def correlate1d(input, weights, axis=-1, output=None, mode="reflect",
cval=0.0, origin=0):
"""Calculate a one-dimensional correlation along the given axis.
The lines of the array along the given axis are correlated with the
given weights.
Parameters
----------
%(input)s
weights : array
One-dimensional sequence of numbers.
%(axis)s
%(output)s
%(mode)s
%(cval)s
%(origin)s
"""
input = numpy.asarray(input)
if numpy.iscomplexobj(input):
raise TypeError('Complex type not supported')
output, return_value = _ni_support._get_output(output, input)
weights = numpy.asarray(weights, dtype=numpy.float64)
if weights.ndim != 1 or weights.shape[0] < 1:
raise RuntimeError('no filter weights given')
if not weights.flags.contiguous:
weights = weights.copy()
axis = _ni_support._check_axis(axis, input.ndim)
if ((len(weights) // 2 + origin < 0) or
(len(weights) // 2 + origin > len(weights))):
raise ValueError('invalid origin')
mode = _ni_support._extend_mode_to_code(mode)
_nd_image.correlate1d(input, weights, axis, output, mode, cval,
origin)
return return_value
def correlate1d(input, weights, axis=-1, output=None, mode="reflect",
cval=0.0, origin=0):
"""Calculate a one-dimensional correlation along the given axis.
The lines of the array along the given axis are correlated with the
given weights.
Parameters
----------
%(input)s
weights : array
One-dimensional sequence of numbers.
%(axis)s
%(output)s
%(mode)s
%(cval)s
%(origin)s
"""
input = numpy.asarray(input)
if numpy.iscomplexobj(input):
raise TypeError('Complex type not supported')
output, return_value = _ni_support._get_output(output, input)
weights = numpy.asarray(weights, dtype=numpy.float64)
if weights.ndim != 1 or weights.shape[0] < 1:
raise RuntimeError('no filter weights given')
if not weights.flags.contiguous:
weights = weights.copy()
axis = _ni_support._check_axis(axis, input.ndim)
if (len(weights) // 2 + origin < 0) or (len(weights) // 2 +
origin > len(weights)):
raise ValueError('invalid origin')
mode = _ni_support._extend_mode_to_code(mode)
_nd_image.correlate1d(input, weights, axis, output, mode, cval,
origin)
return return_value
def __FilterData2D(self, data):
mode = _ni_support._extend_mode_to_code("reflect")
#lowpass filter to suppress noise
#a = ndimage.gaussian_filter(data.astype('f4'), self.filterRadiusLowpass)
#print data.shape
data = data - data.mean()
output, a = _ni_support._get_output(None, data)
if self.PRIaxis == 'x':
_nd_image.correlate1d(data, weightsLowpass, 0, output, mode, 0, 0)
_nd_image.correlate1d(output, weightsHighpass, 1, output, mode, 0,
0)
else:
_nd_image.correlate1d(data, weightsHighpass, 0, output, mode, 0, 0)
_nd_image.correlate1d(output, weightsLowpass, 1, output, mode, 0,
0)
#print numpy.absolute(a - a_).mean()
#lowpass filter again to find background
#b = ndimage.gaussian_filter(a, self.filterRadiusHighpass)
#output, b = _ni_support._get_output(None, data)
#_nd_image.correlate1d(data, weightsHighpass, 0, output, mode, 0,0)
#_nd_image.correlate1d(output, weightsHighpass, 1, output, mode, 0,0)
return a #- b
def __FilterData2D(self,data):
mode = _ni_support._extend_mode_to_code("reflect")
#lowpass filter to suppress noise
#a = ndimage.gaussian_filter(data.astype('f4'), self.filterRadiusLowpass)
#print data.shape
data = data - data.mean()
output, a = _ni_support._get_output(None, data)
if self.PRIaxis == 'x':
_nd_image.correlate1d(data, weightsLowpass, 0, output, mode, 0,0)
_nd_image.correlate1d(output, weightsHighpass, 1, output, mode, 0,0)
else:
_nd_image.correlate1d(data, weightsHighpass, 0, output, mode, 0,0)
_nd_image.correlate1d(output, weightsLowpass, 1, output, mode, 0,0)
#print numpy.absolute(a - a_).mean()
#lowpass filter again to find background
#b = ndimage.gaussian_filter(a, self.filterRadiusHighpass)
#output, b = _ni_support._get_output(None, data)
#_nd_image.correlate1d(data, weightsHighpass, 0, output, mode, 0,0)
#_nd_image.correlate1d(output, weightsHighpass, 1, output, mode, 0,0)
return a #- b
def fastLaplaceNd(arr):
"""A very fast laplace filter for small arrays directly calling the scipy c++ function
Required Inputs
arr :: nd.array :: the array to calculate the n-dim laplace filter
"""
output = np.zeros(arr.shape, 'float64')
if arr.ndim > 0:
# send output as a pointer sio no need for equals sign
_nd_image.correlate1d(arr, laplace_filter, 0, output, 1, 0.0, 0)
if arr.ndim == 1: return output
for ax in xrange(1, arr.ndim):
return_value = np.zeros(arr.shape, dtype=output.dtype)
_nd_image.correlate1d(arr, laplace_filter, ax, output, 1, 0.0, 0)
output += return_value
return output.view(Periodic_Lattice)
def Afunc(self, f):
"""Forward transform - convolve with the PSF"""
fs = np.reshape(f, (self.height, self.width, self.depth))
#d = ndimage.gaussian_filter(fs, self.sigma)
mode = _ni_support._extend_mode_to_code("reflect")
#lowpass filter to suppress noise
#a = ndimage.gaussian_filter(data.astype('f4'), self.filterRadiusLowpass)
#print data.shape
output, a = _ni_support._get_output(None, fs)
_nd_image.correlate1d(fs, self.kernel, 0, output, mode, 0, 0)
_nd_image.correlate1d(output, self.kernel, 1, output, mode, 0, 0)
#ndimage.uniform_filter(output, self.oversamp, output=output)
#d = real(d);
return np.ravel(output) #[::oversamp,::oversamp,:])
def Afunc(self, f):
'''Forward transform - convolve with the PSF'''
fs = reshape(f, (self.height, self.width, self.depth))
#d = ndimage.gaussian_filter(fs, self.sigma)
mode = _ni_support._extend_mode_to_code("reflect")
#lowpass filter to suppress noise
#a = ndimage.gaussian_filter(data.astype('f4'), self.filterRadiusLowpass)
#print data.shape
output, a = _ni_support._get_output(None, fs)
_nd_image.correlate1d(fs, self.kernel, 0, output, mode, 0,0)
_nd_image.correlate1d(output, self.kernel, 1, output, mode, 0,0)
#ndimage.uniform_filter(output, self.oversamp, output=output)
#d = real(d);
return ravel(output)#[::oversamp,::oversamp,:])
def __FilterDataFast(self):
data = self.data[:,:,0]
mode = _ni_support._extend_mode_to_code("reflect")
# lowpass filter to suppress noise
output, a = _ni_support._get_output(None, data)
_nd_image.correlate1d(data, ofdP.weightsLowpass, 0, output, mode, 0, 0)
_nd_image.correlate1d(output, ofdP.weightsLowpass, 1, output, mode, 0, 0)
# lowpass filter again to find background
output, b = _ni_support._get_output(None, data)
_nd_image.correlate1d(data, ofdP.weightsHighpass, 0, output, mode, 0, 0)
_nd_image.correlate1d(output, ofdP.weightsHighpass, 1, output, mode, 0, 0)
return a - b
def __FilterDataFast(self):
data = self.data[:, :, 0]
mode = _ni_support._extend_mode_to_code("reflect")
# lowpass filter to suppress noise
output, a = _ni_support._get_output(None, data)
_nd_image.correlate1d(data, ofdP.weightsLowpass, 0, output, mode, 0, 0)
_nd_image.correlate1d(output, ofdP.weightsLowpass, 1, output, mode, 0,
0)
# lowpass filter again to find background
output, b = _ni_support._get_output(None, data)
_nd_image.correlate1d(data, ofdP.weightsHighpass, 0, output, mode, 0,
0)
_nd_image.correlate1d(output, ofdP.weightsHighpass, 1, output, mode, 0,
0)
return a - b
def __FilterData2D(self, data):
mode = _ni_support._extend_mode_to_code("reflect")
#lowpass filter to suppress noise
#a = ndimage.gaussian_filter(data.astype('f4'), self.filterRadiusLowpass)
#print data.shape
#output, a = _ni_support._get_output(None, data)
a = np.zeros(data.shape, dtype=data.dtype.name)
_nd_image.correlate1d(data, weightsLowpass, 0, a, mode, 0, 0)
_nd_image.correlate1d(a, weightsLowpass, 1, a, mode, 0, 0)
#print np.absolute(a - a_).mean()
#lowpass filter again to find background
#b = ndimage.gaussian_filter(a, self.filterRadiusHighpass)
b = np.zeros(data.shape, dtype=data.dtype.name)
_nd_image.correlate1d(data, weightsHighpass, 0, b, mode, 0, 0)
_nd_image.correlate1d(b, weightsHighpass, 1, b, mode, 0, 0)
return a - b
