From 595d6e5b3c81103f05bede4c802cd59633cf3c75 Mon Sep 17 00:00:00 2001 From: skywateryang <50293686+skywateryang@users.noreply.github.com> Date: Fri, 31 Dec 2021 23:44:16 +0800 Subject: [PATCH] up cp4 --- 第四回：文字图例尽眉目.ipynb | 1460 +++++++--------------------------- 1 file changed, 278 insertions(+), 1182 deletions(-) diff --git a/第四回：文字图例尽眉目.ipynb b/第四回：文字图例尽眉目.ipynb index 1fd78f8..b99f86d 100644 --- a/第四回：文字图例尽眉目.ipynb +++ b/第四回：文字图例尽眉目.ipynb @@ -7,6 +7,16 @@ "# 第四回：文字图例尽眉目" ] }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib\n", + "import matplotlib.pyplot as plt" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -21,85 +31,45 @@ "Matplotlib具有广泛的文本支持，包括对数学表达式的支持、对栅格和矢量输出的TrueType支持、具有任意旋转的换行分隔文本以及Unicode支持。" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.文本API示例" + ] + }, { "cell_type": "markdown", "metadata": {}, "source": [ "下面的命令是介绍了通过pyplot API和objected-oriented API分别创建文本的方式。\n", "\n", - "| [`pyplot`](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.html#module-matplotlib.pyplot) API | OO API | description |\n", - "| :----------------------------------------------------------- | ------------------------------------------------------------ | ------------------------------------------------------------ |\n", - "| [`text`](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.text.html#matplotlib.pyplot.text) | [`text`](https://matplotlib.org/api/_as_gen/matplotlib.axes.Axes.text.html#matplotlib.axes.Axes.text) | 在 [`Axes`](https://matplotlib.org/api/axes_api.html#matplotlib.axes.Axes)的任意位置添加text。 |\n", - "| [`title`](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.title.html#matplotlib.pyplot.title) | [`set_title`](https://matplotlib.org/api/_as_gen/matplotlib.axes.Axes.set_title.html#matplotlib.axes.Axes.set_title) | 在 [`Axes`](https://matplotlib.org/api/axes_api.html#matplotlib.axes.Axes)添加title |\n", - "| [`figtext`](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.figtext.html#matplotlib.pyplot.figtext) | [`text`](https://matplotlib.org/api/_as_gen/matplotlib.figure.Figure.html#matplotlib.figure.Figure.text) | 在[`Figure`](https://matplotlib.org/api/_as_gen/matplotlib.figure.Figure.html#matplotlib.figure.Figure)的任意位置添加text. |\n", - "| [`suptitle`](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.suptitle.html#matplotlib.pyplot.suptitle) | [`suptitle`](https://matplotlib.org/api/_as_gen/matplotlib.figure.Figure.html#matplotlib.figure.Figure.suptitle) | 在 [`Figure`](https://matplotlib.org/api/_as_gen/matplotlib.figure.Figure.html#matplotlib.figure.Figure)添加title |\n", - "| [`xlabel`](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.xlabel.html#matplotlib.pyplot.xlabel) | [`set_xlabel`](https://matplotlib.org/api/_as_gen/matplotlib.axes.Axes.set_xlabel.html#matplotlib.axes.Axes.set_xlabel) | 在[`Axes`](https://matplotlib.org/api/axes_api.html#matplotlib.axes.Axes)的x-axis添加label |\n", - "| [`ylabel`](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.ylabel.html#matplotlib.pyplot.ylabel) | [`set_ylabel`](https://matplotlib.org/api/_as_gen/matplotlib.axes.Axes.set_ylabel.html#matplotlib.axes.Axes.set_ylabel) | 在[`Axes`](https://matplotlib.org/api/axes_api.html#matplotlib.axes.Axes)的y-axis添加label |" + "| pyplot API | OO API | description |\n", + "| ---------- | ------- | ------------ |\n", + "| `text` | `text` | 在子图axes的任意位置添加文本|\n", + "| `annotate` | `annotate` | 在子图axes的任意位置添加注解，包含指向性的箭头|\n", + "| `xlabel` | `set_xlabel` | 为子图axes添加x轴标签 |\n", + "| `ylabel` | `set_ylabel` | 为子图axes添加y轴标签 |\n", + "| `title` | `set_title` | 为子图axes添加标题 |\n", + "| `figtext` | `text` | 在画布figure的任意位置添加文本 |\n", + "| `suptitle` | `suptitle` | 为画布figure添加标题 |" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### 1.text\n", - "pyplot API：matplotlib.pyplot.text(x, y, s, fontdict=None, \\*\\*kwargs) \n", - "OO API:Axes.text(self, x, y, s, fontdict=None, \\*\\*kwargs) \n", - "**参数**：此方法接受以下描述的参数： \n", - "s:此参数是要添加的文本。 \n", - "xy:此参数是放置文本的点(x，y)。 \n", - "fontdict:此参数是一个可选参数，并且是一个覆盖默认文本属性的字典。如果fontdict为None，则由rcParams确定默认值。 \n", - "**返回值**：此方法返回作为创建的文本实例的文本。" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "fontdict主要参数具体介绍，更多参数请参考[官网说明](https://matplotlib.org/api/text_api.html?highlight=text#matplotlib.text.Text)：\n", - "\n", - "\n", - "| Property | Description |\n", - "| ------------------------------------------------------------ | :----------------------------------------------------------- |\n", - "| [`alpha`](https://matplotlib.org/api/_as_gen/matplotlib.artist.Artist.set_alpha.html#matplotlib.artist.Artist.set_alpha) |float or None 该参数指透明度，越接近0越透明，越接近1越不透明 | \n", - "| [`backgroundcolor`](https://matplotlib.org/api/text_api.html#matplotlib.text.Text.set_backgroundcolor) | color [该参数指文本的背景颜色，具体matplotlib支持颜色如下](https://www.cnblogs.com/charliedaifu/p/9957822.html) |\n", - "| [`bbox`](https://matplotlib.org/api/text_api.html#matplotlib.text.Text.set_bbox) | dict with properties for [`patches.FancyBboxPatch`](https://matplotlib.org/api/_as_gen/matplotlib.patches.FancyBboxPatch.html#matplotlib.patches.FancyBboxPatch) 这个是用来设置text周围的box外框 |\n", - "| [`color`](https://matplotlib.org/api/text_api.html#matplotlib.text.Text.set_color) or c | color 指的是字体的颜色 |\n", - "| [`fontfamily`](https://matplotlib.org/api/text_api.html#matplotlib.text.Text.set_fontfamily) or family | {FONTNAME, 'serif', 'sans-serif', 'cursive', 'fantasy', 'monospace'} 该参数指的是字体的类型|\n", - "| [`fontproperties`](https://matplotlib.org/api/text_api.html#matplotlib.text.Text.set_fontproperties) or font or font_properties | [`font_manager.FontProperties`](https://matplotlib.org/api/font_manager_api.html#matplotlib.font_manager.FontProperties) or [`str`](https://docs.python.org/3/library/stdtypes.html#str) or [`pathlib.Path`](https://docs.python.org/3/library/pathlib.html#pathlib.Path) |\n", - "| [`fontsize`](https://matplotlib.org/api/text_api.html#matplotlib.text.Text.set_fontsize) or size | float or {'xx-small', 'x-small', 'small', 'medium', 'large', 'x-large', 'xx-large'} 该参数指字体大小|\n", - "| [`fontstretch`](https://matplotlib.org/api/text_api.html#matplotlib.text.Text.set_fontstretch) or stretch | {a numeric value in range 0-1000, 'ultra-condensed', 'extra-condensed', 'condensed', 'semi-condensed', 'normal', 'semi-expanded', 'expanded', 'extra-expanded', 'ultra-expanded'} 该参数是指从字体中选择正常、压缩或扩展的字体 |\n", - "| [`fontstyle`](https://matplotlib.org/api/text_api.html#matplotlib.text.Text.set_fontstyle) or style | {'normal', 'italic', 'oblique'} 该参数是指字体的样式是否倾斜等 |\n", - "| [`fontweight`](https://matplotlib.org/api/text_api.html#matplotlib.text.Text.set_fontweight) or weight | {a numeric value in range 0-1000, 'ultralight', 'light', 'normal', 'regular', 'book', 'medium', 'roman', 'semibold', 'demibold', 'demi', 'bold', 'heavy', 'extra bold', 'black'} | \n", - "| [`horizontalalignment`](https://matplotlib.org/api/text_api.html#matplotlib.text.Text.set_horizontalalignment) or ha | {'center', 'right', 'left'} 该参数是指选择文本左对齐右对齐还是居中对齐 |\n", - "| [`label`](https://matplotlib.org/api/_as_gen/matplotlib.artist.Artist.set_label.html#matplotlib.artist.Artist.set_label) | object |\n", - "| [`linespacing`](https://matplotlib.org/api/text_api.html#matplotlib.text.Text.set_linespacing) | float (multiple of font size) |\n", - "| [`position`](https://matplotlib.org/api/text_api.html#matplotlib.text.Text.set_position) | (float, float) |\n", - "| [`rotation`](https://matplotlib.org/api/text_api.html#matplotlib.text.Text.set_rotation) | float or {'vertical', 'horizontal'} 该参数是指text逆时针旋转的角度，“horizontal”等于0，“vertical”等于90。我们可以根据自己设定来选择合适角度 |\n", - "| [`verticalalignment`](https://matplotlib.org/api/text_api.html#matplotlib.text.Text.set_verticalalignment) or va | {'center', 'top', 'bottom', 'baseline', 'center_baseline'} |\n", - " |\n", - "\n" + "通过一个综合例子，以OO模式展示这些API是如何控制一个图像中各部分的文本，在之后的章节我们再详细分析这些api的使用技巧" ] }, { "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from matplotlib.font_manager import FontProperties\n", - "import numpy as np" - ] - }, - { - "cell_type": "code", - "execution_count": 1, + "execution_count": 28, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -111,135 +81,69 @@ } ], "source": [ - "#fontdict学习的案例\n", - "#学习的过程中请尝试更换不同的fontdict字典的内容，以便于更好的掌握\n", - "#---------设置字体样式，分别是字体，颜色，宽度，大小\n", - "font1 = {'family': 'SimSun',#华文楷体\n", - " 'alpha':0.7,#透明度\n", - " 'color': 'purple',\n", - " 'weight': 'normal',\n", - " 'size': 16,\n", - " }\n", - "font2 = {'family': 'Times New Roman',\n", - " 'color': 'red',\n", - " 'weight': 'normal',\n", - " 'size': 16,\n", - " }\n", - "font3 = {'family': 'serif',\n", - " 'color': 'blue',\n", - " 'weight': 'bold',\n", - " 'size': 14,\n", - " }\n", - "font4 = {'family': 'Calibri',\n", - " 'color': 'navy',\n", - " 'weight': 'normal',\n", - " 'size': 17,\n", - " }\n", - "#-----------四种不同字体显示风格-----\n", - " \n", - "#-------建立函数----------\n", - "x = np.linspace(0.0, 5.0, 100)\n", - "y = np.cos(2*np.pi*x) * np.exp(-x/3)\n", - "#-------绘制图像，添加标注----------\n", - "plt.plot(x, y, '--')\n", - "plt.title('震荡曲线', fontdict=font1)\n", - "#------添加文本在指定的坐标处------------\n", - "plt.text(2, 0.65, r'$\\cos(2 \\pi x) \\exp(-x/3)$', fontdict=font2)\n", - "#---------设置坐标标签\n", - "plt.xlabel('Y=time (s)', fontdict=font3)\n", - "plt.ylabel('X=voltage(mv)', fontdict=font4)\n", - " \n", - "# 调整图像边距\n", - "plt.subplots_adjust(left=0.15)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2.title和set_title\n", - "pyplot API：matplotlib.pyplot.title(label, fontdict=None, loc=None, pad=None, \\*, y=None, \\*\\*kwargs) \n", - "OO API:Axes.set_title(self, label, fontdict=None, loc=None, pad=None, \\*, y=None, \\*\\*kwargs) \n", - "该命令是用来设置axes的标题。 \n", - "**参数**：此方法接受以下描述的参数: \n", - "label：str，此参数是要添加的文本 \n", - "fontdict：dict，此参数是控制title文本的外观，默认fontdict如下：\n", - "```python\n", - "{'fontsize': rcParams['axes.titlesize'],\n", - " 'fontweight': rcParams['axes.titleweight'],\n", - " 'color': rcParams['axes.titlecolor'],\n", - " 'verticalalignment': 'baseline',\n", - " 'horizontalalignment': loc}\n", - " ```\n", - "loc:str，{'center', 'left', 'right'}默认为center \n", - "pad:float,该参数是指标题偏离图表顶部的距离，默认为6。 \n", - "y:float，该参数是title所在axes垂向的位置。默认值为1，即title位于axes的顶部。 \n", - "kwargs：该参数是指可以设置的一些奇特文本的属性。 \n", - "**返回值**：此方法返回作为创建的title实例的文本。" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 3.figtext和text\n", - "pyplot API：matplotlib.pyplot.figtext(x, y, s, fontdict=None, \\*\\*kwargs) \n", - "OO API:text(self, x, y, s, fontdict=None,\\*\\*kwargs) \n", - "**参数**：此方法接受以下描述的参数: \n", - "x,y：float，此参数是指在figure中放置文本的位置。一般取值是在\\[0,1\\]范围内。使用transform关键字可以更改坐标系。 \n", - "s:str,此参数是指文本 \n", - "fontdict:dict,此参数是一个可选参数，并且是一个覆盖默认文本属性的字典。如果fontdict为None，则由rcParams确定默认值。 \n", - "**返回值**：此方法返回作为创建的文本实例的文本。" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 4.suptitle\n", - "pyplot API：matplotlib.pyplot.suptitle(t, \\*\\*kwargs) \n", - "OO API:suptitle(self, t, \\*\\*kwargs) \n", - "**参数**：此方法接受以下描述的参数: \n", - "t: str,标题的文本 \n", - "x：float,默认值是0.5.该参数是指文本在figure坐标系下的x坐标 \n", - "y：float,默认值是0.95.该参数是指文本在figure坐标系下的y坐标 \n", - "horizontalalignment, ha:该参数是指选择文本水平对齐方式，有三种选择{'center', 'left', right'}，默认值是 'center' \n", - "verticalalignment, va：该参数是指选择文本垂直对齐方式，有四种选择{'top', 'center', 'bottom', 'baseline'}，默认值是 'top' \n", - "fontsize, size：该参数是指文本的大小，默认值是依据rcParams的设置：rcParams[\"figure.titlesize\"] (default: 'large') \n", - "fontweight, weight：该参数是用来设置字重。默认值是依据rcParams的设置：rcParams[\"figure.titleweight\"] (default: 'normal') \n", - "[fontproperties](https://matplotlib.org/api/font_manager_api.html#matplotlib.font_manager.FontProperties):None or dict,该参数是可选参数，如果该参数被指定，字体的大小将从该参数的默认值中提取。 \n", - "**返回值**：此方法返回作为创建的title实例的文本。" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 5.xlabel和ylabel\n", "\n", - "pyplot API：matplotlib.pyplot.xlabel(xlabel, fontdict=None, labelpad=None, \\*, loc=None, \\*\\*kwargs) \n", - "     matplotlib.pyplot.ylabel(ylabel, fontdict=None, labelpad=None,\\*, loc=None, \\*\\*kwargs) \n", - "OO API:  Axes.set_xlabel(self, xlabel, fontdict=None, labelpad=None, \\*, loc=None, \\*\\*kwargs) \n", - "     Axes.set_ylabel(self, ylabel, fontdict=None, labelpad=None,\\*, loc=None, \\*\\*kwargs) \n", - "**参数**：此方法接受以下描述的参数: \n", - "xlabel或者ylabel：label的文本 \n", - "labelpad:设置label距离轴(axis)的距离 \n", - "loc:{'left', 'center', 'right'},默认为center \n", - "\\*\\*kwargs:[文本](https://matplotlib.org/api/text_api.html#matplotlib.text.Text)属性 \n", - "**返回值**：此方法返回作为创建的xlabel和ylabel实例的文本。" + "fig = plt.figure()\n", + "ax = fig.add_subplot()\n", + "\n", + "\n", + "# 分别为figure和ax设置标题，注意两者的位置是不同的\n", + "fig.suptitle('bold figure suptitle', fontsize=14, fontweight='bold')\n", + "ax.set_title('axes title')\n", + "\n", + "# 设置x和y轴标签\n", + "ax.set_xlabel('xlabel')\n", + "ax.set_ylabel('ylabel')\n", + "\n", + "# 设置x和y轴显示范围均为0到10\n", + "ax.axis([0, 10, 0, 10])\n", + "\n", + "# 在子图上添加文本\n", + "ax.text(3, 8, 'boxed italics text in data coords', style='italic',\n", + " bbox={'facecolor': 'red', 'alpha': 0.5, 'pad': 10})\n", + "\n", + "# 在画布上添加文本，一般在子图上添加文本是更常见的操作，这种方法很少用\n", + "fig.text(0.4,0.8,'This is text for figure')\n", + "\n", + "ax.plot([2], [1], 'o')\n", + "# 添加注解\n", + "ax.annotate('annotate', xy=(2, 1), xytext=(3, 4),arrowprops=dict(facecolor='black', shrink=0.05));" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.text - 子图上的文本" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "text的调用方式为`Axes.text(x, y, s, fontdict=None, **kwargs) ` \n", + "其中`x`,`y`为文本出现的位置，默认状态下即为当前坐标系下的坐标值， \n", + "`s`为文本的内容， \n", + "`fontdict`是可选参数，用于覆盖默认的文本属性， \n", + "`**kwargs`为关键字参数，也可以用于传入文本样式参数" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "重点解释下fontdict和\\*\\*kwargs参数，这两种方式都可以用于调整呈现的文本样式，最终效果是一样的，不仅text方法，其他文本方法如set_xlabel,set_title等同样适用这两种方式修改样式。通过一个例子演示这两种方法是如何使用的。" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 46, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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"text/plain": [ - "
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" ] }, "metadata": { @@ -249,24 +153,188 @@ } ], "source": [ - "#文本属性的输入一种是通过**kwargs属性这种方式，一种是通过操作 matplotlib.font_manager.FontProperties 方法\n", - "#该案例中对于x_label采用**kwargs调整字体属性，y_label则采用 matplotlib.font_manager.FontProperties 方法调整字体属性\n", - "#该链接是FontProperties方法的介绍 https://matplotlib.org/api/font_manager_api.html#matplotlib.font_manager.FontProperties\n", - "x1 = np.linspace(0.0, 5.0, 100)\n", - "y1 = np.cos(2 * np.pi * x1) * np.exp(-x1)\n", + "fig = plt.figure(figsize=(10,3))\n", + "axes = fig.subplots(1,2)\n", "\n", - "font = FontProperties()\n", - "font.set_family('serif')\n", - "font.set_name('Times New Roman')\n", - "font.set_style('italic')\n", + "# 使用关键字参数修改文本样式\n", + "axes[0].text(0.3, 0.8, 'modify by **kwargs', style='italic',\n", + " bbox={'facecolor': 'red', 'alpha': 0.5, 'pad': 10});\n", "\n", - "fig, ax = plt.subplots(figsize=(5, 3))\n", - "fig.subplots_adjust(bottom=0.15, left=0.2)\n", - "ax.plot(x1, y1)\n", - "ax.set_xlabel('time [s]', fontsize='large', fontweight='bold')\n", - "ax.set_ylabel('Damped oscillation [V]', fontproperties=font)\n", + "# 使用fontdict参数修改文本样式\n", + "font = {'bbox':{'facecolor': 'red', 'alpha': 0.5, 'pad': 10}, 'style':'italic'}\n", + "axes[1].text(0.3, 0.8, 'modify by fontdict', fontdict=font);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "matplotlib中所有支持的样式参数请参考[官网文档说明](https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.text.html#matplotlib.axes.Axes.text)，大多数时候需要用到的时候再查询即可。 \n", "\n", - "plt.show()" + "下表列举了一些常用的参数供参考。\n", + "\n", + "| Property | Description |\n", + "| ------------------------ | :-------------------------- |\n", + "| `alpha` |float or None 透明度，越接近0越透明，越接近1越不透明 | \n", + "| `backgroundcolor` | color 文本的背景颜色 |\n", + "| `bbox` | dict with properties for patches.FancyBboxPatch 用来设置text周围的box外框 |\n", + "| `color` or c | color 字体的颜色 |\n", + "| `fontfamily` or family | {FONTNAME, 'serif', 'sans-serif', 'cursive', 'fantasy', 'monospace'} 字体的类型|\n", + "| `fontsize` or size | float or {'xx-small', 'x-small', 'small', 'medium', 'large', 'x-large', 'xx-large'} 字体大小|\n", + "| `fontstyle` or style | {'normal', 'italic', 'oblique'} 字体的样式是否倾斜等 |\n", + "| `fontweight` or weight | {a numeric value in range 0-1000, 'ultralight', 'light', 'normal', 'regular', 'book', 'medium', 'roman', 'semibold', 'demibold', 'demi', 'bold', 'heavy', 'extra bold', 'black'} 文本粗细| \n", + "| `horizontalalignment` or ha | {'center', 'right', 'left'} 选择文本左对齐右对齐还是居中对齐 |\n", + "| `linespacing` | float (multiple of font size) 文本间距 |\n", + "| `rotation` | float or {'vertical', 'horizontal'} 指text逆时针旋转的角度，“horizontal”等于0，“vertical”等于90 |\n", + "| `verticalalignment` or va | {'center', 'top', 'bottom', 'baseline', 'center_baseline'} 文本在垂直角度的对齐方式 |\n", + " \n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.xlabel和ylabel - 子图的x，y轴标签" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "xlabel的调用方式为`Axes.set_xlabel(xlabel, fontdict=None, labelpad=None, *, loc=None, **kwargs)` \n", + "ylabel方式类似，这里不重复写出。 \n", + "其中`xlabel`即为标签内容, \n", + "`fontdict`和`**kwargs`用来修改样式，上一小节已介绍, \n", + "`labelpad`为标签和坐标轴的距离，默认为4， \n", + "`loc`为标签位置，可选的值为'left', 'center', 'right'之一，默认为居中" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# 观察labelpad和loc参数的使用效果\n", + "fig = plt.figure(figsize=(10,3))\n", + "axes = fig.subplots(1,2)\n", + "axes[0].set_xlabel('xlabel',labelpad=20,loc='left')\n", + "\n", + "# loc参数仅能提供粗略的位置调整，如果想要更精确的设置标签的位置，可以使用position参数+horizontalalignment参数来定位\n", + "# position由一个元组过程，第一个元素0.2表示x轴标签在x轴的位置，第二个元素对于xlabel其实是无意义的，随便填一个数都可以\n", + "# horizontalalignment='left'表示左对齐，这样设置后x轴标签就能精确定位在x=0.2的位置处\n", + "axes[1].set_xlabel('xlabel', position=(0.2, _), horizontalalignment='left');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4.title和suptitle - 子图和画布的标题" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "title的调用方式为`Axes.set_title(label, fontdict=None, loc=None, pad=None, *, y=None, **kwargs)` \n", + "其中label为子图标签的内容，`fontdict`,`loc`,`**kwargs`和之前小节相同不重复介绍 \n", + "`pad`是指标题偏离图表顶部的距离，默认为6 \n", + "`y`是title所在子图垂向的位置。默认值为1，即title位于子图的顶部。 \n", + "\n", + "suptitle的调用方式为`figure.suptitle(t, **kwargs)` \n", + "其中`t`为画布的标题内容" + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# 观察pad参数的使用效果\n", + "fig = plt.figure(figsize=(10,3))\n", + "fig.suptitle('This is figure title',y=1.2) # 通过参数y设置高度\n", + "axes = fig.subplots(1,2)\n", + "axes[0].set_title('This is title',pad=15)\n", + "axes[1].set_title('This is title',pad=6);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 5.annotate - 子图的注解" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "annotate的调用方式为`Axes.annotate(text, xy, *args, **kwargs)` \n", + "其中`text`为注解的内容， \n", + "`xy`为注解箭头指向的坐标， \n", + "其他常用的参数包括： \n", + "`xytext`为注解文字的坐标， \n", + "`xycoords`用来定义xy参数的坐标系， \n", + "`textcoords`用来定义xytext参数的坐标系， \n", + "`arrowprops`用来定义指向箭头的样式 \n", + "annotate的参数非常复杂，这里仅仅展示一个简单的例子，更多参数可以查看[官方文档中的annotate介绍](https://matplotlib.org/stable/tutorials/text/annotations.html#plotting-guide-annotation)" + ] + }, + { + "cell_type": "code", + "execution_count": 110, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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6C9/MmgCPAUOArsAoM+t6nP1aAf8fKIx2SImtpUuXcu2117JkyRIGDBgQdBwRiZJIZvg9gRJ33+3uh4AFwPEWc2cADwLfRDGfxFheXh7jxo3jtddeIzU1Neg4IhJFkRR+O+DDGttl1fcdZWYXAx3cfel3PZGZTTCzIjMrKi8vP+mw0rhWrlzJ9ddfz2uvvUavXr2CjiMiUdbgN16Z2SnAw0Cdb7t09yx3T3H3lDPPPLOhLy1RVFhYyMiRI1m8eDE9e/YMOo6INIJICn8P0KHGdvvq+77VCugGrDKzUqA3kKsDt4ljy5YtZGRkkJ2dTf/+/YOOIyKNJJLC3wB0MbNzzawZkAnkfvugu3/p7m3cvZO7dwLWA+nuXtQoiSWqSkpK+PnPf86sWbMYNmxY0HFEpBHVWfjuXgHcCiwH3gcWuftWM7vfzNIbO6A0nrKyMgYNGsS9997LqFGjgo4jIo0sojdeufsyYFmt+6afYN/+DY8lja28vJxBgwYxadIkJkyYEHQcEYkBXS0zhL788ksGDx7M8OHDufPOO4OOIyIxosIPmQMHDjBs2DD69OnDjBkzgo4jIjGkwg+RQ4cOMXz4cDp37szs2bMxs6AjiUgMqfBDorKykmuvvZYWLVowb948TjlFX3qRsNHVMkPA3Zk0aRL79u0jLy+Ppk31ZRcJI/3kh8DMmTN5++23WbNmDc2bNw86jogERIWf5F566SX+/Oc/U1BQwGmnnRZ0HBEJkAo/iRUWFjJp0iSWL19O+/btg44jIgHTkbskVVpaypVXXkl2djbdu3cPOo6IxAEVfhLat28fQ4cOZerUqbo+jogcpcJPMocPH2bEiBEMGDCA22+/Peg4IhJHVPhJ5NvTL5s3b86sWbOCjiMicUYHbZPIzJkz2bBhA2vWrKFJkyZBxxGROKPCTxIvv/zy0dMvW7VqFXQcEYlDKvwkUFhYyMSJE3X6pYh8J63hJ7hvT7+cN2+eTr8Uke+kwk9gBw4c4Be/+AV33nknV1xxRdBxRCTOqfATlLszceJEunXrxq9+9aug44hIAtAafoKaO3cuxcXFFBQU6Lr2IhIRFX4CKiwsZPr06eTn59OyZcug44hIgtCSToLZu3cvI0aM4KmnnuKHP/xh0HFEJIGo8BNIRUUFmZmZjB07lvT09KDjiEiCUeEnkLvvvpumTZvy29/+NugoIpKAtIafIF5++WUWLlxIUVGRLpsgIvWiwk8A27dv5+abb+b111+nTZs2QccRkQSlJZ04t3//fq666ioeeOABUlJSgo4jIglMhR/H3J0bbriBPn36cOONNwYdR0QSnJZ04tif/vQndu3axdq1a4OOIiJJQIUfp/Lz8/n9739PYWEhLVq0CDqOiCQBLenEkf379wNV/5N29OjRZGVl0alTp2BDiUjSUOHHicOHD3PuuedSWVnJ+PHjGTZsGBkZGUHHEpEkoiWdOLF7925at25NdnY2H3zwAc8991zQkUQkyWiGHye2b99Ohw4dmDZtGtOmTWPUqFGUlJQEHUtEkogKP05s3ryZTZs20bFjR6ZMmUJaWhqdO3cOOpaIJJGICt/MBpvZDjMrMbOpx3l8spltM7PNZrbCzP41+lGT26uvvsrBgwfJzMxk586djB8/nlNO0e9jEYmeOtfwzawJ8BgwCCgDNphZrrtvq7FbMZDi7gfMbCLwB2BkYwROVvPmzaNt27acffbZQUcRkSQVyRSyJ1Di7rvd/RCwAPgfp4+4+0p3P1C9uR5oH92Yye/HP/6xyl5EGlUkhd8O+LDGdln1fSdyA/D68R4wswlmVmRmReXl5ZGnFBGRBovqIrGZjQFSgJnHe9zds9w9xd1TzjzzzGi+tIiI1CGS8/D3AB1qbLevvu9/MLOBwN1AP3c/GJ14IiISLZHM8DcAXczsXDNrBmQCuTV3MLPuwFwg3d33Rj+miIg0VJ2F7+4VwK3AcuB9YJG7bzWz+83s23+sOhM4DVhsZu+YWe4Jnk5ERAIS0aUV3H0ZsKzWfdNr3B4Y5VwiIhJlemePiEhIqPBFREJChS8iEhIqfBGRkFDhi4iEhApfRCQkVPgiIiGhwhcRCQkVvohISKjwRURCQoUvIhISKnwRkZBQ4YuIhIQKX0QkJFT4IiIhocIXEQkJFb6ISEio8EVEQkKFLyISEip8EZGQUOGLiISECl9EJCRU+CIiIaHCFxEJCRW+iEhIqPBFREJChS8iEhIqfBGRkFDhi4iEhApfRCQkVPgiIiGhwhcRCQkVvohISKjwRURCQoUvIhISERW+mQ02sx1mVmJmU4/zeHMzW1j9eKGZdYp6UhERaZA6C9/MmgCPAUOArsAoM+taa7cbgC/c/YfALODBaAcVEZGGiWSG3xMocffd7n4IWABk1NonA5hfffslYICZWfRiiohIQzWNYJ92wIc1tsuAXifax90rzOxL4P8Cn9XcycwmABOqNw+a2Zb6hE5Cbag1ViGmsThGY3GMxuKYC+r7iZEUftS4exaQBWBmRe6eEsvXj1cai2M0FsdoLI7RWBxjZkX1/dxIlnT2AB1qbLevvu+4+5hZU+D7wN/rG0pERKIvksLfAHQxs3PNrBmQCeTW2icX+H/Vt68G3nR3j15MERFpqDqXdKrX5G8FlgNNgGx332pm9wNF7p4LzAOeM7MS4HOqfinUJasBuZONxuIYjcUxGotjNBbH1HssTBNxEZFw0DttRURCQoUvIhISjV74uizDMRGMxWQz22Zmm81shZn9axA5Y6Gusaix33AzczNL2lPyIhkLM7um+ntjq5m9GOuMsRLBz0hHM1tpZsXVPydpQeRsbGaWbWZ7T/ReJavySPU4bTaziyN6YndvtA+qDvLuAjoDzYB3ga619pkEPFF9OxNY2JiZgvqIcCwuB/5P9e2JYR6L6v1aAW8B64GUoHMH+H3RBSgG/qV6+6ygcwc4FlnAxOrbXYHSoHM30lj0BS4Gtpzg8TTgdcCA3kBhJM/b2DN8XZbhmDrHwt1XuvuB6s31VL3nIRlF8n0BMIOq6zJ9E8twMRbJWIwHHnP3LwDcfW+MM8ZKJGPhQOvq298HPophvphx97eoOuPxRDKAZ73KeuB0Mzunrudt7MI/3mUZ2p1oH3evAL69LEOyiWQsarqBqt/gyajOsaj+E7WDuy+NZbAARPJ9cT5wvpmtM7P1ZjY4ZuliK5KxuA8YY2ZlwDLgtthEizsn2ydAjC+tIJExszFACtAv6CxBMLNTgIeB6wKOEi+aUrWs05+qv/reMrN/d/d9QYYKyCjgGXd/yMwuoer9P93c/UjQwRJBY8/wdVmGYyIZC8xsIHA3kO7uB2OULdbqGotWQDdglZmVUrVGmZukB24j+b4oA3Ld/bC7/xfwAVW/AJJNJGNxA7AIwN0LgBZUXVgtbCLqk9oau/B1WYZj6hwLM+sOzKWq7JN1nRbqGAt3/9Ld27h7J3fvRNXxjHR3r/dFo+JYJD8j/0nV7B4za0PVEs/uGGaMlUjG4m/AAAAz+zeqCr88pinjQy4wtvpsnd7Al+7+cV2f1KhLOt54l2VIOBGOxUzgNGBx9XHrv7l7emChG0mEYxEKEY7FcuBnZrYNqATudPek+ys4wrGYAjxpZndQdQD3umScIJpZDlW/5NtUH6+4FzgVwN2foOr4RRpQAhwAro/oeZNwrERE5Dj0TlsRkZBQ4YuIhIQKX0QkJFT4IiIhocIXEQkJFb6ISEio8EVEQuK/AU6NrDOimaCJAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure()\n", + "ax = fig.add_subplot()\n", + "ax.annotate(\"\",\n", + " xy=(0.2, 0.2), xycoords='data',\n", + " xytext=(0.8, 0.8), textcoords='data',\n", + " arrowprops=dict(arrowstyle=\"->\", connectionstyle=\"arc3,rad=0.2\")\n", + " );" ] }, { @@ -277,829 +345,6 @@ " 字体设置一般有全局字体设置和自定义局部字体设置两种方法。" ] }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "cmmi10\n", - "\n", - "DejaVu Sans Mono\n", - "\n", - "DejaVu Sans\n", - "\n", - "STIXSizeFourSym\n", - "\n", - "DejaVu Sans Display\n", - "\n", - "DejaVu Serif\n", - "\n", - "STIXGeneral\n", - "\n", - "STIXNonUnicode\n", - "\n", - "STIXSizeFourSym\n", - "\n", - "STIXSizeThreeSym\n", - "\n", - "STIXGeneral\n", - "\n", - "cmr10\n", - "\n", - "STIXNonUnicode\n", - "\n", - "cmsy10\n", - "\n", - "DejaVu Sans\n", - "\n", - "STIXSizeFiveSym\n", - "\n", - "DejaVu Sans\n", - "\n", - "STIXSizeThreeSym\n", - "\n", - "DejaVu Serif Display\n", - "\n", - "DejaVu Sans\n", - "\n", - "cmex10\n", - "\n", - "DejaVu Sans Mono\n", - "\n", - "STIXNonUnicode\n", - "\n", - "STIXSizeOneSym\n", - "\n", - "STIXSizeTwoSym\n", - "\n", - "DejaVu Serif\n", - "\n", - "STIXNonUnicode\n", - "\n", - "cmb10\n", - "\n", - "STIXGeneral\n", - "\n", - "STIXGeneral\n", - "\n", - "DejaVu Serif\n", - "\n", - "STIXSizeOneSym\n", - "\n", - "cmss10\n", - "\n", - "STIXSizeTwoSym\n", - "\n", - "cmtt10\n", - "\n", - "DejaVu Sans Mono\n", - "\n", - "DejaVu Serif\n", - "\n", - "DejaVu Sans Mono\n", - "\n", - "Elephant\n", - "\n", - "Trebuchet MS\n", - "\n", - "Dubai\n", - "\n", - "Microsoft New Tai Lue\n", - "\n", - "Ravie\n", - "\n", - "Verdana\n", - "\n", - "Elephant\n", - "\n", - "Microsoft Tai Le\n", - "\n", - "Book Antiqua\n", - "\n", - "Gill Sans MT Ext Condensed Bold\n", - "\n", - "Nirmala UI\n", - "\n", - "Segoe UI\n", - "\n", - "FZShuTi\n", - "\n", - "Lucida Fax\n", - "\n", - "Eras Demi ITC\n", - "\n", - "STHupo\n", - "\n", - "Constantia\n", - "\n", - "Ebrima\n", - "\n", - "Symbol\n", - "\n", - "DengXian\n", - "\n", - "MS Reference Sans Serif\n", - "\n", - "Yu Gothic\n", - "\n", - "Tahoma\n", - "\n", - "Arial\n", - "\n", - "Agency FB\n", - "\n", - "Corbel\n", - "\n", - "Javanese Text\n", - "\n", - "Castellar\n", - "\n", - "Lucida Sans\n", - "\n", - "FZYaoTi\n", - "\n", - "Lucida Sans\n", - "\n", - "Tahoma\n", - "\n", - "Lucida Sans Typewriter\n", - "\n", - "Gill Sans Ultra Bold Condensed\n", - "\n", - "STZhongsong\n", - "\n", - "Palatino Linotype\n", - "\n", - "Algerian\n", - "\n", - "Matura MT Script Capitals\n", - "\n", - "Franklin Gothic Demi\n", - "\n", - "Cooper Black\n", - "\n", - "Lucida Handwriting\n", - "\n", - "Mistral\n", - "\n", - "Sitka Small\n", - "\n", - "Lucida Bright\n", - "\n", - "Bodoni MT\n", - "\n", - "Parchment\n", - "\n", - "Perpetua Titling MT\n", - "\n", - "Segoe UI\n", - "\n", - "Brush Script MT\n", - "\n", - "Freestyle Script\n", - "\n", - "Calibri\n", - "\n", - "Colonna MT\n", - "\n", - "Century Schoolbook\n", - "\n", - "Georgia\n", - "\n", - "Tw Cen MT\n", - "\n", - "Lucida Bright\n", - "\n", - "Gadugi\n", - "\n", - "Constantia\n", - "\n", - "STFangsong\n", - "\n", - "Sitka Small\n", - "\n", - "Gill Sans MT\n", - "\n", - "DejaVu Sans Mono\n", - "\n", - "Calisto MT\n", - "\n", - "Century Schoolbook\n", - "\n", - "Bodoni MT\n", - "\n", - "Mongolian Baiti\n", - "\n", - "Lucida Sans Typewriter\n", - "\n", - "Berlin Sans FB\n", - "\n", - "Perpetua\n", - "\n", - "Lucida Fax\n", - "\n", - "Century Gothic\n", - "\n", - "Gill Sans MT\n", - "\n", - "Times New Roman\n", - "\n", - "Gadugi\n", - "\n", - "Rockwell\n", - "\n", - "Segoe Script\n", - "\n", - "FangSong\n", - "\n", - "Sitka Small\n", - "\n", - "Papyrus\n", - "\n", - "Californian FB\n", - "\n", - "Microsoft YaHei\n", - "\n", - "High Tower Text\n", - "\n", - "Leelawadee\n", - "\n", - "Rockwell Extra Bold\n", - "\n", - "Bodoni MT\n", - "\n", - "Microsoft JhengHei\n", - "\n", - "Times New Roman\n", - "\n", - "Lucida Fax\n", - "\n", - "Microsoft Uighur\n", - "\n", - "Rage Italic\n", - "\n", - "Cambria\n", - "\n", - "Trebuchet MS\n", - "\n", - "Yu Gothic\n", - "\n", - "Perpetua\n", - "\n", - "Bodoni MT\n", - "\n", - "Tw Cen MT Condensed Extra Bold\n", - "\n", - "Segoe UI\n", - "\n", - "Wingdings 3\n", - "\n", - "Segoe UI\n", - "\n", - "Calisto MT\n", - "\n", - "Century Gothic\n", - "\n", - "Arial Rounded MT Bold\n", - "\n", - "Candara\n", - "\n", - "STSong\n", - "\n", - "Maiandra GD\n", - "\n", - "Microsoft Uighur\n", - "\n", - "Engravers MT\n", - "\n", - "Vladimir Script\n", - "\n", - "DengXian\n", - "\n", - "Palatino Linotype\n", - "\n", - "Calibri\n", - "\n", - "Gigi\n", - "\n", - "Book Antiqua\n", - "\n", - "Bernard MT Condensed\n", - "\n", - "Comic Sans MS\n", - "\n", - "Bodoni MT\n", - "\n", - "Modern No. 20\n", - "\n", - "Britannic Bold\n", - "\n", - "Nirmala UI\n", - "\n", - "Haettenschweiler\n", - "\n", - "Book Antiqua\n", - "\n", - "Californian FB\n", - "\n", - "Times New Roman\n", - "\n", - "Calisto MT\n", - "\n", - "Segoe UI\n", - "\n", - "Segoe Print\n", - "\n", - "Franklin Gothic Medium\n", - "\n", - "Bookman Old Style\n", - "\n", - "Lucida Sans Unicode\n", - "\n", - "Consolas\n", - "\n", - "Segoe UI\n", - "\n", - "STKaiti\n", - "\n", - "Monotype Corsiva\n", - "\n", - "Microsoft PhagsPa\n", - "\n", - "Onyx\n", - "\n", - "Sitka Small\n", - "\n", - "DejaVu Sans Mono\n", - "\n", - "Trebuchet MS\n", - "\n", - "Vivaldi\n", - "\n", - "Arial\n", - "\n", - "Franklin Gothic Medium\n", - "\n", - "Bookman Old Style\n", - "\n", - "Bradley Hand ITC\n", - "\n", - "Segoe MDL2 Assets\n", - "\n", - "Centaur\n", - "\n", - "Times New Roman\n", - "\n", - "Microsoft Sans Serif\n", - "\n", - "Script MT Bold\n", - "\n", - "Lucida Bright\n", - "\n", - "Bodoni MT\n", - "\n", - "Myanmar Text\n", - "\n", - "Cambria\n", - "\n", - "Kristen ITC\n", - "\n", - "Sylfaen\n", - "\n", - "Leelawadee\n", - "\n", - "Rockwell Condensed\n", - "\n", - "Calibri\n", - "\n", - "High Tower Text\n", - "\n", - "Cambria\n", - "\n", - "Wingdings\n", - "\n", - "Courier New\n", - "\n", - "Lucida Calligraphy\n", - "\n", - "MT Extra\n", - "\n", - "Microsoft PhagsPa\n", - "\n", - "Trebuchet MS\n", - "\n", - "Calisto MT\n", - "\n", - "Consolas\n", - "\n", - "Lucida Sans\n", - "\n", - "Calibri\n", - "\n", - "Impact\n", - "\n", - "Segoe UI\n", - "\n", - "Tempus Sans ITC\n", - "\n", - "Verdana\n", - "\n", - "Gill Sans MT\n", - "\n", - "French Script MT\n", - "\n", - "Juice ITC\n", - "\n", - "Dubai\n", - "\n", - "Niagara Solid\n", - "\n", - "Lucida Fax\n", - "\n", - "Bodoni MT\n", - "\n", - "Franklin Gothic Heavy\n", - "\n", - "Goudy Old Style\n", - "\n", - "Bell MT\n", - "\n", - "Comic Sans MS\n", - "\n", - "Arial\n", - "\n", - "Goudy Old Style\n", - "\n", - "Calibri\n", - "\n", - "Lucida Sans Typewriter\n", - "\n", - "Copperplate Gothic Bold\n", - "\n", - "Berlin Sans FB Demi\n", - "\n", - "Garamond\n", - "\n", - "Bell MT\n", - "\n", - "Bookman Old Style\n", - "\n", - "Gloucester MT Extra Condensed\n", - "\n", - "Segoe UI Symbol\n", - "\n", - "Arial\n", - "\n", - "Perpetua\n", - "\n", - "Bodoni MT\n", - "\n", - "Bodoni MT\n", - "\n", - "Harlow Solid Italic\n", - "\n", - "Baskerville Old Face\n", - "\n", - "Georgia\n", - "\n", - "Leelawadee UI\n", - "\n", - "Goudy Stout\n", - "\n", - "Franklin Gothic Book\n", - "\n", - "HoloLens MDL2 Assets\n", - "\n", - "STCaiyun\n", - "\n", - "Webdings\n", - "\n", - "Segoe UI\n", - "\n", - "Arial\n", - "\n", - "Poor Richard\n", - "\n", - "Playbill\n", - "\n", - "Franklin Gothic Medium Cond\n", - "\n", - "STLiti\n", - "\n", - "Bauhaus 93\n", - "\n", - "Microsoft JhengHei\n", - "\n", - "Wide Latin\n", - "\n", - "Corbel\n", - "\n", - "Gill Sans MT\n", - "\n", - "Century\n", - "\n", - "Candara\n", - "\n", - "Corbel\n", - "\n", - "SimSun-ExtB\n", - "\n", - "Lucida Sans\n", - "\n", - "Malgun Gothic\n", - "\n", - "Yu Gothic\n", - "\n", - "Corbel\n", - "\n", - "Chiller\n", - "\n", - "Dubai\n", - "\n", - "Candara\n", - "\n", - "Bell MT\n", - "\n", - "Magneto\n", - "\n", - "Palatino Linotype\n", - "\n", - "LiSu\n", - "\n", - "Informal Roman\n", - "\n", - "Consolas\n", - "\n", - "Century Gothic\n", - "\n", - "Dubai\n", - "\n", - "Book Antiqua\n", - "\n", - "Segoe UI Emoji\n", - "\n", - "Segoe UI\n", - "\n", - "Yu Gothic\n", - "\n", - "Courier New\n", - "\n", - "Harrington\n", - "\n", - "Ebrima\n", - "\n", - "Courier New\n", - "\n", - "Perpetua Titling MT\n", - "\n", - "Segoe UI Historic\n", - "\n", - "Palatino Linotype\n", - "\n", - "Agency FB\n", - "\n", - "Old English Text MT\n", - "\n", - "Lucida Bright\n", - "\n", - "Broadway\n", - "\n", - "Leelawadee UI\n", - "\n", - "Stencil\n", - "\n", - "Microsoft YaHei\n", - "\n", - "Segoe Print\n", - "\n", - "Verdana\n", - "\n", - "Ink Free\n", - "\n", - "DejaVu Sans Mono\n", - "\n", - "Eras Bold ITC\n", - "\n", - "Century Gothic\n", - "\n", - "Arial\n", - "\n", - "MS Outlook\n", - "\n", - "Comic Sans MS\n", - "\n", - "Lucida Sans Typewriter\n", - "\n", - "Curlz MT\n", - "\n", - "Century Schoolbook\n", - "\n", - "Tw Cen MT Condensed\n", - "\n", - "Marlett\n", - "\n", - "Georgia\n", - "\n", - "Georgia\n", - "\n", - "OCR A Extended\n", - "\n", - "Jokerman\n", - "\n", - "Blackadder ITC\n", - "\n", - "Lucida Console\n", - "\n", - "Tw Cen MT\n", - "\n", - "Tw Cen MT Condensed\n", - "\n", - "Bookman Old Style\n", - "\n", - "Showcard Gothic\n", - "\n", - "Kunstler Script\n", - "\n", - "MS Gothic\n", - "\n", - "Comic Sans MS\n", - "\n", - "Copperplate Gothic Light\n", - "\n", - "Perpetua\n", - "\n", - "Californian FB\n", - "\n", - "Candara\n", - "\n", - "MS Reference Specialty\n", - "\n", - "Leelawadee UI\n", - "\n", - "Bookshelf Symbol 7\n", - "\n", - "Franklin Gothic Demi Cond\n", - "\n", - "Microsoft YaHei\n", - "\n", - "Garamond\n", - "\n", - "Niagara Engraved\n", - "\n", - "Bodoni MT\n", - "\n", - "Gill Sans MT Condensed\n", - "\n", - "DengXian\n", - "\n", - "SimSun\n", - "\n", - "Gabriola\n", - "\n", - "Eras Light ITC\n", - "\n", - "Nirmala UI\n", - "\n", - "Candara\n", - "\n", - "KaiTi\n", - "\n", - "Constantia\n", - "\n", - "Bodoni MT\n", - "\n", - "YouYuan\n", - "\n", - "Snap ITC\n", - "\n", - "Viner Hand ITC\n", - "\n", - "MV Boli\n", - "\n", - "Consolas\n", - "\n", - "Palace Script MT\n", - "\n", - "Arial\n", - "\n", - "Century Schoolbook\n", - "\n", - "Berlin Sans FB\n", - "\n", - "Constantia\n", - "\n", - "Pristina\n", - "\n", - "Tw Cen MT\n", - "\n", - "Franklin Gothic Heavy\n", - "\n", - "Arial\n", - "\n", - "Microsoft Tai Le\n", - "\n", - "Franklin Gothic Demi\n", - "\n", - "Tw Cen MT\n", - "\n", - "Corbel\n", - "\n", - "Eras Medium ITC\n", - "\n", - "STXinwei\n", - "\n", - "Cambria\n", - "\n", - "Malgun Gothic\n", - "\n", - "Arial\n", - "\n", - "Garamond\n", - "\n", - "Segoe UI\n", - "\n", - "Malgun Gothic\n", - "\n", - "Footlight MT Light\n", - "\n", - "SimHei\n", - "\n", - "Calibri\n", - "\n", - "Forte\n", - "\n", - "Microsoft Yi Baiti\n", - "\n", - "Verdana\n", - "\n", - "Segoe Script\n", - "\n", - "Rockwell\n", - "\n", - "Segoe UI\n", - "\n", - "Microsoft New Tai Lue\n", - "\n", - "Candara\n", - "\n", - "Franklin Gothic Book\n", - "\n", - "Wingdings 2\n", - "\n", - "Bahnschrift\n", - "\n", - "Imprint MT Shadow\n", - "\n", - "Myanmar Text\n", - "\n", - "Rockwell\n", - "\n", - "STXihei\n", - "\n", - "Rockwell\n", - "\n", - "Edwardian Script ITC\n", - "\n", - "Courier New\n", - "\n", - "Gill Sans Ultra Bold\n", - "\n", - "Microsoft Himalaya\n", - "\n", - "Corbel\n", - "\n", - "Rockwell Condensed\n", - "\n", - "MingLiU-ExtB\n", - "\n", - "Segoe UI\n", - "\n", - "Microsoft JhengHei\n", - "\n", - "STXingkai\n", - "\n", - "Felix Titling\n", - "\n", - "DejaVu Sans Mono\n", - "\n", - "Goudy Old Style\n", - "\n" - ] - } - ], - "source": [ - "#首先可以查看matplotlib所有可用的字体\n", - "from matplotlib import font_manager\n", - "font_family = font_manager.fontManager.ttflist\n", - "font_name_list = [i.name for i in font_family]\n", - "for font in font_name_list:\n", - " print(f'{font}\\n')" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -1109,34 +354,23 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 113, "metadata": {}, "outputs": [], "source": [ "#该block讲述如何在matplotlib里面，修改字体默认属性，完成全局字体的更改。\n", - "import matplotlib.pyplot as plt\n", "plt.rcParams['font.sans-serif'] = ['SimSun'] # 指定默认字体为新宋体。\n", "plt.rcParams['axes.unicode_minus'] = False # 解决保存图像时 负号'-' 显示为方块和报错的问题。" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 115, "metadata": {}, "outputs": [ { "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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5fPkuhn2+kmMJyTzRvgF/u6YuRYsEfnmI3GCJwBgT1HYfiWPIjBV8s2YfLWuW5ZU7mtOgSpi/w7qgLBEYY4JSaqryv9+3MeLrNaSkKkM7NaH3FeEUzmflIXKDJQJjTNDZfOA4A6dFsWjzQa6sV4ER3ZpTq0IJf4flN5YIjDFBIzkllf/+vJnX562jaJFCjOwewZ2RNfN1eYjcYInAGBMUVu+OZcC0KKJ2HKF9kyoMv60ZVUqH+jusgGCJwBhToCUkp/D2dxsZ+90GyhQPYUzPVtwaUS3orwJ8WSIwxhRYf2w7xICpUazfd4xurS7imU5NKFeyqL/DCjiWCIwxBc6JxGRen7eO//68mWqlQ3m/92Vc16iyv8MKWJYIjDEFys8bDjBwehTbD8Zx3+W16d+xIWEFrEhcbrNEYIwpEI7EJTHi69VM+X07dSqW5JO+l9Pm4oJZJC63WSIwxuR781buYcjMFcQcT+Rv19TlHzfWJzSk4BaJy22WCIwx+db+owkM+2IlX0XtpnG10ky4/zIiapTxd1j5jiUCY0y+o6rM/HMnz32xihMJKTx5UwMevqYuIYWDo0hcbrNEYIzJV3YejmPwjGi+X7ufS2q5InH1KgdXkbjcZonAGJMvpKYqkxdv4+WvV5Oq8GznJvRqG5xF4nKbJQJjTMDbtP8YA6dFs3jLQa6qX5GXukVQs3zwFonLbQGRCESkKtAF2AXUBt5R1VT/RmWM8bfklFT+s3Azb8xfR7EihXj1jubccWkNKw+RywIiEQD3AO+paqyIdAJaAn/4NyRjjD+t2hVL/2nLWbEzlg5Nq/BC12ZUtiJxeSJQEsEvwKsi0h+4GPjed6WI9AX6AtSqVeuCB2eMuXDik1IY8+0Gxv2wkbIlQhh7zyXcElHN32EVaIGSCJYANwMzgDmqesx3paqOB8YDREZG6oUPzxhzISzdepD+U6PYuP843S+pwdBOjSlbworE5bVASQR/A94EngUmiEgrVV3m55iMMRfI8YRkXp27lkm/bqF6meJM6tOaaxpU8ndYQSNQEkEZIEZVVURmADUBSwTGBIEf1+3n6enR7DoSR6/La/NUx0aUKhYoP03BIVC+7UlAbxHZDzQCRvk3HGNMXjtyIokXvlrF1KU7uLhSST59uC2XhZf3d1hBKSASgapuB973nn7pz1iMMXlvzordDJ21koPHE3nk2ro8foMVifOngEgExpjgsO9oPM/OWsnsFXtoWr007/e+jGYXWZE4f7NEYIzJc6rKtD928sKXq4hLSqF/x4Y8dNXFViQuQFgiMMbkqe0HTzBoRjQ/rT/AZeHleLl7c+pWKuXvsIwPSwTGmDyRmqp88OsWXpm7FgGe79qUe9vUppAViQs4lgiMMbluw75jDJwWxZKth7i6QSVe6taMGuWsSFygskRgjMk1SSmpjP9xE6MXrKdEscK83qMFt19ykRWJC3CWCIwxuWLFziP0nxrFqt2x3BpRjWFdmlIprJi/wzLZYInAGHNe4pNSGP3Nesb/uInyJYsy7t5L6disqr/DMjlgicAYc85+33KQAVOj2HTgOHdG1mDwLU0oUyLE32GZHLJEYIzJsWMJybwyZw0f/LqVGuWK89EDbWhXv6K/wzLnKFuJQEQa4IrCxWSxTTNgtaqm5FZwxpjA893afQyeHs3u2Hj6XFmHJzs0oERRO6fMz7L7tzca2CAi7+N+7ON8V3q3mvwWGAG8kbshGmMCwaHjibzw5SqmL9tJvcqlmPq3K7i0djl/h2VyQU7SeEfgVqC6iCwBpgATgRBgFrAalzCMMQWIqvJ19B6e/XwFh08k8fj19eh3fT2KFbEicQVFThLBCFX9r4iUB24CHgGGAEeALUB3u+G8MQXL3th4hs5cwbxVe4m4qAwfPtCGxtVK+zssk8syTATiZn+MB5YCv/tup6oHRWQzsBdoBRQD5qW/vaQxJv9SVT5dsp3hX60mMTmVp29uxAPt6lDEisQVSJldERQFtuOagl4FSgLFROQOoCWwD5gKPAxUAeaIiKjqo3kesTEmT22LcUXiFm44QOs65RnZvTl1Kpb0d1gmD2WYCFQ1AXgeQESKAp2Bx4AbgWtV9RefzQ+KyPXA7yKyTFUn5HHMxpg8kJKqTPxlC6/NXUvhQsLw25rRs3UtKxIXBDLtI/CGjL4N9FPVaSLSE+gCVPbW3wEsVtVtwCagP6B5H7IxJret33uU/tOiWLbtMNc3qszw25pRvWxxf4dlLpCsOos3AF8DP4tIN6AtMA0vEQBvAQkiMgE3YughVW2Tl8EaY3JXYnIq437YyJhvN1CyWGFG39WSLi2qW5G4IJNVIqiB++H/CdjvbXsL7sw/TQvgU2AgcG8exWiMyQNROw7Tf2oUa/YcpXOL6gzr3IQKpaxIXDDKKhH8zKmmHgEq4jqPk0XkIwBVPSIirwHtgfi8DNQYkzviElMYtWAd7/20iUphxXivVyTtm1Txd1jGjzJNBKpaE0BEmqrqShE5CCwGhgNfApVFpAXwT+Al4HURmaOq1k9gTID6bVMMA6dFsSXmBHe3rsnTtzSmdKgViQt2WXUWV8f1A1wtIpcDCcBMoKeqNhKRvcAMYI+qDhGRq3GjiubnfdjGmJw4Gp/Ey7PXMHnRNmqVL8HHD7bhinpWJM44WTUNJQLLgT5eExC4khKrReRVIBXXR1DD234qcDeWCIwJKN+u2cvgGSvYGxvPg+3q8K+bGlK8qJWHMKdk1TR0AG8ugae2qiaKyLWqGici3VX1KG7EEMDHwCd5GKsxJgcOHk/k+S9WMvPPXTSoUoqx91xBq1pWJM6cKdu1hrwksMhniOi/ReRNVf3YW38gTyI0xuSIqvJF1G6Gfb6So/FJ/OPG+jxybT2KFrHyECZj2b0fQQ/gNyCtA7kN0BSYl3ehGWNyas+ReIbMXMGC1XtpUbMsr3RvTsOqYf4OywS4syYCEWkLvAvc7D0vDPwbV3k0SURmqWrXPI3SGJMlVWXK79t56avVJKWmMuTWxvz1yjoUtvIQJhuyTAQiUhb4H7BdVRd5VUnfwE0wex9IAcqIyF9V9f28DtYYc6atMccZOC2aXzfF0PbiCrzcPYLaFaxInMm+s10RvIybXdzF57V6QHfgWdy9CP4FTBORSXY/AmMunJRU5f2fN/PavLWEFCrEiNsjuOuymlYewuTY2RLBC8AeoJvPa48D/YCxuH6DN/FuTAN8lvshGmPSW7vHFYlbvv0wNzauzPDbIqhaJtTfYZl8KqsJZUVVdaf3uLDXN6C4exE8gbtV5TygNy4p9MUSgTF5KjE5lbHfb+Dt7zYQFhrCm3e3onPzanYVYM5LVlcEL4jIZlUdh6s19AdQRlVjRWQsMAyXACYArYH3RKSsqh7O45iNCUp/bj/MgKlRrN17lK4tq/Ns56aUL1nU32GZAiCrRPAJ7s5j0biCckOBKSJyKzAK2IjrHygG1AV+xFUn/TgvAzYm2MQlpvDv+WuZsHAzlcNCmXB/JDc0tiJxJvdkNbP4D2/+wIdACPAnrnP4LeB+XLPQXbjO5K640hJXYInAmFzzy8YDDJwWzbaDJ7inTS0G3tyIMCsSZ3JZllMNVfUH3A9/daAcro/gHlxdod+A+sC3wHW4m9xH5mWwxgSL2Pgknp4eTc/3FlFIYErfy3mxW4QlAZMnsjOz+C2gEXAIQFV/FZEXga6qepOIlAReB5bh7mFwTkTkOqAo7irjX6p68Fz3ZUx+tmDVXgbPjGb/0QQevvpi/nFjAysSZ/LUWROBqiYDDwPIqaEJY4AHReQOVZ0KzPZe/9e5BCEilYAGqvquiPygqnaTGxN0Yo4lMOyLVXyxfBeNqobxXq9Imtco6++wTBDIdtE5AFWt5v2ZKiLdgS3eFUFpVd19HnF0BMJF5DGgoYgMUNXjaStFpC9ueCq1atU6j8MYE3hUlc+X72LY5ys5lpDMP29swN+vrWtF4swFc9Z/aSLSXES2isgD6VYVxpWb2AW8cp5xVAe2qepbuM7n7r4rVXW8qkaqamSlSpXO81DGBI5dh+N4YNIS/m/Kn9SuUJKvHr+K/7uxviUBc0Fl54ogBEgC/iEic3E3ohmCuxvZF8BfgLnnGUccsNN7vAM3L8GYAis1Vfl48TZenr2GlFRlaKcm9L4i3IrEGb/IbtPQLuB6XJG5T4AFwEPn2Rzk63dc89DnQFVgbS7t15iAs/nAcQZOi2LR5oNcWa8CI7o1p1aFEv4OywSxnPQR1AMuxt2oXoFWItLKZ30isMi7a1mOeCOROorIHUB53GxlYwqU5JRUJizczL/nr6NokUK80r05PSJrWHkI43fZTQQC3ADckcU2lXFNSC3PJRBVffZc3mdMfrB6dywDpkURteMI7ZtUYfhtzahS2orEmcCQVdG5Rrg+gK8AVdW3gbdFpA6wRVU13fal8eYaGGOchOQU3v52A2O/30jZEiG83fMSbomoalcBJqBkdUWwDjca6FkgVETaqOoiXNXR2iIyC/hEVb/1ti8ObMrTaI3JR/7YdogBU6NYv+8Yt7e6iKGdmlDOisSZAJRVraFUEZkCfAM8DXwrIs1UtY2IXIEbLfSxiBwHxqjqG7iSE8YEtROJybw2dx3v/7KZaqVDef+vl3Fdw8r+DsuYTJ2tj2AIcFhVHxKRa1V1s4gUB54EFuOGknYDKuZxnMbkCwvXH+DpGVFsPxhHr7a16d+xEaWK5WjepjEX3Nn+ha7FVRQFqOD9ORo4ALzulZ+wm9GYoHckLokXv1rFp0t2UKdiST59uC2t65T3d1jGZMvZEsEm3A1neuL6CRJxs5GTgXu9Di/BDScVXKeyDYg2QWXuyj0MnbmCmOOJ/P3auvzfDfUJDbEicSb/OFsi2AZsVtV6InIIN6nsPlwJiCW4voHv8jhGYwLS/qMJDPt8JV9F76ZxtdJMuP8yImqU8XdYxuTY2RLBLtxMXwBUdRmwTESeAh4EPhSRdcB9afc3NqagU1VmLNvJ81+u4kRCCk91aEjfqy8mpLDVBzL5U5aJQFXjRaS697STz+spwLsiMgloaEnABIudh+MYPCOa79fu59La5RjZPYJ6lcP8HZYx5yU79yOI9f4846Yz3n0DludBXMYElNRUZfKirbw8ew0KDOvchF5twylkReJMAXDWRCAiG4HnVPWDdK+vymh7VW2SS7EZExA27j/G09OiWbzlIFfVr8hL3SKoWd7GRJiCIzsDnC8CRotIcVV91+f1Brj6Q+BGDH2NqyBqTIGQnJLK+J82MWrBekKLFOLVO5pzx6VWJM4UPNlJBHG4YnNfiMg2VU27LWWqd3N7AEQkWVV/zIsgjbnQVu46woBpUazYGUvHplV5/ramVA6zInGmYMpOIlBV/UZE+gGTRaRpLt6HwJiAEp+UwlvfrmfcD5soV6Io79xzCTdHVPN3WMbkqWzPfVfV90WkC+5eAbfgmoOMKTCWbj1I/6lRbNx/nDsurcGQWxtTtoQViTMFX06LoPwN2CwinXGziY3J944nJPPq3LVM+nUL1csU54M+rbm6gd0b2wSPrO5HcB1QNt3LA3A3rX8xD2My5oL5cd1+np4eza4jcdzfNpynOjSkpBWJM0Emq3/xhXDNQK6gkMgzwN1AW9wIIWPyrcMnEhn+1WqmLt3BxZVK8tnDbYkMtyJxJjhldT+Cb0SkDTBbREbifvw/VdU1IjIOGCIiY33eEioib6tqvzyO2ZjzMjt6N0NnreTQiUT6XVeXx663InEmuJ2txMR6EWkHLABCVPUJb9U7wGBcFdIY7zVrLjIBbd/ReJ6dtZLZK/bQtHppJvW5jKbVrUicMdkpMbFHRDoCP4vIFlV9U1X3isiPwHJVnZD3YRpz7lSVqUt3MPyr1cQlpTCgYyMevKqOFYkzxpOtXjFV3SEid+Kqjb6nqnHAVOAaXD+CMQFp+8ETDJoRzU/rD3BZeDle7t6cupVK+TssYwJKTuYRLBKRJt5dyQA+AibmSVTGnKfUVOWDX7fwyty1CPBC16bc06a2FYkzJgPZSgQiMh4I9R6nX3fac1XtlUuxGXNONuw7xsBpUSzZeohrGlTixW7NqFHOisQZk5nsXhHcDTzqPRZgDOA7OkiAsbgJZ8b4RVJKKuN/3MToBespUaww/76zBd1aXWRF4ow5i+wmAlXVSWlPROTfvs+9195KX6ramAtlxc4j9J8axardsdwaUY1hXZpSKayYv8MyJl/Iambx+5y6KX0xEfmvz+oS6Z6n3cDemAsqPimF0d+sZ/yPmyhfsijj7r2Ujs2qnv2NxpiTsroiWODz+C7gG++xAHf6PE97rXvuhmZM1n7fcpABU6PYdOA4f4msyaBbGlOmRIi/wzIm3xHVs5/Ii0isqpb2eX5QVctntU1eiYyM1CVLluT1YUwAO5aQzCtz1vDBr1upUa44L9/enHb1K/o7LGMCmogsVdXIjNZlt49A0jUFlUz3HFyJif+qap9zitKYbPhu7T4GT49md2w8fa6sw5MdGlCiqBWJM+Z8ZPd/UD/A95r7jBvZZ/KaMbni0PFEXvhyFdOX7aRe5VJM/dsVXFq7nL/DMqZAyM7N66cC/1PVad7zV4ChqpogIoOBoqr6bB7HaYKUqvJ19B6e/XwFh08k8fj19eh3fT2KFbEiccbklqxGDVVU1QNAMaCwiNwOtAK6AikikgRcCoTL6QO1X1PV2LwM2gSHfbHxDJm5gnmr9hJxURk+fKANjavleTeUMUEnw0QgItWB9SKyCqgMVAS2AMeAFOAEkAhsBK4Ejvu83YaRmvOiqny2ZAcvfLWKxORUnr65EQ+0q0MRKxJnTJ7IMBGo6i4RKQ+0A0YBw4F1wHhgF/C6qp4QkWrA34FRqppwYUI2Bdm2GFckbuGGA7SuU56R3ZtTp2JJf4dlTIGW1Y1pEoBvRGQLLhHsAB4CmuOd9avqbhFJBhoCUXkerSmwUlKVib9s4bW5aylcSBh+WzN6tq5lReKMuQCyM2qoD3BcVU+Q8ciga3BNROdNROoAT9pdzoLL+r1H6T8timXbDnNdw0q82C2C6mWL+zssY4JGdm5Ms/8s6xfnXji0AawdIEgkJqcy7oeNjPl2AyWLFWb0XS3p0qK6FYkz5gILmJk4ItIBmA10zGBdX6AvQK1atS5wZCYvRO04TP+pUazZc5TOLaozrHMTKpSyInHG+ENAJAIRqYprfjqS0dmgqo7HdVQTGRlpo5LysbjEFEYtWMd7P22iUlgx3usVSfsmVfwdljFBLduJQESaqurKPIqjBZAgItcCVUWkmaquyKNjGT/5bVMMA6dFsSXmBHe3rsnTtzSmdKgViTPG33JyRRAtIsuAD3AzjfflVhCqOjftsYj0tiRQsByNT+Ll2WuYvGgbtcqX4OMH23BFPSsSZ0ygyEkiaIUrP/0I8KqIzAc+BGaqavz5BuLNTu4ONBWR+qq6/nz3afzv2zV7GTxjBXtj43mwXR3+dVNDihe18hDGBJJslaE+400irYAewB1AFWAq8IGq/pC74Z3JylDnDzHHEnj+y1XM+nMXDaqUYmT35rSqZUXijPGX3ChDfRpVXSYi63CTyP4F/BW435t89kL621ia4KGqfBG1m2Gfr+RofBL/uLE+j1xbj6JFrDyEMYEqR4lARMrhis7dDtwIxABTgAeBnUBvXLNRXVV9JndDNYFuz5F4hsyMZsHqfbSoWZZXujenYdUwf4dljDmLnIwaWgBcBSQA04EuwDd6etvSayKyAvgYsEQQJFJTlSm/b2fE16tJSk1lyK2N+euVdShs5SGMyRdyckWQgDvjn6mqcVlstwWXCEwQ2BpznAHTovht00HaXlyBl7tHULuCTQ43Jj/JdiJQ1Vuzud0a4NFzjsjkCympyvs/b+a1eWsJKVSIEbdHcNdlNa08hDH5UEDMLDb5y9o9rkjc8u2HubFxZYbfFkHVMqH+DssYc44sEZhsS0xO5e3vNjD2+w2UDg3hrbtb0al5NbsKMCafs0RgsuXP7YfpP3U56/Ye47aW1Xmmc1PKlyzq77CMMbnAEoHJUlxiCq/PW8t/f95MldKh/Ld3JNc3siJxxhQklghMpn7ZeICB06LZdvAE97SpxcCbGxFmReKMKXAsEZgzxMYnMeLr1fxv8XbCK5RgSt/LufziCv4OyxiTRywRmNPMX7WXITOj2X80gYevuZh/3tiA0BArEmdMQWaJwABw4FgCwz5fyZdRu2lUNYz3ekXSvEZZf4dljLkALBEEOVVl1p+7eO6LlRxPSOFf7Rvw8DV1rUicMUHEEkEQ23U4jiEzV/Dtmn20quWKxNWvYkXijAk2lgiCUGqq8vHibbw8ew0pqcoznZpw/xXhViTOmCBliSDIbD7gisQt3nyQdvUqMuL2CGqWL+HvsIwxfmSJIEgkp6Tyn4WbeWP+OooWKcQr3ZvTI7KGlYcwxlgiCAardsUyYFoU0TuPcFOTKrxwWzOqlLYiccYYxxJBAZaQnMKYbzfwzvcbKVsihLH3XMLNzaraVYAx5jSWCAqopVsPMWBaFBv2HeP2Sy5i6K1NKGdF4owxGbBEUMAcT0jmtXlrmfjLFqqXKc7Ev17GtQ0r+zssY0wAs0RQgPy0fj9PT49mx6E4erWtTf+OjShVzP6KjTFZs1+JAuDIiSRe/HoVny7ZwcUVS/Lpw21pXae8v8MyxuQTlgjyuTkr9jB01goOHk/kkWvr8vgN9a1InDEmRywR5FP7j7oicV9F76ZJtdK83/syml1Uxt9hGWPyIUsE+YyqMv2PnTz/5SriElN4qkND+l59MSGFrUicMebcWCLIR3YcOsHgGSv4Yd1+Lq1djpHdm1Ovcil/h2WMyecsEeQDqanKR4u2MnL2GhR4rktT7ru8NoWsSJwxJhdYIghwG/cfY+C0KH7fcoir6lfkpW5WJM4Yk7ssEQSopJRU3vtpE6MWrKd4SGFe69GC7pdcZOUhjDG5zhJBAFqx8wgDpkWxclcsNzerynNdm1I5zIrEGWPyhiWCABKflMJb365n3A+bKFeiKO/ccwk3R1Tzd1jGmALOEkGAWLLlIP2nRbFp/3F6XFqDIbc2oUyJEH+HZYwJApYI/OxYQjKvzlnDB79tpXqZ4nzQpzVXN6jk77CMMUHEEoEf/bBuP4OmR7PrSBz3tw3nqQ4NKWlF4owxF1hA/OqISCjQE9gPtAGeUdVU/0aVdw6fSOSFL1cz7Y8d1K1Uks8ebktkuBWJM8b4R0AkAqAjkKKqX4hILaAl8Id/Q8obs6N3M3TWSg6dSOTR6+rx6PX1rEicMcavAiURfA9U8B5XA7b4rhSRvkBfgFq1al3IuHLNvth4npm1kjkr99C0emkm9bmMptWtSJwxxv8CIhGo6mHgsIjUAzao6sF068cD4wEiIyP1wkd47lSVz5buYPiXq4hPTmVAx0Y8dFUdiliROGNMgAiIRAAgIlWBVqo60d+x5JbtB08waEY0P60/QOvw8ozoHkHdSlYkzhgTWAIiEXidxbeo6n9FJARooqrL/R3XuUpJVT74dQuvzl2LAC90bco9baxInDEmMAVEIgAeBK4SkeuB2sDf/RzPOduw7ygDpkWzdOshrmlQiZduj+CissX9HZYxxmQqIBKBqo4Bxvg7jvORlJLKuz9s5M1vNlCiWGH+fWcLurWyInHGmMAXEIkgv4vecYSnpi5nzZ6j3Nq8GsM6N6VSWDF/h2WMMdliieA8xCelMGrBet77aRPlSxbl3fsupUPTqv4Oyxi/S0pKYseOHcTHx/s7lKATGhpKjRo1CAnJfq0ySwTnaNGmGAZOj2bzgeP8JbImg25pbEXijPHs2LGDsLAwwsPDrXn0AlJVYmJi2LFjB3Xq1Mn2+ywR5NDR+CRembOWD3/bSs3yxZn8YBuurFfR32EZE1Di4+MtCfiBiFChQgX279+fo/dZIsiB79bsY/CMaHbHxtPnyjo82aEBJYraV2hMRiwJ+Me5fO/2K5YNB48n8sKXq5ixbCf1K5di2t+v4JJa5fwdljHG5ApLBFlQVb6K3s2zs1ZyJC6Jx6+vR7/r61GsiBWJMybYpKamUqhQwSwNY4kgE3tj4xkycwXzV+0l4qIyfPRgGxpXK+3vsIwx52DixIls2bKFYcOGnfb6NddcQ0JCQobvCQsLY/78+QAsWbKESZMmsWzZMlauXEnTpk1p1aoVb731FlOmTCE+Pp4iRYrw/PPPU7lyZVT1jCaa7du3M2nSJK699lr+85//ULVqVTp16sTNN9/M0aNHT9v2rbfeolWrVkyfPp1+/frRsGFDAB555BH69etH06ZNAWjSpAljx4497+/HEkE6qsqnS7Yz/KvVJCanMuiWRvS50orEGVMQ/fDDD9nabtq0adx7773885//5MUXX2TChAkcP36cV199lZo1a57cbtCgQfTu3ZuhQ4fSvHlzevTocXLd8OHDARg5ciQ//PADJUuWJC4ujtmzZ2d4zKVLl/Ljjz/yyiuvcPvtt1O4cGH+8Y9/sGzZMr7//nvuvffe8/jkp7NE4GNbzAkGTo/il40xtKlTnpHdmxNesaS/wzImX3vui5Ws2hWbq/tsUr00z3ZumuU2qkqnTp2YNWvWydc2bNjAmDFjGDVqVLaPlZyczJQpUxg8eDBTp05l/fr1PPnkk3Tt2pWVK1eelggAPvvsM959912aNGnC22+/zZYtW+jatSuVKrlb0Hbu3Jk//viDdu3a0aZNm0yPu3DhQoYPH87SpUt55513mDFjBkWLFiUlJYWlS5fSunVrGjRokO3PkRVLBLgice//vJnX562jcCHhxW7NuPuyWlYkzph87JtvvqFixYoUKXLqZ65evXosWbKEVatWMXjwYHbv3n1ynaqSnJx82kSsBg0acM011xATEwPAzJkzGTVqFBMnTuTgwdOq5QOwd+9efv/9d4YMGUJycjKlSpVi0aJFvPTSS7zxxhuAa85ZvHgxrVu3ZtmyZfTs2fPke6tUqQLAlVdeyciRI0lNTWXr1q189dVXfPjhhxQvXpyHHnqIHj16ULdu3Vz7roI+Eazbe5T+U6P4c/thrm9UmRe7NaNaGSsSZ0xuOduZe14ZNWoUQ4YMOeP15557jsGDBzNjxgwANm7cSN26dVmyZAljxoxh4sSJbN++/eSZ/n333UeXLl3YsGHDyR/3tWvX8uSTT57cR5pixYrRq1cvfvzxR/bs2QO4mb5jxozhwIEDAMyaNYsKFSoQHR3NoUOHmDp1KlWqVKF9+/bMnz+fw4cPU65cOWbNmsWIESO46KKLSEpKok+fPgAkJCTwySefMHr0aB544AH69et33t9V0CaCxORU3vl+I2O+W0+pYkUYfVdLurSobmOfjSkA/vjjD06cOMHll19+xrobbriBQYMGsWjRIlJSUnj55Zf5/PPPT65PTk7m7rvvZsyYMbRs2ZKxY8fy2GOPMW/ePAYOHMgTTzxBiRIlKF36zMEjR44cITk5mZYtWzJv3jxq167NjTfeSPHixfnqq68AeOedd+jZsycNGjSgQ4cOtGvX7mQ/wcaNG+nXrx9z586lSJEijB8/nmrVqlGpUiUmTJhAjx49KF26NIsWLaJUqVKkpubOrd2Dsgd0+fbDdBmzkDcWrKNjs2oseOIaura0SqHGFBSvvfbaGSOEfA0cOJBPPvmEQYMGMW7cuNPWFSlShI8++ogHH3yQQ4cOERYWBrgRO+3atSM8PJxHHnkkw/3Wrl2bYsWKsWnTJjp06EBERATly5endevWJ5ucxowZczKJzJgxgxtuuIHy5csDrumqfPnyLFiwgFtvvZX4+HgGDRoEuKar2bNno6o89dRTVKxYkYiIiPP6ntIEVSKIS0zhpa9X023szxw6kch7vSJ56+5WVChllUKNKUgmTpzI1VdfffJ5XFzcaSd63bp1o0iRIjzwwANUr179jG3Cw8MZNGgQQ4cOPW2/EyZMID4+nvbt21O2bFkmTpxIcnLyafML2rdvz5133knp0qXZtGkTI0aMYPPmzSfX16tX7+Tj9957jwEDBgCcHMY6ZMgQ/vOf/wDQunVrUlNTOXbsGDfddBNFixZl8uTJdO3a9WR/Qm4ImqahTfuP0Wfi72yJOcHdrWvx9C2NKB1qReKMKYiKFi0KwAcffMDzzz9PbGwsU6ZMOW2bxx9/nBo1avDbb7/Rq1cvjh07xsiRI0+uv/322+natevJ55s3b2bOnDlMnjwZgIMHD9K1a1cSEhKYMGECy5cv56WXXjr5I+7rvvvuY/v27bRr1+601+fNm8e7777LuHHjKFfOVSto2rQpU6ZMYfLkybzzzjsAdOzY8eR79uzZQ8WKFZkxYwZ9+vQ52XdwPkQ1X90LnsjISF2yZEmO3xeflMLfPlpK36sv5oq6ViTOmLy0evVqGjdu7O8wADKc3BVocjvGjL5/EVmqqpEZbR80VwShIYWZ+NfW/g7DGHOBBXoSAP/HGFR9BMYYY85kicAYkyfyW7NzQXEu37slAmNMrgsNDSUmJsaSwQWWdoey0NDQHL0vaPoIjDEXTo0aNdixY0eO75Rlzl/aPYtzwhKBMSbXhYSE5Oieuca/rGnIGGOCnCUCY4wJcpYIjDEmyOW7mcUish/Yeh67qAgcyKVw8jv7Lk5n38fp7Ps4pSB8F7VVtVJGK/JdIjhfIrIks2nWwca+i9PZ93E6+z5OKejfhTUNGWNMkLNEYIwxQS4YE8F4fwcQQOy7OJ19H6ez7+OUAv1dBF0fgTHGmNMF4xWBMcYYH5YIjDEmyFkiMMaYIBdUiUBE/k9E7heRx/0diz+JSKiI9BGRziIyXESC6t9BZkSkjoi87e84AoGIXCciHUTkfREp7+94/EVEqopIXxHpJCL9Cur/lQL5oTIiIvWBaqo6CSgnIo38HZMfdQRSVPULYDfQ0r/hBIw2QEl/B+FvIlIJaKCqc4G/q+pBf8fkR/cAU1T1S1xFg5b+DSdvBE0iAK4DFnuPlwPX+DEWf/seWOg9rgZs8VskAUJEOgCz/R1HgOgIhIvIY8BrIhLMyfEX4FURKQNcDKzzczx5IpgSQUUg1nt8DAjay11VPayqG0WkHrAhyM/4EJGqwHFVPeLvWAJEdWCbqr4FTAO6+zkef1oC7AVmAKGqeszP8eSJYEoEMUCY9zjMex60vB+/Vqo60d+xBIAWQBERuRaoKiLN/BuO38UBO73HO3BXjcHqb8CbwA1AIxFp5ed48kQwJYLvgMu8xy1wzSNBSURCgVtU9TMRCRGRFv6OyZ9Uda6qfq+q3wN7VHWFv2Pys9+BS73HVYG1fozF38oAMepm3s4Aavo5njwRNIlAVdcBe0XkfuCg9zxYPQh0EJGPgG+BFD/H43fi3AE09QYWBC1V/RXA+z4aA1/4NyK/mgT0FpFOQCMKaD+SlZgwxpggFzRXBMYYYzJmicAYY4KcJQJjjAlylghMgSIiYSLSRkQkk/WVRKSEH+KqmFlMZ3tfLsdR9lziMAWbJQJT0NwGfKqZj4L4DLgzOzsSkSdF5J2c/nCKSEkRiRCR7l6tmsLAz8DgHO7nIWCXN78BESkiIkWy2L6zdyxEJDltPoRXX6uUt9lPwGM5icMUfJYITEHTCZh+vjsRkW7AK8B6oIJ3Rn/G4rP9MyLyp4gcwM1cXwy8gJuz0hMIB/6bg+M/DYwCennzGwCGAD+JSJ0Mti8GvAwMT/f6g8BrQEURCQOaAAuyG4cJDpmeXRiTn4jI+7gJg/WAAyLS3lt1Paef8BQFyngzq8EV39ufbl+3AlMAAV73lsyOW1xV44H5wGqgBvB3oKGqqogUB1bg5mp8ncnFxV9Uda23v4rAGFwCaZM2uU1EigLjcBO9/hSRPqo6LW0HqpogIj2BN7ykAO7/96NAd1XdIiJ3AoeAEyIS7nP82GAvMxLsbB6BKRBEpCVwB+7s+2EgFffjGwLMy+KtW1U13Gc/vYD3gFdVdUgmx7rL2+YtVR2Ubt2NwBhVbeQ9/zfwANAHSEi3q05ALyBcVfd5JY534mbzHgIUCPUWgOO4q41juCuMYar6knecW4DWPvt+Bpc4YrzvYSzwLtABSPS2KeEdY7Sq9s/iOzIFnDUNmYAjIj1FRL1mjbTXqorIUREZm9F7VPVPXLPHJOBfQH2vSSUJ2KuqoqoC/AD81Xt8s8/+Q0VkFO4H/nFgodfcIz7blBSRMd4xnvZNAiKyQUQUd2XQ0Iv/MK49/jHv7D0K+AjXTv8NcCvwiqru8z5DKtADdxXTHpckjgHtVLWwqpZW1eqq2gC4Cvjc5ysowqmk0QN3NVMMlwhDgfpAF+BuVS2rqmVxs8pftyRgUFVbbAm4BfdDuR1X8RFgAq4AWulMtq8AxOPOlOcAf/NevxZXPyhtu++B3t7jjsAW7/EQXInhSO95W2A/MBMoDdyHO1tfA1yawfHDgLLAc95+ynrva+OzTTjuDLys9zzC5/MN89ady3K5t49CuH6CdbirgHXAw9666d62T/jEsxLo6++/a1v8v9gVgQlU/YDKwGNes09voJ+qxmay/Z244mhlcT/KNUSkcQ6ONwK4RFWXwMl6O1fhEskuXLv9KKC5qi5N/2ZVPaqqh3Fn48VwnbyxqrpIRJ7wavunCRGRr3FNs/Heay95cYfh7pWxF9fZnPZaOHAFcBD4i8/rYcBir3DgL8CNQDvcj/5zwNNeTam6wNdA2kiiorirhDU5+I5MAWWJwAQkVV2DG+0yAHgLmKGqs7J4S3mgCu5qIBLXJJM2Ske88fNlcU0oJbzHJ2+4oqop6tWaF5HqIjIUmIsryfwiUFtVX1XVtPb1M4hIG1yRthDgCRF5yVvVj1Pt/OCaq8BdBaQdP9E7/m24H+wRqvoMrh3/WU51OPcA3sENRU1R1WPqmpSeA37FJYFqwFRcM9jl3p9tcE1akd4hL8H9/z8jqZkg5O9LEltsyWzBne3G485um+XgfembhrJqVtnibdcEl3QW4n5ws9Usk+64n+KGja7BNfvswHUIK1Abn6YhXH37VKCJ994IXHPYXtyNYNoA43EdxJ/gRkMVwSWaJsAy3G1Gn8aNVCqOSzZNcYmmNaf6DIp6x7jIO34LXHJb6O+/Y1sCY7ErAhPIHgVO4PoKzqdD86ydxbjx931xfQgrgdFAnSyWu30P4F0N3IYbnYOqRuOaXm4CFqnqVt/tVfUbIBp4wnvpBVwTVDvcldAXuKuRtrj+iba44amTgQ24obKvAP+H+2Gf7W2/ApcwFnnP43BXNqjqTu/z98M1tX2a3S/QFGw2j8AEJBFpgmsSeQo3BHKyiHykqhkOBfVG91TGnfVWAG7wbsU5P5uH7Klee72ItAMOq+qWLOILT/fSCOBj3FXAyc1wI3+eymQ343Dj/gfixvqnePu+E/gTd/Z/F27o6RHvGJNwN1CPUNU3RGSMqiYBX3nzH94HGqtqjLev7zh9Atko3A1Wjnj7MsauCEzg8cokvI+b1fuOqn6Ma/8el0WdoE24ppJZQANcO/lxzhy7nyE91Wl7rtZxZgmJe3DJ4ONM3vMRrhnnAZ8k0Ak372AV8Bvus/TG3RTlJ9zoqYVAYxEp7CWBNF1wTT99RKS0iAzB3VFrtM82e7w/N+KuFoyxRGAC0pO4Nu7/U9Vk77XHcW3swzN5z7VACVWtiUsaH6nqs966QiJSQ0Rq4Eb0lPMe52ZBt0e9phdfy4EnVfV4Rm9Q1aO4u8V95PNyNVxyGIC7wrkf16k9G5ccSgItVHVAWvLw2d/DuLkJN+KamV4AHtJTneANcE1OP+ESzAfeJDYT5GxmsSlwRGQOMFNVx3kF277LYvPTZhZ77/8eN4TzrLy+Bt/3njaz2HutKa4zNxJ4G5ewMrxSEZGSQCtcH8CNuMllB3AlL8ak72tI995yuP6E/sBmXGd0G1yfwAZcp/M63Oziq4EvcUnzvqz2awo+OxswweBkZ7Hvwumdxem9jjs7z2zpnoPj98LdEP4tYGwWSSAU90M9AzfZ7XfgKlWtqapPnSUJ/A/Y5x3rCVW9SlWvwQ03jcf1EywEblHVOFWd6x2jEe5qywQxuyIwBY6IVAOOa+aTzy44r3x0qrox/1ltF+Y1GeV0/1fhEt66DNYVArqq6owM1tUADqrqiZwe0xQclgiMMSbIWdOQMcYEOUsExhgT5CwRGGNMkLNEYIwxQc4SgTHGBDlLBMYYE+T+H+HEaMsjM1uKAAAAAElFTkSuQmCC\n", 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\n", 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" ] @@ -1149,159 +383,14 @@ ], "source": [ "#局部字体的修改方法1\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib.font_manager as fontmg\n", - "\n", "x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n", "plt.plot(x, label='小示例图标签')\n", "\n", - "# 直接用字体的名字。\n", + "# 直接用字体的名字\n", "plt.xlabel('x 轴名称参数', fontproperties='Microsoft YaHei', fontsize=16) # 设置x轴名称，采用微软雅黑字体\n", "plt.ylabel('y 轴名称参数', fontproperties='Microsoft YaHei', fontsize=14) # 设置Y轴名称\n", "plt.title('坐标系的标题', fontproperties='Microsoft YaHei', fontsize=20) # 设置坐标系标题的字体\n", - "plt.legend(loc='lower right', prop={\"family\": 'Microsoft YaHei'}, fontsize=10) # 小示例图的字体设置" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "#局部字体的修改方法2\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib.font_manager as fontmg\n", - "\n", - "x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n", - "plt.plot(x, label='小示例图标签')\n", - "#fname为你系统中的字体库路径\n", - "my_font1 = fontmg.FontProperties(fname=r'C:\\Windows\\Fonts\\simhei.ttf') # 读取系统中的 黑体 字体。\n", - "my_font2 = fontmg.FontProperties(fname=r'C:\\Windows\\Fonts\\simkai.ttf') # 读取系统中的 楷体 字体。\n", - "# fontproperties 设置中文显示，fontsize 设置字体大小\n", - "plt.xlabel('x 轴名称参数', fontproperties=my_font1, fontsize=16) # 设置x轴名称\n", - "plt.ylabel('y 轴名称参数', fontproperties=my_font1, fontsize=14) # 设置Y轴名称\n", - "plt.title('坐标系的标题', fontproperties=my_font2, fontsize=20) # 标题的字体设置\n", - "plt.legend(loc='lower right', prop=my_font1, fontsize=10) # 小示例图的字体设置\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 7.数学表达式\n", - "在文本标签中使用数学表达式。有关MathText的概述，请参见 [写数学表达式](https://matplotlib.org/tutorials/text/mathtext.html#sphx-glr-tutorials-text-mathtext-py),但由于数学表达式的练习想必我们都在markdown语法和latex语法中多少有接触，故在此不继续展开，愿意深入学习的可以参看官方文档.下面是一个官方案例，供参考了解。" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "t = np.arange(0.0, 2.0, 0.01)\n", - "s = np.sin(2*np.pi*t)\n", - "\n", - "plt.plot(t, s)\n", - "plt.title(r'$\\alpha_i > \\beta_i$', fontsize=20)\n", - "plt.text(1, -0.6, r'$\\sum_{i=0}^\\infty x_i$', fontsize=20)\n", - "plt.text(0.6, 0.6, r'$\\mathcal{A}\\mathrm{sin}(2 \\omega t)$',\n", - " fontsize=20)\n", - "plt.xlabel('time (s)')\n", - "plt.ylabel('volts (mV)')\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "#这是对前七节学习内容的总结案例\n", - "import matplotlib\n", - "import matplotlib.pyplot as plt\n", - "\n", - "fig = plt.figure()\n", - "ax = fig.add_subplot(111)\n", - "fig.subplots_adjust(top=0.85)\n", - "\n", - "# 分别在figure和subplot上设置title\n", - "fig.suptitle('bold figure suptitle', fontsize=14, fontweight='bold')\n", - "ax.set_title('axes title')\n", - "\n", - "ax.set_xlabel('xlabel')\n", - "ax.set_ylabel('ylabel')\n", - "\n", - "# 设置x-axis和y-axis的范围都是[0, 10]\n", - "ax.axis([0, 10, 0, 10])\n", - "\n", - "ax.text(3, 8, 'boxed italics text in data coords', style='italic',\n", - " bbox={'facecolor': 'red', 'alpha': 0.5, 'pad': 10})\n", - "\n", - "ax.text(2, 6, r'an equation: $E=mc^2$', fontsize=15)\n", - "font1 = {'family': 'Times New Roman',\n", - " 'color': 'purple',\n", - " 'weight': 'normal',\n", - " 'size': 10,\n", - " }\n", - "ax.text(3, 2, 'unicode: Institut für Festkörperphysik',fontdict=font1)\n", - "ax.text(0.95, 0.01, 'colored text in axes coords',\n", - " verticalalignment='bottom', horizontalalignment='right',\n", - " transform=ax.transAxes,\n", - " color='green', fontsize=15)\n", - "\n", - "plt.show()" + "plt.legend(loc='lower right', prop={\"family\": 'Microsoft YaHei'}, fontsize=10) ; # 小示例图的字体设置" ] }, { @@ -1716,7 +805,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 三、[legend](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.legend.html#matplotlib.pyplot.legend)（图例）" + "## 三、legend（图例）" ] }, { @@ -1878,10 +967,17 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 作业\n", - "1.尝试在一张图中运用所讲过的功能，对title、text、xlable、ylabel、数学表达式、tick and ticklabel、legend进行详细的设计. \n", - "2.阅读你可能用到文献或者相关书籍，思考自己如何才能通过学过的例子将自己认为比较好看的图给复现出来." + "## 思考题\n", + "- 请尝试使用两种模仿画出下面的图表(重点是柱状图上的标签)，本文学习的text方法和matplotlib自带的柱状图标签方法bar_label\n", + "![](https://img-blog.csdnimg.cn/99bc6e007eb34fc09015589d56c6eafc.png)" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -1901,7 +997,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.0" + "version": "3.9.4" }, "toc": { "base_numbering": 1,