带有误差线的 matplotlib 直方图的数据处理答案

【问题标题】：Data handling for matplotlib histogram with error bars带有误差线的 matplotlib 直方图的数据处理
【发布时间】：2013-03-20 19:11:10
【问题描述】：

我有一个数据集，它是 python 中的元组列表，如下所示：

dataSet = [(6.1248199999999997, 27), (6.4400500000000003, 4), (5.9150600000000004, 1), (5.5388400000000004, 38), (5.82559, 1), (7.6892199999999997, 2), (6.9047799999999997, 1), (6.3516300000000001, 76), (6.5168699999999999, 1), (7.4382099999999998, 1), (5.4493299999999998, 1), (5.6254099999999996, 1), (6.3227700000000002, 1), (5.3321899999999998, 11), (6.7402300000000004, 4), (7.6701499999999996, 1), (5.4589400000000001, 3), (6.3089700000000004, 1), (6.5926099999999996, 2), (6.0003000000000002, 5), (5.9845800000000002, 1), (6.4967499999999996, 2), (6.51227, 6), (7.0302600000000002, 1), (5.7271200000000002, 49), (7.5311300000000001, 7), (5.9495800000000001, 2), (5.1487299999999996, 18), (5.7637099999999997, 6), (5.5144500000000001, 44), (6.7988499999999998, 1), (5.2578399999999998, 1)]

如果元组的第一个元素是能量，第二个元素是计数器，则影响了多少传感器。

我想创建一个直方图来研究受影响传感器的数量与能量之间的关系。我对 matplotlib（和 python）还很陌生，但这是我到目前为止所做的：

import math
import matplotlib.pyplot as plt

dataSet = [(6.1248199999999997, 27), (6.4400500000000003, 4), (5.9150600000000004, 1), (5.5388400000000004, 38), (5.82559, 1), (7.6892199999999997, 2), (6.9047799999999997, 1), (6.3516300000000001, 76), (6.5168699999999999, 1), (7.4382099999999998, 1), (5.4493299999999998, 1), (5.6254099999999996, 1), (6.3227700000000002, 1), (5.3321899999999998, 11), (6.7402300000000004, 4), (7.6701499999999996, 1), (5.4589400000000001, 3), (6.3089700000000004, 1), (6.5926099999999996, 2), (6.0003000000000002, 5), (5.9845800000000002, 1), (6.4967499999999996, 2), (6.51227, 6), (7.0302600000000002, 1), (5.7271200000000002, 49), (7.5311300000000001, 7), (5.9495800000000001, 2), (5.1487299999999996, 18), (5.7637099999999997, 6), (5.5144500000000001, 44), (6.7988499999999998, 1), (5.2578399999999998, 1)]

binWidth = .2
binnedDataSet = []
#create another list and append the "binning-value"
for item in dataSet:
    binnedDataSet.append((item[0], item[1], math.floor(item[0]/binWidth)*binWidth))

energies, sensorHits, binnedEnergy = [[q[i] for q in binnedDataSet] for i in (0,1,2)]
plt.plot(binnedEnergy, sensorHits, 'ro')
plt.show()

到目前为止，这可行（尽管它甚至看起来不像直方图；-) 但还可以），但现在我想计算每个 bin 的平均值并附加一些误差线。

有什么办法呢？我查看了 matplotlib 的直方图示例，但它们都使用将被计数的一维数据，所以你会得到一个频谱......这不是我真正想要的。

【问题讨论】：

标签： matplotlib histogram

【解决方案1】：

我对你正在尝试做的事情有些困惑，但我认为这（一阶）会做我认为你想要的：

bin_width = .2
bottom = 5.0
top = 8.0

binned_data = [0.0] * int(math.ceil(((top - bottom) / bin_width)))
binned_count = [0] * int(math.ceil(((top - bottom) / bin_width)))
n_bins = len(binned_data)
for E, cnt in dataSet:
    if E < bottom or E > top:
        print 'out of range'
        continue
    bin_id = int(math.floor(n_bins * (E - bottom) / (top - bottom)))
    binned_data[bin_id] += cnt
    binned_count[bin_id] += 1

binned_avergaed_data = [C_sum / hits if hits > 0 else 0 for C_sum, hits in zip(binned_data, binned_count)]

bin_edges = [bottom + j * bin_width for j in range(len(binned_data))]

plt.bar(bin_edges, binned_avergaed_data, width=bin_width)

我还建议研究numpy，这样写起来会更简单。

【讨论】：