Python 使用颜色列Matplotlib绘制矩形

我在画OHLC蜡烛的颜色。这是使用以下可用代码实现的：

但当我在代码中尝试时，出现了一个问题。我会告诉你这里的情况

dataset_train = pd.read_csv('EURUSD_M1TechnicalIndicators.csv',usecols=[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15])
dataset_train.head(10)
dataset_test = pd.read_csv('Test_EURUSD_M1TechnicalIndicators.csv',usecols=[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15],nrows=50)
dataset_test.head(10)

train  = dataset_train.filter(['open','high','low','close','closeTarget',"k","d","atr","macdmain","macdsgnal","bbup","bbmid","bblow"], axis=1)
x = train.values #returns a numpy array
min_max_scaler = preprocessing.MinMaxScaler()
x_scaled = min_max_scaler.fit_transform(x)
datasetTrain = pd.DataFrame(x_scaled)
datasetTrain.columns = ['open','high','low','close','closeTarget',"k","d","atr","macdmain","macdsgnal","bbup","bbmid","bblow"]
datasetTrain.head(10) 

test = dataset_test.filter(['open','high','low','close','closeTarget',"k","d","atr","macdmain","macdsgnal","bbup","bbmid","bblow"], axis=1)
x = test.values #returns a numpy array
min_max_scaler = preprocessing.MinMaxScaler()
x_scaled = min_max_scaler.fit_transform(x)
datasetTest = pd.DataFrame(x_scaled)
datasetTest.columns = ['open','high','low','close','closeTarget',"k","d","atr","macdmain","macdsgnal","bbup","bbmid","bblow"]
datasetTest.head(10)

plt.figure(figsize=(25,5))
plt.plot(xTrain[:,3])
plt.title('Train (' +str(len(xTrain))+' data points)')
plt.show()
plt.figure(figsize=(10,3))
plt.plot(xTest[:,0])
plt.title('Test (' +str(len(xTest))+' data points)')
plt.show()

到目前为止，产量为：

然后我试了一下：

def draw_rects(ax, quotes, width=5., height=1., yloc=1., colorup='g', 
               colordown='r', edgecolor='k', alpha=1.0):

    OFFSET = width / 2.0
    patches = []
    for q in quotes:
        t, open, close, high, low = q[:5]
        if close > open:
            color = colorup
        else:
            color = colordown

        rect = Rectangle(
            xy=(t - OFFSET, yloc),
            width=width,
            height=height,
            facecolor=color,
            edgecolor=edgecolor,
        )
        rect.set_alpha(alpha)
        patches.append(rect)
        ax.add_patch(rect)

    ax.autoscale_view()

    return patches

fig, ax = plt.subplots(1,1)
quotes = xTest
p1 = draw_rects(ax, xTrain, yloc=1)
p2 = draw_rects(ax, xTest, yloc=4)
labels = [item.get_text() for item in ax.get_yticklabels()]
labels[2] = 'Train'
labels[8] = 'Test'
ax.set_yticklabels(labels)
plt.show()

输出：

我正期待着蜡烛的颜色被展示出来。所以上面的过程对我来说不起作用。因此，我在

数据集中创建了另一列作为color和
shiftedcolor

现在我正在考虑使用矩形以烛光方式展示数据集中的颜色数据。请帮我做那件事

这是我使用的数据集
＞P>我没有真正地解决你的问题的复杂性，绘制一些矩形（没有冒犯，如果我忽略了重要的东西，请纠正我，换句话说：TL；Dr.…）
但作为一个例子，我将如何绘制这两行（假设作为一种不同的方法来开始讨论它是否值得…）：

导致：


编辑：
同样的事情也适用于您的数据文件：
（我看到你也想要黑色表示相等的值，所以也添加了这个）

哇，这是我迄今为止得到的一个非常好的答案。你能帮我把它们贴上标签吗？请贴哪种标签？如果您想获得上面发布的x记号标签，可以设置“x=np.arange（-2，4）”。或y轴标签的“plt.yticks（[1,4]，“Train”，“Test']）。或者你想给标记本身加上标签？很明显，我指的是轴标签。我想已经完成了。还有一件事，你能告诉我，如果我想增加两个矩形之间的间距，那么我必须做什么吗？你可以改变矩形的纵横比，它是在
顶点中定义的。我添加了两个参数
rx
和
ry
，以便您可以轻松调整。谢谢您的帮助。我正在查看。通过查看绘图，现在知道您的文件有30202行-难道这个绘图不是很好，只是因为数千个（可能重叠）矩形而难以读取吗？
x = np.arange(10)
y = np.ones(10)

bools = np.random.randint(0, 2, 10).astype(bool)
colors = np.array(bools, str)
colors[bools] = 'g'
colors[~bools] = 'r'

rx = 1
ry = 2
rect = [(-rx, -ry), (rx, -ry), (rx, ry), (-rx, ry)]

plt.figure()
plt.scatter(x, y, facecolor=colors, verts=rect, s=1000)
plt.scatter(x, y+3, facecolor=colors[::-1], verts=rect, s=1000)
plt.ylim(0, 5)
plt.yticks([1, 4], ['Train', 'Test'])

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

dataset_train = pd.read_csv('EURUSD_M1TechnicalIndicators.txt', usecols=['open', 'close'])
dataset_test = pd.read_csv('Test_EURUSD_M1TechnicalIndicators.csv', usecols=['open', 'close'])

gt_train = (dataset_train.open - dataset_train.close) > 0
gt_test = (dataset_test.open - dataset_test.close) > 0
eq_train = (dataset_train.open - dataset_train.close) == 0
eq_test = (dataset_test.open - dataset_test.close) == 0

y_train = np.ones(len(gt_train))
y_test = np.ones(len(gt_test))

colors_train = np.array(gt_train, str)
colors_test = np.array(gt_test, str)
colors_train[gt_train] = 'g'
colors_train[~gt_train] = 'r'
colors_train[eq_train] = 'k'
colors_test[gt_test] = 'g'
colors_test[~gt_test] = 'r'
colors_test[eq_test] = 'k'

rx = .2
ry = 2
rect = [(-rx, -ry), (rx, -ry), (rx, ry), (-rx, ry)]

plt.figure()
plt.scatter(np.arange(y_train.size), y_train, facecolor=colors_train, verts=rect, s=1000)
plt.scatter(np.arange(y_test.size), y_test+3, facecolor=colors_test, verts=rect, s=1000)
plt.ylim(0, 5)
plt.yticks([1, 4], ['Train', 'Test'])