def dNdz_model(sample='qso', z_low=0.1, kind=1):
if sample == 'qso':
dNdz, z = np.loadtxt(
f'/fs/ess/PHS0336/data/dr9v0.57.0/p38fnl/RF_g.txt').T
dNdz_interp = IUS(z, dNdz)
dNdz_interp.set_smoothing_factor(2.0)
z_high = 4.0
nz_low = lambda z: z**kind * dNdz_interp(z_low) / z_low**kind
nz_high = lambda z: np.exp(-z**1.65) * dNdz_interp(z_high) / np.exp(
-z_high**1.65)
z_g = np.linspace(0.0, 10.0, 500)
dNdz_g = dNdz_interp(z_g)
dNdz_g[(z_g < z_low)] = nz_low(z_g[z_g < z_low])
dNdz_g[(z_g > z_high)] = nz_high(z_g[z_g > z_high])
#plt.plot(z, dNdz, 'b--', lw=4, alpha=0.3)
#plt.plot(z_g, dNdz_g, 'r-')
# plt.semilogy()
# plt.ylim(1.0e-8, 1.e3)
return z_g, dNdz_g
elif sample == 'lrg':
zmin, zmax, dNdz = np.loadtxt(
'/fs/ess/PHS0336/data/rongpu/sv3_lrg_dndz_denali.txt',
usecols=(0, 1, 2),
unpack=True)
zmid = 0.5 * (zmin + zmax)
dNdz_interp = IUS(zmid, dNdz, ext=1)
dNdz_interp.set_smoothing_factor(2.0)
z_g = np.linspace(0.0, 5.0, 500)
dNdz_g = dNdz_interp(z_g)
return z_g, dNdz_g
elif sample == 'mock':
z = np.arange(0.0, 3.0, 0.001)
i_lim = 26. # Limiting i-band magnitude
z0 = 0.0417 * i_lim - 0.744
Ngal = 46. * 100.31 * (i_lim - 25.) # Normalisation, galaxies/arcmin^2
pz = 1. / (2. * z0) * (z / z0)**2. * np.exp(
-z / z0) # Redshift distribution, p(z)
dNdz = Ngal * pz # Number density distribution
return z, dNdz
else:
raise NotImplemented(f'{sample}')
def noisefilter(t, signal, avgpts=30, smoothfac=1600):
smooth = rolling_mean(signal[::-1], avgpts)[::-1]
fspline = InterpolatedUnivariateSpline(t[:-avgpts], smooth[:-avgpts], k=4)
fspline.set_smoothing_factor(smoothfac)
return fspline(t)
ax00.axhline(0, color='black', linestyle='--')
def movingaverage(values, window):
weights = np.repeat(1.0, window) / window
sma = np.convolve(values, weights, 'valid')
return sma
# Calculate the mean
mean = movingaverage(var, 12 * 10)
mean_time = movingaverage(time, 12 * 10)
# Smooth the line
mean = InterpolatedUnivariateSpline(mean_time, mean)
mean.set_smoothing_factor(0.2)
mean = mean(mean_time)
# to convert num2date in days sine ..
# since it does not have month as a timedelta
mean_time = mean_time * 30
# Format time axis
mean_time_format = num2date(mean_time[:], units='days since 1856-01-01')
time = num2date(time[:] * 30, units='days since 1856-01-01')
# plot all data
ax00.fill_between(time[:],
var[:],
0,
where=var >= 0.,
print(arr_rho) # form array -> 1D arr_r = np.asarray(arr_r) arr_r = np.asarray(arr_r).squeeze() arr_rho = np.asarray(arr_rho).squeeze() # define axis x = arr_r y = arr_rho # generate spline xs = np.logspace(0, 100, 10000) # spl = UnivariateSpline(x, y) spl = InterpolatedUnivariateSpline(x, y, k=3) spl.set_smoothing_factor(0.00001) # get value from spline # def spline_val(pos): # return spl.__call__(1, nu=0, ext=None) # for x in range(1, 2, 0.1): # print(spline_val(x)) # Plot firts term of rho # plt.plot(listb) # Plot raw_data # plt.plot(x, y, 'ro', ms=5) # Plot spline
import numpy as np
import matplotlib.pyplot as plt
from scipy.interpolate import InterpolatedUnivariateSpline
from scipy import integrate
k = np.inf
file = np.loadtxt('podaci.txt')
tal = file[:,0]
inte = file[:,1]
spl = InterpolatedUnivariateSpline(tal, inte, k=3)
spl.set_smoothing_factor(0.1)
xs = file[:,0]
ys = spl(xs)
plt.plot(tal, inte, '.-')
plt.plot(xs, ys)
plt.show()
print (spl.integral(0, np.inf))
