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Merged
merged 3 commits into from
Jun 22, 2020
Merged

Proximity hot fix_issue44 #45

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9a95314
hot-fix for user_quide
kristinepetrosyan May 28, 2020
2db4b7d
proximity hot fix
kristinepetrosyan Jun 10, 2020
178d8ec
updated changes
kristinepetrosyan Jun 20, 2020
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353 changes: 201 additions & 152 deletions examples/user-guide.ipynb
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11 changes: 6 additions & 5 deletions xrspatial/proximity.py
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Original file line number Diff line number Diff line change
Expand Up @@ -318,7 +318,7 @@ def _proximity(img, x_coords, y_coords, target_values, distance_metric):

# ported from
# https://github.com/OSGeo/gdal/blob/master/gdal/alg/gdalproximity.cpp
def proximity(raster, target_values=[], distance_metric='EUCLIDEAN'):
def proximity(raster, x='x', y='y', target_values=[], distance_metric='EUCLIDEAN'):
"""Compute the proximity of all pixels in the image to a set of pixels in
the source image.

Expand All @@ -333,6 +333,7 @@ def proximity(raster, target_values=[], distance_metric='EUCLIDEAN'):
----------
raster: xarray.DataArray
Input raster image with shape=(height, width)
x, y: 'x' and 'y' coordinates
target_values: list
Target pixel values to measure the distance from. If this option is
not provided, proximity will be computed from non-zero pixel values.
Expand All @@ -349,10 +350,10 @@ def proximity(raster, target_values=[], distance_metric='EUCLIDEAN'):
"""

raster_dims = raster.dims
if raster_dims != ('y', 'x'):
if raster_dims != (y, x):
raise ValueError("In function proximity(). "
"raster.coords should be named as coordinates:"
"('y', 'x')")
"(%s, %s)".format(y, x))

# convert distance metric from string to integer, the correct type
# of argument for function _distance()
Expand All @@ -364,8 +365,8 @@ def proximity(raster, target_values=[], distance_metric='EUCLIDEAN'):
target_values = np.asarray(target_values).astype(np.uint8)

img = raster.values
y_coords = raster.coords['y'].values
x_coords = raster.coords['x'].values
y_coords = raster.coords[y].values
x_coords = raster.coords[x].values

proximity_img = _proximity(img, x_coords, y_coords, target_values,
distance_metric)
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30 changes: 15 additions & 15 deletions xrspatial/tests/test_proximity.py
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Original file line number Diff line number Diff line change
Expand Up @@ -13,16 +13,16 @@


df = pd.DataFrame({
'x': [-10, -10, -4, -4, 1, 3, 7, 7, 7],
'y': [-5, -10, -5, -5, 0, 5, 10, 10, 10]
'lat': [-10, -10, -4, -4, 1, 3, 7, 7, 7],
'lon': [-5, -10, -5, -5, 0, 5, 10, 10, 10]
})

cvs = ds.Canvas(plot_width=width,
plot_height=height,
x_range=(-20, 20),
y_range=(-20, 20))

raster = cvs.points(df, x='x', y='y')
raster = cvs.points(df, x='lat', y='lon')
raster_image = raster.values
nonzeros_raster = np.count_nonzero(raster_image)
zeros_raster = width * height - nonzeros_raster
Expand All @@ -33,7 +33,7 @@ def test_proximity_default():
# DEFAULT SETTINGS
# proximity(img, max_distance=None, target_values=[], dist_units=PIXEL,
# nodata=np.nan)
default_proximity = proximity(raster)
default_proximity = proximity(raster, x='lat', y='lon')
default_proximity_img = default_proximity.values
zeros_default = (default_proximity_img == 0).sum()

Expand All @@ -54,7 +54,7 @@ def test_proximity_target_value():
# TARGET VALUES SETTING
target_values = [2, 3]
num_target = (raster == 2).sum() + (raster == 3).sum()
tv_proximity = proximity(raster, target_values=target_values)
tv_proximity = proximity(raster, x='lat', y='lon', target_values=target_values)
tv_proximity_img = tv_proximity.values
tv_zeros = (tv_proximity_img == 0).sum()

Expand All @@ -71,7 +71,7 @@ def test_proximity_target_value():
def test_proximity_manhattan():

# distance_metric SETTING
dm_proximity = proximity(raster, distance_metric='MANHATTAN')
dm_proximity = proximity(raster, 'lat', 'lon', distance_metric='MANHATTAN')

# output must be an xarray DataArray
assert isinstance(dm_proximity, xa.DataArray)
Expand All @@ -80,18 +80,18 @@ def test_proximity_manhattan():
assert dm_proximity.values.shape[0] == height
assert dm_proximity.values.shape[1] == width
# all output values must be in range [0, max_possible_distance]
max_possible_distance = manhattan_distance(raster.coords['x'].values[0],
raster.coords['x'].values[-1],
raster.coords['y'].values[0],
raster.coords['y'].values[-1])
max_possible_distance = manhattan_distance(raster.coords['lat'].values[0],
raster.coords['lat'].values[-1],
raster.coords['lon'].values[0],
raster.coords['lon'].values[-1])
assert np.nanmax(dm_proximity.values) <= max_possible_distance
assert np.nanmin(dm_proximity.values) == 0


def test_proximity_great_circle():

# distance_metric SETTING
dm_proximity = proximity(raster, distance_metric='GREAT_CIRCLE')
dm_proximity = proximity(raster, 'lat', 'lon', distance_metric='GREAT_CIRCLE')

# output must be an xarray DataArray
assert isinstance(dm_proximity, xa.DataArray)
Expand All @@ -100,10 +100,10 @@ def test_proximity_great_circle():
assert dm_proximity.values.shape[0] == height
assert dm_proximity.values.shape[1] == width
# all output values must be in range [0, max_possible_distance]
max_possible_distance = great_circle_distance(raster.coords['x'].values[0],
raster.coords['x'].values[-1],
raster.coords['y'].values[0],
raster.coords['y'].values[-1])
max_possible_distance = great_circle_distance(raster.coords['lat'].values[0],
raster.coords['lat'].values[-1],
raster.coords['lon'].values[0],
raster.coords['lon'].values[-1])
assert np.nanmax(dm_proximity.values) <= max_possible_distance
assert np.nanmin(dm_proximity.values) == 0

Expand Down