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Matplotlib

wangzf / 2022-09-23

目录

一个 Plot 的生命周期

import numpy as np
import matplotlib.pyplot as plt

print(plt.style.available)
plt.style.use("fivethirtyeight")
plt.rcParams.update({
    "figure.autolayout": True,
})


# data
data = {
    'Barton LLC': 109438.50,
    'Frami, Hills and Schmidt': 103569.59,
    'Fritsch, Russel and Anderson': 112214.71,
    'Jerde-Hilpert': 112591.43,
    'Keeling LLC': 100934.30,
    'Koepp Ltd': 103660.54,
    'Kulas Inc': 137351.96,
    'Trantow-Barrows': 123381.38,
    'White-Trantow': 135841.99,
    'Will LLC': 104437.60
}
group_data = list(data.values())
group_names = list(data.keys())
group_mean = np.mean(group_data)


# plot
def currency(x, pos):
    """
    The two arguments are the value and tick position

    Args:
    x ([type]): [description]
    pos ([type]): [description]
    """
    if x >= 1e6:
        s = "${:1.1f}M".format(x * 1e-6)
    else:
        s = "${:1.0f}K".format(x * 1e-3)
    return s

fig, ax = plt.subplots(figsize = (8, 4))
# bar
ax.barh(group_names, group_data)
labels = ax.get_xticklabels()
plt.setp(labels, rotation = 45, horizontalalignment = "right")
# vertical line
ax.axvline(group_mean, ls = "--", color = "r")
# group text
for group in [3, 5, 8]:
    ax.text(
        145000, 
        group, 
        "New Company", 
        fontsize = 10, 
        verticalalignment = "center"
    )
# 标题设置
ax.title.set(y = 1.05)
# 设置X轴限制、X轴标签、Y轴标签、主标题
ax.set(
    xlim = [-10000, 140000], 
    xlabel = "Total Revenue", 
    ylabel = "Company", 
    title = "Company Revenue"
)
# 设置X轴主刻度标签格式
ax.xaxis.set_major_formatter(currency)
# 设置X轴主刻度标签
ax.set_xticks([0, 25e3, 50e3, 75e3, 100e3, 125e3])
# 微调fig
fig.subplots_adjust(right = 0.1)
# 图片保存
print(fig.canvas.get_supported_filetypes())
fig.savefig("sale.png", transparent = False, dpi = 80, bbox_inches = "tight")
plt.show()

快速开始

def quick_start():
    import numpy as np
    import matplotlib as mpl
    import matplotlib.pyplot as plt

    X = np.linspace(0, 2 * np.pi, 100)
    Y = np.cos(X)

    fig, ax = plt.subplots()
    ax.plot(X, Y, color = "green")

    fig.savefig(
        os.path.join(
            os.path.dirname(__file__), 
            "images/figure.png"
        )
    )
    fig.show()

quick_start()

一张统计图的结构

图形 API

import matplotlib.pyplot as plt

fig, ax = plt.subplots()

Figure

Figure class

fig = plt.fiure()			  # an empty figure with no Axes
fig, ax = plt.subplots()      # a figure with a single Axes
fig, ax = plt.subplots(2, 2) # a figure with a 2x2 grid of Axes

Axes class

Axis class

Artist class

Subplots layout

Subplots layout，子图布局

API

subplots

def subplots_layout():
    fig, ax = plt.subplots(3, 3)
    fig.show()

img

gridsepc

inset_axes

make_axes_locatable

Matplotlib 开发环境

Python Script

plt.show()

IPython shell

IPython Notebook

%matploblib inline

Matplotlib 编程接口

Matplotlib 的文档和示例同时使用 OO 和 pyplot 方法（它们同样强大）， 可以随意使用其中任何一种（但是，最好选择其中之一并坚持使用，而不是混合使用它们）。 通常，建议将 pyplot 限制为交互式绘图（例如，在 Jupyter 笔记本中）， 并且更喜欢 OO 风格的非交互式绘图（在旨在作为更大项目的一部分重用的函数和脚本中）。

下面是两个简单的示例：

fig = plt.figure()
# or plt.figure()
ax = plt.axes()
plt.plot(
    [1, 2, 3, 4],
    [1, 4, 2, 3]
);

fig = plt.figure()
ax = plt.axes()
ax.plot(
    [1, 2, 3, 4],
    [1, 4, 2, 3]
);

fig, ax = plt.subplots()
ax.plot(
    [1, 2, 3, 4],
    [1, 4, 2, 3]
);

pyplot 接口

MatLab 风格接口

pyplot 是使 Matplotlib 像 MATLAB 一样工作的函数集合。每个 pyplot 函数都会对图形进行一些更改： 例如，创建图形、在图形中创建绘图区域、在绘图区域中绘制一些线条、用标签装饰绘图等。 在 pyplot 函数调用中保留各种状态，以便跟踪当前图形(figure)和绘图区域(plotting area)等内容，并且绘图函数指向当前轴(axes)

API

示例

# 图形
fig = plt.figure(num = 2, figsize = (8, 8))
# 坐标轴
# ax = plt.axes()

plt.subplot(211)
plt.plot(x, np.sin(x), label = "sin")
plt.xlabel("x label")
plt.ylabel("y label")
plt.title("Simple Plot")
plt.legend()

plt.subplot(212)
plt.plot(x, np.cos(x), label = "cos")
plt.xlabel("x label")
plt.ylabel("y label")
plt.title("Simple Plot")
plt.legend()
plt.show()

fig.savefig();

plt.gcf() # 获取当前图形(figure)
plt.gca() # 获取当前坐标轴(axes)

OOP 接口

API

示例

# data
x = np.linspace(0, 2, 100)

# plot
fig, ax = plt.subplots(1, 3, figsize = (18, 5), sharey = True)
ax[0].plot(x, x, label = "linear")
ax[0].plot(x, x**2, label = "quadratic")
ax[0].plot(x, x**3, label = "cubic")
ax[0].set_xlabel("x label")
ax[0].set_ylabel("y label")
ax[0].set_title("linear Plot")
ax[0].legend()

ax[1].plot(x, x, label = "linear")
ax[1].plot(x, x**2, label = "quadratic")
ax[1].plot(x, x**3, label = "cubic")
ax[1].set_xlabel("x label")
ax[1].set_ylabel("y label")
ax[1].set_title("quadratic Plot")
ax[1].legend()

ax[2].plot(x, x, label = "linear")
ax[2].plot(x, x**2, label = "quadratic")
ax[2].plot(x, x**3, label = "cubic")
ax[2].set_xlabel("x label")
ax[2].set_ylabel("y label")
ax[2].set_title("cubic Plot")
ax[2].legend();

GUI 应用程序中嵌入 Matplotlib

最佳实践

def my_plotter(ax, data1, data2, param_dict):
    """
    A helper function to make a graph

    Parameters
    ----------
    ax : Axes
        The axes to draw to

    data1 : array
       The x data

    data2 : array
       The y data

    param_dict : dict
       Dictionary of keyword arguments to pass to ax.plot

    Returns
    -------
    out : list
        list of artists added
    """
       out = ax.plot(data1, data2, **param_dict)
    return out

# data
data1, data2, data3, data4 = np.random.randn(4, 100)
# plot
fig, ax = plt.subplots(1, 1)
my_plotter(ax, data1, data2, {"marker": "x"})

![image-20211201235123195](/Users/zfwang/Library/Application Support/typora-user-images/image-20211201235123195.png)

# data
data1, data2, data3, data4 = np.random.randn(4, 100)
# plot
fig, ax = plt.subplots(1, 1)
my_plotter(ax, data1, data2, {"marker": "x"})
my_plotter(ax, data3, data4, {"marker": "o"})

![image-20211201235141268](/Users/zfwang/Library/Application Support/typora-user-images/image-20211201235141268.png)

Matplotlib 个性化

优先级： rcParams > style sheets > matplotlibrc file

rcParams

rc: runtime configuration

API

配置选项

rc = dict(mpl.rcParams)
rc_table = pd.DataFrame({
    "param": rc.keys(),
    "value": rc.values(),
})
# rc_table

全局设置

from cycler import cycler

# lines
mpl.rcParams["lines.linewidth"] = 2
mpl.rcParams["lines.linestyle"] = "--"
mpl.rc("lines", linewidth = 4, linestyle = "--")
# axes
mpl.rcParams["axes.porp_cycle"] = cycler(color = ["r", "g", "b", "y"])

临时设置

with mpl.rc_context({"lines.linewidth": 2, "lines.linestyle": ":"}):
    plt.plot(data)

@mpl.rc_context({"lines.linewidth": 3, "lines.linestyle": "-"})
def plotting_function():
    plt.plot(data)

style sheets

API

所有样式

print(plt.style.available)

['Solarize_Light2',
 '_classic_test_patch',
 '_mpl-gallery',
 '_mpl-gallery-nogrid',
 'bmh',
 'classic',
 'dark_background',
 'fast',
 'fivethirtyeight',
 'ggplot',
 'grayscale',
 'seaborn-v0_8',
 'seaborn-v0_8-bright',
 'seaborn-v0_8-colorblind',
 'seaborn-v0_8-dark',
 'seaborn-v0_8-dark-palette',
 'seaborn-v0_8-darkgrid',
 'seaborn-v0_8-deep',
 'seaborn-v0_8-muted',
 'seaborn-v0_8-notebook',
 'seaborn-v0_8-paper',
 'seaborn-v0_8-pastel',
 'seaborn-v0_8-poster',
 'seaborn-v0_8-talk',
 'seaborn-v0_8-ticks',
 'seaborn-v0_8-white',
 'seaborn-v0_8-whitegrid',
 'tableau-colorblind10']

全局样式

plt.style.use("ggplot")
plt.style.use("./images/presentation.mplstyle")
plt.style.use(["dark_background", "presentation"])

临时样式

with plt.style.context("dark_background"):
    plt.plot(data)

matplotlibrc file

matplotlibrc 文件的位置：

查看当前加载的 matplotlibrc 文件的位置

mpl.matploblib_fname()

'D:\\software\\miniconda3\\envs\\pysci\\lib\\site-packages\\matplotlib\\mpl-data\\matplotlibrc'

Matplotlib 图形保存

图像文件格式

在 savefig() 里面，保存的图片文件格式就是文件的扩展名。 Matplotlib 支持许多图形格式，可以通过 canvas 对象的方法查看系统支持的文件格式

fig = plt.figure()
fig.canvas.get_supported_filetypes()

{'eps': 'Encapsulated Postscript',
 'jpg': 'Joint Photographic Experts Group',
 'jpeg': 'Joint Photographic Experts Group',
 'pdf': 'Portable Document Format',
 'pgf': 'PGF code for LaTeX',
 'png': 'Portable Network Graphics',
 'ps': 'Postscript',
 'raw': 'Raw RGBA bitmap',
 'rgba': 'Raw RGBA bitmap',
 'svg': 'Scalable Vector Graphics',
 'svgz': 'Scalable Vector Graphics',
 'tif': 'Tagged Image File Format',
 'tiff': 'Tagged Image File Format',
 'webp': 'WebP Image Format'}

保存

fig.savefig("my_figure.png")

展示

from IPython.display import Image

Image("my_figure.png")

基本图形

plot

plot([X], Y, [fmt], color, marker, linestyle)

scatter

散点图

scatter(X, Y, [s]izes, [c]olors, markers, alpha, cmap)

bar

bar[h](x, height, width, bottom, align, color)

imshow

imshow(Z, cmap, interpolation, extent, origin)

contour

contour[f]([X], [Y], Z, levels, colors, extent, origin)

pcolormesh

pcolormesh([X], [Y], Z, vmin, vmax, cmap)

quiver

quiver([X], [Y], U, V, C, units, angles)

pie

pie(Z, explode, labels, colors, raidus)

text

text(x, y, text, va, ha, size, weight, transform)

fill

fill[_between][x](X, Y1, Y2, color, where)

高级图形

step

step(X, Y, [fmt], color, marker, where)

boxplot

boxplot(X, notch, sym, bootstrap, widths)

errorbar

errorbar(X, Y, xerr, yerr, fmt)

示例：

# data
dx = 0.1
x = np.linspace(0, 10, 50)
dy = 0.8
y = np.sin(x) + dy * np.random.randn(50)

# plot
fig = plt.figure()
plt.errorbar(
    x, y, 
    yerr = dy, 
    xerr = dx
    fmt = ".k", 
    ecolor = "lightgray", 
    elinewidth = 3, 
    capsize = 0
)

hist

hist(X, bins, range, density, weights)

violinplot

violinplot(D, positions, widths, vert)

barbs

barbs([X], [Y], U, V, C, length, pivot, sizes)

eventplot

eventplot(positions, orientation, lineoffsets)

hexbin

hexbin(X, Y, C, gridsize, bins)

Color, Line, Marker

Color

Sigle Color

常用单色：

character color
'b' blue
'g' green
'r' red
'c' cyan
'm' magenta
'y' yellow
'k' black
'w' white 
# data
x = np.linspace(0, 10, 1000)

# plot
fig, ax = plt.subplots()
ax.plot(x, x + 0, color='blue', label = "blue")  # 标准颜色名称
ax.plot(x, x + 1, color='g', label = "g")  # 缩写颜色代码（rgbcmyk）
ax.plot(x, x + 2, color='0.75', label = "0.75")  # 范围在0~1的灰度值
ax.plot(x, x + 3, color='#FFDD44', label = "#FFDD44")  # 十六进制（RRGGBB，00~FF）
ax.plot(x, x + 4, color=(1.0,0.2,0.3), label = "(1.0,0.2,0.3)")  # RGB元组，范围在0~1
ax.plot(x, x + 5, color='chartreuse', label = "chartreuse")  # HTML颜色名称
ax.legend(loc = "best");

img

Colormaps

img

Line

linestyle 或者 ls：

character description
'-' solid line style 实线
'--' dashed line style 实点线
'-.' dash-dot line style 虚线
':' dotted line style 点划线 
# data
x = np.linspace(0, 10, 1000)

# plot
fig, ax = plt.figure(), plt.axes()
plt.plot(x, x + 0, linestyle='solid', label = "solid")
plt.plot(x, x + 1, linestyle='dashed', label = "dashed")
plt.plot(x, x + 2, linestyle='dashdot', label = "dashdot")
plt.plot(x, x + 3, linestyle='dotted', label = "dotted")
plt.plot(x, x + 4, linestyle='-', label = "-") # 实线
plt.plot(x, x + 5, linestyle='--', label = "--") # 虚线
plt.plot(x, x + 6, linestyle='-.', label = "-.") # 点划线
plt.plot(x, x + 7, linestyle=':', label = ":") # 实点线
plt.legend(loc = "best");

img

Marker

marker:

character description
'.' point marker
',' pixel marker
'o' circle marker
'v' triangle_down marker
'^' triangle_up marker
'<' triangle_left marker
'>' triangle_right marker
'1' tri_down marker
'2' tri_up marker
'3' tri_left marker
'4' tri_right marker
'8' octagon marker
's' square marker
'p' pentagon marker
'P' plus (filled) marker
'*' star marker
'h' hexagon1 marker
'H' hexagon2 marker
'+' plus marker
'x' x marker
'X' x (filled) marker
'D' diamond marker
'd' thin_diamond marker
'|' vline marker
'_' hline marker

示例：

fig, ax = plt.subplots(figsize = (12, 12))
rng = np.random.RandomState(0)
for marker in [".", ",", "o", "v", "^", "<", ">", 
               "1", "2", "3", "4", "8", "s", "p", "P", 
               "*", "h", "H", "x", "X", "+", "d", "D", 
               "|", "_"]:
    plt.plot(rng.rand(5), rng.rand(5), marker, label = f"marker={marker}")
    plt.legend(numpoints = 1)
    plt.xlim(0, 1.8)

marker 参数：

# data
x = np.linspace(0, 10, 30)
y = np.sin(x)

# plot
fig, ax = plt.subplots(figsize = (5, 5))
plt.plot(
    x, y, "-p", color = "gray", linewidth = 4,
    markersize = 15,
    markerfacecolor = "white",
    markeredgecolor = "gray",
    markeredgewidth = 2,
)

Color 加 Line 和 Marker

使用示例：

# data
x = np.linspace(0, 10, 1000)

# plot
fig, ax = plt.figure(), plt.axes()
plt.plot(x, x + 0, '-g') # 绿色实线
plt.plot(x, x + 1, '--c') # 青色虚线
plt.plot(x, x + 2, '-.k') # 黑色点划线
plt.plot(x, x + 3, ':r'); # 红色实点线
plt.plot(x, x + 4, 'or');
plt.plot(x, x + 5, '^k');
plt.legend(loc = "best");

Scales

ax.set_[xy]scale(scale, ...)

Projections

subplot(..., projection = p)

Tick locators

from matplotlib import ticker

ax.[xy]axis.set_[minor|major]_locator(locator)

locator:

Tick formatters

from matplotlib import ticker

ax.[xy]axis.set_[minor|major]_formatter(formatter)

formatter:

Ornaments

Event handling

import matplotlib.pyplot as plt

fig, ax = plt.subplots()

def on_click(event):
    print(event)

fig.canvas.mpl_connect("button_press_event", on_click)

Animation

import matplotlib.animation as mpla

T = np.linspace(0, 2 * np.pi, 100)
S = np.sin(T)
line, = plt.plot(T, S)

def animate(i):
    line.set_ydata(np.sin(T + i / 50))

anim = mpla.FuncAnimation(plt.gcf(), animate, interval = 5)
plt.show()

Quick reminder

import matplotlib.pyplot as plt

fig, ax = plt.subplots()

ax.grid()
ax.set_[xy]lim(vmin, vmax)
ax.set_[xy]label(label)
ax.set_[xy]ticks(ticks, [labels])
ax.set_[xy]ticklabels(labels)
ax.set_title(title)
ax.tick_params(width = 10, ...)
ax.set_axis_[on|off]()

fig.suptitle(title)

fig.tight_layout()

plt.gcf(), plt.gca()

mpl.rc("axes", linewidth = 1, ...)

[fig|ax].patch.set_alpha(0)

text = r"$frac{-e^{i\pi}}{2^n}"

函数输入格式

# pandas.DataFrame 转换
pandas_dataframe = pandas.DataFrame()
array_inputs = pandas_datarframe.values

# numpy.matrix 转换
numpy_matrix = numpy.matrix([1, 2], [3, 4])
array_inputs = numpy.asarray(numpy_matrix)

Image

import matplotlib.image as mpimg

将 image 数据转换为 Numpy array

../../_images/stinkbug.png

img = mpimg.imread("stinkbug.png")
print(img)
print(img.shape)

[[[0.40784314 0.40784314 0.40784314]
  [0.40784314 0.40784314 0.40784314]
  [0.40784314 0.40784314 0.40784314]
  ...
  [0.42745098 0.42745098 0.42745098]
  [0.42745098 0.42745098 0.42745098]
  [0.42745098 0.42745098 0.42745098]]

 [[0.4117647  0.4117647  0.4117647 ]
  [0.4117647  0.4117647  0.4117647 ]
  [0.4117647  0.4117647  0.4117647 ]
  ...
  [0.42745098 0.42745098 0.42745098]
  [0.42745098 0.42745098 0.42745098]
  [0.42745098 0.42745098 0.42745098]]

 [[0.41960785 0.41960785 0.41960785]
  [0.41568628 0.41568628 0.41568628]
  [0.41568628 0.41568628 0.41568628]
  ...
  [0.43137255 0.43137255 0.43137255]
  [0.43137255 0.43137255 0.43137255]
  [0.43137255 0.43137255 0.43137255]]

 ...

 [[0.4392157  0.4392157  0.4392157 ]
  [0.43529412 0.43529412 0.43529412]
  [0.43137255 0.43137255 0.43137255]
  ...
  [0.45490196 0.45490196 0.45490196]
  [0.4509804  0.4509804  0.4509804 ]
  [0.4509804  0.4509804  0.4509804 ]]

 [[0.44313726 0.44313726 0.44313726]
  [0.44313726 0.44313726 0.44313726]
  [0.4392157  0.4392157  0.4392157 ]
  ...
  [0.4509804  0.4509804  0.4509804 ]
  [0.44705883 0.44705883 0.44705883]
  [0.44705883 0.44705883 0.44705883]]

 [[0.44313726 0.44313726 0.44313726]
  [0.4509804  0.4509804  0.4509804 ]
  [0.4509804  0.4509804  0.4509804 ]
  ...
  [0.44705883 0.44705883 0.44705883]
  [0.44705883 0.44705883 0.44705883]
  [0.44313726 0.44313726 0.44313726]]]

(375, 500, 3)

将 Numpy array 绘制成图片

imgplot = plt.imshow(img)

![image-20211204001730928](/Users/zfwang/Library/Application Support/typora-user-images/image-20211204001730928.png)

将伪彩色方案应用于图像

lum_img = img[:, :, 0]
plt.imshow(lum_img)

![image-20211204003701542](/Users/zfwang/Library/Application Support/typora-user-images/image-20211204003701542.png)

lum_img = img[:, : 0]
plt.imshow(lum_img, cmap = "hot")

![image-20211204003910446](/Users/zfwang/Library/Application Support/typora-user-images/image-20211204003910446.png)

lum_img = img[:, :, 0]
imgplot = plt.imshow(lum_img)
imgplot.set_cmap("nipy_spectral")

![image-20211204004118731](/Users/zfwang/Library/Application Support/typora-user-images/image-20211204004118731.png)

色标参考

lum_img = img[:, : 0]
imgplot = plt.imshow(lum_img)
plt.colorbar()

![image-20211204004308940](/Users/zfwang/Library/Application Support/typora-user-images/image-20211204004308940.png)

检查特定数据范围

lum_img = img[:, :, 0]
plt.hist(lum_img.ravel(), bins = 256, range(0.0, 1.0), fc = "k", ec = "k")

![image-20211204005900327](/Users/zfwang/Library/Application Support/typora-user-images/image-20211204005900327.png)

# 调增上限，有效放大直方图中的一部分
lum_img = img[:, :, 0]
imgplot = plt.imshow(lum_img, clim = (0.0, 0.7))

![image-20211204010057096](/Users/zfwang/Library/Application Support/typora-user-images/image-20211204010057096.png)

lum_img = img[:, :, 0]

fig = plt.figure()

ax = fig.add_subplot(1, 2, 1)
imgplot = plt.imshow(lum_img)
ax.set_title('Before')
plt.colorbar(ticks=[0.1, 0.3, 0.5, 0.7], orientation='horizontal')

ax = fig.add_subplot(1, 2, 2)
imgplot = plt.imshow(lum_img)
imgplot.set_clim(0.0, 0.7)
ax.set_title('After')
plt.colorbar(ticks=[0.1, 0.3, 0.5, 0.7], orientation='horizontal')

![image-20211204011743914](/Users/zfwang/Library/Application Support/typora-user-images/image-20211204011743914.png)

数组插值

from PIL import Image

img = Image.open("stinkbug.png")
img.thumbnail((64, 64), Image.ANTIALIAS) # resizes image in-replace
imgplot = plt.imshow(img)

![image-20211204011041795](/Users/zfwang/Library/Application Support/typora-user-images/image-20211204011041795.png)

from PIL import Image

img = Image.open("stinkbug.png")
img.thumbnail((64, 64), Image.ANTIALIAS) # resizes image in-replace
imgplot = plt.imshow(img, interpolation = "nearest")

![image-20211204011222797](/Users/zfwang/Library/Application Support/typora-user-images/image-20211204011222797.png)

from PIL import Image

img = Image.open("stinkbug.png")
img.thumbnail((64, 64), Image.ANTIALIAS) # resizes image in-replace
imgplot = plt.imshow(img, interpolation = "bicubic")

![image-20211204011514588](/Users/zfwang/Library/Application Support/typora-user-images/image-20211204011514588.png)

资料