def transform_lonlats(self, lons, lats):
R = 6370997.0
x_coords = R * da.cos(da.deg2rad(lats)) * da.cos(da.deg2rad(lons))
y_coords = R * da.cos(da.deg2rad(lats)) * da.sin(da.deg2rad(lons))
z_coords = R * da.sin(da.deg2rad(lats))
return da.stack((x_coords, y_coords, z_coords), axis=-1)
def lonlat2xyz(lons, lats):
"""Convert lons and lats to cartesian coordinates."""
R = 6370997.0
x_coords = R * da.cos(da.deg2rad(lats)) * da.cos(da.deg2rad(lons))
y_coords = R * da.cos(da.deg2rad(lats)) * da.sin(da.deg2rad(lons))
z_coords = R * da.sin(da.deg2rad(lats))
return x_coords, y_coords, z_coords
def lonlat2xyz(lons, lats):
"""Convert lons and lats to cartesian coordinates."""
R = 6370997.0
x_coords = R * da.cos(da.deg2rad(lats)) * da.cos(da.deg2rad(lons))
y_coords = R * da.cos(da.deg2rad(lats)) * da.sin(da.deg2rad(lons))
z_coords = R * da.sin(da.deg2rad(lats))
return x_coords, y_coords, z_coords
def ross_part(self, angle_info, global_args):
"""
Calculates the main part of Ross kernel
"""
RossKernelOutputs = namedtuple(
'RossKernelOutputs',
'cos_vza cos_sza sin_vza sin_sza cos_raa ross_element cos_phase_angle phase_angle sin_phase_angle ross'
)
cos_vza = da.cos(angle_info.vza_rad)
cos_sza = da.cos(angle_info.sza_rad)
sin_vza = da.sin(angle_info.vza_rad)
sin_sza = da.sin(angle_info.sza_rad)
cos_raa = da.cos(angle_info.raa_rad)
cos_phase_angle, phase_angle, sin_phase_angle = self.get_phaang(
cos_vza, cos_sza, sin_vza, sin_sza, cos_raa)
ross_element = (global_args.m_pi / 2.0 -
phase_angle) * cos_phase_angle + sin_phase_angle
return RossKernelOutputs(cos_vza=cos_vza,
cos_sza=cos_sza,
sin_vza=sin_vza,
sin_sza=sin_sza,
cos_raa=cos_raa,
ross_element=ross_element,
cos_phase_angle=cos_phase_angle,
phase_angle=phase_angle,
sin_phase_angle=sin_phase_angle,
ross=None)
def slerp(self, val, low, high):
"""Code from https://github.com/soumith/dcgan.torch/issues/14"""
omega = da.arccos(da.clip(da.dot(low / da.linalg.norm(low), high.transpose() / da.linalg.norm(high)), -1, 1))
so = da.sin(omega)
if so == 0:
return (1.0-val) * low + val * high # L'Hopital's rule/LERP
return da.sin((1.0 - val) * omega) / so * low + da.sin(val * omega) / so * high
def lonlat2xyz(lons, lats):
"""Convert geographic coordinates to cartesian 3D coordinates."""
R = 6370997.0
x_coords = R * da.cos(da.deg2rad(lats)) * da.cos(da.deg2rad(lons))
y_coords = R * da.cos(da.deg2rad(lats)) * da.sin(da.deg2rad(lons))
z_coords = R * da.sin(da.deg2rad(lats))
return da.stack(
(x_coords.ravel(), y_coords.ravel(), z_coords.ravel()), axis=-1)
def lonlat2xyz(lons, lats):
R = 6370997.0
x_coords = R * da.cos(da.deg2rad(lats)) * da.cos(da.deg2rad(lons))
y_coords = R * da.cos(da.deg2rad(lats)) * da.sin(da.deg2rad(lons))
z_coords = R * da.sin(da.deg2rad(lats))
return da.stack(
(x_coords.ravel(), y_coords.ravel(), z_coords.ravel()), axis=-1)
def lonlat2xyz(lons, lats):
R = 6370997.0
x_coords = R * da.cos(da.deg2rad(lats)) * da.cos(da.deg2rad(lons))
y_coords = R * da.cos(da.deg2rad(lats)) * da.sin(da.deg2rad(lons))
z_coords = R * da.sin(da.deg2rad(lats))
return da.stack((x_coords.ravel(), y_coords.ravel(), z_coords.ravel()),
axis=-1)
def cos_zen(utc_time, lon, lat):
"""Cosine of the sun-zenith angle for *lon*, *lat* at *utc_time*.
utc_time: datetime.datetime instance of the UTC time
lon and lat in degrees.
"""
lon = da.deg2rad(lon)
lat = da.deg2rad(lat)
r_a, dec = sun_ra_dec(utc_time)
h__ = _local_hour_angle(utc_time, lon, r_a)
return (da.sin(lat) * da.sin(dec) +
da.cos(lat) * da.cos(dec) * da.cos(h__))
def haversine_distance(pickup_latitude, pickup_longitude, dropoff_latitude, dropoff_longitude):
x_1 = pi / 180 * pickup_latitude
y_1 = pi / 180 * pickup_longitude
x_2 = pi / 180 * dropoff_latitude
y_2 = pi / 180 * dropoff_longitude
dlon = y_2 - y_1
dlat = x_2 - x_1
a = sin(dlat / 2)**2 + cos(x_1) * cos(x_2) * sin(dlon / 2)**2
c = 2 * arcsin(sqrt(a))
r = 6371 # Radius of earth in kilometers
return c * r
def get_alt_az(utc_time, lon, lat):
"""Return sun altitude and azimuth from *utc_time*, *lon*, and *lat*.
lon,lat in degrees
What is the unit of the returned angles and heights!? FIXME!
"""
lon = da.deg2rad(lon)
lat = da.deg2rad(lat)
ra_, dec = sun_ra_dec(utc_time)
h__ = _local_hour_angle(utc_time, lon, ra_)
return (da.arcsin(
da.sin(lat) * np.sin(dec) + da.cos(lat) * np.cos(dec) * np.cos(h__)),
da.arctan2(
-np.sin(h__),
(da.cos(lat) * np.tan(dec) - da.sin(lat) * np.cos(h__))))
def phase_rotation(self, darray, rotation, preview=None):
"""
Description
-----------
Rotate the phase of the seismic data by a specified angle
Parameters
----------
darray : Array-like, acceptable inputs include Numpy, HDF5, or Dask Arrays
rotation : Number (degrees), angle of rotation
Keywork Arguments
-----------------
preview : str, enables or disables preview mode and specifies direction
Acceptable inputs are (None, 'inline', 'xline', 'z')
Optimizes chunk size in different orientations to facilitate rapid
screening of algorithm output
Returns
-------
result : Dask Array
"""
phi = np.deg2rad(rotation)
kernel = (1,1,25)
darray, chunks_init = self.create_array(darray, kernel, preview=preview)
analytical_trace = darray.map_blocks(signal.hilbert, dtype=darray.dtype)
result = analytical_trace.real * da.cos(phi) - analytical_trace.imag * da.sin(phi)
result = util.trim_dask_array(result, kernel)
result[da.isnan(result)] = 0
return(result)
def test_dask_sin(df):
X = np.array(df)
Xdv = df.to_dask_array()
sines = np.sin(Xdv).compute()
assert sines[:, 0].tolist() == np.sin(df.x).tolist()
assert sines[:, 1].tolist() == np.sin(df.y).tolist()
# on a column
x = df.x.to_numpy()
xd = da.sin(df['x'].to_dask_array(chunks=(5, ))).compute()
assert xd.tolist() == np.sin(df.x).tolist()
def get_phaang(cos_vza, cos_sza, sin_vza, sin_sza, cos_raa):
"""
Gets the phase angle
"""
cos_phase_angle = da.clip(
cos_vza * cos_sza + sin_vza * sin_sza * cos_raa, -1, 1)
phase_angle = da.arccos(cos_phase_angle)
sin_phase_angle = da.sin(phase_angle)
return cos_phase_angle, phase_angle, sin_phase_angle
def randoms(ns):
zmin = 0
zmax = ns.zmax
dNdZ = read_Nz(ns.nz, ns.ncol, zmin, zmax)
zedges = numpy.linspace(zmin, zmax, 12)
zcenters = 0.5 * (zedges[1:] + zedges[:-1])
dNdZ_ran = lambda z: dNdZ(z) * ns.boost
icdf, Ntotal = geticdf(dNdZ_ran, numpy.linspace(zmin, zmax, 1024))
# drop about 20% of samples to give it enough freedom.
cat = RandomCatalog(csize=int(Ntotal), seed=SEED * 20 + 19)
if cat.comm.rank == 0:
cat.logger.info("Total = %d" % Ntotal)
# generate points uniformly on a sphere.
# FIXME: add uniform sphere to nbodykit's RandomCatalog
z = cat.rng.uniform(-1., 1.)
r = abs(1 - z**2)**0.5
phi = cat.rng.uniform(0, 2 * numpy.pi)
x = da.cos(phi) * r
y = da.sin(phi) * r
z = z
ra, dec = transform.CartesianToEquatorial(da.stack((x, y, z), axis=1),
frame='galactic')
z = icdf(cat.rng.uniform(0, 1))
cat['RA'] = ra.compute()
cat['DEC'] = dec.compute()
cat['Z'] = z
cat['Aemit'] = (1 / (z + 1))
ntarget = numpy.ones_like(z, dtype='i4')
randoms = ArrayCatalog(
{
'RA': cat['RA'].compute().repeat(ntarget, axis=0),
'DEC': cat['DEC'].compute().repeat(ntarget, axis=0),
'Z': cat['Z'].compute().repeat(ntarget, axis=0),
'Aemit': cat['Aemit'].compute().repeat(ntarget, axis=0),
},
comm=cat.comm)
randoms = randoms.sort(('Aemit', ), usecols=['RA', 'DEC', 'Z', 'Aemit'])
randoms.save(ns.output, dataset=ns.odataset)
def SkyToUnitSphere(ra, dec, degrees=True):
"""
Convert sky coordinates (``ra``, ``dec``) to Cartesian coordinates on
the unit sphere.
Parameters
----------
ra : :class:`dask.array.Array`; shape: (N,)
the right ascension angular coordinate
dec : :class:`dask.array.Array`; ; shape: (N,)
the declination angular coordinate
degrees : bool, optional
specifies whether ``ra`` and ``dec`` are in degrees or radians
Returns
-------
pos : :class:`dask.array.Array`; shape: (N,3)
the cartesian position coordinates, where columns represent
``x``, ``y``, and ``z``
Raises
------
TypeError
If the input columns are not dask arrays
"""
if not all(isinstance(col, da.Array) for col in [ra, dec]):
raise TypeError("both ``ra`` and ``dec`` must be dask arrays")
# put into radians from degrees
if degrees:
ra = da.deg2rad(ra)
dec = da.deg2rad(dec)
# cartesian coordinates
x = da.cos( dec ) * da.cos( ra )
y = da.cos( dec ) * da.sin( ra )
z = da.sin( dec )
return da.vstack([x,y,z]).T
def get_li(self, kernel_type, li_recip):
# relative azimuth angle
# ensure it is in a [0,2] pi range
phi = da.fabs(
(self.angle_info.raa_rad % (2.0 * self.global_args.m_pi)))
cos_phi = da.cos(phi)
sin_phi = da.sin(phi)
tanti = da.tan(self.angle_info.sza_rad)
tantv = da.tan(self.angle_info.vza_rad)
cos1, sin1, tan1 = self.get_pangles(tantv, self.global_args.br,
self.global_args.nearly_zero)
cos2, sin2, tan2 = self.get_pangles(tanti, self.global_args.br,
self.global_args.nearly_zero)
# sets cos & sin phase angle terms
cos_phaang, phaang, sin_phaang = self.get_phaang(
cos1, cos2, sin1, sin2, cos_phi)
distance = self.get_distance(tan1, tan2, cos_phi)
overlap_info = self.get_overlap(cos1, cos2, tan1, tan2, sin_phi,
distance, self.global_args.hb,
self.global_args.m_pi)
if kernel_type.lower() == 'sparse':
if li_recip:
li = overlap_info.overlap - overlap_info.temp + 0.5 * (
1.0 + cos_phaang) / cos1 / cos2
else:
li = overlap_info.overlap - overlap_info.temp + 0.5 * (
1.0 + cos_phaang) / cos1
else:
if kernel_type.lower() == 'dense':
if li_recip:
li = (1.0 + cos_phaang) / (
cos1 * cos2 *
(overlap_info.temp - overlap_info.overlap)) - 2.0
else:
li = (1.0 + cos_phaang) / (
cos1 *
(overlap_info.temp - overlap_info.overlap)) - 2.0
return li
def match_lists(ra1, dec1, ra2, dec2, dist, numNei=1):
"""Crossmatches the list of objects (ra1, dec1) with
another list of objects (ra2, dec2) with the matching radius "dist".
The routine searches for up to numNei closest neighbors
the routine, returns the distance to the neighbor and the list
of indices of the neighbor. Everything is in degrees.
If no match is found, the distance is NaN.
Example:
.. code-block:: python
dist, ind = match_lists(ra1,dec1,ra2,dec2, 1./3600)
goodmatch_ind = numpy.isfinite(dist)
plot(ra1[goodmatch_ind],ra2[ind[goodmatch_ind]])
Another example:
.. code-block:: python
print match_lists( [1,1], [2,3], [1.1,1.2,6,], [2.1,2.2,10], 0.3,numNei=2)
(array([[ 0.1413761 , 0.28274768],
[ inf, inf]]),
array([[0, 1],
[3, 3]]))
"""
cosd = lambda x: da.cos(x * np.pi / 180)
sind = lambda x: da.sin(x * np.pi / 180)
mindist = 2 * sind(dist / 2.0)
getxyz = lambda r, d: [cosd(r) * cosd(d), sind(r) * cosd(d), sind(d)]
xyz1 = numpy.array(getxyz(ra1, dec1))
xyz2 = numpy.array(getxyz(ra2, dec2))
if (int(scipy_version[0]) == 0) and (int(scipy_version[1]) < 8):
# If old scipy version is detected then we use KDTree instead of
# cKDTtree because there is a bug in the cKDTree
# http://projects.scipy.org/scipy/ticket/1178
tree2 = scipy.spatial.KDTree(xyz2.T)
else:
tree2 = scipy.spatial.cKDTree(xyz2.T)
del xyz2
ret = tree2.query(xyz1.T, numNei, 0, 2, mindist)
del xyz1
dist, ind = ret
finite = numpy.isfinite(dist)
dist[finite] = (2 * arcsin(dist[finite] / 2)) * 180 / np.pi
return dist, ind
def _transformlng_dask(lng, lat):
ret = 300.0 + lng + 2.0 * lat + 0.1 * lng * lng + \
0.1 * lng * lat + 0.1 * da.sqrt(da.fabs(lng))
ret += (20.0 * da.sin(6.0 * lng * pi) +
20.0 * da.sin(2.0 * lng * pi)) * 2.0 / 3.0
ret += (20.0 * da.sin(lng * pi) +
40.0 * da.sin(lng / 3.0 * pi)) * 2.0 / 3.0
ret += (150.0 * da.sin(lng / 12.0 * pi) +
300.0 * da.sin(lng / 30.0 * pi)) * 2.0 / 3.0
return ret
def _transformlat_dask(lng, lat):
ret = -100.0 + 2.0 * lng + 3.0 * lat + 0.2 * lat * lat + \
0.1 * lng * lat + 0.2 * da.sqrt(da.fabs(lng))
ret += (20.0 * da.sin(6.0 * lng * pi) +
20.0 * da.sin(2.0 * lng * pi)) * 2.0 / 3.0
ret += (20.0 * da.sin(lat * pi) +
40.0 * da.sin(lat / 3.0 * pi)) * 2.0 / 3.0
ret += (160.0 * da.sin(lat / 12.0 * pi) +
320 * da.sin(lat * pi / 30.0)) * 2.0 / 3.0
return ret
def get_pangles(tan1, br, nearly_zero):
"""
Get the prime angles
"""
tanp = br * tan1
tanp = da.where(tanp < 0, 0, tanp)
angp = da.arctan(tanp)
sinp = da.sin(angp)
cosp = da.cos(angp)
# have to make sure c is not 0
cosp = da.where(cosp == 0, nearly_zero, cosp)
return cosp, sinp, tanp
def test_dtype_complex():
x = np.arange(24).reshape((4, 6)).astype('f4')
y = np.arange(24).reshape((4, 6)).astype('i8')
z = np.arange(24).reshape((4, 6)).astype('i2')
a = da.from_array(x, chunks=(2, 3))
b = da.from_array(y, chunks=(2, 3))
c = da.from_array(z, chunks=(2, 3))
def eq(a, b):
return (isinstance(a, np.dtype) and
isinstance(b, np.dtype) and
str(a) == str(b))
assert eq(a._dtype, x.dtype)
assert eq(b._dtype, y.dtype)
assert eq((a + 1)._dtype, (x + 1).dtype)
assert eq((a + b)._dtype, (x + y).dtype)
assert eq(a.T._dtype, x.T.dtype)
assert eq(a[:3]._dtype, x[:3].dtype)
assert eq((a.dot(b.T))._dtype, (x.dot(y.T)).dtype)
assert eq(stack([a, b])._dtype, np.vstack([x, y]).dtype)
assert eq(concatenate([a, b])._dtype, np.concatenate([x, y]).dtype)
assert eq(b.std()._dtype, y.std().dtype)
assert eq(c.sum()._dtype, z.sum().dtype)
assert eq(a.min()._dtype, a.min().dtype)
assert eq(b.std()._dtype, b.std().dtype)
assert eq(a.argmin(axis=0)._dtype, a.argmin(axis=0).dtype)
assert eq(da.sin(c)._dtype, np.sin(z).dtype)
assert eq(da.exp(b)._dtype, np.exp(y).dtype)
assert eq(da.floor(a)._dtype, np.floor(x).dtype)
assert eq(da.isnan(b)._dtype, np.isnan(y).dtype)
with ignoring(ImportError):
assert da.isnull(b)._dtype == 'bool'
assert da.notnull(b)._dtype == 'bool'
x = np.array([('a', 1)], dtype=[('text', 'S1'), ('numbers', 'i4')])
d = da.from_array(x, chunks=(1,))
assert eq(d['text']._dtype, x['text'].dtype)
assert eq(d[['numbers', 'text']]._dtype, x[['numbers', 'text']].dtype)
def gcj02_to_wgs84_dask(lng, lat):
"""
GCJ02(火星坐标系)转GPS84
:param lng:火星坐标系的经度
:param lat:火星坐标系纬度
:return:
"""
dlat = _transformlat_dask(lng - 105.0, lat - 35.0)
dlng = _transformlng_dask(lng - 105.0, lat - 35.0)
radlat = lat / 180.0 * pi
magic = da.sin(radlat)
magic = 1 - ee * magic * magic
sqrtmagic = da.sqrt(magic)
dlat = (dlat * 180.0) / ((a * (1 - ee)) / (magic * sqrtmagic) * pi)
dlng = (dlng * 180.0) / (a / sqrtmagic * da.cos(radlat) * pi)
mglat = lat + dlat
mglng = lng + dlng
return lng * 2 - mglng, lat * 2 - mglat
def test_dtype_complex():
x = np.arange(24).reshape((4, 6)).astype('f4')
y = np.arange(24).reshape((4, 6)).astype('i8')
z = np.arange(24).reshape((4, 6)).astype('i2')
a = da.from_array(x, chunks=(2, 3))
b = da.from_array(y, chunks=(2, 3))
c = da.from_array(z, chunks=(2, 3))
def eq(a, b):
return (isinstance(a, np.dtype) and isinstance(b, np.dtype)
and str(a) == str(b))
assert eq(a._dtype, x.dtype)
assert eq(b._dtype, y.dtype)
assert eq((a + 1)._dtype, (x + 1).dtype)
assert eq((a + b)._dtype, (x + y).dtype)
assert eq(a.T._dtype, x.T.dtype)
assert eq(a[:3]._dtype, x[:3].dtype)
assert eq((a.dot(b.T))._dtype, (x.dot(y.T)).dtype)
assert eq(stack([a, b])._dtype, np.vstack([x, y]).dtype)
assert eq(concatenate([a, b])._dtype, np.concatenate([x, y]).dtype)
assert eq(b.std()._dtype, y.std().dtype)
assert eq(c.sum()._dtype, z.sum().dtype)
assert eq(a.min()._dtype, a.min().dtype)
assert eq(b.std()._dtype, b.std().dtype)
assert eq(a.argmin(axis=0)._dtype, a.argmin(axis=0).dtype)
assert eq(da.sin(c)._dtype, np.sin(z).dtype)
assert eq(da.exp(b)._dtype, np.exp(y).dtype)
assert eq(da.floor(a)._dtype, np.floor(x).dtype)
assert eq(da.isnan(b)._dtype, np.isnan(y).dtype)
with ignoring(ImportError):
assert da.isnull(b)._dtype == 'bool'
assert da.notnull(b)._dtype == 'bool'
x = np.array([('a', 1)], dtype=[('text', 'S1'), ('numbers', 'i4')])
d = da.from_array(x, chunks=(1, ))
assert eq(d['text']._dtype, x['text'].dtype)
assert eq(d[['numbers', 'text']]._dtype, x[['numbers', 'text']].dtype)
def get_overlap(cos1, cos2, tan1, tan2, sin3, distance, hb, m_pi):
"""
Applies the HB ratio transformation
"""
OverlapInfo = namedtuple('OverlapInfo', 'tvar sint overlap temp')
temp = (1.0 / cos1) + (1.0 / cos2)
cost = da.clip(
hb * da.sqrt(distance * distance +
tan1 * tan1 * tan2 * tan2 * sin3 * sin3) / temp, -1,
1)
tvar = da.arccos(cost)
sint = da.sin(tvar)
overlap = 1.0 / m_pi * (tvar - sint * cost) * temp
overlap = da.maximum(overlap, 0)
return OverlapInfo(tvar=tvar, sint=sint, overlap=overlap, temp=temp)
def SkyToUnitSphere(ra, dec, degrees=True, frame='icrs'):
"""
Convert sky coordinates (``ra``, ``dec``) to Cartesian coordinates on
the unit sphere.
Parameters
----------
ra : :class:`dask.array.Array`; shape: (N,)
the right ascension angular coordinate
dec : :class:`dask.array.Array`; ; shape: (N,)
the declination angular coordinate
degrees : bool, optional
specifies whether ``ra`` and ``dec`` are in degrees or radians
frame : string ('icrs' or 'galactic')
speciefies which frame the Cartesian coordinates is. Useful if you know
the simulation (usually cartesian) is in galactic units but you want
to convert to the icrs (ra, dec) usually used in surveys.
Returns
-------
pos : :class:`dask.array.Array`; shape: (N,3)
the cartesian position coordinates, where columns represent
``x``, ``y``, and ``z``
Raises
------
TypeError
If the input columns are not dask arrays
"""
ra, dec = da.broadcast_arrays(ra, dec)
if frame == 'icrs':
# no frame transformation
# put into radians from degrees
if degrees:
ra = da.deg2rad(ra)
dec = da.deg2rad(dec)
# cartesian coordinates
x = da.cos( dec ) * da.cos( ra )
y = da.cos( dec ) * da.sin( ra )
z = da.sin( dec )
return da.vstack([x,y,z]).T
else:
from astropy.coordinates import SkyCoord
if degrees:
ra = da.deg2rad(ra)
dec = da.deg2rad(dec)
def eq_to_cart(ra, dec):
try:
sc = SkyCoord(ra, dec, unit='rad', representation_type='unitspherical', frame='icrs')
except:
sc = SkyCoord(ra, dec, unit='rad', representation='unitspherical', frame='icrs')
scg = sc.transform_to(frame=frame)
scg = scg.cartesian
x, y, z = scg.x.value, scg.y.value, scg.z.value
return numpy.stack([x, y, z], axis=1)
arr = da.apply_gufunc(eq_to_cart, '(),()->(p)', ra, dec, output_dtypes=[ra.dtype], output_sizes={'p': 3})
return arr
def test_arithmetic():
x = np.arange(5).astype('f4') + 2
y = np.arange(5).astype('i8') + 2
z = np.arange(5).astype('i4') + 2
a = da.from_array(x, chunks=(2, ))
b = da.from_array(y, chunks=(2, ))
c = da.from_array(z, chunks=(2, ))
assert eq(a + b, x + y)
assert eq(a * b, x * y)
assert eq(a - b, x - y)
assert eq(a / b, x / y)
assert eq(b & b, y & y)
assert eq(b | b, y | y)
assert eq(b ^ b, y ^ y)
assert eq(a // b, x // y)
assert eq(a**b, x**y)
assert eq(a % b, x % y)
assert eq(a > b, x > y)
assert eq(a < b, x < y)
assert eq(a >= b, x >= y)
assert eq(a <= b, x <= y)
assert eq(a == b, x == y)
assert eq(a != b, x != y)
assert eq(a + 2, x + 2)
assert eq(a * 2, x * 2)
assert eq(a - 2, x - 2)
assert eq(a / 2, x / 2)
assert eq(b & True, y & True)
assert eq(b | True, y | True)
assert eq(b ^ True, y ^ True)
assert eq(a // 2, x // 2)
assert eq(a**2, x**2)
assert eq(a % 2, x % 2)
assert eq(a > 2, x > 2)
assert eq(a < 2, x < 2)
assert eq(a >= 2, x >= 2)
assert eq(a <= 2, x <= 2)
assert eq(a == 2, x == 2)
assert eq(a != 2, x != 2)
assert eq(2 + b, 2 + y)
assert eq(2 * b, 2 * y)
assert eq(2 - b, 2 - y)
assert eq(2 / b, 2 / y)
assert eq(True & b, True & y)
assert eq(True | b, True | y)
assert eq(True ^ b, True ^ y)
assert eq(2 // b, 2 // y)
assert eq(2**b, 2**y)
assert eq(2 % b, 2 % y)
assert eq(2 > b, 2 > y)
assert eq(2 < b, 2 < y)
assert eq(2 >= b, 2 >= y)
assert eq(2 <= b, 2 <= y)
assert eq(2 == b, 2 == y)
assert eq(2 != b, 2 != y)
assert eq(-a, -x)
assert eq(abs(a), abs(x))
assert eq(~(a == b), ~(x == y))
assert eq(~(a == b), ~(x == y))
assert eq(da.logaddexp(a, b), np.logaddexp(x, y))
assert eq(da.logaddexp2(a, b), np.logaddexp2(x, y))
assert eq(da.exp(b), np.exp(y))
assert eq(da.log(a), np.log(x))
assert eq(da.log10(a), np.log10(x))
assert eq(da.log1p(a), np.log1p(x))
assert eq(da.expm1(b), np.expm1(y))
assert eq(da.sqrt(a), np.sqrt(x))
assert eq(da.square(a), np.square(x))
assert eq(da.sin(a), np.sin(x))
assert eq(da.cos(b), np.cos(y))
assert eq(da.tan(a), np.tan(x))
assert eq(da.arcsin(b / 10), np.arcsin(y / 10))
assert eq(da.arccos(b / 10), np.arccos(y / 10))
assert eq(da.arctan(b / 10), np.arctan(y / 10))
assert eq(da.arctan2(b * 10, a), np.arctan2(y * 10, x))
assert eq(da.hypot(b, a), np.hypot(y, x))
assert eq(da.sinh(a), np.sinh(x))
assert eq(da.cosh(b), np.cosh(y))
assert eq(da.tanh(a), np.tanh(x))
assert eq(da.arcsinh(b * 10), np.arcsinh(y * 10))
assert eq(da.arccosh(b * 10), np.arccosh(y * 10))
assert eq(da.arctanh(b / 10), np.arctanh(y / 10))
assert eq(da.deg2rad(a), np.deg2rad(x))
assert eq(da.rad2deg(a), np.rad2deg(x))
assert eq(da.logical_and(a < 1, b < 4), np.logical_and(x < 1, y < 4))
assert eq(da.logical_or(a < 1, b < 4), np.logical_or(x < 1, y < 4))
assert eq(da.logical_xor(a < 1, b < 4), np.logical_xor(x < 1, y < 4))
assert eq(da.logical_not(a < 1), np.logical_not(x < 1))
assert eq(da.maximum(a, 5 - a), np.maximum(a, 5 - a))
assert eq(da.minimum(a, 5 - a), np.minimum(a, 5 - a))
assert eq(da.fmax(a, 5 - a), np.fmax(a, 5 - a))
assert eq(da.fmin(a, 5 - a), np.fmin(a, 5 - a))
assert eq(da.isreal(a + 1j * b), np.isreal(x + 1j * y))
assert eq(da.iscomplex(a + 1j * b), np.iscomplex(x + 1j * y))
assert eq(da.isfinite(a), np.isfinite(x))
assert eq(da.isinf(a), np.isinf(x))
assert eq(da.isnan(a), np.isnan(x))
assert eq(da.signbit(a - 3), np.signbit(x - 3))
assert eq(da.copysign(a - 3, b), np.copysign(x - 3, y))
assert eq(da.nextafter(a - 3, b), np.nextafter(x - 3, y))
assert eq(da.ldexp(c, c), np.ldexp(z, z))
assert eq(da.fmod(a * 12, b), np.fmod(x * 12, y))
assert eq(da.floor(a * 0.5), np.floor(x * 0.5))
assert eq(da.ceil(a), np.ceil(x))
assert eq(da.trunc(a / 2), np.trunc(x / 2))
assert eq(da.degrees(b), np.degrees(y))
assert eq(da.radians(a), np.radians(x))
assert eq(da.rint(a + 0.3), np.rint(x + 0.3))
assert eq(da.fix(a - 2.5), np.fix(x - 2.5))
assert eq(da.angle(a + 1j), np.angle(x + 1j))
assert eq(da.real(a + 1j), np.real(x + 1j))
assert eq((a + 1j).real, np.real(x + 1j))
assert eq(da.imag(a + 1j), np.imag(x + 1j))
assert eq((a + 1j).imag, np.imag(x + 1j))
assert eq(da.conj(a + 1j * b), np.conj(x + 1j * y))
assert eq((a + 1j * b).conj(), (x + 1j * y).conj())
assert eq(da.clip(b, 1, 4), np.clip(y, 1, 4))
assert eq(da.fabs(b), np.fabs(y))
assert eq(da.sign(b - 2), np.sign(y - 2))
l1, l2 = da.frexp(a)
r1, r2 = np.frexp(x)
assert eq(l1, r1)
assert eq(l2, r2)
l1, l2 = da.modf(a)
r1, r2 = np.modf(x)
assert eq(l1, r1)
assert eq(l2, r2)
assert eq(da.around(a, -1), np.around(x, -1))
# Filter
min_P = 0.5
# Displey
vecScale = 1
DISP_TimeZoom = [['2018-04-17T20:00', '2018-04-18T00:00']]
maxGsumMinus1 = 0.3 # 0.1
TimeShiftedFromUTC_s = 0
# Angles in radians:
fPitch = lambda Gxyz: -da.arctan2(Gxyz[0, :], da.sqrt(da.sum(da.square(Gxyz[1:, :]), 0)))
fRoll = lambda Gxyz: da.arctan2(Gxyz[1, :], Gxyz[2,
:]) # da.arctan2(Gxyz[1,:], da.sqrt(da.sum(da.square(Gxyz[(0,2),:]), 0))) #=da.arctan2(Gxyz[1,:], da.sqrt(da.square(Gxyz[0,:])+da.square(Gxyz[2,:])) )
fInclination = lambda Gxyz: da.arctan2(da.sqrt(da.sum(da.square(Gxyz[:-1, :]), 0)), Gxyz[2, :])
fHeading = lambda H, p, r: da.arctan2(H[2, :] * da.sin(r) - H[1, :] * da.cos(r),
H[0, :] * da.cos(p) + (H[1, :] * da.sin(r) + H[2, :] * da.cos(r)) * da.sin(p))
fG = lambda Axyz, Ag, Cg: da.dot(Ag.T, (Axyz - Cg[0, :]).T)
fGi = lambda Ax, Ay, Az, Ag, Cg, i: da.dot(Ag.T, (da.column_stack((Ax, Ay, Az))[slice(*i)] - Cg[0, :]).T)
fbinningClip = lambda x, bin2_iStEn, bin1_nAverage: da.mean(da.reshape(x[slice(*bin2_iStEn)], (-1, bin1_nAverage)), 1)
fbinning = lambda x, bin1_nAverage: da.mean(da.reshape(x, (-1, bin1_nAverage)), 1)
repeat3shift1 = lambda A2: [A2[t:(len(A2) - 2 + t)] for t in range(3)]
median3cols = lambda a, b, c: da.where(a < b, da.where(c < a, a, da.where(b < c, b, c)),
da.where(a < c, a, da.where(c < b, b, c)))
median3 = lambda x: da.hstack((np.NaN, median3cols(*repeat3shift1(x)), np.NaN))
# not convertable to dask easily:
fVabs_old = lambda Gxyz, kVabs: np.polyval(kVabs.flat, np.sqrt(np.tan(fInclination(Gxyz))))
rep2mean = lambda x, bOk: np.interp(np.arange(len(x)), np.flatnonzero(bOk), x[bOk], np.NaN, np.NaN)
fForce2Vabs_fitted = lambda x: da.where(x > 2, 2, da.where(x < 1, 0.25 * x, 0.25 * x + 0.3 * (x - 1) ** 4))
def sinfit(array, periods, dim=None, coord=None, unit='s'):
"""
Least squares sinusoidal fit.
Fit sinusoidal functions ``y = A[p] * sin(2 * pi * ax * f[1] + phi[1])``
Parameters
----------
array : xarray.DataArray
Data to be fitted
periods: float or list of float
The periods of the sinusoidal functions to be fitted
dim : str, optional
The dimension along which the data will be fitted. If not precised,
the first dimension will be used
unit : {'D', 'h', 'm', 's', 'ms', 'us', 'ns'}, optional
If the fit uses a datetime dimension, the unit of the period may be
specified here.
Returns
-------
modes : Dataset
A Dataset with the amplitude and the phase for each periods
"""
if dim is None:
dim = array.dims[0]
if _utils.is_scalar(periods):
periods = [
periods,
]
n = 2 * len(periods) + 1
# Sort frequencies in ascending order
periods.sort(reverse=True)
# Re-order the array to place the fitting dimension as the first dimension
# + stack the other dimensions
array_stacked = _order_and_stack(array, dim)
dim_chunk = array.chunks[array.get_axis_num(dim)][0]
# Check if the dimension is associated with a numpy.datetime
# and normalize to use periods and time in seconds
if coord is None:
coord = array[dim]
if _utils.is_datetime(coord):
# Use the 1e-9 to scale nanoseconds to seconds (by default, xarray use
# datetime in nanoseconds
t = coord.data.astype('f8') * 1e-9
freqs = 1. / pd.to_timedelta(periods, unit=unit).total_seconds()
else:
t = coord.data
freqs = 1. / periods
# Build coefficient matrix for the fit using the exponential form
x = da.vstack([da.cos(2 * np.pi * f * t) for f in reversed(freqs)] + [
da.ones(len(t), chunks=dim_chunk),
] + [da.sin(2 * np.pi * f * t) for f in freqs]).T
x = x.rechunk((dim_chunk, n))
# Solve the least-square system
c, _, _, _ = da.linalg.lstsq(x, array_stacked.data)
# Get cosine (a) and sine (b) ampitudes
b = c[0:n // 2, ][::-1]
a = c[n // 2 + 1:, ]
# Compute amplitude and phase
amplitude = da.sqrt(a**2 + b**2)
phase = da.arctan2(b, a) * 180. / np.pi
# Store the results
new_dims = ('periods', ) + array_stacked.dims[1:]
new_coords = {
co: array_stacked.coords[co]
for co in array_stacked.coords if co != dim
}
var_dict = {
'amplitude': (new_dims, amplitude),
'phase': (new_dims, phase),
'offset': (array_stacked.dims[1:], c[n // 2, ])
}
ds = xr.Dataset(var_dict, coords=new_coords)
ds = ds.assign_coords(periods=periods)
ds['periods'].attrs['units'] = unit
# Unstack the data
modes = _unstack(ds)
return modes
def lengths_and_angles_to_box_vectors(a_length, b_length, c_length, alpha, beta, gamma):
"""Convert from the lengths/angles of the unit cell to the box
vectors (Bravais vectors). The angles should be in degrees.
Mimics mdtraj.core.unitcell.lengths_and_angles_to_box_vectors()
Parameters
----------
a_length : scalar or ndarray
length of Bravais unit vector **a**
b_length : scalar or ndarray
length of Bravais unit vector **b**
c_length : scalar or ndarray
length of Bravais unit vector **c**
alpha : scalar or ndarray
angle between vectors **b** and **c**, in degrees.
beta : scalar or ndarray
angle between vectors **c** and **a**, in degrees.
gamma : scalar or ndarray
angle between vectors **a** and **b**, in degrees.
Returns
-------
a : dask.array
If the inputs are scalar, the vectors will one dimensional (length 3).
If the inputs are one dimension, shape=(n_frames, ), then the output
will be (n_frames, 3)
b : dask.array
If the inputs are scalar, the vectors will one dimensional (length 3).
If the inputs are one dimension, shape=(n_frames, ), then the output
will be (n_frames, 3)
c : dask.array
If the inputs are scalar, the vectors will one dimensional (length 3).
If the inputs are one dimension, shape=(n_frames, ), then the output
will be (n_frames, 3)
This code is adapted from gyroid, which is licensed under the BSD
http://pythonhosted.org/gyroid/_modules/gyroid/unitcell.html
"""
# Fix for da that requires angles and lengths to be arrays
lengths = [a_length, b_length, c_length]
for i, e in enumerate(lengths):
# Use python logic shortcutting to not compute dask Arrays
if not isinstance(e, da.core.Array) and np.isscalar(e):
lengths[i] = np.array([e])
a_length, b_length, c_length = tuple(lengths)
angles = [alpha, beta, gamma]
for i, e in enumerate(angles):
if not isinstance(e, da.core.Array) and np.isscalar(e):
angles[i] = np.array([e])
alpha, beta, gamma = tuple(angles)
if da.all(alpha < 2 * np.pi) and (
da.all(beta < 2 * np.pi) and da.all(gamma < 2 * np.pi)
):
warnings.warn(
"All your angles were less than 2*pi."
" Did you accidentally give me radians?"
)
alpha = alpha * np.pi / 180
beta = beta * np.pi / 180
gamma = gamma * np.pi / 180
a = da.stack([a_length, da.zeros_like(a_length), da.zeros_like(a_length)])
b = da.stack(
[b_length * da.cos(gamma), b_length * da.sin(gamma), da.zeros_like(b_length)]
)
cx = c_length * da.cos(beta)
cy = c_length * (da.cos(alpha) - da.cos(beta) * da.cos(gamma)) / da.sin(gamma)
cz = da.sqrt(c_length * c_length - cx * cx - cy * cy)
c = da.stack([cx, cy, cz])
if not a.shape == b.shape == c.shape:
raise TypeError("Shape is messed up.")
# Make sure that all vector components that are _almost_ 0 are set exactly
# to 0
tol = 1e-6
a[da.logical_and(a > -tol, a < tol)] = 0.0
b[da.logical_and(b > -tol, b < tol)] = 0.0
c[da.logical_and(c > -tol, c < tol)] = 0.0
return a.T, b.T, c.T
def SkyToUnitSphere(ra, dec, degrees=True, frame='icrs'):
"""
Convert sky coordinates (``ra``, ``dec``) to Cartesian coordinates on
the unit sphere.
Parameters
----------
ra : :class:`dask.array.Array`; shape: (N,)
the right ascension angular coordinate
dec : :class:`dask.array.Array`; ; shape: (N,)
the declination angular coordinate
degrees : bool, optional
specifies whether ``ra`` and ``dec`` are in degrees or radians
frame : string ('icrs' or 'galactic')
speciefies which frame the Cartesian coordinates is. Useful if you know
the simulation (usually cartesian) is in galactic units but you want
to convert to the icrs (ra, dec) usually used in surveys.
Returns
-------
pos : :class:`dask.array.Array`; shape: (N,3)
the cartesian position coordinates, where columns represent
``x``, ``y``, and ``z``
Raises
------
TypeError
If the input columns are not dask arrays
"""
ra, dec = da.broadcast_arrays(ra, dec)
if frame == 'icrs':
# no frame transformation
# put into radians from degrees
if degrees:
ra = da.deg2rad(ra)
dec = da.deg2rad(dec)
# cartesian coordinates
x = da.cos( dec ) * da.cos( ra )
y = da.cos( dec ) * da.sin( ra )
z = da.sin( dec )
return da.vstack([x,y,z]).T
else:
from astropy.coordinates import SkyCoord
if degrees:
ra = da.deg2rad(ra)
dec = da.deg2rad(dec)
def eq_to_cart(ra, dec):
try:
sc = SkyCoord(ra, dec, unit='rad', representation_type='unitspherical', frame='icrs')
except:
sc = SkyCoord(ra, dec, unit='rad', representation='unitspherical', frame='icrs')
scg = sc.transform_to(frame=frame)
scg = scg.cartesian
x, y, z = scg.x.value, scg.y.value, scg.z.value
return numpy.stack([x, y, z], axis=1)
arr = da.apply_gufunc(eq_to_cart, '(),()->(p)', ra, dec, output_dtypes=[ra.dtype], output_sizes={'p': 3})
return arr
def apply(self, df):
alpha = ((df[self.column] / 100) * 60 + (df[self.column] % 100)) / 1440
df["cyclic_cos_" + self.column] = da.cos(2 * np.pi * alpha)
df["cyclic_sin_" + self.column] = da.sin(2 * np.pi * alpha)
return df
def test_arithmetic():
x = np.arange(5).astype('f4') + 2
y = np.arange(5).astype('i8') + 2
z = np.arange(5).astype('i4') + 2
a = da.from_array(x, chunks=(2,))
b = da.from_array(y, chunks=(2,))
c = da.from_array(z, chunks=(2,))
assert eq(a + b, x + y)
assert eq(a * b, x * y)
assert eq(a - b, x - y)
assert eq(a / b, x / y)
assert eq(b & b, y & y)
assert eq(b | b, y | y)
assert eq(b ^ b, y ^ y)
assert eq(a // b, x // y)
assert eq(a ** b, x ** y)
assert eq(a % b, x % y)
assert eq(a > b, x > y)
assert eq(a < b, x < y)
assert eq(a >= b, x >= y)
assert eq(a <= b, x <= y)
assert eq(a == b, x == y)
assert eq(a != b, x != y)
assert eq(a + 2, x + 2)
assert eq(a * 2, x * 2)
assert eq(a - 2, x - 2)
assert eq(a / 2, x / 2)
assert eq(b & True, y & True)
assert eq(b | True, y | True)
assert eq(b ^ True, y ^ True)
assert eq(a // 2, x // 2)
assert eq(a ** 2, x ** 2)
assert eq(a % 2, x % 2)
assert eq(a > 2, x > 2)
assert eq(a < 2, x < 2)
assert eq(a >= 2, x >= 2)
assert eq(a <= 2, x <= 2)
assert eq(a == 2, x == 2)
assert eq(a != 2, x != 2)
assert eq(2 + b, 2 + y)
assert eq(2 * b, 2 * y)
assert eq(2 - b, 2 - y)
assert eq(2 / b, 2 / y)
assert eq(True & b, True & y)
assert eq(True | b, True | y)
assert eq(True ^ b, True ^ y)
assert eq(2 // b, 2 // y)
assert eq(2 ** b, 2 ** y)
assert eq(2 % b, 2 % y)
assert eq(2 > b, 2 > y)
assert eq(2 < b, 2 < y)
assert eq(2 >= b, 2 >= y)
assert eq(2 <= b, 2 <= y)
assert eq(2 == b, 2 == y)
assert eq(2 != b, 2 != y)
assert eq(-a, -x)
assert eq(abs(a), abs(x))
assert eq(~(a == b), ~(x == y))
assert eq(~(a == b), ~(x == y))
assert eq(da.logaddexp(a, b), np.logaddexp(x, y))
assert eq(da.logaddexp2(a, b), np.logaddexp2(x, y))
assert eq(da.exp(b), np.exp(y))
assert eq(da.log(a), np.log(x))
assert eq(da.log10(a), np.log10(x))
assert eq(da.log1p(a), np.log1p(x))
assert eq(da.expm1(b), np.expm1(y))
assert eq(da.sqrt(a), np.sqrt(x))
assert eq(da.square(a), np.square(x))
assert eq(da.sin(a), np.sin(x))
assert eq(da.cos(b), np.cos(y))
assert eq(da.tan(a), np.tan(x))
assert eq(da.arcsin(b/10), np.arcsin(y/10))
assert eq(da.arccos(b/10), np.arccos(y/10))
assert eq(da.arctan(b/10), np.arctan(y/10))
assert eq(da.arctan2(b*10, a), np.arctan2(y*10, x))
assert eq(da.hypot(b, a), np.hypot(y, x))
assert eq(da.sinh(a), np.sinh(x))
assert eq(da.cosh(b), np.cosh(y))
assert eq(da.tanh(a), np.tanh(x))
assert eq(da.arcsinh(b*10), np.arcsinh(y*10))
assert eq(da.arccosh(b*10), np.arccosh(y*10))
assert eq(da.arctanh(b/10), np.arctanh(y/10))
assert eq(da.deg2rad(a), np.deg2rad(x))
assert eq(da.rad2deg(a), np.rad2deg(x))
assert eq(da.logical_and(a < 1, b < 4), np.logical_and(x < 1, y < 4))
assert eq(da.logical_or(a < 1, b < 4), np.logical_or(x < 1, y < 4))
assert eq(da.logical_xor(a < 1, b < 4), np.logical_xor(x < 1, y < 4))
assert eq(da.logical_not(a < 1), np.logical_not(x < 1))
assert eq(da.maximum(a, 5 - a), np.maximum(a, 5 - a))
assert eq(da.minimum(a, 5 - a), np.minimum(a, 5 - a))
assert eq(da.fmax(a, 5 - a), np.fmax(a, 5 - a))
assert eq(da.fmin(a, 5 - a), np.fmin(a, 5 - a))
assert eq(da.isreal(a + 1j * b), np.isreal(x + 1j * y))
assert eq(da.iscomplex(a + 1j * b), np.iscomplex(x + 1j * y))
assert eq(da.isfinite(a), np.isfinite(x))
assert eq(da.isinf(a), np.isinf(x))
assert eq(da.isnan(a), np.isnan(x))
assert eq(da.signbit(a - 3), np.signbit(x - 3))
assert eq(da.copysign(a - 3, b), np.copysign(x - 3, y))
assert eq(da.nextafter(a - 3, b), np.nextafter(x - 3, y))
assert eq(da.ldexp(c, c), np.ldexp(z, z))
assert eq(da.fmod(a * 12, b), np.fmod(x * 12, y))
assert eq(da.floor(a * 0.5), np.floor(x * 0.5))
assert eq(da.ceil(a), np.ceil(x))
assert eq(da.trunc(a / 2), np.trunc(x / 2))
assert eq(da.degrees(b), np.degrees(y))
assert eq(da.radians(a), np.radians(x))
assert eq(da.rint(a + 0.3), np.rint(x + 0.3))
assert eq(da.fix(a - 2.5), np.fix(x - 2.5))
assert eq(da.angle(a + 1j), np.angle(x + 1j))
assert eq(da.real(a + 1j), np.real(x + 1j))
assert eq((a + 1j).real, np.real(x + 1j))
assert eq(da.imag(a + 1j), np.imag(x + 1j))
assert eq((a + 1j).imag, np.imag(x + 1j))
assert eq(da.conj(a + 1j * b), np.conj(x + 1j * y))
assert eq((a + 1j * b).conj(), (x + 1j * y).conj())
assert eq(da.clip(b, 1, 4), np.clip(y, 1, 4))
assert eq(da.fabs(b), np.fabs(y))
assert eq(da.sign(b - 2), np.sign(y - 2))
l1, l2 = da.frexp(a)
r1, r2 = np.frexp(x)
assert eq(l1, r1)
assert eq(l2, r2)
l1, l2 = da.modf(a)
r1, r2 = np.modf(x)
assert eq(l1, r1)
assert eq(l2, r2)
assert eq(da.around(a, -1), np.around(x, -1))
def norm_topo(self,
data,
elev,
solar_za,
solar_az,
slope=None,
aspect=None,
method='empirical-rotation',
slope_thresh=2,
nodata=0,
elev_nodata=-32768,
scale_factor=1,
angle_scale=0.01,
n_jobs=1,
robust=False,
min_samples=100,
slope_kwargs=None,
aspect_kwargs=None,
band_coeffs=None):
"""
Applies topographic normalization
Args:
data (2d or 3d DataArray): The data to normalize, in the range 0-1.
elev (2d DataArray): The elevation data.
solar_za (2d DataArray): The solar zenith angles (degrees).
solar_az (2d DataArray): The solar azimuth angles (degrees).
slope (2d DataArray): The slope data. If not given, slope is calculated from ``elev``.
aspect (2d DataArray): The aspect data. If not given, aspect is calculated from ``elev``.
method (Optional[str]): The method to apply. Choices are ['c', 'empirical-rotation'].
slope_thresh (Optional[float or int]): The slope threshold. Any samples with
values < ``slope_thresh`` are not adjusted.
nodata (Optional[int or float]): The 'no data' value for ``data``.
elev_nodata (Optional[float or int]): The 'no data' value for ``elev``.
scale_factor (Optional[float]): A scale factor to apply to the input data.
angle_scale (Optional[float]): The angle scale factor.
n_jobs (Optional[int]): The number of parallel workers for ``LinearRegression.fit``.
robust (Optional[bool]): Whether to fit a robust regression.
min_samples (Optional[int]): The minimum number of samples required to fit a regression.
slope_kwargs (Optional[dict]): Keyword arguments passed to ``gdal.DEMProcessingOptions``
to calculate the slope.
aspect_kwargs (Optional[dict]): Keyword arguments passed to ``gdal.DEMProcessingOptions``
to calculate the aspect.
band_coeffs (Optional[dict]): Slope and intercept coefficients for each band.
References:
See :cite:`teillet_etal_1982` for the C-correction method.
See :cite:`tan_etal_2010` for the Empirical Rotation method.
Returns:
``xarray.DataArray``
Examples:
>>> import geowombat as gw
>>> from geowombat.radiometry import Topo
>>>
>>> topo = Topo()
>>>
>>> # Example where pixel angles are stored in separate GeoTiff files
>>> with gw.config.update(sensor='l7', scale_factor=0.0001, nodata=0):
>>>
>>> with gw.open('landsat.tif') as src,
>>> gw.open('srtm') as elev,
>>> gw.open('solarz.tif') as solarz,
>>> gw.open('solara.tif') as solara:
>>>
>>> src_norm = topo.norm_topo(src, elev, solarz, solara, n_jobs=-1)
"""
method = method.strip().lower()
if method not in ['c', 'empirical-rotation']:
logger.exception(" Currently, the only supported methods are 'c' and 'empirical-rotation'.")
raise NameError
attrs = data.attrs.copy()
if not nodata:
nodata = data.gw.nodata
if scale_factor == 1.0:
scale_factor = data.gw.scale_factor
# Scale the reflectance data
if scale_factor != 1:
data = data * scale_factor
if not slope_kwargs:
slope_kwargs = dict(format='MEM',
computeEdges=True,
alg='ZevenbergenThorne',
slopeFormat='degree')
if not aspect_kwargs:
aspect_kwargs = dict(format='MEM',
computeEdges=True,
alg='ZevenbergenThorne',
trigonometric=False,
zeroForFlat=True)
slope_kwargs['format'] = 'MEM'
slope_kwargs['slopeFormat'] = 'degree'
aspect_kwargs['format'] = 'MEM'
# Force to SRTM resolution
proc_dims = (int((data.gw.ncols*data.gw.cellx) / 30.0),
int((data.gw.nrows*data.gw.celly) / 30.0))
w = int((5 * 30.0) / data.gw.celly)
if w % 2 == 0:
w += 1
if isinstance(slope, xr.DataArray):
slope_deg_fd = slope.squeeze().data
else:
slope_deg = calc_slope_delayed(elev.squeeze().data, proc_dims=proc_dims, w=w, **slope_kwargs)
slope_deg_fd = da.from_delayed(slope_deg, (data.gw.nrows, data.gw.ncols), dtype='float64')
if isinstance(aspect, xr.DataArray):
aspect_deg_fd = aspect.squeeze().data
else:
aspect_deg = calc_aspect_delayed(elev.squeeze().data, proc_dims=proc_dims, w=w, **aspect_kwargs)
aspect_deg_fd = da.from_delayed(aspect_deg, (data.gw.nrows, data.gw.ncols), dtype='float64')
nodata_samps = da.where((elev.data == elev_nodata) |
(data.max(dim='band').data == nodata) |
(slope_deg_fd < slope_thresh), 1, 0)
slope_rad = da.deg2rad(slope_deg_fd)
aspect_rad = da.deg2rad(aspect_deg_fd)
# Convert degrees to radians
solar_za = da.deg2rad(solar_za.squeeze().data * angle_scale)
solar_az = da.deg2rad(solar_az.squeeze().data * angle_scale)
cos_z = da.cos(solar_za)
# Calculate the illumination angle
il = da.cos(slope_rad) * cos_z + da.sin(slope_rad) * da.sin(solar_za) * da.cos(solar_az - aspect_rad)
sr_adj = list()
for band in data.band.values.tolist():
if method == 'c':
sr_adj.append(self._method_c(data.sel(band=band).data,
il,
cos_z,
nodata_samps,
min_samples,
n_jobs,
robust,
band_coeffs,
band))
else:
sr_adj.append(self._method_empirical_rotation(data.sel(band=band).data,
il,
cos_z,
nodata_samps,
min_samples,
n_jobs,
robust,
band_coeffs,
band))
adj_data = xr.DataArray(data=da.concatenate(sr_adj).reshape((data.gw.nbands,
data.gw.nrows,
data.gw.ncols)),
coords={'band': data.band.values.tolist(),
'y': data.y.values,
'x': data.x.values},
dims=('band', 'y', 'x'),
attrs=data.attrs)
attrs['calibration'] = 'Topographic-adjusted'
attrs['nodata'] = nodata
attrs['drange'] = (0, 1)
adj_data.attrs = attrs
return adj_data