def grid(self):
x, y = reshape_2d_arrays(self.x_axis.lo, self.y_axis.lo)
if self.x_axis.is_integrated:
xhi, yhi = reshape_2d_arrays(self.x_axis.hi, self.y_axis.hi)
return x, y, xhi, yhi
else:
return x, y
def grid(self):
x, y = reshape_2d_arrays(self.x_axis.lo, self.y_axis.lo)
if self.x_axis.is_integrated:
xhi, yhi = reshape_2d_arrays(self.x_axis.hi, self.y_axis.hi)
return x, y, xhi, yhi
else:
return x, y
def grid(self):
"""The grid representation of the space.
The x and y arrays in the grid are one-dimensional
representations of the meshgrid obtained from the x and y axis
arrays, as in `numpy.meshgrid(x, y)[0].ravel()`
Returns
-------
tuple
A two element (x, y) or 4 element (x, y, xhi, yhi) tuple.
"""
x, y = reshape_2d_arrays(self.x_axis.lo, self.y_axis.lo)
if self.x_axis.is_integrated:
xhi, yhi = reshape_2d_arrays(self.x_axis.hi, self.y_axis.hi)
return x, y, xhi, yhi
else:
return x, y
def grid(self):
"""
Return the grid representation of this dataset. The grid is always a tuple, even if the
dataset is 1-D and not integrated. This is due to the existing architecture of Sherpa's
model classes and the fact that there is no signature difference among 1-D and 2-D models,
as 1-D models can receive 1 or 2 arrays and 2-D models can receive 2 or 4 arrays.
The x and y arrays in the grid are one-dimentional representations of the meshgrid obtained
from the x and y axis arrays, as in `numpy.meshgrid(x, y)[0].ravel()`
Returns
-------
tuple
A tuple representing the x and y axes. The tuple will contain four arrays if the dataset is
integrated, two otherwise.
"""
x, y = reshape_2d_arrays(self.x_axis.lo, self.y_axis.lo)
if self.x_axis.is_integrated:
xhi, yhi = reshape_2d_arrays(self.x_axis.hi, self.y_axis.hi)
return x, y, xhi, yhi
else:
return x, y
def read_image(arg, coord='logical', dstype=DataIMG):
"""Create an image dataset from a file.
Parameters
----------
arg
The name of the file or a representation of the file
(the type depends on the I/O backend).
coord : {'logical', 'physical', 'world'}, optional
The type of axis coordinates to use. An error is raised if the
file does not contain the necessary metadata for the selected
coordinate system.
dstype : optional
The data type to create (it is expected to follow the
`sherpa.data.BaseData` interface).
Returns
-------
data : a sherpa.data.BaseData derived object
See Also
--------
write_image
Examples
--------
The following examples do not contain argument types specific to
a particular I/O backend.
Create a `sherpa.astro.data.DataIMG` object from the FITS file
``img.fits``:
>>> d = read_image('img.fits')
Select the physical coordinate system from the file:
>>> d = read_image('img.fits', coord='physical')
"""
data, filename = backend.get_image_data(arg)
axlens = data['y'].shape
x0 = numpy.arange(axlens[1], dtype=SherpaFloat) + 1.
x1 = numpy.arange(axlens[0], dtype=SherpaFloat) + 1.
x0, x1 = reshape_2d_arrays(x0, x1)
data['y'] = data['y'].ravel()
data['coord'] = coord
data['shape'] = axlens
if issubclass(dstype, DataIMGInt):
dataset = dstype(filename, x0 - 0.5, x1 - 0.5, x0 + 0.5, x1 + 0.5,
**data)
elif issubclass(dstype, Data2DInt):
for name in ['coord', 'eqpos', 'sky', 'header']:
data.pop(name, None)
dataset = dstype(filename, x0 - 0.5, x1 - 0.5, x0 + 0.5, x1 + 0.5,
**data)
else:
dataset = dstype(filename, x0, x1, **data)
return dataset
def read_image(arg, coord='logical', dstype=DataIMG):
"""Create an image dataset from a file.
Parameters
----------
arg
The name of the file or a representation of the file
(the type depends on the I/O backend).
coord : {'logical', 'physical', 'world'}, optional
The type of axis coordinates to use. An error is raised if the
file does not contain the necessary metadata for the selected
coordinate system.
dstype : optional
The data type to create (it is expected to follow the
`sherpa.data.BaseData` interface).
Returns
-------
data : a sherpa.data.BaseData derived object
See Also
--------
write_image
Examples
--------
The following examples do not contain argument types specific to
a particular I/O backend.
Create a `sherpa.astro.data.DataIMG` object from the FITS file
``img.fits``:
>>> d = read_image('img.fits')
Select the physical coordinate system from the file:
>>> d = read_image('img.fits', coord='physical')
"""
data, filename = backend.get_image_data(arg)
axlens = data['y'].shape
x0 = numpy.arange(axlens[1], dtype=SherpaFloat) + 1.
x1 = numpy.arange(axlens[0], dtype=SherpaFloat) + 1.
x0, x1 = reshape_2d_arrays(x0, x1)
data['y'] = data['y'].ravel()
data['coord'] = coord
data['shape'] = axlens
if issubclass(dstype, DataIMGInt):
dataset = dstype(filename, x0 - 0.5, x1 - 0.5, x0 + 0.5, x1 + 0.5,
**data)
elif issubclass(dstype, Data2DInt):
for name in ['coord', 'eqpos', 'sky', 'header']:
data.pop(name, None)
dataset = dstype(filename, x0 - 0.5, x1 - 0.5, x0 + 0.5, x1 + 0.5,
**data)
else:
dataset = dstype(filename, x0, x1, **data)
return dataset