Hourly rate table

reoptjl/custom_timeseries_table_config.py

@@ -0,0 +1,136 @@
+# custom_timeseries_table_config.py
+from reoptjl.timeseries_table_helpers import safe_get_list, safe_get_value, safe_get
+
+"""
+Timeseries Table Configuration
+===============================
+This file defines configurations for timeseries Excel tables that display hourly or sub-hourly data.
+Each configuration specifies which columns to include and how to extract the data.
+
+Naming Convention:
+------------------
+Structure: custom_timeseries_<feature>
+
+- `custom_timeseries_`: Prefix indicating a timeseries table configuration
+- `<feature>`: Descriptive name for the specific timeseries configuration
+
+Examples:
+- custom_timeseries_energy_demand: Configuration for energy and demand rate timeseries
+- custom_timeseries_emissions: Configuration for emissions timeseries
+- custom_timeseries_loads: Configuration for load profiles
+
+Guidelines:
+- Use lowercase letters and underscores
+- Keep names descriptive and concise
+- Each configuration is a list of column dictionaries
+
+Column Dictionary Structure:
+-----------------------------
+Each column configuration should have:
+{
+    "label": str,               # Column header text
+    "key": str,                 # Unique identifier for the column
+    "timeseries_path": str,     # Dot-separated path to data in the results JSON (e.g., "outputs.ElectricLoad.load_series_kw")
+    "is_base_column": bool,     # True if column comes from first scenario only, False if repeated for all scenarios
+    "units": str                # Optional: Units to display in header (e.g., "($/kWh)", "(kW)")
+}
+
+Note: Formatting (Excel number formats, column widths, colors) is handled in views.py, not in this configuration file.
+
+Special Column Types:
+---------------------
+1. DateTime column: Must have key="datetime" and will be auto-generated based on year and time_steps_per_hour
+2. Base columns: Set is_base_column=True for columns that only use data from the first run_uuid
+3. Scenario columns: Set is_base_column=False for columns that repeat for each run_uuid
+
+Rate Name Headers:
+------------------
+For scenario columns (is_base_column=False), the column header will automatically include the rate name
+from inputs.ElectricTariff.urdb_metadata.rate_name for each scenario.
+"""
+
+# Configuration for energy and demand rate timeseries
+# This configuration specifies which data fields to extract from the results.
+# Formatting (number formats, colors, widths) is handled in views.py
+custom_timeseries_energy_demand = [
+    {
+        "label": "Date Timestep",
+        "key": "datetime",
+        "timeseries_path": lambda df: safe_get(df, "inputs.ElectricLoad.year"),  # Used to generate datetime column based on year and time_steps_per_hour
+        "is_base_column": True
+    },
+    {
+        "label": "Load (kW)",
+        "key": "load_kw",
+        "timeseries_path": lambda df: safe_get(df, "outputs.ElectricLoad.load_series_kw"),
+        "is_base_column": True
+    },
+    {
+        "label": "Peak Monthly Load (kW)",
+        "key": "peak_monthly_load_kw",
+        "timeseries_path": lambda df: safe_get(df, "outputs.ElectricLoad.monthly_peaks_kw"),  # 12-element array, needs special handling to repeat for each timestep
+        "is_base_column": True
+    },
+    {
+        "label": "Energy Charge",
+        "key": "energy_charge",
+        "timeseries_path": lambda df: safe_get(df, "outputs.ElectricTariff.energy_rate_average_series"),
+        "is_base_column": False,  # Repeats for each scenario
+        "units": "($/kWh)"
+    },
+    {
+        "label": "Demand Charge",
+        "key": "demand_charge",
+        "timeseries_path": lambda df: safe_get(df, "outputs.ElectricTariff.demand_rate_average_series"),
+        "is_base_column": False,  # Repeats for each scenario
+        "units": "($/kW)"
+    }
+]
+
+# Example configuration for emissions timeseries (can be expanded as needed)
+custom_timeseries_emissions = [
+    {
+        "label": "Date Timestep",
+        "key": "datetime",
+        "timeseries_path": lambda df: safe_get(df, "inputs.ElectricLoad.year"),
+        "is_base_column": True
+    },
+    {
+        "label": "Grid Emissions",
+        "key": "grid_emissions",
+        "timeseries_path": lambda df: safe_get(df, "inputs.ElectricUtility.emissions_factor_series_lb_CO2_per_kwh"),
+        "is_base_column": True,
+        "units": "(lb CO2/kWh)"
+    },
+    {
+        "label": "Grid Energy",
+        "key": "grid_to_load",
+        "timeseries_path": lambda df: safe_get(df, "outputs.ElectricUtility.electric_to_load_series_kw"),
+        "is_base_column": False,
+        "units": "(kWh)"
+    }
+]
+
+# Example configuration for load profiles (can be expanded as needed)
+custom_timeseries_loads = [
+    {
+        "label": "Date Timestep",
+        "key": "datetime",
+        "timeseries_path": lambda df: safe_get(df, "inputs.ElectricLoad.year"),
+        "is_base_column": True
+    },
+    {
+        "label": "Total Load",
+        "key": "total_load",
+        "timeseries_path": lambda df: safe_get(df, "outputs.ElectricLoad.load_series_kw"),
+        "is_base_column": True,
+        "units": "(kW)"
+    },
+    {
+        "label": "Critical Load",
+        "key": "critical_load",
+        "timeseries_path": lambda df: safe_get(df, "outputs.ElectricLoad.critical_load_series_kw"),
+        "is_base_column": True,
+        "units": "(kW)"
+    }
+]

reoptjl/timeseries_table_helpers.py

@@ -0,0 +1,132 @@
+# timeseries_table_helpers.py
+from typing import Dict, Any, List
+from datetime import datetime, timedelta
+import calendar
+
+def generate_datetime_column(year: int, time_steps_per_hour: int) -> List[str]:
+    """
+    Generate datetime strings for the first column based on year and time_steps_per_hour.
+
+    Args:
+        year: The year for the datetime series
+        time_steps_per_hour: Number of time steps per hour (1, 2, or 4)
+
+    Returns:
+        List of datetime strings formatted as "M/D/YYYY H:MM"
+    """
+    # Check if leap year and adjust days accordingly
+    is_leap = calendar.isleap(year)
+    total_days = 365  # Always use 365 days, even for leap years
+
+    # Calculate time step increment in minutes
+    minutes_per_step = 60 // time_steps_per_hour
+
+    datetime_list = []
+    start_date = datetime(year, 1, 1, 0, 0)
+
+    # Calculate total number of time steps
+    total_steps = total_days * 24 * time_steps_per_hour
+
+    for step in range(total_steps):
+        current_time = start_date + timedelta(minutes=step * minutes_per_step)
+        # Format: M/D/YYYY H:MM (Windows-compatible formatting)
+        month = current_time.month
+        day = current_time.day
+        year = current_time.year
+        hour = current_time.hour
+        minute = current_time.minute
+        formatted_time = f"{month}/{day}/{year} {hour}:{minute:02d}"
+        datetime_list.append(formatted_time)
+
+    return datetime_list
+
+
+def get_monthly_peak_for_timestep(timestep_index: int, monthly_peaks: List[float], time_steps_per_hour: int) -> float:
+    """
+    Get the monthly peak value for a given timestep index.
+
+    Args:
+        timestep_index: The index of the current timestep
+        monthly_peaks: List of 12 monthly peak values
+        time_steps_per_hour: Number of time steps per hour
+
+    Returns:
+        The monthly peak value for the month containing this timestep
+    """
+    # Calculate which month this timestep belongs to
+    # Approximate days per month
+    days_in_months = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]
+
+    steps_per_day = 24 * time_steps_per_hour
+    cumulative_steps = 0
+
+    for month_idx, days in enumerate(days_in_months):
+        cumulative_steps += days * steps_per_day
+        if timestep_index < cumulative_steps:
+            return monthly_peaks[month_idx] if month_idx < len(monthly_peaks) else 0
+
+    # Default to last month if we're beyond December
+    return monthly_peaks[-1] if monthly_peaks else 0
+
+
+def safe_get_list(data: Dict[str, Any], key: str, default: List = None) -> List:
+    """
+    Safely get a list value from nested dictionary.
+
+    Args:
+        data: The dictionary to search
+        key: Dot-separated key path (e.g., "outputs.ElectricLoad.load_series_kw")
+        default: Default value if key not found
+
+    Returns:
+        The found list or default value
+    """
+    if default is None:
+        default = []
+
+    keys = key.split('.')
+    current = data
+
+    try:
+        for k in keys:
+            if isinstance(current, dict):
+                current = current.get(k)
+            else:
+                return default
+
+            if current is None:
+                return default
+
+        return current if isinstance(current, list) else default
+    except (KeyError, TypeError, AttributeError):
+        return default
+
+
+def safe_get_value(data: Dict[str, Any], key: str, default: Any = None) -> Any:
+    """
+    Safely get a value from nested dictionary.
+
+    Args:
+        data: The dictionary to search
+        key: Dot-separated key path
+        default: Default value if key not found
+
+    Returns:
+        The found value or default
+    """
+    keys = key.split('.')
+    current = data
+
+    try:
+        for k in keys:
+            if isinstance(current, dict):
+                current = current.get(k)
+            else:
+                return default
+
+            if current is None:
+                return default
+
+        return current
+    except (KeyError, TypeError, AttributeError):
+        return default

reoptjl/urls.py

@@ -29,5 +29,6 @@
     re_path(r'^pv_cost_defaults/?$', views.pv_cost_defaults),
     re_path(r'^summary_by_runuuids/?$', views.summary_by_runuuids),
     re_path(r'^link_run_to_portfolios/?$', views.link_run_uuids_to_portfolio_uuid),
-    re_path(r'^get_load_metrics/?$', views.get_load_metrics)
+    re_path(r'^get_load_metrics/?$', views.get_load_metrics),
+    re_path(r'^job/get_timeseries_table/?$', views.get_timeseries_table)
 ]