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# -*- coding: utf-8 -*- | ||
""" | ||
Created on Wed May 27 14:18:37 2015 | ||
Updated on July 30 14:18:37 2015 | ||
@author: ke291 | ||
Equivalent and compact port of DP4.jar to python. The results | ||
produced are essentially equivalent, but not identical due to different | ||
floating point precision used in the Python (53 bits) and Java (32 bits) | ||
implementation. | ||
""" | ||
from scipy import stats | ||
import pickle | ||
import re | ||
import bisect | ||
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meanC = 0.0 | ||
meanH = 0.0 | ||
stdevC = 2.269372270818724 | ||
stdevH = 0.18731058105269952 | ||
output = [] | ||
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def main(Clabels, Cvalues, Hlabels, Hvalues, Cexp, Hexp, settings): | ||
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Print(str(Cexp)) | ||
Print(str(Hexp)) | ||
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C_cdp4 = [] | ||
H_cdp4 = [] | ||
Comb_cdp4 = [] | ||
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for isomer in range(0, len(Cvalues)): | ||
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sortedClabels, sortedCvalues, sortedCexp =\ | ||
AssignExpNMR(Clabels, Cvalues[isomer], Cexp) | ||
scaledC = ScaleNMR(sortedCvalues, sortedCexp) | ||
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sortedHlabels, sortedHvalues, sortedHexp = \ | ||
AssignExpNMR(Hlabels, Hvalues[isomer], Hexp) | ||
scaledH = ScaleNMR(sortedHvalues, sortedHexp) | ||
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ScaledErrorsC = [sortedCexp[i] - scaledC[i] | ||
for i in range(0, len(scaledC))] | ||
ScaledErrorsH = [sortedHexp[i] - scaledH[i] | ||
for i in range(0, len(scaledH))] | ||
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Print("\nAssigned shifts for isomer " + str(isomer+1) + ": ") | ||
PrintNMR('C', sortedClabels, sortedCvalues, scaledC, sortedCexp) | ||
Print("Max C error: " + format(max(ScaledErrorsC, key=abs), "6.2f")) | ||
PrintNMR('H', sortedHlabels, sortedHvalues, scaledH, sortedHexp) | ||
Print("Max H error: " + format(max(ScaledErrorsH, key=abs), "6.2f")) | ||
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if settings.PDP4: | ||
C_cdp4.append(CalculateCDP4(ScaledErrorsC, meanC, stdevC)) | ||
H_cdp4.append(CalculateCDP4(ScaledErrorsH, meanH, stdevH)) | ||
elif settings.EP5: | ||
C_cdp4.append(CalculatePDP4(ScaledErrorsC, meanC, stdevC)) | ||
#C_cdp4.append(CalculateKDE(ScaledErrorsC, settings.ScriptDir + '/NucCErr.pkl')) | ||
H_cdp4.append(CalculatePDP4(ScaledErrorsH, meanH, stdevH)) | ||
#H_cdp4.append(CalculateKDE(ScaledErrorsH, settings.ScriptDir + '/NucHErr.pkl')) | ||
Comb_cdp4.append(C_cdp4[-1]*H_cdp4[-1]) | ||
Print("\nDP4 based on C: " + format(C_cdp4[-1], "6.2e")) | ||
Print("DP4 based on H: " + format(H_cdp4[-1], "6.2e")) | ||
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relCDP4 = [(100*x)/sum(C_cdp4) for x in C_cdp4] | ||
relHDP4 = [(100*x)/sum(H_cdp4) for x in H_cdp4] | ||
relCombDP4 = [(100*x)/sum(Comb_cdp4) for x in Comb_cdp4] | ||
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PrintRelDP4('both carbon and proton data', relCombDP4) | ||
PrintRelDP4('carbon data only', relCDP4) | ||
PrintRelDP4('proton data only', relHDP4) | ||
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return output | ||
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def AssignExpNMR(labels, calcShifts, exp): | ||
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#Replace all 'or' and 'OR' with ',', remove all spaces and 'any' | ||
exp = re.sub(r"or|OR", ',', exp, flags=re.DOTALL) | ||
exp = re.sub(r" |any", '', exp, flags=re.DOTALL) | ||
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#Get all assignments, split mulitassignments | ||
ExpLabels = re.findall(r"(?<=\().*?(?=\))", exp, flags=re.DOTALL) | ||
ExpLabels = [x.split(',') for x in ExpLabels] | ||
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#Remove assignments and get shifts | ||
ShiftData = (re.sub(r"\(.*?\)", "", exp, flags=re.DOTALL)).split(',') | ||
expShifts = [float(x) for x in ShiftData] | ||
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#Prepare sorted calculated data with labels and sorted exp data | ||
sortedCalc = sorted(calcShifts) | ||
sortedExp = sorted(expShifts) | ||
sortedExpLabels = SortExpAssignments(expShifts, ExpLabels) | ||
sortedCalcLabels = [] | ||
for v in sortedCalc: | ||
index = calcShifts.index(v) | ||
sortedCalcLabels.append(labels[index]) | ||
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assignedExpLabels = ['' for i in range(0, len(sortedExp))] | ||
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#First pass - assign the unambiguous shifts | ||
for v in range(0, len(sortedExp)): | ||
if len(sortedExpLabels[v]) == 1 and sortedExpLabels[v][0] != '': | ||
#Check that assignment exists in computational data | ||
if sortedExpLabels[v][0] in labels: | ||
assignedExpLabels[v] = sortedExpLabels[v][0] | ||
else: | ||
Print("Label " + sortedExpLabels[v][0] + | ||
" not found in among computed shifts, please check NMR assignment.") | ||
quit() | ||
#Second pass - assign shifts from a limited set | ||
for v in range(0, len(sortedExp)): | ||
if len(sortedExpLabels[v]) != 1 and sortedExpLabels[v][0] != '': | ||
for l in sortedCalcLabels: | ||
if l in sortedExpLabels[v] and l not in assignedExpLabels: | ||
assignedExpLabels[v] = l | ||
break | ||
#Final pass - assign unassigned shifts in order | ||
for v in range(0, len(sortedExp)): | ||
if sortedExpLabels[v][0] == '': | ||
for l in sortedCalcLabels: # Take the first free label | ||
if l not in assignedExpLabels: | ||
assignedExpLabels[v] = l | ||
break | ||
sortedCalc = [] | ||
#Rearrange calc values to match the assigned labels | ||
for l in assignedExpLabels: | ||
index = labels.index(l) | ||
sortedCalc.append(calcShifts[index]) | ||
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return assignedExpLabels, sortedCalc, sortedExp | ||
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def SortExpAssignments(shifts, assignments): | ||
tempshifts = list(shifts) | ||
tempassignments = list(assignments) | ||
sortedassignments = [] | ||
while len(tempassignments) > 0: | ||
index = tempshifts.index(min(tempshifts)) | ||
sortedassignments.append(tempassignments[index]) | ||
tempshifts.pop(index) | ||
tempassignments.pop(index) | ||
return sortedassignments | ||
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#Scale the NMR shifts | ||
def ScaleNMR(calcShifts, expShifts): | ||
slope, intercept, r_value, p_value, std_err = stats.linregress(expShifts, | ||
calcShifts) | ||
scaled = [(x-intercept)/slope for x in calcShifts] | ||
return scaled | ||
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def PrintNMR(nucleus, labels, values, scaled, exp): | ||
Print("\nAssigned " + nucleus + | ||
" shifts: (label, calc, corrected, exp, error)") | ||
for i in range(0, len(labels)): | ||
Print(format(labels[i], "6s") + ' ' + format(values[i], "6.2f") + ' ' | ||
+ format(scaled[i], "6.2f") + ' ' + format(exp[i], "6.2f") + ' ' + | ||
format(exp[i]-scaled[i], "6.2f")) | ||
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def PrintRelDP4(title, RelDP4): | ||
Print("\nResults of DP4 using " + title + ":") | ||
for i in range(0, len(RelDP4)): | ||
Print("Isomer " + str(i+1) + ": " + format(RelDP4[i], "4.1f") + "%") | ||
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def Print(s): | ||
print s | ||
output.append(s) | ||
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def CalculateCDP4(errors, expect, stdev): | ||
cdp4 = 1.0 | ||
for e in errors: | ||
z = abs((e-expect)/stdev) | ||
cdp4 = cdp4*2*stats.norm.cdf(-z) | ||
return cdp4 | ||
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#Alternative function using probability density function instead of cdf | ||
def CalculatePDP4(errors, expect, stdev): | ||
pdp4 = 1.0 | ||
for e in errors: | ||
#z = (e-expect)/stdev | ||
#pdp4 = pdp4*stats.norm.pdf(z) | ||
pdp4 = pdp4*stats.norm(expect, stdev).pdf(e) | ||
return pdp4 | ||
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#use as CalculateKDE(errors, 'NucCErr.pkl') for C or | ||
#CalculateKDE(errors, 'NucHErr.pkl') for H | ||
#load empirical error data from file and use KDE to construct pdf | ||
def CalculateKDE(errors, PickleFile): | ||
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pkl_file = open(PickleFile, 'rb') | ||
ErrorData = pickle.load(pkl_file) | ||
kde = stats.gaussian_kde(ErrorData) | ||
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ep5 = 1.0 | ||
for e in errors: | ||
#z = (e-expect)/stdev | ||
#pdp4 = pdp4*stats.norm.pdf(z) | ||
ep5 = ep5*float(kde(e)[0]) | ||
return ep5 | ||
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#use as CalculateRKDE(errors, 'RKDEC.pkl') for C or | ||
#CalculateKDE(errors, 'RKDEH.pkl') for H | ||
#load empirical error data from file and use KDE to construct several pdfs, | ||
#one for each chemical shift region | ||
def CalculateRKDE(errors, shifts, PickleFile): | ||
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#Load the data | ||
pkl_file = open(PickleFile, 'rb') | ||
regions, RErrors = pickle.load(pkl_file) | ||
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#Reconstruct the distributions for each region | ||
kdes = [] | ||
for es in RErrors: | ||
kdes.append(stats.gaussian_kde(es)) | ||
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ep5 = 1.0 | ||
for i, e in enumerate(errors): | ||
region = bisect.bisect_left(regions, shifts[i]) | ||
ep5 = ep5*float((kdes[region])(e)[0]) | ||
return ep5 |
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