Commit ⋅ MobleyLab/quanformer

Showing 1 of 1 files from the diff.

@@ -31,7 +31,7 @@

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    return qmethod, qbasis


def avg_mol_time(titles, infile, method, basis, tag, mol_slice):
def avg_mol_time(titles, infile, method, basis, tag, mol_slice=[]):
    """
    For an SDF file with all confs of all mols, get the average runtime
        of all conformers for each molecule.

@@ -155,7 +155,8 @@

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            ax.bar(pos, vals, width=width, label=cond)

    # Set the x-axis tick labels to be equal to the x_labels
    #indices = [x - 1 for x in indices]  # OPTIONAL, better placement if many labels
    if len(indices) > 7: # adjust int as needed for better placement of many labels
        indices = [x - 1 for x in indices]
    ax.set_xticks(indices)
    ax.set_xticklabels(x_labels)
    plt.setp(plt.xticks()[1], rotation=50)

@@ -206,7 +207,7 @@

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    plt.show()


def extract_enes(dict1):
def extract_enes(dict1, mol_slice=[]):
    """
    From files in input dictionaries, read in molecules, extract information
    from SD tags for conformer energies and indices.

@@ -216,6 +217,9 @@

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    dict1 : dict
        dictionary of input files and information to extract from SD tags
        keys are: 'theory' 'fname' 'tagkey' 'label'
    mol_slice : list
        list of indices from which to slice mols generator for read_mols
        [start, stop, step]

    Returns
    -------

@@ -230,7 +234,12 @@

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    """

    mols = reader.read_mols(dict1['fname'])
    # Open molecule file.
    if len(mol_slice) == 3:
        mols = reader.read_mols(dict1['fname'], mol_slice)
    else:
        mols = reader.read_mols(dict1['fname'])

    short_tag = dict1['tagkey']
    qmethod, qbasis = separated_theory(dict1['theory'])


@@ -296,20 +305,28 @@

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def avg_coeffvar(enelist):
    """
    For the relative energies of a single conformer, compute coefficient of
    For the ABSOLUTE energies of a single conformer, compute coefficient of
    variation (CV) across methods. Then average the CVs across all conformers
    for each molecule.

    Note: It is incorrect to compute the coefficient of variation on relative
        values. See wikipedia for an example on temperature.
    Note: The units of the input shouldn't matter since the CV is a ratio,
        meaning that units should cancel.

    Parameters
    ----------
    enelist : list of lists
        enelist[i][j] is relative energy of file i, mol j
        enelist[i][j] is absolute energy of file i, mol j

    Returns
    -------
    spread_by_mol : list
        len(spread_by_mol) == number of molecules
        spread_by_mol[i] == avg(CVs(conformer enes from diff methods) of mol i
    cvlist_all_mols : list of lists
        len(cvlist_all_mols) == number of molecules
        len(cvlist_all_mols[i]) == number of conformers for molecule i minus 1

    Notes
    -----

@@ -320,6 +337,7 @@

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    # 1D list for average of CVs of each mol
    spread_by_mol = []
    cvlist_all_mols = []

    # reshape list so that enelist[i][j] is mol i, file j
    enelist = np.array(enelist).T

@@ -332,7 +350,8 @@

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        num_confs = mols_ene[0].shape[0]
        if num_confs <= 1:
            print(f"skipping molecule {i} since only 0 or 1 conformer")
            spread_by_mol.append('n/a')
            spread_by_mol.append(np.nan)
            cvlist_all_mols.append(num_confs*[np.nan])
            continue

        # number of files is mols_ene.shape[0]; should be same for all mols

@@ -346,18 +365,11 @@

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                cv = np.std(confs_ene) / np.average(confs_ene)
            cvlist.append(cv)

        # check that first element is 0. bc energies relative to first conf
        if not np.isnan(cvlist[0]):
            print("\nWARNING: Check input of avg_coeffvar function."
                  " First conformers of each mol should have zero relative "
                  "energies and thus zero standard deviation across files.")
            return
        cvlist = np.delete(cvlist, 0)

        # average the coeffs of variation
        spread_by_mol.append(np.average(cvlist))
        cvlist_all_mols.append(cvlist)

    return spread_by_mol
    return spread_by_mol, cvlist_all_mols


def normalized_deviation(enelist):

@@ -408,7 +420,7 @@

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    return spread_by_mol


def rmsd_two_files(dict1, dict2):
def rmsd_two_files(dict1, dict2, mol_slice=[]):
    """
    From files in input dictionaries, read in molecules, extract information
    from SD tags, compute relative conformer energies wrt to first conformer

@@ -423,6 +435,9 @@

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    dict2 : dict
        dictionary of input files and information to extract from SD tags
        keys are: 'theory' 'fname' 'tagkey' 'label'
    mol_slice : list
        list of indices from which to slice mols generator for read_mols
        [start, stop, step]

    Returns
    -------

@@ -448,8 +463,8 @@

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    compared_enes = []

    # load molecules and extract data
    mols1_titles, mols1_indices, mols1_enes, mols1_nans = extract_enes(dict1)
    mols2_titles, mols2_indices, mols2_enes, mols2_nans = extract_enes(dict2)
    mols1_titles, mols1_indices, mols1_enes, mols1_nans = extract_enes(dict1, mol_slice)
    mols2_titles, mols2_indices, mols2_enes, mols2_nans = extract_enes(dict2, mol_slice)

    # check that number of molecules exist in both files
    if len(mols1_titles) != len(mols2_titles):

@@ -513,7 +528,7 @@

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### ------------------- Script -------------------


def survey_energies_ref(wholedict, ref_index, outfn='relene-rmsd.dat'):
def survey_energies_ref(wholedict, ref_index, mol_slice=[], outfn='relene-rmsd.dat'):
    """
    Compute RMSD of relative conformer energies with respect to the data
    in ref_index spot. The relative energies by conformer are computed first,

@@ -526,6 +541,9 @@

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        keys are: 'theory' 'fname' 'tagkey' 'label'
    ref_index : int
        integer to specify reference file of wholedict[ref_index]
    mol_slice : list
        list of indices from which to slice mols generator for read_mols
        [start, stop, step]
    outfn : str
        name of the output file with RMSDs of energies


@@ -554,7 +572,7 @@

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        # each of the four returned vars is (file) list of (mols) lists
        titleMols, rmsds, confNums, reference_enes, compared_enes =\
            rmsd_two_files(wholedict[ref_index], wholedict[i])
            rmsd_two_files(wholedict[ref_index], wholedict[i], mol_slice)
        wholedict[i]['titleMols'] = titleMols
        wholedict[i]['rmsds'] = rmsds
        wholedict[i]['confNums'] = confNums

@@ -586,7 +604,7 @@

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    return wholedict


def survey_energies(wholedict, outfn='relene.dat'):
def survey_energies(wholedict, mol_slice=[], outfn='relene.dat'):
    """
    Compute the spread of the conformer energies for each molecule in each file.
    The spread can be computed as the coefficient of variation of all methods,

@@ -604,6 +622,9 @@

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    wholedict : OrderedDict
        ordered dictionary of input files and information to extract from SD tags
        keys are: 'theory' 'fname' 'tagkey' 'label'
    mol_slice : list
        list of indices from which to slice mols generator for read_mols
        [start, stop, step]
    outfn : str
        name of the output file with RMSDs of energies


@@ -615,14 +636,13 @@

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    """

    description = 'relative energies (kcal/mol)'
    num_files = len(wholedict)

    # Write description in output file.
    compF = open(outfn, 'w')
    compF.write(f"# Spread of {description} over diff QM methods averaged over conformers\n")
    compF.write(f"# Variability in energies from diff QM methods averaged over conformers\n")
    for i, d in enumerate(wholedict.values()):
        compF.write("# File %d: %s\n" % (i, d['fname']))
        compF.write("# File %d: %s\n" % (i+1, d['fname']))
        compF.write("#   calc=%s, %s\n" % (d['tagkey'], d['theory']))

    enelist = []

@@ -630,7 +650,7 @@

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    nanlist = []
    for i in range(num_files):
        print("Extracting data for file: %s" % wholedict[i]['fname'])
        titleMols, confNums, compared_enes, confNans = extract_enes(wholedict[i])
        titleMols, confNums, compared_enes, confNans = extract_enes(wholedict[i], mol_slice)
        enelist.append(compared_enes)
        idxlist.append(confNums)
        nanlist.append(confNans)

@@ -671,8 +691,8 @@

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            "Cannot calculate energy spread.")
        spreadlist = [-1]*len(relenelist[0])
    else:
        # estimate spread of data
        spreadlist = avg_coeffvar(relenelist)
        # estimate spread of data on ABSOLUTE energies
        spreadlist, cvlist_all_mols = avg_coeffvar(enelist)
        #spreadlist = normalized_deviation(relenelist)



@@ -681,16 +701,20 @@

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        compF.write('\n\n# Mol ' + wholedict[1]['titleMols'][m])

        # for this mol, write the rmsd from each file side by side
        line = ' Spread: {}'.format(spreadlist[m])
        line = ' {:.2f}% averaged CV'.format(spreadlist[m]*100)
        compF.write(line)
        compF.write('\n# ==================================================================')
        compF.write(f'\n# conf\t{description} in column order by file (listed at top)')
        compF.write(f'\n# rel enes (kcal/mol), column i = file i, row j = conformer j')

        # for this mol, write the compared_enes from each file by columns
        for c in range(len(wholedict[1]['confNums'][m])):
            line = '\n' + str(wholedict[1]['confNums'][m][c]) + '\t'
            for i in range(num_files):
                line += (str(wholedict[i]['compared_enes'][m][c]) + '\t')
                line += '{:.4f}\t'.format(wholedict[i]['compared_enes'][m][c])

            # print overall contribution to spread
            line += '# {:.2f}% CV'.format(cvlist_all_mols[m][c]*100)

            compF.write(line)

    compF.close()

@@ -698,7 +722,7 @@

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    return wholedict


def survey_times(wholedict, mol_slice):
def survey_times(wholedict, mol_slice=[]):
    """
    Average all conformers' calculations times for each molecule in each file.
    Generate grouped bar plot where x=QM method, y=time(s). Molecules are

@@ -828,10 +852,9 @@

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    if analyze_energies:
        if ref_index is None:
            wholedict = survey_energies(wholedict)
            wholedict = survey_energies(wholedict, mol_slice)
        else:
            wholedict = survey_energies_ref(wholedict, ref_index)
            wholedict = survey_energies_ref(wholedict, ref_index, mol_slice)
            # TODO move this part inside the survey_energies fx
            # TODO add mol_slice parameter to survey_energies* functions
            if plot_enes:
                arrange_and_plot(wholedict, "RMSDs of relative conformer energies")

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155	155		ax.bar(pos, vals, width=width, label=cond)
156	156
157	157		# Set the x-axis tick labels to be equal to the x_labels
158		-	#indices = [x - 1 for x in indices] # OPTIONAL, better placement if many labels
	158	+	if len(indices) > 7: # adjust int as needed for better placement of many labels
	159	+	indices = [x - 1 for x in indices]
159	160		ax.set_xticks(indices)
160	161		ax.set_xticklabels(x_labels)
161	162		plt.setp(plt.xticks()[1], rotation=50)

206	207		plt.show()
207	208
208	209
209		-	def extract_enes(dict1):
	210	+	def extract_enes(dict1, mol_slice=[]):
210	211		"""
211	212		From files in input dictionaries, read in molecules, extract information
212	213		from SD tags for conformer energies and indices.

216	217		dict1 : dict
217	218		dictionary of input files and information to extract from SD tags
218	219		keys are: 'theory' 'fname' 'tagkey' 'label'
	220	+	mol_slice : list
	221	+	list of indices from which to slice mols generator for read_mols
	222	+	[start, stop, step]
219	223
220	224		Returns
221	225		-------

230	234
231	235		"""
232	236
233		-	mols = reader.read_mols(dict1['fname'])
	237	+	# Open molecule file.
	238	+	if len(mol_slice) == 3:
	239	+	mols = reader.read_mols(dict1['fname'], mol_slice)
	240	+	else:
	241	+	mols = reader.read_mols(dict1['fname'])
	242	+
234	243		short_tag = dict1['tagkey']
235	244		qmethod, qbasis = separated_theory(dict1['theory'])
236	245

296	305
297	306		def avg_coeffvar(enelist):
298	307		"""
299		-	For the relative energies of a single conformer, compute coefficient of
	308	+	For the ABSOLUTE energies of a single conformer, compute coefficient of
300	309		variation (CV) across methods. Then average the CVs across all conformers
301	310		for each molecule.
302	311
	312	+	Note: It is incorrect to compute the coefficient of variation on relative
	313	+	values. See wikipedia for an example on temperature.
	314	+	Note: The units of the input shouldn't matter since the CV is a ratio,
	315	+	meaning that units should cancel.
	316	+
303	317		Parameters
304	318		----------
305	319		enelist : list of lists
306		-	enelist[i][j] is relative energy of file i, mol j
	320	+	enelist[i][j] is absolute energy of file i, mol j
307	321
308	322		Returns
309	323		-------
310	324		spread_by_mol : list
311	325		len(spread_by_mol) == number of molecules
312	326		spread_by_mol[i] == avg(CVs(conformer enes from diff methods) of mol i
	327	+	cvlist_all_mols : list of lists
	328	+	len(cvlist_all_mols) == number of molecules
	329	+	len(cvlist_all_mols[i]) == number of conformers for molecule i minus 1
313	330
314	331		Notes
315	332		-----

31	31		return qmethod, qbasis
32	32
33	33
34		-	def avg_mol_time(titles, infile, method, basis, tag, mol_slice):
	34	+	def avg_mol_time(titles, infile, method, basis, tag, mol_slice=[]):
35	35		"""
36	36		For an SDF file with all confs of all mols, get the average runtime
37	37		of all conformers for each molecule.

320	337
321	338		# 1D list for average of CVs of each mol
322	339		spread_by_mol = []
	340	+	cvlist_all_mols = []
323	341
324	342		# reshape list so that enelist[i][j] is mol i, file j
325	343		enelist = np.array(enelist).T

332	350		num_confs = mols_ene[0].shape[0]
333	351		if num_confs <= 1:
334	352		print(f"skipping molecule {i} since only 0 or 1 conformer")
335		-	spread_by_mol.append('n/a')
	353	+	spread_by_mol.append(np.nan)
	354	+	cvlist_all_mols.append(num_confs*[np.nan])
336	355		continue
337	356
338	357		# number of files is mols_ene.shape[0]; should be same for all mols

346	365		cv = np.std(confs_ene) / np.average(confs_ene)
347	366		cvlist.append(cv)
348	367
349		-	# check that first element is 0. bc energies relative to first conf
350		-	if not np.isnan(cvlist[0]):
351		-	print("\nWARNING: Check input of avg_coeffvar function."
352		-	" First conformers of each mol should have zero relative "
353		-	"energies and thus zero standard deviation across files.")
354		-	return
355		-	cvlist = np.delete(cvlist, 0)
356		-
357	368		# average the coeffs of variation
358	369		spread_by_mol.append(np.average(cvlist))
	370	+	cvlist_all_mols.append(cvlist)
359	371
360		-	return spread_by_mol
	372	+	return spread_by_mol, cvlist_all_mols
361	373
362	374
363	375		def normalized_deviation(enelist):

408	420		return spread_by_mol
409	421
410	422
411		-	def rmsd_two_files(dict1, dict2):
	423	+	def rmsd_two_files(dict1, dict2, mol_slice=[]):
412	424		"""
413	425		From files in input dictionaries, read in molecules, extract information
414	426		from SD tags, compute relative conformer energies wrt to first conformer

423	435		dict2 : dict
424	436		dictionary of input files and information to extract from SD tags
425	437		keys are: 'theory' 'fname' 'tagkey' 'label'
	438	+	mol_slice : list
	439	+	list of indices from which to slice mols generator for read_mols
	440	+	[start, stop, step]
426	441
427	442		Returns
428	443		-------

448	463		compared_enes = []
449	464
450	465		# load molecules and extract data
451		-	mols1_titles, mols1_indices, mols1_enes, mols1_nans = extract_enes(dict1)
452		-	mols2_titles, mols2_indices, mols2_enes, mols2_nans = extract_enes(dict2)
	466	+	mols1_titles, mols1_indices, mols1_enes, mols1_nans = extract_enes(dict1, mol_slice)
	467	+	mols2_titles, mols2_indices, mols2_enes, mols2_nans = extract_enes(dict2, mol_slice)
453	468
454	469		# check that number of molecules exist in both files
455	470		if len(mols1_titles) != len(mols2_titles):

513	528		### ------------------- Script -------------------
514	529
515	530
516		-	def survey_energies_ref(wholedict, ref_index, outfn='relene-rmsd.dat'):
	531	+	def survey_energies_ref(wholedict, ref_index, mol_slice=[], outfn='relene-rmsd.dat'):
517	532		"""
518	533		Compute RMSD of relative conformer energies with respect to the data
519	534		in ref_index spot. The relative energies by conformer are computed first,

526	541		keys are: 'theory' 'fname' 'tagkey' 'label'
527	542		ref_index : int
528	543		integer to specify reference file of wholedict[ref_index]
	544	+	mol_slice : list
	545	+	list of indices from which to slice mols generator for read_mols
	546	+	[start, stop, step]
529	547		outfn : str
530	548		name of the output file with RMSDs of energies
531	549

554	572
555	573		# each of the four returned vars is (file) list of (mols) lists
556	574		titleMols, rmsds, confNums, reference_enes, compared_enes =\
557		-	rmsd_two_files(wholedict[ref_index], wholedict[i])
	575	+	rmsd_two_files(wholedict[ref_index], wholedict[i], mol_slice)
558	576		wholedict[i]['titleMols'] = titleMols
559	577		wholedict[i]['rmsds'] = rmsds
560	578		wholedict[i]['confNums'] = confNums

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586	604		return wholedict
587	605
588	606
589		-	def survey_energies(wholedict, outfn='relene.dat'):
	607	+	def survey_energies(wholedict, mol_slice=[], outfn='relene.dat'):
590	608		"""
591	609		Compute the spread of the conformer energies for each molecule in each file.
592	610		The spread can be computed as the coefficient of variation of all methods,

604	622		wholedict : OrderedDict
605	623		ordered dictionary of input files and information to extract from SD tags
606	624		keys are: 'theory' 'fname' 'tagkey' 'label'
	625	+	mol_slice : list
	626	+	list of indices from which to slice mols generator for read_mols
	627	+	[start, stop, step]
607	628		outfn : str
608	629		name of the output file with RMSDs of energies
609	630

615	636
616	637		"""
617	638
618		-	description = 'relative energies (kcal/mol)'
619	639		num_files = len(wholedict)
620	640
621	641		# Write description in output file.
622	642		compF = open(outfn, 'w')
623		-	compF.write(f"# Spread of {description} over diff QM methods averaged over conformers\n")
	643	+	compF.write(f"# Variability in energies from diff QM methods averaged over conformers\n")
624	644		for i, d in enumerate(wholedict.values()):
625		-	compF.write("# File %d: %s\n" % (i, d['fname']))
	645	+	compF.write("# File %d: %s\n" % (i+1, d['fname']))
626	646		compF.write("# calc=%s, %s\n" % (d['tagkey'], d['theory']))
627	647
628	648		enelist = []

630	650		nanlist = []
631	651		for i in range(num_files):
632	652		print("Extracting data for file: %s" % wholedict[i]['fname'])
633		-	titleMols, confNums, compared_enes, confNans = extract_enes(wholedict[i])
	653	+	titleMols, confNums, compared_enes, confNans = extract_enes(wholedict[i], mol_slice)
634	654		enelist.append(compared_enes)
635	655		idxlist.append(confNums)
636	656		nanlist.append(confNans)

671	691		"Cannot calculate energy spread.")
672	692		spreadlist = [-1]*len(relenelist[0])
673	693		else:
674		-	# estimate spread of data
675		-	spreadlist = avg_coeffvar(relenelist)
	694	+	# estimate spread of data on ABSOLUTE energies
	695	+	spreadlist, cvlist_all_mols = avg_coeffvar(enelist)
676	696		#spreadlist = normalized_deviation(relenelist)
677	697
678	698

681	701		compF.write('\n\n# Mol ' + wholedict[1]['titleMols'][m])
682	702
683	703		# for this mol, write the rmsd from each file side by side
684		-	line = ' Spread: {}'.format(spreadlist[m])
	704	+	line = ' {:.2f}% averaged CV'.format(spreadlist[m]*100)
685	705		compF.write(line)
686	706		compF.write('\n# ==================================================================')
687		-	compF.write(f'\n# conf\t{description} in column order by file (listed at top)')
	707	+	compF.write(f'\n# rel enes (kcal/mol), column i = file i, row j = conformer j')
688	708
689	709		# for this mol, write the compared_enes from each file by columns
690	710		for c in range(len(wholedict[1]['confNums'][m])):
691	711		line = '\n' + str(wholedict[1]['confNums'][m][c]) + '\t'
692	712		for i in range(num_files):
693		-	line += (str(wholedict[i]['compared_enes'][m][c]) + '\t')
	713	+	line += '{:.4f}\t'.format(wholedict[i]['compared_enes'][m][c])
	714	+
	715	+	# print overall contribution to spread
	716	+	line += '# {:.2f}% CV'.format(cvlist_all_mols[m][c]*100)
	717	+
694	718		compF.write(line)
695	719
696	720		compF.close()

698	722		return wholedict
699	723
700	724
701		-	def survey_times(wholedict, mol_slice):
	725	+	def survey_times(wholedict, mol_slice=[]):
702	726		"""
703	727		Average all conformers' calculations times for each molecule in each file.
704	728		Generate grouped bar plot where x=QM method, y=time(s). Molecules are

828	852
829	853		if analyze_energies:
830	854		if ref_index is None:
831		-	wholedict = survey_energies(wholedict)
	855	+	wholedict = survey_energies(wholedict, mol_slice)
832	856		else:
833		-	wholedict = survey_energies_ref(wholedict, ref_index)
	857	+	wholedict = survey_energies_ref(wholedict, ref_index, mol_slice)
834	858		# TODO move this part inside the survey_energies fx
835		-	# TODO add mol_slice parameter to survey_energies* functions
836	859		if plot_enes:
837	860		arrange_and_plot(wholedict, "RMSDs of relative conformer energies")

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16	- "quanformer/match_plot.py"
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18	- "quanformer/quan2modsem.py"
19	- "quanformer/match_minima.py"
20	- "quanformer/opt_vs_spe.py"