From 137b1fa0f9e41b3469d9e99001eea24ebf3b3106 Mon Sep 17 00:00:00 2001 From: sam-watttime Date: Tue, 21 Jan 2025 23:22:54 -0700 Subject: [PATCH 01/85] add report module and notebook --- watttime/new_moer_model_report.ipynb | 207090 ++++++++++++++++++++++++ watttime/report.py | 939 + 2 files changed, 208029 insertions(+) create mode 100644 watttime/new_moer_model_report.ipynb create mode 100644 watttime/report.py diff --git a/watttime/new_moer_model_report.ipynb b/watttime/new_moer_model_report.ipynb new file mode 100644 index 00000000..273a3687 --- /dev/null +++ b/watttime/new_moer_model_report.ipynb @@ -0,0 +1,207090 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 8, + "id": "2e63bd72-6edd-4fe1-b173-3e295259c7a8", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "#setup\n", + "import warnings\n", + "from typing import List, Optional, Union\n", + "from datetime import datetime, date\n", + "from dateutil.parser import parse\n", + "from watttime import api, report\n", + "from itertools import product\n", + "\n", + "warnings.filterwarnings('ignore')\n", + "warnings.filterwarnings('ignore', message=\"Found version mismatch between `dagster`\")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "a3b6b1ed-fda5-4961-b9a5-d7bc54f1b6a6", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "parameters" + ] + }, + "outputs": [], + "source": [ + "#input parameters\n", + "ba_list: List[str] = [\"WEM\"]\n", + "model_date_list: List[str] = [\"2022-10-01\", \"2024-10-16\"]\n", + "signal_type: str = \"co2_moer\"\n", + "eval_start: Union[str, datetime, date] = '2024-01-01T00:00Z'\n", + "eval_end: Union[str, datetime, date] = '2025-01-01T00:00Z'\n", + "\n", + "api_username: Optional[str] = None # if None, will look for env var WATTTIME_USER\n", + "api_password: Optional[str] = None # if None, will look for env var WATTTIME_PASSWORD\n", + "\n", + "forecast_sample_days: int = 5" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "c96c3091", + "metadata": {}, + "outputs": [], + "source": [ + "WT_FORECAST = api.WattTimeForecast(username=api_username, password=api_password)\n", + "WT_HIST = api.WattTimeHistorical(username=api_username, password=api_password)\n", + "\n", + "EVAL_DAYS = report.parse_eval_days(eval_start=eval_start, eval_end=eval_end)\n", + "SAMPLE_DAYS = report.parse_forecast_sample_days(eval_days=EVAL_DAYS, forecast_sample_days=forecast_sample_days)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "e0f70b6a-6a52-489f-a890-4e992eb23ffe", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " Warnings Returned: {'region': 'WEM', 'signal_type': 'co2_moer', 'model': '2022-10-01', 'start': datetime.datetime(2024, 7, 29, 0, 0, tzinfo=), 'end': datetime.datetime(2024, 8, 27, 23, 55, tzinfo=)} {'data_point_period_seconds': 300, 'region': 'WEM', 'signal_type': 'co2_moer', 'units': 'lbs_co2_per_mwh', 'warnings': [{'type': 'MISSING_DATA', 'message': 'You made a valid request, but we have 560 records missing. This can happen for a variety of reasons. If the data is less than a week old, we will most likely fill in this data soon. If it is older than that, we are missing enough important grid data during this time period that we are not able to generate an accurate estimate'}], 'model': {'date': '2022-10-01', 'type': 'binned_regression'}}\n", + "\n", + " Warnings Returned: {'region': 'WEM', 'signal_type': 'co2_moer', 'model': '2022-10-01', 'start': datetime.datetime(2024, 9, 27, 0, 0, tzinfo=), 'end': datetime.datetime(2024, 10, 26, 23, 55, tzinfo=)} {'data_point_period_seconds': 300, 'region': 'WEM', 'signal_type': 'co2_moer', 'units': 'lbs_co2_per_mwh', 'warnings': [{'type': 'MISSING_DATA', 'message': 'You made a valid request, but we have 7200 records missing. This can happen for a variety of reasons. If the data is less than a week old, we will most likely fill in this data soon. If it is older than that, we are missing enough important grid data during this time period that we are not able to generate an accurate estimate'}], 'model': {'date': '2022-10-01', 'type': 'binned_regression'}}\n", + "\n", + " Warnings Returned: {'region': 'WEM', 'signal_type': 'co2_moer', 'model': '2022-10-01', 'start': datetime.datetime(2024, 10, 27, 0, 0, tzinfo=), 'end': datetime.datetime(2024, 11, 25, 23, 55, tzinfo=)} {'data_point_period_seconds': 300, 'region': 'WEM', 'signal_type': 'co2_moer', 'units': 'lbs_co2_per_mwh', 'warnings': [{'type': 'MISSING_DATA', 'message': 'You made a valid request, but we have 6554 records missing. This can happen for a variety of reasons. If the data is less than a week old, we will most likely fill in this data soon. If it is older than that, we are missing enough important grid data during this time period that we are not able to generate an accurate estimate'}], 'model': {'date': '2022-10-01', 'type': 'binned_regression'}}\n", + "\n", + " Warnings Returned: {'region': 'WEM', 'signal_type': 'co2_moer', 'model': '2022-10-01', 'start': datetime.datetime(2024, 11, 26, 0, 0, tzinfo=), 'end': datetime.datetime(2024, 12, 25, 23, 55, tzinfo=)} {'data_point_period_seconds': 300, 'region': 'WEM', 'signal_type': 'co2_moer', 'units': 'lbs_co2_per_mwh', 'warnings': [{'type': 'MISSING_DATA', 'message': 'You made a valid request, but we have 2461 records missing. This can happen for a variety of reasons. If the data is less than a week old, we will most likely fill in this data soon. If it is older than that, we are missing enough important grid data during this time period that we are not able to generate an accurate estimate'}], 'model': {'date': '2022-10-01', 'type': 'binned_regression'}}\n", + "\n", + " Warnings Returned: {'region': 'WEM', 'signal_type': 'co2_moer', 'model': '2024-10-16', 'start': datetime.datetime(2024, 11, 26, 0, 0, tzinfo=), 'end': datetime.datetime(2024, 12, 25, 23, 55, tzinfo=)} {'data_point_period_seconds': 300, 'region': 'WEM', 'signal_type': 'co2_moer', 'units': 'lbs_co2_per_mwh', 'warnings': [{'type': 'MISSING_DATA', 'message': 'You made a valid request, but we have 694 records missing. This can happen for a variety of reasons. If the data is less than a week old, we will most likely fill in this data soon. If it is older than that, we are missing enough important grid data during this time period that we are not able to generate an accurate estimate'}], 'model': {'date': '2024-10-16', 'type': 'binned_regression'}}\n" + ] + } + ], + "source": [ + "jobs = list(\n", + " report.ModelAnalysis(\n", + " ba=i[0],\n", + " model_date=i[1],\n", + " signal_type=signal_type,\n", + " eval_start=eval_start, eval_end=eval_end,\n", + " eval_days=EVAL_DAYS, sample_days=SAMPLE_DAYS,\n", + " wt_forecast=WT_FORECAST, wt_hist=WT_HIST\n", + " ) for i in product(ba_list, model_date_list)\n", + ")\n", + "\n", + "# pull all required data and compile\n", + "for job in jobs:\n", + " job.compile_forecast_v_moer()" + ] + }, + { + "cell_type": "markdown", + "id": "42d2ca67-d48b-4fac-b384-4bab22e32104", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "## Data Distribution Changes\n" + ] + }, + { + "cell_type": "markdown", + "id": "7f46b1a9-936f-4e75-aa16-ebc88110b4f4", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Below is a summary of how you can expect signal values to change between versions." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "83629761-691f-4030-b458-885f412d7113", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "line": { + "width": 2 + }, + "mode": "lines", + "name": "2022-10-01", + "showlegend": true, + "type": "scatter", + "x": [ + "2024-06-13T06:50:00+00:00", + "2024-06-13T06:55:00+00:00", + "2024-06-13T07:00:00+00:00", + "2024-06-13T07:05:00+00:00", + "2024-06-13T07:10:00+00:00", + "2024-06-13T07:15:00+00:00", + "2024-06-13T07:20:00+00:00", + "2024-06-13T07:25:00+00:00", + "2024-06-13T07:30:00+00:00", + 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This represents the magnitude of error to expect from the forecast on average. A model with an MAE closest to 0 is ideal. Norm MAE is MAE divided by the average of the true values. Normalizing MAE allows us to compare models across different datasets. It is expressed as a percentage (%) of the average true value over the validation time period. \n", + "* **Rank Correlation:** A number between -1 and 1 that quantifies the degree to which the rank ordering of two datasets are related. A rank correlation of 1 means the forecast predictions are rank ordered perfectly compared to the true values, making the forecast perfectly effective for time optimization use cases. For many use cases of our forecasts models (such as AER), it is more important that predictions are correctly rank ordered within a forecast window, rather than each forecast being accurate in terms of absolute values.\n", + "* **AER CO2 Savings:** This metric represents the impact in lbs CO2/MWh of using the forecast model for energy usage optimization. We compare these CO2 savings against the \"Max Potential\" case (ie. the case of using perfect forecasts). In this notebook, we model 3 different popular AER Scenarios:\n", + " \n", + " 1. EV-night: Charging an electric vehicle for 3 hours out of a 12-hour overnight window starting at 19:00 local time.\n", + " 2. EV-day: Charging an electric vehicle for 2 hours out of an 8-hour daytime window starting at 09:00 local time.\n", + " 3. 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"name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/watttime/report.py b/watttime/report.py new file mode 100644 index 00000000..8d7ad799 --- /dev/null +++ b/watttime/report.py @@ -0,0 +1,939 @@ +import argparse +import calendar +import os +import sys +from concurrent.futures import ThreadPoolExecutor, as_completed +from datetime import datetime, timedelta, timezone +from dateutil.parser import parse +from functools import cached_property +from operator import attrgetter +from pathlib import Path +from typing import List, Optional, Union +from zoneinfo import ZoneInfo +from dataclasses import dataclass +import random + +import numpy as np +import pandas as pd +import plotly.graph_objects as go +import plotly.subplots as sp +import scipy.stats as stats +from nbconvert.exporters import HTMLExporter + +import papermill as pm +from watttime import api + +# hacky way to allow running this script locally +sys.path.append(str(Path(__file__).parents[1].resolve())) + + +def convert_to_timezone(dt: datetime, tz: Union[str, ZoneInfo]) -> datetime: + """ + Converts datetime to specified timezone. If datetime is naive, assumes UTC. + """ + # Handle string timezone + tz = ZoneInfo(tz) if isinstance(tz, str) else tz + + # Replace the complex tzinfo checking with simple built-in + return (dt.replace(tzinfo=timezone.utc) if dt.tzinfo is None else dt).astimezone(tz) + + +def parse_datetime(date_str: str) -> datetime: + """Parses datetime string and converts to UTC.""" + return convert_to_timezone(parse(date_str), timezone.utc) + + +def round_time(dt: datetime, minutes: int = 5) -> datetime: + """ + Rounds datetime down to nearest interval. + Uses integer division which is more efficient than timedelta operations. + """ + return dt.replace(minute=(dt.minute // minutes) * minutes, second=0, microsecond=0) + + +def parse_eval_days(eval_start, eval_end): + if isinstance(eval_start, str): + eval_start = parse(eval_start) + if isinstance(eval_end, str): + eval_end = parse(eval_end) + + return [ + i.strftime("%Y-%m-%d") + for i in pd.date_range(start=eval_start, end=eval_end, freq="1D") + ] + + +def parse_forecast_sample_days(eval_days, forecast_sample_days): + try: + return random.sample(eval_days, forecast_sample_days) + except ValueError as e: + return eval_days + + +def _fetch_forecast( + day, ba, new_model_date_str, wt_forecast: Optional[api.WattTimeForecast] = None +): + """ + Fetch the historical forecast for a specific day and balancing authority (ba). + """ + + if not wt_forecast: + wt_forecast = api.WattTimeForecast() + + return wt_forecast.get_historical_forecast_pandas( + start=parse(day), + end=parse(day) + pd.Timedelta(hours=23, minutes=55), + region=ba, + signal_type="co2_moer", + model=new_model_date_str, + horizon_hours=72, + ) + + +def _fetch_forecasts( + sample_days: List[str], + ba: str, + model: str, + signal_type: str, + wt_forecast: Optional[api.WattTimeForecast] = None, +): + + _forecasts = [] + out = pd.DataFrame() + + # Use ThreadPoolExecutor for concurrent calls which are I/O bound + with ThreadPoolExecutor(max_workers=os.cpu_count() * 5) as executor: + future_to_day = { + executor.submit(_fetch_forecast, day, ba, model, wt_forecast): day + for day in sample_days + } + for future in as_completed(future_to_day): + try: + forecast = future.result() + _forecasts.append(forecast) + except Exception as e: + print( + f"Error fetching forecast for {future_to_day[future]} in {ba}: {e}" + ) + + # Combine and process the results + if _forecasts: # Ensure there's data before concatenation + out = pd.concat(_forecasts, ignore_index=True) + out["point_time"] = pd.to_datetime(out["point_time"]) + out["horizon_mins"] = ( + out["point_time"] - out["generated_at"] + ).dt.total_seconds() / 60 + out.rename({"value": "predicted_value"}, axis="columns", inplace=True) + + if out.empty: + raise ValueError( + f"Empty dataframe returned for {ba} - {model} - {signal_type} forecast API request" + ) + return out + + +@dataclass +class ModelAnalysis: + ba: str + model_date: str + signal_type: str + eval_start: str + eval_end: str + eval_days: List[str] + sample_days: List[str] + wt_forecast: Optional[api.WattTimeForecast] = api.WattTimeForecast() + wt_hist: Optional[api.WattTimeHistorical] = api.WattTimeHistorical() + + def __post_init__(self): + self.ba = self.ba.upper() + + @cached_property + def moers(self): + return ( + self.wt_hist.get_historical_pandas( + start=self.eval_start, + end=self.eval_end, + region=self.ba, + signal_type=self.signal_type, + model=self.model_date, + ) + .rename(columns={"value": "signal_value"}) + .set_index("point_time", drop=True) + ) + + @cached_property + def forecasts(self): + return _fetch_forecasts( + sample_days=self.sample_days, + ba=self.ba, + model=self.model_date, + signal_type=self.signal_type, + wt_forecast=self.wt_forecast, + ) + + def compile_forecast_v_moer(self): + self.forecasts_v_moers = self.forecasts.merge( + self.moers, + how="left", + left_on="point_time", + right_on="point_time", + ) + + +def get_random_overlapping_period(dfs, max_period="7D"): + """ + Find a random overlapping time period between multiple DataFrames' datetime indices, + maximizing the period up to `max_period`. + + Args: + dfs (list of pd.DataFrame): List of DataFrames with datetime indices. + max_period (str): Maximum time period as a string (e.g., '7D' for 7 days). + + Returns: + pd.DatetimeIndex: List of overlapping datetimes within the defined time period. + """ + if not dfs or len(dfs) < 2: + raise ValueError("You must provide at least two DataFrames.") + + # Find the overlapping range across all DataFrames + start_overlap = max(df.index.min() for df in dfs) + end_overlap = min(df.index.max() for df in dfs) + + if start_overlap > end_overlap: + raise ValueError( + "No overlapping time period found among the provided DataFrames." + ) + + total_overlap_duration = end_overlap - start_overlap + max_timedelta = pd.Timedelta(max_period) + + # If the total overlap duration is less than or equal to max_period, return the full range + if total_overlap_duration <= max_timedelta: + overlap_range = pd.date_range(start=start_overlap, end=end_overlap, freq="5T") + # Check if all indices exist in both DataFrames + if all(df.index.isin(overlap_range).all() for df in dfs): + return overlap_range + else: + raise ValueError("No common overlap found among the provided DataFrames.") + + # Otherwise, select a random period within the overlapping range + max_attempts = 10 + attempts = 0 + while attempts < max_attempts: + random_start_time = pd.Timestamp( + np.random.choice( + pd.date_range(start_overlap, end_overlap - max_timedelta, freq="5T") + ) + ) + random_end_time = random_start_time + max_timedelta + overlap_range = pd.date_range( + start=random_start_time, end=random_end_time, freq="5T" + ) + # Check if all indices exist in both DataFrames + if all(df.index.isin(overlap_range).all() for df in dfs): + return overlap_range + attempts += 1 + raise ValueError(f"No common overlap found after {max_attempts} attempts.") + + +def plot_sample_moers(jobs: List[ModelAnalysis], max_sample_period="7D"): + """ + Plot a sample of old and new MOER values over time, creating a subplot for each unique BA. + + Args: + jobs (list): List of ModelAnalysis objects with a .moers atribute. + + Returns: + plotly.graph_objects.Figure: A Plotly figure with timeseries of values. + """ + + # Create a figure with a subplot for each BA, stacked vertically + unique_bas = set([j.ba for j in jobs]) + fig = sp.make_subplots( + rows=len(unique_bas), + cols=1, + shared_xaxes=True, + vertical_spacing=0.1, + subplot_titles=[f"{ba.upper()}" for ba in unique_bas], + ) + + for i, ba_abbrev in enumerate(unique_bas, start=1): + ba_abbrev = ba_abbrev.upper() + _jobs = [j for j in jobs if j.ba == ba_abbrev] + + times = get_random_overlapping_period( + [j.moers for j in _jobs], max_sample_period + ) + + for _job in _jobs: + fig.add_trace( + go.Scatter( + x=times, + y=_job.moers.loc[times, "signal_value"].to_list(), + mode="lines", + name=_job.model_date, + line=dict(width=2), + showlegend=(i == 1), + ), + row=i, + col=1, + ) + + # Update layout for the figure + fig.update_layout( + height=300 + * len(unique_bas), # Adjust height dynamically based on the number of BAs + title="Data Sample Comparisons by Region", + yaxis_title=f"{jobs[0].signal_type}", + showlegend=True, + margin=dict(l=150, r=20, t=60, b=80), + xaxis=dict( + type="date", + tickformat="%Y-%m-%d %H:%M", + tickangle=45, + showgrid=True, + ), + ) + + return fig + + +def plot_distribution_moers(jobs: List[ModelAnalysis]): + """ + Plot the distribution of old and new MOER values for each BA, creating a stacked subplot. + + Args: + jobs (list): List of ModelAnalysis objects with a .moers atribute. + + Returns: + plotly.graph_objects.Figure: A Plotly figure with stacked subplots of distributions. + """ + # Create a figure with a subplot for each BA, stacked vertically + unique_bas = set([j.ba for j in jobs]) + fig = sp.make_subplots( + rows=len(unique_bas), + cols=1, + shared_xaxes=True, + vertical_spacing=0.2, + subplot_titles=[f"{ba.upper()}" for ba in unique_bas], + ) + + for i, ba_abbrev in enumerate(unique_bas, start=1): + ba_abbrev = ba_abbrev.upper() + _jobs = [j for j in jobs if j.ba == ba_abbrev] + for _job in _jobs: + + # Add a histogram trace for the new MOER distribution + fig.add_trace( + go.Box( + x=_job.moers["signal_value"].dropna(), + name=_job.model_date, + opacity=0.6, + ), + row=i, + col=1, + ) + + # Update layout for the figure + fig.update_layout( + height=300 + * len(unique_bas), # Adjust height dynamically based on the number of BAs + title=f"{jobs[0].signal_type} Distribution Comparisons by Region", + xaxis_title=f"{jobs[0].signal_type} Values", + showlegend=True, + margin=dict(l=150, r=20, t=60, b=80), + xaxis=dict( + type="linear", + tickangle=45, + showgrid=True, + ), + yaxis_visible=False, + yaxis_showticklabels=False, + ) + + return fig + + +def plot_ba_heatmaps(jobs: List[ModelAnalysis], colorscale="Cividis"): + """ + Generate vertically stacked heatmaps for each BA in the ba_list. + + Args: + jobs (list): List of ModelAnalysis objects with a .moers atribute. + + Returns: + plotly.graph_objects.Figure: Figure containing stacked heatmaps. + """ + + jobs = sorted(jobs, key=attrgetter("ba", "model_date")) + + # Initialize a subplot with one row per BA + fig = sp.make_subplots( + rows=len(jobs), + cols=1, + shared_xaxes=True, + subplot_titles=[f"{j.ba} - {j.model_date}" for j in jobs], + vertical_spacing=0.2, + ) + + # share upper and lower bounds between plots + zmin = 1e6 + zmax = 0 + for job in jobs: + jmin = job.moers["signal_value"].quantile(0.01) + jmax = job.moers["signal_value"].quantile(0.99) + + if jmin < zmin: + zmin = jmin + if jmax > zmax: + zmax = jmax + + for i, _job in enumerate(jobs, start=1): + heat = _job.moers.assign( + month=_job.moers.index.month, hour=_job.moers.index.hour + ) + heat = heat.dropna(subset=["signal_value"]) + heat = ( + heat.groupby(["month", "hour"])["signal_value"] + .mean() + .unstack(fill_value=np.nan) + ) + heat.index = [ + calendar.month_abbr[m] for m in heat.index + ] # Map month numbers to month abbreviations + + # Create a heatmap trace + heatmap_trace = go.Heatmap( + z=heat.values, + x=heat.columns, # Hours + y=heat.index, # Month abbreviations + zmin=zmin, + zmax=zmax, + colorscale=colorscale, + colorbar_title="MOER", + showscale=(i == 1), # Show color scale only on the first heatmap + ) + + # Add the heatmap trace to the subplot + fig.add_trace(heatmap_trace, row=i, col=1) + + # Update layout + fig.update_layout( + height=500 * len(jobs), # Adjust the height based on the number of BAs + title=f"Average {jobs[0].signal_type} by Month & Hour", + xaxis_title="Hour of Day", + yaxis_title="Month", + xaxis=dict(title="Hour of Day", tickmode="linear"), + ) + + return fig + + +def calc_norm_mae( + df, horizon_mins, pred_col="predicted_value", truth_col="signal_value" +): + """Returns normalized MAE in lbs/Mwh""" + + filtered_df = df[df["horizon_mins"] == horizon_mins].dropna() + filtered_df["abs_error"] = (filtered_df[truth_col] - filtered_df[pred_col]).abs() + # truth_mean = filtered_df[truth_col].mean() + + norm_mae = (filtered_df["abs_error"].mean() / filtered_df[truth_col].mean()) * 100.0 + + return round(norm_mae, 1) + + +def calc_rank_corr( + df, horizon_mins, pred_col="predicted_value", truth_col="signal_value" +): + """Returns mean daily Rank Correlation""" + + filtered_df = df = df[df["horizon_mins"] <= horizon_mins].dropna() + corr = filtered_df.groupby("generated_at").apply( + lambda group_df: stats.spearmanr( + group_df[truth_col], group_df[pred_col] + ).statistic + ) + + return np.nanmean(corr).round(3) + + +def calc_rank_compare_metrics( + in_df, + charge_mins, + window_mins, + window_starts=None, + pred_col="predicted_value", + truth_col="signal_value", +): + """ + Calculate rank_compare metrics: co2_reduction, co2_potential, co2_pct. + + Parameters: + in_df (pd.DataFrame): DataFrame containing the 'truth' and 'predicted' MOER values. + charge_hours (int): Charge window in minutes. + window_hours (int): Comparison window in minutes. + window_starts (list or None): Start times for windows (e.g., ["09:00"]). + truth_col (str): Column name for the 'truth' MOER values. + pred_col (str): Column name for the 'predicted' MOER values. + + Returns: + dict: co2_reduction, co2_potential, co2_pct metrics. + """ + + df = in_df.copy() + df.set_index("generated_at", inplace=True) + df.dropna(inplace=True) + + # Rank of truth and predicted columns within each window + if window_starts: + # Generate window ranges based on start times and duration + unique_dates = df.index.normalize().unique() # Extract unique dates + window_ranges = [] + for date in unique_dates: + for start_time in window_starts: + start = pd.Timestamp(f"{date} {start_time}") + end = start + pd.Timedelta(minutes=window_mins) + window_ranges.append((start, end)) + + # Assign each row to a window + def assign_window(row): + generated_at = row.name + point_time = row["point_time"] # point_time column + for start, end in window_ranges: + if start <= generated_at < end and point_time < end: + return start # Label window by its start time + return pd.NaT + + df["window"] = df.apply(assign_window, axis=1) + else: + # Default behavior: use rolling windows + def assign_rolling_window(row): + ts = row.name # Index (e.g., generated_at) + point_time = row["point_time"] # point_time column + window_start = ts.floor(f"{window_mins}T") + window_end = window_start + pd.Timedelta(minutes=window_mins) + + # Only assign window if point_time is within the window range + if point_time < window_end: + return window_start + return pd.NaT + + df["window"] = df.apply(assign_rolling_window, axis=1) + + # Drop rows outside any window + df = df.dropna(subset=["window"]) + + df["y_rank"] = df.groupby(["window", "generated_at"])[truth_col].rank( + method="first" + ) + df["y_pred_rank"] = df.groupby(["window", "generated_at"])[pred_col].rank( + method="first" + ) + + # TODO: remove this filter and replace with while loop simulation for max savings + max_rank = df["y_rank"].max() + df = df.groupby(["generated_at", "window"]).filter( + lambda group: group["y_rank"].max() == max_rank + ) + + # Calculate total C02 emissions in lbs/Mwh of each scenario + y_actual_total = ( + df[df["y_pred_rank"] <= (charge_mins / 5)] + .groupby(["window", "generated_at"])[truth_col] + .mean() + ) + y_best_total = ( + df[df["y_rank"] <= (charge_mins / 5)] + .groupby(["window", "generated_at"])[truth_col] + .mean() + ) + y_avg_total = df.groupby(["window", "generated_at"])[truth_col].mean() + + # Calculate savings: co2_reduction, co2_potential + co2_reduction = (y_avg_total - y_actual_total).mean() # actual savings + co2_potential = (y_avg_total - y_best_total).mean() # ideal savings + # co2_pct = (co2_reduction / co2_potential) * 100. # actual / ideal; % savings realized + + # if co2_potential != 0: + # co2_pct = (co2_reduction / co2_potential) * 100. + # else: + # co2_pct = 100. if co2_reduction == 0 else 0 # Handle the edge case when moers are flat + + return { + "co2_reduction": round(co2_reduction, 1), + "co2_potential": round(co2_potential, 1), + } + + +def plot_norm_mae(jobs: List[ModelAnalysis], horizons_hr=[1, 12, 24, 48, 72]): + """ + Create a Plotly bar chart for rank correlation by horizon with one subplot per BA (abbrev). + """ + + # Create subplots + unique_bas = set([j.ba for j in jobs]) + fig = sp.make_subplots( + rows=len(unique_bas), + cols=1, + shared_xaxes=True, + vertical_spacing=0.2, + subplot_titles=list(unique_bas), + ) + + y_min = y_max = 0 + + # Iterate through each BA and create a bar plot + for i, ba_abbrev in enumerate(unique_bas, start=1): + ba_abbrev = ba_abbrev.upper() + _jobs = [j for j in jobs if j.ba == ba_abbrev] + + x_values = [f"{h}hr" for h in horizons_hr] # Add horizon labels + + for _job in _jobs: + y_values = [ + calc_norm_mae(_job.forecasts_v_moers, (h * 60) - 5) for h in horizons_hr + ] + y_max = max(y_values + [y_max]) + y_min = min(y_values + [y_min]) + + fig.add_trace( + go.Bar( + x=x_values, + y=y_values, + name=_job.model_date, + text=[f"{y:.1f}%" for y in y_values], # Add text labels on bars + textposition="outside", + ), + row=i, + col=1, + ) + # Set uniform y-axis range for all subplots + fig.update_yaxes(range=[y_min - (0.25 * y_max), y_max + (0.25 * y_max)]) + + fig.update_layout( + height=300 + * len(unique_bas), # Adjust figure height based on the number of subplots + title_text="Normalized MAE by Horizon", + xaxis_title="Horizon", + yaxis_title="Normalized MAE (%)", + showlegend=True, # Legends appear in individual subplot titles + margin=dict(l=50, r=50, t=50, b=50), + barmode="group", # Ensure bars for each BA are grouped + ) + fig.update_xaxes(title_text="Horizon (Hours)", row=len(unique_bas), col=1) + + return fig + + +def plot_rank_corr(jobs: List[ModelAnalysis], horizons_hr=[12, 24, 48, 72]): + """ + Create a Plotly line plot for rank correlation by horizon with one subplot per BA (abbrev). + + Parameters: + df (pd.DataFrame): DataFrame containing the rank correlation data. + Columns: ['abbrev', 'name', '24hr', '48hr', '72hr']. + metric_name (str): 'Rank Correlation' or 'Normalized MAE' + + Returns: + fig (plotly.graph_objects.Figure): A Plotly figure with subplots. + + """ + + # Create subplots + unique_bas = set([j.ba for j in jobs]) + fig = sp.make_subplots( + rows=len(unique_bas), + cols=1, + shared_xaxes=True, + vertical_spacing=0.2, + subplot_titles=list(unique_bas), + ) + + y_min = y_max = 0 + + # Iterate through each BA and create a line plot + for i, ba_abbrev in enumerate(unique_bas, start=1): + + ba_abbrev = ba_abbrev.upper() + _jobs = [j for j in jobs if j.ba == ba_abbrev] + + # Extract data for the line plot + x_values = [f"{h}hr" for h in horizons_hr] # Add horizon labels + + for _job in _jobs: + y_values = [ + calc_rank_corr(_job.forecasts_v_moers, (h * 60) - 5) + for h in horizons_hr + ] + y_max = max(y_values + [y_max]) + y_min = min(y_values + [y_min]) + + fig.add_trace( + go.Bar( + x=x_values, + y=y_values, + name=f"{_job.ba} - {_job.model_date}", + text=[f"{y:.3f}" for y in y_values], # Add text labels on bars + textposition="outside", + ), + row=i, + col=1, + ) + + # Set uniform y-axis range for all subplots + fig.update_yaxes(range=[y_min - (0.2 * y_max), y_max + (0.2 * y_max)]) + + # Update layout + fig.update_layout( + height=300 + * len(unique_bas), # Adjust figure height based on the number of subplots + title_text=f"Rank Correlation by Horizon", + xaxis_title="Horizon", + yaxis_title="Rank Correlation", + showlegend=True, # Legends appear in individual subplot titles + margin=dict(l=50, r=50, t=50, b=50), + ) + + # Update x-axis for all subplots + fig.update_xaxes(title_text="Horizon (Hours)", row=len(unique_bas), col=1) + + return fig + + +# TODO: translate to local time for start windows? +AER_SCENARIOS = { + "EV-night": { + "charge_mins": 3 * 60, + "window_mins": 12 * 60, + "window_starts": ["19:00"], + }, + "EV-day": { + "charge_mins": 2 * 60, + "window_mins": 8 * 60, + "window_starts": ["09:00"], + }, + "Thermostat": { + "charge_mins": 30, + "window_mins": 60, + "window_starts": None, + }, +} + + +def plot_impact_forecast_metrics( + jobs: List[ModelAnalysis], scenarios=["EV-night", "EV-day", "Thermostat"] +): + + # Create subplots + fig = sp.make_subplots( + rows=len(jobs), + cols=1, + shared_xaxes=True, + vertical_spacing=0.2, + subplot_titles=[f"{j.ba} - {j.model_date}" for j in jobs], + ) + + # Iterate through each BA and create bar plots + for i, _job in enumerate(jobs, start=1): + + _metrics = [ + { + **calc_rank_compare_metrics(_job.forecasts_v_moers, **AER_SCENARIOS[s]), + "scenario": s, + } + for s in scenarios + ] + + _metrics = pd.DataFrame(_metrics) + + # Add the 'co2_potential' bar trace + fig.add_trace( + go.Bar( + x=_metrics["scenario"], + y=_metrics["co2_potential"], + name=f"CO2 Potential Savings", # Legend only in the first subplot + marker=dict( + color="rgba(200, 200, 200, 0.8)" + ), # Light gray for potential + hovertemplate="%{x}: %{y:.1f} lbs CO2/MWh", + ), + row=i, + col=1, + ) + + # Add the 'co2_reduction' bar trace + fig.add_trace( + go.Bar( + x=_metrics["scenario"], + y=_metrics["co2_reduction"], + name="Forecast Achieved CO2 Savings", + text=[ + f"{(r / p) * 100:.1f}%" + for r, p in zip( + _metrics["co2_reduction"], _metrics["co2_potential"] + ) + ], + textposition="outside", + marker=dict(color="rgba(0, 128, 0, 0.8)"), # Green for reduction + hovertemplate="%{x}: %{y:.1f} lbs CO2/MWh", + ), + row=i, + col=1, + ) + + # Update layout + fig.update_layout( + height=400 * len(jobs), # Adjust figure height based on the number of subplots + title_text="Simulated CO2 Savings by AER Scenario and Region", + xaxis_title="Scenario", + yaxis_title="CO2 Savings (lbs/MWh)", + barmode="group", # Grouped bars (side by side) + showlegend=True, # Show legend + margin=dict(l=50, r=50, t=80, b=50), + ) + + # Update axes for all subplots + for i in range(1, len(jobs) + 1): + fig.update_xaxes(title_text="Scenario", row=i, col=1) + fig.update_yaxes(title_text="CO2 Savings (lbs/MWh)", row=i, col=1) + + return fig + + +def parse_report_command_line_args(sys_args): + parser = argparse.ArgumentParser( + description="Parse command line arguments to report_moers script" + ) + + parser.add_argument("-ba", "--ba_list", nargs="+", help="List of ba abbrevs.") + + parser.add_argument( + "-d", + "--signal_type", + type=str, + default="co2_moer", + choices=["co2_moer", "co2_aoer", "health_damages"], + help="signal_type / Signal Type. Default is 'co2_moer'.", + ) + + parser.add_argument( + "-m", + "--models", + nargs="+", + help="The name of the new model version.", + ) + + parser.add_argument( + "-s", + "--start", + type=parse_datetime, + help="Evaluation Start timestamp YYYY-MM-DD HH:MM±HH:MM", + ) + + parser.add_argument( + "-e", + "--end", + type=parse_datetime, + help="Evaluation End timestamp YYYY-MM-DD HH:MM±HH:MM", + ) + + parser.add_argument( + "-o", + "--output_dir", + type=str, + default=str(Path(__file__).parent / "analysis"), + help="Top level directory to save model report. Default is '/research/moer_reports'.", + ) + + parser.add_argument( + "-u", + "--username", + type=str, + default=None, + help="WattTime Username can be provided as a CLI arg or set as an environment variable (WATTTIME_USER)", + ) + + parser.add_argument( + "-p", + "--password", + type=str, + default=None, + help="WattTime Password can be provided as a CLI arg or set as an environment variable (WATTTIME_PASSWORD)", + ) + + parser.add_argument( + "-f", + "--forecast_sample_days", + type=int, + default=12, + help="Number of days to sample from forecast. Default is 12.", + ) + + args = parser.parse_args(sys_args) + + now = datetime.now(timezone.utc) + if args.end is None: + args.end = round_time(now - timedelta(days=7)) + if args.start is None: + args.start = args.end - timedelta(days=7) + + return args + + +def run_report_notebook( + ba_list: List[str], + model_date_list: List[str], + signal_type: str, + eval_start: datetime, + eval_end: datetime, + output_dir: Path, + api_username: Optional[str] = None, + api_password: Optional[str] = None, + forecast_sample_days: Optional[int] = None, +): + filename = f"{signal_type}_{model}_model_stats" + + # run notebook + output_notebook = output_dir / f"{filename}.ipynb" + output_html = output_dir / f"{filename}.html" + pm.execute_notebook( + Path(__file__).parents[0] / "new_moer_model_report.ipynb", + str(output_notebook), + parameters=dict( + ba_list=ba_list, + model_date_list=model_date_list, + signal_type=signal_type, + eval_start=eval_start.isoformat(), + eval_end=eval_end.isoformat(), + api_username=api_username, + api_password=api_password, + forecast_sample_days=forecast_sample_days, + ), + ) + + # HTML exporter with the custom template + html_exporter = HTMLExporter() + html_exporter.exclude_input = True # hide code cells + + # Convert the notebook to HTML + write to file + (body, resources) = html_exporter.from_filename(str(output_notebook)) + with open(str(output_html), "w", encoding="utf-8") as f: + f.write(body) + + +if __name__ == "__main__": + + cli_args = parse_report_command_line_args(sys.argv[1:]) + + run_report_notebook( + ba_list=cli_args.ba_list, + model_date_list=cli_args.model_date_list, + signal_type=cli_args.signal_type, + eval_start=cli_args.start, + eval_end=cli_args.end, + output_dir=Path(cli_args.output_dir), + api_username=cli_args.username, + api_password=cli_args.password, + forecast_sample_days=cli_args.forecast_sample_days, + ) From 72784408aa4097c86a0d2a45464e4d564e6bb0d1 Mon Sep 17 00:00:00 2001 From: sam-watttime Date: Tue, 21 Jan 2025 23:23:13 -0700 Subject: [PATCH 02/85] add tests for report module --- tests/test_report.py | 94 ++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 94 insertions(+) create mode 100644 tests/test_report.py diff --git a/tests/test_report.py b/tests/test_report.py new file mode 100644 index 00000000..67ba9806 --- /dev/null +++ b/tests/test_report.py @@ -0,0 +1,94 @@ +import unittest +from datetime import datetime, date +from watttime import report +from pathlib import Path +from typing import List, Union +from itertools import product + +BA_LIST: List[str] = ["WEM"] +MODEL_DATE_LIST: List[str] = ["2022-10-01", "2024-10-16"] +SIGNAL_TYPE: str = "co2_moer" +EVAL_START: Union[str, datetime, date] = "2024-01-01T00:00Z" +EVAL_END: Union[str, datetime, date] = "2024-03-01T00:00Z" +FORECAST_SAMPLE_DAYS = 3 + + +class TestReport(unittest.TestCase): + def setUp(self): + self.eval_days = report.parse_eval_days( + eval_start=EVAL_START, eval_end=EVAL_END + ) + self.sample_days = report.parse_forecast_sample_days( + eval_days=self.eval_days, forecast_sample_days=FORECAST_SAMPLE_DAYS + ) + + self.jobs = list( + report.ModelAnalysis( + ba=i[0], + model_date=i[1], + signal_type=SIGNAL_TYPE, + eval_start=EVAL_START, + eval_end=EVAL_END, + eval_days=self.eval_days, + sample_days=self.sample_days, + ) + for i in product(BA_LIST, MODEL_DATE_LIST) + ) + + # Compile forecast_v_moer for each job + for job in self.jobs: + job.compile_forecast_v_moer() + + def test_compile_forecast_v_moer(self): + for job in self.jobs: + self.assertFalse(job.moers.empty) + self.assertFalse(job.forecasts.empty) + self.assertFalse(job.forecasts_v_moers.empty) + + def test_plot_sample_moers(self): + fig = report.plot_sample_moers(self.jobs) + self.assertGreater(len(fig.data), 0) + + def test_plot_distribution_moers(self): + fig = report.plot_distribution_moers(self.jobs) + self.assertGreater(len(fig.data), 0) + + def test_plot_ba_heatmaps(self): + fig = report.plot_ba_heatmaps(self.jobs) + self.assertGreater(len(fig.data), 0) + + def test_plot_norm_mae(self): + fig = report.plot_norm_mae(self.jobs) + self.assertGreater(len(fig.data), 0) + + def test_plot_rank_correlation(self): + fig = report.plot_rank_correlation(self.jobs) + self.assertGreater(len(fig.data), 0) + + def test_plot_impact_forecast_metrics(self): + fig = report.plot_impact_forecast_metrics(self.jobs) + self.assertGreater(len(fig.data), 0) + + def test_plot_sample_moers_single_job(self): + fig = report.plot_sample_moers([self.jobs[0]]) + self.assertGreater(len(fig.data), 0) + + def test_plot_distribution_moers_single_job(self): + fig = report.plot_distribution_moers([self.jobs[0]]) + self.assertGreater(len(fig.data), 0) + + def test_plot_ba_heatmaps_single_job(self): + fig = report.plot_ba_heatmaps([self.jobs[0]]) + self.assertGreater(len(fig.data), 0) + + def test_plot_norm_mae_single_job(self): + fig = report.plot_norm_mae([self.jobs[0]]) + self.assertGreater(len(fig.data), 0) + + def test_plot_rank_correlation_single_job(self): + fig = report.plot_rank_correlation([self.jobs[0]]) + self.assertGreater(len(fig.data), 0) + + def test_plot_impact_forecast_metrics_single_job(self): + fig = report.plot_impact_forecast_metrics([self.jobs[0]]) + self.assertGreater(len(fig.data), 0) From f5ce3f0a0e02c33a344469bd58b3ad90bebe8db0 Mon Sep 17 00:00:00 2001 From: sam-watttime Date: Tue, 21 Jan 2025 23:23:38 -0700 Subject: [PATCH 03/85] add .gitkeep for analysis dir --- analysis/.gitkeep | 0 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 analysis/.gitkeep diff --git a/analysis/.gitkeep b/analysis/.gitkeep new file mode 100644 index 00000000..e69de29b From f1d3f21e302df8893e717cf283f0478cbc7bb2b3 Mon Sep 17 00:00:00 2001 From: sam-watttime Date: Tue, 21 Jan 2025 23:24:18 -0700 Subject: [PATCH 04/85] update watttime module init --- watttime/__init__.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/watttime/__init__.py b/watttime/__init__.py index 0cdb9642..016e1dd6 100644 --- a/watttime/__init__.py +++ b/watttime/__init__.py @@ -1,2 +1,3 @@ from watttime.api import * -from watttime.tcy import TCYCalculator \ No newline at end of file +from watttime.tcy import TCYCalculator +from watttime.report import ModelAnalysis \ No newline at end of file From 59c1d8ccdbb40fd8fb119994a20b60cb02194e57 Mon Sep 17 00:00:00 2001 From: sam-watttime Date: Tue, 21 Jan 2025 23:24:33 -0700 Subject: [PATCH 05/85] add extras to setup.py --- setup.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/setup.py b/setup.py index 89710b7b..28ed0613 100644 --- a/setup.py +++ b/setup.py @@ -3,10 +3,11 @@ setup( name="watttime", description="An officially maintained python client for WattTime's API providing access to electricity grid emissions data.", - long_description=open('README.md').read(), + long_description=open("README.md").read(), long_description_content_type="text/markdown", version="v1.3.2", packages=["watttime"], python_requires=">=3.8", install_requires=["requests", "pandas>1.0.0", "holidays", "python-dateutil"], + extras_require={"report": ["papermill", "nbconvert", "plotly", "scipy"]}, ) From ed2d36280cc3c9c660838fe9017c9095959f5121 Mon Sep 17 00:00:00 2001 From: sam-watttime Date: Tue, 21 Jan 2025 23:24:51 -0700 Subject: [PATCH 06/85] update .gitignore to exclude contents of analysis dir --- .gitignore | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/.gitignore b/.gitignore index 68bc17f9..1d7c598c 100644 --- a/.gitignore +++ b/.gitignore @@ -158,3 +158,9 @@ cython_debug/ # and can be added to the global gitignore or merged into this file. For a more nuclear # option (not recommended) you can uncomment the following to ignore the entire idea folder. #.idea/ + +watttime/api_do_not_commit.py +tests/test_do_not_commit.py +analysis/* + +!analysis/.gitkeep \ No newline at end of file From c94c399499b2150754dc5f51e2f778c58afd1a36 Mon Sep 17 00:00:00 2001 From: sam-watttime Date: Tue, 21 Jan 2025 23:39:37 -0700 Subject: [PATCH 07/85] make report.py a script --- setup.py | 1 + watttime/report.py | 2 ++ 2 files changed, 3 insertions(+) diff --git a/setup.py b/setup.py index 28ed0613..2a1084ba 100644 --- a/setup.py +++ b/setup.py @@ -10,4 +10,5 @@ python_requires=">=3.8", install_requires=["requests", "pandas>1.0.0", "holidays", "python-dateutil"], extras_require={"report": ["papermill", "nbconvert", "plotly", "scipy"]}, + scripts=["watttime/report.py"], ) diff --git a/watttime/report.py b/watttime/report.py index 8d7ad799..cb840d01 100644 --- a/watttime/report.py +++ b/watttime/report.py @@ -1,3 +1,5 @@ +#!/usr/bin/env python + import argparse import calendar import os From f62de39cffbe51fe7d995ee52b17f1c57d5b9cfa Mon Sep 17 00:00:00 2001 From: samkoebrich Date: Wed, 12 Feb 2025 16:17:06 -0700 Subject: [PATCH 08/85] use session in WattTimeBase --- watttime/api.py | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/watttime/api.py b/watttime/api.py index b70da778..a650b2be 100644 --- a/watttime/api.py +++ b/watttime/api.py @@ -47,6 +47,8 @@ def __init__( ) # prevent multiple threads from modifying _last_request_times simultaneously self._rate_limit_condition = threading.Condition(self._rate_limit_lock) + self.session = requests.Session() + def _login(self): """ Login to the WattTime API, which provides a JWT valid for 30 minutes @@ -56,7 +58,7 @@ def _login(self): """ url = f"{self.url_base}/login" - rsp = requests.get( + rsp = self.session.get( url, auth=requests.auth.HTTPBasicAuth(self.username, self.password), timeout=20, @@ -147,7 +149,7 @@ def register(self, email: str, organization: Optional[str] = None) -> None: "org": organization, } - rsp = requests.post(url, json=params, timeout=20) + rsp = self.session.post(url, json=params, timeout=20) rsp.raise_for_status() print( f"Successfully registered {self.username}, please check {email} for a verification email" @@ -202,7 +204,7 @@ def _make_rate_limited_request(self, url: str, params: Dict[str, Any]) -> Dict: self._apply_rate_limit(ts) try: - rsp = requests.get(url, headers=self.headers, params=params) + rsp = self.session.get(url, headers=self.headers, params=params) rsp.raise_for_status() j = rsp.json() except requests.exceptions.RequestException as e: From d087931131e055dc42296026e70b313dad889e22 Mon Sep 17 00:00:00 2001 From: samkoebrich Date: Thu, 13 Feb 2025 12:06:58 -0700 Subject: [PATCH 09/85] set max_workers explictily --- watttime/api.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/watttime/api.py b/watttime/api.py index b70da778..cc8207e2 100644 --- a/watttime/api.py +++ b/watttime/api.py @@ -258,7 +258,7 @@ def _fetch_data( responses = [] if self.multithreaded: - with ThreadPoolExecutor(max_workers=os.cpu_count() * 5) as executor: + with ThreadPoolExecutor(max_workers=10) as executor: futures = { executor.submit( self._make_rate_limited_request, url, params From 9c8b1db04cb3af14a93c20a4f9cc868d2c7804b4 Mon Sep 17 00:00:00 2001 From: samkoebrich Date: Thu, 13 Feb 2025 12:08:57 -0700 Subject: [PATCH 10/85] Speed up historical forecast parsing by avoiding pd.json_normalize --- watttime/api.py | 17 ++++++++++------- 1 file changed, 10 insertions(+), 7 deletions(-) diff --git a/watttime/api.py b/watttime/api.py index 4d451dff..eb4e6347 100644 --- a/watttime/api.py +++ b/watttime/api.py @@ -395,13 +395,16 @@ def _parse_historical_forecast_json( Returns: pd.DataFrame: A pandas DataFrame containing the parsed historical forecast data. """ - out = pd.DataFrame() - for json in json_list: - for entry in json.get("data", []): - _df = pd.json_normalize(entry, record_path=["forecast"]) - _df = _df.assign(generated_at=pd.to_datetime(entry["generated_at"])) - out = pd.concat([out, _df], ignore_index=True) - return out + data = [] + for j in json_list: + for gen_at in j["data"]: + for point_time in gen_at["forecast"]: + point_time["generated_at"] = gen_at["generated_at"] + data.append(point_time) + df = pd.DataFrame.from_records(data) + df["point_time"] = pd.to_datetime(df["point_time"]) + df["generated_at"] = pd.to_datetime(df["generated_at"]) + return df def get_forecast_json( self, From 95e8df5ac7a50e599bffbf7bc49e4bc3a2a69894 Mon Sep 17 00:00:00 2001 From: samkoebrich Date: Thu, 13 Feb 2025 16:39:19 -0700 Subject: [PATCH 11/85] don't use session in test (yet) --- tests/test_sdk.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/test_sdk.py b/tests/test_sdk.py index f8bd74ca..220883f3 100644 --- a/tests/test_sdk.py +++ b/tests/test_sdk.py @@ -135,7 +135,7 @@ def test_parse_dates_with_datetime(self): self.assertIsInstance(parsed_end, datetime) self.assertEqual(parsed_end.tzinfo, UTC) - @mock.patch("watttime.requests.Session.post", side_effect=mocked_register) + @mock.patch("requests.post", side_effect=mocked_register) def test_mock_register(self, mock_post): resp = self.base.register(email=os.getenv("WATTTIME_EMAIL")) self.assertEqual(len(mock_post.call_args_list), 1) From 9dc2a357d22a6e9ed3d69a09987f28a5a4c8e216 Mon Sep 17 00:00:00 2001 From: samkoebrich Date: Thu, 13 Feb 2025 16:40:21 -0700 Subject: [PATCH 12/85] use session for register test mock --- tests/test_sdk.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/test_sdk.py b/tests/test_sdk.py index 220883f3..f8bd74ca 100644 --- a/tests/test_sdk.py +++ b/tests/test_sdk.py @@ -135,7 +135,7 @@ def test_parse_dates_with_datetime(self): self.assertIsInstance(parsed_end, datetime) self.assertEqual(parsed_end.tzinfo, UTC) - @mock.patch("requests.post", side_effect=mocked_register) + @mock.patch("watttime.requests.Session.post", side_effect=mocked_register) def test_mock_register(self, mock_post): resp = self.base.register(email=os.getenv("WATTTIME_EMAIL")) self.assertEqual(len(mock_post.call_args_list), 1) From f950b7e6f07ffe8eb783c760790400e9c344da57 Mon Sep 17 00:00:00 2001 From: samkoebrich Date: Thu, 27 Feb 2025 15:59:56 -0700 Subject: [PATCH 13/85] add get_wt_api_forecast_evaluation_data --- .../get_wt_api_forecast_evaluation_data.py | 204 ++++++++++++++++++ 1 file changed, 204 insertions(+) create mode 100644 watttime/evaluation/get_wt_api_forecast_evaluation_data.py diff --git a/watttime/evaluation/get_wt_api_forecast_evaluation_data.py b/watttime/evaluation/get_wt_api_forecast_evaluation_data.py new file mode 100644 index 00000000..b0fd119f --- /dev/null +++ b/watttime/evaluation/get_wt_api_forecast_evaluation_data.py @@ -0,0 +1,204 @@ +from typing import Optional, List, Union +from datetime import datetime +import inspect +import random +import math +from dateutil.parser import parse +from functools import cached_property +from itertools import product + +from dataclasses import dataclass + +import pandas as pd + +from watttime import api + + +@dataclass +class AnalysisDataHandler: + """ + This dataclass performs ETL on WattTime signals including + co2_moer, co2_health_damages, curtailment, and more. Data is also loaded + for forecasts of these underlying signals. + + Args: + region (str): A single BA abbreviation + model_date (str | None): A string representing a released model, if None, will default to latest provided through API + signal_type (str): Such as `co2_moer`, `co2_aoer`, `co2_health_damages`, or `curtailment` + eval_start (str or datetime): The starting point of the analysis to begin pulling signal data. + eval_end (str or datetime): The ending point of the analysis to stop pulling signal data. + forecast_sample_size (float or int): A float will represent a sample of days + to pull forecasts across (between eval_start and eval_end) while an int + greater than 1 will represent an absolute number of days. A value of 1 + will pull all forecasts between eval_start and eval_end, however + this can be time consuming. + forecast_max_horizon (int): The maximum forecast horizon to pull in minutes + forecast_sample_seed (int): Used to ensure consistency in sample dates + between multiple AnalysisDataHandler objects that share an eval_start + and eval_end + wt_forecast (api.WattTimeForecast): a WattTimeForecast API accessor class + may be passed in if you wish to handle login variables or other parameters. + Otherwise, these are loaded from the environment variables. + wt_hist (api.WattTimeHistorical): a WattTimeHistorical API accessor class + may be passed in if you wish to handle login variables or other parameters. + Otherwise, these are loaded from the environment variables. + + Returns: + _type_: _description_ + """ + + region: str + eval_start: Union[datetime, str] + eval_end: Union[datetime, str] + forecast_sample_size: Union[int, float] = 1 + forecast_max_horizon: int = 60 * 24 + forecast_sample_seed: int = 42 + signal_type: str = "co2_moer" + model_date: Optional[ + str + ] = None # if None, will default to latest provided through API + wt_forecast: Optional[api.WattTimeForecast] = api.WattTimeForecast( + multithreaded=True + ) + wt_hist: Optional[api.WattTimeHistorical] = api.WattTimeHistorical() + + def __post_init__(self): + self.region = self.region.upper() + + # parse eval_days + if isinstance(self.eval_start, str): + self.eval_start = parse(self.eval_start) + if isinstance(self.eval_end, str): + self.eval_end = parse(self.eval_end) + self.eval_days = list( + pd.date_range(start=self.eval_start, end=self.eval_end, freq="1D") + ) + + # parse sample_days as int (day count), or float (fraction of eval_days) + if isinstance(self.forecast_sample_size, float): + k = round(len(self.eval_days) * self.forecast_sample_size) + else: + k = self.forecast_sample_size + k = max(k, 1) + random.seed(self.forecast_sample_seed) + self.sample_days = random.sample(self.eval_days, k) + + @cached_property + def moers(self) -> pd.DataFrame: + moers = ( + self.wt_hist.get_historical_pandas( + start=self.eval_start, + end=self.eval_end + + pd.Timedelta( + minutes=self.forecast_max_horizon + ), # include truth_values for final generated_at + region=self.region, + signal_type=self.signal_type, + model=self.model_date, + ) + .rename(columns={"value": "signal_value"}) + .set_index("point_time", drop=True) + ) + + self.returned_meta = self.wt_hist._last_request_meta + self.returned_hist_model_date = self.wt_hist._last_request_meta["model"]["date"] + return moers + + @cached_property + def forecasts(self) -> pd.DataFrame: + + forecasts = self.wt_forecast.get_historical_forecast_pandas_list( + list_of_dates=self.sample_days, + region=self.region, + signal_type=self.signal_type, + model=self.model_date, + horizon_hours=math.ceil(self.forecast_max_horizon / 60), + ) + + forecasts["point_time"] = pd.to_datetime(forecasts["point_time"]) + forecasts["horizon_mins"] = ( + forecasts["point_time"] - forecasts["generated_at"] + ).dt.total_seconds() / 60 + forecasts.rename({"value": "predicted_value"}, axis="columns", inplace=True) + self.returned_meta = self.wt_forecast._last_request_meta + self.returned_forecast_model_date = self.wt_forecast._last_request_meta[ + "model" + ]["date"] + return forecasts + + @cached_property + def forecast_v_moer(self) -> pd.DataFrame: + return self.forecasts.merge( + self.moers, + how="inner", + left_on="point_time", + right_on="point_time", + ).set_index(["generated_at", "point_time"], drop=True) + + def forecast_v_moer_filtered_by_rank( + self, n_rows, filter_on="signal_value", use_highest=False + ): + forecast_ranks = self.forecast_v_moer.groupby("generated_at")[filter_on].rank( + method="first" + ) + if use_highest: + return self.forecast_v_moer[forecast_ranks >= n_rows] + return self.forecast_v_moer[forecast_ranks <= n_rows] + + +class DataHandlerFactory: + """ + Create a list of AnalysisDataHandler objects for the permutations + of bas, model_dates, and signal_types. + + Args: + bas (List[str]): List of bas to analyze + signal_types (List[str]): List of signal types to analyze + model_dates (List[str]): List of model dates to analyze + """ + + def __init__( + self, + eval_start: Union[str, datetime], + eval_end: Union[str, datetime], + bas: Union[str, List[str]], + signal_types: Union[str, List[str]] = "co2_moer", + model_dates: List[str] = None, + **kwargs, + ): + if isinstance(eval_start, str): + eval_start = parse(eval_start) + + if isinstance(eval_end, str): + eval_end = parse(eval_end) + + if not isinstance(bas, list): + bas = [bas] + + if not isinstance(signal_types, list): + signal_types = [signal_types] + + if not isinstance(model_dates, list): + model_dates = [model_dates] + + permutations = list(product(bas, signal_types, model_dates)) + + # only pass through relevant kwargs + _args = inspect.signature(AnalysisDataHandler.__init__).parameters + _kwargs = {k: v for k, v in kwargs.items() if k in _args} + + self.data_handlers = [] + for region, signal_type, model_date in permutations: + dh = AnalysisDataHandler( + eval_start=eval_start, + eval_end=eval_end, + region=region, + signal_type=signal_type, + model_date=model_date, + **_kwargs, + ) + self.data_handlers.append(dh) + + def yield_datahandlers(self) -> AnalysisDataHandler: + for dh in self.data_handlers: + yield dh \ No newline at end of file From 7c51bf200d8cf59da276e2cb7d923aaaa17f6588 Mon Sep 17 00:00:00 2001 From: samkoebrich Date: Thu, 27 Feb 2025 16:00:42 -0700 Subject: [PATCH 14/85] rename "bas" -> "regions" --- .../get_wt_api_forecast_evaluation_data.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/watttime/evaluation/get_wt_api_forecast_evaluation_data.py b/watttime/evaluation/get_wt_api_forecast_evaluation_data.py index b0fd119f..80237e4c 100644 --- a/watttime/evaluation/get_wt_api_forecast_evaluation_data.py +++ b/watttime/evaluation/get_wt_api_forecast_evaluation_data.py @@ -149,10 +149,10 @@ def forecast_v_moer_filtered_by_rank( class DataHandlerFactory: """ Create a list of AnalysisDataHandler objects for the permutations - of bas, model_dates, and signal_types. + of regions, model_dates, and signal_types. Args: - bas (List[str]): List of bas to analyze + regions (List[str]): List of regions to analyze signal_types (List[str]): List of signal types to analyze model_dates (List[str]): List of model dates to analyze """ @@ -161,7 +161,7 @@ def __init__( self, eval_start: Union[str, datetime], eval_end: Union[str, datetime], - bas: Union[str, List[str]], + regions: Union[str, List[str]], signal_types: Union[str, List[str]] = "co2_moer", model_dates: List[str] = None, **kwargs, @@ -172,8 +172,8 @@ def __init__( if isinstance(eval_end, str): eval_end = parse(eval_end) - if not isinstance(bas, list): - bas = [bas] + if not isinstance(regions, list): + regions = [regions] if not isinstance(signal_types, list): signal_types = [signal_types] @@ -181,7 +181,7 @@ def __init__( if not isinstance(model_dates, list): model_dates = [model_dates] - permutations = list(product(bas, signal_types, model_dates)) + permutations = list(product(regions, signal_types, model_dates)) # only pass through relevant kwargs _args = inspect.signature(AnalysisDataHandler.__init__).parameters From dfc31497a237afa8319a4146d2d50dea08c62df6 Mon Sep 17 00:00:00 2001 From: samkoebrich Date: Fri, 28 Feb 2025 16:23:13 -0700 Subject: [PATCH 15/85] first pass at implementing datahandler --- setup.py | 2 +- watttime/__init__.py | 3 +- watttime/evaluation/__init__.py | 0 .../get_wt_api_forecast_evaluation_data.py | 2 +- .../evaluation/new_moer_model_report.ipynb | 225565 +++++++++++++++ watttime/{ => evaluation}/report.py | 271 +- watttime/new_moer_model_report.ipynb | 207090 ------------- 7 files changed, 225635 insertions(+), 207298 deletions(-) create mode 100644 watttime/evaluation/__init__.py create mode 100644 watttime/evaluation/new_moer_model_report.ipynb rename watttime/{ => evaluation}/report.py (74%) delete mode 100644 watttime/new_moer_model_report.ipynb diff --git a/setup.py b/setup.py index b46e2b41..9fd2cd3a 100644 --- a/setup.py +++ b/setup.py @@ -6,7 +6,7 @@ long_description=open("README.md").read(), long_description_content_type="text/markdown", version="v1.3.3", - packages=["watttime"], + packages=["watttime", "watttime.evaluation"], python_requires=">=3.8", install_requires=["requests", "pandas>1.0.0", "holidays", "python-dateutil"], extras_require={"report": ["papermill", "nbconvert", "plotly", "scipy"]}, diff --git a/watttime/__init__.py b/watttime/__init__.py index 016e1dd6..9dc00ada 100644 --- a/watttime/__init__.py +++ b/watttime/__init__.py @@ -1,3 +1,4 @@ from watttime.api import * from watttime.tcy import TCYCalculator -from watttime.report import ModelAnalysis \ No newline at end of file +# from watttime.evaluation.report import ModelAnalysis +# from watttime.evaluation.report.get_wt_api_forecast_evaluation_data import DataHandlerFactory, AnalysisDataHandler \ No newline at end of file diff --git a/watttime/evaluation/__init__.py b/watttime/evaluation/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/watttime/evaluation/get_wt_api_forecast_evaluation_data.py b/watttime/evaluation/get_wt_api_forecast_evaluation_data.py index 80237e4c..60cf2491 100644 --- a/watttime/evaluation/get_wt_api_forecast_evaluation_data.py +++ b/watttime/evaluation/get_wt_api_forecast_evaluation_data.py @@ -127,7 +127,7 @@ def forecasts(self) -> pd.DataFrame: return forecasts @cached_property - def forecast_v_moer(self) -> pd.DataFrame: + def forecasts_v_moers(self) -> pd.DataFrame: return self.forecasts.merge( self.moers, how="inner", diff --git a/watttime/evaluation/new_moer_model_report.ipynb b/watttime/evaluation/new_moer_model_report.ipynb new file mode 100644 index 00000000..4d31ebec --- /dev/null +++ b/watttime/evaluation/new_moer_model_report.ipynb @@ -0,0 +1,225565 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "3c109714", + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "2e63bd72-6edd-4fe1-b173-3e295259c7a8", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "#setup\n", + "import warnings\n", + "from typing import List, Optional, Union\n", + "from datetime import datetime, date\n", + "from watttime import api\n", + "from watttime.evaluation import report\n", + "from watttime.evaluation.get_wt_api_forecast_evaluation_data import AnalysisDataHandler, DataHandlerFactory\n", + "\n", + "warnings.filterwarnings('ignore')\n", + "warnings.filterwarnings('ignore', message=\"Found version mismatch between `dagster`\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "a3b6b1ed-fda5-4961-b9a5-d7bc54f1b6a6", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "parameters" + ] + }, + "outputs": [], + "source": [ + "#input parameters\n", + "region_list: List[str] = [\"ERCOT_SANANTONIO\"]\n", + "model_date_list: List[str] = [\"2022-10-01\", \"2023-03-01\"]\n", + "signal_type: str = \"co2_moer\"\n", + "eval_start: Union[str, datetime, date] = '2022-01-01T00:00Z'\n", + "eval_end: Union[str, datetime, date] = '2022-12-31T23:59Z'\n", + "forecast_sample_days: int = 5" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "7d83e624", + "metadata": {}, + "outputs": [], + "source": [ + "factory = DataHandlerFactory(\n", + " eval_start=eval_start, eval_end=eval_end,\n", + " regions = region_list, model_dates = model_date_list,\n", + " signal_types=signal_type\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6af18b1f", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "42d2ca67-d48b-4fac-b384-4bab22e32104", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "## Data Distribution Changes\n" + ] + }, + { + "cell_type": "markdown", + "id": "7f46b1a9-936f-4e75-aa16-ebc88110b4f4", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Below is a summary of how you can expect signal values to change between versions." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "83629761-691f-4030-b458-885f412d7113", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Warnings Returned: %s | Response: %s {'region': 'ERCOT_SANANTONIO', 'signal_type': 'co2_moer', 'model': '2022-10-01', 'start': datetime.datetime(2022, 1, 1, 0, 0, tzinfo=), 'end': datetime.datetime(2022, 1, 30, 23, 55, tzinfo=)} {'data_point_period_seconds': 300, 'region': 'ERCOT_SANANTONIO', 'signal_type': 'co2_moer', 'units': 'lbs_co2_per_mwh', 'warnings': [{'type': 'MODEL_MARKED_FOR_DEPRECATION', 'message': \"You made a valid request, but the model requested (2022-10-01) is marked for deprecation on 2024-12-04 00:00:00+00:00. After that date, no further data will be created for the requested model. You'll need to specify a newer model from then on, or if you'd like to get data for the newest model, exclude the 'model' parameter from your request.\"}], 'model': {'date': '2022-10-01', 'type': 'binned_regression'}}\n", + "Warnings Returned: %s | Response: %s {'region': 'ERCOT_SANANTONIO', 'signal_type': 'co2_moer', 'model': '2022-10-01', 'start': datetime.datetime(2022, 1, 31, 0, 0, tzinfo=), 'end': datetime.datetime(2022, 3, 1, 23, 55, tzinfo=)} {'data_point_period_seconds': 300, 'region': 'ERCOT_SANANTONIO', 'signal_type': 'co2_moer', 'units': 'lbs_co2_per_mwh', 'warnings': [{'type': 'MODEL_MARKED_FOR_DEPRECATION', 'message': \"You made a valid request, but the model requested (2022-10-01) is marked for deprecation on 2024-12-04 00:00:00+00:00. After that date, no further data will be created for the requested model. You'll need to specify a newer model from then on, or if you'd like to get data for the newest model, exclude the 'model' parameter from your request.\"}], 'model': {'date': '2022-10-01', 'type': 'binned_regression'}}\n", + "Warnings Returned: %s | Response: %s {'region': 'ERCOT_SANANTONIO', 'signal_type': 'co2_moer', 'model': '2022-10-01', 'start': datetime.datetime(2022, 3, 2, 0, 0, tzinfo=), 'end': datetime.datetime(2022, 3, 31, 23, 55, tzinfo=)} {'data_point_period_seconds': 300, 'region': 'ERCOT_SANANTONIO', 'signal_type': 'co2_moer', 'units': 'lbs_co2_per_mwh', 'warnings': [{'type': 'MODEL_MARKED_FOR_DEPRECATION', 'message': \"You made a valid request, but the model requested (2022-10-01) is marked for deprecation on 2024-12-04 00:00:00+00:00. After that date, no further data will be created for the requested model. You'll need to specify a newer model from then on, or if you'd like to get data for the newest model, exclude the 'model' parameter from your request.\"}], 'model': {'date': '2022-10-01', 'type': 'binned_regression'}}\n", + "Warnings Returned: %s | Response: %s {'region': 'ERCOT_SANANTONIO', 'signal_type': 'co2_moer', 'model': '2022-10-01', 'start': datetime.datetime(2022, 4, 1, 0, 0, tzinfo=), 'end': datetime.datetime(2022, 4, 30, 23, 55, tzinfo=)} {'data_point_period_seconds': 300, 'region': 'ERCOT_SANANTONIO', 'signal_type': 'co2_moer', 'units': 'lbs_co2_per_mwh', 'warnings': [{'type': 'MODEL_MARKED_FOR_DEPRECATION', 'message': \"You made a valid request, but the model requested (2022-10-01) is marked for deprecation on 2024-12-04 00:00:00+00:00. After that date, no further data will be created for the requested model. You'll need to specify a newer model from then on, or if you'd like to get data for the newest model, exclude the 'model' parameter from your request.\"}], 'model': {'date': '2022-10-01', 'type': 'binned_regression'}}\n", + "Warnings Returned: %s | Response: %s {'region': 'ERCOT_SANANTONIO', 'signal_type': 'co2_moer', 'model': '2022-10-01', 'start': datetime.datetime(2022, 5, 1, 0, 0, tzinfo=), 'end': datetime.datetime(2022, 5, 30, 23, 55, tzinfo=)} {'data_point_period_seconds': 300, 'region': 'ERCOT_SANANTONIO', 'signal_type': 'co2_moer', 'units': 'lbs_co2_per_mwh', 'warnings': [{'type': 'MODEL_MARKED_FOR_DEPRECATION', 'message': \"You made a valid request, but the model requested (2022-10-01) is marked for deprecation on 2024-12-04 00:00:00+00:00. After that date, no further data will be created for the requested model. You'll need to specify a newer model from then on, or if you'd like to get data for the newest model, exclude the 'model' parameter from your request.\"}], 'model': {'date': '2022-10-01', 'type': 'binned_regression'}}\n", + "Warnings Returned: %s | Response: %s {'region': 'ERCOT_SANANTONIO', 'signal_type': 'co2_moer', 'model': '2022-10-01', 'start': datetime.datetime(2022, 5, 31, 0, 0, tzinfo=), 'end': datetime.datetime(2022, 6, 29, 23, 55, tzinfo=)} {'data_point_period_seconds': 300, 'region': 'ERCOT_SANANTONIO', 'signal_type': 'co2_moer', 'units': 'lbs_co2_per_mwh', 'warnings': [{'type': 'MODEL_MARKED_FOR_DEPRECATION', 'message': \"You made a valid request, but the model requested (2022-10-01) is marked for deprecation on 2024-12-04 00:00:00+00:00. After that date, no further data will be created for the requested model. You'll need to specify a newer model from then on, or if you'd like to get data for the newest model, exclude the 'model' parameter from your request.\"}], 'model': {'date': '2022-10-01', 'type': 'binned_regression'}}\n", + "Warnings Returned: %s | Response: %s {'region': 'ERCOT_SANANTONIO', 'signal_type': 'co2_moer', 'model': '2022-10-01', 'start': datetime.datetime(2022, 6, 30, 0, 0, tzinfo=), 'end': datetime.datetime(2022, 7, 29, 23, 55, tzinfo=)} {'data_point_period_seconds': 300, 'region': 'ERCOT_SANANTONIO', 'signal_type': 'co2_moer', 'units': 'lbs_co2_per_mwh', 'warnings': [{'type': 'MODEL_MARKED_FOR_DEPRECATION', 'message': \"You made a valid request, but the model requested (2022-10-01) is marked for deprecation on 2024-12-04 00:00:00+00:00. After that date, no further data will be created for the requested model. You'll need to specify a newer model from then on, or if you'd like to get data for the newest model, exclude the 'model' parameter from your request.\"}], 'model': {'date': '2022-10-01', 'type': 'binned_regression'}}\n", + "Warnings Returned: %s | Response: %s {'region': 'ERCOT_SANANTONIO', 'signal_type': 'co2_moer', 'model': '2022-10-01', 'start': datetime.datetime(2022, 7, 30, 0, 0, tzinfo=), 'end': datetime.datetime(2022, 8, 28, 23, 55, tzinfo=)} {'data_point_period_seconds': 300, 'region': 'ERCOT_SANANTONIO', 'signal_type': 'co2_moer', 'units': 'lbs_co2_per_mwh', 'warnings': [{'type': 'MODEL_MARKED_FOR_DEPRECATION', 'message': \"You made a valid request, but the model requested (2022-10-01) is marked for deprecation on 2024-12-04 00:00:00+00:00. After that date, no further data will be created for the requested model. You'll need to specify a newer model from then on, or if you'd like to get data for the newest model, exclude the 'model' parameter from your request.\"}], 'model': {'date': '2022-10-01', 'type': 'binned_regression'}}\n", + "Warnings Returned: %s | Response: %s {'region': 'ERCOT_SANANTONIO', 'signal_type': 'co2_moer', 'model': '2022-10-01', 'start': datetime.datetime(2022, 8, 29, 0, 0, tzinfo=), 'end': datetime.datetime(2022, 9, 27, 23, 55, tzinfo=)} {'data_point_period_seconds': 300, 'region': 'ERCOT_SANANTONIO', 'signal_type': 'co2_moer', 'units': 'lbs_co2_per_mwh', 'warnings': [{'type': 'MODEL_MARKED_FOR_DEPRECATION', 'message': \"You made a valid request, but the model requested (2022-10-01) is marked for deprecation on 2024-12-04 00:00:00+00:00. After that date, no further data will be created for the requested model. You'll need to specify a newer model from then on, or if you'd like to get data for the newest model, exclude the 'model' parameter from your request.\"}], 'model': {'date': '2022-10-01', 'type': 'binned_regression'}}\n", + "Warnings Returned: %s | Response: %s {'region': 'ERCOT_SANANTONIO', 'signal_type': 'co2_moer', 'model': '2022-10-01', 'start': datetime.datetime(2022, 9, 28, 0, 0, tzinfo=), 'end': datetime.datetime(2022, 10, 27, 23, 55, tzinfo=)} {'data_point_period_seconds': 300, 'region': 'ERCOT_SANANTONIO', 'signal_type': 'co2_moer', 'units': 'lbs_co2_per_mwh', 'warnings': [{'type': 'MODEL_MARKED_FOR_DEPRECATION', 'message': \"You made a valid request, but the model requested (2022-10-01) is marked for deprecation on 2024-12-04 00:00:00+00:00. After that date, no further data will be created for the requested model. You'll need to specify a newer model from then on, or if you'd like to get data for the newest model, exclude the 'model' parameter from your request.\"}], 'model': {'date': '2022-10-01', 'type': 'binned_regression'}}\n", + "Warnings Returned: %s | Response: %s {'region': 'ERCOT_SANANTONIO', 'signal_type': 'co2_moer', 'model': '2022-10-01', 'start': datetime.datetime(2022, 10, 28, 0, 0, tzinfo=), 'end': datetime.datetime(2022, 11, 26, 23, 55, tzinfo=)} {'data_point_period_seconds': 300, 'region': 'ERCOT_SANANTONIO', 'signal_type': 'co2_moer', 'units': 'lbs_co2_per_mwh', 'warnings': [{'type': 'MODEL_MARKED_FOR_DEPRECATION', 'message': \"You made a valid request, but the model requested (2022-10-01) is marked for deprecation on 2024-12-04 00:00:00+00:00. After that date, no further data will be created for the requested model. You'll need to specify a newer model from then on, or if you'd like to get data for the newest model, exclude the 'model' parameter from your request.\"}], 'model': {'date': '2022-10-01', 'type': 'binned_regression'}}\n", + "Warnings Returned: %s | Response: %s {'region': 'ERCOT_SANANTONIO', 'signal_type': 'co2_moer', 'model': '2022-10-01', 'start': datetime.datetime(2022, 11, 27, 0, 0, tzinfo=), 'end': datetime.datetime(2022, 12, 26, 23, 55, tzinfo=)} {'data_point_period_seconds': 300, 'region': 'ERCOT_SANANTONIO', 'signal_type': 'co2_moer', 'units': 'lbs_co2_per_mwh', 'warnings': [{'type': 'MODEL_MARKED_FOR_DEPRECATION', 'message': \"You made a valid request, but the model requested (2022-10-01) is marked for deprecation on 2024-12-04 00:00:00+00:00. After that date, no further data will be created for the requested model. You'll need to specify a newer model from then on, or if you'd like to get data for the newest model, exclude the 'model' parameter from your request.\"}], 'model': {'date': '2022-10-01', 'type': 'binned_regression'}}\n", + "Warnings Returned: %s | Response: %s {'region': 'ERCOT_SANANTONIO', 'signal_type': 'co2_moer', 'model': '2022-10-01', 'start': datetime.datetime(2022, 12, 27, 0, 0, tzinfo=), 'end': datetime.datetime(2023, 1, 1, 23, 59, tzinfo=)} {'data_point_period_seconds': 300, 'region': 'ERCOT_SANANTONIO', 'signal_type': 'co2_moer', 'units': 'lbs_co2_per_mwh', 'warnings': [{'type': 'MODEL_MARKED_FOR_DEPRECATION', 'message': \"You made a valid request, but the model requested (2022-10-01) is marked for deprecation on 2024-12-04 00:00:00+00:00. After that date, no further data will be created for the requested model. 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This represents the magnitude of error to expect from the forecast on average. A model with an MAE closest to 0 is ideal. Norm MAE is MAE divided by the average of the true values. Normalizing MAE allows us to compare models across different datasets. It is expressed as a percentage (%) of the average true value over the validation time period. \n", + "* **Rank Correlation:** A number between -1 and 1 that quantifies the degree to which the rank ordering of two datasets are related. A rank correlation of 1 means the forecast predictions are rank ordered perfectly compared to the true values, making the forecast perfectly effective for time optimization use cases. For many use cases of our forecasts models (such as AER), it is more important that predictions are correctly rank ordered within a forecast window, rather than each forecast being accurate in terms of absolute values.\n", + "* **AER CO2 Savings:** This metric represents the impact in lbs CO2/MWh of using the forecast model for energy usage optimization. We compare these CO2 savings against the \"Max Potential\" case (ie. the case of using perfect forecasts). In this notebook, we model 3 different popular AER Scenarios:\n", + " \n", + " 1. EV-night: Charging an electric vehicle for 3 hours out of a 12-hour overnight window starting at 19:00 local time.\n", + " 2. EV-day: Charging an electric vehicle for 2 hours out of an 8-hour daytime window starting at 09:00 local time.\n", + " 3. 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}, + "bgcolor": "#E5ECF6", + "caxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "title": { + "x": 0.05 + }, + "xaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + }, + "yaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + } + } + }, + "title": { + "text": "Simulated CO2 Savings by AER Scenario and Region" + }, + "xaxis": { + "anchor": "y", + "domain": [ + 0, + 1 + ], + "matches": "x2", + "showticklabels": false, + "title": { + "text": "Scenario" + } + }, + "xaxis2": { + "anchor": "y2", + "domain": [ + 0, + 1 + ], + "title": { + "text": "Scenario" + } + }, + "yaxis": { + "anchor": "x", + "domain": [ + 0.6000000000000001, + 1 + ], + "title": { + "text": "CO2 Savings (lbs/MWh)" + } + }, + "yaxis2": { + "anchor": "x2", + "domain": [ + 0, + 0.4 + ], + "title": { + "text": "CO2 Savings (lbs/MWh)" + } + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "report.plot_impact_forecast_metrics(factory.data_handlers)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "36d4995c", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "sdk", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/watttime/report.py b/watttime/evaluation/report.py similarity index 74% rename from watttime/report.py rename to watttime/evaluation/report.py index cb840d01..bad4772e 100644 --- a/watttime/report.py +++ b/watttime/evaluation/report.py @@ -1,19 +1,12 @@ -#!/usr/bin/env python - import argparse import calendar -import os import sys -from concurrent.futures import ThreadPoolExecutor, as_completed from datetime import datetime, timedelta, timezone from dateutil.parser import parse -from functools import cached_property from operator import attrgetter from pathlib import Path from typing import List, Optional, Union from zoneinfo import ZoneInfo -from dataclasses import dataclass -import random import numpy as np import pandas as pd @@ -23,7 +16,7 @@ from nbconvert.exporters import HTMLExporter import papermill as pm -from watttime import api +from watttime.evaluation.get_wt_api_forecast_evaluation_data import AnalysisDataHandler # hacky way to allow running this script locally sys.path.append(str(Path(__file__).parents[1].resolve())) @@ -53,135 +46,6 @@ def round_time(dt: datetime, minutes: int = 5) -> datetime: return dt.replace(minute=(dt.minute // minutes) * minutes, second=0, microsecond=0) -def parse_eval_days(eval_start, eval_end): - if isinstance(eval_start, str): - eval_start = parse(eval_start) - if isinstance(eval_end, str): - eval_end = parse(eval_end) - - return [ - i.strftime("%Y-%m-%d") - for i in pd.date_range(start=eval_start, end=eval_end, freq="1D") - ] - - -def parse_forecast_sample_days(eval_days, forecast_sample_days): - try: - return random.sample(eval_days, forecast_sample_days) - except ValueError as e: - return eval_days - - -def _fetch_forecast( - day, ba, new_model_date_str, wt_forecast: Optional[api.WattTimeForecast] = None -): - """ - Fetch the historical forecast for a specific day and balancing authority (ba). - """ - - if not wt_forecast: - wt_forecast = api.WattTimeForecast() - - return wt_forecast.get_historical_forecast_pandas( - start=parse(day), - end=parse(day) + pd.Timedelta(hours=23, minutes=55), - region=ba, - signal_type="co2_moer", - model=new_model_date_str, - horizon_hours=72, - ) - - -def _fetch_forecasts( - sample_days: List[str], - ba: str, - model: str, - signal_type: str, - wt_forecast: Optional[api.WattTimeForecast] = None, -): - - _forecasts = [] - out = pd.DataFrame() - - # Use ThreadPoolExecutor for concurrent calls which are I/O bound - with ThreadPoolExecutor(max_workers=os.cpu_count() * 5) as executor: - future_to_day = { - executor.submit(_fetch_forecast, day, ba, model, wt_forecast): day - for day in sample_days - } - for future in as_completed(future_to_day): - try: - forecast = future.result() - _forecasts.append(forecast) - except Exception as e: - print( - f"Error fetching forecast for {future_to_day[future]} in {ba}: {e}" - ) - - # Combine and process the results - if _forecasts: # Ensure there's data before concatenation - out = pd.concat(_forecasts, ignore_index=True) - out["point_time"] = pd.to_datetime(out["point_time"]) - out["horizon_mins"] = ( - out["point_time"] - out["generated_at"] - ).dt.total_seconds() / 60 - out.rename({"value": "predicted_value"}, axis="columns", inplace=True) - - if out.empty: - raise ValueError( - f"Empty dataframe returned for {ba} - {model} - {signal_type} forecast API request" - ) - return out - - -@dataclass -class ModelAnalysis: - ba: str - model_date: str - signal_type: str - eval_start: str - eval_end: str - eval_days: List[str] - sample_days: List[str] - wt_forecast: Optional[api.WattTimeForecast] = api.WattTimeForecast() - wt_hist: Optional[api.WattTimeHistorical] = api.WattTimeHistorical() - - def __post_init__(self): - self.ba = self.ba.upper() - - @cached_property - def moers(self): - return ( - self.wt_hist.get_historical_pandas( - start=self.eval_start, - end=self.eval_end, - region=self.ba, - signal_type=self.signal_type, - model=self.model_date, - ) - .rename(columns={"value": "signal_value"}) - .set_index("point_time", drop=True) - ) - - @cached_property - def forecasts(self): - return _fetch_forecasts( - sample_days=self.sample_days, - ba=self.ba, - model=self.model_date, - signal_type=self.signal_type, - wt_forecast=self.wt_forecast, - ) - - def compile_forecast_v_moer(self): - self.forecasts_v_moers = self.forecasts.merge( - self.moers, - how="left", - left_on="point_time", - right_on="point_time", - ) - - def get_random_overlapping_period(dfs, max_period="7D"): """ Find a random overlapping time period between multiple DataFrames' datetime indices, @@ -232,36 +96,36 @@ def get_random_overlapping_period(dfs, max_period="7D"): start=random_start_time, end=random_end_time, freq="5T" ) # Check if all indices exist in both DataFrames - if all(df.index.isin(overlap_range).all() for df in dfs): + if all(overlap_range.isin(df.index).all() for df in dfs): return overlap_range attempts += 1 raise ValueError(f"No common overlap found after {max_attempts} attempts.") -def plot_sample_moers(jobs: List[ModelAnalysis], max_sample_period="7D"): +def plot_sample_moers(jobs: List[AnalysisDataHandler], max_sample_period="7D"): """ - Plot a sample of old and new MOER values over time, creating a subplot for each unique BA. + Plot a sample of old and new MOER values over time, creating a subplot for each unique region. Args: - jobs (list): List of ModelAnalysis objects with a .moers atribute. + jobs (list): List of AnalysisDataHandler objects with a .moers atribute. Returns: plotly.graph_objects.Figure: A Plotly figure with timeseries of values. """ - # Create a figure with a subplot for each BA, stacked vertically - unique_bas = set([j.ba for j in jobs]) + # Create a figure with a subplot for each region, stacked vertically + unique_regions = set([j.region for j in jobs]) fig = sp.make_subplots( - rows=len(unique_bas), + rows=len(unique_regions), cols=1, shared_xaxes=True, vertical_spacing=0.1, - subplot_titles=[f"{ba.upper()}" for ba in unique_bas], + subplot_titles=[f"{region.upper()}" for region in unique_regions], ) - for i, ba_abbrev in enumerate(unique_bas, start=1): - ba_abbrev = ba_abbrev.upper() - _jobs = [j for j in jobs if j.ba == ba_abbrev] + for i, region_abbrev in enumerate(unique_regions, start=1): + region_abbrev = region_abbrev.upper() + _jobs = [j for j in jobs if j.region == region_abbrev] times = get_random_overlapping_period( [j.moers for j in _jobs], max_sample_period @@ -284,7 +148,7 @@ def plot_sample_moers(jobs: List[ModelAnalysis], max_sample_period="7D"): # Update layout for the figure fig.update_layout( height=300 - * len(unique_bas), # Adjust height dynamically based on the number of BAs + * len(unique_regions), # Adjust height dynamically regionsed on the number of regions title="Data Sample Comparisons by Region", yaxis_title=f"{jobs[0].signal_type}", showlegend=True, @@ -300,29 +164,29 @@ def plot_sample_moers(jobs: List[ModelAnalysis], max_sample_period="7D"): return fig -def plot_distribution_moers(jobs: List[ModelAnalysis]): +def plot_distribution_moers(jobs: List[AnalysisDataHandler]): """ - Plot the distribution of old and new MOER values for each BA, creating a stacked subplot. + Plot the distribution of old and new MOER values for each region, creating a stacked subplot. Args: - jobs (list): List of ModelAnalysis objects with a .moers atribute. + jobs (list): List of AnalysisDataHandler objects with a .moers atribute. Returns: plotly.graph_objects.Figure: A Plotly figure with stacked subplots of distributions. """ - # Create a figure with a subplot for each BA, stacked vertically - unique_bas = set([j.ba for j in jobs]) + # Create a figure with a subplot for each region, stacked vertically + unique_regions = set([j.region for j in jobs]) fig = sp.make_subplots( - rows=len(unique_bas), + rows=len(unique_regions), cols=1, shared_xaxes=True, vertical_spacing=0.2, - subplot_titles=[f"{ba.upper()}" for ba in unique_bas], + subplot_titles=[f"{region.upper()}" for region in unique_regions], ) - for i, ba_abbrev in enumerate(unique_bas, start=1): - ba_abbrev = ba_abbrev.upper() - _jobs = [j for j in jobs if j.ba == ba_abbrev] + for i, region_abbrev in enumerate(unique_regions, start=1): + region_abbrev = region_abbrev.upper() + _jobs = [j for j in jobs if j.region == region_abbrev] for _job in _jobs: # Add a histogram trace for the new MOER distribution @@ -339,7 +203,7 @@ def plot_distribution_moers(jobs: List[ModelAnalysis]): # Update layout for the figure fig.update_layout( height=300 - * len(unique_bas), # Adjust height dynamically based on the number of BAs + * len(unique_regions), # Adjust height dynamically regionsed on the number of regions title=f"{jobs[0].signal_type} Distribution Comparisons by Region", xaxis_title=f"{jobs[0].signal_type} Values", showlegend=True, @@ -356,25 +220,25 @@ def plot_distribution_moers(jobs: List[ModelAnalysis]): return fig -def plot_ba_heatmaps(jobs: List[ModelAnalysis], colorscale="Cividis"): +def plot_region_heatmaps(jobs: List[AnalysisDataHandler], colorscale="Cividis"): """ - Generate vertically stacked heatmaps for each BA in the ba_list. + Generate vertically stacked heatmaps for each region in the region_list. Args: - jobs (list): List of ModelAnalysis objects with a .moers atribute. + jobs (list): List of AnalysisDataHandler objects with a .moers atribute. Returns: plotly.graph_objects.Figure: Figure containing stacked heatmaps. """ - jobs = sorted(jobs, key=attrgetter("ba", "model_date")) + jobs = sorted(jobs, key=attrgetter("region", "model_date")) - # Initialize a subplot with one row per BA + # Initialize a subplot with one row per region fig = sp.make_subplots( rows=len(jobs), cols=1, shared_xaxes=True, - subplot_titles=[f"{j.ba} - {j.model_date}" for j in jobs], + subplot_titles=[f"{j.region} - {j.model_date}" for j in jobs], vertical_spacing=0.2, ) @@ -421,7 +285,7 @@ def plot_ba_heatmaps(jobs: List[ModelAnalysis], colorscale="Cividis"): # Update layout fig.update_layout( - height=500 * len(jobs), # Adjust the height based on the number of BAs + height=500 * len(jobs), # Adjust the height regionsed on the number of regions title=f"Average {jobs[0].signal_type} by Month & Hour", xaxis_title="Hour of Day", yaxis_title="Month", @@ -484,13 +348,12 @@ def calc_rank_compare_metrics( """ df = in_df.copy() - df.set_index("generated_at", inplace=True) df.dropna(inplace=True) # Rank of truth and predicted columns within each window if window_starts: # Generate window ranges based on start times and duration - unique_dates = df.index.normalize().unique() # Extract unique dates + unique_dates = df.index.get_level_values("generated_at").unique() # Extract unique dates window_ranges = [] for date in unique_dates: for start_time in window_starts: @@ -500,8 +363,7 @@ def calc_rank_compare_metrics( # Assign each row to a window def assign_window(row): - generated_at = row.name - point_time = row["point_time"] # point_time column + point_time, generated_at = row.name for start, end in window_ranges: if start <= generated_at < end and point_time < end: return start # Label window by its start time @@ -511,9 +373,8 @@ def assign_window(row): else: # Default behavior: use rolling windows def assign_rolling_window(row): - ts = row.name # Index (e.g., generated_at) - point_time = row["point_time"] # point_time column - window_start = ts.floor(f"{window_mins}T") + generated_at, point_time = row.name + window_start = generated_at.floor(f"{window_mins}T") window_end = window_start + pd.Timedelta(minutes=window_mins) # Only assign window if point_time is within the window range @@ -568,27 +429,27 @@ def assign_rolling_window(row): } -def plot_norm_mae(jobs: List[ModelAnalysis], horizons_hr=[1, 12, 24, 48, 72]): +def plot_norm_mae(jobs: List[AnalysisDataHandler], horizons_hr=[1, 12, 24, 48, 72]): """ - Create a Plotly bar chart for rank correlation by horizon with one subplot per BA (abbrev). + Create a Plotly bar chart for rank correlation by horizon with one subplot per region (abbrev). """ # Create subplots - unique_bas = set([j.ba for j in jobs]) + unique_regions = set([j.region for j in jobs]) fig = sp.make_subplots( - rows=len(unique_bas), + rows=len(unique_regions), cols=1, shared_xaxes=True, vertical_spacing=0.2, - subplot_titles=list(unique_bas), + subplot_titles=list(unique_regions), ) y_min = y_max = 0 - # Iterate through each BA and create a bar plot - for i, ba_abbrev in enumerate(unique_bas, start=1): - ba_abbrev = ba_abbrev.upper() - _jobs = [j for j in jobs if j.ba == ba_abbrev] + # Iterate through each region and create a bar plot + for i, region_abbrev in enumerate(unique_regions, start=1): + region_abbrev = region_abbrev.upper() + _jobs = [j for j in jobs if j.region == region_abbrev] x_values = [f"{h}hr" for h in horizons_hr] # Add horizon labels @@ -615,22 +476,22 @@ def plot_norm_mae(jobs: List[ModelAnalysis], horizons_hr=[1, 12, 24, 48, 72]): fig.update_layout( height=300 - * len(unique_bas), # Adjust figure height based on the number of subplots + * len(unique_regions), # Adjust figure height regionsed on the number of subplots title_text="Normalized MAE by Horizon", xaxis_title="Horizon", yaxis_title="Normalized MAE (%)", showlegend=True, # Legends appear in individual subplot titles margin=dict(l=50, r=50, t=50, b=50), - barmode="group", # Ensure bars for each BA are grouped + barmode="group", # Ensure bars for each region are grouped ) - fig.update_xaxes(title_text="Horizon (Hours)", row=len(unique_bas), col=1) + fig.update_xaxes(title_text="Horizon (Hours)", row=len(unique_regions), col=1) return fig -def plot_rank_corr(jobs: List[ModelAnalysis], horizons_hr=[12, 24, 48, 72]): +def plot_rank_corr(jobs: List[AnalysisDataHandler], horizons_hr=[12, 24, 48, 72]): """ - Create a Plotly line plot for rank correlation by horizon with one subplot per BA (abbrev). + Create a Plotly line plot for rank correlation by horizon with one subplot per region (abbrev). Parameters: df (pd.DataFrame): DataFrame containing the rank correlation data. @@ -643,22 +504,22 @@ def plot_rank_corr(jobs: List[ModelAnalysis], horizons_hr=[12, 24, 48, 72]): """ # Create subplots - unique_bas = set([j.ba for j in jobs]) + unique_regions = set([j.region for j in jobs]) fig = sp.make_subplots( - rows=len(unique_bas), + rows=len(unique_regions), cols=1, shared_xaxes=True, vertical_spacing=0.2, - subplot_titles=list(unique_bas), + subplot_titles=list(unique_regions), ) y_min = y_max = 0 - # Iterate through each BA and create a line plot - for i, ba_abbrev in enumerate(unique_bas, start=1): + # Iterate through each region and create a line plot + for i, region_abbrev in enumerate(unique_regions, start=1): - ba_abbrev = ba_abbrev.upper() - _jobs = [j for j in jobs if j.ba == ba_abbrev] + region_abbrev = region_abbrev.upper() + _jobs = [j for j in jobs if j.region == region_abbrev] # Extract data for the line plot x_values = [f"{h}hr" for h in horizons_hr] # Add horizon labels @@ -675,7 +536,7 @@ def plot_rank_corr(jobs: List[ModelAnalysis], horizons_hr=[12, 24, 48, 72]): go.Bar( x=x_values, y=y_values, - name=f"{_job.ba} - {_job.model_date}", + name=f"{_job.region} - {_job.model_date}", text=[f"{y:.3f}" for y in y_values], # Add text labels on bars textposition="outside", ), @@ -689,7 +550,7 @@ def plot_rank_corr(jobs: List[ModelAnalysis], horizons_hr=[12, 24, 48, 72]): # Update layout fig.update_layout( height=300 - * len(unique_bas), # Adjust figure height based on the number of subplots + * len(unique_regions), # Adjust figure height based on the number of subplots title_text=f"Rank Correlation by Horizon", xaxis_title="Horizon", yaxis_title="Rank Correlation", @@ -698,7 +559,7 @@ def plot_rank_corr(jobs: List[ModelAnalysis], horizons_hr=[12, 24, 48, 72]): ) # Update x-axis for all subplots - fig.update_xaxes(title_text="Horizon (Hours)", row=len(unique_bas), col=1) + fig.update_xaxes(title_text="Horizon (Hours)", row=len(unique_regions), col=1) return fig @@ -724,7 +585,7 @@ def plot_rank_corr(jobs: List[ModelAnalysis], horizons_hr=[12, 24, 48, 72]): def plot_impact_forecast_metrics( - jobs: List[ModelAnalysis], scenarios=["EV-night", "EV-day", "Thermostat"] + jobs: List[AnalysisDataHandler], scenarios=["EV-night", "EV-day", "Thermostat"] ): # Create subplots @@ -733,10 +594,10 @@ def plot_impact_forecast_metrics( cols=1, shared_xaxes=True, vertical_spacing=0.2, - subplot_titles=[f"{j.ba} - {j.model_date}" for j in jobs], + subplot_titles=[f"{j.region} - {j.model_date}" for j in jobs], ) - # Iterate through each BA and create bar plots + # Iterate through each region and create bar plots for i, _job in enumerate(jobs, start=1): _metrics = [ @@ -808,7 +669,7 @@ def parse_report_command_line_args(sys_args): description="Parse command line arguments to report_moers script" ) - parser.add_argument("-ba", "--ba_list", nargs="+", help="List of ba abbrevs.") + parser.add_argument("-region", "--region_list", nargs="+", help="List of region abbrevs.") parser.add_argument( "-d", @@ -884,7 +745,7 @@ def parse_report_command_line_args(sys_args): def run_report_notebook( - ba_list: List[str], + region_list: List[str], model_date_list: List[str], signal_type: str, eval_start: datetime, @@ -903,7 +764,7 @@ def run_report_notebook( Path(__file__).parents[0] / "new_moer_model_report.ipynb", str(output_notebook), parameters=dict( - ba_list=ba_list, + region_list=region_list, model_date_list=model_date_list, signal_type=signal_type, eval_start=eval_start.isoformat(), @@ -929,7 +790,7 @@ def run_report_notebook( cli_args = parse_report_command_line_args(sys.argv[1:]) run_report_notebook( - ba_list=cli_args.ba_list, + region_list=cli_args.region_list, model_date_list=cli_args.model_date_list, signal_type=cli_args.signal_type, eval_start=cli_args.start, diff --git a/watttime/new_moer_model_report.ipynb b/watttime/new_moer_model_report.ipynb deleted file mode 100644 index 273a3687..00000000 --- a/watttime/new_moer_model_report.ipynb +++ /dev/null @@ -1,207090 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 8, - "id": "2e63bd72-6edd-4fe1-b173-3e295259c7a8", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "#setup\n", - "import warnings\n", - "from typing import List, Optional, Union\n", - "from datetime import datetime, date\n", - "from dateutil.parser import parse\n", - "from watttime import api, report\n", - "from itertools import product\n", - "\n", - "warnings.filterwarnings('ignore')\n", - "warnings.filterwarnings('ignore', message=\"Found version mismatch between `dagster`\")" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "a3b6b1ed-fda5-4961-b9a5-d7bc54f1b6a6", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "parameters" - ] - }, - "outputs": [], - "source": [ - "#input parameters\n", - "ba_list: List[str] = [\"WEM\"]\n", - "model_date_list: List[str] = [\"2022-10-01\", \"2024-10-16\"]\n", - "signal_type: str = \"co2_moer\"\n", - "eval_start: Union[str, datetime, date] = '2024-01-01T00:00Z'\n", - "eval_end: Union[str, datetime, date] = '2025-01-01T00:00Z'\n", - "\n", - "api_username: Optional[str] = None # if None, will look for env var WATTTIME_USER\n", - "api_password: Optional[str] = None # if None, will look for env var WATTTIME_PASSWORD\n", - "\n", - "forecast_sample_days: int = 5" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "c96c3091", - "metadata": {}, - "outputs": [], - "source": [ - "WT_FORECAST = api.WattTimeForecast(username=api_username, password=api_password)\n", - "WT_HIST = api.WattTimeHistorical(username=api_username, password=api_password)\n", - "\n", - "EVAL_DAYS = report.parse_eval_days(eval_start=eval_start, eval_end=eval_end)\n", - "SAMPLE_DAYS = report.parse_forecast_sample_days(eval_days=EVAL_DAYS, forecast_sample_days=forecast_sample_days)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "e0f70b6a-6a52-489f-a890-4e992eb23ffe", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - " Warnings Returned: {'region': 'WEM', 'signal_type': 'co2_moer', 'model': '2022-10-01', 'start': datetime.datetime(2024, 7, 29, 0, 0, tzinfo=), 'end': datetime.datetime(2024, 8, 27, 23, 55, tzinfo=)} {'data_point_period_seconds': 300, 'region': 'WEM', 'signal_type': 'co2_moer', 'units': 'lbs_co2_per_mwh', 'warnings': [{'type': 'MISSING_DATA', 'message': 'You made a valid request, but we have 560 records missing. This can happen for a variety of reasons. If the data is less than a week old, we will most likely fill in this data soon. If it is older than that, we are missing enough important grid data during this time period that we are not able to generate an accurate estimate'}], 'model': {'date': '2022-10-01', 'type': 'binned_regression'}}\n", - "\n", - " Warnings Returned: {'region': 'WEM', 'signal_type': 'co2_moer', 'model': '2022-10-01', 'start': datetime.datetime(2024, 9, 27, 0, 0, tzinfo=), 'end': datetime.datetime(2024, 10, 26, 23, 55, tzinfo=)} {'data_point_period_seconds': 300, 'region': 'WEM', 'signal_type': 'co2_moer', 'units': 'lbs_co2_per_mwh', 'warnings': [{'type': 'MISSING_DATA', 'message': 'You made a valid request, but we have 7200 records missing. This can happen for a variety of reasons. If the data is less than a week old, we will most likely fill in this data soon. If it is older than that, we are missing enough important grid data during this time period that we are not able to generate an accurate estimate'}], 'model': {'date': '2022-10-01', 'type': 'binned_regression'}}\n", - "\n", - " Warnings Returned: {'region': 'WEM', 'signal_type': 'co2_moer', 'model': '2022-10-01', 'start': datetime.datetime(2024, 10, 27, 0, 0, tzinfo=), 'end': datetime.datetime(2024, 11, 25, 23, 55, tzinfo=)} {'data_point_period_seconds': 300, 'region': 'WEM', 'signal_type': 'co2_moer', 'units': 'lbs_co2_per_mwh', 'warnings': [{'type': 'MISSING_DATA', 'message': 'You made a valid request, but we have 6554 records missing. This can happen for a variety of reasons. If the data is less than a week old, we will most likely fill in this data soon. If it is older than that, we are missing enough important grid data during this time period that we are not able to generate an accurate estimate'}], 'model': {'date': '2022-10-01', 'type': 'binned_regression'}}\n", - "\n", - " Warnings Returned: {'region': 'WEM', 'signal_type': 'co2_moer', 'model': '2022-10-01', 'start': datetime.datetime(2024, 11, 26, 0, 0, tzinfo=), 'end': datetime.datetime(2024, 12, 25, 23, 55, tzinfo=)} {'data_point_period_seconds': 300, 'region': 'WEM', 'signal_type': 'co2_moer', 'units': 'lbs_co2_per_mwh', 'warnings': [{'type': 'MISSING_DATA', 'message': 'You made a valid request, but we have 2461 records missing. This can happen for a variety of reasons. If the data is less than a week old, we will most likely fill in this data soon. If it is older than that, we are missing enough important grid data during this time period that we are not able to generate an accurate estimate'}], 'model': {'date': '2022-10-01', 'type': 'binned_regression'}}\n", - "\n", - " Warnings Returned: {'region': 'WEM', 'signal_type': 'co2_moer', 'model': '2024-10-16', 'start': datetime.datetime(2024, 11, 26, 0, 0, tzinfo=), 'end': datetime.datetime(2024, 12, 25, 23, 55, tzinfo=)} {'data_point_period_seconds': 300, 'region': 'WEM', 'signal_type': 'co2_moer', 'units': 'lbs_co2_per_mwh', 'warnings': [{'type': 'MISSING_DATA', 'message': 'You made a valid request, but we have 694 records missing. This can happen for a variety of reasons. If the data is less than a week old, we will most likely fill in this data soon. If it is older than that, we are missing enough important grid data during this time period that we are not able to generate an accurate estimate'}], 'model': {'date': '2024-10-16', 'type': 'binned_regression'}}\n" - ] - } - ], - "source": [ - "jobs = list(\n", - " report.ModelAnalysis(\n", - " ba=i[0],\n", - " model_date=i[1],\n", - " signal_type=signal_type,\n", - " eval_start=eval_start, eval_end=eval_end,\n", - " eval_days=EVAL_DAYS, sample_days=SAMPLE_DAYS,\n", - " wt_forecast=WT_FORECAST, wt_hist=WT_HIST\n", - " ) for i in product(ba_list, model_date_list)\n", - ")\n", - "\n", - "# pull all required data and compile\n", - "for job in jobs:\n", - " job.compile_forecast_v_moer()" - ] - }, - { - "cell_type": "markdown", - "id": "42d2ca67-d48b-4fac-b384-4bab22e32104", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "## Data Distribution Changes\n" - ] - }, - { - "cell_type": "markdown", - "id": "7f46b1a9-936f-4e75-aa16-ebc88110b4f4", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "Below is a summary of how you can expect signal values to change between versions." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "83629761-691f-4030-b458-885f412d7113", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "application/vnd.plotly.v1+json": { - "config": { - "plotlyServerURL": "https://plot.ly" - }, - "data": [ - { - "line": { - "width": 2 - }, - "mode": "lines", - "name": "2022-10-01", - "showlegend": true, - "type": "scatter", - "x": [ - "2024-06-13T06:50:00+00:00", - "2024-06-13T06:55:00+00:00", - "2024-06-13T07:00:00+00:00", - "2024-06-13T07:05:00+00:00", - "2024-06-13T07:10:00+00:00", - "2024-06-13T07:15:00+00:00", - "2024-06-13T07:20:00+00:00", - "2024-06-13T07:25:00+00:00", - "2024-06-13T07:30:00+00:00", - "2024-06-13T07:35:00+00:00", - "2024-06-13T07:40:00+00:00", - 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This represents the magnitude of error to expect from the forecast on average. A model with an MAE closest to 0 is ideal. Norm MAE is MAE divided by the average of the true values. Normalizing MAE allows us to compare models across different datasets. It is expressed as a percentage (%) of the average true value over the validation time period. \n", - "* **Rank Correlation:** A number between -1 and 1 that quantifies the degree to which the rank ordering of two datasets are related. A rank correlation of 1 means the forecast predictions are rank ordered perfectly compared to the true values, making the forecast perfectly effective for time optimization use cases. For many use cases of our forecasts models (such as AER), it is more important that predictions are correctly rank ordered within a forecast window, rather than each forecast being accurate in terms of absolute values.\n", - "* **AER CO2 Savings:** This metric represents the impact in lbs CO2/MWh of using the forecast model for energy usage optimization. We compare these CO2 savings against the \"Max Potential\" case (ie. the case of using perfect forecasts). In this notebook, we model 3 different popular AER Scenarios:\n", - " \n", - " 1. EV-night: Charging an electric vehicle for 3 hours out of a 12-hour overnight window starting at 19:00 local time.\n", - " 2. EV-day: Charging an electric vehicle for 2 hours out of an 8-hour daytime window starting at 09:00 local time.\n", - " 3. 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"nbformat": 4, - "nbformat_minor": 5 -} From 28c5278644c0eb41baf428978d81f526f586b4ce Mon Sep 17 00:00:00 2001 From: samkoebrich Date: Fri, 28 Feb 2025 16:30:14 -0700 Subject: [PATCH 16/85] use log instead of print --- watttime/api.py | 21 +++++++++++++++++---- 1 file changed, 17 insertions(+), 4 deletions(-) diff --git a/watttime/api.py b/watttime/api.py index 175b667c..e3f49fb6 100644 --- a/watttime/api.py +++ b/watttime/api.py @@ -2,6 +2,7 @@ import time import threading import time +import logging from datetime import date, datetime, timedelta, time as dt_time from functools import cache from pathlib import Path @@ -14,8 +15,20 @@ from pytz import UTC +def get_log(): + logging.basicConfig( + format="%(asctime)s [%(levelname)-1s] " "%(message)s", + level=logging.INFO, + handlers=[logging.StreamHandler()], + ) + return logging.getLogger() + + +LOG = get_log() + + class WattTimeBase: - url_base = "https://api.watttime.org" + url_base = "https://api.staging-primary.watttime.org" def __init__( self, @@ -151,7 +164,7 @@ def register(self, email: str, organization: Optional[str] = None) -> None: rsp = self.session.post(url, json=params, timeout=20) rsp.raise_for_status() - print( + LOG.info( f"Successfully registered {self.username}, please check {email} for a verification email" ) @@ -213,7 +226,7 @@ def _make_rate_limited_request(self, url: str, params: Dict[str, Any]) -> Dict: ) from e if j.get("meta", {}).get("warnings"): - print("Warnings Returned: %s | Response: %s", params, j["meta"]) + LOG.warning("Warnings Returned: %s | Response: %s", params, j["meta"]) self._last_request_meta = j.get("meta", {}) @@ -395,7 +408,7 @@ def get_historical_csv( start, end = self._parse_dates(start, end) fp = out_dir / f"{region}_{signal_type}_{start.date()}_{end.date()}.csv" df.to_csv(fp, index=False) - print(f"file written to {fp}") + LOG.info(f"file written to {fp}") class WattTimeMyAccess(WattTimeBase): From 77dd4a14f645e11838e5484673d337d797efec1b Mon Sep 17 00:00:00 2001 From: samkoebrich Date: Tue, 4 Mar 2025 20:41:33 -0700 Subject: [PATCH 17/85] add plot_sample_fuel_mix and use jinja instead of papermill --- watttime/evaluation/report.py | 188 ++++++++++++------ watttime/evaluation/report_card_template.html | 88 ++++++++ 2 files changed, 212 insertions(+), 64 deletions(-) create mode 100644 watttime/evaluation/report_card_template.html diff --git a/watttime/evaluation/report.py b/watttime/evaluation/report.py index bad4772e..1245e873 100644 --- a/watttime/evaluation/report.py +++ b/watttime/evaluation/report.py @@ -5,7 +5,7 @@ from dateutil.parser import parse from operator import attrgetter from pathlib import Path -from typing import List, Optional, Union +from typing import List, Optional, Union, Literal from zoneinfo import ZoneInfo import numpy as np @@ -13,10 +13,9 @@ import plotly.graph_objects as go import plotly.subplots as sp import scipy.stats as stats -from nbconvert.exporters import HTMLExporter -import papermill as pm -from watttime.evaluation.get_wt_api_forecast_evaluation_data import AnalysisDataHandler +from jinja2 import Template +from watttime.evaluation.get_wt_api_forecast_evaluation_data import AnalysisDataHandler, DataHandlerFactory # hacky way to allow running this script locally sys.path.append(str(Path(__file__).parents[1].resolve())) @@ -58,9 +57,6 @@ def get_random_overlapping_period(dfs, max_period="7D"): Returns: pd.DatetimeIndex: List of overlapping datetimes within the defined time period. """ - if not dfs or len(dfs) < 2: - raise ValueError("You must provide at least two DataFrames.") - # Find the overlapping range across all DataFrames start_overlap = max(df.index.min() for df in dfs) end_overlap = min(df.index.max() for df in dfs) @@ -148,7 +144,9 @@ def plot_sample_moers(jobs: List[AnalysisDataHandler], max_sample_period="7D"): # Update layout for the figure fig.update_layout( height=300 - * len(unique_regions), # Adjust height dynamically regionsed on the number of regions + * len( + unique_regions + ), # Adjust height dynamically regionsed on the number of regions title="Data Sample Comparisons by Region", yaxis_title=f"{jobs[0].signal_type}", showlegend=True, @@ -203,7 +201,9 @@ def plot_distribution_moers(jobs: List[AnalysisDataHandler]): # Update layout for the figure fig.update_layout( height=300 - * len(unique_regions), # Adjust height dynamically regionsed on the number of regions + * len( + unique_regions + ), # Adjust height dynamically regionsed on the number of regions title=f"{jobs[0].signal_type} Distribution Comparisons by Region", xaxis_title=f"{jobs[0].signal_type} Values", showlegend=True, @@ -353,7 +353,9 @@ def calc_rank_compare_metrics( # Rank of truth and predicted columns within each window if window_starts: # Generate window ranges based on start times and duration - unique_dates = df.index.get_level_values("generated_at").unique() # Extract unique dates + unique_dates = df.index.get_level_values( + "generated_at" + ).unique() # Extract unique dates window_ranges = [] for date in unique_dates: for start_time in window_starts: @@ -476,7 +478,9 @@ def plot_norm_mae(jobs: List[AnalysisDataHandler], horizons_hr=[1, 12, 24, 48, 7 fig.update_layout( height=300 - * len(unique_regions), # Adjust figure height regionsed on the number of subplots + * len( + unique_regions + ), # Adjust figure height regionsed on the number of subplots title_text="Normalized MAE by Horizon", xaxis_title="Horizon", yaxis_title="Normalized MAE (%)", @@ -663,13 +667,75 @@ def plot_impact_forecast_metrics( return fig +fuel_cp = { + 'coal': 'black', + 'gas': 'brown', +} + +def plot_sample_fuel_mix(jobs: List[AnalysisDataHandler], max_sample_period="7D"): + + # sort jobs by region to group them together + jobs.sort(key=attrgetter("region")) + + # Initialize a subplot with one row per region + fig = sp.make_subplots( + rows=len(jobs), + cols=1, + shared_xaxes=True, + subplot_titles=[f"{j.region} - {j.model_date}" for j in jobs], + vertical_spacing=0.2, + ) + + times = get_random_overlapping_period([j.fuel_mix for j in jobs], max_sample_period) + + for i, _job in enumerate(jobs, start=1): + + # Create cumulative values for stacking + stacked_values = _job.fuel_mix[_job.fuel_mix.index.isin(times)] + for j in range(1, len(stacked_values.columns)): + stacked_values.iloc[:, j] += stacked_values.iloc[:, j - 1] + + # Add each fuel type as an area + for j, fuel in enumerate(_job.fuel_mix.columns): + fig.add_trace( + go.Scatter( + x=stacked_values.index, + y=stacked_values.iloc[:, j], + fill="tonexty" if j > 0 else "tozeroy", + mode="none", # Hide lines to emphasize the filled area + name=f"{_job.region} - {fuel}", + fillcolor=fuel_cp[fuel], + ), + row=i, + col=1, + ) + + # Update layout for the figure + fig.update_layout( + height=300 * len(jobs), # Adjust height dynamically based on regions + title="Marginal Fuel Mix Data Sample Comparisons by Region", + yaxis_title=f"{jobs[0].signal_type}", + showlegend=True, + margin=dict(l=150, r=20, t=60, b=80), + xaxis=dict( + type="date", + tickformat="%Y-%m-%d %H:%M", + tickangle=45, + showgrid=True, + ), + ) + + return fig + def parse_report_command_line_args(sys_args): parser = argparse.ArgumentParser( description="Parse command line arguments to report_moers script" ) - parser.add_argument("-region", "--region_list", nargs="+", help="List of region abbrevs.") + parser.add_argument( + "-region", "--region_list", nargs="+", help="List of region abbrevs." + ) parser.add_argument( "-d", @@ -705,34 +771,19 @@ def parse_report_command_line_args(sys_args): "-o", "--output_dir", type=str, - default=str(Path(__file__).parent / "analysis"), + default=str(Path(__file__).parents[2] / "analysis"), help="Top level directory to save model report. Default is '/research/moer_reports'.", ) parser.add_argument( - "-u", - "--username", + "--steps", type=str, - default=None, - help="WattTime Username can be provided as a CLI arg or set as an environment variable (WATTTIME_USER)", - ) - - parser.add_argument( - "-p", - "--password", - type=str, - default=None, - help="WattTime Password can be provided as a CLI arg or set as an environment variable (WATTTIME_PASSWORD)", - ) - - parser.add_argument( - "-f", - "--forecast_sample_days", - type=int, - default=12, - help="Number of days to sample from forecast. Default is 12.", + nargs="+", + default=["signal", "fuel_mix", "forecast"], + choices=["signal", "fuel_mix", "forecast"], + help="Steps to run. Default is ['signal', 'fuel_mix', 'forecast'].", ) - + args = parser.parse_args(sys_args) now = datetime.now(timezone.utc) @@ -744,6 +795,23 @@ def parse_report_command_line_args(sys_args): return args +PLOTS = { + "signal":[ + plot_sample_moers, + plot_distribution_moers, + plot_region_heatmaps + ], + "fuel_mix":[ + plot_sample_fuel_mix + ], + "forecast":[ + plot_norm_mae, + plot_rank_corr, + plot_impact_forecast_metrics + ] +} + + def run_report_notebook( region_list: List[str], model_date_list: List[str], @@ -751,38 +819,32 @@ def run_report_notebook( eval_start: datetime, eval_end: datetime, output_dir: Path, - api_username: Optional[str] = None, - api_password: Optional[str] = None, - forecast_sample_days: Optional[int] = None, + steps: Literal["signal", "fuel_mix", "forecast"] = ["signal", "fuel_mix", "forecast"], ): - filename = f"{signal_type}_{model}_model_stats" + filename = f"{signal_type}_{'&'.join(region_list)}_{'&'.join(model_date_list)}_model_stats" # run notebook - output_notebook = output_dir / f"{filename}.ipynb" - output_html = output_dir / f"{filename}.html" - pm.execute_notebook( - Path(__file__).parents[0] / "new_moer_model_report.ipynb", - str(output_notebook), - parameters=dict( - region_list=region_list, - model_date_list=model_date_list, - signal_type=signal_type, - eval_start=eval_start.isoformat(), - eval_end=eval_end.isoformat(), - api_username=api_username, - api_password=api_password, - forecast_sample_days=forecast_sample_days, - ), + output_path = output_dir / f"{filename}.html" + input_template_path = Path(__file__).parent / r"report_card_template.html" + + f = DataHandlerFactory( + eval_start=eval_start, + eval_end=eval_end, + regions=region_list, + model_dates=model_date_list, + signal_types=signal_type, ) - # HTML exporter with the custom template - html_exporter = HTMLExporter() - html_exporter.exclude_input = True # hide code cells + plotly_html = {} + for step in steps: + for plot_func in PLOTS[step]: + _plot = plot_func(f.data_handlers) + plotly_html[plot_func.__name__] = _plot.to_html(full_html=False, include_plotlyjs=False) - # Convert the notebook to HTML + write to file - (body, resources) = html_exporter.from_filename(str(output_notebook)) - with open(str(output_html), "w", encoding="utf-8") as f: - f.write(body) + with open(output_path, "w", encoding="utf-8") as output_file: + with open(input_template_path) as template_file: + j2_template = Template(template_file.read()) + output_file.write(j2_template.render(plotly_html)) if __name__ == "__main__": @@ -791,12 +853,10 @@ def run_report_notebook( run_report_notebook( region_list=cli_args.region_list, - model_date_list=cli_args.model_date_list, + model_date_list=cli_args.models, signal_type=cli_args.signal_type, eval_start=cli_args.start, eval_end=cli_args.end, output_dir=Path(cli_args.output_dir), - api_username=cli_args.username, - api_password=cli_args.password, - forecast_sample_days=cli_args.forecast_sample_days, + steps=cli_args.steps ) diff --git a/watttime/evaluation/report_card_template.html b/watttime/evaluation/report_card_template.html new file mode 100644 index 00000000..b8e146b3 --- /dev/null +++ b/watttime/evaluation/report_card_template.html @@ -0,0 +1,88 @@ + + + + + + + + + + + + + + + + + + +

Here's a Plotly graph!

+ +{{ fig }} + +{% if show_moers %} +

Data Distribution Changes

+

We assess the distribution of signal values using three metrics, described below.

+ +
    +
  • Diurnal MOER Values During a Random Week: This plot shows a sample of the signal values over a one-week period. These may not be representative of the signal across all times of the year, but it is helpful to understand diurnal cycles and relative signal periods during a randomly selected period.
    • + +
  • Box and Whiskers of Signal Distribution: This plot shows a "box and whiskers" representation of the distribution of signal values. Outliers are shown beyond the distribution when present.
    • + +
  • Heatmap of Mean Value by Month and Hour: This plot shows the average signal value by month and hour in a heatmap. Darker values represent lower average values during that month and hour, while lighter values represent higher average values.
    • +
+ +{{ plot_sample_moers }} +{{ plot_distribution_moers }} +{{ plot_region_heatmaps }} +{% endif %} + +{% if show_fuel_mix %} +

Marginal Fuel Mix Comparisons

+

Comparing the marginal fuel mix produced by different models before a carbon intensity or damages rate has been applied allows for us to see why signal values have changed. For instance, differences in training data may lead to different margianl fuel mixes being identified (e.g. coal retirements between model training periods).

+ +{{ plot_fuel_mix }} +{% endif %} + +{% if show_forecasts %} +

Forecast Performance

+

We assess the overall performance of our forecasts using a few main metrics, described below.

+ +
    +
  • Normalized Mean Absolute Error (Norm MAE): Mean Absolute Error (MAE) is the average of absolute forecast errors. This represents the magnitude of error to expect from the forecast on average. A model with an MAE closest to 0 is ideal. Norm MAE is MAE divided by the average of the true values. Normalizing MAE allows us to compare models across different datasets. It is expressed as a percentage (%) of the average true value over the validation time period.
    • + +
  • Rank Correlation: A number between -1 and 1 that quantifies the degree to which the rank ordering of two datasets are related. A rank correlation of 1 means the forecast predictions are rank ordered perfectly compared to the true values, making the forecast perfectly effective for time optimization use cases. For many use cases of our forecast models (such as AER), it is more important that predictions are correctly rank ordered within a forecast window, rather than each forecast being accurate in terms of absolute values.
    • + +
  • AER CO2 Savings: This metric represents the impact in lbs CO2/MWh of using the forecast model for energy usage optimization. We compare these CO2 savings against the "Max Potential" case (i.e., the case of using perfect forecasts). In this notebook, we model three different popular AER scenarios: +
      +
    • EV-night: Charging an electric vehicle for 3 hours out of a 12-hour overnight window starting at 19:00 local time.
      • +
    • EV-day: Charging an electric vehicle for 2 hours out of an 8-hour daytime window starting at 09:00 local time.
      • +
    • Thermostat: Optimizing thermostat usage in continual 1-hour windows throughout a 24-hour period, only actively using energy for half of the time.
      • +
    +
    • +
+ +{{ plot_norm_mae }} +{{ plot_rank_corr }} +{{ plot_impact_forecast_metrics }} +{% endif %} + + + + From 32b25e3ca2c52274dbd0f4f3dcecd4efdba80a14 Mon Sep 17 00:00:00 2001 From: samkoebrich Date: Tue, 4 Mar 2025 20:42:08 -0700 Subject: [PATCH 18/85] add fuel_mix attribute to AnalysisDataHandler --- .../get_wt_api_forecast_evaluation_data.py | 75 ++++++++++++++----- 1 file changed, 55 insertions(+), 20 deletions(-) diff --git a/watttime/evaluation/get_wt_api_forecast_evaluation_data.py b/watttime/evaluation/get_wt_api_forecast_evaluation_data.py index 60cf2491..50a9775b 100644 --- a/watttime/evaluation/get_wt_api_forecast_evaluation_data.py +++ b/watttime/evaluation/get_wt_api_forecast_evaluation_data.py @@ -1,6 +1,7 @@ from typing import Optional, List, Union from datetime import datetime import inspect +import warnings import random import math from dateutil.parser import parse @@ -12,6 +13,7 @@ import pandas as pd from watttime import api +from watttime import api_do_not_commit @dataclass @@ -54,13 +56,18 @@ class AnalysisDataHandler: forecast_max_horizon: int = 60 * 24 forecast_sample_seed: int = 42 signal_type: str = "co2_moer" - model_date: Optional[ - str - ] = None # if None, will default to latest provided through API + model_date: Optional[str] = ( + None # if None, will default to latest provided through API + ) wt_forecast: Optional[api.WattTimeForecast] = api.WattTimeForecast( multithreaded=True ) - wt_hist: Optional[api.WattTimeHistorical] = api.WattTimeHistorical() + wt_hist: Optional[api.WattTimeHistorical] = api.WattTimeHistorical( + multithreaded=True + ) + wt_fuel_mix: Optional[api_do_not_commit.WattTimeFuelMix] = ( + api_do_not_commit.WattTimeFuelMix(multithreaded=False) + ) def __post_init__(self): self.region = self.region.upper() @@ -70,6 +77,7 @@ def __post_init__(self): self.eval_start = parse(self.eval_start) if isinstance(self.eval_end, str): self.eval_end = parse(self.eval_end) + assert self.eval_start < self.eval_end self.eval_days = list( pd.date_range(start=self.eval_start, end=self.eval_end, freq="1D") ) @@ -106,25 +114,52 @@ def moers(self) -> pd.DataFrame: @cached_property def forecasts(self) -> pd.DataFrame: + # context manager to only return critical logs + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + forecasts = self.wt_forecast.get_historical_forecast_pandas_list( + list_of_dates=self.sample_days, + region=self.region, + signal_type=self.signal_type, + model=self.model_date, + horizon_hours=math.ceil(self.forecast_max_horizon / 60), + ) - forecasts = self.wt_forecast.get_historical_forecast_pandas_list( - list_of_dates=self.sample_days, + forecasts["point_time"] = pd.to_datetime(forecasts["point_time"]) + forecasts["horizon_mins"] = ( + forecasts["point_time"] - forecasts["generated_at"] + ).dt.total_seconds() / 60 + forecasts.rename({"value": "predicted_value"}, axis="columns", inplace=True) + self.returned_meta = self.wt_forecast._last_request_meta + self.returned_forecast_model_date = self.wt_forecast._last_request_meta[ + "model" + ]["date"] + return forecasts + + @cached_property + def fuel_mix(self) -> pd.DataFrame: + fm = self.wt_fuel_mix.get_fuel_mix_pandas( + start=self.eval_start, + end=self.eval_end + + pd.Timedelta( + minutes=self.forecast_max_horizon + ), # include truth_values for final generated_at region=self.region, - signal_type=self.signal_type, + signal_type="marginal_fuel_mix", model=self.model_date, - horizon_hours=math.ceil(self.forecast_max_horizon / 60), ) - forecasts["point_time"] = pd.to_datetime(forecasts["point_time"]) - forecasts["horizon_mins"] = ( - forecasts["point_time"] - forecasts["generated_at"] - ).dt.total_seconds() / 60 - forecasts.rename({"value": "predicted_value"}, axis="columns", inplace=True) - self.returned_meta = self.wt_forecast._last_request_meta - self.returned_forecast_model_date = self.wt_forecast._last_request_meta[ - "model" - ]["date"] - return forecasts + self.returned_meta = self.wt_fuel_mix._last_request_meta + # self.returned_fuel_mix_model_date = self.wt_fuel_mix._last_request_meta["model"]["date"] + return fm + + def moer_v_fuel_mix(self): + return self.moers.merge( + self.fuel_mix, + how="inner", + left_on="point_time", + right_on="point_time", + ).set_index(["generated_at", "point_time"], drop=True) @cached_property def forecasts_v_moers(self) -> pd.DataFrame: @@ -156,7 +191,7 @@ class DataHandlerFactory: signal_types (List[str]): List of signal types to analyze model_dates (List[str]): List of model dates to analyze """ - + def __init__( self, eval_start: Union[str, datetime], @@ -201,4 +236,4 @@ def __init__( def yield_datahandlers(self) -> AnalysisDataHandler: for dh in self.data_handlers: - yield dh \ No newline at end of file + yield dh From 2831e9e9dfa3b0f6d26b71f4f7e92ea1de9818db Mon Sep 17 00:00:00 2001 From: samkoebrich Date: Tue, 4 Mar 2025 20:42:26 -0700 Subject: [PATCH 19/85] remove papermill req; replace with jinja2 --- setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.py b/setup.py index 9fd2cd3a..5fa3ed7b 100644 --- a/setup.py +++ b/setup.py @@ -9,6 +9,6 @@ packages=["watttime", "watttime.evaluation"], python_requires=">=3.8", install_requires=["requests", "pandas>1.0.0", "holidays", "python-dateutil"], - extras_require={"report": ["papermill", "nbconvert", "plotly", "scipy"]}, + extras_require={"report": ["jinja2", "plotly", "scipy"]}, scripts=["watttime/report.py"], ) From b9370960a6b62e6da32fc4057ce24b35a6ca59c1 Mon Sep 17 00:00:00 2001 From: samkoebrich Date: Tue, 4 Mar 2025 20:42:50 -0700 Subject: [PATCH 20/85] debug logging around requests in api --- watttime/api.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/watttime/api.py b/watttime/api.py index e3f49fb6..56ad26f5 100644 --- a/watttime/api.py +++ b/watttime/api.py @@ -217,9 +217,11 @@ def _make_rate_limited_request(self, url: str, params: Dict[str, Any]) -> Dict: self._apply_rate_limit(ts) try: + LOG.debug(f"Making API Request: {url} | Params: {params}") rsp = self.session.get(url, headers=self.headers, params=params) rsp.raise_for_status() j = rsp.json() + LOG.debug(f"...Request took: {round(time.time() - ts, 2)} seconds") except requests.exceptions.RequestException as e: raise RuntimeError( f"API Request Failed: {e}\nURL: {url}\nParams: {params}" From 46f4743f51f57b5268821d240accb7b4c9c736cd Mon Sep 17 00:00:00 2001 From: samkoebrich Date: Wed, 5 Mar 2025 10:01:29 -0700 Subject: [PATCH 21/85] add styling to report_card_template --- watttime/evaluation/report_card_template.html | 30 ++++++++++++++----- 1 file changed, 23 insertions(+), 7 deletions(-) diff --git a/watttime/evaluation/report_card_template.html b/watttime/evaluation/report_card_template.html index b8e146b3..5ced435a 100644 --- a/watttime/evaluation/report_card_template.html +++ b/watttime/evaluation/report_card_template.html @@ -10,9 +10,12 @@ + + + -

Here's a Plotly graph!

- -{{ fig }} +
+ +
-{% if show_moers %} +
+{% if show_signal %}

Data Distribution Changes

We assess the distribution of signal values using three metrics, described below.

@@ -55,9 +70,9 @@

Data Distribution Changes

{% if show_fuel_mix %}

Marginal Fuel Mix Comparisons

-

Comparing the marginal fuel mix produced by different models before a carbon intensity or damages rate has been applied allows for us to see why signal values have changed. For instance, differences in training data may lead to different margianl fuel mixes being identified (e.g. coal retirements between model training periods).

+

Comparing the marginal fuel mix produced by different models before a carbon intensity or damages rate has been applied allows for us to see why signal values have changed. For instance, differences in training data may lead to different marginal fuel mixes being identified (e.g. coal retirements between model training periods).

-{{ plot_fuel_mix }} +{{ plot_sample_fuel_mix }} {% endif %} {% if show_forecasts %} @@ -82,6 +97,7 @@

Forecast Performance

{{ plot_rank_corr }} {{ plot_impact_forecast_metrics }} {% endif %} +
From c4d2a852c3e67d94b5d374d133c3e169dc3c9353 Mon Sep 17 00:00:00 2001 From: samkoebrich Date: Thu, 6 Mar 2025 10:42:20 -0700 Subject: [PATCH 22/85] updated plots --- watttime/evaluation/fuels_cp.py | 68 ++ watttime/evaluation/report.py | 924 +++++++++++------- watttime/evaluation/report_card_template.html | 405 ++++++-- 3 files changed, 990 insertions(+), 407 deletions(-) create mode 100644 watttime/evaluation/fuels_cp.py diff --git a/watttime/evaluation/fuels_cp.py b/watttime/evaluation/fuels_cp.py new file mode 100644 index 00000000..c4767885 --- /dev/null +++ b/watttime/evaluation/fuels_cp.py @@ -0,0 +1,68 @@ +fuels = ['biomass', 'geothermal', 'hydro', 'solar', 'wind', + 'coal', 'gas', 'nuclear', 'oil'] +other_fuels = ['other', 'waste', 'other_fossil', 'petcoke'] + +fuel_cp = { + 'biomass': '#087243', + 'biomass & waste': '#087243', + 'coal': '#282828', + 'hard_coal_anthricite': '#525252', + 'black_coal': '#525252', + 'coal_anthracite': '#525252', + 'brown_coal_lignite': '#5C5C5C', + 'coal_lignite': '#3D3D3D', + 'coal_bituminous': '#474747', + 'coal_subbituminous': '#333333', + 'coke_oven_gas': '#666666', + 'curtailment': '#ff4050', + 'natgas': '#a76311', + 'combined_cycle': '#a76311', + 'simple_cycle': '#e38009', + 'natural_gas': '#a76311', + 'natural gas': '#a76311', + 'thermal': '#a76311', + 'gas': '#a76311', + 'gas_unspecified': '#a76311', + 'petroleum': '#6f420b', + 'oil': '#6f420b', + 'oil_residual': '#6f420b', + 'oil_diesel': '#6f420b', + 'oil_unspecified': '#6f420b', + 'diesel': '#6f420b', + 'peat': '#0fd27a', + 'petcoke': '#0fd27a', + 'geo': '#cc2851', + 'geothermal': '#cc2851', + 'hydro_pumped_stor': '#054861', + 'pumped': '#054861', + 'PSH': '#054861', + 'pumped hydro': '#054861', + 'hydro_run_of_river': '#0b9fd5', + 'small hydro': '#0b9fd5', + 'hydro_water_res': '#08749b', + 'large hydro': '#08749b', + 'hydro': '#08749b', + 'renewable': '#953766', + 're': '#953766', + 'nuclear': '#9ef274', + 'solar': '#fffc5c', + 'refuse': '#871c37', + 'waste': '#871c37', + 'wind_offshore': '#9deefb', + 'wind_onshore': '#9deefb', + 'wind': '#9deefb', + 'other': '#cccccc', + 'storage': '#cccccc', + 'battery': '#cccccc', + 'marine': '#cccccc', + 'other_fossil': '#cccccc', + 'other fossil': '#cccccc', + 'gas_blastfurnace': '#f78e60', + 'cogen': '#f78e60', + 'net_imports': '#b3a0ba', + 'imports': '#b3a0ba', + 'waste_heat': '#94101F', + 'storage': '#94101F', + 'carbon_free': '#953766', + "imports_exports": "#b3a0ba", +} \ No newline at end of file diff --git a/watttime/evaluation/report.py b/watttime/evaluation/report.py index 1245e873..c1d60d77 100644 --- a/watttime/evaluation/report.py +++ b/watttime/evaluation/report.py @@ -1,21 +1,27 @@ import argparse import calendar import sys +import json from datetime import datetime, timedelta, timezone from dateutil.parser import parse from operator import attrgetter from pathlib import Path -from typing import List, Optional, Union, Literal +from typing import List, Optional, Union, Literal, Dict from zoneinfo import ZoneInfo import numpy as np import pandas as pd import plotly.graph_objects as go import plotly.subplots as sp +import plotly.colors as pc import scipy.stats as stats from jinja2 import Template -from watttime.evaluation.get_wt_api_forecast_evaluation_data import AnalysisDataHandler, DataHandlerFactory +from watttime.evaluation.get_wt_api_forecast_evaluation_data import ( + AnalysisDataHandler, + DataHandlerFactory, +) +from watttime.evaluation.fuels_cp import fuel_cp # hacky way to allow running this script locally sys.path.append(str(Path(__file__).parents[1].resolve())) @@ -45,7 +51,7 @@ def round_time(dt: datetime, minutes: int = 5) -> datetime: return dt.replace(minute=(dt.minute // minutes) * minutes, second=0, microsecond=0) -def get_random_overlapping_period(dfs, max_period="7D"): +def get_random_overlapping_period(dfs, max_period="30D", resample_freq="1H"): """ Find a random overlapping time period between multiple DataFrames' datetime indices, maximizing the period up to `max_period`. @@ -71,7 +77,7 @@ def get_random_overlapping_period(dfs, max_period="7D"): # If the total overlap duration is less than or equal to max_period, return the full range if total_overlap_duration <= max_timedelta: - overlap_range = pd.date_range(start=start_overlap, end=end_overlap, freq="5T") + overlap_range = pd.date_range(start=start_overlap, end=end_overlap, freq=resample_freq) # Check if all indices exist in both DataFrames if all(df.index.isin(overlap_range).all() for df in dfs): return overlap_range @@ -84,12 +90,12 @@ def get_random_overlapping_period(dfs, max_period="7D"): while attempts < max_attempts: random_start_time = pd.Timestamp( np.random.choice( - pd.date_range(start_overlap, end_overlap - max_timedelta, freq="5T") + pd.date_range(start_overlap, end_overlap - max_timedelta, freq=resample_freq) ) ) random_end_time = random_start_time + max_timedelta overlap_range = pd.date_range( - start=random_start_time, end=random_end_time, freq="5T" + start=random_start_time, end=random_end_time, freq=resample_freq ) # Check if all indices exist in both DataFrames if all(overlap_range.isin(df.index).all() for df in dfs): @@ -98,7 +104,7 @@ def get_random_overlapping_period(dfs, max_period="7D"): raise ValueError(f"No common overlap found after {max_attempts} attempts.") -def plot_sample_moers(jobs: List[AnalysisDataHandler], max_sample_period="7D"): +def plot_sample_moers(jobs: List[AnalysisDataHandler], max_sample_period="30D") -> Dict[str, go.Figure]: """ Plot a sample of old and new MOER values over time, creating a subplot for each unique region. @@ -109,60 +115,55 @@ def plot_sample_moers(jobs: List[AnalysisDataHandler], max_sample_period="7D"): plotly.graph_objects.Figure: A Plotly figure with timeseries of values. """ - # Create a figure with a subplot for each region, stacked vertically + figs = {} unique_regions = set([j.region for j in jobs]) - fig = sp.make_subplots( - rows=len(unique_regions), - cols=1, - shared_xaxes=True, - vertical_spacing=0.1, - subplot_titles=[f"{region.upper()}" for region in unique_regions], - ) - for i, region_abbrev in enumerate(unique_regions, start=1): + + fig = go.Figure() + region_abbrev = region_abbrev.upper() _jobs = [j for j in jobs if j.region == region_abbrev] times = get_random_overlapping_period( - [j.moers for j in _jobs], max_sample_period + [j.moers for j in _jobs], max_sample_period, ) for _job in _jobs: + fig.add_trace( go.Scatter( x=times, - y=_job.moers.loc[times, "signal_value"].to_list(), + y=_job.moers.loc[times]['signal_value'], mode="lines", name=_job.model_date, line=dict(width=2), - showlegend=(i == 1), - ), - row=i, - col=1, + showlegend=True, + ) ) - # Update layout for the figure - fig.update_layout( - height=300 - * len( - unique_regions - ), # Adjust height dynamically regionsed on the number of regions - title="Data Sample Comparisons by Region", - yaxis_title=f"{jobs[0].signal_type}", - showlegend=True, - margin=dict(l=150, r=20, t=60, b=80), - xaxis=dict( - type="date", - tickformat="%Y-%m-%d %H:%M", - tickangle=45, - showgrid=True, - ), - ) + # Update layout for the figure + fig.update_layout( + height=300, + yaxis=dict( + title=f"{jobs[0].signal_type}", + fixedrange=True # Disable y-axis panning + ), + showlegend=True, + margin=dict(l=50, r=50, t=50, b=50), + xaxis=dict( + type="date", + tickformat="%Y-%m-%d %H:%M", + tickangle=45, + showgrid=True, + range=[times.min(), times.min() + pd.Timedelta("7D")], # Default 1-week view + ), + ) + figs[region_abbrev] = fig - return fig + return figs -def plot_distribution_moers(jobs: List[AnalysisDataHandler]): +def plot_distribution_moers(jobs: List[AnalysisDataHandler]) -> Dict[str, go.Figure]: """ Plot the distribution of old and new MOER values for each region, creating a stacked subplot. @@ -174,15 +175,10 @@ def plot_distribution_moers(jobs: List[AnalysisDataHandler]): """ # Create a figure with a subplot for each region, stacked vertically unique_regions = set([j.region for j in jobs]) - fig = sp.make_subplots( - rows=len(unique_regions), - cols=1, - shared_xaxes=True, - vertical_spacing=0.2, - subplot_titles=[f"{region.upper()}" for region in unique_regions], - ) + figs = {} for i, region_abbrev in enumerate(unique_regions, start=1): + fig = go.Figure() region_abbrev = region_abbrev.upper() _jobs = [j for j in jobs if j.region == region_abbrev] for _job in _jobs: @@ -193,34 +189,30 @@ def plot_distribution_moers(jobs: List[AnalysisDataHandler]): x=_job.moers["signal_value"].dropna(), name=_job.model_date, opacity=0.6, - ), - row=i, - col=1, + ) ) - # Update layout for the figure - fig.update_layout( - height=300 - * len( - unique_regions - ), # Adjust height dynamically regionsed on the number of regions - title=f"{jobs[0].signal_type} Distribution Comparisons by Region", - xaxis_title=f"{jobs[0].signal_type} Values", - showlegend=True, - margin=dict(l=150, r=20, t=60, b=80), - xaxis=dict( - type="linear", - tickangle=45, - showgrid=True, - ), - yaxis_visible=False, - yaxis_showticklabels=False, - ) + # Update layout for the figure + fig.update_layout( + height=300, + xaxis_title=f"{jobs[0].signal_type} Values", + showlegend=True, + margin=dict(l=50, r=50, t=50, b=50), + xaxis=dict( + type="linear", + tickangle=45, + showgrid=True, + ), + yaxis_visible=False, + yaxis_showticklabels=False, + ) + + figs[region_abbrev] = fig - return fig + return figs -def plot_region_heatmaps(jobs: List[AnalysisDataHandler], colorscale="Cividis"): +def plot_heatmaps(jobs: List[AnalysisDataHandler], colorscale="RdBu_r") -> Dict[str, go.Figure]: """ Generate vertically stacked heatmaps for each region in the region_list. @@ -231,68 +223,73 @@ def plot_region_heatmaps(jobs: List[AnalysisDataHandler], colorscale="Cividis"): plotly.graph_objects.Figure: Figure containing stacked heatmaps. """ - jobs = sorted(jobs, key=attrgetter("region", "model_date")) - - # Initialize a subplot with one row per region - fig = sp.make_subplots( - rows=len(jobs), - cols=1, - shared_xaxes=True, - subplot_titles=[f"{j.region} - {j.model_date}" for j in jobs], - vertical_spacing=0.2, - ) - + unique_regions = set([j.region for j in jobs]) + figs = {} + # share upper and lower bounds between plots - zmin = 1e6 - zmax = 0 - for job in jobs: - jmin = job.moers["signal_value"].quantile(0.01) - jmax = job.moers["signal_value"].quantile(0.99) - - if jmin < zmin: - zmin = jmin - if jmax > zmax: - zmax = jmax - - for i, _job in enumerate(jobs, start=1): - heat = _job.moers.assign( - month=_job.moers.index.month, hour=_job.moers.index.hour - ) - heat = heat.dropna(subset=["signal_value"]) - heat = ( - heat.groupby(["month", "hour"])["signal_value"] - .mean() - .unstack(fill_value=np.nan) - ) - heat.index = [ - calendar.month_abbr[m] for m in heat.index - ] # Map month numbers to month abbreviations - - # Create a heatmap trace - heatmap_trace = go.Heatmap( - z=heat.values, - x=heat.columns, # Hours - y=heat.index, # Month abbreviations - zmin=zmin, - zmax=zmax, - colorscale=colorscale, - colorbar_title="MOER", - showscale=(i == 1), # Show color scale only on the first heatmap + # zmin = 1e6 + # zmax = 0 + # for job in jobs: + # jmin = job.moers["signal_value"].quantile(0.01) + # jmax = job.moers["signal_value"].quantile(0.99) + + # if jmin < zmin: + # zmin = jmin + # if jmax > zmax: + # zmax = jmax + + for i, region_abbrev in enumerate(unique_regions, start=1): + region_abbrev = region_abbrev.upper() + _jobs = [j for j in jobs if j.region == region_abbrev] + + # Initialize a subplot with one row per region + fig = sp.make_subplots( + rows=len(_jobs), + cols=1, + shared_xaxes=True, + subplot_titles=[j.model_date for j in _jobs], + vertical_spacing=0.2, ) - # Add the heatmap trace to the subplot - fig.add_trace(heatmap_trace, row=i, col=1) + for j, _job in enumerate(_jobs, start=1): + heat = _job.moers.assign( + month=_job.moers.index.month, hour=_job.moers.index.hour + ) + heat = heat.dropna(subset=["signal_value"]) + heat = ( + heat.groupby(["month", "hour"])["signal_value"] + .mean() + .unstack(fill_value=np.nan) + ) + heat.index = [ + calendar.month_abbr[m] for m in heat.index + ] # Map month numbers to month abbreviations + + # Create a heatmap trace + heatmap_trace = go.Heatmap( + z=heat.values, + x=heat.columns, # Hours + y=heat.index, # Month abbreviations + # zmin=zmin, + # zmax=zmax, + colorscale=colorscale, + colorbar_title=_job.signal_type, + showscale=True, # Show color scale only on the first heatmap + ) - # Update layout - fig.update_layout( - height=500 * len(jobs), # Adjust the height regionsed on the number of regions - title=f"Average {jobs[0].signal_type} by Month & Hour", - xaxis_title="Hour of Day", - yaxis_title="Month", - xaxis=dict(title="Hour of Day", tickmode="linear"), - ) + # Add the heatmap trace to the subplot + fig.add_trace(heatmap_trace, row=j, col=1) - return fig + # Update layout + fig.update_layout( + height=300 * len(_jobs), # Adjust the height regionsed on the number of regions + xaxis_title="Hour of Day", + yaxis_title="Month", + xaxis=dict(title="Hour of Day", tickmode="linear"), + ) + figs[region_abbrev] = fig + + return figs def calc_norm_mae( @@ -428,31 +425,24 @@ def assign_rolling_window(row): return { "co2_reduction": round(co2_reduction, 1), "co2_potential": round(co2_potential, 1), - } + } -def plot_norm_mae(jobs: List[AnalysisDataHandler], horizons_hr=[1, 12, 24, 48, 72]): +def plot_norm_mae(jobs: List[AnalysisDataHandler], horizons_hr=[1, 6, 12, 18, 24]) -> Dict[str, go.Figure]: """ Create a Plotly bar chart for rank correlation by horizon with one subplot per region (abbrev). """ # Create subplots unique_regions = set([j.region for j in jobs]) - fig = sp.make_subplots( - rows=len(unique_regions), - cols=1, - shared_xaxes=True, - vertical_spacing=0.2, - subplot_titles=list(unique_regions), - ) - y_min = y_max = 0 - + figs = {} + # Iterate through each region and create a bar plot for i, region_abbrev in enumerate(unique_regions, start=1): region_abbrev = region_abbrev.upper() + fig = go.Figure() _jobs = [j for j in jobs if j.region == region_abbrev] - x_values = [f"{h}hr" for h in horizons_hr] # Add horizon labels for _job in _jobs: @@ -469,31 +459,27 @@ def plot_norm_mae(jobs: List[AnalysisDataHandler], horizons_hr=[1, 12, 24, 48, 7 name=_job.model_date, text=[f"{y:.1f}%" for y in y_values], # Add text labels on bars textposition="outside", - ), - row=i, - col=1, + ) ) - # Set uniform y-axis range for all subplots - fig.update_yaxes(range=[y_min - (0.25 * y_max), y_max + (0.25 * y_max)]) - - fig.update_layout( - height=300 - * len( - unique_regions - ), # Adjust figure height regionsed on the number of subplots - title_text="Normalized MAE by Horizon", - xaxis_title="Horizon", - yaxis_title="Normalized MAE (%)", - showlegend=True, # Legends appear in individual subplot titles - margin=dict(l=50, r=50, t=50, b=50), - barmode="group", # Ensure bars for each region are grouped - ) - fig.update_xaxes(title_text="Horizon (Hours)", row=len(unique_regions), col=1) - return fig + fig.update_layout( + height=300, + xaxis_title="Horizon (Hours)", + yaxis_title="Normalized MAE (%)", + showlegend=True, # Legends appear in individual subplot titles + margin=dict(l=50, r=50, t=50, b=50), + barmode="group", # Ensure bars for each region are grouped + ) + + figs[region_abbrev] = fig + + for region, fig in figs.items(): + figs[region] = fig.update_yaxes(range=[y_min - (0.25 * y_max), y_max + (0.25 * y_max)]) + return figs -def plot_rank_corr(jobs: List[AnalysisDataHandler], horizons_hr=[12, 24, 48, 72]): + +def plot_rank_corr(jobs: List[AnalysisDataHandler], horizons_hr=[1, 6, 12, 18, 24]) -> Dict[str, go.Figure]: """ Create a Plotly line plot for rank correlation by horizon with one subplot per region (abbrev). @@ -509,19 +495,12 @@ def plot_rank_corr(jobs: List[AnalysisDataHandler], horizons_hr=[12, 24, 48, 72] # Create subplots unique_regions = set([j.region for j in jobs]) - fig = sp.make_subplots( - rows=len(unique_regions), - cols=1, - shared_xaxes=True, - vertical_spacing=0.2, - subplot_titles=list(unique_regions), - ) - y_min = y_max = 0 + figs = {} # Iterate through each region and create a line plot for i, region_abbrev in enumerate(unique_regions, start=1): - + fig = go.Figure() region_abbrev = region_abbrev.upper() _jobs = [j for j in jobs if j.region == region_abbrev] @@ -540,32 +519,28 @@ def plot_rank_corr(jobs: List[AnalysisDataHandler], horizons_hr=[12, 24, 48, 72] go.Bar( x=x_values, y=y_values, - name=f"{_job.region} - {_job.model_date}", + name=_job.model_date, text=[f"{y:.3f}" for y in y_values], # Add text labels on bars textposition="outside", - ), - row=i, - col=1, + ) ) + + # Update layout + fig.update_layout( + height=300, + xaxis_title="Horizon (Hours)", + yaxis_title="Rank Correlation", + showlegend=True, + margin=dict(l=50, r=50, t=50, b=50), + ) + + figs[region_abbrev] = fig + + for region, fig in figs.items(): + # Set uniform y-axis range for all subplots + figs[region] = fig.update_yaxes(range=[y_min - (0.25 * y_max), y_max + (0.25 * y_max)]) - # Set uniform y-axis range for all subplots - fig.update_yaxes(range=[y_min - (0.2 * y_max), y_max + (0.2 * y_max)]) - - # Update layout - fig.update_layout( - height=300 - * len(unique_regions), # Adjust figure height based on the number of subplots - title_text=f"Rank Correlation by Horizon", - xaxis_title="Horizon", - yaxis_title="Rank Correlation", - showlegend=True, # Legends appear in individual subplot titles - margin=dict(l=50, r=50, t=50, b=50), - ) - - # Update x-axis for all subplots - fig.update_xaxes(title_text="Horizon (Hours)", row=len(unique_regions), col=1) - - return fig + return figs # TODO: translate to local time for start windows? @@ -592,141 +567,399 @@ def plot_impact_forecast_metrics( jobs: List[AnalysisDataHandler], scenarios=["EV-night", "EV-day", "Thermostat"] ): - # Create subplots - fig = sp.make_subplots( - rows=len(jobs), - cols=1, - shared_xaxes=True, - vertical_spacing=0.2, - subplot_titles=[f"{j.region} - {j.model_date}" for j in jobs], - ) + unique_regions = set([j.region for j in jobs]) + figs = {} + y_max = 0 - # Iterate through each region and create bar plots - for i, _job in enumerate(jobs, start=1): + for i, region_abbrev in enumerate(unique_regions, start=1): + + _jobs = [j for j in jobs if j.region == region_abbrev] + fig = sp.make_subplots( + rows=len(_jobs), + cols=1, + shared_xaxes=True, + subplot_titles=[j.model_date for j in jobs], + vertical_spacing=0.2, + ) + + # Iterate through each region and create bar plots + for j, _job in enumerate(_jobs, start=1): + + _metrics = [ + { + **calc_rank_compare_metrics(_job.forecasts_v_moers, **AER_SCENARIOS[s]), + "scenario": s, + } + for s in scenarios + ] - _metrics = [ - { - **calc_rank_compare_metrics(_job.forecasts_v_moers, **AER_SCENARIOS[s]), - "scenario": s, - } - for s in scenarios - ] + _metrics = pd.DataFrame(_metrics) - _metrics = pd.DataFrame(_metrics) - - # Add the 'co2_potential' bar trace - fig.add_trace( - go.Bar( - x=_metrics["scenario"], - y=_metrics["co2_potential"], - name=f"CO2 Potential Savings", # Legend only in the first subplot - marker=dict( - color="rgba(200, 200, 200, 0.8)" - ), # Light gray for potential - hovertemplate="%{x}: %{y:.1f} lbs CO2/MWh", - ), - row=i, - col=1, - ) + # Add the 'co2_potential' bar trace + fig.add_trace( + go.Bar( + x=_metrics["scenario"], + y=_metrics["co2_potential"], + name=f"CO2 Potential Savings", # Legend only in the first subplot + marker=dict( + color="rgba(200, 200, 200, 0.8)" + ), # Light gray for potential + hovertemplate="%{x}: %{y:.1f} lbs CO2/MWh", + ), + row=j, + col=1, + ) - # Add the 'co2_reduction' bar trace - fig.add_trace( - go.Bar( - x=_metrics["scenario"], - y=_metrics["co2_reduction"], - name="Forecast Achieved CO2 Savings", - text=[ - f"{(r / p) * 100:.1f}%" - for r, p in zip( - _metrics["co2_reduction"], _metrics["co2_potential"] - ) - ], - textposition="outside", - marker=dict(color="rgba(0, 128, 0, 0.8)"), # Green for reduction - hovertemplate="%{x}: %{y:.1f} lbs CO2/MWh", - ), - row=i, - col=1, + # Add the 'co2_reduction' bar trace + fig.add_trace( + go.Bar( + x=_metrics["scenario"], + y=_metrics["co2_reduction"], + name="Forecast Achieved", + text=[ + f"{(r / p) * 100:.1f}%" + for r, p in zip( + _metrics["co2_reduction"], _metrics["co2_potential"] + ) + ], + textposition="outside", + marker=dict(color="rgba(0, 128, 0, 0.8)"), # Green for reduction + hovertemplate="%{x}: %{y:.1f} lbs CO2/MWh", + ), + row=j, + col=1, + ) + + y_max = max(_metrics["co2_reduction"].max(), _metrics['co2_potential'].max(), y_max) + + # Update layout + fig.update_layout( + height=300 * len(_jobs), + xaxis_title="Scenario", + yaxis_title="CO2 Savings (lbs/MWh)", + barmode="group", # Grouped bars (side by side) + showlegend=True, # Show legend + margin=dict(l=50, r=50, t=50, b=50), ) - # Update layout - fig.update_layout( - height=400 * len(jobs), # Adjust figure height based on the number of subplots - title_text="Simulated CO2 Savings by AER Scenario and Region", - xaxis_title="Scenario", - yaxis_title="CO2 Savings (lbs/MWh)", - barmode="group", # Grouped bars (side by side) - showlegend=True, # Show legend - margin=dict(l=50, r=50, t=80, b=50), - ) + # Update axes for all subplots + for i in range(1, len(jobs) + 1): + fig.update_xaxes(title_text="Scenario", row=i, col=1) + fig.update_yaxes(title_text="CO2 Savings (lbs/MWh)", row=i, col=1) + + figs[region_abbrev] = fig + + for region, fig in figs.items(): + # Set uniform y-axis range for all subplots + figs[region] = fig.update_yaxes(range=[0, y_max + (0.25 * y_max)]) - # Update axes for all subplots - for i in range(1, len(jobs) + 1): - fig.update_xaxes(title_text="Scenario", row=i, col=1) - fig.update_yaxes(title_text="CO2 Savings (lbs/MWh)", row=i, col=1) + return figs - return fig -fuel_cp = { - 'coal': 'black', - 'gas': 'brown', -} +def plot_sample_fuel_mix(jobs: List[AnalysisDataHandler], max_sample_period="30D") -> Dict[str, go.Figure]: -def plot_sample_fuel_mix(jobs: List[AnalysisDataHandler], max_sample_period="7D"): + figs = {} + unique_regions = set([j.region for j in jobs]) + times = get_random_overlapping_period([j.fuel_mix for j in jobs], max_sample_period) + for i, region_abbrev in enumerate(unique_regions, start=1): + _jobs = [j for j in jobs if j.region == region_abbrev] - # sort jobs by region to group them together - jobs.sort(key=attrgetter("region")) + # Initialize a subplot with one row per region + fig = sp.make_subplots( + rows=len(_jobs), + cols=1, + shared_xaxes=True, + subplot_titles=[j.model_date for j in jobs], + vertical_spacing=0.2, + ) - # Initialize a subplot with one row per region - fig = sp.make_subplots( - rows=len(jobs), - cols=1, - shared_xaxes=True, - subplot_titles=[f"{j.region} - {j.model_date}" for j in jobs], - vertical_spacing=0.2, + for j, _job in enumerate(_jobs, start=1): + + stacked_values = _job.fuel_mix.loc[times] + + # Create cumulative values for stacking + for k in range(1, len(stacked_values.columns)): + stacked_values.iloc[:, k] += stacked_values.iloc[:, k - 1] + + # Add each fuel type as an area + for k, fuel in enumerate(_job.fuel_mix.columns): + fig.add_trace( + go.Scatter( + x=stacked_values.index, + y=stacked_values.iloc[:, k], + fill="tonexty" if j > 0 else "tozeroy", + mode="none", # Hide lines to emphasize the filled area + name=fuel, + fillcolor=fuel_cp[fuel], + ), + row=j, + col=1, + ) + + # Update layout for the figure + fig.update_layout( + height=300 * len(_jobs), + yaxis=dict( + title=f"{jobs[0].signal_type}", + fixedrange=True # Disable y-axis panning + ), + showlegend=True, + margin=dict(l=50, r=50, t=50, b=50), + xaxis=dict( + type="date", + tickformat="%Y-%m-%d %H:%M", + tickangle=45, + showgrid=True, + range=[times.min(), times.min() + pd.Timedelta("7D")], + ), + ) + figs[region_abbrev] = fig + + return figs + + +def calc_max_potential(df, charge_mins, window_mins, window_starts=None, truth_col="signal_value"): + """Predict the maximum potential CO2 Savings, without any forecast to estimate the upper limit of impact from a given signal value.""" + + if window_starts: + # Generate window ranges based on start times and duration + unique_dates = set(df.index.date) + window_ranges = [] + for date in unique_dates: + for start_time in window_starts: + start = pd.Timestamp(f"{date} {start_time}") + end = start + pd.Timedelta(f"{window_mins} min") + window_ranges.append((start, end)) + + # Assign each row to a window + def assign_window(row): + point_time = row.name.replace(tzinfo=None) + for start, end in window_ranges: + if point_time >= start and point_time < end: + return start # Label window by its start time + return pd.NaT + + df["window"] = df.apply(assign_window, axis=1) + + else: + def assign_rolling_window(row): + window_start = row.name.floor(f"{window_mins}T") + # window_end = window_start + pd.Timedelta(f"{window_mins} min") + return window_start + + df["window"] = df.apply(assign_rolling_window, axis=1) + + df = df.dropna(subset=["window"]) + df = df.assign( + y_rank=df.groupby('window')[truth_col].rank(method='first') ) + y_best_total = ( + df[df["y_rank"] <= (charge_mins / 5)] + .groupby("window")[truth_col] + .mean() + ) + y_avg_total = df.groupby("window")[truth_col].mean() + co2_potential = (y_avg_total - y_best_total).sum() + return {"co2_potential": round(co2_potential, 1)} - times = get_random_overlapping_period([j.fuel_mix for j in jobs], max_sample_period) - for i, _job in enumerate(jobs, start=1): +def plot_max_impact_potential(jobs: List[AnalysisDataHandler], scenarios: List[str] = ["EV-night", "EV-day", "Thermostat"]): + unique_regions = set([j.region for j in jobs]) + figs = {} + y_max = 0 + for i, region_abbrev in enumerate(unique_regions, start=1): + _jobs = [j for j in jobs if j.region == region_abbrev] + region_abbrev = region_abbrev.upper() + fig = go.Figure() + + for j, _job in enumerate(_jobs, start=1): + + _metrics = [ + { + **calc_max_potential(_job.moers, **AER_SCENARIOS[s]), + "scenario": s, + } + for s in scenarios + ] - # Create cumulative values for stacking - stacked_values = _job.fuel_mix[_job.fuel_mix.index.isin(times)] - for j in range(1, len(stacked_values.columns)): - stacked_values.iloc[:, j] += stacked_values.iloc[:, j - 1] + _metrics = pd.DataFrame(_metrics) - # Add each fuel type as an area - for j, fuel in enumerate(_job.fuel_mix.columns): + # Add the 'co2_potential' bar trace fig.add_trace( - go.Scatter( - x=stacked_values.index, - y=stacked_values.iloc[:, j], - fill="tonexty" if j > 0 else "tozeroy", - mode="none", # Hide lines to emphasize the filled area - name=f"{_job.region} - {fuel}", - fillcolor=fuel_cp[fuel], - ), - row=i, - col=1, + go.Bar( + x=_metrics["scenario"], + y=_metrics["co2_potential"], + name=_job.model_date, # Legend only in the first subplot + hovertemplate="%{x}: %{y:.1f} lbs CO2/MWh", + ) ) + + y_max = max(_metrics['co2_potential'].max(), y_max) + + figs[region_abbrev] = fig + + return figs - # Update layout for the figure - fig.update_layout( - height=300 * len(jobs), # Adjust height dynamically based on regions - title="Marginal Fuel Mix Data Sample Comparisons by Region", - yaxis_title=f"{jobs[0].signal_type}", - showlegend=True, - margin=dict(l=150, r=20, t=60, b=80), - xaxis=dict( - type="date", - tickformat="%Y-%m-%d %H:%M", - tickangle=45, - showgrid=True, - ), - ) - - return fig +def plot_fuelmix_heatmap(jobs: List[AnalysisDataHandler]): + def create_pivot_table(df, column): + df = df.assign( + month=df.index.month, + hour=df.index.hour + ) + grouped = df.groupby(['month', 'hour'])[column].mean().reset_index() + pivot = grouped.pivot(index='month', columns='hour', values=column) + return pivot + + def create_buttons(column_names, num_subplots): + buttons = [] + for i, name in enumerate(column_names): + # Create visibility array for all traces + visible = [] + for j in range(num_subplots): + # For each subplot, make only the trace for current fuel type visible + for k in range(len(column_names)): + visible.append(True if k == i else False) + + button = dict( + label=name, + method="update", + args=[{"visible": visible}] + ) + buttons.append(button) + return buttons + + def make_alpha_gradient(hex_color, steps=20): + """Generate a Plotly colorscale with an alpha gradient from 0 to 1.""" + rgba_colors = [ + f'rgba{pc.hex_to_rgb(hex_color) + (alpha,)}' + for alpha in [i / (steps - 1) for i in range(steps)] + ] + return [[i / (steps - 1), rgba] for i, rgba in enumerate(rgba_colors)] + + unique_regions = set([j.region for j in jobs]) + figs = {} + + for region_abbrev in unique_regions: + region_abbrev = region_abbrev.upper() + region_jobs = [j for j in jobs if j.region.upper() == region_abbrev] + + # We need to ensure all jobs have the same fuel types for the buttons to work + all_columns = set() + for job in region_jobs: + all_columns.update(job.fuel_mix.columns) + + # Create subplot titles with model dates + subplot_titles = [j.model_date for j in region_jobs] + + # Create subplots + fig = sp.make_subplots( + rows=len(region_jobs), + cols=1, + shared_xaxes=True, + subplot_titles=subplot_titles, + ) + + all_columns_list = sorted(list(all_columns)) # Sort to ensure consistent order + + # Count the total number of traces we'll add + total_traces = len(region_jobs) * len(all_columns_list) + + # Track which trace index we're on + trace_idx = 0 + + # Add traces for each job + for job_idx, job in enumerate(region_jobs, start=1): + for fuel_idx, fuel in enumerate(all_columns_list): + # Check if this fuel exists in the current job + if fuel in job.fuel_mix.columns: + pivot = create_pivot_table(job.fuel_mix, fuel) + + # Determine visibility + visible = (fuel_idx == 0) # Only the first fuel type is visible by default + + # Add the heatmap trace for this fuel + try: + fuel_color = fuel_cp[fuel] # Assuming fuel_cp is a color mapping dictionary + except (NameError, KeyError): + # If fuel_cp doesn't exist or doesn't have this fuel, use a default color + import plotly.express as px + colors = px.colors.qualitative.Plotly + fuel_color = colors[fuel_idx % len(colors)] + + fig.add_trace( + go.Heatmap( + z=pivot.values, + x=pivot.columns, + y=pivot.index, + colorscale=make_alpha_gradient(fuel_color), + name=f"{fuel} ({job.model_date})", + visible=visible, + showscale=(job_idx == 1), # Only show colorscale for the first job + zmin=0, zmax=1, + ), + row=job_idx, col=1 + ) + else: + # Add an empty trace to maintain the correct index structure + fig.add_trace( + go.Heatmap( + z=[[0]], + x=[0], + y=[0], + visible=False, + showscale=False, + name=f"{fuel} ({job.model_date}) - missing" + ), + row=job_idx, col=1 + ) + + trace_idx += 1 + + # Add buttons that control visibility across all subplots + buttons = create_buttons(all_columns_list, len(region_jobs)) + + # Month names mapping + month_names = {1: 'Jan', 2: 'Feb', 3: 'Mar', 4: 'Apr', 5: 'May', 6: 'Jun', + 7: 'Jul', 8: 'Aug', 9: 'Sep', 10: 'Oct', 11: 'Nov', 12: 'Dec'} + + # Update layout + fig.update_layout( + updatemenus=[ + dict( + type="buttons", + direction="right", + active=0, + x=0.1, + y=1.15, + buttons=buttons + ) + ], + height=300 * len(region_jobs), # Adjust height based on number of jobs + xaxis_title="Hour of Day", + ) + + # Update all y-axes to show month names + for i in range(1, len(region_jobs) + 1): + fig.update_yaxes( + title="Month", + tickvals=list(month_names.keys()), + ticktext=list(month_names.values()), + row=i, col=1 + ) + + # Update all x-axes + fig.update_xaxes( + title="Hour of Day" if i == len(region_jobs) else None, # Only show title on bottom plot + tickmode="linear", + dtick=2, # Show every 2 hours + row=i, col=1 + ) + + figs[region_abbrev] = fig + + return figs + def parse_report_command_line_args(sys_args): parser = argparse.ArgumentParser( @@ -783,7 +1016,7 @@ def parse_report_command_line_args(sys_args): choices=["signal", "fuel_mix", "forecast"], help="Steps to run. Default is ['signal', 'fuel_mix', 'forecast'].", ) - + args = parser.parse_args(sys_args) now = datetime.now(timezone.utc) @@ -796,32 +1029,28 @@ def parse_report_command_line_args(sys_args): PLOTS = { - "signal":[ - plot_sample_moers, - plot_distribution_moers, - plot_region_heatmaps - ], - "fuel_mix":[ - plot_sample_fuel_mix - ], - "forecast":[ - plot_norm_mae, - plot_rank_corr, - plot_impact_forecast_metrics - ] + "signal": [plot_sample_moers, plot_distribution_moers, plot_heatmaps, plot_max_impact_potential], + "fuel_mix": [plot_sample_fuel_mix, plot_fuelmix_heatmap], + "forecast": [plot_norm_mae, plot_rank_corr, plot_impact_forecast_metrics], } -def run_report_notebook( +def generate_report( region_list: List[str], model_date_list: List[str], signal_type: str, eval_start: datetime, eval_end: datetime, output_dir: Path, - steps: Literal["signal", "fuel_mix", "forecast"] = ["signal", "fuel_mix", "forecast"], + steps: Literal["signal", "fuel_mix", "forecast"] = [ + "signal", + "fuel_mix", + "forecast", + ], ): - filename = f"{signal_type}_{'&'.join(region_list)}_{'&'.join(model_date_list)}_model_stats" + filename = ( + f"{signal_type}_{'&'.join(region_list)}_{'&'.join(model_date_list)}_model_stats" + ) # run notebook output_path = output_dir / f"{filename}.html" @@ -839,24 +1068,65 @@ def run_report_notebook( for step in steps: for plot_func in PLOTS[step]: _plot = plot_func(f.data_handlers) - plotly_html[plot_func.__name__] = _plot.to_html(full_html=False, include_plotlyjs=False) + + if isinstance(_plot, dict): + plotly_html[plot_func.__name__] = {} + for region_abbrev, _p in _plot.items(): + # for trace in _p.data: + # trace.x = [t.strftime("%Y-%m-%d %H:%M") for t in trace.x] # Keep only date and hour-minute + # trace.y = [round(y, 3) for y in trace.y] # Reduce decimal places + + plotly_html[plot_func.__name__][region_abbrev] = _p.to_html( + full_html=False, include_plotlyjs=False + ) + else: + assert isinstance(_plot, go.Figure) + + # reduce datatype precision in plot + # for trace in _plot.data: + # trace.x = [t.strftime("%Y-%m-%d %H:%M") for t in trace.x] # Keep only date and hour-minute + # trace.y = [round(y, 3) for y in trace.y] # Reduce decimal places + + plotly_html[plot_func.__name__] = _plot.to_html( + full_html=False, include_plotlyjs=False + ) + + plotly_html['report_input_dict'] = json.dumps( + dict( + region_list=region_list, + model_date_list=model_date_list, + signal_type=signal_type, + eval_start=str(eval_start), + eval_end=str(eval_end), + steps=steps + ), indent=4 + ) + + plotly_html['collected_model_meta'] = json.dumps(f.collected_model_meta, indent=4) with open(output_path, "w", encoding="utf-8") as output_file: with open(input_template_path) as template_file: j2_template = Template(template_file.read()) - output_file.write(j2_template.render(plotly_html)) + output_file.write( + j2_template.render( + plotly_html, + show_signal="signal" in steps, + show_fuel_mix="fuel_mix" in steps, + show_forecast="forecast" in steps, + ) + ) if __name__ == "__main__": cli_args = parse_report_command_line_args(sys.argv[1:]) - run_report_notebook( + generate_report( region_list=cli_args.region_list, model_date_list=cli_args.models, signal_type=cli_args.signal_type, eval_start=cli_args.start, eval_end=cli_args.end, output_dir=Path(cli_args.output_dir), - steps=cli_args.steps + steps=cli_args.steps, ) diff --git a/watttime/evaluation/report_card_template.html b/watttime/evaluation/report_card_template.html index 5ced435a..f3d5b65d 100644 --- a/watttime/evaluation/report_card_template.html +++ b/watttime/evaluation/report_card_template.html @@ -1,104 +1,349 @@ - - - + + + + WattTime Analysis Report - - + + - - + + - - + + @keyframes fadeIn { + from { opacity: 0; } + to { opacity: 1; } + } + + .code-block { + background-color: var(--code-bg); + padding: 15px; + border-radius: 5px; + font-family: monospace; + white-space: pre-wrap; + overflow-x: auto; + margin: 1rem 0; + } + + /* Responsive adjustments */ + @media (max-width: 768px) { + .tabs { + flex-direction: column; + border-bottom: none; + } + + .tab { + border: 1px solid var(--border-color); + border-radius: 0; + margin-right: 0; + margin-bottom: -1px; + } + + .tab:first-child { + border-radius: 4px 4px 0 0; + } + + .tab:last-child { + border-radius: 0 0 4px 4px; + } + + .tab.active { + margin-bottom: -1px; + border-bottom: 1px solid var(--border-color); + border-left: 3px solid var(--primary-color); + } + } + + + - -
+
-
- -
-{% if show_signal %} -

Data Distribution Changes

-

We assess the distribution of signal values using three metrics, described below.

- -
    -
  • Diurnal MOER Values During a Random Week: This plot shows a sample of the signal values over a one-week period. These may not be representative of the signal across all times of the year, but it is helpful to understand diurnal cycles and relative signal periods during a randomly selected period.
    • - -
  • Box and Whiskers of Signal Distribution: This plot shows a "box and whiskers" representation of the distribution of signal values. Outliers are shown beyond the distribution when present.
    • - -
  • Heatmap of Mean Value by Month and Hour: This plot shows the average signal value by month and hour in a heatmap. Darker values represent lower average values during that month and hour, while lighter values represent higher average values.
    • -
- -{{ plot_sample_moers }} -{{ plot_distribution_moers }} -{{ plot_region_heatmaps }} -{% endif %} - -{% if show_fuel_mix %} -

Marginal Fuel Mix Comparisons

-

Comparing the marginal fuel mix produced by different models before a carbon intensity or damages rate has been applied allows for us to see why signal values have changed. For instance, differences in training data may lead to different marginal fuel mixes being identified (e.g. coal retirements between model training periods).

- -{{ plot_sample_fuel_mix }} -{% endif %} - -{% if show_forecasts %} -

Forecast Performance

-

We assess the overall performance of our forecasts using a few main metrics, described below.

- -
    -
  • Normalized Mean Absolute Error (Norm MAE): Mean Absolute Error (MAE) is the average of absolute forecast errors. This represents the magnitude of error to expect from the forecast on average. A model with an MAE closest to 0 is ideal. Norm MAE is MAE divided by the average of the true values. Normalizing MAE allows us to compare models across different datasets. It is expressed as a percentage (%) of the average true value over the validation time period.
    • - -
  • Rank Correlation: A number between -1 and 1 that quantifies the degree to which the rank ordering of two datasets are related. A rank correlation of 1 means the forecast predictions are rank ordered perfectly compared to the true values, making the forecast perfectly effective for time optimization use cases. For many use cases of our forecast models (such as AER), it is more important that predictions are correctly rank ordered within a forecast window, rather than each forecast being accurate in terms of absolute values.
    • - -
  • AER CO2 Savings: This metric represents the impact in lbs CO2/MWh of using the forecast model for energy usage optimization. We compare these CO2 savings against the "Max Potential" case (i.e., the case of using perfect forecasts). In this notebook, we model three different popular AER scenarios: -
      -
    • EV-night: Charging an electric vehicle for 3 hours out of a 12-hour overnight window starting at 19:00 local time.
      • -
    • EV-day: Charging an electric vehicle for 2 hours out of an 8-hour daytime window starting at 09:00 local time.
      • -
    • Thermostat: Optimizing thermostat usage in continual 1-hour windows throughout a 24-hour period, only actively using energy for half of the time.
      • -
    -
    • -
- -{{ plot_norm_mae }} -{{ plot_rank_corr }} -{{ plot_impact_forecast_metrics }} -{% endif %} -
+
- +
+ {# Define a reusable macro for tab containers #} + {% macro tab_container(group_name, data_dict, title=None, description=None) %} + {% if title %}

{{ title }}

{% endif %} + {% if description %}

{{ description }}

{% endif %} + +
+
+ {% for tab_title in data_dict.keys() %} +
{{ tab_title }}
+ {% endfor %} +
+ + {% for tab_title, content_html in data_dict.items() %} +
+ {{ content_html|safe }} +
+ {% endfor %} +
+ {% endmacro %} + + {# Historical Signal Data Section #} + {% if show_signal %} +

Analysis of Historical Signal Data

+

We assess the distribution of signal values using metrics plotted below.

+ + {{ tab_container( + "plot_sample_moers", + plot_sample_moers, + "1) Plot of Signal Values Over Time", + "The first thing we look at when examining a new model is a plot of the signal over time, compared against other models for the same region and time period if applicable. Things we are looking for in this plot include: Is there a consistent diurnal pattern (e.g. higher or lower during the day/night)? What is the magintude of the range of values across the day? Does the signal change across seasons as we would expect (e.g. weather often affects demand and renewable generation). You can pan left and right on the plots to see the full range of data." + ) }} - + {{ tab_container( + "plot_distribution_moers", + plot_distribution_moers, + "2) Distribution of Historical Signal", + "A box and whiskers plot is helpful to sucintly examine the entire distribution of signal values. When studying this plot, look for how the median and interquartile range changes between model values. A dramatic shift could be caused by changes to the underlying grid (e.g. coal retirements), or new modelling features (e.g. considering curtailment). Any large changes should be explainable by the modeler." + ) }} + + {{ tab_container( + "plot_heatmaps", + plot_heatmaps, + "3) Heatmaps of Signal Values Over Time", + "These heatmaps provide a succcinct way to look for diurnal and seasonal trends in signal values. To achieve this purpose, the color values are normalized for each model seperately to make the rank order differences more apparent. Generally, we do not expect significant differences in the relative diurnal rank ordering of models without an explainable cause, such as changes to the power system or new modelling features." + ) }} + + {{ tab_container( + "plot_max_impact_potential", + plot_max_impact_potential, + "4) Maximum Achivable Impact Potential of Signal Values", + "Simulations are run for typical customer patterns including charging an EV during the day (3 hours in a 12 hour window starting at 8 AM), the night (2 hours in a 8 hour window starting at 9 PM), and running a thermostat (running for 30 out of 60 minutes throughout the day). Each of these metrics could capture a different source of impact (e.g. solar curtailment may only be accessible to EV day charging). Inspecting these values helps us understand the impact potential of implementing AER with different model versions." + ) }} + {% endif %} + + {# Fuel Mix Section #} + {% if show_fuel_mix %} +

Marginal Fuel Mix Comparisons

+

Comparing the marginal fuel mix produced by different models before a carbon intensity or damages rate has been applied allows for us to see why signal values have changed.

+ + {{ tab_container( + "plot_sample_fuel_mix", + plot_sample_fuel_mix, + "1) Plot of Marginal Fuel Mix Over Time", + "Most models allow for multiple fuel types to be marginal at a given time. Each fuel type is asigned a percentage of the total margianl fuel mix, and these will sum to 100%. Changes in signal value are primarily caused by differences in the marginal fuel mix, but can be affected by other things (such as modeled differences in carbon intensity or damages over time). In addition to fuel types like natural gas, coal, and oil being marginal, interchange can also be a marginal fuel type. Interchange can represent either imports or exports. This plot shows a time series of the marignal fuel mix. It is helpful to look for diurnal and seasonal patterns, along with differences between models. Users can pan to examine different time periods." + ) }} + + {{ tab_container( + "plot_fuelmix_heatmap", + plot_fuelmix_heatmap, + "2) Heatmaps of Marginal Fuel Mix Over Time", + "These heatmaps show the average percentage of the marginal fuel mix for a given fuel type by month and hour. This plot is helpful for examining diurnal and seasonal trends in the marginal fuelmix that can be impacting the signal value. It is also useful to understand which hours of the day curtailment is occuring, indicated by the carbon_free fuel type which may be present." + ) }} + {% endif %} + + {# Forecast Section #} + {% if show_forecast %} +

Forecast Performance

+

Our modeled signal values produce real-time or historical outputs. To achieve AER impact while guaranting an acceptable charge rate / duty factor it is necessary to use a forecast to schedule electricity consumption or charging ahead of time. Our forecast models use traditional machine learning techniques to predict what the signal value will be ahead of time. Our forecast models tend to perform worse at further time horizons from the time they are instatiated. As such, we examine the performance of our forecast models at predicting the signal value over a variety of time horizons.

+ + {{ tab_container( + "plot_norm_mae", + plot_norm_mae, + "1) Normalized Mean Absolute Error by Horizon", + "MAE helps us understand the degree of error in our forecasts. Normalized MAE is a more useful metric because it accounts for the magnitude of the signal values. This metric is reported by varying horizon hours, which are the temporal distance from the time the forecast was produced (generated_at). We expect for models to perform better at lower horizons." + ) }} + + {{ tab_container( + "plot_rank_corr", + plot_rank_corr, + "2) Rank Correlation by Horizon", + "Rank correlation quantifies the degree to which the rank ordering of two datasets are related. We expect for models to perform better at lower horizons." + ) }} + + {{ tab_container( + "plot_impact_forecast_metrics", + plot_impact_forecast_metrics, + "3) Simulation of AER Impact using Forecasts", + "Simulations are run for typical customer patterns including charging an EV during the day (3 hours in a 12 hour window starting at 8 AM), the night (2 hours in a 8 hour window starting at 9 PM), and running a thermostat (running for 30 out of 60 minutes throughout the day). Each of these metrics could capture a different source of impact (e.g. solar curtailment may only be accessible to EV day charging). These metrics assume that the modeled values are accurate, and only quantify the degree of error caused by the forecast model." + ) }} + {% endif %} + + {# Footer Section #} +

About this Report

+ +

This report was generated using the following inputs:

+ 
{{ report_input_dict }}
+ +

The following model metadata was collected when requesting data to generate this report using the WattTime SDK:

+ 
{{ collected_model_meta }}
+
+ + \ No newline at end of file From 3c341629e6e233dff30e35f0ded080ab6e7f8cef Mon Sep 17 00:00:00 2001 From: samkoebrich Date: Thu, 6 Mar 2025 10:42:47 -0700 Subject: [PATCH 23/85] add collected_model_meta prooperty and access last_returned_meta better in DataHandler --- .../get_wt_api_forecast_evaluation_data.py | 20 ++++++++++++++----- 1 file changed, 15 insertions(+), 5 deletions(-) diff --git a/watttime/evaluation/get_wt_api_forecast_evaluation_data.py b/watttime/evaluation/get_wt_api_forecast_evaluation_data.py index 50a9775b..6be4989c 100644 --- a/watttime/evaluation/get_wt_api_forecast_evaluation_data.py +++ b/watttime/evaluation/get_wt_api_forecast_evaluation_data.py @@ -109,7 +109,7 @@ def moers(self) -> pd.DataFrame: ) self.returned_meta = self.wt_hist._last_request_meta - self.returned_hist_model_date = self.wt_hist._last_request_meta["model"]["date"] + self.returned_hist_model_date = self.wt_hist._last_request_meta.get("model", {}).get("date", None) return moers @cached_property @@ -131,9 +131,7 @@ def forecasts(self) -> pd.DataFrame: ).dt.total_seconds() / 60 forecasts.rename({"value": "predicted_value"}, axis="columns", inplace=True) self.returned_meta = self.wt_forecast._last_request_meta - self.returned_forecast_model_date = self.wt_forecast._last_request_meta[ - "model" - ]["date"] + self.returned_forecast_model_date = self.wt_forecast._last_request_meta.get("model", {}).get("date", None) return forecasts @cached_property @@ -150,7 +148,7 @@ def fuel_mix(self) -> pd.DataFrame: ) self.returned_meta = self.wt_fuel_mix._last_request_meta - # self.returned_fuel_mix_model_date = self.wt_fuel_mix._last_request_meta["model"]["date"] + self.returned_fuel_mix_model_date = self.wt_fuel_mix._last_request_meta.get("model", {}).get("date", None) return fm def moer_v_fuel_mix(self): @@ -237,3 +235,15 @@ def __init__( def yield_datahandlers(self) -> AnalysisDataHandler: for dh in self.data_handlers: yield dh + + @property + def collected_model_meta(self): + return [ + { + "region": dh.region, + "requested_model_date": dh.model_date, + "returned_hist_model_date": getattr(dh, "returned_hist_model_date", None), + "returned_fuel_mix_model_date": getattr(dh, "returned_fuel_mix_model_date", None), + "returned_forecast_model_date": getattr(dh, "returned_forecast_model_date", None), + } for dh in self.data_handlers + ] From ec8ff29ad0023502eba6f7691cbea3d630ed0098 Mon Sep 17 00:00:00 2001 From: samkoebrich Date: Sun, 9 Mar 2025 22:22:59 -0600 Subject: [PATCH 24/85] plot cleanup (lengends, colorscales, formatting) --- watttime/evaluation/report.py | 235 ++++++++---------- watttime/evaluation/report_card_template.html | 4 +- 2 files changed, 103 insertions(+), 136 deletions(-) diff --git a/watttime/evaluation/report.py b/watttime/evaluation/report.py index c1d60d77..59aae90d 100644 --- a/watttime/evaluation/report.py +++ b/watttime/evaluation/report.py @@ -84,24 +84,12 @@ def get_random_overlapping_period(dfs, max_period="30D", resample_freq="1H"): else: raise ValueError("No common overlap found among the provided DataFrames.") - # Otherwise, select a random period within the overlapping range - max_attempts = 10 - attempts = 0 - while attempts < max_attempts: - random_start_time = pd.Timestamp( - np.random.choice( - pd.date_range(start_overlap, end_overlap - max_timedelta, freq=resample_freq) - ) - ) - random_end_time = random_start_time + max_timedelta - overlap_range = pd.date_range( - start=random_start_time, end=random_end_time, freq=resample_freq - ) - # Check if all indices exist in both DataFrames - if all(overlap_range.isin(df.index).all() for df in dfs): - return overlap_range - attempts += 1 - raise ValueError(f"No common overlap found after {max_attempts} attempts.") + # Otherwise, return the first overlap range that satisfies the max_period + overlap_range = pd.date_range( + start=start_overlap, end=start_overlap + max_timedelta, freq=resample_freq + ) + assert all(df.index.isin(overlap_range).all() for df in dfs) + return overlap_range def plot_sample_moers(jobs: List[AnalysisDataHandler], max_sample_period="30D") -> Dict[str, go.Figure]: @@ -212,48 +200,33 @@ def plot_distribution_moers(jobs: List[AnalysisDataHandler]) -> Dict[str, go.Fig return figs -def plot_heatmaps(jobs: List[AnalysisDataHandler], colorscale="RdBu_r") -> Dict[str, go.Figure]: +def plot_heatmaps(jobs: List[AnalysisDataHandler], colorscale="Oranges") -> Dict[str, go.Figure]: """ - Generate vertically stacked heatmaps for each region in the region_list. - + Generate vertically stacked heatmaps for each region, with each heatmap having its own colorscale. Args: - jobs (list): List of AnalysisDataHandler objects with a .moers atribute. - + jobs (list): List of AnalysisDataHandler objects with a .moers attribute. Returns: - plotly.graph_objects.Figure: Figure containing stacked heatmaps. + Dict[str, go.Figure]: A dictionary mapping region abbreviations to their heatmap figures. """ - unique_regions = set([j.region for j in jobs]) figs = {} - # share upper and lower bounds between plots - # zmin = 1e6 - # zmax = 0 - # for job in jobs: - # jmin = job.moers["signal_value"].quantile(0.01) - # jmax = job.moers["signal_value"].quantile(0.99) - - # if jmin < zmin: - # zmin = jmin - # if jmax > zmax: - # zmax = jmax - - for i, region_abbrev in enumerate(unique_regions, start=1): + for region_abbrev in unique_regions: region_abbrev = region_abbrev.upper() - _jobs = [j for j in jobs if j.region == region_abbrev] + region_jobs = [j for j in jobs if j.region.upper() == region_abbrev] - # Initialize a subplot with one row per region + # Initialize a subplot with one row per job fig = sp.make_subplots( - rows=len(_jobs), + rows=len(region_jobs), cols=1, shared_xaxes=True, - subplot_titles=[j.model_date for j in _jobs], - vertical_spacing=0.2, + subplot_titles=[j.model_date for j in region_jobs], + vertical_spacing=0.1, # Reduced spacing for better alignment ) - - for j, _job in enumerate(_jobs, start=1): - heat = _job.moers.assign( - month=_job.moers.index.month, hour=_job.moers.index.hour + + for j, job in enumerate(region_jobs, start=1): + heat = job.moers.assign( + month=job.moers.index.month, hour=job.moers.index.hour ) heat = heat.dropna(subset=["signal_value"]) heat = ( @@ -261,34 +234,43 @@ def plot_heatmaps(jobs: List[AnalysisDataHandler], colorscale="RdBu_r") -> Dict[ .mean() .unstack(fill_value=np.nan) ) - heat.index = [ - calendar.month_abbr[m] for m in heat.index - ] # Map month numbers to month abbreviations - - # Create a heatmap trace + heat.index = [calendar.month_abbr[m] for m in heat.index] + heatmap_trace = go.Heatmap( z=heat.values, - x=heat.columns, # Hours - y=heat.index, # Month abbreviations - # zmin=zmin, - # zmax=zmax, + x=heat.columns, + y=heat.index, colorscale=colorscale, - colorbar_title=_job.signal_type, - showscale=True, # Show color scale only on the first heatmap + colorbar_title=None, + showscale=True, + colorbar=dict( + yanchor="middle", + y=0.5, + len=0.9, + thickness=20, + title=dict(side="right") + ), ) - - # Add the heatmap trace to the subplot + fig.add_trace(heatmap_trace, row=j, col=1) - - # Update layout + + fig.update_yaxes(title_text="Month", row=j, col=1) + if j == len(region_jobs): # Only add x-axis title to bottom subplot + fig.update_xaxes(title_text="Hour of Day", row=j, col=1) + fig.update_layout( - height=300 * len(_jobs), # Adjust the height regionsed on the number of regions - xaxis_title="Hour of Day", - yaxis_title="Month", - xaxis=dict(title="Hour of Day", tickmode="linear"), + height=250 * len(region_jobs), # Adjust height per subplot + margin=dict(r=100), # Add right margin for colorbars + # title=f"{region_abbrev} Heat Maps" ) + + # Update colorbar positions to align with subplots + for i, trace in enumerate(fig.data): + trace.colorbar.y = 1 - (2*i + 1)/(2*len(region_jobs)) + trace.colorbar.len = 1/len(region_jobs) * 0.9 + figs[region_abbrev] = fig - + return figs @@ -328,6 +310,7 @@ def calc_rank_compare_metrics( window_starts=None, pred_col="predicted_value", truth_col="signal_value", + load_kw=1000, ): """ Calculate rank_compare metrics: co2_reduction, co2_potential, co2_pct. @@ -385,6 +368,10 @@ def assign_rolling_window(row): # Drop rows outside any window df = df.dropna(subset=["window"]) + + # Normalize units from 1 MWh to actual load + df[pred_col]*= (load_kw / 1000) + df[truth_col]*= (load_kw / 1000) df["y_rank"] = df.groupby(["window", "generated_at"])[truth_col].rank( method="first" @@ -415,12 +402,6 @@ def assign_rolling_window(row): # Calculate savings: co2_reduction, co2_potential co2_reduction = (y_avg_total - y_actual_total).mean() # actual savings co2_potential = (y_avg_total - y_best_total).mean() # ideal savings - # co2_pct = (co2_reduction / co2_potential) * 100. # actual / ideal; % savings realized - - # if co2_potential != 0: - # co2_pct = (co2_reduction / co2_potential) * 100. - # else: - # co2_pct = 100. if co2_reduction == 0 else 0 # Handle the edge case when moers are flat return { "co2_reduction": round(co2_reduction, 1), @@ -549,16 +530,19 @@ def plot_rank_corr(jobs: List[AnalysisDataHandler], horizons_hr=[1, 6, 12, 18, 2 "charge_mins": 3 * 60, "window_mins": 12 * 60, "window_starts": ["19:00"], + "load_kw": 19 }, "EV-day": { "charge_mins": 2 * 60, "window_mins": 8 * 60, "window_starts": ["09:00"], + "load_kw": 19 }, "Thermostat": { "charge_mins": 30, "window_mins": 60, "window_starts": None, + "load_kw": 3 # typical AC }, } @@ -656,7 +640,7 @@ def plot_impact_forecast_metrics( return figs -def plot_sample_fuel_mix(jobs: List[AnalysisDataHandler], max_sample_period="30D") -> Dict[str, go.Figure]: +def plot_sample_fuelmix(jobs: List[AnalysisDataHandler], max_sample_period="30D") -> Dict[str, go.Figure]: figs = {} unique_regions = set([j.region for j in jobs]) @@ -718,7 +702,7 @@ def plot_sample_fuel_mix(jobs: List[AnalysisDataHandler], max_sample_period="30D return figs -def calc_max_potential(df, charge_mins, window_mins, window_starts=None, truth_col="signal_value"): +def calc_max_potential(df, charge_mins, window_mins, window_starts=None, truth_col="signal_value", load_kw=1000): """Predict the maximum potential CO2 Savings, without any forecast to estimate the upper limit of impact from a given signal value.""" if window_starts: @@ -753,6 +737,8 @@ def assign_rolling_window(row): df = df.assign( y_rank=df.groupby('window')[truth_col].rank(method='first') ) + + df[truth_col] *= (load_kw / 1000) y_best_total = ( df[df["y_rank"] <= (charge_mins / 5)] .groupby("window")[truth_col] @@ -781,7 +767,7 @@ def plot_max_impact_potential(jobs: List[AnalysisDataHandler], scenarios: List[s } for s in scenarios ] - + _metrics = pd.DataFrame(_metrics) # Add the 'co2_potential' bar trace @@ -807,8 +793,18 @@ def create_pivot_table(df, column): month=df.index.month, hour=df.index.hour ) - grouped = df.groupby(['month', 'hour'])[column].mean().reset_index() - pivot = grouped.pivot(index='month', columns='hour', values=column) + + if column not in df.columns: + # Create a zero-filled DataFrame with the expected shape + pivot = pd.DataFrame( + 0, + index=range(1, 13), # Months 1-12 + columns=range(24) # Hours 0-23 + ) + else: + grouped = df.groupby(['month', 'hour'])[column].mean().reset_index() + pivot = grouped.pivot(index='month', columns='hour', values=column) + return pivot def create_buttons(column_names, num_subplots): @@ -829,12 +825,13 @@ def create_buttons(column_names, num_subplots): buttons.append(button) return buttons - def make_alpha_gradient(hex_color, steps=20): - """Generate a Plotly colorscale with an alpha gradient from 0 to 1.""" - rgba_colors = [ - f'rgba{pc.hex_to_rgb(hex_color) + (alpha,)}' - for alpha in [i / (steps - 1) for i in range(steps)] - ] + def make_alpha_gradient(hex_color, steps=10): + rgba_colors = [] + + for i in range(steps): + alpha = 0 if i == 0 else min(1, (i / (steps / 1.25))) + rgba_colors.append(f'rgba{pc.hex_to_rgb(hex_color) + (alpha,)}') + return [[i / (steps - 1), rgba] for i, rgba in enumerate(rgba_colors)] unique_regions = set([j.region for j in jobs]) @@ -862,57 +859,26 @@ def make_alpha_gradient(hex_color, steps=20): all_columns_list = sorted(list(all_columns)) # Sort to ensure consistent order - # Count the total number of traces we'll add - total_traces = len(region_jobs) * len(all_columns_list) - - # Track which trace index we're on trace_idx = 0 # Add traces for each job for job_idx, job in enumerate(region_jobs, start=1): for fuel_idx, fuel in enumerate(all_columns_list): - # Check if this fuel exists in the current job - if fuel in job.fuel_mix.columns: - pivot = create_pivot_table(job.fuel_mix, fuel) - - # Determine visibility - visible = (fuel_idx == 0) # Only the first fuel type is visible by default - - # Add the heatmap trace for this fuel - try: - fuel_color = fuel_cp[fuel] # Assuming fuel_cp is a color mapping dictionary - except (NameError, KeyError): - # If fuel_cp doesn't exist or doesn't have this fuel, use a default color - import plotly.express as px - colors = px.colors.qualitative.Plotly - fuel_color = colors[fuel_idx % len(colors)] - - fig.add_trace( - go.Heatmap( - z=pivot.values, - x=pivot.columns, - y=pivot.index, - colorscale=make_alpha_gradient(fuel_color), - name=f"{fuel} ({job.model_date})", - visible=visible, - showscale=(job_idx == 1), # Only show colorscale for the first job - zmin=0, zmax=1, - ), - row=job_idx, col=1 - ) - else: - # Add an empty trace to maintain the correct index structure - fig.add_trace( - go.Heatmap( - z=[[0]], - x=[0], - y=[0], - visible=False, - showscale=False, - name=f"{fuel} ({job.model_date}) - missing" - ), - row=job_idx, col=1 - ) + pivot = create_pivot_table(job.fuel_mix, fuel) + + fig.add_trace( + go.Heatmap( + z=pivot.values, + x=pivot.columns, + y=pivot.index, + colorscale=make_alpha_gradient(fuel_cp[fuel], steps=10), + name=f"{fuel} ({job.model_date})", + visible=(fuel_idx == 0), + showscale=False, # Only show colorscale for the first job + zmin=0, zmax=1, + ), + row=job_idx, col=1 + ) trace_idx += 1 @@ -930,12 +896,13 @@ def make_alpha_gradient(hex_color, steps=20): type="buttons", direction="right", active=0, - x=0.1, - y=1.15, + x=0, + xanchor="left", + y=-0.15, buttons=buttons ) ], - height=300 * len(region_jobs), # Adjust height based on number of jobs + height=(300 * len(region_jobs)) + 50, # Adjust height based on number of jobs xaxis_title="Hour of Day", ) @@ -1030,7 +997,7 @@ def parse_report_command_line_args(sys_args): PLOTS = { "signal": [plot_sample_moers, plot_distribution_moers, plot_heatmaps, plot_max_impact_potential], - "fuel_mix": [plot_sample_fuel_mix, plot_fuelmix_heatmap], + "fuel_mix": [plot_sample_fuelmix, plot_fuelmix_heatmap], "forecast": [plot_norm_mae, plot_rank_corr, plot_impact_forecast_metrics], } diff --git a/watttime/evaluation/report_card_template.html b/watttime/evaluation/report_card_template.html index f3d5b65d..1d002e53 100644 --- a/watttime/evaluation/report_card_template.html +++ b/watttime/evaluation/report_card_template.html @@ -295,8 +295,8 @@

Marginal Fuel Mix Comparisons

Comparing the marginal fuel mix produced by different models before a carbon intensity or damages rate has been applied allows for us to see why signal values have changed.

{{ tab_container( - "plot_sample_fuel_mix", - plot_sample_fuel_mix, + "plot_sample_fuelmix", + plot_sample_fuelmix, "1) Plot of Marginal Fuel Mix Over Time", "Most models allow for multiple fuel types to be marginal at a given time. Each fuel type is asigned a percentage of the total margianl fuel mix, and these will sum to 100%. Changes in signal value are primarily caused by differences in the marginal fuel mix, but can be affected by other things (such as modeled differences in carbon intensity or damages over time). In addition to fuel types like natural gas, coal, and oil being marginal, interchange can also be a marginal fuel type. Interchange can represent either imports or exports. This plot shows a time series of the marignal fuel mix. It is helpful to look for diurnal and seasonal patterns, along with differences between models. Users can pan to examine different time periods." ) }} From a2e6ee10641f3e5e6f5bfda7e27c7cecff31ee7f Mon Sep 17 00:00:00 2001 From: samkoebrich Date: Sun, 9 Mar 2025 22:43:53 -0600 Subject: [PATCH 25/85] localize_tz in get_wt_api_forecast_evaluation_data --- setup.py | 2 +- .../get_wt_api_forecast_evaluation_data.py | 25 ++++++++++++++++++- 2 files changed, 25 insertions(+), 2 deletions(-) diff --git a/setup.py b/setup.py index 5fa3ed7b..7c2c622c 100644 --- a/setup.py +++ b/setup.py @@ -9,6 +9,6 @@ packages=["watttime", "watttime.evaluation"], python_requires=">=3.8", install_requires=["requests", "pandas>1.0.0", "holidays", "python-dateutil"], - extras_require={"report": ["jinja2", "plotly", "scipy"]}, + extras_require={"report": ["jinja2", "plotly", "scipy", "shapely", "timezonefinder"]}, scripts=["watttime/report.py"], ) diff --git a/watttime/evaluation/get_wt_api_forecast_evaluation_data.py b/watttime/evaluation/get_wt_api_forecast_evaluation_data.py index 6be4989c..44171973 100644 --- a/watttime/evaluation/get_wt_api_forecast_evaluation_data.py +++ b/watttime/evaluation/get_wt_api_forecast_evaluation_data.py @@ -11,6 +11,8 @@ from dataclasses import dataclass import pandas as pd +from shapely.geometry import shape +from timezonefinder import TimezoneFinder from watttime import api from watttime import api_do_not_commit @@ -56,6 +58,7 @@ class AnalysisDataHandler: forecast_max_horizon: int = 60 * 24 forecast_sample_seed: int = 42 signal_type: str = "co2_moer" + localize_tz: bool = True model_date: Optional[str] = ( None # if None, will default to latest provided through API ) @@ -90,6 +93,15 @@ def __post_init__(self): k = max(k, 1) random.seed(self.forecast_sample_seed) self.sample_days = random.sample(self.eval_days, k) + + # get tz by looking up centroid of region. + # Note that some regions may contain multiple timezones + if self.localize_tz: + wt_maps = api.WattTimeMaps() + all_maps = wt_maps.get_maps_json() + region = {f['properties']['region']: f['geometry'] for f in all_maps['features']}[self.region] + centroid = shape(region).centroid + self.tz = TimezoneFinder().certain_timezone_at(lat=centroid.y, lng=centroid.x) @cached_property def moers(self) -> pd.DataFrame: @@ -108,6 +120,9 @@ def moers(self) -> pd.DataFrame: .set_index("point_time", drop=True) ) + if self.localize_tz: + moers.index = moers.index.tz_convert(self.tz) + self.returned_meta = self.wt_hist._last_request_meta self.returned_hist_model_date = self.wt_hist._last_request_meta.get("model", {}).get("date", None) return moers @@ -130,6 +145,11 @@ def forecasts(self) -> pd.DataFrame: forecasts["point_time"] - forecasts["generated_at"] ).dt.total_seconds() / 60 forecasts.rename({"value": "predicted_value"}, axis="columns", inplace=True) + + if self.localize_tz: + forecasts['generated_at'] = forecasts['generated_at'].dt.tz_convert(self.tz) + forecasts["point_time"] = forecasts["point_time"].dt.tz_convert(self.tz) + self.returned_meta = self.wt_forecast._last_request_meta self.returned_forecast_model_date = self.wt_forecast._last_request_meta.get("model", {}).get("date", None) return forecasts @@ -146,7 +166,10 @@ def fuel_mix(self) -> pd.DataFrame: signal_type="marginal_fuel_mix", model=self.model_date, ) - + + if self.localize_tz: + fm.index = fm.index.tz_convert(self.tz) + self.returned_meta = self.wt_fuel_mix._last_request_meta self.returned_fuel_mix_model_date = self.wt_fuel_mix._last_request_meta.get("model", {}).get("date", None) return fm From 72b3deccfe36376e6bd76b1534f5d4885974e745 Mon Sep 17 00:00:00 2001 From: samkoebrich Date: Mon, 10 Mar 2025 09:58:05 -0600 Subject: [PATCH 26/85] ix name cleanup --- watttime/evaluation/report.py | 107 +++++++++++++++++----------------- 1 file changed, 52 insertions(+), 55 deletions(-) diff --git a/watttime/evaluation/report.py b/watttime/evaluation/report.py index 59aae90d..399ea2c6 100644 --- a/watttime/evaluation/report.py +++ b/watttime/evaluation/report.py @@ -105,25 +105,25 @@ def plot_sample_moers(jobs: List[AnalysisDataHandler], max_sample_period="30D") figs = {} unique_regions = set([j.region for j in jobs]) - for i, region_abbrev in enumerate(unique_regions, start=1): + for region_abbrev in unique_regions: fig = go.Figure() region_abbrev = region_abbrev.upper() - _jobs = [j for j in jobs if j.region == region_abbrev] + region_models = [j for j in jobs if j.region == region_abbrev] times = get_random_overlapping_period( - [j.moers for j in _jobs], max_sample_period, + [j.moers for j in region_models], max_sample_period, ) - for _job in _jobs: + for model_job in region_models: fig.add_trace( go.Scatter( x=times, - y=_job.moers.loc[times]['signal_value'], + y=model_job.moers.loc[times]['signal_value'], mode="lines", - name=_job.model_date, + name=model_job.model_date, line=dict(width=2), showlegend=True, ) @@ -165,17 +165,17 @@ def plot_distribution_moers(jobs: List[AnalysisDataHandler]) -> Dict[str, go.Fig unique_regions = set([j.region for j in jobs]) figs = {} - for i, region_abbrev in enumerate(unique_regions, start=1): + for region_abbrev in unique_regions: fig = go.Figure() region_abbrev = region_abbrev.upper() - _jobs = [j for j in jobs if j.region == region_abbrev] - for _job in _jobs: + region_models = [j for j in jobs if j.region == region_abbrev] + for model_job in region_models: # Add a histogram trace for the new MOER distribution fig.add_trace( go.Box( - x=_job.moers["signal_value"].dropna(), - name=_job.model_date, + x=model_job.moers["signal_value"].dropna(), + name=model_job.model_date, opacity=0.6, ) ) @@ -265,8 +265,8 @@ def plot_heatmaps(jobs: List[AnalysisDataHandler], colorscale="Oranges") -> Dict ) # Update colorbar positions to align with subplots - for i, trace in enumerate(fig.data): - trace.colorbar.y = 1 - (2*i + 1)/(2*len(region_jobs)) + for model_ix, trace in enumerate(fig.data): + trace.colorbar.y = 1 - (2*model_ix + 1)/(2*len(region_jobs)) trace.colorbar.len = 1/len(region_jobs) * 0.9 figs[region_abbrev] = fig @@ -420,15 +420,15 @@ def plot_norm_mae(jobs: List[AnalysisDataHandler], horizons_hr=[1, 6, 12, 18, 24 figs = {} # Iterate through each region and create a bar plot - for i, region_abbrev in enumerate(unique_regions, start=1): + for region_abbrev in unique_regions: region_abbrev = region_abbrev.upper() fig = go.Figure() - _jobs = [j for j in jobs if j.region == region_abbrev] + region_models = [j for j in jobs if j.region == region_abbrev] x_values = [f"{h}hr" for h in horizons_hr] # Add horizon labels - for _job in _jobs: + for model_job in region_models: y_values = [ - calc_norm_mae(_job.forecasts_v_moers, (h * 60) - 5) for h in horizons_hr + calc_norm_mae(model_job.forecasts_v_moers, (h * 60) - 5) for h in horizons_hr ] y_max = max(y_values + [y_max]) y_min = min(y_values + [y_min]) @@ -437,7 +437,7 @@ def plot_norm_mae(jobs: List[AnalysisDataHandler], horizons_hr=[1, 6, 12, 18, 24 go.Bar( x=x_values, y=y_values, - name=_job.model_date, + name=model_job.model_date, text=[f"{y:.1f}%" for y in y_values], # Add text labels on bars textposition="outside", ) @@ -480,17 +480,17 @@ def plot_rank_corr(jobs: List[AnalysisDataHandler], horizons_hr=[1, 6, 12, 18, 2 figs = {} # Iterate through each region and create a line plot - for i, region_abbrev in enumerate(unique_regions, start=1): + for region_abbrev in unique_regions: fig = go.Figure() region_abbrev = region_abbrev.upper() - _jobs = [j for j in jobs if j.region == region_abbrev] + region_models = [j for j in jobs if j.region == region_abbrev] # Extract data for the line plot x_values = [f"{h}hr" for h in horizons_hr] # Add horizon labels - for _job in _jobs: + for model_job in region_models: y_values = [ - calc_rank_corr(_job.forecasts_v_moers, (h * 60) - 5) + calc_rank_corr(model_job.forecasts_v_moers, (h * 60) - 5) for h in horizons_hr ] y_max = max(y_values + [y_max]) @@ -500,7 +500,7 @@ def plot_rank_corr(jobs: List[AnalysisDataHandler], horizons_hr=[1, 6, 12, 18, 2 go.Bar( x=x_values, y=y_values, - name=_job.model_date, + name=model_job.model_date, text=[f"{y:.3f}" for y in y_values], # Add text labels on bars textposition="outside", ) @@ -555,23 +555,22 @@ def plot_impact_forecast_metrics( figs = {} y_max = 0 - for i, region_abbrev in enumerate(unique_regions, start=1): + for region_abbrev in unique_regions: - _jobs = [j for j in jobs if j.region == region_abbrev] + region_models = [j for j in jobs if j.region == region_abbrev] fig = sp.make_subplots( - rows=len(_jobs), + rows=len(region_models), cols=1, shared_xaxes=True, subplot_titles=[j.model_date for j in jobs], vertical_spacing=0.2, ) - # Iterate through each region and create bar plots - for j, _job in enumerate(_jobs, start=1): + for model_ix, model_job in enumerate(region_models, start=1): _metrics = [ { - **calc_rank_compare_metrics(_job.forecasts_v_moers, **AER_SCENARIOS[s]), + **calc_rank_compare_metrics(model_job.forecasts_v_moers, **AER_SCENARIOS[s]), "scenario": s, } for s in scenarios @@ -579,7 +578,6 @@ def plot_impact_forecast_metrics( _metrics = pd.DataFrame(_metrics) - # Add the 'co2_potential' bar trace fig.add_trace( go.Bar( x=_metrics["scenario"], @@ -590,11 +588,10 @@ def plot_impact_forecast_metrics( ), # Light gray for potential hovertemplate="%{x}: %{y:.1f} lbs CO2/MWh", ), - row=j, + row=model_ix, col=1, ) - # Add the 'co2_reduction' bar trace fig.add_trace( go.Bar( x=_metrics["scenario"], @@ -610,7 +607,7 @@ def plot_impact_forecast_metrics( marker=dict(color="rgba(0, 128, 0, 0.8)"), # Green for reduction hovertemplate="%{x}: %{y:.1f} lbs CO2/MWh", ), - row=j, + row=model_ix, col=1, ) @@ -618,7 +615,7 @@ def plot_impact_forecast_metrics( # Update layout fig.update_layout( - height=300 * len(_jobs), + height=300 * len(region_models), xaxis_title="Scenario", yaxis_title="CO2 Savings (lbs/MWh)", barmode="group", # Grouped bars (side by side) @@ -627,14 +624,14 @@ def plot_impact_forecast_metrics( ) # Update axes for all subplots - for i in range(1, len(jobs) + 1): - fig.update_xaxes(title_text="Scenario", row=i, col=1) - fig.update_yaxes(title_text="CO2 Savings (lbs/MWh)", row=i, col=1) + for model_ix in range(1, len(jobs) + 1): + fig.update_xaxes(title_text="Scenario", row=model_ix, col=1) + fig.update_yaxes(title_text="CO2 Savings (lbs/MWh)", row=model_ix, col=1) figs[region_abbrev] = fig + # Set uniform y-axis range for all subplots for region, fig in figs.items(): - # Set uniform y-axis range for all subplots figs[region] = fig.update_yaxes(range=[0, y_max + (0.25 * y_max)]) return figs @@ -645,28 +642,28 @@ def plot_sample_fuelmix(jobs: List[AnalysisDataHandler], max_sample_period="30D" figs = {} unique_regions = set([j.region for j in jobs]) times = get_random_overlapping_period([j.fuel_mix for j in jobs], max_sample_period) - for i, region_abbrev in enumerate(unique_regions, start=1): - _jobs = [j for j in jobs if j.region == region_abbrev] + for region_abbrev in unique_regions: + region_models = [j for j in jobs if j.region == region_abbrev] # Initialize a subplot with one row per region fig = sp.make_subplots( - rows=len(_jobs), + rows=len(region_models), cols=1, shared_xaxes=True, subplot_titles=[j.model_date for j in jobs], vertical_spacing=0.2, ) - for j, _job in enumerate(_jobs, start=1): + for model_ix, model_job in enumerate(region_models, start=1): - stacked_values = _job.fuel_mix.loc[times] + stacked_values = model_job.fuel_mix.loc[times] # Create cumulative values for stacking for k in range(1, len(stacked_values.columns)): stacked_values.iloc[:, k] += stacked_values.iloc[:, k - 1] # Add each fuel type as an area - for k, fuel in enumerate(_job.fuel_mix.columns): + for k, fuel in enumerate(model_job.fuel_mix.columns): fig.add_trace( go.Scatter( x=stacked_values.index, @@ -676,13 +673,13 @@ def plot_sample_fuelmix(jobs: List[AnalysisDataHandler], max_sample_period="30D" name=fuel, fillcolor=fuel_cp[fuel], ), - row=j, + row=model_ix, col=1, ) # Update layout for the figure fig.update_layout( - height=300 * len(_jobs), + height=300 * len(region_models), yaxis=dict( title=f"{jobs[0].signal_type}", fixedrange=True # Disable y-axis panning @@ -753,16 +750,16 @@ def plot_max_impact_potential(jobs: List[AnalysisDataHandler], scenarios: List[s unique_regions = set([j.region for j in jobs]) figs = {} y_max = 0 - for i, region_abbrev in enumerate(unique_regions, start=1): - _jobs = [j for j in jobs if j.region == region_abbrev] + for region_abbrev in unique_regions: + region_models = [j for j in jobs if j.region == region_abbrev] region_abbrev = region_abbrev.upper() fig = go.Figure() - for j, _job in enumerate(_jobs, start=1): + for model_job in region_models: _metrics = [ { - **calc_max_potential(_job.moers, **AER_SCENARIOS[s]), + **calc_max_potential(model_job.moers, **AER_SCENARIOS[s]), "scenario": s, } for s in scenarios @@ -775,7 +772,7 @@ def plot_max_impact_potential(jobs: List[AnalysisDataHandler], scenarios: List[s go.Bar( x=_metrics["scenario"], y=_metrics["co2_potential"], - name=_job.model_date, # Legend only in the first subplot + name=model_job.model_date, # Legend only in the first subplot hovertemplate="%{x}: %{y:.1f} lbs CO2/MWh", ) ) @@ -907,20 +904,20 @@ def make_alpha_gradient(hex_color, steps=10): ) # Update all y-axes to show month names - for i in range(1, len(region_jobs) + 1): + for model_ix in range(1, len(region_jobs) + 1): fig.update_yaxes( title="Month", tickvals=list(month_names.keys()), ticktext=list(month_names.values()), - row=i, col=1 + row=model_ix, col=1 ) # Update all x-axes fig.update_xaxes( - title="Hour of Day" if i == len(region_jobs) else None, # Only show title on bottom plot + title="Hour of Day" if model_ix == len(region_jobs) else None, # Only show title on bottom plot tickmode="linear", dtick=2, # Show every 2 hours - row=i, col=1 + row=model_ix, col=1 ) figs[region_abbrev] = fig From 97dea545d580d73f11d7c67413a58c231674cf1f Mon Sep 17 00:00:00 2001 From: samkoebrich Date: Tue, 11 Mar 2025 11:05:25 -0600 Subject: [PATCH 27/85] rewrite of calc_rank_potential to ensure consistent behavior --- watttime/evaluation/report.py | 197 ++++++++++++++++++---------------- 1 file changed, 107 insertions(+), 90 deletions(-) diff --git a/watttime/evaluation/report.py b/watttime/evaluation/report.py index 399ea2c6..b0a01cdc 100644 --- a/watttime/evaluation/report.py +++ b/watttime/evaluation/report.py @@ -78,17 +78,13 @@ def get_random_overlapping_period(dfs, max_period="30D", resample_freq="1H"): # If the total overlap duration is less than or equal to max_period, return the full range if total_overlap_duration <= max_timedelta: overlap_range = pd.date_range(start=start_overlap, end=end_overlap, freq=resample_freq) - # Check if all indices exist in both DataFrames - if all(df.index.isin(overlap_range).all() for df in dfs): - return overlap_range - else: - raise ValueError("No common overlap found among the provided DataFrames.") # Otherwise, return the first overlap range that satisfies the max_period - overlap_range = pd.date_range( - start=start_overlap, end=start_overlap + max_timedelta, freq=resample_freq - ) - assert all(df.index.isin(overlap_range).all() for df in dfs) + else: + overlap_range = pd.date_range( + start=start_overlap, end=start_overlap + max_timedelta, freq=resample_freq + ) + assert all([all(t in df.index for t in overlap_range) for df in dfs]), "Not all DataFrames contain the overlapping range." return overlap_range @@ -259,7 +255,7 @@ def plot_heatmaps(jobs: List[AnalysisDataHandler], colorscale="Oranges") -> Dict fig.update_xaxes(title_text="Hour of Day", row=j, col=1) fig.update_layout( - height=250 * len(region_jobs), # Adjust height per subplot + height=300 * len(region_jobs), # Adjust height per subplot margin=dict(r=100), # Add right margin for colorbars # title=f"{region_abbrev} Heat Maps" ) @@ -293,7 +289,7 @@ def calc_rank_corr( ): """Returns mean daily Rank Correlation""" - filtered_df = df = df[df["horizon_mins"] <= horizon_mins].dropna() + filtered_df = df[df["horizon_mins"] <= horizon_mins].dropna() corr = filtered_df.groupby("generated_at").apply( lambda group_df: stats.spearmanr( group_df[truth_col], group_df[pred_col] @@ -312,96 +308,109 @@ def calc_rank_compare_metrics( truth_col="signal_value", load_kw=1000, ): - """ - Calculate rank_compare metrics: co2_reduction, co2_potential, co2_pct. - - Parameters: - in_df (pd.DataFrame): DataFrame containing the 'truth' and 'predicted' MOER values. - charge_hours (int): Charge window in minutes. - window_hours (int): Comparison window in minutes. - window_starts (list or None): Start times for windows (e.g., ["09:00"]). - truth_col (str): Column name for the 'truth' MOER values. - pred_col (str): Column name for the 'predicted' MOER values. - - Returns: - dict: co2_reduction, co2_potential, co2_pct metrics. - """ - df = in_df.copy() df.dropna(inplace=True) - # Rank of truth and predicted columns within each window if window_starts: - # Generate window ranges based on start times and duration - unique_dates = df.index.get_level_values( - "generated_at" - ).unique() # Extract unique dates + # Extract unique dates and create window ranges + unique_dates = df.index.get_level_values("generated_at").unique() window_ranges = [] for date in unique_dates: for start_time in window_starts: start = pd.Timestamp(f"{date} {start_time}") end = start + pd.Timedelta(minutes=window_mins) window_ranges.append((start, end)) - - # Assign each row to a window - def assign_window(row): - point_time, generated_at = row.name - for start, end in window_ranges: - if start <= generated_at < end and point_time < end: - return start # Label window by its start time - return pd.NaT - - df["window"] = df.apply(assign_window, axis=1) + + # Convert to DataFrame + window_df = pd.DataFrame(window_ranges, columns=["window_start", "window_end"]) + window_df = window_df.sort_values("window_start") + tz = df.index.get_level_values('generated_at')[0].tz + window_df = window_df.assign(window_start=pd.to_datetime(window_df["window_start"], errors='coerce').dt.tz_convert(tz)) + window_df = window_df.dropna(subset=["window_start"]) + + # Ensure df is sorted by "generated_at" before merging + df = df.sort_index(level="generated_at") + + # Use merge_asof to efficiently assign windows + df = pd.merge_asof( + df.reset_index().sort_values("generated_at"), + window_df, + left_on="generated_at", + right_on="window_start", + direction="backward" + ).set_index(df.index.names) + else: - # Default behavior: use rolling windows - def assign_rolling_window(row): - generated_at, point_time = row.name - window_start = generated_at.floor(f"{window_mins}T") - window_end = window_start + pd.Timedelta(minutes=window_mins) + # Assign each row to a rolling window of `window_mins` based on `generated_at` + df["window_start"] = df.index.get_level_values("generated_at").floor(f"{window_mins}min") + df['window_end'] = df["window_start"] + pd.Timedelta(f"{window_mins} min") - # Only assign window if point_time is within the window range - if point_time < window_end: - return window_start - return pd.NaT + # Filter out rows that do not fall within the valid window + df.loc[(df.index.get_level_values("point_time") >= df["window_end"]) | + (df.index.get_level_values("generated_at") >= df["window_end"]), "window_start"] = pd.NaT + df = df.dropna(subset=["window_start"]) - df["window"] = df.apply(assign_rolling_window, axis=1) - - # Drop rows outside any window - df = df.dropna(subset=["window"]) - - # Normalize units from 1 MWh to actual load - df[pred_col]*= (load_kw / 1000) - df[truth_col]*= (load_kw / 1000) + def simulate_charge(df, sort_col: Literal[truth_col, pred_col], charge_mins: int): + + df['rank'] = df.groupby(["window_start", "generated_at"])[sort_col].rank(ascending=True) + charge_needed = charge_mins // 5 + df = df.assign(charge_status=False) + df = df.sort_index(ascending=True) + for w in df.groupby("window_start"): + _, w_df = w + + # STATEFULLNESS HEURISTIC: point_time must be equal to generate_at + # TODO: really this should be "less then the next generated_at" in the case + # that point_time and generated_at are not available for every 5 mins + w_df = w_df.loc[w_df.index.get_level_values('point_time') == w_df.index.get_level_values('generated_at')] + + # STATEFULLNESS HEURISTIC: Take charging periods iteratively, + # at each step only consider if rank of value is less than charge_needed + # e.g. charge_needed diminishes overtime + w_df = w_df.sort_values(['generated_at', 'rank'], ascending=[True, True]) + charge_periods = [] + _charge_needed = charge_needed + while (_charge_needed > 0) and (len(w_df) > 0): + if (w_df.iloc[0]['rank'] <= _charge_needed) or (len(w_df) == _charge_needed): + _charge_needed -= 1 + charge_periods.append(w_df.iloc[0].name) + w_df = w_df.iloc[1:] # skip this row + + # Assign charge status only if a full charge is completed + # (e.g. don't have enough data at tail to complete a full charge) + if len(charge_periods) == charge_needed: + df.loc[charge_periods, 'charge_status'] = True + + assert df['charge_status'].sum() >= (len(df['window_start'].unique()) - 1) * charge_needed + assert df['charge_status'].sum() <= (len(df['window_start'].unique())) * charge_needed + return df['charge_status'] - df["y_rank"] = df.groupby(["window", "generated_at"])[truth_col].rank( - method="first" - ) - df["y_pred_rank"] = df.groupby(["window", "generated_at"])[pred_col].rank( - method="first" + df = df.assign( + truth_charge_status=simulate_charge(df, truth_col, charge_mins), + pred_charge_status=simulate_charge(df, pred_col, charge_mins), ) - - # TODO: remove this filter and replace with while loop simulation for max savings - max_rank = df["y_rank"].max() - df = df.groupby(["generated_at", "window"]).filter( - lambda group: group["y_rank"].max() == max_rank + + df = df.assign( + truth_charge_emissions=df[truth_col] * df['truth_charge_status'] * (load_kw / 1000), + pred_charge_emissions=df[truth_col] * df['pred_charge_status'] * (load_kw / 1000), ) + + # baseline: immediate charging rather than AER + df = df.assign(sequential_rank = df.reset_index().groupby(['window_start'])['point_time'].rank(method='first', ascending=True).values) + df = df.assign(baseline_charge_status = df['sequential_rank'] <= charge_mins // 5) + df = df.assign(baseline_charge_emissions = df[truth_col] * df['baseline_charge_status'] * (load_kw / 1000)) - # Calculate total C02 emissions in lbs/Mwh of each scenario - y_actual_total = ( - df[df["y_pred_rank"] <= (charge_mins / 5)] - .groupby(["window", "generated_at"])[truth_col] - .mean() - ) - y_best_total = ( - df[df["y_rank"] <= (charge_mins / 5)] - .groupby(["window", "generated_at"])[truth_col] - .mean() - ) - y_avg_total = df.groupby(["window", "generated_at"])[truth_col].mean() + assert df['baseline_charge_status'].sum() >= (len(df['window_start'].unique()) - 1) * charge_mins // 5 + assert df['baseline_charge_status'].sum() <= (len(df['window_start'].unique())) * charge_mins // 5 + + # Calculate total CO2 emissions ("truth") + y_actual_emissions = df['truth_charge_emissions'].sum() + y_pred_emissions = df['pred_charge_emissions'].sum() + y_base_emissions = df['baseline_charge_emissions'].sum() # Calculate savings: co2_reduction, co2_potential - co2_reduction = (y_avg_total - y_actual_total).mean() # actual savings - co2_potential = (y_avg_total - y_best_total).mean() # ideal savings + co2_reduction = (y_base_emissions - y_pred_emissions).mean() # actual savings + co2_potential = (y_base_emissions - y_actual_emissions).mean() # ideal savings return { "co2_reduction": round(co2_reduction, 1), @@ -598,7 +607,7 @@ def plot_impact_forecast_metrics( y=_metrics["co2_reduction"], name="Forecast Achieved", text=[ - f"{(r / p) * 100:.1f}%" + f"{(r / (p + 1e-6)) * 100:.1f}%" for r, p in zip( _metrics["co2_reduction"], _metrics["co2_potential"] ) @@ -659,16 +668,16 @@ def plot_sample_fuelmix(jobs: List[AnalysisDataHandler], max_sample_period="30D" stacked_values = model_job.fuel_mix.loc[times] # Create cumulative values for stacking - for k in range(1, len(stacked_values.columns)): - stacked_values.iloc[:, k] += stacked_values.iloc[:, k - 1] + for fuel_ix in range(1, len(stacked_values.columns)): + stacked_values.iloc[:, fuel_ix] += stacked_values.iloc[:, fuel_ix - 1] # Add each fuel type as an area - for k, fuel in enumerate(model_job.fuel_mix.columns): + for fuel_ix, fuel in enumerate(model_job.fuel_mix.columns): fig.add_trace( go.Scatter( x=stacked_values.index, - y=stacked_values.iloc[:, k], - fill="tonexty" if j > 0 else "tozeroy", + y=stacked_values.iloc[:, fuel_ix], + fill="tonexty" if model_ix > 0 else "tozeroy", mode="none", # Hide lines to emphasize the filled area name=fuel, fillcolor=fuel_cp[fuel], @@ -724,7 +733,7 @@ def assign_window(row): else: def assign_rolling_window(row): - window_start = row.name.floor(f"{window_mins}T") + window_start = row.name.floor(f"{window_mins}T", ambiguous=True) # window_end = window_start + pd.Timedelta(f"{window_mins} min") return window_start @@ -778,9 +787,17 @@ def plot_max_impact_potential(jobs: List[AnalysisDataHandler], scenarios: List[s ) y_max = max(_metrics['co2_potential'].max(), y_max) + + fig.update_layout( + height=300 * len(region_models), + yaxis=dict( + title="lbs CO2/MWh", + fixedrange=True # Disable y-axis panning + ) + ) figs[region_abbrev] = fig - + return figs From a5e568798649ce411be58685258052dc63afc12b Mon Sep 17 00:00:00 2001 From: samkoebrich Date: Tue, 11 Mar 2025 11:05:42 -0600 Subject: [PATCH 28/85] cleanup language in report_card_template --- watttime/evaluation/report_card_template.html | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/watttime/evaluation/report_card_template.html b/watttime/evaluation/report_card_template.html index 1d002e53..8ff5a21d 100644 --- a/watttime/evaluation/report_card_template.html +++ b/watttime/evaluation/report_card_template.html @@ -325,14 +325,14 @@

Forecast Performance

"plot_rank_corr", plot_rank_corr, "2) Rank Correlation by Horizon", - "Rank correlation quantifies the degree to which the rank ordering of two datasets are related. We expect for models to perform better at lower horizons." + "Rank correlation quantifies the degree to which the rank ordering of two datasets are related. We expect for models to perform better at lower horizons. Please note that this metric should be interpreted with caution, especially for signals with low variability. Rank correlation on a signal where variance is smaller than the noise is not going to give a meaningful metric. For instance, rank correlation could be zero but there's no loss in impact." ) }} {{ tab_container( "plot_impact_forecast_metrics", plot_impact_forecast_metrics, - "3) Simulation of AER Impact using Forecasts", - "Simulations are run for typical customer patterns including charging an EV during the day (3 hours in a 12 hour window starting at 8 AM), the night (2 hours in a 8 hour window starting at 9 PM), and running a thermostat (running for 30 out of 60 minutes throughout the day). Each of these metrics could capture a different source of impact (e.g. solar curtailment may only be accessible to EV day charging). These metrics assume that the modeled values are accurate, and only quantify the degree of error caused by the forecast model." + "3) Estimation of AER Impact using Forecasts", + "These plots simulate a vairiety of AER scenarios for typical customer patterns including charging an EV during the day (3 hours in a 12 hour window starting at 8 AM), the night (2 hours in a 8 hour window starting at 9 PM), and running a thermostat (running for 30 out of 60 minutes throughout the day). Each of these metrics could capture a different source of impact (e.g. solar curtailment may only be accessible to EV day charging). The 'simulation' simply looks at the cleanest timem to use electricity based on the forecasted value, and evaluates the impact of this against the historical signal value as truth. We also show the impact captured if the forecast was perfect by displaying the maximum possible impact if a 'perfect' forecast were used. These metrics assume that the modeled values are accurate, and only quantify the degree of error caused by the forecast model." ) }} {% endif %} From e2cf2f43e9f44af84c0c4962191ab409056e9106 Mon Sep 17 00:00:00 2001 From: samkoebrich Date: Tue, 11 Mar 2025 15:24:37 -0600 Subject: [PATCH 29/85] refactor --- .../get_wt_api_forecast_evaluation_data.py | 15 +- watttime/evaluation/report.py | 413 +++++++++++------- watttime/evaluation/report_card_template.html | 14 +- 3 files changed, 274 insertions(+), 168 deletions(-) diff --git a/watttime/evaluation/get_wt_api_forecast_evaluation_data.py b/watttime/evaluation/get_wt_api_forecast_evaluation_data.py index 44171973..a7f2a49e 100644 --- a/watttime/evaluation/get_wt_api_forecast_evaluation_data.py +++ b/watttime/evaluation/get_wt_api_forecast_evaluation_data.py @@ -1,4 +1,4 @@ -from typing import Optional, List, Union +from typing import Optional, List, Union, Dict from datetime import datetime import inspect import warnings @@ -54,7 +54,7 @@ class AnalysisDataHandler: region: str eval_start: Union[datetime, str] eval_end: Union[datetime, str] - forecast_sample_size: Union[int, float] = 1 + forecast_sample_size: Union[int, float] = 0.1 forecast_max_horizon: int = 60 * 24 forecast_sample_seed: int = 42 signal_type: str = "co2_moer" @@ -122,6 +122,8 @@ def moers(self) -> pd.DataFrame: if self.localize_tz: moers.index = moers.index.tz_convert(self.tz) + + moers = moers.sort_index() self.returned_meta = self.wt_hist._last_request_meta self.returned_hist_model_date = self.wt_hist._last_request_meta.get("model", {}).get("date", None) @@ -174,7 +176,7 @@ def fuel_mix(self) -> pd.DataFrame: self.returned_fuel_mix_model_date = self.wt_fuel_mix._last_request_meta.get("model", {}).get("date", None) return fm - def moer_v_fuel_mix(self): + def moer_v_fuel_mix(self) -> pd.DataFrame: return self.moers.merge( self.fuel_mix, how="inner", @@ -270,3 +272,10 @@ def collected_model_meta(self): "returned_forecast_model_date": getattr(dh, "returned_forecast_model_date", None), } for dh in self.data_handlers ] + + @property + def data_handlers_by_region_dict(self) -> Dict[str, List[AnalysisDataHandler]]: + unique_regions = list(set([dh.region for dh in self.data_handlers])) + out = {region: [dh for dh in self.data_handlers if dh.region == region] for region in unique_regions} + out = {k: sorted(v, key=lambda x: x.model_date, reverse=True) for k, v in out.items()} + return out \ No newline at end of file diff --git a/watttime/evaluation/report.py b/watttime/evaluation/report.py index b0a01cdc..a3b09542 100644 --- a/watttime/evaluation/report.py +++ b/watttime/evaluation/report.py @@ -88,7 +88,7 @@ def get_random_overlapping_period(dfs, max_period="30D", resample_freq="1H"): return overlap_range -def plot_sample_moers(jobs: List[AnalysisDataHandler], max_sample_period="30D") -> Dict[str, go.Figure]: +def plot_sample_moers(factory: DataHandlerFactory, max_sample_period="30D") -> Dict[str, go.Figure]: """ Plot a sample of old and new MOER values over time, creating a subplot for each unique region. @@ -100,17 +100,10 @@ def plot_sample_moers(jobs: List[AnalysisDataHandler], max_sample_period="30D") """ figs = {} - unique_regions = set([j.region for j in jobs]) - for region_abbrev in unique_regions: + times = get_random_overlapping_period([j.moers for j in factory.data_handlers], max_sample_period) + for region_abbrev, region_models in factory.data_handlers_by_region_dict.items(): fig = go.Figure() - - region_abbrev = region_abbrev.upper() - region_models = [j for j in jobs if j.region == region_abbrev] - - times = get_random_overlapping_period( - [j.moers for j in region_models], max_sample_period, - ) for model_job in region_models: @@ -129,7 +122,7 @@ def plot_sample_moers(jobs: List[AnalysisDataHandler], max_sample_period="30D") fig.update_layout( height=300, yaxis=dict( - title=f"{jobs[0].signal_type}", + title=f"{model_job.signal_type}", fixedrange=True # Disable y-axis panning ), showlegend=True, @@ -147,7 +140,7 @@ def plot_sample_moers(jobs: List[AnalysisDataHandler], max_sample_period="30D") return figs -def plot_distribution_moers(jobs: List[AnalysisDataHandler]) -> Dict[str, go.Figure]: +def plot_distribution_moers(factory: DataHandlerFactory) -> Dict[str, go.Figure]: """ Plot the distribution of old and new MOER values for each region, creating a stacked subplot. @@ -158,13 +151,10 @@ def plot_distribution_moers(jobs: List[AnalysisDataHandler]) -> Dict[str, go.Fig plotly.graph_objects.Figure: A Plotly figure with stacked subplots of distributions. """ # Create a figure with a subplot for each region, stacked vertically - unique_regions = set([j.region for j in jobs]) figs = {} - for region_abbrev in unique_regions: + for region_abbrev, region_models in factory.data_handlers_by_region_dict.items(): fig = go.Figure() - region_abbrev = region_abbrev.upper() - region_models = [j for j in jobs if j.region == region_abbrev] for model_job in region_models: # Add a histogram trace for the new MOER distribution @@ -179,7 +169,7 @@ def plot_distribution_moers(jobs: List[AnalysisDataHandler]) -> Dict[str, go.Fig # Update layout for the figure fig.update_layout( height=300, - xaxis_title=f"{jobs[0].signal_type} Values", + xaxis_title=f"{region_models[0].signal_type} Values", showlegend=True, margin=dict(l=50, r=50, t=50, b=50), xaxis=dict( @@ -196,7 +186,7 @@ def plot_distribution_moers(jobs: List[AnalysisDataHandler]) -> Dict[str, go.Fig return figs -def plot_heatmaps(jobs: List[AnalysisDataHandler], colorscale="Oranges") -> Dict[str, go.Figure]: +def plot_heatmaps(factory: DataHandlerFactory, colorscale="Oranges") -> Dict[str, go.Figure]: """ Generate vertically stacked heatmaps for each region, with each heatmap having its own colorscale. Args: @@ -204,23 +194,19 @@ def plot_heatmaps(jobs: List[AnalysisDataHandler], colorscale="Oranges") -> Dict Returns: Dict[str, go.Figure]: A dictionary mapping region abbreviations to their heatmap figures. """ - unique_regions = set([j.region for j in jobs]) - figs = {} - - for region_abbrev in unique_regions: - region_abbrev = region_abbrev.upper() - region_jobs = [j for j in jobs if j.region.upper() == region_abbrev] + figs = {} + for region_abbrev, region_models in factory.data_handlers_by_region_dict.items(): # Initialize a subplot with one row per job fig = sp.make_subplots( - rows=len(region_jobs), + rows=len(region_models), cols=1, shared_xaxes=True, - subplot_titles=[j.model_date for j in region_jobs], + subplot_titles=[j.model_date for j in region_models], vertical_spacing=0.1, # Reduced spacing for better alignment ) - for j, job in enumerate(region_jobs, start=1): + for j, job in enumerate(region_models, start=1): heat = job.moers.assign( month=job.moers.index.month, hour=job.moers.index.hour ) @@ -251,19 +237,19 @@ def plot_heatmaps(jobs: List[AnalysisDataHandler], colorscale="Oranges") -> Dict fig.add_trace(heatmap_trace, row=j, col=1) fig.update_yaxes(title_text="Month", row=j, col=1) - if j == len(region_jobs): # Only add x-axis title to bottom subplot + if j == len(region_models): # Only add x-axis title to bottom subplot fig.update_xaxes(title_text="Hour of Day", row=j, col=1) fig.update_layout( - height=300 * len(region_jobs), # Adjust height per subplot + height=300 * len(region_models), # Adjust height per subplot margin=dict(r=100), # Add right margin for colorbars # title=f"{region_abbrev} Heat Maps" ) # Update colorbar positions to align with subplots for model_ix, trace in enumerate(fig.data): - trace.colorbar.y = 1 - (2*model_ix + 1)/(2*len(region_jobs)) - trace.colorbar.len = 1/len(region_jobs) * 0.9 + trace.colorbar.y = 1 - (2*model_ix + 1)/(2*len(region_models)) + trace.colorbar.len = 1/len(region_models) * 0.9 figs[region_abbrev] = fig @@ -298,6 +284,48 @@ def calc_rank_corr( return np.nanmean(corr).round(3) +def simulate_charge(df: pd.DataFrame, sort_col: str, charge_mins: int): + + assert sort_col in df.columns + include_generated_at = "generated_at" in df.index.names + + df = df.sort_index(ascending=True) + df['rank'] = df.groupby(["window_start"] + (["generated_at"] if include_generated_at else []))[sort_col].rank(ascending=True, method='first') + charge_needed = charge_mins // 5 + df = df.assign(charge_status=False) + for w in df.groupby("window_start"): + _, w_df = w + + # STATEFULLNESS HEURISTIC: point_time must be equal to generate_at + # TODO: really this should be "less then the next generated_at" in the case + # that point_time and generated_at are not available for every 5 mins + if include_generated_at: + w_df = w_df.loc[w_df.index.get_level_values('point_time') == w_df.index.get_level_values('generated_at')] + + # STATEFULLNESS HEURISTIC: Take charging periods iteratively, + # at each step only consider if rank of value is less than charge_needed + # e.g. charge_needed diminishes overtime + w_df = w_df.sort_values((["generated_at"] if include_generated_at else []) +['rank'], ascending=True) + charge_periods = [] + _charge_needed = charge_needed + while (_charge_needed > 0) and (len(w_df) > 0): + # charging conditions: + # 1) currently point_time is in the lowest periods (according to n periods from _charge_needed) + # 2) there are not enough times in the df left to deliver a full charge without charging now + # 3) The current rank is high from the initial sort, but still lower than anything remaining. + if (w_df.iloc[0]['rank'] <= _charge_needed) or (len(w_df) == _charge_needed) or (w_df.iloc[0]['rank'] == w_df['rank'].min()): + _charge_needed -= 1 + charge_periods.append(w_df.iloc[0].name) + w_df = w_df.iloc[1:] # skip this row + + # Assign charge status only if a full charge is completed + # (e.g. don't have enough data at tail to complete a full charge) + if len(charge_periods) == charge_needed: + df.loc[charge_periods, 'charge_status'] = True + + assert df['charge_status'].sum() >= (len(df['window_start'].unique()) - 1) * charge_needed + assert df['charge_status'].sum() <= (len(df['window_start'].unique())) * charge_needed + return df['charge_status'] def calc_rank_compare_metrics( in_df, @@ -309,7 +337,6 @@ def calc_rank_compare_metrics( load_kw=1000, ): df = in_df.copy() - df.dropna(inplace=True) if window_starts: # Extract unique dates and create window ranges @@ -350,41 +377,6 @@ def calc_rank_compare_metrics( (df.index.get_level_values("generated_at") >= df["window_end"]), "window_start"] = pd.NaT df = df.dropna(subset=["window_start"]) - def simulate_charge(df, sort_col: Literal[truth_col, pred_col], charge_mins: int): - - df['rank'] = df.groupby(["window_start", "generated_at"])[sort_col].rank(ascending=True) - charge_needed = charge_mins // 5 - df = df.assign(charge_status=False) - df = df.sort_index(ascending=True) - for w in df.groupby("window_start"): - _, w_df = w - - # STATEFULLNESS HEURISTIC: point_time must be equal to generate_at - # TODO: really this should be "less then the next generated_at" in the case - # that point_time and generated_at are not available for every 5 mins - w_df = w_df.loc[w_df.index.get_level_values('point_time') == w_df.index.get_level_values('generated_at')] - - # STATEFULLNESS HEURISTIC: Take charging periods iteratively, - # at each step only consider if rank of value is less than charge_needed - # e.g. charge_needed diminishes overtime - w_df = w_df.sort_values(['generated_at', 'rank'], ascending=[True, True]) - charge_periods = [] - _charge_needed = charge_needed - while (_charge_needed > 0) and (len(w_df) > 0): - if (w_df.iloc[0]['rank'] <= _charge_needed) or (len(w_df) == _charge_needed): - _charge_needed -= 1 - charge_periods.append(w_df.iloc[0].name) - w_df = w_df.iloc[1:] # skip this row - - # Assign charge status only if a full charge is completed - # (e.g. don't have enough data at tail to complete a full charge) - if len(charge_periods) == charge_needed: - df.loc[charge_periods, 'charge_status'] = True - - assert df['charge_status'].sum() >= (len(df['window_start'].unique()) - 1) * charge_needed - assert df['charge_status'].sum() <= (len(df['window_start'].unique())) * charge_needed - return df['charge_status'] - df = df.assign( truth_charge_status=simulate_charge(df, truth_col, charge_mins), pred_charge_status=simulate_charge(df, pred_col, charge_mins), @@ -396,7 +388,7 @@ def simulate_charge(df, sort_col: Literal[truth_col, pred_col], charge_mins: int ) # baseline: immediate charging rather than AER - df = df.assign(sequential_rank = df.reset_index().groupby(['window_start'])['point_time'].rank(method='first', ascending=True).values) + df = df.assign(sequential_rank=df.groupby('window_start').cumcount()) df = df.assign(baseline_charge_status = df['sequential_rank'] <= charge_mins // 5) df = df.assign(baseline_charge_emissions = df[truth_col] * df['baseline_charge_status'] * (load_kw / 1000)) @@ -408,31 +400,31 @@ def simulate_charge(df, sort_col: Literal[truth_col, pred_col], charge_mins: int y_pred_emissions = df['pred_charge_emissions'].sum() y_base_emissions = df['baseline_charge_emissions'].sum() - # Calculate savings: co2_reduction, co2_potential - co2_reduction = (y_base_emissions - y_pred_emissions).mean() # actual savings - co2_potential = (y_base_emissions - y_actual_emissions).mean() # ideal savings + # Calculate savings: reduction, potential + reduction = (y_base_emissions - y_pred_emissions) / len(set(df.index.date)) + potential = (y_base_emissions - y_actual_emissions) / len(set(df.index.date)) + + # Normalize to mean per window + reduction /= len(df['window_start'].unique()) + potential /= len(df['window_start'].unique()) return { - "co2_reduction": round(co2_reduction, 1), - "co2_potential": round(co2_potential, 1), + "reduction": round(reduction, 1), + "potential": round(potential, 1), } -def plot_norm_mae(jobs: List[AnalysisDataHandler], horizons_hr=[1, 6, 12, 18, 24]) -> Dict[str, go.Figure]: +def plot_norm_mae(factory: DataHandlerFactory, horizons_hr=[1, 6, 12, 18, 24]) -> Dict[str, go.Figure]: """ Create a Plotly bar chart for rank correlation by horizon with one subplot per region (abbrev). """ - # Create subplots - unique_regions = set([j.region for j in jobs]) y_min = y_max = 0 figs = {} # Iterate through each region and create a bar plot - for region_abbrev in unique_regions: - region_abbrev = region_abbrev.upper() + for region_abbrev, region_models in factory.data_handlers_by_region_dict.items(): fig = go.Figure() - region_models = [j for j in jobs if j.region == region_abbrev] x_values = [f"{h}hr" for h in horizons_hr] # Add horizon labels for model_job in region_models: @@ -469,7 +461,7 @@ def plot_norm_mae(jobs: List[AnalysisDataHandler], horizons_hr=[1, 6, 12, 18, 24 return figs -def plot_rank_corr(jobs: List[AnalysisDataHandler], horizons_hr=[1, 6, 12, 18, 24]) -> Dict[str, go.Figure]: +def plot_rank_corr(factory: DataHandlerFactory, horizons_hr=[1, 6, 12, 18, 24]) -> Dict[str, go.Figure]: """ Create a Plotly line plot for rank correlation by horizon with one subplot per region (abbrev). @@ -483,16 +475,12 @@ def plot_rank_corr(jobs: List[AnalysisDataHandler], horizons_hr=[1, 6, 12, 18, 2 """ - # Create subplots - unique_regions = set([j.region for j in jobs]) y_min = y_max = 0 figs = {} # Iterate through each region and create a line plot - for region_abbrev in unique_regions: + for region_abbrev, region_models in factory.data_handlers_by_region_dict.items(): fig = go.Figure() - region_abbrev = region_abbrev.upper() - region_models = [j for j in jobs if j.region == region_abbrev] # Extract data for the line plot x_values = [f"{h}hr" for h in horizons_hr] # Add horizon labels @@ -557,21 +545,19 @@ def plot_rank_corr(jobs: List[AnalysisDataHandler], horizons_hr=[1, 6, 12, 18, 2 def plot_impact_forecast_metrics( - jobs: List[AnalysisDataHandler], scenarios=["EV-night", "EV-day", "Thermostat"] + factory: DataHandlerFactory, scenarios=["EV-night", "EV-day", "Thermostat"] ): - unique_regions = set([j.region for j in jobs]) figs = {} y_max = 0 - for region_abbrev in unique_regions: + for region_abbrev, region_models in factory.data_handlers_by_region_dict.items(): - region_models = [j for j in jobs if j.region == region_abbrev] fig = sp.make_subplots( rows=len(region_models), cols=1, shared_xaxes=True, - subplot_titles=[j.model_date for j in jobs], + subplot_titles=[j.model_date for j in region_models], vertical_spacing=0.2, ) @@ -590,7 +576,7 @@ def plot_impact_forecast_metrics( fig.add_trace( go.Bar( x=_metrics["scenario"], - y=_metrics["co2_potential"], + y=_metrics["potential"], name=f"CO2 Potential Savings", # Legend only in the first subplot marker=dict( color="rgba(200, 200, 200, 0.8)" @@ -604,12 +590,12 @@ def plot_impact_forecast_metrics( fig.add_trace( go.Bar( x=_metrics["scenario"], - y=_metrics["co2_reduction"], + y=_metrics["reduction"], name="Forecast Achieved", text=[ f"{(r / (p + 1e-6)) * 100:.1f}%" for r, p in zip( - _metrics["co2_reduction"], _metrics["co2_potential"] + _metrics["reduction"], _metrics["potential"] ) ], textposition="outside", @@ -620,7 +606,7 @@ def plot_impact_forecast_metrics( col=1, ) - y_max = max(_metrics["co2_reduction"].max(), _metrics['co2_potential'].max(), y_max) + y_max = max(_metrics["reduction"].max(), _metrics['potential'].max(), y_max) # Update layout fig.update_layout( @@ -633,7 +619,7 @@ def plot_impact_forecast_metrics( ) # Update axes for all subplots - for model_ix in range(1, len(jobs) + 1): + for model_ix in range(1, len(region_models) + 1): fig.update_xaxes(title_text="Scenario", row=model_ix, col=1) fig.update_yaxes(title_text="CO2 Savings (lbs/MWh)", row=model_ix, col=1) @@ -646,20 +632,18 @@ def plot_impact_forecast_metrics( return figs -def plot_sample_fuelmix(jobs: List[AnalysisDataHandler], max_sample_period="30D") -> Dict[str, go.Figure]: +def plot_sample_fuelmix(factory: DataHandlerFactory, max_sample_period="30D") -> Dict[str, go.Figure]: figs = {} - unique_regions = set([j.region for j in jobs]) - times = get_random_overlapping_period([j.fuel_mix for j in jobs], max_sample_period) - for region_abbrev in unique_regions: - region_models = [j for j in jobs if j.region == region_abbrev] + times = get_random_overlapping_period([j.fuel_mix for j in factory.data_handlers], max_sample_period) + for region_abbrev, region_models in factory.data_handlers_by_region_dict.items(): # Initialize a subplot with one row per region fig = sp.make_subplots( rows=len(region_models), cols=1, shared_xaxes=True, - subplot_titles=[j.model_date for j in jobs], + subplot_titles=[j.model_date for j in region_models], vertical_spacing=0.2, ) @@ -690,7 +674,7 @@ def plot_sample_fuelmix(jobs: List[AnalysisDataHandler], max_sample_period="30D" fig.update_layout( height=300 * len(region_models), yaxis=dict( - title=f"{jobs[0].signal_type}", + title=f"{region_models[0].signal_type}", fixedrange=True # Disable y-axis panning ), showlegend=True, @@ -708,60 +692,75 @@ def plot_sample_fuelmix(jobs: List[AnalysisDataHandler], max_sample_period="30D" return figs -def calc_max_potential(df, charge_mins, window_mins, window_starts=None, truth_col="signal_value", load_kw=1000): +def calc_max_potential(in_df, charge_mins, window_mins, window_starts=None, truth_col="signal_value", load_kw=1000): """Predict the maximum potential CO2 Savings, without any forecast to estimate the upper limit of impact from a given signal value.""" + df = in_df.copy() + if window_starts: - # Generate window ranges based on start times and duration + # Extract unique dates and create window ranges unique_dates = set(df.index.date) window_ranges = [] for date in unique_dates: for start_time in window_starts: start = pd.Timestamp(f"{date} {start_time}") - end = start + pd.Timedelta(f"{window_mins} min") + end = start + pd.Timedelta(minutes=window_mins) window_ranges.append((start, end)) - # Assign each row to a window - def assign_window(row): - point_time = row.name.replace(tzinfo=None) - for start, end in window_ranges: - if point_time >= start and point_time < end: - return start # Label window by its start time - return pd.NaT - - df["window"] = df.apply(assign_window, axis=1) - - else: - def assign_rolling_window(row): - window_start = row.name.floor(f"{window_mins}T", ambiguous=True) - # window_end = window_start + pd.Timedelta(f"{window_mins} min") - return window_start - - df["window"] = df.apply(assign_rolling_window, axis=1) + # Convert to DataFrame + window_df = pd.DataFrame(window_ranges, columns=["window_start", "window_end"]) + window_df = window_df.sort_values("window_start") + tz = df.index.get_level_values('point_time')[0].tz + window_df = window_df.assign( + window_start=pd.to_datetime(window_df["window_start"], errors='coerce').dt.tz_localize(tz), + window_end=pd.to_datetime(window_df["window_end"], errors='coerce').dt.tz_localize(tz) + ) + window_df = window_df.dropna(subset=["window_start"]) - df = df.dropna(subset=["window"]) - df = df.assign( - y_rank=df.groupby('window')[truth_col].rank(method='first') - ) + # Ensure df is sorted by "point_time" before merging + df = df.sort_index(level="point_time") + + # Use merge_asof to efficiently assign windows + df = pd.merge_asof( + df.reset_index().sort_values("point_time"), + window_df, + left_on="point_time", + right_on="window_start", + direction="backward" + ).set_index(df.index.names) - df[truth_col] *= (load_kw / 1000) - y_best_total = ( - df[df["y_rank"] <= (charge_mins / 5)] - .groupby("window")[truth_col] - .mean() - ) - y_avg_total = df.groupby("window")[truth_col].mean() - co2_potential = (y_avg_total - y_best_total).sum() - return {"co2_potential": round(co2_potential, 1)} + else: + # Assign each row to a rolling window of `window_mins` based on `generated_at` + df["window_start"] = df.index.get_level_values("point_time").floor(f"{window_mins}min") + df['window_end'] = df["window_start"] + pd.Timedelta(f"{window_mins} min") + # Filter out rows that do not fall within the valid window + df = df.dropna(subset=["window_start"]) + df = df.loc[(df.index.get_level_values("point_time") < df["window_end"]) & (df.index.get_level_values("point_time") >= df["window_start"])] + + df = df.assign(charge_status = simulate_charge(df, truth_col, charge_mins)) + df = df.assign(charge_emissions = df[truth_col] * df["charge_status"] * (load_kw / 1000)) + + # baseline: immediate charging rather than AER + df = df.assign(sequential_rank=df.groupby('window_start').cumcount()) + df = df.assign(baseline_charge_status = df['sequential_rank'] < charge_mins // 5) + df = df.assign(baseline_charge_emissions = df[truth_col] * df['baseline_charge_status'] * (load_kw / 1000)) + + assert df['baseline_charge_status'].sum() >= (len(df['window_start'].unique()) - 1) * charge_mins // 5 + assert df['baseline_charge_status'].sum() <= (len(df['window_start'].unique())) * charge_mins // 5 + + y_best_total = df['charge_emissions'].sum() + y_baseline_total = df['baseline_charge_emissions'].sum() + + potential = (y_baseline_total - y_best_total) / len(set(df.index.date)) + + return {"potential": round(potential, 1)} -def plot_max_impact_potential(jobs: List[AnalysisDataHandler], scenarios: List[str] = ["EV-night", "EV-day", "Thermostat"]): - unique_regions = set([j.region for j in jobs]) +# def plot_max_impact_potential(factory: DataHandlerFactory, scenarios: List[str] = ["EV-night", "EV-day", "Thermostat"]): +def plot_max_impact_potential(factory: DataHandlerFactory, scenarios: List[str] = ["EV-night"]): figs = {} y_max = 0 - for region_abbrev in unique_regions: - region_models = [j for j in jobs if j.region == region_abbrev] - region_abbrev = region_abbrev.upper() + for region_abbrev, region_models in factory.data_handlers_by_region_dict.items(): fig = go.Figure() for model_job in region_models: @@ -776,17 +775,17 @@ def plot_max_impact_potential(jobs: List[AnalysisDataHandler], scenarios: List[s _metrics = pd.DataFrame(_metrics) - # Add the 'co2_potential' bar trace + # Add the 'potential' bar trace fig.add_trace( go.Bar( x=_metrics["scenario"], - y=_metrics["co2_potential"], + y=_metrics["potential"], name=model_job.model_date, # Legend only in the first subplot hovertemplate="%{x}: %{y:.1f} lbs CO2/MWh", ) ) - y_max = max(_metrics['co2_potential'].max(), y_max) + y_max = max(_metrics['potential'].max(), y_max) fig.update_layout( height=300 * len(region_models), @@ -801,7 +800,7 @@ def plot_max_impact_potential(jobs: List[AnalysisDataHandler], scenarios: List[s return figs -def plot_fuelmix_heatmap(jobs: List[AnalysisDataHandler]): +def plot_fuelmix_heatmap(factory: DataHandlerFactory): def create_pivot_table(df, column): df = df.assign( month=df.index.month, @@ -848,24 +847,21 @@ def make_alpha_gradient(hex_color, steps=10): return [[i / (steps - 1), rgba] for i, rgba in enumerate(rgba_colors)] - unique_regions = set([j.region for j in jobs]) figs = {} - for region_abbrev in unique_regions: - region_abbrev = region_abbrev.upper() - region_jobs = [j for j in jobs if j.region.upper() == region_abbrev] - + for region_abbrev, region_models in factory.data_handlers_by_region_dict.items(): + # We need to ensure all jobs have the same fuel types for the buttons to work all_columns = set() - for job in region_jobs: + for job in region_models: all_columns.update(job.fuel_mix.columns) # Create subplot titles with model dates - subplot_titles = [j.model_date for j in region_jobs] + subplot_titles = [j.model_date for j in region_models] # Create subplots fig = sp.make_subplots( - rows=len(region_jobs), + rows=len(region_models), cols=1, shared_xaxes=True, subplot_titles=subplot_titles, @@ -876,7 +872,7 @@ def make_alpha_gradient(hex_color, steps=10): trace_idx = 0 # Add traces for each job - for job_idx, job in enumerate(region_jobs, start=1): + for job_idx, job in enumerate(region_models, start=1): for fuel_idx, fuel in enumerate(all_columns_list): pivot = create_pivot_table(job.fuel_mix, fuel) @@ -897,7 +893,7 @@ def make_alpha_gradient(hex_color, steps=10): trace_idx += 1 # Add buttons that control visibility across all subplots - buttons = create_buttons(all_columns_list, len(region_jobs)) + buttons = create_buttons(all_columns_list, len(region_models)) # Month names mapping month_names = {1: 'Jan', 2: 'Feb', 3: 'Mar', 4: 'Apr', 5: 'May', 6: 'Jun', @@ -916,12 +912,12 @@ def make_alpha_gradient(hex_color, steps=10): buttons=buttons ) ], - height=(300 * len(region_jobs)) + 50, # Adjust height based on number of jobs + height=(300 * len(region_models)) + 50, # Adjust height based on number of jobs xaxis_title="Hour of Day", ) # Update all y-axes to show month names - for model_ix in range(1, len(region_jobs) + 1): + for model_ix in range(1, len(region_models) + 1): fig.update_yaxes( title="Month", tickvals=list(month_names.keys()), @@ -931,7 +927,7 @@ def make_alpha_gradient(hex_color, steps=10): # Update all x-axes fig.update_xaxes( - title="Hour of Day" if model_ix == len(region_jobs) else None, # Only show title on bottom plot + title="Hour of Day" if model_ix == len(region_models) else None, # Only show title on bottom plot tickmode="linear", dtick=2, # Show every 2 hours row=model_ix, col=1 @@ -940,7 +936,94 @@ def make_alpha_gradient(hex_color, steps=10): figs[region_abbrev] = fig return figs + + +def plot_bland_altman(factory: DataHandlerFactory, n_samples: int = 2500) -> Dict[str, go.Figure]: + """ + A Bland-Altman plot is useful to see the difference between two measurements (models). + + It includes a scatterplot of the deltas between the two measurements, along with a reference line. + signifying the mean of the deltas, and confidence intervals for the mean. + """ + figs = {} + for region_abbrev, region_models in factory.data_handlers_by_region_dict.items(): + + if len(region_models) == 1: + return None + + merged_moers = pd.concat([dh.moers for dh in region_models], axis='columns').dropna() + merged_moers.columns = [dh.model_date for dh in region_models] + merged_moers = merged_moers.sort_index(axis='columns') # put newest models last + # TODO: consider more than two models + if len(merged_moers.columns) < 2: + merged_moers = merged_moers.iloc[:, :2] + + + merged_moers = merged_moers.assign( + delta=merged_moers.max(axis='columns') - merged_moers.min(axis='columns'), + mean = merged_moers.mean(axis='columns') + ) + merged_moers = merged_moers.sample(n_samples) + + fig = go.Figure() + + # scatter + fig.add_trace(go.Scatter( + x=merged_moers['mean'], y=merged_moers['delta'], mode='markers', + marker=dict(opacity=0.5), + name="Delta of Model Values" + )) + + # reference line + fig.add_trace(go.Scatter( + x=[merged_moers['mean'].min(), merged_moers['mean'].max()], + y=[merged_moers['delta'].mean(), merged_moers['delta'].mean()], + mode='lines', + line=dict(color='red'), + name='Mean Delta' + )) + + # ci + fig.add_trace(go.Scatter( + x=[merged_moers['mean'].min(), merged_moers['mean'].max()], + y=[ + merged_moers['delta'].mean() + merged_moers['delta'].std() * 1.96, + merged_moers['delta'].mean() + merged_moers['delta'].std() * 1.96 + ], + mode='lines', + line=dict(color='red', dash='dash'), + opacity=0.5, + name='95% CI' + )) + + fig.add_trace(go.Scatter( + x=[merged_moers['mean'].min(), merged_moers['mean'].max()], + y=[ + merged_moers['delta'].mean() - merged_moers['delta'].std() * 1.96, + merged_moers['delta'].mean() - merged_moers['delta'].std() * 1.96 + ], + mode='lines', + line=dict(color='red', dash='dash'), + opacity=0.5, + showlegend=False + )) + + fig.update_layout( + height=500, + xaxis_title='Mean', + yaxis_title='Delta', + legend=dict( + orientation="h", + yanchor="bottom", + y=1.02, + xanchor="right", + x=1 + )) + + figs[region_abbrev] = fig + return figs + def parse_report_command_line_args(sys_args): parser = argparse.ArgumentParser( @@ -1010,7 +1093,7 @@ def parse_report_command_line_args(sys_args): PLOTS = { - "signal": [plot_sample_moers, plot_distribution_moers, plot_heatmaps, plot_max_impact_potential], + "signal": [plot_sample_moers, plot_distribution_moers, plot_heatmaps, plot_bland_altman, plot_max_impact_potential], "fuel_mix": [plot_sample_fuelmix, plot_fuelmix_heatmap], "forecast": [plot_norm_mae, plot_rank_corr, plot_impact_forecast_metrics], } @@ -1025,9 +1108,8 @@ def generate_report( output_dir: Path, steps: Literal["signal", "fuel_mix", "forecast"] = [ "signal", - "fuel_mix", "forecast", - ], + ], # no fuel_mix by default ): filename = ( f"{signal_type}_{'&'.join(region_list)}_{'&'.join(model_date_list)}_model_stats" @@ -1037,7 +1119,7 @@ def generate_report( output_path = output_dir / f"{filename}.html" input_template_path = Path(__file__).parent / r"report_card_template.html" - f = DataHandlerFactory( + factory = DataHandlerFactory( eval_start=eval_start, eval_end=eval_end, regions=region_list, @@ -1048,9 +1130,12 @@ def generate_report( plotly_html = {} for step in steps: for plot_func in PLOTS[step]: - _plot = plot_func(f.data_handlers) + _plot = plot_func(factory) + + if _plot is None: + continue - if isinstance(_plot, dict): + elif isinstance(_plot, dict): plotly_html[plot_func.__name__] = {} for region_abbrev, _p in _plot.items(): # for trace in _p.data: @@ -1083,7 +1168,7 @@ def generate_report( ), indent=4 ) - plotly_html['collected_model_meta'] = json.dumps(f.collected_model_meta, indent=4) + plotly_html['collected_model_meta'] = json.dumps(factory.collected_model_meta, indent=4) with open(output_path, "w", encoding="utf-8") as output_file: with open(input_template_path) as template_file: diff --git a/watttime/evaluation/report_card_template.html b/watttime/evaluation/report_card_template.html index 8ff5a21d..344dd3ae 100644 --- a/watttime/evaluation/report_card_template.html +++ b/watttime/evaluation/report_card_template.html @@ -237,6 +237,7 @@
{# Define a reusable macro for tab containers #} {% macro tab_container(group_name, data_dict, title=None, description=None) %} + {% if data_dict and data_dict.keys()|length > 0 %} {% if title %}

{{ title }}

{% endif %} {% if description %}

{{ description }}

{% endif %} @@ -253,7 +254,8 @@
{% endfor %} - {% endmacro %} + {% endif %} + {% endmacro %} {# Historical Signal Data Section #} {% if show_signal %} @@ -287,6 +289,14 @@

Analysis of Historical Signal Data

"4) Maximum Achivable Impact Potential of Signal Values", "Simulations are run for typical customer patterns including charging an EV during the day (3 hours in a 12 hour window starting at 8 AM), the night (2 hours in a 8 hour window starting at 9 PM), and running a thermostat (running for 30 out of 60 minutes throughout the day). Each of these metrics could capture a different source of impact (e.g. solar curtailment may only be accessible to EV day charging). Inspecting these values helps us understand the impact potential of implementing AER with different model versions." ) }} + + {{ tab_container( + "plot_bland_altman", + plot_bland_altman, + "5) Bland Altman Plot for Bias Assesment", + "The Bland Altman plot is a useful tool for comparing the bias between two datasets. This plot is run on a random sample of 2500 datapoints. It contains three elements: 1) A scatterplot of the relative differences between the modeled point_times against the mean of both modeled point_times. We expect to see the y axis of the scatterplot randomly (uniformly) distributed across all means. If there is a trend, it indicates that one model has biases at certain value ranges that the other model doesn't hold. 2) The reference line is the mean delta between the two models. The magnitude and direction of this line indicate if one model has large systemic bias when compared with the other. 3) 95% Confidence Intervals denote the area that we would expect all datapoints to be within if both models had the same bias. Note that in some cases it is expeccted for bias to change between models (e.g. systemic changes to the grid). A suitable explanation should be sought out if bias is apparent." + ) }} + {% endif %} {# Fuel Mix Section #} @@ -328,12 +338,14 @@

Forecast Performance

"Rank correlation quantifies the degree to which the rank ordering of two datasets are related. We expect for models to perform better at lower horizons. Please note that this metric should be interpreted with caution, especially for signals with low variability. Rank correlation on a signal where variance is smaller than the noise is not going to give a meaningful metric. For instance, rank correlation could be zero but there's no loss in impact." ) }} + {{ tab_container( "plot_impact_forecast_metrics", plot_impact_forecast_metrics, "3) Estimation of AER Impact using Forecasts", "These plots simulate a vairiety of AER scenarios for typical customer patterns including charging an EV during the day (3 hours in a 12 hour window starting at 8 AM), the night (2 hours in a 8 hour window starting at 9 PM), and running a thermostat (running for 30 out of 60 minutes throughout the day). Each of these metrics could capture a different source of impact (e.g. solar curtailment may only be accessible to EV day charging). The 'simulation' simply looks at the cleanest timem to use electricity based on the forecasted value, and evaluates the impact of this against the historical signal value as truth. We also show the impact captured if the forecast was perfect by displaying the maximum possible impact if a 'perfect' forecast were used. These metrics assume that the modeled values are accurate, and only quantify the degree of error caused by the forecast model." ) }} + {% endif %} {# Footer Section #} From b11ab17450a800ea9f5517122c7f5be3f87e1d55 Mon Sep 17 00:00:00 2001 From: samkoebrich Date: Tue, 11 Mar 2025 18:11:01 -0600 Subject: [PATCH 30/85] black --- watttime/evaluation/report.py | 646 ++++++++++++++++++++-------------- 1 file changed, 372 insertions(+), 274 deletions(-) diff --git a/watttime/evaluation/report.py b/watttime/evaluation/report.py index a3b09542..d9b78cab 100644 --- a/watttime/evaluation/report.py +++ b/watttime/evaluation/report.py @@ -77,18 +77,24 @@ def get_random_overlapping_period(dfs, max_period="30D", resample_freq="1H"): # If the total overlap duration is less than or equal to max_period, return the full range if total_overlap_duration <= max_timedelta: - overlap_range = pd.date_range(start=start_overlap, end=end_overlap, freq=resample_freq) + overlap_range = pd.date_range( + start=start_overlap, end=end_overlap, freq=resample_freq + ) # Otherwise, return the first overlap range that satisfies the max_period else: overlap_range = pd.date_range( start=start_overlap, end=start_overlap + max_timedelta, freq=resample_freq ) - assert all([all(t in df.index for t in overlap_range) for df in dfs]), "Not all DataFrames contain the overlapping range." + assert all( + [all(t in df.index for t in overlap_range) for df in dfs] + ), "Not all DataFrames contain the overlapping range." return overlap_range -def plot_sample_moers(factory: DataHandlerFactory, max_sample_period="30D") -> Dict[str, go.Figure]: +def plot_sample_moers( + factory: DataHandlerFactory, max_sample_period="30D" +) -> Dict[str, go.Figure]: """ Plot a sample of old and new MOER values over time, creating a subplot for each unique region. @@ -100,17 +106,19 @@ def plot_sample_moers(factory: DataHandlerFactory, max_sample_period="30D") -> D """ figs = {} - times = get_random_overlapping_period([j.moers for j in factory.data_handlers], max_sample_period) + times = get_random_overlapping_period( + [j.moers for j in factory.data_handlers], max_sample_period + ) for region_abbrev, region_models in factory.data_handlers_by_region_dict.items(): - + fig = go.Figure() for model_job in region_models: - + fig.add_trace( go.Scatter( x=times, - y=model_job.moers.loc[times]['signal_value'], + y=model_job.moers.loc[times]["signal_value"], mode="lines", name=model_job.model_date, line=dict(width=2), @@ -123,7 +131,7 @@ def plot_sample_moers(factory: DataHandlerFactory, max_sample_period="30D") -> D height=300, yaxis=dict( title=f"{model_job.signal_type}", - fixedrange=True # Disable y-axis panning + fixedrange=True, # Disable y-axis panning ), showlegend=True, margin=dict(l=50, r=50, t=50, b=50), @@ -132,7 +140,10 @@ def plot_sample_moers(factory: DataHandlerFactory, max_sample_period="30D") -> D tickformat="%Y-%m-%d %H:%M", tickangle=45, showgrid=True, - range=[times.min(), times.min() + pd.Timedelta("7D")], # Default 1-week view + range=[ + times.min(), + times.min() + pd.Timedelta("7D"), + ], # Default 1-week view ), ) figs[region_abbrev] = fig @@ -180,13 +191,15 @@ def plot_distribution_moers(factory: DataHandlerFactory) -> Dict[str, go.Figure] yaxis_visible=False, yaxis_showticklabels=False, ) - + figs[region_abbrev] = fig return figs -def plot_heatmaps(factory: DataHandlerFactory, colorscale="Oranges") -> Dict[str, go.Figure]: +def plot_heatmaps( + factory: DataHandlerFactory, colorscale="Oranges" +) -> Dict[str, go.Figure]: """ Generate vertically stacked heatmaps for each region, with each heatmap having its own colorscale. Args: @@ -194,9 +207,9 @@ def plot_heatmaps(factory: DataHandlerFactory, colorscale="Oranges") -> Dict[str Returns: Dict[str, go.Figure]: A dictionary mapping region abbreviations to their heatmap figures. """ - figs = {} + figs = {} for region_abbrev, region_models in factory.data_handlers_by_region_dict.items(): - + # Initialize a subplot with one row per job fig = sp.make_subplots( rows=len(region_models), @@ -205,7 +218,7 @@ def plot_heatmaps(factory: DataHandlerFactory, colorscale="Oranges") -> Dict[str subplot_titles=[j.model_date for j in region_models], vertical_spacing=0.1, # Reduced spacing for better alignment ) - + for j, job in enumerate(region_models, start=1): heat = job.moers.assign( month=job.moers.index.month, hour=job.moers.index.hour @@ -216,8 +229,8 @@ def plot_heatmaps(factory: DataHandlerFactory, colorscale="Oranges") -> Dict[str .mean() .unstack(fill_value=np.nan) ) - heat.index = [calendar.month_abbr[m] for m in heat.index] - + heat.index = [calendar.month_abbr[m] for m in heat.index] + heatmap_trace = go.Heatmap( z=heat.values, x=heat.columns, @@ -230,29 +243,29 @@ def plot_heatmaps(factory: DataHandlerFactory, colorscale="Oranges") -> Dict[str y=0.5, len=0.9, thickness=20, - title=dict(side="right") + title=dict(side="right"), ), ) - + fig.add_trace(heatmap_trace, row=j, col=1) - + fig.update_yaxes(title_text="Month", row=j, col=1) if j == len(region_models): # Only add x-axis title to bottom subplot fig.update_xaxes(title_text="Hour of Day", row=j, col=1) - + fig.update_layout( height=300 * len(region_models), # Adjust height per subplot margin=dict(r=100), # Add right margin for colorbars # title=f"{region_abbrev} Heat Maps" ) - + # Update colorbar positions to align with subplots for model_ix, trace in enumerate(fig.data): - trace.colorbar.y = 1 - (2*model_ix + 1)/(2*len(region_models)) - trace.colorbar.len = 1/len(region_models) * 0.9 - + trace.colorbar.y = 1 - (2 * model_ix + 1) / (2 * len(region_models)) + trace.colorbar.len = 1 / len(region_models) * 0.9 + figs[region_abbrev] = fig - + return figs @@ -289,44 +302,40 @@ def simulate_charge(df: pd.DataFrame, sort_col: str, charge_mins: int): assert sort_col in df.columns include_generated_at = "generated_at" in df.index.names - df = df.sort_index(ascending=True) - df['rank'] = df.groupby(["window_start"] + (["generated_at"] if include_generated_at else []))[sort_col].rank(ascending=True, method='first') charge_needed = charge_mins // 5 df = df.assign(charge_status=False) - for w in df.groupby("window_start"): - _, w_df = w + df = df.sort_index(ascending=True) + for w in df.reset_index().groupby("window_start"): + window_start, w_df = w - # STATEFULLNESS HEURISTIC: point_time must be equal to generate_at - # TODO: really this should be "less then the next generated_at" in the case - # that point_time and generated_at are not available for every 5 mins if include_generated_at: - w_df = w_df.loc[w_df.index.get_level_values('point_time') == w_df.index.get_level_values('generated_at')] - - # STATEFULLNESS HEURISTIC: Take charging periods iteratively, - # at each step only consider if rank of value is less than charge_needed - # e.g. charge_needed diminishes overtime - w_df = w_df.sort_values((["generated_at"] if include_generated_at else []) +['rank'], ascending=True) - charge_periods = [] - _charge_needed = charge_needed - while (_charge_needed > 0) and (len(w_df) > 0): - # charging conditions: - # 1) currently point_time is in the lowest periods (according to n periods from _charge_needed) - # 2) there are not enough times in the df left to deliver a full charge without charging now - # 3) The current rank is high from the initial sort, but still lower than anything remaining. - if (w_df.iloc[0]['rank'] <= _charge_needed) or (len(w_df) == _charge_needed) or (w_df.iloc[0]['rank'] == w_df['rank'].min()): - _charge_needed -= 1 - charge_periods.append(w_df.iloc[0].name) - w_df = w_df.iloc[1:] # skip this row + charge_periods = [] + _charge_needed = charge_needed + for g in w_df.groupby("generated_at"): + generated_at, g_df = g - # Assign charge status only if a full charge is completed - # (e.g. don't have enough data at tail to complete a full charge) - if len(charge_periods) == charge_needed: - df.loc[charge_periods, 'charge_status'] = True + # how many forecasted points are below the current value? + n_below_now = (g_df[sort_col] <= g_df.iloc[0][sort_col]).sum() + should_charge_now = n_below_now <= _charge_needed + + if should_charge_now: + charge_periods.append((generated_at, generated_at)) # point_time and generated_at must be equal + _charge_needed -= 1 + + if _charge_needed == 0: + break + + else: + w_df['rank'] = w_df[sort_col].rank(ascending=True, method='first') + charge_periods = w_df.loc[w_df['rank'] <= charge_needed, 'point_time'] + + df.loc[charge_periods, 'charge_status'] = True assert df['charge_status'].sum() >= (len(df['window_start'].unique()) - 1) * charge_needed assert df['charge_status'].sum() <= (len(df['window_start'].unique())) * charge_needed return df['charge_status'] + def calc_rank_compare_metrics( in_df, charge_mins, @@ -347,81 +356,107 @@ def calc_rank_compare_metrics( start = pd.Timestamp(f"{date} {start_time}") end = start + pd.Timedelta(minutes=window_mins) window_ranges.append((start, end)) - + # Convert to DataFrame window_df = pd.DataFrame(window_ranges, columns=["window_start", "window_end"]) window_df = window_df.sort_values("window_start") - tz = df.index.get_level_values('generated_at')[0].tz - window_df = window_df.assign(window_start=pd.to_datetime(window_df["window_start"], errors='coerce').dt.tz_convert(tz)) + tz = df.index.get_level_values("generated_at")[0].tz + window_df = window_df.assign( + window_start=pd.to_datetime( + window_df["window_start"], errors="coerce" + ).dt.tz_convert(tz) + ) window_df = window_df.dropna(subset=["window_start"]) - - # Ensure df is sorted by "generated_at" before merging - df = df.sort_index(level="generated_at") # Use merge_asof to efficiently assign windows df = pd.merge_asof( - df.reset_index().sort_values("generated_at"), + df.reset_index().sort_values(["generated_at", "point_time"], ascending=True), window_df, left_on="generated_at", right_on="window_start", - direction="backward" + direction="backward", ).set_index(df.index.names) - + else: # Assign each row to a rolling window of `window_mins` based on `generated_at` - df["window_start"] = df.index.get_level_values("generated_at").floor(f"{window_mins}min") - df['window_end'] = df["window_start"] + pd.Timedelta(f"{window_mins} min") + df["window_start"] = df.index.get_level_values("generated_at").floor( + f"{window_mins}min" + ) + df["window_end"] = df["window_start"] + pd.Timedelta(f"{window_mins} min") # Filter out rows that do not fall within the valid window - df.loc[(df.index.get_level_values("point_time") >= df["window_end"]) | - (df.index.get_level_values("generated_at") >= df["window_end"]), "window_start"] = pd.NaT + df.loc[ + (df.index.get_level_values("point_time") >= df["window_end"]) + | (df.index.get_level_values("generated_at") >= df["window_end"]) | + (df.index.get_level_values("generated_at") <= df['window_start']), + "window_start", + ] = pd.NaT df = df.dropna(subset=["window_start"]) df = df.assign( truth_charge_status=simulate_charge(df, truth_col, charge_mins), pred_charge_status=simulate_charge(df, pred_col, charge_mins), ) - + df = df.assign( - truth_charge_emissions=df[truth_col] * df['truth_charge_status'] * (load_kw / 1000), - pred_charge_emissions=df[truth_col] * df['pred_charge_status'] * (load_kw / 1000), + truth_charge_emissions=df[truth_col] + * df["truth_charge_status"] + * (load_kw / 1000), + pred_charge_emissions=df[truth_col] + * df["pred_charge_status"] + * (load_kw / 1000), ) - + # baseline: immediate charging rather than AER - df = df.assign(sequential_rank=df.groupby('window_start').cumcount()) - df = df.assign(baseline_charge_status = df['sequential_rank'] <= charge_mins // 5) - df = df.assign(baseline_charge_emissions = df[truth_col] * df['baseline_charge_status'] * (load_kw / 1000)) + df = df.sort_index() + df = df.assign(sequential_rank=df.groupby("window_start").cumcount()) + df = df.assign(baseline_charge_status=df["sequential_rank"] < charge_mins // 5) + df = df.assign( + baseline_charge_emissions=df[truth_col] + * df["baseline_charge_status"] + * (load_kw / 1000) + ) - assert df['baseline_charge_status'].sum() >= (len(df['window_start'].unique()) - 1) * charge_mins // 5 - assert df['baseline_charge_status'].sum() <= (len(df['window_start'].unique())) * charge_mins // 5 - - # Calculate total CO2 emissions ("truth") - y_actual_emissions = df['truth_charge_emissions'].sum() - y_pred_emissions = df['pred_charge_emissions'].sum() - y_base_emissions = df['baseline_charge_emissions'].sum() + assert ( + df["baseline_charge_status"].sum() + >= (len(df["window_start"].unique()) - 1) * charge_mins // 5 + ) + assert ( + df["baseline_charge_status"].sum() + <= (len(df["window_start"].unique()) + 1) * charge_mins // 5 + ) - # Calculate savings: reduction, potential - reduction = (y_base_emissions - y_pred_emissions) / len(set(df.index.date)) - potential = (y_base_emissions - y_actual_emissions) / len(set(df.index.date)) + # Calculate total CO2 emissions ("truth") + y_best_emissions = df["truth_charge_emissions"].sum() + y_pred_emissions = df["pred_charge_emissions"].sum() + y_base_emissions = df["baseline_charge_emissions"].sum() - # Normalize to mean per window - reduction /= len(df['window_start'].unique()) - potential /= len(df['window_start'].unique()) + assert y_pred_emissions >= y_best_emissions + assert y_base_emissions >= y_best_emissions + + reduction = (y_base_emissions - y_pred_emissions) / len( + set(df.index.get_level_values("generated_at").date) + ) + potential = (y_base_emissions - y_best_emissions) / len( + set(df.index.get_level_values("generated_at").date) + ) return { "reduction": round(reduction, 1), "potential": round(potential, 1), - } + } -def plot_norm_mae(factory: DataHandlerFactory, horizons_hr=[1, 6, 12, 18, 24]) -> Dict[str, go.Figure]: +def plot_norm_mae( + factory: DataHandlerFactory, horizons_hr=[1, 6, 12, 18, 24] +) -> Dict[str, go.Figure]: """ Create a Plotly bar chart for rank correlation by horizon with one subplot per region (abbrev). """ y_min = y_max = 0 figs = {} - + # Iterate through each region and create a bar plot for region_abbrev, region_models in factory.data_handlers_by_region_dict.items(): fig = go.Figure() @@ -429,7 +464,8 @@ def plot_norm_mae(factory: DataHandlerFactory, horizons_hr=[1, 6, 12, 18, 24]) - for model_job in region_models: y_values = [ - calc_norm_mae(model_job.forecasts_v_moers, (h * 60) - 5) for h in horizons_hr + calc_norm_mae(model_job.forecasts_v_moers, (h * 60) - 5) + for h in horizons_hr ] y_max = max(y_values + [y_max]) y_min = min(y_values + [y_min]) @@ -452,16 +488,20 @@ def plot_norm_mae(factory: DataHandlerFactory, horizons_hr=[1, 6, 12, 18, 24]) - margin=dict(l=50, r=50, t=50, b=50), barmode="group", # Ensure bars for each region are grouped ) - + figs[region_abbrev] = fig for region, fig in figs.items(): - figs[region] = fig.update_yaxes(range=[y_min - (0.25 * y_max), y_max + (0.25 * y_max)]) + figs[region] = fig.update_yaxes( + range=[y_min - (0.25 * y_max), y_max + (0.25 * y_max)] + ) return figs -def plot_rank_corr(factory: DataHandlerFactory, horizons_hr=[1, 6, 12, 18, 24]) -> Dict[str, go.Figure]: +def plot_rank_corr( + factory: DataHandlerFactory, horizons_hr=[1, 6, 12, 18, 24] +) -> Dict[str, go.Figure]: """ Create a Plotly line plot for rank correlation by horizon with one subplot per region (abbrev). @@ -502,7 +542,7 @@ def plot_rank_corr(factory: DataHandlerFactory, horizons_hr=[1, 6, 12, 18, 24]) textposition="outside", ) ) - + # Update layout fig.update_layout( height=300, @@ -511,12 +551,14 @@ def plot_rank_corr(factory: DataHandlerFactory, horizons_hr=[1, 6, 12, 18, 24]) showlegend=True, margin=dict(l=50, r=50, t=50, b=50), ) - + figs[region_abbrev] = fig - + for region, fig in figs.items(): # Set uniform y-axis range for all subplots - figs[region] = fig.update_yaxes(range=[y_min - (0.25 * y_max), y_max + (0.25 * y_max)]) + figs[region] = fig.update_yaxes( + range=[y_min - (0.25 * y_max), y_max + (0.25 * y_max)] + ) return figs @@ -527,19 +569,19 @@ def plot_rank_corr(factory: DataHandlerFactory, horizons_hr=[1, 6, 12, 18, 24]) "charge_mins": 3 * 60, "window_mins": 12 * 60, "window_starts": ["19:00"], - "load_kw": 19 + "load_kw": 19, }, "EV-day": { "charge_mins": 2 * 60, "window_mins": 8 * 60, "window_starts": ["09:00"], - "load_kw": 19 + "load_kw": 19, }, "Thermostat": { "charge_mins": 30, "window_mins": 60, "window_starts": None, - "load_kw": 3 # typical AC + "load_kw": 3, # typical AC }, } @@ -552,7 +594,7 @@ def plot_impact_forecast_metrics( y_max = 0 for region_abbrev, region_models in factory.data_handlers_by_region_dict.items(): - + fig = sp.make_subplots( rows=len(region_models), cols=1, @@ -560,12 +602,14 @@ def plot_impact_forecast_metrics( subplot_titles=[j.model_date for j in region_models], vertical_spacing=0.2, ) - + for model_ix, model_job in enumerate(region_models, start=1): _metrics = [ { - **calc_rank_compare_metrics(model_job.forecasts_v_moers, **AER_SCENARIOS[s]), + **calc_rank_compare_metrics( + model_job.forecasts_v_moers, **AER_SCENARIOS[s] + ), "scenario": s, } for s in scenarios @@ -594,9 +638,7 @@ def plot_impact_forecast_metrics( name="Forecast Achieved", text=[ f"{(r / (p + 1e-6)) * 100:.1f}%" - for r, p in zip( - _metrics["reduction"], _metrics["potential"] - ) + for r, p in zip(_metrics["reduction"], _metrics["potential"]) ], textposition="outside", marker=dict(color="rgba(0, 128, 0, 0.8)"), # Green for reduction @@ -605,8 +647,8 @@ def plot_impact_forecast_metrics( row=model_ix, col=1, ) - - y_max = max(_metrics["reduction"].max(), _metrics['potential'].max(), y_max) + + y_max = max(_metrics["reduction"].max(), _metrics["potential"].max(), y_max) # Update layout fig.update_layout( @@ -622,9 +664,9 @@ def plot_impact_forecast_metrics( for model_ix in range(1, len(region_models) + 1): fig.update_xaxes(title_text="Scenario", row=model_ix, col=1) fig.update_yaxes(title_text="CO2 Savings (lbs/MWh)", row=model_ix, col=1) - + figs[region_abbrev] = fig - + # Set uniform y-axis range for all subplots for region, fig in figs.items(): figs[region] = fig.update_yaxes(range=[0, y_max + (0.25 * y_max)]) @@ -632,10 +674,14 @@ def plot_impact_forecast_metrics( return figs -def plot_sample_fuelmix(factory: DataHandlerFactory, max_sample_period="30D") -> Dict[str, go.Figure]: +def plot_sample_fuelmix( + factory: DataHandlerFactory, max_sample_period="30D" +) -> Dict[str, go.Figure]: figs = {} - times = get_random_overlapping_period([j.fuel_mix for j in factory.data_handlers], max_sample_period) + times = get_random_overlapping_period( + [j.fuel_mix for j in factory.data_handlers], max_sample_period + ) for region_abbrev, region_models in factory.data_handlers_by_region_dict.items(): # Initialize a subplot with one row per region @@ -648,7 +694,7 @@ def plot_sample_fuelmix(factory: DataHandlerFactory, max_sample_period="30D") -> ) for model_ix, model_job in enumerate(region_models, start=1): - + stacked_values = model_job.fuel_mix.loc[times] # Create cumulative values for stacking @@ -675,7 +721,7 @@ def plot_sample_fuelmix(factory: DataHandlerFactory, max_sample_period="30D") -> height=300 * len(region_models), yaxis=dict( title=f"{region_models[0].signal_type}", - fixedrange=True # Disable y-axis panning + fixedrange=True, # Disable y-axis panning ), showlegend=True, margin=dict(l=50, r=50, t=50, b=50), @@ -692,9 +738,16 @@ def plot_sample_fuelmix(factory: DataHandlerFactory, max_sample_period="30D") -> return figs -def calc_max_potential(in_df, charge_mins, window_mins, window_starts=None, truth_col="signal_value", load_kw=1000): +def calc_max_potential( + in_df, + charge_mins, + window_mins, + window_starts=None, + truth_col="signal_value", + load_kw=1000, +): """Predict the maximum potential CO2 Savings, without any forecast to estimate the upper limit of impact from a given signal value.""" - + df = in_df.copy() if window_starts: @@ -706,17 +759,21 @@ def calc_max_potential(in_df, charge_mins, window_mins, window_starts=None, trut start = pd.Timestamp(f"{date} {start_time}") end = start + pd.Timedelta(minutes=window_mins) window_ranges.append((start, end)) - + # Convert to DataFrame window_df = pd.DataFrame(window_ranges, columns=["window_start", "window_end"]) window_df = window_df.sort_values("window_start") - tz = df.index.get_level_values('point_time')[0].tz + tz = df.index.get_level_values("point_time")[0].tz window_df = window_df.assign( - window_start=pd.to_datetime(window_df["window_start"], errors='coerce').dt.tz_localize(tz), - window_end=pd.to_datetime(window_df["window_end"], errors='coerce').dt.tz_localize(tz) + window_start=pd.to_datetime( + window_df["window_start"], errors="coerce" + ).dt.tz_localize(tz), + window_end=pd.to_datetime( + window_df["window_end"], errors="coerce" + ).dt.tz_localize(tz), ) window_df = window_df.dropna(subset=["window_start"]) - + # Ensure df is sorted by "point_time" before merging df = df.sort_index(level="point_time") @@ -726,45 +783,67 @@ def calc_max_potential(in_df, charge_mins, window_mins, window_starts=None, trut window_df, left_on="point_time", right_on="window_start", - direction="backward" + direction="backward", ).set_index(df.index.names) - + else: # Assign each row to a rolling window of `window_mins` based on `generated_at` - df["window_start"] = df.index.get_level_values("point_time").floor(f"{window_mins}min") - df['window_end'] = df["window_start"] + pd.Timedelta(f"{window_mins} min") + df["window_start"] = df.index.get_level_values("point_time").floor( + f"{window_mins}min", ambiguous=False + ) + df["window_end"] = df["window_start"] + pd.Timedelta(f"{window_mins} min") # Filter out rows that do not fall within the valid window df = df.dropna(subset=["window_start"]) - df = df.loc[(df.index.get_level_values("point_time") < df["window_end"]) & (df.index.get_level_values("point_time") >= df["window_start"])] - - df = df.assign(charge_status = simulate_charge(df, truth_col, charge_mins)) - df = df.assign(charge_emissions = df[truth_col] * df["charge_status"] * (load_kw / 1000)) - + df = df.loc[ + (df.index.get_level_values("point_time") < df["window_end"]) + & (df.index.get_level_values("point_time") >= df["window_start"]) + ] + + df = df.assign(charge_status=simulate_charge(df, truth_col, charge_mins)) + df = df.assign( + charge_emissions=df[truth_col] * df["charge_status"] * (load_kw / 1000) + ) + # baseline: immediate charging rather than AER - df = df.assign(sequential_rank=df.groupby('window_start').cumcount()) - df = df.assign(baseline_charge_status = df['sequential_rank'] < charge_mins // 5) - df = df.assign(baseline_charge_emissions = df[truth_col] * df['baseline_charge_status'] * (load_kw / 1000)) - - assert df['baseline_charge_status'].sum() >= (len(df['window_start'].unique()) - 1) * charge_mins // 5 - assert df['baseline_charge_status'].sum() <= (len(df['window_start'].unique())) * charge_mins // 5 - - y_best_total = df['charge_emissions'].sum() - y_baseline_total = df['baseline_charge_emissions'].sum() + df = df.assign(sequential_rank=df.groupby("window_start").cumcount()) + df = df.assign(baseline_charge_status=df["sequential_rank"] < charge_mins // 5) + df = df.assign( + baseline_charge_emissions=df[truth_col] + * df["baseline_charge_status"] + * (load_kw / 1000) + ) + + assert ( + df["baseline_charge_status"].sum() + >= (len(df["window_start"].unique()) - 1) * charge_mins // 5 + ) + assert ( + df["baseline_charge_status"].sum() + <= (len(df["window_start"].unique())) * charge_mins // 5 + ) + + y_best_total = df["charge_emissions"].sum() + y_baseline_total = df["baseline_charge_emissions"].sum() + assert y_best_total <= y_baseline_total + potential = (y_baseline_total - y_best_total) / len(set(df.index.date)) - + return {"potential": round(potential, 1)} -# def plot_max_impact_potential(factory: DataHandlerFactory, scenarios: List[str] = ["EV-night", "EV-day", "Thermostat"]): -def plot_max_impact_potential(factory: DataHandlerFactory, scenarios: List[str] = ["EV-night"]): + +def plot_max_impact_potential( + factory: DataHandlerFactory, + scenarios: List[str] = ["EV-night", "EV-day", "Thermostat"], +): figs = {} y_max = 0 for region_abbrev, region_models in factory.data_handlers_by_region_dict.items(): fig = go.Figure() - + for model_job in region_models: - + _metrics = [ { **calc_max_potential(model_job.moers, **AER_SCENARIOS[s]), @@ -772,7 +851,7 @@ def plot_max_impact_potential(factory: DataHandlerFactory, scenarios: List[str] } for s in scenarios ] - + _metrics = pd.DataFrame(_metrics) # Add the 'potential' bar trace @@ -784,42 +863,36 @@ def plot_max_impact_potential(factory: DataHandlerFactory, scenarios: List[str] hovertemplate="%{x}: %{y:.1f} lbs CO2/MWh", ) ) - - y_max = max(_metrics['potential'].max(), y_max) - + + y_max = max(_metrics["potential"].max(), y_max) + fig.update_layout( height=300 * len(region_models), yaxis=dict( - title="lbs CO2/MWh", - fixedrange=True # Disable y-axis panning - ) + title="lbs CO2/MWh", fixedrange=True # Disable y-axis panning + ), ) - + figs[region_abbrev] = fig - + return figs def plot_fuelmix_heatmap(factory: DataHandlerFactory): def create_pivot_table(df, column): - df = df.assign( - month=df.index.month, - hour=df.index.hour - ) - + df = df.assign(month=df.index.month, hour=df.index.hour) + if column not in df.columns: # Create a zero-filled DataFrame with the expected shape pivot = pd.DataFrame( - 0, - index=range(1, 13), # Months 1-12 - columns=range(24) # Hours 0-23 + 0, index=range(1, 13), columns=range(24) # Months 1-12 # Hours 0-23 ) else: - grouped = df.groupby(['month', 'hour'])[column].mean().reset_index() - pivot = grouped.pivot(index='month', columns='hour', values=column) - + grouped = df.groupby(["month", "hour"])[column].mean().reset_index() + pivot = grouped.pivot(index="month", columns="hour", values=column) + return pivot - + def create_buttons(column_names, num_subplots): buttons = [] for i, name in enumerate(column_names): @@ -829,36 +902,32 @@ def create_buttons(column_names, num_subplots): # For each subplot, make only the trace for current fuel type visible for k in range(len(column_names)): visible.append(True if k == i else False) - - button = dict( - label=name, - method="update", - args=[{"visible": visible}] - ) + + button = dict(label=name, method="update", args=[{"visible": visible}]) buttons.append(button) return buttons - + def make_alpha_gradient(hex_color, steps=10): rgba_colors = [] - + for i in range(steps): alpha = 0 if i == 0 else min(1, (i / (steps / 1.25))) - rgba_colors.append(f'rgba{pc.hex_to_rgb(hex_color) + (alpha,)}') - + rgba_colors.append(f"rgba{pc.hex_to_rgb(hex_color) + (alpha,)}") + return [[i / (steps - 1), rgba] for i, rgba in enumerate(rgba_colors)] - + figs = {} - + for region_abbrev, region_models in factory.data_handlers_by_region_dict.items(): # We need to ensure all jobs have the same fuel types for the buttons to work all_columns = set() for job in region_models: all_columns.update(job.fuel_mix.columns) - + # Create subplot titles with model dates subplot_titles = [j.model_date for j in region_models] - + # Create subplots fig = sp.make_subplots( rows=len(region_models), @@ -866,11 +935,11 @@ def make_alpha_gradient(hex_color, steps=10): shared_xaxes=True, subplot_titles=subplot_titles, ) - + all_columns_list = sorted(list(all_columns)) # Sort to ensure consistent order - + trace_idx = 0 - + # Add traces for each job for job_idx, job in enumerate(region_models, start=1): for fuel_idx, fuel in enumerate(all_columns_list): @@ -885,20 +954,34 @@ def make_alpha_gradient(hex_color, steps=10): name=f"{fuel} ({job.model_date})", visible=(fuel_idx == 0), showscale=False, # Only show colorscale for the first job - zmin=0, zmax=1, + zmin=0, + zmax=1, ), - row=job_idx, col=1 + row=job_idx, + col=1, ) - + trace_idx += 1 - + # Add buttons that control visibility across all subplots buttons = create_buttons(all_columns_list, len(region_models)) - + # Month names mapping - month_names = {1: 'Jan', 2: 'Feb', 3: 'Mar', 4: 'Apr', 5: 'May', 6: 'Jun', - 7: 'Jul', 8: 'Aug', 9: 'Sep', 10: 'Oct', 11: 'Nov', 12: 'Dec'} - + month_names = { + 1: "Jan", + 2: "Feb", + 3: "Mar", + 4: "Apr", + 5: "May", + 6: "Jun", + 7: "Jul", + 8: "Aug", + 9: "Sep", + 10: "Oct", + 11: "Nov", + 12: "Dec", + } + # Update layout fig.update_layout( updatemenus=[ @@ -909,117 +992,132 @@ def make_alpha_gradient(hex_color, steps=10): x=0, xanchor="left", y=-0.15, - buttons=buttons + buttons=buttons, ) ], - height=(300 * len(region_models)) + 50, # Adjust height based on number of jobs + height=(300 * len(region_models)) + + 50, # Adjust height based on number of jobs xaxis_title="Hour of Day", ) - + # Update all y-axes to show month names for model_ix in range(1, len(region_models) + 1): fig.update_yaxes( title="Month", tickvals=list(month_names.keys()), ticktext=list(month_names.values()), - row=model_ix, col=1 + row=model_ix, + col=1, ) - + # Update all x-axes fig.update_xaxes( - title="Hour of Day" if model_ix == len(region_models) else None, # Only show title on bottom plot + title=( + "Hour of Day" if model_ix == len(region_models) else None + ), # Only show title on bottom plot tickmode="linear", dtick=2, # Show every 2 hours - row=model_ix, col=1 + row=model_ix, + col=1, ) - + figs[region_abbrev] = fig - + return figs -def plot_bland_altman(factory: DataHandlerFactory, n_samples: int = 2500) -> Dict[str, go.Figure]: +def plot_bland_altman( + factory: DataHandlerFactory, n_samples: int = 2500 +) -> Dict[str, go.Figure]: """ A Bland-Altman plot is useful to see the difference between two measurements (models). - + It includes a scatterplot of the deltas between the two measurements, along with a reference line. signifying the mean of the deltas, and confidence intervals for the mean. """ figs = {} for region_abbrev, region_models in factory.data_handlers_by_region_dict.items(): - + if len(region_models) == 1: return None - - merged_moers = pd.concat([dh.moers for dh in region_models], axis='columns').dropna() + + merged_moers = pd.concat( + [dh.moers for dh in region_models], axis="columns" + ).dropna() merged_moers.columns = [dh.model_date for dh in region_models] - merged_moers = merged_moers.sort_index(axis='columns') # put newest models last - + merged_moers = merged_moers.sort_index(axis="columns") # put newest models last + # TODO: consider more than two models if len(merged_moers.columns) < 2: merged_moers = merged_moers.iloc[:, :2] - - + merged_moers = merged_moers.assign( - delta=merged_moers.max(axis='columns') - merged_moers.min(axis='columns'), - mean = merged_moers.mean(axis='columns') + delta=merged_moers.max(axis="columns") - merged_moers.min(axis="columns"), + mean=merged_moers.mean(axis="columns"), ) merged_moers = merged_moers.sample(n_samples) fig = go.Figure() # scatter - fig.add_trace(go.Scatter( - x=merged_moers['mean'], y=merged_moers['delta'], mode='markers', - marker=dict(opacity=0.5), - name="Delta of Model Values" - )) + fig.add_trace( + go.Scatter( + x=merged_moers["mean"], + y=merged_moers["delta"], + mode="markers", + marker=dict(opacity=0.5), + name="Delta of Model Values", + ) + ) # reference line - fig.add_trace(go.Scatter( - x=[merged_moers['mean'].min(), merged_moers['mean'].max()], - y=[merged_moers['delta'].mean(), merged_moers['delta'].mean()], - mode='lines', - line=dict(color='red'), - name='Mean Delta' - )) + fig.add_trace( + go.Scatter( + x=[merged_moers["mean"].min(), merged_moers["mean"].max()], + y=[merged_moers["delta"].mean(), merged_moers["delta"].mean()], + mode="lines", + line=dict(color="red"), + name="Mean Delta", + ) + ) # ci - fig.add_trace(go.Scatter( - x=[merged_moers['mean'].min(), merged_moers['mean'].max()], - y=[ - merged_moers['delta'].mean() + merged_moers['delta'].std() * 1.96, - merged_moers['delta'].mean() + merged_moers['delta'].std() * 1.96 - ], - mode='lines', - line=dict(color='red', dash='dash'), - opacity=0.5, - name='95% CI' - )) - - fig.add_trace(go.Scatter( - x=[merged_moers['mean'].min(), merged_moers['mean'].max()], - y=[ - merged_moers['delta'].mean() - merged_moers['delta'].std() * 1.96, - merged_moers['delta'].mean() - merged_moers['delta'].std() * 1.96 - ], - mode='lines', - line=dict(color='red', dash='dash'), - opacity=0.5, - showlegend=False - )) + fig.add_trace( + go.Scatter( + x=[merged_moers["mean"].min(), merged_moers["mean"].max()], + y=[ + merged_moers["delta"].mean() + merged_moers["delta"].std() * 1.96, + merged_moers["delta"].mean() + merged_moers["delta"].std() * 1.96, + ], + mode="lines", + line=dict(color="red", dash="dash"), + opacity=0.5, + name="95% CI", + ) + ) + + fig.add_trace( + go.Scatter( + x=[merged_moers["mean"].min(), merged_moers["mean"].max()], + y=[ + merged_moers["delta"].mean() - merged_moers["delta"].std() * 1.96, + merged_moers["delta"].mean() - merged_moers["delta"].std() * 1.96, + ], + mode="lines", + line=dict(color="red", dash="dash"), + opacity=0.5, + showlegend=False, + ) + ) fig.update_layout( height=500, - xaxis_title='Mean', - yaxis_title='Delta', + xaxis_title="Mean", + yaxis_title="Delta", legend=dict( - orientation="h", - yanchor="bottom", - y=1.02, - xanchor="right", - x=1 - )) + orientation="h", yanchor="bottom", y=1.02, xanchor="right", x=1 + ), + ) figs[region_abbrev] = fig return figs @@ -1093,7 +1191,13 @@ def parse_report_command_line_args(sys_args): PLOTS = { - "signal": [plot_sample_moers, plot_distribution_moers, plot_heatmaps, plot_bland_altman, plot_max_impact_potential], + "signal": [ + plot_sample_moers, + plot_distribution_moers, + plot_heatmaps, + plot_bland_altman, + plot_max_impact_potential, + ], "fuel_mix": [plot_sample_fuelmix, plot_fuelmix_heatmap], "forecast": [plot_norm_mae, plot_rank_corr, plot_impact_forecast_metrics], } @@ -1131,44 +1235,38 @@ def generate_report( for step in steps: for plot_func in PLOTS[step]: _plot = plot_func(factory) - + if _plot is None: continue - + elif isinstance(_plot, dict): plotly_html[plot_func.__name__] = {} for region_abbrev, _p in _plot.items(): - # for trace in _p.data: - # trace.x = [t.strftime("%Y-%m-%d %H:%M") for t in trace.x] # Keep only date and hour-minute - # trace.y = [round(y, 3) for y in trace.y] # Reduce decimal places - plotly_html[plot_func.__name__][region_abbrev] = _p.to_html( - full_html=False, include_plotlyjs=False + full_html=False, include_plotlyjs=False, include_mathjax=False ) else: assert isinstance(_plot, go.Figure) - - # reduce datatype precision in plot - # for trace in _plot.data: - # trace.x = [t.strftime("%Y-%m-%d %H:%M") for t in trace.x] # Keep only date and hour-minute - # trace.y = [round(y, 3) for y in trace.y] # Reduce decimal places - + plotly_html[plot_func.__name__] = _plot.to_html( - full_html=False, include_plotlyjs=False + full_html=False, include_plotlyjs=False, include_mathjax=False ) - - plotly_html['report_input_dict'] = json.dumps( + + plotly_html["report_input_dict"] = json.dumps( dict( region_list=region_list, model_date_list=model_date_list, signal_type=signal_type, eval_start=str(eval_start), eval_end=str(eval_end), - steps=steps - ), indent=4 + steps=steps, + ), + indent=4, + ) + + plotly_html["collected_model_meta"] = json.dumps( + factory.collected_model_meta, indent=4 ) - - plotly_html['collected_model_meta'] = json.dumps(factory.collected_model_meta, indent=4) with open(output_path, "w", encoding="utf-8") as output_file: with open(input_template_path) as template_file: From 7b085c3749bd7cce84836bb4bcfa4ba65c0300b8 Mon Sep 17 00:00:00 2001 From: samkoebrich Date: Tue, 11 Mar 2025 18:11:18 -0600 Subject: [PATCH 31/85] impact_forecast_metrics text --- watttime/evaluation/report_card_template.html | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/watttime/evaluation/report_card_template.html b/watttime/evaluation/report_card_template.html index 344dd3ae..ab514a53 100644 --- a/watttime/evaluation/report_card_template.html +++ b/watttime/evaluation/report_card_template.html @@ -343,7 +343,7 @@

Forecast Performance

"plot_impact_forecast_metrics", plot_impact_forecast_metrics, "3) Estimation of AER Impact using Forecasts", - "These plots simulate a vairiety of AER scenarios for typical customer patterns including charging an EV during the day (3 hours in a 12 hour window starting at 8 AM), the night (2 hours in a 8 hour window starting at 9 PM), and running a thermostat (running for 30 out of 60 minutes throughout the day). Each of these metrics could capture a different source of impact (e.g. solar curtailment may only be accessible to EV day charging). The 'simulation' simply looks at the cleanest timem to use electricity based on the forecasted value, and evaluates the impact of this against the historical signal value as truth. We also show the impact captured if the forecast was perfect by displaying the maximum possible impact if a 'perfect' forecast were used. These metrics assume that the modeled values are accurate, and only quantify the degree of error caused by the forecast model." + "These plots simulate a vairiety of AER scenarios for typical customer patterns including charging an EV during the day (3 hours in a 12 hour window starting at 8 AM), the night (2 hours in a 8 hour window starting at 9 PM), and running a thermostat (running for 30 out of 60 minutes throughout the day). Each of these metrics could capture a different source of impact (e.g. solar curtailment may only be accessible to EV day charging). The 'simulation' simply looks at the cleanest timem to use electricity based on the forecasted value, and evaluates the impact of this against the historical signal value as truth. We also show the impact captured if the forecast was perfect by displaying the maximum possible impact if a 'perfect' forecast were used. These metrics assume that the modeled values are accurate, and only quantify the degree of error caused by the forecast model. Units are normalized to be impact achieved per day (which may or may not be per window)." ) }} {% endif %} From 0e95e1a5ea02bbbe3f3b2605872496f3db9110d3 Mon Sep 17 00:00:00 2001 From: samkoebrich Date: Tue, 11 Mar 2025 18:11:27 -0600 Subject: [PATCH 32/85] tests --- tests/test_report.py | 204 +++++++++++++++++++++++++++++-------------- 1 file changed, 137 insertions(+), 67 deletions(-) diff --git a/tests/test_report.py b/tests/test_report.py index 67ba9806..0da4a078 100644 --- a/tests/test_report.py +++ b/tests/test_report.py @@ -1,94 +1,164 @@ import unittest from datetime import datetime, date -from watttime import report -from pathlib import Path -from typing import List, Union -from itertools import product - -BA_LIST: List[str] = ["WEM"] -MODEL_DATE_LIST: List[str] = ["2022-10-01", "2024-10-16"] +from watttime.evaluation import report +from typing import List, Union, Dict +import plotly.graph_objects as go +import pandas as pd +import numpy as np +from watttime.evaluation.get_wt_api_forecast_evaluation_data import DataHandlerFactory + +REGION_LIST: List[str] = ["WEM"] +MODEL_DATE_LIST: List[str] = ["2022-10-01", "2025-02-01"] SIGNAL_TYPE: str = "co2_moer" -EVAL_START: Union[str, datetime, date] = "2024-01-01T00:00Z" -EVAL_END: Union[str, datetime, date] = "2024-03-01T00:00Z" -FORECAST_SAMPLE_DAYS = 3 +EVAL_START: Union[str, datetime, date] = "2023-10-29T00:00Z" +EVAL_END: Union[str, datetime, date] = "2023-11-03T00:00Z" +FORECAST_SAMPLE = 3 -class TestReport(unittest.TestCase): +class TestPlotCreationMultiModel(unittest.TestCase): def setUp(self): - self.eval_days = report.parse_eval_days( - eval_start=EVAL_START, eval_end=EVAL_END - ) - self.sample_days = report.parse_forecast_sample_days( - eval_days=self.eval_days, forecast_sample_days=FORECAST_SAMPLE_DAYS - ) - self.jobs = list( - report.ModelAnalysis( - ba=i[0], - model_date=i[1], - signal_type=SIGNAL_TYPE, - eval_start=EVAL_START, - eval_end=EVAL_END, - eval_days=self.eval_days, - sample_days=self.sample_days, - ) - for i in product(BA_LIST, MODEL_DATE_LIST) + self.factory = DataHandlerFactory( + eval_start=EVAL_START, + eval_end=EVAL_END, + regions=REGION_LIST, + model_dates=MODEL_DATE_LIST, + signal_types=SIGNAL_TYPE, + forecast_sample_size=FORECAST_SAMPLE ) - # Compile forecast_v_moer for each job - for job in self.jobs: - job.compile_forecast_v_moer() - - def test_compile_forecast_v_moer(self): - for job in self.jobs: - self.assertFalse(job.moers.empty) - self.assertFalse(job.forecasts.empty) - self.assertFalse(job.forecasts_v_moers.empty) - - def test_plot_sample_moers(self): - fig = report.plot_sample_moers(self.jobs) - self.assertGreater(len(fig.data), 0) + def validate_figure(self, fig_dict: Dict[str, go.Figure]): + self.assertIsInstance(fig_dict, dict) + self.assertIsInstance(fig_dict[REGION_LIST[0]], go.Figure) + self.assertGreater(len(fig_dict[REGION_LIST[0]].data), 0) + _ = fig_dict[REGION_LIST[0]].to_html( + full_html=False, include_plotlyjs=False, include_mathjax=False, validate=True + ) def test_plot_distribution_moers(self): - fig = report.plot_distribution_moers(self.jobs) - self.assertGreater(len(fig.data), 0) + fig_dict = report.plot_distribution_moers(self.factory) + self.validate_figure(fig_dict) - def test_plot_ba_heatmaps(self): - fig = report.plot_ba_heatmaps(self.jobs) - self.assertGreater(len(fig.data), 0) + def test_plot_heatmaps(self): + fig_dict = report.plot_heatmaps(self.factory) + self.validate_figure(fig_dict) def test_plot_norm_mae(self): - fig = report.plot_norm_mae(self.jobs) - self.assertGreater(len(fig.data), 0) + fig_dict = report.plot_norm_mae(self.factory) + self.validate_figure(fig_dict) - def test_plot_rank_correlation(self): - fig = report.plot_rank_correlation(self.jobs) - self.assertGreater(len(fig.data), 0) + def test_plot_rank_corr(self): + fig_dict = report.plot_rank_corr(self.factory) + self.validate_figure(fig_dict) def test_plot_impact_forecast_metrics(self): - fig = report.plot_impact_forecast_metrics(self.jobs) - self.assertGreater(len(fig.data), 0) + fig_dict = report.plot_impact_forecast_metrics(self.factory) + self.validate_figure(fig_dict) + + def test_plot_bland_altman(self): + fig_dict = report.plot_bland_altman(self.factory) + self.validate_figure(fig_dict) + + def test_plot_fuelmix_heatmap(self): + fig_dict = report.plot_fuelmix_heatmap(self.factory) + self.validate_figure(fig_dict) + + def test_plot_max_impact_potential(self): + fig_dict = report.plot_max_impact_potential(self.factory) + self.validate_figure(fig_dict) + + def test_plot_sample_fuelmix(self): + fig_dict = report.plot_sample_fuelmix(self.factory) + self.validate_figure(fig_dict) + + +class TestPlotCreationSingleModel(unittest.TestCase): + def setUp(self): + + self.factory = DataHandlerFactory( + eval_start=EVAL_START, + eval_end=EVAL_END, + regions=REGION_LIST, + model_dates=MODEL_DATE_LIST[0], + signal_types=SIGNAL_TYPE, + forecast_sample_size=FORECAST_SAMPLE + ) + + def validate_figure(self, fig_dict: Dict[str, go.Figure]): + self.assertIsInstance(fig_dict, dict) + self.assertIsInstance(fig_dict[REGION_LIST[0]], go.Figure) + self.assertGreater(len(fig_dict[REGION_LIST[0]].data), 0) + _ = fig_dict[REGION_LIST[0]].to_html( + full_html=False, include_plotlyjs=False, include_mathjax=False, validate=True + ) def test_plot_sample_moers_single_job(self): - fig = report.plot_sample_moers([self.jobs[0]]) - self.assertGreater(len(fig.data), 0) + fig_dict = report.plot_sample_moers(self.factory) + self.validate_figure(fig_dict) def test_plot_distribution_moers_single_job(self): - fig = report.plot_distribution_moers([self.jobs[0]]) - self.assertGreater(len(fig.data), 0) + fig_dict = report.plot_distribution_moers(self.factory) + self.validate_figure(fig_dict) - def test_plot_ba_heatmaps_single_job(self): - fig = report.plot_ba_heatmaps([self.jobs[0]]) - self.assertGreater(len(fig.data), 0) + def test_plot_heatmaps_single_job(self): + fig_dict = report.plot_heatmaps(self.factory) + self.validate_figure(fig_dict) def test_plot_norm_mae_single_job(self): - fig = report.plot_norm_mae([self.jobs[0]]) - self.assertGreater(len(fig.data), 0) + fig_dict = report.plot_norm_mae(self.factory) + self.validate_figure(fig_dict) - def test_plot_rank_correlation_single_job(self): - fig = report.plot_rank_correlation([self.jobs[0]]) - self.assertGreater(len(fig.data), 0) + def test_plot_rank_corr_single_job(self): + fig_dict = report.plot_rank_corr(self.factory) + self.validate_figure(fig_dict) def test_plot_impact_forecast_metrics_single_job(self): - fig = report.plot_impact_forecast_metrics([self.jobs[0]]) - self.assertGreater(len(fig.data), 0) + fig_dict = report.plot_impact_forecast_metrics(self.factory) + self.validate_figure(fig_dict) + + +class TestSimulateCharge(unittest.TestCase): + def setUp(self): + """Creates a sample DataFrame where each generated_at has 4 point_time values.""" + generated_at_times = pd.date_range("2025-03-11 00:00:00", periods=4, freq="5T") + point_time_offsets = pd.to_timedelta([0, 5, 10, 15], unit="m") + + index = pd.MultiIndex.from_tuples( + [ + (gen_at + offset, gen_at) + for gen_at in generated_at_times + for offset in point_time_offsets + ], + names=["point_time", "generated_at"] + ) + + data = { + "window_start": [pd.Timestamp("2025-03-11 00:00:00")] * len(index), + "forecast_value": np.random.randint(1, 20, len(index)), # Random forecast values + "sort_col": np.tile([1, 2, 3, 4], len(generated_at_times)) # Ranks within each generated_at + } + + self.df = pd.DataFrame(data, index=index) + + def test_charge_status_output(self): + """Ensure the function outputs a charge_status column with correct type.""" + charge_status = report.simulate_charge(self.df, sort_col="sort_col", charge_mins=30) + self.assertIsInstance(charge_status, pd.Series) + self.assertTrue(charge_status.dtype == bool) + + def test_correct_length(self): + """Check that the charge_status output has the correct length.""" + charge_status = report.simulate_charge(self.df, sort_col="sort_col", charge_mins=30) + self.assertEqual(len(charge_status), len(self.df)) + + def test_all_point_times_gte_generated_at(self): + """Ensure all point_time values are greater than or equal to generated_at.""" + self.assertTrue((self.df.index.get_level_values("point_time") >= self.df.index.get_level_values("generated_at")).all()) + + def test_charging_fully_schedules(self): + """Verify that a full charge is scheduled correctly.""" + charge_status = report.simulate_charge(self.df, sort_col="sort_col", charge_mins=30) + charge_needed = 20 // 5 # Number of charge periods required + charged_windows = self.df.loc[charge_status].groupby("window_start").size() + self.assertTrue((charged_windows >= charge_needed - 1).all()) + self.assertTrue((charged_windows <= charge_needed).all()) From 06ff6c16282c5e1498c95472cad7a1f218d9430d Mon Sep 17 00:00:00 2001 From: samkoebrich Date: Wed, 12 Mar 2025 09:32:28 -0600 Subject: [PATCH 33/85] update units --- watttime/evaluation/report.py | 26 +++++++++++++++----------- 1 file changed, 15 insertions(+), 11 deletions(-) diff --git a/watttime/evaluation/report.py b/watttime/evaluation/report.py index d9b78cab..9c78e38c 100644 --- a/watttime/evaluation/report.py +++ b/watttime/evaluation/report.py @@ -331,8 +331,8 @@ def simulate_charge(df: pd.DataFrame, sort_col: str, charge_mins: int): df.loc[charge_periods, 'charge_status'] = True - assert df['charge_status'].sum() >= (len(df['window_start'].unique()) - 1) * charge_needed assert df['charge_status'].sum() <= (len(df['window_start'].unique())) * charge_needed + assert df['charge_status'].sum() >= (len(df['window_start'].unique()) * 0.97 * charge_needed) return df['charge_status'] @@ -370,7 +370,7 @@ def calc_rank_compare_metrics( # Use merge_asof to efficiently assign windows df = pd.merge_asof( - df.reset_index().sort_values(["generated_at", "point_time"], ascending=True), + df.reset_index().sort_values("generated_at", ascending=True), window_df, left_on="generated_at", right_on="window_start", @@ -392,6 +392,10 @@ def calc_rank_compare_metrics( "window_start", ] = pd.NaT df = df.dropna(subset=["window_start"]) + + # Filter out rows where there aren't enough generated_ats to fulfill charge_mins + n_generated_at_in_window = df.groupby('window_start').transform(lambda x: x.reset_index()['generated_at'].nunique())[pred_col] + df = df.loc[n_generated_at_in_window >= charge_mins // 5] df = df.assign( truth_charge_status=simulate_charge(df, truth_col, charge_mins), @@ -419,12 +423,12 @@ def calc_rank_compare_metrics( assert ( df["baseline_charge_status"].sum() - >= (len(df["window_start"].unique()) - 1) * charge_mins // 5 - ) - assert ( - df["baseline_charge_status"].sum() - <= (len(df["window_start"].unique()) + 1) * charge_mins // 5 + == (len(df["window_start"].unique())) * charge_mins // 5 ) + # assert ( + # df["baseline_charge_status"].sum() + # <= (len(df["window_start"].unique()) + 1) * charge_mins // 5 + # ) # Calculate total CO2 emissions ("truth") y_best_emissions = df["truth_charge_emissions"].sum() @@ -625,7 +629,7 @@ def plot_impact_forecast_metrics( marker=dict( color="rgba(200, 200, 200, 0.8)" ), # Light gray for potential - hovertemplate="%{x}: %{y:.1f} lbs CO2/MWh", + hovertemplate="%{x}: %{y:.1f} lbs CO2", ), row=model_ix, col=1, @@ -642,7 +646,7 @@ def plot_impact_forecast_metrics( ], textposition="outside", marker=dict(color="rgba(0, 128, 0, 0.8)"), # Green for reduction - hovertemplate="%{x}: %{y:.1f} lbs CO2/MWh", + hovertemplate="%{x}: %{y:.1f} lbs CO2", ), row=model_ix, col=1, @@ -860,7 +864,7 @@ def plot_max_impact_potential( x=_metrics["scenario"], y=_metrics["potential"], name=model_job.model_date, # Legend only in the first subplot - hovertemplate="%{x}: %{y:.1f} lbs CO2/MWh", + hovertemplate="%{x}: %{y:.1f} lbs CO2", ) ) @@ -869,7 +873,7 @@ def plot_max_impact_potential( fig.update_layout( height=300 * len(region_models), yaxis=dict( - title="lbs CO2/MWh", fixedrange=True # Disable y-axis panning + title="lbs CO2", fixedrange=True # Disable y-axis panning ), ) From 0279a5ae25131af19783675ef02918973739316b Mon Sep 17 00:00:00 2001 From: samkoebrich Date: Wed, 12 Mar 2025 09:35:54 -0600 Subject: [PATCH 34/85] remove new_moer_mode_report.ipynb --- .../evaluation/new_moer_model_report.ipynb | 225565 --------------- 1 file changed, 225565 deletions(-) delete mode 100644 watttime/evaluation/new_moer_model_report.ipynb diff --git a/watttime/evaluation/new_moer_model_report.ipynb b/watttime/evaluation/new_moer_model_report.ipynb deleted file mode 100644 index 4d31ebec..00000000 --- a/watttime/evaluation/new_moer_model_report.ipynb +++ /dev/null @@ -1,225565 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "3c109714", - "metadata": {}, - "outputs": [], - "source": [ - "%load_ext autoreload\n", - "%autoreload 2" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "2e63bd72-6edd-4fe1-b173-3e295259c7a8", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "#setup\n", - "import warnings\n", - "from typing import List, Optional, Union\n", - "from datetime import datetime, date\n", - "from watttime import api\n", - "from watttime.evaluation import report\n", - "from watttime.evaluation.get_wt_api_forecast_evaluation_data import AnalysisDataHandler, DataHandlerFactory\n", - "\n", - "warnings.filterwarnings('ignore')\n", - "warnings.filterwarnings('ignore', message=\"Found version mismatch between `dagster`\")" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "a3b6b1ed-fda5-4961-b9a5-d7bc54f1b6a6", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "parameters" - ] - }, - "outputs": [], - "source": [ - "#input parameters\n", - "region_list: List[str] = [\"ERCOT_SANANTONIO\"]\n", - "model_date_list: List[str] = [\"2022-10-01\", \"2023-03-01\"]\n", - "signal_type: str = \"co2_moer\"\n", - "eval_start: Union[str, datetime, date] = '2022-01-01T00:00Z'\n", - "eval_end: Union[str, datetime, date] = '2022-12-31T23:59Z'\n", - "forecast_sample_days: int = 5" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "7d83e624", - "metadata": {}, - "outputs": [], - "source": [ - "factory = DataHandlerFactory(\n", - " eval_start=eval_start, eval_end=eval_end,\n", - " regions = region_list, model_dates = model_date_list,\n", - " signal_types=signal_type\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6af18b1f", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "id": "42d2ca67-d48b-4fac-b384-4bab22e32104", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "## Data Distribution Changes\n" - ] - }, - { - "cell_type": "markdown", - "id": "7f46b1a9-936f-4e75-aa16-ebc88110b4f4", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "Below is a summary of how you can expect signal values to change between versions." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "83629761-691f-4030-b458-885f412d7113", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Warnings Returned: %s | Response: %s {'region': 'ERCOT_SANANTONIO', 'signal_type': 'co2_moer', 'model': '2022-10-01', 'start': datetime.datetime(2022, 1, 1, 0, 0, tzinfo=), 'end': datetime.datetime(2022, 1, 30, 23, 55, tzinfo=)} {'data_point_period_seconds': 300, 'region': 'ERCOT_SANANTONIO', 'signal_type': 'co2_moer', 'units': 'lbs_co2_per_mwh', 'warnings': [{'type': 'MODEL_MARKED_FOR_DEPRECATION', 'message': \"You made a valid request, but the model requested (2022-10-01) is marked for deprecation on 2024-12-04 00:00:00+00:00. After that date, no further data will be created for the requested model. You'll need to specify a newer model from then on, or if you'd like to get data for the newest model, exclude the 'model' parameter from your request.\"}], 'model': {'date': '2022-10-01', 'type': 'binned_regression'}}\n", - "Warnings Returned: %s | Response: %s {'region': 'ERCOT_SANANTONIO', 'signal_type': 'co2_moer', 'model': '2022-10-01', 'start': datetime.datetime(2022, 1, 31, 0, 0, tzinfo=), 'end': datetime.datetime(2022, 3, 1, 23, 55, tzinfo=)} {'data_point_period_seconds': 300, 'region': 'ERCOT_SANANTONIO', 'signal_type': 'co2_moer', 'units': 'lbs_co2_per_mwh', 'warnings': [{'type': 'MODEL_MARKED_FOR_DEPRECATION', 'message': \"You made a valid request, but the model requested (2022-10-01) is marked for deprecation on 2024-12-04 00:00:00+00:00. After that date, no further data will be created for the requested model. You'll need to specify a newer model from then on, or if you'd like to get data for the newest model, exclude the 'model' parameter from your request.\"}], 'model': {'date': '2022-10-01', 'type': 'binned_regression'}}\n", - "Warnings Returned: %s | Response: %s {'region': 'ERCOT_SANANTONIO', 'signal_type': 'co2_moer', 'model': '2022-10-01', 'start': datetime.datetime(2022, 3, 2, 0, 0, tzinfo=), 'end': datetime.datetime(2022, 3, 31, 23, 55, tzinfo=)} {'data_point_period_seconds': 300, 'region': 'ERCOT_SANANTONIO', 'signal_type': 'co2_moer', 'units': 'lbs_co2_per_mwh', 'warnings': [{'type': 'MODEL_MARKED_FOR_DEPRECATION', 'message': \"You made a valid request, but the model requested (2022-10-01) is marked for deprecation on 2024-12-04 00:00:00+00:00. After that date, no further data will be created for the requested model. You'll need to specify a newer model from then on, or if you'd like to get data for the newest model, exclude the 'model' parameter from your request.\"}], 'model': {'date': '2022-10-01', 'type': 'binned_regression'}}\n", - "Warnings Returned: %s | Response: %s {'region': 'ERCOT_SANANTONIO', 'signal_type': 'co2_moer', 'model': '2022-10-01', 'start': datetime.datetime(2022, 4, 1, 0, 0, tzinfo=), 'end': datetime.datetime(2022, 4, 30, 23, 55, tzinfo=)} {'data_point_period_seconds': 300, 'region': 'ERCOT_SANANTONIO', 'signal_type': 'co2_moer', 'units': 'lbs_co2_per_mwh', 'warnings': [{'type': 'MODEL_MARKED_FOR_DEPRECATION', 'message': \"You made a valid request, but the model requested (2022-10-01) is marked for deprecation on 2024-12-04 00:00:00+00:00. After that date, no further data will be created for the requested model. You'll need to specify a newer model from then on, or if you'd like to get data for the newest model, exclude the 'model' parameter from your request.\"}], 'model': {'date': '2022-10-01', 'type': 'binned_regression'}}\n", - "Warnings Returned: %s | Response: %s {'region': 'ERCOT_SANANTONIO', 'signal_type': 'co2_moer', 'model': '2022-10-01', 'start': datetime.datetime(2022, 5, 1, 0, 0, tzinfo=), 'end': datetime.datetime(2022, 5, 30, 23, 55, tzinfo=)} {'data_point_period_seconds': 300, 'region': 'ERCOT_SANANTONIO', 'signal_type': 'co2_moer', 'units': 'lbs_co2_per_mwh', 'warnings': [{'type': 'MODEL_MARKED_FOR_DEPRECATION', 'message': \"You made a valid request, but the model requested (2022-10-01) is marked for deprecation on 2024-12-04 00:00:00+00:00. After that date, no further data will be created for the requested model. You'll need to specify a newer model from then on, or if you'd like to get data for the newest model, exclude the 'model' parameter from your request.\"}], 'model': {'date': '2022-10-01', 'type': 'binned_regression'}}\n", - "Warnings Returned: %s | Response: %s {'region': 'ERCOT_SANANTONIO', 'signal_type': 'co2_moer', 'model': '2022-10-01', 'start': datetime.datetime(2022, 5, 31, 0, 0, tzinfo=), 'end': datetime.datetime(2022, 6, 29, 23, 55, tzinfo=)} {'data_point_period_seconds': 300, 'region': 'ERCOT_SANANTONIO', 'signal_type': 'co2_moer', 'units': 'lbs_co2_per_mwh', 'warnings': [{'type': 'MODEL_MARKED_FOR_DEPRECATION', 'message': \"You made a valid request, but the model requested (2022-10-01) is marked for deprecation on 2024-12-04 00:00:00+00:00. After that date, no further data will be created for the requested model. You'll need to specify a newer model from then on, or if you'd like to get data for the newest model, exclude the 'model' parameter from your request.\"}], 'model': {'date': '2022-10-01', 'type': 'binned_regression'}}\n", - "Warnings Returned: %s | Response: %s {'region': 'ERCOT_SANANTONIO', 'signal_type': 'co2_moer', 'model': '2022-10-01', 'start': datetime.datetime(2022, 6, 30, 0, 0, tzinfo=), 'end': datetime.datetime(2022, 7, 29, 23, 55, tzinfo=)} {'data_point_period_seconds': 300, 'region': 'ERCOT_SANANTONIO', 'signal_type': 'co2_moer', 'units': 'lbs_co2_per_mwh', 'warnings': [{'type': 'MODEL_MARKED_FOR_DEPRECATION', 'message': \"You made a valid request, but the model requested (2022-10-01) is marked for deprecation on 2024-12-04 00:00:00+00:00. After that date, no further data will be created for the requested model. You'll need to specify a newer model from then on, or if you'd like to get data for the newest model, exclude the 'model' parameter from your request.\"}], 'model': {'date': '2022-10-01', 'type': 'binned_regression'}}\n", - "Warnings Returned: %s | Response: %s {'region': 'ERCOT_SANANTONIO', 'signal_type': 'co2_moer', 'model': '2022-10-01', 'start': datetime.datetime(2022, 7, 30, 0, 0, tzinfo=), 'end': datetime.datetime(2022, 8, 28, 23, 55, tzinfo=)} {'data_point_period_seconds': 300, 'region': 'ERCOT_SANANTONIO', 'signal_type': 'co2_moer', 'units': 'lbs_co2_per_mwh', 'warnings': [{'type': 'MODEL_MARKED_FOR_DEPRECATION', 'message': \"You made a valid request, but the model requested (2022-10-01) is marked for deprecation on 2024-12-04 00:00:00+00:00. After that date, no further data will be created for the requested model. You'll need to specify a newer model from then on, or if you'd like to get data for the newest model, exclude the 'model' parameter from your request.\"}], 'model': {'date': '2022-10-01', 'type': 'binned_regression'}}\n", - "Warnings Returned: %s | Response: %s {'region': 'ERCOT_SANANTONIO', 'signal_type': 'co2_moer', 'model': '2022-10-01', 'start': datetime.datetime(2022, 8, 29, 0, 0, tzinfo=), 'end': datetime.datetime(2022, 9, 27, 23, 55, tzinfo=)} {'data_point_period_seconds': 300, 'region': 'ERCOT_SANANTONIO', 'signal_type': 'co2_moer', 'units': 'lbs_co2_per_mwh', 'warnings': [{'type': 'MODEL_MARKED_FOR_DEPRECATION', 'message': \"You made a valid request, but the model requested (2022-10-01) is marked for deprecation on 2024-12-04 00:00:00+00:00. After that date, no further data will be created for the requested model. You'll need to specify a newer model from then on, or if you'd like to get data for the newest model, exclude the 'model' parameter from your request.\"}], 'model': {'date': '2022-10-01', 'type': 'binned_regression'}}\n", - "Warnings Returned: %s | Response: %s {'region': 'ERCOT_SANANTONIO', 'signal_type': 'co2_moer', 'model': '2022-10-01', 'start': datetime.datetime(2022, 9, 28, 0, 0, tzinfo=), 'end': datetime.datetime(2022, 10, 27, 23, 55, tzinfo=)} {'data_point_period_seconds': 300, 'region': 'ERCOT_SANANTONIO', 'signal_type': 'co2_moer', 'units': 'lbs_co2_per_mwh', 'warnings': [{'type': 'MODEL_MARKED_FOR_DEPRECATION', 'message': \"You made a valid request, but the model requested (2022-10-01) is marked for deprecation on 2024-12-04 00:00:00+00:00. After that date, no further data will be created for the requested model. You'll need to specify a newer model from then on, or if you'd like to get data for the newest model, exclude the 'model' parameter from your request.\"}], 'model': {'date': '2022-10-01', 'type': 'binned_regression'}}\n", - "Warnings Returned: %s | Response: %s {'region': 'ERCOT_SANANTONIO', 'signal_type': 'co2_moer', 'model': '2022-10-01', 'start': datetime.datetime(2022, 10, 28, 0, 0, tzinfo=), 'end': datetime.datetime(2022, 11, 26, 23, 55, tzinfo=)} {'data_point_period_seconds': 300, 'region': 'ERCOT_SANANTONIO', 'signal_type': 'co2_moer', 'units': 'lbs_co2_per_mwh', 'warnings': [{'type': 'MODEL_MARKED_FOR_DEPRECATION', 'message': \"You made a valid request, but the model requested (2022-10-01) is marked for deprecation on 2024-12-04 00:00:00+00:00. After that date, no further data will be created for the requested model. You'll need to specify a newer model from then on, or if you'd like to get data for the newest model, exclude the 'model' parameter from your request.\"}], 'model': {'date': '2022-10-01', 'type': 'binned_regression'}}\n", - "Warnings Returned: %s | Response: %s {'region': 'ERCOT_SANANTONIO', 'signal_type': 'co2_moer', 'model': '2022-10-01', 'start': datetime.datetime(2022, 11, 27, 0, 0, tzinfo=), 'end': datetime.datetime(2022, 12, 26, 23, 55, tzinfo=)} {'data_point_period_seconds': 300, 'region': 'ERCOT_SANANTONIO', 'signal_type': 'co2_moer', 'units': 'lbs_co2_per_mwh', 'warnings': [{'type': 'MODEL_MARKED_FOR_DEPRECATION', 'message': \"You made a valid request, but the model requested (2022-10-01) is marked for deprecation on 2024-12-04 00:00:00+00:00. After that date, no further data will be created for the requested model. You'll need to specify a newer model from then on, or if you'd like to get data for the newest model, exclude the 'model' parameter from your request.\"}], 'model': {'date': '2022-10-01', 'type': 'binned_regression'}}\n", - "Warnings Returned: %s | Response: %s {'region': 'ERCOT_SANANTONIO', 'signal_type': 'co2_moer', 'model': '2022-10-01', 'start': datetime.datetime(2022, 12, 27, 0, 0, tzinfo=), 'end': datetime.datetime(2023, 1, 1, 23, 59, tzinfo=)} {'data_point_period_seconds': 300, 'region': 'ERCOT_SANANTONIO', 'signal_type': 'co2_moer', 'units': 'lbs_co2_per_mwh', 'warnings': [{'type': 'MODEL_MARKED_FOR_DEPRECATION', 'message': \"You made a valid request, but the model requested (2022-10-01) is marked for deprecation on 2024-12-04 00:00:00+00:00. After that date, no further data will be created for the requested model. You'll need to specify a newer model from then on, or if you'd like to get data for the newest model, exclude the 'model' parameter from your request.\"}], 'model': {'date': '2022-10-01', 'type': 'binned_regression'}}\n" - ] - }, - { - "data": { - "application/vnd.plotly.v1+json": { - "config": { - "plotlyServerURL": "https://plot.ly" - }, - "data": [ - { - "line": { - "width": 2 - }, - "mode": "lines", - "name": "2022-10-01", - "showlegend": true, - "type": "scatter", - "x": [ - "2022-01-24T17:55:00+00:00", - "2022-01-24T18:00:00+00:00", - "2022-01-24T18:05:00+00:00", - "2022-01-24T18:10:00+00:00", - "2022-01-24T18:15:00+00:00", - "2022-01-24T18:20:00+00:00", - "2022-01-24T18:25:00+00:00", - "2022-01-24T18:30:00+00:00", - "2022-01-24T18:35:00+00:00", - "2022-01-24T18:40:00+00:00", - "2022-01-24T18:45:00+00:00", - "2022-01-24T18:50:00+00:00", - "2022-01-24T18:55:00+00:00", - "2022-01-24T19:00:00+00:00", - "2022-01-24T19:05:00+00:00", - "2022-01-24T19:10:00+00:00", - "2022-01-24T19:15:00+00:00", - 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This represents the magnitude of error to expect from the forecast on average. A model with an MAE closest to 0 is ideal. Norm MAE is MAE divided by the average of the true values. Normalizing MAE allows us to compare models across different datasets. It is expressed as a percentage (%) of the average true value over the validation time period. \n", - "* **Rank Correlation:** A number between -1 and 1 that quantifies the degree to which the rank ordering of two datasets are related. A rank correlation of 1 means the forecast predictions are rank ordered perfectly compared to the true values, making the forecast perfectly effective for time optimization use cases. For many use cases of our forecasts models (such as AER), it is more important that predictions are correctly rank ordered within a forecast window, rather than each forecast being accurate in terms of absolute values.\n", - "* **AER CO2 Savings:** This metric represents the impact in lbs CO2/MWh of using the forecast model for energy usage optimization. We compare these CO2 savings against the \"Max Potential\" case (ie. the case of using perfect forecasts). In this notebook, we model 3 different popular AER Scenarios:\n", - " \n", - " 1. EV-night: Charging an electric vehicle for 3 hours out of a 12-hour overnight window starting at 19:00 local time.\n", - " 2. EV-day: Charging an electric vehicle for 2 hours out of an 8-hour daytime window starting at 09:00 local time.\n", - " 3. 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"anchor": "x2", - "domain": [ - 0, - 0.4 - ], - "title": { - "text": "CO2 Savings (lbs/MWh)" - } - } - } - } - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "report.plot_impact_forecast_metrics(factory.data_handlers)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "36d4995c", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "sdk", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} From 5e501c556ec2efcc91d9907e81d22afe62372fa2 Mon Sep 17 00:00:00 2001 From: samkoebrich Date: Wed, 12 Mar 2025 09:52:59 -0600 Subject: [PATCH 35/85] add WattTimeMarginalFuelMix --- tests/test_sdk.py | 41 +++++++++++++++++++++++++++++++++++++ watttime/api.py | 52 +++++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 93 insertions(+) diff --git a/tests/test_sdk.py b/tests/test_sdk.py index f8bd74ca..f65cf927 100644 --- a/tests/test_sdk.py +++ b/tests/test_sdk.py @@ -11,6 +11,7 @@ WattTimeMyAccess, WattTimeForecast, WattTimeMaps, + WattTimeMarginalFuelMix, ) from pathlib import Path @@ -467,5 +468,45 @@ def test_region_from_loc(self): self.assertEqual(region["signal_type"], "co2_moer") +class TestWattTimeMarginalFuelMix(unittest.TestCase): + def setUp(self): + self.fuel_mix = WattTimeMarginalFuelMix() + + def test_fuel_mix_jsons(self): + start = "2024-01-01 00:00Z" + end = "2024-01-07 00:00Z" + fm = self.fuel_mix.get_fuel_mix_jsons(start=start, end=end, region=REGION) + + self.assertIsInstance(fm, list) + self.assertIsInstance(fm[0], dict) + self.assertIn("data", fm[0]) + self.assertIn("meta", fm[0]) + self.assertIsInstance(fm[0]["data"], list) + self.assertIsInstance(fm[0]["data"][0], dict) + self.assertIn("point_time", fm[0]["data"][0]) + self.assertIn("values", fm[0]["data"][0]) + self.assertIsInstance(fm[0]["data"][0]["values"], list) + self.assertIsInstance(fm[0]["data"][0]["values"][0], dict) + self.assertIn("fuel_type", fm[0]["data"][0]["values"][0]) + self.assertIn("value", fm[0]["data"][0]["values"][0]) + self.assertGreaterEqual(len(fm[0]["data"]), 12 * 24 * 5) + self.assertIn("warnings", fm[0]["meta"]) + self.assertIn("model", fm[0]["meta"]) + self.assertEqual("marginal_fuel_mix", fm[0]["meta"]["signal_type"]) + self.assertEqual("proportion", fm[0]["meta"]["units"]) + self.assertEqual(REGION, fm[0]["meta"]["region"]) + + def test_fuel_mix_pandas(self): + start = "2024-01-01 00:00Z" + end = "2024-01-07 00:00Z" + df = self.fuel_mix.get_fuel_mix_pandas(start=start, end=end, region=REGION) + self.assertIsInstance(df, pd.DataFrame) + self.assertGreaterEqual(len(df), 12 * 24 * 5) + self.assertIn("gas", df.columns) + self.assertEqual("point_time", df.index.name) + assert df.index.is_monotonic_increasing + assert all(df.sum(axis="columns") == 1.0) + + if __name__ == "__main__": unittest.main() diff --git a/watttime/api.py b/watttime/api.py index ea8691a4..5b966587 100644 --- a/watttime/api.py +++ b/watttime/api.py @@ -3,6 +3,7 @@ import threading import time from datetime import date, datetime, timedelta, time as dt_time +from collections import defaultdict from functools import cache from pathlib import Path from typing import Any, Dict, List, Literal, Optional, Tuple, Union @@ -690,3 +691,54 @@ def get_maps_json( url = "{}/v3/maps".format(self.url_base) params = {"signal_type": signal_type} return self._make_rate_limited_request(url, params) + + +class WattTimeMarginalFuelMix(WattTimeBase): + + def get_fuel_mix_jsons( + self, + start: Union[str, datetime], + end: Union[str, datetime], + region: str, + signal_type: Optional[Literal["marginal_fuel_mix"]] = "marginal_fuel_mix", + model: Optional[Union[str, date]] = None, + ) -> List[Dict[str, Any]]: + if not self._is_token_valid(): + self._login() + url = f"{self.url_base}/v3/fuel-mix" + responses = [] + params = { + "region": region, + "signal_type": signal_type, + } + + start, end = self._parse_dates(start, end) + chunks = self._get_chunks(start, end, chunk_size=timedelta(days=30)) + + # No model will default to the most recent model version available + if model is not None: + params["model"] = model + + param_chunks = [{**params, "start": c[0], "end": c[1]} for c in chunks] + responses = self._fetch_data(url, param_chunks) + return responses + + def get_fuel_mix_pandas( + self, + start: Union[str, datetime], + end: Union[str, datetime], + region: str, + signal_type: Optional[Literal["marginal_fuel_mix"]] = "marginal_fuel_mix", + model: Optional[Union[str, date]] = None, + ) -> pd.DataFrame: + json_list = self.get_fuel_mix_jsons(start, end, region, signal_type, model) + out = defaultdict(dict) + for json in json_list: + for entry in json["data"]: + for by_fuel in entry["values"]: + out[by_fuel["fuel_type"]][entry["point_time"]] = by_fuel["value"] + + df = pd.DataFrame.from_dict(out).fillna(0).sort_index() + df.index = pd.to_datetime(df.index) + df.index.name = "point_time" + return df From d84f6854e190ab2a8ae8a61f62c48be0882e3a54 Mon Sep 17 00:00:00 2001 From: samkoebrich Date: Wed, 12 Mar 2025 10:18:58 -0600 Subject: [PATCH 36/85] catch 403s if marginal fuel mix api is not scoped for user --- tests/test_sdk.py | 12 ++++++++++++ watttime/api.py | 11 ++++++++++- 2 files changed, 22 insertions(+), 1 deletion(-) diff --git a/tests/test_sdk.py b/tests/test_sdk.py index f65cf927..137eabe3 100644 --- a/tests/test_sdk.py +++ b/tests/test_sdk.py @@ -506,6 +506,18 @@ def test_fuel_mix_pandas(self): self.assertEqual("point_time", df.index.name) assert df.index.is_monotonic_increasing assert all(df.sum(axis="columns") == 1.0) + + @patch.object(WattTimeMarginalFuelMix, "_fetch_data", side_effect=RuntimeError("403 Client Error: Forbidden")) + def test_get_fuel_mix_jsons_handles_403(self, mock_fetch_data): + start = "2024-01-01 00:00Z" + end = "2024-01-07 00:00Z" + + result = self.fuel_mix.get_fuel_mix_jsons(start=start, end=end, region=REGION) + + self.assertEqual(result, []) + mock_fetch_data.assert_called_once() + # TODO: test for logging here once we use log rather than print in api.py + if __name__ == "__main__": diff --git a/watttime/api.py b/watttime/api.py index 5b966587..f4bc271f 100644 --- a/watttime/api.py +++ b/watttime/api.py @@ -720,7 +720,16 @@ def get_fuel_mix_jsons( params["model"] = model param_chunks = [{**params, "start": c[0], "end": c[1]} for c in chunks] - responses = self._fetch_data(url, param_chunks) + + try: + responses = self._fetch_data(url, param_chunks) + except RuntimeError as e: + if "403 Client Error: Forbidden" in str(e): + print( + f"The /v3/fuel-mix endpoint is a beta endpoint that provides *marginal* fuel mix data. This endpoint is not currently available to all users, please reach out to WattTime if you believe accessing marginal fuel mix data could be impactful for your usecase!" + ) + return [] + raise return responses def get_fuel_mix_pandas( From 85016cf8f0fa9a59155d11358125bdc7234b870e Mon Sep 17 00:00:00 2001 From: samkoebrich Date: Wed, 12 Mar 2025 10:23:56 -0600 Subject: [PATCH 37/85] WattTimeFuelMix -> WattTimeMarginalFuelMix --- watttime/evaluation/get_wt_api_forecast_evaluation_data.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/watttime/evaluation/get_wt_api_forecast_evaluation_data.py b/watttime/evaluation/get_wt_api_forecast_evaluation_data.py index a7f2a49e..a9f394ac 100644 --- a/watttime/evaluation/get_wt_api_forecast_evaluation_data.py +++ b/watttime/evaluation/get_wt_api_forecast_evaluation_data.py @@ -68,8 +68,8 @@ class AnalysisDataHandler: wt_hist: Optional[api.WattTimeHistorical] = api.WattTimeHistorical( multithreaded=True ) - wt_fuel_mix: Optional[api_do_not_commit.WattTimeFuelMix] = ( - api_do_not_commit.WattTimeFuelMix(multithreaded=False) + wt_fuel_mix: Optional[api_do_not_commit.WattTimeMarginalFuelMix] = ( + api_do_not_commit.WattTimeMarginalFuelMix(multithreaded=False) ) def __post_init__(self): From 61e49383e45426eb9d48a7ec9f46256f19a77110 Mon Sep 17 00:00:00 2001 From: samkoebrich Date: Wed, 12 Mar 2025 10:37:26 -0600 Subject: [PATCH 38/85] fix import --- watttime/evaluation/get_wt_api_forecast_evaluation_data.py | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/watttime/evaluation/get_wt_api_forecast_evaluation_data.py b/watttime/evaluation/get_wt_api_forecast_evaluation_data.py index a9f394ac..ac7f6145 100644 --- a/watttime/evaluation/get_wt_api_forecast_evaluation_data.py +++ b/watttime/evaluation/get_wt_api_forecast_evaluation_data.py @@ -15,7 +15,6 @@ from timezonefinder import TimezoneFinder from watttime import api -from watttime import api_do_not_commit @dataclass @@ -68,8 +67,8 @@ class AnalysisDataHandler: wt_hist: Optional[api.WattTimeHistorical] = api.WattTimeHistorical( multithreaded=True ) - wt_fuel_mix: Optional[api_do_not_commit.WattTimeMarginalFuelMix] = ( - api_do_not_commit.WattTimeMarginalFuelMix(multithreaded=False) + wt_fuel_mix: Optional[api.WattTimeMarginalFuelMix] = ( + api.WattTimeMarginalFuelMix(multithreaded=False) ) def __post_init__(self): From bbcaee3ddb9f13b431bce113f53505fc214bf2cd Mon Sep 17 00:00:00 2001 From: samkoebrich Date: Wed, 12 Mar 2025 10:37:36 -0600 Subject: [PATCH 39/85] black 22.10 --- .../get_wt_api_forecast_evaluation_data.py | 73 +++++++++++++------ 1 file changed, 49 insertions(+), 24 deletions(-) diff --git a/watttime/evaluation/get_wt_api_forecast_evaluation_data.py b/watttime/evaluation/get_wt_api_forecast_evaluation_data.py index ac7f6145..48598880 100644 --- a/watttime/evaluation/get_wt_api_forecast_evaluation_data.py +++ b/watttime/evaluation/get_wt_api_forecast_evaluation_data.py @@ -67,8 +67,8 @@ class AnalysisDataHandler: wt_hist: Optional[api.WattTimeHistorical] = api.WattTimeHistorical( multithreaded=True ) - wt_fuel_mix: Optional[api.WattTimeMarginalFuelMix] = ( - api.WattTimeMarginalFuelMix(multithreaded=False) + wt_fuel_mix: Optional[api.WattTimeMarginalFuelMix] = api.WattTimeMarginalFuelMix( + multithreaded=False ) def __post_init__(self): @@ -92,15 +92,19 @@ def __post_init__(self): k = max(k, 1) random.seed(self.forecast_sample_seed) self.sample_days = random.sample(self.eval_days, k) - + # get tz by looking up centroid of region. # Note that some regions may contain multiple timezones if self.localize_tz: wt_maps = api.WattTimeMaps() all_maps = wt_maps.get_maps_json() - region = {f['properties']['region']: f['geometry'] for f in all_maps['features']}[self.region] + region = { + f["properties"]["region"]: f["geometry"] for f in all_maps["features"] + }[self.region] centroid = shape(region).centroid - self.tz = TimezoneFinder().certain_timezone_at(lat=centroid.y, lng=centroid.x) + self.tz = TimezoneFinder().certain_timezone_at( + lat=centroid.y, lng=centroid.x + ) @cached_property def moers(self) -> pd.DataFrame: @@ -121,11 +125,13 @@ def moers(self) -> pd.DataFrame: if self.localize_tz: moers.index = moers.index.tz_convert(self.tz) - + moers = moers.sort_index() - + self.returned_meta = self.wt_hist._last_request_meta - self.returned_hist_model_date = self.wt_hist._last_request_meta.get("model", {}).get("date", None) + self.returned_hist_model_date = self.wt_hist._last_request_meta.get( + "model", {} + ).get("date", None) return moers @cached_property @@ -146,13 +152,17 @@ def forecasts(self) -> pd.DataFrame: forecasts["point_time"] - forecasts["generated_at"] ).dt.total_seconds() / 60 forecasts.rename({"value": "predicted_value"}, axis="columns", inplace=True) - + if self.localize_tz: - forecasts['generated_at'] = forecasts['generated_at'].dt.tz_convert(self.tz) + forecasts["generated_at"] = forecasts["generated_at"].dt.tz_convert( + self.tz + ) forecasts["point_time"] = forecasts["point_time"].dt.tz_convert(self.tz) - + self.returned_meta = self.wt_forecast._last_request_meta - self.returned_forecast_model_date = self.wt_forecast._last_request_meta.get("model", {}).get("date", None) + self.returned_forecast_model_date = self.wt_forecast._last_request_meta.get( + "model", {} + ).get("date", None) return forecasts @cached_property @@ -167,12 +177,14 @@ def fuel_mix(self) -> pd.DataFrame: signal_type="marginal_fuel_mix", model=self.model_date, ) - + if self.localize_tz: fm.index = fm.index.tz_convert(self.tz) - + self.returned_meta = self.wt_fuel_mix._last_request_meta - self.returned_fuel_mix_model_date = self.wt_fuel_mix._last_request_meta.get("model", {}).get("date", None) + self.returned_fuel_mix_model_date = self.wt_fuel_mix._last_request_meta.get( + "model", {} + ).get("date", None) return fm def moer_v_fuel_mix(self) -> pd.DataFrame: @@ -259,22 +271,35 @@ def __init__( def yield_datahandlers(self) -> AnalysisDataHandler: for dh in self.data_handlers: yield dh - + @property def collected_model_meta(self): return [ { "region": dh.region, "requested_model_date": dh.model_date, - "returned_hist_model_date": getattr(dh, "returned_hist_model_date", None), - "returned_fuel_mix_model_date": getattr(dh, "returned_fuel_mix_model_date", None), - "returned_forecast_model_date": getattr(dh, "returned_forecast_model_date", None), - } for dh in self.data_handlers + "returned_hist_model_date": getattr( + dh, "returned_hist_model_date", None + ), + "returned_fuel_mix_model_date": getattr( + dh, "returned_fuel_mix_model_date", None + ), + "returned_forecast_model_date": getattr( + dh, "returned_forecast_model_date", None + ), + } + for dh in self.data_handlers ] - + @property def data_handlers_by_region_dict(self) -> Dict[str, List[AnalysisDataHandler]]: unique_regions = list(set([dh.region for dh in self.data_handlers])) - out = {region: [dh for dh in self.data_handlers if dh.region == region] for region in unique_regions} - out = {k: sorted(v, key=lambda x: x.model_date, reverse=True) for k, v in out.items()} - return out \ No newline at end of file + out = { + region: [dh for dh in self.data_handlers if dh.region == region] + for region in unique_regions + } + out = { + k: sorted(v, key=lambda x: x.model_date, reverse=True) + for k, v in out.items() + } + return out From a21b1ea6ae64e0a238af060d57702ebfaa2de75e Mon Sep 17 00:00:00 2001 From: samkoebrich Date: Wed, 12 Mar 2025 10:38:05 -0600 Subject: [PATCH 40/85] black 22.10 for test_report --- tests/test_report.py | 43 ++++++++++++++++++++++++++++++++----------- 1 file changed, 32 insertions(+), 11 deletions(-) diff --git a/tests/test_report.py b/tests/test_report.py index 0da4a078..52c9e10b 100644 --- a/tests/test_report.py +++ b/tests/test_report.py @@ -24,7 +24,7 @@ def setUp(self): regions=REGION_LIST, model_dates=MODEL_DATE_LIST, signal_types=SIGNAL_TYPE, - forecast_sample_size=FORECAST_SAMPLE + forecast_sample_size=FORECAST_SAMPLE, ) def validate_figure(self, fig_dict: Dict[str, go.Figure]): @@ -32,7 +32,10 @@ def validate_figure(self, fig_dict: Dict[str, go.Figure]): self.assertIsInstance(fig_dict[REGION_LIST[0]], go.Figure) self.assertGreater(len(fig_dict[REGION_LIST[0]].data), 0) _ = fig_dict[REGION_LIST[0]].to_html( - full_html=False, include_plotlyjs=False, include_mathjax=False, validate=True + full_html=False, + include_plotlyjs=False, + include_mathjax=False, + validate=True, ) def test_plot_distribution_moers(self): @@ -81,7 +84,7 @@ def setUp(self): regions=REGION_LIST, model_dates=MODEL_DATE_LIST[0], signal_types=SIGNAL_TYPE, - forecast_sample_size=FORECAST_SAMPLE + forecast_sample_size=FORECAST_SAMPLE, ) def validate_figure(self, fig_dict: Dict[str, go.Figure]): @@ -89,7 +92,10 @@ def validate_figure(self, fig_dict: Dict[str, go.Figure]): self.assertIsInstance(fig_dict[REGION_LIST[0]], go.Figure) self.assertGreater(len(fig_dict[REGION_LIST[0]].data), 0) _ = fig_dict[REGION_LIST[0]].to_html( - full_html=False, include_plotlyjs=False, include_mathjax=False, validate=True + full_html=False, + include_plotlyjs=False, + include_mathjax=False, + validate=True, ) def test_plot_sample_moers_single_job(self): @@ -129,35 +135,50 @@ def setUp(self): for gen_at in generated_at_times for offset in point_time_offsets ], - names=["point_time", "generated_at"] + names=["point_time", "generated_at"], ) data = { "window_start": [pd.Timestamp("2025-03-11 00:00:00")] * len(index), - "forecast_value": np.random.randint(1, 20, len(index)), # Random forecast values - "sort_col": np.tile([1, 2, 3, 4], len(generated_at_times)) # Ranks within each generated_at + "forecast_value": np.random.randint( + 1, 20, len(index) + ), # Random forecast values + "sort_col": np.tile( + [1, 2, 3, 4], len(generated_at_times) + ), # Ranks within each generated_at } self.df = pd.DataFrame(data, index=index) def test_charge_status_output(self): """Ensure the function outputs a charge_status column with correct type.""" - charge_status = report.simulate_charge(self.df, sort_col="sort_col", charge_mins=30) + charge_status = report.simulate_charge( + self.df, sort_col="sort_col", charge_mins=30 + ) self.assertIsInstance(charge_status, pd.Series) self.assertTrue(charge_status.dtype == bool) def test_correct_length(self): """Check that the charge_status output has the correct length.""" - charge_status = report.simulate_charge(self.df, sort_col="sort_col", charge_mins=30) + charge_status = report.simulate_charge( + self.df, sort_col="sort_col", charge_mins=30 + ) self.assertEqual(len(charge_status), len(self.df)) def test_all_point_times_gte_generated_at(self): """Ensure all point_time values are greater than or equal to generated_at.""" - self.assertTrue((self.df.index.get_level_values("point_time") >= self.df.index.get_level_values("generated_at")).all()) + self.assertTrue( + ( + self.df.index.get_level_values("point_time") + >= self.df.index.get_level_values("generated_at") + ).all() + ) def test_charging_fully_schedules(self): """Verify that a full charge is scheduled correctly.""" - charge_status = report.simulate_charge(self.df, sort_col="sort_col", charge_mins=30) + charge_status = report.simulate_charge( + self.df, sort_col="sort_col", charge_mins=30 + ) charge_needed = 20 // 5 # Number of charge periods required charged_windows = self.df.loc[charge_status].groupby("window_start").size() self.assertTrue((charged_windows >= charge_needed - 1).all()) From b935c14d9e05b65198ea748bad7d36740de9c67d Mon Sep 17 00:00:00 2001 From: samkoebrich Date: Wed, 12 Mar 2025 10:39:09 -0600 Subject: [PATCH 41/85] remove script utility from report.py; use as CLI with launch --- watttime/evaluation/report.py | 3 --- 1 file changed, 3 deletions(-) diff --git a/watttime/evaluation/report.py b/watttime/evaluation/report.py index 9c78e38c..e442e170 100644 --- a/watttime/evaluation/report.py +++ b/watttime/evaluation/report.py @@ -23,9 +23,6 @@ ) from watttime.evaluation.fuels_cp import fuel_cp -# hacky way to allow running this script locally -sys.path.append(str(Path(__file__).parents[1].resolve())) - def convert_to_timezone(dt: datetime, tz: Union[str, ZoneInfo]) -> datetime: """ From b554d0215f8106ab60fb965044047484ccd8b112 Mon Sep 17 00:00:00 2001 From: samkoebrich Date: Wed, 12 Mar 2025 10:40:10 -0600 Subject: [PATCH 42/85] bump max_period to 365D --- watttime/evaluation/report.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/watttime/evaluation/report.py b/watttime/evaluation/report.py index e442e170..e4836f04 100644 --- a/watttime/evaluation/report.py +++ b/watttime/evaluation/report.py @@ -48,7 +48,7 @@ def round_time(dt: datetime, minutes: int = 5) -> datetime: return dt.replace(minute=(dt.minute // minutes) * minutes, second=0, microsecond=0) -def get_random_overlapping_period(dfs, max_period="30D", resample_freq="1H"): +def get_random_overlapping_period(dfs, max_period="365D", resample_freq="1H"): """ Find a random overlapping time period between multiple DataFrames' datetime indices, maximizing the period up to `max_period`. @@ -90,7 +90,7 @@ def get_random_overlapping_period(dfs, max_period="30D", resample_freq="1H"): def plot_sample_moers( - factory: DataHandlerFactory, max_sample_period="30D" + factory: DataHandlerFactory, max_sample_period="365D" ) -> Dict[str, go.Figure]: """ Plot a sample of old and new MOER values over time, creating a subplot for each unique region. @@ -676,7 +676,7 @@ def plot_impact_forecast_metrics( def plot_sample_fuelmix( - factory: DataHandlerFactory, max_sample_period="30D" + factory: DataHandlerFactory, max_sample_period="365D" ) -> Dict[str, go.Figure]: figs = {} From acac686067e34f8a2b6c4afb278927b89feba0c4 Mon Sep 17 00:00:00 2001 From: samkoebrich Date: Wed, 12 Mar 2025 10:51:43 -0600 Subject: [PATCH 43/85] handle missing data in overlap range --- watttime/evaluation/report.py | 69 +++++++++++++++++++---------------- 1 file changed, 38 insertions(+), 31 deletions(-) diff --git a/watttime/evaluation/report.py b/watttime/evaluation/report.py index e4836f04..a6f00d56 100644 --- a/watttime/evaluation/report.py +++ b/watttime/evaluation/report.py @@ -83,9 +83,7 @@ def get_random_overlapping_period(dfs, max_period="365D", resample_freq="1H"): overlap_range = pd.date_range( start=start_overlap, end=start_overlap + max_timedelta, freq=resample_freq ) - assert all( - [all(t in df.index for t in overlap_range) for df in dfs] - ), "Not all DataFrames contain the overlapping range." + return overlap_range @@ -112,10 +110,12 @@ def plot_sample_moers( for model_job in region_models: + _df = model_job.moers.reindex(times) + fig.add_trace( go.Scatter( - x=times, - y=model_job.moers.loc[times]["signal_value"], + x=_df.index, + y=_df.signal_value, mode="lines", name=model_job.model_date, line=dict(width=2), @@ -294,43 +294,50 @@ def calc_rank_corr( return np.nanmean(corr).round(3) + def simulate_charge(df: pd.DataFrame, sort_col: str, charge_mins: int): - + assert sort_col in df.columns include_generated_at = "generated_at" in df.index.names - + charge_needed = charge_mins // 5 df = df.assign(charge_status=False) df = df.sort_index(ascending=True) for w in df.reset_index().groupby("window_start"): window_start, w_df = w - + if include_generated_at: charge_periods = [] _charge_needed = charge_needed for g in w_df.groupby("generated_at"): generated_at, g_df = g - + # how many forecasted points are below the current value? n_below_now = (g_df[sort_col] <= g_df.iloc[0][sort_col]).sum() should_charge_now = n_below_now <= _charge_needed - + if should_charge_now: - charge_periods.append((generated_at, generated_at)) # point_time and generated_at must be equal + charge_periods.append( + (generated_at, generated_at) + ) # point_time and generated_at must be equal _charge_needed -= 1 - + if _charge_needed == 0: break - + else: - w_df['rank'] = w_df[sort_col].rank(ascending=True, method='first') - charge_periods = w_df.loc[w_df['rank'] <= charge_needed, 'point_time'] - - df.loc[charge_periods, 'charge_status'] = True - - assert df['charge_status'].sum() <= (len(df['window_start'].unique())) * charge_needed - assert df['charge_status'].sum() >= (len(df['window_start'].unique()) * 0.97 * charge_needed) - return df['charge_status'] + w_df["rank"] = w_df[sort_col].rank(ascending=True, method="first") + charge_periods = w_df.loc[w_df["rank"] <= charge_needed, "point_time"] + + df.loc[charge_periods, "charge_status"] = True + + assert ( + df["charge_status"].sum() <= (len(df["window_start"].unique())) * charge_needed + ) + assert df["charge_status"].sum() >= ( + len(df["window_start"].unique()) * 0.97 * charge_needed + ) + return df["charge_status"] def calc_rank_compare_metrics( @@ -384,14 +391,16 @@ def calc_rank_compare_metrics( # Filter out rows that do not fall within the valid window df.loc[ (df.index.get_level_values("point_time") >= df["window_end"]) - | (df.index.get_level_values("generated_at") >= df["window_end"]) | - (df.index.get_level_values("generated_at") <= df['window_start']), + | (df.index.get_level_values("generated_at") >= df["window_end"]) + | (df.index.get_level_values("generated_at") <= df["window_start"]), "window_start", ] = pd.NaT df = df.dropna(subset=["window_start"]) - + # Filter out rows where there aren't enough generated_ats to fulfill charge_mins - n_generated_at_in_window = df.groupby('window_start').transform(lambda x: x.reset_index()['generated_at'].nunique())[pred_col] + n_generated_at_in_window = df.groupby("window_start").transform( + lambda x: x.reset_index()["generated_at"].nunique() + )[pred_col] df = df.loc[n_generated_at_in_window >= charge_mins // 5] df = df.assign( @@ -431,7 +440,7 @@ def calc_rank_compare_metrics( y_best_emissions = df["truth_charge_emissions"].sum() y_pred_emissions = df["pred_charge_emissions"].sum() y_base_emissions = df["baseline_charge_emissions"].sum() - + assert y_pred_emissions >= y_best_emissions assert y_base_emissions >= y_best_emissions @@ -696,7 +705,7 @@ def plot_sample_fuelmix( for model_ix, model_job in enumerate(region_models, start=1): - stacked_values = model_job.fuel_mix.loc[times] + stacked_values = model_job.fuel_mix.reindex(times) # Create cumulative values for stacking for fuel_ix in range(1, len(stacked_values.columns)): @@ -826,7 +835,7 @@ def calc_max_potential( y_best_total = df["charge_emissions"].sum() y_baseline_total = df["baseline_charge_emissions"].sum() - + assert y_best_total <= y_baseline_total potential = (y_baseline_total - y_best_total) / len(set(df.index.date)) @@ -869,9 +878,7 @@ def plot_max_impact_potential( fig.update_layout( height=300 * len(region_models), - yaxis=dict( - title="lbs CO2", fixedrange=True # Disable y-axis panning - ), + yaxis=dict(title="lbs CO2", fixedrange=True), # Disable y-axis panning ) figs[region_abbrev] = fig From ba736fc14d69a2e60293387811fd4a5610e2d728 Mon Sep 17 00:00:00 2001 From: samkoebrich Date: Wed, 19 Mar 2025 12:40:06 -0600 Subject: [PATCH 44/85] pin plotly to working version. TODO: bump before release. --- setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.py b/setup.py index 7c2c622c..678ae2a3 100644 --- a/setup.py +++ b/setup.py @@ -9,6 +9,6 @@ packages=["watttime", "watttime.evaluation"], python_requires=">=3.8", install_requires=["requests", "pandas>1.0.0", "holidays", "python-dateutil"], - extras_require={"report": ["jinja2", "plotly", "scipy", "shapely", "timezonefinder"]}, + extras_require={"report": ["jinja2", "plotly==5.24.1", "scipy", "shapely", "timezonefinder"]}, scripts=["watttime/report.py"], ) From 3682363cd41be71a115d700a50b6697c9462ad22 Mon Sep 17 00:00:00 2001 From: samkoebrich Date: Thu, 3 Apr 2025 09:38:22 -0600 Subject: [PATCH 45/85] consistent file naming; plot first week only --- watttime/evaluation/report.py | 32 ++++++++++++++++++++++++++++---- 1 file changed, 28 insertions(+), 4 deletions(-) diff --git a/watttime/evaluation/report.py b/watttime/evaluation/report.py index a6f00d56..6c3971d2 100644 --- a/watttime/evaluation/report.py +++ b/watttime/evaluation/report.py @@ -48,7 +48,7 @@ def round_time(dt: datetime, minutes: int = 5) -> datetime: return dt.replace(minute=(dt.minute // minutes) * minutes, second=0, microsecond=0) -def get_random_overlapping_period(dfs, max_period="365D", resample_freq="1H"): +def get_random_overlapping_period(dfs, max_period="365D", resample_freq="1H", first_week_of_month_only=True): """ Find a random overlapping time period between multiple DataFrames' datetime indices, maximizing the period up to `max_period`. @@ -56,6 +56,9 @@ def get_random_overlapping_period(dfs, max_period="365D", resample_freq="1H"): Args: dfs (list of pd.DataFrame): List of DataFrames with datetime indices. max_period (str): Maximum time period as a string (e.g., '7D' for 7 days). + resample_freq (str): Frequency for resampling the datetime range. + first_week_of_month_only (bool): If True, filters the results to only include + the first full week (Monday-Sunday) of every month. Returns: pd.DatetimeIndex: List of overlapping datetimes within the defined time period. @@ -77,13 +80,22 @@ def get_random_overlapping_period(dfs, max_period="365D", resample_freq="1H"): overlap_range = pd.date_range( start=start_overlap, end=end_overlap, freq=resample_freq ) - - # Otherwise, return the first overlap range that satisfies the max_period else: overlap_range = pd.date_range( start=start_overlap, end=start_overlap + max_timedelta, freq=resample_freq ) - + + # Filter to only include the first full week (Monday-Sunday) of each month if specified + if first_week_of_month_only: + overlap_df = pd.DataFrame({'date': overlap_range}) + overlap_df['year_month'] = overlap_df['date'].dt.to_period('M') + overlap_df['weekday'] = overlap_df['date'].dt.weekday + + first_monday = overlap_df.groupby('year_month')['date'].transform(lambda x: x[x.dt.weekday == 0].min()) + first_sunday = first_monday + pd.Timedelta(days=6) + + overlap_range = overlap_df.loc[(overlap_df['date'] >= first_monday) & (overlap_df['date'] <= first_sunday), 'date'] + return overlap_range @@ -120,6 +132,7 @@ def plot_sample_moers( name=model_job.model_date, line=dict(width=2), showlegend=True, + connectgaps=False ) ) @@ -721,6 +734,7 @@ def plot_sample_fuelmix( mode="none", # Hide lines to emphasize the filled area name=fuel, fillcolor=fuel_cp[fuel], + connectgaps=False ), row=model_ix, col=1, @@ -1223,6 +1237,16 @@ def generate_report( "forecast", ], # no fuel_mix by default ): + + if isinstance(region_list, str): + region_list = [region_list] + + if isinstance(model_date_list, str): + model_date_list = [model_date_list] + + region_list = sorted(region_list) + model_date_list = sorted(model_date_list) + filename = ( f"{signal_type}_{'&'.join(region_list)}_{'&'.join(model_date_list)}_model_stats" ) From daf2bf4c06c163a577b103fd8a6c22175f0b74a4 Mon Sep 17 00:00:00 2001 From: samkoebrich Date: Thu, 3 Apr 2025 14:00:32 -0600 Subject: [PATCH 46/85] merge updates to api.py --- watttime/api.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/watttime/api.py b/watttime/api.py index 96a91112..e1156aec 100644 --- a/watttime/api.py +++ b/watttime/api.py @@ -3,8 +3,8 @@ import threading import time import logging -from datetime import date, datetime, timedelta, time as dt_time from collections import defaultdict +from datetime import date, datetime, timedelta, time as dt_time from functools import cache from pathlib import Path from typing import Any, Dict, List, Literal, Optional, Tuple, Union @@ -218,18 +218,16 @@ def _make_rate_limited_request(self, url: str, params: Dict[str, Any]) -> Dict: self._apply_rate_limit(ts) try: - LOG.debug(f"Making API Request: {url} | Params: {params}") - rsp = self.session.get(url, headers=self.headers, params=params) + rsp = self.session.get(url, headers=self.headers, params=params, timeout=60) rsp.raise_for_status() j = rsp.json() - LOG.debug(f"...Request took: {round(time.time() - ts, 2)} seconds") except requests.exceptions.RequestException as e: raise RuntimeError( f"API Request Failed: {e}\nURL: {url}\nParams: {params}" ) from e if j.get("meta", {}).get("warnings"): - LOG.warning("Warnings Returned: %s | Response: %s", params, j["meta"]) + print("Warnings Returned: %s | Response: %s", params, j["meta"]) self._last_request_meta = j.get("meta", {}) @@ -278,7 +276,9 @@ def _fetch_data( responses = [] if self.multithreaded: - with ThreadPoolExecutor(max_workers=10) as executor: + with ThreadPoolExecutor( + max_workers=min(10, (os.cpu_count() or 1) * 2) + ) as executor: futures = { executor.submit( self._make_rate_limited_request, url, params From e69f691825ac990b2116a36096cfdd464b51fd3a Mon Sep 17 00:00:00 2001 From: samkoebrich Date: Thu, 3 Apr 2025 14:00:49 -0600 Subject: [PATCH 47/85] make first_week_of_month default to false --- watttime/evaluation/report.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/watttime/evaluation/report.py b/watttime/evaluation/report.py index 6c3971d2..115701e1 100644 --- a/watttime/evaluation/report.py +++ b/watttime/evaluation/report.py @@ -48,7 +48,7 @@ def round_time(dt: datetime, minutes: int = 5) -> datetime: return dt.replace(minute=(dt.minute // minutes) * minutes, second=0, microsecond=0) -def get_random_overlapping_period(dfs, max_period="365D", resample_freq="1H", first_week_of_month_only=True): +def get_random_overlapping_period(dfs, max_period="365D", resample_freq="1H", first_week_of_month_only=False): """ Find a random overlapping time period between multiple DataFrames' datetime indices, maximizing the period up to `max_period`. From 33350ddb72f9c4133f9a57e177e219ad92c57e29 Mon Sep 17 00:00:00 2001 From: matthew mcginn Date: Thu, 3 Apr 2025 16:23:00 -0400 Subject: [PATCH 48/85] correct directory for report.py Signed-off-by: matthew mcginn --- setup.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/setup.py b/setup.py index 678ae2a3..7534461f 100644 --- a/setup.py +++ b/setup.py @@ -9,6 +9,8 @@ packages=["watttime", "watttime.evaluation"], python_requires=">=3.8", install_requires=["requests", "pandas>1.0.0", "holidays", "python-dateutil"], - extras_require={"report": ["jinja2", "plotly==5.24.1", "scipy", "shapely", "timezonefinder"]}, - scripts=["watttime/report.py"], + extras_require={ + "report": ["jinja2", "plotly==5.24.1", "scipy", "shapely", "timezonefinder"] + }, + scripts=["watttime/evaluation/report.py"], ) From 5d2b68cba419ef8f010d6b446cc0a2c4ee0691c4 Mon Sep 17 00:00:00 2001 From: samkoebrich Date: Fri, 4 Apr 2025 09:39:16 -0600 Subject: [PATCH 49/85] allow for report card name to be given a title by passing a tuple of regions --- watttime/evaluation/report.py | 12 +++++++++--- 1 file changed, 9 insertions(+), 3 deletions(-) diff --git a/watttime/evaluation/report.py b/watttime/evaluation/report.py index 115701e1..cf8aeb74 100644 --- a/watttime/evaluation/report.py +++ b/watttime/evaluation/report.py @@ -6,7 +6,7 @@ from dateutil.parser import parse from operator import attrgetter from pathlib import Path -from typing import List, Optional, Union, Literal, Dict +from typing import List, Optional, Union, Literal, Dict, Tuple from zoneinfo import ZoneInfo import numpy as np @@ -1226,7 +1226,7 @@ def parse_report_command_line_args(sys_args): def generate_report( - region_list: List[str], + region_list: Union[Tuple[str, List[str]], List[str]], model_date_list: List[str], signal_type: str, eval_start: datetime, @@ -1239,7 +1239,13 @@ def generate_report( ): if isinstance(region_list, str): + region_title = region_list region_list = [region_list] + elif isinstance(region_list, tuple): + region_title = region_list[0] + region_list = region_list[1] + else: + region_title = '&'.join(region_list) if isinstance(model_date_list, str): model_date_list = [model_date_list] @@ -1248,7 +1254,7 @@ def generate_report( model_date_list = sorted(model_date_list) filename = ( - f"{signal_type}_{'&'.join(region_list)}_{'&'.join(model_date_list)}_model_stats" + f"{signal_type}_{region_title}_{'&'.join(model_date_list)}_model_stats" ) # run notebook From ff8ae0396dee839eacbc74a3e81f1be65461b361 Mon Sep 17 00:00:00 2001 From: samkoebrich Date: Fri, 4 Apr 2025 09:39:35 -0600 Subject: [PATCH 50/85] decrease rate_limit for wt_fuel_mix to increase stability --- watttime/evaluation/get_wt_api_forecast_evaluation_data.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/watttime/evaluation/get_wt_api_forecast_evaluation_data.py b/watttime/evaluation/get_wt_api_forecast_evaluation_data.py index 48598880..5aec3bbe 100644 --- a/watttime/evaluation/get_wt_api_forecast_evaluation_data.py +++ b/watttime/evaluation/get_wt_api_forecast_evaluation_data.py @@ -68,7 +68,7 @@ class AnalysisDataHandler: multithreaded=True ) wt_fuel_mix: Optional[api.WattTimeMarginalFuelMix] = api.WattTimeMarginalFuelMix( - multithreaded=False + multithreaded=False, rate_limit=1 ) def __post_init__(self): From dfa8560fcc20722f8a505744dded8d2acd2f85cc Mon Sep 17 00:00:00 2001 From: samkoebrich Date: Fri, 4 Apr 2025 11:28:53 -0600 Subject: [PATCH 51/85] add bulk_report script --- watttime/evaluation/bulk_report.py | 93 ++++++++++++++++++++++++++++++ 1 file changed, 93 insertions(+) create mode 100644 watttime/evaluation/bulk_report.py diff --git a/watttime/evaluation/bulk_report.py b/watttime/evaluation/bulk_report.py new file mode 100644 index 00000000..61f808fe --- /dev/null +++ b/watttime/evaluation/bulk_report.py @@ -0,0 +1,93 @@ +from watttime.api import WattTimeMyAccess +from watttime.evaluation.report import generate_report +from pathlib import Path +from typing import List, Tuple, Union, Literal +from collections import defaultdict + +ma = WattTimeMyAccess() +MA_DF = ma.get_access_pandas() + + +def get_latest_model_date(abbrev, requested_model_dates: List[str]): + if isinstance(abbrev, tuple): + abbrev = abbrev[1] + if isinstance(abbrev, list): + abbrev = abbrev[0] + models = list( + set( + MA_DF.loc[ + (MA_DF["signal_type"] == "co2_moer") + & (MA_DF["region"] == abbrev) + & (MA_DF["endpoint"] == "v3/historical") + ]["model"] + ) + ) + models = [i for i in models if i not in requested_model_dates] + return max(models) + + +def get_regions_for_model( + model: str, signal_type: str +) -> List[Union[str, Tuple[str, List[str]]]]: + regions = MA_DF.loc[ + (MA_DF["signal_type"] == signal_type) + & (MA_DF["model"] == model) + & (MA_DF["endpoint"] == "v3/historical") + ]["region"].unique() + + regions_dict = defaultdict(list) + for region in regions: + parent = region.split("_")[0] + regions_dict[parent].append(region) + + regions_list = [] + for k, v in regions_dict.items(): + if len(v) > 1: + regions_list.append((k, v)) + else: + regions_list.append(v[0]) + + return regions_list + + +def bulk_generate_reports( + model_date: str, + signal_type: str, + start: str, + end: str, + output_dir: Path, + steps: List[Literal["signal", "fuel_mix", "forecast"]], +): + regions = get_regions_for_model(model_date, signal_type) + + failed = [] + for region in regions: + print(f"working on {region}") + try: + generate_report( + region_list=region, + signal_type=signal_type, + model_date_list=[ + get_latest_model_date(region, [model_date]), + model_date, + ], + eval_start=start, + eval_end=end, + steps=steps, + output_dir=Path(output_dir), + ) + except Exception as e: + print(e) + failed.append(region) + continue + + +MODEL = "2024-10-01" +SIGNAL_TYPE = "co2_moer" +START = "2024-01-01" +END = "2024-12-31" +OUTPUT_DIR = Path("/home/skoeb/Desktop/watttime-python-client/analysis/2024-10-01") +if __name__ == "__main__": + bulk_generate_reports( + MODEL, SIGNAL_TYPE, START, END, OUTPUT_DIR, ["signal", "fuel_mix"] + ) From a0ad60124f0c30f0f2a1997a54c0874bc737c366 Mon Sep 17 00:00:00 2001 From: samkoebrich Date: Fri, 13 Jun 2025 17:31:53 -0600 Subject: [PATCH 52/85] add get_wt_api_forecast_evaluation_data --- watttime/evaluation/get_wt_api_forecast_evaluation_data.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/watttime/evaluation/get_wt_api_forecast_evaluation_data.py b/watttime/evaluation/get_wt_api_forecast_evaluation_data.py index 5aec3bbe..d62f77ee 100644 --- a/watttime/evaluation/get_wt_api_forecast_evaluation_data.py +++ b/watttime/evaluation/get_wt_api_forecast_evaluation_data.py @@ -21,13 +21,13 @@ class AnalysisDataHandler: """ This dataclass performs ETL on WattTime signals including - co2_moer, co2_health_damages, curtailment, and more. Data is also loaded + co2_moer, co2_health_damages and more. Data is also loaded for forecasts of these underlying signals. Args: region (str): A single BA abbreviation model_date (str | None): A string representing a released model, if None, will default to latest provided through API - signal_type (str): Such as `co2_moer`, `co2_aoer`, `co2_health_damages`, or `curtailment` + signal_type (str): Such as `co2_moer`, `co2_aoer`, `co2_health_damages` eval_start (str or datetime): The starting point of the analysis to begin pulling signal data. eval_end (str or datetime): The ending point of the analysis to stop pulling signal data. forecast_sample_size (float or int): A float will represent a sample of days From ade494b04dc96a88654c7a2f757bff327da1f591 Mon Sep 17 00:00:00 2001 From: samkoebrich Date: Fri, 15 Aug 2025 13:54:41 -0600 Subject: [PATCH 53/85] make first_week_of_month a cli arg --- watttime/evaluation/report.py | 25 +++++++++++++++++++++---- 1 file changed, 21 insertions(+), 4 deletions(-) diff --git a/watttime/evaluation/report.py b/watttime/evaluation/report.py index cf8aeb74..7e452b02 100644 --- a/watttime/evaluation/report.py +++ b/watttime/evaluation/report.py @@ -8,6 +8,7 @@ from pathlib import Path from typing import List, Optional, Union, Literal, Dict, Tuple from zoneinfo import ZoneInfo +import inspect import numpy as np import pandas as pd @@ -114,7 +115,8 @@ def plot_sample_moers( figs = {} times = get_random_overlapping_period( - [j.moers for j in factory.data_handlers], max_sample_period + [j.moers for j in factory.data_handlers], max_sample_period, + first_week_of_month_only=True ) for region_abbrev, region_models in factory.data_handlers_by_region_dict.items(): @@ -703,7 +705,8 @@ def plot_sample_fuelmix( figs = {} times = get_random_overlapping_period( - [j.fuel_mix for j in factory.data_handlers], max_sample_period + [j.fuel_mix for j in factory.data_handlers], max_sample_period, + first_week_of_month_only=True ) for region_abbrev, region_models in factory.data_handlers_by_region_dict.items(): @@ -1077,7 +1080,7 @@ def plot_bland_altman( delta=merged_moers.max(axis="columns") - merged_moers.min(axis="columns"), mean=merged_moers.mean(axis="columns"), ) - merged_moers = merged_moers.sample(n_samples) + merged_moers = merged_moers.sample(min(n_samples, len(merged_moers))) fig = go.Figure() @@ -1200,6 +1203,12 @@ def parse_report_command_line_args(sys_args): choices=["signal", "fuel_mix", "forecast"], help="Steps to run. Default is ['signal', 'fuel_mix', 'forecast'].", ) + + parser.add_argument( + "-fw", "--first_week_of_month_only", + action="store_true", + help="If set, only sample the first week of each month for fuel mix plots." + ) args = parser.parse_args(sys_args) @@ -1236,6 +1245,7 @@ def generate_report( "signal", "forecast", ], # no fuel_mix by default + first_week_of_month_only: bool = False, ): if isinstance(region_list, str): @@ -1269,10 +1279,16 @@ def generate_report( signal_types=signal_type, ) + kwargs = { + "first_week_of_month_only": first_week_of_month_only, + } + plotly_html = {} for step in steps: for plot_func in PLOTS[step]: - _plot = plot_func(factory) + _func_params = inspect.signature(plot_func).parameters + _kwargs = {k: v for k, v in kwargs.items() if k in _func_params} + _plot = plot_func(factory, **_kwargs) if _plot is None: continue @@ -1331,4 +1347,5 @@ def generate_report( eval_end=cli_args.end, output_dir=Path(cli_args.output_dir), steps=cli_args.steps, + first_week_of_month_only=cli_args.first_week_of_month_only, ) From 1f6cdb0005f09c461a410acca470b88b95fdb8d9 Mon Sep 17 00:00:00 2001 From: Sam from Cluster Date: Thu, 21 Aug 2025 20:08:54 +0000 Subject: [PATCH 54/85] improve bulk_report, don't regenerate existing reports --- watttime/evaluation/bulk_report.py | 103 ++++++++++++++++++++++------- 1 file changed, 80 insertions(+), 23 deletions(-) diff --git a/watttime/evaluation/bulk_report.py b/watttime/evaluation/bulk_report.py index 61f808fe..87f4ed9b 100644 --- a/watttime/evaluation/bulk_report.py +++ b/watttime/evaluation/bulk_report.py @@ -3,6 +3,7 @@ from pathlib import Path from typing import List, Tuple, Union, Literal from collections import defaultdict +from concurrent.futures import ProcessPoolExecutor, as_completed ma = WattTimeMyAccess() MA_DF = ma.get_access_pandas() @@ -50,6 +51,49 @@ def get_regions_for_model( return regions_list +def _process_region(region_list, signal_type, model_date, start, end, output_dir, steps): + + # TODO: make this DRY + if isinstance(region_list, str): + region_title = region_list + region_list = [region_list] + elif isinstance(region_list, tuple): + region_title = region_list[0] + region_list = region_list[1] + else: + region_title = "&".join(region_list) + + model_date_list = [ + get_latest_model_date(region_list, [model_date]), + model_date, + ] + + region_list = sorted(region_list) + model_date_list = sorted(model_date_list) + + filename = f"{signal_type}_{region_title}_{'&'.join(model_date_list)}_model_stats" + output_path = output_dir / f"{filename}.html" + if output_path.exists(): + print(f"[SKIP] {output_path} already exists, skipping.") + return (region_title, None) + + try: + print(f"[PID] working on {region_title}") + generate_report( + region_list=region_list, + signal_type=signal_type, + model_date_list=model_date_list, + eval_start=start, + eval_end=end, + steps=steps, + output_dir=Path(output_dir), + first_week_of_month_only=isinstance(region_title, str), + ) + return (region_title, None) # success + except Exception as e: + return (region_title, str(e)) # failure + + def bulk_generate_reports( model_date: str, signal_type: str, @@ -57,37 +101,50 @@ def bulk_generate_reports( end: str, output_dir: Path, steps: List[Literal["signal", "fuel_mix", "forecast"]], + max_workers: int = 4, ): regions = get_regions_for_model(model_date, signal_type) - failed = [] - for region in regions: - print(f"working on {region}") - try: - generate_report( - region_list=region, - signal_type=signal_type, - model_date_list=[ - get_latest_model_date(region, [model_date]), - model_date, - ], - eval_start=start, - eval_end=end, - steps=steps, - output_dir=Path(output_dir), + + if max_workers == 1: + # Serial execution + for region in regions: + region, error = _process_region( + region, signal_type, model_date, start, end, output_dir, steps ) - except Exception as e: - print(e) - failed.append(region) - continue + if error: + print(f"[ERROR] {region}: {error}") + failed.append(region) + else: + # Parallel execution + from concurrent.futures import ProcessPoolExecutor, as_completed + + with ProcessPoolExecutor(max_workers=max_workers) as executor: + future_to_region = { + executor.submit( + _process_region, region, signal_type, model_date, start, end, output_dir, steps + ): region + for region in regions + } + for future in as_completed(future_to_region): + region, error = future.result() + if error: + print(f"[ERROR] {region}: {error}") + failed.append(region) + + if failed: + print("\nFailed regions:") + for f in failed: + print(f) MODEL = "2024-10-01" SIGNAL_TYPE = "co2_moer" -START = "2024-01-01" -END = "2024-12-31" -OUTPUT_DIR = Path("/home/skoeb/Desktop/watttime-python-client/analysis/2024-10-01") +START = "2024-01-01T00:00Z" +END = "2024-12-31T00:00Z" +OUTPUT_DIR = Path("/app/watttime-python-client/analysis/2024-10-01") + if __name__ == "__main__": bulk_generate_reports( - MODEL, SIGNAL_TYPE, START, END, OUTPUT_DIR, ["signal", "fuel_mix"] + MODEL, SIGNAL_TYPE, START, END, OUTPUT_DIR, ["signal", "fuel_mix", "forecast"], max_workers=5 ) From 702c7e46daf97dfb381b342744f3055e728fe30b Mon Sep 17 00:00:00 2001 From: Sam from Cluster Date: Thu, 21 Aug 2025 20:09:11 +0000 Subject: [PATCH 55/85] improve opitimization in report --- watttime/evaluation/report.py | 204 ++++++++++++++++++---------------- 1 file changed, 108 insertions(+), 96 deletions(-) diff --git a/watttime/evaluation/report.py b/watttime/evaluation/report.py index 7e452b02..258c2bf2 100644 --- a/watttime/evaluation/report.py +++ b/watttime/evaluation/report.py @@ -49,7 +49,9 @@ def round_time(dt: datetime, minutes: int = 5) -> datetime: return dt.replace(minute=(dt.minute // minutes) * minutes, second=0, microsecond=0) -def get_random_overlapping_period(dfs, max_period="365D", resample_freq="1H", first_week_of_month_only=False): +def get_random_overlapping_period( + dfs, max_period="365D", resample_freq="1h", first_week_of_month_only=False +): """ Find a random overlapping time period between multiple DataFrames' datetime indices, maximizing the period up to `max_period`. @@ -85,18 +87,23 @@ def get_random_overlapping_period(dfs, max_period="365D", resample_freq="1H", fi overlap_range = pd.date_range( start=start_overlap, end=start_overlap + max_timedelta, freq=resample_freq ) - + # Filter to only include the first full week (Monday-Sunday) of each month if specified if first_week_of_month_only: - overlap_df = pd.DataFrame({'date': overlap_range}) - overlap_df['year_month'] = overlap_df['date'].dt.to_period('M') - overlap_df['weekday'] = overlap_df['date'].dt.weekday - - first_monday = overlap_df.groupby('year_month')['date'].transform(lambda x: x[x.dt.weekday == 0].min()) + overlap_df = pd.DataFrame({"date": overlap_range}) + overlap_df["year_month"] = overlap_df["date"].dt.to_period("M") + overlap_df["weekday"] = overlap_df["date"].dt.weekday + + first_monday = overlap_df.groupby("year_month")["date"].transform( + lambda x: x[x.dt.weekday == 0].min() + ) first_sunday = first_monday + pd.Timedelta(days=6) - - overlap_range = overlap_df.loc[(overlap_df['date'] >= first_monday) & (overlap_df['date'] <= first_sunday), 'date'] - + + overlap_range = overlap_df.loc[ + (overlap_df["date"] >= first_monday) & (overlap_df["date"] <= first_sunday), + "date", + ] + return overlap_range @@ -115,8 +122,9 @@ def plot_sample_moers( figs = {} times = get_random_overlapping_period( - [j.moers for j in factory.data_handlers], max_sample_period, - first_week_of_month_only=True + [j.moers for j in factory.data_handlers], + max_sample_period, + first_week_of_month_only=True, ) for region_abbrev, region_models in factory.data_handlers_by_region_dict.items(): @@ -134,7 +142,7 @@ def plot_sample_moers( name=model_job.model_date, line=dict(width=2), showlegend=True, - connectgaps=False + connectgaps=False, ) ) @@ -324,17 +332,18 @@ def simulate_charge(df: pd.DataFrame, sort_col: str, charge_mins: int): if include_generated_at: charge_periods = [] _charge_needed = charge_needed - for g in w_df.groupby("generated_at"): - generated_at, g_df = g + generated_at_groups = list(w_df.groupby("generated_at")) + total_groups = len(generated_at_groups) - # how many forecasted points are below the current value? + for processed_count, (generated_at, g_df) in enumerate(generated_at_groups): n_below_now = (g_df[sort_col] <= g_df.iloc[0][sort_col]).sum() - should_charge_now = n_below_now <= _charge_needed + remaining_generated_at = total_groups - processed_count + should_charge_now = (n_below_now <= _charge_needed) or ( + remaining_generated_at <= _charge_needed + ) if should_charge_now: - charge_periods.append( - (generated_at, generated_at) - ) # point_time and generated_at must be equal + charge_periods.append((generated_at, generated_at)) _charge_needed -= 1 if _charge_needed == 0: @@ -348,10 +357,11 @@ def simulate_charge(df: pd.DataFrame, sort_col: str, charge_mins: int): assert ( df["charge_status"].sum() <= (len(df["window_start"].unique())) * charge_needed - ) + ), "Charge status is too high, check the logic for simulate_charge" assert df["charge_status"].sum() >= ( len(df["window_start"].unique()) * 0.97 * charge_needed - ) + ), "Charge status is too low, check the logic for simulate_charge" + return df["charge_status"] @@ -473,7 +483,7 @@ def calc_rank_compare_metrics( def plot_norm_mae( - factory: DataHandlerFactory, horizons_hr=[1, 6, 12, 18, 24] + factory: DataHandlerFactory, horizons_hr=[1, 6, 12, 18, 24, 72] ) -> Dict[str, go.Figure]: """ Create a Plotly bar chart for rank correlation by horizon with one subplot per region (abbrev). @@ -525,7 +535,7 @@ def plot_norm_mae( def plot_rank_corr( - factory: DataHandlerFactory, horizons_hr=[1, 6, 12, 18, 24] + factory: DataHandlerFactory, horizons_hr=[1, 6, 12, 18, 24, 72] ) -> Dict[str, go.Figure]: """ Create a Plotly line plot for rank correlation by horizon with one subplot per region (abbrev). @@ -705,8 +715,9 @@ def plot_sample_fuelmix( figs = {} times = get_random_overlapping_period( - [j.fuel_mix for j in factory.data_handlers], max_sample_period, - first_week_of_month_only=True + [j.fuel_mix for j in factory.data_handlers], + max_sample_period, + first_week_of_month_only=True, ) for region_abbrev, region_models in factory.data_handlers_by_region_dict.items(): @@ -737,7 +748,7 @@ def plot_sample_fuelmix( mode="none", # Hide lines to emphasize the filled area name=fuel, fillcolor=fuel_cp[fuel], - connectgaps=False + connectgaps=False, ), row=model_ix, col=1, @@ -773,89 +784,91 @@ def calc_max_potential( truth_col="signal_value", load_kw=1000, ): - """Predict the maximum potential CO2 Savings, without any forecast to estimate the upper limit of impact from a given signal value.""" - + """Predict the maximum potential CO2 Savings, without any forecast to estimate the upper limit of impact from a given signal value. + This version is resilient to partial windows (e.g. where the data does not fully cover the expected number of 5-min intervals). + """ df = in_df.copy() + needed_rows = charge_mins // 5 + load_factor = load_kw / 1000 + if window_starts: - # Extract unique dates and create window ranges - unique_dates = set(df.index.date) + unique_dates = df.index.get_level_values("point_time").normalize().unique() window_ranges = [] + window_ranges_extend = window_ranges.extend + for date in unique_dates: - for start_time in window_starts: - start = pd.Timestamp(f"{date} {start_time}") - end = start + pd.Timedelta(minutes=window_mins) - window_ranges.append((start, end)) + window_ranges_extend( + [ + ( + pd.Timestamp.combine( + date.date(), pd.to_datetime(start_time).time() + ), + pd.Timestamp.combine( + date.date(), pd.to_datetime(start_time).time() + ) + + pd.Timedelta(minutes=window_mins), + ) + for start_time in window_starts + ] + ) - # Convert to DataFrame window_df = pd.DataFrame(window_ranges, columns=["window_start", "window_end"]) - window_df = window_df.sort_values("window_start") - tz = df.index.get_level_values("point_time")[0].tz - window_df = window_df.assign( - window_start=pd.to_datetime( - window_df["window_start"], errors="coerce" - ).dt.tz_localize(tz), - window_end=pd.to_datetime( - window_df["window_end"], errors="coerce" - ).dt.tz_localize(tz), - ) - window_df = window_df.dropna(subset=["window_start"]) + window_df.sort_values("window_start", inplace=True) - # Ensure df is sorted by "point_time" before merging - df = df.sort_index(level="point_time") + tz = df.index.get_level_values("point_time")[0].tz + if tz: + window_df["window_start"] = window_df["window_start"].dt.tz_localize(tz) + window_df["window_end"] = window_df["window_end"].dt.tz_localize(tz) - # Use merge_asof to efficiently assign windows + df_reset = df.reset_index().sort_values("point_time") df = pd.merge_asof( - df.reset_index().sort_values("point_time"), + df_reset, window_df, left_on="point_time", right_on="window_start", direction="backward", ).set_index(df.index.names) - else: - # Assign each row to a rolling window of `window_mins` based on `generated_at` - df["window_start"] = df.index.get_level_values("point_time").floor( - f"{window_mins}min", ambiguous=False + # local -> utc -> floor to nearest window -> local to avoid dst issues + point_times = df.index.get_level_values("point_time") + df["window_start"] = ( + point_times.tz_convert("UTC") + .floor(f"{window_mins}min") + .tz_convert(point_times.tz) # <-- use .tz, not .dt.tz ) - df["window_end"] = df["window_start"] + pd.Timedelta(f"{window_mins} min") + df["window_end"] = df["window_start"] + pd.Timedelta(minutes=window_mins) - # Filter out rows that do not fall within the valid window + # Combined filtering operations df = df.dropna(subset=["window_start"]) - df = df.loc[ - (df.index.get_level_values("point_time") < df["window_end"]) - & (df.index.get_level_values("point_time") >= df["window_start"]) - ] + point_times = df.index.get_level_values("point_time") + time_mask = (point_times >= df["window_start"]) & (point_times < df["window_end"]) + df = df.loc[time_mask] - df = df.assign(charge_status=simulate_charge(df, truth_col, charge_mins)) - df = df.assign( - charge_emissions=df[truth_col] * df["charge_status"] * (load_kw / 1000) - ) + window_counts = df.groupby("window_start")[truth_col].transform("count") + df = df[window_counts >= needed_rows] - # baseline: immediate charging rather than AER - df = df.assign(sequential_rank=df.groupby("window_start").cumcount()) - df = df.assign(baseline_charge_status=df["sequential_rank"] < charge_mins // 5) - df = df.assign( - baseline_charge_emissions=df[truth_col] - * df["baseline_charge_status"] - * (load_kw / 1000) - ) + if df.empty: + return {"potential": 0.0} - assert ( - df["baseline_charge_status"].sum() - >= (len(df["window_start"].unique()) - 1) * charge_mins // 5 - ) - assert ( - df["baseline_charge_status"].sum() - <= (len(df["window_start"].unique())) * charge_mins // 5 - ) + df["charge_status"] = simulate_charge(df, truth_col, charge_mins) + + truth_values = df[truth_col] + charge_emissions = truth_values * df["charge_status"] * load_factor + + df["sequential_rank"] = df.groupby("window_start").cumcount() + baseline_charge_status = df["sequential_rank"] < needed_rows + baseline_charge_emissions = truth_values * baseline_charge_status * load_factor - y_best_total = df["charge_emissions"].sum() - y_baseline_total = df["baseline_charge_emissions"].sum() + y_best_total = charge_emissions.sum() + y_baseline_total = baseline_charge_emissions.sum() - assert y_best_total <= y_baseline_total + assert ( + y_best_total <= y_baseline_total + ), f"Best case ({y_best_total}) should not be worse than baseline ({y_baseline_total})" - potential = (y_baseline_total - y_best_total) / len(set(df.index.date)) + num_unique_dates = df.index.get_level_values("point_time").normalize().nunique() + potential = (y_baseline_total - y_best_total) / num_unique_dates return {"potential": round(potential, 1)} @@ -1203,11 +1216,12 @@ def parse_report_command_line_args(sys_args): choices=["signal", "fuel_mix", "forecast"], help="Steps to run. Default is ['signal', 'fuel_mix', 'forecast'].", ) - + parser.add_argument( - "-fw", "--first_week_of_month_only", + "-fw", + "--first_week_of_month_only", action="store_true", - help="If set, only sample the first week of each month for fuel mix plots." + help="If set, only sample the first week of each month for fuel mix plots.", ) args = parser.parse_args(sys_args) @@ -1247,7 +1261,7 @@ def generate_report( ], # no fuel_mix by default first_week_of_month_only: bool = False, ): - + if isinstance(region_list, str): region_title = region_list region_list = [region_list] @@ -1255,17 +1269,15 @@ def generate_report( region_title = region_list[0] region_list = region_list[1] else: - region_title = '&'.join(region_list) + region_title = "&".join(region_list) if isinstance(model_date_list, str): model_date_list = [model_date_list] - + region_list = sorted(region_list) model_date_list = sorted(model_date_list) - - filename = ( - f"{signal_type}_{region_title}_{'&'.join(model_date_list)}_model_stats" - ) + + filename = f"{signal_type}_{region_title}_{'&'.join(model_date_list)}_model_stats" # run notebook output_path = output_dir / f"{filename}.html" @@ -1282,7 +1294,7 @@ def generate_report( kwargs = { "first_week_of_month_only": first_week_of_month_only, } - + plotly_html = {} for step in steps: for plot_func in PLOTS[step]: From 2488f71eac55bd269cf0e967299be433179753cf Mon Sep 17 00:00:00 2001 From: Sam from Cluster Date: Thu, 21 Aug 2025 20:10:15 +0000 Subject: [PATCH 56/85] Retry 500 errors --- watttime/api.py | 12 ++++++++++++ 1 file changed, 12 insertions(+) diff --git a/watttime/api.py b/watttime/api.py index b84b575e..fea4b609 100644 --- a/watttime/api.py +++ b/watttime/api.py @@ -8,6 +8,8 @@ from pathlib import Path from typing import Any, Dict, List, Literal, Optional, Tuple, Union from concurrent.futures import ThreadPoolExecutor, as_completed +from urllib3.util.retry import Retry +from requests.adapters import HTTPAdapter import pandas as pd import requests @@ -54,7 +56,17 @@ def __init__( ) # prevent multiple threads from modifying _last_request_times simultaneously self._rate_limit_condition = threading.Condition(self._rate_limit_lock) + retry_strategy = Retry( + total=3, + status_forcelist=[500, 502, 503, 504], + backoff_factor=1, + raise_on_status=False, + ) + + adapter = HTTPAdapter(max_retries=retry_strategy) self.session = requests.Session() + self.session.mount("http://", adapter) + self.session.mount("https://", adapter) def _login(self): """ From f0a478c6da370bf38d5c6d1d30ff9e490e95f697 Mon Sep 17 00:00:00 2001 From: Sam from Cluster Date: Fri, 22 Aug 2025 01:20:38 +0000 Subject: [PATCH 57/85] add precision/recall plot --- watttime/evaluation/report.py | 188 ++++++++++++++++++++++++++++++++-- 1 file changed, 179 insertions(+), 9 deletions(-) diff --git a/watttime/evaluation/report.py b/watttime/evaluation/report.py index 258c2bf2..f4c0c6ec 100644 --- a/watttime/evaluation/report.py +++ b/watttime/evaluation/report.py @@ -108,7 +108,7 @@ def get_random_overlapping_period( def plot_sample_moers( - factory: DataHandlerFactory, max_sample_period="365D" + factory: DataHandlerFactory, max_sample_period="365D", first_week_of_month_only=True ) -> Dict[str, go.Figure]: """ Plot a sample of old and new MOER values over time, creating a subplot for each unique region. @@ -124,7 +124,7 @@ def plot_sample_moers( times = get_random_overlapping_period( [j.moers for j in factory.data_handlers], max_sample_period, - first_week_of_month_only=True, + first_week_of_month_only=first_week_of_month_only, ) for region_abbrev, region_models in factory.data_handlers_by_region_dict.items(): @@ -140,7 +140,7 @@ def plot_sample_moers( y=_df.signal_value, mode="lines", name=model_job.model_date, - line=dict(width=2), + line=dict(width=2, shape="hv"), showlegend=True, connectgaps=False, ) @@ -618,11 +618,35 @@ def plot_rank_corr( "window_starts": None, "load_kw": 3, # typical AC }, + "10 kW / 24hr / 25% Duty Cycle": { + "charge_mins": 6 * 60, + "window_mins": 24 * 60, # 4 days + "window_starts": ["00:00"], + "load_kw": 10, + }, + "10 kW / 72hr / 25% Duty Cycle": { + "charge_mins": 3 * 3 * 60, + "window_mins": 24 * 3 * 60, # 4 days + "window_starts": ["00:00"], + "load_kw": 10, + }, + "10 kW / 24hr / 50% Duty Cycle": { + "charge_mins": 6 * 60, + "window_mins": 24 * 60, # 4 days + "window_starts": ["00:00"], + "load_kw": 10, + }, + "10 kW / 72hr / 50% Duty Cycle": { + "charge_mins": 12 * 3 * 60, + "window_mins": 24 * 3 * 60, # 4 days + "window_starts": ["00:00"], + "load_kw": 10, + }, } def plot_impact_forecast_metrics( - factory: DataHandlerFactory, scenarios=["EV-night", "EV-day", "Thermostat"] + factory: DataHandlerFactory, scenarios=["EV-night", "EV-day", "Thermostat", "10 kW / 24hr / 25% Duty Cycle", "10 kW / 72hr / 25% Duty Cycle", "10 kW / 24hr / 50% Duty Cycle", "10 kW / 72hr / 50% Duty Cycle"] ): figs = {} @@ -651,7 +675,8 @@ def plot_impact_forecast_metrics( ] _metrics = pd.DataFrame(_metrics) - + _metrics['scenario'] = _metrics['scenario'].apply(lambda x: x.replace(' / ', '\n')) + fig.add_trace( go.Bar( x=_metrics["scenario"], @@ -661,6 +686,8 @@ def plot_impact_forecast_metrics( color="rgba(200, 200, 200, 0.8)" ), # Light gray for potential hovertemplate="%{x}: %{y:.1f} lbs CO2", + legendgroup="Potential Savings", # Group legend for potential savings + showlegend=(model_ix == 1), # Show legend only for the first subplot ), row=model_ix, col=1, @@ -678,6 +705,8 @@ def plot_impact_forecast_metrics( textposition="outside", marker=dict(color="rgba(0, 128, 0, 0.8)"), # Green for reduction hovertemplate="%{x}: %{y:.1f} lbs CO2", + legendgroup="Potential Savings", # Group legend for potential savings + showlegend=(model_ix == 1), # Show legend only for the first subplot ), row=model_ix, col=1, @@ -717,7 +746,7 @@ def plot_sample_fuelmix( times = get_random_overlapping_period( [j.fuel_mix for j in factory.data_handlers], max_sample_period, - first_week_of_month_only=True, + first_week_of_month_only=False, ) for region_abbrev, region_models in factory.data_handlers_by_region_dict.items(): @@ -744,11 +773,14 @@ def plot_sample_fuelmix( go.Scatter( x=stacked_values.index, y=stacked_values.iloc[:, fuel_ix], - fill="tonexty" if model_ix > 0 else "tozeroy", - mode="none", # Hide lines to emphasize the filled area + fill="tonexty" if fuel_ix > 0 else "tozeroy", + mode="none", name=fuel, fillcolor=fuel_cp[fuel], connectgaps=False, + legendgroup=fuel, + showlegend=(model_ix == 1), + line=dict(shape="hv"), ), row=model_ix, col=1, @@ -1161,6 +1193,138 @@ def plot_bland_altman( return figs +def calc_precision_recall( + forecasts_v_moers: pd.DataFrame, horizon_minutes: int, impute_curtail_threshold: int, + pred_col="predicted_value", truth_col="signal_value", +) -> Tuple[float, float]: + """ + Calculate precision and recall for the all forecast values UP TO the specified horizon, + based on the given threshold for curtailment. + + Parameters: + forecasts_v_moers (pd.DataFrame): DataFrame containing forecasts and signal value. + horizon_minutes (int): The horizon in minutes to consider for the calculation. + impute_curtail_threshold (int): Threshold to convert continuous MOER to binary curtailment. + + Returns: + Tuple[float, float]: Precision and recall values. + """ + # Filter forecasts based on the horizon + filtered_forecasts = forecasts_v_moers[ + forecasts_v_moers["horizon_mins"] <= horizon_minutes + ] + + pred_bool = filtered_forecasts[pred_col] <= impute_curtail_threshold + truth_bool = filtered_forecasts[truth_col] <= impute_curtail_threshold + + # TP / (TP + FP) + precision = ( + (pred_bool & truth_bool).sum() / pred_bool.sum() + if pred_bool.sum() > 0 else 0.0 + ) + + # TP / (TP + FN) + recall = ( + (pred_bool & truth_bool).sum() / truth_bool.sum() + if truth_bool.sum() > 0 else 0.0 + ) + + return precision, recall + + +def plot_precision_recall( + factory: DataHandlerFactory, + horizons_hr=[1, 6, 12, 18, 24, 72], + impute_curtail_threshold=100 +) -> Dict[str, go.Figure]: + """ + Create a Plotly figure with subplots: each model in a separate row + showing precision and recall across horizons. + """ + + figs = {} + + for region_abbrev, region_models in factory.data_handlers_by_region_dict.items(): + # One row per model + fig = sp.make_subplots( + rows=len(region_models), + cols=1, + shared_xaxes=True, + subplot_titles=[f"Model {m.model_date}" for m in region_models], + vertical_spacing=0.15, + ) + + precision_y_max = recall_y_max = precision_y_min = recall_y_min = 0 + x_values = [f"{h}hr" for h in horizons_hr] + + for row_idx, model_job in enumerate(region_models, start=1): + values = [ + calc_precision_recall( + model_job.forecasts_v_moers, (h * 60) - 5, + impute_curtail_threshold=impute_curtail_threshold + ) + for h in horizons_hr + ] + + # Convert from fractions to percentages + precision_values, recall_values = zip(*values) + precision_values = [p * 100 for p in precision_values] + recall_values = [r * 100 for r in recall_values] + + precision_y_max = max(precision_values + [precision_y_max]) + recall_y_max = max(recall_values + [recall_y_max]) + + # Precision trace + fig.add_trace( + go.Bar( + x=x_values, + y=precision_values, + name="Precision", + marker_color="blue", + text=[f"{y:.1f}%" for y in precision_values], + textposition="outside", + showlegend=(row_idx == 1), # only show once + ), + row=row_idx, col=1 + ) + + # Recall trace + fig.add_trace( + go.Bar( + x=x_values, + y=recall_values, + name="Recall", + marker_color="red", + text=[f"{y:.1f}%" for y in recall_values], + textposition="outside", + showlegend=(row_idx == 1), # only show once + ), + row=row_idx, col=1 + ) + + # Set uniform y-axis range across subplots + y_range = [ + min(precision_y_min, recall_y_min) - (0.25 * max(precision_y_max, recall_y_max)), + max(precision_y_max, recall_y_max) + (0.25 * max(precision_y_max, recall_y_max)) + ] + + for row_idx in range(1, len(region_models) + 1): + fig.update_yaxes(range=y_range, row=row_idx, col=1) + + fig.update_layout( + height=300 * len(region_models), + xaxis_title="Horizon (Hours)", + yaxis_title="Precision / Recall (%)", + showlegend=True, + margin=dict(l=50, r=50, t=50, b=50), + barmode="group", + ) + + figs[region_abbrev] = fig + + return figs + + def parse_report_command_line_args(sys_args): parser = argparse.ArgumentParser( description="Parse command line arguments to report_moers script" @@ -1244,7 +1408,12 @@ def parse_report_command_line_args(sys_args): plot_max_impact_potential, ], "fuel_mix": [plot_sample_fuelmix, plot_fuelmix_heatmap], - "forecast": [plot_norm_mae, plot_rank_corr, plot_impact_forecast_metrics], + "forecast": [ + plot_norm_mae, + plot_rank_corr, + plot_precision_recall, + plot_impact_forecast_metrics + ], } @@ -1289,6 +1458,7 @@ def generate_report( regions=region_list, model_dates=model_date_list, signal_types=signal_type, + forecast_max_horizon=72 * 60, ) kwargs = { From 7774cce40b9facd16c28488c4bf0719afe1c628d Mon Sep 17 00:00:00 2001 From: Sam from Cluster Date: Fri, 22 Aug 2025 01:20:53 +0000 Subject: [PATCH 58/85] optional to create unified subregion report --- watttime/evaluation/bulk_report.py | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/watttime/evaluation/bulk_report.py b/watttime/evaluation/bulk_report.py index 87f4ed9b..b1c84662 100644 --- a/watttime/evaluation/bulk_report.py +++ b/watttime/evaluation/bulk_report.py @@ -28,14 +28,14 @@ def get_latest_model_date(abbrev, requested_model_dates: List[str]): def get_regions_for_model( - model: str, signal_type: str + model: str, signal_type: str, combine_subregions: bool = False, ) -> List[Union[str, Tuple[str, List[str]]]]: regions = MA_DF.loc[ (MA_DF["signal_type"] == signal_type) & (MA_DF["model"] == model) & (MA_DF["endpoint"] == "v3/historical") ]["region"].unique() - + regions_dict = defaultdict(list) for region in regions: parent = region.split("_")[0] @@ -43,8 +43,11 @@ def get_regions_for_model( regions_list = [] for k, v in regions_dict.items(): - if len(v) > 1: + if len(v) > 1 and combine_subregions: regions_list.append((k, v)) + elif len(v) > 1 and not combine_subregions: + for subregion in v: + regions_list.append(subregion) else: regions_list.append(v[0]) From f8b2036d8d9c5f401013f78191e642553e0b9e6f Mon Sep 17 00:00:00 2001 From: samkoebrich Date: Sun, 24 Aug 2025 09:26:36 -0600 Subject: [PATCH 59/85] simplify window assignment code --- watttime/evaluation/report.py | 289 ++++++++++++++++++---------------- 1 file changed, 150 insertions(+), 139 deletions(-) diff --git a/watttime/evaluation/report.py b/watttime/evaluation/report.py index f4c0c6ec..4dc5f651 100644 --- a/watttime/evaluation/report.py +++ b/watttime/evaluation/report.py @@ -318,40 +318,65 @@ def calc_rank_corr( return np.nanmean(corr).round(3) -def simulate_charge(df: pd.DataFrame, sort_col: str, charge_mins: int): +def pick_k_optimal_charge(in_df: pd.DataFrame, truth_col, charge_mins: int): + df = in_df.copy() + + specify_generated_at = 'generated_at' in df.index.names + + charge_needed = charge_mins // 5 + df["charge_status"] = False + df = df.sort_index(ascending=True) + + for window_start, w_df in df.reset_index().groupby("window_start"): + # Pick the `charge_needed` lowest truth_col values in this window + # If there are ties, 'first' ensures deterministic selection + if specify_generated_at: + w_df = w_df[w_df['generated_at'] == w_df['point_time']] + selected_points = ( + w_df.sort_values(truth_col, ascending=True) + .head(charge_needed) + ) - assert sort_col in df.columns - include_generated_at = "generated_at" in df.index.names + # Mark these point_times as charge periods in the original dataframe + if specify_generated_at: + df.loc[[(p, p) for p in selected_points['point_time']], "charge_status"] = True + else: + df.loc[selected_points['point_time'], "charge_status"] = True + + return df["charge_status"] + + +def simulate_charge(in_df: pd.DataFrame, sort_col: str, charge_mins: int): + + df = in_df.copy() + + assert sort_col in df.columns charge_needed = charge_mins // 5 df = df.assign(charge_status=False) df = df.sort_index(ascending=True) for w in df.reset_index().groupby("window_start"): window_start, w_df = w + w_df = w_df.sort_values(['generated_at', 'point_time'], ascending=True) + charge_periods = [] + _charge_needed = charge_needed + generated_at_groups = list(w_df.groupby("generated_at")) + total_groups = len(generated_at_groups) + + for processed_count, (generated_at, g_df) in enumerate(generated_at_groups): + n_below_now = g_df[sort_col].rank().iloc[0] + remaining_generated_at = total_groups - processed_count + should_charge_now = (n_below_now <= _charge_needed) or ( + remaining_generated_at <= _charge_needed + ) - if include_generated_at: - charge_periods = [] - _charge_needed = charge_needed - generated_at_groups = list(w_df.groupby("generated_at")) - total_groups = len(generated_at_groups) - - for processed_count, (generated_at, g_df) in enumerate(generated_at_groups): - n_below_now = (g_df[sort_col] <= g_df.iloc[0][sort_col]).sum() - remaining_generated_at = total_groups - processed_count - should_charge_now = (n_below_now <= _charge_needed) or ( - remaining_generated_at <= _charge_needed - ) - - if should_charge_now: - charge_periods.append((generated_at, generated_at)) - _charge_needed -= 1 - - if _charge_needed == 0: - break + if should_charge_now: + assert generated_at == g_df.iloc[0]['point_time'] + charge_periods.append((generated_at, generated_at)) + _charge_needed -= 1 - else: - w_df["rank"] = w_df[sort_col].rank(ascending=True, method="first") - charge_periods = w_df.loc[w_df["rank"] <= charge_needed, "point_time"] + if _charge_needed == 0: + break df.loc[charge_periods, "charge_status"] = True @@ -365,71 +390,88 @@ def simulate_charge(df: pd.DataFrame, sort_col: str, charge_mins: int): return df["charge_status"] +def assign_windows(generated_ats: List[pd.Timestamp], window_size: pd.Timedelta) -> List[pd.Timestamp]: + + if isinstance(window_size, int): + window_size=pd.Timedelta(minutes=window_size) + + first_tz = generated_ats[0].tz + if any((ts.tz is None or ts.tz != first_tz) for ts in generated_ats): + raise ValueError("All generated_ats must be tz-aware and share the same timezone") + + # Sort with original indices + indexed = list(enumerate(generated_ats)) + indexed.sort(key=lambda p: p[1]) + + times_sorted = [ts for _, ts in indexed] + signposts_sorted = [None] * len(times_sorted) + + # Current signpost = the earliest timestamp of current window + current_start = times_sorted[0] + signposts_sorted[0] = current_start + + for i in range(1, len(times_sorted)): + ts = times_sorted[i] + # if still within window, map to current_start + if ts < current_start + window_size: + signposts_sorted[i] = current_start + else: + # start a new window here + current_start = ts + signposts_sorted[i] = current_start + + # Map back to original order + result = [None] * len(times_sorted) + for (orig_idx, _), signpost in zip(indexed, signposts_sorted): + result[orig_idx] = signpost + + return result + + def calc_rank_compare_metrics( in_df, charge_mins, window_mins, - window_starts=None, + window_start_time=None, pred_col="predicted_value", truth_col="signal_value", load_kw=1000, ): df = in_df.copy() - - if window_starts: - # Extract unique dates and create window ranges - unique_dates = df.index.get_level_values("generated_at").unique() - window_ranges = [] - for date in unique_dates: - for start_time in window_starts: - start = pd.Timestamp(f"{date} {start_time}") - end = start + pd.Timedelta(minutes=window_mins) - window_ranges.append((start, end)) - - # Convert to DataFrame - window_df = pd.DataFrame(window_ranges, columns=["window_start", "window_end"]) - window_df = window_df.sort_values("window_start") - tz = df.index.get_level_values("generated_at")[0].tz - window_df = window_df.assign( - window_start=pd.to_datetime( - window_df["window_start"], errors="coerce" - ).dt.tz_convert(tz) - ) - window_df = window_df.dropna(subset=["window_start"]) - - # Use merge_asof to efficiently assign windows - df = pd.merge_asof( - df.reset_index().sort_values("generated_at", ascending=True), - window_df, - left_on="generated_at", - right_on="window_start", - direction="backward", - ).set_index(df.index.names) - + + if isinstance(window_mins, int): + window_mins = pd.Timedelta(minutes=window_mins) + + window_starts = assign_windows(df.index.get_level_values('generated_at'), window_mins) + + if window_start_time: + start_offset = pd.to_timedelta(window_start_time + ":00") + adj_window_starts = [i.normalize() + start_offset for i in window_starts] else: - # Assign each row to a rolling window of `window_mins` based on `generated_at` - df["window_start"] = df.index.get_level_values("generated_at").floor( - f"{window_mins}min" - ) - df["window_end"] = df["window_start"] + pd.Timedelta(f"{window_mins} min") - + adj_window_starts = window_starts + + df = df.assign( + window_start=adj_window_starts, + window_end=[i + window_mins for i in adj_window_starts] + ) + # Filter out rows that do not fall within the valid window df.loc[ (df.index.get_level_values("point_time") >= df["window_end"]) | (df.index.get_level_values("generated_at") >= df["window_end"]) - | (df.index.get_level_values("generated_at") <= df["window_start"]), + | (df.index.get_level_values("generated_at") < df["window_start"]), "window_start", ] = pd.NaT - df = df.dropna(subset=["window_start"]) + df = df.dropna(subset=["window_start", "window_end"]) # Filter out rows where there aren't enough generated_ats to fulfill charge_mins n_generated_at_in_window = df.groupby("window_start").transform( lambda x: x.reset_index()["generated_at"].nunique() )[pred_col] - df = df.loc[n_generated_at_in_window >= charge_mins // 5] - + df = df.loc[n_generated_at_in_window >= (charge_mins // 5) - 1] + df = df.assign( - truth_charge_status=simulate_charge(df, truth_col, charge_mins), + truth_charge_status=pick_k_optimal_charge(df, truth_col, charge_mins), pred_charge_status=simulate_charge(df, pred_col, charge_mins), ) @@ -598,55 +640,53 @@ def plot_rank_corr( return figs -# TODO: translate to local time for start windows? AER_SCENARIOS = { "EV-night": { "charge_mins": 3 * 60, "window_mins": 12 * 60, - "window_starts": ["19:00"], + "window_start_time": "19:00", "load_kw": 19, }, "EV-day": { "charge_mins": 2 * 60, "window_mins": 8 * 60, - "window_starts": ["09:00"], + "window_start_time": "09:00", "load_kw": 19, }, "Thermostat": { "charge_mins": 30, "window_mins": 60, - "window_starts": None, "load_kw": 3, # typical AC }, "10 kW / 24hr / 25% Duty Cycle": { "charge_mins": 6 * 60, - "window_mins": 24 * 60, # 4 days - "window_starts": ["00:00"], + "window_mins": 24 * 60, "load_kw": 10, }, "10 kW / 72hr / 25% Duty Cycle": { "charge_mins": 3 * 3 * 60, - "window_mins": 24 * 3 * 60, # 4 days - "window_starts": ["00:00"], + "window_mins": 24 * 3 * 60, "load_kw": 10, }, "10 kW / 24hr / 50% Duty Cycle": { "charge_mins": 6 * 60, - "window_mins": 24 * 60, # 4 days - "window_starts": ["00:00"], + "window_mins": 24 * 60, "load_kw": 10, }, "10 kW / 72hr / 50% Duty Cycle": { "charge_mins": 12 * 3 * 60, - "window_mins": 24 * 3 * 60, # 4 days - "window_starts": ["00:00"], + "window_mins": 24 * 3 * 60, "load_kw": 10, }, } def plot_impact_forecast_metrics( - factory: DataHandlerFactory, scenarios=["EV-night", "EV-day", "Thermostat", "10 kW / 24hr / 25% Duty Cycle", "10 kW / 72hr / 25% Duty Cycle", "10 kW / 24hr / 50% Duty Cycle", "10 kW / 72hr / 50% Duty Cycle"] + factory: DataHandlerFactory, + scenarios=[ + "10 kW / 24hr / 25% Duty Cycle", "10 kW / 72hr / 25% Duty Cycle", + "10 kW / 24hr / 50% Duty Cycle", "10 kW / 72hr / 50% Duty Cycle", + "EV-night", "EV-day", "Thermostat"] ): figs = {} @@ -675,7 +715,6 @@ def plot_impact_forecast_metrics( ] _metrics = pd.DataFrame(_metrics) - _metrics['scenario'] = _metrics['scenario'].apply(lambda x: x.replace(' / ', '\n')) fig.add_trace( go.Bar( @@ -812,7 +851,7 @@ def calc_max_potential( in_df, charge_mins, window_mins, - window_starts=None, + window_start_time=None, truth_col="signal_value", load_kw=1000, ): @@ -824,66 +863,38 @@ def calc_max_potential( needed_rows = charge_mins // 5 load_factor = load_kw / 1000 - if window_starts: - unique_dates = df.index.get_level_values("point_time").normalize().unique() - window_ranges = [] - window_ranges_extend = window_ranges.extend - - for date in unique_dates: - window_ranges_extend( - [ - ( - pd.Timestamp.combine( - date.date(), pd.to_datetime(start_time).time() - ), - pd.Timestamp.combine( - date.date(), pd.to_datetime(start_time).time() - ) - + pd.Timedelta(minutes=window_mins), - ) - for start_time in window_starts - ] - ) - - window_df = pd.DataFrame(window_ranges, columns=["window_start", "window_end"]) - window_df.sort_values("window_start", inplace=True) - - tz = df.index.get_level_values("point_time")[0].tz - if tz: - window_df["window_start"] = window_df["window_start"].dt.tz_localize(tz) - window_df["window_end"] = window_df["window_end"].dt.tz_localize(tz) - - df_reset = df.reset_index().sort_values("point_time") - df = pd.merge_asof( - df_reset, - window_df, - left_on="point_time", - right_on="window_start", - direction="backward", - ).set_index(df.index.names) + if isinstance(window_mins, int): + window_mins = pd.Timedelta(minutes=window_mins) + + window_starts = assign_windows(df.index.get_level_values('point_time'), window_mins) + + if window_start_time: + start_offset = pd.to_timedelta(window_start_time + ":00") + adj_window_starts = [i.normalize() + start_offset for i in window_starts] else: - # local -> utc -> floor to nearest window -> local to avoid dst issues - point_times = df.index.get_level_values("point_time") - df["window_start"] = ( - point_times.tz_convert("UTC") - .floor(f"{window_mins}min") - .tz_convert(point_times.tz) # <-- use .tz, not .dt.tz - ) - df["window_end"] = df["window_start"] + pd.Timedelta(minutes=window_mins) - - # Combined filtering operations - df = df.dropna(subset=["window_start"]) - point_times = df.index.get_level_values("point_time") - time_mask = (point_times >= df["window_start"]) & (point_times < df["window_end"]) - df = df.loc[time_mask] + adj_window_starts = window_starts + + df = df.assign( + window_start=adj_window_starts, + window_end=[i + window_mins for i in adj_window_starts] + ) + + # Filter out rows that do not fall within the valid window + df.loc[ + (df.index.get_level_values("point_time") >= df["window_end"]), "window_start", + ] = pd.NaT + df = df.dropna(subset=["window_start", "window_end"]) - window_counts = df.groupby("window_start")[truth_col].transform("count") - df = df[window_counts >= needed_rows] + # Filter out rows where there aren't enough generated_ats to fulfill charge_mins + n_generated_at_in_window = df.groupby("window_start").transform( + lambda x: x.reset_index()["point_time"].nunique() + )[truth_col] + df = df.loc[n_generated_at_in_window >= (charge_mins // 5) - 1] if df.empty: return {"potential": 0.0} - df["charge_status"] = simulate_charge(df, truth_col, charge_mins) + df["charge_status"] = pick_k_optimal_charge(df, truth_col, charge_mins) truth_values = df[truth_col] charge_emissions = truth_values * df["charge_status"] * load_factor @@ -1280,7 +1291,7 @@ def plot_precision_recall( x=x_values, y=precision_values, name="Precision", - marker_color="blue", + marker_color="#636EFA", text=[f"{y:.1f}%" for y in precision_values], textposition="outside", showlegend=(row_idx == 1), # only show once @@ -1294,7 +1305,7 @@ def plot_precision_recall( x=x_values, y=recall_values, name="Recall", - marker_color="red", + marker_color="#EF553B", text=[f"{y:.1f}%" for y in recall_values], textposition="outside", showlegend=(row_idx == 1), # only show once From 86550fdf35b19c9afcd5c923123b785fa0c1d9bc Mon Sep 17 00:00:00 2001 From: samkoebrich Date: Sun, 24 Aug 2025 09:27:20 -0600 Subject: [PATCH 60/85] black==25.1 --- watttime/evaluation/report.py | 139 ++++++++++++++++++++-------------- 1 file changed, 82 insertions(+), 57 deletions(-) diff --git a/watttime/evaluation/report.py b/watttime/evaluation/report.py index 4dc5f651..83f73af4 100644 --- a/watttime/evaluation/report.py +++ b/watttime/evaluation/report.py @@ -320,8 +320,8 @@ def calc_rank_corr( def pick_k_optimal_charge(in_df: pd.DataFrame, truth_col, charge_mins: int): df = in_df.copy() - - specify_generated_at = 'generated_at' in df.index.names + + specify_generated_at = "generated_at" in df.index.names charge_needed = charge_mins // 5 df["charge_status"] = False @@ -331,33 +331,34 @@ def pick_k_optimal_charge(in_df: pd.DataFrame, truth_col, charge_mins: int): # Pick the `charge_needed` lowest truth_col values in this window # If there are ties, 'first' ensures deterministic selection if specify_generated_at: - w_df = w_df[w_df['generated_at'] == w_df['point_time']] - selected_points = ( - w_df.sort_values(truth_col, ascending=True) - .head(charge_needed) + w_df = w_df[w_df["generated_at"] == w_df["point_time"]] + selected_points = w_df.sort_values(truth_col, ascending=True).head( + charge_needed ) # Mark these point_times as charge periods in the original dataframe if specify_generated_at: - df.loc[[(p, p) for p in selected_points['point_time']], "charge_status"] = True + df.loc[[(p, p) for p in selected_points["point_time"]], "charge_status"] = ( + True + ) else: - df.loc[selected_points['point_time'], "charge_status"] = True + df.loc[selected_points["point_time"], "charge_status"] = True return df["charge_status"] - - + + def simulate_charge(in_df: pd.DataFrame, sort_col: str, charge_mins: int): - + df = in_df.copy() - assert sort_col in df.columns + assert sort_col in df.columns charge_needed = charge_mins // 5 df = df.assign(charge_status=False) df = df.sort_index(ascending=True) for w in df.reset_index().groupby("window_start"): window_start, w_df = w - w_df = w_df.sort_values(['generated_at', 'point_time'], ascending=True) + w_df = w_df.sort_values(["generated_at", "point_time"], ascending=True) charge_periods = [] _charge_needed = charge_needed generated_at_groups = list(w_df.groupby("generated_at")) @@ -371,7 +372,7 @@ def simulate_charge(in_df: pd.DataFrame, sort_col: str, charge_mins: int): ) if should_charge_now: - assert generated_at == g_df.iloc[0]['point_time'] + assert generated_at == g_df.iloc[0]["point_time"] charge_periods.append((generated_at, generated_at)) _charge_needed -= 1 @@ -386,18 +387,22 @@ def simulate_charge(in_df: pd.DataFrame, sort_col: str, charge_mins: int): assert df["charge_status"].sum() >= ( len(df["window_start"].unique()) * 0.97 * charge_needed ), "Charge status is too low, check the logic for simulate_charge" - + return df["charge_status"] -def assign_windows(generated_ats: List[pd.Timestamp], window_size: pd.Timedelta) -> List[pd.Timestamp]: +def assign_windows( + generated_ats: List[pd.Timestamp], window_size: pd.Timedelta +) -> List[pd.Timestamp]: if isinstance(window_size, int): - window_size=pd.Timedelta(minutes=window_size) + window_size = pd.Timedelta(minutes=window_size) first_tz = generated_ats[0].tz if any((ts.tz is None or ts.tz != first_tz) for ts in generated_ats): - raise ValueError("All generated_ats must be tz-aware and share the same timezone") + raise ValueError( + "All generated_ats must be tz-aware and share the same timezone" + ) # Sort with original indices indexed = list(enumerate(generated_ats)) @@ -438,23 +443,25 @@ def calc_rank_compare_metrics( load_kw=1000, ): df = in_df.copy() - + if isinstance(window_mins, int): window_mins = pd.Timedelta(minutes=window_mins) - - window_starts = assign_windows(df.index.get_level_values('generated_at'), window_mins) - + + window_starts = assign_windows( + df.index.get_level_values("generated_at"), window_mins + ) + if window_start_time: start_offset = pd.to_timedelta(window_start_time + ":00") adj_window_starts = [i.normalize() + start_offset for i in window_starts] else: adj_window_starts = window_starts - + df = df.assign( window_start=adj_window_starts, - window_end=[i + window_mins for i in adj_window_starts] + window_end=[i + window_mins for i in adj_window_starts], ) - + # Filter out rows that do not fall within the valid window df.loc[ (df.index.get_level_values("point_time") >= df["window_end"]) @@ -469,7 +476,7 @@ def calc_rank_compare_metrics( lambda x: x.reset_index()["generated_at"].nunique() )[pred_col] df = df.loc[n_generated_at_in_window >= (charge_mins // 5) - 1] - + df = df.assign( truth_charge_status=pick_k_optimal_charge(df, truth_col, charge_mins), pred_charge_status=simulate_charge(df, pred_col, charge_mins), @@ -663,7 +670,7 @@ def plot_rank_corr( "window_mins": 24 * 60, "load_kw": 10, }, - "10 kW / 72hr / 25% Duty Cycle": { + "10 kW / 72hr / 25% Duty Cycle": { "charge_mins": 3 * 3 * 60, "window_mins": 24 * 3 * 60, "load_kw": 10, @@ -673,7 +680,7 @@ def plot_rank_corr( "window_mins": 24 * 60, "load_kw": 10, }, - "10 kW / 72hr / 50% Duty Cycle": { + "10 kW / 72hr / 50% Duty Cycle": { "charge_mins": 12 * 3 * 60, "window_mins": 24 * 3 * 60, "load_kw": 10, @@ -684,9 +691,14 @@ def plot_rank_corr( def plot_impact_forecast_metrics( factory: DataHandlerFactory, scenarios=[ - "10 kW / 24hr / 25% Duty Cycle", "10 kW / 72hr / 25% Duty Cycle", - "10 kW / 24hr / 50% Duty Cycle", "10 kW / 72hr / 50% Duty Cycle", - "EV-night", "EV-day", "Thermostat"] + "10 kW / 24hr / 25% Duty Cycle", + "10 kW / 72hr / 25% Duty Cycle", + "10 kW / 24hr / 50% Duty Cycle", + "10 kW / 72hr / 50% Duty Cycle", + "EV-night", + "EV-day", + "Thermostat", + ], ): figs = {} @@ -715,7 +727,7 @@ def plot_impact_forecast_metrics( ] _metrics = pd.DataFrame(_metrics) - + fig.add_trace( go.Bar( x=_metrics["scenario"], @@ -726,7 +738,9 @@ def plot_impact_forecast_metrics( ), # Light gray for potential hovertemplate="%{x}: %{y:.1f} lbs CO2", legendgroup="Potential Savings", # Group legend for potential savings - showlegend=(model_ix == 1), # Show legend only for the first subplot + showlegend=( + model_ix == 1 + ), # Show legend only for the first subplot ), row=model_ix, col=1, @@ -745,7 +759,9 @@ def plot_impact_forecast_metrics( marker=dict(color="rgba(0, 128, 0, 0.8)"), # Green for reduction hovertemplate="%{x}: %{y:.1f} lbs CO2", legendgroup="Potential Savings", # Group legend for potential savings - showlegend=(model_ix == 1), # Show legend only for the first subplot + showlegend=( + model_ix == 1 + ), # Show legend only for the first subplot ), row=model_ix, col=1, @@ -865,23 +881,24 @@ def calc_max_potential( if isinstance(window_mins, int): window_mins = pd.Timedelta(minutes=window_mins) - - window_starts = assign_windows(df.index.get_level_values('point_time'), window_mins) - + + window_starts = assign_windows(df.index.get_level_values("point_time"), window_mins) + if window_start_time: start_offset = pd.to_timedelta(window_start_time + ":00") adj_window_starts = [i.normalize() + start_offset for i in window_starts] else: adj_window_starts = window_starts - + df = df.assign( window_start=adj_window_starts, - window_end=[i + window_mins for i in adj_window_starts] + window_end=[i + window_mins for i in adj_window_starts], ) - + # Filter out rows that do not fall within the valid window df.loc[ - (df.index.get_level_values("point_time") >= df["window_end"]), "window_start", + (df.index.get_level_values("point_time") >= df["window_end"]), + "window_start", ] = pd.NaT df = df.dropna(subset=["window_start", "window_end"]) @@ -1205,8 +1222,11 @@ def plot_bland_altman( def calc_precision_recall( - forecasts_v_moers: pd.DataFrame, horizon_minutes: int, impute_curtail_threshold: int, - pred_col="predicted_value", truth_col="signal_value", + forecasts_v_moers: pd.DataFrame, + horizon_minutes: int, + impute_curtail_threshold: int, + pred_col="predicted_value", + truth_col="signal_value", ) -> Tuple[float, float]: """ Calculate precision and recall for the all forecast values UP TO the specified horizon, @@ -1227,17 +1247,17 @@ def calc_precision_recall( pred_bool = filtered_forecasts[pred_col] <= impute_curtail_threshold truth_bool = filtered_forecasts[truth_col] <= impute_curtail_threshold - + # TP / (TP + FP) precision = ( - (pred_bool & truth_bool).sum() / pred_bool.sum() - if pred_bool.sum() > 0 else 0.0 + (pred_bool & truth_bool).sum() / pred_bool.sum() if pred_bool.sum() > 0 else 0.0 ) - + # TP / (TP + FN) recall = ( (pred_bool & truth_bool).sum() / truth_bool.sum() - if truth_bool.sum() > 0 else 0.0 + if truth_bool.sum() > 0 + else 0.0 ) return precision, recall @@ -1246,7 +1266,7 @@ def calc_precision_recall( def plot_precision_recall( factory: DataHandlerFactory, horizons_hr=[1, 6, 12, 18, 24, 72], - impute_curtail_threshold=100 + impute_curtail_threshold=100, ) -> Dict[str, go.Figure]: """ Create a Plotly figure with subplots: each model in a separate row @@ -1271,8 +1291,9 @@ def plot_precision_recall( for row_idx, model_job in enumerate(region_models, start=1): values = [ calc_precision_recall( - model_job.forecasts_v_moers, (h * 60) - 5, - impute_curtail_threshold=impute_curtail_threshold + model_job.forecasts_v_moers, + (h * 60) - 5, + impute_curtail_threshold=impute_curtail_threshold, ) for h in horizons_hr ] @@ -1296,7 +1317,8 @@ def plot_precision_recall( textposition="outside", showlegend=(row_idx == 1), # only show once ), - row=row_idx, col=1 + row=row_idx, + col=1, ) # Recall trace @@ -1310,13 +1332,16 @@ def plot_precision_recall( textposition="outside", showlegend=(row_idx == 1), # only show once ), - row=row_idx, col=1 + row=row_idx, + col=1, ) # Set uniform y-axis range across subplots y_range = [ - min(precision_y_min, recall_y_min) - (0.25 * max(precision_y_max, recall_y_max)), - max(precision_y_max, recall_y_max) + (0.25 * max(precision_y_max, recall_y_max)) + min(precision_y_min, recall_y_min) + - (0.25 * max(precision_y_max, recall_y_max)), + max(precision_y_max, recall_y_max) + + (0.25 * max(precision_y_max, recall_y_max)), ] for row_idx in range(1, len(region_models) + 1): @@ -1334,7 +1359,7 @@ def plot_precision_recall( figs[region_abbrev] = fig return figs - + def parse_report_command_line_args(sys_args): parser = argparse.ArgumentParser( @@ -1423,7 +1448,7 @@ def parse_report_command_line_args(sys_args): plot_norm_mae, plot_rank_corr, plot_precision_recall, - plot_impact_forecast_metrics + plot_impact_forecast_metrics, ], } From 9c2fb615fa40e6f522896efbf2b828e9741e161c Mon Sep 17 00:00:00 2001 From: Sam Koebrich Date: Mon, 25 Aug 2025 15:18:52 +0000 Subject: [PATCH 61/85] simplify tz localization --- .../get_wt_api_forecast_evaluation_data.py | 29 +---- watttime/evaluation/report.py | 121 +++++++++++------- 2 files changed, 81 insertions(+), 69 deletions(-) diff --git a/watttime/evaluation/get_wt_api_forecast_evaluation_data.py b/watttime/evaluation/get_wt_api_forecast_evaluation_data.py index d62f77ee..3d7ff364 100644 --- a/watttime/evaluation/get_wt_api_forecast_evaluation_data.py +++ b/watttime/evaluation/get_wt_api_forecast_evaluation_data.py @@ -11,8 +11,7 @@ from dataclasses import dataclass import pandas as pd -from shapely.geometry import shape -from timezonefinder import TimezoneFinder + from watttime import api @@ -57,7 +56,6 @@ class AnalysisDataHandler: forecast_max_horizon: int = 60 * 24 forecast_sample_seed: int = 42 signal_type: str = "co2_moer" - localize_tz: bool = True model_date: Optional[str] = ( None # if None, will default to latest provided through API ) @@ -93,18 +91,6 @@ def __post_init__(self): random.seed(self.forecast_sample_seed) self.sample_days = random.sample(self.eval_days, k) - # get tz by looking up centroid of region. - # Note that some regions may contain multiple timezones - if self.localize_tz: - wt_maps = api.WattTimeMaps() - all_maps = wt_maps.get_maps_json() - region = { - f["properties"]["region"]: f["geometry"] for f in all_maps["features"] - }[self.region] - centroid = shape(region).centroid - self.tz = TimezoneFinder().certain_timezone_at( - lat=centroid.y, lng=centroid.x - ) @cached_property def moers(self) -> pd.DataFrame: @@ -123,9 +109,6 @@ def moers(self) -> pd.DataFrame: .set_index("point_time", drop=True) ) - if self.localize_tz: - moers.index = moers.index.tz_convert(self.tz) - moers = moers.sort_index() self.returned_meta = self.wt_hist._last_request_meta @@ -153,12 +136,6 @@ def forecasts(self) -> pd.DataFrame: ).dt.total_seconds() / 60 forecasts.rename({"value": "predicted_value"}, axis="columns", inplace=True) - if self.localize_tz: - forecasts["generated_at"] = forecasts["generated_at"].dt.tz_convert( - self.tz - ) - forecasts["point_time"] = forecasts["point_time"].dt.tz_convert(self.tz) - self.returned_meta = self.wt_forecast._last_request_meta self.returned_forecast_model_date = self.wt_forecast._last_request_meta.get( "model", {} @@ -178,8 +155,8 @@ def fuel_mix(self) -> pd.DataFrame: model=self.model_date, ) - if self.localize_tz: - fm.index = fm.index.tz_convert(self.tz) + # if self.localize_tz: + # fm.index = fm.index.tz_convert(self.tz) self.returned_meta = self.wt_fuel_mix._last_request_meta self.returned_fuel_mix_model_date = self.wt_fuel_mix._last_request_meta.get( diff --git a/watttime/evaluation/report.py b/watttime/evaluation/report.py index 83f73af4..d9ec06a4 100644 --- a/watttime/evaluation/report.py +++ b/watttime/evaluation/report.py @@ -9,6 +9,7 @@ from typing import List, Optional, Union, Literal, Dict, Tuple from zoneinfo import ZoneInfo import inspect +from functools import lru_cache import numpy as np import pandas as pd @@ -16,6 +17,8 @@ import plotly.subplots as sp import plotly.colors as pc import scipy.stats as stats +from shapely.geometry import shape +from timezonefinder import TimezoneFinder from jinja2 import Template from watttime.evaluation.get_wt_api_forecast_evaluation_data import ( @@ -23,6 +26,20 @@ DataHandlerFactory, ) from watttime.evaluation.fuels_cp import fuel_cp +from watttime import api + +@lru_cache +def get_tz_from_centroid(region): + wt_maps = api.WattTimeMaps() + all_maps = wt_maps.get_maps_json() + region = { + f["properties"]["region"]: f["geometry"] for f in all_maps["features"] + }[region] + centroid = shape(region).centroid + tz = TimezoneFinder().certain_timezone_at( + lat=centroid.y, lng=centroid.x + ) + return tz def convert_to_timezone(dt: datetime, tz: Union[str, ZoneInfo]) -> datetime: @@ -129,11 +146,14 @@ def plot_sample_moers( for region_abbrev, region_models in factory.data_handlers_by_region_dict.items(): fig = go.Figure() + + tz = get_tz_from_centroid(region_abbrev) for model_job in region_models: _df = model_job.moers.reindex(times) - + _df = _df.tz_convert(tz) + fig.add_trace( go.Scatter( x=_df.index, @@ -394,49 +414,47 @@ def simulate_charge(in_df: pd.DataFrame, sort_col: str, charge_mins: int): def assign_windows( generated_ats: List[pd.Timestamp], window_size: pd.Timedelta ) -> List[pd.Timestamp]: - + if isinstance(window_size, int): window_size = pd.Timedelta(minutes=window_size) - - first_tz = generated_ats[0].tz - if any((ts.tz is None or ts.tz != first_tz) for ts in generated_ats): - raise ValueError( - "All generated_ats must be tz-aware and share the same timezone" - ) - - # Sort with original indices - indexed = list(enumerate(generated_ats)) - indexed.sort(key=lambda p: p[1]) - - times_sorted = [ts for _, ts in indexed] - signposts_sorted = [None] * len(times_sorted) - - # Current signpost = the earliest timestamp of current window - current_start = times_sorted[0] - signposts_sorted[0] = current_start - - for i in range(1, len(times_sorted)): - ts = times_sorted[i] - # if still within window, map to current_start - if ts < current_start + window_size: - signposts_sorted[i] = current_start + + n = len(generated_ats) + if n == 0: + return [] + + # Create array of (timestamp, original_index) and sort by timestamp + ts_with_idx = np.array([(ts.value, i) for i, ts in enumerate(generated_ats)], + dtype=[('ts', 'i8'), ('idx', 'i4')]) + ts_with_idx.sort(order='ts') + + # Convert window_size to nanoseconds for direct comparison + window_ns = window_size.value + + # Assign signposts using vectorized operations where possible + signposts = np.empty(n, dtype='i8') + current_start_ns = ts_with_idx[0]['ts'] + signposts[0] = current_start_ns + + for i in range(1, n): + ts_ns = ts_with_idx[i]['ts'] + if ts_ns < current_start_ns + window_ns: + signposts[i] = current_start_ns else: - # start a new window here - current_start = ts - signposts_sorted[i] = current_start - - # Map back to original order - result = [None] * len(times_sorted) - for (orig_idx, _), signpost in zip(indexed, signposts_sorted): - result[orig_idx] = signpost - - return result - + current_start_ns = ts_ns + signposts[i] = current_start_ns + + # Map back to original order using fancy indexing + result = np.empty(n, dtype='i8') + result[ts_with_idx['idx']] = signposts + + # Convert back to Timestamps + return [pd.Timestamp(ns, tz='UTC') for ns in result] def calc_rank_compare_metrics( in_df, charge_mins, window_mins, + tz=None, window_start_time=None, pred_col="predicted_value", truth_col="signal_value", @@ -452,8 +470,12 @@ def calc_rank_compare_metrics( ) if window_start_time: - start_offset = pd.to_timedelta(window_start_time + ":00") - adj_window_starts = [i.normalize() + start_offset for i in window_starts] + utc_offset = pd.to_timedelta(window_start_time + ":00") + local_dt = (pd.Timestamp("1999-01-01T00:00Z") + utc_offset).tz_convert(tz) + local_offset = pd.to_timedelta(local_dt.hour, unit="h") \ + + pd.to_timedelta(local_dt.minute, unit="m") \ + + pd.to_timedelta(local_dt.second, unit="s") + adj_window_starts = [i.normalize() + local_offset for i in window_starts] else: adj_window_starts = window_starts @@ -713,13 +735,16 @@ def plot_impact_forecast_metrics( subplot_titles=[j.model_date for j in region_models], vertical_spacing=0.2, ) + + tz = get_tz_from_centroid(region_abbrev) for model_ix, model_job in enumerate(region_models, start=1): _metrics = [ { **calc_rank_compare_metrics( - model_job.forecasts_v_moers, **AER_SCENARIOS[s] + model_job.forecasts_v_moers, **AER_SCENARIOS[s], + tz=tz ), "scenario": s, } @@ -813,10 +838,14 @@ def plot_sample_fuelmix( subplot_titles=[j.model_date for j in region_models], vertical_spacing=0.2, ) + + tz = get_tz_from_centroid(region_abbrev) for model_ix, model_job in enumerate(region_models, start=1): stacked_values = model_job.fuel_mix.reindex(times) + + stacked_values = stacked_values.tz_convert(tz) # Create cumulative values for stacking for fuel_ix in range(1, len(stacked_values.columns)): @@ -868,6 +897,7 @@ def calc_max_potential( charge_mins, window_mins, window_start_time=None, + tz=None, truth_col="signal_value", load_kw=1000, ): @@ -885,8 +915,12 @@ def calc_max_potential( window_starts = assign_windows(df.index.get_level_values("point_time"), window_mins) if window_start_time: - start_offset = pd.to_timedelta(window_start_time + ":00") - adj_window_starts = [i.normalize() + start_offset for i in window_starts] + utc_offset = pd.to_timedelta(window_start_time + ":00") + local_dt = (pd.Timestamp("1999-01-01T00:00Z") + utc_offset).tz_convert(tz) + local_offset = pd.to_timedelta(local_dt.hour, unit="h") \ + + pd.to_timedelta(local_dt.minute, unit="m") \ + + pd.to_timedelta(local_dt.second, unit="s") + adj_window_starts = [i.normalize() + local_offset for i in window_starts] else: adj_window_starts = window_starts @@ -941,13 +975,14 @@ def plot_max_impact_potential( y_max = 0 for region_abbrev, region_models in factory.data_handlers_by_region_dict.items(): fig = go.Figure() + tz = get_tz_from_centroid(region_abbrev) for model_job in region_models: _metrics = [ { - **calc_max_potential(model_job.moers, **AER_SCENARIOS[s]), - "scenario": s, + **calc_max_potential(model_job.moers, **AER_SCENARIOS[s], tz=tz), + "scenario": s, } for s in scenarios ] From 4324e9f52cab1e9f1fdb0f127602108daee259d0 Mon Sep 17 00:00:00 2001 From: sam_watttime Date: Wed, 8 Oct 2025 10:58:03 -0600 Subject: [PATCH 62/85] WattTimeAPIWarning class and better warning handeling in api.py --- watttime/api.py | 39 +++++++++++++++++++++++++++++++++++---- 1 file changed, 35 insertions(+), 4 deletions(-) diff --git a/watttime/api.py b/watttime/api.py index 31b5c47c..a7837391 100644 --- a/watttime/api.py +++ b/watttime/api.py @@ -19,6 +19,28 @@ from pytz import UTC +class WattTimeAPIWarning: + def __init__(self, url: str, params: Dict[str, Any], warning_message: str): + self.url = url + self.params = params + self.warning_message = warning_message + + def __repr__(self): + return f"\n" + + def to_dict(self) -> Dict[str, Any]: + def stringify(value: Any) -> Any: + if isinstance(value, datetime): + return value.isoformat() + return value + + return { + "url": self.url, + "params": {k: stringify(v) for k, v in self.params.items()}, + "warning_message": self.warning_message, + } + + def get_log(): logging.basicConfig( format="%(asctime)s [%(levelname)-1s] " "%(message)s", @@ -63,6 +85,7 @@ def __init__( self.rate_limit = rate_limit self._last_request_times = [] self.worker_count = worker_count + self.raised_warnings: List[WattTimeAPIWarning] = [] if self.multithreaded: self._rate_limit_lock = ( @@ -248,10 +271,19 @@ def _make_rate_limited_request(self, url: str, params: Dict[str, Any]) -> Dict: f"API Request Failed: {e}\nURL: {url}\nParams: {params}" ) from e - if j.get("meta", {}).get("warnings"): - print("Warnings Returned: %s | Response: %s", params, j["meta"]) + meta = j.get("meta", {}) + warnings = meta.get("warnings") + if warnings: + for warning_message in warnings: + warning = WattTimeAPIWarning( + url=url, params=params, warning_message=warning_message + ) + self.raised_warnings.append(warning) + LOG.warning( + f"API Warning: {warning_message} | URL: {url} | Params: {params}" + ) - self._last_request_meta = j.get("meta", {}) + self._last_request_meta = meta return j @@ -738,7 +770,6 @@ def get_maps_json( class WattTimeMarginalFuelMix(WattTimeBase): - def get_fuel_mix_jsons( self, start: Union[str, datetime], From e613249220c63cebcacef420b09e6f4b4e39f703 Mon Sep 17 00:00:00 2001 From: sam_watttime Date: Wed, 8 Oct 2025 10:58:28 -0600 Subject: [PATCH 63/85] improve warning handelling in report.py --- watttime/evaluation/report.py | 151 +++++++++++++++++++++------------- 1 file changed, 92 insertions(+), 59 deletions(-) diff --git a/watttime/evaluation/report.py b/watttime/evaluation/report.py index d9ec06a4..ef526fee 100644 --- a/watttime/evaluation/report.py +++ b/watttime/evaluation/report.py @@ -2,6 +2,7 @@ import calendar import sys import json +import warnings from datetime import datetime, timedelta, timezone from dateutil.parser import parse from operator import attrgetter @@ -21,24 +22,20 @@ from timezonefinder import TimezoneFinder from jinja2 import Template -from watttime.evaluation.get_wt_api_forecast_evaluation_data import ( - AnalysisDataHandler, - DataHandlerFactory, -) +from watttime.evaluation.get_wt_api_forecast_evaluation_data import DataHandlerFactory from watttime.evaluation.fuels_cp import fuel_cp from watttime import api + @lru_cache def get_tz_from_centroid(region): wt_maps = api.WattTimeMaps() all_maps = wt_maps.get_maps_json() - region = { - f["properties"]["region"]: f["geometry"] for f in all_maps["features"] - }[region] + region = {f["properties"]["region"]: f["geometry"] for f in all_maps["features"]}[ + region + ] centroid = shape(region).centroid - tz = TimezoneFinder().certain_timezone_at( - lat=centroid.y, lng=centroid.x - ) + tz = TimezoneFinder().certain_timezone_at(lat=centroid.y, lng=centroid.x) return tz @@ -146,14 +143,14 @@ def plot_sample_moers( for region_abbrev, region_models in factory.data_handlers_by_region_dict.items(): fig = go.Figure() - + tz = get_tz_from_centroid(region_abbrev) for model_job in region_models: _df = model_job.moers.reindex(times) _df = _df.tz_convert(tz) - + fig.add_trace( go.Scatter( x=_df.index, @@ -326,16 +323,22 @@ def calc_norm_mae( def calc_rank_corr( df, horizon_mins, pred_col="predicted_value", truth_col="signal_value" ): - """Returns mean daily Rank Correlation""" + """Compute mean daily Spearman rank correlation up to a given forecast horizon.""" - filtered_df = df[df["horizon_mins"] <= horizon_mins].dropna() - corr = filtered_df.groupby("generated_at").apply( - lambda group_df: stats.spearmanr( - group_df[truth_col], group_df[pred_col] - ).statistic + filtered_df = df[df["horizon_mins"] <= horizon_mins].dropna( + subset=[pred_col, truth_col] ) + if filtered_df.empty: + return np.nan - return np.nanmean(corr).round(3) + def safe_spearmanr(group_df): + if group_df[truth_col].nunique() < 2 or group_df[pred_col].nunique() < 2: + return np.nan + return stats.spearmanr(group_df[truth_col], group_df[pred_col]).statistic + + corr = filtered_df.groupby("generated_at").apply(safe_spearmanr) + + return float(np.nanmean(corr).round(3)) def pick_k_optimal_charge(in_df: pd.DataFrame, truth_col, charge_mins: int): @@ -358,9 +361,9 @@ def pick_k_optimal_charge(in_df: pd.DataFrame, truth_col, charge_mins: int): # Mark these point_times as charge periods in the original dataframe if specify_generated_at: - df.loc[[(p, p) for p in selected_points["point_time"]], "charge_status"] = ( - True - ) + df.loc[ + [(p, p) for p in selected_points["point_time"]], "charge_status" + ] = True else: df.loc[selected_points["point_time"], "charge_status"] = True @@ -392,7 +395,14 @@ def simulate_charge(in_df: pd.DataFrame, sort_col: str, charge_mins: int): ) if should_charge_now: - assert generated_at == g_df.iloc[0]["point_time"] + + if generated_at != g_df.iloc[0]["point_time"]: + warnings.warn( + "The generated_at and first point_time for a forecast do not match, look closely to understand why!", + RuntimeWarning, + ) + continue + charge_periods.append((generated_at, generated_at)) _charge_needed -= 1 @@ -414,41 +424,44 @@ def simulate_charge(in_df: pd.DataFrame, sort_col: str, charge_mins: int): def assign_windows( generated_ats: List[pd.Timestamp], window_size: pd.Timedelta ) -> List[pd.Timestamp]: - + if isinstance(window_size, int): window_size = pd.Timedelta(minutes=window_size) - + n = len(generated_ats) if n == 0: return [] - + # Create array of (timestamp, original_index) and sort by timestamp - ts_with_idx = np.array([(ts.value, i) for i, ts in enumerate(generated_ats)], - dtype=[('ts', 'i8'), ('idx', 'i4')]) - ts_with_idx.sort(order='ts') - + ts_with_idx = np.array( + [(ts.value, i) for i, ts in enumerate(generated_ats)], + dtype=[("ts", "i8"), ("idx", "i4")], + ) + ts_with_idx.sort(order="ts") + # Convert window_size to nanoseconds for direct comparison window_ns = window_size.value - + # Assign signposts using vectorized operations where possible - signposts = np.empty(n, dtype='i8') - current_start_ns = ts_with_idx[0]['ts'] + signposts = np.empty(n, dtype="i8") + current_start_ns = ts_with_idx[0]["ts"] signposts[0] = current_start_ns - + for i in range(1, n): - ts_ns = ts_with_idx[i]['ts'] + ts_ns = ts_with_idx[i]["ts"] if ts_ns < current_start_ns + window_ns: signposts[i] = current_start_ns else: current_start_ns = ts_ns signposts[i] = current_start_ns - + # Map back to original order using fancy indexing - result = np.empty(n, dtype='i8') - result[ts_with_idx['idx']] = signposts - + result = np.empty(n, dtype="i8") + result[ts_with_idx["idx"]] = signposts + # Convert back to Timestamps - return [pd.Timestamp(ns, tz='UTC') for ns in result] + return [pd.Timestamp(ns, tz="UTC") for ns in result] + def calc_rank_compare_metrics( in_df, @@ -472,9 +485,11 @@ def calc_rank_compare_metrics( if window_start_time: utc_offset = pd.to_timedelta(window_start_time + ":00") local_dt = (pd.Timestamp("1999-01-01T00:00Z") + utc_offset).tz_convert(tz) - local_offset = pd.to_timedelta(local_dt.hour, unit="h") \ - + pd.to_timedelta(local_dt.minute, unit="m") \ - + pd.to_timedelta(local_dt.second, unit="s") + local_offset = ( + pd.to_timedelta(local_dt.hour, unit="h") + + pd.to_timedelta(local_dt.minute, unit="m") + + pd.to_timedelta(local_dt.second, unit="s") + ) adj_window_starts = [i.normalize() + local_offset for i in window_starts] else: adj_window_starts = window_starts @@ -499,6 +514,12 @@ def calc_rank_compare_metrics( )[pred_col] df = df.loc[n_generated_at_in_window >= (charge_mins // 5) - 1] + # Filter out rows where generated_at != first point_time in the window + df = df.loc[ + df.index.get_level_values("generated_at") + == df.reset_index().groupby("generated_at")["point_time"].transform("first") + ] + df = df.assign( truth_charge_status=pick_k_optimal_charge(df, truth_col, charge_mins), pred_charge_status=simulate_charge(df, pred_col, charge_mins), @@ -540,12 +561,15 @@ def calc_rank_compare_metrics( assert y_pred_emissions >= y_best_emissions assert y_base_emissions >= y_best_emissions - reduction = (y_base_emissions - y_pred_emissions) / len( - set(df.index.get_level_values("generated_at").date) - ) - potential = (y_base_emissions - y_best_emissions) / len( - set(df.index.get_level_values("generated_at").date) - ) + dates = df.index.get_level_values("generated_at") + n_days = len(set(dates.dropna().date)) + + if n_days == 0: + reduction = np.nan + potential = np.nan + else: + reduction = (y_base_emissions - y_pred_emissions) / n_days + potential = (y_base_emissions - y_best_emissions) / n_days return { "reduction": round(reduction, 1), @@ -735,7 +759,7 @@ def plot_impact_forecast_metrics( subplot_titles=[j.model_date for j in region_models], vertical_spacing=0.2, ) - + tz = get_tz_from_centroid(region_abbrev) for model_ix, model_job in enumerate(region_models, start=1): @@ -743,8 +767,7 @@ def plot_impact_forecast_metrics( _metrics = [ { **calc_rank_compare_metrics( - model_job.forecasts_v_moers, **AER_SCENARIOS[s], - tz=tz + model_job.forecasts_v_moers, **AER_SCENARIOS[s], tz=tz ), "scenario": s, } @@ -838,13 +861,16 @@ def plot_sample_fuelmix( subplot_titles=[j.model_date for j in region_models], vertical_spacing=0.2, ) - + tz = get_tz_from_centroid(region_abbrev) + # Track which fuel types have been added to legend + fuels_in_legend = set() + for model_ix, model_job in enumerate(region_models, start=1): stacked_values = model_job.fuel_mix.reindex(times) - + stacked_values = stacked_values.tz_convert(tz) # Create cumulative values for stacking @@ -853,6 +879,11 @@ def plot_sample_fuelmix( # Add each fuel type as an area for fuel_ix, fuel in enumerate(model_job.fuel_mix.columns): + # Only show in legend if not already added + show_in_legend = fuel not in fuels_in_legend + if show_in_legend: + fuels_in_legend.add(fuel) + fig.add_trace( go.Scatter( x=stacked_values.index, @@ -863,7 +894,7 @@ def plot_sample_fuelmix( fillcolor=fuel_cp[fuel], connectgaps=False, legendgroup=fuel, - showlegend=(model_ix == 1), + showlegend=show_in_legend, line=dict(shape="hv"), ), row=model_ix, @@ -917,9 +948,11 @@ def calc_max_potential( if window_start_time: utc_offset = pd.to_timedelta(window_start_time + ":00") local_dt = (pd.Timestamp("1999-01-01T00:00Z") + utc_offset).tz_convert(tz) - local_offset = pd.to_timedelta(local_dt.hour, unit="h") \ - + pd.to_timedelta(local_dt.minute, unit="m") \ - + pd.to_timedelta(local_dt.second, unit="s") + local_offset = ( + pd.to_timedelta(local_dt.hour, unit="h") + + pd.to_timedelta(local_dt.minute, unit="m") + + pd.to_timedelta(local_dt.second, unit="s") + ) adj_window_starts = [i.normalize() + local_offset for i in window_starts] else: adj_window_starts = window_starts @@ -982,7 +1015,7 @@ def plot_max_impact_potential( _metrics = [ { **calc_max_potential(model_job.moers, **AER_SCENARIOS[s], tz=tz), - "scenario": s, + "scenario": s, } for s in scenarios ] From 87e5f3e7b8ee47303e9f6675a4a683e86df34722 Mon Sep 17 00:00:00 2001 From: sam_watttime Date: Wed, 8 Oct 2025 10:59:00 -0600 Subject: [PATCH 64/85] black 22.10 --- watttime/evaluation/bulk_report.py | 31 +++++-- watttime/evaluation/fuels_cp.py | 140 +++++++++++++++-------------- 2 files changed, 99 insertions(+), 72 deletions(-) diff --git a/watttime/evaluation/bulk_report.py b/watttime/evaluation/bulk_report.py index b1c84662..1687abdb 100644 --- a/watttime/evaluation/bulk_report.py +++ b/watttime/evaluation/bulk_report.py @@ -28,14 +28,16 @@ def get_latest_model_date(abbrev, requested_model_dates: List[str]): def get_regions_for_model( - model: str, signal_type: str, combine_subregions: bool = False, + model: str, + signal_type: str, + combine_subregions: bool = False, ) -> List[Union[str, Tuple[str, List[str]]]]: regions = MA_DF.loc[ (MA_DF["signal_type"] == signal_type) & (MA_DF["model"] == model) & (MA_DF["endpoint"] == "v3/historical") ]["region"].unique() - + regions_dict = defaultdict(list) for region in regions: parent = region.split("_")[0] @@ -54,8 +56,10 @@ def get_regions_for_model( return regions_list -def _process_region(region_list, signal_type, model_date, start, end, output_dir, steps): - +def _process_region( + region_list, signal_type, model_date, start, end, output_dir, steps +): + # TODO: make this DRY if isinstance(region_list, str): region_title = region_list @@ -79,7 +83,7 @@ def _process_region(region_list, signal_type, model_date, start, end, output_dir if output_path.exists(): print(f"[SKIP] {output_path} already exists, skipping.") return (region_title, None) - + try: print(f"[PID] working on {region_title}") generate_report( @@ -125,7 +129,14 @@ def bulk_generate_reports( with ProcessPoolExecutor(max_workers=max_workers) as executor: future_to_region = { executor.submit( - _process_region, region, signal_type, model_date, start, end, output_dir, steps + _process_region, + region, + signal_type, + model_date, + start, + end, + output_dir, + steps, ): region for region in regions } @@ -149,5 +160,11 @@ def bulk_generate_reports( if __name__ == "__main__": bulk_generate_reports( - MODEL, SIGNAL_TYPE, START, END, OUTPUT_DIR, ["signal", "fuel_mix", "forecast"], max_workers=5 + MODEL, + SIGNAL_TYPE, + START, + END, + OUTPUT_DIR, + ["signal", "fuel_mix", "forecast"], + max_workers=5, ) diff --git a/watttime/evaluation/fuels_cp.py b/watttime/evaluation/fuels_cp.py index c4767885..c626b450 100644 --- a/watttime/evaluation/fuels_cp.py +++ b/watttime/evaluation/fuels_cp.py @@ -1,68 +1,78 @@ -fuels = ['biomass', 'geothermal', 'hydro', 'solar', 'wind', - 'coal', 'gas', 'nuclear', 'oil'] -other_fuels = ['other', 'waste', 'other_fossil', 'petcoke'] +fuels = [ + "biomass", + "geothermal", + "hydro", + "solar", + "wind", + "coal", + "gas", + "nuclear", + "oil", +] +other_fuels = ["other", "waste", "other_fossil", "petcoke"] fuel_cp = { - 'biomass': '#087243', - 'biomass & waste': '#087243', - 'coal': '#282828', - 'hard_coal_anthricite': '#525252', - 'black_coal': '#525252', - 'coal_anthracite': '#525252', - 'brown_coal_lignite': '#5C5C5C', - 'coal_lignite': '#3D3D3D', - 'coal_bituminous': '#474747', - 'coal_subbituminous': '#333333', - 'coke_oven_gas': '#666666', - 'curtailment': '#ff4050', - 'natgas': '#a76311', - 'combined_cycle': '#a76311', - 'simple_cycle': '#e38009', - 'natural_gas': '#a76311', - 'natural gas': '#a76311', - 'thermal': '#a76311', - 'gas': '#a76311', - 'gas_unspecified': '#a76311', - 'petroleum': '#6f420b', - 'oil': '#6f420b', - 'oil_residual': '#6f420b', - 'oil_diesel': '#6f420b', - 'oil_unspecified': '#6f420b', - 'diesel': '#6f420b', - 'peat': '#0fd27a', - 'petcoke': '#0fd27a', - 'geo': '#cc2851', - 'geothermal': '#cc2851', - 'hydro_pumped_stor': '#054861', - 'pumped': '#054861', - 'PSH': '#054861', - 'pumped hydro': '#054861', - 'hydro_run_of_river': '#0b9fd5', - 'small hydro': '#0b9fd5', - 'hydro_water_res': '#08749b', - 'large hydro': '#08749b', - 'hydro': '#08749b', - 'renewable': '#953766', - 're': '#953766', - 'nuclear': '#9ef274', - 'solar': '#fffc5c', - 'refuse': '#871c37', - 'waste': '#871c37', - 'wind_offshore': '#9deefb', - 'wind_onshore': '#9deefb', - 'wind': '#9deefb', - 'other': '#cccccc', - 'storage': '#cccccc', - 'battery': '#cccccc', - 'marine': '#cccccc', - 'other_fossil': '#cccccc', - 'other fossil': '#cccccc', - 'gas_blastfurnace': '#f78e60', - 'cogen': '#f78e60', - 'net_imports': '#b3a0ba', - 'imports': '#b3a0ba', - 'waste_heat': '#94101F', - 'storage': '#94101F', - 'carbon_free': '#953766', + "biomass": "#087243", + "biomass & waste": "#087243", + "coal": "#282828", + "hard_coal_anthricite": "#525252", + "black_coal": "#525252", + "coal_anthracite": "#525252", + "brown_coal_lignite": "#5C5C5C", + "coal_lignite": "#3D3D3D", + "coal_bituminous": "#474747", + "coal_subbituminous": "#333333", + "coke_oven_gas": "#666666", + "curtailment": "#ff4050", + "natgas": "#a76311", + "combined_cycle": "#a76311", + "simple_cycle": "#e38009", + "natural_gas": "#a76311", + "natural gas": "#a76311", + "thermal": "#a76311", + "gas": "#a76311", + "gas_unspecified": "#a76311", + "petroleum": "#6f420b", + "oil": "#6f420b", + "oil_residual": "#6f420b", + "oil_diesel": "#6f420b", + "oil_unspecified": "#6f420b", + "diesel": "#6f420b", + "peat": "#0fd27a", + "petcoke": "#0fd27a", + "geo": "#cc2851", + "geothermal": "#cc2851", + "hydro_pumped_stor": "#054861", + "pumped": "#054861", + "PSH": "#054861", + "pumped hydro": "#054861", + "hydro_run_of_river": "#0b9fd5", + "small hydro": "#0b9fd5", + "hydro_water_res": "#08749b", + "large hydro": "#08749b", + "hydro": "#08749b", + "renewable": "#953766", + "renewable_curtailment": "#953766", + "re": "#953766", + "nuclear": "#9ef274", + "solar": "#fffc5c", + "refuse": "#871c37", + "waste": "#871c37", + "wind_offshore": "#9deefb", + "wind_onshore": "#9deefb", + "wind": "#9deefb", + "other": "#cccccc", + "storage": "#cccccc", + "battery": "#cccccc", + "marine": "#cccccc", + "other_fossil": "#cccccc", + "other fossil": "#cccccc", + "gas_blastfurnace": "#f78e60", + "cogen": "#f78e60", + "net_imports": "#b3a0ba", + "imports": "#b3a0ba", + "waste_heat": "#94101F", + "storage": "#94101F", + "carbon_free": "#953766", "imports_exports": "#b3a0ba", -} \ No newline at end of file +} From 966fbcc8e67f60b6f0766822e033232382b9f207 Mon Sep 17 00:00:00 2001 From: sam_watttime Date: Wed, 8 Oct 2025 10:59:17 -0600 Subject: [PATCH 65/85] gather warnings in AnalysisDataHandler --- .../get_wt_api_forecast_evaluation_data.py | 68 +++++++++++++------ 1 file changed, 46 insertions(+), 22 deletions(-) diff --git a/watttime/evaluation/get_wt_api_forecast_evaluation_data.py b/watttime/evaluation/get_wt_api_forecast_evaluation_data.py index 3d7ff364..10c63de9 100644 --- a/watttime/evaluation/get_wt_api_forecast_evaluation_data.py +++ b/watttime/evaluation/get_wt_api_forecast_evaluation_data.py @@ -56,9 +56,9 @@ class AnalysisDataHandler: forecast_max_horizon: int = 60 * 24 forecast_sample_seed: int = 42 signal_type: str = "co2_moer" - model_date: Optional[str] = ( - None # if None, will default to latest provided through API - ) + model_date: Optional[ + str + ] = None # if None, will default to latest provided through API wt_forecast: Optional[api.WattTimeForecast] = api.WattTimeForecast( multithreaded=True ) @@ -91,7 +91,6 @@ def __post_init__(self): random.seed(self.forecast_sample_seed) self.sample_days = random.sample(self.eval_days, k) - @cached_property def moers(self) -> pd.DataFrame: moers = ( @@ -111,6 +110,7 @@ def moers(self) -> pd.DataFrame: moers = moers.sort_index() + self.returned_moer_warnings = self.wt_hist.raised_warnings self.returned_meta = self.wt_hist._last_request_meta self.returned_hist_model_date = self.wt_hist._last_request_meta.get( "model", {} @@ -136,6 +136,7 @@ def forecasts(self) -> pd.DataFrame: ).dt.total_seconds() / 60 forecasts.rename({"value": "predicted_value"}, axis="columns", inplace=True) + self.returned_forecast_warnings = self.wt_forecast.raised_warnings self.returned_meta = self.wt_forecast._last_request_meta self.returned_forecast_model_date = self.wt_forecast._last_request_meta.get( "model", {} @@ -155,9 +156,7 @@ def fuel_mix(self) -> pd.DataFrame: model=self.model_date, ) - # if self.localize_tz: - # fm.index = fm.index.tz_convert(self.tz) - + self.returned_fuel_mix_warnings = self.wt_fuel_mix.raised_warnings self.returned_meta = self.wt_fuel_mix._last_request_meta self.returned_fuel_mix_model_date = self.wt_fuel_mix._last_request_meta.get( "model", {} @@ -251,22 +250,47 @@ def yield_datahandlers(self) -> AnalysisDataHandler: @property def collected_model_meta(self): - return [ - { - "region": dh.region, - "requested_model_date": dh.model_date, - "returned_hist_model_date": getattr( - dh, "returned_hist_model_date", None - ), - "returned_fuel_mix_model_date": getattr( - dh, "returned_fuel_mix_model_date", None - ), - "returned_forecast_model_date": getattr( - dh, "returned_forecast_model_date", None - ), + meta = [] + for dh in self.data_handlers: + # Helper: safely convert warnings to list[dict] + def serialize_warnings(warnings): + if warnings is None: + return None + return [w.to_dict() if hasattr(w, "to_dict") else w for w in warnings] + + # Build the warnings dict, excluding None values + warnings = { + k: serialize_warnings(v) + for k, v in { + "moer": getattr(dh, "returned_moer_warnings", None), + "fuel_mix": getattr(dh, "returned_fuel_mix_warnings", None), + "forecast": getattr(dh, "returned_forecast_warnings", None), + }.items() + if v is not None } - for dh in self.data_handlers - ] + + # Build the main meta dict, excluding None values and empty warnings + m = { + k: v + for k, v in { + "region": dh.region, + "requested_model_date": dh.model_date, + "returned_hist_model_date": getattr( + dh, "returned_hist_model_date", None + ), + "returned_fuel_mix_model_date": getattr( + dh, "returned_fuel_mix_model_date", None + ), + "returned_forecast_model_date": getattr( + dh, "returned_forecast_model_date", None + ), + "returned_warnings": warnings if warnings else None, + }.items() + if v is not None + } + + meta.append(m) + return meta @property def data_handlers_by_region_dict(self) -> Dict[str, List[AnalysisDataHandler]]: From 756d422a8f997c35245cec5055649d71bb4ff8e5 Mon Sep 17 00:00:00 2001 From: matthew mcginn Date: Fri, 17 Oct 2025 17:58:32 -0400 Subject: [PATCH 66/85] black Signed-off-by: matthew mcginn --- watttime/evaluation/get_wt_api_forecast_evaluation_data.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/watttime/evaluation/get_wt_api_forecast_evaluation_data.py b/watttime/evaluation/get_wt_api_forecast_evaluation_data.py index 10c63de9..7a843c5f 100644 --- a/watttime/evaluation/get_wt_api_forecast_evaluation_data.py +++ b/watttime/evaluation/get_wt_api_forecast_evaluation_data.py @@ -56,9 +56,9 @@ class AnalysisDataHandler: forecast_max_horizon: int = 60 * 24 forecast_sample_seed: int = 42 signal_type: str = "co2_moer" - model_date: Optional[ - str - ] = None # if None, will default to latest provided through API + model_date: Optional[str] = ( + None # if None, will default to latest provided through API + ) wt_forecast: Optional[api.WattTimeForecast] = api.WattTimeForecast( multithreaded=True ) From 6f5b97ccd918b99f495f98070158aca28528423a Mon Sep 17 00:00:00 2001 From: matthew mcginn Date: Fri, 17 Oct 2025 17:59:16 -0400 Subject: [PATCH 67/85] grab each day and +2 days after Signed-off-by: matthew mcginn --- .../get_wt_api_forecast_evaluation_data.py | 24 ++++++++++++------- 1 file changed, 15 insertions(+), 9 deletions(-) diff --git a/watttime/evaluation/get_wt_api_forecast_evaluation_data.py b/watttime/evaluation/get_wt_api_forecast_evaluation_data.py index 7a843c5f..ab207e3c 100644 --- a/watttime/evaluation/get_wt_api_forecast_evaluation_data.py +++ b/watttime/evaluation/get_wt_api_forecast_evaluation_data.py @@ -1,18 +1,15 @@ -from typing import Optional, List, Union, Dict -from datetime import datetime import inspect -import warnings -import random import math -from dateutil.parser import parse +import random +import warnings +from dataclasses import dataclass +from datetime import datetime from functools import cached_property from itertools import product - -from dataclasses import dataclass +from typing import Dict, List, Optional, Union import pandas as pd - - +from dateutil.parser import parse from watttime import api @@ -55,6 +52,7 @@ class AnalysisDataHandler: forecast_sample_size: Union[int, float] = 0.1 forecast_max_horizon: int = 60 * 24 forecast_sample_seed: int = 42 + forecast_data_horizon_days: int = 3 signal_type: str = "co2_moer" model_date: Optional[str] = ( None # if None, will default to latest provided through API @@ -90,6 +88,14 @@ def __post_init__(self): k = max(k, 1) random.seed(self.forecast_sample_seed) self.sample_days = random.sample(self.eval_days, k) + sample_days_extended = set() + for day in sample_days: + sample_days_extended = sample_days_extended | set( + pd.date_range( + start=day, periods=self.forecast_data_horizon_days, freq="1D" + ) + ) + self.sample_days = list(sample_days_extended) @cached_property def moers(self) -> pd.DataFrame: From 4f47ec96eec92048d6afeecc4eb77e638aa773f9 Mon Sep 17 00:00:00 2001 From: matthew mcginn Date: Mon, 20 Oct 2025 11:25:31 -0400 Subject: [PATCH 68/85] adjust sampling percentage and anchor broken var Signed-off-by: matthew mcginn --- watttime/evaluation/get_wt_api_forecast_evaluation_data.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/watttime/evaluation/get_wt_api_forecast_evaluation_data.py b/watttime/evaluation/get_wt_api_forecast_evaluation_data.py index ab207e3c..c3494ebd 100644 --- a/watttime/evaluation/get_wt_api_forecast_evaluation_data.py +++ b/watttime/evaluation/get_wt_api_forecast_evaluation_data.py @@ -49,7 +49,7 @@ class AnalysisDataHandler: region: str eval_start: Union[datetime, str] eval_end: Union[datetime, str] - forecast_sample_size: Union[int, float] = 0.1 + forecast_sample_size: Union[int, float] = 0.03 forecast_max_horizon: int = 60 * 24 forecast_sample_seed: int = 42 forecast_data_horizon_days: int = 3 @@ -89,7 +89,7 @@ def __post_init__(self): random.seed(self.forecast_sample_seed) self.sample_days = random.sample(self.eval_days, k) sample_days_extended = set() - for day in sample_days: + for day in self.sample_days: sample_days_extended = sample_days_extended | set( pd.date_range( start=day, periods=self.forecast_data_horizon_days, freq="1D" From 4a2b3fddee6f84ea7e1f8a0d29b244b076c3a6f7 Mon Sep 17 00:00:00 2001 From: matthew mcginn Date: Mon, 20 Oct 2025 11:25:53 -0400 Subject: [PATCH 69/85] parametrize some of the hard-coded values for easier overriding Signed-off-by: matthew mcginn --- watttime/evaluation/bulk_report.py | 21 ++++++++++++++------- 1 file changed, 14 insertions(+), 7 deletions(-) diff --git a/watttime/evaluation/bulk_report.py b/watttime/evaluation/bulk_report.py index 1687abdb..07145602 100644 --- a/watttime/evaluation/bulk_report.py +++ b/watttime/evaluation/bulk_report.py @@ -1,9 +1,11 @@ -from watttime.api import WattTimeMyAccess -from watttime.evaluation.report import generate_report -from pathlib import Path -from typing import List, Tuple, Union, Literal +import os from collections import defaultdict from concurrent.futures import ProcessPoolExecutor, as_completed +from pathlib import Path +from typing import List, Literal, Tuple, Union + +from watttime.api import WattTimeMyAccess +from watttime.evaluation.report import generate_report ma = WattTimeMyAccess() MA_DF = ma.get_access_pandas() @@ -110,7 +112,9 @@ def bulk_generate_reports( steps: List[Literal["signal", "fuel_mix", "forecast"]], max_workers: int = 4, ): - regions = get_regions_for_model(model_date, signal_type) + regions = os.getenv("REGIONS", "").split(",") + if regions == [""]: + regions = get_regions_for_model(model_date, signal_type) failed = [] if max_workers == 1: @@ -156,7 +160,10 @@ def bulk_generate_reports( SIGNAL_TYPE = "co2_moer" START = "2024-01-01T00:00Z" END = "2024-12-31T00:00Z" -OUTPUT_DIR = Path("/app/watttime-python-client/analysis/2024-10-01") +OUTPUT_DIR = Path( + os.getenv("OUTPUT_DIR", "/app/watttime-python-client/analysis/2024-10-01") +) +MAX_WORKERS = int(os.getenv("MAX_WORKERS"), 5) if __name__ == "__main__": bulk_generate_reports( @@ -166,5 +173,5 @@ def bulk_generate_reports( END, OUTPUT_DIR, ["signal", "fuel_mix", "forecast"], - max_workers=5, + max_workers=MAX_WORKERS, ) From 0c11ac90e7492b2aaaf75017a93cd0f5d89994f0 Mon Sep 17 00:00:00 2001 From: matthew mcginn Date: Mon, 20 Oct 2025 11:28:35 -0400 Subject: [PATCH 70/85] properly override the var Signed-off-by: matthew mcginn --- watttime/evaluation/bulk_report.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/watttime/evaluation/bulk_report.py b/watttime/evaluation/bulk_report.py index 07145602..8919ec80 100644 --- a/watttime/evaluation/bulk_report.py +++ b/watttime/evaluation/bulk_report.py @@ -163,7 +163,7 @@ def bulk_generate_reports( OUTPUT_DIR = Path( os.getenv("OUTPUT_DIR", "/app/watttime-python-client/analysis/2024-10-01") ) -MAX_WORKERS = int(os.getenv("MAX_WORKERS"), 5) +MAX_WORKERS = int(os.getenv("MAX_WORKERS", 5)) if __name__ == "__main__": bulk_generate_reports( From 25519aae278a3c7aa0cc45354714608850663657 Mon Sep 17 00:00:00 2001 From: matthew mcginn Date: Tue, 21 Oct 2025 12:13:44 -0400 Subject: [PATCH 71/85] small typo in report-card Signed-off-by: matthew mcginn --- watttime/evaluation/report_card_template.html | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/watttime/evaluation/report_card_template.html b/watttime/evaluation/report_card_template.html index ab514a53..dac83845 100644 --- a/watttime/evaluation/report_card_template.html +++ b/watttime/evaluation/report_card_template.html @@ -286,7 +286,7 @@

Analysis of Historical Signal Data

{{ tab_container( "plot_max_impact_potential", plot_max_impact_potential, - "4) Maximum Achivable Impact Potential of Signal Values", + "4) Maximum Achievable Impact Potential of Signal Values", "Simulations are run for typical customer patterns including charging an EV during the day (3 hours in a 12 hour window starting at 8 AM), the night (2 hours in a 8 hour window starting at 9 PM), and running a thermostat (running for 30 out of 60 minutes throughout the day). Each of these metrics could capture a different source of impact (e.g. solar curtailment may only be accessible to EV day charging). Inspecting these values helps us understand the impact potential of implementing AER with different model versions." ) }} @@ -358,4 +358,4 @@

About this Report

{{ collected_model_meta }}
- \ No newline at end of file + From 8a554d6c7e6c6b01d04f5f426a031e208d5a8ae3 Mon Sep 17 00:00:00 2001 From: matthew mcginn Date: Tue, 21 Oct 2025 15:09:15 -0400 Subject: [PATCH 72/85] last typo in report card Signed-off-by: matthew mcginn --- watttime/evaluation/report_card_template.html | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/watttime/evaluation/report_card_template.html b/watttime/evaluation/report_card_template.html index dac83845..b2322183 100644 --- a/watttime/evaluation/report_card_template.html +++ b/watttime/evaluation/report_card_template.html @@ -280,7 +280,7 @@

Analysis of Historical Signal Data

"plot_heatmaps", plot_heatmaps, "3) Heatmaps of Signal Values Over Time", - "These heatmaps provide a succcinct way to look for diurnal and seasonal trends in signal values. To achieve this purpose, the color values are normalized for each model seperately to make the rank order differences more apparent. Generally, we do not expect significant differences in the relative diurnal rank ordering of models without an explainable cause, such as changes to the power system or new modelling features." + "These heatmaps provide a succinct way to look for diurnal and seasonal trends in signal values. To achieve this purpose, the color values are normalized for each model seperately to make the rank order differences more apparent. Generally, we do not expect significant differences in the relative diurnal rank ordering of models without an explainable cause, such as changes to the power system or new modelling features." ) }} {{ tab_container( From 659f73873b907b330c3312ed4dc03bf23e5570ec Mon Sep 17 00:00:00 2001 From: sam_watttime Date: Fri, 24 Oct 2025 15:28:21 -0600 Subject: [PATCH 73/85] update distribution plot to be a violin --- watttime/evaluation/report.py | 61 +++++++++++++++++++++++------------ 1 file changed, 40 insertions(+), 21 deletions(-) diff --git a/watttime/evaluation/report.py b/watttime/evaluation/report.py index ef526fee..0ff88096 100644 --- a/watttime/evaluation/report.py +++ b/watttime/evaluation/report.py @@ -188,45 +188,64 @@ def plot_sample_moers( return figs -def plot_distribution_moers(factory: DataHandlerFactory) -> Dict[str, go.Figure]: +def plot_distribution_moers(factory: DataHandlerFactory, subsample_size=500) -> Dict[str, go.Figure]: """ - Plot the distribution of old and new MOER values for each region, creating a stacked subplot. + Plot the distribution of MOER values for each region using horizontal Plotly violin plots. Args: - jobs (list): List of AnalysisDataHandler objects with a .moers atribute. + factory: DataHandlerFactory that exposes .data_handlers_by_region_dict Returns: - plotly.graph_objects.Figure: A Plotly figure with stacked subplots of distributions. + dict mapping region abbreviation to a Plotly Figure """ - # Create a figure with a subplot for each region, stacked vertically + figs = {} + # Use Plotly's qualitative color palette + palette = pc.qualitative.Plotly + for region_abbrev, region_models in factory.data_handlers_by_region_dict.items(): fig = go.Figure() - for model_job in region_models: - # Add a histogram trace for the new MOER distribution + for ix, model_job in enumerate(region_models): + series = model_job.moers["signal_value"].dropna() + color = palette[ix % len(palette)] + + # Calculate exact min/max for span + min_val = series.min() + max_val = series.max() + fig.add_trace( - go.Box( - x=model_job.moers["signal_value"].dropna(), - name=model_job.model_date, - opacity=0.6, + go.Violin( + x=series.values, # horizontal violin + name=str(model_job.model_date), + box=dict(visible=True), + meanline=dict(visible=False), + points='outliers', + scalemode='width', + spanmode='manual', # Manually set the span + span=[min_val, max_val], # Don't extend beyond data range + opacity=0.5, # Fill opacity + line=dict( + color='black', + width=1.5, # Slightly thicker line for better visibility + ), + fillcolor=color, ) ) - # Update layout for the figure + if not fig.data: + figs[region_abbrev] = go.Figure() + continue + fig.update_layout( - height=300, - xaxis_title=f"{region_models[0].signal_type} Values", + height=350, + xaxis_title=f"{region_models[0].signal_type} Values", # x axis is now the value showlegend=True, margin=dict(l=50, r=50, t=50, b=50), - xaxis=dict( - type="linear", - tickangle=45, - showgrid=True, - ), - yaxis_visible=False, - yaxis_showticklabels=False, + violingap=0.2, + violingroupgap=0.1, + violinmode='group', ) figs[region_abbrev] = fig From d95ead5c852948c78326ef98aaa3e7ab3d92ca8b Mon Sep 17 00:00:00 2001 From: sam_watttime Date: Fri, 24 Oct 2025 15:28:57 -0600 Subject: [PATCH 74/85] clean up time zones on heatmaps (local not UTC) --- watttime/evaluation/report.py | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/watttime/evaluation/report.py b/watttime/evaluation/report.py index 0ff88096..2ed2add2 100644 --- a/watttime/evaluation/report.py +++ b/watttime/evaluation/report.py @@ -274,10 +274,12 @@ def plot_heatmaps( subplot_titles=[j.model_date for j in region_models], vertical_spacing=0.1, # Reduced spacing for better alignment ) + tz = get_tz_from_centroid(region_abbrev) for j, job in enumerate(region_models, start=1): - heat = job.moers.assign( - month=job.moers.index.month, hour=job.moers.index.hour + moers = job.moers.tz_convert(tz) + heat = moers.assign( + month=moers.index.month, hour=moers.index.hour ) heat = heat.dropna(subset=["signal_value"]) heat = ( @@ -1128,7 +1130,8 @@ def make_alpha_gradient(hex_color, steps=10): # Add traces for each job for job_idx, job in enumerate(region_models, start=1): for fuel_idx, fuel in enumerate(all_columns_list): - pivot = create_pivot_table(job.fuel_mix, fuel) + fuel_mix = job.fuel_mix.tz_convert(tz) + pivot = create_pivot_table(fuel_mix, fuel) fig.add_trace( go.Heatmap( From 700b3777172869b5730f1ff792512309d39b380c Mon Sep 17 00:00:00 2001 From: sam_watttime Date: Fri, 24 Oct 2025 15:29:11 -0600 Subject: [PATCH 75/85] another UTC to local --- watttime/evaluation/report.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/watttime/evaluation/report.py b/watttime/evaluation/report.py index 2ed2add2..ccc97d6a 100644 --- a/watttime/evaluation/report.py +++ b/watttime/evaluation/report.py @@ -1107,6 +1107,8 @@ def make_alpha_gradient(hex_color, steps=10): for region_abbrev, region_models in factory.data_handlers_by_region_dict.items(): + tz = get_tz_from_centroid(region_abbrev) + # We need to ensure all jobs have the same fuel types for the buttons to work all_columns = set() for job in region_models: From c7e23143b195860b8dd8e6339d5aba18faf0a03f Mon Sep 17 00:00:00 2001 From: sam_watttime Date: Fri, 24 Oct 2025 15:29:43 -0600 Subject: [PATCH 76/85] add forecast_v_signal plot --- watttime/evaluation/report.py | 122 ++++++++++++++++++ watttime/evaluation/report_card_template.html | 14 +- 2 files changed, 133 insertions(+), 3 deletions(-) diff --git a/watttime/evaluation/report.py b/watttime/evaluation/report.py index ccc97d6a..0828ca4e 100644 --- a/watttime/evaluation/report.py +++ b/watttime/evaluation/report.py @@ -1453,6 +1453,127 @@ def plot_precision_recall( return figs +def plot_forecasts_vs_signal( + factory: DataHandlerFactory, + horizons=['15min', '1h', '8h'], +) -> Dict[str, go.Figure]: + """ + Plot forecasts vs signal for different forecast pull frequencies. + Assumes that forecast is pulled every increment of `horizons`. (e.g. once every 15 minutes, once every hour, once every 8 hours), + and these forecasts are stringed together to form a continuous forecast time series. + + Only plots the longest contiguous block of forecasts to avoid gaps in the forecast time series. + """ + + + figs = {} + + for region_abbrev, region_models in factory.data_handlers_by_region_dict.items(): + + fig = sp.make_subplots( + rows=len(region_models), + cols=1, + shared_xaxes=True, + subplot_titles=[j.model_date for j in region_models], + vertical_spacing=0.1, # Reduced spacing for better alignment + ) + tz = get_tz_from_centroid(region_abbrev) + + for j, model_job in enumerate(region_models, start=1): + + # Before plotting, identify the longest contiguous generated_at block (5-min cadence) + # to ensure we plot a continuous series even when forecast_sample_size < 1.0 + fh = model_job.forecasts_v_moers + if fh is None or fh.empty: + # nothing to plot for this model_job + continue + + # Extract generated_at level (expected freq 5T) and sort unique values + gen_at = fh.index.get_level_values('generated_at').unique() + gen_at = pd.DatetimeIndex(gen_at).sort_values() + + # compute gaps between successive generated_at timestamps in minutes + deltas = gen_at.to_series().diff().dt.total_seconds().div(60) + + # treat a gap as > 5 minutes (allow a small tolerance) + gap_mask = deltas > 6 # >6 minutes considered a gap + + # find contiguous blocks by grouping on cumulative gaps + block_id = gap_mask.cumsum() + blocks = pd.DataFrame({'ts': gen_at, 'block': block_id.values}) + + # compute block lengths and pick the longest block + block_lengths = blocks.groupby('block').size() + longest_block = block_lengths.idxmax() + block_times = blocks[blocks['block'] == longest_block]['ts'] + + if block_times.empty: + # fallback: use the full range + start_ts, end_ts = gen_at.min(), gen_at.max() + else: + start_ts, end_ts = block_times.min(), block_times.max() + + # filter forecasts to only generated_at within the longest contiguous block + fh_mask = (fh.index.get_level_values('generated_at') >= start_ts) & ( + fh.index.get_level_values('generated_at') <= end_ts + ) + fh_filtered = fh[fh_mask].sort_index() + + # plot each resample_horizon as its own trace using the filtered forecasts + + max_point_time = pd.Timestamp('1999-01-01T00:00Z') + for ix, resample_horizon in enumerate(horizons): + + # filter df down to only rows where 'generated_at' is the first one in each resample_horizon period + gen_at_f = fh_filtered.index.get_level_values('generated_at') + first_gen_at = pd.DatetimeIndex(gen_at_f.to_series().resample(resample_horizon).first().dropna()) + + # include final forecast in first_gen_at to show horizon + first_gen_at = first_gen_at.append(gen_at[-1:]) + df_filtered = fh_filtered[fh_filtered.index.get_level_values('generated_at').isin(first_gen_at)] + + # filter df to avoid overlapping forecasts, only keep rows where horizon_mins < resample_horizon in minutes + df_filtered = df_filtered[df_filtered['horizon_mins'] < pd.Timedelta(resample_horizon).total_seconds() / 60] + df_filtered = df_filtered.sort_index() + + max_point_time = max(max_point_time, df_filtered.index.get_level_values('point_time').max()) + + if df_filtered.empty: + continue + + fig.add_trace(go.Scatter( + x=df_filtered.index.get_level_values('point_time').tz_convert(tz), + y=df_filtered['predicted_value'], + mode='lines', + name=f'Forecast Pull Freq: {resample_horizon}', + opacity=0.9, + line=dict(color=pc.qualitative.Plotly[ix % len(pc.qualitative.Plotly)], width=3, dash='dash'), + ), row=j, col=1) + + # get unique point_time values, and plot the signal_value as a solid line + signal = fh_filtered.reset_index().drop_duplicates('point_time').set_index('point_time').sort_index()['signal_value'] + signal = signal.loc['1999-01-01T00:00Z':max_point_time] + fig.add_trace(go.Scatter( + x=signal.index.tz_convert(tz), + y=signal.values, + mode='lines', + name='Signal Value', + opacity=0.9, + line=dict(color='black', width=1), + ), row=j, col=1) + + fig.update_layout( + height=300, + xaxis_title='Point Time', + yaxis_title='Value', + showlegend=True + ) + + figs[region_abbrev] = fig + + return figs + + def parse_report_command_line_args(sys_args): parser = argparse.ArgumentParser( description="Parse command line arguments to report_moers script" @@ -1537,6 +1658,7 @@ def parse_report_command_line_args(sys_args): ], "fuel_mix": [plot_sample_fuelmix, plot_fuelmix_heatmap], "forecast": [ + plot_forecasts_vs_signal, plot_norm_mae, plot_rank_corr, plot_precision_recall, diff --git a/watttime/evaluation/report_card_template.html b/watttime/evaluation/report_card_template.html index b2322183..955334f1 100644 --- a/watttime/evaluation/report_card_template.html +++ b/watttime/evaluation/report_card_template.html @@ -323,18 +323,26 @@

Marginal Fuel Mix Comparisons

{% if show_forecast %}

Forecast Performance

Our modeled signal values produce real-time or historical outputs. To achieve AER impact while guaranting an acceptable charge rate / duty factor it is necessary to use a forecast to schedule electricity consumption or charging ahead of time. Our forecast models use traditional machine learning techniques to predict what the signal value will be ahead of time. Our forecast models tend to perform worse at further time horizons from the time they are instatiated. As such, we examine the performance of our forecast models at predicting the signal value over a variety of time horizons.

+ + {{ tab_container( + "plot_forecasts_vs_signal", + plot_forecasts_vs_signal, + "1) Sample of Forecast vs. Signal Value at Different Forecast \"Pull\" Frequencies", + "We expect our users to operationalize AER using forecasts by \"pulling\" our forecast at a set frequency and creating a charging plan based on the forecasted values. The plot below visualizes this charging behavior by comparing the true signal value against forecasts pulled or refreshed at various frequencies. We expect that shorter frequencies (e.g., 15 min or 5 min) provide greater accuracy to the user, though at the cost of added complexity." + ) }} + {{ tab_container( "plot_norm_mae", plot_norm_mae, - "1) Normalized Mean Absolute Error by Horizon", + "2) Normalized Mean Absolute Error by Horizon", "MAE helps us understand the degree of error in our forecasts. Normalized MAE is a more useful metric because it accounts for the magnitude of the signal values. This metric is reported by varying horizon hours, which are the temporal distance from the time the forecast was produced (generated_at). We expect for models to perform better at lower horizons." ) }} {{ tab_container( "plot_rank_corr", plot_rank_corr, - "2) Rank Correlation by Horizon", + "3) Rank Correlation by Horizon", "Rank correlation quantifies the degree to which the rank ordering of two datasets are related. We expect for models to perform better at lower horizons. Please note that this metric should be interpreted with caution, especially for signals with low variability. Rank correlation on a signal where variance is smaller than the noise is not going to give a meaningful metric. For instance, rank correlation could be zero but there's no loss in impact." ) }} @@ -342,7 +350,7 @@

Forecast Performance

{{ tab_container( "plot_impact_forecast_metrics", plot_impact_forecast_metrics, - "3) Estimation of AER Impact using Forecasts", + "4) Estimation of AER Impact using Forecasts", "These plots simulate a vairiety of AER scenarios for typical customer patterns including charging an EV during the day (3 hours in a 12 hour window starting at 8 AM), the night (2 hours in a 8 hour window starting at 9 PM), and running a thermostat (running for 30 out of 60 minutes throughout the day). Each of these metrics could capture a different source of impact (e.g. solar curtailment may only be accessible to EV day charging). The 'simulation' simply looks at the cleanest timem to use electricity based on the forecasted value, and evaluates the impact of this against the historical signal value as truth. We also show the impact captured if the forecast was perfect by displaying the maximum possible impact if a 'perfect' forecast were used. These metrics assume that the modeled values are accurate, and only quantify the degree of error caused by the forecast model. Units are normalized to be impact achieved per day (which may or may not be per window)." ) }} From d3052de435bd4b815eb918dfff665bb1d2e57da5 Mon Sep 17 00:00:00 2001 From: sam_watttime Date: Fri, 24 Oct 2025 15:30:35 -0600 Subject: [PATCH 77/85] fix units (MOER is hourly, time stamps are 5 min) --- watttime/evaluation/report.py | 28 ++++++++++++++++------------ 1 file changed, 16 insertions(+), 12 deletions(-) diff --git a/watttime/evaluation/report.py b/watttime/evaluation/report.py index 0828ca4e..c51edee4 100644 --- a/watttime/evaluation/report.py +++ b/watttime/evaluation/report.py @@ -549,10 +549,12 @@ def calc_rank_compare_metrics( df = df.assign( truth_charge_emissions=df[truth_col] * df["truth_charge_status"] - * (load_kw / 1000), + * (load_kw / 1000) + * (5 / 60), # normalize hourly MOER to 5-minute intervals, pred_charge_emissions=df[truth_col] * df["pred_charge_status"] - * (load_kw / 1000), + * (load_kw / 1000) + * (5 / 60), ) # baseline: immediate charging rather than AER @@ -563,16 +565,17 @@ def calc_rank_compare_metrics( baseline_charge_emissions=df[truth_col] * df["baseline_charge_status"] * (load_kw / 1000) + * (5 / 60) ) assert ( df["baseline_charge_status"].sum() == (len(df["window_start"].unique())) * charge_mins // 5 ) - # assert ( - # df["baseline_charge_status"].sum() - # <= (len(df["window_start"].unique()) + 1) * charge_mins // 5 - # ) + assert ( + df["baseline_charge_status"].sum() + <= (len(df["window_start"].unique()) + 1) * charge_mins // 5 + ) # Calculate total CO2 emissions ("truth") y_best_emissions = df["truth_charge_emissions"].sum() @@ -592,6 +595,7 @@ def calc_rank_compare_metrics( reduction = (y_base_emissions - y_pred_emissions) / n_days potential = (y_base_emissions - y_best_emissions) / n_days + return { "reduction": round(reduction, 1), "potential": round(potential, 1), @@ -733,22 +737,22 @@ def plot_rank_corr( "load_kw": 3, # typical AC }, "10 kW / 24hr / 25% Duty Cycle": { - "charge_mins": 6 * 60, + "charge_mins": int(24 * 60 * 0.25), "window_mins": 24 * 60, "load_kw": 10, }, "10 kW / 72hr / 25% Duty Cycle": { - "charge_mins": 3 * 3 * 60, + "charge_mins": int(24 * 3 * 60 * 0.25), "window_mins": 24 * 3 * 60, "load_kw": 10, }, "10 kW / 24hr / 50% Duty Cycle": { - "charge_mins": 6 * 60, + "charge_mins": int(24 * 60 * 0.5), "window_mins": 24 * 60, "load_kw": 10, }, "10 kW / 72hr / 50% Duty Cycle": { - "charge_mins": 12 * 3 * 60, + "charge_mins": (int(24 * 3 * 60 * 0.5)), "window_mins": 24 * 3 * 60, "load_kw": 10, }, @@ -842,7 +846,7 @@ def plot_impact_forecast_metrics( fig.update_layout( height=300 * len(region_models), xaxis_title="Scenario", - yaxis_title="CO2 Savings (lbs/MWh)", + yaxis_title="CO2 Savings (lbs per day)", barmode="group", # Grouped bars (side by side) showlegend=True, # Show legend margin=dict(l=50, r=50, t=50, b=50), @@ -851,7 +855,7 @@ def plot_impact_forecast_metrics( # Update axes for all subplots for model_ix in range(1, len(region_models) + 1): fig.update_xaxes(title_text="Scenario", row=model_ix, col=1) - fig.update_yaxes(title_text="CO2 Savings (lbs/MWh)", row=model_ix, col=1) + fig.update_yaxes(title_text="CO2 Savings (lbs per day)", row=model_ix, col=1) figs[region_abbrev] = fig From b33aaee762c8b33a004bbeaddfd8c3521fa9674a Mon Sep 17 00:00:00 2001 From: sam_watttime Date: Mon, 27 Oct 2025 11:56:16 -0600 Subject: [PATCH 78/85] remove context catcher from get_forecasts --- .../get_wt_api_forecast_evaluation_data.py | 48 +++++++++---------- 1 file changed, 23 insertions(+), 25 deletions(-) diff --git a/watttime/evaluation/get_wt_api_forecast_evaluation_data.py b/watttime/evaluation/get_wt_api_forecast_evaluation_data.py index c3494ebd..9573a42e 100644 --- a/watttime/evaluation/get_wt_api_forecast_evaluation_data.py +++ b/watttime/evaluation/get_wt_api_forecast_evaluation_data.py @@ -54,9 +54,9 @@ class AnalysisDataHandler: forecast_sample_seed: int = 42 forecast_data_horizon_days: int = 3 signal_type: str = "co2_moer" - model_date: Optional[str] = ( - None # if None, will default to latest provided through API - ) + model_date: Optional[ + str + ] = None # if None, will default to latest provided through API wt_forecast: Optional[api.WattTimeForecast] = api.WattTimeForecast( multithreaded=True ) @@ -125,29 +125,27 @@ def moers(self) -> pd.DataFrame: @cached_property def forecasts(self) -> pd.DataFrame: - # context manager to only return critical logs - with warnings.catch_warnings(): - warnings.simplefilter("ignore") - forecasts = self.wt_forecast.get_historical_forecast_pandas_list( - list_of_dates=self.sample_days, - region=self.region, - signal_type=self.signal_type, - model=self.model_date, - horizon_hours=math.ceil(self.forecast_max_horizon / 60), - ) - forecasts["point_time"] = pd.to_datetime(forecasts["point_time"]) - forecasts["horizon_mins"] = ( - forecasts["point_time"] - forecasts["generated_at"] - ).dt.total_seconds() / 60 - forecasts.rename({"value": "predicted_value"}, axis="columns", inplace=True) - - self.returned_forecast_warnings = self.wt_forecast.raised_warnings - self.returned_meta = self.wt_forecast._last_request_meta - self.returned_forecast_model_date = self.wt_forecast._last_request_meta.get( - "model", {} - ).get("date", None) - return forecasts + forecasts = self.wt_forecast.get_historical_forecast_pandas_list( + list_of_dates=self.sample_days, + region=self.region, + signal_type=self.signal_type, + model=self.model_date, + horizon_hours=math.ceil(self.forecast_max_horizon / 60), + ) + + forecasts["point_time"] = pd.to_datetime(forecasts["point_time"]) + forecasts["horizon_mins"] = ( + forecasts["point_time"] - forecasts["generated_at"] + ).dt.total_seconds() / 60 + forecasts.rename({"value": "predicted_value"}, axis="columns", inplace=True) + + self.returned_forecast_warnings = self.wt_forecast.raised_warnings + self.returned_meta = self.wt_forecast._last_request_meta + self.returned_forecast_model_date = self.wt_forecast._last_request_meta.get( + "model", {} + ).get("date", None) + return forecasts @cached_property def fuel_mix(self) -> pd.DataFrame: From 410db2dd4ba9e9efcdb53025b0c78852ef0edf97 Mon Sep 17 00:00:00 2001 From: sam_watttime Date: Mon, 27 Oct 2025 11:58:10 -0600 Subject: [PATCH 79/85] formatting in forecasts v moer --- watttime/evaluation/report.py | 192 +++++++++++++++++++++------------- 1 file changed, 119 insertions(+), 73 deletions(-) diff --git a/watttime/evaluation/report.py b/watttime/evaluation/report.py index c51edee4..417e26c0 100644 --- a/watttime/evaluation/report.py +++ b/watttime/evaluation/report.py @@ -188,7 +188,9 @@ def plot_sample_moers( return figs -def plot_distribution_moers(factory: DataHandlerFactory, subsample_size=500) -> Dict[str, go.Figure]: +def plot_distribution_moers( + factory: DataHandlerFactory, subsample_size=500 +) -> Dict[str, go.Figure]: """ Plot the distribution of MOER values for each region using horizontal Plotly violin plots. @@ -210,24 +212,24 @@ def plot_distribution_moers(factory: DataHandlerFactory, subsample_size=500) -> for ix, model_job in enumerate(region_models): series = model_job.moers["signal_value"].dropna() color = palette[ix % len(palette)] - + # Calculate exact min/max for span min_val = series.min() max_val = series.max() - + fig.add_trace( go.Violin( x=series.values, # horizontal violin name=str(model_job.model_date), box=dict(visible=True), meanline=dict(visible=False), - points='outliers', - scalemode='width', - spanmode='manual', # Manually set the span + points="outliers", + scalemode="width", + spanmode="manual", # Manually set the span span=[min_val, max_val], # Don't extend beyond data range opacity=0.5, # Fill opacity line=dict( - color='black', + color="black", width=1.5, # Slightly thicker line for better visibility ), fillcolor=color, @@ -245,7 +247,7 @@ def plot_distribution_moers(factory: DataHandlerFactory, subsample_size=500) -> margin=dict(l=50, r=50, t=50, b=50), violingap=0.2, violingroupgap=0.1, - violinmode='group', + violinmode="group", ) figs[region_abbrev] = fig @@ -278,9 +280,7 @@ def plot_heatmaps( for j, job in enumerate(region_models, start=1): moers = job.moers.tz_convert(tz) - heat = moers.assign( - month=moers.index.month, hour=moers.index.hour - ) + heat = moers.assign(month=moers.index.month, hour=moers.index.hour) heat = heat.dropna(subset=["signal_value"]) heat = ( heat.groupby(["month", "hour"])["signal_value"] @@ -550,7 +550,7 @@ def calc_rank_compare_metrics( truth_charge_emissions=df[truth_col] * df["truth_charge_status"] * (load_kw / 1000) - * (5 / 60), # normalize hourly MOER to 5-minute intervals, + * (5 / 60), # normalize hourly MOER to 5-minute intervals, pred_charge_emissions=df[truth_col] * df["pred_charge_status"] * (load_kw / 1000) @@ -595,7 +595,6 @@ def calc_rank_compare_metrics( reduction = (y_base_emissions - y_pred_emissions) / n_days potential = (y_base_emissions - y_best_emissions) / n_days - return { "reduction": round(reduction, 1), "potential": round(potential, 1), @@ -855,7 +854,9 @@ def plot_impact_forecast_metrics( # Update axes for all subplots for model_ix in range(1, len(region_models) + 1): fig.update_xaxes(title_text="Scenario", row=model_ix, col=1) - fig.update_yaxes(title_text="CO2 Savings (lbs per day)", row=model_ix, col=1) + fig.update_yaxes( + title_text="CO2 Savings (lbs per day)", row=model_ix, col=1 + ) figs[region_abbrev] = fig @@ -1458,8 +1459,8 @@ def plot_precision_recall( def plot_forecasts_vs_signal( - factory: DataHandlerFactory, - horizons=['15min', '1h', '8h'], + factory: DataHandlerFactory, + horizons=["15min", "1h", "8h"], ) -> Dict[str, go.Figure]: """ Plot forecasts vs signal for different forecast pull frequencies. @@ -1468,12 +1469,13 @@ def plot_forecasts_vs_signal( Only plots the longest contiguous block of forecasts to avoid gaps in the forecast time series. """ - + + horizons = sorted([pd.Timedelta(h) for h in horizons]) figs = {} for region_abbrev, region_models in factory.data_handlers_by_region_dict.items(): - + fig = sp.make_subplots( rows=len(region_models), cols=1, @@ -1485,96 +1487,140 @@ def plot_forecasts_vs_signal( for j, model_job in enumerate(region_models, start=1): - # Before plotting, identify the longest contiguous generated_at block (5-min cadence) - # to ensure we plot a continuous series even when forecast_sample_size < 1.0 fh = model_job.forecasts_v_moers if fh is None or fh.empty: # nothing to plot for this model_job continue - # Extract generated_at level (expected freq 5T) and sort unique values - gen_at = fh.index.get_level_values('generated_at').unique() - gen_at = pd.DatetimeIndex(gen_at).sort_values() + # Before plotting, identify the longest contiguous generated_at block (5-min cadence) + # to ensure we plot a continuous series even when forecast_sample_size < 1.0 + if j == 1: + # Extract generated_at level (expected freq 5T) and sort unique values + gen_at = fh.index.get_level_values("generated_at").unique() + gen_at = pd.DatetimeIndex(gen_at).sort_values() - # compute gaps between successive generated_at timestamps in minutes - deltas = gen_at.to_series().diff().dt.total_seconds().div(60) + # compute gaps between successive generated_at timestamps in minutes + deltas = gen_at.to_series().diff().dt.total_seconds().div(60) - # treat a gap as > 5 minutes (allow a small tolerance) - gap_mask = deltas > 6 # >6 minutes considered a gap + # treat a gap as > 5 minutes (allow a small tolerance) + gap_mask = deltas > 6 # >6 minutes considered a gap - # find contiguous blocks by grouping on cumulative gaps - block_id = gap_mask.cumsum() - blocks = pd.DataFrame({'ts': gen_at, 'block': block_id.values}) + # find contiguous blocks by grouping on cumulative gaps + block_id = gap_mask.cumsum() + blocks = pd.DataFrame({"ts": gen_at, "block": block_id.values}) - # compute block lengths and pick the longest block - block_lengths = blocks.groupby('block').size() - longest_block = block_lengths.idxmax() - block_times = blocks[blocks['block'] == longest_block]['ts'] + # compute block lengths and pick the longest block + block_lengths = blocks.groupby("block").size() + longest_block = block_lengths.idxmax() + block_times = blocks[blocks["block"] == longest_block]["ts"] - if block_times.empty: - # fallback: use the full range - start_ts, end_ts = gen_at.min(), gen_at.max() - else: - start_ts, end_ts = block_times.min(), block_times.max() + if block_times.empty: + # fallback: use the full range + start_ts, end_ts = gen_at.min(), gen_at.max() + else: + start_ts, end_ts = block_times.min(), block_times.max() # filter forecasts to only generated_at within the longest contiguous block - fh_mask = (fh.index.get_level_values('generated_at') >= start_ts) & ( - fh.index.get_level_values('generated_at') <= end_ts + fh_mask = (fh.index.get_level_values("generated_at") >= start_ts) & ( + fh.index.get_level_values("generated_at") <= end_ts ) - fh_filtered = fh[fh_mask].sort_index() + fh = fh[fh_mask].sort_index() # plot each resample_horizon as its own trace using the filtered forecasts - max_point_time = pd.Timestamp('1999-01-01T00:00Z') for ix, resample_horizon in enumerate(horizons): # filter df down to only rows where 'generated_at' is the first one in each resample_horizon period - gen_at_f = fh_filtered.index.get_level_values('generated_at') - first_gen_at = pd.DatetimeIndex(gen_at_f.to_series().resample(resample_horizon).first().dropna()) + gen_at_f = fh.index.get_level_values("generated_at") + first_gen_at = pd.DatetimeIndex( + gen_at_f.to_series().resample(resample_horizon).first().dropna() + ) # include final forecast in first_gen_at to show horizon - first_gen_at = first_gen_at.append(gen_at[-1:]) - df_filtered = fh_filtered[fh_filtered.index.get_level_values('generated_at').isin(first_gen_at)] + first_gen_at = first_gen_at.append(gen_at_f[-1:]) + df_filtered = fh[ + fh.index.get_level_values("generated_at").isin(first_gen_at) + ] # filter df to avoid overlapping forecasts, only keep rows where horizon_mins < resample_horizon in minutes - df_filtered = df_filtered[df_filtered['horizon_mins'] < pd.Timedelta(resample_horizon).total_seconds() / 60] + df_filtered = df_filtered[ + df_filtered["horizon_mins"] <= resample_horizon.total_seconds() / 60 + ] df_filtered = df_filtered.sort_index() - - max_point_time = max(max_point_time, df_filtered.index.get_level_values('point_time').max()) + df_filtered = ( + df_filtered.reset_index() + .drop_duplicates(subset="point_time", keep="first") + .set_index(["generated_at", "point_time"]) + ) if df_filtered.empty: continue - fig.add_trace(go.Scatter( - x=df_filtered.index.get_level_values('point_time').tz_convert(tz), - y=df_filtered['predicted_value'], - mode='lines', - name=f'Forecast Pull Freq: {resample_horizon}', - opacity=0.9, - line=dict(color=pc.qualitative.Plotly[ix % len(pc.qualitative.Plotly)], width=3, dash='dash'), - ), row=j, col=1) + fig.add_trace( + go.Scatter( + x=df_filtered.index.get_level_values("point_time").tz_convert( + tz + ), + y=df_filtered["predicted_value"], + mode="lines", + name=f"Forecast Pull Freq: {resample_horizon}", + opacity=0.9, + line=dict( + color=pc.qualitative.Plotly[ + ix % len(pc.qualitative.Plotly) + ], + width=3, + dash="dash", + ), + ), + row=j, + col=1, + ) # get unique point_time values, and plot the signal_value as a solid line - signal = fh_filtered.reset_index().drop_duplicates('point_time').set_index('point_time').sort_index()['signal_value'] - signal = signal.loc['1999-01-01T00:00Z':max_point_time] - fig.add_trace(go.Scatter( - x=signal.index.tz_convert(tz), - y=signal.values, - mode='lines', - name='Signal Value', - opacity=0.9, - line=dict(color='black', width=1), - ), row=j, col=1) + signal = ( + fh.reset_index() + .drop_duplicates("point_time") + .set_index("point_time") + .sort_index()["signal_value"] + ) + signal = signal.loc[start_ts : end_ts + max(horizons)].sort_index() + fig.add_trace( + go.Scatter( + x=signal.index.tz_convert(tz), + y=signal.values, + mode="lines", + name="Signal Value", + opacity=0.9, + line=dict(color="black", width=1), + ), + row=j, + col=1, + ) + # Update layout for the figure fig.update_layout( - height=300, - xaxis_title='Point Time', - yaxis_title='Value', - showlegend=True + height=500, + yaxis=dict( + title=f"{model_job.signal_type}", + fixedrange=True, # Disable y-axis panning + ), + showlegend=True, + margin=dict(l=50, r=50, t=50, b=50), + xaxis=dict( + type="date", + tickformat="%Y-%m-%d %H:%M", + tickangle=45, + showgrid=True, + range=[ + start_ts, + end_ts + max(horizons), + ], + ), ) figs[region_abbrev] = fig - + return figs From a11334d155374cd372e8e3e45cb95f6c711e8be8 Mon Sep 17 00:00:00 2001 From: sam_watttime Date: Tue, 28 Oct 2025 23:40:37 -0600 Subject: [PATCH 80/85] add caching to AnalysisDataHandler; AnalysisDataHandler returns local tz dataframes --- .../get_wt_api_forecast_evaluation_data.py | 265 +++++++++++++----- watttime/evaluation/report.py | 16 +- 2 files changed, 193 insertions(+), 88 deletions(-) diff --git a/watttime/evaluation/get_wt_api_forecast_evaluation_data.py b/watttime/evaluation/get_wt_api_forecast_evaluation_data.py index 9573a42e..8233bbf7 100644 --- a/watttime/evaluation/get_wt_api_forecast_evaluation_data.py +++ b/watttime/evaluation/get_wt_api_forecast_evaluation_data.py @@ -1,18 +1,78 @@ + import inspect import math import random -import warnings +import os +import hashlib +import pickle +from functools import lru_cache + from dataclasses import dataclass from datetime import datetime from functools import cached_property from itertools import product from typing import Dict, List, Optional, Union +from zoneinfo import ZoneInfo +from shapely.geometry import shape +from timezonefinder import TimezoneFinder import pandas as pd from dateutil.parser import parse from watttime import api +def remap_model_date( + signal_type: str, step_type: str, requested_model_date: Optional[str] +) -> Optional[str]: + if len(signal_type) == 11 and 'tail' in signal_type and requested_model_date == "2023-03-01": + return "2023-01-21" + return requested_model_date + +@lru_cache +def get_tz_from_centroid(region): + wt_maps = api.WattTimeMaps() + all_maps = wt_maps.get_maps_json() + region = {f["properties"]["region"]: f["geometry"] for f in all_maps["features"]}[ + region + ] + centroid = shape(region).centroid + tz = TimezoneFinder().certain_timezone_at(lat=centroid.y, lng=centroid.x) + return tz + +def localize_dataframe_index(df: pd.DataFrame, tz: str) -> pd.DataFrame: + """ + Localize or convert all DatetimeIndex levels and columns named 'generated_at' or 'point_time' to the given timezone. + """ + + def localize(dtobj): + # For DatetimeIndex + if isinstance(dtobj, pd.DatetimeIndex): + if dtobj.tz is None: + return dtobj.tz_localize(tz) + return dtobj.tz_convert(tz) + # For Series with .dt accessor + elif isinstance(dtobj, pd.Series) and pd.api.types.is_datetime64_any_dtype(dtobj): + if dtobj.dt.tz is None: + return dtobj.dt.tz_localize(tz) + return dtobj.dt.tz_convert(tz) + return dtobj + + # Localize index levels + if isinstance(df.index, pd.MultiIndex): + new_levels = [localize(level) if isinstance(level, pd.DatetimeIndex) else level for level in df.index.levels] + df.index = pd.MultiIndex.from_arrays([ + new_levels[i][df.index.codes[i]] for i in range(len(df.index.levels)) + ], names=df.index.names) + elif isinstance(df.index, pd.DatetimeIndex): + df.index = localize(df.index) + + # Localize columns if present + for col in ["generated_at", "point_time"]: + if col in df.columns and pd.api.types.is_datetime64_any_dtype(df[col]): + df[col] = localize(df[col]) + + return df + @dataclass class AnalysisDataHandler: """ @@ -49,33 +109,73 @@ class AnalysisDataHandler: region: str eval_start: Union[datetime, str] eval_end: Union[datetime, str] - forecast_sample_size: Union[int, float] = 0.03 + forecast_sample_size: Union[int, float] = 0.1 forecast_max_horizon: int = 60 * 24 forecast_sample_seed: int = 42 forecast_data_horizon_days: int = 3 signal_type: str = "co2_moer" - model_date: Optional[ - str - ] = None # if None, will default to latest provided through API - wt_forecast: Optional[api.WattTimeForecast] = api.WattTimeForecast( - multithreaded=True - ) - wt_hist: Optional[api.WattTimeHistorical] = api.WattTimeHistorical( - multithreaded=True - ) - wt_fuel_mix: Optional[api.WattTimeMarginalFuelMix] = api.WattTimeMarginalFuelMix( - multithreaded=False, rate_limit=1 - ) + model_date: Optional[str] = None # if None, will default to latest provided through API + wt_forecast: Optional[api.WattTimeForecast] = api.WattTimeForecast(multithreaded=True) + wt_hist: Optional[api.WattTimeHistorical] = api.WattTimeHistorical(multithreaded=True) + wt_fuel_mix: Optional[api.WattTimeMarginalFuelMix] = api.WattTimeMarginalFuelMix(multithreaded=False, rate_limit=1) + use_disk_cache: bool = True + def _get_cache_path(self, kind: str, params: dict) -> str: + """ + Generate a unique cache file path in /tmp/watttime_cache/ for the given kind and parameters. + """ + cache_dir = "/tmp/watttime_cache" + os.makedirs(cache_dir, exist_ok=True) + # Use sorted keys for deterministic hash + key_str = f"{kind}_" + "_".join(f"{k}={v}" for k, v in sorted(params.items())) + key_hash = hashlib.sha256(key_str.encode()).hexdigest() + return os.path.join(cache_dir, f"{kind}_{key_hash}.pkl") + + def _load_or_compute(self, kind: str, params: dict, compute_fn): + """ + Load a DataFrame from disk if present, otherwise compute and cache it. + """ + if not self.use_disk_cache: + return compute_fn() + cache_path = self._get_cache_path(kind, params) + if os.path.exists(cache_path): + try: + with open(cache_path, "rb") as f: + return pickle.load(f) + except Exception: + # If loading fails, fall back to recompute + pass + df = compute_fn() + try: + with open(cache_path, "wb") as f: + pickle.dump(df, f) + except Exception: + pass + return df def __post_init__(self): self.region = self.region.upper() + self.tz = get_tz_from_centroid(self.region) # parse eval_days + # Ensure eval_start and eval_end are datetime before timezone logic if isinstance(self.eval_start, str): self.eval_start = parse(self.eval_start) if isinstance(self.eval_end, str): self.eval_end = parse(self.eval_end) assert self.eval_start < self.eval_end + + # localize if no timezone info + if getattr(self.eval_start, 'tzinfo', None) is None: + self.eval_start = self.eval_start.replace(tzinfo=ZoneInfo(self.tz)) + if getattr(self.eval_end, 'tzinfo', None) is None: + self.eval_end = self.eval_end.replace(tzinfo=ZoneInfo(self.tz)) + + # convert tz if not matching + if getattr(self.eval_start.tzinfo, "key", None) != self.tz: + self.eval_start = self.eval_start.astimezone(ZoneInfo(self.tz)) + if getattr(self.eval_end.tzinfo, "key", None) != self.tz: + self.eval_end = self.eval_end.astimezone(ZoneInfo(self.tz)) + self.eval_days = list( pd.date_range(start=self.eval_start, end=self.eval_end, freq="1D") ) @@ -99,73 +199,88 @@ def __post_init__(self): @cached_property def moers(self) -> pd.DataFrame: - moers = ( - self.wt_hist.get_historical_pandas( - start=self.eval_start, - end=self.eval_end - + pd.Timedelta( - minutes=self.forecast_max_horizon - ), # include truth_values for final generated_at - region=self.region, - signal_type=self.signal_type, - model=self.model_date, + model = remap_model_date(self.signal_type, "signal", self.model_date) + params = { + "region": self.region, + "eval_start": str(self.eval_start), + "eval_end": str(self.eval_end), + "signal_type": self.signal_type, + "model_date": model, + "forecast_max_horizon": self.forecast_max_horizon, + } + def compute(): + moers = ( + self.wt_hist.get_historical_pandas( + start=self.eval_start, + end=self.eval_end + pd.Timedelta(minutes=self.forecast_max_horizon), + region=self.region, + signal_type=self.signal_type, + model=model, + ) + .rename(columns={"value": "signal_value"}) + .set_index("point_time", drop=True) ) - .rename(columns={"value": "signal_value"}) - .set_index("point_time", drop=True) - ) - - moers = moers.sort_index() - - self.returned_moer_warnings = self.wt_hist.raised_warnings - self.returned_meta = self.wt_hist._last_request_meta - self.returned_hist_model_date = self.wt_hist._last_request_meta.get( - "model", {} - ).get("date", None) - return moers + moers = moers.sort_index() + moers = localize_dataframe_index(moers, self.tz) + self.returned_moer_warnings = self.wt_hist.raised_warnings + self.returned_meta = self.wt_hist._last_request_meta + self.returned_hist_model_date = self.wt_hist._last_request_meta.get("model", {}).get("date", None) + return moers + return self._load_or_compute("moers", params, compute) @cached_property def forecasts(self) -> pd.DataFrame: - - forecasts = self.wt_forecast.get_historical_forecast_pandas_list( - list_of_dates=self.sample_days, - region=self.region, - signal_type=self.signal_type, - model=self.model_date, - horizon_hours=math.ceil(self.forecast_max_horizon / 60), - ) - - forecasts["point_time"] = pd.to_datetime(forecasts["point_time"]) - forecasts["horizon_mins"] = ( - forecasts["point_time"] - forecasts["generated_at"] - ).dt.total_seconds() / 60 - forecasts.rename({"value": "predicted_value"}, axis="columns", inplace=True) - - self.returned_forecast_warnings = self.wt_forecast.raised_warnings - self.returned_meta = self.wt_forecast._last_request_meta - self.returned_forecast_model_date = self.wt_forecast._last_request_meta.get( - "model", {} - ).get("date", None) - return forecasts + model = remap_model_date(self.signal_type, "forecast", self.model_date) + params = { + "region": self.region, + "sample_days": str(sorted(self.sample_days)), + "signal_type": self.signal_type, + "model_date": model, + "forecast_max_horizon": self.forecast_max_horizon, + } + def compute(): + forecasts = self.wt_forecast.get_historical_forecast_pandas_list( + list_of_dates=self.sample_days, + region=self.region, + signal_type=self.signal_type, + model=model, + horizon_hours=math.ceil(self.forecast_max_horizon / 60), + ) + forecasts["point_time"] = pd.to_datetime(forecasts["point_time"]) + forecasts["horizon_mins"] = ( + forecasts["point_time"] - forecasts["generated_at"] + ).dt.total_seconds() / 60 + forecasts.rename({"value": "predicted_value"}, axis="columns", inplace=True) + forecasts = localize_dataframe_index(forecasts, self.tz) + self.returned_forecast_warnings = self.wt_forecast.raised_warnings + self.returned_meta = self.wt_forecast._last_request_meta + self.returned_forecast_model_date = self.wt_forecast._last_request_meta.get("model", {}).get("date", None) + return forecasts[['point_time', 'generated_at', 'horizon_mins', 'predicted_value']] + return self._load_or_compute("forecasts", params, compute) @cached_property def fuel_mix(self) -> pd.DataFrame: - fm = self.wt_fuel_mix.get_fuel_mix_pandas( - start=self.eval_start, - end=self.eval_end - + pd.Timedelta( - minutes=self.forecast_max_horizon - ), # include truth_values for final generated_at - region=self.region, - signal_type="marginal_fuel_mix", - model=self.model_date, - ) - - self.returned_fuel_mix_warnings = self.wt_fuel_mix.raised_warnings - self.returned_meta = self.wt_fuel_mix._last_request_meta - self.returned_fuel_mix_model_date = self.wt_fuel_mix._last_request_meta.get( - "model", {} - ).get("date", None) - return fm + params = { + "region": self.region, + "eval_start": str(self.eval_start), + "eval_end": str(self.eval_end), + "model_date": self.model_date, + "forecast_max_horizon": self.forecast_max_horizon, + } + def compute(): + fm = self.wt_fuel_mix.get_fuel_mix_pandas( + start=self.eval_start, + end=self.eval_end + pd.Timedelta(minutes=self.forecast_max_horizon), + region=self.region, + signal_type="marginal_fuel_mix", + model=self.model_date, + ) + fm = localize_dataframe_index(fm, self.tz) + self.returned_fuel_mix_warnings = self.wt_fuel_mix.raised_warnings + self.returned_meta = self.wt_fuel_mix._last_request_meta + self.returned_fuel_mix_model_date = self.wt_fuel_mix._last_request_meta.get("model", {}).get("date", None) + return fm + return self._load_or_compute("fuel_mix", params, compute) def moer_v_fuel_mix(self) -> pd.DataFrame: return self.moers.merge( @@ -182,7 +297,7 @@ def forecasts_v_moers(self) -> pd.DataFrame: how="inner", left_on="point_time", right_on="point_time", - ).set_index(["generated_at", "point_time"], drop=True) + ).set_index(["generated_at", "point_time"], drop=True)[['horizon_mins', 'predicted_value', 'signal_value']] def forecast_v_moer_filtered_by_rank( self, n_rows, filter_on="signal_value", use_highest=False diff --git a/watttime/evaluation/report.py b/watttime/evaluation/report.py index 417e26c0..2933b4c2 100644 --- a/watttime/evaluation/report.py +++ b/watttime/evaluation/report.py @@ -791,7 +791,7 @@ def plot_impact_forecast_metrics( _metrics = [ { **calc_rank_compare_metrics( - model_job.forecasts_v_moers, **AER_SCENARIOS[s], tz=tz + model_job.forecasts_v_moers, **AER_SCENARIOS[s], ), "scenario": s, } @@ -888,8 +888,6 @@ def plot_sample_fuelmix( vertical_spacing=0.2, ) - tz = get_tz_from_centroid(region_abbrev) - # Track which fuel types have been added to legend fuels_in_legend = set() @@ -897,8 +895,6 @@ def plot_sample_fuelmix( stacked_values = model_job.fuel_mix.reindex(times) - stacked_values = stacked_values.tz_convert(tz) - # Create cumulative values for stacking for fuel_ix in range(1, len(stacked_values.columns)): stacked_values.iloc[:, fuel_ix] += stacked_values.iloc[:, fuel_ix - 1] @@ -954,7 +950,6 @@ def calc_max_potential( charge_mins, window_mins, window_start_time=None, - tz=None, truth_col="signal_value", load_kw=1000, ): @@ -1034,13 +1029,12 @@ def plot_max_impact_potential( y_max = 0 for region_abbrev, region_models in factory.data_handlers_by_region_dict.items(): fig = go.Figure() - tz = get_tz_from_centroid(region_abbrev) for model_job in region_models: _metrics = [ { - **calc_max_potential(model_job.moers, **AER_SCENARIOS[s], tz=tz), + **calc_max_potential(model_job.moers, **AER_SCENARIOS[s]), "scenario": s, } for s in scenarios @@ -1112,8 +1106,6 @@ def make_alpha_gradient(hex_color, steps=10): for region_abbrev, region_models in factory.data_handlers_by_region_dict.items(): - tz = get_tz_from_centroid(region_abbrev) - # We need to ensure all jobs have the same fuel types for the buttons to work all_columns = set() for job in region_models: @@ -1137,8 +1129,7 @@ def make_alpha_gradient(hex_color, steps=10): # Add traces for each job for job_idx, job in enumerate(region_models, start=1): for fuel_idx, fuel in enumerate(all_columns_list): - fuel_mix = job.fuel_mix.tz_convert(tz) - pivot = create_pivot_table(fuel_mix, fuel) + pivot = create_pivot_table(job.fuel_mix, fuel) fig.add_trace( go.Heatmap( @@ -1483,7 +1474,6 @@ def plot_forecasts_vs_signal( subplot_titles=[j.model_date for j in region_models], vertical_spacing=0.1, # Reduced spacing for better alignment ) - tz = get_tz_from_centroid(region_abbrev) for j, model_job in enumerate(region_models, start=1): From 13686b442b845d05e6599daaddfdd79565b450c8 Mon Sep 17 00:00:00 2001 From: sam_watttime Date: Tue, 28 Oct 2025 23:43:54 -0600 Subject: [PATCH 81/85] improve assign_window now that local_tz; fix tests --- tests/test_report.py | 230 ++++++++++++++++++++++++++++++- watttime/evaluation/report.py | 251 +++++++++++++++++++++------------- 2 files changed, 384 insertions(+), 97 deletions(-) diff --git a/tests/test_report.py b/tests/test_report.py index 52c9e10b..857a1ed3 100644 --- a/tests/test_report.py +++ b/tests/test_report.py @@ -3,15 +3,16 @@ from watttime.evaluation import report from typing import List, Union, Dict import plotly.graph_objects as go +import random import pandas as pd import numpy as np -from watttime.evaluation.get_wt_api_forecast_evaluation_data import DataHandlerFactory +from watttime.evaluation.get_wt_api_forecast_evaluation_data import DataHandlerFactory, AnalysisDataHandler -REGION_LIST: List[str] = ["WEM"] -MODEL_DATE_LIST: List[str] = ["2022-10-01", "2025-02-01"] +REGION_LIST: List[str] = ["CAISO_NORTH"] +MODEL_DATE_LIST: List[str] = ["2023-03-01", "2024-10-01"] SIGNAL_TYPE: str = "co2_moer" -EVAL_START: Union[str, datetime, date] = "2023-10-29T00:00Z" -EVAL_END: Union[str, datetime, date] = "2023-11-03T00:00Z" +EVAL_START: Union[str, datetime, date] = "2024-10-01T00:00-02:00" +EVAL_END: Union[str, datetime, date] = "2024-11-01T00:00Z" FORECAST_SAMPLE = 3 @@ -183,3 +184,222 @@ def test_charging_fully_schedules(self): charged_windows = self.df.loc[charge_status].groupby("window_start").size() self.assertTrue((charged_windows >= charge_needed - 1).all()) self.assertTrue((charged_windows <= charge_needed).all()) + + def test_assign_windows_daily_overnight(self): + """assign_windows should include timestamps in [start, start+window) even when the window crosses midnight.""" + tz = "America/Los_Angeles" + timestamps = pd.DatetimeIndex( + [ + "2025-03-11 18:55", # before daily start + "2025-03-11 19:00", # at start + "2025-03-11 22:00", # inside + "2025-03-12 00:10", # inside (next day) + "2025-03-12 02:55", # inside (next day) + "2025-03-12 03:00", # at end (excluded) + "2025-03-12 03:05", # after end + ], + tz=tz, + ) + + anchors = report.assign_windows(timestamps, window_size=8 * 60, window_start_time="19:00") + + expected_anchor = pd.Timestamp("2025-03-11 19:00", tz=tz) + + self.assertTrue(pd.isna(anchors[0])) + self.assertEqual(anchors[1], expected_anchor) + self.assertEqual(anchors[2], expected_anchor) + self.assertEqual(anchors[3], expected_anchor) + self.assertEqual(anchors[4], expected_anchor) + self.assertTrue(pd.isna(anchors[5])) + self.assertTrue(pd.isna(anchors[6])) + + def test_assign_windows_continuous_tiling(self): + """Without window_start_time, assign continuous non-overlapping windows of given length from first local midnight.""" + tz = "America/Los_Angeles" + start = pd.Timestamp("2025-03-11 00:00", tz=tz) + timestamps = pd.date_range(start=start, periods=25, freq="5T") # 2 hours + 5 minutes + + anchors = report.assign_windows(timestamps, window_size=30) + + # 00:00-00:30 -> anchor 00:00, 00:30-01:00 -> anchor 00:30, 01:00-01:30 -> anchor 01:00, etc. + self.assertEqual(anchors[0], start) # 00:00 -> anchor 00:00 + self.assertEqual(anchors[6], start + pd.Timedelta(minutes=30)) # 00:30 -> anchor 00:30 + self.assertEqual(anchors[7], start + pd.Timedelta(minutes=30)) # 00:35 -> anchor 00:30 + self.assertEqual(anchors[12], start + pd.Timedelta(minutes=60)) # 01:00 -> anchor 01:00 + + def test_calc_max_potential_equals_rank_compare_potential_when_constrained_inputs_match(self): + """When both functions operate over the same constrained candidate set (earliest-per-pull rows after + window filtering), their 'potential' should match. + + Steps: + - Build a synthetic forecasts_v_moers across a single 2-hour window. + - Reproduce the window filtering used by calc_rank_compare_metrics, then reduce to earliest-per-pull rows. + - Feed that constrained candidate set as a moers DataFrame into calc_max_potential. + - Assert that calc_max_potential['potential'] equals calc_rank_compare_metrics()['potential']. + """ + + tz = "America/Los_Angeles" + start = pd.Timestamp("2024-01-01 19:00", tz=tz) + window_mins = 60 + charge_mins = 10 # 2 five-minute slots + load_kw = 100 + + # Build forecasts_v_moers: signal high then low to make ordering visible + rows = [] + for ga in pd.date_range(start, start + pd.Timedelta(hours=2), freq="5T"): + for pt_i in pd.date_range(ga, ga + pd.Timedelta(minutes=55), freq="5T"): + rows.append( + { + "generated_at": ga, + "point_time": pt_i, + "predicted_value": random.random(), + "signal_value": 1000 - pt_i.minute * 10 + } + ) + fvm = ( + pd.DataFrame(rows) + .set_index(["generated_at", "point_time"]) + .sort_index() + ) + + # Compute rank-compare potential + rc = report.calc_rank_compare_metrics( + fvm, + charge_mins=charge_mins, + window_mins=window_mins, + window_start_time="19:00", + pred_col="predicted_value", + truth_col="signal_value", + load_kw=load_kw, + ) + rc_potential = rc["potential"] + + # Reproduce the same candidate set (earliest-per-pull after window filtering) + wm = pd.Timedelta(minutes=window_mins) + window_starts = report.assign_windows( + fvm.index.get_level_values("generated_at"), wm, "19:00" + ) + df = fvm.assign( + window_start=window_starts, + window_end=[i + wm for i in window_starts], + ) + # out-of-window filter + df.loc[ + (df.index.get_level_values("point_time") >= df["window_end"]) + | (df.index.get_level_values("generated_at") >= df["window_end"]) + | (df.index.get_level_values("generated_at") < df["window_start"]), + "window_start", + ] = pd.NaT + df = df.dropna(subset=["window_start", "window_end"]).copy() + # sufficient generated_at per window + n_gen = df.groupby("window_start").transform( + lambda x: x.reset_index()["generated_at"].nunique() + )["predicted_value"] + df = df.loc[n_gen >= (charge_mins // 5) - 1] + + mp = report.calc_max_potential( + df, + charge_mins=charge_mins, + window_mins=window_mins, + window_start_time="19:00", + truth_col="signal_value", + load_kw=load_kw, + )["potential"] + + self.assertAlmostEqual(mp, rc_potential, places=6) + +class TestAnalysisDataHandler(unittest.TestCase): + + def setUp(self) -> None: + self.handler = AnalysisDataHandler( + region=REGION_LIST[0], + model_date=MODEL_DATE_LIST[0], + signal_type=SIGNAL_TYPE, + eval_start=EVAL_START, + eval_end=EVAL_END, + forecast_sample_size=2, + forecast_max_horizon=72*60 + ) + + def test_data_handler_initialization(self): + """Test that AnalysisDataHandler initializes correctly with given parameters.""" + + self.assertEqual(self.handler.region, REGION_LIST[0]) + self.assertEqual(self.handler.model_date, MODEL_DATE_LIST[0]) + self.assertEqual(self.handler.signal_type, SIGNAL_TYPE) + self.assertEqual(self.handler.tz, "America/Los_Angeles") + self.assertEqual(len(self.handler.sample_days), 2*3) + + def test_fetch_moer_data(self): + """Test that MOER data is fetched and returned as a DataFrame.""" + self.assertIsInstance(self.handler.moers, pd.DataFrame) + self.assertEqual(["signal_value"], self.handler.moers.columns) + self.assertIsInstance(self.handler.moers.index, pd.DatetimeIndex) + self.assertEqual(self.handler.moers.index.tz.zone, self.handler.tz) + expected_date_range = pd.date_range( + start=pd.to_datetime(EVAL_START).tz_convert(self.handler.tz), + end=pd.to_datetime(EVAL_END).tz_convert(self.handler.tz) + pd.Timedelta(minutes=self.handler.forecast_max_horizon), + freq="5min", + ) + self.assertTrue( + self.handler.moers.index.equals(expected_date_range), + "MOER DataFrame index does not match expected date range.", + ) + def test_fetch_forecast_data(self): + """Test that forecast data is fetched and returned as a DataFrame.""" + self.assertIsInstance(self.handler.forecasts, pd.DataFrame) + self.assertEqual( + ['point_time', 'generated_at', 'horizon_mins', 'predicted_value'], + list(self.handler.forecasts.columns), + ) + self.assertEqual( + self.handler.forecasts['point_time'].dt.tz.zone, + self.handler.tz, + ) + self.assertEqual( + self.handler.forecasts['generated_at'].dt.tz.zone, + self.handler.tz, + ) + expected_gen_ats = len(self.handler.sample_days) * ((60 // 5) * 24) + actual_gen_ats = self.handler.forecasts['generated_at'].nunique() + self.assertEqual( + actual_gen_ats, + expected_gen_ats, + "Number of unique generated_at timestamps does not match expected count.", + ) + + expected_length = expected_gen_ats * (self.handler.forecast_max_horizon // 5) + self.assertEqual( + len(self.handler.forecasts), + expected_length, + "Forecast DataFrame length does not match expected number of samples.", + ) + + def test_forecasts_v_moers(self): + """Test that forecasted values align correctly with MOER data.""" + self.assertEqual( + len(self.handler.forecasts_v_moers), + len(self.handler.forecasts), + "Forecasts vs MOERs DataFrame length does not match Forecasts DataFrame length.", + ) + self.assertEqual( + list(self.handler.forecasts_v_moers.columns), + ['horizon_mins', 'predicted_value', 'signal_value'], + "Forecasts vs MOERs DataFrame columns do not match expected columns.", + ) + self.assertEqual( + self.handler.forecasts_v_moers.index.names, + ['generated_at', 'point_time'], + "Forecasts vs MOERs DataFrame index names do not match expected names.", + ) + self.assertEqual( + self.handler.forecasts_v_moers.index.get_level_values('generated_at').tz.zone, + self.handler.tz, + "generated_at index level does not have the expected timezone.", + ) + self.assertEqual( + self.handler.forecasts_v_moers.index.get_level_values('point_time').tz.zone, + self.handler.tz, + "point_time index level does not have the expected timezone.", + ) + diff --git a/watttime/evaluation/report.py b/watttime/evaluation/report.py index 2933b4c2..2778d35f 100644 --- a/watttime/evaluation/report.py +++ b/watttime/evaluation/report.py @@ -188,9 +188,7 @@ def plot_sample_moers( return figs -def plot_distribution_moers( - factory: DataHandlerFactory, subsample_size=500 -) -> Dict[str, go.Figure]: +def plot_distribution_moers(factory: DataHandlerFactory) -> Dict[str, go.Figure]: """ Plot the distribution of MOER values for each region using horizontal Plotly violin plots. @@ -432,63 +430,118 @@ def simulate_charge(in_df: pd.DataFrame, sort_col: str, charge_mins: int): df.loc[charge_periods, "charge_status"] = True + # Compute the feasible number of charge periods given available generated_at groups per window + # We can decide at most once per generated_at (we charge at the forecast's first point_time), + # so the upper bound per-window is min(charge_needed, n_generated_at_in_window). + window_group_counts = ( + df.reset_index().groupby("window_start")["generated_at"].nunique() + ) + expected_total_needed = int( + sum(min(charge_needed, c) for c in window_group_counts.values) + ) + + charged_total = int(df["charge_status"].sum()) + + # Upper bound: cannot exceed feasible slots assert ( - df["charge_status"].sum() <= (len(df["window_start"].unique())) * charge_needed + charged_total <= expected_total_needed ), "Charge status is too high, check the logic for simulate_charge" - assert df["charge_status"].sum() >= ( - len(df["window_start"].unique()) * 0.97 * charge_needed - ), "Charge status is too low, check the logic for simulate_charge" + + # Lower bound: be within 3% of feasible slots (or equal when small counts) + if expected_total_needed > 0: + assert ( + charged_total >= int(np.floor(0.97 * expected_total_needed)) + ), "Charge status is too low, check the logic for simulate_charge" return df["charge_status"] def assign_windows( - generated_ats: List[pd.Timestamp], window_size: pd.Timedelta + timestamps: List[pd.Timestamp] | pd.DatetimeIndex, + window_size: Union[int, pd.Timedelta], + window_start_time: Optional[str] = None, ) -> List[pd.Timestamp]: + """ + Assign a window_start timestamp for each input time based on: + - A fixed window length (window_size) + - An optional daily start time (window_start_time in "HH:MM"). + + Behavior: + - If window_start_time is provided: one window per day starting at that local time. + For each timestamp t, compute daily_start = local midnight + window_start_time and + daily_end = daily_start + window_size. Return daily_start only if daily_start <= t < daily_end; + otherwise return NaT for that row (the row is outside the window). + - If window_start_time is None: windows are tiled back-to-back every `window_size` + from a fixed base (the first timestamp's local midnight), producing non-overlapping + windows of length `window_size` across the entire timeline. + + All input timestamps are expected to be timezone-aware and already localized. + Returns a list of tz-aware pandas Timestamps in the same timezone as inputs. + """ + # Normalize inputs if isinstance(window_size, int): window_size = pd.Timedelta(minutes=window_size) - n = len(generated_ats) - if n == 0: - return [] - - # Create array of (timestamp, original_index) and sort by timestamp - ts_with_idx = np.array( - [(ts.value, i) for i, ts in enumerate(generated_ats)], - dtype=[("ts", "i8"), ("idx", "i4")], - ) - ts_with_idx.sort(order="ts") - - # Convert window_size to nanoseconds for direct comparison - window_ns = window_size.value - - # Assign signposts using vectorized operations where possible - signposts = np.empty(n, dtype="i8") - current_start_ns = ts_with_idx[0]["ts"] - signposts[0] = current_start_ns - - for i in range(1, n): - ts_ns = ts_with_idx[i]["ts"] - if ts_ns < current_start_ns + window_ns: - signposts[i] = current_start_ns - else: - current_start_ns = ts_ns - signposts[i] = current_start_ns - - # Map back to original order using fancy indexing - result = np.empty(n, dtype="i8") - result[ts_with_idx["idx"]] = signposts + if isinstance(timestamps, list): + if len(timestamps) == 0: + return [] + ts_index = pd.DatetimeIndex(timestamps) + elif isinstance(timestamps, pd.DatetimeIndex): + if len(timestamps) == 0: + return [] + ts_index = timestamps + else: + # Fallback: try to construct a DatetimeIndex + ts_index = pd.DatetimeIndex(timestamps) + if len(ts_index) == 0: + return [] - # Convert back to Timestamps - return [pd.Timestamp(ns, tz="UTC") for ns in result] + # Branch 1: daily-anchored windows at specific local time + if window_start_time: + try: + hour_str, minute_str = window_start_time.split(":") + start_hours, start_minutes = int(hour_str), int(minute_str) + except Exception as e: + raise ValueError( + f"Invalid window_start_time '{window_start_time}'. Expected 'HH:MM'." + ) from e + + # For each timestamp, find the most recent daily anchor whose window [start, start+window_size) + # contains t. This supports windows that cross midnight and windows longer than 24h by + # checking up to ceil(window_size/1day) days back. + anchors = [] + start_offset = pd.Timedelta(hours=start_hours, minutes=start_minutes) + one_day = pd.Timedelta(days=1) + days_back_to_check = int(np.ceil(window_size / one_day)) + for t in ts_index: + # Start with today's anchor; if t is before it, start from previous day's anchor + base_anchor = t.normalize() + start_offset + candidate = base_anchor if t >= base_anchor else base_anchor - one_day + assigned = pd.NaT + for k in range(days_back_to_check): + a = candidate - (k * one_day) + if (t >= a) and (t < a + window_size): + assigned = a + break + anchors.append(assigned) + + return anchors + + # Branch 2: continuous tiling of windows across the timeline + # Use a stable base so windows are non-overlapping and consistent across days + base = ts_index.min().normalize() + delta = ts_index - base # TimedeltaIndex + # Floor-divide to count how many full windows have elapsed since base + steps = delta // window_size + anchor_idx = base + (steps * window_size) + return list(pd.DatetimeIndex(anchor_idx)) def calc_rank_compare_metrics( in_df, charge_mins, window_mins, - tz=None, window_start_time=None, pred_col="predicted_value", truth_col="signal_value", @@ -499,25 +552,14 @@ def calc_rank_compare_metrics( if isinstance(window_mins, int): window_mins = pd.Timedelta(minutes=window_mins) + window_starts = assign_windows( - df.index.get_level_values("generated_at"), window_mins + df.index.get_level_values("generated_at"), window_mins, window_start_time ) - if window_start_time: - utc_offset = pd.to_timedelta(window_start_time + ":00") - local_dt = (pd.Timestamp("1999-01-01T00:00Z") + utc_offset).tz_convert(tz) - local_offset = ( - pd.to_timedelta(local_dt.hour, unit="h") - + pd.to_timedelta(local_dt.minute, unit="m") - + pd.to_timedelta(local_dt.second, unit="s") - ) - adj_window_starts = [i.normalize() + local_offset for i in window_starts] - else: - adj_window_starts = window_starts - df = df.assign( - window_start=adj_window_starts, - window_end=[i + window_mins for i in adj_window_starts], + window_start=window_starts, + window_end=[i + window_mins for i in window_starts], ) # Filter out rows that do not fall within the valid window @@ -536,10 +578,10 @@ def calc_rank_compare_metrics( df = df.loc[n_generated_at_in_window >= (charge_mins // 5) - 1] # Filter out rows where generated_at != first point_time in the window - df = df.loc[ - df.index.get_level_values("generated_at") - == df.reset_index().groupby("generated_at")["point_time"].transform("first") - ] + # df = df.loc[ + # df.index.get_level_values("generated_at") + # == df.reset_index().groupby("generated_at")["point_time"].transform("first") + # ] df = df.assign( truth_charge_status=pick_k_optimal_charge(df, truth_col, charge_mins), @@ -720,12 +762,12 @@ def plot_rank_corr( AER_SCENARIOS = { "EV-night": { "charge_mins": 3 * 60, - "window_mins": 12 * 60, + "window_mins": 8 * 60, "window_start_time": "19:00", "load_kw": 19, }, "EV-day": { - "charge_mins": 2 * 60, + "charge_mins": 3 * 60, "window_mins": 8 * 60, "window_start_time": "09:00", "load_kw": 19, @@ -784,8 +826,6 @@ def plot_impact_forecast_metrics( vertical_spacing=0.2, ) - tz = get_tz_from_centroid(region_abbrev) - for model_ix, model_job in enumerate(region_models, start=1): _metrics = [ @@ -964,23 +1004,13 @@ def calc_max_potential( if isinstance(window_mins, int): window_mins = pd.Timedelta(minutes=window_mins) - window_starts = assign_windows(df.index.get_level_values("point_time"), window_mins) - - if window_start_time: - utc_offset = pd.to_timedelta(window_start_time + ":00") - local_dt = (pd.Timestamp("1999-01-01T00:00Z") + utc_offset).tz_convert(tz) - local_offset = ( - pd.to_timedelta(local_dt.hour, unit="h") - + pd.to_timedelta(local_dt.minute, unit="m") - + pd.to_timedelta(local_dt.second, unit="s") - ) - adj_window_starts = [i.normalize() + local_offset for i in window_starts] - else: - adj_window_starts = window_starts + window_starts = assign_windows( + df.index.get_level_values("point_time"), window_mins, window_start_time + ) df = df.assign( - window_start=adj_window_starts, - window_end=[i + window_mins for i in adj_window_starts], + window_start=window_starts, + window_end=[i + window_mins for i in window_starts], ) # Filter out rows that do not fall within the valid window @@ -1002,11 +1032,11 @@ def calc_max_potential( df["charge_status"] = pick_k_optimal_charge(df, truth_col, charge_mins) truth_values = df[truth_col] - charge_emissions = truth_values * df["charge_status"] * load_factor + charge_emissions = truth_values * df["charge_status"] * load_factor * (5 / 60) df["sequential_rank"] = df.groupby("window_start").cumcount() baseline_charge_status = df["sequential_rank"] < needed_rows - baseline_charge_emissions = truth_values * baseline_charge_status * load_factor + baseline_charge_emissions = truth_values * baseline_charge_status * load_factor * (5 / 60) y_best_total = charge_emissions.sum() y_baseline_total = baseline_charge_emissions.sum() @@ -1023,7 +1053,15 @@ def calc_max_potential( def plot_max_impact_potential( factory: DataHandlerFactory, - scenarios: List[str] = ["EV-night", "EV-day", "Thermostat"], + scenarios: List[str] = [ + "10 kW / 24hr / 25% Duty Cycle", + "10 kW / 72hr / 25% Duty Cycle", + "10 kW / 24hr / 50% Duty Cycle", + "10 kW / 72hr / 50% Duty Cycle", + "EV-night", + "EV-day", + "Thermostat", + ], ): figs = {} y_max = 0 @@ -1546,14 +1584,18 @@ def plot_forecasts_vs_signal( if df_filtered.empty: continue + # format resample_horizon for legend + if resample_horizon >= pd.Timedelta("1h"): + rh_str = f"{int(resample_horizon.total_seconds() // 3600)}h" + else: + rh_str = f"{int(resample_horizon.total_seconds() // 60)}min" + fig.add_trace( go.Scatter( - x=df_filtered.index.get_level_values("point_time").tz_convert( - tz - ), + x=df_filtered.index.get_level_values("point_time"), y=df_filtered["predicted_value"], mode="lines", - name=f"Forecast Pull Freq: {resample_horizon}", + name=f"Forecast Pull Freq: {rh_str}", opacity=0.9, line=dict( color=pc.qualitative.Plotly[ @@ -1572,12 +1614,12 @@ def plot_forecasts_vs_signal( fh.reset_index() .drop_duplicates("point_time") .set_index("point_time") - .sort_index()["signal_value"] + .sort_index(ascending=True)["signal_value"] ) - signal = signal.loc[start_ts : end_ts + max(horizons)].sort_index() + signal = signal.loc[start_ts : end_ts + max(horizons)] fig.add_trace( go.Scatter( - x=signal.index.tz_convert(tz), + x=signal, y=signal.values, mode="lines", name="Signal Value", @@ -1688,7 +1730,7 @@ def parse_report_command_line_args(sys_args): return args -PLOTS = { +MOER_PLOTS = { "signal": [ plot_sample_moers, plot_distribution_moers, @@ -1701,11 +1743,28 @@ def parse_report_command_line_args(sys_args): plot_forecasts_vs_signal, plot_norm_mae, plot_rank_corr, - plot_precision_recall, plot_impact_forecast_metrics, ], } +OTHER_PLOTS = { + "signal": [ + plot_sample_moers, + plot_heatmaps, + ], + "forecast": [ + plot_forecasts_vs_signal, + plot_norm_mae, + plot_precision_recall, + ], +} + + +PLOTS = { + 'co2_moer': MOER_PLOTS, + 'co2_health_damages': MOER_PLOTS, +} + def generate_report( region_list: Union[Tuple[str, List[str]], List[str]], @@ -1756,8 +1815,16 @@ def generate_report( } plotly_html = {} + + if signal_type in PLOTS: + plot_dict = PLOTS[signal_type] + elif len(signal_type) == 11 and 'tail' in signal_type: # shh + plot_dict = OTHER_PLOTS + else: + raise NotImplementedError(f"Plots not defined for signal_type {signal_type}") + for step in steps: - for plot_func in PLOTS[step]: + for plot_func in plot_dict[step]: _func_params = inspect.signature(plot_func).parameters _kwargs = {k: v for k, v in kwargs.items() if k in _func_params} _plot = plot_func(factory, **_kwargs) From 1e97bb48512e1efaaaeb68a80a19d587695e6885 Mon Sep 17 00:00:00 2001 From: sam_watttime Date: Tue, 28 Oct 2025 23:44:55 -0600 Subject: [PATCH 82/85] new plot in template --- watttime/evaluation/report_card_template.html | 9 ++++++++- 1 file changed, 8 insertions(+), 1 deletion(-) diff --git a/watttime/evaluation/report_card_template.html b/watttime/evaluation/report_card_template.html index 955334f1..10379091 100644 --- a/watttime/evaluation/report_card_template.html +++ b/watttime/evaluation/report_card_template.html @@ -346,11 +346,18 @@

Forecast Performance

"Rank correlation quantifies the degree to which the rank ordering of two datasets are related. We expect for models to perform better at lower horizons. Please note that this metric should be interpreted with caution, especially for signals with low variability. Rank correlation on a signal where variance is smaller than the noise is not going to give a meaningful metric. For instance, rank correlation could be zero but there's no loss in impact." ) }} + {{ tab_container( + "plot_precision_recall", + plot_precision_recall, + "4) Precision and Recall by Horizon", + "Lorem ipsum." + ) }} + {{ tab_container( "plot_impact_forecast_metrics", plot_impact_forecast_metrics, - "4) Estimation of AER Impact using Forecasts", + "5) Estimation of AER Impact using Forecasts", "These plots simulate a vairiety of AER scenarios for typical customer patterns including charging an EV during the day (3 hours in a 12 hour window starting at 8 AM), the night (2 hours in a 8 hour window starting at 9 PM), and running a thermostat (running for 30 out of 60 minutes throughout the day). Each of these metrics could capture a different source of impact (e.g. solar curtailment may only be accessible to EV day charging). The 'simulation' simply looks at the cleanest timem to use electricity based on the forecasted value, and evaluates the impact of this against the historical signal value as truth. We also show the impact captured if the forecast was perfect by displaying the maximum possible impact if a 'perfect' forecast were used. These metrics assume that the modeled values are accurate, and only quantify the degree of error caused by the forecast model. Units are normalized to be impact achieved per day (which may or may not be per window)." ) }} From daef440e68128fbcdc9ab0f1f12a5cac38371b69 Mon Sep 17 00:00:00 2001 From: sam_watttime Date: Wed, 29 Oct 2025 11:50:10 -0600 Subject: [PATCH 83/85] add CIs to plots, discount rate kwarg to simulate_charge --- tests/test_report.py | 428 +++++++++++--- .../get_wt_api_forecast_evaluation_data.py | 10 +- watttime/evaluation/report.py | 524 +++++++++++++++--- 3 files changed, 811 insertions(+), 151 deletions(-) diff --git a/tests/test_report.py b/tests/test_report.py index 857a1ed3..e51006bf 100644 --- a/tests/test_report.py +++ b/tests/test_report.py @@ -127,7 +127,7 @@ def test_plot_impact_forecast_metrics_single_job(self): class TestSimulateCharge(unittest.TestCase): def setUp(self): """Creates a sample DataFrame where each generated_at has 4 point_time values.""" - generated_at_times = pd.date_range("2025-03-11 00:00:00", periods=4, freq="5T") + generated_at_times = pd.date_range("2025-03-11 00:00:00", periods=4, freq="5min") point_time_offsets = pd.to_timedelta([0, 5, 10, 15], unit="m") index = pd.MultiIndex.from_tuples( @@ -227,86 +227,6 @@ def test_assign_windows_continuous_tiling(self): self.assertEqual(anchors[7], start + pd.Timedelta(minutes=30)) # 00:35 -> anchor 00:30 self.assertEqual(anchors[12], start + pd.Timedelta(minutes=60)) # 01:00 -> anchor 01:00 - def test_calc_max_potential_equals_rank_compare_potential_when_constrained_inputs_match(self): - """When both functions operate over the same constrained candidate set (earliest-per-pull rows after - window filtering), their 'potential' should match. - - Steps: - - Build a synthetic forecasts_v_moers across a single 2-hour window. - - Reproduce the window filtering used by calc_rank_compare_metrics, then reduce to earliest-per-pull rows. - - Feed that constrained candidate set as a moers DataFrame into calc_max_potential. - - Assert that calc_max_potential['potential'] equals calc_rank_compare_metrics()['potential']. - """ - - tz = "America/Los_Angeles" - start = pd.Timestamp("2024-01-01 19:00", tz=tz) - window_mins = 60 - charge_mins = 10 # 2 five-minute slots - load_kw = 100 - - # Build forecasts_v_moers: signal high then low to make ordering visible - rows = [] - for ga in pd.date_range(start, start + pd.Timedelta(hours=2), freq="5T"): - for pt_i in pd.date_range(ga, ga + pd.Timedelta(minutes=55), freq="5T"): - rows.append( - { - "generated_at": ga, - "point_time": pt_i, - "predicted_value": random.random(), - "signal_value": 1000 - pt_i.minute * 10 - } - ) - fvm = ( - pd.DataFrame(rows) - .set_index(["generated_at", "point_time"]) - .sort_index() - ) - - # Compute rank-compare potential - rc = report.calc_rank_compare_metrics( - fvm, - charge_mins=charge_mins, - window_mins=window_mins, - window_start_time="19:00", - pred_col="predicted_value", - truth_col="signal_value", - load_kw=load_kw, - ) - rc_potential = rc["potential"] - - # Reproduce the same candidate set (earliest-per-pull after window filtering) - wm = pd.Timedelta(minutes=window_mins) - window_starts = report.assign_windows( - fvm.index.get_level_values("generated_at"), wm, "19:00" - ) - df = fvm.assign( - window_start=window_starts, - window_end=[i + wm for i in window_starts], - ) - # out-of-window filter - df.loc[ - (df.index.get_level_values("point_time") >= df["window_end"]) - | (df.index.get_level_values("generated_at") >= df["window_end"]) - | (df.index.get_level_values("generated_at") < df["window_start"]), - "window_start", - ] = pd.NaT - df = df.dropna(subset=["window_start", "window_end"]).copy() - # sufficient generated_at per window - n_gen = df.groupby("window_start").transform( - lambda x: x.reset_index()["generated_at"].nunique() - )["predicted_value"] - df = df.loc[n_gen >= (charge_mins // 5) - 1] - - mp = report.calc_max_potential( - df, - charge_mins=charge_mins, - window_mins=window_mins, - window_start_time="19:00", - truth_col="signal_value", - load_kw=load_kw, - )["potential"] - - self.assertAlmostEqual(mp, rc_potential, places=6) class TestAnalysisDataHandler(unittest.TestCase): @@ -403,3 +323,349 @@ def test_forecasts_v_moers(self): "point_time index level does not have the expected timezone.", ) + +class TestBootstrapConfidenceInterval(unittest.TestCase): + """Tests for the bootstrap_confidence_interval function.""" + + def setUp(self): + """Create sample daily data for testing.""" + np.random.seed(42) + dates = pd.date_range("2024-01-01", periods=30, freq="D") + self.daily_data = pd.DataFrame({ + "date": dates, + "emissions": np.random.normal(100, 20, 30), + "count": np.random.randint(10, 50, 30), + }) + + def test_bootstrap_returns_dict_with_ci_keys(self): + """Test that bootstrap returns a dict with CI lower/upper keys.""" + def metric_func(df): + return {"mean_emissions": df["emissions"].mean()} + + result = report.bootstrap_confidence_interval( + df_with_date=self.daily_data, + metric_func=metric_func, + effective_forecast_samp_rate=1.0, + n_bootstrap=100, + ) + + self.assertIsInstance(result, dict) + self.assertIn("mean_emissions_ci_lower", result) + self.assertIn("mean_emissions_ci_upper", result) + + def test_bootstrap_ci_bounds_sensible(self): + """Test that CI lower < upper and bounds are reasonable.""" + def metric_func(df): + return {"mean_emissions": df["emissions"].mean()} + + result = report.bootstrap_confidence_interval( + df_with_date=self.daily_data, + metric_func=metric_func, + effective_forecast_samp_rate=1.0, + n_bootstrap=100, + ) + + self.assertLess( + result["mean_emissions_ci_lower"], + result["mean_emissions_ci_upper"], + "CI lower bound should be less than upper bound" + ) + + # Check that bounds are within reasonable range of data + mean_val = self.daily_data["emissions"].mean() + self.assertLess(result["mean_emissions_ci_lower"], mean_val * 1.5) + self.assertGreater(result["mean_emissions_ci_upper"], mean_val * 0.5) + + def test_bootstrap_multiple_metrics(self): + """Test that bootstrap works with multiple metrics returned.""" + def metric_func(df): + return { + "mean": df["emissions"].mean(), + "total": df["emissions"].sum(), + "max": df["emissions"].max(), + } + + result = report.bootstrap_confidence_interval( + df_with_date=self.daily_data, + metric_func=metric_func, + effective_forecast_samp_rate=1.0, + n_bootstrap=100, + ) + + # Check all metrics have CIs + self.assertIn("mean_ci_lower", result) + self.assertIn("mean_ci_upper", result) + self.assertIn("total_ci_lower", result) + self.assertIn("total_ci_upper", result) + self.assertIn("max_ci_lower", result) + self.assertIn("max_ci_upper", result) + + # All should be valid + for metric in ["mean", "total", "max"]: + self.assertLess(result[f"{metric}_ci_lower"], result[f"{metric}_ci_upper"]) + + def test_bootstrap_with_low_sample_rate(self): + """Test that low sample rate inflates CIs (wider intervals).""" + def metric_func(df): + return {"mean": df["emissions"].mean()} + + # Full sample + result_full = report.bootstrap_confidence_interval( + df_with_date=self.daily_data, + metric_func=metric_func, + effective_forecast_samp_rate=1.0, + n_bootstrap=200, + random_seed=42, + ) + + # Low sample rate (10%) + result_sampled = report.bootstrap_confidence_interval( + df_with_date=self.daily_data, + metric_func=metric_func, + effective_forecast_samp_rate=0.1, + n_bootstrap=200, + random_seed=42, + ) + + width_full = result_full["mean_ci_upper"] - result_full["mean_ci_lower"] + width_sampled = result_sampled["mean_ci_upper"] - result_sampled["mean_ci_lower"] + + self.assertGreater( + width_sampled, + width_full, + "Low sample rate should produce wider confidence intervals" + ) + + def test_bootstrap_reproducible_with_seed(self): + """Test that results are reproducible with same random seed.""" + def metric_func(df): + return {"mean": df["emissions"].mean()} + + result1 = report.bootstrap_confidence_interval( + df_with_date=self.daily_data, + metric_func=metric_func, + effective_forecast_samp_rate=1.0, + n_bootstrap=100, + random_seed=123, + ) + + result2 = report.bootstrap_confidence_interval( + df_with_date=self.daily_data, + metric_func=metric_func, + effective_forecast_samp_rate=1.0, + n_bootstrap=100, + random_seed=123, + ) + + self.assertEqual(result1["mean_ci_lower"], result2["mean_ci_lower"]) + self.assertEqual(result1["mean_ci_upper"], result2["mean_ci_upper"]) + + def test_bootstrap_raises_on_empty_data(self): + """Test that bootstrap raises ValueError on empty DataFrame.""" + def metric_func(df): + return {"mean": df["emissions"].mean()} + + empty_df = pd.DataFrame({"date": [], "emissions": []}) + + with self.assertRaises(ValueError) as context: + report.bootstrap_confidence_interval( + df_with_date=empty_df, + metric_func=metric_func, + effective_forecast_samp_rate=1.0, + ) + + self.assertIn("empty", str(context.exception).lower()) + + def test_bootstrap_confidence_level(self): + """Test that different confidence levels produce different intervals.""" + def metric_func(df): + return {"mean": df["emissions"].mean()} + + result_95 = report.bootstrap_confidence_interval( + df_with_date=self.daily_data, + metric_func=metric_func, + effective_forecast_samp_rate=1.0, + confidence_level=0.95, + n_bootstrap=200, + random_seed=42, + ) + + result_90 = report.bootstrap_confidence_interval( + df_with_date=self.daily_data, + metric_func=metric_func, + effective_forecast_samp_rate=1.0, + confidence_level=0.90, + n_bootstrap=200, + random_seed=42, + ) + + width_95 = result_95["mean_ci_upper"] - result_95["mean_ci_lower"] + width_90 = result_90["mean_ci_upper"] - result_90["mean_ci_lower"] + + self.assertGreater( + width_95, + width_90, + "95% CI should be wider than 90% CI" + ) + + def test_bootstrap_effective_sample_size_calculation(self): + """Test that effective sample size is calculated correctly for different rates.""" + def metric_func(df): + return {"count": len(df)} + + # With 30 days and 0.1 sample rate, effective n should be max(2, int(30*0.1)) = 3 + result = report.bootstrap_confidence_interval( + df_with_date=self.daily_data, + metric_func=metric_func, + effective_forecast_samp_rate=0.1, + n_bootstrap=50, + random_seed=42, + ) + + # The count metric should reflect bootstrap sampling + # With very low effective sample size, CIs should be quite wide + self.assertIn("count_ci_lower", result) + self.assertIn("count_ci_upper", result) + + +class TestCalcNormMaeWithCI(unittest.TestCase): + """Test calc_norm_mae with confidence intervals enabled.""" + + def setUp(self): + """Create a mock AnalysisDataHandler with synthetic data.""" + # Create synthetic forecasts_v_moers data + np.random.seed(42) + dates = pd.date_range("2024-01-01", periods=10, freq="D", tz="America/Los_Angeles") + + rows = [] + for date in dates: + for hour in range(24): + generated_at = date + pd.Timedelta(hours=hour) + for horizon in [55, 115, 175]: # 55min, 115min, 175min horizons + point_time = generated_at + pd.Timedelta(minutes=horizon) + predicted = 100 + np.random.normal(0, 10) + signal = 100 + np.random.normal(0, 10) + rows.append({ + "generated_at": generated_at, + "point_time": point_time, + "horizon_mins": horizon, + "predicted_value": predicted, + "signal_value": signal, + }) + + forecasts_v_moers = pd.DataFrame(rows).set_index(["generated_at", "point_time"]) + + # Create a mock data handler + self.mock_handler = type('MockHandler', (), { + 'forecasts_v_moers': forecasts_v_moers, + 'effective_forecast_sample_rate': 1.0, + })() + + def test_calc_norm_mae_without_ci_returns_scalar(self): + """Test that calc_norm_mae returns scalar when ci=False.""" + result = report.calc_norm_mae( + self.mock_handler, + horizon_mins=55, + ci=False, + ) + + self.assertIsInstance(result, (int, float, np.floating)) + + def test_calc_norm_mae_with_ci_returns_dict(self): + """Test that calc_norm_mae returns dict with CIs when ci=True.""" + result = report.calc_norm_mae( + self.mock_handler, + horizon_mins=55, + ci=True, + ) + + self.assertIsInstance(result, dict) + self.assertIn("norm_mae", result) + self.assertIn("norm_mae_ci_lower", result) + self.assertIn("norm_mae_ci_upper", result) + + def test_calc_norm_mae_ci_bounds_sensible(self): + """Test that MAE CIs are sensible (lower < value < upper).""" + result = report.calc_norm_mae( + self.mock_handler, + horizon_mins=55, + ci=True, + ) + + self.assertLessEqual(result["norm_mae_ci_lower"], result["norm_mae"]) + self.assertLessEqual(result["norm_mae"], result["norm_mae_ci_upper"]) + + +class TestCalcRankCorrWithCI(unittest.TestCase): + """Test calc_rank_corr with confidence intervals enabled.""" + + def setUp(self): + """Create a mock AnalysisDataHandler with synthetic data.""" + np.random.seed(42) + dates = pd.date_range("2024-01-01", periods=10, freq="D", tz="America/Los_Angeles") + + rows = [] + for date in dates: + for hour in range(24): + generated_at = date + pd.Timedelta(hours=hour) + # Create correlated predictions and signals + signal_base = 100 + hour * 2 # Time-varying pattern + for horizon in [55, 115, 175]: + point_time = generated_at + pd.Timedelta(minutes=horizon) + signal = signal_base + np.random.normal(0, 5) + predicted = signal_base + np.random.normal(0, 8) # Correlated with signal + rows.append({ + "generated_at": generated_at, + "point_time": point_time, + "horizon_mins": horizon, + "predicted_value": predicted, + "signal_value": signal, + }) + + forecasts_v_moers = pd.DataFrame(rows).set_index(["generated_at", "point_time"]) + + self.mock_handler = type('MockHandler', (), { + 'forecasts_v_moers': forecasts_v_moers, + 'effective_forecast_sample_rate': 1.0, + })() + + def test_calc_rank_corr_without_ci_returns_scalar(self): + """Test that calc_rank_corr returns scalar when ci=False.""" + result = report.calc_rank_corr( + self.mock_handler, + horizon_mins=175, + ci=False, + ) + + self.assertIsInstance(result, (int, float, np.floating)) + + def test_calc_rank_corr_with_ci_returns_dict(self): + """Test that calc_rank_corr returns dict with CIs when ci=True.""" + result = report.calc_rank_corr( + self.mock_handler, + horizon_mins=175, + ci=True, + ) + + self.assertIsInstance(result, dict) + self.assertIn("rank_corr", result) + self.assertIn("rank_corr_ci_lower", result) + self.assertIn("rank_corr_ci_upper", result) + + def test_calc_rank_corr_ci_bounds_sensible(self): + """Test that correlation CIs are sensible (within [-1, 1] and ordered).""" + result = report.calc_rank_corr( + self.mock_handler, + horizon_mins=175, + ci=True, + ) + + # CIs should be ordered + self.assertLessEqual(result["rank_corr_ci_lower"], result["rank_corr"]) + self.assertLessEqual(result["rank_corr"], result["rank_corr_ci_upper"]) + + # All values should be in valid correlation range [-1, 1] + self.assertGreaterEqual(result["rank_corr_ci_lower"], -1) + self.assertLessEqual(result["rank_corr_ci_upper"], 1) + + diff --git a/watttime/evaluation/get_wt_api_forecast_evaluation_data.py b/watttime/evaluation/get_wt_api_forecast_evaluation_data.py index 8233bbf7..29f26db3 100644 --- a/watttime/evaluation/get_wt_api_forecast_evaluation_data.py +++ b/watttime/evaluation/get_wt_api_forecast_evaluation_data.py @@ -109,7 +109,7 @@ class AnalysisDataHandler: region: str eval_start: Union[datetime, str] eval_end: Union[datetime, str] - forecast_sample_size: Union[int, float] = 0.1 + forecast_sample_size: Union[int, float] = 0.03 forecast_max_horizon: int = 60 * 24 forecast_sample_seed: int = 42 forecast_data_horizon_days: int = 3 @@ -308,6 +308,14 @@ def forecast_v_moer_filtered_by_rank( if use_highest: return self.forecast_v_moer[forecast_ranks >= n_rows] return self.forecast_v_moer[forecast_ranks <= n_rows] + + @cached_property + def effective_forecast_sample_rate(self) -> float: + df_reset_for_sampling = self.forecasts_v_moers.reset_index() + generated_at_dates = df_reset_for_sampling["generated_at"].dt.normalize() + unique_forecast_days = generated_at_dates.nunique() + date_range_days = (generated_at_dates.max() - generated_at_dates.min()).days + 1 + return unique_forecast_days / date_range_days if date_range_days > 0 else 1.0 class DataHandlerFactory: diff --git a/watttime/evaluation/report.py b/watttime/evaluation/report.py index 2778d35f..c0059810 100644 --- a/watttime/evaluation/report.py +++ b/watttime/evaluation/report.py @@ -326,24 +326,97 @@ def plot_heatmaps( def calc_norm_mae( - df, horizon_mins, pred_col="predicted_value", truth_col="signal_value" + data_handler, + horizon_mins, + pred_col="predicted_value", + truth_col="signal_value", + ci=False, ): - """Returns normalized MAE in lbs/Mwh""" + """ + Calculate normalized MAE in lbs/Mwh. + + Parameters: + data_handler: AnalysisDataHandler object with forecasts_v_moers DataFrame + horizon_mins: Forecast horizon in minutes + pred_col: Column name for predictions + truth_col: Column name for ground truth + ci: If True, returns dict with confidence intervals; if False, returns scalar (default False) + + Returns: + float or dict: If ci=False, returns normalized MAE value. + If ci=True, returns {"norm_mae": value, "norm_mae_ci_lower": value, "norm_mae_ci_upper": value} + """ + df = data_handler.forecasts_v_moers filtered_df = df[df["horizon_mins"] == horizon_mins].dropna() filtered_df["abs_error"] = (filtered_df[truth_col] - filtered_df[pred_col]).abs() - # truth_mean = filtered_df[truth_col].mean() - norm_mae = (filtered_df["abs_error"].mean() / filtered_df[truth_col].mean()) * 100.0 - - return round(norm_mae, 1) + norm_mae = round(norm_mae, 1) + + if not ci: + return norm_mae + + effective_forecast_samp_rate = data_handler.effective_forecast_sample_rate + df_with_date = filtered_df.reset_index() + df_with_date["date"] = df_with_date["generated_at"].dt.normalize() + + daily_errors = df_with_date.groupby("date", as_index=False).apply( + lambda g: pd.Series({ + "abs_error_sum": (g[truth_col] - g[pred_col]).abs().sum(), + "truth_sum": g[truth_col].sum(), + "count": len(g), + }), + include_groups=False + ) + + def calc_mae_metric(daily_df): + total_abs_error = daily_df["abs_error_sum"].sum() + total_truth = daily_df["truth_sum"].sum() + return {"norm_mae": (total_abs_error / total_truth) * 100.0 if total_truth > 0 else 0.0} + + try: + ci_dict = bootstrap_confidence_interval( + df_with_date=daily_errors, + metric_func=calc_mae_metric, + effective_forecast_samp_rate=effective_forecast_samp_rate, + ) + return { + "norm_mae": norm_mae, + "norm_mae_ci_lower": round(ci_dict["norm_mae_ci_lower"], 1), + "norm_mae_ci_upper": round(ci_dict["norm_mae_ci_upper"], 1), + } + except ValueError: + # Not enough data for bootstrap + return { + "norm_mae": norm_mae, + "norm_mae_ci_lower": np.nan, + "norm_mae_ci_upper": np.nan, + } def calc_rank_corr( - df, horizon_mins, pred_col="predicted_value", truth_col="signal_value" + data_handler, + horizon_mins, + pred_col="predicted_value", + truth_col="signal_value", + ci=False, ): - """Compute mean daily Spearman rank correlation up to a given forecast horizon.""" + """ + Compute mean daily Spearman rank correlation up to a given forecast horizon. + + Parameters: + data_handler: AnalysisDataHandler object with forecasts_v_moers DataFrame + horizon_mins: Maximum forecast horizon in minutes + pred_col: Column name for predictions + truth_col: Column name for ground truth + ci: If True, returns dict with confidence intervals; if False, returns scalar (default False) + + Returns: + float or dict: If ci=False, returns rank correlation value. + If ci=True, returns {"rank_corr": value, "rank_corr_ci_lower": value, "rank_corr_ci_upper": value} + """ + df = data_handler.forecasts_v_moers filtered_df = df[df["horizon_mins"] <= horizon_mins].dropna( subset=[pred_col, truth_col] ) @@ -355,9 +428,123 @@ def safe_spearmanr(group_df): return np.nan return stats.spearmanr(group_df[truth_col], group_df[pred_col]).statistic - corr = filtered_df.groupby("generated_at").apply(safe_spearmanr) + corr_by_forecast = filtered_df.groupby("generated_at").apply( + safe_spearmanr, include_groups=False + ) + + rank_corr = float(np.nanmean(corr_by_forecast)) + if not np.isnan(rank_corr): + rank_corr = round(rank_corr, 3) + + if not ci: + return rank_corr + + # block bootstrap + df_reset = corr_by_forecast.reset_index() + df_reset.columns = ["generated_at", "correlation"] + df_reset = df_reset.dropna() + df_reset["date"] = df_reset["generated_at"].dt.normalize() + + # Group by date and average correlations within each day + daily_corr = df_reset.groupby("date")["correlation"].mean().reset_index() + + effective_forecast_samp_rate = data_handler.effective_forecast_sample_rate + + def calc_corr_metric(daily_df): + return {"rank_corr": daily_df["correlation"].mean()} + + try: + ci_dict = bootstrap_confidence_interval( + df_with_date=daily_corr, + metric_func=calc_corr_metric, + effective_forecast_samp_rate=effective_forecast_samp_rate, + ) + return { + "rank_corr": rank_corr, + "rank_corr_ci_lower": round(ci_dict["rank_corr_ci_lower"], 3), + "rank_corr_ci_upper": round(ci_dict["rank_corr_ci_upper"], 3), + } + except ValueError: + # Not enough data for bootstrap + return { + "rank_corr": rank_corr, + "rank_corr_ci_lower": np.nan, + "rank_corr_ci_upper": np.nan, + } - return float(np.nanmean(corr).round(3)) + +def bootstrap_confidence_interval( + df_with_date: pd.DataFrame, + metric_func, + effective_forecast_samp_rate: float, + n_bootstrap: int = 1000, + confidence_level: float = 0.95, + random_seed: int = 42, +): + """ + Calculate bootstrap confidence intervals for a metric, accounting for forecast sampling. + + This function implements a day-level block bootstrap that appropriately handles + uncertainty when forecasts are sampled on entire days (not all days have forecasts). + + Parameters: + df_with_date: DataFrame with a 'date' column (normalized datetime) for grouping + metric_func: Function that takes a DataFrame and returns a dict of metrics + Example: lambda df: {"metric": df["value"].sum()} + effective_forecast_samp_rate: Fraction of days in date range that have forecasts (0-1) + n_bootstrap: Number of bootstrap iterations (default 1000) + confidence_level: Confidence level for intervals (default 0.95 = 95% CI) + random_seed: Random seed for reproducibility (default 42) + + Returns: + dict: Point estimates and CIs for each metric returned by metric_func + Keys: {metric}_ci_lower, {metric}_ci_upper for each metric + + Algorithm: + - If effective_forecast_samp_rate < 0.95: Use reduced effective sample size to inflate CIs + - If effective_forecast_samp_rate >= 0.95: Use standard bootstrap (full sample size) + - Both approaches use block resampling of entire days to preserve temporal structure + + """ + + n_days = len(df_with_date) + + if n_days == 0: + raise ValueError("DataFrame is empty, cannot calculate confidence intervals") + + # Determine bootstrap strategy based on sampling rate + if effective_forecast_samp_rate < 0.95: + # Sampled forecasts: use effective sample size to account for sampling uncertainty + effective_n_days = max(2, int(n_days * effective_forecast_samp_rate)) + else: + # Full or nearly-full data: standard bootstrap + effective_n_days = n_days + + np.random.seed(random_seed) + bootstrap_results = [] + + for _ in range(n_bootstrap): + # Block resample: sample entire days with replacement + sampled_days = df_with_date.sample(n=effective_n_days, replace=True) + metrics = metric_func(sampled_days) + bootstrap_results.append(metrics) + + bootstrap_df = pd.DataFrame(bootstrap_results) + + alpha = 1 - confidence_level + lower_percentile = (alpha / 2) * 100 + upper_percentile = (1 - alpha / 2) * 100 + + ci_dict = {} + for metric_name in bootstrap_df.columns: + ci_dict[f"{metric_name}_ci_lower"] = np.percentile( + bootstrap_df[metric_name], lower_percentile + ) + ci_dict[f"{metric_name}_ci_upper"] = np.percentile( + bootstrap_df[metric_name], upper_percentile + ) + + return ci_dict def pick_k_optimal_charge(in_df: pd.DataFrame, truth_col, charge_mins: int): @@ -389,7 +576,44 @@ def pick_k_optimal_charge(in_df: pd.DataFrame, truth_col, charge_mins: int): return df["charge_status"] -def simulate_charge(in_df: pd.DataFrame, sort_col: str, charge_mins: int): +def simulate_charge( + in_df: pd.DataFrame, + sort_col: str, + charge_mins: int, + discount_factor: float = 0.0, + discount_horizon_hours: float = 6.0 +): + """ + Simulate a greedy charging strategy that makes sequential decisions at each forecast time. + + At each decision point (generated_at), determines whether to charge immediately based on: + 1. Current forecast's prediction for "now" + 2. How that prediction ranks among all forecasted values + 3. Remaining opportunities to charge before window ends + + The algorithm processes forecasts chronologically and makes irrevocable charging decisions + without knowledge of future forecasts (causally valid simulation). + + Parameters: + in_df: DataFrame with MultiIndex (generated_at, point_time) + sort_col: Column name to use for ranking (e.g., 'predicted_value') + charge_mins: Total minutes of charging needed in each window + discount_factor: If > 0, applies exponential time-discounting to future predictions + to account for increasing uncertainty (default 0.0 = no discounting) + discount_horizon_hours: Reference horizon for discounting (default 6h) + + Returns: + Series of boolean charge_status values + + Algorithm: + For each window and each generated_at time T: + 1. Get current forecast value for time T + 2. Count how many forecasted values are better: + - Without discounting: simple count of values < current + - With discounting: weighted count using exp(-discount * hours_ahead) + 3. Charge if: (better_count <= remaining_charge_needed) OR (running_out_of_time) + 4. Continue to next decision point + """ df = in_df.copy() @@ -398,6 +622,7 @@ def simulate_charge(in_df: pd.DataFrame, sort_col: str, charge_mins: int): charge_needed = charge_mins // 5 df = df.assign(charge_status=False) df = df.sort_index(ascending=True) + for w in df.reset_index().groupby("window_start"): window_start, w_df = w w_df = w_df.sort_values(["generated_at", "point_time"], ascending=True) @@ -407,7 +632,19 @@ def simulate_charge(in_df: pd.DataFrame, sort_col: str, charge_mins: int): total_groups = len(generated_at_groups) for processed_count, (generated_at, g_df) in enumerate(generated_at_groups): - n_below_now = g_df[sort_col].rank().iloc[0] + current_value = g_df.iloc[0][sort_col] + current_time = generated_at + + if discount_factor > 0: + # Apply time-based discounting to future predictions + g_df_reset = g_df.reset_index() + hours_ahead = (g_df_reset["point_time"] - current_time).dt.total_seconds() / 3600 + weights = np.exp(-discount_factor * hours_ahead / discount_horizon_hours) + better_mask = g_df[sort_col] < current_value + n_below_now = (better_mask * weights).sum() + else: + n_below_now = (g_df[sort_col] < current_value).sum() + remaining_generated_at = total_groups - processed_count should_charge_now = (n_below_now <= _charge_needed) or ( remaining_generated_at <= _charge_needed @@ -535,11 +772,11 @@ def assign_windows( # Floor-divide to count how many full windows have elapsed since base steps = delta // window_size anchor_idx = base + (steps * window_size) - return list(pd.DatetimeIndex(anchor_idx)) + return list(anchor_idx) def calc_rank_compare_metrics( - in_df, + data_handler, charge_mins, window_mins, window_start_time=None, @@ -547,41 +784,56 @@ def calc_rank_compare_metrics( truth_col="signal_value", load_kw=1000, ): + in_df = data_handler.forecasts_v_moers df = in_df.copy() if isinstance(window_mins, int): window_mins = pd.Timedelta(minutes=window_mins) + effective_forecast_samp_rate = data_handler.effective_forecast_sample_rate + window_starts = assign_windows( df.index.get_level_values("generated_at"), window_mins, window_start_time ) - df = df.assign( - window_start=window_starts, - window_end=[i + window_mins for i in window_starts], - ) + df = df.assign(window_start=window_starts) + + window_ends = [] + for ws in window_starts: + if pd.isna(ws): + window_ends.append(pd.NaT) + else: + window_ends.append(ws + window_mins) + + df = df.assign(window_end=window_ends) + + # reset index before filtering + df = df.reset_index() # Filter out rows that do not fall within the valid window df.loc[ - (df.index.get_level_values("point_time") >= df["window_end"]) - | (df.index.get_level_values("generated_at") >= df["window_end"]) - | (df.index.get_level_values("generated_at") < df["window_start"]), + (df["point_time"] >= df["window_end"]) + | (df["generated_at"] >= df["window_end"]) + | (df["generated_at"] < df["window_start"]), "window_start", ] = pd.NaT df = df.dropna(subset=["window_start", "window_end"]) - # Filter out rows where there aren't enough generated_ats to fulfill charge_mins - n_generated_at_in_window = df.groupby("window_start").transform( - lambda x: x.reset_index()["generated_at"].nunique() - )[pred_col] - df = df.loc[n_generated_at_in_window >= (charge_mins // 5) - 1] + # Filter out windows where we don't have generated_ats covering the full window duration + expected_gen_ats = window_mins // pd.Timedelta(minutes=5) + window_gen_at_counts = df.groupby("window_start")["generated_at"].nunique() + valid_windows = window_gen_at_counts[window_gen_at_counts >= expected_gen_ats].index + df = df[df["window_start"].isin(valid_windows)] # Filter out rows where generated_at != first point_time in the window - # df = df.loc[ - # df.index.get_level_values("generated_at") - # == df.reset_index().groupby("generated_at")["point_time"].transform("first") - # ] + # e.g. we pulled an incomplete forecast (not sure why this would happen, but appe) + df = df.loc[ + df['generated_at'] + == df.groupby('generated_at')["point_time"].transform("first") + ] + + df = df.set_index(['generated_at', 'point_time']).sort_index(ascending=True) df = df.assign( truth_charge_status=pick_k_optimal_charge(df, truth_col, charge_mins), @@ -619,35 +871,81 @@ def calc_rank_compare_metrics( <= (len(df["window_start"].unique()) + 1) * charge_mins // 5 ) - # Calculate total CO2 emissions ("truth") - y_best_emissions = df["truth_charge_emissions"].sum() - y_pred_emissions = df["pred_charge_emissions"].sum() - y_base_emissions = df["baseline_charge_emissions"].sum() - - assert y_pred_emissions >= y_best_emissions - assert y_base_emissions >= y_best_emissions - - dates = df.index.get_level_values("generated_at") - n_days = len(set(dates.dropna().date)) + # Calculate daily-level emissions to enable bootstrap confidence intervals + df_with_date = df.reset_index() + df_with_date["date"] = df_with_date["generated_at"].dt.normalize() + + daily_emissions = df_with_date.groupby("date").agg({ + "truth_charge_emissions": "sum", + "pred_charge_emissions": "sum", + "baseline_charge_emissions": "sum", + }).reset_index() + + n_days = len(daily_emissions) if n_days == 0: reduction = np.nan potential = np.nan + reduction_ci_lower = np.nan + reduction_ci_upper = np.nan + potential_ci_lower = np.nan + potential_ci_upper = np.nan else: + # Calculate point estimates + y_best_emissions = daily_emissions["truth_charge_emissions"].sum() + y_pred_emissions = daily_emissions["pred_charge_emissions"].sum() + y_base_emissions = daily_emissions["baseline_charge_emissions"].sum() + + assert y_pred_emissions >= y_best_emissions + assert y_base_emissions >= y_best_emissions + reduction = (y_base_emissions - y_pred_emissions) / n_days potential = (y_base_emissions - y_best_emissions) / n_days + + # Bootstrap confidence intervals (95% CI) + # Use the general bootstrap function to calculate CIs + def calc_emissions_metrics(daily_df): + """Calculate reduction and potential from daily emissions DataFrame""" + base = daily_df["baseline_charge_emissions"].sum() + pred = daily_df["pred_charge_emissions"].sum() + best = daily_df["truth_charge_emissions"].sum() + + return { + "reduction": (base - pred) / n_days, + "potential": (base - best) / n_days, + } + + ci_dict = bootstrap_confidence_interval( + df_with_date=daily_emissions, + metric_func=calc_emissions_metrics, + effective_forecast_samp_rate=effective_forecast_samp_rate, + ) + + reduction_ci_lower = ci_dict["reduction_ci_lower"] + reduction_ci_upper = ci_dict["reduction_ci_upper"] + potential_ci_lower = ci_dict["potential_ci_lower"] + potential_ci_upper = ci_dict["potential_ci_upper"] return { "reduction": round(reduction, 1), "potential": round(potential, 1), + "reduction_ci_lower": round(reduction_ci_lower, 1), + "reduction_ci_upper": round(reduction_ci_upper, 1), + "potential_ci_lower": round(potential_ci_lower, 1), + "potential_ci_upper": round(potential_ci_upper, 1), } def plot_norm_mae( - factory: DataHandlerFactory, horizons_hr=[1, 6, 12, 18, 24, 72] + factory: DataHandlerFactory, horizons_hr=[1, 6, 12, 18, 24, 72], ci=True ) -> Dict[str, go.Figure]: """ - Create a Plotly bar chart for rank correlation by horizon with one subplot per region (abbrev). + Create a Plotly bar chart for normalized MAE by horizon with one subplot per region (abbrev). + + Parameters: + factory: DataHandlerFactory with data handlers + horizons_hr: List of horizons in hours + ci: If True, adds confidence interval error bars (default True) """ y_min = y_max = 0 @@ -659,13 +957,40 @@ def plot_norm_mae( x_values = [f"{h}hr" for h in horizons_hr] # Add horizon labels for model_job in region_models: - y_values = [ - calc_norm_mae(model_job.forecasts_v_moers, (h * 60) - 5) + # Calculate metrics with or without CIs + results = [ + calc_norm_mae(model_job, (h * 60) - 5, ci=ci) for h in horizons_hr ] + + if ci: + # Extract values and CIs from dict results + y_values = [r["norm_mae"] for r in results] + ci_lower = [r["norm_mae_ci_lower"] for r in results] + ci_upper = [r["norm_mae_ci_upper"] for r in results] + + # Calculate error bar arrays (distance from point estimate) + error_minus = [y - lower if not np.isnan(lower) else 0 for y, lower in zip(y_values, ci_lower)] + error_plus = [upper - y if not np.isnan(upper) else 0 for y, upper in zip(y_values, ci_upper)] + + # Update y_max to include CI upper bounds + y_max = max([u for u in ci_upper if not np.isnan(u)] + [y_max]) + else: + # Scalar results + y_values = results + error_minus = None + error_plus = None + y_max = max(y_values + [y_max]) y_min = min(y_values + [y_min]) + error_y_config = dict( + type="data", + symmetric=False, + array=error_plus, + arrayminus=error_minus, + ) if ci else None + fig.add_trace( go.Bar( x=x_values, @@ -673,62 +998,90 @@ def plot_norm_mae( name=model_job.model_date, text=[f"{y:.1f}%" for y in y_values], # Add text labels on bars textposition="outside", + error_y=error_y_config, ) ) - fig.update_layout( - height=300, - xaxis_title="Horizon (Hours)", - yaxis_title="Normalized MAE (%)", - showlegend=True, # Legends appear in individual subplot titles - margin=dict(l=50, r=50, t=50, b=50), - barmode="group", # Ensure bars for each region are grouped - ) + fig.update_layout( + height=300, + xaxis_title="Horizon (Hours)", + yaxis_title="Normalized MAE (%)", + showlegend=True, # Legends appear in individual subplot titles + margin=dict(l=50, r=50, t=50, b=50), + barmode="group", # Ensure bars for each region are grouped + ) - figs[region_abbrev] = fig + figs[region_abbrev] = fig - for region, fig in figs.items(): - figs[region] = fig.update_yaxes( - range=[y_min - (0.25 * y_max), y_max + (0.25 * y_max)] - ) + for region, fig in figs.items(): + figs[region] = fig.update_yaxes( + range=[y_min - (0.25 * y_max), y_max + (0.25 * y_max)] + ) return figs def plot_rank_corr( - factory: DataHandlerFactory, horizons_hr=[1, 6, 12, 18, 24, 72] + factory: DataHandlerFactory, horizons_hr=[1, 6, 12, 18, 24, 72], ci=True ) -> Dict[str, go.Figure]: """ - Create a Plotly line plot for rank correlation by horizon with one subplot per region (abbrev). + Create a Plotly bar plot for rank correlation by horizon with one subplot per region (abbrev). Parameters: - df (pd.DataFrame): DataFrame containing the rank correlation data. - Columns: ['abbrev', 'name', '24hr', '48hr', '72hr']. - metric_name (str): 'Rank Correlation' or 'Normalized MAE' + factory: DataHandlerFactory with data handlers + horizons_hr: List of horizons in hours + ci: If True, adds confidence interval error bars (default True) Returns: fig (plotly.graph_objects.Figure): A Plotly figure with subplots. - """ y_min = y_max = 0 figs = {} - # Iterate through each region and create a line plot + # Iterate through each region and create a bar plot for region_abbrev, region_models in factory.data_handlers_by_region_dict.items(): fig = go.Figure() - # Extract data for the line plot + # Extract data for the bar plot x_values = [f"{h}hr" for h in horizons_hr] # Add horizon labels for model_job in region_models: - y_values = [ - calc_rank_corr(model_job.forecasts_v_moers, (h * 60) - 5) + # Calculate metrics with or without CIs + results = [ + calc_rank_corr(model_job, (h * 60) - 5, ci=ci) for h in horizons_hr ] + + if ci: + # Extract values and CIs from dict results + y_values = [r["rank_corr"] for r in results] + ci_lower = [r["rank_corr_ci_lower"] for r in results] + ci_upper = [r["rank_corr_ci_upper"] for r in results] + + # Calculate error bar arrays (distance from point estimate) + error_minus = [y - lower if not np.isnan(lower) else 0 for y, lower in zip(y_values, ci_lower)] + error_plus = [upper - y if not np.isnan(upper) else 0 for y, upper in zip(y_values, ci_upper)] + + # Update y_max/y_min to include CI bounds + y_max = max([u for u in ci_upper if not np.isnan(u)] + [y_max]) + y_min = min([l for l in ci_lower if not np.isnan(l)] + [y_min]) + else: + # Scalar results + y_values = results + error_minus = None + error_plus = None + y_max = max(y_values + [y_max]) y_min = min(y_values + [y_min]) + error_y_config = dict( + type="data", + symmetric=False, + array=error_plus, + arrayminus=error_minus, + ) if ci else None + fig.add_trace( go.Bar( x=x_values, @@ -736,6 +1089,7 @@ def plot_rank_corr( name=model_job.model_date, text=[f"{y:.3f}" for y in y_values], # Add text labels on bars textposition="outside", + error_y=error_y_config, ) ) @@ -753,7 +1107,7 @@ def plot_rank_corr( for region, fig in figs.items(): # Set uniform y-axis range for all subplots figs[region] = fig.update_yaxes( - range=[y_min - (0.25 * y_max), y_max + (0.25 * y_max)] + range=[y_min - (0.25 * abs(y_max - y_min)), y_max + (0.25 * abs(y_max - y_min))] ) return figs @@ -831,7 +1185,7 @@ def plot_impact_forecast_metrics( _metrics = [ { **calc_rank_compare_metrics( - model_job.forecasts_v_moers, **AER_SCENARIOS[s], + model_job, **AER_SCENARIOS[s], ), "scenario": s, } @@ -840,10 +1194,26 @@ def plot_impact_forecast_metrics( _metrics = pd.DataFrame(_metrics) + # Calculate error bar values for potential (symmetric CIs) + potential_error = [ + [ + _metrics["potential"].iloc[i] - _metrics["potential_ci_lower"].iloc[i], + _metrics["potential_ci_upper"].iloc[i] - _metrics["potential"].iloc[i], + ] + for i in range(len(_metrics)) + ] + potential_error_symmetric = list(zip(*potential_error)) + fig.add_trace( go.Bar( x=_metrics["scenario"], y=_metrics["potential"], + error_y=dict( + type="data", + symmetric=False, + array=potential_error_symmetric[1], # upper errors + arrayminus=potential_error_symmetric[0], # lower errors + ), name=f"CO2 Potential Savings", # Legend only in the first subplot marker=dict( color="rgba(200, 200, 200, 0.8)" @@ -858,10 +1228,26 @@ def plot_impact_forecast_metrics( col=1, ) + # Calculate error bar values for reduction + reduction_error = [ + [ + _metrics["reduction"].iloc[i] - _metrics["reduction_ci_lower"].iloc[i], + _metrics["reduction_ci_upper"].iloc[i] - _metrics["reduction"].iloc[i], + ] + for i in range(len(_metrics)) + ] + reduction_error_symmetric = list(zip(*reduction_error)) + fig.add_trace( go.Bar( x=_metrics["scenario"], y=_metrics["reduction"], + error_y=dict( + type="data", + symmetric=False, + array=reduction_error_symmetric[1], # upper errors + arrayminus=reduction_error_symmetric[0], # lower errors + ), name="Forecast Achieved", text=[ f"{(r / (p + 1e-6)) * 100:.1f}%" From 18887d6d38b9fce05976fc60c71b869b6bf06e93 Mon Sep 17 00:00:00 2001 From: sam_watttime Date: Thu, 30 Oct 2025 13:44:21 -0600 Subject: [PATCH 84/85] use SE instead of bootstrapping, more simulate_charge logic cleanup allowing different forecast_pull frequencies --- watttime/evaluation/report.py | 608 ++++++++++++++++++++++------------ 1 file changed, 404 insertions(+), 204 deletions(-) diff --git a/watttime/evaluation/report.py b/watttime/evaluation/report.py index c0059810..a9447122 100644 --- a/watttime/evaluation/report.py +++ b/watttime/evaluation/report.py @@ -356,42 +356,41 @@ def calc_norm_mae( if not ci: return norm_mae - effective_forecast_samp_rate = data_handler.effective_forecast_sample_rate + # Calculate per-forecast normalized MAE for confidence intervals df_with_date = filtered_df.reset_index() df_with_date["date"] = df_with_date["generated_at"].dt.normalize() - daily_errors = df_with_date.groupby("date", as_index=False).apply( - lambda g: pd.Series({ - "abs_error_sum": (g[truth_col] - g[pred_col]).abs().sum(), - "truth_sum": g[truth_col].sum(), - "count": len(g), - }), + # Group by date and calculate daily normalized MAE + daily_mae = df_with_date.groupby("date").apply( + lambda g: (g[truth_col] - g[pred_col]).abs().sum() / g[truth_col].sum() * 100.0 if g[truth_col].sum() > 0 else np.nan, include_groups=False ) + daily_mae = daily_mae.dropna() - def calc_mae_metric(daily_df): - total_abs_error = daily_df["abs_error_sum"].sum() - total_truth = daily_df["truth_sum"].sum() - return {"norm_mae": (total_abs_error / total_truth) * 100.0 if total_truth > 0 else 0.0} - - try: - ci_dict = bootstrap_confidence_interval( - df_with_date=daily_errors, - metric_func=calc_mae_metric, - effective_forecast_samp_rate=effective_forecast_samp_rate, - ) - return { - "norm_mae": norm_mae, - "norm_mae_ci_lower": round(ci_dict["norm_mae_ci_lower"], 1), - "norm_mae_ci_upper": round(ci_dict["norm_mae_ci_upper"], 1), - } - except ValueError: - # Not enough data for bootstrap + n = len(daily_mae) + if n < 2: return { "norm_mae": norm_mae, "norm_mae_ci_lower": np.nan, "norm_mae_ci_upper": np.nan, } + + # Calculate 95% CI using t-distribution + # Use the module-level `stats` import to avoid creating a local binding that + # interferes with nested functions (see NameError closure issues). + mae_se = daily_mae.sem() + t_critical = stats.t.ppf(0.975, df=n-1) + + # Note: using daily_mae.mean() instead of norm_mae for consistency with CI calculation + mae_mean = daily_mae.mean() + mae_ci_lower = mae_mean - (t_critical * mae_se) + mae_ci_upper = mae_mean + (t_critical * mae_se) + + return { + "norm_mae": round(mae_mean, 1), + "norm_mae_ci_lower": round(mae_ci_lower, 1), + "norm_mae_ci_upper": round(mae_ci_upper, 1), + } def calc_rank_corr( @@ -439,38 +438,38 @@ def safe_spearmanr(group_df): if not ci: return rank_corr - # block bootstrap + # Calculate per-day correlation for confidence intervals df_reset = corr_by_forecast.reset_index() df_reset.columns = ["generated_at", "correlation"] df_reset = df_reset.dropna() df_reset["date"] = df_reset["generated_at"].dt.normalize() # Group by date and average correlations within each day - daily_corr = df_reset.groupby("date")["correlation"].mean().reset_index() - - effective_forecast_samp_rate = data_handler.effective_forecast_sample_rate + daily_corr = df_reset.groupby("date")["correlation"].mean() - def calc_corr_metric(daily_df): - return {"rank_corr": daily_df["correlation"].mean()} - - try: - ci_dict = bootstrap_confidence_interval( - df_with_date=daily_corr, - metric_func=calc_corr_metric, - effective_forecast_samp_rate=effective_forecast_samp_rate, - ) - return { - "rank_corr": rank_corr, - "rank_corr_ci_lower": round(ci_dict["rank_corr_ci_lower"], 3), - "rank_corr_ci_upper": round(ci_dict["rank_corr_ci_upper"], 3), - } - except ValueError: - # Not enough data for bootstrap + n = len(daily_corr) + if n < 2: return { "rank_corr": rank_corr, "rank_corr_ci_lower": np.nan, "rank_corr_ci_upper": np.nan, } + + # Calculate 95% CI using t-distribution + # Use the module-level `stats` import to avoid creating a local binding + corr_se = daily_corr.sem() + t_critical = stats.t.ppf(0.975, df=n-1) + + # Note: using daily_corr.mean() instead of rank_corr for consistency with CI calculation + corr_mean = daily_corr.mean() + corr_ci_lower = corr_mean - (t_critical * corr_se) + corr_ci_upper = corr_mean + (t_critical * corr_se) + + return { + "rank_corr": round(corr_mean, 3), + "rank_corr_ci_lower": round(corr_ci_lower, 3), + "rank_corr_ci_upper": round(corr_ci_upper, 3), + } def bootstrap_confidence_interval( @@ -548,30 +547,40 @@ def bootstrap_confidence_interval( def pick_k_optimal_charge(in_df: pd.DataFrame, truth_col, charge_mins: int): + """ + Select the k optimal charging periods (lowest truth_col values) within each window. + + This represents the theoretical best-case scenario where we have perfect knowledge + of future values and can select the optimal charging times. + + Parameters: + in_df: DataFrame with window_start column and point_time as index (or in MultiIndex) + truth_col: Column name containing the actual signal values + charge_mins: Total minutes of charging needed per window + + Returns: + Series of boolean charge_status values indicating optimal charge periods + """ df = in_df.copy() - specify_generated_at = "generated_at" in df.index.names - charge_needed = charge_mins // 5 df["charge_status"] = False df = df.sort_index(ascending=True) for window_start, w_df in df.reset_index().groupby("window_start"): # Pick the `charge_needed` lowest truth_col values in this window - # If there are ties, 'first' ensures deterministic selection - if specify_generated_at: - w_df = w_df[w_df["generated_at"] == w_df["point_time"]] + # If there are ties, 'first' ensures deterministic selection selected_points = w_df.sort_values(truth_col, ascending=True).head( charge_needed ) # Mark these point_times as charge periods in the original dataframe - if specify_generated_at: - df.loc[ - [(p, p) for p in selected_points["point_time"]], "charge_status" - ] = True - else: + # Handle both simple index (point_time) and MultiIndex (generated_at, point_time) + try: df.loc[selected_points["point_time"], "charge_status"] = True + except KeyError: + # MultiIndex case - use the full index from selected_points + df.loc[selected_points.set_index(['generated_at', 'point_time']).index, "charge_status"] = True return df["charge_status"] @@ -586,14 +595,20 @@ def simulate_charge( """ Simulate a greedy charging strategy that makes sequential decisions at each forecast time. - At each decision point (generated_at), determines whether to charge immediately based on: - 1. Current forecast's prediction for "now" - 2. How that prediction ranks among all forecasted values + At each decision point (generated_at), determines whether to charge based on: + 1. The forecast's predictions for future point_times + 2. How those predictions rank among all forecasted values 3. Remaining opportunities to charge before window ends The algorithm processes forecasts chronologically and makes irrevocable charging decisions without knowledge of future forecasts (causally valid simulation). + The function automatically detects the forecast frequency (interval between generated_at times) + and determines how many 5-minute periods each forecast can schedule. For example: + - 5-minute frequency: each forecast schedules 1 period + - 30-minute frequency: each forecast schedules up to 6 periods + - 60-minute frequency: each forecast schedules up to 12 periods + Parameters: in_df: DataFrame with MultiIndex (generated_at, point_time) sort_col: Column name to use for ranking (e.g., 'predicted_value') @@ -607,88 +622,157 @@ def simulate_charge( Algorithm: For each window and each generated_at time T: - 1. Get current forecast value for time T - 2. Count how many forecasted values are better: - - Without discounting: simple count of values < current - - With discounting: weighted count using exp(-discount * hours_ahead) - 3. Charge if: (better_count <= remaining_charge_needed) OR (running_out_of_time) - 4. Continue to next decision point + 1. Determine how many periods this forecast can schedule (based on forecast frequency) + 2. From the available forecasted periods, select the best N periods + 3. Count how many forecasted values are better (with optional discounting) + 4. Decide whether to schedule charging for the selected periods + 5. Continue to next decision point """ df = in_df.copy() assert sort_col in df.columns - charge_needed = charge_mins // 5 + charge_needed = charge_mins // 5 # Total number of 5-min periods needed df = df.assign(charge_status=False) df = df.sort_index(ascending=True) for w in df.reset_index().groupby("window_start"): window_start, w_df = w w_df = w_df.sort_values(["generated_at", "point_time"], ascending=True) + + # Detect forecast frequency by looking at time between consecutive generated_at values + unique_generated_at = sorted(w_df["generated_at"].unique()) + if len(unique_generated_at) > 1: + # Calculate median interval to be robust to missing forecasts + intervals = pd.Series(unique_generated_at).diff().dropna() + forecast_freq_minutes = int(intervals.median().total_seconds() / 60) + else: + # Only one forecast, assume 5-minute frequency + forecast_freq_minutes = 5 + + # Calculate how many 5-minute periods each forecast can schedule + periods_per_forecast = max(1, forecast_freq_minutes // 5) + charge_periods = [] + scheduled_periods = set() # Track which periods are already scheduled _charge_needed = charge_needed generated_at_groups = list(w_df.groupby("generated_at")) total_groups = len(generated_at_groups) for processed_count, (generated_at, g_df) in enumerate(generated_at_groups): - current_value = g_df.iloc[0][sort_col] - current_time = generated_at + if _charge_needed == 0: + break + + # Filter to schedulable periods: not already scheduled, within [generated_at, generated_at + forecast_freq) + # This represents the "decision window" - we can only schedule for the next forecast period + g_df_reset = g_df.reset_index() + next_generated_at = generated_at + pd.Timedelta(minutes=forecast_freq_minutes) + schedulable_mask = ( + ~g_df_reset["point_time"].isin(scheduled_periods) & + (g_df_reset["point_time"] >= generated_at) & + (g_df_reset["point_time"] < next_generated_at) + ) + schedulable_df = g_df_reset[schedulable_mask] + + if len(schedulable_df) == 0: + continue + + # Determine how many periods to schedule in this decision + # Can schedule up to periods_per_forecast, but limited by remaining need and available periods + max_to_schedule = min(periods_per_forecast, _charge_needed, len(schedulable_df)) + + # Apply discounting if specified if discount_factor > 0: - # Apply time-based discounting to future predictions - g_df_reset = g_df.reset_index() - hours_ahead = (g_df_reset["point_time"] - current_time).dt.total_seconds() / 3600 + hours_ahead = (schedulable_df["point_time"] - generated_at).dt.total_seconds() / 3600 weights = np.exp(-discount_factor * hours_ahead / discount_horizon_hours) - better_mask = g_df[sort_col] < current_value - n_below_now = (better_mask * weights).sum() + schedulable_df = schedulable_df.copy() + schedulable_df["weighted_value"] = schedulable_df[sort_col] / weights + sort_column = "weighted_value" + else: + sort_column = sort_col + + # Select the best max_to_schedule periods from schedulable periods + best_periods = schedulable_df.nsmallest(max_to_schedule, sort_column) + + # Evaluate: Count how many periods in the ENTIRE forecast (all of g_df) are better than worst selected + # This gives us the ranking quality without the scheduling constraint + worst_selected_value = best_periods[sort_col].max() + + if discount_factor > 0: + # For discounting, evaluate against all forecasted periods with weights + all_future_mask = g_df_reset["point_time"] > best_periods["point_time"].max() + all_future_df = g_df_reset[all_future_mask] + if len(all_future_df) > 0: + hours_ahead_all = (all_future_df["point_time"] - generated_at).dt.total_seconds() / 3600 + weights_all = np.exp(-discount_factor * hours_ahead_all / discount_horizon_hours) + better_mask = all_future_df[sort_col] < worst_selected_value + n_below_worst = (better_mask * weights_all).sum() + else: + n_below_worst = 0 else: - n_below_now = (g_df[sort_col] < current_value).sum() + # Simple count: how many periods in the entire forecast are better than worst selected + all_future_mask = g_df_reset["point_time"] > best_periods["point_time"].max() + n_below_worst = (g_df_reset[all_future_mask][sort_col] < worst_selected_value).sum() - remaining_generated_at = total_groups - processed_count - should_charge_now = (n_below_now <= _charge_needed) or ( - remaining_generated_at <= _charge_needed + remaining_generated_at = total_groups - processed_count - 1 + remaining_capacity = remaining_generated_at * periods_per_forecast + should_charge_now = (n_below_worst <= _charge_needed) or ( + remaining_capacity <= _charge_needed ) if should_charge_now: - - if generated_at != g_df.iloc[0]["point_time"]: - warnings.warn( - "The generated_at and first point_time for a forecast do not match, look closely to understand why!", - RuntimeWarning, - ) - continue - - charge_periods.append((generated_at, generated_at)) - _charge_needed -= 1 - - if _charge_needed == 0: - break - + # Schedule charging for the selected periods + for _, row in best_periods.iterrows(): + pt = row["point_time"] + charge_periods.append((generated_at, pt)) + scheduled_periods.add(pt) + _charge_needed -= 1 + + # Mark the scheduled periods in the dataframe df.loc[charge_periods, "charge_status"] = True - # Compute the feasible number of charge periods given available generated_at groups per window - # We can decide at most once per generated_at (we charge at the forecast's first point_time), - # so the upper bound per-window is min(charge_needed, n_generated_at_in_window). - window_group_counts = ( - df.reset_index().groupby("window_start")["generated_at"].nunique() - ) - expected_total_needed = int( - sum(min(charge_needed, c) for c in window_group_counts.values) - ) - + # Validation: check that we achieved reasonable charging coverage charged_total = int(df["charge_status"].sum()) + + # Calculate expected capacity based on forecast frequency + window_group_counts = df.reset_index().groupby("window_start")["generated_at"].nunique() + + # For each window, calculate its forecast frequency and expected capacity + expected_total_needed = 0 + for window_start, n_forecasts in window_group_counts.items(): + # Get forecasts for this window + window_mask = df.reset_index()["window_start"] == window_start + window_df = df.reset_index()[window_mask] + unique_gen_at = sorted(window_df["generated_at"].unique()) + + if len(unique_gen_at) > 1: + intervals = pd.Series(unique_gen_at).diff().dropna() + freq_mins = int(intervals.median().total_seconds() / 60) + else: + freq_mins = 5 + + periods_per_fc = max(1, freq_mins // 5) + window_capacity = n_forecasts * periods_per_fc + expected_total_needed += min(charge_needed, window_capacity) - # Upper bound: cannot exceed feasible slots + # Upper bound: cannot exceed expected capacity (with small buffer for edge cases) assert ( - charged_total <= expected_total_needed - ), "Charge status is too high, check the logic for simulate_charge" - - # Lower bound: be within 3% of feasible slots (or equal when small counts) + charged_total <= expected_total_needed + (len(window_group_counts) * 2) + ), f"Charge status is too high ({charged_total} > {expected_total_needed}), check the logic for simulate_charge" + + # Lower bound: be within 10% of expected (relaxed to account for greedy scheduling constraints) + # The greedy algorithm may not perfectly achieve theoretical capacity due to: + # - Period conflicts from overlapping forecast coverage + # - Suboptimal early decisions in the greedy algorithm + # - Edge effects at window boundaries + # - Timing misalignments between forecast pulls and optimal charge periods if expected_total_needed > 0: + min_acceptable = int(np.floor(0.90 * expected_total_needed)) assert ( - charged_total >= int(np.floor(0.97 * expected_total_needed)) - ), "Charge status is too low, check the logic for simulate_charge" + charged_total >= min_acceptable + ), f"Charge status is too low ({charged_total} < {min_acceptable}, which is 90% of {expected_total_needed}), check the logic for simulate_charge" return df["charge_status"] @@ -783,18 +867,47 @@ def calc_rank_compare_metrics( pred_col="predicted_value", truth_col="signal_value", load_kw=1000, + forecast_pull_mins=30, ): + """ + Calculate emissions reduction metrics comparing forecast-based charging to baseline. + + Parameters: + data_handler: AnalysisDataHandler with forecasts_v_moers data + charge_mins: Total minutes of charging needed per window + window_mins: Window duration in minutes + window_start_time: Optional time string (HH:MM) for daily window start + pred_col: Column name for forecast predictions + truth_col: Column name for ground truth values + load_kw: Load in kilowatts + forecast_pull_mins: Forecast pull frequency in minutes (default 30) + Controls how often forecasts are retrieved. + E.g., 30 means forecasts pulled every 30 minutes. + + Returns: + dict: Reduction and potential metrics with confidence intervals + """ in_df = data_handler.forecasts_v_moers df = in_df.copy() if isinstance(window_mins, int): window_mins = pd.Timedelta(minutes=window_mins) + + forecast_pull_delta = pd.Timedelta(minutes=forecast_pull_mins) effective_forecast_samp_rate = data_handler.effective_forecast_sample_rate + # reset index before filtering + df = df.reset_index() + + # Filter generated_at times to match the forecast_pull frequency BEFORE assigning windows + # This significantly reduces the volume of data that needs window assignment + # Keep only forecasts where generated_at minute aligns with the forecast pull frequency + # e.g., if forecast_pull_mins=30, keep forecasts at :00 and :30 minutes + df = df[df["generated_at"].dt.minute % forecast_pull_mins == 0] window_starts = assign_windows( - df.index.get_level_values("generated_at"), window_mins, window_start_time + df["generated_at"], window_mins, window_start_time ) df = df.assign(window_start=window_starts) @@ -808,82 +921,143 @@ def calc_rank_compare_metrics( df = df.assign(window_end=window_ends) - # reset index before filtering - df = df.reset_index() - # Filter out rows that do not fall within the valid window - df.loc[ - (df["point_time"] >= df["window_end"]) - | (df["generated_at"] >= df["window_end"]) - | (df["generated_at"] < df["window_start"]), - "window_start", - ] = pd.NaT - df = df.dropna(subset=["window_start", "window_end"]) + # Instead of using .loc to set values then dropna, directly filter the dataframe + # This is much faster as it avoids the expensive .loc assignment operation + valid_mask = ( + (df["point_time"] < df["window_end"]) + & (df["generated_at"] < df["window_end"]) + & (df["generated_at"] >= df["window_start"]) + ) + df = df[valid_mask] - # Filter out windows where we don't have generated_ats covering the full window duration - expected_gen_ats = window_mins // pd.Timedelta(minutes=5) + # Filter out windows where we don't have enough generated_ats to cover the full window duration + # With the new forecast_pull frequency, we need fewer forecasts + expected_gen_ats = int(np.ceil(window_mins / forecast_pull_delta)) window_gen_at_counts = df.groupby("window_start")["generated_at"].nunique() - valid_windows = window_gen_at_counts[window_gen_at_counts >= expected_gen_ats].index + # Allow some tolerance (90% of expected) + min_required_gen_ats = max(1, int(np.floor(0.9 * expected_gen_ats))) + valid_windows = window_gen_at_counts[window_gen_at_counts >= min_required_gen_ats].index df = df[df["window_start"].isin(valid_windows)] # Filter out rows where generated_at != first point_time in the window - # e.g. we pulled an incomplete forecast (not sure why this would happen, but appe) - df = df.loc[ - df['generated_at'] - == df.groupby('generated_at')["point_time"].transform("first") - ] + # e.g. we pulled an incomplete forecast (not sure why this would happen, but safety check) + # Use direct boolean indexing instead of .loc for better performance + first_point_times = df.groupby('generated_at')["point_time"].transform("first") + df = df[df['generated_at'] == first_point_times] df = df.set_index(['generated_at', 'point_time']).sort_index(ascending=True) + # For pick_k_optimal_charge, we need ALL nowcast values (every 5 minutes), not just those from filtered forecasts + # Go back to the ORIGINAL unfiltered data to get complete nowcast coverage + df_for_optimal = in_df.reset_index() + df_for_optimal = df_for_optimal[df_for_optimal['generated_at'] == df_for_optimal['point_time']] + + # Apply window assignment + window_starts_optimal = assign_windows( + df_for_optimal["point_time"], window_mins, window_start_time + ) + df_for_optimal = df_for_optimal.assign(window_start=window_starts_optimal) + + # Keep only windows that have forecasts (from valid_windows), but DON'T validate + # based on nowcast count - we need ALL available nowcast data for baseline/optimal + df_for_optimal = df_for_optimal[df_for_optimal["window_start"].isin(valid_windows)] + + # NOW validate that each window has enough nowcast data for baseline calculation + # We need at least charge_mins // 5 periods per window + min_periods_needed = charge_mins // 5 + nowcast_counts = df_for_optimal.groupby('window_start').size() + windows_with_enough_data = nowcast_counts[nowcast_counts >= min_periods_needed].index + df_for_optimal = df_for_optimal[df_for_optimal["window_start"].isin(windows_with_enough_data)] + + # Update valid_windows to only include windows that have both forecasts AND enough nowcast data + valid_windows = windows_with_enough_data + df = df.reset_index() + df = df[df["window_start"].isin(valid_windows)] + df = df.set_index(['generated_at', 'point_time']).sort_index(ascending=True) + + df_for_optimal = df_for_optimal[['point_time', truth_col, 'window_start']].copy() + df_for_optimal = df_for_optimal.drop_duplicates(subset=['point_time'], keep='first').set_index('point_time').sort_index() + + # Calculate optimal charge status and emissions on the optimal dataset + df_for_optimal['charge_status'] = pick_k_optimal_charge(df_for_optimal, truth_col, charge_mins) + df_for_optimal['truth_charge_emissions'] = ( + df_for_optimal[truth_col] + * df_for_optimal['charge_status'] + * (load_kw / 1000) + * (5 / 60) + ) + + # Calculate baseline: immediate charging on the same optimal dataset + # This ensures baseline uses the same complete set of point_times as optimal + df_for_optimal = df_for_optimal.sort_index() + df_for_optimal['sequential_rank'] = df_for_optimal.groupby('window_start').cumcount() + df_for_optimal['baseline_charge_status'] = df_for_optimal['sequential_rank'] < charge_mins // 5 + df_for_optimal['baseline_charge_emissions'] = ( + df_for_optimal[truth_col] + * df_for_optimal['baseline_charge_status'] + * (load_kw / 1000) + * (5 / 60) + ) + + # Aggregate optimal and baseline results by window + optimal_by_window = df_for_optimal.groupby('window_start').agg({ + 'charge_status': 'sum', + 'truth_charge_emissions': 'sum', + 'baseline_charge_status': 'sum', + 'baseline_charge_emissions': 'sum' + }).rename(columns={ + 'charge_status': 'truth_charge_count', + 'baseline_charge_status': 'baseline_charge_count' + }) + + # Calculate pred_charge_status on the forecast dataframe df = df.assign( - truth_charge_status=pick_k_optimal_charge(df, truth_col, charge_mins), pred_charge_status=simulate_charge(df, pred_col, charge_mins), ) df = df.assign( - truth_charge_emissions=df[truth_col] - * df["truth_charge_status"] - * (load_kw / 1000) - * (5 / 60), # normalize hourly MOER to 5-minute intervals, pred_charge_emissions=df[truth_col] * df["pred_charge_status"] * (load_kw / 1000) * (5 / 60), ) - # baseline: immediate charging rather than AER - df = df.sort_index() - df = df.assign(sequential_rank=df.groupby("window_start").cumcount()) - df = df.assign(baseline_charge_status=df["sequential_rank"] < charge_mins // 5) - df = df.assign( - baseline_charge_emissions=df[truth_col] - * df["baseline_charge_status"] - * (load_kw / 1000) - * (5 / 60) - ) - - assert ( - df["baseline_charge_status"].sum() - == (len(df["window_start"].unique())) * charge_mins // 5 - ) - assert ( - df["baseline_charge_status"].sum() - <= (len(df["window_start"].unique()) + 1) * charge_mins // 5 - ) - - # Calculate daily-level emissions to enable bootstrap confidence intervals - df_with_date = df.reset_index() - df_with_date["date"] = df_with_date["generated_at"].dt.normalize() + # Aggregate forecast results by window and merge with optimal/baseline results + by_window = df.groupby("window_start").agg({ + 'pred_charge_status': 'sum', + 'pred_charge_emissions': 'sum', + }).rename(columns={ + 'pred_charge_status': 'pred_charge_count', + }) - daily_emissions = df_with_date.groupby("date").agg({ - "truth_charge_emissions": "sum", - "pred_charge_emissions": "sum", - "baseline_charge_emissions": "sum", - }).reset_index() + by_window = by_window.join(optimal_by_window, how='inner') - n_days = len(daily_emissions) + # Validate that charge counts are correct for each window + expected_charge_count = charge_mins // 5 + for window_start, row in by_window.iterrows(): + assert row['baseline_charge_count'] == expected_charge_count, \ + f"Window {window_start}: baseline_charge_count={row['baseline_charge_count']} != {expected_charge_count}" + assert row['truth_charge_count'] == expected_charge_count, \ + f"Window {window_start}: truth_charge_count={row['truth_charge_count']} != {expected_charge_count}" + # pred_charge_count may be less due to forecast limitations, but should be close + assert row['pred_charge_count'] >= int(0.9 * expected_charge_count), \ + f"Window {window_start}: pred_charge_count={row['pred_charge_count']} < 90% of {expected_charge_count}" + assert row['pred_charge_count'] <= expected_charge_count + 2, \ + f"Window {window_start}: pred_charge_count={row['pred_charge_count']} > {expected_charge_count}" + - if n_days == 0: + # Validate sum of emissions follows order baseline >= pred >= truth + summed_window = by_window[['baseline_charge_emissions', 'pred_charge_emissions', 'truth_charge_emissions']].sum() + assert summed_window['baseline_charge_emissions'] >= summed_window['pred_charge_emissions'], \ + "Total baseline emissions should be >= total predicted emissions. Although this may be effected by forecast_pull_mins" + assert summed_window['pred_charge_emissions'] >= summed_window['truth_charge_emissions'], \ + "Total predicted emissions should be >= total truth (optimal) emissions" + + by_window = by_window.reset_index() + n = len(by_window) + + if n == 0: reduction = np.nan potential = np.nan reduction_ci_lower = np.nan @@ -892,39 +1066,37 @@ def calc_rank_compare_metrics( potential_ci_upper = np.nan else: # Calculate point estimates - y_best_emissions = daily_emissions["truth_charge_emissions"].sum() - y_pred_emissions = daily_emissions["pred_charge_emissions"].sum() - y_base_emissions = daily_emissions["baseline_charge_emissions"].sum() + y_best_emissions = by_window["truth_charge_emissions"].sum() + y_pred_emissions = by_window["pred_charge_emissions"].sum() + y_base_emissions = by_window["baseline_charge_emissions"].sum() assert y_pred_emissions >= y_best_emissions assert y_base_emissions >= y_best_emissions - reduction = (y_base_emissions - y_pred_emissions) / n_days - potential = (y_base_emissions - y_best_emissions) / n_days + reduction = (y_base_emissions - y_pred_emissions) / n + potential = (y_base_emissions - y_best_emissions) / n + + # Calculate confidence intervals using standard error (95% CI) + # Each window represents an independent sample + by_window['reduction_per_window'] = ( + by_window['baseline_charge_emissions'] - by_window['pred_charge_emissions'] + ) + by_window['potential_per_window'] = ( + by_window['baseline_charge_emissions'] - by_window['truth_charge_emissions'] + ) - # Bootstrap confidence intervals (95% CI) - # Use the general bootstrap function to calculate CIs - def calc_emissions_metrics(daily_df): - """Calculate reduction and potential from daily emissions DataFrame""" - base = daily_df["baseline_charge_emissions"].sum() - pred = daily_df["pred_charge_emissions"].sum() - best = daily_df["truth_charge_emissions"].sum() - - return { - "reduction": (base - pred) / n_days, - "potential": (base - best) / n_days, - } + # Standard error of the mean + reduction_se = by_window['reduction_per_window'].sem() + potential_se = by_window['potential_per_window'].sem() - ci_dict = bootstrap_confidence_interval( - df_with_date=daily_emissions, - metric_func=calc_emissions_metrics, - effective_forecast_samp_rate=effective_forecast_samp_rate, - ) + # 95% CI using t-distribution (more conservative for small samples) + # Use module-level `stats` to avoid creating a local binding + t_critical = stats.t.ppf(0.975, df=n-1) # 95% CI, two-tailed - reduction_ci_lower = ci_dict["reduction_ci_lower"] - reduction_ci_upper = ci_dict["reduction_ci_upper"] - potential_ci_lower = ci_dict["potential_ci_lower"] - potential_ci_upper = ci_dict["potential_ci_upper"] + reduction_ci_lower = reduction - (t_critical * reduction_se) + reduction_ci_upper = reduction + (t_critical * reduction_se) + potential_ci_lower = potential - (t_critical * potential_se) + potential_ci_upper = potential + (t_critical * potential_se) return { "reduction": round(reduction, 1), @@ -989,6 +1161,9 @@ def plot_norm_mae( symmetric=False, array=error_plus, arrayminus=error_minus, + width=20, + thickness=1, + color="rgba(0, 0, 0, 0.3)" # black with some transparency ) if ci else None fig.add_trace( @@ -996,11 +1171,11 @@ def plot_norm_mae( x=x_values, y=y_values, name=model_job.model_date, - text=[f"{y:.1f}%" for y in y_values], # Add text labels on bars + text=[f"{y:.1f}%" for y in y_values], textposition="outside", error_y=error_y_config, ) - ) + ) fig.update_layout( height=300, @@ -1080,6 +1255,9 @@ def plot_rank_corr( symmetric=False, array=error_plus, arrayminus=error_minus, + color="rgba(0, 0, 0, 0.3)", # black with some transparency + width=20, + thickness=1, ) if ci else None fig.add_trace( @@ -1087,7 +1265,7 @@ def plot_rank_corr( x=x_values, y=y_values, name=model_job.model_date, - text=[f"{y:.3f}" for y in y_values], # Add text labels on bars + text=[f"{y:.3f}" for y in y_values], textposition="outside", error_y=error_y_config, ) @@ -1119,37 +1297,44 @@ def plot_rank_corr( "window_mins": 8 * 60, "window_start_time": "19:00", "load_kw": 19, + "forecast_pull_mins": 30, }, "EV-day": { "charge_mins": 3 * 60, "window_mins": 8 * 60, "window_start_time": "09:00", "load_kw": 19, + "forecast_pull_mins": 30, }, "Thermostat": { "charge_mins": 30, "window_mins": 60, "load_kw": 3, # typical AC + "forecast_pull_mins": 30, }, "10 kW / 24hr / 25% Duty Cycle": { "charge_mins": int(24 * 60 * 0.25), "window_mins": 24 * 60, "load_kw": 10, + "forecast_pull_mins": 30, }, "10 kW / 72hr / 25% Duty Cycle": { "charge_mins": int(24 * 3 * 60 * 0.25), "window_mins": 24 * 3 * 60, "load_kw": 10, + "forecast_pull_mins": 30, }, "10 kW / 24hr / 50% Duty Cycle": { "charge_mins": int(24 * 60 * 0.5), "window_mins": 24 * 60, "load_kw": 10, + "forecast_pull_mins": 30, }, "10 kW / 72hr / 50% Duty Cycle": { "charge_mins": (int(24 * 3 * 60 * 0.5)), "window_mins": 24 * 3 * 60, "load_kw": 10, + "forecast_pull_mins": 30, }, } @@ -1158,12 +1343,12 @@ def plot_impact_forecast_metrics( factory: DataHandlerFactory, scenarios=[ "10 kW / 24hr / 25% Duty Cycle", - "10 kW / 72hr / 25% Duty Cycle", + # "10 kW / 72hr / 25% Duty Cycle", "10 kW / 24hr / 50% Duty Cycle", - "10 kW / 72hr / 50% Duty Cycle", + # "10 kW / 72hr / 50% Duty Cycle", "EV-night", "EV-day", - "Thermostat", + # "Thermostat", ], ): @@ -1194,7 +1379,8 @@ def plot_impact_forecast_metrics( _metrics = pd.DataFrame(_metrics) - # Calculate error bar values for potential (symmetric CIs) + # Calculate error bar values for potential + # CI values are absolute, so we need to compute distance from the mean potential_error = [ [ _metrics["potential"].iloc[i] - _metrics["potential_ci_lower"].iloc[i], @@ -1213,6 +1399,9 @@ def plot_impact_forecast_metrics( symmetric=False, array=potential_error_symmetric[1], # upper errors arrayminus=potential_error_symmetric[0], # lower errors + color="rgba(0, 0, 0, 0.25)", # black + width=20, + thickness=1, ), name=f"CO2 Potential Savings", # Legend only in the first subplot marker=dict( @@ -1223,12 +1412,15 @@ def plot_impact_forecast_metrics( showlegend=( model_ix == 1 ), # Show legend only for the first subplot + text=[f"{y:.1f} lbs" for y in _metrics["potential"]], + textposition="outside", ), row=model_ix, col=1, ) # Calculate error bar values for reduction + # CI values are absolute, so we need to compute distance from the mean reduction_error = [ [ _metrics["reduction"].iloc[i] - _metrics["reduction_ci_lower"].iloc[i], @@ -1238,6 +1430,12 @@ def plot_impact_forecast_metrics( ] reduction_error_symmetric = list(zip(*reduction_error)) + # Calculate text positions above error bars + text_labels = [ + f"{(r / (p + 1e-6)) * 100:.1f}%" + for r, p in zip(_metrics["reduction"], _metrics["potential"]) + ] + fig.add_trace( go.Bar( x=_metrics["scenario"], @@ -1247,16 +1445,16 @@ def plot_impact_forecast_metrics( symmetric=False, array=reduction_error_symmetric[1], # upper errors arrayminus=reduction_error_symmetric[0], # lower errors + color="rgba(0, 0, 0, 0.25)", # black for reduction + width=20, + thickness=1, ), name="Forecast Achieved", - text=[ - f"{(r / (p + 1e-6)) * 100:.1f}%" - for r, p in zip(_metrics["reduction"], _metrics["potential"]) - ], - textposition="outside", marker=dict(color="rgba(0, 128, 0, 0.8)"), # Green for reduction hovertemplate="%{x}: %{y:.1f} lbs CO2", legendgroup="Potential Savings", # Group legend for potential savings + text=text_labels, + textposition="outside", showlegend=( model_ix == 1 ), # Show legend only for the first subplot @@ -1378,6 +1576,7 @@ def calc_max_potential( window_start_time=None, truth_col="signal_value", load_kw=1000, + **kwargs ): """Predict the maximum potential CO2 Savings, without any forecast to estimate the upper limit of impact from a given signal value. This version is resilient to partial windows (e.g. where the data does not fully cover the expected number of 5-min intervals). @@ -1441,12 +1640,12 @@ def plot_max_impact_potential( factory: DataHandlerFactory, scenarios: List[str] = [ "10 kW / 24hr / 25% Duty Cycle", - "10 kW / 72hr / 25% Duty Cycle", + # "10 kW / 72hr / 25% Duty Cycle", "10 kW / 24hr / 50% Duty Cycle", - "10 kW / 72hr / 50% Duty Cycle", + # "10 kW / 72hr / 50% Duty Cycle", "EV-night", "EV-day", - "Thermostat", + # "Thermostat", ], ): figs = {} @@ -1875,7 +2074,7 @@ def plot_precision_recall( def plot_forecasts_vs_signal( factory: DataHandlerFactory, - horizons=["15min", "1h", "8h"], + horizons=["30min", "2h", "8h"], ) -> Dict[str, go.Figure]: """ Plot forecasts vs signal for different forecast pull frequencies. @@ -2005,7 +2204,7 @@ def plot_forecasts_vs_signal( signal = signal.loc[start_ts : end_ts + max(horizons)] fig.add_trace( go.Scatter( - x=signal, + x=signal.index, y=signal.values, mode="lines", name="Signal Value", @@ -2161,6 +2360,7 @@ def generate_report( output_dir: Path, steps: Literal["signal", "fuel_mix", "forecast"] = [ "signal", + "fuel_mix", "forecast", ], # no fuel_mix by default first_week_of_month_only: bool = False, From b0ba4b1f1f0f4a2cb78c9942839f50e2d343fef8 Mon Sep 17 00:00:00 2001 From: sam_watttime Date: Thu, 30 Oct 2025 13:44:33 -0600 Subject: [PATCH 85/85] bump default forecast_sample_size --- watttime/evaluation/get_wt_api_forecast_evaluation_data.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/watttime/evaluation/get_wt_api_forecast_evaluation_data.py b/watttime/evaluation/get_wt_api_forecast_evaluation_data.py index 29f26db3..674ed1c2 100644 --- a/watttime/evaluation/get_wt_api_forecast_evaluation_data.py +++ b/watttime/evaluation/get_wt_api_forecast_evaluation_data.py @@ -109,7 +109,7 @@ class AnalysisDataHandler: region: str eval_start: Union[datetime, str] eval_end: Union[datetime, str] - forecast_sample_size: Union[int, float] = 0.03 + forecast_sample_size: Union[int, float] = 0.1 forecast_max_horizon: int = 60 * 24 forecast_sample_seed: int = 42 forecast_data_horizon_days: int = 3