Source code for agpy.downsample
import numpy
try:
from scipy.stats import nanmean as mean
except ImportError:
from numpy import mean
[docs]def downsample(myarr,factor,estimator=mean):
"""
Downsample a 2D array by averaging over *factor* pixels in each axis.
Crops upper edge if the shape is not a multiple of factor.
This code is pure numpy and should be fast.
keywords:
estimator - default to mean. You can downsample by summing or
something else if you want a different estimator
(e.g., downsampling error: you want to sum & divide by sqrt(n))
"""
ys,xs = myarr.shape
crarr = myarr[:ys-(ys % int(factor)),:xs-(xs % int(factor))]
dsarr = estimator( numpy.concatenate([[crarr[i::factor,j::factor]
for i in range(factor)]
for j in range(factor)]), axis=0)
return dsarr
[docs]def downsample_cube(myarr,factor,ignoredim=0):
"""
Downsample a 3D array by averaging over *factor* pixels on the last two
axes.
"""
if ignoredim > 0: myarr = myarr.swapaxes(0,ignoredim)
zs,ys,xs = myarr.shape
crarr = myarr[:,:ys-(ys % int(factor)),:xs-(xs % int(factor))]
dsarr = mean(numpy.concatenate([[crarr[:,i::factor,j::factor]
for i in range(factor)]
for j in range(factor)]), axis=0)
if ignoredim > 0: dsarr = dsarr.swapaxes(0,ignoredim)
return dsarr
[docs]def downsample_1d(myarr,factor,estimator=mean):
"""
Downsample a 1D array by averaging over *factor* pixels.
Crops right side if the shape is not a multiple of factor.
This code is pure numpy and should be fast.
keywords:
estimator - default to mean. You can downsample by summing or
something else if you want a different estimator
(e.g., downsampling error: you want to sum & divide by sqrt(n))
"""
xs = myarr.shape
crarr = myarr[:xs-(xs % int(factor))]
dsarr = estimator( numpy.concatenate([[crarr[i::factor]
for i in range(factor)] ]),axis=0)
return dsarr