mbclient 1.0.1

#mbclient Client software and on the fly visualisation tools for the moessbauer effect.

##The client software The client software is a command line tool capable of connecting to the Red-Pitaya running the MBFilter

program in server mode, providing the server on the Red-Pitaya with all the neccesary information to configure itself properly and to store the data received from the Red-Pitaya in a .csv file for Analysis by the student and also Visualise the data as a pulse-height spektrum and a 2d spektrum of pulse height vs. Digital Function Generator Address. The Visualisation is updated continuously and can be exited without interfering with the data taking procedures.

##The program structure The mbclient package consists of the cli application located at cli.py and associated functions in the mbclient.py file. The command line program operates asynchronously with asyncio. There are a total of three tasks.

1. The user facing task waits for a stop input from the user and sends a terminate signal to the other running tasks.
2. The process_data task opens a websocket connection to the Red-Pitaya experiment, reads in the data from there and passes it along to 'consumer' tasks.
3. The write_to_file task writes the decoded data it receives from the task 2 and writes it into an csv file.
4. The plot_data task starts a second process, that is responsible for 'live' plotting of the data and forwards the data to it via a PIPE.

The mbdatatypes.py file contains the Class that represents the result coming from the Red-Pitaya. It contains methods to decode the raw data from the websocket and methods to transform the data read to a csv entry.

The mbplotter.py file contains the Class that is spawned off into the second process to plot the data, as well as a class that acts as an API for sending data to the plotting process via the plot(data) funciton. The characteristics and details of the plot are encoded in the ProcessPlotter class.

##Notes on Matplotlib Matplotlib does quite a lot of things. One of them being the implementation of it's own event loop similar to the one used from asyncio. With the event loop comes the ability to set a timer that fires a callback function that updates the plot. All this is implemented in the ProcessPlotter class. The Pipe between the processes is used as a data buffer.