def show_wfs(dg):
"""
show waveforms in different enery regions.
use the hit file to select events
"""
# get file list and load hit data
lh5_dir = os.path.expandvars(dg.config['lh5_dir'])
hit_list = lh5_dir + dg.file_keys['hit_path'] + '/' + dg.file_keys[
'hit_file']
df_hit = lh5.load_dfs(hit_list, ['trapEmax'],
'ORSIS3302DecoderForEnergy/hit')
print(df_hit)
print(df_hit.columns)
# settings
etype = 'trapEmax_cal'
nwfs = 20
# elo, ehi, epb = 0, 100, 0.2 # low-e region
elo, ehi, epb = 0, 20, 0.2 # noise region
# elo, ehi, epb = 1458, 1468, 1 # good physics events
# elo, ehi, epb = 6175, 6250, 1 # overflow peak
# elo, ehi, epb = 5000, 5200, 0.2 # lower overflow peak
# # diagnostic plot
# hE, xE, vE = pgh.get_hist(df_hit[etype], range=(elo, ehi), dx=epb)
# plt.plot(xE[1:], hE, c='b', ds='steps')
# plt.show()
# exit()
# select waveforms
idx = df_hit[etype].loc[(df_hit[etype] >= elo)
& (df_hit[etype] <= ehi)].index[:nwfs]
raw_store = lh5.Store()
tb_name = 'ORSIS3302DecoderForEnergy/raw'
raw_list = lh5_dir + dg.file_keys['raw_path'] + '/' + dg.file_keys[
'raw_file']
f_raw = raw_list.values[0] # fixme, only works for one file rn
data_raw = raw_store.read_object(tb_name,
f_raw,
start_row=0,
n_rows=idx[-1] + 1)
wfs_all = data_raw['waveform']['values'].nda
wfs = wfs_all[idx.values, :]
ts = np.arange(0, wfs.shape[1], 1)
# plot wfs
for iwf in range(wfs.shape[0]):
plt.plot(ts, wfs[iwf, :], lw=1)
plt.xlabel('time (clock ticks)', ha='right', x=1)
plt.ylabel('ADC', ha='right', y=1)
plt.show()
def peak_drift(dg):
"""
show any drift of the 1460 peak (5 minute bins)
"""
lh5_dir = os.path.expandvars(dg.config['lh5_dir'])
hit_list = lh5_dir + dg.file_keys['hit_path'] + '/' + dg.file_keys[
'hit_file']
df_hit = lh5.load_dfs(hit_list, ['trapEmax'],
'ORSIS3302DecoderForEnergy/hit')
df_hit.reset_index(inplace=True)
rt_min = dg.file_keys['runtime'].sum()
print(f'runtime: {rt_min:.2f} min')
# settings
elo, ehi, epb, etype = 1450, 1470, 1, 'trapEmax_cal'
df_hit = df_hit.query(
f'trapEmax_cal > {elo} and trapEmax_cal < {ehi}').copy()
# # diagnostic plot
# hE, xE, vE = pgh.get_hist(df_hit[etype], range=(elo, ehi), dx=epb)
# plt.plot(xE[1:], hE, c='b', ds='steps')
# plt.show()
t0 = df_hit['ts_glo'].values[0]
df_hit['ts_adj'] = (df_hit['ts_glo'] - t0) / 60 # minutes after 0
tlo, thi, tpb = 0, df_hit['ts_adj'].max(), 1
nbx = int((thi - tlo) / tpb)
nby = int((ehi - elo) / epb)
h = plt.hist2d(df_hit['ts_adj'],
df_hit['trapEmax_cal'],
bins=[nbx, nby],
range=[[tlo, thi], [elo, ehi]],
cmap='jet')
plt.xlabel(f'Time ({tpb:.1f} min/bin)', ha='right', x=1)
plt.ylabel('trapEmax_cal', ha='right', y=1)
plt.tight_layout()
# plt.show()
plt.savefig('./plots/oppi_1460_drift.png', dpi=300)
def timepoints(runs):
for run in runs:
# get run files
dg = DataGroup('cage.json', load=True)
str_query = f'run=={run} and skip==False'
dg.fileDB.query(str_query, inplace=True)
#get runtime, startime, runtype
runtype_list = np.array(dg.fileDB['runtype'])
runtype = runtype_list[0]
rt_min = dg.fileDB['runtime'].sum()
u_start = dg.fileDB.iloc[0]['startTime']
t_start = pd.to_datetime(u_start, unit='s')
# get scan position
if runtype == 'alp':
alphaDB = pd.read_hdf('alphaDB.h5')
scan_pos = alphaDB.loc[alphaDB['run']==run]
radius = np.array(scan_pos['radius'])[0]
angle = np.array(scan_pos['angle'])[0]
angle_det = 270 + angle
print(f'Radius: {radius}; Angle: {angle}')
else:
radius = 'n/a'
angle = 'n/a'
angle_det = 'n/a'
# get hit df
lh5_dir = dg.lh5_user_dir #if user else dg.lh5_dir
hit_list = lh5_dir + dg.fileDB['hit_path'] + '/' + dg.fileDB['hit_file']
df_hit = lh5.load_dfs(hit_list, ['trapEmax', 'trapEmax_cal', 'bl','bl_sig','A_10','AoE', 'ts_sec', 'dcr_raw', 'dcr_ftp', 'dcr_max', 'tp_0', 'tp_10', 'tp_50', 'tp_90', 'tp_max'], 'ORSIS3302DecoderForEnergy/hit')
# use baseline cut
df_cut = df_hit.query('bl > 8500 and bl < 10000').copy()
#creat new DCR
const = 0.0555
df_cut['dcr_linoff'] = df_cut['dcr_raw'] + const*df_cut['trapEmax']
#create 0-50
df_cut['tp0_50'] = df_cut['tp_50']- df_cut['tp_0']
#create 10-90
df_cut['10-90'] = df_cut['tp_90']- df_cut['tp_10']
#create 50-100
df_cut['50-100'] = df_cut['tp_max']- df_cut['tp_50']
#-------------------------------------
# Plots before alpha cuts
#--------------------
# DCR vs tp_50___________
fig, ax = plt.subplots()
fig.suptitle(f'DCR vs 50% rise time', horizontalalignment='center', fontsize=16)
dlo, dhi, dpb = -100, 200, 0.6
tlo, thi, tpb = 0, 700, 10
nbx = int((dhi-dlo)/dpb)
nby = int((thi-tlo)/tpb)
alpha_dcr_hist = plt.hist2d(df_cut['dcr_linoff'], df_cut['tp0_50'], bins=[nbx,nby],
range=[[dlo, dhi], [tlo, thi]], cmap='viridis', norm=LogNorm())
cb = plt.colorbar()
cb.set_label("counts", ha = 'right', va='center', rotation=270, fontsize=14)
cb.ax.tick_params(labelsize=12)
ax.set_xlabel('DCR (arb)', fontsize=16)
ax.set_ylabel('tp 0-50 (ns)', fontsize=16)
plt.setp(ax.get_xticklabels(), fontsize=14)
plt.setp(ax.get_yticklabels(), fontsize=14)
# plt.legend()
ax.text(0.95, 0.83, f'r = {radius} mm \ntheta = {angle_det} deg', verticalalignment='bottom',
horizontalalignment='right', transform=ax.transAxes, color='green', fontsize=14, bbox={'facecolor': 'white', 'alpha': 0.95, 'pad': 10})
plt.title(f'\n{runtype} run {run}, {rt_min:.2f} mins', fontsize=12)
plt.tight_layout()
plt.savefig(f'./plots/normScan/cal_normScan/{runtype}_dcr_vs_tp0_50_run{run}.png', dpi=200)
# plt.show()
plt.clf()
plt.close()
# DCR vs 10-90___________
fig, ax = plt.subplots()
fig.suptitle(f'DCR vs 10-90% rise time', horizontalalignment='center', fontsize=16)
dlo, dhi, dpb = -100, 200, 0.6
tlo, thi, tpb = 0, 600, 10
nbx = int((dhi-dlo)/dpb)
nby = int((thi-tlo)/tpb)
alpha_dcr_hist = plt.hist2d(df_cut['dcr_linoff'], df_cut['10-90'], bins=[nbx,nby],
range=[[dlo, dhi], [tlo, thi]], cmap='viridis', norm=LogNorm())
cb = plt.colorbar()
cb.set_label("counts", ha = 'right', va='center', rotation=270, fontsize=14)
cb.ax.tick_params(labelsize=12)
ax.set_xlabel('DCR (arb)', fontsize=16)
ax.set_ylabel('tp 10-90 (ns)', fontsize=16)
plt.setp(ax.get_xticklabels(), fontsize=14)
plt.setp(ax.get_yticklabels(), fontsize=14)
# plt.legend()
ax.text(0.95, 0.83, f'r = {radius} mm \ntheta = {angle_det} deg', verticalalignment='bottom',
horizontalalignment='right', transform=ax.transAxes, color='green', fontsize=14, bbox={'facecolor': 'white', 'alpha': 0.95, 'pad': 10})
plt.title(f'\n{runtype} run {run}, {rt_min:.2f} mins', fontsize=12)
plt.tight_layout()
plt.savefig(f'./plots/normScan/cal_normScan/{runtype}_dcr_vs_tp10_90_run{run}.png', dpi=200)
# plt.show()
plt.clf()
plt.close()
# DCR vs 50-100___________
fig, ax = plt.subplots()
fig.suptitle(f'DCR vs 50-100% rise time', horizontalalignment='center', fontsize=16)
dlo, dhi, dpb = -100, 200, 0.6
tlo, thi, tpb = 0, 1000, 10
nbx = int((dhi-dlo)/dpb)
nby = int((thi-tlo)/tpb)
alpha_dcr_hist = plt.hist2d(df_cut['dcr_linoff'], df_cut['50-100'], bins=[nbx,nby],
range=[[dlo, dhi], [tlo, thi]], cmap='viridis', norm=LogNorm())
cb = plt.colorbar()
cb.set_label("counts", ha = 'right', va='center', rotation=270, fontsize=14)
cb.ax.tick_params(labelsize=12)
ax.set_xlabel('DCR (arb)', fontsize=16)
ax.set_ylabel('tp 10-90 (ns)', fontsize=16)
plt.setp(ax.get_xticklabels(), fontsize=14)
plt.setp(ax.get_yticklabels(), fontsize=14)
# plt.legend()
ax.text(0.95, 0.83, f'r = {radius} mm \ntheta = {angle_det} deg', verticalalignment='bottom',
horizontalalignment='right', transform=ax.transAxes, color='green', fontsize=14, bbox={'facecolor': 'white', 'alpha': 0.95, 'pad': 10})
plt.title(f'\n{runtype} run {run}, {rt_min:.2f} mins', fontsize=12)
plt.tight_layout()
plt.savefig(f'./plots/normScan/cal_normScan/{runtype}_dcr_vs_tp50_100_run{run}.png', dpi=200)
# plt.show()
plt.clf()
plt.close()
def pole_zero(dg):
"""
"""
# load hit data
lh5_dir = os.path.expandvars(dg.config['lh5_dir'])
hit_list = lh5_dir + dg.file_keys['hit_path'] + '/' + dg.file_keys[
'hit_file']
df_hit = lh5.load_dfs(hit_list, ['trapEmax'],
'ORSIS3302DecoderForEnergy/hit')
df_hit.reset_index(inplace=True)
rt_min = dg.file_keys['runtime'].sum()
# print(f'runtime: {rt_min:.2f} min')
# load waveforms
etype = 'trapEmax_cal'
nwfs = 20
elo, ehi = 1455, 1465
# select waveforms
idx = df_hit[etype].loc[(df_hit[etype] >= elo)
& (df_hit[etype] <= ehi)].index[:nwfs]
raw_store = lh5.Store()
tb_name = 'ORSIS3302DecoderForEnergy/raw'
raw_list = lh5_dir + dg.file_keys['raw_path'] + '/' + dg.file_keys[
'raw_file']
f_raw = raw_list.values[0] # fixme, only works for one file rn
data_raw = raw_store.read_object(tb_name,
f_raw,
start_row=0,
n_rows=idx[-1] + 1)
wfs_all = data_raw['waveform']['values'].nda
wfs = wfs_all[idx.values, :]
df_wfs = pd.DataFrame(wfs)
# print(df_wfs)
# simple test function to compute pole-zero constant for a few wfs.
# the final one should become a dsp processor
clock = 1e8 # 100 MHz
istart = 5000
iwinlo, iwinhi, iwid = 500, 2500, 20 # two-point slope
# ts = np.arange(istart, df_wfs.shape[1]-1, 1) / 1e3 # usec
ts = np.arange(0, df_wfs.shape[1] - 1 - istart, 1) / 1e3 # usec
def get_rc(row):
# two-point method
wf = row[istart:-1].values
wflog = np.log(wf)
win1 = np.mean(np.log(row[istart + iwinlo:istart + iwinlo + iwid]))
win2 = np.mean(np.log(row[istart + iwinhi:istart + iwinhi + iwid]))
slope = (win2 - win1) / (ts[iwinhi] - ts[iwinlo])
tau = 1 / slope
# # diagnostic plot: check against expo method
# guess_tau = 60
# a = wf.max()
# expdec = lambda x : a * np.exp(-x / guess_tau)
# logdec = lambda x : np.log(a * np.exp(-x / guess_tau))
# slopeway = lambda x: wflog[0] + x / tau
# plt.plot(ts, wflog, '-r', lw=1)
# plt.plot(ts, logdec(ts), '-b', lw=1)
# plt.plot(ts, slopeway(ts), '-k', lw=1)
# plt.show()
# exit()
return tau
# return tau
res = df_wfs.apply(get_rc, axis=1)
tau_avg, tau_std = res.mean(), res.std()
print(f'average RC decay constant: {tau_avg:.2f} pm {tau_std:.2f}')
def data_cleaning(dg):
"""
using parameters in the hit file, plot 1d and 2d spectra to find cut values.
columns in file:
['trapE', 'bl', 'bl_sig', 'A_10', 'AoE', 'packet_id', 'ievt', 'energy',
'energy_first', 'timestamp', 'crate', 'card', 'channel', 'energy_cal',
'trapE_cal']
note, 'energy_first' from first value of energy gate.
"""
i_plot = 0 # run all plots after this number
# get file list and load hit data
lh5_dir = os.path.expandvars(dg.config['lh5_dir'])
hit_list = lh5_dir + dg.file_keys['hit_path'] + '/' + dg.file_keys[
'hit_file']
df_hit = lh5.load_dfs(hit_list, ['trapEmax'],
'ORSIS3302DecoderForEnergy/hit')
# print(df_hit)
print(df_hit.columns)
# get info about df -- 'describe' is very convenient
dsc = df_hit[['bl', 'bl_sig', 'A_10', 'ts_sec']].describe()
# print(dsc)
# exit()
if i_plot <= 0:
# bl vs energy
elo, ehi, epb = 0, 50, 1
blo, bhi, bpb = 0, 10000, 100
nbx = int((ehi - elo) / epb)
nby = int((bhi - blo) / bpb)
h = plt.hist2d(df_hit['trapEmax_cal'],
df_hit['bl'],
bins=[nbx, nby],
range=[[elo, ehi], [blo, bhi]],
cmap='jet')
cb = plt.colorbar(h[3], ax=plt.gca())
plt.xlabel('trapEmax_cal', ha='right', x=1)
plt.ylabel('bl', ha='right', y=1)
plt.tight_layout()
# plt.show()
plt.savefig('./plots/oppi_bl_vs_e.png', dpi=300)
cb.remove()
plt.cla()
# make a formal baseline cut from 1d histogram
hE, bins, vE = pgh.get_hist(df_hit['bl'], range=(blo, bhi), dx=bpb)
xE = bins[1:]
plt.semilogy(xE, hE, c='b', ds='steps')
bl_cut_lo, bl_cut_hi = 8000, 9500
plt.axvline(bl_cut_lo, c='r', lw=1)
plt.axvline(bl_cut_hi, c='r', lw=1)
plt.xlabel('bl', ha='right', x=1)
plt.ylabel('counts', ha='right', y=1)
# plt.show()
plt.savefig('./plots/oppi_bl_cut.png')
plt.cla()
if i_plot <= 1:
# A_10/trapEmax_cal vs trapEmax_cal (A/E vs E)
# use baseline cut
df_cut = df_hit.query('bl > 8000 and bl < 9500').copy()
# add new A/E column
df_cut['aoe'] = df_cut['A_10'] / df_cut['trapEmax_cal']
# alo, ahi, apb = -1300, 350, 1
# elo, ehi, epb = 0, 250, 1
alo, ahi, apb = 0, 0.4, 0.005
# elo, ehi, epb = 0, 3000, 10
elo, ehi, epb = 0, 6000, 10
nbx = int((ehi - elo) / epb)
nby = int((ahi - alo) / apb)
h = plt.hist2d(df_cut['trapEmax_cal'],
df_cut['aoe'],
bins=[nbx, nby],
range=[[elo, ehi], [alo, ahi]],
cmap='jet',
norm=LogNorm())
plt.xlabel('trapEmax_cal', ha='right', x=1)
plt.ylabel('A/E', ha='right', y=1)
plt.tight_layout()
# plt.show()
plt.savefig('./plots/oppi_aoe_vs_e.png', dpi=300)
plt.cla()
if i_plot <= 2:
# show effect of baseline cut on low-energy spectrum
df_cut = df_hit.query('bl > 8000 and bl < 9500')
etype = 'trapEmax_cal'
elo, ehi, epb = 0, 250, 0.5
# no cuts
h1, x1, v1 = pgh.get_hist(df_hit[etype], range=(elo, ehi), dx=epb)
x1 = x1[1:]
plt.plot(x1, h1, c='k', lw=1, ds='steps', label='raw')
# baseline cut
h2, x2, v2 = pgh.get_hist(df_cut[etype], range=(elo, ehi), dx=epb)
plt.plot(x1, h2, c='b', lw=1, ds='steps', label='bl cut')
plt.xlabel(etype, ha='right', x=1)
plt.ylabel('counts', ha='right', y=1)
plt.legend()
# plt.show()
plt.savefig('./plots/oppi_lowe_cut.png')
plt.cla()
if i_plot <= 3:
# show DCR vs E
etype = 'trapEmax_cal'
elo, ehi, epb = 0, 6000, 10
dlo, dhi, dpb = -1000, 1000, 10
nbx = int((ehi - elo) / epb)
nby = int((dhi - dlo) / dpb)
h = plt.hist2d(df_cut['trapEmax_cal'],
df_cut['dcr'],
bins=[nbx, nby],
range=[[elo, ehi], [dlo, dhi]],
cmap='jet',
norm=LogNorm())
plt.xlabel('trapEmax_cal', ha='right', x=1)
plt.ylabel('DCR', ha='right', y=1)
plt.tight_layout()
# plt.show()
plt.savefig('./plots/oppi_dcr_vs_e.png', dpi=300)
plt.cla()