### Setup for pcolormesh examples

Source: https://matplotlib.org/stable/gallery/images_contours_and_fields/pcolormesh_grids.html

Imports necessary libraries and defines plotting parameters. This setup is common across multiple examples.

```python
import matplotlib.pyplot as plt
import numpy as np

```

--------------------------------

### Get verbose installation details

Source: https://matplotlib.org/stable/users/faq.html

Use this command to output detailed platform information during installation.

```bash
pip install --verbose
```

--------------------------------

### Example Installation Output

Source: https://matplotlib.org/stable/install/index.html

Expected output format when verifying a successful Matplotlib installation.

```text
3.10.0 /Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/matplotlib/__init__.py
```

--------------------------------

### Initialize Example Data

Source: https://matplotlib.org/stable/_downloads/4a8db41bb850567e9fd7e426f443dba4/subplots_demo.ipynb

Setup code for generating sample data used in subsequent plotting examples.

```python
import matplotlib.pyplot as plt
import numpy as np

# Some example data to display
x = np.linspace(0, 2 * np.pi, 400)
y = np.sin(x ** 2)
```

--------------------------------

### Setup for Marker Examples

Source: https://matplotlib.org/stable/gallery/lines_bars_and_markers/marker_reference.html

Imports necessary modules and defines helper functions for formatting axes and splitting lists, used across various marker examples.

```python
import matplotlib.pyplot as plt

from matplotlib.lines import Line2D
from matplotlib.markers import MarkerStyle
from matplotlib.transforms import Affine2D

text_style = dict(horizontalalignment='right', verticalalignment='center',
                  fontsize=12, fontfamily='monospace')
marker_style = dict(linestyle=':', color='0.8', markersize=10,
                    markerfacecolor="tab:blue", markeredgecolor="tab:blue")


def format_axes(ax):
    ax.margins(0.2)
    ax.set_axis_off()
    ax.invert_yaxis()


def split_list(a_list):
    i_half = len(a_list) // 2
    return a_list[:i_half], a_list[i_half:]

```

--------------------------------

### Linking to Documentation Pages

Source: https://matplotlib.org/stable/devel/document.html

Use the `:doc:` role with absolute paths (starting with '/') to cross-link to other pages in the documentation. This ensures clear navigation to specific sections like installation guides.

```rst
See the :doc:`/install/index`
```

--------------------------------

### Cleaned Up Setup Logic in setup.py

Source: https://matplotlib.org/stable/users/prev_whats_new/changelog.html

The setup script (`setup.py`) has undergone a cleanup of its logic, streamlining the installation and configuration process for Matplotlib.

```python
# setup.py: cleaned up setup logic - NN
```

--------------------------------

### Import Libraries and Setup

Source: https://matplotlib.org/stable/gallery/images_contours_and_fields/image_demo.html

Imports necessary libraries and sets a random seed for reproducibility. This is a common setup for Matplotlib examples.

```python
import matplotlib.pyplot as plt
import numpy as np

import matplotlib.cbook as cbook
import matplotlib.cm as cm
from matplotlib.patches import PathPatch
from matplotlib.path import Path

# Fixing random state for reproducibility
np.random.seed(19680801)

```

--------------------------------

### Setup GTK Toolbar

Source: https://matplotlib.org/stable/gallery/user_interfaces/mplcvd.html

Configures a GTK toolbar with a dropdown menu for selecting display options. Requires GTK to be installed.

```python
def _setup_gtk(tb):
    from matplotlib.backends.backend_gtk3 import Gtk
    from pathlib import Path

    menu = Gtk.Menu()
    for name in _MENU_ENTRIES:
        item = Gtk.MenuItem(name)
        item.connect("activate", lambda w, _name=name: _set_menu_entry(tb, _name))
        menu.append(item)
    menu.show_all()

    tbutton = Gtk.MenuToolButton.new(image, _BUTTON_NAME)
    tbutton.set_menu(menu)
    tb.insert(tbutton, idx + 1)

tb.show_all()
```

--------------------------------

### Import Libraries and Setup

Source: https://matplotlib.org/stable/_downloads/baca3c3513a3e3102673c4c4bf6dfd44/colorbar_tick_labelling_demo.ipynb

Imports necessary libraries and sets up a random number generator for reproducible data. This is boilerplate code for the examples.

```python
import matplotlib.pyplot as plt
import numpy as np

import matplotlib.ticker as mticker

# Fixing random state for reproducibility
rng = np.random.default_rng(seed=19680801)
```

--------------------------------

### HTMLWriter.setup()

Source: https://matplotlib.org/stable/api/_as_gen/matplotlib.animation.HTMLWriter.html

Prepares the environment for writing the movie file, setting up the figure, output file, and DPI.

```APIDOC
## HTMLWriter.setup()

### Description
Setup for writing the movie file.

### Parameters
* **fig** (`Figure`) - The figure to grab the rendered frames from.
* **outfile** (str) - The filename of the resulting movie file.
* **dpi** (float) - Optional - The dpi of the output file. This, with the figure size, controls the size in pixels of the resulting movie file. Defaults to `fig.dpi`.
* **frame_dir** (str) - Optional - The directory to save temporary frames.

### Method
`setup(fig, outfile, dpi=None, frame_dir=None)`
```

--------------------------------

### Get Matplotlib Install Location

Source: https://matplotlib.org/stable/install/index.html

Locate the directory where the Matplotlib package is installed.

```python
>>> import matplotlib
>>> matplotlib.__file__
'/home/jdhunter/dev/lib64/python2.5/site-packages/matplotlib/__init__.pyc'
```

--------------------------------

### WebAggApplication.start

Source: https://matplotlib.org/stable/api/backend_webagg_api.html

Starts the WebAggApplication.

```APIDOC
## WebAggApplication.start

### Description
Starts the WebAggApplication.
```

--------------------------------

### MovieWriter.setup Method

Source: https://matplotlib.org/stable/api/_as_gen/matplotlib.animation.MovieWriter.html

Prepares the MovieWriter for writing by setting up the figure, output file, and DPI.

```APIDOC
## MovieWriter.setup

### Description
Setup for writing the movie file.

### Parameters
- **fig** (`Figure`) - The figure object that contains the information for frames.
- **outfile** (str) - The filename of the resulting movie file.
- **dpi** (float, optional) - The DPI (or resolution) for the file. This controls the size in pixels of the resulting movie file. Defaults to `fig.dpi`.
```

--------------------------------

### Setup for Contour Plotting

Source: https://matplotlib.org/stable/gallery/images_contours_and_fields/contour_demo.html

Initializes numpy arrays and creates a Z matrix for contour plotting. This setup is common for all subsequent examples.

```python
import matplotlib.pyplot as plt
import numpy as np

import matplotlib.cm as cm

delta = 0.025
x = np.arange(-3.0, 3.0, delta)
y = np.arange(-2.0, 2.0, delta)
X, Y = np.meshgrid(x, y)
Z1 = np.exp(-X**2 - Y**2)
Z2 = np.exp(-(X - 1)**2 - (Y - 1)**2)
Z = (Z1 - Z2) * 2

```

--------------------------------

### Basic Grid Configuration

Source: https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.grid.html

Demonstrates how to turn on grid lines with custom properties. This example shows setting color, linestyle, and linewidth for the grid.

```python
import matplotlib.pyplot as plt

plt.plot([1, 2, 3], [4, 5, 6])
plt.grid(color='r', linestyle='-', linewidth=2)
plt.show()
```

--------------------------------

### Build HTML Documentation

Source: https://matplotlib.org/stable/devel/document.html

Navigate to the doc/ directory and run 'make html' to build the documentation in HTML format. The initial build may take a significant amount of time.

```bash
make html

```

--------------------------------

### Setup and Event Connection

Source: https://matplotlib.org/stable/gallery/event_handling/pick_event_demo.html

Imports necessary libraries and sets up a figure with two subplots. It configures artists for picking and connects a 'pick_event' callback.

```python
import matplotlib.pyplot as plt
import numpy as np
from numpy.random import rand

from matplotlib.image import AxesImage
from matplotlib.lines import Line2D
from matplotlib.patches import Rectangle
from matplotlib.text import Text

# Fixing random state for reproducibility
np.random.seed(19680801)

fig, (ax1, ax2) = plt.subplots(2, 1)
ax1.set_title('click on points, rectangles or text', picker=True)
ax1.set_ylabel('ylabel', picker=True, bbox=dict(facecolor='red'))
line, = ax1.plot(rand(100), 'o', picker=True, pickradius=5)

# Pick the rectangle.
ax2.bar(range(10), rand(10), picker=True)
for label in ax2.get_xticklabels():  # Make the xtick labels pickable.
    label.set_picker(True)


def onpick1(event):
    if isinstance(event.artist, Line2D):
        thisline = event.artist
        xdata = thisline.get_xdata()
        ydata = thisline.get_ydata()
        ind = event.ind
        print('onpick1 line:', np.column_stack([xdata[ind], ydata[ind]]))
    elif isinstance(event.artist, Rectangle):
        patch = event.artist
        print('onpick1 patch:', patch.get_path())
    elif isinstance(event.artist, Text):
        text = event.artist
        print('onpick1 text:', text.get_text())


fig.canvas.mpl_connect('pick_event', onpick1)

```

--------------------------------

### Get all figure numbers

Source: https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.get_fignums.html

This example demonstrates how to use `matplotlib.pyplot.get_fignums()` to get a list of all currently open figure numbers. It first creates a few figures to ensure there are numbers to retrieve.

```APIDOC
## matplotlib.pyplot.get_fignums

### Description
Return a list of existing figure numbers.

### Method
```python
matplotlib.pyplot.get_fignums()
```

### Parameters
This function does not take any parameters.

### Returns
- list: A list of integers, where each integer is a figure number.

### Example
```python
import matplotlib.pyplot as plt

# Create a few figures
plt.figure(1)
plt.figure(2)

# Get all figure numbers
fignums = plt.get_fignums()
print(fignums) # Output: [1, 2]

# Create another figure
plt.figure()

fignums = plt.get_fignums()
print(fignums) # Output: [1, 2, 3]
```
```

--------------------------------

### FileMovieWriter.setup()

Source: https://matplotlib.org/stable/api/_as_gen/matplotlib.animation.FileMovieWriter.html

Sets up the writer for saving the movie file, specifying the figure, output file, and DPI.

```APIDOC
## FileMovieWriter.setup()

### Description
Prepares the writer to begin saving the movie file. This method must be called before grabbing frames.

### Parameters
* **fig** (`Figure`) - The matplotlib Figure object to capture frames from.
* **outfile** (str) - The path to the output movie file.
* **dpi** (float, default: `fig.dpi`) - The resolution of the output file in dots per inch.
* **frame_prefix** (str, optional) - A prefix for temporary frame filenames. If None, frames are saved to a temporary directory.
```

--------------------------------

### Get Matplotlib Version

Source: https://matplotlib.org/stable/install/index.html

Retrieve the version number of the currently installed Matplotlib package.

```python
>>> import matplotlib
>>> matplotlib.__version__
'0.98.0'
```

--------------------------------

### Import Matplotlib and NumPy

Source: https://matplotlib.org/stable/gallery/style_sheets/style_sheets_reference.html

Imports necessary libraries for plotting and numerical operations. This is a common setup for Matplotlib examples.

```python
import matplotlib.pyplot as plt
import numpy as np

import matplotlib.colors as mcolors
from matplotlib.patches import Rectangle
```

--------------------------------

### Import Libraries and Setup

Source: https://matplotlib.org/stable/_downloads/3521362dc0e73684626f9a6f0a9ec8a0/hist.ipynb

Imports necessary libraries and sets up a random number generator for reproducible data generation.

```python
import matplotlib.pyplot as plt
import numpy as np

from matplotlib import colors
from matplotlib.ticker import PercentFormatter

# Create a random number generator with a fixed seed for reproducibility
rng = np.random.default_rng(19680801)
```

--------------------------------

### Setup for Zorder Demo

Source: https://matplotlib.org/stable/gallery/misc/zorder_demo.html

Imports necessary libraries and generates sample data for plotting.

```python
import matplotlib.pyplot as plt
import numpy as np

r = np.linspace(0.3, 1, 30)
theta = np.linspace(0, 4*np.pi, 30)
x = r * np.sin(theta)
y = r * np.cos(theta)

```

--------------------------------

### Show Locally Built Documentation

Source: https://matplotlib.org/stable/devel/document.html

Use the 'make show' command to open the locally built HTML documentation in your default web browser.

```bash
make show

```

--------------------------------

### Import Matplotlib Modules

Source: https://matplotlib.org/stable/_downloads/210021ff118a667d0bea86dcb158be37/fancybox_demo.ipynb

Imports necessary modules from Matplotlib for plotting and creating patches. This is a common setup for Matplotlib examples.

```python
import inspect

import matplotlib.pyplot as plt

import matplotlib.patches as mpatch
from matplotlib.patches import FancyBboxPatch
import matplotlib.transforms as mtransforms
```

--------------------------------

### Unit Definitions and Conversions

Source: https://matplotlib.org/stable/gallery/units/basic_units.html

Example setup for specific units like centimeters, inches, radians, and time units with conversion factors.

```python
cm = BasicUnit('cm', 'centimeters')
inch = BasicUnit('inch', 'inches')
inch.add_conversion_factor(cm, 2.54)
cm.add_conversion_factor(inch, 1/2.54)

radians = BasicUnit('rad', 'radians')
degrees = BasicUnit('deg', 'degrees')
radians.add_conversion_factor(degrees, 180.0/np.pi)
degrees.add_conversion_factor(radians, np.pi/180.0)

secs = BasicUnit('s', 'seconds')
hertz = BasicUnit('Hz', 'Hertz')
minutes = BasicUnit('min', 'minutes')

secs.add_conversion_fn(hertz, lambda x: 1./x)
secs.add_conversion_factor(minutes, 1/60.0)
```

--------------------------------

### Setup Qt Toolbar

Source: https://matplotlib.org/stable/gallery/user_interfaces/mplcvd.html

Sets up a Qt toolbar with a QToolButton that displays a QMenu. This is compatible with both Qt5 and Qt6.

```python
def _setup_qt(tb):
    from matplotlib.backends.qt_compat import QtGui, QtWidgets

    menu = QtWidgets.QMenu()
    try:
        QActionGroup = QtGui.QActionGroup  # Qt6
    except AttributeError:
        QActionGroup = QtWidgets.QActionGroup  # Qt5
    group = QActionGroup(menu)
    group.triggered.connect(lambda action: _set_menu_entry(tb, action.text()))

    for name in _MENU_ENTRIES:
        action = menu.addAction(name)
        action.setCheckable(True)
        action.setActionGroup(group)
        action.setChecked(name == "None")

    actions = tb.actions()
    before = next(
        (action for action in actions
         if isinstance(tb.widgetForAction(action), QtWidgets.QLabel)), None)

    tb.insertSeparator(before)
    button = QtWidgets.QToolButton()
    # FIXME: _icon needs public API.
    button.setIcon(tb._icon(str(Path(__file__).parent / "images/eye.png")))
    button.setText(_BUTTON_NAME)
    button.setToolTip(_BUTTON_HELP)
    button.setPopupMode(QtWidgets.QToolButton.ToolButtonPopupMode.InstantPopup)
    button.setMenu(menu)
    tb.insertWidget(before, button)
```

--------------------------------

### Infinite Lines Example

Source: https://matplotlib.org/stable/gallery/index.html

Demonstrates the creation of infinite lines in a plot. No specific setup is required beyond standard Matplotlib imports.

```python
import matplotlib.pyplot as plt
import numpy as np

fig, ax = plt.subplots()

ax.plot([0, 1], [0, 1], transform=ax.transAxes)
ax.plot([0, 1], [1, 0], transform=ax.transAxes)

plt.show()
```

--------------------------------

### WebAggApplication.initialize

Source: https://matplotlib.org/stable/api/backend_webagg_api.html

Initializes the WebAggApplication.

```APIDOC
## WebAggApplication.initialize

### Description
Initializes the WebAggApplication.

### Parameters
#### Keyword Parameters
- **url_prefix** (str, optional) - The URL prefix for the application.
- **port** (int, optional) - The port to run the application on.
- **address** (str, optional) - The address to bind the application to.
```

--------------------------------

### matplotlib.testing.setup

Source: https://matplotlib.org/stable/api/testing_api.html

General setup function for Matplotlib testing.

```APIDOC
## matplotlib.testing.setup

### Description
General setup function for Matplotlib testing. This may include environment variable configurations or other necessary preparations for running tests.

### Usage
Typically called at the beginning of a test suite.
```

--------------------------------

### Setup fake data for box plots

Source: https://matplotlib.org/stable/_downloads/43dfdc3242d6e3f7d2a54971e771b799/boxplot.ipynb

Initializes random lognormal data and labels for use in subsequent box plot examples.

```python
import matplotlib.pyplot as plt
import numpy as np

# fake data
np.random.seed(19680801)
data = np.random.lognormal(size=(37, 4), mean=1.5, sigma=1.75)
labels = list('ABCD')
fs = 10  # fontsize
```

--------------------------------

### AbstractMovieWriter.setup

Source: https://matplotlib.org/stable/api/_as_gen/matplotlib.animation.AbstractMovieWriter.html

Abstract method to set up the movie writing process. It takes the figure object, output filename, and an optional DPI.

```APIDOC
abstractmethod setup(_fig_ , _outfile_ , _dpi =None_)[source]#
    
Setup for writing the movie file. 

Parameters:
     
**fig**`Figure`
    
The figure object that contains the information for frames. 

**outfile** str
    
The filename of the resulting movie file. 

**dpi** float, default: `fig.dpi`
    
The DPI (or resolution) for the file. This controls the size in pixels of the resulting movie file.
```