垂直 "broken" 条形图，以数组作为条形高度和颜色编码

Question

我正在尝试创建一个如下所示的条形图：

x轴是重合命中的探测器数量（即多重性）对于每个多重性，我都有几个事件。 y 轴包含每个 event.The 颜色的平均脉冲高度应对应于具有所示脉冲高度并出现在具有相应多重性的事件中的命中数

我有一个字典，它以多重性作为键，以平均脉冲高度的数组作为值。 :

averages = {2 : [...],
        3 : [...],
        4 : [...],
        5 : [...],
        6 : [...],}

for key in averages:
plt.bar(key,averages[key] ,width = 0.8)

我只知道如何制作简单版的条形图，如下所示：

有人能告诉我如何使条形图“断开以显示所有脉冲高度并添加颜色编码吗？

Answer 1

不完全清楚，但我想你想要这样的东西

import seaborn as sns
from scipy import stats
import matplotlib as mpl
import matplotlib.pyplot as plt

# Create some fake data that looks roughly like what you have
tips = sns.load_dataset("tips")
weights = stats.gaussian_kde(tips["total_bill"])(tips["total_bill"])
tips = tips.sample(frac=50, weights=weights, replace=True)

days = []
segments = []
counts = []
for day, x in tips["total_bill"].groupby(tips["day"]):
    days.append(day)
    segments.append(np.sort(x.unique()))
    counts.append(x.value_counts().sort_index())

# Map from counts to colors
norm = mpl.colors.Normalize(0, np.concatenate(counts).max())
colors = [mpl.cm.viridis(norm(c)) for c in counts]

f, ax = plt.subplots()

# Draw each horizontal line
events = ax.eventplot(segments, colors=colors, orientation="vertical", zorder=.5)
events[0].set_norm(norm)
f.colorbar(events[0])

# Add the mean/std for each x position
sns.pointplot(data=tips, x="day", y="total_bill", ci="sd", order=days, join=False, color=".1")

我的问题是需要每条水平线代表每个数据值，但如果你对直方图感到满意，这是 seaborn 中的两个函数调用 (>=0.11)

sns.histplot(
    data=tips, x="day", y="total_bill",
    discrete=(True, False), binwidth=(1, .5),
    cmap="viridis", cbar=True, zorder=.5, alpha=.75,
)

sns.pointplot(
    data=tips, x="day", y="total_bill",
    ci="sd", order=days, join=False, color=".1",
)

Answer 2

这是一个使用 imshow 生成列式“颜色直方图”的解决方案：

import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np

# Create dummy data
coincidences = [2, 3, 4, 5, 6]
n_list = [10000, 8000, 6000, 4000, 2000]
mu_list = np.array([200, 300, 400, 500, 600])
scale = 100
averages = {c: np.random.normal(loc=mu_list[i], scale=scale, size=n_list[i])
            for i, c in enumerate(coincidences)}

# Calculate histogram for each column
bins = np.linspace(0, 1000, 1000)
hist_img = np.array([np.histogram(averages[c], bins=bins)[0]
                     for c in coincidences]).T

# Create Normalized colormap
# norm = mpl.colors.Normalize()
norm = mpl.colors.LogNorm(vmin=1, vmax=hist_img.max())
sm = mpl.cm.ScalarMappable(cmap='viridis', norm=norm)

# Use colormap for img_hist and make zeros transparent
hist_img2 = sm.to_rgba(hist_img, bytes=True)
hist_img2[hist_img == 0, 3] = 0

# Plot
fig, ax = plt.subplots()
cc = ax.imshow(hist_img2, aspect='auto', interpolation='none', origin='lower',
               extent=[1.5, 6.5, 0, 1000])
plt.colorbar(sm)

mean = [np.mean(averages[c]) for c in coincidences]
std = [np.std(averages[c]) for c in coincidences]
ax.errorbar(coincidences, mean, yerr=std, ls='', c='k', capsize=3, label='std')
ax.plot(coincidences, mean, ls='', marker='o', c='b', label='mean')
ax.legend()

垂直 "broken" 条形图，以数组作为条形高度和颜色编码

Vertical "broken" bar plot with arrays as bar heights and color coding

python

matplotlib

color-coding

histogram

bar-chart