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iFAMS_Inverse_Fourier_Transform.py
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import numpy as np
import sys
import ast
import argparse
from scipy import interpolate as interpolate
import matplotlib.pyplot as plt
import os
chargestatesr=[]
###### system arguments ################
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--input", required=True, help="name of the file")
ap.add_argument("-sm", "--subunit_mass", required=True, help="The mass of the subunit")
ap.add_argument("-cs", "--charge_states", required=True, help="the charge states")
ap.add_argument("-p", "--plot", default = "no", help="would you like the data plotted?")
ap.add_argument("-d", "--domain", default = "abs", help="would you like real or absolute values. Default is Absolute,"
"type real for real values")
args = vars(ap.parse_args())
submass= float(args["subunit_mass"])
chargestatesr= ast.literal_eval(args["charge_states"]) #the charge states
file_name = args["input"]
###### system arguments ################
f = open(file_name,'r')
x,y=np.loadtxt (f,
unpack = True,
delimiter = '\t')
namebase = os.path.splitext(file_name)[0]
datainterpolation = interpolate.InterpolatedUnivariateSpline(x, y, k=3)
xnew = np.linspace(np.min(x), np.max(x), np.size(x), endpoint=True) #number of equally-spaced m/z values is equal to number of m/z values in original data
ynew = datainterpolation(xnew)
next2power = np.ceil(np.log2(len(ynew)))
newpadding = pow(2,next2power)-len(ynew)
paddedynew = np.pad(ynew,(0,np.int_(newpadding)),'constant',constant_values=(0,0))
paddedxnew = np.pad(xnew,(0,np.int_(newpadding)),'constant',constant_values=(0,0))
span = np.max(x)-np.min(x) #m/z span of mass spec
expandedspan = span + newpadding/len(ynew)*span
temp = np.array(paddedynew)
yflip = np.fliplr([temp])[0]
np.append(yflip,paddedynew)
yfull = np.append(yflip,paddedynew)
maxfreq = np.size(yfull)/expandedspan/2
ftx = np.linspace(0, maxfreq, np.size(yfull), endpoint=True)
FT = np.fft.fft(yfull)
if args["domain"] == "real":
ABFT=np.real(FT)
if args["domain"] == "abs":
ABFT=np.absolute(FT)
ftspacing = maxfreq/(np.size(yfull)-1)
chargestateints = [int(chargestatesr[i]) for i in range(0,len(chargestatesr))]
omegafinal = expandedspan/ submass * 2
ABIFT = []
ABIFTmax = []
ABIFTmaxfinal = 0
ABIFTintegral = 0
msintegral = sum(y)
for i in range(0, len(chargestatesr)):
freqmax = (chargestatesr[i] + 1 / 2) * omegafinal
freqmin = (chargestatesr[i] - 1 / 2) * omegafinal
condition = np.logical_and((ftx/ ftspacing) > freqmin, (ftx / ftspacing) < freqmax)
csdata = np.extract(condition, FT) # extracts the FFT data from the FFT spectrum that are within 1/2
# the peak spacing of each maximum
extlen = np.size(csdata)
leftzeroes = np.ceil(freqmin)
rightzeroes = np.size(FT) - extlen - leftzeroes
paddedcsdata = np.lib.pad(csdata, (np.int_(leftzeroes), np.int_(rightzeroes)), 'constant', constant_values=(0, 0))
IFT = np.fft.ifft(paddedcsdata)
if args["domain"] == "real":
ABIFT.append(np.real(IFT[int((len(IFT)) / 2):]))
if args["domain"] == "abs":
ABIFT.append(abs(IFT[int((len(IFT)) / 2):]))
############### Normalization of the IFFT Data ##########################
if args["domain"] == "abs":
for i in range(0, len(chargestatesr)):
ABIFTmax.append(max(ABIFT[i]))
ABIFTmaxfinal = max(ABIFTmax)
for i in range(0, len(chargestatesr)):
for j in range(0, len(ABIFT[i])):
ABIFT[i][j] = ABIFT[i][j] / ABIFTmaxfinal
for i in range(0, len(chargestatesr)):
ABIFTintegral += sum(ABIFT[i])
for i in range(0, len(chargestatesr)):
for j in range(0, len(ABIFT[i])):
ABIFT[i][j] = ABIFT[i][j] / ABIFTintegral * msintegral
if args["domain"] == "real":
for i in range(0, len(chargestatesr)):
ABIFTmax.append(max(ABIFT[i]))
ABIFTmaxfinal = max(ABIFTmax)
for i in range(0, len(chargestatesr)):
for j in range(0, len(ABIFT[i])):
ABIFT[i][j] = ABIFT[i][j] / ABIFTmaxfinal
for i in range(0, len(chargestatesr)):
for j in range(0, len(ABIFT[i])):
if ABIFT[i][j] >= 0:
ABIFTintegral += ABIFT[i][j]
else:
ABIFTintegral += 0
for i in range(0, len(chargestatesr)):
for j in range(0, len(ABIFT[i])):
ABIFT[i][j] = ABIFT[i][j] / ABIFTintegral * msintegral/2
############### Normalization of the IFFT Data ##########################
for i in range (0, len(chargestateints)):
ifftfilename = 0
iftstring = "IFFT"
ifftfilename = namebase + iftstring + str(chargestateints[i]) + ".csv"
ifftforexport = 0
ifftforexport = np.transpose([xnew,ABIFT[i][0:int(len(xnew))]])
np.savetxt(ifftfilename,ifftforexport,fmt='%10.6f', delimiter=',') #outputs each charge-state-specific spectrum to its own csv file
print("file was exported")
if args["plot"] == "yes":
plt.plot(x,y,color='k')
colors = ['b', 'g', 'r', 'c', 'm', 'y', 'k', 'b', 'g', 'r', 'c', 'm', 'y', 'k', 'b', 'g', 'r', 'c', 'm', 'y', 'k',
'b', 'g', 'r', 'c', 'm', 'y', 'k', 'b', 'g', 'r', 'c', 'm', 'y', 'k']
for i in range(0, len(chargestateints)):
plt.plot(xnew, ABIFT[i][0:int(len(xnew))],label=chargestateints[i],color = colors[i])
plt.legend(loc='upper right', title='Charge State')
plt.show()