Question

我想很好地说明某个输出（x轴）发生的频率（y轴）...
我的代码产生以下图：这不好，因为这些值显然四舍五入为整数，例如，不超过100个输出的100％，但实际上我认为其中大多数是99％。

代码：

#!/usr/bin/env python3
import matplotlib
import matplotlib.pyplot as plt
import numpy as np

trajectoryIds = [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0, 25.0, 26.0, 27.0, 28.0, 29.0, 30.0, 31.0, 32.0, 33.0, 34.0, 35.0, 36.0, 37.0, 38.0, 39.0, 40.0, 41.0, 42.0, 43.0, 44.0, 45.0, 46.0, 47.0, 48.0, 49.0, 50.0, 51.0, 52.0, 53.0, 54.0, 55.0, 56.0, 57.0, 58.0, 59.0, 60.0, 61.0, 62.0, 63.0, 64.0, 65.0, 66.0, 67.0, 68.0, 69.0, 70.0, 71.0, 72.0, 73.0, 74.0, 75.0, 76.0, 77.0, 78.0, 79.0, 80.0, 81.0, 82.0, 83.0, 84.0, 85.0, 86.0, 87.0, 88.0, 89.0, 90.0, 91.0, 92.0, 93.0, 94.0, 95.0, 96.0, 97.0, 98.0, 99.0, 100.0, 101.0, 102.0, 103.0, 104.0, 105.0, 106.0, 107.0, 108.0, 109.0, 110.0, 111.0, 112.0, 113.0, 114.0, 115.0, 116.0, 117.0, 118.0, 119.0, 120.0, 121.0, 122.0, 123.0, 124.0, 125.0, 126.0, 127.0, 128.0, 129.0, 130.0, 131.0, 132.0, 133.0, 134.0, 135.0, 136.0, 137.0, 138.0, 139.0, 140.0, 141.0, 142.0, 143.0, 144.0, 145.0, 146.0, 147.0, 148.0, 149.0, 150.0, 151.0, 152.0, 153.0, 154.0, 155.0, 156.0, 157.0, 158.0, 159.0, 160.0, 161.0, 162.0, 163.0, 164.0, 165.0, 166.0, 167.0, 168.0, 169.0, 170.0, 171.0, 172.0, 173.0, 174.0, 175.0, 176.0, 177.0, 178.0, 179.0, 180.0, 181.0, 182.0, 183.0, 184.0, 185.0, 186.0, 187.0, 188.0, 189.0, 190.0, 191.0, 192.0, 193.0, 194.0, 195.0, 196.0, 197.0, 198.0]
avgSolutionPercentages = [20.6256, 99.1448, 15.6764, 21.8231, 16.3733, 17.7502, 20.0055, 86.6873, 11.3105, 15.6693, 10.3449, 81.8921, 11.6745, 92.6031, 11.8787, 23.0229, 37.9636, 2.3903, 15.1727, 14.7088, 10.0426, 59.6758, 8.0042, 12.4174, 10.0585, 46.0567, 90.2376, 98.3273, 52.8645, 49.3027, 62.4136, 32.6199, 19.0642, 10.3319, 74.6157, 22.5771, 22.4118, 11.2017, 16.5053, 11.2021, 30.8376, 24.5255, 83.1072, 10.1529, 14.3991, 46.3459, 16.2137, 4.5773, 44.9549, 1.0719, 76.5605, 42.6589, 13.6209, 34.2856, 1.3574, 29.0465, 66.8146, 16.4796, 32.9564, 62.0732, 3.7047, 13.8828, 31.6088, 60.1141, 3.3247, 45.0796, 13.7862, 26.4498, 93.6806, 10.3245, 62.5157, 10.9833, 42.5908, 37.3208, 27.4115, 84.1648, 13.9058, 13.9065, 67.8918, 27.9075, 3.6116, 10.9091, 41.0988, 24.2177, 50.2762, 61.3869, 15.5915, 27.6536, 0.7993, 22.9483, 22.3393, 88.1832, 25.1604, 18.3625, 15.7212, 56.9646, 4.0434, 11.8431, 56.0613, 32.5472, 97.8757, 21.8233, 14.8162, 38.8259, 20.5676, 72.7201, 17.7987, 35.8117, 15.1699, 17.0359, 14.0621, 35.9655, 11.9095, 10.5691, 23.3259, 16.1746, 10.1936, 12.5084, 24.1494, 16.4727, 21.0687, 15.7495, 28.8929, 11.0135, 13.3133, 14.6639, 50.1304, 21.0346, 5.1604, 53.5107, 20.0712, 41.5111, 12.1633, 74.3263, 17.7904, 17.1684, 25.3977, 21.5871, 21.9332, 22.6674, 36.6634, 99.1179, 15.3213, 16.3999, 12.0147, 57.5163, 4.2062, 17.3874, 10.7132, 17.4919, 17.8457, 29.3538, 26.1468, 75.1234, 16.4368, 21.6191, 61.1394, 12.9972, 73.5746, 72.5788, 41.6835, 39.9912, 20.1648, 11.7097, 11.5203, 36.7387, 5.0694, 30.8129, 12.0922, 22.5419, 12.3569, 54.6776, 28.3561, 26.1219, 44.7455, 1.3281, 46.5064, 13.6016, 23.5483, 11.7151, 44.3669, 3.2577, 75.0943, 10.8634, 14.8226, 45.7661, 19.7319, 30.7981, 3.5965, 47.8161, 14.5996, 39.4484, 13.0693, 24.9947, 97.4253, 76.7901, 73.1183, 4.0922]
solutionPercentages = [99.2537, 99.8467, 96.4718, 99.6637, 99.6633, 97.1289, 9.7373, 99.5126, 97.3251, 96.0545, 99.6756, 75.6587, 61.1496, 96.7575, 97.1969, 96.5258, 99.7409, 99.8641, 99.8821, 98.5401, 99.7833, 99.6314, 99.7899, 99.9117, 99.5754, 99.5868, 99.7919, 99.9127, 0.0001, 99.7297, 40.8438, 99.8559, 99.6591, 99.8917, 99.3622, 0.0001, 0.0001, 99.4828, 0.0001, 99.8559, 0.0001, 0.0001, 99.6714, 9.9635, 99.8744, 93.8854, 67.3692, 96.3229, 98.4899, 66.9173, 98.2533, 99.8318, 73.9904, 99.8431, 6.2614, 97.2776, 96.0938, 71.9457, 99.9211, 96.1596, 99.8405, 99.6314, 95.4566, 98.4786, 99.8217, 96.1014, 99.0391, 94.6034, 99.8403, 99.9093, 9.8096, 97.8549, 98.7041, 19.9098, 86.3154, 21.5302, 99.2769, 99.0496, 99.7266, 99.8602, 86.7925, 96.3197, 99.9226, 9.4447, 97.9722, 50.4884, 92.2358, 87.4311, 74.2156, 97.8819, 93.2483, 96.3186, 77.9828, 80.2446, 47.1835, 40.8011, 90.5123, 85.7852, 9.8074, 95.9032, 98.5906, 12.5081, 97.0264, 9.9166, 73.6486, 97.8634, 8.4403, 97.7592, 97.9933, 95.8486, 49.7977, 95.1031, 76.1712, 96.1552, 89.0059, 79.6172, 96.7383, 90.8518, 95.8096, 98.2061, 96.3314, 97.5753, 97.9857, 9.0739, 66.9977, 86.5744, 76.8124, 8.6195, 81.3285, 91.0891, 87.3345, 65.3729, 86.7354, 89.9558, 3.1401, 83.4993, 75.1529, 83.5419, 78.3002, 89.8564, 82.2419, 19.3794, 88.2163, 87.9032, 97.8686, 95.0742, 12.3542, 84.7324, 99.4753, 76.1753, 99.5386, 99.8664, 85.7785, 9.9933, 99.7167, 99.9328, 74.4693, 99.7531, 99.0579, 99.5994, 99.7785, 19.2743, 54.7251, 91.7269, 99.5033, 98.9247, 97.6214, 0.0001, 97.7027, 98.6832, 98.4691, 98.9759, 99.7087, 99.9244, 99.4908, 82.1103, 67.6125, 78.2363, 93.5725, 91.5612, 99.8865, 68.5426, 79.0635, 76.8951, 99.3555, 98.9196, 6.1157, 75.8655, 83.8525, 86.1269, 83.3388, 96.1854, 87.1961, 81.7453, 9.2689, 95.2765, 9.0809, 99.8599]

avgSuccess = sum(avgSolutionPercentages)/len(trajectoryIds)

y = solutionPercentages

#Plot
fig, ax = plt.subplots()
ax.hist(y)
ax.set_ylabel('Number of Motions (Total: '+ str(len(trajectoryIds)) + ')')
ax.set_xlabel('Planning Solution (%)')
ax.set_title('Planning Success Rate (Avg: ' + str(round(avgSuccess,2)) + '%)')
plt.legend(loc='upper left')
plt.show()

因此，我发现了如何使x轴上的值更精确：我将ax.hist(y)更改为ax.hist(y, bins = 1000)。但这也不是很好：所以现在我需要：

摆脱我的小节之间的空白（是否有办法摆脱这些空白x值？）
同时将所有条形保持相同的宽度
随时更改精度，例如，将每个小节的精度从1更改为0.01步

关于如何使绘图（和代码）看起来更好的任何建议都受到赞赏：）也许不是.hist函数最适合此方法...但是我不知道有什么更好的方法-这样做失败到目前为止的条形图：（

Answer 1

您可能会使用一些与箱宽度的非线性相关性，例如

b  = 5
bins = (np.linspace(np.min(y)**b, np.max(y)**b))**(1/b)

fig, ax = plt.subplots()
ax.hist(y, bins=bins, edgecolor="k")

或者您可以定义完全定制的垃圾箱，例如使用10到60的料斗宽度，然后使用5到90的料斗宽度，最后使用1到100的料斗。

bins = np.concatenate((np.linspace(0,60,7),
                       np.linspace(60,90,7),
                       np.linspace(90,100,11)))

fig, ax = plt.subplots()
ax.hist(y, bins=bins, edgecolor="k")

Answer 2

类似的东西

#!/usr/bin/env python3
import matplotlib.pyplot as plt
import numpy as np

trajectoryIds = [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0, 25.0, 26.0, 27.0, 28.0, 29.0, 30.0, 31.0, 32.0, 33.0, 34.0, 35.0, 36.0, 37.0, 38.0, 39.0, 40.0, 41.0, 42.0, 43.0, 44.0, 45.0, 46.0, 47.0, 48.0, 49.0, 50.0, 51.0, 52.0, 53.0, 54.0, 55.0, 56.0, 57.0, 58.0, 59.0, 60.0, 61.0, 62.0, 63.0, 64.0, 65.0, 66.0, 67.0, 68.0, 69.0, 70.0, 71.0, 72.0, 73.0, 74.0, 75.0, 76.0, 77.0, 78.0, 79.0, 80.0, 81.0, 82.0, 83.0, 84.0, 85.0, 86.0, 87.0, 88.0, 89.0, 90.0, 91.0, 92.0, 93.0, 94.0, 95.0, 96.0, 97.0, 98.0, 99.0, 100.0, 101.0, 102.0, 103.0, 104.0, 105.0, 106.0, 107.0, 108.0, 109.0, 110.0, 111.0, 112.0, 113.0, 114.0, 115.0, 116.0, 117.0, 118.0, 119.0, 120.0, 121.0, 122.0, 123.0, 124.0, 125.0, 126.0, 127.0, 128.0, 129.0, 130.0, 131.0, 132.0, 133.0, 134.0, 135.0, 136.0, 137.0, 138.0, 139.0, 140.0, 141.0, 142.0, 143.0, 144.0, 145.0, 146.0, 147.0, 148.0, 149.0, 150.0, 151.0, 152.0, 153.0, 154.0, 155.0, 156.0, 157.0, 158.0, 159.0, 160.0, 161.0, 162.0, 163.0, 164.0, 165.0, 166.0, 167.0, 168.0, 169.0, 170.0, 171.0, 172.0, 173.0, 174.0, 175.0, 176.0, 177.0, 178.0, 179.0, 180.0, 181.0, 182.0, 183.0, 184.0, 185.0, 186.0, 187.0, 188.0, 189.0, 190.0, 191.0, 192.0, 193.0, 194.0, 195.0, 196.0, 197.0, 198.0]
avgSolutionPercentages = [20.6256, 99.1448, 15.6764, 21.8231, 16.3733, 17.7502, 20.0055, 86.6873, 11.3105, 15.6693, 10.3449, 81.8921, 11.6745, 92.6031, 11.8787, 23.0229, 37.9636, 2.3903, 15.1727, 14.7088, 10.0426, 59.6758, 8.0042, 12.4174, 10.0585, 46.0567, 90.2376, 98.3273, 52.8645, 49.3027, 62.4136, 32.6199, 19.0642, 10.3319, 74.6157, 22.5771, 22.4118, 11.2017, 16.5053, 11.2021, 30.8376, 24.5255, 83.1072, 10.1529, 14.3991, 46.3459, 16.2137, 4.5773, 44.9549, 1.0719, 76.5605, 42.6589, 13.6209, 34.2856, 1.3574, 29.0465, 66.8146, 16.4796, 32.9564, 62.0732, 3.7047, 13.8828, 31.6088, 60.1141, 3.3247, 45.0796, 13.7862, 26.4498, 93.6806, 10.3245, 62.5157, 10.9833, 42.5908, 37.3208, 27.4115, 84.1648, 13.9058, 13.9065, 67.8918, 27.9075, 3.6116, 10.9091, 41.0988, 24.2177, 50.2762, 61.3869, 15.5915, 27.6536, 0.7993, 22.9483, 22.3393, 88.1832, 25.1604, 18.3625, 15.7212, 56.9646, 4.0434, 11.8431, 56.0613, 32.5472, 97.8757, 21.8233, 14.8162, 38.8259, 20.5676, 72.7201, 17.7987, 35.8117, 15.1699, 17.0359, 14.0621, 35.9655, 11.9095, 10.5691, 23.3259, 16.1746, 10.1936, 12.5084, 24.1494, 16.4727, 21.0687, 15.7495, 28.8929, 11.0135, 13.3133, 14.6639, 50.1304, 21.0346, 5.1604, 53.5107, 20.0712, 41.5111, 12.1633, 74.3263, 17.7904, 17.1684, 25.3977, 21.5871, 21.9332, 22.6674, 36.6634, 99.1179, 15.3213, 16.3999, 12.0147, 57.5163, 4.2062, 17.3874, 10.7132, 17.4919, 17.8457, 29.3538, 26.1468, 75.1234, 16.4368, 21.6191, 61.1394, 12.9972, 73.5746, 72.5788, 41.6835, 39.9912, 20.1648, 11.7097, 11.5203, 36.7387, 5.0694, 30.8129, 12.0922, 22.5419, 12.3569, 54.6776, 28.3561, 26.1219, 44.7455, 1.3281, 46.5064, 13.6016, 23.5483, 11.7151, 44.3669, 3.2577, 75.0943, 10.8634, 14.8226, 45.7661, 19.7319, 30.7981, 3.5965, 47.8161, 14.5996, 39.4484, 13.0693, 24.9947, 97.4253, 76.7901, 73.1183, 4.0922]
solutionPercentages = [99.2537, 99.8467, 96.4718, 99.6637, 99.6633, 97.1289, 9.7373, 99.5126, 97.3251, 96.0545, 99.6756, 75.6587, 61.1496, 96.7575, 97.1969, 96.5258, 99.7409, 99.8641, 99.8821, 98.5401, 99.7833, 99.6314, 99.7899, 99.9117, 99.5754, 99.5868, 99.7919, 99.9127, 0.0001, 99.7297, 40.8438, 99.8559, 99.6591, 99.8917, 99.3622, 0.0001, 0.0001, 99.4828, 0.0001, 99.8559, 0.0001, 0.0001, 99.6714, 9.9635, 99.8744, 93.8854, 67.3692, 96.3229, 98.4899, 66.9173, 98.2533, 99.8318, 73.9904, 99.8431, 6.2614, 97.2776, 96.0938, 71.9457, 99.9211, 96.1596, 99.8405, 99.6314, 95.4566, 98.4786, 99.8217, 96.1014, 99.0391, 94.6034, 99.8403, 99.9093, 9.8096, 97.8549, 98.7041, 19.9098, 86.3154, 21.5302, 99.2769, 99.0496, 99.7266, 99.8602, 86.7925, 96.3197, 99.9226, 9.4447, 97.9722, 50.4884, 92.2358, 87.4311, 74.2156, 97.8819, 93.2483, 96.3186, 77.9828, 80.2446, 47.1835, 40.8011, 90.5123, 85.7852, 9.8074, 95.9032, 98.5906, 12.5081, 97.0264, 9.9166, 73.6486, 97.8634, 8.4403, 97.7592, 97.9933, 95.8486, 49.7977, 95.1031, 76.1712, 96.1552, 89.0059, 79.6172, 96.7383, 90.8518, 95.8096, 98.2061, 96.3314, 97.5753, 97.9857, 9.0739, 66.9977, 86.5744, 76.8124, 8.6195, 81.3285, 91.0891, 87.3345, 65.3729, 86.7354, 89.9558, 3.1401, 83.4993, 75.1529, 83.5419, 78.3002, 89.8564, 82.2419, 19.3794, 88.2163, 87.9032, 97.8686, 95.0742, 12.3542, 84.7324, 99.4753, 76.1753, 99.5386, 99.8664, 85.7785, 9.9933, 99.7167, 99.9328, 74.4693, 99.7531, 99.0579, 99.5994, 99.7785, 19.2743, 54.7251, 91.7269, 99.5033, 98.9247, 97.6214, 0.0001, 97.7027, 98.6832, 98.4691, 98.9759, 99.7087, 99.9244, 99.4908, 82.1103, 67.6125, 78.2363, 93.5725, 91.5612, 99.8865, 68.5426, 79.0635, 76.8951, 99.3555, 98.9196, 6.1157, 75.8655, 83.8525, 86.1269, 83.3388, 96.1854, 87.1961, 81.7453, 9.2689, 95.2765, 9.0809, 99.8599]

avgSuccess = sum(avgSolutionPercentages)/len(trajectoryIds)

y = solutionPercentages

BIN_COUNT = 15
BAR_WIDTH = 0.75

fig, ax = plt.subplots()
# use numpy histogram so we can perform filtering
hist, bin_edges = np.histogram(y, bins=BIN_COUNT)
# so we can remove bins with zero entries
non_zero = np.nonzero(hist)
# take only entries where bin is non-zero
hist = hist[non_zero]
bin_edges = bin_edges[non_zero]
# generate labels based on bin edge values (maybe use centers?)
x_ticks = [str(int(edge)) for edge in bin_edges]
indices = np.arange(len(bin_edges))

plt.bar(indices, hist, BAR_WIDTH, align='center')
plt.xticks(indices, x_ticks)
ax.set_ylabel('Number of Motions (Total: '+ str(len(trajectoryIds)) + ')')
ax.set_xlabel('Planning Solution (%)')
ax.set_title('Planning Success Rate (Avg: ' + str(round(avgSuccess,2)) + '%)')
plt.show()

产生情节

直方图隐藏空箱

2 个答案: