def test_apod_gmb(self): spec = fromBruker(self.filename, False, False) spec = apod(spec, w=lambda s: GMB(s, 2, 0.5)) C = converter() u = guess_udic(self.dic,self.data) C.from_bruker(self.dic, self.data, u) pipe_dic, pipe_data = C.to_pipe() test_data = pipep.gmb(pipe_dic, pipe_data, lb=2, gb=0.5)[1] ts.assert_allclose(spec, test_data, 1e-7,1e-3, 'GBM apodization not equal to NMRPipe processed one.')
def test_apod_gm(self): spec = fromBruker(self.filename, False, False) spec = apod(spec, w=lambda s: GM(s, 1, 10, 5)) C = converter() u = guess_udic(self.dic,self.data) C.from_bruker(self.dic, self.data, u) pipe_dic, pipe_data = C.to_pipe() test_data = pipep.gm(pipe_dic, pipe_data, g1=1, g2=10, g3=5)[1] ts.assert_allclose(spec, test_data, 1e-7,1e-3, 'GM apodization not equal to NMRPipe processed one.')
def test_apod_sp(self): spec = fromBruker(self.filename, False, False) spec = apod(spec, w=lambda s: SP(s, off=0.5, power=2)) C = converter() u = guess_udic(self.dic,self.data) C.from_bruker(self.dic, self.data, u) pipe_dic, pipe_data = C.to_pipe() test_data = pipep.sp(pipe_dic, pipe_data, off=0.5, pow=2)[1] ts.assert_allclose(spec, test_data, 1e-7,1e-3, 'SP apodization not equal to NMRPipe processed one.')
def test_apod_jmod(self): spec = fromBruker(self.filename, False, False) spec = apod(spec, w=lambda s: JMOD(s, 0.5, 5, 1)) C = converter() u = guess_udic(self.dic,self.data) C.from_bruker(self.dic, self.data, u) pipe_dic, pipe_data = C.to_pipe() test_data = pipep.jmod(pipe_dic, pipe_data, off=0.5, j=5, lb=1)[1] ts.assert_allclose(spec, test_data, 1e-7,1e-3, 'JMOD apodization not equal to NMRPipe processed one.')
def test_apod_tri(self): spec = fromBruker(self.filename, False, False) # Loc < 1 results in dimension reduction of the apodization vector with self.assertRaises(NMRShapeError): apod(spec, w=lambda s: TRI(s, 0.5, 0.7, 0.5)) spec = fromBruker(self.filename, False, False) spec = apod(spec, w=lambda s: TRI(s, 5000, 0.7, 0.5)) C = converter() u = guess_udic(self.dic,self.data) C.from_bruker(self.dic, self.data, u) pipe_dic, pipe_data = C.to_pipe() test_data = pipep.tri(pipe_dic, pipe_data, loc=5000, lHi=0.7, rHi=0.5)[1] ts.assert_allclose(spec, test_data, 1e-7,1e-3, 'TRI apodization not equal to NMRPipe processed one.')
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))