metadata

tags:
  - gradio-custom-component
  - Plot
  - med
  - medicine
  - bio
  - biology
  - chem
  - chemistry
  - MSA
  - multiple sequence alignment
  - seqlogo
  - annotation
  - consensus histogram
  - visualize
title: gradio_msaplot
short_description: MSAplot is customizable panels for plotting MSA.
colorFrom: blue
colorTo: yellow
sdk: gradio
pinned: false
app_file: demo/space.py

`gradio_msaplot`

MSAplot is customizable panels for plotting MSA, seqlogo, annotation, and consensus histograms.

Installation

pip install gradio_msaplot

Usage


import gradio as gr
from gradio_msaplot import MSAPlot, MSAPlotData
import matplotlib
matplotlib.use('Agg')

example = MSAPlot().example_value()

with gr.Blocks() as demo:
    with gr.Row():
        MSAPlot(label="Blank"),  # blank component
        MSAPlot(value=example, label="Populated"),  # populated component


if __name__ == "__main__":
    demo.launch()

`MSAPlot`

Initialization

name	type	default	description
`value`	`typing.Any \| None`	`None`	None
`label`	`str \| None`	`None`	None
`every`	`float \| None`	`None`	None
`show_label`	`bool \| None`	`None`	None
`container`	`bool`	`True`	None
`scale`	`int \| None`	`None`	None
`min_width`	`int`	`160`	None
`visible`	`bool`	`True`	None
`elem_id`	`str \| None`	`None`	None
`elem_classes`	`list[str] \| str \| None`	`None`	None
`render`	`bool`	`True`	None
`key`	`int \| str \| None`	`None`	None

Events

name	description
`change`	Triggered when the value of the MSAPlot changes either because of user input (e.g. a user types in a textbox) OR because of a function update (e.g. an image receives a value from the output of an event trigger). See `.input()` for a listener that is only triggered by user input.
`clear`	Triggered when the plot is cleared.

User function

The impact on the users predict function varies depending on whether the component is used as an input or output for an event (or both).

When used as an Input, the component only impacts the input signature of the user function.
When used as an output, the component only impacts the return signature of the user function.

The code snippet below is accurate in cases where the component is used as both an input and an output.

As output: Is passed, the preprocessed input data sent to the user's function in the backend.
As input: Should return, the output data received by the component from the user's function in the backend.

def predict(
    value: MSAPlotData | None
) -> MSAPlotData:
    return value

`MSAPlotData`

Initialization

name	type	default	description
`data`	`typing.Any`	`None`	None