def z_hists(data, dz):
from matplotlib.ticker import AutoMinorLocator, MultipleLocator
x, y = [], []
for k in dz:
for t in data:
xx, yy = data_hist(t, k)
x.append(xx)
y.append(yy)
fig = plt.figure(figsize=(15, 8))
axes = []
for i in range(8):
axes.append(fig.add_subplot(4, 2, i + 1))
axes[i].hist(x[i][:-1], bins=x[i], weights=y[i])
axes[i].set_xlim(0, 6)
axes[i].xaxis.set_major_locator(MultipleLocator(1))
axes[i].xaxis.set_minor_locator(AutoMinorLocator(2))
axes[0].set_ylabel('N(z)')
axes[2].set_ylabel('N(z)')
axes[4].set_ylabel('N(z)')
axes[6].set_ylabel('N(z)')
axes[6].set_xlabel('z')
axes[7].set_xlabel('z')
axes[0].set_title('Z_MIN_CHI2')
axes[1].set_title('Z_MED_PDZ')
fig.tight_layout()
plt.show()
def structures_old(data, dz, kind, save=False, path=None):
xb, y = data_hist(data, dz, bi='edges')
x = [xb[i] + dz / 2 for i in range(len(xb) - 1)]
x, y = np.array(x), np.array(y)
par = ap.fast(x, y, kind)[0]
delta = (y - y_app(xb, dz, par, kind)) / y_app(xb, dz, par, kind)
sigma = 1 / y_app(xb, dz, par, kind)
number, *d = my_main_old(x, delta, sigma)
table_dict = {
'n': d[0],
'z start': d[1],
'st err com': d[2],
'st err pro': d[3],
'z finish': d[4],
'fin err com': d[5],
'fin err pro': d[6],
'z middle': d[7],
'r comoving': d[8],
'r proper': d[9],
'sigma fluct': d[10], # несмещённая дисперсия
'sigma fluct err': d[11],
'sigma Poisson': d[12],
'sigma obs': d[13]
}
table = pd.DataFrame(table_dict, index=number)
table.index.name = 'j'
if save:
if path is not None:
filename = path + 'dz={:.3f} kind {}.csv'.format(dz, kind)
else:
filename = 'dz={:.3f} kind {}.csv'.format(dz, kind)
table.to_csv(filename)
else:
pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 1000)
print(table)
def fluct_table(data, dz, kind, save=False, path=None):
xb, y = data_hist(data, dz, bi='edges')
x = [xb[i] + dz / 2 for i in range(len(xb) - 1)]
x, y = np.array(x), np.array(y)
par = ap.fast(x, y, kind)[0]
delta = (y - y_app(xb, dz, par, kind)) / y_app(xb, dz, par, kind)
sigma = 5 / np.sqrt(y_app(xb, dz, par, kind))
r = [
di.r_comoving(xb[i]) - di.r_comoving(xb[i - 1])
for i in range(1, len(xb))
]
d_l = [
di.r_proper(xb[i]) - di.r_proper(xb[i - 1]) for i in range(1, len(xb))
]
table_dict = {
'z': x,
'R': r,
'd_L': d_l,
'delta': delta,
'5 sigma Poisson': sigma
}
table = pd.DataFrame(table_dict)
table.index.name = 'i'
if save:
if path is not None:
filename = path + 'dz={:.3f} kind {}.csv'.format(dz, kind)
else:
filename = 'dz={:.3f} kind {}.csv'.format(dz, kind)
table.to_csv(filename)
else:
pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 1000)
print(table)
def structures(data, dz, kind, save=False, path=None):
xb, y = data_hist(data, dz, bi='edges')
x = [xb[i] + dz / 2 for i in range(len(xb) - 1)]
x, y = np.array(x), np.array(y)
par = ap.fast(x, y, kind)[0]
delta = (y - y_app(xb, dz, par, kind)) / y_app(xb, dz, par, kind)
integrals = y_app(xb, dz, par, kind)
j, *d = my_main(x, delta, integrals)
t = {
'n': d[0] + [''],
'z middle': np.around(d[3] + [sum(d[3]) / j[-1]], 2),
'z err': np.around(d[4] + [sum(d[4]) / j[-1]], 2),
'r comoving': d[5],
'st err com': d[6],
'fin err com': d[7],
'sigma Poisson': d[8] + [sum(d[8]) / j[-1]],
'sigma obs': d[9] + [sum(d[9]) / j[-1]],
'sigma obs err': d[10],
'sigma dm': d[11] + [sum(d[11]) / j[-1]],
'b dm': d[12],
'sigma b dm': [],
'sigma pl': d[13] + [sum(d[13]) / j[-1]],
'b pl': d[14],
'sigma b pl': []
}
zero_count = 0
for i in range(j[-1]):
if t['b dm'][i] == 0:
zero_count += 1
t['b dm'] = t['b dm'] + [sum(t['b dm']) / (j[-1] - zero_count)]
t['b pl'] = t['b pl'] + [sum(t['b pl']) / (j[-1] - zero_count)]
r_mean = sum(t['r comoving']) / j[-1]
r_sigma = 0
sigma_sigma_obs = 0
sigma_sigma_b_dm = 0
sigma_sigma_b_pl = 0
for i in range(j[-1]):
r_sigma += (t['r comoving'][i] - r_mean)**2
sigma_sigma_obs += (t['sigma obs'][i] - t['sigma obs'][-1])**2
sigma_sigma_b_dm += (t['b dm'][i] - t['b dm'][-1])**2
sigma_sigma_b_pl += (t['b pl'][i] - t['b pl'][-1])**2
t['sigma b dm'].append(t['b dm'][i] * t['sigma obs err'][i] /
t['sigma obs'][i])
t['sigma b pl'].append(t['b pl'][i] * t['sigma obs err'][i] /
t['sigma obs'][i])
r_sigma /= j[-1] - 1
sigma_sigma_obs /= j[-1] - 1
sigma_sigma_b_dm /= j[-1] - 1
sigma_sigma_b_pl /= j[-1] - 1
r_st_err_mean = np.sqrt(
(sum(t['st err com'])**2 / (j[-1] - 1) + r_sigma) /
j[-1]) # the calculating for standard error of the means
r_fin_err_mean = np.sqrt(
(sum(t['fin err com'])**2 / (j[-1] - 1) + r_sigma) /
j[-1]) # the calculating for standard error of the means
sigma_obs_err_mean = np.sqrt(
(t['sigma Poisson'][-1]**2 + sigma_sigma_obs) /
j[-1]) # the calculating for standard error of the means
# r_st_err_mean = np.sqrt((sum(t['st err com']) / j[-1]) ** 2 + r_sigma) / np.sqrt(j[-1]) # the calculating for standard error of the means
# r_fin_err_mean = np.sqrt((sum(t['fin err com']) / j[-1]) ** 2 + r_sigma) / np.sqrt(j[-1]) # the calculating for standard error of the means
# sigma_obs_err_mean = np.sqrt(t['sigma Poisson'][-1] ** 2 + sigma_sigma_obs) / np.sqrt(j[-1]) # the calculating for standard error of the means
sigma_sigma_b_dm /= j[
-1] # the calculating for standard error of the means
sigma_sigma_b_pl /= j[
-1] # the calculating for standard error of the means
t['r comoving'] = [int(i) for i in t['r comoving'] + [r_mean]]
t['st err com'] = [int(i) for i in t['st err com'] + [r_st_err_mean]]
t['fin err com'] = [int(i) for i in t['fin err com'] + [r_fin_err_mean]]
t['sigma obs err'] = t['sigma obs err'] + [sigma_obs_err_mean]
t['b dm'] = np.around(t['b dm'], 2)
t['sigma b dm'] = np.around(t['sigma b dm'] + [np.sqrt(sigma_sigma_b_dm)],
2)
t['b pl'] = np.around(t['b pl'], 2)
t['sigma b pl'] = np.around(t['sigma b pl'] + [np.sqrt(sigma_sigma_b_pl)],
2)
j += ['means:']
table = pd.DataFrame(t, index=j)
table.index.name = 'j'
if save:
if path is not None:
filename = path + 'dz={:.3f} kind {}.csv'.format(dz, kind)
else:
filename = 'dz={:.3f} kind {}.csv'.format(dz, kind)
table.to_csv(filename)
else:
pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 1000)
print(table)
def z_c_graph(data, func, dz, w, save=False, path=None):
print('dz={}, w>{}'.format(str(dz), str(w)))
x, y = data_hist(data, dz, bi='center')
par1, s1 = fast(x, y, 1)
par2, s2 = fast(x, y, 2)
par3, s3 = fast(x, y, 3)
a1, b1, c1 = par1
a2, b2, c2 = par2
a3, b3, c3 = par3
p = ['{:.2f}'.format(k) for k in [a1, b1, a2, b2, a3, b3]]
a1, b1, a2, b2, a3, b3 = p
p = ['{:.0f}'.format(k) for k in [c1, c2, c3]]
c1, c2, c3 = p
x0 = np.linspace(x[0], x[-1], 1000)
fig, ax = plt.subplots(figsize=(12.8, 7.2))
ax.scatter(x, y, s=10, c=[[1, 0, 1] for i in x])
ax.plot(x, y)
label1 = r'$\mathrm{' + c1 + 'x^{' + a1 + r'}e^{\left(-\frac{x}{' + '1' + r'}\right)^{' + b1 + '}}}$'
label1 += ', ' + '%.3e' % s1
ax.plot(x0,
f(x0, par1, 1),
color='orange',
linestyle='--',
linewidth=0.8,
label=label1)
label2 = r'$\mathrm{' + str(
int(c2)
) + 'x^{' + a2 + r'}e^{\left(-\frac{x}{' + '3' + r'}\right)^{' + b2 + '}}}$'
label2 += ', ' + '%.3e' % s2
ax.plot(x0,
f(x0, par2, 2),
color='limegreen',
linestyle='--',
linewidth=0.8,
label=label2)
label3 = r'$\mathrm{' + c3 + 'x^{' + a3 + r'}e^{\left(-\frac{x}{' + '6' + r'}\right)^{' + b3 + '}}}$'
label3 += ', ' + '%.3e' % s3
ax.plot(x0,
f(x0, par3, 3),
color='orangered',
linestyle='--',
linewidth=0.8,
label=label3)
ax.set_xlabel('z', fontsize='x-large')
ax.set_ylabel('N(z)', fontsize='x-large')
ax.set_xlim(0)
ax.set_ylim(0)
if w == -100:
plt.title('Approximations for {} with dz={}, all w, N={:d}'.format(
func.upper(), str(dz), sum(y)),
fontsize='x-large')
else:
plt.title('Approximations for {} with dz={}, w>{}, N={:d}'.format(
func.upper(), str(dz), str(w), sum(y)),
fontsize='x-large')
ax.legend(fontsize='x-large')
ax.tick_params(labelsize='x-large')
if save:
if path is not None:
filename = path + 'dz={:.3f}.png'.format(dz)
else:
filename = 'dz={:.3f}.png'.format(dz)
plt.savefig(filename, dpi=150)
else:
plt.show()
plt.close()
def z_c_fluct(data, func, dz, w, save=False, path=None):
from matplotlib.ticker import FixedLocator
from scipy import integrate
def y_app(bins, par, kind):
yapp = []
for i in range(1, len(bins)):
yapp.append(
integrate.quad(f, bins[i - 1], bins[i], args=(par, kind))[0] /
dz)
return np.array(yapp)
print('dz={}, w>{}'.format(dz, w))
xb, y = data_hist(data, dz, bi='edges')
x = [xb[i] + dz / 2 for i in range(len(xb) - 1)]
x, y = np.array(x), np.array(y)
par1, s1 = fast(x, y, 1)
par2, s2 = fast(x, y, 2)
par3, s3 = fast(x, y, 3)
a1, b1, c1 = par1
a2, b2, c2 = par2
a3, b3, c3 = par3
p = ['{:.2f}'.format(k) for k in [a1, b1, a2, b2, a3, b3]]
a1, b1, a2, b2, a3, b3 = p
p = ['{:.0f}'.format(k) for k in [c1, c2, c3]]
c1, c2, c3 = p
ig, ax = plt.subplots(figsize=(12.8, 7.2))
label1 = r'$\mathrm{' + c1 + 'x^{' + a1 + r'}e^{\left(-\frac{x}{' + '1' + r'}\right)^{' + b1 + '}}}$'
label1 += ', ' + '%.3e' % s1
ax.plot(x, (y - y_app(xb, par1, 1)) / y_app(xb, par1, 1),
color='orange',
linewidth=0.9,
label=label1)
ax.plot(x,
5 / np.sqrt(y_app(xb, par1, 1)),
color='orange',
linestyle='--',
linewidth=0.5)
ax.plot(x,
-5 / np.sqrt(y_app(xb, par1, 1)),
color='orange',
linestyle='--',
linewidth=0.5)
label2 = r'$\mathrm{' + c2 + 'x^{' + a2 + r'}e^{\left(-\frac{x}{' + '3' + r'}\right)^{' + b2 + '}}}$'
label2 += ', ' + '%.3e' % s2
ax.plot(x, (y - y_app(xb, par2, 2)) / y_app(xb, par2, 2),
color='limegreen',
linewidth=0.9,
label=label2)
ax.plot(x,
5 / np.sqrt(y_app(xb, par2, 2)),
color='limegreen',
linestyle='--',
linewidth=0.5)
ax.plot(x,
-5 / np.sqrt(y_app(xb, par2, 2)),
color='limegreen',
linestyle='--',
linewidth=0.5)
label3 = r'$\mathrm{' + c3 + 'x^{' + a3 + r'}e^{\left(-\frac{x}{' + '6' + r'}\right)^{' + b3 + '}}}$'
label3 += ', ' + '%.3e' % s3
ax.plot(x, (y - y_app(xb, par3, 3)) / y_app(xb, par3, 3),
color='orangered',
linewidth=0.9,
label=label3)
ax.plot(x,
5 / np.sqrt(y_app(xb, par3, 3)),
color='orangered',
linestyle='--',
linewidth=0.5)
ax.plot(x,
-5 / np.sqrt(y_app(xb, par3, 3)),
color='orangered',
linestyle='--',
linewidth=0.5)
ax.tick_params(axis='x', direction='inout')
ax.xaxis.set_major_locator(FixedLocator([1, 2, 3, 4, 5, 6]))
ax.tick_params(labelsize='x-large')
ax.spines['bottom'].set_position(('data', 0.0))
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.set_xlabel('z', fontsize='x-large')
ax.xaxis.set_label_coords(1.01, 0.516)
ax.set_ylabel(
r'$\mathrm{\delta=\frac{\Delta N_{obs} - \Delta N_{approx}}{\Delta N_{approx}}}$',
fontsize='xx-large')
ax.set_xlim(0, 6.1)
if w != 'all w' and float(w) > 0.8:
ax.set_ylim(-1, 1)
else:
limits = plt.axis()
ylim = max(abs(limits[2]), abs(limits[3]))
ax.set_ylim(-ylim, ylim)
if w == -100:
plt.title('Fluctuations for {} with dz={}, all w, N={:d}'.format(
func.upper(), dz, sum(y)),
fontsize='x-large')
else:
plt.title('Fluctuations for {} with dz={}, w>{}, N={:d}'.format(
func.upper(), dz, w, sum(y)),
fontsize='x-large')
ax.legend(fontsize='x-large')
if save:
if path is not None:
filename = path + 'dz={:.3f}.png'.format(dz)
else:
filename = 'dz={:.3f}.png'.format(dz)
plt.savefig(filename, dpi=150)
else:
plt.show()
plt.close()
def app_graph_r(data, dr, w, save=False, path=None):
print(f'dz={dr}, w>{w}')
x, y = data_hist(data, dr, bi='center')
# par1, s1 = ap.fast(x, y, 1)
# par2, s2 = ap.fast(x, y, 2)
par3, s3 = ap.fast(x, y, 3)
# a1, b1, c1 = par1
# a2, b2, z2, c2 = par2
a3, b3, d3, c3 = par3
# p = ['{:.2f}'.format(k) for k in [a3, b3]]
# a3, b3 = p
# d3 = f'{d3:.0e}'
# c3 = f'{c3:.0e}'
x0 = np.linspace(x[0], x[-1], 1000)
fig, ax = plt.subplots(figsize=(12.8 * frac, 7.2 * frac))
ax.scatter(x, y, s=10, c=[[1, 0, 1] for i in x])
ax.plot(x, y)
# label1 = r'$\mathrm{' + str(int(c1)) + 'z^{' + a1 + '}e^{-' + b1 + 'z}}$'
# label1 += ', ' + '%.3e' % s1
# ax.plot(x0, ap.f(x0, par1, 1), color='orange', linestyle='--', linewidth=0.8, label=label1)
# label2 = r'$\mathrm{' + str(int(c2)) + 'z^{' + a2 + r'}e^{\left(-\frac{z}{' + z2 + r'}\right)^{' + b2 + '}}}$'
# label2 += ', ' + '%.3e' % s2
# ax.plot(x0, ap.f(x0, par2, 2), color='limegreen', linestyle='--', linewidth=0.8, label=label2)
# label3 = r'$\mathrm{' + c3 + r'\left(\frac{R^{' + a3 + '}+R^{' + a3 + r'\cdot' + b3 + '}}{R^{' + b3 + \
# '}+' + d3 + r'}\right)}$'
# label3 += ', ' + '%.3e' % s3
ax.plot(x0,
ap.f(x0, par3, 3),
color='orangered',
linestyle='--',
linewidth=0.8)
ax.set_xlabel('R', fontsize='x-large')
ax.set_ylabel(r'$\mathrm{\Delta N(R,\Delta R)}$', fontsize='x-large')
ax.set_xlim(0)
ax.set_ylim(0)
if w == -100:
plt.title('Approximations with dR={}, all w, N={:d}'.format(
str(dr), sum(y)),
fontsize='x-large')
else:
plt.title('Approximations with dR={}, w>{}, N={:d}'.format(
str(dr), str(w), sum(y)),
fontsize='x-large')
# ax.legend(fontsize='x-large')
# if the legend is superimposed on the graph
# ax.legend(fontsize='x-large', loc='lower left', bbox_to_anchor=(0.05, 0))
ax.tick_params(labelsize='x-large')
if save:
if path is not None:
filename = path + 'dR={:.3f}.png'.format(dr)
else:
filename = 'dR={:.3f}.png'.format(dr)
plt.savefig(filename, dpi=150)
else:
plt.show()
plt.close()
def r_hists(dr, w, save=False, path=None):
from data_processing import distance
from data_processing import z_final
from distance import r_comoving
print('dr=' + str(dr))
x = []
y = []
for wi in w:
data = distance(w=wi)
# data = [r_comoving(i) for i in z_med_pdz(w=wi)]
xx, yy = data_hist(data, dr)
x.append(xx)
y.append(yy)
fig, ax = plt.subplots(figsize=(12.8 * frac, 7.2 * frac))
if w[0] == -100:
label = 'all w, N={:d}'.format(sum(y[0]))
ax.hist(x[0][:-1],
bins=x[0],
weights=y[0],
histtype='step',
label=label)
else:
label = 'w>{}, N={:d}'.format(str(w[0]), sum(y[0]))
ax.hist(x[0][:-1],
bins=x[0],
weights=y[0],
histtype='stepfilled',
alpha=0.9,
label=label)
for i in range(1, len(w)):
label = 'w>{}, N={:d}'.format(str(w[i]), sum(y[i]))
if i == 4:
ax.hist(x[i][:-1],
bins=x[i],
weights=y[i],
histtype='stepfilled',
color='#FFD94A',
alpha=0.9,
label=label)
else:
ax.hist(x[i][:-1],
bins=x[i],
weights=y[i],
histtype='stepfilled',
alpha=0.9,
label=label)
ax.set_xlabel('r', fontsize='x-large')
ax.set_ylabel(r'$\mathrm{\Delta N(R,\Delta R)}$', fontsize='x-large')
ax.set_xlim(0, max(x[0]))
ax.set_ylim(0)
ax.legend(fontsize='x-large')
ax.tick_params(labelsize='x-large')
plt.title(r'Histograms with $\mathrm{\Delta R=' + str(dr) + '}$',
fontsize='x-large')
if save:
if path is not None:
filename = f'{path}dr={dr}.png'
else:
filename = f'dr={dr}.png'
plt.savefig(filename, dpi=150)
else:
plt.show()
plt.close()
def w_hists(dz, w, integral=False, save=False, path=None, file=None):
from data_processing import z_final
# from data_processing import z_med_pdz
print('dz={}'.format(dz))
x = []
y = []
y_sum = []
for wi in w:
data = z_final(w=wi, file=file)
# data = z_med_pdz(w=wi)
xx, yy = data_hist(data, dz)
if integral:
yyy = [yy[0]]
for i in range(1, len(yy)):
yyy.append(yyy[-1] + yy[i])
y.append(yyy)
y_sum.append(yyy[-1])
else:
y.append(yy)
y_sum.append(sum(yy))
x.append(xx)
fig, ax = plt.subplots(figsize=(12.8 * frac, 7.2 * frac))
if w[0] == -100:
label = 'all w, N={:d}'.format(y_sum[0])
ax.hist(x[0][:-1],
bins=x[0],
weights=y[0],
histtype='step',
label=label)
else:
label = 'w>{}, N={:d}'.format(str(w[0]), y_sum[0])
ax.hist(x[0][:-1],
bins=x[0],
weights=y[0],
histtype='stepfilled',
alpha=0.9,
label=label)
for i in range(1, len(w)):
label = 'w>{}, N={:d}'.format(str(w[i]), y_sum[i])
if i == 4:
ax.hist(x[i][:-1],
bins=x[i],
weights=y[i],
histtype='stepfilled',
color='#FFD94A',
alpha=0.9,
label=label)
else:
ax.hist(x[i][:-1],
bins=x[i],
weights=y[i],
histtype='stepfilled',
alpha=0.9,
label=label)
ax.set_xlabel('z', fontsize='x-large')
ax.set_ylabel(r'$\mathrm{\Delta N(z,\Delta z)}$', fontsize='x-large')
ax.set_xlim(0, max(x[0]))
ax.set_ylim(0)
ax.legend(fontsize='x-large')
ax.tick_params(labelsize='x-large')
plt.title(r'Histograms with $\mathrm{\Delta z=' + str(dz) + '}$',
fontsize='x-large')
if save:
if path is not None:
filename = f'{path}dz={dz:.3f}.png'
else:
filename = f'dz={dz:.3f}.png'
plt.savefig(filename, dpi=150)
else:
plt.show()
plt.close()
def fluct_graph(data, func, dz, w, save=False, path=None):
from matplotlib.ticker import FixedLocator
from scipy import integrate
def y_app(bins, par, kind):
yapp = []
for i in range(1, len(bins)):
yapp.append(
integrate.quad(ap.f, bins[i - 1], bins[i], args=(par, kind))[0]
/ dz)
return np.array(yapp)
print(f'dz={dz}, w>{w}')
xb, y = data_hist(data, dz, bi='edges')
x = [xb[i] + dz / 2 for i in range(len(xb) - 1)]
x, y = np.array(x), np.array(y)
par1, s1 = ap.fast(x, y, 1)
par2, s2 = ap.fast(x, y, 2)
par3, s3 = ap.fast(x, y, 3)
a1, b1, c1 = par1
a2, b2, z2, c2 = par2
a3, b3, d3, c3 = par3
p = ['{:.2f}'.format(k) for k in [a1, b1, a2, b2, z2, a3, b3, d3]]
a1, b1, a2, b2, z2, a3, b3, d3 = p
p = ['{:.0f}'.format(k) for k in [c1, c2, c3]]
c1, c2, c3 = p
ig, ax = plt.subplots(figsize=(12.8 * frac, 7.2 * frac))
# label_sigma = r'$5\sigma$'
label1 = r'$\mathrm{' + c1 + 'z^{' + a1 + '}e^{-' + b1 + 'z}}$'
label1 += ', ' + '%.3e' % s1
ax.plot(x, (y - y_app(xb, par1, 1)) / y_app(xb, par1, 1),
color='orange',
linewidth=0.9,
label=label1)
ax.plot(x,
5 / np.sqrt(y_app(xb, par1, 1)),
color='orange',
linestyle='--',
linewidth=0.5)
ax.plot(x,
-5 / np.sqrt(y_app(xb, par1, 1)),
color='orange',
linestyle='--',
linewidth=0.5)
label2 = r'$\mathrm{' + c2 + 'z^{' + a2 + r'}e^{\left(-\frac{z}{' + z2 + r'}\right)^{' + b2 + '}}}$'
label2 += ', ' + '%.3e' % s2
ax.plot(x, (y - y_app(xb, par2, 2)) / y_app(xb, par2, 2),
color='limegreen',
linewidth=0.9,
label=label2)
ax.plot(x,
5 / np.sqrt(y_app(xb, par2, 2)),
color='limegreen',
linestyle='--',
linewidth=0.5)
ax.plot(x,
-5 / np.sqrt(y_app(xb, par2, 2)),
color='limegreen',
linestyle='--',
linewidth=0.5)
label3 = r'$\mathrm{' + c3 + r'\left(\frac{z^{' + a3 + '}+z^{' + a3 + r'\cdot' + b3 + '}}{z^{' + b3 + '}+' + d3 + \
r'}\right)}$'
label3 += ', ' + '%.3e' % s3
ax.plot(x, (y - y_app(xb, par3, 3)) / y_app(xb, par3, 3),
color='orangered',
linewidth=0.9,
label=label3)
ax.plot(x,
5 / np.sqrt(y_app(xb, par3, 3)),
color='orangered',
linestyle='--',
linewidth=0.5)
ax.plot(x,
-5 / np.sqrt(y_app(xb, par3, 3)),
color='orangered',
linestyle='--',
linewidth=0.5)
ax.tick_params(axis='x', direction='inout')
ax.xaxis.set_major_locator(FixedLocator([1, 2, 3, 4, 5, 6]))
ax.tick_params(labelsize='x-large')
ax.spines['bottom'].set_position(('data', 0.0))
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.set_xlabel('z', fontsize='x-large')
ax.xaxis.set_label_coords(1.01, 0.516)
# if the name of the x axis is superimposed on numbers
if frac < 1:
ax.set_ylabel(
r'$\mathrm{\delta=\frac{\Delta N_{obs} - \Delta N_{approx}}{\Delta N_{approx}}}$',
fontsize='xx-large',
labelpad=-10)
else:
ax.set_ylabel(
r'$\mathrm{\delta=\frac{\Delta N_{obs} - \Delta N_{approx}}{\Delta N_{approx}}}$',
fontsize='xx-large')
ax.set_xlim(0)
if float(w) > 0.8:
ax.set_ylim(-1.05, 1.05)
else:
limits = plt.axis()
ylim = max(abs(limits[2]), abs(limits[3]))
ax.set_ylim(-ylim, ylim)
if w == -100:
plt.title('Fluctuations with dz={}, all w, N={:d}'.format(dz, sum(y)),
fontsize='x-large')
else:
plt.title('Fluctuations with dz={}, w>{}, N={:d}'.format(
dz, w, sum(y)),
fontsize='x-large')
ax.legend(fontsize='x-large')
# if the legend is superimposed on the graph
# ax.legend(fontsize='x-large', loc='lower left', bbox_to_anchor=(0.02, 0))
# ax.set_ylim(-1.6, 1.6)
if save:
if path is not None:
filename = path + 'dz={:.3f}.png'.format(dz)
else:
filename = 'dz={:.3f}.png'.format(dz)
plt.savefig(filename, dpi=150)
else:
plt.show()
plt.close()