"""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 proteobench.plotting.plot_quant import PlotDataPoint
from .filter import filter_data_using_slider
[docs]
def initialize_submitted_slider(slider_id_submitted_uuid, default_val_slider) -> None:
"""
Initialize the slider for the submitted data.
"""
id_key_in_state = slider_id_submitted_uuid
if id_key_in_state not in st.session_state.keys():
st.session_state[id_key_in_state] = uuid.uuid4()
if st.session_state[id_key_in_state] not in st.session_state.keys():
value_key_in_state = st.session_state[id_key_in_state] # the uuid4 as key
st.session_state[value_key_in_state] = default_val_slider
[docs]
def generate_submitted_slider(variables_quant) -> None:
"""
Create a slider input.
"""
if variables_quant.slider_id_submitted_uuid not in st.session_state:
st.session_state[variables_quant.slider_id_submitted_uuid] = uuid.uuid4()
slider_key = st.session_state[variables_quant.slider_id_submitted_uuid]
st.markdown(open(variables_quant.description_slider_md, "r", encoding="utf-8").read())
st.select_slider(
label="Minimal precursor quantifications (# samples)",
options=[1, 2, 3, 4, 5, 6],
value=st.session_state.get(slider_key, variables_quant.default_val_slider),
key=slider_key,
)
[docs]
def generate_submitted_selectbox(variables_quant) -> None:
"""
Create the selectbox for the Streamlit UI.
"""
if variables_quant.selectbox_id_submitted_uuid not in st.session_state.keys():
st.session_state[variables_quant.selectbox_id_submitted_uuid] = uuid.uuid4()
try:
st.selectbox(
"Select label to plot",
["None", "precursor_mass_tolerance", "fragment_mass_tolerance"],
key=st.session_state[variables_quant.selectbox_id_submitted_uuid],
)
except Exception as e:
st.error(f"Unable to create the selectbox: {e}", icon="🚨")
[docs]
def display_submitted_results(variables_quant, 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_quant.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_quant.slider_id_submitted_uuid,
all_datapoints=key_all_datapoints_submitted,
filter_data_point=ionmodule.filter_data_point,
)
metric = st.radio(
"Select metric to plot",
options=["Median", "Mean"],
help="Toggle between median and mean absolute difference metrics.",
key="placeholder2", # TODO: add to variables
)
if len(data_points_filtered) == 0:
st.error("No datapoints available for plotting", icon="🚨")
return
try:
fig_metric = PlotDataPoint.plot_metric(
data_points_filtered,
metric=metric,
label=st.session_state[st.session_state[variables_quant.selectbox_id_submitted_uuid]],
)
try:
st.plotly_chart(fig_metric, use_container_width=True)
except Exception:
st.error("No (new) datapoints available for plotting", icon="🚨")
return
except Exception as e:
st.error(f"Unable to plot the datapoints: {e}", icon="🚨")
st.session_state[variables_quant.table_id_uuid] = uuid.uuid4()
st.data_editor(
st.session_state[variables_quant.all_datapoints_submitted],
key=st.session_state[variables_quant.table_id_uuid],
on_change=handle_submitted_table_edits,
args=(variables_quant,),
)
st.title("Public submission")
st.markdown(
"If you want to make this point — and the associated data —"
"publicly available, please go to “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 handle_submitted_table_edits(variables_quant) -> None:
"""Callback function for handling edits made to the data table in the UI."""
edits = st.session_state[st.session_state[variables_quant.table_id_uuid]]["edited_rows"].items()
for k, v in edits:
try:
# ToDo: check if this can be done more clearly
# ? gets row k from the all_datapoints_submitted DataFrame
# ? and ... modifies the first column specified in the keys of dictionary v
# df = st.session_state[variables_quant.all_datapoints_submitted]
# df[list(v.keys())[0]].iloc[k] = list(v.values())[0]
st.session_state[variables_quant.all_datapoints_submitted][list(v.keys())[0]].iloc[k] = list(v.values())[0]
except TypeError:
return
st.session_state[variables_quant.highlight_list_submitted] = list(
st.session_state[variables_quant.all_datapoints_submitted]["Highlight"]
)
st.session_state[variables_quant.placeholder_table] = st.session_state[variables_quant.all_datapoints_submitted]
if len(st.session_state[variables_quant.all_datapoints]) == 0:
st.error("No datapoints available for plotting", icon="🚨")
try:
fig_metric = PlotDataPoint.plot_metric(
st.session_state[variables_quant.all_datapoints],
label=st.session_state[st.session_state[variables_quant.selectbox_id_uuid]],
)
except Exception as e:
st.error(f"Unable to plot the datapoints: {e}", icon="🚨")
st.session_state[variables_quant.fig_metric] = fig_metric
