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Update data/climate_data.py
Browse files- data/climate_data.py +378 -275
data/climate_data.py
CHANGED
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@@ -1,10 +1,6 @@
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"""
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ASHRAE 169 climate data module for HVAC Load Calculator.
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This module provides access to climate data for various locations based on ASHRAE 169 standard.
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Author: Dr Majed Abuseif
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Date: March 2025
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Version: 1.0.0
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"""
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from typing import Dict, List, Any, Optional, Tuple
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import os
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import json
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from dataclasses import dataclass
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import streamlit as st
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import plotly.graph_objects as go
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from io import StringIO
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# Define paths
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DATA_DIR = os.path.dirname(os.path.abspath(__file__))
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@@ -35,10 +28,14 @@ class ClimateLocation:
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climate_zone: str
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heating_degree_days: float # base 18°C
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cooling_degree_days: float # base 18°C
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winter_design_temp: float # 99.6% heating design temperature (°C)
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summer_design_temp_db: float # 0.4% cooling design dry-bulb temperature (°C)
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summer_design_temp_wb: float # 0.4% cooling design wet-bulb temperature (°C)
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summer_daily_range: float # Mean daily temperature range in summer (°C)
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monthly_temps: Dict[str, float] # Average monthly temperatures (°C)
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monthly_humidity: Dict[str, float] # Average monthly relative humidity (%)
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@@ -69,303 +66,409 @@ class ClimateData:
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def __init__(self):
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"""Initialize climate data."""
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self.locations =
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self.countries =
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self.country_states =
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def _group_locations_by_country_state(self) -> Dict[str, Dict[str, List[str]]]:
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"""
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result = {}
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for loc in self.locations.values():
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if loc.country not in result:
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result[loc.country] = {}
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if loc.state_province not in result[loc.country]:
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result[loc.country][loc.state_province] = []
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result[loc.country][loc.state_province].append(loc.city)
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for country in result:
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for state in result[country]:
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result[country][state] = sorted(result[country][state])
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return result
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def
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"""
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self.countries = sorted(list(set(loc.country for loc in self.locations.values())))
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self.country_states = self._group_locations_by_country_state()
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def display_climate_input(self, session_state: Dict[str, Any]):
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"""Display form for manual input or EPW upload in Streamlit."""
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st.title("Climate Data")
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epw_data = pd.read_csv(StringIO("\n".join(epw_lines[8:])), header=None)
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if len(epw_data) != 8760:
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raise ValueError("EPW file must contain 8760 hourly records.")
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months = epw_data[1].values # Month column
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dry_bulb = epw_data[6].values # Dry-bulb temperature
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wet_bulb = epw_data[8].values # Wet-bulb temperature
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humidity = epw_data[9].values # Relative humidity
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# Calculate daily averages for HDD/CDD
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daily_temps = dry_bulb.reshape(-1, 24).mean(axis=1)
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hdd = round(sum(max(18 - temp, 0) for temp in daily_temps))
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cdd = round(sum(max(temp - 18, 0) for temp in daily_temps))
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# Design conditions (ASHRAE standards)
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winter_design_temp = round(np.percentile(dry_bulb, 0.4), 1) # 99.6% heating design
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summer_design_temp_db = round(np.percentile(dry_bulb, 99.6), 1) # 0.4% cooling design DB
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summer_idx = np.argmax(dry_bulb >= summer_design_temp_db)
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summer_design_temp_wb = round(wet_bulb[summer_idx], 1) # Corresponding WB
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summer_mask = (months >= 6) & (months <= 8)
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summer_temps = dry_bulb[summer_mask].reshape(-1, 24)
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summer_daily_range = round(np.mean(summer_temps.max(axis=1) - summer_temps.min(axis=1)), 1)
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# Monthly averages
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monthly_temps = {}
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monthly_humidity = {}
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month_names = ["Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"]
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for i in range(1, 13):
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month_mask = (months == i)
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monthly_temps[month_names[i-1]] = round(np.mean(dry_bulb[month_mask]), 1)
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monthly_humidity[month_names[i-1]] = round(np.mean(humidity[month_mask]), 1)
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# Assign climate zone
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avg_humidity = np.mean(humidity)
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climate_zone = self.assign_climate_zone(hdd, cdd, avg_humidity)
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location = ClimateLocation(
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id=f"{session_state.building_info['country'][:2].upper()}-{session_state.building_info['city'][:3].upper()}",
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country=session_state.building_info["country"],
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state_province="N/A",
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city=session_state.building_info["city"],
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latitude=latitude,
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longitude=longitude,
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elevation=elevation,
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climate_zone=climate_zone,
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heating_degree_days=hdd,
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cooling_degree_days=cdd,
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winter_design_temp=winter_design_temp,
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summer_design_temp_db=summer_design_temp_db,
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summer_design_temp_wb=summer_design_temp_wb,
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summer_daily_range=summer_daily_range,
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monthly_temps=monthly_temps,
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monthly_humidity=monthly_humidity
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)
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self.add_location(location)
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st.success("Climate data extracted from EPW file!")
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self.display_design_conditions(location)
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self.visualize_data(location)
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except Exception as e:
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st.error(f"Error processing EPW file: {str(e)}. Ensure it has 8760 hourly records and correct format.")
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# Navigation buttons
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col1, col2 = st.columns(2)
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with col1:
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st.button("Back to Building Information", on_click=lambda: setattr(session_state, "page", "Building Information"))
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with col2:
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if self.locations:
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st.button("Continue to Building Components", on_click=lambda: setattr(session_state, "page", "Building Components"))
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else:
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st.button("Continue to Building Components", disabled=True)
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def display_design_conditions(self, location: ClimateLocation):
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"""Display a table of design conditions for calculations."""
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st.subheader("Design Conditions for HVAC Calculations")
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design_data = pd.DataFrame({
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"Parameter": [
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"Climate Zone",
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"Heating Degree Days (base 18°C)",
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"Cooling Degree Days (base 18°C)",
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"Winter Design Temperature (99.6%)",
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"Summer Design Dry-Bulb Temp (0.4%)",
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"Summer Design Wet-Bulb Temp (0.4%)",
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"Summer Daily Temperature Range"
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],
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"Value": [
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location.climate_zone,
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f"{location.heating_degree_days} HDD",
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f"{location.cooling_degree_days} CDD",
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f"{location.winter_design_temp} °C",
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f"{location.summer_design_temp_db} °C",
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f"{location.summer_design_temp_wb} °C",
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f"{location.summer_daily_range} °C"
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]
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})
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st.table(design_data)
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@staticmethod
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def assign_climate_zone(hdd: float, cdd: float, avg_humidity: float) -> str:
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"""Assign ASHRAE 169 climate zone based on HDD, CDD, and humidity."""
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if cdd > 10000:
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return "0A" if avg_humidity > 60 else "0B"
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elif cdd > 5000:
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return "1A" if avg_humidity > 60 else "1B"
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elif cdd > 2500:
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return "2A" if avg_humidity > 60 else "2B"
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elif hdd < 2000 and cdd > 1000:
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return "3A" if avg_humidity > 60 else "3B" if avg_humidity < 40 else "3C"
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elif hdd < 3000:
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return "4A" if avg_humidity > 60 else "4B" if avg_humidity < 40 else "4C"
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elif hdd < 4000:
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return "5A" if avg_humidity > 60 else "5B" if avg_humidity < 40 else "5C"
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elif hdd < 5000:
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return "6A" if avg_humidity > 60 else "6B"
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elif hdd < 7000:
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return "7"
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else:
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return "8"
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@staticmethod
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def visualize_data(location: ClimateLocation):
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"""Visualize monthly temperature and humidity data."""
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st.subheader("Monthly Climate Data Visualization")
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months = list(range(1, 13))
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month_names = ["Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"]
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temps = [location.monthly_temps[m] for m in month_names]
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humidity = [location.monthly_humidity[m] for m in month_names]
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# Temperature Plot
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fig_temp = go.Figure()
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fig_temp.add_trace(go.Scatter(
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x=months,
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y=temps,
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mode='lines+markers',
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name='Temperature (°C)',
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line=dict(color='red')
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))
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fig_temp.update_layout(
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title='Monthly Temperatures',
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xaxis_title='Month',
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yaxis_title='Temperature (°C)',
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xaxis=dict(tickmode='array', tickvals=months, ticktext=month_names)
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)
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st.plotly_chart(fig_temp, use_container_width=True)
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# Humidity Plot
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fig_hum = go.Figure()
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fig_hum.add_trace(go.Scatter(
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x=months,
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y=humidity,
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mode='lines+markers',
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name='Humidity (%)',
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line=dict(color='blue')
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))
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fig_hum.update_layout(
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title='Monthly Relative Humidity',
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xaxis_title='Month',
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yaxis_title='Relative Humidity (%)',
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xaxis=dict(tickmode='array', tickvals=months, ticktext=month_names)
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yaxis=dict(range=[0, 100]), # Set y-axis range from 0 to 100
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)
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st.plotly_chart(fig_hum, use_container_width=True)
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def export_to_json(self, file_path: str) -> None:
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"""
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data = {loc_id: loc.to_dict() for loc_id, loc in self.locations.items()}
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with open(file_path, 'w') as f:
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json.dump(data, f, indent=4)
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| 355 |
@classmethod
|
| 356 |
def from_json(cls, file_path: str) -> 'ClimateData':
|
| 357 |
-
"""
|
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|
| 358 |
with open(file_path, 'r') as f:
|
| 359 |
data = json.load(f)
|
|
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|
| 360 |
climate_data = cls()
|
| 361 |
climate_data.locations = {}
|
|
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|
| 362 |
for loc_id, loc_dict in data.items():
|
| 363 |
-
climate_data.locations[loc_id] = ClimateLocation(
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|
| 364 |
climate_data.countries = sorted(list(set(loc.country for loc in climate_data.locations.values())))
|
| 365 |
climate_data.country_states = climate_data._group_locations_by_country_state()
|
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|
| 366 |
return climate_data
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| 368 |
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|
| 369 |
if __name__ == "__main__":
|
| 370 |
-
climate_data
|
| 371 |
-
climate_data.display_climate_input(st.session_state)
|
|
|
|
| 1 |
"""
|
| 2 |
ASHRAE 169 climate data module for HVAC Load Calculator.
|
| 3 |
This module provides access to climate data for various locations based on ASHRAE 169 standard.
|
|
|
|
|
|
|
|
|
|
|
|
|
| 4 |
"""
|
| 5 |
|
| 6 |
from typing import Dict, List, Any, Optional, Tuple
|
|
|
|
| 9 |
import os
|
| 10 |
import json
|
| 11 |
from dataclasses import dataclass
|
|
|
|
|
|
|
|
|
|
| 12 |
|
| 13 |
# Define paths
|
| 14 |
DATA_DIR = os.path.dirname(os.path.abspath(__file__))
|
|
|
|
| 28 |
climate_zone: str
|
| 29 |
heating_degree_days: float # base 18°C
|
| 30 |
cooling_degree_days: float # base 18°C
|
| 31 |
+
|
| 32 |
+
# Design conditions
|
| 33 |
winter_design_temp: float # 99.6% heating design temperature (°C)
|
| 34 |
summer_design_temp_db: float # 0.4% cooling design dry-bulb temperature (°C)
|
| 35 |
summer_design_temp_wb: float # 0.4% cooling design wet-bulb temperature (°C)
|
| 36 |
summer_daily_range: float # Mean daily temperature range in summer (°C)
|
| 37 |
+
|
| 38 |
+
# Monthly data
|
| 39 |
monthly_temps: Dict[str, float] # Average monthly temperatures (°C)
|
| 40 |
monthly_humidity: Dict[str, float] # Average monthly relative humidity (%)
|
| 41 |
|
|
|
|
| 66 |
|
| 67 |
def __init__(self):
|
| 68 |
"""Initialize climate data."""
|
| 69 |
+
self.locations = self._load_climate_locations()
|
| 70 |
+
self.countries = sorted(list(set(loc.country for loc in self.locations.values())))
|
| 71 |
+
self.country_states = self._group_locations_by_country_state()
|
| 72 |
+
|
| 73 |
+
def _load_climate_locations(self) -> Dict[str, ClimateLocation]:
|
| 74 |
+
"""
|
| 75 |
+
Load climate location data.
|
| 76 |
+
|
| 77 |
+
Returns:
|
| 78 |
+
Dictionary of climate locations indexed by ID
|
| 79 |
+
"""
|
| 80 |
+
# This would typically load from a JSON or CSV file with ASHRAE 169 data
|
| 81 |
+
# For now, we'll define some sample locations inline
|
| 82 |
+
|
| 83 |
+
# Sample monthly data (for all locations in this example)
|
| 84 |
+
months = ["Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"]
|
| 85 |
+
|
| 86 |
+
# New York monthly temperatures (°C)
|
| 87 |
+
ny_temps = {
|
| 88 |
+
"Jan": 0.5, "Feb": 2.1, "Mar": 6.3, "Apr": 12.5, "May": 18.2,
|
| 89 |
+
"Jun": 23.1, "Jul": 25.8, "Aug": 24.9, "Sep": 20.7, "Oct": 14.3,
|
| 90 |
+
"Nov": 8.2, "Dec": 2.4
|
| 91 |
+
}
|
| 92 |
+
|
| 93 |
+
# New York monthly humidity (%)
|
| 94 |
+
ny_humidity = {
|
| 95 |
+
"Jan": 65, "Feb": 62, "Mar": 58, "Apr": 55, "May": 60,
|
| 96 |
+
"Jun": 65, "Jul": 68, "Aug": 70, "Sep": 68, "Oct": 63,
|
| 97 |
+
"Nov": 67, "Dec": 68
|
| 98 |
+
}
|
| 99 |
+
|
| 100 |
+
# Los Angeles monthly temperatures (°C)
|
| 101 |
+
la_temps = {
|
| 102 |
+
"Jan": 14.6, "Feb": 15.1, "Mar": 15.8, "Apr": 17.1, "May": 18.3,
|
| 103 |
+
"Jun": 20.1, "Jul": 22.3, "Aug": 22.9, "Sep": 22.1, "Oct": 20.3,
|
| 104 |
+
"Nov": 17.2, "Dec": 14.9
|
| 105 |
+
}
|
| 106 |
+
|
| 107 |
+
# Los Angeles monthly humidity (%)
|
| 108 |
+
la_humidity = {
|
| 109 |
+
"Jan": 63, "Feb": 67, "Mar": 70, "Apr": 71, "May": 74,
|
| 110 |
+
"Jun": 75, "Jul": 76, "Aug": 76, "Sep": 74, "Oct": 70,
|
| 111 |
+
"Nov": 65, "Dec": 63
|
| 112 |
+
}
|
| 113 |
+
|
| 114 |
+
# Chicago monthly temperatures (°C)
|
| 115 |
+
chi_temps = {
|
| 116 |
+
"Jan": -3.5, "Feb": -1.2, "Mar": 4.1, "Apr": 10.3, "May": 16.5,
|
| 117 |
+
"Jun": 22.1, "Jul": 24.8, "Aug": 23.9, "Sep": 19.7, "Oct": 12.8,
|
| 118 |
+
"Nov": 5.2, "Dec": -1.4
|
| 119 |
+
}
|
| 120 |
+
|
| 121 |
+
# Chicago monthly humidity (%)
|
| 122 |
+
chi_humidity = {
|
| 123 |
+
"Jan": 72, "Feb": 70, "Mar": 65, "Apr": 60, "May": 64,
|
| 124 |
+
"Jun": 67, "Jul": 70, "Aug": 73, "Sep": 71, "Oct": 68,
|
| 125 |
+
"Nov": 72, "Dec": 75
|
| 126 |
+
}
|
| 127 |
+
|
| 128 |
+
# London monthly temperatures (°C)
|
| 129 |
+
lon_temps = {
|
| 130 |
+
"Jan": 5.2, "Feb": 5.5, "Mar": 7.4, "Apr": 9.9, "May": 13.3,
|
| 131 |
+
"Jun": 16.7, "Jul": 18.7, "Aug": 18.3, "Sep": 15.9, "Oct": 12.2,
|
| 132 |
+
"Nov": 8.3, "Dec": 5.9
|
| 133 |
+
}
|
| 134 |
+
|
| 135 |
+
# London monthly humidity (%)
|
| 136 |
+
lon_humidity = {
|
| 137 |
+
"Jan": 84, "Feb": 80, "Mar": 76, "Apr": 72, "May": 70,
|
| 138 |
+
"Jun": 70, "Jul": 71, "Aug": 72, "Sep": 75, "Oct": 80,
|
| 139 |
+
"Nov": 84, "Dec": 86
|
| 140 |
+
}
|
| 141 |
+
|
| 142 |
+
# Sydney monthly temperatures (°C)
|
| 143 |
+
syd_temps = {
|
| 144 |
+
"Jan": 23.5, "Feb": 23.4, "Mar": 22.1, "Apr": 19.5, "May": 16.5,
|
| 145 |
+
"Jun": 14.1, "Jul": 13.4, "Aug": 14.5, "Sep": 16.6, "Oct": 18.8,
|
| 146 |
+
"Nov": 20.6, "Dec": 22.6
|
| 147 |
+
}
|
| 148 |
+
|
| 149 |
+
# Sydney monthly humidity (%)
|
| 150 |
+
syd_humidity = {
|
| 151 |
+
"Jan": 65, "Feb": 68, "Mar": 68, "Apr": 67, "May": 70,
|
| 152 |
+
"Jun": 70, "Jul": 68, "Aug": 63, "Sep": 60, "Oct": 60,
|
| 153 |
+
"Nov": 62, "Dec": 63
|
| 154 |
+
}
|
| 155 |
+
|
| 156 |
+
# Create sample locations
|
| 157 |
+
locations = {
|
| 158 |
+
"US-NY-NYC": ClimateLocation(
|
| 159 |
+
id="US-NY-NYC",
|
| 160 |
+
country="United States",
|
| 161 |
+
state_province="New York",
|
| 162 |
+
city="New York",
|
| 163 |
+
latitude=40.7128,
|
| 164 |
+
longitude=-74.0060,
|
| 165 |
+
elevation=10.0,
|
| 166 |
+
climate_zone="4A",
|
| 167 |
+
heating_degree_days=2600,
|
| 168 |
+
cooling_degree_days=1200,
|
| 169 |
+
winter_design_temp=-8.3,
|
| 170 |
+
summer_design_temp_db=32.8,
|
| 171 |
+
summer_design_temp_wb=25.6,
|
| 172 |
+
summer_daily_range=8.3,
|
| 173 |
+
monthly_temps=ny_temps,
|
| 174 |
+
monthly_humidity=ny_humidity
|
| 175 |
+
),
|
| 176 |
+
"US-CA-LAX": ClimateLocation(
|
| 177 |
+
id="US-CA-LAX",
|
| 178 |
+
country="United States",
|
| 179 |
+
state_province="California",
|
| 180 |
+
city="Los Angeles",
|
| 181 |
+
latitude=34.0522,
|
| 182 |
+
longitude=-118.2437,
|
| 183 |
+
elevation=93.0,
|
| 184 |
+
climate_zone="3B",
|
| 185 |
+
heating_degree_days=800,
|
| 186 |
+
cooling_degree_days=1200,
|
| 187 |
+
winter_design_temp=8.3,
|
| 188 |
+
summer_design_temp_db=32.2,
|
| 189 |
+
summer_design_temp_wb=23.3,
|
| 190 |
+
summer_daily_range=6.7,
|
| 191 |
+
monthly_temps=la_temps,
|
| 192 |
+
monthly_humidity=la_humidity
|
| 193 |
+
),
|
| 194 |
+
"US-IL-CHI": ClimateLocation(
|
| 195 |
+
id="US-IL-CHI",
|
| 196 |
+
country="United States",
|
| 197 |
+
state_province="Illinois",
|
| 198 |
+
city="Chicago",
|
| 199 |
+
latitude=41.8781,
|
| 200 |
+
longitude=-87.6298,
|
| 201 |
+
elevation=179.0,
|
| 202 |
+
climate_zone="5A",
|
| 203 |
+
heating_degree_days=3500,
|
| 204 |
+
cooling_degree_days=1000,
|
| 205 |
+
winter_design_temp=-16.7,
|
| 206 |
+
summer_design_temp_db=33.3,
|
| 207 |
+
summer_design_temp_wb=25.6,
|
| 208 |
+
summer_daily_range=8.9,
|
| 209 |
+
monthly_temps=chi_temps,
|
| 210 |
+
monthly_humidity=chi_humidity
|
| 211 |
+
),
|
| 212 |
+
"UK-LDN": ClimateLocation(
|
| 213 |
+
id="UK-LDN",
|
| 214 |
+
country="United Kingdom",
|
| 215 |
+
state_province="England",
|
| 216 |
+
city="London",
|
| 217 |
+
latitude=51.5074,
|
| 218 |
+
longitude=-0.1278,
|
| 219 |
+
elevation=35.0,
|
| 220 |
+
climate_zone="4A",
|
| 221 |
+
heating_degree_days=2500,
|
| 222 |
+
cooling_degree_days=200,
|
| 223 |
+
winter_design_temp=-3.9,
|
| 224 |
+
summer_design_temp_db=28.3,
|
| 225 |
+
summer_design_temp_wb=20.0,
|
| 226 |
+
summer_daily_range=10.0,
|
| 227 |
+
monthly_temps=lon_temps,
|
| 228 |
+
monthly_humidity=lon_humidity
|
| 229 |
+
),
|
| 230 |
+
"AU-NSW-SYD": ClimateLocation(
|
| 231 |
+
id="AU-NSW-SYD",
|
| 232 |
+
country="Australia",
|
| 233 |
+
state_province="New South Wales",
|
| 234 |
+
city="Sydney",
|
| 235 |
+
latitude=-33.8688,
|
| 236 |
+
longitude=151.2093,
|
| 237 |
+
elevation=3.0,
|
| 238 |
+
climate_zone="3C",
|
| 239 |
+
heating_degree_days=600,
|
| 240 |
+
cooling_degree_days=900,
|
| 241 |
+
winter_design_temp=7.2,
|
| 242 |
+
summer_design_temp_db=31.1,
|
| 243 |
+
summer_design_temp_wb=24.4,
|
| 244 |
+
summer_daily_range=7.8,
|
| 245 |
+
monthly_temps=syd_temps,
|
| 246 |
+
monthly_humidity=syd_humidity
|
| 247 |
+
)
|
| 248 |
+
}
|
| 249 |
+
|
| 250 |
+
return locations
|
| 251 |
|
| 252 |
def _group_locations_by_country_state(self) -> Dict[str, Dict[str, List[str]]]:
|
| 253 |
+
"""
|
| 254 |
+
Group locations by country and state/province.
|
| 255 |
+
|
| 256 |
+
Returns:
|
| 257 |
+
Nested dictionary of countries, states, and cities
|
| 258 |
+
"""
|
| 259 |
result = {}
|
| 260 |
+
|
| 261 |
for loc in self.locations.values():
|
| 262 |
if loc.country not in result:
|
| 263 |
result[loc.country] = {}
|
| 264 |
+
|
| 265 |
if loc.state_province not in result[loc.country]:
|
| 266 |
result[loc.country][loc.state_province] = []
|
| 267 |
+
|
| 268 |
result[loc.country][loc.state_province].append(loc.city)
|
| 269 |
+
|
| 270 |
+
# Sort states and cities
|
| 271 |
for country in result:
|
| 272 |
for state in result[country]:
|
| 273 |
result[country][state] = sorted(result[country][state])
|
| 274 |
+
|
| 275 |
return result
|
| 276 |
|
| 277 |
+
def get_location(self, location_id: str) -> Optional[ClimateLocation]:
|
| 278 |
+
"""
|
| 279 |
+
Get climate location by ID.
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 280 |
|
| 281 |
+
Args:
|
| 282 |
+
location_id: Location identifier
|
| 283 |
+
|
| 284 |
+
Returns:
|
| 285 |
+
ClimateLocation object or None if not found
|
| 286 |
+
"""
|
| 287 |
+
return self.locations.get(location_id)
|
| 288 |
+
|
| 289 |
+
def find_location(self, country: str, state_province: str = None, city: str = None) -> Optional[ClimateLocation]:
|
| 290 |
+
"""
|
| 291 |
+
Find a climate location by country, state/province, and city.
|
| 292 |
+
|
| 293 |
+
Args:
|
| 294 |
+
country: Country name
|
| 295 |
+
state_province: State or province name (optional)
|
| 296 |
+
city: City name (optional)
|
| 297 |
+
|
| 298 |
+
Returns:
|
| 299 |
+
ClimateLocation object or None if not found
|
| 300 |
+
"""
|
| 301 |
+
for loc in self.locations.values():
|
| 302 |
+
if loc.country == country:
|
| 303 |
+
if state_province is None or loc.state_province == state_province:
|
| 304 |
+
if city is None or loc.city == city:
|
| 305 |
+
return loc
|
| 306 |
+
return None
|
| 307 |
+
|
| 308 |
+
def find_locations_by_climate_zone(self, climate_zone: str) -> List[ClimateLocation]:
|
| 309 |
+
"""
|
| 310 |
+
Find climate locations by climate zone.
|
| 311 |
+
|
| 312 |
+
Args:
|
| 313 |
+
climate_zone: ASHRAE climate zone
|
| 314 |
+
|
| 315 |
+
Returns:
|
| 316 |
+
List of ClimateLocation objects
|
| 317 |
+
"""
|
| 318 |
+
return [loc for loc in self.locations.values() if loc.climate_zone == climate_zone]
|
| 319 |
+
|
| 320 |
+
def get_states_for_country(self, country: str) -> List[str]:
|
| 321 |
+
"""
|
| 322 |
+
Get states/provinces for a country.
|
| 323 |
+
|
| 324 |
+
Args:
|
| 325 |
+
country: Country name
|
| 326 |
+
|
| 327 |
+
Returns:
|
| 328 |
+
List of state/province names
|
| 329 |
+
"""
|
| 330 |
+
if country in self.country_states:
|
| 331 |
+
return sorted(self.country_states[country].keys())
|
| 332 |
+
return []
|
| 333 |
+
|
| 334 |
+
def get_cities_for_state(self, country: str, state_province: str) -> List[str]:
|
| 335 |
+
"""
|
| 336 |
+
Get cities for a state/province.
|
| 337 |
+
|
| 338 |
+
Args:
|
| 339 |
+
country: Country name
|
| 340 |
+
state_province: State or province name
|
| 341 |
+
|
| 342 |
+
Returns:
|
| 343 |
+
List of city names
|
| 344 |
+
"""
|
| 345 |
+
if country in self.country_states and state_province in self.country_states[country]:
|
| 346 |
+
return self.country_states[country][state_province]
|
| 347 |
+
return []
|
| 348 |
+
|
| 349 |
+
def get_location_id(self, country: str, state_province: str, city: str) -> Optional[str]:
|
| 350 |
+
"""
|
| 351 |
+
Get location ID for a city.
|
| 352 |
+
|
| 353 |
+
Args:
|
| 354 |
+
country: Country name
|
| 355 |
+
state_province: State or province name
|
| 356 |
+
city: City name
|
| 357 |
+
|
| 358 |
+
Returns:
|
| 359 |
+
Location ID or None if not found
|
| 360 |
+
"""
|
| 361 |
+
for loc_id, loc in self.locations.items():
|
| 362 |
+
if (loc.country == country and
|
| 363 |
+
loc.state_province == state_province and
|
| 364 |
+
loc.city == city):
|
| 365 |
+
return loc_id
|
| 366 |
+
return None
|
| 367 |
+
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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| 368 |
def export_to_json(self, file_path: str) -> None:
|
| 369 |
+
"""
|
| 370 |
+
Export all climate data to a JSON file.
|
| 371 |
+
|
| 372 |
+
Args:
|
| 373 |
+
file_path: Path to the output JSON file
|
| 374 |
+
"""
|
| 375 |
data = {loc_id: loc.to_dict() for loc_id, loc in self.locations.items()}
|
| 376 |
+
|
| 377 |
with open(file_path, 'w') as f:
|
| 378 |
json.dump(data, f, indent=4)
|
| 379 |
+
|
| 380 |
@classmethod
|
| 381 |
def from_json(cls, file_path: str) -> 'ClimateData':
|
| 382 |
+
"""
|
| 383 |
+
Create a ClimateData instance from a JSON file.
|
| 384 |
+
|
| 385 |
+
Args:
|
| 386 |
+
file_path: Path to the input JSON file
|
| 387 |
+
|
| 388 |
+
Returns:
|
| 389 |
+
A new ClimateData instance
|
| 390 |
+
"""
|
| 391 |
with open(file_path, 'r') as f:
|
| 392 |
data = json.load(f)
|
| 393 |
+
|
| 394 |
climate_data = cls()
|
| 395 |
climate_data.locations = {}
|
| 396 |
+
|
| 397 |
for loc_id, loc_dict in data.items():
|
| 398 |
+
climate_data.locations[loc_id] = ClimateLocation(
|
| 399 |
+
id=loc_dict["id"],
|
| 400 |
+
country=loc_dict["country"],
|
| 401 |
+
state_province=loc_dict["state_province"],
|
| 402 |
+
city=loc_dict["city"],
|
| 403 |
+
latitude=loc_dict["latitude"],
|
| 404 |
+
longitude=loc_dict["longitude"],
|
| 405 |
+
elevation=loc_dict["elevation"],
|
| 406 |
+
climate_zone=loc_dict["climate_zone"],
|
| 407 |
+
heating_degree_days=loc_dict["heating_degree_days"],
|
| 408 |
+
cooling_degree_days=loc_dict["cooling_degree_days"],
|
| 409 |
+
winter_design_temp=loc_dict["winter_design_temp"],
|
| 410 |
+
summer_design_temp_db=loc_dict["summer_design_temp_db"],
|
| 411 |
+
summer_design_temp_wb=loc_dict["summer_design_temp_wb"],
|
| 412 |
+
summer_daily_range=loc_dict["summer_daily_range"],
|
| 413 |
+
monthly_temps=loc_dict["monthly_temps"],
|
| 414 |
+
monthly_humidity=loc_dict["monthly_humidity"]
|
| 415 |
+
)
|
| 416 |
+
|
| 417 |
climate_data.countries = sorted(list(set(loc.country for loc in climate_data.locations.values())))
|
| 418 |
climate_data.country_states = climate_data._group_locations_by_country_state()
|
| 419 |
+
|
| 420 |
return climate_data
|
| 421 |
+
|
| 422 |
+
def display_climate_input(self, session_state):
|
| 423 |
+
"""Display climate data input form in Streamlit and store selection in session state."""
|
| 424 |
+
import streamlit as st
|
| 425 |
+
|
| 426 |
+
st.title("Climate Data")
|
| 427 |
+
st.write("Select a location to load its ASHRAE 169 climate data.")
|
| 428 |
+
|
| 429 |
+
# Dropdown for country selection
|
| 430 |
+
country = st.selectbox("Country", self.countries, key="climate_country")
|
| 431 |
+
|
| 432 |
+
# Dropdown for state/province selection
|
| 433 |
+
states = self.get_states_for_country(country)
|
| 434 |
+
state = st.selectbox("State/Province", states, key="climate_state") if states else None
|
| 435 |
+
|
| 436 |
+
# Dropdown for city selection
|
| 437 |
+
cities = self.get_cities_for_state(country, state) if state else []
|
| 438 |
+
city = st.selectbox("City", cities, key="climate_city") if cities else None
|
| 439 |
+
|
| 440 |
+
# Button to confirm selection
|
| 441 |
+
if st.button("Load Climate Data") and country and state and city:
|
| 442 |
+
location_id = self.get_location_id(country, state, city)
|
| 443 |
+
if location_id:
|
| 444 |
+
location = self.get_location(location_id)
|
| 445 |
+
if location:
|
| 446 |
+
# Store the selected location in session state
|
| 447 |
+
session_state["climate_data"] = location.to_dict()
|
| 448 |
+
st.success(f"Loaded climate data for {city}, {state}, {country}")
|
| 449 |
+
|
| 450 |
+
# Display key climate data
|
| 451 |
+
st.subheader("Selected Location Climate Data")
|
| 452 |
+
st.write(f"Climate Zone: {location.climate_zone}")
|
| 453 |
+
st.write(f"Winter Design Temperature: {location.winter_design_temp}°C")
|
| 454 |
+
st.write(f"Summer Design Dry-Bulb Temperature: {location.summer_design_temp_db}°C")
|
| 455 |
+
st.write(f"Summer Design Wet-Bulb Temperature: {location.summer_design_temp_wb}°C")
|
| 456 |
+
st.write(f"Heating Degree Days: {location.heating_degree_days}")
|
| 457 |
+
st.write(f"Cooling Degree Days: {location.cooling_degree_days}")
|
| 458 |
+
else:
|
| 459 |
+
st.error("Location data not found.")
|
| 460 |
+
else:
|
| 461 |
+
st.error("Invalid location selection.")
|
| 462 |
+
|
| 463 |
+
# Display existing selection if available
|
| 464 |
+
if "climate_data" in session_state and session_state["climate_data"]:
|
| 465 |
+
st.subheader("Current Selection")
|
| 466 |
+
st.json(session_state["climate_data"])
|
| 467 |
+
|
| 468 |
|
| 469 |
+
# Create a singleton instance
|
| 470 |
+
climate_data = ClimateData()
|
| 471 |
|
| 472 |
+
# Export climate data to JSON if needed
|
| 473 |
if __name__ == "__main__":
|
| 474 |
+
climate_data.export_to_json(os.path.join(DATA_DIR, "climate_data.json"))
|
|
|