/usr/lib/python2.7/dist-packages/photutils/aperture_funcs.py is in python-photutils 0.2.1-2.
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"""Functions for performing aperture photometry on 2-D arrays."""
from __future__ import (absolute_import, division, print_function,
unicode_literals)
import numpy as np
import warnings
import astropy.units as u
from astropy.utils.exceptions import AstropyUserWarning
__all__ = []
def get_phot_extents(data, positions, extents):
"""
Get the photometry extents and check if the apertures is fully out of data.
Parameters
----------
data : array_like
The 2-d array on which to perform photometry.
Returns
-------
extents : dict
The ``extents`` dictionary contains 3 elements:
* ``'ood_filter'``
A boolean array with `True` elements where the aperture is
falling out of the data region.
* ``'pixel_extent'``
x_min, x_max, y_min, y_max : Refined extent of apertures with
data coverage.
* ``'phot_extent'``
x_pmin, x_pmax, y_pmin, y_pmax: Extent centered to the 0, 0
positions as required by the `~photutils.geometry` functions.
"""
# Check if an aperture is fully out of data
ood_filter = np.logical_or(extents[:, 0] >= data.shape[1],
extents[:, 1] <= 0)
np.logical_or(ood_filter, extents[:, 2] >= data.shape[0],
out=ood_filter)
np.logical_or(ood_filter, extents[:, 3] <= 0, out=ood_filter)
# TODO check whether it makes sense to have negative pixel
# coordinate, one could imagine a stackes image where the reference
# was a bit offset from some of the images? Or in those cases just
# give Skycoord to the Aperture and it should deal with the
# conversion for the actual case?
x_min = np.maximum(extents[:, 0], 0)
x_max = np.minimum(extents[:, 1], data.shape[1])
y_min = np.maximum(extents[:, 2], 0)
y_max = np.minimum(extents[:, 3], data.shape[0])
x_pmin = x_min - positions[:, 0] - 0.5
x_pmax = x_max - positions[:, 0] - 0.5
y_pmin = y_min - positions[:, 1] - 0.5
y_pmax = y_max - positions[:, 1] - 0.5
# TODO: check whether any pixel is nan in data[y_min[i]:y_max[i],
# x_min[i]:x_max[i])), if yes return something valid rather than nan
pixel_extent = [x_min, x_max, y_min, y_max]
phot_extent = [x_pmin, x_pmax, y_pmin, y_pmax]
return ood_filter, pixel_extent, phot_extent
def find_fluxvar(data, fraction, error, flux, effective_gain, imin, imax,
jmin, jmax, pixelwise_error):
if isinstance(error, u.Quantity):
zero_variance = 0 * error.unit**2
else:
zero_variance = 0
if pixelwise_error:
subvariance = error[jmin:jmax,
imin:imax] ** 2
if effective_gain is not None:
subvariance += (data[jmin:jmax, imin:imax] /
effective_gain[jmin:jmax, imin:imax])
# Make sure variance is > 0
fluxvar = np.maximum(np.sum(subvariance * fraction), zero_variance)
else:
local_error = error[int((jmin + jmax) / 2 + 0.5),
int((imin + imax) / 2 + 0.5)]
fluxvar = np.maximum(local_error ** 2 * np.sum(fraction),
zero_variance)
if effective_gain is not None:
local_effective_gain = effective_gain[
int((jmin + jmax) / 2 + 0.5), int((imin + imax) / 2 + 0.5)]
fluxvar += flux / local_effective_gain
return fluxvar
def do_circular_photometry(data, positions, radius, error, effective_gain,
pixelwise_error, method, subpixels, r_in=None):
extents = np.zeros((len(positions), 4), dtype=int)
extents[:, 0] = positions[:, 0] - radius + 0.5
extents[:, 1] = positions[:, 0] + radius + 1.5
extents[:, 2] = positions[:, 1] - radius + 0.5
extents[:, 3] = positions[:, 1] + radius + 1.5
ood_filter, extent, phot_extent = get_phot_extents(data, positions,
extents)
flux = u.Quantity(np.zeros(len(positions), dtype=np.float), unit=data.unit)
if error is not None:
fluxvar = u.Quantity(np.zeros(len(positions), dtype=np.float),
unit=error.unit ** 2)
# TODO: flag these objects
if np.sum(ood_filter):
flux[ood_filter] = np.nan
warnings.warn("The aperture at position {0} does not have any "
"overlap with the data"
.format(positions[ood_filter]),
AstropyUserWarning)
if np.sum(ood_filter) == len(positions):
return (flux, )
x_min, x_max, y_min, y_max = extent
x_pmin, x_pmax, y_pmin, y_pmax = phot_extent
if method == 'center':
use_exact = 0
subpixels = 1
elif method == 'subpixel':
use_exact = 0
else:
use_exact = 1
subpixels = 1
from .geometry import circular_overlap_grid
for i in range(len(flux)):
if not np.isnan(flux[i]):
fraction = circular_overlap_grid(x_pmin[i], x_pmax[i],
y_pmin[i], y_pmax[i],
x_max[i] - x_min[i],
y_max[i] - y_min[i],
radius, use_exact, subpixels)
if r_in is not None:
fraction -= circular_overlap_grid(x_pmin[i], x_pmax[i],
y_pmin[i], y_pmax[i],
x_max[i] - x_min[i],
y_max[i] - y_min[i],
r_in, use_exact, subpixels)
flux[i] = np.sum(data[y_min[i]:y_max[i],
x_min[i]:x_max[i]] * fraction)
if error is not None:
fluxvar[i] = find_fluxvar(data, fraction, error, flux[i],
effective_gain, x_min[i], x_max[i],
y_min[i], y_max[i], pixelwise_error)
if error is None:
return (flux, )
else:
return (flux, np.sqrt(fluxvar))
def do_elliptical_photometry(data, positions, a, b, theta, error,
effective_gain, pixelwise_error, method,
subpixels, a_in=None):
extents = np.zeros((len(positions), 4), dtype=int)
# TODO: we can be more efficient in terms of bounding box
radius = max(a, b)
extents[:, 0] = positions[:, 0] - radius + 0.5
extents[:, 1] = positions[:, 0] + radius + 1.5
extents[:, 2] = positions[:, 1] - radius + 0.5
extents[:, 3] = positions[:, 1] + radius + 1.5
ood_filter, extent, phot_extent = get_phot_extents(data, positions,
extents)
flux = u.Quantity(np.zeros(len(positions), dtype=np.float), unit=data.unit)
if error is not None:
fluxvar = u.Quantity(np.zeros(len(positions), dtype=np.float),
unit=error.unit ** 2)
# TODO: flag these objects
if np.sum(ood_filter):
flux[ood_filter] = np.nan
warnings.warn("The aperture at position {0} does not have any "
"overlap with the data"
.format(positions[ood_filter]),
AstropyUserWarning)
if np.sum(ood_filter) == len(positions):
return (flux, )
x_min, x_max, y_min, y_max = extent
x_pmin, x_pmax, y_pmin, y_pmax = phot_extent
if method == 'center':
use_exact = 0
subpixels = 1
elif method == 'subpixel':
use_exact = 0
else:
use_exact = 1
subpixels = 1
from .geometry import elliptical_overlap_grid
for i in range(len(flux)):
if not np.isnan(flux[i]):
fraction = elliptical_overlap_grid(x_pmin[i], x_pmax[i],
y_pmin[i], y_pmax[i],
x_max[i] - x_min[i],
y_max[i] - y_min[i],
a, b, theta, use_exact,
subpixels)
if a_in is not None:
b_in = a_in * b / a
fraction -= elliptical_overlap_grid(x_pmin[i], x_pmax[i],
y_pmin[i], y_pmax[i],
x_max[i] - x_min[i],
y_max[i] - y_min[i],
a_in, b_in, theta,
use_exact, subpixels)
flux[i] = np.sum(data[y_min[i]:y_max[i],
x_min[i]:x_max[i]] * fraction)
if error is not None:
fluxvar[i] = find_fluxvar(data, fraction, error, flux[i],
effective_gain, x_min[i], x_max[i],
y_min[i], y_max[i], pixelwise_error)
if error is None:
return (flux, )
else:
return (flux, np.sqrt(fluxvar))
def do_rectangular_photometry(data, positions, w, h, theta, error,
effective_gain, pixelwise_error, method,
subpixels, reduce='sum', w_in=None):
extents = np.zeros((len(positions), 4), dtype=int)
# TODO: this is an overestimate by up to sqrt(2) unless theta = 45 deg
radius = max(h, w) * (2 ** -0.5)
extents[:, 0] = positions[:, 0] - radius + 0.5
extents[:, 1] = positions[:, 0] + radius + 1.5
extents[:, 2] = positions[:, 1] - radius + 0.5
extents[:, 3] = positions[:, 1] + radius + 1.5
ood_filter, extent, phot_extent = get_phot_extents(data, positions,
extents)
flux = u.Quantity(np.zeros(len(positions), dtype=np.float), unit=data.unit)
if error is not None:
fluxvar = u.Quantity(np.zeros(len(positions), dtype=np.float),
unit=error.unit ** 2)
# TODO: flag these objects
if np.sum(ood_filter):
flux[ood_filter] = np.nan
warnings.warn("The aperture at position {0} does not have any "
"overlap with the data"
.format(positions[ood_filter]),
AstropyUserWarning)
if np.sum(ood_filter) == len(positions):
return (flux, )
x_min, x_max, y_min, y_max = extent
x_pmin, x_pmax, y_pmin, y_pmax = phot_extent
if method in ('center', 'subpixel'):
if method == 'center':
method = 'subpixel'
subpixels = 1
from .geometry import rectangular_overlap_grid
for i in range(len(flux)):
if not np.isnan(flux[i]):
fraction = rectangular_overlap_grid(x_pmin[i], x_pmax[i],
y_pmin[i], y_pmax[i],
x_max[i] - x_min[i],
y_max[i] - y_min[i],
w, h, theta, 0, subpixels)
if w_in is not None:
h_in = w_in * h / w
fraction -= rectangular_overlap_grid(x_pmin[i], x_pmax[i],
y_pmin[i], y_pmax[i],
x_max[i] - x_min[i],
y_max[i] - y_min[i],
w_in, h_in, theta,
0, subpixels)
flux[i] = np.sum(data[y_min[i]:y_max[i],
x_min[i]:x_max[i]] * fraction)
if error is not None:
fluxvar[i] = find_fluxvar(data, fraction, error,
flux[i], effective_gain,
x_min[i], x_max[i],
y_min[i], y_max[i],
pixelwise_error)
if error is None:
return (flux, )
else:
return (flux, np.sqrt(fluxvar))
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