def plot(sample_file_name, directory):
max_sample_idx = 200
fallPercentage = 0.97
if os.path.isfile(directory + "fit_params.npz"):
data = np.load(directory + "fit_params.npz", encoding="latin1")
max_sample_idx = data['max_sample_idx']
fallPercentage = data['fallPercentage']
wf_idxs = data['wf_idxs']
wf_file_name = data['wf_file_name']
field_file_name = data['field_file_name']
doMaxInterp = data['doMaxInterp']
# det_name = data['det_name']
wf_file_name = "%s" % wf_file_name
field_file_name = "%s" % field_file_name
else:
print("Saved fit param %s not available" %
(directory + "fit_params.npz"))
exit(0)
if os.path.isfile(wf_file_name):
data = np.load(wf_file_name)
wfs = data['wfs']
wfs = wfs[wf_idxs]
numWaveforms = wfs.size
else:
print("Saved waveform file %s not available" % wfFileName)
exit(0)
wfLengths = np.empty(numWaveforms)
wfMaxes = np.empty(numWaveforms)
if doInitPlot: plt.figure(500)
baselineLengths = np.empty(numWaveforms)
for (wf_idx, wf) in enumerate(wfs):
wf.WindowWaveformAroundMax(fallPercentage=fallPercentage,
rmsMult=2,
earlySamples=max_sample_idx)
baselineLengths[wf_idx] = wf.t0Guess
print("wf %d length %d (entry %d from run %d)" %
(wf_idx, wf.wfLength, wf.entry_number, wf.runNumber))
wfLengths[wf_idx] = wf.wfLength
wfMaxes[wf_idx] = np.argmax(wf.windowedWf)
if doInitPlot:
if len(colors) < numWaveforms:
color = "red"
else:
color = colors[wf_idx]
plt.plot(wf.windowedWf, color=color)
if doInitPlot:
plt.show()
exit()
siggen_wf_length = (max_sample_idx - np.amin(baselineLengths) + 10) * 10
output_wf_length = np.amax(wfLengths) + 1
#Create a detector model
timeStepSize = 1 #ns
detName = "conf/P42574A_bull.conf"
det = Detector(detName,
timeStep=timeStepSize,
numSteps=siggen_wf_length,
maxWfOutputLength=output_wf_length,
t0_padding=100)
det.LoadFieldsGrad(field_file_name)
fig1 = plt.figure(0, figsize=(20, 10))
plt.clf()
gs = gridspec.GridSpec(2, 1, height_ratios=[4, 1])
ax0 = plt.subplot(gs[0])
ax1 = plt.subplot(gs[1], sharex=ax0)
ax1.set_xlabel("Digitizer Time [ns]")
ax0.set_ylabel("Voltage [Arb.]")
ax1.set_ylabel("Residual")
for wf in wfs:
dataLen = wf.wfLength
t_data = np.arange(dataLen) * 10
ax0.plot(t_data, wf.windowedWf, color="black")
sample_file_name = directory + sample_file_name
if sample_file_name == directory + "sample.txt":
shutil.copy(directory + "sample.txt", directory + "sample_plot.txt")
sample_file_name = directory + "sample_plot.txt"
data = np.loadtxt(sample_file_name)
num_samples = len(data)
data_len = num_samples
# data = pd.read_csv("sample_plot.txt", delim_whitespace=True, header=None)
# num_samples = len(data.index)
print("found %d samples" % num_samples)
if sample_file_name == (directory + "sample_plot.txt"):
if num_samples > plotNum: num_samples = plotNum
if doWaveformPlot:
if num_samples > plotNum: num_samples = plotNum
print("plotting %d samples" % num_samples)
r_arr = np.empty((numWaveforms, num_samples))
z_arr = np.empty((numWaveforms, num_samples))
tf = np.empty((6, num_samples))
velo = np.empty((6, num_samples))
wf_params = np.empty((numWaveforms, 6, num_samples))
det_params = np.empty((4, num_samples))
tf_first_idx, velo_first_idx, grad_idx, trap_idx = global_get_indices()
for (idx, params) in enumerate(data[-num_samples:]):
# params = data.iloc[-(idx+1)]
# print params
tf_phi, tf_omega, d, rc1, rc2, rcfrac, aliasrc = params[
tf_first_idx:tf_first_idx + 7]
#tf_d = tf_c * tf_dc
c = -d * np.cos(tf_omega)
b_ov_a = c - np.tan(tf_phi) * np.sqrt(d**2 - c**2)
a = 1. / (1 + b_ov_a)
tf_b = a * b_ov_a
tf_c = c
tf_d = d
h_111_va, h_111_vmax, h_100_multa, h_100_multmax, h_100_beta, h_111_beta, = params[
velo_first_idx:velo_first_idx + 6]
charge_trapping = params[trap_idx]
grad = (params[grad_idx])
imp_avg = (params[grad_idx + 1])
# rc1 = -1./np.log(e_rc1)
# rc2 = -1./np.log(e_rc2)
# charge_trapping = -1./np.log(e_charge_trapping)
h_100_va = h_100_multa * h_111_va
h_100_vmax = h_100_multmax * h_111_vmax
h_100_mu0, h_100_beta, h_100_e0 = get_velo_params(
h_100_va, h_100_vmax, h_100_beta)
h_111_mu0, h_111_beta, h_111_e0 = get_velo_params(
h_111_va, h_111_vmax, h_111_beta)
tf[:, idx] = tf_phi, tf_omega, tf_d, rc1, rc2, rcfrac
velo[:, idx] = params[velo_first_idx:velo_first_idx + 6]
det_params[:, idx] = grad, imp_avg, charge_trapping, aliasrc
det.SetTransferFunction(tf_b, tf_c, tf_d, rc1, rc2, rcfrac)
det.siggenInst.set_hole_params(h_100_mu0, h_100_beta, h_100_e0,
h_111_mu0, h_111_beta, h_111_e0)
det.trapping_rc = charge_trapping
det.SetGrads(grad, imp_avg)
det.SetAntialiasingRC(aliasrc)
rad_arr, phi_arr, theta_arr, scale_arr, t0_arr, smooth_arr, = params[
trap_idx + 1:].reshape((6, numWaveforms))
for (wf_idx, wf) in enumerate(wfs):
rad, phi, theta = rad_arr[wf_idx], phi_arr[wf_idx], theta_arr[
wf_idx]
scale, t0, smooth = scale_arr[wf_idx], t0_arr[wf_idx], smooth_arr[
wf_idx]
# m, b = m_arr[wf_idx], b_arr[wf_idx]
wf_params[wf_idx, :, idx] = rad, phi, theta, scale, t0, smooth,
r = rad * np.cos(theta)
z = rad * np.sin(theta)
r_arr[wf_idx, idx], z_arr[wf_idx, idx] = r, z
if doWaveformPlot:
color_idx = wf_idx % len(colors)
ml_wf = det.MakeSimWaveform(r,
phi,
z,
scale,
t0,
np.int(output_wf_length),
h_smoothing=smooth,
alignPoint="max",
doMaxInterp=True)
if ml_wf is None:
continue
dataLen = wf.wfLength
t_data = np.arange(dataLen) * 10
ax0.plot(t_data,
ml_wf[:dataLen],
color=colors[color_idx],
alpha=0.1)
ax1.plot(t_data,
ml_wf[:dataLen] - wf.windowedWf,
color=colors[color_idx],
alpha=0.1)
ax0.set_ylim(-20, wf.wfMax * 1.1)
ax1.set_ylim(-20, 20)
if not doHists:
plt.tight_layout()
plt.savefig("waveforms.png")
plt.show()
exit()
vFig = plt.figure(2, figsize=(20, 10))
tfLabels = ['tf_phi', 'tf_omega', 'tf_d', 'rc1', 'rc2', 'rcfrac']
vLabels = [
'h_111_va', 'h_111_vmax', 'h_100_multa', 'h_100_multmax', 'h_100_beta',
'h_111_beta'
]
vmodes, tfmodes = np.empty(6), np.empty(6)
num_bins = 100
for i in range(6):
idx = (i + 1) * 3
axis = vFig.add_subplot(6, 3, idx - 1)
axis.set_ylabel(vLabels[i])
[n, b, p] = axis.hist(velo[i, :], bins=num_bins)
max_idx = np.argmax(n)
print("%s mode: %f" % (vLabels[i], b[max_idx]))
axis = vFig.add_subplot(6, 3, idx - 2)
axis.set_ylabel(tfLabels[i])
[n, b, p] = axis.hist(tf[i, :], bins=num_bins)
max_idx = np.argmax(n)
print("%s mode: %f" % (tfLabels[i], b[max_idx]))
if i == 0:
axis = vFig.add_subplot(6, 3, idx)
axis.set_ylabel("imp grad")
[n, b, p] = axis.hist(det_params[i, :], bins=num_bins)
max_idx = np.argmax(n)
print("%s mode: %f" % ("imp_grad", b[max_idx]))
if i == 1:
axis = vFig.add_subplot(6, 3, idx)
axis.set_ylabel("imp average")
[n, b, p] = axis.hist(det_params[i, :], bins=num_bins)
max_idx = np.argmax(n)
print("%s mode: %f" % ("imp_avg", b[max_idx]))
if i == 2:
axis = vFig.add_subplot(6, 3, idx)
axis.set_ylabel("trapping_rc")
[n, b, p] = axis.hist(det_params[i, :], bins=num_bins)
max_idx = np.argmax(n)
print("%s mode: %f" % ("trapping_rc", b[max_idx]))
if i == 3:
axis = vFig.add_subplot(6, 3, idx)
axis.set_ylabel("alias_rc")
[n, b, p] = axis.hist(det_params[i, :], bins=num_bins)
max_idx = np.argmax(n)
print("%s mode: %f" % ("alias_rc", b[max_idx]))
if doPositionHist:
positionFig = plt.figure(3, figsize=(10, 10))
plt.clf()
colorbars = [
"Reds", "Blues", "Greens", "Purples", "Oranges", "Greys", "YlOrBr",
"PuRd"
]
if not doContourHist:
for wf_idx in range(numWaveforms):
color_idx = wf_idx % len(colorbars)
xedges = np.linspace(
0, np.around(det.detector_radius, 1),
np.around(det.detector_radius, 1) * 10 + 1)
yedges = np.linspace(
0, np.around(det.detector_length, 1),
np.around(det.detector_length, 1) * 10 + 1)
plt.hist2d(r_arr[wf_idx, :],
z_arr[wf_idx, :],
bins=[xedges, yedges],
cmap=plt.get_cmap(colorbars[color_idx])) #cmin=0.1
rad_mean = np.mean(wf_params[wf_idx, 0, :])
# print "--> guess was at %f" % (np.sqrt(results[wf_idx]['x'][0]**2 + results[wf_idx]['x'][2]**2))
# plt.colorbar()
plt.xlabel("r from Point Contact (mm)")
plt.ylabel("z from Point Contact (mm)")
plt.xlim(0, det.detector_radius)
plt.ylim(0, det.detector_length)
plt.gca().set_aspect('equal', adjustable='box')
else:
for wf_idx in range(numWaveforms):
xedges = np.linspace(
0, np.around(det.detector_radius, 1),
np.around(det.detector_radius, 1) * 10 + 1)
yedges = np.linspace(
0, np.around(det.detector_length, 1),
np.around(det.detector_length, 1) * 10 + 1)
z, xe, ye = np.histogram2d(r_arr[wf_idx, :],
z_arr[wf_idx, :],
bins=[xedges, yedges])
z /= z.sum()
n = 100
t = np.linspace(0, z.max(), n)
integral = ((z >= t[:, None, None]) * z).sum(axis=(1, 2))
from scipy import interpolate
f = interpolate.interp1d(integral, t)
t_contours = f(np.array([0.9, 0.8]))
cs = plt.contourf(
z.T,
t_contours,
extent=[0, det.detector_radius, 0, det.detector_length],
alpha=1,
colors=(colors[wf_idx]),
extend='max')
# cs.cmap.set_over(colors[wf_idx])
plt.xlabel("r from Point Contact (mm)")
plt.ylabel("z from Point Contact (mm)")
# plt.axis('equal')
plt.xlim(0, det.detector_radius)
plt.ylim(0, det.detector_length)
plt.gca().set_aspect('equal', adjustable='box')
plt.tight_layout()
plt.savefig("credible_intervals.pdf")
if numWaveforms == 1:
#TODO: make this plot work for a bunch of wfs
vFig = plt.figure(4, figsize=(20, 10))
wfLabels = ['rad', 'phi', 'theta', 'scale', 't0', 'smooth', 'm', 'b']
num_bins = 100
for i in range(8):
axis = vFig.add_subplot(4, 2, i + 1)
axis.set_ylabel(wfLabels[i])
[n, b, p] = axis.hist(wf_params[0, i, :], bins=num_bins)
# if i == 4:
# axis.axvline(x=t0_min, color="r")
# axis.axvline(x=t0_max, color="r")
# axis.axvline(x=t0_guess, color="g")
if False:
velo_plot = plt.figure(5)
fields_log = np.linspace(np.log(100), np.log(10000), 100)
fields = np.exp(fields_log)
for idx in range(num_samples):
h_111_va, h_111_vmax, h_100_multa, h_100_multmax, h_100_beta, h_111_beta, = velo[:,
idx]
# print "v params: ",
# print mu_0, h_100_lnbeta, h_111_lnbeta, h_111_emu, h_100_mult
h_100_va = h_100_multa * h_111_va
h_100_vmax = h_100_multmax * h_111_vmax
h100 = np.empty_like(fields)
h111 = np.empty_like(fields)
h_100_mu0, h_100_beta, h_100_e0 = get_velo_params(
h_100_va, h_100_vmax, h_100_beta)
h_111_mu0, h_111_beta, h_111_e0 = get_velo_params(
h_111_va, h_111_vmax, h_111_beta)
for (idx, field) in enumerate(fields):
h100[idx] = find_drift_velocity_bruyneel(
field, h_100_mu0, h_100_beta, h_100_e0)
h111[idx] = find_drift_velocity_bruyneel(
field, h_111_mu0, h_111_beta, h_111_e0)
plt.plot(fields, h100, color="r", alpha=1. / 100)
plt.plot(fields, h111, color="b", alpha=1. / 100)
h100_reg = np.zeros_like(h100)
h111_reg = np.zeros_like(h100)
h100_bruy = np.zeros_like(h100)
h111_bruy = np.zeros_like(h100)
for (idx, field) in enumerate(fields):
h100_reg[idx] = find_drift_velocity_bruyneel(
field, 66333., 0.744, 181.)
h111_reg[idx] = find_drift_velocity_bruyneel(
field, 107270., 0.580, 100.)
h100_bruy[idx] = find_drift_velocity_bruyneel(
field, 61824., 0.942, 185.)
h111_bruy[idx] = find_drift_velocity_bruyneel(
field, 61215., 0.662, 182.)
plt.plot(fields, h100_reg, color="g")
plt.plot(fields, h111_reg, color="g", ls="--")
plt.plot(fields, h100_bruy, color="orange")
plt.plot(fields, h111_bruy, color="orange", ls="--")
# plt.axvline(x=250, color="black", ls=":")
plt.axvline(x=500, color="black", ls=":")
plt.xscale('log')
# plt.yscale('log')
# plt.xlim(.45, 1E5)
# plt.ylim(1E4, 1E8)
plt.show()
b_ov_a = c - np.tan(tf_phi) * np.sqrt(d**2-c**2)
a = 1./(1+b_ov_a)
tf_b = a * b_ov_a
tf_c = c
tf_d = d
h_100_va = h_100_multa * h_111_va
h_100_vmax = h_100_multmax * h_111_vmax
h_100_mu0, h_100_beta, h_100_e0 = get_velo_params(h_100_va, h_100_vmax, h_100_beta)
h_111_mu0, h_111_beta, h_111_e0 = get_velo_params(h_111_va, h_111_vmax, h_111_beta)
det.SetTransferFunction(tf_b, tf_c, tf_d, rc1, rc2, rcfrac)
det.siggenInst.set_hole_params(h_100_mu0, h_100_beta, h_100_e0, h_111_mu0, h_111_beta, h_111_e0)
det.trapping_rc = charge_trapping
det.SetGrads(grad, imp_avg)
det.SetAntialiasingRC(aliasrc)
def main(argv):
# numThreads = 8
wfFileName = "fep_event_set_runs11510-11539_channel626.npz"
#wfFileName = "P42574A_24_spread.npz"
if os.path.isfile(wfFileName):
data = np.load(wfFileName)
wfs = data['wfs']
numWaveforms = wfs.size
else:
print "No saved waveforms available."
exit(0)
def plot_waveforms():
#the pysiggen Detector object is a wrapper around siggen that also does all the elctronics processing. Let's set one up.
wf_length = 350 #in tens of ns. This sets how long a wf you will simulate (after post-processing for electronics etc)
fitSamples = 800 #number of samples siggen will calculate (before post-processing, in 1 ns increments)
timeStepSize = 1 #don't change this, you'll break everything
detName = "conf/P42574A_ben.conf"
detector = Detector(detName, timeStep=timeStepSize, numSteps=fitSamples, maxWfOutputLength=5000)
#Load field information
detector.LoadFieldsGrad("P42574A_fields_impAndAvg_21by21.npz")
#Set a specific impurity gradient, impurity level combo
imp_grad =detector.gradList[3];
avg_imp = detector.impAvgList[3];
detector.SetGrads(imp_grad, avg_imp)
#hole drift mobilities
detector.siggenInst.set_hole_params(66333., 0.744, 181., 107270., 0.580, 100.,)
#Electronics transfer function
rc_decay = 72.6 #us
detector.SetTransferFunction(1, -0.814072377576, 0.82162729751, rc_decay, 1, 1)
#position we're gonna simulate
r, phi,z = 30, np.pi/8 ,30
#energy just scales the simulated waveform amplitude linearly
energy = 10
#align time is where the signal is aligned. If alignPoint="max" is set, this is at the signal max. Else, its at the signal t0 (with flat baseline before t0)
align_t = 200
smooth = 10 #sigma of the gaussian charge size convolution
#Set up some plotz
fig1 = plt.figure(0, figsize=(15,8))
gs = gridspec.GridSpec(2, 2, height_ratios=[4, 1])
ax0 = plt.subplot(gs[0])
ax1 = plt.subplot(gs[2], sharex=ax0)
ax1.set_xlabel("Digitizer Time [ns]")
ax0.set_ylabel("Voltage [A.U.]")
ax1.set_ylabel("Residual [A.U]")
ax0.set_title("Raw hole signal (no processing)")
ax2 = plt.subplot(gs[1])
ax3 = plt.subplot(gs[3], sharex=ax2)
ax3.set_xlabel("Digitizer Time [ns]")
ax2.set_ylabel("Voltage [A.U.]")
ax3.set_ylabel("Residual [A.U]")
ax2.set_title("After electronics processing")
timesteps_raw = np.arange(0, fitSamples) #for raw signal
timesteps = np.arange(0, wf_length)*10. #for processed signal
#need to do a np.copy because the memory allocated for wf sim gets rewritten with each sim
wf_raw = np.copy(detector.MakeRawSiggenWaveform(r, phi, z,1)) #the 1 is to do holes - a -1 would do electrons
wf_proc = np.copy(detector.MakeSimWaveform(r, phi, z, energy, align_t, wf_length, h_smoothing=smooth, trapType="fullSignal", alignPoint="max"))
ax0.plot(timesteps_raw, wf_raw, color="green")
ax2.plot(timesteps, wf_proc, color="green")
#take a look at how changing average impurity level changes the signal
num = 7
imp_vals = np.linspace(detector.impAvgList[0], detector.impAvgList[-1], num)
imp_grads = np.linspace(detector.gradList[0], detector.gradList[-1], num)
#just to make sure the residual color is the same as the corresponding wf
colorList = ["red", "blue", "purple", "orange", "black", "brown", "magenta", "cyan"]
# for (idx, grad) in enumerate(imp_grads):
for (idx, avg_imp) in enumerate(imp_vals):
detector.SetGrads(imp_grad, avg_imp)
new_wf_raw = np.copy(detector.MakeRawSiggenWaveform(r, phi, z, 1))
ax0.plot(timesteps_raw, new_wf_raw, color=colorList[idx])
ax1.plot(timesteps_raw, wf_raw - new_wf_raw, color=colorList[idx])
new_wf_proc= np.copy(detector.MakeSimWaveform(r, phi, z, energy, align_t, wf_length, h_smoothing=smooth, trapType="fullSignal", alignPoint="max"))
ax2.plot(timesteps, new_wf_proc, color=colorList[idx])
ax3.plot(timesteps, wf_proc - new_wf_proc, color=colorList[idx])
plt.show()