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from typing import List, Dict, Any
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import logging
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import pandas as pd
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from fiscalyear import FiscalYear
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from streamlit.delta_generator import DeltaGenerator
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import streamlit as st
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from constants_module import TRAINING_COUNT_COLUMNS, NEOSERRA_COLUMNS, OUT_COLUMNS
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from streamlit_constants import DASHBOARD_CONFIG_OBJECT_KEY
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from utility_classes.base_report_page import BaseReportPage
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from cached_function_wrappers.shared import get_df_centers
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from cached_function_wrappers.trainings_cached_functions import cached_generate_cleaned_trainings_dataset
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from section_1_graph_library_module import (
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make_attendee_bins_statistics_charts
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)
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from shared_tools_module import StatChartVariants
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from utility_classes.figure_with_max_y import find_fig_max_y_and_generate_wrapper, FigureWithMaxY
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from utility_classes.dashboard_config_parser import DashboardConfig, ExportModulePair
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class TrainingAttendeeRanges(BaseReportPage):
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"""
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Concrete implementation of a report page analyzing training attendee size distributions.
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This class manages the pipeline for categorizing training events into attendee size brackets (bins).
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It isolates specific training types—such as 'First Steps' and 'Preplanning'—to evaluate how
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introductory courses impact the overall network attendee size distributions.
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:param kwargs: Arbitrary keyword arguments passed to the parent BaseReportPage constructor.
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"""
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def __init__(self, **kwargs):
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"""
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Initializes the temporal boundaries and configuration state for the attendee ranges report.
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Captures current and previous fiscal years to manage report filtering and extracts
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the global application configuration to resolve the appropriate external data endpoints.
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:param kwargs: Arbitrary keyword arguments.
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"""
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super().__init__("Network Wide Training Attendee Ranges")
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self.fiscal_year = FiscalYear.current()
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self.prev_fiscal_year = self.fiscal_year.prev_fiscal_year
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self.fiscal_year_text = f'FY{str(self.fiscal_year.fiscal_year)[2:]}'
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self.prev_fiscal_year_text = f'FY{str(self.prev_fiscal_year.fiscal_year)[2:]}'
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# Grab the app config so we can use it to get the export module urls
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self.app_config: DashboardConfig = st.session_state[DASHBOARD_CONFIG_OBJECT_KEY]
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self.logger = logging.getLogger(__name__)
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def get_fiscal_year_export_url(self, selected_fiscal_year):
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"""
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Resolves the external dataset endpoint based on the active temporal state.
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Maps the user's selected fiscal year to the appropriate data URL, ensuring the pipeline
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fetches the correct historical or current training records.
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:param selected_fiscal_year: The string representation of the chosen fiscal year.
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:type selected_fiscal_year: Any
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:return: The URL for the corresponding dataset export.
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:rtype: str
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"""
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export_urls:ExportModulePair = self.app_config.get_trainings_urls()
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if selected_fiscal_year == self.fiscal_year_text:
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return export_urls.current_fy
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else:
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return export_urls.prev_fy
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@staticmethod
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def get_page_name():
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"""
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Provides the static display identifier for this report module.
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Utilized by dashboard orchestrators to construct routing and navigation menus.
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:return: The human-readable name of the report.
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:rtype: str
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"""
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return "Training Attendee Ranges"
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def render_controls(self, container: DeltaGenerator) -> Dict[str, Any]:
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"""
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Defines the user input interface and establishes a safe execution boundary.
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Renders selection widgets for fiscal year and target centers. Implements a strict fail-fast
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pattern that halts the Streamlit execution sequence if the baseline dataset fails to load,
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preventing downstream rendering errors.
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:param container: The Streamlit container to attach the input widgets to.
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:type container: DeltaGenerator
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:return: A dictionary containing the user-selected fiscal year and centers.
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:rtype: Dict[str, Any]
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"""
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report_settings_expander = container.expander(
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label="Report Options",
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expanded=True,
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key=self.get_widget_key("report_settings_expander")
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)
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report_settings_expander.markdown("## Dataset Options")
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report_settings_expander.markdown("These settings will modify the input dataset used to generate the graphs.")
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selected_fiscal_year = report_settings_expander.selectbox(
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label="Fiscal Year",
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options=[self.prev_fiscal_year_text, self.fiscal_year_text],
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index=1,
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key=self.get_widget_key("selected_fiscal_year_selectbox")
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)
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reportable_only = report_settings_expander.checkbox(label="Reportable only?", value=True, key=self.get_widget_key("reportable_only_checkbox"))
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include_future_events = report_settings_expander.checkbox(label="Include Future Events?", value=False, key=self.get_widget_key("include_future_events_checkbox"))
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include_on_demand = report_settings_expander.checkbox(label="Include On-Demand Events?", value=True, key=self.get_widget_key("include_on_demand_checkbox"))
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export_url = self.get_fiscal_year_export_url(selected_fiscal_year)
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try:
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all_centers = get_df_centers(export_url)
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except Exception as e:
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self.logger.exception(f"Failed to fetch the dataset for this page: {e}")
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container.error(
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"Failed to get the list of all centers for the dataset for this page. A detailed error message has been added to the logs.")
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st.stop()
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selected_centers = report_settings_expander.multiselect(label="Centers", options=all_centers,
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default=all_centers,
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key=self.get_widget_key("selected_centers_multiselect"))
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return {
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"selected_fiscal_year":selected_fiscal_year,
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"selected_centers":selected_centers,
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"reportable_only":reportable_only,
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"include_future_events":include_future_events,
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"include_on_demand":include_on_demand
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}
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def generate_figures(self, parameters: Dict[str, Any]) -> Dict[str, Any]:
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"""
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Executes the analytical data pipeline and constructs the binned visualization objects.
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Fetches the trainings dataset and generates a suite of chart variants comparing absolute
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counts against percentages across different training subsets. Computes strict max-Y values
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for the quantity-based charts to support external dynamic axis synchronization.
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:param parameters: The parameter state dictionary captured from the render_controls phase.
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:type parameters: Dict[str, Any]
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:return: A dictionary mapping identifiers to FigureWithMaxY objects and the raw dataframe.
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:rtype: Dict[str, Any]
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"""
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selected_fiscal_year: str = parameters["selected_fiscal_year"]
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selected_centers: List[str] = parameters["selected_centers"]
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reportable_only:bool = parameters["reportable_only"]
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include_future_events:bool = parameters["include_future_events"]
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include_on_demand:bool = parameters["include_on_demand"]
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export_url = self.get_fiscal_year_export_url(selected_fiscal_year)
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trainings_df = cached_generate_cleaned_trainings_dataset(
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export_url,
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reportable_only=reportable_only,
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allowed_centers=selected_centers,
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include_future_events=include_future_events,
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include_on_demand=include_on_demand
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)
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self.logger.error(f"{trainings_df.info()}")
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try:
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bins_figs = make_attendee_bins_statistics_charts(
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trainings_df,
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center="Network Wide",
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network_label="PASBDC*",
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fiscal_year_tag=selected_fiscal_year,
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first_steps_vals=['First Steps', 'Next Steps'],
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preplanning_val=OUT_COLUMNS.val_preplanning,
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col_neo_attendees_total=NEOSERRA_COLUMNS.attendees_total,
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col_attendees_range=OUT_COLUMNS.attendees_range,
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col_neo_primary_topic=NEOSERRA_COLUMNS.primary_training_topic
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)
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except Exception as e:
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self.logger.exception(f"Failed to generate figures for this page: {e}")
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st.error(f"Failed to generate the figures for this page. A detailed error has been added to the logs. {self.app_config.get_errors_contact_string()}")
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st.stop()
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return {
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'total_count':find_fig_max_y_and_generate_wrapper(bins_figs[StatChartVariants.TOTAL_COUNT]),
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'total_percent':FigureWithMaxY(figure=bins_figs[StatChartVariants.TOTAL_PERCENT], max_y=0.0),
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'no_first_no_pre_count':find_fig_max_y_and_generate_wrapper(bins_figs[StatChartVariants.NO_FIRST_NO_PREPLANNNG_COUNT]),
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'no_first_no_pre_percent':FigureWithMaxY(figure=bins_figs[StatChartVariants.NO_FIRST_NO_PREPLANNNG_PERCENT], max_y=0.0),
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'first_pre_only_count':find_fig_max_y_and_generate_wrapper(bins_figs[StatChartVariants.FIRST_AND_PREPLANNING_ONLY]),
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'first_pre_only_percent':FigureWithMaxY(figure=bins_figs[StatChartVariants.FIRST_AND_PREPLANNING_ONLY_PERCENT], max_y=0.0),
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'trainings_df':trainings_df
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}
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def render_figures(self, container: DeltaGenerator, output_data: Dict[str, Any]):
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"""
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Iteratively maps the generated paired visual artifacts to the Streamlit layout.
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Arranges the charts sequentially using a repetitive 2-column layout to directly contrast
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absolute counts with proportional percentages for each training subset. Exposes the raw
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underlying dataset via an expander module to ensure data auditing and transparency.
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:param container: The Streamlit layout container for the visuals.
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:type container: DeltaGenerator
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:param output_data: The dictionary of computed figures and dataframes from generate_figures.
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:type output_data: Dict[str, Any]
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"""
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chart_pairs = [
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("All Trainings", "total_count", "total_percent"),
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("Excluding First Steps & Preplanning", "no_first_no_pre_count", "no_first_no_pre_percent"),
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("First Steps & Preplanning Only", "first_pre_only_count", "first_pre_only_percent"),
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]
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trainings_df:pd.DataFrame = output_data.get("trainings_df")
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for title, count_key, percent_key in chart_pairs:
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# Add a subheader for the section
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container.subheader(title)
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# Create a 2-column layout
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col1, col2 = container.columns(2)
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# Extract and render the count figure
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count_fig = output_data.get(count_key)['figure']
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if count_fig:
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col1.plotly_chart(count_fig, use_container_width=True, key=self.get_widget_key(count_key))
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# Extract and render the percentage figure
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percent_fig = output_data.get(percent_key)['figure']
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if percent_fig:
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col2.plotly_chart(percent_fig, use_container_width=True, key=self.get_widget_key(percent_key))
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# Add a horizontal line to separate sections cleanly
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container.divider()
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dataset_expander = container.expander(
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label="Source Datasets",
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expanded=True,
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key=self.get_widget_key("dataset_expander")
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)
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dataset_expander.markdown("## Source Data")
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dataset_expander.markdown("### Neoserra Trainings Dataset")
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dataset_expander.write(trainings_df)
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def get_syncable_figure_keys(self) -> List[str]:
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"""
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Declares the specific figures that permit dynamic external Y-axis scaling.
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Explicitly isolates the absolute count charts for synchronization, filtering out the
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percentage-based charts to ensure external axis scaling does not distort proportional data.
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:return: A list of dictionary keys corresponding to absolute count figures.
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:rtype: List[str]
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"""
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return ["total_count", "no_first_no_pre_count", "first_pre_only_count"]
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