elif g == 'own':
# Take each experiment on its own
experiments_groups.append([[i] for i in range(nexperiments[i])])
del i, g
#%% LOAD DATA
# Define variables and begin loop
all_groups = []
all_data = []
for i, pi in enumerate(paths):
these_groups = []
this_data = []
for j, pij in enumerate(pi):
# Load data
results, header, footer = ivs.loadTxt(os.path.join(pij, 'Results.txt'))
# Export data outside loop
these_groups.append(footer['experiments_groups'])
this_data.append(results)
all_groups.append(these_groups)
all_data.append(this_data)
del i, pi, footer, results, these_groups, this_data
'''
# Make list of failed groups too
all_failed_groups = []
for groups, egroups in zip(all_groups, experiments_groups):
failed_groups = []
for g in groups:
for eg in egroups:
if g not in groups:
print(str(g) + '\n')
index = [params_filenames.index(f) for f in filenames]
params = params[index, :]
params_header = [
'Amplitud (mVpp)', 'Potencia Pump post-MOA (muW)', 'Longitud de onda (nm)',
'Ancho medio de la campana (nm)'
]
del params_filenames, index, amplitude, power, wavelength, spectral_width
# Now create a list of folders for each filename
fits_filenames = [ivs.filenameToFitsFilename(file, home) for file in filenames]
# Load data from each fit
fits_data = []
fits_footer = []
for file in fits_filenames:
data, fits_header, footer = ivs.loadTxt(file)
fits_data.append(data)
fits_footer.append(footer)
del file, data, footer, fits_filenames
# Keep only the fit term that has the closest frequency to the desired one
fits_new_data = []
for rod, fit in zip(rods, fits_data):
try:
i = np.argmin(
abs(fit[:, 0] - desired_frequency * np.ones(fit.shape[0])))
fits_new_data.append([*fit[i, :]])
except IndexError:
fits_new_data.append([*fit])
fits_data = np.array(fits_new_data)
frequency = fits_data[:, 0] * 1e9 # Hz
Created on Wed Oct 23 15:26:46 2019
@author: Valeria
"""
import iv_save_module as ivs
import iv_utilities_module as ivu
import matplotlib.pyplot as plt
#%% Parameters
this_filename = 'C:\\Users\\Valeria\\OneDrive\\Labo 6 y 7\\Análisis\\Potencia_M_20191018_10\\Resultados.txt'
#%% Load data
this_data, this_header, this_footer = ivs.loadTxt(this_filename)
#%% Plot
# Plot results for the different rods
fig, ax1 = plt.subplots()
# Frequency plot, right axis
ax1.set_xlabel('Repetición')
ax1.set_ylabel('Frecuencia (GHz)', color='tab:red')
ax1.plot(this_data[:, 1], 'ro')
ax1.tick_params(axis='y', labelcolor='tab:red')
# Quality factor, left axis
ax2 = ax1.twinx() # Second axes that shares the same x-axis
ax2.set_ylabel('Tiempo de decaimiento (ps)', color='tab:blue')
elif groups_mode == 'own':
# Take each experiment on its own
experiments_groups = [[i] for i in range(nexperiments)]
#%% LOAD DATA
# Define variables and begin loop
all_groups = []
all_results = []
all_other_results = []
all_fit_params = []
#all_tables = []
for file in filenames:
# Load data from a base fit made by hand
results, header, footer = ivs.loadTxt(file)
# Reorganize data
other_results_keys = ['Nsingular_values', 'chi_squared']
other_results = {k: footer[k] for k in other_results_keys}
fit_params = dict(footer)
for k in other_results_keys:
fit_params.pop(k)
del k, other_results_keys
fit_params = ivu.InstancesDict(fit_params)
del footer
# # Generate fit tables
# tables = iva.linearPredictionTables(fit_params,
# results,
# other_results))
params_header = [
'Amplitud (mVpp)', 'Potencia Pump post-MOA (muW)',
'Longitud de onda (nm)', 'Ancho medio de la campana (nm)'
]
del params_filenames, index, amplitude, power, wavelength, spectral_width
# Now create a list of folders for each filename
fits_filenames = [
ivs.filenameToFitsFilename(file, home) for file in sfilenames
]
# Load data from each fit
sfits_data = []
sfits_footer = []
for file in fits_filenames:
data, fits_header, footer = ivs.loadTxt(file)
sfits_data.append(data)
sfits_footer.append(footer)
del file, data, footer, fits_filenames
# Keep only the fit term that has the closest frequency to the desired one
fits_new_data = []
for rod, fit in zip(srods, sfits_data):
try:
i = np.argmin(abs(fit[:, 0] - f * np.ones(fit.shape[0])))
fits_new_data.append([*fit[i, :]])
except IndexError:
fits_new_data.append([*fit])
sfits_data = np.array(fits_new_data)
sfrequency = sfits_data[:, 0] * 1e9 # Hz
sdamping_time = sfits_data[:, 1] * 1e-12 # s
#%% LOAD DATA
# Load data from files
data = []
for f in filenames:
t, V, details = ivs.loadNicePumpProbe(f)
data.append(np.array([t, *V.T]).T)
del t, V, details, f
# Load data from fit filenames
other_results_keys = ['Nsingular_values', 'chi_squared']
fit_params = []
for f in fit_filenames:
# Load data from a base fit made by hand
r, fit_header, ft = ivs.loadTxt(f)
# Reorganize data
others = {k: ft[k] for k in other_results_keys}
fp = dict(ft)
for k in other_results_keys:
fp.pop(k)
del k
fp = ivu.InstancesDict(fp)
del ft
# Add data to external variables
fit_params.append(fp)
del r, others, fp, f
#%% DATA
home = r'C:\Users\Valeria\OneDrive\Labo 6 y 7'
figs_folder = 'Informe L7\Figuras\Figuras análisis\Modelos (G, E, etc)'
data_folder = 'Informe L7\Datos Iván'
file = os.path.join(home, data_folder,
'Resultados_Comparados_LIGO1 sin outl.txt')
file2 = os.path.join(home, data_folder,
'Resultados_Comparados_LIGO1_PostUSA sin outl.txt')
file3 = os.path.join(home, data_folder, 'Resultados_Comparados_LIGO5bis.txt')
# Load data
data, header, footer = ivs.loadTxt(file) # Fused Silica + Air
data2, header, footer2 = ivs.loadTxt(file2) # Fused Silica + Ta2O5
data3, header, footer3 = ivs.loadTxt(file3) # Ta2O5 + Air
# Parameters
rhoAu = 19300 # kg/m3
rhoTa = 8180 # kg/m3
gammaAu = 2e-3 # Pa/s
cLTa = 4920 # m/s
f0 = data[:, 6] * 1e9 # from GHz to Hz
d = data[:, 0] * 1e-9
L = data[:, 2] * 1e-9 # from nm to m
f = data2[:, 6] * 1e9
d2 = data2[:, 0] * 1e-9
make_boxplot_of = [[0], [1], [0, 1]]
overwrite = True
#%% LOAD DATA
# Organize paths
paths = [os.path.join(home, r'Muestras\SEM', f) for f in folders]
filenames = [
os.path.join(home, r'Muestras\SEM', f, 'Resultados_SEM_{}.txt'.format(s))
for f, s in zip(folders, series)
]
data = []
rods = []
for f in filenames:
d, header, ft = ivs.loadTxt(f)
data.append(d)
rods.append(ft['rods'])
del d, ft
if filter_ta2o5_outliers:
index = np.argsort(data[1][:, 2])[:-1]
rods[1] = [rods[1][i] for i in index]
data[1] = data[1][index, :]
#%% VALUES
variables = ['Longitud L', 'Diámetro d', 'Relación de aspecto', 'Ángulo']
variables_units = ['nm', 'nm', '', 'º']
variables_data = lambda i: [
iva.getValueError(data[i][:, 2], data[i][:, 3]),
# Plot parameters
plot_params = dict(plot=True,
interactive=False,
autoclose=True,
extension='.png')
plot_params = ivu.InstancesDict(plot_params)
#%% LOAD DATA
# Make filenames routs
filename = ivs.filenameToMeasureFilename(name, home=home)
fit_filename = ivs.filenameToFitsFilename(name, home=home)
other_fit_filename = os.path.join(path, name + '.txt')
# Load data from a base fit made by hand
results, header, footer = ivs.loadTxt(fit_filename)
# Reorganize data
other_results_keys = ['Nsingular_values', 'chi_squared']
other_results = {k: footer[k] for k in other_results_keys}
fit_params = dict(footer)
for k in other_results_keys:
fit_params.pop(k)
fit_params = ivu.InstancesDict(fit_params)
del footer
# New parameters
fit_params.use_full_mean = False
fit_params.choose_t0 = False
fit_params.choose_tf = False
t0 = fit_params.time_range[0]
@author: Lec """ import numpy as np import iv_save_module as ivs import matplotlib.pyplot as plt from scipy.optimize import curve_fit #%% DATA file = r'C:\Users\Usuario\OneDrive\Labo 6 y 7\OneDrive\Labo 6 y 7\Análisis\ComparedAnalysis_FusedSilica\Resultados_Comparados_LIGO1.txt' file2 = r'C:\Users\Usuario\OneDrive\Labo 6 y 7\OneDrive\Labo 6 y 7\Análisis\ComparedAnalysis_FusedSilica\Resultados_Comparados_LIGO1_PostUSA.txt' # Load data data, header, footer = ivs.loadTxt(file) # In air data2, header, footer2 = ivs.loadTxt(file2) # In Ta2O5 filter_outliers = False filter_notcommon = False # Parameters rhoAu = 19300 # kg/m3 rhoTa = 8180 # kg/m3 gammaAu = 2e-3 # Pa/s r = data[:, 0] * 1e-9 / 2 A = np.pi * (r**2) L = data[:, 2] * 1e-9 # from nm to m L2 = data2[:, 2] * 1e-9 # from nm to m
chi = [] # Chi Squared
meanqdiff = [] # Mean Squared Difference
nterms = [] # Number of fit terms
fit_params = []
# Now, begin iteration on files
for n in names:
print("---> File {}/{}".format(names.index(n) + 1, len(names)))
# Load data
t_n, V, details = ivs.loadNicePumpProbe(
ivs.filenameToMeasureFilename(n, home))
# Load fit parameters
results, header, fit_params_n = ivs.loadTxt(
ivs.filenameToFitsFilename(n, home))
fit_params_n = ivu.InstancesDict(fit_params_n)
del results, header
# Choose data to fit
if fit_params_n.use_full_mean:
data_n = np.mean(V, axis=1)
else:
data_n = np.mean(V[:, fit_params_n.use_experiments], axis=1)
# Make a vertical shift
data_n = data_n - fit_params_n.voltage_zero
# Choose time interval to fit
t0_n = fit_params_n.time_range[0] # Initial time assumed to optimize it
i = np.argmin(np.abs(t_n -
