Strip Detector

this notebook loads data acquired with a segmented silicon detector

import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mp
import copy
import re
import holoviews as hv
from holoviews import opts
hv.extension('bokeh')

#shot_no = 37021
shot_no = 43753

Download logfile and time of plasma start and plasma end

log_URL = "http://golem.fjfi.cvut.cz/shots/{shot_no}/Diagnostics/StripDetector/PH32Det{det}_{shot_no}.log"
plasma_URL = "http://golem.fjfi.cvut.cz/shots/{}/Diagnostics/PlasmaDetection/Results/{}"

ds = np.DataSource(None)

# get logs from web
logfile0 = ds.open(log_URL.format(shot_no=shot_no, det=0))
logfile1 = ds.open(log_URL.format(shot_no=shot_no, det=1))

# get logs from local storage
#logfile0 = ds.open(f"PH32Det0_{shot_no}.log")
#logfile1 = ds.open(f"PH32Det1_{shot_no}.log")

try:
    t_plasma_start = ds.open(plasma_URL.format(shot_no, "t_plasma_start"))
    t_plasma_start = np.loadtxt(t_plasma_start)

    t_plasma_end = ds.open(plasma_URL.format(shot_no, "t_plasma_end"))
    t_plasma_end = np.loadtxt(t_plasma_end)
except OSError:
    t_plasma_start = -1.0
    t_plasma_end = -1.0

Reading log file

Communication between rPi (or any other computer) and PH32 readout chip is achieved with SURE (Simple USB Readout Equipment).

SURE uses NMEA 0183 protocol for communication. Each message for configuring PH32 is acknowledged. These 'echos' are saved in the log file.
For example $ACKNL,SEATM -- acquisition time set to 100us*1b is response to $SEATM,100 command which sets acquisition time.
$STARB,[wait-for-trigger],[delay] sets detector for measurement and is fololowed by 200 frames of measured data eg.: $FRAME,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0*5d

From log file we extract

• acquisition time of one frame
• delay between frames

• acquisition mode

  • hit count
  • ToT = Time over threshold $\sim$ deposited energy
  • ToT-First $\sim$ deposited energy of first detected particle
  • ToA = Time of Arrival (for time of flight measeruement)

• frames

  • for each strip, we get a value in the range 0-65534. Depending on the acquisition mode, this represents
    • number of hist
    • number of clock cycles
    • value 65535 is used in case of overflow, channel saturation, or when a channel is disabled

def parseLog(logfile):

    frames = []

    # defaults
    acc_time    = 0.15 #ms
    delay       = 0.10 #ms
    start_delay = 0.50 #ms
    acc_mode = ['hit count'] * 32

    # read logfile
    for line in logfile:
        regex_seatm = re.match("\$ACKNL,SEATM -- acquisition time set to (\d+)us", line)
        if regex_seatm:
            acc_time = regex_seatm.group(1) 
            acc_time = int(acc_time)/1000.0 ## ms
                
        regex_delay = re.match("\$ACKNL,DELAY -- delay set to (\d+)us", line)
        if regex_delay:
            delay = regex_delay.group(1)
            delay = int(delay)/1000.0 ## ms
        
        regex_selo1= re.match(
            "\$ACKNL,SELO1 -- local configuration for channel (\d+) was set to value (\d+)", line)
        if regex_selo1:
            channel = int(regex_selo1.group(1))
            if (int(regex_selo1.group(2)) & 0x60) == 0x60 :
                acc_mode[channel] = "ToA"
            elif (int(regex_selo1.group(2)) & 0x20) == 0x20:
                acc_mode[channel] = "ToT-First"
            elif (int(regex_selo1.group(2)) & 0x40) == 0x40:
                acc_mode[channel] = "ToT"
            else:
                acc_mode[channel] = "hit count"

        regex_startb = re.match("\$STARB,(\d+),(\d+)", line)
        if regex_startb:
            start_delay = regex_startb.group(2)
            start_delay = int(start_delay)/1000.0 ## ms
                            

        regex_frame = re.match("\$FRAME,((?:\d+,)+)(\d+)\*[0-9a-f]{2}", line)
        if regex_frame:
            line = np.fromstring(''.join(regex_frame.groups()), sep=',')
            ### saturation or error in measeurement or chanel is disabled
            line[line == 65535] = np.nan
            frames.append(line)
                
    frames = np.array(frames)
    
    time_btw_frames = acc_time + delay
    
    return {'start_delay':start_delay,
            'acc_time':acc_time,
            'delay':delay,
            'acc_mode':acc_mode,
            'time_btw_frames': time_btw_frames,
            'frames':frames}

Plot raw data

detector0 = parseLog(logfile0)
detector1 = parseLog(logfile1)

framesCombined = np.concatenate((detector0["frames"].T,detector1["frames"].T))
plt.pcolormesh(framesCombined)
plt.axhline(32,color = 'k')
plt.axis('off')
plt.savefig('rawdata.png')

Rearrange channels

The order of channels on the PH32 chip does not correspond to the order of strips on the sensor; we need to rearrange them.
Strip n. 0 is on channel 15, strip n. 1 on channel 16, ...

channelOrder = [15,16,14,17,13,18,12,19,11,20,10,21,9,22,8,23,24,7,25,6,26,5,27,4,28,3,29,2,30,1,31,0]

def rearrangeFrames(frames):
    permutation_matrix = np.zeros((len(channelOrder), len(channelOrder)))
    for idx, i in enumerate(channelOrder):
        permutation_matrix[i, idx] = 1

    frames = np.dot(frames, permutation_matrix)
    return frames

## rearange acc_mode
def rearrangeModes(acc_mode):
    new_acc_mode = [''] *32
    for new, old in enumerate(channelOrder):
        new_acc_mode[new] = acc_mode[old]

    acc_mode = new_acc_mode
    return acc_mode

for detector in [detector0, detector1]:
    detector['frames'] = rearrangeFrames(detector['frames'])
    detector['acc_mode'] = rearrangeModes(detector['acc_mode'])

def addMasks(detector):
    acc_mode = detector['acc_mode']
    detector["mask_hit"]  = np.array([ch == 'hit count' for ch in acc_mode])
    detector["mask_ToT"]  = np.array([ch == 'ToT' for ch in acc_mode])
    detector["mask_fToT"] = np.array([ch == 'ToT-First' for ch in acc_mode])
    detector["mask_ToA"]  = np.array([ch == 'ToA' for ch in acc_mode])

# test if list (mask) consists of same values
def all_eq(iterable, val):
    return all([i == val for i in iterable])

[addMasks(detector) for detector in [detector0, detector1]];

[None, None]

For energy measeuremets convert number of clock cycles to deposited energy

energy_calib0 = 115.26 #keV/clock
energy_calib1 = 79.82 #keV/clock

def applyECalib(detector, calib):
    mask_energy = detector["mask_ToT"] & detector["mask_fToT"]
    detector["frames"][:,mask_energy] *= calib
    
applyECalib(detector0, energy_calib0)
applyECalib(detector1, energy_calib1)

Convert absolute ToA values to relative
from each frame subtract minimal ToA -> compare ToA between channels

def shiftToA(detector):
    frames = detector['frames']
    mask_ToA = detector["mask_ToA"]
    if not all_eq(mask_ToA, False):
        detector["frames"][:,mask_ToA] -= np.nanmin(frames[:,mask_ToA],axis = 1)[:, np.newaxis]

Plot result

About half of the frames are usually empty because the detector records after plasma end.
Plot only when plasma was present

def mplPlot(ax, frames, acc_mode, start_delay, time_btw_frames, acc_time, **kwargs):
    y_tics = np.arange(0,32,1)
    t_tics = np.arange(0, frames.shape[0]*time_btw_frames, time_btw_frames)
    t_tics += start_delay

    cmap = copy.copy(mp.cm.get_cmap("viridis"))
    
    # bad values in grey
    cmap.set_under("grey") 

    im = ax.pcolormesh(t_tics, y_tics, frames.T, vmin = 0, vmax = np.amax(frames[5:80]),
                    cmap=cmap, shading = 'auto')
    ax.set_ylabel("strip n.")
    ax.set_xlabel("time [ms]")

    if t_plasma_start != -1.0 and t_plasma_start != -1.0:
        ax.set_xlim(start_delay, t_plasma_end + 2)
        ax.axvline(t_plasma_start, color = "white", dashes=[1, 2], label='plasma start')
        ax.axvline(t_plasma_end, color = "white", dashes=[1, 2], label='plasma end')
    else:
        ax.set_xlim(start_delay, 30.0)
    cbar = fig.colorbar(im, ax=ax)
    if all_eq(acc_mode, "hit count"):
        cbar.set_label(acc_mode)
    elif all_eq(acc_mode, "ToT"):
        cbar.set_label("Deposited energy [keV/{}ms]".format(acc_time))
    elif all_eq(acc_mode,"first hit"):
        cbar.set_label("First hit energy [kev]")
    else:
        cbar.set_label("mix")


fig, axes = plt.subplots(nrows=2, figsize = (8,10))
[mplPlot(ax, **detector) for ax, detector in zip(axes, [detector0, detector1])]
fig.savefig('icon-fig.png')

def hvCurvePlot(frames, acc_mode, start_delay, time_btw_frames, mask_hit, 
                mask_ToT, mask_fToT, mask_ToA, **kwargs):

    t_tics = np.arange(0, frames.shape[0]*time_btw_frames, time_btw_frames)
    t_tics += start_delay

    hit_curves       = [hv.Curve((t_tics,ch), 't [ms]', 'hit count') for ch in frames[:,mask_hit].T]
    tot_curves       = [hv.Curve((t_tics,ch), 't [ms]', 'E_dep [keV]') for ch in frames[:,mask_ToT].T]
    first_hit_curves = [hv.Curve((t_tics,ch), 't [ms]', 'E_first [keV]') for ch in frames[:,mask_fToT].T]
    toa_curves       = [hv.Curve((t_tics,ch), 't [ms]', '\Delta t') for ch in frames[:,mask_ToA].T]


    hit_overlay = hv.Overlay(hit_curves) if hit_curves else hv.Empty()
    tot_overlay = hv.Overlay(tot_curves) if tot_curves else hv.Empty()
    first_hit_overlay = hv.Overlay(first_hit_curves) if first_hit_curves else hv.Empty()
    toa_overlay = hv.Overlay(toa_curves) if toa_curves else hv.Empty()

    curve_plot = hv.Layout(hit_overlay + tot_overlay + first_hit_overlay + toa_overlay).cols(1)

    x_max = t_plasma_end + 2 if t_plasma_end != -1.0 else 30.0
    curve_plot.opts(opts.Overlay(height=200, width=450, xlim=(start_delay, x_max)))
    return curve_plot

hv.Layout(hvCurvePlot(**detector0) + hvCurvePlot(**detector1)).opts(transpose=True)

def hvMeshPlot(frames, acc_mode, start_delay, time_btw_frames, mask_hit, 
                mask_ToT, mask_fToT, mask_ToA, **kwargs):
        
    t_tics = np.arange(0, frames.shape[0]*time_btw_frames, time_btw_frames)
    t_tics += start_delay            

    masks  = [mask_hit, mask_ToT, mask_fToT, mask_ToA]
    titles = ['hit count', 'E_dep', 'E_first-dep',  '\Delta t' ]
    meshes = [hv.Image(frames[:,mask].T, bounds=(t_tics[0],0,t_tics[-1],np.sum(mask))).opts(
                    title = title, ylabel='channel', xlabel='t [ms]')
            if frames[:,mask].size else hv.Empty() for mask, title in zip(masks,titles)]

    mesh_plot = hv.Layout(meshes).cols(1)

    x_max = t_plasma_end + 2 if t_plasma_end != -1.0 else 30.0
    mesh_plot.opts(opts.Image(height=200, width=450, xlim=(start_delay, x_max)))
    return mesh_plot

hv.Layout(hvMeshPlot(**detector0) + hvMeshPlot(**detector1)).opts(transpose=True)