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- # -*- coding: UTF-8 -*-
- """
- Copyright 2020 Tianshu AI Platform. All Rights Reserved.
-
- Licensed under the Apache License, Version 2.0 (the "License");
- you may not use this file except in compliance with the License.
- You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
- Unless required by applicable law or agreed to in writing, software
- distributed under the License is distributed on an "AS IS" BASIS,
- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- See the License for the specific language governing permissions and
- limitations under the License.
- =============================================================
- """
- import numpy as np
-
-
- def histogram(data, buckets_count=None):
- def auto_ranges(_min, _max):
- _range = [_min]
- temp = _min * 1.1
- while temp < _max:
- _range.append(temp)
- temp = temp * 1.1
- _range.append(_max)
- return np.array(_range)
-
- def merge_zero(counts, buckets_limit):
- # 合并相邻统计值为0的区间，减少写入日志大小
- delete_index = []
- for index, x in enumerate(counts[1:], start=1):
- if x == 0 and counts[index - 1] == 0:
- delete_index.append(index)
- counts = np.delete(counts, delete_index)
- buckets_limit = np.delete(buckets_limit, delete_index)
-
- return buckets_limit, counts
-
- def auto_buckets(data):
- # 根据数据分布自动设置区间大小
- _min = data.min()
- _max = data.max()
- abs_min = np.min(np.abs(data))
-
- if _min < 0 and _max > 0:
- _range = auto_ranges(abs_min, _max)
- neg_range = 0 - auto_ranges(abs_min, -_min)[::-1] # reverse
- ranges = np.append(neg_range, _range)
- elif _min >= 0:
- ranges = auto_ranges(_min, _max)
- elif _max <= 0:
- ranges = 0 - auto_ranges(0 - _max, 0 - _min)[::-1]
-
- if len(ranges) == 1: # 统计数据都为一个常数
- ranges.append(ranges[0])
- counts, buckets_limit = np.histogram(data, bins=ranges)
- buckets_limit, counts = merge_zero(counts, buckets_limit)
-
- return _min, _max, buckets_limit[1:], counts
-
- def specified_buckets(data, buckets_count):
- counts, buckets_limit = np.histogram(data, bins=buckets_count)
- buckets_limit, counts = merge_zero(counts, buckets_limit)
- _min = buckets_limit[0]
- _max = buckets_limit[-1]
- return _min, _max, buckets_limit[1:], counts
-
- data = np.array(data).ravel() # flatten
- if data.size == 0:
- return [0], [0]
-
- if buckets_count is None:
- # 自动设置区间
- return auto_buckets(data)
- else:
- return specified_buckets(data, buckets_count)
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