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model_scans.py
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#!/usr/bin/env python
import optparse
import pickle
import pylab
import numpy as np
import matplotlib.pyplot as plt
from numpy import exp, sqrt
from scipy.constants import elementary_charge, electron_mass, speed_of_light, pi, Planck
from scipy.special import yn, jv, jn
from scipy.optimize import minimize
import glob
import pandas as pd
import re
import seaborn as sns
sns.set(color_codes=True)
from matplotlib import rc
rc('font', **{'family':'serif','serif':['Palatino']})
rc('text', usetex=True)
from plot_scans import get_gap, get_slit_opening, get_ring_current
def residual(x, data_matrix):
k0, k1, k2 = x
data = pickle.load(open(data_matrix))
theory = []
for gap, n in data[:, 0:2]:
theory.append(undulator_peak_energy(gap, n, k0=k0, k1=k1, k2=k2))
theory = np.array(theory)
experiment = data[:, 2]
return 1. / (2 * len(theory)) * np.sum((experiment - theory) ** 2)
def residual2(x, gaps, bs):
k0, k1, k2 = x
theory = undulator_magnetic_field(gaps, k0, k1, k2)
return 1. / 2 * (len(theory)) * np.sum((bs - theory) ** 2)
def F(K, n):
# K *= 1.5
# k = (n * K) / (1. + K ** 2 / 2)
chi = n / (1. + 0.5 * K ** 2)
Y = 0.25 * (K ** 2) * chi
return (chi ** 2) * (K ** 2) * (jv((n + 1) / 2., Y) - jv((n - 1) / 2., Y)) ** 2
def central_cone_flux(K, E=2.75, I=0.5, N=80):
return 2.86e14 * N * I * (K ** 2) / (1 + K ** 2)
def angular_flux_density(K, n, E=2.75, I=0.5, N=80):
return 1.74e14 * (N ** 2) * (E ** 2) * I * F(K, n)
def get_lambda_harmonic(lambda_peak, n, N=80, detune=False):
if detune:
detune_parameter = 1 - 1 / (n * N)
else:
detune_parameter = 1
return lambda_peak * detune_parameter
def undulator_peak_energy(gap, n, k0=2.72142696, k1=-3.83321034, k2=0.6322541, N=80, detune=False):
if detune:
detune_parameter = 1 - 1 / (n * N)
else:
detune_parameter = 1
return undulator_harmonic_energy(gap, n, k0=k0, k1=k1, k2=k2) * detune_parameter
def undulator_magnetic_field(gap, k0=2.72142696, k1=-3.83321034, k2=0.6322541, period_length=24.):
x = gap / period_length
return k0 * exp(k1 * x + k2 * x ** 2)
def undulator_strength(B, period_length=24.):
k = 1e-3 * elementary_charge / (electron_mass * speed_of_light * 2 * pi)
return k * period_length * B
# return 0.0934 * period_length * B
def undulator_magnetic_field_from_K(K, period_length=24.):
k = 1e-3 * elementary_charge / (electron_mass * speed_of_light * 2 * pi)
return K / ( k * period_length)
def undulator_strength_from_peak_position(peak_energy, n, electron_energy=2.75, period_length=24.0):
#return 9.5 * n * electron_energy ** 2 / ((1 + K ** 2 / 2.) * period_length)
return sqrt(2 * 9.5 * n * electron_energy ** 2 / (period_length * peak_energy) - 2)
def undulator_harmonic_energy(gap, n, k0=2.72142696, k1=-3.83321034, k2=0.6322541, period_length=24.0, electron_energy=2.75):
k = 1e-3 * elementary_charge / (electron_mass * speed_of_light * 2 * pi)
B = undulator_magnetic_field(
gap, k0=k0, k1=k1, k2=k2, period_length=period_length)
return 1000 * 9.5 * n * electron_energy ** 2 / (period_length * (1 + undulator_strength(B) ** 2 / 2))
def undulator_peak_intensity(gap, n, k0=2.72142696, k1=-3.83321034, k2=0.6322541, period_length=24., N=80):
k = 1e-3 * elementary_charge / (electron_mass * speed_of_light * 2 * pi)
B = undulator_magnetic_field(gap, k0=k0, k1=k1, k2=k2)
K = undulator_strength(B)
return angular_flux_density(K, n)
def get_flux_vs_energy(filename):
r = np.array(pickle.load(open(filename)))
energy = r[:, 0]
flux = r[:, 2]
xbpm1 = r[:, 4]
return energy, flux, xbpm1
def get_experimental_peaks(theory, exp_energy, exp_flux, gap, data):
peak_positions = []
#if gap < 8.1:
#lim = 75
#elif gap > 8.1 and gap < 8.5:
#lim = 105
#elif gap > 8.3 and gap < 10.3:
#lim = 255
#elif gap > 10.5 and gap < 11.9:
#lim = 155
#else:
#lim = 205
lim = 75
total_max = exp_flux.max()
for harmonic, n in theory[::3]:
if harmonic > 4000. and harmonic < 20001: # .max():
# print('harmonic')
# print(harmonic)
maximum_flux = exp_flux[(exp_energy > harmonic - lim) & (exp_energy < harmonic + lim)].max()
# print('maximum_flux', maximum_flux)
me = exp_energy[(exp_energy > harmonic - lim) & (exp_energy < harmonic + lim)]
fe = exp_flux[(exp_energy > harmonic - lim) & (exp_energy < harmonic + lim)]
# print('me', me)
# print('fe', fe)
maximum_energy = me[np.where(np.abs(maximum_flux - fe) < 1)]
# print('maximum_energy', maximum_energy)
energies = me # exp_energy[np.where(np.abs(exp_energy - maximum_energy) < 75)]
weights = fe # exp_flux[np.where(np.abs(exp_energy - maximum_energy) < 75)]
position = np.average(energies, axis=0, weights=weights)
if maximum_flux > 0.02 * total_max:
peak_positions.append([position, harmonic])
data.append([gap, n, position, maximum_flux])
return peak_positions
def fit(data_matrix, method='powell'):
x0 = 2.73096921, -3.84082989, 0.60382274
result = minimize(residual, x0, args=(data_matrix,), method=method)
print(result)
return result
xkcd_colors_that_i_like = ["pale purple", "coral", "moss green", "windows blue", "amber", "greyish", "faded green", "dusty purple", "crimson", "custard", "orangeish", "dusk blue", "ugly purple", "carmine", "faded blue", "dark aquamarine", "cool grey", "faded blue"]
from sklearn.linear_model import LinearRegression
def plot(data_matrix):
colormap = plt.cm.gist_ncar
data = pickle.load(open(data_matrix))
harmonics = list(set(map(int, data[:, 1])))
harmonics.sort()
#k0, k1, k2 = 2.71504025, -3.80924779, 0.55774775
#k0, k1, k2 = 2.73081755, -3.84032394, 0.60509254
k0, k1, k2 = 2.73096921, -3.84082989, 0.60382274
#k0, k1, k2 = 2.7289875 , -3.8387636 , 0.60923686
pylab.figure(figsize=(16, 9))
#plt.gca().set_color_cycle([colormap(i) for i in np.linspace(0, 0.9, len(harmonics))])
#plt.gca().set_color_cycle(sns.color_palette("cubehelix", len(harmonics)))
#plt.gca().set_color_cycle(sns.diverging_palette(255, 133, l=60, n=len(harmonics), center="dark"))
#plt.gca().set_color_cycle(sns.color_palette("hls", len(harmonics)))
#plt.gca().set_color_cycle(sns.hls_palette(len(harmonics), l=.33, s=.9))
#plt.gca().set_color_cycle(sns.color_palette("Set1", len(harmonics)))
plt.gca().set_color_cycle(sns.xkcd_palette(xkcd_colors_that_i_like))
print('sns.xkcd_palette(xkcd_colors_that_i_like)')
print(sns.xkcd_palette(xkcd_colors_that_i_like))
intercepts = []
coeffs = []
ns = []
max_gap = 7.8
for n in harmonics:
# gap = []
# energy = []
selection = list(data[data[:, 1] == n])
selection.sort(key=lambda x: x[2])
selection = np.array(selection)
gaps = selection[:, 0]
if gaps.max() > max_gap:
max_gap = gaps.max()
energies = selection[:, 2]
ens = energies
X = np.vstack([ens/1.e3, gaps]).T
np.savetxt('GAP_ENERGY_HARMONIC%s.txt' % n, X, fmt='%6.3f', delimiter=' ', header='%d\n%d\nENERGY GAP' % X.shape[::-1], comments='')
modeled_energies = undulator_peak_energy(gaps, n, k0=k0, k1=k1, k2=k2)
X_model = np.vstack([modeled_energies, gaps]).T
np.savetxt('fit_GAP_ENERGY_HARMONIC%s.txt' % n, X_model, fmt='%6.3f', delimiter=' ', header='%d\n%d\nENERGY GAP' % X_model.shape[::-1], comments='')
#lm = LinearRegression()
#lm.fit(np.array([ens, ens**2]).T, gaps)
#ens_fit = np.linspace(ens[0], ens[-1], 50)
#gap_fit = lm.predict(np.array([ens_fit, ens_fit**2]).T)
#print('%s score' % n, lm.score(np.array([ens, ens**2]).T, gaps))
#print('%s intercept' % n, lm.intercept_ )
#print('%s coeff' % n, lm.coef_)
#intercepts.append(lm.intercept_)
#coeffs.append(lm.coef_* 1e3)
#ns.append(n)
#X_fit = X = np.vstack([ens_fit/1.e3, gap_fit]).T
#np.savetxt('fit_GAP_ENERGY_HARMONIC%s.txt' % n, X_fit, fmt='%6.3f', delimiter=' ', header='%d\n%d\nENERGY GAP' % X_fit.shape[::-1], comments='')
#Bs = undulator_magnetic_field(gaps, n, 2.72898056, -3.83864548, 0.60969562)
#Ks = undulator_strength(Bs)
#print('n', n)
#print('gaps')
#print(gaps)
#print('energies')
#print(energies)
pylab.plot(energies, gaps, 'o-', color=sns.xkcd_rgb[xkcd_colors_that_i_like[n-min(harmonics)]], label='%d' % n)
pylab.plot(modeled_energies, gaps, 'v-', color=sns.xkcd_rgb[xkcd_colors_that_i_like[n-min(harmonics)]])
#pylab.plot(ens_fit, gap_fit,'d--', color=sns.xkcd_rgb[xkcd_colors_that_i_like[n-min(harmonics)]])
pylab.title('Proxima 2A U24 undulator harmonic peak positions as function of gap and energy', fontsize=22)
pylab.xlabel('energy [eV]', fontsize=18)
pylab.ylabel('gap [mm]', fontsize=18)
pylab.ylim([7., max_gap + 0.5])
pylab.grid(True)
pylab.legend(loc='best', fontsize=16)
ax = pylab.gca()
for label in (ax.get_xticklabels() + ax.get_yticklabels()):
label.set_fontsize(16)
pylab.savefig('U24_harmonic_peak_positions_gap_vs_energy.png')
#pylab.figure(figsize=(16, 9))
#pylab.plot(ns[:-1], coeffs[:-1], 'o-', label='coefs')
#pylab.plot(ns[:-1], intercepts[:-1], 'd-', label='intercepts')
#pylab.legend(loc='best', fontsize=16)
#pylab.xlabel('harmonic number', fontsize=18)
#pylab.ylabel('regression parameters', fontsize=18)
#pylab.title('Linear regression parameters (gap vs. energy) as function of harmonic number', fontsize=22)
#pylab.savefig('linear_regression_paramters_as_function_of_harmonic_number.png')
pylab.figure(figsize=(16, 9))
#plt.gca().set_color_cycle(sns.diverging_palette(255, 133, l=60, n=len(harmonics), center="dark"))
#plt.gca().set_color_cycle(sns.color_palette("cubehelix", len(harmonics)))
#plt.gca().set_color_cycle(sns.color_palette("hls", len(harmonics)))
#plt.gca().set_color_cycle(sns.hls_palette(len(harmonics), l=.33, s=.9))
#plt.gca().set_color_cycle(sns.color_palette("Set1", n_colors=len(harmonics)))
plt.gca().set_color_cycle(sns.xkcd_palette(xkcd_colors_that_i_like))
for n in harmonics:
selection = list(data[data[:, 1] == n])
selection.sort(key=lambda x: x[2])
selection = np.array(selection)
energies = selection[:, 2]
fluxes = selection[:, 3]
#print('n', n)
#print('energies')
#print(energies)
#print('fluxes')
#print(fluxes)
pylab.plot(energies, fluxes, 'o-', color=sns.xkcd_rgb[xkcd_colors_that_i_like[n-min(harmonics)]], label='%d' % n)
pylab.title(
'Proxima 2A U24 undulator tuning curves', fontsize=22)
pylab.xlabel('energy [eV]', fontsize=18)
pylab.ylabel('flux [ph/s]', fontsize=18)
# pylab.ylim([7., 12.5])
pylab.grid(True)
pylab.legend(loc='best', fontsize=16)
ax = pylab.gca()
for label in (ax.get_xticklabels() + ax.get_yticklabels()):
label.set_fontsize(16)
pylab.savefig('U24_tuning_curves_all_harmonics.png')
pylab.figure(figsize=(16, 9))
#plt.gca().set_color_cycle(sns.diverging_palette(255, 133, l=60, n=len(harmonics), center="dark"))
#plt.gca().set_color_cycle(sns.color_palette("cubehelix", len(harmonics)))
#plt.gca().set_color_cycle(sns.color_palette("hls", len(harmonics)))
odd_harmonics = [n for n in harmonics if n % 2 == 1]
#plt.gca().set_color_cycle(sns.hls_palette(len(odd_harmonics), l=.33, s=.9))
#plt.gca().set_color_cycle(sns.color_palette("Set1", n_colors=len(odd_harmonics)))
plt.gca().set_color_cycle(sns.xkcd_palette(xkcd_colors_that_i_like))
for n in odd_harmonics:
selection = list(data[data[:, 1] == n])
selection.sort(key=lambda x: x[2])
selection = np.array(selection)
energies = selection[:, 2]
fluxes = selection[:, 3]
#print('n', n)
#print('energies')
#print(energies)
#print('fluxes')
#print(fluxes)
pylab.plot(energies, fluxes, 'o-', color=sns.xkcd_rgb[xkcd_colors_that_i_like[n-min(harmonics)]], label='%d' % n)
pylab.title(
'Proxima 2A U24 undulator tuning curves, odd harmonics', fontsize=22)
pylab.xlabel('energy [eV]', fontsize=18)
pylab.ylabel('flux [ph/s]', fontsize=18)
# pylab.ylim([7., 12.5])
pylab.grid(True)
pylab.legend(loc='best', fontsize=16)
ax = pylab.gca()
for label in (ax.get_xticklabels() + ax.get_yticklabels()):
label.set_fontsize(16)
pylab.savefig('U24_tuning_curves_odd_harmonics.png')
pylab.figure(figsize=(16, 9))
#plt.gca().set_color_cycle(sns.diverging_palette(255, 133, l=60, n=len(harmonics), center="dark"))
#plt.gca().set_color_cycle(sns.color_palette("cubehelix", len(harmonics)))
#plt.gca().set_color_cycle(sns.color_palette("hls", len(harmonics)))
even_harmonics = [n for n in harmonics if n % 2 == 0]
#plt.gca().set_color_cycle(sns.hls_palette(len(even_harmonics), l=.33, s=.9))
#plt.gca().set_color_cycle(sns.color_palette("Set1", n_colors=len(even_harmonics)))
plt.gca().set_color_cycle(sns.xkcd_palette(xkcd_colors_that_i_like))
for n in even_harmonics:
selection = list(data[data[:, 1] == n])
selection.sort(key=lambda x: x[2])
selection = np.array(selection)
energies = selection[:, 2]
fluxes = selection[:, 3]
#print('n', n)
#print('energies')
#print(energies)
#print('fluxes')
#print(fluxes)
pylab.plot(energies, fluxes, 'o-', color=sns.xkcd_rgb[xkcd_colors_that_i_like[n-min(harmonics)]], label='%d' % n)
pylab.title(
'Proxima 2A U24 undulator tuning curves, even harmonics', fontsize=22)
pylab.xlabel('energy [eV]', fontsize=18)
pylab.ylabel('flux [ph/s]', fontsize=18)
# pylab.ylim([7., 12.5])
pylab.grid(True)
pylab.legend(loc='best', fontsize=16)
ax = pylab.gca()
for label in (ax.get_xticklabels() + ax.get_yticklabels()):
label.set_fontsize(16)
pylab.savefig('U24_tuning_curves_even_harmonics.png')
pylab.figure(figsize=(16, 9))
#plt.gca().set_color_cycle(sns.color_palette("cubehelix", len(harmonics)))
#plt.gca().set_color_cycle(sns.diverging_palette(255, 133, l=60, n=len(harmonics), center="dark"))
#plt.gca().set_color_cycle(sns.color_palette("hls", len(harmonics)))
#plt.gca().set_color_cycle(sns.hls_palette(len(harmonics), l=.33, s=.9))
#plt.gca().set_color_cycle(sns.color_palette("Set1", n_colors=len(harmonics)))
plt.gca().set_color_cycle(sns.xkcd_palette(xkcd_colors_that_i_like))
for n in harmonics:
selection = list(data[data[:, 1] == n])
selection.sort(key=lambda x: x[2])
selection = np.array(selection)
energies = selection[:, 2]
fluxes = selection[:, 3]
gaps = selection[:, 0]
#print('n', n)
#print('gaps')
#print(gaps)
#print('energies')
#print(energies)
#print('fluxes')
#print(fluxes)
pylab.plot(gaps, fluxes, 'o-', color=sns.xkcd_rgb[xkcd_colors_that_i_like[n-min(harmonics)]], label='%d' % n)
pylab.title(
'Proxima 2A U24 undulator flux vs. gap', fontsize=22)
pylab.xlabel('gap [mm]', fontsize=18)
pylab.ylabel('flux [ph/s]', fontsize=18)
# pylab.ylim([7., 12.5])
pylab.grid(True)
pylab.legend(loc='best', fontsize=16)
ax = pylab.gca()
for label in (ax.get_xticklabels() + ax.get_yticklabels()):
label.set_fontsize(16)
pylab.savefig('U24_flux_vs_gap_all_harmonics.png')
pylab.figure(figsize=(16, 9))
#plt.gca().set_color_cycle(sns.color_palette("cubehelix", len(harmonics)))
#plt.gca().set_color_cycle(sns.diverging_palette(255, 133, l=60, n=len(harmonics), center="dark"))
#plt.gca().set_color_cycle(sns.color_palette("hls", len(harmonics)))
#plt.gca().set_color_cycle(sns.hls_palette(len(harmonics), l=.33, s=.9))
#plt.gca().set_color_cycle(sns.color_palette("Set1", n_colors=len(harmonics)))
plt.gca().set_color_cycle(sns.xkcd_palette(xkcd_colors_that_i_like))
for n in harmonics: # [4, 5, 7, 8, 10, 11, 12]:
selection = list(data[data[:, 1] == n])
selection.sort(key=lambda x: x[2])
selection = np.array(selection)
energies = selection[:, 2]
fluxes = selection[:, 3]
gaps = selection[:, 0]
Bs = undulator_magnetic_field(gaps, k0, k1, k2) #, k0=3.8, k1=-4.47, k2=1.83)
Ks = undulator_strength(Bs)
#print('gaps')
#print(gaps)
#print('Bs')
#print(Bs)
#print('Ks')
#print(Ks)
theoric_fluxes = angular_flux_density(Ks, n, N=80)
#theoric_fluxes = central_cone_flux(Ks)
pylab.plot(energies, theoric_fluxes, 'o-', color=sns.xkcd_rgb[xkcd_colors_that_i_like[n-min(harmonics)]], label='%d' % n)
pylab.title(
'Proxima 2A U24 theoretic undulator tuning curves', fontsize=22)
pylab.xlabel('energy [eV]', fontsize=18)
pylab.ylabel('flux [ph/s]', fontsize=18)
# pylab.ylim([7., 12.5])
pylab.grid(True)
pylab.legend(loc='best', fontsize=16)
ax = pylab.gca()
for label in (ax.get_xticklabels() + ax.get_yticklabels()):
label.set_fontsize(16)
pylab.savefig('U24_theoretic_tuning_curves_all_harmonics.png')
pylab.figure(figsize=(16, 9))
#plt.gca().set_color_cycle(sns.hls_palette(len(data), l=.33, s=.9))
plt.gca().set_color_cycle(sns.color_palette("Set1", n_colors=len(data)))
sns.set_color_codes()
gaps = list(set(data[:, 0]))
gaps.sort()
gaps = np.array(gaps)
#bs1 = undulator_magnetic_field(gaps)
#bs2 = undulator_magnetic_field(gaps, 3.81815378, -5.53642081, 2.71692885)
#bs3 = undulator_magnetic_field(gaps, 3.33, -5.47, 1.8)
bs = []
gs = []
ks = []
for k, result in enumerate(data):
gap, n, energy, flux = result
gs.append(gap)
#energy *= (1 + 2/(n*80))
K = undulator_strength_from_peak_position(energy/1e3, n)
ks.append(K)
B = undulator_magnetic_field_from_K(K)
if k == len(data)-1:
pylab.plot(gap, B, 'bo', label='experiment')
else:
pylab.plot(gap, B, 'bo')
#pylab.plot(gap, K, 'ro')
bs.append(B)
#x0 = [3.3, -5.47, 1.8]
#print(residual((3.33, -5.47, 1.8)))
gs = np.array(gs)
bs = np.array(bs)
ks = np.array(ks)
data = list(zip(gs, bs))
data.sort(key=lambda x: x[0])
data = np.array(data)
d = pd.DataFrame()
#data['order'] = np.arange(len(gs))
d['gap'] = data[:,0]
d['B'] = data[:,1] + 0.3
#print('data')
#print(data)
#sns.tsplot(data=d, time='gap', value='B', legend='sns.tsplot') #, time="gap", unit="B", legend='sns.tsplot')
#ax = sns.tsplot(data=
#res = minimize(residual2, x0, args=(gs, bs), method='trust-ncg')
#print(res)
# nelder-mead 2.71502275, -3.80920849, 0.55769274, error=0.061607012818753311
# powell 2.72898056, -3.83864548, 0.60969562, error= 0.061607014855154206
# L-BFGS-B 2.71503557, -3.80922939, 0.55771415, error=0.06160701240774303
# TNC 3.19156135, -4.60888915, 1.52785286, error = 0.11672641054304266
# COBYLA 3.60484283, -5.23766814, 2.32703523, error = 0.2112670192219884
# SLSQP 2.71504711, -3.80925158, 0.55774223, error= 0.061607012467943152
#2.71504025, -3.80924779, 0.55774775
bs3 = undulator_magnetic_field(gaps, k0, k1, k2)
##pylab.plot(gaps, bs2, 'r-')
##pylab.plot(gaps, bs3, 'c^-')
pylab.xlabel('gap [mm]', fontsize=18)
h = pylab.ylabel('B [T]', fontsize=18, labelpad=35)
h.set_rotation(0)
pylab.title('Proxima 2A U24 undulator peak magnetic field and strength as function of gap', fontsize=22)
pylab.grid(True)
ax = pylab.gca()
ax.text(0.83, 0.8, '\# data points = %d' % len(data), color='b', fontsize=18, transform=ax.transAxes)
ax.text(0.05, 0.15, 'model function: $B(x) = k_{0} \\exp(k_{1} x + k_{2} x^{2}); x = \\frac{gap}{\lambda_{u}}$', fontsize=20, color='green', transform=ax.transAxes)
ax.text(0.05, 0.08, 'fit parameters: k_{0} = %6.3f, k_{1} = %6.3f, k_{2} = %6.3f' % (k0, k1, k2), fontsize=20, color='green', transform=ax.transAxes)
#ax.text(0.83, 0.45, '$K = \\frac{eB\lambda_{u}}{m_{e}c2\\pi}$', fontsize=20, transform=ax.transAxes)
#ax.text(0.35, 0.65, '$x = \\frac{gap}{\lambda_{u}}$', fontsize=16, transform=ax.transAxes)
for label in (ax.get_xticklabels() + ax.get_yticklabels()):
label.set_fontsize(16)
ax_k = ax.twinx()
ax_k.plot(gs, ks, 'bo')
h = ax_k.set_ylabel('$K = \\frac{eB\lambda_{u}}{m_{e}c2\\pi}$', fontsize=18, labelpad=40)
h.set_rotation(0)
ax_k.grid(False)
for label in (ax_k.get_yticklabels()):
label.set_fontsize(16)
ax.plot(gaps, bs3, 'gv-', label='fit')
ax.legend(loc='best', fontsize=18)
#pylab.xlim([7.5, 12.3])
pylab.savefig('B_and_K_vs_gap.png')
#pylab.figure()
#ax = sns.tsplot(data=d, time='gap', value='B')
##ax.legend()
pylab.show()
def main():
parser = optparse.OptionParser()
parser.add_option('-d', '--data_matrix', default='data_0.1x0.1mm_450mA.pkl', type=str, help='Data matrix file')
parser.add_option('-f', '--fit', action='store_true', help='Perform a fit and print out the fitted parameters')
parser.add_option('-p', '--plot', action='store_true', help='Show the results and generate figures')
options, args = parser.parse_args()
print(options, args)
if options.fit:
fit(options.data_matrix)
else:
plot(options.data_matrix)
if __name__ == '__main__':
main()