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Refactor hourly rate table to generic timeseries table
Renamed hourly_rate_helpers.py to timeseries_table_helpers.py and updated all references accordingly. Added (but not currently used) custom_timeseries_table_config.py to define flexible timeseries table configurations. Refactored the hourly_rate_table view and URL to get_timeseries_table, enhanced Excel formatting with colored headers, and improved code organization for future extensibility.
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‎reoptjl/custom_timeseries_table_config.py‎

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# custom_timeseries_table_config.py
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from reoptjl.timeseries_table_helpers import safe_get_list, safe_get_value, safe_get
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"""
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Timeseries Table Configuration
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===============================
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This file defines configurations for timeseries Excel tables that display hourly or sub-hourly data.
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Each configuration specifies which columns to include and how to extract the data.
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Naming Convention:
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------------------
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Structure: custom_timeseries_<feature>
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- `custom_timeseries_`: Prefix indicating a timeseries table configuration
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- `<feature>`: Descriptive name for the specific timeseries configuration
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Examples:
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- custom_timeseries_energy_demand: Configuration for energy and demand rate timeseries
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- custom_timeseries_emissions: Configuration for emissions timeseries
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- custom_timeseries_loads: Configuration for load profiles
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Guidelines:
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- Use lowercase letters and underscores
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- Keep names descriptive and concise
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- Each configuration is a list of column dictionaries
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Column Dictionary Structure:
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-----------------------------
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Each column configuration should have:
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{
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"label": str, # Column header text
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"key": str, # Unique identifier for the column
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"timeseries_path": str, # Dot-separated path to data in the results JSON (e.g., "outputs.ElectricLoad.load_series_kw")
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"is_base_column": bool, # True if column comes from first scenario only, False if repeated for all scenarios
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"units": str # Optional: Units to display in header (e.g., "($/kWh)", "(kW)")
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}
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Note: Formatting (Excel number formats, column widths, colors) is handled in views.py, not in this configuration file.
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Special Column Types:
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---------------------
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1. DateTime column: Must have key="datetime" and will be auto-generated based on year and time_steps_per_hour
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2. Base columns: Set is_base_column=True for columns that only use data from the first run_uuid
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3. Scenario columns: Set is_base_column=False for columns that repeat for each run_uuid
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Rate Name Headers:
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------------------
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For scenario columns (is_base_column=False), the column header will automatically include the rate name
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from inputs.ElectricTariff.urdb_metadata.rate_name for each scenario.
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"""
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# Configuration for energy and demand rate timeseries
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# This configuration specifies which data fields to extract from the results.
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# Formatting (number formats, colors, widths) is handled in views.py
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custom_timeseries_energy_demand = [
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{
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"label": "Date Timestep",
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"key": "datetime",
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"timeseries_path": lambda df: safe_get(df, "inputs.ElectricLoad.year"), # Used to generate datetime column based on year and time_steps_per_hour
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"is_base_column": True
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},
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{
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"label": "Load (kW)",
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"key": "load_kw",
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"timeseries_path": lambda df: safe_get(df, "outputs.ElectricLoad.load_series_kw"),
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"is_base_column": True
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},
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{
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"label": "Peak Monthly Load (kW)",
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"key": "peak_monthly_load_kw",
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"timeseries_path": lambda df: safe_get(df, "outputs.ElectricLoad.monthly_peaks_kw"), # 12-element array, needs special handling to repeat for each timestep
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"is_base_column": True
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},
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{
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"label": "Energy Charge",
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"key": "energy_charge",
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"timeseries_path": lambda df: safe_get(df, "outputs.ElectricTariff.energy_rate_average_series"),
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"is_base_column": False, # Repeats for each scenario
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"units": "($/kWh)"
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},
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{
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"label": "Demand Charge",
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"key": "demand_charge",
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"timeseries_path": lambda df: safe_get(df, "outputs.ElectricTariff.demand_rate_average_series"),
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"is_base_column": False, # Repeats for each scenario
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"units": "($/kW)"
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}
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]
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# Example configuration for emissions timeseries (can be expanded as needed)
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custom_timeseries_emissions = [
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{
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"label": "Date Timestep",
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"key": "datetime",
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"timeseries_path": lambda df: safe_get(df, "inputs.ElectricLoad.year"),
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"is_base_column": True
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},
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{
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"label": "Grid Emissions",
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"key": "grid_emissions",
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"timeseries_path": lambda df: safe_get(df, "inputs.ElectricUtility.emissions_factor_series_lb_CO2_per_kwh"),
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"is_base_column": True,
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"units": "(lb CO2/kWh)"
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},
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{
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"label": "Grid Energy",
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"key": "grid_to_load",
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"timeseries_path": lambda df: safe_get(df, "outputs.ElectricUtility.electric_to_load_series_kw"),
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"is_base_column": False,
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"units": "(kWh)"
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}
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]
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# Example configuration for load profiles (can be expanded as needed)
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custom_timeseries_loads = [
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{
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"label": "Date Timestep",
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"key": "datetime",
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"timeseries_path": lambda df: safe_get(df, "inputs.ElectricLoad.year"),
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"is_base_column": True
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},
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{
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"label": "Total Load",
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"key": "total_load",
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"timeseries_path": lambda df: safe_get(df, "outputs.ElectricLoad.load_series_kw"),
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"is_base_column": True,
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"units": "(kW)"
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},
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{
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"label": "Critical Load",
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"key": "critical_load",
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"timeseries_path": lambda df: safe_get(df, "outputs.ElectricLoad.critical_load_series_kw"),
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"is_base_column": True,
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"units": "(kW)"
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}
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]

‎reoptjl/hourly_rate_helpers.py‎ renamed to ‎reoptjl/timeseries_table_helpers.py‎

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@@ -1,4 +1,4 @@
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# hourly_rate_helpers.py
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# timeseries_table_helpers.py
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from typing import Dict, Any, List
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from datetime import datetime, timedelta
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import calendar

‎reoptjl/urls.py‎

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re_path(r'^summary_by_runuuids/?$', views.summary_by_runuuids),
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re_path(r'^link_run_to_portfolios/?$', views.link_run_uuids_to_portfolio_uuid),
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re_path(r'^get_load_metrics/?$', views.get_load_metrics),
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re_path(r'^job/hourly_rate_table/?$', views.hourly_rate_table)
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re_path(r'^job/get_timeseries_table/?$', views.get_timeseries_table)
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]

‎reoptjl/views.py‎

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@@ -2366,10 +2366,10 @@ def get_bau_column(col):
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##############################################################################################################################
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##############################################################################################################################
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################################################# START Hourly Rate Table #####################################################
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################################################# START Get Timeseries Table #####################################################
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##############################################################################################################################
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def hourly_rate_table(request: Any) -> HttpResponse:
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def get_timeseries_table(request: Any) -> HttpResponse:
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"""
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Generate an Excel file with hourly rate data for one or more scenarios.
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Accepts multiple run_uuid values via GET request parameters.
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- Column 5: Demand Charge from first run_uuid ($/kW)
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- Columns 6-7, 8-9, etc.: Energy and Demand charges for additional run_uuids
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"""
2385-
from reoptjl.hourly_rate_helpers import (
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from reoptjl.timeseries_table_helpers import (
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generate_datetime_column,
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get_monthly_peak_for_timestep,
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safe_get_list,
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monthly_peaks = safe_get_list(first_scenario, 'outputs.ElectricLoad.monthly_peaks_kw', [])
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# Log for debugging
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log.info(f"hourly_rate_table - year: {year}, time_steps_per_hour: {time_steps_per_hour}")
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log.info(f"hourly_rate_table - load_series length: {len(load_series)}, monthly_peaks length: {len(monthly_peaks)}")
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log.info(f"hourly_rate_table - datetime_col length: {len(datetime_col)}")
2442+
log.info(f"get_timeseries_table - year: {year}, time_steps_per_hour: {time_steps_per_hour}")
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log.info(f"get_timeseries_table - load_series length: {len(load_series)}, monthly_peaks length: {len(monthly_peaks)}")
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log.info(f"get_timeseries_table - datetime_col length: {len(datetime_col)}")
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# Create monthly peak column (repeat monthly peak for all timesteps in that month)
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monthly_peak_col = [
@@ -2465,6 +2465,31 @@ def hourly_rate_table(request: Any) -> HttpResponse:
24652465
'text_wrap': True
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})
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# Define different header colors for each rate
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rate_header_colors = [
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"#50AEE9", # Blue (first rate)
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'#2E7D32', # Green (second rate)
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'#D32F2F', # Red (third rate)
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'#F57C00', # Orange (fourth rate)
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'#7B1FA2', # Purple (fifth rate)
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'#0097A7', # Cyan (sixth rate)
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'#C2185B', # Pink (seventh rate)
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'#5D4037', # Brown (eighth rate)
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]
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# Create header formats for each rate
2481+
rate_header_formats = []
2482+
for color in rate_header_colors:
2483+
rate_header_formats.append(workbook.add_format({
2484+
'bold': True,
2485+
'bg_color': color,
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'font_color': 'white',
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'border': 1,
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'align': 'center',
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'valign': 'vcenter',
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'text_wrap': True
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}))
2492+
24682493
datetime_format = workbook.add_format({
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'border': 1,
24702495
'align': 'center',
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25232548
# Get rate name from urdb_metadata
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rate_name = safe_get_value(scenario['data'], 'inputs.ElectricTariff.urdb_metadata.rate_name', f'Scenario {scenario_idx + 1}')
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2526-
worksheet.write(0, col_offset, f'Energy Charge {rate_name} ($/kWh)', header_format)
2527-
worksheet.write(0, col_offset + 1, f'Demand Charge {rate_name} ($/kW)', header_format)
2551+
# Use different colored header for each rate (cycle through colors if more than 8 rates)
2552+
rate_header = rate_header_formats[scenario_idx % len(rate_header_formats)]
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2554+
# Add a return after "Charge"
2555+
worksheet.write(0, col_offset, f'Energy Charge: \n{rate_name} ($/kWh)', rate_header)
2556+
worksheet.write(0, col_offset + 1, f'Demand Charge: \n{rate_name} ($/kW)', rate_header)
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col_offset += 2
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# Write data rows
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25502579
# Log on first row for debugging
25512580
if row_idx == 0:
2552-
log.info(f"hourly_rate_table - scenario {scenario_idx}: energy_rates length: {len(energy_rates)}, demand_rates length: {len(demand_rates)}")
2581+
log.info(f"get_timeseries_table - scenario {scenario_idx}: energy_rates length: {len(energy_rates)}, demand_rates length: {len(demand_rates)}")
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25542583
energy_rate = energy_rates[row_idx] if row_idx < len(energy_rates) else 0
25552584
demand_rate = demand_rates[row_idx] if row_idx < len(demand_rates) else 0
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25712600
output,
25722601
content_type='application/vnd.openxmlformats-officedocument.spreadsheetml.sheet'
25732602
)
2574-
response['Content-Disposition'] = 'attachment; filename="hourly_rate_table.xlsx"'
2603+
response['Content-Disposition'] = 'attachment; filename="get_timeseries_table.xlsx"'
25752604
return response
25762605

25772606
except Exception as e:
2578-
log.error(f"Error in hourly_rate_table: {e}")
2607+
log.error(f"Error in get_timeseries_table: {e}")
25792608
return JsonResponse({"Error": f"An unexpected error occurred: {str(e)}"}, status=500)
25802609

25812610
##############################################################################################################################
2582-
################################################### END Hourly Rate Table #####################################################
2611+
################################################### END Get Timeseries Table #####################################################
25832612
##############################################################################################################################

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