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Remove deprecated warning for numpy>=1.20.0
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@@ -413,7 +413,7 @@ class TSDataSampler:
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# 1) for better performance, use the last nan line for padding the lost date
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# 1) for better performance, use the last nan line for padding the lost date
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# 2) In case of precision problems. We use np.float64. # TODO: I'm not sure if whether np.float64 will result in
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# 2) In case of precision problems. We use np.float64. # TODO: I'm not sure if whether np.float64 will result in
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# precision problems. It will not cause any problems in my tests at least
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# precision problems. It will not cause any problems in my tests at least
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indices = np.nan_to_num(indices.astype(np.float64), nan=self.nan_idx).astype(np.int)
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indices = np.nan_to_num(indices.astype(np.float64), nan=self.nan_idx).astype(int)
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data = self.data_arr[indices]
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data = self.data_arr[indices]
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if isinstance(idx, mtit):
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if isinstance(idx, mtit):
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@@ -64,7 +64,7 @@ def np_ffill(arr: np.array):
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arr : np.array
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arr : np.array
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Input numpy 1D array
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Input numpy 1D array
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"""
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"""
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mask = np.isnan(arr.astype(np.float)) # np.isnan only works on np.float
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mask = np.isnan(arr.astype(float)) # np.isnan only works on np.float
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# get fill index
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# get fill index
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idx = np.where(~mask, np.arange(mask.shape[0]), 0)
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idx = np.where(~mask, np.arange(mask.shape[0]), 0)
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np.maximum.accumulate(idx, out=idx)
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np.maximum.accumulate(idx, out=idx)
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