def f(self, array, warp):
"""
A sampling function, responsible for returning a sampled set of values
from the given array.
Parameters
----------
array: nd-array
Input array for sampling.
warp: nd-array
Deformation coordinates.
Returns
-------
sample: nd-array
Sampled array data.
"""
if self.coordinates is None:
raise ValueError('Appropriately defined coordinates not provided.')
result = np.zeros_like(array)
arg0 = c_ndarray(warp, dtype=np.double, ndim=3)
arg1 = c_ndarray(array, dtype=np.double, ndim=2)
arg2 = c_ndarray(result, dtype=np.double, ndim=2)
libsampler.cubicConvolution(arg0, arg1, arg2)
return result.flatten()
def f(self, array, warp):
"""
A sampling function, responsible for returning a sampled set of values
from the given array.
Parameters
----------
array: nd-array
Input array for sampling.
warp: nd-array
Deformation coordinates.
Returns
-------
sample: nd-array
Sampled array data.
"""
if self.coordinates is None:
raise ValueError("Appropriately defined coordinates not provided.")
result = np.zeros_like(warp[0])
arg0 = c_ndarray(warp, dtype=np.double, ndim=3)
arg1 = c_ndarray(array, dtype=np.double, ndim=2)
arg2 = c_ndarray(result, dtype=np.double, ndim=2)
libsampler.nearest(
arg0, arg1, arg2, ctypes.c_char(EXTRAPOLATION_MODE[0]), ctypes.c_double(EXTRAPOLATION_CVALUE)
)
return result.flatten()
def f(self, array, warp):
"""
A sampling function, responsible for returning a sampled set of values
from the given array.
Parameters
----------
array: nd-array
Input array for sampling.
warp: nd-array
Deformation coordinates.
Returns
-------
sample: nd-array
Sampled array data.
"""
if self.coordinates is None:
raise ValueError("Appropriately defined coordinates not provided.")
result = np.zeros_like(array)
arg0 = c_ndarray(warp, dtype=np.double, ndim=3)
arg1 = c_ndarray(array, dtype=np.double, ndim=2)
arg2 = c_ndarray(result, dtype=np.double, ndim=2)
libsampler.cubicConvolution(arg0, arg1, arg2)
return result.flatten()
def run(vis, model = 'gaussian', flux_ext=1e-3, dflux_ext=1e-4,
flux_ps=0e-3, dflux_ps=1e-4,
sigma=1*arcsec, dsigma=1*arcsec, x=0., y=0., nscan=10):
libpath = os.path.join(os.path.abspath(__path__[0]), 'build', 'libchi2_scan.so')
libchi2 = cdll.LoadLibrary(libpath)
if model =='gaussian':
shape = [nscan, nscan]
else:
shape = [nscan]*3
chi2 = np.zeros(shape)
xs = np.zeros(shape)
ys = np.zeros(shape)
if model == 'gaussian':
fluxes_ext = np.linspace(flux_ext-dflux_ext, flux_ext+dflux_ext, nscan)
sigmas = np.linspace(sigma-dsigma, sigma+dsigma, nscan)
fluxes_ext,sigmas = np.meshgrid(fluxes_ext, sigmas)
parameters = np.zeros(shape+[4])
parameters[:,:,0] = fluxes_ext
parameters[:,:,1] = xs
parameters[:,:,2] = ys
parameters[:,:,3] = sigmas
elif model == 'gaussian_ps':
fluxes_ext = np.linspace(flux_ext-dflux_ext, flux_ext+dflux_ext, nscan)
fluxes_ps = np.linspace(flux_ps-dflux_ps, flux_ps+dflux_ps, nscan)
sigmas = np.linspace(sigma-dsigma, sigma+dsigma, nscan)
fluxes_ext, fluxes_ps, sigmas = meshgrid(fluxes_ext, fluxes_ps, sigmas, indexing='xy')
parameters = np.zeros(shape+[5])
parameters[:,:,:,0] = fluxes_ext
parameters[:,:,:,1] = xs
parameters[:,:,:,2] = ys
parameters[:,:,:,3] = sigmas
parameters[:,:,:,4] = fluxes_ps
# fluxes,sigmas = np.meshgrid(fluxes, sigmas)
chi2_scan = libchi2.c_chi2_scan
c_chi2 = c_ndarray(chi2, dtype=chi2.dtype, ndim=chi2.ndim)
c_parameters = c_ndarray(parameters, dtype=parameters.dtype, ndim=parameters.ndim)
chi2_scan(c_chi2, c_int(chi2.ndim), c_char_p(vis), c_model[model], c_parameters)
return parameters, chi2
def f(self, array, warp):
"""
A sampling function, responsible for returning a sampled set of values
from the given array.
@param array: an n-dimensional array (representing an image or volume).
@param coords: array coordinates in cartesian form (n by p).
"""
if self.coordinates is None:
raise ValueError('Appropriately defined coordinates not provided.')
result = np.zeros_like(array)
arg0 = c_ndarray(warp, dtype=np.double, ndim=3)
arg1 = c_ndarray(array, dtype=np.double, ndim=2)
arg2 = c_ndarray(result, dtype=np.double, ndim=2)
libsampler.nearest(arg0, arg1, arg2)
return result.flatten()
def f(self, array, warp):
"""
A sampling function, responsible for returning a sampled set of values
from the given array.
Parameters
----------
array: nd-array
Input array for sampling.
warp: nd-array
Deformation coordinates.
Returns
-------
sample: nd-array
Sampled array data.
"""
if self.coordinates is None:
raise ValueError('Appropriately defined coordinates not provided.')
result = np.zeros_like(warp[0])
arg0 = c_ndarray(warp, dtype=np.double, ndim=3)
arg1 = c_ndarray(array, dtype=np.double, ndim=2)
arg2 = c_ndarray(result, dtype=np.double, ndim=2)
libsampler.nearest(
arg0,
arg1,
arg2,
ctypes.c_char(EXTRAPOLATION_MODE[0]),
ctypes.c_double(EXTRAPOLATION_CVALUE)
)
return result.flatten()
import numpy as np from numpyctypes import c_ndarray c_myarray = c_ndarray(myarray, dtype=np.double, ndim=3)
def DbbWrapper(array1, array2, aShape):
arg1 = c_ndarray(array1, dtype=N.uint8, ndim=2, shape=aShape)
arg2 = c_ndarray(array2, dtype=N.uint8, ndim=2, shape=aShape)
return mylib.DbbWrapper(arg1, arg2)