def fillnodata(
image,
mask=None,
max_search_distance=100.0,
smoothing_iterations=0):
"""Fill holes in raster data by interpolation
This algorithm will interpolate values for all designated nodata
pixels (marked by zeros in `mask`). For each pixel a four direction
conic search is done to find values to interpolate from (using
inverse distance weighting). Once all values are interpolated, zero
or more smoothing iterations (3x3 average filters on interpolated
pixels) are applied to smooth out artifacts.
This algorithm is generally suitable for interpolating missing
regions of fairly continuously varying rasters (such as elevation
models for instance). It is also suitable for filling small holes
and cracks in more irregularly varying images (like aerial photos).
It is generally not so great for interpolating a raster from sparse
point data.
Parameters
----------
image : numpy ndarray
The source image with holes to be filled. If a MaskedArray, the
inverse of its mask will define the pixels to be filled --
unless the ``mask`` argument is not None (see below).`
mask : numpy ndarray or None
A mask band indicating which pixels to interpolate. Pixels to
interpolate into are indicated by the value 0. Values
> 0 indicate areas to use during interpolation. Must be same
shape as image. This array always takes precedence over the
image's mask (see above). If None, the inverse of the image's
mask will be used if available.
max_search_distance : float, optional
The maxmimum number of pixels to search in all directions to
find values to interpolate from. The default is 100.
smoothing_iterations : integer, optional
The number of 3x3 smoothing filter passes to run. The default is
0.
Returns
-------
out : numpy ndarray
The filled raster array.
"""
if mask is None and isinstance(image, MaskedArray):
mask = ~image.mask
if not dtypes.is_ndarray(mask):
raise ValueError("An mask array is required")
if isinstance(image, MaskedArray):
image = image.data
if not dtypes.is_ndarray(image):
raise ValueError("An image array is required")
max_search_distance = float(max_search_distance)
smoothing_iterations = int(smoothing_iterations)
return _fillnodata(
image, mask, max_search_distance, smoothing_iterations)
def fillnodata(
image,
mask=None,
max_search_distance=100.0,
smoothing_iterations=0):
"""
Fill holes in a raster dataset by interpolation from the edges.
This algorithm will interpolate values for all designated nodata
pixels (marked by zeros in `mask`). For each pixel a four direction
conic search is done to find values to interpolate from (using
inverse distance weighting). Once all values are interpolated, zero
or more smoothing iterations (3x3 average filters on interpolated
pixels) are applied to smooth out artifacts.
This algorithm is generally suitable for interpolating missing
regions of fairly continuously varying rasters (such as elevation
models for instance). It is also suitable for filling small holes
and cracks in more irregularly varying images (like aerial photos).
It is generally not so great for interpolating a raster from sparse
point data.
Parameters
----------
image : numpy ndarray
The source containing nodata holes.
mask : numpy ndarray or None
A mask band indicating which pixels to interpolate. Pixels to
interpolate into are indicated by the value 0. Values > 0
indicate areas to use during interpolation. Must be same shape
as image. If `None`, a mask will be diagnosed from the source
data.
max_search_distance : float, optional
The maxmimum number of pixels to search in all directions to
find values to interpolate from. The default is 100.
smoothing_iterations : integer, optional
The number of 3x3 smoothing filter passes to run. The default is
0.
Returns
-------
out : numpy ndarray
The filled raster array.
"""
max_search_distance = float(max_search_distance)
smoothing_iterations = int(smoothing_iterations)
with rasterio.drivers():
return _fillnodata(
image, mask, max_search_distance, smoothing_iterations)
def fillnodata(image,
mask=None,
max_search_distance=100.0,
smoothing_iterations=0):
"""
Fill holes in a raster dataset by interpolation from the edges.
This algorithm will interpolate values for all designated nodata
pixels (marked by zeros in `mask`). For each pixel a four direction
conic search is done to find values to interpolate from (using
inverse distance weighting). Once all values are interpolated, zero
or more smoothing iterations (3x3 average filters on interpolated
pixels) are applied to smooth out artifacts.
This algorithm is generally suitable for interpolating missing
regions of fairly continuously varying rasters (such as elevation
models for instance). It is also suitable for filling small holes
and cracks in more irregularly varying images (like aerial photos).
It is generally not so great for interpolating a raster from sparse
point data.
Parameters
----------
image : numpy ndarray
The source containing nodata holes.
mask : numpy ndarray or None
A mask band indicating which pixels to interpolate. Pixels to
interpolate into are indicated by the value 0. Values > 0
indicate areas to use during interpolation. Must be same shape
as image. If `None`, a mask will be diagnosed from the source
data.
max_search_distance : float, optional
The maxmimum number of pixels to search in all directions to
find values to interpolate from. The default is 100.
smoothing_iterations : integer, optional
The number of 3x3 smoothing filter passes to run. The default is
0.
Returns
-------
out : numpy ndarray
The filled raster array.
"""
max_search_distance = float(max_search_distance)
smoothing_iterations = int(smoothing_iterations)
with rasterio.drivers():
return _fillnodata(image, mask, max_search_distance,
smoothing_iterations)
def interpolate_high_values(self, max_value):
"""Creates raster with smoothed outliers"""
template = rasterio.open(self.rad_raster)
profile = template.profile
profile.update(count=1,
compress='lzw',
predictor=2,
bigtiff='yes',
nodata=-99999)
template.close()
with rasterio.open(str(self.rad_raster)) as src, rasterio.open(str(self.out_raster), 'w', **profile) as dst:
rad_data = src.read()
mask = np.full((rad_data.shape), 1)
mask[np.where(rad_data > max_value)] = 0
print(mask[np.where(mask == 0)])
rad_data[np.where(rad_data > max_value)] = -99999
result = _fillnodata(rad_data, mask=mask)
dst.write(result)