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[api change] directional_weighted_semivariance is no longer public function
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CHANGELOG.rst

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@@ -24,6 +24,7 @@ Changes - from version >= 1.x
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* [enhancement] `build_experimental_variogram()` function has the new parameters `values` and `geometries`, that might be provided instead of `ds` parameter
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* [tests] Added basic tests for `build_experimental_variogram()` function
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* [enhancement] `VariogramCloud()` class has the new parameters `values` and `geometries`, that might be provided instead of `ds` parameter
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* [api change] `directional_weighted_semivariance()` function is now private: `_directional_weighted_semivariance()`
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2025-10-11

dev/profile/semivariogram/experimental/profile_from_ellipse_weighted.py

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import numpy as np
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from pyinterpolate.semivariogram.experimental.functions.directional import \
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directional_weighted_semivariance
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_directional_weighted_semivariance
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def profile_select_in_ellipse():
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step_size = 0.05
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max_range = 0.6
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_ = directional_weighted_semivariance(
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_ = _directional_weighted_semivariance(
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points=points,
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lags=np.linspace(step_size, max_range, 10),
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custom_weights=weights,

src/pyinterpolate/semivariogram/experimental/experimental_semivariogram.py

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validate_semivariance_weights, validate_direction_and_tolerance, \
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validate_bins
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from pyinterpolate.semivariogram.experimental.functions.directional import \
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directional_weighted_semivariance, from_ellipse, from_ellipse_cloud
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_directional_weighted_semivariance, from_ellipse, from_ellipse_cloud
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from pyinterpolate.semivariogram.experimental.functions.general import \
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omnidirectional_variogram, omnidirectional_semivariogram_cloud
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from pyinterpolate.semivariogram.experimental.functions.semivariance import \
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direction,
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tolerance)
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else:
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output_semivariances = directional_weighted_semivariance(
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output_semivariances = _directional_weighted_semivariance(
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points,
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lags,
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custom_weights,

src/pyinterpolate/semivariogram/experimental/functions/directional.py

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return point_cloud
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def directional_weighted_semivariance(
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def _directional_weighted_semivariance(
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points: np.array,
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lags: np.array,
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custom_weights: np.array,

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