Source code for webinterface.pages.base_pages.denovo_tabs.tab3

import streamlit as st

from proteobench.plotting.plot_denovo import PlotDataPoint




def generate_ptm_plots(variables, df, modifications):
    st.markdown("# PTMs")
    st.markdown("### Overview PTM precision")
    with st.expander("Description"):
        st.markdown(variables.texts.Description.ptm_overview)

    fig = PlotDataPoint.plot_ptm_overview(self=PlotDataPoint(), benchmark_metrics_df=df, mod_labels=modifications)

    st.plotly_chart(fig, use_container_width=True, key=variables.fig_ptm_overview)

    st.markdown("### Precision per modification")
    with st.expander("Description"):
        st.markdown(variables.texts.Description.ptm_specific)
    tabs = st.tabs(modifications)
    tab_dict = {mod_label: tab for mod_label, tab in zip(modifications, tabs)}

    for mod_label, tab in tab_dict.items():
        with tab:
            st.header(mod_label)
            fig = PlotDataPoint.plot_ptm_specific(self=PlotDataPoint(), benchmark_metrics_df=df, mod_label=mod_label)
            st.plotly_chart(fig, use_container_width=True, key=variables.fig_ptm_specific + mod_label)





def generate_spectrum_feature_plots(variables, df, feature_names):
    st.markdown("# Spectrum features")

    exact_mode = st.toggle(
        label="Exact evaluation mode", value=False, key=variables.evaluation_mode_toggle_tab3_features
    )
    if exact_mode:
        evaluation_type = "exact"
    else:
        evaluation_type = "mass"

    with st.expander("Description"):
        st.markdown(variables.texts.Description.spectrum_features_overview)

    tabs = st.tabs(feature_names)
    tab_dict = {feature_name: tab for feature_name, tab in zip(feature_names, tabs)}

    for feature_name, tab in tab_dict.items():
        with tab:
            st.header(feature_name)
            fig = PlotDataPoint.plot_spectrum_feature(
                self=PlotDataPoint(), benchmark_metrics_df=df, feature=feature_name, evaluation_type=evaluation_type
            )
            st.plotly_chart(fig, use_container_width=True, key=variables.fig_spectrum_feature + feature_name)





def generate_species_plot(variables, df):
    st.markdown("# Species")

    with st.expander("Description"):
        st.markdown(variables.texts.Description.species)

    exact_mode = st.toggle(
        label="Exact evaluation mode", value=False, key=variables.evaluation_mode_toggle_tab3_species
    )
    if exact_mode:
        evaluation_type = "exact"
    else:
        evaluation_type = "mass"

    fig = PlotDataPoint.plot_species_overview(
        self=PlotDataPoint(), benchmark_metrics_df=df, evaluation_type=evaluation_type
    )

    st.plotly_chart(fig, use_container_width=True, key=variables.fig_species_overview)