picodaqa 1.1.4

Data AcQuisition and analysis with PicoScope usb-oscilloscopes

# picoDAQ

python Data AcQuisition and analysis with PicoScope usb-oscilloscopes

The usb-oscilloscope series PicoSpope by Pico Technology (see <https://www.picotech.com>) offers universal instruments that come with great software support, not only a graphical interface offering the functionality known from oscilloscopes, but - most importantly for this project - also with a software development kit (SDK) which makes it possible to use the devices with a wide range of high-level languages.

Provided here is a data acquisition system as is needed to record, analyze, classify and count the occurrence of wave forms such as provided for example by single-photon counters or typical detectors common in quantum mechanical measurements or in nuclear, particle physics and astro particle physics, e.g. photo tubes, Geiger counters, avalanche photo-diodes or modern SiPMs.

The random nature of such processes and the need to keep read-out dead times low requires an input buffer and a buffer manager running as a background process. Data are provided via the buffer manager interface to several consumer processes to analyze, check or visualize data and analysis results. Such consumers may be obligatory ones, i.e. data acquisition pauses if all input buffers are full and an obligatory consumer is still busy processing. A second type of random consumers receives an event copy from the buffer manager upon request, without pausing the data acquisition process. Typical examples of random consumers are displays of a subset of the wave forms or of intermediate analysis results. Examples for such consumers are provided as part of this package, running either as a ‘thread’ within the python interpreter or as sub-processes using the ‘multiprocessing’ package.

This project originated as a demonstration to analyze pulses from a photomultiplier (PM) or a Silicon Photo Multiplier (SiPM) registering optical signals from a detector, in the simplest case a coffeepot filled with water and equipped with a PM to count muons from cosmic rays.

## List of implemented Functions:

• class picoConfig: - set up PicoScope time base, channel ranges and trigger - set up the internal signal generator - PicoScope configuration read from json or yaml file - data acquisition of raw data from device

• class BufferMan: - acquire data (implemented as background thread) - manage event data buffer and distribute to consumers - configuration read from json or yaml file - supports

  • obligatory consumers: data acquisition pauses until consumer done

  • and random consumers: receive a copy of one event, data acquisition continues

• module AnimatedInstruments (deprecated, to be removed soon) - examples of animated graphical devices: a Buffer Manager display (using class plotBufManInfo), a VoltMeter (class *VoltMeter), an Oscilloscope (class Ocscilloscope and a ratemeter (class RMeter). The module must run as a python thread in the same python interpreter as BufferMan

• module mpBufManCntrl - this sub-process receives status and logging information from the Buffer Manager via a multiprocessing Queue and displays input rate history, filling level of the buffers and the data-acquisition lifetime. Buttons at the bottom of the window allow status changes (from RUNNING to PAUSED or vice versa) or to exit. A log-file at the end contains a summary and, optionally, logging information.

• module mpOsci runs an instance of Oscilloscpe as a sub-process, and receives data from BufferMan via a multiprocessing Queue.

• module mpRMeter runs an instance of the RMeter class as a sub-process, receiving via a multiprocessing Queue.

• module mpVMeter runs an instance of the VoltMeter class as a sub-process, receiving data via a multiprocessing Queue.

• module mpHists runs an instance of the animHists class as a sub-process; receives input data via a multiprocessing Queue. Data are formatted as lists of values. A normalized frequency distribution is then updated and displayed.

• module mpBDisplay - runs an instance of class BarDisplay and shows one (signed or unsigned) value per Channel (e.g. peak Voltage, effective Voltage etc.). Values are passed to the sub-process via a multiprocessing Queue.

• module mpDataLogger runs an instance of the DataLogger class as a sub-process, displaying values passed via a multiprocessing Queue as a history plot. This module is not implemented as a BufferMan* client (see example runDataLogger).

• module mpDataGraphs runs an instance of the DataGraphs class as a sub-process, displaying values passed via a multiprocessing Queue as a bar graph, a history plot or optionally - if two channels are enabled - as xy-display. This module ist not implemented as a BufferMan client (see example runDataGraphs)

The script runDAQ.py gives an example of how to use all of the above. For a full demo, connect the output of a PicoScope’s signal generator to channel B, and eventually an open cable to Channel A to see random noise. Use the configuration file DAQconfig.yaml, which specifies the configuration files`BMconfig.yaml` for the Buffer Manager and PSConfig.yaml for the PicoScope. As a hook for own extensions, user code may be included. An example for this is shown in the configuration file DAQ_Cosmo.json, which points to a code snippet anaDAQ.py to start some example consumers (code in`exampleConsumers.py`).

## Examples

The directory examples/ contains configuration files and some special applications.

The script runDataLogger.py implements a data logger for rates below 20 Hz. Signals are sampled with a PicoSocpe at a rate of 10 kHz over 20 ms and then averaged. 50 Hz noise is thus eliminated, and a clean voltage signal is obtained. The history of the recorded voltages is displayed using the module mpDataLogger. Similarly, runDataGraphs uses the same sampling mechanism to display the effective voltage as bar graph, a history plot, and, optionally, channel B vs. Channel A as an xy-graph if two channels are enabled. These examples directly read from the hardware device and therefore do not rely on the BufferMan class. As a third simple example the script runOsci.py provides a simple oscilloscpe independent of BufferMan.

The script runCosmo.py is a modified version of runDAQ.py and depends on the code in pulseFilter.py, which implements a convolution filter to search for characteristic signal shapes in an input waveform. The example is tailored to identify short pulses from muon detectors (e. g. the scintillator panels of the CosMO-experiment by “Netzwerk Teilchenwelt”, <http://www.teilchenwelt.de>, or the Kamiokanne-Experiment with photomultiplier readout and pulses shaped to a length of approx. 150ns). A more complete and updated version has been moved to the project picoCosmo, see <https://github.com/GuenterQuast/picoCosmo>.

## Installation of the package

This python code is compatible with python versions and >=3.5. It was tested with PicoScope device classes PS2000, PS2000a, PS3000a and PS4000 under Ubuntu, openSUSE Leap and on RaspberryPi. Graphical displays are implemented with matplotlib.

Requirements:

• The low-level drivers and C-libraries contained in the Pico Technology Software Development Kit are required, SDK by Pico Technology, see https://www.picotech.com/downloads

• python bindings of the pico-python project by Colin O’Flynn and Mark Harfouche, https://github.com/colinoflynn/pico-python

picoDAQ presently consists of the modules in the direcoctry picodaqa, mentioned above, and an example python script (runDAQ.py) with configuration examples (.yaml files) for the data acquisition (DAQconfig.yaml), for the PicoScope Device (PSconfig.yaml) and for the Buffer Mananger (BMconfig.yaml).

After downloading all files from the git repository, connect your PicoScope and start from the command line, e. g. python runDAQ.py.

You may run the script make_dist.sh to generate a .whl file in the subdirectory dist, which can be installed via pip install picodaqa_<vers>_<type>.whl. Once this is done, the provided examples can be copied to any directory.