JupyterPiDAQ 0.6.0

Data Acquisition in Jupyter notebook on Raspberry Pi

JupyterPiDAQ

This software allows live collection and plotting of analog data in a Jupyter notebook. The package was initially developed to provide an inexpensive laboratory system for teaching based on the Raspberry Pi. However, as the development has progressed the data acquisition board drivers have been separated out of the user interface, so that the software has potential to work on other computers running Jupyter with A-to-D board specific connector code. Presently the compatible A-to-Ds are for Raspberry Pis:

• Adafruit compliant ADS1115 boards (example, also available from other vendors);
• The π-Plates DAQC2 plate.
• A demo mode will run on any computer with a Jupyter notebook install and Python 3.6+.

The goal is for the user interface to be as close to self-explanatory as possible. However, documentation is being developed along with some example experiments.

Sensors: Like many commercial educational packages the software knows about the properties of some sensors, so can collect data directly in the units appropriate for the sensor, in addition to the raw voltage signal returned by the sensor. Not all sensors are compatible with all boards. The developer(s) attempt to keep this list of known sensors up-to-date, but the code may provide additional sensors not listed here:

• ADS1115 compatible (board can provide 3.3 V of power/reference to sensors):
  • voltage reading (V, mV) from any sensor that puts out a voltage in the range +/-3.3 V.
  • built-in thermistor (V, mV, K, C, F).
  • Vernier SS temperature probe (V, mV, K, C, F).
• DAQC2 compatible (board can provide 5.0 V of power/reference to sensors):
  • voltage reading (V, mV) from any sensor that puts out a voltage in the range +/- 12 V.
  • Vernier SS temperature probe (V, mV, K, C, F).
  • Vernier standard pH probe (V, mV, pH).
  • Vernier flat (tris compatible) pH probe (V, mV, pH).
  • Compatible with standard Vernier analog probes. Default calibrations being added as time and sensors become available.

You can also hook up your own sensors and manually convert the raw voltage readings or write and submit a new sensor definition to the project.

Initial Author: Jonathan Gutow [e-mail]

License: GPL V3+

Installation

Installation is meant to be done into a virtual environment from the PyPi repository. There are two modes "Production" for end users and "Development" for those who want to improve the package.

NOTE: If a binary distribution (whl or wheel) is not available for your platform, some of the required packages may need to be compiled. If you get compilation errors when installing try getting the python header and development files for your platform. To get them on most *nix platforms use the command $ sudo apt install python3-dev.

Production

1. If not installed, install pipenv:$ pip3 install --user pipenv. You may need to add ~/.local/bin to your PATH to make pipenv available in your command shell. More discussion: The Hitchhiker's Guide to Python.
2. Create a directory for the virtual environment you will be installing into (example: $ mkdir JupyterPiDAQ).
3. Navigate into the directory $ cd JupyterPiDAQ.
4. Create the virtual environment and enter it $ pipenv shell. To get out of the environment you can issue the $ exit command on the command line.
5. While still in the shell install the latest JupyterPiDAQ and all its requirements $ pip install -U JupyterPiDAQ. This can take a long time, especially on a Raspberry Pi. On a Pi 3B+ (minimum requirement) it will probably not run without at least 1 GB of swap. See: Build Jupyter on a Pi for a discussion of adding swap space on a Pi.
6. Still within the environment shell test this by starting jupyter $ jupyter notebook. Jupyter should launch in your browser.
   1. Open a new notebook using the default (Python 3) kernel.
   2. In the first cell import all from DAQinstance.py: from jupyterpidaq.DAQinstance import *. When run this cell should load the DAQmenu at the end of the Jupyter notebook menu/icon bar. If you do not have an appropriate A-to-D board installed you will get a message and the software will default to demo mode, substituting a random number generator for the A-to-D. Because of the demo mode it is possible to run this on any computer, not just a Pi.

Development

Basic requirements: Python 3.6+, associated pip and a Jupyter notebook. See: python.org and Jupyter.org.

1. If not installed, install pipenv:$ pip3 install --user pipenv. You may need to add ~/.local/bin to your PATH to make pipenv available in your command shell. More discussion: The Hitchhiker's Guide to Python.
2. Navigate to the directory where this package will be or has been downloaded to. Use pipenvto install an "editable" package inside the directory as described below:
   1. Start a shell in the environment $ pipenv shell.
   2. Install using pip.
      1. If you downloaded the git repository named "JupyterPiDAQ" and have used that directory to build your virtual environment: $ pip install -e ../JupyterPiDAQ/.
      2. If you are downloading from PyPi $ pip install -e JupyterPiDAQ
      3. Either should install all the additional packages this package depends upon. On a Raspberry Pi this will take a long time. It probably will not run without at least 1 GB of swap. See: Build Jupyter on a Pi .
   3. Still within the environment shell test this by starting jupyter $ jupyter notebook. Jupyter should launch in your browser.
      1. Open a new notebook using the default (Python 3) kernel.
      2. In the first cell import all from DAQinstance.py: from jupyterpidaq.DAQinstance import *. When run this cell should load the DAQmenu at the end of the Jupyter notebook menu/icon bar. If you do not have an appropriate A-to-D board installed you will get a message and the software will default to demo mode, substituting a random number generator for the A-to-D. Because of the demo mode it is possible to run this on any computer, not just a Pi.
3. If you wish, you can make this environment available to an alternate Jupyter install as a special kernel when you are the user.
   1. Make sure you are running in your virtual environment $ pipenv shell in the directory for virtual environment will do that.
   2. Issue the command to add this as a kernel to your personal space: $ python -m ipykernel install --user --name=<name-you-want-for-kernel>.
   3. More information is available in the Jupyter/Ipython documentation. A simple tutorial from Nikolai Jankiev (Parametric Thoughts) can be found here.