Basis for subplot2grid, ticker, enumerate, seaborn, plt/ax/fig

Posted on 2020-08-20 In 学习笔记 Word count in article: 1.9k Reading time ≈ 7 mins.

一些在读别人代码的时候发现自己还没有掌握的东西。

matplotlib.ticker

定位

Tick locating

The Locator class is the base class for all tick locators. The locators handle autoscaling of the view limits based on the data limits, and the choosing of tick locations. A useful semi-automatic tick locator is MultipleLocator. It is initialized with a base, e.g., 10, and it picks axis limits and ticks that are multiples of that base.

The Locator subclasses defined here are

AutoLocator

MaxNLocator with simple defaults. This is the default tick locator for most plotting.
MaxNLocator

Finds up to a max number of intervals with ticks at nice locations.
LinearLocator

Space ticks evenly from min to max.
LogLocator

Space ticks logarithmically from min to max.
MultipleLocator

Ticks and range are a multiple of base; either integer or float.
FixedLocator

Tick locations are fixed.
IndexLocator

Locator for index plots (e.g., where x = range(len(y))).
NullLocator

No ticks.
SymmetricalLogLocator

Locator for use with with the symlog norm; works like LogLocator for the part outside of the threshold and adds 0 if inside the limits.
LogitLocator

Locator for logit scaling.
OldAutoLocator

Choose a MultipleLocator and dynamically reassign it for intelligent ticking during navigation.
AutoMinorLocator

Locator for minor ticks when the axis is linear and the major ticks are uniformly spaced. Subdivides the major tick interval into a specified number of minor intervals, defaulting to 4 or 5 depending on the major interval.

There are a number of locators specialized for date locations - see the dates module.

You can define your own locator by deriving from Locator. You must override the __call__ method, which returns a sequence of locations, and you will probably want to override the autoscale method to set the view limits from the data limits.

If you want to override the default locator, use one of the above or a custom locator and pass it to the x or y axis instance. The relevant methods are:

ax.xaxis.set_major_locator(xmajor_locator)
ax.xaxis.set_minor_locator(xminor_locator)
ax.yaxis.set_major_locator(ymajor_locator)
ax.yaxis.set_minor_locator(yminor_locator)

The default minor locator is NullLocator, i.e., no minor ticks on by default.

格式

Tick formatting

Tick formatting is controlled by classes derived from Formatter. The formatter operates on a single tick value and returns a string to the axis.

NullFormatter

No labels on the ticks.
IndexFormatter

Set the strings from a list of labels.
FixedFormatter

Set the strings manually for the labels.
FuncFormatter

User defined function sets the labels.
StrMethodFormatter

Use string format method.
FormatStrFormatter

Use an old-style sprintf format string.
ScalarFormatter

Default formatter for scalars: autopick the format string.
LogFormatter

Formatter for log axes.
LogFormatterExponent

Format values for log axis using exponent = log_base(value).
LogFormatterMathtext

Format values for log axis using exponent = log_base(value) using Math text.
LogFormatterSciNotation

Format values for log axis using scientific notation.
LogitFormatter

Probability formatter.
EngFormatter

Format labels in engineering notation
PercentFormatter

Format labels as a percentage

You can derive your own formatter from the Formatter base class by simply overriding the __call__ method. The formatter class has access to the axis view and data limits.

To control the major and minor tick label formats, use one of the following methods:

ax.xaxis.set_major_formatter(xmajor_formatter)
ax.xaxis.set_minor_formatter(xminor_formatter)
ax.yaxis.set_major_formatter(ymajor_formatter)
ax.yaxis.set_minor_formatter(yminor_formatter)

See Major and minor ticks for an example of setting major and minor ticks. See the matplotlib.dates module for more information and examples of using date locators and formatters.

参考

https://matplotlib.org/3.1.1/api/ticker_api.html

enumerate

描述

enumerate() 函数用于将一个可遍历的数据对象(如列表、元组或字符串)组合为一个索引序列，同时列出数据和数据下标，一般用在 for 循环当中。

Python 2.3. 以上版本可用，2.6 添加 start 参数。

语法

以下是 enumerate() 方法的语法:

1	enumerate(sequence, [start=0])

参数

sequence -- 一个序列、迭代器或其他支持迭代对象。
start -- 下标起始位置。

返回值

返回 enumerate(枚举) 对象。

实例

以下展示了使用 enumerate() 方法的实例：

>>>seasons = ['Spring', 'Summer', 'Fall', 'Winter']
>>> list(enumerate(seasons))
[(0, 'Spring'), (1, 'Summer'), (2, 'Fall'), (3, 'Winter')]
>>> list(enumerate(seasons, start=1))       # 下标从 1 开始
[(1, 'Spring'), (2, 'Summer'), (3, 'Fall'), (4, 'Winter')]

普通的 for 循环

>>>i = 0
>>> seq = ['one', 'two', 'three']
>>> for element in seq:
...     print i, seq[i]
...     i +=1
... 
0 one
1 two
2 three

for 循环使用 enumerate

>>>seq = ['one', 'two', 'three']
>>> for i, element in enumerate(seq):
...     print i, element
... 
0 one参考
1 two
2 three

参考

https://www.runoob.com/python/python-func-enumerate.html

subplot2grid

这个可以自定义子图的位置，并且可以跨越原来大小。

原文https://wizardforcel.gitbooks.io/matplotlib-user-guide/content/3.3.html

GridSpec

指定子图将放置的网格的几何位置。需要设置网格的行数和列数。子图布局参数（例如，左，右等）可以选择性调整。

1	SubplotSpec

指定在给定GridSpec中的子图位置。

1	subplot2grid

一个辅助函数，类似于pyplot.subplot，但是使用基于 0 的索引，并可使子图跨越多个格子。

subplot2grid基本示例

要使用subplot2grid，你需要提供网格的几何形状和网格中子图的位置。对于简单的单网格子图：

1	ax = plt.subplot2grid((2,2),(0, 0))

等价于：

ax = plt.subplot(2,2,1)
       nRow=2, nCol=2
(0,0) +-------+-------+
      |   1   |       |
      +-------+-------+
      |       |       |
      +-------+-------+

要注意不像subplot，gridspec中的下标从 0 开始。

为了创建跨越多个格子的子图，

1 2	ax2 = plt.subplot2grid((3,3), (1, 0), colspan=2) ax3 = plt.subplot2grid((3,3), (1, 2), rowspan=2)

例如，下列命令：

ax1 = plt.subplot2grid((3,3), (0,0), colspan=3)
ax2 = plt.subplot2grid((3,3), (1,0), colspan=2)
ax3 = plt.subplot2grid((3,3), (1, 2), rowspan=2)
ax4 = plt.subplot2grid((3,3), (2, 0))
ax5 = plt.subplot2grid((3,3), (2, 1))

会创建：

GridSpec和SubplotSpec

你可以显式创建GridSpec并用它们创建子图。

例如，

1	ax = plt.subplot2grid((2,2),(0, 0))

等价于：

1
2
3

import matplotlib.gridspec as gridspec
gs = gridspec.GridSpec(2, 2)
ax = plt.subplot(gs[0, 0])

gridspec示例提供类似数组（一维或二维）的索引，并返回SubplotSpec实例。例如，使用切片来返回跨越多个格子的SubplotSpec实例。

上面的例子会变成：

gs = gridspec.GridSpec(3, 3)
ax1 = plt.subplot(gs[0, :])
ax2 = plt.subplot(gs[1,:-1])
ax3 = plt.subplot(gs[1:, -1])
ax4 = plt.subplot(gs[-1,0])
ax5 = plt.subplot(gs[-1,-2])

调整 GridSpec布局

在显式使用GridSpec的时候，你可以调整子图的布局参数，子图由gridspec创建。

1 2	gs1 = gridspec.GridSpec(3, 3) gs1.update(left=0.05, right=0.48, wspace=0.05)

这类似于subplots_adjust，但是他只影响从给定GridSpec创建的子图。

下面的代码

gs1 = gridspec.GridSpec(3, 3)
gs1.update(left=0.05, right=0.48, wspace=0.05)
ax1 = plt.subplot(gs1[:-1, :])
ax2 = plt.subplot(gs1[-1, :-1])
ax3 = plt.subplot(gs1[-1, -1])

gs2 = gridspec.GridSpec(3, 3)
gs2.update(left=0.55, right=0.98, hspace=0.05)
ax4 = plt.subplot(gs2[:, :-1])
ax5 = plt.subplot(gs2[:-1, -1])
ax6 = plt.subplot(gs2[-1, -1])

会产生

使用 SubplotSpec创建 GridSpec

你可以从SubplotSpec创建GridSpec，其中它的布局参数设置为给定SubplotSpec的位置的布局参数。

gs0 = gridspec.GridSpec(1, 2)

gs00 = gridspec.GridSpecFromSubplotSpec(3, 3, subplot_spec=gs0[0])
gs01 = gridspec.GridSpecFromSubplotSpec(3, 3, subplot_spec=gs0[1])

使用SubplotSpec创建复杂嵌套的GridSpec

这里有一个更复杂的嵌套gridspec的示例，我们通过在每个 3x3 内部网格中隐藏适当的脊线，在 4x4 外部网格的每个单元格周围放置一个框。

网格尺寸可变的GridSpec

通常，GridSpec创建大小相等的网格。你可以调整行和列的相对高度和宽度，要注意绝对高度值是无意义的，有意义的只是它们的相对比值。

gs = gridspec.GridSpec(2, 2,
                       width_ratios=[1,2],
                       height_ratios=[4,1]
                       )

ax1 = plt.subplot(gs[0])
ax2 = plt.subplot(gs[1])
ax3 = plt.subplot(gs[2])
ax4 = plt.subplot(gs[3])

seaborn

seaborn是对matplotlib进一步的封装，简单点来说就是更简单了。

官网https://seaborn.pydata.org/

我这里放几个我感觉用得上的代码。

lineplot

seaborn.lineplot(x=None, y=None, hue=None, size=None, style=None, data=None, palette=None, hue_order=None, hue_norm=None, sizes=None, size_order=None, size_norm=None, dashes=True, markers=None, style_order=None, units=None, estimator='mean', ci=95, n_boot=1000, seed=None, sort=True, err_style='band', err_kws=None, legend='brief', ax=None, **kwargs)

https://seaborn.pydata.org/generated/seaborn.lineplot.html

heatmap

seaborn.heatmap(data, vmin=None, vmax=None, cmap=None, center=None, robust=False, annot=None, fmt='.2g', annot_kws=None, linewidths=0, linecolor='white', cbar=True, cbar_kws=None, cbar_ax=None, square=False, xticklabels='auto', yticklabels='auto', mask=None, ax=None, **kwargs)

https://zhuanlan.zhihu.com/p/35494575

implot

eaborn.lmplot(x, y, data, hue=None, col=None, row=None, palette=None, col_wrap=None, size=5, aspect=1, markers='o', sharex=True, sharey=True, hue_order=None, col_order=None, row_order=None, legend=True, legend_out=True, x_estimator=None, x_bins=None, x_ci='ci', scatter=True, fit_reg=True, ci=95, n_boot=1000, units=None, order=1, logistic=False, lowess=False, robust=False, logx=False, x_partial=None, y_partial=None, truncate=False, x_jitter=None, y_jitter=None, scatter_kws=None, line_kws=None)

https://zhuanlan.zhihu.com/p/25909753

常见统计图片基本都可以在这里面看到

https://seaborn.pydata.org/examples/index.html

subplot_adjust

matplotlib.pyplot.subplots_adjust(*args, **kwargs)
subplots_adjust(left=None, bottom=None, right=None, top=None,wspace=None, hspace=None)

left  = 0.125  # 子图(subplot)距画板(figure)左边的距离
right = 0.9    # 右边
bottom = 0.1   # 底部
top = 0.9      # 顶部
wspace = 0.2   # 子图水平间距
hspace = 0.2   # 子图垂直间距

plt/ax/fig

尽量避免直接使用plt

https://zhuanlan.zhihu.com/p/93423829