def test_unravel_index():
for nindices, shape, order in [
(0, (15, ), "C"),
(1, (15, ), "C"),
(3, (15, ), "C"),
(3, (15, ), "F"),
(2, (15, 16), "C"),
(2, (15, 16), "F"),
]:
arr = np.random.random(shape)
darr = da.from_array(arr, chunks=1)
findices = np.random.randint(np.prod(shape, dtype=int), size=nindices)
d_findices = da.from_array(findices, chunks=1)
indices = np.unravel_index(findices, shape, order)
d_indices = da.unravel_index(d_findices, shape, order)
assert isinstance(d_indices, type(indices))
assert len(d_indices) == len(indices)
for i in range(len(indices)):
assert_eq(d_indices[i], indices[i])
assert_eq(darr.vindex[d_indices], arr[indices])
def test_unravel_index_empty():
shape = tuple()
findices = np.array(0, dtype=int)
d_findices = da.from_array(findices, chunks=1)
indices = np.unravel_index(findices, shape)
d_indices = da.unravel_index(d_findices, shape)
assert isinstance(d_indices, type(indices))
assert len(d_indices) == len(indices) == 0
def test_unravel_index_empty():
shape = tuple()
findices = np.array(0, dtype=int)
d_findices = da.from_array(findices, chunks=1)
indices = np.unravel_index(findices, shape)
d_indices = da.unravel_index(d_findices, shape)
assert isinstance(d_indices, type(indices))
assert len(d_indices) == len(indices) == 0
def test_unravel_index():
for nindices, shape, order in [(0, (15,), 'C'),
(1, (15,), 'C'),
(3, (15,), 'C'),
(3, (15,), 'F'),
(2, (15, 16), 'C'),
(2, (15, 16), 'F')]:
arr = np.random.random(shape)
darr = da.from_array(arr, chunks=1)
findices = np.random.randint(np.prod(shape, dtype=int), size=nindices)
d_findices = da.from_array(findices, chunks=1)
indices = np.unravel_index(findices, shape, order)
d_indices = da.unravel_index(d_findices, shape, order)
assert isinstance(d_indices, type(indices))
assert len(d_indices) == len(indices)
for i in range(len(indices)):
assert_eq(d_indices[i], indices[i])
assert_eq(darr.vindex[d_indices], arr[indices])
def statistics(xds, dv='IMAGE', name='statistics', compute=False):
"""
Generate statistics on specified image data contents
Resulting data is placed in the attributes section of the dataset
Parameters
----------
xds : xarray.core.dataset.Dataset
input Image Dataset
dv : str
name of data_var in xds to compute statistics on. Default is 'IMAGE'
name : str
name of the attribute in xds to hold statistics dictionary. Default is 'statistics'
compute : bool
execute the DAG to compute the statistics. Default False returns lazy DAG
(statistics can then be retrieved via xds.<name>.<key>.values)
Returns
-------
xarray.core.dataset.Dataset
output Image
"""
import numpy as np
import dask.array as da
assert dv in xds.data_vars, "axis not present in input image"
intensity = xds[dv]
# the number of unmasked points used
# don't use a big loop, vectorized mathematics is faster
#nps = 1
#for i in intensity.shape:
# nps = nps * i
nps = (intensity != np.nan).astype(int).sum() #.values
# the sum of the pixel values
sum = intensity.sum()
# the sum of the squares of the pixel value
sumsq = (intensity * intensity).sum()
# the mean of pixel values
mean = intensity.mean()
# the standard deviation about the mean
sigma = intensity.std()
# the root mean sqaure
rms = (sumsq / nps)**0.5
# minimum pixel value
min = intensity.min()
# maximum pixel value
max = intensity.max()
# the median pixel value
median = intensity.median(
dim=['l', 'm', 'chan', 'pol'])[0] # one median. not median array.
#median = np.median(intensity)
# the median of the absolute deviations from the median # median value, not median array for each channel
medabsdevmed = np.abs(intensity - intensity.median(
dim=['l', 'm', 'chan', 'pol'])).median(
dim=['l', 'm', 'chan', 'pol'])[0]
#medabsdevmed = np.median(np.abs(intensity - np.median(intensity)))
# the first quartile
q1 = intensity.chunk({'chan': -1}).quantile(0.25)
# the third quartile
q3 = intensity.chunk({'chan': -1}).quantile(0.75)
# the inter-quartile range (if robust=T). Find the points which are 25% largest and 75% largest (the median is 50% largest).
quartile = (q3 - q1)
# the absolute pixel coordinate of the bottom left corner of the bounding box of the region of interest.
# If ’region’ is unset, this will be the bottom left corner of the whole image.
blc = da.from_array([0] * len(intensity.dims))
# the formatted absolute world coordinate of the bottom left corner of the bounding box of the region of interest.
blcf = getWCSvalueFromIndex(xds, blc, compute)
# trc - the absolute pixel coordinate of the top right corner of the bounding box of the region of interest.
trc = da.from_array(intensity.shape) - 1
# trcf - the formatted absolute world coordinate of the top right corner of the bounding box of the region of interest.
trcf = getWCSvalueFromIndex(xds, trc, compute)
# absolute pixel coordinate of minimum pixel value
#minIndex = np.where(intensity == np.amin(intensity))
#minPos = [minIndex[0][0], minIndex[1][0], minIndex[2][0], minIndex[3][0], minIndex[4][0]]
minPos = da.unravel_index(intensity.argmin().data, intensity.shape)
minPosf = getWCSvalueFromIndex(xds, minPos, compute)
# absolute pixel coordinate of maximum pixel value
#maxIndex = np.where(intensity == np.amax(intensity))
#maxPos = [maxIndex[0][0], maxIndex[1][0], maxIndex[2][0], maxIndex[3][0], maxIndex[4][0]]
maxPos = da.unravel_index(intensity.argmax().data, intensity.shape)
maxPosf = getWCSvalueFromIndex(xds, maxPos, compute)
if compute:
sum = sum.values.item()
sumsq = sumsq.values.item()
mean = mean.values.item()
sigma = sigma.values.item()
rms = rms.values.item()
min = min.values.item()
max = max.values.item()
median = median.values.item()
medabsdevmed = medabsdevmed.values.item()
q1 = q1.values.item()
q3 = q3.values.item()
quartile = quartile.values.item()
trc = trc.compute()
blc = blc.compute()
nps = nps.values.item()
minPos = [mm.compute() for mm in minPos]
maxPos = [mm.compute() for mm in maxPos]
statisticsResults = {
# "flux": flux,
"max": max,
"maxpos": maxPos,
"maxposf": maxPosf,
"mean": mean,
"medabsdevmed": medabsdevmed,
"median": median,
"min": min,
"minpos": minPos,
"minposf": minPosf,
"npts": nps,
"q1": q1,
"q3": q3,
"quartile": quartile,
"rms": rms,
"sigma": sigma,
"sum": sum,
"sumsq": sumsq,
"blc": blc,
"blcf": blcf,
"trc": trc,
"trcf": trcf
}
return xds.assign_attrs({name: statisticsResults})
