# how do i adjust the size and aspect ratio of matplotlib radio buttons?

• Last Update :
• Techknowledgy :
```plt.figure()
rax = plt.axes([0.1, 0.1, 0.6, 0.6], frameon = True, aspect = 'equal')
labels = [str(i) for i in range(10)]
default is 0.05
plt.show()```

Suggestion : 2

Controlling the position and size of colorbars with Inset Axes ,Using radio buttons to choose properties of your plot.,Radio buttons let you choose between multiple options in a visualization. In this case, the buttons let the user choose one of the three different sine waves to be shown in the plot., Contouring the solution space of optimizations

```import numpy as np
import matplotlib.pyplot as plt
from matplotlib.widgets

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')

axcolor = 'lightgoldenrodyellow'
rax = plt.axes([0.05, 0.7, 0.15, 0.15], facecolor = axcolor)

def hzfunc(label):
hzdict = {
'2 Hz': s0,
'4 Hz': s1,
'8 Hz': s2
}
ydata = hzdict[label]
l.set_ydata(ydata)
plt.draw()

rax = plt.axes([0.05, 0.4, 0.15, 0.15], facecolor = axcolor)

def colorfunc(label):
l.set_color(label)
plt.draw()

rax = plt.axes([0.05, 0.1, 0.15, 0.15], facecolor = axcolor)

def stylefunc(label):
l.set_linestyle(label)
plt.draw()

plt.show()```

Suggestion : 3

RadioItems is a component for rendering a set of radio (or option) buttons. Users can select one option from the set. See Checklist for selecting multiple options at a time, and Dropdown for a more compact view.,The options and value properties are the first two arguments of dcc.RadioItems. There are multiple ways to set options. The following examples define the same RadioItems component:,To create a basic radioitems, provide options and a value to the dcc.RadioItems component in that order.,We’ve seen how options can be set using a list, a dictionary, or a list of dictionaries. options also accepts Pandas and NumPy data structures.

```from dash
import Dash, dcc

app = Dash(__name__)

app.layout = dcc.RadioItems(['New York City', 'Montreal', 'San Francisco'], 'Montreal')

if __name__ == "__main__":
app.run_server(debug = True)```
```from dash
import Dash, dcc

app = Dash(__name__)

app.layout = dcc.RadioItems(['New York City', 'Montreal', 'San Francisco'], 'Montreal', inline = True)

if __name__ == "__main__":
app.run_server(debug = True)```

The options and value properties are the first two arguments of `dcc.RadioItems`. There are multiple ways to set `options`. The following examples define the same RadioItems component:

`dcc.RadioItems(['NYC', 'MTL', 'SF'], 'NYC')`
``````dcc.RadioItems(['NYC', 'MTL', 'SF'], 'NYC')
``````
```dcc.RadioItems(
options = ['New York City', 'Montreal', 'San Francisco'],
value = 'Montreal'
)```
``````dcc.RadioItems(
options=['New York City', 'Montreal', 'San Francisco'],
value='Montreal'
)
``````
```dcc.RadioItems(
options = [{
'label': 'New York City',
'value': 'New York City'
},
{
'label': 'Montreal',
'value': 'Montreal'
},
{
'label': 'San Francisco',
'value': 'San Francisco'
},
],
value = 'Montreal'
)```

In these examples, the option’s label (what the user sees) and the value (what’s passed into the callback) are equivalent. Often it is helpful for these to be separate so that you can easily change the label without changing the callback logic that uses the value:

```dcc.RadioItems(
options = {
'NYC': 'New York City',
'MTL': 'Montreal',
'SF': 'San Francisco'
},
value = 'MTL'
)```

Options provided as a single dictionary render in no particular order in the browser.
Providing a list that contains a dictionary for each option ensures the options render in the order provided.

```dcc.RadioItems(
options = [{
'label': 'New York City',
'value': 'NYC'
},
{
'label': 'Montreal',
'value': 'MTL'
},
{
'label': 'San Francisco',
'value': 'SF'
},
],
value = 'MTL'
)```
```from dash
import Dash, dcc

app = Dash(__name__)

'label': 'New York City',
'value': 'NYC'
},
{
'label': 'Montréal',
'value': 'MTL'
},
{
'label': 'San Francisco',
'value': 'SF',
'disabled': True
}
],
'MTL'
)

if __name__ == "__main__":
app.run_server(debug = True)```
```from dash
import Dash, dcc
from plotly.express
import data
import pandas as pd

df = data.medals_long()

app = Dash(__name__)

if __name__ == "__main__":
app.run_server(debug = True)```
```from dash
import Dash, dcc
from plotly.express
import data
import pandas as pd

df = data.medals_long()

app = Dash(__name__)