#!/usr/bin/env python
import streamlit as st

from utils import fetch_resource, get_degs, ui_model_selection

st.set_page_config(layout="wide")

st.markdown("""
    # Differentially expressed genes
    
    Genes below have been determined using `sc.tl.rank_genes_groups` where `t-test_overestim_var`
    is the default method.
    """)

ui_model_selection()

filter_flag = []

if st.session_state["SPECIE"] and st.session_state["VERSION"]:
    adata = fetch_resource(st.session_state["SPECIE"], st.session_state["VERSION"])

    degs_by = st.sidebar.selectbox(
        "**Select by**",
        [x for x in adata.uns_keys() if "degs" in x],
        index=None,
        placeholder="Differentially expressed genes by",
    )

    if degs_by:
        degs = get_degs(adata, degs_by)

        group = st.sidebar.multiselect(
            "**Cell type**", degs["group"].unique(), placeholder="Select group ..."
        )

        genes = st.sidebar.multiselect(
            "**Gene**", sorted(degs["names"].unique()), placeholder="Select genes ..."
        )

        foldchange = st.sidebar.number_input(
            "**Log2 fold-change**",
            value=1,
        )

        pval_adj = st.sidebar.number_input(
            "**Adjusted p-value**",
            value=0.05,
        )

        if group:
            filter_flag.append("group == @group")

        if genes:
            filter_flag.append("@genes in names")

        if foldchange:
            filter_flag.append(
                "logfoldchanges > @foldchange"
                if foldchange > 0
                else "logfoldchanges < @foldchange"
            )

        if pval_adj:
            filter_flag.append("pvals_adj < @pval_adj")

        subset = degs.query(" & ".join(filter_flag)) if filter_flag else degs
        st.dataframe(subset, use_container_width=True, height=650)