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matplotlib & pandas

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# 按钮
构建一个简单的按钮GUI来修改正弦波。
``下一个``和``上一个``按钮小部件有助于以新频率显示波形。
![按钮示例](https://matplotlib.org/_images/sphx_glr_buttons_001.png)
```python
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.widgets import Button
freqs = np.arange(2, 20, 3)
fig, ax = plt.subplots()
plt.subplots_adjust(bottom=0.2)
t = np.arange(0.0, 1.0, 0.001)
s = np.sin(2*np.pi*freqs[0]*t)
l, = plt.plot(t, s, lw=2)
class Index(object):
ind = 0
def next(self, event):
self.ind += 1
i = self.ind % len(freqs)
ydata = np.sin(2*np.pi*freqs[i]*t)
l.set_ydata(ydata)
plt.draw()
def prev(self, event):
self.ind -= 1
i = self.ind % len(freqs)
ydata = np.sin(2*np.pi*freqs[i]*t)
l.set_ydata(ydata)
plt.draw()
callback = Index()
axprev = plt.axes([0.7, 0.05, 0.1, 0.075])
axnext = plt.axes([0.81, 0.05, 0.1, 0.075])
bnext = Button(axnext, 'Next')
bnext.on_clicked(callback.next)
bprev = Button(axprev, 'Previous')
bprev.on_clicked(callback.prev)
plt.show()
```
## 下载这个示例
- [下载python源码: buttons.py](https://matplotlib.org/_downloads/buttons.py)
- [下载Jupyter notebook: buttons.ipynb](https://matplotlib.org/_downloads/buttons.ipynb)

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# 复选按钮
使用复选按钮打开和关闭视觉元素。
该程序显示了“检查按钮”的使用，类似于复选框。 显示了3种不同的正弦波，我们可以选择使用复选按钮显示哪些波形。
![复选按钮示例](https://matplotlib.org/_images/sphx_glr_check_buttons_001.png)
```python
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.widgets import CheckButtons
t = np.arange(0.0, 2.0, 0.01)
s0 = np.sin(2*np.pi*t)
s1 = np.sin(4*np.pi*t)
s2 = np.sin(6*np.pi*t)
fig, ax = plt.subplots()
l0, = ax.plot(t, s0, visible=False, lw=2, color='k', label='2 Hz')
l1, = ax.plot(t, s1, lw=2, color='r', label='4 Hz')
l2, = ax.plot(t, s2, lw=2, color='g', label='6 Hz')
plt.subplots_adjust(left=0.2)
lines = [l0, l1, l2]
# Make checkbuttons with all plotted lines with correct visibility
rax = plt.axes([0.05, 0.4, 0.1, 0.15])
labels = [str(line.get_label()) for line in lines]
visibility = [line.get_visible() for line in lines]
check = CheckButtons(rax, labels, visibility)
def func(label):
index = labels.index(label)
lines[index].set_visible(not lines[index].get_visible())
plt.draw()
check.on_clicked(func)
plt.show()
```
## 下载这个示例
- [下载python源码: check_buttons.py](https://matplotlib.org/_downloads/check_buttons.py)
- [下载Jupyter notebook: check_buttons.ipynb](https://matplotlib.org/_downloads/check_buttons.ipynb)

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# 光标
![光标示例](https://matplotlib.org/_images/sphx_glr_cursor_001.png)
```python
from matplotlib.widgets import Cursor
import numpy as np
import matplotlib.pyplot as plt
# Fixing random state for reproducibility
np.random.seed(19680801)
fig = plt.figure(figsize=(8, 6))
ax = fig.add_subplot(111, facecolor='#FFFFCC')
x, y = 4*(np.random.rand(2, 100) - .5)
ax.plot(x, y, 'o')
ax.set_xlim(-2, 2)
ax.set_ylim(-2, 2)
# Set useblit=True on most backends for enhanced performance.
cursor = Cursor(ax, useblit=True, color='red', linewidth=2)
plt.show()
```
## 下载这个示例
- [下载python源码: cursor.py](https://matplotlib.org/_downloads/cursor.py)
- [下载Jupyter notebook: cursor.ipynb](https://matplotlib.org/_downloads/cursor.ipynb)

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# 小部件
如何在matplotlib中编写原始的、但与GUI无关的小部件的示例

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# 套索选择器演示
使用套索工具以交互方式选择数据点。
此示例绘制散点图。 然后，您可以通过在图表上的点周围绘制套索循环来选择几个点。 要绘制，只需单击图形，按住，然后将其拖动到需要选择的点周围。
```python
import numpy as np
from matplotlib.widgets import LassoSelector
from matplotlib.path import Path
class SelectFromCollection(object):
"""Select indices from a matplotlib collection using `LassoSelector`.
Selected indices are saved in the `ind` attribute. This tool fades out the
points that are not part of the selection (i.e., reduces their alpha
values). If your collection has alpha < 1, this tool will permanently
alter the alpha values.
Note that this tool selects collection objects based on their *origins*
(i.e., `offsets`).
Parameters
----------
ax : :class:`~matplotlib.axes.Axes`
Axes to interact with.
collection : :class:`matplotlib.collections.Collection` subclass
Collection you want to select from.
alpha_other : 0 <= float <= 1
To highlight a selection, this tool sets all selected points to an
alpha value of 1 and non-selected points to `alpha_other`.
"""
def __init__(self, ax, collection, alpha_other=0.3):
self.canvas = ax.figure.canvas
self.collection = collection
self.alpha_other = alpha_other
self.xys = collection.get_offsets()
self.Npts = len(self.xys)
# Ensure that we have separate colors for each object
self.fc = collection.get_facecolors()
if len(self.fc) == 0:
raise ValueError('Collection must have a facecolor')
elif len(self.fc) == 1:
self.fc = np.tile(self.fc, (self.Npts, 1))
self.lasso = LassoSelector(ax, onselect=self.onselect)
self.ind = []
def onselect(self, verts):
path = Path(verts)
self.ind = np.nonzero(path.contains_points(self.xys))[0]
self.fc[:, -1] = self.alpha_other
self.fc[self.ind, -1] = 1
self.collection.set_facecolors(self.fc)
self.canvas.draw_idle()
def disconnect(self):
self.lasso.disconnect_events()
self.fc[:, -1] = 1
self.collection.set_facecolors(self.fc)
self.canvas.draw_idle()
if __name__ == '__main__':
import matplotlib.pyplot as plt
# Fixing random state for reproducibility
np.random.seed(19680801)
data = np.random.rand(100, 2)
subplot_kw = dict(xlim=(0, 1), ylim=(0, 1), autoscale_on=False)
fig, ax = plt.subplots(subplot_kw=subplot_kw)
pts = ax.scatter(data[:, 0], data[:, 1], s=80)
selector = SelectFromCollection(ax, pts)
def accept(event):
if event.key == "enter":
print("Selected points:")
print(selector.xys[selector.ind])
selector.disconnect()
ax.set_title("")
fig.canvas.draw()
fig.canvas.mpl_connect("key_press_event", accept)
ax.set_title("Press enter to accept selected points.")
plt.show()
```
## 下载这个示例
- [下载python源码: lasso_selector_demo_sgskip.py](https://matplotlib.org/_downloads/lasso_selector_demo_sgskip.py)
- [下载Jupyter notebook: lasso_selector_demo_sgskip.ipynb](https://matplotlib.org/_downloads/lasso_selector_demo_sgskip.ipynb)

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# 菜单
![菜单示例](https://matplotlib.org/_images/sphx_glr_menu_001.png)
输出：
```python
100 371 91 29
100 342 91 29
100 313 91 29
100 284 91 29
100 255 91 29
```
```python
import numpy as np
import matplotlib.colors as colors
import matplotlib.patches as patches
import matplotlib.mathtext as mathtext
import matplotlib.pyplot as plt
import matplotlib.artist as artist
import matplotlib.image as image
class ItemProperties(object):
def __init__(self, fontsize=14, labelcolor='black', bgcolor='yellow',
alpha=1.0):
self.fontsize = fontsize
self.labelcolor = labelcolor
self.bgcolor = bgcolor
self.alpha = alpha
self.labelcolor_rgb = colors.to_rgba(labelcolor)[:3]
self.bgcolor_rgb = colors.to_rgba(bgcolor)[:3]
class MenuItem(artist.Artist):
parser = mathtext.MathTextParser("Bitmap")
padx = 5
pady = 5
def __init__(self, fig, labelstr, props=None, hoverprops=None,
on_select=None):
artist.Artist.__init__(self)
self.set_figure(fig)
self.labelstr = labelstr
if props is None:
props = ItemProperties()
if hoverprops is None:
hoverprops = ItemProperties()
self.props = props
self.hoverprops = hoverprops
self.on_select = on_select
x, self.depth = self.parser.to_mask(
labelstr, fontsize=props.fontsize, dpi=fig.dpi)
if props.fontsize != hoverprops.fontsize:
raise NotImplementedError(
'support for different font sizes not implemented')
self.labelwidth = x.shape[1]
self.labelheight = x.shape[0]
self.labelArray = np.zeros((x.shape[0], x.shape[1], 4))
self.labelArray[:, :, -1] = x/255.
self.label = image.FigureImage(fig, origin='upper')
self.label.set_array(self.labelArray)
# we'll update these later
self.rect = patches.Rectangle((0, 0), 1, 1)
self.set_hover_props(False)
fig.canvas.mpl_connect('button_release_event', self.check_select)
def check_select(self, event):
over, junk = self.rect.contains(event)
if not over:
return
if self.on_select is not None:
self.on_select(self)
def set_extent(self, x, y, w, h):
print(x, y, w, h)
self.rect.set_x(x)
self.rect.set_y(y)
self.rect.set_width(w)
self.rect.set_height(h)
self.label.ox = x + self.padx
self.label.oy = y - self.depth + self.pady/2.
self.hover = False
def draw(self, renderer):
self.rect.draw(renderer)
self.label.draw(renderer)
def set_hover_props(self, b):
if b:
props = self.hoverprops
else:
props = self.props
r, g, b = props.labelcolor_rgb
self.labelArray[:, :, 0] = r
self.labelArray[:, :, 1] = g
self.labelArray[:, :, 2] = b
self.label.set_array(self.labelArray)
self.rect.set(facecolor=props.bgcolor, alpha=props.alpha)
def set_hover(self, event):
'check the hover status of event and return true if status is changed'
b, junk = self.rect.contains(event)
changed = (b != self.hover)
if changed:
self.set_hover_props(b)
self.hover = b
return changed
class Menu(object):
def __init__(self, fig, menuitems):
self.figure = fig
fig.suppressComposite = True
self.menuitems = menuitems
self.numitems = len(menuitems)
maxw = max(item.labelwidth for item in menuitems)
maxh = max(item.labelheight for item in menuitems)
totalh = self.numitems*maxh + (self.numitems + 1)*2*MenuItem.pady
x0 = 100
y0 = 400
width = maxw + 2*MenuItem.padx
height = maxh + MenuItem.pady
for item in menuitems:
left = x0
bottom = y0 - maxh - MenuItem.pady
item.set_extent(left, bottom, width, height)
fig.artists.append(item)
y0 -= maxh + MenuItem.pady
fig.canvas.mpl_connect('motion_notify_event', self.on_move)
def on_move(self, event):
draw = False
for item in self.menuitems:
draw = item.set_hover(event)
if draw:
self.figure.canvas.draw()
break
fig = plt.figure()
fig.subplots_adjust(left=0.3)
props = ItemProperties(labelcolor='black', bgcolor='yellow',
fontsize=15, alpha=0.2)
hoverprops = ItemProperties(labelcolor='white', bgcolor='blue',
fontsize=15, alpha=0.2)
menuitems = []
for label in ('open', 'close', 'save', 'save as', 'quit'):
def on_select(item):
print('you selected %s' % item.labelstr)
item = MenuItem(fig, label, props=props, hoverprops=hoverprops,
on_select=on_select)
menuitems.append(item)
menu = Menu(fig, menuitems)
plt.show()
```
## 下载这个示例
- [下载python源码: menu.py](https://matplotlib.org/_downloads/menu.py)
- [下载Jupyter notebook: menu.ipynb](https://matplotlib.org/_downloads/menu.ipynb)

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# 多光标
同时在多个图上显示光标。
此示例生成两个子图，并将光标悬停在一个子图中的数据上，该数据点的值分别显示在两个子图中。
![多光标示例](https://matplotlib.org/_images/sphx_glr_multicursor_001.png)
```python
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.widgets import MultiCursor
t = np.arange(0.0, 2.0, 0.01)
s1 = np.sin(2*np.pi*t)
s2 = np.sin(4*np.pi*t)
fig, (ax1, ax2) = plt.subplots(2, sharex=True)
ax1.plot(t, s1)
ax2.plot(t, s2)
multi = MultiCursor(fig.canvas, (ax1, ax2), color='r', lw=1)
plt.show()
```
## 下载这个示例
- [下载python源码: multicursor.py](https://matplotlib.org/_downloads/multicursor.py)
- [下载Jupyter notebook: multicursor.ipynb](https://matplotlib.org/_downloads/multicursor.ipynb)

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# 多边形选择器演示
显示如何以交互方式选择多边形的索引。
![多边形选择器演示](https://matplotlib.org/_images/sphx_glr_polygon_selector_demo_001.png)
输出：
```python
Select points in the figure by enclosing them within a polygon.
Press the 'esc' key to start a new polygon.
Try holding the 'shift' key to move all of the vertices.
Try holding the 'ctrl' key to move a single vertex.
Selected points:
[]
```
```python
import numpy as np
from matplotlib.widgets import PolygonSelector
from matplotlib.path import Path
class SelectFromCollection(object):
"""Select indices from a matplotlib collection using `PolygonSelector`.
Selected indices are saved in the `ind` attribute. This tool fades out the
points that are not part of the selection (i.e., reduces their alpha
values). If your collection has alpha < 1, this tool will permanently
alter the alpha values.
Note that this tool selects collection objects based on their *origins*
(i.e., `offsets`).
Parameters
----------
ax : :class:`~matplotlib.axes.Axes`
Axes to interact with.
collection : :class:`matplotlib.collections.Collection` subclass
Collection you want to select from.
alpha_other : 0 <= float <= 1
To highlight a selection, this tool sets all selected points to an
alpha value of 1 and non-selected points to `alpha_other`.
"""
def __init__(self, ax, collection, alpha_other=0.3):
self.canvas = ax.figure.canvas
self.collection = collection
self.alpha_other = alpha_other
self.xys = collection.get_offsets()
self.Npts = len(self.xys)
# Ensure that we have separate colors for each object
self.fc = collection.get_facecolors()
if len(self.fc) == 0:
raise ValueError('Collection must have a facecolor')
elif len(self.fc) == 1:
self.fc = np.tile(self.fc, (self.Npts, 1))
self.poly = PolygonSelector(ax, self.onselect)
self.ind = []
def onselect(self, verts):
path = Path(verts)
self.ind = np.nonzero(path.contains_points(self.xys))[0]
self.fc[:, -1] = self.alpha_other
self.fc[self.ind, -1] = 1
self.collection.set_facecolors(self.fc)
self.canvas.draw_idle()
def disconnect(self):
self.poly.disconnect_events()
self.fc[:, -1] = 1
self.collection.set_facecolors(self.fc)
self.canvas.draw_idle()
if __name__ == '__main__':
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
grid_size = 5
grid_x = np.tile(np.arange(grid_size), grid_size)
grid_y = np.repeat(np.arange(grid_size), grid_size)
pts = ax.scatter(grid_x, grid_y)
selector = SelectFromCollection(ax, pts)
print("Select points in the figure by enclosing them within a polygon.")
print("Press the 'esc' key to start a new polygon.")
print("Try holding the 'shift' key to move all of the vertices.")
print("Try holding the 'ctrl' key to move a single vertex.")
plt.show()
selector.disconnect()
# After figure is closed print the coordinates of the selected points
print('\nSelected points:')
print(selector.xys[selector.ind])
```
## 下载这个示例
- [下载python源码: polygon_selector_demo.py](https://matplotlib.org/_downloads/polygon_selector_demo.py)
- [下载Jupyter notebook: polygon_selector_demo.ipynb](https://matplotlib.org/_downloads/polygon_selector_demo.ipynb)

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# 单选按钮
使用单选按钮选择绘图的属性。
单选按钮允许您在可视化中选择多个选项。在这种情况下，按钮允许用户选择要在图中显示的三种不同正弦波中的一种。
![单选按钮示例](https://matplotlib.org/_images/sphx_glr_radio_buttons_001.png)
```python
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.widgets import RadioButtons
t = np.arange(0.0, 2.0, 0.01)
s0 = np.sin(2*np.pi*t)
s1 = np.sin(4*np.pi*t)
s2 = np.sin(8*np.pi*t)
fig, ax = plt.subplots()
l, = ax.plot(t, s0, lw=2, color='red')
plt.subplots_adjust(left=0.3)
axcolor = 'lightgoldenrodyellow'
rax = plt.axes([0.05, 0.7, 0.15, 0.15], facecolor=axcolor)
radio = RadioButtons(rax, ('2 Hz', '4 Hz', '8 Hz'))
def hzfunc(label):
hzdict = {'2 Hz': s0, '4 Hz': s1, '8 Hz': s2}
ydata = hzdict[label]
l.set_ydata(ydata)
plt.draw()
radio.on_clicked(hzfunc)
rax = plt.axes([0.05, 0.4, 0.15, 0.15], facecolor=axcolor)
radio2 = RadioButtons(rax, ('red', 'blue', 'green'))
def colorfunc(label):
l.set_color(label)
plt.draw()
radio2.on_clicked(colorfunc)
rax = plt.axes([0.05, 0.1, 0.15, 0.15], facecolor=axcolor)
radio3 = RadioButtons(rax, ('-', '--', '-.', 'steps', ':'))
def stylefunc(label):
l.set_linestyle(label)
plt.draw()
radio3.on_clicked(stylefunc)
plt.show()
```
## 下载这个示例
- [下载python源码: radio_buttons.py](https://matplotlib.org/_downloads/radio_buttons.py)
- [下载Jupyter notebook: radio_buttons.ipynb](https://matplotlib.org/_downloads/radio_buttons.ipynb)

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# 矩形选择器
在某处鼠标点击，将鼠标移动到某个目的地，然后释放按钮。 此类提供单击和释放事件，并从点击点到实际鼠标位置（在相同轴内）绘制一条线或一个框，直到释放按钮。 在方法'self.ignore()'中，检查来自eventpress和eventrelease的按钮是否相同。
![矩形选择器示例](https://matplotlib.org/_images/sphx_glr_rectangle_selector_001.png)
输出：
```python
click --> release
```
```python
from matplotlib.widgets import RectangleSelector
import numpy as np
import matplotlib.pyplot as plt
def line_select_callback(eclick, erelease):
'eclick and erelease are the press and release events'
x1, y1 = eclick.xdata, eclick.ydata
x2, y2 = erelease.xdata, erelease.ydata
print("(%3.2f, %3.2f) --> (%3.2f, %3.2f)" % (x1, y1, x2, y2))
print(" The button you used were: %s %s" % (eclick.button, erelease.button))
def toggle_selector(event):
print(' Key pressed.')
if event.key in ['Q', 'q'] and toggle_selector.RS.active:
print(' RectangleSelector deactivated.')
toggle_selector.RS.set_active(False)
if event.key in ['A', 'a'] and not toggle_selector.RS.active:
print(' RectangleSelector activated.')
toggle_selector.RS.set_active(True)
fig, current_ax = plt.subplots() # make a new plotting range
N = 100000 # If N is large one can see
x = np.linspace(0.0, 10.0, N) # improvement by use blitting!
plt.plot(x, +np.sin(.2*np.pi*x), lw=3.5, c='b', alpha=.7) # plot something
plt.plot(x, +np.cos(.2*np.pi*x), lw=3.5, c='r', alpha=.5)
plt.plot(x, -np.sin(.2*np.pi*x), lw=3.5, c='g', alpha=.3)
print("\n click --> release")
# drawtype is 'box' or 'line' or 'none'
toggle_selector.RS = RectangleSelector(current_ax, line_select_callback,
drawtype='box', useblit=True,
button=[1, 3], # don't use middle button
minspanx=5, minspany=5,
spancoords='pixels',
interactive=True)
plt.connect('key_press_event', toggle_selector)
plt.show()
```
## 下载这个示例
- [下载python源码: rectangle_selector.py](https://matplotlib.org/_downloads/rectangle_selector.py)
- [下载Jupyter notebook: rectangle_selector.ipynb](https://matplotlib.org/_downloads/rectangle_selector.ipynb)

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# 滑块演示
使用滑块小部件来控制绘图的可视属性。
在此示例中，滑块用于选择正弦波的频率。 您可以通过这种方式控制绘图的许多连续变化属性。
![滑块演示](https://matplotlib.org/_images/sphx_glr_slider_demo_001.png)
```python
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.widgets import Slider, Button, RadioButtons
fig, ax = plt.subplots()
plt.subplots_adjust(left=0.25, bottom=0.25)
t = np.arange(0.0, 1.0, 0.001)
a0 = 5
f0 = 3
delta_f = 5.0
s = a0*np.sin(2*np.pi*f0*t)
l, = plt.plot(t, s, lw=2, color='red')
plt.axis([0, 1, -10, 10])
axcolor = 'lightgoldenrodyellow'
axfreq = plt.axes([0.25, 0.1, 0.65, 0.03], facecolor=axcolor)
axamp = plt.axes([0.25, 0.15, 0.65, 0.03], facecolor=axcolor)
sfreq = Slider(axfreq, 'Freq', 0.1, 30.0, valinit=f0, valstep=delta_f)
samp = Slider(axamp, 'Amp', 0.1, 10.0, valinit=a0)
def update(val):
amp = samp.val
freq = sfreq.val
l.set_ydata(amp*np.sin(2*np.pi*freq*t))
fig.canvas.draw_idle()
sfreq.on_changed(update)
samp.on_changed(update)
resetax = plt.axes([0.8, 0.025, 0.1, 0.04])
button = Button(resetax, 'Reset', color=axcolor, hovercolor='0.975')
def reset(event):
sfreq.reset()
samp.reset()
button.on_clicked(reset)
rax = plt.axes([0.025, 0.5, 0.15, 0.15], facecolor=axcolor)
radio = RadioButtons(rax, ('red', 'blue', 'green'), active=0)
def colorfunc(label):
l.set_color(label)
fig.canvas.draw_idle()
radio.on_clicked(colorfunc)
plt.show()
```
## 下载这个示例
- [下载python源码: slider_demo.py](https://matplotlib.org/_downloads/slider_demo.py)
- [下载Jupyter notebook: slider_demo.ipynb](https://matplotlib.org/_downloads/slider_demo.ipynb)

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# 跨度选择器
SpanSelector是一个鼠标小部件，用于选择xmin / xmax范围并绘制下轴中所选区域的详细视图
![跨度选择器示例](https://matplotlib.org/_images/sphx_glr_span_selector_001.png)
```python
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.widgets import SpanSelector
# Fixing random state for reproducibility
np.random.seed(19680801)
fig, (ax1, ax2) = plt.subplots(2, figsize=(8, 6))
ax1.set(facecolor='#FFFFCC')
x = np.arange(0.0, 5.0, 0.01)
y = np.sin(2*np.pi*x) + 0.5*np.random.randn(len(x))
ax1.plot(x, y, '-')
ax1.set_ylim(-2, 2)
ax1.set_title('Press left mouse button and drag to test')
ax2.set(facecolor='#FFFFCC')
line2, = ax2.plot(x, y, '-')
def onselect(xmin, xmax):
indmin, indmax = np.searchsorted(x, (xmin, xmax))
indmax = min(len(x) - 1, indmax)
thisx = x[indmin:indmax]
thisy = y[indmin:indmax]
line2.set_data(thisx, thisy)
ax2.set_xlim(thisx[0], thisx[-1])
ax2.set_ylim(thisy.min(), thisy.max())
fig.canvas.draw()
# Set useblit=True on most backends for enhanced performance.
span = SpanSelector(ax1, onselect, 'horizontal', useblit=True,
rectprops=dict(alpha=0.5, facecolor='red'))
plt.show()
```
## 下载这个示例
- [下载python源码: span_selector.py](https://matplotlib.org/_downloads/span_selector.py)
- [下载Jupyter notebook: span_selector.ipynb](https://matplotlib.org/_downloads/span_selector.ipynb)

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# 文本框
允许使用Textbox小部件输入文本。
您可以使用“文本框”小部件让用户提供需要显示的任何文本，包括公式。 您可以使用提交按钮创建具有给定输入的绘图。
![文本框示例](https://matplotlib.org/_images/sphx_glr_textbox_001.png)
```python
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.widgets import TextBox
fig, ax = plt.subplots()
plt.subplots_adjust(bottom=0.2)
t = np.arange(-2.0, 2.0, 0.001)
s = t ** 2
initial_text = "t ** 2"
l, = plt.plot(t, s, lw=2)
def submit(text):
ydata = eval(text)
l.set_ydata(ydata)
ax.set_ylim(np.min(ydata), np.max(ydata))
plt.draw()
axbox = plt.axes([0.1, 0.05, 0.8, 0.075])
text_box = TextBox(axbox, 'Evaluate', initial=initial_text)
text_box.on_submit(submit)
plt.show()
```
## 下载这个示例
- [下载python源码: textbox.py](https://matplotlib.org/_downloads/textbox.py)
- [下载Jupyter notebook: textbox.ipynb](https://matplotlib.org/_downloads/textbox.ipynb)