Misc Utilities

Getting Started

radial_distance

The radial_distance() function returns an array where each value is the Euclidean distance from a given position. In this simple example we set the origin position at (40, 30) ((y, x)) and get an array of shape (100, 100) ((ny, nx)):

>>> from astroimtools import radial_distance
>>> data = radial_distance((40, 30), (100, 100))

Let’s plot the result:

>>> import matplotlib.pylab as plt
>>> plt.imshow(data, cmap='Blues_r', origin='lower',
...            interpolation='nearest')

(Source code, png, hires.png, pdf)

_images/utils-1.png

Here’s a cut along y=40 of the data array:

(Source code, png, hires.png, pdf)

_images/utils-2.png

listpixels

The listpixels() function returns an Astropy Table listing the (y, x) positions and data values for a subarray (or the entire array):

>>> import numpy as np
>>> from astroimtools import listpixels
>>> np.random.seed(12345)
>>> data = np.random.random((25, 25))
>>> tbl = listpixels(data, (8, 11), (3, 3))
>>> for col in tbl.colnames:
...     tbl[col].info.format = '%.8g'  # for consistent table output
>>> tbl.pprint(max_lines=-1)
 x   y     value
--- --- -----------
 10   7  0.75857204
 11   7 0.069529666
 12   7  0.70547344
 10   8   0.8406625
 11   8  0.46931469
 12   8  0.56264343
 10   9 0.034131584
 11   9  0.23049655
 12   9  0.22835371

listpixels also supports NDData objects as input.

mask_databounds

The mask_databounds() function creates or updates a mask by masking data values that are below a lower bound, above an upper bound, equal to particular value, or are invalid (e.g. np.nan or np.inf).

Here is a simple example of creating a mask array where data is less than 2, greater than 5, or equal to 3:

>>> import numpy as np
>>> from astroimtools import mask_databounds
>>> data = np.arange(7)
>>> data
array([0, 1, 2, 3, 4, 5, 6])
>>> mask_databounds(data, lower_bound=2, upper_bound=5, value=3)
array([ True,  True, False,  True, False, False,  True]...)

If mask is input, then it will be updated:

>>> mask = [False, False, True, False, False, False, False]
>>> mask_databounds(data, mask=mask, lower_bound=2, upper_bound=5, value=3)
array([ True,  True,  True,  True, False, False,  True]...)

Additionally, invalid data values (e.g., NaN and inf) are masked if mask_invalid is True (the default):

>>> data = np.arange(7.)
>>> data[2] = np.nan
>>> data
array([  0.,   1.,  nan,   3.,   4.,   5.,   6.])
>>> mask_databounds(data, upper_bound=5, mask_invalid=True)
array([False, False,  True, False, False, False,  True]...)

nddata_cutout2d

The nddata_cutout2d() function creates a 2D cutout of a 2D NDData object. Specifically, cutouts will made for the nddata.data and nddata.mask (if present) arrays. If nddata.wcs exists, then it will also be updated. Note that cutouts will not be made for nddata.uncertainty (if present) because they are general (unstandardized) objects and not arrays.

Let’s start by creating a simple NDData object with units, a mask, and a meta dict:

>>> import numpy as np
>>> from astropy.nddata import NDData
>>> import astropy.units as u
>>> from astroimtools import nddata_cutout2d
>>> data = np.random.random((500, 500))
>>> unit = u.electron / u.s
>>> mask = (data > 0.7)
>>> meta = {'exptime': 1234 * u.s}
>>> nddata = NDData(data, mask=mask, unit=unit, meta=meta)

Now let’s create a 2D cutout centered at (y, x) of (100, 100) and with a shape of (10, 10) ((ny, nx)):

>>> cutout = nddata_cutout2d(nddata, (100, 100), (10, 10))
>>> cutout.data.shape
(10, 10)
>>> cutout.mask.shape
(10, 10)
>>> cutout.unit
Unit("electron / s")

Reference/API

Misc utility functions.

Functions

radial_distance(position, shape)

Return an array where each value is the Euclidean distance from a given position.

listpixels(data, position, shape[, ...])

Return a Table listing the (y, x) positions and data values for a subarray.

mask_databounds(data[, mask, lower_bound, ...])

Create or update a mask by masking data values that are below a lower bound, above an upper bound, equal to particular value, or are invalid (e.g.

nddata_cutout2d(nddata, position, size[, ...])

Create a 2D cutout of a NDData object.