def convolve_template():
from plot_Profile_evolution import all_obs
import numpy as np
date_list, observations = all_obs()
x = np.arange(512 - 25 - 50, 512 + 45 + 51)
delay = []
for n in observations:
best_mu = 0
best_corr = 0
for mu in np.linspace(512 - 25, 512 + 45, 701):
template = np.exp(-(x - 512) ** 2 / (2.0 * 6.2 ** 2)) + 0.09 * np.exp(-(x - mu) ** 2 / (2.0 * 8.0 ** 2))
template /= template.max()
corr = np.correlate(n, template, mode="valid").max()
if corr > best_corr:
best_corr = corr
best_mu = mu
delay.append(best_mu - 512)
plt.plot(date_list, delay, "ko")
plt.show()
def fit_all_good():
from plot_Profile_evolution import all_obs
import fitting_position
import numpy as np
import matplotlib.pyplot as plt
date_list, observations = all_obs()
idx = np.array([3, 6, 7, 8, 9, 10, 11, 12, 14, 25, 26, 28, 84, 94, 97, 98, 99])
idx = np.hstack([idx, range(100, 155)])
date_list = date_list[idx]
observations = observations[idx]
observations = observations[:, 450:700]
params, params_err = fitting_position.fitting(observations)
delay = (params[:, 3] - params[:, 0]) / 1024.0 * 0.5384688219194 * 1000
delay_err = np.sqrt(params_err[:, 3] ** 2 + params_err[:, 0] ** 2) / 1024.0 * 0.5384688219194 * 1000
f, (ax1, ax2) = plt.subplots(2, sharex=True)
ax1.errorbar(date_list, delay, yerr=delay_err, fmt="ko")
ax2.errorbar(date_list, params[:, 2], yerr=params_err[:, 2], fmt="ko")
f.subplots_adjust(hspace=0)
plt.setp([a.get_xticklabels() for a in f.axes[:-1]], visible=False)
plt.show()
def fit_all():
from plot_Profile_evolution import all_obs
import fitting_position
import numpy as np
import matplotlib.pyplot as plt
date_list, observations = all_obs()
def gauss(x, *p):
mu1, mu2 = p
return np.exp(-(x - mu1) ** 2 / (2.0 * 6.2 ** 2)) + 0.09 * np.exp(-(x - mu2) ** 2 / (2.0 * 8.0 ** 2))
def fit(prof):
x = np.arange(prof.size)
coeff, var_matrix = curve_fit(gauss, x, prof, p0=p0, maxfev=10000)
return coeff[1] - coeff[0]
delay = []
for n in observations:
d = fit(n)
delay.append(d)
plt.plot(date_list, n, "ko")
plt.show()
import plot_Profile_evolution import DMvariation import matplotlib.pyplot as plt f, (ax1, ax2) = plt.subplots(1, 2, sharey=True) date_list, observations = plot_Profile_evolution.all_obs() plot_Profile_evolution.plot_image(date_list, observations, ax=ax1) DMvariation.DMvariation(ax=ax2, horizontal=False) plt.show()
