CircuitPython Histogram Library

uhistogram

CircuitPython library to calculate and graph histograms

• Author(s): Jose D. Montoya

Implementation Notes

Depends on CircuitPyton version 8.0.0

class uhistogram.Histogram(*args: Any, **kwargs: Any)

A Histogram TileGrid. The origin is set using x and y.

Parameters:

• data ((list, tuple)) – source data to calculate the histogram

• x (int) – x position of the histogram origin

• y (int) – y position of the histogram origin

• width (int) – requested width, in pixels. Defaults to 100 pixels.

• height (int) – requested height, in pixels. Defaults to 100 pixels.

• line_color (int) – background color to use defaults to white (0xFFFFFF)

Quickstart: Importing and using uhistogram

Here is one way of importing the uhistogram.Histogram class so you can use it as the name uhistogram.Histogram:

from uhistogram import Histogram
import displayio

Now you can create a boxplot at pixel position x=50, y=50 using:

a=[1, 1, 4, 5, 6, 7, 7, 7, 8, 9, 10, 15, 16, 17, 24, 56, 76, 87, 87]
my_histogram=Histogram(a, x=50, y=50) # instance the histogram at x=50, y=50
my_group = displayio.Group()

Once you set up your display, you can now add my_boxplot to your display.Group() using:

my_group.append(my_histogram)
display.show(my_group) # add the group to the display

Summary: Histogram Features and input variables

The uhistogram TileGrid has some options for controlling its position, visible appearance, through a collection of input variables:

• position: x, y

• size: width and height

• color: line_color

This is a diagram of a histogram

draw()

This function draws the histogram

static get_numberbins(data, rule=0)

Function that calculate the number of bins for the sample. you could use two rules, changing the parameter rule. rule = 0 applies Sturge’s rule rule = 1 applies square root choice rule = 2 will choose the best criteria to be applied. for data size < 30 –> Square Root Choice for data size > 30 –> Sturg’s Rule

Parameters:

• data – data to be analyzed

• rule – desired rule. Defaults to 0.

Returns:

bin calculated data

static normalize(oldrangemin, oldrangemax, newrangemin, newrangemax, value)

This function converts the original value into a new defined value in the new range

Parameters:

• oldrangemin – minimum of the original range

• oldrangemax – maximum of the original range

• newrangemin – minimum of the new range

• newrangemax – maximum of the new range

• value – value to be converted

Returns:

converted value

print_data()

This function prints the bins data

Returns:

None