Correct examples in docstrings (#703)

* curvature

* focal tools

* proximity

* viewshed

* zonal tools

Author: thuydotm

xrspatial/curvature.py

@@ -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)
@@ -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)>

xrspatial/focal.py

@@ -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
     """
 
@@ -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      ],
@@ -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)
@@ -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],

xrspatial/proximity.py

@@ -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
@@ -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],

xrspatial/viewshed.py

@@ -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

xrspatial/zonal.py

@@ -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))
@@ -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)
@@ -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
@@ -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
@@ -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]])
@@ -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")
@@ -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")
@@ -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