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merged 5 commits into from
May 4, 2022
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Correct examples in docstrings #703

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8901951
curvature
thuydotm May 4, 2022
3ca0239
focal tools
thuydotm May 4, 2022
3f8ba90
proximity
thuydotm May 4, 2022
4992f49
viewshed
thuydotm May 4, 2022
5def4b7
zonal tools
thuydotm May 4, 2022
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3 changes: 2 additions & 1 deletion xrspatial/curvature.py
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Original file line number Diff line number Diff line change
Expand Up @@ -121,6 +121,7 @@ def curvature(agg: xr.DataArray,
.. sourcecode:: python

>>> import numpy as np
>>> import dask.array as da
>>> import xarray as xr
>>> from xrspatial import curvature
>>> flat_data = np.zeros((5, 5), dtype=np.float32)
Expand Down Expand Up @@ -187,7 +188,7 @@ def curvature(agg: xr.DataArray,
cupy.asarray(concave_data),
attrs={'res': (10, 10)}, name='concave_cupy_raster')
>>> concave_curv = curvature(concave_raster)
>>> print(type(concave_curv))
>>> print(type(concave_curv.data))
<class 'cupy.core.core.ndarray'>
>>> print(concave_curv)
<xarray.DataArray 'curvature' (dim_0: 5, dim_1: 5)>
Expand Down
28 changes: 15 additions & 13 deletions xrspatial/focal.py
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Expand Up @@ -193,11 +193,11 @@ def mean(agg, passes=1, excludes=[np.nan], name='mean'):
<class 'cupy.core.core.ndarray'>
>>> print(mean_cupy)
<xarray.DataArray 'mean' (dim_0: 5, dim_1: 5)>
array([[0.47928994, 0.47928994, 0.47928994, 0.47928994, 0.47928994],
[0.47928994, 0.47928994, 0.47928994, 0.47928994, 0.47928994],
[0.47928994, 0.47928994, 0.47928994, 0.47928994, 0.47928994],
[0.47928994, 0.47928994, 0.47928994, 0.47928994, 0.47928994],
[0.47928994, 0.47928994, 0.47928994, 0.47928994, 0.47928994]])
array([[0.47928995, 0.47928995, 0.47928995, 0.47928995, 0.47928995],
[0.47928995, 0.47928995, 0.47928995, 0.47928995, 0.47928995],
[0.47928995, 0.47928995, 0.47928995, 0.47928995, 0.47928995],
[0.47928995, 0.47928995, 0.47928995, 0.47928995, 0.47928995],
[0.47928995, 0.47928995, 0.47928995, 0.47928995, 0.47928995]])
Dimensions without coordinates: dim_0, dim_1
"""

Expand Down Expand Up @@ -358,6 +358,7 @@ def apply(raster, kernel, func=_calc_mean, name='focal_apply'):
[0., 1., 0.]])
>>> # apply kernel mean by default
>>> apply_mean_agg = apply(raster, kernel)
>>> apply_mean_agg
<xarray.DataArray 'focal_apply' (y: 4, x: 5)>
array([[ 2. , 2.25 , 3.25 , 4.25 , 5.33333333],
[ 5.25 , 6. , 7. , 8. , 8.75 ],
Expand Down Expand Up @@ -387,13 +388,14 @@ def apply(raster, kernel, func=_calc_mean, name='focal_apply'):
])
>>> @ngjit
>>> def func(kernel_data):
>>> weight = np.array([
[0, 0.5, 0],
[0, 1, 0.5],
[0, 0.5, 0],
])
>>> return np.nansum(kernel_data * weight)

... weight = np.array([
... [0, 0.5, 0],
... [0, 1, 0.5],
... [0, 0.5, 0],
... ])
... return np.nansum(kernel_data * weight)

>>> import dask.array as da
>>> data_da = da.from_array(np.ones((6, 4), dtype=np.float64), chunks=(3, 2))
>>> raster_da = xr.DataArray(data_da, dims=['y', 'x'], name='raster_da')
>>> print(raster_da)
Expand Down Expand Up @@ -713,7 +715,7 @@ def hotspots(raster, kernel):
>>> import numpy as np
>>> import xarray as xr
>>> from xrspatial.convolution import custom_kernel
>>> kernel = custom_kernel(np.array([1, 1, 0]))
>>> kernel = custom_kernel(np.array([[1, 1, 0]]))
>>> data = np.array([
... [0, 1000, 1000, 0, 0, 0],
... [0, 0, 0, -1000, -1000, 0],
Expand Down
6 changes: 3 additions & 3 deletions xrspatial/proximity.py
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Original file line number Diff line number Diff line change
Expand Up @@ -112,14 +112,14 @@ def manhattan_distance(x1: float, x2: float, y1: float, y2: float) -> float:
>>> from xrspatial import manhattan_distance
>>> point_a = (142.32, 23.23)
>>> point_b = (312.54, 432.01)
>>> # Calculate Euclidean Distance
>>> # Calculate Manhattan Distance
>>> dist = manhattan_distance(
... point_a[0],
... point_b[0],
... point_a[1],
... point_b[1])
>>> print(dist)
196075.9368
579.0
"""

x = x1 - x2
Expand Down Expand Up @@ -165,7 +165,7 @@ def great_circle_distance(
>>> from xrspatial import great_circle_distance
>>> point_a = (123.2, 82.32)
>>> point_b = (178.0, 65.09)
>>> # Calculate Euclidean Distance
>>> # Calculate Great Circle Distance
>>> dist = great_circle_distance(
... point_a[0],
... point_b[0],
Expand Down
2 changes: 1 addition & 1 deletion xrspatial/viewshed.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1637,7 +1637,7 @@ def viewshed(raster: xarray.DataArray,
array([[ 0, 0, 1, 0, 0],
[ 1, 3, 0, 0, 0],
[10, 2, 5, 2, -1],
[20, 1, 2, 9, 0]])
[11, 1, 2, 9, 0]])
Coordinates:
* y (y) float64 1.0 2.0 3.0 4.0
* x (x) float64 1.0 2.0 3.0 4.0 5.0
Expand Down
37 changes: 14 additions & 23 deletions xrspatial/zonal.py
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Original file line number Diff line number Diff line change
Expand Up @@ -517,9 +517,8 @@ def stats(
stats() works with Dask with NumPy backed DataArray
>>> import dask.array as da
>>> import dask.array as da
>>> values_dask = xr.DataArray(da.from_array(values, chunks=(3, 3)))
>>> zones_dask = xr.DataArray(da.from_array(zones, chunks=(3, 3)))

>>> values_dask = xr.DataArray(da.from_array(values_data, chunks=(3, 3)))
>>> zones_dask = xr.DataArray(da.from_array(zones_data, chunks=(3, 3)))
>>> # Calculate Stats with dask backed xarray DataArrays
>>> dask_stats_df = stats(zones=zones_dask, values=values_dask)
>>> print(type(dask_stats_df))
Expand All @@ -530,20 +529,6 @@ def stats(
1 10 27.0 49 5 675 14.21267 202.0 25
2 20 72.0 94 50 1800 14.21267 202.0 25
3 30 77.0 99 55 1925 14.21267 202.0 25

>>> # Custom Stats with dask backed xarray DataArrays
>>> dask_custom_stats ={'double_sum': lambda val: val.sum()*2}
>>> dask_custom_stats_df = stats(
>>> zones=zones_dask, values=values_dask, stats_funcs=custom_stats
>>> )
>>> print(type(dask_custom_stats_df))
<class 'dask.dataframe.core.DataFrame'>
>>> print(dask_custom_stats_df.compute())
zone double_sum
0 0 1100
1 10 1350
2 20 3600
3 30 3850
"""

validate_arrays(zones, values)
Expand Down Expand Up @@ -968,10 +953,10 @@ def crosstab(
.. sourcecode:: python

>>> import dask.array as da
>>> values_dask = xr.DataArray(da.from_array(values, chunks=(3, 3)))
>>> zones_dask = xr.DataArray(da.from_array(zones, chunks=(3, 3)))
>>> df = crosstab(zones=zones_dask, values=values_dask)
>>> print(df)
>>> values_dask = xr.DataArray(da.from_array(values_data, chunks=(3, 3)))
>>> zones_dask = xr.DataArray(da.from_array(zones_data, chunks=(3, 3)))
>>> dask_df = crosstab(zones=zones_dask, values=values_dask)
>>> print(dask_df)
Dask DataFrame Structure:
zone 0.0 10.0 20.0 30.0 40.0 50.0
npartitions=5
Expand All @@ -981,7 +966,7 @@ def crosstab(
4 ... ... ... ... ... ... ...
5 ... ... ... ... ... ... ...
Dask Name: astype, 1186 tasks
>>> print(dask_df.compute)
>>> print(dask_df.compute())
zone 0.0 10.0 20.0 30.0 40.0 50.0
0 0 1 0 0 0 0 0
1 1 3 0 0 0 0 0
Expand Down Expand Up @@ -1120,6 +1105,7 @@ def apply(

>>> import numpy as np
>>> import xarray as xr
>>> from xrspatial.zonal import apply
>>> zones_val = np.array([
[1, 1, 0, 2],
[0, 2, 1, 2]])
Expand All @@ -1132,7 +1118,7 @@ def apply(
>>> apply(zones, agg, func)
>>> agg
array([[0, 0, 5, 0],
[3, 0, 0, 0]])
[3, np.nan, 0, 0]])
"""
if not isinstance(zones, xr.DataArray):
raise TypeError("zones must be instance of DataArray")
Expand Down Expand Up @@ -1276,6 +1262,7 @@ def suggest_zonal_canvas(
>>> from spatialpandas import GeoDataFrame
>>> import geopandas as gpd
>>> import datashader as ds
>>> from xrspatial.zonal import suggest_zonal_canvas

>>> df = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))
>>> df = df.to_crs("EPSG:3857")
Expand All @@ -1298,12 +1285,16 @@ def suggest_zonal_canvas(
crs='Mercator',
min_pixels=min_pixels,
)
>>> height, width
(1537, 2376)
>>> cvs = ds.Canvas(x_range=x_range, y_range=y_range,
>>> plot_height=height, plot_width=width)
>>> spatial_df = GeoDataFrame(df, geometry='geometry')
>>> agg = cvs.polygons(spatial_df, 'geometry', agg=ds.max('id'))
>>> min_poly_id = df.area.argmin()
>>> actual_min_pixels = len(np.where(agg.data==min_poly_id)[0])
>>> actual_min_pixels
22
"""
full_xrange, full_yrange = get_full_extent(crs)
xmin, xmax = full_xrange
Expand Down