Hugging Face's logo

Datasets:
compasszzn
/
Molecular

Dataset card Files Community
compasszzn commited on
Commit
47d0344
•
1 Parent(s): 3d6b902

Upload 5 files

Browse files
Files changed (5) hide show
  1. molecular_dataset/process.ipynb +556 -0
  2. molecular_dataset/processed_data/lipo/lipo.npz +3 -0
  3. molecular_dataset/processed_data/lips/lips.npz +3 -0
  4. molecular_dataset/raw_data/lipo/lipo.npz +3 -0
  5. molecular_dataset/raw_data/lips/lips.npz +3 -0
molecular_dataset/process.ipynb ADDED
@@ -0,0 +1,556 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "cells": [
3
+ {
4
+ "cell_type": "code",
5
+ "execution_count": 1,
6
+ "metadata": {},
7
+ "outputs": [],
8
+ "source": [
9
+ "import numpy as np\n",
10
+ "a=np.load('/home/zinanzheng/project/KD/nbody/nbody/mole/lipo/lipo.npz')\n",
11
+ "# a['coords'].shape\n",
12
+ "# loc=a['coords']\n",
13
+ "# loc_next=a['coords'][1:]\n",
14
+ "# loc_next = np.vstack((loc_next, loc[-1][np.newaxis, :, :]))\n",
15
+ "# vel=loc_next-loc\n",
16
+ "# loc_vel=np.concatenate([loc,vel],axis=2)"
17
+ ]
18
+ },
19
+ {
20
+ "cell_type": "code",
21
+ "execution_count": 5,
22
+ "metadata": {},
23
+ "outputs": [
24
+ {
25
+ "name": "stdout",
26
+ "output_type": "stream",
27
+ "text": [
28
+ "(25000, 208, 3)\n"
29
+ ]
30
+ }
31
+ ],
32
+ "source": [
33
+ "print(a['coords'].shape)"
34
+ ]
35
+ },
36
+ {
37
+ "cell_type": "code",
38
+ "execution_count": 6,
39
+ "metadata": {},
40
+ "outputs": [
41
+ {
42
+ "name": "stdout",
43
+ "output_type": "stream",
44
+ "text": [
45
+ "[[ 3 3 3 ... 16 16 16]\n",
46
+ " [ 3 3 3 ... 16 16 16]\n",
47
+ " [ 3 3 3 ... 16 16 16]\n",
48
+ " ...\n",
49
+ " [ 3 3 3 ... 16 16 16]\n",
50
+ " [ 3 3 3 ... 16 16 16]\n",
51
+ " [ 3 3 3 ... 16 16 16]]\n"
52
+ ]
53
+ }
54
+ ],
55
+ "source": [
56
+ "print(a['atom_types'][0])"
57
+ ]
58
+ },
59
+ {
60
+ "cell_type": "code",
61
+ "execution_count": 2,
62
+ "metadata": {},
63
+ "outputs": [
64
+ {
65
+ "data": {
66
+ "text/plain": [
67
+ "array([[10.575768, -0.049882, 0.042349],\n",
68
+ " [-0.066807, 13.964852, 0.052307],\n",
69
+ " [ 0.073029, 0.059637, 16.077265]])"
70
+ ]
71
+ },
72
+ "execution_count": 2,
73
+ "metadata": {},
74
+ "output_type": "execute_result"
75
+ }
76
+ ],
77
+ "source": [
78
+ "a['cell']"
79
+ ]
80
+ },
81
+ {
82
+ "cell_type": "code",
83
+ "execution_count": 9,
84
+ "metadata": {},
85
+ "outputs": [
86
+ {
87
+ "data": {
88
+ "text/plain": [
89
+ "(25000, 208, 6)"
90
+ ]
91
+ },
92
+ "execution_count": 9,
93
+ "metadata": {},
94
+ "output_type": "execute_result"
95
+ }
96
+ ],
97
+ "source": [
98
+ "loc_vel.shape"
99
+ ]
100
+ },
101
+ {
102
+ "cell_type": "code",
103
+ "execution_count": 4,
104
+ "metadata": {},
105
+ "outputs": [
106
+ {
107
+ "data": {
108
+ "text/plain": [
109
+ "(25000, 208, 3)"
110
+ ]
111
+ },
112
+ "execution_count": 4,
113
+ "metadata": {},
114
+ "output_type": "execute_result"
115
+ }
116
+ ],
117
+ "source": [
118
+ "a['coords'].shape"
119
+ ]
120
+ },
121
+ {
122
+ "cell_type": "code",
123
+ "execution_count": 12,
124
+ "metadata": {},
125
+ "outputs": [
126
+ {
127
+ "data": {
128
+ "text/plain": [
129
+ "(25000, 208, 1)"
130
+ ]
131
+ },
132
+ "execution_count": 12,
133
+ "metadata": {},
134
+ "output_type": "execute_result"
135
+ }
136
+ ],
137
+ "source": [
138
+ "np.expand_dims(a['atom_types'],-1).shape"
139
+ ]
140
+ },
141
+ {
142
+ "cell_type": "code",
143
+ "execution_count": 2,
144
+ "metadata": {},
145
+ "outputs": [
146
+ {
147
+ "data": {
148
+ "text/plain": [
149
+ "array([[10.575768, -0.049882, 0.042349],\n",
150
+ " [-0.066807, 13.964852, 0.052307],\n",
151
+ " [ 0.073029, 0.059637, 16.077265]])"
152
+ ]
153
+ },
154
+ "execution_count": 2,
155
+ "metadata": {},
156
+ "output_type": "execute_result"
157
+ }
158
+ ],
159
+ "source": [
160
+ "a['cell']"
161
+ ]
162
+ },
163
+ {
164
+ "cell_type": "code",
165
+ "execution_count": null,
166
+ "metadata": {},
167
+ "outputs": [],
168
+ "source": [
169
+ "loc=a['coords']"
170
+ ]
171
+ },
172
+ {
173
+ "cell_type": "code",
174
+ "execution_count": 5,
175
+ "metadata": {},
176
+ "outputs": [
177
+ {
178
+ "data": {
179
+ "image/png": "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",
180
+ "text/plain": [
181
+ "<Figure size 640x480 with 1 Axes>"
182
+ ]
183
+ },
184
+ "metadata": {},
185
+ "output_type": "display_data"
186
+ }
187
+ ],
188
+ "source": [
189
+ "import matplotlib.pyplot as plt\n",
190
+ "for i in loc:\n",
191
+ " plt.scatter(i[:,0],i[:,1],c='r')"
192
+ ]
193
+ },
194
+ {
195
+ "cell_type": "code",
196
+ "execution_count": 6,
197
+ "metadata": {},
198
+ "outputs": [
199
+ {
200
+ "data": {
201
+ "image/png": "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",
202
+ "text/plain": [
203
+ "<Figure size 640x480 with 1 Axes>"
204
+ ]
205
+ },
206
+ "metadata": {},
207
+ "output_type": "display_data"
208
+ }
209
+ ],
210
+ "source": [
211
+ "import matplotlib.pyplot as plt\n",
212
+ "for i in loc:\n",
213
+ " plt.scatter(i[:,0],i[:,2],c='r')"
214
+ ]
215
+ },
216
+ {
217
+ "cell_type": "code",
218
+ "execution_count": 7,
219
+ "metadata": {},
220
+ "outputs": [
221
+ {
222
+ "data": {
223
+ "image/png": "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",
224
+ "text/plain": [
225
+ "<Figure size 640x480 with 1 Axes>"
226
+ ]
227
+ },
228
+ "metadata": {},
229
+ "output_type": "display_data"
230
+ }
231
+ ],
232
+ "source": [
233
+ "import matplotlib.pyplot as plt\n",
234
+ "for i in loc:\n",
235
+ " plt.scatter(i[:,1],i[:,2],c='r')"
236
+ ]
237
+ },
238
+ {
239
+ "cell_type": "code",
240
+ "execution_count": 8,
241
+ "metadata": {},
242
+ "outputs": [
243
+ {
244
+ "data": {
245
+ "text/plain": [
246
+ "array([[12.04948385, 0.01410079, -2.8262677 ],\n",
247
+ " [-3.57193123, 11.14554125, -3.65390947],\n",
248
+ " [ 0.02336308, 0.08415787, 12.43668805]])"
249
+ ]
250
+ },
251
+ "execution_count": 8,
252
+ "metadata": {},
253
+ "output_type": "execute_result"
254
+ }
255
+ ],
256
+ "source": [
257
+ "a['cell']"
258
+ ]
259
+ },
260
+ {
261
+ "cell_type": "code",
262
+ "execution_count": 9,
263
+ "metadata": {},
264
+ "outputs": [
265
+ {
266
+ "name": "stdout",
267
+ "output_type": "stream",
268
+ "text": [
269
+ "4.25715072450114 5.6282853971045945 3.0417149519272244\n"
270
+ ]
271
+ }
272
+ ],
273
+ "source": [
274
+ "mean_x = np.mean(loc[:, :, 0])\n",
275
+ "mean_y = np.mean(loc[:, :, 1])\n",
276
+ "mean_z = np.mean(loc[:, :, 2])\n",
277
+ "print(mean_x,mean_y,mean_z)"
278
+ ]
279
+ },
280
+ {
281
+ "cell_type": "code",
282
+ "execution_count": 109,
283
+ "metadata": {},
284
+ "outputs": [
285
+ {
286
+ "name": "stdout",
287
+ "output_type": "stream",
288
+ "text": [
289
+ "10.66875 14.04255 16.14281\n"
290
+ ]
291
+ },
292
+ {
293
+ "ename": "",
294
+ "evalue": "",
295
+ "output_type": "error",
296
+ "traceback": [
297
+ "\u001b[1;31mThe Kernel crashed while executing code in the the current cell or a previous cell. Please review the code in the cell(s) to identify a possible cause of the failure. Click <a href='https://aka.ms/vscodeJupyterKernelCrash'>here</a> for more info. View Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
298
+ ]
299
+ }
300
+ ],
301
+ "source": [
302
+ "min_x = np.min(loc[:, :, 0])\n",
303
+ "min_y = np.min(loc[:, :, 1])\n",
304
+ "min_z = np.min(loc[:, :, 2])\n",
305
+ "max_x = np.max(loc[:, :, 0])\n",
306
+ "max_y = np.max(loc[:, :, 1])\n",
307
+ "max_z = np.max(loc[:, :, 2])\n",
308
+ "print(max_x-min_x,max_y-min_y,max_z-min_z)"
309
+ ]
310
+ },
311
+ {
312
+ "cell_type": "code",
313
+ "execution_count": null,
314
+ "metadata": {},
315
+ "outputs": [],
316
+ "source": []
317
+ },
318
+ {
319
+ "cell_type": "code",
320
+ "execution_count": 64,
321
+ "metadata": {},
322
+ "outputs": [],
323
+ "source": [
324
+ "atom_types=a['atom_types']\n",
325
+ "cell=a['cell']"
326
+ ]
327
+ },
328
+ {
329
+ "cell_type": "code",
330
+ "execution_count": 66,
331
+ "metadata": {},
332
+ "outputs": [
333
+ {
334
+ "data": {
335
+ "text/plain": [
336
+ "array([[12.04948385, 0.01410079, -2.8262677 ],\n",
337
+ " [-3.57193123, 11.14554125, -3.65390947],\n",
338
+ " [ 0.02336308, 0.08415787, 12.43668805]])"
339
+ ]
340
+ },
341
+ "execution_count": 66,
342
+ "metadata": {},
343
+ "output_type": "execute_result"
344
+ }
345
+ ],
346
+ "source": [
347
+ "cell"
348
+ ]
349
+ },
350
+ {
351
+ "cell_type": "code",
352
+ "execution_count": 65,
353
+ "metadata": {},
354
+ "outputs": [],
355
+ "source": [
356
+ "np.savez(\"/home/zinanzheng/project/KD/nbody/nbody/mole/process/lips/lips.npz\",loc_vel=loc_vel, atom_types=atom_types,cell=cell)"
357
+ ]
358
+ },
359
+ {
360
+ "cell_type": "code",
361
+ "execution_count": 43,
362
+ "metadata": {},
363
+ "outputs": [
364
+ {
365
+ "name": "stdout",
366
+ "output_type": "stream",
367
+ "text": [
368
+ "(25000, 208)\n"
369
+ ]
370
+ }
371
+ ],
372
+ "source": [
373
+ "b=np.load('/home/zinanzheng/project/KD/nbody/nbody/mole/process/lipo/lipo.npz')\n",
374
+ "print(b['atom_types'].shape)"
375
+ ]
376
+ },
377
+ {
378
+ "cell_type": "code",
379
+ "execution_count": 56,
380
+ "metadata": {},
381
+ "outputs": [
382
+ {
383
+ "data": {
384
+ "text/plain": [
385
+ "array([[[10.01499, 12.92582, 13.99406],\n",
386
+ " [ 6.77695, 9.49916, 4.94717],\n",
387
+ " [ 6.15642, 6.26985, 3.71857],\n",
388
+ " ...,\n",
389
+ " [ 8.60587, 7.0621 , 5.91124],\n",
390
+ " [ 3.76817, 4.45782, 12.22634],\n",
391
+ " [ 3.22248, 11.38739, 13.47051]],\n",
392
+ "\n",
393
+ " [[10.01193, 12.94079, 13.96517],\n",
394
+ " [ 6.80638, 9.48081, 4.93982],\n",
395
+ " [ 6.13662, 6.29387, 3.72921],\n",
396
+ " ...,\n",
397
+ " [ 8.62039, 7.06226, 5.92296],\n",
398
+ " [ 3.77395, 4.45026, 12.21999],\n",
399
+ " [ 3.23641, 11.38371, 13.46024]],\n",
400
+ "\n",
401
+ " [[10.00113, 12.95469, 13.92655],\n",
402
+ " [ 6.83531, 9.46051, 4.92919],\n",
403
+ " [ 6.11969, 6.3101 , 3.74004],\n",
404
+ " ...,\n",
405
+ " [ 8.63769, 7.06303, 5.9398 ],\n",
406
+ " [ 3.78027, 4.44348, 12.20823],\n",
407
+ " [ 3.2494 , 11.38427, 13.44482]],\n",
408
+ "\n",
409
+ " ...,\n",
410
+ "\n",
411
+ " [[ 6.67581, 11.37063, 13.47172],\n",
412
+ " [ 5.90004, 8.8403 , 4.1025 ],\n",
413
+ " [ 5.70681, 6.12366, 4.00624],\n",
414
+ " ...,\n",
415
+ " [ 8.28422, 7.19436, 5.53361],\n",
416
+ " [ 5.22054, 4.71746, 11.43566],\n",
417
+ " [ 3.68254, 11.5736 , 13.60964]],\n",
418
+ "\n",
419
+ " [[ 6.63964, 11.35052, 13.44687],\n",
420
+ " [ 5.89198, 8.84278, 4.08125],\n",
421
+ " [ 5.69845, 6.15116, 4.0084 ],\n",
422
+ " ...,\n",
423
+ " [ 8.28411, 7.20164, 5.5359 ],\n",
424
+ " [ 5.20641, 4.70505, 11.41667],\n",
425
+ " [ 3.68398, 11.57309, 13.59649]],\n",
426
+ "\n",
427
+ " [[ 6.60604, 11.33111, 13.42436],\n",
428
+ " [ 5.88576, 8.84788, 4.06055],\n",
429
+ " [ 5.6933 , 6.17839, 4.00447],\n",
430
+ " ...,\n",
431
+ " [ 8.2857 , 7.20819, 5.54052],\n",
432
+ " [ 5.19352, 4.69318, 11.39725],\n",
433
+ " [ 3.68629, 11.57334, 13.58418]]])"
434
+ ]
435
+ },
436
+ "execution_count": 56,
437
+ "metadata": {},
438
+ "output_type": "execute_result"
439
+ }
440
+ ],
441
+ "source": [
442
+ "b['loc_vel'][:,:,0:3]"
443
+ ]
444
+ },
445
+ {
446
+ "cell_type": "code",
447
+ "execution_count": 57,
448
+ "metadata": {},
449
+ "outputs": [
450
+ {
451
+ "data": {
452
+ "text/plain": [
453
+ "array([[[10.01499, 12.92582, 13.99406],\n",
454
+ " [ 6.77695, 9.49916, 4.94717],\n",
455
+ " [ 6.15642, 6.26985, 3.71857],\n",
456
+ " ...,\n",
457
+ " [ 8.60587, 7.0621 , 5.91124],\n",
458
+ " [ 3.76817, 4.45782, 12.22634],\n",
459
+ " [ 3.22248, 11.38739, 13.47051]],\n",
460
+ "\n",
461
+ " [[10.01193, 12.94079, 13.96517],\n",
462
+ " [ 6.80638, 9.48081, 4.93982],\n",
463
+ " [ 6.13662, 6.29387, 3.72921],\n",
464
+ " ...,\n",
465
+ " [ 8.62039, 7.06226, 5.92296],\n",
466
+ " [ 3.77395, 4.45026, 12.21999],\n",
467
+ " [ 3.23641, 11.38371, 13.46024]],\n",
468
+ "\n",
469
+ " [[10.00113, 12.95469, 13.92655],\n",
470
+ " [ 6.83531, 9.46051, 4.92919],\n",
471
+ " [ 6.11969, 6.3101 , 3.74004],\n",
472
+ " ...,\n",
473
+ " [ 8.63769, 7.06303, 5.9398 ],\n",
474
+ " [ 3.78027, 4.44348, 12.20823],\n",
475
+ " [ 3.2494 , 11.38427, 13.44482]],\n",
476
+ "\n",
477
+ " ...,\n",
478
+ "\n",
479
+ " [[ 6.67581, 11.37063, 13.47172],\n",
480
+ " [ 5.90004, 8.8403 , 4.1025 ],\n",
481
+ " [ 5.70681, 6.12366, 4.00624],\n",
482
+ " ...,\n",
483
+ " [ 8.28422, 7.19436, 5.53361],\n",
484
+ " [ 5.22054, 4.71746, 11.43566],\n",
485
+ " [ 3.68254, 11.5736 , 13.60964]],\n",
486
+ "\n",
487
+ " [[ 6.63964, 11.35052, 13.44687],\n",
488
+ " [ 5.89198, 8.84278, 4.08125],\n",
489
+ " [ 5.69845, 6.15116, 4.0084 ],\n",
490
+ " ...,\n",
491
+ " [ 8.28411, 7.20164, 5.5359 ],\n",
492
+ " [ 5.20641, 4.70505, 11.41667],\n",
493
+ " [ 3.68398, 11.57309, 13.59649]],\n",
494
+ "\n",
495
+ " [[ 6.60604, 11.33111, 13.42436],\n",
496
+ " [ 5.88576, 8.84788, 4.06055],\n",
497
+ " [ 5.6933 , 6.17839, 4.00447],\n",
498
+ " ...,\n",
499
+ " [ 8.2857 , 7.20819, 5.54052],\n",
500
+ " [ 5.19352, 4.69318, 11.39725],\n",
501
+ " [ 3.68629, 11.57334, 13.58418]]])"
502
+ ]
503
+ },
504
+ "execution_count": 57,
505
+ "metadata": {},
506
+ "output_type": "execute_result"
507
+ }
508
+ ],
509
+ "source": [
510
+ "a['coords']"
511
+ ]
512
+ },
513
+ {
514
+ "cell_type": "code",
515
+ "execution_count": 58,
516
+ "metadata": {},
517
+ "outputs": [
518
+ {
519
+ "data": {
520
+ "text/plain": [
521
+ "True"
522
+ ]
523
+ },
524
+ "execution_count": 58,
525
+ "metadata": {},
526
+ "output_type": "execute_result"
527
+ }
528
+ ],
529
+ "source": [
530
+ "np.array_equal(a['coords'],b['loc_vel'][:,:,0:3])"
531
+ ]
532
+ }
533
+ ],
534
+ "metadata": {
535
+ "kernelspec": {
536
+ "display_name": "eqgnn",
537
+ "language": "python",
538
+ "name": "python3"
539
+ },
540
+ "language_info": {
541
+ "codemirror_mode": {
542
+ "name": "ipython",
543
+ "version": 3
544
+ },
545
+ "file_extension": ".py",
546
+ "mimetype": "text/x-python",
547
+ "name": "python",
548
+ "nbconvert_exporter": "python",
549
+ "pygments_lexer": "ipython3",
550
+ "version": "3.10.13"
551
+ },
552
+ "orig_nbformat": 4
553
+ },
554
+ "nbformat": 4,
555
+ "nbformat_minor": 2
556
+ }
molecular_dataset/processed_data/lipo/lipo.npz ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:04c8cfbe57b1b51fd661a622fb2c3b1d8745d92870bae8555aa826c81d45e137
3
+ size 291200832
molecular_dataset/processed_data/lips/lips.npz ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:4870f3791c15093f06014e7cd077565eafdd1bac708cb63666cad59846a338df
3
+ size 116205480
molecular_dataset/raw_data/lipo/lipo.npz ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:1fc187b45cca6480d66e15573829e5c7932d008e6337e20ab0825e21dee66a94
3
+ size 166400830
molecular_dataset/raw_data/lips/lips.npz ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:36ded73efe833f86a701d5250d69faa0f0c8c4e79a4a84233eae83816447b1ec
3
+ size 66403486