plot_expression.py

import pandas as pd
import numpy as np
from plotly.subplots import make_subplots
import plotly.express as px
from itertools import cycle
from plotly.graph_objs import Bar
import streamlit as st
import plotly.io as pio
from io import BytesIO
from st_aggrid import AgGrid


@st.cache(allow_output_mutation=True)
def default_expression_data():
    sample = pd.read_csv(r'./cache/sample_data.csv')
    return sample


def reduce(ls):
    new_ls = list(dict.fromkeys(ls))
    return new_ls


def expression_plot():
    df = default_expression_data().copy()
    st.sidebar.markdown('## Fetch default format file')
    st.sidebar.download_button('Download Sample file', df.to_csv(index=False).encode('utf-8'), 'expression_sample.csv')
    st.sidebar.markdown('## Input formatted file')
    file = st.sidebar.file_uploader(label='Only csv file is available:', type='csv', accept_multiple_files=False)

    if not file:
        st.markdown('### Sample Data: \n > Press the "Draw" button to get sample result.')
        data = df
        name = 'Test_data'
    else:
        st.markdown('### Input Data: \n > Press the "Draw" button to get result.')
        data = pd.read_csv(file)
        name = file.name.split('.')[0]
    grid_return = AgGrid(data, editable=True, fit_columns_on_grid_load=True, height=data.shape[0] * 28 + 40,
                         GridUpdateMode='VALUE_CHANGED', theme='streamlit')
    grid = grid_return["data"]
    data = grid
    st.sidebar.markdown('## Draw Figure by:')
    divide = st.sidebar.radio('Default to be Target Gene', ['Target', 'Sample', 'Biological Set Name'])
    # st.dataframe(data, width=1000)
    st.sidebar.markdown('## Available Format:')
    output_format = st.sidebar.radio('SVG Format Recommended', ('svg', 'jpg', 'png', 'pdf'))

    if st.button('Draw'):
        looplist = reduce(list(data[divide].values))
        # st.text(looplist)
        ls = ['Target', 'Sample', 'Biological Set Name']
        ls.remove(divide)

        for a in ls:
            if a not in data.columns:
                ls.remove(a)

        st.subheader("Relative Gene Expression")
        columns = ['Expression']
        error_column = 'Expression SD'
        count = 0

        if len(looplist) == 1:
            row = 1
            col = 1
            wid = 400
            hei = 400
        else:
            row = int(np.ceil(len(looplist) / 2))
            col = 2
            wid = 500 * col
            hei = 500 * row
        fig = make_subplots(rows=row, cols=col)

        pio.templates.default = "simple_white"

        # st.dataframe(data)
        # st.text(len(ls))

        for target in looplist:
            count += 1
            row_count = int(np.ceil(count / 2))
            col_count = 2 - int(count % 2)

            tmp = data[data[divide] == target]

            palette = cycle(px.colors.qualitative.Alphabet)
            colors = {c: next(palette) for c in looplist}
            # st.text(ls)
            # st.text('Succeed')

            for cols in columns:
                # st.text([tmp[ls[0]], tmp[ls[1]]])
                # st.text(tmp[cols])
                # st.text('Succeed')

                if len(ls)>=2:
                    fig.add_trace(Bar(x=[tmp[ls[0]], tmp[ls[1]]], y=tmp[cols], name=target, legendgroup=cols,
                                      marker_color=colors[target], showlegend=True,
                                      error_y={'array': tmp[error_column].to_list(), 'type': 'data', 'visible': True}),
                                  row=row_count, col=col_count)
                else:
                    fig.add_trace(Bar(x=tmp[ls[0]], y=tmp[cols], name=target, legendgroup=cols,
                                      marker_color=colors[target], showlegend=True,
                                      error_y={'array': tmp[error_column].to_list(), 'type': 'data', 'visible': True}),
                                  row=row_count, col=col_count)

        fig.update_layout(barmode='group', width=wid, height=hei)

        st.plotly_chart(fig, False)

        st.subheader('Download Figure Above')
        fn = '{0}.{1}'.format(name,output_format)
        img = BytesIO()
        fig.write_image(img, format=output_format)

        btn = st.download_button(
            label="Download image",
            data=img,
            file_name=fn
        )