def test_median_absolute_deviation_masked():
# Based on the changes introduces in #4658
# normal masked arrays without masked values are handled like normal
# numpy arrays
array = np.ma.array([1, 2, 3])
assert funcs.median_absolute_deviation(array) == 1
# masked numpy arrays return something different (rank 0 masked array)
# but one can still compare it without np.all!
array = np.ma.array([1, 4, 3], mask=[0, 1, 0])
assert funcs.median_absolute_deviation(array) == 1
# Just cross check if that's identical to the function on the unmasked
# values only
assert funcs.median_absolute_deviation(array) == (
funcs.median_absolute_deviation(array[~array.mask]))
# Multidimensional masked array
array = np.ma.array([[1, 4], [2, 2]], mask=[[1, 0], [0, 0]])
funcs.median_absolute_deviation(array)
assert funcs.median_absolute_deviation(array) == 0
# Just to compare it with the data without mask:
assert funcs.median_absolute_deviation(array.data) == 0.5
# And check if they are also broadcasted correctly
np.testing.assert_array_equal(
funcs.median_absolute_deviation(array, axis=0).data, [0, 1])
np.testing.assert_array_equal(
funcs.median_absolute_deviation(array, axis=1).data, [0, 0])
def test_median_absolute_deviation_masked():
# Based on the changes introduces in #4658
# normal masked arrays without masked values are handled like normal
# numpy arrays
array = np.ma.array([1, 2, 3])
assert funcs.median_absolute_deviation(array) == 1
# masked numpy arrays return something different (rank 0 masked array)
# but one can still compare it without np.all!
array = np.ma.array([1, 4, 3], mask=[0, 1, 0])
assert funcs.median_absolute_deviation(array) == 1
# Just cross check if that's identical to the function on the unmasked
# values only
assert funcs.median_absolute_deviation(array) == (
funcs.median_absolute_deviation(array[~array.mask]))
# Multidimensional masked array
array = np.ma.array([[1, 4], [2, 2]], mask=[[1, 0], [0, 0]])
funcs.median_absolute_deviation(array)
assert funcs.median_absolute_deviation(array) == 0
# Just to compare it with the data without mask:
assert funcs.median_absolute_deviation(array.data) == 0.5
# And check if they are also broadcasted correctly
np.testing.assert_array_equal(
funcs.median_absolute_deviation(array, axis=0).data, [0, 1])
np.testing.assert_array_equal(
funcs.median_absolute_deviation(array, axis=1).data, [0, 0])
def test_median_absolute_deviation_nans():
array = np.array([[1, 4, 3, np.nan],
[2, 5, np.nan, 4]])
assert_equal(funcs.median_absolute_deviation(array, func=np.nanmedian,
axis=1), [1, 1])
array = np.ma.masked_invalid(array)
assert funcs.median_absolute_deviation(array) == 1
def test_median_absolute_deviation_nans():
array = np.array([[1, 4, 3, np.nan], [2, 5, np.nan, 4]])
assert_equal(
funcs.median_absolute_deviation(array, func=np.nanmedian, axis=1),
[1, 1])
array = np.ma.masked_invalid(array)
assert funcs.median_absolute_deviation(array) == 1
def test_median_absolute_deviation_quantity():
# Based on the changes introduces in #4658
# Just a small test that this function accepts Quantities and returns a
# quantity
a = np.array([1, 16, 5]) * u.m
mad = funcs.median_absolute_deviation(a)
# Check for the correct unit and that the result is identical to the
# result without units.
assert mad.unit == a.unit
assert mad.value == funcs.median_absolute_deviation(a.value)
def test_median_absolute_deviation_nans_masked():
"""
Regression test to ensure ignore_nan=True gives same results for
ndarray and masked arrays that contain +/-inf.
"""
data1 = np.array([1., np.nan, 2, np.inf])
data2 = np.ma.masked_array(data1, mask=False)
mad1 = funcs.median_absolute_deviation(data1, ignore_nan=True)
mad2 = funcs.median_absolute_deviation(data2, ignore_nan=True)
assert_equal(mad1, mad2)
def test_median_absolute_deviation_quantity():
# Based on the changes introduces in #4658
# Just a small test that this function accepts Quantities and returns a
# quantity
a = np.array([1, 16, 5]) * u.m
mad = funcs.median_absolute_deviation(a)
# Check for the correct unit and that the result is identical to the
# result without units.
assert mad.unit == a.unit
assert mad.value == funcs.median_absolute_deviation(a.value)
def aperture_mask(self, snr_threshold=5):
"""Returns an aperture photometry mask.
snr_threshold : float
Background detection threshold.
"""
# Find the pixels that are above the threshold in the median flux image
median = self.median_flux()
mad = median_absolute_deviation(median[np.isfinite(median)])
mad_cut = 1.4826 * mad * snr_threshold # 1.4826 turns MAD into STDEV for a Gaussian
region = np.where(median > mad_cut, 1, 0)
# Label all contiguous regions above the threshold
labels = scipy.ndimage.label(region)[0]
# Central pixel coordinate
centralpix = [1 + median.shape[0] // 2,
1 + median.shape[1] // 2] # "//" is the integer division
# find brightest pix within margin of central pix
margin = 4
central_img = median[centralpix[0] - margin: centralpix[0] + margin,
centralpix[1] - margin: centralpix[1] + margin]
# unravel_index converts indices into a tuple of coordinate arrays
brightestpix = np.unravel_index(central_img.argmax(), central_img.shape)
bpixy, bpixx = brightestpix
# Which label corresponds to the brightest pixel?
regnum = labels[centralpix[0] - 4 + bpixy, centralpix[1] - 4 + bpixx]
if regnum == 0: # No pixels above threshold?
print('WARNING, no star was found in light curve, \
{} light curve will be junk!'.format(fn))
aperture_mask = labels == regnum
return aperture_mask
def aperture_mask(self, snr_threshold=5):
"""Returns an aperture photometry mask.
snr_threshold : float
Background detection threshold.
"""
# Find the pixels that are above the threshold in the median flux image
median = self.median_flux()
mad = median_absolute_deviation(median[np.isfinite(median)])
mad_cut = 1.4826 * mad * snr_threshold # 1.4826 turns MAD into STDEV for a Gaussian
region = np.where(median > mad_cut, 1, 0)
# Label all contiguous regions above the threshold
labels = scipy.ndimage.label(region)[0]
# Central pixel coordinate
centralpix = [1 + median.shape[0] // 2,
1 + median.shape[1] // 2] # "//" is the integer division
# find brightest pix within margin of central pix
margin = 4
central_img = median[centralpix[0] - margin:centralpix[0] + margin,
centralpix[1] - margin:centralpix[1] + margin]
# unravel_index converts indices into a tuple of coordinate arrays
brightestpix = np.unravel_index(central_img.argmax(),
central_img.shape)
bpixy, bpixx = brightestpix
# Which label corresponds to the brightest pixel?
regnum = labels[centralpix[0] - 4 + bpixy, centralpix[1] - 4 + bpixx]
if regnum == 0: # No pixels above threshold?
print('WARNING, no star was found in light curve, \
{} light curve will be junk!'.format(fn))
aperture_mask = labels == regnum
return aperture_mask
def test_median_absolute_deviation():
with NumpyRNGContext(12345):
# test that it runs
randvar = np.random.randn(10000)
mad = funcs.median_absolute_deviation(randvar)
# test whether an array is returned if an axis is used
randvar = randvar.reshape((10, 1000))
mad = funcs.median_absolute_deviation(randvar, axis=1)
assert len(mad) == 10
assert mad.size < randvar.size
mad = funcs.median_absolute_deviation(randvar, axis=0)
assert len(mad) == 1000
assert mad.size < randvar.size
# Test some actual values in a 3 dimensional array
x = np.arange(3 * 4 * 5)
a = np.array([sum(x[:i + 1]) for i in range(len(x))]).reshape(3, 4, 5)
mad = funcs.median_absolute_deviation(a)
assert mad == 389.5
mad = funcs.median_absolute_deviation(a, axis=0)
assert_allclose(mad, [[210., 230., 250., 270., 290.],
[310., 330., 350., 370., 390.],
[410., 430., 450., 470., 490.],
[510., 530., 550., 570., 590.]])
mad = funcs.median_absolute_deviation(a, axis=1)
assert_allclose(mad, [[27.5, 32.5, 37.5, 42.5, 47.5],
[127.5, 132.5, 137.5, 142.5, 147.5],
[227.5, 232.5, 237.5, 242.5, 247.5]])
mad = funcs.median_absolute_deviation(a, axis=2)
assert_allclose(mad, [[3., 8., 13., 18.],
[23., 28., 33., 38.],
[43., 48., 53., 58.]])
def test_median_absolute_deviation():
with NumpyRNGContext(12345):
# test that it runs
randvar = np.random.randn(10000)
mad = funcs.median_absolute_deviation(randvar)
# test whether an array is returned if an axis is used
randvar = randvar.reshape((10, 1000))
mad = funcs.median_absolute_deviation(randvar, axis=1)
assert len(mad) == 10
assert mad.size < randvar.size
mad = funcs.median_absolute_deviation(randvar, axis=0)
assert len(mad) == 1000
assert mad.size < randvar.size
# Test some actual values in a 3 dimensional array
x = np.arange(3 * 4 * 5)
a = np.array([sum(x[:i + 1]) for i in range(len(x))]).reshape(3, 4, 5)
mad = funcs.median_absolute_deviation(a)
assert mad == 389.5
mad = funcs.median_absolute_deviation(a, axis=0)
assert_allclose(
mad,
[[210., 230., 250., 270., 290.], [310., 330., 350., 370., 390.],
[410., 430., 450., 470., 490.], [510., 530., 550., 570., 590.]])
mad = funcs.median_absolute_deviation(a, axis=1)
assert_allclose(mad, [[27.5, 32.5, 37.5, 42.5, 47.5],
[127.5, 132.5, 137.5, 142.5, 147.5],
[227.5, 232.5, 237.5, 242.5, 247.5]])
mad = funcs.median_absolute_deviation(a, axis=2)
assert_allclose(
mad,
[[3., 8., 13., 18.], [23., 28., 33., 38.], [43., 48., 53., 58.]])
def aperture_mask(self, snr_threshold=5, margin=4):
"""Returns an aperture photometry mask.
Parameters
----------
snr_threshold : float
Background detection threshold.
"""
# Find the pixels that are above the threshold in the median flux image
median = np.nanmedian(self.flux, axis=0)
mad = median_absolute_deviation(median[np.isfinite(median)])
# 1.4826 turns MAD into STDEV for a Gaussian
mad_cut = 1.4826 * mad * snr_threshold
region = np.where(median > mad_cut, 1, 0)
# Label all contiguous regions above the threshold
labels = scipy.ndimage.label(region)[0]
# Central pixel coordinate
centralpix = [1 + median.shape[0] // 2, 1 + median.shape[1] // 2]
# find brightest pix within margin of central pix
central_img = median[centralpix[0] - margin:centralpix[0] + margin,
centralpix[1] - margin:centralpix[1] + margin]
# unravel_index converts indices into a tuple of coordinate arrays
brightestpix = np.unravel_index(central_img.argmax(),
central_img.shape)
bpixy, bpixx = brightestpix
# Which label corresponds to the brightest pixel?
regnum = labels[centralpix[0] - margin + bpixy,
centralpix[1] - margin + bpixx]
if regnum == 0:
warnings.warn(
'No star were found in light curve {}, '
'light curve will be junk!'.format(self.path), UserWarning)
aperture_mask = labels == regnum
return aperture_mask
def extract_lightcurve(fn, qual_cut=False, toss_resat=True,
bg_cut=5, skip=None):
if skip is None:
skip = 0
# Read the data into time, fluxarr, and quality
with fits.open(fn) as f:
time = f[1].data['TIME'][skip:] - f[1].data['TIME'][0]
fluxarr = f[1].data['FLUX'][skip:]
quality = f[1].data['QUALITY'][skip:]
# Remove data that does not meet the quality criteria
if qual_cut:
time = time[quality == 0]
fluxarr = fluxarr[quality == 0, :, :]
elif toss_resat:
# data the cadences where there is a wheel
# resetuation event
time = time[quality != 32800]
fluxarr = fluxarr[quality != 32800, :, :]
# fix dodgy data: the C0 data release included zeros
# this will be changed later but we need this
# fix for now
fluxarr[fluxarr == 0] = np.nan
# subtract background
flux_b = median_subtract(fluxarr)
# create a median image to calculate where
# the pixels to use are
flatim = np.nanmedian(flux_b, axis=0)
# find pixels that are X MAD above the median
vals = flatim[np.isfinite(flatim)].flatten()
# 1.4826 turns a MAD into a STDEV for a Gaussian
mad_cut = 1.4826 * median_absolute_deviation(vals) * bg_cut
region = np.where(flatim > mad_cut, 1, 0)
lab = scipy.ndimage.label(region)[0]
# find the central pixel ("//" is the integer division)
imshape = np.shape(flatim)
centralpix = [1 + imshape[0] // 2, 1 + imshape[1] // 2]
# find brightest pix within 9x9 of central pix
centflatim = flatim[centralpix[0] - 4: centralpix[0] + 4,
centralpix[1] - 4: centralpix[1] + 4]
flatimfix = np.where(np.isfinite(centflatim), centflatim, 0)
# unravel_index converts indices into a tuple of coordinate arrays
brightestpix = np.unravel_index(flatimfix.argmax(), centflatim.shape)
bpixy, bpixx = brightestpix
regnum = lab[centralpix[0] - 4 + bpixy, centralpix[1] - 4 + bpixx]
if regnum == 0:
print('WARNING, no star was found in light curve, \
{} light curve will be junk!'.format(fn))
# Initialize return values
lc = np.zeros_like(time)
xbar = np.zeros_like(time)
ybar = np.zeros_like(time)
# there is a loop that performs the aperture photometry
# lets also calcualte the moments of the image
# make a rectangular aperture for the moments thing
ymin = np.min(np.where(lab == regnum)[0])
ymax = np.max(np.where(lab == regnum)[0])
xmin = np.min(np.where(lab == regnum)[1])
xmax = np.max(np.where(lab == regnum)[1])
momlims = [ymin, ymax + 1, xmin, xmax + 1]
for i, fl in enumerate(fluxarr):
lc[i] = np.sum(fl[lab == regnum])
momim = fl[momlims[0]:momlims[1],
momlims[2]:momlims[3]]
momim[~np.isfinite(momim)] == 0.0
xbar[i], ybar[i], cov = intertial_axis(momim)
return (time, lc, xbar - np.mean(xbar), ybar - np.mean(ybar), regnum)
def test_median_absolute_deviation_multidim_axis():
array = np.ones((5, 4, 3)) * np.arange(5)[:, np.newaxis, np.newaxis]
assert_equal(funcs.median_absolute_deviation(array, axis=(1, 2)),
np.zeros(5))
assert_equal(funcs.median_absolute_deviation(array, axis=np.array([1, 2])),
np.zeros(5))
def test_median_absolute_deviation_multidim_axis():
array = np.ones((5, 4, 3)) * np.arange(5)[:, np.newaxis, np.newaxis]
mad1 = funcs.median_absolute_deviation(array, axis=(1, 2))
mad2 = funcs.median_absolute_deviation(array, axis=(2, 1))
assert_equal(mad1, np.zeros(5))
assert_equal(mad1, mad2)
def test_median_absolute_deviation_multidim_axis():
array = np.ones((5, 4, 3)) * np.arange(5)[:, np.newaxis, np.newaxis]
assert_equal(funcs.median_absolute_deviation(array, axis=(1, 2)),
np.zeros(5))
assert_equal(funcs.median_absolute_deviation(
array, axis=np.array([1, 2])), np.zeros(5))
def extract_lightcurve(fn,
qual_cut=False,
toss_resat=True,
bg_cut=5,
skip=None):
if skip is None:
skip = 0
# Read the data into time, fluxarr, and quality
with fits.open(fn) as f:
time = f[1].data['TIME'][skip:] - f[1].data['TIME'][0]
fluxarr = f[1].data['FLUX'][skip:]
quality = f[1].data['QUALITY'][skip:]
# Remove data that does not meet the quality criteria
if qual_cut:
time = time[quality == 0]
fluxarr = fluxarr[quality == 0, :, :]
elif toss_resat:
# data the cadences where there is a wheel
# resetuation event
time = time[quality != 32800]
fluxarr = fluxarr[quality != 32800, :, :]
# fix dodgy data: the C0 data release included zeros
# this will be changed later but we need this
# fix for now
fluxarr[fluxarr == 0] = np.nan
# subtract background
flux_b = median_subtract(fluxarr)
# create a median image to calculate where
# the pixels to use are
flatim = np.nanmedian(flux_b, axis=0)
# find pixels that are X MAD above the median
vals = flatim[np.isfinite(flatim)].flatten()
# 1.4826 turns a MAD into a STDEV for a Gaussian
mad_cut = 1.4826 * median_absolute_deviation(vals) * bg_cut
region = np.where(flatim > mad_cut, 1, 0)
lab = scipy.ndimage.label(region)[0]
# find the central pixel ("//" is the integer division)
imshape = np.shape(flatim)
centralpix = [1 + imshape[0] // 2, 1 + imshape[1] // 2]
# find brightest pix within 9x9 of central pix
centflatim = flatim[centralpix[0] - 4:centralpix[0] + 4,
centralpix[1] - 4:centralpix[1] + 4]
flatimfix = np.where(np.isfinite(centflatim), centflatim, 0)
# unravel_index converts indices into a tuple of coordinate arrays
brightestpix = np.unravel_index(flatimfix.argmax(), centflatim.shape)
bpixy, bpixx = brightestpix
regnum = lab[centralpix[0] - 4 + bpixy, centralpix[1] - 4 + bpixx]
if regnum == 0:
print('WARNING, no star was found in light curve, \
{} light curve will be junk!'.format(fn))
# Initialize return values
lc = np.zeros_like(time)
xbar = np.zeros_like(time)
ybar = np.zeros_like(time)
# there is a loop that performs the aperture photometry
# lets also calcualte the moments of the image
# make a rectangular aperture for the moments thing
ymin = np.min(np.where(lab == regnum)[0])
ymax = np.max(np.where(lab == regnum)[0])
xmin = np.min(np.where(lab == regnum)[1])
xmax = np.max(np.where(lab == regnum)[1])
momlims = [ymin, ymax + 1, xmin, xmax + 1]
for i, fl in enumerate(fluxarr):
lc[i] = np.sum(fl[lab == regnum])
momim = fl[momlims[0]:momlims[1], momlims[2]:momlims[3]]
momim[~np.isfinite(momim)] == 0.0
xbar[i], ybar[i], cov = intertial_axis(momim)
return (time, lc, xbar - np.mean(xbar), ybar - np.mean(ybar), regnum)
