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__author__ = 'matt'
from . import ch
import numpy as np
from .utils import row, col
import scipy.sparse as sp
import scipy.special
class Interp3D(ch.Ch):
dterms = 'locations'
terms = 'image'
def on_changed(self, which):
if 'image' in which:
self.gx, self.gy, self.gz = np.gradient(self.image)
def compute_r(self):
locations = self.locations.r.copy()
for i in range(3):
locations[:,i] = np.clip(locations[:,i], 0, self.image.shape[i]-1)
locs = np.floor(locations).astype(np.uint32)
result = self.image[locs[:,0], locs[:,1], locs[:,2]]
offset = (locations - locs)
dr = self.dr_wrt(self.locations).dot(offset.ravel())
return result + dr
def compute_dr_wrt(self, wrt):
if wrt is self.locations:
locations = self.locations.r.copy()
for i in range(3):
locations[:,i] = np.clip(locations[:,i], 0, self.image.shape[i]-1)
locations = locations.astype(np.uint32)
xc = col(self.gx[locations[:,0], locations[:,1], locations[:,2]])
yc = col(self.gy[locations[:,0], locations[:,1], locations[:,2]])
zc = col(self.gz[locations[:,0], locations[:,1], locations[:,2]])
data = np.vstack([xc.ravel(), yc.ravel(), zc.ravel()]).T.copy()
JS = np.arange(locations.size)
IS = JS // 3
return sp.csc_matrix((data.ravel(), (IS, JS)))
class gamma(ch.Ch):
dterms = 'x',
def compute_r(self):
return scipy.special.gamma(self.x.r)
def compute_dr_wrt(self, wrt):
if wrt is self.x:
d = scipy.special.polygamma(0, self.x.r)*self.r
return sp.diags([d.ravel()], [0])
# This function is based directly on the "moment" function
# in scipy, specifically in mstats_basic.py.
def moment(a, moment=1, axis=0):
if moment == 1:
# By definition the first moment about the mean is 0.
shape = list(a.shape)
del shape[axis]
if shape:
# return an actual array of the appropriate shape
return ch.zeros(shape, dtype=float)
else:
# the input was 1D, so return a scalar instead of a rank-0 array
return np.float64(0.0)
else:
mn = ch.expand_dims(a.mean(axis=axis), axis)
s = ch.power((a-mn), moment)
return s.mean(axis=axis)
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