load_arrays
- sherpa.astro.ui.load_arrays(id, *args)
Create a data set from array values.
- Parameters:
Warning
Sherpa currently does not support numpy masked arrays. Use the set_filter function and note that it follows a different convention by default (a positive value or True for a “bad” channel, 0 or False for a good channel).
See also
copy_data
Copy a data set to a new identifier.
delete_data
Delete a data set by identifier.
get_data
Return the data set by identifier.
load_data
Create a data set from a file.
set_data
Set a data set.
unpack_arrays
Create a sherpa data object from arrays of data.
Notes
The data type identifier, which defaults to
Data1D
, determines the number, and order, of the required inputs.Identifier
Required Fields
Optional Fields
Data1D
x, y
statistical error, systematic error
Data1DInt
xlo, xhi, y
statistical error, systematic error
Data2D
x0, x1, y
shape, statistical error, systematic error
Data2DInt
x0lo, x1lo, x0hi, x1hi, y
shape, statistical error, systematic error
DataPHA
channel, counts
statistical error, systematic error, bin_lo, bin_hi, grouping, quality
DataIMG
x0, x1, y
shape, statistical error, systematic error
The
shape
argument should be a tuple giving the size of the data(ny,nx)
, and for theDataIMG
case the arrays are 1D, not 2D.Examples
Create a 1D data set with three points:
>>> load_arrays(1, [10, 12, 15], [4.2, 12.1, 8.4])
Create a 1D data set, with the identifier ‘prof’, from the arrays
x
(independent axis),y
(dependent axis), anddy
(statistical error on the dependent axis):>>> load_arrays('prof', x, y, dy)
Explicitly define the type of the data set:
>>> load_arrays('prof', x, y, dy, Data1D)
Data set 1 is a histogram, where the bins cover the range 1-3, 3-5, and 5-7 with values 4, 5, and 9 respectively.
>>> load_arrays(1, [1, 3, 5], [3, 5, 7], [4, 5, 9], Data1DInt)
Create an image data set:
>>> ivals = np.arange(12) >>> y, x = np.mgrid[0:3, 0:4] >>> x = x.flatten() >>> y = y.flatten() >>> load_arrays('img', x, y, ivals, (3, 4), DataIMG)