/usr/lib/python3/dist-packages/pyresample/geometry.py is in python3-pyresample 1.8.1-1.
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#
# Copyright (C) 2010-2016
#
# Authors:
# Esben S. Nielsen
# Thomas Lavergne
# Adam Dybbroe
#
# This program is free software: you can redistribute it and/or modify it under
# the terms of the GNU Lesser General Public License as published by the Free
# Software Foundation, either version 3 of the License, or (at your option) any
# later version.
#
# This program is distributed in the hope that it will be useful, but WITHOUT
# ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
# FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more
# details.
#
# You should have received a copy of the GNU Lesser General Public License along
# with this program. If not, see <http://www.gnu.org/licenses/>.
"""Classes for geometry operations"""
import warnings
from collections import OrderedDict
from logging import getLogger
import hashlib
import numpy as np
import yaml
from pyproj import Geod, Proj
from pyresample import _spatial_mp, utils, CHUNK_SIZE
try:
from xarray import DataArray
except ImportError:
DataArray = np.ndarray
logger = getLogger(__name__)
class DimensionError(ValueError):
pass
class IncompatibleAreas(ValueError):
"""Error when the areas to combine are not compatible."""
class Boundary(object):
"""Container for geometry boundary.
Labelling starts in upper left corner and proceeds clockwise"""
def __init__(self, side1, side2, side3, side4):
self.side1 = side1
self.side2 = side2
self.side3 = side3
self.side4 = side4
class BaseDefinition(object):
"""Base class for geometry definitions
.. versionchanged:: 1.8.0
`BaseDefinition` no longer checks the validity of the provided
longitude and latitude coordinates to improve performance. Longitude
arrays are expected to be between -180 and 180 degrees, latitude -90
to 90 degrees. Use `pyresample.utils.check_and_wrap` to preprocess
your arrays.
"""
def __init__(self, lons=None, lats=None, nprocs=1):
if type(lons) != type(lats):
raise TypeError('lons and lats must be of same type')
elif lons is not None:
if not isinstance(lons, (np.ndarray, DataArray)):
lons = np.asanyarray(lons)
lats = np.asanyarray(lats)
if lons.shape != lats.shape:
raise ValueError('lons and lats must have same shape')
self.nprocs = nprocs
self.lats = lats
self.lons = lons
self.ndim = None
self.cartesian_coords = None
def __eq__(self, other):
"""Test for approximate equality"""
if other.lons is None or other.lats is None:
other_lons, other_lats = other.get_lonlats()
else:
other_lons = other.lons
other_lats = other.lats
if self.lons is None or self.lats is None:
self_lons, self_lats = self.get_lonlats()
else:
self_lons = self.lons
self_lats = self.lats
try:
return (np.allclose(self_lons, other_lons, atol=1e-6,
rtol=5e-9) and
np.allclose(self_lats, other_lats, atol=1e-6,
rtol=5e-9))
except (AttributeError, ValueError):
return False
def __ne__(self, other):
"""Test for approximate equality"""
return not self.__eq__(other)
def get_area_extent_for_subset(self, row_LR, col_LR, row_UL, col_UL):
"""Calculate extent for a subdomain of this area
Rows are counted from upper left to lower left and columns are
counted from upper left to upper right.
Args:
row_LR (int): row of the lower right pixel
col_LR (int): col of the lower right pixel
row_UL (int): row of the upper left pixel
col_UL (int): col of the upper left pixel
Returns:
area_extent (tuple):
Area extent (LL_x, LL_y, UR_x, UR_y) of the subset
Author:
Ulrich Hamann
"""
(a, b) = self.get_proj_coords(data_slice=(row_LR, col_LR))
a = a - 0.5 * self.pixel_size_x
b = b - 0.5 * self.pixel_size_y
(c, d) = self.get_proj_coords(data_slice=(row_UL, col_UL))
c = c + 0.5 * self.pixel_size_x
d = d + 0.5 * self.pixel_size_y
return a, b, c, d
def get_lonlat(self, row, col):
"""Retrieve lon and lat of single pixel
Parameters
----------
row : int
col : int
Returns
-------
(lon, lat) : tuple of floats
"""
if self.ndim != 2:
raise DimensionError(('operation undefined '
'for %sD geometry ') % self.ndim)
elif self.lons is None or self.lats is None:
raise ValueError('lon/lat values are not defined')
return self.lons[row, col], self.lats[row, col]
def get_lonlats(self, data_slice=None, **kwargs):
"""Base method for lon lat retrieval with slicing"""
if self.lons is None or self.lats is None:
raise ValueError('lon/lat values are not defined')
elif data_slice is None:
return self.lons, self.lats
else:
return self.lons[data_slice], self.lats[data_slice]
def get_boundary_lonlats(self):
"""Returns Boundary objects"""
side1 = self.get_lonlats(data_slice=(0, slice(None)))
side2 = self.get_lonlats(data_slice=(slice(None), -1))
side3 = self.get_lonlats(data_slice=(-1, slice(None)))
side4 = self.get_lonlats(data_slice=(slice(None), 0))
return (Boundary(side1[0], side2[0], side3[0][::-1], side4[0][::-1]),
Boundary(side1[1], side2[1], side3[1][::-1], side4[1][::-1]))
def get_cartesian_coords(self, nprocs=None, data_slice=None, cache=False):
"""Retrieve cartesian coordinates of geometry definition
Parameters
----------
nprocs : int, optional
Number of processor cores to be used.
Defaults to the nprocs set when instantiating object
data_slice : slice object, optional
Calculate only cartesian coordnates for the defined slice
cache : bool, optional
Store result the result. Requires data_slice to be None
Returns
-------
cartesian_coords : numpy array
"""
if self.cartesian_coords is None:
# Coordinates are not cached
if nprocs is None:
nprocs = self.nprocs
if data_slice is None:
# Use full slice
data_slice = slice(None)
lons, lats = self.get_lonlats(nprocs=nprocs, data_slice=data_slice)
if nprocs > 1:
cartesian = _spatial_mp.Cartesian_MP(nprocs)
else:
cartesian = _spatial_mp.Cartesian()
cartesian_coords = cartesian.transform_lonlats(np.ravel(lons),
np.ravel(lats))
if isinstance(lons, np.ndarray) and lons.ndim > 1:
# Reshape to correct shape
cartesian_coords = cartesian_coords.reshape(lons.shape[0],
lons.shape[1], 3)
if cache and data_slice is None:
self.cartesian_coords = cartesian_coords
else:
# Coordinates are cached
if data_slice is None:
cartesian_coords = self.cartesian_coords
else:
cartesian_coords = self.cartesian_coords[data_slice]
return cartesian_coords
@property
def corners(self):
"""Returns the corners of the current area.
"""
from pyresample.spherical_geometry import Coordinate
return [Coordinate(*self.get_lonlat(0, 0)),
Coordinate(*self.get_lonlat(0, -1)),
Coordinate(*self.get_lonlat(-1, -1)),
Coordinate(*self.get_lonlat(-1, 0))]
def __contains__(self, point):
"""Is a point inside the 4 corners of the current area? This uses
great circle arcs as area boundaries.
"""
from pyresample.spherical_geometry import point_inside, Coordinate
corners = self.corners
if isinstance(point, tuple):
return point_inside(Coordinate(*point), corners)
else:
return point_inside(point, corners)
def overlaps(self, other):
"""Tests if the current area overlaps the *other* area. This is based
solely on the corners of areas, assuming the boundaries to be great
circles.
Parameters
----------
other : object
Instance of subclass of BaseDefinition
Returns
-------
overlaps : bool
"""
from pyresample.spherical_geometry import Arc
self_corners = self.corners
other_corners = other.corners
for i in self_corners:
if i in other:
return True
for i in other_corners:
if i in self:
return True
self_arc1 = Arc(self_corners[0], self_corners[1])
self_arc2 = Arc(self_corners[1], self_corners[2])
self_arc3 = Arc(self_corners[2], self_corners[3])
self_arc4 = Arc(self_corners[3], self_corners[0])
other_arc1 = Arc(other_corners[0], other_corners[1])
other_arc2 = Arc(other_corners[1], other_corners[2])
other_arc3 = Arc(other_corners[2], other_corners[3])
other_arc4 = Arc(other_corners[3], other_corners[0])
for i in (self_arc1, self_arc2, self_arc3, self_arc4):
for j in (other_arc1, other_arc2, other_arc3, other_arc4):
if i.intersects(j):
return True
return False
def get_area(self):
"""Get the area of the convex area defined by the corners of the current
area.
"""
from pyresample.spherical_geometry import get_polygon_area
return get_polygon_area(self.corners)
def intersection(self, other):
"""Returns the corners of the intersection polygon of the current area
with *other*.
Parameters
----------
other : object
Instance of subclass of BaseDefinition
Returns
-------
(corner1, corner2, corner3, corner4) : tuple of points
"""
from pyresample.spherical_geometry import intersection_polygon
return intersection_polygon(self.corners, other.corners)
def overlap_rate(self, other):
"""Get how much the current area overlaps an *other* area.
Parameters
----------
other : object
Instance of subclass of BaseDefinition
Returns
-------
overlap_rate : float
"""
from pyresample.spherical_geometry import get_polygon_area
other_area = other.get_area()
inter_area = get_polygon_area(self.intersection(other))
return inter_area / other_area
class CoordinateDefinition(BaseDefinition):
"""Base class for geometry definitions defined by lons and lats only"""
def __init__(self, lons, lats, nprocs=1):
if not isinstance(lons, (np.ndarray, DataArray)):
lons = np.asanyarray(lons)
lats = np.asanyarray(lats)
super(CoordinateDefinition, self).__init__(lons, lats, nprocs)
if lons.shape == lats.shape and lons.dtype == lats.dtype:
self.shape = lons.shape
self.size = lons.size
self.ndim = lons.ndim
self.dtype = lons.dtype
else:
raise ValueError(('%s must be created with either '
'lon/lats of the same shape with same dtype') %
self.__class__.__name__)
def concatenate(self, other):
if self.ndim != other.ndim:
raise DimensionError(('Unable to concatenate %sD and %sD '
'geometries') % (self.ndim, other.ndim))
klass = _get_highest_level_class(self, other)
lons = np.concatenate((self.lons, other.lons))
lats = np.concatenate((self.lats, other.lats))
nprocs = min(self.nprocs, other.nprocs)
return klass(lons, lats, nprocs=nprocs)
def append(self, other):
if self.ndim != other.ndim:
raise DimensionError(('Unable to append %sD and %sD '
'geometries') % (self.ndim, other.ndim))
self.lons = np.concatenate((self.lons, other.lons))
self.lats = np.concatenate((self.lats, other.lats))
self.shape = self.lons.shape
self.size = self.lons.size
def __str__(self):
# Rely on numpy's object printing
return ('Shape: %s\nLons: %s\nLats: %s') % (str(self.shape),
str(self.lons),
str(self.lats))
class GridDefinition(CoordinateDefinition):
"""Grid defined by lons and lats
Parameters
----------
lons : numpy array
lats : numpy array
nprocs : int, optional
Number of processor cores to be used for calculations.
Attributes
----------
shape : tuple
Grid shape as (rows, cols)
size : int
Number of elements in grid
lons : object
Grid lons
lats : object
Grid lats
cartesian_coords : object
Grid cartesian coordinates
"""
def __init__(self, lons, lats, nprocs=1):
super(GridDefinition, self).__init__(lons, lats, nprocs)
if lons.shape != lats.shape:
raise ValueError('lon and lat grid must have same shape')
elif lons.ndim != 2:
raise ValueError('2 dimensional lon lat grid expected')
def get_array_hashable(arr):
"""Compute a hashable form of the array `arr`.
Works with numpy arrays, dask.array.Array, and xarray.DataArray.
"""
# look for precomputed value
if isinstance(arr, DataArray) and np.ndarray is not DataArray:
return arr.attrs.get('hash', get_array_hashable(arr.data))
else:
try:
return arr.name.encode('utf-8') # dask array
except AttributeError:
return np.asarray(arr).view(np.uint8) # np array
class SwathDefinition(CoordinateDefinition):
"""Swath defined by lons and lats.
Parameters
----------
lons : numpy array
lats : numpy array
nprocs : int, optional
Number of processor cores to be used for calculations.
Attributes
----------
shape : tuple
Swath shape
size : int
Number of elements in swath
ndims : int
Swath dimensions
lons : object
Swath lons
lats : object
Swath lats
cartesian_coords : object
Swath cartesian coordinates
"""
def __init__(self, lons, lats, nprocs=1):
if not isinstance(lons, (np.ndarray, DataArray)):
lons = np.asanyarray(lons)
lats = np.asanyarray(lats)
super(SwathDefinition, self).__init__(lons, lats, nprocs)
if lons.shape != lats.shape:
raise ValueError('lon and lat arrays must have same shape')
elif lons.ndim > 2:
raise ValueError('Only 1 and 2 dimensional swaths are allowed')
self.hash = None
def __hash__(self):
"""Compute the hash of this object."""
if self.hash is None:
hasher = hashlib.sha1()
hasher.update(get_array_hashable(self.lons))
hasher.update(get_array_hashable(self.lats))
try:
if self.lons.mask is not np.bool_(False):
hasher.update(get_array_hashable(self.lons.mask))
except AttributeError:
pass
self.hash = int(hasher.hexdigest(), 16)
return self.hash
def get_lonlats_dask(self, chunks=CHUNK_SIZE):
"""Get the lon lats as a single dask array."""
import dask.array as da
lons, lats = self.get_lonlats()
if isinstance(lons.data, da.Array):
return lons.data, lats.data
else:
lons = da.from_array(np.asanyarray(lons),
chunks=chunks)
lats = da.from_array(np.asanyarray(lats),
chunks=chunks)
return lons, lats
def _compute_omerc_parameters(self, ellipsoid):
"""Compute the oblique mercator projection bouding box parameters."""
lines, cols = self.lons.shape
lon1, lon2 = np.asanyarray(self.lons[[0, -1], int(cols / 2)])
lat1, lat, lat2 = np.asanyarray(
self.lats[[0, int(lines / 2), -1], int(cols / 2)])
proj_dict2points = {'proj': 'omerc', 'lat_0': lat, 'ellps': ellipsoid,
'lat_1': lat1, 'lon_1': lon1,
'lat_2': lat2, 'lon_2': lon2}
lonc, lat0 = Proj(**proj_dict2points)(0, 0, inverse=True)
az1, az2, dist = Geod(**proj_dict2points).inv(lonc, lat0, lon1, lat1)
del az2, dist
return {'proj': 'omerc', 'alpha': float(az1),
'lat_0': float(lat0), 'lonc': float(lonc),
'no_rot': True, 'ellps': ellipsoid}
def _compute_generic_parameters(self, projection, ellipsoid):
"""Compute the projection bb parameters for most projections."""
lines, cols = self.lons.shape
lat_0 = self.lats[int(lines / 2), int(cols / 2)]
lon_0 = self.lons[int(lines / 2), int(cols / 2)]
return {'proj': projection, 'ellps': ellipsoid,
'lat_0': lat_0, 'lon_0': lon_0}
def get_edge_lonlats(self):
"""Get the concatenated boundary of the current swath."""
lons, lats = self.get_boundary_lonlats()
blons = np.ma.concatenate([lons.side1, lons.side2,
lons.side3, lons.side4])
blats = np.ma.concatenate([lats.side1, lats.side2,
lats.side3, lats.side4])
return blons, blats
def compute_bb_proj_params(self, proj_dict):
projection = proj_dict['proj']
ellipsoid = proj_dict.get('ellps', 'WGS84')
if projection == 'omerc':
return self._compute_omerc_parameters(ellipsoid)
else:
new_proj = self._compute_generic_parameters(projection, ellipsoid)
new_proj.update(proj_dict)
return new_proj
def compute_optimal_bb_area(self, proj_dict=None):
"""Compute the "best" bounding box area for this swath with `proj_dict`.
By default, the projection is Oblique Mercator (`omerc` in proj.4), in
which case the right projection angle `alpha` is computed from the
swath centerline. For other projections, only the appropriate center of
projection and area extents are computed.
"""
if proj_dict is None:
proj_dict = {}
projection = proj_dict.setdefault('proj', 'omerc')
area_id = projection + '_otf'
description = 'On-the-fly ' + projection + ' area'
lines, cols = self.lons.shape
x_size = int(cols * 1.1)
y_size = int(lines * 1.1)
proj_dict = self.compute_bb_proj_params(proj_dict)
if projection == 'omerc':
x_size, y_size = y_size, x_size
area = DynamicAreaDefinition(area_id, description, proj_dict)
lons, lats = self.get_edge_lonlats()
return area.freeze((lons, lats), size=(x_size, y_size))
class DynamicAreaDefinition(object):
"""An AreaDefintion containing just a subset of the needed parameters.
The purpose of this class is to be able to adapt the area extent and size
of the area to a given set of longitudes and latitudes, such that e.g.
polar satellite granules can be resampled optimaly to a give projection.
"""
def __init__(self, area_id=None, description=None, proj_dict=None,
x_size=None, y_size=None, area_extent=None,
optimize_projection=False, rotation=None):
"""Initialize the DynamicAreaDefinition.
area_id:
The name of the area.
description:
The description of the area.
proj_dict:
The dictionary of projection parameters. Doesn't have to be complete.
x_size, y_size:
The size of the resulting area.
area_extent:
The area extent of the area.
optimize_projection:
Whether the projection parameters have to be optimized.
rotation:
Rotation in degrees (negative is cw)
"""
self.area_id = area_id
self.description = description
self.proj_dict = proj_dict
self.x_size = x_size
self.y_size = y_size
self.area_extent = area_extent
self.optimize_projection = optimize_projection
self.rotation = rotation
def compute_domain(self, corners, resolution=None, size=None):
"""Compute size and area_extent from corners and [size or resolution]
info."""
if resolution is not None and size is not None:
raise ValueError("Both resolution and size can't be provided.")
if size:
x_size, y_size = size
x_resolution = (corners[2] - corners[0]) * 1.0 / (x_size - 1)
y_resolution = (corners[3] - corners[1]) * 1.0 / (y_size - 1)
if resolution:
try:
x_resolution, y_resolution = resolution
except TypeError:
x_resolution = y_resolution = resolution
x_size = int(np.rint((corners[2] - corners[0]) * 1.0 /
x_resolution + 1))
y_size = int(np.rint((corners[3] - corners[1]) * 1.0 /
y_resolution + 1))
area_extent = (corners[0] - x_resolution / 2,
corners[1] - y_resolution / 2,
corners[2] + x_resolution / 2,
corners[3] + y_resolution / 2)
return area_extent, x_size, y_size
def freeze(self, lonslats=None,
resolution=None, size=None,
proj_info=None, rotation=None):
"""Create an AreaDefintion from this area with help of some extra info.
lonlats:
the geographical coordinates to contain in the resulting area.
resolution:
the resolution of the resulting area.
size:
the size of the resulting area.
proj_info:
complementing parameters to the projection info.
rotation:
rotation in degrees (negative is cw)
Resolution and Size parameters are ignored if the instance is created
with the `optimize_projection` flag set to True.
"""
if proj_info is not None:
self.proj_dict.update(proj_info)
if self.optimize_projection:
return lonslats.compute_optimal_bb_area(self.proj_dict)
if not self.area_extent or not self.x_size or not self.y_size:
proj4 = Proj(**self.proj_dict)
try:
lons, lats = lonslats
except (TypeError, ValueError):
lons, lats = lonslats.get_lonlats()
xarr, yarr = proj4(np.asarray(lons), np.asarray(lats))
corners = [np.min(xarr), np.min(yarr), np.max(xarr), np.max(yarr)]
domain = self.compute_domain(corners, resolution, size)
self.area_extent, self.x_size, self.y_size = domain
return AreaDefinition(self.area_id, self.description, '',
self.proj_dict, self.x_size, self.y_size,
self.area_extent, self.rotation)
class AreaDefinition(BaseDefinition):
"""Holds definition of an area.
Parameters
----------
area_id : str
ID of area
name : str
Name of area
proj_id : str
ID of projection
proj_dict : dict
Dictionary with Proj.4 parameters
x_size : int
x dimension in number of pixels
y_size : int
y dimension in number of pixels
rotation: float
rotation in degrees (negative is cw)
area_extent : list
Area extent as a list (LL_x, LL_y, UR_x, UR_y)
nprocs : int, optional
Number of processor cores to be used
lons : numpy array, optional
Grid lons
lats : numpy array, optional
Grid lats
Attributes
----------
area_id : str
ID of area
name : str
Name of area
proj_id : str
ID of projection
proj_dict : dict
Dictionary with Proj.4 parameters
x_size : int
x dimension in number of pixels
y_size : int
y dimension in number of pixels
rotation: float
rotation in degrees (negative is cw)
shape : tuple
Corresponding array shape as (rows, cols)
size : int
Number of points in grid
area_extent : tuple
Area extent as a tuple (LL_x, LL_y, UR_x, UR_y)
area_extent_ll : tuple
Area extent in lons lats as a tuple (LL_lon, LL_lat, UR_lon, UR_lat)
pixel_size_x : float
Pixel width in projection units
pixel_size_y : float
Pixel height in projection units
pixel_upper_left : list
Coordinates (x, y) of center of upper left pixel in projection units
pixel_offset_x : float
x offset between projection center and upper left corner of upper
left pixel in units of pixels.
pixel_offset_y : float
y offset between projection center and upper left corner of upper
left pixel in units of pixels..
proj4_string : str
Projection defined as Proj.4 string
lons : object
Grid lons
lats : object
Grid lats
cartesian_coords : object
Grid cartesian coordinates
projection_x_coords : object
Grid projection x coordinate
projection_y_coords : object
Grid projection y coordinate
"""
def __init__(self, area_id, name, proj_id, proj_dict, x_size, y_size,
area_extent, rotation=None, nprocs=1, lons=None, lats=None,
dtype=np.float64):
if not isinstance(proj_dict, dict):
raise TypeError('Wrong type for proj_dict: %s. Expected dict.'
% type(proj_dict))
super(AreaDefinition, self).__init__(lons, lats, nprocs)
self.area_id = area_id
self.name = name
self.proj_id = proj_id
self.x_size = int(x_size)
self.y_size = int(y_size)
self.shape = (y_size, x_size)
try:
self.rotation = float(rotation)
except TypeError:
self.rotation = 0
if lons is not None:
if lons.shape != self.shape:
raise ValueError('Shape of lon lat grid must match '
'area definition')
self.size = y_size * x_size
self.ndim = 2
self.pixel_size_x = (area_extent[2] - area_extent[0]) / float(x_size)
self.pixel_size_y = (area_extent[3] - area_extent[1]) / float(y_size)
self.proj_dict = proj_dict
self.area_extent = tuple(area_extent)
# Calculate area_extent in lon lat
proj = _spatial_mp.Proj(**proj_dict)
corner_lons, corner_lats = proj((area_extent[0], area_extent[2]),
(area_extent[1], area_extent[3]),
inverse=True)
self.area_extent_ll = (corner_lons[0], corner_lats[0],
corner_lons[1], corner_lats[1])
# Calculate projection coordinates of center of upper left pixel
self.pixel_upper_left = \
(float(area_extent[0]) +
float(self.pixel_size_x) / 2,
float(area_extent[3]) -
float(self.pixel_size_y) / 2)
# Pixel_offset defines the distance to projection center from origen
# (UL) of image in units of pixels.
self.pixel_offset_x = -self.area_extent[0] / self.pixel_size_x
self.pixel_offset_y = self.area_extent[3] / self.pixel_size_y
self._projection_x_coords = None
self._projection_y_coords = None
self.dtype = dtype
@property
def proj_str(self):
return utils.proj4_dict_to_str(self.proj_dict, sort=True)
def __str__(self):
# We need a sorted dictionary for a unique hash of str(self)
proj_dict = self.proj_dict
proj_str = ('{' +
', '.join(["'%s': '%s'" % (str(k), str(proj_dict[k]))
for k in sorted(proj_dict.keys())]) +
'}')
if not self.proj_id:
third_line = ""
else:
third_line = "Projection ID: {0}\n".format(self.proj_id)
return ('Area ID: {0}\nDescription: {1}\n{2}'
'Projection: {3}\nNumber of columns: {4}\nNumber of rows: {5}\n'
'Area extent: {6}').format(self.area_id, self.name, third_line,
proj_str, self.x_size, self.y_size,
self.area_extent)
def create_areas_def(self):
to_dump = OrderedDict()
res = OrderedDict()
to_dump[self.area_id] = res
res['description'] = self.name
res['shape'] = OrderedDict([('height', self.y_size),
('width', self.x_size)])
res['area_extent'] = OrderedDict([('lower_left_xy',
list(self.area_extent[:2])),
('upper_right_xy',
list(self.area_extent[2:])),
('units', 'm')
])
return ordered_dump(to_dump)
def create_areas_def_legacy(self):
proj_dict = self.proj_dict
proj_str = ','.join(["%s=%s" % (str(k), str(proj_dict[k]))
for k in sorted(proj_dict.keys())])
fmt = "REGION: {name} {{\n"
fmt += "\tNAME:\t{name}\n"
fmt += "\tPCS_ID:\t{area_id}\n"
fmt += "\tPCS_DEF:\t{proj_str}\n"
fmt += "\tXSIZE:\t{x_size}\n"
fmt += "\tYSIZE:\t{y_size}\n"
fmt += "\tROTATION:\t{rotation}\n"
fmt += "\tAREA_EXTENT: {area_extent}\n}};\n"
area_def_str = fmt.format(name=self.name, area_id=self.area_id,
proj_str=proj_str, x_size=self.x_size,
y_size=self.y_size,
area_extent=self.area_extent)
return area_def_str
__repr__ = __str__
def __eq__(self, other):
"""Test for equality"""
try:
return ((self.proj_str == other.proj_str) and
(self.shape == other.shape) and
(np.allclose(self.area_extent, other.area_extent)))
except AttributeError:
return super(AreaDefinition, self).__eq__(other)
def __ne__(self, other):
"""Test for equality"""
return not self.__eq__(other)
def __hash__(self):
return hash((
self.proj_str,
self.shape,
self.area_extent
))
def colrow2lonlat(self, cols, rows):
"""
Return longitudes and latitudes for the given image columns
and rows. Both scalars and arrays are supported.
To be used with scarse data points instead of slices
(see get_lonlats).
"""
p = _spatial_mp.Proj(self.proj4_string)
x = self.projection_x_coords
y = self.projection_y_coords
return p(y[y.size - cols], x[x.size - rows], inverse=True)
def lonlat2colrow(self, lons, lats):
"""
Return image columns and rows for the given longitudes
and latitudes. Both scalars and arrays are supported.
Same as get_xy_from_lonlat, renamed for convenience.
"""
return self.get_xy_from_lonlat(lons, lats)
def get_xy_from_lonlat(self, lon, lat):
"""Retrieve closest x and y coordinates (column, row indices) for the
specified geolocation (lon,lat) if inside area. If lon,lat is a point a
ValueError is raised if the return point is outside the area domain. If
lon,lat is a tuple of sequences of longitudes and latitudes, a tuple of
masked arrays are returned.
:Input:
lon : point or sequence (list or array) of longitudes
lat : point or sequence (list or array) of latitudes
:Returns:
(x, y) : tuple of integer points/arrays
"""
if isinstance(lon, list):
lon = np.array(lon)
if isinstance(lat, list):
lat = np.array(lat)
if ((isinstance(lon, np.ndarray) and
not isinstance(lat, np.ndarray)) or
(not isinstance(lon, np.ndarray) and
isinstance(lat, np.ndarray))):
raise ValueError("Both lon and lat needs to be of " +
"the same type and have the same dimensions!")
if isinstance(lon, np.ndarray) and isinstance(lat, np.ndarray):
if lon.shape != lat.shape:
raise ValueError("lon and lat is not of the same shape!")
pobj = _spatial_mp.Proj(self.proj4_string)
xm_, ym_ = pobj(lon, lat)
return self.get_xy_from_proj_coords(xm_, ym_)
def get_xy_from_proj_coords(self, xm, ym):
"""Find closest grid cell index for a specified projection coordinate.
If xm, ym is a tuple of sequences of projection coordinates, a tuple
of masked arrays are returned.
Args:
xm (list or array): point or sequence of x-coordinates in
meters (map projection)
ym (list or array): point or sequence of y-coordinates in
meters (map projection)
Returns:
x, y : column and row grid cell indexes as 2 scalars or arrays
Raises:
ValueError: if the return point is outside the area domain
"""
if isinstance(xm, list):
xm = np.array(xm)
if isinstance(ym, list):
ym = np.array(ym)
if ((isinstance(xm, np.ndarray) and
not isinstance(ym, np.ndarray)) or
(not isinstance(xm, np.ndarray) and
isinstance(ym, np.ndarray))):
raise ValueError("Both projection coordinates xm and ym needs to be of " +
"the same type and have the same dimensions!")
if isinstance(xm, np.ndarray) and isinstance(ym, np.ndarray):
if xm.shape != ym.shape:
raise ValueError(
"projection coordinates xm and ym is not of the same shape!")
upl_x = self.area_extent[0]
upl_y = self.area_extent[3]
xscale = (self.area_extent[2] -
self.area_extent[0]) / float(self.x_size)
# because rows direction is the opposite of y's
yscale = (self.area_extent[1] -
self.area_extent[3]) / float(self.y_size)
x__ = (xm - upl_x) / xscale
y__ = (ym - upl_y) / yscale
if isinstance(x__, np.ndarray) and isinstance(y__, np.ndarray):
mask = (((x__ < 0) | (x__ > self.x_size)) |
((y__ < 0) | (y__ > self.y_size)))
return (np.ma.masked_array(x__.astype('int'), mask=mask,
fill_value=-1, copy=False),
np.ma.masked_array(y__.astype('int'), mask=mask,
fill_value=-1, copy=False))
else:
if ((x__ < 0 or x__ > self.x_size) or
(y__ < 0 or y__ > self.y_size)):
raise ValueError('Point outside area:( %f %f)' % (x__, y__))
return int(x__), int(y__)
def get_lonlat(self, row, col):
"""Retrieves lon and lat values of single point in area grid
Parameters
----------
row : int
col : int
Returns
-------
(lon, lat) : tuple of floats
"""
return self.get_lonlats(nprocs=None, data_slice=(row, col))
def get_proj_vectors_dask(self, chunks=CHUNK_SIZE, dtype=None):
import dask.array as da
if dtype is None:
dtype = self.dtype
target_x = da.arange(self.x_size, chunks=chunks, dtype=dtype) * \
self.pixel_size_x + self.pixel_upper_left[0]
target_y = da.arange(self.y_size, chunks=chunks, dtype=dtype) * - \
self.pixel_size_y + self.pixel_upper_left[1]
return target_x, target_y
def get_proj_coords_dask(self, chunks=CHUNK_SIZE, dtype=None):
# TODO: Add rotation
import dask.array as da
target_x, target_y = self.get_proj_vectors_dask(chunks, dtype)
return da.meshgrid(target_x, target_y)
def get_proj_coords(self, data_slice=None, cache=False, dtype=None):
"""Get projection coordinates of grid.
Parameters
----------
data_slice : slice object, optional
Calculate only coordinates for specified slice
cache : bool, optional
Store the result. Requires data_slice to be None
Returns
-------
(target_x, target_y) : tuple of numpy arrays
Grids of area x- and y-coordinates in projection units
"""
def do_rotation(xspan, yspan, rot_deg=0):
rot_rad = np.radians(rot_deg)
rot_mat = np.array([[np.cos(rot_rad), np.sin(rot_rad)],
[-np.sin(rot_rad), np.cos(rot_rad)]])
x, y = np.meshgrid(xspan, yspan)
return np.einsum('ji, mni -> jmn', rot_mat, np.dstack([x, y]))
def get_val(val, sub_val, max):
# Get value with substitution and wrapping
if val is None:
return sub_val
else:
if val < 0:
# Wrap index
return max + val
else:
return val
if self._projection_x_coords is not None and self._projection_y_coords is not None:
# Projection coords are cached
if data_slice is None:
return self._projection_x_coords, self._projection_y_coords
else:
return self._projection_x_coords[data_slice], self._projection_y_coords[data_slice]
is_single_value = False
is_1d_select = False
if dtype is None:
dtype = self.dtype
# create coordinates of local area as ndarrays
if data_slice is None or data_slice == slice(None):
# Full slice
rows = self.y_size
cols = self.x_size
row_start = 0
col_start = 0
else:
if isinstance(data_slice, slice):
# Row slice
row_start = get_val(data_slice.start, 0, self.y_size)
col_start = 0
rows = get_val(
data_slice.stop, self.y_size, self.y_size) - row_start
cols = self.x_size
elif isinstance(data_slice[0], slice) and isinstance(data_slice[1], slice):
# Block slice
row_start = get_val(data_slice[0].start, 0, self.y_size)
col_start = get_val(data_slice[1].start, 0, self.x_size)
rows = get_val(
data_slice[0].stop, self.y_size, self.y_size) - row_start
cols = get_val(
data_slice[1].stop, self.x_size, self.x_size) - col_start
elif isinstance(data_slice[0], slice):
# Select from col
is_1d_select = True
row_start = get_val(data_slice[0].start, 0, self.y_size)
col_start = get_val(data_slice[1], 0, self.x_size)
rows = get_val(
data_slice[0].stop, self.y_size, self.y_size) - row_start
cols = 1
elif isinstance(data_slice[1], slice):
# Select from row
is_1d_select = True
row_start = get_val(data_slice[0], 0, self.y_size)
col_start = get_val(data_slice[1].start, 0, self.x_size)
rows = 1
cols = get_val(
data_slice[1].stop, self.x_size, self.x_size) - col_start
else:
# Single element select
is_single_value = True
row_start = get_val(data_slice[0], 0, self.y_size)
col_start = get_val(data_slice[1], 0, self.x_size)
rows = 1
cols = 1
# Calculate coordinates
target_x = np.arange(col_start, col_start + cols, dtype=dtype) * \
self.pixel_size_x + self.pixel_upper_left[0]
target_y = np.arange(row_start, row_start + rows, dtype=dtype) * - \
self.pixel_size_y + self.pixel_upper_left[1]
if self.rotation != 0:
res = do_rotation(target_x, target_y, self.rotation)
target_x, target_y = res[0, :, :], res[1, :, :]
else:
target_x, target_y = np.meshgrid(target_x, target_y)
if is_single_value:
# Return single values
target_x = float(target_x)
target_y = float(target_y)
elif is_1d_select:
# Reshape to 1D array
target_x = target_x.reshape((target_x.size,))
target_y = target_y.reshape((target_y.size,))
if cache and data_slice is None:
# Cache the result if requested
self._projection_x_coords = target_x
self._projection_y_coords = target_y
return target_x, target_y
@property
def projection_x_coords(self):
return self.get_proj_coords(data_slice=(0, slice(None)))[0]
@property
def projection_y_coords(self):
return self.get_proj_coords(data_slice=(slice(None), 0))[1]
@property
def proj_x_coords(self):
warnings.warn(
"Deprecated, use 'projection_x_coords' instead", DeprecationWarning)
return self.projection_x_coords
@property
def proj_y_coords(self):
warnings.warn(
"Deprecated, use 'projection_y_coords' instead", DeprecationWarning)
return self.projection_y_coords
@property
def outer_boundary_corners(self):
"""Returns the lon,lat of the outer edges of the corner points
"""
from pyresample.spherical_geometry import Coordinate
proj = _spatial_mp.Proj(**self.proj_dict)
corner_lons, corner_lats = proj((self.area_extent[0], self.area_extent[2],
self.area_extent[2], self.area_extent[0]),
(self.area_extent[3], self.area_extent[3],
self.area_extent[1], self.area_extent[1]),
inverse=True)
return [Coordinate(corner_lons[0], corner_lats[0]),
Coordinate(corner_lons[1], corner_lats[1]),
Coordinate(corner_lons[2], corner_lats[2]),
Coordinate(corner_lons[3], corner_lats[3])]
def get_lonlats_dask(self, chunks=CHUNK_SIZE, dtype=None):
from dask.array import map_blocks
dtype = dtype or self.dtype
target_x, target_y = self.get_proj_coords_dask(chunks, dtype)
target_proj = Proj(**self.proj_dict)
def invproj(data1, data2):
return np.dstack(target_proj(data1, data2, inverse=True))
res = map_blocks(invproj, target_x, target_y, chunks=(target_x.chunks[0],
target_x.chunks[1],
2),
new_axis=[2])
return res[:, :, 0], res[:, :, 1]
def get_lonlats(self, nprocs=None, data_slice=None, cache=False, dtype=None):
"""Returns lon and lat arrays of area.
Parameters
----------
nprocs : int, optional
Number of processor cores to be used.
Defaults to the nprocs set when instantiating object
data_slice : slice object, optional
Calculate only coordinates for specified slice
cache : bool, optional
Store result the result. Requires data_slice to be None
Returns
-------
(lons, lats) : tuple of numpy arrays
Grids of area lons and and lats
"""
if dtype is None:
dtype = self.dtype
if self.lons is None or self.lats is None:
# Data is not cached
if nprocs is None:
nprocs = self.nprocs
# Proj.4 definition of target area projection
if nprocs > 1:
target_proj = _spatial_mp.Proj_MP(**self.proj_dict)
else:
target_proj = _spatial_mp.Proj(**self.proj_dict)
# Get coordinates of local area as ndarrays
target_x, target_y = self.get_proj_coords(
data_slice=data_slice, dtype=dtype)
# Get corresponding longitude and latitude values
lons, lats = target_proj(target_x, target_y, inverse=True,
nprocs=nprocs)
lons = np.asanyarray(lons, dtype=dtype)
lats = np.asanyarray(lats, dtype=dtype)
if cache and data_slice is None:
# Cache the result if requested
self.lons = lons
self.lats = lats
# Free memory
del(target_x)
del(target_y)
else:
# Data is cached
if data_slice is None:
# Full slice
lons = self.lons
lats = self.lats
else:
lons = self.lons[data_slice]
lats = self.lats[data_slice]
return lons, lats
@property
def proj4_string(self):
"""Returns projection definition as Proj.4 string"""
items = self.proj_dict.items()
return '+' + ' +'.join([t[0] + '=' + str(t[1]) for t in items])
def combine_area_extents_vertical(area1, area2):
"""Combine the area extents of areas 1 and 2."""
if (area1.area_extent[0] == area2.area_extent[0] and
area1.area_extent[2] == area2.area_extent[2]):
current_extent = list(area1.area_extent)
if np.isclose(area1.area_extent[1], area2.area_extent[3]):
current_extent[1] = area2.area_extent[1]
elif np.isclose(area1.area_extent[3], area2.area_extent[1]):
current_extent[3] = area2.area_extent[3]
else:
raise IncompatibleAreas(
"Can't concatenate area definitions with "
"incompatible area extents: "
"{0} and {1}".format(area1, area2))
return current_extent
def concatenate_area_defs(area1, area2, axis=0):
"""Append *area2* to *area1* and return the results"""
different_items = (set(area1.proj_dict.items()) ^
set(area2.proj_dict.items()))
if axis == 0:
same_size = area1.x_size == area2.x_size
else:
raise NotImplementedError('Only vertical contatenation is supported.')
if different_items or not same_size:
raise IncompatibleAreas("Can't concatenate area definitions with "
"different projections: "
"{0} and {1}".format(area1, area2))
if axis == 0:
area_extent = combine_area_extents_vertical(area1, area2)
x_size = int(area1.x_size)
y_size = int(area1.y_size + area2.y_size)
else:
raise NotImplementedError('Only vertical contatenation is supported.')
return AreaDefinition(area1.area_id, area1.name, area1.proj_id,
area1.proj_dict, x_size, y_size,
area_extent)
class StackedAreaDefinition(BaseDefinition):
"""Definition based on muliple vertically stacked AreaDefinitions."""
def __init__(self, *definitions, **kwargs):
"""Base this instance on *definitions*.
*kwargs* used here are `nprocs` and `dtype` (see AreaDefinition).
"""
nprocs = kwargs.get('nprocs', 1)
super(StackedAreaDefinition, self).__init__(nprocs=nprocs)
self.dtype = kwargs.get('dtype', np.float64)
self.defs = []
self.proj_dict = {}
for definition in definitions:
self.append(definition)
@property
def x_size(self):
return self.defs[0].x_size
@property
def y_size(self):
return sum(definition.y_size for definition in self.defs)
@property
def size(self):
return self.y_size * self.x_size
def append(self, definition):
"""Append another definition to the area."""
if isinstance(definition, StackedAreaDefinition):
for area in definition.defs:
self.append(area)
return
if definition.y_size == 0:
return
if not self.defs:
self.proj_dict = definition.proj_dict
elif self.proj_dict != definition.proj_dict:
raise NotImplementedError('Cannot append areas:'
' Proj.4 dict mismatch')
try:
self.defs[-1] = concatenate_area_defs(self.defs[-1], definition)
except (IncompatibleAreas, IndexError):
self.defs.append(definition)
def get_lonlats(self, nprocs=None, data_slice=None, cache=False, dtype=None):
"""Return lon and lat arrays of the area."""
llons = []
llats = []
try:
row_slice, col_slice = data_slice
except TypeError:
row_slice = slice(0, self.y_size)
col_slice = slice(0, self.x_size)
offset = 0
for definition in self.defs:
local_row_slice = slice(max(row_slice.start - offset, 0),
min(max(row_slice.stop - offset, 0),
definition.y_size),
row_slice.step)
lons, lats = definition.get_lonlats(nprocs=nprocs,
data_slice=(local_row_slice,
col_slice),
cache=cache,
dtype=dtype)
llons.append(lons)
llats.append(lats)
offset += lons.shape[0]
self.lons = np.vstack(llons)
self.lats = np.vstack(llats)
return self.lons, self.lats
def get_lonlats_dask(self, chunks=CHUNK_SIZE, dtype=None):
""""Return lon and lat dask arrays of the area."""
import dask.array as da
llons = []
llats = []
for definition in self.defs:
lons, lats = definition.get_lonlats_dask(chunks=chunks,
dtype=dtype)
llons.append(lons)
llats.append(lats)
self.lons = da.concatenate(llons, axis=0)
self.lats = da.concatenate(llats, axis=0)
return self.lons, self.lats
def squeeze(self):
"""Generate a single AreaDefinition if possible."""
if len(self.defs) == 1:
return self.defs[0]
else:
return self
@property
def proj4_string(self):
"""Returns projection definition as Proj.4 string"""
return self.defs[0].proj4_string
def _get_slice(segments, shape):
"""Generator for segmenting a 1D or 2D array"""
if not (1 <= len(shape) <= 2):
raise ValueError('Cannot segment array of shape: %s' % str(shape))
else:
size = shape[0]
slice_length = int(np.ceil(float(size) / segments))
start_idx = 0
end_idx = slice_length
while start_idx < size:
if len(shape) == 1:
yield slice(start_idx, end_idx)
else:
yield (slice(start_idx, end_idx), slice(None))
start_idx = end_idx
end_idx = min(start_idx + slice_length, size)
def _flatten_cartesian_coords(cartesian_coords):
"""Flatten array to (n, 3) shape"""
shape = cartesian_coords.shape
if len(shape) > 2:
cartesian_coords = cartesian_coords.reshape(shape[0] *
shape[1], 3)
return cartesian_coords
def _get_highest_level_class(obj1, obj2):
if (not issubclass(obj1.__class__, obj2.__class__) or
not issubclass(obj2.__class__, obj1.__class__)):
raise TypeError('No common superclass for %s and %s' %
(obj1.__class__, obj2.__class__))
if obj1.__class__ == obj2.__class__:
klass = obj1.__class__
elif issubclass(obj1.__class__, obj2.__class__):
klass = obj2.__class__
else:
klass = obj1.__class__
return klass
def ordered_dump(data, stream=None, Dumper=yaml.Dumper, **kwds):
class OrderedDumper(Dumper):
pass
def _dict_representer(dumper, data):
return dumper.represent_mapping(
yaml.resolver.BaseResolver.DEFAULT_MAPPING_TAG,
data.items(), flow_style=False)
OrderedDumper.add_representer(OrderedDict, _dict_representer)
return yaml.dump(data, stream, OrderedDumper, **kwds)
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