measpy (MEASurements with PYthon)

A package for easy data acquisition and signal processing/analysis

(c) 2021-2023 Olivier Doaré Contact: olivier.doare@ensta-paris.fr

measpy is a set of classes and methods that

• helps signal processing and analysis using rapid and compact Python scripting, thanks to the functional programming paradigm proposed by this package,
• ease and unify the data acquisition process with various DAQ cards

Documentation: https://odoare.github.io/measpy

WARNING: major incompatible changes have been made to the measurement class for v0.1, if backward compatibility is needed, the old measurement class system is kept in the pre v0.1 branch. Changes in this branch will only concern eventual bug fixing.

The base classes defined by measpy are:

• Signal: This is the core class of the package. It defines a signal through a sampling frequency, a physical unit and a list of samples and a description. Additional properties can be defined in order to take into account calibration or time shifting of the signal with respect to a reference time.
• Spectral: This class represents the signals in the Fourier space. A spectral object contains the complex amplitudes as a 1D numpy array for frequencies up to the Nyquist frequency or the sampling frequency, and some properties as sampling frequency, unit and description.
• Measurement : A class that describe a data acquisition process, its outputs (Signal objects), its inputs (Signal objects)...
• Weighting : A weighting class holds complex values for a list of frequencies, and methods to to smoothing, interpolation, etc.

For now, data acquisition with these daq devices are implemented :

• Audio cards, via the sounddevice package,
• NI DAQ cards, via the nidaqmx package.
• Picoscope scopes, via the picosdk-python-wrappers package.

To import the package and perform data acquisition with sound cards:

import measpy as mp

This will import the classes mp.Measurement, mp.Signal, mp.Spectral and mp.Weighting.

To do data acquisition one has to select the module that corresponds to the target device. If it is a soundcard:

from measpy.audio import audio_run_measurement

If it is a NI daq card:

from measpy.ni import ni_run_measurement

If it is a Picoscope of the ps2000 series:

from measpy.ps2000 import ps2000_run_measurement

If it is a Picoscope of the ps4000 series:

from measpy.ps4000 import ps4000_run_measurement

In theses modules, there's also the audio_get_devices and ni_get_devices functions to get a list of devices present in the system. To get the list of devices, do for example:

from measpy.audio import audio_get_devices
l = audio_get_devices()
print(l)

Usage example

Consider the following experiment in which we want to record a pressure and an acceleration while we send a white noise at the sound card output (typical sound and vibration transfer function measurement):

• a white noise between 20Hz and 20kHz is sent to output 1 of the soundcard
• a pressure is acquired at input 1 (Unit pascals)
• an acceleration is acquired at input 2 (unit m/s^2)
• the sampling frequency is 44100Hz
• the calibration of signal conditionners are : 1V/pascal, 0.1V/(m/s^2)
• the soundcard input gain is such that for 5V at its inputs, the sample amplitude is 1.0 (input or output). This value is referred to as 0dB full scale (0dBFS). It given in the soundcard specifications or has to be calibrated using known signals.
• the duration of the measurement is 5s
• the soundcard name is 'My card', as given by measpy.audio.audio_get_devices()

First step is to import the modules and prepare the signals. We create a five seconds noise signal for output, and two empty signals with the correct properties for the inputs.

import measpy as mp
from measpy.audio import audio_run_measurement
sout = mp.Signal.noise(fs=44100, freq_min=20, freq_max=20000, dur=5)
sin1 = mp.Signal(desc = 'Pressure', dbfs=5.0, cal=1.0, unit='Pa' )
sin2 = mp.Signal(desc = 'Acceleration', dbfs=5.0, cal=0.1, unit='m*s**(-2)' )

We then setup and run the measurement:

M1 = mp.Measurement(out_sig=[sout],
                    out_map=[1],
                    in_sig=[sin1,sin2],
                    in_map=[1,2],
                    dur=5,
                    in_device='My card',
                    out_device='My card',
                    device_type='audio')
audio_run_measurement(M1)

The data is stored in the list of Signal objects M1.in_sig. To plot the pressure:

M1.in_sig[0].plot()
plt.show()

The measurement can be saved in a directory:

M1.to_dir('my_measurement')

The created directory contains the measurement parameters in a file params.csv, individual signals as pairs of csv and wav files, one pair for each signal. The csv contains the signal parameters, the wav file contains the raw data points.

The measurement can then be restored using:

M2=mp.Measurement.from_dir('my_measurement')

An example of analysis consists in computing transfer function between sent signal and acquired signals. For instance, the Welch method can be implemented using:

H=M2.in_sig[0].tfe_welch(M2.out_sig[0])

The output of tfe_welch() is an object of the measpy.signal.Spectral class. This class has also a plotting method:

H.plot()
plt.show()

This Spectral object can then be used to compute an impulse response:

G = H.irfft()

Functional programing paradigm

Most signal processing methods of Signal and Spectral classes return a Signal or Spectral object. This allows to write signal processing scripts by chaining these methods. For instance the impulse response calculation above can be done in one step:

G = M2.in_sig[0].tfe_welch(M2.out_sig[0]).irfft()

We might want to remove frequencies below 20Hz and above 20kHz before computing the impulse. This can be done in the same line of code, in the functional programing way:

G = M2.in_sig[0].tfe_welch(M2.out_sig[0]).filter_out([20,20000]).irfft()

Units

Units are preserved during the operations:

print(Gap.unit)

should give something like pascal * second**2 / m

Documentation

Additionnal documentation and examples can be found in the ./docs and ./examples directories of the project. The main page for web documentation is https://odoare.github.io/measpy.

Releasing

Releases are published automatically when a tag is pushed to GitHub.

Example:

   # Set next version number
   export RELEASE=x.x.x

   # Create tags
   git commit --allow-empty -m "Release $RELEASE"
   git tag -a $RELEASE -m "Version $RELEASE"

   # Push
   git push upstream --tags