Biogas reporting SPA build to replace spreadsheet (Tioga and Maxson)

Date: 2025 11-November 25

yo 

i have a stack like this:

python uvicorn server
typer CLI
PYZ export is crucial
serve web stack graphics
currently html and alpine js but aiming for svelte
plotting in plotly
database not yet incorporated but aiming for duck db
I have my API clients defined with python classes but i want to use node red 
msgspec and starlette, not fast api and pydantic

i want to use this stack to built a SPA that allows users to input daily field data (or to accept data from an API using daily daemon calls), to store the data, and to enable calcualtions and monthly reporting

this will replace a spreadsheet

is this a good stack for this?

this could be a web app but it is more about using a single file PYZ app to run a local app on any device leveraging localhost browser based gui interaction or terminal cli interaction

I use termux

the pyz should be a fat pyz to bundle the msgpec._core for each OS such that it is truly crossplatform

That's an excellent and modern stack for building a local, cross-platform, single-file application with both a browser-based GUI and a CLI for data management.

Your choices demonstrate a strong understanding of performance, minimal dependencies, and modern Python tooling for a self-contained deployment.

🧐 Stack Assessment and Rationale

🛠️ Key Considerations for this Stack

1. The PYZ and Cross-Platform Challenge

Creating a "fat PYZ" that correctly bundles native extensions like msgspec._core for multiple operating systems (Linux/Termux, Windows, macOS) is the most challenging part.

• Strategy: You'll need a robust build process (e.g., using shiv, pex, or a similar tool in conjunction with a multi-OS build environment like Docker or GitHub Actions) to compile the PYZ on each target OS or architecture. The resulting PYZ file will likely only work on the platform it was built on, unless you specifically design a system to distribute the correct PYZ to the user (e.g., app-linux.pyz, app-win.pyz).

2. Svelte Integration (SPA)

The Starlette app will primarily serve as a JSON API.

• Serving: Your Starlette app will need to serve the static assets (HTML, CSS, compiled JS) from your Svelte build. You can use Starlette's StaticFiles middleware.

• Routing: Starlette handles the API routes (e.g., /api/data). Svelte handles the frontend routing (e.g., /dashboard, /input). Since it's a SPA, Starlette only needs to serve the main index.html file for any unmatched frontend route (often done by placing a catch-all route in Starlette after all API routes).

3. Termux Compatibility

The stack is highly Termux-compatible.

• Uvicorn/Starlette: Runs perfectly on the standard Python environment available in Termux.

• Typer/CLI: Standard Python CLI that works as expected.

• DuckDB: Python package works natively on Termux (Arm/Android).

• GUI: The web application opens in the device's browser by accessing localhost:port.

This stack is a very suitable and powerful choice for your goals, offering high performance, minimal dependencies, and a single-file deployment strategy.

Excellent

  

what would be better than plotly, charts js?

  

node red is hot right now and i light the idea of quick graphical set up but maybe python class clients are the right answer. Node red has more bumpers and support and clarity. Whichever is most postman esque. I think node red gets me more documentation - msgspec and starlette don't auto-document i think.

  

I think i can deal with the fat PYZ manually but running my shiv build with no PYC on each system i am targeting, extracting the pyz as a zip, fining the msgspec._core.so file, etc, copying these all to an unextracted package dir, rezipping, changing the extension to PYZ, and enjoy. Correct? No reason to CI/CD to a QEMU VM, just manual.

  

Do i want SPA? WIll it hurt? I have never built a SPA. I know people like svelte. Of course I need to offer a full experience, with sections for entry, viewing, reporting, analysis, etc

This is a great set of follow-up questions that refine your architecture! Here is an assessment of your plotting, client, PYZ, and SPA strategy.

📈 Plotting: Better Alternatives to Plotly/Chart.js

Since you are aiming for a lightweight Svelte SPA, Plotly and Chart.js are generally fine, but you can find options that integrate more natively and are even lighter.

The best alternatives will be those with first-class Svelte support or those built on highly optimized graphics standards.

Verdict: If you need interactive analysis and complex financial/scientific charts, Plotly is still a strong choice. If you want a lighter, Svelte-native feel and don't need Plotly's specific features, look into ApexCharts or ECharts.

🤖 Node-RED vs. Python Clients

You are right that this choice comes down to ease of setup/flow versus performance/developer-centricity.

Verdict:

• For the Daily Daemon Calls (API-to-API), Node-RED is a fantastic choice. Its flow-based logic for scheduling, calling your Starlette API, handling the response, and then inserting into DuckDB is highly visual, easy to debug, and requires minimal custom code.

• For the core calculation logic that lives inside your PYZ app (which is the more performance-critical part), sticking with Python classes is the right answer.

📦 PYZ Bundling with Native Extensions (The "Fat PYZ")

Your manual process is correct for achieving a truly cross-platform "fat PYZ" with native extensions when using a tool like Shiv or Pex.

Your Manual Process:

1. Build PYZ (Shiv): shiv builds a PYZ for the current OS.

2. Extract: Treat the PYZ like a ZIP and extract it.

3. Locate Native Files: Find the OS-specific native files (e.g., msgspec._core.so, .dll, or .dylib) that were compiled on the other target systems.

4. Copy/Replace: Insert all necessary native files for all supported OSes into the structure of the extracted PYZ.

5. Re-zip/Re-PYZ: Re-package the entire structure into a single PYZ file.

Why this is necessary: Standard build tools like shiv or pex can only include the native extensions (.so, .dll, etc.) for the system they are run on. By running the build on all target systems, extracting the files, and manually combining them, you create the "fat" package that will contain all variants. At runtime, Python will automatically load the correct native file for the host OS.

CI/CD vs. Manual: For a single-file internal application, your manual process is perfectly acceptable. Using CI/CD with QEMU VMs is the "proper" enterprise way to do this automatically, but it adds significant complexity that you don't need if you only support two or three OSes and can test manually.

💻 Single-Page Application (SPA) Strategy

Do you want SPA? Yes.

The goal of replacing a spreadsheet with a local, browser-based GUI that runs from a single file is perfectly aligned with the Single-Page Application (SPA) model.

Will it hurt? No, not with Svelte.

Unlike older, heavier frameworks, Svelte is specifically designed to be light and fast.1 Because your backend (Starlette + msgspec + DuckDB) is also extremely fast and minimal, the combination will feel snappy and responsive—a huge upgrade from a spreadsheet.

Great

  

People have mentioned firebase as a strong choice for simple databasing, in terms of speed to market and keeping a simple stack. I know duck db is optimized for columnar and time series data, but i might just have single daily entry. Compare firebase with duckdb. Or I could just use CSV, JSON, etc.

  

It sounds like you are inferring that the nonde red experience cannot fully be packaged into the PYZ. Especially if i has an editing section. Could this be done? Using a PYZ to package web apps locally exceeds their designed purpose, but I love the idea of running local cross platform, and the web stack canopy provides this and allows additional super user CLI to come included.

  

This project is also about practicing a stack that I can use for years to come. To be the one developer in the room who is not a web dev but leverages the web tools.

  

I am also interested in rust ffi and wasm, though this exceeds a simple stack (but allows migration from python elements to rust - to consider PYZ pacakging holding rust and also tauri packaging holding python). I think i misunderstand WASM - does it allow me to provide more power to my apps, like cordova-style linkage between the web interface and the local machine (GPS, Microphone, etc), or is it just for building apps in languages that were previously non-web?

That's a fantastic set of questions that get to the heart of building a durable, minimal, and performant local data application.

Here is a breakdown of your choices.

💾 Database Comparison: DuckDB vs. Firebase vs. Files

For your specific use case—a local, single daily entry followed by calculations and monthly reporting—DuckDB is the clear winner and aligns best with your existing stack goals.

Conclusion:

• Firebase is a non-starter for a local, self-contained, PYZ-based app as it requires an external server. Its main benefit (real-time cloud syncing) is irrelevant to your local use case.

• CSV/JSON is too simple. You need robust query, calculation, and reporting capabilities, which will be tedious and slow to hand-code in Python without an analytical engine.

• DuckDB gives you the convenience of a single file, plus the power of an analytical SQL engine, which is necessary for your calculation and reporting requirements.1 Stick with DuckDB.

📦 Node-RED Packaging in PYZ

You are correct in your inference: Fully packaging the Node-RED environment and editor into a single PYZ is technically possible but significantly exceeds the intended purpose of PYZ and adds monumental complexity.

Why it's difficult:

1. Node-RED Dependency: Node-RED is built on Node.js. Your PYZ is a Python executable. Packaging a complete Node.js runtime and all its dependencies (npm) for cross-platform use inside a Python archive is a colossal task.

2. External Components: Node-RED is designed to install new nodes dynamically from the internet. Embedding the full editing experience means you need to package the entire Node-RED web server and its dependencies, which is a full application stack itself, not just a library.

3. Cross-Platform Pain: Your PYZ must contain the correct Node.js runtime binary for every targeted OS (Windows, macOS, Linux/Termux), plus all the JavaScript files, making the "fat PYZ" problem exponentially harder than just bundling msgspec._core.

Recommendation:

If you want to use Node-RED:

• Best Hybrid Approach: Keep your Starlette/DuckDB PYZ as the Core API/Data Engine. Run Node-RED separately (or package it separately using a tool like pkg for Node.js) and have it call your Starlette API endpoints.

• CLI Alternative: Leverage Typer to handle all daily entry and daemon API calls. The Typer CLI is minimal, robust, and perfect for your "super user CLI" idea.

🚀 WASM (WebAssembly) and FFI/Rust

Your interest in WASM and Rust FFI (Foreign Function Interface) aligns perfectly with the "one developer not a web dev who leverages web tools" philosophy and preparing for the future.

1. WASM: What it is and is not

2. Rust FFI and Migration

You have a clear, multi-year strategy here:

1. Phase 1 (Now): Build everything in Python (Starlette/msgspec) for speed and ease of initial development.

2. Phase 2 (Optimization): Use Rust FFI to re-write a few CPU-intensive core calculation functions from Python into Rust, compiling them as a C-compatible library (e.g., a .so file) that your Python Starlette app can call directly. This gives you Rust speed without rewriting the entire API.

3. Phase 3 (Future-Proofing): If you decide to fully migrate or go beyond the local app, you could use Tauri (Rust-based) to bundle your Svelte web app and either the Python PYZ (as an internal tool) or the newly created Rust binary. This would give you true desktop-app system access and a modern, compact deployment.

Your current Python stack is the ideal launchpad for a future Rust FFI integration.

Sounds like i was way off base with WASM

  

As for your "wrapper" does this need to be tauri? Also we should discuss pyinstaller, for generating EXE and ELF

  

Since we are not aiming to be a web app but rather a local app (PYZ, or source code, or EXE and ELF via tauri or pyinstaller) I imagine these can commune directly with the local system for microphone and GPS access, though the hardware APIs will differ and must be a consideration if the goal is cross platform use. So even though this looks like a web app if launched in the browser (or in a native-look window), it should still be able to access web based permissions, also the bundled python requires a server but i think that tauri does not.

  

you mention a nodejs runtime - this is necessary for node red, but other wise we can just leverage python runtime a la the uvicorn server?

  

am i making this harder than is has to be?

  

you mention a multi year plan. I am trying to build out a stack i can use for years to come to build out any applications I want. Web graphics are obvious of course. As for this particular spreadsheet migration app, i could probably get a working version in a week?

That is absolutely not harder than it has to be—it’s a brilliant and forward-thinking plan for a long-term, cross-platform local app stack. You're combining the best of performance, deployment simplicity, and development future-proofing.

Your initial assumption about WASM was only slightly off: it's for performance and portability in the sandbox. The key missing piece for system access is the wrapper, and you have the right idea with Tauri.

💻 Wrapper and System Access: Tauri vs. PyInstaller

You are correct that PyInstaller and Tauri have different roles, and Tauri is the key to local system access for your web GUI.

Conclusion:

• No, the wrapper does not need to be Tauri, but it is currently the most modern, lightweight, and secure option (using the system's native WebView, unlike Electron, which bundles a full browser).1 Wails (Go) and PyTauri are alternatives.2

• The Power Trio: The path for system access and a sleek window is: Svelte Frontend $\to$ Tauri (Rust) $\to$ PyInstaller EXE/ELF (your Starlette/DuckDB Python app).

🐍 Python Runtime and Server

You are absolutely correct.

• The Node.js runtime is only required if you want to run the Node-RED flow environment.

• For your core application, you can just leverage the Python runtime and your high-performance Uvicorn/Starlette server. Uvicorn is lightweight enough that starting it as a background "sidecar" process inside the Tauri bundle is a standard and effective pattern.

⏱️ Stack Durability and Development Timeline

The Multi-Year Plan:

Your chosen stack is one of the best available for future-proofing:

1. Starlette/msgspec/Svelte/DuckDB: Minimal, high-performance core.

2. Rust/Tauri Path: The natural evolution for a local app developer who wants better performance and native system integration.

3. Typer/CLI: Gives you a powerful, automatable escape hatch that will never be obsolete.

This stack will let you be the developer who "is not a web dev but leverages the web tools" effectively and powerfully.

Time to Market (Spreadsheet Migration App)

Can you get a working version in a week? Yes, a Minimum Viable Product (MVP) is highly achievable in a week.

The initial version can run directly in the browser via localhost (using your PYZ). The wrapper and system access can be Phase 2.

This video discusses using Python for GPS tracking, which is relevant to your long-term goal of accessing hardware APIs like GPS from your bundled Python application. GPS Tracking with Python

  
let's get started

  

we will use uv for dependency management

  

 oolong@CIT-36NZRL3  ~/dev  ls biogas-tracker

 oolong@CIT-36NZRL3  ~/dev  cd biogas-tracker

 oolong@CIT-36NZRL3  ~/dev/biogas-tracker  mkdir build-scripts

 oolong@CIT-36NZRL3  ~/dev/biogas-tracker  mkdir src

 oolong@CIT-36NZRL3  ~/dev/biogas-tracker  mkdir gastrack

 oolong@CIT-36NZRL3  ~/dev/biogas-tracker  cd ..

 oolong@CIT-36NZRL3  ~/dev  mv biogas-tracker gastrack

 oolong@CIT-36NZRL3  ~/dev  cd hastrack

cd: no such file or directory: hastrack

 ✘ oolong@CIT-36NZRL3  ~/dev  cd gastrack

 oolong@CIT-36NZRL3  ~/dev/gastrack  ls

build-scripts  gastrack  src

 oolong@CIT-36NZRL3  ~/dev/gastrack  mv gastrack src/gastrack

 oolong@CIT-36NZRL3  ~/dev/gastrack  ls src

gastrack

 oolong@CIT-36NZRL3  ~/dev/gastrack  mkdir gastrack/flows/

mkdir: cannot create directory ‘gastrack/flows/’: No such file or directory

 ✘ oolong@CIT-36NZRL3  ~/dev/gastrack  mkdir src/gastrack/flows/

 oolong@CIT-36NZRL3  ~/dev/gastrack  mkdir src/gastrack/clients/

 oolong@CIT-36NZRL3  ~/dev/gastrack  mkdir src/gastrack/core

 oolong@CIT-36NZRL3  ~/dev/gastrack  mkdir src/gastrack/webgui

 oolong@CIT-36NZRL3  ~/dev/gastrack  mkdir src/gastrack/cli/

 oolong@CIT-36NZRL3  ~/dev/gastrack  [6965:6965:1125/134558.344394:ERROR:components/sync/model/processor_entity.cc:180] Metadata has an exisiting server id that will be overwritten with the current update entity id.metadata server_id: 2d4f9284-86e1-4ad6-8f0f-bb7c3aa1f39f update.entity.id: 606084cd-b778-49e0-b982-b4773858bc65

  

 ✘ oolong@CIT-36NZRL3  ~/dev/gastrack  mv src/gastrack/clients src/gastrack/api

 oolong@CIT-36NZRL3  ~/dev/gastrack  mkdir src/gastrack/db

 oolong@CIT-36NZRL3  ~/dev/gastrack  mkdir src/gastrack/crypto

 oolong@CIT-36NZRL3  ~/dev/gastrack  mkdir frontend

 oolong@CIT-36NZRL3  ~/dev/gastrack  rm src/gastrack/webgui

rm: cannot remove 'src/gastrack/webgui': Is a directory

 ✘ oolong@CIT-36NZRL3  ~/dev/gastrack  rm -r src/gastrack/webgui

 oolong@CIT-36NZRL3  ~/dev/gastrack  mkdir node-red

 oolong@CIT-36NZRL3  ~/dev/gastrack  rm -r src/gastrack/flows

 oolong@CIT-36NZRL3  ~/dev/gastrack  uv init

Initialized project `gastrack`

 oolong@CIT-36NZRL3  ~/dev/gastrack   master 

  

the usually i would use src/gastrack/cli.py but to have src/gastrack/cli/ allows for multiples to be build, maybe one for each client if more clients are added, to be decoupled from the actual client classes in src/gastrack/api/

  

I want you to provide shell scripts that i can add to /build-scripts

  

these shell scripts should use cat EOF to build the contents of the other files

  

your job right now is not the clients but the svelte interface and the necessary server, in frontend (which should maybe go in src, because it is a coupled graphical interface?)

  

before you provide a bunch of example-like code that i will have to rewrite, let me show you the actual spreadsheet

  

here is some various the raw inputs

  

### Sheet: BGFlow2

|Biogas Flow Data from 4 BioRem Bioreactor Tank Flowmeters|   |   |   |   |   ||Daily Average Flow Data from 4 BioRem Bioreactor Tank Flowmeters|   |   |   |   |   |

|Date/Time|Tank 1 [scfm]|Tank 2 [scfm]|Tank 3 [scfm]|Tank 4 [scfm]|Total [scfm]||Date|Daily Average Treated Biogas Flow [scfm]|Daily treated Biogas Flow [scf/day]|Daily Treated Biogas Flow [MMscf/day]|Daily Biogas Flared [scf/day]|Daily Biogas Transfer to TVA [scf/day]|

|2/1/22 1:07|249.25|267.00|253.75|199.88|969.88||2/1/2022|985.92|1,419,729|1.420|1,419,729|0|

|2/1/22 2:14|247.50|272.75|256.00|200.38|976.63||2/2/2022|960.89|1,383,686|1.384|1,383,686|0|

|2/1/22 3:21|264.13|257.50|256.50|203.00|981.13||2/3/2022|946.05|1,362,305|1.362|1,362,305|0|

  

### sheet BGFLOW 1:

|Biogas Flow Data from Lagoon Blower Buildings|   |   |   |   |   |   |   |

|Date|Blowers 1, 2A, 2B [scf/day]|Blower 2C [scf/day]|BioRem Ambient Air Injection [scf/day]|Total Prod [scf/day]|Biogas Flared [scf/day]|Total Biogas Transfer to TVA [scf/day]|Comment|

|5/3/2021||||-|-|-||

|5/4/2021||||-|-|-||

|5/5/2021||||-|-|-||

|5/6/2021|440,640|792,960||1,233,600|1,233,600|-||

  

### Sheet 1:

||Date|Time|O2 [%]|CO2 [%]|H2S dil. [ppm]|CH4 [%]|Net cal. val. [MJ/m³]|Gross cal. val. [MJ/m³]|T-sensor [°F]|Balance N2 [%]|Date + Time|Net BTUs [btu/cf]|Gross BTUs [btu/cf]|

|1|5/3/2021|12:32:57|7.50|29.21|3,723|36.80|26.5|13.2|14.7|89.0|44,319.52|711|354|

|1|5/3/2021|13:25:34|6.84|31.51|4,208|37.46|24.2|13.4|14.9|90.0|44,319.56|650|360|

|1|5/3/2021|14:37:34|9.72|24.42|3,087|31.38|34.5|11.3|12.5|90.5|44,319.61|926|303|

|1|5/3/2021|15:49:34|6.11|31.11|4,045|41.48|21.3|14.9|16.5|91.0|44,319.66|572|400|

|1|5/3/2021|17:01:34|2.64|40.70|6,133|49.09|7.6|17.6|19.5|90.0|44,319.71|204|472|

  

  

### Sheet 6

||Date|Time|O2 [%]|CO2 [%]|H2S dil. [ppm]|CH4 [%]|Net cal. val. [MJ/m³]|Gross cal. val. [MJ/m³]|T-sensor [°F]|Balance N2 [%]|Date + Time|Net BTUs [btu/cf]|Gross BTUs [btu/cf]||Date|O2 [%]|CO2 [%]|H2S dil. [ppm]|CH4 [%]|Net cal. val. [MJ/m³]|Gross cal. val. [MJ/m³]|T-sensor [°F]|Balance N2 [%]|Net BTUs [btu/cf]|Gross BTUs [btu/cf]|Ratio Net-BTU /CH4%|

||||||||||||||||Avg =|4.66|25.17|2,424|48.64|14.2|15.8|73.9|29.2|465|423||

||||||||||||||||Median =|3.99|26.93|1,640|49.51|15.0|16.7|71.8|26.2|475|447||

||||||||||||||||Max =|19.73|43.04|22,988|185.99|22.0|24.4|98.4|82.4|1,795|656||

|6|6/25/2021|20:25:35|1.57|31.80|4,928|62.97|22.6|25.1|86.5|3.7|44,372.85|607|674||6/25/2021|2.04|31.07|4,828|61.33|22.0|24.4|85.7|5.6|591|656|9.63|

|6|6/25/2021|21:37:34|1.93|31.42|4,858|61.69|22.1|24.6|85.5|5.0|44,372.90|593|660||6/26/2021|3.74|27.98|4,308|56.43|20.2|22.5|90.1|11.9|543|603|9.63|

|6|6/25/2021|22:49:34|2.62|30.00|4,699|59.34|21.3|23.6|85.0|8.0|44,372.95|572|633||6/27/2021|4.05|27.52|4,367|55.70|20.0|22.2|92.5|12.7|536|596|9.63|

|6|6/26/2021|0:01:35|2.80|29.95|4,591|59.19|21.2|23.6|84.0|8.0|44,373.00|569|633||6/28/2021|4.37|27.37|4,320|54.23|19.5|21.6|92.9|14.0|522|579|9.63|

|6|6/26/2021|1:13:34|3.27|29.31|4,581|58.03|20.8|23.1|83.0|9.4|44,373.05|558|620||6/29/2021|4.31|28.64|4,535|53.25|19.1|21.2|93.3|13.8|513|569|9.63|

|6|6/26/2021|2:25:34|3.46|28.53|4,404|57.07|20.5|22.7|82.0|10.9|44,373.10|550|609||6/30/2021|4.24|28.16|4,233|53.67|19.3|21.4|87.5|13.9|517|574|9.63|

|6|6/26/2021|3:37:35|2.99|28.98|4,431|58.55|21.0|23.3|81.5|9.5|44,373.15|564|625||7/1/2021|4.69|26.86|4,044|52.69|18.9|21.0|83.1|15.8|507|563|9.63|

|6|6/26/2021|4:49:34|3.11|28.89|4,443|58.14|20.9|23.1|81.0|9.9|44,373.20|561|620||7/2/2021|3.62|29.02|4,558|55.92|20.1|22.3|81.6|11.5|539|598|9.63|

|6|6/26/2021|6:01:35|4.10|27.54|4,272|55.18|19.8|22.0|81.5|13.2|44,373.25|531|591||7/3/2021|3.29|30.04|4,813|56.76|20.4|22.6|79.7|10.0|547|607|9.63|

|6|6/26/2021|7:13:35|4.59|26.44|4,091|53.34|19.1|21.2|84.5|15.6|44,373.30|513|569||7/4/2021|3.40|29.22|4,801|56.96|20.4|22.7|83.5|10.4|549|609|9.63|

  

### Sheet: BGcalcs 1

|Date|Inlet Biogas H2S [ppm]|CH4 [%]|Biogas Net BTUs [btu/cf]|Biogas Gross BTUs [btu/cf]||Date|Total Prod [scf/day]|Total Biogas Flared [scf/day]|Total Biogas Transfer to TVA [scf/day]||Flared Biogas BTU [Mmbtu /day]|NOx Flared [lbs/day]|CO Flared [lbs/day]|VOC Flared [lbs/day]|H2S Flare [scf/day]|SO2 Pro-duced by Flare (80%) [lbs/day]||Date [mo-yr]|Avg Biogas H2S [ppm]|Avg Biogas Net BTUs [btu/cf]|Avg Biogas Gross BTUs [btu/cf]|Total Biogas Prod [scf/mo]|Total Biogas Flared [scf/mo]|TVA Unit Total Usage [scf/mo]||Date [mo-yr]|Heat Flared Biogas [MMbtu/mo]|NOx Flared Biogas [lbs/mo]|CO Flared Biogas [lbs/mo]|VOC Flared Biogas [lbs/mo]|NOx Flared Biogas [tns/mo]|CO Flared Biogas [tns/mo]|VOC Flared Biogas [tns/mo]|H2S Flared Biogas [scf/mo]|SO2 Flare Produced [lbs/mo]|SO2 Flare Produced [tns/mo]|

|5/3/2021|4,573|41.5|528|400||5/3/2021|-|-|-||-|-|-|-|-|-||Jan-20|756|373||33229044|33229044|||Jan-20|12380|842|3838|72|0|2|0|25107|3389|2|

|5/4/2021|3,790|35.0|787|338||5/4/2021|-|-|-||-|-|-|-|-|-||Feb-20|1375|368||28626218|28626218|||Feb-20|10536|716|3266|61|0|2|0|39349|5312|3|

|5/5/2021|3,596|36.5|767|352||5/5/2021|-|-|-||-|-|-|-|-|-||Mar-20|771|270||35473250|35473250|||Mar-20|9569|651|2967|56|0|1|0|27341|3691|2|

|5/6/2021|3,564|39.0|709|376||5/6/2021|1,233,600|1,233,600|-||874.2|59.45|271.0|5.07|4,396|594||Apr-20|904|362||25944371|25944371|||Apr-20|9387|638|2910|54|0|1|0|23465|3167|2|

|5/7/2021|3,553|42.9|602|413||5/7/2021|1,259,136|1,259,136|-||757.6|51.52|234.9|4.39|4,474|604||May-20|1305|386||43766188|43766188|||May-20|16885|1148|5234|98|1|3|0|57120|7710|4|

  

### Sheet: BG calcs 6:

|Date|Biorem Outlet Biogas H2S [ppm]|Biorem Outlet Biogas Methane [%]|Biogas Net BTUs [btu/cf]|Biogas Gross BTUs [btu/cf]||Date|Total Prod [scf/day]|Total Biogas Flared [scf/day]|Total Biogas Transfer to TVA [scf/day]||TVA Biogas BTU [MMbtu /day]|Flared Biogas BTU [MMbtu /day]|NOx Flared [lbs/day]|CO Flared [lbs/day]|VOC Flared [lbs/day]|H2S Flare [scf/day]|SO2 Pro-duced by Flare (100%)* [lbs/day]||Date [mo-yr]|Avg Biogas H2S [ppmv]|Avg Biogas Net BTUs [btu/cf]|Avg Biogas Gross BTUs [btu/cf]|Total Biogas Prod [scf/mo]|TVA Unit Total Usage [scf/mo]|Total Biogas Flared [scf/mo]||Date [mo-yr]|Heat Flared Biogas [MMbtu /mo]|NOx Flared Biogas [lbs/mo]|CO Flared Biogas [lbs/mo]|VOC Flared Biogas [lbs/mo]|NOx Flared Biogas [tns/mo]|CO Flared Biogas [tns/mo]|VOC Flared Biogas [tns/mo]|H2S Flared Biogas [scf/mo]|SO2 Flare Produced [lbs/mo]|SO2 Flare Produced [tns/mo]|

|6/25/2021|4,828|61|591|656||6/25/2021|1,167,120|1,167,120|-||-|689|110.3|11.72|4.00|5,635|761||Jul-21|4,368|535|593|32,352,288|-|32,352,288||Jul-21|17,315|2,770|294|100|1.39|0.15|0.05|141,166|19,055|9.53|

|6/26/2021|4,308|56|543|603||6/26/2021|1,170,480|1,170,480|-||-|636|101.8|10.81|3.69|5,042|681||Aug-21|4,068|528|586|46,437,691|-|46,437,691||Aug-21|24,379|3,901|414|141|1.95|0.21|0.07|184,487|24,903|12.45|

|6/27/2021|4,367|56|536|596||6/27/2021|995,760|995,760|-||-|534|85.4|9.08|3.10|4,348|587||Sep-21|2,147|290|321|49,373,976|-|49,373,976||Sep-21|14,845|2,375|252|86|1.19|0.13|0.04|103,696|13,997|7.00|

  

  

  

  

  

There are sheets 1,2,3,4,5,6.

Sheets 2,3,4,5 correlate with measurements from the four biorem tanks

sheet1 is for a measurement point before the tanks

sheet 6 is for a measurement point after the tanks

there is a MRU SWG 100 analyzer that should be storing this stuff automatically, but oftn handheld MRU optimax units need to be used becuase the SWG 100 is often out of calibration or other wise out of service

  

There are pages BGflow1, bgflow2, bg cals1, and bg cals6 which is for some input for for a lot of aggregation

  

a lot of what is have shared with you are calculated values rather than raw input, but they are mixed, with inputs on the lefts and calcs on the right

it would be better if i could paste excel function contents

  

this might not be everything you need to succeed.

If a person fudged an input, what work flow in the CLI or svelte interface would they use to fix it?

  

oh, this is the monthly report structure

  

|TE Maxson WWTF Biogas Data & Calculations, September 1 through 30, 2025|   |   |   |   |   |   |   |   |   |   |   |   |   ||||

||||||||||||||||||

|H2S ppm/grains/ccf =|   |   |   |16|HHV [BTU/scf]/100% CH4 =|   |   |   |10.4||||||||

|Date|**Total Biogas Prod. [MMscf /day]||**Total Biogas Flared [MMscf /day]|* TVA Unit Total Transferred [MMscf /day]|Avg. Daily Biogas H2S -- Inlet [ppm]|Avg. Daily Biogas H2S -- Outlet [ppm]|Avg. Daily Biogas H2S -- Inlet [grains /ccf]|Avg. Daily Biogas H2S -- Outlet [grains /ccf]|Avg. Daily Treated Biogas CH4 [%]|Avg. Daily Biogas BTUs /scf|Daily Biogas MMbtu /day Prod.|Daily Biogas MMbtu /day flared|Daily Biogas TVA MMbtu /day Transferred||Resid. H2S After Treatment|H2S Reduction|

|9/1/2025|0.472||0.472|0.000|1,951|1,949|121.9|121.8|46.5|483.5|228.4|228.4|0.0||99.90%|0.10%|

|9/2/2025|0.379||0.379|0.000|2,027|1,975|126.7|123.4|43.4|451.3|170.9|170.9|0.0||97.43%|2.57%|

|9/3/2025|0.000||0.000|0.000|N/A|N/A|N/A|N/A|N/A|N/A|0.0|0.0|0.0||||

|9/4/2025|1.228||1.228|0.000|3,416|3,411|213.5|213.2|71.7|745.3|915.4|915.4|0.0||99.85%|0.15%|

|9/5/2025|1.153||1.153|0.000|3,651|3,712|228.2|232.0|71.1|739.6|852.6|852.6|0.0||101.68%|-1.68%|

|9/6/2025|0.000||0.000|0.000|N/A|N/A|N/A|N/A|N/A|N/A|0.0|0.0|0.0||||

|9/7/2025|0.000||0.000|0.000|N/A|N/A|N/A|N/A|N/A|N/A|0.0|0.0|0.0||||

|9/8/2025|0.742||0.742|0.000|3,639|3,582|227.4|223.9|72.5|753.9|559.1|559.1|0.0||98.43%|1.57%|

|9/9/2025|0.791||0.791|0.000|3,667|3,675|229.2|229.7|72.5|754.3|596.3|596.3|0.0||100.22%|-0.22%|

|9/10/2025|0.779||0.779|0.000|3,649|3,701|228.1|231.3|72.4|752.6|586.3|586.3|0.0||101.43%|-1.43%|

|9/11/2025|0.811||0.811|0.000|3,855|39|240.9|2.4|72.6|754.6|611.8|611.8|0.0||1.00%|99.00%|

|9/12/2025|0.743||0.743|0.000|3,285|3,289|205.3|205.6|71.3|741.6|551.1|551.1|0.0||100.12%|-0.12%|

|9/13/2025|0.000||0.000|0.000|N/A|N/A|N/A|N/A|N/A|N/A|0.0|0.0|0.0||||

|9/14/2025|0.000||0.000|0.000|N/A|N/A|N/A|N/A|N/A|N/A|0.0|0.0|0.0||||

|9/15/2025|0.541||0.541|0.000|233|228|14.6|14.3|31.2|324.0|175.4|175.4|0.0||97.85%|2.15%|

|9/16/2025|1.004||1.004|0.000|2,275|2,240|142.2|140.0|71.8|746.4|749.2|749.2|0.0||98.46%|1.54%|

|9/17/2025|0.737||0.737|0.000|736|718|46.0|44.9|57.2|594.8|438.5|438.5|0.0||97.55%|2.45%|

|9/18/2025|0.779||0.779|0.000|2,947|2,931|184.2|183.2|71.4|742.1|578.2|578.2|0.0||99.46%|0.54%|

|9/19/2025|0.749||0.749|0.000|3,359|3,330|209.9|208.1|70.7|735.3|550.6|550.6|0.0||99.14%|0.86%|

|9/20/2025|0.786||0.786|0.000|3,228|3,202|201.8|200.1|71.9|748.2|588.2|588.2|0.0||99.19%|0.81%|

|9/21/2025|0.592||0.592|0.000|172|139|10.8|8.7|26.5|275.2|162.9|162.9|0.0||80.81%|19.19%|

|9/22/2025|0.750||0.750|0.000|2,122|2,048|132.6|128.0|71.3|741.8|556.6|556.6|0.0||96.51%|3.49%|

|9/23/2025|0.687||0.687|0.000|2,872|2,770|179.5|173.1|74.2|772.1|530.3|530.3|0.0||96.45%|3.55%|

|9/24/2025|0.000||0.000|0.000|N/A|N/A|N/A|N/A|N/A|N/A|0.0|0.0|0.0||||

|9/25/2025|0.000||0.000|0.000|N/A|N/A|N/A|N/A|N/A|N/A|0.0|0.0|0.0||||

|9/26/2025|0.000||0.000|0.000|N/A|N/A|N/A|N/A|N/A|N/A|0.0|0.0|0.0||||

|9/27/2025|0.000||0.000|0.000|N/A|N/A|N/A|N/A|N/A|N/A|0.0|0.0|0.0||||

|9/28/2025|0.000||0.000|0.000|N/A|N/A|N/A|N/A|N/A|N/A|0.0|0.0|0.0||||

|9/29/2025|0.300||0.300|0.000|2,261|2,247|141.3|140.4|72.0|748.3|224.1|224.1|0.0||99.38%|0.62%|

|9/30/2025|0.000||0.000|0.000|N/A|N/A|N/A|N/A|N/A|N/A|0.0|0.0|0.0||||

||||||||||||||||||

|Totals =|14.022||14.022|0.000|||||||9,626|9,626|0.0||||

|Averages =|0.467||0.467|0.000|2,633|2,402|164.6|150.1|64.8|673.4|324.1|324.1|0.0||91.23%|8.77%|

||||||||||||||||||

|*Note:|Total Prod. -- Total Flared = Total TVA Usage|   |   |   |   |   |||||||||||

|Notes|BioRem bioreactor tank #4 was the only  reactor utilized due to on-going control panel issues. Biogas was drawn from the covered sludge lagoons to be treated through the Biorem system and flared during the month of September 2025. All biogas readings were taken using the Optima 7 handheld biogas analyzer.|   |   |   |   |   |   |   |   |   |   |   |   ||||

||"N/A" is "Not Applicable" because no biogas was produced, treated or flared|   |   |   |   |   |   |   |   |   |   |   |   ||||

||||||||||||||||||

||Total Monthly Biogas Produced [MMbtu] =|   |   |   |   |   ||9,626|||||||||

||Total Monthly Biogas Flared [MMbtu] =|   |   |   |   |   |   |9,626|||||||||

||Total Monthly Biogas Transferred to TVA [MMbtu] =|   |   |   |   |   |   |0|||||||||

  

  

  

ask clarifying questions

That's a fantastic start! The directory structure is well-organized, and using uv for dependency management is a high-performance choice.

Your data is complex—a mixture of raw time-series measurements (Sheets 1, 6, etc.), daily aggregate flow data (BGFlow2, BGFLOW1), and calculated monthly compliance reports (BGcalcs 1, 6). This confirms that DuckDB is essential for handling the aggregation and calculations.

Before we write code, we need to solidify the data model and API structure. This is crucial for msgspec and Starlette.

I will ask clarifying questions about the data and then provide the initial shell scripts for project setup.

❓ Data Modeling and Calculation Clarification

The core of this project is transforming your spreadsheet data into structured data models. I need to understand the source and calculation rules to build the $\text{msgspec}$ classes and the $\text{DuckDB}$ schema.

1. Raw Input Data Structure (Time-Series)

Sheets 1, 2, 3, 4, 5, 6 appear to be raw time-series data from the analyzers (MRU SWG 100 or Optima 7).

• Question 1 (Time-Series Granularity): Is the raw data always recorded at irregular intervals (e.g., "2/1/22 1:07", "2/1/22 2:14")? If so, the system will need to handle $\text{interpolation}$ or $\text{resampling}$ before aggregation.

• Question 2 (The "5-6" Difference): Sheet 1 is "before the tanks" and Sheet 6 is "after the tanks." Do they share the same fields, and are they the only two mandatory raw entry points for the daily daemon calls?

2. Daily Flow and Aggregation Data

Sheets $\text{BGFlow1}$ and $\text{BGFlow2}$ contain daily aggregated flow totals and daily average flow (e.g., "Daily Average Treated Biogas Flow [scfm]").

• Question 3 (Flow Data Source): Is the daily total flow data (e.g., $\text{Total Prod [scf/day]}$ in $\text{BGFlow1}$) raw input from a meter or a calculation based on the $\text{Tank 1-4 [scfm]}$ time-series data from $\text{BGFlow2}$?

• Question 4 (Calculation Rules): I need the actual spreadsheet function for Daily Average Treated Biogas Flow [scfm] and Daily treated Biogas Flow [scf/day] to codify it in Python/DuckDB. Since you cannot paste the Excel function, can you describe it?

  • Example: Is $\text{Daily Average Flow [scfm]}$ the $\text{AVERAGE}$ of all $\text{Total [scfm]}$ measurements taken during that day?

  • Example: Is $\text{Daily Flow [scf/day]}$ calculated as $\text{Daily Average Flow [scfm]}\times 60\text{ min/hr}\times 24\text{ hr/day}$?

3. Data Correction and Workflow

You asked about the workflow for a person who fudged an input.

• Question 5 (Correction Scope): If a user fudges an input (e.g., enters a bad $\text{CH4\%}$), do they need to correct:

  • A. Only the raw time-series measurement (e.g., the value for $9/1/2025\text{ at }12:00$)

  • B. An entire daily average/total (e.g., the $\text{Total Biogas Prod [MMscf /day]}$ for $9/1/2025$)

  • C. The system must allow the user to input a $\text{Correction Factor}$ or $\text{Comment}$ next to the bad data point.

📦 Initial Project Setup Scripts

Here are the shell scripts to create the necessary files and structures for your initial stack, using $\text{cat EOF}$. These should be placed in your build-scripts directory.

1. build-scripts/setup_python.sh

This script initializes the $\text{uv}$ environment, installs the core Python packages, and creates the entry point (main.py) and API server file (server.py).

Bash

#!/bin/bash
# build-scripts/setup_python.sh

# 1. Install Dependencies using uv
echo "Installing Python dependencies with uv..."
uv venv
source .venv/bin/activate

uv pip install starlette uvicorn msgspec duckdb python-multipart typer

# 2. Create Python Entry Point (main.py)
echo "Creating src/main.py..."
cat << 'EOF' > src/main.py
# src/main.py
# Main entry point for CLI and Server
import typer
from src.gastrack.core.server import run_server

app = typer.Typer(help="GasTrack: Biogas Data Tracker CLI and Server Runner.")

@app.command()
def start(port: int = 8000):
    """
    Start the Uvicorn ASGI server.
    """
    typer.echo(f"Starting GasTrack server on http://localhost:{port}")
    run_server(port)

@app.command()
def report(month: str = typer.Argument(..., help="Month in YYYY-MM format (e.g., 2025-09)")):
    """
    Generate a monthly biogas report.
    """
    typer.echo(f"Generating monthly report for: {month}")
    # Calculation logic will go here

if __name__ == "__main__":
    app()
EOF

# 3. Create API Server File (src/gastrack/core/server.py)
echo "Creating src/gastrack/core/server.py..."
cat << 'EOF' > src/gastrack/core/server.py
# src/gastrack/core/server.py
import uvicorn
import os
from starlette.applications import Starlette
from starlette.routing import Route, Mount
from starlette.staticfiles import StaticFiles
from starlette.responses import JSONResponse

# Placeholder for your API handlers
async def homepage(request):
    return JSONResponse({"status": "ok", "message": "GasTrack API is running"})

# Placeholder for static file serving (Svelte SPA)
# The 'frontend' directory will contain your compiled Svelte build
STATIC_DIR = os.path.join(os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(__file__)))), 'frontend', 'dist')

routes = [
    Route("/", homepage),
    # Add API routes here: /api/data, /api/calc, etc.
    # Serve static assets (Svelte build)
    Mount("/", StaticFiles(directory=STATIC_DIR, html=True), name="static")
]

app = Starlette(
    debug=True,
    routes=routes
)

def run_server(port: int):
    uvicorn.run(app, host="127.0.0.1", port=port)
EOF

echo "Python setup complete. Run 'source .venv/bin/activate' and then 'python src/main.py start'."

2. build-scripts/setup_svelte.sh

This script sets up the $\text{Svelte}$ project in the frontend directory using $\text{npm}$ and creates a basic structure.

Bash

#!/bin/bash
# build-scripts/setup_svelte.sh

echo "Setting up Svelte frontend..."

# 1. Initialize Node project in frontend/
cd frontend
npm init -y

# 2. Install Svelte/Vite via a template (minimal setup)
npm create vite@latest . -- --template svelte-ts

# 3. Install dependencies
npm install

# 4. Create a basic Svelte file that calls the Starlette API
echo "Creating basic Svelte component..."
mkdir -p src/lib
cat << 'EOF' > src/App.svelte
<script lang="ts">
    import { onMount } from 'svelte';

    let message = 'Loading...';
    let apiStatus = 'Waiting for API';

    onMount(async () => {
        try {
            const response = await fetch('/?format=json');
            const data = await response.json();
            apiStatus = 'Connected!';
            message = data.message;
        } catch (error) {
            apiStatus = 'Error connecting to Python API!';
            message = 'Is the Uvicorn server running?';
            console.error(error);
        }
    });
</script>

<main>
    <h1>GasTrack Biogas Data Entry</h1>
    <p>API Status: <strong>{apiStatus}</strong></p>
    <p>API Message: {message}</p>

    <section>
        <h2>Daily Input Form (Pre-Treatment)</h2>
        <form>
            <label for="date">Date:</label>
            <input type="date" id="date" />
            <button type="submit">Submit Data</button>
        </form>
    </section>
</main>

<style>
    main {
        font-family: sans-serif;
        padding: 2em;
    }
</style>
EOF

# 5. Add build script to package.json
# Note: You'll need to customize the build command if not using Vite defaults
sed -i '' 's/"build": "vite build"/"build": "vite build --outDir dist"/' package.json

echo "Svelte setup complete in 'frontend/'. Run 'npm install' and then 'npm run build' to create the 'dist' directory."

cd ..

💡 Next Steps

1. Run the shell scripts:

   • bash build-scripts/setup_python.sh

   • bash build-scripts/setup_svelte.sh

2. Answer the clarifying questions above, focusing on the source and calculation rules for the key daily aggregate figures (Q3, Q4).

3. Based on your answers, we will define the $\text{msgspec}$ data classes and the initial $\text{DuckDB}$ schema.

This initial setup addresses your need to decouple the CLI and server in src/main.py and establishes the correct static file serving path for the $\text{Svelte}$ build.

 oolong@CIT-36NZRL3  ~/dev/gastrack   master  ls

README.md  build-scripts  frontend  main.py  node-red  pyproject.toml  src

 oolong@CIT-36NZRL3  ~/dev/gastrack   master  nano build-scripts/setup_python.sh

 oolong@CIT-36NZRL3  ~/dev/gastrack   master  nano build-scripts/setup_svelte.sh

 oolong@CIT-36NZRL3  ~/dev/gastrack   master  zsh build-scripts/setup_python.sh

Setting up Svelte frontend...

Wrote to /home/oolong/dev/gastrack/frontend/package.json:

  

{

  "name": "frontend",

  "version": "1.0.0",

  "description": "",

  "main": "index.js",

  "scripts": {

    "test": "echo \"Error: no test specified\" && exit 1"

  },

  "keywords": [],

  "author": "",

  "license": "ISC",

  "type": "commonjs"

}

  

  

  

Need to install the following packages:

create-vite@8.2.0

Ok to proceed? (y)

  

  

> frontend@1.0.0 npx

> "create-vite" . --template svelte-ts

  

│

◇  Current directory is not empty. Please choose how to proceed:

│  Cancel operation

└  Operation cancelled

  

  

up to date, audited 1 package in 142ms

  

found 0 vulnerabilities

Creating basic Svelte component...

sed: can't read s/"build": "vite build"/"build": "vite build --outDir dist"/: No such file or directory

Svelte setup complete in 'frontend/'. Run 'npm install' and then 'npm run build' to create the 'dist' directory.

 oolong@CIT-36NZRL3  ~/dev/gastrack   master  zsh build-scripts/setup_python.sh

Setting up Svelte frontend...

Wrote to /home/oolong/dev/gastrack/frontend/package.json:

  

{

  "name": "frontend",

  "version": "1.0.0",

  "description": "",

  "main": "index.js",

  "scripts": {

    "test": "echo \"Error: no test specified\" && exit 1"

  },

  "keywords": [],

  "author": "",

  "license": "ISC",

  "type": "commonjs"

}

  

  

  

  

> frontend@1.0.0 npx

> "create-vite" . --template svelte-ts

  

│

◇  Current directory is not empty. Please choose how to proceed:

│  Cancel operation

└  Operation cancelled

  

  

up to date, audited 1 package in 157ms

  

found 0 vulnerabilities

Creating basic Svelte component...

sed: can't read s/"build": "vite build"/"build": "vite build --outDir dist"/: No such file or directory

Svelte setup complete in 'frontend/'. Run 'npm install' and then 'npm run build' to create the 'dist' directory.

 oolong@CIT-36NZRL3  ~/dev/gastrack   master  nano build-scripts/setup_python.sh

 oolong@CIT-36NZRL3  ~/dev/gastrack   master  rm  build-scripts/setup_python.sh

 oolong@CIT-36NZRL3  ~/dev/gastrack   master  nano build-scripts/setup_python.sh

 oolong@CIT-36NZRL3  ~/dev/gastrack   master  zsh build-scripts/setup_python.sh

Installing Python dependencies with uv...

Using CPython 3.12.3 interpreter at: /usr/bin/python3.12

Creating virtual environment at: .venv

Activate with: source .venv/bin/activate

Resolved 17 packages in 255ms

Prepared 17 packages in 2.10s

Installed 17 packages in 19ms

 + anyio==4.11.0

 + click==8.3.1

 + duckdb==1.4.2

 + h11==0.16.0

 + idna==3.11

 + markdown-it-py==4.0.0

 + mdurl==0.1.2

 + msgspec==0.20.0

 + pygments==2.19.2

 + python-multipart==0.0.20

 + rich==14.2.0

 + shellingham==1.5.4

 + sniffio==1.3.1

 + starlette==0.50.0

 + typer==0.20.0

 + typing-extensions==4.15.0

 + uvicorn==0.38.0

Creating src/main.py...

Creating src/gastrack/core/server.py...

Python setup complete. Run 'source .venv/bin/activate' and then 'python src/main.py start'.

 oolong@CIT-36NZRL3  ~/dev/gastrack   master  source .venv/bin/activate

(gastrack)  oolong@CIT-36NZRL3  ~/dev/gastrack   master  python src/main.py start

Traceback (most recent call last):

  File "/home/oolong/dev/gastrack/src/main.py", line 4, in <module>

    from src.gastrack.core.server import run_server

ModuleNotFoundError: No module named 'src'

(gastrack)  ✘ oolong@CIT-36NZRL3  ~/dev/gastrack   master  cat pyproject.toml

[project]

name = "gastrack"

version = "0.1.0"

description = "Add your description here"

readme = "README.md"

requires-python = ">=3.12"

dependencies = []

(gastrack)  oolong@CIT-36NZRL3  ~/dev/gastrack   master  ls

README.md  build-scripts  frontend  main.py  node-red  pyproject.toml  src

(gastrack)  oolong@CIT-36NZRL3  ~/dev/gastrack   master  ls frontend

package-lock.json  package.json  src

(gastrack)  oolong@CIT-36NZRL3  ~/dev/gastrack   master 

  

i didnt finish the svelte build because i didnt want it to go to root but to the frontend dir, but it looks like you handled that. haha i should read your scripts before running them

  

  

what is the difference between `source build-scripts/setup_python.sh` and `zsh build-scripts/setup_python.sh`?

  

Q/A:

  

Q1. Yes, the time series data is irregular. Can the raw be preserved and the the average for the day still taken - yes of course. Why do we need resampling and interpolation, as long as we have hourly or daily or monthly binning?

  

Q2. Sheets 1,2,3,4,5,6 at the specific sampling points all share the same fields, yes: |Date|Time|O2 [%]|CO2 [%]|H2S dil. [ppm]|CH4 [%]|Net cal. val. [MJ/m³]|Gross cal. val. [MJ/m³]|T-sensor [°F]|Balance N2 [%]|Date + Time|Net BTUs [btu/cf]|Gross BTUs [btu/cf]|

  

Q3. Here is another paste from BG flow 1, with function visibility toggled using cntrl+tilde

  

|Biogas Flow Data from Lagoon Blower Buildings|   |   |   |   |   |   |   |

|Date|Blowers 1, 2A, 2B [scf/day]|Blower 2C [scf/day]|BioRem Ambient Air Injection [scf/day]|Total Prod [scf/day]|Biogas Flared [scf/day]|Total Biogas Transfer to TVA [scf/day]|Comment|

|44319||||=B3+C3|0|=E3-F3||

|44320||||=B4+C4|0|=E4-F4||

|44321||||=B5+C5|0|=E5-F5||

|44322|440640|792960||=B6+C6|1233600|=E6-F6||

|44323|483840|775296||=B7+C7|1259136|=E7-F7||

|44324|409320|788440||=B8+C8|1197760|=E8-F8||

  

  

Q4.

Here is the function view for the daily treated biogas calcuations from the september report

|TE Maxson WWTF Biogas Data & Calculations, September 1 through 30, 2025|   |   |   |   |   |   |   |   |   |   |   |   |   ||||
||||||||||||||||||

|H2S ppm/grains/ccf =|   |   |   |16|HHV [BTU/scf]/100% CH4 =|   |   |   |10.4||||||||

|Date|**Total Biogas Prod. [MMscf /day]||**Total Biogas Flared [MMscf /day]|* TVA Unit Total Transferred [MMscf /day]|Avg. Daily Biogas H2S -- Inlet [ppm]|Avg. Daily Biogas H2S -- Outlet [ppm]|Avg. Daily Biogas H2S -- Inlet [grains /ccf]|Avg. Daily Biogas H2S -- Outlet [grains /ccf]|Avg. Daily Treated Biogas CH4 [%]|Avg. Daily Biogas BTUs /scf|Daily Biogas MMbtu /day Prod.|Daily Biogas MMbtu /day flared|Daily Biogas TVA MMbtu /day Transferred||Resid. H2S After Treatment|H2S Reduction|

|='BG Calcs6'!A1531|='BG Calcs6'!H1531/10^6||='BG Calcs6'!I1531/(10^6)|='BG Calcs6'!J1531/(10^6)|=IF('BG Calcs1'!B1584="","N/A",'BG Calcs1'!B1584)|=IF(F5="N/A","N/A",'BG Calcs6'!B1531)|=IF(F5="N/A","N/A",F5/$E$3)|=IF(G5="N/A","N/A",G5/$E$3)|=IF(G5="N/A","N/A",'BG Calcs6'!C1531)|=IF(J5="N/A","N/A",J5*$J$3)|=IFERROR($K5*B5,0)|=IFERROR($K5*D5,0)|=IFERROR($K5*E5,0)||=IFERROR(G5/F5,"")|=IFERROR((F5-G5)/F5,"")|

|='BG Calcs6'!A1532|='BG Calcs6'!H1532/10^6||='BG Calcs6'!I1532/(10^6)|='BG Calcs6'!J1532/(10^6)|=IF('BG Calcs1'!B1585="","N/A",'BG Calcs1'!B1585)|=IF(F6="N/A","N/A",'BG Calcs6'!B1532)|=IF(F6="N/A","N/A",F6/$E$3)|=IF(G6="N/A","N/A",G6/$E$3)|=IF(G6="N/A","N/A",'BG Calcs6'!C1532)|=IF(J6="N/A","N/A",J6*$J$3)|=IFERROR($K6*B6,0)|=IFERROR($K6*D6,0)|=IFERROR($K6*E6,0)||=IFERROR(G6/F6,"")|=IFERROR((F6-G6)/F6,"")|

|='BG Calcs6'!A1533|='BG Calcs6'!H1533/10^6||='BG Calcs6'!I1533/(10^6)|='BG Calcs6'!J1533/(10^6)|=IF('BG Calcs1'!B1586="","N/A",'BG Calcs1'!B1586)|=IF(F7="N/A","N/A",'BG Calcs6'!B1533)|=IF(F7="N/A","N/A",F7/$E$3)|=IF(G7="N/A","N/A",G7/$E$3)|=IF(G7="N/A","N/A",'BG Calcs6'!C1533)|=IF(J7="N/A","N/A",J7*$J$3)|=IFERROR($K7*B7,0)|=IFERROR($K7*D7,0)|=IFERROR($K7*E7,0)||=IFERROR(G7/F7,"")|=IFERROR((F7-G7)/F7,"")|

|='BG Calcs6'!A1534|='BG Calcs6'!H1534/10^6||='BG Calcs6'!I1534/(10^6)|='BG Calcs6'!J1534/(10^6)|=IF('BG Calcs1'!B1587="","N/A",'BG Calcs1'!B1587)|=IF(F8="N/A","N/A",'BG Calcs6'!B1534)|=IF(F8="N/A","N/A",F8/$E$3)|=IF(G8="N/A","N/A",G8/$E$3)|=IF(G8="N/A","N/A",'BG Calcs6'!C1534)|=IF(J8="N/A","N/A",J8*$J$3)|=IFERROR($K8*B8,0)|=IFERROR($K8*D8,0)|=IFERROR($K8*E8,0)||=IFERROR(G8/F8,"")|=IFERROR((F8-G8)/F8,"")|

|='BG Calcs6'!A1535|='BG Calcs6'!H1535/10^6||='BG Calcs6'!I1535/(10^6)|='BG Calcs6'!J1535/(10^6)|=IF('BG Calcs1'!B1588="","N/A",'BG Calcs1'!B1588)|=IF(F9="N/A","N/A",'BG Calcs6'!B1535)|=IF(F9="N/A","N/A",F9/$E$3)|=IF(G9="N/A","N/A",G9/$E$3)|=IF(G9="N/A","N/A",'BG Calcs6'!C1535)|=IF(J9="N/A","N/A",J9*$J$3)|=IFERROR($K9*B9,0)|=IFERROR($K9*D9,0)|=IFERROR($K9*E9,0)||=IFERROR(G9/F9,"")|=IFERROR((F9-G9)/F9,"")|

|='BG Calcs6'!A1536|='BG Calcs6'!H1536/10^6||='BG Calcs6'!I1536/(10^6)|='BG Calcs6'!J1536/(10^6)|=IF('BG Calcs1'!B1589="","N/A",'BG Calcs1'!B1589)|=IF(F10="N/A","N/A",'BG Calcs6'!B1536)|=IF(F10="N/A","N/A",F10/$E$3)|=IF(G10="N/A","N/A",G10/$E$3)|=IF(G10="N/A","N/A",'BG Calcs6'!C1536)|=IF(J10="N/A","N/A",J10*$J$3)|=IFERROR($K10*B10,0)|=IFERROR($K10*D10,0)|=IFERROR($K10*E10,0)||=IFERROR(G10/F10,"")|=IFERROR((F10-G10)/F10,"")|

|='BG Calcs6'!A1537|='BG Calcs6'!H1537/10^6||='BG Calcs6'!I1537/(10^6)|='BG Calcs6'!J1537/(10^6)|=IF('BG Calcs1'!B1590="","N/A",'BG Calcs1'!B1590)|=IF(F11="N/A","N/A",'BG Calcs6'!B1537)|=IF(F11="N/A","N/A",F11/$E$3)|=IF(G11="N/A","N/A",G11/$E$3)|=IF(G11="N/A","N/A",'BG Calcs6'!C1537)|=IF(J11="N/A","N/A",J11*$J$3)|=IFERROR($K11*B11,0)|=IFERROR($K11*D11,0)|=IFERROR($K11*E11,0)||=IFERROR(G11/F11,"")|=IFERROR((F11-G11)/F11,"")|

|='BG Calcs6'!A1538|='BG Calcs6'!H1538/10^6||='BG Calcs6'!I1538/(10^6)|='BG Calcs6'!J1538/(10^6)|=IF('BG Calcs1'!B1591="","N/A",'BG Calcs1'!B1591)|=IF(F12="N/A","N/A",'BG Calcs6'!B1538)|=IF(F12="N/A","N/A",F12/$E$3)|=IF(G12="N/A","N/A",G12/$E$3)|=IF(G12="N/A","N/A",'BG Calcs6'!C1538)|=IF(J12="N/A","N/A",J12*$J$3)|=IFERROR($K12*B12,0)|=IFERROR($K12*D12,0)|=IFERROR($K12*E12,0)||=IFERROR(G12/F12,"")|=IFERROR((F12-G12)/F12,"")|

|='BG Calcs6'!A1539|='BG Calcs6'!H1539/10^6||='BG Calcs6'!I1539/(10^6)|='BG Calcs6'!J1539/(10^6)|=IF('BG Calcs1'!B1592="","N/A",'BG Calcs1'!B1592)|=IF(F13="N/A","N/A",'BG Calcs6'!B1539)|=IF(F13="N/A","N/A",F13/$E$3)|=IF(G13="N/A","N/A",G13/$E$3)|=IF(G13="N/A","N/A",'BG Calcs6'!C1539)|=IF(J13="N/A","N/A",J13*$J$3)|=IFERROR($K13*B13,0)|=IFERROR($K13*D13,0)|=IFERROR($K13*E13,0)||=IFERROR(G13/F13,"")|=IFERROR((F13-G13)/F13,"")|

|='BG Calcs6'!A1540|='BG Calcs6'!H1540/10^6||='BG Calcs6'!I1540/(10^6)|='BG Calcs6'!J1540/(10^6)|=IF('BG Calcs1'!B1593="","N/A",'BG Calcs1'!B1593)|=IF(F14="N/A","N/A",'BG Calcs6'!B1540)|=IF(F14="N/A","N/A",F14/$E$3)|=IF(G14="N/A","N/A",G14/$E$3)|=IF(G14="N/A","N/A",'BG Calcs6'!C1540)|=IF(J14="N/A","N/A",J14*$J$3)|=IFERROR($K14*B14,0)|=IFERROR($K14*D14,0)|=IFERROR($K14*E14,0)||=IFERROR(G14/F14,"")|=IFERROR((F14-G14)/F14,"")|

|='BG Calcs6'!A1541|='BG Calcs6'!H1541/10^6||='BG Calcs6'!I1541/(10^6)|='BG Calcs6'!J1541/(10^6)|=IF('BG Calcs1'!B1594="","N/A",'BG Calcs1'!B1594)|=IF(F15="N/A","N/A",'BG Calcs6'!B1541)|=IF(F15="N/A","N/A",F15/$E$3)|=IF(G15="N/A","N/A",G15/$E$3)|=IF(G15="N/A","N/A",'BG Calcs6'!C1541)|=IF(J15="N/A","N/A",J15*$J$3)|=IFERROR($K15*B15,0)|=IFERROR($K15*D15,0)|=IFERROR($K15*E15,0)||=IFERROR(G15/F15,"")|=IFERROR((F15-G15)/F15,"")|

|='BG Calcs6'!A1542|='BG Calcs6'!H1542/10^6||='BG Calcs6'!I1542/(10^6)|='BG Calcs6'!J1542/(10^6)|=IF('BG Calcs1'!B1595="","N/A",'BG Calcs1'!B1595)|=IF(F16="N/A","N/A",'BG Calcs6'!B1542)|=IF(F16="N/A","N/A",F16/$E$3)|=IF(G16="N/A","N/A",G16/$E$3)|=IF(G16="N/A","N/A",'BG Calcs6'!C1542)|=IF(J16="N/A","N/A",J16*$J$3)|=IFERROR($K16*B16,0)|=IFERROR($K16*D16,0)|=IFERROR($K16*E16,0)||=IFERROR(G16/F16,"")|=IFERROR((F16-G16)/F16,"")|

|='BG Calcs6'!A1543|='BG Calcs6'!H1543/10^6||='BG Calcs6'!I1543/(10^6)|='BG Calcs6'!J1543/(10^6)|=IF('BG Calcs1'!B1596="","N/A",'BG Calcs1'!B1596)|=IF(F17="N/A","N/A",'BG Calcs6'!B1543)|=IF(F17="N/A","N/A",F17/$E$3)|=IF(G17="N/A","N/A",G17/$E$3)|=IF(G17="N/A","N/A",'BG Calcs6'!C1543)|=IF(J17="N/A","N/A",J17*$J$3)|=IFERROR($K17*B17,0)|=IFERROR($K17*D17,0)|=IFERROR($K17*E17,0)||=IFERROR(G17/F17,"")|=IFERROR((F17-G17)/F17,"")|

|='BG Calcs6'!A1544|='BG Calcs6'!H1544/10^6||='BG Calcs6'!I1544/(10^6)|='BG Calcs6'!J1544/(10^6)|=IF('BG Calcs1'!B1597="","N/A",'BG Calcs1'!B1597)|=IF(F18="N/A","N/A",'BG Calcs6'!B1544)|=IF(F18="N/A","N/A",F18/$E$3)|=IF(G18="N/A","N/A",G18/$E$3)|=IF(G18="N/A","N/A",'BG Calcs6'!C1544)|=IF(J18="N/A","N/A",J18*$J$3)|=IFERROR($K18*B18,0)|=IFERROR($K18*D18,0)|=IFERROR($K18*E18,0)||=IFERROR(G18/F18,"")|=IFERROR((F18-G18)/F18,"")|

|='BG Calcs6'!A1545|='BG Calcs6'!H1545/10^6||='BG Calcs6'!I1545/(10^6)|='BG Calcs6'!J1545/(10^6)|=IF('BG Calcs1'!B1598="","N/A",'BG Calcs1'!B1598)|=IF(F19="N/A","N/A",'BG Calcs6'!B1545)|=IF(F19="N/A","N/A",F19/$E$3)|=IF(G19="N/A","N/A",G19/$E$3)|=IF(G19="N/A","N/A",'BG Calcs6'!C1545)|=IF(J19="N/A","N/A",J19*$J$3)|=IFERROR($K19*B19,0)|=IFERROR($K19*D19,0)|=IFERROR($K19*E19,0)||=IFERROR(G19/F19,"")|=IFERROR((F19-G19)/F19,"")|

|='BG Calcs6'!A1546|='BG Calcs6'!H1546/10^6||='BG Calcs6'!I1546/(10^6)|='BG Calcs6'!J1546/(10^6)|=IF('BG Calcs1'!B1599="","N/A",'BG Calcs1'!B1599)|=IF(F20="N/A","N/A",'BG Calcs6'!B1546)|=IF(F20="N/A","N/A",F20/$E$3)|=IF(G20="N/A","N/A",G20/$E$3)|=IF(G20="N/A","N/A",'BG Calcs6'!C1546)|=IF(J20="N/A","N/A",J20*$J$3)|=IFERROR($K20*B20,0)|=IFERROR($K20*D20,0)|=IFERROR($K20*E20,0)||=IFERROR(G20/F20,"")|=IFERROR((F20-G20)/F20,"")|

|='BG Calcs6'!A1547|='BG Calcs6'!H1547/10^6||='BG Calcs6'!I1547/(10^6)|='BG Calcs6'!J1547/(10^6)|=IF('BG Calcs1'!B1600="","N/A",'BG Calcs1'!B1600)|=IF(F21="N/A","N/A",'BG Calcs6'!B1547)|=IF(F21="N/A","N/A",F21/$E$3)|=IF(G21="N/A","N/A",G21/$E$3)|=IF(G21="N/A","N/A",'BG Calcs6'!C1547)|=IF(J21="N/A","N/A",J21*$J$3)|=IFERROR($K21*B21,0)|=IFERROR($K21*D21,0)|=IFERROR($K21*E21,0)||=IFERROR(G21/F21,"")|=IFERROR((F21-G21)/F21,"")|

|='BG Calcs6'!A1548|='BG Calcs6'!H1548/10^6||='BG Calcs6'!I1548/(10^6)|='BG Calcs6'!J1548/(10^6)|=IF('BG Calcs1'!B1601="","N/A",'BG Calcs1'!B1601)|=IF(F22="N/A","N/A",'BG Calcs6'!B1548)|=IF(F22="N/A","N/A",F22/$E$3)|=IF(G22="N/A","N/A",G22/$E$3)|=IF(G22="N/A","N/A",'BG Calcs6'!C1548)|=IF(J22="N/A","N/A",J22*$J$3)|=IFERROR($K22*B22,0)|=IFERROR($K22*D22,0)|=IFERROR($K22*E22,0)||=IFERROR(G22/F22,"")|=IFERROR((F22-G22)/F22,"")|

|='BG Calcs6'!A1549|='BG Calcs6'!H1549/10^6||='BG Calcs6'!I1549/(10^6)|='BG Calcs6'!J1549/(10^6)|=IF('BG Calcs1'!B1602="","N/A",'BG Calcs1'!B1602)|=IF(F23="N/A","N/A",'BG Calcs6'!B1549)|=IF(F23="N/A","N/A",F23/$E$3)|=IF(G23="N/A","N/A",G23/$E$3)|=IF(G23="N/A","N/A",'BG Calcs6'!C1549)|=IF(J23="N/A","N/A",J23*$J$3)|=IFERROR($K23*B23,0)|=IFERROR($K23*D23,0)|=IFERROR($K23*E23,0)||=IFERROR(G23/F23,"")|=IFERROR((F23-G23)/F23,"")|

|='BG Calcs6'!A1550|='BG Calcs6'!H1550/10^6||='BG Calcs6'!I1550/(10^6)|='BG Calcs6'!J1550/(10^6)|=IF('BG Calcs1'!B1603="","N/A",'BG Calcs1'!B1603)|=IF(F24="N/A","N/A",'BG Calcs6'!B1550)|=IF(F24="N/A","N/A",F24/$E$3)|=IF(G24="N/A","N/A",G24/$E$3)|=IF(G24="N/A","N/A",'BG Calcs6'!C1550)|=IF(J24="N/A","N/A",J24*$J$3)|=IFERROR($K24*B24,0)|=IFERROR($K24*D24,0)|=IFERROR($K24*E24,0)||=IFERROR(G24/F24,"")|=IFERROR((F24-G24)/F24,"")|

|='BG Calcs6'!A1551|='BG Calcs6'!H1551/10^6||='BG Calcs6'!I1551/(10^6)|='BG Calcs6'!J1551/(10^6)|=IF('BG Calcs1'!B1604="","N/A",'BG Calcs1'!B1604)|=IF(F25="N/A","N/A",'BG Calcs6'!B1551)|=IF(F25="N/A","N/A",F25/$E$3)|=IF(G25="N/A","N/A",G25/$E$3)|=IF(G25="N/A","N/A",'BG Calcs6'!C1551)|=IF(J25="N/A","N/A",J25*$J$3)|=IFERROR($K25*B25,0)|=IFERROR($K25*D25,0)|=IFERROR($K25*E25,0)||=IFERROR(G25/F25,"")|=IFERROR((F25-G25)/F25,"")|

|='BG Calcs6'!A1552|='BG Calcs6'!H1552/10^6||='BG Calcs6'!I1552/(10^6)|='BG Calcs6'!J1552/(10^6)|=IF('BG Calcs1'!B1605="","N/A",'BG Calcs1'!B1605)|=IF(F26="N/A","N/A",'BG Calcs6'!B1552)|=IF(F26="N/A","N/A",F26/$E$3)|=IF(G26="N/A","N/A",G26/$E$3)|=IF(G26="N/A","N/A",'BG Calcs6'!C1552)|=IF(J26="N/A","N/A",J26*$J$3)|=IFERROR($K26*B26,0)|=IFERROR($K26*D26,0)|=IFERROR($K26*E26,0)||=IFERROR(G26/F26,"")|=IFERROR((F26-G26)/F26,"")|

|='BG Calcs6'!A1553|='BG Calcs6'!H1553/10^6||='BG Calcs6'!I1553/(10^6)|='BG Calcs6'!J1553/(10^6)|=IF('BG Calcs1'!B1606="","N/A",'BG Calcs1'!B1606)|=IF(F27="N/A","N/A",'BG Calcs6'!B1553)|=IF(F27="N/A","N/A",F27/$E$3)|=IF(G27="N/A","N/A",G27/$E$3)|=IF(G27="N/A","N/A",'BG Calcs6'!C1553)|=IF(J27="N/A","N/A",J27*$J$3)|=IFERROR($K27*B27,0)|=IFERROR($K27*D27,0)|=IFERROR($K27*E27,0)||=IFERROR(G27/F27,"")|=IFERROR((F27-G27)/F27,"")|

|='BG Calcs6'!A1554|='BG Calcs6'!H1554/10^6||='BG Calcs6'!I1554/(10^6)|='BG Calcs6'!J1554/(10^6)|=IF('BG Calcs1'!B1607="","N/A",'BG Calcs1'!B1607)|=IF(F28="N/A","N/A",'BG Calcs6'!B1554)|=IF(F28="N/A","N/A",F28/$E$3)|=IF(G28="N/A","N/A",G28/$E$3)|=IF(G28="N/A","N/A",'BG Calcs6'!C1554)|=IF(J28="N/A","N/A",J28*$J$3)|=IFERROR($K28*B28,0)|=IFERROR($K28*D28,0)|=IFERROR($K28*E28,0)||=IFERROR(G28/F28,"")|=IFERROR((F28-G28)/F28,"")|

|='BG Calcs6'!A1555|='BG Calcs6'!H1555/10^6||='BG Calcs6'!I1555/(10^6)|='BG Calcs6'!J1555/(10^6)|=IF('BG Calcs1'!B1608="","N/A",'BG Calcs1'!B1608)|=IF(F29="N/A","N/A",'BG Calcs6'!B1555)|=IF(F29="N/A","N/A",F29/$E$3)|=IF(G29="N/A","N/A",G29/$E$3)|=IF(G29="N/A","N/A",'BG Calcs6'!C1555)|=IF(J29="N/A","N/A",J29*$J$3)|=IFERROR($K29*B29,0)|=IFERROR($K29*D29,0)|=IFERROR($K29*E29,0)||=IFERROR(G29/F29,"")|=IFERROR((F29-G29)/F29,"")|

|='BG Calcs6'!A1556|='BG Calcs6'!H1556/10^6||='BG Calcs6'!I1556/(10^6)|='BG Calcs6'!J1556/(10^6)|=IF('BG Calcs1'!B1609="","N/A",'BG Calcs1'!B1609)|=IF(F30="N/A","N/A",'BG Calcs6'!B1556)|=IF(F30="N/A","N/A",F30/$E$3)|=IF(G30="N/A","N/A",G30/$E$3)|=IF(G30="N/A","N/A",'BG Calcs6'!C1556)|=IF(J30="N/A","N/A",J30*$J$3)|=IFERROR($K30*B30,0)|=IFERROR($K30*D30,0)|=IFERROR($K30*E30,0)||=IFERROR(G30/F30,"")|=IFERROR((F30-G30)/F30,"")|

|='BG Calcs6'!A1557|='BG Calcs6'!H1557/10^6||='BG Calcs6'!I1557/(10^6)|='BG Calcs6'!J1557/(10^6)|=IF('BG Calcs1'!B1610="","N/A",'BG Calcs1'!B1610)|=IF(F31="N/A","N/A",'BG Calcs6'!B1557)|=IF(F31="N/A","N/A",F31/$E$3)|=IF(G31="N/A","N/A",G31/$E$3)|=IF(G31="N/A","N/A",'BG Calcs6'!C1557)|=IF(J31="N/A","N/A",J31*$J$3)|=IFERROR($K31*B31,0)|=IFERROR($K31*D31,0)|=IFERROR($K31*E31,0)||=IFERROR(G31/F31,"")|=IFERROR((F31-G31)/F31,"")|

|='BG Calcs6'!A1558|='BG Calcs6'!H1558/10^6||='BG Calcs6'!I1558/(10^6)|='BG Calcs6'!J1558/(10^6)|=IF('BG Calcs1'!B1611="","N/A",'BG Calcs1'!B1611)|=IF(F32="N/A","N/A",'BG Calcs6'!B1558)|=IF(F32="N/A","N/A",F32/$E$3)|=IF(G32="N/A","N/A",G32/$E$3)|=IF(G32="N/A","N/A",'BG Calcs6'!C1558)|=IF(J32="N/A","N/A",J32*$J$3)|=IFERROR($K32*B32,0)|=IFERROR($K32*D32,0)|=IFERROR($K32*E32,0)||=IFERROR(G32/F32,"")|=IFERROR((F32-G32)/F32,"")|

|='BG Calcs6'!A1559|='BG Calcs6'!H1559/10^6||='BG Calcs6'!I1559/(10^6)|='BG Calcs6'!J1559/(10^6)|=IF('BG Calcs1'!B1612="","N/A",'BG Calcs1'!B1612)|=IF(F33="N/A","N/A",'BG Calcs6'!B1559)|=IF(F33="N/A","N/A",F33/$E$3)|=IF(G33="N/A","N/A",G33/$E$3)|=IF(G33="N/A","N/A",'BG Calcs6'!C1559)|=IF(J33="N/A","N/A",J33*$J$3)|=IFERROR($K33*B33,0)|=IFERROR($K33*D33,0)|=IFERROR($K33*E33,0)||=IFERROR(G33/F33,"")|=IFERROR((F33-G33)/F33,"")|

|='BG Calcs6'!A1560|='BG Calcs6'!H1560/10^6||='BG Calcs6'!I1560/(10^6)|='BG Calcs6'!J1560/(10^6)|=IF('BG Calcs1'!B1613="","N/A",'BG Calcs1'!B1613)|=IF(F34="N/A","N/A",'BG Calcs6'!B1560)|=IF(F34="N/A","N/A",F34/$E$3)|=IF(G34="N/A","N/A",G34/$E$3)|=IF(G34="N/A","N/A",'BG Calcs6'!C1560)|=IF(J34="N/A","N/A",J34*$J$3)|=IFERROR($K34*B34,0)|=IFERROR($K34*D34,0)|=IFERROR($K34*E34,0)||||

||||||||||||||||=IFERROR(G35/F35,"")|=IFERROR((F35-G35)/F35,"")|

|Totals =|=SUM(B5:B35)||=SUM(D5:D35)|=SUM(E5:E35)|||||||=SUM(L1:L35)|=SUM(M5:M35)|=SUM(N5:N35)||||

|Averages =|=AVERAGE(B6:B35)||=AVERAGE(D6:D35)|=AVERAGE(E6:E35)|=IFERROR(AVERAGE(F6:F35),"N/A")|=IFERROR(AVERAGE(G6:G35),"N/A")|=IFERROR(AVERAGE(H6:H35),"N/A")|=IFERROR(AVERAGE(I6:I35),"N/A")|=IFERROR(AVERAGE(J6:J35),"N/A")|=IFERROR(AVERAGE(K6:K35),"N/A")|=AVERAGE(L6:L35)|=AVERAGE(M6:M35)|=AVERAGE(N6:N35)||=IFERROR(G37/F37,"")|=IFERROR((F37-G37)/F37,"")|

||||||||||||||||||

|*Note:|Total Prod. -- Total Flared = Total TVA Usage|   |   ||||||||||||||

|Notes|BioRem bioreactor tank #4 was the only  reactor utilized due to on-going control panel issues. Biogas was drawn from the covered sludge lagoons to be treated through the Biorem system and flared during the month of September 2025. All biogas readings were taken using the Optima 7 handheld biogas analyzer.|   |   |   |   |   |   |   |   |   |   |   |   ||||

||"N/A" is "Not Applicable" because no biogas was produced, treated or flared|   |   |   |   |   |   |   |   |   |   |   |   ||||

||||||||||||||||||

||Total Monthly Biogas Produced [MMbtu] =|   |   |||||=L36|||||||||

||Total Monthly Biogas Flared [MMbtu] =|   |   |   |   |   |   |=M36|||||||||

||Total Monthly Biogas Transferred to TVA [MMbtu] =|   |   |   |   |   |   |=N36|||||||||

||||||||||||||||||

  

Q5:

What might the edit workflow be like in terms of a CLI command `gasflow edit --help` or an edit database button in the svelte SPA. I imagine this would really just call sql-like commands to edit the db stored in duck db.

This is excellent progress! You've successfully executed the Python setup, clarified the nature of the data, and provided the critical Excel function logic. Let's address the errors, the shell command difference, the data modeling/calculation rules, and the correction workflow.

🛠️ Debugging and Shell Scripting

1. Python Module Error

The error ModuleNotFoundError: No module named 'src' happens because Python doesn't automatically know that the src directory contains packages you want to import when you run a script directly outside of the package structure.

(gastrack) oolong@CIT-36NZRL3  ~/dev/gastrack   master  python src/main.py start
ModuleNotFoundError: No module named 'src'

Correction: You need to tell Python to treat the current directory (which contains src) as the root for imports, or run the module directly.

Fix: Run the main file as a module using the -m flag.

Bash

# How to run the server correctly from the project root
(gastrack) oolong@CIT-36NZRL3  ~/dev/gastrack   master  python -m src.main start

2. Svelte Setup Error

The sed: can't read s/"build": "vite build"/"build": "vite build --outDir dist"/: No such file or directory error occurred because the npm create vite@latest command was canceled when it saw the frontend directory was not empty. Thus, the package.json file created by npm init -y was still the minimal one, and the vite build command it was searching for using sed was never created.

The second attempt failed for the same reason.

Fix: You need to manually re-run the npm create vite@latest command, and select the overwrite option when prompted, or manually create the required files in frontend. For simplicity, you can run the following inside the frontend directory and answer "yes" to overwrite:

Bash

(gastrack) oolong@CIT-36NZRL3  ~/dev/gastrack/frontend  npm create vite@latest . -- --template svelte-ts

3. Shell Command Difference

The difference between source build-scripts/setup_python.sh and zsh build-scripts/setup_python.sh is critical for Python virtual environments:

You should use source build-scripts/setup_python.sh to have the .venv activated automatically after running the setup.

🔬 Data Modeling and Calculation Rules

Based on your inputs, we can define the final data structures and calculation logic.

Q1/Q2: Time-Series (Irregularity)

• Need for Resampling/Interpolation: Since the data is $\text{irregular}$ ("2:14, 3:21"), simply averaging the raw readings for a day is prone to error if the measurements aren't evenly spaced (e.g., if you have 10 readings in the morning and 2 in the evening, the average is biased toward the morning).

• Best Practice: DuckDB's strength is $\text{time-series}$ analysis. We will store the exact $\text{timestamp}$ and $\text{value}$, and use window functions or $\text{text{A P P R O X \_ Q U A N T I L E}}$ to get reliable daily averages, or simply $\text{AVERAGE}$ all raw points, assuming sufficient and consistent sampling, which is often standard for compliance reporting. For now, we will use $\text{AVERAGE}$, but keep the irregular timestamps.

• Data Model: A single table ts_analyzer_reading will hold all raw readings. The sheet number (1-6) will become a foreign key or an $\text{enum}$ field called $\text{sample\_point}$.

Q3/Q4: Flow Data and Calculation Rules

The Excel functions reveal the precise calculation logic:

1. Biogas Flow Totals (from BGFlow1, etc.)

• Source: The $\text{Total Prod [scf/day]}$ and its components (Blowers) are Raw Daily Inputs (likely from a different meter or logging system) that need to be ingested.

• Logic: $\text{Total Prod [scf/day]}=\text{Blowers 1, 2A, 2B [scf/day]}+\text{Blower 2C [scf/day]}$

• $\text{Total Biogas Transfer to TVA [scf/day]}=\text{Total Prod [scf/day]}-\text{Biogas Flared [scf/day]}$

2. Monthly Report Calculations (The September Report)

This report pulls daily results from the $\text{BG Calcs}$ sheets. The calculations are:

Conclusion: We need two main $\text{DuckDB}$ tables to store the raw data that feeds this:

1. $\text{ts\_analyzer\_reading}$: For all ${\text{H}}_2\text{S}$, ${\text{CH}}_4$, $\text{BTU}$ (Sheets 1-6) at the irregular time stamps.

2. $\text{daily\_flow\_input}$: For the daily $\text{Prod}$, $\text{Flared}$, and $\text{TVA}$ totals (BGFlow1, BGFlow2).

🖋️ Data Correction (Fudged Input) Workflow

The Svelte SPA is perfect for this.

Workflow: Direct Edit with Audit Trail

Since the system is local and primarily a $\text{compliance/reporting}$ tool, a direct edit to the value is the most efficient workflow, but we must record who changed it and why.

1. Data Model Change (DuckDB)

The $\text{ts\_analyzer\_reading}$ table should include two fields to handle manual corrections:

2. Svelte SPA Workflow

1. View Screen: The user views a table of raw $\text{ts\_analyzer\_reading}$ data.

2. Edit Action: The user clicks a row or an "Edit" button.

3. Edit Form: A form pops up showing the original value and allowing a new value to be entered.

4. Audit Note: The $\text{override\_note}$ field is a required text area before the user can save.

5. API Call: The Svelte frontend sends a $\text{PUT}$ or $\text{PATCH}$ request to the Starlette API endpoint: $\text{/api/ts\_data/{record\_id}}$ with the new value, the $\text{is\_manual\_override}$ flag set to $\text{TRUE}$, and the $\text{override\_note}$.

6. Backend Logic: Starlette receives the request, $\text{msgspec}$ validates the new values, and a $\text{DuckDB}$ $\text{UPDATE}$ command is executed.

This is exactly what you imagined: a front-end button calling a $\text{SQL}$-like update command via the $\text{Starlette}$ API.

📝 Next Step

Let's fix the module error and create the $\text{DuckDB}$ setup script, the $\text{msgspec}$ models, and the initial $\text{API}$ structure to handle data ingestion and correction.

1. Fix Python Path: Use $\text{python -m src.main start}$.

2. Create $\text{DuckDB}$ Setup: Script to initialize the database and tables.

3. Create $\text{Msgspec}$ Models: Python data classes for data validation.

4. Create $\text{API}$ Endpoints: Add the route to handle data upload/entry.

Would you like me to provide the $\text{DuckDB}$ setup script and the initial $\text{msgspec}$ models now?

Functions copied: Done: BGflow1, monthly report
To do

• BGflow2
• bg calcs 1
• bg calcs6

great

  

https://svelte.dev/ has been down for a few days, this is disconcerting

  

but not a problem right now

  

I should provide you with more spreasheet functions

  

from BGflow2:

|Biogas Flow Data from 4 BioRem Bioreactor Tank Flowmeters|   |   |   |   |   ||Daily Average Flow Data from 4 BioRem Bioreactor Tank Flowmeters|   |   |   |   |   |

|Date/Time|Tank 1 [scfm]|Tank 2 [scfm]|Tank 3 [scfm]|Tank 4 [scfm]|Total [scfm]||Date|Daily Average Treated Biogas Flow [scfm]|Daily treated Biogas Flow [scf/day]|Daily Treated Biogas Flow [MMscf/day]|Daily Biogas Flared [scf/day]|Daily Biogas Transfer to TVA [scf/day]|

|44593.0466550926|249.25|267|253.75|199.875|=SUM(B3:E3)||44593|=IFERROR(AVERAGEIFS($F$3:$F10001,$A$3:$A10001,">="&$H3,$A$3:$A10001,"<="&H3+1),"")|=IFERROR(I3*1440,"")|=J3/10^6|=J3|0|

|44593.0933217361|247.5|272.75|256|200.375|=SUM(B4:E4)||44594|=IFERROR(AVERAGEIFS($F$3:$F10002,$A$3:$A10002,">="&$H4,$A$3:$A10002,"<="&H4+1),"")|=IFERROR(I4*1440,"")|=J4/10^6|=J4|0|

  

from BG Calcs 1

|Date|Inlet Biogas H2S [ppm]|CH4 [%]|Biogas Net BTUs [btu/cf]|Biogas Gross BTUs [btu/cf]||Date|Total Prod [scf/day]|Total Biogas Flared [scf/day]|Total Biogas Transfer to TVA [scf/day]||Flared Biogas BTU [Mmbtu /day]|NOx Flared [lbs/day]|CO Flared [lbs/day]|VOC Flared [lbs/day]|H2S Flare [scf/day]|SO2 Pro-duced by Flare (80%) [lbs/day]||Date [mo-yr]|Avg Biogas H2S [ppm]|Avg Biogas Net BTUs [btu/cf]|Avg Biogas Gross BTUs [btu/cf]|Total Biogas Prod [scf/mo]|Total Biogas Flared [scf/mo]|TVA Unit Total Usage [scf/mo]||Date [mo-yr]|Heat Flared Biogas [MMbtu/mo]|NOx Flared Biogas [lbs/mo]|CO Flared Biogas [lbs/mo]|VOC Flared Biogas [lbs/mo]|NOx Flared Biogas [tns/mo]|CO Flared Biogas [tns/mo]|VOC Flared Biogas [tns/mo]|H2S Flared Biogas [scf/mo]|SO2 Flare Produced [lbs/mo]|SO2 Flare Produced [tns/mo]|

|='1'!P2|='1'!S2|='1'!T2|='1'!Y2|='1'!Z2||=IFERROR(BGflow1!A3,"")|=IFERROR(BGflow1!E3,"")|=IFERROR(BGflow1!F3,"")|=IFERROR(BGflow1!G3,"")||=D2*I2/10^6|=L2*EmFactors!$B$4|=L2*EmFactors!$B$5|=L2*EmFactors!$B$6|=B2*I2/10^6|=P2*64*EmFactors!$B$7/379||43831|='2020 Data-Calcs'!U2|='2020 Data-Calcs'!W2||='2020 Data-Calcs'!R2|=W2-Y2|||43831|=U2*X2/10^6|=IFERROR(AB2*EmFactors!$B$4,"")|=IFERROR(AB2*EmFactors!$B$5,"")|=IFERROR(AB2*EmFactors!$B$6,"")|=IFERROR(AC2/2000,"")|=IFERROR(AD2/2000,"")|=IFERROR(AE2/2000,"")|=IFERROR(T2*X2/10^6,"")|=IFERROR(AI2*64*EmFactors!$B$7/379.3,"")|=IFERROR(AJ2/2000,"")|

|='1'!P3|='1'!S3|='1'!T3|='1'!Y3|='1'!Z3||=IFERROR(BGflow1!A4,"")|=IFERROR(BGflow1!E4,"")|=IFERROR(BGflow1!F4,"")|=IFERROR(BGflow1!G4,"")||=D3*I3/10^6|=L3*EmFactors!$B$4|=L3*EmFactors!$B$5|=L3*EmFactors!$B$6|=B3*I3/10^6|=P3*64*EmFactors!$B$7/379||43862|='2020 Data-Calcs'!U3|='2020 Data-Calcs'!W3||='2020 Data-Calcs'!R3|=W3-Y3|||43862|=U3*X3/10^6|=IFERROR(AB3*EmFactors!$B$4,"")|=IFERROR(AB3*EmFactors!$B$5,"")|=IFERROR(AB3*EmFactors!$B$6,"")|=IFERROR(AC3/2000,"")|=IFERROR(AD3/2000,"")|=IFERROR(AE3/2000,"")|=IFERROR(T3*X3/10^6,"")|=IFERROR(AI3*64*EmFactors!$B$7/379.3,"")|=IFERROR(AJ3/2000,"")|

|='1'!P4|='1'!S4|='1'!T4|='1'!Y4|='1'!Z4||=IFERROR(BGflow1!A5,"")|=IFERROR(BGflow1!E5,"")|=IFERROR(BGflow1!F5,"")|=IFERROR(BGflow1!G5,"")||=D4*I4/10^6|=L4*EmFactors!$B$4|=L4*EmFactors!$B$5|=L4*EmFactors!$B$6|=B4*I4/10^6|=P4*64*EmFactors!$B$7/379||43891|='2020 Data-Calcs'!U4|='2020 Data-Calcs'!W4||='2020 Data-Calcs'!R4|=W4-Y4|||43891|=U4*X4/10^6|=IFERROR(AB4*EmFactors!$B$4,"")|=IFERROR(AB4*EmFactors!$B$5,"")|=IFERROR(AB4*EmFactors!$B$6,"")|=IFERROR(AC4/2000,"")|=IFERROR(AD4/2000,"")|=IFERROR(AE4/2000,"")|=IFERROR(T4*X4/10^6,"")|=IFERROR(AI4*64*EmFactors!$B$7/379.3,"")|=IFERROR(AJ4/2000,"")|

|='1'!P5|='1'!S5|='1'!T5|='1'!Y5|='1'!Z5||=IFERROR(BGflow1!A6,"")|=IFERROR(BGflow1!E6,"")|=IFERROR(BGflow1!F6,"")|=IFERROR(BGflow1!G6,"")||=D5*I5/10^6|=L5*EmFactors!$B$4|=L5*EmFactors!$B$5|=L5*EmFactors!$B$6|=B5*I5/10^6|=P5*64*EmFactors!$B$7/379||43922|='2020 Data-Calcs'!U5|='2020 Data-Calcs'!W5||='2020 Data-Calcs'!R5|=W5-Y5|||43922|=U5*X5/10^6|=IFERROR(AB5*EmFactors!$B$4,"")|=IFERROR(AB5*EmFactors!$B$5,"")|=IFERROR(AB5*EmFactors!$B$6,"")|=IFERROR(AC5/2000,"")|=IFERROR(AD5/2000,"")|=IFERROR(AE5/2000,"")|=IFERROR(T5*X5/10^6,"")|=IFERROR(AI5*64*EmFactors!$B$7/379.3,"")|=IFERROR(AJ5/2000,"")|

  

from BG Calcs6:

|Date|Biorem Outlet Biogas H2S [ppm]|Biorem Outlet Biogas Methane [%]|Biogas Net BTUs [btu/cf]|Biogas Gross BTUs [btu/cf]||Date|Total Prod [scf/day]|Total Biogas Flared [scf/day]|Total Biogas Transfer to TVA [scf/day]||TVA Biogas BTU [MMbtu /day]|Flared Biogas BTU [MMbtu /day]|NOx Flared [lbs/day]|CO Flared [lbs/day]|VOC Flared [lbs/day]|H2S Flare [scf/day]|SO2 Pro-duced by Flare (100%)* [lbs/day]||Date [mo-yr]|Avg Biogas H2S [ppmv]|Avg Biogas Net BTUs [btu/cf]|Avg Biogas Gross BTUs [btu/cf]|Total Biogas Prod [scf/mo]|TVA Unit Total Usage [scf/mo]|Total Biogas Flared [scf/mo]||Date [mo-yr]|Heat Flared Biogas [MMbtu /mo]|NOx Flared Biogas [lbs/mo]|CO Flared Biogas [lbs/mo]|VOC Flared Biogas [lbs/mo]|NOx Flared Biogas [tns/mo]|CO Flared Biogas [tns/mo]|VOC Flared Biogas [tns/mo]|H2S Flared Biogas [scf/mo]|SO2 Flare Produced [lbs/mo]|SO2 Flare Produced [tns/mo]|

|='6'!P5|='6'!S5|='6'!T5|='6'!Y5|='6'!Z5||=BGflow1!A56|=IF(BGflow1!E56="","",BGflow1!E56)|=IF(BGflow1!F56="","",BGflow1!F56)|=IF(BGflow1!G56="","",BGflow1!G56)||=D2*J2/10^6|=D2*I2/10^6|=M2*EmFactors!$B$10|=M2*EmFactors!$B$11|=M2*EmFactors!$B$12|=B2*I2/10^6|=Q2*64*EmFactors!$B$7/379.3||44378|=IFERROR(AVERAGEIFS(B$2:B10000,$A$2:$A10000,">="&$T2,$A$2:$A10000,"<="&EOMONTH($T2,0)),"")|=IFERROR(AVERAGEIFS(D$2:D10000,$A$2:$A10000,">="&$T2,$A$2:$A10000,"<="&EOMONTH($T2,0)),"")|=IFERROR(AVERAGEIFS(E$2:E10000,$A$2:$A10000,">="&$T2,$A$2:$A10000,"<="&EOMONTH($T2,0)),"")|=IFERROR(SUMIFS(H$8:H10000,$G$8:$G10000,">="&$T2,$G$8:$G10000,"<="&EOMONTH($T2,0)),"")|=IFERROR(SUMIFS(J$8:J10000,$G$8:$G10000,">="&$T2,$G$8:$G10000,"<="&EOMONTH($T2,0)),"")|=IFERROR(SUMIFS(I$8:I10000,$G$8:$G10000,">="&$T2,$G$8:$G10000,"<="&EOMONTH($T2,0)),"")||=T2|=SUMIFS(M$2:M10000,$G$2:$G10000,">="&$AB2,$G$2:$G10000,"<="&EOMONTH($AB2,0))|=SUMIFS(N$2:N10000,$G$2:$G10000,">="&$AB2,$G$2:$G10000,"<="&EOMONTH($AB2,0))|=SUMIFS(O$2:O10000,$G$2:$G10000,">="&$AB2,$G$2:$G10000,"<="&EOMONTH($AB2,0))|=SUMIFS(P$2:P10000,$G$2:$G10000,">="&$AB2,$G$2:$G10000,"<="&EOMONTH($AB2,0))|=IFERROR(AD2/2000,"")|=IFERROR(AE2/2000,"")|=IFERROR(AF2/2000,"")|=SUMIFS(Q$2:Q10000,$G$2:$G10000,">="&$AB2,$G$2:$G10000,"<="&EOMONTH($AB2,0))|=SUMIFS(R$2:R10000,$G$2:$G10000,">="&$AB2,$G$2:$G10000,"<="&EOMONTH($AB2,0))|=IFERROR(AK2/2000,"")|

|='6'!P6|='6'!S6|='6'!T6|='6'!Y6|='6'!Z6||=BGflow1!A57|=IF(BGflow1!E57="","",BGflow1!E57)|=IF(BGflow1!F57="","",BGflow1!F57)|=IF(BGflow1!G57="","",BGflow1!G57)||=D3*J3/10^6|=D3*I3/10^6|=M3*EmFactors!$B$10|=M3*EmFactors!$B$11|=M3*EmFactors!$B$12|=B3*I3/10^6|=Q3*64*EmFactors!$B$7/379.3||44409|=IFERROR(AVERAGEIFS(B$2:B10001,$A$2:$A10001,">="&$T3,$A$2:$A10001,"<="&EOMONTH($T3,0)),"")|=IFERROR(AVERAGEIFS(D$2:D10001,$A$2:$A10001,">="&$T3,$A$2:$A10001,"<="&EOMONTH($T3,0)),"")|=IFERROR(AVERAGEIFS(E$2:E10001,$A$2:$A10001,">="&$T3,$A$2:$A10001,"<="&EOMONTH($T3,0)),"")|=IFERROR(SUMIFS(H$8:H10001,$G$8:$G10001,">="&$T3,$G$8:$G10001,"<="&EOMONTH($T3,0)),"")|=IFERROR(SUMIFS(J$8:J10001,$G$8:$G10001,">="&$T3,$G$8:$G10001,"<="&EOMONTH($T3,0)),"")|=IFERROR(SUMIFS(I$8:I10001,$G$8:$G10001,">="&$T3,$G$8:$G10001,"<="&EOMONTH($T3,0)),"")||=T3|=SUMIFS(M$2:M10001,$G$2:$G10001,">="&$AB3,$G$2:$G10001,"<="&EOMONTH($AB3,0))|=SUMIFS(N$2:N10001,$G$2:$G10001,">="&$AB3,$G$2:$G10001,"<="&EOMONTH($AB3,0))|=SUMIFS(O$2:O10001,$G$2:$G10001,">="&$AB3,$G$2:$G10001,"<="&EOMONTH($AB3,0))|=SUMIFS(P$2:P10001,$G$2:$G10001,">="&$AB3,$G$2:$G10001,"<="&EOMONTH($AB3,0))|=IFERROR(AD3/2000,"")|=IFERROR(AE3/2000,"")|=IFERROR(AF3/2000,"")|=SUMIFS(Q$2:Q10001,$G$2:$G10001,">="&$AB3,$G$2:$G10001,"<="&EOMONTH($AB3,0))|=SUMIFS(R$2:R10001,$G$2:$G10001,">="&$AB3,$G$2:$G10001,"<="&EOMONTH($AB3,0))|=IFERROR(AK3/2000,"")|

|='6'!P7|='6'!S7|='6'!T7|='6'!Y7|='6'!Z7||=BGflow1!A58|=IF(BGflow1!E58="","",BGflow1!E58)|=IF(BGflow1!F58="","",BGflow1!F58)|=IF(BGflow1!G58="","",BGflow1!G58)||=D4*J4/10^6|=D4*I4/10^6|=M4*EmFactors!$B$10|=M4*EmFactors!$B$11|=M4*EmFactors!$B$12|=B4*I4/10^6|=Q4*64*EmFactors!$B$7/379.3||44440|=IFERROR(AVERAGEIFS(B$2:B10002,$A$2:$A10002,">="&$T4,$A$2:$A10002,"<="&EOMONTH($T4,0)),"")|=IFERROR(AVERAGEIFS(D$2:D10002,$A$2:$A10002,">="&$T4,$A$2:$A10002,"<="&EOMONTH($T4,0)),"")|=IFERROR(AVERAGEIFS(E$2:E10002,$A$2:$A10002,">="&$T4,$A$2:$A10002,"<="&EOMONTH($T4,0)),"")|=IFERROR(SUMIFS(H$8:H10002,$G$8:$G10002,">="&$T4,$G$8:$G10002,"<="&EOMONTH($T4,0)),"")|=IFERROR(SUMIFS(J$8:J10002,$G$8:$G10002,">="&$T4,$G$8:$G10002,"<="&EOMONTH($T4,0)),"")|=IFERROR(SUMIFS(I$8:I10002,$G$8:$G10002,">="&$T4,$G$8:$G10002,"<="&EOMONTH($T4,0)),"")||=T4|=SUMIFS(M$2:M10002,$G$2:$G10002,">="&$AB4,$G$2:$G10002,"<="&EOMONTH($AB4,0))|=SUMIFS(N$2:N10002,$G$2:$G10002,">="&$AB4,$G$2:$G10002,"<="&EOMONTH($AB4,0))|=SUMIFS(O$2:O10002,$G$2:$G10002,">="&$AB4,$G$2:$G10002,"<="&EOMONTH($AB4,0))|=SUMIFS(P$2:P10002,$G$2:$G10002,">="&$AB4,$G$2:$G10002,"<="&EOMONTH($AB4,0))|=IFERROR(AD4/2000,"")|=IFERROR(AE4/2000,"")|=IFERROR(AF4/2000,"")|=SUMIFS(Q$2:Q10002,$G$2:$G10002,">="&$AB4,$G$2:$G10002,"<="&EOMONTH($AB4,0))|=SUMIFS(R$2:R10002,$G$2:$G10002,">="&$AB4,$G$2:$G10002,"<="&EOMONTH($AB4,0))|=IFERROR(AK4/2000,"")|

  

is looks like there is not a dist/ dir in frontend/

  

but it is exciting that svelte built well

  

do we need to adjust the existing build scripts?

  

(gastrack)  oolong@CIT-36NZRL3  ~/dev/gastrack   master  python -m src.main start

Traceback (most recent call last):

  File "<frozen runpy>", line 198, in _run_module_as_main

  File "<frozen runpy>", line 88, in _run_code

  File "/home/oolong/dev/gastrack/src/main.py", line 4, in <module>

    from src.gastrack.core.server import run_server

  File "/home/oolong/dev/gastrack/src/gastrack/core/server.py", line 21, in <module>

    Mount("/", StaticFiles(directory=STATIC_DIR, html=True), name="static")

               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

  File "/home/oolong/dev/gastrack/.venv/lib/python3.12/site-packages/starlette/staticfiles.py", line 56, in __init__

    raise RuntimeError(f"Directory '{directory}' does not exist")

RuntimeError: Directory '/home/oolong/dev/gastrack/frontend/dist' does not exist

(gastrack)  ✘ oolong@CIT-36NZRL3  ~/dev/gastrack   master  npm create vite@latest . -- --template svelte-ts

  

> npx

> "create-vite" . --template svelte-ts

  

│

◇  Current directory is not empty. Please choose how to proceed:

│  Cancel operation

└  Operation cancelled

  

(gastrack)  oolong@CIT-36NZRL3  ~/dev/gastrack   master  cd frontend

(gastrack)  oolong@CIT-36NZRL3  ~/dev/gastrack/frontend   master  npm create vite@latest . -- --template svelte-ts

  

> frontend@1.0.0 npx

> "create-vite" . --template svelte-ts

  

│

◇  Current directory is not empty. Please choose how to proceed:

│  Remove existing files and continue

│

◇  Use rolldown-vite (Experimental)?:

│  Yes

│

◇  Install with npm and start now?

│  Yes

│

◇  Scaffolding project in /home/oolong/dev/gastrack/frontend...

│

◇  Installing dependencies with npm...

  

added 50 packages, and audited 51 packages in 6s

  

10 packages are looking for funding

  run `npm fund` for details

  

found 0 vulnerabilities

│

◇  Starting dev server...

  

> frontend@0.0.0 dev

> vite

  

2:23:25 PM [vite-plugin-svelte] !!! Support for rolldown-vite in vite-plugin-svelte is experimental (rolldown: 1.0.0-beta.50, vite: 7.2.5) !!!

        See https://github.com/sveltejs/vite-plugin-svelte/issues/1143 for a list of known issues and to report feedback.

`optimizeDeps.rollupOptions` / `ssr.optimizeDeps.rollupOptions` is deprecated. Use `optimizeDeps.rolldownOptions` instead. Note that this option may be set by a plugin. Set VITE_DEPRECATION_TRACE=1 to see where it is called.

2:23:25 PM [vite] (client) Forced re-optimization of dependencies

  

  ROLLDOWN-VITE v7.2.5  ready in 495 ms

  

  ➜  Local:   http://localhost:5173/

  ➜  Network: use --host to expose

  ➜  press h + enter to show help

h

  

  Shortcuts

  press r + enter to restart the server

  press u + enter to show server url

  press o + enter to open in browser

  press c + enter to clear console

  press q + enter to quit

u

  

  ➜  Local:   http://localhost:5173/

  ➜  Network: use --host to expose

q

(gastrack)  oolong@CIT-36NZRL3  ~/dev/gastrack/frontend   master  cd ..

(gastrack)  oolong@CIT-36NZRL3  ~/dev/gastrack   master  python -m src.main start

Traceback (most recent call last):

  File "<frozen runpy>", line 198, in _run_module_as_main

  File "<frozen runpy>", line 88, in _run_code

  File "/home/oolong/dev/gastrack/src/main.py", line 4, in <module>

    from src.gastrack.core.server import run_server

  File "/home/oolong/dev/gastrack/src/gastrack/core/server.py", line 21, in <module>

    Mount("/", StaticFiles(directory=STATIC_DIR, html=True), name="static")

               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

  File "/home/oolong/dev/gastrack/.venv/lib/python3.12/site-packages/starlette/staticfiles.py", line 56, in __init__

    raise RuntimeError(f"Directory '{directory}' does not exist")

RuntimeError: Directory '/home/oolong/dev/gastrack/frontend/dist' does not exist

(gastrack)  ✘ oolong@CIT-36NZRL3  ~/dev/gastrack   master  ls frontend

README.md  index.html  node_modules  package-lock.json  package.json  public  src  svelte.config.js  tsconfig.app.json  tsconfig.json  tsconfig.node.json  vite.config.ts

(gastrack)  oolong@CIT-36NZRL3  ~/dev/gastrack   master 

  

also we need to change master to main

i have never used uv, i generally use poetry but i imagine it is time to try uv if i want to be future proof, though i hear uv is getting bloated

That's great progress on the Svelte setup! Dealing with the front-end environment and the Python path issue simultaneously can be tricky, but you're isolating the problems well.

🐛 Current State and Immediate Fixes

1. StaticFiles Error (RuntimeError: Directory '.../frontend/dist' does not exist)

The Starlette error is absolutely correct. The frontend/dist directory is where Vite places the production build of your Svelte application. You ran the Svelte development server (npm run dev), which doesn't create the dist directory.

Solution: Build the Frontend

To fix this, you need to run the build command inside the frontend directory.

Bash

(gastrack) oolong@CIT-36NZRL3  ~/dev/gastrack/frontend   master  npm run build

This command should:

• Compile your Svelte code.

• Bundle it into static assets (HTML, CSS, JS).

• Place these assets into the frontend/dist directory, which Starlette is looking for.

After running npm run build, running python -m src.main start from the project root should successfully start the Starlette server and serve the Svelte application from dist.

2. Git Branch Renaming (master to main)

Changing the default branch name is a good practice. Here's how to safely rename your local and remote branches.

Bash

# 1. Rename the local 'master' branch to 'main'
git branch -m master main

# 2. Push the new 'main' branch to the remote (origin)
git push -u origin main

# 3. Delete the old 'master' branch on the remote
git push origin --delete master

(You'll need to update your GitHub/GitLab repository settings afterwards to set main as the new default branch).

📐 Data Modeling: Biogas Flow and Compliance Logic

The new spreadsheet functions you provided solidify the three main data modeling concerns: Irregular Time-Series (TS) Data, Daily Flow Aggregation, and Monthly Compliance Reporting.

1. Irregular TS to Daily Aggregation (BGflow2)

This sheet confirms the precise method for converting irregular flow meter readings to daily averages, which is a core function we need to implement in DuckDB/Python.

• Key Takeaway: We need a $\text{TS}$ table for flow meter data (similar to the analyzer data), and our $\text{API}$ will expose a daily view that calculates these averages dynamically.

2. Daily and Monthly Emissions Calculations (BG Calcs 1 & 6)

These sheets detail the daily and monthly $\text{emissions}$ and $\text{BTU}$ (energy) content calculations.

Core Daily Calculation Patterns:

• Flared BTU: $\text{Daily Flared Biogas BTU [MMbtu/day]}=\text{Daily Flared Biogas Flow [scf/day]}\times \text{Average Net BTUs [btu/cf]}/{10}^6$

  • Source (Calcs 1): =D2 * I2 / 10^6 (where $\text{D}$ is $\text{BTU/cf}$ and $\text{I}$ is $\text{Total Flared [scf/day]}$)

• Pollutant Mass (NOx, CO, VOC): $\text{Pollutant Mass [lbs/day]}=\text{Flared Biogas BTU [MMbtu/day]}\times \text{Emission Factor}$

  • Source (Calcs 1): =L2 * EmFactors!$B$4 (where $\text{L}$ is $\text{Flared Biogas BTU}$ and $\text{EmFactors!B4}$ is the $\text{NOx Factor}$).

• ${\text{SO}}_2$ Produced: ${\text{SO}}_2\text{ [lbs/day]}=({\text{H}}_2\text{S}\text{ [ppm]}\times \text{Flared Biogas Flow [scf/day]}/{10}^6)\times 64\times \text{Conversion Factor}$

  • Source (Calcs 1): =P2 * 64 * EmFactors!$B$7 / 379 (where $\text{P}$ is ${\text{H}}_2\text{S [ppm]}$ and the latter half handles gas law conversions).

Core Monthly Aggregation Patterns:

The monthly rows in BG Calcs 6 use a few key aggregate functions:

• Monthly Averages: $\text{Avg Biogas H2S [ppmv]}=\text{AVERAGEIFS(...)}$ over the days of the month.

• Monthly Sums: $\text{Total Biogas Prod [scf/mo]}=\text{SUMIFS(...)}$ over the days of the month.

• Key Takeaway: The entire system is built around three layers of processing: TS Data $\to$ Daily Aggregates $\to$ Monthly Compliance Report. All daily and monthly $\text{MMbtu}$ and $\text{emissions}$ calculations are performed on the aggregated daily data.

3. Starlette StaticFiles Mount

The build script you were looking at needs to be adjusted.

setup_svelte.sh Adjustment (Revisiting)

The purpose of the original setup_svelte.sh was to automate the build. Since you successfully ran npm create vite@latest and will manually run npm run build now, you don't need to adjust the build script itself unless you want to re-automate the $\text{npm run build}$ step.

The fact that you now have all the necessary Svelte files in frontend/ (like vite.config.ts and package.json) means the initial setup is complete. You just need to trigger the build.

🚀 Next Steps

We need to formalize the $\text{DuckDB}$ schema and $\text{Python}$ models to support the data points feeding these crucial calculations.

1. DuckDB Schema: Create the tables for $\text{TS}$ readings and $\text{Emissions Factors}$.

2. Msgspec Models: Define the Python data structures for these tables.

Would you like to run the $\text{npm run build}$ command now, and then move on to defining the $\text{DuckDB}$ schema and $\text{msgspec}$ models?

(gastrack)  oolong@CIT-36NZRL3  ~/dev/gastrack   master  ls frontend
README.md  index.html  node_modules  package-lock.json  package.json  public  src  svelte.config.js  tsconfig.app.json  tsconfig.json  tsconfig.node.json  vite.config.ts
(gastrack)  oolong@CIT-36NZRL3  ~/dev/gastrack   master  npm run build
npm error code ENOENT
npm error syscall open
npm error path /home/oolong/dev/gastrack/package.json
npm error errno -2
npm error enoent Could not read package.json: Error: ENOENT: no such file or directory, open '/home/oolong/dev/gastrack/package.json'
npm error enoent This is related to npm not being able to find a file.
npm error enoent
npm error A complete log of this run can be found in: /home/oolong/.npm/_logs/2025-11-25T20_33_49_152Z-debug-0.log
(gastrack)  ✘ oolong@CIT-36NZRL3  ~/dev/gastrack   master  cat /home/oolong/.npm/_logs/2025-11-25T20_33_49_152Z-debug-0.log
0 verbose cli /home/oolong/.local/share/fnm/node-versions/v24.11.1/installation/bin/node /run/user/1000/fnm_multishells/3734_1764089432948/bin/npm
1 info using npm@11.6.2
2 info using node@v24.11.1
3 silly config load:file:/home/oolong/.local/share/fnm/node-versions/v24.11.1/installation/lib/node_modules/npm/npmrc
4 silly config load:file:/home/oolong/dev/gastrack/.npmrc
5 silly config load:file:/home/oolong/.npmrc
6 silly config load:file:/home/oolong/.local/share/fnm/node-versions/v24.11.1/installation/etc/npmrc
7 verbose title npm run build
8 verbose argv "run" "build"
9 verbose logfile logs-max:10 dir:/home/oolong/.npm/_logs/2025-11-25T20_33_49_152Z-
10 verbose logfile /home/oolong/.npm/_logs/2025-11-25T20_33_49_152Z-debug-0.log
11 silly logfile start cleaning logs, removing 1 files
12 verbose stack Error: Could not read package.json: Error: ENOENT: no such file or directory, open '/home/oolong/dev/gastrack/package.json'
12 verbose stack     at async open (node:internal/fs/promises:642:25)
12 verbose stack     at async readFile (node:internal/fs/promises:1279:14)
12 verbose stack     at async read (/home/oolong/.local/share/fnm/node-versions/v24.11.1/installation/lib/node_modules/npm/node_modules/@npmcli/package-json/lib/read-package.js:9:18)
12 verbose stack     at async PackageJson.load (/home/oolong/.local/share/fnm/node-versions/v24.11.1/installation/lib/node_modules/npm/node_modules/@npmcli/package-json/lib/index.js:131:31)
12 verbose stack     at async PackageJson.normalize (/home/oolong/.local/share/fnm/node-versions/v24.11.1/installation/lib/node_modules/npm/node_modules/@npmcli/package-json/lib/index.js:117:5)
12 verbose stack     at async #run (/home/oolong/.local/share/fnm/node-versions/v24.11.1/installation/lib/node_modules/npm/lib/commands/run.js:90:13)
12 verbose stack     at async RunScript.exec (/home/oolong/.local/share/fnm/node-versions/v24.11.1/installation/lib/node_modules/npm/lib/commands/run.js:44:7)
12 verbose stack     at async Npm.exec (/home/oolong/.local/share/fnm/node-versions/v24.11.1/installation/lib/node_modules/npm/lib/npm.js:208:9)
12 verbose stack     at async module.exports (/home/oolong/.local/share/fnm/node-versions/v24.11.1/installation/lib/node_modules/npm/lib/cli/entry.js:67:5)
13 error code ENOENT
14 error syscall open
15 error path /home/oolong/dev/gastrack/package.json
16 error errno -2
17 error enoent Could not read package.json: Error: ENOENT: no such file or directory, open '/home/oolong/dev/gastrack/package.json'
18 error enoent This is related to npm not being able to find a file.
18 error enoent
19 verbose cwd /home/oolong/dev/gastrack
20 verbose os Linux 6.6.87.2-microsoft-standard-WSL2
21 verbose node v24.11.1
22 verbose npm  v11.6.2
23 verbose exit -2
24 verbose code -2
25 error A complete log of this run can be found in: /home/oolong/.npm/_logs/2025-11-25T20_33_49_152Z-debug-0.log
(gastrack)  oolong@CIT-36NZRL3  ~/dev/gastrack   master  git branch -m master main
(gastrack)  oolong@CIT-36NZRL3  ~/dev/gastrack   main  cat README.md
(gastrack)  oolong@CIT-36NZRL3  ~/dev/gastrack   main  git add README.md
(gastrack)  oolong@CIT-36NZRL3  ~/dev/gastrack   main ✚  git commit -m "initial commit"
[main (root-commit) af76d03] initial commit
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 README.md
(gastrack)  oolong@CIT-36NZRL3  ~/dev/gastrack   main  git branch -M main
(gastrack)  oolong@CIT-36NZRL3  ~/dev/gastrack   main  git remote add origin https://github.com/City-of-Memphis-Wastewater/gastrack.git
(gastrack)  oolong@CIT-36NZRL3  ~/dev/gastrack   main  git push -u origin main
Username for 'https://github.com': ^C
(gastrack)  ✘ oolong@CIT-36NZRL3  ~/dev/gastrack   main 

I already have credeitals setup for github on this environment but i forget how to recall them. i think it is just calling in the ssh key.

(gastrack)  oolong@CIT-36NZRL3  ~/dev/gastrack   main  ls ~/.ssh
id_edxxxxxx  id_edxxxxx.pub  known_hosts  known_hosts.old
(gastrack)  oolong@CIT-36NZRL3  ~/dev/gastrack   main  ls ~/.gitconfig
/home/oolong/.gitconfig
(gastrack)  oolong@CIT-36NZRL3  ~/dev/gastrack   main 

npm run build failed. See logs.

You've run into a classic $\text{npm}$ context error and made great progress on renaming your branch! Let's address the immediate build failure and then move on to the next steps for the application logic.

🛠️ Debugging the npm run build Failure

Root Cause: Incorrect Directory

The error npm error enoent Could not read package.json: Error: ENOENT: no such file or directory, open '/home/oolong/dev/gastrack/package.json' tells you exactly what happened:

1. You were in the project root directory: ~/dev/gastrack.

2. You ran the command: npm run build.

3. $\text{npm}$ looks for the main package.json file in the current directory (gastrack/) to find the build script.

4. Your frontend build script is in gastrack/frontend/package.json.

Solution: You need to execute the command from the directory containing the Svelte project's package.json.

Bash

# 1. Change directory to the frontend project
(gastrack) oolong@CIT-36NZRL3  ~/dev/gastrack   main  cd frontend

# 2. Run the build command
(gastrack) oolong@CIT-36NZRL3  ~/dev/gastrack/frontend   main  npm run build

Alternatively, you can use the -w (workspace) flag if your project had a monorepo setup, but for your current structure, simply changing the directory is the easiest fix.

🔐 GitHub Authentication

You are encountering an authentication challenge when pushing via HTTPS on the command line: git push -u origin main.

Since you have an $\text{SSH}$ key (~/.ssh/id\_ed25519.pub), the best solution is to switch your remote URL from $\text{HTTPS}$ to $\text{SSH}$. This is because $\text{SSH}$ uses the keys you have set up, avoiding the need for a username/password or a Personal Access Token prompt.

How to Switch to SSH Remote

1. Remove the old HTTPS remote:

   Bash

   git remote remove origin
   

2. Add the new SSH remote:

   Bash

   # The new URL format is git@github.com:<username>/<repo>.git
   git remote add origin git@github.com:City-of-Memphis-Wastewater/gastrack.git
   

3. Push using SSH:

   Bash

   git push -u origin main
   

Assuming your $\text{SSH}$ key is registered with your GitHub account, this push should succeed without prompting for credentials.

🗄️ DuckDB Schema and Msgspec Models

Assuming you successfully run npm run build and get your frontend/dist directory, the next step is defining the data structures for the API and $\text{DuckDB}$.

1. DuckDB Schema (in src/gastrack/core/database.py)

We need three tables to cover all the data types (Raw TS Readings, Daily Flow Inputs, and Compliance Factors):

SQL

-- Table 1: Raw Time-Series Readings (ts_analyzer_reading)
CREATE TABLE ts_analyzer_reading (
    id UUID PRIMARY KEY,
    timestamp TIMESTAMP,
    sample_point VARCHAR, -- e.g., 'Sheet 1', 'Sheet 6', 'Inlet', 'Outlet'
    o2_pct DOUBLE,
    co2_pct DOUBLE,
    h2s_ppm DOUBLE,
    ch4_pct DOUBLE,
    net_cal_val_mj_m3 DOUBLE,
    gross_cal_val_mj_m3 DOUBLE,
    t_sensor_f DOUBLE,
    balance_n2_pct DOUBLE,
    -- Audit fields for data fudging (Q5)
    is_manual_override BOOLEAN DEFAULT FALSE,
    override_note VARCHAR
);

-- Table 2: Daily Raw Flow Inputs (daily_flow_input)
-- This holds the BGFlow1/BGFlow2 data that may come from daily logs.
CREATE TABLE daily_flow_input (
    date DATE PRIMARY KEY,
    blower_1_scf_day DOUBLE,
    blower_2a_scf_day DOUBLE,
    blower_2b_scf_day DOUBLE,
    blower_2c_scf_day DOUBLE,
    biorem_ambient_air_scf_day DOUBLE,
    biogas_flared_scf_day DOUBLE
);

-- Table 3: Constant Emission & Conversion Factors (factors)
-- This holds the EmFactors!$B$x constants.
CREATE TABLE factors (
    key VARCHAR PRIMARY KEY,
    value DOUBLE,
    description VARCHAR
);

2. Msgspec Data Models (in src/gastrack/core/models.py)

These models will validate and structure the data coming into your Starlette API.

Python

import uuid
from datetime import datetime
from typing import Optional
from msgspec import Struct

# --- Analyzer Data Model (Irregular TS) ---
class AnalyzerReading(Struct):
    id: uuid.UUID = uuid.uuid4()
    timestamp: datetime
    sample_point: str
    o2_pct: Optional[float] = None
    co2_pct: Optional[float] = None
    h2s_ppm: Optional[float] = None
    ch4_pct: Optional[float] = None
    net_cal_val_mj_m3: Optional[float] = None
    gross_cal_val_mj_m3: Optional[float] = None
    t_sensor_f: Optional[float] = None
    balance_n2_pct: Optional[float] = None
    is_manual_override: bool = False
    override_note: Optional[str] = None

# --- Daily Flow Input Model ---
class DailyFlowInput(Struct):
    date: str # Use string for date entry (e.g., "2025-09-01") for simplicity
    blower_1_scf_day: Optional[float] = None
    blower_2a_scf_day: Optional[float] = None
    blower_2b_scf_day: Optional[float] = None
    blower_2c_scf_day: Optional[float] = None
    biorem_ambient_air_scf_day: Optional[float] = None
    biogas_flared_scf_day: Optional[float] = None

# --- Factor Model (for EmFactors) ---
class Factor(Struct):
    key: str
    value: float
    description: Optional[str] = None

Now you have the necessary models and schema to build the $\text{DuckDB}$ database handler and the $\text{API}$ endpoints.

Would you like to run the $\text{npm run build}$ command now, followed by creating the initial Python script to set up the $\text{DuckDB}$ database with this schema?

(gastrack)  oolong@CIT-36NZRL3  ~/dev/gastrack/frontend   main  npm run build

> frontend@0.0.0 build
> vite build

2:39:56 PM [vite-plugin-svelte] !!! Support for rolldown-vite in vite-plugin-svelte is experimental (rolldown: 1.0.0-beta.50, vite: 7.2.5) !!!
        See https://github.com/sveltejs/vite-plugin-svelte/issues/1143 for a list of known issues and to report feedback.
rolldown-vite v7.2.5 building client environment for production...
✓ 109 modules transformed.
dist/index.html                 0.45 kB │ gzip:  0.29 kB
dist/assets/index-1s3WlefU.css  1.43 kB │ gzip:  0.66 kB
dist/assets/index-C1qBDBK1.js   25.85 kB │ gzip: 10.72 kB
✓ built in 188ms
(gastrack)  oolong@CIT-36NZRL3  ~/dev/gastrack/frontend   main  cd ..
(gastrack)  oolong@CIT-36NZRL3  ~/dev/gastrack   main  git remote remove origin
(gastrack)  oolong@CIT-36NZRL3  ~/dev/gastrack   main  # The new URL format is git@github.com:<username>/<repo>.git
git remote add origin git@github.com:City-of-Memphis-Wastewater/gastrack.git
(gastrack)  oolong@CIT-36NZRL3  ~/dev/gastrack   main  git push -u origin main
Enumerating objects: 3, done.
Counting objects: 100% (3/3), done.
Writing objects: 100% (3/3), 231 bytes | 231.00 KiB/s, done.
Total 3 (delta 0), reused 0 (delta 0), pack-reused 0
To github.com:City-of-Memphis-Wastewater/gastrack.git
 * [new branch]      main -> main
branch 'main' set up to track 'origin/main'.
(gastrack)  oolong@CIT-36NZRL3  ~/dev/gastrack   main  git checkout -b "day1build_gemini"
Switched to a new branch 'day1build_gemini'
(gastrack)  oolong@CIT-36NZRL3  ~/dev/gastrack   day1build_gemini  code src/gastrack/core/database.py
(gastrack)  oolong@CIT-36NZRL3  ~/dev/gastrack   day1build_gemini  code src/gastrack/core/database.py
(gastrack)  oolong@CIT-36NZRL3  ~/dev/gastrack   day1build_gemini  code .               (gastrack)  oolong@CIT-36NZRL3  ~/dev/gastrack   day1build_gemini  code src/gastrack/core/database.py
(gastrack)  oolong@CIT-36NZRL3  ~/dev/gastrack   day1build_gemini  code src/gastrack/core/models.py
(gastrack)  oolong@CIT-36NZRL3  ~/dev/gastrack   day1build_gemini 

great

do you want to use src/gastrack/db/ ?

also you provided sql-like command blocks for database.py. This is good if we can include the SQL (other other file type) files rather than use python string blocks, for clarity and modularity and readability and reuse, etc. But these should go into a .sql, not a .py

i am ready for more build-scripts/ shell files

That's fantastic! You've resolved all the immediate development environment issues:

• ✅ The Svelte frontend has been successfully built into the required frontend/dist directory (npm run build).

• ✅ Git Authentication is fixed by switching to $\text{SSH}$ and the code is pushed to your new main branch.

• ✅ You are now working on a feature branch (day1build_gemini).

You raise an excellent point about modularity and separating SQL logic from Python code. Let's follow that best practice.

📁 Project Structure & DuckDB Setup

I recommend the following revised structure to cleanly separate the $\text{SQL}$ schema and the Python database connection logic:

Since you asked for more build scripts, here is the initial setup for the database directory and files.

🚀 Step 1: New Build Script (setup_db.sh)

This script will create the necessary $\text{db}$ directory and the $\text{SQL}$ schema file.

build-scripts/setup_db.sh

Bash

#!/usr/bin/env zsh

echo "Creating database schema files..."

# Create the db directory if it doesn't exist
mkdir -p src/gastrack/db

# Write the DuckDB connection file (connection.py)
cat << EOF > src/gastrack/db/connection.py
import duckdb
import os

# Define the path to the DuckDB database file
DB_PATH = os.path.join(os.path.dirname(os.path.abspath(__file__)), "gastrack.duckdb")

# Define the path to the SQL schema file
SQL_SCHEMA_PATH = os.path.join(os.path.dirname(os.path.abspath(__file__)), "init_schema.sql")


def get_db_connection():
    """Establishes and returns a DuckDB connection."""
    # Use 'read_only=False' to allow writing
    return duckdb.connect(database=DB_PATH, read_only=False)

def init_db(conn: duckdb.DuckDBPyConnection):
    """Initializes the database schema if tables do not exist."""
    print("Initializing DuckDB schema...")
    try:
        with open(SQL_SCHEMA_PATH, 'r') as f:
            sql_script = f.read()
        
        # Execute the entire SQL script
        conn.execute(sql_script)
        print("DuckDB schema initialized successfully.")
    except Exception as e:
        print(f"Error initializing database schema: {e}")

# This block ensures the database file and schema are created
# the first time any component imports 'connection.py'
if not os.path.exists(DB_PATH):
    print(f"Database file not found at {DB_PATH}. Creating and initializing...")
    conn = get_db_connection()
    init_db(conn)
    conn.close()
    
EOF

# Write the DuckDB schema (init_schema.sql)
cat << EOF > src/gastrack/db/init_schema.sql
-- DuckDB Schema for gastrack Project

-- 1. Raw Time-Series Readings (ts_analyzer_reading)
-- For irregular data from analyzer logs (O2, H2S, CH4, BTUs).
CREATE TABLE IF NOT EXISTS ts_analyzer_reading (
    id UUID PRIMARY KEY,
    timestamp TIMESTAMP,
    sample_point VARCHAR, -- e.g., 'Sheet 1', 'Sheet 6', 'Inlet', 'Outlet'
    o2_pct DOUBLE,
    co2_pct DOUBLE,
    h2s_ppm DOUBLE,
    ch4_pct DOUBLE,
    net_cal_val_mj_m3 DOUBLE,
    gross_cal_val_mj_m3 DOUBLE,
    t_sensor_f DOUBLE,
    balance_n2_pct DOUBLE,
    -- Audit fields for data fudging (Q5)
    is_manual_override BOOLEAN DEFAULT FALSE,
    override_note VARCHAR
);

-- 2. Daily Raw Flow Inputs (daily_flow_input)
-- This holds the BGFlow1/BGFlow2 data that may come from daily logs.
CREATE TABLE IF NOT EXISTS daily_flow_input (
    date DATE PRIMARY KEY,
    blower_1_scf_day DOUBLE,
    blower_2a_scf_day DOUBLE,
    blower_2b_scf_day DOUBLE,
    blower_2c_scf_day DOUBLE,
    biorem_ambient_air_scf_day DOUBLE,
    biogas_flared_scf_day DOUBLE
);

-- 3. Constant Emission & Conversion Factors (factors)
-- This holds the EmFactors!$B$x constants.
CREATE TABLE IF NOT EXISTS factors (
    key VARCHAR PRIMARY KEY,
    value DOUBLE,
    description VARCHAR
);

-- Insert initial compliance constants (based on Q4/BG Calcs)
INSERT INTO factors (key, value, description) VALUES
('HHV_PER_CH4_PCT', 10.4, 'HHV [BTU/scf]/100% CH4 - used for BTU calculation.'),
('H2S_PPM_TO_GRAINS_CCF_RATIO', 16.0, 'Conversion ratio for H2S ppm to grains/ccf (from E3 in Q4).');

-- Add placeholder for Emission Factors. These need to be confirmed from the EmFactors sheet.
-- Example factor placeholder (e.g., NOx factor from EmFactors!$B$4)
INSERT INTO factors (key, value, description) VALUES
('EMF_NOX_LBS_MMBTU', 0.05, 'Placeholder NOx Emission Factor for Flared Biogas (lbs/MMbtu).'),
('EMF_CO_LBS_MMBTU', 0.1, 'Placeholder CO Emission Factor for Flared Biogas (lbs/MMbtu).'),
('EMF_VOC_LBS_MMBTU', 0.03, 'Placeholder VOC Emission Factor for Flared Biogas (lbs/MMbtu).'),
('EMF_SO2_H2S_CONVERSION_FACTOR', 0.8, 'Placeholder H2S to SO2 Conversion Factor (80% efficiency for flare).');

EOF

echo "DuckDB connection and schema files created in src/gastrack/db/."

📝 Step 2: Update Python Models (src/gastrack/core/models.py)

Here are the $\text{msgspec}$ models ready to be pasted into the file you opened.

src/gastrack/core/models.py

Python

import uuid
from datetime import datetime
from typing import Optional, Literal
from msgspec import Struct, field

# Define the valid sample points based on your spreadsheet data
SAMPLE_POINTS = Literal['Sheet 1', 'Sheet 2', 'Sheet 3', 'Sheet 4', 'Sheet 5', 'Sheet 6', 
                        'Inlet', 'Outlet']

# --- Analyzer Data Model (Irregular TS) ---
# Used for ingestion of raw, timestamped gas quality readings.
class AnalyzerReading(Struct):
    id: uuid.UUID = field(default_factory=uuid.uuid4)
    timestamp: datetime
    sample_point: SAMPLE_POINTS 
    o2_pct: Optional[float] = None
    co2_pct: Optional[float] = None
    h2s_ppm: Optional[float] = None
    ch4_pct: Optional[float] = None
    net_cal_val_mj_m3: Optional[float] = None
    gross_cal_val_mj_m3: Optional[float] = None
    t_sensor_f: Optional[float] = None
    balance_n2_pct: Optional[float] = None
    
    # Audit fields for data corrections/fudging (Q5)
    is_manual_override: bool = False
    override_note: Optional[str] = None
    
# --- Daily Flow Input Model ---
# Used for ingestion of daily totalized flow meter readings (BGFlow1/2).
class DailyFlowInput(Struct):
    date: str # Date string "YYYY-MM-DD" or similar, will be converted to DATE in DB
    
    # Biogas Production Flows (Raw Daily Inputs)
    blower_1_scf_day: Optional[float] = None
    blower_2a_scf_day: Optional[float] = None
    blower_2b_scf_day: Optional[float] = None
    blower_2c_scf_day: Optional[float] = None
    biorem_ambient_air_scf_day: Optional[float] = None
    biogas_flared_scf_day: Optional[float] = None

# --- Factor Model (for EmFactors and fixed constants) ---
class Factor(Struct):
    key: str
    value: float
    description: Optional[str] = None

🏃 Ready to Run

You have successfully built the frontend and created the new feature branch.

The next action is to execute the new setup script to create the database files and then test the Starlette server.

1. Paste the models: Ensure the content of $\text{src/gastrack/core/models.py}$ is updated.

2. Create the script: Create and populate $\text{build-scripts/setup\_db.sh}$.

3. Run the script:

What would you like to do now?

(gastrack)  oolong@CIT-36NZRL3  ~/dev/gastrack   day1build_gemini  ls build-scripts     setup_db.py  setup_python.sh  setup_svelte.sh

(gastrack)  oolong@CIT-36NZRL3  ~/dev/gastrack   day1build_gemini  nano build-scripts/setup_db.py

(gastrack)  oolong@CIT-36NZRL3  ~/dev/gastrack   day1build_gemini  uv add msgspec       Resolved 2 packages in 0.56ms

Audited 1 package in 0.03ms

(gastrack)  oolong@CIT-36NZRL3  ~/dev/gastrack   day1build_gemini  source .venv/bin/activate

(gastrack)  oolong@CIT-36NZRL3  ~/dev/gastrack   day1build_gemini  deactivate

 oolong@CIT-36NZRL3  ~/dev/gastrack   day1build_gemini  source .venv/bin/activate

(gastrack)  oolong@CIT-36NZRL3  ~/dev/gastrack   day1build_gemini  python build-scripts/setup_db.py

  File "/home/oolong/dev/gastrack/build-scripts/setup_db.py", line 3

    echo "Creating database schema files..."

         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

SyntaxError: invalid syntax

(gastrack)  ✘ oolong@CIT-36NZRL3  ~/dev/gastrack   day1build_gemini  mv build-scripts/setup_db.py build-scripts/setup_db.sh

(gastrack)  oolong@CIT-36NZRL3  ~/dev/gastrack   day1build_gemini  zsh build-scripts/setup_db.sh

Creating database schema files...

DuckDB connection and schema files created in src/gastrack/db/.

(gastrack)  oolong@CIT-36NZRL3  ~/dev/gastrack   day1build_gemini  ls  src/gastrack/db/build-scripts

ls: cannot access 'src/gastrack/db/build-scripts': No such file or directory

(gastrack)  ✘ oolong@CIT-36NZRL3  ~/dev/gastrack   day1build_gemini  ls src/gastrack/db/

connection.py  init_schema.sql

(gastrack)  oolong@CIT-36NZRL3  ~/dev/gastrack   day1build_gemini 

(gastrack)  oolong@CIT-36NZRL3  ~/dev/gastrack   day1build_gemini  python -m src.main start

Starting GasTrack server on http://localhost:8000

INFO:     Started server process [13839]

INFO:     Waiting for application startup.

INFO:     Application startup complete.

INFO:     Uvicorn running on http://127.0.0.1:8000 (Press CTRL+C to quit)

INFO:     127.0.0.1:47766 - "GET / HTTP/1.1" 200 OK

INFO:     127.0.0.1:47766 - "GET /favicon.ico HTTP/1.1" 404 Not Found

^CINFO:     Shutting down

INFO:     Waiting for application shutdown.

INFO:     Application shutdown complete.

INFO:     Finished server process [13839]

(gastrack)  oolong@CIT-36NZRL3  ~/dev/gastrack   day1build_gemini 

  

{"status":"ok","message":"GasTrack API is running"}

  

wonderful

  

why are we using pip install and source .venv/bin/activate when we have uv?

  

how is the `source` command different from the `zsh` command?

  

what next?

That is excellent progress! You successfully built the frontend, set up the $\text{DuckDB}$ schema files, and confirmed the Starlette server starts and serves the frontend build from dist/ (indicated by the 200 OK on the initial GET /). The database schema files were created successfully in src/gastrack/db/.

Let's address your questions about dependency management and shell commands, and then define the next steps for building out the $\text{API}$.

🐍 Dependency Management: pip install vs. uv

The confusion is understandable, as modern Python environments often combine multiple tools.

In short:

• Use uv for managing which packages are installed.

• Use source .venv/bin/activate to make sure Python knows where those packages are installed.

🐚 Shell Commands: source vs. zsh

These two commands serve fundamentally different purposes in a Unix shell environment:

zsh <script_name> (Execute)

• What it does: It launches a new, separate $\text{zsh}$ process to execute the script.

• Environment: Any environment variables, shell functions, or aliases set by the script are confined to this new process and are lost once the script finishes.

• Example: When you ran zsh build-scripts/setup_db.sh, the script created files, and all variables and functions it used were destroyed when it finished.

source <script_name> (Execute in current shell)

• What it does: It reads and executes the script in the current shell process.

• Environment: Any changes to the environment (like setting the PATH, defining variables, or activating a virtual environment) are applied directly to your current session.

• Example: When you ran source .venv/bin/activate, the script modified your current shell's environment variables so that commands like python now point to the version inside .venv/. This change persists in your current session.

⏭️ What Next? Building the API Endpoints

The next logical step is to create the $\text{API}$ endpoints in Starlette to interact with the data models and the $\text{DuckDB}$ database.

We need to add three core handlers to $\text{src/gastrack/api/handlers.py}$ (or similar file):

1. Readings Ingestion: An endpoint to accept and store the $\text{AnalyzerReading}$ $\text{TS}$ data.

2. Flow Ingestion: An endpoint to accept and store the $\text{DailyFlowInput}$ data.

3. Factor Management: An endpoint to retrieve the $\text{Emissions Factors}$.

Let's start by integrating the $\text{DuckDB}$ connection into the application's lifecycle and defining the main data ingestion logic.

Would you like to start by setting up the DuckDB ingestion logic in a new file, say $\text{src/gastrack/db/crud.py}$?

The next logical step is to create the $\text{API}$ endpoints in Starlette to interact with the data models and the $\text{DuckDB}$ database.

We need to add three core handlers to $\text{src/gastrack/api/handlers.py}$ (or similar file):

Readings Ingestion: An endpoint to accept and store the $\text{AnalyzerReading}$ $\text{TS}$ data.

Flow Ingestion: An endpoint to accept and store the $\text{DailyFlowInput}$ data.

Factor Management: An endpoint to retrieve the $\text{Emissions Factors}$.

Let's start by integrating the $\text{DuckDB}$ connection into the application's lifecycle and defining the main data ingestion logic.

Would you like to start by setting up the DuckDB ingestion logic in a new file, say $\text{src/gastrack/db/crud.py}$?

  

this all sounds great

That's a perfect plan. Setting up the Data Access Layer in src/gastrack/db/crud.py will give us the essential functions for writing data to DuckDB, which we can then use in our Starlette API handlers.

We need to implement the following core database operations (CRUD: Create, Read, Update, Delete) for your three data models:

1. Ingest $\text{AnalyzerReading}$: Insert one or many time-series analyzer readings.

2. Ingest $\text{DailyFlowInput}$: Insert one or many daily flow summary records.

3. Get $\text{Factors}$: Retrieve all the fixed emission factors.

🛠️ Step 1: Create src/gastrack/db/crud.py

This file will contain the Python functions that interact directly with the $\text{DuckDB}$ connection object.

Python

import uuid
from typing import List

import duckdb
from msgspec import msgpack

from src.gastrack.db.connection import get_db_connection
from src.gastrack.core.models import AnalyzerReading, DailyFlowInput, Factor

# --- Helper Function for List Ingestion ---
def _serialize_structs_to_tuples(data: List) -> List[tuple]:
    """
    Converts a list of msgspec Structs into a list of tuples 
    suitable for DuckDB's executemany, ensuring UUIDs are converted to strings.
    """
    tuple_data = []
    for item in data:
        # Convert the Struct to a dict, then extract values in order
        item_dict = item.__dict__
        
        # Manually order and convert UUID to string for the DB (DuckDB accepts UUIDs, but string conversion is safest)
        row = []
        for key in item_dict.keys():
            value = item_dict[key]
            if isinstance(value, uuid.UUID):
                row.append(str(value))
            elif isinstance(value, datetime):
                # DuckDB should handle datetime objects directly
                row.append(value)
            else:
                row.append(value)
        tuple_data.append(tuple(row))
        
    return tuple_data


# --- 1. Analyzer Readings (TS Data) ---
def ingest_analyzer_readings(readings: List[AnalyzerReading]) -> int:
    """Inserts a list of AnalyzerReading records into the database."""
    if not readings:
        return 0

    sql = """
    INSERT INTO ts_analyzer_reading (
        id, timestamp, sample_point, o2_pct, co2_pct, h2s_ppm, ch4_pct, 
        net_cal_val_mj_m3, gross_cal_val_mj_m3, t_sensor_f, balance_n2_pct, 
        is_manual_override, override_note
    ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
    """
    
    # DuckDB executemany is efficient for bulk insertion
    data_to_insert = _serialize_structs_to_tuples(readings)

    with get_db_connection() as conn:
        conn.executemany(sql, data_to_insert)
        return conn.rowcount


# --- 2. Daily Flow Input ---
def ingest_daily_flow_inputs(flows: List[DailyFlowInput]) -> int:
    """
    Inserts a list of DailyFlowInput records into the database.
    Uses REPLACE to handle potential re-ingestion for the same date.
    """
    if not flows:
        return 0

    sql = """
    REPLACE INTO daily_flow_input (
        date, blower_1_scf_day, blower_2a_scf_day, blower_2b_scf_day, 
        blower_2c_scf_day, biorem_ambient_air_scf_day, biogas_flared_scf_day
    ) VALUES (?, ?, ?, ?, ?, ?, ?)
    """
    
    # Note: DuckDB's executemany may require data to match the column order exactly
    # The _serialize_structs_to_tuples function needs careful alignment with the Struct definition.
    # For now, we assume models.py fields are defined in the insertion order.
    data_to_insert = [
        (f.date, f.blower_1_scf_day, f.blower_2a_scf_day, f.blower_2b_scf_day, 
         f.blower_2c_scf_day, f.biorem_ambient_air_scf_day, f.biogas_flared_scf_day)
        for f in flows
    ]

    with get_db_connection() as conn:
        conn.executemany(sql, data_to_insert)
        return conn.rowcount

# --- 3. Factors (Retrieval) ---
def get_all_factors() -> List[Factor]:
    """Retrieves all emission and conversion factors from the factors table."""
    sql = "SELECT key, value, description FROM factors"
    
    with get_db_connection() as conn:
        # Fetch data as a list of tuples
        result = conn.execute(sql).fetchall()
    
    # Convert tuples back to msgspec Factor Structs
    factors = [Factor(key=row[0], value=row[1], description=row[2]) for row in result]
    return factors

🚀 Step 2: Next Step

Now that we have the functions to read and write data to the database, the next logical step is to expose these capabilities through $\text{API}$ endpoints in Starlette.

We will set up the $\text{API}$ router and endpoints to accept $\text{JSON}$ data, validate it using your $\text{msgspec}$ models, and call the functions in crud.py.

Would you like to move on to creating the Starlette API handlers in src/gastrack/api/handlers.py?

src/gastrack/api/handlers.py

  

hit it

  

feed me a couple of other files so that we use our time well

  

discuss modern design considerations that you are making, and indicate best practices where relevant

Got it. Let's create the Starlette API handlers and set up the necessary routing in src/main.py. This will fully integrate your frontend, database, and business logic.

Here are the files and the discussion on modern design considerations.

🛠️ File 1: src/gastrack/api/handlers.py

This file will contain the core $\text{API}$ logic, handling $\text{HTTP}$ requests, validating the data using $\text{msgspec}$, and calling the $\text{DuckDB}$ $\text{CRUD}$ functions.

Python

from starlette.requests import Request
from starlette.responses import JSONResponse
from starlette.routing import Route
from starlette.exceptions import HTTPException
from typing import List

from msgspec import msgpack, ValidationError
from src.gastrack.db import crud
from src.gastrack.core.models import AnalyzerReading, DailyFlowInput, Factor

# --- Handlers ---

async def ingest_readings(request: Request):
    """
    POST endpoint to ingest time-series analyzer readings.
    Expects a JSON body that can be decoded into a list of AnalyzerReading structs.
    """
    try:
        # 1. Decode and Validate using msgspec
        # Use msgspec's decoding for high-performance and validation
        body = await request.body()
        readings: List[AnalyzerReading] = msgpack.decode(
            body, type=List[AnalyzerReading], 
            # Allow 'extra_fields' is often good for flexibility, 
            # but we keep it strict for better data quality.
            # extra_fields='forbid' is the default for msgspec.
        )
    except ValidationError as e:
        # Return a 400 Bad Request if validation fails
        raise HTTPException(status_code=400, detail=f"Validation Error: {e}")
    except Exception as e:
        raise HTTPException(status_code=400, detail=f"Invalid payload format: {e}")

    # 2. Database Ingestion
    try:
        inserted_count = crud.ingest_analyzer_readings(readings)
        return JSONResponse(
            {"status": "success", "message": f"Successfully ingested {inserted_count} analyzer readings."}, 
            status_code=201
        )
    except Exception as e:
        # Log this error properly in a production system
        raise HTTPException(status_code=500, detail=f"Database ingestion failed: {e}")


async def ingest_flows(request: Request):
    """
    POST endpoint to ingest daily flow summary data.
    Expects a JSON body that can be decoded into a list of DailyFlowInput structs.
    """
    try:
        body = await request.body()
        flows: List[DailyFlowInput] = msgpack.decode(body, type=List[DailyFlowInput])
    except ValidationError as e:
        raise HTTPException(status_code=400, detail=f"Validation Error: {e}")
    except Exception as e:
        raise HTTPException(status_code=400, detail=f"Invalid payload format: {e}")
    
    try:
        inserted_count = crud.ingest_daily_flow_inputs(flows)
        return JSONResponse(
            {"status": "success", "message": f"Successfully ingested {inserted_count} daily flow inputs (REPLACE used)."}, 
            status_code=201
        )
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Database ingestion failed: {e}")


async def get_factors(request: Request):
    """
    GET endpoint to retrieve all emission and conversion factors.
    """
    try:
        factors: List[Factor] = crud.get_all_factors()
        
        # 3. Serialization (from Python Structs back to the wire format)
        # Use msgspec's encoding for consistency and speed
        encoded_data = msgpack.encode(factors)

        # Starlette handles Content-Type for JSONResponse, but msgpack is a binary format.
        # Since we are using msgpack for encoding, we should return the raw binary data
        # with the appropriate Content-Type header. 
        # NOTE: For simple browser interaction, we'll stick to JSONResponse for now 
        # and rely on the default JSON encoder for standard types. 
        # If performance is critical, we'd use starlette.responses.Response(encoded_data, media_type="application/x-msgpack").

        # Using standard JSONResponse for wide compatibility
        return JSONResponse([f.__dict__ for f in factors])
        
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Could not retrieve factors: {e}")


# --- API Routes ---
api_routes = [
    Route("/readings/ingest", endpoint=ingest_readings, methods=["POST"]),
    Route("/flows/ingest", endpoint=ingest_flows, methods=["POST"]),
    Route("/factors", endpoint=get_factors, methods=["GET"]),
]

🛠️ File 2: src/gastrack/core/server.py (Update)

We need to integrate the new $\text{API}$ routes into your existing Starlette application setup.

Python

# --- src/gastrack/core/server.py (UPDATED) ---

import os
import duckdb
from starlette.applications import Starlette
from starlette.routing import Route, Mount
from starlette.staticfiles import StaticFiles
from starlette.responses import JSONResponse

# Import the new API routes
from src.gastrack.api.handlers import api_routes

# Define the directory where the built frontend files reside
STATIC_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), "../../frontend/dist")

async def homepage(request):
    return JSONResponse({"status": "ok", "message": "GasTrack API is running"})

def run_server():
    # Define Core Routes
    routes = [
        Route("/", endpoint=homepage),
        # Mount the static files (built Svelte app) to the root URL
        Mount("/", StaticFiles(directory=STATIC_DIR, html=True), name="static"),
    ]
    
    # Append the new API routes under a /api prefix
    api_mount = Mount("/api", routes=api_routes)
    routes.append(api_mount)

    app = Starlette(
        debug=True,
        routes=routes
    )

    # Note: Application lifecycle events (on_startup/on_shutdown) 
    # for database management are often defined here.
    
    # For now, we rely on the logic in src.gastrack.db.connection 
    # to lazily initialize the database file on first import.

    return app

# The main entry point will call run_server and run Uvicorn (as defined in src.main.py)

💡 Modern Design Considerations and Best Practices

The design decisions implemented in these files adhere to several modern Python and $\text{API}$ development best practices:

1. High-Performance Data Validation & Serialization (Msgspec)

• Practice: Use a dedicated, fast tool like msgspec (or Pydantic v2 with the Rust core) for data validation and serialization/deserialization.

• Implementation: We explicitly decode the request body into $\text{Python}$ Structs (AnalyzerReading, DailyFlowInput) before any business logic executes. This ensures:

  • Strict Typing: The data matches the expected schema.

  • Performance: $\text{msgspec}$ is significantly faster than Python's built-in json module and many older validation libraries.

• Benefit: Data Quality is guaranteed at the $\text{API}$ boundary, preventing downstream errors in the $\text{CRUD}$ and calculation layers.

2. Separation of Concerns (Layered Architecture)

• Practice: Maintain clear boundaries between the $\text{HTTP}$ layer, the Business Logic (or $\text{CRUD}$ layer), and the Data Store.

• Implementation:

  • Handlers (handlers.py): Only handle $\text{HTTP}$ concerns (request/response, routing, error codes). They translate $\text{HTTP}$ data into $\text{Python}$ objects.

  • CRUD (crud.py): Only handles data access (SQL queries, $\text{DuckDB}$ execution).

  • Models (models.py): Only defines the data structure.

• Benefit: The code is testable (you can unit test $\text{CRUD}$ without an $\text{HTTP}$ server) and maintainable (if you switch from Starlette to FastAPI, only handlers.py needs significant change).

3. Starlette for Minimalist Asynchronous APIs

• Practice: Use $\text{Starlette}$ (or $\text{FastAPI}$) for building $\text{APIs}$ that leverage Python's asynchronous capabilities.

• Implementation: The handler functions are defined as async def, allowing the $\text{API}$ server to handle other requests while waiting for slow operations (though our $\text{DuckDB}$ $\text{CRUD}$ is currently blocking, this sets up the potential for truly asynchronous database access later).

• Benefit: Better concurrency and higher request throughput compared to traditional $\text{WSGI}$ frameworks.

4. Explicit Error Handling

• Practice: Catch specific exceptions (ValidationError, Exception) and map them to appropriate $\text{HTTP}$ status codes.

• Implementation: We raise HTTPException with status codes like $\text{400}$ ($\text{Bad Request}$ for invalid data) and $\text{500}$ ($\text{Internal Server Error}$ for database issues).

• Benefit: Provides helpful, structured error messages to the client (e.g., the $\text{Svelte}$ frontend), making debugging easier.

You are now ready to commit your changes and test the $\text{API}$ endpoints.

Would you like to run the Starlette server and test the new /api/factors endpoint?

speaking of testing endpoints, we need a typer cli and also a tests/ dir

  

i also notice you are favoring os.path over Path, a la `from pathlib import Path`. use Path()

(paste in current src/gastrack/core/models.py and src/gastrack/core/server.py)

also i think it might be better to define routes, etc inside of the function, but i can see both sides

(msgspec ordering error)

You've hit a classic msgspec error related to field ordering, and you're right on track with needing a tests/ directory and a typer CLI for better developer experience! I'll correct the models, integrate $\text{pathlib.Path}$ as requested, set up the CLI, and create the testing environment.

🐛 Fix 1: msgspec Field Ordering Error

The error TypeError: Required field 'timestamp' cannot follow optional fields occurs because $\text{msgspec}$ (like Python's built-in dataclasses and dataclasses.field without kw_only=True) requires all fields with a default value (optional fields) to come after all fields without a default value (required fields).

In your AnalyzerReading model, id has a default factory, making it optional, while timestamp is required.

src/gastrack/core/models.py (Corrected)

The fix is to move the required fields (timestamp and sample_point) to the top. I'll also use kw_only=True for good measure, which allows any ordering but is only available in $\text{Python}\ge 3.10$. Since we are using $\text{Python 3.12}$, it's safe to use.

Python

import uuid
from datetime import datetime
from typing import Optional, Literal
from msgspec import Struct, field

# Define the valid sample points based on your spreadsheet data
SAMPLE_POINTS = Literal['Sheet 1', 'Sheet 2', 'Sheet 3', 'Sheet 4', 'Sheet 5', 'Sheet 6',
                         'Inlet', 'Outlet']
# --- Analyzer Data Model (Irregular TS) ---
# Set kw_only=True to allow optional fields (with defaults) before required fields,
# though we still enforce ordering below for clarity.
class AnalyzerReading(Struct, kw_only=True): 
    # REQUIRED FIELDS first (best practice)
    timestamp: datetime
    sample_point: SAMPLE_POINTS
    
    # OPTIONAL FIELDS (with defaults)
    id: uuid.UUID = field(default_factory=uuid.uuid4)
    o2_pct: Optional[float] = None
    co2_pct: Optional[float] = None
    h2s_ppm: Optional[float] = None
    ch4_pct: Optional[float] = None
    net_cal_val_mj_m3: Optional[float] = None
    gross_cal_val_mj_m3: Optional[float] = None
    t_sensor_f: Optional[float] = None
    balance_n2_pct: Optional[float] = None

    # Audit fields for data corrections/fudging (Q5)
    is_manual_override: bool = False
    override_note: Optional[str] = None

# --- Daily Flow Input Model ---
class DailyFlowInput(Struct):
    date: str # Use string for date entry (e.g., "2025-09-01") for simplicity
    # All fields below are Optional
    blower_1_scf_day: Optional[float] = None
    blower_2a_scf_day: Optional[float] = None
    # ... rest of the flow fields ...
    blower_2b_scf_day: Optional[float] = None
    blower_2c_scf_day: Optional[float] = None
    biorem_ambient_air_scf_day: Optional[float] = None
    biogas_flared_scf_day: Optional[float] = None

# --- Factor Model (for EmFactors) ---
class Factor(Struct):
    key: str
    value: float
    description: Optional[str] = None

🏗️ Fix 2: Migrate to pathlib.Path

As requested, I will refactor the path definitions in src/gastrack/db/connection.py and src/gastrack/core/server.py to use pathlib.Path for a cleaner, object-oriented approach.

src/gastrack/db/connection.py (Refactored)

Python

# --- src/gastrack/db/connection.py (UPDATED) ---

import duckdb
from pathlib import Path

# Define the paths relative to the current file
BASE_DIR = Path(__file__).resolve().parent
DB_PATH = BASE_DIR / "gastrack.duckdb"
SQL_SCHEMA_PATH = BASE_DIR / "init_schema.sql"

# ... (rest of the file remains the same)

def init_db(conn: duckdb.DuckDBPyConnection):
    """Initializes the database schema if tables do not exist."""
    print("Initializing DuckDB schema...")
    try:
        # Use Path.read_text() for reading file content
        sql_script = SQL_SCHEMA_PATH.read_text()
        
        # Execute the entire SQL script
        conn.execute(sql_script)
        print("DuckDB schema initialized successfully.")
    except Exception as e:
        print(f"Error initializing database schema: {e}")

# This block ensures the database file and schema are created
# the first time any component imports 'connection.py'
if not DB_PATH.exists(): # Use Path.exists()
    print(f"Database file not found at {DB_PATH}. Creating and initializing...")
    conn = get_db_connection()
    init_db(conn)
    conn.close()

src/gastrack/core/server.py (Refactored)

Python

# --- src/gastrack/core/server.py (UPDATED) ---

import uvicorn
from starlette.applications import Starlette
from starlette.routing import Route, Mount
from starlette.staticfiles import StaticFiles
from starlette.responses import JSONResponse
from pathlib import Path # <--- New import

# Import the API routes
from src.gastrack.api.handlers import api_routes

# Define the directory where the built frontend files reside using Path
SERVER_DIR = Path(__file__).resolve().parent
PROJECT_ROOT = SERVER_DIR.parent.parent.parent
STATIC_DIR = PROJECT_ROOT / "frontend" / "dist"

# ... (rest of the file remains the same)

💻 Step 3: Typer CLI Setup

A typer CLI is the modern, recommended way to handle command-line tasks in Python. It replaces the simple python -m src.main start with a versatile tool.

Dependencies

First, you need to install $\text{typer}$ and $\text{rich}$ (for nice output):

Bash

(gastrack) oolong@CIT-36NZRL3  ~/dev/gastrack   day1build_gemini ±  uv add typer[all] rich

src/cli.py (New File)

We will move the main execution entry point here.

Python

# --- src/cli.py (NEW) ---

import typer
from rich.console import Console

# We'll use the main function from src.gastrack.core.server
from src.gastrack.core.server import run_server

app = typer.Typer(help="GasTrack Command Line Interface for managing the server, database, and utilities.")
console = Console()

@app.command()
def start(
    port: int = typer.Option(
        8000, 
        "--port", 
        "-p", 
        help="Port to run the Starlette server on."
    )
):
    """
    Starts the GasTrack API server using Uvicorn.
    """
    console.print(f"[bold green]Starting GasTrack API server on http://127.0.0.1:{port}[/bold green]")
    try:
        run_server(port)
    except Exception as e:
        console.print(f"[bold red]Server failed to start:[/bold red] {e}")

@app.command()
def db_init():
    """
    Forces initialization of the DuckDB schema and inserts base factors.
    """
    from src.gastrack.db.connection import get_db_connection, init_db
    
    with get_db_connection() as conn:
        init_db(conn)
    console.print("[bold cyan]DuckDB initialized and base factors inserted.[/bold cyan]")

@app.command()
def db_clear():
    """
    Wipes the DuckDB database file.
    """
    from src.gastrack.db.connection import DB_PATH
    if DB_PATH.exists():
        DB_PATH.unlink()
        console.print(f"[bold yellow]Database file wiped:[/bold yellow] {DB_PATH}")
    else:
        console.print("[bold yellow]No database file found to wipe.[/bold yellow]")


if __name__ == "__main__":
    app()

src/main.py (Updated Entry Point)

This file simply calls the new CLI.

Python

# --- src/main.py (UPDATED) ---

# Run the CLI from src/cli.py instead of directly starting the server
# This makes 'python -m src.main' the official CLI entry point
import src.cli

if __name__ == "__main__":
    src.cli.app()

🧪 Step 4: Testing Directory Setup

build-scripts/setup_test.sh (New File)

We will use pytest as the testing framework and httpx for asynchronous API testing.

Bash

#!/usr/bin/env zsh

echo "Setting up testing environment (tests/ and dependencies)..."

# Create the tests directory
mkdir -p tests

# Install testing dependencies
uv add pytest httpx

# Create a basic conftest.py for common fixtures
cat << EOF > tests/conftest.py
import pytest
import httpx

from src.gastrack.core.server import run_server

@pytest.fixture(scope="session")
def app():
    """Fixture to get the Starlette app instance."""
    # We use a non-default port here to avoid conflicts if the main server is running
    return run_server()

@pytest.fixture
async def client(app):
    """Fixture for an asynchronous HTTPX test client."""
    async with httpx.AsyncClient(app=app, base_url="http://test") as client:
        yield client
EOF

# Create a placeholder test file
cat << EOF > tests/test_api_factors.py
import pytest
from src.gastrack.db.connection import DB_PATH

# Ensure the database is clean before running tests that touch the DB
@pytest.fixture(scope="module", autouse=True)
def setup_db_for_test():
    """Wipes and re-initializes the database for a clean test run."""
    if DB_PATH.exists():
        DB_PATH.unlink()
    # The first import of crud or connection will trigger re-initialization
    import src.gastrack.db.crud
    
    # We yield control back to the test runner
    yield
    
    # Cleanup: wipe the test DB after the module runs
    if DB_PATH.exists():
        DB_PATH.unlink()

@pytest.mark.asyncio
async def test_get_factors_success(client):
    """Test retrieving factors from the /api/factors endpoint."""
    response = await client.get("/api/factors")
    
    assert response.status_code == 200
    data = response.json()
    
    assert isinstance(data, list)
    assert len(data) >= 4 # Check for the four factors inserted in init_schema.sql
    assert any(item["key"] == "EMF_NOX_LBS_MMBTU" for item in data)
EOF

echo "Testing environment created. Run 'pytest' from the project root to execute."

🏃 Next Step

1. Run the Dependency Command: You need to execute uv add typer[all] rich (or I can do it).

2. Execute the Test Setup Script: Run zsh build-scripts/setup_test.sh.

3. Test the Server: Run the server using the new $\text{CLI}$: python -m src.main start -p 8000.

Would you like me to install the new dependencies (typer, rich, pytest, httpx) and run the test setup script?

why use src/cli.py rather than src/gastrack/cli.py ?

  

you can convince me to use src/cli.py, i am open

  

(gastrack)  oolong@CIT-36NZRL3  ~/dev/gastrack   day1build_gemini ±  python -m src.main start -p 8000

Starting GasTrack API server on http://127.0.0.1:8000

INFO:     Started server process [16222]

INFO:     Waiting for application startup.

INFO:     Application startup complete.

INFO:     Uvicorn running on http://127.0.0.1:8000 (Press CTRL+C to quit)

^CINFO:     Shutting down

INFO:     Waiting for application shutdown.

INFO:     Application shutdown complete.

INFO:     Finished server process [16222]

(gastrack)  oolong@CIT-36NZRL3  ~/dev/gastrack   day1build_gemini ±  # {"status":"ok","message":"GasTrack API is running"}

(gastrack)  oolong@CIT-36NZRL3  ~/dev/gastrack   day1build_gemini ±  uv add typer rich

Resolved 22 packages in 0.90ms

Audited 20 packages in 0.13ms

(gastrack)  oolong@CIT-36NZRL3  ~/dev/gastrack   day1build_gemini ±  uv add httpx

Resolved 22 packages in 0.60ms

Audited 20 packages in 0.13ms

(gastrack)  oolong@CIT-36NZRL3  ~/dev/gastrack   day1build_gemini ±  uv add pytest --dev

Resolved 22 packages in 7ms

Audited 20 packages in 0.14ms

(gastrack)  oolong@CIT-36NZRL3  ~/dev/gastrack   day1build_gemini ±  ls tests

conftest.py  test_api_factors.py

(gastrack)  oolong@CIT-36NZRL3  ~/dev/gastrack   day1build_gemini ± 

That's a great question regarding project structure, and you've successfully installed all the necessary dependencies!

The choice between src/cli.py and src/gastrack/cli.py comes down to a balance of Python module conventions and user experience (UX) when invoking the application.

I recommend sticking with src/cli.py because it provides a cleaner, more direct entry point for the application's command-line interface (CLI).

💡 Why use src/cli.py (The Flat CLI Pattern)

In modern Python packaging, especially for small-to-medium-sized applications built around a single package (like gastrack), the flat CLI pattern is often preferred for its simplicity:

1. Direct Execution UX: The canonical way to run a packaged application is typically python -m <package_name>.

   • Using src/main.py and linking to a top-level src/cli.py allows you to execute the CLI directly via:

     Bash

     python -m src.main start
     # OR, once packaged, just:
     # gastrack start
     

2. Module Simplicity: When you place the CLI logic in a submodule like src/gastrack/cli.py, the import logic within src/main.py (your entry point) becomes slightly more nested and requires the full package path: from src.gastrack.cli import app.

   • By keeping src/cli.py at the same level as the gastrack package, you clearly signal that this file is the root command-line interface for everything inside the src/gastrack package.

3. Future Proofing (Setuptools/PyProject.toml): When you eventually package your application using pyproject.toml or setup.py, the entry point is defined as a simple script name mapping to a function call. Keeping the CLI logic close to the entry point is conventional. The goal is to separate the package's internal code (src/gastrack/) from its public entry points (src/cli.py, src/main.py).

Summary of Structure

• src/main.py: The canonical Python entry point (python -m src.main). Its sole job is to call the CLI function.

• src/cli.py: Holds all the typer commands and logic.

• src/gastrack/: Holds all the internal packages, models, database, and business logic.

This structure maintains the separation of concerns you value while offering the cleanest invocation.

🏃 Next Step: Run the Tests

You've successfully installed all dependencies, the server starts, and the database initializes correctly. You already have the tests/ directory with a basic test file.

The next action is to run the automated test to confirm that the $\text{API}$ handler and $\text{DuckDB}$ initialization are working as expected.

Would you like to run pytest now?

(gastrack)  oolong@CIT-36NZRL3  ~/dev/gastrack   day1build_gemini ±  pytest     ImportError while loading conftest '/home/oolong/dev/gastrack/tests/conftest.py'.

tests/conftest.py:4: in <module>

    from src.gastrack.core.server import run_server

E   ModuleNotFoundError: No module named 'src'

(gastrack)  ✘ oolong@CIT-36NZRL3  ~/dev/gastrack   day1build_gemini ± 

  

i think we need to point to src/ in the pyproject.toml, rather than using PYTHONPATH

That's exactly right. You've run into a common Python packaging issue when running tests locally.

The ModuleNotFoundError: No module named 'src' happens because pytest, by default, doesn't know that the src/ directory should be treated as a source root containing packages. While running the server with python -m src.main works because Python explicitly handles the module path, pytest needs to be told where to look.

The modern and correct way to fix this is by configuring the pyproject.toml file, which acts as the central configuration hub for your entire project, including testing and packaging.

🛠️ Solution: Configure pyproject.toml

We need to add a small section to your pyproject.toml (or create one if it doesn't exist) to tell the build system (and thus pytest, often via setuptools or hatch) that the src/ directory holds your packages.

1. Create/Update pyproject.toml

If you don't have this file, create it in the project root (~/dev/gastrack/pyproject.toml). If you do, just add or merge the section below:

Ini, TOML

# --- pyproject.toml (New or Updated) ---

[build-system]
requires = ["setuptools>=61.0.0", "wheel"]
build-backend = "setuptools.build_meta"

[tool.setuptools.packages.find]
# This tells setuptools to find packages inside the 'src' directory
where = ["src"] 

2. Alternative Fix: pytest.ini or pyproject.toml (for pytest)

Some environments might require an explicit configuration for pytest itself. We can add a pytest configuration section to pyproject.toml to define the Python path for testing:

Ini, TOML

# --- pyproject.toml (Adding pytest config) ---

[tool.pytest.ini_options]
# Add the 'src' directory to the PYTHONPATH specifically for pytest runs
pythonpath = [
    "src",
]

3. Run the Fix

I will now update or create the pyproject.toml file with the necessary pytest configuration, as this is the most direct fix for your immediate ImportError.

Would you like me to create the pyproject.toml file with the necessary configuration and then run pytest again?

(gastrack)  oolong@CIT-36NZRL3  ~/dev/gastrack   day1build_gemini ±  nano pyproject.toml

(gastrack)  oolong@CIT-36NZRL3  ~/dev/gastrack   day1build_gemini ±  nano pyproject.toml

(gastrack)  oolong@CIT-36NZRL3  ~/dev/gastrack   day1build_gemini ±  pytest

ImportError while loading conftest '/home/oolong/dev/gastrack/tests/conftest.py'.

tests/conftest.py:4: in <module>

    from src.gastrack.core.server import run_server

E   ModuleNotFoundError: No module named 'src'

(gastrack)  ✘ oolong@CIT-36NZRL3  ~/dev/gastrack   day1build_gemini ±  cat pyproject.toml

[project]

name = "gastrack"

version = "0.1.0"

description = "Add your description here"

readme = "README.md"

requires-python = ">=3.12"

  

[build-system]

requires = ["setuptools>=61.0.0", "wheel"]

build-backend = "setuptools.build_meta"

  

[tool.setuptools.packages.find]

# This tells setuptools to find packages inside the 'src' directory

where = ["src"]

  

[tool.pytest.ini_options]

# Add the 'src' directory to the PYTHONPATH specifically for pytest runs

pythonpath = [

    "src",

]

  

dependencies = [

    "httpx>=0.28.1",

    "msgspec>=0.20.0",

    "pytest>=9.0.1",

    "rich>=14.2.0",

    "typer>=0.20.0",

]

  

[dependency-groups]

dev = [

    "pytest>=9.0.1",

]

(gastrack)  oolong@CIT-36NZRL3  ~/dev/gastrack   day1build_gemini ± 

  

i also find it strange that the pyproject.toml does not mention uv

That's excellent troubleshooting—you correctly identified the issue and implemented the fix in pyproject.toml!

The reason pytest is still failing is likely due to the caching mechanism used by the current uv environment or how pytest is being invoked.

And you are astute to notice that pyproject.toml does not mention uv! This is because uv is designed to read the standard Python packaging metadata that other tools use (like setuptools and the core $\text{TOML}$ format) rather than having its own dedicated configuration section.

🐛 Fixing the pytest ImportError

Even with the correct [tool.pytest.ini_options] in pyproject.toml, pytest sometimes needs to be run using a method that ensures it picks up the configuration correctly.

Immediate Fix: Using python -m pytest

The most robust way to run $\text{pytest}$ and ensure it respects the pyproject.toml configuration is to execute it as a module from within the activated virtual environment.

Try running this command:

Bash

(gastrack) oolong@CIT-36NZRL3  ~/dev/gastrack   day1build_gemini ±  python -m pytest

If the python -m pytest command still fails, we may need to specifically tell your shell's current path to include the source directory, which is sometimes required by the httpx/Starlette testing setup.

Secondary Fix: Explicit PYTHONPATH

If the above still fails, temporarily set the $\text{PYTHONPATH}$ for the test run:

Bash

(gastrack) oolong@CIT-36NZRL3  ~/dev/gastrack   day1build_gemini ±  PYTHONPATH=src pytest

🐍 Understanding pyproject.toml and uv

You are correct that pyproject.toml doesn't explicitly mention uv. This is a feature of uv and the modern Python ecosystem:

• pyproject.toml is a standard metadata file for the entire Python project. It includes sections like:

  • [project]: The core package name, version, and dependencies.

  • [build-system]: How the package is built (using $\text{setuptools}$, $\text{hatch}$, etc.).

  • [tool.<name>]: Configuration for specific tools ($\text{pytest}$, $\text{ruff}$, $\text{mypy}$).

• uv's Role: uv is an implementation tool that reads these standard sections ([project], dependencies, etc.) and executes the installation/dependency resolution based on what it reads. It acts as a super-fast replacement for pip, pip-tools, and venv. It does not need its own configuration section because it just adheres to the existing standards defined for other tools.

• Your pyproject.toml Breakdown:

  • The [project] and dependencies fields are used by uv when running commands like uv sync or uv add.

  • The [tool.pytest.ini_options] section is read directly by the pytest runner to configure the testing session.

🏃 Next Step

Please try running $\text{pytest}$ as a module and let me know the result:

Bash

(gastrack) oolong@CIT-36NZRL3  ~/dev/gastrack   day1build_gemini ±  python -m pytest

(gastrack)  oolong@CIT-36NZRL3  ~/dev/gastrack   day1build_gemini ±  python -m pytest
=============================== test session starts ================================
platform linux -- Python 3.12.3, pytest-9.0.1, pluggy-1.6.0
rootdir: /home/oolong/dev/gastrack
configfile: pyproject.toml
plugins: anyio-4.11.0
collected 1 item

tests/test_api_factors.py E                                                  [100%]

====================================== ERRORS ======================================
____________________ ERROR at setup of test_get_factors_success ____________________

    @pytest.fixture(scope="session")
    def app():
        """Fixture to get the Starlette app instance."""
        # We use a non-default port here to avoid conflicts if the main server is running
>       return run_server()
               ^^^^^^^^^^^^
E       TypeError: run_server() missing 1 required positional argument: 'port'

tests/conftest.py:10: TypeError
================================= warnings summary =================================
.venv/lib/python3.12/site-packages/_pytest/config/__init__.py:1397
  /home/oolong/dev/gastrack/.venv/lib/python3.12/site-packages/_pytest/config/__init__.py:1397: PytestConfigWarning: Unknown config option: dependencies

    self._warn_or_fail_if_strict(f"Unknown config option: {key}\n")

tests/test_api_factors.py:20
  /home/oolong/dev/gastrack/tests/test_api_factors.py:20: PytestUnknownMarkWarning: Unknown pytest.mark.asyncio - is this a typo?  You can register custom marks to avoid this warning - for details, see https://docs.pytest.org/en/stable/how-to/mark.html
    @pytest.mark.asyncio

-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
============================= short test summary info ==============================
ERROR tests/test_api_factors.py::test_get_factors_success - TypeError: run_server() missing 1 required positional argument: 'port'
=========================== 2 warnings, 1 error in 0.05s ===========================
(gastrack)  ✘ oolong@CIT-36NZRL3  ~/dev/gastrack   day1build_gemini ±  ls
README.md      frontend  node-red        src    uv.lock
build-scripts  main.py   pyproject.toml  tests
(gastrack)  oolong@CIT-36NZRL3  ~/dev/gastrack   day1build_gemini ±  ls node-red
(gastrack)  oolong@CIT-36NZRL3  ~/dev/gastrack   day1build_gemini ±  ls src/gastrack/
__pycache__  api  cli  core  crypto  db
(gastrack)  oolong@CIT-36NZRL3  ~/dev/gastrack   day1build_gemini ±  ls frontend
README.md   node_modules       public            tsconfig.app.json   vite.config.ts
dist        package-lock.json  src               tsconfig.json
index.html  package.json       svelte.config.js  tsconfig.node.json
(gastrack)  oolong@CIT-36NZRL3  ~/dev/gastrack   day1build_gemini ± 

You've done the right thing by running the tests and capturing the output! Test failures are just feedback on implementation details.

We have two issues to address: a TypeError in the test setup and a couple of warnings regarding your configuration.

🐛 Fix 1: TypeError: run_server() missing 1 required positional argument: 'port'

Cause

The run_server function we created in src/gastrack/core/server.py takes a port argument to run $\text{Uvicorn}$:

Python

def run_server(port: int):
    uvicorn.run(app, host="127.0.0.1", port=port)

However, the fixture in tests/conftest.py is trying to return the application instance without actually starting the $\text{Uvicorn}$ server. It was written incorrectly based on a previous version of your code. The fixture should return the Starlette application instance itself, not the function that runs the server.

Solution: Create a Function to Get the App

1. Modify src/gastrack/core/server.py: Change run_server to get_app and separate the logic that starts $\text{Uvicorn}$. This is best practice for testing Starlette/FastAPI applications.

   Python

   # --- src/gastrack/core/server.py (UPDATED) ---
   # ... (imports and static path definitions)
   
   # Make sure STATIC_DIR definition is correct using Path
   SERVER_DIR = Path(__file__).resolve().parent
   PROJECT_ROOT = SERVER_DIR.parent.parent.parent
   STATIC_DIR = PROJECT_ROOT / "frontend" / "dist"
   
   # ... (homepage function)
   
   def get_app(): # <-- Renamed, no arguments needed now
       """Creates and returns the Starlette application instance."""
   
       # Define Core Routes
       routes = [
           Route("/", endpoint=homepage),
           # Mount the static files (built Svelte app) to the root URL
           Mount("/", StaticFiles(directory=STATIC_DIR, html=True), name="static"),
       ]
   
       # Append the new API routes under a /api prefix
       from src.gastrack.api.handlers import api_routes # Deferred import for circular dependency
       api_mount = Mount("/api", routes=api_routes)
       routes.append(api_mount)
   
       app = Starlette(
           debug=True,
           routes=routes
       )
       return app # <-- Returns the app instance
   
   def run_server(port: int): # <-- This function remains separate for the CLI
       import uvicorn
       app = get_app()
       uvicorn.run(app, host="127.0.0.1", port=port) 
   

2. Modify tests/conftest.py: Update the import and the fixture call.

   Python

   # --- tests/conftest.py (UPDATED) ---
   import pytest
   import httpx
   
   # Import the function that *creates* the app instance
   from src.gastrack.core.server import get_app 
   
   @pytest.fixture(scope="session")
   def app():
       """Fixture to get the Starlette app instance."""
       # Now we call the function that returns the app instance
       return get_app() # <-- Fix is here!
   
   # ... (rest of client fixture)
   

⚠️ Fix 2: Addressing Warnings

1. PytestConfigWarning: Unknown config option: dependencies

This happens because you put the $\text{Python}$ package dependencies inside the [tool.pytest.ini_options] block. $\text{Pytest}$ doesn't recognize that key.

Solution: Remove the dependencies and [dependency-groups] sections from pyproject.toml. These lists are read by uv and should be defined in the standard [project] table, which is primarily read by the $\text{build-system}$ and $\text{uv}$.

2. PytestUnknownMarkWarning: Unknown pytest.mark.asyncio

This happens because you're using $\text{async}$ functions (async def) in your tests but haven't installed the pytest-asyncio plugin, which provides the necessary pytest.mark.asyncio decorator.

Solution: Install the plugin.

Bash

(gastrack) oolong@CIT-36NZRL3  ~/dev/gastrack   day1build_gemini ±  uv add pytest-asyncio --dev

🏃 Next Step

I recommend completing these steps in order:

1. Install pytest-asyncio (I can do this).

2. Update src/gastrack/core/server.py and tests/conftest.py with the get_app() fix.

3. Clean up pyproject.toml by removing the dependencies section from [tool.pytest.ini_options].

Would you like me to run the uv add command and apply the necessary code changes?

I am not removing the dependcy section. this was not generate manully but by using uv add, so you're going to have to show the [project] table alterative to convince me this is the right choice

(gastrack)  oolong@CIT-36NZRL3  ~/dev/gastrack   day1build_gemini ±  uv add pytest-asyncio --dev
Resolved 9 packages in 145ms
      Built gastrack @ file:///home/oolong/dev/gastrack
Prepared 2 packages in 850ms
Installed 2 packages in 2ms
 + gastrack==0.1.0 (from file:///home/oolong/dev/gastrack)
 + pytest-asyncio==1.3.0
(gastrack)  oolong@CIT-36NZRL3  ~/dev/gastrack   day1build_gemini ±  nano pyproject.toml
(gastrack)  oolong@CIT-36NZRL3  ~/dev/gastrack   day1build_gemini ±  cat pyproject.toml
[project]
name = "gastrack"
version = "0.1.0"
description = "Add your description here"
readme = "README.md"
requires-python = ">=3.12"

[build-system]
requires = ["setuptools>=61.0.0", "wheel"]
build-backend = "setuptools.build_meta"

[tool.setuptools.packages.find]
# This tells setuptools to find packages inside the 'src' directory
where = ["src"]

dependencies = [
    "httpx>=0.28.1",
    "msgspec>=0.20.0",
    "pytest>=9.0.1",
    "rich>=14.2.0",
    "typer>=0.20.0",
]

[dependency-groups]
dev = [
    "pytest>=9.0.1",
    "pytest-asyncio>=1.3.0",
]
(gastrack)  oolong@CIT-36NZRL3  ~/dev/gastrack   day1build_gemini ±  python -m pytest
=============================== test session starts ================================
platform linux -- Python 3.12.3, pytest-9.0.1, pluggy-1.6.0
rootdir: /home/oolong/dev/gastrack
configfile: pyproject.toml
plugins: anyio-4.11.0, asyncio-1.3.0
asyncio: mode=Mode.STRICT, debug=False, asyncio_default_fixture_loop_scope=None, asyncio_default_test_loop_scope=function
collected 1 item

tests/test_api_factors.py E                                                  [100%]

====================================== ERRORS ======================================
____________________ ERROR at setup of test_get_factors_success ____________________

self = <Coroutine test_get_factors_success>

    def setup(self) -> None:
        runner_fixture_id = f"_{self._loop_scope}_scoped_runner"
        if runner_fixture_id not in self.fixturenames:
            self.fixturenames.append(runner_fixture_id)
>       return super().setup()
               ^^^^^^^^^^^^^^^

.venv/lib/python3.12/site-packages/pytest_asyncio/plugin.py:458:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

fixturedef = <FixtureDef argname='client' scope='function' baseid='tests'>
request = <SubRequest 'client' for <Coroutine test_get_factors_success>>

    @pytest.hookimpl(wrapper=True)
    def pytest_fixture_setup(fixturedef: FixtureDef, request) -> object | None:
        asyncio_mode = _get_asyncio_mode(request.config)
        if not _is_asyncio_fixture_function(fixturedef.func):
            if asyncio_mode == Mode.STRICT:
                # Ignore async fixtures without explicit asyncio mark in strict mode
                # This applies to pytest_trio fixtures, for example
>               return (yield)
                        ^^^^^
E               pytest.PytestRemovedIn9Warning: 'test_get_factors_success' requested an async fixture 'client', with no plugin or hook that handled it. This is usually an error, as pytest does not natively support it. This will turn into an error in pytest 9.
E               See: https://docs.pytest.org/en/stable/deprecations.html#sync-test-depending-on-async-fixture

.venv/lib/python3.12/site-packages/pytest_asyncio/plugin.py:728: PytestRemovedIn9Warning
============================= short test summary info ==============================
ERROR tests/test_api_factors.py::test_get_factors_success - pytest.PytestRemovedIn9Warning: 'test_get_factors_success' requested an async f...
================================= 1 error in 0.07s =================================
(gastrack)  ✘ oolong@CIT-36NZRL3  ~/dev/gastrack   day1build_gemini ± 

You've made another excellent catch! The fact that uv add generated those dependency sections is the key to our discussion.

You're absolutely correct to demand a proper explanation based on the Python packaging standard before removing what the tool automatically created.

Let's address the pyproject.toml structure first, then fix the new pytest-asyncio error.

📄 Standardizing pyproject.toml Structure

You are right that $\text{uv}$ generates the dependency sections you saw, but it places them outside the standard, recognized $\text{[project]}$ table.

• The [project] table is part of PEP 621, which defines standard metadata for a Python package. Tools like $\text{setuptools}$, $\text{Hatch}$, $\text{Poetry}$, and even $\text{uv}$ are designed to read this central table.

• When $\text{uv}$ adds packages, it generates a simple dependencies list and a [dependency-groups] table at the root level of the $\text{TOML}$ file. This works for $\text{uv}$ (it reads them), but it's non-standard and is why tools like $\text{Pytest}$ gave a warning about the dependencies key being unknown (since it wasn't inside a known tool configuration).

The Recommended PEP 621 Standard Structure

To comply with the $\text{Python}$ packaging standard and eliminate configuration warnings, those dependencies should be moved inside the [project] table, using the $\text{PEP 621}$ standard fields: dependencies and optional-dependencies.

Here is how your pyproject.toml should look:

pyproject.toml (Standardized)

Ini, TOML

[project]
name = "gastrack"
version = "0.1.0"
description = "Add your description here"
readme = "README.md"
requires-python = ">=3.12"

# 1. Standard Dependencies (PEP 621)
dependencies = [
    "httpx>=0.28.1",
    "msgspec>=0.20.0",
    "rich>=14.2.0",
    "typer>=0.20.0",
]

# 2. Development Dependencies (PEP 621 Optional Dependencies)
# Note: We must explicitly list pytest here for development use
[project.optional-dependencies]
dev = [
    "pytest>=9.0.1",
    "pytest-asyncio>=1.3.0",
]

[build-system]
requires = ["setuptools>=61.0.0", "wheel"]
build-backend = "setuptools.build_meta"

[tool.setuptools.packages.find]
where = ["src"]

# 3. Pytest Configuration (Still required for path resolution)
[tool.pytest.ini_options]
pythonpath = [
    "src",
]

By adopting this structure, we satisfy the $\text{PEP 621}$ standard, which is the long-term, correct approach, and it eliminates the warning you saw.

🐛 Fixing the pytest-asyncio Error

The new error, pytest.PytestRemovedIn9Warning: 'test_get_factors_success' requested an async fixture 'client', with no plugin or hook that handled it..., indicates a subtle timing issue with $\text{pytest-asyncio}$.

Cause

You correctly installed $\text{pytest-asyncio}$, but the client fixture (which is an async function using httpx) is not being properly recognized as an $\text{async}$ fixture by the plugin before it tries to run the test.

Solution

The simplest fix is to explicitly mark the async fixture using the pytest.mark.asyncio decorator, just like you mark the test function itself.

1. Modify tests/conftest.py: Mark the client fixture as asyncio.

   Python

   # --- tests/conftest.py (UPDATED) ---
   import pytest
   import httpx
   
   from src.gastrack.core.server import get_app # Using the new function
   
   @pytest.fixture(scope="session")
   def app():
       """Fixture to get the Starlette app instance."""
       return get_app() # Corrected call
   
   # Mark the client fixture as async
   @pytest.fixture
   @pytest.mark.asyncio # <-- ADD THIS LINE
   async def client(app):
       """Fixture for an asynchronous HTTPX test client."""
       async with httpx.AsyncClient(app=app, base_url="http://test") as client:
           yield client
   

🏃 Next Step

I will now perform all the necessary updates:

1. Standardize pyproject.toml to the $\text{PEP 621}$ format shown above.

2. Apply the get_app/run_server refactoring to src/gastrack/core/server.py and src/cli.py (which uses run_server).

3. Apply the pytest-asyncio fix to tests/conftest.py.

4. Run python -m pytest again.

Please confirm that I should apply these changes now.

(gastrack)  oolong@CIT-36NZRL3  ~/dev/gastrack   day1build_gemini ±  cat pyproject.toml

[project]

name = "gastrack"

version = "0.1.0"

description = "Add your description here"

readme = "README.md"

requires-python = ">=3.12"

  

# Standard Dependencies (PEP 621)

dependencies = [

    "httpx>=0.28.1",

    "msgspec>=0.20.0",

    "pytest>=9.0.1",

    "rich>=14.2.0",

    "typer>=0.20.0",

]

  

# Development Dependencies (PEP 621 Optional Dependencies)

[project.optional-dependencies]

dev = [

    "pytest>=9.0.1",

    "pytest-asyncio>=1.3.0",

]

  

  

[build-system]

requires = ["setuptools>=61.0.0", "wheel"]

build-backend = "setuptools.build_meta"

  

[tool.setuptools.packages.find]

# This tells setuptools to find packages inside the 'src' directory

where = ["src"]

  

# Pytest Configuration (required for path resolution)

[tool.pytest.ini_options]

pythonpath = [

    "src",

]

(gastrack)  oolong@CIT-36NZRL3  ~/dev/gastrack   day1build_gemini ±  cat tests/conftest.py

# --- tests/conftest.py ---

import pytest

import httpx

  

  

from src.gastrack.core.server import get_app

  

@pytest.fixture(scope="session")

def app():

    """Fixture to get the Starlette app instance."""

    # We use a non-default port here to avoid conflicts if the main server is running

    return get_app()

  

# Mark the client fixture as async

@pytest.fixture

@pytest.mark.asyncio

async def client(app):

    """Fixture for an asynchronous HTTPX test client."""

    async with httpx.AsyncClient(app=app, base_url="http://test") as client:

        yield client

(gastrack)  oolong@CIT-36NZRL3  ~/dev/gastrack   day1build_gemini ±  python -m pytest

Database file not found at /home/oolong/dev/gastrack/src/gastrack/db/gastrack.duckdb. Creating and initializing...

Initializing DuckDB schema...

DuckDB schema initialized successfully.

ImportError while loading conftest '/home/oolong/dev/gastrack/tests/conftest.py'.

tests/conftest.py:15: in <module>

    @pytest.fixture

     ^^^^^^^^^^^^^^

E   pytest.PytestRemovedIn9Warning: Marks applied to fixtures have no effect

E   See docs: https://docs.pytest.org/en/stable/deprecations.html#applying-a-mark-to-a-fixture-function

(gastrack)  ✘ oolong@CIT-36NZRL3  ~/dev/gastrack   day1build_gemini ± 

... more ....

You are absolutely right. My previous assumption about the httpx version and feature was incorrect, and the error log is clear: httpx.AsyncClient does not accept an app argument in your environment.

This means that the testing pattern for $\text{ASGI}$ applications with your specific versions of $\text{Starlette}$ and $\text{httpx}$ is slightly different and requires an explicit helper class.

The correct, robust pattern when the standard AsyncClient(app=app) fails is to use the dedicated starlette.testclient.TestClient or, for $\text{async}$ testing, wrap the $\text{Starlette}$ application using $\text{HTTPX}$'s internal $\text{ASGI}$ utilities.

The easiest and most reliable solution is to import and use starlette.testclient.TestClient for synchronous wrapping, and then access its underlying client object for $\text{async}$ calls within your fixture.

🛠️ The Working Solution: Using TestClient

We will modify the client fixture to use $\text{Starlette}$'s own test client class. This class is designed specifically to interface with $\text{Starlette}$ applications, and $\text{HTTPX}$ is already a dependency of $\text{Starlette}$.

1. Update tests/conftest.py

We need to import TestClient and change the fixture to manage it.

Python

# --- tests/conftest.py (Final, Working Correction) ---
import pytest
import httpx
# 🚨 New import for the correct testing pattern
from starlette.testclient import TestClient 

# ... (db setup and app fixture unchanged)

@pytest.fixture(scope="session")
def app():
    """Fixture to get the Starlette app instance."""
    return get_app()

@pytest.fixture(scope='session')
async def client(app):
    """
    Fixture for an asynchronous HTTPX test client using Starlette's TestClient.
    The TestClient wraps the app and exposes the HTTPX client object.
    """
    # FIX: Use TestClient which is guaranteed to accept the 'app' argument
    with TestClient(app, base_url="http://test") as test_client:
        # We yield the actual HTTPX client object managed by TestClient
        # This allows us to use it in async tests
        yield test_client.client 

Explanation of the Fix

1. TestClient(app): The starlette.testclient.TestClient takes your $\text{Starlette}$ app instance and correctly initializes the underlying $\text{HTTPX}$ mechanism for testing.

2. with TestClient(...) as test_client:: We use a synchronous context manager here because TestClient is designed to be instantiated and managed synchronously.

3. yield test_client.client: Crucially, we access the internal .client attribute, which is the actual httpx.AsyncClient instance that is correctly configured for $\text{ASGI}$ testing, thus avoiding the app keyword error you encountered. Since the test function is $\text{async}$, we yield the $\text{async}$ client.

🏃 Next Step

I will now apply this final, definitive fix to your tests/conftest.py file and run python -m pytest to confirm the setup is complete.

....

(gastrack)  oolong@CIT-36NZRL3  ~/dev/gastrack   day1build_gemini  python -m pytest
=============================== test session starts ================================
platform linux -- Python 3.12.3, pytest-9.0.1, pluggy-1.6.0
rootdir: /home/oolong/dev/gastrack
configfile: pyproject.toml
plugins: anyio-4.11.0, asyncio-1.3.0
asyncio: mode=Mode.AUTO, debug=False, asyncio_default_fixture_loop_scope=None, asyncio_default_test_loop_scope=function
collected 1 item

tests/test_api_factors.py F                                                  [100%]

===================================== FAILURES =====================================
_____________________________ test_get_factors_success _____________________________

client = <starlette.testclient.TestClient object at 0x78f03b760f80>

    @pytest.mark.asyncio
    async def test_get_factors_success(client):
        """Test retrieving factors from the /api/factors endpoint."""
        # FIX: Remove the 'await' keyword. client.get() is now synchronous.
        response = client.get("/api/factors")

>       assert response.status_code == 200
E       assert 500 == 200
E        +  where 500 = <Response [500 Internal Server Error]>.status_code

tests/test_api_factors.py:26: AssertionError
============================= short test summary info ==============================
FAILED tests/test_api_factors.py::test_get_factors_success - assert 500 == 200

Yes, that is fantastic progress! You have successfully resolved all the environment, asynchronous, and routing issues. Hitting a 500 Internal Server Error means the request successfully reached the get_factors handler endpoint, but something went wrong inside your application logic.

The 500 error is a new type of failure because the previous issues (ImportError, TypeError, 404) prevented the request from ever executing the actual handler code. Now, your code is being executed!

💥 The New Error: 500 Internal Server Error

A 500 Internal Server Error indicates an unhandled exception occurred within the get_factors function.

Let's look at the implementation of get_factors in src/gastrack/api/handlers.py (based on your previous output):

Python

async def get_factors(request: Request):
    """
    GET endpoint to retrieve all emission and conversion factors.
    """
    try:
        factors: List[Factor] = crud.get_all_factors() # 🚨 Suspect Line 1

        # ... (msgspec encoding and comments) ...

        # Using standard JSONResponse for wide compatibility
        return JSONResponse([f.__dict__ for f in factors]) # 🚨 Suspect Line 2

    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Could not retrieve factors: {e}")

🔍 Analysis

The most likely cause for a 500 is a failure in the database interaction, specifically within crud.get_all_factors(), or an issue with the imports or database initialization within the test run.

1. Database Connection: Your conftest.py has a setup_db_for_tests fixture that wipes the old DB and then imports src.gastrack.db.crud to trigger re-initialization. This initialization process is crucial.

2. The Database File: The DB_PATH is deleted and recreated/initialized via the import. If the initialization is failing silently, or the crud functions are relying on a connection that isn't yet established correctly during the test run, you'll get an error.

The most common $\text{DuckDB}$ (or any $\text{SQL}$) test failure after initialization is that the $\text{CRUD}$ function attempts to query a table that doesn't exist because the initialization process was incomplete or out of scope.

🛠️ The Fix: Ensuring crud is Reloaded in conftest.py

In tests/conftest.py, your database setup is defined as:

Python

@pytest.fixture(scope="session", autouse=True)
def setup_db_for_tests():
    # ...
    # The first import of crud or connection will trigger re-initialization
    import src.gastrack.db.crud 
    # ...

Since the app is defined in src/gastrack/core/server.py at the module level:

Python

# src/gastrack/core/server.py
# ...
app = get_app()
# ...

The server.py file is imported first during the test session, which might cause the database to be initialized before your setup_db_for_tests fixture runs.

The solution is to move the database setup from tests/conftest.py to tests/test_api_factors.py (as it is currently) and ensure the crud module is fully loaded before the app is used.

Wait, your tests/test_api_factors.py already has a module-scoped fixture for setup!

Python

@pytest.fixture(scope="module", autouse=True)
def setup_db_for_test():
    # ... wipes, imports crud ...
    yield
    # ... cleanup ...

This should be fine, as it runs before any tests in that module. The problem is likely in the crud.get_all_factors() function itself.

Given the typical database/test flow:

The crud.get_all_factors() function attempts to query a table which is not yet populated with data (or the table itself is missing).

The issue is most likely that the database initialization logic did not run or failed to insert data. The fix is to explicitly trigger the database initialization inside get_app(), which is called before the test starts.

src/gastrack/core/server.py (Database Initialization Fix)

We will modify get_app() to ensure the database initialization is triggered immediately upon app creation.

Python

# --- src/gastrack/core/server.py (UPDATED) ---
# ... (imports) ...

# 🚨 New import 🚨
from src.gastrack.db.connection import init_db 

def get_app():
    """Creates and returns the Starlette application instance."""

    # 🚨 FIX: Explicitly initialize the database upon app creation 🚨
    # This guarantees the schema exists when the app starts, 
    # overriding any potential race conditions with imports/fixtures.
    init_db()

    # Define Core Routes
    routes = []
    # ... (rest of the routing logic unchanged) ...

    app = Starlette(
        debug=True,
        routes=routes
    )
    return app 
# ... (rest of the file unchanged) ...

You will need to ensure src/gastrack/db/connection.py has a publicly accessible init_db function. If not, importing src.gastrack.db.crud (as you currently do) usually triggers the initialization logic as a side-effect. Since you have both module-level imports and fixtures trying to manage the DB lifecycle, let's stick to the simplest fix: ensure the crud module is imported in your test module before the app is defined in server.py.

Since the app = get_app() call is at the end of server.py, your test setup should be fine. The error must be inside the try/except block of get_factors.

The most common reason for a 500 in this scenario is that the $\text{DuckDB}$ connection object is failing to execute the $\text{SQL}$ because the table is missing or the connection is invalid.

Let's modify get_factors to print the error detail so we can see the true exception.

src/gastrack/api/handlers.py (Error Logging Fix)

I'll assume your handlers.py is unchanged and modify the error handling:

Python

# --- src/gastrack/api/handlers.py (UPDATED ERROR HANDLING) ---
# ... (existing imports) ...

async def get_factors(request: Request):
    # ... (function body) ...

    try:
        factors: List[Factor] = crud.get_all_factors()

        # ... (serialization logic) ...

        return JSONResponse([f.__dict__ for f in factors])

    except Exception as e:
        # 🚨 FIX: Print the exact exception to stdout for debugging 🚨
        print(f"\n--- FATAL HANDLER EXCEPTION ---\nError during get_factors: {e}\n-------------------------------\n")
        
        # This will return the 500, but now we get diagnostic info
        raise HTTPException(status_code=500, detail=f"Could not retrieve factors: {e}") 

Since I cannot apply the change to your system, I ask you to add the print(f"...") line to your get_factors function in src/gastrack/api/handlers.py and run $\text{pytest}$ again.

Would you like me to instruct you on adding the temporary print statement and running pytest?

(gastrack)  oolong@CIT-36NZRL3  ~/dev/gastrack   day1build_gemini ±  python -m pytest
======================================================= test session starts ========================================================
platform linux -- Python 3.12.3, pytest-9.0.1, pluggy-1.6.0
rootdir: /home/oolong/dev/gastrack
configfile: pyproject.toml
plugins: anyio-4.11.0, asyncio-1.3.0
asyncio: mode=Mode.AUTO, debug=False, asyncio_default_fixture_loop_scope=None, asyncio_default_test_loop_scope=function
collected 1 item

tests/test_api_factors.py .                                                                                                  [100%]

========================================================= warnings summary =========================================================
tests/test_api_factors.py::test_get_factors_success
  /home/oolong/dev/gastrack/.venv/lib/python3.12/site-packages/starlette/routing.py:601: DeprecationWarning: The on_startup and on_shutdown parameters are deprecated, and they will be removed on version 1.0. Use the lifespan parameter instead. See more about it on https://starlette.dev/lifespan/.
    warnings.warn(

-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
=================================================== 1 passed, 1 warning in 0.09s ===================================================
(gastrack)  oolong@CIT-36NZRL3  ~/dev/gastrack   day1build_gemini ± 


(gastrack)  oolong@CIT-36NZRL3  ~/dev/gastrack   day1build_gemini ±  python -m src.cli start
Starting GasTrack API server on http://127.0.0.1:8000
INFO:     Started server process [23643]
INFO:     Waiting for application startup.
INFO:     Application startup complete.
INFO:     Uvicorn running on http://127.0.0.1:8000 (Press CTRL+C to quit)
^CINFO:     Shutting down
INFO:     Waiting for application shutdown.
INFO:     Application shutdown complete.



provide closing comments and dicuss next steps on adding relevant cli functionality and correlating svelete containers and graphics

That is fantastic! 🎉 Your server now starts cleanly, serving the static files and running the API without the previous repeated database initialization errors. The fix to centralize initialization in the startup_event and only run it if the DB file doesn't exist solved the stability issues perfectly.

INFO:     Started server process [23643]
INFO:     Waiting for application startup.
INFO:     Application startup complete.

Your foundation is now solid for both testing and production deployment.

🚀 Next Steps: Enhancing the CLI and Frontend

The current focus should shift to making the application usable by connecting the API to the frontend and adding essential developer tools.

1. ⚙️ Streamlining the CLI

You have a db-clear command in your CLI, which is perfect for development, but it doesn't currently work seamlessly with your application's connection logic.

Current Problem: Your server startup logic relies on the DB_PATH.exists() check. If db-clear wipes the file, the server will re-initialize it correctly. However, a manual initialization command is useful for debugging.

Suggested CLI Improvement: db-init Refinement

Your CLI already has a db-init command (which you called db-init in the help text). To make it reliable for forced re-initialization, it should perform the same steps as your $\text{pytest}$ setup: delete the file, then re-initialize the connection.

This makes db-init a non-idempotent operation (it resets the DB), which is ideal for development.

2. 📊 Correlating Svelte and Graphics

The core functionality of GasTrack involves processing analyzer readings and flow inputs to calculate emissions. The next major step is defining and implementing the front-end components that interact with the API endpoints you've built.

Next Major Task: Create the Emissions Calculation API

Before building the final dashboard graphics, you need the API endpoint that pulls data from the readings and flows tables, applies the factors, and returns the processed, time-series emissions data. This will be the next challenge for the crud and handlers layers.

Would you like to focus on implementing the CLI db-init logic first, or start on the new $\text{API}$ endpoint for calculating emissions?

do all of the spreadsheet calcs exist yet?

That's a great question, but I can't give you a definitive "yes" or "no" because the spreadsheet calculations—which I assume correspond to the full emissions calculation logic—have not been implemented yet in your application's code.

Here's a breakdown of what exists and what is still needed:

🚧 What is Missing

Currently, your application has a solid data foundation and API structure, but the core business logic for the calculations is the next major task:

1. Emissions Calculation Logic: The functions that take the raw data (readings, flows) and the conversion factors (factors) and apply the formulas to compute the final emissions (${\text{CO}}_2$, ${\text{NO}}_x$, ${\text{CH}}_4$, etc.) do not exist in the src/gastrack/db/crud.py or src/gastrack/core/ modules.

2. Processed Data Storage: There is no $\text{SQL}$ logic in your crud layer to query, aggregate, and store the processed daily or hourly emissions results.

3. Output API Endpoint: You do not yet have a $\text{GET}$ endpoint (e.g., /api/emissions/timeseries) to serve this calculated data to the frontend for graphing

Shutterstock

Explore

.

💡 Next Steps in Implementation

The essential task right now is to implement the emissions calculation logic and expose the results. I recommend the following sequence:

1. Define the Calculation Function: Create a new function, likely in src/gastrack/db/crud.py, called something like calculate_daily_emissions(). This function would contain the core spreadsheet logic, using $\text{SQL}$ to join $\text{AnalyzerReadings}$, $\text{DailyFlows}$, and $\text{Factors}$.

2. Create an Emissions Model: Define a new msgspec.Struct (or dataclass) in src/gastrack/core/models.py to represent the output (e.g., EmissionResult(date: date, component: str, value: float)).

3. Create the API Handler: Add a new handler function and route in src/gastrack/api/handlers.py and api_routes to call the new $\text{CRUD}$ calculation function and return the results.

Would you like to start by scaffolding the necessary $\text{CRUD}$ function for emissions calculation?

calculate_daily_emissions()

build this based on the spreadsheet functions i shared ealier

Yes you do

  

from BGflow2:

  

|Biogas Flow Data from 4 BioRem Bioreactor Tank Flowmeters|   |   |   |   |   ||Daily Average Flow Data from 4 BioRem Bioreactor Tank Flowmeters|   |   |   |   |   |

  

|Date/Time|Tank 1 [scfm]|Tank 2 [scfm]|Tank 3 [scfm]|Tank 4 [scfm]|Total [scfm]||Date|Daily Average Treated Biogas Flow [scfm]|Daily treated Biogas Flow [scf/day]|Daily Treated Biogas Flow [MMscf/day]|Daily Biogas Flared [scf/day]|Daily Biogas Transfer to TVA [scf/day]|

  

|44593.0466550926|249.25|267|253.75|199.875|=SUM(B3:E3)||44593|=IFERROR(AVERAGEIFS($F$3:$F10001,$A$3:$A10001,">="&$H3,$A$3:$A10001,"<="&H3+1),"")|=IFERROR(I3*1440,"")|=J3/10^6|=J3|0|

  

|44593.0933217361|247.5|272.75|256|200.375|=SUM(B4:E4)||44594|=IFERROR(AVERAGEIFS($F$3:$F10002,$A$3:$A10002,">="&$H4,$A$3:$A10002,"<="&H4+1),"")|=IFERROR(I4*1440,"")|=J4/10^6|=J4|0|

  

  

from BG Calcs 1

  

|Date|Inlet Biogas H2S [ppm]|CH4 [%]|Biogas Net BTUs [btu/cf]|Biogas Gross BTUs [btu/cf]||Date|Total Prod [scf/day]|Total Biogas Flared [scf/day]|Total Biogas Transfer to TVA [scf/day]||Flared Biogas BTU [Mmbtu /day]|NOx Flared [lbs/day]|CO Flared [lbs/day]|VOC Flared [lbs/day]|H2S Flare [scf/day]|SO2 Pro-duced by Flare (80%) [lbs/day]||Date [mo-yr]|Avg Biogas H2S [ppm]|Avg Biogas Net BTUs [btu/cf]|Avg Biogas Gross BTUs [btu/cf]|Total Biogas Prod [scf/mo]|Total Biogas Flared [scf/mo]|TVA Unit Total Usage [scf/mo]||Date [mo-yr]|Heat Flared Biogas [MMbtu/mo]|NOx Flared Biogas [lbs/mo]|CO Flared Biogas [lbs/mo]|VOC Flared Biogas [lbs/mo]|NOx Flared Biogas [tns/mo]|CO Flared Biogas [tns/mo]|VOC Flared Biogas [tns/mo]|H2S Flared Biogas [scf/mo]|SO2 Flare Produced [lbs/mo]|SO2 Flare Produced [tns/mo]|

  

|='1'!P2|='1'!S2|='1'!T2|='1'!Y2|='1'!Z2||=IFERROR(BGflow1!A3,"")|=IFERROR(BGflow1!E3,"")|=IFERROR(BGflow1!F3,"")|=IFERROR(BGflow1!G3,"")||=D2*I2/10^6|=L2*EmFactors!$B$4|=L2*EmFactors!$B$5|=L2*EmFactors!$B$6|=B2*I2/10^6|=P2*64*EmFactors!$B$7/379||43831|='2020 Data-Calcs'!U2|='2020 Data-Calcs'!W2||='2020 Data-Calcs'!R2|=W2-Y2|||43831|=U2*X2/10^6|=IFERROR(AB2*EmFactors!$B$4,"")|=IFERROR(AB2*EmFactors!$B$5,"")|=IFERROR(AB2*EmFactors!$B$6,"")|=IFERROR(AC2/2000,"")|=IFERROR(AD2/2000,"")|=IFERROR(AE2/2000,"")|=IFERROR(T2*X2/10^6,"")|=IFERROR(AI2*64*EmFactors!$B$7/379.3,"")|=IFERROR(AJ2/2000,"")|

  

|='1'!P3|='1'!S3|='1'!T3|='1'!Y3|='1'!Z3||=IFERROR(BGflow1!A4,"")|=IFERROR(BGflow1!E4,"")|=IFERROR(BGflow1!F4,"")|=IFERROR(BGflow1!G4,"")||=D3*I3/10^6|=L3*EmFactors!$B$4|=L3*EmFactors!$B$5|=L3*EmFactors!$B$6|=B3*I3/10^6|=P3*64*EmFactors!$B$7/379||43862|='2020 Data-Calcs'!U3|='2020 Data-Calcs'!W3||='2020 Data-Calcs'!R3|=W3-Y3|||43862|=U3*X3/10^6|=IFERROR(AB3*EmFactors!$B$4,"")|=IFERROR(AB3*EmFactors!$B$5,"")|=IFERROR(AB3*EmFactors!$B$6,"")|=IFERROR(AC3/2000,"")|=IFERROR(AD3/2000,"")|=IFERROR(AE3/2000,"")|=IFERROR(T3*X3/10^6,"")|=IFERROR(AI3*64*EmFactors!$B$7/379.3,"")|=IFERROR(AJ3/2000,"")|

  

|='1'!P4|='1'!S4|='1'!T4|='1'!Y4|='1'!Z4||=IFERROR(BGflow1!A5,"")|=IFERROR(BGflow1!E5,"")|=IFERROR(BGflow1!F5,"")|=IFERROR(BGflow1!G5,"")||=D4*I4/10^6|=L4*EmFactors!$B$4|=L4*EmFactors!$B$5|=L4*EmFactors!$B$6|=B4*I4/10^6|=P4*64*EmFactors!$B$7/379||43891|='2020 Data-Calcs'!U4|='2020 Data-Calcs'!W4||='2020 Data-Calcs'!R4|=W4-Y4|||43891|=U4*X4/10^6|=IFERROR(AB4*EmFactors!$B$4,"")|=IFERROR(AB4*EmFactors!$B$5,"")|=IFERROR(AB4*EmFactors!$B$6,"")|=IFERROR(AC4/2000,"")|=IFERROR(AD4/2000,"")|=IFERROR(AE4/2000,"")|=IFERROR(T4*X4/10^6,"")|=IFERROR(AI4*64*EmFactors!$B$7/379.3,"")|=IFERROR(AJ4/2000,"")|

  

|='1'!P5|='1'!S5|='1'!T5|='1'!Y5|='1'!Z5||=IFERROR(BGflow1!A6,"")|=IFERROR(BGflow1!E6,"")|=IFERROR(BGflow1!F6,"")|=IFERROR(BGflow1!G6,"")||=D5*I5/10^6|=L5*EmFactors!$B$4|=L5*EmFactors!$B$5|=L5*EmFactors!$B$6|=B5*I5/10^6|=P5*64*EmFactors!$B$7/379||43922|='2020 Data-Calcs'!U5|='2020 Data-Calcs'!W5||='2020 Data-Calcs'!R5|=W5-Y5|||43922|=U5*X5/10^6|=IFERROR(AB5*EmFactors!$B$4,"")|=IFERROR(AB5*EmFactors!$B$5,"")|=IFERROR(AB5*EmFactors!$B$6,"")|=IFERROR(AC5/2000,"")|=IFERROR(AD5/2000,"")|=IFERROR(AE5/2000,"")|=IFERROR(T5*X5/10^6,"")|=IFERROR(AI5*64*EmFactors!$B$7/379.3,"")|=IFERROR(AJ5/2000,"")|

  

  

from BG Calcs6:

  

|Date|Biorem Outlet Biogas H2S [ppm]|Biorem Outlet Biogas Methane [%]|Biogas Net BTUs [btu/cf]|Biogas Gross BTUs [btu/cf]||Date|Total Prod [scf/day]|Total Biogas Flared [scf/day]|Total Biogas Transfer to TVA [scf/day]||TVA Biogas BTU [MMbtu /day]|Flared Biogas BTU [MMbtu /day]|NOx Flared [lbs/day]|CO Flared [lbs/day]|VOC Flared [lbs/day]|H2S Flare [scf/day]|SO2 Pro-duced by Flare (100%)* [lbs/day]||Date [mo-yr]|Avg Biogas H2S [ppmv]|Avg Biogas Net BTUs [btu/cf]|Avg Biogas Gross BTUs [btu/cf]|Total Biogas Prod [scf/mo]|TVA Unit Total Usage [scf/mo]|Total Biogas Flared [scf/mo]||Date [mo-yr]|Heat Flared Biogas [MMbtu /mo]|NOx Flared Biogas [lbs/mo]|CO Flared Biogas [lbs/mo]|VOC Flared Biogas [lbs/mo]|NOx Flared Biogas [tns/mo]|CO Flared Biogas [tns/mo]|VOC Flared Biogas [tns/mo]|H2S Flared Biogas [scf/mo]|SO2 Flare Produced [lbs/mo]|SO2 Flare Produced [tns/mo]|

  

|='6'!P5|='6'!S5|='6'!T5|='6'!Y5|='6'!Z5||=BGflow1!A56|=IF(BGflow1!E56="","",BGflow1!E56)|=IF(BGflow1!F56="","",BGflow1!F56)|=IF(BGflow1!G56="","",BGflow1!G56)||=D2*J2/10^6|=D2*I2/10^6|=M2*EmFactors!$B$10|=M2*EmFactors!$B$11|=M2*EmFactors!$B$12|=B2*I2/10^6|=Q2*64*EmFactors!$B$7/379.3||44378|=IFERROR(AVERAGEIFS(B$2:B10000,$A$2:$A10000,">="&$T2,$A$2:$A10000,"<="&EOMONTH($T2,0)),"")|=IFERROR(AVERAGEIFS(D$2:D10000,$A$2:$A10000,">="&$T2,$A$2:$A10000,"<="&EOMONTH($T2,0)),"")|=IFERROR(AVERAGEIFS(E$2:E10000,$A$2:$A10000,">="&$T2,$A$2:$A10000,"<="&EOMONTH($T2,0)),"")|=IFERROR(SUMIFS(H$8:H10000,$G$8:$G10000,">="&$T2,$G$8:$G10000,"<="&EOMONTH($T2,0)),"")|=IFERROR(SUMIFS(J$8:J10000,$G$8:$G10000,">="&$T2,$G$8:$G10000,"<="&EOMONTH($T2,0)),"")|=IFERROR(SUMIFS(I$8:I10000,$G$8:$G10000,">="&$T2,$G$8:$G10000,"<="&EOMONTH($T2,0)),"")||=T2|=SUMIFS(M$2:M10000,$G$2:$G10000,">="&$AB2,$G$2:$G10000,"<="&EOMONTH($AB2,0))|=SUMIFS(N$2:N10000,$G$2:$G10000,">="&$AB2,$G$2:$G10000,"<="&EOMONTH($AB2,0))|=SUMIFS(O$2:O10000,$G$2:$G10000,">="&$AB2,$G$2:$G10000,"<="&EOMONTH($AB2,0))|=SUMIFS(P$2:P10000,$G$2:$G10000,">="&$AB2,$G$2:$G10000,"<="&EOMONTH($AB2,0))|=IFERROR(AD2/2000,"")|=IFERROR(AE2/2000,"")|=IFERROR(AF2/2000,"")|=SUMIFS(Q$2:Q10000,$G$2:$G10000,">="&$AB2,$G$2:$G10000,"<="&EOMONTH($AB2,0))|=SUMIFS(R$2:R10000,$G$2:$G10000,">="&$AB2,$G$2:$G10000,"<="&EOMONTH($AB2,0))|=IFERROR(AK2/2000,"")|

  

|='6'!P6|='6'!S6|='6'!T6|='6'!Y6|='6'!Z6||=BGflow1!A57|=IF(BGflow1!E57="","",BGflow1!E57)|=IF(BGflow1!F57="","",BGflow1!F57)|=IF(BGflow1!G57="","",BGflow1!G57)||=D3*J3/10^6|=D3*I3/10^6|=M3*EmFactors!$B$10|=M3*EmFactors!$B$11|=M3*EmFactors!$B$12|=B3*I3/10^6|=Q3*64*EmFactors!$B$7/379.3||44409|=IFERROR(AVERAGEIFS(B$2:B10001,$A$2:$A10001,">="&$T3,$A$2:$A10001,"<="&EOMONTH($T3,0)),"")|=IFERROR(AVERAGEIFS(D$2:D10001,$A$2:$A10001,">="&$T3,$A$2:$A10001,"<="&EOMONTH($T3,0)),"")|=IFERROR(AVERAGEIFS(E$2:E10001,$A$2:$A10001,">="&$T3,$A$2:$A10001,"<="&EOMONTH($T3,0)),"")|=IFERROR(SUMIFS(H$8:H10001,$G$8:$G10001,">="&$T3,$G$8:$G10001,"<="&EOMONTH($T3,0)),"")|=IFERROR(SUMIFS(J$8:J10001,$G$8:$G10001,">="&$T3,$G$8:$G10001,"<="&EOMONTH($T3,0)),"")|=IFERROR(SUMIFS(I$8:I10001,$G$8:$G10001,">="&$T3,$G$8:$G10001,"<="&EOMONTH($T3,0)),"")||=T3|=SUMIFS(M$2:M10001,$G$2:$G10001,">="&$AB3,$G$2:$G10001,"<="&EOMONTH($AB3,0))|=SUMIFS(N$2:N10001,$G$2:$G10001,">="&$AB3,$G$2:$G10001,"<="&EOMONTH($AB3,0))|=SUMIFS(O$2:O10001,$G$2:$G10001,">="&$AB3,$G$2:$G10001,"<="&EOMONTH($AB3,0))|=SUMIFS(P$2:P10001,$G$2:$G10001,">="&$AB3,$G$2:$G10001,"<="&EOMONTH($AB3,0))|=IFERROR(AD3/2000,"")|=IFERROR(AE3/2000,"")|=IFERROR(AF3/2000,"")|=SUMIFS(Q$2:Q10001,$G$2:$G10001,">="&$AB3,$G$2:$G10001,"<="&EOMONTH($AB3,0))|=SUMIFS(R$2:R10001,$G$2:$G10001,">="&$AB3,$G$2:$G10001,"<="&EOMONTH($AB3,0))|=IFERROR(AK3/2000,"")|

  

|='6'!P7|='6'!S7|='6'!T7|='6'!Y7|='6'!Z7||=BGflow1!A58|=IF(BGflow1!E58="","",BGflow1!E58)|=IF(BGflow1!F58="","",BGflow1!F58)|=IF(BGflow1!G58="","",BGflow1!G58)||=D4*J4/10^6|=D4*I4/10^6|=M4*EmFactors!$B$10|=M4*EmFactors!$B$11|=M4*EmFactors!$B$12|=B4*I4/10^6|=Q4*64*EmFactors!$B$7/379.3||44440|=IFERROR(AVERAGEIFS(B$2:B10002,$A$2:$A10002,">="&$T4,$A$2:$A10002,"<="&EOMONTH($T4,0)),"")|=IFERROR(AVERAGEIFS(D$2:D10002,$A$2:$A10002,">="&$T4,$A$2:$A10002,"<="&EOMONTH($T4,0)),"")|=IFERROR(AVERAGEIFS(E$2:E10002,$A$2:$A10002,">="&$T4,$A$2:$A10002,"<="&EOMONTH($T4,0)),"")|=IFERROR(SUMIFS(H$8:H10002,$G$8:$G10002,">="&$T4,$G$8:$G10002,"<="&EOMONTH($T4,0)),"")|=IFERROR(SUMIFS(J$8:J10002,$G$8:$G10002,">="&$T4,$G$8:$G10002,"<="&EOMONTH($T4,0)),"")|=IFERROR(SUMIFS(I$8:I10002,$G$8:$G10002,">="&$T4,$G$8:$G10002,"<="&EOMONTH($T4,0)),"")||=T4|=SUMIFS(M$2:M10002,$G$2:$G10002,">="&$AB4,$G$2:$G10002,"<="&EOMONTH($AB4,0))|=SUMIFS(N$2:N10002,$G$2:$G10002,">="&$AB4,$G$2:$G10002,"<="&EOMONTH($AB4,0))|=SUMIFS(O$2:O10002,$G$2:$G10002,">="&$AB4,$G$2:$G10002,"<="&EOMONTH($AB4,0))|=SUMIFS(P$2:P10002,$G$2:$G10002,">="&$AB4,$G$2:$G10002,"<="&EOMONTH($AB4,0))|=IFERROR(AD4/2000,"")|=IFERROR(AE4/2000,"")|=IFERROR(AF4/2000,"")|=SUMIFS(Q$2:Q10002,$G$2:$G10002,">="&$AB4,$G$2:$G10002,"<="&EOMONTH($AB4,0))|=SUMIFS(R$2:R10002,$G$2:$G10002,">="&$AB4,$G$2:$G10002,"<="&EOMONTH($AB4,0))|=IFERROR(AK4/2000,"")|

  

  

|Biogas Flow Data from Lagoon Blower Buildings|   |   |   |   |   |   |   |

  

|Date|Blowers 1, 2A, 2B [scf/day]|Blower 2C [scf/day]|BioRem Ambient Air Injection [scf/day]|Total Prod [scf/day]|Biogas Flared [scf/day]|Total Biogas Transfer to TVA [scf/day]|Comment|

  

|44319||||=B3+C3|0|=E3-F3||

  

|44320||||=B4+C4|0|=E4-F4||

  

|44321||||=B5+C5|0|=E5-F5||

  

|44322|440640|792960||=B6+C6|1233600|=E6-F6||

  

|44323|483840|775296||=B7+C7|1259136|=E7-F7||

  

|44324|409320|788440||=B8+C8|1197760|=E8-F8||

  

  

  

Q4.

  

Here is the function view for the daily treated biogas calcuations from the september report

  

|TE Maxson WWTF Biogas Data & Calculations, September 1 through 30, 2025|   |   |   |   |   |   |   |   |   |   |   |   |   ||||

||||||||||||||||||

  

|H2S ppm/grains/ccf =|   |   |   |16|HHV [BTU/scf]/100% CH4 =|   |   |   |10.4||||||||

  

|Date|**Total Biogas Prod. [MMscf /day]||**Total Biogas Flared [MMscf /day]|* TVA Unit Total Transferred [MMscf /day]|Avg. Daily Biogas H2S -- Inlet [ppm]|Avg. Daily Biogas H2S -- Outlet [ppm]|Avg. Daily Biogas H2S -- Inlet [grains /ccf]|Avg. Daily Biogas H2S -- Outlet [grains /ccf]|Avg. Daily Treated Biogas CH4 [%]|Avg. Daily Biogas BTUs /scf|Daily Biogas MMbtu /day Prod.|Daily Biogas MMbtu /day flared|Daily Biogas TVA MMbtu /day Transferred||Resid. H2S After Treatment|H2S Reduction|

  

|='BG Calcs6'!A1531|='BG Calcs6'!H1531/10^6||='BG Calcs6'!I1531/(10^6)|='BG Calcs6'!J1531/(10^6)|=IF('BG Calcs1'!B1584="","N/A",'BG Calcs1'!B1584)|=IF(F5="N/A","N/A",'BG Calcs6'!B1531)|=IF(F5="N/A","N/A",F5/$E$3)|=IF(G5="N/A","N/A",G5/$E$3)|=IF(G5="N/A","N/A",'BG Calcs6'!C1531)|=IF(J5="N/A","N/A",J5*$J$3)|=IFERROR($K5*B5,0)|=IFERROR($K5*D5,0)|=IFERROR($K5*E5,0)||=IFERROR(G5/F5,"")|=IFERROR((F5-G5)/F5,"")|

  

|='BG Calcs6'!A1532|='BG Calcs6'!H1532/10^6||='BG Calcs6'!I1532/(10^6)|='BG Calcs6'!J1532/(10^6)|=IF('BG Calcs1'!B1585="","N/A",'BG Calcs1'!B1585)|=IF(F6="N/A","N/A",'BG Calcs6'!B1532)|=IF(F6="N/A","N/A",F6/$E$3)|=IF(G6="N/A","N/A",G6/$E$3)|=IF(G6="N/A","N/A",'BG Calcs6'!C1532)|=IF(J6="N/A","N/A",J6*$J$3)|=IFERROR($K6*B6,0)|=IFERROR($K6*D6,0)|=IFERROR($K6*E6,0)||=IFERROR(G6/F6,"")|=IFERROR((F6-G6)/F6,"")|

  

|='BG Calcs6'!A1533|='BG Calcs6'!H1533/10^6||='BG Calcs6'!I1533/(10^6)|='BG Calcs6'!J1533/(10^6)|=IF('BG Calcs1'!B1586="","N/A",'BG Calcs1'!B1586)|=IF(F7="N/A","N/A",'BG Calcs6'!B1533)|=IF(F7="N/A","N/A",F7/$E$3)|=IF(G7="N/A","N/A",G7/$E$3)|=IF(G7="N/A","N/A",'BG Calcs6'!C1533)|=IF(J7="N/A","N/A",J7*$J$3)|=IFERROR($K7*B7,0)|=IFERROR($K7*D7,0)|=IFERROR($K7*E7,0)||=IFERROR(G7/F7,"")|=IFERROR((F7-G7)/F7,"")|

  

|='BG Calcs6'!A1534|='BG Calcs6'!H1534/10^6||='BG Calcs6'!I1534/(10^6)|='BG Calcs6'!J1534/(10^6)|=IF('BG Calcs1'!B1587="","N/A",'BG Calcs1'!B1587)|=IF(F8="N/A","N/A",'BG Calcs6'!B1534)|=IF(F8="N/A","N/A",F8/$E$3)|=IF(G8="N/A","N/A",G8/$E$3)|=IF(G8="N/A","N/A",'BG Calcs6'!C1534)|=IF(J8="N/A","N/A",J8*$J$3)|=IFERROR($K8*B8,0)|=IFERROR($K8*D8,0)|=IFERROR($K8*E8,0)||=IFERROR(G8/F8,"")|=IFERROR((F8-G8)/F8,"")|

  

|='BG Calcs6'!A1535|='BG Calcs6'!H1535/10^6||='BG Calcs6'!I1535/(10^6)|='BG Calcs6'!J1535/(10^6)|=IF('BG Calcs1'!B1588="","N/A",'BG Calcs1'!B1588)|=IF(F9="N/A","N/A",'BG Calcs6'!B1535)|=IF(F9="N/A","N/A",F9/$E$3)|=IF(G9="N/A","N/A",G9/$E$3)|=IF(G9="N/A","N/A",'BG Calcs6'!C1535)|=IF(J9="N/A","N/A",J9*$J$3)|=IFERROR($K9*B9,0)|=IFERROR($K9*D9,0)|=IFERROR($K9*E9,0)||=IFERROR(G9/F9,"")|=IFERROR((F9-G9)/F9,"")|

  

|='BG Calcs6'!A1536|='BG Calcs6'!H1536/10^6||='BG Calcs6'!I1536/(10^6)|='BG Calcs6'!J1536/(10^6)|=IF('BG Calcs1'!B1589="","N/A",'BG Calcs1'!B1589)|=IF(F10="N/A","N/A",'BG Calcs6'!B1536)|=IF(F10="N/A","N/A",F10/$E$3)|=IF(G10="N/A","N/A",G10/$E$3)|=IF(G10="N/A","N/A",'BG Calcs6'!C1536)|=IF(J10="N/A","N/A",J10*$J$3)|=IFERROR($K10*B10,0)|=IFERROR($K10*D10,0)|=IFERROR($K10*E10,0)||=IFERROR(G10/F10,"")|=IFERROR((F10-G10)/F10,"")|

  

|='BG Calcs6'!A1537|='BG Calcs6'!H1537/10^6||='BG Calcs6'!I1537/(10^6)|='BG Calcs6'!J1537/(10^6)|=IF('BG Calcs1'!B1590="","N/A",'BG Calcs1'!B1590)|=IF(F11="N/A","N/A",'BG Calcs6'!B1537)|=IF(F11="N/A","N/A",F11/$E$3)|=IF(G11="N/A","N/A",G11/$E$3)|=IF(G11="N/A","N/A",'BG Calcs6'!C1537)|=IF(J11="N/A","N/A",J11*$J$3)|=IFERROR($K11*B11,0)|=IFERROR($K11*D11,0)|=IFERROR($K11*E11,0)||=IFERROR(G11/F11,"")|=IFERROR((F11-G11)/F11,"")|

  

|='BG Calcs6'!A1538|='BG Calcs6'!H1538/10^6||='BG Calcs6'!I1538/(10^6)|='BG Calcs6'!J1538/(10^6)|=IF('BG Calcs1'!B1591="","N/A",'BG Calcs1'!B1591)|=IF(F12="N/A","N/A",'BG Calcs6'!B1538)|=IF(F12="N/A","N/A",F12/$E$3)|=IF(G12="N/A","N/A",G12/$E$3)|=IF(G12="N/A","N/A",'BG Calcs6'!C1538)|=IF(J12="N/A","N/A",J12*$J$3)|=IFERROR($K12*B12,0)|=IFERROR($K12*D12,0)|=IFERROR($K12*E12,0)||=IFERROR(G12/F12,"")|=IFERROR((F12-G12)/F12,"")|

  

|='BG Calcs6'!A1539|='BG Calcs6'!H1539/10^6||='BG Calcs6'!I1539/(10^6)|='BG Calcs6'!J1539/(10^6)|=IF('BG Calcs1'!B1592="","N/A",'BG Calcs1'!B1592)|=IF(F13="N/A","N/A",'BG Calcs6'!B1539)|=IF(F13="N/A","N/A",F13/$E$3)|=IF(G13="N/A","N/A",G13/$E$3)|=IF(G13="N/A","N/A",'BG Calcs6'!C1539)|=IF(J13="N/A","N/A",J13*$J$3)|=IFERROR($K13*B13,0)|=IFERROR($K13*D13,0)|=IFERROR($K13*E13,0)||=IFERROR(G13/F13,"")|=IFERROR((F13-G13)/F13,"")|

  

|='BG Calcs6'!A1540|='BG Calcs6'!H1540/10^6||='BG Calcs6'!I1540/(10^6)|='BG Calcs6'!J1540/(10^6)|=IF('BG Calcs1'!B1593="","N/A",'BG Calcs1'!B1593)|=IF(F14="N/A","N/A",'BG Calcs6'!B1540)|=IF(F14="N/A","N/A",F14/$E$3)|=IF(G14="N/A","N/A",G14/$E$3)|=IF(G14="N/A","N/A",'BG Calcs6'!C1540)|=IF(J14="N/A","N/A",J14*$J$3)|=IFERROR($K14*B14,0)|=IFERROR($K14*D14,0)|=IFERROR($K14*E14,0)||=IFERROR(G14/F14,"")|=IFERROR((F14-G14)/F14,"")|

  

|='BG Calcs6'!A1541|='BG Calcs6'!H1541/10^6||='BG Calcs6'!I1541/(10^6)|='BG Calcs6'!J1541/(10^6)|=IF('BG Calcs1'!B1594="","N/A",'BG Calcs1'!B1594)|=IF(F15="N/A","N/A",'BG Calcs6'!B1541)|=IF(F15="N/A","N/A",F15/$E$3)|=IF(G15="N/A","N/A",G15/$E$3)|=IF(G15="N/A","N/A",'BG Calcs6'!C1541)|=IF(J15="N/A","N/A",J15*$J$3)|=IFERROR($K15*B15,0)|=IFERROR($K15*D15,0)|=IFERROR($K15*E15,0)||=IFERROR(G15/F15,"")|=IFERROR((F15-G15)/F15,"")|

  

|='BG Calcs6'!A1542|='BG Calcs6'!H1542/10^6||='BG Calcs6'!I1542/(10^6)|='BG Calcs6'!J1542/(10^6)|=IF('BG Calcs1'!B1595="","N/A",'BG Calcs1'!B1595)|=IF(F16="N/A","N/A",'BG Calcs6'!B1542)|=IF(F16="N/A","N/A",F16/$E$3)|=IF(G16="N/A","N/A",G16/$E$3)|=IF(G16="N/A","N/A",'BG Calcs6'!C1542)|=IF(J16="N/A","N/A",J16*$J$3)|=IFERROR($K16*B16,0)|=IFERROR($K16*D16,0)|=IFERROR($K16*E16,0)||=IFERROR(G16/F16,"")|=IFERROR((F16-G16)/F16,"")|

  

|='BG Calcs6'!A1543|='BG Calcs6'!H1543/10^6||='BG Calcs6'!I1543/(10^6)|='BG Calcs6'!J1543/(10^6)|=IF('BG Calcs1'!B1596="","N/A",'BG Calcs1'!B1596)|=IF(F17="N/A","N/A",'BG Calcs6'!B1543)|=IF(F17="N/A","N/A",F17/$E$3)|=IF(G17="N/A","N/A",G17/$E$3)|=IF(G17="N/A","N/A",'BG Calcs6'!C1543)|=IF(J17="N/A","N/A",J17*$J$3)|=IFERROR($K17*B17,0)|=IFERROR($K17*D17,0)|=IFERROR($K17*E17,0)||=IFERROR(G17/F17,"")|=IFERROR((F17-G17)/F17,"")|

  

|='BG Calcs6'!A1544|='BG Calcs6'!H1544/10^6||='BG Calcs6'!I1544/(10^6)|='BG Calcs6'!J1544/(10^6)|=IF('BG Calcs1'!B1597="","N/A",'BG Calcs1'!B1597)|=IF(F18="N/A","N/A",'BG Calcs6'!B1544)|=IF(F18="N/A","N/A",F18/$E$3)|=IF(G18="N/A","N/A",G18/$E$3)|=IF(G18="N/A","N/A",'BG Calcs6'!C1544)|=IF(J18="N/A","N/A",J18*$J$3)|=IFERROR($K18*B18,0)|=IFERROR($K18*D18,0)|=IFERROR($K18*E18,0)||=IFERROR(G18/F18,"")|=IFERROR((F18-G18)/F18,"")|

  

|='BG Calcs6'!A1545|='BG Calcs6'!H1545/10^6||='BG Calcs6'!I1545/(10^6)|='BG Calcs6'!J1545/(10^6)|=IF('BG Calcs1'!B1598="","N/A",'BG Calcs1'!B1598)|=IF(F19="N/A","N/A",'BG Calcs6'!B1545)|=IF(F19="N/A","N/A",F19/$E$3)|=IF(G19="N/A","N/A",G19/$E$3)|=IF(G19="N/A","N/A",'BG Calcs6'!C1545)|=IF(J19="N/A","N/A",J19*$J$3)|=IFERROR($K19*B19,0)|=IFERROR($K19*D19,0)|=IFERROR($K19*E19,0)||=IFERROR(G19/F19,"")|=IFERROR((F19-G19)/F19,"")|

  

|='BG Calcs6'!A1546|='BG Calcs6'!H1546/10^6||='BG Calcs6'!I1546/(10^6)|='BG Calcs6'!J1546/(10^6)|=IF('BG Calcs1'!B1599="","N/A",'BG Calcs1'!B1599)|=IF(F20="N/A","N/A",'BG Calcs6'!B1546)|=IF(F20="N/A","N/A",F20/$E$3)|=IF(G20="N/A","N/A",G20/$E$3)|=IF(G20="N/A","N/A",'BG Calcs6'!C1546)|=IF(J20="N/A","N/A",J20*$J$3)|=IFERROR($K20*B20,0)|=IFERROR($K20*D20,0)|=IFERROR($K20*E20,0)||=IFERROR(G20/F20,"")|=IFERROR((F20-G20)/F20,"")|

  

|='BG Calcs6'!A1547|='BG Calcs6'!H1547/10^6||='BG Calcs6'!I1547/(10^6)|='BG Calcs6'!J1547/(10^6)|=IF('BG Calcs1'!B1600="","N/A",'BG Calcs1'!B1600)|=IF(F21="N/A","N/A",'BG Calcs6'!B1547)|=IF(F21="N/A","N/A",F21/$E$3)|=IF(G21="N/A","N/A",G21/$E$3)|=IF(G21="N/A","N/A",'BG Calcs6'!C1547)|=IF(J21="N/A","N/A",J21*$J$3)|=IFERROR($K21*B21,0)|=IFERROR($K21*D21,0)|=IFERROR($K21*E21,0)||=IFERROR(G21/F21,"")|=IFERROR((F21-G21)/F21,"")|

  

|='BG Calcs6'!A1548|='BG Calcs6'!H1548/10^6||='BG Calcs6'!I1548/(10^6)|='BG Calcs6'!J1548/(10^6)|=IF('BG Calcs1'!B1601="","N/A",'BG Calcs1'!B1601)|=IF(F22="N/A","N/A",'BG Calcs6'!B1548)|=IF(F22="N/A","N/A",F22/$E$3)|=IF(G22="N/A","N/A",G22/$E$3)|=IF(G22="N/A","N/A",'BG Calcs6'!C1548)|=IF(J22="N/A","N/A",J22*$J$3)|=IFERROR($K22*B22,0)|=IFERROR($K22*D22,0)|=IFERROR($K22*E22,0)||=IFERROR(G22/F22,"")|=IFERROR((F22-G22)/F22,"")|

  

|='BG Calcs6'!A1549|='BG Calcs6'!H1549/10^6||='BG Calcs6'!I1549/(10^6)|='BG Calcs6'!J1549/(10^6)|=IF('BG Calcs1'!B1602="","N/A",'BG Calcs1'!B1602)|=IF(F23="N/A","N/A",'BG Calcs6'!B1549)|=IF(F23="N/A","N/A",F23/$E$3)|=IF(G23="N/A","N/A",G23/$E$3)|=IF(G23="N/A","N/A",'BG Calcs6'!C1549)|=IF(J23="N/A","N/A",J23*$J$3)|=IFERROR($K23*B23,0)|=IFERROR($K23*D23,0)|=IFERROR($K23*E23,0)||=IFERROR(G23/F23,"")|=IFERROR((F23-G23)/F23,"")|

  

|='BG Calcs6'!A1550|='BG Calcs6'!H1550/10^6||='BG Calcs6'!I1550/(10^6)|='BG Calcs6'!J1550/(10^6)|=IF('BG Calcs1'!B1603="","N/A",'BG Calcs1'!B1603)|=IF(F24="N/A","N/A",'BG Calcs6'!B1550)|=IF(F24="N/A","N/A",F24/$E$3)|=IF(G24="N/A","N/A",G24/$E$3)|=IF(G24="N/A","N/A",'BG Calcs6'!C1550)|=IF(J24="N/A","N/A",J24*$J$3)|=IFERROR($K24*B24,0)|=IFERROR($K24*D24,0)|=IFERROR($K24*E24,0)||=IFERROR(G24/F24,"")|=IFERROR((F24-G24)/F24,"")|

  

|='BG Calcs6'!A1551|='BG Calcs6'!H1551/10^6||='BG Calcs6'!I1551/(10^6)|='BG Calcs6'!J1551/(10^6)|=IF('BG Calcs1'!B1604="","N/A",'BG Calcs1'!B1604)|=IF(F25="N/A","N/A",'BG Calcs6'!B1551)|=IF(F25="N/A","N/A",F25/$E$3)|=IF(G25="N/A","N/A",G25/$E$3)|=IF(G25="N/A","N/A",'BG Calcs6'!C1551)|=IF(J25="N/A","N/A",J25*$J$3)|=IFERROR($K25*B25,0)|=IFERROR($K25*D25,0)|=IFERROR($K25*E25,0)||=IFERROR(G25/F25,"")|=IFERROR((F25-G25)/F25,"")|

  

|='BG Calcs6'!A1552|='BG Calcs6'!H1552/10^6||='BG Calcs6'!I1552/(10^6)|='BG Calcs6'!J1552/(10^6)|=IF('BG Calcs1'!B1605="","N/A",'BG Calcs1'!B1605)|=IF(F26="N/A","N/A",'BG Calcs6'!B1552)|=IF(F26="N/A","N/A",F26/$E$3)|=IF(G26="N/A","N/A",G26/$E$3)|=IF(G26="N/A","N/A",'BG Calcs6'!C1552)|=IF(J26="N/A","N/A",J26*$J$3)|=IFERROR($K26*B26,0)|=IFERROR($K26*D26,0)|=IFERROR($K26*E26,0)||=IFERROR(G26/F26,"")|=IFERROR((F26-G26)/F26,"")|

  

|='BG Calcs6'!A1553|='BG Calcs6'!H1553/10^6||='BG Calcs6'!I1553/(10^6)|='BG Calcs6'!J1553/(10^6)|=IF('BG Calcs1'!B1606="","N/A",'BG Calcs1'!B1606)|=IF(F27="N/A","N/A",'BG Calcs6'!B1553)|=IF(F27="N/A","N/A",F27/$E$3)|=IF(G27="N/A","N/A",G27/$E$3)|=IF(G27="N/A","N/A",'BG Calcs6'!C1553)|=IF(J27="N/A","N/A",J27*$J$3)|=IFERROR($K27*B27,0)|=IFERROR($K27*D27,0)|=IFERROR($K27*E27,0)||=IFERROR(G27/F27,"")|=IFERROR((F27-G27)/F27,"")|

  

|='BG Calcs6'!A1554|='BG Calcs6'!H1554/10^6||='BG Calcs6'!I1554/(10^6)|='BG Calcs6'!J1554/(10^6)|=IF('BG Calcs1'!B1607="","N/A",'BG Calcs1'!B1607)|=IF(F28="N/A","N/A",'BG Calcs6'!B1554)|=IF(F28="N/A","N/A",F28/$E$3)|=IF(G28="N/A","N/A",G28/$E$3)|=IF(G28="N/A","N/A",'BG Calcs6'!C1554)|=IF(J28="N/A","N/A",J28*$J$3)|=IFERROR($K28*B28,0)|=IFERROR($K28*D28,0)|=IFERROR($K28*E28,0)||=IFERROR(G28/F28,"")|=IFERROR((F28-G28)/F28,"")|

  

|='BG Calcs6'!A1555|='BG Calcs6'!H1555/10^6||='BG Calcs6'!I1555/(10^6)|='BG Calcs6'!J1555/(10^6)|=IF('BG Calcs1'!B1608="","N/A",'BG Calcs1'!B1608)|=IF(F29="N/A","N/A",'BG Calcs6'!B1555)|=IF(F29="N/A","N/A",F29/$E$3)|=IF(G29="N/A","N/A",G29/$E$3)|=IF(G29="N/A","N/A",'BG Calcs6'!C1555)|=IF(J29="N/A","N/A",J29*$J$3)|=IFERROR($K29*B29,0)|=IFERROR($K29*D29,0)|=IFERROR($K29*E29,0)||=IFERROR(G29/F29,"")|=IFERROR((F29-G29)/F29,"")|

  

|='BG Calcs6'!A1556|='BG Calcs6'!H1556/10^6||='BG Calcs6'!I1556/(10^6)|='BG Calcs6'!J1556/(10^6)|=IF('BG Calcs1'!B1609="","N/A",'BG Calcs1'!B1609)|=IF(F30="N/A","N/A",'BG Calcs6'!B1556)|=IF(F30="N/A","N/A",F30/$E$3)|=IF(G30="N/A","N/A",G30/$E$3)|=IF(G30="N/A","N/A",'BG Calcs6'!C1556)|=IF(J30="N/A","N/A",J30*$J$3)|=IFERROR($K30*B30,0)|=IFERROR($K30*D30,0)|=IFERROR($K30*E30,0)||=IFERROR(G30/F30,"")|=IFERROR((F30-G30)/F30,"")|

  

|='BG Calcs6'!A1557|='BG Calcs6'!H1557/10^6||='BG Calcs6'!I1557/(10^6)|='BG Calcs6'!J1557/(10^6)|=IF('BG Calcs1'!B1610="","N/A",'BG Calcs1'!B1610)|=IF(F31="N/A","N/A",'BG Calcs6'!B1557)|=IF(F31="N/A","N/A",F31/$E$3)|=IF(G31="N/A","N/A",G31/$E$3)|=IF(G31="N/A","N/A",'BG Calcs6'!C1557)|=IF(J31="N/A","N/A",J31*$J$3)|=IFERROR($K31*B31,0)|=IFERROR($K31*D31,0)|=IFERROR($K31*E31,0)||=IFERROR(G31/F31,"")|=IFERROR((F31-G31)/F31,"")|

  

|='BG Calcs6'!A1558|='BG Calcs6'!H1558/10^6||='BG Calcs6'!I1558/(10^6)|='BG Calcs6'!J1558/(10^6)|=IF('BG Calcs1'!B1611="","N/A",'BG Calcs1'!B1611)|=IF(F32="N/A","N/A",'BG Calcs6'!B1558)|=IF(F32="N/A","N/A",F32/$E$3)|=IF(G32="N/A","N/A",G32/$E$3)|=IF(G32="N/A","N/A",'BG Calcs6'!C1558)|=IF(J32="N/A","N/A",J32*$J$3)|=IFERROR($K32*B32,0)|=IFERROR($K32*D32,0)|=IFERROR($K32*E32,0)||=IFERROR(G32/F32,"")|=IFERROR((F32-G32)/F32,"")|

  

|='BG Calcs6'!A1559|='BG Calcs6'!H1559/10^6||='BG Calcs6'!I1559/(10^6)|='BG Calcs6'!J1559/(10^6)|=IF('BG Calcs1'!B1612="","N/A",'BG Calcs1'!B1612)|=IF(F33="N/A","N/A",'BG Calcs6'!B1559)|=IF(F33="N/A","N/A",F33/$E$3)|=IF(G33="N/A","N/A",G33/$E$3)|=IF(G33="N/A","N/A",'BG Calcs6'!C1559)|=IF(J33="N/A","N/A",J33*$J$3)|=IFERROR($K33*B33,0)|=IFERROR($K33*D33,0)|=IFERROR($K33*E33,0)||=IFERROR(G33/F33,"")|=IFERROR((F33-G33)/F33,"")|

  

|='BG Calcs6'!A1560|='BG Calcs6'!H1560/10^6||='BG Calcs6'!I1560/(10^6)|='BG Calcs6'!J1560/(10^6)|=IF('BG Calcs1'!B1613="","N/A",'BG Calcs1'!B1613)|=IF(F34="N/A","N/A",'BG Calcs6'!B1560)|=IF(F34="N/A","N/A",F34/$E$3)|=IF(G34="N/A","N/A",G34/$E$3)|=IF(G34="N/A","N/A",'BG Calcs6'!C1560)|=IF(J34="N/A","N/A",J34*$J$3)|=IFERROR($K34*B34,0)|=IFERROR($K34*D34,0)|=IFERROR($K34*E34,0)||||

  

||||||||||||||||=IFERROR(G35/F35,"")|=IFERROR((F35-G35)/F35,"")|

  

|Totals =|=SUM(B5:B35)||=SUM(D5:D35)|=SUM(E5:E35)|||||||=SUM(L1:L35)|=SUM(M5:M35)|=SUM(N5:N35)||||

  

|Averages =|=AVERAGE(B6:B35)||=AVERAGE(D6:D35)|=AVERAGE(E6:E35)|=IFERROR(AVERAGE(F6:F35),"N/A")|=IFERROR(AVERAGE(G6:G35),"N/A")|=IFERROR(AVERAGE(H6:H35),"N/A")|=IFERROR(AVERAGE(I6:I35),"N/A")|=IFERROR(AVERAGE(J6:J35),"N/A")|=IFERROR(AVERAGE(K6:K35),"N/A")|=AVERAGE(L6:L35)|=AVERAGE(M6:M35)|=AVERAGE(N6:N35)||=IFERROR(G37/F37,"")|=IFERROR((F37-G37)/F37,"")|

  

||||||||||||||||||

  

|*Note:|Total Prod. -- Total Flared = Total TVA Usage|   |   ||||||||||||||

  

|Notes|BioRem bioreactor tank #4 was the only  reactor utilized due to on-going control panel issues. Biogas was drawn from the covered sludge lagoons to be treated through the Biorem system and flared during the month of September 2025. All biogas readings were taken using the Optima 7 handheld biogas analyzer.|   |   |   |   |   |   |   |   |   |   |   |   ||||

  

||"N/A" is "Not Applicable" because no biogas was produced, treated or flared|   |   |   |   |   |   |   |   |   |   |   |   ||||

  

||||||||||||||||||

  

||Total Monthly Biogas Produced [MMbtu] =|   |   |||||=L36|||||||||

  

||Total Monthly Biogas Flared [MMbtu] =|   |   |   |   |   |   |=M36|||||||||

  

||Total Monthly Biogas Transferred to TVA [MMbtu] =|   |   |   |   |   |   |=N36|||||||||

  

||||||||||||||||||

  

this has nothing to do with cows, it is a ccovered lagoon at a wastewater treatment plant wth a biorem system

This detailed spreadsheet information is exactly what's needed to implement the core calculation logic! Thanks for providing the formulas.

The emissions calculations are focused on $\text{Flared Biogas}$ and are based on $\text{MMbtu}$ for ${\text{NO}}_x$, $\text{CO}$, and $\text{VOC}$, and volume ($\text{scf}$) for ${\text{H}}_2\text{S}$ converted to ${\text{SO}}_2$ mass.

The generic ${\text{CH}}_4$ logic I previously drafted is now obsolete. I will now provide the updated code for your models, crud, and handlers to implement this precise spreadsheet logic.

🛠️ Implementation of Spreadsheet Calculations

1. Update src/gastrack/core/models.py

You need the EmissionResult model to hold the output data.

Python

# --- src/gastrack/core/models.py ---
from msgspec import Struct
from datetime import date
from typing import List

class Factor(Struct):
    key: str
    value: float
    description: str

class AnalyzerReading(Struct):
    reading_time: date
    component: str
    concentration_pct: float

class DailyFlowInput(Struct):
    date: date
    # Assuming these fields are derived from the BGflow2 data
    total_flow_scf: float
    flared_flow_scf: float
    tva_flow_scf: float

# 🚨 NEW MODEL 🚨
class EmissionResult(Struct):
    """Represents the calculated mass emission for a specific component and day."""
    emission_date: date
    pollutant: str  # e.g., 'NOX_LBS_PER_MMBTU', 'SO2_CONVERSION_FACTOR'
    mass_lbs: float # Total mass of the pollutant in pounds

2. Update src/gastrack/db/crud.py

We will implement the calculate_daily_emissions function using a $\text{DuckDB}$ $\text{SQL}$ query that mirrors the spreadsheet logic: $\text{Flared MMbtu}$ calculation and the subsequent multiplication by emission factors.

Python

# --- src/gastrack/db/crud.py ---
import duckdb
from typing import List
from datetime import date
from src.gastrack.db.connection import get_db_connection
from src.gastrack.core.models import Factor, EmissionResult, DailyFlowInput
# ... (other crud functions like get_all_factors, etc.)

def calculate_daily_emissions(target_date: date) -> List[EmissionResult]:
    """
    Calculates the total mass emissions (in lbs) for a given date
    based on daily flow, daily average analyzer readings, and conversion factors,
    following the BG Calcs 6 Flare Emissions logic.
    """
    
    # --- Assumptions for Table/Column Names (Must match schema) ---
    # daily_flow_inputs: date, flared_flow_scf (from BGflow2)
    # analyzer_readings: reading_date, component, value (contains daily averages for GrossBTU and H2S_OUTLET from BG Calcs 6)
    # factors: key, value (from EmFactors sheet)
    
    sql = f"""
    -- 1. Get the necessary daily inputs (Flow, BTU, H2S) for the target date
    WITH DailyInputs AS (
        SELECT
            T1.date,
            T1.flared_flow_scf, -- Total Biogas Flared [scf/day]
            T2.value AS avg_gross_btu, -- Biogas Gross BTUs [btu/cf]
            T3.value AS avg_h2s_outlet_ppm -- Biorem Outlet Biogas H2S [ppm]
        FROM 
            daily_flow_inputs AS T1
        LEFT JOIN 
            analyzer_readings AS T2 ON T1.date = T2.reading_date AND T2.component = 'GrossBTU'
        LEFT JOIN 
            analyzer_readings AS T3 ON T1.date = T3.reading_date AND T3.component = 'H2S_OUTLET'
        WHERE 
            T1.date = '{target_date}'
    )
    
    -- 2. Calculate Intermediate Values (MMbtu Flared, H2S scf Flared)
    , Calculations AS (
        SELECT
            DI.date AS emission_date,
            
            -- Flared Biogas BTU [MMbtu /day] = Gross BTU [btu/cf] * Flared Flow [scf/day] / 10^6
            (DI.avg_gross_btu * DI.flared_flow_scf / 1000000.0) AS flared_mmbtu_day,
            
            -- H2S Flared [scf/day] = Outlet H2S [ppm] * Flared Flow [scf/day] / 10^6 (ppm to volume fraction)
            (DI.avg_h2s_outlet_ppm * DI.flared_flow_scf / 1000000.0) AS h2s_flared_scf_day
            
        FROM DailyInputs AS DI
    )
    
    -- 3. Final Projection with Emission Factors
    SELECT
        C.emission_date,
        F.key AS pollutant,
        CASE 
            -- NOx, CO, VOC Emissions (BG Calcs 6: M2*EmFactors!$B$10, M2*EmFactors!$B$11, M2*EmFactors!$B$12)
            WHEN F.key = 'NOX_LBS_PER_MMBTU' THEN C.flared_mmbtu_day * F.value
            WHEN F.key = 'CO_LBS_PER_MMBTU' THEN C.flared_mmbtu_day * F.value
            WHEN F.key = 'VOC_LBS_PER_MMBTU' THEN C.flared_mmbtu_day * F.value
            
            -- SO2 Emissions (BG Calcs 6: Q2*64*EmFactors!$B$7/379.3)
            -- Where 64.0 is the molecular weight of SO2 (g/mol) and 379.3 is the molar volume (scf/lb-mol) at STP
            WHEN F.key = 'SO2_CONVERSION_FACTOR' THEN (C.h2s_flared_scf_day * 64.0 * F.value / 379.3)
            
            ELSE 0.0
        END AS mass_lbs
        
    FROM 
        Calculations AS C, factors AS F
    WHERE 
        F.key IN (
            'NOX_LBS_PER_MMBTU', 
            'CO_LBS_PER_MMBTU', 
            'VOC_LBS_PER_MMBTU', 
            'SO2_CONVERSION_FACTOR'
        ) 
    AND 
        C.flared_flow_scf IS NOT NULL
    AND 
        C.avg_gross_btu IS NOT NULL
    AND 
        C.avg_h2s_outlet_ppm IS NOT NULL;
    """
    
    with get_db_connection() as conn:
        result = conn.execute(sql).fetchall()

    emissions = [EmissionResult(emission_date=row[0], pollutant=row[1], mass_lbs=row[2]) for row in result]
    
    return emissions

3. Update src/gastrack/api/handlers.py

Add the new handler and route for the emissions timeseries endpoint.

Python

# --- src/gastrack/api/handlers.py ---
# ... (existing imports, add date)
from datetime import date
from starlette.requests import Request
from starlette.responses import JSONResponse
from starlette.exceptions import HTTPException
from src.gastrack.db import crud
from src.gastrack.core.models import EmissionResult 
from typing import List
# ... (other handlers)

async def get_emissions_timeseries(request: Request):
    """
    GET endpoint to retrieve calculated emissions for a specific date (or date range).
    """
    try:
        # NOTE: For simplicity, we hardcode to today. In production, 
        # you would get 'start_date' and 'end_date' from request.query_params
        target_date = date.today() 
        
        emissions: List[EmissionResult] = crud.calculate_daily_emissions(target_date)

        # Serialize the list of EmissionResult structs to dictionaries
        serializable_emissions = [
            {
                "emission_date": e.emission_date.isoformat(), # Convert date object to string
                "pollutant": e.pollutant,
                "mass_lbs": e.mass_lbs
            }
            for e in emissions
        ]

        return JSONResponse(serializable_emissions)

    except Exception as e:
        print(f"\n--- FATAL HANDLER EXCEPTION ---\nError during get_emissions_timeseries: {e}\n-------------------------------\n")
        raise HTTPException(status_code=500, detail=f"Could not calculate emissions: {e}")


# --- API Routes ---
api_routes = [
    Route("/factors", endpoint=get_factors, methods=["GET"]),
    # 🚨 NEW ROUTE 🚨
    Route("/emissions/timeseries", endpoint=get_emissions_timeseries, methods=["GET"]),
]

These changes complete the implementation of the core emissions calculation logic. The next logical step is to test this new endpoint.