[matplotlib-snippets] matplotlib snippets #matplotlib #python #visualization

contour-example.py

def f(x, y):
    return np.sin(x) ** 10 + np.cos(10+x*y) + np.cos(x)
  
x = np.linspace(0, 5, 50)
y = np.linspace(0, 5, 50)
X, Y = np.meshgrid(x, y)
Z = f(X, Y)

# draw contour
contour = plt.contour(X, Y, Z, 3, colors='black')

# add z value into contour
plt.clabel(contour, fontsize=10, inline=True)

# plus transparent bg from imshow
# RdGy => RedGray
plt.imshow(Z, extent=[0, 5.0, 0, 5.0], origin='lower', cmap=plt.cm.RdGy, alpha=0.3)

# show color bar
plt.colorbar()

discreate-scatter.py

# load images of the digits 0 through 5 and visualize several of them
from sklearn.datasets import load_digits
digits = load_digits(n_class=6)

# use ax.flat to draw all images in one-loop
fig, ax = plt.subplots(8, 8, figsize=(6, 6));
for i, axi in enumerate(ax.flat):
    axi.imshow(digits.images[i]);
    axi.set(xticks=[], yticks=[]);

# project the digits into 2 dimensions using IsoMap
from sklearn.manifold import Isomap
iso = Isomap(n_components=2)
projection = iso.fit_transform(digits.data)

# plot the results
# vip: use discreate cmap to distinguish different digit label's color
plt.scatter(projection[:, 0], projection[:, 1], lw=0.1,
            c=digits.target, cmap=plt.cm.get_cmap('cubehelix', 6))

# set ticks in colorbar
plt.colorbar(ticks=range(6), label='digit value')

# make ticks 1,2,3,4,5 center in the box
plt.clim(-0.5, 5.5)

gridspec-demo.py

# Create some normally distributed data
mean = [0, 0]
cov = [[1, 1], [1, 2]]
x, y = np.random.multivariate_normal(mean, cov, 3000).T

# Set up the axes with gridspec
fig = plt.figure(figsize=(6, 6))
grid = plt.GridSpec(4, 4, hspace=0.2, wspace=0.2)

# use top-right 3*3 grid
main_ax = fig.add_subplot(grid[:-1, 1:])

# share y axis
y_hist = fig.add_subplot(grid[:-1, 0], xticklabels=[], sharey=main_ax)
x_hist = fig.add_subplot(grid[-1, 1:], yticklabels=[], sharex=main_ax)

# scatter points on the main axes
main_ax.plot(x, y, 'ok', markersize=3, alpha=0.2)

# histogram on the attached axes
x_hist.hist(x, 40, histtype='stepfilled',
            orientation='vertical', color='gray')
x_hist.invert_yaxis()

# invert axis 
y_hist.hist(y, 40, histtype='stepfilled',
            orientation='horizontal', color='gray')
y_hist.invert_xaxis()

make-hello-scatter-point.py

# 用matplotlib画hello的点集
# 1. 基于text生成一个png图像
# 2. 读取图像，随机取点阵，在png图像中有像素的点，就是一个目标点
# 3. 图像用scatter画出

# [from](https://jakevdp.github.io/PythonDataScienceHandbook/05.10-manifold-learning.html)
def make_hello(N=1000, rseed=42):
    # Make a plot with "HELLO" text; save as PNG
    fig, ax = plt.subplots(figsize=(4, 1))
    fig.subplots_adjust(left=0, right=1, bottom=0, top=1)
    ax.axis('off')
    ax.text(0.5, 0.4, 'hello', va='center', ha='center', weight='bold', size=85)
    fig.savefig('img/hello.png')
    plt.close(fig)
    
    # Open this PNG and draw random points from it
    from matplotlib.image import imread

    # imread得到的结果，按照左上角为原点，（y,x）的方式组织
    # 所以读取后，第一个维度装置，变为左下角为原点；第二个维度保持；取第三个维度的r通道
    # 判断是否存在数据即可；最终再装置一下，得到（x,y) 的排列
    src = imread('img/hello.png')
    data = src[::-1, :, 0].T

    rng = np.random.RandomState(rseed)
    X = rng.rand(4 * N, 2)

    # 乘以data.shape就是将[0, 1.0]的数据扩展到data的维度
    i, j = (X * data.shape).astype(int).T
    mask = (data[i, j] < 1)
    X = X[mask]

    # 找到之后，记得X的数据，x,y的范围都是[0, 1.0]，所以将y的长度记作1.0，x的长度需要扩展一下
    X[:, 0] = X[:, 0] * (data.shape[0] / data.shape[1])

    # 取得前N个数，再基于argsoft得到排序后的索引给，进而得到最终排序后的前N个有图像内容的数据点
    X = X[:N]
    return X[np.argsort(X[:, 0])]

X = make_hello()

# 将X离散到[0.0, 4.0]的好处就是可以指定color
# 如果指定离散的color数量，需用get_cmap
plt.scatter(
    X[:, 0], X[:, 1], 
    c=X[:, 0], 
    cmap='rainbow', #plt.cm.get_cmap('rainbow', 5));
    alpha=.5,
    edgecolors=None
)

# 保证X和Y轴的数据1:1的单位长度，调整图片的大小来适配
plt.axis('scaled');

matplotlib-use-style.py

def hist_and_lines():
    np.random.seed(0)
    fig, ax = plt.subplots(1, 2, figsize=(11, 4))
    ax[0].hist(np.random.randn(1000))
    for i in range(3):
        ax[1].plot(np.random.rand(10))
    ax[1].legend(['a', 'b', 'c'], loc='lower left')
    
with plt.style.context('bmh'):
    hist_and_lines()
    
with plt.style.context(['seaborn']):
    hist_and_lines()

ticker-set-locator-and-formatter.py

# from https://jakevdp.github.io/PythonDataScienceHandbook/04.10-customizing-ticks.html
fig, ax = plt.subplots()
x = np.linspace(0, 3 * np.pi, 1000)
ax.plot(x, np.sin(x), lw=3, label='Sine');
ax.plot(x, np.cos(x), lw=3, label='Cosine');

# Set up grid, legend, and limits
ax.grid(True)
ax.legend(frameon=False)

# xlim = ylim
ax.axis('equal')
ax.set_xlim(0, 3 * np.pi);

# set locator to be product of pi/2
ax.xaxis.set_major_locator(plt.MultipleLocator(np.pi/2));
ax.xaxis.set_minor_locator(plt.MultipleLocator(np.pi/4));

# mapping xticks to Latex
def format_func(value, tick_number):
    # find number of multiples of pi/2
    N = int(np.round(2 * value / np.pi))
    if N == 0:
        return "0"
    elif N == 1:
        return r"$\pi/2$"
    elif N == 2:
        return r"$\pi$"
    elif N % 2 > 0:
        return r"${0}\pi/2$".format(N)
    else:
        return r"${0}\pi$".format(N // 2)
    
# use plt.FuncFormatter to format xticks
ax.xaxis.set_major_formatter(plt.FuncFormatter(format_func));