"""Display similar to Tab1 in the quant benchmarking modules, all of the data
next to the submitted data."""
import uuid
from typing import Callable
import streamlit as st
from .filter import filter_data_using_slider
from .metricplot import render_metric_plot
from .resulttable import configure_aggrid, prepare_display_dataframe, render_aggrid
[docs]
def initialize_submitted_slider(slider_id_submitted_uuid, default_val_slider) -> None:
"""
Initialize the slider for the submitted data.
"""
key = slider_id_submitted_uuid
if key not in st.session_state.keys():
st.session_state[key] = uuid.uuid4()
_id_of_key = st.session_state[key]
if _id_of_key not in st.session_state.keys():
st.session_state[_id_of_key] = default_val_slider
[docs]
def generate_submitted_slider(variables) -> None:
"""
Create a slider input.
"""
if variables.slider_id_submitted_uuid not in st.session_state:
st.session_state[variables.slider_id_submitted_uuid] = uuid.uuid4()
slider_key = st.session_state[variables.slider_id_submitted_uuid]
st.select_slider(
label="Minimal precursor quantifications (# samples)",
options=[1, 2, 3, 4, 5, 6],
value=st.session_state.get(slider_key, variables.default_val_slider),
key=slider_key,
help="Use the slider to set the minimum number of raw files in which a precursor must be quantified (e.g., 3 = ≥3 files).",
)
[docs]
def generate_submitted_selectbox(variables) -> None:
"""
Create the selectbox for the Streamlit UI.
"""
if variables.selectbox_id_submitted_uuid not in st.session_state.keys():
st.session_state[variables.selectbox_id_submitted_uuid] = uuid.uuid4()
try:
st.selectbox(
"Label",
variables.metric_plot_labels,
key=st.session_state[variables.selectbox_id_submitted_uuid],
)
except Exception as e:
st.error(f"Unable to create the selectbox: {e}", icon="🚨")
[docs]
def display_submitted_results(variables, ionmodule) -> None:
"""
Display the results section of the page for submitted data.
"""
# handled_submission = self.process_submission_form()
# if handled_submission == False:
# return
key_all_datapoints_submitted = variables.all_datapoints_submitted
initialize_submitted_data_points(key_all_datapoints_submitted, ionmodule.obtain_all_data_points)
data_points_filtered = filter_data_using_slider(
slider_id_uuid=variables.slider_id_submitted_uuid,
all_datapoints=key_all_datapoints_submitted,
filter_data_point=ionmodule.filter_data_point,
)
metric = display_metric_selector(variables)
# ROC-AUC has no mode variants (it's already species-aware by design)
if metric == "ROC-AUC":
mode = None
else:
mode = display_metric_calc_approach_selector(variables)
colorblind_mode = display_colorblindmode_selector(variables)
if len(data_points_filtered) == 0:
st.error("No datapoints available for plotting", icon="🚨")
return
# prepare plot key explicitly for tab 4
key = variables.result_submitted_plot_uuid
if key not in st.session_state.keys():
st.session_state[key] = uuid.uuid4()
_id_of_key = st.session_state[key]
plot_generator = ionmodule.get_plot_generator()
highlight_point_id = render_metric_plot(
data_points_filtered,
metric,
mode=mode,
label=st.session_state[st.session_state[variables.selectbox_id_submitted_uuid]],
colorblind_mode=colorblind_mode,
key=_id_of_key,
plot_generator=plot_generator,
)
df_display = prepare_display_dataframe(st.session_state[variables.all_datapoints_submitted], highlight_point_id)
grid_options = configure_aggrid(df_display)
# prepare df key explicitly for tab 4
key = variables.table_new_results_uuid
if key not in st.session_state.keys():
st.session_state[key] = uuid.uuid4()
_id_of_key = st.session_state[key]
render_aggrid(df_display, grid_options, key=str(_id_of_key))
st.title("Public submission")
st.markdown(
"If you want to make this point — and the associated data —"
"publicly available, please go to the tab 'Public Submission'"
)
########################################################################################
# helper functions
[docs]
def initialize_submitted_data_points(
all_datapoints_submitted: str,
obtain_all_data_points: Callable,
) -> None:
"""
Initialize the all_datapoints variable in the session state.
"""
key_in_state = all_datapoints_submitted
if key_in_state not in st.session_state.keys():
st.session_state[key_in_state] = None
# ? So all_datapoints argument is always None?
st.session_state[key_in_state] = obtain_all_data_points(
all_datapoints=st.session_state[key_in_state],
)
[docs]
def display_metric_selector(variables) -> str:
key = variables.metric_selector_submitted_uuid
if key not in st.session_state.keys():
st.session_state[key] = uuid.uuid4()
_id_of_key = st.session_state[key]
# TODO: Add "ROC-AUC" to options list to enable ROC-AUC metric display
return st.radio(
"Metric",
options=["Median", "Mean"],
help="Toggle between median and mean absolute difference metrics.",
key=_id_of_key,
)
[docs]
def display_colorblindmode_selector(variables) -> str:
key = variables.colorblind_mode_selector_submitted_uuid
if key not in st.session_state.keys():
st.session_state[key] = uuid.uuid4()
_id_of_key = st.session_state[key]
return st.toggle(
"Colorblind Mode",
help="Toggle colorblind mode on or off.",
key=_id_of_key,
)
[docs]
def display_metric_calc_approach_selector(variables) -> str:
key = variables.metric_calc_approach_selector_submitted_uuid
if key not in st.session_state.keys():
st.session_state[key] = uuid.uuid4()
_id_of_key = st.session_state[key]
return st.radio(
"Weighting approach",
options=["Global", "Species-weighted"],
help="Toggle between Species-weighted and global absolute difference metrics.",
key=_id_of_key,
)
