WIP: Signal Distribution and Forecast Accuracy Analysis Report

.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

setup.py

@@ -3,10 +3,12 @@
 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"]},
+    scripts=["watttime/report.py"],
 )

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)

watttime/__init__.py

@@ -1,2 +1,3 @@
 from watttime.api import *
-from watttime.tcy import TCYCalculator
+from watttime.tcy import TCYCalculator
+from watttime.report import ModelAnalysis