def test_settingerr():
ref1 = np.seterr()
ref2 = {"divide": "ignore", "invalid": "ignore", "over": "ignore", "under": "ignore"}
ref3 = {"divide": "raise", "invalid": "ignore", "over": "warn", "under": "ignore"}
with settingerr(all="ignore"):
assert_eq(np.seterr(), ref2)
with settingerr(divide="raise", over="warn"):
assert_eq(np.seterr(), ref3)
assert_eq(np.seterr(), ref2)
assert_eq(np.seterr(), ref1)
def test_settingerr():
ref1 = np.seterr()
ref2 = {'divide': 'ignore', 'invalid': 'ignore', 'over': 'ignore',
'under': 'ignore'}
ref3 = {'divide': 'raise', 'invalid': 'ignore', 'over': 'warn',
'under': 'ignore'}
with settingerr(all='ignore'):
assert_eq(np.seterr(), ref2)
with settingerr(divide='raise', over='warn'):
assert_eq(np.seterr(), ref3)
assert_eq(np.seterr(), ref2)
assert_eq(np.seterr(), ref1)
def primary_beam(theta, phi):
import numpy as np
with settingerr(invalid='ignore'):
return theta <= np.radians(9)
def semilogy_spectra(ell,
spectra=None,
lmax=None,
Dl=False,
yerr=None,
xerr=None,
loc='best',
**keywords):
"""
Plot Cl power spectra as sqrt(l(l+1)Cl/2pi) Cl with log scaling
on the y axis.
semilogy_spectra([ell,] spectra)
Parameters
----------
ell : integer array, optional
The l values of the spectra. If not provided, it is assumed that
the Cls start from 0 and are not binned.
spectra : array-like, 4-tuple or 6-tuple
The Cl spectrum or the TT, EE, BB and TE or TT, EE, BB, TE, EB, TB
Cl spectra.
Dl : boolean, optional
Use `D_l` as label instead of `l(l+1)Cl/2pi`.
lmax : integer
Plot upper x limit.
loc : string
keyword passed to `legend` if there is more than one input spectrum.
"""
import matplotlib.pyplot as mp
if spectra is None:
spectra = ell
ell = None
if lmax is None:
lmax = np.max(ell)
if not isinstance(spectra, (list, np.ndarray, tuple)):
raise TypeError('Invalid type for the power spectra.')
if not isinstance(spectra[0], (list, np.ndarray, tuple)):
nspectra = 1
spectra = (spectra, )
else:
nspectra = len(spectra)
if nspectra not in (1, 4, 6):
raise ValueError('Invalid number of spectra.')
if Dl:
label = '$\sqrt{{\mathcal{{D}}_\ell^{{{}}}}}$'
else:
label = '$\sqrt{{\ell(\ell+1)C_\ell^{{{}}}/2\pi}}$'
if ell is None:
ell = np.arange(spectra[0].size)
fact = ell * (ell + 1) / (2 * np.pi)
with settingerr(invalid='ignore'):
if nspectra == 1:
c = fact * spectra[0]
sc = np.sqrt(c)
if xerr is None and yerr is None:
p = mp.plot(ell, np.sqrt(c), **keywords)
else:
if yerr is not None:
yerr = [
sc - np.sqrt(c - fact * yerr),
np.sqrt(c + fact * yerr) - sc
]
p = mp.errorbar(ell, sc, yerr=yerr, xerr=xerr, **keywords)
mp.plot(ell, np.sqrt(-c), linestyle='--', color=p[0].get_color())
else:
if nspectra == 6:
ctt, cee, cbb, cte, ceb, ctb = spectra
mp.plot(ell,
np.sqrt(fact * ceb),
color='brown',
label=label.format('EB'))
mp.plot(ell,
np.sqrt(-fact * ceb),
color='brown',
linestyle='--')
mp.plot(ell,
np.sqrt(fact * ctb),
color='cyan',
label=label.format('TB'))
mp.plot(ell,
np.sqrt(-fact * ctb),
color='cyan',
linestyle='--')
else:
ctt, cee, cbb, cte = spectra
mp.plot(ell, np.sqrt(fact * ctt), 'k', label=label.format('TT'))
mp.plot(ell, np.sqrt(fact * cte), 'g', label=label.format('TE'))
mp.plot(ell, np.sqrt(-fact * cte), 'g', linestyle='--')
mp.plot(ell, np.sqrt(fact * cee), 'b', label=label.format('EE'))
c = fact * cbb
sc = np.sqrt(c)
if 'color' not in keywords:
keywords['color'] = 'r'
if 'label' not in keywords:
keywords['label'] = label.format('BB')
if xerr is None and yerr is None:
mp.plot(ell, sc, **keywords)
else:
if yerr is not None:
yerr = [
sc - np.sqrt(c - fact * yerr),
np.sqrt(c + fact * yerr) - sc
]
mp.errorbar(ell, sc, yerr=yerr, xerr=xerr, **keywords)
mp.yscale('log')
mp.xlim(0, lmax)
mp.ylim(0.01, 100)
mp.xlabel('$\ell$')
mp.ylabel(label.format('') + ' [$\mu$ K]')
if nspectra > 1:
mp.legend(loc=loc, frameon=False)
def primary_beam(theta, phi):
import numpy as np
with settingerr(invalid='ignore'):
return theta <= np.radians(9)
def semilogy_spectra(ell, spectra=None, lmax=None, Dl=False, yerr=None,
xerr=None, loc='best', **keywords):
"""
Plot Cl power spectra as sqrt(l(l+1)Cl/2pi) Cl with log scaling
on the y axis.
semilogy_spectra([ell,] spectra)
Parameters
----------
ell : integer array, optional
The l values of the spectra. If not provided, it is assumed that
the Cls start from 0 and are not binned.
spectra : array-like, 4-tuple or 6-tuple
The Cl spectrum or the TT, EE, BB and TE or TT, EE, BB, TE, EB, TB
Cl spectra.
Dl : boolean, optional
Use `D_l` as label instead of `l(l+1)Cl/2pi`.
lmax : integer
Plot upper x limit.
loc : string
keyword passed to `legend` if there is more than one input spectrum.
"""
import matplotlib.pyplot as mp
if spectra is None:
spectra = ell
ell = None
if lmax is None:
lmax = np.max(ell)
if not isinstance(spectra, (list, np.ndarray, tuple)):
raise TypeError('Invalid type for the power spectra.')
if not isinstance(spectra[0], (list, np.ndarray, tuple)):
nspectra = 1
spectra = (spectra,)
else:
nspectra = len(spectra)
if nspectra not in (1, 4, 6):
raise ValueError('Invalid number of spectra.')
if Dl:
label = '$\sqrt{{\mathcal{{D}}_\ell^{{{}}}}}$'
else:
label = '$\sqrt{{\ell(\ell+1)C_\ell^{{{}}}/2\pi}}$'
if ell is None:
ell = np.arange(spectra[0].size)
fact = ell * (ell + 1) / (2 * np.pi)
with settingerr(invalid='ignore'):
if nspectra == 1:
c = fact * spectra[0]
sc = np.sqrt(c)
if xerr is None and yerr is None:
p = mp.plot(ell, np.sqrt(c), **keywords)
else:
if yerr is not None:
yerr = [sc - np.sqrt(c - fact * yerr),
np.sqrt(c + fact * yerr) - sc]
p = mp.errorbar(ell, sc, yerr=yerr, xerr=xerr, **keywords)
mp.plot(ell, np.sqrt(-c), linestyle='--', color=p[0].get_color())
else:
if nspectra == 6:
ctt, cee, cbb, cte, ceb, ctb = spectra
mp.plot(ell, np.sqrt(fact * ceb), color='brown',
label=label.format('EB'))
mp.plot(ell, np.sqrt(-fact * ceb), color='brown',
linestyle='--')
mp.plot(ell, np.sqrt(fact * ctb), color='cyan',
label=label.format('TB'))
mp.plot(ell, np.sqrt(-fact * ctb), color='cyan',
linestyle='--')
else:
ctt, cee, cbb, cte = spectra
mp.plot(ell, np.sqrt(fact * ctt), 'k', label=label.format('TT'))
mp.plot(ell, np.sqrt(fact * cte), 'g', label=label.format('TE'))
mp.plot(ell, np.sqrt(-fact * cte), 'g', linestyle='--')
mp.plot(ell, np.sqrt(fact * cee), 'b', label=label.format('EE'))
c = fact * cbb
sc = np.sqrt(c)
if 'color' not in keywords:
keywords['color'] = 'r'
if 'label' not in keywords:
keywords['label'] = label.format('BB')
if xerr is None and yerr is None:
mp.plot(ell, sc, **keywords)
else:
if yerr is not None:
yerr = [sc - np.sqrt(c - fact * yerr),
np.sqrt(c + fact * yerr) - sc]
mp.errorbar(ell, sc, yerr=yerr, xerr=xerr, **keywords)
mp.yscale('log')
mp.xlim(0, lmax)
mp.ylim(0.01, 100)
mp.xlabel('$\ell$')
mp.ylabel(label.format('') + ' [$\mu$ K]')
if nspectra > 1:
mp.legend(loc=loc, frameon=False)
