axis.hist(scatter[i][1] - scatter[i][0],
histtype='step',
bins=bins,
normed=normed,
color='C%d' % (i % 9),
label='%0.2e' % mbins[i])
if normed:
x0 = [1 / (bins[-1] - bins[0]), mean, std]
#if normed: x0 = [scatter[i][0].size, mean, std]
else:
x0 = [scatter[i][0].size, mean, std]
#if normed: x0 = [10, 0, 1]
#else: x0 = [scatter[i][1].size/10., 0, 1]
xx, yy, res = dg.fitpdf(scatter[i][1] - scatter[i][0],
func,
bins=bins,
normed=normed,
x0=x0)
axis.plot(xx,
func(xx, *res.x),
'k--',
label='%0.2f, %0.2f' % (res.x[1], res.x[2]))
tosave.append([msave[i], msave[i + 1], res.x[1], res.x[2]])
axis.legend()
ff = mtpath + 'noisehist_ovd_M%02d_gal.png' % (10 * np.log10(M0))
if Rsm != 3: ff = ff[:-4] + '_R%d.png' % Rsm
fig.savefig(ff)
fpath = mtpath + 'hist_ovd_M%02d_gal.txt' % (10 * np.log10(M0))
if Rsm != 3: fpath = fpath[:-4] + 'R%d.txt' % Rsm
mbins, datapR[...])
tosave = []
#bins = np.linspace(-0.5, 0.7)
fig, ax = plt.subplots(4, 4, figsize=(15, 15))
for i in range(16):
bins = np.linspace(-10 / (i + 1), 10 / (i + 1))
axis = ax.flatten()[i]
axis.hist(scatter[i][1] - scatter[i][0],
histtype='step',
bins=bins,
normed=True,
color='C%d' % (i % 9),
label='%0.2e' % mbins[i])
xx, yy, res = dg.fitpdf(scatter[i][1] - scatter[i][0],
dg.normal,
bins=bins,
normed=True,
x0=[10, 0., 1])
axis.plot(xx,
dg.normal(xx, *res.x),
'k--',
label='%0.2f, %0.2f' % (res.x[1], res.x[2]))
tosave.append([msave[i], msave[i + 1], res.x[1], res.x[2]])
axis.legend()
ff = mtpath + 'noisehist_ovd_M%02d_na.png' % (10 * np.log10(M0))
if Rsm != 3: ff = ff[:-4] + '_R%d.png' % Rsm
fig.savefig(ff)
fpath = mtpath + 'hist_ovd_M%02d_na.txt' % (10 * np.log10(M0))
if Rsm != 3: fpath = fpath[:-4] + 'R%d.txt' % Rsm