PSEC Analysis

Miles Lucas - Iowa State University

This code serves as a simple parsing tool for the PSEC data created by Dr. Eric Oberla's PSEC acdc-daq.

Requirements

• Python 3.4+
• numpy
• matplotlib (only for plotting)

Data Format

Eric's data output is laid out in a text file with the following format

	0	1	2-31	32	k*32 + 1	k*32 + (2-31)	k*32 + 32
0 to 255	Count	0	Ped data	0	0	Ped data	0
256 to (n+1) x 256 - 1	Count	Wraparound	Data	Metadata	Wraparound	Data	Metadata

Where n is the event number and k is the board number

The metadata is stored in the 33rd column of each event and has the following values

Value	Name	Description
0	count
1	aa
2	time
3	datetime
4	events
7	bin count rise
8	self trig settings 2
9	sys coinc width
10	coinc num chips
11	coinc num chans
12	self trig settings
13	trig en
14	trig wait for sys
15	trig rate only
16	trig sign
17	use sma trig in
18	use coinc settings
19	use trig valid as reset
20	coinc window
21	reg self trig
22	counts of sys no local
23	sys trig count
24	resets from firmw
25	firmware version
26	self trig mask
27	dig timestamp lo
28	dig timestamp mid
29	dig timestamp hi
30	dig event count
31	event count
32	timestamp hi
33	timestamp mid
34	timestamp lo
35	CC bin count
36	CC event count
37	CC timestamp lo
38	CC timestamp mid
39	CC timestamp hi
40-44	RO count	RO count for each chip
45-49	RO target count	for each chip
50-54	vbias	for each chip
55-59	trigger threshold	for each chip
60-64	RO DAC value	for each chip
70-99	self trig scalar	for each ac channel
110	time from valid to trig
111	firmware reset time
112	last coinc num chans

Usage

In [1]: import Analysis as ana

In [2]: data = ana.load_data('data/test2.acdc.dat')

In [3]: ccTimeStampLo = data['Meta'].loc[0, 5, 37] # 0th board, 5th event

In [4]: print(ccTimeStampLo)
1617

In [5]: plot = ana.plot_event(data, board=0, event=25, channel=2)