colors = ['r', 'b', 'g', 'y', 'm', 'orange', 'brown', 'k']
normed = True
mms = [1e6, 1e7, 1e8, 1e9, 1e10]
for M0 in mms:
print('For M0 = %0.2e' % M0)
#mbins = np.linspace(np.log10(M0)-1, min(10, np.log10(M0) + 4), 10)[::-1]
#mbins = 10**mbins
mbins = np.logspace(7, 10, 16)[::-1]
msave = [mbins[0] * 100] + list(mbins)
scatter = dg.gridscatter(datapR[...] / (datapR[...].mean() + M0),
predictR[...] / (predictR[...].mean() + M0),
mbins, datapR[...])
tosave = []
#bins = np.linspace(-0.5, 0.7)
fig, ax = plt.subplots(4, 4, figsize=(15, 15))
for i in range(16):
#Asymmettric
mean, std = (scatter[i][1] -
scatter[i][0]).mean(), (scatter[i][1] -
scatter[i][0]).std()
bins = np.linspace(mean - 3 * std, mean + 3 * std)
#bins = np.linspace(-(4*ii+5)/(i+1), (3*ii+5)/(i+1))
axis = ax.flatten()[i]
axis.hist(scatter[i][1] - scatter[i][0],
histtype='step',
print('Data generated')
colors = ['r', 'b', 'g', 'y', 'm', 'orange', 'brown', 'k']
###########################################
mbins = np.logspace(10, 13, 16)[::-1]
msave = [mbins[0] * 100] + list(mbins)
print('mbins -- ', mbins)
func = dg.normal
normed = True
####
scatter = dg.gridscatter(datapR[...], predictR[...], mbins, datamR[...])
tosave = []
fig, ax = plt.subplots(4, 4, figsize=(16, 16))
for i in range(ax.size):
axis = ax.flatten()[i]
mean, std = (scatter[i][1] - scatter[i][0]).mean(), (scatter[i][1] -
scatter[i][0]).std()
bins = np.linspace(mean - 3 * std, mean + 3 * std)
#if normed: x0 = [1/(bins[-1]-bins[0]), mean, std]
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]
####
mms = [1e6, 1e7, 1e8, 1e9, 1e10]
for ii, M0 in enumerate(mms[::-1]):
print('For M0 = %0.2e' % M0)
#mbins = np.linspace(np.log10(M0)-1, min(10, np.log10(M0) + 2), 16)[::-1]
mbins = np.logspace(7, 10, 16)[::-1]
#mbins = 10**mbins
msave = [mbins[0] * 100] + list(mbins)
print(mbins)
normed = False
normp, normd = predictR.cmean(), datapR.cmean()
scatter = dg.gridscatter(np.log((datapR[...] + M0) / (normd)),
np.log((predictR[...] + M0) / (normp)), mbins,
datapR[...])
#scatter = dg.gridscatter(datapR[...]/(datapR[...].mean()+M0), predictR[...]/(predictR[...].mean()+M0), mbins, datapR[...])
tosave = []
#bins = np.linspace(-0.5, 0.7)
fig, ax = plt.subplots(4, 4, figsize=(15, 15))
for i in range(16):
#Asymmettric
mean, std = (scatter[i][1] -
scatter[i][0]).mean(), (scatter[i][1] -
scatter[i][0]).std()
bins = np.linspace(mean - 3 * std, mean + 3 * std)
#bins = np.linspace(-(4*ii+5)/(i+1), (3*ii+5)/(i+1))
axis = ax.flatten()[i]
axis.hist(scatter[i][1] - scatter[i][0],
mbins = np.logspace(10, 13, 16)[::-1]
msave = [mbins[0] * 100] + list(mbins)
print('mbins -- ', mbins)
####
mms = [1e10, 1e11, 1e12]
for M0 in mms:
print('For M0 = %0.2e' % M0)
#normp, normd = predictR.cmean(), datapR.cmean()
#scatter = dg.gridscatter(np.log((datapR[...]+M0)/(normd)), np.log((predictR[...]+M0)/(normp)), mbins, datapR[...])
normd, normp = np.log((datapR[...] + M0)).mean(), np.log(
(predictR[...] + M0)).mean()
scatter = dg.gridscatter(
np.log((datapR[...] + M0)) / normd,
np.log((predictR[...] + M0)) / normp, mbins, datapR[...])
tosave = []
#bins = np.linspace(-0.5, 0.7)
fig, ax = plt.subplots(4, 4, figsize=(15, 15))
for i in range(16):
mean, std = (scatter[i][1] -
scatter[i][0]).mean(), (scatter[i][1] -
scatter[i][0]).std()
bins = np.linspace(mean - 3 * std, mean + 3 * std)
#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,