diff --git a/.devcontainer/Dockerfile b/.devcontainer/Dockerfile new file mode 100644 index 0000000..a52f9cd --- /dev/null +++ b/.devcontainer/Dockerfile @@ -0,0 +1,30 @@ +FROM mcr.microsoft.com/devcontainers/python:1-3.12-bullseye + +# Raise file descriptor limits +RUN echo "* soft nofile 65536\n* hard nofile 65536" >> /etc/security/limits.conf + +# Install just +RUN curl --proto '=https' --tlsv1.2 -sSf https://just.systems/install.sh | bash -s -- --to /usr/local/bin + +# Install Quarto CLI +RUN QUARTO_VERSION=1.6.42 && \ + curl -LO "https://github.com/quarto-dev/quarto-cli/releases/download/v${QUARTO_VERSION}/quarto-${QUARTO_VERSION}-linux-arm64.deb" && \ + dpkg -i "quarto-${QUARTO_VERSION}-linux-arm64.deb" && \ + rm "quarto-${QUARTO_VERSION}-linux-arm64.deb" + +# Install AWS CLI v2 +RUN curl "https://awscli.amazonaws.com/awscli-exe-linux-aarch64.zip" -o "awscliv2.zip" && \ + unzip awscliv2.zip && \ + ./aws/install && \ + rm -rf aws awscliv2.zip + +# Install Chromium (remove stale yarn repo first to unblock apt-get update) +RUN rm -f /etc/apt/sources.list.d/yarn.list && \ + apt-get update && \ + apt-get install -y chromium && \ + apt-get clean + +# Create workspaces directory expected by devcontainer +RUN mkdir -p /workspaces/smart-meter-analysis +RUN mkdir -p /opt/venv && chown vscode:vscode /opt/venv +WORKDIR /workspaces/smart-meter-analysis diff --git a/.devcontainer/devcontainer.json b/.devcontainer/devcontainer.json index 9c3b51d..756fd53 100644 --- a/.devcontainer/devcontainer.json +++ b/.devcontainer/devcontainer.json @@ -1,25 +1,11 @@ -// .devcontainer/devcontainer.json - { "name": "smart_meter_analysis", - "image": "mcr.microsoft.com/devcontainers/python:1-3.12-bullseye", - - // Install Quarto CLI and Chromium - "features": { - "ghcr.io/rocker-org/devcontainer-features/quarto-cli:1": { - "installChromium": true - }, - "ghcr.io/rocker-org/devcontainer-features/apt-packages:1": { - "packages": "chromium" - }, - "ghcr.io/guiyomh/features/just:0.1.0": { - "version": "1.40.0" - }, - "ghcr.io/devcontainers/features/aws-cli:1": { - "version": "2.27.4" - } + "workspaceFolder": "/workspaces/smart-meter-analysis", + "runArgs": ["--ulimit", "nofile=65536:65536"], + "build": { + "dockerfile": "Dockerfile", + "context": ".." }, - "initializeCommand": "mkdir -p ${localEnv:HOME}/.aws", "mounts": [ "source=${localEnv:HOME}/.aws,target=/home/vscode/.aws,type=bind,consistency=cached" @@ -27,15 +13,11 @@ "remoteEnv": { "AWS_REGION": "us-west-2" }, - - // Run after the container is created to install uv, create .venv, and install deps "postCreateCommand": "./.devcontainer/postCreateCommand.sh", - - // Tell Quarto to use the Python interpreter in the uv-managed virtual environment. "containerEnv": { - "QUARTO_PYTHON": "/workspaces/smart-meter-analysis/.venv/bin/python" + "UV_PROJECT_ENVIRONMENT": "/opt/venv", + "QUARTO_PYTHON": "/opt/venv/bin/python" }, - "customizations": { "vscode": { "extensions": [ @@ -48,12 +30,8 @@ "python.testing.pytestArgs": ["tests"], "python.testing.unittestEnabled": false, "python.testing.pytestEnabled": true, - - // Use the uv-created virtual environment - "python.defaultInterpreterPath": "/workspaces/smart-meter-analysis/.venv/bin/python", - "python.testing.pytestPath": "/workspaces/smart-meter-analysis/.venv/bin/pytest", - - // Let the Python extension auto-activate the environment in its own terminals + "python.defaultInterpreterPath": "/opt/venv/bin/python", + "python.testing.pytestPath": "/opt/venv/bin/pytest", "python.terminal.activateEnvironment": true } } diff --git a/.dockerignore b/.dockerignore new file mode 100644 index 0000000..96f3fda --- /dev/null +++ b/.dockerignore @@ -0,0 +1,10 @@ +data/ +archive/ +tmp_polars_*/ +.mypy_cache/ +.ruff_cache/ +.venv/ +__pycache__/ +*.parquet +*.csv +.git/ diff --git a/.gitignore b/.gitignore index 15eb7d1..042079b 100644 --- a/.gitignore +++ b/.gitignore @@ -18,7 +18,6 @@ dist/ downloads/ eggs/ .eggs/ -lib/ lib64/ parts/ sdist/ @@ -143,6 +142,7 @@ cython_debug/ *.xlsx *.csv *.parquet +*.geojson *.tsv *.kml *.zip @@ -156,7 +156,10 @@ data/ # Temporary files scratch/ *_sample_*.csv +# Ignore ad-hoc test scripts in the project root (test_foo.py scratch files) +# but keep real test files in the tests/ directory via negation pattern. test_*.py +!tests/test_*.py debug_*.py # Debug files @@ -166,9 +169,28 @@ archive/ # generated artifacts out/ +output/ results/ # benchmark artificats profiles/ docs/*.html docs/index_files/ + +# Local run artifacts (shard lists, input lists, temp dirs) +*.txt +.tmp/ +archive_quarantine/ +tmp_polars_run_*/ +subagent_packages/ + +# Operator-only env (do not commit) +.env.comed + +# Pricing pilot data +data/pilot_interval_parquet/ +CLAUDE.md +.cursor/ + +/.quarto/ +_manuscript/ diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 23d8811..6b277b4 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -13,10 +13,19 @@ repos: args: [--autofix, --no-sort-keys] - id: end-of-file-fixer - id: trailing-whitespace + - id: detect-private-key - repo: https://github.com/astral-sh/ruff-pre-commit - rev: "v0.11.5" + rev: "v0.14.4" hooks: - id: ruff args: [--exit-non-zero-on-fix] - id: ruff-format + + - repo: local + hooks: + - id: forbid-secrets + name: Block secrets and credential files + entry: "bash -c 'echo BLOCKED: secrets/credential file staged for commit >&2; exit 1'" + language: system + files: '(\.env$|\.env\.|\.secrets|\.secret|credentials\.json|\.pem$|\.key$|\.p12$|\.pfx$|\.jks$)' diff --git a/.python-version b/.python-version new file mode 100644 index 0000000..e4fba21 --- /dev/null +++ b/.python-version @@ -0,0 +1 @@ +3.12 diff --git a/.ruffignore b/.ruffignore index 6ce45d2..a012ec2 100644 --- a/.ruffignore +++ b/.ruffignore @@ -1,2 +1 @@ archive/ -archive/ diff --git a/.style/fonts/ft-bold.otf b/.style/fonts/ft-bold.otf new file mode 100644 index 0000000..28a7a2b Binary files /dev/null and b/.style/fonts/ft-bold.otf differ diff --git a/.style/fonts/ft-regular.otf b/.style/fonts/ft-regular.otf new file mode 100644 index 0000000..c418da5 Binary files /dev/null and b/.style/fonts/ft-regular.otf differ diff --git a/.style/fonts/gtp-black.otf b/.style/fonts/gtp-black.otf new file mode 100644 index 0000000..0f2988b Binary files /dev/null and b/.style/fonts/gtp-black.otf differ diff --git a/.style/fonts/gtp-bold.otf b/.style/fonts/gtp-bold.otf new file mode 100644 index 0000000..33653a9 Binary files /dev/null and b/.style/fonts/gtp-bold.otf differ diff --git a/.style/fonts/gtp-regular.otf b/.style/fonts/gtp-regular.otf new file mode 100644 index 0000000..73c1dc7 Binary files /dev/null and b/.style/fonts/gtp-regular.otf differ diff --git a/.style/fonts/ips-bold.otf b/.style/fonts/ips-bold.otf new file mode 100644 index 0000000..5ae5057 Binary files /dev/null and b/.style/fonts/ips-bold.otf differ diff --git a/.style/fonts/ips-regular.otf b/.style/fonts/ips-regular.otf new file mode 100644 index 0000000..51b38a2 Binary files /dev/null and b/.style/fonts/ips-regular.otf differ diff --git a/.style/inline_svgs.py b/.style/inline_svgs.py new file mode 100644 index 0000000..286316e --- /dev/null +++ b/.style/inline_svgs.py @@ -0,0 +1,244 @@ +"""Inline SVG figures into Quarto-rendered HTML files. + +Replaces tags with the raw markup so that inline +SVGs participate in the page's CSS cascade and the browser's text renderer +(with font hinting) handles chart text. + +Also patches lightbox links wrapping inlined SVGs so that clicking them +opens a modal showing a clone of the inline SVG (preserving page fonts), +rather than loading the external SVG file (which would lose fonts). + +Usage (from a report directory, after quarto render): + + uv run python ../.style/inline_svgs.py docs/ + +The script processes every .html file under the given directory. +""" + +from __future__ import annotations + +import re +import sys +from pathlib import Path + +# Lightweight modal for inline SVGs. Clones the SVG so page @font-face +# rules apply. Supports click-to-close, Escape, and left/right navigation +# between figures on the same page. +_SVG_LIGHTBOX_SCRIPT = """ + +' + + title.substring(0, colonIdx + 1) + '' + + '' + title.substring(colonIdx + 1) + ''; + } else { + cap.innerHTML = '' + title + ''; + } + overlay.appendChild(cap); + } + overlay.addEventListener('click', close); + document.body.appendChild(overlay); + } + + function close() { + if (overlay) { overlay.remove(); overlay = null; current = null; } + } + + function nav(dir) { + if (!current) return; + var i = ordered.indexOf(current) + dir; + if (i >= 0 && i < ordered.length) show(ordered[i]); + } + + document.addEventListener('keydown', function(e) { + if (!overlay) return; + if (e.key === 'Escape') close(); + else if (e.key === 'ArrowLeft') nav(-1); + else if (e.key === 'ArrowRight') nav(1); + }); + + links.forEach(function(a) { + a.addEventListener('click', function(e) { + e.preventDefault(); + e.stopPropagation(); + show(a); + }); + }); +})(); + +""" + + +def _read_svg_body(svg_path: Path) -> str: + """Read an SVG file and strip the XML declaration and DOCTYPE.""" + raw = svg_path.read_text(encoding="utf-8") + raw = re.sub(r"<\?xml[^?]*\?>", "", raw).strip() + raw = re.sub(r"]*>", "", raw).strip() + return raw + + +def _make_svg_fixed_width(svg_markup: str, classes: str) -> str: + """Modify the root element for fixed-width display. + + Uses the viewBox width (in pt, which maps 1:1 to CSS px) as the display + width so that matplotlib font sizes render at the intended pixel size + regardless of container width. ``max-width: 100%`` prevents overflow on + narrow viewports. The SVG is left-aligned in the column (no centering). + """ + vb_match = re.search(r'viewBox="([^"]*)"', svg_markup) + if vb_match: + parts = vb_match.group(1).split() + vb_width = parts[2] if len(parts) >= 3 else None + else: + vb_width = None + + def replace_svg_tag(m: re.Match[str]) -> str: + tag_content = m.group(1) + tag_content = re.sub(r'\bwidth="[^"]*"', "", tag_content) + tag_content = re.sub(r'\bheight="[^"]*"', "", tag_content) + tag_content = tag_content.strip() + if vb_width: + style = "max-width:100%;height:auto;display:block;margin:0;" + return f'' + return f'' + + return re.sub(r"]*)>", replace_svg_tag, svg_markup, count=1) + + +_IMG_SVG_RE = re.compile( + r']*/?>", + re.IGNORECASE, +) + + +def inline_svgs_in_html(html_path: Path) -> int: + """Replace with inline in a single HTML file. + + Also renames the wrapping lightbox class so GLightbox ignores these links, + and injects a lightweight modal script for SVG zoom. + + Returns the number of replacements made. + """ + html_text = html_path.read_text(encoding="utf-8") + html_dir = html_path.parent + count = 0 + + def _replace(m: re.Match[str]) -> str: + nonlocal count + src = m.group(1) + classes = m.group(2) or "" + svg_path = html_dir / src + if not svg_path.exists(): + return m.group(0) + svg_body = _read_svg_body(svg_path) + svg_body = _make_svg_fixed_width(svg_body, classes) + count += 1 + return svg_body + + new_html = _IMG_SVG_RE.sub(_replace, html_text) + + if count > 0: + # Rename lightbox class only on tags that now contain an inline + # (i.e. formerly had an that was replaced). + # GLightbox uses selector ".lightbox" — renaming to "lightbox-svg" + # keeps GLightbox from intercepting these clicks while leaving + # lightbox links wrapping untouched. + new_html = re.sub( + r'(class="lightbox )([^"]*"[^>]*>)\s*(", _SVG_LIGHTBOX_SCRIPT + "") + + html_path.write_text(new_html, encoding="utf-8") + return count + + +def main() -> None: + if len(sys.argv) != 2: + print(f"Usage: {sys.argv[0]} ", file=sys.stderr) + sys.exit(1) + + docs_dir = Path(sys.argv[1]) + if not docs_dir.is_dir(): + print(f"Error: {docs_dir} is not a directory", file=sys.stderr) + sys.exit(1) + + total = 0 + for html_file in sorted(docs_dir.rglob("*.html")): + n = inline_svgs_in_html(html_file) + if n > 0: + print(f" {html_file.relative_to(docs_dir)}: inlined {n} SVG(s)") + total += n + + # Remove external SVG figure files — they're now redundant since the + # SVGs are inlined in the HTML and the lightbox modal clones from there. + removed = 0 + for svg_file in sorted(docs_dir.rglob("figure-html/*.svg")): + svg_file.unlink() + removed += 1 + + print(f"Inlined {total} SVG(s), removed {removed} external SVG(s) in {docs_dir}") + + +if __name__ == "__main__": + main() diff --git a/.style/notebook-preview.html b/.style/notebook-preview.html new file mode 100644 index 0000000..a9c5357 --- /dev/null +++ b/.style/notebook-preview.html @@ -0,0 +1,40 @@ + + + + + + + + + diff --git a/.style/switchbox.html b/.style/switchbox.html new file mode 100644 index 0000000..f5c0d09 --- /dev/null +++ b/.style/switchbox.html @@ -0,0 +1,62 @@ + diff --git a/.style/switchbox.scss b/.style/switchbox.scss new file mode 100644 index 0000000..1be58f3 --- /dev/null +++ b/.style/switchbox.scss @@ -0,0 +1,507 @@ +/*-- scss:defaults --*/ + +/* switchbox colors */ +$sky: #68bed8; +$carrot: #fc9706; +$midnight: #023047; +$midnight_lighter: #0b6082; +$saffron: #ffc729; +$pistachio: #a0af12; +$pistachio_darker: #546800; +$black: #000000; +$grey: #a9aaae; +$white: #ffffff; + +$link-color: $midnight_lighter !default; +$link-decoration: underline !default; + +$h1-font-size: 2.5em; +$h2-font-size: 1em; +$h3-font-size: 1em; + +// The left hand sidebar +$grid-sidebar-width: 300px !default; + +// The main body +$grid-body-width: 750px !default; + +// The right hand margin bar +$grid-margin-width: 300px !default; + +// The gutter that appears between the above columns +$width: screen and + ( + min-width: 768px, + ); + +$grid-column-gutter-width: 5rem; + +@if $width == true { + $grid-column-gutter-width: 1rem; +} + +/*-- scss:rules --*/ + +/* farnham_text */ +@font-face { + font-display: swap; + font-family: "Farnham"; + font-style: normal; + src: url("https://switchbox-data.github.io/reports/fonts/farnham_text/FarnhamText-Regular.otf") + format("opentype"), + url("https://switchbox-data.github.io/reports/fonts/farnham_text/FarnhamText-Regular.otf") format("opentype"); +} + +/* farnham_text_bold */ +@font-face { + font-display: swap; + font-family: "Farnham-Bold"; + font-style: normal; + src: url("https://switchbox-data.github.io/reports/fonts/farnham_text/FarnhamText-Bold.otf") format("opentype"), + url("https://switchbox-data.github.io/reports/fonts/farnham_text/FarnhamText-Bold.otf") format("opentype"); +} + +/* gt_planar */ +@font-face { + font-display: swap; + font-family: "GT-Planar"; + font-style: normal; + src: url("https://switchbox-data.github.io/reports/fonts/gt_planar/GT-Planar-Regular.otf") format("opentype"), + url("https://switchbox-data.github.io/reports/fonts/gt_planar/GT-Planar-Regular.otf") format("opentype"); +} + +/* gt_planar_bold */ +@font-face { + font-display: swap; + font-family: "GT-Planar-Bold"; + src: url("https://switchbox-data.github.io/reports/fonts/gt_planar/GT-Planar-Bold.otf") format("opentype"), + url("https://switchbox-data.github.io/reports/fonts/gt_planar/GT-Planar-Bold.otf") format("opentype"); +} + +/* gt_planar_black */ +@font-face { + font-display: swap; + font-family: "GT-Planar-Black"; + src: url("https://switchbox-data.github.io/reports/fonts/gt_planar/GT-Planar-Black.otf") format("opentype"), + url("https://switchbox-data.github.io/reports/fonts/gt_planar/GT-Planar-Black.otf") format("opentype"); +} + +/* ibm_plex_sans */ +@font-face { + font-display: swap; + font-family: "IBM-Plex-Sans"; + src: url("https://switchbox-data.github.io/reports/fonts/ibm_plex_sans/IBMPlexSans-Regular.otf") + format("opentype"), + url("https://switchbox-data.github.io/reports/fonts/ibm_plex_sans/IBMPlexSans-Regular.otf") + format("opentype"); +} + +/* sf_mono */ +@font-face { + font-display: swap; + font-family: "SFMono"; + font-style: normal; + font-weight: 400; + src: url("https://switchbox-data.github.io/reports/fonts/sf_mono/SFMono-Regular.otf") format("opentype"), + url("https://switchbox-data.github.io/reports/fonts/sf_mono/SFMono-Regular.otf") format("opentype"); +} + +/* Matplotlib SVG font aliases — match the internal OTF names that matplotlib + outputs in elements when svg.fonttype = "none". These allow inline + SVG chart text to resolve to the correct Switchbox brand fonts via the + page's CSS cascade. */ +@font-face { + font-display: swap; + font-family: "IBM Plex Sans"; + font-style: normal; + font-weight: 400; + src: url("https://switchbox-data.github.io/reports/fonts/ibm_plex_sans/IBMPlexSans-Regular.otf") + format("opentype"); +} +@font-face { + font-display: swap; + font-family: "IBM Plex Sans"; + font-style: normal; + font-weight: 700; + src: url("https://switchbox-data.github.io/reports/fonts/ibm_plex_sans/IBMPlexSans-Bold.otf") + format("opentype"); +} +@font-face { + font-display: swap; + font-family: "GT Planar"; + font-style: normal; + font-weight: 400; + src: url("https://switchbox-data.github.io/reports/fonts/gt_planar/GT-Planar-Regular.otf") + format("opentype"); +} +@font-face { + font-display: swap; + font-family: "GT Planar"; + font-style: normal; + font-weight: 700; + src: url("https://switchbox-data.github.io/reports/fonts/gt_planar/GT-Planar-Bold.otf") + format("opentype"); +} +@font-face { + font-display: swap; + font-family: "Farnham Text"; + font-style: normal; + font-weight: 400; + src: url("https://switchbox-data.github.io/reports/fonts/farnham_text/FarnhamText-Regular.otf") + format("opentype"); +} +@font-face { + font-display: swap; + font-family: "Farnham Text"; + font-style: normal; + font-weight: 700; + src: url("https://switchbox-data.github.io/reports/fonts/farnham_text/FarnhamText-Bold.otf") + format("opentype"); +} + +sup { + color: $carrot; + font-family: "GT-Planar-Black"; + font-size: 13px; +} + +main { + line-height: 1.7; + + a { + text-decoration-color: $sky; + } + + h1 { + font-family: "GT-Planar-Bold"; + } + + h2 { + font-family: "GT-Planar-Black"; + text-transform: uppercase; + border-bottom: 0; + border-top: 0.25em solid $black; + letter-spacing: 0.06em; + padding: 10px 0 16px; + } + + h3 { + font-family: "GT-Planar-Bold"; + border-top: 1px black solid; + padding: 10px 0 20px; + } + + h4 { + font-family: "Farnham-Bold"; + font-size: 1.07em; + } + + p { + font-family: "Farnham"; + font-size: 1.07em; + } + + strong { + font-family: "Farnham-Bold"; + font-size: 1.07em; + } + + div { + font-family: "Farnham"; + font-size: 1.07em; + } + + code { + font-family: "SFMono"; + font-size: .9em; + } + + .column { + p { + font-family: "Farnham" !important; + font-size: 1.07em !important; + } + } + + ol { + list-style-position: outside; + padding: 0; + + li { + font-family: "Farnham"; + font-size: 1.07em; + + strong { + font-family: "Farnham-Bold"; + } + + li { + font-size: inherit; + } + } + + li::marker { + color: $sky; + font-family: "GT-Planar"; + } + + li > p { + font-size: inherit; + margin-top: 0; + margin-bottom: 0; + } + } + + ul { + list-style: none; + display: table-cell; + padding: 0; + margin: 0; + + li { + font-family: "Farnham"; + font-size: 1.07em; + padding-left: 2.2em; + text-indent: -1.8em; + + strong { + font-family: "Farnham-Bold"; + } + + li { + font-size: inherit; + } + } + + li:before { + content: "\25CB"; + color: $sky; + padding-right: 20px; + } + + li > p:first-child { + display: inline; + } + + li > p { + font-size: inherit; + margin-top: 0; + margin-bottom: 0; + } + } + + li { + margin: 10px 0px; + } + + table { + font-size: 0.75rem; + + thead { + border-top: 3px $black solid; + font-family: "GT-Planar"; + + th:first-of-type { + text-align: start; + } + + th { + vertical-align: top; + text-align: end; + } + } + + th, + td { + border-right: 1px solid rgba(0, 0, 0, 0.08); + + &:last-child { + border-right: none; + } + } + + td:first-of-type { + font-family: "GT-Planar"; + text-align: start; + + strong { + font-family: "GT-Planar-Bold"; + } + } + + td { + font-family: "IBM-Plex-Sans"; + text-align: end; + + strong { + font-family: "IBM-Plex-Sans"; + font-weight: bold; + font-size: inherit; + } + } + } +} + +.cell-output-display.page-full, +.quarto-float.page-full { + z-index: 0 !important; +} + +.column-margin { + z-index: 998 !important; + sup { + top: 0; + margin-right: 4em; + font-size: 1em; + line-height: 1.4em; + } + p { + font-family: "GT-Planar" !important; + color: $grey; + font-size: 1em; + line-height: 1.4em; + } + + strong { + font-family: "GT-Planar-Bold" !important; + } + a { + color: $sky; + text-decoration: none; + } +} + +.sidebar { + h2 { + font-family: "GT-Planar-Black" !important; + text-transform: uppercase; + color: $midnight_lighter; + } + + a { + font-family: "GT-Planar" !important; + color: $grey !important; + :active { + color: $sky !important; + } + :hover { + color: $sky !important; + } + } +} + +.quarto-title-banner { + a { + color: $sky; + } + + h1 { + color: $sky; + font-family: "GT-Planar-Bold"; + } + + .subtitle { + font-family: "GT-Planar"; + } + + .quarto-title-meta-heading { + font-family: "GT-Planar-Black"; + } + + background-color: $midnight; + color: $sky; + font-family: "GT-Planar"; + background-image: url("https://switchbox-data.github.io/reports/icon-white.png"); + background-size: 100px; + background-repeat: no-repeat, no-repeat; + background-position: 80%; +} + +.quarto-appendix-heading { + margin-bottom: 17px; +} + +.quarto-appendix-contents { + .csl-entry { + margin: 18px 0; + } +} + +.references { + .csl-entry { + margin: 18px 0; + } +} + +code { + font-family: "SFMono"; +} + +figcaption { + display: flex; + + .figure { + font-family: "GT-Planar-Black" !important; + color: $carrot; + margin-right: 5px; + font-size: 13px; + } + + p { + font-family: "GT-Planar" !important; + color: $grey; + font-size: 1em; + line-height: 1.4em; + } + + p strong { + font-family: "GT-Planar-Bold" !important; + } +} + +dt { + font-family: "GT-Planar-bold" !important; + color: $grey !important; + font-size: 1em !important; +} +dd { + font-family: "GT-Planar" !important; + color: $grey !important; + font-size: 1em !important; +} + +.sidebar nav[role="doc-toc"] ul > li > a.active, +.sidebar nav[role="doc-toc"] ul > li > ul > li > a.active { + border-left: 1px solid $sky; + color: $sky !important; +} + +.sidebar nav[role="doc-toc"] ul > li > a:hover, +.sidebar nav[role="doc-toc"] ul > li > ul > li > a:hover { + color: $sky !important; +} + + +div.callout-tip.callout { + border-left-color: $pistachio; +} + +div.callout-tip.callout-style-default>.callout-header { + background-color: #f7fbd4; +} + +div.callout-warning.callout { + border-left-color: $carrot; +} + +div.callout-warning.callout-style-default>.callout-header { + background-color: #ffefda; +} + +div.callout-note.callout { + border-left-color: $sky; +} + +div.callout-note.callout-style-default>.callout-header { + background-color: #e8f5f9; +} diff --git a/AGENTS.md b/AGENTS.md new file mode 100644 index 0000000..0e8222d --- /dev/null +++ b/AGENTS.md @@ -0,0 +1,696 @@ +# Agent guide: reports2 + +This file orients AI agents so they can work effectively in this repo — writing reports, building analysis notebooks, and managing data — without reading the entire codebase. + +## What this repo is + +**reports2** is [Switchbox's](https://switch.box/) report repository. Switchbox is a nonprofit think tank that produces rigorous, accessible data on U.S. state climate policy for advocates, policymakers, and the public. + +Each report is a [Quarto Manuscript](https://quarto.org/docs/manuscripts/) project that combines a **policy narrative** (`index.qmd`) with **reproducible data analysis** (`notebooks/analysis.qmd`), using R (tidyverse) and Python (polars). Reports are published as static HTML via GitHub Pages, reviewed as Word documents, and typeset as PDFs via InDesign. + +The main inputs are data from S3 (`s3://data.sb/`): NREL ResStock building simulations, Cambium marginal costs, EIA energy data, Census PUMS, and utility tariff data. The main outputs are publication-quality reports on energy policy — heat pump rates, grid impacts, gas infrastructure, LMI programs, and building electrification. + +The companion repo [rate-design-platform](https://github.com/switchbox-data/rate-design-platform) runs the CAIRO rate simulations whose outputs many reports analyze. See its AGENTS.md for simulation-side conventions. + +## Layout + +| Path | Purpose | +| ------------------------------ | ----------------------------------------------------------------------------------------------------------------- | +| `reports/` | Source code for all report projects. Each subdirectory is a self-contained Quarto Manuscript project. | +| `reports/.style/` | Shared SCSS theme (`switchbox.scss`) and HTML includes (`switchbox.html`) used by all reports. | +| `reports/references.bib` | Shared BibTeX bibliography used by all reports. | +| `lib/` | Shared R and Python libraries used across reports. | +| `lib/ggplot/switchbox_theme.R` | Custom ggplot2 theme (IBM Plex Sans, white background, Switchbox colors). Source this in every analysis notebook. | +| `lib/rates_analysis/` | Shared R functions for heat pump rate analysis (bill calculation, tariff assignment, plotting). | +| `lib/eia/` | Python scripts for fetching EIA data (fuel prices, state profiles). | +| `docs/` | Published HTML reports served via GitHub Pages at `switchbox-data.github.io/reports2`. | +| `tests/` | Pytest test suite. | +| `.devcontainer/` | Dev container configuration (Dockerfile, devcontainer.json). | +| `Justfile` | Root task runner: `install`, `check`, `test`, `new_report`, `aws`, `clean`. | +| `pyproject.toml` | Python dependencies (managed by uv). | +| `DESCRIPTION` | R dependencies (managed by pak). | + +## Report architecture + +Every report project lives in `reports//` and follows the Quarto Manuscript structure. This separation between narrative and analysis is the core architectural pattern — understand it before touching any report. + +### Anatomy of a report project + +```text +reports// +├── index.qmd # The publication narrative (what readers see) +├── notebooks/ +│ └── analysis.qmd # The data analysis (the engine room) +├── _quarto.yml # Quarto project config +├── Justfile # render, draft, typeset, publish, clean +├── cache/ # Gitignored: .RData files, intermediate outputs +└── docs/ # Gitignored: rendered HTML/DOCX/ICML output +``` + +- **`index.qmd`**: The report's narrative. Contains prose, embedded charts, inline computed values, and margin citations. This is what the reader sees. It loads pre-computed variables and embeds figures from the analysis notebook. It never loads raw data or runs heavy computation. +- **`notebooks/analysis.qmd`**: The data analysis. Loads data from S3, computes statistics, generates labeled figures, and exports variables to `.RData`. Readers don't see this directly — its outputs flow into `index.qmd`. Prefer a single `analysis.qmd`; consult the team before adding multiple notebooks. +- **`_quarto.yml`**: Project config. Type is always `manuscript`. Theme always references `../.style/switchbox.scss`. The `render` list must include all notebooks needed for the build. + +### Data flow: analysis to narrative + +```mermaid +flowchart LR + S3["S3 data\n(parquet)"] --> Analysis["analysis.qmd\n(R/Python)"] + Analysis -->|"save(vars, file='cache/report_variables.RData')"| RData["cache/\nreport_variables.RData"] + Analysis -->|"#| label: fig-xxx"| Figures["Labeled figures"] + RData -->|"load('cache/report_variables.RData')"| Index["index.qmd\n(narrative)"] + Figures -->|"{{< embed notebooks/analysis.qmd#fig-xxx >}}"| Index + Index --> HTML["Rendered report"] +``` + +1. `analysis.qmd` loads data from S3, computes, and `save()`s variables to a `.RData` file in `cache/`. +2. `analysis.qmd` creates labeled figures using chunk options like `#| label: fig-energy-savings`. +3. `index.qmd` loads variables via `load(file = "cache/report_variables.RData")` and uses them inline: `` `r total_savings |> scales::dollar()` ``. +4. `index.qmd` embeds figures from the analysis notebook: `{{< embed notebooks/analysis.qmd#fig-energy-savings >}}`. + +Never put raw data loading or heavy computation in `index.qmd`. Never put narrative prose in `analysis.qmd`. + +### YAML frontmatter template + +Every `index.qmd` uses this frontmatter (adapt title, authors, date, keywords): + +```yaml +--- +title: "Report Title" +subtitle: "Descriptive subtitle" +date: YYYY-MM-DD +author: + - name: Author Name + orcid: 0000-0000-0000-0000 + email: name@switch.box + affiliations: + - Switchbox + +keywords: [keyword1, keyword2] + +bibliography: ../references.bib +license: "CC BY-NC" + +toc: true +notebook-links: false +reference-location: margin +fig-cap: true +fig-cap-location: margin +tbl-cap-location: margin + +appendix-style: default +citation-location: document +citation: + container-title: Switchbox + +# Uncomment when PDF is ready: +#other-links: +# - text: PDF +# icon: file-earmark-pdf +# href: switchbox_.pdf +--- +``` + +### Embedding figures and variables + +In `analysis.qmd`, create a labeled figure: + +````markdown +```{r} +#| label: fig-energy-savings +#| fig-cap: "Annual energy savings by heating fuel type" + +ggplot(data, aes(x = fuel_type, y = savings)) + + geom_col() + + theme_minimal() +``` +```` + +In `index.qmd`, embed it: + +```markdown +:::{.column-page-inset-right} +{{< embed notebooks/analysis.qmd#fig-energy-savings >}} +::: +``` + +Use `:::{.column-page-inset-right}` or `:::{.column-page-inset}` for full-width layout (the standard for all charts). + +For inline values, always use R inline code. Never hardcode statistics in prose: + +```markdown +Gas-heated homes pay a median annual energy bill of **`r pre_hp_total_bill |> scales::dollar(accuracy = 1)`**. +``` + +## Analysis notebook conventions + +This section covers the **literate programming style** of `notebooks/analysis.qmd` — the engine room that powers each report. All analysis notebooks are open-sourced alongside the report, so they must be readable and followable by anyone who knows the language. + +The guiding principle: **a reader who knows R (or Python) should be able to follow the analysis without external documentation.** They should understand what data is being loaded, what it looks like, what transformations are applied and why, and how each output connects to the report. The notebook is not a script with comments — it is a document that happens to execute. + +For polished reference implementations, see [tdr-model/notebooks/analysis.qmd](https://github.com/switchbox-data/tdr-model/blob/main/notebooks/analysis.qmd) (LMI discount modeling) and [ny_heat/notebooks/analysis.qmd](https://github.com/switchbox-data/reports/blob/main/src/ny_heat/notebooks/analysis.qmd) (Census PUMS energy burden analysis). + +### Top-level structure + +Analysis notebooks follow a consistent arc that mirrors the research process: + +1. **Introduction** — A short, reader-facing welcome. Since these notebooks are open-sourced, the intro orients an external reader: what this notebook contains, how it relates to the report, and how to navigate (e.g., "click Download Source above"). It can also list caveats and limitations up front. From ny_heat: "This notebook, written in the R programming language, contains all of the code used to produce the findings in our report, starting from raw data." + +2. **Setup** — Import libraries and define top-level parameters. Each parameter gets a brief comment or prose explanation. Group related parameters together and show their values (e.g., render a discount rate table with `gt()`). Parameters like `state_code`, `burdened_cutoff`, and `pums_year` should be defined here so the notebook can be rerun for a different state or scenario by changing a few values. If the analysis can be rerun with different parameters, include a "How to run this for X" subsection with numbered steps. + +3. **Import data** — Load each dataset, explain what it contains, print it. This is the single most important section for readability. (See "Show the data on import" below.) + +4. **Data preparation** — Filtering, joining, reweighting, tier assignment. Each transformation gets its own cell with a prose explanation of _what_ is being done and _why_. + +5. **Core analysis** — The analytical functions and computations that produce the report's findings. Functions are defined in one cell, then called in subsequent cells. Complex functions get docstring-style prose above them. + +6. **Visualization** — Figure-producing cells, each with `#| label: fig-xxx` and `#| fig-cap:` options. Group figures by the story they tell, not by chart type. + +7. **Report variables** — A clearly labeled section at the end that computes the summary metrics used in `index.qmd` and exports them via `save.image()` or `save()`. + +### Show the data on import + +This is a policy, not a suggestion. When you load a dataset, **immediately show it** so the reader can reason about subsequent code. The pattern is: + +1. Load the data. +2. Explain what it represents in prose. +3. Print a sample (first few rows via `gt()`, `head()`, or `glimpse()`). +4. If the schema isn't obvious, include a markdown table documenting the columns. + +From the TDR model: + +> "Each row in this table represents a housing unit. It could be a single-family home, or an apartment in a multi-family building. The ResStock data contains the following columns:" +> +> | Column | Description | +> | ----------------- | --------------------------------------- | +> | `bldg_id` | Unique identifier for each housing unit | +> | `assigned_income` | Annual household income (2024 dollars) | +> | ... | ... | + +Then later: + +> "Let's take a look at the electricity and gas tariff data." + +followed by a `gt()` table rendering the tariff data with formatted currency and percentages. + +This applies equally to intermediate datasets. After a complex join or transformation, use a **checkpoint** — print a few rows and walk the reader through the new columns: + +> "Let's take a look at where we stand." + +Then explain what each new variable means in prose: "We know whether each household in our sample `is_energy_burdened`: whether they pay more than `r scales::label_percent()(burdened_cutoff)` of their annual income on energy." Note the use of inline R code even in the analysis notebook's own prose — this keeps the notebook parameterized. + +When a dataset has encoding quirks, explain them. From ny_heat, on Census PUMS data: + +> "ACS uses low values of these columns to denote different reasons for zeros, not actual values, so we need to set them to zero." + +Without that comment, the `case_when(GASP <= 4 ~ 0)` would be mystifying. + +### Cell size and atomicity + +Each code cell should do **one logical thing**. If you're loading data, load data. If you're computing survey weights, compute survey weights. If you're making a plot, make a plot. Do not combine unrelated operations in a single cell. + +Good: + +```r +# Cell 1: Load electricity tariff data +elec_tariff <- googlesheets4::read_sheet(url, sheet = elec_sheet) |> + select(utility, customer_charge, volumetric_rate, month, current_discount) +``` + +```r +# Cell 2: Show it +elec_tariff |> gt() |> + fmt_currency(columns = c(customer_charge, volumetric_rate), decimals = 4) +``` + +Bad: A single cell that loads three datasets, joins them, filters, computes a summary, and makes a plot. + +### Prose between cells + +The prose between code cells is **informal, conversational, and directional**. It tells the reader what is about to happen and why it matters. It is not a formal methods section — it is a running commentary from a colleague walking you through their work. + +Characteristic phrases: + +- "First, we define a function to..." +- "Next, we read the ResStock data, excluding buildings with..." +- "We now need to match each housing unit to the correct HEAP tier, based on..." +- "Let's take a look at..." +- "Let's run a quick sanity check, ensuring that..." +- "These target percentages are stored in the Google Sheets and would need to be updated to model a different utility." + +The tone is second-person-inclusive ("we") and present-tense ("we define", "we load", "we now need to"). It reads like pair programming. + +### Introduce domain concepts where they're needed + +Do not assume the reader knows what a HEAP tier is, or how survey weights work, or what a volumetric rate means. Introduce domain concepts **at the point in the code where they first matter**, not in a separate glossary. + +From the TDR model, right before the tier-assignment code: + +> "HEAP tiers are defined as:" +> +> | HEAP Tier | Income Level | +> | ------------ | -------------------- | +> | Lowest tier | Less than 100% FPL | +> | Middle tier | 100%-200% FPL | +> | Highest tier | 200% FPL to 60% SMI | +> | Non-LMI | Greater than 60% SMI | +> +> _FPL = federal poverty level, SMI = state median income._ + +Then the code that implements this mapping follows immediately. The reader learns the concept and sees the implementation in one scroll. + +Sometimes domain education needs more than a definition table. From ny_heat, the explanation of Census PUMAs spans several paragraphs — what they are, why they matter for the analysis, how they relate to counties, why the allocation factor is needed — before the join code appears. The reader gets a mini-lesson, not just a glossary entry. This is appropriate when the concept is central to the analysis and would be confusing without context. + +When a concept has external documentation that the curious reader might want, **link to it**: + +> "For definitions of other PUMS variables, consult the official [data dictionary](https://www2.census.gov/programs-surveys/acs/tech_docs/pums/data_dict/PUMS_Data_Dictionary_2021.pdf). To learn how to work with PUMS data, check out [this tutorial](https://walker-data.com/tidycensus/articles/pums-data.html)." + +This keeps the notebook self-contained for the casual reader while giving the motivated reader a path to go deeper. + +### Orient the reader to what matters vs. boilerplate + +Not every cell is equally important. Some are setup boilerplate (library imports, DB connection functions); others are the analytical core. Use prose to **signal which is which**: + +- Before boilerplate: "First, we import the libraries we'll use in this notebook." (Then the cell. No further explanation needed.) +- Before core logic: Multiple paragraphs explaining the analytical approach, what the function computes and why, what the inputs and outputs represent. + +The TDR model's `eval_discount_rate` function, for example, gets a full prose section ("Core Analysis Functions") with a numbered list of what the functions calculate — monthly bills, program costs, energy burdens, impact on non-LMI customers — before any code appears. + +For visualization sections, annotate each figure group with what it demonstrates: + +> "### Figure 3: Impact of Increasing Discounts — +> Shows how different discount rates affect +> lowest tier households and middle tier households +> (who get intermediate discount). +> Key findings discussed in report section 'Discount Rate Analysis'" + +### Verification and sanity checks + +Include assertions and verification steps throughout, not just at the end. After loading data, after joining, after reweighting — anywhere a silent error could propagate. Present them conversationally: + +> "Let's run a quick sanity check, ensuring that each housing unit only appears in one row of each of these datasets." + +```r +stopifnot(dim(bldgs_elec)[1] == length(unique(bldgs_elec$bldg_id))) +``` + +For survey weight verification, print a comparison table of weighted vs. target percentages and label it clearly: + +```r +print("Electric - Weighted vs Target Percentages") +``` + +### Comments in code cells + +Comments inside code cells should explain **why**, not **what**. The prose between cells handles the "what." + +Good comments: + +```r +filter(!bldg_id %in% exclude_bldgs) # Remove buildings with negative electricity consumption +bill * (1 - current_discount) # Apply current discount to LMI bills only +sum((bill - bill_discounted) * survey_weight) # Must be weighted for cost-per-kwh calculation +``` + +Comments that explain **data encoding quirks** are especially valuable: + +```r +GASP = case_when(GASP <= 4 ~ 0, .default = GASP), # ACS uses low values to denote reasons for zeros, not actual dollar amounts +``` + +Bad comments: + +```r +# Read the data +# Filter the data +# Calculate the sum +``` + +### Inline computed values in prose + +Use inline R code (`` `r expr` ``) in the notebook's own prose — not just in `index.qmd`. This keeps the notebook parameterized and self-updating: + +> "We know whether each household in our sample `is_energy_burdened`: whether they pay more than `` `r scales::label_percent()(burdened_cutoff)` `` of their annual income on energy." + +If `burdened_cutoff` changes from 0.06 to 0.10, the prose updates automatically. + +### Caching expensive fetches + +When a data fetch is expensive (e.g., Census API calls), use a conditional download pattern so the notebook doesn't re-fetch every time it renders: + +```r +if (file.exists(pums_path)) { + raw_data <- readRDS(pums_path) +} else { + raw_data <- get_pums(variables = vars, state = state_fips, ...) + saveRDS(raw_data, pums_path) +} +``` + +Explain the pattern briefly in prose so the reader knows what's happening. + +### Define metrics before computing them + +Before a block of aggregation code, list the exact metrics you're about to compute. This gives the reader a roadmap so they can map each line of code to its purpose. From ny_heat: + +> "All that's left now is to report the following metrics for different geographies, starting with the entire state: +> +> - `households_included`: the number of households +> - `median_income`: the median income of households across the state +> - `pct_energy_burdened`: the percent of households that are energy burdened +> - `avg_monthly_bill_of_burdened`: the average monthly energy bills of those households, before NY HEAT +> - `utility_burden_of_burdened`: how much utility burdened households stand to save every month, after NY HEAT" + +Then the `summarise()` cell follows, and every column is already explained. + +### Repeat-and-slice with progressive shortening + +Many analyses compute the same metrics across multiple slicing dimensions — by building type, by fuel type, by income level, by ownership status. The pattern is always: **count → aggregate → plot → table**. + +The first time through (e.g., building type), give the full treatment: explain what you're doing, why, how the categories are defined, and what the results show. By the second and third time (fuel type, income), the reader already knows the pattern. Shorten the prose to telegraphic transitions: + +> "Next, we crunch the same numbers for each economic region:" +> +> "Now we do it for counties:" +> +> "Plot counts." +> +> "Aggregate results by fuel type." +> +> "Place results in a table." + +This progressive shortening respects the reader's time. They learned the pattern once; they don't need it re-explained for every dimension. + +### The report variables section + +Every analysis notebook ends with a clearly labeled section that computes summary metrics for `index.qmd`. This section should: + +1. Be explicitly labeled (e.g., `# Report variables`). +2. Include a prose note explaining its purpose: "Each variable calculated here corresponds to a metric in the report. You can see where they are used by searching for the variable name in Index.qmd." +3. Compute formatted values using `scales::dollar()`, `scales::percent()`, etc. +4. Export everything via `save.image(file = "cache/report_variables.RData")` or a targeted `save()`. + +### Figure cells + +Figure-producing cells always include these Knitr chunk options: + +```r +#| label: fig-descriptive-name +#| fig-cap: "Human-readable caption that stands alone" +#| fig-width: 10 +#| fig-cap-location: margin +``` + +Group figures by the story they tell, not by chart type. Use markdown headers and prose before each figure group to orient the reader to what the figure shows and what the key findings are. + +### What NOT to do in analysis notebooks + +- Do not write a wall of code with no prose. Every 2-3 cells should have connecting text. +- Do not load data without showing it. The reader cannot follow joins and filters on data they've never seen. +- Do not define 10 functions in one massive cell. Break them into logical groups with prose between. +- Do not rely on comments alone to explain logic. If it needs more than a one-line comment, write prose above the cell. +- Do not put narrative conclusions in the analysis notebook. State what the _code_ is doing and what the _data_ shows; save the policy interpretation for `index.qmd`. +- Do not hardcode file paths that only work in one environment. Use relative paths or environment variables. +- Do not skip the report variables section. If `index.qmd` uses computed values, they must be exported from `analysis.qmd`. + +## Shared resources and branding + +### Theme and styling + +- `reports/.style/switchbox.scss`: Custom Quarto theme. Switchbox brand colors: sky (`#68bed8`), carrot (`#fc9706`), midnight (`#023047`), saffron (`#ffc729`), pistachio (`#a0af12`). Fonts: Farnham (body text), GT Planar (headings), IBM Plex Sans (tables/charts), SF Mono (code). Do not override these in individual reports. +- `reports/.style/switchbox.html`: Shared HTML include for figure caption formatting. + +### ggplot2 theme + +Source `lib/ggplot/switchbox_theme.R` at the top of every R-based analysis notebook: + +```r +source("/workspaces/reports2/lib/ggplot/switchbox_theme.R") +``` + +This sets `theme_minimal()` as the base, uses IBM Plex Sans at 12pt, white panel background, and axis lines/ticks. Do not create custom themes or override these defaults. + +### Switchbox color palette for charts + +When using Switchbox colors in ggplot code, define them explicitly: + +```r +sb_sky <- "#68bed8" +sb_carrot <- "#fc9706" +sb_midnight <- "#023047" +sb_saffron <- "#ffc729" +sb_pistachio <- "#a0af12" +``` + +### Shared R libraries + +- `lib/rates_analysis/heat_pump_rate_funcs.R`: Bill calculation, tariff assignment, monthly/annual bill aggregation, LMI discount application, ResStock data processing. +- `lib/rates_analysis/heat_pump_rate_plots.R`: Plotting functions for rate analysis (histograms, supply rate plots). +- `lib/rates_analysis/create_sb_housing_units.R`: Creates standardized housing unit datasets from ResStock. + +### Bibliography + +`reports/references.bib` is the **single shared bibliography** used by every report (each report's YAML front matter points to it via `bibliography: ../references.bib`). It is auto-exported by Zotero from the "Reports" subcollection on JP's laptop — adding a reference to that Zotero collection automatically updates the `.bib` file in the local repo, but it only becomes available to others once committed and pushed to `main`. If you need to add a citation and don't have Zotero access, add the entry manually to `references.bib` following the key format below, and it will be reconciled on the next Zotero export. + +Citation key format: `{author_short_title_year}`. When adding citations, follow this pattern: + +```bibtex +@article{adams_BeingRebuffedRegulators_2024, + title = {Being Rebuffed by Regulators...}, + author = {Adams, John}, + ... +} +``` + +## When to use R vs Python + +- **R** (default): Data analysis, statistical modeling, data visualization, report notebooks. Use tidyverse for data manipulation, ggplot2 for charts, arrow for parquet I/O, gt for tables. +- **Python**: Data engineering scripts, numerical simulations, when a specific Python library is needed (e.g., geopandas for geospatial work, polars for large-scale data processing). +- Within a single analysis notebook, prefer consistency (usually all R). +- Both languages use Arrow/Parquet for data exchange and lazy evaluation for S3 reads. + +## Working with data + +All data lives on S3 (`s3://data.sb/`). Never store data files in git. + +### Reading data + +**R (preferred for analysis notebooks):** + +```r +library(arrow) +library(dplyr) + +lf <- open_dataset("s3://data.sb/eia/heating_oil_prices/") +result <- lf |> + filter(state == "RI") |> + group_by(year) |> + summarize(avg_price = mean(price)) +df <- result |> collect() +``` + +**Python:** + +```python +import polars as pl + +lf = pl.scan_parquet("s3://data.sb/eia/heating_oil_prices/*.parquet") +result = lf.filter(pl.col("state") == "RI").group_by("year").agg(pl.col("price").mean()) +df = result.collect() +``` + +Stay in lazy execution as long as possible. Only `collect()` / `compute()` when you need the data in memory. + +### S3 naming conventions + +```text +s3://data.sb/// +``` + +- Lowercase with underscores. Date suffix reflects when data was downloaded. +- Always use a dataset directory, even for single files. +- Prefer Parquet format. + +### Local caching + +`data/` and `cache/` directories are gitignored. Use them for caching downloads and intermediate results, but the analysis must be reproducible from S3 alone. Never reference local-only files in committed code without a clear download/generation step. + +## Code quality + +Before considering any change done: + +- **`just check`**: Runs lock validation (`uv lock --locked`) and pre-commit hooks (ruff-check, ruff-format, ty-check, trailing whitespace, end-of-file newline, YAML/JSON/TOML validation, no large files >600KB, no merge conflict markers). +- **`just test`**: Runs pytest suite. Add or extend tests for new or changed behavior. +- **`just render`** (from report directory): Verifies the report renders without errors. This is the reproducibility check — unique to a reports repo. Run it after any change to a report. + +R formatting: Use the [air](https://github.com/posit-dev/air) formatter via the Posit.air-vscode editor extension (pre-installed in devcontainer). Not yet integrated with pre-commit hooks. + +Python: Ruff for formatting and linting, ty for type checking. + +## How to work in this repo + +### Tasks + +Use `just` as the main interface. Root `Justfile` for dev tasks, report `Justfile`s for rendering. + +### Dependencies + +- **Python**: `uv add ` (updates `pyproject.toml` + `uv.lock`). Never use `pip install`. +- **R**: Add to `DESCRIPTION` Imports section, then `just install`. + +### Creating a new report + +```bash +just new_report +``` + +Naming convention: `state_topic` (e.g., `ny_aeba_grid`, `ri_hp_rates`). Reuse topic names across states for consistency. + +### Rendering + +From the report directory: + +```bash +just render # HTML for web publishing +just draft # DOCX for content review +just typeset # ICML for InDesign +just publish # Copy rendered HTML to root docs/ for GitHub Pages +``` + +### Publishing + +1. `just render` and `just publish` from the report directory. +2. Return to repo root: `cd ../..` +3. `git add -f docs/` (force-add; `docs/` is gitignored in report dirs). +4. Commit, push, and merge to `main`. GitHub Pages deploys automatically. + +### Computing contexts + +- Data scientists' laptops (Mac with Apple Silicon) +- Devcontainers via DevPod (local Docker or AWS EC2 in us-west-2) +- Be aware of which context you're in (affects S3 latency and data access patterns). + +### AWS + +Data is on S3 in `us-west-2`. Refresh credentials with `just aws`. + +## Commits, branches, and PRs + +### Commits + +- **Atomic**: One logical change per commit. +- **Message format**: Imperative verb, <50 char summary (e.g., "Add winter peak analysis"). +- **WIP commits**: Prefix with `WIP:` for work-in-progress snapshots. + +### Branches and PRs + +- **PR title** MUST start with `[project_code]` (e.g., `[ny_aeba] Add peak analysis`) — this becomes the squash-merge commit message on `main`. +- **Create PRs early** (draft is fine). This gives the team visibility into in-flight work. +- PRs should **merge within the sprint**; break large work into smaller PRs if needed. +- **Delete branches** after merging. +- **Description**: Don't duplicate the issue. Write: high-level overview, reviewer focus, non-obvious implementation details. +- **Close the GitHub issue**: Include `Closes #` (not the Linear identifier). +- Do not add "Made with Cursor" or LLM attribution. + +## Issue conventions + +All work is tracked via Linear issues (which sync to GitHub Issues). When creating or updating tickets, use the Linear MCP tools. Every new issue MUST satisfy the following before it is created: + +### Issue fields + +- **Type**: One of **Code** (delivered via commits/PRs), **Research** (starts with a question, findings documented in issue comments), or **Other** (proposals, graphics, coordination — deliverables vary). +- **Title**: `[project_code] Brief description` starting with a verb (e.g., `[ny_aeba] Add winter peak analysis`). +- **What**: High-level description. Anyone can understand scope at a glance. +- **Why**: Context, importance, value. +- **How** (skip only when the What is self-explanatory and implementation is trivial): + - For Code issues: numbered implementation steps, trade-offs, dependencies. + - For Research issues: background context, options to consider, evaluation criteria. +- **Deliverables**: Concrete, verifiable outputs that define "done": + - Code: "PR that adds ...", "Tests for ...", "Updated `data/` directory with ..." + - Research: "Comment in this issue documenting ... with rationale and sources" + - Other: "Google Doc at ...", "Slide deck for ...", link to external deliverable + - Never vague ("Finish the analysis") or unmeasurable ("Make it better"). +- **Project**: Must be set. Should match `reports//`. +- **Status**: Default to Backlog. Options: Backlog, To Do, In Progress, Under Review, Done. +- **Milestone**: Set when applicable (strongly encouraged). +- **Assignee**: Set if known. +- **Priority**: Set when urgency/importance is clear. + +### Status transitions + +Keep status updated as work progresses — this is critical for team visibility: + +- **Backlog** -> **To Do**: Picked for the current sprint +- **To Do** -> **In Progress**: Work has started (branch created for code issues) +- **In Progress** -> **Under Review**: PR ready for review, or findings documented +- **Under Review** -> **Done**: PR merged (auto-closes), or reviewer approves and closes + +## Conventions agents should follow + +1. **Never hardcode computed values in prose.** Always use inline R code (`` `r var |> scales::dollar()` ``). +2. **Keep analysis in `notebooks/analysis.qmd`, narrative in `index.qmd`.** This separation is non-negotiable. +3. **Source `switchbox_theme.R`** in every analysis notebook. Use the Switchbox color palette. +4. **Add new citations** to `reports/references.bib` with `{author_short_title_year}` keys. +5. **Use `{{< embed >}}`** for figures. Never copy-paste chart code into `index.qmd`. +6. **Don't commit** `data/`, `cache/`, or report `docs/` directories. +7. **Prefer R** for analysis and visualization. Use Python only when there's a specific reason. +8. **Run `just check`** before considering a change done. +9. **Follow the writing conventions** in this file. Clear, direct, accessible, policy-oriented. No academic prose. No vague quantification. No passive voice for findings. +10. **Technical details go in the Appendix**, not the main text. +11. **Every figure needs a sentence before it** telling the reader what to look for. +12. **Use the conditional "would"** for modeled outcomes, never "will." +13. **When adding or removing files under `reports/`**, verify `_quarto.yml` render lists are updated. +14. **Respect data boundaries.** Don't assume large data is in git. Follow S3 paths documented in existing notebooks. +15. **Never under any circumstances** make a commit with agent attribution. Commits should never include "co-authored by Claude +or any similar message. +16. **Always** make sure commits are made with an appropriate message pass all pre-commit hooks, mypy tests, deptry tests, and ruff tests + +## Quarto reference + +Reports are built with [Quarto](https://quarto.org/) using the Manuscript project type. When writing or editing reports, consult these pages for authoritative syntax and options. Do not guess at Quarto syntax from training data -- fetch the docs at runtime via Context7 or web fetch. + +| When you need to... | Consult | +| ---------------------------------------------------------- | --------------------------------------------------------------------------------------------- | +| Understand the Manuscript project type | [Quarto Manuscripts](https://quarto.org/docs/manuscripts/) | +| Write markdown (text, lists, footnotes, tables) | [Markdown Basics](https://quarto.org/docs/authoring/markdown-basics.html) | +| Add or configure figures | [Figures](https://quarto.org/docs/authoring/figures.html) | +| Embed output from analysis notebooks | [Embedding from Other Documents](https://quarto.org/docs/authoring/notebook-embed.html) | +| Use callout boxes (note, warning, tip) | [Callout Blocks](https://quarto.org/docs/authoring/callouts.html) | +| Control page layout (margin, page-inset, screen columns) | [Article Layout](https://quarto.org/docs/authoring/article-layout.html) | +| Set up front matter (authors, abstract, license, citation) | [Front Matter](https://quarto.org/docs/authoring/front-matter.html) | +| Add citations and bibliographies | [Citations](https://quarto.org/docs/authoring/citations.html) | +| Create cross-references to figures, tables, sections | [Cross References](https://quarto.org/docs/authoring/cross-references.html) | +| Make the report itself citeable | [Creating Citeable Articles](https://quarto.org/docs/authoring/create-citeable-articles.html) | +| Configure appendices | [Appendices](https://quarto.org/docs/authoring/appendices.html) | +| Add Mermaid or Graphviz diagrams | [Diagrams](https://quarto.org/docs/authoring/diagrams.html) | +| Set Jupyter code cell options | [Code Cells: Jupyter](https://quarto.org/docs/reference/cells/cells-jupyter.html) | +| Set Knitr (R) code cell options | [Code Cells: Knitr](https://quarto.org/docs/reference/cells/cells-knitr.html) | +| Configure HTML format options | [HTML Options](https://quarto.org/docs/reference/formats/html.html) | + +The Article Layout page is especially important -- it documents the column classes (`column-page-inset-right`, `column-margin`, etc.) that we use for figure placement and margin content throughout our reports. + +## MCP Tools + +### Context7 + +When writing or modifying code that uses a library, use the Context7 MCP server to fetch up-to-date documentation. Do not rely on training data for API signatures or usage patterns. + +### Linear + +When a task involves creating, updating, or referencing issues, use the Linear MCP server to interact with the workspace directly. Follow the issue conventions above. + +## Quick reference + +| Command | Where | What it does | +| ----------------- | ---------- | ------------------------------------- | +| `just install` | Root | Set up dev environment | +| `just check` | Root | Lint, format, typecheck | +| `just test` | Root | Run pytest suite | +| `just new_report` | Root | Create report from template | +| `just aws` | Root | Refresh AWS SSO credentials | +| `just clean` | Root | Remove generated files and caches | +| `just render` | Report dir | Render HTML | +| `just draft` | Report dir | Render DOCX | +| `just typeset` | Report dir | Render ICML for InDesign | +| `just publish` | Report dir | Copy HTML to `docs/` for GitHub Pages | +| `just clean` | Report dir | Remove report caches | diff --git a/Justfile b/Justfile index e38229e..93f5c42 100644 --- a/Justfile +++ b/Justfile @@ -9,7 +9,6 @@ default: # ============================================================================= install: - echo "🚀 Creating virtual environment using uv" uv sync uv run pre-commit install @@ -20,8 +19,6 @@ update: # 🔍 AWS # ============================================================================= -# Authenticate with AWS via SSO (for manual AWS CLI usage like S3 access) -# Automatically configures SSO if not already configured aws: .devcontainer/devpod/aws.sh @@ -29,159 +26,248 @@ aws: # 🚀 DEVELOPMENT ENVIRONMENT # ============================================================================= -# Ensure Terraform is installed (internal dependency). Depends on aws so credentials -# are valid before any Terraform or infra script runs. _terraform: aws bash infra/install-terraform.sh -# Set up EC2 instance (run once by admin) -# Idempotent: safe to run multiple times dev-setup: _terraform bash infra/dev-setup.sh -# Destroy EC2 instance but preserve data volume (to recreate, run dev-setup again) dev-teardown: _terraform bash infra/dev-teardown.sh -# Destroy everything including data volume (WARNING: destroys all data!) dev-teardown-all: _terraform bash infra/dev-teardown-all.sh -# User login (run by any authorized user) dev-login: aws bash infra/dev-login.sh # ============================================================================= -# 🔄 DATA PIPELINE +# 🔄 DATA PIPELINE (ANALYTICS) # ============================================================================= -test-pipeline-local: - uv run python scripts/run_comed_pipeline.py --source local - pipeline YEAR_MONTH: uv run python scripts/run_comed_pipeline.py --year-month {{YEAR_MONTH}} --source s3 -test-pipeline YEAR_MONTH MAX_FILES="10": - uv run mprof run scripts/run_comed_pipeline.py --year-month {{YEAR_MONTH}} --max-files {{MAX_FILES}} --source s3 - pipeline-skip-download YEAR_MONTH: uv run python scripts/run_comed_pipeline.py --year-month {{YEAR_MONTH}} --skip-download --source s3 pipeline-debug YEAR_MONTH: uv run python scripts/run_comed_pipeline.py --year-month {{YEAR_MONTH}} --debug --source s3 -download-transform YEAR_MONTH MAX_FILES="": - uv run python -m smart_meter_analysis.aws_loader {{YEAR_MONTH}} {{MAX_FILES}} - # ============================================================================= -# 🧪 SAMPLE DATA (S3 + Synthetic) +# 🔄 CSV → PARQUET MIGRATION (PORTABLE + OPEN SOURCE SAFE) # ============================================================================= +# Operator configuration: +# .env.comed (gitignored) may define: +# COMED_S3_PREFIX +# COMED_MIGRATE_OUT_BASE +# COMED_MIGRATE_BATCH_SIZE +# COMED_MIGRATE_WORKERS +# CONTINUE_ON_ERROR +# COMED_ORCHESTRATOR_LOG_DIR + +S3_PREFIX := env_var_or_default("COMED_S3_PREFIX", "") +MIGRATE_OUT_BASE := env_var_or_default("COMED_MIGRATE_OUT_BASE", "") +MIGRATE_BATCH_SIZE := env_var_or_default("COMED_MIGRATE_BATCH_SIZE", "100") +MIGRATE_WORKERS := env_var_or_default("COMED_MIGRATE_WORKERS", "6") +CONTINUE_ON_ERROR := env_var_or_default("CONTINUE_ON_ERROR", "") +ORCHESTRATOR_LOG_DIR := env_var_or_default("COMED_ORCHESTRATOR_LOG_DIR", "") +OUT_ROOT_TEMPLATE := env_var_or_default("COMED_OUT_ROOT_TEMPLATE", "") + +# ----------------------------------------------------------------------------- +# List available YYYYMM months from S3 +# ----------------------------------------------------------------------------- + +months-from-s3 OUT_FILE PREFIX=S3_PREFIX: + #!/usr/bin/env bash + set -euo pipefail + if [ -f ".env.comed" ]; then source ".env.comed"; fi + + prefix="{{PREFIX}}" + if [ -z "$prefix" ]; then prefix="${COMED_S3_PREFIX:-}"; fi + if [ -z "$prefix" ]; then + echo "ERROR: S3 prefix not set. Use COMED_S3_PREFIX or PREFIX=..." >&2 + exit 1 + fi + prefix="${prefix%/}/" -download-samples YEAR_MONTH="202308" NUM_FILES="5": - uv run python scripts/testing/download_samples_from_s3.py --year-month {{YEAR_MONTH}} --num-files {{NUM_FILES}} + AWS_PAGER="" aws s3 ls "$prefix" \ + | awk '/PRE/ {gsub(/\//,"",$2); if ($2 ~ /^[0-9]{6}$/) print $2}' \ + | sort -u > "{{OUT_FILE}}" -download-samples-small YEAR_MONTH="202308": - uv run python scripts/testing/download_samples_from_s3.py --year-month {{YEAR_MONTH}} --num-files 3 + echo "Wrote $(wc -l < "{{OUT_FILE}}") months to {{OUT_FILE}}" -download-samples-large YEAR_MONTH="202308": - uv run python scripts/testing/download_samples_from_s3.py --year-month {{YEAR_MONTH}} --num-files 10 +# ----------------------------------------------------------------------------- +# Single-month migration (EC2 only) +# ----------------------------------------------------------------------------- -generate-samples: - uv run python scripts/testing/generate_sample_data.py +migrate-month YEAR_MONTH: + #!/usr/bin/env bash + set -euo pipefail + if [ -f ".env.comed" ]; then source ".env.comed"; fi -generate-samples-custom ACCOUNTS DAYS START_DATE: - uv run python scripts/testing/generate_sample_data.py --num-accounts {{ACCOUNTS}} --num-days {{DAYS}} --start-date {{START_DATE}} + if [ ! -d /ebs ]; then + echo "ERROR: /ebs not found. Must run on EC2 with EBS mounted." >&2 + exit 1 + fi -validate-local: - uv run python scripts/diagnostics/validate_pipeline.py --input data/processed/comed_samples.parquet + prefix="{{S3_PREFIX}}" + if [ -z "$prefix" ]; then prefix="${COMED_S3_PREFIX:-}"; fi + if [ -z "$prefix" ]; then + echo "ERROR: S3 prefix not set. Use COMED_S3_PREFIX or S3_PREFIX=..." >&2 + exit 1 + fi + prefix="${prefix%/}/" -inspect-dst-local: - uv run python scripts/diagnostics/inspect_dst_days.py --input data/processed/comed_samples.parquet --start 2023-11-01 --end 2023-11-10 + bucket=$(echo "$prefix" | sed 's|^s3://||' | cut -d/ -f1) -view-sample: - @ls data/samples/*.csv 2>/dev/null | head -1 | xargs head -n 5 || echo "No samples found. Run: just download-samples" + out_base="{{MIGRATE_OUT_BASE}}" + if [ -z "$out_base" ]; then out_base="${COMED_MIGRATE_OUT_BASE:-}"; fi + if [ -z "$out_base" ]; then out_base="/ebs/home/$(whoami)/runs"; fi -clean-samples: - rm -rf data/samples/*.csv - @echo "Sample data cleaned" + INPUT_LIST="$HOME/s3_paths_{{YEAR_MONTH}}_full.txt" + OUT_ROOT="${out_base}/out_{{YEAR_MONTH}}_production" -# ============================================================================= -# 🗄️ DATA COLLECTION -# ============================================================================= + AWS_PAGER="" aws s3 ls "${prefix}{{YEAR_MONTH}}/" --recursive \ + | awk -v b="s3://${bucket}/" -v m="{{YEAR_MONTH}}" 'match($4,/ANONYMOUS_DATA_([0-9]{6})_/,a) && a[1]==m {print b $4}' \ + | sort -u > "$INPUT_LIST" -download-ameren: - uv run python scripts/data_collection/ameren_scraper.py + if [ "$(wc -l < "$INPUT_LIST")" -eq 0 ]; then + echo "ERROR: No CSVs found for {{YEAR_MONTH}}" >&2 + exit 1 + fi -download-ameren-force: - uv run python scripts/data_collection/ameren_scraper.py --force + echo "Wrote $(wc -l < "$INPUT_LIST") CSVs to $INPUT_LIST" -download-ameren-debug: - uv run python scripts/data_collection/ameren_scraper.py --debug + uv run python scripts/csv_to_parquet/migrate_month_runner.py \ + --input-list "$INPUT_LIST" \ + --out-root "$OUT_ROOT" \ + --year-month "{{YEAR_MONTH}}" \ + --batch-size "{{MIGRATE_BATCH_SIZE}}" \ + --workers "{{MIGRATE_WORKERS}}" \ + --resume \ + --exec-mode lazy_sink -# ============================================================================= -# 🏙️ CHICAGO-WIDE SAMPLER -# ============================================================================= +# ----------------------------------------------------------------------------- +# Multi-month migration (sequential) +# ----------------------------------------------------------------------------- -sample-city zips start end out bucket prefix target="200" cm90="": +migrate-months MONTHS_FILE: #!/usr/bin/env bash set -euo pipefail - CM90="{{cm90}}" - if [ -n "$CM90" ]; then EXTRA="--cm90 $CM90"; else EXTRA=""; fi - python scripts/tasks/task_runner.py sample \ - --zips "{{zips}}" \ - --start "{{start}}" \ - --end "{{end}}" \ - --bucket "{{bucket}}" \ - --prefix-base "{{prefix}}" \ - --target-per-zip {{target}} \ - --out "{{out}}" \ - $EXTRA - -sample-city-file zips_file start end out bucket prefix target="100" cm90="": + if [ -f ".env.comed" ]; then source ".env.comed"; fi + + if [ ! -d /ebs ]; then + echo "ERROR: /ebs not found. Must run on EC2." >&2 + exit 1 + fi + + log_dir="{{ORCHESTRATOR_LOG_DIR}}" + if [ -z "$log_dir" ]; then log_dir="/ebs/home/$(whoami)/runs/_orchestrator_logs"; fi + mkdir -p "$log_dir" + + ts=$(date -u +%Y%m%dT%H%M%SZ) + log_file="$log_dir/migrate_${ts}.log" + + succeeded=0; failed=0; skipped=0; failures="" + + log() { echo "[$(date -u +%Y-%m-%dT%H:%M:%SZ)] $*" | tee -a "$log_file"; } + + while IFS= read -r line || [ -n "$line" ]; do + month=$(echo "$line" | sed 's/#.*//' | tr -d '[:space:]') + [ -z "$month" ] && continue + if ! echo "$month" | grep -qE '^[0-9]{6}$'; then + log "SKIP invalid month: $month" + skipped=$((skipped + 1)) + continue + fi + + rc=0 + log "START $month" + just migrate-month "$month" 2>&1 | tee -a "$log_file" || rc=$? + log "END $month rc=$rc" + + if [ "$rc" -eq 0 ]; then + succeeded=$((succeeded + 1)) + else + failed=$((failed + 1)) + failures="$failures $month" + if [ "{{CONTINUE_ON_ERROR}}" != "1" ]; then + log "ABORT on first failure" + break + fi + fi + done < "{{MONTHS_FILE}}" + + log "DONE succeeded=$succeeded failed=$failed skipped=$skipped" + [ "$failed" -eq 0 ] + +# ----------------------------------------------------------------------------- +# Validation +# ----------------------------------------------------------------------------- + +validate-month YEAR_MONTH OUT_ROOT MAX_FILES="50" CHECK_MODE="sample" DST="1": #!/usr/bin/env bash set -euo pipefail - CM90="{{cm90}}" - if [ -n "$CM90" ]; then EXTRA="--cm90 $CM90"; else EXTRA=""; fi - python scripts/tasks/task_runner.py sample \ - --zips-file "{{zips_file}}" \ - --start "{{start}}" \ - --end "{{end}}" \ - --bucket "{{bucket}}" \ - --prefix-base "{{prefix}}" \ - --target-per-zip {{target}} \ - --out "{{out}}" \ - $EXTRA - -viz inp out: - python scripts/tasks/task_runner.py viz --inp "{{inp}}" --out "{{out}}" + run_base="{{OUT_ROOT}}/_runs/{{YEAR_MONTH}}" + run_dir=$(ls -1dt "$run_base"/*/ 2>/dev/null | head -1 || true) + run_dir="${run_dir%/}" -# ============================================================================= -# 📊 BENCHMARKS (eager vs lazy) -# ============================================================================= + if [ -z "$run_dir" ]; then + run_dir="$run_base/_unknown" + mkdir -p "$run_dir" + fi + + ts=$(date -u +%Y%m%dT%H%M%SZ) + report="$run_dir/validation_${ts}.json" + + dst_flag="" + if [ "{{DST}}" = "1" ]; then dst_flag="--dst-month-check"; fi + + python3 scripts/csv_to_parquet/validate_month_output.py \ + --out-root "{{OUT_ROOT}}" \ + --check-mode "{{CHECK_MODE}}" \ + --max-files "{{MAX_FILES}}" \ + $dst_flag \ + --run-dir "$run_dir" \ + --output-report "$report" + + echo "Report: $report" + +validate-months MONTHS_FILE OUT_BASE_DIR="/ebs/home/$(whoami)/runs": + #!/usr/bin/env bash + set -euo pipefail + + log_dir="{{ORCHESTRATOR_LOG_DIR}}" + if [ -z "$log_dir" ]; then log_dir="$OUT_BASE_DIR/_orchestrator_logs"; fi + mkdir -p "$log_dir" + + ts=$(date -u +%Y%m%dT%H%M%SZ) + log_file="$log_dir/validate_${ts}.log" -# Run a specific benchmark: N in {100, 1000, 10000} -bench-run N MODE="lazy": - uv run python scripts/bench/eager_vs_lazy_benchmarks.py run \ - --mode {{MODE}} \ - --n {{N}} - -# Build summary CSV from stored profiles -bench-summary: - uv run python scripts/bench/eager_vs_lazy_benchmarks.py summary - -# Plot memory curves (requires existing profiles) -bench-plot: - uv run python scripts/bench/eager_vs_lazy_benchmarks.py plot - -# Run all benchmarks for eager + lazy -bench-all: - just bench-run 100 eager - just bench-run 100 lazy - just bench-run 1000 eager - just bench-run 1000 lazy - just bench-run 10000 eager - # lazy 10k intentionally omitted (8+ hrs) - @echo "✔ Benchmark suite complete" + while read -r month; do + [ -z "$month" ] && continue + out_root="$OUT_BASE_DIR/out_${month}_production" + just validate-month "$month" "$out_root" 2>&1 | tee -a "$log_file" + done < "{{MONTHS_FILE}}" + +# ----------------------------------------------------------------------------- +# Status dashboard +# ----------------------------------------------------------------------------- + +migration-status OUT_BASE_DIR="/ebs/home/$(whoami)/runs": + #!/usr/bin/env bash + for d in "$OUT_BASE_DIR"/out_*_production; do + [ -d "$d" ] || continue + m=$(basename "$d" | grep -oE '[0-9]{6}') + files=$(find "$d" -name "*.parquet" | wc -l) + run=$(ls -1dt "$d/_runs/$m/"* 2>/dev/null | head -1) + if [ -f "$run/run_summary.json" ]; then + python3 -c 'import json; s=json.load(open("$run/run_summary.json")); print(f"{m} files={files} success={s['total_success']} failure={s['total_failure']}")' + else + echo "$m files=$files (no run_summary.json)" + fi + done # ============================================================================= # 🔍 CODE QUALITY & TESTING @@ -217,118 +303,14 @@ test-coverage: uv run pytest --cov=smart_meter_analysis --cov-report=html # ============================================================================= -# 📚 DOCUMENTATION +# 📄 REPORT RENDERING # ============================================================================= -docs-test: - uv run mkdocs build -s - -docs: - uv run mkdocs serve - -docs-serve: - uv run pdoc smart_meter_analysis - -# ============================================================================= -# 📊 DATA EXPLORATION -# ============================================================================= +render: + quarto render -notebook: - uv run jupyter notebook - -lab: - uv run jupyter lab - -inspect-data FILE N="10": - uv run python -c "import polars as pl; df = pl.scan_parquet('{{FILE}}').limit({{N}}).collect(); print(df)" - -inspect-schema FILE: - uv run python -c "import polars as pl; print(pl.scan_parquet('{{FILE}}').collect_schema())" - -count-rows FILE: - uv run python -c "import polars as pl; print(pl.scan_parquet('{{FILE}}').select(pl.len()).collect())" - -# ============================================================================= -# 🧹 UTILITIES -# ============================================================================= +draft: + quarto render --to docx clean: - rm -rf .pytest_cache - rm -rf .mypy_cache - rm -rf .ruff_cache - rm -rf htmlcov - rm -rf dist - rm -rf *.egg-info - find . -type d -name __pycache__ -exec rm -rf {} + - find . -type f -name "*.pyc" -delete - -clean-data: - #!/usr/bin/env bash - echo "This will delete processed data files!" - echo "Raw data in S3 will not be affected." - read -p "Are you sure? (y/N) " -n 1 -r - if [[ $$REPLY =~ ^[Yy]$ ]]; then - rm -rf data/processed/* - echo "Data cleaned" - fi - -du: - @echo "Data directory sizes:" - @du -sh data/* 2>/dev/null || echo "No data directories found" - -# ============================================================================= -# 📦 BUILD & RELEASE -# ============================================================================= - -clean-build: - #!/usr/bin/env bash - echo "🚀 Removing build artifacts" - rm -rf dist - echo "Removed 'dist' (if it existed)." - -build: clean-build - echo "🚀 Creating wheel file" - uvx --from build pyproject-build --installer uv - -publish: - echo "🚀 Publishing." - uvx twine upload --repository-url https://upload.pypi.org/legacy/ dist/* - -build-and-publish: build publish - -# ============================================================================= -# 💡 EXAMPLES -# ============================================================================= - -example-quick: - @echo "Step 1: Download 5 sample files from S3..." - just download-samples-small 202308 - @echo "" - @echo "Step 2: Run pipeline on samples..." - just test-pipeline-local - @echo "" - @echo "Step 3: Inspect results..." - just inspect-data data/processed/comed_samples.parquet 10 - -example-quick-offline: - @echo "Step 1: Generate synthetic sample data..." - just generate-samples - @echo "" - @echo "Step 2: Run pipeline on samples..." - just test-pipeline-local - @echo "" - @echo "Step 3: Inspect results..." - just inspect-data data/processed/comed_samples.parquet 10 - -example-test: - @echo "Running test pipeline with 10 files from S3..." - just test-pipeline 202308 10 - -example-full: - @echo "Running full pipeline for August 2023..." - @echo "This will take approximately 5-8 hours." - just pipeline 202308 - -example-rerun: - @echo "Re-running analysis on existing August 2023 data..." - just pipeline-skip-download 202308 + rm -rf docs cache/_freeze diff --git a/README.md b/README.md index ebdd931..022508a 100644 --- a/README.md +++ b/README.md @@ -239,7 +239,7 @@ just dev-teardown-all ``` ⚠️ WARNING: This will destroy EVERYTHING including the data volume! All data on the EBS volume will be permanently deleted. -Are you sure? Type 'yes' to confirm: +Are you sure? Type 'yes' to confirm: ``` Type `yes` to confirm, then the cleanup proceeds. diff --git a/_quarto.yml b/_quarto.yml new file mode 100644 index 0000000..50712dd --- /dev/null +++ b/_quarto.yml @@ -0,0 +1,12 @@ +project: + type: manuscript + render: + - index.qmd + - notebooks/analysis.qmd + +format: + html: + theme: .style/switchbox.scss + include-in-header: .style/switchbox.html + +fig-format: svg diff --git a/analysis/clustering/euclidean_clustering_k_search.py b/analysis/clustering/euclidean_clustering_k_search.py index b655065..2adf5d5 100644 --- a/analysis/clustering/euclidean_clustering_k_search.py +++ b/analysis/clustering/euclidean_clustering_k_search.py @@ -273,7 +273,7 @@ def plot_centroids(centroids: np.ndarray, output_path: Path) -> None: x = np.arange(n_timepoints) xlabel = "Time Interval" - fig, ax = plt.subplots(figsize=(12, 6)) + _fig, ax = plt.subplots(figsize=(12, 6)) for i in range(k): ax.plot(x, centroids[i], label=f"Cluster {i}", linewidth=2) diff --git a/analysis/clustering/euclidean_clustering_minibatch.py b/analysis/clustering/euclidean_clustering_minibatch.py index 49a690d..18ace6d 100644 --- a/analysis/clustering/euclidean_clustering_minibatch.py +++ b/analysis/clustering/euclidean_clustering_minibatch.py @@ -306,7 +306,7 @@ def plot_centroids(centroids: np.ndarray, output_path: Path) -> None: x = np.arange(n_timepoints) xlabel = "Time Interval" - fig, ax = plt.subplots(figsize=(12, 6)) + _fig, ax = plt.subplots(figsize=(12, 6)) for i in range(k): ax.plot(x, centroids[i], label=f"Cluster {i}", linewidth=2) diff --git a/analysis/rtp/build_regression_dataset.py b/analysis/rtp/build_regression_dataset.py new file mode 100644 index 0000000..9fc9fe9 --- /dev/null +++ b/analysis/rtp/build_regression_dataset.py @@ -0,0 +1,764 @@ +#!/usr/bin/env python3 +"""Build block-group regression dataset from household bills and run OLS. + +Produces three BG-level outcome tables: + - bg_month_outcomes.parquet (BG x month) + - bg_annual_outcomes.parquet (BG, summed over all months) + - bg_season_outcomes.parquet (BG x season) + +Then joins the appropriate table to census demographics and fits OLS. + +Crosswalk logic mirrors the R script +``analysis/stage2/stage2_multinom_blockgroup_weighted.R`` exactly: + +1. Normalize ZIP+4: accept ``#####-####`` or ``#########``; convert to ``#####-####``. +2. Crosswalk TSV with Zip, Zip4, CensusKey2023; derive + ``block_group_geoid = substr(CensusKey2023, 1, 12)``. +3. Deterministic 1:1 mapping: smallest ``block_group_geoid`` per ``zip4``. +4. Fail-loud if mapping coverage is low or required columns are absent. + +"Chicago" throughout this codebase means the IANA timezone **America/Chicago** +(Central Time). The analysis covers the **full ComEd service territory** +across northern Illinois, not just the City of Chicago. + +Typical usage:: + + python analysis/rtp/build_regression_dataset.py \\ + --bills data/bills/run123/all_months_household_bills.parquet \\ + --crosswalk data/reference/comed_bg_zip4_crosswalk.txt \\ + --census data/reference/census_17_2023.parquet \\ + --output-dir data/bills/run123/regression + + python analysis/rtp/build_regression_dataset.py \\ + --bills data/bills/run123/all_months_household_bills.parquet \\ + --crosswalk data/reference/comed_bg_zip4_crosswalk.txt \\ + --census data/reference/census_17_2023.parquet \\ + --output-dir data/bills/run123/regression \\ + --regression-level bg_month +""" + +from __future__ import annotations + +import argparse +import json +import logging +import sys +from datetime import datetime, timezone +from pathlib import Path +from typing import Any + +import pandas as pd +import polars as pl + +try: + import statsmodels.api as sm +except ImportError: + sys.exit("statsmodels is required but not installed. Install with:\n uv add statsmodels") + +logging.basicConfig( + level=logging.INFO, + format="%(asctime)s - %(levelname)s - %(message)s", +) +log = logging.getLogger(__name__) + +# Preferred and fallback column names for the two OLS outcomes. +# The pipeline orchestrator may or may not add capacity/admin adjustments, +# so we prefer the "net" column (which accounts for those) but fall back +# to the gross column when the caller didn't compute net values. +SAVINGS_COLS = ("net_pct_savings", "pct_savings") +BILL_DIFF_COLS = ("net_bill_diff_dollars", "bill_diff_dollars") +CORE_PREDICTORS = ("median_household_income", "old_building_pct") + + +# --------------------------------------------------------------------------- +# ZIP+4 normalisation (mirrors R normalize_zip4) +# --------------------------------------------------------------------------- +# ComEd meter data carries 9-digit ZIP+4, not 5-digit ZIP. Using ZIP+4 +# gives us ~10x more geographic resolution for the BG crosswalk—most +# ZIP+4s map to a single Census block group, whereas a 5-digit ZIP can +# span dozens of BGs and would require probabilistic allocation. + + +def _normalize_zip4_expr() -> pl.Expr: + """Polars expression: derive ``zip4`` (#####-####) from ``zip_code``. + + Handles both ``#####-####`` (already correct) and ``#########`` (9-digit). + """ + raw = pl.col("zip_code").cast(pl.Utf8).str.strip_chars() + # Already has dash -> keep as-is; 9-digit -> insert dash; else null + return ( + pl.when(raw.str.contains(r"^\d{5}-\d{4}$")) + .then(raw) + .when(raw.str.contains(r"^\d{9}$")) + .then(raw.str.slice(0, 5) + pl.lit("-") + raw.str.slice(5, 4)) + .otherwise(pl.lit(None)) + .alias("zip4") + ) + + +# --------------------------------------------------------------------------- +# Crosswalk loading (duplicated from stage2_logratio_regression.py:184-226) +# --------------------------------------------------------------------------- +# This logic is intentionally duplicated rather than shared: the R-based +# stage-2 code and this Python pipeline must stay 1:1 identical in their +# crosswalk semantics. A shared helper would tempt divergence when one +# side is updated but the other isn't. + + +def load_crosswalk_one_to_one( + crosswalk_path: Path, +) -> tuple[pl.LazyFrame, dict[str, Any]]: + """Load ZIP+4 -> block group crosswalk with deterministic 1:1 linkage. + + Returns: + mapping_lf: one row per zip4, with ``block_group_geoid`` = smallest BG. + metrics: size / fanout metrics for provenance. + """ + log.info("Loading crosswalk: %s", crosswalk_path) + + lf = ( + pl.scan_csv(crosswalk_path, separator="\t", infer_schema_length=10_000) + .with_columns( + (pl.col("Zip").cast(pl.Utf8).str.zfill(5) + pl.lit("-") + pl.col("Zip4").cast(pl.Utf8).str.zfill(4)).alias( + "zip4" + ), + pl.col("CensusKey2023").cast(pl.Utf8).str.zfill(15).str.slice(0, 12).alias("block_group_geoid"), + ) + .select("zip4", "block_group_geoid") + .drop_nulls() + ) + + metrics = ( + lf.select( + pl.len().alias("n_rows"), + pl.col("zip4").n_unique().alias("n_zip4"), + pl.col("block_group_geoid").n_unique().alias("n_bg"), + ) + .collect() + .to_dicts()[0] + ) + + fanout = ( + lf.group_by("zip4") + .agg(pl.col("block_group_geoid").n_unique().alias("n_bg")) + .filter(pl.col("n_bg") > 1) + .select(pl.len().alias("n")) + .collect() + .item() + ) + metrics["n_zip4_multi_bg"] = int(fanout) + + if fanout: + log.warning( + "Crosswalk fan-out: %s zip4s map to multiple block groups; using smallest GEOID per zip4.", + f"{fanout:,}", + ) + + # Deterministic 1:1: smallest GEOID per zip4. + # min() is arbitrary but reproducible—it guarantees the same mapping + # across runs without depending on row order in the crosswalk file. + mapping = lf.group_by("zip4").agg( + pl.col("block_group_geoid").min().alias("block_group_geoid"), + ) + return mapping, metrics + + +# --------------------------------------------------------------------------- +# Join bills -> BG +# --------------------------------------------------------------------------- + + +def attach_block_groups( + bills: pl.DataFrame, + crosswalk_lf: pl.LazyFrame, + *, + max_drop_pct: float, +) -> tuple[pl.DataFrame, dict[str, Any]]: + """Left-join bills to crosswalk on ``zip4``. Fail-loud on high drop rate.""" + n_before = bills.height + crosswalk = crosswalk_lf.collect() + + joined = bills.join(crosswalk, on="zip4", how="left") + n_matched = joined.filter(pl.col("block_group_geoid").is_not_null()).height + n_dropped = n_before - n_matched + pct_dropped = (n_dropped / n_before * 100) if n_before else 0.0 + + metrics = { + "households_before_crosswalk": n_before, + "households_matched": n_matched, + "households_dropped": n_dropped, + "pct_dropped": round(pct_dropped, 3), + } + + if n_dropped: + log.warning( + "Crosswalk join: dropped %s households (%.2f%%) with no BG match.", + f"{n_dropped:,}", + pct_dropped, + ) + + if pct_dropped > max_drop_pct: + raise RuntimeError( + f"Crosswalk coverage too low: {pct_dropped:.2f}% of households " + f"have no match (threshold: {max_drop_pct}%). " + f"Dropped {n_dropped:,} of {n_before:,}." + ) + + return joined.drop_nulls("block_group_geoid"), metrics + + +# --------------------------------------------------------------------------- +# BG x month outcomes + rollups +# --------------------------------------------------------------------------- + + +def build_bg_month_outcomes( + bills_bg: pl.DataFrame, + *, + bill_diff_col: str, +) -> pl.DataFrame: + """Aggregate household bills to BG x month outcomes. + + Returns one row per (block_group_geoid, month) with additive sums and + bill-weighted savings percentages. ``n_household_months`` counts unique + account_identifiers per BG-month (not persistent across months). + """ + always_sum = ["total_kwh", "bill_a_dollars"] + optional_sum = ["bill_b_dollars", "bill_diff_dollars", "net_bill_diff_dollars"] + sum_cols = always_sum + [c for c in optional_sum if c in bills_bg.columns] + + agg_exprs: list[pl.Expr] = [pl.col(c).sum().alias(f"sum_{c}") for c in sum_cols] + agg_exprs.append(pl.col("account_identifier").n_unique().alias("n_household_months")) + + result = bills_bg.group_by(["block_group_geoid", "month"]).agg(agg_exprs) + + # Bill-weighted pct savings: sum_diff / sum_bill_a * 100 + diff_sum_col = f"sum_{bill_diff_col}" + result = result.with_columns( + pl.when(pl.col("sum_bill_a_dollars") > 0) + .then(pl.col(diff_sum_col) / pl.col("sum_bill_a_dollars") * 100) + .otherwise(None) + .alias("pct_savings_weighted"), + ) + + # Also compute net-weighted pct if net is present and wasn't the resolved col + if "net_bill_diff_dollars" in bills_bg.columns and bill_diff_col != "net_bill_diff_dollars": + result = result.with_columns( + pl.when(pl.col("sum_bill_a_dollars") > 0) + .then(pl.col("sum_net_bill_diff_dollars") / pl.col("sum_bill_a_dollars") * 100) + .otherwise(None) + .alias("net_pct_savings_weighted"), + ) + + return result.sort(["block_group_geoid", "month"]) + + +def _rollup_bg_outcomes( + bg_month: pl.DataFrame, + group_cols: list[str], + *, + bill_diff_col: str, +) -> pl.DataFrame: + """Roll up BG x month outcomes by summing additive columns and recomputing pct. + + Works for both annual (group by block_group_geoid) and seasonal + (group by block_group_geoid + season) rollups. + """ + additive = [c for c in bg_month.columns if c.startswith("sum_")] + + agg_exprs: list[pl.Expr] = [pl.col(c).sum() for c in additive] + agg_exprs.append(pl.col("n_household_months").sum()) + + result = bg_month.group_by(group_cols).agg(agg_exprs) + + # Recompute weighted pct savings from ratio of sums + diff_sum_col = f"sum_{bill_diff_col}" + if diff_sum_col in result.columns and "sum_bill_a_dollars" in result.columns: + result = result.with_columns( + pl.when(pl.col("sum_bill_a_dollars") > 0) + .then(pl.col(diff_sum_col) / pl.col("sum_bill_a_dollars") * 100) + .otherwise(None) + .alias("pct_savings_weighted"), + ) + + if "sum_net_bill_diff_dollars" in result.columns and bill_diff_col != "net_bill_diff_dollars": + result = result.with_columns( + pl.when(pl.col("sum_bill_a_dollars") > 0) + .then(pl.col("sum_net_bill_diff_dollars") / pl.col("sum_bill_a_dollars") * 100) + .otherwise(None) + .alias("net_pct_savings_weighted"), + ) + + return result.sort(group_cols) + + +def _derive_season_expr() -> pl.Expr: + """Polars expression: derive season from YYYYMM ``month`` column. + + Winter=12,01,02 Spring=03,04,05 Summer=06,07,08 Fall=09,10,11 + Deterministic; no locale or timezone dependence. + """ + mm = pl.col("month").str.slice(4, 2) + return ( + pl.when(mm.is_in(["12", "01", "02"])) + .then(pl.lit("Winter")) + .when(mm.is_in(["03", "04", "05"])) + .then(pl.lit("Spring")) + .when(mm.is_in(["06", "07", "08"])) + .then(pl.lit("Summer")) + .when(mm.is_in(["09", "10", "11"])) + .then(pl.lit("Fall")) + .otherwise(pl.lit(None)) + .alias("season") + ) + + +# --------------------------------------------------------------------------- +# Predictor detection +# --------------------------------------------------------------------------- + +EXCLUDE_COLS = {"block_group_geoid", "GEOID", "NAME"} + + +def detect_predictors( + census: pl.DataFrame, + *, + mode: str, +) -> tuple[list[str], list[str]]: + """Determine predictor columns from census DataFrame. + + Args: + census: Census DataFrame (must include block_group_geoid + features). + mode: ``"auto"`` | ``"core"`` | ``"col1,col2,..."`` + + Returns: + (predictors_used, excluded_all_null) + """ + if mode == "core": + preds = [c for c in CORE_PREDICTORS if c in census.columns] + if not preds: + raise RuntimeError( + f"--predictors core requested but none of {CORE_PREDICTORS} " + f"found in census columns: {sorted(census.columns)}" + ) + return preds, [] + + if mode != "auto": + # Explicit comma-separated list + requested = [c.strip() for c in mode.split(",") if c.strip()] + missing = [c for c in requested if c not in census.columns] + if missing: + raise RuntimeError( + f"Requested predictors not found in census: {missing}. Available: {sorted(census.columns)}" + ) + return requested, [] + + # Auto-infer: all numeric columns minus id/name/all-null. + # This lets the census file evolve (add new demographic columns) + # without requiring code changes—new numeric columns are picked up + # automatically, which is the right default for exploratory modeling. + numeric_types = {pl.Float64, pl.Float32, pl.Int64, pl.Int32, pl.Int16, pl.Int8, pl.UInt64, pl.UInt32} + candidates = [c for c, dt in census.schema.items() if c not in EXCLUDE_COLS and dt in numeric_types] + # Drop all-null columns + null_counts = census.select(candidates).null_count() + all_null = [c for c in candidates if null_counts[c][0] == census.height] + predictors = [c for c in candidates if c not in all_null] + + return sorted(predictors), sorted(all_null) + + +# --------------------------------------------------------------------------- +# OLS fitting +# --------------------------------------------------------------------------- + + +def fit_ols( + df: pl.DataFrame, + *, + y_col: str, + predictors: list[str], + label: str, + month_fe_col: str | None = None, +) -> dict[str, Any]: + """Fit OLS: y_col ~ predictors [+ month fixed effects]. + + When ``month_fe_col`` is set, month dummies (drop_first=True) are appended + to the predictor matrix. Month FE columns are recorded in the result but + kept separate from the census predictors list. + """ + select_cols = [y_col, *predictors] + if month_fe_col: + select_cols.append(month_fe_col) + + pdf = df.select(select_cols).to_pandas().dropna() + n_obs = len(pdf) + + month_fe_names: list[str] = [] + if month_fe_col: + month_dummies = pd.get_dummies(pdf[month_fe_col], drop_first=True, prefix="month") + month_fe_names = list(month_dummies.columns) + X_df = pd.concat([pdf[predictors], month_dummies], axis=1) + else: + X_df = pdf[predictors] + + all_predictor_names = list(X_df.columns) + n_total_params = len(all_predictor_names) + 1 # +1 for constant + + if n_obs < n_total_params + 1: + raise RuntimeError( + f"OLS '{label}': only {n_obs} complete observations for " + f"{n_total_params} parameters (incl. constant). " + f"Need at least {n_total_params + 1}." + ) + + y = pdf[y_col].values + X = sm.add_constant(X_df.values) + full_names = ["const", *all_predictor_names] + + model = sm.OLS(y, X).fit(cov_type="HC1") + + coefficients = {} + for i, name in enumerate(full_names): + coefficients[name] = { + "estimate": float(model.params[i]), + "std_error": float(model.bse[i]), + "t_stat": float(model.tvalues[i]), + "p_value": float(model.pvalues[i]), + } + + return { + "label": label, + "outcome": y_col, + "n_obs": n_obs, + "n_predictors": len(predictors), + "n_month_fe": len(month_fe_names), + "month_fe_columns": month_fe_names, + "r_squared": float(model.rsquared), + "adj_r_squared": float(model.rsquared_adj), + "f_statistic": float(model.fvalue), + "f_pvalue": float(model.f_pvalue), + "condition_number": float(model.condition_number), + "coefficients": coefficients, + "summary_text": str(model.summary()), + } + + +# --------------------------------------------------------------------------- +# Outcome column resolution +# --------------------------------------------------------------------------- + + +def _resolve_col( + columns: set[str], + preferred: str, + fallback: str, + description: str, +) -> tuple[str, bool]: + """Pick preferred column; fall back if absent. Fail if neither exists.""" + if preferred in columns: + return preferred, False + if fallback in columns: + log.warning( + "%s: preferred column '%s' not found; falling back to '%s'.", + description, + preferred, + fallback, + ) + return fallback, True + raise RuntimeError( + f"{description}: neither '{preferred}' nor '{fallback}' found in bills columns: {sorted(columns)}" + ) + + +# --------------------------------------------------------------------------- +# CLI + main +# --------------------------------------------------------------------------- + + +def parse_args(argv: list[str] | None = None) -> argparse.Namespace: + p = argparse.ArgumentParser( + description="Build BG regression dataset from household bills and run OLS.", + ) + p.add_argument("--bills", type=Path, required=True, help="Household bills parquet.") + p.add_argument("--crosswalk", type=Path, required=True, help="ZIP+4 -> BG crosswalk TSV.") + p.add_argument("--census", type=Path, required=True, help="Census demographics parquet.") + p.add_argument("--output-dir", type=Path, required=True, help="Output directory.") + p.add_argument( + "--predictors", + type=str, + default="auto", + help="'auto' (default) | 'core' (income + building_age) | 'col1,col2,...'", + ) + p.add_argument( + "--max-crosswalk-drop-pct", + type=float, + default=5.0, + help="Fail if crosswalk drop rate exceeds this %% (default: 5.0).", + ) + p.add_argument( + "--min-obs-per-bg", + type=int, + default=3, + help="Minimum household-months per BG for regression (default: 3).", + ) + p.add_argument( + "--regression-level", + type=str, + choices=["annual", "bg_month"], + default="annual", + help="Regression granularity: 'annual' (BG annual, default) or 'bg_month' (BG x month + month FE).", + ) + return p.parse_args(argv) + + +def main(argv: list[str] | None = None) -> int: # noqa: C901 + args = parse_args(argv) + + # -- Validate input files ------------------------------------------------- + for path, label in [ + (args.bills, "bills"), + (args.crosswalk, "crosswalk"), + (args.census, "census"), + ]: + if not path.exists(): + log.error("%s file not found: %s", label.title(), path) + return 1 + + # -- Load bills ----------------------------------------------------------- + log.info("Loading bills: %s", args.bills) + bills_lf = pl.scan_parquet(args.bills) + + # Validate required columns before collecting into memory + cols = set(bills_lf.collect_schema().names()) + for req in ("account_identifier", "zip_code", "total_kwh", "bill_a_dollars", "month"): + if req not in cols: + log.error("Bills missing required column: '%s'. Found: %s", req, sorted(cols)) + return 1 + + # Schema OK — collect into memory + bills = bills_lf.collect() + n_bills_total = bills.height + log.info("Bills: %d rows, columns: %s", n_bills_total, bills.columns) + + # Validate month format: YYYYMM, 6 digits + months_unique = bills.select("month").unique().to_series().to_list() + for m in months_unique: + if not isinstance(m, str) or len(m) != 6 or not m.isdigit(): + raise ValueError(f"Invalid month value '{m}'; expected YYYYMM (6 digits).") + log.info("Months in bills: %s", sorted(months_unique)) + + # -- Resolve outcome columns ---------------------------------------------- + savings_col, savings_fallback = _resolve_col( + cols, + SAVINGS_COLS[0], + SAVINGS_COLS[1], + "Savings outcome", + ) + bill_diff_col, bill_diff_fallback = _resolve_col( + cols, + BILL_DIFF_COLS[0], + BILL_DIFF_COLS[1], + "Bill diff outcome", + ) + log.info("Outcome columns: savings='%s', bill_diff='%s'", savings_col, bill_diff_col) + + # -- Derive zip4 from zip_code -------------------------------------------- + bills = bills.with_columns(_normalize_zip4_expr()) + n_null_zip4 = bills.filter(pl.col("zip4").is_null()).height + if n_null_zip4: + log.warning("%d rows have un-parseable zip_code -> null zip4; these will be dropped.", n_null_zip4) + bills = bills.drop_nulls("zip4") + + # -- Load crosswalk ------------------------------------------------------- + crosswalk_lf, crosswalk_metrics = load_crosswalk_one_to_one(args.crosswalk) + log.info("Crosswalk: %s", crosswalk_metrics) + + # -- Join bills -> BG ----------------------------------------------------- + bills_bg, join_metrics = attach_block_groups( + bills, + crosswalk_lf, + max_drop_pct=args.max_crosswalk_drop_pct, + ) + log.info("Join metrics: %s", join_metrics) + + # -- Build BG x month outcomes -------------------------------------------- + bg_month = build_bg_month_outcomes(bills_bg, bill_diff_col=bill_diff_col) + args.output_dir.mkdir(parents=True, exist_ok=True) + bg_month_path = args.output_dir / "bg_month_outcomes.parquet" + bg_month.write_parquet(bg_month_path) + log.info("Wrote BG x month outcomes: %s (%d rows)", bg_month_path, bg_month.height) + + # -- BG annual rollup ----------------------------------------------------- + bg_annual = _rollup_bg_outcomes( + bg_month, + ["block_group_geoid"], + bill_diff_col=bill_diff_col, + ) + bg_annual_path = args.output_dir / "bg_annual_outcomes.parquet" + bg_annual.write_parquet(bg_annual_path) + log.info("Wrote BG annual outcomes: %s (%d rows)", bg_annual_path, bg_annual.height) + + # -- BG seasonal rollup --------------------------------------------------- + bg_month_with_season = bg_month.with_columns(_derive_season_expr()) + bg_season = _rollup_bg_outcomes( + bg_month_with_season, + ["block_group_geoid", "season"], + bill_diff_col=bill_diff_col, + ) + bg_season_path = args.output_dir / "bg_season_outcomes.parquet" + bg_season.write_parquet(bg_season_path) + log.info("Wrote BG seasonal outcomes: %s (%d rows)", bg_season_path, bg_season.height) + + # -- Load census + rename GEOID ------------------------------------------- + log.info("Loading census: %s", args.census) + census = pl.read_parquet(args.census) + if "GEOID" in census.columns and "block_group_geoid" not in census.columns: + census = census.rename({"GEOID": "block_group_geoid"}) + elif "block_group_geoid" not in census.columns: + log.error("Census has no 'GEOID' or 'block_group_geoid' column.") + return 1 + + # -- Choose regression table based on --regression-level ------------------ + if args.regression_level == "annual": + bg_data = bg_annual.join(census, on="block_group_geoid", how="left") + else: + bg_data = bg_month.join(census, on="block_group_geoid", how="left") + + # -- Census join match rate (robust: check any non-key col is not null) --- + census_nonkey = [c for c in census.columns if c != "block_group_geoid"] + n_census_match = bg_data.filter( + pl.any_horizontal(pl.col(c).is_not_null() for c in census_nonkey), + ).height + n_bg_total = bg_data.height + log.info( + "Census join: matched %d/%d BGs (%.1f%%)", + n_census_match, + n_bg_total, + n_census_match / n_bg_total * 100 if n_bg_total else 0, + ) + + # -- Detect predictors ---------------------------------------------------- + predictors, excluded_null = detect_predictors(census, mode=args.predictors) + if not predictors: + log.error("No usable predictors found (mode='%s').", args.predictors) + return 1 + log.info("Predictors (%d): %s", len(predictors), predictors) + if excluded_null: + log.info("Excluded (all null): %s", excluded_null) + + # -- Filter: min obs + complete case -------------------------------------- + n_before_filter = bg_data.height + bg_data = bg_data.filter(pl.col("n_household_months") >= args.min_obs_per_bg) + n_after_min_obs = bg_data.height + log.info( + "Min-obs filter (%d): %d -> %d", + args.min_obs_per_bg, + n_before_filter, + n_after_min_obs, + ) + + bg_data = bg_data.drop_nulls(subset=predictors) + n_complete = bg_data.height + log.info("Complete-case filter: %d -> %d", n_after_min_obs, n_complete) + + if n_complete == 0: + log.error("No block groups remain after filtering.") + return 1 + + # -- Write regression dataset --------------------------------------------- + dataset_path = args.output_dir / "regression_dataset_bg.parquet" + bg_data.write_parquet(dataset_path) + log.info("Wrote regression dataset: %s (%d rows)", dataset_path, bg_data.height) + + # -- Fit OLS models ------------------------------------------------------- + diff_sum_col = f"sum_{bill_diff_col}" + month_fe_col = "month" if args.regression_level == "bg_month" else None + + results: dict[str, Any] = {} + summary_texts: list[str] = [] + + for y_col, label in [ + ("pct_savings_weighted", "model_1_pct_savings_weighted"), + (diff_sum_col, "model_2_sum_bill_diff"), + ]: + log.info( + "Fitting OLS: %s ~ %d predictors%s", + y_col, + len(predictors), + " + month FE" if month_fe_col else "", + ) + res = fit_ols( + bg_data, + y_col=y_col, + predictors=predictors, + label=label, + month_fe_col=month_fe_col, + ) + results[label] = {k: v for k, v in res.items() if k != "summary_text"} + summary_texts.append(f"{'=' * 70}\n{label}: {y_col}\n{'=' * 70}\n{res['summary_text']}\n") + log.info( + " %s: R2=%.4f, adj_R2=%.4f, F=%.2f, n=%d", + label, + res["r_squared"], + res["adj_r_squared"], + res["f_statistic"], + res["n_obs"], + ) + + # -- Write results JSON --------------------------------------------------- + results_path = args.output_dir / "regression_results.json" + with open(results_path, "w") as f: + json.dump(results, f, indent=2) + log.info("Wrote regression results: %s", results_path) + + # -- Write summary text --------------------------------------------------- + summary_path = args.output_dir / "regression_summary.txt" + with open(summary_path, "w") as f: + f.write("\n".join(summary_texts)) + log.info("Wrote regression summary: %s", summary_path) + + # -- Write metadata JSON -------------------------------------------------- + metadata: dict[str, Any] = { + "created_utc": datetime.now(timezone.utc).replace(microsecond=0).isoformat(), + "regression_level": args.regression_level, + "months_included": sorted(months_unique), + "bills_path": str(args.bills), + "crosswalk_path": str(args.crosswalk), + "census_path": str(args.census), + "crosswalk_metrics": crosswalk_metrics, + "join_metrics": join_metrics, + "crosswalk_exposure": { + "n_bills_total": n_bills_total, + "n_zip4_parse_dropped": n_null_zip4, + "pct_zip4_parse_dropped": round(n_null_zip4 / n_bills_total * 100, 3) if n_bills_total else 0.0, + "pct_crosswalk_no_bg_match": join_metrics["pct_dropped"], + }, + "savings_column_used": savings_col, + "savings_fallback_used": savings_fallback, + "bill_diff_column_used": bill_diff_col, + "bill_diff_fallback_used": bill_diff_fallback, + "predictor_mode": args.predictors, + "predictors_used": predictors, + "predictors_excluded_all_null": excluded_null, + "min_obs_per_bg": args.min_obs_per_bg, + "max_crosswalk_drop_pct": args.max_crosswalk_drop_pct, + "n_bg_before_filter": n_before_filter, + "n_bg_after_min_obs": n_after_min_obs, + "n_bg_complete_case": n_complete, + "files_written": { + "bg_month_outcomes": str(bg_month_path), + "bg_annual_outcomes": str(bg_annual_path), + "bg_season_outcomes": str(bg_season_path), + "regression_dataset": str(dataset_path), + }, + } + metadata_path = args.output_dir / "regression_metadata.json" + with open(metadata_path, "w") as f: + json.dump(metadata, f, indent=2) + log.info("Wrote metadata: %s", metadata_path) + + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/analysis/rtp/compute_hourly_loads.py b/analysis/rtp/compute_hourly_loads.py index b623eb8..e2d626a 100644 --- a/analysis/rtp/compute_hourly_loads.py +++ b/analysis/rtp/compute_hourly_loads.py @@ -2,148 +2,356 @@ """ Compute hourly household loads for RTP billing analysis. -Takes interval-level ComEd smart meter data (5- or 30-minute intervals) -and aggregates to hourly kWh per household, *restricted to* the set of -households that appear in the clustering assignments. - -Typical usage: - - python analysis/rtp/compute_hourly_loads.py \ - --input data/validation_runs/202308_1000/processed/comed_202308.parquet \ - --cluster-assignments data/validation_runs/202308_1000/clustering/results/cluster_assignments.parquet \ - --output data/validation_runs/202308_1000/rtp/hourly_loads_202308.parquet +Takes interval-level ComEd smart meter data (30-minute intervals in this pipeline) +and aggregates to hourly kWh per household. + +If --cluster-assignments is provided, restrict to households that appear +in the clustering assignments. + +Large files are processed in sub-file row batches via PyArrow's +iter_batches() so peak memory is O(batch_size), not O(file). + +Expected input columns (from processed interval parquet): + - account_identifier + - zip_code + - datetime (naive local time, Datetime[us], tz=None) + - energy_kwh + +Output columns: + - account_identifier + - zip_code + - hour_chicago (datetime truncated to hour) + - kwh_hour (sum of energy_kwh within that hour) """ from __future__ import annotations +import gc +import os + +# Constrain Polars to a single thread so parallel workers don't each +# allocate full-dataset copies. Must be set before polars is imported. +os.environ["POLARS_MAX_THREADS"] = "1" + import argparse +import glob as _glob import logging +import shutil +import tempfile from pathlib import Path import polars as pl +import pyarrow as pa +import pyarrow.parquet as pq -logging.basicConfig( - level=logging.INFO, - format="%(asctime)s - %(levelname)s - %(message)s", -) +logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s") logger = logging.getLogger(__name__) +logger.info("Polars thread_pool_size = %d", pl.thread_pool_size()) + +_GROUP_KEYS = ["account_identifier", "zip_code", "hour_chicago"] +_INPUT_COLS = ["account_identifier", "zip_code", "datetime", "energy_kwh"] +_BATCH_SIZE = 100_000 +_MERGE_FAN_IN = 8 # max intermediate files per re-aggregation pass +_MERGE_SHARDS = 16 # hash partitions for final dict merge + + +def _resolve_parquet_paths(input_path: Path) -> list[str]: + """Resolve input to a list of parquet paths (file, directory, or glob).""" + input_str = str(input_path) + if any(ch in input_str for ch in ["*", "?", "["]): + paths = sorted(_glob.glob(input_str)) + if not paths: + raise FileNotFoundError(f"Input parquet glob matched 0 files: {input_str}") + return paths + if input_path.is_dir(): + paths = sorted(str(p) for p in input_path.glob("*.parquet")) + if not paths: + raise FileNotFoundError(f"Input parquet directory has 0 *.parquet files: {input_path}") + return paths + if not input_path.exists(): + raise FileNotFoundError(f"Input parquet not found: {input_path}") + return [str(input_path)] -def load_sampled_accounts(assignments_path: Path) -> pl.Series: - """ - Load the set of sampled households from cluster_assignments.parquet. +def _validate_schema(lf: pl.LazyFrame) -> None: + """Raise ValueError if required columns are missing.""" + required = {"account_identifier", "zip_code", "datetime", "energy_kwh"} + missing = required - set(lf.collect_schema().names()) + if missing: + raise ValueError(f"Input file missing required columns: {sorted(missing)}") + + +def _prepare_account_filter(assignments_path: Path) -> pl.Series: + """Load cluster assignments and return account identifiers as a Series. - Returns: - Series of unique account_identifier values. + Returning a pl.Series (rather than a Python set) lets Polars perform + ``is_in`` filtering natively without repeated Python→Rust conversion. """ if not assignments_path.exists(): raise FileNotFoundError(f"Cluster assignments not found: {assignments_path}") - - logger.info("Loading sampled households from %s", assignments_path) lf_assign = pl.scan_parquet(assignments_path) - if "account_identifier" not in lf_assign.collect_schema().names(): raise ValueError("Cluster assignments file has no 'account_identifier' column") + return lf_assign.select(pl.col("account_identifier")).unique().collect().to_series() - df_accounts = lf_assign.select(pl.col("account_identifier").unique()).collect() - accounts = df_accounts["account_identifier"] - logger.info("Found %d unique sampled households", len(accounts)) - return accounts +def _aggregate_file_chunked( + path: str, + account_filter: pl.Series | None, + tmp_dir: Path, + chunk_offset: int, + batch_size: int = _BATCH_SIZE, +) -> int: + """Read a parquet file in row batches, aggregate each, write intermediates. + Only the four required columns are read from disk (_INPUT_COLS) so extra + columns in the source file never touch memory. -def compute_hourly_loads( - input_path: Path, - assignments_path: Path | None, + Returns the number of intermediate chunks written. + """ + pf = pq.ParquetFile(path) + n_written = 0 + for batch in pf.iter_batches(batch_size=batch_size, columns=_INPUT_COLS): + df = pl.from_arrow(batch) + if account_filter is not None: + df = df.filter(pl.col("account_identifier").is_in(account_filter.implode())) + if df.is_empty(): + del df + continue + agg = ( + df.select( + pl.col("account_identifier"), + pl.col("zip_code"), + pl.col("datetime").dt.truncate("1h").alias("hour_chicago"), + pl.col("energy_kwh"), + ) + .group_by(_GROUP_KEYS) + .agg(pl.col("energy_kwh").sum().alias("kwh_hour")) + ) + del df + agg.write_parquet(tmp_dir / f"chunk_{chunk_offset + n_written:04d}.parquet") + del agg + gc.collect() + n_written += 1 + return n_written + + +def _scatter_to_shards( + paths: list[str], + shard_dir: Path, + n_shards: int, +) -> dict[int, pq.ParquetWriter]: + """Read input files and partition rows into per-shard parquet files.""" + from collections import defaultdict + + cols = [*_GROUP_KEYS, "kwh_hour"] + writers: dict[int, pq.ParquetWriter] = {} + for p in paths: + pf = pq.ParquetFile(p) + for batch in pf.iter_batches(batch_size=50_000, columns=cols): + tbl = pa.Table.from_batches([batch]) + accts = tbl.column("account_identifier").to_pylist() + buckets: dict[int, list[int]] = defaultdict(list) + for i, a in enumerate(accts): + buckets[hash(a) % n_shards].append(i) + for sid, indices in buckets.items(): + sub = tbl.take(indices) + if sid not in writers: + writers[sid] = pq.ParquetWriter( + str(shard_dir / f"shard_{sid:03d}.parquet"), + sub.schema, + ) + writers[sid].write_table(sub) + for w in writers.values(): + w.close() + return writers + + +def _aggregate_shard(shard_path: Path, agg_path: Path) -> int: + """Aggregate a single shard file via Polars group_by. Returns unique key count.""" + df = pl.read_parquet(shard_path) + # Re-truncate to hourly grain so that any sub-hour timestamps surviving + # upstream streaming passes are collapsed before the final group_by. + df = df.with_columns(pl.col("hour_chicago").dt.truncate("1h")) + agg = df.group_by(_GROUP_KEYS).agg(pl.col("kwh_hour").sum()) + agg.write_parquet(agg_path) + n_keys = agg.height + del df, agg + gc.collect() + return n_keys + + +def _dict_merge_sharded( + paths: list[str], output_path: Path, + *, + sort_output: bool, + n_shards: int = _MERGE_SHARDS, ) -> None: + """Merge intermediate parquets via sharded dict accumulation. + + Partitions rows by ``hash(account_identifier) % n_shards`` into temp + shard files, then dict-aggregates each shard independently. Peak memory + is ``O(unique_keys / n_shards)`` instead of ``O(unique_keys)``. """ - Aggregate interval-level kWh to hourly totals per household. - - If assignments_path is provided, restrict to households that appear - in the clustering assignments (to keep memory manageable). - - Expects input schema to include at least: - - account_identifier - - zip_code - - datetime (naive local time, at 5- or 30-minute resolution) - - kwh - - Produces: - - account_identifier - - zip_code - - hour_chicago (datetime truncated to the top of the hour) - - kwh_hour (sum of kWh within that hour) + shard_dir = Path(tempfile.mkdtemp(prefix="merge_shards_")) + + try: + logger.info( + "Sharded dict merge: scattering %d files into %d shards", + len(paths), + n_shards, + ) + writers = _scatter_to_shards(paths, shard_dir, n_shards) + + agg_parts: list[str] = [] + for sid in sorted(writers): + shard_path = shard_dir / f"shard_{sid:03d}.parquet" + agg_path = shard_dir / f"agg_{sid:03d}.parquet" + n_keys = _aggregate_shard(shard_path, agg_path) + agg_parts.append(str(agg_path)) + logger.info(" shard %d: %d unique keys", sid, n_keys) + + if not agg_parts: + pl.DataFrame( + schema={ + "account_identifier": pl.Utf8, + "zip_code": pl.Utf8, + "hour_chicago": pl.Datetime("us"), + "kwh_hour": pl.Float64, + } + ).write_parquet(output_path) + return + + lf = pl.scan_parquet(agg_parts) + if sort_output: + lf = lf.sort(_GROUP_KEYS) + lf.sink_parquet(output_path) + finally: + shutil.rmtree(shard_dir, ignore_errors=True) + + +def _merge_aggregate( + tmp_dir: Path, + output_path: Path, + *, + sort_output: bool, + fan_in: int = _MERGE_FAN_IN, +) -> None: + """Re-aggregate intermediate parquets in bounded passes. + + Each pass groups at most ``fan_in`` files, collects, re-aggregates, and + writes a merged file. Repeats until the file count is small enough for a + single final pass that writes ``output_path``. """ - if not input_path.exists(): - raise FileNotFoundError(f"Input parquet not found: {input_path}") + current_dir = tmp_dir + paths = sorted(str(p) for p in current_dir.glob("*.parquet")) + + if not paths: + logger.warning("No data survived filtering; writing empty output.") + pl.DataFrame( + schema={ + "account_identifier": pl.Utf8, + "zip_code": pl.Utf8, + "hour_chicago": pl.Datetime("us"), + "kwh_hour": pl.Float64, + } + ).write_parquet(output_path) + return + + pass_num = 0 + while len(paths) > fan_in: + pass_num += 1 + next_dir = tmp_dir / f"pass{pass_num}" + next_dir.mkdir() + n_groups = (len(paths) + fan_in - 1) // fan_in + logger.info( + "Merge pass %d: %d files -> %d groups of <=%d", + pass_num, + len(paths), + n_groups, + fan_in, + ) + for g in range(n_groups): + group = paths[g * fan_in : (g + 1) * fan_in] + dest = next_dir / f"merged_{g:04d}.parquet" + pl.scan_parquet(group).with_columns(pl.col("hour_chicago").dt.truncate("1h")).group_by(_GROUP_KEYS).agg( + pl.col("kwh_hour").sum() + ).sink_parquet(dest) + gc.collect() + + # Free previous tier (but not the top-level tmp_dir — caller owns that) + if current_dir != tmp_dir: + shutil.rmtree(current_dir, ignore_errors=True) + current_dir = next_dir + paths = sorted(str(p) for p in current_dir.glob("*.parquet")) + + # Sharded dict merge: partition rows by hash(account_identifier) into + # N shards, then dict-aggregate each shard independently. Peak memory + # is O(unique_keys / N) instead of O(unique_keys), which avoids OOM + # when the hourly-grain key space is large (e.g. 328K accounts x 720h). + _dict_merge_sharded(paths, output_path, sort_output=sort_output) - logger.info("Loading interval data from %s", input_path) - lf = pl.scan_parquet(input_path) - required_cols = {"account_identifier", "zip_code", "datetime", "kwh"} - missing = required_cols - set(lf.collect_schema().names()) - if missing: - raise ValueError(f"Input file missing required columns: {sorted(missing)}") +def compute_hourly_loads( + input_path: Path, + assignments_path: Path | None, + output_path: Path, + *, + sort_output: bool, +) -> None: + scan_paths = _resolve_parquet_paths(input_path) + logger.info("Scanning interval data (%d file(s)): %s", len(scan_paths), scan_paths[0]) + + # Validate schema from the first file + _validate_schema(pl.scan_parquet(scan_paths[0])) - # Optionally restrict to sampled / clustered accounts + account_filter: pl.Series | None = None if assignments_path is not None: - accounts = load_sampled_accounts(assignments_path) - logger.info("Filtering interval data to sampled households only...") - lf = lf.filter(pl.col("account_identifier").is_in(accounts)) + account_filter = _prepare_account_filter(assignments_path) + logger.info( + "Restricting to %d accounts from cluster assignments: %s", + len(account_filter), + assignments_path, + ) - logger.info("Aggregating to hourly loads per (account_identifier, zip_code, hour)...") + output_path.parent.mkdir(parents=True, exist_ok=True) - lf_hourly = ( - lf.with_columns(pl.col("datetime").dt.truncate("1h").alias("hour_chicago")) - .group_by(["account_identifier", "zip_code", "hour_chicago"]) - .agg(pl.col("kwh").sum().alias("kwh_hour")) - .sort(["account_identifier", "hour_chicago"]) - ) + tmp_dir = Path(tempfile.mkdtemp(prefix="hourly_loads_")) + try: + chunk_offset = 0 + for i, path in enumerate(scan_paths): + logger.info("Aggregating file %d/%d in batches: %s", i + 1, len(scan_paths), path) + n = _aggregate_file_chunked(path, account_filter, tmp_dir, chunk_offset) + logger.info(" -> wrote %d intermediate chunks", n) + chunk_offset += n - # Materialize and write - df_hourly = lf_hourly.collect() - output_path.parent.mkdir(parents=True, exist_ok=True) - df_hourly.write_parquet(output_path) - - logger.info("Wrote %d hourly rows to %s", len(df_hourly), output_path) - logger.info( - "Hourly load summary: kwh_hour min=%.4f, max=%.4f, mean=%.4f", - df_hourly["kwh_hour"].min(), - df_hourly["kwh_hour"].max(), - df_hourly["kwh_hour"].mean(), - ) + _merge_aggregate(tmp_dir, output_path, sort_output=sort_output) + logger.info("Wrote hourly loads to %s", output_path) + finally: + shutil.rmtree(tmp_dir, ignore_errors=True) def main() -> int: - parser = argparse.ArgumentParser( - description="Aggregate interval-level ComEd data to hourly loads per household.", - ) - parser.add_argument( - "--input", - type=Path, - required=True, - help="Path to comed_YYYYMM.parquet (interval-level data).", - ) + parser = argparse.ArgumentParser(description="Aggregate interval-level ComEd data to hourly loads per household.") + parser.add_argument("--input", type=Path, required=True, help="Path to comed_YYYYMM.parquet (interval-level data).") parser.add_argument( "--cluster-assignments", type=Path, default=None, - help=( - "Optional: cluster_assignments.parquet to restrict to sampled households (recommended for large months)." - ), + help="Optional: cluster_assignments.parquet to restrict to sampled households.", ) + parser.add_argument("--output", type=Path, required=True, help="Output parquet for hourly loads.") parser.add_argument( - "--output", - type=Path, - required=True, - help="Output parquet for hourly loads.", + "--sort-output", + action="store_true", + help="If set, sort output rows by (zip_code, account_identifier, hour_chicago).", ) args = parser.parse_args() try: - compute_hourly_loads(args.input, args.cluster_assignments, args.output) + compute_hourly_loads(args.input, args.cluster_assignments, args.output, sort_output=args.sort_output) except Exception as e: logger.error("Failed to compute hourly loads: %s", e) return 1 diff --git a/analysis/rtp/compute_household_bills.py b/analysis/rtp/compute_household_bills.py index 1f48b97..36b404c 100644 --- a/analysis/rtp/compute_household_bills.py +++ b/analysis/rtp/compute_household_bills.py @@ -1,6 +1,6 @@ #!/usr/bin/env python3 """ -Compute monthly household bills under flat vs RTP pricing for a single month. +Compute monthly household bills comparing two hourly tariffs for a single month. Inputs: 1) Hourly household loads for that month @@ -11,41 +11,53 @@ - kwh_hour - zip_code (optional but recommended) - 2) Hourly flat-vs-RTP spreads for the year - - output of scripts/build_rtp_spreads.py - - expected columns: + 2) Two hourly tariff price files (baseline A and alternative B) + - output of scripts/build_tariff_hourly_prices.py (or any file with + the same schema) + - required columns per file: - datetime_chicago (local naive datetime, hourly) - - rtp_price_cents - - flat_price_cents + - price_cents_per_kwh + +The script is deliberately generalized to "tariff A vs tariff B" rather +than hard-coded as "flat vs RTP". This lets us reuse the same billing +logic for any pair of rate structures (e.g., STOU vs DTOU) without code +changes—only the input price files differ. This script: - * Joins hourly loads to hourly prices on local time - * Computes hourly costs under RTP and flat pricing + * Joins hourly loads to BOTH tariff price calendars on local time + (fail-loud: every load hour must match in both tariffs, no silent drops) + * Computes hourly costs under each tariff and the hourly difference * Aggregates to monthly totals per household - * Optionally subtracts: - - a capacity charge based on each household's peak hourly kW - - a fixed monthly admin fee - -Key outputs per household: - - total_kwh - - rtp_bill_dollars - - flat_bill_dollars - - bill_diff_dollars (flat - RTP, energy component only) - - capacity_kw (approx. peak hourly kWh) - - capacity_charge_dollars (if capacity rate > 0) - - admin_fee_dollars (if admin fee > 0) - - net_bill_diff_dollars (bill_diff_dollars - capacity - admin) - - pct_savings (bill_diff_dollars / flat_bill) - - net_pct_savings (net_bill_diff_dollars / flat_bill) + * Optionally applies capacity charge and admin fee as costs specific to + tariff B (the alternative), reducing the savings from switching A → B + +Output schema per household: + - account_identifier (str) + - zip_code (str, if present in loads) + - total_kwh (float) + - bill_a_dollars (float, baseline tariff energy cost) + - bill_b_dollars (float, alternative tariff energy cost) + - bill_diff_dollars (float, bill_a - bill_b; positive = B is cheaper) + - peak_kwh_hour (float) + - capacity_kw (float, = peak_kwh_hour) + - capacity_charge_dollars (float, applied to tariff B) + - admin_fee_dollars (float, applied to tariff B) + - net_bill_diff_dollars (float, bill_diff - capacity - admin) + - pct_savings (float, bill_diff / bill_a * 100) + - net_pct_savings (float, net_bill_diff / bill_a * 100) """ from __future__ import annotations import argparse +import gc import logging +import shutil from pathlib import Path import polars as pl +import pyarrow as pa +import pyarrow.parquet as pq logging.basicConfig( level=logging.INFO, @@ -70,62 +82,112 @@ def load_hourly_loads(path: Path) -> pl.DataFrame: return df -def load_spreads(path: Path) -> pl.DataFrame: - """Load hourly flat-vs-RTP spreads.""" +def load_tariff_prices(path: Path, label: str) -> pl.DataFrame: + """Load hourly tariff prices and rename price column with label suffix.""" if not path.exists(): - raise FileNotFoundError(f"Spreads file not found: {path}") + raise FileNotFoundError(f"Tariff prices file not found: {path}") - logger.info(f"Loading hourly spreads from {path}") + logger.info(f"Loading tariff prices ({label}) from {path}") df = pl.read_parquet(path) - required = {"datetime_chicago", "rtp_price_cents", "flat_price_cents"} + required = {"datetime_chicago", "price_cents_per_kwh"} missing = required - set(df.columns) if missing: - raise ValueError(f"Spreads file missing required columns: {sorted(missing)}") + raise ValueError(f"Tariff prices ({label}) missing required columns: {sorted(missing)}") - # Only keep what we need return df.select([ pl.col("datetime_chicago"), - pl.col("rtp_price_cents"), - pl.col("flat_price_cents"), + pl.col("price_cents_per_kwh").alias(f"price_{label}_cents"), ]) +def _join_tariff( + df: pl.DataFrame, + tariff: pl.DataFrame, + price_col: str, + label: str, +) -> pl.DataFrame: + """Left-join a tariff onto df, fail-loud on nulls or row-count changes.""" + n_before = df.height + + # Left join so we can detect unmatched hours explicitly via nulls, + # rather than silently dropping rows with an inner join. + joined = df.join( + tariff, + left_on="hour_chicago", + right_on="datetime_chicago", + how="left", + ) + + # Fail-loud: any null price means the tariff calendar has gaps + # (e.g. missing DST hours). Better to crash here than produce + # bills with silently missing hours. + n_null = joined.select(pl.col(price_col).is_null().sum()).item() + if n_null > 0: + unmatched = joined.filter(pl.col(price_col).is_null()).select("hour_chicago").unique().sort("hour_chicago") + n_unmatched = unmatched.height + hour_min = unmatched["hour_chicago"][0] + hour_max = unmatched["hour_chicago"][-1] + preview = unmatched.head(24) + raise ValueError( + f"Tariff {label}: {n_null} load rows have no matching price. " + f"{n_unmatched} distinct unmatched hours (min={hour_min}, max={hour_max}). " + f"First {min(24, n_unmatched)}:\n{preview}" + ) + + # Row-count check catches duplicate datetime_chicago in the tariff, + # which would silently inflate bills via a many-to-one fan-out. + if joined.height != n_before: + raise RuntimeError( + f"Tariff {label}: join changed row count {n_before} → {joined.height}. " + "Tariff prices may have duplicate datetime_chicago values." + ) + + return joined + + def compute_household_bills( hourly_loads: pl.DataFrame, - spreads: pl.DataFrame, + prices_a: pl.DataFrame, + prices_b: pl.DataFrame, capacity_rate_dollars_per_kw_month: float = 0.0, admin_fee_dollars: float = 0.0, ) -> pl.DataFrame: """ - Join hourly loads to prices and compute monthly bills per household. + Join hourly loads to two tariff price calendars and compute monthly bills. + + Tariff A is the baseline; tariff B is the alternative. + bill_diff = bill_a - bill_b (positive means B is cheaper). + Capacity charge and admin fee are costs specific to tariff B. Args: hourly_loads: Hourly kWh per household for a single month. - spreads: Hourly RTP vs flat price data for the year. + prices_a: Baseline tariff with price_A_cents column. + prices_b: Alternative tariff with price_B_cents column. capacity_rate_dollars_per_kw_month: Capacity charge rate ($/kW-month). admin_fee_dollars: Fixed monthly admin fee per household. Returns: - DataFrame with one row per household and billing fields. + DataFrame with one row per household and billing/comparison fields. """ - logger.info("Joining hourly loads with hourly spreads on local hour...") + n_loads = hourly_loads.height + logger.info("Joining %d load rows with two tariff price calendars...", n_loads) - joined = hourly_loads.join( - spreads, - left_on="hour_chicago", - right_on="datetime_chicago", - how="inner", - ) + joined = _join_tariff(hourly_loads, prices_a, "price_A_cents", "A") + joined = _join_tariff(joined, prices_b, "price_B_cents", "B") if joined.is_empty(): raise RuntimeError("Join produced no rows. Check datetime alignment and inputs.") - # Compute hourly cost under each tariff + # Compute hourly costs and difference. + # Sign convention: bill_diff = A - B, so positive means B is cheaper. + # This matches the intuition "savings from switching TO the alternative." joined = joined.with_columns( - (pl.col("kwh_hour") * pl.col("rtp_price_cents")).alias("bill_rtp_cents"), - (pl.col("kwh_hour") * pl.col("flat_price_cents")).alias("bill_flat_cents"), - ).with_columns((pl.col("bill_flat_cents") - pl.col("bill_rtp_cents")).alias("bill_diff_cents")) + (pl.col("kwh_hour") * pl.col("price_A_cents")).alias("bill_a_cents"), + (pl.col("kwh_hour") * pl.col("price_B_cents")).alias("bill_b_cents"), + ).with_columns( + (pl.col("bill_a_cents") - pl.col("bill_b_cents")).alias("bill_diff_cents"), + ) # Grouping keys: always by account, include zip_code if present group_cols: list[str] = ["account_identifier"] @@ -136,35 +198,37 @@ def compute_household_bills( monthly = joined.group_by(group_cols).agg( pl.col("kwh_hour").sum().alias("total_kwh"), - pl.col("bill_rtp_cents").sum().alias("rtp_bill_cents"), - pl.col("bill_flat_cents").sum().alias("flat_bill_cents"), + pl.col("bill_a_cents").sum().alias("bill_a_cents"), + pl.col("bill_b_cents").sum().alias("bill_b_cents"), pl.col("bill_diff_cents").sum().alias("bill_diff_cents"), pl.col("kwh_hour").max().alias("peak_kwh_hour"), ) # Convert to dollars and define capacity_kw monthly = monthly.with_columns( - (pl.col("rtp_bill_cents") / 100).alias("rtp_bill_dollars"), - (pl.col("flat_bill_cents") / 100).alias("flat_bill_dollars"), + (pl.col("bill_a_cents") / 100).alias("bill_a_dollars"), + (pl.col("bill_b_cents") / 100).alias("bill_b_dollars"), (pl.col("bill_diff_cents") / 100).alias("bill_diff_dollars"), pl.col("peak_kwh_hour").alias("capacity_kw"), - ).drop(["rtp_bill_cents", "flat_bill_cents", "bill_diff_cents"]) + ).drop(["bill_a_cents", "bill_b_cents", "bill_diff_cents"]) - # Gross % savings (energy spread only) + # Gross % savings (energy only, relative to baseline A) monthly = monthly.with_columns( - pl.when(pl.col("flat_bill_dollars") > 0) - .then(pl.col("bill_diff_dollars") / pl.col("flat_bill_dollars") * 100) + pl.when(pl.col("bill_a_dollars") > 0) + .then(pl.col("bill_diff_dollars") / pl.col("bill_a_dollars") * 100) .otherwise(None) - .alias("pct_savings") + .alias("pct_savings"), ) - # Capacity + admin adjustments + # Capacity + admin adjustments are costs unique to tariff B (the + # alternative), not present in baseline A. They reduce the savings + # from switching A→B, modeling real-world RTP program fees. apply_capacity = capacity_rate_dollars_per_kw_month > 0 apply_admin = admin_fee_dollars > 0 if apply_capacity or apply_admin: logger.info( - "Applying capacity/admin adjustments: " + "Applying capacity/admin adjustments (tariff B costs): " f"capacity_rate=${capacity_rate_dollars_per_kw_month:.3f}/kW-month, " f"admin_fee=${admin_fee_dollars:.2f}/month" ) @@ -174,7 +238,8 @@ def compute_household_bills( pl.lit(admin_fee_dollars).alias("admin_fee_dollars"), ) else: - # Keep columns explicit but zeroed so schema is stable + # Keep columns explicit but zeroed so downstream code never has + # to branch on "does this column exist?" — stable schema always. monthly = monthly.with_columns( pl.lit(0.0).alias("capacity_charge_dollars"), pl.lit(0.0).alias("admin_fee_dollars"), @@ -187,17 +252,17 @@ def compute_household_bills( ) monthly = monthly.with_columns( - pl.when(pl.col("flat_bill_dollars") > 0) - .then(pl.col("net_bill_diff_dollars") / pl.col("flat_bill_dollars") * 100) + pl.when(pl.col("bill_a_dollars") > 0) + .then(pl.col("net_bill_diff_dollars") / pl.col("bill_a_dollars") * 100) .otherwise(None) - .alias("net_pct_savings") + .alias("net_pct_savings"), ) return monthly def summarize_results(df: pl.DataFrame) -> None: - """Print a textual summary of the billing results.""" + """Print a textual summary of the billing comparison.""" n_households = df.height gross_mean = df["bill_diff_dollars"].mean() @@ -216,22 +281,54 @@ def summarize_results(df: pl.DataFrame) -> None: print("HOUSEHOLD BILL SUMMARY (MONTH)") print("=" * 70) print(f"Households: {n_households:,}") - print("\nEnergy spread only (flat - RTP):") + print("\nEnergy savings switching A → B (bill_a - bill_b):") print(f" Mean bill difference: ${gross_mean:8.2f}") print(f" Median bill difference: ${gross_median:8.2f}") - print(f" % saving with RTP: {pct_saving_gross:6.1f}%") - print(f" % paying more on RTP: {pct_paying_more_gross:6.1f}%") - print("\nAfter capacity + admin adjustments:") + print(f" % saving with B: {pct_saving_gross:6.1f}%") + print(f" % paying more on B: {pct_paying_more_gross:6.1f}%") + print("\nAfter capacity + admin adjustments (tariff B costs):") print(f" Mean NET bill difference: ${net_mean:8.2f}") print(f" Median NET bill difference: ${net_median:8.2f}") - print(f" % NET saving with RTP: {pct_saving_net:6.1f}%") - print(f" % NET paying more on RTP: {pct_paying_more_net:6.1f}%") + print(f" % NET saving with B: {pct_saving_net:6.1f}%") + print(f" % NET paying more on B: {pct_paying_more_net:6.1f}%") print("=" * 70) +_BILL_SHARDS = 16 + + +def _scatter_loads_to_shards( + loads_path: Path, + shard_dir: Path, + n_shards: int, +) -> list[Path]: + """Hash-partition hourly loads by account_identifier into shard files.""" + from collections import defaultdict + + writers: dict[int, pq.ParquetWriter] = {} + pf = pq.ParquetFile(str(loads_path)) + for batch in pf.iter_batches(batch_size=50_000): + tbl = pa.Table.from_batches([batch]) + accts = tbl.column("account_identifier").to_pylist() + buckets: dict[int, list[int]] = defaultdict(list) + for i, a in enumerate(accts): + buckets[hash(a) % n_shards].append(i) + for sid, indices in buckets.items(): + sub = tbl.take(indices) + if sid not in writers: + writers[sid] = pq.ParquetWriter( + str(shard_dir / f"shard_{sid:03d}.parquet"), + sub.schema, + ) + writers[sid].write_table(sub) + for w in writers.values(): + w.close() + return sorted(shard_dir / f"shard_{sid:03d}.parquet" for sid in writers) + + def main() -> int: parser = argparse.ArgumentParser( - description="Compute monthly household bills under flat vs RTP pricing.", + description="Compute monthly household bills comparing two hourly tariffs.", ) parser.add_argument( "--hourly-loads", @@ -240,10 +337,16 @@ def main() -> int: help="Parquet file with hourly loads (output of compute_hourly_loads.py)", ) parser.add_argument( - "--spreads", + "--tariff-prices-a", + type=Path, + required=True, + help="Baseline tariff: Parquet with datetime_chicago + price_cents_per_kwh.", + ) + parser.add_argument( + "--tariff-prices-b", type=Path, required=True, - help="Parquet file with hourly flat vs RTP spreads.", + help="Alternative tariff: Parquet with datetime_chicago + price_cents_per_kwh.", ) parser.add_argument( "--output", @@ -255,35 +358,74 @@ def main() -> int: "--capacity-rate-dollars-per-kw-month", type=float, default=0.0, - help="Capacity charge rate in $/kW-month (default: 0.0 = disabled).", + help="Capacity charge rate in $/kW-month applied to tariff B (default: 0.0 = disabled).", ) parser.add_argument( "--admin-fee-dollars", type=float, default=0.0, - help="Fixed monthly admin fee per household in $ (default: 0.0 = disabled).", + help="Fixed monthly admin fee for tariff B in $ (default: 0.0 = disabled).", ) args = parser.parse_args() logger.info("Starting household bill computation...") - hourly_loads = load_hourly_loads(args.hourly_loads) - spreads = load_spreads(args.spreads) + if not args.hourly_loads.exists(): + raise FileNotFoundError(f"Hourly loads file not found: {args.hourly_loads}") - bills = compute_household_bills( - hourly_loads, - spreads, - capacity_rate_dollars_per_kw_month=args.capacity_rate_dollars_per_kw_month, - admin_fee_dollars=args.admin_fee_dollars, - ) + # Validate schema without loading data into memory + schema = pq.read_schema(str(args.hourly_loads)) + required = {"account_identifier", "hour_chicago", "kwh_hour"} + missing = required - set(schema.names) + if missing: + raise ValueError(f"Hourly loads missing required columns: {sorted(missing)}") - summarize_results(bills) + prices_a = load_tariff_prices(args.tariff_prices_a, "A") + prices_b = load_tariff_prices(args.tariff_prices_b, "B") - # Save output + # Shard hourly loads by account hash so the full file is never in memory args.output.parent.mkdir(parents=True, exist_ok=True) - bills.write_parquet(args.output) - logger.info(f"Wrote household bills to {args.output}") + shard_dir = args.output.parent / "_shards" + if shard_dir.exists(): + shutil.rmtree(shard_dir) + shard_dir.mkdir(parents=True) + + try: + logger.info("Scattering hourly loads into %d shards...", _BILL_SHARDS) + shard_paths = _scatter_loads_to_shards(args.hourly_loads, shard_dir, _BILL_SHARDS) + logger.info("Scattered into %d shard files", len(shard_paths)) + + bill_parts: list[Path] = [] + for i, sp in enumerate(shard_paths): + logger.info("Computing bills for shard %d/%d: %s", i + 1, len(shard_paths), sp.name) + shard_loads = pl.read_parquet(sp) + + shard_bills = compute_household_bills( + shard_loads, + prices_a, + prices_b, + capacity_rate_dollars_per_kw_month=args.capacity_rate_dollars_per_kw_month, + admin_fee_dollars=args.admin_fee_dollars, + ) + + part_path = shard_dir / f"bills_{i:03d}.parquet" + shard_bills.write_parquet(part_path) + bill_parts.append(part_path) + logger.info(" shard %d: %d households", i + 1, shard_bills.height) + del shard_loads, shard_bills + gc.collect() + + # Concat shard results (small: one row per household) + logger.info("Concatenating %d shard bill files...", len(bill_parts)) + bills = pl.concat([pl.read_parquet(p) for p in bill_parts], how="vertical") + + summarize_results(bills) + + bills.write_parquet(args.output) + logger.info(f"Wrote household bills to {args.output}") + finally: + shutil.rmtree(shard_dir, ignore_errors=True) return 0 diff --git a/scripts/bench/eager_vs_lazy_benchmarks.py b/archive/bench/eager_vs_lazy_benchmarks.py similarity index 100% rename from scripts/bench/eager_vs_lazy_benchmarks.py rename to archive/bench/eager_vs_lazy_benchmarks.py diff --git a/analysis/pipelines/chicago_sampler.py b/archive/chicago_sampler.py similarity index 99% rename from analysis/pipelines/chicago_sampler.py rename to archive/chicago_sampler.py index 513f87e..bc26826 100644 --- a/analysis/pipelines/chicago_sampler.py +++ b/archive/chicago_sampler.py @@ -1,7 +1,8 @@ #!/usr/bin/env python """ -CLI wrapper for Chicago smart meter sampling. +CLI wrapper for ComEd smart meter sampling. Handles multiple ZIP codes and writes output for each. +Pass --zips or --zips-file to specify which ZIP codes to sample. """ import argparse diff --git a/scripts/analysis/create_chicago_visualizations.py b/archive/create_chicago_visualizations.py similarity index 64% rename from scripts/analysis/create_chicago_visualizations.py rename to archive/create_chicago_visualizations.py index f0d543a..816112c 100644 --- a/scripts/analysis/create_chicago_visualizations.py +++ b/archive/create_chicago_visualizations.py @@ -1,15 +1,25 @@ #!/usr/bin/env python """ -Final visualizations for Chicago smart meter data (CM90 dataset): +Visualizations for ComEd smart meter data (CM90 dataset): - Heatmap shows MEAN kWh per 30-min per customer. - Monthly bar chart annotates each bar with the month's mean kWh. - Hourly profile with peak/baseload annotations. - Weekend vs weekday comparison. -Usage: +Illinois is the default geographic scope. Pass --geography and --zip-codes +to produce a named subset (e.g. Chicago). + +Usage (Illinois, all ZIPs): + python scripts/analysis/create_chicago_visualizations.py \ + --input data/illinois_2024/CLIPPED_CM90.parquet \ + --output figures/illinois_2024 + +Usage (Chicago subset): python scripts/analysis/create_chicago_visualizations.py \ - --input analysis/chicago_2024/final/CLIPPED_CM90.parquet \ - --output analysis/chicago_2024/visualizations + --input data/illinois_2024/CLIPPED_CM90.parquet \ + --output figures/chicago_2024 \ + --geography Chicago \ + --zip-codes 60601 60602 60603 """ import argparse @@ -33,11 +43,18 @@ }) -def create_heatmap(data_path: str, output_path: Path): +def _apply_zip_filter(lf: pl.LazyFrame, zip_codes: list[str] | None) -> pl.LazyFrame: + """Restrict to a named ZIP code subset when zip_codes is provided.""" + if zip_codes is not None: + lf = lf.filter(pl.col("zip_code").is_in(zip_codes)) + return lf + + +def create_heatmap(data_path: str, output_path: Path, geography: str, zip_codes: list[str] | None): """Monthly-hourly heatmap (MEAN kWh per customer).""" print("\n📊 Creating heatmap (MEAN kWh per customer)...") - lf = pl.scan_parquet(data_path) + lf = _apply_zip_filter(pl.scan_parquet(data_path), zip_codes) stats = lf.select([ pl.col("account_identifier").n_unique().alias("n_customers"), @@ -54,6 +71,10 @@ def create_heatmap(data_path: str, output_path: Path): .collect(engine="streaming") ) + if monthly_hourly.is_empty(): + print(f"⚠️ No data for geography '{geography}' — skipping heatmap.") + return + matrix = monthly_hourly.pivot(index="hour", columns="sample_month", values="mean_kwh").fill_null(0) hour_labels = matrix.select("hour").to_series().to_list() @@ -83,7 +104,7 @@ def create_heatmap(data_path: str, output_path: Path): ax.set_ylabel("Hour of Day", fontsize=15, fontweight="bold", labelpad=12) ax.set_title( "Residential Electricity Load Patterns: Temporal Heat Map\n" - f"Chicago • {date_range} • {n_customers:,} Households", + f"{geography} • {date_range} • {n_customers:,} Households", fontsize=18, fontweight="bold", pad=25, @@ -92,17 +113,18 @@ def create_heatmap(data_path: str, output_path: Path): ax.invert_yaxis() plt.tight_layout(rect=[0, 0.03, 1, 1]) - output_file = output_path / "chicago_heatmap.png" + geo_slug = geography.lower().replace(" ", "_") + output_file = output_path / f"{geo_slug}_heatmap.png" plt.savefig(output_file, dpi=300, bbox_inches="tight", facecolor="white") print(f"✅ Saved: {output_file}") plt.close() -def create_hourly_profile(data_path: str, output_path: Path): +def create_hourly_profile(data_path: str, output_path: Path, geography: str, zip_codes: list[str] | None): """Average hourly profile across the year (mean and IQR).""" print("\n📊 Creating hourly profile...") - lf = pl.scan_parquet(data_path) + lf = _apply_zip_filter(pl.scan_parquet(data_path), zip_codes) n_customers = lf.select(pl.col("account_identifier").n_unique()).collect(engine="streaming")[0, 0] hourly = ( @@ -116,6 +138,10 @@ def create_hourly_profile(data_path: str, output_path: Path): .collect(engine="streaming") ) + if hourly.is_empty(): + print(f"⚠️ No data for geography '{geography}' — skipping hourly profile.") + return + _fig, ax = plt.subplots(figsize=(15, 8)) hours = hourly["hour"].to_list() mean = hourly["mean_kwh"].to_list() @@ -128,7 +154,7 @@ def create_hourly_profile(data_path: str, output_path: Path): ax.set_xlabel("Hour of Day", fontsize=15, fontweight="bold", labelpad=12) ax.set_ylabel("Energy Consumption (kWh per 30-min)", fontsize=15, fontweight="bold", labelpad=12) ax.set_title( - f"Average Hourly Electricity Usage Profile\n{n_customers:,} Chicago Households", + f"Average Hourly Electricity Usage Profile\n{n_customers:,} {geography} Households", fontsize=18, fontweight="bold", pad=25, @@ -167,17 +193,18 @@ def create_hourly_profile(data_path: str, output_path: Path): ax.legend(loc="upper left", framealpha=0.95, edgecolor="#000", fancybox=True, shadow=True, fontsize=12) plt.tight_layout() - output_file = output_path / "chicago_hourly_profile.png" + geo_slug = geography.lower().replace(" ", "_") + output_file = output_path / f"{geo_slug}_hourly_profile.png" plt.savefig(output_file, dpi=300, bbox_inches="tight", facecolor="white") print(f"✅ Saved: {output_file}") plt.close() -def create_monthly_profile(data_path: str, output_path: Path): +def create_monthly_profile(data_path: str, output_path: Path, geography: str, zip_codes: list[str] | None): """Monthly average bar chart with mean kWh annotations.""" print("\n📊 Creating monthly profile...") - lf = pl.scan_parquet(data_path) + lf = _apply_zip_filter(pl.scan_parquet(data_path), zip_codes) monthly = ( lf.group_by("sample_month") @@ -190,6 +217,10 @@ def create_monthly_profile(data_path: str, output_path: Path): .collect(engine="streaming") ) + if monthly.is_empty(): + print(f"⚠️ No data for geography '{geography}' — skipping monthly profile.") + return + _fig, ax = plt.subplots(figsize=(15, 8)) months = monthly["sample_month"].to_list() mean_kwh = monthly["mean_kwh"].to_list() @@ -214,7 +245,7 @@ def create_monthly_profile(data_path: str, output_path: Path): ax.set_xlabel("Month", fontsize=15, fontweight="bold", labelpad=12) ax.set_ylabel("Average Energy (kWh per 30-min)", fontsize=15, fontweight="bold", labelpad=12) - ax.set_title("Monthly Average Electricity Consumption\nChicago", fontsize=18, fontweight="bold", pad=25) + ax.set_title(f"Monthly Average Electricity Consumption\n{geography}", fontsize=18, fontweight="bold", pad=25) ax.set_xticks(range(len(months))) ax.set_xticklabels(month_labels, fontsize=12) @@ -222,17 +253,18 @@ def create_monthly_profile(data_path: str, output_path: Path): ax.grid(True, alpha=0.3, axis="y") plt.tight_layout(rect=[0, 0.03, 1, 1]) - output_file = output_path / "chicago_monthly_profile.png" + geo_slug = geography.lower().replace(" ", "_") + output_file = output_path / f"{geo_slug}_monthly_profile.png" plt.savefig(output_file, dpi=300, bbox_inches="tight", facecolor="white") print(f"✅ Saved: {output_file}") plt.close() -def create_weekend_comparison(data_path: str, output_path: Path): +def create_weekend_comparison(data_path: str, output_path: Path, geography: str, zip_codes: list[str] | None): """Weekday vs weekend mean kWh per 30-min.""" print("\n📊 Creating weekend comparison...") - lf = pl.scan_parquet(data_path) + lf = _apply_zip_filter(pl.scan_parquet(data_path), zip_codes) comparison = ( lf.group_by(["hour", "is_weekend"]) .agg(pl.col("kwh").mean().alias("mean_kwh")) @@ -240,9 +272,13 @@ def create_weekend_comparison(data_path: str, output_path: Path): .collect(engine="streaming") ) - weekday = comparison.filter(not pl.col("is_weekend")) + weekday = comparison.filter(~pl.col("is_weekend")) weekend = comparison.filter(pl.col("is_weekend")) + if weekday.is_empty() or weekend.is_empty(): + print(f"⚠️ Missing weekday or weekend data for geography '{geography}' — skipping weekend comparison.") + return + _fig, ax = plt.subplots(figsize=(15, 9)) sns.lineplot( @@ -268,7 +304,7 @@ def create_weekend_comparison(data_path: str, output_path: Path): ax.set_xlabel("Hour of Day", fontsize=15, fontweight="bold", labelpad=12) ax.set_ylabel("Average Energy (kWh per 30-min)", fontsize=15, fontweight="bold", labelpad=12) - ax.set_title("Weekday vs Weekend Load Profiles\nChicago", fontsize=18, fontweight="bold", pad=30) + ax.set_title(f"Weekday vs Weekend Load Profiles\n{geography}", fontsize=18, fontweight="bold", pad=30) ax.grid(True, alpha=0.4, linestyle="--", linewidth=0.8) ax.set_xticks(range(0, 24, 2)) ax.set_xlim(-0.5, 23.5) @@ -279,50 +315,86 @@ def create_weekend_comparison(data_path: str, output_path: Path): ax.legend(loc="upper left", framealpha=0.95, edgecolor="#000", fancybox=True, shadow=True, fontsize=13) plt.tight_layout() - output_file = output_path / "chicago_weekend_comparison.png" + geo_slug = geography.lower().replace(" ", "_") + output_file = output_path / f"{geo_slug}_weekend_comparison.png" plt.savefig(output_file, dpi=300, bbox_inches="tight", facecolor="white") print(f"✅ Saved: {output_file}") plt.close() def main(): - parser = argparse.ArgumentParser(description="Create visualizations from Chicago smart meter data") + parser = argparse.ArgumentParser( + description="Create visualizations from ComEd smart meter data. " + "Illinois is the default geographic scope; use --geography and --zip-codes " + "to produce a named subset (e.g. Chicago)." + ) parser.add_argument( "--input", required=True, - help="Path to input parquet file (e.g., analysis/chicago_2024/final/CLIPPED_CM90.parquet)", + help="Path to input parquet file (must contain a zip_code column).", ) parser.add_argument( "--output", required=True, - help="Output directory for visualizations (e.g., analysis/chicago_2024/visualizations)", + help="Output directory for visualizations.", + ) + parser.add_argument( + "--geography", + default="Illinois", + help="Geographic label used in figure titles and output filenames (default: Illinois).", + ) + parser.add_argument( + "--zip-codes", + nargs="+", + default=None, + metavar="ZIP", + help="Optional list of 5-digit ZIP codes to restrict analysis to a subset " + "(e.g. --zip-codes 60601 60602). Omit to include all ZIPs in the input file.", ) args = parser.parse_args() data_path = Path(args.input) output_dir = Path(args.output) + geography: str = args.geography + zip_codes: list[str] | None = args.zip_codes - # Validate input + # Validate input file exists if not data_path.exists(): print(f"❌ File not found: {data_path}") raise SystemExit(1) + # Preflight: check required columns before running any visualizations. + # sample_month and date are produced by chicago_sampler.py; hour/is_weekend + # come from add_time_columns(); kwh/account_identifier/zip_code are canonical. + required_cols = {"account_identifier", "zip_code", "date", "sample_month", "hour", "kwh", "is_weekend"} + actual_cols = set(pl.scan_parquet(data_path).collect_schema().names()) + missing_cols = required_cols - actual_cols + if missing_cols: + print(f"❌ Input file is missing required columns: {sorted(missing_cols)}") + print(f" Found: {sorted(actual_cols)}") + raise SystemExit(1) + # Create output directory output_dir.mkdir(parents=True, exist_ok=True) print("=" * 80) - print("CHICAGO SMART METER VISUALIZATIONS") + print(f"{geography.upper()} SMART METER VISUALIZATIONS") print("=" * 80) - print(f"Input: {data_path}") - print(f"Output: {output_dir}") + print(f"Input: {data_path}") + print(f"Output: {output_dir}") + print(f"Geography: {geography}") + if zip_codes: + print(f"ZIP filter: {', '.join(zip_codes)}") + else: + print("ZIP filter: none (all ZIPs in input file)") print("=" * 80) # Create all visualizations - create_heatmap(str(data_path), output_dir) - create_hourly_profile(str(data_path), output_dir) - create_monthly_profile(str(data_path), output_dir) - create_weekend_comparison(str(data_path), output_dir) + create_heatmap(str(data_path), output_dir, geography, zip_codes) + create_hourly_profile(str(data_path), output_dir, geography, zip_codes) + create_monthly_profile(str(data_path), output_dir, geography, zip_codes) + create_weekend_comparison(str(data_path), output_dir, geography, zip_codes) print("\n" + "=" * 80) print("✅ ALL VISUALIZATIONS COMPLETE!") diff --git a/scripts/data_collection/ameren_scraper.py b/archive/data_collection/ameren_scraper.py similarity index 100% rename from scripts/data_collection/ameren_scraper.py rename to archive/data_collection/ameren_scraper.py diff --git a/archive/edit_geojson.py b/archive/edit_geojson.py new file mode 100644 index 0000000..8ebe5cd --- /dev/null +++ b/archive/edit_geojson.py @@ -0,0 +1,140 @@ +import argparse +import glob +import json +import os +import shutil +from typing import Any, Optional + +ANCHOR_MIN_ID = "__DOMAIN_ANCHOR_MIN__" +ANCHOR_MAX_ID = "__DOMAIN_ANCHOR_MAX__" + + +def _load_bound_sym(folder: str, bound_override: Optional[float]) -> float: + if bound_override is not None: + return float(bound_override) + + range_path = os.path.join(folder, "range_global.json") + if os.path.exists(range_path): + with open(range_path) as f: + meta = json.load(f) + if "bound_sym" not in meta: + raise RuntimeError(f"{range_path} exists but has no 'bound_sym' key.") + return float(meta["bound_sym"]) + + # Fallback: last known bound from your run (range_global.json you pasted) + return 24.143564317219816 + + +def _has_anchor(features: list[dict[str, Any]]) -> bool: + for feat in features: + props = feat.get("properties") or {} + geoid = props.get("geoid_bg") + if geoid in (ANCHOR_MIN_ID, ANCHOR_MAX_ID): + return True + return False + + +def _make_anchor(geoid_bg: str, value: float) -> dict[str, Any]: + return { + "type": "Feature", + "geometry": None, + "properties": { + "geoid_bg": geoid_bg, + "n_households": 0, + "mean_delta": None, + "mean_delta_cap_global_sym": value, + "is_domain_anchor": True, + }, + } + + +def _warn_on_invariant(path: str, features: list[dict[str, Any]]) -> None: + # Fail-loud would be better upstream; here we just warn. + bad = 0 + for feat in features: + props = feat.get("properties") or {} + geoid = props.get("geoid_bg") + if geoid in (ANCHOR_MIN_ID, ANCHOR_MAX_ID): + continue + nh = props.get("n_households") + v = props.get("mean_delta_cap_global_sym") + try: + nh_i = int(nh) if nh is not None else 0 + except Exception: + nh_i = 0 + if nh_i > 0: + try: + float(v) + except Exception: + bad += 1 + if bad: + print( + f"[WARN] {os.path.basename(path)}: {bad} features have n_households>0 but invalid mean_delta_cap_global_sym" + ) + + +def process_file(path: str, bound_sym: float, make_backup: bool) -> bool: + with open(path) as f: + data = json.load(f) + + if data.get("type") != "FeatureCollection": + print(f"[SKIP] {path}: not a FeatureCollection") + return False + + features = data.get("features") + if not isinstance(features, list): + print(f"[SKIP] {path}: missing/invalid 'features' array") + return False + + if _has_anchor(features): + print(f"[OK] {os.path.basename(path)}: anchors already present (skipping)") + _warn_on_invariant(path, features) + return False + + features.append(_make_anchor(ANCHOR_MIN_ID, -bound_sym)) + features.append(_make_anchor(ANCHOR_MAX_ID, bound_sym)) + + _warn_on_invariant(path, features) + + if make_backup: + shutil.copy2(path, path + ".bak") + + # Write compact JSON (smaller files, fast upload). Use indent=2 if you prefer readable. + tmp_path = path + ".tmp" + with open(tmp_path, "w") as f: + json.dump(data, f, ensure_ascii=False) + os.replace(tmp_path, path) + + print(f"[WRITE] {os.path.basename(path)}: added anchors ±{bound_sym:.6f}") + return True + + +def main() -> None: + ap = argparse.ArgumentParser(description="Append ±bound_sym domain anchor features to all GeoJSONs in a folder.") + ap.add_argument("folder", help="Folder containing *.geojson files (and optionally range_global.json).") + ap.add_argument( + "--bound-sym", type=float, default=None, help="Override bound_sym (else read range_global.json if present)." + ) + ap.add_argument("--no-backup", action="store_true", help="Do not write .bak backups.") + args = ap.parse_args() + + folder = os.path.abspath(os.path.expanduser(args.folder)) + if not os.path.isdir(folder): + raise SystemExit(f"Not a directory: {folder}") + + bound_sym = _load_bound_sym(folder, args.bound_sym) + geojson_paths = sorted(glob.glob(os.path.join(folder, "*.geojson"))) + + if not geojson_paths: + raise SystemExit(f"No *.geojson files found in {folder}") + + changed = 0 + for p in geojson_paths: + if process_file(p, bound_sym, make_backup=(not args.no_backup)): + changed += 1 + + print(f"Done. Changed {changed} files out of {len(geojson_paths)}. bound_sym={bound_sym}") + + +if __name__ == "__main__": + main() diff --git a/archive/export_delta_geojson.py b/archive/export_delta_geojson.py new file mode 100644 index 0000000..d944172 --- /dev/null +++ b/archive/export_delta_geojson.py @@ -0,0 +1,135 @@ +import glob +import os + +import geopandas as gpd +import polars as pl + +ROOT = "/ebs/home/griffin_switch_box" +REPO = f"{ROOT}/smart-meter-analysis" + +BILLS_DIR = f"{ROOT}/pricing_pilot/bills" +MAP_DIR = f"{ROOT}/pricing_pilot" +SHP_PATH = f"{REPO}/data/shapefiles/tiger2023_il_bg/tl_2023_17_bg.shp" +OUT_DIR = f"{ROOT}/pricing_pilot/regression/maps" + +os.makedirs(OUT_DIR, exist_ok=True) + +jobs = [ + ( + "jan_2023_dtou", + f"{BILLS_DIR}/chicago_202301_*vs_dtou_scaled_allin_*.parquet", + f"{MAP_DIR}/account_bg_map_202301.parquet", + ), + ( + "jan_2023_stou", + f"{BILLS_DIR}/chicago_202301_*vs_rate_best*_scaled*.parquet", + f"{MAP_DIR}/account_bg_map_202301.parquet", + ), + ( + "jul_2023_dtou", + f"{BILLS_DIR}/chicago_202307_*vs_dtou*_scaled*.parquet", + f"{MAP_DIR}/account_bg_map_202307.parquet", + ), + ( + "jul_2023_stou", + f"{BILLS_DIR}/chicago_202307_*vs_rate_best*_scaled*.parquet", + f"{MAP_DIR}/account_bg_map_202307.parquet", + ), +] + + +def pick_many(pattern: str) -> list[str]: + hits = sorted(glob.glob(pattern)) + if not hits: + raise RuntimeError(f"No files matched glob: {pattern}") + return hits + + +def choose_delta(cols: list[str]) -> pl.Expr: + # IMPORTANT: these columns are already Alternative - Flat in your billing outputs. + if "net_bill_diff_dollars" in cols: + return pl.col("net_bill_diff_dollars") + if "bill_diff_dollars" in cols: + return pl.col("bill_diff_dollars") + if "bill_b_dollars" in cols and "bill_a_dollars" in cols: + return pl.col("bill_b_dollars") - pl.col("bill_a_dollars") + raise RuntimeError(f"No valid delta columns found. cols={cols}") + + +if not os.path.exists(SHP_PATH): + raise FileNotFoundError(SHP_PATH) + +g = gpd.read_file(SHP_PATH) +if "GEOID" not in g.columns: + raise RuntimeError("Shapefile missing GEOID") +g["GEOID"] = g["GEOID"].astype(str) + +for label, bills_glob, map_path in jobs: + bill_files = pick_many(bills_glob) + if not os.path.exists(map_path): + raise FileNotFoundError(map_path) + + lf = pl.scan_parquet(bill_files) + cols = lf.collect_schema().names() + if "account_identifier" not in cols: + raise RuntimeError(f"Missing account_identifier in one of: {bills_glob}") + + delta_expr = choose_delta(cols).cast(pl.Float64).alias("delta_dollars") + + bills = lf.select([ + pl.col("account_identifier").cast(pl.Utf8), + delta_expr, + ]).collect() + + amap = pl.read_parquet(map_path).select([ + pl.col("account_identifier").cast(pl.Utf8), + pl.col("geoid_bg").cast(pl.Utf8), + ]) + + joined = bills.join(amap, on="account_identifier", how="inner").filter(pl.col("geoid_bg").is_not_null()) + + bg = joined.group_by("geoid_bg").agg([ + pl.col("delta_dollars").mean().alias("mean_delta"), + pl.len().alias("n_households"), + ]) + + stats = bg.select([ + pl.len().alias("n_bgs"), + pl.col("mean_delta").mean().alias("mean_of_bg_means"), + pl.col("mean_delta").min().alias("min"), + pl.col("mean_delta").max().alias("max"), + (pl.col("mean_delta") > 0).mean().alias("share_pos"), + (pl.col("mean_delta") < 0).mean().alias("share_neg"), + ]).to_dict(as_series=False) + + print(f"\n[{label}] bills_files={len(bill_files)}") + for k, v in stats.items(): + print(f" {k}: {v[0]}") + + # Fail-loud guardrail: if everything is one sign AND magnitude is big, something is wrong. + if (stats["share_pos"][0] == 0.0 or stats["share_neg"][0] == 0.0) and abs(stats["mean_of_bg_means"][0]) > 5.0: + raise RuntimeError(f"Suspicious BG deltas for {label}: all one sign with large magnitude. Aborting export.") + + bg_pd = bg.to_pandas() + bg_pd["geoid_bg"] = bg_pd["geoid_bg"].astype(str) + + # Scope geometry to only the counties present in the bill data, then left-join + # so every BG in those counties appears in the output — null values for BGs + # with no household data (no sentinel values). + county_fips_set = set(bg_pd["geoid_bg"].str[:5]) + g_counties = g[g["GEOID"].str[:5].isin(county_fips_set)] + merged = g_counties.merge(bg_pd, left_on="GEOID", right_on="geoid_bg", how="left") + # Fill geoid_bg from GEOID for rows with no matching household data. + merged["geoid_bg"] = merged["geoid_bg"].fillna(merged["GEOID"]) + + out_path = f"{OUT_DIR}/{label}_delta.geojson" + merged.to_file(out_path, driver="GeoJSON") + + n_with_data = int(merged["n_households"].notna().sum()) + used = ",".join([os.path.basename(x) for x in bill_files]) + print( + f"WROTE {out_path} | BG count: {len(merged)} ({n_with_data} with data, {len(merged) - n_with_data} null-fill)" + ) + print(f" bills: {used}") + +print("\nDONE: 4 Felt-ready GeoJSON layers created.") diff --git a/analysis/stage2/stage2_multinom_blockgroup_weighted.R b/archive/stage2_R/stage2_multinom_blockgroup_weighted.R similarity index 100% rename from analysis/stage2/stage2_multinom_blockgroup_weighted.R rename to archive/stage2_R/stage2_multinom_blockgroup_weighted.R diff --git a/scripts/tasks/task_runner.py b/archive/tasks/task_runner.py similarity index 100% rename from scripts/tasks/task_runner.py rename to archive/tasks/task_runner.py diff --git a/tests/test_ameren_scraper.py b/archive/test_ameren_scraper.py similarity index 100% rename from tests/test_ameren_scraper.py rename to archive/test_ameren_scraper.py diff --git a/archive/test_files/fix_final.py b/archive/test_files/fix_final.py deleted file mode 100644 index b12ee10..0000000 --- a/archive/test_files/fix_final.py +++ /dev/null @@ -1,11 +0,0 @@ -with open("smart_meter_analysis/census.py") as f: - lines = f.readlines() - -# Find and fix the _import_cenpy line -for i, line in enumerate(lines): - if "def _import_cenpy()" in line: - lines[i] = "def _import_cenpy(): # type: ignore[no-untyped-def]\n" - break - -with open("smart_meter_analysis/census.py", "w") as f: - f.writelines(lines) diff --git a/analysis/viz_test/visualizations/chicago_heatmap.png b/archive/viz_test/visualizations/chicago_heatmap.png similarity index 100% rename from analysis/viz_test/visualizations/chicago_heatmap.png rename to archive/viz_test/visualizations/chicago_heatmap.png diff --git a/analysis/viz_test/visualizations/chicago_hourly_profile.png b/archive/viz_test/visualizations/chicago_hourly_profile.png similarity index 100% rename from analysis/viz_test/visualizations/chicago_hourly_profile.png rename to archive/viz_test/visualizations/chicago_hourly_profile.png diff --git a/analysis/viz_test/visualizations/chicago_monthly_profile.png b/archive/viz_test/visualizations/chicago_monthly_profile.png similarity index 100% rename from analysis/viz_test/visualizations/chicago_monthly_profile.png rename to archive/viz_test/visualizations/chicago_monthly_profile.png diff --git a/analysis/viz_test/visualizations/chicago_weekend_comparison.png b/archive/viz_test/visualizations/chicago_weekend_comparison.png similarity index 100% rename from analysis/viz_test/visualizations/chicago_weekend_comparison.png rename to archive/viz_test/visualizations/chicago_weekend_comparison.png diff --git a/cache/report_variables.pkl b/cache/report_variables.pkl new file mode 100644 index 0000000..a4ac0dd Binary files /dev/null and b/cache/report_variables.pkl differ diff --git a/docs/modules.md b/docs/modules.md index 611d322..6fb2e43 100644 --- a/docs/modules.md +++ b/docs/modules.md @@ -1 +1 @@ -::: smart_meter_analysis.foo +::: smart_meter_analysis diff --git a/docs/pricing_simulation_test_plan.md b/docs/pricing_simulation_test_plan.md new file mode 100644 index 0000000..5db5f8b --- /dev/null +++ b/docs/pricing_simulation_test_plan.md @@ -0,0 +1,131 @@ +# Pricing Simulation Test Plan + +Reviewer-facing checklist for the RTP billing pipeline (`analysis/rtp/`) and +orchestrator (`scripts/run_billing_pipeline.py`). + +## Quick Reference: Running Tests + +```bash +# All pricing simulation tests (< 3 min on sample data) +pytest tests/test_billing_pipeline_e2e.py tests/test_regression_predictor_modes.py tests/test_fail_loud_conditions.py -v + +# Individual modules +pytest tests/test_billing_pipeline_e2e.py -v # E2E orchestrator (~90s) +pytest tests/test_regression_predictor_modes.py -v # Predictor modes (~2s) +pytest tests/test_fail_loud_conditions.py -v # Fail-loud conditions (~15s) + +# DST roll-in tests (already merged) +pytest tests/test_dst_rollin.py -v +``` + +## Test Modules + +### 1. `tests/test_billing_pipeline_e2e.py` + +End-to-end orchestrator test using real sample data in `data/processed/` and +`data/reference/`. Runs via subprocess, writes to `tmp_path`. + +| Test class | What it checks | Key assertions | +|---|---|---| +| `TestDirectoryLayout` | Expected dir tree: `_tmp/`, `month=YYYYMM/`, manifest, log | Files/dirs exist | +| `TestPerMonthOutputs` | Hourly loads and bills are non-empty, have required columns | Column presence, no nulls in price columns | +| `TestAnnualAggregate` | Annual totals equal monthly sums; household count matches | Dollar-sum equality, pct_savings recomputed | +| `TestJoinCoverage` | No duplicate accounts in bills; no silent drops from loads | Set comparison of account IDs | +| `TestRegressionOutputs` | Regression artifacts exist; JSON has both models, R-sq, predictors | Schema of results + metadata JSON | +| `TestManifest` | git SHA, months, month_summary, parameters, steps_completed | Required keys + value validation | + +**Runtime:** ~90 seconds (two subprocess runs: with and without regression). + +**Prerequisite data:** +- `data/processed/comed_202308.parquet` (145,824 rows, 98 accounts) +- `data/reference/comed_flat_hourly_prices_2023.parquet` +- `data/reference/comed_stou_hourly_prices_2023.parquet` +- `data/reference/comed_bg_zip4_crosswalk.txt` +- `data/reference/census_17_2023.parquet` + +If any file is missing, the module is skipped with a clear message. + +### 2. `tests/test_regression_predictor_modes.py` + +Unit tests for `detect_predictors()`, `_normalize_zip4_expr()`, and +`_resolve_col()` using synthetic DataFrames (no subprocess, no disk I/O). + +| Test class | What it checks | +|---|---| +| `TestAutoMode` | Auto-infer numeric cols, exclude IDs/NAME/all-null, sorted output | +| `TestCoreMode` | Returns income + building_pct; partial match; fails if neither present | +| `TestExplicitMode` | Validates comma-separated list against census; fails on missing cols | +| `TestNormalizeZip4` | Formats: `#####-####`, `#########`, 5-digit, empty, whitespace, mixed | +| `TestResolveCol` | Preferred found, fallback used, neither raises RuntimeError | + +**Runtime:** < 2 seconds. + +### 3. `tests/test_fail_loud_conditions.py` + +Negative tests that verify non-zero exit codes and clear error messages. + +| Test class | What it checks | +|---|---| +| `TestMissingBillsColumns` | Missing `account_identifier`; missing both savings columns | +| `TestCrosswalkCoverageThreshold` | 100% drop rate vs 5% threshold; 100% threshold allows proceeding | +| `TestZeroPredictors` | Nonexistent explicit predictor; core mode with no core cols in census | +| `TestMissingInputFiles` | Nonexistent bills file; nonexistent census file | +| `TestOrchestratorFailLoud` | Missing interval data; invalid month format (`2023-08` vs `202308`) | +| `TestOutcomeColumnFallback` | `pct_savings` fallback when `net_pct_savings` absent; `net_bill_diff_dollars` fallback | + +**Runtime:** ~15 seconds (small synthetic data, subprocess calls). + +## Failure Semantics + +All scripts use **fail-loud** semantics: exit non-zero with a clear error +message rather than silently producing wrong results. + +| Condition | Script | Expected behavior | +|---|---|---| +| Required column missing from bills | `build_regression_dataset.py` | Exit 1, log error naming the column | +| Neither savings column found | `build_regression_dataset.py` | RuntimeError: "neither 'net_pct_savings' nor 'pct_savings'" | +| Crosswalk drop rate > threshold | `build_regression_dataset.py` | RuntimeError with actual vs threshold pct | +| Zero BGs after filtering | `build_regression_dataset.py` | Exit 1: "No block groups remain" | +| Explicit predictor not in census | `build_regression_dataset.py` | RuntimeError: "not found in census" | +| Too few observations for OLS | `build_regression_dataset.py` | RuntimeError: "only N complete observations for M predictors" | +| statsmodels not installed | `build_regression_dataset.py` | `sys.exit()` with install instructions | +| Interval file missing for month | `run_billing_pipeline.py` | Exit 1: "Interval data not found for YYYYMM" | +| Invalid YYYYMM format | `run_billing_pipeline.py` | ValueError: "Invalid month format" | +| Tariff file missing | `run_billing_pipeline.py` | Exit 1: "tariff-a not found" | +| Monthly bills missing during aggregate | `run_billing_pipeline.py` | FileNotFoundError | + +## Pipeline Data Flow + +``` +interval parquet (per month) + | + v +compute_hourly_loads.py + | -> hourly_loads.parquet (account_identifier, zip_code, hour_chicago, kwh_hour) + v +compute_household_bills.py (x2 tariffs) + | -> household_bills.parquet (13 cols: account_identifier, zip_code, ...) + v +build_annual_aggregate() (concat months, group_by account) + | -> annual_household_aggregate.parquet + v +build_regression_dataset.py (ZIP+4 -> BG crosswalk -> census join -> OLS) + | -> regression_dataset_bg.parquet + | -> regression_results.json + | -> regression_summary.txt + | -> regression_metadata.json +``` + +## Reviewer Checklist + +- [ ] All three test modules pass: `pytest tests/test_billing_pipeline_e2e.py tests/test_regression_predictor_modes.py tests/test_fail_loud_conditions.py -v` +- [ ] DST roll-in tests still pass: `pytest tests/test_dst_rollin.py -v` +- [ ] Regression script produces two OLS models (savings + bill_diff) with non-zero observations +- [ ] Annual aggregate dollar sums match monthly sums (verified by `TestAnnualAggregate`) +- [ ] `pct_savings` is recomputed from annual totals, not averaged from monthly values +- [ ] Manifest JSON includes git SHA, month-by-month row counts, all parameters +- [ ] No writes to tracked data directories (all test output via `tmp_path`) +- [ ] `--predictors core` restricts to `median_household_income` + `old_building_pct` only +- [ ] `--predictors auto` excludes `block_group_geoid`, `GEOID`, `NAME`, and all-null columns +- [ ] Crosswalk join uses `zip4` (9-char `#####-####`) not 5-digit `zip_code` +- [ ] Outcome column fallback: `net_pct_savings` -> `pct_savings` with metadata flag diff --git a/index.qmd b/index.qmd new file mode 100644 index 0000000..55d2ad7 --- /dev/null +++ b/index.qmd @@ -0,0 +1,783 @@ +--- +title: "Who would benefit from ComEd's new TOU programs?" +subtitle: "A statewide simulation of bill impacts under ComEd's time-of-use rate structures" +date: 2026-XX-XX +author: + - name: Griffin Sharps + email: griffin@switch.box + affiliations: + - Switchbox + +keywords: [time-of-use rates, ComEd, rate equity, smart meter data, DTOU, STOU] + +bibliography: references.bib +license: "CC BY-NC" + +toc: true +notebook-links: false +reference-location: margin +fig-cap: true +fig-cap-location: margin +tbl-cap-location: margin + +appendix-style: default +citation-location: document +citation: + container-title: Switchbox +--- + +```{python} +#| echo: false +#| message: false + +import pickle +from pathlib import Path +from types import SimpleNamespace + +v = SimpleNamespace(**pickle.loads(Path("cache/report_variables.pkl").read_bytes())) + +def dollar(x, accuracy=0): + return f"${x:,.{accuracy}f}" + +def pct(x, accuracy=0): + return f"{x * 100:,.{accuracy}f}%" +``` + + +## Introduction + +Today, most of ComEd's residential customers pay a **flat rate** for their +electricity. That means that they pay the same amount of money for a given +amount of electrical usage, at any time of day, on any date throughout the +year. + +This year though, ComEd is introducing two new **time-of-use** (TOU) rates. +Under TOU rates, customers pay more for energy during the times when the power +grid is most likely to be under strain, and less when demand is typically +smaller. Electricity is expensive at times everyone when everyone is likely to +be blasting their AC on a hot summer afternoon, but cheaper at 1 AM that same +night. This is designed to incentivize people to shift their usage to times +that create less strain on the grid. + +These new rates are the product of a years-long regulatory process. Illinois's +2021 **Climate and Equitable Jobs Act** (CEJA) directed utility companies to +promote electrification and to shift electricity demand away from peak hours: +making the grid more resilient and reducing costs for customers. This +legislation created the context for the Illinois Commerce Commission (ICC) +to approve both TOU programs in 2025 after extensive stakeholder negotiations +involving ComEd, the Citizens Utility Board (CUB), environmental groups, +and competitive energy suppliers.[^fn-reg-context] + +[^fn-reg-context]: For a fuller account of the regulatory history, see the +Regulatory Context section of the Appendix. + +Residential customers who sign up for one of these two programs have an +opportunity to save on their electricity bills. They can pay less for energy by +using it during the cheaper off-peak hours and avoiding the more expensive peak +hours. But how much money could they save, exactly? And who would save the most? + +This memo analyzes data from **smart meters** installed across ComEd's service +territory. Smart meters track how much electricity a customer uses on a +half-hourly basis, making it easy to compare the costs of the same behavior +under different rate structures. + +Three questions guide the analysis: + +1. How would different households' bills change under ComEd's new TOU rates? +2. Would lower-income households be negatively affected? +3. If one of the two new TOU rates outperforms the other, why? + +A full analysis would model how customer behavior might change in response to +changes in the rate structure. In this case, we are assuming that customers sign +up, but then continue to use electricity as usual. As a result, these are +conservative estimates on how much households might save. + + +## Executive Summary + +We used smart meter data from January 2023[^fn-july-also] to simulate monthly +electricity bills for `{python} f"{v.n_households_jan:,}"` ComEd residential +accounts under the company's two new time-of-use rate structures: **Delivery +Time-of-Use (DTOU)** and **Supply Time-of-Use (STOU)**.[^fn-commercial-names] +We then compared these simulated bills to what each account would have paid +under the current flat rate, assuming that customers kept their behavior +identical across the two scenarios. The geographic unit of analysis throughout +was a Census block group: an area of around 600–3,000 people. + +[^fn-july-also]: We also ran our simulation using data from July 2023, the peak +cooling month. A comparison between the two months is given later in the memo, +and a full account of the July results can be found in the appendix. + +[^fn-commercial-names]: Commercially, DTOU is marketed as DTOD +(Delivery Time of Day). In ComEd's tariff filings, STOU is referred to as "Rate +BEST" (Basic Electric Service Time-of-use), though that name and program are +still only proposed. We use the shorthand DTOU and STOU throughout this memo. + +**Both TOU rates would save the vast majority of households money.** Under DTOU, +**`{python} f"{v.pct_save_dtou_jan:.1f}"`%** of households would pay less than +they do today. Under STOU, that figure rises to +**`{python} f"{v.pct_save_stou_jan:.1f}"`%**. + +**STOU would save dramatically more than DTOU.** The average block group would +save **$`{python} f"{v.bg_mean_stou_jan:.2f}"`** per month under STOU, compared +to **$`{python} f"{v.bg_mean_dtou_jan:.2f}"`** under DTOU. That's approximately +three to four times more. These savings are driven by STOU's supply charges, +which are far below the current flat supply rate during off-peak hours. + +**STOU's large projected savings depend on placeholder supply rates that ComEd has not yet finalized.** +The supply charges used in our simulation come from illustrative values ComEd +submitted to the ICC last year. They are not binding tariff rates. STOU launches +in June 2026, and ComEd has not announced its actual +rates. The savings we model should not be treated as a forecast. + +**Neither rate would disproportionately burden lower-income communities.** We +measured savings as a share of each household's total bill to account for the +fact that wealthier households tend to have larger bills. In our simulation, a +household's income was not a good predictor of its savings under either rate +structure. + +**Block groups in central Chicago show smaller savings, but this reflects building stock, not geography.** +Roughly **`{python} f"{v.core_chicago_mf_share*100:.0f}"`%** of accounts in +centralChicago are multi-family, compared to +**`{python} f"{v.suburban_mf_share*100:.0f}"`%** in suburban areas. ComEd +charges lower base rates to customers in multi-family buildings than to those in +single-family homes, and this can result in smaller savings for these customers +under TOU rates. + + +## Background: How electricity bills work + +ComEd residential bills have two main components: **supply** and **delivery**. +Supply is the cost of generating the electricity itself. Delivery is the cost of +the **poles and wires** that transport that electricity to your home. In other +words, you pay for both the energy you use and the cost of getting it to you. + +:::{.column-margin} +**Supply** is the cost of generating power. **Delivery** is the cost of the +infrastructure that transports it to your home. +::: + +Both of these components cost more at some points during the day than others. A +flat rate doesn't reflect this difference and simply charges a single "average" +price for all hours of the day. + +**Time-of-use (TOU) rates** replace that single flat price with prices that vary +across different periods of the day. Prices are higher during **peak usage** +hours, when the grid is under strain and the cost of providing electricity is +high. Prices are lower during **off-peak** hours when the grid is not under +strain and the cost of providing electricity is low. + +Both of ComEd's new TOU programs divide the day into four time blocks: Morning +(6 AM–1 PM), Mid-Day Peak (1–7 PM), Evening (7–9 PM), and Overnight (9 PM–6 AM). +But the two programs differ in which part of the bill they change from the +standard flat rate. + +:::{.column-margin} +**DTOU (Delivery Time-of-Use)** varies only the delivery charge by time of day. +The supply charge stays the same as the flat rate. +::: + +The **Delivery Time-of-Use (DTOU)** program changes only the delivery side of +the bill.[^fn-dtou] Supply stays the same as ComEd's flat rate. During the +expensive mid-day peak window, the delivery charge can rise to approximately +10.7¢ per unit of electricity, compared to about 5.9¢ under the flat rate. +During overnight hours however, DTOU rates drop by about half. + +[^fn-dtou]: DTOU was approved in the ICC's Final Order of January 16, 2025 +(Docket 24-0378). DTOU took effect in January 2026 as a voluntary, opt-in +program. + +:::{.column-margin} +**STOU (Supply Time-of-Use)** is a bundled time-of-use structure that varies +both supply and delivery charges by time of day and season. +::: + +The **Supply Time-of-Use (STOU)** program goes further.[^fn-rate-best] It varies + **both** supply and delivery charges by the time of day. While the delivery +side follows the same four time blocks as DTOU, the supply side adds in +additional factors. Firstly, STOU's supply side adds seasonal differentiation +(summer rates are higher than non-summer rates). Timing-wise, supply charges +obey the same logic as DTOU's delivery charges: high costs during the peak, low +ones in the off peak periods. + +[^fn-rate-best]: STOU was approved on rehearing (ICC Order of July 17, 2025, +Docket 24-0378). STOU launches in June 2026 as a voluntary, bundled package: +customers who opt in receive both the time-varying supply rate and DTOU delivery +together. + +So what's the difference for your electricity bill? The more parts of your bill +that vary by time of day, the more **when** you use electricity (and not just +how much) matters. Because DTOU varies only delivery charges — which don't swing +as sharply between peak and off-peak as supply charges do — bill impacts +under DTOU are modest in absolute terms. But as this memo shows, under STOU, +timing could produce big savings for ComEd customers. + +:::{.column-page-inset-right} +{{< embed notebooks/analysis.qmd#fig-rate-structures >}} +::: + + +## Scope and approach + +We simulate what `{python} f"{v.n_households_jan:,}"` ComEd residential accounts +would have paid under each TOU rate structure, using their 2023 half-hour usage +data.[^fn-july-scope] We then compare those simulated bills to what each +household would have paid under the current flat rate to measure who saves, who +pays more, and whether the answer differs meaningfully by income or geography. + +[^fn-july-scope]: We also conducted the same simulation for July 2023, the peak +cooling month. Full results can be found in the appendix. + +The analysis makes the following assumptions: + +- **One month, representing a seasonal extreme.** We model January 2023 (peak +heating). The rates' effects in other seasons and annual net impact cannot be +determined from one month alone. + +- **2025/2026 rates applied to 2023 usage.** For flat rates, we use the 2026 +non-summer prices. The TOU rate schedules are also from 2026. These present-day +rates are applied to 2023 consumption patterns. Our findings are the result of a +constructed simulation: not a report on historical behavior. + +- **No behavioral response.** We assume households do not change when or how +much electricity they use in response to TOU pricing. In practice, increased +costs might cause customers to shift usage away from peak hours, which would +increase their savings. Our estimates are conservative and represent a floor on +potential savings rather than a ceiling. + +- **Anonymized smart meter data.** ComEd's interval data are released under +ICC-approved privacy rules that strip all customer identifiers (name, address, +account number) and apply an anonymity screen. A customer's data can only be +included if their geographic area contains at least 15 customers in the same +delivery class, and no single customer represents 15% or more of that area's +total load.[^fn-anon] Customers who do not pass this screen are excluded from +the dataset entirely. In practice, ComEd's dense service territory (northern +Illinois, including Chicago) means most customers pass the screen — though +sparsely populated Census areas are excluded. + +[^fn-anon]: The anonymity screen was adopted by the ICC in Docket No. 13-0506 +and is commonly known as the 15/15 Rule. + +- **Percentage of bill as the equity metric.** To assess whether TOU rates +disproportionately benefit or cost certain income groups, we measure savings as +a percentage of each household's total flat-rate bill. This controls for the +fact that higher-income households tend to have larger bills (and therefore +larger dollar savings) regardless of rate design. + +- **Census block group income as the income proxy.** We use median household +income at the Census block group level (from the American Community Survey +5-year estimates) as our measure of neighborhood income. This captures +neighborhood-level patterns but does not identify individual low-income +households within high-income block groups, or vice versa.[^fn-bg-income] + +[^fn-bg-income]: Approximately 6.6% of modeled households fall in block groups +without valid Census income data and are excluded from the regression and +quintile analyses. Household-level statistics (e.g., the share of households +that save) use the full account counts. + +- **STOU supply rates are illustrative.** The supply charges used for STOU come +from ComEd's Compromise Proposal (ICC Docket 24-0378), not from a final tariff. +These rates have not been finalized. + + +## Findings + +### Most households would save money under either rate + +Under both DTOU and STOU, the vast majority of ComEd residential customers in +January would pay less than they do under the current flat rate, even with no +change in their behavior. + +Under DTOU, **`{python} f"{v.pct_save_dtou_jan:.1f}"`%** of households would +save money. Under STOU, those savings are near-universal: +**`{python} f"{v.pct_save_stou_jan:.1f}"`%** of households would save. + + +### Under STOU, those savings are substantially larger + +The average block group using DTOU would save +**$`{python} f"{v.bg_mean_dtou_jan:.2f}"`** in January. On the map below, blue +indicates block groups where the average household would save money under DTOU. +The darker the blue is, the larger the average savings. Gray block groups lacked +sufficient data to model. + + + + +But as discussed earlier, delivery charges are relatively stable throughout +the day compared to supply charges. So while DTOU savings are widespread but +relatively small, shouldn't we expect to see the potential for larger savings +under STOU? + +And that's exactly what the data show. Because STOU varies both supply and +delivery charges by time of day, its bill impacts are categorically larger. +The average block group would save +**$`{python} f"{v.bg_mean_stou_jan:.2f}"`** per month under STOU — approximately +three to four times more than under DTOU. The STOU map below uses the same color +scale, and the contrast is stark. Where DTOU showed light blue, STOU is +deep blue across nearly the entire territory: every single block group saves +money on average. + + + +That difference is not about customer behavior. It is about the structure of +STOU's supply rates. + + +### Why does STOU save so much more? + +Under the current flat rate, ComEd customers pay approximately +**`{python} f"{v.flat_ptc_nonsummer:.3f}"`¢** per unit of electricity for +supply. Under the placeholder rates that ComEd showed the ICC, the off-peak +supply charges (morning, evening, and overnight) range from approximately +**`{python} f"{v.bestec_low:.1f}"`¢ to `{python} f"{v.bestec_high:.1f}"`¢**. +That is a discount of **60–70%** below the flat rate during most hours of the +day. + +Most residential electricity consumption falls outside the expensive mid-day +peak window (1–7 PM). The math is simple: when off-peak rates are 60–70% below +the flat rate, and most consumption is off-peak, nearly every household saves +money. The mid-day peak supply charge is high, but it applies to a relatively +small share of the total consumption. For the vast majority of households, the +big off-peak savings overwhelm the peak penalty. + +This is why `{python} f"{v.pct_save_stou_jan:.1f}"`% of households would save +under STOU in January. It is not because those households have unusually +favorable usage patterns. It is because the off-peak supply rates ComEd has +provided so far are so low. + + +### STOU's savings depend on placeholder rates + +::: {.callout-important} +**These placeholder rates are illustrative, not final.** The values used in this +simulation were submitted by ComEd as part of stakeholder negotiations (ICC +Docket 24-0378). They are not legally binding tariff rates. STOU launches in +June 2026, and ComEd has not announced its actual supply rates. If the final +off-peak values were set at 7–8¢ per unit instead of 3–4¢, the savings +documented in this memo would shrink dramatically and the equity patterns could +shift. +::: + + +### Neither rate burdens lower-income communities + +To assess whether TOU rates are regressive (that is, whether they place +disproportionate costs on lower-income households) we measured savings as a +percentage of each household's total flat-rate bill. This controls for the fact +that higher-income households tend to have larger bills, and therefore larger +dollar savings, regardless of rate design. + +:::{.column-page-inset-right} +{{< embed notebooks/analysis.qmd#fig-scatter-stou-jan-pct >}} +::: + +Under STOU in January, savings hover around +`{python} f"{v.stou_jan_mean_pct:.1f}"`% of the bill across all income +quintiles, with no meaningful difference between lower-income and higher-income +neighborhoods.[^fn-stou-jan-pvalue] + +[^fn-stou-jan-pvalue]: We tested whether there is a statistically significant +relationship between block-group income and percentage savings. The result +(p = `{python} f"{v.stou_jan_p_value:.3f}"`) indicates that any apparent trend +could easily be due to chance. By convention, a p-value above 0.05 means the +relationship is not statistically significant. + +Under DTOU in January, there is a tiny trend in the opposite direction — +higher-income neighborhoods save a marginally larger share of their bill — but +the difference is so small as to have no practical significance. The +lowest-income quintile would save +**`{python} f"{v.dtou_jan_q1_pct:.1f}"`%** versus +**`{python} f"{v.dtou_jan_q5_pct:.1f}"`%** for the highest — a gap of +`{python} f"{v.dtou_jan_q1q5_gap:.1f}"` percentage points.[^fn-dtou-jan-r2] + +[^fn-dtou-jan-r2]: This relationship is statistically significant (p < 0.0001) +but income explains approximately 1% of the variation in savings across block +groups. A statistically significant result can still be practically meaningless +when the effect size is negligible. + +In short, **neither rate is meaningfully regressive.** + + +### What changes in summer + +We also conducted the same simulation for July 2023 (the peak cooling month) +using `{python} f"{v.n_households_jul:,}"` accounts. As in January, both rates +save the vast majority of households money and STOU continues to outperform +DTOU. + +But there are two notable differences. + +**Savings are somewhat smaller in summer.** The average block group would save +**$`{python} f"{v.bg_mean_stou_jul:.2f}"`** per month under STOU in July, +compared to **$`{python} f"{v.bg_mean_stou_jan:.2f}"`** in January. Under DTOU, +the average savings are **$`{python} f"{v.bg_mean_dtou_jul:.2f}"`** in July +versus **$`{python} f"{v.bg_mean_dtou_jan:.2f}"`** in January. Summer air +conditioning typically pushes more electricity consumption into the hottest part +of the day: the expensive mid-day peak window. That change would offset the +discounts customers get on their off-peak usage under TOU rates. + +**The equity gradient becomes visible.** In summer, both rates are mildly +progressive — lower-income neighborhoods save a larger share of their bill than +higher-income ones. Under STOU in July, the lowest-income quintile of block +groups would save **`{python} f"{v.stou_jul_q1_pct_savings:.1f}"`%** of their +bill, compared to **`{python} f"{v.stou_jul_q5_pct_savings:.1f}"`%** for the +highest-income quintile: a gap of +**`{python} f"{v.stou_jul_q1q5_gap:.1f}"` percentage points**. Under DTOU in +July, the lowest-income quintile would save +**`{python} f"{v.dtou_jul_q1_pct:.1f}"`%** versus +**`{python} f"{v.dtou_jul_q5_pct:.1f}"`%** for the highest — a gap of +`{python} f"{v.dtou_jul_q1q5_gap:.1f}"` percentage points. + +Under DTOU in July, +**`{python} f"{v.pct_save_dtou_jul:.1f}"`%** of households would save — still an +overwhelming majority, but the lowest share of any scenario. Under STOU, +**`{python} f"{v.pct_save_stou_jul:.1f}"`%** would save. + + + + + +The July maps also reveal more geographic texture than January. This leads us to +our final question: does location matter? + + +### Geographic patterns + +Looking closer at the July maps, some block groups — particularly in central +Chicago — show smaller savings than the surrounding area.[^fn-core-chicago] + +[^fn-core-chicago]: "Central Chicago" refers to block groups falling within 25 +ZIP codes along the lakefront and near downtown, running from Rogers Park south +to South Shore and west through Humboldt Park and the West Loop. These +neighborhoods were selected because they correspond to the lighter-colored areas +visible on both sets of maps. + + + +What's going on here? + +The pattern reflects building stock, not geography. Roughly +**`{python} f"{v.core_chicago_mf_share*100:.0f}"`%** of accounts in central +Chicago are multi-family, compared to +**`{python} f"{v.suburban_mf_share*100:.0f}"`%** in surrounding suburban areas. + +ComEd charges lower base rates to customers in multi-family buildings than to +those in single-family homes.[^fn-delivery-classes] TOU pricing sets its +peak and off-peak rates relative to these base rates, and the lower a base rate +is, the smaller the gap between peak and off-peak rates. As a consequence, the +potential savings are smaller too. + +[^fn-delivery-classes]: ComEd assigns residential customers to "delivery +classes" based on their building type and heating source. The two largest +classes are C23 (single-family homes without electric heat) and C24 +(multi-family buildings without electric heat). Each class has its own +per-kilowatt-hour (kWh) delivery rate — the charge for each unit of electricity +delivered to the customer. Under the current flat rate, C23 customers pay +6.228¢/kWh for delivery, while C24 customers pay 4.791¢/kWh. A kilowatt-hour is +the standard unit for measuring electricity consumption: approximately the +energy used by running ten 100-watt light bulbs for one hour. + +[^fn-suburban]: "Suburban areas" refers to block groups within 45 ZIP codes +spanning near-west suburbs (Oak Park, Berwyn, Cicero, Elmwood Park) and far +northwest suburbs (Crystal Lake, McHenry, Woodstock, Schaumburg, Barrington, +Elgin, and surrounding communities). These areas were selected to represent a +range of suburban densities across ComEd's service territory. + +When we compare single-family homes only, the pattern reverses. Single-family +households in central Chicago would save +**$`{python} f"{v.core_chicago_sf_mean_stou_jul:.2f}"`** per month under STOU in +July, compared to **$`{python} f"{v.suburban_sf_mean_stou_jul:.2f}"`** in +suburban areas.[^fn-suburban] The maps look different in central Chicago not +because of where those customers live, but because of what kind of buildings +they live in. + + +## The bottom line + +Both of ComEd's new TOU rate structures would save almost all of their +residential customers money, and neither would disproportionately burden +lower-income communities or specific places in Illinois. STOU would produce +dramatically larger savings than DTOU, but those savings depend on illustrative +supply rates that ComEd has not yet finalized. The actual impact of STOU on +customers' bills will depend on the rates ComEd sets when the program launches +in June 2026. + + +## Appendix {.appendix} + +### Acknowledgments + + + + +### Data and Methods + +This section describes the data sources, transformations, and assumptions +underlying each major finding in sufficient detail to reproduce the analysis. + +#### Smart meter data + +**Source:** ComEd Anonymous Data Service, accessed under the ICC-approved +privacy framework (Docket No. 13-0506). The dataset provides half-hourly +interval energy consumption (48 readings per day) for residential customers with +AMI meters, anonymized under the 15/15 Rule. + +**Months modeled:** January 2023 and July 2023. + +**Account counts:** + +| Month | Accounts in raw data | Accounts in Block Group-level analysis | +|-------|---------------------|-------------------------------| +| January 2023 | `{python} f"{v.n_households_jan:,}"` | `{python} f"{v.n_bg_analysis_jan:,}"` | +| July 2023 | `{python} f"{v.n_households_jul:,}"` | `{python} f"{v.n_bg_analysis_jul:,}"` | + +The difference between raw and Block Group-level counts reflects two filters: +(1) aninner join to the account-to-block-group crosswalk, which drops +approximately 0.04% of accounts with no block group match, and (2) a Census +income filter that excludes block groups with null or invalid median household +income. Approximately 6.6% of modeled households fall in block groups without +valid income data and are excluded from regressions and quintile +analyses. Household-level statistics (e.g., share saving) use the full raw +counts. + +**Delivery classes included:** C23 (single-family, no electric space heat), C24 +(multi-family, no electric space heat), C26 (single-family, +electric space heat), C28 (multi-family, electric space heat). Classes C25 +(dusk-to-dawn/outdoor lighting) and C27 (electric heating with thermal storage) +are excluded throughout because ComEd publishes no TOU delivery facility charges +for these non-standard residential classes. Without published rates, no bill +comparison can be computed. + +**Anonymization constraints:** Account identifiers are regenerated each month. +There is no persistent ID linking a household's January record to its July +record. Each month is analyzed as an independent cross-section. + +**Data pipeline:** Raw CSVs were converted to Parquet format, with naming +convention `YYYY/MM/part-NNNNN.parquet`. Conversion resulted in files +approximately 1 GiB in size. All 49 months of available data were restructured +and verified, though only January and July 2023 are used in this analysis. + + +#### Geographic crosswalk + +**Source:** A ZIP+4-to-Census-block crosswalk was purchased from Melissa, a +commercial data firm. Census blocks were then aggregated to block groups in a +subsequent pipeline stage. + +**Match rate:** 1.34 million accounts matched to 4,840 block groups, with a +99.97% match rate. + +**Income data:** Median household income at the block group level is drawn from +the American Community Survey 2023 5-year estimates. + +| Statistic | Value | +|-----------|-------| +| Mean Block Group median income | $96,345 | +| Median Block Group median income | $88,710 | +| Minimum | $2,500 | +| Maximum | $250,002 (Census top-code) | +| Total Block Groups with data (Jan) | `{python} f"{v.n_bgs_jan:,}"` | +| Total Block Groups with data (Jul) | `{python} f"{v.n_bgs_jul:,}"` | + + +#### Bill calculation + +For each household and each month, three bills are computed: + +$$ +\begin{aligned} +\text{Flat bill} &= \text{PTC}_{\text{flat}} \times \text{kWh}_{\text{total}} + \text{DFC}_{\text{flat}} \times \text{kWh}_{\text{total}} \\ +\text{DTOU bill} &= \text{PTC}_{\text{flat}} \times \text{kWh}_{\text{total}} + \sum_{p} \text{DFC}_{\text{TOU}}^{(p)} \times \text{kWh}^{(p)} \\ +\text{STOU bill} &= \sum_{p} \text{BESTEC}^{(p)} \times \text{kWh}^{(p)} + \sum_{p} \text{DFC}_{\text{TOU}}^{(p)} \times \text{kWh}^{(p)} +\end{aligned} +$$ + +**Sign convention:** `delta = flat bill − alternative bill`. Positive delta means +the customer saves money under TOU. + +The four TOU time periods are: Morning (6 AM–1 PM), Mid-Day Peak (1–7 PM), +Evening (7–9 PM), Overnight (9 PM–6 AM). These apply year-round for delivery. +Supply periods under STOU additionally vary by season (summer: June– +September; non-summer: October–May). + + +#### Rate inputs + +**Flat supply — Price to Compare (PTC):** + +| Season | PTC (¢/kWh) | Source | +|--------|------------|--------| +| Non-summer (Oct–May) | 9.660 | ComEd Price to Compare (2026), confirmed by CUB | +| Summer (Jun–Sep) | 10.028 | ComEd Price to Compare (2025 summer; 2026 summer not yet released), confirmed by CUB | + +**Flat delivery — Distribution Facilities Charge (DFC):** + +| Class | DFC (¢/kWh) | Source | +|-------|------------|--------| +| C23 | 6.228 | CUB DTOD Fact Sheet (January 2026) | +| C24 | 4.791 | CUB DTOD Fact Sheet (January 2026) | +| C26 | 3.165 | CUB DTOD Fact Sheet (January 2026) | +| C28 | 2.996 | CUB DTOD Fact Sheet (January 2026) | + +These are billed rates inclusive of all rider adjustment factors. + +**TOU delivery rates (used by both DTOU and STOU):** + +| Class | Morning | Mid-Day Peak | Evening | Overnight | +|-------|---------|-------------|---------|-----------| +| C23 | 4.009 | 10.712 | 3.747 | 2.984 | +| C24 | 3.073 | 8.689 | 2.856 | 2.251 | +| C26 | 1.999 | 5.329 | 1.890 | 1.550 | +| C28 | 1.925 | 4.975 | 1.823 | 1.512 | + +All values ¢/kWh. No seasonal variation. Base rates from ComEd's current +Ratebook (p. 852, Informational Sheet No. 67), which lists rates as "& ADJ" +(before rider adjustments). The pipeline uses billed rates inclusive of all +rider adjustments, as published in the CUB DTOD Fact Sheet (January 2026). + +**STOU supply rates — BESTECs (Basic Electric Service Time-of-use Energy +Charges):** + +| Period | Non-summer (¢/kWh) | Summer (¢/kWh) | +|--------|-------------------|----------------| +| Morning | 4.095 | 4.279 | +| Mid-Day Peak | 18.080 | 19.485 | +| Evening | 4.352 | 4.356 | +| Overnight | 3.278 | 3.136 | + +Source: ComEd Ex. 19.0, ICC Docket 24-0378 — Compromise Proposal rates plus +1.266¢/kWh Transmission & Miscellaneous Procurement (T&MP) charge. These rates +are illustrative and do not represent final tariff rates. + + +#### Equity analysis: regressions + +**Method:** OLS regression of block-group-level mean percentage savings on +block-group median household income, with HC1 (heteroskedasticity-consistent) +robust standard errors. + +**Specification:** +`mean_pct_savings = β₀ + β₁ × (median_income / 10000) + ε` + +Income is scaled by $10,000 so that β₁ represents the change in percentage +savings per $10,000 increase in block-group median income. A negative β₁ +indicates a progressive pattern. A positive β₁ indicates a regressive pattern. + +**Groupings:** Regressions were run for each of 4 scenarios (2 rate structures × +2 months) across 5 class groupings (pooled + 4 individual delivery classes), for +a total of 40 regressions. All 40 were independently reproduced from the raw +Block Group-level CSVs with exact numerical agreement. + +**Key pooled results (percentage savings):** + +| Scenario | β₁ (per $10K) | p-value | R² | Interpretation | +|----------|--------------|---------|-----|---------------| +| STOU Jan | +0.007 | 0.357 | 0.02% | Not significant | +| STOU Jul | −0.245 | <0.001 | 7.04% | Progressive | +| DTOU Jan | +0.031 | <0.001 | 1.02% | Significant but trivial | +| DTOU Jul | −0.055 | <0.001 | 5.04% | Progressive | + +The complete 40-regression table, including per-class results and +absolute-dollar regressions, is available in the project repository. + + +#### Equity analysis: income quintiles + +Block groups are sorted by median household income and divided into five +equal-sized groups. For each quintile, mean dollar savings and mean percentage +savings are computed. + +**Key result — STOU July, pooled:** + +| Quintile | Income range | Mean Δ ($) | Mean % savings | n Block Groups | +|----------|-------------|-----------|----------------|-------| +| Q1 (lowest) | $2,500–$59,077 | $18.94 | 18.48% | 878 | +| Q2 | $59,101–$78,682 | $19.81 | 16.26% | 878 | +| Q3 | $78,719–$99,689 | $20.42 | 15.74% | 878 | +| Q4 | $99,733–$130,918 | $21.39 | 14.71% | 878 | +| Q5 (highest) | $130,930–$250,002 | $25.54 | 14.67% | 879 | + +The complete quintile tables for all 4 scenarios and all class groupings are +available in the project repository. + + +#### Choropleth maps + +**Platform:** Felt (felt.com). + +**Data preparation:** Block-group-level mean deltas are clamped and exported as +GeoJSON. The pipeline's sign convention is `delta = flat − TOU` (positive = +customer saves). Felt's default diverging color ramp maps high values to red. To +align red with "pays more," the map field is inverted: +`cost_change = -mean_delta_clamped`. This way blue = saves and red = pays more, +matching intuitive expectations. + +**Color range:** ±$25.45, computed as the 80th percentile of |mean_delta| across +all 20 scenario files. This symmetric bound ensures cross-map comparability. + +**Domain anchoring:** Before upload, a script injects two synthetic GeoJSON +features with `cost_change` set to ±$25.45 and `n_households = 0` to force +Felt's color ramp to use the full symmetric range and center on zero. + + +#### Limitations + +1. **Two months do not capture full annual impacts.** Shoulder months likely +show smaller bill impacts. The annual net equity effect cannot be determined +from January and July alone. + +2. **No behavioral response modeled.** Some households would shift load in +response to TOU pricing, increasing their savings. Our estimates represent a +floor. + +3. **Block-group-level income proxy.** Within-Block Group income heterogeneity +is notcaptured. Individual low-income households in high-income Block Groups +are not separately identified. + +4. **Census income top-coding.** Approximately 40 Block Groups have median +income at or near $250,001, understating true income and potentially +attenuating the estimated income gradient. + +5. **Small-sample delivery classes.** C26 and C28 regressions have 282–341 Block +Groups and limited statistical power. Results for these classes are directional +only. + +6. **Rate vintage mixing.** Supply uses 2025/2026 flat PTCs; delivery uses 2026 +flat and TOU DFCs; STOU BESTECs are illustrative from the Compromise Proposal. +The analysis applies these rates to 2023 usage patterns. + +7. **STOU supply rates are not final.** Both the direction and magnitude of STOU +findings depend on the actual supply rates ComEd sets for the June 2026 launch. + + +### Regulatory context + +ComEd's TOU rates are the product of a multi-year legislative and regulatory +process rooted in the Climate and Equitable Jobs Act (CEJA, P.A. 102-0662), +signed into law on September 15, 2021. Among many other provisions, CEJA +required Illinois utility companies to file Beneficial Electrification (BE) +Plans to promote vehicle electrification and grid flexibility. ComEd's first BE +Plan was approved in March 2023 (ICC Docket 22-0432/22-0442), and that order +directed ComEd to propose a residential TOU delivery rate. + +The proceeding, ICC Docket 24-0378, was filed by ComEd in May 2024. Its +statutory scope was narrow: to reallocate ComEd's delivery service revenue +requirement among customer classes on a revenue-neutral basis. The ICC's Final +Order (January 16, 2025) approved the Delivery Time-of-Use (DTOU) rate as a +voluntary, opt-in program with four time blocks. The Commission initially +declined to approve a supply-side TOU rate, citing competitive market concerns +that had not been fully litigated. + +ComEd petitioned for rehearing to obtain approval for a combined +supply-and-delivery TOU rate. The ICC granted rehearing on March 6, 2025. After +stakeholder negotiations involving ComEd, CUB, the Environmental Defense Fund +(EDF), and others, the Commission approved STOU in its Order on Rehearing +(July 17, 2025). STOU uses the same four time blocks as DTOU and is available +only bundled with DTOU. + + +### References + +:::{#refs} +::: diff --git a/infra/README.md b/infra/README.md index db2bac6..d14e7e8 100644 --- a/infra/README.md +++ b/infra/README.md @@ -197,7 +197,7 @@ just dev-teardown-all ``` ⚠️ WARNING: This will destroy EVERYTHING including the data volume! All data on the EBS volume will be permanently deleted. -Are you sure? Type 'yes' to confirm: +Are you sure? Type 'yes' to confirm: ``` Type `yes` to confirm, then the cleanup proceeds. diff --git a/infra/dev-teardown-all.sh b/infra/dev-teardown-all.sh index 51d0991..4b0328d 100755 --- a/infra/dev-teardown-all.sh +++ b/infra/dev-teardown-all.sh @@ -1,20 +1,29 @@ #!/usr/bin/env bash set -euo pipefail -# Destroy everything including data volume (WARNING: destroys all data!) +# Destroy ALL resources including the EBS data volume. PERMANENT DATA LOSS. +# +# Strategy: +# 1. Load AWS credentials (same pattern as dev-setup.sh) +# 2. Initialize Terraform if needed +# 3. Import any resources that exist in AWS but are missing from Terraform state +# (self-healing for manual terminations or partial previous teardowns) +# 4. Run terraform destroy to clean up everything +# # Run from repo root: infra/dev-teardown-all.sh (or from infra: ./dev-teardown-all.sh) SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" REPO_ROOT="$(cd "$SCRIPT_DIR/.." && pwd)" +# ── AWS credentials ────────────────────────────────────────────────────────── + CONFIG_FILE="$REPO_ROOT/.secrets/aws-sso-config.sh" if [ -f "$CONFIG_FILE" ]; then + # shellcheck source=.secrets/aws-sso-config.sh . "$CONFIG_FILE" fi -# When run via `just dev-teardown-all`, `aws` already ran (Justfile dependency). - -export AWS_DEFAULT_REGION="${AWS_DEFAULT_REGION:-us-west-2}"="${AWS_DEFAULT_REGION:-us-west-2}" +export AWS_DEFAULT_REGION="${AWS_DEFAULT_REGION:-us-west-2}" export_aws_creds() { eval "$(aws configure export-credentials --format env 2>/dev/null)" @@ -32,10 +41,12 @@ fi PROJECT_NAME="${PROJECT_NAME:-smart-meter-analysis}" -echo "⚠️ WARNING: This will destroy EVERYTHING including the data volume!" +# ── Confirmation prompt ────────────────────────────────────────────────────── + +echo "⚠️ WARNING: This will destroy EVERYTHING including the EBS data volume!" echo " All data on the EBS volume will be permanently deleted." echo -read -p "Are you sure? Type 'yes' to confirm: " CONFIRM +read -p "Type 'yes' to confirm: " CONFIRM if [ "$CONFIRM" != "yes" ]; then echo "Aborted." exit 1 @@ -47,90 +58,100 @@ echo cd "$SCRIPT_DIR" +# ── Terraform init ─────────────────────────────────────────────────────────── + if [ ! -d ".terraform" ]; then echo "📦 Initializing Terraform..." terraform init echo fi -terraform destroy -auto-approve || true -echo +# ── State drift recovery ──────────────────────────────────────────────────── +# +# If someone terminated the instance via the AWS console, Terraform state may +# be out of sync. Import any resources that exist in AWS but not in state so +# that terraform destroy can clean them up properly. -echo "🧹 Cleaning up any orphaned AWS resources..." -echo +import_if_missing() { + local addr="$1" + local import_id="$2" -INSTANCE_ID=$(aws ec2 describe-instances \ - --filters "Name=tag:Project,Values=$PROJECT_NAME" "Name=instance-state-name,Values=pending,running,stopping,stopped" \ - --query 'Reservations[0].Instances[0].InstanceId' \ - --output text 2>/dev/null || echo "None") + if terraform state show "$addr" >/dev/null 2>&1; then + echo " ℹ️ $addr — already in state, skipping" + return 0 + fi -if [ -n "$INSTANCE_ID" ] && [ "$INSTANCE_ID" != "None" ]; then - echo " Terminating EC2 instance: $INSTANCE_ID" - aws ec2 terminate-instances --instance-ids "$INSTANCE_ID" >/dev/null 2>&1 || true - echo " Waiting for instance to terminate..." - aws ec2 wait instance-terminated --instance-ids "$INSTANCE_ID" 2>/dev/null || true -fi + echo " 🔍 $addr — not in state, attempting import..." + if terraform import "$addr" "$import_id" >/dev/null 2>&1; then + echo " ✅ $addr — imported successfully" + else + echo " ⚠️ $addr — import failed (resource may not exist in AWS)" + fi +} -VOLUME_ID=$(aws ec2 describe-volumes \ - --filters "Name=tag:Name,Values=${PROJECT_NAME}-data" \ - --query 'Volumes[0].VolumeId' \ - --output text 2>/dev/null || echo "None") +echo "🔍 Checking for state drift..." -if [ -n "$VOLUME_ID" ] && [ "$VOLUME_ID" != "None" ]; then - echo " Deleting EBS volume: $VOLUME_ID" - for i in {1..30}; do - STATE=$(aws ec2 describe-volumes --volume-ids "$VOLUME_ID" --query 'Volumes[0].State' --output text 2>/dev/null || echo "deleted") - if [ "$STATE" = "available" ] || [ "$STATE" = "deleted" ]; then - break - fi - sleep 2 - done - aws ec2 delete-volume --volume-id "$VOLUME_ID" 2>/dev/null || true -fi +# IAM resources (static names) +import_if_missing "aws_iam_role.ec2_role" \ + "${PROJECT_NAME}-ec2-role" + +import_if_missing "aws_iam_instance_profile.ec2_profile" \ + "${PROJECT_NAME}-ec2-profile" +import_if_missing "aws_iam_role_policy.ssm_managed_instance" \ + "${PROJECT_NAME}-ec2-role:${PROJECT_NAME}-ssm-managed-instance" + +import_if_missing 'aws_iam_role_policy.s3_access[0]' \ + "${PROJECT_NAME}-ec2-role:${PROJECT_NAME}-s3-access" + +import_if_missing "aws_iam_role_policy_attachment.ssm_managed_instance_core" \ + "${PROJECT_NAME}-ec2-role/arn:aws:iam::aws:policy/AmazonSSMManagedInstanceCore" + +# Security group (need to look up sg-xxxx ID) SG_ID=$(aws ec2 describe-security-groups \ --filters "Name=group-name,Values=${PROJECT_NAME}-sg" \ --query 'SecurityGroups[0].GroupId' \ --output text 2>/dev/null || echo "None") if [ -n "$SG_ID" ] && [ "$SG_ID" != "None" ]; then - echo " Deleting security group: $SG_ID" - for i in {1..10}; do - if aws ec2 delete-security-group --group-id "$SG_ID" 2>/dev/null; then - break - fi - sleep 3 - done + import_if_missing "aws_security_group.ec2_sg" "$SG_ID" fi -ROLE_NAME="${PROJECT_NAME}-ec2-role" -PROFILE_NAME="${PROJECT_NAME}-ec2-profile" - -if aws iam get-role --role-name "$ROLE_NAME" >/dev/null 2>&1; then - echo " Cleaning up IAM role: $ROLE_NAME" - - aws iam remove-role-from-instance-profile \ - --instance-profile-name "$PROFILE_NAME" \ - --role-name "$ROLE_NAME" 2>/dev/null || true +# EBS volume (need to look up vol-xxxx ID) +VOLUME_ID=$(aws ec2 describe-volumes \ + --filters "Name=tag:Name,Values=${PROJECT_NAME}-data" \ + --query 'Volumes[0].VolumeId' \ + --output text 2>/dev/null || echo "None") - aws iam delete-instance-profile --instance-profile-name "$PROFILE_NAME" 2>/dev/null || true +if [ -n "$VOLUME_ID" ] && [ "$VOLUME_ID" != "None" ]; then + import_if_missing "aws_ebs_volume.data" "$VOLUME_ID" +fi - POLICIES=$(aws iam list-role-policies --role-name "$ROLE_NAME" --query 'PolicyNames[]' --output text 2>/dev/null || echo "") - for policy in $POLICIES; do - aws iam delete-role-policy --role-name "$ROLE_NAME" --policy-name "$policy" 2>/dev/null || true - done +# EC2 instance (may already be terminated — that's fine) +INSTANCE_ID=$(aws ec2 describe-instances \ + --filters "Name=tag:Project,Values=${PROJECT_NAME}" "Name=instance-state-name,Values=pending,running,stopping,stopped" \ + --query 'Reservations[0].Instances[0].InstanceId' \ + --output text 2>/dev/null || echo "None") - ATTACHED=$(aws iam list-attached-role-policies --role-name "$ROLE_NAME" --query 'AttachedPolicies[].PolicyArn' --output text 2>/dev/null || echo "") - for policy_arn in $ATTACHED; do - aws iam detach-role-policy --role-name "$ROLE_NAME" --policy-arn "$policy_arn" 2>/dev/null || true - done +if [ -n "$INSTANCE_ID" ] && [ "$INSTANCE_ID" != "None" ]; then + import_if_missing "aws_instance.main" "$INSTANCE_ID" - aws iam delete-role --role-name "$ROLE_NAME" 2>/dev/null || true + # Volume attachment only exists if both instance and volume are present + if [ -n "$VOLUME_ID" ] && [ "$VOLUME_ID" != "None" ]; then + import_if_missing "aws_volume_attachment.data" \ + "/dev/sdf:${VOLUME_ID}:${INSTANCE_ID}" + fi fi -aws iam delete-instance-profile --instance-profile-name "$PROFILE_NAME" 2>/dev/null || true +echo + +# ── Destroy everything ────────────────────────────────────────────────────── +echo "🏗️ Running terraform destroy..." +terraform destroy -auto-approve echo + +# ── Done ───────────────────────────────────────────────────────────────────── + echo "✅ Complete teardown finished (all resources destroyed)" -echo -echo "To recreate everything from scratch, run: just dev-setup" +echo " To recreate everything from scratch, run: just dev-setup" diff --git a/infra/dev-teardown.sh b/infra/dev-teardown.sh index 7a1adae..c6f8007 100755 --- a/infra/dev-teardown.sh +++ b/infra/dev-teardown.sh @@ -1,30 +1,43 @@ #!/usr/bin/env bash set -euo pipefail -# Destroy EC2 instance but preserve data volume (to recreate, run dev-setup again) +# Destroy EC2 instance and supporting resources, but PRESERVE the EBS data volume. +# +# Strategy: +# 1. Load AWS credentials (same pattern as dev-setup.sh) +# 2. Initialize Terraform if needed +# 3. Import any resources that exist in AWS but are missing from Terraform state +# (self-healing for manual terminations or partial previous teardowns) +# 4. Remove the EBS volume from Terraform state so destroy won't delete it +# 5. Run terraform destroy to clean up everything else +# 6. Re-import the EBS volume so state is ready for the next dev-setup +# # Run from repo root: infra/dev-teardown.sh (or from infra: ./dev-teardown.sh) SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" REPO_ROOT="$(cd "$SCRIPT_DIR/.." && pwd)" +# ── AWS credentials ────────────────────────────────────────────────────────── + CONFIG_FILE="$REPO_ROOT/.secrets/aws-sso-config.sh" if [ -f "$CONFIG_FILE" ]; then + # shellcheck source=.secrets/aws-sso-config.sh . "$CONFIG_FILE" fi -# When run via `just dev-teardown`, `aws` already ran (Justfile dependency). - -export AWS_DEFAULT_REGION="${AWS_DEFAULT_REGION:-us-west-2}"="${AWS_DEFAULT_REGION:-us-west-2}" +export AWS_DEFAULT_REGION="${AWS_DEFAULT_REGION:-us-west-2}" export_aws_creds() { eval "$(aws configure export-credentials --format env 2>/dev/null)" } -if ! export_aws_creds || [ -z "${AWS_ACCESS_KEY_ID:-}" ]; then - echo "⚠️ Credentials not exported (SSO may be expired). Running 'aws sso login'..." - aws sso login || true +if [ -z "${AWS_ACCESS_KEY_ID:-}" ]; then if ! export_aws_creds || [ -z "${AWS_ACCESS_KEY_ID:-}" ]; then - echo "❌ Could not export AWS credentials for Terraform. Run 'just aws' to log in, then run this script again." >&2 - exit 1 + echo "⚠️ Credentials not exported (SSO may be expired). Running 'aws sso login'..." + aws sso login || true + if ! export_aws_creds || [ -z "${AWS_ACCESS_KEY_ID:-}" ]; then + echo "❌ Could not export AWS credentials for Terraform. Run 'just aws' to log in, then run this script again." >&2 + exit 1 + fi fi fi @@ -35,104 +48,142 @@ echo cd "$SCRIPT_DIR" -# Check if Terraform is initialized +# ── Terraform init ─────────────────────────────────────────────────────────── + if [ ! -d ".terraform" ]; then echo "📦 Initializing Terraform..." terraform init echo fi -# Destroy only instance-related resources, keeping the EBS volume -echo "🏗️ Destroying instance resources (keeping data volume)..." -TERRAFORM_DESTROY_SUCCESS=false -if terraform destroy -auto-approve \ - -target=aws_volume_attachment.data \ - -target=aws_instance.main \ - -target=aws_security_group.ec2_sg \ - -target=aws_iam_instance_profile.ec2_profile \ - -target=aws_iam_role_policy.s3_access \ - -target=aws_iam_role_policy.ssm_managed_instance \ - -target=aws_iam_role.ec2_role; then - TERRAFORM_DESTROY_SUCCESS=true -fi -echo +# ── State drift recovery ──────────────────────────────────────────────────── +# +# If someone terminated the instance via the AWS console, Terraform state may +# be out of sync. Import any resources that exist in AWS but not in state so +# that terraform destroy can clean them up properly. -# Clean up any orphaned AWS resources that might exist outside Terraform state -echo "🧹 Cleaning up any orphaned AWS resources..." -echo +import_if_missing() { + local addr="$1" + local import_id="$2" -# 1. Terminate EC2 instance by tag (if exists) -INSTANCE_ID=$(aws ec2 describe-instances \ - --filters "Name=tag:Project,Values=$PROJECT_NAME" "Name=instance-state-name,Values=pending,running,stopping,stopped" \ - --query 'Reservations[0].Instances[0].InstanceId' \ - --output text 2>/dev/null || echo "None") + if terraform state show "$addr" >/dev/null 2>&1; then + echo " ℹ️ $addr — already in state, skipping" + return 0 + fi -if [ -n "$INSTANCE_ID" ] && [ "$INSTANCE_ID" != "None" ]; then - echo " Terminating EC2 instance: $INSTANCE_ID" - aws ec2 terminate-instances --instance-ids "$INSTANCE_ID" >/dev/null 2>&1 || true - echo " Waiting for instance to terminate..." - aws ec2 wait instance-terminated --instance-ids "$INSTANCE_ID" 2>/dev/null || true -fi + echo " 🔍 $addr — not in state, attempting import..." + if terraform import "$addr" "$import_id" >/dev/null 2>&1; then + echo " ✅ $addr — imported successfully" + else + echo " ⚠️ $addr — import failed (resource may not exist in AWS)" + fi +} + +echo "🔍 Checking for state drift..." + +# IAM resources (static names) +import_if_missing "aws_iam_role.ec2_role" \ + "${PROJECT_NAME}-ec2-role" + +import_if_missing "aws_iam_instance_profile.ec2_profile" \ + "${PROJECT_NAME}-ec2-profile" + +import_if_missing "aws_iam_role_policy.ssm_managed_instance" \ + "${PROJECT_NAME}-ec2-role:${PROJECT_NAME}-ssm-managed-instance" + +import_if_missing 'aws_iam_role_policy.s3_access[0]' \ + "${PROJECT_NAME}-ec2-role:${PROJECT_NAME}-s3-access" + +import_if_missing "aws_iam_role_policy_attachment.ssm_managed_instance_core" \ + "${PROJECT_NAME}-ec2-role/arn:aws:iam::aws:policy/AmazonSSMManagedInstanceCore" -# 2. Delete security group (if exists) - NOT the EBS volume! +# Security group (need to look up sg-xxxx ID) SG_ID=$(aws ec2 describe-security-groups \ --filters "Name=group-name,Values=${PROJECT_NAME}-sg" \ --query 'SecurityGroups[0].GroupId' \ --output text 2>/dev/null || echo "None") if [ -n "$SG_ID" ] && [ "$SG_ID" != "None" ]; then - echo " Deleting security group: $SG_ID" - for i in {1..10}; do - if aws ec2 delete-security-group --group-id "$SG_ID" 2>/dev/null; then - break - fi - sleep 3 - done + import_if_missing "aws_security_group.ec2_sg" "$SG_ID" fi -# 3. Clean up IAM resources -ROLE_NAME="${PROJECT_NAME}-ec2-role" -PROFILE_NAME="${PROJECT_NAME}-ec2-profile" +# EBS volume (need to look up vol-xxxx ID) +VOLUME_ID=$(aws ec2 describe-volumes \ + --filters "Name=tag:Name,Values=${PROJECT_NAME}-data" \ + --query 'Volumes[0].VolumeId' \ + --output text 2>/dev/null || echo "None") -if aws iam get-role --role-name "$ROLE_NAME" >/dev/null 2>&1; then - echo " Cleaning up IAM role: $ROLE_NAME" +if [ -n "$VOLUME_ID" ] && [ "$VOLUME_ID" != "None" ]; then + import_if_missing "aws_ebs_volume.data" "$VOLUME_ID" +fi - aws iam remove-role-from-instance-profile \ - --instance-profile-name "$PROFILE_NAME" \ - --role-name "$ROLE_NAME" 2>/dev/null || true +# EC2 instance (may already be terminated — that's fine) +INSTANCE_ID=$(aws ec2 describe-instances \ + --filters "Name=tag:Project,Values=${PROJECT_NAME}" "Name=instance-state-name,Values=pending,running,stopping,stopped" \ + --query 'Reservations[0].Instances[0].InstanceId' \ + --output text 2>/dev/null || echo "None") - aws iam delete-instance-profile --instance-profile-name "$PROFILE_NAME" 2>/dev/null || true +if [ -n "$INSTANCE_ID" ] && [ "$INSTANCE_ID" != "None" ]; then + import_if_missing "aws_instance.main" "$INSTANCE_ID" - POLICIES=$(aws iam list-role-policies --role-name "$ROLE_NAME" --query 'PolicyNames[]' --output text 2>/dev/null || echo "") - for policy in $POLICIES; do - aws iam delete-role-policy --role-name "$ROLE_NAME" --policy-name "$policy" 2>/dev/null || true - done + # Volume attachment only exists if both instance and volume are present + if [ -n "$VOLUME_ID" ] && [ "$VOLUME_ID" != "None" ]; then + import_if_missing "aws_volume_attachment.data" \ + "/dev/sdf:${VOLUME_ID}:${INSTANCE_ID}" + fi +fi - ATTACHED=$(aws iam list-attached-role-policies --role-name "$ROLE_NAME" --query 'AttachedPolicies[].PolicyArn' --output text 2>/dev/null || echo "") - for policy_arn in $ATTACHED; do - aws iam detach-role-policy --role-name "$ROLE_NAME" --policy-arn "$policy_arn" 2>/dev/null || true - done +echo + +# ── Save EBS volume ID ────────────────────────────────────────────────────── +# Try Terraform state first, fall back to AWS CLI lookup above. + +EBS_VOL_ID="" +EBS_VOL_ID=$(terraform state show aws_ebs_volume.data 2>/dev/null \ + | grep '^\s*id\s*=' | head -1 | sed 's/.*= *"//;s/".*//' || true) - aws iam delete-role --role-name "$ROLE_NAME" 2>/dev/null || true +if [ -z "$EBS_VOL_ID" ] && [ -n "$VOLUME_ID" ] && [ "$VOLUME_ID" != "None" ]; then + EBS_VOL_ID="$VOLUME_ID" fi -aws iam delete-instance-profile --instance-profile-name "$PROFILE_NAME" 2>/dev/null || true +if [ -z "$EBS_VOL_ID" ]; then + echo "⚠️ Could not find EBS data volume — nothing to preserve." + echo " Proceeding with destroy anyway." + echo +fi -VOLUME_ID=$(aws ec2 describe-volumes \ - --filters "Name=tag:Name,Values=${PROJECT_NAME}-data" \ - --query 'Volumes[0].VolumeId' \ - --output text 2>/dev/null || echo "None") +# ── Remove EBS volume from state so destroy won't delete it ───────────────── + +if [ -n "$EBS_VOL_ID" ]; then + echo "📦 Removing EBS volume from Terraform state (to preserve it)..." + terraform state rm aws_volume_attachment.data 2>/dev/null || true + terraform state rm aws_ebs_volume.data 2>/dev/null || true + echo +fi + +# ── Destroy everything else ───────────────────────────────────────────────── +echo "🏗️ Running terraform destroy..." +terraform destroy -auto-approve echo -if [ "$TERRAFORM_DESTROY_SUCCESS" = true ]; then - echo "✅ Teardown complete" - if [ -n "$VOLUME_ID" ] && [ "$VOLUME_ID" != "None" ]; then - echo " 📦 Data volume preserved: $VOLUME_ID" + +# ── Re-import EBS volume into state ───────────────────────────────────────── + +if [ -n "$EBS_VOL_ID" ]; then + echo "📦 Re-importing EBS volume into Terraform state..." + if terraform import aws_ebs_volume.data "$EBS_VOL_ID" >/dev/null 2>&1; then + echo " ✅ Volume re-imported: $EBS_VOL_ID" + else + echo " ⚠️ Could not re-import volume. Run manually before next dev-setup:" + echo " cd infra && terraform import aws_ebs_volume.data $EBS_VOL_ID" fi echo - echo "To recreate the instance, run: just dev-setup" -else - echo "❌ Teardown failed - Terraform destroy encountered errors" - echo " Check the error messages above and fix any issues before retrying" - exit 1 fi + +# ── Done ───────────────────────────────────────────────────────────────────── + +echo "✅ Teardown complete" +if [ -n "$EBS_VOL_ID" ]; then + echo " 📦 Data volume preserved: $EBS_VOL_ID" +fi +echo " To recreate the instance, run: just dev-setup" diff --git a/lib/__init__.py b/lib/__init__.py new file mode 100644 index 0000000..9d1d68f --- /dev/null +++ b/lib/__init__.py @@ -0,0 +1 @@ +"""Shared library modules for reports2.""" diff --git a/lib/cairo.py b/lib/cairo.py new file mode 100644 index 0000000..708ed6d --- /dev/null +++ b/lib/cairo.py @@ -0,0 +1,66 @@ +"""CAIRO post-processing: add delivered fuel bills to combined bill data.""" + +from __future__ import annotations + +import polars as pl + +_KWH_PER_GAL_HEATING_OIL = 40.6 +_KWH_PER_GAL_PROPANE = 26.8 +_OIL_CONSUMPTION_COL = "out.fuel_oil.total.energy_consumption" +_PROPANE_CONSUMPTION_COL = "out.propane.total.energy_consumption" +_MONTH_INT_TO_STR: dict[int, str] = { + 1: "Jan", + 2: "Feb", + 3: "Mar", + 4: "Apr", + 5: "May", + 6: "Jun", + 7: "Jul", + 8: "Aug", + 9: "Sep", + 10: "Oct", + 11: "Nov", + 12: "Dec", +} + + +def add_delivered_fuel_bills( + comb_bills: pl.LazyFrame, + load_curve_monthly: pl.LazyFrame, + monthly_prices: pl.DataFrame, +) -> pl.LazyFrame: + """Top up combined bills with oil/propane costs from monthly consumption x EIA prices.""" + fuel_with_prices = ( + load_curve_monthly.select( + pl.col("bldg_id"), + pl.col("month"), + pl.col(_OIL_CONSUMPTION_COL).fill_null(0), + pl.col(_PROPANE_CONSUMPTION_COL).fill_null(0), + ) + .join(monthly_prices.lazy(), on="month", how="left") + .with_columns( + (pl.col(_OIL_CONSUMPTION_COL) / _KWH_PER_GAL_HEATING_OIL * pl.col("oil_price_per_gallon")).alias( + "oil_bill" + ), + (pl.col(_PROPANE_CONSUMPTION_COL) / _KWH_PER_GAL_PROPANE * pl.col("propane_price_per_gallon")).alias( + "propane_bill" + ), + ) + .with_columns((pl.col("oil_bill") + pl.col("propane_bill")).alias("delivered_fuel_bill")) + ) + fuel_monthly = fuel_with_prices.select( + pl.col("bldg_id"), + pl.col("month").replace_strict(_MONTH_INT_TO_STR, return_dtype=pl.String), + pl.col("delivered_fuel_bill"), + ) + fuel_annual = ( + fuel_monthly.group_by("bldg_id") + .agg(pl.col("delivered_fuel_bill").sum()) + .with_columns(pl.lit("Annual").alias("month")) + .select("bldg_id", "month", "delivered_fuel_bill") + ) + all_fuel = pl.concat([fuel_monthly, fuel_annual]) + combined = comb_bills.join(all_fuel, on=["bldg_id", "month"], how="left") + return combined.with_columns((pl.col("bill_level") + pl.col("delivered_fuel_bill")).alias("bill_level")).drop( + "delivered_fuel_bill" + ) diff --git a/lib/eia/fetch_delivered_fuels_prices_eia.py b/lib/eia/fetch_delivered_fuels_prices_eia.py new file mode 100644 index 0000000..390c51f --- /dev/null +++ b/lib/eia/fetch_delivered_fuels_prices_eia.py @@ -0,0 +1,255 @@ +import os + +import pandas as pd +import requests +from requests.exceptions import RequestException + + +def get_eia_petroleum_data( + api_key: str, + state_abbrev: str, + duoarea: str, + fuel_type: str = "heating_oil", + retail_or_wholesale: str | None = "retail", + frequency: str = "weekly", + path_csv: str = "/workspaces/reports2/data/eia/delivered_fuels/xx_eia_delivered_fuels_prices_weekly.csv", + path_parquet: str = "/workspaces/reports2/data/eia/delivered_fuels/xx_eia_delivered_fuels_prices_weekly.parquet", + sort_column: str = "period", + sort_direction: str = "desc", + offset: int = 0, + length: int = 5000, +) -> str: + """ + Fetch petroleum pricing data from EIA API and save to CSV. + + Args: + api_key: Your EIA API key + state_abbrev: State abbreviation (e.g., 'RI', 'PADD_1A') + duoarea: EIA duoarea code (e.g., 'R1X' or 'SRI') + fuel_type: Fuel type (default: "heating_oil", or 'propane') + retail_or_wholesale: 'retail' or 'wholesale' (default: 'retail') + frequency: Data frequency (default: "weekly") + path_csv: Path where to save the CSV file (default: "/workspaces/reports2/data/eia/delivered_fuels/xx_eia_heating_oil_prices_weekly.csv") + path_parquet: Path where to save the Parquet file (default: "/workspaces/reports2/data/eia/delivered_fuels/xx_eia_heating_oil_prices_weekly.parquet") + sort_column: Column to sort by (default: "period") + sort_direction: Sort direction "asc" or "desc" (default: "desc") + offset: Starting offset for pagination (default: 0) + length: Number of records to return (default: 5000) + + Returns: + Path to the saved Parquet file + + Raises: + requests.exceptions.RequestException: If the API request fails + OSError: If there's an issue creating directories or saving the file + """ + + # Base URL + base_url = "https://api.eia.gov/v2/petroleum/pri/wfr/data/" + + # Build parameters + params = { + "frequency": frequency, + "data[0]": "value", + "sort[0][column]": sort_column, + "sort[0][direction]": sort_direction, + "offset": offset, + "length": length, + "api_key": api_key, + } + + # Add duoarea + params["facets[duoarea][0]"] = duoarea + + # Add processes + # "Process" is a EIA "process" for collecting and aggregating data + # 1. "PRS" (Price Delivered to Residential Consumers) + # 2. "PWR" (Price Delivered to Wholesale Consumers) + if retail_or_wholesale == "wholesale" and fuel_type == "propane": + params["facets[process][0]"] = "PWR" + else: + params["facets[process][0]"] = "PRS" + + # Add series + # "Series" is an EIA time series + # Composed of... + # 1. "W" (Weekly) + # 2. "EPD2F" (No.2 Heating Oild) + # 3. "PRS" (Price Delivered to Residential Consumers) + # 4. "duaoarea" (duoarea: state (e.g. 'RI') or region (eg 'R1X' for PADD_1A)) + # 5. "DPG" (Dollars per Gallon) + fuel_code_eia = "EPD2F" if fuel_type == "heating_oil" else "EPLLPA" + + params["facets[series][0]"] = f"W_{fuel_code_eia}_{params['facets[process][0]']}_{duoarea}_DPG" + + try: + # Make API request + response = requests.get( + base_url, params=params, headers={"Accept": "application/json", "User-Agent": "reports"}, timeout=30 + ) + response.raise_for_status() + data = response.json() + + # Extract data records + records = data.get("response", {}).get("data", []) + + if not records: + print("No data returned from API") + return data + + # Convert to DataFrame + df = pd.DataFrame(records) + + # Create directory if it doesn't exist + path_csv = path_csv.replace("xx", state_abbrev.lower()).replace("delivered_fuels", fuel_type) + path_parquet = path_parquet.replace("xx", state_abbrev.lower()).replace("delivered_fuels", fuel_type) + os.makedirs(os.path.dirname(path_csv), exist_ok=True) + os.makedirs(os.path.dirname(path_parquet), exist_ok=True) + + # Save to CSV + df.to_csv(path_csv, index=False) + print(f"Data saved to {path_csv}") + + # Save to Parquet + df.to_parquet(path_parquet, index=False) + print(f"Data saved to {path_parquet}") + + return path_parquet + + except RequestException as e: + print(f"Error fetching data from EIA API: {e}") + raise + except Exception as e: + print(f"Error processing or saving data: {e}") + raise + + +def clean_eia_petroleum_data(path_parquet, state_abbrev, fuel_type): + """ + Clean the EIA petroleum data. + """ + heating_oil_df = pd.read_parquet(path_parquet) + + heating_oil_df["year"] = heating_oil_df["period"].str.split("-").str[0].astype(int) + heating_oil_df["month"] = heating_oil_df["period"].str.split("-").str[1].astype(int) + heating_oil_df["day"] = heating_oil_df["period"].str.split("-").str[2].astype(int) + + # Fuel Oil: 145.945 MJ per gallon (https://www.eia.gov/energyexplained/units-and-calculators/energy-conversion-calculators.php) + # 40.2778 kWh/gallon + if (heating_oil_df["units"] == "$/GAL").all(): + if fuel_type == "heating_oil": + heating_oil_df["value"] = heating_oil_df["value"].astype(float) / 40.2778 + heating_oil_df["units"] = "dollars_per_kwh" + else: # fuel_type == 'propane': + # propane = 26.8kWh per gallon (1 kWh = 3.41214163312794 BTU, 1 gallon of propane = 91,452 BTU) + # # https://www.eia.gov/energyexplained/units-and-calculators/british-thermal-units.php + heating_oil_df["value"] = heating_oil_df["value"].astype(float) / 26.8 + heating_oil_df["units"] = "dollars_per_kwh" + else: + print(f"Expected '$/GAL' in 'units' column, but got something else. Please check the data at {path_parquet}.") + + # Group by year and month and calculate mean prices + heating_oil_df = ( + heating_oil_df.groupby(["year", "month"]) + .agg({ + "value": "mean", + "units": "first", + "duoarea": "first", + "area-name": "first", + "product": "first", + "process": "first", + "process-name": "first", + "series": "first", + "series-description": "first", + }) + .reset_index() + ) + + # Rename 'value' to 'supply_rate' to match expected column naming convention + heating_oil_df = heating_oil_df.rename(columns={"value": "supply_rate"}) + + # add a "fuel_oil_utility" column + if fuel_type == "heating_oil": + heating_oil_df["fuel_oil_utility"] = "generic_retail" + else: # fuel_type == 'propane': + heating_oil_df["propane_utility"] = "generic_retail" + + # add a "state" column + heating_oil_df["state"] = state_abbrev + + # To-Do: Linearly interpolate the data for missing values + + # Save + # ---- + # parquet + heating_oil_df.to_parquet( + path_parquet.replace("xx", state_abbrev.lower()).replace("weekly", "monthly"), index=False + ) + print(f"Saved {path_parquet}") + + # csv + path_csv = path_parquet.replace("xx", state_abbrev.lower()).replace("weekly", "monthly").replace(".parquet", ".csv") + heating_oil_df.to_csv(path_csv, index=False) + print(f"Saved {path_csv}") + + return heating_oil_df + + +# def plot_eia_petroleum_data(path_parquet, state_abbrev): +# """ +# Plot the EIA petroleum data. +# """ +# path_parquet = path_parquet.replace('xx', state_abbrev.lower()) +# heating_oil_df = pd.read_parquet(path_parquet) + +# import matplotlib.pyplot as plt +# # Plot the data +# ax = heating_oil_df.plot(x='year', y=['heating_oil_residential_price_dollars_per_gallon_r1x', 'heating_oil_residential_price_dollars_per_gallon_sri'], +# label=['R1X', 'SRI'], kind='line') +# ax.legend(['R1X', 'SRI']) +# plt.show() + + +def main(state_abbrev, duoarea, fuel_type): + try: + with open("/workspaces/reports2/.secrets/config") as f: + secrets = dict(line.strip().split("=", 1) for line in f if "=" in line.strip()) + EIA_API_KEY = secrets["EIA_API_KEY"] + except Exception as e: + print(f"Error: {e}") + return None + + path_parquet = get_eia_petroleum_data( + state_abbrev=state_abbrev, + duoarea=duoarea, + api_key=EIA_API_KEY, + frequency="weekly", + retail_or_wholesale="retail", + fuel_type=fuel_type, + ) + + clean_eia_petroleum_data(path_parquet, state_abbrev, fuel_type) + + +if __name__ == "__main__": + import sys + + state_to_duoarea = {"CT": "SCT", "RI": "SRI", "PADD_1A": "R1X"} + + # Get list of area codes from command line or use default ['SRI', 'R1X'] + if len(sys.argv) > 3: + print(len(sys.argv)) + print( + "Input is a state abbreviation, PADD code (regional), or 'list' to list all available states and PADD codes" + ) + + elif sys.argv[1] == "list": + print("Available states and PADD codes (EIA alias):") + print("--------------------------------") + [print(f"{key} ({state_to_duoarea[key]})") for key in sorted(state_to_duoarea.keys())] + elif sys.argv[1].upper() in list(state_to_duoarea.keys()): + state_abbrev = sys.argv[1].upper() + duoarea = state_to_duoarea[state_abbrev] + main(state_abbrev, duoarea, sys.argv[2]) + else: + print(f"Invalid state abbreviation: {sys.argv[1]}") diff --git a/lib/eia/fetch_eia_state_profile.py b/lib/eia/fetch_eia_state_profile.py new file mode 100644 index 0000000..f3665c0 --- /dev/null +++ b/lib/eia/fetch_eia_state_profile.py @@ -0,0 +1,162 @@ +import os # Add os module import +import re +from datetime import datetime # Add datetime import + +import pandas as pd +import requests +from bs4 import BeautifulSoup +from requests.exceptions import RequestException + + +def fetch_heating_data(state_abbrev): + """ + Fetch home heating source data for a given state from EIA website. + + Parameters: + state_abbrev (str): Two-letter state abbreviation + + Returns: + list: dataframe of heating sources + """ + url = f"https://www.eia.gov/state/print.php?sid={state_abbrev}" + try: + # Fetch the HTML content + response = requests.get(url) + response.raise_for_status() # Raise an exception for HTTP errors + html_content = response.text + except RequestException as e: + raise Exception(f"Failed to fetch data from URL: {e}") from e + + # Parse the HTML content + soup = BeautifulSoup(html_content, "html.parser") + + # Find all tables + tables = soup.find_all("table", class_="contable") + + # Look for the specific table with heating sources + # Find the consumption & expenditures table + consumption_table = None + tables = soup.find_all("table", class_="contable") + + for table in tables: + headers = table.find_all("th") + for header in headers: + if "Consumption & Expenditures" in header.text: + consumption_table = table + break + if consumption_table: + break + + if not consumption_table: + raise ValueError(f"Consumption & Expenditures table not found for state {state_abbrev}") + + # Find the section with home heating data + heating_section = None + summary_rows = consumption_table.find_all("tr", class_="summary") + + for row in summary_rows: + if "Energy Source Used for Home Heating" in row.text: + heating_section = row + break + + if not heating_section: + raise ValueError(f"Home heating section not found for state {state_abbrev}") + + # Extract all rows for the home heating section + data = [] + current_row = heating_section.find_next_sibling("tr") + + # Loop until we hit another summary row or end of table + while current_row and current_row.get("class", "") != "summary": + cells = current_row.find_all("td") + + if len(cells) >= 3: # Ensuring we have enough cells + energy_source = cells[0].text.strip() + state_value = cells[1].text.strip() + us_value = cells[2].text.strip() + + # Get period (if available) + period = cells[4].text.strip() if len(cells) > 4 else "" + + # Add to data list + data.append({ + "Energy Source": energy_source, + f"{state_abbrev} (%)": state_value, + "U.S. Average (%)": us_value, + "Period": period, + }) + + # Move to next row + current_row = current_row.find_next_sibling("tr") + if not current_row: + break + + return pd.DataFrame(data) + + +def clean_percentage_data(df, state_abbrev): + """ + Clean the percentage data in the DataFrame: + - Remove whitespace and % symbols + - Convert percentages to decimal fractions + + Parameters: + df (pandas.DataFrame): DataFrame with the original data + state_abbrev (str): Two-letter state abbreviation + + Returns: + pandas.DataFrame: Cleaned DataFrame + """ + # Make a copy to avoid modifying the original DataFrame + cleaned_df = df.copy() + + # Clean state percentage column + state_col = f"{state_abbrev} (%)" + cleaned_df[state_col] = cleaned_df[state_col].apply(lambda x: re.sub(r"[^\d.]", "", x) if isinstance(x, str) else x) + + # Clean US average percentage column + cleaned_df["U.S. Average (%)"] = cleaned_df["U.S. Average (%)"].apply( + lambda x: re.sub(r"[^\d.]", "", x) if isinstance(x, str) else x + ) + + # Convert to float and divide by 100 to get decimal fractions + cleaned_df[state_col] = cleaned_df[state_col].astype(float) / 100 + cleaned_df["U.S. Average (%)"] = cleaned_df["U.S. Average (%)"].astype(float) / 100 + + # Rename columns to reflect that they're now decimal fractions + cleaned_df = cleaned_df.rename(columns={state_col: state_abbrev, "U.S. Average (%)": "U.S. Average"}) + + return cleaned_df + + +def main(state_abbrev="MA"): + try: + df_raw = fetch_heating_data(state_abbrev) + df_cleaned = clean_percentage_data(df_raw, state_abbrev) + + # Create output directory if it doesn't exist + output_dir = "/workspaces/reports2/data/eia/state_energy_profiles" + os.makedirs(output_dir, exist_ok=True) + + # Get current date in YYYYMMDD format + current_date = datetime.now().strftime("%Y%m%d") + + # Save to CSV with date in filename + output_file = f"{output_dir}/{state_abbrev.lower()}_heating_sources_{current_date}.csv" + df_cleaned.to_csv(output_file, index=False) + print(rf"\EIA state profile ({state_abbrev}) saved to '{output_file}'") + + return df_cleaned + + except Exception as e: + print(f"Error: {e}") + return None + + +if __name__ == "__main__": + import sys + + # Get state abbreviation from command line or use 'MA' as default + state_abbrev = sys.argv[1].upper() if len(sys.argv) > 1 else "MA" + + main(state_abbrev) diff --git a/lib/ggplot/IBMPlexSans-Bold.otf b/lib/ggplot/IBMPlexSans-Bold.otf new file mode 100644 index 0000000..5ae5057 Binary files /dev/null and b/lib/ggplot/IBMPlexSans-Bold.otf differ diff --git a/lib/ggplot/IBMPlexSans-Regular.otf b/lib/ggplot/IBMPlexSans-Regular.otf new file mode 100644 index 0000000..51b38a2 Binary files /dev/null and b/lib/ggplot/IBMPlexSans-Regular.otf differ diff --git a/lib/ggplot/switchbox_theme.R b/lib/ggplot/switchbox_theme.R new file mode 100644 index 0000000..6a108ea --- /dev/null +++ b/lib/ggplot/switchbox_theme.R @@ -0,0 +1,84 @@ +# Load and register IBM Plex Sans font + +library(sysfonts) +library(showtext) +library(ggplot2) + +# Download fonts to the same directory as this theme file +# Get project root (go up from current working directory to find project root) +project_root <- getwd() +while (!file.exists(file.path(project_root, "lib"))) { + parent <- dirname(project_root) + if (parent == project_root) { + # Reached filesystem root, use current directory as fallback + project_root <- getwd() + break + } + project_root <- parent +} + +theme_dir <- file.path(project_root, "lib", "ggplot") + +# Download fonts if they don't exist +regular_path <- file.path(theme_dir, "IBMPlexSans-Regular.otf") +bold_path <- file.path(theme_dir, "IBMPlexSans-Bold.otf") + +if (!file.exists(regular_path)) { + download.file( + "https://switchbox-data.github.io/reports/fonts/ibm_plex_sans/IBMPlexSans-Regular.otf", + regular_path, + mode = "wb" + ) +} + +if (!file.exists(bold_path)) { + download.file( + "https://switchbox-data.github.io/reports/fonts/ibm_plex_sans/IBMPlexSans-Bold.otf", + bold_path, + mode = "wb" + ) +} + +font_add( + family = "IBM-Plex-Sans", + regular = regular_path, + bold = bold_path +) +showtext_auto() +showtext::showtext_opts(dpi = 300) +theme_set(theme_minimal()) +theme_update( + panel.background = element_rect(fill = "white", color = "white"), + legend.title = element_text(hjust = 0.5), # Centers the legend title + axis.line = element_line(linewidth = 0.5), + axis.ticks = element_line(color = "black"), + # panel.grid.minor.x = element_blank(), + text = element_text(family = "IBM-Plex-Sans", size = 12), + axis.text = element_text( + family = "IBM-Plex-Sans", + size = 12 + ), + axis.title = element_text( + family = "IBM-Plex-Sans", + size = 12 + ), + strip.text = element_text( + size = 12, # Font size + family = "IBM-Plex-Sans", + ), + axis.title.x = element_text(margin = margin(t = 3)), + axis.title.y = element_text(margin = margin(r = 3)) +) + + +sb_colors <- c( + "sky" = "#68BED8", # primary + "midnight" = "#023047", # primary + "carrot" = "#FC9706", # primary + "saffron" = "#FFC729", # secondary + "pistachio" = "#A0AF12", # secondary + "black" = "#000000", # utilitarian + "white" = "#FFFFFF", # utilitarian + "midnight_text" = "#0B6082", # lighter, text only + "pistachio_text" = "#546800" # darker, text only +) diff --git a/lib/inflation.R b/lib/inflation.R new file mode 100644 index 0000000..4a0f4b9 --- /dev/null +++ b/lib/inflation.R @@ -0,0 +1,30 @@ +library(fredr) +library(tidyverse) + +get_inflation_index <- function(series, start, end, api_key, freq = "q") { + fredr_set_key(api_key) + fredr( + series_id = series, + observation_start = as_date(start), + observation_end = as_date(end), + frequency = freq + ) |> + filter(!is.na(value)) |> + filter(month(date) == month(max(date))) |> # filter to the most recent reported month + mutate( + pct_change = last(value) / value, + year = year(date) + ) |> + select(year, pct_change) +} + +get_inflation_factor <- function(inflation_index, input_year, target_year) { + # scaling factor to convert input_year dollars to target_year dollars + + if (max(inflation_index) != target_year) { + stop("inflation_index is not referenced to target year.") + } + inflation_index |> + filter(year == input_year) |> + pull(pct_change) +} diff --git a/lib/just/__init__.py b/lib/just/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/lib/just/clean.py b/lib/just/clean.py new file mode 100644 index 0000000..7c72d1f --- /dev/null +++ b/lib/just/clean.py @@ -0,0 +1,44 @@ +#!/usr/bin/env python3 +"""Clean all generated caches and artifacts from a report directory. + +Usage (from a report directory): + uv run python -m lib.just.clean +""" + +from __future__ import annotations + +import shutil +from pathlib import Path + + +def main() -> None: + print("🧹 Cleaning generated files...") + removed = 0 + + for name in [".quarto", ".diff", "prerendered_old", "prerendered_new"]: + p = Path(name) + if p.is_dir(): + shutil.rmtree(p) + removed += 1 + + for d in Path("docs").glob("*_files"): + if d.is_dir(): + shutil.rmtree(d) + removed += 1 + + notebooks = Path("notebooks") + if notebooks.is_dir(): + for pattern in ["*.html", "*.ipynb", "*.rmarkdown"]: + for f in notebooks.glob(pattern): + f.unlink() + removed += 1 + for d in notebooks.glob("*_files"): + if d.is_dir(): + shutil.rmtree(d) + removed += 1 + + print(f"✅ Removed {removed} item(s)") + + +if __name__ == "__main__": + main() diff --git a/lib/just/diff.py b/lib/just/diff.py new file mode 100644 index 0000000..f5b4472 --- /dev/null +++ b/lib/just/diff.py @@ -0,0 +1,109 @@ +#!/usr/bin/env python3 +"""Diff all HTML pages between a baseline and current render. + +Creates a temporary hub page linking to every common HTML file so that +website_diff's crawler discovers all pages — not just those reachable +from index.html. Past diffs are archived under .diff/diffs/. + +Usage (from a report directory): + uv run python -m lib.just.diff # timestamped diff + uv run python -m lib.just.diff my-label # timestamped + label +""" + +from __future__ import annotations + +import os +import platform +import shutil +import subprocess +import sys +from datetime import datetime +from pathlib import Path + +BASELINE = Path(".diff/baseline") +DIFFS = Path(".diff/diffs") + + +def _find_html(directory: Path) -> set[str]: + return {str(p.relative_to(directory)) for p in directory.rglob("*.html")} + + +def main() -> None: + label = sys.argv[1] if len(sys.argv) > 1 and sys.argv[1] else None + + if not BASELINE.is_dir(): + print(f"❌ No baseline at {BASELINE}. Run 'just render' first to create one.", file=sys.stderr) + sys.exit(1) + + docs = Path("docs") + if not docs.is_dir(): + print("❌ No rendered docs at docs/.", file=sys.stderr) + sys.exit(1) + + print("🔍 Comparing baseline and current render...") + old_files = _find_html(BASELINE) + new_files = _find_html(docs) + added = sorted(new_files - old_files) + removed = sorted(old_files - new_files) + common = sorted(old_files & new_files) + + if added: + print(f"🆕 New pages (not in baseline): {', '.join(added)}") + if removed: + print(f"🗑️ Removed pages (not in new render): {', '.join(removed)}") + if not common: + print("❌ No common HTML files to diff.") + sys.exit(1) + + print(f"📄 Diffing {len(common)} page(s)...") + + timestamp = datetime.now().strftime("%Y-%m-%d_%H-%M-%S") + diff_name = f"{timestamp}_{label}" if label else timestamp + diff_dir = DIFFS / diff_name + + hub = "_diff_hub.html" + links = "\n".join(f'{f}
' for f in common) + hub_html = f"diff hub\n{links}\n\n" + (BASELINE / hub).write_text(hub_html) + (docs / hub).write_text(hub_html) + + DIFFS.mkdir(parents=True, exist_ok=True) + + # Symlink docs/ into .diff/ so website_diff creates both prerendered + # working directories inside .diff/ (it places them next to -o and -n). + docs_link = BASELINE.parent / "current" + if docs_link.is_symlink() or docs_link.exists(): + docs_link.unlink() + docs_link.symlink_to(docs.resolve()) + + temp_paths = [ + BASELINE.parent / "prerendered_old", + BASELINE.parent / "prerendered_new", + docs_link, + ] + + try: + subprocess.run( + ["website_diff", "-o", str(BASELINE), "-n", str(docs_link), "-d", str(diff_dir), "-i", hub], + check=True, + ) + finally: + (BASELINE / hub).unlink(missing_ok=True) + (docs / hub).unlink(missing_ok=True) + (diff_dir / hub).unlink(missing_ok=True) + for d in temp_paths: + if d.is_symlink(): + d.unlink() + elif d.exists(): + shutil.rmtree(d) + + index = diff_dir / "index.html" + print(f"✅ Diff saved to {diff_dir}") + if platform.system() == "Darwin": + os.execlp("open", "open", str(index)) + else: + print(f"👉 Open {index} to view") + + +if __name__ == "__main__": + main() diff --git a/lib/just/publish.py b/lib/just/publish.py new file mode 100644 index 0000000..fa20c36 --- /dev/null +++ b/lib/just/publish.py @@ -0,0 +1,35 @@ +#!/usr/bin/env python3 +"""Publish rendered docs to the root docs/ directory for GitHub Pages. + +Copies docs/ to ../../docs//, where is +the name of the current directory (i.e. the report project). + +Usage (from a report directory): + uv run python -m lib.just.publish +""" + +from __future__ import annotations + +import shutil +import sys +from pathlib import Path + + +def main() -> None: + docs = Path("docs") + if not docs.is_dir(): + print("❌ No docs/ directory. Run 'just render' first.", file=sys.stderr) + sys.exit(1) + + project = Path.cwd().name + pub_path = Path("../..") / "docs" / project + + print(f"📦 Publishing docs/ → {pub_path}") + if pub_path.exists(): + shutil.rmtree(pub_path) + shutil.copytree(docs, pub_path) + print(f"✅ Published to {pub_path}") + + +if __name__ == "__main__": + main() diff --git a/lib/just/render.py b/lib/just/render.py new file mode 100644 index 0000000..89e0796 --- /dev/null +++ b/lib/just/render.py @@ -0,0 +1,87 @@ +#!/usr/bin/env python3 +"""Render a Quarto report with baseline snapshot for diffing. + +Snapshots current docs/ to .diff/baseline/ before rendering, inlines +any SVG figures into the HTML (removing the standalone .svg files), +and cleans up .ipynb artifacts from docs/ after rendering. + +Usage (from a report directory): + uv run python -m lib.just.render +""" + +from __future__ import annotations + +import platform +import shutil +import subprocess +import sys +from pathlib import Path + +BASELINE = Path(".diff/baseline") +INLINE_SVGS = Path("../.style/inline_svgs.py") + + +def _clean_quarto_artifacts(docs: Path) -> None: + """Remove Quarto-generated intermediates from the source and docs trees.""" + removed = 0 + + for pattern in ("**/*.out.ipynb", "**/*.embed.ipynb"): + for f in Path(".").glob(pattern): + f.unlink() + removed += 1 + + for f in docs.rglob("*.ipynb"): + f.unlink() + removed += 1 + + if removed: + print(f"🗑️ Removed {removed} .ipynb artifact(s)") + + +def main() -> None: + docs = Path("docs") + + if docs.is_dir(): + print("📸 Snapshotting docs/ → .diff/baseline/") + if BASELINE.exists(): + shutil.rmtree(BASELINE) + BASELINE.parent.mkdir(parents=True, exist_ok=True) + shutil.copytree(docs, BASELINE) + + render_failed = False + print("📖 Rendering Quarto project...") + try: + result = subprocess.run(["quarto", "render", "."]) + if result.returncode != 0: + render_failed = True + print("💥 Quarto render failed!", file=sys.stderr) + + if not render_failed and INLINE_SVGS.exists(): + print("🖼️ Inlining SVGs into HTML...") + result = subprocess.run([sys.executable, str(INLINE_SVGS), "docs"]) + if result.returncode != 0: + render_failed = True + + svgs = list(docs.rglob("*.svg")) + if svgs: + for f in svgs: + f.unlink() + print(f"🗑️ Removed {len(svgs)} standalone SVG file(s)") + finally: + _clean_quarto_artifacts(docs) + + if render_failed: + sys.exit(1) + + print("✅ Render complete!") + + index = docs / "index.html" + if index.exists(): + if platform.system() == "Darwin": + subprocess.Popen(["open", str(index)]) + else: + print(f"👉 Open {index} to view") + + +if __name__ == "__main__": + main() diff --git a/lib/nyserda_cef_utils.R b/lib/nyserda_cef_utils.R new file mode 100644 index 0000000..964e9b4 --- /dev/null +++ b/lib/nyserda_cef_utils.R @@ -0,0 +1,50 @@ +library(tidyverse) +library(readxl) +library(janitor) + + +read_nyserda_cef_data <- function(prog_path, part_path) { + programs <- read_csv(prog_path, col_types = cols(Year = col_integer())) |> + clean_names() + participants <- read_csv(part_path, col_types = cols(Year = col_integer())) |> + clean_names() + + cols <- c( + "program_administrator", + "fuel_type_funding_source", + "portfolio", + "primary_end_use_sector", + "program_name", + "nys_clean_heat", + "new_efficiency_new_york", + "lmi_market_rate", + "active_inactive", + "year", + "reporting_period" + ) + + programs_by_quarter <- programs |> + group_by(across(all_of(cols))) |> + summarize( + expenditures = sum(total_program_dollars_expenditures_this_quarter), + co2e_reductions_annual = sum( + direct_annual_co2e_emission_reductions_metric_tons_acquired_this_quarter + ), + co2e_reductions_gross_lifetime = sum( + direct_gross_lifetime_co2e_emission_reductions_metric_tons_acquired_this_quarter + ) + ) |> + ungroup() + + participants_by_quarter <- participants |> + group_by(across(all_of(cols))) |> + summarize( + participants = sum(participants_acquired_this_quarter) + ) |> + ungroup() + + joined <- programs_by_quarter |> + left_join(participants_by_quarter, by = cols) + + return(joined) +} diff --git a/lib/plotnine/__init__.py b/lib/plotnine/__init__.py new file mode 100644 index 0000000..155f38a --- /dev/null +++ b/lib/plotnine/__init__.py @@ -0,0 +1,3 @@ +from lib.plotnine.switchbox_theme import SB_COLORS, theme_switchbox + +__all__ = ["SB_COLORS", "theme_switchbox"] diff --git a/lib/plotnine/switchbox_theme.py b/lib/plotnine/switchbox_theme.py new file mode 100644 index 0000000..1e54d45 --- /dev/null +++ b/lib/plotnine/switchbox_theme.py @@ -0,0 +1,171 @@ +"""Switchbox brand theme for plotnine. + +Applies Switchbox brand fonts, colors, and sizes to all chart text +elements, producing a consistent visual hierarchy across reports. + +Typography guide +---------------- +- **Title** (GT Planar Bold 15pt black): chart headline, echoes H3 headings. +- **Labeling layer** (GT Planar 13pt #333333): subtitle, axis titles, strip + (facet) labels, legend title. All share the same font/size/color. +- **Data-reference layer** (IBM Plex Sans 11pt #4D4D4D): axis tick labels, + legend text — smallest text, for reading values off axes. + +Usage:: + + from lib.plotnine import theme_switchbox, SB_COLORS + + ( + ggplot(df, aes("x", "y")) + + geom_col(fill=SB_COLORS["sky"]) + + theme_switchbox() + ) +""" + +from __future__ import annotations + +from pathlib import Path +from typing import Any, Literal + +import matplotlib as mpl +from matplotlib import font_manager +from plotnine import element_line, element_rect, element_text, theme +from plotnine.themes.theme_minimal import theme_minimal + +_MarginKey = Literal["t", "b", "l", "r", "unit"] + +_FONT_DIR = Path(__file__).resolve().parent.parent.parent / "reports" / ".style" / "fonts" +_FONT_IBM_PLEX = "IBM Plex Sans" +_FONT_GT_PLANAR = "GT Planar" +_FONT_FARNHAM = "Farnham Text" +_FONTS_REGISTERED = False + +_FONT_FILES: list[str] = [ + "ips-regular.otf", + "ips-bold.otf", + "gtp-regular.otf", + "gtp-bold.otf", + "gtp-black.otf", + "ft-regular.otf", + "ft-bold.otf", +] + + +def _register_fonts() -> None: + """Register all brand fonts with matplotlib (idempotent).""" + global _FONTS_REGISTERED + if _FONTS_REGISTERED: + return + + for filename in _FONT_FILES: + path = _FONT_DIR / filename + if path.exists(): + font_manager.fontManager.addfont(str(path)) + + mpl.rcParams["svg.fonttype"] = "none" + _FONTS_REGISTERED = True + + +SB_COLORS: dict[str, str] = { + "sky": "#68BED8", + "midnight": "#023047", + "carrot": "#FC9706", + "saffron": "#FFC729", + "pistachio": "#A0AF12", + "black": "#000000", + "white": "#FFFFFF", + "midnight_text": "#0B6082", + "pistachio_text": "#546800", +} + + +_COLOR_LABEL = "#333333" +_COLOR_DATA = "#4D4D4D" + + +class theme_switchbox(theme_minimal): + """Switchbox brand theme for plotnine. + + Three-tier text hierarchy: + + ==== ==================== =========================== + Tier Elements Spec + ==== ==================== =========================== + 1 plot_title GT Planar Bold · 15pt · black + 2 subtitle, axis GT Planar · 13pt · #333333 + titles, strip text, + legend title + 3 axis tick labels, IBM Plex Sans · 11pt · #4D4D4D + legend text + ==== ==================== =========================== + """ + + def __init__(self, base_size: int = 11): + _register_fonts() + super().__init__(base_size=base_size, base_family=_FONT_IBM_PLEX) + margin_title: dict[_MarginKey, Any] = {"b": 8, "unit": "pt"} + margin_x: dict[_MarginKey, Any] = {"t": 8, "unit": "pt"} + margin_y: dict[_MarginKey, Any] = {"r": 8, "unit": "pt"} + self += theme( + panel_background=element_rect(fill="white", color="white"), + # Tier 1 — title + plot_title=element_text( + family=_FONT_GT_PLANAR, + fontweight="bold", + size=15, + color="black", + margin=margin_title, + ), + # Tier 2 — labeling layer + plot_subtitle=element_text( + family=_FONT_GT_PLANAR, + size=13, + color=_COLOR_LABEL, + ), + axis_title_x=element_text( + family=_FONT_GT_PLANAR, + size=13, + color=_COLOR_LABEL, + margin=margin_x, + ), + axis_title_y=element_text( + family=_FONT_GT_PLANAR, + size=13, + color=_COLOR_LABEL, + margin=margin_y, + ), + strip_text=element_text( + family=_FONT_GT_PLANAR, + size=13, + color=_COLOR_LABEL, + ), + legend_title=element_text( + family=_FONT_GT_PLANAR, + size=13, + color=_COLOR_LABEL, + ), + # Tier 3 — data-reference layer (explicit x/y so all axis tick labels match) + axis_text=element_text( + family=_FONT_IBM_PLEX, + size=11, + color=_COLOR_DATA, + ), + axis_text_x=element_text( + family=_FONT_IBM_PLEX, + size=11, + color=_COLOR_DATA, + ), + axis_text_y=element_text( + family=_FONT_IBM_PLEX, + size=11, + color=_COLOR_DATA, + ), + legend_text=element_text( + family=_FONT_IBM_PLEX, + size=11, + color=_COLOR_DATA, + ), + # Structure + axis_line=element_line(size=0.5), + axis_ticks=element_line(color="black"), + ) diff --git a/lib/quarto.py b/lib/quarto.py new file mode 100644 index 0000000..288e13c --- /dev/null +++ b/lib/quarto.py @@ -0,0 +1,27 @@ +"""Helpers for Quarto Manuscript rendering.""" + +from __future__ import annotations + +import io +from typing import TYPE_CHECKING + +if TYPE_CHECKING: + from matplotlib.figure import Figure + + +def display_svg(fig: Figure) -> None: + """Render a matplotlib figure as SVG and display it via IPython. + + Quarto Manuscript's ``{{< embed >}}`` breaks on cells that produce + multi-MIME output (e.g. a raw matplotlib Figure emits both PNG and + text/plain). Finishing the cell with a single ``IPython.display.SVG`` + object avoids this by producing only an ``image/svg+xml`` MIME type. + + After saving to SVG the figure is closed to free memory. + """ + import matplotlib.pyplot as plt + from IPython.display import SVG, display + + fig.savefig(buf := io.BytesIO(), format="svg", bbox_inches="tight") + plt.close(fig) + display(SVG(data=buf.getvalue())) diff --git a/lib/rates_analysis/create_sb_housing_units.R b/lib/rates_analysis/create_sb_housing_units.R new file mode 100644 index 0000000..bef5fb8 --- /dev/null +++ b/lib/rates_analysis/create_sb_housing_units.R @@ -0,0 +1,448 @@ +library(tidyverse) +library(scales) +library(viridis) +library(ggplot2) +library(patchwork) +library(lubridate) +library(arrow) + + +######################################################## +# Constants and Conversions +######################################################## +# Gas +# https://www.rienergy.com/site/-/media/rie-jss-app/home/ways-to-save/rates-and-shopping/service-rates/residential-rates/Rates-5-01-25.ashx?sc_lang=en&hash=EB0DA36E10360398E10E6365C5AD7FBB +convert_gas_therm_to_mmbtu <- 1 +convert_gas_therm_to_ccf <- 1 / 1.028 +convert_gas_ccf_to_therm <- 1.028 +convert_gas_therm_to_kwh <- 29.3 + +# Delivered Fuels +convert_gal_fuel_oil_to_kwh <- 40.2778 +convert_propane_gal_to_kwh <- 27 + + +######################################################## +# Misc helper Functions +######################################################## +print_all_column_names <- function(table) { + print(colnames(table)) +} + +######################################################## +# Preferred Labels +######################################################## +add_baseline_heating_type <- function(housing_units) { + housing_units <- housing_units |> + mutate( + baseline_heating_type = case_when( + `in.hvac_heating_type_and_fuel` == "Electricity ASHP" ~ "Heat Pump", + `in.hvac_heating_type_and_fuel` == + "Electricity Baseboard" ~ "Electric Resistance", + `in.hvac_heating_type_and_fuel` == + "Electricity Electric Boiler" ~ "Electric Resistance", + `in.hvac_heating_type_and_fuel` == + "Electricity Electric Furnace" ~ "Electric Resistance", + `in.hvac_heating_type_and_fuel` == + "Electricity Electric Wall Furnace" ~ "Electric Resistance", + `in.hvac_heating_type_and_fuel` == "Electricity MSHP" ~ "Heat Pump", + `in.hvac_heating_type_and_fuel` == + "Electricity Shared Heating" ~ "Electric Resistance", + `in.hvac_heating_type_and_fuel` == "Fuel Oil Fuel Boiler" ~ "Fuel Oil", + `in.hvac_heating_type_and_fuel` == "Fuel Oil Fuel Furnace" ~ "Fuel Oil", + `in.hvac_heating_type_and_fuel` == + "Fuel Oil Fuel Wall/Floor Furnace" ~ "Fuel Oil", + `in.hvac_heating_type_and_fuel` == + "Fuel Oil Shared Heating" ~ "Fuel Oil", + `in.hvac_heating_type_and_fuel` == + "Natural Gas Fuel Boiler" ~ "Natural Gas", + `in.hvac_heating_type_and_fuel` == + "Natural Gas Fuel Furnace" ~ "Natural Gas", + `in.hvac_heating_type_and_fuel` == + "Natural Gas Fuel Wall/Floor Furnace" ~ "Natural Gas", + `in.hvac_heating_type_and_fuel` == + "Natural Gas Shared Heating" ~ "Natural Gas", + `in.hvac_heating_type_and_fuel` == "None" ~ "Other/None", + `in.hvac_heating_type_and_fuel` == + "Other Fuel Fuel Boiler" ~ "Other/None", + `in.hvac_heating_type_and_fuel` == + "Other Fuel Fuel Furnace" ~ "Other/None", + `in.hvac_heating_type_and_fuel` == + "Other Fuel Fuel Wall/Floor Furnace" ~ "Other/None", + `in.hvac_heating_type_and_fuel` == + "Other Fuel Shared Heating" ~ "Other/None", + `in.hvac_heating_type_and_fuel` == "Propane Fuel Boiler" ~ "Propane", + `in.hvac_heating_type_and_fuel` == "Propane Fuel Furnace" ~ "Propane", + `in.hvac_heating_type_and_fuel` == + "Propane Fuel Wall/Floor Furnace" ~ "Propane", + `in.hvac_heating_type_and_fuel` == "Propane Shared Heating" ~ "Propane", + TRUE ~ "Other/None" + ) + ) |> + select(-`in.hvac_heating_type_and_fuel`) + return(housing_units) +} + +add_baseline_cooling_type <- function(housing_units) { + housing_units <- housing_units |> + mutate( + baseline_cooling_type = case_when( + `in.hvac_cooling_type` == "Central AC" ~ "Central AC", + `in.hvac_cooling_type` == "Room AC" ~ "Room AC", + `in.hvac_cooling_type` == "Ducted Heat Pump" ~ "Heat Pump", + `in.hvac_cooling_type` == "Non-ducted Heat Pump" ~ "Heat Pump", + `in.hvac_cooling_type` == "None" ~ "None", + TRUE ~ "Other Cooling" + ) + ) |> + select(-`in.hvac_cooling_type`) + return(housing_units) +} + +add_hvac_appliances_shell <- function(housing_units) { + housing_units <- housing_units |> + mutate( + hvac = case_when( + upgrade == 0 ~ baseline_heating_type, + upgrade == 1 ~ "hp_low", + upgrade == 2 ~ "hp_high", + upgrade == 3 ~ "hp_best", + upgrade == 4 ~ "hp_low", + upgrade == 5 ~ "hp_geo", + upgrade == 6 ~ "hp_low", + upgrade == 7 ~ "hp_high", + upgrade == 8 ~ "hp_best", + upgrade == 9 ~ "hp_high", + upgrade == 10 ~ "hp_geo", + upgrade == 11 ~ "hp_low", + upgrade == 12 ~ "hp_high", + upgrade == 13 ~ "hp_best", + upgrade == 14 ~ "hp_low", + upgrade == 15 ~ "hp_geo", + upgrade == 16 ~ baseline_heating_type, + TRUE ~ "missed_hvac" + ) + ) |> + mutate( + hvac_backup = case_when( + upgrade == 0 ~ baseline_heating_type, + upgrade == 1 ~ "electric_resistance", + upgrade == 2 ~ "electric_resistance", + upgrade == 3 ~ "electric_resistance", + upgrade == 4 ~ baseline_heating_type, + upgrade == 5 ~ "none", + upgrade == 6 ~ "electric_resistance", + upgrade == 7 ~ "electric_resistance", + upgrade == 8 ~ "electric_resistance", + upgrade == 9 ~ baseline_heating_type, + upgrade == 10 ~ "none", + upgrade == 11 ~ "electric_resistance", + upgrade == 12 ~ "electric_resistance", + upgrade == 13 ~ "electric_resistance", + upgrade == 14 ~ baseline_heating_type, + upgrade == 15 ~ "none", + upgrade == 16 ~ baseline_heating_type, + TRUE ~ "missed_hvac_backup" + ) + ) |> + mutate( + shell = case_when( + upgrade == 0 ~ "baseline", + upgrade == 1 ~ "baseline", + upgrade == 2 ~ "baseline", + upgrade == 3 ~ "baseline", + upgrade == 4 ~ "baseline", + upgrade == 5 ~ "baseline", + upgrade == 6 ~ "light_touch", + upgrade == 7 ~ "light_touch", + upgrade == 8 ~ "light_touch", + upgrade == 9 ~ "light_touch", + upgrade == 10 ~ "light_touch", + upgrade == 11 ~ "light_touch", + upgrade == 12 ~ "light_touch", + upgrade == 13 ~ "light_touch", + upgrade == 14 ~ "light_touch", + upgrade == 15 ~ "light_touch", + upgrade == 16 ~ "light_touch", + TRUE ~ "missed_shell" + ) + ) |> + mutate( + appliances = case_when( + upgrade == 0 ~ "baseline", + upgrade == 1 ~ "baseline", + upgrade == 2 ~ "baseline", + upgrade == 3 ~ "baseline", + upgrade == 4 ~ "baseline", + upgrade == 5 ~ "baseline", + upgrade == 6 ~ "baseline", + upgrade == 7 ~ "baseline", + upgrade == 8 ~ "baseline", + upgrade == 9 ~ "baseline", + upgrade == 10 ~ "baseline", + upgrade == 11 ~ "all_electric", + upgrade == 12 ~ "all_electric", + upgrade == 13 ~ "all_electric", + upgrade == 14 ~ "all_electric", + upgrade == 15 ~ "all_electric", + upgrade == 16 ~ "baseline", + TRUE ~ "missed_appliances" + ) + ) + return(housing_units) +} + + +######################################################## +# Preferred Groupings (building type, etc) +######################################################## + +# Building Type +update_building_type_group <- function(housing_units) { + housing_units <- housing_units |> + mutate( + building_type_group = case_when( + `in.geometry_building_type_acs` %in% + c( + "Single-Family Detached", + "Mobile Home", + "Single-Family Attached" + ) ~ "Single-Family", + `in.geometry_building_type_acs` %in% + c("2 Unit", "3 or 4 Unit") ~ "2-4 Units", + `in.geometry_building_type_acs` %in% + c( + "5 to 9 Unit", + "10 to 19 Unit", + "20 to 49 Unit", + "50 or more Unit" + ) ~ "5+ Units", + TRUE ~ "Other" + ) + ) |> + select(-`in.geometry_building_type_acs`) + + return(housing_units) +} + +# Occupants +change_occupants_to_number <- function(housing_units) { + housing_units <- housing_units |> + mutate( + occupants = case_when( + in.occupants == "10+" ~ 10, + .default = as.numeric(in.occupants) + ) + ) |> + select(-in.occupants) + return(housing_units) +} + +add_occupants_group <- function(housing_units) { + housing_units <- housing_units |> + mutate( + occupants_group = case_when( + occupants == 0 ~ "Vacant", + occupants == 1 ~ "Single", + occupants == 2 ~ "Couple", + occupants == 3 ~ "3-4 Occupants", + occupants == 4 ~ "3-4 Occupants", + occupants == 5 ~ "5+ Occupants", + occupants == 6 ~ "5+ Occupants", + occupants == 7 ~ "5+ Occupants", + occupants == 8 ~ "5+ Occupants", + occupants == 9 ~ "5+ Occupants", + occupants == 10 ~ "5+ Occupants", + TRUE ~ "Other" + ) + ) + return(housing_units) +} + + +######################################################## +# Income and LMI Discounts +######################################################## +inflate_income_to_2024 <- function(housing_units, from_year = 2018) { + # load inflation factors + + inflation_adj_factors <- readRDS( + "/workspaces/reports2/data/fred/inflation_factors.rds" + ) + + inflation_factor <- inflation_adj_factors$inflation_factor[ + inflation_adj_factors$year == from_year + ] + + # inflation adjustment based on the Employment Cost Index + housing_units <- housing_units |> + mutate( + in.representative_income = in.representative_income * inflation_factor + ) + return(housing_units) +} + + +group_income_by_smi <- function( + housing_units, + table_name, + url_smi_thresholds, + smi_tiers = c(0.6, 0.8) +) { + library(googlesheets4) + googlesheets4::gs4_deauth() + smi_thresholds <- googlesheets4::read_sheet( + url_smi_thresholds, + sheet = table_name + ) + smi_thresholds <- smi_thresholds |> + select(-source, -note) + + housing_units <- housing_units |> + left_join( + smi_thresholds, + by = join_by( + `occupants` == occupants_min + ) + ) |> + + # calculate percent of SMI + mutate(percent_of_smi = `in.representative_income` / smi) |> + select(-smi) |> + + # group into SMI tiers + mutate( + smi_tier = case_when( + percent_of_smi < 0.01 ~ "No Income", + percent_of_smi >= 0.01 & percent_of_smi < smi_tiers[1] ~ "Low Income", + percent_of_smi >= smi_tiers[1] & + percent_of_smi < smi_tiers[2] ~ "Moderate Income", + percent_of_smi >= smi_tiers[2] ~ "Not LMI", + TRUE ~ "Not LMI" + ) + ) + return(housing_units) +} + +group_income_by_dollars <- function( + housing_units, + dollar_tiers = c(1000, 55374, 92290) +) { + housing_units <- housing_units |> + mutate( + dollar_tier = case_when( + `in.representative_income` < dollar_tiers[1] ~ "No Income", + `in.representative_income` >= dollar_tiers[1] & + `in.representative_income` < dollar_tiers[2] ~ "Low Income", + `in.representative_income` >= dollar_tiers[2] & + `in.representative_income` < dollar_tiers[3] ~ "Moderate Income", + `in.representative_income` >= dollar_tiers[3] ~ "Not LMI", + TRUE ~ "Not LMI" + ) + ) + return(housing_units) +} + + +add_liheap_eligibility <- function( + housing_units, + electric_lmi_thresholds, + gas_lmi_thresholds +) { + housing_units <- housing_units |> + mutate(lmi = (discount_rate_elec > 0 | `discount_rate_gas` > 0)) + return(housing_units) +} + +add_lmi_discount <- function( + housing_units, + electric_lmi_thresholds, + gas_lmi_thresholds +) { + housing_units_with_electric <- housing_units |> + left_join( + electric_lmi_thresholds, + by = join_by( + electric_utility, + `in.representative_income` >= income_threshold_lower, + `in.representative_income` < income_threshold_upper, + `occupants` == occupants_min + ), + suffix = c("", "_electric") + ) |> + # Create electric_discount_rate column, use 0 if no match + mutate(discount_rate_elec = coalesce(discount_rate, 0)) |> + # Remove the intermediate discount_rate column from the join + select( + -discount_rate, + -customer_class, + -income_threshold_lower, + -income_threshold_upper + ) + + housing_units_with_both <- housing_units_with_electric |> + left_join( + gas_lmi_thresholds, + by = join_by( + electric_utility, # Both threshold tables use electric_utility as the key + `in.representative_income` >= income_threshold_lower, + `in.representative_income` <= income_threshold_upper, + `occupants` == occupants_min + ), + suffix = c("", "_gas") + ) |> + # Create gas_discount_rate column (note: user requested "gas_discount-rate" with hyphen) + mutate(`discount_rate_gas` = coalesce(discount_rate, 0)) |> + # Remove the intermediate discount_rate column from the join + select( + -discount_rate, + -customer_class, + -income_threshold_lower, + -income_threshold_upper + ) + + # Finally, create the lmi column + housing_units_final <- housing_units_with_both |> + mutate( + lmi = (discount_rate_elec > 0 | `discount_rate_gas` > 0) + ) + + return(housing_units_final) +} + + +######################################################## +# Utility Assignment +######################################################## +assign_utilities <- function( + housing_units, + path_to_bldg_utility_crosswalk = NULL +) { + #' Assign electricity utility to housing units + #' + #' @param housing_units A dataframe containing housing units + #' @param path_to_bldg_utility_crosswalk A path to a CSV file containing a mapping of building IDs to electricity utilities + #' @return A dataframe containing housing units with electricity utilities assigned + #' @examples + #' assign_electricity_utility(housing_units, path_to_bldg_utility_crosswalk = "/workspaces/reports2/data/resstock/2024_resstock_amy2018_release_2/rs_2024_bldg_utility_crosswalk.csv") + + if (is.null(path_to_bldg_utility_crosswalk)) { + path_to_bldg_utility_crosswalk <- "/workspaces/reports2/data/resstock/2024_release2_tmy3/metadata/rs2024_bldg_utility_crosswalk.feather" + } + + if (!file.exists(path_to_bldg_utility_crosswalk)) { + #make_empty_utility_crosswalk(path_to_rs2024_metadata) + stop( + "Utility crosswalk file does not exist. Please run /data/resstock/just/make_empty_utility_crosswalk_2024() to create it." + ) + } + + bldg_utility_mapping <- read_feather(path_to_bldg_utility_crosswalk) + + housing_units <- housing_units |> + left_join( + bldg_utility_mapping, + by = c("bldg_id", "in.state") + ) + return(housing_units) +} diff --git a/lib/rates_analysis/heat_pump_rate_funcs.R b/lib/rates_analysis/heat_pump_rate_funcs.R new file mode 100644 index 0000000..9048890 --- /dev/null +++ b/lib/rates_analysis/heat_pump_rate_funcs.R @@ -0,0 +1,2668 @@ +library(tidyverse) +library(scales) +library(viridis) +library(ggplot2) +library(patchwork) +library(lubridate) +library(arrow) + + +######################################################## +# Constants and Conversions +######################################################## + +# Gas +# https://www.rienergy.com/site/-/media/rie-jss-app/home/ways-to-save/rates-and-shopping/service-rates/residential-rates/Rates-5-01-25.ashx?sc_lang=en&hash=EB0DA36E10360398E10E6365C5AD7FBB +convert_gas_therm_to_mmbtu <- 1 +convert_gas_therm_to_ccf <- 1 / 1.028 +convert_gas_ccf_to_therm <- 1.028 +convert_gas_therm_to_kwh <- 29.3 + +# Delivered Fuels +convert_gal_fuel_oil_to_kwh <- 40.2778 +convert_propane_gal_to_kwh <- 26.8 + + +######################################################## +# Misc Utility Functions +######################################################## + +## --- Adding/Manipulating housing_units table --- +add_upgrade_alias_columns <- function(path_rs_db, table) { + # Connect to database + con <- DBI::dbConnect(duckdb::duckdb(), path_rs_db) + + # Pull table + df <- tbl(con, table) |> collect() + + # Add columns + df <- df |> + mutate( + hvac = case_when( + upgrade == '0' ~ "current", + upgrade == '3' ~ "low_hp", + upgrade == '4' ~ "high_hp", + upgrade == '7' ~ "low_hp", + upgrade == '8' ~ "high_hp", + upgrade == '9' ~ "high_hp", + upgrade == '10' ~ "high_hp" + ) + ) |> + mutate( + shell = case_when( + upgrade == '0' ~ "current", + upgrade == '3' ~ "current", + upgrade == '4' ~ "current", + upgrade == '7' ~ "current", + upgrade == '8' ~ "current", + upgrade == '9' ~ "basic_shell", + upgrade == '10' ~ "enhanced_shell" + ) + ) |> + mutate( + appliances = case_when( + upgrade == '0' ~ "current", + upgrade == '3' ~ "current", + upgrade == '4' ~ "current", + upgrade == '7' ~ "low_elec", + upgrade == '8' ~ "high_elec", + upgrade == '9' ~ "high_elec", + upgrade == '10' ~ "high_elec" + ) + ) + + # Write back to database + DBI::dbWriteTable(con, table, df, overwrite = TRUE) + + # Close connection + DBI::dbDisconnect(con) +} + + +add_season_column <- function(df) { + # Winter/Summer Months Enumerations + summer_months_eversource <- c(6, 7, 8, 9) + winter_months_eversource <- c(10, 11, 12, 1, 2, 3, 4, 5) + + summer_months_nationalgrid <- c(5, 6, 7, 8, 9, 10) + winter_months_nationalgrid <- c(11, 12, 1, 2, 3, 4) + + summer_months_unitil <- c(5, 6, 7, 8, 9, 10) + winter_months_unitil <- c(11, 12, 1, 2, 3, 4) + + summer_months_municipal <- c(6, 7, 8, 9) + winter_months_municipal <- c(10, 11, 12, 1, 2, 3, 4, 5) + + df |> + mutate( + season = case_when( + electric_utility == "eversource" ~ if_else( + month %in% summer_months_eversource, + "summer", + "winter" + ), + electric_utility == "nationalgrid" ~ if_else( + month %in% summer_months_nationalgrid, + "summer", + "winter" + ), + electric_utility == "unitil" ~ if_else( + month %in% summer_months_unitil, + "summer", + "winter" + ), + electric_utility == "municipal" ~ if_else( + month %in% summer_months_municipal, + "summer", + "winter" + ) + ) + ) +} + + +add_baseline_heating_type <- function(path_rs_db) { + con <- DBI::dbConnect(duckdb::duckdb(), path_rs_db) + + housing_units <- tbl(con, "housing_units") |> collect() + + housing_units <- housing_units |> + select(-baseline_heating_type) + + housing_units <- housing_units |> + mutate( + baseline_heating_type = case_when( + `in.heating_fuel` == 'Natural Gas' ~ 'Natural Gas', + `in.heating_fuel` == 'Electricity' & + `in.hvac_heating_type` %in% + c('Ducted Heating', 'Non-Ducted Heating') ~ 'Resistance', + `in.heating_fuel` == 'Electricity' & + `in.hvac_heating_type` == 'Ducted Heat Pump' ~ 'Heat Pump', + `in.heating_fuel` == 'Fuel Oil' ~ 'Fuel Oil', + `in.heating_fuel` == 'Propane' ~ 'Propane', + `in.heating_fuel` == 'Other Fuel' ~ 'Other Fuel', + TRUE ~ 'None' + ) + ) + + DBI::dbWriteTable(con, "housing_units", housing_units, overwrite = TRUE) + + DBI::dbDisconnect(con) +} + +add_baseline_cooling_type <- function(path_rs_db) { + con <- DBI::dbConnect(duckdb::duckdb(), path_rs_db) + + housing_units <- tbl(con, "housing_units") |> collect() + + # housing_units <- housing_units |> + # select(-baseline_cooling_type) + + housing_units <- housing_units |> + mutate( + baseline_cooling_type = case_when( + `in.hvac_cooling_type` == 'Central AC' ~ 'Yes AC', + `in.hvac_cooling_type` == 'Room AC' ~ 'Yes AC', + `in.hvac_cooling_type` == 'None' ~ 'No AC', + TRUE ~ 'No AC' + ) + ) + + DBI::dbWriteTable(con, "housing_units", housing_units, overwrite = TRUE) + + DBI::dbDisconnect(con) +} + + +add_building_type_group <- function(path_rs_db) { + con <- DBI::dbConnect(duckdb::duckdb(), path_rs_db) + + housing_units <- tbl(con, "housing_units") |> collect() + + housing_units <- housing_units |> + mutate(building_type = `in.geometry_building_type_acs`) |> + mutate( + building_type_group = case_when( + `in.geometry_building_type_acs` %in% + c( + "Single-Family Detached", + "Mobile Home", + "Single-Family Attached" + ) ~ "Single-Family", + `in.geometry_building_type_acs` %in% + c("2 Unit", "3 or 4 Unit") ~ "2-4 Units", + `in.geometry_building_type_acs` %in% + c( + "5 to 9 Unit", + "10 to 19 Unit", + "20 to 49 Unit", + "50 or more Unit" + ) ~ "5+ Units", + TRUE ~ "Other" + ) + ) + + DBI::dbWriteTable(con, "housing_units", housing_units, overwrite = TRUE) + + DBI::dbDisconnect(con) +} + + +# get_income_distribution() - get the distribution within bounds of income +get_income_distribution <- function( + min_income = 0, + max_income = 500000, + state = "MA", + year = 2022 +) { + pums_path <- paste0( + "/workspaces/reports2/data/census/pums/pums_", + state, + "_", + year, + ".Rds" + ) + + if (file.exists(pums_path)) { + message("Loading PUMS data from cache...") + pums_data <- readRDS(pums_path) + } else { + message("Fetching PUMS data from Census API...") + # Define variables we need + vars <- c("HINCP", "WGTP") + + # Fetch PUMS data + pums_data <- get_pums( + variables = vars, + state = state, + year = year, + survey = "acs1", + rep_weights = "housing", + recode = TRUE + ) |> + distinct(SERIALNO, .keep_all = TRUE) + + saveRDS(pums_data, pums_path) + } + + # Filter to our income bracket and remove NAs + min_income <- max(8000, min_income) + income_bracket <- pums_data |> + filter(HINCP > min_income, HINCP <= max_income) |> + filter(!is.na(HINCP), !is.na(WGTP)) +} + + +assign_precise_income_dollars <- function( + path_rs_db, + state = 'MA', + year = 2022 +) { + # Setting precise dollar income levels + source("/workspaces/reports2/lib/inflation.R") + library(fredr) + library(tidycensus) + + # 3 Steps + # ------- + # 1. For each ResStock income bracket of N, get the "real" income distribution from the Census; from the Census PUMS + # income distribution, take N random samples and assign them to bldg_ids in the ResStock database + # 2. Inflate those income values from 2019 (year of ResStock data) to 2024 (current year) + # 3. Write the housing_units table back to the database + + # 0. Get the housing_units table + con <- DBI::dbConnect(duckdb::duckdb(), path_rs_db) + on.exit(DBI::dbDisconnect(con)) + + housing_units <- tbl(con, "housing_units") |> + collect() + + # Drop income-related columns if they exist + cols_to_drop <- c( + "income_dollars", + "income_low", + "income_high", + "income_sim", + "assigned_income", + "assigned_income_uninflated", + "assigned_income_uninflated.x", + "assigned_income_uninflated.y" + ) + + housing_units <- housing_units |> + select(-any_of(cols_to_drop)) + + housing_units_with_ranges <- housing_units |> + mutate( + income = case_match( + in.income, + "<10000" ~ "0-10000", + "200000+" ~ "200000-500000", + "Not Available" ~ NA, + .default = in.income + ) + ) |> + mutate( + income_low = if_else( + !is.na(income), + as.numeric(str_extract(income, "^[0-9]+")), + NA_real_ + ), + income_high = if_else( + !is.na(income), + as.numeric(str_extract(income, "[0-9]+$")), + NA_real_ + ) + ) + + # Create an empty tibble to store results + housing_with_incomes <- tibble() + + # Process each income bracket separately + for (income_bracket in unique(housing_units_with_ranges$in.income)) { + # Get buildings in this income bracket + buildings_in_bracket <- housing_units_with_ranges |> + filter(in.income == income_bracket) |> + select(bldg_id, income_low, income_high) + + # # Skip if missing income bounds + # if (nrow(buildings_in_bracket) == 0 || + # is.na(buildings_in_bracket$income_low[1]) || + # is.na(buildings_in_bracket$income_high[1])) { + # next + # } + + # Get income distribution for this bracket + distribution <- get_income_distribution( + min_income = buildings_in_bracket$income_low[1], + max_income = buildings_in_bracket$income_high[1] + ) + + # Skip if no valid distribution + # if (nrow(distribution) == 0) { + # next + # } + + # Sample incomes directly + n_buildings <- nrow(buildings_in_bracket) + sampled_incomes <- sample( + x = distribution$HINCP, + size = n_buildings, + replace = TRUE, + prob = distribution$WGTP + ) + + # Assign to buildings + bracket_result <- buildings_in_bracket |> + select(bldg_id, income_low, income_high) |> # Just keep bldg_id + mutate(assigned_income_uninflated = sampled_incomes) + + # Add to results + housing_with_incomes <- bind_rows(housing_with_incomes, bracket_result) + } + + # Join back to housing_units to add the assigned incomes + housing_units_with_incomes <- housing_units |> + left_join(housing_with_incomes, by = "bldg_id") + + # ------------------------------------------ + # 2. Inflate those income values from 2019 (year of ResStock data) to 2024 (current year) + #census_api_key(api_key = "c79b508d59918868944680000000000000000000") + FRED_KEY <- file("/workspaces/reports2/.secrets/fred.key") |> readLines() # Needs API key + fredr_set_key(FRED_KEY) + + # inflation adjustment based on the Employment Cost Index + # source: https://fred.stlouisfed.org/series/CIU2020000000212I + ECI <- "CIU2020000000212I" # Employment Cost Index: Wages and salaries for Private industry workers in the Middle Atlantic + wage_index <- get_inflation_index( + series = ECI, + start = "2019-01-01", + end = "2024-12-31", + api_key = FRED_KEY + ) + + # pull the 2019 inflation rate + inflation_adj_resstock <- get_inflation_factor( + wage_index, + input_year = 2019, + target_year = 2024 + ) + + # Apply inflation adjustment to assigned incomes + housing_units_with_incomes <- housing_units_with_incomes |> + mutate( + assigned_income = assigned_income_uninflated * inflation_adj_resstock + ) + + # ------------------------------------------ + # 3. Write the housing_units table back to the database + DBI::dbWriteTable( + con, + "housing_units", + housing_units_with_incomes, + overwrite = TRUE + ) + + DBI::dbDisconnect(con) +} + +get_housing_units_column_counts <- function( + column_tariff_name, + county_code_rs +) { + con <- DBI::dbConnect(duckdb::duckdb(), path_rs_db) + + if (county_code_rs == 'statewide') { + counts <- DBI::dbGetQuery( + con, + sprintf( + " + SELECT + %s, + COUNT(*) as count + FROM housing_units + GROUP BY %s + ORDER BY count DESC + ", + column_tariff_name, + column_tariff_name + ) + ) + } else { + counts <- DBI::dbGetQuery( + con, + sprintf( + " + SELECT + %s, + COUNT(*) as count + FROM housing_units + WHERE \"in.county\" IN ('%s') + GROUP BY %s + ORDER BY count DESC + ", + column_tariff_name, + county_code_rs, + column_tariff_name + ) + ) + } + + DBI::dbDisconnect(con) + + # Add percentage column + counts <- counts |> + mutate(pct = round((count / sum(count)) * 100, 1)) + + return(counts) +} + +get_bldgs_by_heating_fuel <- function(path_rs_db, heating_fuel) { + con <- DBI::dbConnect(duckdb::duckdb(), path_rs_db) + on.exit(DBI::dbDisconnect(con), add = TRUE) + + heated_bldgs <- tbl(con, "housing_units") |> + filter( + (`in.heating_fuel` == heating_fuel) & + (`in.hvac_cooling_type` != "Heat Pump") + ) |> + select(bldg_id) |> + collect() + + return(heated_bldgs) +} + +get_bldgs_by_building_type <- function(path_rs_db, building_types) { + con <- DBI::dbConnect(duckdb::duckdb(), path_rs_db) + on.exit(DBI::dbDisconnect(con), add = TRUE) + + heated_bldgs <- tbl(con, "housing_units") |> + filter(`in.geometry_building_type_acs` %in% building_types) |> + select(bldg_id) |> + collect() + + return(heated_bldgs) +} + +## --- Resampling functions --- +resample_to_match_housing_distribution <- function( + annual_change_table, + target_pcts, + print_summary_table = FALSE +) { + # Get the order of the building type groups from the target percentages + # This function resamples a dataset to match target housing type distributions + # Steps: + # 1. The the current count and pct of each building type group + # 2. Calculate "availability": pct_original / target_pcts + # 3. Calculate weights: The group with the lowest availability will keep all its samples, so weight = 1.0 + # Other groups will have weights < 1.0 (min_availability / availability) + # 4. Calculate target counts: weights * n_buildings_original + # 5. Sample the building IDs for each type + # 6. Return the filtered dataframe containing only the sampled building IDs that match the target distribution + + # Args: + # annual_change_table: A dataframe containing building data with columns: + # - building_type_group: The type of building (Single-Family, 2-4 Units, 5+ Units) + # - bldg_id: Unique identifier for each building + # target_pcts: tariff_named vector of target percentages for each building type group + # print_summary_table: Boolean, whether to print summary statistics + # Returns: + # A filtered dataframe containing only the sampled building IDs that match + # the target distribution + building_type_counts_order <- tariff_names(target_pcts) + + # First get counts of each building type in original data + building_type_counts <- annual_change_table |> + group_by(building_type_group) |> + summarise(n_buildings_original = n_distinct(bldg_id)) |> + mutate(pct_original = n_buildings_original / sum(n_buildings_original)) |> + arrange(match(building_type_group, building_type_counts_order)) + + # Calculate how many samples we can keep for each type + # We'll use the smallest group as our baseline to ensure we don't exceed any group's count + availability <- building_type_counts$pct_original / + target_pcts[building_type_counts$building_type_group] + + min_availability <- min(availability) + + weights <- c( + (min_availability / availability[1]), + (min_availability / availability[2]), + (min_availability / availability[3]) + ) + + # Calculate target counts for each type + target_counts <- c( + floor(weights[1] * building_type_counts$n_buildings_original[1]), + floor(weights[2] * building_type_counts$n_buildings_original[2]), + floor(weights[3] * building_type_counts$n_buildings_original[3]) + ) + tariff_names(target_counts) <- building_type_counts_order + + # Create a tariff_named vector of target counts that matches the building_type_group tariff_names + target_counts_tariff_named <- settariff_names( + target_counts, + building_type_counts_order + ) + + # Use map_dfr to create the sampled IDs data frame in one operation + sampled_bldg_ids <- map_dfr( + building_type_counts_order, + function(group) { + sampled_ids <- annual_change_table |> + filter(building_type_group == group) |> + distinct(bldg_id) |> + pull(bldg_id) |> + sample(size = target_counts_tariff_named[group]) + + tibble( + bldg_id = sampled_ids, + building_type_group = group + ) + } + ) + + # Then get all rows for those buildings + annual_change_table_resampled <- annual_change_table |> + inner_join(sampled_bldg_ids, by = c("bldg_id", "building_type_group")) + + if (print_summary_table) { + building_type_counts_resampled <- annual_change_table_resampled |> + group_by(building_type_group) |> + summarise(n_buildings_resampled = n_distinct(bldg_id)) |> + mutate( + pct_resampled = n_buildings_resampled / sum(n_buildings_resampled) + ) |> + arrange(match(building_type_group, building_type_counts_order)) + + # Create a summary table combining all the information + summary_table <- building_type_counts |> + # Add availability and weights + mutate( + availability = availability, + weights = weights, + target_counts = target_counts + ) |> + # Join with resampled counts + left_join( + building_type_counts_resampled, + by = "building_type_group" + ) |> + # Format percentages + mutate( + pct_original = scales::percent(pct_original, accuracy = 0.1), + pct_resampled = scales::percent(pct_resampled, accuracy = 0.1) + ) |> + # Select and arrange columns + select( + building_type_group, + n_buildings_original, + pct_original, + availability, + weights, + target_counts, + n_buildings_resampled, + pct_resampled + ) |> + # Arrange by building type order + arrange(match(building_type_group, building_type_counts_order)) + + # Print the summary table + print(summary_table) + } + + return(annual_change_table_resampled) +} + +######################################################## +# Rate Analysis Functions +######################################################## +calc_stats_by_rate_version <- function( + annual_change_table, + hp_eff_for_stats, + print_table = TRUE +) { + scenario_stats <- annual_change_table |> + filter(hvac == hp_eff_for_stats) |> + group_by(version_elec) |> + summarise( + median_change = median(annual_bill_change), + pct_that_save = mean(annual_bill_change < 0) * 100, + pct_that_save_big = mean(annual_bill_change < -1000) * 100, + pct_that_lose = mean(annual_bill_change > 0) * 100, + pct_that_lose_big = mean(annual_bill_change > 1000) * 100, + median_savings = median(annual_bill_change[annual_bill_change < 0]), + median_loss = median(annual_bill_change[annual_bill_change > 0]) + ) + + if (print_table) { + print(knitr::kable( + scenario_stats, + caption = glue::glue( + "Summary Statistics by Rate Version (HP Efficiency = {hp_eff_for_stats})" + ), + format = "pipe", + digits = 1, + col.tariff_names = c( + "Rate Version", + "Median Bill Change ($)", + "% That Save", + "Median Savings ($)", + "Median Loss ($)" + ) + )) + } + + return(scenario_stats) +} + +######################################################## +# Supply Rate Functions +######################################################## +get_month_hour_supply <- function( + supply_rates, + start_year, + end_year, + dynamic_or_all_hours, + all_hours_tariff_name, + on_peak_tariff_name, + off_peak_tariff_name +) { + # Extract supply rates for given year + supply_rates_year <- supply_rates |> + filter( + year >= start_year & year <= end_year, + tariff_name %in% + c(all_hours_tariff_name, on_peak_tariff_name, off_peak_tariff_name) + ) |> + select( + month = month, + hour = 0, + tariff_name = tariff_name, + rate = rate + ) + + # Define peak/off-peak hours + on_peak_hours <- c( + 8, + 9, + 10, + 11, + 12, + 13, + 14, + 15, + 16, + 17, + 18, + 19, + 20, + 21, + 22, + 23 + ) + off_peak_hours <- c(0, 1, 2, 3, 4, 5, 6, 7) + + # Create month-hour grid + month_hour_grid <- expand.grid( + month = 1:12, + hour = 0:23 + ) |> + arrange(month, hour) + + month_hour_supply <- month_hour_grid |> + mutate( + rate_type = if (dynamic_or_all_hours == "dynamic") { + case_when( + hour %in% on_peak_hours ~ on_peak_tariff_name, + hour %in% off_peak_hours ~ off_peak_tariff_name, + TRUE ~ all_hours_tariff_name # Fallback to all-hours rate + ) + } else { + all_hours_tariff_name # Use all_hours for every row when not dynamic + } + ) |> + left_join( + supply_rates_year, + by = c("month", "rate_type" = "tariff_name") # Join on both month and rate type + ) |> + select(month, hour, supply_rate = rate) # Select and rename the rate column + return(month_hour_supply) +} + +get_supply_rates_monthly <- function( + supply_rates, + target_year = 2024, + type = NULL, + zone = NULL, + electric_utility = NULL +) { + supply_rates_year <- supply_rates |> + filter(year == as.numeric(target_year)) |> + mutate( + year = year, + month = month, + type = type, + zone = zone, + electric_utility = electric_utility, + supply_rate = supply_rate + ) |> + select(year, month, type, zone, electric_utility, supply_rate) + + if (!is.null(type)) { + supply_rates_year <- supply_rates_year |> filter(type == !!type) + } + if (!is.null(zone)) { + supply_rates_year <- supply_rates_year |> filter(zone == !!zone) + } + if (!is.null(electric_utility)) { + supply_rates_year <- supply_rates_year |> + filter(electric_utility == !!electric_utility) + } + + return(supply_rates_year) +} + +######################################################## +# Delivery Tariff Functions +######################################################## +assign_tariffs_to_month_hour_grid <- function(tariffs) { + #' Assign tariffs to a month-hour grid + #' + #' Takes a data frame of tariffs with month and hour ranges and expands them into a complete + #' month-hour grid with assigned rates. Handles tariffs that wrap around calendar boundaries. + #' + #' @param tariffs A data frame containing tariff definitions with columns: + #' \itemize{ + #' \item start_month - Starting month (1-12) + #' \item end_month - Ending month (1-12) + #' \item start_hour - Starting hour (0-23) + #' \item end_hour - Ending hour (0-23) + #' \item tariff_tariff_name - tariff_name/identifier of the tariff + #' \item value - Rate value for the tariff + #' } + #' + #' @return A data frame with columns: + #' \itemize{ + #' \item month - Month number (1-12) + #' \item hour - Hour number (0-23) + #' \item tariff_tariff_name - tariff_name of the tariff + #' \item rate - Rate value for that month-hour combination + #' } + #' + #' @details + #' The function handles tariffs that wrap around calendar boundaries (e.g. Nov-Feb) + #' or day boundaries (e.g. 22:00-06:00). It first creates a complete month-hour grid, + #' then joins the expanded tariff definitions to assign rates to each time slot. + #' The result is pivoted to create separate columns for each tariff type before + #' being converted back to long format. + #' + #' @examples + #' tariffs <- data.frame( + #' start_month = c(6, 10), + #' end_month = c(9, 5), + #' start_hour = c(14, 0), + #' end_hour = c(20, 23), + #' tariff_tariff_name = c("r3", "r4"), + #' value = c(0.25, 0.15) + #' ) + #' month_hour_rates <- assign_tariffs_to_month_hour_grid(tariffs) + + # Build the month-hour grid + month_hour_grid <- expand.grid( + month = 1:12, + hour = 0:23 + ) |> + arrange(month, hour) + + # Process each tariff and create a lookup table + lookup_table <- tariffs |> + # Must operate rowwise() + rowwise() |> + # create lists for months and hours + mutate( + months = list( + if (start_month <= end_month) { + start_month:end_month + } else { + c(start_month:12, 1:end_month) + } + ), + hours = list( + if (start_hour <= end_hour) { + start_hour:end_hour + } else { + c(start_hour:23, 0:end_hour) + } + ) + ) |> + # Expand the months and hours + unnest(months) |> + unnest(hours) |> + # Keep relevant columns including tariff_tariff_name + select(month = months, hour = hours, tariff_tariff_name, value) + + # First pivot to wide format by tariff_tariff_name + month_hour_tariff_wide <- month_hour_grid |> + left_join(lookup_table, by = c("month", "hour")) |> + pivot_wider( + tariff_names_from = tariff_tariff_name, + values_from = value + ) + + # Then pivot back to long format + month_hour_tariff_long <- month_hour_tariff_wide |> + pivot_longer( + cols = c(r3, r4), # specify the rate columns + tariff_names_to = "tariff_tariff_name", + values_to = "rate" + ) |> + # replace any NA values with 0 + mutate(rate = replace_na(rate, 0)) |> + # Ensure all columns have the correct type + mutate( + month = as.integer(month), + hour = as.integer(hour), + tariff_tariff_name = as.character(tariff_tariff_name), + rate = as.numeric(rate) + ) + + return(month_hour_tariff_long) +} + +assign_tariffs_to_month_grid <- function(tariffs) { + # Create a lookup table by expanding the months for each tariff + lookup_table <- tariffs |> + # Must operate rowwise() + rowwise() |> + + # Create a list of months covered by this tariff + mutate( + months = list( + if (start_month <= end_month) { + start_month:end_month + } else { + c(start_month:12, 1:end_month) + } + ) + ) |> + + # Expand the months + unnest(months) |> + + # Select only the columns we need for the final output + select( + utility, + version, + customer_class, + tariff_tariff_name, + domain, + class, + type, + tariff_name, + value, + month = months + ) + + # Check for duplicate month combinations + duplicate_check <- lookup_table |> + group_by( + utility, + version, + customer_class, + tariff_tariff_name, + type, + month + ) |> + summarize( + count = n(), + values = paste(unique(value), collapse = ", "), + .groups = "drop" + ) |> + filter(count > 1) + + if (nrow(duplicate_check) > 0) { + warning("Found duplicate values for some month combinations:") + print(duplicate_check) + + # Use the first value for each month combination + lookup_table <- lookup_table |> + group_by( + utility, + version, + customer_class, + tariff_tariff_name, + type, + month + ) |> + slice(1) |> + ungroup() + } + + return(lookup_table) +} + + +######################################################## +# Bill Calculation Functions +######################################################## +get_monthly_consumption <- function( + path_monthly_data, + fuel, + functional_group, + use_these_states, + use_these_upgrades = c("00") +) { + #' Calculate Monthly Energy Consumption + #' + #' This function calculates monthly energy consumption for a given fuel and functional group. + #' + #' @param path_monthly_data Character string. Path to the directory containing monthly + #' load data in Arrow dataset format. + #' @param fuel Character string. The fuel to calculate consumption for. + #' @param functional_group Character string. The functional group to calculate consumption for. + #' @param use_these_upgrades Character vector. The upgrades to use for the calculation. + #' + #' @return A data frame containing monthly energy consumption with the following columns: + #' \item bldg_id - Building identifier + #' \item upgrade - Upgrade scenario identifier + #' \item month - Month number (1-12) + #' \item consumption - Energy consumption in kWh + #' + #' @examples + #' \dontrun{ + #' monthly_consumption <- get_monthly_consumption( + #' path_monthly_data = "/workspaces/reports2/data/ResStock/2024_release2_tmy3/load_curve_monthly_10/state=RI", + #' fuel = "natural_gas", + #' functional_group = "heating", + #' use_these_upgrades = c("00", "01", "02", "03") + #' ) + #' } + + # load the data dictionary labeled (ddl) from feather + ddl <- read_feather( + "/workspaces/reports2/lib/resstock/2024/end_use_groups.feather" + ) + + # filter ddl by fuel and functional_group to get the target columns + ddl_filtered <- ddl |> + filter( + .data$fuel == .env$fuel, + .data$functional_group == .env$functional_group + ) + + # target columns + target_columns <- ddl_filtered$timeseries_field_name + + # Read the dataset from the parquet directory + data <- open_dataset(path_monthly_data) + + monthly_consumption <- data |> + #filter(year(timestamp) == 2018) |> + filter(state %in% use_these_states) |> + filter(upgrade %in% use_these_upgrades) |> + mutate( + bldg_id = as.integer(bldg_id), + month = as.integer(month(timestamp)) + ) |> + select(all_of(c("bldg_id", "upgrade", "month", target_columns))) |> + collect() + + monthly_consumption <- monthly_consumption |> + mutate( + "consumption_kwh" := rowSums(across(all_of(target_columns)), na.rm = TRUE) + ) |> + select(all_of(c("bldg_id", "upgrade", "month", "consumption_kwh"))) + + return(monthly_consumption) +} + + +# monthly bills +calc_monthly_bills <- function( + monthly_consumption, + fuel_type, + delivery_tariffs, + supply_rates, + housing_units, + supply_year, + state = NULL, + enable_lmi_discount = TRUE, + use_these_upgrades = c("00") +) { + #' Calculate Monthly Electric Bills + #' + #' This function calculates monthly electric bills for housing units based on their + #' electricity consumption, delivery tariffs, and supply rates. It handles different + #' utility companies, customer classes, and Low to Moderate Income (LMI) discounts. + #' + #' @param path_monthly_data Character string. Path to the directory containing monthly + #' load data in Arrow dataset format. + #' @param fuel_type Character string. The type of fuel to calculate bills for. + #' @param delivery_tariffs Data frame. Contains electric delivery tariffs + #' with columns for utility, customer_class, type (customer_charge, delivery_rate, + #' sales_tax_rate), value, month, and lmi status. + #' @param supply_rates Data frame. Contains electric supply rates with + #' columns for electric_utility, month, year, and electric_supply_rate. + #' @param housing_units Data frame. Contains housing unit metadata including bldg_id, + #' electric_utility, baseline_heating_type, lmi status, and discount_rate. + #' @param supply_year Integer. The year for which to calculate bills (used to filter + #' supply rates). + #' @param state Character string. The state to use for the calculation. + #' @param use_these_upgrades Character vector. The upgrades to use for the calculation. + #' + #' @return A data frame containing monthly electric bills with the following columns: + #' \itemize{ + #' \item bldg_id - Building identifier + #' \item upgrade - Upgrade scenario identifier + #' \item month - Month number (1-12) + #' \item consumption - {fuel_type} consumption in kWh + #' \item utility - Utility company tariff_name + #' \item baseline_heating_type - Type of heating system + #' \item lmi - Low to Moderate Income status (logical) + #' \item discount_rate - {fuel_type} discount rate for LMI customers + #' \item customer_charge - Fixed monthly customer charge + #' \item delivery_rate - {fuel_type} delivery rate per kWh + #' \item delivery_charge - Total delivery charges + #' \item supply_charge - Total supply charges + #' \item total_pretax_bill - Total bill before taxes + #' \item sales_tax_charge - Sales tax amount + #' \item total_bill - Final total bill including taxes + #' } + #' + #' @details The function performs the following operations: + #' \enumerate{ + #' \item Loads monthly electricity consumption data from the specified path + #' \item Filters for baseline upgrades (upgrade "00") and year 2018 data + #' \item Joins consumption data with housing unit metadata + #' \item Applies appropriate delivery tariffs based on utility and LMI status + #' \item Calculates delivery charges, supply charges, and taxes + #' \item Returns comprehensive billing information for each building-month + #' } + #' + #' @note The function uses Arrow datasets for efficient processing of large monthly + #' load files. It assumes the monthly load data contains columns for bldg_id, + #' upgrade, timestamp, and out.electricity.total.energy_consumption. + #' + #' @seealso \code{\link{calc_annual_bills_from_monthly}} for aggregating monthly + #' bills to annual totals + #' + #' @examples + #' \dontrun{ + #' monthly_bills <- calc_monthly_bills_elec( + #' path_monthly_data = "/path/to/monthly/loads", + #' electric_delivery_tariffs = delivery_tariffs, + #' electric_supply_rates = supply_rates, + #' housing_units = housing_data, + #' year = 2024 + #' ) + #' } + # upgrade_filters <- paste0("upgrade == '", use_these_upgrades, "'", collapse = " | ") + # upgrade_filter_expr <- parse(text = upgrade_filters)[[1]] + + # Set some columns names based on fuel_type + # ------------ + # Electricity + if (fuel_type == "electricity") { + fuel_consumption_column <- "out.electricity.total.energy_consumption" + discount_rate <- "discount_rate_elec" + utility <- "electric_utility" + # ------------ + # Gas + } else if (fuel_type == "natural_gas") { + fuel_consumption_column <- "out.natural_gas.total.energy_consumption" + discount_rate <- "discount_rate_gas" + utility <- "gas_utility" + # ------------ + # Fuel Oil + } else if (fuel_type == "fuel_oil") { + fuel_consumption_column <- "out.fuel_oil.total.energy_consumption" + discount_rate <- "discount_rate_fuel_oil" + utility <- "fuel_oil_utility" + # ------------ + # Propane + } else if (fuel_type == "propane") { + fuel_consumption_column <- "out.propane.total.energy_consumption" + discount_rate <- "discount_rate_propane" + utility <- "propane_utility" + } + # ------------ + + final_result <- monthly_consumption |> + left_join( + housing_units |> + mutate(bldg_id = as.integer(bldg_id)) |> + select( + bldg_id, + upgrade, + hvac, + !!sym(utility), + lmi, + !!sym(discount_rate) + ) |> + mutate( + !!sym(discount_rate) := if (enable_lmi_discount) { + !!sym(discount_rate) + } else { + 0 + } + ), + by = c("bldg_id", "upgrade") + ) |> + left_join( + delivery_tariffs |> + filter(type == "customer_charge") |> + select( + month, + !!sym(utility), + customer_charge = value, + tariff_name, + version, + lmi + ), + by = c("month", utility, "lmi") + ) |> + left_join( + delivery_tariffs |> + filter(type == "delivery_rate") |> + select( + month, + !!sym(utility), + delivery_rate = value, + tariff_name, + version, + lmi + ), + by = c("month", utility, "version", "lmi", "tariff_name") + ) |> + left_join( + delivery_tariffs |> + filter(type == "sales_tax_rate") |> + select( + month, + !!sym(utility), + sales_tax_rate = value, + tariff_name, + version, + lmi + ), + by = c("month", utility, "version", "lmi", "tariff_name") + ) |> + left_join( + supply_rates |> + filter(year == supply_year) |> + select(month, !!sym(utility), supply_rate, year), + by = c("month", utility) + ) |> + mutate( + customer_charge = customer_charge * (1 - !!sym(discount_rate)), + delivery_charge = consumption_kwh * + delivery_rate * + (1 - !!sym(discount_rate)), + supply_charge = consumption_kwh * + supply_rate * + (1 - !!sym(discount_rate)), + total_pretax_bill = delivery_charge + supply_charge + customer_charge, + sales_tax_charge = total_pretax_bill * sales_tax_rate, + monthly_bill = total_pretax_bill + sales_tax_charge, + monthly_bill_undiscounted = monthly_bill / (1 - !!sym(discount_rate)) + ) |> + select( + bldg_id, + upgrade, + hvac, + month, + year, + !!sym(utility), + consumption_kwh, + version, + tariff_name, + delivery_rate, + supply_rate, + customer_charge, + delivery_charge, + supply_charge, + total_pretax_bill, + sales_tax_charge, + monthly_bill, + monthly_bill_undiscounted + ) |> + arrange(bldg_id, month, year, !!sym(utility), version, tariff_name) |> + collect() + + return(final_result) +} + +calc_monthly_bills_gas <- function( + path_monthly_data, + gas_delivery_tariffs, + gas_supply_rates, + housing_units, + supply_year, + state = NULL, + enable_lmi_discount = TRUE, + use_these_upgrades = c("00") +) { + #' Calculate Monthly Gas Bills + #' + #' This function calculates monthly electric bills for housing units based on their + #' gas consumption, delivery tariffs, and supply rates. It handles different + #' utility companies, customer classes, and Low to Moderate Income (LMI) discounts. + #' + #' @param path_monthly_data Character string. Path to the directory containing monthly + #' load data in Arrow dataset format. + #' @param gas_delivery_tariffs Data frame. Contains gas delivery tariffs + #' with columns for utility, customer_class, type (customer_charge, delivery_rate, + #' sales_tax_rate), value, month, and lmi status. + #' @param gas_supply_rates Data frame. Contains gas supply rates with + #' columns for gas_utility, month, year, and gas_supply_rate. + #' @param housing_units Data frame. Contains housing unit metadata including bldg_id, + #' gas_utility, baseline_heating_type, lmi status, and discount_rate_gas. + #' @param supply_year Integer. The year for which to calculate bills (used to filter + #' supply rates). + #' @param state Character string. The state to use for the calculation. + #' @param use_these_upgrades Character vector. The upgrades to use for the calculation. + #' + #' @return A data frame containing monthly electric bills with the following columns: + #' \itemize{ + #' \item bldg_id - Building identifier + #' \item upgrade - Upgrade scenario identifier + #' \item month - Month number (1-12) + #' \item gas_consumption - Gas consumption in Therms + #' \item gas_utility - Utility company tariff_name + #' \item baseline_heating_type - Type of heating system + #' \item lmi - Low to Moderate Income status (logical) + #' \item discount_rate_gas - Gas discount rate for LMI customers + #' \item customer_charge - Fixed monthly customer charge + #' \item delivery_rate - Delivery rate per kWh <- YES, per kWh + #' \item delivery_charge - Total delivery charges + #' \item supply_charge - Total supply charges + #' \item total_pretax_bill - Total bill before taxes + #' \item sales_tax_charge - Sales tax amount + #' \item total_bill - Final total bill including taxes + #' } + #' + #' @details The function performs the following operations: + #' \enumerate{ + #' \item Loads monthly electricity consumption data from the specified path + #' \item Filters for baseline upgrades (upgrade "00") and year 2018 data + #' \item Joins consumption data with housing unit metadata + #' \item Applies appropriate delivery tariffs based on utility and LMI status + #' \item Calculates delivery charges, supply charges, and taxes + #' \item Returns comprehensive billing information for each building-month + #' } + #' + #' @note The function uses Arrow datasets for efficient processing of large monthly + #' load files. It assumes the monthly load data contains columns for bldg_id, + #' upgrade, timestamp, and out.electricity.total.energy_consumption. + #' + #' @seealso \code{\link{calc_annual_bills_from_monthly}} for aggregating monthly + #' bills to annual totals + #' + #' @examples + #' \dontrun{ + #' monthly_bills <- calc_monthly_bills_elec( + #' path_monthly_data = "/path/to/monthly/loads", + #' electric_delivery_tariffs = delivery_tariffs, + #' electric_supply_rates = supply_rates, + #' housing_units = housing_data, + #' year = 2024 + #' ) + #' } + + # Build the dataset path based on state if provided + if (!is.null(state)) { + # For partitioned datasets, Arrow can filter more efficiently if we point + # directly to the state partition + data_path <- file.path(path_monthly_data, paste0("state=", toupper(state))) + if (!dir.exists(data_path)) { + stop("State partition not found: ", data_path) + } + print(data_path) + + # Option 1: Open the dataset at the state level and filter by upgrades + # Arrow will automatically detect the upgrade partitions + monthly_load_ds <- open_dataset(data_path) |> + filter(upgrade %in% use_these_upgrades) + } else { + # Open the full dataset if no state specified + monthly_load_ds <- open_dataset(path_monthly_data) + + # Need to filter by upgrades when no state is specified + # Build a filter expression + if (length(use_these_upgrades) == 1) { + monthly_load_ds <- monthly_load_ds |> + filter(upgrade == use_these_upgrades[1]) + } else if (length(use_these_upgrades) == 2) { + monthly_load_ds <- monthly_load_ds |> + filter( + upgrade == use_these_upgrades[1] | upgrade == use_these_upgrades[2] + ) + } else { + # For more upgrades, build dynamically + filter_expr <- paste0( + "upgrade == '", + use_these_upgrades, + "'", + collapse = " | " + ) + monthly_load_ds <- monthly_load_ds |> + filter(eval(parse(text = paste0("(", filter_expr, ")")))) + } + } + + # Now do all operations in a single pipeline + final_result <- monthly_load_ds |> + filter(year(timestamp) == 2018) |> + mutate( + month = as.integer(month(timestamp)), + bldg_id = as.integer(bldg_id) + ) |> + rename(gas_consumption = out.natural_gas.total.energy_consumption) |> + select(bldg_id, upgrade, month, gas_consumption) |> + + # Add housing unit metadata + left_join( + housing_units |> + mutate(bldg_id = as.integer(bldg_id)) |> + + # for gas, we need to flag heat_non_heat + mutate( + heat_non_heat = case_when( + hvac == "natural_gas" ~ "heat", + TRUE ~ "non_heat" + ) + ) |> + select( + bldg_id, + upgrade, + gas_utility, + lmi, + hvac, + heat_non_heat, + discount_rate_gas + ) |> + mutate( + discount_rate_gas = if (enable_lmi_discount) discount_rate_gas else 0 + ), + by = c("bldg_id", "upgrade") + ) |> + + # Inline the tariff filtering and selection directly in joins + left_join( + gas_delivery_tariffs |> + filter(type == "customer_charge") |> + select( + month, + gas_utility, + customer_charge = value, + lmi, + heat_non_heat, + tariff_name, + version + ), + by = c("month", "gas_utility", "lmi", "heat_non_heat") + ) |> + left_join( + gas_delivery_tariffs |> + filter(type == "delivery_rate") |> + select( + month, + gas_utility, + delivery_rate = value, + lmi, + heat_non_heat, + tariff_name, + version + ), + by = c( + "month", + "gas_utility", + "lmi", + "heat_non_heat", + "tariff_name", + "version" + ) + ) |> + left_join( + gas_delivery_tariffs |> + filter(type == "sales_tax_rate") |> + select( + month, + gas_utility, + sales_tax_rate = value, + lmi, + heat_non_heat, + tariff_name, + version + ), + by = c( + "month", + "gas_utility", + "lmi", + "heat_non_heat", + "tariff_name", + "version" + ) + ) |> + left_join( + gas_supply_rates |> + select(month, gas_utility, heat_non_heat, gas_supply_rate, year), + by = c("month", "gas_utility", "heat_non_heat") + ) |> + # handle NAs and invalid numbers + mutate( + year = if_else(is.na(year), supply_year, year), + delivery_rate = if_else(is.na(delivery_rate), 0, delivery_rate), + gas_supply_rate = if_else(is.na(gas_supply_rate), 0, gas_supply_rate), + customer_charge = if_else(is.na(customer_charge), 0, customer_charge), + sales_tax_rate = if_else(is.na(sales_tax_rate), 0, sales_tax_rate) + ) |> + # calculate monthly bills by component + mutate( + customer_charge = customer_charge * (1 - discount_rate_gas), + delivery_charge = gas_consumption * + delivery_rate * + (1 - discount_rate_gas), + supply_charge = gas_consumption * + gas_supply_rate * + (1 - discount_rate_gas), + total_pretax_bill = delivery_charge + supply_charge + customer_charge, + sales_tax_charge = total_pretax_bill * sales_tax_rate, + monthly_bill = total_pretax_bill + sales_tax_charge, + monthly_bill_undiscounted = monthly_bill / (1 - discount_rate_gas) + ) |> + # handle NAs and invalid numbers + mutate( + delivery_charge = if_else(is.na(delivery_charge), 0, delivery_charge), + supply_charge = if_else(is.na(supply_charge), 0, supply_charge), + total_pretax_bill = if_else( + is.na(total_pretax_bill), + 0, + total_pretax_bill + ), + sales_tax_charge = if_else(is.na(sales_tax_charge), 0, sales_tax_charge), + monthly_bill = if_else(is.na(monthly_bill), 0, monthly_bill), + monthly_bill_undiscounted = if_else( + is.na(monthly_bill_undiscounted), + 0, + monthly_bill_undiscounted + ) + ) |> + select( + bldg_id, + upgrade, + heat_non_heat, + hvac, + month, + year, + gas_utility, + gas_consumption, + version, + tariff_name, + delivery_rate, + gas_supply_rate, + customer_charge, + delivery_charge, + supply_charge, + total_pretax_bill, + sales_tax_charge, + monthly_bill, + monthly_bill_undiscounted + ) |> + arrange(bldg_id, month, year, version, tariff_name) |> + collect() # Only collect at the very end + + return(final_result) +} + +calc_monthly_changes <- function(monthly_bills, fuel_type) { + # Set some columns names based on fuel_type + # ------------ + # Electricity + if (fuel_type == "electricity") { + discount_rate <- "discount_rate_elec" + utility <- "electric_utility" + # ------------ + # Gas + } else if (fuel_type == "natural_gas") { + discount_rate <- "discount_rate_gas" + utility <- "gas_utility" + # ------------ + # Fuel Oil + } else if (fuel_type == "fuel_oil") { + discount_rate <- "discount_rate_fuel_oil" + utility <- "fuel_oil_utility" + # ------------ + # Propane + } else if (fuel_type == "propane") { + discount_rate <- "discount_rate_propane" + utility <- "propane_utility" + } + # ------------ + + monthly_changes <- monthly_bills |> + # first get the baseline bill for comparison + filter(upgrade %in% c(0, "00") & version == "baseline") |> + select(bldg_id, year, month, !!sym(utility), monthly_bill) |> + rename(baseline_bill = monthly_bill) |> + # join the baseline bill column to the full dataset + right_join(monthly_bills, by = c("bldg_id", "year", "month", utility)) |> + mutate(monthly_change = monthly_bill - baseline_bill) |> + select( + bldg_id, + upgrade, + hvac, + year, + month, + !!sym(utility), + version, + tariff_name, + baseline_bill, + electrified_bill = monthly_bill, + monthly_change + ) + + return(monthly_changes) +} + +# Annual Bills +calc_annual_bills_from_monthly <- function( + monthly_bills, + fuel_type, + months = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12) +) { + # First assign groups_cols based on fuel_type + groups_cols <- if (fuel_type == "electricity") { + c( + "bldg_id", + "upgrade", + "hvac", + "year", + "electric_utility", + "version", + "tariff_name" + ) + } else if (fuel_type == "natural_gas") { + c( + "bldg_id", + "upgrade", + "hvac", + "year", + "gas_utility", + "version", + "tariff_name" + ) + } else if (fuel_type == "fuel_oil") { + c( + "bldg_id", + "upgrade", + "hvac", + "year", + "fuel_oil_utility", + "version", + "tariff_name" + ) + } else if (fuel_type == "propane") { + c( + "bldg_id", + "upgrade", + "hvac", + "year", + "propane_utility", + "version", + "tariff_name" + ) + } + + monthly_bills |> + filter(month %in% months) |> + group_by(across(all_of(groups_cols))) |> + summarize( + annual_bill = sum(monthly_bill[ + !is.na(monthly_bill) & is.finite(monthly_bill) + ]), + .groups = "drop" + ) |> + ungroup() +} + +calc_annual_change_from_monthly <- function( + monthly_changes, + fuel_type, + months = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12) +) { + # First assign groups_cols based on fuel_type + groups_cols <- if (fuel_type == "electricity") { + c( + "bldg_id", + "upgrade", + "hvac", + "year", + "electric_utility", + "version", + "tariff_name" + ) + } else if (fuel_type == "natural_gas") { + c( + "bldg_id", + "upgrade", + "hvac", + "year", + "gas_utility", + "version", + "tariff_name" + ) + } else if (fuel_type == "fuel_oil") { + c( + "bldg_id", + "upgrade", + "hvac", + "year", + "fuel_oil_utility", + "version", + "tariff_name" + ) + } else if (fuel_type == "propane") { + c( + "bldg_id", + "upgrade", + "hvac", + "year", + "propane_utility", + "version", + "tariff_name" + ) + } + + monthly_changes |> + filter(month %in% months) |> + group_by(across(all_of(groups_cols))) |> + summarize( + annual_bill = sum(electrified_bill[ + !is.na(electrified_bill) & is.finite(electrified_bill) + ]), + annual_bill_baseline = sum(baseline_bill[ + !is.na(baseline_bill) & is.finite(baseline_bill) + ]), + annual_change = sum(monthly_change[ + !is.na(monthly_change) & is.finite(monthly_change) + ]), + .groups = "drop" + ) |> + ungroup() +} + +calc_annual_bills_total <- function( + annual_bills_elec, + annual_bills_gas, + annual_bills_fuel_oil, + annual_bills_propane +) { + annual_bills_total <- annual_bills_elec |> + select(bldg_id, upgrade, year, annual_bill, version, tariff_name) |> + rename( + annual_bill_elec = annual_bill, + version_elec = version, + tariff_name_elec = tariff_name + ) |> + # gas bills + left_join( + annual_bills_gas |> + select(bldg_id, upgrade, year, annual_bill, version, tariff_name) |> + rename( + annual_bill_gas = annual_bill, + version_gas = version, + tariff_name_gas = tariff_name + ), + by = c("bldg_id", "upgrade", "year") + ) |> + # fuel oil bills + left_join( + annual_bills_fuel_oil |> + select(bldg_id, upgrade, year, annual_bill, version, tariff_name) |> + rename( + annual_bill_fuel_oil = annual_bill, + version_fuel_oil = version, + tariff_name_fuel_oil = tariff_name + ), + by = c("bldg_id", "upgrade", "year") + ) |> + # propane bills + left_join( + annual_bills_propane |> + select(bldg_id, upgrade, year, annual_bill, version, tariff_name) |> + rename( + annual_bill_propane = annual_bill, + version_propane = version, + tariff_name_propane = tariff_name + ), + by = c("bldg_id", "upgrade", "year") + ) |> + # total bills + mutate( + annual_bill_total = annual_bill_elec + + annual_bill_gas + + annual_bill_fuel_oil + + annual_bill_propane + ) |> + # add some metadata from housing_units + left_join( + housing_units |> + select( + bldg_id, + upgrade, + hvac, + in.representative_income, + baseline_heating_type, + building_type_group, + baseline_cooling_type, + dollar_tier, + smi_tier, + occupants_group + ), + by = c("bldg_id", "upgrade") + ) |> + + # Remove homes with no income + filter(in.representative_income > 1000) |> + filter(occupants_group != "Vacant") |> + + # Energy Burdens + mutate( + burden_elec = annual_bill_elec / in.representative_income, + burden_gas = annual_bill_gas / in.representative_income, + burden_fuel_oil = annual_bill_fuel_oil / in.representative_income, + burden_propane = annual_bill_propane / in.representative_income, + burden_total = annual_bill_total / in.representative_income + ) + + return(annual_bills_total) +} + +calc_annual_changes_total <- function( + annual_changes_elec, + annual_changes_gas, + annual_changes_fuel_oil, + annual_changes_propane +) { + annual_changes_total <- annual_changes_elec |> + select(bldg_id, upgrade, year, annual_change, version, tariff_name) |> + rename( + annual_change_elec = annual_change, + version_elec = version, + tariff_name_elec = tariff_name + ) |> + # gas bills + left_join( + annual_changes_gas |> + select(bldg_id, upgrade, year, annual_change, version, tariff_name) |> + rename( + annual_change_gas = annual_change, + version_gas = version, + tariff_name_gas = tariff_name + ), + by = c("bldg_id", "upgrade", "year") + ) |> + # fuel oil bills + left_join( + annual_changes_fuel_oil |> + select(bldg_id, upgrade, year, annual_change, version, tariff_name) |> + rename( + annual_change_fuel_oil = annual_change, + version_fuel_oil = version, + tariff_name_fuel_oil = tariff_name + ), + by = c("bldg_id", "upgrade", "year") + ) |> + # propane bills + left_join( + annual_changes_propane |> + select(bldg_id, upgrade, year, annual_change, version, tariff_name) |> + rename( + annual_change_propane = annual_change, + version_propane = version, + tariff_name_propane = tariff_name + ), + by = c("bldg_id", "upgrade", "year") + ) |> + # total bills + mutate( + annual_change_total = annual_change_elec + + annual_change_gas + + annual_change_fuel_oil + + annual_change_propane + ) |> + # add some metadata from housing_units + left_join( + housing_units |> + select( + bldg_id, + upgrade, + hvac, + baseline_heating_type, + building_type_group, + baseline_cooling_type, + dollar_tier, + smi_tier, + occupants_group + ), + by = c("bldg_id", "upgrade") + ) + + return(annual_changes_total) +} + +# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx +# Bill Calculation Functions OLD +# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx +get_monthly_bills_elec_old <- function(path_rs_db, year) { + con <- DBI::dbConnect(duckdb::duckdb(), path_rs_db) + on.exit(DBI::dbDisconnect(con), add = TRUE) + + query <- sprintf( + " + WITH base_data AS ( + SELECT m.bldg_id, m.month, m.upgrade, m.hvac, m.shell, m.appliances, + m.total_elec_kwh as elec_consumption, + h.electric_utility, h.gas_utility, h.city, h.county, h.county_and_puma, h.building_type, h.occupants + FROM ma_monthly m + RIGHT JOIN ( + SELECT bldg_id, electric_utility, gas_utility, \"in.city\" as city, \"in.county\" as county, \"in.county_and_puma\" as county_and_puma, \"in.geometry_building_type_acs\" as building_type, \"in.occupants\" as occupants + FROM housing_units + ) h ON m.bldg_id = h.bldg_id + ), + with_delivery AS ( + SELECT b.*, + d1.value as customer_charge, + d2.value as delivery_rate, + d3.value as sales_tax_rate, + d1.version, + d1.tariff_id + FROM base_data b + LEFT JOIN ( + SELECT month, electric_utility, value, version, tariff_id + FROM delivery_tariffs_elec + WHERE type = 'customer_charge' + ) d1 ON b.month = d1.month AND b.electric_utility = d1.electric_utility + LEFT JOIN ( + SELECT month, electric_utility, value, version, tariff_id + FROM delivery_tariffs_elec + WHERE type = 'delivery_rate' + ) d2 ON b.month = d2.month + AND b.electric_utility = d2.electric_utility + AND d2.version = d1.version + AND d2.tariff_id = d1.tariff_id + LEFT JOIN ( + SELECT month, electric_utility, value, version, tariff_id + FROM delivery_tariffs_elec + WHERE type = 'sales_tax_rate' + ) d3 ON b.month = d3.month + AND b.electric_utility = d3.electric_utility + AND d3.version = d1.version + AND d3.tariff_id = d1.tariff_id + ), + final AS ( + SELECT w.*, + s.supply_rate, + s.year, + w.elec_consumption * w.delivery_rate as delivery_charge, + w.elec_consumption * s.supply_rate as supply_charge, + (w.elec_consumption * w.delivery_rate + w.elec_consumption * s.supply_rate + w.customer_charge) as total_pretax_bill, + (w.elec_consumption * w.delivery_rate + w.elec_consumption * s.supply_rate + w.customer_charge) * w.sales_tax_rate as sales_tax_charge, + (w.elec_consumption * w.delivery_rate + w.elec_consumption * s.supply_rate + w.customer_charge) + + ((w.elec_consumption * w.delivery_rate + w.elec_consumption * s.supply_rate + w.customer_charge) * w.sales_tax_rate) as monthly_bill + FROM with_delivery w + LEFT JOIN ( + SELECT month, electric_utility, supply_rate, year + FROM supply_rates_elec + WHERE year = %d + ) s ON w.month = s.month AND w.electric_utility = s.electric_utility + ) + SELECT + bldg_id, + upgrade, + hvac, + shell, + appliances, + month, + year, + electric_utility, + gas_utility, + city, + county, + county_and_puma, + building_type, + occupants, + elec_consumption, + version, + tariff_id, + delivery_rate, + supply_rate, + customer_charge, + delivery_charge, + supply_charge, + total_pretax_bill, + sales_tax_charge, + monthly_bill + FROM final + ORDER BY bldg_id, month, year, hvac, shell, appliances, electric_utility, version, tariff_id + ", + year + ) + + monthly_bills_elec <- DBI::dbGetQuery(con, query) + return(monthly_bills_elec) +} + +get_monthly_bills_gas <- function(path_rs_db, year) { + con <- DBI::dbConnect(duckdb::duckdb(), path_rs_db) + on.exit(DBI::dbDisconnect(con), add = TRUE) + + query <- sprintf( + " + WITH base_data AS ( + SELECT m.bldg_id, m.month, m.upgrade, m.hvac, m.shell, m.appliances, + m.total_gas_kwh as gas_consumption, + h.electric_utility, h.gas_utility, h.city, h.county, h.county_and_puma, h.building_type, h.occupants, + CASE + WHEN m.hvac = 'current' THEN 'R-3' + WHEN m.hvac IN ('low_hp','mid_hp','high_hp') THEN 'R-1' + END as customer_class + FROM ma_monthly m + RIGHT JOIN ( + SELECT bldg_id, electric_utility, gas_utility, \"in.city\" as city, \"in.county\" as county, \"in.county_and_puma\" as county_and_puma, \"in.geometry_building_type_acs\" as building_type, \"in.occupants\" as occupants + FROM housing_units + ) h ON m.bldg_id = h.bldg_id + ), + with_delivery AS ( + SELECT b.*, + d1.value as customer_charge, + d2.value as delivery_rate, + d3.value as sales_tax_rate, + d1.version, + d1.tariff_id, + d1.customer_class + FROM base_data b + LEFT JOIN ( + SELECT month, gas_utility, value, version, tariff_id, customer_class + FROM delivery_tariffs_gas + WHERE type = 'customer_charge' + ) d1 ON b.month = d1.month AND b.gas_utility = d1.gas_utility AND b.customer_class = d1.customer_class + LEFT JOIN ( + SELECT month, gas_utility, value, version, tariff_id, customer_class + FROM delivery_tariffs_gas + WHERE type = 'delivery_rate' + ) d2 ON b.month = d2.month + AND b.gas_utility = d2.gas_utility + AND b.customer_class = d2.customer_class + AND d2.version = d1.version + AND d2.tariff_id = d1.tariff_id + LEFT JOIN ( + SELECT month, gas_utility, value, version, tariff_id, customer_class + FROM delivery_tariffs_gas + WHERE type = 'sales_tax_rate' + ) d3 ON b.month = d3.month + AND b.gas_utility = d3.gas_utility + AND b.customer_class = d3.customer_class + AND d3.version = d1.version + AND d3.tariff_id = d1.tariff_id + ), + final AS ( + SELECT w.*, + s.supply_rate, + s.year, + s.rate_class, + w.gas_consumption * w.delivery_rate as delivery_charge, + w.gas_consumption * s.supply_rate as supply_charge, + (w.gas_consumption * w.delivery_rate + w.gas_consumption * s.supply_rate + w.customer_charge) as total_pretax_bill, + (w.gas_consumption * w.delivery_rate + w.gas_consumption * s.supply_rate + w.customer_charge) * w.sales_tax_rate as sales_tax_charge, + (w.gas_consumption * w.delivery_rate + w.gas_consumption * s.supply_rate + w.customer_charge) + + ((w.gas_consumption * w.delivery_rate + w.gas_consumption * s.supply_rate + w.customer_charge) * w.sales_tax_rate) as monthly_bill + FROM with_delivery w + LEFT JOIN ( + SELECT month, gas_utility, supply_rate, year, rate_class + FROM supply_rates_gas + WHERE year = %d + ) s ON w.month = s.month AND w.gas_utility = s.gas_utility AND s.rate_class = w.customer_class + ) + SELECT + bldg_id, + upgrade, + hvac, + shell, + appliances, + month, + year, + electric_utility, + gas_utility, + city, + county, + county_and_puma, + building_type, + occupants, + gas_consumption, + version, + tariff_id, + delivery_rate, + supply_rate, + customer_charge, + delivery_charge, + supply_charge, + total_pretax_bill, + sales_tax_charge, + monthly_bill + FROM final + ORDER BY bldg_id, month, year, hvac, shell, appliances, gas_utility, version, tariff_id + ", + year + ) + + # Print the query before executing it + #cat("About to execute query:\n", query, "\n") + + monthly_bills_gas <- DBI::dbGetQuery(con, query) + + # Force all rows to have the year passed as argument + monthly_bills_gas$year <- year + + return(monthly_bills_gas) +} + + +get_monthly_bills_fueloil <- function(path_rs_db, year) { + con <- DBI::dbConnect(duckdb::duckdb(), path_rs_db) + on.exit(DBI::dbDisconnect(con), add = TRUE) + + query <- sprintf( + " + WITH base_data AS ( + SELECT m.bldg_id, m.month, m.upgrade, m.hvac, m.shell, m.appliances, + m.total_fuel_oil_kwh as fueloil_consumption, + h.electric_utility, h.gas_utility, h.city, h.county, h.county_and_puma, h.building_type, h.occupants + FROM ma_monthly m + LEFT JOIN ( + SELECT bldg_id, electric_utility, gas_utility, \"in.city\" as city, \"in.county\" as county, \"in.county_and_puma\" as county_and_puma, \"in.geometry_building_type_acs\" as building_type, \"in.occupants\" as occupants + FROM housing_units + ) h ON m.bldg_id = h.bldg_id + ), + with_delivery AS ( + SELECT b.*, + d1.value as customer_charge, + d2.value as delivery_rate, + d3.value as sales_tax_rate + FROM base_data b + LEFT JOIN ( + SELECT month, value + FROM delivery_tariffs_fueloil + WHERE type = 'customer_charge' + ) d1 ON b.month = d1.month + LEFT JOIN ( + SELECT month, value + FROM delivery_tariffs_fueloil + WHERE type = 'delivery_rate' + ) d2 ON b.month = d2.month + LEFT JOIN ( + SELECT month, value + FROM delivery_tariffs_fueloil + WHERE type = 'sales_tax_rate' + ) d3 ON b.month = d3.month + ), + final AS ( + SELECT w.*, + fuel_oil_dollars_per_kwh as supply_rate, + %d as year, + w.fueloil_consumption * supply_rate as supply_charge, + w.fueloil_consumption * w.delivery_rate as delivery_charge, + (w.fueloil_consumption * w.delivery_rate + w.fueloil_consumption * supply_rate + w.customer_charge) as total_pretax_bill, + (w.fueloil_consumption * w.delivery_rate + w.fueloil_consumption * supply_rate + w.customer_charge) * w.sales_tax_rate as sales_tax_charge, + (w.fueloil_consumption * w.delivery_rate + w.fueloil_consumption * supply_rate + w.customer_charge) + + ((w.fueloil_consumption * w.delivery_rate + w.fueloil_consumption * supply_rate + w.customer_charge) * w.sales_tax_rate) as monthly_bill + FROM with_delivery w + LEFT JOIN ( + SELECT month, fuel_oil_dollars_per_kwh + FROM supply_rates_fueloil + WHERE year = %d + ) s ON w.month = s.month + ) + SELECT + bldg_id, + upgrade, + hvac, + shell, + appliances, + month, + year, + electric_utility, + gas_utility, + city, + county, + county_and_puma, + building_type, + occupants, + fueloil_consumption, + delivery_rate, + supply_rate, + customer_charge, + delivery_charge, + supply_charge, + total_pretax_bill, + sales_tax_charge, + monthly_bill + FROM final + ORDER BY bldg_id, month + ", + year, + year + ) + + # Print the query before executing it + #cat("About to execute query:\n", query, "\n") + + monthly_bills_fueloil <- DBI::dbGetQuery(con, query) + + return(monthly_bills_fueloil) +} + +get_monthly_bills_propane <- function(path_rs_db, year) { + con <- DBI::dbConnect(duckdb::duckdb(), path_rs_db) + on.exit(DBI::dbDisconnect(con), add = TRUE) + + query <- sprintf( + " + WITH base_data AS ( + SELECT m.bldg_id, m.month, m.upgrade, m.hvac, m.shell, m.appliances, + m.total_propane_kwh as propane_consumption, + h.electric_utility, h.gas_utility, h.city, h.county, h.county_and_puma, h.building_type, h.occupants + FROM ma_monthly m + LEFT JOIN ( + SELECT bldg_id, electric_utility, gas_utility, \"in.city\" as city, \"in.county\" as county, \"in.county_and_puma\" as county_and_puma, \"in.geometry_building_type_acs\" as building_type, \"in.occupants\" as occupants + FROM housing_units + ) h ON m.bldg_id = h.bldg_id + ), + with_delivery AS ( + SELECT b.*, + d1.value as customer_charge, + d2.value as delivery_rate, + d3.value as sales_tax_rate + FROM base_data b + LEFT JOIN ( + SELECT month, value + FROM delivery_tariffs_propane + WHERE type = 'customer_charge' + ) d1 ON b.month = d1.month + LEFT JOIN ( + SELECT month, value + FROM delivery_tariffs_propane + WHERE type = 'delivery_rate' + ) d2 ON b.month = d2.month + LEFT JOIN ( + SELECT month, value + FROM delivery_tariffs_propane + WHERE type = 'sales_tax_rate' + ) d3 ON b.month = d3.month + ), + final AS ( + SELECT w.*, + propane_dollars_per_kwh as supply_rate, + %d as year, + w.propane_consumption * supply_rate as supply_charge, + w.propane_consumption * w.delivery_rate as delivery_charge, + (w.propane_consumption * w.delivery_rate + w.propane_consumption * supply_rate + w.customer_charge) as total_pretax_bill, + (w.propane_consumption * w.delivery_rate + w.propane_consumption * supply_rate + w.customer_charge) * w.sales_tax_rate as sales_tax_charge, + (w.propane_consumption * w.delivery_rate + w.propane_consumption * supply_rate + w.customer_charge) + + ((w.propane_consumption * w.delivery_rate + w.propane_consumption * supply_rate + w.customer_charge) * w.sales_tax_rate) as monthly_bill + FROM with_delivery w + LEFT JOIN ( + SELECT month, propane_dollars_per_kwh + FROM supply_rates_propane + WHERE year = %d + ) s ON w.month = s.month + ) + SELECT + bldg_id, + upgrade, + hvac, + shell, + appliances, + month, + year, + electric_utility, + gas_utility, + city, + county, + county_and_puma, + building_type, + occupants, + propane_consumption, + delivery_rate, + supply_rate, + customer_charge, + delivery_charge, + supply_charge, + total_pretax_bill, + sales_tax_charge, + monthly_bill + FROM final + ORDER BY bldg_id, month, year + ", + year, + year + ) + + # Print the query before executing it + #cat("About to execute query:\n", query, "\n") + + monthly_bills_propane <- DBI::dbGetQuery(con, query) + + return(monthly_bills_propane) +} + + +## Apply low-income discounts +apply_low_income_discounts <- function( + path_rs_db, + url_sheet_low_income_thresholds, + sheet_tariff_name, + monthly_bills, + fuel_type +) { + # Get low income thresholds from Google Sheet + googlesheets4::gs4_deauth() + low_income_thresholds <- googlesheets4::read_sheet( + url_sheet_low_income_thresholds, + sheet = sheet_tariff_name + ) |> + select(-source, -note) + + # Get housing units data + con <- DBI::dbConnect(duckdb::duckdb(), path_rs_db) + housing_units <- DBI::dbReadTable(con, "housing_units") + DBI::dbDisconnect(con) + + # Calculate discount rates for each building + result <- housing_units |> + # Cross join with low_income_thresholds to get all possible combinations + cross_join(low_income_thresholds) |> + # Filter to keep only rows where conditions are met + filter( + if (fuel_type == "electricity") { + electric_utility.x == electric_utility.y + } else if (fuel_type == "gas") { + gas_utility.x == gas_utility.y + } else if (fuel_type == "fueloil") { + TRUE + } else if (fuel_type == "propane") { + TRUE + }, + in.occupants == occupants_min, + assigned_income >= income_threshold_lower, + assigned_income <= income_threshold_upper + ) |> + # Group by housing unit to handle multiple matching thresholds + group_by(bldg_id) |> + # Take highest matching discount rate or 0 if none match + summarize( + discount_rate = if (n() > 0) max(discount_rate) else 0, + .groups = "drop" + ) |> + # Right join to housing_units to keep all buildings + right_join(housing_units, by = "bldg_id") |> + # Fill NA discounts with 0 + mutate(discount_rate = coalesce(discount_rate, 0)) |> + select(bldg_id, in.occupants, assigned_income, discount_rate) + + # Apply discounts to annual bills + monthly_bills_discounted <- monthly_bills |> + left_join(result, by = c("bldg_id", "occupants" = "in.occupants")) |> + mutate(monthly_bill_raw = monthly_bill) |> + mutate(discount = -(monthly_bill_raw * discount_rate)) |> + mutate(monthly_bill = monthly_bill_raw + discount) + + return(monthly_bills_discounted) +} + + +## Annual Bills +calc_annual_bills_from_monthly_old <- function(monthly_bills, fuel_type) { + # First assign groups_cols based on fuel_type + groups_cols <- if (fuel_type == "electricity") { + c( + "bldg_id", + "year", + "hvac", + "shell", + "appliances", + "county", + "county_and_puma", + "building_type", + "occupants", + "electric_utility", + "version", + "tariff_id" + ) + } else if (fuel_type == "gas") { + c( + "bldg_id", + "year", + "hvac", + "shell", + "appliances", + "county", + "building_type", + "occupants", + "gas_utility", + "version", + "tariff_id" + ) + } else if (fuel_type == "fueloil") { + c( + "bldg_id", + "year", + "hvac", + "shell", + "appliances", + "county", + "building_type", + "occupants" + ) + } else if (fuel_type == "propane") { + c( + "bldg_id", + "year", + "hvac", + "shell", + "appliances", + "county", + "building_type", + "occupants" + ) + } + + # monthly_bills |> + # group_by(across(all_of(groups_cols))) |> + # summarize( + # annual_bill = sum(monthly_bill), + # annual_bill_raw = sum(monthly_bill_raw) + # )|> + # ungroup() + + monthly_bills |> + group_by(across(all_of(groups_cols))) |> + summarize( + annual_bill = sum(monthly_bill[ + !is.na(monthly_bill) & is.finite(monthly_bill) + ]), + annual_bill_raw = sum(monthly_bill_raw[ + !is.na(monthly_bill_raw) & is.finite(monthly_bill_raw) + ]) + ) |> + ungroup() +} + +calculate_annual_elec_bills_from_monthly <- function(monthly_bills) { + monthly_bills |> + group_by(bldg_id, year, upgrade, electric_utility, version, tariff_id) |> + summarize( + annual_bill = sum(monthly_bill[ + !is.na(monthly_bill) & is.finite(monthly_bill) + ]) + ) |> + filter(annual_bill > 0) |> + ungroup() +} + + +calculate_annual_gas_bills_from_monthly <- function(monthly_bills) { + monthly_bills |> + group_by(bldg_id, year, upgrade, gas_utility, version, tariff_id) |> + summarize( + annual_bill = sum(total_bill[!is.na(total_bill) & is.finite(total_bill)]) + ) |> + filter(annual_bill > 0) |> + ungroup() +} + + +######################################################## +# Plotting Functions +######################################################## + +## Supply Plots ---------------------- +### NY Supply Rates +plot_supply_rates <- function( + supply_rates, + y, + start_year, + end_year = 2024, + highlight_years = c("2020", "2024") +) { + # Filter data to desired year range + supply_rates_filtered <- supply_rates |> + filter(year >= start_year & year <= end_year, tariff_name == y) + + p <- ggplot(supply_rates_filtered, aes(x = month, y = rate, group = year)) + + + # Add grey lines for years not in highlight years + geom_line( + data = filter( + supply_rates_filtered, + !as.character(year) %in% highlight_years + ), + color = "grey80" + ) + + geom_point( + data = filter( + supply_rates_filtered, + !as.character(year) %in% highlight_years + ), + color = "grey80", + size = 0.1 + ) + + + # Add colored lines for highlight years + geom_line( + data = filter( + supply_rates_filtered, + as.character(year) %in% highlight_years + ), + aes(color = as.factor(year)) + ) + + geom_point( + data = filter( + supply_rates_filtered, + as.character(year) %in% highlight_years + ), + aes(color = as.factor(year)), + size = 0.25 + ) + + + scale_x_continuous(breaks = 1:12, labels = month.abb) + + scale_y_continuous(labels = scales::label_dollar()) + + scale_color_viridis_d() + + labs( + title = paste("ConEd", y, "Supply Rates by Year"), + x = "", + y = "Wholesale Supply Cost ($/kWh)", + color = "Year" + ) + + theme(legend.position = "right") + + return(p) +} + +plot_supply_rates_ribbon <- function( + supply_rates, + first_year, + last_year, + ribbon_top, + ribbon_bottom, + middle +) { + # Create color palette + year_range <- last_year - first_year + 1 + year_colors <- viridis::viridis( + year_range, + begin = 0, + end = 1, + direction = -1 + ) |> + settariff_names(first_year:last_year) + + # Filter and reshape data + plot_data <- supply_rates |> + filter(year >= first_year & year <= last_year) |> + mutate(date = make_date(year, month, 1)) |> + pivot_wider( + tariff_names_from = tariff_name, + values_from = rate + ) + + # Create plot + p <- ggplot(plot_data, aes(x = date, group = year)) + + # Add ribbon between specified rates + geom_ribbon( + aes( + ymin = .data[[ribbon_bottom]], + ymax = .data[[ribbon_top]], + fill = as.factor(year) + ), + alpha = 0.3 + ) + + # Add middle line + geom_line( + aes( + y = .data[[middle]], + color = as.factor(year) + ), + linewidth = 1 + ) + + # Formatting + scale_x_date(date_breaks = "1 year", date_labels = "%Y") + + scale_y_continuous(labels = scales::label_dollar()) + + scale_fill_manual(values = year_colors) + + scale_color_manual(values = year_colors) + + labs( + title = paste("Supply Rates:", first_year, "-", last_year), + subtitle = "Showing peak/off-peak spread and all-hours rate", + x = "", + y = "Supply Rate ($/kWh)", + fill = "Year", + color = "Year" + ) + + theme(legend.position = "None") + + return(p) +} + +### MA Gas Supply Rates +plot_gas_supply_rates <- function(data, save_path, width = 6, height = 3) { + # Get year range for shading + start_year <- as.numeric(format(min(data$effective_date), "%Y")) + end_year <- as.numeric(format(max(data$effective_date), "%Y")) + + # Create data frame for summer period rectangles + summer_periods <- data.frame( + xmin = as.Date(sprintf("%d-05-01", start_year:end_year)), + xmax = as.Date(sprintf("%d-10-31", start_year:end_year)) + ) + + # Create the plot + p <- ggplot(data, aes(x = effective_date, y = gaf, color = gas_utility)) + + # Add summer period shading + theme( + panel.grid.minor.x = element_blank(), # Remove minor gridlines + panel.grid.major.y = element_line(linewidth = 0.5, color = "grey80"), # Explicit y gridlines + panel.grid.minor.y = element_line(linewidth = 0.25, color = "grey90") # Explicit minor y gridlines + ) + + # geom_rect(data = summer_periods, + # aes(xmin = xmin, xmax = xmax, + # ymin = -Inf, ymax = Inf), + # fill = "blue", + # color = NA, + # inherit.aes = FALSE) + + geom_point(size = 0.25) + + geom_line() + + labs( + title = "Gas Supply (GAF) Rates by Utility", + x = "Effective Date", + y = "GAF Rate ($/Therm)", + color = "gas_utility" + ) + + scale_color_viridis_d(option = "viridis") + + scale_x_date( + date_breaks = "1 year", + date_labels = "%Y", + limits = c( + as.Date(paste0(min(format(data$effective_date, "%Y")), "-01-01")), + max(data$effective_date) + ) + ) + + scale_y_continuous(limits = c(0, NA)) + + # Save the plot + ggsave(save_path, p, width = width, height = height, bg = "white") + + # Display the plot + print(p) + + # Return the plot object invisibly + invisible(p) +} + +### Plot supply rates for 12 months +plot_supply_rates_12_months <- function( + supply_rates_monthly_long, + electric_utility, + highlight_years = c("2025") +) { + # Spaghetti plot of supply rates for 12 months + # X: Month + # Y: Supply Rate + # Color: Year + # Add grey lines for years not in highlight years + # Add colored lines for highlight years + + # Filter data for the selected utility + filtered_data <- supply_rates_monthly_long |> + filter(electric_utility == !!electric_utility) + + ggplot(filtered_data, aes(x = month, y = supply_rate, group = year)) + + # Add grey lines for years not in highlight years + geom_line( + data = filter(filtered_data, !year %in% highlight_years), + color = "grey80" + ) + + geom_point( + data = filter(filtered_data, !year %in% highlight_years), + color = "grey80", + size = 0.1 + ) + + + # Add colored lines for highlight years + geom_line( + data = filter(filtered_data, year %in% highlight_years), + aes(color = factor(year)) + ) + + geom_point( + data = filter(filtered_data, year %in% highlight_years), + aes(color = factor(year)), + size = 0.25 + ) + + + scale_x_continuous(breaks = 1:12, labels = month.abb) + + scale_y_continuous(labels = label_dollar()) + + scale_color_viridis_d() + + labs( + title = paste( + "Massachusetts Basic Service Rates:", + gsub("_", " ", electric_utility) + ), + x = "", + y = "Wholesale Supply Cost ($/kWh)" + ) + + theme(legend.position = "right") +} + +## Annual Bill Plots -------------------- diff --git a/lib/rates_analysis/heat_pump_rate_plots.R b/lib/rates_analysis/heat_pump_rate_plots.R new file mode 100644 index 0000000..afeaba7 --- /dev/null +++ b/lib/rates_analysis/heat_pump_rate_plots.R @@ -0,0 +1,814 @@ +library(lubridate) +library(patchwork) +library(tidyverse) +library(ggrepel) + +hist_for_single_rate_version <- function( + annual_change_table, + bill_type, + version_elec, + baseline_heating_type_option, + hvac_option, + supply_year, + season = 'Annual', + title = "auto", + second_subtitle = NULL, + x_limits = c(-3000, 3000), + y_limits = c(0, 1000), + binwidth = 100, + show_category_labels = TRUE +) { + # -------------------------------------- + # Plot parameters + x_min <- x_limits[1] + x_max <- x_limits[2] + + # Define the breaks for binning + breaks <- seq( + floor(x_min / binwidth) * binwidth, + ceiling(x_max / binwidth) * binwidth, + by = binwidth + ) + + # More balanced color palette with less contrast + dark_red <- "#db8b87" # dark red + light_red <- "#eaada9" # light red + light_green <- "#8ebd85" # light green + dark_green <- "#5e8a5e" # dark green + # -------------------------------------- + + # Filter data for mid_hp + plot_data <- annual_change_table |> + filter(hvac == hvac_option, version_elec == !!version_elec) |> + filter( + if (baseline_heating_type_option != "All Fuels") { + baseline_heating_type == !!baseline_heating_type_option + } else { + TRUE + } + ) |> + # Add category column before binning with new cutoffs + mutate( + bill_category = case_when( + annual_change_total <= -1000 ~ "large_savings", + annual_change_total < 0 ~ "small_savings", + annual_change_total < 1000 ~ "small_increase", + TRUE ~ "large_increase" + ) + ) + + # Calculate percentages for each category + category_percentages <- plot_data |> + group_by(bill_category) |> + summarise(count = n(), .groups = "drop") |> + mutate(percentage = round(count / sum(count) * 100, 1)) + + # Add a row for 'pct_that_save' as the sum of 'small_savings' and 'large_savings' + category_percentages <- category_percentages |> + bind_rows( + tibble( + bill_category = "pct_that_save", + count = sum(category_percentages$count[ + category_percentages$bill_category %in% + c("small_savings", "large_savings") + ]), + percentage = sum(category_percentages$percentage[ + category_percentages$bill_category %in% + c("small_savings", "large_savings") + ]) + ) + ) |> + bind_rows( + tibble( + bill_category = "pct_that_lose", + count = sum(category_percentages$count[ + category_percentages$bill_category %in% + c("small_increase", "large_increase") + ]), + percentage = sum(category_percentages$percentage[ + category_percentages$bill_category %in% + c("small_increase", "large_increase") + ]) + ) + ) + + # Get percentages for annotation + pct_large_savings <- category_percentages |> + filter(bill_category == "large_savings") |> + pull(percentage) |> + round(1) + if (length(pct_large_savings) == 0) { + pct_large_savings <- 0 + } + + pct_small_savings <- category_percentages |> + filter(bill_category == "small_savings") |> + pull(percentage) |> + round(1) + if (length(pct_small_savings) == 0) { + pct_small_savings <- 0 + } + + pct_that_save <- pct_large_savings + pct_small_savings + + pct_small_increase <- category_percentages |> + filter(bill_category == "small_increase") |> + pull(percentage) |> + round(1) + if (length(pct_small_increase) == 0) { + pct_small_increase <- 0 + } + + pct_large_increase <- category_percentages |> + filter(bill_category == "large_increase") |> + pull(percentage) |> + round(1) + if (length(pct_large_increase) == 0) { + pct_large_increase <- 0 + } + + pct_that_lose <- pct_small_increase + pct_large_increase + + # Manually bin the data and calculate counts - this preserves the bill_category + binned_data <- plot_data |> + mutate( + bin = cut( + annual_change_total, + breaks = breaks, + include.lowest = TRUE, + right = FALSE + ) + ) |> + group_by(bin, bill_category) |> + summarise(count = n(), .groups = "drop") |> + # Add bin midpoint for plotting + mutate( + bin_mid = as.numeric(as.character( + sapply(bin, function(b) { + # Extract bin boundaries and calculate midpoint + vals <- as.numeric(gsub( + "\\(|\\]|\\[|\\)", + "", + strsplit(as.character(b), ",")[[1]] + )) + return(mean(vals)) + }) + )) + ) + + # Position the bars at the very top and text just below + bar_y_pos <- y_limits[2] # Exactly at the top + text_y_pos <- y_limits[2] * 0.95 # Just below the colored line + + # Calculate x positions for percentage labels (center of each category) + x_large_savings <- mean(c(x_min, -1000)) + x_small_savings <- mean(c(-1000, 0)) + x_small_increase <- mean(c(0, 1000)) + x_large_increase <- mean(c(1000, x_max)) + + # Create the plot using geom_col() with binned data + px_hist_by_rate_version <- ggplot( + binned_data, + aes(x = bin_mid, y = count, fill = bill_category) + ) + + # Add vertical dotted lines at the category boundaries + geom_vline( + xintercept = c(-1000, 0, 1000), + linetype = "dotted", + color = "gray50", + size = 0.5 + ) + + + # Add the bars + geom_col(position = "stack", width = binwidth * 0.9) + + + # Set the fill colors manually with expanded categories + scale_fill_manual( + values = c( + "large_savings" = "#5e8a5e", # dark green + "small_savings" = "#8ebd85", # light green + "small_increase" = "#eaada9", # light red + "large_increase" = "#db8b87" # dark red + ), + guide = "none" + ) + + + # Add horizontal lines at the very top of the plot + # With colors matching their respective categories + annotate( + "segment", + x = x_min, + xend = -1000, + y = bar_y_pos, + yend = bar_y_pos, + color = dark_green, + size = 1 + ) + + + annotate( + "segment", + x = -1000, + xend = 0, + y = bar_y_pos, + yend = bar_y_pos, + color = light_green, + size = 1 + ) + + + annotate( + "segment", + x = 0, + xend = 1000, + y = bar_y_pos, + yend = bar_y_pos, + color = light_red, + size = 1 + ) + + + annotate( + "segment", + x = 1000, + xend = x_max, + y = bar_y_pos, + yend = bar_y_pos, + color = dark_red, + size = 1 + ) + + + # Add percentage labels for each category directly below the lines + annotate( + "text", + x = x_large_savings, + y = text_y_pos * 0.95, + label = paste0(pct_large_savings, "%"), + size = 3, + fontface = "bold" + ) + + annotate( + "text", + x = x_small_savings, + y = text_y_pos * 0.95, + label = paste0(pct_small_savings, "%"), + size = 3, + fontface = "bold" + ) + + annotate( + "text", + x = x_small_increase, + y = text_y_pos * 0.95, + label = paste0(pct_small_increase, "%"), + size = 3, + fontface = "bold" + ) + + annotate( + "text", + x = x_large_increase, + y = text_y_pos * 0.95, + label = paste0(pct_large_increase, "%"), + size = 3, + fontface = "bold" + ) + + + # Rest of the plot styling + labs( + title = if (title == "auto") { + title = case_when( + bill_type == "annual_change_total" ~ glue::glue( + "Change in Total {season} Energy Bills after Switching to Heat Pump from {baseline_heating_type_option}" + ), + bill_type == "annual_change_elec" ~ glue::glue( + "Switching from {baseline_heating_type_option} to Heat Pump: Change in {season} Electric Bill" + ), + bill_type == "annual_change_gas" ~ glue::glue( + "Switching from {baseline_heating_type_option} to Heat Pump: Change in {season} Gas Bill" + ), + bill_type == "annual_change_fuel_oil" ~ glue::glue( + "Switching from {baseline_heating_type_option} to Heat Pump: Change in {season} Fuel Oil Bill" + ), + bill_type == "annual_change_propane" ~ glue::glue( + "Switching from {baseline_heating_type_option} to Heat Pump: Change in {season} Propane Bill" + ), + TRUE ~ "Heat Pump Rate Bill Changes" # Default case + ) + } else if (title == "No Title") { + title = NULL + } else { + title = title + }, + x = glue::glue("{season} Bill Change ($)"), + y = "# of Homes" + ) + + scale_y_continuous( + labels = function(x) paste0(round(x * 242.13 * 0.001), "k"), + name = "# of Homes", + limits = y_limits, + breaks = seq(0, y_limits[2] * 242.13, by = 20000) / 242.13 + ) + + scale_x_continuous( + labels = scales::dollar_format(), + limits = x_limits, + breaks = seq(x_min, x_max, by = 500) + ) + + theme_minimal() + + theme( + plot.title = element_text(hjust = 0.5, size = 10, face = "bold"), + panel.grid.major = element_line(linewidth = 0.2), + panel.grid.minor = element_blank(), + axis.text.x = element_text(angle = 45, hjust = 1), + legend.position = "none", + aspect.ratio = 0.6 + ) + + { + if (show_category_labels) { + list( + annotate( + "text", + x = x_large_savings, + y = y_limits[2] * 0.97, + label = "savings > $1k", + size = 3, + fontface = "bold" + ), + annotate( + "text", + x = x_small_savings, + y = y_limits[2] * 0.97, + label = "savings $0–1k", + size = 3, + fontface = "bold" + ), # make bold + annotate( + "text", + x = x_small_increase, + y = y_limits[2] * 0.97, + label = "losses $0–1k", + size = 3, + fontface = "bold" + ), # make bold + annotate( + "text", + x = x_large_increase, + y = y_limits[2] * 0.97, + label = "losses > $1k", + size = 3, + fontface = "bold" + ) + ) + } + } + + coord_cartesian(clip = "off") + + return(list(px_hist_by_rate_version, category_percentages)) +} + + +plot_bill_change_histograms <- function( + annual_change_table, + bill_type, + baseline_heating_type_option, + season = 'Annual', + version_elec = c("baseline", "baseline", "baseline"), + hvac_option = c("hp_low", "hp_mid", "hp_best"), + supply_year = 2024, + second_subtitle = NULL, + x_limits = c(-2500, 2500), + y_limits = c(0, 100000) / 242.13, + binwidth = 100 +) { + # Create nicer version labels + version_labels <- c( + "hp_low" = "HSPF 9.2 - Energy Star Minimum", + "hp_high" = "HSPF 11 - Minimum for Climate Zone 5", + "hp_best" = "HSPF 13 - Best Available" + ) + + # Create three histograms without individual titles + + # Plot 1: hp_low + # ----------------------------------------------- + result_1 <- hist_for_single_rate_version( + annual_change_table = annual_change_table, + bill_type = bill_type, + version_elec = version_elec[1], + baseline_heating_type_option = baseline_heating_type_option, + hvac_option = hvac_option[1], + supply_year = supply_year, + title = 'auto', + x_limits = x_limits, + y_limits = y_limits, + binwidth = binwidth, + show_category_labels = FALSE + ) + + p1 <- result_1[[1]] + + labs(title = NULL) + # Remove title + ylab(NULL) + # Remove y-axis label + theme( + axis.title.x = element_blank(), # Remove x axis title from top two plots + axis.text.x = element_blank(), # Remove x tick labels from top two plots + axis.ticks.x = element_blank(), # Remove x ticks from top two plots + plot.margin = margin(5, 5, 0, 5), # Consistent margins (top, right, bottom, left) + axis.title.y = element_blank(), # Remove y title from top plot + aspect.ratio = 0.18 + ) + + # Add elegant version label on right side + annotate( + "text", + x = x_limits[2] * 0.95, + y = y_limits[2] * 0.7, + label = glue::glue( + "{version_labels[hvac_option[1]]}\n{version_elec[1]} rates" + ), + hjust = 1, + fontface = "bold", + size = 4, + color = "#023047" + ) + + # Plot 2: hp_mid + # ----------------------------------------------- + result_2 <- hist_for_single_rate_version( + annual_change_table = annual_change_table, + bill_type = bill_type, + version_elec = version_elec[2], + baseline_heating_type_option = baseline_heating_type_option, + hvac_option = hvac_option[2], + supply_year = supply_year, + title = 'auto', + x_limits = x_limits, + y_limits = y_limits, + binwidth = binwidth, + show_category_labels = FALSE + ) + + p2 <- result_2[[1]] + + labs(title = NULL) + # Remove title + ylab(NULL) + + theme( + axis.title.x = element_blank(), # Remove x axis title from top two plots + axis.text.x = element_blank(), # Remove x tick labels from top two plots + axis.ticks.x = element_blank(), # Remove x ticks from top two plots + axis.title.y = element_blank(), # Remove y title from middle plot + plot.margin = margin(0, 5, 0, 5), # Consistent margins (no top/bottom margin) + aspect.ratio = 0.18 + ) + + # Add elegant version label on right side + annotate( + "text", + x = x_limits[2] * 0.95, + y = y_limits[2] * 0.7, + label = glue::glue( + "{version_labels[hvac_option[2]]}\n{version_elec[2]} rates" + ), + hjust = 1, + fontface = "bold", + size = 4, + color = "#FC9706" + ) + + # Plot 3: hp_best + # ----------------------------------------------- + result_3 <- hist_for_single_rate_version( + annual_change_table = annual_change_table, + bill_type = bill_type, + version_elec = version_elec[3], + baseline_heating_type_option = baseline_heating_type_option, + hvac_option = hvac_option[3], + supply_year = supply_year, + title = 'auto', + x_limits = x_limits, + y_limits = y_limits, + binwidth = binwidth, + show_category_labels = FALSE + ) + + p3 <- result_3[[1]] + + labs(title = NULL) + # Remove title + ylab(NULL) + + theme( + axis.title.y = element_blank(), # Remove y title from bottom plot + plot.margin = margin(0, 5, 5, 5), # Consistent margins + axis.title.x = element_text(size = 9), + aspect.ratio = 0.18 + ) + + # Add elegant version label on right side + annotate( + "text", + x = x_limits[2] * 0.95, + y = y_limits[2] * 0.7, + label = glue::glue( + "{version_labels[hvac_option[3]]}\n{version_elec[3]} rates" + ), + hjust = 1, + fontface = "bold", + size = 4, + color = "#68BED8" + ) + + # Combine plots with shared x and y axes + combined_plot <- p1 / + p2 / + p3 + + plot_layout(heights = c(1, 1, 1)) + # Equal heights for all plots + plot_annotation( + title = case_when( + bill_type == "annual_change_total" ~ glue::glue( + "Change in Total Annual Energy Bills after Switching to Heat Pump from {baseline_heating_type_option}" + ), + bill_type == "annual_change_elec" ~ glue::glue( + "Switching from {baseline_heating_type_option} to Heat Pump: Change in Annual Electric Bill" + ), + bill_type == "annual_change_gas" ~ glue::glue( + "Switching from {baseline_heating_type_option} to Heat Pump: Change in Annual Gas Bill" + ), + bill_type == "annual_change_fuel_oil" ~ glue::glue( + "Switching from {baseline_heating_type_option} to Heat Pump: Change in Annual Fuel Oil Bill" + ), + bill_type == "annual_change_propane" ~ glue::glue( + "Switching from {baseline_heating_type_option} to Heat Pump: Change in Annual Propane Bill" + ), + TRUE ~ "Heat Pump Rate Bill Changes" # Default case + ), + subtitle = second_subtitle, + theme = theme( + plot.margin = margin(10, 25, 10, 10), # Add more right margin for labels + plot.title = element_text(hjust = 0.5, size = 10, face = "bold"), + plot.subtitle = element_text(hjust = 0.5, size = 8), + ) + ) + + # Apply consistent theming to all plots + theme( + panel.grid.minor = element_blank(), + panel.grid.major.x = element_line(linewidth = 0.2, color = "gray90"), + panel.grid.major.y = element_line(linewidth = 0.2, color = "gray90"), + ) + + # Wrap the combined plot to add a shared y-axis label + combined_plot <- patchwork::wrap_elements(combined_plot) + + labs(tag = "# of Homes") + + theme( + plot.tag = element_text( + angle = 90, + vjust = 0.5, + hjust = 0.5, + face = "plain", + size = 9 + ), + plot.tag.position = "left" + ) + + return(list(combined_plot, result_1[[2]], result_2[[2]], result_3[[2]])) +} + + +plot_energy_burden_histogram_standalone <- function( + data, + name, + x_limits = c(0, 1.0), + y_limits = c(0, 50000 / 242.13), + binwidth = 0.01, + show_x_axis = TRUE, + show_y_axis = TRUE +) { + low_burden_threshold <- 0.04 + moderate_burden_threshold <- 0.06 + high_burden_threshold <- 0.12 + + # Calculate bill categories + data <- data |> + mutate( + burden_group = case_when( + burden_total < low_burden_threshold ~ "low_energy_burden", + burden_total < moderate_burden_threshold ~ "moderate_energy_burden", + burden_total < high_burden_threshold ~ "high_energy_burden", + TRUE ~ "very_high_energy_burden" + ) + ) + + # Calculate percentages for each category + category_percentages <- data |> + group_by(burden_group) |> + summarise(count = n(), .groups = "drop") |> + mutate(percentage = count / sum(count) * 100) + + # Get percentages for annotation + pct_low_energy_burden <- category_percentages |> + filter(burden_group == "low_energy_burden") |> + pull(percentage) |> + round(1) + + pct_moderate_energy_burden <- category_percentages |> + filter(burden_group == "moderate_energy_burden") |> + pull(percentage) |> + round(1) + + pct_high_energy_burden <- category_percentages |> + filter(burden_group == "high_energy_burden") |> + pull(percentage) |> + round(1) + + pct_very_high_energy_burden <- category_percentages |> + filter(burden_group == "very_high_energy_burden") |> + pull(percentage) |> + round(1) + + # Calculate bin breaks and midpoints + breaks <- seq( + floor(x_limits[1] / binwidth) * binwidth, + ceiling(x_limits[2] / binwidth) * binwidth, + by = binwidth + ) + + # Manually bin the data + binned_data <- data |> + mutate( + bin = cut( + burden_total, + breaks = breaks, + include.lowest = TRUE, + right = FALSE + ) + ) |> + group_by(bin, burden_group) |> + summarise(count = n(), .groups = "drop") |> + mutate( + bin_mid = as.numeric(as.character( + sapply(bin, function(b) { + vals <- as.numeric(gsub( + "\\(|\\]|\\[|\\)", + "", + strsplit(as.character(b), ",")[[1]] + )) + return(mean(vals)) + }) + )) + ) + + # Position the bars at the top and text just below + bar_y_pos <- y_limits[2] + text_y_pos <- y_limits[2] * 0.90 + + # Calculate x positions for percentage labels + x_low_energy_burden <- mean(c(0, low_burden_threshold - 0.002)) + x_moderate_energy_burden <- mean(c( + low_burden_threshold, + moderate_burden_threshold + )) + x_high_energy_burden <- mean(c( + moderate_burden_threshold, + high_burden_threshold + )) + x_very_high_energy_burden <- mean(c(high_burden_threshold, x_limits[2])) + + # Create the plot + plot <- ggplot( + binned_data, + aes(x = bin_mid, y = count, fill = burden_group) + ) + + # Add vertical dotted lines at the category boundaries + geom_vline( + xintercept = c( + low_burden_threshold, + moderate_burden_threshold, + high_burden_threshold + ), + linetype = "dotted", + color = "gray50", + size = 0.5 + ) + + # Add bars + geom_col(position = "stack", width = binwidth * 0.9) + + + # Set the fill colors for the bars + scale_fill_manual( + values = energy_burden_colors, + guide = "none" + ) + + # Add vertical lines at the category boundaries + annotate( + "segment", + x = x_limits[1], + xend = low_burden_threshold, + y = bar_y_pos, + yend = bar_y_pos, + color = energy_burden_colors["low_energy_burden"], + size = 1 + ) + + annotate( + "segment", + x = low_burden_threshold, + xend = moderate_burden_threshold, + y = bar_y_pos, + yend = bar_y_pos, + color = energy_burden_colors["moderate_energy_burden"], + size = 1 + ) + + annotate( + "segment", + x = moderate_burden_threshold, + xend = high_burden_threshold, + y = bar_y_pos, + yend = bar_y_pos, + color = energy_burden_colors["high_energy_burden"], + size = 1 + ) + + annotate( + "segment", + x = high_burden_threshold, + xend = x_limits[2], + y = bar_y_pos, + yend = bar_y_pos, + color = energy_burden_colors["very_high_energy_burden"], + size = 1 + ) + + annotate( + "text", + x = x_low_energy_burden, + y = text_y_pos * 0.95, + label = paste0(pct_low_energy_burden, "%"), + size = 3, + fontface = "bold" + ) + + annotate( + "text", + x = x_moderate_energy_burden, + y = text_y_pos * 0.95, + label = paste0(pct_moderate_energy_burden, "%"), + size = 3, + fontface = "bold" + ) + + annotate( + "text", + x = x_high_energy_burden, + y = text_y_pos * 0.95, + label = paste0(pct_high_energy_burden, "%"), + size = 3, + fontface = "bold" + ) + + annotate( + "text", + x = x_very_high_energy_burden, + y = text_y_pos * 0.95, + label = paste0(pct_very_high_energy_burden, "%"), + size = 3, + fontface = "bold" + ) + + scale_y_continuous( + labels = function(x) paste0(round(x * 242.13 * 0.001), "k"), + #name = "# of Homes", + limits = y_limits, + breaks = c( + 0, + 25000, + 50000, + 75000, + 100000, + 125000, + 150000, + 175000, + 200000, + 225000, + 250000 + ) / + 242.13 + ) + + scale_x_continuous( + labels = scales::percent_format(accuracy = 1), + name = "Annual Energy Burden (All fuels, no transportation costs)", + limits = x_limits, + breaks = c( + low_burden_threshold, + moderate_burden_threshold, + high_burden_threshold + ), + ) + + theme_minimal() + + theme( + panel.grid.major = element_line(linewidth = 0.2), + panel.grid.minor = element_blank(), + aspect.ratio = 0.18, + axis.title.x = element_text(size = 8) + ) + + # Remove x-axis elements if requested + if (!show_x_axis) { + plot <- plot + + theme( + axis.title.x = element_blank(), + axis.text.x = element_blank(), + axis.ticks.x = element_blank() + ) + } + + # Remove y-axis elements if requested + if (!show_y_axis) { + plot <- plot + + theme( + axis.title.y = element_blank(), + axis.text.y = element_blank(), + axis.ticks.y = element_blank() + ) + } + + return(list(plot = plot, category_percentages = category_percentages)) +} diff --git a/lib/rdp.py b/lib/rdp.py new file mode 100644 index 0000000..249b0f9 --- /dev/null +++ b/lib/rdp.py @@ -0,0 +1,601 @@ +"""Shared reporting utilities and rate-design-platform fetch helpers.""" + +from __future__ import annotations + +import base64 +import io +import json +import os +import urllib.request +from collections.abc import Iterable, Sequence +from pathlib import Path +from typing import TYPE_CHECKING, cast + +if TYPE_CHECKING: + import pandas as pd + import polars as pl + + +DEFAULT_RESSTOCK_DATA_ROOT_NAMES = ("resstock", "ResStock") +DIST_PARAM_KEYS = ( + "annual_future_distr_costs", + "distr_peak_hrs", + "nc_ratio_baseline", +) + + +def repo_root() -> Path: + """Return the repository root for the current checkout.""" + return Path(__file__).resolve().parent.parent + + +def _choose_existing_path(candidates: Iterable[Path], description: str) -> Path: + checked = [] + for candidate in candidates: + checked.append(candidate) + if candidate.exists(): + return candidate + raise FileNotFoundError(f"No {description} found. Checked: " + ", ".join(str(path) for path in checked)) + + +def _normalize_upgrade(upgrade: str | int) -> str: + if isinstance(upgrade, int): + return f"{upgrade:02d}" + if upgrade.isdigit(): + return f"{int(upgrade):02d}" + return upgrade + + +def resolve_resstock_data_root( + candidates: Sequence[Path] | None = None, + root: Path | None = None, +) -> Path: + """Return the first existing local ResStock data root under the repo.""" + root = root or repo_root() + candidates = list(candidates or []) + if not candidates: + candidates = [root / "data" / name for name in DEFAULT_RESSTOCK_DATA_ROOT_NAMES] + return _choose_existing_path(candidates, "local ResStock data root") + + +def resolve_resstock_release_root( + release: str, + data_root: Path | None = None, + root: Path | None = None, +) -> Path: + """Return the existing directory for a ResStock release.""" + base = data_root or resolve_resstock_data_root(root=root) + return _choose_existing_path([base / release], f"ResStock release directory for {release!r}") + + +def resolve_resstock_metadata_dir( + release: str, + state: str, + upgrade: str | int, + data_root: Path | None = None, + root: Path | None = None, +) -> Path: + """Return the existing metadata directory for a release/state/upgrade.""" + release_root = resolve_resstock_release_root(release, data_root=data_root, root=root) + upgrade_str = _normalize_upgrade(upgrade) + candidates = [ + release_root / "metadata" / f"state={state}" / f"upgrade={upgrade_str}", + release_root / "metadata" / state / f"up{upgrade_str}", + release_root / "metadata" / state / f"upgrade={upgrade_str}", + release_root / "metadata" / f"state={state}" / f"up{upgrade_str}", + ] + return _choose_existing_path( + candidates, + f"ResStock metadata directory for release={release!r}, state={state!r}, upgrade={upgrade_str!r}", + ) + + +def resolve_resstock_metadata_file(base: Path) -> Path: + """Return the preferred metadata parquet file from a metadata directory.""" + candidates = [ + base / "metadata-sb.parquet", + base / "metadata-sb-with-utilities.parquet", + base / "metadata.parquet", + ] + return _choose_existing_path(candidates, f"metadata parquet in {base}") + + +def resolve_resstock_hourly_loads_dir( + release: str, + state: str | None = None, + upgrade: str | int | None = None, + data_root: Path | None = None, + root: Path | None = None, +) -> Path: + """Return the best available hourly-load directory for a ResStock release.""" + release_root = resolve_resstock_release_root(release, data_root=data_root, root=root) + hourly_root = release_root / "load_curve_hourly" + candidates = [] + + if state is not None and upgrade is not None: + upgrade_str = _normalize_upgrade(upgrade) + candidates.extend([ + hourly_root / f"state={state}" / f"upgrade={upgrade_str}", + hourly_root / state / f"up{upgrade_str}", + hourly_root / state / f"upgrade={upgrade_str}", + hourly_root / f"state={state}" / f"up{upgrade_str}", + ]) + + candidates.append(hourly_root) + return _choose_existing_path( + candidates, + f"ResStock hourly-load directory for release={release!r}", + ) + + +def build_hp_flag_expr(schema_cols: list[str]) -> pl.Expr: + """Return a Polars expression that identifies heat-pump homes.""" + import polars as pl + + if "postprocess_group.has_hp" in schema_cols: + return pl.col("postprocess_group.has_hp") + + if "in.hvac_heating_and_fuel_type" in schema_cols: + return pl.col("in.hvac_heating_and_fuel_type").str.to_lowercase().str.contains("hp").fill_null(False) + + if "in.hvac_heating_type_and_fuel" in schema_cols: + return pl.col("in.hvac_heating_type_and_fuel").str.to_lowercase().str.contains("hp").fill_null(False) + + raise ValueError( + "Could not determine HP flag from metadata columns. " + "Expected `postprocess_group.has_hp`, `in.hvac_heating_and_fuel_type`, " + "or `in.hvac_heating_type_and_fuel`." + ) + + +def resolve_heating_type_column(schema_cols: list[str]) -> str: + """Return the metadata column that stores the home heating type.""" + if "in.hvac_heating_and_fuel_type" in schema_cols: + return "in.hvac_heating_and_fuel_type" + if "in.hvac_heating_type_and_fuel" in schema_cols: + return "in.hvac_heating_type_and_fuel" + raise ValueError( + "Could not determine heating-type column from metadata. " + "Expected `in.hvac_heating_and_fuel_type` or `in.hvac_heating_type_and_fuel`." + ) + + +def build_hp_group_expr( + hp_flag_col: str = "hp_flag", + heating_type_col: str = "heating_type", +) -> pl.Expr: + """Return a Polars expression that buckets homes into HP/non-HP groups.""" + import polars as pl + + return ( + pl.when(pl.col(hp_flag_col)) + .then(pl.lit("HP")) + .when(pl.col(heating_type_col).str.to_lowercase().str.contains("electric")) + .then(pl.lit("Electric (non-HP heating)")) + .otherwise(pl.lit("Non-HP")) + ) + + +def _parse_s3_uri(uri: str) -> tuple[str, str]: + """Split an ``s3://bucket/key`` URI into ``(bucket, key)``.""" + without_prefix = uri[len("s3://") :] + bucket, _, key = without_prefix.partition("/") + return bucket, key + + +def _read_s3_bytes(s3_uri: str) -> bytes: + """Read raw bytes from an S3 URI using boto3.""" + import boto3 + + bucket, key = _parse_s3_uri(s3_uri) + response = boto3.client("s3").get_object(Bucket=bucket, Key=key) + return response["Body"].read() + + +def _to_polars_frame(frame) -> pl.DataFrame: + """Convert a pandas or Polars DataFrame into a Polars DataFrame.""" + import polars as pl + + if isinstance(frame, pl.DataFrame): + return frame + + if frame.__class__.__module__.startswith("pandas"): + return pl.from_pandas(frame) + + raise TypeError(f"Unsupported frame type: {type(frame)!r}") + + +def _reset_index_if_needed(frame: pd.DataFrame | pl.DataFrame) -> pd.DataFrame | pl.DataFrame: + """Reset index for pandas-like frames that expose ``reset_index``.""" + reset_index = getattr(frame, "reset_index", None) + if callable(reset_index): + return reset_index() + return frame + + +def read_s3_csv(s3_uri: str, **kwargs) -> pl.DataFrame: + """Read a CSV from an S3 URI into Polars.""" + import polars as pl + + return pl.read_csv(io.BytesIO(_read_s3_bytes(s3_uri)), **kwargs) + + +def read_s3_json(s3_uri: str) -> dict: + """Read a JSON object from an S3 URI using boto3 (no s3fs required).""" + import boto3 + + bucket, key = _parse_s3_uri(s3_uri) + response = boto3.client("s3").get_object(Bucket=bucket, Key=key) + return json.loads(response["Body"].read()) + + +def find_latest_run_dir(run_base: str, run_name: str) -> str: + """Return the S3 URI of the most recent output directory matching ``run_name``.""" + import boto3 + + bucket, prefix = _parse_s3_uri(run_base) + prefix = prefix.rstrip("/") + "/" + + paginator = boto3.client("s3").get_paginator("list_objects_v2") + matching: list[str] = [] + for page in paginator.paginate(Bucket=bucket, Prefix=prefix, Delimiter="/"): + for entry in page.get("CommonPrefixes", []): + dir_prefix = entry["Prefix"] + dir_name = dir_prefix[len(prefix) :].rstrip("/") + if dir_name.endswith(f"_{run_name}"): + matching.append(dir_name) + + if not matching: + raise FileNotFoundError( + f"No output directory matching run_name={run_name!r} found under {run_base}. " + "Re-run the scenario to generate outputs." + ) + + latest = sorted(matching)[-1] + return f"{run_base.rstrip('/')}/{latest}" + + +def resolve_dist_params(defaults: dict, candidates: list[Path] | None = None) -> dict: + """Return distribution-cost parameters from the first existing JSON candidate.""" + candidates = candidates or [] + for path in candidates: + if not path.exists(): + continue + with open(path) as f: + loaded = json.load(f) + return {key: loaded[key] for key in DIST_PARAM_KEYS} + return defaults + + +def choose_latest_run(run_root: Path) -> Path: + """Return the lexicographically latest run directory under ``run_root``.""" + runs = sorted(path for path in run_root.iterdir() if path.is_dir()) + if not runs: + raise FileNotFoundError(f"No run directories found in {run_root}") + return runs[-1] + + +def load_dist_mc_from_run(run_dir: Path | str) -> pl.DataFrame: + """Load ``distribution_marginal_costs.csv`` into a timezone-aware Polars DataFrame.""" + import polars as pl + + csv_path = f"{str(run_dir).rstrip('/')}/distribution_marginal_costs.csv" + if csv_path.startswith("s3://"): + df = read_s3_csv(csv_path, try_parse_dates=True) + else: + df = pl.read_csv(csv_path, try_parse_dates=True) + + value_col = df.columns[1] + return force_timezone_est_polars( + df.select( + pl.col("time"), + pl.col(value_col).alias("Marginal Distribution Costs ($/kWh)"), + ), + timestamp_col="time", + ) + + +def load_cambium_from_parquet_s3(s3_uri: str, target_year: int) -> pl.DataFrame: + """Load Cambium marginal costs from an S3 parquet file into Polars.""" + import polars as pl + + frame = cast( + pl.DataFrame, + pl.scan_parquet(s3_uri) + .filter(pl.col("t") == target_year) + .select(["timestamp_local", "energy_cost_enduse", "capacity_cost_enduse"]) + .rename({ + "timestamp_local": "time", + "energy_cost_enduse": "Marginal Energy Costs ($/kWh)", + "capacity_cost_enduse": "Marginal Capacity Costs ($/kWh)", + }) + .with_columns( + (pl.col("Marginal Energy Costs ($/kWh)") / 1000.0).alias("Marginal Energy Costs ($/kWh)"), + (pl.col("Marginal Capacity Costs ($/kWh)") / 1000.0).alias("Marginal Capacity Costs ($/kWh)"), + ) + .collect(), + ) + return force_timezone_est_polars(frame, timestamp_col="time") + + +def force_timezone_est_polars( + frame: pl.DataFrame, + timestamp_col: str = "timestamp", +) -> pl.DataFrame: + """Ensure a Polars datetime column is timezone-aware and converted to EST.""" + import polars as pl + + if timestamp_col not in frame.columns: + raise ValueError(f"{timestamp_col} not found in frame columns") + + dtype = frame.schema[timestamp_col] + if isinstance(dtype, pl.Datetime) and dtype.time_zone is not None: + expr = pl.col(timestamp_col).dt.convert_time_zone("EST") + else: + expr = pl.col(timestamp_col).cast(pl.Datetime, strict=False).dt.replace_time_zone("EST") + return frame.with_columns(expr.alias(timestamp_col)) + + +def build_bldg_id_to_load_filepath( + path_resstock_loads: Path, + building_ids: list[int], +) -> dict[int, Path]: + """Map requested building IDs to their ResStock load parquet paths.""" + bldg_set = {int(i) for i in building_ids} + mapping: dict[int, Path] = {} + for parquet_file in path_resstock_loads.rglob("*.parquet"): + try: + bldg_id = int(parquet_file.stem.split("-")[0]) + except ValueError: + continue + if bldg_id in bldg_set: + mapping[bldg_id] = parquet_file + missing = bldg_set - set(mapping) + if missing: + print(f"Warning: missing load files for {len(missing)} building IDs") + return mapping + + +_CROSS_SUBSIDY_WEIGHTED_COLS: dict[str, str] = { + "BAT_vol": "BAT_vol_weighted_avg", + "BAT_peak": "BAT_peak_weighted_avg", + "BAT_percustomer": "BAT_percustomer_weighted_avg", + "customer_level_residual_share_volumetric": "residual_vol_weighted_avg", + "customer_level_residual_share_peak": "residual_peak_weighted_avg", + "customer_level_residual_share_percustomer": "residual_percustomer_weighted_avg", + "Annual": "Annual_bill_weighted_avg", + "customer_level_economic_burden": "Economic_burden_weighted_avg", +} + + +def summarize_cross_subsidy(cross: pd.DataFrame | pl.DataFrame, metadata: pd.DataFrame | pl.DataFrame) -> pl.DataFrame: + """Compute weighted cross-subsidy metrics for HP and Non-HP groups.""" + import polars as pl + + cross_pl = _to_polars_frame(cross) + metadata_pl = _to_polars_frame(metadata) + + merged = cross_pl.join( + metadata_pl.select(["bldg_id", "postprocess_group.has_hp", "weight"]), + on=["bldg_id", "weight"], + how="left", + ) + + agg_exprs = [pl.col("weight").sum().alias("customers_weighted")] + agg_exprs.extend( + ((pl.col(source_col) * pl.col("weight")).sum() / pl.col("weight").sum()).alias(output_col) + for source_col, output_col in _CROSS_SUBSIDY_WEIGHTED_COLS.items() + ) + + return ( + merged.group_by("postprocess_group.has_hp") + .agg(agg_exprs) + .with_columns( + pl.when(pl.col("postprocess_group.has_hp")).then(pl.lit("HP")).otherwise(pl.lit("Non-HP")).alias("group") + ) + .select("postprocess_group.has_hp", "customers_weighted", "group", *_CROSS_SUBSIDY_WEIGHTED_COLS.values()) + .sort("postprocess_group.has_hp", descending=True) + ) + + +def summarize_cross_subsidy_by_heating_type( + cross: pd.DataFrame | pl.DataFrame, + metadata: pd.DataFrame | pl.DataFrame, +) -> pl.DataFrame: + """Compute weighted cross-subsidy metrics grouped by heating type.""" + import polars as pl + + cross_pl = _to_polars_frame(cross) + metadata_pl = _to_polars_frame(metadata) + + agg_exprs = [pl.col("weight").sum().alias("customers_weighted")] + agg_exprs.extend( + ((pl.col(source_col) * pl.col("weight")).sum() / pl.col("weight").sum()).alias(output_col) + for source_col, output_col in _CROSS_SUBSIDY_WEIGHTED_COLS.items() + ) + + return ( + cross_pl.join( + metadata_pl.select(["bldg_id", "postprocess_group.heating_type", "weight"]), + on=["bldg_id", "weight"], + how="left", + ) + .group_by("postprocess_group.heating_type") + .agg(agg_exprs) + .with_columns(pl.col("postprocess_group.heating_type").cast(pl.String).alias("group")) + .select("postprocess_group.heating_type", "customers_weighted", "group", *_CROSS_SUBSIDY_WEIGHTED_COLS.values()) + .sort("customers_weighted", descending=True) + ) + + +def build_hourly_group_loads( + raw_load_elec: pd.DataFrame | pl.DataFrame, + metadata: pd.DataFrame | pl.DataFrame, +) -> pl.DataFrame: + """Aggregate weighted hourly electricity load by HP flag and total.""" + import polars as pl + + raw_prepared = _reset_index_if_needed(raw_load_elec) + raw_pl = _to_polars_frame(raw_prepared).select("time", "bldg_id", "electricity_net") + metadata_pl = _to_polars_frame(metadata).select("bldg_id", "postprocess_group.has_hp", "weight") + + return ( + raw_pl.join(metadata_pl, on="bldg_id", how="left") + .with_columns((pl.col("electricity_net") * pl.col("weight")).alias("weighted_load_kwh")) + .group_by("time") + .agg( + pl.when(pl.col("postprocess_group.has_hp")) + .then(pl.col("weighted_load_kwh")) + .otherwise(0.0) + .sum() + .alias("hp_load_kwh"), + pl.when(~pl.col("postprocess_group.has_hp").fill_null(False)) + .then(pl.col("weighted_load_kwh")) + .otherwise(0.0) + .sum() + .alias("non_hp_load_kwh"), + ) + .sort("time") + .with_columns((pl.col("non_hp_load_kwh") + pl.col("hp_load_kwh")).alias("total_load_kwh")) + ) + + +def build_hourly_heating_type_loads( + raw_load_elec: pd.DataFrame | pl.DataFrame, + metadata: pd.DataFrame | pl.DataFrame, +) -> pl.DataFrame: + """Aggregate weighted hourly electricity load by heating type.""" + import polars as pl + + raw_prepared = _reset_index_if_needed(raw_load_elec) + raw_pl = _to_polars_frame(raw_prepared).select("time", "bldg_id", "electricity_net") + metadata_pl = _to_polars_frame(metadata).select("bldg_id", "postprocess_group.heating_type", "weight") + + return ( + raw_pl.join(metadata_pl, on="bldg_id", how="left") + .with_columns((pl.col("electricity_net") * pl.col("weight")).alias("weighted_load_kwh")) + .group_by(["time", "postprocess_group.heating_type"]) + .agg(pl.col("weighted_load_kwh").sum().alias("load_kwh")) + .rename({"postprocess_group.heating_type": "heating_type"}) + .sort(["time", "heating_type"]) + ) + + +def build_cross_components(cross_summary: pd.DataFrame | pl.DataFrame) -> pl.DataFrame: + """Build benchmark component contributions for charting cross-subsidy impacts.""" + import polars as pl + + cross_summary_pl = _to_polars_frame(cross_summary) + component_labels = { + "BAT_vol_weighted_avg": "Volumetric benchmark", + "BAT_peak_weighted_avg": "Peak benchmark", + "BAT_percustomer_weighted_avg": "Per-customer benchmark", + } + + return pl.concat( + [ + cross_summary_pl.select( + "group", + "customers_weighted", + pl.lit(component).alias("component"), + pl.col(component).alias("weighted_avg_bat_usd_per_customer_year"), + ).with_columns( + pl.lit(label).alias("component_label"), + (pl.col("weighted_avg_bat_usd_per_customer_year") * pl.col("customers_weighted") / 1e6).alias( + "component_transfer_total_musd_per_year" + ), + ) + for component, label in component_labels.items() + ], + how="vertical", + ) + + +def summarize_positive_distribution_hours( + hourly: pd.DataFrame | pl.DataFrame, + customer_count_map: dict[str, float], +) -> pl.DataFrame: + """Summarize per-customer load behavior in positive marginal distribution-cost hours.""" + import polars as pl + + hourly_pl = _to_polars_frame(hourly) + rows = [] + for col, label in [("hp_load_kwh", "HP"), ("non_hp_load_kwh", "Non-HP")]: + customer_count = float(customer_count_map[label]) + stats = hourly_pl.select( + pl.col(col).sum().alias("annual"), + pl.when(pl.col("mdc_positive")).then(pl.col(col)).otherwise(0.0).sum().alias("positive"), + pl.when(pl.col("mdc_positive")).then(pl.col(col)).otherwise(None).mean().alias("positive_mean"), + pl.when(~pl.col("mdc_positive")).then(pl.col(col)).otherwise(None).mean().alias("zero_mean"), + ).row(0, named=True) + annual = float(stats["annual"]) + positive = float(stats["positive"]) + rows.append({ + "group": label, + "weighted_customers": customer_count, + "annual_load_mwh_per_customer": (annual / customer_count) / 1000, + "positive_dist_cost_hours_load_mwh_per_customer": (positive / customer_count) / 1000, + "share_of_annual_load_in_positive_dist_cost_hours": positive / annual, + "avg_hourly_load_kwh_during_positive_dist_hours": (float(stats["positive_mean"]) / customer_count), + "avg_hourly_load_kwh_during_zero_dist_hours": (float(stats["zero_mean"]) / customer_count), + }) + return pl.DataFrame(rows) + + +def build_tariff_components( + hourly: pd.DataFrame | pl.DataFrame, + cross_summary: pd.DataFrame | pl.DataFrame, + fixed_monthly: float, + vol_rate: float, +) -> pl.DataFrame: + """Compute annual fixed and volumetric charges collected by customer group.""" + import polars as pl + + hourly_pl = _to_polars_frame(hourly) + cross_summary_pl = _to_polars_frame(cross_summary) + + group_load = pl.DataFrame({ + "group": ["Non-HP", "HP"], + "annual_load_kwh": [ + hourly_pl.select(pl.col("non_hp_load_kwh").sum()).item(), + hourly_pl.select(pl.col("hp_load_kwh").sum()).item(), + ], + }) + + return group_load.join( + cross_summary_pl.select( + "group", + pl.col("customers_weighted").alias("weighted_customers"), + ), + on="group", + how="left", + ).with_columns( + (pl.lit(fixed_monthly * 12) * pl.col("weighted_customers")).alias("annual_fixed_charge_collected_usd"), + (pl.lit(vol_rate) * pl.col("annual_load_kwh")).alias("annual_volumetric_charge_collected_usd"), + ) + + +def fetch_rdp_file(path: str, ref: str) -> str: + """Fetch a file from rate-design-platform on GitHub; return contents as string. + + Uses the GitHub API with ``GITHUB_TOKEN`` when available (required for + private repos), otherwise falls back to the public raw URL. + """ + token = os.environ.get("GITHUB_TOKEN") + if token: + url = f"https://api.github.com/repos/switchbox-data/rate-design-platform/contents/{path}?ref={ref}" + req = urllib.request.Request(url) + req.add_header("Authorization", f"Bearer {token}") + with urllib.request.urlopen(req) as resp: + data = json.loads(resp.read().decode()) + return base64.b64decode(data["content"]).decode() + url = f"https://raw.githubusercontent.com/switchbox-data/rate-design-platform/{ref}/{path}" + with urllib.request.urlopen(url) as resp: + return resp.read().decode() + + +def parse_urdb_json(content: str | bytes) -> dict: + """Parse URDB tariff JSON (string or bytes) into a dict.""" + if isinstance(content, bytes): + content = content.decode() + return json.loads(content) diff --git a/lib/resstock/2024/end_use_groups.feather b/lib/resstock/2024/end_use_groups.feather new file mode 100644 index 0000000..4c6db70 Binary files /dev/null and b/lib/resstock/2024/end_use_groups.feather differ diff --git a/lib/resstock/2024/mix_and_match_2024.qmd b/lib/resstock/2024/mix_and_match_2024.qmd new file mode 100644 index 0000000..6c28513 --- /dev/null +++ b/lib/resstock/2024/mix_and_match_2024.qmd @@ -0,0 +1,30 @@ +```{r} +library(dplyr) +library(arrow) +library(feather) + +# Read data dictionary and create column lists +ddl <- read.csv("/workspaces/reports2/lib/resstock/2024/data_dictionary_2024_labeled.csv") + +# Split the "field_name" column by "." and extract "fuel" and "end_use" +ddl <- ddl |> + filter(functional_group != "") |> + mutate( + timeseries_field_name = sapply( + strsplit(field_name, "\\."), + function(x) if(length(x) >= 3) paste0(paste(x[1:3], collapse = "."), ".energy_consumption") else NA + ), + fuel = sapply(strsplit(field_name, "\\."), function(x) if(length(x) >= 2) x[2] else NA), + end_use = sapply(strsplit(field_name, "\\."), function(x) if(length(x) >= 3) x[3] else NA) + ) |> + select(functional_group, fuel, end_use, timeseries_field_name) + +View(ddl) + + +# save ddl as a csv and an RData file +write.csv(ddl, "/workspaces/reports2/lib/resstock/2024/end_use_groups.csv", row.names = FALSE) + +# save as feather +write_feather(ddl, "/workspaces/reports2/lib/resstock/2024/end_use_groups.feather") +``` diff --git a/lib/utility_mapping.R b/lib/utility_mapping.R new file mode 100644 index 0000000..165222f --- /dev/null +++ b/lib/utility_mapping.R @@ -0,0 +1,679 @@ +library(yaml) +library(tidyverse) +library(sf) +library(tigris) +library(arrow) + +######################################################## +# Making or updating utility crosswalks +######################################################## +make_empty_utility_crosswalk <- function(path_to_rs2024_metadata) { + #' Make an empty utility crosswalk CSV file + #' + #' @param path_to_rs2024_metadata A path to the ResStock metadata parquet file + #' @return A CSV file containing a list of all Restock 2024.2 bldg_ids, their states, and their heating fuels + #' @examples + #' make_empty_utility_crosswalk("/workspaces/reports2/data/resstock/2024_resstock_amy2018_release_2/res_2024_tmy3_2/metadata/RI/up00/metadata.parquet") + + use_these_columns <- c( + "bldg_id", + 'in.state', + 'in.heating_fuel', + 'out.natural_gas.total.energy_consumption' + ) + + # Read metadata parquet file with selected columns + bldg_utility_mapping <- arrow::read_parquet( + file.path(path_to_rs2024_metadata, "metadata.parquet"), + col_select = use_these_columns + ) + + # Add empty utility columns + bldg_utility_mapping <- bldg_utility_mapping |> + mutate( + electric_utility = NA_character_, + gas_utility = NA_character_ + ) + bldg_utility_mapping |> head() |> print() + + # Write to feather + write_feather( + bldg_utility_mapping, + file.path(path_to_rs2024_metadata, "rs2024_bldg_utility_crosswalk.feather") + ) + + # Write to csv + write_csv( + bldg_utility_mapping, + file.path(path_to_rs2024_metadata, "rs2024_bldg_utility_crosswalk.csv") + ) +} + +######################################################## +# Forced Utility Mapping +######################################################## +forced_utility_crosswalk_ri <- function(path_to_rs2024_metadata) { + use_these_columns <- c( + "bldg_id", + 'in.state', + 'in.heating_fuel', + 'out.natural_gas.total.energy_consumption' + ) + + # Read metadata parquet file with selected columns + bldg_utility_mapping <- read_parquet( + file.path(path_to_rs2024_metadata, "metadata.parquet"), + col_select = use_these_columns + ) + + # Add empty utility columns + bldg_utility_mapping <- bldg_utility_mapping |> + mutate( + electric_utility = NA_character_, + gas_utility = NA_character_ + ) + + bldg_utility_mapping <- bldg_utility_mapping |> + mutate( + electric_utility = case_when( + `in.state` == "RI" ~ "rhode_island_energy", + TRUE ~ electric_utility + ), + gas_utility = case_when( + `in.state` == "RI" & + `out.natural_gas.total.energy_consumption` > + 10 ~ "rhode_island_energy", + TRUE ~ gas_utility + ) + ) + + # Write to CSV + write_feather( + bldg_utility_mapping, + file.path(path_to_rs2024_metadata, "rs2024_bldg_utility_crosswalk.feather") + ) + + # Write to CSV + write_csv( + bldg_utility_mapping, + file.path(path_to_rs2024_metadata, "rs2024_bldg_utility_crosswalk.csv") + ) +} + +######################################################## +# GIS Utility Mapping +######################################################## +state_configs <- list( + "NY" = list( + state_fips = "36", + state_crs = 2260, # New York state plane (meters) + hh_utilities_path = "/workspaces/reports2/data/resstock/utility_lookups/NY_hh_utilities.csv", + resstock_path = "/workspaces/reports2/data/resstock/2022_resstock_amy2018_release_1.1/20230922.db", + electric_poly_path = "/workspaces/reports2/data/buildings2/Utilities/NYS_Electric_Utility_Service_Territories.csv", + gas_poly_path = "/workspaces/reports2/data/buildings2/Utilities/NYS_Gas_Utility_Service_Territories.csv", + utility_name_map = tribble( + ~state_name , ~std_name , + "Bath Electric Gas and Water" , "bath" , + "Central Hudson Gas and Electric" , "cenhud" , + "Chautauqua Utilities, Inc." , "chautauqua" , + "Consolidated Edison" , "coned" , + "Corning Natural Gas" , "corning" , + "Fillmore Gas Company" , "fillmore" , + "National Grid - NYC" , "kedny" , + "National Grid - Long Island" , "kedli" , + "National Grid" , "nimo" , + "None" , "none" , + "National Fuel Gas Distribution" , "nationalfuel" , + "NYS Electric and Gas" , "nyseg" , + "Orange and Rockland Utilities" , "or" , + "Long Island Power Authority" , "pseg-li" , + "Reserve Gas Company" , "reserve" , + "Rochester Gas and Electric" , "rge" , + "St. Lawrence Gas" , "stlawrence" , + "Valley Energy" , "valley" , + "Woodhull Municipal Gas Company" , "woodhull" + ) + ), + "MA" = list( + state_fips = "25", + state_crs = 26986, # Massachusetts state plane (meters) + hh_utilities_path = "/workspaces/reports2/data/resstock/utility_lookups/MA_hh_utilities.csv", + resstock_path = "/workspaces/reports2/data/resstock/2022_resstock_amy2018_release_1.1/rs_20250326.db", + electric_poly_path = "/workspaces/reports2/data/datamagov/MA_utility_territory_shapefiles_20250326/TOWNS_POLY_V_ELEC.shp", + gas_poly_path = "/workspaces/reports2/data/datamagov/MA_utility_territory_shapefiles_20250326/TOWNS_POLY_V_GAS.shp", + utility_name_map = tribble( + ~state_name , ~std_name , + "The Berkshire Gas Company" , "berkshire" , + "Eversource Energy" , "eversource" , + "NSTAR Electric d/b/a Eversource Energy" , "eversource" , + "Liberty Utilities" , "liberty" , + "Municipal" , "municipal" , + "National Grid" , "nationalgrid" , + "Massachusetts Electric d/b/a National Grid" , "nationalgrid" , + "Nantucket Electric Company d/b/a National Grid" , "nationalgrid" , + "No Natural Gas Service" , "none" , + "Unitil" , "unitil" , + "UNITIL" , "unitil" + ) + ) +) + + +# puma_centroids_default <- "/workspaces/reports2/data/buildings2/2010_Gaz_PUMAs_national.tsv" # Census PUMA centroids +# tract_lookup_default <- "/workspaces/reports2/data/resstock/spatial_tract_lookup_table.csv" + +get_bldg_by_utility <- function( + state_code, + utility_electric = NULL, + utility_gas = NULL, + config = state_configs +) { + #' Get buildings by utility service area + #' + #' @description + #' Returns buildings that are served by the specified utilities in the given state. + #' If only electric utility is specified, returns all buildings served by those electric utility. + #' If only gas utility is specified, returns all buildings served by those gas utility. + #' If both are specified, returns buildings served by both utilities. + #' If neither is specified, returns all buildings in the state and their associated utilities. + #' + #' @param state (str) State code. Must be one of: + #' - "NY" (New York) + #' - "MA" (Massachusetts) #not ready yet + #' + #' @param utility_electric (list) Electric utility identifier. For NY, must be any of: + #' - "nimo" (National Grid) + #' - "cenhud" (Central Hudson Gas and Electric) + #' - "coned" (Consolidated Edison) + #' - "rge" (Rochester Gas and Electric) + #' - "nyseg" (NYS Electric and Gas) + #' - "pseg-li" (Long Island Power Authority) + #' - "or" (Orange and Rockland Utilities) + #' + #' @param utility_gas (list) Gas utility identifier. For NY, must be any of: + #' - "kedny" (National Grid - NYC) + #' - "kedli" (National Grid - Long Island) + #' - "nimo" (National Grid) + #' - "coned" (Consolidated Edison) + #' - "nationalfuel" (National Fuel Gas Distribution) + #' - "rge" (Rochester Gas and Electric) + #' - "nyseg" (NYS Electric and Gas) + #' + #' @return A dataframe containing: + #' - bldg_id: ResStock building identifier + #' - std_name.electric: Standardized electric utility name + #' - std_name.gas: Standardized gas utility name + #' + #' @examples + #' # Get all buildings served by National Grid electric + #' get_bldg_by_utility("NY", utility_electric = "nimo") + #' + #' # Get buildings served by both ConEd electric and gas + #' get_bldg_by_utility("NY", utility_electric = "coned", utility_gas = "coned") + #' + #' # Get all buildings in NY with their utility assignments + #' get_bldg_by_utility("NY") + + state_config <- config[[state_code]] + if (is.null(state_config)) { + stop(sprintf("No configuration available for state: %s", state_code)) + } + + if (file.exists(state_config$hh_utilities_path)) { + print("Loading existing hh_utilities file") + hh_utilities <- read_csv( + state_config$hh_utilities_path, + show_col_types = FALSE + ) + } else { + print("Creating new hh_utilities file") + hh_utilities <- create_hh_utilities( + state_code = state_code, + config = config + ) + } + + hh_utilities |> + filter( + (is.null(utility_electric) | electric_utility %in% utility_electric) & + (is.null(utility_gas) | gas_utility %in% utility_gas) + ) |> + select(bldg_id, electric_utility, gas_utility) +} + +create_hh_utilities <- function( + state_code, + config = state_configs, + puma_year = 2019, + save_file = TRUE, + db_path = NULL +) { + #' Create a dataframe of households with their associated utilities + #' + #' @description + #' Returns a dataframe of households with their associated utilities. + #' + #' @param state_code (str) State code. Must be one of: + #' - "NY" (New York) + #' - "MA" (Massachusetts) #not ready yet + #' + #' @param state_config (list) State configuration. + #' + #' @param save_file (bool) Whether to save the file to the state_config$hh_utilities_path. + #' + #' @return A dataframe of households with their associated utilities. + + state_config <- config[[state_code]] + if (is.null(state_config)) { + stop(sprintf("No configuration available for state: %s", state)) + } + + utility_name_map <- state_config$utility_name_map + + if (is.null(db_path)) { + db_path <- state_config$resstock_path + } + + # load PUMAS + pumas <- pumas( + state = state_code, + year = puma_year, + cb = TRUE # Use cartographic boundaries (simplified) + ) + + if (state_code == "MA") { + electric_utility_polygons <- merge_ma_electric_polygons( + state_config$electric_poly_path + ) + + gas_utility_polygons <- merge_ma_gas_polygons(state_config$gas_poly_path) + } else { + electric_utility_polygons <- read_csv( + state_config$electric_poly_path, + show_col_types = FALSE + ) |> + st_as_sf(wkt = "the_geom") |> + st_set_crs(4326) |> # Set WGS84 as the CRS for the input data|> + rename(utility = COMP_FULL) + + gas_utility_polygons <- read_csv( + state_config$gas_poly_path, + show_col_types = FALSE + ) |> + st_as_sf(wkt = "the_geom") |> + st_set_crs(4326) |> # Set WGS84 as the CRS for the input data|> + rename(utility = COMP_FULL) + } + + # calculate overlap between PUMAS and utilities + puma_elec_overlap <- pumas |> + st_transform(state_config$state_crs) |> + mutate(puma_area = st_area(geometry)) |> # Calculate total area of each PUMA + st_intersection( + electric_utility_polygons |> st_transform(state_config$state_crs) + ) |> # Intersect with utilities + mutate( + overlap_area = st_area(geometry), # Calculate area of each overlap + pct_overlap = as.numeric(overlap_area / puma_area * 100) # Calculate percentage + ) |> + st_drop_geometry() |> + select(puma_id = PUMACE10, pct_overlap, contains("utility")) + puma_gas_overlap <- pumas |> + st_transform(state_config$state_crs) |> # Transform to Massachusetts state plane (meters) + mutate(puma_area = st_area(geometry)) |> # Calculate total area of each PUMA + st_intersection( + gas_utility_polygons |> st_transform(state_config$state_crs) + ) |> # Intersect with utilities + mutate( + overlap_area = st_area(geometry), # Calculate area of each overlap + pct_overlap = as.numeric(overlap_area / puma_area * 100) # Calculate percentage + ) |> + st_drop_geometry() |> + select(puma_id = PUMACE10, pct_overlap, contains("utility")) + + if (state_code == "MA") { + puma_elec_overlap <- split_multi_service_areas(puma_elec_overlap) + puma_gas_overlap <- split_multi_service_areas(puma_gas_overlap) + } + puma_elec_probs <- puma_elec_overlap |> + left_join(utility_name_map, by = c("utility" = "state_name")) |> + mutate(utility = coalesce(std_name, utility)) |> + select(-std_name) |> + mutate( + utility = case_when( + str_detect(utility, "^Municipal Utility:") ~ paste0( + "muni-", + str_to_lower(str_trim(str_remove(utility, "^Municipal Utility:"))) + ), + .default = utility + ) + ) |> + group_by(puma_id) |> + mutate( + probability = pct_overlap / sum(pct_overlap) + ) |> + ungroup() |> + select(puma_id, utility, probability) |> + pivot_wider( + names_from = utility, + values_from = probability, + values_fill = 0 + ) + + puma_gas_probs <- puma_gas_overlap |> + left_join(utility_name_map, by = c("utility" = "state_name")) |> + mutate(utility = coalesce(std_name, utility)) |> + select(-std_name) |> + group_by(puma_id) |> + mutate( + probability = pct_overlap / sum(pct_overlap) + ) |> + ungroup() |> + select(puma_id, utility, probability) |> + filter(utility != "none") |> + pivot_wider( + names_from = utility, + values_from = probability, + values_fill = 0 + ) + # get resstock data + # Create connection when needed + con <- DBI::dbConnect( + duckdb::duckdb(), + dbdir = db_path, + read_only = TRUE + ) + on.exit(DBI::dbDisconnect(con), add = TRUE) # Ensure connection is closed + + bldgs <- tbl(con, "housing_units") |> + select(bldg_id, puma = in.puma, heating_fuel = `in.heating_fuel`) |> + mutate(puma = str_sub(puma, start = -5)) |> + collect() + + # assign elec to bldgs + building_elec <- bldgs |> + left_join(puma_elec_probs, by = c("puma" = "puma_id")) |> + rowwise() |> + mutate( + utility = sample( + names(pick(everything()))[-(1:3)], # Get utility names from columns, skip first 3 (bldg_id, puma, heating_fuel) + size = 1, + prob = c_across(-(1:3)) # Get probabilities, skip first 3 columns + ) + ) |> + ungroup() |> + select(bldg_id, electric_utility = utility) + + # assign gas to bldgs + building_gas <- bldgs |> + left_join(puma_gas_probs, by = c("puma" = "puma_id")) |> + rowwise() |> + mutate( + utility = case_when( + heating_fuel == "Natural Gas" ~ sample( + names(pick(everything()))[-(1:3)], # Get utility names from columns, skip first 3 (bldg_id, puma, heating_fuel) + size = 1, + prob = c_across(-(1:3)) # Get probabilities, skip first 3 columns + ), + .default = NA + ) + ) |> + ungroup() |> + select(bldg_id, gas_utility = utility) + + building_utilities <- building_elec |> + left_join(building_gas, by = "bldg_id") + + if (save_file) { + write_csv(building_utilities, state_config$hh_utilities_path) + } + + return(building_utilities) +} + + +# create_hh_utilities <- function( +# state_code, +# state_config, +# puma_centroids_path = puma_centroids_default, +# tract_lookup = tract_lookup_default, +# save_file = TRUE) { +# # Get state-specific configs +# state_config <- state_configs[[state_code]] +# if (is.null(state_config)) { +# stop(sprintf("No configuration available for state: %s", state)) +# } + +# # Use state_config values +# state_fips <- state_config$state_fips +# electric_poly_path <- state_config$electric_poly_path +# gas_poly_path <- state_config$gas_poly_path + +# # Create connection when needed +# resstock <- DBI::dbConnect( +# duckdb::duckdb(), +# dbdir = state_config$resstock_path, +# read_only = TRUE +# ) +# on.exit(DBI::dbDisconnect(resstock), add = TRUE) # Ensure connection is closed + +# # # We need to map households to gas and electric utility service territories, via their PUMA. +# # # ResStock PUMA codes don't match Census PUMA codes, so we map those first. + +# puma_centroids <- read_tsv(puma_centroids_path, show_col_types = FALSE) |> # Census PUMA centroids +# select(GEOID, INTPTLAT, INTPTLONG) + +# resstock_puma_mapping <- read_csv(tract_lookup, show_col_types = FALSE) |> # Census tracts with ResStock and Census PUMAS +# select(nhgis_2010_puma_gisjoin, puma_tsv) |> +# separate_wider_delim(puma_tsv, delim = ", ", names = c("state", "puma")) |> # Fix FIPS code: "NY, 001341" -> "NY", "36001341" +# mutate(puma = paste0(state_fips, puma)) |> +# filter(state == state_code) |> +# select(-state) |> +# distinct() |> # from 1 per tract to 1 per PUMA +# left_join(puma_centroids, by = c("puma" = "GEOID")) |> +# st_as_sf(coords = c("INTPTLONG", "INTPTLAT")) + +# electric_utility_polygons <- read_csv(electric_poly_path, show_col_types = FALSE) |> +# st_as_sf(wkt = "the_geom") + +# gas_utility_polygons <- read_csv(gas_poly_path, show_col_types = FALSE) |> +# st_as_sf(wkt = "the_geom") + +# utility_name_mapping <- tribble( +# ~std_name, ~bills_name, ~state_name, +# "kedny", "KEDNY", "National Grid - NYC", +# "kedli", "KEDLI", "National Grid - Long Island", +# "nimo", "NiMO", "National Grid", +# "cenhud", "CenHud", "Central Hudson Gas and Electric", +# "coned", "ConEd", "Consolidated Edison", +# "nationalfuel", "NF", "National Fuel Gas Distribution", +# "rge", "RGE", "Rochester Gas and Electric", +# "nyseg", "NYSEG", "NYS Electric and Gas", +# "pseg-li", "PSEG-LI", "Long Island Power Authority", +# "or", "O&R", "Orange and Rockland Utilities", +# ) + +# puma_electric <- resstock_puma_mapping |> +# st_join(electric_utility_polygons, +# join = st_covered_by +# ) |> +# select(-puma) |> +# rename( +# puma = nhgis_2010_puma_gisjoin, +# utility_full_name = COMP_FULL, +# utility_short_name = COMP_SHORT +# ) |> +# select(puma, utility_full_name, utility_short_name) |> +# st_drop_geometry() |> +# left_join(utility_name_mapping, by = c("utility_full_name" = "state_name")) + +# puma_gas <- resstock_puma_mapping |> +# st_join(gas_utility_polygons, +# join = st_covered_by +# ) |> +# select(-puma) |> +# rename( +# puma = nhgis_2010_puma_gisjoin, +# utility_full_name = COMP_FULL, +# utility_short_name = COMP_SHORT +# ) |> +# select(puma, utility_full_name, utility_short_name) |> +# st_drop_geometry() |> +# left_join(utility_name_mapping, by = c("utility_full_name" = "state_name")) + +# hh_utilities <- tbl(resstock, "housing_units") |> +# left_join(puma_gas, by = c("in.puma" = "puma"), copy = TRUE) |> +# left_join(puma_electric, by = c("in.puma" = "puma"), copy = TRUE, suffix = c(".gas", ".electric")) |> +# select(bldg_id, std_name.gas, std_name.electric, bills_name.gas, bills_name.electric) |> +# mutate( +# gas_missing = is.na(bills_name.gas), +# electric_missing = is.na(bills_name.electric), +# bills_name.gas = coalesce(bills_name.gas, "NiMO"), +# bills_name.electric = coalesce(bills_name.electric, "NiMO"), +# std_name.gas = coalesce(std_name.gas, "nimo"), +# std_name.electric = coalesce(std_name.electric, "nimo") +# ) |> +# collect() + +# if (save_file) { +# write_csv(hh_utilities, state_config$hh_utilities_path) +# } + +# return(hh_utilities) +# } + +merge_ma_electric_polygons <- function(electric_poly_path) { + # MA electric utility polygons are mapped to municipalities, we want to merge polygons by utility + + electric_utility_poly <- st_read(electric_poly_path) |> + mutate( + utility_1 = str_extract(ELEC_LABEL, "^[^,]+"), # Extract everything before first comma + utility_2 = str_extract(ELEC_LABEL, "(?<=, ).+") # Extract everything after comma and space + ) |> + # Clean up by trimming any whitespace + mutate( + across(c(utility_1, utility_2), str_trim), + multi_utility = ifelse(is.na(utility_2), 0, 1) + # utility_1 = case_when(utility_1 == "Municipal" ~ paste0("muni-", str_to_lower(TOWN)), + # .default = utility_1 + # ) + ) + + merged_utilities <- electric_utility_poly |> + rename(utility = ELEC_LABEL) |> + group_by(utility) |> + summarise( + n_towns = n(), # Count number of towns per utility + utility_1 = first(utility_1), + utility_2 = first(utility_2), + multi_utility = first(multi_utility) + ) |> + ungroup() + + return(merged_utilities) +} + +merge_ma_gas_polygons <- function(gas_poly_path) { + # MA gas utility polygons are mapped to municipalities, we want to merge polygons by utility + + gas_utility_poly <- st_read(gas_poly_path) |> + mutate( + utility_1 = str_extract(GAS_LABEL, "^[^,]+"), # Extract everything before first comma + utility_2 = str_extract(GAS_LABEL, "(?<=, ).+") # Extract everything after comma and space + ) |> + # Clean up by trimming any whitespace + mutate( + across(c(utility_1, utility_2), str_trim), + multi_utility = ifelse(is.na(utility_2), 0, 1) + # utility_1 = case_when(utility_1 == "Municipal" ~ paste0("muni-", str_to_lower(TOWN)), + # .default = utility_1 + # ) + ) + + merged_utilities <- gas_utility_poly |> + rename(utility = GAS_LABEL) |> + group_by(utility) |> + summarise( + n_towns = n(), # Count number of towns per utility + utility_1 = first(utility_1), + utility_2 = first(utility_2), + multi_utility = first(multi_utility) + ) |> + ungroup() + return(merged_utilities) +} + +split_multi_service_areas <- function(puma_utility_overlap) { + # some service areas are labeled with 2 utilities, we want to split them into 2 rows and split the area + mutli_utils <- puma_utility_overlap |> + filter(multi_utility == 1) |> + select(-utility, -multi_utility) |> + pivot_longer(-c(puma_id, pct_overlap), values_to = "utility") |> + mutate(pct_overlap = pct_overlap / 2) |> + select(-name) + + single_utils <- puma_utility_overlap |> + filter(multi_utility == 0) |> + select(puma_id, utility, pct_overlap) + + pct_overlap_final <- bind_rows(mutli_utils, single_utils) |> + summarise(.by = c(puma_id, utility), pct_overlap = sum(pct_overlap)) + + return(pct_overlap_final) +} +write_utilities_to_db <- function(state_code, db_path, config = state_configs) { + if (!state_code %in% names(config)) { + message(sprintf( + "Cannot add utilities, state %s not supported. Only %s are currently supported.", + state_code, + paste(names(config), collapse = " and ") + )) + return(NULL) + } + + building_utilities <- create_hh_utilities(state_code, db_path = db_path) + + con <- DBI::dbConnect( + duckdb::duckdb(), + dbdir = db_path, + read_only = FALSE + ) + on.exit(DBI::dbDisconnect(con), add = TRUE) # Ensure connection is closed + # Write the results to the housing_units table + # Add columns one at a time + DBI::dbExecute( + con, + "ALTER TABLE housing_units ADD COLUMN IF NOT EXISTS electric_utility VARCHAR;" + ) + DBI::dbExecute( + con, + "ALTER TABLE housing_units ADD COLUMN IF NOT EXISTS gas_utility VARCHAR;" + ) + + # Update the housing_units table with utility assignments + # Write utilities to temporary table + DBI::dbWriteTable( + con, + "temp_utilities", + building_utilities |> select(bldg_id, electric_utility, gas_utility), + temporary = TRUE, + overwrite = TRUE + ) + + # Update housing_units with utility assignments + DBI::dbExecute( + con, + " + UPDATE housing_units AS h + SET + electric_utility = t.electric_utility, + gas_utility = t.gas_utility + FROM temp_utilities AS t + WHERE h.bldg_id = t.bldg_id; + " + ) + + # Remove columns one at a time + # DBI::dbExecute(con, "ALTER TABLE housing_units DROP COLUMN IF EXISTS electric_utility;") + # DBI::dbExecute(con, "ALTER TABLE housing_units DROP COLUMN IF EXISTS gas_utility;") +} diff --git a/notebooks/analysis.qmd b/notebooks/analysis.qmd new file mode 100644 index 0000000..61dfe09 --- /dev/null +++ b/notebooks/analysis.qmd @@ -0,0 +1,1010 @@ +--- +title: "CUB TOU Equity Analysis" +subtitle: "Computing report variables from pre-processed billing simulation outputs" +date: 2026-03-23 +author: + - name: Switchbox + affiliations: + - Switchbox + +keywords: [ComEd, TOU, DTOU, STOU, Rate BEST, equity, CUB, billing simulation] +license: "CC BY-NC" + +format: + html: + code-fold: true + code-summary: "Show code" + toc: true + +execute: + echo: true + warning: false +--- + +# Introduction + +This notebook contains all of the code used to produce the findings in +Switchbox's equity analysis of ComEd's proposed time-of-use (TOU) rate +structures for the Citizens Utility Board (CUB) of Illinois. It starts from +pre-computed billing simulation outputs — not raw smart meter data — and +computes the summary statistics, income quintile breakdowns, and figures that +appear in the report narrative (`index.qmd`). + +The upstream pipeline (documented in `analysis/rtp/` and `scripts/`) applied +January and July 2023 smart meter interval data to 2026 rate inputs for two +alternative rate structures: + +- **DTOU** (Delivery Time-of-Use) — a delivery-side TOU rate with time-varying + distribution facility charges; the supply charge stays at the flat Price to + Compare +- **Rate BEST** (referred to as **STOU** in the pipeline code) — a bundled + supply-and-delivery TOU rate with time-varying energy charges based on the + Compromise Proposal in ICC Docket 24-0378 + +The sign convention throughout is **`delta = flat − alternative`**, so +**positive delta means the customer saves under TOU**. + +To download the source code and run the analysis yourself, click +**Download Source** above. + + +# Setup + +```{python} +#| echo: false +#| message: false + +import pickle +from pathlib import Path + +import numpy as np +import polars as pl +from plotnine import ( + aes, + element_text, + geom_col, + geom_point, + geom_smooth, + geom_text, + ggplot, + labs, + position_dodge, + scale_fill_manual, + scale_x_continuous, + scale_y_continuous, + theme, +) + +from lib.plotnine import SB_COLORS, theme_switchbox +from lib.quarto import display_svg +``` + +We accumulate every report variable into a single dictionary as we go. The +final cell serializes it to `cache/report_variables.pkl`, where `index.qmd` +picks it up via `SimpleNamespace`. + +```{python} +#| label: setup-report-vars + +report_vars: dict = {} +``` + +```{python} +#| label: setup-paths + +DATA_DIR = Path("../data") +CACHE_DIR = Path("../cache") +CACHE_DIR.mkdir(exist_ok=True) +``` + + +# Import data + +This section loads each dataset and shows it, so the reader can reason about +the computations that follow. All files live in `data/` at the project root. + +## Household-level combined parquets + +These are the core billing simulation outputs — one row per household per +month, with bills computed under both the flat rate and the alternative TOU +rate. We have four files: STOU and DTOU, each for January and July 2023. + +```{python} +#| label: load-stou-jan + +stou_jan = pl.read_parquet(DATA_DIR / "stou_combined_202301.parquet") +print(f"STOU January: {stou_jan.height:,} households × {stou_jan.width} columns") +print(f"Columns: {stou_jan.columns}") +stou_jan.head(5) +``` + +```{python} +#| label: load-stou-jul + +stou_jul = pl.read_parquet(DATA_DIR / "stou_combined_202307.parquet") +print(f"STOU July: {stou_jul.height:,} households × {stou_jul.width} columns") +stou_jul.head(5) +``` + +```{python} +#| label: load-dtou-jan + +dtou_jan = pl.read_parquet(DATA_DIR / "dtou_combined_202301.parquet") +print(f"DTOU January: {dtou_jan.height:,} households × {dtou_jan.width} columns") +dtou_jan.head(5) +``` + +```{python} +#| label: load-dtou-jul + +dtou_jul = pl.read_parquet(DATA_DIR / "dtou_combined_202307.parquet") +print(f"DTOU July: {dtou_jul.height:,} households × {dtou_jul.width} columns") +dtou_jul.head(5) +``` + +Each row represents one household's monthly billing outcome. The key savings +column differs by rate type: STOU parquets use `total_delta_dollars`, DTOU +parquets use `dtou_total_delta_dollars`. In both cases positive means the +household saves under TOU (flat minus alternative). + +::: {.callout-note} +## Parquet column names +STOU parquets store flat-minus-alternative savings in `total_delta_dollars`. +DTOU parquets store the same quantity in `dtou_total_delta_dollars`. Both +follow the same sign convention: positive = customer saves under TOU. +::: + + +## Block-group-level CSVs + +The pipeline aggregates household outcomes to Census block groups, joining +demographics via a ZIP+4-to-block-group crosswalk (purchased from Melissa). +Each row is one block group with mean savings, household counts, and +demographic predictors. + +```{python} +#| label: load-bg-stou-jan + +bg_stou_jan = pl.read_csv(DATA_DIR / "bg_level_stou_jan.csv") +print(f"BG STOU January: {bg_stou_jan.height:,} block groups × {bg_stou_jan.width} columns") +print(f"Columns: {bg_stou_jan.columns}") +bg_stou_jan.head(5) +``` + +```{python} +#| label: load-bg-stou-jul + +bg_stou_jul = pl.read_csv(DATA_DIR / "bg_level_stou_jul.csv") +print(f"BG STOU July: {bg_stou_jul.height:,} block groups × {bg_stou_jul.width} columns") +bg_stou_jul.head(5) +``` + +```{python} +#| label: load-bg-dtou-jan + +bg_dtou_jan = pl.read_csv(DATA_DIR / "bg_level_dtou_jan.csv") +print(f"BG DTOU January: {bg_dtou_jan.height:,} block groups × {bg_dtou_jan.width} columns") +bg_dtou_jan.head(5) +``` + +```{python} +#| label: load-bg-dtou-jul + +bg_dtou_jul = pl.read_csv(DATA_DIR / "bg_level_dtou_jul.csv") +print(f"BG DTOU July: {bg_dtou_jul.height:,} block groups × {bg_dtou_jul.width} columns") +bg_dtou_jul.head(5) +``` + +Each row represents one Census block group. Key columns: + +| Column | Description | +|--------|-------------| +| `mean_delta` | Mean household savings (flat − alternative), dollars | +| `mean_pct_savings` | Mean household percentage savings | +| `median_household_income` | Block group median household income (check units — may be natural log) | +| `n_households` | Number of households in this block group | + + +## C23 single-family subset (STOU July) + +For the C23 (single-family, no electric space heating) delivery class subset, +we have a separate BG-level file. This lets us report quintile results for the +largest delivery class in isolation. + +```{python} +#| label: load-bg-stou-jul-c23 + +bg_stou_jul_c23 = pl.read_csv(DATA_DIR / "bg_level_stou_jul_sf_no_esh.csv") +print(f"BG STOU July (C23): {bg_stou_jul_c23.height:,} block groups × {bg_stou_jul_c23.width} columns") +bg_stou_jul_c23.head(5) +``` + + +## Regression summary + +The OLS regression results from the upstream pipeline — 40 regressions across +rate types, months, delivery classes, and outcome variables. Each row is one +regression with its coefficients, p-values, and fit statistics. + +```{python} +#| label: load-regression + +regression_df = pl.read_csv(DATA_DIR / "regression_summary.csv") +print(f"Regression summary: {regression_df.height} models × {regression_df.width} columns") +print(f"Columns: {regression_df.columns}") +regression_df.head(10) +``` + + +# Household-level statistics + +We start with the broadest metrics: how many households are in the sample, and +what share would save under each rate? + +## Sample sizes + +The DTOU and STOU parquets should contain the same households for each month +(the pipeline runs both rates on the same smart meter data). We use the DTOU +files as the canonical household count. + +```{python} +#| label: compute-sample-sizes + +report_vars["n_households_jan"] = dtou_jan.height +report_vars["n_households_jul"] = dtou_jul.height + +print(f"January households: {report_vars['n_households_jan']:,}") +print(f"July households: {report_vars['n_households_jul']:,}") + +# Sanity check: STOU should have the same counts +assert stou_jan.height == dtou_jan.height, ( + f"STOU/DTOU January mismatch: {stou_jan.height:,} vs {dtou_jan.height:,}" +) +assert stou_jul.height == dtou_jul.height, ( + f"STOU/DTOU July mismatch: {stou_jul.height:,} vs {dtou_jul.height:,}" +) +print("✓ STOU and DTOU household counts match within each month") +``` + +## Share of households saving + +For each rate × month combination, we compute the percentage of households +with positive delta (i.e., they would save under the alternative rate). + +```{python} +#| label: compute-pct-saving + +# ── STOU ───────────────────────────────────────────────────────────────── +# STOU parquets store flat-minus-alternative savings in total_delta_dollars. +STOU_DELTA_COL = "total_delta_dollars" + +report_vars["pct_save_stou_jan"] = round( + (stou_jan[STOU_DELTA_COL] > 0).mean() * 100, 1 +) +report_vars["pct_save_stou_jul"] = round( + (stou_jul[STOU_DELTA_COL] > 0).mean() * 100, 1 +) + +# ── DTOU ───────────────────────────────────────────────────────────────── +# DTOU parquets store flat-minus-alternative savings in dtou_total_delta_dollars. +DTOU_DELTA_COL = "dtou_total_delta_dollars" + +report_vars["pct_save_dtou_jan"] = round( + (dtou_jan[DTOU_DELTA_COL] > 0).mean() * 100, 1 +) +report_vars["pct_save_dtou_jul"] = round( + (dtou_jul[DTOU_DELTA_COL] > 0).mean() * 100, 1 +) + +# ── Worst-case floor stated in the executive summary ───────────────────── +# The report claims "more than X% of households save under either rate." This +# is set to the nearest 5% floor below the actual minimum across all four +# scenarios — a conservative, defensible floor rather than a computed value. +report_vars["pct_save_worst_case"] = 95 + +print("Share of households saving under TOU:") +print(f" STOU January: {report_vars['pct_save_stou_jan']}%") +print(f" STOU July: {report_vars['pct_save_stou_jul']}%") +print(f" DTOU January: {report_vars['pct_save_dtou_jan']}%") +print(f" DTOU July: {report_vars['pct_save_dtou_jul']}%") +print(f" Worst case: {report_vars['pct_save_worst_case']}% (conservative floor)") +``` + + +# Block-group-level statistics + +Next we compute summary statistics from the block-group-level data. These +capture the geographic dimension: how many block groups are in the analysis, +and what is the average savings at the block group level? + +## BG counts and household coverage + +The BG-level files contain fewer households than the combined parquets because +some households couldn't be mapped to block groups via the ZIP+4 crosswalk. + +```{python} +#| label: compute-bg-counts + +# NOTE: Adjust column name if household count is stored differently. +# Check bg_dtou_jan.columns if this fails. +N_HH_COL = "n_households" + +report_vars["n_bgs_jan"] = bg_dtou_jan.height +report_vars["n_bgs_jul"] = bg_dtou_jul.height +report_vars["n_bg_analysis_jan"] = bg_dtou_jan[N_HH_COL].sum() +report_vars["n_bg_analysis_jul"] = bg_dtou_jul[N_HH_COL].sum() + +print(f"January: {report_vars['n_bgs_jan']:,} block groups, " + f"{report_vars['n_bg_analysis_jan']:,} households") +print(f"July: {report_vars['n_bgs_jul']:,} block groups, " + f"{report_vars['n_bg_analysis_jul']:,} households") + +# Coverage check +jan_coverage = report_vars["n_bg_analysis_jan"] / report_vars["n_households_jan"] +jul_coverage = report_vars["n_bg_analysis_jul"] / report_vars["n_households_jul"] +print(f"BG coverage: January {jan_coverage:.1%}, July {jul_coverage:.1%}") +``` + + +## Mean BG-level savings + +The mean of `mean_delta` across block groups gives us the unweighted average +BG-level savings in dollars. + +```{python} +#| label: compute-bg-means + +MEAN_DELTA_COL = "mean_delta" + +report_vars["bg_mean_stou_jan"] = round(bg_stou_jan[MEAN_DELTA_COL].mean(), 2) +report_vars["bg_mean_stou_jul"] = round(bg_stou_jul[MEAN_DELTA_COL].mean(), 2) +report_vars["bg_mean_dtou_jan"] = round(bg_dtou_jan[MEAN_DELTA_COL].mean(), 2) +report_vars["bg_mean_dtou_jul"] = round(bg_dtou_jul[MEAN_DELTA_COL].mean(), 2) + +print("Mean BG-level savings ($/month, unweighted across BGs):") +print(f" STOU January: ${report_vars['bg_mean_stou_jan']:.2f}") +print(f" STOU July: ${report_vars['bg_mean_stou_jul']:.2f}") +print(f" DTOU January: ${report_vars['bg_mean_dtou_jan']:.2f}") +print(f" DTOU July: ${report_vars['bg_mean_dtou_jul']:.2f}") +``` + + +# Quintile analysis + +To assess equity, we sort block groups by median household income and divide +them into five equal groups (quintiles). Q1 is the lowest-income quintile, Q5 +is the highest. We then compute the mean percentage savings within each +quintile. If lower-income communities see larger percentage savings, the rate +is progressive; if they see smaller savings, it's regressive. + +## Income column check + +The upstream Census parquet stores `median_household_income` as a natural log. +The BG-level CSVs _may_ have already exponentiated it. We check here and +exponentiate if necessary. + +```{python} +#| label: check-income-units + +INCOME_COL = "median_household_income" +PCT_SAVINGS_COL = "mean_pct_savings" + +# Heuristic: if the max income value is < 15, it's probably in log units +# (ln(150000) ≈ 11.9). If it's > 1000, it's in dollar units. +sample_max = bg_stou_jul[INCOME_COL].max() +income_is_log = sample_max < 20 # generous threshold + +if income_is_log: + print(f"Income appears to be in log units (max = {sample_max:.2f}). Exponentiating.") + INCOME_TRANSFORM = "exp" +else: + print(f"Income appears to be in dollar units (max = {sample_max:,.0f}). No transform needed.") + INCOME_TRANSFORM = "none" +``` + + +## Quintile helper + +```{python} +#| label: quintile-helper + +def compute_quintiles( + df: pl.DataFrame, + income_col: str = INCOME_COL, + pct_col: str = PCT_SAVINGS_COL, + transform: str = INCOME_TRANSFORM, +) -> pl.DataFrame: + """Sort BGs by income, cut into 5 equal groups, return mean pct savings per quintile. + + Q1 = lowest income, Q5 = highest income. + """ + working = df.filter(pl.col(income_col).is_not_null()) + + if transform == "exp": + working = working.with_columns( + pl.col(income_col).exp().alias("_income_natural") + ) + sort_col = "_income_natural" + else: + sort_col = income_col + + working = working.sort(sort_col) + n = working.height + quintile_labels = [] + for i in range(n): + quintile_labels.append((i * 5) // n + 1) + + working = working.with_columns( + pl.Series("quintile", quintile_labels, dtype=pl.Int8) + ) + + result = ( + working.group_by("quintile") + .agg( + pl.col(pct_col).mean().alias("mean_pct_savings"), + pl.len().alias("n_bgs"), + ) + .sort("quintile") + ) + + return result +``` + + +## STOU July quintiles (all households) + +```{python} +#| label: quintiles-stou-jul + +q_stou_jul = compute_quintiles(bg_stou_jul) +print("STOU July — income quintile breakdown:") +q_stou_jul + +report_vars["stou_jul_q1_pct_savings"] = round( + q_stou_jul.filter(pl.col("quintile") == 1)["mean_pct_savings"][0], 2 +) +report_vars["stou_jul_q5_pct_savings"] = round( + q_stou_jul.filter(pl.col("quintile") == 5)["mean_pct_savings"][0], 2 +) +# Computed from Q1 and Q5 to stay consistent with the values above +report_vars["stou_jul_q1q5_gap"] = round( + report_vars["stou_jul_q1_pct_savings"] - report_vars["stou_jul_q5_pct_savings"], 1 +) + +print(f"\nQ1 (lowest income): {report_vars['stou_jul_q1_pct_savings']}%") +print(f"Q5 (highest income): {report_vars['stou_jul_q5_pct_savings']}%") +print(f"Gap (Q1 − Q5): {report_vars['stou_jul_q1q5_gap']} pp") +``` + + +## DTOU January quintiles + +```{python} +#| label: quintiles-dtou-jan + +q_dtou_jan = compute_quintiles(bg_dtou_jan) +print("DTOU January — income quintile breakdown:") +q_dtou_jan + +report_vars["dtou_jan_q1_pct"] = round( + q_dtou_jan.filter(pl.col("quintile") == 1)["mean_pct_savings"][0], 2 +) +report_vars["dtou_jan_q5_pct"] = round( + q_dtou_jan.filter(pl.col("quintile") == 5)["mean_pct_savings"][0], 2 +) +report_vars["dtou_jan_q1q5_gap"] = round( + report_vars["dtou_jan_q1_pct"] - report_vars["dtou_jan_q5_pct"], 1 +) + +print(f"\nQ1: {report_vars['dtou_jan_q1_pct']}%") +print(f"Q5: {report_vars['dtou_jan_q5_pct']}%") +print(f"Gap: {report_vars['dtou_jan_q1q5_gap']} pp") +``` + + +## DTOU July quintiles + +```{python} +#| label: quintiles-dtou-jul + +q_dtou_jul = compute_quintiles(bg_dtou_jul) +print("DTOU July — income quintile breakdown:") +q_dtou_jul + +report_vars["dtou_jul_q1_pct"] = round( + q_dtou_jul.filter(pl.col("quintile") == 1)["mean_pct_savings"][0], 2 +) +report_vars["dtou_jul_q5_pct"] = round( + q_dtou_jul.filter(pl.col("quintile") == 5)["mean_pct_savings"][0], 2 +) +report_vars["dtou_jul_q1q5_gap"] = round( + report_vars["dtou_jul_q1_pct"] - report_vars["dtou_jul_q5_pct"], 1 +) + +print(f"\nQ1: {report_vars['dtou_jul_q1_pct']}%") +print(f"Q5: {report_vars['dtou_jul_q5_pct']}%") +print(f"Gap: {report_vars['dtou_jul_q1q5_gap']} pp") +``` + + +## C23 STOU July quintiles (single-family, no electric space heating) + +The C23 delivery class is the largest residential segment. We report its +quintile breakdown separately to show that the equity pattern holds within the +most common customer type. + +```{python} +#| label: quintiles-c23-stou-jul + +q_c23_stou_jul = compute_quintiles(bg_stou_jul_c23) +print("C23 STOU July — income quintile breakdown:") +q_c23_stou_jul + +report_vars["c23_stou_jul_q1_pct"] = round( + q_c23_stou_jul.filter(pl.col("quintile") == 1)["mean_pct_savings"][0], 2 +) +report_vars["c23_stou_jul_q5_pct"] = round( + q_c23_stou_jul.filter(pl.col("quintile") == 5)["mean_pct_savings"][0], 2 +) +report_vars["c23_stou_jul_q1q5_gap"] = round( + report_vars["c23_stou_jul_q1_pct"] - report_vars["c23_stou_jul_q5_pct"], 1 +) + +print(f"\nQ1: {report_vars['c23_stou_jul_q1_pct']}%") +print(f"Q5: {report_vars['c23_stou_jul_q5_pct']}%") +print(f"Gap: {report_vars['c23_stou_jul_q1q5_gap']} pp") +``` + + +# Regression results + +The upstream pipeline ran 40 OLS regressions with HC1 robust standard errors. +We extract three specific results that appear in the report narrative. + +```{python} +#| label: extract-regression + +# Regression summary columns: scenario, month, rate, delivery_class, dep_var, +# beta_0, beta_1, se_beta_1, t_stat, p_value, r_squared, n_obs. +# Filter on "rate" (not "rate_type") and "dep_var" (not "outcome"). + +print("Regression summary columns for reference:") +print(regression_df.columns) +print() +regression_df +``` + +```{python} +#| label: assign-regression-vars + +# ── STOU January p-value (for income coefficient on pct savings) ───────── +# The report states: "STOU January percentage savings is NOT progressive +# (p=0.357)." This p-value comes from the income coefficient in the +# STOU January pct_savings regression. + +stou_jan_row = regression_df.filter( + (pl.col("rate") == "stou") + & (pl.col("month") == 202301) + & (pl.col("dep_var").str.contains("pct")) +) +if stou_jan_row.height > 0: + report_vars["stou_jan_p_value"] = round(stou_jan_row["p_value"][0], 3) +else: + report_vars["stou_jan_p_value"] = 0.357 + print("⚠ No matching row for STOU January p-value — using hardcoded fallback") + +# There is no mean_pct column in regression_summary.csv — hardcode from pipeline output. +report_vars["stou_jan_mean_pct"] = 25.66 + +# ── STOU July R² ───────────────────────────────────────────────────────── +stou_jul_row = regression_df.filter( + (pl.col("rate") == "stou") + & (pl.col("month") == 202307) + & (pl.col("dep_var").str.contains("pct")) +) +if stou_jul_row.height > 0: + report_vars["stou_jul_r2"] = round(stou_jul_row["r_squared"][0], 2) +else: + report_vars["stou_jul_r2"] = 7.04 + print("⚠ No matching row for STOU July R² — using hardcoded fallback") + +print(f"STOU January p-value (income → pct savings): {report_vars['stou_jan_p_value']}") +print(f"STOU January mean pct savings: {report_vars['stou_jan_mean_pct']}%") +print(f"STOU July R² (income → pct savings): {report_vars['stou_jul_r2']}%") +``` + + +# Rate constants + +These are the tariff values used in the report narrative and the rate +structure comparison figure. They come from ComEd's tariff filings and the ICC +Docket 24-0378 Compromise Proposal. All values are ¢/kWh inclusive of rider +adjustments unless otherwise noted. + +```{python} +#| label: rate-constants + +# ── Flat supply rate (Price to Compare, non-summer) ─────────────────────── +report_vars["flat_ptc_nonsummer"] = 9.660 # ¢/kWh; 2026 ComEd PTC confirmed by CUB + +# ── Rate BEST (STOU) supply rate range ─────────────────────────────────── +# These are the lowest and highest off-peak supply rates in the Compromise +# Proposal (ComEd Ex. 19.0), including the 1.266 ¢/kWh T&MP adder. +# Overnight non-summer = 3.278¢ (lowest); evening non-summer = 4.352¢ (highest off-peak). +# The report rounds these to one decimal for narrative clarity. +report_vars["bestec_low"] = 3.1 # ¢/kWh, approx. overnight non-summer +report_vars["bestec_high"] = 4.4 # ¢/kWh, approx. highest off-peak non-summer +report_vars["bestec_peak_summer"] = 19.5 # ¢/kWh, mid-day peak summer (19.485¢ exact) + +print("Rate constants (¢/kWh):") +print(f" Flat PTC (non-summer): {report_vars['flat_ptc_nonsummer']}") +print(f" BEST/STOU off-peak low: {report_vars['bestec_low']}") +print(f" BEST/STOU off-peak high: {report_vars['bestec_high']}") +print(f" BEST/STOU peak summer: {report_vars['bestec_peak_summer']}") +``` + + +# Figures + +## Scatterplot: STOU July percentage savings by income + +This figure shows the relationship between block group median household income +and mean percentage savings under Rate BEST in July. Each point is one Census +block group. The OLS regression line with 95% confidence band visualizes the +income–savings gradient — a downward slope means lower-income neighborhoods +save a proportionally larger share of their bill. + +```{python} +#| label: fig-scatter-stou-jul-pct +#| fig-cap: "Block group percentage savings under Rate BEST (July) vs. median household income. Each point is one Census block group. The regression line shows a modest progressive gradient — lower-income communities see slightly larger percentage savings." +#| fig-width: 10 + +# Prepare data — exponentiate income if needed +scatter_df = bg_stou_jul.filter( + pl.col(INCOME_COL).is_not_null() & pl.col(PCT_SAVINGS_COL).is_not_null() +) +if INCOME_TRANSFORM == "exp": + scatter_df = scatter_df.with_columns( + (pl.col(INCOME_COL).exp() / 1000).alias("income_thousands") + ) +else: + scatter_df = scatter_df.with_columns( + (pl.col(INCOME_COL) / 1000).alias("income_thousands") + ) + +p = ( + ggplot(scatter_df, aes(x="income_thousands", y=PCT_SAVINGS_COL)) + + geom_point(alpha=0.25, size=1, color=SB_COLORS["sky"]) + + geom_smooth( + method="lm", + color=SB_COLORS["midnight"], + fill=SB_COLORS["sky"], + alpha=0.3, + ) + + labs( + title="Rate BEST (July): percentage savings by block group income", + x="Median household income ($, thousands)", + y="Mean percentage savings (%)", + ) + + scale_x_continuous(labels=lambda lst: [f"${x:.0f}k" for x in lst]) + + theme_switchbox() + + theme(figure_size=(10.5, 4.5)) +) + +fig = p.draw() +display_svg(fig) +``` + + +## Scatterplot: STOU January percentage savings by income + +This figure mirrors the July scatterplot for the January billing period, showing +how block group median household income relates to mean percentage savings under +Rate BEST in a non-summer month. Comparing the two seasons reveals how the +income gradient shifts when the peak window overlaps with morning and evening +routines rather than afternoon air-conditioning. + +```{python} +#| label: fig-scatter-stou-jan-pct +#| fig-cap: "Block group percentage savings under Rate BEST (January) vs. median household income. Each point is one Census block group. The regression line captures the income–savings gradient across the non-summer billing period." +#| fig-width: 10 + +scatter_jan_df = bg_stou_jan.filter( + pl.col(INCOME_COL).is_not_null() & pl.col(PCT_SAVINGS_COL).is_not_null() +) +if INCOME_TRANSFORM == "exp": + scatter_jan_df = scatter_jan_df.with_columns( + (pl.col(INCOME_COL).exp() / 1000).alias("income_thousands") + ) +else: + scatter_jan_df = scatter_jan_df.with_columns( + (pl.col(INCOME_COL) / 1000).alias("income_thousands") + ) + +p = ( + ggplot(scatter_jan_df, aes(x="income_thousands", y=PCT_SAVINGS_COL)) + + geom_point(alpha=0.25, size=1, color=SB_COLORS["sky"]) + + geom_smooth( + method="lm", + color=SB_COLORS["midnight"], + fill=SB_COLORS["sky"], + alpha=0.3, + ) + + labs( + title="STOU (January): precentage savings by block group income", + x="Median household income ($, thousands)", + y="Mean percentage savings (%)", + ) + + scale_x_continuous(labels=lambda lst: [f"${x:.0f}k" for x in lst]) + + theme_switchbox() + + theme(figure_size=(10.5, 4.5)) +) + +fig = p.draw() +display_svg(fig) +``` + + +## Rate structure comparison + +This grouped bar chart compares the total all-in electricity rate (supply + +delivery) that a C23 customer would pay per kWh in each time block under three +structures: flat rate, DTOU, and Rate BEST (non-summer). It makes the key +trade-off visible: under Rate BEST, three of the four daily time blocks are +dramatically cheaper than the flat rate, while the mid-day peak is +substantially more expensive. Because most residential consumption falls +outside the 1–7 PM peak, the large off-peak discount drives overall savings. + +Rate values used here are the billed C23 rates (inclusive of all rider +adjustments) from the CUB DTOD Fact Sheet (January 2026) and the ComEd Ex. +19.0 Compromise Proposal, as documented in the Data and Methods section of the +report. + +```{python} +#| label: fig-rate-structures +#| fig-cap: "Total electricity rate (supply + delivery, C23 class) by time-of-day period for three rate structures, non-summer season. Under Rate BEST, off-peak periods are 40–60% cheaper than the flat rate; the mid-day peak is roughly twice as expensive." +#| fig-width: 10 + +# ── C23 non-summer all-in rates (¢/kWh = supply + delivery) ───────────── +# +# Flat: PTC (9.660) + flat DFC (6.228) = 15.888¢ all periods +# +# DTOU: flat PTC (9.660) + TOU DFC (varies) +# Overnight: 9.660 + 2.984 = 12.644 +# Morning: 9.660 + 4.009 = 13.669 +# Mid-Day Peak: 9.660 + 10.712 = 20.372 +# Evening: 9.660 + 3.747 = 13.407 +# +# Rate BEST: BESTEC non-summer (varies) + TOU DFC (varies) +# Overnight: 3.278 + 2.984 = 6.262 +# Morning: 4.095 + 4.009 = 8.104 +# Mid-Day Peak: 18.080 + 10.712 = 28.792 +# Evening: 4.352 + 3.747 = 8.099 + +rate_data = pl.DataFrame({ + "period": ["Overnight", "Morning", "Mid-Day Peak", "Evening"] * 3, + "rate_type": ( + ["Flat rate"] * 4 + + ["DTOU"] * 4 + + ["Rate BEST"] * 4 + ), + "rate_cents": [ + # Flat — same rate all day (non-summer PTC + flat DFC) + 15.888, 15.888, 15.888, 15.888, + # DTOU — flat supply + TOU delivery + 12.644, 13.669, 20.372, 13.407, + # Rate BEST — TOU supply + TOU delivery (non-summer BESTECs + DTOU DFC) + 6.262, 8.104, 28.792, 8.099, + ], +}) + +# Set period order for the x-axis (chronological flow of the day) +period_order = ["Overnight", "Morning", "Mid-Day Peak", "Evening"] +rate_data = rate_data.with_columns( + pl.col("period").cast(pl.Enum(period_order)) +) + +# Set rate type order for grouping +type_order = ["Flat rate", "DTOU", "Rate BEST"] +rate_data = rate_data.with_columns( + pl.col("rate_type").cast(pl.Enum(type_order)) +) + +p = ( + ggplot(rate_data, aes(x="period", y="rate_cents", fill="rate_type")) + + geom_col(position=position_dodge(width=0.8), width=0.7) + + scale_fill_manual( + values=[SB_COLORS["midnight"], SB_COLORS["sky"], SB_COLORS["carrot"]] + ) + + labs( + title="Total single-family home without electric heat by time-of-day period (non-summer)", + x="Time-of-day period", + y="All-in rate (¢/kWh)", + fill="Rate structure", + ) + + theme_switchbox() + + theme(figure_size=(10.5, 4.5)) +) + +fig = p.draw() +display_svg(fig) +``` + + +# Spot-check verification + +Before exporting, we verify a handful of key values against the expected +results from the verified pipeline run. + +```{python} +#| label: spot-checks + +checks = { + "n_households_jan": 658_959, + "pct_save_dtou_jul": 95.7, + "bg_mean_stou_jan": 26.84, + "stou_jul_q1_pct_savings": 18.48, + "stou_jan_p_value": 0.357, +} + +all_pass = True +for var, expected in checks.items(): + actual = report_vars.get(var) + if actual is None: + print(f" ✗ {var}: MISSING from report_vars") + all_pass = False + elif isinstance(expected, float): + if abs(actual - expected) > 0.02: + print(f" ✗ {var}: expected {expected}, got {actual}") + all_pass = False + else: + print(f" ✓ {var}: {actual} (expected {expected})") + elif actual != expected: + print(f" ✗ {var}: expected {expected}, got {actual}") + all_pass = False + else: + print(f" ✓ {var}: {actual}") + +if all_pass: + print("\n✓ All spot-checks pass") +else: + print("\n✗ Some spot-checks failed — review before proceeding") +``` + + +# Geographic comparison: core Chicago vs. suburban + +To understand how savings vary by geography, we compare mean STOU July bill +impacts across two groups of ZIP codes: the dense, majority-multifamily +neighborhoods of core Chicago, and the lower-density suburban communities +in ComEd's outer service territory. + +We build the ZIP-to-block-group mapping from the ComEd ZIP+4 crosswalk, +then compute the mean of block-group mean deltas (treating each block group +equally) and the multi-family share (pooled households minus SF-only +households, divided by pooled households). + +```{python} +#| label: geo-comparison + +core_chicago_zips = [ + "60617", "60649", "60637", "60615", "60653", "60616", "60605", "60604", + "60603", "60602", "60601", "60611", "60610", "60614", "60657", "60613", + "60640", "60622", "60612", "60607", "60642", "60647", "60661", "60606", "60654", +] + +suburban_zips = [ + "60014", "60012", "60050", "60051", "60098", "60030", "60084", "60013", + "60047", "60060", "60073", "60156", "60102", "60010", "60110", "60067", + "60074", "60004", "60005", "60120", "60123", "60124", "60177", "60173", + "60194", "60195", "60107", "60103", "60172", "60108", + "60068", "60712", "60706", "60707", "60131", "60160", "60153", + "60301", "60302", "60304", "60402", "60804", "60513", "60534", "60501", "60455", +] + +# Build ZIP -> block group mapping from the ComEd ZIP+4 crosswalk. +# CensusKey2023 is a 15-character block FIPS; the first 12 characters are the +# block group GEOID (state + county + tract + BG). +zip_bg_xwalk = ( + pl.read_csv( + DATA_DIR / "reference/comed_bg_zip4_crosswalk.txt", + separator="\t", + schema_overrides={"CensusKey2023": pl.Utf8}, + ) + .with_columns( + pl.col("Zip").cast(pl.Utf8).str.zfill(5), + pl.col("CensusKey2023").str.slice(0, 12).alias("geoid_bg"), + ) + .select(["Zip", "geoid_bg"]) + .unique() +) + +bg_jul_pooled = pl.read_csv( + DATA_DIR / "bg_level_stou_jul.csv", + schema_overrides={"geoid_bg": pl.Utf8}, +) +bg_jul_sf = pl.read_csv( + DATA_DIR / "statewide_analysis_regressions/bg_level_stou_jul_sf_no_esh.csv", + schema_overrides={"geoid_bg": pl.Utf8}, +) + + +def geo_stats( + zips: list[str], + pooled: pl.DataFrame, + sf: pl.DataFrame, + xwalk: pl.DataFrame, +) -> dict[str, float]: + """Compute mean STOU July savings and MF share for a set of ZIP codes. + + Returns a dict with: + - pooled_mean: unweighted mean of block-group mean_delta (pooled) + - sf_mean: unweighted mean of block-group mean_delta (SF-only) + - mf_share: 1 - (SF households / pooled households), left-joining pooled + to SF and filling missing SF counts with 0 + """ + bgs = xwalk.filter(pl.col("Zip").is_in(zips))["geoid_bg"].unique().to_list() + + pooled_geo = pooled.filter(pl.col("geoid_bg").is_in(bgs)) + sf_geo = sf.filter(pl.col("geoid_bg").is_in(bgs)) + + pooled_mean = pooled_geo["mean_delta"].mean() + sf_mean = sf_geo["mean_delta"].mean() + + joined = pooled_geo.select(["geoid_bg", "n_households"]).join( + sf_geo.select(["geoid_bg", pl.col("n_households").alias("n_households_sf")]), + on="geoid_bg", + how="left", + ).with_columns(pl.col("n_households_sf").fill_null(0)) + + mf_share = 1 - joined["n_households_sf"].sum() / joined["n_households"].sum() + + return {"pooled_mean": pooled_mean, "sf_mean": sf_mean, "mf_share": mf_share} + + +cc = geo_stats(core_chicago_zips, bg_jul_pooled, bg_jul_sf, zip_bg_xwalk) +sub = geo_stats(suburban_zips, bg_jul_pooled, bg_jul_sf, zip_bg_xwalk) + +print("Core Chicago:") +print(f" pooled mean STOU July savings: ${cc['pooled_mean']:.2f}") +print(f" SF-only mean STOU July savings: ${cc['sf_mean']:.2f}") +print(f" multi-family share: {cc['mf_share']:.1%}") +print() +print("Suburban:") +print(f" pooled mean STOU July savings: ${sub['pooled_mean']:.2f}") +print(f" SF-only mean STOU July savings: ${sub['sf_mean']:.2f}") +print(f" multi-family share: {sub['mf_share']:.1%}") + +report_vars.update( + { + "core_chicago_pooled_mean_stou_jul": cc["pooled_mean"], + "suburban_pooled_mean_stou_jul": sub["pooled_mean"], + "core_chicago_sf_mean_stou_jul": cc["sf_mean"], + "suburban_sf_mean_stou_jul": sub["sf_mean"], + "core_chicago_mf_share": cc["mf_share"], + "suburban_mf_share": sub["mf_share"], + } +) +``` + + +# Export + +We serialize all report variables to `cache/report_variables.pkl`. The +report narrative (`index.qmd`) loads this file and accesses variables via +`SimpleNamespace`. Each variable calculated here corresponds to a metric in +the report — you can find where each is used by searching for the variable +name in `index.qmd`. + +```{python} +#| label: export-report-vars + +print(f"Exporting {len(report_vars)} report variables to {CACHE_DIR / 'report_variables.pkl'}") +print() +for k, v in sorted(report_vars.items()): + print(f" {k}: {v}") + +CACHE_DIR.mkdir(exist_ok=True) +Path(CACHE_DIR / "report_variables.pkl").write_bytes(pickle.dumps(report_vars)) +print(f"\n✓ Wrote cache/report_variables.pkl ({len(report_vars)} variables)") +``` diff --git a/pyproject.toml b/pyproject.toml index c7297bb..6c58d29 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -21,10 +21,13 @@ dependencies = [ "botocore>=1.40.47", "cenpy>=1.0.1", "ipykernel>=6.30.1", + "jupyter>=1.1.1", "matplotlib>=3.9.4", "memory-profiler>=0.61.0", + "nbformat>=5.10.4", "numpy>=2.2.6", "openpyxl>=3.1.5", + "plotnine>=0.15.3", "polars>=1.31.0", "pyarrow>=14.0.0", "pyyaml>=6.0.3", @@ -46,10 +49,12 @@ Documentation = "https://switchbox-data.github.io/smart-meter-analysis/" exclude = ["archive", ".venv", "tests"] [tool.deptry.per_rule_ignores] +DEP001 = ["compact_month_output", "build_tariff_hourly_prices", "compute_delivery_deltas", "gspread", "dotenv"] DEP002 = [ "ipykernel", "cenpy", "openpyxl", + "plotnine", "scikit-learn", "quarto", "beautifulsoup4", @@ -58,8 +63,9 @@ DEP002 = [ "memory-profiler", "snakeviz", "tslearn", + "selenium", ] -DEP003 = ["botocore", "analysis", "smart_meter_analysis", "pandas", "scipy"] +DEP003 = ["botocore", "analysis", "smart_meter_analysis", "pandas", "scipy", "geopandas", "bs4", "IPython"] DEP004 = ["botocore"] [dependency-groups] @@ -70,7 +76,7 @@ dev = [ "tox-uv>=1.11.3", "deptry>=0.23.0", "mypy>=0.991", - "ruff>=0.11.5", + "ruff>=0.14.4", "mkdocs>=1.4.2", "mkdocs-material>=8.5.10", "mkdocstrings[python]>=0.26.1", @@ -120,6 +126,8 @@ line-length = 120 fix = true exclude = [ "archive/", + "lib/", + "_manuscript/", ".git", ".venv", "__pycache__", @@ -137,18 +145,22 @@ extend-ignore = ["TRY003", "TRY300", "TRY400"] "scripts/run_comed_pipeline.py" = ["C901", "S603", "S607"] "scripts/diagnostics/*.py" = ["C901"] "smart_meter_analysis/pipeline_validator.py" = ["C901", "PGH003"] +"smart_meter_analysis/wide_to_long.py" = ["C901"] "smart_meter_analysis/run_manifest.py" = ["S603", "S607"] "smart_meter_analysis/aws_loader.py" = ["C901", "TRY301"] "smart_meter_analysis/census.py" = ["C901"] "tests/test_aws_transform.py" = ["E402"] "tests/test_census.py" = ["E402"] -"scripts/data_collection/*" = ["C901"] -"analysis/clustering/clustering_validation.py" = ["C901", "F841", "RUF015"] "analysis/clustering/stage2_logratio_regression.py" = ["C901"] -"analysis/clustering/stage2_multinomial.py" = ["C901"] -"tests/validate_total_comed_pipeline.py" = ["C901", "S603", "RUF001"] "scripts/testing/generate_sample_data.py" = ["UP035", "UP006", "UP007", "S311"] -"tests/*" = ["S101", "RUF001"] +# S603/S607: subprocess calls with variable args are inherent to the +# orchestrator design (each pipeline step runs as a child process). +"scripts/run_billing_pipeline.py" = ["S603", "S607"] +"scripts/pricing_pilot/*.py" = ["C901"] +"scripts/csv_to_parquet/migrate_month_runner.py" = ["C901"] +"scripts/csv_to_parquet/compact_month_output.py" = ["C901", "S603", "S607"] +"scripts/csv_to_parquet/restructure_for_export.py" = ["C901", "S603", "S607"] +"tests/*" = ["S101", "S603", "RUF001"] [tool.ruff.format] diff --git a/rate_structures/.gitkeep b/rate_structures/.gitkeep new file mode 100644 index 0000000..e69de29 diff --git a/rate_structures/comed_dtou_2026.yaml b/rate_structures/comed_dtou_2026.yaml new file mode 100644 index 0000000..42d5a76 --- /dev/null +++ b/rate_structures/comed_dtou_2026.yaml @@ -0,0 +1,69 @@ +# ComEd Dynamic Time-of-Use (DTOU) rate structure for 2026 — window definitions. +# +# IMPORTANT: Season boundaries and period hour windows MUST remain identical +# to those in comed_stou_2026.yaml. Only the price values may differ between +# the two files. The automated test +# tests/test_rate_structure_window_alignment_real_yaml.py +# enforces this invariant. If you shift any hour boundary or season date in +# one file you MUST make the same change in the other. +# +# TODO: Replace every price: 0.000 placeholder below with the actual DTOU +# all-in supply+delivery cents/kWh values before running production +# calendar builds. +# +# Price structure note: +# DTOU delivery is TOU (same four windows as STOU). +# DTOU supply is flat (uniform rate across all periods). +# The price field here must be the ALL-IN (delivery TOU + flat supply) +# cents/kWh so billing math is consistent with the STOU and flat-rate +# calendars produced by scripts/build_tariff_hourly_prices.py. +# +name: comed_dtou_2026_rate +timezone: America/Chicago +unit: cents_per_kwh +holiday_rule: treat_as_weekend + +# Hour ranges are half-open [start_hour, end_hour) — MUST match comed_stou_2026.yaml. +seasons: + - name: summer + start_mmdd: "06-01" + end_mmdd: "09-30" + periods: + - period: morning + start_hour: 6 + end_hour: 13 + price: 0.000 # TODO: actual DTOU summer morning cents/kWh + - period: midday_peak + start_hour: 13 + end_hour: 19 + price: 0.000 # TODO: actual DTOU summer midday_peak cents/kWh + - period: evening + start_hour: 19 + end_hour: 21 + price: 0.000 # TODO: actual DTOU summer evening cents/kWh + - period: overnight + start_hour: 21 + end_hour: 6 + price: 0.000 # TODO: actual DTOU summer overnight cents/kWh + + # Nonsummer wraps around the calendar year (Oct 1 → May 31) — same as STOU. + - name: nonsummer + start_mmdd: "10-01" + end_mmdd: "05-31" + periods: + - period: morning + start_hour: 6 + end_hour: 13 + price: 0.000 # TODO: actual DTOU nonsummer morning cents/kWh + - period: midday_peak + start_hour: 13 + end_hour: 19 + price: 0.000 # TODO: actual DTOU nonsummer midday_peak cents/kWh + - period: evening + start_hour: 19 + end_hour: 21 + price: 0.000 # TODO: actual DTOU nonsummer evening cents/kWh + - period: overnight + start_hour: 21 + end_hour: 6 + price: 0.000 # TODO: actual DTOU nonsummer overnight cents/kWh diff --git a/rate_structures/comed_stou_2026.yaml b/rate_structures/comed_stou_2026.yaml new file mode 100644 index 0000000..3c0b3c5 --- /dev/null +++ b/rate_structures/comed_stou_2026.yaml @@ -0,0 +1,70 @@ +# ComEd Space-Time-of-Use rate structure for 2026 (BES supply). +# All prices are in cents/kWh—matches the pipeline's internal convention +# and avoids floating-point confusion between cents and dollars. +# +# Source: ComEd Ex. 19.0, Figure 1 (Kremer rebuttal testimony, May 12, 2025) +# ICC Docket No. 24-0378 (on Rehearing) +# URL: https://www.icc.illinois.gov/docket/P2024-0378/documents/365088/files/639688.pdf +# +# Compromise Proposal rates plus 1.266 cents/kWh Transmission & Misc. Procurement +# (T&MP from same exhibit). Illustrative rates with 100% capacity costs rolled +# into midday peak. +# +# Original Compromise rates (without T&MP): Summer morning 3.013, +# midday_peak 18.219, evening 3.090, overnight 1.870; +# Nonsummer morning 2.829, midday_peak 16.814, evening 3.086, overnight 2.012. +# +# Raw BESTECs (for future sensitivity analysis only, NOT used here): +# Source: ComEd Ex. 19.01, page 1, columns g and h (corrected illustrative BESTEC) +# URL: https://icc.illinois.gov/docket/P2024-0378/documents/365088/files/639689.pdf +name: comed_stou_2026_rate_best_supply +timezone: America/Chicago +unit: cents_per_kwh +holiday_rule: treat_as_weekend + +# Hour ranges are half-open [start_hour, end_hour) so adjacent periods +# tile without overlap. Wrap-around (e.g. 21→6) is handled in code. +seasons: + - name: summer + start_mmdd: "06-01" + end_mmdd: "09-30" + periods: + - period: morning + start_hour: 6 + end_hour: 13 + price: 4.279 + - period: midday_peak + start_hour: 13 + end_hour: 19 + price: 19.485 + - period: evening + start_hour: 19 + end_hour: 21 + price: 4.356 + - period: overnight + start_hour: 21 + end_hour: 6 + price: 3.136 + + # Nonsummer wraps around the calendar year (Oct 1 → May 31). + # _in_season() handles this wrap via day-of-year comparison. + - name: nonsummer + start_mmdd: "10-01" + end_mmdd: "05-31" + periods: + - period: morning + start_hour: 6 + end_hour: 13 + price: 4.095 + - period: midday_peak + start_hour: 13 + end_hour: 19 + price: 18.080 + - period: evening + start_hour: 19 + end_hour: 21 + price: 4.352 + - period: overnight + start_hour: 21 + end_hour: 6 + price: 3.278 diff --git a/references.bib b/references.bib new file mode 100644 index 0000000..f60cb3d --- /dev/null +++ b/references.bib @@ -0,0 +1,15 @@ +@misc{icc_FinalOrderDocket24_2025, + title = {Final Order, Docket No. 24-0378}, + author = {{Illinois Commerce Commission}}, + year = {2025}, + month = jan, + url = {https://www.icc.illinois.gov/docket/P2024-0378/documents/360200/files/630772.pdf} +} + +@misc{icc_OrderRehearingDocket24_2025, + title = {Order on Rehearing, Docket No. 24-0378}, + author = {{Illinois Commerce Commission}}, + year = {2025}, + month = jul, + url = {https://www.icc.illinois.gov/docket/P2024-0378/documents/368065/files/644892.pdf} +} diff --git a/scripts/audit/phase0_repo_audit.sh b/scripts/audit/phase0_repo_audit.sh new file mode 100755 index 0000000..9460f08 --- /dev/null +++ b/scripts/audit/phase0_repo_audit.sh @@ -0,0 +1,148 @@ +#!/usr/bin/env bash +# phase0_repo_audit.sh — Audit the smart-meter-analysis repo and write /tmp/repo_audit.md +# Designed to run on EC2 where ripgrep is NOT installed. Uses grep -rn throughout. +set -euo pipefail + +REPO="/ebs/home/griffin_switch_box/smart-meter-analysis" +OUT="/tmp/repo_audit.md" +RUNS_ROOT="/ebs/home/griffin_switch_box/runs" + +# ── helpers ────────────────────────────────────────────────────────────────── +section() { printf '\n## %s\n\n' "$1" >> "$OUT"; } +subsection() { printf '\n### %s\n\n' "$1" >> "$OUT"; } +fence() { echo '```' >> "$OUT"; } +fenced() { fence; cat >> "$OUT"; fence; } + +grep_patterns() { + # $1 = label, $2 = extended-regex pattern + subsection "$1" + grep -rn --include='*.py' --include='*.sh' -E "$2" "$REPO" 2>/dev/null \ + | sed "s|$REPO/||" \ + | fenced \ + || echo '_No matches._' >> "$OUT" +} + +# ── start report ───────────────────────────────────────────────────────────── +cat > "$OUT" <<'HEADER' +# Smart-Meter-Analysis — Repo Audit (Phase 0) + +HEADER +printf 'Generated: %s\n\n' "$(date -u '+%Y-%m-%d %H:%M:%S UTC')" >> "$OUT" + +# ── 1. Git state ───────────────────────────────────────────────────────────── +section "Git State" +( + cd "$REPO" + echo "HEAD: $(git rev-parse --short HEAD)" + echo "Branch: $(git branch --show-current)" + echo "" + echo "Last commit:" + git log -1 --format=' %h %s (%ai)' + echo "" + echo "Status:" + git status --short || echo '(clean)' +) | fenced + +# ── 2. Repo tree (depth 2) ────────────────────────────────────────────────── +section "Repo Tree (depth 2)" +(cd "$REPO" && find . -maxdepth 2 -not -path './.git/*' -not -path './.git' | sort) | fenced + +# ── 3. Directory listings ──────────────────────────────────────────────────── +section "Directory Listings" + +for dir in scripts scripts/csv_to_parquet config; do + subsection "$dir/" + if [ -d "$REPO/$dir" ]; then + ls -la "$REPO/$dir" | fenced + else + echo "_Directory does not exist._" >> "$OUT" + fi +done + +# ── 4. Justfile ────────────────────────────────────────────────────────────── +section "Justfile" +if [ -f "$REPO/Justfile" ]; then + cat "$REPO/Justfile" | fenced +else + echo '_No Justfile found._' >> "$OUT" +fi + +# ── 5. Pattern searches ───────────────────────────────────────────────────── +section "Pattern Searches" + +grep_patterns "S3 Input List Generation" \ + 's3_paths|s3_list|input_list|aws s3 ls' + +grep_patterns "Wide→Long Conversion" \ + 'wide.*long|melt|unpivot|pivot' + +grep_patterns "Parquet Writing" \ + 'write_parquet|sink_parquet|ParquetWriter' + +grep_patterns "Compaction" \ + 'compact|merge.*parquet|k.way' + +grep_patterns "Validation" \ + 'validate|check_sort|check_dup|schema_check|iter_batches' + +grep_patterns "Overwrite / Resume Flags" \ + 'overwrite|resume|--force|_SUCCESS|state_file' + +# ── 6. Production output inventory ────────────────────────────────────────── +section "Production Output Inventory" + +prod_dirs=() +while IFS= read -r d; do + prod_dirs+=("$d") +done < <(find "$RUNS_ROOT" -maxdepth 1 -type d -name 'out_*_production' 2>/dev/null | sort) + +if [ ${#prod_dirs[@]} -eq 0 ]; then + echo '_No out\_\*\_production directories found._' >> "$OUT" +else + echo "Count: ${#prod_dirs[@]}" >> "$OUT" + echo "" >> "$OUT" + + subsection "Directory List" + printf '%s\n' "${prod_dirs[@]}" | sed "s|$RUNS_ROOT/||" | fenced + + subsection "Per-Month File Counts and Sizes" + { + printf '%-40s %8s %s\n' "MONTH (YYYY/MM)" "FILES" "SIZE" + printf '%-40s %8s %s\n' "---" "---" "---" + for prod_dir in "${prod_dirs[@]}"; do + base=$(basename "$prod_dir") + # Walk plain YYYY/MM directories (not Hive-style) + find "$prod_dir" -mindepth 2 -maxdepth 2 -type d 2>/dev/null \ + | grep -E '/[0-9]{4}/[0-9]{2}$' | sort | while read -r mdir; do + rel=$(echo "$mdir" | grep -oE '[0-9]{4}/[0-9]{2}$') + nfiles=$(find "$mdir" -maxdepth 1 -name 'part-*.parquet' -type f 2>/dev/null | wc -l) + sz=$(du -sh "$mdir" 2>/dev/null | cut -f1) + printf '%-40s %8d %s\n' "$base/$rel" "$nfiles" "$sz" + done + done + } | fenced +fi + +# ── 7. s3_paths_*.txt inventory ────────────────────────────────────────────── +section "s3_paths_*.txt Files" + +for search_dir in /home/griffin_switch_box /ebs/home/griffin_switch_box; do + subsection "$search_dir" + if [ -d "$search_dir" ]; then + found=$(find "$search_dir" -maxdepth 1 -name 's3_paths_*.txt' 2>/dev/null | sort) + if [ -n "$found" ]; then + ls -lh $found | fenced + else + echo '_None found._' >> "$OUT" + fi + else + echo "_Directory does not exist._" >> "$OUT" + fi +done + +# ── done ───────────────────────────────────────────────────────────────────── +echo "" >> "$OUT" +echo "---" >> "$OUT" +echo "_End of audit._" >> "$OUT" + +echo "Audit written to $OUT ($(wc -l < "$OUT") lines)" diff --git a/scripts/audit_stou_dtou_windows.py b/scripts/audit_stou_dtou_windows.py new file mode 100644 index 0000000..7f77dd1 --- /dev/null +++ b/scripts/audit_stou_dtou_windows.py @@ -0,0 +1,152 @@ +#!/usr/bin/env python3 +"""Audit: verify STOU and DTOU calendar parquets assign identical TOU periods. + +STOU (Space-Time-of-Use) and DTOU (Dynamic Time-of-Use) use the same four +TOU period windows (morning, midday_peak, evening, overnight) — only prices +differ. This script loads both calendar parquets and fails loudly if any +datetime_chicago maps to a different period label in the two files. + +The tariff calendars are produced by scripts/build_tariff_hourly_prices.py; +each has a ``period`` column and a ``datetime_chicago`` join key. + +Usage (EC2, pointing at the "allin" calendar parquets):: + + uv run python scripts/tmp/audit_stou_dtou_windows.py \\ + --stou ~/pricing_pilot/comed_stou_allin_202307_sf_no_esh.parquet \\ + --dtou ~/pricing_pilot/comed_dtou_allin_202307_sf_no_esh.parquet + + # Or compare entire directories of calendar parquets (one per file) + uv run python scripts/tmp/audit_stou_dtou_windows.py \\ + --stou ~/pricing_pilot/stou_calendars/ \\ + --dtou ~/pricing_pilot/dtou_calendars/ + +Exit codes: + 0 — all period assignments match + 1 — at least one mismatch (details printed to stdout) + 2 — input error (file not found, missing column, row-count mismatch) +""" + +from __future__ import annotations + +import argparse +import sys +from pathlib import Path + +import polars as pl + +PERIOD_COL = "period" +KEY_COL = "datetime_chicago" + +REQUIRED_COLS = {KEY_COL, PERIOD_COL} + + +def _load(path: Path) -> pl.DataFrame: + """Load a single parquet or scan a directory of parquets.""" + if path.is_dir(): + parts = sorted(path.glob("*.parquet")) + if not parts: + print(f"ERROR: no parquet files in directory: {path}", file=sys.stderr) + sys.exit(2) + df = pl.read_parquet(parts) + elif path.is_file(): + df = pl.read_parquet(path) + else: + print(f"ERROR: path not found: {path}", file=sys.stderr) + sys.exit(2) + + missing = REQUIRED_COLS - set(df.columns) + if missing: + print( + f"ERROR: {path} is missing required columns: {sorted(missing)}. Found: {sorted(df.columns)}", + file=sys.stderr, + ) + sys.exit(2) + + return df.select([KEY_COL, PERIOD_COL]).sort(KEY_COL) + + +def audit(stou_path: Path, dtou_path: Path) -> int: + """Compare period columns; return 0 on match, 1 on mismatch.""" + stou = _load(stou_path) + dtou = _load(dtou_path) + + print(f"STOU: {stou.height} rows ({stou_path})") + print(f"DTOU: {dtou.height} rows ({dtou_path})") + + if stou.height != dtou.height: + print( + f"ERROR: row count mismatch — STOU={stou.height}, DTOU={dtou.height}. " + "Calendars must cover the same datetime range.", + file=sys.stderr, + ) + return 2 + + # Join on datetime_chicago; unmatched → null period_dtou + joined = stou.join( + dtou.rename({PERIOD_COL: "period_dtou"}), + on=KEY_COL, + how="left", + suffix="_dtou", + ).rename({PERIOD_COL: "period_stou"}) + + # Rows where keys don't align (nulls in period_dtou after left join) + n_unjoined = joined.filter(pl.col("period_dtou").is_null()).height + if n_unjoined: + sample = joined.filter(pl.col("period_dtou").is_null()).head(5)[KEY_COL].to_list() + print( + f"ERROR: {n_unjoined} datetime(s) in STOU have no match in DTOU. Sample: {sample}", + file=sys.stderr, + ) + return 2 + + mismatches = joined.filter(pl.col("period_stou") != pl.col("period_dtou")) + n_mismatch = mismatches.height + n_total = joined.height + + if n_mismatch == 0: + print(f"OK — all {n_total} period assignments match between STOU and DTOU.") + # Print period distribution as a quick sanity check + dist = stou.group_by(PERIOD_COL).len().sort(PERIOD_COL).rename({"len": "hours"}) + print("\nPeriod distribution (STOU):") + print(dist) + return 0 + + print(f"\nFAIL — {n_mismatch}/{n_total} period assignments differ between STOU and DTOU:\n") + print(mismatches.head(20)) + + # Summary by (stou_period, dtou_period) pair + summary = ( + mismatches.group_by(["period_stou", "period_dtou"]) + .len() + .sort(["period_stou", "period_dtou"]) + .rename({"len": "n_rows"}) + ) + print("\nMismatch summary (by period pair):") + print(summary) + return 1 + + +def main(argv: list[str] | None = None) -> int: + parser = argparse.ArgumentParser( + description="Verify STOU and DTOU TOU period assignments are identical.", + ) + parser.add_argument( + "--stou", + type=Path, + required=True, + metavar="PATH", + help="STOU calendar parquet file or directory of parquets.", + ) + parser.add_argument( + "--dtou", + type=Path, + required=True, + metavar="PATH", + help="DTOU calendar parquet file or directory of parquets.", + ) + args = parser.parse_args(argv) + return audit(args.stou, args.dtou) + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/scripts/build_flat_hourly_prices.py b/scripts/build_flat_hourly_prices.py new file mode 100644 index 0000000..d86d16f --- /dev/null +++ b/scripts/build_flat_hourly_prices.py @@ -0,0 +1,202 @@ +#!/usr/bin/env python3 +"""Build an hourly flat-rate price calendar Parquet from a CSV of monthly rates. + +Generates one row per *local* hour in America/Chicago for the requested year, +assigning the flat supply rate for each hour's month. Output is suitable for +direct comparison against STOU/DTOU hourly tariffs produced by +``build_tariff_hourly_prices.py``. + +DST behaviour (matches build_tariff_hourly_prices.py exactly) +───────────────────────────────────────────────────────────── +• Spring-forward (Mar): the skipped local hour is absent from the output. +• Fall-back (Nov): the ambiguous hour appears twice in the tz-aware range; + the script keeps the *first* UTC occurrence (CDT) and drops the second (CST), + logging a warning. +• ``datetime_chicago`` uniqueness is enforced; the script fails loudly on + any violation. +""" + +from __future__ import annotations + +import argparse +import logging +import sys +from datetime import datetime +from pathlib import Path + +import polars as pl + +log = logging.getLogger(__name__) + +TZ = "America/Chicago" + + +# --------------------------------------------------------------------------- +# Core builder +# --------------------------------------------------------------------------- + + +def build_hourly_prices(rates: pl.DataFrame, year: int) -> pl.DataFrame: + """Build the hourly flat-price DataFrame for *year*.""" + + # --- Generate tz-aware hourly range for the full year ---------------- + dt_range = pl.datetime_range( + datetime(year, 1, 1), + datetime(year + 1, 1, 1), + interval="1h", + time_zone=TZ, + eager=True, + closed="left", + ) + df = pl.DataFrame({"datetime_aware": dt_range}) + + # Naive local column + df = df.with_columns( + pl.col("datetime_aware").dt.replace_time_zone(None).alias("datetime_chicago"), + ) + + # --- DST fall-back deduplication (keep earliest UTC per local hour) --- + # Must exactly mirror the STOU builder's dedup strategy so the two + # price calendars have identical datetime_chicago keys—otherwise the + # billing join would fail on one tariff but not the other. + n_total = df.height + n_unique = df.select(pl.col("datetime_chicago").n_unique()).item() + if n_unique != n_total: + n_dupes = n_total - n_unique + dupes = df.group_by("datetime_chicago").len().filter(pl.col("len") > 1).sort("datetime_chicago") + log.warning( + "DST fall-back: %d duplicate naive timestamp(s) detected. " + "Keeping first UTC occurrence, dropping later:\n%s", + n_dupes, + dupes, + ) + # Sort by the aware column (which sorts by UTC instant) then dedup + df = df.sort("datetime_aware").unique(subset=["datetime_chicago"], keep="first") + log.info("After dedup: %d rows (dropped %d).", df.height, n_dupes) + + # Drop the tz-aware helper column + df = df.drop("datetime_aware") + + # --- Enforce uniqueness ----------------------------------------------- + # Belt-and-suspenders: the dedup above should have handled it, but a + # bug in the Polars range or an unexpected DST rule change could + # reintroduce duplicates—fail loudly rather than let them propagate + # into the billing join. + final_unique = df.select(pl.col("datetime_chicago").n_unique()).item() + if final_unique != df.height: + raise ValueError( + f"datetime_chicago is not unique after dedup: {df.height} rows but {final_unique} distinct values" + ) + + # --- Extract helper columns ------------------------------------------- + df = df.with_columns( + pl.col("datetime_chicago").dt.year().cast(pl.Int32).alias("year"), + pl.col("datetime_chicago").dt.month().cast(pl.Int32).alias("month"), + pl.col("datetime_chicago").dt.hour().cast(pl.Int32).alias("hour"), + # Polars weekday: Mon=1 … Sun=7 → convert to Mon=0 … Sun=6 + (pl.col("datetime_chicago").dt.weekday() - 1).cast(pl.Int32).alias("day_of_week"), + ) + df = df.with_columns((pl.col("day_of_week") >= 5).alias("is_weekend")) + + # --- Join flat rates -------------------------------------------------- + rates_join = rates.select( + pl.col("year").cast(pl.Int32), + pl.col("month").cast(pl.Int32), + pl.col("flat_price_cents").cast(pl.Float64).alias("price_cents_per_kwh"), + ) + df = df.join(rates_join, on=["year", "month"], how="left") + + # --- Fail-loud checks ------------------------------------------------- + null_prices = df.filter(pl.col("price_cents_per_kwh").is_null()).height + if null_prices > 0: + missing = df.filter(pl.col("price_cents_per_kwh").is_null()).select("year", "month").unique() + raise ValueError(f"{null_prices} rows have null price_cents_per_kwh. Missing rates for:\n{missing}") + if df.height == 0: + raise ValueError("Output DataFrame is empty") + + # --- Final select & sort ---------------------------------------------- + df = df.select( + "datetime_chicago", + "year", + "month", + "price_cents_per_kwh", + "hour", + "day_of_week", + "is_weekend", + ).sort("datetime_chicago") + + return df + + +# --------------------------------------------------------------------------- +# CLI +# --------------------------------------------------------------------------- + + +def parse_args(argv: list[str] | None = None) -> argparse.Namespace: + p = argparse.ArgumentParser( + description="Build hourly flat-rate price calendar from monthly CSV.", + ) + p.add_argument("--year", type=int, required=True, help="Calendar year to generate.") + p.add_argument( + "--flat-rates-csv", + type=Path, + required=True, + help="Path to CSV with columns: year, month, flat_price_cents.", + ) + p.add_argument( + "--output", + type=Path, + default=None, + help="Output Parquet path (default: data/reference/comed_flat_hourly_prices_{YEAR}.parquet).", + ) + p.add_argument("--check", action="store_true", help="Build and validate but do not write output.") + p.add_argument("--verbose", "-v", action="store_true", help="Enable debug logging.") + return p.parse_args(argv) + + +def main(argv: list[str] | None = None) -> None: + args = parse_args(argv) + logging.basicConfig( + level=logging.DEBUG if args.verbose else logging.INFO, + format="%(asctime)s %(levelname)-8s %(message)s", + ) + + # --- Load and validate flat rates ------------------------------------- + csv_path: Path = args.flat_rates_csv + if not csv_path.exists(): + sys.exit(f"Flat rates CSV not found: {csv_path}") + + rates = pl.read_csv(csv_path) + log.info("Loaded %d rows from %s", rates.height, csv_path) + + # Ensure every month 1-12 has a rate for the requested year + expected_months = set(range(1, 13)) + year_rates = rates.filter(pl.col("year") == args.year) + present_months = set(year_rates["month"].to_list()) + missing_months = sorted(expected_months - present_months) + if missing_months: + sys.exit(f"Missing flat rates for year={args.year}, months={missing_months}") + + # --- Build hourly calendar -------------------------------------------- + log.info("Building hourly flat prices for year %d …", args.year) + df = build_hourly_prices(year_rates, args.year) + log.info("Built %d rows.", df.height) + + # Summary + summary = df.group_by("month").agg(pl.len().alias("hours"), pl.col("price_cents_per_kwh").first()).sort("month") + log.info("Monthly summary:\n%s", summary) + + if args.check: + log.info("--check mode: skipping write.") + return + + # --- Write output ----------------------------------------------------- + out_path = args.output or Path(f"data/reference/comed_flat_hourly_prices_{args.year}.parquet") + out_path.parent.mkdir(parents=True, exist_ok=True) + df.write_parquet(out_path) + log.info("Wrote %s (%d rows)", out_path, df.height) + + +if __name__ == "__main__": + main() diff --git a/scripts/build_tariff_hourly_prices.py b/scripts/build_tariff_hourly_prices.py new file mode 100644 index 0000000..27476ef --- /dev/null +++ b/scripts/build_tariff_hourly_prices.py @@ -0,0 +1,459 @@ +#!/usr/bin/env python3 +"""Build an hourly tariff price calendar Parquet from a YAML rate structure. + +Generates one row per *local* hour in America/Chicago for the requested year, +mapping each hour to its season and TOU period with the associated price. + +DST behaviour (v1) +────────────────── +The script builds a timezone-aware hourly range in America/Chicago, then strips +the timezone to produce naive local timestamps (``datetime_chicago``). A UTC +column (``datetime_utc``) is also emitted for auditability. + +• Spring-forward (Mar): the 2 AM hour does not exist locally → 8 759 rows. +• Fall-back (Nov): the 1 AM hour occurs twice → two distinct UTC instants map + to the *same* naive local timestamp. + +Because downstream joins key on ``datetime_chicago``-naive, duplicate values +would silently corrupt results. **v1 policy:** when a fall-back duplicate is +detected, the script keeps the *first* UTC occurrence (the CDT instant before +the clock falls back) and drops the second (the CST instant). A warning is +logged listing every dropped row. The ``datetime_utc`` column is included so +that the deduplication is auditable. + +This means the output always has exactly one row per unique ``datetime_chicago`` +value, with either 8 759 (spring-forward year) or 8 760 rows for a full year. +""" + +from __future__ import annotations + +import argparse +import logging +import sys +from datetime import datetime +from pathlib import Path +from typing import Any + +import polars as pl + +try: + import yaml +except ImportError: + sys.exit("PyYAML is required but not installed. Install it with:\n uv add pyyaml") + +log = logging.getLogger(__name__) + +# --------------------------------------------------------------------------- +# YAML helpers +# --------------------------------------------------------------------------- + + +def load_config(path: Path) -> dict[str, Any]: + """Load and minimally validate the YAML rate structure.""" + if not path.exists(): + raise FileNotFoundError(f"Config not found: {path}") + with open(path) as f: + cfg = yaml.safe_load(f) + if not isinstance(cfg, dict): + raise TypeError("YAML root must be a mapping") + for key in ("name", "timezone", "unit", "seasons"): + if key not in cfg: + raise ValueError(f"Missing required key: {key}") + if not cfg["seasons"]: + raise ValueError("At least one season is required") + return cfg + + +def _parse_mmdd(mmdd: str) -> tuple[int, int]: + """Parse 'MM-DD' → (month, day).""" + parts = mmdd.split("-") + if len(parts) != 2: + raise ValueError(f"Invalid MM-DD: {mmdd!r}") + return int(parts[0]), int(parts[1]) + + +# --------------------------------------------------------------------------- +# Validation +# --------------------------------------------------------------------------- + + +def validate_period_coverage(seasons: list[dict[str, Any]]) -> None: + """Ensure every hour 0-23 maps to exactly one period per season.""" + for season in seasons: + name = season["name"] + hour_map: dict[int, str] = {} + for p in season["periods"]: + start_h = p["start_hour"] + end_h = p["end_hour"] + hours = _expand_hour_range(start_h, end_h) + for h in hours: + if h in hour_map: + raise ValueError(f"Season '{name}': hour {h} mapped to both '{hour_map[h]}' and '{p['period']}'") + hour_map[h] = p["period"] + missing = sorted(set(range(24)) - set(hour_map)) + if missing: + raise ValueError(f"Season '{name}': hours {missing} have no period mapping") + + +def _expand_hour_range(start: int, end: int) -> list[int]: + """Expand [start, end) with midnight wrap-around into a list of hours. + + Half-open [start, end) so adjacent periods tile without overlap: + e.g. [6,13) + [13,19) + [19,21) + [21,6) covers all 24 hours exactly once. + """ + if end > start: + return list(range(start, end)) + # wrap: e.g. 21->6 means [21..24) U [0..6) + return list(range(start, 24)) + list(range(0, end)) + + +# --------------------------------------------------------------------------- +# Window comparison +# --------------------------------------------------------------------------- + + +def _extract_window_definitions(cfg: dict[str, Any]) -> dict[str, Any]: + """Extract season+period window definitions from a config, stripping prices. + + Returns a dict keyed by season name; each value contains the season date + range and a sub-dict of period → {start_hour, end_hour}. Price values are + intentionally excluded so comparisons are price-agnostic. + """ + result: dict[str, Any] = {} + for season in cfg.get("seasons", []): + result[season["name"]] = { + "start_mmdd": season["start_mmdd"], + "end_mmdd": season["end_mmdd"], + "periods": { + p["period"]: {"start_hour": p["start_hour"], "end_hour": p["end_hour"]} + for p in season.get("periods", []) + }, + } + return result + + +def _diff_season( + sname: str, + sa: dict[str, Any], + sb: dict[str, Any], + name_a: str, + name_b: str, +) -> list[str]: + """Return diff lines for a single season (date range + period hour boundaries).""" + lines: list[str] = [] + for field in ("start_mmdd", "end_mmdd"): + if sa[field] != sb[field]: + lines.append(f" season '{sname}' {field}: {name_a}={sa[field]!r} vs {name_b}={sb[field]!r}") + periods_a: dict[str, Any] = sa["periods"] + periods_b: dict[str, Any] = sb["periods"] + for pname in sorted(set(periods_a) | set(periods_b)): + if pname not in periods_a: + lines.append(f" season '{sname}' period '{pname}': only in {name_b}") + continue + if pname not in periods_b: + lines.append(f" season '{sname}' period '{pname}': only in {name_a}") + continue + pa, pb = periods_a[pname], periods_b[pname] + for field in ("start_hour", "end_hour"): + if pa[field] != pb[field]: + lines.append( + f" season '{sname}' period '{pname}' {field}: {name_a}={pa[field]} vs {name_b}={pb[field]}" + ) + return lines + + +def compare_window_definitions( + cfg_a: dict[str, Any], + cfg_b: dict[str, Any], + name_a: str = "config_a", + name_b: str = "config_b", +) -> tuple[bool, str]: + """Compare TOU window definitions (season dates + period hour ranges) between two configs. + + Prices are ignored — only season boundaries and period start/end hours matter. + + Returns ``(True, "")`` when windows are identical, else ``(False, human_readable_diff)`` + listing every differing field. + """ + windows_a = _extract_window_definitions(cfg_a) + windows_b = _extract_window_definitions(cfg_b) + if windows_a == windows_b: + return True, "" + + lines: list[str] = [f"TOU window mismatch between '{name_a}' and '{name_b}':"] + for sname in sorted(set(windows_a) | set(windows_b)): + if sname not in windows_a: + lines.append(f" season '{sname}': only in {name_b}") + elif sname not in windows_b: + lines.append(f" season '{sname}': only in {name_a}") + else: + lines.extend(_diff_season(sname, windows_a[sname], windows_b[sname], name_a, name_b)) + return False, "\n".join(lines) + + +# --------------------------------------------------------------------------- +# Season / period resolution +# --------------------------------------------------------------------------- + + +def _mmdd_to_dayofyear(month: int, day: int, year: int) -> int: + return datetime(year, month, day).timetuple().tm_yday + + +def _in_season(month: int, day: int, start_mmdd: str, end_mmdd: str, year: int) -> bool: + """Check if month/day falls within [start_mmdd, end_mmdd] inclusive, with wrap.""" + sm, sd = _parse_mmdd(start_mmdd) + em, ed = _parse_mmdd(end_mmdd) + s_doy = _mmdd_to_dayofyear(sm, sd, year) + e_doy = _mmdd_to_dayofyear(em, ed, year) + cur = _mmdd_to_dayofyear(month, day, year) + if s_doy <= e_doy: + return s_doy <= cur <= e_doy + # wrap-around (e.g. Oct-May) + return cur >= s_doy or cur <= e_doy + + +def resolve_season(month: int, day: int, year: int, seasons: list[dict[str, Any]]) -> dict[str, Any]: + """Return the season dict for a given date.""" + for s in seasons: + if _in_season(month, day, s["start_mmdd"], s["end_mmdd"], year): + return s + raise ValueError(f"No season covers {month:02d}-{day:02d}") + + +def resolve_period(hour: int, season: dict[str, Any]) -> tuple[str, float]: + """Return (period_name, price) for a given hour within a season.""" + for p in season["periods"]: + hours = _expand_hour_range(p["start_hour"], p["end_hour"]) + if hour in hours: + return p["period"], p["price"] + raise ValueError(f"Season '{season['name']}': no period covers hour {hour}") + + +# --------------------------------------------------------------------------- +# Core builder +# --------------------------------------------------------------------------- + + +def build_hourly_prices(cfg: dict[str, Any], year: int) -> pl.DataFrame: + """Build the hourly price DataFrame for *year*.""" + tz = cfg["timezone"] + seasons = cfg["seasons"] + + # Timezone-aware hourly range: start of year → start of next year + start = datetime(year, 1, 1) + end = datetime(year + 1, 1, 1) + dt_range = pl.datetime_range( + start, + end, + interval="1h", + time_zone=tz, + eager=True, + closed="left", + ) + + # Build a minimal frame with the aware column + df = pl.DataFrame({"datetime_aware": dt_range}) + + # UTC column (for auditability) + df = df.with_columns( + pl.col("datetime_aware").dt.convert_time_zone("UTC").dt.replace_time_zone(None).alias("datetime_utc"), + ) + + # Naive local column + df = df.with_columns( + pl.col("datetime_aware").dt.replace_time_zone(None).alias("datetime_chicago"), + ) + + # DST fall-back deduplication: keep the *first* UTC instant (the CDT + # occurrence before the clock falls back). This is arbitrary but + # consistent with the flat-rate builder, and downstream joins key on + # naive datetime_chicago which must be unique. + n_total = df.height + n_unique = df.select(pl.col("datetime_chicago").n_unique()).item() + if n_unique != n_total: + n_dupes = n_total - n_unique + dupes = df.group_by("datetime_chicago").len().filter(pl.col("len") > 1).sort("datetime_chicago") + log.warning( + "DST fall-back: %d duplicate naive timestamp(s) detected. " + "Keeping first UTC occurrence, dropping later:\n%s", + n_dupes, + dupes, + ) + # Keep first UTC occurrence (sort by datetime_utc, then unique on datetime_chicago) + df = df.sort("datetime_utc").unique(subset=["datetime_chicago"], keep="first") + log.info("After dedup: %d rows (dropped %d).", df.height, n_dupes) + + # Extract helper columns + df = df.with_columns( + pl.col("datetime_chicago").dt.year().cast(pl.Int32).alias("year"), + pl.col("datetime_chicago").dt.month().cast(pl.Int32).alias("month"), + pl.col("datetime_chicago").dt.day().cast(pl.Int32).alias("day"), + pl.col("datetime_chicago").dt.hour().cast(pl.Int32).alias("hour"), + pl.col("datetime_chicago").dt.weekday().cast(pl.Int32).alias("day_of_week"), # Mon=1 in Polars + ) + + # Polars weekday: Monday=1 … Sunday=7 → convert to Monday=0 … Sunday=6 + df = df.with_columns( + (pl.col("day_of_week") - 1).alias("day_of_week"), + ) + + df = df.with_columns( + (pl.col("day_of_week") >= 5).alias("is_weekend"), + ) + + # Map each row to season / period / price via Python UDF. A pure-Polars + # expression would need nested when/then chains for season date ranges + # with wrap-around + hour ranges per period—fragile and hard to audit. + # The Python loop is ~1s for 8760 rows and trivially matches the YAML. + season_names: list[str] = [] + period_names: list[str] = [] + prices: list[float] = [] + + months = df["month"].to_list() + days = df["day"].to_list() + hours = df["hour"].to_list() + + for m, d, h in zip(months, days, hours): + s = resolve_season(m, d, year, seasons) + pname, price = resolve_period(h, s) + season_names.append(s["name"]) + period_names.append(pname) + prices.append(price) + + df = df.with_columns( + pl.Series("season", season_names, dtype=pl.Utf8), + pl.Series("period", period_names, dtype=pl.Utf8), + pl.Series("price_cents_per_kwh", prices, dtype=pl.Float64), + ) + + # Final column selection & sort + df = df.select( + "datetime_chicago", + "datetime_utc", + "year", + "month", + "hour", + "day_of_week", + "is_weekend", + "season", + "period", + "price_cents_per_kwh", + ).sort("datetime_chicago") + + # Null check + null_counts = df.null_count() + total_nulls = sum(null_counts.row(0)) + if total_nulls > 0: + raise ValueError(f"Output contains nulls:\n{null_counts}") + if df.height == 0: + raise ValueError("Output DataFrame is empty") + + return df + + +# --------------------------------------------------------------------------- +# CLI +# --------------------------------------------------------------------------- + + +def parse_args(argv: list[str] | None = None) -> argparse.Namespace: + p = argparse.ArgumentParser( + description="Build hourly tariff price calendar from YAML rate structure.", + ) + p.add_argument( + "--config", + type=Path, + required=True, + help="Path to YAML rate structure file.", + ) + p.add_argument( + "--year", + type=int, + required=True, + help="Calendar year to generate.", + ) + p.add_argument( + "--output", + type=Path, + default=None, + help="Output Parquet path (default: data/reference/comed_stou_hourly_prices_{YEAR}.parquet).", + ) + p.add_argument( + "--check", + action="store_true", + help="Validate config and build DataFrame, but do not write output.", + ) + p.add_argument( + "--verbose", + "-v", + action="store_true", + help="Enable debug logging.", + ) + p.add_argument( + "--validate-windows-against", + type=Path, + default=None, + metavar="OTHER_YAML", + help=( + "After loading --config, compare its TOU window definitions (season dates " + "and period hour boundaries, not prices) against OTHER_YAML. " + "Exit non-zero if any boundary differs." + ), + ) + return p.parse_args(argv) + + +def main(argv: list[str] | None = None) -> None: + args = parse_args(argv) + logging.basicConfig( + level=logging.DEBUG if args.verbose else logging.INFO, + format="%(asctime)s %(levelname)-8s %(message)s", + ) + + log.info("Loading config: %s", args.config) + cfg = load_config(args.config) + log.info("Rate structure: %s tz=%s", cfg["name"], cfg["timezone"]) + + log.info("Validating period coverage …") + validate_period_coverage(cfg["seasons"]) + log.info("Period coverage OK — all hours 0-23 mapped in every season.") + + if args.validate_windows_against: + log.info("Comparing window definitions against: %s", args.validate_windows_against) + other_cfg = load_config(args.validate_windows_against) + ok, diff_msg = compare_window_definitions( + cfg, + other_cfg, + name_a=str(args.config), + name_b=str(args.validate_windows_against), + ) + if not ok: + log.error("%s", diff_msg) + sys.exit(1) + log.info("Window definitions match: %s <-> %s", args.config, args.validate_windows_against) + + log.info("Building hourly prices for year %d …", args.year) + df = build_hourly_prices(cfg, args.year) + log.info("Built %d rows.", df.height) + + # Summary stats + summary = ( + df.group_by("season", "period") + .agg(pl.len().alias("hours"), pl.col("price_cents_per_kwh").first()) + .sort("season", "period") + ) + log.info("Summary:\n%s", summary) + + if args.check: + log.info("--check mode: skipping write.") + return + + out_path = args.output or Path(f"data/reference/comed_stou_hourly_prices_{args.year}.parquet") + out_path.parent.mkdir(parents=True, exist_ok=True) + df.write_parquet(out_path) + log.info("Wrote %s (%d rows)", out_path, df.height) + + +if __name__ == "__main__": + main() diff --git a/scripts/csv_to_parquet/PREFLIGHT_200.md b/scripts/csv_to_parquet/PREFLIGHT_200.md new file mode 100644 index 0000000..16ddf4e --- /dev/null +++ b/scripts/csv_to_parquet/PREFLIGHT_200.md @@ -0,0 +1,181 @@ +# Preflight Validation Checklist: 200-File Run (202307) + +Target: Validate 200-file shard run before scaling to full ~30k month. + +## Prerequisites + +- 25-file batch (shard 100) completed successfully +- Output at: `/ebs/home/griffin_switch_box/runs/out_test_output_ec2/` +- Run artifacts at: `/ebs/home/griffin_switch_box/runs/out_test_output_ec2/_runs/202307//` + +--- + +## Step 1: Run the 200-file migration + +```bash +# Prepare input list (200 files from the sorted CSV inventory) +head -200 /path/to/all_csvs_202307_sorted.txt > /tmp/shard_200.txt +wc -l /tmp/shard_200.txt # confirm 200 + +# Run migration (adjust paths as needed) +python scripts/csv_to_parquet/migrate_month_runner.py \ + --input-list /tmp/shard_200.txt \ + --out-root /ebs/home/griffin_switch_box/runs/out_200_preflight \ + --year-month 202307 \ + --shard-id 200 \ + --batch-size 50 \ + --workers 4 \ + --exec-mode lazy_sink \ + --fail-fast +``` + +Expect: 4 batch files (200 / 50 = 4 batches). + +--- + +## Step 2: Quick sanity (before full validation) + +```bash +# Confirm output structure +find /ebs/home/griffin_switch_box/runs/out_200_preflight/year=2023/month=07/ \ + -name '*.parquet' | sort + +# Expected: 4 files named shard_200_batch_0000.parquet through shard_200_batch_0003.parquet + +# Confirm run completed cleanly +cat /ebs/home/griffin_switch_box/runs/out_200_preflight/_runs/202307/*/run_summary.json \ + | python -m json.tool | grep -E '"total_(success|failure|skip)|batches_written|stop_requested"' + +# Expected: total_failure=0, total_success=200, batches_written=4, stop_requested=false +``` + +--- + +## Step 3: Identify the run-dir + +```bash +# List run directories to find the run_id +ls /ebs/home/griffin_switch_box/runs/out_200_preflight/_runs/202307/ + +# Set variable for convenience (replace with actual) +RUN_DIR="/ebs/home/griffin_switch_box/runs/out_200_preflight/_runs/202307/" +OUT_ROOT="/ebs/home/griffin_switch_box/runs/out_200_preflight" +``` + +--- + +## Step 4: Full validation (all checks) + +```bash +python scripts/csv_to_parquet/validate_month_output.py \ + --out-root "$OUT_ROOT" \ + --check-mode full \ + --dst-month-check \ + --run-dir "$RUN_DIR" \ + --output-report "$RUN_DIR/validation_report_200.json" +``` + +This single command validates all of the following: + +| Check | What it verifies | +|---|---| +| Schema contract | All 10 columns present, exact dtypes | +| Partition integrity | year=2023, month=7 in every file | +| No duplicates | No duplicate (zip_code, account_identifier, datetime) within any batch | +| Datetime invariants | No nulls, min=00:00, max=23:30, no spillover | +| DST Option B | Exactly 48 slots/day, no timestamps beyond 23:30 | +| Sortedness (full) | Lexicographic order by (zip_code, account_identifier, datetime) | +| Run artifact integrity | plan.json valid, run_summary.json clean, manifests 0 failures | +| Row counts | Per-file and total row counts reported | + +Expected output on success: +``` +OK: validated 4 parquet files across 1 partitions (discovered total parquet files=4, total rows validated=NNNNNN). +Validation report written to: .../validation_report_200.json +``` + +--- + +## Step 5: Review the validation report + +```bash +python -m json.tool "$RUN_DIR/validation_report_200.json" +``` + +Checklist for the report JSON: + +- [ ] `"status": "pass"` +- [ ] `"files_validated": 4` +- [ ] `"total_rows_validated"` is reasonable (expect ~200 files * ~N accounts * 48 slots * 31 days) +- [ ] `"checks_passed"` contains all 7 checks: + - `schema_contract` + - `partition_integrity` + - `no_duplicates` + - `datetime_invariants` + - `sortedness_full` + - `dst_option_b` + - `run_artifact_integrity` +- [ ] `"per_file_rows"` shows all 4 batch files with non-zero row counts +- [ ] `"run_artifacts"."summary_total_failure"` is 0 +- [ ] `"run_artifacts"."manifest_success_count"` is 200 + +--- + +## Step 6: Spot-check a parquet file interactively + +```python +import polars as pl + +f = "/ebs/home/griffin_switch_box/runs/out_200_preflight/year=2023/month=07/shard_200_batch_0000.parquet" +df = pl.read_parquet(f) + +print("Shape:", df.shape) +print("Schema:", df.schema) +print("Head:\n", df.head(5)) +print("Tail:\n", df.tail(5)) + +# Verify sort order visually +print("Sorted check:", df.select([ + pl.col("zip_code"), + pl.col("account_identifier"), + pl.col("datetime"), +]).head(20)) + +# Unique accounts +print("Unique accounts:", df["account_identifier"].n_unique()) +print("Date range:", df["datetime"].min(), "to", df["datetime"].max()) +``` + +--- + +## Step 7: Cross-check with 25-file run (optional determinism) + +If the 200-file input list's first 25 files overlap with the original 25-file shard: + +```bash +python scripts/csv_to_parquet/validate_month_output.py \ + --out-root "$OUT_ROOT" \ + --compare-root /ebs/home/griffin_switch_box/runs/out_test_output_ec2 \ + --check-mode sample +``` + +Note: This will only work if both roots share identical partition structure. +If shard IDs differ, compare individual batch files manually instead. + +--- + +## Go/No-Go Decision + +| Criterion | Required | +|---|---| +| Step 4 prints `OK` | YES | +| Validation report `status: pass` | YES | +| All 7 checks in `checks_passed` | YES | +| `total_rows_validated > 0` | YES | +| `run_artifacts.summary_total_failure == 0` | YES | +| `run_artifacts.manifest_success_count == 200` | YES | +| No unexpected files in output directory | YES | +| Spot-check schema + sort order looks correct | YES | + +If all criteria pass: proceed to full-month sharded run. +If any fail: investigate, fix, re-run the 200-file batch. diff --git a/scripts/csv_to_parquet/compact_month_output.py b/scripts/csv_to_parquet/compact_month_output.py new file mode 100644 index 0000000..8231a8f --- /dev/null +++ b/scripts/csv_to_parquet/compact_month_output.py @@ -0,0 +1,1167 @@ +#!/usr/bin/env python3 +"""Month-level Parquet compaction for the ComEd CSV→Parquet pipeline. + +Compacts all ``batch_*.parquet`` files produced by ``migrate_month_runner.py`` +into deterministic ``compacted_NNNN.parquet`` files targeting ~1 GiB each. +Invoked by the runner after all batches for a month complete with zero failures. + +Design decisions +---------------- +1. **Memory-safe file-by-file streaming** — reads one batch Parquet file at a + time, accumulates rows until a per-row-count budget is reached, then flushes. + Maximum in-memory footprint is approximately two batch files simultaneously + (current file + carry-over slice from the previous boundary). No global + collect of the entire month's data is performed. + +2. **No re-sort** — relies on the invariant (enforced by the runner) that batch + files are already globally sorted by ``(zip_code, account_identifier, + datetime)`` and that lexicographic filename order (batch_0000 < batch_0001 + < …) preserves global sort across file boundaries. The adjacent-key + validation pass verifies this contract on the *output* files. + +3. **Atomic directory swap** — staging output is written under + ``/compaction_staging/year=YYYY/month=MM/``. After validation the + original month directory is renamed to ``month=MM_precompact_`` and + the staging directory is renamed to the canonical month directory. Both + renames use ``os.replace()`` (single rename(2) syscall on Linux), which is + atomic within the same filesystem. A failed phase-2 rename triggers an + automatic rollback of phase 1. + +4. **Fail-loud** — any validation failure raises ``RuntimeError`` before the + atomic swap, leaving the original batch files completely untouched. + +5. **Audit trail** — five JSON artifacts are written under + ``/compaction/`` regardless of outcome (where possible). + +6. **Self-contained** — this module does NOT import from + ``migrate_month_runner`` to avoid circular imports (the runner imports this + module at its top level). Shared constants (``FINAL_LONG_COLS``, + ``SORT_KEYS``) are re-declared here with a cross-reference comment. +""" + +from __future__ import annotations + +import contextlib +import datetime as dt +import hashlib +import json +import os +import shutil +import subprocess +import time +import traceback +from dataclasses import dataclass +from pathlib import Path +from typing import Any + +import polars as pl +import pyarrow.parquet as pq + +# --------------------------------------------------------------------------- +# Canonical schema contract +# These constants MUST stay in sync with migrate_month_runner.py and +# validate_month_output.py. They are re-declared here (not imported) to +# prevent a circular import: the runner imports this module at top level, so +# this module cannot import the runner. +# --------------------------------------------------------------------------- + +FINAL_LONG_COLS: tuple[str, ...] = ( + "zip_code", + "delivery_service_class", + "delivery_service_name", + "account_identifier", + "datetime", + "energy_kwh", + "plc_value", + "nspl_value", + "year", + "month", +) + +SORT_KEYS: tuple[str, str, str] = ("zip_code", "account_identifier", "datetime") + +DEFAULT_COMPACT_TARGET_SIZE_BYTES: int = 1_073_741_824 # 1 GiB + +JsonDict = dict[str, Any] + +STREAMING_VALIDATOR_VERSION: str = "2.0.0-streaming-pyarrow" +DEFAULT_VALIDATION_BATCH_SIZE: int = 1_000_000 # rows per PyArrow iter_batches call +ROWS_PER_ROW_GROUP: int = 50_000_000 # rows per row group; bounds peak RSS to ~2 GiB per group + + +# --------------------------------------------------------------------------- +# Data model +# --------------------------------------------------------------------------- + + +@dataclass(frozen=True) +class CompactionConfig: + """Immutable configuration for one compaction run. + + Constructed by ``migrate_month_runner.main()`` from parsed CLI args and the + existing ``RunnerConfig``, then passed to ``run_compaction()``. + """ + + year_month: str # YYYYMM — must match the runner's target month + run_id: str # same run_id as the enclosing migration run + out_root: Path # dataset root (Hive partitions live here) + run_dir: Path # _runs/YYYYMM// — audit artifacts go here + target_size_bytes: int # target on-disk size per output Parquet file + max_files: int | None # optional cap on number of output compacted files + overwrite: bool # allow overwriting existing compacted_*.parquet + dry_run: bool # plan only: write plan + original inventory + summary; skip write/validate/swap + no_swap: bool # run compaction + validation + write artifacts; skip atomic swap + validation_batch_size: int = DEFAULT_VALIDATION_BATCH_SIZE # rows per batch for streaming validation + + +# --------------------------------------------------------------------------- +# Utilities (self-contained; no runner imports) +# --------------------------------------------------------------------------- + + +def _now_utc_iso() -> str: + return dt.datetime.now(dt.timezone.utc).isoformat(timespec="seconds") + + +def _elapsed_ms(t0: float, t1: float) -> int: + return round((t1 - t0) * 1000.0) + + +def _write_json(path: Path, obj: Any) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + path.write_text(json.dumps(obj, indent=2, sort_keys=True), encoding="utf-8") + + +def _try_git_sha() -> str | None: + """Return the current git HEAD SHA, or None if git is unavailable.""" + try: + cp = subprocess.run( + ["git", "rev-parse", "HEAD"], + check=False, + capture_output=True, + text=True, + ) + return cp.stdout.strip() if cp.returncode == 0 else None + except Exception: + return None + + +def _year_month_dirs(year_month: str) -> tuple[str, str]: + y = int(year_month[:4]) + m = int(year_month[4:6]) + return f"{y:04d}", f"{m:02d}" + + +def _file_list_hash(paths: list[Path]) -> str: + """Stable SHA-256 fingerprint of a sorted list of file paths.""" + content = "\n".join(str(p) for p in paths) + return hashlib.sha256(content.encode("utf-8")).hexdigest()[:16] + + +# --------------------------------------------------------------------------- +# Parquet metadata helpers — row counts from file footer; no data loaded +# --------------------------------------------------------------------------- + + +def _parquet_row_count(path: Path) -> int: + """Read row count from Parquet file footer metadata without loading data.""" + return pq.read_metadata(str(path)).num_rows + + +def _parquet_schema_names(path: Path) -> list[str]: + """Read column names from Parquet file schema without loading data.""" + arrow_schema = pq.read_schema(str(path)) + return list(arrow_schema.names) + + +def _file_inventory_entry(path: Path) -> JsonDict: + """Build a metadata inventory entry for a single Parquet file.""" + meta = pq.read_metadata(str(path)) + return { + "path": str(path), + "size_bytes": int(path.stat().st_size), + "num_rows": int(meta.num_rows), + "num_row_groups": int(meta.num_row_groups), + } + + +def _write_success_marker( + month_dir: Path, + output_names: list[str], + year_month: str, + run_id: str, + pre_rows: int, + post_rows: int, + total_output_bytes: int, + git_sha: str | None, + sort_keys: tuple[str, ...] = SORT_KEYS, + schema: tuple[str, ...] = FINAL_LONG_COLS, +) -> None: + """Write a _SUCCESS.json marker with compaction metadata. + + Follows the Spark convention of a per-partition success marker. Contains + metadata for downstream validation without needing to read Parquet footers. + """ + files_manifest = [] + for name in output_names: + path = month_dir / name + meta = pq.read_metadata(str(path)) + files_manifest.append({ + "name": name, + "size_bytes": int(path.stat().st_size), + "num_rows": int(meta.num_rows), + "num_row_groups": int(meta.num_row_groups), + }) + + marker: JsonDict = { + "timestamp": _now_utc_iso(), + "git_sha": git_sha, + "year_month": year_month, + "compaction_run_id": run_id, + "n_files": len(output_names), + "total_rows": post_rows, + "total_bytes": total_output_bytes, + "sort_keys": list(sort_keys), + "schema": list(schema), + "files": files_manifest, + } + _write_json(month_dir / "_SUCCESS.json", marker) + + +# --------------------------------------------------------------------------- +# Core: memory-safe streaming write (multi-row-group ParquetWriter) +# --------------------------------------------------------------------------- + + +def _stream_write_chunks( + sorted_input_files: list[Path], + staging_month_dir: Path, + rows_per_row_group: int, + target_size_bytes: int, + max_files: int | None, + logger: Any, + log_ctx: JsonDict, +) -> list[Path]: + """Stream through sorted batch files and write compacted output files. + + Memory-safety guarantee + ----------------------- + Reads one batch Parquet file at a time. Maintains a ``carry`` DataFrame + of rows that didn't fill the previous row group. Maximum RSS footprint is + approximately two row groups in memory simultaneously (~2 x 1.5 GiB). + + No global sort is performed here. The function relies on the runner's + invariant that input batch files are already globally sorted by SORT_KEYS + and that lexicographic filename order preserves global sort order. The + post-write adjacent-key validation pass verifies this contract. + + Row-group and file-rollover semantics + ------------------------------------- + Rows are flushed to the current ``pq.ParquetWriter`` as row groups of up + to ``rows_per_row_group`` rows. After each flush, if the on-disk file + size reaches ``target_size_bytes`` the writer is closed and a new file is + opened for the next row group. The final partial row group is always + flushed regardless of size. + + Parameters + ---------- + sorted_input_files: + Input batch Parquet files in lexicographic order (deterministic). + staging_month_dir: + Directory to write ``compacted_NNNN.parquet`` files. + rows_per_row_group: + Maximum rows per row group; bounds peak RSS to ~2 GiB per group. + target_size_bytes: + Close the current file and open a new one when on-disk size reaches + this threshold after a row-group flush. + max_files: + Optional hard cap on the number of output files written. + logger: + JsonlLogger-compatible object (``logger.log(dict)``). + log_ctx: + Context fields included in every log event. + + Returns + ------- + list[Path] + Paths of written output Parquet files in write order. + """ + output_files: list[Path] = [] + file_idx = 0 + carry: pl.DataFrame | None = None + writer: pq.ParquetWriter | None = None + current_file_path: Path | None = None + arrow_schema: Any = None # pa.Schema, derived from first flush + + def flush_row_group(rg_df: pl.DataFrame) -> None: + nonlocal writer, file_idx, current_file_path, arrow_schema + + t0 = time.time() + arrow_table = rg_df.to_arrow() + if arrow_schema is None: + arrow_schema = arrow_table.schema + + if writer is None: + current_file_path = staging_month_dir / f"part-{file_idx:05d}.parquet" + writer = pq.ParquetWriter( + str(current_file_path), + arrow_schema, + compression="snappy", + write_statistics=True, + ) + output_files.append(current_file_path) + + writer.write_table(arrow_table) + if current_file_path is None: + raise RuntimeError("current_file_path is None after writer was opened") + size = int(current_file_path.stat().st_size) + + logger.log({ + **log_ctx, + "event": "compaction_write_row_group", + "status": "info", + "file_idx": file_idx, + "file_path": str(current_file_path), + "num_rows": rg_df.height, + "file_size_bytes": size, + "elapsed_ms": _elapsed_ms(t0, time.time()), + }) + + if size >= target_size_bytes: + writer.close() + writer = None + file_idx += 1 + current_file_path = None + + staging_month_dir.mkdir(parents=True, exist_ok=True) + logger.log({ + **log_ctx, + "event": "compaction_scan_start", + "status": "info", + "n_input_files": len(sorted_input_files), + "rows_per_row_group": rows_per_row_group, + "target_size_bytes": target_size_bytes, + }) + + try: + for input_path in sorted_input_files: + if max_files is not None and file_idx >= max_files: + break + + df = pl.read_parquet(str(input_path)) + + if carry is not None and carry.height > 0: + df = pl.concat([carry, df], how="vertical", rechunk=False) + carry = None + + while df.height >= rows_per_row_group: + if max_files is not None and file_idx >= max_files: + break + flush_row_group(df.slice(0, rows_per_row_group)) + df = df.slice(rows_per_row_group) + + carry = df if df.height > 0 else None + + if carry is not None and carry.height > 0 and (max_files is None or file_idx < max_files): + flush_row_group(carry) + + if writer is not None: + writer.close() + writer = None + except Exception: + if writer is not None: + with contextlib.suppress(Exception): + writer.close() + raise + + return output_files + + +# --------------------------------------------------------------------------- +# Validation: pre-flight check for existing compacted files +# --------------------------------------------------------------------------- + + +def _pre_validate_no_existing_compacted( + canonical_dir: Path, + overwrite: bool, +) -> None: + """Fail-loud if part-*.parquet already exist in the canonical dir. + + Called early in run_compaction() before any writes to prevent accidental + overwrites. Skipped when ``overwrite=True``. + """ + if overwrite: + return + existing = sorted(canonical_dir.glob("part-*.parquet")) + if existing: + names = [p.name for p in existing[:5]] + suffix = f" (and {len(existing) - 5} more)" if len(existing) > 5 else "" + raise RuntimeError( + f"Canonical directory already contains {len(existing)} part-*.parquet " + f"file(s): {names}{suffix}. Use --overwrite-compact to allow replacement, " + f"or remove them manually before re-running compaction." + ) + + +# --------------------------------------------------------------------------- +# Validation: adjacent-key sort order and uniqueness — streaming (no group_by) +# --------------------------------------------------------------------------- + + +def _validate_adjacent_keys_streaming( + sorted_files: list[Path], + label: str, + batch_size: int = DEFAULT_VALIDATION_BATCH_SIZE, + logger: Any | None = None, + log_ctx: JsonDict | None = None, +) -> JsonDict: + """Validate global sort order and key uniqueness via true streaming batches. + + Algorithm + --------- + Uses ``pyarrow.ParquetFile.iter_batches()`` to read only the three key + columns in fixed-size batches. Memory is bounded to one batch (~1M rows + x 3 columns ~30-50 MB) plus a single carry-forward key tuple. + + For each batch: + + 1. **Cross-boundary check** — first key of current batch must be strictly + greater than ``prev_key`` (last key of previous batch/file). + 2. **Within-batch check** — vectorized Polars ``shift(1)`` on the three + key columns, bounded to ``batch_size`` rows. No ``group_by``, no + Python-level row iteration. + + The compound sort key is ``(zip_code, account_identifier, datetime)`` + with lexicographic ordering. + + Parameters + ---------- + sorted_files : + Files to validate, in logical sort order (filename order). + label : + Human-readable name for the dataset being validated. + batch_size : + Rows per PyArrow batch. Default 1,000,000. + logger : + Optional JsonlLogger for structured event logging. + log_ctx : + Optional base dict merged into every log event. + + Returns + ------- + dict with keys: ``passed``, ``error``, ``error_location``, ``n_files``, + ``total_rows``, ``key_min``, ``key_max``, ``validator_version``, + ``validator_method``, ``batch_size``. + """ + KEY_COLS: list[str] = list(SORT_KEYS) + _log_ctx: JsonDict = log_ctx or {} + + prev_key: tuple[Any, ...] | None = None + total_rows: int = 0 + n_files: int = 0 + key_min: tuple[Any, ...] | None = None + key_max: tuple[Any, ...] | None = None + error: str | None = None + error_location: JsonDict | None = None + + for file_idx, path in enumerate(sorted_files): + pf = pq.ParquetFile(str(path)) + n_files += 1 + file_rows: int = 0 + batch_idx: int = 0 + + if logger is not None: + logger.log({ + **_log_ctx, + "event": "validation_file_start", + "status": "info", + "file": path.name, + "file_idx": file_idx, + "n_row_groups": pf.metadata.num_row_groups, + }) + + for batch in pf.iter_batches(batch_size=batch_size, columns=KEY_COLS): + n = batch.num_rows + if n == 0: + batch_idx += 1 + continue + + # Convert PyArrow RecordBatch → Polars DataFrame (zero-copy where possible). + df = pl.from_arrow(batch) + + # First/last key as Python tuples — constant cost per batch. + first_row = df.row(0) + last_row = df.row(df.height - 1) + batch_first_key: tuple[Any, ...] = (first_row[0], first_row[1], first_row[2]) + batch_last_key: tuple[Any, ...] = (last_row[0], last_row[1], last_row[2]) + + if key_min is None: + key_min = batch_first_key + key_max = batch_last_key + + # ── Cross-boundary check ────────────────────────────────────── + if prev_key is not None: + if batch_first_key < prev_key: + error = ( + f"{label}: sort violation at boundary: " + f"file={path.name} batch={batch_idx} " + f"first_key={batch_first_key!r} < prev_key={prev_key!r}" + ) + error_location = { + "file": path.name, + "file_idx": file_idx, + "batch_idx": batch_idx, + "row_offset_in_file": file_rows, + "row_offset_global": total_rows + file_rows, + } + break + if batch_first_key == prev_key: + error = ( + f"{label}: duplicate key at boundary: " + f"file={path.name} batch={batch_idx} " + f"key={batch_first_key!r}" + ) + error_location = { + "file": path.name, + "file_idx": file_idx, + "batch_idx": batch_idx, + "row_offset_in_file": file_rows, + "row_offset_global": total_rows + file_rows, + } + break + + # ── Within-batch adjacent-pair check (vectorized, bounded) ──── + if n > 1: + zip_prev = pl.col("zip_code").shift(1) + acct_prev = pl.col("account_identifier").shift(1) + dt_prev = pl.col("datetime").shift(1) + + zip_eq = zip_prev == pl.col("zip_code") + acct_eq = acct_prev == pl.col("account_identifier") + dt_eq = dt_prev == pl.col("datetime") + + zip_gt = zip_prev > pl.col("zip_code") + acct_gt = acct_prev > pl.col("account_identifier") + dt_gt = dt_prev > pl.col("datetime") + + sort_violation = zip_gt | (zip_eq & acct_gt) | (zip_eq & acct_eq & dt_gt) + dup_violation = zip_eq & acct_eq & dt_eq + + check = ( + df.with_row_index("_row_idx") + .with_columns([ + sort_violation.alias("_sort_viol"), + dup_violation.alias("_dup_viol"), + ]) + .slice(1) # row 0 has null shift values + ) + + sort_bad = check.filter(pl.col("_sort_viol")).head(1) + if sort_bad.height > 0: + bad = sort_bad.select([*KEY_COLS, "_row_idx"]).to_dicts()[0] + row_in_file = file_rows + int(bad["_row_idx"]) + error = ( + f"{label}: sort violation in file={path.name} " + f"batch={batch_idx} row_in_file={row_in_file} " + f"bad_row={bad}" + ) + error_location = { + "file": path.name, + "file_idx": file_idx, + "batch_idx": batch_idx, + "row_in_batch": int(bad["_row_idx"]), + "row_offset_in_file": row_in_file, + "row_offset_global": total_rows + row_in_file, + "bad_key": {k: str(bad[k]) for k in KEY_COLS}, + } + break + + dup_bad = check.filter(pl.col("_dup_viol")).head(1) + if dup_bad.height > 0: + bad = dup_bad.select([*KEY_COLS, "_row_idx"]).to_dicts()[0] + row_in_file = file_rows + int(bad["_row_idx"]) + error = ( + f"{label}: duplicate key in file={path.name} " + f"batch={batch_idx} row_in_file={row_in_file} " + f"bad_row={bad}" + ) + error_location = { + "file": path.name, + "file_idx": file_idx, + "batch_idx": batch_idx, + "row_in_batch": int(bad["_row_idx"]), + "row_offset_in_file": row_in_file, + "row_offset_global": total_rows + row_in_file, + "bad_key": {k: str(bad[k]) for k in KEY_COLS}, + } + break + + prev_key = batch_last_key + file_rows += n + batch_idx += 1 + + # Count rows scanned even on partial files. + total_rows += file_rows + + if error is not None: + break + + if logger is not None: + logger.log({ + **_log_ctx, + "event": "validation_file_end", + "status": "info", + "file": path.name, + "file_idx": file_idx, + "file_rows": file_rows, + "running_total_rows": total_rows, + }) + + return { + "passed": error is None, + "error": error, + "error_location": error_location, + "n_files": n_files, + "total_rows": total_rows, + "key_min": str(key_min) if key_min is not None else None, + "key_max": str(key_max) if key_max is not None else None, + "validator_version": STREAMING_VALIDATOR_VERSION, + "validator_method": "adjacent_key_streaming_pyarrow_iter_batches", + "batch_size": batch_size, + } + + +# --------------------------------------------------------------------------- +# Validation: schema conformance +# --------------------------------------------------------------------------- + + +def _validate_schema(files: list[Path], label: str) -> JsonDict: + """Verify every file has exactly FINAL_LONG_COLS in canonical column order. + + Uses Parquet file footer metadata — no row data is loaded. + """ + errors: list[str] = [] + for path in files: + actual = _parquet_schema_names(path) + # pyarrow may surface internal ``__null_dask_index__`` or similar names; + # filter them out before comparing. + actual_clean = [c for c in actual if not c.startswith("__")] + if tuple(actual_clean) != FINAL_LONG_COLS: + errors.append(f"{path.name}: expected {list(FINAL_LONG_COLS)} got {actual_clean}") + return {"passed": len(errors) == 0, "errors": errors, "label": label} + + +# --------------------------------------------------------------------------- +# Validation: partition uniformity +# --------------------------------------------------------------------------- + + +def _validate_partition_uniformity(files: list[Path], year_month: str) -> JsonDict: + """Verify all rows belong to the target (year, month) partition. + + Reads only the ``year`` and ``month`` columns. A violation here would mean + cross-month row leakage — a serious data corruption that must block swap. + """ + y = int(year_month[:4]) + m = int(year_month[4:6]) + errors: list[str] = [] + for path in files: + df = pl.read_parquet(str(path), columns=["year", "month"]) + bad = df.filter((pl.col("year") != y) | (pl.col("month") != m)).height + if bad > 0: + errors.append(f"{path.name}: {bad} rows with wrong (year,month); expected ({y},{m})") + return {"passed": len(errors) == 0, "errors": errors, "year": y, "month": m} + + +# --------------------------------------------------------------------------- +# Atomic directory swap +# --------------------------------------------------------------------------- + + +def _atomic_swap( + month_canonical_dir: Path, + staging_month_dir: Path, + precompact_dir: Path, + logger: Any, + log_ctx: JsonDict, +) -> None: + """Atomically swap staging compacted files into the canonical month directory. + + Two-phase rename sequence + ------------------------- + Phase 1 (atomic): + ``os.replace(month_canonical_dir → precompact_dir)`` + The canonical directory disappears; batch files are now in + ``precompact_dir`` and remain safe. + + Phase 2 (atomic): + ``os.replace(staging_month_dir → month_canonical_dir)`` + Staging becomes the new canonical directory. + + Rollback: + If phase 2 fails, phase 1 is reversed by renaming ``precompact_dir`` + back to ``month_canonical_dir``. If the rollback also fails, a + ``RuntimeError`` with manual recovery instructions is raised — the + original files are in ``precompact_dir`` and can be restored manually. + + Filesystem constraint: + ``os.replace()`` is atomic only within the same filesystem. We + assert both directories share the same device before proceeding. + Cross-device moves are refused with a clear error. + + Note: this function must NEVER touch ``/_runs/`` — the run + artifacts directory is completely separate from Hive partition directories. + """ + # Guard: same-filesystem requirement for atomic rename(2). + canonical_dev = os.stat(str(month_canonical_dir.parent)).st_dev + staging_dev = os.stat(str(staging_month_dir.parent)).st_dev + if canonical_dev != staging_dev: + raise RuntimeError( + f"Staging and canonical parent directories are on different " + f"filesystems (canonical_dev={canonical_dev}, " + f"staging_dev={staging_dev}). Atomic rename is not possible. " + f"Move staging under the same mount point as the output root." + ) + + logger.log({ + **log_ctx, + "event": "compaction_atomic_swap", + "status": "start", + "month_canonical_dir": str(month_canonical_dir), + "staging_month_dir": str(staging_month_dir), + "precompact_dir": str(precompact_dir), + }) + + # Phase 1: canonical → precompact (atomic rename; canonical dir vanishes). + os.replace(str(month_canonical_dir), str(precompact_dir)) + + try: + # Phase 2: staging → canonical (atomic rename; staging dir vanishes). + os.replace(str(staging_month_dir), str(month_canonical_dir)) + + except Exception as swap_err: + # Phase 2 failed. Attempt rollback of phase 1 to restore original state. + try: + os.replace(str(precompact_dir), str(month_canonical_dir)) + except Exception as rollback_err: + # Both phase-2 and rollback failed. The original batch files are + # in precompact_dir; manual intervention is required. + raise RuntimeError( + "CRITICAL: compaction phase-2 swap failed AND rollback failed. " + "Manual recovery required: " + f"rename {precompact_dir} → {month_canonical_dir}. " + f"swap_err={swap_err!r} rollback_err={rollback_err!r}" + ) from swap_err + + # Rollback succeeded; original batch files restored. + raise RuntimeError( + f"Compaction phase-2 swap failed (rollback succeeded; original " + f"batch files are intact): {swap_err!r}. " + f"Staging files remain at: {staging_month_dir.parent}" + ) from swap_err + + logger.log({ + **log_ctx, + "event": "compaction_atomic_swap", + "status": "success", + "month_canonical_dir": str(month_canonical_dir), + "precompact_dir": str(precompact_dir), + }) + + +# --------------------------------------------------------------------------- +# Public entry point +# --------------------------------------------------------------------------- + + +def run_compaction(cfg: CompactionConfig, logger: Any) -> JsonDict: + """Run month-level Parquet compaction. + + Caller contract + --------------- + - All batches for ``cfg.year_month`` completed with ``total_failure == 0``. + - The cooperative stop flag is not set. + - ``logger`` is a ``JsonlLogger``-compatible object exposing ``.log(dict)``. + + Stages + ------ + 1. Locate and sort input ``batch_*.parquet`` files (lexicographic order). + 2. Idempotency guard (fail if ``compacted_*`` files exist without + ``--overwrite-compact``). + 3. Read pre-compaction row counts from Parquet footer metadata (no I/O). + 4. Validate input schema against ``FINAL_LONG_COLS``. + 5. Derive ``rows_per_row_group`` and estimate output file count. + 6. Write audit ``compaction_plan.json`` and ``original_file_inventory.json``. + 7. Stream-write compacted chunks to staging directory. + 8. Post-write validations: row count, schema, sort order, duplicates, + partition uniformity. + 9. Write audit ``compaction_validation.json`` and + ``compacted_file_inventory.json``. + 10. Atomic directory swap (skip if ``cfg.dry_run``). + 11. Write audit ``compaction_summary.json``. + + Returns + ------- + Summary dict (also written to ``/compaction/compaction_summary.json``). + + Raises + ------ + RuntimeError + On any unrecoverable failure. Staging files are cleaned up before + raising. Original batch files are never modified. + """ + t_start = time.time() + git_sha = _try_git_sha() + ydir, mdir = _year_month_dirs(cfg.year_month) + + month_canonical_dir = cfg.out_root / ydir / mdir + staging_base = cfg.run_dir / "compaction_staging" + staging_month_dir = staging_base / ydir / mdir + audit_dir = cfg.run_dir / "compaction" + # Precompact backup name: month=MM_precompact_ — lives adjacent to + # the canonical month dir so it is on the same filesystem. + precompact_dir = month_canonical_dir.parent / f"{mdir}_precompact_{cfg.run_id}" + + log_ctx: JsonDict = { + "ts_utc": _now_utc_iso(), + "year_month": cfg.year_month, + "run_id": cfg.run_id, + } + + logger.log({ + **log_ctx, + "event": "compaction_start", + "status": "start", + "month_canonical_dir": str(month_canonical_dir), + "staging_month_dir": str(staging_month_dir), + "audit_dir": str(audit_dir), + "target_size_bytes": cfg.target_size_bytes, + "dry_run": cfg.dry_run, + "overwrite": cfg.overwrite, + }) + + # ── 1. Locate and sort input files ────────────────────────────────────── + if not month_canonical_dir.exists(): + raise RuntimeError(f"Month directory does not exist: {month_canonical_dir}") + + all_parquet = sorted(month_canonical_dir.glob("*.parquet")) + if not all_parquet: + raise RuntimeError(f"No .parquet files found in {month_canonical_dir}") + + # ── 2. Idempotency guard ───────────────────────────────────────────────── + existing_compacted = [p for p in all_parquet if p.name.startswith("part-")] + if existing_compacted and not cfg.overwrite: + raise RuntimeError( + f"Compacted files already exist in {month_canonical_dir}: " + f"{[p.name for p in existing_compacted]}. " + f"Pass --overwrite-compact to re-compact." + ) + + # Input files: only batch_*.parquet (never pre-existing compacted_* files). + input_files: list[Path] = sorted(p for p in all_parquet if p.name.startswith("batch_")) + if not input_files: + raise RuntimeError( + f"No batch_*.parquet input files found in {month_canonical_dir}. " + f"All files present: {[p.name for p in all_parquet]}" + ) + + # ── 3. Pre-compaction row count from Parquet footer metadata ───────────── + pre_rows = sum(_parquet_row_count(p) for p in input_files) + total_input_bytes = sum(p.stat().st_size for p in input_files) + + if pre_rows == 0: + raise RuntimeError("Input batch files contain zero rows; nothing to compact.") + + # ── 4. Input schema validation ─────────────────────────────────────────── + input_schema_result = _validate_schema(input_files, label="input") + if not input_schema_result["passed"]: + raise RuntimeError(f"Input schema validation failed: {input_schema_result['errors']}") + + # ── Pre-flight: check for existing compacted files in canonical dir ── + _pre_validate_no_existing_compacted(month_canonical_dir, cfg.overwrite) + + # ── 5. Derive row-group size and estimate output file count ────────────── + # rows_per_row_group is a fixed constant bounding peak RSS to ~2 GiB. + # estimated_n_output_files is for audit/logging only; actual file count is + # determined at runtime by on-disk size rollover in _stream_write_chunks. + bytes_per_row: float = total_input_bytes / pre_rows + rows_per_row_group: int = ROWS_PER_ROW_GROUP + estimated_n_output_files: int = max(1, round(total_input_bytes / cfg.target_size_bytes)) + + # ── 6. Write audit plan ────────────────────────────────────────────────── + audit_dir.mkdir(parents=True, exist_ok=True) + original_inventory = [_file_inventory_entry(p) for p in input_files] + compaction_plan: JsonDict = { + "ts_utc": _now_utc_iso(), + "git_sha": git_sha, + "year_month": cfg.year_month, + "run_id": cfg.run_id, + "month_canonical_dir": str(month_canonical_dir), + "staging_month_dir": str(staging_month_dir), + "precompact_dir": str(precompact_dir), + "n_input_files": len(input_files), + "pre_rows": pre_rows, + "total_input_bytes": total_input_bytes, + "bytes_per_row_estimate": round(bytes_per_row, 4), + "rows_per_row_group": rows_per_row_group, + "estimated_n_output_files": estimated_n_output_files, + "target_size_bytes": cfg.target_size_bytes, + "max_files": cfg.max_files, + "dry_run": cfg.dry_run, + "overwrite": cfg.overwrite, + "sort_keys": list(SORT_KEYS), + "final_long_cols": list(FINAL_LONG_COLS), + "input_file_list_hash": _file_list_hash(input_files), + } + _write_json(audit_dir / "compaction_plan.json", compaction_plan) + _write_json(audit_dir / "original_file_inventory.json", original_inventory) + + # ── Dry-run: stop before writing any data ──────────────────────────────── + if cfg.dry_run: + logger.log({ + **log_ctx, + "event": "compaction_complete", + "status": "info", + "msg": "dry_run=True; validation plan written; swap skipped", + "pre_rows": pre_rows, + "rows_per_row_group": rows_per_row_group, + "n_input_files": len(input_files), + }) + dry_summary: JsonDict = { + "ts_utc": _now_utc_iso(), + "git_sha": git_sha, + "year_month": cfg.year_month, + "run_id": cfg.run_id, + "status": "dry_run", + "pre_rows": pre_rows, + "n_input_files": len(input_files), + "rows_per_row_group": rows_per_row_group, + "target_size_bytes": cfg.target_size_bytes, + "elapsed_ms": _elapsed_ms(t_start, time.time()), + } + _write_json(audit_dir / "compaction_summary.json", dry_summary) + return dry_summary + + # ── 7. Stream-write compacted chunks to staging ────────────────────────── + staging_month_dir.mkdir(parents=True, exist_ok=True) + try: + output_files = _stream_write_chunks( + sorted_input_files=input_files, + staging_month_dir=staging_month_dir, + rows_per_row_group=rows_per_row_group, + target_size_bytes=cfg.target_size_bytes, + max_files=cfg.max_files, + logger=logger, + log_ctx=log_ctx, + ) + except Exception as write_err: + logger.log({ + **log_ctx, + "event": "compaction_failure", + "status": "failure", + "phase": "write", + "exception_type": type(write_err).__name__, + "exception_msg": str(write_err), + "traceback": traceback.format_exc(), + }) + shutil.rmtree(str(staging_month_dir), ignore_errors=True) + raise RuntimeError(f"Compaction write phase failed: {write_err}") from write_err + + if not output_files: + shutil.rmtree(str(staging_month_dir), ignore_errors=True) + raise RuntimeError("Compaction produced zero output files; aborting before swap.") + + # ── 8. Post-write validations ──────────────────────────────────────────── + # Row count: sum footer metadata (no data load). + post_rows = sum(_parquet_row_count(p) for p in output_files) + row_count_ok = post_rows == pre_rows + + output_schema_result = _validate_schema(output_files, label="output") + sort_dup_result = _validate_adjacent_keys_streaming( + output_files, + label="compacted", + batch_size=cfg.validation_batch_size, + logger=logger, + log_ctx=log_ctx, + ) + partition_result = _validate_partition_uniformity(output_files, cfg.year_month) + + total_output_bytes_staging = sum(p.stat().st_size for p in output_files) + + # ── 9. Write validation audit artifacts ────────────────────────────────── + compacted_inventory = [_file_inventory_entry(p) for p in output_files] + all_passed = ( + row_count_ok and output_schema_result["passed"] and sort_dup_result["passed"] and partition_result["passed"] + ) + validation_result: JsonDict = { + "ts_utc": _now_utc_iso(), + "git_sha": git_sha, + "year_month": cfg.year_month, + "run_id": cfg.run_id, + "pre_rows": pre_rows, + "post_rows": post_rows, + "row_count_match": row_count_ok, + "schema_validation": output_schema_result, + "sort_dup_validation": sort_dup_result, + "partition_validation": partition_result, + "passed": all_passed, + } + _write_json(audit_dir / "compaction_validation.json", validation_result) + _write_json(audit_dir / "compacted_file_inventory.json", compacted_inventory) + + if not all_passed: + failure_reasons: list[str] = [] + if not row_count_ok: + failure_reasons.append(f"row_count_mismatch: pre={pre_rows} post={post_rows}") + if not output_schema_result["passed"]: + failure_reasons.append(f"schema: {output_schema_result['errors']}") + if not sort_dup_result["passed"]: + failure_reasons.append(f"sort_or_dup: {sort_dup_result['error']}") + if not partition_result["passed"]: + failure_reasons.append(f"partition: {partition_result['errors']}") + + logger.log({ + **log_ctx, + "event": "compaction_failure", + "status": "failure", + "phase": "validation", + "reasons": failure_reasons, + }) + shutil.rmtree(str(staging_month_dir), ignore_errors=True) + raise RuntimeError( + f"Compaction post-write validation failed: {failure_reasons}. " + f"Original batch files are untouched at {month_canonical_dir}." + ) + + logger.log({ + **log_ctx, + "event": "compaction_validation_pass", + "status": "success", + "pre_rows": pre_rows, + "post_rows": post_rows, + "n_output_files": len(output_files), + "total_output_bytes_staging": total_output_bytes_staging, + }) + + # ── No-swap mode: keep staged outputs, skip atomic swap ────────────────── + if cfg.no_swap: + t_end = time.time() + summary: JsonDict = { + "ts_utc": _now_utc_iso(), + "git_sha": git_sha, + "year_month": cfg.year_month, + "run_id": cfg.run_id, + "status": "no_swap", + "n_input_files": len(input_files), + "n_output_files": len(output_files), + "pre_rows": pre_rows, + "post_rows": post_rows, + "total_input_bytes": total_input_bytes, + "total_output_bytes_staging": total_output_bytes_staging, + "rows_per_row_group": rows_per_row_group, + "target_size_bytes": cfg.target_size_bytes, + "month_canonical_dir": str(month_canonical_dir), + "staging_month_dir": str(staging_month_dir), + "precompact_dir": str(precompact_dir), + "elapsed_ms": _elapsed_ms(t_start, t_end), + "sort_keys": list(SORT_KEYS), + "input_file_list_hash": _file_list_hash(input_files), + "staged_output_file_list_hash": _file_list_hash(output_files), + } + _write_json(audit_dir / "compaction_summary.json", summary) + + logger.log({ + **log_ctx, + "event": "compaction_complete", + "status": "info", + "msg": "no_swap=True; staging+validation complete; swap skipped", + "pre_rows": pre_rows, + "post_rows": post_rows, + "n_input_files": len(input_files), + "n_output_files": len(output_files), + "total_output_bytes_staging": total_output_bytes_staging, + "elapsed_ms": _elapsed_ms(t_start, t_end), + }) + return summary + + # ── 10. Atomic directory swap ───────────────────────────────────────────── + try: + _atomic_swap( + month_canonical_dir=month_canonical_dir, + staging_month_dir=staging_month_dir, + precompact_dir=precompact_dir, + logger=logger, + log_ctx=log_ctx, + ) + except Exception as swap_err: + logger.log({ + **log_ctx, + "event": "compaction_failure", + "status": "failure", + "phase": "atomic_swap", + "exception_type": type(swap_err).__name__, + "exception_msg": str(swap_err), + "traceback": traceback.format_exc(), + }) + # Staging files are still in staging_month_dir (swap failed before or + # during phase-2; _atomic_swap rolls back phase-1 automatically). + shutil.rmtree(str(staging_month_dir), ignore_errors=True) + raise + + # After successful swap the output files now live under month_canonical_dir. + # Compute output bytes from the canonical location (staging dir is gone). + output_names = [p.name for p in output_files] + total_output_bytes = sum((month_canonical_dir / name).stat().st_size for name in output_names) + + # Write _SUCCESS.json marker (Spark convention) with compaction metadata. + _write_success_marker( + month_dir=month_canonical_dir, + output_names=output_names, + year_month=cfg.year_month, + run_id=cfg.run_id, + pre_rows=pre_rows, + post_rows=post_rows, + total_output_bytes=total_output_bytes, + git_sha=git_sha, + ) + + # ── 11. Final summary ───────────────────────────────────────────────────── + t_end = time.time() + summary: JsonDict = { + "ts_utc": _now_utc_iso(), + "git_sha": git_sha, + "year_month": cfg.year_month, + "run_id": cfg.run_id, + "status": "success", + "n_input_files": len(input_files), + "n_output_files": len(output_files), + "pre_rows": pre_rows, + "post_rows": post_rows, + "total_input_bytes": total_input_bytes, + "total_output_bytes": total_output_bytes, + "rows_per_row_group": rows_per_row_group, + "target_size_bytes": cfg.target_size_bytes, + "month_canonical_dir": str(month_canonical_dir), + "precompact_dir": str(precompact_dir), + "elapsed_ms": _elapsed_ms(t_start, t_end), + "sort_keys": list(SORT_KEYS), + "input_file_list_hash": _file_list_hash(input_files), + "output_file_list_hash": _file_list_hash([month_canonical_dir / n for n in output_names]), + } + _write_json(audit_dir / "compaction_summary.json", summary) + + logger.log({ + **log_ctx, + "event": "compaction_complete", + "status": "success", + "pre_rows": pre_rows, + "post_rows": post_rows, + "n_input_files": len(input_files), + "n_output_files": len(output_files), + "total_output_bytes": total_output_bytes, + "elapsed_ms": _elapsed_ms(t_start, t_end), + }) + + return summary diff --git a/scripts/csv_to_parquet/migrate_month_runner.py b/scripts/csv_to_parquet/migrate_month_runner.py new file mode 100644 index 0000000..9f59f79 --- /dev/null +++ b/scripts/csv_to_parquet/migrate_month_runner.py @@ -0,0 +1,1465 @@ +#!/usr/bin/env python3 +"""Deterministic, resumable CSV-to-Parquet month migration runner. + +Architecture +------------ +Orchestrates conversion of ~30k wide-format ComEd smart-meter CSVs per month +into Hive-partitioned Parquet (year=YYYY/month=MM). + +Key design decisions: + +1. **Batch-level atomicity** — Files are grouped into fixed-size batches. Each + batch produces exactly one Parquet file, written to a staging directory and + atomically published via ``os.replace()``. Readers never see partial output. + +2. **Resume / checkpointing** — Per-file success is recorded in JSONL manifests. + Re-running with ``--resume`` skips already-succeeded inputs, enabling safe + restarts after crashes or OOMs without re-processing the entire month. + +3. **Deterministic output** — Inputs are sorted lexicographically before batching + so batch composition is reproducible. Within each batch, rows are globally + sorted by ``(zip_code, account_identifier, datetime)`` before writing. + +4. **Lazy-then-collect execution** — The ``lazy_sink`` mode builds LazyFrames + per file, concatenates them, then *materializes* via ``.collect()`` before + sorting and writing. This is required because Polars' streaming + ``sink_parquet`` does not honor ``.sort()`` — it processes data in unordered + chunks. Explicit collect → sort → write guarantees sorted output at the cost + of batch-level memory. + +5. **Thread-pool parallelism** — Batches execute concurrently via + ``ThreadPoolExecutor``. Threads (not processes) are chosen because the + per-batch workload is I/O-bound (CSV read → transform → Parquet write) and + Polars releases the GIL during its native Rust operations. + +6. **Full audit trail** — Every file- and batch-level event is logged to + structured JSONL. ``plan.json``, ``run_summary.json``, and per-batch manifest + files provide complete post-hoc reproducibility evidence. + +Usage (via Justfile):: + + just migrate-month 202307 +""" + +from __future__ import annotations + +import argparse +import concurrent.futures as cf +import dataclasses +import datetime as dt +import hashlib +import json +import os +import platform +import shutil +import signal +import subprocess +import sys +import tempfile +import threading +import time +import traceback +from collections.abc import Sequence +from dataclasses import dataclass +from pathlib import Path +from typing import Any, Literal + +import polars as pl +from compact_month_output import ( + DEFAULT_COMPACT_TARGET_SIZE_BYTES, + DEFAULT_VALIDATION_BATCH_SIZE, + CompactionConfig, + run_compaction, +) + +from smart_meter_analysis.wide_to_long import transform_wide_to_long, transform_wide_to_long_lf + +JsonDict = dict[str, Any] +Status = Literal["start", "success", "failure", "skip", "warning", "info"] + +# Canonical sort order for all batch output. This three-column key is the +# contract shared with validate_month_output.py — both must agree exactly. +# Ordering rationale: zip_code groups geographically co-located accounts, +# account_identifier within zip provides stable per-meter ordering, and +# datetime gives the natural time series within each meter. +SORT_KEYS: tuple[str, str, str] = ("zip_code", "account_identifier", "datetime") + +# Minimum columns required in the upstream wide CSV to proceed with transform. +# If any are absent, the CSV is structurally invalid — fail-loud rather than +# attempting partial output that would silently corrupt downstream analysis. +REQUIRED_WIDE_COLS: tuple[str, ...] = ( + "ZIP_CODE", + "DELIVERY_SERVICE_CLASS", + "DELIVERY_SERVICE_NAME", + "ACCOUNT_IDENTIFIER", + "INTERVAL_READING_DATE", + "INTERVAL_LENGTH", + "TOTAL_REGISTERED_ENERGY", + "PLC_VALUE", + "NSPL_VALUE", +) + +# Canonical output schema (exact column order). All Parquet files produced by +# this runner must have exactly these columns in this order. The validator +# (validate_month_output.py) cross-checks against this contract. +FINAL_LONG_COLS: tuple[str, ...] = ( + "zip_code", + "delivery_service_class", + "delivery_service_name", + "account_identifier", + "datetime", + "energy_kwh", + "plc_value", + "nspl_value", + "year", + "month", +) + +DEFAULT_WORKERS = 4 +DEFAULT_BATCH_SIZE = 50 +DEFAULT_MAX_ERRORS = 1000 +DEFAULT_PRINT_FAILURES = 10 +DEFAULT_EXEC_MODE = "lazy_sink" # default streaming sink +DEFAULT_SKIP_EXISTING_BATCH_OUTPUTS = True + + +# ----------------------------- +# Data models +# ----------------------------- + + +@dataclass(frozen=True) +class RunnerConfig: + """Immutable run configuration resolved from CLI arguments at startup. + + Frozen to prevent accidental mutation during concurrent batch execution. + All filesystem paths are resolved to absolute at construction time so that + batch workers can operate independently of working-directory changes. + """ + + compact_no_swap: bool + + year_month: str # YYYYMM + input_list: Path + out_root: Path # dataset root (Hive partitions live here) + run_id: str # unique per invocation; used for artifact directory naming + + workers: int + batch_size: int + resume: bool # when True, skip inputs already logged as success in manifests + dry_run: bool + fail_fast: bool + max_errors: int # per-batch error budget before aborting + max_files: int | None # optional cap on total inputs (for testing) + + shard_id: int | None # enables filename-safe parallel sharding + + skip_existing_batch_outputs: bool # batch-level idempotence guard + overwrite: bool # opt-in to overwrite existing batch outputs + + run_dir: Path # _runs/// — all artifacts live here + log_jsonl: Path # structured event log (append-only) + manifest_dir: Path # per-batch JSONL manifests for resume + staging_dir: Path # temp write location for atomic publish + + print_failures: int + + exec_mode: Literal["eager", "lazy_sink"] + debug_mem: bool + debug_temp_scan: bool + polars_temp_dir: str | None + + # Compaction stage (optional; runs after all batches complete). + compact_month: bool + compact_target_size_bytes: int + compact_max_files: int | None + overwrite_compact: bool + compact_dry_run: bool + validation_batch_size: int + + +@dataclass(frozen=True) +class BatchPlan: + """A unit of work: a group of input CSVs that will produce one Parquet file. + + batch_id is zero-padded (batch_0000, batch_0001, ...) for deterministic + filesystem ordering and human readability in logs. + """ + + batch_id: str + inputs: list[str] + + +# ----------------------------- +# Logging +# ----------------------------- + + +class JsonlLogger: + """Thread-safe, append-only structured event logger. + + Uses a threading.Lock to serialize writes from concurrent batch workers. + JSONL (one JSON object per line) is chosen over CSV or multi-line JSON + because it is append-safe, grep-friendly, and trivially parseable for + post-hoc analysis (e.g., extracting failure events from a 100k-line log). + """ + + def __init__(self, path: Path) -> None: + self._path = path + self._lock = threading.Lock() + self._path.parent.mkdir(parents=True, exist_ok=True) + + def log(self, event: JsonDict) -> None: + line = json.dumps(event, ensure_ascii=False, sort_keys=True) + with self._lock, self._path.open("a", encoding="utf-8") as f: + f.write(line + "\n") + + +def now_utc_iso() -> str: + return dt.datetime.now(dt.timezone.utc).isoformat(timespec="seconds") + + +def elapsed_ms(t0: float, t1: float) -> int: + return round((t1 - t0) * 1000.0) + + +def stable_hash(s: str) -> str: + return hashlib.sha256(s.encode("utf-8")).hexdigest()[:16] + + +def try_git_info() -> JsonDict: + """Capture git SHA and dirty state for the audit trail. + + Best-effort: returns None fields if git is unavailable (e.g., in Docker + images without git). This is logged in plan.json to tie output artifacts + back to the exact code version that produced them. + """ + + def _run(args: list[str]) -> str | None: + try: + cp = subprocess.run(args, check=False, capture_output=True, text=True) # noqa: S603 + if cp.returncode != 0: + return None + return cp.stdout.strip() + except Exception: + return None + + sha = _run(["git", "rev-parse", "HEAD"]) + dirty = _run(["git", "status", "--porcelain"]) + return {"sha": sha, "is_dirty": bool(dirty) if dirty is not None else None} + + +def build_env_info() -> JsonDict: + return { + "python": sys.version.replace("\n", " "), + "platform": platform.platform(), + "polars": pl.__version__, + "cwd": str(Path.cwd()), + } + + +# ----------------------------- +# Debug helpers (RSS / disk / temp) +# ----------------------------- + + +def _read_rss_bytes() -> int | None: + """Read resident set size from /proc/self/status (Linux only). + + Used with --debug-mem to track per-batch memory growth and detect leaks + during long-running migrations. Returns None on non-Linux platforms. + """ + try: + with open("/proc/self/status", encoding="utf-8") as f: + for line in f: + if line.startswith("VmRSS:"): + parts = line.split() + if len(parts) >= 2 and parts[1].isdigit(): + kb = int(parts[1]) + return kb * 1024 + except Exception: + return None + return None + + +def _disk_usage_bytes(path: Path) -> JsonDict: + try: + du = shutil.disk_usage(str(path)) + return {"free": int(du.free), "total": int(du.total), "used": int(du.used)} + except Exception as e: + return {"error": type(e).__name__, "msg": str(e)} + + +def _snapshot_dir(path: Path, limit: int = 2000) -> dict[str, int]: + out: dict[str, int] = {} + try: + if not path.exists() or not path.is_dir(): + return out + for i, p in enumerate(path.iterdir()): + if i >= limit: + break + try: + if p.is_file(): + out[p.name] = int(p.stat().st_size) + except Exception: # noqa: S112 + continue + except Exception: + return out + return out + + +# ----------------------------- +# Planning / inputs +# ----------------------------- + + +def normalize_input_path(p: str) -> str: + """Canonicalize an input path for deterministic deduplication. + + S3 URIs are kept as-is (already canonical); local paths are resolved to + absolute so that the same file referenced via different relative paths + (e.g., ./foo.csv vs ../dir/foo.csv) produces the same manifest key. + """ + p = p.strip() + if not p: + return p + if p.startswith("s3://"): + return p + return str(Path(p).expanduser().resolve()) + + +def load_inputs(input_list: Path) -> list[str]: + """Load and sort the input file list. + + Sorting is critical for determinism: it ensures that the same set of inputs + always produces the same batch assignments, regardless of the order in which + ``aws s3 ls`` or ``find`` emits them. This makes runs reproducible across + retries and enables meaningful determinism comparisons between outputs. + """ + if not input_list.exists(): + raise SystemExit(f"--input-list not found: {input_list}") + raw = input_list.read_text(encoding="utf-8").splitlines() + inputs = [normalize_input_path(x) for x in raw if x.strip() and not x.strip().startswith("#")] + inputs_sorted = sorted(inputs) + if not inputs_sorted: + raise SystemExit("No inputs found in --input-list after filtering comments/blank lines.") + return inputs_sorted + + +def make_batches(inputs_sorted: list[str], batch_size: int) -> list[BatchPlan]: + """Partition the sorted input list into fixed-size, sequentially-numbered batches. + + Sequential numbering (batch_0000, batch_0001, ...) is required for: + - deterministic output filenames that sort naturally on disk + - resume correctness (batch_id is the checkpoint key) + - human readability in logs and manifest files + """ + if batch_size <= 0: + raise SystemExit("--batch-size must be > 0") + out: list[BatchPlan] = [] + n = len(inputs_sorted) + for i in range(0, n, batch_size): + j = i // batch_size + out.append(BatchPlan(batch_id=f"batch_{j:05d}", inputs=inputs_sorted[i : i + batch_size])) + return out + + +def write_json(path: Path, obj: Any) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + path.write_text(json.dumps(obj, indent=2, sort_keys=True), encoding="utf-8") + + +def to_jsonable(x: Any) -> Any: + if isinstance(x, Path): + return str(x) + if dataclasses.is_dataclass(x) and not isinstance(x, type): + return to_jsonable(dataclasses.asdict(x)) + if isinstance(x, dict): + return {str(k): to_jsonable(v) for k, v in x.items()} + if isinstance(x, (list, tuple)): + return [to_jsonable(v) for v in x] + return x + + +# ----------------------------- +# Resume / checkpointing +# ----------------------------- + + +def iter_manifest_success_inputs(manifest_dir: Path) -> set[str]: + """Build the set of input paths that previously succeeded (for --resume). + + Scans all manifest JSONL files and collects input_path values with + status=success. This set is used as a skip-list so that resumed runs + don't re-transform files that already completed. The manifest is the + source of truth — not the presence of output files — because a crash + could leave partial staging files without a success record. + """ + if not manifest_dir.exists(): + return set() + + success: set[str] = set() + for p in sorted(manifest_dir.glob("manifest_*.jsonl")): + with p.open("r", encoding="utf-8") as f: + for line in f: + line = line.strip() + if not line: + continue + try: + rec = json.loads(line) + except json.JSONDecodeError: + continue + if rec.get("status") == "success" and isinstance(rec.get("input_path"), str): + success.add(rec["input_path"]) + return success + + +# ----------------------------- +# Schema / validation helpers +# ----------------------------- + + +def build_wide_schema() -> dict[str, pl.DataType]: + """Construct an explicit Polars schema for the upstream wide CSV. + + An explicit schema is used instead of inference because: + - Inference is nondeterministic across files (a column that happens to have + all-integer values in one file may be inferred as Int64, while another + file with the same column may be inferred as Float64). + - ZIP_CODE and ACCOUNT_IDENTIFIER must be read as Utf8 to preserve leading + zeros (e.g., ZIP 01234). + - INTERVAL_READING_DATE is read as Utf8 and parsed downstream with an + explicit date format to avoid DD/MM vs MM/DD ambiguity. + """ + schema: dict[str, pl.DataType] = { + "ZIP_CODE": pl.Utf8, + "DELIVERY_SERVICE_CLASS": pl.Utf8, + "DELIVERY_SERVICE_NAME": pl.Utf8, + "ACCOUNT_IDENTIFIER": pl.Utf8, + "INTERVAL_READING_DATE": pl.Utf8, + "INTERVAL_LENGTH": pl.Int32, + "TOTAL_REGISTERED_ENERGY": pl.Float64, + "PLC_VALUE": pl.Float64, + "NSPL_VALUE": pl.Float64, + } + + # Standard 0030..2400 (48 cols) + DST extras 2430/2500 (2 cols) + for minutes in [*list(range(30, 1441, 30)), 1470, 1500]: + hh, mm = divmod(minutes, 60) + schema[f"INTERVAL_HR{hh:02d}{mm:02d}_ENERGY_QTY"] = pl.Float64 + + return schema + + +def validate_wide_contract(df: pl.DataFrame) -> None: + """Fail-loud pre-transform contract check on an eager DataFrame. + + Checks required columns exist and INTERVAL_LENGTH is uniformly 1800s (30 min). + This is the authoritative guard against upstream schema drift — catching it + here prevents corrupt long output from being written to the batch Parquet file. + """ + missing = [c for c in REQUIRED_WIDE_COLS if c not in df.columns] + if missing: + raise ValueError(f"Missing required wide columns: {missing}") + + if df.schema.get("INTERVAL_LENGTH") not in { + pl.Int8, + pl.Int16, + pl.Int32, + pl.Int64, + pl.UInt8, + pl.UInt16, + pl.UInt32, + pl.UInt64, + }: + raise ValueError(f"INTERVAL_LENGTH dtype must be integer seconds. observed={df.schema.get('INTERVAL_LENGTH')}") + + bad = df.filter(pl.col("INTERVAL_LENGTH").is_null() | (pl.col("INTERVAL_LENGTH") != 1800)).height + if bad > 0: + sample = ( + df.filter(pl.col("INTERVAL_LENGTH").is_null() | (pl.col("INTERVAL_LENGTH") != 1800)) + .select(["ZIP_CODE", "ACCOUNT_IDENTIFIER", "INTERVAL_READING_DATE", "INTERVAL_LENGTH"]) + .head(10) + .to_dicts() + ) + raise ValueError( + f"INTERVAL_LENGTH contract violation: expected 1800 everywhere. bad_rows={bad} sample={sample}" + ) + + +def validate_wide_contract_lf(lf: pl.LazyFrame) -> None: + """Lazy-mode equivalent of validate_wide_contract. + + Both eager and lazy variants exist because the runner supports two execution + modes. The lazy variant avoids full materialization — it collects only the + bad-row count and a small diagnostic sample. + """ + cols = lf.collect_schema().names() + missing = [c for c in REQUIRED_WIDE_COLS if c not in cols] + if missing: + raise ValueError(f"Missing required wide columns: {missing}") + + bad = ( + lf.filter(pl.col("INTERVAL_LENGTH").is_null() | (pl.col("INTERVAL_LENGTH") != 1800)) + .select(pl.len().alias("bad_rows")) + .collect(engine="streaming") + .item() + ) + if int(bad) > 0: + sample = ( + lf.filter(pl.col("INTERVAL_LENGTH").is_null() | (pl.col("INTERVAL_LENGTH") != 1800)) + .select(["ZIP_CODE", "ACCOUNT_IDENTIFIER", "INTERVAL_READING_DATE", "INTERVAL_LENGTH"]) + .head(10) + .collect(engine="streaming") + .to_dicts() + ) + raise ValueError( + f"INTERVAL_LENGTH contract violation: expected 1800 everywhere. bad_rows={int(bad)} sample={sample}" + ) + + +def shape_long_after_transform(df: pl.DataFrame) -> pl.DataFrame: + """Enforce canonical column names, dtypes, and order on the transform output. + + This is a defensive layer between wide_to_long.py (which owns the transform + logic) and the Parquet writer (which requires an exact schema). It handles: + - Legacy column naming (interval_energy → energy_kwh) + - Dtype coercion to the canonical 10-column schema + - Adding year/month partition columns derived from datetime + - Projecting to FINAL_LONG_COLS in exact order + """ + out = df + if "energy_kwh" not in out.columns and "interval_energy" in out.columns: + out = out.rename({"interval_energy": "energy_kwh"}) + + required = [ + "zip_code", + "delivery_service_class", + "delivery_service_name", + "account_identifier", + "datetime", + "energy_kwh", + "plc_value", + "nspl_value", + ] + missing = [c for c in required if c not in out.columns] + if missing: + raise ValueError(f"Transform output missing required columns: {missing} present_cols={out.columns}") + + if out.schema.get("datetime") == pl.Utf8: + raise ValueError("datetime is Utf8. transform must output Datetime.") + + out = out.with_columns([ + pl.col("zip_code").cast(pl.Utf8), + pl.col("account_identifier").cast(pl.Utf8), + pl.col("delivery_service_class").cast(pl.Categorical), + pl.col("delivery_service_name").cast(pl.Categorical), + pl.col("energy_kwh").cast(pl.Float64, strict=False), + pl.col("plc_value").cast(pl.Float64, strict=False), + pl.col("nspl_value").cast(pl.Float64, strict=False), + pl.col("datetime").cast(pl.Datetime("us")), + ]).with_columns([ + pl.col("datetime").dt.year().cast(pl.Int32).alias("year"), + pl.col("datetime").dt.month().cast(pl.Int8).alias("month"), + ]) + + return out.select(list(FINAL_LONG_COLS)) + + +def shape_long_after_transform_lf(lf: pl.LazyFrame) -> pl.LazyFrame: + """Lazy-mode equivalent of shape_long_after_transform.""" + cols = lf.collect_schema().names() + if "energy_kwh" not in cols and "interval_energy" in cols: + lf = lf.rename({"interval_energy": "energy_kwh"}) + + cols = lf.collect_schema().names() + required = [ + "zip_code", + "delivery_service_class", + "delivery_service_name", + "account_identifier", + "datetime", + "energy_kwh", + "plc_value", + "nspl_value", + ] + missing = [c for c in required if c not in cols] + if missing: + raise ValueError(f"Transform output missing required columns: {missing} present_cols={cols}") + + lf = lf.with_columns([ + pl.col("zip_code").cast(pl.Utf8), + pl.col("account_identifier").cast(pl.Utf8), + pl.col("delivery_service_class").cast(pl.Categorical), + pl.col("delivery_service_name").cast(pl.Categorical), + pl.col("energy_kwh").cast(pl.Float64, strict=False), + pl.col("plc_value").cast(pl.Float64, strict=False), + pl.col("nspl_value").cast(pl.Float64, strict=False), + pl.col("datetime").cast(pl.Datetime("us")), + ]).with_columns([ + pl.col("datetime").dt.year().cast(pl.Int32).alias("year"), + pl.col("datetime").dt.month().cast(pl.Int8).alias("month"), + ]) + + return lf.select(list(FINAL_LONG_COLS)) + + +def validate_year_month(df: pl.DataFrame, year_month: str) -> None: + """Guard against partition spillover: every row must belong to the target month. + + A CSV file dated in July that contains even one row with a datetime in August + would corrupt the Hive partition. Catching this at transform time (rather + than post-hoc validation) prevents bad data from being written to disk. + """ + y = int(year_month[:4]) + m = int(year_month[4:6]) + bad = df.filter((pl.col("year") != y) | (pl.col("month") != m)).height + if bad > 0: + raise ValueError(f"--year-month {year_month} validation failed: bad_rows={bad}") + + +def validate_year_month_lf(lf: pl.LazyFrame, year_month: str) -> None: + """Lazy-mode equivalent of validate_year_month.""" + y = int(year_month[:4]) + m = int(year_month[4:6]) + bad = ( + lf.filter((pl.col("year") != y) | (pl.col("month") != m)) + .select(pl.len().alias("bad_rows")) + .collect(engine="streaming") + .item() + ) + if int(bad) > 0: + raise ValueError(f"--year-month {year_month} validation failed: bad_rows={int(bad)}") + + +# --------------------------------------------------------------------------- +# Paths / deterministic output naming +# +# Output path structure: /year=YYYY/month=MM/.parquet +# Staging path structure: //year=YYYY/month=MM/.parquet +# +# The staging directory mirrors the final path hierarchy so that atomic_publish +# can use a single os.replace() call. When sharding is enabled, filenames +# include the shard_id prefix to avoid collisions between parallel shards. +# --------------------------------------------------------------------------- + + +def year_month_dirs(year_month: str) -> tuple[str, str]: + y = int(year_month[:4]) + m = int(year_month[4:6]) + return f"{y:04d}", f"{m:02d}" + + +def batch_output_filename(batch_id: str, shard_id: int | None) -> str: + if shard_id is None: + return f"{batch_id}.parquet" + return f"shard_{shard_id:02d}_{batch_id}.parquet" + + +def canonical_batch_out_path(cfg: RunnerConfig, batch_id: str) -> Path: + ydir, mdir = year_month_dirs(cfg.year_month) + return cfg.out_root / ydir / mdir / batch_output_filename(batch_id, cfg.shard_id) + + +def staging_batch_out_path(cfg: RunnerConfig, batch_id: str) -> Path: + ydir, mdir = year_month_dirs(cfg.year_month) + return cfg.staging_dir / batch_id / ydir / mdir / batch_output_filename(batch_id, cfg.shard_id) + + +def atomic_publish(staging_path: Path, final_path: Path, overwrite: bool) -> None: + """Move a completed batch file from staging to its final location. + + Uses os.replace() for atomicity on POSIX filesystems: the destination either + has the old file or the new file, never a partially-written one. This is + essential because downstream readers (validators, queries) may access the + output directory concurrently during long-running migrations. + """ + final_path.parent.mkdir(parents=True, exist_ok=True) + if overwrite: + os.replace(str(staging_path), str(final_path)) + return + if final_path.exists(): + raise FileExistsError(f"Refusing to overwrite existing output: {final_path}") + os.replace(str(staging_path), str(final_path)) + + +# ----------------------------- +# Batch execution +# ----------------------------- + + +def batch_manifest_paths(cfg: RunnerConfig, batch_id: str) -> tuple[Path, Path]: + manifest = cfg.manifest_dir / f"manifest_{batch_id}.jsonl" + summary = cfg.manifest_dir / f"summary_{batch_id}.json" + return manifest, summary + + +def _raise_batch_multi_year_month(uniq: pl.DataFrame) -> None: + """Raise ValueError with batch (year,month) values for diagnostics.""" + raise ValueError(f"Batch contains multiple (year,month) values: {uniq.sort(['year', 'month']).to_dicts()}") + + +def run_batch( + *, + cfg: RunnerConfig, + batch: BatchPlan, + logger: JsonlLogger, + skip_set: set[str], + stop_flag: threading.Event, +) -> JsonDict: + """Execute one batch: read CSVs, transform, sort, write Parquet. + + This is the core unit of work. Each batch: + 1. Checks whether the final output already exists (batch-level idempotence). + 2. Iterates over input files, transforming each wide CSV to long format. + 3. Concatenates all long DataFrames within the batch. + 4. Sorts by SORT_KEYS and writes a single Parquet file to staging. + 5. Atomically publishes the staging file to the final output location. + 6. Records per-file status in the batch manifest for resume support. + + Returns a batch summary dict logged to both the JSONL log and a JSON file. + """ + t_batch0 = time.time() + manifest_path, summary_path = batch_manifest_paths(cfg, batch.batch_id) + manifest_path.parent.mkdir(parents=True, exist_ok=True) + + batch_ctx: JsonDict = { + "ts_utc": now_utc_iso(), + "year_month": cfg.year_month, + "run_id": cfg.run_id, + "batch_id": batch.batch_id, + "shard_id": cfg.shard_id, + } + + final_out = canonical_batch_out_path(cfg, batch.batch_id) + + # Batch-level checkpoint (default ON): + if cfg.skip_existing_batch_outputs and final_out.exists() and not cfg.overwrite: + summary: JsonDict = { + **batch_ctx, + "status": "skip", + "skip_reason": "existing_batch_output", + "n_inputs": len(batch.inputs), + "n_success": 0, + "n_failure": 0, + "n_skip": 0, + "elapsed_ms": elapsed_ms(t_batch0, time.time()), + "manifest_jsonl": str(manifest_path), + "final_out_path": str(final_out), + "wrote_file": False, + "exec_mode": cfg.exec_mode, + "sort_keys": list(SORT_KEYS), + } + write_json(summary_path, summary) + logger.log({ + **batch_ctx, + "event": "batch_skip_existing_output", + "status": "skip", + "final_out_path": str(final_out), + }) + return summary + + logger.log({ + **batch_ctx, + "event": "batch_start", + "status": "start", + "n_inputs": len(batch.inputs), + "final_out_path": str(final_out), + }) + + tmp_dir = Path(tempfile.gettempdir()) + polars_tmp = os.environ.get("POLARS_TEMP_DIR") + wide_schema = build_wide_schema() + + frames: list[pl.DataFrame] = [] + lfs: list[pl.LazyFrame] = [] + + n_success = 0 + n_failure = 0 + n_skip = 0 + errors: list[JsonDict] = [] + + with manifest_path.open("a", encoding="utf-8") as mf: + for input_path in batch.inputs: + if stop_flag.is_set(): + break + + if input_path in skip_set: + n_skip += 1 + mf.write( + json.dumps( + {**batch_ctx, "input_path": input_path, "status": "skip", "reason": "resume_success"}, + sort_keys=True, + ) + + "\n" + ) + logger.log({ + **batch_ctx, + "event": "file_skip", + "status": "skip", + "input_path": input_path, + "reason": "resume_success", + }) + continue + + t0 = time.time() + file_ctx: JsonDict = {**batch_ctx, "input_path": input_path} + logger.log({**file_ctx, "event": "file_start", "status": "start"}) + + try: + if cfg.exec_mode == "eager": + df_wide = pl.read_csv( + input_path, + schema=wide_schema, + has_header=True, + infer_schema_length=0, + ignore_errors=False, + try_parse_dates=False, + ) + validate_wide_contract(df_wide) + + df_long = transform_wide_to_long(df_wide, strict=True, sort_output=False) + df_long = shape_long_after_transform(df_long) + validate_year_month(df_long, cfg.year_month) + + frames.append(df_long) + rows_wide = int(df_wide.height) + rows_long = int(df_long.height) + else: + lf_wide = pl.scan_csv( + input_path, + schema=wide_schema, + has_header=True, + ignore_errors=False, + try_parse_dates=False, + ) + validate_wide_contract_lf(lf_wide) + + lf_long = transform_wide_to_long_lf(lf_wide, strict=True, sort_output=False) + lf_long = shape_long_after_transform_lf(lf_long) + validate_year_month_lf(lf_long, cfg.year_month) + + rows_wide = int(lf_wide.select(pl.len()).collect(engine="streaming").item()) + rows_long = int(lf_long.select(pl.len()).collect(engine="streaming").item()) + lfs.append(lf_long) + + n_success += 1 + t1 = time.time() + mf.write( + json.dumps( + { + **file_ctx, + "status": "success", + "elapsed_ms": elapsed_ms(t0, t1), + "rows_wide": rows_wide, + "rows_long": rows_long, + }, + sort_keys=True, + ) + + "\n" + ) + logger.log({ + **file_ctx, + "event": "file_success", + "status": "success", + "elapsed_ms": elapsed_ms(t0, t1), + "rows_wide": rows_wide, + "rows_long": rows_long, + }) + + except Exception as e: + n_failure += 1 + t1 = time.time() + mf.write( + json.dumps( + { + **file_ctx, + "status": "failure", + "elapsed_ms": elapsed_ms(t0, t1), + "exception_type": type(e).__name__, + "exception_msg": str(e), + }, + sort_keys=True, + ) + + "\n" + ) + logger.log({ + **file_ctx, + "event": "file_failure", + "status": "failure", + "elapsed_ms": elapsed_ms(t0, t1), + "exception_type": type(e).__name__, + "exception_msg": str(e), + "traceback": traceback.format_exc(), + }) + errors.append({"input_path": input_path, "exception_type": type(e).__name__, "exception_msg": str(e)}) + + if cfg.fail_fast or n_failure >= cfg.max_errors: + break + + wrote_file = False + write_bytes = 0 + staging_out = staging_batch_out_path(cfg, batch.batch_id) + staging_out.parent.mkdir(parents=True, exist_ok=True) + + if cfg.debug_mem: + logger.log({ + **batch_ctx, + "event": "debug_env", + "status": "info", + "exec_mode": cfg.exec_mode, + "tmp_dir": str(tmp_dir), + "polars_temp_dir_env": polars_tmp, + "rss_bytes": _read_rss_bytes(), + "disk_tmp": _disk_usage_bytes(tmp_dir), + "disk_out_root": _disk_usage_bytes(cfg.out_root), + "final_out_path": str(final_out), + "staging_out_path": str(staging_out), + }) + + try: + if cfg.exec_mode == "eager" and frames: + df_batch = pl.concat(frames, how="vertical", rechunk=False) + df_batch = df_batch.sort(list(SORT_KEYS), maintain_order=True) + + uniq = df_batch.select(["year", "month"]).unique() + if uniq.height != 1: + _raise_batch_multi_year_month(uniq) + + df_batch.write_parquet(str(staging_out), compression="snappy", statistics=True, use_pyarrow=False) + wrote_file = True + + if cfg.exec_mode == "lazy_sink" and lfs: + lf_batch = pl.concat(lfs, how="vertical") + uniq = lf_batch.select(["year", "month"]).unique().collect(engine="streaming") + if uniq.height != 1: + _raise_batch_multi_year_month(uniq) + + # Collect before sort+write: sink_parquet uses the streaming engine + # which does not honor .sort() — it processes data in unordered chunks, + # silently producing unsorted output. Materializing first guarantees + # write_parquet emits rows in sorted order. + df_batch = lf_batch.collect(engine="streaming") + df_batch = df_batch.sort(list(SORT_KEYS), maintain_order=True) + df_batch.write_parquet(str(staging_out), compression="snappy", statistics=True, use_pyarrow=False) + wrote_file = True + + if wrote_file: + write_bytes = staging_out.stat().st_size + atomic_publish(staging_out, final_out, overwrite=cfg.overwrite) + + try: + staging_batch_root = cfg.staging_dir / batch.batch_id + if staging_batch_root.exists(): + shutil.rmtree(staging_batch_root, ignore_errors=True) + except Exception: # noqa: S110 + pass + + except FileExistsError as e: + logger.log({ + **batch_ctx, + "event": "batch_publish_collision", + "status": "warning", + "exception_msg": str(e), + "final_out_path": str(final_out), + "staging_out_path": str(staging_out), + }) + n_failure += 1 + wrote_file = False + except Exception as e: + logger.log({ + **batch_ctx, + "event": "batch_write_failure", + "status": "failure", + "exception_type": type(e).__name__, + "exception_msg": str(e), + "traceback": traceback.format_exc(), + }) + n_failure += 1 + wrote_file = False + + t_batch1 = time.time() + batch_summary: JsonDict = { + "ts_utc": now_utc_iso(), + "year_month": cfg.year_month, + "run_id": cfg.run_id, + "batch_id": batch.batch_id, + "shard_id": cfg.shard_id, + "n_inputs": len(batch.inputs), + "n_success": n_success, + "n_failure": n_failure, + "n_skip": n_skip, + "elapsed_ms": elapsed_ms(t_batch0, t_batch1), + "errors_sample": errors[:10], + "manifest_jsonl": str(manifest_path), + "final_out_path": str(final_out), + "staging_out_path": str(staging_out), + "wrote_file": wrote_file, + "write_bytes": write_bytes, + "sort_keys": list(SORT_KEYS), + "exec_mode": cfg.exec_mode, + "tmp_dir": str(tmp_dir), + "polars_temp_dir_env": polars_tmp, + } + write_json(summary_path, batch_summary) + logger.log({**batch_ctx, "event": "batch_end", "status": "info", **batch_summary}) + return batch_summary + + +# ----------------------------- +# CLI / main +# ----------------------------- + + +def parse_args(argv: Sequence[str]) -> RunnerConfig: + ap = argparse.ArgumentParser( + prog="migrate_month_runner", + description="Deterministic, resumable CSV→Parquet month runner (single-file per batch; shard-safe filenames).", + ) + ap.add_argument("--input-list", required=True, type=Path, help="Newline-delimited input paths (local or s3://).") + ap.add_argument("--out-root", required=True, type=Path, help="Output dataset root (Hive partitions).") + ap.add_argument("--year-month", required=True, help="Target month in YYYYMM, e.g. 202307") + ap.add_argument("--run-id", default=None, help="Optional run id. Default: UTC timestamp + stable hash.") + ap.add_argument("--workers", type=int, default=DEFAULT_WORKERS) + ap.add_argument("--batch-size", type=int, default=DEFAULT_BATCH_SIZE) + ap.add_argument("--resume", action="store_true") + ap.add_argument("--fail-fast", action="store_true") + ap.add_argument("--dry-run", action="store_true") + ap.add_argument("--max-errors", type=int, default=DEFAULT_MAX_ERRORS) + ap.add_argument("--max-files", type=int, default=None) + ap.add_argument("--shard-id", type=int, default=None, help="Shard identifier (used in output filenames).") + ap.add_argument( + "--skip-existing-batch-outputs", + action="store_true", + default=DEFAULT_SKIP_EXISTING_BATCH_OUTPUTS, + help="Skip a batch if its expected output file already exists (default: on).", + ) + ap.add_argument( + "--no-skip-existing-batch-outputs", + action="store_false", + dest="skip_existing_batch_outputs", + help="Disable skip-existing behavior (not recommended).", + ) + ap.add_argument( + "--overwrite", + action="store_true", + help="Overwrite existing batch output files (dangerous; opt-in).", + ) + ap.add_argument( + "--exec-mode", + choices=["eager", "lazy_sink"], + default=DEFAULT_EXEC_MODE, + help="Execution mode. Default is lazy_sink (sort+sink_parquet in streaming).", + ) + ap.add_argument("--debug-mem", action="store_true", help="Log RSS/disk/timing per batch stage.") + ap.add_argument("--debug-temp-scan", action="store_true", help="Snapshot temp dir before/after sink.") + ap.add_argument( + "--polars-temp-dir", + default=None, + help="If set, exports POLARS_TEMP_DIR for this process (helps prove spill location).", + ) + ap.add_argument("--print-failures", type=int, default=DEFAULT_PRINT_FAILURES) + + # Compaction flags (all optional; compaction is off by default). + ap.add_argument( + "--compact-month", + action="store_true", + help="Run month-level compaction after all batches complete successfully.", + ) + ap.add_argument( + "--compact-target-size-bytes", + type=int, + default=DEFAULT_COMPACT_TARGET_SIZE_BYTES, + help="Target on-disk size per compacted Parquet file (default 1 GiB).", + ) + ap.add_argument( + "--compact-max-files", + type=int, + default=None, + help="Optional cap on the number of compacted output files.", + ) + ap.add_argument( + "--overwrite-compact", + action="store_true", + help="Allow overwriting existing compacted_*.parquet files.", + ) + ap.add_argument( + "--compact-dry-run", + action="store_true", + help="Plan-only: write compaction_plan.json + original inventory + summary; do not write compacted outputs.", + ) + ap.add_argument( + "--compact-no-swap", + action="store_true", + help=( + "Run full month compaction into staging and perform all post-write validations, " + "but DO NOT atomically swap staged outputs into the canonical month directory." + ), + ) + ap.add_argument( + "--validation-batch-size", + type=int, + default=DEFAULT_VALIDATION_BATCH_SIZE, + help=( + "Rows per PyArrow batch during streaming adjacent-key validation. " + "Lower values reduce peak memory; higher values improve throughput. " + f"Default: {DEFAULT_VALIDATION_BATCH_SIZE:,}." + ), + ) + + ns = ap.parse_args(list(argv)) + + ym = ns.year_month.strip() + if len(ym) != 6 or (not ym.isdigit()): + raise SystemExit("--year-month must be YYYYMM (6 digits)") + + out_root = ns.out_root.expanduser().resolve() + out_root.mkdir(parents=True, exist_ok=True) + + if ns.polars_temp_dir is not None: + os.environ["POLARS_TEMP_DIR"] = str(Path(ns.polars_temp_dir).expanduser().resolve()) + + if ns.run_id is None: + ts = dt.datetime.now(dt.timezone.utc).strftime("%Y%m%dT%H%M%SZ") + rid = f"{ts}_{stable_hash(ym + '|' + str(out_root))}" + else: + rid = ns.run_id.strip() + + run_dir = out_root / "_runs" / ym / rid + log_jsonl = run_dir / "logs" / "run_log.jsonl" + manifest_dir = run_dir / "manifests" + staging_dir = run_dir / "staging" + + return RunnerConfig( + year_month=ym, + input_list=ns.input_list.expanduser().resolve(), + out_root=out_root, + run_id=rid, + workers=ns.workers, + batch_size=ns.batch_size, + resume=ns.resume, + dry_run=ns.dry_run, + fail_fast=ns.fail_fast, + max_errors=ns.max_errors, + max_files=ns.max_files, + shard_id=ns.shard_id, + skip_existing_batch_outputs=ns.skip_existing_batch_outputs, + overwrite=ns.overwrite, + run_dir=run_dir, + log_jsonl=log_jsonl, + manifest_dir=manifest_dir, + staging_dir=staging_dir, + print_failures=ns.print_failures, + exec_mode=ns.exec_mode, + debug_mem=ns.debug_mem, + debug_temp_scan=ns.debug_temp_scan, + polars_temp_dir=ns.polars_temp_dir, + compact_month=ns.compact_month, + compact_target_size_bytes=ns.compact_target_size_bytes, + compact_max_files=ns.compact_max_files, + overwrite_compact=ns.overwrite_compact, + compact_dry_run=ns.compact_dry_run, + compact_no_swap=ns.compact_no_swap, + validation_batch_size=ns.validation_batch_size, + ) + + +def sample_failures_from_log(log_path: Path, n: int) -> list[dict[str, Any]]: + """Extract a bounded sample of failure events from the run log for stderr output. + + Provides immediate diagnostic visibility at the end of a run without + requiring the operator to manually parse the full JSONL log. + """ + if n <= 0 or (not log_path.exists()): + return [] + out: list[dict[str, Any]] = [] + for line in log_path.read_text(encoding="utf-8").splitlines(): + line = line.strip() + if not line: + continue + try: + rec = json.loads(line) + except (json.JSONDecodeError, ValueError): + continue + if ( + rec.get("event") in ("file_failure", "batch_write_failure", "batch_publish_collision") + or rec.get("status") == "failure" + ): + out.append({ + k: rec.get(k) + for k in ["batch_id", "shard_id", "input_path", "exception_type", "exception_msg", "final_out_path"] + }) + if len(out) >= n: + break + return out + + +def main(argv: Sequence[str]) -> int: + """Entry point: plan → (optionally resume) → execute batches → summarize. + + Signal handling: SIGINT/SIGTERM set a cooperative stop flag rather than + killing workers abruptly. In-flight batches complete their current file + and exit cleanly, ensuring manifests reflect actual work done. This is + critical for resume correctness — an unrecorded partial write would cause + duplicate processing on retry. + """ + cfg = parse_args(argv) + + # Cooperative shutdown: workers check stop_flag between files. + stop_flag = threading.Event() + + def _handle_signal(_signum: int, _frame: Any) -> None: + stop_flag.set() + + signal.signal(signal.SIGINT, _handle_signal) + signal.signal(signal.SIGTERM, _handle_signal) + + cfg.run_dir.mkdir(parents=True, exist_ok=True) + cfg.manifest_dir.mkdir(parents=True, exist_ok=True) + cfg.staging_dir.mkdir(parents=True, exist_ok=True) + + logger = JsonlLogger(cfg.log_jsonl) + + inputs_sorted = load_inputs(cfg.input_list) + if cfg.max_files is not None: + inputs_sorted = inputs_sorted[: cfg.max_files] + + batches = make_batches(inputs_sorted, cfg.batch_size) + + plan = { + "ts_utc": now_utc_iso(), + "year_month": cfg.year_month, + "run_id": cfg.run_id, + "inputs_sorted": inputs_sorted, + "batches": [{"batch_id": b.batch_id, "n_inputs": len(b.inputs)} for b in batches], + "config": to_jsonable(cfg) | {"sort_keys": list(SORT_KEYS)}, + "env": build_env_info(), + "git": try_git_info(), + "notes": { + "deterministic_sort_keys": "zip_code, account_identifier, datetime", + "single_file_per_batch_month": True, + "lazy_sink_note": "lazy_sink uses LazyFrame.sink_parquet (streaming mode).", + "skip_existing_batch_outputs_default": DEFAULT_SKIP_EXISTING_BATCH_OUTPUTS, + }, + } + plan_path = cfg.run_dir / "plan.json" + write_json(plan_path, plan) + + logger.log({ + "ts_utc": now_utc_iso(), + "event": "run_start", + "status": "start", + "year_month": cfg.year_month, + "run_id": cfg.run_id, + "shard_id": cfg.shard_id, + "n_inputs": len(inputs_sorted), + "n_batches": len(batches), + "workers": cfg.workers, + "batch_size": cfg.batch_size, + "resume": cfg.resume, + "dry_run": cfg.dry_run, + "out_root": str(cfg.out_root), + "plan_path": str(plan_path), + "log_jsonl": str(cfg.log_jsonl), + "manifest_dir": str(cfg.manifest_dir), + "staging_dir": str(cfg.staging_dir), + "sort_keys": list(SORT_KEYS), + "exec_mode": cfg.exec_mode, + "polars_temp_dir_env": os.environ.get("POLARS_TEMP_DIR"), + "skip_existing_batch_outputs": cfg.skip_existing_batch_outputs, + "overwrite": cfg.overwrite, + }) + + if cfg.dry_run: + print( + json.dumps( + { + "year_month": cfg.year_month, + "run_id": cfg.run_id, + "shard_id": cfg.shard_id, + "n_inputs": len(inputs_sorted), + "n_batches": len(batches), + "first_inputs": inputs_sorted[:5], + "first_batches": [{"batch_id": b.batch_id, "n_inputs": len(b.inputs)} for b in batches[:3]], + "plan_path": str(plan_path), + "log_jsonl": str(cfg.log_jsonl), + "manifest_dir": str(cfg.manifest_dir), + "out_root": str(cfg.out_root), + "sort_keys": list(SORT_KEYS), + "exec_mode": cfg.exec_mode, + "skip_existing_batch_outputs": cfg.skip_existing_batch_outputs, + "overwrite": cfg.overwrite, + }, + indent=2, + sort_keys=True, + ) + ) + logger.log({"ts_utc": now_utc_iso(), "event": "run_end", "status": "info", "msg": "dry_run complete"}) + return 0 + + skip_set: set[str] = set() + if cfg.resume: + skip_set = iter_manifest_success_inputs(cfg.manifest_dir) + logger.log({ + "ts_utc": now_utc_iso(), + "event": "resume_loaded", + "status": "info", + "run_id": cfg.run_id, + "year_month": cfg.year_month, + "shard_id": cfg.shard_id, + "n_success_already": len(skip_set), + }) + + t0 = time.time() + summaries: list[JsonDict] = [] + + # ThreadPoolExecutor is preferred over ProcessPoolExecutor because: + # - Polars releases the GIL during native Rust operations (CSV parse, sort, + # Parquet write), so threads achieve true parallelism for the heavy work. + # - Threads share memory, avoiding the serialization overhead of passing + # DataFrames between processes. + # - Simpler error propagation and signal handling. + with cf.ThreadPoolExecutor(max_workers=cfg.workers) as ex: + futs: dict[cf.Future[JsonDict], BatchPlan] = {} + for b in batches: + futs[ex.submit(run_batch, cfg=cfg, batch=b, logger=logger, skip_set=skip_set, stop_flag=stop_flag)] = b + + for fut in cf.as_completed(futs): + b = futs[fut] + try: + summary = fut.result() + summaries.append(summary) + + if cfg.fail_fast and int(summary.get("n_failure", 0)) > 0: + stop_flag.set() + + except Exception as e: + logger.log({ + "ts_utc": now_utc_iso(), + "event": "batch_future_failure", + "status": "failure", + "year_month": cfg.year_month, + "run_id": cfg.run_id, + "shard_id": cfg.shard_id, + "batch_id": b.batch_id, + "exception_type": type(e).__name__, + "exception_msg": str(e), + "traceback": traceback.format_exc(), + }) + if cfg.fail_fast: + stop_flag.set() + + try: + if cfg.staging_dir.exists() and not any(cfg.staging_dir.iterdir()): + cfg.staging_dir.rmdir() + except Exception: # noqa: S110 + pass + + t1 = time.time() + total_success = sum(int(x.get("n_success", 0)) for x in summaries) + total_failure = sum(int(x.get("n_failure", 0)) for x in summaries) + total_skip = sum(int(x.get("n_skip", 0)) for x in summaries) + + # ── Optional month-level compaction ────────────────────────────────────── + # Runs only when explicitly requested AND the month completed cleanly: + # - zero file-level failures across all batches + # - cooperative stop flag was never set (no mid-run abort) + # - every planned batch produced a summary (no futures dropped silently) + compaction_summary: JsonDict | None = None + if cfg.compact_month: + compaction_eligible = total_failure == 0 and not stop_flag.is_set() and len(summaries) == len(batches) + if not compaction_eligible: + logger.log({ + "ts_utc": now_utc_iso(), + "event": "compaction_skipped", + "status": "warning", + "year_month": cfg.year_month, + "run_id": cfg.run_id, + "reason": ("total_failure > 0 or stop_flag set or incomplete batches"), + "total_failure": total_failure, + "stop_requested": stop_flag.is_set(), + "n_summaries": len(summaries), + "n_batches_planned": len(batches), + }) + else: + compact_cfg = CompactionConfig( + year_month=cfg.year_month, + run_id=cfg.run_id, + out_root=cfg.out_root, + run_dir=cfg.run_dir, + target_size_bytes=cfg.compact_target_size_bytes, + max_files=cfg.compact_max_files, + overwrite=cfg.overwrite_compact, + dry_run=cfg.compact_dry_run, + no_swap=cfg.compact_no_swap, + validation_batch_size=cfg.validation_batch_size, + ) + try: + compaction_summary = run_compaction(compact_cfg, logger) + except Exception as compact_err: + logger.log({ + "ts_utc": now_utc_iso(), + "event": "compaction_failure", + "status": "failure", + "year_month": cfg.year_month, + "run_id": cfg.run_id, + "exception_type": type(compact_err).__name__, + "exception_msg": str(compact_err), + "traceback": traceback.format_exc(), + }) + # Compaction failure is surfaced in the run summary but does + # NOT retroactively fail the batch migration exit code — the + # batch Parquet files are intact and usable. + compaction_summary = { + "status": "failure", + "exception_type": type(compact_err).__name__, + "exception_msg": str(compact_err), + } + + t1 = time.time() + batches_written = sum(1 for x in summaries if x.get("wrote_file") is True) + batches_skipped_existing_output = sum(1 for x in summaries if x.get("skip_reason") == "existing_batch_output") + batches_with_failures = sum(1 for x in summaries if int(x.get("n_failure", 0)) > 0) + + run_summary = { + "ts_utc": now_utc_iso(), + "year_month": cfg.year_month, + "run_id": cfg.run_id, + "shard_id": cfg.shard_id, + "out_root": str(cfg.out_root), + "n_inputs": len(inputs_sorted), + "n_batches_planned": len(batches), + "n_batches_completed": len(summaries), + "batches_written": batches_written, + "batches_skipped_existing_output": batches_skipped_existing_output, + "batches_with_failures": batches_with_failures, + "total_success": total_success, + "total_failure": total_failure, + "total_skip": total_skip, + "elapsed_ms": elapsed_ms(t0, t1), + "plan_path": str(plan_path), + "log_jsonl": str(cfg.log_jsonl), + "manifest_dir": str(cfg.manifest_dir), + "stop_requested": stop_flag.is_set(), + "sort_keys": list(SORT_KEYS), + "exec_mode": cfg.exec_mode, + "polars_temp_dir_env": os.environ.get("POLARS_TEMP_DIR"), + "skip_existing_batch_outputs": cfg.skip_existing_batch_outputs, + "overwrite": cfg.overwrite, + "compaction": compaction_summary, + } + write_json(cfg.run_dir / "run_summary.json", run_summary) + logger.log({"ts_utc": now_utc_iso(), "event": "run_end", "status": "info", **run_summary}) + + print(json.dumps(run_summary, indent=2, sort_keys=True)) + fails = sample_failures_from_log(cfg.log_jsonl, cfg.print_failures) + if fails: + print("Sample failures:") + for r in fails: + print(json.dumps(r, ensure_ascii=False)) + + return 1 if total_failure > 0 else 0 + + +if __name__ == "__main__": + raise SystemExit(main(sys.argv[1:])) diff --git a/scripts/csv_to_parquet/restructure_for_export.py b/scripts/csv_to_parquet/restructure_for_export.py new file mode 100644 index 0000000..3d9880c --- /dev/null +++ b/scripts/csv_to_parquet/restructure_for_export.py @@ -0,0 +1,580 @@ +#!/usr/bin/env python3 +"""Restructure 49 months of compacted Parquet data for export. + +Copies files from the blessed-run layout into a clean, Spark-compatible layout: + + SOURCE: /out_YYYYMM_blessed/year=YYYY/month=MM/compacted_NNNN.parquet + EXPORT: /YYYY/MM/part-NNNNN.parquet + _SUCCESS.json + +Three transformations applied during copy: + 1. year=YYYY/month=MM/ → YYYY/MM/ (drop Hive prefixes) + 2. compacted_NNNN → part-NNNNN (Spark naming, 5-digit zero-pad) + 3. _SUCCESS.json generated per month from pyarrow footer metadata + +Source files are NEVER modified or deleted. The export directory is a fresh +copy. Use --force to overwrite an existing export. + +Usage +----- + uv run python scripts/csv_to_parquet/restructure_for_export.py \\ + --source-root /ebs/.../runs_bs0500_w2 \\ + --export-root /ebs/.../runs_bs0500_w2/export \\ + [--dry-run] [--force] +""" + +from __future__ import annotations + +import argparse +import datetime as dt +import json +import re +import shutil +import subprocess +import sys +from dataclasses import dataclass, field +from pathlib import Path +from typing import Any + +import pyarrow.parquet as pq + +# --------------------------------------------------------------------------- +# Constants — must stay in sync with the pipeline schema contract +# --------------------------------------------------------------------------- + +SORT_KEYS: tuple[str, ...] = ("zip_code", "account_identifier", "datetime") + +FINAL_LONG_COLS: tuple[str, ...] = ( + "zip_code", + "delivery_service_class", + "delivery_service_name", + "account_identifier", + "datetime", + "energy_kwh", + "plc_value", + "nspl_value", + "year", + "month", +) + +EXPECTED_MONTHS: tuple[str, ...] = ( + "202103", + "202104", + "202105", + "202106", + "202107", + "202108", + "202202", + "202203", + "202204", + "202205", + "202206", + "202207", + "202208", + "202209", + "202210", + "202211", + "202212", + "202301", + "202302", + "202303", + "202304", + "202305", + "202306", + "202307", + "202308", + "202309", + "202310", + "202311", + "202312", + "202401", + "202402", + "202403", + "202404", + "202405", + "202406", + "202407", + "202408", + "202409", + "202410", + "202411", + "202412", + "202501", + "202502", + "202503", + "202504", + "202505", + "202506", + "202507", + "202508", +) + +EXPECTED_MONTH_COUNT: int = 49 + +# Pattern: out_YYYYMM_blessed +_BLESSED_DIR_RE = re.compile(r"^out_(\d{6})_blessed$") + +# Pattern: compacted_NNNN.parquet → capture the 4-digit index +_COMPACTED_RE = re.compile(r"^compacted_(\d{4})\.parquet$") + +JsonDict = dict[str, Any] + + +# --------------------------------------------------------------------------- +# Helpers +# --------------------------------------------------------------------------- + + +def _now_utc_iso() -> str: + return dt.datetime.now(dt.timezone.utc).isoformat(timespec="seconds") + + +def _git_sha() -> str | None: + try: + result = subprocess.run( + ["git", "rev-parse", "HEAD"], + capture_output=True, + text=True, + check=True, + ) + return result.stdout.strip() or None + except Exception: + return None + + +def _write_json(path: Path, obj: JsonDict) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + path.write_text(json.dumps(obj, indent=2) + "\n", encoding="utf-8") + + +def _compacted_to_part_name(compacted_name: str) -> str: + """Convert compacted_NNNN.parquet → part-NNNNN.parquet (4-digit → 5-digit).""" + m = _COMPACTED_RE.match(compacted_name) + if not m: + raise ValueError(f"Unexpected filename (expected compacted_NNNN.parquet): {compacted_name!r}") + idx = int(m.group(1)) + return f"part-{idx:05d}.parquet" + + +# --------------------------------------------------------------------------- +# Data model +# --------------------------------------------------------------------------- + + +@dataclass +class MonthPlan: + year_month: str # YYYYMM + year: str # YYYY + month: str # MM + source_dir: Path # .../year=YYYY/month=MM/ + dest_dir: Path # export/YYYY/MM/ + # sorted list of (src_path, dest_name) pairs + files: list[tuple[Path, str]] = field(default_factory=list) + + @property + def n_files(self) -> int: + return len(self.files) + + +@dataclass +class MonthResult: + year_month: str + n_files: int + total_rows: int + total_bytes: int + status: str # "OK" or "FAIL: " + + +# --------------------------------------------------------------------------- +# Discovery: scan source root for blessed dirs +# --------------------------------------------------------------------------- + + +def discover_months(source_root: Path) -> list[MonthPlan]: + """Find all out_YYYYMM_blessed dirs and build MonthPlan list.""" + plans: dict[str, MonthPlan] = {} + + blessed_dirs = sorted(d for d in source_root.iterdir() if d.is_dir() and _BLESSED_DIR_RE.match(d.name)) + + if not blessed_dirs: + sys.exit(f"ERROR: No out_*_blessed directories found under {source_root}\n Check --source-root path.") + + for bdir in blessed_dirs: + m = _BLESSED_DIR_RE.match(bdir.name) + if m is None: + raise RuntimeError(f"BUG: {bdir.name!r} passed filter but didn't match regex") + year_month = m.group(1) + year = year_month[:4] + month = year_month[4:6] + + # Locate year=YYYY/month=MM/ sub-path + hive_year_dirs = sorted(bdir.glob("year=*")) + if not hive_year_dirs: + sys.exit(f"ERROR: {bdir} has no year=* subdirectory.\n Expected: {bdir}/year={year}/month={month}/") + + found_source: Path | None = None + for ydir in hive_year_dirs: + candidate = ydir / f"month={month}" + if candidate.is_dir(): + found_source = candidate + break + + if found_source is None: + sys.exit( + f"ERROR: {bdir} has no month={month} subdirectory under any year=* dir.\n" + f" Dirs present: {[d.name for d in hive_year_dirs]}" + ) + + compacted = sorted(found_source.glob("compacted_*.parquet")) + if not compacted: + sys.exit( + f"ERROR: {found_source} contains no compacted_*.parquet files.\n" + f" Files present: {[p.name for p in found_source.iterdir()]}" + ) + + # Validate all filenames match expected pattern + file_pairs: list[tuple[Path, str]] = [] + for src in compacted: + dest_name = _compacted_to_part_name(src.name) + file_pairs.append((src, dest_name)) + + if year_month in plans: + sys.exit(f"ERROR: Duplicate year_month {year_month} found in source root.") + + plans[year_month] = MonthPlan( + year_month=year_month, + year=year, + month=month, + source_dir=found_source, + dest_dir=Path("__placeholder__"), # set below after export_root known + files=file_pairs, + ) + + return list(plans.values()) + + +def attach_dest_dirs(plans: list[MonthPlan], export_root: Path) -> None: + for p in plans: + p.dest_dir = export_root / p.year / p.month + + +# --------------------------------------------------------------------------- +# Validation: expected months +# --------------------------------------------------------------------------- + + +def validate_month_set(plans: list[MonthPlan]) -> None: + found = {p.year_month for p in plans} + expected = set(EXPECTED_MONTHS) + + missing = sorted(expected - found) + extra = sorted(found - expected) + + errors: list[str] = [] + if missing: + errors.append(f"Missing months ({len(missing)}): {missing}") + if extra: + errors.append(f"Unexpected months ({len(extra)}): {extra}") + if len(found) != EXPECTED_MONTH_COUNT: + errors.append(f"Expected {EXPECTED_MONTH_COUNT} months, found {len(found)}") + + if errors: + sys.exit("ERROR: Month set mismatch:\n " + "\n ".join(errors)) + + +# --------------------------------------------------------------------------- +# Dry-run: print plan without copying +# --------------------------------------------------------------------------- + + +def print_dry_run(plans: list[MonthPlan], export_root: Path) -> None: + total_files = sum(p.n_files for p in plans) + total_bytes = sum(src.stat().st_size for p in plans for src, _ in p.files) + + print(f"\nDRY-RUN — {len(plans)} months, {total_files} files, {total_bytes / 1_073_741_824:.1f} GiB total\n") + print(f" export root: {export_root}\n") + print(f" {'MONTH':<8} {'FILES':>5} {'BYTES (GiB)':>11} DEST DIR") + print(f" {'-' * 8} {'-' * 5} {'-' * 11} {'-' * 50}") + + for p in sorted(plans, key=lambda x: x.year_month): + month_bytes = sum(src.stat().st_size for src, _ in p.files) + print(f" {p.year_month:<8} {p.n_files:>5} {month_bytes / 1_073_741_824:>10.3f} {p.dest_dir}") + for src, dest_name in p.files: + print(f" {src.name:>20} → {dest_name}") + + print(f"\n TOTAL: {len(plans)} months / {total_files} files / {total_bytes / 1_073_741_824:.2f} GiB\n") + + +# --------------------------------------------------------------------------- +# Copy: one month +# --------------------------------------------------------------------------- + + +def copy_month(plan: MonthPlan, force: bool, git_sha: str | None) -> MonthResult: + dest_dir = plan.dest_dir + dest_dir.mkdir(parents=True, exist_ok=True) + + # Check for existing part files + existing_parts = sorted(dest_dir.glob("part-*.parquet")) + if existing_parts and not force: + sys.exit( + f"ERROR: {dest_dir} already contains {len(existing_parts)} part-*.parquet file(s).\n" + f" Use --force to overwrite." + ) + if existing_parts and force: + for p in existing_parts: + p.unlink() + success_marker = dest_dir / "_SUCCESS.json" + if success_marker.exists(): + success_marker.unlink() + + # Copy files + for src, dest_name in plan.files: + dest_path = dest_dir / dest_name + shutil.copy2(src, dest_path) + + # Read metadata from destination files for _SUCCESS.json + files_manifest: list[JsonDict] = [] + total_rows = 0 + total_bytes = 0 + + for _src, dest_name in plan.files: + dest_path = dest_dir / dest_name + meta = pq.read_metadata(str(dest_path)) + size = int(dest_path.stat().st_size) + rows = int(meta.num_rows) + total_rows += rows + total_bytes += size + files_manifest.append({ + "name": dest_name, + "size_bytes": size, + "num_rows": rows, + "num_row_groups": int(meta.num_row_groups), + }) + + marker: JsonDict = { + "timestamp": _now_utc_iso(), + "git_sha": git_sha, + "year_month": plan.year_month, + "n_files": plan.n_files, + "total_rows": total_rows, + "total_bytes": total_bytes, + "sort_keys": list(SORT_KEYS), + "schema": list(FINAL_LONG_COLS), + "files": files_manifest, + } + _write_json(dest_dir / "_SUCCESS.json", marker) + + return MonthResult( + year_month=plan.year_month, + n_files=plan.n_files, + total_rows=total_rows, + total_bytes=total_bytes, + status="OK", + ) + + +# --------------------------------------------------------------------------- +# Post-copy verification +# --------------------------------------------------------------------------- + + +def verify_export(plans: list[MonthPlan], export_root: Path) -> list[MonthResult]: + """Verify all destination files: count, size, readability.""" + results: list[MonthResult] = [] + failures: list[str] = [] + + for plan in sorted(plans, key=lambda x: x.year_month): + dest_dir = plan.dest_dir + dest_parts = sorted(dest_dir.glob("part-*.parquet")) + + # 1. File count + if len(dest_parts) != plan.n_files: + msg = f"{plan.year_month}: file count mismatch — expected {plan.n_files}, found {len(dest_parts)}" + failures.append(msg) + results.append(MonthResult(plan.year_month, plan.n_files, 0, 0, f"FAIL: {msg}")) + continue + + month_rows = 0 + month_bytes = 0 + month_ok = True + + for (src, dest_name), dest_path in zip(plan.files, dest_parts): + # 2. Byte-for-byte size match + src_size = src.stat().st_size + dst_size = dest_path.stat().st_size + if src_size != dst_size: + msg = f"{plan.year_month}/{dest_name}: size mismatch — src {src_size}, dst {dst_size}" + failures.append(msg) + month_ok = False + continue + + # 3. Parquet readability + try: + meta = pq.read_metadata(str(dest_path)) + month_rows += int(meta.num_rows) + month_bytes += dst_size + except Exception as exc: + msg = f"{plan.year_month}/{dest_name}: pq.read_metadata failed — {exc}" + failures.append(msg) + month_ok = False + + status = "OK" if month_ok else "FAIL: see above" + results.append(MonthResult(plan.year_month, plan.n_files, month_rows, month_bytes, status)) + + # 4. Total month count + export_month_dirs = list(export_root.rglob("_SUCCESS.json")) + if len(export_month_dirs) != EXPECTED_MONTH_COUNT: + failures.append( + f"Total month count: expected {EXPECTED_MONTH_COUNT}, found {len(export_month_dirs)} _SUCCESS.json files" + ) + + if failures: + print("\nVERIFICATION FAILURES:") + for f in failures: + print(f" ✗ {f}") + return results + + return results + + +# --------------------------------------------------------------------------- +# Summary table +# --------------------------------------------------------------------------- + + +def print_summary(results: list[MonthResult]) -> bool: + """Print summary table. Returns True if all OK.""" + header = f"{'MONTH':<8} | {'FILES':>5} | {'ROWS':>16} | {'BYTES':>14} | STATUS" + sep = "-" * len(header) + print(f"\n{header}") + print(sep) + + total_files = 0 + total_rows = 0 + total_bytes = 0 + n_ok = 0 + + for r in sorted(results, key=lambda x: x.year_month): + row = f"{r.year_month:<8} | {r.n_files:>5} | {r.total_rows:>16,} | {r.total_bytes:>14,} | {r.status}" + print(row) + total_files += r.n_files + total_rows += r.total_rows + total_bytes += r.total_bytes + if r.status == "OK": + n_ok += 1 + + print(sep) + all_ok = n_ok == len(results) + overall = f"{n_ok}/{len(results)} OK" + print(f"{'TOTAL':<8} | {total_files:>5} | {total_rows:>16,} | {total_bytes:>14,} | {overall}") + print() + return all_ok + + +# --------------------------------------------------------------------------- +# Entry point +# --------------------------------------------------------------------------- + + +def main() -> None: + parser = argparse.ArgumentParser( + description="Restructure 49 months of compacted Parquet for export.", + formatter_class=argparse.RawDescriptionHelpFormatter, + ) + parser.add_argument( + "--source-root", + required=True, + type=Path, + help="Root containing out_YYYYMM_blessed/ directories.", + ) + parser.add_argument( + "--export-root", + required=True, + type=Path, + help="Destination root for YYYY/MM/ export layout.", + ) + parser.add_argument( + "--dry-run", + action="store_true", + help="Print plan without copying any files.", + ) + parser.add_argument( + "--force", + action="store_true", + help="Overwrite existing part-*.parquet files in export dir.", + ) + args = parser.parse_args() + + source_root: Path = args.source_root.resolve() + export_root: Path = args.export_root.resolve() + + # Safety guard: export_root must be on /ebs + if not str(export_root).startswith("/ebs"): + sys.exit(f"ERROR: export-root must be under /ebs. Got: {export_root}") + if not str(source_root).startswith("/ebs"): + sys.exit(f"ERROR: source-root must be under /ebs. Got: {source_root}") + + # Export root must not be the same as source root + if export_root == source_root: + sys.exit("ERROR: --export-root must differ from --source-root.") + + # Export root must not be inside a blessed dir + if any(part.endswith("_blessed") for part in export_root.parts): + sys.exit("ERROR: --export-root must not be inside a blessed directory.") + + print(f"source-root : {source_root}") + print(f"export-root : {export_root}") + print(f"dry-run : {args.dry_run}") + print(f"force : {args.force}") + + # ── 1. Discover months ──────────────────────────────────────────────────── + print("\nDiscovering source months...") + plans = discover_months(source_root) + attach_dest_dirs(plans, export_root) + + # ── 2. Validate month set ───────────────────────────────────────────────── + validate_month_set(plans) + print(f"Found {len(plans)} months ({sum(p.n_files for p in plans)} files total). ✓") + + # ── 3. Dry-run shortcut ─────────────────────────────────────────────────── + if args.dry_run: + print_dry_run(plans, export_root) + return + + # ── 4. Copy months ──────────────────────────────────────────────────────── + git_sha = _git_sha() + print(f"\nCopying {len(plans)} months to {export_root} ...") + results: list[MonthResult] = [] + + for i, plan in enumerate(sorted(plans, key=lambda x: x.year_month), 1): + month_bytes = sum(src.stat().st_size for src, _ in plan.files) + print( + f" [{i:>2}/{len(plans)}] {plan.year_month} " + f"{plan.n_files} file(s) {month_bytes / 1_073_741_824:.3f} GiB ...", + end="", + flush=True, + ) + try: + result = copy_month(plan, force=args.force, git_sha=git_sha) + print(" OK") + results.append(result) + except Exception as exc: + print(f" FAILED: {exc}") + sys.exit(f"\nERROR: Copy failed for {plan.year_month}: {exc}") + + # ── 5. Verify ───────────────────────────────────────────────────────────── + print("\nVerifying export...") + verify_results = verify_export(plans, export_root) + + # ── 6. Summary ──────────────────────────────────────────────────────────── + all_ok = print_summary(verify_results) + + if not all_ok: + sys.exit("EXPORT FAILED — see verification failures above.") + + print(f"Export complete. {len(plans)} months written to {export_root}") + + +if __name__ == "__main__": + main() diff --git a/scripts/csv_to_parquet/validate_month_output.py b/scripts/csv_to_parquet/validate_month_output.py new file mode 100644 index 0000000..5088ded --- /dev/null +++ b/scripts/csv_to_parquet/validate_month_output.py @@ -0,0 +1,1138 @@ +# scripts/csv_to_parquet/validate_month_output.py +from __future__ import annotations + +import argparse +import datetime as dt_mod +import json +import random +import re +import sys +from collections.abc import Sequence +from dataclasses import dataclass, field +from pathlib import Path +from typing import Any, NoReturn + +import polars as pl +import pyarrow.parquet as pq + +JsonDict = dict[str, Any] + +""" +Month-output Validator (QA + determinism + contract enforcement) for ComEd CSV->Parquet migration. + +What this validates (fail-loud; raises ValueError with actionable diagnostics): +1) Discovery: + - Walks --out-root recursively and discovers Hive partitions year=YYYY/month=MM (no filename/count assumptions). + - Finds all parquet files under discovered partitions; fails if none found. + +2) Schema contract (metadata-first where possible): + - Required columns exist exactly (no silent passing on missing columns). + - Dtypes match contract: + zip_code Utf8 + delivery_service_class Categorical + delivery_service_name Categorical + account_identifier Utf8 + datetime Datetime + energy_kwh/plc_value/nspl_value Float64 + year Int32 + month Int8 + Note: year/month accept Int16/Int32 only if explicitly allowed via flags is NOT implemented; contract default is strict. + +3) Partition integrity (per-file): + - year/month columns exist, non-null, and min/max match partition directory year=... month=... + - Detects mismatches and reports offending files. + +4) Datetime invariants (per partition, collected per-file and merged): + - No null datetime. + - min(datetime) has (hour,minute)==(0,0) + - max(datetime) has (hour,minute)==(23,30) + - All datetime values fall within (partition year, partition month) (no spillover). + +5) DST Option B invariants (optional: --dst-month-check, collected per-file and merged): + - Exactly 48 distinct time slots per day (no 49/50 slot days). + - Ensures no timestamps beyond 23:30. + - Spot-checks that (23:00 and 23:30) exist on at least one day with non-null energy_kwh (coarse sanity). + +6) Sortedness + Uniqueness (non-tautological): + - Validates strict lexicographic ordering by (zip_code, account_identifier, datetime). + - Modes: + --check-mode full : PyArrow streaming pass across files; O(batch_size) memory; checks strictly + increasing composite key (sortedness + no duplicates in one pass). + --check-mode sample : checks first/last K rows and deterministic random windows per file; + also checks boundaries and strictly-increasing keys within windows. + +7) Determinism compare (optional: --compare-root): + - Compares directory trees (relative paths) and per-file sizes between two outputs. + - Optionally row-counts for a limited number of files (controlled by --max-files in compare pass). + +How to run: + python scripts/csv_to_parquet/validate_month_output.py --out-root /path/to/month_output_root --check-mode sample + python scripts/csv_to_parquet/validate_month_output.py --out-root ... --check-mode sample --dst-month-check + python scripts/csv_to_parquet/validate_month_output.py --out-root run1 --compare-root run2 --check-mode sample +""" + + +RE_YEAR_DIR = re.compile(r"^year=(?P\d{4})$") +RE_MONTH_DIR = re.compile(r"^month=(?P\d{1,2})$") + + +REQUIRED_SCHEMA: dict[str, pl.DataType] = { + "zip_code": pl.Utf8, + "delivery_service_class": pl.Categorical, + "delivery_service_name": pl.Categorical, + "account_identifier": pl.Utf8, + "datetime": pl.Datetime, + "energy_kwh": pl.Float64, + "plc_value": pl.Float64, + "nspl_value": pl.Float64, + "year": pl.Int32, + "month": pl.Int8, +} + +SORT_KEY_COLS: tuple[str, str, str] = ("zip_code", "account_identifier", "datetime") + + +# --------------------------------------------------------------------------- +# Data classes +# --------------------------------------------------------------------------- + + +@dataclass(frozen=True) +class Partition: + year: int + month: int + path: Path + + +@dataclass +class _DtStats: + """Aggregated datetime statistics for a single file or merged partition.""" + + dt_nulls: int = 0 + dt_min: dt_mod.datetime | None = None + dt_max: dt_mod.datetime | None = None + year_min: int | None = None + year_max: int | None = None + month_min: int | None = None + month_max: int | None = None + + +@dataclass +class _DstFileStats: + """Per-file DST statistics for merge across a partition.""" + + day_slots: dict[dt_mod.date, set[tuple[int, int]]] = field(default_factory=dict) + day_nonnull_late_slots: dict[dt_mod.date, set[tuple[int, int]]] = field(default_factory=dict) + has_beyond_2330: bool = False + + +# --------------------------------------------------------------------------- +# Helpers +# --------------------------------------------------------------------------- + + +def _fail(msg: str) -> NoReturn: + """Abort validation with a diagnostic message. + + Typed as NoReturn so that mypy narrows Optional types after guard clauses + that call _fail() — e.g., after ``if x is None: _fail(...)``, mypy knows + x is not None on the subsequent line. + """ + raise ValueError(msg) + + +def _is_parquet(p: Path) -> bool: + return p.is_file() and p.suffix.lower() == ".parquet" + + +def _read_parquet_schema(path: Path) -> dict[str, pl.DataType]: + """Extract column names and dtypes without reading row data. + + Uses a two-level fallback chain because Polars' ``read_parquet_schema`` + API has varied across versions; ``scan_parquet(...).schema`` is the + reliable alternative. Both are metadata-only operations (O(1) data I/O). + """ + try: + schema = pl.read_parquet_schema(str(path)) + return dict(schema) + except Exception: + try: + return dict(pl.scan_parquet(str(path)).schema) + except Exception as e: + _fail(f"Failed to read parquet schema for {path}: {e}") + return {} + + +def _dtype_eq(observed: pl.DataType, expected: pl.DataType) -> bool: + """Compare observed dtype against the schema contract. + + Special-cases Datetime because the contract requires "is a Datetime" without + constraining time_unit (us/ns/ms) or time_zone. Polars' Datetime is + parameterized, so a naive ``==`` check would reject valid ``Datetime('us')`` + when the contract specifies the unparameterized ``pl.Datetime``. + """ + if expected == pl.Datetime: + return isinstance(observed, pl.Datetime) or observed == pl.Datetime + return observed == expected + + +def _composite_key_expr() -> pl.Expr: + """Build a single-string composite key for sortedness and uniqueness checks. + + Uses U+001F (Unit Separator) as delimiter because it is a non-printable + control character that cannot appear in zip codes, account identifiers, or + datetime strings. This guarantees the composite key comparison is equivalent + to a lexicographic tuple comparison of the three sort-key columns, without + the overhead of maintaining and comparing three separate columns. + """ + return pl.concat_str([ + pl.col("zip_code").cast(pl.Utf8), + pl.lit("\u001f"), # unit separator + pl.col("account_identifier").cast(pl.Utf8), + pl.lit("\u001f"), + pl.col("datetime").cast(pl.Utf8), + ]).alias("_k") + + +def _get_row_count_metadata(path: Path) -> int: + """Get row count from parquet file metadata (O(1), no data scan).""" + pf = pq.ParquetFile(str(path)) + return pf.metadata.num_rows + + +# --------------------------------------------------------------------------- +# Phase 1: Discovery +# --------------------------------------------------------------------------- + + +def _discover_partitions(out_root: Path) -> list[Partition]: # noqa: C901 + if not out_root.exists(): + _fail(f"--out-root does not exist: {out_root}") + if not out_root.is_dir(): + _fail(f"--out-root is not a directory: {out_root}") + + parts: list[Partition] = [] + # Walk directories; find .../year=YYYY/month=MM + for year_dir in out_root.rglob("*"): + if not year_dir.is_dir(): + continue + # Skip _runs/ artifact directories + if "_runs" in year_dir.parts: + continue + m_y = RE_YEAR_DIR.match(year_dir.name) + if not m_y: + continue + year = int(m_y.group("year")) + for month_dir in year_dir.iterdir(): + if not month_dir.is_dir(): + continue + m_m = RE_MONTH_DIR.match(month_dir.name) + if not m_m: + continue + month = int(m_m.group("month")) + if not (1 <= month <= 12): + _fail(f"Invalid month directory detected: {month_dir} (month={month})") + parts.append(Partition(year=year, month=month, path=month_dir)) + + if not parts: + _fail(f"No Hive partitions found under out-root={out_root}. Expected directories like year=YYYY/month=MM.") + # Deterministic ordering + parts.sort(key=lambda p: (p.year, p.month, str(p.path))) + return parts + + +def _discover_parquet_files(partitions: Sequence[Partition]) -> dict[Partition, list[Path]]: + mapping: dict[Partition, list[Path]] = {} + total = 0 + for part in partitions: + files = [p for p in part.path.rglob("*.parquet") if _is_parquet(p)] + files.sort() + mapping[part] = files + total += len(files) + + if total == 0: + _fail( + "Discovery succeeded but found zero parquet files under discovered partitions. " + "Check out-root and conversion output." + ) + return mapping + + +# --------------------------------------------------------------------------- +# Phase 2: Metadata checks (schema + partition integrity) +# --------------------------------------------------------------------------- + + +def _validate_schema_on_file(path: Path) -> None: + """Validate that a single Parquet file conforms to the canonical schema. + + Runs metadata-only (no row data read). Checks both column presence and + dtype compatibility. Fails on the first file that violates the contract, + providing the exact filename and mismatch details for rapid diagnosis. + """ + schema = _read_parquet_schema(path) + + missing = [c for c in REQUIRED_SCHEMA if c not in schema] + if missing: + _fail( + f"Schema missing required columns in file {path}:\n missing={missing}\n observed_cols={sorted(schema.keys())}" + ) + + mismatches: list[str] = [] + for col, expected in REQUIRED_SCHEMA.items(): + observed = schema[col] + if not _dtype_eq(observed, expected): + mismatches.append(f"{col}: expected={expected}, observed={observed}") + + if mismatches: + _fail(f"Dtype mismatches in file {path}:\n " + "\n ".join(mismatches)) + + +def _validate_partition_integrity_file(path: Path, part: Partition) -> None: + """Verify that year/month column values match the Hive directory they reside in. + + A row with year=2023 in a ``year=2024`` directory would silently corrupt + queries that rely on Hive partition pruning. This check reads only six + scalar aggregates (min/max/null_count for year and month) — negligible I/O. + """ + # Read tiny aggregates only. + lf = pl.scan_parquet(str(path)).select([ + pl.col("year").null_count().alias("year_nulls"), + pl.col("month").null_count().alias("month_nulls"), + pl.col("year").min().alias("year_min"), + pl.col("year").max().alias("year_max"), + pl.col("month").min().alias("month_min"), + pl.col("month").max().alias("month_max"), + ]) + try: + row = lf.collect(engine="streaming").row(0) + except Exception as e: + _fail(f"Failed to collect partition integrity stats for {path}: {e}") + + year_nulls, month_nulls, year_min, year_max, month_min, month_max = row + if year_nulls != 0 or month_nulls != 0: + _fail(f"Null partition keys in file {path}: year_nulls={year_nulls}, month_nulls={month_nulls}") + if year_min != part.year or year_max != part.year or month_min != part.month or month_max != part.month: + _fail( + f"Partition key mismatch in file {path} (dir year={part.year}, month={part.month}) " + f"but columns have year_min={year_min}, year_max={year_max}, month_min={month_min}, month_max={month_max}" + ) + + +# --------------------------------------------------------------------------- +# Phase 3a: Streaming sort + duplicate check (full mode) +# --------------------------------------------------------------------------- + + +def _streaming_sort_and_dup_check( + files: Sequence[Path], + batch_size: int = 65_536, +) -> tuple[int, list[dict[str, object]]]: + """Combined streaming sortedness + uniqueness check across ordered files. + + Leverages the global sort order: data sorted by (zip_code, account_identifier, datetime) + means duplicates are always adjacent. Checks each composite key is strictly greater than + the previous (sort order AND uniqueness in a single pass). + + Uses PyArrow iter_batches for O(batch_size) memory per pass. + + Returns (total_rows, per_file_rows). + """ + prev_key: str | None = None + total_rows = 0 + per_file_rows: list[dict[str, object]] = [] + + for fpath in files: + pf = pq.ParquetFile(str(fpath)) + file_rows = 0 + + for batch in pf.iter_batches(batch_size=batch_size, columns=list(SORT_KEY_COLS)): + n = batch.num_rows + if n == 0: + continue + + # Convert PyArrow batch -> Polars DataFrame for composite key + df = pl.from_arrow(batch) + keys = df.select(_composite_key_expr())["_k"] + + # -- Cross-batch/file boundary check -- + first_key = str(keys[0]) + if prev_key is not None: + if first_key < prev_key: + _fail( + f"Sort violation at batch boundary (row ~{total_rows + file_rows}) " + f"in {fpath}: prev_key={prev_key!r} > first_key={first_key!r}" + ) + elif first_key == prev_key: + _fail( + f"Duplicate key at batch boundary (row ~{total_rows + file_rows}) in {fpath}: key={first_key!r}" + ) + + # -- Within-batch: strictly increasing check -- + if n > 1: + violations = ( + df.select([_composite_key_expr()]) + .with_row_index("_idx") + .with_columns(pl.col("_k").shift(1).alias("_kp")) + .filter(pl.col("_kp").is_not_null() & (pl.col("_k") <= pl.col("_kp"))) + .head(1) + ) + if violations.height > 0: + r = violations.row(0) + idx_in_batch, k, kp = r + abs_row = total_rows + file_rows + idx_in_batch + kind = "Duplicate key" if k == kp else "Sort violation" + _fail(f"{kind} at row ~{abs_row} in {fpath}: prev_key={kp!r}, key={k!r}") + + prev_key = str(keys[-1]) + file_rows += n + + if file_rows == 0: + _fail(f"Empty parquet file (0 rows): {fpath}") + + per_file_rows.append({"file": fpath.name, "rows": file_rows}) + total_rows += file_rows + + return total_rows, per_file_rows + + +# --------------------------------------------------------------------------- +# Phase 3b: Sample-mode sort + duplicate check +# --------------------------------------------------------------------------- + + +def _slice_keys(path: Path, offset: int, length: int) -> pl.DataFrame: + # Do NOT use engine="streaming" here: streaming may reorder rows for + # sliced reads, which defeats the purpose of sortedness validation. + # Slices are small (head_k / window_k rows of 3 key cols) so default + # engine is both correct and fast enough. + lf = pl.scan_parquet(str(path)).select([pl.col(c) for c in SORT_KEY_COLS]).slice(offset, length) + try: + return lf.collect() + except Exception as e: + _fail(f"Failed to slice keys for {path} offset={offset} length={length}: {e}") + + +def _keys_strictly_increasing_df(df: pl.DataFrame) -> bool: + """Check that composite keys in df are strictly increasing (sorted + unique). + + "Strictly increasing" (k[i] < k[i+1] for all i) validates both sortedness + AND uniqueness in a single pass: if any adjacent pair has k[i] == k[i+1], + the check fails. This is more efficient than separate sort + deduplicate + checks and is sound because the data is globally sorted by SORT_KEY_COLS. + """ + if df.height <= 1: + return True + violations = ( + df.select([_composite_key_expr()]) + .with_row_index("_idx") + .with_columns(pl.col("_k").shift(1).alias("_kp")) + .filter(pl.col("_kp").is_not_null() & (pl.col("_k") <= pl.col("_kp"))) + ) + return violations.height == 0 + + +def _first_last_key(df: pl.DataFrame) -> tuple[str, str]: + k = df.select( + pl.concat_str([ + pl.col("zip_code").cast(pl.Utf8), + pl.lit("\u001f"), + pl.col("account_identifier").cast(pl.Utf8), + pl.lit("\u001f"), + pl.col("datetime").cast(pl.Utf8), + ]).alias("_k") + )["_k"] + return str(k[0]), str(k[-1]) + + +def _check_sorted_sample(path: Path, seed: int, max_windows: int, window_k: int, head_k: int) -> None: + """Probabilistic sortedness check: validate sort order in sampled windows. + + Checks head, tail, and several deterministic random windows within a file. + Each window validates strictly-increasing composite keys internally, and + cross-window boundary checks confirm ordering between adjacent non-overlapping + windows. + + The overlap guard (``off >= prev_end``) is essential: random windows may + overlap with the head/tail slices or with each other. Comparing the last + key of slice A to the first key of slice B is only valid when B starts at + or after the end of A; otherwise the "boundary" is inside A and the + comparison is semantically meaningless (and produces false positives). + """ + # Get row count cheaply from parquet metadata + n = _get_row_count_metadata(path) + if n <= 1: + return + + rng = random.Random(seed) # noqa: S311 + + slices: list[tuple[int, int, str]] = [] + # Head and tail + slices.append((0, min(head_k, n), "head")) + tail_len = min(head_k, n) + slices.append((max(0, n - tail_len), tail_len, "tail")) + + # Deterministic random windows + if n > window_k and max_windows > 0: + for i in range(max_windows): + off = rng.randrange(0, n - window_k + 1) + slices.append((off, window_k, f"win{i}")) + + # Sort slices by offset to allow boundary checks + slices.sort(key=lambda t: t[0]) + + prev_last_key: str | None = None + prev_tag: str | None = None + prev_end: int = 0 # offset + length of previous slice + + for off, length, tag in slices: + df = _slice_keys(path, off, length) + + if not _keys_strictly_increasing_df(df): + # Locate the violation for diagnostics + viol = ( + df.select([_composite_key_expr()]) + .with_row_index("_idx") + .with_columns(pl.col("_k").shift(1).alias("_kp")) + .filter(pl.col("_kp").is_not_null() & (pl.col("_k") <= pl.col("_kp"))) + .head(1) + ) + if viol.height > 0: + r = viol.row(0) + idx, k, kp = r + kind = "Duplicate key" if k == kp else "Sort violation" + _fail( + f"{kind} in slice tag={tag} offset={off}+{idx} in file {path}: " + f"prev_key={kp!r}, key={k!r}. " + f"Re-run with --check-mode full for exact break index." + ) + _fail( + f"Strictly-increasing violation in slice tag={tag} offset={off} in file {path}. " + f"Re-run with --check-mode full for exact break index." + ) + + first_k, last_k = _first_last_key(df) + # Cross-slice boundary check: only valid when slices do NOT overlap. + # Random windows can overlap with head/tail or each other; comparing + # last-key-of-A to first-key-of-B is meaningless if B starts inside A. + if prev_last_key is not None and off >= prev_end: + if first_k < prev_last_key: + _fail( + f"Sort violation across slice boundary in file {path}: " + f"prev_slice={prev_tag} last_key={prev_last_key!r} > " + f"slice={tag} first_key={first_k!r}. " + f"Re-run with --check-mode full for exact break index." + ) + elif first_k == prev_last_key: + _fail( + f"Duplicate key across slice boundary in file {path}: " + f"prev_slice={prev_tag} key={first_k!r}. " + f"Re-run with --check-mode full for exact break index." + ) + + prev_last_key = last_k + prev_tag = tag + prev_end = off + length + + +# --------------------------------------------------------------------------- +# Phase 4: Datetime invariants (per-file collect + merge) +# --------------------------------------------------------------------------- + + +def _collect_datetime_stats_file(path: Path) -> _DtStats: + """Collect datetime aggregate stats from a single file (cheap aggregates).""" + lf = pl.scan_parquet(str(path)).select([ + pl.col("datetime").null_count().alias("dt_nulls"), + pl.col("datetime").min().alias("dt_min"), + pl.col("datetime").max().alias("dt_max"), + pl.col("datetime").dt.year().min().alias("dt_year_min"), + pl.col("datetime").dt.year().max().alias("dt_year_max"), + pl.col("datetime").dt.month().min().alias("dt_month_min"), + pl.col("datetime").dt.month().max().alias("dt_month_max"), + ]) + try: + row = lf.collect(engine="streaming").row(0) + except Exception as e: + _fail(f"Failed to collect datetime stats for {path}: {e}") + + return _DtStats( + dt_nulls=row[0], + dt_min=row[1], + dt_max=row[2], + year_min=row[3], + year_max=row[4], + month_min=row[5], + month_max=row[6], + ) + + +def _merge_dt_stats(stats_list: Sequence[_DtStats]) -> _DtStats: + """Merge per-file datetime stats into partition-level stats.""" + merged = _DtStats() + for s in stats_list: + merged.dt_nulls += s.dt_nulls + if s.dt_min is not None: + merged.dt_min = min(merged.dt_min, s.dt_min) if merged.dt_min is not None else s.dt_min + if s.dt_max is not None: + merged.dt_max = max(merged.dt_max, s.dt_max) if merged.dt_max is not None else s.dt_max + if s.year_min is not None: + merged.year_min = min(merged.year_min, s.year_min) if merged.year_min is not None else s.year_min + if s.year_max is not None: + merged.year_max = max(merged.year_max, s.year_max) if merged.year_max is not None else s.year_max + if s.month_min is not None: + merged.month_min = min(merged.month_min, s.month_min) if merged.month_min is not None else s.month_min + if s.month_max is not None: + merged.month_max = max(merged.month_max, s.month_max) if merged.month_max is not None else s.month_max + return merged + + +def _validate_datetime_stats_for_partition(merged: _DtStats, part: Partition) -> None: + """Validate merged datetime stats against partition expectations.""" + if merged.dt_nulls != 0: + _fail(f"Null datetime found in partition {part.path}: dt_nulls={merged.dt_nulls}") + + if merged.dt_min is None or merged.dt_max is None: + _fail(f"Datetime min/max unexpectedly None in partition {part.path}") + + # Ensure within partition month + if ( + merged.year_min != part.year + or merged.year_max != part.year + or merged.month_min != part.month + or merged.month_max != part.month + ): + _fail( + f"Datetime spillover in partition {part.path} (dir year={part.year}, month={part.month}) " + f"but datetime year range=({merged.year_min},{merged.year_max}) " + f"month range=({merged.month_min},{merged.month_max})" + ) + + # Time-of-day checks + if (merged.dt_min.hour, merged.dt_min.minute) != (0, 0): + _fail(f"Partition {part.path} has dt_min={merged.dt_min} but expected time-of-day 00:00") + if (merged.dt_max.hour, merged.dt_max.minute) != (23, 30): + _fail(f"Partition {part.path} has dt_max={merged.dt_max} but expected time-of-day 23:30") + + +# --------------------------------------------------------------------------- +# Phase 5: DST Option B (per-file collect + merge) +# --------------------------------------------------------------------------- + + +def _collect_dst_stats_file(path: Path) -> _DstFileStats: + """Collect DST-relevant stats from a single file. + + Returns per-day unique (h,m) slot sets and spot-check data. + Memory: O(days_in_month * 48) = ~1500 entries max. + """ + lf_base = ( + pl.scan_parquet(str(path)) + .select(["datetime", "energy_kwh"]) + .with_columns([ + pl.col("datetime").dt.date().alias("d"), + pl.col("datetime").dt.hour().alias("h"), + pl.col("datetime").dt.minute().alias("m"), + ]) + ) + + # Unique (d, h, m) — at most 31 * 48 = 1488 rows regardless of account count + slots_df = lf_base.select(["d", "h", "m"]).unique().collect(engine="streaming") + day_slots: dict[dt_mod.date, set[tuple[int, int]]] = {} + for row in slots_df.iter_rows(): + d, h, m = row + day_slots.setdefault(d, set()).add((h, m)) + + # Beyond 23:30 check + beyond_count = int( + lf_base.filter((pl.col("h") > 23) | ((pl.col("h") == 23) & (pl.col("m") > 30))) + .select(pl.len()) + .collect(engine="streaming") + .row(0)[0] + ) + + # Non-null energy at 23:00 and 23:30 (for spot-check merge) + late_df = ( + lf_base.filter((pl.col("h") == 23) & pl.col("m").is_in([0, 30]) & pl.col("energy_kwh").is_not_null()) + .select(["d", "h", "m"]) + .unique() + .collect(engine="streaming") + ) + day_nonnull: dict[dt_mod.date, set[tuple[int, int]]] = {} + for row in late_df.iter_rows(): + d, h, m = row + day_nonnull.setdefault(d, set()).add((h, m)) + + return _DstFileStats( + day_slots=day_slots, + day_nonnull_late_slots=day_nonnull, + has_beyond_2330=beyond_count > 0, + ) + + +def _validate_dst_for_partition(part: Partition, files: Sequence[Path]) -> None: + """Validate DST Option B by collecting per-file stats and merging.""" + merged_slots: dict[dt_mod.date, set[tuple[int, int]]] = {} + merged_nonnull: dict[dt_mod.date, set[tuple[int, int]]] = {} + any_beyond = False + + for f in files: + stats = _collect_dst_stats_file(f) + for d, s in stats.day_slots.items(): + merged_slots.setdefault(d, set()).update(s) + for d, s in stats.day_nonnull_late_slots.items(): + merged_nonnull.setdefault(d, set()).update(s) + if stats.has_beyond_2330: + any_beyond = True + + # Check 1: exactly 48 unique time slots per day + bad_days = [(d, len(s)) for d, s in merged_slots.items() if len(s) != 48] + if bad_days: + bad_days.sort() + sample = [{"date": str(d), "slots": n} for d, n in bad_days[:10]] + _fail(f"DST Option B violation: days with slots!=48 in partition {part.path}. Examples (up to 10): {sample}") + + # Check 2: no timestamps beyond 23:30 + if any_beyond: + _fail(f"DST Option B violation: found datetime beyond 23:30 in partition {part.path}.") + + # Check 3: at least one day has non-null energy_kwh at both 23:00 and 23:30 + days_with_both = sum(1 for s in merged_nonnull.values() if (23, 0) in s and (23, 30) in s) + if days_with_both == 0: + _fail( + f"DST Option B spot-check failed in partition {part.path}: " + f"did not find any day with non-null energy_kwh at both 23:00 and 23:30." + ) + + +# --------------------------------------------------------------------------- +# File selection +# --------------------------------------------------------------------------- + + +def _select_files_for_mode(files: Sequence[Path], mode: str, max_files: int | None, seed: int) -> list[Path]: + """Select a subset of files for validation when --max-files is set. + + Full mode uses deterministic first-N selection (reproducible, bias toward + early batches). Sample mode uses seeded random selection to provide + coverage across the full output without examining every file. The seed + ensures that repeated runs with the same arguments validate the same files. + """ + if max_files is None or max_files <= 0 or max_files >= len(files): + return list(files) + + if mode == "full": + return list(files)[:max_files] + + rng = random.Random(seed) # noqa: S311 + idxs = list(range(len(files))) + rng.shuffle(idxs) + chosen = sorted(idxs[:max_files]) + return [files[i] for i in chosen] + + +# --------------------------------------------------------------------------- +# Determinism compare +# --------------------------------------------------------------------------- + + +def _compare_roots(root_a: Path, root_b: Path, max_files: int | None, seed: int) -> None: # noqa: C901 + """Compare two output directories for determinism (same code + inputs → same output). + + Three-tier comparison strategy, each progressively more expensive: + 1. Directory tree structure (file paths must match exactly) + 2. File sizes (cheap; catches most non-determinism from different row counts + or compression differences) + 3. Row counts for a sample of Parquet files (controlled by --max-files) + + This does NOT do byte-for-byte comparison because Parquet writer versions + and compression settings may produce bitwise-different files with identical + logical content. Size + row count is sufficient for migration QA purposes. + """ + if not root_b.exists() or not root_b.is_dir(): + _fail(f"--compare-root is not a directory: {root_b}") + + def list_rel_files(root: Path) -> list[Path]: + rels = [] + for p in root.rglob("*"): + if p.is_file(): + rels.append(p.relative_to(root)) + rels.sort() + return rels + + a_files = list_rel_files(root_a) + b_files = list_rel_files(root_b) + + a_set = set(a_files) + b_set = set(b_files) + if a_set != b_set: + only_a = sorted(a_set - b_set)[:20] + only_b = sorted(b_set - a_set)[:20] + _fail( + "Determinism compare failed: directory trees differ.\n" + f" only_in_out_root (up to 20): {only_a}\n" + f" only_in_compare_root (up to 20): {only_b}" + ) + + # Compare sizes (cheap and stable) + mismatches: list[str] = [] + for rel in a_files: + pa = root_a / rel + pb = root_b / rel + sa = pa.stat().st_size + sb = pb.stat().st_size + if sa != sb: + mismatches.append(f"{rel}: size_out={sa}, size_compare={sb}") + if len(mismatches) >= 50: + break + + if mismatches: + _fail( + "Determinism compare failed: file sizes differ (note: writer versions may legitimately differ; " + "this check is intentionally strict on size).\n " + "\n ".join(mismatches) + ) + + # Optional: row counts for up to max_files parquet files (controlled) + parquet_rels = [rel for rel in a_files if rel.suffix.lower() == ".parquet"] + if not parquet_rels: + return + + chosen: list[Path] + if max_files is None or max_files <= 0 or max_files >= len(parquet_rels): + chosen = parquet_rels + else: + rng = random.Random(seed) # noqa: S311 + idxs = list(range(len(parquet_rels))) + rng.shuffle(idxs) + chosen = [parquet_rels[i] for i in sorted(idxs[:max_files])] + + row_mismatches: list[str] = [] + for rel in chosen: + pa = root_a / rel + pb = root_b / rel + try: + na = int(pl.scan_parquet(str(pa)).select(pl.len()).collect(engine="streaming").row(0)[0]) + nb = int(pl.scan_parquet(str(pb)).select(pl.len()).collect(engine="streaming").row(0)[0]) + except Exception as e: + _fail(f"Determinism compare failed reading row counts for {rel}: {e}") + if na != nb: + row_mismatches.append(f"{rel}: rows_out={na}, rows_compare={nb}") + if len(row_mismatches) >= 50: + break + + if row_mismatches: + _fail("Determinism compare failed: row counts differ.\n " + "\n ".join(row_mismatches)) + + +# --------------------------------------------------------------------------- +# Run artifact validation +# --------------------------------------------------------------------------- + + +def _validate_run_artifacts(run_dir: Path, expected_parquet_count: int | None = None) -> JsonDict: # noqa: C901 + """Validate runner artifacts under a _runs/// directory. + + Checks: + - plan.json exists and is valid JSON + - run_summary.json exists, is valid JSON, and reports total_failure=0 + - Manifest JSONL files exist; all file-level entries are success or skip + - If expected_parquet_count is provided, cross-checks batches_written + + Returns a dict of artifact-check results for inclusion in the validation report. + """ + if not run_dir.exists() or not run_dir.is_dir(): + _fail(f"--run-dir does not exist or is not a directory: {run_dir}") + + results: JsonDict = {"run_dir": str(run_dir)} + + # -- plan.json -- + plan_path = run_dir / "plan.json" + if not plan_path.exists(): + _fail(f"Missing plan.json in run artifacts: {plan_path}") + try: + plan = json.loads(plan_path.read_text(encoding="utf-8")) + except (json.JSONDecodeError, OSError) as e: + _fail(f"Invalid plan.json: {plan_path}: {e}") + results["plan_n_inputs"] = len(plan.get("inputs_sorted", [])) + results["plan_n_batches"] = len(plan.get("batches", [])) + + # -- run_summary.json -- + summary_path = run_dir / "run_summary.json" + if not summary_path.exists(): + _fail(f"Missing run_summary.json in run artifacts: {summary_path}") + try: + summary = json.loads(summary_path.read_text(encoding="utf-8")) + except (json.JSONDecodeError, OSError) as e: + _fail(f"Invalid run_summary.json: {summary_path}: {e}") + + total_failure = int(summary.get("total_failure", -1)) + total_success = int(summary.get("total_success", 0)) + total_skip = int(summary.get("total_skip", 0)) + batches_written = int(summary.get("batches_written", 0)) + stop_requested = summary.get("stop_requested", False) + + if total_failure != 0: + _fail( + f"run_summary.json reports total_failure={total_failure} (must be 0). " + f"total_success={total_success}, total_skip={total_skip}. " + f"Investigate logs at: {run_dir / 'logs' / 'run_log.jsonl'}" + ) + + if stop_requested: + _fail(f"run_summary.json reports stop_requested=True. Run was interrupted: {summary_path}") + + results["summary_total_success"] = total_success + results["summary_total_failure"] = total_failure + results["summary_total_skip"] = total_skip + results["summary_batches_written"] = batches_written + + if expected_parquet_count is not None and batches_written != expected_parquet_count: + _fail( + f"Batch count mismatch: run_summary.json reports batches_written={batches_written} " + f"but discovered {expected_parquet_count} parquet files on disk." + ) + + # -- manifest JSONL -- + manifest_dir = run_dir / "manifests" + if not manifest_dir.exists(): + _fail(f"Missing manifests directory: {manifest_dir}") + + manifest_files = sorted(manifest_dir.glob("manifest_*.jsonl")) + if not manifest_files: + _fail(f"No manifest_*.jsonl files found in {manifest_dir}") + + manifest_failures: list[str] = [] + manifest_success_count = 0 + manifest_skip_count = 0 + + for mf in manifest_files: + try: + for line in mf.read_text(encoding="utf-8").splitlines(): + line = line.strip() + if not line: + continue + rec = json.loads(line) + status = rec.get("status", "") + if status == "success": + manifest_success_count += 1 + elif status == "skip": + manifest_skip_count += 1 + elif status == "failure": + inp = rec.get("input_path", "?") + exc = rec.get("exception_msg", "?") + manifest_failures.append(f"{inp}: {exc}") + except (json.JSONDecodeError, OSError) as e: + _fail(f"Error reading manifest file {mf}: {e}") + + if manifest_failures: + sample = manifest_failures[:10] + _fail(f"Manifest contains {len(manifest_failures)} failure entries (must be 0). Sample (up to 10): {sample}") + + results["manifest_files_checked"] = len(manifest_files) + results["manifest_success_count"] = manifest_success_count + results["manifest_skip_count"] = manifest_skip_count + + # -- batch summaries -- + summary_files = sorted(manifest_dir.glob("summary_*.json")) + batch_failures = [] + for sf in summary_files: + try: + bs = json.loads(sf.read_text(encoding="utf-8")) + if int(bs.get("n_failure", 0)) > 0: + batch_failures.append(f"{sf.name}: n_failure={bs.get('n_failure')}") + except (json.JSONDecodeError, OSError): + batch_failures.append(f"{sf.name}: unreadable") + + if batch_failures: + _fail(f"Batch summary files report failures: {batch_failures[:10]}") + + results["batch_summaries_checked"] = len(summary_files) + + return results + + +# --------------------------------------------------------------------------- +# Main (phase-based architecture) +# --------------------------------------------------------------------------- + + +def main(argv: Sequence[str] | None = None) -> int: # noqa: C901 + """Orchestrate validation in sequential phases. + + Phase architecture rationale: phases are ordered from cheapest to most + expensive. If a cheap check (schema, partition integrity) fails, expensive + checks (streaming sort, DST) are never reached. This fail-fast approach + minimizes wall-clock time when data is corrupt. + + Phases: + 1. Discovery — find partitions and Parquet files + 1b. Compare — structural determinism check (optional, fail-fast) + 2. Metadata — schema contract + partition integrity (metadata-only I/O) + 3. Sortedness + duplicates — streaming or sample-based (configurable) + 4. Datetime invariants — per-file collect + merge (all files) + 5. DST Option B — per-file collect + merge (optional) + 6. Run artifacts — plan.json, manifests, summaries (optional) + 7. Report — build and write validation summary + """ + p = argparse.ArgumentParser(description="Validate ComEd month-output parquet dataset contract.") + p.add_argument( + "--out-root", required=True, help="Converted dataset output root containing year=YYYY/month=MM partitions." + ) + p.add_argument( + "--check-mode", choices=["full", "sample"], default="sample", help="Validation intensity for sortedness checks." + ) + p.add_argument( + "--dst-month-check", action="store_true", help="Enable DST Option B shape checks (48 slots/day; no extras)." + ) + p.add_argument( + "--compare-root", default=None, help="Optional second output root to compare for determinism invariants." + ) + p.add_argument( + "--max-files", type=int, default=None, help="Max parquet files to validate (selection depends on mode)." + ) + p.add_argument("--seed", type=int, default=42, help="Deterministic seed for sampling selection/windows.") + p.add_argument("--output-report", default=None, help="Write validation report JSON to this path.") + p.add_argument( + "--run-dir", + default=None, + help="Runner artifact directory (_runs/YYYYMM//) to validate plan.json, run_summary.json, manifests.", + ) + args = p.parse_args(list(argv) if argv is not None else None) + + out_root = Path(args.out_root).resolve() + + # ── Phase 1: Discovery ────────────────────────────────────────────── + partitions = _discover_partitions(out_root) + mapping = _discover_parquet_files(partitions) + + # ── Phase 1b: Compare mode (structural, fail fast) ────────────────── + if args.compare_root is not None: + _compare_roots(out_root, Path(args.compare_root).resolve(), args.max_files, args.seed) + + # ── Phase 2: Metadata checks (schema + partition integrity) ───────── + total_files = sum(len(v) for v in mapping.values()) + checked_files = 0 + + for part in partitions: + files = mapping[part] + if not files: + _fail( + f"Discovered partition {part.path} (year={part.year}, month={part.month}) " + f"but found zero parquet files under it." + ) + + selected = _select_files_for_mode(files, args.check_mode, args.max_files, args.seed) + for f in selected: + _validate_schema_on_file(f) + _validate_partition_integrity_file(f, part) + checked_files += 1 + + if checked_files == 0: + _fail("No files validated (unexpected). Check --max-files and discovered outputs.") + + # ── Phase 3: Sortedness + duplicates + row counts ─────────────────── + total_rows = 0 + per_file_rows: list[dict[str, object]] = [] + + for part in partitions: + files = mapping[part] + selected = _select_files_for_mode(files, args.check_mode, args.max_files, args.seed) + + if args.check_mode == "full": + # Combined streaming sort+dup check — O(batch_size) memory + partition_rows, partition_per_file = _streaming_sort_and_dup_check(selected) + total_rows += partition_rows + per_file_rows.extend(partition_per_file) + else: + # Sample mode: enhanced strict-increasing check per file + for f in selected: + _check_sorted_sample( + f, + seed=args.seed, + max_windows=3, + window_k=5_000, + head_k=5_000, + ) + frows = _get_row_count_metadata(f) + total_rows += frows + per_file_rows.append({"file": f.name, "rows": frows}) + + # ── Phase 4: Datetime invariants (all files, per-file + merge) ────── + for part in partitions: + files = mapping[part] + dt_stats_list = [_collect_datetime_stats_file(f) for f in files] + merged = _merge_dt_stats(dt_stats_list) + _validate_datetime_stats_for_partition(merged, part) + + # ── Phase 5: DST Option B (all files, per-file + merge) ──────────── + if args.dst_month_check: + for part in partitions: + _validate_dst_for_partition(part, mapping[part]) + + # ── Phase 6: Run artifact integrity (optional) ───────────────────── + run_artifact_results: JsonDict | None = None + if args.run_dir is not None: + run_artifact_results = _validate_run_artifacts( + Path(args.run_dir).resolve(), + expected_parquet_count=total_files, + ) + + # ── Phase 7: Build validation report ──────────────────────────────── + checks_passed = [ + "schema_contract", + "partition_integrity", + "no_duplicates", + "datetime_invariants", + f"sortedness_{args.check_mode}", + ] + + if args.dst_month_check: + checks_passed.append("dst_option_b") + + if args.compare_root: + checks_passed.append("determinism_compare") + + if run_artifact_results is not None: + checks_passed.append("run_artifact_integrity") + + report: JsonDict = { + "status": "pass", + "timestamp": dt_mod.datetime.now(dt_mod.timezone.utc).isoformat(), + "out_root": str(out_root), + "partitions_validated": len(partitions), + "partition_details": [{"year": p.year, "month": p.month, "files": len(mapping[p])} for p in partitions], + "files_validated": checked_files, + "total_files_discovered": total_files, + "total_rows_validated": total_rows, + "per_file_rows": per_file_rows, + "check_mode": args.check_mode, + "dst_month_check": args.dst_month_check, + "checks_passed": checks_passed, + "sort_order": list(SORT_KEY_COLS), + } + + if args.compare_root: + report["compare_root"] = str(Path(args.compare_root).resolve()) + + if run_artifact_results is not None: + report["run_artifacts"] = run_artifact_results + + # Write report if requested + if args.output_report: + report_path = Path(args.output_report) + report_path.parent.mkdir(parents=True, exist_ok=True) + with open(report_path, "w") as outfile: + json.dump(report, outfile, indent=2) + print(f"Validation report written to: {report_path}") + + # Minimal success signal (no prints during failure). + print( + f"OK: validated {checked_files} parquet files across {len(partitions)} partitions " + f"(discovered total parquet files={total_files}, total rows validated={total_rows})." + ) + + return 0 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/scripts/generate_flat_ptc_parquet.py b/scripts/generate_flat_ptc_parquet.py new file mode 100644 index 0000000..c4a7507 --- /dev/null +++ b/scripts/generate_flat_ptc_parquet.py @@ -0,0 +1,58 @@ +#!/usr/bin/env python3 +"""Generate the flat PTC (price-to-compare) hourly parquet for 2026 rates. + +Reads the existing comed_flat_hourly_prices_2023.parquet, keeps its exact +schema and datetime spine (8759 rows, DST-aware), and replaces the +price_cents_per_kwh column with 2026 PTC values: + + - Nonsummer (months 1-5, 10-12): 9.660 cents/kWh + - Summer (months 6-9): 10.028 cents/kWh + +Usage: + python scripts/generate_flat_ptc_parquet.py +""" + +from __future__ import annotations + +from pathlib import Path + +import polars as pl + +PARQUET_PATH = Path("data/reference/comed_flat_hourly_prices_2023.parquet") + +# 2026 flat PTC rates (cents/kWh) +# Source: Client instruction (Eric, CUB) — current 2026 ComEd PTCs +# No public URL; values provided via email +SUMMER_PTC = 10.028 # months 6-9 +NONSUMMER_PTC = 9.660 # months 1-5, 10-12 + + +def main() -> None: + df = pl.read_parquet(PARQUET_PATH) + original_shape = df.shape + original_schema = df.schema + + df = df.with_columns( + pl.when(pl.col("month").is_between(6, 9)) + .then(pl.lit(SUMMER_PTC)) + .otherwise(pl.lit(NONSUMMER_PTC)) + .alias("price_cents_per_kwh") + ) + + # Verify schema and shape are unchanged + if df.shape != original_shape: + raise ValueError(f"Shape changed: {original_shape} -> {df.shape}") + if df.schema != original_schema: + raise ValueError(f"Schema changed: {original_schema} -> {df.schema}") + + df.write_parquet(PARQUET_PATH) + print(f"Wrote {PARQUET_PATH} ({df.shape[0]} rows)") + + # Print verification summary + summary = df.group_by("month").agg(pl.col("price_cents_per_kwh").first()).sort("month") + for row in summary.iter_rows(): + print(f" Month {row[0]:2d}: {row[1]:.3f} cents/kWh") + + +if __name__ == "__main__": + main() diff --git a/scripts/pricing_pilot/account_count_chain_of_custody.py b/scripts/pricing_pilot/account_count_chain_of_custody.py new file mode 100644 index 0000000..e059809 --- /dev/null +++ b/scripts/pricing_pilot/account_count_chain_of_custody.py @@ -0,0 +1,318 @@ +#!/usr/bin/env python3 +"""Account count chain-of-custody check across all delivery classes. + +Verify no accounts are dropped between raw interval data and bill outputs. +For each of the four delivery classes (C23=sf_no_esh, C24=mf_no_esh, C26=sf_esh, C28=mf_esh), +compare distinct account counts at each pipeline stage for January and July 2023. + +Stages: + 1) Raw interval data: ~/pricing_pilot/interval_data/year=2023/month=01|07/ + 2) Bill files: ~/pricing_pilot/bills_unscaled/ (DTOU and STOU per class; flag if counts differ) + 3) Account-BG map: join bills to ~/pricing_pilot/account_bg_map_{yyyymm}.parquet + +Output: Summary table + sample dropped account IDs when counts drop between stages. + +Usage: + uv run python scripts/pricing_pilot/account_count_chain_of_custody.py + uv run python scripts/pricing_pilot/account_count_chain_of_custody.py --data-root ~/pricing_pilot/interval_data --bills-dir ~/pricing_pilot/bills_unscaled +""" + +from __future__ import annotations + +import argparse +import sys +from pathlib import Path + +import polars as pl + +# Delivery class: code (raw) <-> filename suffix (bills) +DELIVERY_CLASSES = ["C23", "C24", "C26", "C28"] # sf_no_esh, mf_no_esh, sf_esh, mf_esh +CODE_TO_SUFFIX = { + "C23": "sf_no_esh", + "C24": "mf_no_esh", + "C26": "sf_esh", + "C28": "mf_esh", +} +SUFFIX_TO_CODE = {v: k for k, v in CODE_TO_SUFFIX.items()} + +MONTHS = [("2023", "01", "202301"), ("2023", "07", "202307")] +MIN_PART_BYTES = 20 * 1024 * 1024 # match compute_household_bills_bounded stub filter + + +def _filter_valid_parquet(paths: list[Path], min_bytes: int) -> list[Path]: + return [p for p in paths if p.stat().st_size >= min_bytes] + + +def stage1_raw_counts(data_root: Path) -> dict[tuple[str, str], int]: + """Stage 1: distinct accounts per (month_yyyymm, delivery_class) from raw interval parquets.""" + class_col = "delivery_service_class" + account_col = "account_identifier" + out: dict[tuple[str, str], int] = {} + + for year, month, yyyymm in MONTHS: + dir_path = data_root / f"year={year}" / f"month={month}" + if not dir_path.exists(): + print(f"[Stage 1] Directory not found: {dir_path}", file=sys.stderr) + for dc in DELIVERY_CLASSES: + out[(yyyymm, dc)] = 0 + continue + parts = sorted(dir_path.glob("*.parquet")) + parts = _filter_valid_parquet(parts, MIN_PART_BYTES) + if not parts: + print(f"[Stage 1] No valid parquet parts in {dir_path} (min {MIN_PART_BYTES} bytes)", file=sys.stderr) + for dc in DELIVERY_CLASSES: + out[(yyyymm, dc)] = 0 + continue + + first = pl.scan_parquet(parts[0]) + schema_names = first.collect_schema().names() + print(f"[Stage 1] Raw interval schema ({yyyymm}): {schema_names}") + if class_col not in schema_names: + print(f"[Stage 1] WARNING: column '{class_col}' not in schema for {yyyymm}", file=sys.stderr) + + # Single lazy scan across all parts — Polars pushes unique + group_by down + print(f"[Stage 1] Counting distinct accounts for {yyyymm} ({len(parts)} parts)...", flush=True) + counts = ( + pl.scan_parquet(parts) + .select(pl.col(account_col), pl.col(class_col)) + .group_by(class_col) + .agg(pl.col(account_col).n_unique().alias("n")) + .collect() + ) + for row in counts.iter_rows(named=True): + dc = str(row[class_col]).strip() + if dc in DELIVERY_CLASSES: + out[(yyyymm, dc)] = row["n"] + for dc in DELIVERY_CLASSES: + if (yyyymm, dc) not in out: + out[(yyyymm, dc)] = 0 + return out + + +def _parse_bill_path(path: Path) -> tuple[str, str, str] | None: + """(yyyymm, 'dtou'|'stou', delivery_class_suffix) or None.""" + stem = path.stem + if "_flat_vs_" not in stem: + return None + month = stem[:6] + if not month.isdigit() or len(month) != 6: + return None + rate = "dtou" if "dtou" in stem else "stou" + for suffix in CODE_TO_SUFFIX.values(): + if stem.endswith(suffix): + return (month, rate, suffix) + return None + + +def stage2_bill_counts(bills_dir: Path) -> tuple[dict[tuple[str, str], int], dict[tuple[str, str], int], list[str]]: + """Stage 2: distinct accounts per (month, delivery_class) for DTOU and STOU. Returns (dtou, stou, flags).""" + account_col = "account_identifier" + dtou_counts: dict[tuple[str, str], int] = {} + stou_counts: dict[tuple[str, str], int] = {} + flags: list[str] = [] + + paths = sorted(bills_dir.glob("*_flat_vs_*_*.parquet")) + for path in paths: + parsed = _parse_bill_path(path) + if not parsed: + continue + yyyymm, rate, suffix = parsed + dc = SUFFIX_TO_CODE[suffix] + n = pl.scan_parquet(path).select(pl.col(account_col).n_unique()).collect().item() + if rate == "dtou": + dtou_counts[(yyyymm, dc)] = n + else: + stou_counts[(yyyymm, dc)] = n + + for yyyymm, dc in set(dtou_counts) | set(stou_counts): + key = (yyyymm, dc) + d = dtou_counts.get(key, 0) + s = stou_counts.get(key, 0) + if d != s: + flags.append(f"DTOU vs STOU count mismatch: {yyyymm} {dc} -> DTOU={d}, STOU={s}") + + return dtou_counts, stou_counts, flags + + +def stage3_bg_join_counts( + bills_dir: Path, + map_pattern: str, +) -> tuple[dict[tuple[str, str], int], dict[tuple[str, str], int]]: + """Stage 3: for each bill file, join to account_bg_map; count with/without geoid_bg match.""" + account_col = "account_identifier" + with_bg: dict[tuple[str, str], int] = {} + without_bg: dict[tuple[str, str], int] = {} + + paths = sorted(bills_dir.glob("*_flat_vs_dtou_*.parquet")) # one rate is enough for account set per class + for path in paths: + parsed = _parse_bill_path(path) + if not parsed: + continue + yyyymm, _, suffix = parsed + dc = SUFFIX_TO_CODE[suffix] + map_path = Path(map_pattern.replace("{yyyymm}", yyyymm)) + if not map_path.exists(): + print(f"[Stage 3] Map not found: {map_path}", file=sys.stderr) + with_bg[(yyyymm, dc)] = 0 + without_bg[(yyyymm, dc)] = 0 + continue + bills = pl.scan_parquet(path).select(account_col).unique().collect() + amap = pl.read_parquet(map_path, columns=[account_col, "geoid_bg"]) + joined = bills.join(amap, on=account_col, how="left") + n_with = joined.filter(pl.col("geoid_bg").is_not_null()).height + n_without = joined.filter(pl.col("geoid_bg").is_null()).height + with_bg[(yyyymm, dc)] = n_with + without_bg[(yyyymm, dc)] = n_without + + return with_bg, without_bg + + +def sample_missing( + earlier_set: set[str], + later_set: set[str], + n: int = 5, +) -> list[str]: + """Return up to n account IDs that are in earlier_set but not in later_set.""" + missing = list(earlier_set - later_set)[:n] + return missing + + +def main() -> int: + """Run chain-of-custody check: compare distinct account counts across pipeline stages.""" + default_root = Path.home() / "pricing_pilot" / "interval_data" + default_bills = Path.home() / "pricing_pilot" / "bills_unscaled" + default_map = str(Path.home() / "pricing_pilot" / "account_bg_map_{yyyymm}.parquet") + + parser = argparse.ArgumentParser(description="Account count chain-of-custody across delivery classes.") + parser.add_argument( + "--data-root", + type=Path, + default=default_root, + help="Root of Hive-partitioned interval parquets (default: ~/pricing_pilot/interval_data). Layout: /year=YYYY/month=MM/*.parquet", + ) + parser.add_argument("--bills-dir", type=Path, default=default_bills, help="Directory of bill parquets") + parser.add_argument( + "--account-bg-map-pattern", type=str, default=default_map, help="Path with {yyyymm} for account_bg_map" + ) + args = parser.parse_args() + + data_root = args.data_root + bills_dir = args.bills_dir + map_pattern = args.account_bg_map_pattern + + if not bills_dir.exists(): + print(f"Bills directory not found: {bills_dir}", file=sys.stderr) + return 1 + + print("=== Stage 1: Raw interval data (distinct accounts per delivery class) ===") + raw = stage1_raw_counts(data_root) + + print("\n=== Stage 2: Bill files (DTOU and STOU distinct accounts per class) ===") + dtou, stou, flags = stage2_bill_counts(bills_dir) + for f in flags: + print(f" FLAG: {f}") + + print("\n=== Stage 3: Join to account_bg_map (with vs without geoid_bg) ===") + with_bg, without_bg = stage3_bg_join_counts(bills_dir, map_pattern) + + # Build summary table: month, delivery_class, raw_interval_accounts, bill_dtou_accounts, bill_stou_accounts, accounts_with_bg_match, accounts_without_bg_match + rows = [] + for yyyymm in ["202301", "202307"]: + for dc in DELIVERY_CLASSES: + rows.append({ + "month": yyyymm, + "delivery_class": dc, + "raw_interval_accounts": raw.get((yyyymm, dc), 0), + "bill_dtou_accounts": dtou.get((yyyymm, dc), 0), + "bill_stou_accounts": stou.get((yyyymm, dc), 0), + "accounts_with_bg_match": with_bg.get((yyyymm, dc), 0), + "accounts_without_bg_match": without_bg.get((yyyymm, dc), 0), + }) + table = pl.DataFrame(rows) + + print("\n--- Summary table ---") + print(table) + + # Drops: raw -> bills; bills -> bg_match + print("\n--- Drops (sample account IDs when count decreases) ---") + any_drops = False + + # Load account sets for sampling (only where we need them) + for yyyymm in ["202301", "202307"]: + map_path = Path(map_pattern.replace("{yyyymm}", yyyymm)) + amap = ( + pl.read_parquet(map_path, columns=["account_identifier", "geoid_bg"]) + if map_path.exists() + else pl.DataFrame({"account_identifier": [], "geoid_bg": []}) + ) + + for dc in DELIVERY_CLASSES: + r = table.filter((pl.col("month") == yyyymm) & (pl.col("delivery_class") == dc)).to_dicts()[0] + raw_n = r["raw_interval_accounts"] + bill_d = r["bill_dtou_accounts"] + bill_s = r["bill_stou_accounts"] + without_bg_n = r["accounts_without_bg_match"] + + # Drop from raw to bill (use DTOU as bill reference) + if raw_n > 0 and bill_d < raw_n: + any_drops = True + year, month = yyyymm[:4], yyyymm[4:6] + dir_path = data_root / f"year={year}" / f"month={month}" + parts = ( + _filter_valid_parquet(sorted(dir_path.glob("*.parquet")), MIN_PART_BYTES) + if dir_path.exists() + else [] + ) + raw_accounts = set() + if parts: + raw_df = ( + pl.scan_parquet(parts) + .filter(pl.col("delivery_service_class") == dc) + .select("account_identifier") + .unique() + .collect() + ) + raw_accounts = set(raw_df["account_identifier"].cast(pl.Utf8).to_list()) + bill_path = bills_dir / f"{yyyymm}_flat_vs_dtou_{CODE_TO_SUFFIX[dc]}.parquet" + bill_accounts = set() + if bill_path.exists(): + bill_accounts = set( + pl.scan_parquet(bill_path) + .select(pl.col("account_identifier").cast(pl.Utf8)) + .unique() + .collect() + .to_series() + .to_list() + ) + samples = sample_missing(raw_accounts, bill_accounts, 5) + print( + f" {yyyymm} {dc}: raw ({raw_n}) -> bill ({bill_d}); sample accounts in raw but not in bills: {samples}" + ) + + # Drop from bill to bg_match (accounts in bills without geoid_bg) + if (bill_d > 0 or bill_s > 0) and without_bg_n > 0: + any_drops = True + bill_path = bills_dir / f"{yyyymm}_flat_vs_dtou_{CODE_TO_SUFFIX[dc]}.parquet" + if bill_path.exists(): + bills_df = pl.scan_parquet(bill_path).select("account_identifier").collect() + joined = bills_df.join(amap, on="account_identifier", how="left") + no_bg = ( + joined.filter(pl.col("geoid_bg").is_null()) + .select("account_identifier") + .to_series() + .cast(pl.Utf8) + .to_list() + ) + samples = no_bg[:5] + print( + f" {yyyymm} {dc}: bill accounts without BG match: {without_bg_n}; sample account IDs: {samples}" + ) + + if not any_drops: + print(" (No drops between stages.)") + + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/pricing_pilot/bill_stats_and_bg_correlation.py b/scripts/pricing_pilot/bill_stats_and_bg_correlation.py new file mode 100644 index 0000000..38b8abf --- /dev/null +++ b/scripts/pricing_pilot/bill_stats_and_bg_correlation.py @@ -0,0 +1,261 @@ +#!/usr/bin/env python3 +""" +Report for ~/pricing_pilot/bills_unscaled/: +1. Per file: row count, mean bill difference, median bill difference. +2. Join to account_bg_map, aggregate mean bill_diff_dollars by block group (geoid_bg). +3. For each (month, delivery_class): Pearson correlation between DTOU and Rate BEST (STOU) + mean deltas at block group level — report all 8 in a table. +4. For each of the 16 files: share of block groups where mean delta is positive (savings) + vs negative (losses). + +Sign convention (canonical, matches compute_household_bills.py): + delta = flat_rate - alternative_rate (bill_a - bill_b) + Positive = customer SAVES under the alternative (TOU is cheaper) + Negative = customer PAYS MORE under the alternative (TOU is worse) + +Usage:: + + uv run python scripts/pricing_pilot/bill_stats_and_bg_correlation.py + + # Override paths: + uv run python scripts/pricing_pilot/bill_stats_and_bg_correlation.py \\ + --bills-dir ~/pricing_pilot/bills_unscaled \\ + --account-bg-map-pattern ~/pricing_pilot/account_bg_map_{yyyymm}.parquet +""" + +from __future__ import annotations + +import argparse +import math +import sys +from pathlib import Path + +import polars as pl + +CLASSES_ORDER = ("sf_no_esh", "mf_no_esh", "sf_esh", "mf_esh") + + +def _add_delta_col(df: pl.DataFrame) -> tuple[pl.DataFrame, str]: + """Add a ``delta`` column using the same priority as export_delta_geojson_by_class. + + Priority: bill_diff_dollars → net_bill_diff_dollars → bill_a_dollars - bill_b_dollars. + Resolved per-file so heterogeneous schemas across files are handled correctly. + + Sign convention: delta = flat - alt (bill_a - bill_b); positive = saves. + + Returns: + Tuple of (DataFrame with ``delta`` column, source column label). + """ + cols = df.columns + if "bill_diff_dollars" in cols: + return df.with_columns(pl.col("bill_diff_dollars").alias("delta")), "bill_diff_dollars" + if "net_bill_diff_dollars" in cols: + return df.with_columns(pl.col("net_bill_diff_dollars").alias("delta")), "net_bill_diff_dollars" + if "bill_b_dollars" in cols and "bill_a_dollars" in cols: + return df.with_columns( + (pl.col("bill_a_dollars") - pl.col("bill_b_dollars")).alias("delta") + ), "bill_a_dollars - bill_b_dollars" + raise ValueError( + f"No delta column in {cols}; expected bill_diff_dollars, " + "net_bill_diff_dollars, or both of bill_b_dollars/bill_a_dollars" + ) + + +def parse_name(name: str) -> tuple[str, str, str] | None: + """Parse {yyyymm}_flat_vs_{dtou|stou}_{delivery_class}.parquet -> (yyyymm, rate, dc) or None.""" + stem = name.replace(".parquet", "") + month = stem[:6] + if not month.isdigit() or len(month) != 6 or "_flat_vs_" not in stem: + return None + rate = "dtou" if "dtou" in stem else "stou" + if "mf_esh" in stem: + dc = "mf_esh" + elif "mf_no_esh" in stem: + dc = "mf_no_esh" + elif "sf_esh" in stem: + dc = "sf_esh" + elif "sf_no_esh" in stem: + dc = "sf_no_esh" + else: + return None + return month, rate, dc + + +def main() -> int: + """Run bill stats and block-group correlation report.""" + default_bills_dir = Path.home() / "pricing_pilot" / "bills_unscaled" + default_map_pattern = str(Path.home() / "pricing_pilot" / "account_bg_map_{yyyymm}.parquet") + + parser = argparse.ArgumentParser(description="Bill stats and block-group correlation report.") + parser.add_argument( + "--bills-dir", + type=Path, + default=default_bills_dir, + help=f"Directory containing *_flat_vs_*_*.parquet bill files (default: {default_bills_dir}).", + ) + parser.add_argument( + "--account-bg-map-pattern", + type=str, + default=default_map_pattern, + help=( + "Path with {yyyymm} placeholder for account->BG crosswalk " + "(default: ~/pricing_pilot/account_bg_map_{yyyymm}.parquet)." + ), + ) + args = parser.parse_args() + + bills_dir: Path = args.bills_dir + map_pattern: str = args.account_bg_map_pattern + + if not bills_dir.exists(): + print(f"Bills directory not found: {bills_dir}", file=sys.stderr) + return 1 + + # Same glob as export_delta_geojson_by_class to avoid ingesting unrelated parquets + files = sorted(bills_dir.glob("*_flat_vs_*_*.parquet")) + files = [f for f in files if parse_name(f.name) is not None] + if not files: + print(f"No bill files matching *_flat_vs_*_*.parquet in {bills_dir}", file=sys.stderr) + return 1 + + # Cache maps; skips months without a map file instead of raising KeyError + map_cache: dict[str, pl.DataFrame | None] = {} + + def _load_map(yyyymm: str) -> pl.DataFrame | None: + if yyyymm not in map_cache: + p = Path(map_pattern.replace("{yyyymm}", yyyymm)) + if not p.exists(): + print(f" WARNING: account-BG map not found for {yyyymm}: {p}", file=sys.stderr) + map_cache[yyyymm] = None + else: + map_cache[yyyymm] = pl.read_parquet(p) + return map_cache[yyyymm] + + # --- 1. Per-file stats --- + print("=" * 70) + print("1. PER-FILE STATS (row count, mean bill diff, median bill diff)") + print(" Delta column resolved per file: bill_diff_dollars → net_bill_diff_dollars → bill_a - bill_b") + print("=" * 70) + + for f in files: + df = pl.read_parquet(f) + n = df.height + if n == 0: + print(f" {f.name}") + print(" row_count=0, mean_bill_diff=NaN, median_bill_diff=NaN [col: N/A]") + else: + df, col_label = _add_delta_col(df) + mean_d = df["delta"].mean() + median_d = df["delta"].median() + print(f" {f.name}") + print(f" row_count={n}, mean_bill_diff={mean_d:.4f}, median_bill_diff={median_d:.4f} [col: {col_label}]") + + # --- 2. Aggregate to block group (mean delta per BG) --- + print() + print("=" * 70) + print("2. BLOCK GROUP MEAN DELTAS (per file, joined with account_bg_map)") + print("=" * 70) + + bg_means: dict[tuple[str, str, str], pl.DataFrame] = {} + + for f in files: + parsed = parse_name(f.name) + if not parsed: + continue + month, rate, dc = parsed + + df = pl.read_parquet(f) + if df.height == 0: + bg_means[(month, rate, dc)] = pl.DataFrame(schema={"geoid_bg": pl.Utf8, "mean_delta": pl.Float64}) + continue + + m = _load_map(month) + if m is None: + continue + + df, _ = _add_delta_col(df) + bg = ( + df.select("account_identifier", "delta") + .join( + m.select("account_identifier", "geoid_bg"), + on="account_identifier", + how="inner", + ) + .group_by("geoid_bg") + .agg(pl.col("delta").mean().alias("mean_delta")) + ) + bg_means[(month, rate, dc)] = bg + print(f" {f.name} -> {bg.height} block groups") + + # --- 3. Pearson correlation table: DTOU vs Rate BEST (STOU) per (month, delivery_class) --- + print() + print("=" * 70) + print("3. PEARSON CORRELATION: DTOU vs Rate BEST (STOU) mean deltas by block group") + print(" (2 months x 4 classes = 8 correlations)") + print("=" * 70) + + corr_rows: list[dict[str, object]] = [] + for month in ("202301", "202307"): + for dc in CLASSES_ORDER: + bg_dtou = bg_means.get((month, "dtou", dc)) + bg_stou = bg_means.get((month, "stou", dc)) + if bg_dtou is None or bg_stou is None or bg_dtou.height == 0 or bg_stou.height == 0: + r: float = float("nan") + n_bg = 0 + else: + combined = bg_dtou.rename({"mean_delta": "mean_delta_dtou"}).join( + bg_stou.rename({"mean_delta": "mean_delta_stou"}), + on="geoid_bg", + how="inner", + ) + n_bg = combined.height + if n_bg >= 2: + r = combined.select(pl.pearson_corr("mean_delta_dtou", "mean_delta_stou")).item() + else: + r = float("nan") + corr_rows.append({"month": month, "delivery_class": dc, "pearson_r": r, "n_bg": n_bg}) + + # Print as pivoted table (month x delivery_class) + header = f"{'month':<10}" + "".join(f"{dc:>14}" for dc in CLASSES_ORDER) + print(header) + for month_label in ("202301", "202307"): + vals = [] + for dc in CLASSES_ORDER: + r_val = next( + row["pearson_r"] for row in corr_rows if row["month"] == month_label and row["delivery_class"] == dc + ) + vals.append(f"{r_val:.4f}" if not math.isnan(r_val) else "N/A") + print(f"{month_label:<10}" + "".join(f"{v:>14}" for v in vals)) + print() + + # --- 4. Share of block groups with savings (mean_delta > 0) vs losses (mean_delta < 0) --- + print("=" * 70) + print("4. SHARE OF BLOCK GROUPS: positive mean delta (savings) vs negative (losses)") + print(" Per file (16 files). July all-losses check: DTOU and/or Rate BEST.") + print("=" * 70) + + for f in files: + parsed = parse_name(f.name) + if not parsed: + continue + month, rate, dc = parsed + bg = bg_means.get((month, rate, dc)) + if bg is None or bg.height == 0: + n_bg = 0 + pct_s = pct_l = "N/A" + else: + n_bg = bg.height + pos = bg.filter(pl.col("mean_delta") > 0).height + neg = bg.filter(pl.col("mean_delta") < 0).height + pct_s = f"{100 * pos / n_bg:.2f}%" + pct_l = f"{100 * neg / n_bg:.2f}%" + print(f" {f.name}") + print(f" n_bg={n_bg}, share BGs with savings (Δ>0)={pct_s}, share BGs with losses (Δ<0)={pct_l}") + + print() + print("Done.") + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/pricing_pilot/build_account_bg_map.py b/scripts/pricing_pilot/build_account_bg_map.py new file mode 100644 index 0000000..c679310 --- /dev/null +++ b/scripts/pricing_pilot/build_account_bg_map.py @@ -0,0 +1,370 @@ +#!/usr/bin/env python3 +"""Build account→block-group map for the pricing pilot analysis. + +Replaces ad-hoc shell one-liners used to generate +~/pricing_pilot/account_bg_map_{yyyymm}.parquet on EC2. + +Pipeline +-------- +1. Locate canonical long Parquet shards for the given month under a + Hive-partitioned tree: /year=YYYY/month=MM/*.parquet + (default: ~/pricing_pilot/interval_data). +2. Optionally filter to a sub-geography by providing a CSV of ZIP codes + via --zip-file. When omitted, all accounts in the input shards are + processed (full service territory / statewide). +3. Extract unique (account_identifier, zip_code) pairs (column-projected + scan: only these two columns are read from disk). +4. Normalise zip_code → zip4 (#####-####). Handles both the + "#####-####" and "#########" variants present in meter data. + Mirrors analysis/rtp/build_regression_dataset.py/_normalize_zip4_expr() + exactly so mappings are byte-for-byte identical across the codebase. +5. Load the ZIP+4→BG crosswalk (data/reference/comed_bg_zip4_crosswalk.txt, + tab-separated, columns Zip/Zip4/CensusKey2023). Derive geoid_bg as + CensusKey2023.zfill(15)[:12]. +6. Resolve crosswalk fan-out (one zip4 → multiple BGs) with + min(geoid_bg) per zip4 — same deterministic tie-break used by + build_regression_dataset.py and the stage-2 R scripts. +7. Left-join (account, zip4) pairs to the crosswalk. For accounts + that appear with multiple zip4s, apply a second min(geoid_bg) + tie-break per account_identifier. +8. Log counts and any accounts that fail to join to any BG. + Abort if the unmatched rate exceeds --max-drop-pct. +9. Assert strict 1:1 account→BG output; fail loudly on violation. +10. Write account_bg_map_{yyyymm}.parquet with columns: + account_identifier (Utf8), geoid_bg (Utf8, 12-digit FIPS). + +Output consumers +---------------- +- bill_stats_and_bg_correlation.py (reads account_identifier, geoid_bg) +- export_delta_geojson_by_class.py (reads account_identifier, geoid_bg) +- account_count_chain_of_custody.py (reads account_identifier, geoid_bg) +- validate_mf_esh_bg_count.py (reads account_identifier, geoid_bg) + +Usage:: + + # Default — all accounts (statewide): + uv run python scripts/pricing_pilot/build_account_bg_map.py --month 202307 + + # Filtered to Chicago: + uv run python scripts/pricing_pilot/build_account_bg_map.py --month 202307 \\ + --zip-file data/reference/geography/chicago_zip_codes.csv +""" + +from __future__ import annotations + +import argparse +import logging +from pathlib import Path + +import polars as pl + +REPO_ROOT = Path(__file__).resolve().parents[2] + +logging.basicConfig( + level=logging.INFO, + format="%(asctime)s - %(levelname)s - %(message)s", +) +log = logging.getLogger(__name__) + + +# --------------------------------------------------------------------------- +# ZIP normalisation (mirrors build_regression_dataset.py/_normalize_zip4_expr) +# --------------------------------------------------------------------------- + + +def _normalize_zip4_expr() -> pl.Expr: + """Polars expression: derive zip4 (#####-####) from zip_code. + + Handles both ``#####-####`` (already correct) and ``#########`` (9-digit). + Mirrors analysis/rtp/build_regression_dataset.py/_normalize_zip4_expr() + exactly so the join key is identical across all pipeline scripts. + """ + raw = pl.col("zip_code").cast(pl.Utf8).str.strip_chars() + return ( + pl.when(raw.str.contains(r"^\d{5}-\d{4}$")) + .then(raw) + .when(raw.str.contains(r"^\d{9}$")) + .then(raw.str.slice(0, 5) + pl.lit("-") + raw.str.slice(5, 4)) + .otherwise(pl.lit(None)) + .alias("zip4") + ) + + +# --------------------------------------------------------------------------- +# Crosswalk loading (mirrors build_regression_dataset.py/load_crosswalk_one_to_one) +# --------------------------------------------------------------------------- + + +def _load_crosswalk(crosswalk_path: Path) -> pl.DataFrame: + """Load ZIP+4→BG crosswalk with deterministic 1:1 linkage. + + Fan-out (one zip4 → multiple BGs) is resolved by taking the smallest + ``geoid_bg`` per ``zip4``. This is arbitrary but reproducible and + matches the R stage-2 scripts and build_regression_dataset.py exactly. + + Returns a collected DataFrame with columns ``zip4`` and ``geoid_bg``. + """ + lf = ( + pl.scan_csv(crosswalk_path, separator="\t", infer_schema_length=10_000) + .with_columns( + (pl.col("Zip").cast(pl.Utf8).str.zfill(5) + pl.lit("-") + pl.col("Zip4").cast(pl.Utf8).str.zfill(4)).alias( + "zip4" + ), + pl.col("CensusKey2023").cast(pl.Utf8).str.zfill(15).str.slice(0, 12).alias("geoid_bg"), + ) + .select("zip4", "geoid_bg") + .drop_nulls() + ) + + n_rows, n_zip4, n_bg = ( + lf.select(pl.len(), pl.col("zip4").n_unique(), pl.col("geoid_bg").n_unique()).collect().row(0) + ) + log.info("Crosswalk: %d rows, %d unique zip4s, %d unique block groups", n_rows, n_zip4, n_bg) + + fanout = ( + lf.group_by("zip4") + .agg(pl.col("geoid_bg").n_unique().alias("n_bg")) + .filter(pl.col("n_bg") > 1) + .select(pl.len()) + .collect() + .item() + ) + if fanout: + log.warning( + "Crosswalk fan-out: %d zip4(s) map to multiple block groups; " + "resolving with min(geoid_bg) per zip4 (mirrors build_regression_dataset.py).", + fanout, + ) + + mapping = lf.group_by("zip4").agg(pl.col("geoid_bg").min()).collect() + log.info("Crosswalk 1:1 mapping: %d zip4 → geoid_bg entries", mapping.height) + return mapping + + +# --------------------------------------------------------------------------- +# CLI + main +# --------------------------------------------------------------------------- + + +def main() -> int: + repo_root = REPO_ROOT + default_parquet_dir = Path.home() / "pricing_pilot" / "interval_data" + default_crosswalk = repo_root / "data/reference/comed_bg_zip4_crosswalk.txt" + default_output_dir = Path.home() / "pricing_pilot" + + parser = argparse.ArgumentParser( + description="Build account→block-group map (account_bg_map_{yyyymm}.parquet).", + ) + parser.add_argument( + "--month", + required=True, + help="Month to process in YYYYMM format (e.g. 202307).", + ) + parser.add_argument( + "--parquet-dir", + type=Path, + default=default_parquet_dir, + help=( + "Root of Hive-partitioned canonical long parquets " + f"(default: {default_parquet_dir}). " + "Expected layout: /year=YYYY/month=MM/*.parquet" + ), + ) + parser.add_argument( + "--crosswalk", + type=Path, + default=default_crosswalk, + help=f"ZIP+4→BG crosswalk TSV (default: {default_crosswalk}).", + ) + parser.add_argument( + "--zip-file", + type=Path, + default=None, + help=( + "Optional CSV with a zip5 column to filter accounts by ZIP code. " + "When omitted, all accounts are included (full service territory). " + "Example: data/reference/geography/chicago_zip_codes.csv for Chicago only." + ), + ) + parser.add_argument( + "--output-dir", + type=Path, + default=default_output_dir, + help=f"Output directory for account_bg_map_{{yyyymm}}.parquet (default: {default_output_dir}).", + ) + parser.add_argument( + "--max-drop-pct", + type=float, + default=5.0, + help="Abort if more than this %% of accounts have no BG match (default: 5.0).", + ) + args = parser.parse_args() + + # ------------------------------------------------------------------ + # Validate --month + # ------------------------------------------------------------------ + month: str = args.month + if len(month) != 6 or not month.isdigit(): + log.error("--month must be 6 digits (YYYYMM), got: %r", month) + return 1 + + # ------------------------------------------------------------------ + # Validate input paths + # ------------------------------------------------------------------ + for path, label in [ + (args.crosswalk, "--crosswalk"), + (args.parquet_dir, "--parquet-dir"), + ]: + if not path.exists(): + log.error("%s not found: %s", label, path) + return 1 + if args.zip_file and not args.zip_file.exists(): + log.error("--zip-file not found: %s", args.zip_file) + return 1 + + # ------------------------------------------------------------------ + # 1. Locate monthly shard directory + # ------------------------------------------------------------------ + year, mm = month[:4], month[4:6] + month_dir = args.parquet_dir / f"year={year}" / f"month={mm}" + if not month_dir.exists(): + log.error("Month directory not found: %s", month_dir) + return 1 + + parts = sorted(month_dir.glob("*.parquet")) + if not parts: + log.error("No parquet shards found in %s", month_dir) + return 1 + log.info("Found %d parquet shard(s) in %s", len(parts), month_dir) + + # ------------------------------------------------------------------ + # 2. Schema check: verify required columns exist before full scan + # ------------------------------------------------------------------ + peek_schema = pl.scan_parquet(parts[0]).collect_schema() + required_cols = {"account_identifier", "zip_code"} + missing = required_cols - set(peek_schema.names()) + if missing: + log.error( + "Parquet shards missing required columns: %s. Found: %s", + sorted(missing), + sorted(peek_schema.names()), + ) + return 1 + + # ------------------------------------------------------------------ + # 3. Lazy scan: project to needed columns, optionally filter by ZIP, + # deduplicate (account_identifier, zip_code) pairs. + # Column projection avoids loading all 60 canonical columns. + # ------------------------------------------------------------------ + lf = pl.scan_parquet(parts).select(["account_identifier", "zip_code"]) + + if args.zip_file is not None: + zip_list = pl.read_csv(args.zip_file, schema_overrides={"zip5": pl.Utf8}).get_column("zip5").to_list() + log.info("ZIP filter: %d codes from %s", len(zip_list), args.zip_file) + zip5_expr = pl.col("zip_code").cast(pl.Utf8).str.replace_all(r"[^0-9]", "").str.slice(0, 5).str.zfill(5) + lf = lf.with_columns(zip5_expr.alias("_zip5")).filter(pl.col("_zip5").is_in(zip_list)).drop("_zip5") + else: + log.info("No ZIP filter — processing all accounts in %s", args.parquet_dir) + + pairs = lf.unique(subset=["account_identifier", "zip_code"]).collect() + + n_pairs = pairs.height + n_accounts_raw = pairs.select(pl.col("account_identifier").n_unique()).item() + log.info( + "After dedup: %d unique (account, zip_code) pairs from %d distinct accounts.", + n_pairs, + n_accounts_raw, + ) + if n_accounts_raw == 0: + log.error("No accounts remain. Check --parquet-dir and --zip-file.") + return 1 + + # ------------------------------------------------------------------ + # 4. Normalise zip_code → zip4 (#####-####) + # ------------------------------------------------------------------ + pairs = pairs.with_columns(_normalize_zip4_expr()) + n_null_zip4 = pairs.filter(pl.col("zip4").is_null()).height + if n_null_zip4: + log.warning( + "%d (account, zip_code) pair(s) produced a null zip4 (unparseable format); dropping.", + n_null_zip4, + ) + pairs = pairs.drop_nulls("zip4") + + # ------------------------------------------------------------------ + # 5. Load crosswalk (already 1:1 zip4→geoid_bg via min tie-break) + # ------------------------------------------------------------------ + crosswalk = _load_crosswalk(args.crosswalk) + + # ------------------------------------------------------------------ + # 6. Join pairs to crosswalk on zip4 (left join to detect misses) + # ------------------------------------------------------------------ + joined = pairs.join(crosswalk, on="zip4", how="left") + + n_matched_pairs = joined.filter(pl.col("geoid_bg").is_not_null()).height + n_unmatched_pairs = joined.height - n_matched_pairs + log.info( + "Crosswalk join: %d matched pairs, %d unmatched pairs (zip4 not in crosswalk).", + n_matched_pairs, + n_unmatched_pairs, + ) + + # Accounts whose zip4s are ALL unmatched — they get no BG at all. + matched_accounts = set(joined.filter(pl.col("geoid_bg").is_not_null()).get_column("account_identifier").to_list()) + all_accounts = set(joined.get_column("account_identifier").to_list()) + no_bg_accounts = sorted(all_accounts - matched_accounts) + n_no_bg = len(no_bg_accounts) + if n_no_bg: + pct_no_bg = n_no_bg / len(all_accounts) * 100 + log.warning( + "%d account(s) (%.2f%%) have no BG match across any of their zip4s. Sample: %s", + n_no_bg, + pct_no_bg, + no_bg_accounts[:10], + ) + if pct_no_bg > args.max_drop_pct: + log.error( + "Unmatched account rate %.2f%% exceeds --max-drop-pct %.2f%%. Aborting.", + pct_no_bg, + args.max_drop_pct, + ) + return 1 + else: + log.info("All accounts matched to at least one BG.") + + # ------------------------------------------------------------------ + # 7. Resolve accounts with multiple zip4s → min(geoid_bg) per account + # (second tie-break, same logic as the crosswalk-level tie-break) + # ------------------------------------------------------------------ + mapping = joined.drop_nulls("geoid_bg").group_by("account_identifier").agg(pl.col("geoid_bg").min()) + n_mapped = mapping.height + log.info( + "Final mapping: %d of %d accounts have a 1:1 BG assignment (%d dropped — no BG match).", + n_mapped, + n_accounts_raw, + n_accounts_raw - n_mapped, + ) + + # ------------------------------------------------------------------ + # 8. Enforce strict 1:1 output (fail loudly on data integrity violations) + # ------------------------------------------------------------------ + n_unique_acct = mapping.select(pl.col("account_identifier").n_unique()).item() + if mapping.height != n_unique_acct: + raise RuntimeError(f"BUG: mapping is not 1:1 — {mapping.height} rows but {n_unique_acct} unique accounts.") + n_null_geoid_bg = mapping.filter(pl.col("geoid_bg").is_null()).height + if n_null_geoid_bg: + raise RuntimeError(f"BUG: {n_null_geoid_bg} null geoid_bg value(s) in final mapping.") + + # ------------------------------------------------------------------ + # 9. Write output + # ------------------------------------------------------------------ + args.output_dir.mkdir(parents=True, exist_ok=True) + out_path = args.output_dir / f"account_bg_map_{month}.parquet" + mapping.select("account_identifier", "geoid_bg").write_parquet(out_path) + log.info("Wrote %s (%d rows).", out_path, mapping.height) + + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/pricing_pilot/build_delivery_class_lookup.py b/scripts/pricing_pilot/build_delivery_class_lookup.py new file mode 100644 index 0000000..3b838a1 --- /dev/null +++ b/scripts/pricing_pilot/build_delivery_class_lookup.py @@ -0,0 +1,177 @@ +#!/usr/bin/env python3 +"""Build account → delivery_service_class lookup from raw interval Parquet. + +Reads the compacted production output for 202301 and 202307, +extracts the unique (account_identifier, delivery_service_class) pairs, +validates 1:1 mapping within each month and cross-month consistency, +then writes a single deduplicated lookup to Parquet. + +Usage:: + + uv run python scripts/pricing_pilot/build_delivery_class_lookup.py \\ + --base-dir ~/pricing_pilot/runs \\ + --output ~/pricing_pilot/runs/billing_output/delivery_class_lookup.parquet +""" + +from __future__ import annotations + +import argparse +import sys +from pathlib import Path + +import polars as pl + +MONTHS = ["202301", "202307"] + + +def load_lookup(base_dir: Path, month: str) -> pl.DataFrame: + """Extract unique (account_identifier, delivery_service_class) pairs for one month. + + Streams each compacted part file in 500K-row batches via PyArrow's + iter_batches(). Each batch's pairs are added to a plain Python set, + keeping peak memory at ~50 MB regardless of input size. The set is + converted to a Polars DataFrame only after all files are processed. + """ + import pyarrow.parquet as pq + + mm = month[4:] + parquet_dir = base_dir / f"out_{month}_production" + part_dir = parquet_dir / "2023" / mm + files = sorted(part_dir.glob("part-*.parquet")) + if not files: + raise FileNotFoundError(f"No part-*.parquet files in {part_dir}") + if len(files) != 3: + print(f" WARNING: expected 3 part files, found {len(files)} in {part_dir}") + print(f" Found {len(files)} compacted part files in {part_dir}") + + pairs: set[tuple[str, str]] = set() + + for i, f in enumerate(files): + pf = pq.ParquetFile(str(f)) + batch_count = 0 + for batch in pf.iter_batches( + batch_size=500_000, + columns=["account_identifier", "delivery_service_class"], + ): + acct_col = batch.column("account_identifier") + dsc_col = batch.column("delivery_service_class") + # Cast dictionary-encoded / categorical columns to plain strings + if hasattr(dsc_col, "dictionary_decode"): + dsc_col = dsc_col.dictionary_decode() + pairs.update(zip(acct_col.to_pylist(), (str(v) for v in dsc_col.to_pylist()))) + batch_count += 1 + if batch_count % 100 == 0: + print(f" batch {batch_count}, unique pairs so far: {len(pairs):,}") + + print(f" File {i + 1}/{len(files)}: {f.name} ({batch_count} batches, unique pairs: {len(pairs):,})") + + return pl.DataFrame( + {"account_identifier": [p[0] for p in pairs], "delivery_service_class": [p[1] for p in pairs]}, + schema={"account_identifier": pl.Utf8, "delivery_service_class": pl.Utf8}, + ) + + +def validate_one_class_per_account(df: pl.DataFrame, label: str) -> None: + """Assert each account maps to exactly one delivery class.""" + multi = ( + df.group_by("account_identifier") + .agg(pl.col("delivery_service_class").n_unique().alias("n_classes")) + .filter(pl.col("n_classes") > 1) + ) + if len(multi) > 0: + print(f"\nERROR [{label}]: {len(multi)} accounts map to multiple classes:") + print(multi.head(20)) + raise ValueError(f"{label}: accounts with multiple delivery classes") + print(f" [{label}] All {len(df)} account→class pairs are 1:1.") + + +def main() -> int: + """Build and validate a deduplicated account → delivery_service_class lookup.""" + default_base = Path.home() / "pricing_pilot" / "runs" + default_output = default_base / "billing_output" / "delivery_class_lookup.parquet" + + parser = argparse.ArgumentParser(description="Build account → delivery_service_class lookup") + parser.add_argument( + "--base-dir", + type=Path, + default=default_base, + help=f"Root directory containing out_YYYYMM_production/ folders (default: {default_base}).", + ) + parser.add_argument( + "--output", + type=Path, + default=default_output, + help=f"Output path for the lookup Parquet file (default: {default_output}).", + ) + args = parser.parse_args() + + lookups: dict[str, pl.DataFrame] = {} + + for month in MONTHS: + print(f"\nLoading {month} from {args.base_dir} ...") + df = load_lookup(args.base_dir, month) + print(f" Raw unique pairs: {len(df):,}") + validate_one_class_per_account(df, month) + + # Null check + null_acct = df["account_identifier"].null_count() + null_class = df["delivery_service_class"].null_count() + if null_acct > 0 or null_class > 0: + print( + f"ERROR: {month}: found nulls — account_identifier: {null_acct}, delivery_service_class: {null_class}", + file=sys.stderr, + ) + return 1 + print(" No nulls in either column.") + + vc = df["delivery_service_class"].value_counts().sort("delivery_service_class") + print(f" Value counts:\n{vc}") + lookups[month] = df + del df, null_acct, null_class, vc + + # ------------------------------------------------------------------ + # Cross-month consistency + # ------------------------------------------------------------------ + jan, jul = lookups["202301"], lookups["202307"] + overlap = jan.join(jul, on="account_identifier", how="inner", suffix="_jul") + + print("\n--- Cross-month consistency ---") + print(f" Accounts in Jan only: {len(jan) - len(overlap):,}") + print(f" Accounts in Jul only: {len(jul) - len(overlap):,}") + print(f" Overlapping accounts: {len(overlap):,}") + + mismatches = overlap.filter(pl.col("delivery_service_class") != pl.col("delivery_service_class_jul")) + if len(mismatches) > 0: + print(f" MISMATCH: {len(mismatches)} accounts changed class between months:") + print(mismatches.head(20)) + else: + print(" All overlapping accounts have consistent delivery class.") + + del overlap, mismatches + + # ------------------------------------------------------------------ + # Merge and deduplicate + # ------------------------------------------------------------------ + combined = pl.concat([jan, jul]).unique() + del jan, jul, lookups + + validate_one_class_per_account(combined, "combined") + + vc_final = combined["delivery_service_class"].value_counts().sort("delivery_service_class") + print("\n--- Final lookup ---") + print(f" Total unique accounts: {len(combined):,}") + print(f" Value counts:\n{vc_final}") + + # ------------------------------------------------------------------ + # Write + # ------------------------------------------------------------------ + args.output.parent.mkdir(parents=True, exist_ok=True) + combined.sort("account_identifier").write_parquet(args.output) + print(f"\n Saved to {args.output}") + print(f" File size: {args.output.stat().st_size / 1024:.1f} KB") + + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/pricing_pilot/build_statewide_account_bg_map.py b/scripts/pricing_pilot/build_statewide_account_bg_map.py new file mode 100644 index 0000000..940b232 --- /dev/null +++ b/scripts/pricing_pilot/build_statewide_account_bg_map.py @@ -0,0 +1,298 @@ +#!/usr/bin/env python3 +"""Build statewide account→block-group map from combined billing Parquets. + +Unlike build_account_bg_map.py (which reads raw interval Parquets for a single +month), this script reads the already-combined STOU/DTOU billing Parquets that +already contain account_identifier and zip_code columns. This avoids scanning +hundreds of GB of interval data. + +Pipeline +-------- +1. Read account_identifier and zip_code from four combined Parquet files: + {billing-output-dir}/stou_combined/stou_combined_202301.parquet + {billing-output-dir}/stou_combined/stou_combined_202307.parquet + {billing-output-dir}/dtou_combined/dtou_combined_202301.parquet + {billing-output-dir}/dtou_combined/dtou_combined_202307.parquet +2. Deduplicate to unique (account_identifier, zip_code) pairs. +3. Normalise zip_code → zip4 (#####-####). +4. Load ZIP+4→BG crosswalk and resolve fan-out with min(geoid_bg) per zip4, + reusing the exact crosswalk logic from build_account_bg_map.py. +5. Left-join accounts to crosswalk on zip4. +6. Resolve accounts with multiple BGs via min(geoid_bg) per account. +7. Assert strict 1:1 mapping; abort if unmatched rate exceeds 5%. +8. Write {billing-output-dir}/account_bg_map_statewide.parquet. + +Usage:: + + python scripts/pricing_pilot/build_statewide_account_bg_map.py \\ + --billing-output-dir /ebs/home/griffin_switch_box/runs/billing_output \\ + --crosswalk data/reference/comed_bg_zip4_crosswalk.txt +""" + +from __future__ import annotations + +import argparse +import logging +from pathlib import Path + +import polars as pl + +REPO_ROOT = Path(__file__).resolve().parents[2] + +logging.basicConfig( + level=logging.INFO, + format="%(asctime)s - %(levelname)s - %(message)s", +) +log = logging.getLogger(__name__) + +# Expected combined Parquet files (relative to --billing-output-dir) +INPUT_FILES = [ + "stou_combined/stou_combined_202301.parquet", + "stou_combined/stou_combined_202307.parquet", + "dtou_combined/dtou_combined_202301.parquet", + "dtou_combined/dtou_combined_202307.parquet", +] + + +# --------------------------------------------------------------------------- +# ZIP normalisation (mirrors build_account_bg_map.py/_normalize_zip4_expr) +# --------------------------------------------------------------------------- + + +def _normalize_zip4_expr() -> pl.Expr: + """Polars expression: derive zip4 (#####-####) from zip_code. + + Handles both ``#####-####`` (already correct) and ``#########`` (9-digit). + Mirrors build_account_bg_map.py/_normalize_zip4_expr() exactly so the + join key is identical across all pipeline scripts. + """ + raw = pl.col("zip_code").cast(pl.Utf8).str.strip_chars() + return ( + pl.when(raw.str.contains(r"^\d{5}-\d{4}$")) + .then(raw) + .when(raw.str.contains(r"^\d{9}$")) + .then(raw.str.slice(0, 5) + pl.lit("-") + raw.str.slice(5, 4)) + .otherwise(pl.lit(None)) + .alias("zip4") + ) + + +# --------------------------------------------------------------------------- +# Crosswalk loading (mirrors build_account_bg_map.py/_load_crosswalk) +# --------------------------------------------------------------------------- + + +def _load_crosswalk(crosswalk_path: Path) -> pl.DataFrame: + """Load ZIP+4→BG crosswalk with deterministic 1:1 linkage. + + Fan-out (one zip4 → multiple BGs) is resolved by taking the smallest + ``geoid_bg`` per ``zip4``. This is arbitrary but reproducible and + matches build_account_bg_map.py and the R stage-2 scripts exactly. + + Returns a collected DataFrame with columns ``zip4`` and ``geoid_bg``. + """ + lf = ( + pl.scan_csv(crosswalk_path, separator="\t", infer_schema_length=10_000) + .with_columns( + (pl.col("Zip").cast(pl.Utf8).str.zfill(5) + pl.lit("-") + pl.col("Zip4").cast(pl.Utf8).str.zfill(4)).alias( + "zip4" + ), + pl.col("CensusKey2023").cast(pl.Utf8).str.zfill(15).str.slice(0, 12).alias("geoid_bg"), + ) + .select("zip4", "geoid_bg") + .drop_nulls() + ) + + n_rows, n_zip4, n_bg = ( + lf.select(pl.len(), pl.col("zip4").n_unique(), pl.col("geoid_bg").n_unique()).collect().row(0) + ) + log.info("Crosswalk: %d rows, %d unique zip4s, %d unique block groups", n_rows, n_zip4, n_bg) + + fanout = ( + lf.group_by("zip4") + .agg(pl.col("geoid_bg").n_unique().alias("n_bg")) + .filter(pl.col("n_bg") > 1) + .select(pl.len()) + .collect() + .item() + ) + if fanout: + log.warning( + "Crosswalk fan-out: %d zip4(s) map to multiple block groups; resolving with min(geoid_bg) per zip4.", + fanout, + ) + + mapping = lf.group_by("zip4").agg(pl.col("geoid_bg").min()).collect() + log.info("Crosswalk 1:1 mapping: %d zip4 → geoid_bg entries", mapping.height) + return mapping + + +# --------------------------------------------------------------------------- +# CLI + main +# --------------------------------------------------------------------------- + + +def main() -> int: + default_crosswalk = REPO_ROOT / "data/reference/comed_bg_zip4_crosswalk.txt" + + parser = argparse.ArgumentParser( + description="Build statewide account→block-group map from combined billing Parquets.", + ) + parser.add_argument( + "--billing-output-dir", + type=Path, + required=True, + help="Root of billing output directory containing stou_combined/ and dtou_combined/ subdirectories.", + ) + parser.add_argument( + "--crosswalk", + type=Path, + default=default_crosswalk, + help=f"ZIP+4→BG crosswalk TSV (default: {default_crosswalk}).", + ) + parser.add_argument( + "--max-drop-pct", + type=float, + default=5.0, + help="Abort if more than this %% of accounts have no BG match (default: 5.0).", + ) + args = parser.parse_args() + + # ------------------------------------------------------------------ + # Validate input paths + # ------------------------------------------------------------------ + if not args.crosswalk.exists(): + log.error("--crosswalk not found: %s", args.crosswalk) + return 1 + if not args.billing_output_dir.exists(): + log.error("--billing-output-dir not found: %s", args.billing_output_dir) + return 1 + + input_paths: list[Path] = [] + for rel in INPUT_FILES: + p = args.billing_output_dir / rel + if not p.exists(): + log.error("Input file not found: %s", p) + return 1 + input_paths.append(p) + log.info("Reading %d combined Parquet files from %s", len(input_paths), args.billing_output_dir) + + # ------------------------------------------------------------------ + # 1. Read account_identifier + zip_code from all four files + # ------------------------------------------------------------------ + frames: list[pl.LazyFrame] = [] + for p in input_paths: + lf = pl.scan_parquet(p).select("account_identifier", "zip_code") + frames.append(lf) + pairs = pl.concat(frames).unique(subset=["account_identifier", "zip_code"]).collect() + + n_pairs = pairs.height + n_accounts = pairs.select(pl.col("account_identifier").n_unique()).item() + log.info("Total unique accounts across all input files: %d", n_accounts) + log.info("Unique (account, zip_code) pairs: %d", n_pairs) + + if n_accounts == 0: + log.error("No accounts found in input files.") + return 1 + + # ------------------------------------------------------------------ + # 2. Normalise zip_code → zip4 (#####-####) + # ------------------------------------------------------------------ + pairs = pairs.with_columns(_normalize_zip4_expr()) + n_null_zip4 = pairs.filter(pl.col("zip4").is_null()).height + if n_null_zip4: + log.warning( + "%d (account, zip_code) pair(s) produced a null zip4 (unparseable format); dropping.", + n_null_zip4, + ) + pairs = pairs.drop_nulls("zip4") + + # ------------------------------------------------------------------ + # 3. Load crosswalk (1:1 zip4→geoid_bg via min tie-break) + # ------------------------------------------------------------------ + crosswalk = _load_crosswalk(args.crosswalk) + + # ------------------------------------------------------------------ + # 4. Left-join accounts to crosswalk on zip4 + # ------------------------------------------------------------------ + joined = pairs.join(crosswalk, on="zip4", how="left") + + n_matched_pairs = joined.filter(pl.col("geoid_bg").is_not_null()).height + n_unmatched_pairs = joined.height - n_matched_pairs + log.info("Crosswalk join: %d matched, %d unmatched pairs.", n_matched_pairs, n_unmatched_pairs) + + # ------------------------------------------------------------------ + # 5. Check unmatched accounts + # ------------------------------------------------------------------ + matched_accounts: set[str] = set( + joined.filter(pl.col("geoid_bg").is_not_null()).get_column("account_identifier").to_list() + ) + all_accounts: set[str] = set(joined.get_column("account_identifier").to_list()) + no_bg_accounts = sorted(all_accounts - matched_accounts) + n_no_bg = len(no_bg_accounts) + + if n_no_bg: + pct_no_bg = n_no_bg / len(all_accounts) * 100 + log.warning( + "%d account(s) (%.2f%%) have no BG match. Sample: %s", + n_no_bg, + pct_no_bg, + no_bg_accounts[:10], + ) + if pct_no_bg > args.max_drop_pct: + log.error( + "Unmatched account rate %.2f%% exceeds --max-drop-pct %.2f%%. Aborting.", + pct_no_bg, + args.max_drop_pct, + ) + return 1 + else: + log.info("All accounts matched to at least one BG.") + + # ------------------------------------------------------------------ + # 6. Resolve accounts with multiple BGs → min(geoid_bg) + # ------------------------------------------------------------------ + mapping = joined.drop_nulls("geoid_bg").group_by("account_identifier").agg(pl.col("geoid_bg").min()) + + # ------------------------------------------------------------------ + # 7. Assert strict 1:1 mapping + # ------------------------------------------------------------------ + n_unique_acct = mapping.select(pl.col("account_identifier").n_unique()).item() + if mapping.height != n_unique_acct: + raise RuntimeError(f"BUG: mapping is not 1:1 — {mapping.height} rows but {n_unique_acct} unique accounts.") + n_null_geoid = mapping.filter(pl.col("geoid_bg").is_null()).height + if n_null_geoid: + raise RuntimeError(f"BUG: {n_null_geoid} null geoid_bg value(s) in final mapping.") + + # ------------------------------------------------------------------ + # 8. Validation summary + # ------------------------------------------------------------------ + n_mapped = mapping.height + n_bg_out = mapping.select(pl.col("geoid_bg").n_unique()).item() + match_rate = n_mapped / n_accounts * 100 + + print(f"\n{'=' * 50}") + print("Statewide account→BG map — validation summary") + print(f"{'=' * 50}") + print(f"Total unique accounts across input files: {n_accounts:,}") + print(f"Crosswalk rows loaded: {crosswalk.height:,}") + print(f"Crosswalk unique zip4 values: {crosswalk.select(pl.col('zip4').n_unique()).item():,}") + print(f"Match count: {n_mapped:,}") + print(f"Match rate: {match_rate:.2f}%") + print(f"Unmatched count: {n_no_bg:,}") + print(f"Unique block groups in output: {n_bg_out:,}") + print(f"Output row count: {n_mapped:,}") + + # ------------------------------------------------------------------ + # 9. Write output + # ------------------------------------------------------------------ + out_path = args.billing_output_dir / "account_bg_map_statewide.parquet" + mapping.select("account_identifier", "geoid_bg").write_parquet(out_path) + log.info("Wrote %s (%d rows).", out_path, n_mapped) + print(f"Output: {out_path}") + + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/pricing_pilot/compute_delivery_deltas.py b/scripts/pricing_pilot/compute_delivery_deltas.py new file mode 100644 index 0000000..ccb7cab --- /dev/null +++ b/scripts/pricing_pilot/compute_delivery_deltas.py @@ -0,0 +1,487 @@ +#!/usr/bin/env python3 +"""Compute delivery + supply deltas for STOU (Rate BEST) analysis. + +Rate BEST shifts both supply AND delivery charges by time of day. +This script computes both components inline from hourly load data, +using the current rate constants (no dependency on pre-computed +household_bills.parquet from the core pipeline). + +Steps: + +1. Loads hourly loads from the v2 pipeline _tmp/ output +2. Assigns each hour to a TOU period +3. Joins the delivery class lookup +4. Computes delivery delta per household (flat delivery - TOU delivery) +5. Computes supply delta per household inline: + flat supply = total_kwh * flat PTC + STOU supply = sum(kwh_period * STOU rate[period]) +6. Combines delivery + supply into total delta output + +Usage:: + + uv run python scripts/pricing_pilot/compute_delivery_deltas.py \\ + --month 202301 \\ + --billing-output-dir ~/pricing_pilot/billing_output + + # Run both months back-to-back: + uv run python scripts/pricing_pilot/compute_delivery_deltas.py \\ + --both \\ + --billing-output-dir ~/pricing_pilot/billing_output +""" + +from __future__ import annotations + +import argparse +import sys +from pathlib import Path + +import polars as pl +import yaml + +# --------------------------------------------------------------------------- +# TOU period definitions (Chicago local time, from hour_chicago) +# --------------------------------------------------------------------------- + +PERIOD_MAP: dict[int, str] = { + 0: "overnight", + 1: "overnight", + 2: "overnight", + 3: "overnight", + 4: "overnight", + 5: "overnight", + 6: "morning", + 7: "morning", + 8: "morning", + 9: "morning", + 10: "morning", + 11: "morning", + 12: "morning", + 13: "midday_peak", + 14: "midday_peak", + 15: "midday_peak", + 16: "midday_peak", + 17: "midday_peak", + 18: "midday_peak", + 19: "evening", + 20: "evening", + 21: "overnight", + 22: "overnight", + 23: "overnight", +} + +PERIODS = ("morning", "midday_peak", "evening", "overnight") + +# --------------------------------------------------------------------------- +# Delivery rates (raw, no adjustment factors, no uncollectible multipliers) +# --------------------------------------------------------------------------- + +# TOU DFCs by delivery class (cents/kWh) +# Source: CUB DTOD Fact Sheet (January 2026), cross-verified against +# ComEd Info Sheet 67 (2026 column) +# URL (CUB): https://www.citizensutilityboard.org/wp-content/uploads/2025/12/ComEdDeliveryTOD.pdf +# URL (Info Sheet 67, in 2026 Ratebook): https://www.comed.com/cdn/assets/v3/assets/blt3ebb3fed6084be2a/blt86ebee5fe6ed02f8/69ab092748ce0ef3e48504df/2026_Ratebook.pdf +# Note: C28 overnight = 1.512 (CUB PDF has typo of 2.512; Info Sheet 67 confirms 1.512) +TOU_DFCS: dict[str, dict[str, float]] = { + "C23": {"morning": 4.009, "midday_peak": 10.712, "evening": 3.747, "overnight": 2.984}, + "C24": {"morning": 3.073, "midday_peak": 8.689, "evening": 2.856, "overnight": 2.251}, + "C26": {"morning": 1.999, "midday_peak": 5.329, "evening": 1.890, "overnight": 1.550}, + "C28": {"morning": 1.925, "midday_peak": 4.975, "evening": 1.823, "overnight": 1.512}, +} + +# Flat DFCs by delivery class (cents/kWh) +# Source: CUB DTOD Fact Sheet (January 2026) +# URL: https://www.citizensutilityboard.org/wp-content/uploads/2025/12/ComEdDeliveryTOD.pdf +FLAT_DFCS: dict[str, float] = { + "C23": 6.228, + "C24": 4.791, + "C26": 3.165, + "C28": 2.996, +} + +# --------------------------------------------------------------------------- +# Supply rates +# --------------------------------------------------------------------------- + +# Flat PTCs by season (cents/kWh) +# Source: Client instruction (Eric, CUB) — current 2026 ComEd PTCs +# No public URL; values provided via email +FLAT_PTCS: dict[str, float] = {"summer": 10.028, "nonsummer": 9.660} + +# STOU supply rates loaded at runtime from this YAML (avoids rate drift) +STOU_YAML_PATH = Path(__file__).resolve().parent.parent.parent / "rate_structures" / "comed_stou_2026.yaml" + +MONTHS = ["202301", "202307"] + + +def _month_to_season(month: str) -> str: + """Map YYYYMM to 'summer' (Jun-Sep) or 'nonsummer'.""" + mm = int(month[4:6]) + return "summer" if 6 <= mm <= 9 else "nonsummer" + + +def _load_stou_supply_rates(yaml_path: Path) -> dict[str, dict[str, float]]: + """Parse STOU YAML into {season: {period: rate_cents_per_kwh}}.""" + with open(yaml_path) as f: + data = yaml.safe_load(f) + rates: dict[str, dict[str, float]] = {} + for season in data["seasons"]: + rates[season["name"]] = {p["period"]: p["price"] for p in season["periods"]} + return rates + + +def _resolve_paths(billing_output_dir: Path, month: str) -> tuple[Path, Path, Path]: + """Derive all file paths from billing-output-dir and month.""" + run_name = f"statewide_stou_{month}_v2" + run_dir = billing_output_dir / run_name / run_name + + hourly_loads_path = run_dir / "_tmp" / f"month={month}" / "hourly_loads.parquet" + delivery_lookup_path = billing_output_dir / "delivery_class_lookup.parquet" + output_dir = billing_output_dir / "stou_combined" + + return hourly_loads_path, delivery_lookup_path, output_dir + + +def _aggregate_hourly_to_periods(hourly_loads_path: Path) -> pl.DataFrame: + """Aggregate hourly kWh into TOU periods per household. + + Returns a DataFrame with columns: + account_identifier, zip_code, period, kwh_period + + Uses PyArrow streaming to avoid OOM on large files. + """ + print(f" Scanning hourly loads: {hourly_loads_path}") + return _aggregate_hourly_pyarrow(hourly_loads_path) + + +def _aggregate_hourly_pyarrow(hourly_loads_path: Path) -> pl.DataFrame: + """Fallback: stream hourly loads via PyArrow iter_batches().""" + import pyarrow.parquet as pq + + pf = pq.ParquetFile(str(hourly_loads_path)) + # Accumulate (account_identifier, zip_code, period) -> kwh sum + sums: dict[tuple[str, str, str], float] = {} + batch_count = 0 + + for batch in pf.iter_batches( + batch_size=500_000, + columns=["account_identifier", "zip_code", "hour_chicago", "kwh_hour"], + ): + chunk = pl.from_arrow(batch) + # Extract hour and assign period + hours = chunk["hour_chicago"].dt.hour().to_list() + accts = chunk["account_identifier"].to_list() + zips = chunk["zip_code"].to_list() + kwhs = chunk["kwh_hour"].to_list() + + for acct, zc, hr, kwh in zip(accts, zips, hours, kwhs): + period = PERIOD_MAP[hr] + key = (acct, zc, period) + sums[key] = sums.get(key, 0.0) + (kwh or 0.0) + + batch_count += 1 + if batch_count % 100 == 0: + print(f" Processed {batch_count} batches, {len(sums):,} unique keys...") + + print(f" PyArrow streaming complete: {batch_count} batches, {len(sums):,} unique keys") + + rows = [{"account_identifier": k[0], "zip_code": k[1], "period": k[2], "kwh_period": v} for k, v in sums.items()] + return pl.DataFrame(rows) + + +def _compute_delivery_deltas(period_kwh: pl.DataFrame, delivery_lookup: pl.DataFrame) -> pl.DataFrame: + """Compute flat and TOU delivery costs, then delivery delta per household. + + Returns one row per household with columns: + account_identifier, zip_code, delivery_service_class, total_kwh, + flat_delivery_dollars, tou_delivery_dollars, delivery_delta_dollars + """ + # Pivot periods to columns: one row per (account_identifier, zip_code) + pivoted = period_kwh.pivot( + on="period", + index=["account_identifier", "zip_code"], + values="kwh_period", + ).fill_null(0.0) + + # Ensure all period columns exist + for p in PERIODS: + if p not in pivoted.columns: + pivoted = pivoted.with_columns(pl.lit(0.0).alias(p)) + + # Join delivery class + pivoted = pivoted.join(delivery_lookup, on="account_identifier", how="inner") + + # Compute total kWh + pivoted = pivoted.with_columns( + (pl.col("morning") + pl.col("midday_peak") + pl.col("evening") + pl.col("overnight")).alias("total_kwh") + ) + + # Compute TOU delivery cost in cents using map over delivery classes + tou_cents_expr = pl.lit(0.0) + for dc, rates in TOU_DFCS.items(): + dc_contribution = pl.lit(0.0) + for period, rate in rates.items(): + dc_contribution = dc_contribution + pl.col(period) * rate + tou_cents_expr = pl.when(pl.col("delivery_service_class") == dc).then(dc_contribution).otherwise(tou_cents_expr) + + # Compute flat delivery cost in cents + flat_cents_expr = pl.lit(0.0) + for dc, rate in FLAT_DFCS.items(): + flat_cents_expr = ( + pl.when(pl.col("delivery_service_class") == dc).then(pl.col("total_kwh") * rate).otherwise(flat_cents_expr) + ) + + pivoted = pivoted.with_columns( + (flat_cents_expr / 100.0).alias("flat_delivery_dollars"), + (tou_cents_expr / 100.0).alias("tou_delivery_dollars"), + ) + + # Delivery delta: positive = TOU delivery is cheaper + pivoted = pivoted.with_columns( + (pl.col("flat_delivery_dollars") - pl.col("tou_delivery_dollars")).alias("delivery_delta_dollars") + ) + + return pivoted.select( + "account_identifier", + "zip_code", + "delivery_service_class", + "total_kwh", + "flat_delivery_dollars", + "tou_delivery_dollars", + "delivery_delta_dollars", + ) + + +def _compute_supply_inline( + period_kwh: pl.DataFrame, month: str, stou_rates: dict[str, dict[str, float]] +) -> pl.DataFrame: + """Compute flat and STOU supply costs per household from period kWh. + + Returns a DataFrame with columns: + account_identifier, zip_code, bill_a_dollars (flat supply), + bill_b_dollars (STOU supply), supply_delta_dollars + """ + season = _month_to_season(month) + flat_ptc = FLAT_PTCS[season] + season_rates = stou_rates[season] + + print(f" Supply season: {season}") + print(f" Flat PTC: {flat_ptc} ¢/kWh") + print(f" STOU rates: {season_rates}") + + # Build STOU rate expression: map each period to its cents/kWh rate + stou_rate_expr = pl.lit(0.0) + for period, rate in season_rates.items(): + stou_rate_expr = pl.when(pl.col("period") == period).then(pl.lit(rate)).otherwise(stou_rate_expr) + + supply = ( + period_kwh.with_columns( + (pl.col("kwh_period") * flat_ptc / 100.0).alias("flat_supply_contrib"), + (pl.col("kwh_period") * stou_rate_expr / 100.0).alias("stou_supply_contrib"), + ) + .group_by("account_identifier", "zip_code") + .agg( + pl.col("flat_supply_contrib").sum().alias("bill_a_dollars"), + pl.col("stou_supply_contrib").sum().alias("bill_b_dollars"), + ) + .with_columns((pl.col("bill_a_dollars") - pl.col("bill_b_dollars")).alias("supply_delta_dollars")) + ) + + print(f" Supply computed for {len(supply):,} households") + return supply + + +def _combine_delivery_and_supply(delivery: pl.DataFrame, supply: pl.DataFrame) -> pl.DataFrame: + """Join delivery deltas with inline-computed supply costs. + + Returns combined output with supply + delivery deltas and totals. + """ + combined = delivery.join(supply, on=["account_identifier", "zip_code"], how="inner") + + combined = combined.with_columns( + (pl.col("bill_a_dollars") + pl.col("flat_delivery_dollars")).alias("total_bill_a_dollars"), + (pl.col("bill_b_dollars") + pl.col("tou_delivery_dollars")).alias("total_bill_b_dollars"), + (pl.col("supply_delta_dollars") + pl.col("delivery_delta_dollars")).alias("total_delta_dollars"), + ) + + combined = combined.with_columns( + pl.when(pl.col("total_bill_a_dollars") != 0) + .then(pl.col("total_delta_dollars") / pl.col("total_bill_a_dollars") * 100) + .otherwise(None) + .alias("total_pct_savings") + ) + + return combined.select( + "account_identifier", + "zip_code", + "delivery_service_class", + "total_kwh", + "bill_a_dollars", + "bill_b_dollars", + "supply_delta_dollars", + "flat_delivery_dollars", + "tou_delivery_dollars", + "delivery_delta_dollars", + "total_bill_a_dollars", + "total_bill_b_dollars", + "total_delta_dollars", + "total_pct_savings", + ) + + +def _validate(combined: pl.DataFrame, n_hourly_hh: int) -> None: + """Print validation checks to stdout.""" + n_out = len(combined) + + # 1. Row counts + print("\n --- Validation ---") + print(f" Hourly loads households: {n_hourly_hh:,}") + print(f" Combined output rows: {n_out:,}") + drops_from_delivery = n_hourly_hh - n_out if n_hourly_hh > n_out else 0 + if drops_from_delivery > 0: + print(f" Dropped by delivery class join: {drops_from_delivery:,}") + + # 2. Delivery delta summary + dd = combined["delivery_delta_dollars"] + print("\n Delivery delta ($/month):") + print(f" mean={dd.mean():.4f} median={dd.median():.4f} min={dd.min():.4f} max={dd.max():.4f}") + n_pos = (dd > 0).sum() + n_neg = (dd < 0).sum() + print(f" % positive (TOU cheaper): {100 * n_pos / n_out:.2f}%") + print(f" % negative (TOU costlier): {100 * n_neg / n_out:.2f}%") + + # 3. Total delta summary + td = combined["total_delta_dollars"] + print("\n Total delta ($/month):") + print(f" mean={td.mean():.4f} median={td.median():.4f} min={td.min():.4f} max={td.max():.4f}") + n_pos_t = (td > 0).sum() + n_neg_t = (td < 0).sum() + print(f" % positive (TOU cheaper): {100 * n_pos_t / n_out:.2f}%") + print(f" % negative (TOU costlier): {100 * n_neg_t / n_out:.2f}%") + + # 4. Sanity: total_delta = supply_delta + delivery_delta + check = ( + combined["total_delta_dollars"] - combined["supply_delta_dollars"] - combined["delivery_delta_dollars"] + ).abs() + max_diff = check.max() + if max_diff >= 1e-10: + raise ValueError(f"total_delta != supply_delta + delivery_delta, max diff = {max_diff}") + print(f"\n Sanity check: total_delta = supply + delivery (max diff: {max_diff:.2e}) OK") + + # 5. Delivery class value counts + vc = combined["delivery_service_class"].value_counts().sort("delivery_service_class") + print(f"\n Delivery class value counts:\n{vc}") + + # 6. Null checks + for col in ("delivery_delta_dollars", "total_delta_dollars", "delivery_service_class"): + nc = combined[col].null_count() + if nc > 0: + print(f" WARNING: {nc} nulls in {col}") + else: + print(f" No nulls in {col}") + + +def process_month(month: str, billing_output_dir: Path) -> int: + """Process a single month end-to-end. Returns 0 on success, 1 on error.""" + print(f"\n{'=' * 60}") + print(f"Processing {month}") + print(f"{'=' * 60}") + + hourly_loads_path, delivery_lookup_path, output_dir = _resolve_paths(billing_output_dir, month) + + # Check delivery class lookup exists + if not delivery_lookup_path.exists(): + print( + f"ERROR: delivery_class_lookup.parquet not found at {delivery_lookup_path}\n" + "Run scripts/pricing_pilot/build_delivery_class_lookup.py first.", + file=sys.stderr, + ) + return 1 + + # Check input files exist + if not hourly_loads_path.exists(): + print(f"ERROR: hourly loads not found at {hourly_loads_path}", file=sys.stderr) + return 1 + + # Load STOU supply rates from YAML + stou_rates = _load_stou_supply_rates(STOU_YAML_PATH) + print(f" Loaded STOU supply rates from {STOU_YAML_PATH.name}") + + # Load delivery class lookup + delivery_lookup = pl.read_parquet(delivery_lookup_path) + print(f" Delivery class lookup: {len(delivery_lookup):,} accounts") + + # Step 1: Aggregate hourly loads to TOU periods + period_kwh = _aggregate_hourly_to_periods(hourly_loads_path) + n_hourly_hh = period_kwh["account_identifier"].n_unique() + print(f" Unique households in hourly loads: {n_hourly_hh:,}") + + # Step 2: Compute delivery deltas + delivery = _compute_delivery_deltas(period_kwh, delivery_lookup) + print(f" Delivery deltas computed for {len(delivery):,} households") + + # Step 3: Compute supply deltas inline (no household_bills.parquet dependency) + supply = _compute_supply_inline(period_kwh, month, stou_rates) + + # Step 4: Combine delivery + supply + combined = _combine_delivery_and_supply(delivery, supply) + + # Step 5: Validate + _validate(combined, n_hourly_hh) + + # Step 6: Write output + output_dir.mkdir(parents=True, exist_ok=True) + output_path = output_dir / f"stou_combined_{month}.parquet" + combined.sort("account_identifier").write_parquet(output_path) + print(f"\n Saved to {output_path}") + print(f" File size: {output_path.stat().st_size / (1024 * 1024):.1f} MB") + print(f" Rows: {len(combined):,}") + + return 0 + + +def main() -> int: + """Parse CLI args and dispatch to process_month for each requested month.""" + default_billing_output = Path.home() / "pricing_pilot" / "billing_output" + + parser = argparse.ArgumentParser( + description="Compute delivery deltas and combine with supply deltas for STOU analysis." + ) + parser.add_argument( + "--month", + type=str, + help="Month to process in YYYYMM format (e.g. 202301).", + ) + parser.add_argument( + "--both", + action="store_true", + help="Process both January 2023 and July 2023.", + ) + parser.add_argument( + "--billing-output-dir", + type=Path, + default=default_billing_output, + help=f"Root billing output directory (default: {default_billing_output}).", + ) + args = parser.parse_args() + + if not args.month and not args.both: + parser.error("Specify --month YYYYMM or --both") + if args.month and args.both: + parser.error("Specify --month or --both, not both") + + months = MONTHS if args.both else [args.month] + + for month in months: + if len(month) != 6 or not month.isdigit(): + print(f"ERROR: Invalid month format '{month}', expected YYYYMM", file=sys.stderr) + return 1 + rc = process_month(month, args.billing_output_dir) + if rc != 0: + return rc + + print("\nDone.") + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/pricing_pilot/export_delta_geojson_by_class.py b/scripts/pricing_pilot/export_delta_geojson_by_class.py new file mode 100644 index 0000000..9d9c185 --- /dev/null +++ b/scripts/pricing_pilot/export_delta_geojson_by_class.py @@ -0,0 +1,577 @@ +#!/usr/bin/env python3 +"""Export GeoJSON layers by delivery class for Felt maps (save/lose by customer class). + +Reads household bill parquets from ~/pricing_pilot/bills_unscaled/ with naming: + {yyyymm}_flat_vs_{dtou|stou}_{delivery_class}.parquet +e.g. 202301_flat_vs_dtou_sf_no_esh.parquet + +For each file: joins to account_bg_map_{yyyymm}.parquet (202301 for January, 202307 for July), +aggregates mean bill delta to block group (geoid_bg), joins to BG geometry, writes one GeoJSON. +Delta computed as bill_a_dollars - bill_b_dollars when available; fallback to +net_bill_diff_dollars, then bill_diff_dollars. + +Sign convention (canonical, matches compute_household_bills.py): + delta = flat_rate - alternative_rate (bill_a - bill_b) + - positive (+) means customer SAVES under the alternative rate (TOU is cheaper) + - negative (-) means customer PAYS MORE under the alternative rate (TOU is worse) + +BG universe: by default, all block groups in the shapefile (Illinois statewide). +When --zip-file is provided, the universe is narrowed to block groups reachable +from those ZIPs via the crosswalk (e.g. Chicago-only analysis). + +Produces 16 GeoJSON files (2 months x 2 rate comparisons x 4 delivery classes). + +Each GeoJSON contains: + - geoid_bg: string, 12-digit Census block group ID (FIPS) + - mean_delta: float, mean monthly bill delta ($, positive=saves); null for BGs with no data + - n_households: int, count of simulated households; null for BGs with no data + - mean_delta_cap_global_sym: float, mean_delta clamped to [-X, +X] where X is + the p80 of |mean_delta| across ALL scenarios; null where mean_delta is null. + Use this field for Felt choropleth with a consistent legend range. + - geometry: polygon, Census BG boundary + +Domain anchors: two synthetic features (__DOMAIN_ANCHOR_MIN__, __DOMAIN_ANCHOR_MAX__) +are injected by default to force Felt to use the full symmetric color domain across +maps. Disable with --no-domain-anchors. + +Output files: {output_dir}/{yyyymm}_{dtou|stou}_{delivery_class}.geojson +e.g. 202301_dtou_sf_no_esh.geojson, 202307_stou_mf_esh.geojson + +Sidecar: {output_dir}/range_global.json — contains bound_sym (X), method, sign convention. + +Geometry: Block group shapefile (default repo data/shapefiles/...). If missing, script +exits with error; provide a path via --shapefile. + +Usage:: + + # Statewide (default): all BGs in the shapefile + uv run python scripts/pricing_pilot/export_delta_geojson_by_class.py + + # Chicago-filtered: restrict to BGs reachable from Chicago ZIPs + uv run python scripts/pricing_pilot/export_delta_geojson_by_class.py \\ + --zip-file data/reference/geography/chicago_zip_codes.csv + + # Override paths: + uv run python scripts/pricing_pilot/export_delta_geojson_by_class.py \\ + --bills-dir ~/pricing_pilot/bills_unscaled \\ + --account-bg-map-pattern ~/pricing_pilot/account_bg_map_{yyyymm}.parquet \\ + --shapefile /path/to/tl_2023_17_bg.shp \\ + --output-dir ~/pricing_pilot/geojson_out + + # Disable global range (omits mean_delta_cap_global_sym and range_global.json): + uv run python scripts/pricing_pilot/export_delta_geojson_by_class.py --no-global-range + +Felt usage: + - Set numericAttribute to mean_delta_cap_global_sym (NOT mean_delta) + - Set legend min = -bound_sym, max = +bound_sym for ALL layers + - bound_sym is printed at run end and stored in range_global.json +""" + +from __future__ import annotations + +import argparse +import datetime +import json +import sys +from dataclasses import dataclass +from pathlib import Path + +import geopandas as gpd +import numpy as np +import pandas as pd +import polars as pl + +REPO_ROOT = Path(__file__).resolve().parents[2] + +# Sign convention (canonical, matches compute_household_bills.py): +# delta = flat_rate - alternative_rate (bill_a - bill_b) +# Positive = customer SAVES under the alternative (TOU is cheaper) +# Negative = customer PAYS MORE under the alternative (TOU is worse) +SIGN_CONVENTION = "delta = flat_rate - alternative_rate (bill_a - bill_b); positive = customer saves under alt rate; negative = customer pays more under alt rate" + + +@dataclass +class _ScenarioData: + month: str + rate: str + delivery_class: str + bill_path: Path + # pandas DataFrame with columns: geoid_bg (str), mean_delta (float), n_households (int) + bg_pd: pd.DataFrame + county_fips_set: frozenset + + +def _choose_delta(cols: list[str]) -> pl.Expr: + """Return a Polars expression for bill delta with the canonical sign convention. + + Preference order: + 1. bill_a_dollars - bill_b_dollars (explicit; matches sign convention by construction) + 2. net_bill_diff_dollars (fallback; semantics assumed to be flat - alt) + 3. bill_diff_dollars (last resort; semantics may vary — Pass 1 consistency + check will warn if sign is flipped) + + Sign convention: delta = flat_rate - alternative_rate (bill_a - bill_b). + Positive = customer SAVES under alt rate; negative = customer pays more. + """ + if "bill_b_dollars" in cols and "bill_a_dollars" in cols: + return pl.col("bill_a_dollars") - pl.col("bill_b_dollars") + if "net_bill_diff_dollars" in cols: + # Assumed semantics: flat - alt. Caller must verify. + return pl.col("net_bill_diff_dollars") + if "bill_diff_dollars" in cols: + # Last resort. Semantics may vary; consistency check below will warn if sign-flipped. + return pl.col("bill_diff_dollars") + raise ValueError(f"No delta column found. Have: {cols}") + + +def _parse_bill_filename(path: Path) -> tuple[str, str, str] | None: + """Parse {yyyymm}_flat_vs_{dtou|stou}_{delivery_class}.parquet -> (yyyymm, rate, delivery_class).""" + stem = path.name.replace(".parquet", "") + if "_flat_vs_" not in stem or stem.count("_") < 4: + return None + month = stem[:6] + if not month.isdigit() or len(month) != 6: + return None + rate = "dtou" if "dtou" in stem else "stou" + if "mf_esh" in stem: + dc = "mf_esh" + elif "mf_no_esh" in stem: + dc = "mf_no_esh" + elif "sf_esh" in stem: + dc = "sf_esh" + elif "sf_no_esh" in stem: + dc = "sf_no_esh" + else: + return None + return (month, rate, dc) + + +def main() -> int: + """Export GeoJSON layers by delivery class from household bill parquets.""" + default_bills_dir = Path.home() / "pricing_pilot" / "bills_unscaled" + default_map_pattern = str(Path.home() / "pricing_pilot" / "account_bg_map_{yyyymm}.parquet") + default_out = Path.home() / "pricing_pilot" / "geojson_out" + + parser = argparse.ArgumentParser( + description="Export GeoJSON by delivery class from household bills (one file per month/rate/class).", + ) + parser.add_argument( + "--bills-dir", + type=Path, + default=default_bills_dir, + help=f"Directory containing *_flat_vs_*_*.parquet bill files (default: {default_bills_dir}).", + ) + parser.add_argument( + "--account-bg-map-pattern", + type=str, + default=default_map_pattern, + help="Path with {yyyymm} placeholder for account->BG crosswalk (default: ~/pricing_pilot/account_bg_map_{{yyyymm}}.parquet).", + ) + parser.add_argument( + "--shapefile", + type=Path, + default=REPO_ROOT / "data/shapefiles/tiger2023_il_bg/tl_2023_17_bg.shp", + help="Block group shapefile (default: repo data/shapefiles/...). If missing, script exits; provide path or add shapefile.", + ) + parser.add_argument( + "--zip-file", + type=Path, + default=None, + help=( + "Optional CSV with a zip5 column to restrict the BG universe to block groups " + "reachable from those ZIPs via the crosswalk. When omitted, all BGs in the " + "shapefile are included. Example: data/reference/geography/chicago_zip_codes.csv" + ), + ) + parser.add_argument( + "--crosswalk", + type=Path, + default=REPO_ROOT / "data/reference/comed_bg_zip4_crosswalk.txt", + help="ZIP+4→BG crosswalk TSV. Required when --zip-file is used.", + ) + parser.add_argument( + "--output-dir", + type=Path, + default=default_out, + help=f"Output directory for GeoJSON files (default: {default_out}).", + ) + parser.add_argument( + "--no-global-range", + dest="global_range", + action="store_false", + default=True, + help="Disable global symmetric range: omits mean_delta_cap_global_sym column and range_global.json.", + ) + parser.add_argument( + "--global-range-quantile", + type=float, + default=0.80, + metavar="Q", + help="Quantile of |mean_delta| used for global symmetric bound X (default: 0.80 = p80).", + ) + parser.add_argument( + "--no-domain-anchors", + dest="domain_anchors", + action="store_false", + default=True, + help="Disable domain anchor injection (two synthetic features that force Felt to use the full symmetric color domain).", + ) + parser.add_argument( + "--expected-bg-count", + type=int, + default=None, + metavar="N", + help="Expected BG feature count per output file. If provided, fails if any file differs. " + "If omitted, the first file's count becomes the expectation and subsequent files are checked against it.", + ) + args = parser.parse_args() + + bills_dir = args.bills_dir + if not bills_dir.exists(): + print(f"Bills directory not found: {bills_dir}", file=sys.stderr) + return 1 + bill_paths = sorted(bills_dir.glob("*_flat_vs_*_*.parquet")) + bill_paths = [p for p in bill_paths if _parse_bill_filename(p) is not None] + if not bill_paths: + print( + f"No bill files matching *_flat_vs_*_*.parquet (with parseable month/rate/class) in {bills_dir}", + file=sys.stderr, + ) + return 1 + + if not args.shapefile.exists(): + print(f"Shapefile not found: {args.shapefile}", file=sys.stderr) + print("Provide a block group shapefile via --shapefile (e.g. Census TIGER tl_2023_17_bg.shp).", file=sys.stderr) + return 1 + + args.output_dir.mkdir(parents=True, exist_ok=True) + g = gpd.read_file(args.shapefile) + if "GEOID" not in g.columns: + print("Shapefile missing GEOID column.", file=sys.stderr) + return 1 + g["GEOID"] = g["GEOID"].astype(str).str.strip() + + # ========================================================================= + # Build BG universe. + # When --zip-file is provided, universe = BG GEOIDs reachable from those + # ZIP5s via the crosswalk. Otherwise, all BGs in the shapefile are included. + # BGs outside the universe are excluded entirely; BGs inside with no billing + # data are kept as gray "no-data" polygons (n_households=0, metric null). + # ========================================================================= + if args.zip_file is not None: + # Sub-geography mode: build BG universe from ZIP filter via crosswalk + if not args.zip_file.exists(): + print(f"--zip-file not found: {args.zip_file}", file=sys.stderr) + return 1 + if not args.crosswalk.exists(): + print(f"--crosswalk not found: {args.crosswalk}", file=sys.stderr) + return 1 + _zip5_raw = pl.read_csv(args.zip_file, infer_schema_length=0) + _zip5s = set(_zip5_raw["zip5"].str.strip_chars().str.zfill(5).to_list()) + _xw = pl.read_csv(args.crosswalk, separator="\t", infer_schema_length=0) + _xw_filtered = _xw.filter(pl.col("Zip").str.strip_chars().str.zfill(5).is_in(_zip5s)) + _bg_universe_geoids = frozenset( + _xw_filtered["CensusKey2023"].str.strip_chars().str.zfill(15).str.slice(0, 12).unique().to_list() + ) + g_universe = g[g["GEOID"].isin(_bg_universe_geoids)].copy() + if len(g_universe) == 0: + print( + "ERROR: ZIP-based BG universe produced 0 geometry features." + " Check --zip-file, --crosswalk, and shapefile GEOID format.", + file=sys.stderr, + ) + return 1 + geo_scope = f"zip_filter:{args.zip_file.name}" + print(f"[geo] BG universe (ZIP-filtered): kept {len(g_universe)}/{len(g)} features") + else: + # Statewide mode: all BGs in shapefile + g_universe = g.copy() + geo_scope = "statewide" + print(f"[geo] BG universe (statewide): all {len(g_universe)} features from shapefile") + + # ========================================================================= + # PASS 1: load all scenarios and aggregate to BG level. + # We do this before writing any files so we can compute the global range X + # from ALL scenarios before committing any output. + # ========================================================================= + print(f"Pass 1: aggregating {len(bill_paths)} bill file(s) to block-group level...") + scenarios: list[_ScenarioData] = [] + all_mean_deltas: list[float] = [] # collected across every scenario for global X + + for bill_path in bill_paths: + bill_path = Path(bill_path) + parsed = _parse_bill_filename(bill_path) + if not parsed: + continue + month, rate, delivery_class = parsed + + map_path = Path(args.account_bg_map_pattern.format(yyyymm=month)) + if not map_path.exists(): + print(f" Account-BG map not found for {month}: {map_path}", file=sys.stderr) + continue + + lf = pl.scan_parquet(bill_path) + cols = lf.collect_schema().names() + # Only read columns needed for delta computation and join. + _needed = {"account_identifier"} & set(cols) + for c in ("bill_a_dollars", "bill_b_dollars", "bill_diff_dollars", "net_bill_diff_dollars"): + if c in cols: + _needed.add(c) + _needed.add("account_identifier") + df = lf.select(sorted(_needed)).collect() + if "account_identifier" not in cols: + print(f" Bills missing account_identifier: {bill_path}", file=sys.stderr) + continue + + # Internal consistency check: if both bill_a/b_dollars AND bill_diff_dollars + # are present, verify that bill_diff_dollars matches (a - b) and is not sign-flipped. + # This catches upstream sign errors early and loudly. + if {"bill_a_dollars", "bill_b_dollars", "bill_diff_dollars"}.issubset(cols): + _chk = df.select( + (pl.col("bill_a_dollars") - pl.col("bill_b_dollars")).alias("d_ab"), + pl.col("bill_diff_dollars").alias("d_col"), + ).with_columns( + (pl.col("d_ab") - pl.col("d_col")).abs().alias("diff_same"), + (pl.col("d_ab") + pl.col("d_col")).abs().alias("diff_flip"), + ) + mean_same = _chk["diff_same"].mean() + mean_flip = _chk["diff_flip"].mean() + if mean_flip < mean_same: + print( + f" WARNING [{bill_path.name}]: bill_diff_dollars is OPPOSITE-SIGN to" + f" (bill_a - bill_b) [mean|a-b - diff|={mean_same:.4g}," + f" mean|a-b + diff|={mean_flip:.4g}]." + " Using (bill_a_dollars - bill_b_dollars); bill_diff_dollars IGNORED.", + file=sys.stderr, + ) + elif mean_same > 1e-6: + print( + f" WARNING [{bill_path.name}]: bill_diff_dollars differs from (bill_a - bill_b)" + f" but is not a clean sign-flip [mean|same|={mean_same:.4g}," + f" mean|flip|={mean_flip:.4g}]. Using (bill_a - bill_b).", + file=sys.stderr, + ) + # else: agree within tolerance — no warning + + # Sign convention: delta = flat_rate - alternative_rate (bill_a - bill_b) + delta_expr = _choose_delta(cols) + df = df.with_columns(delta_expr.alias("delta_dollars")) + + amap = pl.read_parquet(map_path).select( + pl.col("account_identifier").cast(pl.Utf8), + pl.col("geoid_bg").cast(pl.Utf8), + ) + joined = ( + df.select( + pl.col("account_identifier").cast(pl.Utf8), + pl.col("delta_dollars").cast(pl.Float64), + ) + .join(amap, on="account_identifier", how="inner") + .filter(pl.col("geoid_bg").is_not_null()) + ) + + bg = joined.group_by("geoid_bg").agg( + pl.col("delta_dollars").mean().alias("mean_delta"), + pl.len().alias("n_households"), + ) + if bg.height == 0: + print(f" No BG data after join for {bill_path.name}; skipping.", file=sys.stderr) + continue + + bg_pd = bg.to_pandas() + bg_pd["geoid_bg"] = bg_pd["geoid_bg"].astype(str).str.strip() + bg_pd["mean_delta"] = bg_pd["mean_delta"].astype("float64") + bg_pd["n_households"] = bg_pd["n_households"].astype("int64") + + county_fips_set = frozenset(bg_pd["geoid_bg"].str[:5]) + all_mean_deltas.extend(bg_pd["mean_delta"].dropna().tolist()) + scenarios.append( + _ScenarioData( + month=month, + rate=rate, + delivery_class=delivery_class, + bill_path=bill_path, + bg_pd=bg_pd, + county_fips_set=county_fips_set, + ) + ) + print(f" Loaded {bill_path.name}: {bg.height} BGs with data, counties={sorted(county_fips_set)}") + + if not scenarios: + print("No scenarios were successfully processed.", file=sys.stderr) + return 1 + + # ========================================================================= + # Compute global symmetric bound X. + # X = quantile(|mean_delta|, q) across all non-null BG values in all scenarios. + # Using a symmetric bound ensures +$5 means the same thing on every layer. + # ========================================================================= + bound_sym: float | None = None + if args.global_range: + if not all_mean_deltas: + print("WARNING: global range requested but no mean_delta values collected.", file=sys.stderr) + else: + q = args.global_range_quantile + bound_sym = float(np.quantile(np.abs(all_mean_deltas), q)) + print( + f"\nGlobal symmetric bound: X = {bound_sym:.4f}" + f" (p{int(q * 100)} of |mean_delta| across {len(scenarios)} scenarios," + f" {len(all_mean_deltas)} BG values)" + ) + print(f" => Felt legend: min = -{bound_sym:.2f} max = +{bound_sym:.2f}") + print(f" => Sign convention: {SIGN_CONVENTION}\n") + + # ========================================================================= + # PASS 2: join geometry, add mean_delta_cap_global_sym, validate, write. + # ========================================================================= + print(f"Pass 2: joining geometry and writing {len(scenarios)} GeoJSON file(s)...") + written_paths: list[Path] = [] + expected_bg_count: int | None = args.expected_bg_count # None until first file sets it + + for s in scenarios: + # Scope geometry to BG universe (statewide or ZIP-filtered). + # g_universe is fixed across all scenarios; computed once before Pass 1. + g_bg_universe = g_universe + + # Left join: every BG in the universe appears; null for BGs with no household data. + merged = g_bg_universe.merge(s.bg_pd, left_on="GEOID", right_on="geoid_bg", how="left") + # Fill geoid_bg from GEOID for rows with no matching household data. + merged["geoid_bg"] = merged["geoid_bg"].fillna(merged["GEOID"]) + # Fill n_households to 0 for BGs with no modeled households. + merged["n_households"] = merged["n_households"].fillna(0).astype("int64") + + # ---- Fail-loud validation ---------------------------------------- + + # 1. geoid_bg uniqueness + if merged["geoid_bg"].duplicated().any(): + dupes = merged.loc[merged["geoid_bg"].duplicated(keep=False), "geoid_bg"].tolist() + print( + f"ERROR: duplicate geoid_bg in {s.bill_path.name}: {dupes[:10]}", + file=sys.stderr, + ) + return 1 + + # 2. Feature count consistency across all files + n_f = len(merged) + if expected_bg_count is None: + expected_bg_count = n_f # first file sets the expectation + elif n_f != expected_bg_count: + print( + f"ERROR: BG feature count mismatch in {s.bill_path.name}: got {n_f}, expected {expected_bg_count}.", + file=sys.stderr, + ) + return 1 + + # 3. Warn if any BGs with data had no matching geometry (data is lost). + n_unmatched = int((~s.bg_pd["geoid_bg"].isin(g_bg_universe["GEOID"])).sum()) + if n_unmatched > 0: + print( + f" WARNING: {n_unmatched}/{len(s.bg_pd)} BG(s) with data in {s.bill_path.name}" + " had no matching GEOID in shapefile and were excluded.", + file=sys.stderr, + ) + + # ---- Build output GeoDataFrame ----------------------------------- + + out_cols = ["geoid_bg", "mean_delta", "n_households"] + + if bound_sym is not None: + # Clamp mean_delta to [-X, +X]; NaN stays NaN (no data BGs remain null). + merged["mean_delta_cap_global_sym"] = merged["mean_delta"].clip(-bound_sym, bound_sym) + out_cols.append("mean_delta_cap_global_sym") + + out_cols.append("geometry") + out_gdf = merged[out_cols].copy() + out_gdf = gpd.GeoDataFrame(out_gdf, geometry="geometry") + + # ---- Domain anchor injection ------------------------------------- + # Two synthetic features that pin the color domain to [-bound_sym, +bound_sym] + # so Felt uses the full symmetric range across all maps. + if bound_sym is not None and args.domain_anchors: + # Fail-loud: no real BG (n_households > 0) should have null capped delta + real_with_null_cap = out_gdf[(out_gdf["n_households"] > 0) & (out_gdf["mean_delta_cap_global_sym"].isna())] + if len(real_with_null_cap) > 0: + print( + f"ERROR: {len(real_with_null_cap)} BG(s) with households have null " + f"mean_delta_cap_global_sym in {s.bill_path.name}", + file=sys.stderr, + ) + return 1 + + anchor_min = { + "geoid_bg": "__DOMAIN_ANCHOR_MIN__", + "mean_delta": None, + "n_households": 0, + "mean_delta_cap_global_sym": -bound_sym, + "geometry": None, + } + anchor_max = { + "geoid_bg": "__DOMAIN_ANCHOR_MAX__", + "mean_delta": None, + "n_households": 0, + "mean_delta_cap_global_sym": bound_sym, + "geometry": None, + } + anchors_gdf = gpd.GeoDataFrame([anchor_min, anchor_max], geometry="geometry", crs=out_gdf.crs) + out_gdf = gpd.GeoDataFrame(pd.concat([out_gdf, anchors_gdf], ignore_index=True), geometry="geometry") + + out_path = args.output_dir / f"{s.month}_{s.rate}_{s.delivery_class}.geojson" + out_gdf.to_file(out_path, driver="GeoJSON") + + # ---- Per-file summary -------------------------------------------- + n_nonnull = int(out_gdf["mean_delta"].notna().sum()) + mean_d = float(out_gdf["mean_delta"].mean()) + min_d = float(out_gdf["mean_delta"].min()) + max_d = float(out_gdf["mean_delta"].max()) + n_hh = int(out_gdf["n_households"].dropna().sum()) + + summary_parts = [ + f"n_features_total={n_f}", + f"n_features_nonnull={n_nonnull}", + f"mean_delta min={min_d:.2f} mean={mean_d:.2f} max={max_d:.2f}", + ] + if bound_sym is not None: + cap_min = float(out_gdf["mean_delta_cap_global_sym"].min()) + cap_max = float(out_gdf["mean_delta_cap_global_sym"].max()) + summary_parts.append(f"cap=[{cap_min:.2f}, {cap_max:.2f}]") + summary_parts.append(f"n_households_nonnull={n_hh}") + + print(f" Wrote {out_path.name} | " + " | ".join(summary_parts)) + written_paths.append(out_path) + + # ========================================================================= + # Write range_global.json sidecar. + # ========================================================================= + if bound_sym is not None: + q = args.global_range_quantile + range_meta = { + "bound_sym": bound_sym, + "method": f"p{int(q * 100)}_abs_over_all_scenarios", + "global_range_quantile": q, + "geo_scope": geo_scope, + "domain_anchors": args.domain_anchors, + "created_at": datetime.datetime.now(datetime.timezone.utc).isoformat(), + "sign_convention": SIGN_CONVENTION, + "n_scenarios": len(scenarios), + "n_bg_values_used": len(all_mean_deltas), + } + range_path = args.output_dir / "range_global.json" + range_path.write_text(json.dumps(range_meta, indent=2) + "\n") + print(f"\nWrote {range_path}") + + # ========================================================================= + # Final summary. + # ========================================================================= + print(f"\nDone. {len(written_paths)} GeoJSON file(s) in {args.output_dir}") + if bound_sym is not None: + print(f" All layers share bound_sym = ±{bound_sym:.4f}") + print( + f" Felt: set numericAttribute=mean_delta_cap_global_sym, legend min=-{bound_sym:.2f}, max=+{bound_sym:.2f}" + ) + if expected_bg_count is not None: + print(f" All layers have {expected_bg_count} BG features (consistent coverage).") + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/pricing_pilot/export_statewide_geojson.py b/scripts/pricing_pilot/export_statewide_geojson.py new file mode 100644 index 0000000..71e2900 --- /dev/null +++ b/scripts/pricing_pilot/export_statewide_geojson.py @@ -0,0 +1,293 @@ +#!/usr/bin/env python3 +"""Export GeoJSON choropleth layers for statewide pricing simulation maps. + +Reads BG-level CSVs produced by run_statewide_analysis.py and joins them to +Census block group geometry to produce GeoJSON files suitable for web map +embedding (e.g. Felt, Mapbox). + +Inputs: + - BG-level CSVs: {analysis-dir}/bg_level_{stou|dtou}_{jan|jul}.csv + Expected columns: geoid_bg, mean_delta, mean_pct_savings, n_households, + median_household_income (plus others that are ignored). + - TIGER shapefile: block group boundaries with a GEOID column. + +Outputs (one per scenario): + {out-dir}/{scenario}.geojson with fields: + geoid_bg, mean_delta, mean_pct_savings, n_households, + median_household_income, mean_delta_clamped, geometry + + {out-dir}/color_range.json — symmetric color bounds for consistent legends. + +Sign convention (matches run_statewide_analysis.py / compute_household_bills.py): + delta = flat_rate - alternative_rate + Positive = customer SAVES under the alternative rate (TOU is cheaper) + Negative = customer PAYS MORE under the alternative rate (TOU is worse) + +All IL block groups appear in the output (left join from shapefile). BGs with +no modeled data have null metric values and n_households = 0. + +Usage:: + + python scripts/pricing_pilot/export_statewide_geojson.py \\ + --analysis-dir /ebs/home/griffin_switch_box/runs/billing_output/statewide_analysis \\ + --shapefile data/shapefiles/tiger2023_il_bg/tl_2023_17_bg.shp \\ + --out-dir /ebs/home/griffin_switch_box/runs/billing_output/statewide_geojson +""" + +from __future__ import annotations + +import argparse +import datetime +import json +import sys +from pathlib import Path + +import geopandas as gpd +import numpy as np +import pandas as pd + +REPO_ROOT = Path(__file__).resolve().parents[2] + +SIGN_CONVENTION = ( + "delta = flat_rate - alternative_rate; " + "positive = customer saves under alt rate; " + "negative = customer pays more under alt rate" +) + +# The 20 scenarios we expect: 4 pooled + 16 per-class, mapped to CSV filenames. +SCENARIOS = { + "stou_jan": "bg_level_stou_jan.csv", + "stou_jul": "bg_level_stou_jul.csv", + "dtou_jan": "bg_level_dtou_jan.csv", + "dtou_jul": "bg_level_dtou_jul.csv", + "stou_jan_sf_no_esh": "bg_level_stou_jan_sf_no_esh.csv", + "stou_jan_mf_no_esh": "bg_level_stou_jan_mf_no_esh.csv", + "stou_jan_sf_esh": "bg_level_stou_jan_sf_esh.csv", + "stou_jan_mf_esh": "bg_level_stou_jan_mf_esh.csv", + "stou_jul_sf_no_esh": "bg_level_stou_jul_sf_no_esh.csv", + "stou_jul_mf_no_esh": "bg_level_stou_jul_mf_no_esh.csv", + "stou_jul_sf_esh": "bg_level_stou_jul_sf_esh.csv", + "stou_jul_mf_esh": "bg_level_stou_jul_mf_esh.csv", + "dtou_jan_sf_no_esh": "bg_level_dtou_jan_sf_no_esh.csv", + "dtou_jan_mf_no_esh": "bg_level_dtou_jan_mf_no_esh.csv", + "dtou_jan_sf_esh": "bg_level_dtou_jan_sf_esh.csv", + "dtou_jan_mf_esh": "bg_level_dtou_jan_mf_esh.csv", + "dtou_jul_sf_no_esh": "bg_level_dtou_jul_sf_no_esh.csv", + "dtou_jul_mf_no_esh": "bg_level_dtou_jul_mf_no_esh.csv", + "dtou_jul_sf_esh": "bg_level_dtou_jul_sf_esh.csv", + "dtou_jul_mf_esh": "bg_level_dtou_jul_mf_esh.csv", +} + +# Columns carried from the BG-level CSV into the GeoJSON output. +METRIC_COLS = [ + "mean_delta", + "mean_pct_savings", + "n_households", + "median_household_income", +] + + +def main() -> int: + """Export statewide GeoJSON choropleth layers from BG-level analysis CSVs.""" + parser = argparse.ArgumentParser( + description="Export statewide GeoJSON choropleth layers from BG-level analysis CSVs.", + ) + parser.add_argument( + "--analysis-dir", + type=Path, + required=True, + help="Directory containing bg_level_*.csv files from run_statewide_analysis.py.", + ) + parser.add_argument( + "--shapefile", + type=Path, + default=REPO_ROOT / "data/shapefiles/tiger2023_il_bg/tl_2023_17_bg.shp", + help="Block group shapefile with GEOID column (default: repo data/shapefiles/...).", + ) + parser.add_argument( + "--out-dir", + type=Path, + required=True, + help="Output directory for GeoJSON files and color_range.json.", + ) + parser.add_argument( + "--quantile", + type=float, + default=0.80, + metavar="Q", + help="Quantile of |mean_delta| for symmetric color bound (default: 0.80).", + ) + args = parser.parse_args() + + # ------------------------------------------------------------------ + # Validate inputs + # ------------------------------------------------------------------ + if not args.analysis_dir.is_dir(): + print(f"Analysis directory not found: {args.analysis_dir}", file=sys.stderr) + return 1 + if not args.shapefile.exists(): + print(f"Shapefile not found: {args.shapefile}", file=sys.stderr) + return 1 + + # ------------------------------------------------------------------ + # Load shapefile + # ------------------------------------------------------------------ + print(f"Loading shapefile: {args.shapefile}") + geo = gpd.read_file(args.shapefile) + if "GEOID" not in geo.columns: + print("Shapefile missing GEOID column.", file=sys.stderr) + return 1 + geo["GEOID"] = geo["GEOID"].astype(str).str.strip() + print(f" {len(geo)} block group features loaded.") + + # ------------------------------------------------------------------ + # Pass 1: load all scenario CSVs, collect mean_delta values + # ------------------------------------------------------------------ + print(f"\nPass 1: loading BG-level CSVs from {args.analysis_dir} ...") + loaded: dict[str, pd.DataFrame] = {} + all_mean_deltas: list[float] = [] + + for scenario_name, csv_name in SCENARIOS.items(): + csv_path = args.analysis_dir / csv_name + if not csv_path.exists(): + print(f" WARNING: {csv_path} not found; skipping scenario '{scenario_name}'.", file=sys.stderr) + continue + + df = pd.read_csv(csv_path, dtype={"geoid_bg": str}) + df["geoid_bg"] = df["geoid_bg"].astype(str).str.strip().str.zfill(12) + + # Validate expected columns are present + missing = [c for c in METRIC_COLS if c not in df.columns] + if missing: + print(f" ERROR: {csv_name} missing columns: {missing}", file=sys.stderr) + return 1 + + loaded[scenario_name] = df + non_null_deltas = df["mean_delta"].dropna().tolist() + all_mean_deltas.extend(non_null_deltas) + print(f" {csv_name}: {len(df)} BGs, {len(non_null_deltas)} with mean_delta values") + + if not loaded: + print("No scenario CSVs were loaded.", file=sys.stderr) + return 1 + + # ------------------------------------------------------------------ + # Compute global symmetric color bound + # ------------------------------------------------------------------ + if not all_mean_deltas: + print("No mean_delta values across any scenario.", file=sys.stderr) + return 1 + + q = args.quantile + bound = float(np.quantile(np.abs(all_mean_deltas), q)) + print(f"\nGlobal symmetric bound: {bound:.4f}") + print(f" (p{int(q * 100)} of |mean_delta| across {len(loaded)} scenarios, {len(all_mean_deltas)} BG values)") + print(f" Legend range: [-{bound:.2f}, +{bound:.2f}]") + + # ------------------------------------------------------------------ + # Pass 2: join geometry, clamp, validate, write GeoJSON + # ------------------------------------------------------------------ + args.out_dir.mkdir(parents=True, exist_ok=True) + print(f"\nPass 2: writing GeoJSON to {args.out_dir} ...") + written: list[Path] = [] + expected_count: int | None = None + + for scenario_name, df in loaded.items(): + # Left join: all BGs in shapefile appear; unmatched get null metrics. + merged = geo.merge(df[["geoid_bg", *METRIC_COLS]], left_on="GEOID", right_on="geoid_bg", how="left") + + # Fill join key and n_households for unmatched BGs. + merged["geoid_bg"] = merged["geoid_bg"].fillna(merged["GEOID"]) + merged["n_households"] = merged["n_households"].fillna(0).astype("int64") + + # Validate geoid_bg uniqueness. + if merged["geoid_bg"].duplicated().any(): + dupes = merged.loc[merged["geoid_bg"].duplicated(keep=False), "geoid_bg"].unique()[:10] + print(f"ERROR: duplicate geoid_bg in {scenario_name}: {dupes.tolist()}", file=sys.stderr) + return 1 + + # Validate consistent feature count. + n_features = len(merged) + if expected_count is None: + expected_count = n_features + elif n_features != expected_count: + print( + f"ERROR: feature count mismatch in {scenario_name}: {n_features} vs expected {expected_count}", + file=sys.stderr, + ) + return 1 + + # Warn about data BGs that had no geometry match. + n_unmatched = int((~df["geoid_bg"].isin(geo["GEOID"])).sum()) + if n_unmatched > 0: + print( + f" WARNING: {n_unmatched}/{len(df)} BG(s) in {scenario_name} had no matching GEOID in shapefile.", + file=sys.stderr, + ) + + # Clamp mean_delta to symmetric bound; NaN stays NaN. + merged["mean_delta_clamped"] = merged["mean_delta"].clip(-bound, bound) + + # Invert sign: positive = cost increase, negative = savings. + # Aligns with Felt's default diverging ramp (red=high=bad, blue=low=good). + merged["cost_change"] = -merged["mean_delta_clamped"] + + # Select output columns. + out_cols = [ + "geoid_bg", + "mean_delta", + "mean_pct_savings", + "n_households", + "median_household_income", + "mean_delta_clamped", + "cost_change", + "geometry", + ] + out_gdf = gpd.GeoDataFrame(merged[out_cols], geometry="geometry") + + out_path = args.out_dir / f"{scenario_name}.geojson" + out_gdf.to_file(out_path, driver="GeoJSON") + + # Per-file summary. + n_nonnull = int(out_gdf["mean_delta"].notna().sum()) + mean_d = out_gdf["mean_delta"].mean() + min_d = out_gdf["mean_delta"].min() + max_d = out_gdf["mean_delta"].max() + n_hh = int(out_gdf["n_households"].sum()) + print( + f" {out_path.name}: {n_features} features ({n_nonnull} with data), " + f"mean_delta [{min_d:.2f}, {max_d:.2f}] mean={mean_d:.2f}, " + f"n_households={n_hh:,}" + ) + written.append(out_path) + + # ------------------------------------------------------------------ + # Write color_range.json sidecar + # ------------------------------------------------------------------ + range_meta = { + "bound_sym": bound, + "method": f"p{int(q * 100)}_abs_over_all_scenarios", + "quantile": q, + "legend_min": -bound, + "legend_max": bound, + "n_scenarios": len(loaded), + "n_bg_values_used": len(all_mean_deltas), + "sign_convention": SIGN_CONVENTION, + "created_at": datetime.datetime.now(datetime.timezone.utc).isoformat(), + } + range_path = args.out_dir / "color_range.json" + range_path.write_text(json.dumps(range_meta, indent=2) + "\n") + print(f"\nWrote {range_path}") + + # ------------------------------------------------------------------ + # Final summary + # ------------------------------------------------------------------ + print(f"\nDone. {len(written)} GeoJSON file(s) in {args.out_dir}") + print(f" Symmetric bound: +/-{bound:.4f}") + if expected_count is not None: + print(f" All layers have {expected_count} BG features.") + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/pricing_pilot/generate_scatterplots.py b/scripts/pricing_pilot/generate_scatterplots.py new file mode 100644 index 0000000..e0fcb19 --- /dev/null +++ b/scripts/pricing_pilot/generate_scatterplots.py @@ -0,0 +1,453 @@ +#!/usr/bin/env python3 +"""Generate income-vs-savings scatterplots from corrected combined parquets. + +Produces 8 scatterplot PNGs (4 scenarios x 2 metrics) and a regression +summary CSV. Reads from the corrected stou_combined and dtou_combined +parquets (steps 1-2 of the pricing pilot pipeline), NOT from the stale +bills_unscaled/ directory. + +Scenarios: + DTOU January, DTOU July, STOU January, STOU July + +Metrics: + Absolute delta ($) and percent savings (%) + +Usage:: + + uv run python scripts/pricing_pilot/generate_scatterplots.py \\ + --billing-output-dir ~/pricing_pilot/billing_output \\ + --census ~/pricing_pilot/billing_output/census_17_2023.parquet \\ + --out-dir ~/pricing_pilot/scatterplots + + # With custom options: + uv run python scripts/pricing_pilot/generate_scatterplots.py \\ + --billing-output-dir ~/pricing_pilot/billing_output \\ + --census ~/pricing_pilot/billing_output/census_17_2023.parquet \\ + --out-dir ~/pricing_pilot/scatterplots \\ + --dpi 300 --min-accounts-per-bg 10 +""" + +from __future__ import annotations + +import argparse +import sys +from pathlib import Path + +import matplotlib +import matplotlib.pyplot as plt +import numpy as np +import polars as pl +import statsmodels.api as sm +from matplotlib.ticker import FuncFormatter + +matplotlib.rcParams.update({ + "font.family": "serif", + "font.serif": ["Times New Roman"], + "font.size": 12, +}) + +# --------------------------------------------------------------------------- +# Scenario definitions +# --------------------------------------------------------------------------- + +ALLOWED_CLASSES = {"C23", "C24", "C26", "C28"} + +# (relative path, scenario tag, display name, month label, +# delta column, pct savings column) +SCENARIOS: list[tuple[str, str, str, str, str, str]] = [ + ( + "dtou_combined/dtou_combined_202301.parquet", + "dtou_jan", + "DTOU", + "January", + "dtou_total_delta_dollars", + "dtou_total_pct_savings", + ), + ( + "dtou_combined/dtou_combined_202307.parquet", + "dtou_jul", + "DTOU", + "July", + "dtou_total_delta_dollars", + "dtou_total_pct_savings", + ), + ( + "stou_combined/stou_combined_202301.parquet", + "stou_jan", + "STOU", + "January", + "total_delta_dollars", + "total_pct_savings", + ), + ( + "stou_combined/stou_combined_202307.parquet", + "stou_jul", + "STOU", + "July", + "total_delta_dollars", + "total_pct_savings", + ), +] + + +# --------------------------------------------------------------------------- +# Data loading + aggregation +# --------------------------------------------------------------------------- + + +def _load_combined(path: Path, delta_col: str, pct_col: str) -> pl.DataFrame: + """Load a combined parquet and select/rename to canonical columns.""" + df = pl.read_parquet(path) + print(f" Loaded {df.height:,} rows from {path.name}") + + # Filter to allowed delivery classes + n_before = df.height + df = df.filter(pl.col("delivery_service_class").is_in(list(ALLOWED_CLASSES))) + n_dropped = n_before - df.height + if n_dropped > 0: + print(f" Dropped {n_dropped:,} rows with excluded delivery classes") + + return df.select( + "account_identifier", + pl.col(delta_col).alias("delta_dollars"), + pl.col(pct_col).alias("pct_savings"), + ) + + +def _aggregate_to_bg( + accounts: pl.DataFrame, + bg_map: pl.DataFrame, + census: pl.DataFrame, + min_accounts: int, +) -> pl.DataFrame: + """Join accounts → BG map → Census, aggregate to block-group level.""" + # Join to BG map + joined = accounts.join(bg_map, on="account_identifier", how="inner") + print(f" Matched {joined.height:,}/{accounts.height:,} accounts to block groups") + + # Aggregate to block group + bg = joined.group_by("geoid_bg").agg( + pl.col("delta_dollars").mean().alias("mean_delta"), + pl.col("pct_savings").mean().alias("mean_pct_savings"), + pl.len().alias("n_accounts"), + ) + + # Filter by min accounts + n_before = bg.height + bg = bg.filter(pl.col("n_accounts") >= min_accounts) + print(f" Block groups: {bg.height:,} (dropped {n_before - bg.height:,} with < {min_accounts} accounts)") + + # Join Census income + bg = bg.join(census, left_on="geoid_bg", right_on="GEOID", how="left") + + # Census stores income as natural log — exponentiate to raw dollars + bg = bg.rename({"median_household_income": "log_median_household_income"}).with_columns( + pl.col("log_median_household_income").exp().alias("median_household_income"), + ) + + # Drop BGs with missing or non-positive income + n_before = bg.height + bg = bg.filter(pl.col("median_household_income").is_not_null() & (pl.col("median_household_income") > 0)) + print(f" Block groups with valid income: {bg.height:,} (dropped {n_before - bg.height:,})") + + total_accounts = bg["n_accounts"].sum() + print(f" Total accounts represented: {total_accounts:,}") + + return bg + + +# --------------------------------------------------------------------------- +# OLS regression +# --------------------------------------------------------------------------- + + +def _run_ols( + bg: pl.DataFrame, + y_col: str, +) -> dict[str, object]: + """OLS of y_col on median_household_income (scaled by /10,000), HC1 robust SEs.""" + x = bg["median_household_income"].to_numpy() / 10_000 + y = bg[y_col].to_numpy() + + # Drop non-finite values + mask = np.isfinite(x) & np.isfinite(y) + x, y = x[mask], y[mask] + + X = sm.add_constant(x) + model = sm.OLS(y, X).fit(cov_type="HC1") + + return { + "slope": float(model.params[1]), + "intercept": float(model.params[0]), + "r_squared": float(model.rsquared), + "p_value": float(model.pvalues[1]), + "se_slope": float(model.bse[1]), + "n_obs": int(model.nobs), + "model": model, + } + + +# --------------------------------------------------------------------------- +# Plotting +# --------------------------------------------------------------------------- + + +def _dollar_fmt(x: float, _pos: int) -> str: + if abs(x) >= 1000: + return f"${x:,.0f}" + return f"${x:,.0f}" + + +def _pct_fmt(x: float, _pos: int) -> str: + return f"{x:.1f}%" + + +def _income_fmt(x: float, _pos: int) -> str: + return f"${x:,.0f}" + + +def _plot_scatter( + bg: pl.DataFrame, + ols_result: dict[str, object], + *, + rate_name: str, + month_label: str, + y_col: str, + metric_label: str, + y_axis_label: str, + out_path: Path, + dpi: int, +) -> None: + """Produce one scatterplot with OLS regression line + 95% CI band.""" + fig, ax = plt.subplots(figsize=(10, 7)) + + income = bg["median_household_income"].to_numpy() + y = bg[y_col].to_numpy() + n_bg = len(income) + n_accounts = int(bg["n_accounts"].sum()) + + # Scatter + ax.scatter(income, y, s=8, alpha=0.3, color="steelblue", zorder=2) + + # Regression line + CI band + x_sorted = np.linspace(income.min(), income.max(), 300) + X_pred = sm.add_constant(x_sorted / 10_000) + model = ols_result["model"] + pred = model.get_prediction(X_pred) + pred_summary = pred.summary_frame(alpha=0.05) + + ax.plot(x_sorted, pred_summary["mean"], color="red", linewidth=2, zorder=3) + ax.fill_between( + x_sorted, + pred_summary["mean_ci_lower"], + pred_summary["mean_ci_upper"], + color="red", + alpha=0.2, + zorder=1, + ) + + # Stats text box + r2 = ols_result["r_squared"] + slope = ols_result["slope"] + p_val = ols_result["p_value"] + stats_text = f"R² = {r2:.4f}\nSlope = {slope:.4g}\np = {p_val:.4g}" + ax.text( + 0.97, + 0.97, + stats_text, + transform=ax.transAxes, + ha="right", + va="top", + fontsize=10, + bbox={"boxstyle": "round,pad=0.4", "facecolor": "white", "edgecolor": "0.8", "alpha": 0.95}, + ) + + # Labels + is_pct = metric_label == "pct_savings" + ax.set_xlabel("Median Household Income ($)") + ax.set_ylabel(y_axis_label) + ax.set_title(f"{rate_name} — {month_label} 2023", fontsize=14, fontweight="bold") + ax.text( + 0.5, + 1.01, + f"{n_bg:,} block groups | {n_accounts:,} accounts", + transform=ax.transAxes, + ha="center", + va="bottom", + fontsize=10, + color="gray", + ) + + ax.xaxis.set_major_formatter(FuncFormatter(_income_fmt)) + ax.yaxis.set_major_formatter(FuncFormatter(_pct_fmt if is_pct else _dollar_fmt)) + plt.setp(ax.get_xticklabels(), rotation=45, ha="right") + + fig.tight_layout() + out_path.parent.mkdir(parents=True, exist_ok=True) + fig.savefig(out_path, dpi=dpi) + plt.close(fig) + print(f" Saved {out_path.name}") + + +# --------------------------------------------------------------------------- +# Main +# --------------------------------------------------------------------------- + + +def main() -> int: + parser = argparse.ArgumentParser( + description="Generate income-vs-savings scatterplots from corrected combined parquets.", + ) + parser.add_argument( + "--billing-output-dir", + type=Path, + required=True, + help="Root billing output directory containing stou_combined/ and dtou_combined/.", + ) + parser.add_argument( + "--census", + type=Path, + required=True, + help="Path to census_17_2023.parquet (block-group demographics, log-transformed income).", + ) + parser.add_argument( + "--out-dir", + type=Path, + required=True, + help="Output directory for scatterplot PNGs and regression summary CSV.", + ) + parser.add_argument( + "--dpi", + type=int, + default=150, + help="DPI for output PNGs (default: 150).", + ) + parser.add_argument( + "--min-accounts-per-bg", + type=int, + default=5, + help="Minimum accounts per block group to include (default: 5).", + ) + args = parser.parse_args() + + # Validate inputs + if not args.billing_output_dir.exists(): + print(f"ERROR: --billing-output-dir not found: {args.billing_output_dir}", file=sys.stderr) + return 1 + if not args.census.exists(): + print(f"ERROR: --census not found: {args.census}", file=sys.stderr) + return 1 + + bg_map_path = args.billing_output_dir / "account_bg_map_statewide.parquet" + if not bg_map_path.exists(): + print( + f"ERROR: account_bg_map_statewide.parquet not found at {bg_map_path}\n" + "Run scripts/pricing_pilot/build_statewide_account_bg_map.py first.", + file=sys.stderr, + ) + return 1 + + for rel, _tag, _name, _month, _dc, _pc in SCENARIOS: + p = args.billing_output_dir / rel + if not p.exists(): + print(f"ERROR: Input file not found: {p}", file=sys.stderr) + return 1 + + # Load shared data + print("Loading account→BG map...") + bg_map = pl.read_parquet(bg_map_path).select( + pl.col("account_identifier").cast(pl.Utf8), + pl.col("geoid_bg").cast(pl.Utf8), + ) + bg_map = bg_map.unique(subset=["account_identifier"], keep="first") + print(f" {bg_map.height:,} unique account→BG mappings") + + print("Loading Census block-group demographics...") + census = pl.read_parquet(args.census).select("GEOID", "median_household_income") + print(f" {census.height:,} block groups in Census data") + + args.out_dir.mkdir(parents=True, exist_ok=True) + regression_rows: list[dict[str, object]] = [] + + # Process each scenario + for rel, tag, rate_name, month_label, delta_col, pct_col in SCENARIOS: + print(f"\n{'=' * 60}") + print(f"Scenario: {tag} ({rate_name} — {month_label})") + print(f"{'=' * 60}") + + input_path = args.billing_output_dir / rel + accounts = _load_combined(input_path, delta_col, pct_col) + bg = _aggregate_to_bg(accounts, bg_map, census, args.min_accounts_per_bg) + + # --- Absolute delta scatterplot --- + ols_abs = _run_ols(bg, "mean_delta") + _plot_scatter( + bg, + ols_abs, + rate_name=rate_name, + month_label=month_label, + y_col="mean_delta", + metric_label="absolute_delta", + y_axis_label=f"Mean Monthly Savings Under {rate_name} ($)", + out_path=args.out_dir / f"{tag}_absolute_delta.png", + dpi=args.dpi, + ) + regression_rows.append({ + "scenario": tag, + "month": month_label, + "metric": "absolute_delta", + "slope": ols_abs["slope"], + "intercept": ols_abs["intercept"], + "r_squared": ols_abs["r_squared"], + "p_value": ols_abs["p_value"], + "n_block_groups": ols_abs["n_obs"], + "n_accounts": int(bg["n_accounts"].sum()), + }) + + # --- Percent savings scatterplot --- + ols_pct = _run_ols(bg, "mean_pct_savings") + _plot_scatter( + bg, + ols_pct, + rate_name=rate_name, + month_label=month_label, + y_col="mean_pct_savings", + metric_label="pct_savings", + y_axis_label=f"Mean Monthly Savings Under {rate_name} (%)", + out_path=args.out_dir / f"{tag}_pct_savings.png", + dpi=args.dpi, + ) + regression_rows.append({ + "scenario": tag, + "month": month_label, + "metric": "pct_savings", + "slope": ols_pct["slope"], + "intercept": ols_pct["intercept"], + "r_squared": ols_pct["r_squared"], + "p_value": ols_pct["p_value"], + "n_block_groups": ols_pct["n_obs"], + "n_accounts": int(bg["n_accounts"].sum()), + }) + + # Export regression summary CSV + reg_df = pl.DataFrame(regression_rows) + csv_path = args.out_dir / "scatterplot_regression_summary.csv" + reg_df.write_csv(csv_path) + print(f"\n{'=' * 60}") + print(f"Regression summary: {csv_path}") + print(f"{'=' * 60}") + + for row in regression_rows: + print( + f" {row['scenario']:<10s} {row['metric']:<16s} " + f"slope={row['slope']:>10.6f} R²={row['r_squared']:>8.4f} " + f"p={row['p_value']:>8.4f} n_bg={row['n_block_groups']} n_acct={row['n_accounts']:,}" + ) + + print(f"\nAll outputs written to {args.out_dir}") + print("Done.") + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/pricing_pilot/package_dtou_results.py b/scripts/pricing_pilot/package_dtou_results.py new file mode 100644 index 0000000..71b95ef --- /dev/null +++ b/scripts/pricing_pilot/package_dtou_results.py @@ -0,0 +1,186 @@ +#!/usr/bin/env python3 +"""Package DTOU (Delivery Time-of-Use) results from existing STOU combined files. + +DTOU changes ONLY delivery charges -- supply stays flat. Since DTOU and STOU +use the same TOU delivery rates (Info Sheet 67, same four time blocks), the +DTOU total bill delta equals the delivery delta alone. This script repackages +existing STOU combined data into DTOU-specific output files with correct total +bill columns. + +Usage:: + + uv run python scripts/pricing_pilot/package_dtou_results.py \\ + --month 202301 \\ + --billing-output-dir ~/pricing_pilot/billing_output + + # Run both months back-to-back: + uv run python scripts/pricing_pilot/package_dtou_results.py \\ + --both \\ + --billing-output-dir ~/pricing_pilot/billing_output +""" + +from __future__ import annotations + +import argparse +import sys +from pathlib import Path + +import polars as pl + +MONTHS = ["202301", "202307"] + + +def _resolve_paths(billing_output_dir: Path, month: str) -> tuple[Path, Path]: + """Derive input and output file paths from billing-output-dir and month.""" + input_path = billing_output_dir / "stou_combined" / f"stou_combined_{month}.parquet" + output_dir = billing_output_dir / "dtou_combined" + return input_path, output_dir + + +def _build_dtou(stou: pl.DataFrame) -> pl.DataFrame: + """Derive DTOU-specific columns from the STOU combined data. + + DTOU keeps supply flat on both sides, so: + - dtou_total_bill_a = flat_supply + flat_delivery (baseline) + - dtou_total_bill_b = flat_supply + tou_delivery (DTOU scenario) + - dtou_total_delta = delivery_delta (supply cancels) + """ + return stou.select( + "account_identifier", + "zip_code", + "delivery_service_class", + "total_kwh", + pl.col("bill_a_dollars").alias("flat_supply_dollars"), + "flat_delivery_dollars", + "tou_delivery_dollars", + "delivery_delta_dollars", + (pl.col("bill_a_dollars") + pl.col("flat_delivery_dollars")).alias("dtou_total_bill_a_dollars"), + (pl.col("bill_a_dollars") + pl.col("tou_delivery_dollars")).alias("dtou_total_bill_b_dollars"), + pl.col("delivery_delta_dollars").alias("dtou_total_delta_dollars"), + pl.when(pl.col("bill_a_dollars") + pl.col("flat_delivery_dollars") != 0) + .then(pl.col("delivery_delta_dollars") / (pl.col("bill_a_dollars") + pl.col("flat_delivery_dollars")) * 100) + .otherwise(None) + .alias("dtou_total_pct_savings"), + ) + + +def _validate(dtou: pl.DataFrame) -> None: + """Print validation checks to stdout.""" + n = len(dtou) + + # 1. Row count + print("\n --- Validation ---") + print(f" Output rows: {n:,}") + + # 2. Total delta summary + td = dtou["dtou_total_delta_dollars"] + print("\n DTOU total delta ($/month):") + print(f" mean={td.mean():.4f} median={td.median():.4f} min={td.min():.4f} max={td.max():.4f}") + + # 3. Percent savings summary + ps = dtou["dtou_total_pct_savings"].drop_nulls() + print("\n DTOU total pct savings:") + print(f" mean={ps.mean():.4f}% median={ps.median():.4f}%") + + # 4. Savings vs paying more + n_saving = (td > 0).sum() + n_paying = (td < 0).sum() + n_even = (td == 0).sum() + print(f"\n % households saving: {100 * n_saving / n:.2f}%") + print(f" % households paying more: {100 * n_paying / n:.2f}%") + print(f" % households even: {100 * n_even / n:.2f}%") + + # 5. Delivery class value counts + vc = dtou["delivery_service_class"].value_counts().sort("delivery_service_class") + print(f"\n Delivery class value counts:\n{vc}") + + # 6. Sanity: dtou_total_delta == delivery_delta (must be exact) + diff = (dtou["dtou_total_delta_dollars"] - dtou["delivery_delta_dollars"]).abs() + max_diff = diff.max() + if max_diff > 0: + raise ValueError(f"dtou_total_delta_dollars != delivery_delta_dollars, max diff = {max_diff}") + print(f"\n Sanity check: dtou_total_delta == delivery_delta (max diff: {max_diff:.2e}) OK") + + +def process_month(month: str, billing_output_dir: Path) -> int: + """Process a single month end-to-end. Returns 0 on success, 1 on error.""" + print(f"\n{'=' * 60}") + print(f"Processing {month}") + print(f"{'=' * 60}") + + input_path, output_dir = _resolve_paths(billing_output_dir, month) + + # Check input exists + if not input_path.exists(): + print( + f"ERROR: STOU combined file not found at {input_path}\n" + "Run scripts/pricing_pilot/compute_delivery_deltas.py first.", + file=sys.stderr, + ) + return 1 + + # Read STOU combined data + stou = pl.read_parquet(input_path) + print(f" STOU combined input: {len(stou):,} rows") + + # Build DTOU output + dtou = _build_dtou(stou) + + # Validate + _validate(dtou) + + # Write output + output_dir.mkdir(parents=True, exist_ok=True) + output_path = output_dir / f"dtou_combined_{month}.parquet" + dtou.sort("account_identifier").write_parquet(output_path) + print(f"\n Saved to {output_path}") + print(f" File size: {output_path.stat().st_size / (1024 * 1024):.1f} MB") + print(f" Rows: {len(dtou):,}") + + return 0 + + +def main() -> int: + """Parse CLI args and dispatch to process_month for each requested month.""" + default_billing_output = Path.home() / "pricing_pilot" / "billing_output" + + parser = argparse.ArgumentParser(description="Package DTOU results from existing STOU combined files.") + parser.add_argument( + "--month", + type=str, + help="Month to process in YYYYMM format (e.g. 202301).", + ) + parser.add_argument( + "--both", + action="store_true", + help="Process both January 2023 and July 2023.", + ) + parser.add_argument( + "--billing-output-dir", + type=Path, + default=default_billing_output, + help=f"Root billing output directory (default: {default_billing_output}).", + ) + args = parser.parse_args() + + if not args.month and not args.both: + parser.error("Specify --month YYYYMM or --both") + if args.month and args.both: + parser.error("Specify --month or --both, not both") + + months = MONTHS if args.both else [args.month] + + for month in months: + if len(month) != 6 or not month.isdigit(): + print(f"ERROR: Invalid month format '{month}', expected YYYYMM", file=sys.stderr) + return 1 + rc = process_month(month, args.billing_output_dir) + if rc != 0: + return rc + + print("\nDone.") + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/pricing_pilot/run_statewide_analysis.py b/scripts/pricing_pilot/run_statewide_analysis.py new file mode 100644 index 0000000..de18fcd --- /dev/null +++ b/scripts/pricing_pilot/run_statewide_analysis.py @@ -0,0 +1,497 @@ +#!/usr/bin/env python3 +"""Statewide block-group-level pricing analysis: filter, aggregate, regress, export. + +Pipeline +-------- +1. Load four combined billing Parquets (STOU Jan/Jul, DTOU Jan/Jul). +2. Filter out delivery classes C25 and C27 (keep C23, C24, C26, C28). +3. Inner-join each to the statewide account→BG map. +4. Aggregate to block-group level (mean delta, mean pct savings, median delta, + mean kWh, household count). +5. Left-join Census block-group demographics; drop BGs with null/non-positive + median household income. +6. Run OLS regressions (HC1 robust SEs) of mean_delta and mean_pct_savings on + median_household_income, both pooled across all classes and per-class. +7. Export regression summary CSV, per-scenario BG-level CSVs, and print results. + +Sign convention (matches compute_delivery_deltas.py): + delta = flat - alternative (bill_a - bill_b) + Positive = customer SAVES under TOU + A positive beta_1 on income → higher-income BGs save more (regressive in + absolute terms). A negative beta_1 → lower-income BGs save more (progressive). + +Usage:: + + python scripts/pricing_pilot/run_statewide_analysis.py \\ + --billing-output-dir /ebs/home/griffin_switch_box/runs/billing_output \\ + --census data/reference/census_17_2023.parquet \\ + --out-dir /ebs/home/griffin_switch_box/runs/billing_output/statewide_analysis +""" + +from __future__ import annotations + +import argparse +import sys +from pathlib import Path + +import numpy as np +import polars as pl +import statsmodels.api as sm + +# --------------------------------------------------------------------------- +# Constants +# --------------------------------------------------------------------------- + +ALLOWED_CLASSES = {"C23", "C24", "C26", "C28"} +CLASS_LABELS: dict[str, str] = { + "C23": "sf_no_esh", + "C24": "mf_no_esh", + "C26": "sf_esh", + "C28": "mf_esh", +} + +# (relative path from billing-output-dir, scenario label, rate type, month, +# delta column, pct savings column) +SCENARIO_SPECS: list[tuple[str, str, str, str, str, str]] = [ + ( + "stou_combined/stou_combined_202301.parquet", + "stou_jan", + "stou", + "202301", + "total_delta_dollars", + "total_pct_savings", + ), + ( + "stou_combined/stou_combined_202307.parquet", + "stou_jul", + "stou", + "202307", + "total_delta_dollars", + "total_pct_savings", + ), + ( + "dtou_combined/dtou_combined_202301.parquet", + "dtou_jan", + "dtou", + "202301", + "dtou_total_delta_dollars", + "dtou_total_pct_savings", + ), + ( + "dtou_combined/dtou_combined_202307.parquet", + "dtou_jul", + "dtou", + "202307", + "dtou_total_delta_dollars", + "dtou_total_pct_savings", + ), +] + + +# --------------------------------------------------------------------------- +# Step 1 — Filter +# --------------------------------------------------------------------------- + + +def _filter_classes(df: pl.DataFrame, file_label: str) -> pl.DataFrame: + """Drop rows where delivery_service_class is not in ALLOWED_CLASSES.""" + n_before = df.height + df_filtered = df.filter(pl.col("delivery_service_class").is_in(list(ALLOWED_CLASSES))) + n_after = df_filtered.height + n_dropped = n_before - n_after + print(f" {file_label}: {n_before:,} rows → {n_after:,} after filter ({n_dropped:,} dropped)") + return df_filtered + + +# --------------------------------------------------------------------------- +# Step 2 — Join account→BG +# --------------------------------------------------------------------------- + + +def _join_bg_map(df: pl.DataFrame, bg_map: pl.DataFrame, file_label: str) -> pl.DataFrame: + """Inner-join to account→BG map on account_identifier. Log match rate.""" + n_before = df.height + joined = df.join(bg_map, on="account_identifier", how="inner") + n_after = joined.height + match_rate = 100 * n_after / n_before if n_before > 0 else 0.0 + print(f" {file_label}: {n_after:,}/{n_before:,} accounts matched to BG ({match_rate:.2f}%)") + return joined + + +# --------------------------------------------------------------------------- +# Step 3 — Aggregate to block group +# --------------------------------------------------------------------------- + + +def _aggregate_to_bg( + df: pl.DataFrame, + delta_col: str, + pct_col: str, +) -> pl.DataFrame: + """Group by geoid_bg and compute BG-level metrics.""" + return df.group_by("geoid_bg").agg( + pl.col(delta_col).mean().alias("mean_delta"), + pl.col(pct_col).mean().alias("mean_pct_savings"), + pl.col(delta_col).median().alias("median_delta"), + pl.col("total_kwh").mean().alias("mean_kwh"), + pl.len().alias("n_households"), + ) + + +# --------------------------------------------------------------------------- +# Step 4 — Join Census +# --------------------------------------------------------------------------- + + +def _join_census(bg_df: pl.DataFrame, census: pl.DataFrame) -> pl.DataFrame: + """Left-join Census on geoid_bg=GEOID; drop null/non-positive income.""" + n_before = bg_df.height + joined = bg_df.join(census, left_on="geoid_bg", right_on="GEOID", how="left") + joined = joined.filter(pl.col("median_household_income").is_not_null() & (pl.col("median_household_income") > 0)) + n_after = joined.height + print(f" BGs with valid income: {n_after:,}/{n_before:,}") + # Census stores income as natural log; exponentiate to recover raw dollars. + joined = joined.rename({"median_household_income": "log_median_household_income"}).with_columns( + pl.col("log_median_household_income").exp().alias("median_household_income"), + ) + return joined + + +# --------------------------------------------------------------------------- +# Step 5 — OLS regressions +# --------------------------------------------------------------------------- + + +def _run_regression( + y: np.ndarray, + x_income: np.ndarray, + scenario: str, + month: str, + rate: str, + delivery_class: str, + dep_var: str, +) -> dict[str, object]: + """Run OLS with HC1 robust SEs. Returns a dict row for the summary CSV.""" + x = sm.add_constant(x_income) + model = sm.OLS(y, x).fit(cov_type="HC1") + + return { + "scenario": scenario, + "month": month, + "rate": rate, + "delivery_class": delivery_class, + "dep_var": dep_var, + "beta_0": model.params[0], + "beta_1": model.params[1], + "se_beta_1": model.bse[1], + "t_stat": model.tvalues[1], + "p_value": model.pvalues[1], + "r_squared": model.rsquared, + "n_obs": int(model.nobs), + } + + +def _interpret(dep_var: str, beta_1: float) -> str: + """Return a one-line interpretation of the income coefficient.""" + if dep_var == "mean_delta": + direction = ( + "regressive (higher-income BGs save more)" if beta_1 > 0 else "progressive (lower-income BGs save more)" + ) + return f" → Absolute savings: {direction} (beta_1={beta_1:.4f} $/10k income)" + else: + direction = "regressive" if beta_1 > 0 else "progressive" + return f" → Pct savings: {direction} (beta_1={beta_1:.4f} pct-pts/10k income)" + + +# --------------------------------------------------------------------------- +# Step 6 — Income quintiles +# --------------------------------------------------------------------------- + + +def compute_income_quintiles( + bg: pl.DataFrame, + label: str, +) -> pl.DataFrame: + """Compute income-quintile summary statistics from BG-level data. + + Assigns each block group to an income quintile (1 = lowest income), + then aggregates billing and demographic metrics within each quintile. + + Parameters + ---------- + bg : pl.DataFrame + BG-level DataFrame with columns: geoid_bg, mean_delta, + mean_pct_savings, median_delta, mean_kwh, n_households, + median_household_income. + label : str + Scenario label for logging (e.g., "stou_jan", "stou_jan_sf_no_esh"). + """ + # Filter out non-positive income (mirrors regression filter) + bg = bg.filter(pl.col("median_household_income") > 0) + + # Sort deterministically: income ascending, geoid_bg as tiebreaker + bg = bg.sort("median_household_income", "geoid_bg") + + n = bg.height + base_size = n // 5 + + # First 4 quintiles get base_size rows; quintile 5 absorbs remainder + assignments: list[int] = [] + for q in range(1, 6): + if q < 5: + assignments.extend([q] * base_size) + else: + assignments.extend([q] * (n - len(assignments))) + + bg = bg.with_columns(pl.Series("quintile", assignments)) + + return ( + bg.group_by("quintile") + .agg( + pl.col("median_household_income").min().alias("income_min"), + pl.col("median_household_income").max().alias("income_max"), + pl.col("median_household_income").mean().alias("income_mean"), + pl.col("mean_delta").mean().alias("mean_delta_mean"), + pl.col("mean_pct_savings").mean().alias("mean_pct_savings_mean"), + pl.col("median_delta").mean().alias("median_delta_mean"), + pl.col("mean_kwh").mean().alias("mean_kwh_mean"), + pl.len().alias("n_bgs"), + pl.col("n_households").sum().alias("n_households"), + (pl.col("mean_delta") * pl.col("n_households")).sum().alias("_wt_delta"), + (pl.col("mean_pct_savings") * pl.col("n_households")).sum().alias("_wt_pct"), + ) + .with_columns( + (pl.col("_wt_delta") / pl.col("n_households")).alias("hh_weighted_mean_delta"), + (pl.col("_wt_pct") / pl.col("n_households")).alias("hh_weighted_mean_pct_savings"), + ) + .drop("_wt_delta", "_wt_pct") + .sort("quintile") + ) + + +# --------------------------------------------------------------------------- +# Main +# --------------------------------------------------------------------------- + + +def main() -> int: + parser = argparse.ArgumentParser( + description="Statewide block-group-level pricing analysis: filter, aggregate, regress, export.", + ) + parser.add_argument( + "--billing-output-dir", + type=Path, + required=True, + help="Root billing output directory containing stou_combined/ and dtou_combined/.", + ) + parser.add_argument( + "--census", + type=Path, + required=True, + help="Path to census_17_2023.parquet (block-group demographics).", + ) + parser.add_argument( + "--out-dir", + type=Path, + required=True, + help="Output directory for regression_summary.csv and bg_level_*.csv files.", + ) + args = parser.parse_args() + + # ------------------------------------------------------------------ + # Validate inputs + # ------------------------------------------------------------------ + if not args.billing_output_dir.exists(): + print(f"ERROR: --billing-output-dir not found: {args.billing_output_dir}", file=sys.stderr) + return 1 + if not args.census.exists(): + print(f"ERROR: --census not found: {args.census}", file=sys.stderr) + return 1 + + bg_map_path = args.billing_output_dir / "account_bg_map_statewide.parquet" + if not bg_map_path.exists(): + print( + f"ERROR: account_bg_map_statewide.parquet not found at {bg_map_path}\n" + "Run scripts/pricing_pilot/build_statewide_account_bg_map.py first.", + file=sys.stderr, + ) + return 1 + + for rel, _scenario, _rate, _month, _dc, _pc in SCENARIO_SPECS: + p = args.billing_output_dir / rel + if not p.exists(): + print(f"ERROR: Input file not found: {p}", file=sys.stderr) + return 1 + + # ------------------------------------------------------------------ + # Load shared data + # ------------------------------------------------------------------ + print("Loading account→BG map...") + bg_map = pl.read_parquet(bg_map_path) + print(f" {bg_map.height:,} account→BG mappings") + + print("Loading Census block-group demographics...") + census = pl.read_parquet(args.census).select("GEOID", "median_household_income") + print(f" {census.height:,} block groups in Census data") + + args.out_dir.mkdir(parents=True, exist_ok=True) + regression_rows: list[dict[str, object]] = [] + # Collect BG-level DataFrames for quintile analysis (second pass) + bg_datasets: list[tuple[str, str, str, str, pl.DataFrame]] = [] + + # ------------------------------------------------------------------ + # Process each scenario + # ------------------------------------------------------------------ + for rel, scenario, rate, month, delta_col, pct_col in SCENARIO_SPECS: + print(f"\n{'=' * 60}") + print(f"Scenario: {scenario} (rate={rate}, month={month})") + print(f"{'=' * 60}") + + # Load + input_path = args.billing_output_dir / rel + df = pl.read_parquet(input_path) + print(f" Loaded {df.height:,} rows from {input_path.name}") + + # Step 1: Filter + df = _filter_classes(df, scenario) + + # Step 2: Join BG + df = _join_bg_map(df, bg_map, scenario) + + # Step 3: Aggregate to BG + bg_all = _aggregate_to_bg(df, delta_col, pct_col) + print(f" Aggregated to {bg_all.height:,} block groups (all classes)") + + # Step 4: Join Census + bg_all = _join_census(bg_all, census) + + # Export BG-level CSV + bg_csv_path = args.out_dir / f"bg_level_{scenario}.csv" + bg_all.sort("geoid_bg").write_csv(bg_csv_path) + print(f" Wrote {bg_csv_path.name} ({bg_all.height:,} rows)") + bg_datasets.append((scenario, month, rate, "all", bg_all)) + + # Step 5: OLS regressions — pooled (all classes) + income_scaled = bg_all["median_household_income"].to_numpy() / 10_000 + + for dep_var, col_name in [("mean_delta", "mean_delta"), ("mean_pct_savings", "mean_pct_savings")]: + y = bg_all[col_name].to_numpy() + row = _run_regression(y, income_scaled, scenario, month, rate, "all", dep_var) + regression_rows.append(row) + + # Per-class regressions + for class_code, class_label in CLASS_LABELS.items(): + df_class = df.filter(pl.col("delivery_service_class") == class_code) + if df_class.height == 0: + continue + + bg_class = _aggregate_to_bg(df_class, delta_col, pct_col) + bg_class = _join_census(bg_class, census) + + if bg_class.height < 3: # need at least 3 obs for meaningful regression + print(f" Skipping {class_label}: only {bg_class.height} BGs with valid income") + continue + + bg_class_csv = args.out_dir / f"bg_level_{scenario}_{class_label}.csv" + bg_class.sort("geoid_bg").write_csv(bg_class_csv) + print(f" Wrote {bg_class_csv.name} ({bg_class.height:,} rows)") + bg_datasets.append((scenario, month, rate, class_label, bg_class)) + + income_class = bg_class["median_household_income"].to_numpy() / 10_000 + for dep_var, col_name in [("mean_delta", "mean_delta"), ("mean_pct_savings", "mean_pct_savings")]: + y = bg_class[col_name].to_numpy() + row = _run_regression(y, income_class, scenario, month, rate, class_label, dep_var) + regression_rows.append(row) + + # ------------------------------------------------------------------ + # Export regression summary + # ------------------------------------------------------------------ + reg_df = pl.DataFrame(regression_rows) + reg_csv_path = args.out_dir / "regression_summary.csv" + reg_df.write_csv(reg_csv_path) + print(f"\n{'=' * 60}") + print(f"Regression summary: {reg_csv_path}") + print(f"{'=' * 60}") + + # Print full table to stdout + for row in regression_rows: + label = f"{row['scenario']}/{row['delivery_class']}/{row['dep_var']}" + print( + f" {label:<40s} β₁={row['beta_1']:>10.6f} SE={row['se_beta_1']:>10.6f} " + f"t={row['t_stat']:>8.3f} p={row['p_value']:>8.4f} R²={row['r_squared']:>8.4f} n={row['n_obs']}" + ) + print(_interpret(str(row["dep_var"]), float(row["beta_1"]))) + + # ------------------------------------------------------------------ + # Quintile analysis (second pass over stored BG DataFrames) + # ------------------------------------------------------------------ + print(f"\n{'=' * 60}") + print("Income quintile analysis") + print(f"{'=' * 60}") + + all_quintile_dfs: list[pl.DataFrame] = [] + pooled_quintiles: list[tuple[str, pl.DataFrame]] = [] + + for scenario, month, rate, delivery_class, bg_df in bg_datasets: + label = scenario if delivery_class == "all" else f"{scenario}_{delivery_class}" + + # Check minimum BG count after filtering non-positive income + bg_valid = bg_df.filter(pl.col("median_household_income") > 0) + if bg_valid.height < 25: + print(f" WARNING: {label} has only {bg_valid.height} BGs (< 25), skipping quintiles") + continue + + q_df = compute_income_quintiles(bg_df, label) + + # Write individual CSV + q_csv_path = args.out_dir / f"quintiles_{label}.csv" + q_df.write_csv(q_csv_path) + print(f" Wrote {q_csv_path.name}") + + # Add metadata columns for combined CSV + q_with_meta = q_df.select( + pl.lit(scenario).alias("scenario"), + pl.lit(month).alias("month"), + pl.lit(rate).alias("rate"), + pl.lit(delivery_class).alias("delivery_class"), + pl.all(), + ) + all_quintile_dfs.append(q_with_meta) + + if delivery_class == "all": + pooled_quintiles.append((scenario, q_df)) + + # Write combined quintile CSV + if all_quintile_dfs: + combined_q = pl.concat(all_quintile_dfs) + combined_csv_path = args.out_dir / "quintile_summary.csv" + combined_q.write_csv(combined_csv_path) + print(f"\n Combined quintile summary: {combined_csv_path}") + + # Print pooled quintile summary table + if pooled_quintiles: + print(f"\n{'=' * 60}") + print("Pooled quintile summary (all delivery classes)") + print(f"{'=' * 60}") + print( + f" {'Scenario':<12s} {'Q':>2s} {'Income Range':>27s}" + f" {'Mean Δ':>10s} {'Mean %':>8s} {'BGs':>6s} {'HH':>10s}" + ) + for scenario, q_df in pooled_quintiles: + for row in q_df.iter_rows(named=True): + inc_range = f"${row['income_min']:,.0f}-${row['income_max']:,.0f}" + print( + f" {scenario:<12s} {row['quintile']:>2d} {inc_range:>27s}" + f" ${row['mean_delta_mean']:>8.2f}" + f" {row['mean_pct_savings_mean']:>7.2f}%" + f" {row['n_bgs']:>6,d}" + f" {row['n_households']:>10,d}" + ) + + print(f"\nAll outputs written to {args.out_dir}") + print("Done.") + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/pricing_pilot/validate_mf_esh_bg_count.py b/scripts/pricing_pilot/validate_mf_esh_bg_count.py new file mode 100644 index 0000000..7a85c92 --- /dev/null +++ b/scripts/pricing_pilot/validate_mf_esh_bg_count.py @@ -0,0 +1,146 @@ +#!/usr/bin/env python3 +"""Validate mf_esh block-group counts across pipeline stages. + +Compares three counts for mf_esh (C28) with DTOU rate: + A) Distinct accounts in bill parquets + B) Distinct block groups after joining to account_bg_map + C) Features in the output GeoJSON + +Reports mismatches with sample geoid_bg values for debugging join/type issues. + +Usage:: + + uv run python scripts/pricing_pilot/validate_mf_esh_bg_count.py + + # Override paths: + uv run python scripts/pricing_pilot/validate_mf_esh_bg_count.py \\ + --bills-dir ~/pricing_pilot/bills_unscaled \\ + --map-dir ~/pricing_pilot \\ + --geojson-dir ~/pricing_pilot/geojson_out +""" + +from __future__ import annotations + +import argparse +import json +import sys +from pathlib import Path + +import polars as pl + +MONTHS = [ + ("202301", "January"), + ("202307", "July"), +] + + +def main() -> int: + """Validate mf_esh block-group count consistency across bills, BG map, and GeoJSON.""" + default_base = Path.home() / "pricing_pilot" + + parser = argparse.ArgumentParser( + description="Validate mf_esh block-group counts: bills vs BG map vs GeoJSON.", + ) + parser.add_argument( + "--bills-dir", + type=Path, + default=default_base / "bills_unscaled", + help="Directory containing bill parquets (default: ~/pricing_pilot/bills_unscaled).", + ) + parser.add_argument( + "--map-dir", + type=Path, + default=default_base, + help="Directory containing account_bg_map_{yyyymm}.parquet files (default: ~/pricing_pilot).", + ) + parser.add_argument( + "--geojson-dir", + type=Path, + default=default_base / "geojson_out", + help="Directory containing output GeoJSON files (default: ~/pricing_pilot/geojson_out).", + ) + args = parser.parse_args() + + rows = [] + for yyyymm, label in MONTHS: + bill_path = args.bills_dir / f"{yyyymm}_flat_vs_dtou_mf_esh.parquet" + map_path = args.map_dir / f"account_bg_map_{yyyymm}.parquet" + geojson_path = args.geojson_dir / f"{yyyymm}_dtou_mf_esh.geojson" + + # A) Distinct account_identifier in bills + bills = pl.read_parquet(bill_path) + if "account_identifier" not in bills.columns: + # try common alternatives + id_col = next((c for c in bills.columns if "account" in c.lower() or c == "account_id"), None) + if id_col is None: + print(f"No account column in {bill_path}. Columns: {bills.columns}", file=sys.stderr) + return 1 + account_col = id_col + else: + account_col = "account_identifier" + a_count = bills.select(pl.col(account_col).n_unique()).item() + + # B) Join to account_bg_map, count distinct geoid_bg + amap = ( + pl.read_parquet(map_path) + .select( + pl.col("account_identifier").cast(pl.Utf8), + pl.col("geoid_bg").cast(pl.Utf8), + ) + .unique(subset=["account_identifier"], keep="first") + ) + accounts = bills.select(pl.col(account_col).cast(pl.Utf8).alias("account_identifier")) + joined = accounts.join(amap, on="account_identifier", how="inner").filter( + pl.col("geoid_bg").is_not_null() & (pl.col("geoid_bg").str.strip_chars() != "") + ) + b_count = joined.select(pl.col("geoid_bg").n_unique()).item() + b_geoids = set(joined["geoid_bg"].unique().to_list()) + + # C) Count features in GeoJSON + with open(geojson_path) as f: + gj = json.load(f) + features = gj.get("features", []) + c_count = len(features) + c_geoids = set() + for feat in features: + props = feat.get("properties", {}) + g = props.get("geoid_bg") or props.get("GEOID") or props.get("geoid") + if g is not None: + c_geoids.add(str(g).strip()) + + rows.append((label, yyyymm, a_count, b_count, c_count)) + print(f"--- {label} ({yyyymm}) ---") + print(f" A) Distinct accounts (bills): {a_count}") + print(f" B) Distinct geoid_bg (join): {b_count}") + print(f" C) GeoJSON feature count: {c_count}") + if b_count != c_count: + print(" >>> B ≠ C: checking for join/type mismatches") + only_in_join = b_geoids - c_geoids + only_in_geojson = c_geoids - b_geoids + if only_in_join: + sample_join = sorted(only_in_join)[:10] + print(f" Sample geoid_bg only in join (not in GeoJSON): {sample_join}") + if sample_join: + print(f" Types/repr from join: {[repr(x) for x in sample_join]}") + if only_in_geojson: + sample_gj = sorted(only_in_geojson)[:10] + print(f" Sample geoid_bg only in GeoJSON (not in join): {sample_gj}") + if sample_gj: + print(f" Types/repr from GeoJSON: {[repr(x) for x in sample_gj]}") + print() + + # Side-by-side summary + print("=" * 60) + print("Summary (side by side)") + print("=" * 60) + print(f"{'Month':<12} {'A (accounts)':>14} {'B (BGs join)':>14} {'C (GeoJSON)':>14}") + print("-" * 60) + for label, _yyyymm, a, b, c in rows: + print(f"{label:<12} {a:>14} {b:>14} {c:>14}") + print("=" * 60) + + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/run_billing_pipeline.py b/scripts/run_billing_pipeline.py new file mode 100644 index 0000000..bea80e5 --- /dev/null +++ b/scripts/run_billing_pipeline.py @@ -0,0 +1,501 @@ +#!/usr/bin/env python3 +"""Multi-month RTP billing pipeline orchestrator. + +Chains the full billing analysis pipeline for one or more months: + +1. Per month: compute_hourly_loads -> compute_household_bills +2. Concatenate monthly bills into all_months_household_bills.parquet (no aggregation) +3. (Optional) build_regression_dataset on the concatenated bills + +Directory layout:: + + data/bills// + _tmp/ + month=YYYYMM/hourly_loads.parquet + month=YYYYMM/household_bills.parquet + all_months_household_bills.parquet + regression/ + bg_month_outcomes.parquet + bg_annual_outcomes.parquet + bg_season_outcomes.parquet + regression_dataset_bg.parquet + regression_results.json + regression_summary.txt + regression_metadata.json + run_manifest.json + pipeline.log + +Typical usage:: + + python scripts/run_billing_pipeline.py \\ + --months 202301,202302,202303 \\ + --interval-pattern "data/processed/comed_{yyyymm}.parquet" \\ + --tariff-a data/reference/comed_flat_hourly_prices_2023.parquet \\ + --tariff-b data/reference/comed_stou_hourly_prices_2023.parquet + + python scripts/run_billing_pipeline.py \\ + --months-file months.txt \\ + --interval-pattern "data/processed/comed_{yyyymm}.parquet" \\ + --cluster-assignments-pattern "data/clustering/{yyyymm}/assignments.parquet" \\ + --tariff-a data/reference/comed_flat_hourly_prices_2023.parquet \\ + --tariff-b data/reference/comed_stou_hourly_prices_2023.parquet +""" + +from __future__ import annotations + +import argparse +import hashlib +import json +import logging +import os +import subprocess +import sys +from datetime import datetime, timezone +from pathlib import Path +from typing import Any + +import polars as pl + +logger = logging.getLogger(__name__) + + +# --------------------------------------------------------------------------- +# Helpers (ported from scripts/run_comed_pipeline.py) +# --------------------------------------------------------------------------- + + +def _utc_now_iso() -> str: + return datetime.now(timezone.utc).replace(microsecond=0).isoformat() + + +def _configure_logging(log_path: Path) -> None: + log_path.parent.mkdir(parents=True, exist_ok=True) + handlers: list[logging.Handler] = [ + logging.StreamHandler(sys.stdout), + logging.FileHandler(str(log_path), encoding="utf-8"), + ] + logging.basicConfig( + level=logging.INFO, + format="%(asctime)s - %(levelname)s - %(message)s", + handlers=handlers, + force=True, + ) + + +def _get_git_sha(repo_root: Path) -> str | None: + try: + r = subprocess.run( + ["git", "rev-parse", "HEAD"], + cwd=str(repo_root), + check=False, + capture_output=True, + text=True, + ) + sha = (r.stdout or "").strip() + return sha if r.returncode == 0 and sha else None + except Exception: + return None + + +def _run_subprocess(cmd: list[str], *, label: str) -> None: + # Each pipeline step runs as a subprocess so that (a) memory is fully + # released between steps (Polars/Arrow can be hungry), and (b) a step + # crash produces a clean exit code without taking down the orchestrator. + # Polars must be single-threaded: set POLARS_MAX_THREADS in the subprocess + # env so the Rayon thread pool is constrained before any import. + env = {**os.environ, "POLARS_MAX_THREADS": "1"} + logger.info("[%s] Running: %s", label, " ".join(cmd)) + r = subprocess.run(cmd, env=env, check=False) + if r.returncode != 0: + raise RuntimeError(f"[{label}] Command failed (exit {r.returncode}): {' '.join(cmd)}") + + +def generate_run_id(months: list[str]) -> str: + # Hash includes both the month list and a timestamp so that (a) re-runs + # of the same months get distinct directories, and (b) the ID is short + # enough for friendly directory names while still collision-resistant. + ts = datetime.now(timezone.utc).strftime("%Y%m%dT%H%M%SZ") + hash_input = ",".join(sorted(months)) + "|" + ts + short_hash = hashlib.sha256(hash_input.encode()).hexdigest()[:12] + return f"{ts}_{short_hash}" + + +# --------------------------------------------------------------------------- +# Pipeline steps +# --------------------------------------------------------------------------- + + +def step_compute_hourly_loads( + *, + input_path: Path, + cluster_assignments_path: Path | None, + output_path: Path, +) -> None: + output_path.parent.mkdir(parents=True, exist_ok=True) + cmd = [ + sys.executable, + "analysis/rtp/compute_hourly_loads.py", + "--input", + str(input_path), + "--output", + str(output_path), + ] + if cluster_assignments_path is not None: + cmd.extend(["--cluster-assignments", str(cluster_assignments_path)]) + _run_subprocess(cmd, label="hourly_loads") + + +def step_compute_bills( + *, + hourly_loads_path: Path, + tariff_a_path: Path, + tariff_b_path: Path, + output_path: Path, + capacity_rate: float, + admin_fee: float, +) -> None: + output_path.parent.mkdir(parents=True, exist_ok=True) + cmd = [ + sys.executable, + "analysis/rtp/compute_household_bills.py", + "--hourly-loads", + str(hourly_loads_path), + "--tariff-prices-a", + str(tariff_a_path), + "--tariff-prices-b", + str(tariff_b_path), + "--output", + str(output_path), + "--capacity-rate-dollars-per-kw-month", + str(capacity_rate), + "--admin-fee-dollars", + str(admin_fee), + ] + _run_subprocess(cmd, label="bills") + + +def step_build_regression( + *, + bills_path: Path, + crosswalk_path: Path, + census_path: Path, + output_dir: Path, + predictors: str, + max_crosswalk_drop_pct: float, + min_obs_per_bg: int, + regression_level: str = "annual", +) -> None: + output_dir.mkdir(parents=True, exist_ok=True) + cmd = [ + sys.executable, + "analysis/rtp/build_regression_dataset.py", + "--bills", + str(bills_path), + "--crosswalk", + str(crosswalk_path), + "--census", + str(census_path), + "--output-dir", + str(output_dir), + "--predictors", + predictors, + "--max-crosswalk-drop-pct", + str(max_crosswalk_drop_pct), + "--min-obs-per-bg", + str(min_obs_per_bg), + "--regression-level", + regression_level, + ] + _run_subprocess(cmd, label="regression") + + +# --------------------------------------------------------------------------- +# Concatenate monthly bills (no cross-month aggregation by account_identifier) +# --------------------------------------------------------------------------- + + +def build_all_months_bills( + run_dir: Path, + months: list[str], + out_path: Path, +) -> int: + """Concatenate per-month household bills with a ``month`` column. + + Household IDs do NOT persist across months, so we must NOT aggregate + by account_identifier across months. This function simply stacks the + monthly bill files and tags each row with its YYYYMM month string. + + Returns: + Number of rows in the concatenated output. + """ + lfs: list[pl.LazyFrame] = [] + for ym in months: + path = run_dir / f"month={ym}" / "household_bills.parquet" + if not path.exists(): + raise FileNotFoundError(f"Monthly bills not found: {path}") + lf = pl.scan_parquet(path).with_columns(pl.lit(ym).alias("month")) + lfs.append(lf) + + combined = pl.concat(lfs, how="vertical") + out_path.parent.mkdir(parents=True, exist_ok=True) + combined.sink_parquet(out_path) + n_rows = pl.scan_parquet(out_path).select(pl.len()).collect().item() + logger.info( + "Concatenated %d monthly bill files: %d total rows -> %s", + len(lfs), + n_rows, + out_path, + ) + return n_rows + + +# --------------------------------------------------------------------------- +# Month resolution +# --------------------------------------------------------------------------- + + +def resolve_months(args: argparse.Namespace) -> list[str]: + """Parse and validate month list from CLI args.""" + if args.months: + months = [m.strip() for m in args.months.split(",") if m.strip()] + elif args.months_file: + p = Path(args.months_file) + if not p.exists(): + raise FileNotFoundError(f"Months file not found: {p}") + months = [line.strip() for line in p.read_text().splitlines() if line.strip()] + else: + raise ValueError("Provide either --months or --months-file.") + + # Validate YYYYMM format + for m in months: + if len(m) != 6 or not m.isdigit(): + raise ValueError(f"Invalid month format '{m}'; expected YYYYMM.") + + return sorted(set(months)) + + +def resolve_path(pattern: str, yyyymm: str) -> Path: + """Replace {yyyymm} placeholder in a path pattern.""" + return Path(pattern.replace("{yyyymm}", yyyymm)) + + +# --------------------------------------------------------------------------- +# CLI +# --------------------------------------------------------------------------- + + +def parse_args(argv: list[str] | None = None) -> argparse.Namespace: + p = argparse.ArgumentParser( + description="Multi-month RTP billing pipeline orchestrator.", + formatter_class=argparse.RawDescriptionHelpFormatter, + ) + + # Month selection (mutually exclusive group) + mg = p.add_mutually_exclusive_group(required=True) + mg.add_argument("--months", type=str, help="Comma-separated YYYYMM list.") + mg.add_argument("--months-file", type=str, help="Path to file with one YYYYMM per line.") + + # Input patterns + p.add_argument( + "--interval-pattern", + type=str, + required=True, + help="Path pattern with {yyyymm} for interval parquet files.", + ) + p.add_argument( + "--cluster-assignments-pattern", + type=str, + default=None, + help="Optional path pattern with {yyyymm} for cluster assignments.", + ) + + # Tariff files + p.add_argument("--tariff-a", type=Path, required=True, help="Baseline tariff prices parquet.") + p.add_argument("--tariff-b", type=Path, required=True, help="Alternative tariff prices parquet.") + + # Reference data + p.add_argument( + "--crosswalk", + type=Path, + default=Path("data/reference/comed_bg_zip4_crosswalk.txt"), + help="ZIP+4 -> BG crosswalk TSV.", + ) + p.add_argument( + "--census", + type=Path, + default=Path("data/reference/census_17_2023.parquet"), + help="Census demographics parquet.", + ) + + # Billing parameters + p.add_argument("--capacity-rate", type=float, default=0.0, help="$/kW-month for tariff B.") + p.add_argument("--admin-fee", type=float, default=0.0, help="$/month admin fee for tariff B.") + + # Regression parameters + p.add_argument("--predictors", type=str, default="auto", help="'auto' | 'core' | 'col1,col2,...'") + p.add_argument("--max-crosswalk-drop-pct", type=float, default=5.0) + p.add_argument("--min-obs-per-bg", type=int, default=3) + + # Pipeline control + p.add_argument("--run-name", type=str, default=None, help="Override run ID.") + p.add_argument("--output-dir", type=Path, default=Path("data/bills"), help="Base output dir.") + p.add_argument("--skip-regression", action="store_true", help="Skip regression step.") + p.add_argument( + "--regression-level", + type=str, + choices=["annual", "bg_month"], + default="annual", + help="Regression granularity: 'annual' or 'bg_month' (BG x month + month FE).", + ) + + return p.parse_args(argv) + + +# --------------------------------------------------------------------------- +# Main orchestration +# --------------------------------------------------------------------------- + + +def main(argv: list[str] | None = None) -> int: + args = parse_args(argv) + + # ── Resolve months ─────────────────────────────────────────────────── + months = resolve_months(args) + + # ── Run ID + directories ───────────────────────────────────────────── + run_id = args.run_name or generate_run_id(months) + run_dir = args.output_dir / run_id + # Hourly loads are large intermediate files only needed during the run; + # _tmp/ signals they can be deleted after the pipeline completes. + tmp_dir = run_dir / "_tmp" + regression_dir = run_dir / "regression" + + run_dir.mkdir(parents=True, exist_ok=True) + # Configure logging BEFORE first logger.info() call + _configure_logging(run_dir / "pipeline.log") + logger.info("Months to process: %s", months) + logger.info("Run ID: %s", run_id) + logger.info("Output directory: %s", run_dir) + + # ── Validate tariff files exist ────────────────────────────────────── + for path, label in [(args.tariff_a, "tariff-a"), (args.tariff_b, "tariff-b")]: + if not path.exists(): + logger.error("%s not found: %s", label, path) + return 1 + + # ── Per-month processing ───────────────────────────────────────────── + manifest: dict[str, Any] = { + "run_id": run_id, + "created_utc": _utc_now_iso(), + "git_sha": _get_git_sha(Path(".")), + "months": months, + "month_summary": {}, + "inputs": { + "interval_pattern": args.interval_pattern, + "cluster_assignments_pattern": args.cluster_assignments_pattern, + "tariff_a": str(args.tariff_a), + "tariff_b": str(args.tariff_b), + "crosswalk": str(args.crosswalk), + "census": str(args.census), + }, + "parameters": { + "capacity_rate": args.capacity_rate, + "admin_fee": args.admin_fee, + "predictors": args.predictors, + "max_crosswalk_drop_pct": args.max_crosswalk_drop_pct, + "min_obs_per_bg": args.min_obs_per_bg, + "skip_regression": args.skip_regression, + "regression_level": args.regression_level, + }, + "steps_completed": [], + } + + for ym in months: + logger.info("════ Processing month %s ════", ym) + + # Resolve input path + input_path = resolve_path(args.interval_pattern, ym) + input_str = str(input_path) + is_glob = any(ch in input_str for ch in ["*", "?", "["]) + if not is_glob and not input_path.exists(): + logger.error("Interval data not found for %s: %s", ym, input_path) + return 1 + + # Resolve optional cluster assignments + cluster_path = None + if args.cluster_assignments_pattern: + cluster_path = resolve_path(args.cluster_assignments_pattern, ym) + if not cluster_path.exists(): + logger.warning( + "Cluster assignments not found for %s: %s (proceeding without).", + ym, + cluster_path, + ) + cluster_path = None + + # Step 1: hourly loads + loads_path = tmp_dir / f"month={ym}" / "hourly_loads.parquet" + step_compute_hourly_loads( + input_path=input_path, + cluster_assignments_path=cluster_path, + output_path=loads_path, + ) + + # Step 2: household bills + bills_path = run_dir / f"month={ym}" / "household_bills.parquet" + step_compute_bills( + hourly_loads_path=loads_path, + tariff_a_path=args.tariff_a, + tariff_b_path=args.tariff_b, + output_path=bills_path, + capacity_rate=args.capacity_rate, + admin_fee=args.admin_fee, + ) + + # Record row counts + loads_rows = pl.scan_parquet(loads_path).select(pl.len()).collect().item() + bills_rows = pl.scan_parquet(bills_path).select(pl.len()).collect().item() + manifest["month_summary"][ym] = { + "rows_hourly_loads": int(loads_rows), + "rows_bills": int(bills_rows), + } + manifest["steps_completed"].append(ym) + logger.info("Month %s complete: %d loads rows, %d bills rows.", ym, loads_rows, bills_rows) + + # ── Concatenate all months bills ──────────────────────────────────── + logger.info("════ Building all-months household bills ════") + all_months_bills_path = run_dir / "all_months_household_bills.parquet" + n_all = build_all_months_bills(run_dir, months, all_months_bills_path) + manifest["all_months_bills_rows"] = n_all + manifest["steps_completed"].append("all_months_bills") + + # ── Regression ─────────────────────────────────────────────────────── + if not args.skip_regression: + logger.info("════ Running regression ════") + step_build_regression( + bills_path=all_months_bills_path, + crosswalk_path=args.crosswalk, + census_path=args.census, + output_dir=regression_dir, + predictors=args.predictors, + max_crosswalk_drop_pct=args.max_crosswalk_drop_pct, + min_obs_per_bg=args.min_obs_per_bg, + regression_level=args.regression_level, + ) + manifest["steps_completed"].append("regression") + else: + logger.info("Skipping regression (--skip-regression).") + + # ── Write manifest ─────────────────────────────────────────────────── + manifest["completed_utc"] = _utc_now_iso() + manifest_path = run_dir / "run_manifest.json" + with open(manifest_path, "w") as f: + json.dump(manifest, f, indent=2) + logger.info("Wrote manifest: %s", manifest_path) + + logger.info("Pipeline complete. Output: %s", run_dir) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/run_pricing_pilot_bg_regressions.py b/scripts/run_pricing_pilot_bg_regressions.py new file mode 100644 index 0000000..2aea49a --- /dev/null +++ b/scripts/run_pricing_pilot_bg_regressions.py @@ -0,0 +1,839 @@ +#!/usr/bin/env python3 + +from __future__ import annotations + +import argparse +import json +import logging +from pathlib import Path +from typing import Any + +import matplotlib +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +import polars as pl +import seaborn as sns +import statsmodels.api as sm +from matplotlib.ticker import FuncFormatter, MaxNLocator + +from smart_meter_analysis.census import fetch_acs_data + +# Use Times New Roman throughout all figures. +# NOTE: re-applied inside _plot_scatter after sns.set_theme(), which resets rcParams. +matplotlib.rcParams.update({ + "font.family": "serif", + "font.serif": ["Times New Roman"], + "font.size": 12, +}) + +logger = logging.getLogger(__name__) + + +# ---------------------------- +# Defaults (EC2) +# ---------------------------- + +DEFAULT_BILLS_DIR = Path("/ebs/home/griffin_switch_box/pricing_pilot/bills_unscaled") +DEFAULT_OUT_DIR = Path("/ebs/home/griffin_switch_box/pricing_pilot/regression") + +DEFAULT_ACCOUNT_BG_MAP_202301 = Path("/ebs/home/griffin_switch_box/pricing_pilot/account_bg_map_202301.parquet") +DEFAULT_ACCOUNT_BG_MAP_202307 = Path("/ebs/home/griffin_switch_box/pricing_pilot/account_bg_map_202307.parquet") + +DEFAULT_CENSUS_CACHE = Path("/ebs/home/griffin_switch_box/pricing_pilot/census_min_income_acs2023.parquet") + + +# ---------------------------- +# Label maps +# ---------------------------- + +CLASS_LABELS: dict[str, str] = { + "sf_no_esh": "Single-Family, No Electric Space Heat", + "mf_no_esh": "Multifamily, No Electric Space Heat", + "sf_esh": "Single-Family, Electric Space Heat", + "mf_esh": "Multifamily, Electric Space Heat", +} + +COMPARISON_LABELS: dict[str, str] = { + "stou_vs_flat": "STOU vs Flat Rate", + "dtou_vs_flat": "DTOU vs Flat Rate", +} + +MONTH_PREFIXES: dict[str, str] = { + "202301": "jan_2023", + "202307": "jul_2023", +} + + +# ---------------------------- +# Small utilities +# ---------------------------- + + +def _configure_logging() -> None: + logging.basicConfig( + level=logging.INFO, + format="%(asctime)s - %(levelname)s - %(message)s", + force=True, + ) + + +def _dollar_formatter(x: float, _pos: int) -> str: + return f"${x:,.0f}" + + +def _pct_formatter(x: float, _pos: int) -> str: + return f"{x:.1f}%" + + +def _month_title(month: str) -> str: + if month == "202301": + return "January 2023" + if month == "202307": + return "July 2023" + return month + + +def _y_label(*, pct: bool = False) -> str: + if pct: + return "Average Monthly Bill Change (%)" + # mean_delta = block-group average of (Flat - Alt); positive = savings under alternative. + return "Average Monthly Savings (Flat \u2212 Alt, $)" + + +def _class_label(code: str) -> str: + """Human-readable delivery class name, e.g. 'sf_no_esh' → 'Single-Family, No Electric Space Heat'.""" + return CLASS_LABELS.get(code, code) + + +def _comparison_label(lbl: str) -> str: + """Human-readable comparison label, e.g. 'stou_vs_flat' → 'STOU vs Flat Rate'.""" + return COMPARISON_LABELS.get(lbl, lbl) + + +def _month_prefix(ym: str) -> str: + """Short filename prefix for a month, e.g. '202301' → 'jan_2023'.""" + return MONTH_PREFIXES.get(ym, ym) + + +def _extract_class_from_path(path: Path) -> str | None: + """Extract delivery class code from a bill filename. + + Expected pattern: {yyyymm}_flat_vs_{dtou|stou}_{class_code}.parquet + Returns the class code segment, e.g. 'sf_no_esh', or None if unrecognised. + """ + stem = path.stem + for marker in ("vs_dtou_", "vs_stou_"): + idx = stem.find(marker) + if idx >= 0: + return stem[idx + len(marker) :] + return None + + +def _comparison_label_from_filename(name: str) -> str | None: + """ + Pilot policy: + - DTOU: filenames containing 'vs_dtou' (e.g. {yyyymm}_flat_vs_dtou_{sf_no_esh,...}.parquet) + - STOU: filenames containing 'vs_stou' or 'vs_rate_best' (rate_best alias, only when _scaled_) + """ + n = name.lower() + if "vs_dtou" in n: + return "dtou_vs_flat" + if "vs_stou" in n: + return "stou_vs_flat" + if "vs_rate_best" in n: + return "stou_vs_flat" if "_scaled_" in n else None + return None + + +def _discover_scenarios(bills_dir: Path) -> dict[tuple[str, str], list[Path]]: + """ + Returns mapping (yyyymm, comparison_label) -> list[parquet paths]. + File pattern: {yyyymm}_flat_vs_{dtou|stou}_{sf_no_esh,mf_no_esh,sf_esh,mf_esh}.parquet + Months: 202301, 202307. Each scenario gets 4 delivery-class files. + """ + out: dict[tuple[str, str], list[Path]] = {} + for ym in ("202301", "202307"): + for comp in ("dtou", "stou"): + lbl = f"{comp}_vs_flat" + files = sorted(bills_dir.glob(f"{ym}_flat_vs_{comp}_*.parquet")) + if files: + out[(ym, lbl)] = files + return out + + +# ---------------------------- +# Data prep +# ---------------------------- + + +def _compute_household_delta(hh: pl.DataFrame) -> pl.DataFrame: + """ + Standardizes household-level delta for pilot bills. + + ΔBill = Flat - Alternative (positive = savings under alternative), prefer: + 1) net_bill_diff_dollars + 2) bill_diff_dollars + 3) bill_b_dollars - bill_a_dollars + """ + cols = set(hh.columns) + + if "net_bill_diff_dollars" in cols: + delta = pl.col("net_bill_diff_dollars").cast(pl.Float64) + elif "bill_diff_dollars" in cols: + delta = pl.col("bill_diff_dollars").cast(pl.Float64) + elif "bill_b_dollars" in cols and "bill_a_dollars" in cols: + delta = pl.col("bill_a_dollars").cast(pl.Float64) - pl.col("bill_b_dollars").cast(pl.Float64) + else: + raise RuntimeError( + "Could not compute household delta. Expected one of: net_bill_diff_dollars, bill_diff_dollars, " + "or (bill_b_dollars & bill_a_dollars). " + f"Got columns: {sorted(cols)}" + ) + + if "total_kwh" not in cols: + raise RuntimeError("Expected total_kwh column in pilot bills (for mean_kwh hue/diagnostics).") + + return hh.with_columns([ + delta.alias("delta_dollars"), + pl.col("total_kwh").cast(pl.Float64).alias("kwh"), + pl.col("account_identifier").cast(pl.Utf8).alias("account_identifier"), + ]) + + +def _compute_household_pct_change(hh: pl.DataFrame) -> pl.DataFrame: + """Standardizes household-level percentage change for pilot bills. + + pct_change = (flat_bill - alt_bill) / flat_bill * 100 + (positive = savings under alternative) + + Households where flat_bill <= 0 yield null and are dropped before return. + + Column priority mirrors _compute_household_delta: + 1) net_pct_savings (pre-computed; matches net_bill_diff_dollars priority) + 2) pct_savings (pre-computed; matches bill_diff_dollars priority) + 3) net_bill_diff_dollars / bill_a_dollars * 100 + 4) bill_diff_dollars / bill_a_dollars * 100 + 5) (bill_a_dollars - bill_b_dollars) / bill_a_dollars * 100 + """ + cols = set(hh.columns) + + if "net_pct_savings" in cols: + pct_expr = pl.col("net_pct_savings").cast(pl.Float64) + elif "pct_savings" in cols: + pct_expr = pl.col("pct_savings").cast(pl.Float64) + elif "net_bill_diff_dollars" in cols and "bill_a_dollars" in cols: + pct_expr = ( + pl.when(pl.col("bill_a_dollars").cast(pl.Float64) > 0) + .then(pl.col("net_bill_diff_dollars").cast(pl.Float64) / pl.col("bill_a_dollars").cast(pl.Float64) * 100) + .otherwise(None) + ) + elif "bill_diff_dollars" in cols and "bill_a_dollars" in cols: + pct_expr = ( + pl.when(pl.col("bill_a_dollars").cast(pl.Float64) > 0) + .then(pl.col("bill_diff_dollars").cast(pl.Float64) / pl.col("bill_a_dollars").cast(pl.Float64) * 100) + .otherwise(None) + ) + elif "bill_a_dollars" in cols and "bill_b_dollars" in cols: + pct_expr = ( + pl.when(pl.col("bill_a_dollars").cast(pl.Float64) > 0) + .then( + (pl.col("bill_a_dollars").cast(pl.Float64) - pl.col("bill_b_dollars").cast(pl.Float64)) + / pl.col("bill_a_dollars").cast(pl.Float64) + * 100 + ) + .otherwise(None) + ) + else: + raise RuntimeError( + "Could not compute household pct change. Expected one of: net_pct_savings, pct_savings, " + "(net_bill_diff_dollars & bill_a_dollars), (bill_diff_dollars & bill_a_dollars), " + "or (bill_a_dollars & bill_b_dollars). " + f"Got columns: {sorted(cols)}" + ) + + if "total_kwh" not in cols: + raise RuntimeError("Expected total_kwh column in pilot bills (for mean_kwh hue/diagnostics).") + + # Reuse delta_dollars column name so all downstream aggregation/regression code + # works without modification; units are percent, not dollars. + out = hh.with_columns([ + pct_expr.alias("delta_dollars"), + pl.col("total_kwh").cast(pl.Float64).alias("kwh"), + pl.col("account_identifier").cast(pl.Utf8).alias("account_identifier"), + ]) + n_before = out.height + out = out.drop_nulls(["delta_dollars"]) + n_dropped = n_before - out.height + if n_dropped: + logger.info("_compute_household_pct_change: dropped %d/%d rows with flat_bill <= 0.", n_dropped, n_before) + return out + + +def _load_account_bg_map(path: Path) -> pl.DataFrame: + if not path.exists(): + raise FileNotFoundError(f"Account->BG map not found: {path}") + df = pl.read_parquet(path).select([ + pl.col("account_identifier").cast(pl.Utf8), + pl.col("geoid_bg").cast(pl.Utf8), + ]) + # De-dupe deterministically + return df.unique(subset=["account_identifier"], keep="first") + + +def _attach_geoid_bg(hh: pl.DataFrame, acct_bg: pl.DataFrame) -> pl.DataFrame: + out = hh.join(acct_bg, on="account_identifier", how="left") + miss = out.select(pl.col("geoid_bg").is_null().mean()).item() + if miss > 0: + logger.warning("Missing geoid_bg for %.6f%% of household rows after map join.", miss * 100.0) + return out.drop_nulls(["geoid_bg"]) + + +def _aggregate_bg(hh: pl.DataFrame) -> pl.DataFrame: + return hh.group_by("geoid_bg").agg([ + pl.mean("delta_dollars").alias("mean_delta"), + pl.median("delta_dollars").alias("median_delta"), + pl.mean("kwh").alias("mean_kwh"), + pl.len().alias("n_households"), + ]) + + +def _assert_no_bg_duplicates(bg: pl.DataFrame, *, tag: str) -> None: + n = bg.height + n_unique = bg.select(pl.col("geoid_bg").n_unique()).item() + if n != n_unique: + raise RuntimeError(f"{tag}: BG duplicates detected: rows={n} unique_geoid_bg={n_unique}") + + +# ---------------------------- +# Census income (minimal cache) +# ---------------------------- + + +def _load_or_build_income_cache( + *, + cache_path: Path, + needed_geoids: set[str], + acs_year: int, + state_fips: str, + county_fips: str | None, +) -> pl.DataFrame: + """ + Builds (or loads) a minimal census table: + geoid_bg (12-digit) + median_income (float) + using smart_meter_analysis.census.fetch_acs_data() and the engineered + 'median_household_income' feature (from B19013_001E). + + If county_fips is provided, only that county is fetched (faster). + After fetching, we filter to needed_geoids and cache to parquet. + """ + if cache_path.exists(): + logger.info("Loading cached income parquet: %s", cache_path) + df = pl.read_parquet(cache_path) + return df.select([pl.col("geoid_bg").cast(pl.Utf8), pl.col("median_income").cast(pl.Float64)]) + + logger.info( + "Building income cache via ACS API (year=%d state=%s county=%s).", + acs_year, + state_fips, + county_fips or "*", + ) + + acs = fetch_acs_data(state_fips=state_fips, year=acs_year, county_fips=county_fips) + + if "GEOID" not in acs.columns or "median_household_income" not in acs.columns: + raise RuntimeError(f"ACS pull did not contain required columns. Found columns: {acs.columns}") + + df = acs.select([ + pl.col("GEOID").cast(pl.Utf8).alias("geoid_bg"), + pl.col("median_household_income").cast(pl.Float64, strict=False).alias("median_income"), + ]) + + if needed_geoids: + df = df.filter(pl.col("geoid_bg").is_in(list(needed_geoids))) + + cache_path.parent.mkdir(parents=True, exist_ok=True) + df.write_parquet(cache_path) + logger.info("Wrote income cache: %s (rows=%d)", cache_path, df.height) + return df + + +def _attach_income(bg: pl.DataFrame, income: pl.DataFrame, *, tag: str) -> pl.DataFrame: + """ + Attach income via left join, then drop null income (and null mean_delta defensively), + with explicit logging. No silent data loss. + """ + n0 = bg.height + out = bg.join(income, on="geoid_bg", how="left") + n_missing_join = out.select(pl.col("median_income").is_null().sum()).item() + + # Drop nulls per requirements (income + mean_delta) + out2 = out.drop_nulls(["median_income", "mean_delta"]) + n1 = out2.height + + logger.info( + "%s: BG income join+sanitize: n0=%d missing_income=%d dropped=%d n=%d", + tag, + n0, + int(n_missing_join), + int(n0 - n1), + n1, + ) + + if out2.height == 0: + raise RuntimeError(f"{tag}: zero BG rows after dropping null income/mean_delta") + + # Fail-loud + if out2.select(pl.col("median_income").is_null().any()).item(): + raise RuntimeError(f"{tag}: median_income nulls remain after drop_nulls") + if out2.select(pl.col("mean_delta").is_null().any()).item(): + raise RuntimeError(f"{tag}: mean_delta nulls remain after drop_nulls") + + return out2 + + +# ---------------------------- +# Regression + plotting +# ---------------------------- + + +def _sanitize_for_statsmodels(pdf: pd.DataFrame, *, tag: str) -> pd.DataFrame: + """ + Drop NaNs and assert finite X/Y before OLS. Log counts. + """ + n0 = len(pdf) + + # Drop NaNs (defensive; should already be gone) + null_x = int(pd.isna(pdf["median_income"]).sum()) + null_y = int(pd.isna(pdf["mean_delta"]).sum()) + pdf2 = pdf.dropna(subset=["median_income", "mean_delta"]).copy() + + # Finite check + x = pdf2["median_income"].to_numpy(dtype=float, copy=False) + y = pdf2["mean_delta"].to_numpy(dtype=float, copy=False) + finite = np.isfinite(x) & np.isfinite(y) + bad = int((~finite).sum()) + if bad: + pdf2 = pdf2.loc[finite].copy() + + n2 = len(pdf2) + + logger.info( + "%s: statsmodels sanitize: n0=%d null_x=%d null_y=%d nonfinite_rows=%d n=%d", + tag, + n0, + null_x, + null_y, + bad, + n2, + ) + + if n2 == 0: + raise RuntimeError(f"{tag}: empty regression frame after sanitization") + + # Fail-loud invariants + if pdf2["median_income"].isna().any(): + raise RuntimeError(f"{tag}: median_income NaNs remain") + if pdf2["mean_delta"].isna().any(): + raise RuntimeError(f"{tag}: mean_delta NaNs remain") + if not np.isfinite(pdf2["median_income"].to_numpy(dtype=float)).all(): + raise RuntimeError(f"{tag}: median_income contains non-finite values") + if not np.isfinite(pdf2["mean_delta"].to_numpy(dtype=float)).all(): + raise RuntimeError(f"{tag}: mean_delta contains non-finite values") + + return pdf2 + + +def _run_ols_hc1(df: pl.DataFrame, *, tag: str) -> tuple[Any, pd.DataFrame]: + """ + OLS(mean_delta ~ const + median_income) with HC1 robust SE. + Logs N, no silent drops. + """ + pdf = df.select(["median_income", "mean_delta", "mean_kwh", "n_households", "geoid_bg"]).to_pandas() + pdf["median_income"] = pdf["median_income"] / 10_000 + pdf = _sanitize_for_statsmodels(pdf, tag=tag) + + y = pdf["mean_delta"].astype(float) + X = sm.add_constant(pdf["median_income"].astype(float), has_constant="add") + + model = sm.OLS(y, X).fit(cov_type="HC1") + + logger.info( + "%s: OLS(HC1) beta1=%.6g se=%.6g p=%.4g r2=%.4f n=%d", + tag, + float(model.params["median_income"]), + float(model.bse["median_income"]), + float(model.pvalues["median_income"]), + float(model.rsquared), + int(model.nobs), + ) + + return model, pdf + + +def _save_regression_json(model: Any, out_path: Path) -> None: + res = { + "beta_0": float(model.params["const"]), + "beta_1": float(model.params["median_income"]), + "se_beta_1": float(model.bse["median_income"]), + "p_value_beta_1": float(model.pvalues["median_income"]), + "r_squared": float(model.rsquared), + "n": int(model.nobs), + } + out_path.parent.mkdir(parents=True, exist_ok=True) + out_path.write_text(json.dumps(res, indent=2)) + + +def _plot_scatter( + pdf: pd.DataFrame, + model: Any, + title: str, + out_path: Path, + *, + pct: bool = False, +) -> None: + """Produce one publication-ready scatterplot with OLS overlay. + + Parameters + ---------- + title: + Full two-line title already formatted as ``"Line1\\nBlock Group Level"``. + pct: + When True the y-axis is percentage change; adjusts label and tick formatter. + """ + sns.set_theme(style="whitegrid") + # sns.set_theme() resets rcParams; re-apply font settings immediately after. + matplotlib.rcParams.update({ + "font.family": "serif", + "font.serif": ["Times New Roman"], + "font.size": 12, + }) + + fig, (ax, ax_stats) = plt.subplots( + 1, + 2, + figsize=(10.5, 6.5), + gridspec_kw={"width_ratios": [4.5, 1.5]}, + ) + + sns.scatterplot( + data=pdf, + x="median_income", + y="mean_delta", + hue="mean_kwh", + palette="viridis", + edgecolor=None, + alpha=0.8, + legend=False, + ax=ax, + ) + + # Colorbar: dot colour encodes block-group mean electricity use + cbar = fig.colorbar(ax.collections[0], ax=ax, shrink=0.8) + cbar.set_label("Block-Group Mean Usage (kWh)", fontsize=10, fontfamily="serif") + + # Regression line with 95% CI band + sns.regplot( + data=pdf, + x="median_income", + y="mean_delta", + scatter=False, + ci=95, + ax=ax, + ) + + # Remove any legend seaborn may have attached + legend = ax.get_legend() + if legend is not None: + legend.remove() + + ax.set_xlabel("Median Household Income ($10K)") + ax.set_ylabel(_y_label(pct=pct)) + ax.xaxis.set_major_locator(MaxNLocator(nbins=6)) + ax.xaxis.set_major_formatter(FuncFormatter(_dollar_formatter)) + ax.yaxis.set_major_formatter(FuncFormatter(_pct_formatter if pct else _dollar_formatter)) + plt.setp(ax.get_xticklabels(), rotation=45, ha="right") + ax.set_title(title) + + # ── OLS stats panel (right side, out of the way) ────────────────────── + beta1 = float(model.params["median_income"]) + se = float(model.bse["median_income"]) + pval = float(model.pvalues["median_income"]) + r2 = float(model.rsquared) + + stats_parts = [ + "Ordinary Least Squares Regression", + "", + f"\u03b2\u2081 = {beta1:.4g}", + f"SE = {se:.4g}", + f"p = {pval:.4g}", + f"R\u00b2 = {r2:.4f}", + f"N = {len(pdf)}", + ] + + ax_stats.axis("off") + ax_stats.text( + 0.0, + 1.0, + "\n".join(stats_parts), + ha="left", + va="top", + fontsize=10, + fontfamily="serif", + bbox={"boxstyle": "round,pad=0.4", "facecolor": "white", "edgecolor": "0.8", "alpha": 0.95}, + ) + + # Safety net: force Times New Roman on every text element of the main axes. + for item in [ax.title, ax.xaxis.label, ax.yaxis.label, *ax.get_xticklabels(), *ax.get_yticklabels()]: + item.set_fontfamily("serif") + item.set_fontname("Times New Roman") + + fig.tight_layout() + out_path.parent.mkdir(parents=True, exist_ok=True) + fig.savefig(out_path, dpi=300) + plt.close(fig) + + +# ---------------------------- +# BG-table construction helpers +# ---------------------------- + + +def _build_aggregate_bg( + paths: list[Path], + acct_bg: pl.DataFrame, + tag: str, + *, + pct: bool = False, +) -> tuple[pl.DataFrame, float]: + """Pool all delivery-class files for one scenario; aggregate to BG level. + + Returns (bg_table, household_mean_delta_or_pct). + """ + hh = pl.scan_parquet(paths).collect() + hh = _compute_household_pct_change(hh) if pct else _compute_household_delta(hh) + hh_mean = float(hh.select(pl.col("delta_dollars").mean()).item()) + logger.info( + "%s: household mean(delta_%s)=%.6g (should be ~0; upstream authoritative)", + tag, + "pct" if pct else "dollars", + hh_mean, + ) + hh = _attach_geoid_bg(hh, acct_bg) + bg = _aggregate_bg(hh) + _assert_no_bg_duplicates(bg, tag=tag) + return bg, hh_mean + + +def _build_per_class_bg( + path: Path, + acct_bg: pl.DataFrame, + tag: str, + *, + pct: bool = False, +) -> pl.DataFrame: + """Aggregate a single delivery-class bill file to BG level.""" + hh = pl.read_parquet(path) + hh = _compute_household_pct_change(hh) if pct else _compute_household_delta(hh) + hh = _attach_geoid_bg(hh, acct_bg) + bg = _aggregate_bg(hh) + _assert_no_bg_duplicates(bg, tag=tag) + return bg + + +# ---------------------------- +# Main +# ---------------------------- + + +def parse_args() -> argparse.Namespace: + p = argparse.ArgumentParser() + p.add_argument("--bills-dir", type=Path, default=DEFAULT_BILLS_DIR) + p.add_argument("--out-dir", type=Path, default=DEFAULT_OUT_DIR) + + p.add_argument("--account-bg-map-202301", type=Path, default=DEFAULT_ACCOUNT_BG_MAP_202301) + p.add_argument("--account-bg-map-202307", type=Path, default=DEFAULT_ACCOUNT_BG_MAP_202307) + + # Census fetch controls (we only need income) + p.add_argument("--acs-year", type=int, default=2023) + p.add_argument("--state-fips", type=str, default="17") + # Default Cook only for speed; set to None to pull all IL counties if needed. + p.add_argument( + "--county-fips", + type=str, + default="031", + help="County FIPS for ACS pull (default Cook=031). Use '' to pull all counties.", + ) + p.add_argument("--income-cache", type=Path, default=DEFAULT_CENSUS_CACHE) + + p.add_argument( + "--pct", + action="store_true", + default=False, + help=( + "Compute percentage change instead of absolute dollars. " + "pct_change = (flat_bill - alt_bill) / flat_bill * 100. " + "Households with flat_bill <= 0 are excluded. " + "Appends _pct to all output filenames (PNGs, JSONs) and writes " + "regression_summary_pct.txt instead of regression_summary.txt." + ), + ) + + return p.parse_args() + + +def main() -> int: # noqa: C901 + _configure_logging() + args = parse_args() + + pct: bool = args.pct + file_suffix = "_pct" if pct else "" + + bills_dir: Path = args.bills_dir + out_dir: Path = args.out_dir + out_reg_dir = out_dir + out_fig_dir = out_dir / "figures" + + if not bills_dir.exists(): + raise FileNotFoundError(f"--bills-dir not found: {bills_dir}") + + scenarios = _discover_scenarios(bills_dir) + if not scenarios: + raise RuntimeError(f"No scenarios discovered in {bills_dir}. Expected files like 202301_flat_vs_dtou_*.parquet") + + logger.info("Discovered %d scenarios.", len(scenarios)) + + # Load maps (fail-loud) + acct_bg_202301 = _load_account_bg_map(args.account_bg_map_202301) + acct_bg_202307 = _load_account_bg_map(args.account_bg_map_202307) + + # Collect needed BGs from all scenarios, then load income once. + needed_geoids: set[str] = set() + bg_agg: dict[tuple[str, str], pl.DataFrame] = {} + bg_class: dict[tuple[str, str, str], pl.DataFrame] = {} + hh_mean_deltas: dict[tuple[str, str], float] = {} + + for (ym, lbl), paths in sorted(scenarios.items()): + tag = f"{ym}_{lbl}" + logger.info("Scenario month=%s label=%s inputs=%d", ym, lbl, len(paths)) + acct_bg = acct_bg_202301 if ym == "202301" else acct_bg_202307 + + # Aggregate (pool all delivery classes) + bg, hh_mean = _build_aggregate_bg(paths, acct_bg, tag, pct=pct) + bg_agg[(ym, lbl)] = bg + hh_mean_deltas[(ym, lbl)] = hh_mean + needed_geoids.update(bg["geoid_bg"].to_list()) + + # Per delivery class + for path in paths: + class_code = _extract_class_from_path(path) + if class_code is None: + logger.warning("Cannot extract class code from %s; skipping per-class plot.", path.name) + continue + class_tag = f"{tag}_{class_code}" + logger.info("Building per-class BG table: %s", class_tag) + bg_class[(ym, lbl, class_code)] = _build_per_class_bg(path, acct_bg, class_tag, pct=pct) + + county_fips = args.county_fips.strip() or None + + income = _load_or_build_income_cache( + cache_path=args.income_cache, + needed_geoids=needed_geoids, + acs_year=args.acs_year, + state_fips=args.state_fips, + county_fips=county_fips, + ) + + out_reg_dir.mkdir(parents=True, exist_ok=True) + out_fig_dir.mkdir(parents=True, exist_ok=True) + + summary_lines: list[str] = [] + ran_any = False + + # ── Aggregate plots (4: 2 months x 2 comparisons) ──────────────────── + for (ym, lbl), bg in sorted(bg_agg.items()): + tag = f"{ym}_{lbl}_aggregate" + bg2 = _attach_income(bg, income, tag=tag) + + # N reasonable (~1,600-1,900 BGs) -- log, don't hard fail unless extreme + if not (1000 <= bg2.height <= 2500): + logger.warning("%s: aggregate BG N outside expected 1000-2500: n=%d", tag, bg2.height) + + model, pdf = _run_ols_hc1(bg2, tag=tag) + + month_title = _month_title(ym) + comp_label = _comparison_label(lbl) + mp = _month_prefix(ym) + + title = f"{month_title} — {comp_label} — All Delivery Classes\nBlock Group Level" + fig_stem = f"{mp}_{lbl}_aggregate{file_suffix}" + + _plot_scatter(pdf, model, title, out_fig_dir / f"{fig_stem}_scatter.png", pct=pct) + _save_regression_json(model, out_reg_dir / f"{fig_stem}_regression.json") + + summary_lines.append(f"=== AGGREGATE: {month_title} — {comp_label} — All Delivery Classes ===") + summary_lines.append(model.summary().as_text()) + summary_lines.append("") + ran_any = True + + # ── Per-class plots (up to 16: 4 classes x 2 months x 2 comparisons) ─ + for (ym, lbl, class_code), bg in sorted(bg_class.items()): + tag = f"{ym}_{lbl}_{class_code}" + bg2 = _attach_income(bg, income, tag=tag) + + if bg2.height < 10: + logger.warning("%s: only %d BG rows — skipping (too few for OLS).", tag, bg2.height) + continue + + model, pdf = _run_ols_hc1(bg2, tag=tag) + + month_title = _month_title(ym) + comp_label = _comparison_label(lbl) + class_lbl = _class_label(class_code) + mp = _month_prefix(ym) + + title = f"{month_title} — {comp_label} — {class_lbl}\nBlock Group Level" + fig_stem = f"{mp}_{lbl}_{class_code}{file_suffix}" + + _plot_scatter( + pdf, + model, + title, + out_fig_dir / f"{fig_stem}_scatter.png", + pct=pct, + ) + _save_regression_json(model, out_reg_dir / f"{fig_stem}_regression.json") + + summary_lines.append(f"=== {class_lbl}: {month_title} — {comp_label} ===") + summary_lines.append(model.summary().as_text()) + summary_lines.append("") + ran_any = True + + if not ran_any: + raise RuntimeError("No regressions were run (likely due to too few BGs after income join). Check logs above.") + + if pct: + summary_lines = [ + "=== Percentage-change regressions (--pct) ===", + "Y variable: mean_pct_change = mean((flat_bill - alt_bill) / flat_bill * 100) per block group", + "Households with flat_bill <= 0 excluded.", + "", + *summary_lines, + ] + + summary_fname = f"regression_summary{file_suffix}.txt" + (out_reg_dir / summary_fname).write_text("\n".join(summary_lines), encoding="utf-8") + + logger.info("Wrote %s -> %s", summary_fname, out_reg_dir / summary_fname) + logger.info("Figures dir -> %s", out_fig_dir) + logger.info("Income cache -> %s", args.income_cache) + + # Final validation summary (explicit, no silent assumptions) + for (ym, lbl), v in sorted(hh_mean_deltas.items()): + logger.info("VALIDATION: %s_%s household mean(delta_%s)=%.6g", ym, lbl, "pct" if pct else "dollars", v) + + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/test_pipeline_audit.py b/scripts/test_pipeline_audit.py new file mode 100644 index 0000000..9e040fd --- /dev/null +++ b/scripts/test_pipeline_audit.py @@ -0,0 +1,699 @@ +#!/usr/bin/env python3 +"""End-to-end audit: synthetic data → pipeline → hand-calculated verification. + +Generates synthetic hourly loads with known kWh patterns, runs the actual +compute_delivery_deltas.py and package_dtou_results.py pipeline code on +them, and asserts that every output column matches hand-calculated expected +values within floating-point tolerance. + +Usage:: + + uv run python scripts/test_pipeline_audit.py +""" + +from __future__ import annotations + +import shutil +import subprocess +import sys +from datetime import datetime +from pathlib import Path + +import polars as pl + +# ── Paths ───────────────────────────────────────────────────────────────── + +REPO_ROOT = Path(__file__).resolve().parent.parent +AUDIT_DIR = Path("/tmp/pipeline_audit") # noqa: S108 +COMPUTE_SCRIPT = REPO_ROOT / "scripts" / "pricing_pilot" / "compute_delivery_deltas.py" +PACKAGE_SCRIPT = REPO_ROOT / "scripts" / "pricing_pilot" / "package_dtou_results.py" + +# ── Test configuration ──────────────────────────────────────────────────── + +ZIP_CODE = "60601" +TOLERANCE = 1e-6 + +DELIVERY_CLASSES = ["C23", "C24", "C26", "C28"] + +# (month_str, season, days_in_month) +MONTHS_CFG = [ + ("202301", "nonsummer", 31), + ("202307", "summer", 31), +] + +# kWh per hour for each TOU period — chosen to exercise period weighting +KWH_PER_HOUR: dict[str, float] = { + "morning": 2.0, + "midday_peak": 3.0, + "evening": 1.5, + "overnight": 1.0, +} + +# ── Period mapping (must match compute_delivery_deltas.py PERIOD_MAP) ───── + +PERIOD_MAP: dict[int, str] = { + 0: "overnight", + 1: "overnight", + 2: "overnight", + 3: "overnight", + 4: "overnight", + 5: "overnight", + 6: "morning", + 7: "morning", + 8: "morning", + 9: "morning", + 10: "morning", + 11: "morning", + 12: "morning", + 13: "midday_peak", + 14: "midday_peak", + 15: "midday_peak", + 16: "midday_peak", + 17: "midday_peak", + 18: "midday_peak", + 19: "evening", + 20: "evening", + 21: "overnight", + 22: "overnight", + 23: "overnight", +} + +# Hours per period per day (derived from PERIOD_MAP for explicitness) +HOURS_PER_PERIOD_PER_DAY: dict[str, int] = { + "morning": 7, # hours 6-12 + "midday_peak": 6, # hours 13-18 + "evening": 2, # hours 19-20 + "overnight": 9, # hours 0-5, 21-23 +} +assert sum(HOURS_PER_PERIOD_PER_DAY.values()) == 24 # noqa: S101 + +# ── Rate constants (must match pipeline code exactly) ───────────────────── + +FLAT_PTCS: dict[str, float] = {"summer": 10.028, "nonsummer": 9.660} + +STOU_SUPPLY: dict[str, dict[str, float]] = { + "summer": { + "morning": 4.279, + "midday_peak": 19.485, + "evening": 4.356, + "overnight": 3.136, + }, + "nonsummer": { + "morning": 4.095, + "midday_peak": 18.080, + "evening": 4.352, + "overnight": 3.278, + }, +} + +FLAT_DFCS: dict[str, float] = { + "C23": 6.228, + "C24": 4.791, + "C26": 3.165, + "C28": 2.996, +} + +TOU_DFCS: dict[str, dict[str, float]] = { + "C23": {"morning": 4.009, "midday_peak": 10.712, "evening": 3.747, "overnight": 2.984}, + "C24": {"morning": 3.073, "midday_peak": 8.689, "evening": 2.856, "overnight": 2.251}, + "C26": {"morning": 1.999, "midday_peak": 5.329, "evening": 1.890, "overnight": 1.550}, + "C28": {"morning": 1.925, "midday_peak": 4.975, "evening": 1.823, "overnight": 1.512}, +} + + +# ── Helpers ─────────────────────────────────────────────────────────────── + + +def _acct_name(dc: str, month_str: str) -> str: + """Generate a deterministic account name for a delivery class + month.""" + return f"ACCT_{dc}_{month_str}" + + +# ── Step 1: Generate synthetic data ────────────────────────────────────── + + +def generate_synthetic_data() -> None: + """Create hourly_loads parquet files and delivery_class_lookup.parquet. + + Directory layout matches what compute_delivery_deltas.py expects: + + {AUDIT_DIR}/ + statewide_stou_{month}_v2/ + statewide_stou_{month}_v2/ + _tmp/ + month={month}/ + hourly_loads.parquet + delivery_class_lookup.parquet + """ + print("\n" + "=" * 60) + print("STEP 1: Generate synthetic data") + print("=" * 60) + + if AUDIT_DIR.exists(): + shutil.rmtree(AUDIT_DIR) + + # --- Hourly loads (one file per month) --- + for month_str, _season, days in MONTHS_CFG: + run_name = f"statewide_stou_{month_str}_v2" + hourly_dir = AUDIT_DIR / run_name / run_name / "_tmp" / f"month={month_str}" + hourly_dir.mkdir(parents=True) + + year = int(month_str[:4]) + month_int = int(month_str[4:6]) + + rows: list[dict] = [] + for dc in DELIVERY_CLASSES: + acct = _acct_name(dc, month_str) + for day in range(1, days + 1): + for hour in range(24): + period = PERIOD_MAP[hour] + rows.append({ + "account_identifier": acct, + "zip_code": ZIP_CODE, + "hour_chicago": datetime(year, month_int, day, hour, 0, 0), + "kwh_hour": KWH_PER_HOUR[period], + }) + + df = pl.DataFrame(rows).with_columns( + pl.col("hour_chicago").cast(pl.Datetime("us")), + pl.col("kwh_hour").cast(pl.Float64), + ) + + total_hours = days * 24 + assert len(df) == len(DELIVERY_CLASSES) * total_hours, ( # noqa: S101 + f"Expected {len(DELIVERY_CLASSES) * total_hours} rows, got {len(df)}" + ) + + hourly_path = hourly_dir / "hourly_loads.parquet" + df.write_parquet(hourly_path) + print(f" {month_str}: {len(df):,} rows → {hourly_path}") + + # --- Delivery class lookup (all 8 accounts) --- + lookup_rows = [] + for month_str, _, _ in MONTHS_CFG: + for dc in DELIVERY_CLASSES: + lookup_rows.append({ + "account_identifier": _acct_name(dc, month_str), + "delivery_service_class": dc, + }) + + lookup_df = pl.DataFrame(lookup_rows) + lookup_path = AUDIT_DIR / "delivery_class_lookup.parquet" + lookup_df.write_parquet(lookup_path) + print(f" Lookup: {len(lookup_df)} rows → {lookup_path}") + + +# ── Step 2: Hand-calculate expected values ─────────────────────────────── + + +def hand_calculate_expected() -> dict[str, dict]: + """Compute expected values with explicit arithmetic — no pipeline code. + + Returns {account_name: {column: value}} for all 8 test accounts. + """ + print("\n" + "=" * 60) + print("STEP 2: Hand-calculate expected values") + print("=" * 60) + + expected: dict[str, dict] = {} + + for month_str, season, days in MONTHS_CFG: + # ── Period kWh totals (same for all accounts in this month) ── + + period_kwh: dict[str, float] = {} + for period in ("morning", "midday_peak", "evening", "overnight"): + hours_in_period = HOURS_PER_PERIOD_PER_DAY[period] * days + period_kwh[period] = hours_in_period * KWH_PER_HOUR[period] + + total_kwh = sum(period_kwh.values()) + + print(f"\n {month_str} ({season}, {days} days):") + print( + f" Hours per period: morning={HOURS_PER_PERIOD_PER_DAY['morning'] * days}, " + f"midday_peak={HOURS_PER_PERIOD_PER_DAY['midday_peak'] * days}, " + f"evening={HOURS_PER_PERIOD_PER_DAY['evening'] * days}, " + f"overnight={HOURS_PER_PERIOD_PER_DAY['overnight'] * days}" + ) + print( + f" kWh per period: morning={period_kwh['morning']:.1f}, " + f"midday_peak={period_kwh['midday_peak']:.1f}, " + f"evening={period_kwh['evening']:.1f}, " + f"overnight={period_kwh['overnight']:.1f}" + ) + print(f" Total kWh: {total_kwh:.1f}") + + # ── Supply (same for all delivery classes in this month) ── + + flat_ptc = FLAT_PTCS[season] + + # flat_supply = total_kwh x flat_ptc / 100 + flat_supply = total_kwh * flat_ptc / 100.0 + + # stou_supply = Σ(period_kwh x stou_rate) / 100 + stou_supply_cents = 0.0 + for p in ("morning", "midday_peak", "evening", "overnight"): + contrib = period_kwh[p] * STOU_SUPPLY[season][p] + stou_supply_cents += contrib + stou_supply = stou_supply_cents / 100.0 + + # supply_delta = flat - stou (positive = customer saves under TOU) + supply_delta = flat_supply - stou_supply + + print(f" Flat supply: {total_kwh} x {flat_ptc}¢ / 100 = ${flat_supply:.6f}") + print(f" STOU supply: ${stou_supply:.6f}") + print(f" Supply delta: ${supply_delta:.6f}") + + # ── Per delivery class: delivery + combined totals ── + + for dc in DELIVERY_CLASSES: + acct = _acct_name(dc, month_str) + + # flat_delivery = total_kwh x flat_dfc / 100 + flat_dfc = FLAT_DFCS[dc] + flat_delivery = total_kwh * flat_dfc / 100.0 + + # tou_delivery = Σ(period_kwh x tou_dfc) / 100 + tou_delivery_cents = 0.0 + for p in ("morning", "midday_peak", "evening", "overnight"): + tou_delivery_cents += period_kwh[p] * TOU_DFCS[dc][p] + tou_delivery = tou_delivery_cents / 100.0 + + # delivery_delta = flat - tou (positive = customer saves under TOU) + delivery_delta = flat_delivery - tou_delivery + + # ── STOU combined ── + total_bill_a = flat_supply + flat_delivery + total_bill_b = stou_supply + tou_delivery + total_delta = supply_delta + delivery_delta + total_pct_savings = total_delta / total_bill_a * 100.0 + + # ── DTOU (supply cancels: same flat PTC on both sides) ── + dtou_total_bill_a = flat_supply + flat_delivery # = total_bill_a + dtou_total_bill_b = flat_supply + tou_delivery # flat supply, not STOU + dtou_total_delta = delivery_delta # supply cancels + dtou_total_pct_savings = delivery_delta / dtou_total_bill_a * 100.0 + + expected[acct] = { + "month": month_str, + "season": season, + "delivery_service_class": dc, + "total_kwh": total_kwh, + # Supply + "bill_a_dollars": flat_supply, + "bill_b_dollars": stou_supply, + "supply_delta_dollars": supply_delta, + # Delivery + "flat_delivery_dollars": flat_delivery, + "tou_delivery_dollars": tou_delivery, + "delivery_delta_dollars": delivery_delta, + # STOU combined + "total_bill_a_dollars": total_bill_a, + "total_bill_b_dollars": total_bill_b, + "total_delta_dollars": total_delta, + "total_pct_savings": total_pct_savings, + # DTOU + "flat_supply_dollars": flat_supply, # = bill_a_dollars + "dtou_total_bill_a_dollars": dtou_total_bill_a, + "dtou_total_bill_b_dollars": dtou_total_bill_b, + "dtou_total_delta_dollars": dtou_total_delta, + "dtou_total_pct_savings": dtou_total_pct_savings, + } + + print(f"\n {acct} ({dc}):") + print(f" Flat delivery: {total_kwh} x {flat_dfc}¢ / 100 = ${flat_delivery:.6f}") + print( + f" TOU delivery: " + f"({period_kwh['morning']}x{TOU_DFCS[dc]['morning']} + " + f"{period_kwh['midday_peak']}x{TOU_DFCS[dc]['midday_peak']} + " + f"{period_kwh['evening']}x{TOU_DFCS[dc]['evening']} + " + f"{period_kwh['overnight']}x{TOU_DFCS[dc]['overnight']}) / 100 " + f"= ${tou_delivery:.6f}" + ) + print(f" Delivery delta: ${delivery_delta:.6f}") + print( + f" STOU: bill_a=${total_bill_a:.6f} bill_b=${total_bill_b:.6f} " + f"delta=${total_delta:.6f} pct={total_pct_savings:.6f}%" + ) + print( + f" DTOU: bill_a=${dtou_total_bill_a:.6f} bill_b=${dtou_total_bill_b:.6f} " + f"delta=${dtou_total_delta:.6f} pct={dtou_total_pct_savings:.6f}%" + ) + + return expected + + +# ── Step 3: Run the actual pipeline ────────────────────────────────────── + + +def run_pipeline() -> None: + """Invoke compute_delivery_deltas.py and package_dtou_results.py.""" + print("\n" + "=" * 60) + print("STEP 3: Run pipeline") + print("=" * 60) + + # --- compute_delivery_deltas.py --both --- + cmd_compute = [ + sys.executable, + str(COMPUTE_SCRIPT), + "--both", + "--billing-output-dir", + str(AUDIT_DIR), + ] + print(f" Running: {' '.join(cmd_compute)}") + result = subprocess.run(cmd_compute, capture_output=True, text=True) # noqa: S603 + print(result.stdout) + if result.returncode != 0: + print(f"STDERR:\n{result.stderr}", file=sys.stderr) + raise RuntimeError(f"compute_delivery_deltas.py failed with rc={result.returncode}") + + # --- package_dtou_results.py --both --- + cmd_package = [ + sys.executable, + str(PACKAGE_SCRIPT), + "--both", + "--billing-output-dir", + str(AUDIT_DIR), + ] + print(f" Running: {' '.join(cmd_package)}") + result = subprocess.run(cmd_package, capture_output=True, text=True) # noqa: S603 + print(result.stdout) + if result.returncode != 0: + print(f"STDERR:\n{result.stderr}", file=sys.stderr) + raise RuntimeError(f"package_dtou_results.py failed with rc={result.returncode}") + + +# ── Step 4: Compare actual vs expected ─────────────────────────────────── + + +def _compare_column( + label: str, + actual: float | None, + expected_val: float, + tol: float, +) -> bool: + """Compare one value; return True if PASS.""" + if actual is None: + diff = float("inf") + status = "FAIL" + else: + diff = abs(actual - expected_val) + status = "PASS" if diff < tol else "FAIL" + print(f" {label:30s} expected={expected_val:>14.6f} actual={actual!s:>14s} diff={diff:.2e} {status}") + return status == "PASS" + + +def _compare_stou_account( + df: pl.DataFrame, + dc: str, + month_str: str, + expected: dict[str, dict], + numeric_cols: list[str], +) -> int: + """Compare one account's STOU row against expected values. Returns failure count.""" + acct = _acct_name(dc, month_str) + exp = expected[acct] + + row = df.filter(pl.col("account_identifier") == acct) + if len(row) == 0: + print(f"\n FAIL: {acct} not found in output") + return 1 + if len(row) > 1: + print(f"\n FAIL: {acct} has {len(row)} rows (expected 1)") + return 1 + + row_dict = row.to_dicts()[0] + print(f"\n {acct} ({dc}, {month_str}):") + + failures = 0 + if row_dict["delivery_service_class"] != dc: + print(f" FAIL: delivery_service_class expected={dc} actual={row_dict['delivery_service_class']}") + failures += 1 + + for col in numeric_cols: + if not _compare_column(col, row_dict[col], exp[col], TOLERANCE): + failures += 1 + + return failures + + +def compare_stou_output(expected: dict[str, dict]) -> int: + """Read STOU combined parquets and compare against expected values. + + Returns number of failures. + """ + print("\n" + "=" * 60) + print("STEP 4a: Compare STOU combined output vs expected") + print("=" * 60) + + # Expected STOU output schema (14 columns, exact order) + stou_schema = [ + "account_identifier", + "zip_code", + "delivery_service_class", + "total_kwh", + "bill_a_dollars", + "bill_b_dollars", + "supply_delta_dollars", + "flat_delivery_dollars", + "tou_delivery_dollars", + "delivery_delta_dollars", + "total_bill_a_dollars", + "total_bill_b_dollars", + "total_delta_dollars", + "total_pct_savings", + ] + + numeric_cols = [ + "total_kwh", + "bill_a_dollars", + "bill_b_dollars", + "supply_delta_dollars", + "flat_delivery_dollars", + "tou_delivery_dollars", + "delivery_delta_dollars", + "total_bill_a_dollars", + "total_bill_b_dollars", + "total_delta_dollars", + "total_pct_savings", + ] + + failures = 0 + + for month_str, _, _ in MONTHS_CFG: + path = AUDIT_DIR / "stou_combined" / f"stou_combined_{month_str}.parquet" + if not path.exists(): + print(f" FAIL: output file missing: {path}") + failures += 1 + continue + + df = pl.read_parquet(path) + + # Verify schema + if df.columns != stou_schema: + print(f" FAIL: schema mismatch for {month_str}") + print(f" expected: {stou_schema}") + print(f" actual: {df.columns}") + failures += 1 + continue + print(f"\n {month_str}: schema OK ({len(df.columns)} columns)") + + # Verify row count: 4 accounts per month + if len(df) != len(DELIVERY_CLASSES): + print(f" FAIL: expected {len(DELIVERY_CLASSES)} rows, got {len(df)}") + failures += 1 + continue + print(f" {month_str}: row count OK ({len(df)} rows)") + + for dc in DELIVERY_CLASSES: + failures += _compare_stou_account(df, dc, month_str, expected, numeric_cols) + + return failures + + +def compare_dtou_output(expected: dict[str, dict]) -> int: + """Read DTOU combined parquets and compare against expected values. + + Returns number of failures. + """ + print("\n" + "=" * 60) + print("STEP 4b: Compare DTOU combined output vs expected") + print("=" * 60) + + # Expected DTOU output schema (12 columns) + dtou_schema = [ + "account_identifier", + "zip_code", + "delivery_service_class", + "total_kwh", + "flat_supply_dollars", + "flat_delivery_dollars", + "tou_delivery_dollars", + "delivery_delta_dollars", + "dtou_total_bill_a_dollars", + "dtou_total_bill_b_dollars", + "dtou_total_delta_dollars", + "dtou_total_pct_savings", + ] + + failures = 0 + + for month_str, _, _ in MONTHS_CFG: + path = AUDIT_DIR / "dtou_combined" / f"dtou_combined_{month_str}.parquet" + if not path.exists(): + print(f" FAIL: output file missing: {path}") + failures += 1 + continue + + df = pl.read_parquet(path) + + # Verify schema + if df.columns != dtou_schema: + print(f" FAIL: schema mismatch for {month_str}") + print(f" expected: {dtou_schema}") + print(f" actual: {df.columns}") + failures += 1 + continue + print(f"\n {month_str}: schema OK ({len(df.columns)} columns)") + + # Row count + if len(df) != len(DELIVERY_CLASSES): + print(f" FAIL: expected {len(DELIVERY_CLASSES)} rows, got {len(df)}") + failures += 1 + continue + print(f" {month_str}: row count OK ({len(df)} rows)") + + # DTOU numeric columns to check + dtou_numeric_cols = [ + "total_kwh", + "flat_supply_dollars", + "flat_delivery_dollars", + "tou_delivery_dollars", + "delivery_delta_dollars", + "dtou_total_bill_a_dollars", + "dtou_total_bill_b_dollars", + "dtou_total_delta_dollars", + "dtou_total_pct_savings", + ] + + for dc in DELIVERY_CLASSES: + acct = _acct_name(dc, month_str) + exp = expected[acct] + + row = df.filter(pl.col("account_identifier") == acct) + if len(row) != 1: + print(f"\n FAIL: {acct} has {len(row)} rows (expected 1)") + failures += 1 + continue + + row_dict = row.to_dicts()[0] + print(f"\n {acct} ({dc}, {month_str}):") + + for col in dtou_numeric_cols: + if not _compare_column(col, row_dict[col], exp[col], TOLERANCE): + failures += 1 + + # Key DTOU invariant: dtou_total_delta == delivery_delta (supply cancels) + supply_residual = abs(row_dict["dtou_total_delta_dollars"] - row_dict["delivery_delta_dollars"]) + if supply_residual > TOLERANCE: + print(f" FAIL: supply did not cancel — dtou_total_delta - delivery_delta = {supply_residual:.2e}") + failures += 1 + else: + print(f" Supply cancellation check: residual={supply_residual:.2e} PASS") + + return failures + + +# ── Step 5: Verify YAML rate loading ───────────────────────────────────── + + +def verify_yaml_rates() -> int: + """Import _load_stou_supply_rates and verify against known constants.""" + print("\n" + "=" * 60) + print("STEP 5: Verify YAML rate loading") + print("=" * 60) + + # Import the function from the pipeline module + sys.path.insert(0, str(REPO_ROOT / "scripts" / "pricing_pilot")) + from compute_delivery_deltas import STOU_YAML_PATH, _load_stou_supply_rates + + rates = _load_stou_supply_rates(STOU_YAML_PATH) + print(f" Loaded rates from: {STOU_YAML_PATH}") + print(f" Seasons: {list(rates.keys())}") + + failures = 0 + + for season in ("summer", "nonsummer"): + if season not in rates: + print(f" FAIL: missing season '{season}'") + failures += 1 + continue + + print(f"\n {season}:") + for period in ("morning", "midday_peak", "evening", "overnight"): + if period not in rates[season]: + print(f" FAIL: missing period '{period}'") + failures += 1 + continue + + actual = rates[season][period] + expected_val = STOU_SUPPLY[season][period] + match = "PASS" if abs(actual - expected_val) < 1e-10 else "FAIL" + print(f" {period:15s} expected={expected_val:.3f} actual={actual:.3f} {match}") + if match == "FAIL": + failures += 1 + + # Also verify we get exactly the expected periods (no extras) + for season in rates: + extra = set(rates[season].keys()) - {"morning", "midday_peak", "evening", "overnight"} + if extra: + print(f" FAIL: unexpected periods in {season}: {extra}") + failures += 1 + + return failures + + +# ── Main ───────────────────────────────────────────────────────────────── + + +def main() -> int: + """Run the full audit end-to-end.""" + print("=" * 60) + print("PIPELINE AUDIT: Synthetic data end-to-end verification") + print("=" * 60) + + # Step 1: Generate synthetic data + generate_synthetic_data() + + # Step 2: Hand-calculate expected values + expected = hand_calculate_expected() + + # Step 3: Run the actual pipeline + run_pipeline() + + # Step 4: Compare actual vs expected + stou_failures = compare_stou_output(expected) + dtou_failures = compare_dtou_output(expected) + + # Step 5: Verify YAML rate loading + yaml_failures = verify_yaml_rates() + + # ── Summary ── + total_failures = stou_failures + dtou_failures + yaml_failures + print("\n" + "=" * 60) + print("AUDIT SUMMARY") + print("=" * 60) + print(f" STOU comparison failures: {stou_failures}") + print(f" DTOU comparison failures: {dtou_failures}") + print(f" YAML rate check failures: {yaml_failures}") + print(f" Total failures: {total_failures}") + + if total_failures == 0: + print("\n ALL CHECKS PASSED") + else: + print(f"\n {total_failures} CHECK(S) FAILED") + + return 1 if total_failures > 0 else 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/testing/generate_sample_data.py b/scripts/testing/generate_sample_data.py index 26dc9be..04c2cfb 100644 --- a/scripts/testing/generate_sample_data.py +++ b/scripts/testing/generate_sample_data.py @@ -17,7 +17,7 @@ from dataclasses import dataclass from datetime import date, timedelta from pathlib import Path -from typing import List, Optional +from typing import List import polars as pl @@ -39,7 +39,7 @@ class GenerationConfig: num_days: int start_date: date out_dir: Path - seed: Optional[int] + seed: int | None def _iter_days(start: date, num_days: int) -> Iterable[date]: diff --git a/scripts/validate_tou_windows_yaml.py b/scripts/validate_tou_windows_yaml.py new file mode 100644 index 0000000..84555d7 --- /dev/null +++ b/scripts/validate_tou_windows_yaml.py @@ -0,0 +1,68 @@ +#!/usr/bin/env python3 +"""Validate that two YAML rate structures share identical TOU window definitions. + +Loads two YAML configs, extracts their season date ranges and period hour +boundaries (stripping prices), and exits non-zero on any mismatch. + +Usage:: + + uv run python scripts/tmp/validate_tou_windows_yaml.py \\ + rate_structures/comed_stou_2026.yaml \\ + rate_structures/comed_dtou_2026.yaml + +Exit codes: + 0 — windows are identical + 1 — mismatch found (diff printed to stdout) + 2 — input error (file not found or invalid YAML structure) +""" + +from __future__ import annotations + +import sys +from pathlib import Path + +_SCRIPTS_DIR = Path(__file__).resolve().parent.parent # .../scripts/ +sys.path.insert(0, str(_SCRIPTS_DIR)) + +from build_tariff_hourly_prices import compare_window_definitions, load_config # noqa: E402 + + +def main(argv: list[str] | None = None) -> int: + args = sys.argv[1:] if argv is None else argv + if len(args) != 2: + print( + "Usage: validate_tou_windows_yaml.py ", + file=sys.stderr, + ) + return 2 + + path_a, path_b = Path(args[0]), Path(args[1]) + + try: + cfg_a = load_config(path_a) + except (FileNotFoundError, ValueError, TypeError) as exc: + print(f"ERROR loading {path_a}: {exc}", file=sys.stderr) + return 2 + + try: + cfg_b = load_config(path_b) + except (FileNotFoundError, ValueError, TypeError) as exc: + print(f"ERROR loading {path_b}: {exc}", file=sys.stderr) + return 2 + + ok, diff_msg = compare_window_definitions( + cfg_a, + cfg_b, + name_a=str(path_a), + name_b=str(path_b), + ) + if ok: + print(f"OK — window definitions match: {path_a.name} <-> {path_b.name}") + return 0 + + print(diff_msg) + return 1 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/scripts/validate_wide_to_long_batched.py b/scripts/validate_wide_to_long_batched.py new file mode 100644 index 0000000..38dad10 --- /dev/null +++ b/scripts/validate_wide_to_long_batched.py @@ -0,0 +1,479 @@ +from __future__ import annotations + +import argparse +import hashlib +import json +import time +from collections.abc import Sequence +from dataclasses import asdict, dataclass +from datetime import datetime, timezone +from pathlib import Path +from typing import Any + +import polars as pl + +from smart_meter_analysis.wide_to_long import transform_wide_to_long_lf + +# -------------------------------------------------------------------------------------- +# Batched wide_to_long validator (resumable, JSONL checkpoints) +# +# What is this? +# - Month-scale Zip4 validation in the Docker devcontainer can wedge Docker if we do +# a global sort or force full materialization of a full month. Many checks are +# expensive if they require multiple passes over the dataset. +# - This script validates correctness (schema contracts, Daylight Saving Time +# behavior, datetime bounds, and 48-interval invariants) at full-month scale +# by processing input CSVs in bounded batches with checkpoints. +# If the container crashes or Docker becomes unstable, we can resume without +# redoing completed work. +# -------------------------------------------------------------------------------------- + + +@dataclass(frozen=True) +class BatchResult: + """ + One record for one batch attempt. + + Stored as JSONL for two reasons: + 1) resume: a successful batch is a stable checkpoint (skip on rerun) + 2) audit: gives an append-only provenance trail of what was validated + + We record both configuration (strict/sort/engine) and outcomes (rows, datetime bounds), + because "it passed" is not meaningful unless we can tie it to the exact validation mode. + """ + + run_id: str + batch_id: str + batch_index: int + batch_size: int + n_files: int + first_path: str + last_path: str + started_at_utc: str + finished_at_utc: str + elapsed_sec: float + + strict: bool + sort_output: bool + engine: str + infer_schema_length: int + + # Validation outputs + long_rows: int | None + long_rows_mod_48: int | None + min_datetime: str | None + max_datetime: str | None + any_null_datetime: bool | None + schema_fingerprint: str | None + + ok: bool + error_type: str | None + error_message: str | None + + +def _utc_now_iso() -> str: + """ + Return a stable, timezone-aware UTC timestamp for logs. + + Why: datetime.utcnow() is deprecated in newer Python versions and produces naive + datetimes. We intentionally write Zulu timestamps into JSONL to keep log output + consistent across environments and to avoid local-time ambiguity. + """ + return datetime.now(timezone.utc).replace(microsecond=0).isoformat().replace("+00:00", "Z") + + +def _read_paths(list_path: str) -> list[str]: + """ + Read an input manifest of CSV paths (S3 or local), one path per line. + + Why: + - We want the driver/orchestrator to own discovery. This tool should be deterministic + and replayable: given a manifest, it validates exactly that set of files. + - We support comment lines (# ...) to allow simple manifest curation. + """ + p = Path(list_path) + if not p.exists(): + raise FileNotFoundError(f"Input list file not found: {list_path}") + + paths: list[str] = [] + for line in p.read_text().splitlines(): + s = line.strip() + if not s or s.startswith("#"): + continue + paths.append(s) + + if not paths: + raise ValueError(f"No usable paths found in list file: {list_path}") + + return paths + + +def _chunk_paths(paths: Sequence[str], batch_size: int) -> list[list[str]]: + """ + Partition the manifest into batches of bounded size. + + Why: + - Memory and swap pressure is the primary failure mode in Docker Desktop devcontainers. + Batching is the simplest, most reliable control for peak memory use. + - We prefer deterministic partitioning (simple slicing) so resume behavior is stable: + batch_00000 always contains the same files for the same manifest and batch_size. + """ + if batch_size <= 0: + raise ValueError(f"batch_size must be positive; got {batch_size}") + return [list(paths[i : i + batch_size]) for i in range(0, len(paths), batch_size)] + + +def _schema_fingerprint(schema: pl.Schema) -> str: + """ + Compute a stable fingerprint of the output schema (name + dtype). + + Why: + - When validating at scale, we want a compact way to detect drift across batches. + If one file has a surprising type coercion behavior, schema fingerprints will diverge. + - This fingerprint is not meant to be cryptographic security; SHA256 is convenient, + ubiquitous, and stable. + """ + pairs = [(name, str(dtype)) for name, dtype in schema.items()] + payload = json.dumps(pairs, sort_keys=False, separators=(",", ":")).encode("utf-8") + return hashlib.sha256(payload).hexdigest() + + +def _load_completed_batches(checkpoint_jsonl: Path) -> set[str]: + """ + Read JSONL checkpoints and return the set of batch_ids that completed successfully. + + Why: + - We treat successful batches as durable checkpoints, allowing safe resume after + a wedge/crash without reprocessing. + - We ignore malformed lines rather than failing the resume path; the checkpoint + file is append-only and may be truncated in a crash scenario. + """ + completed: set[str] = set() + if not checkpoint_jsonl.exists(): + return completed + + for line in checkpoint_jsonl.read_text().splitlines(): + line = line.strip() + if not line: + continue + try: + rec = json.loads(line) + except json.JSONDecodeError: + continue + if rec.get("ok") is True and isinstance(rec.get("batch_id"), str): + completed.add(rec["batch_id"]) + + return completed + + +def _append_jsonl(checkpoint_jsonl: Path, rec: dict[str, Any]) -> None: + """ + Append one JSON record to the JSONL checkpoint file. + + Why: + - Append-only writing is resilient: if a run is interrupted, prior records remain valid. + - JSONL is easy to inspect, grep, parse, and archive for audit trails. + """ + checkpoint_jsonl.parent.mkdir(parents=True, exist_ok=True) + with checkpoint_jsonl.open("a", encoding="utf-8") as f: + f.write(json.dumps(rec, sort_keys=True) + "\n") + + +def _validate_batch( + *, + run_id: str, + batch_id: str, + batch_index: int, + batch_paths: Sequence[str], + batch_size: int, + strict: bool, + sort_output: bool, + engine: str, + infer_schema_length: int, +) -> BatchResult: + """ + Validate a single batch of CSV files. + + Validation strategy: + - Build one LazyFrame scan over the batch. + - Apply wide_to_long (transform-only). + - Compute all validation metrics in one select + one collect to avoid multiple + passes over potentially large datasets. + + Why one collect: + - In Polars, separate .collect() calls can trigger separate executions. At scale, + multiple passes are expensive and can increase peak memory usage and I/O. + - We constrain validation to the minimum set of invariants that provide strong + correctness guarantees without needing to materialize the full long table. + + Note on sorting: + - sort_output is intentionally configurable. Month-scale semantic validation uses + sort_output=False to avoid global sorts that can wedge Docker Desktop. + - Deterministic ordering can be validated separately on bounded samples with + sort_output=True. + """ + t0 = time.time() + started_iso = _utc_now_iso() + + # Batching against resource exhaustion. + wide_lf = pl.scan_csv(batch_paths, infer_schema_length=infer_schema_length) + out_lf = transform_wide_to_long_lf(lf=wide_lf, strict=strict, sort_output=sort_output) + + # Single-pass metrics collection (streaming engine recommended). + metrics_df = out_lf.select([ + pl.len().alias("long_rows"), + (pl.len() % 48).alias("long_rows_mod_48"), + pl.col("datetime").min().alias("min_datetime"), + pl.col("datetime").max().alias("max_datetime"), + pl.col("datetime").is_null().any().alias("any_null_datetime"), + ]).collect(engine=engine) + + long_rows = int(metrics_df["long_rows"][0]) + long_rows_mod_48 = int(metrics_df["long_rows_mod_48"][0]) + mn = metrics_df["min_datetime"][0] + mx = metrics_df["max_datetime"][0] + any_null = bool(metrics_df["any_null_datetime"][0]) + + mn_s = mn.isoformat() if mn is not None else None + mx_s = mx.isoformat() if mx is not None else None + + # Schema fingerprint is cheap (no data scan); it uses the logical schema post-transform. + schema_fp = _schema_fingerprint(out_lf.collect_schema()) + + # Invariants (fail-loud): + # + # These checks are intentionally chosen because they have high diagnostic value: + # - long_rows % 48 == 0 catches interval count issues (missing/extra intervals). + # - min/max datetime validate the core datetime semantics and DST folding behavior. + # - null datetime indicates parsing or datetime math failures (must never happen in strict mode). + if long_rows == 0: + raise ValueError("Batch produced 0 long rows (unexpected).") + + if long_rows_mod_48 != 0: + raise ValueError(f"Batch long_rows not divisible by 48: long_rows={long_rows} mod_48={long_rows_mod_48}") + + if any_null: + raise ValueError("Batch contains null datetime values.") + + if mn is None or mx is None: + raise ValueError("Batch min/max datetime is null (unexpected).") + + if (mn.hour, mn.minute) != (0, 0): + raise ValueError(f"Batch min datetime not at 00:00: {mn!r}") + + if (mx.hour, mx.minute) != (23, 30): + raise ValueError(f"Batch max datetime not at 23:30: {mx!r}") + + finished_iso = _utc_now_iso() + elapsed = time.time() - t0 + + return BatchResult( + run_id=run_id, + batch_id=batch_id, + batch_index=batch_index, + batch_size=batch_size, + n_files=len(batch_paths), + first_path=batch_paths[0], + last_path=batch_paths[-1], + started_at_utc=started_iso, + finished_at_utc=finished_iso, + elapsed_sec=elapsed, + strict=strict, + sort_output=sort_output, + engine=engine, + infer_schema_length=infer_schema_length, + long_rows=long_rows, + long_rows_mod_48=long_rows_mod_48, + min_datetime=mn_s, + max_datetime=mx_s, + any_null_datetime=any_null, + schema_fingerprint=schema_fp, + ok=True, + error_type=None, + error_message=None, + ) + + +def main(argv: Sequence[str] | None = None) -> int: + """ + CLI entrypoint. + + This tool is intentionally a validator rather than a writer: + - it proves correctness at scale without entangling file output concerns + - it is safe to run repeatedly (idempotent resume semantics) + - it produces a stable, append-only audit log (JSONL checkpoints) + """ + ap = argparse.ArgumentParser(description="Batched wide_to_long validator with JSONL checkpoints (resumable).") + + ap.add_argument("--input-list", required=True, help="Text file of CSV paths (one per line).") + ap.add_argument("--batch-size", type=int, default=25, help="Files per batch (e.g., 25, 10).") + ap.add_argument( + "--out-dir", + default="/workspaces/smart-meter-analysis/data/validation", + help="Directory for checkpoints/logs.", + ) + ap.add_argument("--run-id", default=None, help="Run identifier (default: timestamp-based).") + ap.add_argument( + "--resume", + action="store_true", + help="Skip batches already marked ok in checkpoints.jsonl.", + ) + + ap.add_argument("--strict", action="store_true", help="Enable strict wide_to_long validations.") + ap.add_argument("--no-strict", dest="strict", action="store_false", help="Disable strict mode.") + ap.set_defaults(strict=True) + + ap.add_argument( + "--sort-output", + action="store_true", + help="Enable global sort inside wide_to_long (use for determinism checks on bounded samples).", + ) + ap.add_argument( + "--no-sort-output", + dest="sort_output", + action="store_false", + help="Disable global sort inside wide_to_long (recommended for month-scale semantic validation).", + ) + ap.set_defaults(sort_output=False) + + ap.add_argument( + "--engine", + default="streaming", + choices=["streaming", "in_memory"], + help="Polars collect engine.", + ) + ap.add_argument( + "--infer-schema-length", + type=int, + default=0, + help="Polars scan_csv infer_schema_length (0 = full scan of types).", + ) + + ap.add_argument("--max-batches", type=int, default=None, help="Process at most this many batches.") + ap.add_argument("--start-batch", type=int, default=0, help="Start at this batch index (0-based).") + ap.add_argument( + "--continue-on-error", + action="store_true", + help="Log failure and continue to next batch.", + ) + + args = ap.parse_args(list(argv) if argv is not None else None) + + paths = _read_paths(args.input_list) + batches = _chunk_paths(paths, args.batch_size) + + run_id = args.run_id or datetime.now(timezone.utc).strftime("wide_to_long_validate_%Y%m%dT%H%M%SZ") + out_dir = Path(args.out_dir) / run_id + checkpoint_jsonl = out_dir / "checkpoints.jsonl" + + completed = _load_completed_batches(checkpoint_jsonl) if args.resume else set() + + total_files = len(paths) + print(f"run_id={run_id}") + print(f"input_list={args.input_list}") + print(f"total_files={total_files}") + print(f"batch_size={args.batch_size}") + print(f"n_batches={len(batches)}") + print(f"out_dir={out_dir}") + print(f"checkpoint_jsonl={checkpoint_jsonl}") + print( + f"strict={args.strict} sort_output={args.sort_output} engine={args.engine} infer_schema_length={args.infer_schema_length}" + ) + print(f"resume={args.resume} completed_batches={len(completed)}") + + out_dir.mkdir(parents=True, exist_ok=True) + + batches_ok = 0 + batches_failed = 0 + files_ok = 0 + files_failed = 0 + + for i in range(args.start_batch, len(batches)): + if args.max_batches is not None and (batches_ok + batches_failed) >= args.max_batches: + print(f"Reached --max-batches={args.max_batches}; stopping.") + break + + batch_paths = batches[i] + batch_id = f"batch_{i:05d}" + + # Resume behavior: skip batches already confirmed OK by prior runs. + if args.resume and batch_id in completed: + print(f"[SKIP] {batch_id} already ok (resume).") + continue + + print(f"[RUN ] {batch_id} n_files={len(batch_paths)} first={batch_paths[0]}") + + try: + res = _validate_batch( + run_id=run_id, + batch_id=batch_id, + batch_index=i, + batch_paths=batch_paths, + batch_size=args.batch_size, + strict=args.strict, + sort_output=args.sort_output, + engine=args.engine, + infer_schema_length=args.infer_schema_length, + ) + _append_jsonl(checkpoint_jsonl, asdict(res)) + batches_ok += 1 + files_ok += res.n_files + + print( + f"[OK ] {batch_id} files={res.n_files} files_ok={files_ok}/{total_files} " + f"long_rows={res.long_rows} min={res.min_datetime} max={res.max_datetime} " + f"elapsed_sec={res.elapsed_sec:.2f}" + ) + except Exception as e: + # On failure, we still checkpoint the error. This makes failures reproducible + # and supports later triage without rerunning the full month. + batches_failed += 1 + files_failed += len(batch_paths) + + rec: dict[str, Any] = { + "run_id": run_id, + "batch_id": batch_id, + "batch_index": i, + "batch_size": args.batch_size, + "n_files": len(batch_paths), + "first_path": batch_paths[0] if batch_paths else "", + "last_path": batch_paths[-1] if batch_paths else "", + "started_at_utc": None, + "finished_at_utc": _utc_now_iso(), + "elapsed_sec": None, + "strict": args.strict, + "sort_output": args.sort_output, + "engine": args.engine, + "infer_schema_length": args.infer_schema_length, + "ok": False, + "error_type": type(e).__name__, + "error_message": str(e), + } + _append_jsonl(checkpoint_jsonl, rec) + + print( + f"[FAIL] {batch_id} files={len(batch_paths)} files_failed={files_failed}/{total_files} " + f"error_type={type(e).__name__} error={e}" + ) + + if not args.continue_on_error: + print("Stopping on first failure (use --continue-on-error to proceed).") + print( + "Summary: " + f"batches_ok={batches_ok} batches_failed={batches_failed} " + f"files_ok={files_ok} files_failed={files_failed} total_files={total_files} " + f"checkpoint_jsonl={checkpoint_jsonl}" + ) + return 2 + + print( + "Done. " + f"batches_ok={batches_ok} batches_failed={batches_failed} " + f"files_ok={files_ok} files_failed={files_failed} total_files={total_files} " + f"checkpoint_jsonl={checkpoint_jsonl}" + ) + return 0 if batches_failed == 0 else 2 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/smart_meter_analysis/foo.py b/smart_meter_analysis/foo.py deleted file mode 100644 index 8b7396d..0000000 --- a/smart_meter_analysis/foo.py +++ /dev/null @@ -1,17 +0,0 @@ -def foo(bar: str) -> str: - """Summary line. - - Extended description of function. - - Args: - bar: Description of input argument. - - Returns: - Description of return value - """ - - return bar - - -if __name__ == "__main__": # pragma: no cover - pass diff --git a/smart_meter_analysis/wide_to_long.py b/smart_meter_analysis/wide_to_long.py new file mode 100644 index 0000000..c979e55 --- /dev/null +++ b/smart_meter_analysis/wide_to_long.py @@ -0,0 +1,514 @@ +from __future__ import annotations + +import re +from collections.abc import Iterable, Sequence +from dataclasses import dataclass + +import polars as pl + +__all__ = ["IntervalColSpec", "transform_wide_to_long", "transform_wide_to_long_lf"] + +# ------------------------------------------------------------------------------------------------- +# Zip4 wide → long canonicalization (ComEd smart-meter interval data) +# +# Context: +# - Source data arrives as one row per account per day with 48 interval "end time" columns +# (0030..2400). DST anomalies may appear as extra end-time columns (2430, 2500). +# - Downstream clustering and regression require a canonical "long" representation: +# one row per account per interval with a true interval START timestamp. +# +# Primary design goals: +# 1) Fail-loud contract enforcement in strict mode (regulatory defensibility / auditability). +# 2) Stable, canonical output schema (order + dtypes) independent of input inference quirks. +# 3) DST policy is: fold extras into their base intervals with null-preserving semantics. +# 4) Determinism is required for partitioned Parquet writing, but global sorts are operationally +# expensive at month scale. We therefore gate sorting behind sort_output. +# +# Operational note: +# - For month-scale validation in constrained environments (e.g., Docker devcontainer), +# prefer sort_output=False and validate determinism separately on bounded samples or +# in a higher-memory runtime. +# ------------------------------------------------------------------------------------------------- + + +# Exact header match only (no IGNORECASE). +_INTERVAL_COL_RE = re.compile(r"^INTERVAL_HR(?P\d{4})_ENERGY_QTY$") + +# Standard 48 end-times: 0030...2400 (0000 absent) at 30-min cadence. +# Expressed as minutes since midnight for simple set arithmetic. +_STANDARD_END_MINUTES: set[int] = set(range(30, 1441, 30)) + +# DST extras appear as end-times 24:30 and 25:00 (minutes 1470, 1500). +_DST_EXTRA_END_MINUTES: set[int] = {1470, 1500} # 2430, 2500 + +# DST fold-in map +_DST_FOLD_MAP = { + "INTERVAL_HR2430_ENERGY_QTY": "INTERVAL_HR2330_ENERGY_QTY", + "INTERVAL_HR2500_ENERGY_QTY": "INTERVAL_HR2400_ENERGY_QTY", +} + +# Used historically for dtype enforcement; kept as an immutable set for defensive checks if needed. +_INTEGER_DTYPES = frozenset({ + pl.Int8, + pl.Int16, + pl.Int32, + pl.Int64, + pl.UInt8, + pl.UInt16, + pl.UInt32, + pl.UInt64, +}) + + +@dataclass(frozen=True) +class IntervalColSpec: + """ + Parsed metadata for one interval column. + + Why keep this structure: + - We want a durable, inspectable representation of the interval headers rather than + relying on ad hoc string slicing scattered across the transform. + - Using end_minutes/start_minutes (not just HHMM) makes validation and datetime math simpler. + """ + + colname: str + hhmm: int + end_minutes: int + start_minutes: int + + +def _format_list_preview(x: list, max_items: int = 10) -> str: + """ + Format a bounded preview of a python list for error messages. + + Why: + - When strict validation fails, we want diagnostic information without dumping huge values. + - This keeps exceptions readable in CI logs and in interactive debugging sessions. + """ + if len(x) <= max_items: + return str(x) + return str(x[:max_items])[:-1] + f", ...] (n={len(x)})" + + +def _require_columns_from_names(schema_names: set[str], required: Iterable[str]) -> None: + """ + Fail-loud if required wide columns are missing. + + Why: + - Missing required columns almost always indicates upstream schema drift, not something + we should guess at or attempt to "repair" inside the transform. + """ + missing = [c for c in required if c not in schema_names] + if missing: + raise ValueError(f"Missing required columns: {missing}") + + +def _enforce_total_columns_59_from_names(schema_names_in_order: Sequence[str]) -> None: + """ + Enforce the "59 columns total" wide schema contract in strict mode. + + Why: + - The source format is treated as a contract. Silent acceptance of schema changes + is a common root cause of hard-to-debug downstream failures. + - This is deliberately an exact count check, not "at least" or "contains". + """ + if len(schema_names_in_order) != 59: + cols = list(schema_names_in_order) + raise ValueError( + "Contract violation: expected exactly 59 columns in the wide CSV schema.\n" + f"- observed_n_columns={len(cols)}\n" + f"- first_20_columns={cols[:20]}\n" + ) + + +def _parse_interval_specs_from_columns(columns: Sequence[str]) -> list[IntervalColSpec]: + """ + Parse all interval columns (including possible DST extras) from exact headers. + + Why: + - Header parsing is the authoritative place to enforce interval-label invariants. + - We do not attempt to normalize invalid HHMMs; invalid headers are treated as data contract + violations and should fail loudly. + """ + specs: list[IntervalColSpec] = [] + for c in columns: + m = _INTERVAL_COL_RE.match(c) + if not m: + continue + + hhmm = int(m.group("hhmm")) + hh = hhmm // 100 + mm = hhmm % 100 + + # Reject invalid minutes and invalid hour range. + if mm not in (0, 30): + raise ValueError(f"Contract violation: invalid interval column minutes (expected 00/30).\n- column={c}\n") + if hh < 0 or hh > 25: + raise ValueError(f"Contract violation: invalid interval column hour (expected 00..25).\n- column={c}\n") + + # Authoritative 0000 rejection (locked contract). + # Why: the dataset is defined in terms of end-times 0030..2400; 0000 must not appear. + if hhmm == 0: + raise ValueError( + f"Contract violation: found an interval ending at 0000 (HHMM=0000). Do not guess.\n- column={c}\n" + ) + + end_minutes = 60 * hh + mm + start_minutes = end_minutes - 30 + + # Defensive redundancy. This should be unreachable given the explicit 0000 rejection, + # but it provides additional protection against malformed headers. + if start_minutes < 0: + raise ValueError( + "Contract violation: interval implies start_minutes < 0 (likely 0000), which must not exist.\n" + f"- column={c}\n" + ) + + specs.append( + IntervalColSpec( + colname=c, + hhmm=hhmm, + end_minutes=end_minutes, + start_minutes=start_minutes, + ) + ) + + specs.sort(key=lambda s: (s.end_minutes, s.colname)) + return specs + + +def _validate_interval_set(interval_specs: Sequence[IntervalColSpec], *, strict: bool) -> None: + """ + Validate the observed end-time set against locked contract requirements. + + Why: + - Most downstream correctness depends on having exactly the expected interval grid. + Missing or unexpected interval headers cannot be fixed reliably downstream. + - We validate in terms of end-minutes, which is robust to header ordering and avoids + string-based comparisons. + """ + observed_end = {s.end_minutes for s in interval_specs} + + if strict: + missing_standard = sorted(_STANDARD_END_MINUTES - observed_end) + if missing_standard: + raise ValueError( + "Interval HHMM set missing standard end-minutes (expected 48 columns).\n" + f"- missing_end_minutes={missing_standard}\n" + ) + + allowed = set(_STANDARD_END_MINUTES) | set(_DST_EXTRA_END_MINUTES) + unexpected = sorted(observed_end - allowed) + if unexpected: + raise ValueError( + "Interval HHMM set has unexpected end-minutes (allowed extras only 1470/1500).\n" + f"- unexpected_end_minutes={unexpected}\n" + ) + + observed_standard = observed_end & _STANDARD_END_MINUTES + if len(observed_standard) != 48: + raise ValueError( + "Contract violation: standard interval end-times are not exactly 48 distinct values.\n" + f"- observed_standard_count={len(observed_standard)}\n" + ) + + +def _validate_interval_length_1800_lf(lf: pl.LazyFrame) -> None: + """ + Contract check: INTERVAL_LENGTH must represent constant 1800 seconds (fail-loud). + + Why keep this validation even though INTERVAL_LENGTH is dropped from output: + - It's assumed in the datetime semantics. If intervals aren't 30 minutes, + the entire long representation becomes invalid. + - In practice, S3 scans often infer INTERVAL_LENGTH as String. The contract is the *value*, + not the storage dtype, so we accept either as long as it parses to 1800 everywhere. + """ + col = "INTERVAL_LENGTH" + schema = lf.collect_schema() + if col not in schema.names(): + raise ValueError("Missing required column: INTERVAL_LENGTH") + + # Accept either integer-typed or string-typed inputs; enforce that the value parses to 1800. + # We use strict=False cast to avoid blowing up on benign string representations like "1800". + il_int = pl.col(col).cast(pl.Int32, strict=False) + invalid = il_int.is_null() | (il_int != pl.lit(1800, dtype=pl.Int32)) + + any_invalid = bool(lf.select(invalid.any()).collect().item()) + if not any_invalid: + return + + # Provide a small raw sample of offending values for debugging (bounded to 30). + bad_vals = lf.filter(invalid).select(pl.col(col).cast(pl.Utf8).unique().head(30)).collect().to_series().to_list() + raise ValueError( + "INTERVAL_LENGTH contract violation: values must be 1800 seconds (string or integer accepted).\n" + f"- raw_values_sample={_format_list_preview(bad_vals, max_items=30)}\n" + ) + + +def _validate_reading_date_parses_strict_lf(lf: pl.LazyFrame, *, colname: str) -> None: + """ + Locked contract: INTERVAL_READING_DATE parses with %m/%d/%Y only (fail-loud). + + Why: + - Date parsing ambiguity is a classic source of silent data corruption (e.g., DD/MM vs MM/DD). + - We do not accept "best effort" parsing here; strictness is intentional. + """ + parsed = pl.col(colname).cast(pl.Utf8).str.strptime(pl.Date, format="%m/%d/%Y", strict=False) + bad_mask = parsed.is_null() & pl.col(colname).is_not_null() + any_bad = bool(lf.select(bad_mask.any()).collect().item()) + if not any_bad: + return + + bad_vals = lf.filter(bad_mask).select(pl.col(colname).unique().head(30)).collect().to_series().to_list() + raise ValueError( + f"Failed to parse {colname} into Date for some rows using %m/%d/%Y.\n" + f"- raw_values_failed_parse_sample={_format_list_preview(bad_vals, max_items=30)}\n" + ) + + +def _fold_in_preserve_nulls(base: pl.Expr, extra: pl.Expr) -> pl.Expr: + """ + Policy: + - HR2330 := HR2330 + HR2430 + - HR2400 := HR2400 + HR2500 + - Drop extras after fold-in. + + Null semantics are important: + - If both base and extra are null, output must remain null (unknown). + - Otherwise treat null as 0.0 for summation. + + Why: + - This reflects how DST extras behave operationally: an extra interval is additive if present, + but we must not turn a fully-missing pair into a synthetic 0. + """ + base_f = base.cast(pl.Float64, strict=False) + extra_f = extra.cast(pl.Float64, strict=False) + return ( + pl.when(base_f.is_null() & extra_f.is_null()) + .then(pl.lit(None, dtype=pl.Float64)) + .otherwise(base_f.fill_null(0.0) + extra_f.fill_null(0.0)) + ) + + +def transform_wide_to_long_lf( + lf: pl.LazyFrame, + *, + strict: bool = True, + sort_output: bool = True, +) -> pl.LazyFrame: + """ + Wide CSV -> Long (canonical) LazyFrame transform (transform-only; no writing). + + This function is intentionally "pure transform": + - It does not read/write files directly. + - It does not manage batching. + - It does not choose execution resources. + Those concerns belong to the driver/orchestrator layer. + + Determinism: + - sort_output=True enforces deterministic global ordering on + (zip_code, account_identifier, datetime). + - Month-scale validation in constrained environments should typically use + sort_output=False and validate determinism separately on bounded samples. + + Final output schema (exact order + dtypes): + 1) zip_code: Utf8 + 2) delivery_service_class: Categorical + 3) delivery_service_name: Categorical + 4) account_identifier: Utf8 + 5) datetime: Datetime(us) + 6) energy_kwh: Float64 + 7) plc_value: Float64 + 8) nspl_value: Float64 + 9) year: Int32 + 10) month: Int8 + """ + required = [ + "ZIP_CODE", + "DELIVERY_SERVICE_CLASS", + "DELIVERY_SERVICE_NAME", + "ACCOUNT_IDENTIFIER", + "INTERVAL_READING_DATE", + "INTERVAL_LENGTH", + "PLC_VALUE", + "NSPL_VALUE", + ] + + # Collecting schema is metadata-only and does not scan data. We use it to make + # validation decisions without triggering a full execution. + schema = lf.collect_schema() + schema_cols_in_order = schema.names() + schema_names = set(schema_cols_in_order) + + _require_columns_from_names(schema_names, required) + + if strict: + _enforce_total_columns_59_from_names(schema_cols_in_order) + + interval_specs_all = _parse_interval_specs_from_columns(schema_cols_in_order) + if not interval_specs_all: + raise ValueError("Contract violation: no interval columns found matching ^INTERVAL_HR\\d{4}_ENERGY_QTY$.\n") + + _validate_interval_set(interval_specs_all, strict=strict) + + if strict: + _validate_interval_length_1800_lf(lf) + _validate_reading_date_parses_strict_lf(lf, colname="INTERVAL_READING_DATE") + + # Derive the canonical "standard" interval columns from the observed schema. We do not + # hardcode the headers to avoid dependence on input ordering; strict mode ensures the set. + standard_specs = [s for s in interval_specs_all if s.end_minutes in _STANDARD_END_MINUTES] + standard_cols = [s.colname for s in standard_specs] + + if strict and len(standard_cols) != 48: + raise ValueError( + "Contract violation: expected exactly 48 standard interval columns.\n" + f"- observed_n_standard_cols={len(standard_cols)}\n" + ) + + # Fail-loud if fold targets missing. We must always have base columns 2330 and 2400 + # since DST fold-in adds into them. + if "INTERVAL_HR2330_ENERGY_QTY" not in schema_names or "INTERVAL_HR2400_ENERGY_QTY" not in schema_names: + raise ValueError("Contract violation: missing required standard columns HR2330 or HR2400.\n") + + # Parse date as Date (not Datetime) first; this keeps semantics explicit and avoids + # timezone ambiguity. We later cast to Datetime(us) for interval math. + reading_date_expr = ( + pl.col("INTERVAL_READING_DATE") + .cast(pl.Utf8) + .str.strptime(pl.Date, format="%m/%d/%Y", strict=True) + .alias("interval_reading_date") + ) + + # Project early to minimize memory pressure: + # - keep only identifier columns + PLC/NSPL + reading_date + interval columns + # - drop filler columns and any other wide fields not needed for the canonical long output + dst_extra_cols = [extra for extra in _DST_FOLD_MAP if extra in schema_names] + wide = lf.select([ + pl.col("ZIP_CODE").cast(pl.Utf8).alias("zip_code"), + pl.col("DELIVERY_SERVICE_CLASS").cast(pl.Categorical).alias("delivery_service_class"), + pl.col("DELIVERY_SERVICE_NAME").cast(pl.Categorical).alias("delivery_service_name"), + pl.col("ACCOUNT_IDENTIFIER").cast(pl.Utf8).alias("account_identifier"), + pl.col("PLC_VALUE").cast(pl.Float64, strict=False).alias("plc_value"), + pl.col("NSPL_VALUE").cast(pl.Float64, strict=False).alias("nspl_value"), + reading_date_expr, + *[pl.col(c).cast(pl.Float64, strict=False) for c in standard_cols], + *[pl.col(c).cast(pl.Float64, strict=False) for c in dst_extra_cols], + ]) + + # Apply DST Option B fold-in via mapping, then drop the extra columns. + fold_exprs: list[pl.Expr] = [] + for extra_col, base_col in _DST_FOLD_MAP.items(): + if extra_col in schema_names: + fold_exprs.append(_fold_in_preserve_nulls(pl.col(base_col), pl.col(extra_col)).alias(base_col)) + if fold_exprs: + wide = wide.with_columns(fold_exprs) + + if dst_extra_cols: + wide = wide.drop(dst_extra_cols) + + # id_vars define the "identity" columns that are repeated for each unpivoted interval. + # interval_reading_date is kept only until we compute datetime; it is not part of final output. + id_vars = [ + "zip_code", + "delivery_service_class", + "delivery_service_name", + "account_identifier", + "plc_value", + "nspl_value", + "interval_reading_date", + ] + + # Unpivot produces one row per (id_vars, interval_col). We immediately cast energy_kwh + # to Float64 to enforce canonical dtype regardless of upstream inference. + long = wide.unpivot( + index=id_vars, + on=standard_cols, + variable_name="interval_col", + value_name="energy_kwh", + ).with_columns(pl.col("energy_kwh").cast(pl.Float64, strict=False)) + + # Extract end-time HHMM from the interval column label. This is intentionally strict: + # interval headers are part of the upstream contract; if they don't match, we should fail. + long = long.with_columns( + pl.col("interval_col") + .str.extract(r"^INTERVAL_HR(\d{4})_ENERGY_QTY$", 1) + .cast(pl.Int32, strict=True) + .alias("hhmm") + ).with_columns((((pl.col("hhmm") // 100) * 60) + (pl.col("hhmm") % 100)).alias("end_minutes")) + + if strict: + # After unpivot, ensure only standard end-times remain. + allowed = sorted(_STANDARD_END_MINUTES) + any_bad_end = bool(long.select((~pl.col("end_minutes").is_in(allowed)).any()).collect().item()) + if any_bad_end: + bad_cols = ( + long.filter(~pl.col("end_minutes").is_in(allowed)) + .select(pl.col("interval_col").unique().head(30)) + .collect() + .to_series() + .to_list() + ) + raise ValueError( + "Contract violation: unexpected interval columns appeared after unpivot.\n" + f"- unexpected_interval_cols_sample={_format_list_preview(bad_cols, max_items=30)}\n" + ) + + # datetime = interval START time: + # - Input labels are end-times (e.g., HR0030 ends at 00:30). + # - We subtract 30 minutes to get the interval start. + # - HR2400 therefore maps to 23:30 same day (no rollover), matching the locked semantics. + long = long.with_columns( + ( + pl.col("interval_reading_date").cast(pl.Datetime("us")) + + pl.duration(minutes=pl.col("end_minutes").cast(pl.Int64) - pl.lit(30)) + ).alias("datetime") + ) + + # Derived partition columns. These must come from datetime (not from INTERVAL_READING_DATE), + # because datetime semantics are the canonical time representation. + long = long.with_columns([ + pl.col("datetime").dt.year().cast(pl.Int32).alias("year"), + pl.col("datetime").dt.month().cast(pl.Int8).alias("month"), + ]) + + # Drop helper columns promptly to reduce downstream memory footprint. + long = long.drop(["interval_col", "hhmm", "end_minutes"]) + + if sort_output: + # Sorting is intentionally optional: + # - required for deterministic output in write paths + # - avoided in month-scale validation in constrained environments + long = long.sort(["zip_code", "account_identifier", "datetime"]) + + # Authoritative final projection: + # - enforces schema order and dtypes + # - ensures interval_reading_date is not in the final output + return long.select([ + pl.col("zip_code").cast(pl.Utf8), + pl.col("delivery_service_class").cast(pl.Categorical), + pl.col("delivery_service_name").cast(pl.Categorical), + pl.col("account_identifier").cast(pl.Utf8), + pl.col("datetime").cast(pl.Datetime("us")), + pl.col("energy_kwh").cast(pl.Float64, strict=False), + pl.col("plc_value").cast(pl.Float64, strict=False), + pl.col("nspl_value").cast(pl.Float64, strict=False), + pl.col("year").cast(pl.Int32), + pl.col("month").cast(pl.Int8), + ]) + + +def transform_wide_to_long( + df: pl.DataFrame, + *, + strict: bool = True, + sort_output: bool = True, +) -> pl.DataFrame: + """ + Backward-compatible DataFrame API wrapper. + + Why keep this: + - Some call sites prefer an eager DataFrame API (e.g., unit tests, small local files). + - We keep the LazyFrame transform as the source of truth and collect at the boundary. + """ + return transform_wide_to_long_lf(df.lazy(), strict=strict, sort_output=sort_output).collect() diff --git a/tests/diagnose_bg_density_v2.py b/tests/diagnose_bg_density_v2.py deleted file mode 100644 index b36d397..0000000 --- a/tests/diagnose_bg_density_v2.py +++ /dev/null @@ -1,360 +0,0 @@ -#!/usr/bin/env python3 -""" -Diagnostic: Check block-group sampling density for Stage 2 regression. - -This script analyzes whether your household sample is dense enough at the -block-group level to detect meaningful demographic patterns. - -Usage: - python diagnose_bg_density.py \ - --clusters data/clustering/results/cluster_assignments.parquet \ - --crosswalk data/reference/2023_comed_zip4_census_crosswalk.txt \ - --output diagnostics.txt -""" - -from __future__ import annotations - -import argparse -from pathlib import Path - -import polars as pl - - -def inspect_cluster_file(clusters_path: Path) -> None: - """Inspect what's actually in the cluster assignments file.""" - print("=" * 70) - print("CLUSTER FILE INSPECTION") - print("=" * 70) - - df = pl.read_parquet(clusters_path) - - print(f"\nFile: {clusters_path}") - print(f"Rows: {len(df):,}") - print(f"Columns: {df.columns}") - print("\nSchema:") - for col, dtype in df.schema.items(): - print(f" {col:30s} {dtype}") - - print("\nFirst few rows:") - print(df.head()) - - print("\n" + "=" * 70) - - -def load_and_join_to_blockgroups( - clusters_path: Path, - crosswalk_path: Path, -) -> pl.DataFrame: - """Join cluster assignments to block groups.""" - print("\nLoading cluster assignments...") - clusters = pl.read_parquet(clusters_path) - print(f" {len(clusters):,} household-day observations") - - # Check which ID columns are present - id_col = None - if "account_identifier" in clusters.columns: - id_col = "account_identifier" - elif "household_id" in clusters.columns: - id_col = "household_id" - elif "meter_id" in clusters.columns: - id_col = "meter_id" - else: - print(" ⚠️ WARNING: No household identifier column found!") - print(f" Available columns: {clusters.columns}") - id_col = None - - if id_col: - print(f" {clusters[id_col].n_unique():,} unique households (using column: {id_col})") - - if "zip_code" not in clusters.columns: - raise ValueError(f"'zip_code' column not found in {clusters_path}") - - if "cluster" not in clusters.columns: - raise ValueError(f"'cluster' column not found in {clusters_path}") - - print(f" {clusters['cluster'].n_unique()} clusters") - - print("\nLoading crosswalk...") - zip_codes = clusters["zip_code"].unique().to_list() - - crosswalk = ( - pl.scan_csv(crosswalk_path, separator="\t", infer_schema_length=10000) - .with_columns([ - (pl.col("Zip").cast(pl.Utf8).str.zfill(5) + "-" + pl.col("Zip4").cast(pl.Utf8).str.zfill(4)).alias( - "zip_code" - ), - pl.col("CensusKey2023").cast(pl.Utf8).str.zfill(15).str.slice(0, 12).alias("block_group_geoid"), - ]) - .filter(pl.col("zip_code").is_in(zip_codes)) - .select(["zip_code", "block_group_geoid"]) - .collect() - ) - - print(f" {crosswalk['zip_code'].n_unique():,} ZIP+4s matched") - - # Check for fan-out - fanout = crosswalk.group_by("zip_code").agg(pl.n_unique("block_group_geoid").alias("n_bg")) - max_fanout = fanout["n_bg"].max() - if max_fanout > 1: - pct_fanout = (fanout.filter(pl.col("n_bg") > 1).height / len(fanout)) * 100 - print(f" ⚠️ {pct_fanout:.1f}% of ZIP+4s map to multiple block groups (max={max_fanout})") - - print("\nJoining to block groups...") - df = clusters.join(crosswalk, on="zip_code", how="left") - - missing = df.filter(pl.col("block_group_geoid").is_null()).height - if missing > 0: - print(f" ⚠️ {missing:,} observations ({missing / len(df) * 100:.1f}%) missing block group") - - df = df.filter(pl.col("block_group_geoid").is_not_null()) - print(f" ✓ {len(df):,} observations across {df['block_group_geoid'].n_unique():,} block groups") - - # Store the ID column name for later use - df = df.with_columns(pl.lit(id_col).alias("_id_col_name")) - - return df - - -def diagnose_household_density(df: pl.DataFrame) -> dict: - """Check households per block group.""" - print("\n" + "=" * 70) - print("DIAGNOSTIC 1: HOUSEHOLD DENSITY PER BLOCK GROUP") - print("=" * 70) - - # Get the ID column name (stored during join) - id_col = df["_id_col_name"][0] if "_id_col_name" in df.columns else None - - if not id_col or id_col not in df.columns: - print("\n⚠️ WARNING: Cannot compute household density - no household ID column") - print(" Skipping this diagnostic.") - return {} - - hh_per_bg = df.group_by("block_group_geoid").agg(pl.col(id_col).n_unique().alias("n_households")) - - stats = { - "n_block_groups": hh_per_bg.height, - "mean_hh_per_bg": hh_per_bg["n_households"].mean(), - "median_hh_per_bg": hh_per_bg["n_households"].median(), - "min_hh_per_bg": hh_per_bg["n_households"].min(), - "max_hh_per_bg": hh_per_bg["n_households"].max(), - "p10_hh_per_bg": hh_per_bg["n_households"].quantile(0.10), - "p90_hh_per_bg": hh_per_bg["n_households"].quantile(0.90), - } - - print(f"\nBlock groups: {stats['n_block_groups']:,}") - print("Households per block group:") - print(f" Mean: {stats['mean_hh_per_bg']:.1f}") - print(f" Median: {stats['median_hh_per_bg']:.1f}") - print(f" Min: {stats['min_hh_per_bg']}") - print(f" Max: {stats['max_hh_per_bg']}") - print(f" P10: {stats['p10_hh_per_bg']:.1f}") - print(f" P90: {stats['p90_hh_per_bg']:.1f}") - - # Distribution - print("\nDistribution:") - dist = ( - hh_per_bg.with_columns( - pl.when(pl.col("n_households") == 1) - .then(pl.lit("1 household")) - .when(pl.col("n_households") == 2) - .then(pl.lit("2 households")) - .when(pl.col("n_households").is_between(3, 5)) - .then(pl.lit("3-5 households")) - .when(pl.col("n_households").is_between(6, 10)) - .then(pl.lit("6-10 households")) - .when(pl.col("n_households").is_between(11, 20)) - .then(pl.lit("11-20 households")) - .otherwise(pl.lit("20+ households")) - .alias("bucket") - ) - .group_by("bucket") - .agg(pl.len().alias("n_bg")) - .sort("n_bg", descending=True) - ) - - for row in dist.iter_rows(named=True): - pct = row["n_bg"] / stats["n_block_groups"] * 100 - print(f" {row['bucket']:20s}: {row['n_bg']:5,} BGs ({pct:5.1f}%)") - - # Assessment - print("\nASSESSMENT:") - if stats["median_hh_per_bg"] < 3: - print(" ❌ CRITICAL: Median < 3 households per block group") - print(" → Most block groups have too few households for stable cluster shares") - print(" → Stage 2 regression will have very weak signal") - elif stats["median_hh_per_bg"] < 10: - print(" ⚠️ WARNING: Median < 10 households per block group") - print(" → Cluster shares will be noisy") - print(" → Consider increasing sample size or aggregating to ZIP-level") - else: - print(" ✓ GOOD: Median ≥ 10 households per block group") - print(" → Should have reasonable signal for Stage 2") - - return stats - - -def diagnose_obs_density(df: pl.DataFrame) -> dict: - """Check household-day observations per block group.""" - print("\n" + "=" * 70) - print("DIAGNOSTIC 2: OBSERVATION DENSITY PER BLOCK GROUP") - print("=" * 70) - - obs_per_bg = df.group_by("block_group_geoid").agg(pl.len().alias("n_obs")) - - stats = { - "mean_obs_per_bg": obs_per_bg["n_obs"].mean(), - "median_obs_per_bg": obs_per_bg["n_obs"].median(), - "min_obs_per_bg": obs_per_bg["n_obs"].min(), - "max_obs_per_bg": obs_per_bg["n_obs"].max(), - } - - print("\nObservations (household-days) per block group:") - print(f" Mean: {stats['mean_obs_per_bg']:.1f}") - print(f" Median: {stats['median_obs_per_bg']:.1f}") - print(f" Min: {stats['min_obs_per_bg']}") - print(f" Max: {stats['max_obs_per_bg']}") - - # After Stage 2 filtering (≥50 obs, ≥2 clusters) - filtered = obs_per_bg.filter(pl.col("n_obs") >= 50) - n_filtered = filtered.height - pct_surviving = n_filtered / obs_per_bg.height * 100 if obs_per_bg.height > 0 else 0 - - print("\nAfter Stage 2 filtering (≥50 obs per BG):") - print(f" {n_filtered:,} block groups ({pct_surviving:.1f}%) survive") - - if pct_surviving < 20: - print("\n ⚠️ WARNING: <20% of block groups survive filtering") - print(" → You're throwing away most of your data") - print(" → Consider lowering threshold or increasing sample size") - - return stats - - -def diagnose_cluster_share_variance(df: pl.DataFrame) -> dict: - """Check variance in cluster shares across block groups.""" - print("\n" + "=" * 70) - print("DIAGNOSTIC 3: CLUSTER SHARE VARIANCE") - print("=" * 70) - - # Compute cluster shares per block group - bg_counts = df.group_by(["block_group_geoid", "cluster"]).agg(pl.len().alias("n_obs")) - - bg_totals = df.group_by("block_group_geoid").agg(pl.len().alias("total_obs")) - - shares = bg_counts.join(bg_totals, on="block_group_geoid").with_columns( - (pl.col("n_obs") / pl.col("total_obs")).alias("cluster_share") - ) - - # Pivot to wide format for variance calculation - wide = shares.pivot( - index="block_group_geoid", - columns="cluster", - values="cluster_share", - ).fill_null(0) - - cluster_cols = [c for c in wide.columns if c != "block_group_geoid"] - - print("\nCluster share variance across block groups:") - stats = {} - for col in sorted(cluster_cols): - if col in wide.columns: - var = wide[col].var() - mean = wide[col].mean() - stats[col] = {"mean": mean, "variance": var} - print(f" Cluster {col}: mean={mean:.3f}, variance={var:.4f}") - - if stats: - avg_var = sum(s["variance"] for s in stats.values()) / len(stats) - - print(f"\nAverage variance: {avg_var:.4f}") - - print("\nASSESSMENT:") - if avg_var < 0.01: - print(" ❌ CRITICAL: Variance < 0.01") - print(" → Cluster shares barely differ across block groups") - print(" → No demographic signal can be detected") - print(" → MUST increase sample size or change aggregation level") - elif avg_var < 0.02: - print(" ⚠️ WARNING: Variance < 0.02") - print(" → Weak signal; demographic effects will be hard to detect") - else: - print(" ✓ GOOD: Variance ≥ 0.02") - print(" → Sufficient variation for regression") - - return stats - - -def generate_recommendations( - hh_stats: dict, - obs_stats: dict, - share_stats: dict, -) -> None: - """Generate actionable recommendations.""" - print("\n" + "=" * 70) - print("RECOMMENDATIONS") - print("=" * 70) - - if not hh_stats: - print("\n⚠️ Could not assess household density (no ID column)") - print(" Assess based on observation density instead.") - return - - median_hh = hh_stats.get("median_hh_per_bg", 0) - - if median_hh < 3: - print("\n🔴 CRITICAL ISSUE: Sample too sparse for block-group analysis") - print("\nOptions:") - print(" 1. Increase household sample to 50k-100k+") - print(" 2. Switch to ZIP-level or ZIP+4-level aggregation") - print(" 3. Use stratified sampling (population-weighted by block group)") - print(" 4. Aggregate to county-level if only interested in broad patterns") - - elif median_hh < 10: - print("\n⚠️ WARNING: Marginal sample density") - print("\nOptions:") - print(" 1. Increase sample to 30k-50k households") - print(" 2. Consider ZIP-level aggregation for more stable estimates") - print(" 3. Use hierarchical modeling to pool information across similar BGs") - - else: - print("\n✓ Sample density looks reasonable") - print("\nOptional improvements:") - print(" 1. Still consider stratified sampling for better coverage") - print(" 2. Add more days if household-day counts are low") - - print("\n" + "=" * 70) - - -def main() -> None: - parser = argparse.ArgumentParser(description="Diagnose block-group sampling density for Stage 2") - parser.add_argument("--clusters", type=Path, required=True, help="Path to cluster_assignments.parquet") - parser.add_argument("--crosswalk", type=Path, required=True, help="Path to ZIP+4 crosswalk file") - parser.add_argument("--inspect-only", action="store_true", help="Only inspect the cluster file schema and exit") - parser.add_argument("--output", type=Path, default=None, help="Optional: save report to file") - - args = parser.parse_args() - - # Inspect the cluster file first - inspect_cluster_file(args.clusters) - - if args.inspect_only: - return - - # Load and join - df = load_and_join_to_blockgroups(args.clusters, args.crosswalk) - - # Run diagnostics - hh_stats = diagnose_household_density(df) - obs_stats = diagnose_obs_density(df) - share_stats = diagnose_cluster_share_variance(df) - - # Recommendations - generate_recommendations(hh_stats, obs_stats, share_stats) - - # TODO: Save to file if requested - if args.output: - print(f"\nReport saved to: {args.output}") - - -if __name__ == "__main__": - main() diff --git a/tests/investigate_dst_values.py b/tests/investigate_dst_values.py deleted file mode 100644 index c2e76a7..0000000 --- a/tests/investigate_dst_values.py +++ /dev/null @@ -1,171 +0,0 @@ -#!/usr/bin/env python3 -""" -Investigate HR2430 and HR2500 columns in source data. - -Questions: -1. Are these columns always populated or only on DST days? -2. What values do they contain? -3. How do they compare to adjacent intervals? -4. Can we safely ignore them? -""" - -from pathlib import Path - -import polars as pl - - -def investigate_dst_columns(): - """Analyze HR2430 and HR2500 in sample CSVs.""" - - sample_dir = Path("data/samples") - csv_files = list(sample_dir.glob("*.csv")) - - if not csv_files: - print("No sample files found. Run: just download-samples 202308 5") - return - - print("=" * 80) - print("INVESTIGATING HR2430 AND HR2500 COLUMNS") - print("=" * 80) - - for csv_file in csv_files[:3]: # Check first 3 files - print(f"\n📁 File: {csv_file.name}") - print("-" * 80) - - df = pl.read_csv(csv_file) - - # Check if these columns exist - has_2430 = "INTERVAL_HR2430_ENERGY_QTY" in df.columns - has_2500 = "INTERVAL_HR2500_ENERGY_QTY" in df.columns - - print(f"Has HR2430: {has_2430}") - print(f"Has HR2500: {has_2500}") - - if not (has_2430 and has_2500): - print("⚠️ Expected columns not found!") - continue - - # Count non-null values - null_2430 = df["INTERVAL_HR2430_ENERGY_QTY"].null_count() - null_2500 = df["INTERVAL_HR2500_ENERGY_QTY"].null_count() - total_rows = df.height - - print(f"\nTotal rows: {total_rows}") - print(f"HR2430 null count: {null_2430} ({null_2430 / total_rows * 100:.1f}%)") - print(f"HR2500 null count: {null_2500} ({null_2500 / total_rows * 100:.1f}%)") - print(f"HR2430 non-null: {total_rows - null_2430} ({(total_rows - null_2430) / total_rows * 100:.1f}%)") - print(f"HR2500 non-null: {total_rows - null_2500} ({(total_rows - null_2500) / total_rows * 100:.1f}%)") - - # Sample non-null values - if total_rows - null_2430 > 0: - print("\n📊 Sample HR2430 values (non-null):") - sample_2430 = df.filter(pl.col("INTERVAL_HR2430_ENERGY_QTY").is_not_null()).head(5) - print( - sample_2430.select([ - "INTERVAL_READING_DATE", - "ACCOUNT_IDENTIFIER", - "INTERVAL_HR2330_ENERGY_QTY", - "INTERVAL_HR2400_ENERGY_QTY", - "INTERVAL_HR2430_ENERGY_QTY", - ]) - ) - - if total_rows - null_2500 > 0: - print("\n📊 Sample HR2500 values (non-null):") - sample_2500 = df.filter(pl.col("INTERVAL_HR2500_ENERGY_QTY").is_not_null()).head(5) - print( - sample_2500.select([ - "INTERVAL_READING_DATE", - "ACCOUNT_IDENTIFIER", - "INTERVAL_HR2400_ENERGY_QTY", - "INTERVAL_HR2430_ENERGY_QTY", - "INTERVAL_HR2500_ENERGY_QTY", - ]) - ) - - # Statistics on non-null values - if total_rows - null_2430 > 0: - print("\n📈 HR2430 Statistics (non-null values):") - stats = df.filter(pl.col("INTERVAL_HR2430_ENERGY_QTY").is_not_null())[ - "INTERVAL_HR2430_ENERGY_QTY" - ].describe() - print(stats) - - if total_rows - null_2500 > 0: - print("\n📈 HR2500 Statistics (non-null values):") - stats = df.filter(pl.col("INTERVAL_HR2500_ENERGY_QTY").is_not_null())[ - "INTERVAL_HR2500_ENERGY_QTY" - ].describe() - print(stats) - - # Compare to adjacent regular intervals - print("\n🔍 Comparing to adjacent intervals:") - comparison = df.select([ - "INTERVAL_READING_DATE", - pl.col("INTERVAL_HR2330_ENERGY_QTY").alias("23:30"), - pl.col("INTERVAL_HR2400_ENERGY_QTY").alias("24:00"), - pl.col("INTERVAL_HR2430_ENERGY_QTY").alias("24:30_DST"), - pl.col("INTERVAL_HR2500_ENERGY_QTY").alias("25:00_DST"), - pl.col("INTERVAL_HR0030_ENERGY_QTY").alias("00:30"), - pl.col("INTERVAL_HR0100_ENERGY_QTY").alias("01:00"), - ]).head(10) - print(comparison) - - # Check August dates specifically - print("\n" + "=" * 80) - print("AUGUST 2023 DST CHECK") - print("=" * 80) - - # DST transitions in 2023 - dst_spring = "2023-03-12" # Spring forward - dst_fall = "2023-11-05" # Fall back - - print("\n2023 DST Transitions:") - print(f" Spring Forward: {dst_spring} (not in August)") - print(f" Fall Back: {dst_fall} (not in August)") - print("\nAugust 2023 has NO DST transitions!") - print("Therefore, HR2430 and HR2500 should be NULL for all August dates.") - - # Check if any August date has these values - for csv_file in csv_files[:1]: - df = pl.read_csv(csv_file) - - august_dates = ( - df.filter( - (pl.col("INTERVAL_HR2430_ENERGY_QTY").is_not_null()) - | (pl.col("INTERVAL_HR2500_ENERGY_QTY").is_not_null()) - ) - .select("INTERVAL_READING_DATE") - .unique() - ) - - if august_dates.height > 0: - print("\n⚠️ Found non-null HR2430/HR2500 on these dates:") - print(august_dates) - else: - print("\n✅ All HR2430/HR2500 are null (as expected)") - - # RECOMMENDATION - print("\n" + "=" * 80) - print("RECOMMENDATION") - print("=" * 80) - - print("\nBased on the data above:") - print("\nIF HR2430/HR2500 are mostly/all NULL:") - print(" ✅ These are DST-only columns") - print(" ✅ Safe to exclude from schema (use 48 columns)") - print(" ✅ Filter would have removed them anyway") - - print("\nIF HR2430/HR2500 have actual values:") - print(" ⚠️ ComEd may be using these for something else") - print(" ⚠️ Need to understand what they represent") - print(" ⚠️ May need special handling") - - print("\nTo decide:") - print(" 1. Check the percentages above") - print(" 2. If >95% null → exclude from schema") - print(" 3. If <95% null → investigate further with ComEd") - - -if __name__ == "__main__": - investigate_dst_columns() diff --git a/tests/investigate_duplicates.py b/tests/investigate_duplicates.py deleted file mode 100644 index 16d2bd7..0000000 --- a/tests/investigate_duplicates.py +++ /dev/null @@ -1,142 +0,0 @@ -#!/usr/bin/env python3 -""" -Memory-efficient duplicate investigation using streaming. -""" - -import polars as pl - - -def investigate_duplicates_efficient(): - """ - Memory-efficient duplicate detection using lazy evaluation. - """ - - print("=" * 80) - print("DUPLICATE TIMESTAMP INVESTIGATION (Memory Efficient)") - print("=" * 80) - - lf = pl.scan_parquet("data/processed/comed_202308.parquet") - - # Strategy 1: Check a larger sample (10M rows) - print("\n🔍 Strategy 1: Checking 10M row sample...") - - sample_dups = ( - lf.head(10_000_000) - .group_by(["account_identifier", "datetime"]) - .agg(pl.len().alias("count")) - .filter(pl.col("count") > 1) - .collect() - ) - - print(f"Duplicates in 10M sample: {sample_dups.height:,}") - - if sample_dups.height > 0: - # Extrapolate to full dataset - sample_ratio = 10_000_000 / 34_340_581 - estimated_total = int(sample_dups.height / sample_ratio) - print(f"Estimated total duplicates: ~{estimated_total:,}") - - print("\n📊 Sample of duplicate pairs:") - print(sample_dups.head(10)) - - # Check if same or different kWh values - print("\n🔬 Checking if duplicates have same kWh...") - - dup_sample = sample_dups.head(20) - - for row in dup_sample.iter_rows(named=True): - account = row["account_identifier"] - dt = row["datetime"] - - # Get all rows for this duplicate - dup_rows = ( - lf.filter((pl.col("account_identifier") == account) & (pl.col("datetime") == dt)) - .select(["account_identifier", "datetime", "zip_code", "kwh"]) - .collect() - ) - - unique_kwh = dup_rows["kwh"].n_unique() - - status = "✅ SAME kWh (true duplicate)" if unique_kwh == 1 else "⚠️ DIFFERENT kWh (data issue!)" - - print(f"\nAccount {account}, {dt}:") - print(f" Occurrences: {row['count']}, {status}") - print(dup_rows) - - # Strategy 2: Check specific accounts that had duplicates in sample - print("\n" + "=" * 80) - print("🔍 Strategy 2: Checking specific problematic accounts...") - print("=" * 80) - - if sample_dups.height > 0: - # Get accounts with most duplicates - problem_accounts = sample_dups.sort("count", descending=True).head(5)["account_identifier"] - - for account in problem_accounts: - print(f"\nAnalyzing account: {account}") - - account_data = ( - lf.filter(pl.col("account_identifier") == account) - .select(["account_identifier", "datetime", "zip_code", "kwh"]) - .collect() - ) - - # Count duplicates for this account - account_dups = ( - account_data.group_by(["account_identifier", "datetime"]) - .agg(pl.len().alias("count")) - .filter(pl.col("count") > 1) - ) - - print(f" Total rows: {account_data.height}") - print(f" Duplicate timestamps: {account_dups.height}") - - if account_dups.height > 0: - print(" Sample duplicates:") - - for dup_row in account_dups.head(3).iter_rows(named=True): - dt = dup_row["datetime"] - dup_instances = account_data.filter(pl.col("datetime") == dt) - print(f"\n Datetime: {dt}") - print(dup_instances) - - # Strategy 3: Partition-based check (most efficient) - print("\n" + "=" * 80) - print("🔍 Strategy 3: Partition-based duplicate count...") - print("=" * 80) - - # Count unique (account, datetime) pairs vs total rows - unique_pairs = lf.select(["account_identifier", "datetime"]).unique().select(pl.len()).collect()[0, 0] - total_rows = lf.select(pl.len()).collect()[0, 0] - - duplicate_rows = total_rows - unique_pairs - - print(f"\nTotal rows: {total_rows:,}") - print(f"Unique (account, datetime) pairs: {unique_pairs:,}") - print(f"Duplicate rows: {duplicate_rows:,}") - print(f"Duplicate percentage: {duplicate_rows / total_rows * 100:.2f}%") - - # RECOMMENDATION - print("\n" + "=" * 80) - print("RECOMMENDATION") - print("=" * 80) - - if duplicate_rows > 0: - print(f"\n⚠️ You have {duplicate_rows:,} duplicate rows ({duplicate_rows / total_rows * 100:.2f}% of data)") - print("\nThis is likely caused by:") - print(" 1. Same account appearing in multiple source files") - print(" 2. ZIP+4 overlap causing duplicate account entries") - print(" 3. Data corrections in source system") - print("\n✅ RECOMMENDED FIX:") - print(" Add deduplication to your pipeline:") - print("\n In aws_loader.py, after concatenation:") - print(" lf_combined = lf_combined.unique(subset=['account_identifier', 'datetime'], keep='last')") - print("\n This will keep the most recent value for each (account, datetime) pair") - else: - print("\n✅ No duplicates found!") - - return duplicate_rows - - -if __name__ == "__main__": - investigate_duplicates_efficient() diff --git a/tests/test_billing_pipeline_e2e.py b/tests/test_billing_pipeline_e2e.py new file mode 100644 index 0000000..9dedd5e --- /dev/null +++ b/tests/test_billing_pipeline_e2e.py @@ -0,0 +1,995 @@ +#!/usr/bin/env python3 +"""End-to-end tests for the multi-month billing pipeline orchestrator. + +Runs the full pipeline on existing sample artifacts (no synthetic data +generation) and validates: + +1. Orchestrator produces expected directory layout +2. Per-month outputs exist and have non-empty row counts +3. All-months concatenated bills have correct schema and month tagging +4. Join coverage: no null prices, no row inflation +5. Required columns exist in all outputs +6. Regression outputs exist with correct schemas and mathematical invariants +7. Manifest contains git SHA, parameters, month-by-month counts, steps +8. Skip-regression mode produces no regression artifacts +9. Both regression modes (annual, bg_month) produce consistent outputs + +All outputs go to tmp_path; no writes into tracked data directories. +""" + +from __future__ import annotations + +import json +import subprocess +import sys +from pathlib import Path + +import polars as pl +import pytest + +# ── Sample data paths (committed reference data) ──────────────────────── +_RAW_INTERVAL_DATA = Path("data/processed/comed_202308.parquet") +TARIFF_A = Path("data/reference/comed_flat_hourly_prices_2023.parquet") +TARIFF_B = Path("data/reference/comed_stou_hourly_prices_2023.parquet") +CROSSWALK = Path("data/reference/comed_bg_zip4_crosswalk.txt") +CENSUS = Path("data/reference/census_17_2023.parquet") + +# Skip entire module if sample data is missing. Module-level skip keeps +# test collection clean in CI environments without the full data checkout. +pytestmark = pytest.mark.skipif( + not _RAW_INTERVAL_DATA.exists() or not TARIFF_A.exists() or not TARIFF_B.exists(), + reason="Sample data files not found; skipping E2E pipeline tests.", +) + +# Regression model keys produced by build_regression_dataset.py +MODEL_KEY_1 = "model_1_pct_savings_weighted" +MODEL_KEY_2 = "model_2_sum_bill_diff" + +# Regression parquet artifacts (under regression/) +REGRESSION_PARQUETS = [ + "bg_month_outcomes.parquet", + "bg_annual_outcomes.parquet", + "bg_season_outcomes.parquet", + "regression_dataset_bg.parquet", +] +REGRESSION_JSONS = [ + "regression_results.json", + "regression_metadata.json", +] +REGRESSION_TEXTS = [ + "regression_summary.txt", +] +ALL_REGRESSION_FILES = REGRESSION_PARQUETS + REGRESSION_JSONS + REGRESSION_TEXTS + + +# ── Helper to prepare test data ───────────────────────────────────────── +# The refactored pipeline expects the canonical column name ``energy_kwh`` +# but the committed sample parquet uses the legacy name ``kwh``. Rather +# than modifying the core pipeline or the committed data, we create a +# lightweight temp copy with the column renamed once per test session. +# +# The committed sample data maps to only 2 Census block groups—too few for +# OLS regression (needs n_obs >= n_params + 2). We synthesize a handful +# of extra households at diverse ZIP+4 values so that the regression step +# can run with ≥ 6 BGs. The synthetic rows reuse real hourly timestamps +# so that tariff joins succeed without gaps. + +# ZIP+4 values that map to distinct block groups with non-null census +# predictors, chosen from the crosswalk. We only need a few extra BGs +# beyond the 2 already in the committed test data. +_SYNTHETIC_ZIP4S = [ + "60068-5766", + "60430-1950", + "60946-0128", + "62401-4817", + "62650-6533", + "61048-0270", +] + + +def _prepare_interval_data(tmp_dir: Path) -> Path: + """Copy interval parquet to *tmp_dir* with fixes for the current pipeline. + + 1. Rename ``kwh`` → ``energy_kwh`` if needed (canonical column name). + 2. Append synthetic households at diverse ZIP+4s so the crosswalk join + yields enough block groups for OLS regression (≥ 6 BGs). + + Returns the **pattern** path suitable for ``--interval-pattern``. + """ + dest = tmp_dir / "comed_202308.parquet" + if dest.exists(): + return tmp_dir / "comed_{yyyymm}.parquet" + + df = pl.read_parquet(_RAW_INTERVAL_DATA) + if "energy_kwh" not in df.columns and "kwh" in df.columns: + df = df.rename({"kwh": "energy_kwh"}) + + # Take one real household's hourly timestamps as a template so + # tariff joins match. Each synthetic household gets identical + # timestamps but a different zip_code and account_identifier. + template_acct = df["account_identifier"][0] + template = df.filter(pl.col("account_identifier") == template_acct) + + synth_frames = [] + for i, zip4 in enumerate(_SYNTHETIC_ZIP4S): + synth = template.with_columns( + pl.lit(f"SYNTH_{i:04d}").alias("account_identifier"), + pl.lit(zip4).alias("zip_code"), + ) + synth_frames.append(synth) + + augmented = pl.concat([df, *synth_frames], how="diagonal_relaxed") + augmented.write_parquet(dest) + return tmp_dir / "comed_{yyyymm}.parquet" + + +# ── Helper to build pipeline command ──────────────────────────────────── + + +def _build_pipeline_cmd( + *, + months: str, + interval_pattern: Path, + out_dir: Path, + run_name: str, + skip_regression: bool = False, + regression_level: str = "annual", + include_crosswalk: bool = True, +) -> list[str]: + """Build a CLI command list for the billing pipeline orchestrator.""" + cmd = [ + sys.executable, + "scripts/run_billing_pipeline.py", + "--months", + months, + "--interval-pattern", + str(interval_pattern), + "--tariff-a", + str(TARIFF_A), + "--tariff-b", + str(TARIFF_B), + "--run-name", + run_name, + "--output-dir", + str(out_dir), + ] + if skip_regression: + cmd.append("--skip-regression") + else: + # Regression requires crosswalk + census + if include_crosswalk: + cmd.extend(["--crosswalk", str(CROSSWALK)]) + cmd.extend(["--census", str(CENSUS)]) + # Relaxed thresholds for small test fixture + cmd.extend(["--predictors", "median_household_income"]) + cmd.extend(["--min-obs-per-bg", "1"]) + cmd.extend(["--max-crosswalk-drop-pct", "100"]) + cmd.extend(["--regression-level", regression_level]) + return cmd + + +# ── Fixtures ───────────────────────────────────────────────────────────── + + +@pytest.fixture(scope="module") +def interval_pattern(tmp_path_factory: pytest.TempPathFactory) -> Path: + """Prepare interval data with canonical column names (session-shared).""" + data_dir = tmp_path_factory.mktemp("interval_data") + return _prepare_interval_data(data_dir) + + +@pytest.fixture(scope="module") +def pipeline_run(tmp_path_factory: pytest.TempPathFactory, interval_pattern: Path) -> dict: + """Run the orchestrator with regression (annual, default level).""" + out_dir = tmp_path_factory.mktemp("billing_e2e") + run_name = "test_e2e" + cmd = _build_pipeline_cmd( + months="202308", + interval_pattern=interval_pattern, + out_dir=out_dir, + run_name=run_name, + regression_level="annual", + ) + result = subprocess.run(cmd, capture_output=True, text=True, timeout=300) + return { + "run_dir": out_dir / run_name, + "returncode": result.returncode, + "stdout": result.stdout, + "stderr": result.stderr, + "month": "202308", + } + + +@pytest.fixture(scope="module") +def pipeline_run_bg_month(tmp_path_factory: pytest.TempPathFactory, interval_pattern: Path) -> dict: + """Run the orchestrator with --regression-level bg_month.""" + out_dir = tmp_path_factory.mktemp("billing_bgmonth") + run_name = "test_bgmonth" + cmd = _build_pipeline_cmd( + months="202308", + interval_pattern=interval_pattern, + out_dir=out_dir, + run_name=run_name, + regression_level="bg_month", + ) + result = subprocess.run(cmd, capture_output=True, text=True, timeout=300) + return { + "run_dir": out_dir / run_name, + "returncode": result.returncode, + "stdout": result.stdout, + "stderr": result.stderr, + "month": "202308", + } + + +@pytest.fixture(scope="module") +def pipeline_skip_regression(tmp_path_factory: pytest.TempPathFactory, interval_pattern: Path) -> dict: + """Run orchestrator with --skip-regression for faster validation.""" + out_dir = tmp_path_factory.mktemp("billing_skip_reg") + run_name = "test_skip_reg" + cmd = _build_pipeline_cmd( + months="202308", + interval_pattern=interval_pattern, + out_dir=out_dir, + run_name=run_name, + skip_regression=True, + ) + result = subprocess.run(cmd, capture_output=True, text=True, timeout=300) + return { + "run_dir": out_dir / run_name, + "returncode": result.returncode, + "stdout": result.stdout, + "stderr": result.stderr, + } + + +# ═══════════════════════════════════════════════════════════════════════════ +# 1. Directory layout +# ═══════════════════════════════════════════════════════════════════════════ + + +class TestDirectoryLayout: + """Verify that the orchestrator creates the expected directory tree.""" + + def test_pipeline_succeeds(self, pipeline_skip_regression: dict) -> None: + r = pipeline_skip_regression + assert r["returncode"] == 0, ( + f"Pipeline exited {r['returncode']}.\nstdout: {r['stdout'][-2000:]}\nstderr: {r['stderr'][-2000:]}" + ) + + def test_run_dir_exists(self, pipeline_skip_regression: dict) -> None: + assert pipeline_skip_regression["run_dir"].is_dir() + + def test_tmp_dir_exists(self, pipeline_skip_regression: dict) -> None: + tmp = pipeline_skip_regression["run_dir"] / "_tmp" + assert tmp.is_dir(), "Missing _tmp directory for hourly loads" + + def test_hourly_loads_exists(self, pipeline_skip_regression: dict) -> None: + path = pipeline_skip_regression["run_dir"] / "_tmp" / "month=202308" / "hourly_loads.parquet" + assert path.exists(), f"Missing hourly loads: {path}" + + def test_monthly_bills_exist(self, pipeline_skip_regression: dict) -> None: + path = pipeline_skip_regression["run_dir"] / "month=202308" / "household_bills.parquet" + assert path.exists(), f"Missing monthly bills: {path}" + + def test_all_months_bills_exists(self, pipeline_skip_regression: dict) -> None: + path = pipeline_skip_regression["run_dir"] / "all_months_household_bills.parquet" + assert path.exists(), f"Missing all-months bills: {path}" + + def test_manifest_exists(self, pipeline_skip_regression: dict) -> None: + path = pipeline_skip_regression["run_dir"] / "run_manifest.json" + assert path.exists(), f"Missing manifest: {path}" + + def test_log_exists(self, pipeline_skip_regression: dict) -> None: + path = pipeline_skip_regression["run_dir"] / "pipeline.log" + assert path.exists(), f"Missing pipeline log: {path}" + + +# ═══════════════════════════════════════════════════════════════════════════ +# 2. Per-month outputs: non-empty row counts & required columns +# ═══════════════════════════════════════════════════════════════════════════ + + +class TestPerMonthOutputs: + """Verify per-month outputs have non-empty row counts and correct schema.""" + + def test_hourly_loads_not_empty(self, pipeline_skip_regression: dict) -> None: + path = pipeline_skip_regression["run_dir"] / "_tmp" / "month=202308" / "hourly_loads.parquet" + df = pl.read_parquet(path) + assert df.height > 0, "Hourly loads parquet is empty" + + def test_monthly_bills_not_empty(self, pipeline_skip_regression: dict) -> None: + path = pipeline_skip_regression["run_dir"] / "month=202308" / "household_bills.parquet" + df = pl.read_parquet(path) + assert df.height > 0, "Monthly bills parquet is empty" + + def test_hourly_loads_required_columns(self, pipeline_skip_regression: dict) -> None: + path = pipeline_skip_regression["run_dir"] / "_tmp" / "month=202308" / "hourly_loads.parquet" + df = pl.read_parquet(path) + required = {"account_identifier", "zip_code", "hour_chicago", "kwh_hour"} + missing = required - set(df.columns) + assert not missing, f"Hourly loads missing columns: {missing}" + + def test_monthly_bills_required_columns(self, pipeline_skip_regression: dict) -> None: + path = pipeline_skip_regression["run_dir"] / "month=202308" / "household_bills.parquet" + df = pl.read_parquet(path) + required = { + "account_identifier", + "zip_code", + "total_kwh", + "bill_a_dollars", + "bill_b_dollars", + "bill_diff_dollars", + "pct_savings", + "net_bill_diff_dollars", + "net_pct_savings", + } + missing = required - set(df.columns) + assert not missing, f"Monthly bills missing columns: {missing}" + + def test_no_null_prices_in_bills(self, pipeline_skip_regression: dict) -> None: + path = pipeline_skip_regression["run_dir"] / "month=202308" / "household_bills.parquet" + df = pl.read_parquet(path) + for col in ("bill_a_dollars", "bill_b_dollars"): + n_null = df.select(pl.col(col).is_null().sum()).item() + assert n_null == 0, f"Column {col} has {n_null} null values (join coverage failure)" + + +# ═══════════════════════════════════════════════════════════════════════════ +# 3. All-months household bills (replaces annual aggregate) +# ═══════════════════════════════════════════════════════════════════════════ + + +class TestAllMonthsBills: + """Validate the concatenated all_months_household_bills.parquet artifact.""" + + def test_not_empty(self, pipeline_skip_regression: dict) -> None: + path = pipeline_skip_regression["run_dir"] / "all_months_household_bills.parquet" + df = pl.read_parquet(path) + assert df.height > 0, "All-months bills is empty" + + def test_readable_by_polars(self, pipeline_skip_regression: dict) -> None: + path = pipeline_skip_regression["run_dir"] / "all_months_household_bills.parquet" + df = pl.read_parquet(path) + assert isinstance(df, pl.DataFrame) + + def test_required_columns(self, pipeline_skip_regression: dict) -> None: + path = pipeline_skip_regression["run_dir"] / "all_months_household_bills.parquet" + df = pl.read_parquet(path) + required = { + "account_identifier", + "zip_code", + "month", + "total_kwh", + "bill_a_dollars", + "bill_diff_dollars", + } + missing = required - set(df.columns) + assert not missing, f"All-months bills missing columns: {missing}" + + def test_month_column_format(self, pipeline_skip_regression: dict) -> None: + """month must be a 6-digit YYYYMM string for all rows.""" + path = pipeline_skip_regression["run_dir"] / "all_months_household_bills.parquet" + df = pl.read_parquet(path) + # Check dtype is string + assert df.schema["month"] == pl.Utf8, f"month dtype should be Utf8, got {df.schema['month']}" + # Every value must be exactly 6 digits + bad = df.filter(~pl.col("month").str.contains(r"^\d{6}$")) + assert bad.height == 0, ( + f"{bad.height} rows have non-YYYYMM month values: {bad['month'].unique().to_list()[:10]}" + ) + + def test_month_column_no_nulls(self, pipeline_skip_regression: dict) -> None: + path = pipeline_skip_regression["run_dir"] / "all_months_household_bills.parquet" + df = pl.read_parquet(path) + n_null = df.select(pl.col("month").is_null().sum()).item() + assert n_null == 0, f"month column has {n_null} nulls" + + def test_month_values_match_requested(self, pipeline_skip_regression: dict) -> None: + """Set of months in output should exactly match requested months.""" + path = pipeline_skip_regression["run_dir"] / "all_months_household_bills.parquet" + df = pl.read_parquet(path) + actual_months = set(df["month"].unique().to_list()) + expected_months = {"202308"} + assert actual_months == expected_months, ( + f"Month set mismatch: actual={actual_months}, expected={expected_months}" + ) + + def test_row_count_matches_monthly(self, pipeline_skip_regression: dict) -> None: + """For single-month run, all-months rows should equal monthly rows.""" + run_dir = pipeline_skip_regression["run_dir"] + monthly = pl.read_parquet(run_dir / "month=202308" / "household_bills.parquet") + all_months = pl.read_parquet(run_dir / "all_months_household_bills.parquet") + assert all_months.height == monthly.height, ( + f"Row count mismatch: all_months={all_months.height}, monthly={monthly.height}" + ) + + def test_household_count_matches(self, pipeline_skip_regression: dict) -> None: + """Unique households should match between monthly and all-months.""" + run_dir = pipeline_skip_regression["run_dir"] + monthly = pl.read_parquet(run_dir / "month=202308" / "household_bills.parquet") + all_months = pl.read_parquet(run_dir / "all_months_household_bills.parquet") + assert monthly["account_identifier"].n_unique() == all_months["account_identifier"].n_unique() + + def test_additive_totals_match_monthly(self, pipeline_skip_regression: dict) -> None: + """For single-month run, sum of additive columns should match exactly.""" + run_dir = pipeline_skip_regression["run_dir"] + monthly = pl.read_parquet(run_dir / "month=202308" / "household_bills.parquet") + all_months = pl.read_parquet(run_dir / "all_months_household_bills.parquet") + sum_cols = ["total_kwh", "bill_a_dollars", "bill_b_dollars", "bill_diff_dollars"] + for col in sum_cols: + monthly_total = monthly[col].sum() + all_months_total = all_months[col].sum() + assert abs(monthly_total - all_months_total) < 0.01, ( + f"Column {col}: monthly sum={monthly_total:.4f} != all_months sum={all_months_total:.4f}" + ) + + +# ═══════════════════════════════════════════════════════════════════════════ +# 4. Join coverage: no row inflation +# ═══════════════════════════════════════════════════════════════════════════ + + +class TestJoinCoverage: + """Verify joins don't inflate or drop rows unexpectedly.""" + + def test_no_row_inflation_in_bills(self, pipeline_skip_regression: dict) -> None: + """Each account should appear exactly once in monthly bills.""" + path = pipeline_skip_regression["run_dir"] / "month=202308" / "household_bills.parquet" + df = pl.read_parquet(path) + dupes = df.group_by("account_identifier").len().filter(pl.col("len") > 1) + assert dupes.height == 0, f"Duplicate accounts in monthly bills: {dupes.height}" + + def test_loads_accounts_preserved_in_bills(self, pipeline_skip_regression: dict) -> None: + """All accounts in loads should appear in bills (no silent drops).""" + run_dir = pipeline_skip_regression["run_dir"] + loads = pl.read_parquet(run_dir / "_tmp" / "month=202308" / "hourly_loads.parquet") + bills = pl.read_parquet(run_dir / "month=202308" / "household_bills.parquet") + loads_accounts = set(loads["account_identifier"].unique().to_list()) + bills_accounts = set(bills["account_identifier"].unique().to_list()) + dropped = loads_accounts - bills_accounts + assert not dropped, f"Accounts in loads but not in bills: {len(dropped)}" + + +# ═══════════════════════════════════════════════════════════════════════════ +# 5. Regression artifacts: existence and schemas +# ═══════════════════════════════════════════════════════════════════════════ + + +class TestRegressionArtifacts: + """Verify all regression outputs exist and have correct schemas.""" + + def test_full_pipeline_succeeds(self, pipeline_run: dict) -> None: + r = pipeline_run + assert r["returncode"] == 0, ( + f"Full pipeline exited {r['returncode']}.\nstdout: {r['stdout'][-2000:]}\nstderr: {r['stderr'][-2000:]}" + ) + + def test_regression_dir_exists(self, pipeline_run: dict) -> None: + reg_dir = pipeline_run["run_dir"] / "regression" + assert reg_dir.is_dir(), f"Regression directory missing: {reg_dir}" + + @pytest.mark.parametrize("filename", ALL_REGRESSION_FILES) + def test_regression_file_exists(self, pipeline_run: dict, filename: str) -> None: + path = pipeline_run["run_dir"] / "regression" / filename + assert path.exists(), f"Missing regression artifact: {path}" + + @pytest.mark.parametrize("filename", REGRESSION_PARQUETS) + def test_regression_parquet_readable(self, pipeline_run: dict, filename: str) -> None: + path = pipeline_run["run_dir"] / "regression" / filename + df = pl.read_parquet(path) + assert isinstance(df, pl.DataFrame) + assert df.height > 0, f"{filename} is empty" + + def test_bg_month_outcomes_schema(self, pipeline_run: dict) -> None: + df = pl.read_parquet(pipeline_run["run_dir"] / "regression" / "bg_month_outcomes.parquet") + required = { + "block_group_geoid", + "month", + "sum_total_kwh", + "sum_bill_a_dollars", + "sum_bill_diff_dollars", + "pct_savings_weighted", + "n_household_months", + } + missing = required - set(df.columns) + assert not missing, f"bg_month_outcomes missing columns: {missing}" + + def test_bg_annual_outcomes_schema(self, pipeline_run: dict) -> None: + df = pl.read_parquet(pipeline_run["run_dir"] / "regression" / "bg_annual_outcomes.parquet") + required = { + "block_group_geoid", + "sum_total_kwh", + "sum_bill_a_dollars", + "sum_bill_diff_dollars", + "pct_savings_weighted", + "n_household_months", + } + missing = required - set(df.columns) + assert not missing, f"bg_annual_outcomes missing columns: {missing}" + + def test_bg_season_outcomes_schema(self, pipeline_run: dict) -> None: + df = pl.read_parquet(pipeline_run["run_dir"] / "regression" / "bg_season_outcomes.parquet") + required = { + "block_group_geoid", + "season", + "sum_total_kwh", + "sum_bill_a_dollars", + "sum_bill_diff_dollars", + "pct_savings_weighted", + "n_household_months", + } + missing = required - set(df.columns) + assert not missing, f"bg_season_outcomes missing columns: {missing}" + + def test_bg_annual_n_household_months_definition(self, pipeline_run: dict) -> None: + """n_household_months is household-months, not unique households.""" + bg_month = pl.read_parquet(pipeline_run["run_dir"] / "regression" / "bg_month_outcomes.parquet") + bg_annual = pl.read_parquet(pipeline_run["run_dir"] / "regression" / "bg_annual_outcomes.parquet") + # For single-month run, the values should match (no multi-month aggregation) + expected = bg_month.group_by("block_group_geoid").agg(pl.col("n_household_months").sum()) + check = expected.join( + bg_annual.select("block_group_geoid", "n_household_months"), + on="block_group_geoid", + suffix="_annual", + ) + diff = (check["n_household_months"] - check["n_household_months_annual"]).abs().max() + assert diff == 0, f"n_household_months mismatch between month and annual: max diff={diff}" + + +# ═══════════════════════════════════════════════════════════════════════════ +# 6. Mathematical invariants (strong correctness checks) +# ═══════════════════════════════════════════════════════════════════════════ + + +class TestRegressionMathInvariants: + """Verify mathematical relationships across regression outputs.""" + + def test_pct_savings_weighted_definition_annual(self, pipeline_run: dict) -> None: + """pct_savings_weighted ≈ sum_diff / sum_bill_a * 100 on annual table.""" + reg_dir = pipeline_run["run_dir"] / "regression" + # Read metadata to know which diff column the pipeline resolved + with open(reg_dir / "regression_metadata.json") as f: + meta = json.load(f) + diff_col = meta["bill_diff_column_used"] + sum_diff_col = f"sum_{diff_col}" + + df = pl.read_parquet(reg_dir / "bg_annual_outcomes.parquet") + valid = df.filter(pl.col("sum_bill_a_dollars") > 0) + assert valid.height > 0, "No rows with positive sum_bill_a_dollars" + + expected = valid[sum_diff_col] / valid["sum_bill_a_dollars"] * 100 + actual = valid["pct_savings_weighted"] + max_diff = (expected - actual).abs().max() + assert max_diff < 1e-6, f"pct_savings_weighted invariant violated on annual table: max diff={max_diff}" + + def test_pct_savings_weighted_null_when_zero_denom(self, pipeline_run: dict) -> None: + """When sum_bill_a_dollars <= 0, pct_savings_weighted should be null.""" + df = pl.read_parquet(pipeline_run["run_dir"] / "regression" / "bg_annual_outcomes.parquet") + zero_denom = df.filter(pl.col("sum_bill_a_dollars") <= 0) + if zero_denom.height > 0: + non_null = zero_denom.filter(pl.col("pct_savings_weighted").is_not_null()) + assert non_null.height == 0, ( + f"{non_null.height} rows have pct_savings_weighted despite zero/negative bill_a" + ) + + def test_annual_rollup_equals_sum_of_months(self, pipeline_run: dict) -> None: + """Annual additive sums should equal sum over monthly BG outcomes.""" + reg_dir = pipeline_run["run_dir"] / "regression" + bg_month = pl.read_parquet(reg_dir / "bg_month_outcomes.parquet") + bg_annual = pl.read_parquet(reg_dir / "bg_annual_outcomes.parquet") + + additive_cols = [c for c in bg_month.columns if c.startswith("sum_")] + expected = bg_month.group_by("block_group_geoid").agg( + [pl.col(c).sum() for c in additive_cols] + [pl.col("n_household_months").sum()] + ) + + check = expected.join(bg_annual, on="block_group_geoid", suffix="_annual") + for col in additive_cols: + diff = (check[col] - check[f"{col}_annual"]).abs().max() + assert diff < 1e-6, f"Annual rollup mismatch for {col}: max diff={diff}" + nhm_diff = (check["n_household_months"] - check["n_household_months_annual"]).abs().max() + assert nhm_diff == 0, f"n_household_months mismatch: max diff={nhm_diff}" + + def test_annual_pct_recomputed_from_sums(self, pipeline_run: dict) -> None: + """Annual pct_savings_weighted should be recomputed from rolled-up sums.""" + reg_dir = pipeline_run["run_dir"] / "regression" + with open(reg_dir / "regression_metadata.json") as f: + meta = json.load(f) + diff_col = meta["bill_diff_column_used"] + sum_diff_col = f"sum_{diff_col}" + + bg_month = pl.read_parquet(reg_dir / "bg_month_outcomes.parquet") + bg_annual = pl.read_parquet(reg_dir / "bg_annual_outcomes.parquet") + + # Compute expected annual pct from rolled-up month sums + rolled = bg_month.group_by("block_group_geoid").agg( + pl.col(sum_diff_col).sum(), + pl.col("sum_bill_a_dollars").sum(), + ) + rolled = rolled.with_columns( + pl.when(pl.col("sum_bill_a_dollars") > 0) + .then(pl.col(sum_diff_col) / pl.col("sum_bill_a_dollars") * 100) + .otherwise(None) + .alias("expected_pct"), + ) + check = rolled.join( + bg_annual.select("block_group_geoid", "pct_savings_weighted"), + on="block_group_geoid", + ) + valid = check.filter(pl.col("expected_pct").is_not_null()) + max_diff = (valid["expected_pct"] - valid["pct_savings_weighted"]).abs().max() + assert max_diff < 1e-6, f"Annual pct not recomputed from sums: max diff={max_diff}" + + def test_season_values_valid(self, pipeline_run: dict) -> None: + """Seasons must be from the canonical set.""" + df = pl.read_parquet(pipeline_run["run_dir"] / "regression" / "bg_season_outcomes.parquet") + valid_seasons = {"Winter", "Spring", "Summer", "Fall"} + actual_seasons = set(df["season"].unique().to_list()) + assert actual_seasons <= valid_seasons, f"Invalid seasons: {actual_seasons - valid_seasons}" + + def test_season_mapping_consistent_for_sample(self, pipeline_run: dict) -> None: + """August (202308) should map to Summer.""" + df = pl.read_parquet(pipeline_run["run_dir"] / "regression" / "bg_season_outcomes.parquet") + # With single month 202308, only Summer should appear + actual = set(df["season"].unique().to_list()) + assert actual == {"Summer"}, f"Expected only Summer for month 202308, got {actual}" + + def test_season_rollup_matches_months(self, pipeline_run: dict) -> None: + """Season additive sums should equal sum of constituent months.""" + reg_dir = pipeline_run["run_dir"] / "regression" + bg_month = pl.read_parquet(reg_dir / "bg_month_outcomes.parquet") + bg_season = pl.read_parquet(reg_dir / "bg_season_outcomes.parquet") + + # Derive season from month (mirrors _derive_season_expr logic) + mm = bg_month["month"].str.slice(4, 2) + bg_month_with_season = bg_month.with_columns( + pl.when(mm.is_in(["12", "01", "02"])) + .then(pl.lit("Winter")) + .when(mm.is_in(["03", "04", "05"])) + .then(pl.lit("Spring")) + .when(mm.is_in(["06", "07", "08"])) + .then(pl.lit("Summer")) + .when(mm.is_in(["09", "10", "11"])) + .then(pl.lit("Fall")) + .otherwise(pl.lit(None)) + .alias("season") + ) + + additive_cols = [c for c in bg_month.columns if c.startswith("sum_")] + expected = bg_month_with_season.group_by(["block_group_geoid", "season"]).agg( + [pl.col(c).sum() for c in additive_cols] + [pl.col("n_household_months").sum()] + ) + + check = expected.join(bg_season, on=["block_group_geoid", "season"], suffix="_season") + for col in additive_cols: + diff = (check[col] - check[f"{col}_season"]).abs().max() + assert diff < 1e-6, f"Season rollup mismatch for {col}: max diff={diff}" + + +# ═══════════════════════════════════════════════════════════════════════════ +# 7. Crosswalk coverage & regression metadata +# ═══════════════════════════════════════════════════════════════════════════ + + +class TestRegressionCrosswalkAndMetadata: + """Validate crosswalk coverage and regression metadata provenance.""" + + def test_crosswalk_metrics_in_metadata(self, pipeline_run: dict) -> None: + path = pipeline_run["run_dir"] / "regression" / "regression_metadata.json" + with open(path) as f: + meta = json.load(f) + cw = meta["crosswalk_metrics"] + assert "n_zip4" in cw, "Missing n_zip4 in crosswalk_metrics" + assert "n_bg" in cw, "Missing n_bg in crosswalk_metrics" + assert "n_zip4_multi_bg" in cw, "Missing n_zip4_multi_bg in crosswalk_metrics" + assert cw["n_zip4"] > 0, "n_zip4 should be positive" + assert cw["n_bg"] > 0, "n_bg should be positive" + + def test_join_metrics_in_metadata(self, pipeline_run: dict) -> None: + path = pipeline_run["run_dir"] / "regression" / "regression_metadata.json" + with open(path) as f: + meta = json.load(f) + jm = meta["join_metrics"] + assert "pct_dropped" in jm, "Missing pct_dropped in join_metrics" + assert "households_before_crosswalk" in jm + assert "households_matched" in jm + assert "households_dropped" in jm + + def test_pct_dropped_within_threshold(self, pipeline_run: dict) -> None: + """pct_dropped should be <= max_crosswalk_drop_pct and not NaN.""" + path = pipeline_run["run_dir"] / "regression" / "regression_metadata.json" + with open(path) as f: + meta = json.load(f) + pct = meta["join_metrics"]["pct_dropped"] + max_pct = meta["max_crosswalk_drop_pct"] + assert isinstance(pct, (int, float)), f"pct_dropped is not numeric: {pct!r}" + assert pct == pct, "pct_dropped is NaN" # NaN != NaN + assert pct <= max_pct, f"pct_dropped={pct:.2f}% exceeds max_crosswalk_drop_pct={max_pct}%" + + def test_predictors_match_explicit(self, pipeline_run: dict) -> None: + """--predictors median_household_income should use exactly that predictor.""" + path = pipeline_run["run_dir"] / "regression" / "regression_metadata.json" + with open(path) as f: + meta = json.load(f) + assert meta["predictors_used"] == ["median_household_income"] + + def test_regression_level_recorded(self, pipeline_run: dict) -> None: + path = pipeline_run["run_dir"] / "regression" / "regression_metadata.json" + with open(path) as f: + meta = json.load(f) + assert meta["regression_level"] == "annual" + + def test_months_included_recorded(self, pipeline_run: dict) -> None: + path = pipeline_run["run_dir"] / "regression" / "regression_metadata.json" + with open(path) as f: + meta = json.load(f) + assert "months_included" in meta + assert "202308" in meta["months_included"] + + +# ═══════════════════════════════════════════════════════════════════════════ +# 8. Regression results JSON: schema & model structure +# ═══════════════════════════════════════════════════════════════════════════ + + +class TestRegressionResults: + """Validate regression_results.json structure and content.""" + + def test_model_keys_exist(self, pipeline_run: dict) -> None: + path = pipeline_run["run_dir"] / "regression" / "regression_results.json" + with open(path) as f: + results = json.load(f) + assert MODEL_KEY_1 in results, f"Missing {MODEL_KEY_1} in regression results" + assert MODEL_KEY_2 in results, f"Missing {MODEL_KEY_2} in regression results" + + @pytest.mark.parametrize("model_key", [MODEL_KEY_1, MODEL_KEY_2]) + def test_model_required_fields(self, pipeline_run: dict, model_key: str) -> None: + path = pipeline_run["run_dir"] / "regression" / "regression_results.json" + with open(path) as f: + results = json.load(f) + m = results[model_key] + for field in ("r_squared", "adj_r_squared", "n_obs", "n_predictors"): + assert field in m, f"{model_key} missing {field}" + assert m["n_obs"] > 0, f"{model_key} has 0 observations" + assert "coefficients" in m, f"{model_key} missing coefficients" + + @pytest.mark.parametrize("model_key", [MODEL_KEY_1, MODEL_KEY_2]) + def test_model_coefficients_include_const(self, pipeline_run: dict, model_key: str) -> None: + path = pipeline_run["run_dir"] / "regression" / "regression_results.json" + with open(path) as f: + results = json.load(f) + coeffs = results[model_key]["coefficients"] + assert "const" in coeffs, f"{model_key} coefficients missing 'const'" + + @pytest.mark.parametrize("model_key", [MODEL_KEY_1, MODEL_KEY_2]) + def test_model_has_predictor_coefficients(self, pipeline_run: dict, model_key: str) -> None: + path = pipeline_run["run_dir"] / "regression" / "regression_results.json" + with open(path) as f: + results = json.load(f) + coeffs = results[model_key]["coefficients"] + non_const = {k for k in coeffs if k != "const"} + assert len(non_const) >= 1, f"{model_key} has no predictor coefficients (only const)" + + def test_regression_summary_text(self, pipeline_run: dict) -> None: + path = pipeline_run["run_dir"] / "regression" / "regression_summary.txt" + assert path.exists(), f"Missing regression summary: {path}" + text = path.read_text() + assert len(text) > 100, "Regression summary text suspiciously short" + assert "R-squared" in text, "Regression summary should contain R-squared" + + +# ═══════════════════════════════════════════════════════════════════════════ +# 9. Regression: bg_month mode +# ═══════════════════════════════════════════════════════════════════════════ + + +class TestRegressionBgMonth: + """Tests specific to --regression-level bg_month.""" + + def test_bg_month_pipeline_succeeds(self, pipeline_run_bg_month: dict) -> None: + r = pipeline_run_bg_month + assert r["returncode"] == 0, ( + f"bg_month pipeline exited {r['returncode']}.\nstdout: {r['stdout'][-2000:]}\nstderr: {r['stderr'][-2000:]}" + ) + + @pytest.mark.parametrize("filename", ALL_REGRESSION_FILES) + def test_bg_month_regression_artifacts_exist(self, pipeline_run_bg_month: dict, filename: str) -> None: + path = pipeline_run_bg_month["run_dir"] / "regression" / filename + assert path.exists(), f"Missing bg_month regression artifact: {path}" + + def test_bg_month_metadata_records_level(self, pipeline_run_bg_month: dict) -> None: + path = pipeline_run_bg_month["run_dir"] / "regression" / "regression_metadata.json" + with open(path) as f: + meta = json.load(f) + assert meta["regression_level"] == "bg_month" + + def test_bg_month_month_fixed_effects_in_results(self, pipeline_run_bg_month: dict) -> None: + """In bg_month mode, model results should record month FE metadata. + + With a single-month fixture and drop_first=True, n_month_fe will be 0 + (degenerate case), but the structural fields must still be present. + """ + path = pipeline_run_bg_month["run_dir"] / "regression" / "regression_results.json" + with open(path) as f: + results = json.load(f) + for model_key in (MODEL_KEY_1, MODEL_KEY_2): + m = results[model_key] + assert "n_month_fe" in m, f"{model_key} missing n_month_fe" + assert "month_fe_columns" in m, f"{model_key} missing month_fe_columns" + assert isinstance(m["month_fe_columns"], list) + + @pytest.mark.parametrize( + "filename", + # regression_dataset_bg.parquet is excluded: bg_month mode joins to + # the BG x month table (which has a ``month`` column), while annual + # mode joins to the BG annual table (no ``month``). + [f for f in REGRESSION_PARQUETS if f != "regression_dataset_bg.parquet"], + ) + def test_bg_month_outcome_parquets_consistent_schema( + self, pipeline_run: dict, pipeline_run_bg_month: dict, filename: str + ) -> None: + """Both modes should produce the same outcome parquet schemas.""" + annual_df = pl.read_parquet(pipeline_run["run_dir"] / "regression" / filename) + bgm_df = pl.read_parquet(pipeline_run_bg_month["run_dir"] / "regression" / filename) + assert set(annual_df.columns) == set(bgm_df.columns), ( + f"Schema mismatch in {filename}: annual={sorted(annual_df.columns)}, bg_month={sorted(bgm_df.columns)}" + ) + + def test_bg_month_dataset_has_month_column(self, pipeline_run_bg_month: dict) -> None: + """In bg_month mode, regression_dataset_bg.parquet should include month.""" + df = pl.read_parquet(pipeline_run_bg_month["run_dir"] / "regression" / "regression_dataset_bg.parquet") + assert "month" in df.columns, "regression_dataset_bg.parquet should include month in bg_month mode" + + def test_bg_month_manifest_records_level(self, pipeline_run_bg_month: dict) -> None: + path = pipeline_run_bg_month["run_dir"] / "run_manifest.json" + with open(path) as f: + m = json.load(f) + assert m["parameters"]["regression_level"] == "bg_month" + + +# ═══════════════════════════════════════════════════════════════════════════ +# 10. Manifest correctness +# ═══════════════════════════════════════════════════════════════════════════ + + +class TestManifest: + """Verify the run manifest JSON has complete provenance.""" + + def test_manifest_required_fields(self, pipeline_skip_regression: dict) -> None: + path = pipeline_skip_regression["run_dir"] / "run_manifest.json" + with open(path) as f: + m = json.load(f) + for key in ( + "run_id", + "created_utc", + "months", + "month_summary", + "inputs", + "parameters", + "steps_completed", + "all_months_bills_rows", + ): + assert key in m, f"Manifest missing key: {key}" + + def test_manifest_git_sha(self, pipeline_skip_regression: dict) -> None: + path = pipeline_skip_regression["run_dir"] / "run_manifest.json" + with open(path) as f: + m = json.load(f) + sha = m.get("git_sha") + assert sha is not None, "Manifest missing git_sha" + assert len(sha) == 40, f"git_sha should be 40 hex chars, got: {sha!r}" + + def test_manifest_months(self, pipeline_skip_regression: dict) -> None: + path = pipeline_skip_regression["run_dir"] / "run_manifest.json" + with open(path) as f: + m = json.load(f) + assert m["months"] == ["202308"], f"Expected months=['202308'], got {m['months']}" + + def test_manifest_all_months_bills_rows(self, pipeline_skip_regression: dict) -> None: + path = pipeline_skip_regression["run_dir"] / "run_manifest.json" + with open(path) as f: + m = json.load(f) + assert m["all_months_bills_rows"] > 0, "all_months_bills_rows should be > 0" + + def test_manifest_all_months_bills_rows_equals_sum(self, pipeline_skip_regression: dict) -> None: + """all_months_bills_rows should equal sum of per-month rows_bills.""" + path = pipeline_skip_regression["run_dir"] / "run_manifest.json" + with open(path) as f: + m = json.load(f) + total = sum(v["rows_bills"] for v in m["month_summary"].values()) + assert m["all_months_bills_rows"] == total, ( + f"all_months_bills_rows={m['all_months_bills_rows']} != sum(rows_bills)={total}" + ) + + def test_manifest_month_summary_counts(self, pipeline_skip_regression: dict) -> None: + path = pipeline_skip_regression["run_dir"] / "run_manifest.json" + with open(path) as f: + m = json.load(f) + summary = m["month_summary"] + assert "202308" in summary, "Month 202308 not in month_summary" + counts = summary["202308"] + assert counts["rows_hourly_loads"] > 0, "rows_hourly_loads should be > 0" + assert counts["rows_bills"] > 0, "rows_bills should be > 0" + + def test_manifest_steps_completed_skip_regression(self, pipeline_skip_regression: dict) -> None: + path = pipeline_skip_regression["run_dir"] / "run_manifest.json" + with open(path) as f: + m = json.load(f) + steps = m["steps_completed"] + assert "202308" in steps, "Month 202308 not in steps_completed" + assert "all_months_bills" in steps, "all_months_bills not in steps_completed" + assert "regression" not in steps, "regression should NOT be in steps_completed when skipped" + + def test_manifest_steps_completed_with_regression(self, pipeline_run: dict) -> None: + path = pipeline_run["run_dir"] / "run_manifest.json" + with open(path) as f: + m = json.load(f) + steps = m["steps_completed"] + assert "all_months_bills" in steps, "all_months_bills not in steps_completed" + assert "regression" in steps, "regression not in steps_completed" + + def test_manifest_parameters(self, pipeline_skip_regression: dict) -> None: + path = pipeline_skip_regression["run_dir"] / "run_manifest.json" + with open(path) as f: + m = json.load(f) + params = m["parameters"] + assert "capacity_rate" in params, "Missing capacity_rate in parameters" + assert "admin_fee" in params, "Missing admin_fee in parameters" + assert params["skip_regression"] is True, "skip_regression should be True" + + def test_manifest_inputs(self, pipeline_skip_regression: dict) -> None: + path = pipeline_skip_regression["run_dir"] / "run_manifest.json" + with open(path) as f: + m = json.load(f) + inputs = m["inputs"] + assert "interval_pattern" in inputs, "Missing interval_pattern in inputs" + assert "tariff_a" in inputs, "Missing tariff_a in inputs" + assert "tariff_b" in inputs, "Missing tariff_b in inputs" + + +# ═══════════════════════════════════════════════════════════════════════════ +# 11. Skip-regression mode +# ═══════════════════════════════════════════════════════════════════════════ + + +class TestSkipRegression: + """Verify behavior when --skip-regression is set.""" + + def test_skip_pipeline_succeeds(self, pipeline_skip_regression: dict) -> None: + r = pipeline_skip_regression + assert r["returncode"] == 0, ( + f"Skip-reg pipeline exited {r['returncode']}.\nstdout: {r['stdout'][-2000:]}\nstderr: {r['stderr'][-2000:]}" + ) + + def test_regression_dir_absent(self, pipeline_skip_regression: dict) -> None: + """Regression directory should not exist or should be empty.""" + reg = pipeline_skip_regression["run_dir"] / "regression" + assert not reg.exists() or not any(reg.iterdir()), ( + "Regression artifacts should not exist with --skip-regression" + ) + + def test_manifest_skip_regression_true(self, pipeline_skip_regression: dict) -> None: + path = pipeline_skip_regression["run_dir"] / "run_manifest.json" + with open(path) as f: + m = json.load(f) + assert m["parameters"]["skip_regression"] is True + + def test_steps_completed_no_regression(self, pipeline_skip_regression: dict) -> None: + path = pipeline_skip_regression["run_dir"] / "run_manifest.json" + with open(path) as f: + m = json.load(f) + assert "regression" not in m["steps_completed"] + + def test_all_months_bills_still_produced(self, pipeline_skip_regression: dict) -> None: + """Even with skip-regression, all_months_household_bills should exist.""" + path = pipeline_skip_regression["run_dir"] / "all_months_household_bills.parquet" + assert path.exists(), "all_months_household_bills.parquet missing with skip-regression" + df = pl.read_parquet(path) + assert df.height > 0, "all_months_household_bills is empty with skip-regression" + + def test_all_months_bills_has_month_column(self, pipeline_skip_regression: dict) -> None: + """month column must be present even when regression is skipped.""" + path = pipeline_skip_regression["run_dir"] / "all_months_household_bills.parquet" + df = pl.read_parquet(path) + assert "month" in df.columns, "month column missing from all_months_household_bills" diff --git a/tests/test_compact_month_output.py b/tests/test_compact_month_output.py new file mode 100644 index 0000000..892b976 --- /dev/null +++ b/tests/test_compact_month_output.py @@ -0,0 +1,284 @@ +#!/usr/bin/env python3 +"""Tests for _stream_write_chunks and related constants in compact_month_output.py. + +Covers: +- ROWS_PER_ROW_GROUP constant value +- compaction_plan.json contains rows_per_row_group and estimated_n_output_files keys +- _stream_write_chunks: single small batch -> one file +- _stream_write_chunks: multi-row-group packing into a single file +- _stream_write_chunks: file rollover when target_size_bytes is reached +- _stream_write_chunks: total row count preserved across all output files +- _stream_write_chunks: sort order within a single output file +- _stream_write_chunks: sort order preserved across rolled-over files +""" + +from __future__ import annotations + +import datetime as dt +import json +import sys +from pathlib import Path +from typing import Any + +import polars as pl +import pyarrow.parquet as pq +import pytest + +# The scripts/ directory is not an installed package; add project root to path +# so pytest can resolve the namespace package on both local and CI runs. +sys.path.insert(0, str(Path(__file__).parents[1])) + +from scripts.csv_to_parquet.compact_month_output import ( + DEFAULT_COMPACT_TARGET_SIZE_BYTES, + ROWS_PER_ROW_GROUP, + SORT_KEYS, + CompactionConfig, + _stream_write_chunks, + run_compaction, +) + +# --------------------------------------------------------------------------- +# Helpers +# --------------------------------------------------------------------------- + +YEAR_MONTH = "202307" +RUN_ID = "test_run_001" + + +def _canonical_df(n_rows: int = 20) -> pl.DataFrame: + """Return a minimal DataFrame matching the canonical 10-column schema.""" + base_ts = dt.datetime(2023, 7, 1, 0, 0, 0) + return pl.DataFrame({ + "zip_code": ["60601"] * n_rows, + "delivery_service_class": ["DS1"] * n_rows, + "delivery_service_name": ["Residential"] * n_rows, + "account_identifier": [f"ACCT{i:04d}" for i in range(n_rows)], + "datetime": [base_ts + dt.timedelta(hours=i) for i in range(n_rows)], + "energy_kwh": [float(i) * 0.5 for i in range(n_rows)], + "plc_value": [0.0] * n_rows, + "nspl_value": [0.0] * n_rows, + "year": [2023] * n_rows, + "month": [7] * n_rows, + }).with_columns( + pl.col("zip_code").cast(pl.Utf8), + pl.col("delivery_service_class").cast(pl.Categorical), + pl.col("delivery_service_name").cast(pl.Categorical), + pl.col("account_identifier").cast(pl.Utf8), + pl.col("datetime").cast(pl.Datetime("us")), + pl.col("energy_kwh").cast(pl.Float64), + pl.col("plc_value").cast(pl.Float64), + pl.col("nspl_value").cast(pl.Float64), + pl.col("year").cast(pl.Int32), + pl.col("month").cast(pl.Int8), + ) + + +def _write_batch(df: pl.DataFrame, month_dir: Path, name: str = "batch_0000.parquet") -> Path: + month_dir.mkdir(parents=True, exist_ok=True) + path = month_dir / name + df.write_parquet(path) + return path + + +def _make_cfg(tmp_path: Path, target_size_bytes: int, dry_run: bool = True) -> CompactionConfig: + return CompactionConfig( + year_month=YEAR_MONTH, + run_id=RUN_ID, + out_root=tmp_path / "out", + run_dir=tmp_path / "_runs" / YEAR_MONTH / RUN_ID, + target_size_bytes=target_size_bytes, + max_files=None, + overwrite=False, + dry_run=dry_run, + no_swap=False, + ) + + +class _SilentLogger: + """No-op logger compatible with run_compaction's logger.log(dict) protocol.""" + + def log(self, event: dict[str, Any]) -> None: + pass + + +def _setup_month_dir(tmp_path: Path) -> Path: + """Write one batch_0000.parquet to the canonical month directory.""" + month_dir = tmp_path / "out" / "2023" / "07" + _write_batch(_canonical_df(), month_dir) + return month_dir + + +# --------------------------------------------------------------------------- +# Unit tests — constants and plan keys +# --------------------------------------------------------------------------- + + +def test_rows_per_row_group_constant() -> None: + """ROWS_PER_ROW_GROUP must be exactly 50_000_000.""" + assert ROWS_PER_ROW_GROUP == 50_000_000 + + +def test_plan_contains_all_new_keys(tmp_path: pytest.TempPathFactory) -> None: + """compaction_plan.json must contain rows_per_row_group and estimated_n_output_files.""" + _setup_month_dir(tmp_path) + cfg = _make_cfg(tmp_path, target_size_bytes=DEFAULT_COMPACT_TARGET_SIZE_BYTES) + run_compaction(cfg, _SilentLogger()) + + plan_path = tmp_path / "_runs" / YEAR_MONTH / RUN_ID / "compaction" / "compaction_plan.json" + plan = json.loads(plan_path.read_text()) + + assert "rows_per_row_group" in plan, "Missing key 'rows_per_row_group' in compaction_plan.json" + assert "estimated_n_output_files" in plan, "Missing key 'estimated_n_output_files' in compaction_plan.json" + assert plan["rows_per_row_group"] == ROWS_PER_ROW_GROUP + assert plan["estimated_n_output_files"] >= 1 + + +# --------------------------------------------------------------------------- +# Unit tests — _stream_write_chunks directly +# --------------------------------------------------------------------------- + + +def test_single_small_batch_one_file(tmp_path: Path) -> None: + """5 rows, rows_per_row_group=10, huge target -> 1 output file with 1 row group.""" + staging = tmp_path / "staging" + batch_path = _write_batch(_canonical_df(5), tmp_path / "input", "batch_0000.parquet") + + output_files = _stream_write_chunks( + sorted_input_files=[batch_path], + staging_month_dir=staging, + rows_per_row_group=10, + target_size_bytes=10 * 1024**3, # 10 GiB — never triggers rollover + max_files=None, + logger=_SilentLogger(), + log_ctx={}, + ) + + assert len(output_files) == 1 + meta = pq.read_metadata(str(output_files[0])) + assert meta.num_row_groups == 1 + assert meta.num_rows == 5 + + +def test_multi_row_group_single_file(tmp_path: Path) -> None: + """25 rows, rows_per_row_group=10, huge target -> 1 file with 3 row groups (10+10+5).""" + staging = tmp_path / "staging" + batch_path = _write_batch(_canonical_df(25), tmp_path / "input", "batch_0000.parquet") + + output_files = _stream_write_chunks( + sorted_input_files=[batch_path], + staging_month_dir=staging, + rows_per_row_group=10, + target_size_bytes=10 * 1024**3, # 10 GiB — never triggers rollover + max_files=None, + logger=_SilentLogger(), + log_ctx={}, + ) + + assert len(output_files) == 1 + meta = pq.read_metadata(str(output_files[0])) + assert meta.num_row_groups == 3 + assert meta.num_rows == 25 + + +def test_file_rollover_at_target_size(tmp_path: Path) -> None: + """30 rows, rows_per_row_group=10, target_size_bytes=1 -> rollover after each rg -> 3 files.""" + staging = tmp_path / "staging" + batch_path = _write_batch(_canonical_df(30), tmp_path / "input", "batch_0000.parquet") + + output_files = _stream_write_chunks( + sorted_input_files=[batch_path], + staging_month_dir=staging, + rows_per_row_group=10, + target_size_bytes=1, # always triggers rollover + max_files=None, + logger=_SilentLogger(), + log_ctx={}, + ) + + assert len(output_files) == 3 + for f in output_files: + meta = pq.read_metadata(str(f)) + assert meta.num_row_groups == 1 + assert meta.num_rows == 10 + + +def test_row_count_preserved(tmp_path: Path) -> None: + """2 batch files of 15 rows each -> total 30 rows across all output files.""" + staging = tmp_path / "staging" + input_dir = tmp_path / "input" + b0 = _write_batch(_canonical_df(15), input_dir, "batch_0000.parquet") + # Second batch: distinct account_identifiers to avoid duplicate key issues + df2 = _canonical_df(15).with_columns(pl.Series("account_identifier", [f"ACCT{i:04d}" for i in range(15, 30)])) + b1 = _write_batch(df2, input_dir, "batch_0001.parquet") + + output_files = _stream_write_chunks( + sorted_input_files=[b0, b1], + staging_month_dir=staging, + rows_per_row_group=10, + target_size_bytes=10 * 1024**3, + max_files=None, + logger=_SilentLogger(), + log_ctx={}, + ) + + total = sum(pq.read_metadata(str(f)).num_rows for f in output_files) + assert total == 30 + + +def test_sort_order_within_file(tmp_path: Path) -> None: + """Rows read back from a single output file are sorted by SORT_KEYS.""" + staging = tmp_path / "staging" + input_dir = tmp_path / "input" + b0 = _write_batch(_canonical_df(15), input_dir, "batch_0000.parquet") + df2 = _canonical_df(15).with_columns(pl.Series("account_identifier", [f"ACCT{i:04d}" for i in range(15, 30)])) + b1 = _write_batch(df2, input_dir, "batch_0001.parquet") + + output_files = _stream_write_chunks( + sorted_input_files=[b0, b1], + staging_month_dir=staging, + rows_per_row_group=10, + target_size_bytes=10 * 1024**3, + max_files=None, + logger=_SilentLogger(), + log_ctx={}, + ) + + for f in output_files: + df = pl.read_parquet(str(f)) + sort_keys = list(SORT_KEYS) + sorted_df = df.sort(sort_keys) + assert df.select(sort_keys).equals(sorted_df.select(sort_keys)), ( + f"Rows in {f.name} are not sorted by {sort_keys}" + ) + + +def test_sort_order_across_files(tmp_path: Path) -> None: + """Last sort key of file N < first sort key of file N+1 when rollover occurs.""" + staging = tmp_path / "staging" + input_dir = tmp_path / "input" + b0 = _write_batch(_canonical_df(15), input_dir, "batch_0000.parquet") + df2 = _canonical_df(15).with_columns(pl.Series("account_identifier", [f"ACCT{i:04d}" for i in range(15, 30)])) + b1 = _write_batch(df2, input_dir, "batch_0001.parquet") + + output_files = _stream_write_chunks( + sorted_input_files=[b0, b1], + staging_month_dir=staging, + rows_per_row_group=10, + target_size_bytes=1, # rollover after each row group -> multiple files + max_files=None, + logger=_SilentLogger(), + log_ctx={}, + ) + + assert len(output_files) > 1, "Expected multiple output files with target_size_bytes=1" + sort_keys = list(SORT_KEYS) + prev_last: tuple[Any, ...] | None = None + for f in output_files: + df = pl.read_parquet(str(f)) + first = tuple(df.select(sort_keys).row(0)) + last = tuple(df.select(sort_keys).row(-1)) + if prev_last is not None: + assert prev_last <= first, ( + f"Sort order broken across files: last of prev={prev_last} >= first of next={first}" + ) + prev_last = last diff --git a/tests/test_compute_hourly_loads.py b/tests/test_compute_hourly_loads.py new file mode 100644 index 0000000..fbc5aee --- /dev/null +++ b/tests/test_compute_hourly_loads.py @@ -0,0 +1,456 @@ +#!/usr/bin/env python3 +"""Unit tests for analysis/rtp/compute_hourly_loads.py. + +Tests the compute_hourly_loads() function directly using synthetic DataFrames +so that the suite requires no large data files and runs in milliseconds. +""" + +from __future__ import annotations + +import datetime as dt +from pathlib import Path + +import polars as pl +import pytest + +from analysis.rtp.compute_hourly_loads import compute_hourly_loads + +# ── Helpers ─────────────────────────────────────────────────────────────────── + + +def _make_interval_df( + accounts: list[str], + start: dt.datetime, + n_intervals: int, + *, + kwh_per_interval: float = 0.5, + zip_code: str = "60601-1234", +) -> pl.DataFrame: + """Build a minimal 30-minute interval DataFrame for testing.""" + rows = [] + for acct in accounts: + for i in range(n_intervals): + ts = start + dt.timedelta(minutes=30 * i) + rows.append({ + "account_identifier": acct, + "zip_code": zip_code, + "datetime": ts, + "energy_kwh": kwh_per_interval, + }) + return pl.DataFrame(rows).with_columns( + pl.col("datetime").cast(pl.Datetime("us")), + pl.col("energy_kwh").cast(pl.Float64), + ) + + +def _write_parquet(df: pl.DataFrame, path: Path) -> Path: + path.parent.mkdir(parents=True, exist_ok=True) + df.write_parquet(path) + return path + + +# ═══════════════════════════════════════════════════════════════════════════ +# 1. Basic aggregation +# ═══════════════════════════════════════════════════════════════════════════ + + +class TestBasicAggregation: + """Core summation behaviour: 30-min intervals → hourly kWh.""" + + def test_two_intervals_sum_to_one_hour(self, tmp_path: Path) -> None: + """Two 0.5 kWh 30-min intervals within the same hour → kwh_hour = 1.0.""" + start = dt.datetime(2023, 7, 1, 0, 0) + df = _make_interval_df(["A001"], start, n_intervals=2, kwh_per_interval=0.5) + inp = _write_parquet(df, tmp_path / "interval.parquet") + out = tmp_path / "loads.parquet" + + compute_hourly_loads(inp, None, out, sort_output=False) + + result = pl.read_parquet(out) + assert result.height == 1 + assert result["kwh_hour"][0] == pytest.approx(1.0) + + def test_48_intervals_produce_24_hours(self, tmp_path: Path) -> None: + """One full day of 30-min data for a single account → 24 hourly rows.""" + start = dt.datetime(2023, 7, 1, 0, 0) + df = _make_interval_df(["A001"], start, n_intervals=48, kwh_per_interval=1.0) + inp = _write_parquet(df, tmp_path / "interval.parquet") + out = tmp_path / "loads.parquet" + + compute_hourly_loads(inp, None, out, sort_output=False) + + result = pl.read_parquet(out) + assert result.height == 24 + + def test_each_hour_sums_two_intervals(self, tmp_path: Path) -> None: + """Each hour must equal exactly 2 x kwh_per_interval = 2.0.""" + start = dt.datetime(2023, 7, 1, 0, 0) + df = _make_interval_df(["A001"], start, n_intervals=48, kwh_per_interval=1.0) + inp = _write_parquet(df, tmp_path / "interval.parquet") + out = tmp_path / "loads.parquet" + + compute_hourly_loads(inp, None, out, sort_output=False) + + result = pl.read_parquet(out) + for v in result["kwh_hour"].to_list(): + assert v == pytest.approx(2.0) + + def test_multiple_accounts_independent(self, tmp_path: Path) -> None: + """Three accounts each get their own 24 hourly rows — no cross-contamination.""" + start = dt.datetime(2023, 7, 1, 0, 0) + df = _make_interval_df(["A001", "A002", "A003"], start, n_intervals=48) + inp = _write_parquet(df, tmp_path / "interval.parquet") + out = tmp_path / "loads.parquet" + + compute_hourly_loads(inp, None, out, sort_output=False) + + result = pl.read_parquet(out) + assert result.height == 3 * 24 + for acct in ["A001", "A002", "A003"]: + acct_rows = result.filter(pl.col("account_identifier") == acct) + assert acct_rows.height == 24 + + def test_hour_chicago_truncated_to_hour(self, tmp_path: Path) -> None: + """hour_chicago must have zero minutes/seconds — truncated to the hour.""" + start = dt.datetime(2023, 7, 1, 0, 0) + df = _make_interval_df(["A001"], start, n_intervals=4) + inp = _write_parquet(df, tmp_path / "interval.parquet") + out = tmp_path / "loads.parquet" + + compute_hourly_loads(inp, None, out, sort_output=False) + + result = pl.read_parquet(out) + minutes = result["hour_chicago"].dt.minute().to_list() + assert all(m == 0 for m in minutes) + seconds = result["hour_chicago"].dt.second().to_list() + assert all(s == 0 for s in seconds) + + +# ═══════════════════════════════════════════════════════════════════════════ +# 2. Output schema +# ═══════════════════════════════════════════════════════════════════════════ + + +class TestOutputSchema: + """Validate the columns emitted by compute_hourly_loads.""" + + def test_required_columns_present(self, tmp_path: Path) -> None: + start = dt.datetime(2023, 7, 1, 0, 0) + df = _make_interval_df(["A001"], start, n_intervals=2) + inp = _write_parquet(df, tmp_path / "interval.parquet") + out = tmp_path / "loads.parquet" + + compute_hourly_loads(inp, None, out, sort_output=False) + + result = pl.read_parquet(out) + required = {"account_identifier", "zip_code", "hour_chicago", "kwh_hour"} + assert required <= set(result.columns) + + def test_no_null_values_in_key_columns(self, tmp_path: Path) -> None: + start = dt.datetime(2023, 7, 1, 0, 0) + df = _make_interval_df(["A001", "A002"], start, n_intervals=4) + inp = _write_parquet(df, tmp_path / "interval.parquet") + out = tmp_path / "loads.parquet" + + compute_hourly_loads(inp, None, out, sort_output=False) + + result = pl.read_parquet(out) + for col in ("account_identifier", "zip_code", "hour_chicago", "kwh_hour"): + n_null = result[col].null_count() + assert n_null == 0, f"Column {col} has {n_null} nulls" + + def test_kwh_hour_non_negative(self, tmp_path: Path) -> None: + start = dt.datetime(2023, 7, 1, 0, 0) + df = _make_interval_df(["A001"], start, n_intervals=4, kwh_per_interval=0.25) + inp = _write_parquet(df, tmp_path / "interval.parquet") + out = tmp_path / "loads.parquet" + + compute_hourly_loads(inp, None, out, sort_output=False) + + result = pl.read_parquet(out) + assert all(v >= 0 for v in result["kwh_hour"].to_list()) + + +# ═══════════════════════════════════════════════════════════════════════════ +# 3. DST fall-back: duplicate naive timestamps aggregate correctly +# ═══════════════════════════════════════════════════════════════════════════ + + +class TestDSTFallback: + """ + During fall-back, two meter reads at e.g. 01:00 CDT and 01:00 CST share + the same naive local time. compute_hourly_loads must sum all intervals + within the same naive hour — including duplicates — without dropping rows. + """ + + def test_fallback_intervals_both_summed(self, tmp_path: Path) -> None: + """Four 30-min reads at the same naive hour (fall-back) → kwh_hour = 2.0.""" + # 2023-11-05: fall-back at 2 AM CDT → 1 AM CST + # Both the CDT 01:00/01:30 and CST 01:00/01:30 map to the same naive hour. + ts_01_00 = dt.datetime(2023, 11, 5, 1, 0) + ts_01_30 = dt.datetime(2023, 11, 5, 1, 30) + + # Simulate 4 intervals that all truncate to 01:00 (the CDT set + CST set) + rows = [ + { + "account_identifier": "A001", + "zip_code": "60601-0001", + "datetime": ts_01_00, + "energy_kwh": 0.5, + }, + { + "account_identifier": "A001", + "zip_code": "60601-0001", + "datetime": ts_01_30, + "energy_kwh": 0.5, + }, + { + "account_identifier": "A001", + "zip_code": "60601-0001", + "datetime": ts_01_00, + "energy_kwh": 0.5, + }, + { + "account_identifier": "A001", + "zip_code": "60601-0001", + "datetime": ts_01_30, + "energy_kwh": 0.5, + }, + ] + df = pl.DataFrame(rows).with_columns( + pl.col("datetime").cast(pl.Datetime("us")), + pl.col("energy_kwh").cast(pl.Float64), + ) + inp = _write_parquet(df, tmp_path / "interval.parquet") + out = tmp_path / "loads.parquet" + + compute_hourly_loads(inp, None, out, sort_output=False) + + result = pl.read_parquet(out) + # Two distinct naive hours: 01:00 and 01:30 (after truncation to hour, + # ts_01_30 truncates to 01:00 too — so actually all 4 truncate to 01:00) + # All four 0.5 kWh intervals truncate to 01:00 → kwh_hour = 2.0 + assert result.height == 1 + assert result["kwh_hour"][0] == pytest.approx(2.0) + + +# ═══════════════════════════════════════════════════════════════════════════ +# 4. Sort output +# ═══════════════════════════════════════════════════════════════════════════ + + +class TestSortOutput: + """Verify sort_output=True produces (zip_code, account_identifier, hour_chicago) order.""" + + def test_sorted_output_is_ordered(self, tmp_path: Path) -> None: + start = dt.datetime(2023, 7, 1, 0, 0) + # Deliberately put accounts in reverse order so unsorted would fail + df = _make_interval_df(["Z999", "A001", "M500"], start, n_intervals=4) + inp = _write_parquet(df, tmp_path / "interval.parquet") + out = tmp_path / "loads.parquet" + + compute_hourly_loads(inp, None, out, sort_output=True) + + result = pl.read_parquet(out) + expected_order = result.sort(["zip_code", "account_identifier", "hour_chicago"]) + assert result.rows() == expected_order.rows() + + +# ═══════════════════════════════════════════════════════════════════════════ +# 5. Multi-file chunked processing +# ═══════════════════════════════════════════════════════════════════════════ + + +class TestChunkedMultiFile: + """When input is a directory with multiple files, chunked aggregation must + produce the same result as a single-file run.""" + + def test_multi_file_matches_single_file(self, tmp_path: Path) -> None: + """Splitting data across 3 files must give identical output to one file.""" + start = dt.datetime(2023, 7, 1, 0, 0) + df = _make_interval_df(["A001", "A002"], start, n_intervals=48) + + # Single-file reference + single_dir = tmp_path / "single" + _write_parquet(df, single_dir / "all.parquet") + out_single = tmp_path / "out_single.parquet" + compute_hourly_loads(single_dir / "all.parquet", None, out_single, sort_output=True) + + # Split across 3 files in a directory (triggers chunked path) + multi_dir = tmp_path / "multi" + multi_dir.mkdir() + chunks = [df[:32], df[32:64], df[64:]] + for i, chunk in enumerate(chunks): + chunk.write_parquet(multi_dir / f"part_{i}.parquet") + out_multi = tmp_path / "out_multi.parquet" + compute_hourly_loads(multi_dir, None, out_multi, sort_output=True) + + ref = pl.read_parquet(out_single) + got = pl.read_parquet(out_multi) + assert ref.shape == got.shape + assert ref.rows() == got.rows() + + def test_multi_file_re_aggregates_split_keys(self, tmp_path: Path) -> None: + """Same (account, zip, hour) key split across two files must sum correctly.""" + ts = dt.datetime(2023, 7, 1, 10, 0) # 10:00 + row_a = { + "account_identifier": "A001", + "zip_code": "60601-1234", + "datetime": ts, + "energy_kwh": 0.3, + } + row_b = {**row_a, "energy_kwh": 0.7} + + df_a = pl.DataFrame([row_a]).with_columns( + pl.col("datetime").cast(pl.Datetime("us")), + pl.col("energy_kwh").cast(pl.Float64), + ) + df_b = pl.DataFrame([row_b]).with_columns( + pl.col("datetime").cast(pl.Datetime("us")), + pl.col("energy_kwh").cast(pl.Float64), + ) + + multi_dir = tmp_path / "multi" + multi_dir.mkdir() + df_a.write_parquet(multi_dir / "part_0.parquet") + df_b.write_parquet(multi_dir / "part_1.parquet") + + out = tmp_path / "loads.parquet" + compute_hourly_loads(multi_dir, None, out, sort_output=False) + + result = pl.read_parquet(out) + assert result.height == 1 + assert result["kwh_hour"][0] == pytest.approx(1.0) + + def test_multi_file_with_cluster_filter(self, tmp_path: Path) -> None: + """Cluster assignment filtering works with chunked multi-file path.""" + start = dt.datetime(2023, 7, 1, 0, 0) + df = _make_interval_df(["A001", "A002", "A003"], start, n_intervals=4) + + multi_dir = tmp_path / "multi" + multi_dir.mkdir() + df[:6].write_parquet(multi_dir / "part_0.parquet") + df[6:].write_parquet(multi_dir / "part_1.parquet") + + assignments = pl.DataFrame({"account_identifier": ["A001"]}) + asgn_path = tmp_path / "assignments.parquet" + assignments.write_parquet(asgn_path) + + out = tmp_path / "loads.parquet" + compute_hourly_loads(multi_dir, asgn_path, out, sort_output=False) + + result = pl.read_parquet(out) + assert set(result["account_identifier"].unique().to_list()) == {"A001"} + + def test_multi_file_sorted(self, tmp_path: Path) -> None: + """sort_output=True works with chunked multi-file path.""" + start = dt.datetime(2023, 7, 1, 0, 0) + df = _make_interval_df(["Z999", "A001"], start, n_intervals=4) + + multi_dir = tmp_path / "multi" + multi_dir.mkdir() + df[:4].write_parquet(multi_dir / "part_0.parquet") + df[4:].write_parquet(multi_dir / "part_1.parquet") + + out = tmp_path / "loads.parquet" + compute_hourly_loads(multi_dir, None, out, sort_output=True) + + result = pl.read_parquet(out) + expected_order = result.sort(["zip_code", "account_identifier", "hour_chicago"]) + assert result.rows() == expected_order.rows() + + +# ═══════════════════════════════════════════════════════════════════════════ +# 6. Cluster assignment filtering +# ═══════════════════════════════════════════════════════════════════════════ + + +class TestClusterAssignmentFilter: + """When cluster_assignments is given, only those accounts are retained.""" + + def test_semi_join_restricts_accounts(self, tmp_path: Path) -> None: + start = dt.datetime(2023, 7, 1, 0, 0) + df = _make_interval_df(["A001", "A002", "A003"], start, n_intervals=2) + inp = _write_parquet(df, tmp_path / "interval.parquet") + + # Only A001 in cluster assignments + assignments = pl.DataFrame({"account_identifier": ["A001"]}) + asgn_path = tmp_path / "assignments.parquet" + assignments.write_parquet(asgn_path) + + out = tmp_path / "loads.parquet" + compute_hourly_loads(inp, asgn_path, out, sort_output=False) + + result = pl.read_parquet(out) + accounts = set(result["account_identifier"].unique().to_list()) + assert accounts == {"A001"} + + def test_no_rows_when_no_accounts_match(self, tmp_path: Path) -> None: + start = dt.datetime(2023, 7, 1, 0, 0) + df = _make_interval_df(["A001", "A002"], start, n_intervals=2) + inp = _write_parquet(df, tmp_path / "interval.parquet") + + assignments = pl.DataFrame({"account_identifier": ["ZZZZ"]}) + asgn_path = tmp_path / "assignments.parquet" + assignments.write_parquet(asgn_path) + + out = tmp_path / "loads.parquet" + compute_hourly_loads(inp, asgn_path, out, sort_output=False) + + result = pl.read_parquet(out) + assert result.height == 0 + + +# ═══════════════════════════════════════════════════════════════════════════ +# 7. Fail-loud conditions +# ═══════════════════════════════════════════════════════════════════════════ + + +class TestFailLoud: + """compute_hourly_loads must raise clearly on bad inputs.""" + + def test_missing_input_file_raises(self, tmp_path: Path) -> None: + with pytest.raises(FileNotFoundError): + compute_hourly_loads( + tmp_path / "nonexistent.parquet", + None, + tmp_path / "out.parquet", + sort_output=False, + ) + + def test_missing_energy_kwh_column_raises(self, tmp_path: Path) -> None: + df = pl.DataFrame({ + "account_identifier": ["A001"], + "zip_code": ["60601-0001"], + "datetime": [dt.datetime(2023, 7, 1, 0, 0)], + # energy_kwh deliberately absent + }) + inp = _write_parquet(df, tmp_path / "interval.parquet") + + with pytest.raises(ValueError, match="missing required columns"): + compute_hourly_loads(inp, None, tmp_path / "out.parquet", sort_output=False) + + def test_missing_datetime_column_raises(self, tmp_path: Path) -> None: + df = pl.DataFrame({ + "account_identifier": ["A001"], + "zip_code": ["60601-0001"], + "energy_kwh": [0.5], + # datetime deliberately absent + }) + inp = _write_parquet(df, tmp_path / "interval.parquet") + + with pytest.raises(ValueError, match="missing required columns"): + compute_hourly_loads(inp, None, tmp_path / "out.parquet", sort_output=False) + + def test_missing_cluster_assignments_file_raises(self, tmp_path: Path) -> None: + start = dt.datetime(2023, 7, 1, 0, 0) + df = _make_interval_df(["A001"], start, n_intervals=2) + inp = _write_parquet(df, tmp_path / "interval.parquet") + + with pytest.raises(FileNotFoundError): + compute_hourly_loads( + inp, + tmp_path / "nonexistent_assignments.parquet", + tmp_path / "out.parquet", + sort_output=False, + ) diff --git a/tests/test_compute_household_bills.py b/tests/test_compute_household_bills.py new file mode 100644 index 0000000..32af29f --- /dev/null +++ b/tests/test_compute_household_bills.py @@ -0,0 +1,453 @@ +#!/usr/bin/env python3 +"""Unit tests for analysis/rtp/compute_household_bills.py. + +Tests compute_household_bills() and _join_tariff() directly using +synthetic DataFrames. No large data files required; runs in milliseconds. + +NOTE ON TARIFF FORMAT +--------------------- +compute_household_bills() expects tariff DataFrames that have already been +processed by load_tariff_prices(), which renames the price column: + tariff A → price_A_cents (column expected by _join_tariff(..., "price_A_cents", "A")) + tariff B → price_B_cents (column expected by _join_tariff(..., "price_B_cents", "B")) + +The _make_tariff() helper below accepts a label ("A" or "B") to produce the +correctly named column. +""" + +from __future__ import annotations + +import datetime as dt +from typing import ClassVar + +import polars as pl +import pytest + +from analysis.rtp.compute_household_bills import _join_tariff, compute_household_bills + +# ── Helpers ─────────────────────────────────────────────────────────────────── + + +def _make_loads( + accounts: list[str], + hours: list[dt.datetime], + kwh_per_hour: float = 1.0, + zip_code: str = "60601-1234", +) -> pl.DataFrame: + """Build a minimal hourly loads DataFrame.""" + rows = [] + for acct in accounts: + for h in hours: + rows.append({ + "account_identifier": acct, + "zip_code": zip_code, + "hour_chicago": h, + "kwh_hour": kwh_per_hour, + }) + return pl.DataFrame(rows).with_columns( + pl.col("hour_chicago").cast(pl.Datetime("us")), + pl.col("kwh_hour").cast(pl.Float64), + ) + + +def _make_tariff(hours: list[dt.datetime], price: float, label: str) -> pl.DataFrame: + """Build a flat-price hourly tariff with the pre-renamed price column. + + load_tariff_prices(path, label) renames price_cents_per_kwh → price_{label}_cents. + compute_household_bills() expects this already-renamed form. + """ + return pl.DataFrame({ + "datetime_chicago": pl.Series(hours).cast(pl.Datetime("us")), + f"price_{label}_cents": pl.Series([price] * len(hours), dtype=pl.Float64), + }) + + +def _hours(start: dt.datetime, n: int) -> list[dt.datetime]: + return [start + dt.timedelta(hours=i) for i in range(n)] + + +# ═══════════════════════════════════════════════════════════════════════════ +# 1. Bill arithmetic +# ═══════════════════════════════════════════════════════════════════════════ + + +class TestBillArithmetic: + """Verify the core dollar math is correct.""" + + def test_bill_a_equals_kwh_times_price_a(self) -> None: + """bill_a_dollars = sum(kwh_hour * price_A_cents) / 100.""" + hrs = _hours(dt.datetime(2023, 7, 1, 0), 24) + loads = _make_loads(["A001"], hrs, kwh_per_hour=2.0) + tariff_a = _make_tariff(hrs, price=10.0, label="A") # 10 cents/kWh + tariff_b = _make_tariff(hrs, price=5.0, label="B") # 5 cents/kWh + + result = compute_household_bills(loads, tariff_a, tariff_b) + + # 24 hours x 2 kWh x 10 c/kWh / 100 = $4.80 + assert result["bill_a_dollars"][0] == pytest.approx(24 * 2.0 * 10.0 / 100.0) + + def test_bill_b_equals_kwh_times_price_b(self) -> None: + hrs = _hours(dt.datetime(2023, 7, 1, 0), 24) + loads = _make_loads(["A001"], hrs, kwh_per_hour=2.0) + tariff_a = _make_tariff(hrs, price=10.0, label="A") + tariff_b = _make_tariff(hrs, price=5.0, label="B") + + result = compute_household_bills(loads, tariff_a, tariff_b) + + assert result["bill_b_dollars"][0] == pytest.approx(24 * 2.0 * 5.0 / 100.0) + + def test_bill_diff_is_a_minus_b(self) -> None: + """bill_diff_dollars = bill_a - bill_b (positive when B is cheaper).""" + hrs = _hours(dt.datetime(2023, 7, 1, 0), 24) + loads = _make_loads(["A001"], hrs, kwh_per_hour=1.0) + tariff_a = _make_tariff(hrs, price=10.0, label="A") + tariff_b = _make_tariff(hrs, price=6.0, label="B") + + result = compute_household_bills(loads, tariff_a, tariff_b) + + bill_a = result["bill_a_dollars"][0] + bill_b = result["bill_b_dollars"][0] + expected_diff = bill_a - bill_b + assert result["bill_diff_dollars"][0] == pytest.approx(expected_diff) + assert result["bill_diff_dollars"][0] > 0 # B is cheaper + + def test_bill_diff_negative_when_b_more_expensive(self) -> None: + hrs = _hours(dt.datetime(2023, 7, 1, 0), 24) + loads = _make_loads(["A001"], hrs, kwh_per_hour=1.0) + tariff_a = _make_tariff(hrs, price=5.0, label="A") + tariff_b = _make_tariff(hrs, price=15.0, label="B") + + result = compute_household_bills(loads, tariff_a, tariff_b) + + assert result["bill_diff_dollars"][0] < 0 # B is more expensive + + def test_total_kwh_correct(self) -> None: + hrs = _hours(dt.datetime(2023, 7, 1, 0), 24) + loads = _make_loads(["A001"], hrs, kwh_per_hour=1.5) + tariff_a = _make_tariff(hrs, price=10.0, label="A") + tariff_b = _make_tariff(hrs, price=10.0, label="B") + + result = compute_household_bills(loads, tariff_a, tariff_b) + + assert result["total_kwh"][0] == pytest.approx(24 * 1.5) + + def test_peak_kwh_hour_is_max_hourly(self) -> None: + """peak_kwh_hour should be the maximum single hourly load.""" + hrs = _hours(dt.datetime(2023, 7, 1, 0), 3) + rows = [ + { + "account_identifier": "A001", + "zip_code": "60601-0001", + "hour_chicago": hrs[0], + "kwh_hour": 1.0, + }, + { + "account_identifier": "A001", + "zip_code": "60601-0001", + "hour_chicago": hrs[1], + "kwh_hour": 5.0, + }, + { + "account_identifier": "A001", + "zip_code": "60601-0001", + "hour_chicago": hrs[2], + "kwh_hour": 2.0, + }, + ] + loads = pl.DataFrame(rows).with_columns( + pl.col("hour_chicago").cast(pl.Datetime("us")), + pl.col("kwh_hour").cast(pl.Float64), + ) + tariff_a = _make_tariff(hrs, price=10.0, label="A") + tariff_b = _make_tariff(hrs, price=10.0, label="B") + + result = compute_household_bills(loads, tariff_a, tariff_b) + + assert result["peak_kwh_hour"][0] == pytest.approx(5.0) + + +# ═══════════════════════════════════════════════════════════════════════════ +# 2. pct_savings and net_pct_savings +# ═══════════════════════════════════════════════════════════════════════════ + + +class TestPercentSavings: + """pct_savings = bill_diff / bill_a x 100; null when bill_a <= 0.""" + + def test_pct_savings_definition(self) -> None: + hrs = _hours(dt.datetime(2023, 7, 1, 0), 24) + loads = _make_loads(["A001"], hrs, kwh_per_hour=1.0) + tariff_a = _make_tariff(hrs, price=10.0, label="A") + tariff_b = _make_tariff(hrs, price=8.0, label="B") + + result = compute_household_bills(loads, tariff_a, tariff_b) + + bill_a = result["bill_a_dollars"][0] + bill_diff = result["bill_diff_dollars"][0] + expected_pct = bill_diff / bill_a * 100 + assert result["pct_savings"][0] == pytest.approx(expected_pct) + + def test_pct_savings_null_when_zero_kwh(self) -> None: + """Zero usage → bill_a = 0 → pct_savings must be null (no division by zero).""" + hrs = _hours(dt.datetime(2023, 7, 1, 0), 24) + loads = _make_loads(["A001"], hrs, kwh_per_hour=0.0) + tariff_a = _make_tariff(hrs, price=10.0, label="A") + tariff_b = _make_tariff(hrs, price=8.0, label="B") + + result = compute_household_bills(loads, tariff_a, tariff_b) + + assert result["pct_savings"][0] is None + + def test_net_pct_savings_with_capacity_charge(self) -> None: + """net_pct_savings = (bill_diff - capacity - admin) / bill_a x 100.""" + hrs = _hours(dt.datetime(2023, 7, 1, 0), 24) + loads = _make_loads(["A001"], hrs, kwh_per_hour=1.0) + tariff_a = _make_tariff(hrs, price=10.0, label="A") + tariff_b = _make_tariff(hrs, price=8.0, label="B") + + result = compute_household_bills( + loads, + tariff_a, + tariff_b, + capacity_rate_dollars_per_kw_month=2.0, + admin_fee_dollars=1.0, + ) + + bill_a = result["bill_a_dollars"][0] + bill_diff = result["bill_diff_dollars"][0] + cap = result["capacity_charge_dollars"][0] + admin = result["admin_fee_dollars"][0] + expected_net_pct = (bill_diff - cap - admin) / bill_a * 100 + assert result["net_pct_savings"][0] == pytest.approx(expected_net_pct) + + +# ═══════════════════════════════════════════════════════════════════════════ +# 3. Capacity and admin fee +# ═══════════════════════════════════════════════════════════════════════════ + + +class TestCapacityAndAdminFee: + """Verify capacity charge and admin fee are applied correctly to tariff B.""" + + def test_capacity_charge_equals_peak_times_rate(self) -> None: + hrs = _hours(dt.datetime(2023, 7, 1, 0), 3) + # Peak hour = 5 kWh + rows = [ + { + "account_identifier": "A001", + "zip_code": "60601-0001", + "hour_chicago": hrs[0], + "kwh_hour": 5.0, + }, + { + "account_identifier": "A001", + "zip_code": "60601-0001", + "hour_chicago": hrs[1], + "kwh_hour": 2.0, + }, + { + "account_identifier": "A001", + "zip_code": "60601-0001", + "hour_chicago": hrs[2], + "kwh_hour": 1.0, + }, + ] + loads = pl.DataFrame(rows).with_columns( + pl.col("hour_chicago").cast(pl.Datetime("us")), + pl.col("kwh_hour").cast(pl.Float64), + ) + tariff_a = _make_tariff(hrs, price=10.0, label="A") + tariff_b = _make_tariff(hrs, price=10.0, label="B") + + result = compute_household_bills(loads, tariff_a, tariff_b, capacity_rate_dollars_per_kw_month=3.0) + + # capacity_kw = peak_kwh_hour = 5.0; capacity_charge = 5 x $3 = $15 + assert result["capacity_charge_dollars"][0] == pytest.approx(15.0) + + def test_admin_fee_fixed_per_household(self) -> None: + hrs = _hours(dt.datetime(2023, 7, 1, 0), 2) + loads = _make_loads(["A001", "A002"], hrs, kwh_per_hour=1.0) + tariff_a = _make_tariff(hrs, price=10.0, label="A") + tariff_b = _make_tariff(hrs, price=10.0, label="B") + + result = compute_household_bills(loads, tariff_a, tariff_b, admin_fee_dollars=5.0) + + for val in result["admin_fee_dollars"].to_list(): + assert val == pytest.approx(5.0) + + def test_no_charges_when_defaults(self) -> None: + hrs = _hours(dt.datetime(2023, 7, 1, 0), 2) + loads = _make_loads(["A001"], hrs) + tariff_a = _make_tariff(hrs, price=10.0, label="A") + tariff_b = _make_tariff(hrs, price=10.0, label="B") + + result = compute_household_bills(loads, tariff_a, tariff_b) + + assert result["capacity_charge_dollars"][0] == pytest.approx(0.0) + assert result["admin_fee_dollars"][0] == pytest.approx(0.0) + + def test_net_bill_diff_reduced_by_charges(self) -> None: + hrs = _hours(dt.datetime(2023, 7, 1, 0), 24) + loads = _make_loads(["A001"], hrs, kwh_per_hour=1.0) + tariff_a = _make_tariff(hrs, price=10.0, label="A") + tariff_b = _make_tariff(hrs, price=8.0, label="B") + + result_no_charges = compute_household_bills(loads, tariff_a, tariff_b) + result_with_charges = compute_household_bills( + loads, tariff_a, tariff_b, capacity_rate_dollars_per_kw_month=1.0, admin_fee_dollars=0.5 + ) + + assert result_with_charges["net_bill_diff_dollars"][0] < result_no_charges["net_bill_diff_dollars"][0] + + +# ═══════════════════════════════════════════════════════════════════════════ +# 4. Multiple accounts +# ═══════════════════════════════════════════════════════════════════════════ + + +class TestMultipleAccounts: + """Each account gets its own row; no cross-account contamination.""" + + def test_one_row_per_account(self) -> None: + hrs = _hours(dt.datetime(2023, 7, 1, 0), 24) + loads = _make_loads(["A001", "A002", "A003"], hrs) + tariff_a = _make_tariff(hrs, price=10.0, label="A") + tariff_b = _make_tariff(hrs, price=8.0, label="B") + + result = compute_household_bills(loads, tariff_a, tariff_b) + + assert result.height == 3 + assert result["account_identifier"].n_unique() == 3 + + def test_accounts_dont_share_kwh(self) -> None: + """A001 uses 1 kWh/hr, A002 uses 3 kWh/hr — bills must differ proportionally.""" + hrs = _hours(dt.datetime(2023, 7, 1, 0), 24) + rows_a1 = [ + { + "account_identifier": "A001", + "zip_code": "60601-0001", + "hour_chicago": h, + "kwh_hour": 1.0, + } + for h in hrs + ] + rows_a2 = [ + { + "account_identifier": "A002", + "zip_code": "60601-0001", + "hour_chicago": h, + "kwh_hour": 3.0, + } + for h in hrs + ] + loads = pl.DataFrame(rows_a1 + rows_a2).with_columns( + pl.col("hour_chicago").cast(pl.Datetime("us")), + pl.col("kwh_hour").cast(pl.Float64), + ) + tariff_a = _make_tariff(hrs, price=10.0, label="A") + tariff_b = _make_tariff(hrs, price=10.0, label="B") + + result = compute_household_bills(loads, tariff_a, tariff_b) + + a1_kwh = result.filter(pl.col("account_identifier") == "A001")["total_kwh"][0] + a2_kwh = result.filter(pl.col("account_identifier") == "A002")["total_kwh"][0] + assert a2_kwh == pytest.approx(3 * a1_kwh) + + +# ═══════════════════════════════════════════════════════════════════════════ +# 5. _join_tariff fail-loud conditions +# ═══════════════════════════════════════════════════════════════════════════ + + +class TestJoinTariffFailLoud: + """_join_tariff must raise on null prices (gap) or row inflation (duplicate hours).""" + + def test_null_price_raises_value_error(self) -> None: + """A tariff missing one hour produces null prices → ValueError.""" + hrs = _hours(dt.datetime(2023, 7, 1, 0), 3) + loads = _make_loads(["A001"], hrs) + + # Tariff only covers 2 of the 3 hours, and has the price col named correctly + tariff = pl.DataFrame({ + "datetime_chicago": pl.Series(hrs[:2]).cast(pl.Datetime("us")), + "price_A_cents": [10.0, 10.0], + }) + + with pytest.raises(ValueError, match="no matching price"): + _join_tariff(loads, tariff, "price_A_cents", "A") + + def test_duplicate_tariff_hour_raises_runtime_error(self) -> None: + """Duplicate datetime_chicago in tariff inflates row count → RuntimeError.""" + hrs = _hours(dt.datetime(2023, 7, 1, 0), 2) + loads = _make_loads(["A001"], hrs) + + # Tariff has a duplicate at hrs[0] + tariff = pl.DataFrame({ + "datetime_chicago": pl.Series([hrs[0], hrs[0], hrs[1]]).cast(pl.Datetime("us")), + "price_A_cents": [10.0, 10.0, 10.0], + }) + + with pytest.raises(RuntimeError, match="row count"): + _join_tariff(loads, tariff, "price_A_cents", "A") + + def test_full_coverage_does_not_raise(self) -> None: + """A tariff that covers all load hours must not raise.""" + hrs = _hours(dt.datetime(2023, 7, 1, 0), 24) + loads = _make_loads(["A001"], hrs) + tariff = pl.DataFrame({ + "datetime_chicago": pl.Series(hrs).cast(pl.Datetime("us")), + "price_A_cents": [10.0] * 24, + }) + result = _join_tariff(loads, tariff, "price_A_cents", "A") + assert result.height == loads.height + + +# ═══════════════════════════════════════════════════════════════════════════ +# 6. Output schema +# ═══════════════════════════════════════════════════════════════════════════ + + +class TestOutputSchema: + """All expected output columns must be present regardless of optional params.""" + + REQUIRED_COLS: ClassVar[set[str]] = { + "account_identifier", + "zip_code", + "total_kwh", + "bill_a_dollars", + "bill_b_dollars", + "bill_diff_dollars", + "peak_kwh_hour", + "capacity_kw", + "capacity_charge_dollars", + "admin_fee_dollars", + "net_bill_diff_dollars", + "pct_savings", + "net_pct_savings", + } + + def test_all_columns_present_no_charges(self) -> None: + hrs = _hours(dt.datetime(2023, 7, 1, 0), 2) + loads = _make_loads(["A001"], hrs) + tariff_a = _make_tariff(hrs, price=10.0, label="A") + tariff_b = _make_tariff(hrs, price=8.0, label="B") + + result = compute_household_bills(loads, tariff_a, tariff_b) + missing = self.REQUIRED_COLS - set(result.columns) + assert not missing, f"Missing columns: {missing}" + + def test_all_columns_present_with_charges(self) -> None: + hrs = _hours(dt.datetime(2023, 7, 1, 0), 2) + loads = _make_loads(["A001"], hrs) + tariff_a = _make_tariff(hrs, price=10.0, label="A") + tariff_b = _make_tariff(hrs, price=8.0, label="B") + + result = compute_household_bills( + loads, + tariff_a, + tariff_b, + capacity_rate_dollars_per_kw_month=1.0, + admin_fee_dollars=0.5, + ) + missing = self.REQUIRED_COLS - set(result.columns) + assert not missing, f"Missing columns with charges: {missing}" diff --git a/tests/test_dst_rollin.py b/tests/test_dst_rollin.py new file mode 100644 index 0000000..3c02b90 --- /dev/null +++ b/tests/test_dst_rollin.py @@ -0,0 +1,504 @@ +#!/usr/bin/env python3 +""" +End-to-end DST roll-in validation for the RTP billing pipeline. + +Validates that the chain: + + interval data → compute_hourly_loads (hour_chicago) + YAML/CSV → build_tariff_hourly_prices / build_flat_hourly_prices (datetime_chicago) + join → compute_household_bills (hour_chicago = datetime_chicago) + +produces correct, unique, fully-covered join keys—including across DST +spring-forward and fall-back transitions. + +Timezone note +───────────── +"Chicago" throughout this codebase means the IANA timezone **America/Chicago** +(Central Time: CT = CDT in summer / CST in winter). It does NOT refer to the +City of Chicago specifically. The analysis covers the **full ComEd service +territory**, which spans most of northern Illinois and operates entirely within +the America/Chicago timezone. + +Assertions +────────── +1. Hourly loads: ``(account_identifier, hour_chicago)`` is unique. +2. Tariffs A and B: ``datetime_chicago`` is unique. +3. Coverage: every ``hour_chicago`` in the loads joins to exactly one row in + both tariff A and tariff B (no null prices after join). + +On failure the script exits non-zero with a message identifying the offending +keys. + +Usage as script (exit-code based): + + python tests/test_dst_rollin.py # synthetic + sample data + python tests/test_dst_rollin.py --sample-only # only real files + +Usage as pytest: + + pytest tests/test_dst_rollin.py -v +""" + +from __future__ import annotations + +import argparse +import logging +import sys +from datetime import datetime +from pathlib import Path + +import polars as pl +import pytest + +# ── Paths to sample artefacts built by the existing pipeline ────────────── +SAMPLE_LOADS = Path("data/reference/hourly_loads_202308_50.parquet") +SAMPLE_TARIFF_STOU = Path("data/reference/comed_stou_hourly_prices_2023.parquet") +SAMPLE_TARIFF_FLAT = Path("data/reference/comed_flat_hourly_prices_2023.parquet") + +log = logging.getLogger(__name__) + +TZ = "America/Chicago" + + +# ═══════════════════════════════════════════════════════════════════════════ +# Assertion helpers (shared by script mode and pytest) +# ═══════════════════════════════════════════════════════════════════════════ + + +def assert_loads_unique(df: pl.DataFrame) -> None: + """(account_identifier, hour_chicago) must be unique in hourly loads.""" + dupes = ( + df.group_by(["account_identifier", "hour_chicago"]) + .len() + .filter(pl.col("len") > 1) + .sort(["account_identifier", "hour_chicago"]) + ) + if dupes.height > 0: + sample = dupes.head(20) + raise AssertionError( + f"Hourly loads: {dupes.height} duplicate (account_identifier, hour_chicago) keys.\n" + f"First offenders:\n{sample}" + ) + log.info(" PASS loads uniqueness: %d rows, 0 duplicates.", df.height) + + +def assert_tariff_unique(df: pl.DataFrame, label: str) -> None: + """datetime_chicago must be unique in a tariff price calendar.""" + dupes = df.group_by("datetime_chicago").len().filter(pl.col("len") > 1).sort("datetime_chicago") + if dupes.height > 0: + sample = dupes.head(20) + raise AssertionError( + f"Tariff {label}: {dupes.height} duplicate datetime_chicago values.\nFirst offenders:\n{sample}" + ) + log.info( + " PASS tariff %s uniqueness: %d rows, 0 duplicates.", + label, + df.height, + ) + + +def assert_coverage( + loads: pl.DataFrame, + tariff: pl.DataFrame, + label: str, +) -> None: + """Every hour_chicago in loads must match exactly one datetime_chicago in tariff.""" + load_hours = loads.select("hour_chicago").unique() + tariff_hours = tariff.select("datetime_chicago").unique() + + # Hours present in loads but missing from tariff + missing = load_hours.join( + tariff_hours, + left_on="hour_chicago", + right_on="datetime_chicago", + how="anti", + ) + if missing.height > 0: + sample = missing.sort("hour_chicago").head(20) + raise AssertionError( + f"Coverage gap: {missing.height} hour(s) in loads have no price in tariff {label}.\n" + f"Missing hours:\n{sample}" + ) + + # Simulate the actual left join and check for nulls + joined = loads.join( + tariff.select("datetime_chicago", "price_cents_per_kwh"), + left_on="hour_chicago", + right_on="datetime_chicago", + how="left", + ) + null_prices = joined.filter(pl.col("price_cents_per_kwh").is_null()).height + if null_prices > 0: + unmatched = ( + joined.filter(pl.col("price_cents_per_kwh").is_null()) + .select("hour_chicago") + .unique() + .sort("hour_chicago") + .head(20) + ) + raise AssertionError( + f"Tariff {label}: {null_prices} load rows produced null prices after join.\nUnmatched hours:\n{unmatched}" + ) + + # Check that no row was duplicated (tariff uniqueness should prevent this, + # but belt-and-suspenders) + if joined.height != loads.height: + raise AssertionError( + f"Tariff {label}: join changed row count {loads.height} → {joined.height}. " + "Tariff may have duplicate datetime_chicago values." + ) + + log.info( + " PASS coverage tariff %s: %d unique load hours all matched.", + label, + load_hours.height, + ) + + +# ═══════════════════════════════════════════════════════════════════════════ +# Synthetic data builders (for DST edge-case testing) +# ═══════════════════════════════════════════════════════════════════════════ + + +def _make_tariff_for_month(year: int, month: int) -> pl.DataFrame: + """Build a minimal hourly tariff calendar for a single month. + + Handles DST exactly the same way as build_tariff_hourly_prices.py: + tz-aware range → strip to naive → deduplicate fall-back duplicates. + """ + start = datetime(year, month, 1) + end = datetime(year + 1, 1, 1) if month == 12 else datetime(year, month + 1, 1) + + dt_range = pl.datetime_range( + start, + end, + interval="1h", + time_zone=TZ, + eager=True, + closed="left", + ) + df = pl.DataFrame({"datetime_aware": dt_range}) + + df = df.with_columns( + pl.col("datetime_aware").dt.replace_time_zone(None).alias("datetime_chicago"), + ) + + # Deduplicate fall-back (same logic as build_tariff_hourly_prices.py) + df = df.sort("datetime_aware").unique(subset=["datetime_chicago"], keep="first") + df = df.drop("datetime_aware") + + # Assign a dummy price so coverage checks can verify non-null join + df = df.with_columns(pl.lit(5.0).alias("price_cents_per_kwh")) + return df.sort("datetime_chicago") + + +def _make_interval_data(year: int, month: int, n_accounts: int = 3) -> pl.DataFrame: + """Build synthetic 30-minute interval data for *n_accounts* over one month. + + Mimics the naive-local datetime column that compute_hourly_loads expects. + Includes every half-hour in the month using the tz-aware → naive approach + so that DST transitions are correctly represented. + """ + start = datetime(year, month, 1) + end = datetime(year + 1, 1, 1) if month == 12 else datetime(year, month + 1, 1) + + dt_range = pl.datetime_range( + start, + end, + interval="30m", + time_zone=TZ, + eager=True, + closed="left", + ) + df_times = pl.DataFrame({"datetime_aware": dt_range}) + df_times = df_times.with_columns( + pl.col("datetime_aware").dt.replace_time_zone(None).alias("datetime"), + ) + # During fall-back, two aware instants map to the same naive value. + # Real meter data would show two intervals at e.g. 01:00 and 01:30 (CDT) + # and then *again* at 01:00 and 01:30 (CST) — all naive-identical. + # We keep all rows here to let compute_hourly_loads aggregate them. + df_times = df_times.drop("datetime_aware") + + accounts = [f"ACCT_{i:04d}" for i in range(n_accounts)] + df_accts = pl.DataFrame({ + "account_identifier": accounts, + "zip_code": [f"6000{i}" for i in range(n_accounts)], + }) + + # Cross join: every account x every interval + df = df_times.join(df_accts, how="cross") + df = df.with_columns(pl.lit(0.5).alias("kwh")) + return df + + +def _aggregate_hourly(df: pl.DataFrame) -> pl.DataFrame: + """Replicate the aggregation logic from compute_hourly_loads.py.""" + return ( + df.with_columns( + pl.col("datetime").dt.truncate("1h").alias("hour_chicago"), + ) + .group_by(["account_identifier", "zip_code", "hour_chicago"]) + .agg(pl.col("kwh").sum().alias("kwh_hour")) + .sort(["account_identifier", "hour_chicago"]) + ) + + +# ═══════════════════════════════════════════════════════════════════════════ +# Pytest tests +# ═══════════════════════════════════════════════════════════════════════════ + + +class TestDSTRollInSynthetic: + """Synthetic end-to-end checks for DST edge-case months. + + Three months are tested: November (fall-back), March (spring-forward), + and August (no DST). The first two exercise the two DST edge cases; + August provides a "nothing special happens" baseline that would catch + regressions in the non-DST path. + """ + + # -- November 2023: fall-back (2 AM → 1 AM on Nov 5) -------------------- + + def test_november_loads_unique(self) -> None: + intervals = _make_interval_data(2023, 11) + loads = _aggregate_hourly(intervals) + assert_loads_unique(loads) + + def test_november_tariff_unique(self) -> None: + tariff = _make_tariff_for_month(2023, 11) + assert_tariff_unique(tariff, "synthetic_nov") + + def test_november_coverage(self) -> None: + intervals = _make_interval_data(2023, 11) + loads = _aggregate_hourly(intervals) + tariff = _make_tariff_for_month(2023, 11) + assert_coverage(loads, tariff, "synthetic_nov") + + def test_november_fall_back_hour_count(self) -> None: + """November tariff should have 720 hours (30 days x 24), NOT 721. + + The fall-back duplicate (1 AM CST) is dropped so that + datetime_chicago stays unique. + """ + tariff = _make_tariff_for_month(2023, 11) + assert tariff.height == 30 * 24, f"Expected 720 hours for Nov 2023 (fall-back deduped), got {tariff.height}" + + # -- March 2023: spring-forward (2 AM → 3 AM on Mar 12) ───────────────── + + def test_march_loads_unique(self) -> None: + intervals = _make_interval_data(2023, 3) + loads = _aggregate_hourly(intervals) + assert_loads_unique(loads) + + def test_march_tariff_unique(self) -> None: + tariff = _make_tariff_for_month(2023, 3) + assert_tariff_unique(tariff, "synthetic_mar") + + def test_march_coverage(self) -> None: + intervals = _make_interval_data(2023, 3) + loads = _aggregate_hourly(intervals) + tariff = _make_tariff_for_month(2023, 3) + assert_coverage(loads, tariff, "synthetic_mar") + + def test_march_spring_forward_hour_count(self) -> None: + """March tariff should have 743 hours (31x24 - 1 skipped).""" + tariff = _make_tariff_for_month(2023, 3) + assert tariff.height == 31 * 24 - 1, f"Expected 743 hours for Mar 2023 (spring-forward), got {tariff.height}" + + # -- August 2023: no DST transition (baseline sanity) ─────────────────── + + def test_august_loads_unique(self) -> None: + intervals = _make_interval_data(2023, 8) + loads = _aggregate_hourly(intervals) + assert_loads_unique(loads) + + def test_august_tariff_unique(self) -> None: + tariff = _make_tariff_for_month(2023, 8) + assert_tariff_unique(tariff, "synthetic_aug") + + def test_august_coverage(self) -> None: + intervals = _make_interval_data(2023, 8) + loads = _aggregate_hourly(intervals) + tariff = _make_tariff_for_month(2023, 8) + assert_coverage(loads, tariff, "synthetic_aug") + + def test_august_hour_count(self) -> None: + tariff = _make_tariff_for_month(2023, 8) + assert tariff.height == 31 * 24 + + +class TestDSTRollInSampleData: + """Validate the real sample artefacts sitting in data/reference/.""" + + @pytest.fixture(autouse=True) + def _skip_if_missing(self) -> None: + for p in (SAMPLE_LOADS, SAMPLE_TARIFF_STOU, SAMPLE_TARIFF_FLAT): + if not p.exists(): + pytest.skip(f"Sample file not found: {p}") + + @pytest.fixture() + def loads(self) -> pl.DataFrame: + return pl.read_parquet(SAMPLE_LOADS) + + @pytest.fixture() + def tariff_stou(self) -> pl.DataFrame: + return pl.read_parquet(SAMPLE_TARIFF_STOU) + + @pytest.fixture() + def tariff_flat(self) -> pl.DataFrame: + return pl.read_parquet(SAMPLE_TARIFF_FLAT) + + def test_loads_unique(self, loads: pl.DataFrame) -> None: + assert_loads_unique(loads) + + def test_tariff_stou_unique(self, tariff_stou: pl.DataFrame) -> None: + assert_tariff_unique(tariff_stou, "STOU") + + def test_tariff_flat_unique(self, tariff_flat: pl.DataFrame) -> None: + assert_tariff_unique(tariff_flat, "flat") + + def test_coverage_stou( + self, + loads: pl.DataFrame, + tariff_stou: pl.DataFrame, + ) -> None: + assert_coverage(loads, tariff_stou, "STOU") + + def test_coverage_flat( + self, + loads: pl.DataFrame, + tariff_flat: pl.DataFrame, + ) -> None: + assert_coverage(loads, tariff_flat, "flat") + + def test_full_year_hour_count_stou(self, tariff_stou: pl.DataFrame) -> None: + """2023 has spring-forward → 8760 - 1 = 8759 unique hours.""" + assert tariff_stou.height == 8759, f"Expected 8759 rows in STOU 2023 tariff, got {tariff_stou.height}" + + def test_full_year_hour_count_flat(self, tariff_flat: pl.DataFrame) -> None: + assert tariff_flat.height == 8759, f"Expected 8759 rows in flat 2023 tariff, got {tariff_flat.height}" + + +# ═══════════════════════════════════════════════════════════════════════════ +# Script-mode runner (exit non-zero on first failure) +# ═══════════════════════════════════════════════════════════════════════════ +# Dual mode: importable as pytest tests AND runnable as a standalone +# script (python tests/test_dst_rollin.py). The script mode is useful +# for ad-hoc validation on EC2 where pytest may not be configured. + + +def _run_synthetic_checks() -> list[str]: + """Run synthetic DST month checks, returning failure messages.""" + failures: list[str] = [] + for year, month, label in [ + (2023, 11, "Nov-2023-fall-back"), + (2023, 3, "Mar-2023-spring-forward"), + (2023, 8, "Aug-2023-no-DST"), + ]: + log.info("── Synthetic %s ──", label) + try: + intervals = _make_interval_data(year, month) + loads = _aggregate_hourly(intervals) + tariff = _make_tariff_for_month(year, month) + assert_loads_unique(loads) + assert_tariff_unique(tariff, label) + assert_coverage(loads, tariff, label) + except AssertionError as exc: + failures.append(f"[{label}] {exc}") + return failures + + +def _run_sample_checks() -> list[str]: + """Validate real sample artefacts in data/reference/.""" + failures: list[str] = [] + log.info("── Sample artefacts (data/reference/) ──") + for p in (SAMPLE_LOADS, SAMPLE_TARIFF_STOU, SAMPLE_TARIFF_FLAT): + if not p.exists(): + failures.append(f"Sample file not found: {p}") + return failures + + loads = pl.read_parquet(SAMPLE_LOADS) + tariff_stou = pl.read_parquet(SAMPLE_TARIFF_STOU) + tariff_flat = pl.read_parquet(SAMPLE_TARIFF_FLAT) + + try: + assert_loads_unique(loads) + except AssertionError as exc: + failures.append(f"[sample-loads] {exc}") + + for df, label in [(tariff_stou, "STOU"), (tariff_flat, "flat")]: + try: + assert_tariff_unique(df, label) + except AssertionError as exc: + failures.append(f"[sample-tariff-{label}] {exc}") + + for df, label in [(tariff_stou, "STOU"), (tariff_flat, "flat")]: + try: + assert_coverage(loads, df, label) + except AssertionError as exc: + failures.append(f"[sample-coverage-{label}] {exc}") + return failures + + +def _run_checks(sample_only: bool = False) -> list[str]: + """Run all checks and return a list of failure messages (empty = success).""" + failures: list[str] = [] + if not sample_only: + failures.extend(_run_synthetic_checks()) + failures.extend(_run_sample_checks()) + return failures + + +def main(argv: list[str] | None = None) -> int: + parser = argparse.ArgumentParser( + description=( + "Validate DST roll-in semantics end-to-end for the RTP billing pipeline.\n\n" + "TIMEZONE NOTE: 'Chicago' means the IANA timezone America/Chicago " + "(Central Time), NOT the City of Chicago. The analysis covers the " + "full ComEd service territory across northern Illinois." + ), + formatter_class=argparse.RawDescriptionHelpFormatter, + ) + parser.add_argument( + "--sample-only", + action="store_true", + help="Skip synthetic tests; only validate real sample artefacts.", + ) + parser.add_argument( + "--verbose", + "-v", + action="store_true", + help="Enable debug logging.", + ) + args = parser.parse_args(argv) + + logging.basicConfig( + level=logging.DEBUG if args.verbose else logging.INFO, + format="%(asctime)s %(levelname)-8s %(message)s", + ) + + print( + "╔══════════════════════════════════════════════════════════════╗\n" + "║ DST roll-in validation — RTP billing pipeline ║\n" + "║ ║\n" + "║ 'Chicago' = America/Chicago timezone (Central Time). ║\n" + "║ Analysis covers the full ComEd territory, not just the ║\n" + "║ City of Chicago. ║\n" + "╚══════════════════════════════════════════════════════════════╝" + ) + + failures = _run_checks(sample_only=args.sample_only) + + if failures: + print(f"\nFAILED — {len(failures)} check(s):\n", file=sys.stderr) + for f in failures: + print(f" ✗ {f}\n", file=sys.stderr) + return 1 + + log.info("All DST roll-in checks passed.") + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/tests/test_fail_loud_conditions.py b/tests/test_fail_loud_conditions.py new file mode 100644 index 0000000..03e3df7 --- /dev/null +++ b/tests/test_fail_loud_conditions.py @@ -0,0 +1,431 @@ +#!/usr/bin/env python3 +"""Negative tests for fail-loud conditions in the billing pipeline. + +Verifies that the pipeline and regression script exit non-zero with clear +error messages when given invalid inputs or when thresholds are breached. +All tests use synthetic data via tmp_path; no network, no real data required. +""" + +from __future__ import annotations + +import json +import subprocess +import sys +from pathlib import Path + +import polars as pl + +# ── Helpers ────────────────────────────────────────────────────────────── +# All tests use tiny synthetic DataFrames so the suite runs in seconds +# with no network, no real data, and no side effects. This is important +# because these are negative tests that intentionally trigger errors—we +# don't want real data files accidentally corrupted or large downloads. + + +def _write_minimal_bills(path: Path, *, include_savings: bool = True, include_bill_diff: bool = True) -> None: + """Write a minimal valid bills parquet.""" + data: dict = { + "account_identifier": ["A001", "A002", "A003"], + "zip_code": ["60002-1102", "60002-1102", "60002-1103"], + "total_kwh": [100.0, 200.0, 150.0], + } + if include_savings: + data["net_pct_savings"] = [5.0, 3.0, 7.0] + if include_bill_diff: + data["bill_diff_dollars"] = [10.0, 6.0, 14.0] + pl.DataFrame(data).write_parquet(path) + + +def _write_minimal_crosswalk(path: Path) -> None: + """Write a minimal crosswalk TSV matching the test bills.""" + lines = [ + "Zip\tZip4\tCensusKey2023\n", + "60002\t1102\t170310101001234\n", + "60002\t1103\t170310101002345\n", + ] + path.write_text("".join(lines)) + + +def _write_minimal_census(path: Path) -> None: + """Write a minimal census parquet with GEOID + numeric columns.""" + pl.DataFrame({ + "GEOID": ["170310101001", "170310101002"], + "median_household_income": [55000.0, 72000.0], + "old_building_pct": [0.35, 0.50], + }).write_parquet(path) + + +# 120s timeout is generous but necessary: on slow CI runners, subprocess +# startup + Polars import + parquet I/O can exceed the default 30s. +def _run_regression(args: list[str], *, timeout: int = 120) -> subprocess.CompletedProcess: + """Run build_regression_dataset.py as subprocess.""" + cmd = [sys.executable, "analysis/rtp/build_regression_dataset.py", *args] + return subprocess.run(cmd, capture_output=True, text=True, timeout=timeout) + + +def _run_orchestrator(args: list[str], *, timeout: int = 120) -> subprocess.CompletedProcess: + """Run run_billing_pipeline.py as subprocess.""" + cmd = [sys.executable, "scripts/run_billing_pipeline.py", *args] + return subprocess.run(cmd, capture_output=True, text=True, timeout=timeout) + + +# ═══════════════════════════════════════════════════════════════════════════ +# 1. Missing columns in bills +# ═══════════════════════════════════════════════════════════════════════════ + + +class TestMissingBillsColumns: + """Regression script should fail when required columns are absent.""" + + def test_missing_account_identifier(self, tmp_path: Path) -> None: + bills_path = tmp_path / "bills.parquet" + pl.DataFrame({ + "zip_code": ["60002-1102"], + "total_kwh": [100.0], + "net_pct_savings": [5.0], + "bill_diff_dollars": [10.0], + }).write_parquet(bills_path) + + xwalk_path = tmp_path / "crosswalk.txt" + _write_minimal_crosswalk(xwalk_path) + census_path = tmp_path / "census.parquet" + _write_minimal_census(census_path) + + r = _run_regression([ + "--bills", + str(bills_path), + "--crosswalk", + str(xwalk_path), + "--census", + str(census_path), + "--output-dir", + str(tmp_path / "out"), + ]) + assert r.returncode != 0, "Should fail when account_identifier is missing" + + def test_missing_both_outcome_columns(self, tmp_path: Path) -> None: + bills_path = tmp_path / "bills.parquet" + _write_minimal_bills(bills_path, include_savings=False, include_bill_diff=True) + # Remove savings columns entirely + df = pl.read_parquet(bills_path) + # This file has bill_diff_dollars but no savings column at all + df_no_savings = df.select([c for c in df.columns if c not in ("net_pct_savings", "pct_savings")]) + df_no_savings.write_parquet(bills_path) + + xwalk_path = tmp_path / "crosswalk.txt" + _write_minimal_crosswalk(xwalk_path) + census_path = tmp_path / "census.parquet" + _write_minimal_census(census_path) + + r = _run_regression([ + "--bills", + str(bills_path), + "--crosswalk", + str(xwalk_path), + "--census", + str(census_path), + "--output-dir", + str(tmp_path / "out"), + ]) + assert r.returncode != 0, "Should fail when neither savings column exists" + + +# ═══════════════════════════════════════════════════════════════════════════ +# 2. Crosswalk coverage threshold +# ═══════════════════════════════════════════════════════════════════════════ + + +class TestCrosswalkCoverageThreshold: + """Fail-loud when crosswalk drop rate exceeds threshold.""" + + def test_exceeds_drop_threshold(self, tmp_path: Path) -> None: + """With a crosswalk that matches nothing, drop rate is 100%.""" + bills_path = tmp_path / "bills.parquet" + _write_minimal_bills(bills_path) + + # Crosswalk with ZIP+4 that won't match any bills + xwalk_path = tmp_path / "crosswalk.txt" + xwalk_path.write_text("Zip\tZip4\tCensusKey2023\n99999\t9999\t170310101001234\n") + + census_path = tmp_path / "census.parquet" + _write_minimal_census(census_path) + + r = _run_regression([ + "--bills", + str(bills_path), + "--crosswalk", + str(xwalk_path), + "--census", + str(census_path), + "--output-dir", + str(tmp_path / "out"), + "--max-crosswalk-drop-pct", + "5.0", + ]) + assert r.returncode != 0, "Should fail when crosswalk drop rate exceeds threshold" + + def test_high_threshold_allows_low_coverage(self, tmp_path: Path) -> None: + """With threshold=100, even total mismatch should still try to proceed. + + This verifies that the threshold is a gate (not a hard-coded constant) + and that a different error ("No block groups remain") fires downstream. + """ + bills_path = tmp_path / "bills.parquet" + _write_minimal_bills(bills_path) + + # Crosswalk matches nothing + xwalk_path = tmp_path / "crosswalk.txt" + xwalk_path.write_text("Zip\tZip4\tCensusKey2023\n99999\t9999\t170310101001234\n") + + census_path = tmp_path / "census.parquet" + _write_minimal_census(census_path) + + r = _run_regression([ + "--bills", + str(bills_path), + "--crosswalk", + str(xwalk_path), + "--census", + str(census_path), + "--output-dir", + str(tmp_path / "out"), + "--max-crosswalk-drop-pct", + "100.0", + ]) + # Should fail with "No block groups remain" (not crosswalk threshold) + assert r.returncode != 0 + + +# ═══════════════════════════════════════════════════════════════════════════ +# 3. Zero usable predictors +# ═══════════════════════════════════════════════════════════════════════════ + + +class TestZeroPredictors: + """Fail when no usable predictors found.""" + + def test_explicit_nonexistent_predictor(self, tmp_path: Path) -> None: + bills_path = tmp_path / "bills.parquet" + _write_minimal_bills(bills_path) + xwalk_path = tmp_path / "crosswalk.txt" + _write_minimal_crosswalk(xwalk_path) + census_path = tmp_path / "census.parquet" + _write_minimal_census(census_path) + + r = _run_regression([ + "--bills", + str(bills_path), + "--crosswalk", + str(xwalk_path), + "--census", + str(census_path), + "--output-dir", + str(tmp_path / "out"), + "--predictors", + "totally_fake_column", + ]) + assert r.returncode != 0, "Should fail when explicit predictor not in census" + + def test_core_mode_no_core_cols(self, tmp_path: Path) -> None: + """Census without income or building_pct should fail in core mode.""" + bills_path = tmp_path / "bills.parquet" + _write_minimal_bills(bills_path) + xwalk_path = tmp_path / "crosswalk.txt" + _write_minimal_crosswalk(xwalk_path) + + # Census with NO core predictor columns + census_path = tmp_path / "census.parquet" + pl.DataFrame({ + "GEOID": ["170310101001", "170310101002"], + "pct_renter": [0.60, 0.35], + }).write_parquet(census_path) + + r = _run_regression([ + "--bills", + str(bills_path), + "--crosswalk", + str(xwalk_path), + "--census", + str(census_path), + "--output-dir", + str(tmp_path / "out"), + "--predictors", + "core", + ]) + assert r.returncode != 0, "Should fail when no core predictor columns exist in census" + + +# ═══════════════════════════════════════════════════════════════════════════ +# 4. Input file not found +# ═══════════════════════════════════════════════════════════════════════════ + + +class TestMissingInputFiles: + """Fail-loud when input files don't exist.""" + + def test_missing_bills_file(self, tmp_path: Path) -> None: + r = _run_regression([ + "--bills", + str(tmp_path / "nonexistent_bills.parquet"), + "--crosswalk", + str(tmp_path / "xwalk.txt"), + "--census", + str(tmp_path / "census.parquet"), + "--output-dir", + str(tmp_path / "out"), + ]) + assert r.returncode != 0 + + def test_missing_census_file(self, tmp_path: Path) -> None: + bills_path = tmp_path / "bills.parquet" + _write_minimal_bills(bills_path) + xwalk_path = tmp_path / "crosswalk.txt" + _write_minimal_crosswalk(xwalk_path) + + r = _run_regression([ + "--bills", + str(bills_path), + "--crosswalk", + str(xwalk_path), + "--census", + str(tmp_path / "nonexistent_census.parquet"), + "--output-dir", + str(tmp_path / "out"), + ]) + assert r.returncode != 0 + + +# ═══════════════════════════════════════════════════════════════════════════ +# 5. Orchestrator: missing interval data +# ═══════════════════════════════════════════════════════════════════════════ + + +class TestOrchestratorFailLoud: + """Orchestrator should fail when interval data is missing.""" + + def test_missing_interval_file(self, tmp_path: Path) -> None: + r = _run_orchestrator([ + "--months", + "202301", + "--interval-pattern", + str(tmp_path / "nonexistent_{yyyymm}.parquet"), + "--tariff-a", + str(tmp_path / "tariff_a.parquet"), + "--tariff-b", + str(tmp_path / "tariff_b.parquet"), + "--skip-regression", + "--run-name", + "test_fail", + "--output-dir", + str(tmp_path / "out"), + ]) + assert r.returncode != 0, "Should fail when interval data file is missing" + + def test_invalid_month_format(self, tmp_path: Path) -> None: + r = _run_orchestrator([ + "--months", + "2023-08", + "--interval-pattern", + str(tmp_path / "{yyyymm}.parquet"), + "--tariff-a", + str(tmp_path / "tariff_a.parquet"), + "--tariff-b", + str(tmp_path / "tariff_b.parquet"), + "--skip-regression", + "--output-dir", + str(tmp_path / "out"), + ]) + assert r.returncode != 0, "Should fail on invalid YYYYMM format" + + +# ═══════════════════════════════════════════════════════════════════════════ +# 6. Outcome column fallback +# ═══════════════════════════════════════════════════════════════════════════ + + +class TestOutcomeColumnFallback: + """Test that fallback columns work when preferred is absent.""" + + def test_fallback_to_pct_savings(self, tmp_path: Path) -> None: + """When net_pct_savings absent, should fall back to pct_savings.""" + bills_path = tmp_path / "bills.parquet" + pl.DataFrame({ + "account_identifier": ["A001", "A002", "A003"], + "zip_code": ["60002-1102", "60002-1102", "60002-1103"], + "total_kwh": [100.0, 200.0, 150.0], + "pct_savings": [5.0, 3.0, 7.0], + "bill_diff_dollars": [10.0, 6.0, 14.0], + }).write_parquet(bills_path) + + xwalk_path = tmp_path / "crosswalk.txt" + _write_minimal_crosswalk(xwalk_path) + census_path = tmp_path / "census.parquet" + _write_minimal_census(census_path) + out_dir = tmp_path / "out" + + _run_regression([ + "--bills", + str(bills_path), + "--crosswalk", + str(xwalk_path), + "--census", + str(census_path), + "--output-dir", + str(out_dir), + "--predictors", + "core", + "--max-crosswalk-drop-pct", + "100", + "--min-obs-per-bg", + "1", + ]) + + # May fail at OLS step (too few obs) but should NOT fail at column resolution + # Check metadata if it was written + meta_path = out_dir / "regression_metadata.json" + if meta_path.exists(): + with open(meta_path) as f: + meta = json.load(f) + assert meta["savings_column_used"] == "pct_savings" + assert meta["savings_fallback_used"] is True + + def test_fallback_to_net_bill_diff(self, tmp_path: Path) -> None: + """When bill_diff_dollars absent, should fall back to net_bill_diff_dollars.""" + bills_path = tmp_path / "bills.parquet" + pl.DataFrame({ + "account_identifier": ["A001", "A002", "A003"], + "zip_code": ["60002-1102", "60002-1102", "60002-1103"], + "total_kwh": [100.0, 200.0, 150.0], + "net_pct_savings": [5.0, 3.0, 7.0], + "net_bill_diff_dollars": [10.0, 6.0, 14.0], + }).write_parquet(bills_path) + + xwalk_path = tmp_path / "crosswalk.txt" + _write_minimal_crosswalk(xwalk_path) + census_path = tmp_path / "census.parquet" + _write_minimal_census(census_path) + out_dir = tmp_path / "out" + + _run_regression([ + "--bills", + str(bills_path), + "--crosswalk", + str(xwalk_path), + "--census", + str(census_path), + "--output-dir", + str(out_dir), + "--predictors", + "core", + "--max-crosswalk-drop-pct", + "100", + "--min-obs-per-bg", + "1", + ]) + + meta_path = out_dir / "regression_metadata.json" + if meta_path.exists(): + with open(meta_path) as f: + meta = json.load(f) + assert meta["bill_diff_column_used"] == "net_bill_diff_dollars" + assert meta["bill_diff_fallback_used"] is True diff --git a/tests/test_foo.py b/tests/test_foo.py deleted file mode 100644 index bcea4cd..0000000 --- a/tests/test_foo.py +++ /dev/null @@ -1,5 +0,0 @@ -from smart_meter_analysis.foo import foo - - -def test_foo(): - assert foo("foo") == "foo" diff --git a/tests/test_rate_structure_window_alignment_real_yaml.py b/tests/test_rate_structure_window_alignment_real_yaml.py new file mode 100644 index 0000000..d83f682 --- /dev/null +++ b/tests/test_rate_structure_window_alignment_real_yaml.py @@ -0,0 +1,98 @@ +"""Guard: STOU and DTOU REAL YAML files must have identical TOU window definitions. + +If someone edits hour boundaries in comed_stou_2026.yaml but forgets to +mirror the change in comed_dtou_2026.yaml (or vice versa), these tests fail +loudly before any calendar parquet is built. + +What is checked: + - Both YAML files exist in rate_structures/ + - Season names, start_mmdd, end_mmdd are identical + - Period names (morning, midday_peak, evening, overnight) are identical + - Period start_hour and end_hour are identical in every season + - Prices are intentionally NOT compared (they differ by design) +""" + +from __future__ import annotations + +import importlib.util +import sys +from pathlib import Path + +import pytest + +REPO_ROOT = Path(__file__).resolve().parents[1] +sys.path.insert(0, str(REPO_ROOT / "scripts")) + +from build_tariff_hourly_prices import compare_window_definitions, load_config # noqa: E402 + +STOU_YAML = REPO_ROOT / "rate_structures" / "comed_stou_2026.yaml" +DTOU_YAML = REPO_ROOT / "rate_structures" / "comed_dtou_2026.yaml" + +_YAML_AVAILABLE = importlib.util.find_spec("yaml") is not None + +pytestmark = pytest.mark.skipif(not _YAML_AVAILABLE, reason="PyYAML not installed") + +_EXPECTED_PERIODS = {"morning", "midday_peak", "evening", "overnight"} +_EXPECTED_SEASONS = {"summer", "nonsummer"} + + +def test_both_yaml_files_exist() -> None: + """Guard: DTOU YAML must be committed alongside STOU YAML.""" + assert STOU_YAML.exists(), f"STOU YAML not found: {STOU_YAML}" + assert DTOU_YAML.exists(), f"DTOU YAML not found: {DTOU_YAML}" + + +def test_stou_dtou_window_definitions_are_identical() -> None: + """Core invariant: every season date and period hour boundary must match.""" + stou_cfg = load_config(STOU_YAML) + dtou_cfg = load_config(DTOU_YAML) + + ok, diff_msg = compare_window_definitions( + stou_cfg, + dtou_cfg, + name_a=STOU_YAML.name, + name_b=DTOU_YAML.name, + ) + assert ok, f"\n{diff_msg}" + + +def test_stou_dtou_have_expected_season_names() -> None: + """Both files must define summer and nonsummer seasons.""" + for label, yaml_path in [("STOU", STOU_YAML), ("DTOU", DTOU_YAML)]: + cfg = load_config(yaml_path) + actual = {s["name"] for s in cfg["seasons"]} + missing = _EXPECTED_SEASONS - actual + unexpected = actual - _EXPECTED_SEASONS + assert not missing, f"{label}: missing seasons {missing}" + assert not unexpected, f"{label}: unexpected seasons {unexpected}" + + +def test_stou_dtou_have_expected_period_names() -> None: + """Both files must define the four canonical period labels in every season.""" + for label, yaml_path in [("STOU", STOU_YAML), ("DTOU", DTOU_YAML)]: + cfg = load_config(yaml_path) + for season in cfg["seasons"]: + actual = {p["period"] for p in season["periods"]} + missing = _EXPECTED_PERIODS - actual + unexpected = actual - _EXPECTED_PERIODS + assert not missing, f"{label} season '{season['name']}': missing periods {missing}" + assert not unexpected, f"{label} season '{season['name']}': unexpected periods {unexpected}" + + +def test_stou_dtou_prices_differ() -> None: + """Sanity: once DTOU placeholder prices are filled in, they must differ from STOU. + + Skipped when DTOU prices are still all-zero placeholders (see TODO in + rate_structures/comed_dtou_2026.yaml). + """ + stou_cfg = load_config(STOU_YAML) + dtou_cfg = load_config(DTOU_YAML) + + dtou_prices = {p["price"] for s in dtou_cfg["seasons"] for p in s["periods"]} + if dtou_prices == {0.0} or dtou_prices == {0}: + pytest.skip("DTOU prices are still placeholder zeros — skipping price diff check") + + stou_prices = {p["price"] for s in stou_cfg["seasons"] for p in s["periods"]} + assert stou_prices != dtou_prices, ( + "STOU and DTOU prices are identical — is comed_dtou_2026.yaml correctly filled in?" + ) diff --git a/tests/test_regression_predictor_modes.py b/tests/test_regression_predictor_modes.py new file mode 100644 index 0000000..d2e9b50 --- /dev/null +++ b/tests/test_regression_predictor_modes.py @@ -0,0 +1,220 @@ +#!/usr/bin/env python3 +"""Unit tests for predictor selection modes in build_regression_dataset.py. + +Tests the three predictor modes (auto / core / explicit comma-separated) and +their edge cases, without running the full subprocess pipeline. Uses synthetic +DataFrames that mirror the Census schema so tests are fast and deterministic. +""" + +from __future__ import annotations + +import polars as pl +import pytest + +# Direct imports (not subprocess) so we can test individual functions in +# isolation without the overhead and fragility of CLI round-trips. The +# fail-loud tests in test_fail_loud_conditions.py cover subprocess behavior. +from analysis.rtp.build_regression_dataset import ( + EXCLUDE_COLS, + _normalize_zip4_expr, + _resolve_col, + detect_predictors, +) + +# ═══════════════════════════════════════════════════════════════════════════ +# Fixtures +# ═══════════════════════════════════════════════════════════════════════════ + + +# Fixture includes both CORE_PREDICTORS (income, building_pct), additional +# numeric columns, an all-null column, and an EXCLUDE_COLS member (NAME). +# This exercises every branch in detect_predictors with a single fixture. +@pytest.fixture() +def census_df() -> pl.DataFrame: + """Synthetic census DataFrame with known columns and types.""" + return pl.DataFrame({ + "block_group_geoid": ["170310101001", "170310101002", "170310101003"], + "NAME": ["BG 1", "BG 2", "BG 3"], + "median_household_income": [55000.0, 72000.0, 43000.0], + "old_building_pct": [0.35, 0.50, 0.20], + "pct_college_degree": [0.40, 0.65, 0.30], + "pct_renter_occupied": [0.60, 0.35, 0.55], + "total_population": [1500, 2200, 900], + "all_null_col": [None, None, None], + }).cast({ + "median_household_income": pl.Float64, + "old_building_pct": pl.Float64, + "pct_college_degree": pl.Float64, + "pct_renter_occupied": pl.Float64, + "total_population": pl.Int64, + "all_null_col": pl.Float64, + }) + + +@pytest.fixture() +def census_no_core() -> pl.DataFrame: + """Census DataFrame without any CORE_PREDICTORS columns.""" + return pl.DataFrame({ + "block_group_geoid": ["170310101001", "170310101002"], + "pct_college_degree": [0.40, 0.65], + "total_population": [1500, 2200], + }).cast({ + "pct_college_degree": pl.Float64, + "total_population": pl.Int64, + }) + + +# ═══════════════════════════════════════════════════════════════════════════ +# 1. Auto mode +# ═══════════════════════════════════════════════════════════════════════════ + + +class TestAutoMode: + """--predictors auto: infer all numeric cols minus excluded/all-null.""" + + def test_auto_includes_numeric(self, census_df: pl.DataFrame) -> None: + preds, _ = detect_predictors(census_df, mode="auto") + # Should include all numeric columns except EXCLUDE_COLS and all-null + assert "median_household_income" in preds + assert "old_building_pct" in preds + assert "pct_college_degree" in preds + assert "total_population" in preds + + def test_auto_excludes_id_columns(self, census_df: pl.DataFrame) -> None: + preds, _ = detect_predictors(census_df, mode="auto") + for col in EXCLUDE_COLS: + assert col not in preds, f"Auto mode should exclude {col}" + assert "NAME" not in preds + + def test_auto_excludes_all_null(self, census_df: pl.DataFrame) -> None: + preds, excluded = detect_predictors(census_df, mode="auto") + assert "all_null_col" not in preds + assert "all_null_col" in excluded + + def test_auto_returns_sorted(self, census_df: pl.DataFrame) -> None: + preds, _ = detect_predictors(census_df, mode="auto") + assert preds == sorted(preds), "Auto predictors should be sorted" + + def test_auto_excluded_sorted(self, census_df: pl.DataFrame) -> None: + _, excluded = detect_predictors(census_df, mode="auto") + assert excluded == sorted(excluded), "Excluded columns should be sorted" + + +# ═══════════════════════════════════════════════════════════════════════════ +# 2. Core mode +# ═══════════════════════════════════════════════════════════════════════════ + + +class TestCoreMode: + """--predictors core: just median_household_income + old_building_pct.""" + + def test_core_returns_both(self, census_df: pl.DataFrame) -> None: + preds, excluded = detect_predictors(census_df, mode="core") + assert set(preds) == {"median_household_income", "old_building_pct"} + assert excluded == [] + + def test_core_partial_match(self) -> None: + """If only one core predictor exists, return just that one.""" + df = pl.DataFrame({ + "block_group_geoid": ["170310101001"], + "median_household_income": [55000.0], + }) + preds, _ = detect_predictors(df, mode="core") + assert preds == ["median_household_income"] + + def test_core_fails_when_none_present(self, census_no_core: pl.DataFrame) -> None: + with pytest.raises(RuntimeError, match="--predictors core requested but none of"): + detect_predictors(census_no_core, mode="core") + + +# ═══════════════════════════════════════════════════════════════════════════ +# 3. Explicit mode (comma-separated) +# ═══════════════════════════════════════════════════════════════════════════ + + +class TestExplicitMode: + """--predictors col1,col2: validate against census columns.""" + + def test_explicit_valid_columns(self, census_df: pl.DataFrame) -> None: + preds, excluded = detect_predictors(census_df, mode="pct_college_degree,total_population") + assert preds == ["pct_college_degree", "total_population"] + assert excluded == [] + + def test_explicit_single_column(self, census_df: pl.DataFrame) -> None: + preds, _ = detect_predictors(census_df, mode="median_household_income") + assert preds == ["median_household_income"] + + def test_explicit_with_spaces(self, census_df: pl.DataFrame) -> None: + preds, _ = detect_predictors(census_df, mode=" median_household_income , old_building_pct ") + assert preds == ["median_household_income", "old_building_pct"] + + def test_explicit_missing_column(self, census_df: pl.DataFrame) -> None: + with pytest.raises(RuntimeError, match="Requested predictors not found in census"): + detect_predictors(census_df, mode="nonexistent_col") + + def test_explicit_partial_missing(self, census_df: pl.DataFrame) -> None: + with pytest.raises(RuntimeError, match="Requested predictors not found"): + detect_predictors(census_df, mode="median_household_income,fake_column") + + +# ═══════════════════════════════════════════════════════════════════════════ +# 4. ZIP+4 normalization +# ═══════════════════════════════════════════════════════════════════════════ + + +class TestNormalizeZip4: + """Test _normalize_zip4_expr for various input formats.""" + + def test_already_formatted(self) -> None: + df = pl.DataFrame({"zip_code": ["60002-1102"]}) + result = df.with_columns(_normalize_zip4_expr()) + assert result["zip4"][0] == "60002-1102" + + def test_nine_digit_no_dash(self) -> None: + df = pl.DataFrame({"zip_code": ["600021102"]}) + result = df.with_columns(_normalize_zip4_expr()) + assert result["zip4"][0] == "60002-1102" + + def test_five_digit_returns_null(self) -> None: + df = pl.DataFrame({"zip_code": ["60002"]}) + result = df.with_columns(_normalize_zip4_expr()) + assert result["zip4"][0] is None + + def test_empty_string_returns_null(self) -> None: + df = pl.DataFrame({"zip_code": [""]}) + result = df.with_columns(_normalize_zip4_expr()) + assert result["zip4"][0] is None + + def test_whitespace_stripped(self) -> None: + df = pl.DataFrame({"zip_code": [" 60002-1102 "]}) + result = df.with_columns(_normalize_zip4_expr()) + assert result["zip4"][0] == "60002-1102" + + def test_mixed_batch(self) -> None: + df = pl.DataFrame({"zip_code": ["60002-1102", "600021102", "60002", None]}) + result = df.with_columns(_normalize_zip4_expr()) + expected = ["60002-1102", "60002-1102", None, None] + assert result["zip4"].to_list() == expected + + +# ═══════════════════════════════════════════════════════════════════════════ +# 5. Outcome column resolution +# ═══════════════════════════════════════════════════════════════════════════ + + +class TestResolveCol: + """Test _resolve_col fallback logic.""" + + def test_preferred_found(self) -> None: + col, fallback = _resolve_col({"net_pct_savings", "pct_savings"}, "net_pct_savings", "pct_savings", "test") + assert col == "net_pct_savings" + assert fallback is False + + def test_fallback_used(self) -> None: + col, fallback = _resolve_col({"pct_savings"}, "net_pct_savings", "pct_savings", "test") + assert col == "pct_savings" + assert fallback is True + + def test_neither_raises(self) -> None: + with pytest.raises(RuntimeError, match="neither 'net_pct_savings' nor 'pct_savings'"): + _resolve_col({"other_col"}, "net_pct_savings", "pct_savings", "test") diff --git a/tests/test_statewide_validation.py b/tests/test_statewide_validation.py new file mode 100644 index 0000000..53ebd44 --- /dev/null +++ b/tests/test_statewide_validation.py @@ -0,0 +1,682 @@ +"""Comprehensive validation suite for statewide pricing simulation outputs. + +Validates the pricing pilot pipeline outputs using locally available data: +- Household bills parquets: output/statewide_{stou,dtou}_{202301,202307}/ +- BG-level regression parquets and JSON: output/statewide_*/regression/ +- Rate configuration YAML: rate_structures/comed_{stou,dtou}_2026.yaml +- Rate constants in Python source: scripts/pricing_pilot/compute_delivery_deltas.py + +Sign convention: delta = flat - alternative (positive = saves under TOU) +""" + +from __future__ import annotations + +import importlib +import json +from pathlib import Path + +import polars as pl +import pytest +import yaml + +# --------------------------------------------------------------------------- +# Paths +# --------------------------------------------------------------------------- + +REPO = Path("/workspaces/smart-meter-analysis") +OUTPUT = REPO / "output" +STOU_YAML = REPO / "rate_structures" / "comed_stou_2026.yaml" +DTOU_YAML = REPO / "rate_structures" / "comed_dtou_2026.yaml" + +SCENARIOS = [ + ("stou", "202301"), + ("stou", "202307"), + ("dtou", "202301"), + ("dtou", "202307"), +] + +# Expected row counts (from pipeline run manifests) +EXPECTED_ROWS = { + ("stou", "202301"): 658_959, + ("stou", "202307"): 686_430, + ("dtou", "202301"): 658_959, + ("dtou", "202307"): 686_430, +} + + +def _bills_path(rate: str, month: str) -> Path: + return OUTPUT / f"statewide_{rate}_{month}" / f"month={month}" / "household_bills.parquet" + + +def _regression_dir(rate: str, month: str) -> Path: + return OUTPUT / f"statewide_{rate}_{month}" / "regression" + + +def _load_yaml(path: Path) -> dict: + with open(path) as f: + return yaml.safe_load(f) + + +def _load_rate_constants(): + """Import rate constants from compute_delivery_deltas.py.""" + spec_path = REPO / "scripts" / "pricing_pilot" / "compute_delivery_deltas.py" + spec = importlib.util.spec_from_file_location("compute_delivery_deltas", spec_path) + mod = importlib.util.module_from_spec(spec) + spec.loader.exec_module(mod) + return mod + + +# ============================================================================ +# Check 1 — Rate table consistency +# ============================================================================ + + +class TestRateTableConsistency: + """Verify rate configs match expected tariff values (¢/kWh).""" + + @pytest.fixture(scope="class") + def stou_yaml(self): + return _load_yaml(STOU_YAML) + + @pytest.fixture(scope="class") + def dtou_yaml(self): + return _load_yaml(DTOU_YAML) + + @pytest.fixture(scope="class") + def rate_mod(self): + return _load_rate_constants() + + # -- Flat PTCs -- + + def test_flat_ptc_nonsummer(self, rate_mod): + assert rate_mod.FLAT_PTCS["nonsummer"] == pytest.approx(9.660) + + def test_flat_ptc_summer(self, rate_mod): + assert rate_mod.FLAT_PTCS["summer"] == pytest.approx(10.028) + + # -- Flat DFCs -- + + @pytest.mark.parametrize( + "cls,expected", + [("C23", 6.228), ("C24", 4.791), ("C26", 3.165), ("C28", 2.996)], + ) + def test_flat_dfc(self, rate_mod, cls, expected): + assert rate_mod.FLAT_DFCS[cls] == pytest.approx(expected) + + # -- TOU DFCs -- + + def test_tou_dfc_c28_overnight(self, rate_mod): + assert rate_mod.TOU_DFCS["C28"]["overnight"] == pytest.approx(1.512) + + @pytest.mark.parametrize( + "cls,period,expected", + [ + ("C23", "morning", 4.009), + ("C23", "midday_peak", 10.712), + ("C24", "overnight", 2.251), + ("C26", "midday_peak", 5.329), + ("C28", "morning", 1.925), + ("C28", "midday_peak", 4.975), + ("C28", "evening", 1.823), + ], + ) + def test_tou_dfc_spot_checks(self, rate_mod, cls, period, expected): + assert rate_mod.TOU_DFCS[cls][period] == pytest.approx(expected) + + # -- STOU supply rates -- + + def test_stou_midday_peak_summer(self, stou_yaml): + summer = next(s for s in stou_yaml["seasons"] if s["name"] == "summer") + peak = next(p for p in summer["periods"] if p["period"] == "midday_peak") + assert peak["price"] == pytest.approx(19.485) + + def test_stou_midday_peak_nonsummer(self, stou_yaml): + nonsummer = next(s for s in stou_yaml["seasons"] if s["name"] == "nonsummer") + peak = next(p for p in nonsummer["periods"] if p["period"] == "midday_peak") + assert peak["price"] == pytest.approx(18.080) + + def test_stou_tmp_component_summer(self, stou_yaml): + """T&MP = 1.266: midday_peak(19.485) - original_compromise(18.219) = 1.266.""" + summer = next(s for s in stou_yaml["seasons"] if s["name"] == "summer") + peak = next(p for p in summer["periods"] if p["period"] == "midday_peak") + # Original Compromise summer midday_peak = 18.219 (from YAML header comment) + tmp = peak["price"] - 18.219 + assert tmp == pytest.approx(1.266, abs=1e-4) + + def test_stou_tmp_component_nonsummer(self, stou_yaml): + """T&MP = 1.266: midday_peak(18.080) - original_compromise(16.814) = 1.266.""" + nonsummer = next(s for s in stou_yaml["seasons"] if s["name"] == "nonsummer") + peak = next(p for p in nonsummer["periods"] if p["period"] == "midday_peak") + tmp = peak["price"] - 16.814 + assert tmp == pytest.approx(1.266, abs=1e-4) + + # -- No C25/C27 in configs -- + + def test_no_c25_c27_in_flat_dfcs(self, rate_mod): + assert "C25" not in rate_mod.FLAT_DFCS + assert "C27" not in rate_mod.FLAT_DFCS + + def test_no_c25_c27_in_tou_dfcs(self, rate_mod): + assert "C25" not in rate_mod.TOU_DFCS + assert "C27" not in rate_mod.TOU_DFCS + + def test_no_c25_c27_in_stou_yaml(self, stou_yaml): + yaml_str = yaml.dump(stou_yaml) + assert "C25" not in yaml_str + assert "C27" not in yaml_str + + def test_no_c25_c27_in_dtou_yaml(self, dtou_yaml): + yaml_str = yaml.dump(dtou_yaml) + assert "C25" not in yaml_str + assert "C27" not in yaml_str + + # -- DTOU YAML has placeholder prices -- + + def test_dtou_yaml_prices_are_zero_placeholders(self, dtou_yaml): + """DTOU YAML has 0.000 placeholders; actual DTOU uses Python DFC constants.""" + for season in dtou_yaml["seasons"]: + for period in season["periods"]: + assert period["price"] == 0.0, ( + f"DTOU {season['name']}/{period['period']} has non-zero price " + f"{period['price']}; expected 0.000 placeholder" + ) + + # -- Window alignment (STOU and DTOU share same time blocks) -- + + def test_stou_dtou_same_season_dates(self, stou_yaml, dtou_yaml): + for s_season, d_season in zip(stou_yaml["seasons"], dtou_yaml["seasons"]): + assert s_season["name"] == d_season["name"] + assert s_season["start_mmdd"] == d_season["start_mmdd"] + assert s_season["end_mmdd"] == d_season["end_mmdd"] + + def test_stou_dtou_same_period_hours(self, stou_yaml, dtou_yaml): + for s_season, d_season in zip(stou_yaml["seasons"], dtou_yaml["seasons"]): + for s_period, d_period in zip(s_season["periods"], d_season["periods"]): + assert s_period["period"] == d_period["period"] + assert s_period["start_hour"] == d_period["start_hour"] + assert s_period["end_hour"] == d_period["end_hour"] + + +# ============================================================================ +# Check 2 — Universe counts +# ============================================================================ + + +class TestUniverseCounts: + """Verify expected row counts and cross-scenario consistency.""" + + @pytest.mark.parametrize("rate,month", SCENARIOS) + def test_row_count(self, rate, month): + path = _bills_path(rate, month) + n = pl.scan_parquet(path).select(pl.len()).collect().item() + assert n == EXPECTED_ROWS[(rate, month)], f"{rate} {month}: {n} != {EXPECTED_ROWS[(rate, month)]}" + + def test_stou_dtou_jan_same_count(self): + n_stou = pl.scan_parquet(_bills_path("stou", "202301")).select(pl.len()).collect().item() + n_dtou = pl.scan_parquet(_bills_path("dtou", "202301")).select(pl.len()).collect().item() + assert n_stou == n_dtou, f"Jan mismatch: STOU={n_stou}, DTOU={n_dtou}" + + def test_stou_dtou_jul_same_count(self): + n_stou = pl.scan_parquet(_bills_path("stou", "202307")).select(pl.len()).collect().item() + n_dtou = pl.scan_parquet(_bills_path("dtou", "202307")).select(pl.len()).collect().item() + assert n_stou == n_dtou, f"Jul mismatch: STOU={n_stou}, DTOU={n_dtou}" + + def test_jul_more_than_jan(self): + n_jan = pl.scan_parquet(_bills_path("stou", "202301")).select(pl.len()).collect().item() + n_jul = pl.scan_parquet(_bills_path("stou", "202307")).select(pl.len()).collect().item() + assert n_jul > n_jan, f"Jul ({n_jul}) should exceed Jan ({n_jan})" + + +# ============================================================================ +# Check 3 — Household-level bill sanity +# ============================================================================ + + +class TestHouseholdBillSanity: + """Verify bill arithmetic at the household level.""" + + @pytest.fixture(scope="class") + def stou_jan(self): + return pl.read_parquet(_bills_path("stou", "202301")) + + @pytest.fixture(scope="class") + def stou_jul(self): + return pl.read_parquet(_bills_path("stou", "202307")) + + @pytest.fixture(scope="class") + def dtou_jan(self): + return pl.read_parquet(_bills_path("dtou", "202301")) + + # -- bill_diff = bill_a - bill_b -- + + @pytest.mark.parametrize("rate,month", SCENARIOS) + def test_bill_diff_equals_bill_a_minus_bill_b(self, rate, month): + df = pl.read_parquet(_bills_path(rate, month)) + err = (df["bill_a_dollars"] - df["bill_b_dollars"] - df["bill_diff_dollars"]).abs().max() + assert err < 1e-8, f"max |bill_a - bill_b - bill_diff| = {err}" + + # -- pct_savings = bill_diff / bill_a * 100 -- + + def test_stou_pct_savings_definition(self, stou_jan): + pos_bill = stou_jan.filter(pl.col("bill_a_dollars") > 0) + computed = pos_bill["bill_diff_dollars"] / pos_bill["bill_a_dollars"] * 100 + err = (computed - pos_bill["pct_savings"]).abs().max() + assert err < 1e-8, f"pct_savings mismatch: max err = {err}" + + def test_stou_pct_savings_null_when_zero_bill(self, stou_jan): + zero_bill = stou_jan.filter(pl.col("bill_a_dollars") == 0) + if zero_bill.height > 0: + n_null = zero_bill["pct_savings"].null_count() + assert n_null == zero_bill.height, "pct_savings should be null when bill_a = 0" + + # -- net_bill_diff = bill_diff - capacity_charge - admin_fee -- + + @pytest.mark.parametrize("rate,month", SCENARIOS) + def test_net_bill_diff_definition(self, rate, month): + df = pl.read_parquet(_bills_path(rate, month)) + computed = df["bill_diff_dollars"] - df["capacity_charge_dollars"] - df["admin_fee_dollars"] + err = (computed - df["net_bill_diff_dollars"]).abs().max() + assert err < 1e-8, f"net_bill_diff mismatch: max err = {err}" + + # -- DTOU: bill_b = 0 (DTOU YAML prices are all 0.000 placeholders) -- + + def test_dtou_bill_b_always_zero(self, dtou_jan): + assert dtou_jan["bill_b_dollars"].max() == 0.0 + assert dtou_jan["bill_b_dollars"].min() == 0.0 + + def test_dtou_pct_savings_always_100(self, dtou_jan): + non_null = dtou_jan["pct_savings"].drop_nulls() + assert non_null.min() == pytest.approx(100.0) + assert non_null.max() == pytest.approx(100.0) + + # -- bill_a non-negative -- + + @pytest.mark.parametrize("rate,month", SCENARIOS) + def test_bill_a_non_negative(self, rate, month): + df = pl.read_parquet(_bills_path(rate, month)) + assert df["bill_a_dollars"].min() >= 0.0 + + # -- STOU and DTOU Jan have same flat supply (bill_a) since both use same flat PTC -- + + def test_stou_dtou_jan_bill_a_identical(self, stou_jan, dtou_jan): + """Both STOU and DTOU core runs use the same flat PTC for bill_a (flat supply).""" + stou_sorted = stou_jan.sort("account_identifier") + dtou_sorted = dtou_jan.sort("account_identifier") + # Same accounts + assert stou_sorted["account_identifier"].to_list() == dtou_sorted["account_identifier"].to_list() + # Same bill_a (flat supply charge) — allow floating-point tolerance + err = (stou_sorted["bill_a_dollars"] - dtou_sorted["bill_a_dollars"]).abs().max() + assert err < 1e-8, f"bill_a should be identical; max diff = {err}" + + # -- Spot-check: random households have reasonable values -- + + def test_stou_jan_no_extreme_outliers(self, stou_jan): + """No single household bill_diff exceeds $10,000.""" + assert stou_jan["bill_diff_dollars"].abs().max() < 10_000 + + def test_stou_pct_savings_range(self, stou_jan): + """pct_savings bounded to [-100, 100] for positive bills.""" + pos_bill = stou_jan.filter(pl.col("bill_a_dollars") > 0) + assert pos_bill["pct_savings"].min() >= -100.0 + assert pos_bill["pct_savings"].max() <= 100.0 + + +# ============================================================================ +# Check 4 — BG-level parquet consistency +# ============================================================================ + + +class TestBGLevelConsistency: + """Cross-scenario BG-level consistency checks using regression parquets.""" + + @pytest.fixture(scope="class") + def bg_data(self): + """Load bg_month_outcomes for all 4 scenarios.""" + data = {} + for rate, month in SCENARIOS: + path = _regression_dir(rate, month) / "bg_month_outcomes.parquet" + data[(rate, month)] = pl.read_parquet(path) + return data + + def test_stou_dtou_jan_same_bg_set(self, bg_data): + stou_bgs = set(bg_data[("stou", "202301")]["block_group_geoid"].to_list()) + dtou_bgs = set(bg_data[("dtou", "202301")]["block_group_geoid"].to_list()) + assert stou_bgs == dtou_bgs, f"Jan BG mismatch: |diff| = {len(stou_bgs ^ dtou_bgs)}" + + def test_stou_dtou_jul_same_bg_set(self, bg_data): + stou_bgs = set(bg_data[("stou", "202307")]["block_group_geoid"].to_list()) + dtou_bgs = set(bg_data[("dtou", "202307")]["block_group_geoid"].to_list()) + assert stou_bgs == dtou_bgs, f"Jul BG mismatch: |diff| = {len(stou_bgs ^ dtou_bgs)}" + + def test_jul_more_bgs_than_jan(self, bg_data): + n_jan = bg_data[("stou", "202301")]["block_group_geoid"].n_unique() + n_jul = bg_data[("stou", "202307")]["block_group_geoid"].n_unique() + assert n_jul > n_jan, f"Jul ({n_jul}) should have more BGs than Jan ({n_jan})" + + def test_all_n_household_months_positive(self, bg_data): + for key, df in bg_data.items(): + min_n = df["n_household_months"].min() + assert min_n > 0, f"{key} has n_household_months = {min_n}" + + def test_no_duplicate_bgs(self, bg_data): + for key, df in bg_data.items(): + n_total = df.height + n_unique = df["block_group_geoid"].n_unique() + assert n_total == n_unique, f"{key} has {n_total - n_unique} duplicate BGs" + + def test_stou_dtou_jan_same_n_households_per_bg(self, bg_data): + """Same households → same n_household_months per BG.""" + stou = bg_data[("stou", "202301")].sort("block_group_geoid") + dtou = bg_data[("dtou", "202301")].sort("block_group_geoid") + assert stou["n_household_months"].to_list() == dtou["n_household_months"].to_list(), ( + "STOU and DTOU Jan should have identical per-BG household counts" + ) + + @pytest.mark.parametrize("rate,month", SCENARIOS) + def test_bg_geoid_format(self, bg_data, rate, month): + """All GEOIDs should be 12-character strings (FIPS state+county+tract+BG).""" + geoids = bg_data[(rate, month)]["block_group_geoid"] + lengths = geoids.str.len_chars() + # Illinois FIPS = 17, so GEOIDs should be 12 chars (2+3+6+1) + assert lengths.min() >= 11, f"GEOID too short: min len = {lengths.min()}" + assert lengths.max() <= 12, f"GEOID too long: max len = {lengths.max()}" + + +# ============================================================================ +# Check 5 — Regression sanity +# ============================================================================ + + +class TestRegressionSanity: + """Verify regression results are statistically reasonable.""" + + @pytest.fixture(scope="class") + def reg_results(self): + """Load all regression_results.json files.""" + results = {} + for rate, month in SCENARIOS: + path = _regression_dir(rate, month) / "regression_results.json" + with open(path) as f: + results[(rate, month)] = json.load(f) + return results + + @pytest.mark.parametrize("rate,month", SCENARIOS) + def test_p_values_valid(self, reg_results, rate, month): + for model_name, model in reg_results[(rate, month)].items(): + for coeff_name, coeff in model["coefficients"].items(): + p = coeff["p_value"] + assert 0 <= p <= 1, f"{rate}/{month}/{model_name}/{coeff_name}: p={p}" + + @pytest.mark.parametrize("rate,month", SCENARIOS) + def test_r_squared_reasonable(self, reg_results, rate, month): + """R² should be non-negative (or -inf for DTOU pct_savings which is constant).""" + for model_name, model in reg_results[(rate, month)].items(): + r2 = model["r_squared"] + if rate == "dtou" and "pct_savings" in model_name: + # DTOU pct_savings is exactly 100% for all BGs → R²=-inf is expected + assert r2 == float("-inf"), f"{rate}/{month}/{model_name}: R²={r2}, expected -inf" + else: + assert 0 <= r2 <= 1, f"{rate}/{month}/{model_name}: R²={r2}" + + def test_stou_jul_pct_savings_beta1_negative(self, reg_results): + """STOU Jul pooled pct_savings beta_1 should be negative (progressive).""" + model = reg_results[("stou", "202307")]["model_1_pct_savings_weighted"] + income_coeff = next(v for k, v in model["coefficients"].items() if k != "const") + assert income_coeff["estimate"] < 0, ( + f"STOU Jul pct_savings beta_1 = {income_coeff['estimate']}, expected negative (progressive)" + ) + + def test_stou_jul_bill_diff_beta1_negative(self, reg_results): + """STOU Jul bill_diff beta_1 should be negative (progressive in absolute $).""" + model = reg_results[("stou", "202307")]["model_2_sum_bill_diff"] + income_coeff = next(v for k, v in model["coefficients"].items() if k != "const") + assert income_coeff["estimate"] < 0, ( + f"STOU Jul bill_diff beta_1 = {income_coeff['estimate']}, expected negative" + ) + + def test_dtou_pct_savings_constant_100(self, reg_results): + """DTOU pct_savings intercept should be ~100 (all savings are 100%).""" + for month in ["202301", "202307"]: + model = reg_results[("dtou", month)]["model_1_pct_savings_weighted"] + const = model["coefficients"]["const"]["estimate"] + assert const == pytest.approx(100.0, abs=0.01), f"DTOU {month} pct_savings const = {const}, expected ~100" + + @pytest.mark.parametrize("rate,month", SCENARIOS) + def test_n_obs_reasonable(self, reg_results, rate, month): + """Each regression should have 1000+ observations.""" + for model_name, model in reg_results[(rate, month)].items(): + assert model["n_obs"] > 1000, f"{rate}/{month}/{model_name}: n_obs={model['n_obs']}" + + @pytest.mark.parametrize("rate,month", SCENARIOS) + def test_model_keys_present(self, reg_results, rate, month): + """Each scenario should have both pct_savings and bill_diff models.""" + keys = set(reg_results[(rate, month)].keys()) + assert "model_1_pct_savings_weighted" in keys + assert "model_2_sum_bill_diff" in keys + + +# ============================================================================ +# Check 6 — Numeric plausibility +# ============================================================================ + + +class TestNumericPlausibility: + """Range and distribution sanity checks.""" + + def test_stou_jan_mean_bill_diff_positive(self): + """STOU Jan: overall positive savings (flat > TOU in winter).""" + df = pl.read_parquet(_bills_path("stou", "202301")) + mean_diff = df["bill_diff_dollars"].mean() + assert mean_diff > 0, f"STOU Jan mean bill_diff = {mean_diff}, expected positive" + + def test_stou_jul_mean_bill_diff_near_zero_or_negative(self): + """STOU Jul: smaller or negative savings (summer TOU peak penalty).""" + df = pl.read_parquet(_bills_path("stou", "202307")) + mean_diff = df["bill_diff_dollars"].mean() + # Summer should have smaller savings than winter due to peak pricing + assert mean_diff < 10, f"STOU Jul mean bill_diff = {mean_diff}, expected < 10" + + def test_stou_jan_larger_savings_than_jul(self): + """Winter STOU savings should exceed summer.""" + jan = pl.read_parquet(_bills_path("stou", "202301"))["bill_diff_dollars"].mean() + jul = pl.read_parquet(_bills_path("stou", "202307"))["bill_diff_dollars"].mean() + assert jan > jul, f"Jan mean ({jan:.2f}) should exceed Jul mean ({jul:.2f})" + + def test_no_extreme_bill_outliers(self): + """No household bill_diff exceeds $15,000 in absolute value. + + A few very high-consumption accounts (e.g., commercial-scale usage on + residential class C28) can produce large deltas; $15k is a reasonable + ceiling that allows genuine outliers while catching data corruption. + """ + for rate, month in SCENARIOS: + df = pl.read_parquet(_bills_path(rate, month)) + max_abs = df["bill_diff_dollars"].abs().max() + assert max_abs < 15_000, f"{rate}/{month} max |bill_diff| = {max_abs}" + + def test_income_range_in_regression_dataset(self): + """median_household_income in BG regression data should be in valid range. + + Census stores income as natural log; the regression dataset uses log scale. + Valid range: ln($2,500) ≈ 7.8 to ln($500,000) ≈ 13.1. + """ + import math + + path = _regression_dir("stou", "202301") / "regression_dataset_bg.parquet" + df = pl.read_parquet(path) + inc = df["median_household_income"].drop_nulls() + # Detect if values are in log scale (all < 20) or dollar scale (all > 1000) + if inc.mean() < 20: + # Log scale: ln($2,500)=7.82, ln($500,000)=13.12 + assert inc.min() >= math.log(2_500), f"min log income = {inc.min()}" + assert inc.max() < math.log(500_000), f"max log income = {inc.max()}" + else: + # Dollar scale + assert inc.min() > 5_000, f"min income = {inc.min()}, expected > $5k" + assert inc.max() < 500_000, f"max income = {inc.max()}, expected < $500k" + + def test_bg_pct_savings_weighted_range(self): + """BG-level pct_savings_weighted should be in reasonable range.""" + for rate, month in SCENARIOS: + path = _regression_dir(rate, month) / "bg_month_outcomes.parquet" + df = pl.read_parquet(path) + pct = df["pct_savings_weighted"].drop_nulls() + if rate == "dtou": + # DTOU pct_savings_weighted should be ~100% + assert pct.mean() == pytest.approx(100.0, abs=1.0) + else: + # STOU: should be between -100 and 100 + assert pct.min() > -100, f"{rate}/{month} min pct = {pct.min()}" + assert pct.max() < 100, f"{rate}/{month} max pct = {pct.max()}" + + def test_sum_bill_diff_all_positive_for_stou_jan(self): + """Most BGs in STOU Jan should have positive sum_bill_diff (aggregate savings).""" + path = _regression_dir("stou", "202301") / "bg_month_outcomes.parquet" + df = pl.read_parquet(path) + pct_positive = (df["sum_bill_diff_dollars"] > 0).mean() + assert pct_positive > 0.5, f"Only {pct_positive:.1%} of BGs have positive sum_bill_diff" + + +# ============================================================================ +# Check 7 — Cross-validation: household → BG consistency +# ============================================================================ + + +class TestHouseholdToBGConsistency: + """Verify that BG-level aggregates are consistent with household bills.""" + + @pytest.fixture(scope="class") + def stou_jan_bills(self): + return pl.read_parquet(_bills_path("stou", "202301")) + + @pytest.fixture(scope="class") + def stou_jan_bg(self): + return pl.read_parquet(_regression_dir("stou", "202301") / "bg_month_outcomes.parquet") + + def test_total_bill_diff_matches(self, stou_jan_bills, stou_jan_bg): + """Sum of household bill_diff should ~ match sum of BG sum_bill_diff.""" + hh_total = stou_jan_bills["bill_diff_dollars"].sum() + bg_total = stou_jan_bg["sum_bill_diff_dollars"].sum() + # BG data may drop some households (crosswalk match), allow 20% tolerance + ratio = bg_total / hh_total + assert 0.5 < ratio < 1.5, f"HH total={hh_total:.0f}, BG total={bg_total:.0f}, ratio={ratio:.3f}" + + def test_bg_household_count_below_total(self, stou_jan_bills, stou_jan_bg): + """Total households in BG data should be <= total household bills.""" + bg_hh = stou_jan_bg["n_household_months"].sum() + hh_total = stou_jan_bills.height + # BG data loses some HHs in crosswalk, but should not exceed + assert bg_hh <= hh_total * 1.01, f"BG HHs ({bg_hh}) > total HHs ({hh_total})" + + def test_bg_count_reasonable(self, stou_jan_bg): + """Expect 4000-5000 unique BGs for Illinois.""" + n_bg = stou_jan_bg["block_group_geoid"].n_unique() + assert 4000 <= n_bg <= 5000, f"BG count = {n_bg}, expected 4000-5000" + + +# ============================================================================ +# Check 8 — Schema consistency across scenarios +# ============================================================================ + + +class TestSchemaConsistency: + """Verify all scenarios produce consistent schemas.""" + + @pytest.mark.parametrize("rate,month", SCENARIOS) + def test_household_bills_schema(self, rate, month): + expected_cols = { + "account_identifier", + "zip_code", + "total_kwh", + "peak_kwh_hour", + "bill_a_dollars", + "bill_b_dollars", + "bill_diff_dollars", + "capacity_kw", + "pct_savings", + "capacity_charge_dollars", + "admin_fee_dollars", + "net_bill_diff_dollars", + "net_pct_savings", + } + df = pl.scan_parquet(_bills_path(rate, month)) + actual_cols = set(df.collect_schema().names()) + missing = expected_cols - actual_cols + assert not missing, f"{rate}/{month} missing columns: {missing}" + + @pytest.mark.parametrize("rate,month", SCENARIOS) + def test_bg_outcomes_schema(self, rate, month): + expected_cols = { + "block_group_geoid", + "month", + "sum_total_kwh", + "sum_bill_a_dollars", + "sum_bill_b_dollars", + "sum_bill_diff_dollars", + "sum_net_bill_diff_dollars", + "n_household_months", + "pct_savings_weighted", + } + path = _regression_dir(rate, month) / "bg_month_outcomes.parquet" + df = pl.scan_parquet(path) + actual_cols = set(df.collect_schema().names()) + missing = expected_cols - actual_cols + assert not missing, f"{rate}/{month} bg_month_outcomes missing: {missing}" + + @pytest.mark.parametrize("rate,month", SCENARIOS) + def test_regression_json_has_required_fields(self, rate, month): + path = _regression_dir(rate, month) / "regression_results.json" + with open(path) as f: + data = json.load(f) + required = {"r_squared", "n_obs", "coefficients", "f_statistic", "f_pvalue"} + for model_name, model in data.items(): + missing = required - set(model.keys()) + assert not missing, f"{rate}/{month}/{model_name} missing fields: {missing}" + + +# ============================================================================ +# Check 9 — Regression metadata consistency +# ============================================================================ + + +class TestRegressionMetadata: + """Verify regression metadata JSON files are internally consistent.""" + + @pytest.mark.parametrize("rate,month", SCENARIOS) + def test_metadata_exists_and_valid(self, rate, month): + path = _regression_dir(rate, month) / "regression_metadata.json" + with open(path) as f: + meta = json.load(f) + assert "months_included" in meta + assert "predictors" in meta or "predictor_columns" in meta or "predictor_mode" in meta + + @pytest.mark.parametrize("rate,month", SCENARIOS) + def test_regression_dataset_has_census_predictors(self, rate, month): + """Regression dataset should contain Census predictors (joined from census data).""" + path = _regression_dir(rate, month) / "regression_dataset_bg.parquet" + df = pl.scan_parquet(path) + cols = set(df.collect_schema().names()) + assert "median_household_income" in cols + # At least a few other census predictors + census_cols = cols & { + "pct_below_poverty", + "pct_owner_occupied", + "pct_renter_occupied", + "pct_heat_electric", + "median_age", + } + assert len(census_cols) >= 3, f"Only {len(census_cols)} census predictors found" + + @pytest.mark.parametrize("rate,month", SCENARIOS) + def test_regression_dataset_bg_count_matches_outcomes(self, rate, month): + """regression_dataset_bg should have same BGs as bg_month_outcomes (after filters).""" + ds_path = _regression_dir(rate, month) / "regression_dataset_bg.parquet" + bg_path = _regression_dir(rate, month) / "bg_month_outcomes.parquet" + ds = pl.read_parquet(ds_path) + bg = pl.read_parquet(bg_path) + # Dataset may have fewer BGs (dropped nulls), but should not exceed + assert ds.height <= bg.height, f"regression_dataset ({ds.height}) > bg_month_outcomes ({bg.height})" + # Should retain at least 80% of BGs + assert ds.height > bg.height * 0.8, ( + f"regression_dataset only has {ds.height}/{bg.height} BGs ({ds.height / bg.height:.0%})" + ) diff --git a/tests/test_tou_window_alignment.py b/tests/test_tou_window_alignment.py new file mode 100644 index 0000000..4652632 --- /dev/null +++ b/tests/test_tou_window_alignment.py @@ -0,0 +1,337 @@ +"""Unit tests: STOU and DTOU TOU period windows are identical. + +STOU and DTOU share the same four period window definitions +(morning, midday_peak, evening, overnight) and only differ in price. +These tests verify that ``build_tariff_hourly_prices.build_hourly_prices`` +assigns identical ``period`` values for every hour of 202301 (nonsummer) +and 202307 (summer) regardless of which price schedule is used. + +Root-cause note +--------------- +The single source of truth for hour→period mapping is the YAML config loaded +by ``scripts/build_tariff_hourly_prices.load_config`` / ``resolve_period``. +If DTOU were ever given a *different* YAML (e.g. with shifted hour boundaries +for a pilot variant), ``resolve_period`` would silently produce a diverging +``period`` column, breaking the assumption in ``bill_stats_and_bg_correlation`` +and the BG-correlation analysis that STOU/DTOU deltas are comparable on the +same TOU dimension. These tests catch that divergence. +""" + +from __future__ import annotations + +import sys +from pathlib import Path + +import polars as pl +import pytest + +# Make the repo root importable when running with pytest from the workspace root. +REPO_ROOT = Path(__file__).resolve().parents[1] +sys.path.insert(0, str(REPO_ROOT / "scripts")) + +from build_tariff_hourly_prices import build_hourly_prices # noqa: E402 + +# --------------------------------------------------------------------------- +# Inline YAML fixtures — same period windows as comed_stou_2026.yaml +# but with different prices to simulate a DTOU schedule. +# --------------------------------------------------------------------------- + +_STOU_YAML = """\ +name: stou_test +timezone: America/Chicago +unit: cents_per_kwh +seasons: + - name: summer + start_mmdd: "06-01" + end_mmdd: "09-30" + periods: + - period: morning + start_hour: 6 + end_hour: 13 + price: 3.013 + - period: midday_peak + start_hour: 13 + end_hour: 19 + price: 18.219 + - period: evening + start_hour: 19 + end_hour: 21 + price: 3.090 + - period: overnight + start_hour: 21 + end_hour: 6 + price: 1.870 + - name: nonsummer + start_mmdd: "10-01" + end_mmdd: "05-31" + periods: + - period: morning + start_hour: 6 + end_hour: 13 + price: 2.829 + - period: midday_peak + start_hour: 13 + end_hour: 19 + price: 16.814 + - period: evening + start_hour: 19 + end_hour: 21 + price: 3.086 + - period: overnight + start_hour: 21 + end_hour: 6 + price: 2.012 +""" + +# DTOU: identical period windows, different prices +_DTOU_YAML = """\ +name: dtou_test +timezone: America/Chicago +unit: cents_per_kwh +seasons: + - name: summer + start_mmdd: "06-01" + end_mmdd: "09-30" + periods: + - period: morning + start_hour: 6 + end_hour: 13 + price: 2.500 + - period: midday_peak + start_hour: 13 + end_hour: 19 + price: 20.000 + - period: evening + start_hour: 19 + end_hour: 21 + price: 4.000 + - period: overnight + start_hour: 21 + end_hour: 6 + price: 1.500 + - name: nonsummer + start_mmdd: "10-01" + end_mmdd: "05-31" + periods: + - period: morning + start_hour: 6 + end_hour: 13 + price: 2.200 + - period: midday_peak + start_hour: 13 + end_hour: 19 + price: 17.000 + - period: evening + start_hour: 19 + end_hour: 21 + price: 3.500 + - period: overnight + start_hour: 21 + end_hour: 6 + price: 1.800 +""" + +# DTOU with a deliberately wrong window (morning shifted to 7-13) — used to +# confirm the test actually catches divergence. +_DTOU_WRONG_YAML = """\ +name: dtou_wrong_test +timezone: America/Chicago +unit: cents_per_kwh +seasons: + - name: summer + start_mmdd: "06-01" + end_mmdd: "09-30" + periods: + - period: morning + start_hour: 7 + end_hour: 13 + price: 2.500 + - period: midday_peak + start_hour: 13 + end_hour: 19 + price: 20.000 + - period: evening + start_hour: 19 + end_hour: 21 + price: 4.000 + - period: overnight + start_hour: 21 + end_hour: 7 + price: 1.500 + - name: nonsummer + start_mmdd: "10-01" + end_mmdd: "05-31" + periods: + - period: morning + start_hour: 7 + end_hour: 13 + price: 2.200 + - period: midday_peak + start_hour: 13 + end_hour: 19 + price: 17.000 + - period: evening + start_hour: 19 + end_hour: 21 + price: 3.500 + - period: overnight + start_hour: 21 + end_hour: 7 + price: 1.800 +""" + + +# --------------------------------------------------------------------------- +# Helpers +# --------------------------------------------------------------------------- + +try: + import yaml # type: ignore[import-untyped] + + _YAML_AVAILABLE = True +except ImportError: + _YAML_AVAILABLE = False + + +def _parse_cfg(yaml_str: str) -> dict: + return yaml.safe_load(yaml_str) + + +def _build(yaml_str: str, year: int) -> pl.DataFrame: + """Build a tariff calendar from an inline YAML string.""" + cfg = _parse_cfg(yaml_str) + return build_hourly_prices(cfg, year) + + +def _period_col(df: pl.DataFrame) -> pl.Series: + return df.sort("datetime_chicago")["period"] + + +def _compare_periods(stou_df: pl.DataFrame, dtou_df: pl.DataFrame) -> pl.DataFrame: + """Return rows where period differs; empty DataFrame means full match.""" + joined = ( + stou_df.select(["datetime_chicago", "period"]) + .sort("datetime_chicago") + .rename({"period": "period_stou"}) + .join( + dtou_df.select(["datetime_chicago", "period"]).sort("datetime_chicago").rename({"period": "period_dtou"}), + on="datetime_chicago", + how="inner", + ) + ) + return joined.filter(pl.col("period_stou") != pl.col("period_dtou")) + + +# --------------------------------------------------------------------------- +# Tests +# --------------------------------------------------------------------------- + +pytestmark = pytest.mark.skipif(not _YAML_AVAILABLE, reason="PyYAML not installed") + + +class TestTouWindowAlignment: + """STOU and DTOU period windows must be identical hour-for-hour.""" + + @pytest.mark.parametrize( + "year, month_label", + [ + (2023, "202301"), # nonsummer + (2023, "202307"), # summer + ], + ) + def test_period_windows_match_full_year(self, year: int, month_label: str) -> None: + """Period column must be identical across all hours of the year.""" + stou = _build(_STOU_YAML, year) + dtou = _build(_DTOU_YAML, year) + + mismatches = _compare_periods(stou, dtou) + assert mismatches.height == 0, ( + f"Period mismatch for year={year}: {mismatches.height} rows differ.\nSample:\n{mismatches.head(10)}" + ) + + @pytest.mark.parametrize("year", [2023]) + def test_prices_differ(self, year: int) -> None: + """Sanity check: STOU and DTOU prices must actually differ so we know + the test is exercising two distinct rate structures.""" + stou = _build(_STOU_YAML, year) + dtou = _build(_DTOU_YAML, year) + + stou_prices = set(stou["price_cents_per_kwh"].unique().to_list()) + dtou_prices = set(dtou["price_cents_per_kwh"].unique().to_list()) + assert stou_prices != dtou_prices, "STOU and DTOU prices should differ in the test fixtures" + + @pytest.mark.parametrize("year", [2023]) + def test_wrong_window_is_detected(self, year: int) -> None: + """The comparison must flag divergence when windows genuinely differ.""" + stou = _build(_STOU_YAML, year) + dtou_wrong = _build(_DTOU_WRONG_YAML, year) + + mismatches = _compare_periods(stou, dtou_wrong) + # Hour 6 is in morning for STOU, overnight for wrong DTOU → should find mismatches + assert mismatches.height > 0, ( + "Expected period mismatches when DTOU morning window is shifted to hour 7, " + "but none were found — the comparison logic may be broken." + ) + + @pytest.mark.parametrize("year", [2023]) + def test_period_values_are_expected_labels(self, year: int) -> None: + """Both calendars must only contain the four canonical period labels.""" + expected = {"morning", "midday_peak", "evening", "overnight"} + for label, yaml_str in [("STOU", _STOU_YAML), ("DTOU", _DTOU_YAML)]: + df = _build(yaml_str, year) + actual = set(df["period"].unique().to_list()) + unexpected = actual - expected + assert not unexpected, f"{label} calendar contains unexpected period labels: {unexpected}" + + def test_period_coverage_nonsummer_jan_hours(self) -> None: + """Spot-check January 2023 hour→period mapping against expected windows.""" + stou = _build(_STOU_YAML, 2023) + dtou = _build(_DTOU_YAML, 2023) + + import datetime as dt + + # Check a representative hour from each period on a January weekday + # (all nonsummer, no DST complications in January) + expected: list[tuple[dt.datetime, str]] = [ + (dt.datetime(2023, 1, 10, 5, 0), "overnight"), # hour 5 → [21,6) + (dt.datetime(2023, 1, 10, 6, 0), "morning"), # hour 6 → [6,13) + (dt.datetime(2023, 1, 10, 12, 0), "morning"), # hour 12 → [6,13) + (dt.datetime(2023, 1, 10, 13, 0), "midday_peak"), # hour 13 → [13,19) + (dt.datetime(2023, 1, 10, 18, 0), "midday_peak"), # hour 18 → [13,19) + (dt.datetime(2023, 1, 10, 19, 0), "evening"), # hour 19 → [19,21) + (dt.datetime(2023, 1, 10, 20, 0), "evening"), # hour 20 → [19,21) + (dt.datetime(2023, 1, 10, 21, 0), "overnight"), # hour 21 → [21,6) + ] + + for ts, expected_period in expected: + for label, df in [("STOU", stou), ("DTOU", dtou)]: + row = df.filter(pl.col("datetime_chicago") == ts) + assert row.height == 1, f"{label}: expected 1 row for {ts}, got {row.height}" + actual = row["period"].item() + assert actual == expected_period, ( + f"{label}: hour={ts.hour} → expected '{expected_period}', got '{actual}'" + ) + + def test_period_coverage_summer_jul_hours(self) -> None: + """Spot-check July 2023 hour→period mapping (summer season).""" + stou = _build(_STOU_YAML, 2023) + dtou = _build(_DTOU_YAML, 2023) + + import datetime as dt + + expected: list[tuple[dt.datetime, str]] = [ + (dt.datetime(2023, 7, 10, 5, 0), "overnight"), + (dt.datetime(2023, 7, 10, 6, 0), "morning"), + (dt.datetime(2023, 7, 10, 13, 0), "midday_peak"), + (dt.datetime(2023, 7, 10, 19, 0), "evening"), + (dt.datetime(2023, 7, 10, 21, 0), "overnight"), + ] + + for ts, expected_period in expected: + for label, df in [("STOU", stou), ("DTOU", dtou)]: + row = df.filter(pl.col("datetime_chicago") == ts) + assert row.height == 1, f"{label}: expected 1 row for {ts}, got {row.height}" + actual = row["period"].item() + assert actual == expected_period, ( + f"{label}: hour={ts.hour} → expected '{expected_period}', got '{actual}'" + ) diff --git a/tests/test_wide_to_long.py b/tests/test_wide_to_long.py new file mode 100644 index 0000000..957715c --- /dev/null +++ b/tests/test_wide_to_long.py @@ -0,0 +1,230 @@ +from __future__ import annotations + +import datetime as dt + +import polars as pl +import pytest + +from smart_meter_analysis.wide_to_long import transform_wide_to_long_lf + + +def _standard_interval_cols() -> list[str]: + cols: list[str] = [] + # 0030..2400 inclusive, step 30 minutes + for end_minutes in range(30, 1441, 30): + hh = end_minutes // 60 + mm = end_minutes % 60 + hhmm = f"{hh:02d}{mm:02d}" + cols.append(f"INTERVAL_HR{hhmm}_ENERGY_QTY") + return cols + + +def _add_filler_columns_to_reach_59(df: pl.DataFrame) -> pl.DataFrame: + """Add deterministic non-contract filler columns so total wide schema is exactly 59 columns. + + Important: Filler columns MUST NOT match the interval column regex and MUST NOT overlap required columns. + """ + target = 59 + current = len(df.columns) + if current > target: + raise ValueError(f"Test fixture bug: wide df already has {current} columns (>59).") + + needed = target - current + if needed == 0: + return df + + existing = set(df.columns) + # Find the next filler index after any existing FILLER_XX columns to avoid overwrites. + max_idx = 0 + for c in existing: + if c.startswith("FILLER_"): + try: + max_idx = max(max_idx, int(c.split("_", 1)[1])) + except ValueError: + continue + + added = 0 + i = max_idx + 1 + while added < needed: + name = f"FILLER_{i:02d}" + if name not in existing: + df = df.with_columns(pl.lit(f"filler_{i:02d}").alias(name)) + existing.add(name) + added += 1 + i += 1 + + assert len(df.columns) == 59 + return df + + +def _minimal_wide_lf(n: int = 3, *, with_dst_extras: bool) -> pl.LazyFrame: + reading_date = "07/01/2023" + + base = pl.DataFrame({ + "ZIP_CODE": ["60601"] * n, + "DELIVERY_SERVICE_CLASS": ["RES"] * n, + "DELIVERY_SERVICE_NAME": ["COMED"] * n, + "ACCOUNT_IDENTIFIER": [f"acct_{i:03d}" for i in range(n)], + "INTERVAL_READING_DATE": [reading_date] * n, + # Keep as integer dtype to satisfy strict INTERVAL_LENGTH contract. + "INTERVAL_LENGTH": [1800] * n, + "PLC_VALUE": [1.0] * n, + "NSPL_VALUE": [2.0] * n, + }) + + # Standard 48 interval columns: default 0.0 + for c in _standard_interval_cols(): + base = base.with_columns(pl.lit(0.0).alias(c)) + + if with_dst_extras: + # Provide extras (2430/2500) and set specific values for fold-in testing. + base = base.with_columns([ + pl.lit(2.0).alias("INTERVAL_HR2430_ENERGY_QTY"), + pl.lit(4.0).alias("INTERVAL_HR2500_ENERGY_QTY"), + ]) + # Set base values for 2330/2400 so we can assert sums: + base = base.with_columns([ + pl.lit(1.0).alias("INTERVAL_HR2330_ENERGY_QTY"), + pl.lit(3.0).alias("INTERVAL_HR2400_ENERGY_QTY"), + ]) + + # Enforce exact 59-column wide schema required by strict mode. + base = _add_filler_columns_to_reach_59(base) + return base.lazy() + + +def _assert_schema_contract(out: pl.DataFrame) -> None: + assert out.columns == [ + "zip_code", + "delivery_service_class", + "delivery_service_name", + "account_identifier", + "datetime", + "energy_kwh", + "plc_value", + "nspl_value", + "year", + "month", + ] + + sch = out.schema + assert sch["zip_code"] == pl.Utf8 + assert sch["delivery_service_class"] == pl.Categorical + assert sch["delivery_service_name"] == pl.Categorical + assert sch["account_identifier"] == pl.Utf8 + assert sch["datetime"] == pl.Datetime("us") + assert sch["energy_kwh"] == pl.Float64 + assert sch["plc_value"] == pl.Float64 + assert sch["nspl_value"] == pl.Float64 + assert sch["year"] == pl.Int32 + assert sch["month"] == pl.Int8 + + +def test_rows_out_equals_rows_wide_times_48() -> None: + lf = _minimal_wide_lf(n=3, with_dst_extras=True) + out = transform_wide_to_long_lf(lf, strict=True).collect() + assert out.height == 3 * 48 + + +def test_dst_option_b_foldin_and_no_extras_representation() -> None: + lf = _minimal_wide_lf(n=1, with_dst_extras=True) + out = transform_wide_to_long_lf(lf, strict=True).collect() + + # Strong invariant: max datetime is 23:30 and there are exactly 48 unique datetimes for the day. + assert out.select(pl.col("datetime").max()).item() == dt.datetime(2023, 7, 1, 23, 30) + assert out.select(pl.col("datetime").n_unique()).item() == 48 + + # Fold-in checks: + # HR2330 label corresponds to interval start at 23:00 + v_2300 = out.filter(pl.col("datetime") == dt.datetime(2023, 7, 1, 23, 0)).select("energy_kwh").item() + assert v_2300 == pytest.approx(3.0) + + # HR2400 label corresponds to interval start at 23:30 + v_2330 = out.filter(pl.col("datetime") == dt.datetime(2023, 7, 1, 23, 30)).select("energy_kwh").item() + assert v_2330 == pytest.approx(7.0) + + +def test_output_schema_and_dtypes() -> None: + lf = _minimal_wide_lf(n=2, with_dst_extras=False) + out = transform_wide_to_long_lf(lf, strict=True).collect() + _assert_schema_contract(out) + + +def test_deterministic_sort_order_default_true() -> None: + # Default behavior must remain deterministic (sorted). + lf = _minimal_wide_lf(n=5, with_dst_extras=False) + out = transform_wide_to_long_lf(lf, strict=True).collect() + out_sorted = out.sort(["zip_code", "account_identifier", "datetime"]) + assert out.rows() == out_sorted.rows() + + +def test_sort_output_false_preserves_semantics_and_schema() -> None: + # No-sort mode is intended for month-scale validation in constrained environments. + lf = _minimal_wide_lf(n=4, with_dst_extras=True) + out = transform_wide_to_long_lf(lf, strict=True, sort_output=False).collect() + + # Schema/dtypes remain contractually identical. + _assert_schema_contract(out) + + # Row multiplier holds. + assert out.height == 4 * 48 + + # Datetime invariants: max is 23:30, no nulls, 48 unique per day (since all rows are same day). + assert out.select(pl.col("datetime").max()).item() == dt.datetime(2023, 7, 1, 23, 30) + assert out.select(pl.col("datetime").is_null().any()).item() is False + assert out.select(pl.col("datetime").n_unique()).item() == 48 + + # IMPORTANT: do not assert ordering in no-sort mode. + + +def test_strict_mode_rejects_bad_interval_length() -> None: + df = _minimal_wide_lf(n=1, with_dst_extras=False).collect() + + # Keep total columns == 59 by replacing the existing column (no add/drop). + df = df.with_columns(pl.lit(3600).cast(pl.Int32).alias("INTERVAL_LENGTH")) + with pytest.raises(ValueError, match="INTERVAL_LENGTH"): + transform_wide_to_long_lf(df.lazy(), strict=True).collect() + + +def test_strict_mode_rejects_bad_date_format() -> None: + df = _minimal_wide_lf(n=1, with_dst_extras=False).collect() + # Replace only; keep 59 columns invariant. + df = df.with_columns(pl.lit("2023-07-01").alias("INTERVAL_READING_DATE")) + with pytest.raises(ValueError, match="Failed to parse INTERVAL_READING_DATE"): + transform_wide_to_long_lf(df.lazy(), strict=True).collect() + + +def test_strict_mode_rejects_missing_standard_interval() -> None: + df = _minimal_wide_lf(n=1, with_dst_extras=False).collect() + + # Dropping a required standard interval reduces column count. + # To keep 59 columns invariant, drop one filler column as well and then add back a filler column. + df = df.drop(["INTERVAL_HR0030_ENERGY_QTY", "FILLER_01"]) + df = _add_filler_columns_to_reach_59(df) + + with pytest.raises(ValueError, match="missing standard"): + transform_wide_to_long_lf(df.lazy(), strict=True).collect() + + +def test_strict_mode_rejects_unexpected_extra_interval() -> None: + df = _minimal_wide_lf(n=1, with_dst_extras=False).collect() + + # Adding a bogus interval would make 60 columns; keep invariant by dropping a filler column first. + df = df.drop(["FILLER_01"]) + df = df.with_columns(pl.lit(0.0).alias("INTERVAL_HR2415_ENERGY_QTY")) + assert len(df.columns) == 59 + + with pytest.raises(ValueError, match="invalid interval column minutes"): + transform_wide_to_long_lf(df.lazy(), strict=True).collect() + + +def test_forbid_0000_interval_label() -> None: + df = _minimal_wide_lf(n=1, with_dst_extras=False).collect() + + # Adding 0000 interval would make 60 columns; keep invariant by dropping a filler column first. + df = df.drop(["FILLER_01"]) + df = df.with_columns(pl.lit(0.0).alias("INTERVAL_HR0000_ENERGY_QTY")) + assert len(df.columns) == 59 + + with pytest.raises(ValueError, match="ending at 0000"): + transform_wide_to_long_lf(df.lazy(), strict=True).collect() diff --git a/tests/validated_enrichment.py b/tests/validated_enrichment.py deleted file mode 100644 index 2aeaabf..0000000 --- a/tests/validated_enrichment.py +++ /dev/null @@ -1,262 +0,0 @@ -""" -Validation script for enriched ComEd data. - -Performs data quality checks on enriched parquet files to ensure: -- Schema correctness -- Data completeness -- Geographic coverage -- Demographic data integrity -- Time feature preservation - -Usage: - python tests/validate_enriched_data.py data/analysis/202308/enriched.parquet -""" - -from __future__ import annotations - -import argparse -import sys -from pathlib import Path - -import polars as pl - - -class EnrichedDataValidator: - """Validates enriched energy data quality.""" - - def __init__(self, filepath: Path): - self.filepath = filepath - self.df: pl.DataFrame | None = None - self.errors: list[str] = [] - self.warnings: list[str] = [] - - def load_data(self) -> bool: - """Load the parquet file.""" - try: - self.df = pl.read_parquet(self.filepath) - print(f"✓ Loaded {self.filepath}") - print(f" Shape: {self.df.shape[0]:,} rows × {self.df.shape[1]} columns") - return True - except Exception as e: - self.errors.append(f"Failed to load file: {e}") - return False - - def check_required_columns(self) -> bool: - """Verify all required columns are present.""" - required_energy_cols = [ - "zip_code", - "account_identifier", - "delivery_service_class", - "delivery_service_name", - "datetime", - "kwh", - ] - - required_time_cols = ["date", "hour", "weekday", "is_weekend"] - - required_geo_cols = ["block_group_geoid"] - - missing_energy = [c for c in required_energy_cols if c not in self.df.columns] - missing_time = [c for c in required_time_cols if c not in self.df.columns] - missing_geo = [c for c in required_geo_cols if c not in self.df.columns] - - if missing_energy: - self.errors.append(f"Missing energy columns: {missing_energy}") - - if missing_time: - self.errors.append(f"Missing time columns: {missing_time}") - - if missing_geo: - self.errors.append(f"Missing geographic columns: {missing_geo}") - - if not (missing_energy or missing_time or missing_geo): - print("✓ All required columns present") - return True - return False - - def check_data_completeness(self) -> bool: - """Check for null values in critical columns.""" - critical_cols = ["zip_code", "account_identifier", "datetime", "kwh"] - - for col in critical_cols: - if col not in self.df.columns: - continue - - null_count = self.df[col].null_count() - null_pct = (null_count / len(self.df)) * 100 - - if null_pct > 5: - self.errors.append(f"{col}: {null_pct:.1f}% null values (critical)") - elif null_pct > 0: - self.warnings.append(f"{col}: {null_pct:.1f}% null values") - - if not self.errors: - print("✓ Data completeness check passed") - return True - return False - - def check_geographic_coverage(self) -> bool: - """Verify geographic enrichment coverage.""" - total_rows = len(self.df) - matched_rows = self.df.filter(pl.col("block_group_geoid").is_not_null()).shape[0] - match_rate = (matched_rows / total_rows) * 100 - - print(f"✓ Geographic coverage: {match_rate:.1f}%") - print(f" Matched: {matched_rows:,} / {total_rows:,} records") - - if match_rate < 90: - self.errors.append(f"Low geographic match rate: {match_rate:.1f}%") - return False - elif match_rate < 95: - self.warnings.append(f"Geographic match rate below 95%: {match_rate:.1f}%") - - # Check unique block groups - n_block_groups = self.df["block_group_geoid"].n_unique() - print(f" Block groups: {n_block_groups:,}") - - return True - - def check_demographic_data(self) -> bool: - """Verify demographic variables are present and valid.""" - expected_demo_vars = ["Total_Households", "Median_Household_Income", "Avg_Household_Size"] - - present_vars = [v for v in expected_demo_vars if v in self.df.columns] - - if not present_vars: - self.errors.append("No demographic variables found") - return False - - # Count total demographic columns - excluded_cols = { - "zip_code", - "account_identifier", - "datetime", - "kwh", - "date", - "hour", - "weekday", - "is_weekend", - "block_group_geoid", - "delivery_service_class", - "delivery_service_name", - "is_spring_forward_day", - "is_fall_back_day", - "is_dst_day", - } - demo_cols = [c for c in self.df.columns if c not in excluded_cols] - - print(f"✓ Demographic variables: {len(demo_cols)}") - print(f" Examples: {', '.join(present_vars)}") - - return True - - def check_time_features(self) -> bool: - """Verify time features are correctly populated.""" - checks_passed = True - - # Check hour range - if "hour" in self.df.columns: - hour_min = self.df["hour"].min() - hour_max = self.df["hour"].max() - if hour_min < 0 or hour_max > 23: - self.errors.append(f"Invalid hour values: {hour_min} to {hour_max}") - checks_passed = False - else: - print(f"✓ Hour range: {hour_min}-{hour_max}") - - # Check weekday range - if "weekday" in self.df.columns: - weekday_min = self.df["weekday"].min() - weekday_max = self.df["weekday"].max() - if weekday_min < 1 or weekday_max > 7: - self.errors.append(f"Invalid weekday values: {weekday_min} to {weekday_max}") - checks_passed = False - else: - print(f"✓ Weekday range: {weekday_min}-{weekday_max}") - - return checks_passed - - def check_energy_data(self) -> bool: - """Validate energy usage values.""" - if "kwh" not in self.df.columns: - return True - - print("✓ Energy data statistics:") - print(f" Mean: {self.df['kwh'].mean():.3f} kWh") - print(f" Median: {self.df['kwh'].median():.3f} kWh") - print(f" Max: {self.df['kwh'].max():.3f} kWh") - - # Check for negative values - negative_count = self.df.filter(pl.col("kwh") < 0).shape[0] - if negative_count > 0: - self.warnings.append(f"Negative kWh values: {negative_count} records") - - # Check for unreasonably high values (>100 kWh per 30-min interval = 200 kW) - high_count = self.df.filter(pl.col("kwh") > 100).shape[0] - if high_count > 0: - self.warnings.append(f"Very high kWh values (>100): {high_count} records") - - return True - - def run_all_checks(self) -> bool: - """Run all validation checks.""" - print("\n" + "=" * 80) - print(f"VALIDATING: {self.filepath.name}") - print("=" * 80 + "\n") - - if not self.load_data(): - return False - - print() - self.check_required_columns() - print() - self.check_data_completeness() - print() - self.check_geographic_coverage() - print() - self.check_demographic_data() - print() - self.check_time_features() - print() - self.check_energy_data() - - # Print summary - print("\n" + "=" * 80) - print("VALIDATION SUMMARY") - print("=" * 80) - - if self.errors: - print(f"\n❌ FAILED with {len(self.errors)} error(s):") - for err in self.errors: - print(f" - {err}") - else: - print("\n✅ PASSED all critical checks") - - if self.warnings: - print(f"\n⚠️ {len(self.warnings)} warning(s):") - for warn in self.warnings: - print(f" - {warn}") - - print() - - return len(self.errors) == 0 - - -def main(): - parser = argparse.ArgumentParser(description="Validate enriched ComEd data quality") - parser.add_argument("filepath", type=Path, help="Path to enriched parquet file to validate") - - args = parser.parse_args() - - if not args.filepath.exists(): - print(f"ERROR: File not found: {args.filepath}") - sys.exit(1) - - validator = 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