87 lines
1.9 KiB
Python
87 lines
1.9 KiB
Python
"""
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aggregations, two special case need to consider:
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1. extra 0s
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2. full of 0s
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"""
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import numpy as np
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def sum_agg(z):
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z = np.where(np.isnan(z), 0, z)
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return np.sum(z, axis=0)
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def product_agg(z):
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z = np.where(np.isnan(z), 1, z)
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return np.prod(z, axis=0)
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def max_agg(z):
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z = np.where(np.isnan(z), -np.inf, z)
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return np.max(z, axis=0)
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def min_agg(z):
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z = np.where(np.isnan(z), np.inf, z)
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return np.min(z, axis=0)
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def maxabs_agg(z):
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z = np.where(np.isnan(z), 0, z)
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abs_z = np.abs(z)
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max_abs_index = np.argmax(abs_z)
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return z[max_abs_index]
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def median_agg(z):
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non_zero_mask = ~np.isnan(z)
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n = np.sum(non_zero_mask, axis=0)
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z = np.where(np.isnan(z), np.inf, z)
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sorted_valid_values = np.sort(z)
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if n % 2 == 0:
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return (sorted_valid_values[n // 2 - 1] + sorted_valid_values[n // 2]) / 2
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else:
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return sorted_valid_values[n // 2]
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def mean_agg(z):
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non_zero_mask = ~np.isnan(z)
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valid_values_sum = sum_agg(z)
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valid_values_count = np.sum(non_zero_mask, axis=0)
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mean_without_zeros = valid_values_sum / valid_values_count
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return mean_without_zeros
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AGG_TOTAL_LIST = [sum_agg, product_agg, max_agg, min_agg, maxabs_agg, median_agg, mean_agg]
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agg_name2key = {
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'sum': 0,
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'product': 1,
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'max': 2,
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'min': 3,
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'maxabs': 4,
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'median': 5,
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'mean': 6,
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}
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def agg(idx, z):
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idx = np.asarray(idx, dtype=np.int32)
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if np.all(z == 0.):
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return 0
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else:
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return AGG_TOTAL_LIST[idx](z)
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if __name__ == '__main__':
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array = np.asarray([1, 2, np.nan, np.nan, 3, 4, 5, np.nan, np.nan, np.nan, np.nan], dtype=np.float32)
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for names in agg_name2key.keys():
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print(names, agg(agg_name2key[names], array))
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array2 = np.asarray([0, 0, 0, 0], dtype=np.float32)
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for names in agg_name2key.keys():
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print(names, agg(agg_name2key[names], array2))
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