def test_aggregation_partitions(s_points_frame, x_range, y_range):
# Get original as pandas
df = s_points_frame.compute()
# Query subset
query_ddf = s_points_frame.spatial_query(x_range, y_range)
# Create canvas
cvs = Canvas(x_range=x_range, y_range=y_range)
# Aggregate with full pandas frame
agg_expected = cvs.points(df, 'x', 'y')
agg_query = cvs.points(query_ddf, 'x', 'y')
agg = cvs.points(s_points_frame, 'x', 'y')
assert agg.equals(agg_expected)
assert agg.equals(agg_query)
def generate_terrain(x_range: tuple = (0, 500),
y_range: tuple = (0, 500),
width: int = 25,
height: int = 30,
canvas: ds.Canvas = None,
seed: int = 10,
zfactor: int = 4000,
full_extent: Optional[str] = None) -> xr.DataArray:
"""
Generates a pseudo-random terrain which can be helpful
for testing raster functions
Parameters:
----------
x_range: tuple (default = (0, 500))
Range of x values.
x_range: tuple (default = (0, 500))
Range of y values.
width: int (default = 25)
Width of output data array in pixels.
height: int (default = 30)
Height of output data array in pixels.
canvas: ds.Canvas (default = None)
Instance for passing output dimensions / ranges
seed: int (default = 10)
Seed for random number generator.
zfactor: int (default = 4000)
Multipler for z values.
full_extent: str, optional (default = None)
bbox<xmin, ymin, xmax, ymax>. Full extent of coordinate system.
Returns:
----------
terrain: xarray.DataArray
2D array of generated terrain.
Notes:
----------
Algorithm References:
- This was inspired by Michael McHugh's 2016 PyCon Canada talk:
https://www.youtube.com/watch?v=O33YV4ooHSo
- https://www.redblobgames.com/maps/terrain-from-noise/
Examples:
----------
Imports
>>> import datashader as ds
>>> from datashader.transfer_functions import shade
>>> from xrspatial import generate_terrain
Create Canvas
>>> cvs = ds.Canvas(plot_width=800,
>>> plot_height=600,
>>> x_range=(-20e6, 20e6),
>>> y_range=(-20e6, 20e6))
Generate Terrain Data Array
>>> terrain = generate_terrain(canvas = cvs)
>>> print(terrain)
<xarray.DataArray 'terrain' (y: 600, x: 800)>
array([[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
...,
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.]])
Coordinates:
* x (x) float64 -1.998e+07 -1.992e+07 ... 1.992e+07 1.997e+07
* y (y) float64 -1.997e+07 -1.99e+07 -1.983e+07 ... 1.99e+07 1.997e+07
Attributes:
res: 1
"""
def _gen_heights(bumps):
out = np.zeros(len(bumps))
for i, b in enumerate(bumps):
x = b[0]
y = b[1]
val = agg.data[y, x]
if val >= 0.33 and val <= 3:
out[i] = 0.1
return out
def _scale(value, old_range, new_range):
d = (value - old_range[0]) / (old_range[1] - old_range[0])
return d * (new_range[1] - new_range[0]) + new_range[0]
mercator_extent = (-np.pi * 6378137, -np.pi * 6378137, np.pi * 6378137,
np.pi * 6378137)
crs_extents = {'3857': mercator_extent}
if isinstance(full_extent, str):
full_extent = crs_extents[full_extent]
elif full_extent is None:
full_extent = (canvas.x_range[0], canvas.y_range[0], canvas.x_range[1],
canvas.y_range[1])
elif not isinstance(full_extent, (list, tuple)) and len(full_extent) != 4:
raise TypeError('full_extent must be tuple(4) or str wkid')
full_xrange = (full_extent[0], full_extent[2])
full_yrange = (full_extent[1], full_extent[3])
x_range_scaled = (_scale(canvas.x_range[0], full_xrange, (0.0, 1.0)),
_scale(canvas.x_range[1], full_xrange, (0.0, 1.0)))
y_range_scaled = (_scale(canvas.y_range[0], full_yrange, (0.0, 1.0)),
_scale(canvas.y_range[1], full_yrange, (0.0, 1.0)))
data = _gen_terrain(canvas.plot_width,
canvas.plot_height,
seed,
x_range=x_range_scaled,
y_range=y_range_scaled)
data = (data - np.min(data)) / np.ptp(data)
data[data < 0.3] = 0 # create water
data *= zfactor
# DataArray coords were coming back different from cvs.points...
hack_agg = canvas.points(pd.DataFrame({'x': [], 'y': []}), 'x', 'y')
agg = DataArray(data,
name='terrain',
coords=hack_agg.coords,
dims=hack_agg.dims,
attrs={'res': 1})
return agg
def generate_terrain(x_range: tuple = (0, 500),
y_range: tuple = (0, 500),
width: int = 25,
height: int = 30,
canvas: ds.Canvas = None,
seed: int = 10,
zfactor: int = 4000,
full_extent: Optional[str] = None) -> xr.DataArray:
"""
Generates a pseudo-random terrain which can be helpful for testing
raster functions.
Parameters
----------
x_range : tuple, default=(0, 500)
Range of x values.
x_range : tuple, default=(0, 500)
Range of y values.
width : int, default=25
Width of output data array in pixels.
height : int, default=30
Height of output data array in pixels.
canvas : ds.Canvas, default=None
Instance for passing output dimensions / ranges.
seed : int, default=10
Seed for random number generator.
zfactor : int, default=4000
Multipler for z values.
full_extent : str, default=None
bbox<xmin, ymin, xmax, ymax>. Full extent of coordinate system.
Returns
-------
terrain : xr.DataArray
2D array of generated terrain values.
References
----------
- Michael McHugh: https://www.youtube.com/watch?v=O33YV4ooHSo
- Red Blob Games: https://www.redblobgames.com/maps/terrain-from-noise/
Examples
--------
.. plot::
:include-source:
import datashader as ds
import matplotlib.pyplot as plt
from xrspatial import generate_terrain, aspect
# Create Canvas
W = 500
H = 300
cvs = ds.Canvas(plot_width = W,
plot_height = H,
x_range = (-20e6, 20e6),
y_range = (-20e6, 20e6))
# Generate Example Terrain
terrain_agg = generate_terrain(canvas = cvs)
# Edit Attributes
terrain_agg = terrain_agg.assign_attrs(
{
'Description': 'Example Terrain',
'units': 'km',
'Max Elevation': '4000',
}
)
terrain_agg = terrain_agg.rename({'x': 'lon', 'y': 'lat'})
terrain_agg = terrain_agg.rename('Elevation')
# Plot Terrain
terrain_agg.plot(cmap = 'terrain', aspect = 2, size = 4)
plt.title("Terrain")
plt.ylabel("latitude")
plt.xlabel("longitude")
.. sourcecode:: python
>>> print(terrain_agg[200:203, 200:202])
<xarray.DataArray 'Elevation' (lat: 3, lon: 2)>
array([[1264.02249454, 1261.94748873],
[1285.37061171, 1282.48046696],
[1306.02305679, 1303.40657515]])
Coordinates:
* lon (lon) float64 -3.96e+06 -3.88e+06
* lat (lat) float64 6.733e+06 6.867e+06 7e+06
Attributes:
res: 1
Description: Example Terrain
units: km
Max Elevation: 4000
"""
def _gen_heights(bumps):
out = np.zeros(len(bumps))
for i, b in enumerate(bumps):
x = b[0]
y = b[1]
val = agg.data[y, x]
if val >= 0.33 and val <= 3:
out[i] = 0.1
return out
def _scale(value, old_range, new_range):
d = (value - old_range[0]) / (old_range[1] - old_range[0])
return d * (new_range[1] - new_range[0]) + new_range[0]
mercator_extent = (-np.pi * 6378137, -np.pi * 6378137,
np.pi * 6378137, np.pi * 6378137)
crs_extents = {'3857': mercator_extent}
if isinstance(full_extent, str):
full_extent = crs_extents[full_extent]
elif full_extent is None:
full_extent = (canvas.x_range[0], canvas.y_range[0],
canvas.x_range[1], canvas.y_range[1])
elif not isinstance(full_extent, (list, tuple)) and len(full_extent) != 4:
raise TypeError('full_extent must be tuple(4) or str wkid')
full_xrange = (full_extent[0], full_extent[2])
full_yrange = (full_extent[1], full_extent[3])
x_range_scaled = (_scale(canvas.x_range[0], full_xrange, (0.0, 1.0)),
_scale(canvas.x_range[1], full_xrange, (0.0, 1.0)))
y_range_scaled = (_scale(canvas.y_range[0], full_yrange, (0.0, 1.0)),
_scale(canvas.y_range[1], full_yrange, (0.0, 1.0)))
data = _gen_terrain(canvas.plot_width, canvas.plot_height, seed,
x_range=x_range_scaled, y_range=y_range_scaled)
data = (data - np.min(data))/np.ptp(data)
data[data < 0.3] = 0 # create water
data *= zfactor
# DataArray coords were coming back different from cvs.points...
hack_agg = canvas.points(pd.DataFrame({'x': [], 'y': []}), 'x', 'y')
agg = DataArray(data,
name='terrain',
coords=hack_agg.coords,
dims=hack_agg.dims,
attrs={'res': 1})
return agg