Automatic Tracking of Pressure, Temperature, and Humidity Data

Data Driven Organic Materials Lab

Materials

• Dracal VCP-PTH200 (https://www.dracal.com/en/product/pth200/)
• Raspberry Pi
• DigitalOcean Droplet running Flask Server

Workflow

The PTH monitoring system is composed of two major components: the sensor assembly and the server. The sensor assembly, a Raspberry Pi connected to the VCP-PTH200, posts HTTP requests to the server on a fixed schedule (in our case, every 15 minutes) containing pressure, temperature, and humidity readings.

The Flask server, hosted on a DigitalOcean Droplet, exposes several endpoints for data ingestion and retrieval, all under a /pth prefix. It's a Flask Blueprint, distributed as a pip-installable package (server/) — it can run as its own standalone server (server/app.py) or be mounted into an existing Flask application. Incoming PTH data is accepted at /pth/api/store_data and stored in a SQLite database, tagged with the identifier of the device that sent it.

A browser-based dashboard is served at /pth/dashboard on the same DigitalOcean Droplet. The interface is built in vanilla HTML, CSS, and JavaScript, using Chart.js for visualization. On load, it fetches the past 7 days of data from /pth/api/ndays and renders a time-series line plot. Each sensor channel (pressure, temperature, humidity) is assigned its own Y-axis, allowing channels with disparate units and scales to be displayed simultaneously without compression.

The interface exposes several controls: the displayed date range can be narrowed using start and end datetime pickers, individual sensor channels can be toggled on or off via a checkbox panel, and a point lookup tool queries /pth/api/closest to retrieve the recorded values nearest to a user-specified timestamp. A statistics panel below the chart displays the minimum, maximum, mean, and total point count for each active channel over the selected time window. Data for the current view can also be exported as a CSV file directly from the browser.

Data can be retrieved through the API in three ways: /pth/api/ndays returns all records from the past N days, /pth/api/range returns all records between two given timestamps, and /pth/api/closest accepts a Unix timestamp or ISO 8601 datetime string and returns the record with the nearest matching timestamp. All three accept Unix epoch integers or ISO 8601 datetime strings wherever a timestamp is expected. Full endpoint reference: server/API.md.

import requests

BASE_URL = "https://.../pth"

# Unix epoch integer
response = requests.get(f"{BASE_URL}/api/closest", params={"time": 1740000000})
print(response.json())
## {'MS5611 Pressure': 100840.8, 'SHT31 Relative Humidity': 31.26, 'SHT31 Temperature': 20.58, 'device_id': 'greenhouse-1', 'time': 1762489563}

# ISO 8601 datetime string
response = requests.get(f"{BASE_URL}/api/closest", params={"time": "2025-02-19T14:30:00"})
print(response.json())

# Get the last N days of data
response = requests.get(f"{BASE_URL}/api/ndays", params={"days": 7})
data = response.json()  # Returns a list of dicts, one per recorded interval

# Get all data between two timestamps
response = requests.get(f"{BASE_URL}/api/range", params={"start": 1740000000, "end": "2025-03-01T00:00:00"})
data = response.json()

Installation

Sensor Setup

The sensor installation script creates a systemd service that takes a reading at a fixed interval (default 15 minutes) and posts it to the server.

In the terminal on your sensor device (Raspberry Pi), follow the instructions at sensor/INSTALL.md to install and configure the sensor.

Server Setup

On your server, clone this repository. To download only the server code (not the sensor code), run:

git clone --no-checkout https://github.com/ddomlab/pth_analysis.git && cd pth_analysis

git sparse-checkout init --cone && git sparse-checkout set server && git checkout main

Then, pip install the server blueprint

pip install ./server

The server is a pip-installable Flask Blueprint (pth_server), usable two ways:

• As a standalone server Simply run the provided server/app.py (python3 app.py, for testing or for production, gunicorn app:app). This registers the blueprint on its own dedicated Flask app. See this guide for help setting up Gunicorn.
• Mounted on an existing Flask app: In your app's setup code:

  import pth_server 
  
  app.register_blueprint(pth_server.pth_bp)
  pth_server.init_app_db(app)

Either way, every PTH route lives under the /pth prefix (/pth/dashboard, /pth/api/...). Set app.config["PTH_DB_PATH"] to control where the SQLite database file is created (defaults to pth_data.db in the working directory).

If you're migrating an existing flat pth_data.csv from an older version of this project, see server/migrate_csv_to_db.py.