def aliasing_filter(det):
'''
We know there's a 1-pole antialiasing filter at ~1/(2 * 49.9 * 33E-12) Hz
How big a difference should that make?
'''
lowpass = DigitalFilter(2)
lowpass.num = [1,2,1]
lowpass.set_poles(0.975, 0.007)
hipass = DigitalFilter(1)
hipass.num, hipass.den = rc_decay(72, 1E9)
det.AddDigitalFilter(lowpass)
det.AddDigitalFilter(hipass)
wf_proc = np.copy(det.MakeSimWaveform(25, 0, 25, 1, 125, 0.95, wf_length, smoothing=20))
wf_compare = np.copy(wf_proc)
f, ax = plt.subplots(2,1,figsize=(15,8))
ax[0].plot (wf_compare, color="r")
lopass2 = DigitalFilter(1)
lopass2.num = [1,1]
rc = (2 * 49.9 * 33E-12)
lopass2.den = [1, -np.exp( -1./rc/1E9)]
det.AddDigitalFilter(lopass2)
wf_proc = np.copy(det.MakeSimWaveform(25, 0, 25, 1, 125, 0.95, wf_length, smoothing=20))
ax[0].plot (wf_proc, color="g")
ax[1].plot (wf_compare-wf_proc, color="g")
plt.show()
exit()
def skew():
lowpass = DigitalFilter(2)
lowpass.num = [1, 2, 1]
lowpass.set_poles(0.975, 0.007)
hipass = DigitalFilter(2)
hipass.num = [1, -2, 1]
hipass.set_poles(1. - 10.**-7, np.pi**-13.3)
det.AddDigitalFilter(lowpass)
det.AddDigitalFilter(hipass)
wf_proc = np.copy(
det.MakeSimWaveform(25, 0, 25, 1, 125, 0.95, 1000, smoothing=25))
wf_compare = np.copy(wf_proc)
f, ax = plt.subplots(2, 1, figsize=(15, 8), sharex=True)
ax[0].plot(wf_compare, color="r")
overshoot = GretinaOvershootFilter(1)
overshoot.num, overshoot.den = rc_decay(1.7, 1E9)
det.AddDigitalFilter(overshoot)
for frac in np.linspace(0.01, 0.05, 5):
overshoot.overshoot_frac = frac
wf_proc = np.copy(
det.MakeSimWaveform(25, 0, 25, 1, 125, 0.95, 1000, smoothing=25))
ax[0].plot(wf_proc)
ax[1].plot(wf_proc - wf_compare)
plt.show()
def min_func(x):
rc_decay, overshoot_decay, overshoot_pole_rel, energy = x
# rc_decay, overshoot_decay, overshoot_pole_rel, energy, long_rc = x
rc_num, rc_den = filt.rc_decay(rc_decay*10)
wf_proc1 = signal.lfilter(rc_den, rc_num, wf_data)
# long_rc_num, long_rc_den = filt.rc_decay(long_rc*1000)
# wf_proc1 = signal.lfilter(long_rc_den, long_rc_num, wf_proc1)
overshoot_num, overshoot_den = filt.gretina_overshoot(overshoot_decay, overshoot_pole_rel)
wf_proc = signal.lfilter(overshoot_den, overshoot_num, wf_proc1)
tail_data = wf_proc[max_idx:]
flat_line = np.ones(len(tail_data))*energy*1000
return np.sum((tail_data-flat_line)**2)
def min_func(x):
rc_decay, overshoot_decay, overshoot_pole_rel, energy = x
# rc_decay, overshoot_decay, overshoot_pole_rel, energy, long_rc = x
rc_num, rc_den = filt.rc_decay(rc_decay * 10)
wf_proc1 = signal.lfilter(rc_den, rc_num, wf_data)
# long_rc_num, long_rc_den = filt.rc_decay(long_rc*1000)
# wf_proc1 = signal.lfilter(long_rc_den, long_rc_num, wf_proc1)
overshoot_num, overshoot_den = filt.gretina_overshoot(
overshoot_decay, overshoot_pole_rel)
wf_proc = signal.lfilter(overshoot_den, overshoot_num, wf_proc1)
tail_data = wf_proc[max_idx:]
flat_line = np.ones(len(tail_data)) * energy * 1000
return np.sum((tail_data - flat_line)**2)
def fit_tail(wf_data):
'''
try to fit out the best tail parameters to flatten the top
'''
max_idx = np.argmax(wf_data)
def min_func(x):
rc_decay, overshoot_decay, overshoot_pole_rel, energy = x
# rc_decay, overshoot_decay, overshoot_pole_rel, energy, long_rc = x
rc_num, rc_den = filt.rc_decay(rc_decay*10)
wf_proc1 = signal.lfilter(rc_den, rc_num, wf_data)
# long_rc_num, long_rc_den = filt.rc_decay(long_rc*1000)
# wf_proc1 = signal.lfilter(long_rc_den, long_rc_num, wf_proc1)
overshoot_num, overshoot_den = filt.gretina_overshoot(overshoot_decay, overshoot_pole_rel)
wf_proc = signal.lfilter(overshoot_den, overshoot_num, wf_proc1)
tail_data = wf_proc[max_idx:]
flat_line = np.ones(len(tail_data))*energy*1000
return np.sum((tail_data-flat_line)**2)
wf_max = wf_data.max()/1000
res = optimize.minimize(min_func, [7.2, 2, -4, wf_max], method="Powell")
# print(res["x"])
rc1, rc2, f, e = res["x"]
rc1*=10
rc_num, rc_den = filt.rc_decay(rc1)
wf_proc1 = signal.lfilter(rc_den, rc_num, wf_data)
# long_rc*=1000
# long_rc_num, long_rc_den = filt.rc_decay(long_rc)
# wf_proc1 = signal.lfilter(long_rc_den, long_rc_num, wf_proc1)
overshoot_num, overshoot_den = filt.gretina_overshoot(rc2, f)
wf_proc = signal.lfilter(overshoot_den, overshoot_num, wf_proc1)
return wf_proc, (e*1000), res["fun"]
def two_rc(det):
'''
WHATS IT LOOK LIKE WITH A 72 US AND 2 MS DECAY?
'''
lowpass = DigitalFilter(2)
lowpass.num = [1, 2, 1]
lowpass.set_poles(0.975, 0.007)
hipass = DigitalFilter(1)
hipass.num, hipass.den = rc_decay(82, 1E9)
det.AddDigitalFilter(lowpass)
det.AddDigitalFilter(hipass)
wf_proc = np.copy(
det.MakeSimWaveform(25, 0, 25, 1, 125, 0.95, wf_length, smoothing=20))
wf_compare = np.copy(wf_proc)
f, ax = plt.subplots(2, 1, figsize=(15, 8))
ax[0].plot(wf_compare, color="r")
# hipass2 = DigitalFilter(1)
# hipass2.num, hipass2.den = rc_decay(2000, 1E9)
# hipass.num, hipass.den = rc_decay(74.75, 1E9)
# det.AddDigitalFilter(hipass2)
mag = 1. - 10.**-5.22
phi = np.pi**-13.3
det.RemoveDigitalFilter(hipass)
hipass = DigitalFilter(2)
hipass.num = [1, -2, 1]
hipass.set_poles(mag, phi)
det.AddDigitalFilter(hipass)
wf_proc = np.copy(
det.MakeSimWaveform(25, 0, 25, 1, 125, 0.95, wf_length, smoothing=20))
ax[0].plot(wf_proc, color="g")
ax[1].plot(wf_compare - wf_proc, color="g")
plt.show()
exit()
def fit_tail(wf_data):
'''
try to fit out the best tail parameters to flatten the top
'''
max_idx = np.argmax(wf_data)
def min_func(x):
rc_decay, overshoot_decay, overshoot_pole_rel, energy = x
# rc_decay, overshoot_decay, overshoot_pole_rel, energy, long_rc = x
rc_num, rc_den = filt.rc_decay(rc_decay * 10)
wf_proc1 = signal.lfilter(rc_den, rc_num, wf_data)
# long_rc_num, long_rc_den = filt.rc_decay(long_rc*1000)
# wf_proc1 = signal.lfilter(long_rc_den, long_rc_num, wf_proc1)
overshoot_num, overshoot_den = filt.gretina_overshoot(
overshoot_decay, overshoot_pole_rel)
wf_proc = signal.lfilter(overshoot_den, overshoot_num, wf_proc1)
tail_data = wf_proc[max_idx:]
flat_line = np.ones(len(tail_data)) * energy * 1000
return np.sum((tail_data - flat_line)**2)
wf_max = wf_data.max() / 1000
res = optimize.minimize(min_func, [7.2, 2, -4, wf_max], method="Powell")
# print(res["x"])
rc1, rc2, f, e = res["x"]
rc1 *= 10
rc_num, rc_den = filt.rc_decay(rc1)
wf_proc1 = signal.lfilter(rc_den, rc_num, wf_data)
# long_rc*=1000
# long_rc_num, long_rc_den = filt.rc_decay(long_rc)
# wf_proc1 = signal.lfilter(long_rc_den, long_rc_num, wf_proc1)
overshoot_num, overshoot_den = filt.gretina_overshoot(rc2, f)
wf_proc = signal.lfilter(overshoot_den, overshoot_num, wf_proc1)
return wf_proc, (e * 1000), res["fun"]
def main():
runList = [848]
plt.ion()
f = plt.figure(figsize=(12,9))
rc_num, rc_den = filt.rc_decay(72)
overshoot_num, overshoot_den = filt.gretina_overshoot(2, -3.5)
for runNumber in runList:
t1_file = "t1_run{}.h5".format(runNumber)
df = pd.read_hdf(t1_file,key="ORGretina4MWaveformDecoder")
g4 = dl.Gretina4MDecoder(t1_file)
chanList = [49]#np.unique(df["channel"])
for chan in chanList:
df_chan = df[df.channel == chan]
for i, (index, row) in enumerate(df_chan.iterrows()):
if i<10: continue
wf = g4.parse_event_data(row)
wf_dat = wf.data - np.mean(wf.data[:200])
if np.amax(wf_dat) < 200: continue
if np.count_nonzero( wf_dat > 0.5*wf_dat.max()) < 10: continue
max_idx = np.argmax(wf_dat>20)
wf_corr, model, t0 = fit_tail(wf_dat, max_idx)
plt.plot(wf_dat)
plt.plot(wf_corr)
plt.plot(np.arange(max_idx+t0, max_idx+t0+len(model)), model, c="r")
plt.axvline(max_idx,c="r", ls=":")
inp = input("q to continue, else to quit")
if inp == "q": exit()
def main():
runList = np.arange(11515, 11516)
proc = DataProcessor()
plt.ion()
f = plt.figure(figsize=(12,9))
rc_num, rc_den = filt.rc_decay(72)
overshoot_num, overshoot_den = filt.gretina_overshoot(2, -3.5)
# print(overshoot_num, overshoot_den)
# exit()
ds_inf = pd.read_csv("ds1_run_info.csv")
for runNumber in runList:
t1_file = os.path.join(proc.t1_data_dir, "t1_run{}.h5".format(runNumber))
df = pd.read_hdf(t1_file,key="ORGretina4MWaveformDecoder")
g4 = dl.Gretina4MDecoder(t1_file)
chanList = np.unique(df["channel"])
chanList = [ 580]
pz_fun = []
masses = []
resolutions = []
for chan in chanList:
print ("Channel {}".format(chan))
try:
ds_inf_det = ds_inf[(ds_inf.LG==chan) | (ds_inf.HG==chan) ]
det_mass = ds_inf_det.iloc[0].Mass
det_res = ds_inf_det.iloc[0].Resolution
det_ctres = ds_inf_det.iloc[0].ct_resolution
trap_factor = (det_res-det_ctres)/det_ctres
except IndexError:
print ("...couldn't find channel info")
if chan%2 == 1: continue
if not is_mj(chan): continue
plt.clf()
ax1 = plt.subplot(2,2,2)
ax2 = plt.subplot(2,2,4)
ax3 = plt.subplot(2,2,3)
ax4 = plt.subplot(2,2,1)
df_chan = df[ (df.channel == chan) & (df.energy > 0.2E9) ]
e_min = df_chan.energy.min()
e_max = df_chan.energy.max()
e_cut = 0.9*(e_max-e_min) + e_min
df_cut = df_chan[df_chan.energy > e_cut]
bl_idx = 800
baseline = np.zeros(bl_idx)
flat_top = np.zeros(800)
# plt.figure()
num_wfs = 0
for i, (index, row) in enumerate(df_cut.iterrows()):
wf = g4.parse_event_data(row)
wf_dat = wf.data - np.mean(wf.data[:bl_idx])
try:
wf_corr, energy, gof = fit_tail(wf_dat)
align_idx = np.argmax(wf_corr/energy>0.999)+20
flat_top_wf = wf_corr[align_idx:align_idx+800] - energy
baseline += wf_dat[:bl_idx]
flat_top += flat_top_wf
num_wfs +=1
except ValueError as e:
print(e)
continue
ax1.plot(wf_corr[:800], c="b", alpha=0.1 )
ax2.plot(flat_top_wf, c="b", alpha=0.1 )
ax4.plot(wf_corr[align_idx-400:align_idx+805]/energy, c="b", alpha=0.1 )
# plt.plot(flat_top_wf, c="b", alpha=0.1)
# if num_wfs < 5: continue
flat_top /=num_wfs
baseline /= num_wfs
pz_fun.append( np.sum(flat_top**2) )
masses.append(det_res)
ax1.set_title("Baseline")
ax1.plot(baseline, c="r")
ax2.set_title("Decay (PZ corrected)")
ax2.plot(flat_top, c="r")
# plt.plot(flat_top, c="r")
plt.title("Channel {} (mass {}, res {})".format(chan, det_mass, det_res))
# ax1.plot(baseline, label="baseline")
# ax1.plot(flat_top - np.mean(flat_top), label="flat top")
xf,power = signal.periodogram(baseline, fs=1E8, detrend="linear", scaling="spectrum")
x_idx = np.argmax(xf>0.2E7)
ax3.semilogx(xf[x_idx:],power[x_idx:], label="baseline")
max_pwr = power.max()
# ax2.plot(flat_top)
xf,power = signal.periodogram(flat_top, fs=1E8, detrend="constant", scaling="spectrum")
x_idx = np.argmax(xf>0.2E7)
ax3.semilogx(xf[x_idx:],power[x_idx:], label="flat top")
ax3.legend()
plt.savefig("fft_plots/channel{}_fft.png".format(chan))
# inp = input("q to continue, else to quit")
# if inp == "q": exit()
plt.figure()
plt.scatter(masses, pz_fun)
inp = input("q to continue, else to quit")
if inp == "q": exit()
def overshoot(det):
'''
How do I get me a decaying overshoot?
'''
lowpass = DigitalFilter(2)
lowpass.num = [1, 2, 1]
lowpass.set_poles(0.975, 0.007)
hipass = DigitalFilter(1)
hipass.num, hipass.den = rc_decay(82, 1E9)
det.AddDigitalFilter(lowpass)
# det.AddDigitalFilter(hipass)
wf_proc = np.copy(
det.MakeSimWaveform(25, 0, 25, 1, 125, 0.95, wf_length, smoothing=20))
wf_compare = np.copy(wf_proc)
f, ax = plt.subplots(2, 2, figsize=(15, 8))
# plt.figure()
ax[0, 0].plot(wf_compare, color="r")
cmap = cm.get_cmap('viridis')
new_filt = DigitalFilter(1)
det.AddDigitalFilter(new_filt)
p_mags = 1 - np.logspace(-4, -2, 100, base=10)
z_mags = 1 - np.logspace(-4, -2, 20, base=10)
for zero_mag in [5E-4]: #z_mags:
for pole_mag in np.logspace(-7, -5, 100, base=10): #p_mags:
zero_mag = 1 - zero_mag
pole_mag = zero_mag - pole_mag
if zero_mag == pole_mag: continue
color = "b"
# color = cmap( (mag2 - mags[0])/(mags[-1] - mags[0]) )
new_filt.set_zeros(zero_mag, 0)
new_filt.set_poles(pole_mag, 0)
# print (new_filt.num, np.sum(new_filt.num))
# print (new_filt.den, np.sum(new_filt.den))
# exit()
wf_proc = np.copy(
det.MakeSimWaveform(25,
0,
25,
1,
125,
0.95,
1000,
smoothing=20))
try:
if wf_proc[155] < 1.00001: continue
if np.amax(wf_proc) > 1.1: continue
print(zero_mag, pole_mag)
p = ax[0, 0].plot(wf_proc)
color = p[0].get_color()
ax[1, 0].plot(wf_proc - wf_compare, color=color)
except (TypeError, IndexError) as e:
continue
w, h2 = get_freq_resp(new_filt,
w=np.logspace(-15, 0, 500, base=np.pi))
ax[0, 1].loglog(w, h2, color=color)
p[0] = ax[1, 1].scatter(zero_mag, 0, color=color)
ax[1, 1].scatter(pole_mag, 0, color=color, marker="x")
an = np.linspace(0, np.pi, 200)
ax[1, 1].plot(np.cos(an), np.sin(an), c="k")
ax[1, 1].plot(np.cos(an), -np.sin(an), c="k")
ax[1, 1].axis("equal")
plt.show()
def oscillation(det):
'''
How do I get me a decaying oscillation?
'''
lowpass = DigitalFilter(2)
lowpass.num = [1, 2, 1]
lowpass.set_poles(0.975, 0.007)
hipass = DigitalFilter(1)
hipass.num, hipass.den = rc_decay(82, 1E9)
det.AddDigitalFilter(lowpass)
# det.AddDigitalFilter(hipass)
wf_proc = np.copy(
det.MakeSimWaveform(25, 0, 25, 1, 125, 0.95, wf_length, smoothing=20))
wf_compare = np.copy(wf_proc)
f, ax = plt.subplots(2, 2, figsize=(15, 8))
# plt.figure()
ax[0, 0].plot(wf_compare, color="r")
cmap = cm.get_cmap('viridis')
new_filt = DigitalFilter(2)
det.AddDigitalFilter(new_filt)
new_filt.num = [1, 2, 1]
p_mags = 1 - np.logspace(-3, -2, 20, base=10)
p_phis = [0.5 * np.pi**-3]
# p_phis = np.logspace(-5, 1, 4, base=np.pi)
# z_mags = 1 - np.logspace(-4, -2, 20, base=10)
pole_phi = 0.5 * np.pi**-3
pole_mag = 0.995
for i in range(3):
# for pole_phi in p_phis:
# for pole_mag in p_mags:
# zero_mag = 1-zero_mag
# pole_mag = zero_mag + pole_mag
# if zero_mag == pole_mag: continue
color = get_color(cmap, pole_mag, p_mags)
if i == 0:
color = "k"
elif i == 1:
new_filt.set_zeros(0.99, 0.01)
color = "b"
elif i == 2:
new_filt.set_zeros(0.995, 0.001)
color = "g"
new_filt.set_poles(pole_mag, pole_phi)
# print (new_filt.num, np.sum(new_filt.num))
# print (new_filt.den, np.sum(new_filt.den))
# exit()
wf_proc = np.copy(
det.MakeSimWaveform(25, 0, 25, 1, 125, 0.95, 1000, smoothing=20))
try:
# if wf_proc[155] < 1.00001: continue
# # if np.amax(wf_proc) > 1.1: continue
# print( zero_mag, pole_mag)
p = ax[0, 0].plot(wf_proc, color=color)
# color = p[0].get_color()
ax[1, 0].plot(wf_proc - wf_compare, color=color)
except (TypeError, IndexError) as e:
continue
w, h2 = get_freq_resp(new_filt, w=np.logspace(-15, 0, 500, base=np.pi))
ax[0, 1].loglog(w, h2, color=color)
ax[0, 1].axvline(pole_phi / (np.pi / nyq_freq))
# p[0] = ax[1,1].scatter(zero_mag, 0, color=color)
ax[1, 1].scatter(pole_mag * np.cos(pole_phi),
pole_mag * np.sin(pole_phi),
color=color,
marker="x")
an = np.linspace(0, np.pi, 200)
ax[1, 1].plot(np.cos(an), np.sin(an), c="k")
ax[1, 1].plot(np.cos(an), -np.sin(an), c="k")
ax[1, 1].axis("equal")
plt.show()
def main():
runList = np.arange(11515, 11516)
proc = DataProcessor()
plt.ion()
f = plt.figure(figsize=(12, 9))
rc_num, rc_den = filt.rc_decay(72)
overshoot_num, overshoot_den = filt.gretina_overshoot(2, -3.5)
# print(overshoot_num, overshoot_den)
# exit()
ds_inf = pd.read_csv("ds1_run_info.csv")
for runNumber in runList:
t1_file = os.path.join(proc.t1_data_dir,
"t1_run{}.h5".format(runNumber))
df = pd.read_hdf(t1_file, key="ORGretina4MWaveformDecoder")
g4 = dl.Gretina4MDecoder(t1_file)
chanList = np.unique(df["channel"])
chanList = [580]
pz_fun = []
masses = []
resolutions = []
for chan in chanList:
print("Channel {}".format(chan))
try:
ds_inf_det = ds_inf[(ds_inf.LG == chan) | (ds_inf.HG == chan)]
det_mass = ds_inf_det.iloc[0].Mass
det_res = ds_inf_det.iloc[0].Resolution
det_ctres = ds_inf_det.iloc[0].ct_resolution
trap_factor = (det_res - det_ctres) / det_ctres
except IndexError:
print("...couldn't find channel info")
if chan % 2 == 1: continue
if not is_mj(chan): continue
plt.clf()
ax1 = plt.subplot(2, 2, 2)
ax2 = plt.subplot(2, 2, 4)
ax3 = plt.subplot(2, 2, 3)
ax4 = plt.subplot(2, 2, 1)
df_chan = df[(df.channel == chan) & (df.energy > 0.2E9)]
e_min = df_chan.energy.min()
e_max = df_chan.energy.max()
e_cut = 0.9 * (e_max - e_min) + e_min
df_cut = df_chan[df_chan.energy > e_cut]
bl_idx = 800
baseline = np.zeros(bl_idx)
flat_top = np.zeros(800)
# plt.figure()
num_wfs = 0
for i, (index, row) in enumerate(df_cut.iterrows()):
wf = g4.parse_event_data(row)
wf_dat = wf.data - np.mean(wf.data[:bl_idx])
try:
wf_corr, energy, gof = fit_tail(wf_dat)
align_idx = np.argmax(wf_corr / energy > 0.999) + 20
flat_top_wf = wf_corr[align_idx:align_idx + 800] - energy
baseline += wf_dat[:bl_idx]
flat_top += flat_top_wf
num_wfs += 1
except ValueError as e:
print(e)
continue
ax1.plot(wf_corr[:800], c="b", alpha=0.1)
ax2.plot(flat_top_wf, c="b", alpha=0.1)
ax4.plot(wf_corr[align_idx - 400:align_idx + 805] / energy,
c="b",
alpha=0.1)
# plt.plot(flat_top_wf, c="b", alpha=0.1)
# if num_wfs < 5: continue
flat_top /= num_wfs
baseline /= num_wfs
pz_fun.append(np.sum(flat_top**2))
masses.append(det_res)
ax1.set_title("Baseline")
ax1.plot(baseline, c="r")
ax2.set_title("Decay (PZ corrected)")
ax2.plot(flat_top, c="r")
# plt.plot(flat_top, c="r")
plt.title("Channel {} (mass {}, res {})".format(
chan, det_mass, det_res))
# ax1.plot(baseline, label="baseline")
# ax1.plot(flat_top - np.mean(flat_top), label="flat top")
xf, power = signal.periodogram(baseline,
fs=1E8,
detrend="linear",
scaling="spectrum")
x_idx = np.argmax(xf > 0.2E7)
ax3.semilogx(xf[x_idx:], power[x_idx:], label="baseline")
max_pwr = power.max()
# ax2.plot(flat_top)
xf, power = signal.periodogram(flat_top,
fs=1E8,
detrend="constant",
scaling="spectrum")
x_idx = np.argmax(xf > 0.2E7)
ax3.semilogx(xf[x_idx:], power[x_idx:], label="flat top")
ax3.legend()
plt.savefig("fft_plots/channel{}_fft.png".format(chan))
# inp = input("q to continue, else to quit")
# if inp == "q": exit()
plt.figure()
plt.scatter(masses, pz_fun)
inp = input("q to continue, else to quit")
if inp == "q": exit()