def gaussian_fit(x, y, num_gauss, range_1, ax, peak):
x = np.array(x)
y = np.array(y)
for n_gaussians in range(1, num_gauss):
# compute the best-fit linear combination
w_best, rms, locs, widths = sum_of_norms(x,
y,
n_gaussians,
spacing='linear',
full_output=True)
norms = w_best * norm(x[:, None], locs, widths)
if rms <= 0.00000001:
print('Number of Gaussians: ', n_gaussians)
break
y_1 = norms.sum(1)
grad = np.gradient(y_1)
ax.plot(x, y_1)
peaks, _ = find_peaks(y_1[:-800], height=0.01,
width=1) #Set 0.19 for GeSe_flake
results_half = peak_widths(y_1, peaks, rel_height=0.005)
print('Total number of peaks: ', len(peaks))
print('-----------------------------------')
lines_gaus = []
for i in range(len(peaks)):
peak_start = np.where(
x >= abs(x[peaks[i]] - float(results_half[0][i]) / 2))[0]
peak_end = np.where(
x >= abs(x[peaks[i]] + float(results_half[0][i]) / 2))[0]
ax.plot(x[peaks[i]], y[peaks[i]], 'o', markersize=3, color='black')
mu = x[peaks[i]]
sigma = np.std(x[peak_start[0]:peak_end[0]], ddof=1)
Gaus = 0.5 * y_1[peaks[i]] * np.exp(-(x - mu)**2 / (2 * sigma**2))
gaus_shift = [i - min(Gaus) for i in Gaus]
lines = ax.fill_between(x, gaus_shift, alpha=0.4)
lines_gaus.append(lines)
return lines_gaus
# Fetch the data
x, y = fetch_vega_spectrum()
# truncate the spectrum
mask = (x >= 2000) & (x < 10000)
x = x[mask]
y = y[mask]
for n_gaussians in (10, 50, 100):
# compute the best-fit linear combination
w_best, rms, locs, widths = sum_of_norms(x, y, n_gaussians,
spacing='linear',
full_output=True)
norms = w_best * norm(x[:, None], locs, widths)
# plot the results
plt.figure()
plt.plot(x, y, '-k', label='input spectrum')
ylim = plt.ylim()
plt.plot(x, norms, ls='-', c='#FFAAAA')
plt.plot(x, norms.sum(1), '-r', label='sum of gaussians')
plt.ylim(-0.1 * ylim[1], ylim[1])
plt.legend(loc=0)
plt.text(0.97, 0.8,
"rms error = %.2g" % rms,
ha='right', va='top', transform=plt.gca().transAxes)
def gaussian_fit(x, y, num_gauss, range_1, peak, sigma):
x = np.array(x)
y = np.array(y)
y1 = minmax_scale(np.array(y))
for n_gaussians in range(1, num_gauss):
# compute the best-fit linear combination
w_best, rms, locs, widths = sum_of_norms(x,
y1,
n_gaussians,
spacing='linear',
full_output=True)
norms = w_best * norm(x[:, None], locs, widths)
if rms <= 10e-10:
print('Number of Gaussians: ', n_gaussians)
break
y_1 = norms.sum(1)
# compute second derivative
#second_derivative = np.gradient(np.gradient(y_1[:-1100]))
# find switching points
#infls = np.where(np.diff(np.sign(second_derivative)))[0]
#print(np.sign(second_derivative))
#ax.plot([x[infls], x[infls]], [0,1])
peaks, _ = find_peaks(y_1[:-930], height=0.01, width=1)
results_half = peak_widths(y_1, peaks, rel_height=0.001)
#peaks = np.delete(peaks, 2) #Deleting peaks by index
print('Total number of peaks: ', len(peaks))
print('-----------------------------------')
#peaks = peaks[:3] + peaks[3+1 :]
center = []
mu_1 = []
sigma_1 = []
y_peak = []
Gaussian_func = []
for i in range(len(peaks)):
peak_start = np.where(
x >= abs(x[peaks[i]] - float(results_half[0][i]) / 2))[0]
peak_end = np.where(
x >= abs(x[peaks[i]] + float(results_half[0][i]) / 2))[0]
mu = x[peaks[i]]
#sigma = np.std(x[peak_start[0]: peak_end[0]], ddof=1)
Gaus = 0.5 * y[peaks[i]] * np.exp(-(x - mu)**2 / (2 * sigma[i]**2))
Gaussian_func.append(Gaus)
center.append(x[peaks[i]])
mu_1.append(mu)
sigma_1.append(sigma[i])
y_peak.append(y[peaks[i]])
print('-------------------------')
print('Gaussian peak nr: ', i + 1)
print('Center: ', x[peaks[i]])
print('mu: ', mu)
print('sigma: ', sigma)
print('rms: ', rms)
print('x-span: ', x[peak_start[0]], x[peak_end[0]])
print('Rel Intensity: ', y_1[peaks[i]])
xGaus = x[peaks[peak] - range_1:peaks[peak] + range_1]
norm_new = y_1[peaks[peak] - range_1:peaks[peak] + range_1]
return xGaus, y_1, center, mu_1, sigma_1, y_peak, n_gaussians, rms, Gaussian_func
def extend_sens(sens_file='/Users/brammer/3DHST/Spectra/Work/CONF/WFC3.IR.G141.3rd.sens.2.fits'):
"""
Some of the "v2" sensitivity files are cut off at the wavelength extremes for no apparent reason.
Here, extend them with multi-gaussian component fits
"""
import os
import matplotlib.pyplot as plt
from astroML.sum_of_norms import sum_of_norms, norm
from threedhst import catIO
#sens = catIO.Table('/Users/brammer/3DHST/Spectra/Work/CONF/WFC3.IR.G102.2nd.sens.2.fits')
#sens = catIO.Table('/Users/brammer/3DHST/Spectra/Work/CONF/WFC3.IR.G102.3rd.sens.2.fits')
if 'G102' in sens_file:
xarr = np.arange(7100,1.19e4)
n_gaussians, spacing = 20, 'linear'
else:
xarr = np.arange(9700,1.75e4)
n_gaussians, spacing = 20, 'log'
#sens = catIO.Table('/Users/brammer/3DHST/Spectra/Work/CONF/WFC3.IR.G141.2nd.sens.2.fits')
sens = catIO.Table(sens_file)
ok = sens['SENSITIVITY'] > 0
x = sens['WAVELENGTH'][ok]
y = sens['SENSITIVITY'][ok]
ye = sens['ERROR'][ok]
out = {}
fig = plt.figure()
for i, col in enumerate(['SENSITIVITY', 'ERROR']):
ax = fig.add_subplot(211+i)
y = sens[col][ok]
w_best, rms, locs, widths = sum_of_norms(x, y, n_gaussians,
spacing=spacing,
full_output=True)
norms = w_best * norm(xarr[:, None], locs, widths)
out[col] = norms*1
ax.plot(sens['WAVELENGTH'], sens[col], '-k', linewidth=3, label=col)
ylim = ax.get_ylim()
if col == 'SENSITIVITY':
g = out[col].sum(1)
keep = g > 1.e-4*g.max()
ax.set_title(os.path.basename(sens.filename))
ax.plot(xarr[keep], out[col][keep,:], ls='-', c='#FFAAAA')
ax.plot(xarr[keep], out[col][keep,:].sum(1), '-r', linewidth=3, alpha=0.7)
ax.set_ylim(-0.1 * ylim[1], 1.05*ylim[1])
ax.legend(loc='upper right', fontsize=9)
ax.set_xlabel('wavelength')
fig.tight_layout(pad=1)
t = catIO.table_base()
t.add_column(catIO.Column(name='WAVELENGTH', data=xarr[keep]))
for i, col in enumerate(['SENSITIVITY', 'ERROR']):
t.add_column(catIO.Column(name=col, data=out[col][keep,:].sum(1)))
outfile = sens.filename.replace('sens.2.fits', 'sens.X.fits')
if os.path.exists(outfile):
os.remove(outfile)
t.write(outfile)
print outfile
plt.savefig(outfile.replace('.fits', '.png'))
