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 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 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 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 radiance_to_toar(radiance, solar_za, global_args):
"""
Converts radiance to top-of-atmosphere reflectance
Args:
radiance (DataArray): The radiance data to calibrate.
solar_za (DataArray): The solar zenith angle.
global_args (namedtuple): Global arguments.
Returns:
``xarray.DataArray``
"""
attrs = radiance.attrs.copy()
solar_zenith_angle = solar_za * 0.01
toar_data = (global_args.pi * radiance * global_args.d**2) / (
global_args.esun * da.cos(solar_zenith_angle))
attrs['calibration'] = 'top-of-atmosphere reflectance'
toar_data.attrs = attrs
return toar_data
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 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 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 haversines(x1, x2, y1, y2, z1=None, z2=None):
x1, x2 = da.deg2rad(x1), da.deg2rad(x2)
y1, y2 = da.deg2rad(y1), da.deg2rad(y2)
x = (x2 - x1) * da.cos((y1 + y2) * 0.5) * cst.r_earth
y = (y2 - y1) * cst.r_earth * da.ones_like(x1) * da.ones_like(x2)
if z1 is None or z2 is None:
return da.stack((x, y), axis=-1)
else:
z1 = da.where(da.isnan(z1), 0, z1)
z2 = da.where(da.isnan(z2), 0, z2)
z = (z2 - z1) * da.ones_like(x)
return da.stack((x, y, z), axis=-1)
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 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 run_crefl(refl,
coeffs,
lon,
lat,
sensor_azimuth,
sensor_zenith,
solar_azimuth,
solar_zenith,
avg_elevation=None,
percent=False,
use_abi=False):
"""Run main crefl algorithm.
All input parameters are per-pixel values meaning they are the same size
and shape as the input reflectance data, unless otherwise stated.
:param reflectance_bands: tuple of reflectance band arrays
:param coefficients: tuple of coefficients for each band (see `get_coefficients`)
:param lon: input swath longitude array
:param lat: input swath latitude array
:param sensor_azimuth: input swath sensor azimuth angle array
:param sensor_zenith: input swath sensor zenith angle array
:param solar_azimuth: input swath solar azimuth angle array
:param solar_zenith: input swath solar zenith angle array
:param avg_elevation: average elevation (usually pre-calculated and stored in CMGDEM.hdf)
:param percent: True if input reflectances are on a 0-100 scale instead of 0-1 scale (default: False)
"""
# FUTURE: Find a way to compute the average elevation before hand
# Get digital elevation map data for our granule, set ocean fill value to 0
if avg_elevation is None:
LOG.debug("No average elevation information provided in CREFL")
#height = np.zeros(lon.shape, dtype=np.float)
height = 0.
else:
LOG.debug("Using average elevation information provided to CREFL")
lat[(lat <= -90) | (lat >= 90)] = np.nan
lon[(lon <= -180) | (lon >= 180)] = np.nan
row = ((90.0 - lat) * avg_elevation.shape[0] / 180.0).astype(np.int32)
col = ((lon + 180.0) * avg_elevation.shape[1] / 360.0).astype(np.int32)
space_mask = da.isnull(lon) | da.isnull(lat)
row[space_mask] = 0
col[space_mask] = 0
def _avg_elevation_index(avg_elevation, row, col):
return avg_elevation[row, col]
height = da.map_blocks(_avg_elevation_index,
avg_elevation,
row,
col,
dtype=avg_elevation.dtype)
height = xr.DataArray(height, dims=['y', 'x'])
# negative heights aren't allowed, clip to 0
height = height.where((height >= 0.) & ~space_mask, 0.0)
del lat, lon, row, col
mus = da.cos(da.deg2rad(solar_zenith))
mus = mus.where(mus >= 0)
muv = da.cos(da.deg2rad(sensor_zenith))
phi = solar_azimuth - sensor_azimuth
if use_abi:
LOG.debug("Using ABI CREFL algorithm")
a_O3 = [268.45, 0.5, 115.42, -3.2922]
a_H2O = [0.0311, 0.1, 92.471, -1.3814]
a_O2 = [0.4567, 0.007, 96.4884, -1.6970]
G_O3 = G_calc(solar_zenith, a_O3) + G_calc(sensor_zenith, a_O3)
G_H2O = G_calc(solar_zenith, a_H2O) + G_calc(sensor_zenith, a_H2O)
G_O2 = G_calc(solar_zenith, a_O2) + G_calc(sensor_zenith, a_O2)
# Note: bh2o values are actually ao2 values for abi
sphalb, rhoray, TtotraytH2O, tOG = get_atm_variables_abi(
mus, muv, phi, height, G_O3, G_H2O, G_O2, *coeffs)
else:
LOG.debug("Using original VIIRS CREFL algorithm")
sphalb, rhoray, TtotraytH2O, tOG = get_atm_variables(
mus, muv, phi, height, *coeffs)
del solar_azimuth, solar_zenith, sensor_zenith, sensor_azimuth
# Note: Assume that fill/invalid values are either NaN or we are dealing
# with masked arrays
if percent:
corr_refl = ((refl / 100.) / tOG - rhoray) / TtotraytH2O
else:
corr_refl = (refl / tOG - rhoray) / TtotraytH2O
corr_refl /= (1.0 + corr_refl * sphalb)
return corr_refl.clip(REFLMIN, REFLMAX)
def G_calc(zenith, a_coeff):
return (da.cos(da.deg2rad(zenith)) +
(a_coeff[0] * (zenith**a_coeff[1]) *
(a_coeff[2] - zenith)**a_coeff[3]))**-1
def chand(phi, muv, mus, taur):
# FROM FUNCTION CHAND
# phi: azimuthal difference between sun and observation in degree
# (phi=0 in backscattering direction)
# mus: cosine of the sun zenith angle
# muv: cosine of the observation zenith angle
# taur: molecular optical depth
# rhoray: molecular path reflectance
# constant xdep: depolarization factor (0.0279)
# xfd = (1-xdep/(2-xdep)) / (1 + 2*xdep/(2-xdep)) = 2 * (1 - xdep) / (2 + xdep) = 0.958725775
# */
xfd = 0.958725775
xbeta2 = 0.5
# float pl[5];
# double fs01, fs02, fs0, fs1, fs2;
as0 = [
0.33243832, 0.16285370, -0.30924818, -0.10324388, 0.11493334,
-6.777104e-02, 1.577425e-03, -1.240906e-02, 3.241678e-02, -3.503695e-02
]
as1 = [0.19666292, -5.439061e-02]
as2 = [0.14545937, -2.910845e-02]
# float phios, xcos1, xcos2, xcos3;
# float xph1, xph2, xph3, xitm1, xitm2;
# float xlntaur, xitot1, xitot2, xitot3;
# int i, ib;
xph1 = 1.0 + (3.0 * mus * mus - 1.0) * (3.0 * muv * muv - 1.0) * xfd / 8.0
xph2 = -xfd * xbeta2 * 1.5 * mus * muv * da.sqrt(
1.0 - mus * mus) * da.sqrt(1.0 - muv * muv)
xph3 = xfd * xbeta2 * 0.375 * (1.0 - mus * mus) * (1.0 - muv * muv)
# pl[0] = 1.0
# pl[1] = mus + muv
# pl[2] = mus * muv
# pl[3] = mus * mus + muv * muv
# pl[4] = mus * mus * muv * muv
fs01 = as0[0] + (mus + muv) * as0[1] + (mus * muv) * as0[2] + (
mus * mus + muv * muv) * as0[3] + (mus * mus * muv * muv) * as0[4]
fs02 = as0[5] + (mus + muv) * as0[6] + (mus * muv) * as0[7] + (
mus * mus + muv * muv) * as0[8] + (mus * mus * muv * muv) * as0[9]
# for (i = 0; i < 5; i++) {
# fs01 += (double) (pl[i] * as0[i]);
# fs02 += (double) (pl[i] * as0[5 + i]);
# }
# for refl, (ah2o, bh2o, ao3, tau) in zip(reflectance_bands, coefficients):
# ib = find_coefficient_index(center_wl)
# if ib is None:
# raise ValueError("Can't handle band with wavelength '{}'".format(center_wl))
xlntaur = da.log(taur)
fs0 = fs01 + fs02 * xlntaur
fs1 = as1[0] + xlntaur * as1[1]
fs2 = as2[0] + xlntaur * as2[1]
del xlntaur, fs01, fs02
trdown = da.exp(-taur / mus)
trup = da.exp(-taur / muv)
xitm1 = (1.0 - trdown * trup) / 4.0 / (mus + muv)
xitm2 = (1.0 - trdown) * (1.0 - trup)
xitot1 = xph1 * (xitm1 + xitm2 * fs0)
xitot2 = xph2 * (xitm1 + xitm2 * fs1)
xitot3 = xph3 * (xitm1 + xitm2 * fs2)
del xph1, xph2, xph3, xitm1, xitm2, fs0, fs1, fs2
phios = da.deg2rad(phi + 180.0)
xcos1 = 1.0
xcos2 = da.cos(phios)
xcos3 = da.cos(2.0 * phios)
del phios
rhoray = xitot1 * xcos1 + xitot2 * xcos2 * 2.0 + xitot3 * xcos3 * 2.0
return rhoray, trdown, trup
def new_grid_mapping_from_coords(
x_coords: xr.DataArray,
y_coords: xr.DataArray,
crs: Union[str, pyproj.crs.CRS],
*,
tile_size: Union[int, Tuple[int, int]] = None,
tolerance: float = DEFAULT_TOLERANCE,
) -> GridMapping:
crs = _normalize_crs(crs)
assert_instance(x_coords, xr.DataArray, name='x_coords')
assert_instance(y_coords, xr.DataArray, name='y_coords')
assert_true(x_coords.ndim in (1, 2),
'x_coords and y_coords must be either 1D or 2D arrays')
assert_instance(tolerance, float, name='tolerance')
assert_true(tolerance > 0.0, 'tolerance must be greater zero')
if x_coords.name and y_coords.name:
xy_var_names = str(x_coords.name), str(y_coords.name)
else:
xy_var_names = _default_xy_var_names(crs)
tile_size = _normalize_int_pair(tile_size, default=None)
is_lon_360 = None # None means "not yet known"
if crs.is_geographic:
is_lon_360 = bool(np.any(x_coords > 180))
x_res = 0
y_res = 0
if x_coords.ndim == 1:
# We have 1D x,y coordinates
cls = Coords1DGridMapping
assert_true(x_coords.size >= 2 and y_coords.size >= 2,
'sizes of x_coords and y_coords 1D arrays must be >= 2')
size = x_coords.size, y_coords.size
x_dim, y_dim = x_coords.dims[0], y_coords.dims[0]
x_diff = _abs_no_zero(x_coords.diff(dim=x_dim).values)
y_diff = _abs_no_zero(y_coords.diff(dim=y_dim).values)
if not is_lon_360 and crs.is_geographic:
is_anti_meridian_crossed = np.any(np.nanmax(x_diff) > 180)
if is_anti_meridian_crossed:
x_coords = to_lon_360(x_coords)
x_diff = _abs_no_zero(x_coords.diff(dim=x_dim))
is_lon_360 = True
x_res, y_res = x_diff[0], y_diff[0]
x_diff_equal = np.allclose(x_diff, x_res, atol=tolerance)
y_diff_equal = np.allclose(y_diff, y_res, atol=tolerance)
is_regular = x_diff_equal and y_diff_equal
if is_regular:
x_res = round_to_fraction(x_res, 5, 0.25)
y_res = round_to_fraction(y_res, 5, 0.25)
else:
x_res = round_to_fraction(float(np.nanmedian(x_diff)), 2, 0.5)
y_res = round_to_fraction(float(np.nanmedian(y_diff)), 2, 0.5)
if tile_size is None \
and x_coords.chunks is not None \
and y_coords.chunks is not None:
tile_size = (max(0,
*x_coords.chunks[0]), max(0, *y_coords.chunks[0]))
# Guess j axis direction
is_j_axis_up = bool(y_coords[0] < y_coords[-1])
else:
# We have 2D x,y coordinates
cls = Coords2DGridMapping
assert_true(
x_coords.shape == y_coords.shape, 'shapes of x_coords and y_coords'
' 2D arrays must be equal')
assert_true(
x_coords.dims == y_coords.dims,
'dimensions of x_coords and y_coords'
' 2D arrays must be equal')
y_dim, x_dim = x_coords.dims
height, width = x_coords.shape
size = width, height
x = da.asarray(x_coords)
y = da.asarray(y_coords)
x_x_diff = _abs_no_nan(da.diff(x, axis=1))
x_y_diff = _abs_no_nan(da.diff(x, axis=0))
y_x_diff = _abs_no_nan(da.diff(y, axis=1))
y_y_diff = _abs_no_nan(da.diff(y, axis=0))
if not is_lon_360 and crs.is_geographic:
is_anti_meridian_crossed = da.any(da.max(x_x_diff) > 180) \
or da.any(da.max(x_y_diff) > 180)
if is_anti_meridian_crossed:
x_coords = to_lon_360(x_coords)
x = da.asarray(x_coords)
x_x_diff = _abs_no_nan(da.diff(x, axis=1))
x_y_diff = _abs_no_nan(da.diff(x, axis=0))
is_lon_360 = True
is_regular = False
if da.all(x_y_diff == 0) and da.all(y_x_diff == 0):
x_res = x_x_diff[0, 0]
y_res = y_y_diff[0, 0]
is_regular = \
da.allclose(x_x_diff[0, :], x_res, atol=tolerance) \
and da.allclose(x_x_diff[-1, :], x_res, atol=tolerance) \
and da.allclose(y_y_diff[:, 0], y_res, atol=tolerance) \
and da.allclose(y_y_diff[:, -1], y_res, atol=tolerance)
if not is_regular:
# Let diff arrays have same shape as original by
# doubling last rows and columns.
x_x_diff_c = da.concatenate([x_x_diff, x_x_diff[:, -1:]], axis=1)
y_x_diff_c = da.concatenate([y_x_diff, y_x_diff[:, -1:]], axis=1)
x_y_diff_c = da.concatenate([x_y_diff, x_y_diff[-1:, :]], axis=0)
y_y_diff_c = da.concatenate([y_y_diff, y_y_diff[-1:, :]], axis=0)
# Find resolution via area
x_abs_diff = da.sqrt(da.square(x_x_diff_c) + da.square(x_y_diff_c))
y_abs_diff = da.sqrt(da.square(y_x_diff_c) + da.square(y_y_diff_c))
if crs.is_geographic:
# Convert degrees into meters
x_abs_diff_r = da.radians(x_abs_diff)
y_abs_diff_r = da.radians(y_abs_diff)
x_abs_diff = _ER * da.cos(x_abs_diff_r) * y_abs_diff_r
y_abs_diff = _ER * y_abs_diff_r
xy_areas = (x_abs_diff * y_abs_diff).flatten()
xy_areas = da.where(xy_areas > 0, xy_areas, np.nan)
# Get indices of min and max area
xy_area_index_min = da.nanargmin(xy_areas)
xy_area_index_max = da.nanargmax(xy_areas)
# Convert area to edge length
xy_res_min = math.sqrt(xy_areas[xy_area_index_min])
xy_res_max = math.sqrt(xy_areas[xy_area_index_max])
# Empirically weight min more than max
xy_res = 0.7 * xy_res_min + 0.3 * xy_res_max
if crs.is_geographic:
# Convert meters back into degrees
# print(f'xy_res in meters: {xy_res}')
xy_res = math.degrees(xy_res / _ER)
# print(f'xy_res in degrees: {xy_res}')
# Because this is an estimation, we can round to a nice number
xy_res = round_to_fraction(xy_res, digits=1, resolution=0.5)
x_res, y_res = float(xy_res), float(xy_res)
if tile_size is None and x_coords.chunks is not None:
j_chunks, i_chunks = x_coords.chunks
tile_size = max(0, *i_chunks), max(0, *j_chunks)
if tile_size is not None:
tile_width, tile_height = tile_size
x_coords = x_coords.chunk((tile_height, tile_width))
y_coords = y_coords.chunk((tile_height, tile_width))
# Guess j axis direction
is_j_axis_up = np.all(y_coords[0, :] < y_coords[-1, :]) or None
assert_true(x_res > 0 and y_res > 0,
'internal error: x_res and y_res could not be determined',
exception_type=RuntimeError)
x_res, y_res = _to_int_or_float(x_res), _to_int_or_float(y_res)
x_res_05, y_res_05 = x_res / 2, y_res / 2
x_min = _to_int_or_float(x_coords.min() - x_res_05)
y_min = _to_int_or_float(y_coords.min() - y_res_05)
x_max = _to_int_or_float(x_coords.max() + x_res_05)
y_max = _to_int_or_float(y_coords.max() + y_res_05)
return cls(x_coords=x_coords,
y_coords=y_coords,
crs=crs,
size=size,
tile_size=tile_size,
xy_bbox=(x_min, y_min, x_max, y_max),
xy_res=(x_res, y_res),
xy_var_names=xy_var_names,
xy_dim_names=(str(x_dim), str(y_dim)),
is_regular=is_regular,
is_lon_360=is_lon_360,
is_j_axis_up=is_j_axis_up)
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
# 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 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
def get_reflectance(self, sun_zenith, sat_zenith, azidiff, bandname, redband=None):
"""Get the reflectance from the three sun-sat angles"""
# Get wavelength in nm for band:
if isinstance(bandname, float):
LOG.warning('A wavelength is provided instead of band name - ' +
'disregard the relative spectral responses and assume ' +
'it is the effective wavelength: %f (micro meter)', bandname)
wvl = bandname * 1000.0
else:
wvl = self.get_effective_wavelength(bandname)
wvl = wvl * 1000.0
rayl, wvl_coord, azid_coord, satz_sec_coord, sunz_sec_coord = self.get_reflectance_lut()
# force dask arrays
compute = False
if HAVE_DASK and not isinstance(sun_zenith, Array):
compute = True
sun_zenith = from_array(sun_zenith, chunks=sun_zenith.shape)
sat_zenith = from_array(sat_zenith, chunks=sat_zenith.shape)
azidiff = from_array(azidiff, chunks=azidiff.shape)
if redband is not None:
redband = from_array(redband, chunks=redband.shape)
clip_angle = rad2deg(arccos(1. / sunz_sec_coord.max()))
sun_zenith = clip(sun_zenith, 0, clip_angle)
sunzsec = 1. / cos(deg2rad(sun_zenith))
clip_angle = rad2deg(arccos(1. / satz_sec_coord.max()))
sat_zenith = clip(sat_zenith, 0, clip_angle)
satzsec = 1. / cos(deg2rad(sat_zenith))
shape = sun_zenith.shape
if not(wvl_coord.min() < wvl < wvl_coord.max()):
LOG.warning(
"Effective wavelength for band %s outside 400-800 nm range!",
str(bandname))
LOG.info(
"Set the rayleigh/aerosol reflectance contribution to zero!")
if HAVE_DASK:
chunks = sun_zenith.chunks if redband is None else redband.chunks
res = zeros(shape, chunks=chunks)
return res.compute() if compute else res
else:
return zeros(shape)
idx = np.searchsorted(wvl_coord, wvl)
wvl1 = wvl_coord[idx - 1]
wvl2 = wvl_coord[idx]
fac = (wvl2 - wvl) / (wvl2 - wvl1)
raylwvl = fac * rayl[idx - 1, :, :, :] + (1 - fac) * rayl[idx, :, :, :]
tic = time.time()
smin = [sunz_sec_coord[0], azid_coord[0], satz_sec_coord[0]]
smax = [sunz_sec_coord[-1], azid_coord[-1], satz_sec_coord[-1]]
orders = [
len(sunz_sec_coord), len(azid_coord), len(satz_sec_coord)]
f_3d_grid = atleast_2d(raylwvl.ravel())
if HAVE_DASK and isinstance(smin[0], Array):
# compute all of these at the same time before passing to the interpolator
# otherwise they are computed separately
smin, smax, orders, f_3d_grid = da.compute(smin, smax, orders, f_3d_grid)
minterp = MultilinearInterpolator(smin, smax, orders)
minterp.set_values(f_3d_grid)
if HAVE_DASK:
ipn = map_blocks(self._do_interp, minterp, sunzsec, azidiff,
satzsec, dtype=raylwvl.dtype, chunks=azidiff.chunks)
else:
ipn = self._do_interp(minterp, sunzsec, azidiff, satzsec)
LOG.debug("Time - Interpolation: {0:f}".format(time.time() - tic))
ipn *= 100
res = ipn
if redband is not None:
res = where(redband < 20., res,
(1 - (redband - 20) / 80) * res)
res = clip(res, 0, 100)
if compute:
res = res.compute()
return res
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 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 get_reflectance(self, sun_zenith, sat_zenith, azidiff, bandname, redband=None):
"""Get the reflectance from the three sun-sat angles"""
# Get wavelength in nm for band:
if isinstance(bandname, float):
LOG.warning('A wavelength is provided instead of band name - ' +
'disregard the relative spectral responses and assume ' +
'it is the effective wavelength: %f (micro meter)', bandname)
wvl = bandname * 1000.0
else:
wvl = self.get_effective_wavelength(bandname)
if wvl is None:
LOG.error("Can't get effective wavelength for band %s on platform %s and sensor %s",
str(bandname), self.platform_name, self.sensor)
return None
else:
wvl = wvl * 1000.0
rayl, wvl_coord, azid_coord, satz_sec_coord, sunz_sec_coord = \
self.get_reflectance_lut()
# force dask arrays
compute = False
if HAVE_DASK and not isinstance(sun_zenith, Array):
compute = True
sun_zenith = from_array(sun_zenith, chunks=sun_zenith.shape)
sat_zenith = from_array(sat_zenith, chunks=sat_zenith.shape)
azidiff = from_array(azidiff, chunks=azidiff.shape)
if redband is not None:
redband = from_array(redband, chunks=redband.shape)
clip_angle = rad2deg(arccos(1. / sunz_sec_coord.max()))
sun_zenith = clip(sun_zenith, 0, clip_angle)
sunzsec = 1. / cos(deg2rad(sun_zenith))
clip_angle = rad2deg(arccos(1. / satz_sec_coord.max()))
sat_zenith = clip(sat_zenith, 0, clip_angle)
satzsec = 1. / cos(deg2rad(sat_zenith))
shape = sun_zenith.shape
if not(wvl_coord.min() < wvl < wvl_coord.max()):
LOG.warning(
"Effective wavelength for band %s outside 400-800 nm range!",
str(bandname))
LOG.info(
"Set the rayleigh/aerosol reflectance contribution to zero!")
if HAVE_DASK:
chunks = sun_zenith.chunks if redband is None \
else redband.chunks
res = zeros(shape, chunks=chunks)
return res.compute() if compute else res
else:
return zeros(shape)
idx = np.searchsorted(wvl_coord, wvl)
wvl1 = wvl_coord[idx - 1]
wvl2 = wvl_coord[idx]
fac = (wvl2 - wvl) / (wvl2 - wvl1)
raylwvl = fac * rayl[idx - 1, :, :, :] + (1 - fac) * rayl[idx, :, :, :]
tic = time.time()
smin = [sunz_sec_coord[0], azid_coord[0], satz_sec_coord[0]]
smax = [sunz_sec_coord[-1], azid_coord[-1], satz_sec_coord[-1]]
orders = [
len(sunz_sec_coord), len(azid_coord), len(satz_sec_coord)]
f_3d_grid = atleast_2d(raylwvl.ravel())
if HAVE_DASK and isinstance(smin[0], Array):
# compute all of these at the same time before passing to the interpolator
# otherwise they are computed separately
smin, smax, orders, f_3d_grid = da.compute(smin, smax, orders, f_3d_grid)
minterp = MultilinearInterpolator(smin, smax, orders)
minterp.set_values(f_3d_grid)
def _do_interp(minterp, sunzsec, azidiff, satzsec):
interp_points2 = np.vstack((sunzsec.ravel(),
180 - azidiff.ravel(),
satzsec.ravel()))
res = minterp(interp_points2)
return res.reshape(sunzsec.shape)
if HAVE_DASK:
ipn = map_blocks(_do_interp, minterp, sunzsec, azidiff,
satzsec, dtype=raylwvl.dtype,
chunks=azidiff.chunks)
else:
ipn = _do_interp(minterp, sunzsec, azidiff, satzsec)
LOG.debug("Time - Interpolation: {0:f}".format(time.time() - tic))
ipn *= 100
res = ipn
if redband is not None:
res = where(redband < 20., res,
(1 - (redband - 20) / 80) * res)
res = clip(res, 0, 100)
if compute:
res = res.compute()
return res
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 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 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 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
