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Merge pull request #149 from crawfordsm/cr_names
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Removed variance names from CR tasks
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@mwcraig
mwcraig committed Jul 23, 2014
2 parents df3e2d2 + 2c7f03c commit b7750f9
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Showing 2 changed files with 76 additions and 78 deletions.
106 changes: 52 additions & 54 deletions ccdproc/core.py
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Original file line number Diff line number Diff line change
Expand Up @@ -19,7 +19,7 @@
from .utils.slices import slice_from_string
from .log_meta import log_to_metadata

__all__ = ['background_variance_box', 'background_variance_filter',
__all__ = ['background_deviation_box', 'background_deviation_filter',
'cosmicray_median', 'cosmicray_lacosmic',
'create_variance', 'flat_correct', 'gain_correct', 'rebin',
'sigma_func', 'subtract_bias', 'subtract_dark', 'subtract_overscan',
Expand All @@ -28,8 +28,8 @@
# The dictionary below is used to translate actual function names to names
# that are FITS compliant, i.e. 8 characters or less.
_short_names = {
'background_variance_box': 'bakvarbx',
'background_variance_filter': 'bakvfilt',
'background_deviation_box': 'bakdevbx',
'background_deviation_filter': 'bakdfilt',
'cosmicray_median': 'crmedian',
'create_variance': 'creatvar',
'flat_correct': 'flatcor',
Expand Down Expand Up @@ -116,7 +116,7 @@ def subtract_overscan(ccd, overscan=None, overscan_axis=1, fits_section=None,
Parameters
----------
ccd : `~ccdproc.ccddata.CCDData`
Data to have variance frame corrected
Data to have overscan frame corrected
overscan : `~ccdproc.ccddata.CCDData`
Slice from `ccd` that contains the overscan. Must provide either
Expand Down Expand Up @@ -412,7 +412,7 @@ def gain_correct(ccd, gain, gain_unit=None):
Parameters
----------
ccd : `~ccdproc.ccddata.CCDData`
Data to have variance frame corrected
Data to have gain corrected
gain : `~astropy.units.Quantity` or `~ccdproc.ccdproc.Keyword`
gain value for the image expressed in electrons per adu
Expand Down Expand Up @@ -562,7 +562,7 @@ def transform_image(ccd, transform_func, **kwargs):

def sigma_func(arr):
"""
Robust method for calculating the variance of an array. ``sigma_func`` uses
Robust method for calculating the deviation of an array. ``sigma_func`` uses
the median absolute deviation to determine the variance.
Parameters
Expand Down Expand Up @@ -623,20 +623,20 @@ def setbox(x, y, mbox, xmax, ymax):
return x1, x2, y1, y2


def background_variance_box(data, bbox):
def background_deviation_box(data, bbox):
"""
Determine the background variance with a box size of bbox. The algorithm
steps through the image and calculates the variance within each box.
It returns an array with the pixels in each box filled with the variance
Determine the background deviation with a box size of bbox. The algorithm
steps through the image and calculates the deviation within each box.
It returns an array with the pixels in each box filled with the deviation
value.
Parameters
----------
data : `~numpy.ndarray` or `~numpy.ma.MaskedArray`
Data to measure background variance
Data to measure background deviation
bbox : int
Box size for calculating background variance
Box size for calculating background deviation
Raises
------
Expand All @@ -646,7 +646,7 @@ def background_variance_box(data, bbox):
Returns
-------
background : `~numpy.ndarray` or `~numpy.ma.MaskedArray`
An array with the measured background variance in each pixel
An array with the measured background deviation in each pixel
"""
# Check to make sure the background box is an appropriate size
Expand All @@ -665,18 +665,18 @@ def background_variance_box(data, bbox):
return barr


def background_variance_filter(data, bbox):
def background_deviation_filter(data, bbox):
"""
Determine the background variance for each pixel from a box with size of
Determine the background deviation for each pixel from a box with size of
bbox.
Parameters
----------
data : `~numpy.ndarray`
Data to measure background variance
Data to measure background deviation
bbox : int
Box size for calculating background variance
Box size for calculating background deviation
Raises
------
Expand All @@ -686,7 +686,7 @@ def background_variance_filter(data, bbox):
Returns
-------
background : `~numpy.ndarray` or `~numpy.ma.MaskedArray`
An array with the measured background variance in each pixel
An array with the measured background deviation in each pixel
"""
# Check to make sure the background box is an appropriate size
Expand Down Expand Up @@ -831,7 +831,7 @@ def _blkavg(data, newshape):
return eval(''.join(evList))


def cosmicray_lacosmic(ccd, variance_image=None, thresh=5, fthresh=5,
def cosmicray_lacosmic(ccd, error_image=None, thresh=5, fthresh=5,
gthresh=1.5, b_factor=2, mbox=5, min_limit=0.01,
gbox=0, rbox=0,
f_conv=np.array([[0, -1, 0], [-1, 4, -1], [0, -1, 0]])):
Expand All @@ -847,8 +847,8 @@ def cosmicray_lacosmic(ccd, variance_image=None, thresh=5, fthresh=5,
ccd: `~ccdproc.CCDData` or `numpy.ndarray`
Data to have cosmic ray cleaned
variance_image : `numpy.ndarray`
Background variance level. It should have the same shape as data
error_image : `numpy.ndarray`
Error level in the image. It should have the same shape as data
as data. This is the same as the noise array in lacosmic.cl
thresh : float
Expand Down Expand Up @@ -913,11 +913,11 @@ def cosmicray_lacosmic(ccd, variance_image=None, thresh=5, fthresh=5,
1) Given an numpy.ndarray object, the syntax for running
cosmicrar_lacosmic would be:
>>> newdata, mask = cosmicray_clean(data, variance_image=variance_image,
>>> newdata, mask = cosmicray_clean(data, error_image=error_image,
thresh=5, mbox=11, rbox=11, gbox=5)
where the variance is an array that is the same shape as data but
includes the pixel variance. This would return a data array, newdata,
where the error is an array that is the same shape as data but
includes the pixel error. This would return a data array, newdata,
with the bad pixels replaced by the local median from a box of 11
pixels; and it would return a mask indicating the bad pixels.
Expand All @@ -935,10 +935,10 @@ def cosmicray_lacosmic(ccd, variance_image=None, thresh=5, fthresh=5,
if isinstance(ccd, np.ndarray):
data = ccd

if not isinstance(variance_image, np.ndarray):
raise TypeError('variance_image is not a ndarray object')
if data.shape != variance_image.shape:
raise ValueError('variance_image is not the same shape as data')
if not isinstance(error_image, np.ndarray):
raise TypeError('error_image is not a ndarray object')
if data.shape != error_image.shape:
raise ValueError('error_image is not the same shape as data')

#set up a copy of the array and original shape
shape = data.shape
Expand All @@ -955,7 +955,7 @@ def cosmicray_lacosmic(ccd, variance_image=None, thresh=5, fthresh=5,
ldata = _blkavg(ldata, shape)

#median the noise image
med_noise = ndimage.median_filter(variance_image, size=(mbox, mbox))
med_noise = ndimage.median_filter(error_image, size=(mbox, mbox))

#create S/N image
sndata = ldata / med_noise / b_factor
Expand All @@ -974,9 +974,7 @@ def cosmicray_lacosmic(ccd, variance_image=None, thresh=5, fthresh=5,

# set a minimum value for all pixels so no divide by zero problems
fdata[fdata < min_limit] = min_limit

fdata = sndata * masks / fdata
maskf = (fdata > fthresh)

#make the list of cosmic rays
crarr = masks * (fdata > fthresh)
Expand All @@ -1000,14 +998,14 @@ def cosmicray_lacosmic(ccd, variance_image=None, thresh=5, fthresh=5,

elif isinstance(ccd, CCDData):

#set up the variance image
if variance_image is None and ccd.uncertainty is not None:
variance_image = ccd.uncertainty.array
if ccd.data.shape != variance_image.shape:
raise ValueError('variance_image is not the same shape as data')
#set up the error image
if error_image is None and ccd.uncertainty is not None:
error_image = ccd.uncertainty.array
if ccd.data.shape != error_image.shape:
raise ValueError('error_image is not the same shape as data')

data, crarr = cosmicray_lacosmic(ccd.data,
variance_image=variance_image,
error_image=error_image,
thresh=thresh, fthresh=fthresh,
gthresh=gthresh, b_factor=b_factor,
mbox=mbox, min_limit=min_limit,
Expand All @@ -1026,7 +1024,7 @@ def cosmicray_lacosmic(ccd, variance_image=None, thresh=5, fthresh=5,
raise TypeError('ccddata is not a CCDData or ndarray object')


def cosmicray_median(ccd, variance_image=None, thresh=5, mbox=11, gbox=0,
def cosmicray_median(ccd, error_image=None, thresh=5, mbox=11, gbox=0,
rbox=0):
"""
Identify cosmic rays through median technique. The median technique
Expand All @@ -1042,8 +1040,8 @@ def cosmicray_median(ccd, variance_image=None, thresh=5, mbox=11, gbox=0,
thresh : float
Threshhold for detecting cosmic rays
variance_image : None, float, or `~numpy.ndarray`
Background variance level. If None, the task will use the standard
error_image : None, float, or `~numpy.ndarray`
Error level. If None, the task will use the standard
deviation of the data. If an ndarray, it should have the same shape
as data.
Expand Down Expand Up @@ -1080,11 +1078,11 @@ def cosmicray_median(ccd, variance_image=None, thresh=5, mbox=11, gbox=0,
1) Given an numpy.ndarray object, the syntax for running
cosmicray_lacosmic would be:
>>> newdata, mask = cosmicray_lacosmic(data, variance_image=variance,
>>> newdata, mask = cosmicray_lacosmic(data, error_image=error,
thresh=5, mbox=11, rbox=11, gbox=5)
where variance is an array that is the same shape as data but
includes the pixel variance. This would return a data array, newdata,
where error is an array that is the same shape as data but
includes the pixel error. This would return a data array, newdata,
with the bad pixels replaced by the local median from a box of 11
pixels; and it would return a mask indicating the bad pixels.
Expand All @@ -1102,11 +1100,11 @@ def cosmicray_median(ccd, variance_image=None, thresh=5, mbox=11, gbox=0,
if isinstance(ccd, np.ndarray):
data = ccd

if variance_image is None:
variance_image = data.std()
if error_image is None:
error_image = data.std()
else:
if not isinstance(variance_image, (float, np.ndarray)):
raise TypeError('variance_image is not a float or ndarray')
if not isinstance(error_image, (float, np.ndarray)):
raise TypeError('error_image is not a float or ndarray')

# create the median image
marr = ndimage.median_filter(data, size=(mbox, mbox))
Expand All @@ -1116,7 +1114,7 @@ def cosmicray_median(ccd, variance_image=None, thresh=5, mbox=11, gbox=0,
data = data.data

# Find the residual image
rarr = (data - marr) / variance_image
rarr = (data - marr) / error_image

# identify all sources
crarr = (rarr > thresh)
Expand All @@ -1135,13 +1133,13 @@ def cosmicray_median(ccd, variance_image=None, thresh=5, mbox=11, gbox=0,
return ndata, crarr
elif isinstance(ccd, CCDData):

#set up the variance image
if variance_image is None and ccd.uncertainty is not None:
variance_image = ccd.uncertainty.array
if ccd.data.shape != variance_image.shape:
raise ValueError('variance_image is not the same shape as data')
#set up the error image
if error_image is None and ccd.uncertainty is not None:
error_image = ccd.uncertainty.array
if ccd.data.shape != error_image.shape:
raise ValueError('error_image is not the same shape as data')

data, crarr = cosmicray_median(ccd.data, variance_image=variance_image,
data, crarr = cosmicray_median(ccd.data, error_image=error_image,
thresh=thresh, mbox=mbox, gbox=gbox,
rbox=rbox)

Expand Down

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