d, a = pipe.read("freq_real.ft2") d, a = p.rft(d, a) pipe.write("rft11.glue", d, a, overwrite=True) # HT # ps90-180 mode doesn't match d, a = pipe.read("1D_freq_real.dat") d, a = p.ht(d, a, mode="ps90-180") pipe.write("ht4.glue", d, a, overwrite=True) # Integration tests # process 2D mixed mode data d, a = pipe.read("time_real.fid") d, a = p.gmb(d, a, gb=0.1, lb=-8, c=0.5) d, a = p.zf(d, a, auto=True) d, a = p.ft(d, a, alt=True) # BUG glue seems to double the data...? d, a = p.ps(d, a, p0=0, p1=0) d, a = p.tp(d, a, hyper=True) d, a = p.sp(d, a, off=0.5, pow=2, c=0.5) d, a = p.zf(d, a, auto=True) d, a = p.ft(d, a, auto=True) d, a = p.ps(d, a, p0=0, p1=0) d, a = p.di(d, a) pipe.write("2d_mixed_processing1.glue", d, a, overwrite=True) # process 2D mixed mode data d, a = pipe.read("time_real.fid") d, a = p.em(d, a, lb=8) d, a = p.zf(d, a, auto=True) d, a = p.ft(d, a, auto=True)
#! /usr/bin/env python import nmrglue.fileio.pipe as pipe import nmrglue.process.pipe_proc as p d,a = pipe.read("time_complex.fid") d,a = p.zf(d,a,zf=2) pipe.write("zf.glue",d,a,overwrite=True) d,a = pipe.read("time_complex.fid") d,a = p.zf(d,a,pad=200) pipe.write("zf2.glue",d,a,overwrite=True) d,a = pipe.read("time_complex.fid") d,a = p.zf(d,a,size=8000) pipe.write("zf3.glue",d,a,overwrite=True) d,a = pipe.read("time_complex.fid") d,a = p.zf(d,a,size=4096,mid=True) pipe.write("zf4.glue",d,a,overwrite=True) d,a = pipe.read("time_complex.fid") d,a = p.zf(d,a,size=4096,inter=True) pipe.write("zf5.glue",d,a,overwrite=True)
def read_varian_spec_raw(spectrum_directory, fid_filename, procpar_filename, zero_filling=True, apodization=True): from nmrglue.fileio import varian, convert from nmrglue.process import pipe_proc #Read varian files: res = varian.read(dir=spectrum_directory, fid_file=fid_filename, procpar_file=procpar_filename) varian_data = res[1] varian_dic = res[0] #Get main parameters for ppm scale: sw = float(varian_dic["procpar"]["sw"]["values"][0]) obs = float(varian_dic["procpar"]["sfrq"]["values"][0]) car = float(varian_dic["procpar"]["reffrq"]["values"][0]) #Convert varian to pipe: universal_varian_dic = varian.guess_udic(varian_dic, varian_data) universal_varian_dic[0]["sw"] = sw universal_varian_dic[0]["obs"] = obs universal_varian_dic[0]["label"] = varian_dic["procpar"]["tn"]["values"][0] universal_varian_dic[0]["car"] = car C = convert.converter() C.from_varian(varian_dic, varian_data, universal_varian_dic) pipe_dic, varian_pipe_data = C.to_pipe() pipe_dic["FDF1SW"] = sw pipe_dic["FDF1OBS"] = obs pipe_dic["FDF1LABEL"] = universal_varian_dic[0]["label"] pipe_dic["FDF1CAR"] = car #If zero filling: if zero_filling: pipe_dic, varian_data = pipe_proc.zf(pipe_dic, varian_data) #If apodization: if apodization: lb_d = float(varian_dic["procpar"]["lb"]["values"][0]) pipe_dic, varian_data = pipe_proc.em(pipe_dic, varian_data, lb=lb_d) #Fourier Transform: pipe_dic, varian_data = pipe_proc.ft(pipe_dic, varian_data, auto=True, inv=True) #Phase correction: p_zero = float(varian_dic["procpar"]["rp"]["values"][0]) p_one = float(varian_dic["procpar"]["lp"]["values"][0]) pipe_dic, varian_data = pipe_proc.ps(pipe_dic, varian_data, p0=p_zero, p1=p_one, inv=True) #Baseline Correction: pipe_dic, varian_data = pipe_proc.cbf(pipe_dic, varian_data) #Remove imaginary numbers: dic, data = pipe_proc.di(pipe_dic, varian_data) #Calculate ppm scale: ppm = list() ppm_f = (obs * 1000000 + sw / 2 - (car * 1000000)) / car ppm_width = sw / car ppm_i = ppm_f - ppm_width n = int(len(data)) ppm_step = ppm_width / n ppm.append(ppm_i) for i in range(1, n): ppm.append(ppm[i - 1] + ppm_step) return (ppm, abs(data))
def read_varian_spec2d_raw(spectrum_directory, fid_filename, procpar_filename, zero_filling=True, apodization=True): from nmrglue.fileio import varian, convert from nmrglue.process import pipe_proc # Read varian files res = varian.read(dir=spectrum_directory, fid_file=fid_filename, procpar_file=procpar_filename) varian_data = res[1] varian_dic = res[0] # Get main parameters for ppm scale: sw = float(varian_dic["procpar"]["sw"]["values"][0]) #direct dimension sw1 = float(varian_dic["procpar"]["sw1"]["values"][0]) #indirect dimension obs = float(varian_dic["procpar"]["sfrq"]["values"][0]) #direct dimension obs1 = float( varian_dic["procpar"]["dfrq"]["values"][0]) #indirect dimension car = float( varian_dic["procpar"]["reffrq"]["values"][0]) #direct dimension car1 = float( varian_dic["procpar"]["reffrq1"]["values"][0]) #indirect dimension # Convert varian to pipe universal_varian_dic = varian.guess_udic(varian_dic, varian_data) ## direct dimension universal_varian_dic[0]["sw"] = sw universal_varian_dic[0]["obs"] = obs universal_varian_dic[0]["label"] = varian_dic["procpar"]["tn"]["values"][0] universal_varian_dic[0]["car"] = car ## indirect dimension universal_varian_dic[1]["sw"] = sw1 universal_varian_dic[1]["obs"] = obs1 universal_varian_dic[1]["label"] = varian_dic["procpar"]["dn"]["values"][0] universal_varian_dic[1]["car"] = car1 C = convert.converter() C.from_varian(varian_dic, varian_data, universal_varian_dic) pipe_dic, varian_pipe_data = C.to_pipe() ## direct dimension pipe_dic["FDF2SW"] = sw pipe_dic["FDF2OBS"] = obs pipe_dic["FDF2LABEL"] = universal_varian_dic[0]["label"] pipe_dic["FDF2CAR"] = car ## indirect dimension pipe_dic["FDF1SW"] = sw1 pipe_dic["FDF1OBS"] = obs1 pipe_dic["FDF1LABEL"] = universal_varian_dic[1]["label"] pipe_dic["FDF1CAR"] = car1 # process the direct dimension ## zero filling if zero_filling: pipe_dic, varian_pipe_data = pipe_proc.zf(pipe_dic, varian_pipe_data) ## apodization if apodization: lb_d = float(varian_dic["procpar"]["lb"]["values"][0]) pipe_dic, varian_pipe_data = pipe_proc.em(pipe_dic, varian_pipe_data, lb=lb_d) # for lorentz-to-gauss # pipe_dic, varian_pipe_data = pipe_proc.gm(pipe_dic, varian_pipe_data) ## Fourier transform pipe_dic, varian_pipe_data = pipe_proc.ft(pipe_dic, varian_pipe_data, auto=True) ## Phase Correction p_zero = float(varian_dic["procpar"]["rp"]["values"][0]) p_one = float(varian_dic["procpar"]["lp"]["values"][0]) pipe_dic, varian_pipe_data = pipe_proc.ps(pipe_dic, varian_pipe_data, p0=p_zero, p1=p_one) ## Remove imaginary numbers: pipe_dic, varian_pipe_data = pipe_proc.di(pipe_dic, varian_pipe_data) # process the indirect dimension pipe_dic, varian_pipe_data = pipe_proc.tp(pipe_dic, varian_pipe_data) ## zero filling if zero_filling: pipe_dic, varian_pipe_data = pipe_proc.zf(pipe_dic, varian_pipe_data) ## apodization if apodization: lb_d1 = float(varian_dic["procpar"]["lb1"]["values"][0]) pipe_dic, varian_pipe_data = pipe_proc.em(pipe_dic, varian_pipe_data, lb=lb_d1) # for lorentz-to-gauss # pipe_dic, varian_pipe_data = pipe_proc.gm(pipe_dic, varian_pipe_data) ## Fourier transform pipe_dic, varian_pipe_data = pipe_proc.ft(pipe_dic, varian_pipe_data, auto=True) ## Phase Correction p_zero1 = float(varian_dic["procpar"]["rp1"]["values"][0]) p_one1 = float(varian_dic["procpar"]["lp1"]["values"][0]) pipe_dic, varian_pipe_data = pipe_proc.ps(pipe_dic, varian_pipe_data, p0=p_zero1, p1=p_one1) ## Remove imaginary numbers: pipe_dic, varian_pipe_data = pipe_proc.di(pipe_dic, varian_pipe_data) dic, data = pipe_proc.tp(pipe_dic, varian_pipe_data) # Calculate both ppm scales: ## direct dimension ppm = list() ppm_f = (obs * 1000000 + sw / 2 - (car * 1000000)) / car ppm_width = sw / car ppm_i = ppm_f - ppm_width n = int(data.shape[1]) ppm_step = ppm_width / n ppm.append(ppm_i) for i in range(1, n): ppm.append(ppm[i - 1] + ppm_step) new_ppm = ppm[::-1] ## indirect dimension # handling homonuclear cases if car1 == car and sw == sw1: ppm1 = list() ppm1.append(ppm_i) n1 = int(data.shape[0]) ppm_step1 = ppm_width / n1 for i in range(1, n1): ppm1.append(ppm1[i - 1] + ppm_step1) new_ppm1 = ppm1[::-1] else: ppm1 = list() ppm_f1 = (obs1 * 1000000 + sw1 / 2 - (car1 * 1000000)) / car1 ppm_width1 = sw1 / car1 ppm_i1 = ppm_f1 - ppm_width1 n1 = int(data.shape[0]) ppm_step1 = ppm_width1 / n1 ppm1.append(ppm_i1) for i in range(1, n1): ppm1.append(ppm1[i - 1] + ppm_step1) new_ppm1 = ppm1[::-1] ppms = [new_ppm, new_ppm1] return (ppms, abs(data))