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Commit 0e58088a authored by Marco Selig's avatar Marco Selig
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interpolate_power implemented; several minor adjustments.

parent 7cf3fd1c
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nifty_core.py
View file @ 0e58088a
......@@ -2393,10 +2393,10 @@ class rg_space(space):
if(not np.all(np.isfinite(spec))):
about.warnings.cprint("WARNING: infinite value(s).")
## check positivity (excluding null)
if(np.any(spec==0)):
about.warnings.cprint("WARNING: nonpositive value(s).")
if(np.any(spec<0)):
raise ValueError(about._errors.cstring("ERROR: nonpositive value(s)."))
elif(np.any(spec==0)):
about.warnings.cprint("WARNING: nonpositive value(s).")
if(size is None):
## explicit kindex
......@@ -3488,10 +3488,10 @@ class lm_space(space):
if(not np.all(np.isfinite(spec))):
about.warnings.cprint("WARNING: infinite value(s).")
## check positivity (excluding null)
if(np.any(spec==0)):
about.warnings.cprint("WARNING: nonpositive value(s).")
if(np.any(spec<0)):
raise ValueError(about._errors.cstring("ERROR: nonpositive value(s)."))
elif(np.any(spec==0)):
about.warnings.cprint("WARNING: nonpositive value(s).")
size = self.para[0]+1 ## lmax+1
## drop imaginary part
......@@ -3689,7 +3689,7 @@ class lm_space(space):
if(self.datatype==np.complex64):
x = gl.synalm_f(arg[1],lmax=lmax,mmax=lmax)
else:
#x = gl.synalm(spec,lmax=lmax,mmax=lmax)
#x = gl.synalm(arg[1],lmax=lmax,mmax=lmax)
x = hp.synalm(arg[1],lmax=lmax,mmax=lmax)
return x
......@@ -4307,10 +4307,10 @@ class gl_space(space):
if(not np.all(np.isfinite(spec))):
about.warnings.cprint("WARNING: infinite value(s).")
## check positivity (excluding null)
if(np.any(spec==0)):
about.warnings.cprint("WARNING: nonpositive value(s).")
if(np.any(spec<0)):
raise ValueError(about._errors.cstring("ERROR: nonpositive value(s)."))
elif(np.any(spec==0)):
about.warnings.cprint("WARNING: nonpositive value(s).")
size = self.para[0] ## nlat
......@@ -4932,10 +4932,10 @@ class hp_space(space):
if(not np.all(np.isfinite(spec))):
about.warnings.cprint("WARNING: infinite value(s).")
## check positivity (excluding null)
if(np.any(spec==0)):
about.warnings.cprint("WARNING: nonpositive value(s).")
if(np.any(spec<0)):
raise ValueError(about._errors.cstring("ERROR: nonpositive value(s)."))
elif(np.any(spec==0)):
about.warnings.cprint("WARNING: nonpositive value(s).")
size = 3*self.para[0] ## 3*nside
......
nifty_power.py
View file @ 0e58088a
......@@ -41,6 +41,7 @@
"""
from __future__ import division
from scipy.interpolate import interp1d as ip
#import numpy as np
from nifty_core import *
import smoothing as gs
......@@ -127,7 +128,7 @@ def weight_power(domain,spec,power=1,pindex=None,pundex=None,**kwargs):
elif(len(pundex)!=np.size(domain.dim(split=True))):
raise ValueError(about._errors.cstring("ERROR: dimension mismatch ( "+str(len(pundex))+" <> "+str(np.size(domain.dim(split=True)))+" )."))
return np.real(domain.calc_weight(domain.enforce_power(spec,size=len(set(pindex.flatten(order='C'))))[pindex],power=power)[pundex])
return np.real(domain.calc_weight(domain.enforce_power(spec,size=np.max(pindex,axis=None,out=None)+1)[pindex],power=power)[pundex])
##-----------------------------------------------------------------------------
......@@ -198,11 +199,47 @@ def smooth_power(spec,domain=None,kindex=None,mode="2s",exclude=1,sigma=-1,**kwa
raise ValueError(about._errors.cstring("ERROR: invalid input."))
else:
kindex = domain.power_indices.get("kindex")
else:
raise TypeError(about._errors.cstring("ERROR: insufficient input."))
## check explicit power indices
## check power spectrum
spec = domain.enforce_power(spec,size=np.size(kindex))
## check explicit kindex
else:
kindex = np.array(kindex,dtype=domain.vol.dtype)
kindex = np.sort(np.real(np.array(kindex,dtype=None).flatten(order='C')),axis=0,kind="quicksort",order=None)
## check power spectrum
if(isinstance(spec,field)):
spec = spec.val.astype(kindex.dtype)
elif(callable(spec)):
try:
spec = spec(kindex)
except:
TypeError(about._errors.cstring("ERROR: invalid power spectra function.")) ## exception in ``spec(kindex)``
elif(np.isscalar(spec)):
spec = np.array([spec],dtype=kindex.dtype)
else:
spec = np.array(spec,dtype=kindex.dtype)
## drop imaginary part
spec = np.real(spec)
## check finiteness and positivity (excluding null)
if(not np.all(np.isfinite(spec))):
raise ValueError(about._errors.cstring("ERROR: infinite value(s)."))
elif(np.any(spec<0)):
raise ValueError(about._errors.cstring("ERROR: nonpositive value(s)."))
elif(np.any(spec==0)):
about.warnings.cprint("WARNING: nonpositive value(s).")
size = np.size(kindex)
## extend
if(np.size(spec)==1):
spec = spec*np.ones(size,dtype=spec.dtype,order='C')
## size check
elif(np.size(spec)<size):
raise ValueError(about._errors.cstring("ERROR: size mismatch ( "+str(np.size(spec))+" < "+str(size)+" )."))
elif(np.size(spec)>size):
about.warnings.cprint("WARNING: power spectrum cut to size ( == "+str(size)+" ).")
spec = spec[:size]
## smoothing
if(mode=="2s"):
return gs.smooth_power_2s(spec,kindex,exclude=exclude,smooth_length=sigma)
......@@ -381,7 +418,7 @@ def infer_power(m,domain=None,Sk=None,D=None,pindex=None,pundex=None,kindex=None
Raises
------
IndexError, TypeError, ValueError
Exception, IndexError, TypeError, ValueError
If some input is invalid.
"""
......@@ -391,7 +428,7 @@ def infer_power(m,domain=None,Sk=None,D=None,pindex=None,pundex=None,kindex=None
## check domain
if(domain is None):
if(Sk is None):
raise TypeError(about._errors.cstring("ERROR: insufficient input."))
raise Exception(about._errors.cstring("ERROR: insufficient input."))
else:
domain = Sk.domain
elif(not isinstance(domain,space)):
......@@ -417,7 +454,7 @@ def infer_power(m,domain=None,Sk=None,D=None,pindex=None,pundex=None,kindex=None
pindex = np.array(pindex,dtype=np.int)
if(not np.all(np.array(np.shape(pindex))==domain.dim(split=True))):
raise ValueError(about._errors.cstring("ERROR: shape mismatch ( "+str(np.array(np.shape(pindex)))+" <> "+str(domain.dim(split=True))+" )."))
kindex = np.array(kindex,dtype=domain.vol.dtype)
kindex = np.sort(np.real(np.array(kindex,dtype=domain.vol.dtype).flatten(order='C')),axis=0,kind="quicksort",order=None)
rho = np.array(rho,dtype=np.int)
if(pundex is None):
## quick pundex
......@@ -509,3 +546,162 @@ def infer_power(m,domain=None,Sk=None,D=None,pindex=None,pundex=None,kindex=None
##=============================================================================
##-----------------------------------------------------------------------------
def interpolate_power(spec,mode="linear",domain=None,kindex=None,newkindex=None,**kwargs):
"""
Interpolates a given power spectrum at new k(-indices).
Parameters
----------
spec : {scalar, array}
The power spectrum. A scalars is interpreted as a constant
spectrum.
mode : string
String specifying the interpolation scheme, supported
schemes are (default: "linear"):
- "linear"
- "nearest"
- "zero"
- "slinear"
- "quadratic"
- "cubic"
domain : space, *optional*
The space wherein the power spectrum is defined (default: None).
kindex : numpy.ndarray, *optional*
Scales corresponding to each band in the old power spectrum;
can be retrieved from `domain` (default: None).
newkindex : numpy.ndarray, *optional*
Scales corresponding to each band in the new power spectrum;
can be retrieved from `domain` if `kindex` is given
(default: None).
Returns
-------
newspec : numpy.ndarray
The interpolated power spectrum.
Other Parameters
----------------
log : bool, *optional*
Flag specifying if the spectral binning is performed on logarithmic
scale or not; if set, the number of used bins is set
automatically (if not given otherwise); by default no binning
is done (default: None).
nbin : integer, *optional*
Number of used spectral bins; if given `log` is set to ``False``;
integers below the minimum of 3 induce an automatic setting;
by default no binning is done (default: None).
binbounds : {list, array}, *optional*
User specific inner boundaries of the bins, which are preferred
over the above parameters; by default no binning is done
(default: None). vmin : {scalar, list, ndarray, field}, *optional*
Lower limit of the uniform distribution if ``random == "uni"``
(default: 0).
See Also
--------
scipy.interpolate.interp1d
Raises
------
Exception, IndexError, TypeError, ValueError
If some input is invalid.
ValueError
If an interpolation is flawed.
"""
## check implicit kindex
if(kindex is None):
if(isinstance(domain,space)):
try:
domain.set_power_indices(**kwargs)
except:
raise ValueError(about._errors.cstring("ERROR: invalid input."))
else:
kindex = domain.power_indices.get("kindex")
else:
raise TypeError(about._errors.cstring("ERROR: insufficient input."))
## check power spectrum
spec = domain.enforce_power(spec,size=np.size(kindex))
## check explicit newkindex
if(newkindex is None):
raise Exception(about._errors.cstring("ERROR: insufficient input."))
else:
newkindex = np.sort(np.real(np.array(newkindex,dtype=domain.vol.dtype).flatten(order='C')),axis=0,kind="quicksort",order=None)
## check explicit kindex
else:
kindex = np.sort(np.real(np.array(kindex,dtype=None).flatten(order='C')),axis=0,kind="quicksort",order=None)
## check power spectrum
if(isinstance(spec,field)):
spec = spec.val.astype(kindex.dtype)
elif(callable(spec)):
try:
spec = spec(kindex)
except:
TypeError(about._errors.cstring("ERROR: invalid power spectra function.")) ## exception in ``spec(kindex)``
elif(np.isscalar(spec)):
spec = np.array([spec],dtype=kindex.dtype)
else:
spec = np.array(spec,dtype=kindex.dtype)
## drop imaginary part
spec = np.real(spec)
## check finiteness and positivity (excluding null)
if(not np.all(np.isfinite(spec))):
raise ValueError(about._errors.cstring("ERROR: infinite value(s)."))
elif(np.any(spec<0)):
raise ValueError(about._errors.cstring("ERROR: nonpositive value(s)."))
elif(np.any(spec==0)):
about.warnings.cprint("WARNING: nonpositive value(s).")
size = np.size(kindex)
## extend
if(np.size(spec)==1):
spec = spec*np.ones(size,dtype=spec.dtype,order='C')
## size check
elif(np.size(spec)<size):
raise ValueError(about._errors.cstring("ERROR: size mismatch ( "+str(np.size(spec))+" < "+str(size)+" )."))
elif(np.size(spec)>size):
about.warnings.cprint("WARNING: power spectrum cut to size ( == "+str(size)+" ).")
spec = spec[:size]
## check implicit newkindex
if(newkindex is None):
if(isinstance(domain,space)):
try:
domain.set_power_indices(**kwargs)
except:
raise ValueError(about._errors.cstring("ERROR: invalid input."))
else:
newkindex = domain.power_indices.get("kindex")
else:
raise TypeError(about._errors.cstring("ERROR: insufficient input."))
## check explicit newkindex
else:
newkindex = np.sort(np.real(np.array(newkindex,dtype=None).flatten(order='C')),axis=0,kind="quicksort",order=None)
## check bounds
if(kindex[0]<0)or(newkindex[0]<0):
raise ValueError(about._errors.cstring("ERROR: invalid input."))
if(np.any(newkindex>kindex[-1])):
about.warnings.cprint("WARNING: interpolation beyond upper bound.")
## continuation extension by point mirror
nmirror = np.size(kindex)-np.searchsorted(kindex,2*kindex[-1]-newkindex[-1],side='left')+1
spec = np.r_[spec,np.exp(2*np.log(spec[-1])-np.log(spec[-nmirror:-1][::-1]))]
kindex = np.r_[kindex,(2*kindex[-1]-kindex[-nmirror:-1][::-1])]
## interpolation
newspec = ip(kindex,spec,kind=mode,axis=0,copy=True,bounds_error=True,fill_value=np.NAN)(newkindex)
## check new power spectrum
if(not np.all(np.isfinite(newspec))):
raise ValueError(about._errors.cstring("ERROR: infinite value(s)."))
elif(np.any(newspec<0)):
raise ValueError(about._errors.cstring("ERROR: nonpositive value(s)."))
elif(np.any(newspec==0)):
about.warnings.cprint("WARNING: nonpositive value(s).")
return newspec
##-----------------------------------------------------------------------------
smoothing.py
View file @ 0e58088a
......@@ -64,8 +64,8 @@ def smooth_power(power, k, exclude=1, smooth_length=None):
pbinned = pbinned / counter
nmirror = int(5 * smooth_length / dk) + 2
zpbinned = np.r_[(2 * pbinned[0] - pbinned[1:nmirror][::-1]), pbinned,
(2 * pbinned[-1] - pbinned[-nmirror:-1][::-1])]
zpbinned = np.r_[np.exp(2 * np.log(pbinned[0]) - np.log(pbinned[1:nmirror][::-1])), pbinned,
np.exp(2 * np.log(pbinned[-1])- np.log(pbinned[-nmirror:-1][::-1]))]
zpbinned = np.maximum(0, zpbinned)
tpbinned = np.fft.fftshift(np.fft.fft(zpbinned))
......@@ -101,7 +101,7 @@ def smooth_power_bf(power, k, exclude=1, smooth_length=None):
smooth_length = k[1]-k[0]
nmirror = int(5*smooth_length/(k[1]-k[0]))+2
mpower = np.r_[(2*power[0]-power[1:nmirror][::-1]),power,(2*power[-1]-power[-nmirror:-1][::-1])]
mpower = np.r_[np.exp(2*np.log(power[0])-np.log(power[1:nmirror][::-1])),power,np.exp(2*np.log(power[-1])-np.log(power[-nmirror:-1][::-1]))]
mk = np.r_[(2*k[0]-k[1:nmirror][::-1]),k,(2*k[-1]-k[-nmirror:-1][::-1])]
mdk = np.r_[0.5*(mk[1]-mk[0]),0.5*(mk[2:]-mk[:-2]),0.5*(mk[-1]-mk[-2])]
......@@ -143,7 +143,7 @@ def smooth_power_2s(power, k, exclude=1, smooth_length=None):
smooth_length = k[1]-k[0]
nmirror = int(5*smooth_length/(k[1]-k[0]))+2
mpower = np.r_[(2*power[0]-power[1:nmirror][::-1]),power,(2*power[-1]-power[-nmirror:-1][::-1])]
mpower = np.r_[np.exp(2*np.log(power[0])-np.log(power[1:nmirror][::-1])),power,np.exp(2*np.log(power[-1])-np.log(power[-nmirror:-1][::-1]))]
mk = np.r_[(2*k[0]-k[1:nmirror][::-1]),k,(2*k[-1]-k[-nmirror:-1][::-1])]
mdk = np.r_[0.5*(mk[1]-mk[0]),0.5*(mk[2:]-mk[:-2]),0.5*(mk[-1]-mk[-2])]
......
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