# Source code for verde.coordinates

"""
Functions for generating and manipulating coordinates.
"""
import numpy as np
from sklearn.utils import check_random_state

try:
from pykdtree.kdtree import KDTree
except ImportError:
from scipy.spatial import cKDTree as KDTree  # pylint: disable=no-name-in-module

from .utils import n_1d_arrays

def check_region(region):
"""
Check that the given region dimensions are valid.

For example, the west limit should not be greater than the east and there
must be exactly 4 values given.

Parameters
----------
region : list = [W, E, S, N]
The boundaries of a given region in Cartesian or geographic
coordinates.

Raises
------
ValueError
If the region doesn't have exactly 4 entries, W > E, or S > N.

"""
if len(region) != 4:
raise ValueError("Invalid region '{}'. Only 4 values allowed.".format(region))
w, e, s, n = region
if w > e:
raise ValueError(
"Invalid region '{}' (W, E, S, N). Must have W =< E.".format(region)
)
if s > n:
raise ValueError(
"Invalid region '{}' (W, E, S, N). Must have S =< N.".format(region)
)

[docs]def get_region(coordinates):
"""
Get the bounding region of the given coordinates.

Parameters
----------
coordinates : tuple of arrays
Arrays with the coordinates of each data point. Should be in the
following order: (easting, northing, vertical, ...). Only easting and
northing will be used, all subsequent coordinates will be ignored.

Returns
-------
region : tuple = (W, E, S, N)
The boundaries of a given region in Cartesian or geographic
coordinates.

Examples
--------

>>> coords = grid_coordinates((0, 1, -10, -6), shape=(10, 10))
>>> print(get_region(coords))
(0.0, 1.0, -10.0, -6.0)

"""
easting, northing = coordinates[:2]
region = (np.min(easting), np.max(easting), np.min(northing), np.max(northing))
return region

"""
Extend the borders of a region by the given amount.

Parameters
----------
region : list = [W, E, S, N]
The boundaries of a given region in Cartesian or geographic
coordinates.
The amount of padding to add to the region. If it's a single number,
add this to all boundaries of region equally. If it's a tuple of
East-West dimensions.

Returns
-------
padded_region : list = [W, E, S, N]

Examples
--------

>>> pad_region((0, 1, -5, -3), 1)
(-1, 2, -6, -2)
>>> pad_region((0, 1, -5, -3), (3, 2))
(-2, 3, -8, 0)

"""
w, e, s, n = region

[docs]def project_region(region, projection):
"""
Calculate the bounding box of a region in projected coordinates.

Parameters
----------
region : list = [W, E, S, N]
The boundaries of a given region in Cartesian or geographic
coordinates.
projection : callable or None
If not None, then should be a callable object (like a function)
projection(easting, northing) -> (proj_easting, proj_northing) that
takes in easting and northing coordinate arrays and returns projected
northing and easting coordinate arrays.

Returns
-------
proj_region : list = [W, E, S, N]
The bounding box of the projected region.

Examples
--------

>>> def projection(x, y):
...     return (2*x, -1*y)
>>> project_region((3, 5, -9, -4), projection)
(6.0, 10.0, 4.0, 9.0)

"""
east, north = grid_coordinates(region, shape=(101, 101))
east, north = projection(east.ravel(), north.ravel())
return (east.min(), east.max(), north.min(), north.max())

[docs]def scatter_points(region, size, random_state=None):
"""
Generate the coordinates for a random scatter of points.

The points are drawn from a uniform distribution.

Parameters
----------
region : list = [W, E, S, N]
The boundaries of a given region in Cartesian or geographic
coordinates.
size : int
The number of points to generate.
random_state : numpy.random.RandomState or an int seed
A random number generator used to define the state of the random
permutations. Use a fixed seed to make sure computations are
reproducible. Use None to choose a seed automatically (resulting in
different numbers with each run).

Returns
-------
easting, northing : 1d arrays
The West-East and South-North coordinates of each point.

Examples
--------

>>> # We'll use a seed value will ensure that the same will be generated
>>> # every time.
>>> easting, northing = scatter_points((0, 10, -2, -1), 4, random_state=0)
>>> print(', '.join(['{:.4f}'.format(i) for i in easting]))
5.4881, 7.1519, 6.0276, 5.4488
>>> print(', '.join(['{:.4f}'.format(i) for i in northing]))
-1.5763, -1.3541, -1.5624, -1.1082

--------
grid_coordinates : Generate coordinates for each point on a regular grid
profile_coordinates : Coordinates for a profile between two points

"""
check_region(region)
random = check_random_state(random_state)
w, e, s, n = region
easting = random.uniform(w, e, size)
northing = random.uniform(s, n, size)
return easting, northing

[docs]def grid_coordinates(
):
"""
Generate the coordinates for each point on a regular grid.

The grid can be specified by either the number of points in each dimension
(the *shape*) or by the grid node spacing.

If the given region is not divisible by the desired spacing, either the
region or the spacing will have to be adjusted. By default, the spacing
will be rounded to the nearest multiple. Optionally, the East and North
boundaries of the region can be adjusted to fit the exact spacing given.
See the examples below.

Parameters
----------
region : list = [W, E, S, N]
The boundaries of a given region in Cartesian or geographic
coordinates.
shape : tuple = (n_north, n_east) or None
The number of points in the South-North and West-East directions,
respectively.
spacing : float, tuple = (s_north, s_east), or None
The grid spacing in the South-North and West-East directions,
respectively. A single value means that the spacing is equal in both
directions.
Whether to adjust the spacing or the region if required. Ignored if
spacing.
pixel_register : bool
If True, the coordinates will refer to the center of each grid pixel
instead of the grid lines. In practice, this means that there will be
one less element per dimension of the grid when compared to grid line
registered. Default is False.

Returns
-------
easting, northing : 2d arrays
The West-East and South-North coordinates of each point in the grid.
The arrays have the specified *shape*.

Examples
--------

>>> east, north = grid_coordinates(region=(0, 5, 0, 10), shape=(5, 3))
>>> print(east.shape, north.shape)
(5, 3) (5, 3)
>>> # Lower printing precision to shorten this example
>>> import numpy as np; np.set_printoptions(precision=1, suppress=True)
>>> print(east)
[[0.  2.5 5. ]
[0.  2.5 5. ]
[0.  2.5 5. ]
[0.  2.5 5. ]
[0.  2.5 5. ]]
>>> print(north)
[[ 0.   0.   0. ]
[ 2.5  2.5  2.5]
[ 5.   5.   5. ]
[ 7.5  7.5  7.5]
[10.  10.  10. ]]
>>> # The grid can also be specified using the spacing between points
>>> # instead of the shape.
>>> east, north = grid_coordinates(region=(0, 5, 0, 10), spacing=2.5)
>>> print(east.shape, north.shape)
(5, 3) (5, 3)
>>> print(east)
[[0.  2.5 5. ]
[0.  2.5 5. ]
[0.  2.5 5. ]
[0.  2.5 5. ]
[0.  2.5 5. ]]
>>> print(north)
[[ 0.   0.   0. ]
[ 2.5  2.5  2.5]
[ 5.   5.   5. ]
[ 7.5  7.5  7.5]
[10.  10.  10. ]]
>>> # The spacing can be different for northing and easting, respectively
>>> east, north = grid_coordinates(region=(-5, 1, 0, 10), spacing=(2.5, 1))
>>> print(east.shape, north.shape)
(5, 7) (5, 7)
>>> print(east)
[[-5. -4. -3. -2. -1.  0.  1.]
[-5. -4. -3. -2. -1.  0.  1.]
[-5. -4. -3. -2. -1.  0.  1.]
[-5. -4. -3. -2. -1.  0.  1.]
[-5. -4. -3. -2. -1.  0.  1.]]
>>> print(north)
[[ 0.   0.   0.   0.   0.   0.   0. ]
[ 2.5  2.5  2.5  2.5  2.5  2.5  2.5]
[ 5.   5.   5.   5.   5.   5.   5. ]
[ 7.5  7.5  7.5  7.5  7.5  7.5  7.5]
[10.  10.  10.  10.  10.  10.  10. ]]
>>> # If the region can't be divided into the desired spacing, the spacing
>>> # will be adjusted to conform to the region
>>> east, north = grid_coordinates(region=(-5, 0, 0, 5), spacing=2.6)
>>> print(east.shape, north.shape)
(3, 3) (3, 3)
>>> print(east)
[[-5.  -2.5  0. ]
[-5.  -2.5  0. ]
[-5.  -2.5  0. ]]
>>> print(north)
[[0.  0.  0. ]
[2.5 2.5 2.5]
[5.  5.  5. ]]
>>> east, north = grid_coordinates(region=(-5, 0, 0, 5), spacing=2.4)
>>> print(east.shape, north.shape)
(3, 3) (3, 3)
>>> print(east)
[[-5.  -2.5  0. ]
[-5.  -2.5  0. ]
[-5.  -2.5  0. ]]
>>> print(north)
[[0.  0.  0. ]
[2.5 2.5 2.5]
[5.  5.  5. ]]
>>> # You can also choose to adjust the East and North boundaries of the
>>> east, north = grid_coordinates(region=(-5, 0, 0, 5), spacing=2.6,
>>> print(east.shape, north.shape)
(3, 3) (3, 3)
>>> print(east)
[[-5.  -2.4  0.2]
[-5.  -2.4  0.2]
[-5.  -2.4  0.2]]
>>> print(north)
[[0.  0.  0. ]
[2.6 2.6 2.6]
[5.2 5.2 5.2]]
>>> east, north = grid_coordinates(region=(-5, 0, 0, 5), spacing=2.4,
>>> print(east.shape, north.shape)
(3, 3) (3, 3)
>>> print(east)
[[-5.  -2.6 -0.2]
[-5.  -2.6 -0.2]
[-5.  -2.6 -0.2]]
>>> print(north)
[[0.  0.  0. ]
[2.4 2.4 2.4]
[4.8 4.8 4.8]]
>>> # We can optionally generate coordinates for the center of each grid
>>> # pixel instead of the corner (default)
>>> east, north = grid_coordinates(region=(0, 5, 0, 10), spacing=2.5,
...                                pixel_register=True)
>>> # Lower printing precision to shorten this example
>>> import numpy as np; np.set_printoptions(precision=2, suppress=True)
>>> print(east.shape, north.shape)
(4, 2) (4, 2)
>>> print(east)
[[1.25 3.75]
[1.25 3.75]
[1.25 3.75]
[1.25 3.75]]
>>> print(north)
[[1.25 1.25]
[3.75 3.75]
[6.25 6.25]
[8.75 8.75]]

--------
scatter_points : Generate the coordinates for a random scatter of points
profile_coordinates : Coordinates for a profile between two points

"""
check_region(region)
if shape is not None and spacing is not None:
raise ValueError("Both grid shape and spacing provided. Only one is allowed.")
if shape is None and spacing is None:
raise ValueError("Either a grid shape or a spacing must be provided.")
if spacing is not None:
shape, region = spacing_to_shape(region, spacing, adjust)
nnorth, neast = shape
w, e, s, n = region
east_lines = np.linspace(w, e, neast)
north_lines = np.linspace(s, n, nnorth)
if pixel_register:
east_lines = east_lines[:-1] + (east_lines[1] - east_lines[0]) / 2
north_lines = north_lines[:-1] + (north_lines[1] - north_lines[0]) / 2
easting, northing = np.meshgrid(east_lines, north_lines)
return easting, northing

"""
Convert the grid spacing to a grid shape.

Adjusts the spacing or the region if the desired spacing is not a multiple
of the grid dimensions.

Parameters
----------
region : list = [W, E, S, N]
The boundaries of a given region in Cartesian or geographic
coordinates.
spacing : float, tuple = (s_north, s_east), or None
The grid spacing in the South-North and West-East directions,
respectively. A single value means that the spacing is equal in both
directions.
Whether to adjust the spacing or the region if required. Ignored if
spacing.

Returns
-------
shape, region : tuples
The calculated shape and region that best fits the desired spacing.
Spacing or region may be adjusted.

"""
if adjust not in ["spacing", "region"]:
raise ValueError(
"Invalid value for *adjust* '{}'. Should be 'spacing' or 'region'".format(
)
)

spacing = np.atleast_1d(spacing)
if len(spacing) == 1:
deast = dnorth = spacing[0]
elif len(spacing) == 2:
dnorth, deast = spacing
else:
raise ValueError(
"Only two values allowed for grid spacing: {}".format(str(spacing))
)

w, e, s, n = region
# Add 1 to get the number of nodes, not segments
nnorth = int(round((n - s) / dnorth)) + 1
neast = int(round((e - w) / deast)) + 1
# The shape is the same but we adjust the region so that the spacing
# isn't altered when we do the linspace.
n = s + (nnorth - 1) * dnorth
e = w + (neast - 1) * deast
return (nnorth, neast), (w, e, s, n)

[docs]def profile_coordinates(point1, point2, size, coordinate_system="cartesian"):
"""
Coordinates for a profile along a line between two points.

If on a geographic coordinate system, will calculate along a great circle.
Otherwise, will use a straight line.

Parameters
----------
point1 : tuple or list
(easting, northing) West-East and South-North coordinates of the
first point, respectively.
point2 : tuple or list
(easting, northing) West-East and South-North coordinates of the
second point, respectively.
size : int
Number of points to sample along the line.
coordinate_system : str
The coordinate system used to define the points and the line. Either
'cartesian' or 'geographic'.

Returns
-------
easting, northing, distances : 1d arrays
The easting and northing coordinates of points along the straight line
and the distances from the first point.

Examples
--------

>>> east, north, dist = profile_coordinates((1, 10), (1, 20), size=11)
>>> print('easting:', ', '.join('{:.1f}'.format(i) for i in east))
easting: 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0
>>> print('northing:', ', '.join('{:.1f}'.format(i) for i in north))
northing: 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0
>>> print('distance:', ', '.join('{:.1f}'.format(i) for i in dist))
distance: 0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0

--------
scatter_points : Generate the coordinates for a random scatter of points
grid_coordinates : Generate coordinates for each point on a regular grid

"""
valid_coordinate_systems = ["cartesian", "geographic"]
if coordinate_system not in valid_coordinate_systems:
raise ValueError(
"Invalid coordinate system '{}'. Must be one of {}.".format(
coordinate_system, str(valid_coordinate_systems)
)
)
if size <= 0:
raise ValueError("Invalid profile size '{}'. Must be > 0.".format(size))
if coordinate_system == "geographic":
raise NotImplementedError()
elif coordinate_system == "cartesian":
east1, north1 = point1
east2, north2 = point2
separation = np.sqrt((east1 - east2) ** 2 + (north1 - north2) ** 2)
distances = np.linspace(0, separation, size)
angle = np.arctan2(north2 - north1, east2 - east1)
easting = east1 + distances * np.cos(angle)
northing = north1 + distances * np.sin(angle)
return easting, northing, distances

[docs]def inside(coordinates, region, out=None, tmp=None):
"""
Determine which points fall inside a given region.

Points at the boundary are counted as being outsize.

Parameters
----------
coordinates : tuple of arrays
Arrays with the coordinates of each data point. Should be in the
following order: (easting, northing, vertical, ...). Only easting and
northing will be used, all subsequent coordinates will be ignored.
region : list = [W, E, S, N]
The boundaries of a given region in Cartesian or geographic
coordinates.
out : None or array of booleans
Numpy array to be used as output. The contents will be overwritten and
the same array will be returned.
tmp : None or tuple
Numpy arrays used to store the outputs of temporary logical operations.
Passing in pre-allocated arrays avoids the overhead of allocation when
calling this function repeatedly. If not None, then should be a tuple
of 4 numpy arrays of boolean type and a shape equal to or broadcast
from the input coordinates.

Returns
-------
are_inside : array of booleans
An array of booleans with the same shape as the input coordinate
arrays. Will be True if the respective coordinates fall inside the
area, False otherwise.

Examples
--------

>>> import numpy as np
>>> east = np.array([1, 2, 3, 4, 5, 6])
>>> north = np.array([10, 11, 12, 13, 14, 15])
>>> region = [2.5, 5.5, 12, 15]
>>> print(inside((east, north), region))
[False False  True  True  True False]
>>> # This also works for 2D-arrays
>>> east = np.array([[1, 1, 1],
...                  [2, 2, 2],
...                  [3, 3, 3]])
>>> north = np.array([[5, 7, 9],
...                   [5, 7, 9],
...                   [5, 7, 9]])
>>> region = [0.5, 2.5, 6, 9]
>>> print(inside((east, north), region))
[[False  True  True]
[False  True  True]
[False False False]]

"""
check_region(region)
w, e, s, n = region
easting, northing = coordinates[:2]
# Allocate temporary arrays to minimize memory allocation overhead
if out is None:
out = np.empty_like(easting, dtype=np.bool)
if tmp is None:
tmp = tuple(np.empty_like(easting, dtype=np.bool) for i in range(4))
# Using the logical functions is a lot faster than & > < for some reason
# Plus, this way avoids repeated allocation of intermediate arrays
in_we = np.logical_and(
np.greater_equal(easting, w, out=tmp[0]),
np.less_equal(easting, e, out=tmp[1]),
out=tmp[2],
)
in_ns = np.logical_and(
np.greater_equal(northing, s, out=tmp[0]),
np.less_equal(northing, n, out=tmp[1]),
out=tmp[3],
)
are_inside = np.logical_and(in_we, in_ns, out=out)
return are_inside

"""
Split a region into blocks and label points according to where they fall.

The labels are integers corresponding to the index of the block. The same
index is used for the coordinates of each block.

.. note::

If installed, package pykdtree will be used instead of
:class:scipy.spatial.cKDTree for better performance.

Parameters
----------
coordinates : tuple of arrays
Arrays with the coordinates of each data point. Should be in the
following order: (easting, northing, vertical, ...). Only easting and
northing will be used, all subsequent coordinates will be ignored.
spacing : float, tuple = (s_north, s_east), or None
The block size in the South-North and West-East directions,
respectively. A single value means that the size is equal in both
directions.
Whether to adjust the spacing or the region if required. Ignored if
spacing.
region : list = [W, E, S, N]
The boundaries of a given region in Cartesian or geographic
coordinates. If not region is given, will use the bounding region of
the given points.

Returns
-------
block_coordinates : tuple of arrays
(easting, northing) arrays with the coordinates of the center of each
block.
labels : array
integer label for each data point. The label is the index of the block
to which that point belongs.

--------
BlockReduce : Apply a reduction operation to the data in blocks (windows).

Examples
--------

>>> from verde import grid_coordinates
>>> coords = grid_coordinates((-5, 0, 5, 10), spacing=1)
>>> block_coords, labels = block_split(coords, spacing=2.5)
>>> for coord in block_coords:
...     print(', '.join(['{:.2f}'.format(i) for i in coord]))
-3.75, -1.25, -3.75, -1.25
6.25, 6.25, 8.75, 8.75
>>> print(labels.reshape(coords[0].shape))
[[0 0 0 1 1 1]
[0 0 0 1 1 1]
[0 0 0 1 1 1]
[2 2 2 3 3 3]
[2 2 2 3 3 3]
[2 2 2 3 3 3]]

"""
if region is None:
region = get_region(coordinates)
block_coords = tuple(
i.ravel()
for i in grid_coordinates(