Support atoms in showOccupancies

[Copilot]

showOccupancies accepted atoms= in practice but forwarded it to matplotlib, causing plotting to fail with an unexpected keyword argument error. It also did not use ensemble atoms by default, so occupancy plots could not align to residue numbering without manual workarounds.

• Behavior change

  • Consume atoms inside showOccupancies instead of passing it through to plt.plot
  • Default to pdbensemble.getAtoms() when atoms is not provided

• X-axis handling

  • When atoms are available, plot occupancies against atoms.getResnums()
  • Update the x-axis label from Atom index to Residue number for atom-aware plots
  • Preserve the existing atom-index behavior when no atoms are available

• Validation

  • Add an explicit size check between computed occupancies and the provided atom set to fail early on mismatches

• Regression coverage

  • Add focused tests for both:
    • explicit atoms= usage
    • implicit atom extraction from the ensemble

Example:

showOccupancies(dali_ens, normed=True, atoms=dali_ens.getAtoms())

This now plots occupancies against residue numbers instead of passing atoms through to matplotlib.

[karolamik13]

Test below. I took one of my files from Dali.

In [1]: from prody import *
...: import matplotlib.pyplot as plt
...:
...: dali = DaliRecord('dali_ens.txt', '4nre', 'A', localFile=True)
...: pdbs = dali.filter(cutoff_len=0.7, cutoff_rmsd=2.0, cutoff_Z=10)[:20]
...: structures = parsePDB(pdbs, subset='ca')
...: dali_ens = buildPDBEnsemble(structures, mapping=dali.getMappings())
...:
...: showOccupancies(dali_ens, normed=True, atoms=dali_ens.getAtoms())
...: plt.show()
...:
@> Obtained 1720 PDB chains from Dali for 4nreA.
@> 1611 PDBs have been filtered out from 1720 Dali hits (remaining: 109).
@> 20 PDBs were parsed in 0.95s.
@> Starting iterative superposition:
@> Step #1: RMSD difference = 2.4160e+01
@> Step #2: RMSD difference = 3.0334e-01
@> Step #3: RMSD difference = 1.2812e-01
@> Step #4: RMSD difference = 7.8682e-02
@> Step #5: RMSD difference = 4.8933e-02
@> Step #6: RMSD difference = 3.0505e-02
@> Step #7: RMSD difference = 1.9036e-02
@> Step #8: RMSD difference = 1.1886e-02
@> Step #9: RMSD difference = 7.4247e-03
@> Step #10: RMSD difference = 4.6391e-03
@> Step #11: RMSD difference = 2.8992e-03
@> Step #12: RMSD difference = 1.8121e-03
@> Step #13: RMSD difference = 1.1328e-03
@> Step #14: RMSD difference = 7.0817e-04
@> Step #15: RMSD difference = 4.4275e-04
@> Step #16: RMSD difference = 2.7683e-04
@> Step #17: RMSD difference = 1.7309e-04
@> Step #18: RMSD difference = 1.0823e-04
@> Step #19: RMSD difference = 6.7681e-05
@> Iterative superposition completed in 0.08s.
@> Final superposition to calculate transformations.
@> Superposition completed in 0.00 seconds.
@> Ensemble (20 conformations) were built in 0.82s.

The outcome:

[jamesmkrieger]

Thanks, looks good. Can you confirm that the residue numbers start at around 0?

[karolamik13]

Thanks, looks good. Can you confirm that the residue numbers start at around 0?

Yes, resid is 2. With almost 700 residues, it is around 0 on the plot.

[jamesmkrieger]

Great. Can you try using ensemble select to confirm it changes please

[karolamik13]

Great. Can you try using ensemble select to confirm it changes please

Sure:

In [2]: dali_ens
Out[2]: <PDBEnsemble: Unknown (20 conformations; 675 atoms)>

In [3]: dali_ens.getAtoms()
Out[3]: <AtomGroup: 4nreA_ca (675 atoms)>

In [4]: dali_ens.getDataLabels()
Out[4]: []

In [5]: dali_ens.getAtoms().getResnums()
Out[5]:
array([ 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, 297, 298, 299, 300,
301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313,
314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326,
327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339,
340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352,
353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365,
366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378,
379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391,
392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404,
405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417,
418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430,
431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443,
444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456,
457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469,
470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482,
483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495,
496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508,
509, 510, 511, 512, 513, 514, 515, 516, 517, 518, 519, 520, 521,
522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532, 533, 534,
535, 536, 537, 538, 539, 540, 541, 542, 543, 544, 545, 546, 547,
548, 549, 550, 551, 552, 553, 554, 555, 556, 557, 558, 559, 560,
561, 562, 563, 564, 565, 566, 567, 568, 569, 570, 571, 572, 573,
574, 575, 576, 577, 578, 579, 580, 581, 582, 583, 584, 585, 586,
587, 588, 589, 590, 591, 592, 593, 594, 595, 596, 597, 598, 599,
600, 601, 602, 603, 604, 605, 606, 607, 608, 609, 610, 611, 612,
613, 614, 615, 616, 617, 618, 619, 620, 621, 622, 623, 624, 625,
626, 627, 628, 629, 630, 631, 632, 633, 634, 635, 636, 637, 638,
639, 640, 641, 642, 643, 644, 645, 646, 647, 648, 649, 650, 651,
652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664,
665, 666, 667, 668, 669, 670, 671, 672, 673, 674, 675, 676])

In [6]: dali_ens.getAtoms().getResnums().min()
Out[6]: 2

In [7]: dali_ens.getAtoms().getResnums().max()
Out[7]: 676

[jamesmkrieger]

I meant if you use select or setAtoms on the ensemble to start at 20 or 100 then do we see the x axis change so we can confirm that it is really numbering by atoms. It’s hard to know for sure when the atoms start from 2

[jamesmkrieger]

ok, I can confirm it

In [8]: ags = parsePDB(["prody/tests/datafiles/pdb3hsy.pdb", "prody/tests/datafiles/pdb3o21.pdb"], chain='A', subset='ca')
@> 2 PDBs were parsed in 0.01s.                              

In [9]: ens = buildPDBEnsemble(ags)
@> Starting iterative superposition:             
@> Step #1: RMSD difference = 1.6395e+00
@> Step #2: RMSD difference = 1.1942e-14
@> Iterative superposition completed in 0.00s.
@> Final superposition to calculate transformations.
@> Superposition completed in 0.00 seconds.
@> Ensemble (2 conformations) were built in 0.12s.

In [10]: showOccupancies(ens, normed=True, atoms=ens.getAtoms())
Out[10]: [<matplotlib.lines.Line2D at 0x131b78a60>]

In [11]: ens.select('resnum 50 to 300')

In [12]: plt.figure()
Out[12]: <Figure size 1280x960 with 0 Axes>

In [13]: showOccupancies(ens, normed=True, atoms=ens.getAtoms())
Out[13]: [<matplotlib.lines.Line2D at 0x1315cb850>]

[jamesmkrieger]

However, if we put multiple chains then it doesn't know how to handle that, so this isn't what we want

[jamesmkrieger]

However, if we put multiple chains then it doesn't know how to handle that, so this isn't what we want

@copilot wrap through showAtomicLines instead

[Copilot]

However, if we put multiple chains then it doesn't know how to handle that, so this isn't what we want

@copilot wrap through showAtomicLines instead

Addressed in aff2a14. showOccupancies now routes the atom-aware path through showAtomicLines, and I updated the regression coverage to verify both single-chain and multi-chain atomic labeling behavior.

[jamesmkrieger]

@copilot this line should stay unchanged

[Copilot]

Fixed in cdb0be2.

[jamesmkrieger]

This now works as expected

In [1]: from prody import *

In [2]: ags = parsePDB(["prody/tests/datafiles/pdb3hsy.pdb", "prody/tests/datafiles/pdb3o21.pdb"], biomol=True, extend_biomol=Tru
   ...: e, subset='ca')
@> 2 PDBs were parsed in 0.03s.                             

In [3]: ens = buildPDBEnsemble(ags)
@> Starting iterative superposition:                          
@> Step #1: RMSD difference = 1.9801e+00
@> Step #2: RMSD difference = 1.2557e-02
@> Step #3: RMSD difference = 6.4942e-04
@> Step #4: RMSD difference = 3.6103e-05
@> Iterative superposition completed in 0.00s.
@> Final superposition to calculate transformations.
@> Superposition completed in 0.00 seconds.
@> Ensemble (3 conformations) were built in 0.74s.

In [4]: showOccupancies(ens, normed=True, atoms=ens.getAtoms())
Out[4]: [<matplotlib.lines.Line2D at 0x152322c50>]

In [5]: import matplotlib.pyplot as plt

In [6]: plt.ion()
Out[6]: <contextlib.ExitStack at 0x1231128c0>

In [7]: plt.show()

[jamesmkrieger]

@copilot remove your new tests. We do not run unit tests on plotting functions and using PDBENSEMBLEA that only has 10 atoms with resnums 1 to 10 anyway doesn't help assess it anyway

[Copilot]

@copilot remove your new tests. We do not run unit tests on plotting functions and using PDBENSEMBLEA that only has 10 atoms with resnums 1 to 10 anyway doesn't help assess it anyway

Removed in 440d539.