theme_timetk

theme_timetk(base_size=11, base_family=['Arial', 'Helvetica', 'sans-serif'], dpi=100, width=700, height=500)

Returns a plotnine theme with timetk styles applied, allowing for customization of the appearance of plots in Python.

Parameters

Name	Type	Description	Default
`base_size`	int	The `base_size` parameter determines the base font size for the theme. It is set to 11 by default, but you can change it to any desired value.	`11`
`base_family`	list	The `base_family` parameter is a list of font families that will be used as the base font for the theme. The default value is `['Arial', 'Helvetica', 'sans-serif']`, which means that the theme will use Arial font if available, otherwise it will try Helvetica, and if that is not available either, it will use the generic sans-serif font.	`['Arial', 'Helvetica', 'sans-serif']`
`dpi`	int	The `dpi` parameter stands for dots per inch and determines the resolution of the plot. It specifies the number of pixels per inch in the output image. Higher dpi values result in higher resolution images.	`100`
`width`	int	The `width` parameter is used to specify the width of the plot in pixels at dpi. It determines the horizontal size of the plot. The default value is 700 pixels.	`700`
`height`	int	The `height` parameter is used to specify the height of the plot in inches. It is an optional parameter, so if you don’t provide a value for it, the default height will be 5 inches (500 pixels).	`500`

Returns

Type	Description
A theme object that can be used to customize the appearance of plots in	Python. The theme object contains various elements such as line, rect, axis, panel, legend, strip, and plot, each with their own set of properties that can be customized.

Examples

import pytimetk as tk
import pandas as pd

from plotnine import ggplot, aes, geom_line, labs, scale_x_date, facet_wrap

data = {
    'date': pd.date_range(start='2023-01-01', end='2023-01-10'),
    'value': [1, 3, 7, 9, 11, 14, 18, 21, 24, 29]
}
df = pd.DataFrame(data)

# Plotnine chart without styling
fig = (
    ggplot(df, aes(x='date', y='value')) +
        geom_line(color='blue') +
        labs(title='Time Series Plot', x='Date', y='Value') +
        scale_x_date(date_labels='%a')
)
fig

<Figure Size: (640 x 480)>

# Plotnine chart with timetk styling
fig + tk.theme_timetk()

<Figure Size: (700 x 500)>

# Faceted plot with timetk styling
data = {
    'date': pd.date_range(start='2023-01-01', end='2023-01-10').tolist() * 2,
    'value': [1, 3, 7, 9, 11, 14, 18, 21, 24, 29] * 2,
    'category': ['A'] * 10 + ['B'] * 10,
}
df = pd.DataFrame(data)

(
    ggplot(df, aes(x='date', y='value')) +
        geom_line(color='blue') +
        labs(title='Faceted Time Series Plot', x='Date', y='Value') +
        facet_wrap('~category') +
        scale_x_date(date_labels='%a') +
        tk.theme_timetk()
)

<Figure Size: (700 x 500)>