc,by_rc,bz_rc],[cx_rc,cy_rc,cz_rc]]).T
frames_append([rc_coords, rc_unit_cell])
return frames
def write_scaling_callback_animation(frames, prefix):
G = nx.Graph()
coord_frames = []
coord_frames_append = coord_frames.append
inds = []
inds_append = inds.append
norm_coord_dict = {}
fcoord_dict = {}
for l in frames[0][0]:
name, cif, fcoords, edges = l
inds_append(int(nl(name)))
max_ind = max(inds)
for l in frames[0][0]:
name, cif, fcoords, edges = l
norm_coord_dict[name] = fcoords
fcoord_dict[name] = np.array(fcoords)
transform_dict = dict((k,[]) for k in fcoord_dict)
for l in frames[0][0]:
name, cif, fcoords, edges = l
for e in edges:
a1,a2 = e[1]
dist, sym = PBC3DF_sym(fcoord_dict[a1], fcoord_dict[a2])
if sym == '.':
G.add_edge(*e[1])
transform_dict[a1].append((a1, (0,0,0)))
transform_dict[a2].append((a2, (0,0,0)))
else:
sym = sym.split('_')[1]
sym = np.array([float(sym[0]),float(sym[1]),float(sym[2])])
sym = sym - 5.0
max_ind += 1
na = nn(a2) + str(max_ind)
G.add_edge(a1, na)
transform_dict[a2].append((na, (sym[0],sym[1],sym[2])))
duplicate_vecs = []
for dim in (0,1,2):
init = [0,0,0]
if abs(fcoords[dim] - 1.0) < 0.05:
init[dim] -= 1.0
elif abs(fcoords[dim] ) < 0.05:
init[dim] += 1.0
duplicate_vecs.append(init)
for dv in duplicate_vecs:
dup_vec = np.array(dv)
if np.any(dup_vec):
max_ind += 1
new_name = nn(name) + str(max_ind)
transform_dict[name].append((new_name, (dv[0], dv[1], dv[2])))
for e in edges:
a1,a2 = e[1]
dist, sym = PBC3DF_sym(fcoords + dup_vec, fcoord_dict[a2])
if sym == '.':
G.add_edge(new_name, a2)
for a in transform_dict:
transform_dict[a] = set(transform_dict[a])
for f in frames:
coords = []
coords_append = coords.append
fc, rc_unit_cell = f
fcoord_dict = dict((s[0],np.array(s[2])) for s in fc)
for l in fc:
name, cif, fcoords, edges = l
fcoords = PBC3DF(norm_coord_dict[name], fcoords)
ccoords = np.dot(rc_unit_cell, fcoords)
coords_append([name, ccoords ])
if len(transform_dict[name]) > 1:
for na in transform_dict[name]:
new_name, tvec = na
if new_name != name:
tvec = np.asarray(tvec)
new_ccoords = np.dot(rc_unit_cell, fcoords + tvec)
coords_append([new_name, new_ccoords])
coord_frames_append(coords)
for i in range(len(coord_frames)):
f = coord_frames[i]
coord_frames[i] = sorted(f, key = lambda x : int(nl(x[0])))
with open(prefix + '_scale_animation.xyz', 'w') as out:
fc = 0
for f in coord_frames:
fc += 1
out.write(str(len(f)) + '\n')
out.write('frame: ' + str(fc) + '\n')
for l in f:
name, coord = l
x,y,z = coord
out.write('{:5} {:>10.5f} {:>10.5f} {:>10.5f}'.format(name, x, y, z))
out.write('\n')
with open(prefix + '_add_bonds.tcl', 'w') as out:
for b in G.edges():
ind0 = int(nl(b[0])) - 1
ind1 = int(nl(b[1])) - 1
out.write('topo addbond ' + str(ind0) + ' ' + str(ind1) + '\n')
def animate_objective_minimization(callbackresults, prefix, bitrate=3000, fps=30, font_size=16, time=5.0):
plt.rcParams.update({'font.size': font_size})
Writer = animation.PillowWriter
writer = Writer(fps=fps, metadata=dict(artist='Me'), bitrate=bitrate)
uc_params = []
fX_vals = []
res_vals = []
max_len = 0
max_angle = 0
for l in callbackresults:
X, fX = l
a,b,c,alpha,beta,gamma = X[0:6]
max_len_current = max((a,b,c))
if max_len_current > max_len:
max_len = max_len_current
max_angle_current = max((alpha,beta,gamma))
if max_angle_current > max_angle:
max_angle = max_angle_current
res = X[6:]
avg_res = np.average(res)
uc_params.append([a, b, c, alpha, beta, gamma])
fX_vals.append(fX)
res_vals.append(avg_res)
iterations = np.array(range(len(fX_vals)))
uc_params = np.asarray(uc_params)
fX_vals = np.asarray(fX_vals)/max(fX_vals)
res_vals = np.asarray(res_vals)
nframes = len(iterations)
time *= 1000
delay = time/nframes
class SubplotAnimation(animation.TimedAnimation):
def __init__(self):
fig = plt.figure(figsize=(16.0,5.5))
matplotlib.pyplot.subplots_adjust(left=None, bottom=0.2, right=None, top=None, wspace=0.30, hspace=None)
ax1 = fig.add_subplot(1, 3, 1)
ax2 = fig.add_subplot(1, 3, 2)
ax3 = fig.add_subplot(1, 3, 3)
self.t = iterations
self.x = iterations
self.fX = fX_vals
self.a = uc_params[:,0]
self.b = uc_params[:,1]
self.c = uc_params[:,2]
self.alpha = uc_params[:,3]
self.beta = uc_params[:,4]
self.gamma = uc_params[:,5]
ax1.set_xlabel('Iterations')
ax1.set_ylabel('Normalized Objective Function')
self.line11 = Line2D([], [], color='black', linewidth=2)
ax1.add_line(self.line11)
ax1.set_xlim(0, max(iterations))
ax1.set_ylim(0, 0.10)
ax1.xaxis.set_major_formatter(plt.NullFormatter())
ax2.set_xlabel('Iterations')
ax2.set_ylabel('Unit Cell Lengths / ' + r'$\AA$')
self.line21 = Line2D([], [], color='black', linewidth=2)
self.line22 = Line2D([], [], color='blue' , linewidth=2)
self.line23 = Line2D([], [], color='red' , linewidth=2)
ax2.add_line(self.line21)
ax2.add_line(self.line22)
ax2.add_line(self.line23)
ax2.set_xlim(0, max(iterations))
ax2.set_ylim(5, max_len + 5.0)
ax2.xaxis.set_major_formatter(plt.NullFormatter())
ax3.set_xlabel('Iterations')
ax3.set_ylabel('Unit Cell Angles / degrees')
self.line31 = Line2D([], [], color='black', linewidth=2)
self.line32 = Line2D([], [], color='blue' , linewidth=2)
self.line33 = Line2D([], [], color='red' , linewidth=2)
ax3.add_line(self.line31)
ax3.add_line(self.line32)
ax3.add_line(self.line33)
ax3.set_xlim(0 , max(iterations))
ax3.set_ylim(60, max_angle + 10.0)
ax3.xaxis.set_major_formatter(plt.NullFormatter())
animation.TimedAnimation.__init__(self, fig, interval=delay, blit=True)
def _draw_frame(self, framedata):
i = framedata
self.line11.set_data(self.x[:i], self.fX[:i])
self.line21.set_data(self.x[:i], self.a[:i])
self.line22.set_data(self.x[:i], self.b[:i])
self.line23.set_data(self.x[:i], self.c[:i])
self.line31.set_data(self.x[:i], self.alpha[:i])
self.line32.set_data(self.x[:i], self.beta[:i])
self.line33.set_data(self.x[:i], self.gamma[:i])
self._drawn_artists = [self.line11, self.line21, self.line22,
self.line23, self.line31, self.line32,
self.line33]
def new_frame_seq(self):
return iter(range(self.t.size))
def _init_draw(self):
lines = [self.line11, self.line21, self.line22,
self.line23, self.line31, self.line32,
self.line33]
for l in lines:
l.set_data([], [])
ani = SubplotAnimation()
ani.save(prefix + '_ts.gif')