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Simplified gridcount

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master
Per A Brodtkorb 8 years ago
parent 8140ec785b
commit 110d3df086
1 changed files with 67 additions and 64 deletions
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131
wafo/kdetools/gridding.py
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@ -12,6 +12,11 @@ from itertools import product
__all__ = ['accum', 'gridcount']
def _assert(cond, msg):
if not cond:
raise ValueError(msg)
def bitget(int_type, offset):
"""Returns the value of the bit at the offset position in int_type.
@ -24,7 +29,7 @@ def bitget(int_type, offset):
return np.bitwise_and(int_type, 1 << offset) >> offset
def accumsum(accmap, a, shape, dtype=None):
def accumsum_sparse(accmap, a, shape, dtype=None):
"""
Example
-------
@ -36,7 +41,7 @@ def accumsum(accmap, a, shape, dtype=None):
[-1, 8, 9]])
>>> # Sum the diagonals.
>>> accmap = array([[0,1,2],[2,0,1],[1,2,0]])
>>> s = accumsum(accmap, a, (3,))
>>> s = accumsum_sparse(accmap, a, (3,))
>>> np.allclose(s.toarray().T, [ 9, 7, 15])
True
@ -57,7 +62,7 @@ def accumsum(accmap, a, shape, dtype=None):
return out
def accumsum2(accmap, a, shape):
def accumsum(accmap, a, shape):
"""
Example
-------
@ -69,7 +74,7 @@ def accumsum2(accmap, a, shape):
[-1, 8, 9]])
>>> accmap = array([[0,1,2],[2,0,1],[1,2,0]]) # Sum the diagonals.
>>> s = accumsum2(accmap, a, (3,))
>>> s = accumsum(accmap, a, (3,))
>>> np.allclose(s, [ 9, 7, 15])
True
@ -191,6 +196,46 @@ def accum(accmap, a, func=None, shape=None, fill_value=0, dtype=None):
return out
def _gridcount_nd(acfun, data, x, y, w, binx):
d, inc = x.shape
c_shape = np.repeat(inc, d)
nc = c_shape.prod() # inc ** d
c = np.zeros((nc, ))
fact2 = np.asarray(np.reshape(inc * np.arange(d), (d, -1)), dtype=int)
fact1 = np.asarray(np.reshape(c_shape.cumprod() / inc, (d, -1)), dtype=int)
bt0 = [0, 0]
X1 = x.ravel()
for ir in range(2 ** (d - 1)):
bt0[0] = np.reshape(bitget(ir, np.arange(d)), (d, -1))
bt0[1] = 1 - bt0[0]
for ix in range(2):
one = np.mod(ix, 2)
two = np.mod(ix + 1, 2)
# Convert to linear index
# linear index to c
b1 = np.sum((binx + bt0[one]) * fact1, axis=0)
bt2 = bt0[two] + fact2
b2 = binx + bt2 # linear index to X
c += acfun(b1, np.abs(np.prod(X1[b2] - data, axis=0)) * y,
shape=(nc, ))
c = np.reshape(c / w, c_shape, order='F')
T = np.arange(d).tolist()
T[1], T[0] = T[0], T[1]
# make sure c is stored in the same way as meshgrid
c = c.transpose(*T)
return c
def _gridcount_1d(acfun, data, x, y, w, binx, inc):
x.shape = -1,
c = (acfun(binx, (x[binx + 1] - data) * y, shape=(inc, )) +
acfun(binx + 1, (data - x[binx]) * y, shape=(inc, ))).ravel()
return c / w
def gridcount(data, X, y=1):
'''
Returns D-dimensional histogram using linear binning.
@ -224,7 +269,6 @@ def gridcount(data, X, y=1):
-------
>>> import numpy as np
>>> import wafo.kdetools as wk
>>> import pylab as plb
>>> N = 20
>>> data = np.random.rayleigh(1,N)
>>> data = np.array(
@ -243,8 +287,9 @@ def gridcount(data, X, y=1):
>>> np.allclose(np.trapz(pdf, x), 1)
True
h = plb.plot(x,c,'.') # 1D histogram
h1 = plb.plot(x, pdf) # 1D probability density plot
import matplotlib.pyplot as plt
h = plt.plot(x,c,'.') # 1D histogram
h1 = plt.plot(x, pdf) # 1D probability density plot
See also
--------
@ -256,70 +301,28 @@ def gridcount(data, X, y=1):
'Kernel smoothing'
Chapman and Hall, pp 182-192
'''
dat = np.atleast_2d(data)
x = np.atleast_2d(X)
data2, x = np.atleast_2d(data, X)
y = np.atleast_1d(y).ravel()
d = dat.shape[0]
d1, inc = x.shape
d, inc = x.shape
if d != d1:
raise ValueError('Dimension 0 of data and X do not match.')
_assert(d == data2.shape[0], 'Dimension 0 of data and X do not match.')
dx = np.diff(x[:, :2], axis=1)
xlo = x[:, 0]
xup = x[:, -1]
datlo = dat.min(axis=1)
datup = dat.max(axis=1)
if ((datlo < xlo) | (xup < datup)).any():
raise ValueError('X does not include whole range of the data!')
csiz = np.repeat(inc, d)
use_sparse = False
if use_sparse:
acfun = accumsum # faster than accum
else:
acfun = accumsum2 # accum
binx = np.asarray(np.floor((dat - xlo[:, np.newaxis]) / dx), dtype=int)
w = dx.prod()
if d == 1:
x.shape = (-1,)
c = np.asarray((acfun(binx, (x[binx + 1] - dat) * y, shape=(inc, )) +
acfun(binx + 1, (dat - x[binx]) * y, shape=(inc, ))) /
w).ravel()
else: # d>2
Nc = csiz.prod()
c = np.zeros((Nc,))
xlo, xup = x[:, 0], x[:, -1]
fact2 = np.asarray(np.reshape(inc * np.arange(d), (d, -1)), dtype=int)
fact1 = np.asarray(
np.reshape(csiz.cumprod() / inc, (d, -1)), dtype=int)
# fact1 = fact1(ones(n,1),:);
bt0 = [0, 0]
X1 = X.ravel()
for ir in range(2 ** (d - 1)):
bt0[0] = np.reshape(bitget(ir, np.arange(d)), (d, -1))
bt0[1] = 1 - bt0[0]
for ix in range(2):
one = np.mod(ix, 2)
two = np.mod(ix + 1, 2)
# Convert to linear index
# linear index to c
b1 = np.sum((binx + bt0[one]) * fact1, axis=0)
bt2 = bt0[two] + fact2
b2 = binx + bt2 # linear index to X
c += acfun(b1, np.abs(np.prod(X1[b2] - dat, axis=0)) * y,
shape=(Nc,))
datlo, datup = data2.min(axis=1), data2.max(axis=1)
_assert(not ((datlo < xlo) | (xup < datup)).any(),
'X does not include whole range of the data!')
c = np.reshape(c / w, csiz, order='F')
# acfun = accumsum_sparse # faster than accum
acfun = accumsum # faster than accumsum_sparse
T = [i for i in range(d)]
T[1], T[0] = T[0], T[1]
# make sure c is stored in the same way as meshgrid
c = c.transpose(*T)
return c
binx = np.asarray(np.floor((data2 - xlo[:, np.newaxis]) / dx), dtype=int)
w = dx.prod()
if d == 1:
return _gridcount_1d(acfun, data2, x, y, w, binx, inc)
# else: # d>1
return _gridcount_nd(acfun, data2, x, y, w, binx)
if __name__ == '__main__':

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