带有错误条的matplotlib直方图的数据处理

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)]

元组的第一个元素是能量，第二个元素是计数器，受影响的传感器数量

我想创建一个直方图来研究受影响传感器的数量和能量之间的关系。我对matplotlib（和python）非常陌生，但到目前为止，我做了以下工作：

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，这是可行的（虽然它甚至不像直方图；-），但还行），但现在我想计算每个箱子的平均值，并附加一些错误条

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怎么做？我查看了matplotlib的直方图示例，但它们都使用一维数据，这些数据将被计数，因此您可以得到频谱…这并不是我真正想要的。

我对您正试图做的有点困惑，但我认为（首先）这将实现我认为您想要的：

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)

bin_宽度=.2
底部=5.0
top=8.0
binned_data=[0.0]*int（math.ceil（（（顶部-底部）/bin_宽度）））
binned_count=[0]*int（math.ceil（（（顶部-底部）/bin_宽度）））
n\u bins=len（装箱数据）
对于E，数据集中的cnt：
如果E<底部或E>顶部：
打印“超出范围”
持续
bin_id=int（数学层（n_bin*（E-bottom）/（top-bottom）））
binned_数据[bin_id]+=cnt
binned_计数[bin_id]+=1
binned_avergaed_data=[C_sum/hits if hits>0否则C_sum为0，hits in zip（binned_data，binned_count）]
bin_边=[底部+j*范围内j的bin_宽度（len（binned_数据））]
plt.bar（箱边，箱平均数据，宽度=箱宽）

我还建议研究一下

numpy

，这将使编写更简单