def fit_stage2_decay(det=None):
f, d1, d2 = read_second_stage()
f_min_idx = 0
f_max_idx = np.argmax(f> 1E6)
freqs = f.values[f_min_idx:f_max_idx]
spice_volts = d1[f_min_idx:f_max_idx]
mod1 = DigitalFilter(1)
# mod2 = DigitalFilter(2)
mod1.num = [1,-1]
# mod2.num = [1]
if det is not None:
det.AddDigitalFilter(mod1)
def get_filter(mag):
mag = 1 - 10**mag
ws = freqs *(np.pi / 0.5E9)
mod1.set_poles(mag)
h1 = get_freqz(mod1, w = ws )
return np.abs(h1)
res = optimize.minimize(lsq, [-3], method="L-BFGS-B", args=(get_filter, spice_volts), bounds=[(-8,-1)])
v = get_filter(*res["x"])
f,ax = plt.subplots(2,1, sharex=True)
ax[0].semilogx(freqs, db(spice_volts), label="EAGLE")
ax[0].semilogx(freqs, db( v ), label="4th order best fit")
ax[1].plot(freqs, db(spice_volts)-db( v ))
plt.legend()
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 fit_second_stage_lowpass(det=None):
f, d1, d2 = read_second_stage()
f_min_idx = np.argmax(f > 10**6)
f_max_idx = np.argmax(f > 5E8)
freqs = f.values[f_min_idx:f_max_idx]
spice_volts = d1[f_min_idx:f_max_idx]
mod1 = DigitalFilter(2)
mod2 = DigitalFilter(1)
# mod1.num = [1,1,0]
# mod2.num = [1]
if det is not None:
det.AddDigitalFilter(mod1)
# det.AddDigitalFilter(mod2)
def get_filter(
mag,
phi,
zmag,
zphi,
):
ws = freqs * (np.pi / 0.5E9)
mag = 1 - 10**mag
zmag = 1 - 10**zmag
mod1.set_zeros(zmag, zphi)
# mod1.num=[1,0.]
mod1.set_poles(mag, phi)
# mod2.set_zeros(zmag2)
# mag2 = 1-10**mag2
# zmag2 = 1-10**zmag2
# mod2.set_poles(mag2)
h1 = get_freqz(mod1, w=ws)
# h2 = get_freqz(mod2, w = ws )
h2 = 1
return np.abs(h1 * h2)
res = optimize.minimize(lsq, [-2, 0.1, -2, 0.1],
method="L-BFGS-B",
args=(get_filter, spice_volts),
bounds=[(-8, 0), (0, np.pi), (-8, 0), (0, np.pi)])
v = get_filter(*res["x"])
print(res["x"])
print(mod1.num)
f, ax = plt.subplots(2, 1, sharex=True)
ax[0].semilogx(freqs, db(spice_volts), label="EAGLE")
ax[0].semilogx(freqs, db(v), label="3rd order filter")
ax[1].plot(freqs, db(spice_volts) - db(v))
ax[0].legend()
def main():
df = pd.read_csv("front_end_1k_to_1g.txt", sep="\s+")
f = df.frequency
f_min_idx = np.argmax(df.frequency > 10**6)
# f_max_idx = -1
f_max_idx = np.argmax(df.frequency > 5E8)
freqs = df.frequency.values[f_min_idx:f_max_idx]
volts = df.vm[f_min_idx:f_max_idx]/df.vm.max()
mod1 = DigitalFilter(2)
mod2 = DigitalFilter(2)
mod1.num = [1,1]
mod2.num = [1]
def get_filter(mag, phi):
ws = freqs *(np.pi / 0.5E9)
mod1.set_poles(mag, phi)
mod2.set_poles(mag, phi)
h1 = get_freqz(mod1, w = ws )
h2 = get_freqz(mod2, w = ws)
return np.abs(h1)*np.abs(h2)
def min_func(x):
v = get_filter(*x)
return np.sum((volts-v)**2)
res = optimize.minimize(min_func, [0.9, 0.1*np.pi], method="L-BFGS-B", bounds=[(0,1), (0,np.pi)])
v = get_filter(*res["x"])
print(res["x"])
# plt.figure()
f,ax = plt.subplots(2,1, sharex=True)
ax[0].semilogx(freqs, db(volts), label="EAGLE")
ax[0].semilogx(freqs, db( v ), label="4th order best fit")
ax[1].plot(freqs, db(volts)-db( v ))
plt.legend()
plt.show()
def fit_digitizer(det=None):
f, d1, d2 = read_digitzer()
f_min_idx = np.argmax(f > 10**6)
f_max_idx = np.argmax(f> 5E8)
freqs = f.values[f_min_idx:f_max_idx]
spice_volts = d1[f_min_idx:f_max_idx]
mod1 = DigitalFilter(2)
mod2 = DigitalFilter(2)
mod1.num = [1,1]
mod2.num = [1]
if det is not None:
det.AddDigitalFilter(mod1)
det.AddDigitalFilter(mod2)
def get_filter(mag, phi):
ws = freqs *(np.pi / 0.5E9)
mod1.set_poles(mag, phi)
mod2.set_poles(mag, phi)
h1 = get_freqz(mod1, w = ws )
h2 = get_freqz(mod2, w = ws)
return np.abs(h1)*np.abs(h2)
res = optimize.minimize(lsq, [0.9, 0.1*np.pi], method="L-BFGS-B", args=(get_filter, spice_volts), bounds=[(0,1), (0,np.pi)])
v = get_filter(*res["x"])
print(res["x"])
f,ax = plt.subplots(2,1, sharex=True)
ax[0].semilogx(freqs, db(spice_volts), label="EAGLE")
ax[0].semilogx(freqs, db( v ), label="4th order best fit")
ax[1].plot(freqs, db(spice_volts)-db( v ))
plt.legend()
def fit_stage2_decay(det=None):
f, d1, d2 = read_second_stage()
f_min_idx = 0
f_max_idx = np.argmax(f > 1E6)
freqs = f.values[f_min_idx:f_max_idx]
spice_volts = d1[f_min_idx:f_max_idx]
mod1 = DigitalFilter(1)
# mod2 = DigitalFilter(2)
mod1.num = [1, -1]
# mod2.num = [1]
if det is not None:
det.AddDigitalFilter(mod1)
def get_filter(mag):
mag = 1 - 10**mag
ws = freqs * (np.pi / 0.5E9)
mod1.set_poles(mag)
h1 = get_freqz(mod1, w=ws)
return np.abs(h1)
res = optimize.minimize(lsq, [-3],
method="L-BFGS-B",
args=(get_filter, spice_volts),
bounds=[(-8, -1)])
v = get_filter(*res["x"])
f, ax = plt.subplots(2, 1, sharex=True)
ax[0].semilogx(freqs, db(spice_volts), label="EAGLE")
ax[0].semilogx(freqs, db(v), label="4th order best fit")
ax[1].plot(freqs, db(spice_volts) - db(v))
plt.legend()
def plot_pulser():
from waffle.models import PulserGenerator
from siggen.electronics import DigitalFilter
pg = PulserGenerator(1000)
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)
pg.digital_filters.append(lowpass)
pg.digital_filters.append(hipass)
pg.energy = 1000
p = pg.make_pulser(50, 500, 250)
plt.figure()
plt.plot(p)
plt.show()
def main():
df = pd.read_csv("front_end_1k_to_1g.txt", sep="\s+")
f = df.frequency
f_min_idx = np.argmax(df.frequency > 10**6)
# f_max_idx = -1
f_max_idx = np.argmax(df.frequency > 5E8)
freqs = df.frequency.values[f_min_idx:f_max_idx]
volts = df.vm[f_min_idx:f_max_idx] / df.vm.max()
mod1 = DigitalFilter(2)
mod2 = DigitalFilter(2)
mod1.num = [1, 1]
mod2.num = [1]
def get_filter(mag, phi):
ws = freqs * (np.pi / 0.5E9)
mod1.set_poles(mag, phi)
mod2.set_poles(mag, phi)
h1 = get_freqz(mod1, w=ws)
h2 = get_freqz(mod2, w=ws)
return np.abs(h1) * np.abs(h2)
def min_func(x):
v = get_filter(*x)
return np.sum((volts - v)**2)
res = optimize.minimize(min_func, [0.9, 0.1 * np.pi],
method="L-BFGS-B",
bounds=[(0, 1), (0, np.pi)])
v = get_filter(*res["x"])
print(res["x"])
# plt.figure()
f, ax = plt.subplots(2, 1, sharex=True)
ax[0].semilogx(freqs, db(volts), label="EAGLE")
ax[0].semilogx(freqs, db(v), label="4th order best fit")
ax[1].plot(freqs, db(volts) - db(v))
plt.legend()
plt.show()
def fit_digitizer(det=None):
f, d1, d2 = read_digitzer()
f_min_idx = np.argmax(f > 10**6)
f_max_idx = np.argmax(f > 5E8)
freqs = f.values[f_min_idx:f_max_idx]
spice_volts = d1[f_min_idx:f_max_idx]
mod1 = DigitalFilter(2)
mod2 = DigitalFilter(2)
mod1.num = [1, 1]
mod2.num = [1]
if det is not None:
det.AddDigitalFilter(mod1)
det.AddDigitalFilter(mod2)
def get_filter(mag, phi):
ws = freqs * (np.pi / 0.5E9)
mod1.set_poles(mag, phi)
mod2.set_poles(mag, phi)
h1 = get_freqz(mod1, w=ws)
h2 = get_freqz(mod2, w=ws)
return np.abs(h1) * np.abs(h2)
res = optimize.minimize(lsq, [0.9, 0.1 * np.pi],
method="L-BFGS-B",
args=(get_filter, spice_volts),
bounds=[(0, 1), (0, np.pi)])
v = get_filter(*res["x"])
print(res["x"])
f, ax = plt.subplots(2, 1, sharex=True)
ax[0].semilogx(freqs, db(spice_volts), label="EAGLE")
ax[0].semilogx(freqs, db(v), label="4th order best fit")
ax[1].plot(freqs, db(spice_volts) - db(v))
plt.legend()
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 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 poles(det):
lowpass = DigitalFilter(2)
lowpass.num = [1, 2, 1]
lowpass.set_poles(0.975, 0.007)
mag = 1. - 10.**-7
phi = np.pi**-13.3
hipass = DigitalFilter(2)
hipass.num = [1, -2, 1]
hipass.set_poles(mag, phi)
det.AddDigitalFilter(lowpass)
det.AddDigitalFilter(hipass)
wf_proc = np.copy(
det.MakeSimWaveform(25, 0, 25, 1, 125, 0.95, 1000, smoothing=20))
wf_compare = np.copy(wf_proc)
f, ax = plt.subplots(2, 2, figsize=(15, 8))
ax[0, 0].plot(wf_compare, color="r")
cmap = cm.get_cmap('viridis')
phis = np.logspace(-20, -8, 100, base=np.pi)
mags = 1 - np.logspace(-7.5, -6.5, 100, base=10)
# phis = np.pi - phis
w, h = get_freq_resp(hipass)
ax[0, 1].loglog(w, h, color="r")
# for phi2 in phis:
# color = cmap( (phi2 - phis[0])/(phis[-1] - phis[0]) )
# mag2=mag
for mag2 in mags:
phi2 = phi
color = cmap((mag2 - mags[0]) / (mags[-1] - mags[0]))
hipass.set_poles(mag2, phi2)
wf_proc = np.copy(
det.MakeSimWaveform(25, 0, 25, 1, 125, 0.95, 1000, smoothing=20))
try:
ax[0, 0].plot(wf_proc,
label="{}, {}".format(phi, mag),
color=color)
ax[1, 0].plot(wf_proc - wf_compare,
label="{}, {}".format(phi, mag),
color=color)
except TypeError:
continue
w, h2 = get_freq_resp(hipass)
ax[0, 1].loglog(w, h2, color=color)
# plt.legend()
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 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()