def process(targ):
if P(targ).is_file():
targ = P(targ).parent
regex = re.compile(r"_Scan(\d+).dat")
experiments = list(set([P("_".join(str(ii).split("_")[:-1]))
for ii in P(targ).iterdir() if regex.search(ii.name)]))
for ind, exp in enumerate(experiments):
files = [ii for ii in P(targ).iterdir() if ii.name.startswith(
exp.name) and regex.search(ii.name)]
for idx, f in enumerate(files):
header, data = read(f, flipX=False)
if idx == 0:
outdata = np.zeros_like(data)
for row in header.split("\n"):
if 'Wavelength:' in row:
wv = int(
float(("".join([ii for ii in row if ii.isdigit() or ii == "."]))))
outdata += data
outdata /= len(files)
try:
os.mkdir(str(exp.parent) + '/processed_data/')
except FileExistsError:
pass
fileout = exp.parent.joinpath(
"processed_data/" + exp.name + f"_{wv}.csv")
outstr = ""
for row in outdata:
if row[0] > 0:
outstr += f"{row[0]}, {row[1]}\n"
fileout.write_text(outstr)
statusBar((ind + 1) / len(experiments) * 100)
def process(filename):
header, data = read(filename)
data_array = np.array(data)
data_dict = {
'param': 1E6 * data_array[:, 0],
'echo': data_array[:, 3],
}
popt, pcov = cf(func, data_dict['param'], data_dict['echo'])
min_idx = np.argmin(func(data_dict['param'], *popt))
min_time = data_dict['param'][min_idx]
rabi_freq = 1 / (2 * min_time *
1E-6) / 1E6 # convert the us to s then get freq
# print(popt)
fig, ax = plt.subplots()
ax.plot(data_dict['param'], data_dict['echo'], label='Raw data', c='black')
ax.plot(data_dict['param'],
func(data_dict['param'], *popt),
label=f"Min time: {min_time:.2f} $\mu$s\nFreq: {popt[0]:.2f} MHz",
c='red')
ax.legend()
ax.set_ylabel('Echo intensity (arb. u)')
ax.set_yticks([])
ax.set_xlabel('$P_1$ length ($\mu$s)')
ax.set_title('Rabi echo intensity vs. inversion pulse length')
for s in ['top', 'right']:
ax.spines[s].set_visible(False)
plt.savefig(P(filename).parent.joinpath('fixedSourceRabi.png'), dpi=300)
plt.show()
def fit(filename, fitax, difax):
folder = P(filename).parent
_, data = read(filename)
field = str(P(filename).stem).split("data_")[-1]
if TESTING:
popt, pcov = cf(double_debye,
data[:, 0],
data[:, 1],
method='trf',
verbose=0)
print(field)
else:
popt, pcov = cf(double_debye,
data[:, 0],
data[:, 1],
method='trf',
verbose=2,
p0=[50, 10, 200, 10])
leg_popt = [f"{ii:.1f}" for ii in popt]
fitax.plot(data[:, 0],
double_debye(data[:, 0], *popt),
label=f'fit: {field}')
fitax.plot(data[:, 0], data[:, 1], label=f'raw: {field}')
fitax.legend()
difax.plot(data[:, 0],
data[:, 1] - double_debye(data[:, 0], *popt),
label=f'{field}')
difax.legend()
return popt
def fit(filename, debye_T, approx=False, dims=False):
header, data = read(filename)
max_T = 15
data = data[data[:, 0] < max_T]
if not approx:
if dims:
popt, pcov = cf(debye_ndim, data[:,0], data[:, 1], bounds=(([55, 0, 1/2],[65,np.inf,4])))
else:
popt, pcov = cf(debye, data[:,0], data[:, 1], bounds=(([55, 0],[65,np.inf])))
else:
popt, pcov = cf(low_T_approx, data[:,0], data[:, 1], p0=[150, 10E-3])
fig, ax = plt.subplots()
if not approx:
if dims:
ax.plot(data[:,0], debye_ndim(data[:, 0], *popt),label=r"$T_D$" + f"={popt[0]:.1f} K\nA={popt[1]:.1e}\nn={popt[2]:.1f}")
else:
ax.plot(data[:,0], debye(data[:, 0], *popt),label=r"$T_D$" + f"={popt[0]:.1f} K\nA={popt[1]:.1e}")
else:
ax.plot(data[:,0], low_T_approx(data[:, 0], *popt),label=f"A={popt[0]:.3e}; B={popt[1]:.3e}")
ax.plot(data[:,0], data[:,1], label="raw")
ax.legend()
plt.savefig(P(filename).parent.joinpath('fit_to_0T.png'),dpi=300)
# ax.set_yscale('log')
figg, axx = plt.subplots()
for field in [0, 1, 3, 6, 9]:
_, data = read(f"/Users/Brad/Library/Containers/com.eltima.cloudmounter.mas/Data/.CMVolumes/Brad Price/Research/Data/2021/04/1/data_{field}T.dat")
data = data[data[:, 0] < max_T]
if not approx:
if dims:
plt.plot(data[:, 0], data[:, 1] - debye_ndim(data[:, 0], *popt), label=f"{field} T")
else:
plt.plot(data[:, 0], data[:, 1] - debye(data[:, 0], *popt), label=f"{field} T")
else:
plt.plot(data[:, 0], data[:, 1] - low_T_approx(data[:, 0], *popt), label=f"{field} T")
axx.legend()
plt.savefig(P(filename).parent.joinpath('all_fields_subtract_0T_fit.png'),dpi=300)
plt.show()
def compare(targ='./'):
if not targ.endswith('/'):
targ += '/'
filelist = [
targ + ii for ii in os.listdir(targ)
if (ii.startswith('dispersion') or ii.startswith('absorption'))
and ii.endswith('_exp.txt')
]
disp_add = False
abs_add = False
for file in filelist:
legend = ' '.join([
ii.title() for ii in P(file).stem.split('_') if
('absorption' in ii or 'dispersion' in ii or 'light' in ii.lower())
]).replace('light', '').replace('Absorption',
'Abs').replace('Dispersion', 'Disp')
header, data = read(file)
plt.figure('Comparison')
# data[:, 1] = subtract(data[:, 1])
if not disp_add:
if 'Disp' in legend:
disp_add = np.max(data[:, 1])
if not abs_add:
if 'Abs' in legend:
abs_add = np.min(data[:, 1])
if 'Disp' in legend:
data[:, 1] += disp_add
elif 'Abs' in legend:
data[:, 1] += abs_add
plt.plot(data[:, 0], data[:, 1], label=legend)
plt.legend()
plt.title('Light on/off comparison')
plt.xlabel('Field (T)')
plt.ylabel('Signal (arb. u)')
plt.yticks([])
plt.ticklabel_format(axis='y', style='sci', scilimits=(-2, 2))
plt.savefig(targ + 'compared.png', dpi=200)
plt.show()
def importData(filename):
"""importData.
:param filename: file
"""
file = open(filename, 'r').read()
if file[0] == '{':
data = ast.literal_eval(file)
else:
data = {}
header, npdata = read(filename)
for item in header.strip().split('\n')[-1].split(','):
itemdata = list(
npdata[:,
header.strip().split('\n')[-1].split(',').index(item)])
data[item.strip()] = itemdata
return data
def create(targ):
files = [
ii for ii in P(targ).iterdir()
if ii.name.endswith('_exp.txt') and ii.name.startswith('dispersion')
]
for f in files:
outstr = ""
header, data = read(f)
data[:, 1] = -1 * np.imag(signal.hilbert(data[:, 1]))
frac = 100
deltaB = (data[-1, 0] - data[0, 0]) / len(data[:, 0])
leftB = np.linspace(data[0, 0] - (data[-1, 0] - data[0, 0]) / frac,
data[0, 0],
int(((data[-1, 0] - data[0, 0]) / frac) // deltaB))
rightB = np.linspace(
data[-1, 0] + (data[-1, 0] - data[0, 0]) / frac, data[-1, 0],
int(((data[-1, 0] - data[0, 0]) / frac) // deltaB))
left_zeros = np.zeros_like(leftB)
right_zeros = np.zeros_like(rightB)
B = np.zeros(len(data[:, 0]) + len(leftB) + len(rightB))
Data = np.zeros_like(B)
B[:len(leftB)] = leftB
B[len(leftB):len(leftB) + len(data[:, 0])] = data[:, 0]
B[len(leftB) + len(data[:, 0]):len(leftB) + len(data[:, 0]) +
len(rightB)] = rightB
Data[:len(leftB)] = left_zeros
Data[len(leftB):len(leftB) + len(data[:, 1])] = data[:, 1]
Data[len(leftB) + len(data[:, 1]):len(leftB) + len(data[:, 1]) +
len(rightB)] = right_zeros
outdat = np.transpose(np.array([B, Data]))
for index, row in enumerate(outdat):
outstr += f"{row[0]}, {row[1]}\n"
P(targ).joinpath(f.name.replace('dispersion',
'hilbert_abs')).write_text(outstr)
def process(filename, on, off, window_frac=10, order=2):
"""process.
:param filename: input filename
:param on: on-time for laser
:param off: off-time for laser
:window_frac: reciprocal of data length for window choice
:order: savgol_filter polyfit order
"""
header, data = read(filename)
t = data[:, 0]
r = data[:, 2] + 1j * data[:, 3]
i = data[:, 4] + 1j * data[:, 5]
sig = np.abs(r) + 1j * np.abs(i)
index = 0
avgsig = []
avgr = []
avgi = []
prevlen = 0
while index < len(sig):
lo = np.where(t >= index * (on + off))[0][0]
hi = np.where(t < (index + 1) * (on + off))[0][-1]
if np.abs(hi - lo) < prevlen * 0.9:
break
if window_frac == 0: # use 0 to not do savgol filtering
avgsig.append(np.abs(sig[lo:hi]))
avgr.append(np.real(r[lo:hi]) + 1j * np.imag(r[lo:hi]))
avgi.append(np.real(i[lo:hi]) + 1j * np.imag(i[lo:hi]))
else:
avgsig.append(
savgol_filter(np.abs(sig[lo:hi]),
(2 * (np.abs(hi - lo) // window_frac) + 1),
order))
avgr.append(
savgol_filter(
np.real(r)[lo:hi], (2 * (np.abs(hi - lo) // window_frac) +
1), order) +
1j * savgol_filter(
np.imag(r)[lo:hi],
(2 * (np.abs(hi - lo) // window_frac) + 1), order))
avgi.append(
savgol_filter(
np.real(i)[lo:hi], (2 * (np.abs(hi - lo) // window_frac) +
1), order) +
1j * savgol_filter(
np.imag(i)[lo:hi],
(2 * (np.abs(hi - lo) // window_frac) + 1), order))
prevlen = np.abs(hi - lo)
index += 1
smallen = np.inf
for row in avgsig:
if len(row) < smallen:
smallen = len(row)
full = np.zeros(smallen)
plots = {
"Average mag": avgsig,
"Ch1 real": avgr,
"Ch1 imag": avgr,
"Ch2 real": avgi,
"Ch2 imag": avgi
}
# avgr, "Ch2 real": avgi, "Ch2 imag": avgi}
for plot, dat in plots.items():
plt.figure(plot)
for row in dat:
if "real" in plot:
row = np.real(row)
elif "imag" in plot:
row = np.imag(row)
if np.min(row) < 0:
row -= np.min(row)
plt.plot(t[:len(row)],
row / np.max(row),
alpha=0.3,
color='lightgray')
full += row[:smallen] / np.max(row[:smallen])
full = full / np.max(full)
popt, pcov = curve_fit(biexponential,
t[:smallen],
full,
maxfev=1000000)
abovelas = np.where(t > on)[0][0]
plt.plot(t[:smallen], full, label="Raw data")
plt.plot(np.linspace(t[abovelas], t[smallen]),
biexponential(np.linspace(t[abovelas], t[smallen]), *popt),
label=r"Fit: $\tau_1$=" + f"{popt[2]:.2f}" +
r" $s^{-1}$;$\tau_2$=" + f"{popt[4]:.2f}" + r" $s^{-1}$")
plt.legend()
rang = np.max(full) - np.min(full)
plt.annotate('Laser\npulse', (on / 2, np.max(full)),
color='gray',
horizontalalignment='center')
plt.ylim(np.min(full) - rang / 4, np.max(full) + rang / 4)
plt.axvspan(0, on, facecolor='palegreen')
plt.title(plot)
if plot == "Ch2 imag":
plt.title("Time-resolved EPR absoption vs. time")
plt.ylabel('Normalized signal')
plt.xlabel('Time (s)')
full = np.zeros(smallen)
plt.savefig(P(filename).parent.joinpath(f"{plot}.png"))
plt.show()
def shrink(filename, start=0, end=-1, directory='./'):
header, data = read(directory + filename)
data = data[start:end, :]
savename = directory + '/short/' + filename
np.savetxt(savename, data, header=header, delimiter=', ')
def subtract(targ='./', background=0, experiment=1, downsample=10):
if not targ.endswith('/'):
targ += '/'
filelist = [
targ + ii for ii in os.listdir(targ)
if ii.endswith('.dat') and ii.startswith('M')
]
background_file = ''
data_file = ''
for file in filelist:
m = re.search('[M][0-9][0-9]', file)
num = int(m.group(0).lstrip('M'))
if num == background and 'AverageLog' in file:
background_file = file
if num == experiment and 'AverageLog' in file:
data_file = file
if not background_file:
print('No background average found. Attempting to calculate it.')
background_list = [
ii for ii in filelist
if int(re.search('[M][0-9][0-9]', ii).group(0).lstrip('M')) ==
background
]
first_head, first_data = read(background_list[0])
background_sum = np.copy(first_data)
count = 1
for file in background_list:
h, d = read(file)
background_sum += d
count += 1
b_data = background_sum / count
else:
b_header, b_data = read(background_file)
if not data_file:
print('No data average found. Attempting to calculate it.')
data_list = [
ii for ii in filelist
if int(re.search('[M][0-9][0-9]', ii).group(0).lstrip('M')) == data
]
first_head, first_data = read(data_list[0])
data_sum = np.copy(first_data)
count = 1
for file in data_list:
h, d = read(file)
data_sum += d
count += 1
data = data_sum / count
else:
header, data = read(data_file)
datatypes = header.strip().split(', ')
title = ' '.join(data_file.split('/')[-1].split('_')[:-1]).title() + \
' spectrum w/o background'
true_data = data[2:, 1] - b_data[2:, 1]
plt.figure('True data')
plt.plot(data[2:, 0], true_data, label=f"{targ.split('/')[-2]}nm")
plt.plot(data[2::downsample, 0],
decimate(true_data, downsample),
label=f"{targ.split('/')[-2]}nm {downsample}x downsample")
yy = savgol_filter(true_data, len(true_data) // downsample, 2)
plt.plot(data[2:, 0], yy, label='savgol_filter')
plt.ticklabel_format(axis='y', style='sci', scilimits=(-2, 2))
plt.ylabel(datatypes[1])
plt.yticks([])
plt.xlabel(datatypes[0])
plt.title(title)
plt.legend()
plt.savefig(targ + 'subtracted.png')
if __name__ == "__main__":
_, ax = plt.subplots()
if FITALL:
fields = [0, 3, 6, 9]
_, dax = plt.subplots()
results = {}
for field in fields:
filename = f"/Users/Brad/Library/Containers/com.eltima.cloudmounter.mas/Data/.CMVolumes/Brad Price/Research/Data/2021/04/13/BDPA_{field}T.csv"
results[f"{field}T"] = list(fit(filename, ax, dax))
P(filename).parent.joinpath('fit_params.txt').write_text(repr(results))
else:
fields = [3, 6, 9]
filename = f"/Users/Brad/Library/Containers/com.eltima.cloudmounter.mas/Data/.CMVolumes/Brad Price/Research/Data/2021/04/13/BDPA_0T.csv"
params = P(filename).parent.joinpath("fit_params.txt").read_text()
popt = ast.literal_eval(params)["0T"]
_, dat = read(filename)
f = interp1d(dat[:, 0], dat[:, 1])
for field in fields:
# popt = ast.literal_eval(params)[f"{field}T"]
filename = f"/Users/Brad/Library/Containers/com.eltima.cloudmounter.mas/Data/.CMVolumes/Brad Price/Research/Data/2021/04/13/BDPA_{field}T.csv"
_, data = read(filename)
data = data[(data[:, 0] <= dat[-1, 0]) & (data[:, 0] >= dat[0, 0])]
bdata = f(data[:, 0])
# ax.plot(data[:, 0], (data[:, 1]-double_debye(data[:, 0], *popt))/(double_debye(data[:,0], *popt))*100, label=f"{field}T")
# ax.scatter(data[:, 0], (data[:, 1]-bdata)/bdata*100, label=f"{field}T", s=10)
ax.plot(data[:, 0], (data[:, 1] - bdata) / bdata * 100,
label=f"{field}T")
# ax.plot(data[:, 0], double_debye(data[:, 0], *popt), label="1T fit")
# ax.set_title(r"$T_{D_1}$=" + f"{popt[0]:.1f}, " + f"A={popt[1]:.1f}, " + r"$T_{D_2}$=" + f"{popt[2]:.1f}, B={popt[3]:.1f}")
ax.set_title("Field dependent magnetic heat capacity")
# plot =
