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slide script wip
This commit is contained in:
Khalim Conn-Kowlessar
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commit
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2 changed files with 149 additions and 38 deletions
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@ -1,65 +1,119 @@
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import matplotlib.pyplot as plt
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from sqlalchemy.orm import Session
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from backend.app.db.utils import row2dict
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from backend.app.db.models.portfolio import PropertyModel
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from backend.app.db.models.portfolio import PropertyModel, PropertyDetailsEpcModel
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from backend.app.db.models.recommendations import Recommendation
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EPC_COLOURS = {
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"A": "#008054",
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"B": "#1ab559",
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"C": "#8ccf45",
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"D": "#ffd600",
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"E": "#fcab66",
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"F": "#f08024",
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"G": "#e8143b"
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"A": "#028051",
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"B": "#14b759",
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"C": "#8ecd46",
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"D": "#fdd401",
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"E": "#fdab67",
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"F": "#ee8023",
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"G": "#e71437"
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}
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def get_properties_by_portfolio_id(session: Session, portfolio_id: int):
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def get_properties_with_default_recommendations(session: Session, portfolio_id: int):
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"""
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This function retrieves all properties associated with a given portfolio_id.
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Fetch properties for a given portfolio_id along with their default recommendations,
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minimizing database queries.
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:param session: The SQLAlchemy session used to execute the query.
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:param portfolio_id: The ID of the portfolio for which to retrieve properties.
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:return: A list of dictionaries, where each dictionary represents a property.
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Returns an empty list if no properties are found.
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:param portfolio_id: The ID of the portfolio for which to retrieve properties and recommendations.
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:return: A list of dictionaries, where each dictionary represents a property including
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its associated default recommendations.
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"""
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properties = session.query(PropertyModel).filter(PropertyModel.portfolio_id == portfolio_id).all()
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# Perform a query that joins PropertyModel and Recommendation, filtering by portfolio_id and default=True
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query = session.query(PropertyModel, Recommendation).join(Recommendation, isouter=True) \
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.filter(PropertyModel.portfolio_id == portfolio_id, Recommendation.default == True) \
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.all()
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# Process the query results
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properties = {}
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for property, recommendation in query:
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# Check if the property is already added to the dictionary
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if property.id not in properties:
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properties[property.id] = row2dict(property)
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properties[property.id]['recommendations'] = []
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# Add recommendation to the property if it exists
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if recommendation:
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properties[property.id]['recommendations'].append(row2dict(recommendation))
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return list(properties.values())
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def get_property_details_by_portfolio_id(session: Session, portfolio_id: int):
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"""
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This function retrieves all property details associated with a given portfolio_id.
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:param session: The SQLAlchemy session used to execute the query.
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:param portfolio_id: The ID of the portfolio for which to retrieve property details.
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:return: A list of dictionaries, where each dictionary represents a property's details.
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Returns an empty list if no property details are found.
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"""
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property_details = session.query(PropertyDetailsEpcModel).filter(
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PropertyDetailsEpcModel.portfolio_id == portfolio_id).all()
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# Convert the SQLAlchemy objects to dictionaries
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properties_dict = [row2dict(p) for p in properties] if properties else []
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property_details_dict = [row2dict(pd) for pd in property_details] if property_details else []
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return properties_dict
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return property_details_dict
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def plot_epc_distribution(df, title='Your units', figsize=(10, 6)):
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def plot_epc_distribution(df, title='Your Units', background_color='white', bar_height=0.4, font_size=15):
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"""
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Plots a horizontal bar chart of EPC rating distribution with percentages annotated on the bars.
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Plots a horizontal bar chart of EPC rating distribution with adjustable bar thickness and text sizes.
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Allows setting the plot background color and dynamically adjusts text size and bar spacing.
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:param df: DataFrame with columns ['current_epc_rating', 'count', 'percentage']
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:param title: Title of the plot (default is 'EPC Rating Distribution by Percentage')
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:param figsize: Figure size as a tuple (default is (10, 6))
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:param title: Title of the plot
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:param background_color: Background color of the plot
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:param bar_height: Thickness of the bars (default 0.4)
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:param font_size: Base font size for text annotations (default 15)
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"""
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# Sort the DataFrame for a consistent plotting order
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# Calculate dynamic figure size or adjust based on preferences
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square_size = max(6, len(df) * 0.6) # Ensure minimum size and adjust based on number of entries
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fig, ax = plt.subplots(figsize=(square_size, square_size))
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fig.patch.set_facecolor(background_color) # Set figure background color
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ax.set_facecolor(background_color) # Set axes background color
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df['percentage'] = df['percentage'].round(1) # Round the percentage values to 1 decimal place
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df_sorted = df.sort_values('percentage', ascending=True)
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colors = df_sorted['current_epc_rating'].map(EPC_COLOURS) # Map the EPC ratings to colors
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# Plot bars with specified height for adjustable thickness
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bars = ax.barh(df_sorted['current_epc_rating'], df_sorted['percentage'],
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color=df_sorted['current_epc_rating'].map(EPC_COLOURS), edgecolor='none', height=bar_height)
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# Create the horizontal bar chart
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plt.figure(figsize=figsize)
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bars = plt.barh(df_sorted['current_epc_rating'], df_sorted['percentage'], color=colors)
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epc_rating_font_size = font_size * 2 # EPC rating font size larger than base font size
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count_percentage_font_size = font_size # Count (percentage) font size as base font size
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# Annotate the bars with percentage values
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for bar in bars:
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# Annotate bars with EPC ratings inside and count with percentage values outside
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for index, bar in enumerate(bars):
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width = bar.get_width()
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label_x_pos = width + 1 # Adjust the offset for the label if necessary
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plt.text(label_x_pos, bar.get_y() + bar.get_height() / 2, f'{width}%', va='center')
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epc_rating = df_sorted.iloc[index]['current_epc_rating']
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count = df_sorted.iloc[index]['count']
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percentage = df_sorted.iloc[index]['percentage']
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# Customize the plot aesthetics for better readability and presentation
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plt.xlabel('Percentage')
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plt.ylabel('EPC Rating')
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plt.title(title)
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plt.tight_layout() # Adjust layout to not cut off labels
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plt.grid(axis='x', linestyle='--') # Add a light grid for better readability
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# EPC rating inside the bar with increased font size
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ax.text(width - (width * 0.05), bar.get_y() + bar.get_height() / 2,
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f"{epc_rating}", va='center', ha='right', color='white', fontsize=epc_rating_font_size)
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# Show the plot
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# Count and percentage outside the bar, original font size
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ax.text(width + 1, bar.get_y() + bar.get_height() / 2,
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f"{count} ({percentage}%)", va='center', color='black', fontsize=count_percentage_font_size)
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ax.set_title(title, fontsize=font_size * 1.2) # Adjust title font size proportionally
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ax.tick_params(axis='x', which='both', bottom=False, top=False,
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labelbottom=False) # Remove x-axis tick marks and values
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ax.tick_params(axis='y', which='both', left=False, right=False,
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labelleft=False) # Remove y-axis tick marks and labels
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ax.spines['top'].set_visible(False) # Remove top spine
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ax.spines['right'].set_visible(False) # Remove right spine
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ax.spines['left'].set_visible(False) # Remove left spine
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ax.spines['bottom'].set_visible(False) # Remove bottom spine
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plt.tight_layout() # Adjust layout
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plt.show()
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@ -6,7 +6,11 @@ a environment akin to the backend to run this script
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import pandas as pd
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from backend.app.db.connection import db_engine
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from sqlalchemy.orm import sessionmaker
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from etl.customers.slide_utils import get_properties_by_portfolio_id, plot_epc_distribution
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from etl.customers.slide_utils import (
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plot_epc_distribution,
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get_property_details_by_portfolio_id,
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get_properties_with_default_recommendations
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)
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PORTFOLIO_ID = 66
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@ -15,8 +19,12 @@ def app():
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# Connect to database
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session = sessionmaker(bind=db_engine)()
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########################################################################
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# Get the data we need
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########################################################################
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# Get the properties for the portfolio
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properties = get_properties_by_portfolio_id(session, PORTFOLIO_ID)
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properties = get_properties_with_default_recommendations(session, PORTFOLIO_ID)
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# The first visual we want to produce is a horizontal bar chart showing the number of properties at each current
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# EPC band
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@ -24,3 +32,52 @@ def app():
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properties_df = pd.DataFrame(properties)
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epc_rating_summary = properties_df.groupby("current_epc_rating").size().reset_index(name="count")
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epc_rating_summary["percentage"] = epc_rating_summary["count"] / epc_rating_summary["count"].sum() * 100
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# We now pull the data for the property details
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property_details = get_property_details_by_portfolio_id(session, PORTFOLIO_ID)
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property_details_df = pd.DataFrame(property_details)
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########################################################################
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# We pull out the data for the slides
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########################################################################
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############
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# Slide 1:
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############
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# visual
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plot_epc_distribution(epc_rating_summary, title="", background_color="white", bar_height=0.75, font_size=15)
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# floor area - upper and lower bounds
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min_area, max_area, average_area = (
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property_details_df["total_floor_area"].min(),
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property_details_df["total_floor_area"].max(),
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property_details_df["total_floor_area"].median()
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)
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# Annual energy consumption - upper and lower bounds
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min_energy_consumption, max_energy_consumption, average_consumption = (
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property_details_df["adjusted_energy_consumption"].min(),
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property_details_df["adjusted_energy_consumption"].max(),
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property_details_df["adjusted_energy_consumption"].median()
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)
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# Co2 emissions - upper and lower bounds
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min_co2, max_co2, average_co2 = (
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property_details_df["co2_emissions"].min(),
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property_details_df["co2_emissions"].max(),
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property_details_df["co2_emissions"].median()
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)
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# Valuation: upper and lower bounds - TODO!
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min_valuation, max_valuation, average_valuation = 0, 0, 0
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############
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# Slide 2:
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############
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# What it would take to hit EPC C
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# This is the number of properties that are below a C
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n_units = properties_df[properties_df["current_epc_rating"].isin(["D", "E", "F", "G"])].shape[0]
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# We calculate the number of units that will make it to an EPC C
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n_units_to_epc_c = ()
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