def quantile(a, q, axis=None):
'''Quantile (or inverse cumulative distribution) function based on input
a -- data
q -- probability value(s)
axis -- can be None'''
from pycore import toList
from math import floor
q = toList(q)
sa = _np.sort(a, axis=axis)
if axis is None:
size = sa.size
else:
size = sa.shape[axis]
x = [ (size - 1)*p for p in q ]
n = [ int(floor(f)) for f in x ]
x = [ f - i for f, i in zip(x,n) ]
if axis is None:
return [ sa[i]*(1-f) + sa[i+1]*f for f, i in zip(x,n) ]
else:
slices = [ slice(d) for d in sa.shape ]
result = []
for f, i in zip(x, n):
slices[axis] = i
r = sa[slices]*(1-f)
slices[axis] = i+1
r += sa[slices]*f
result.append(r)
return result
def quantile(a, q, axis=None):
'''Quantile (or inverse cumulative distribution) function based on input
a -- data
q -- probability value(s)
axis -- can be None'''
from pycore import toList
from math import floor
q = toList(q)
sa = _np.sort(a, axis=axis)
if axis is None:
size = sa.size
else:
size = sa.shape[axis]
x = [(size - 1) * p for p in q]
n = [int(floor(f)) for f in x]
x = [f - i for f, i in zip(x, n)]
if axis is None:
return [sa[i] * (1 - f) + sa[i + 1] * f for f, i in zip(x, n)]
else:
slices = [slice(d) for d in sa.shape]
result = []
for f, i in zip(x, n):
slices[axis] = i
r = sa[slices] * (1 - f)
slices[axis] = i + 1
r += sa[slices] * f
result.append(r)
return result
def centroid(weights, coords=None):
'''Calculate the centroid of an array with its (half) indexes or
coordinates (list of 1D arrays), if given, and returns it as a list
'''
if coords is None:
coords = [ _np.arange(d, dtype=_np.float) + 0.5 for d in weights.shape ]
else:
from pycore import toList
coords = toList(coords)
rank = weights.ndim
if rank > len(coords):
raise ValueError, "Number of coordinate arrays must match rank"
total = weights.sum()
cshape = [1,]*rank
result = []
for i in range(rank):
cdata = coords[i]
if cdata.ndim != 1:
raise ValueError, "All coordinate arrays must be 1D"
cshape[i] = cdata.shape[0]
result.append(_np.multiply(weights, cdata.reshape(cshape)).sum()/total)
cshape[i] = 1
return result
def centroid(weights, coords=None):
'''Calculate the centroid of an array with its (half) indexes or
coordinates (list of 1D arrays), if given, and returns it as a list
'''
if coords is None:
coords = [_np.arange(d, dtype=_np.float) + 0.5 for d in weights.shape]
else:
from pycore import toList
coords = toList(coords)
rank = weights.ndim
if rank > len(coords):
raise ValueError, "Number of coordinate arrays must match rank"
total = weights.sum()
cshape = [
1,
] * rank
result = []
for i in range(rank):
cdata = coords[i]
if cdata.ndim != 1:
raise ValueError, "All coordinate arrays must be 1D"
cshape[i] = cdata.shape[0]
result.append(
_np.multiply(weights, cdata.reshape(cshape)).sum() / total)
cshape[i] = 1
return result