first commit

section_3_graph_library_module/pyproject.toml

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+# scripts/graph_generation_library/pyproject.toml
+[build-system]
+requires = ["setuptools", "wheel"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "section_3_graph_library_module"
+version = "0.1.0"
+description = "Internal PASBDC graph creation functions used to generate figures for the network wide desk reviews."
+
+[tool.setuptools]
+packages = ["section_3_graph_library_module"]
+
+

section_3_graph_library_module/section_3_graph_library_module.egg-info/PKG-INFO

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+Metadata-Version: 2.4
+Name: section_3_graph_library_module
+Version: 0.1.0
+Summary: Internal PASBDC graph creation functions used to generate figures for the network wide desk reviews.

section_3_graph_library_module/section_3_graph_library_module.egg-info/SOURCES.txt

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+pyproject.toml
+section_3_graph_library_module/__init__.py
+section_3_graph_library_module/trainings_topic_per_center_analysis.py
+section_3_graph_library_module.egg-info/PKG-INFO
+section_3_graph_library_module.egg-info/SOURCES.txt
+section_3_graph_library_module.egg-info/dependency_links.txt
+section_3_graph_library_module.egg-info/top_level.txt

section_3_graph_library_module/section_3_graph_library_module.egg-info/dependency_links.txt

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+

section_3_graph_library_module/section_3_graph_library_module.egg-info/top_level.txt

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+section_3_graph_library_module

section_3_graph_library_module/section_3_graph_library_module/__init__.py

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+from .trainings_topic_per_center_analysis import make_center_topic_analysis
+
+__all__ = [
+    'make_center_topic_analysis'
+]

section_3_graph_library_module/section_3_graph_library_module/trainings_topic_per_center_analysis.py

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+# FILE: trainings_topic_per_center_analysis.py
+# CREATED: 12/24/25
+# AUTHOR: Vincent Allen
+# CONTACT: vincent@vtallen.com valle276@live.kutztown.edu
+# PURPOSE:
+# This file contains plotly figure generating library functions that produce a report for a single center containing info about the topics
+# that appear within trainings for that center.
+# This includes a view of how many events per topic and how many attendees per topic 
+
+# A percentage graph is provided for each view that simply uses percents out of all events in the center instead of a raw count
+
+# third party libraries
+import pandas as pd
+import plotly.graph_objects as go
+import plotly.express as px
+
+# python modules
+from enum import Enum
+from typing import Dict
+
+# Custom modules
+from section_1_graph_library_module import (
+    StatChartVariants
+)
+
+from constants_module import NEOSERRA_COLUMNS, OUT_COLUMNS
+
+def make_center_topic_analysis(
+    trainings_df:pd.DataFrame, 
+    center_name:str,
+    fiscal_year_tag:str="FY25",
+    col_neo_center:str=NEOSERRA_COLUMNS.center,
+    col_neo_primary_topic:str=NEOSERRA_COLUMNS.primary_training_topic
+) -> Dict[StatChartVariants, go.Figure]:
+    """
+    parameters:
+        trainings_df:pd.DataFrame - The cleaned trainings data from Neoserra
+        center_name:str - The name of the center being processed (For graph titles)
+        fiscal_year_tag:str - The fiscal year of the provided data (for graph titles)
+        col_neo_center:str - The column at which the center name can be found in the trainings_df
+        col_primary_topic:str - The column at which the primary training topic can be found in trainings_df
+
+    returns: Dict[CenterTopicAnalysisVarients, go.Figure]
+
+    description:
+        Using the cleaned and prepped list of all training events in Neoserra, this function generates a center specific report containing
+        bar charts where the x axis is the primary training topics found in that center's events and the y axis is either the number of events 
+        that had that topic, or the number of attendees who went to events with that topic.
+
+        Both total count and percentage views are provided.
+
+        The returned dictionary will have a figure object for each enum key defined in CenterTopicAnalysisVarients
+    """
+
+    collected_graphs = {}
+
+    center_data = trainings_df[trainings_df[col_neo_center] == center_name]
+
+    # Attended event counts only
+    event_counts_attended = center_data[center_data[NEOSERRA_COLUMNS.attendees_total] >= 1].groupby(NEOSERRA_COLUMNS.primary_training_topic).size().reset_index(name='Event Counts') #pyright:ignore
+    attended_count_fig = px.bar(
+        event_counts_attended,
+        x=col_neo_primary_topic,
+        y='Event Counts',
+        text='Event Counts',
+        title=f'Count of Events for {center_name} by Primary Training Topic Excluding Zero Attendee Events {fiscal_year_tag}',
+        width=1250,
+        height=600
+    )
+    total_events = event_counts_attended['Event Counts'].sum()
+    attended_count_fig.add_annotation(
+            showarrow=False,
+            text=f'Sum of Events: {total_events}',
+            xref='paper',
+            yref='paper',
+            x=0,
+            y=1.04
+        )
+    attended_count_fig.update_layout(font_family="Futura",title_font_family="Futura") 
+    attended_count_fig.update_traces(showlegend=False, marker_color='#197f60') 
+    collected_graphs[StatChartVariants.TOTAL_ATTENDED] = attended_count_fig
+
+    # Percent graph
+    attended_percent_fig = px.bar(
+        event_counts_attended.assign(Percent=lambda x: x['Event Counts'] / x['Event Counts'].sum() * 100),
+        x=col_neo_primary_topic,
+        y='Percent',
+        text='Percent',
+        title=f'Percent of Events for {center_name} by Primary Training Topic Excluding Zero Attendee Events {fiscal_year_tag}',
+        width=1250,
+        height=600
+    )
+    total_events = event_counts_attended['Event Counts'].sum()
+    attended_percent_fig.add_annotation(
+            showarrow=False,
+            text=f'Sum of Events: {total_events}',
+            xref='paper',
+            yref='paper',
+            x=0,
+            y=1.04
+        )
+    attended_percent_fig.update_layout(
+        yaxis_title='Percent of Events',
+        font_family="Futura",
+        title_font_family="Futura"
+    ) 
+    attended_percent_fig.update_traces(
+        texttemplate='%{text:.1f}%', 
+        showlegend=False, 
+        marker_color='#197f60') 
+    collected_graphs[StatChartVariants.PERCENT_ATTENDED] = attended_percent_fig
+
+    # Attendee counts including 0 attendee events
+    attendee_counts = center_data.groupby(col_neo_primary_topic)[NEOSERRA_COLUMNS.attendees_total].sum().reset_index(name='Attendee Counts') #pyright:ignore
+    total_attendees_fig = px.bar(
+        attendee_counts,
+        x=col_neo_primary_topic,
+        y='Attendee Counts',
+        text='Attendee Counts',
+        title=f'Count of Attendees for {center_name} by Primary Training Topic {fiscal_year_tag}',
+        width=1250,
+        height=600
+    )
+    
+    total_attendees = attendee_counts['Attendee Counts'].sum()
+    total_attendees_fig.add_annotation(
+            showarrow=False,
+            text=f'Total Attendees: {total_attendees}',
+            xref='paper',
+            yref='paper',
+            x=0,
+            y=1.04
+        )
+    total_attendees_fig.update_layout(font_family="Futura",title_font_family="Futura") 
+    total_attendees_fig.update_traces(showlegend=False, marker_color='#71bf44') 
+    collected_graphs[StatChartVariants.TOTAL_COUNT] = total_attendees_fig
+
+    # Percent graph
+    percent_attendees_fig = px.bar(
+        attendee_counts.assign(Percent=lambda x: x['Attendee Counts'] / x['Attendee Counts'].sum() * 100),
+        x=col_neo_primary_topic,
+        y='Percent',
+        text='Percent',
+        title=f'Percent of Attendees for {center_name} by Primary Training Topic {fiscal_year_tag}',
+        width=1250,
+        height=600
+    )
+    
+    total_attendees = attendee_counts['Attendee Counts'].sum()
+    percent_attendees_fig.add_annotation(
+            showarrow=False,
+            text=f'Total Attendees: {total_attendees}',
+            xref='paper',
+            yref='paper',
+            x=0,
+            y=1.04
+        )
+    percent_attendees_fig.update_layout(
+        yaxis_title='Percent of Total Attendees',
+        font_family="Futura",
+        title_font_family="Futura") 
+    percent_attendees_fig.update_traces(
+        texttemplate='%{text:.1f}%', 
+        showlegend=False,
+        marker_color='#71bf44') 
+
+    collected_graphs[StatChartVariants.TOTAL_PERCENT] = percent_attendees_fig
+
+    return collected_graphs