def exponential(mean, shape=[]):
"""exponential(mean, n) or exponential(mean, [n, m, ...])
Returns array of random numbers exponentially distributed with
specified mean
"""
# If U is a random number uniformly distributed on [0,1], then
# -ln(U) is exponentially distributed with mean 1, and so
# -ln(U)*M is exponentially distributed with mean M.
(mean,), shape = _broadcast_arguments((mean,), shape)
x = random(shape)
num.log(x, x)
num.subtract(0.0, x, x)
num.multiply(mean, x, x)
return x
def exponential(mean, shape=[]):
"""exponential(mean, n) or exponential(mean, [n, m, ...])
Returns array of random numbers exponentially distributed with
specified mean
"""
# If U is a random number uniformly distributed on [0,1], then
# -ln(U) is exponentially distributed with mean 1, and so
# -ln(U)*M is exponentially distributed with mean M.
(mean, ), shape = _broadcast_arguments((mean, ), shape)
x = random(shape)
num.log(x, x)
num.subtract(0.0, x, x)
num.multiply(mean, x, x)
return x
def _correlate2d_fft(data0, kernel0, output=None, mode="nearest", cval=0.0):
"""_correlate2d_fft does 2d correlation of 'data' with 'kernel', storing
the result in 'output' using the FFT to perform the correlation.
supported 'mode's include:
'nearest' elements beyond boundary come from nearest edge pixel.
'wrap' elements beyond boundary come from the opposite array edge.
'reflect' elements beyond boundary come from reflection on same array edge.
'constant' elements beyond boundary are set to 'cval'
"""
shape = data0.shape
kshape = kernel0.shape
oversized = (num.array(shape) + num.array(kshape))
dy = kshape[0] // 2
dx = kshape[1] // 2
kernel = num.zeros(oversized, typecode=num.Float64)
kernel[:kshape[0], :kshape[1]] = kernel0[::-1, ::
-1] # convolution <-> correlation
data = iraf_frame.frame(data0, oversized, mode=mode, cval=cval)
complex_result = (isinstance(data, _nt.ComplexType)
or isinstance(kernel, _nt.ComplexType))
Fdata = fft.fft2d(data)
del data
Fkernel = fft.fft2d(kernel)
del kernel
num.multiply(Fdata, Fkernel, Fdata)
del Fkernel
if complex_result:
convolved = fft.inverse_fft2d(Fdata, s=oversized)
else:
convolved = fft.inverse_real_fft2d(Fdata, s=oversized)
result = convolved[kshape[0] - 1:shape[0] + kshape[0] - 1,
kshape[1] - 1:shape[1] + kshape[1] - 1]
if output is not None:
output._copyFrom(result)
else:
return result
def _correlate2d_fft(data0, kernel0, output=None, mode="nearest", cval=0.0):
"""_correlate2d_fft does 2d correlation of 'data' with 'kernel', storing
the result in 'output' using the FFT to perform the correlation.
supported 'mode's include:
'nearest' elements beyond boundary come from nearest edge pixel.
'wrap' elements beyond boundary come from the opposite array edge.
'reflect' elements beyond boundary come from reflection on same array edge.
'constant' elements beyond boundary are set to 'cval'
"""
shape = data0.shape
kshape = kernel0.shape
oversized = (num.array(shape) + num.array(kshape))
dy = kshape[0] // 2
dx = kshape[1] // 2
kernel = num.zeros(oversized, typecode=num.Float64)
kernel[:kshape[0], :kshape[1]] = kernel0[::-1,::-1] # convolution <-> correlation
data = iraf_frame.frame(data0, oversized, mode=mode, cval=cval)
complex_result = (isinstance(data, _nt.ComplexType) or
isinstance(kernel, _nt.ComplexType))
Fdata = fft.fft2d(data)
del data
Fkernel = fft.fft2d(kernel)
del kernel
num.multiply(Fdata, Fkernel, Fdata)
del Fkernel
if complex_result:
convolved = fft.inverse_fft2d( Fdata, s=oversized)
else:
convolved = fft.inverse_real_fft2d( Fdata, s=oversized)
result = convolved[ kshape[0]-1:shape[0]+kshape[0]-1, kshape[1]-1:shape[1]+kshape[1]-1 ]
if output is not None:
output._copyFrom( result )
else:
return result
