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A/E related pargen updates #578

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merged 3 commits into from
May 8, 2024
Merged

A/E related pargen updates #578

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9224c19
fix dt corr and add modes for timecorr
ggmarshall May 7, 2024
15de1ca
fix bounds as dict
ggmarshall May 7, 2024
33e31b6
Merge branch 'legend-exp:main' into main
ggmarshall May 7, 2024
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2 changes: 1 addition & 1 deletion src/pygama/math/unbinned_fitting.py
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Original file line number Diff line number Diff line change
Expand Up @@ -66,7 +66,7 @@ def fit_unbinned(
m = Minuit(cost_func, *guess)
if bounds is not None:
if isinstance(bounds, dict):
for arg, key in bounds:
for arg, key in bounds.items():
m.limits[arg] = key
else:
m.limits = bounds
Expand Down
58 changes: 41 additions & 17 deletions src/pygama/pargen/AoE_cal.py
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Original file line number Diff line number Diff line change
Expand Up @@ -28,6 +28,7 @@
hpge_peak,
nb_erfc,
)
from pygama.math.functions.gauss import nb_gauss_amp
from pygama.math.functions.hpge_peak import hpge_get_fwfm, hpge_get_fwhm, hpge_get_mode
from pygama.math.functions.sum_dists import SumDists
from pygama.pargen.utils import convert_to_minuit, return_nans
Expand Down Expand Up @@ -877,7 +878,9 @@ def update_cal_dicts(self, update_dict):
else:
self.cal_dicts.update(update_dict)

def time_correction(self, df, aoe_param, output_name="AoE_Timecorr", display=0):
def time_correction(
self, df, aoe_param, mode="full", output_name="AoE_Timecorr", display=0
):
log.info("Starting A/E time correction")
self.timecorr_df = pd.DataFrame()
try:
Expand Down Expand Up @@ -938,11 +941,24 @@ def time_correction(self, df, aoe_param, output_name="AoE_Timecorr", display=0):
)
self.timecorr_df.set_index("run_timestamp", inplace=True)
if len(self.timecorr_df) > 1:
time_dict = fit_time_means(
np.array(self.timecorr_df.index),
np.array(self.timecorr_df["mean"]),
np.array(self.timecorr_df["sigma"]),
)
if mode == "partial":
time_dict = fit_time_means(
np.array(self.timecorr_df.index),
np.array(self.timecorr_df["mean"]),
np.array(self.timecorr_df["sigma"]),
)

elif mode == "full":
time_dict = {
time: mean
for time, mean in zip(
np.array(self.timecorr_df.index),
np.array(self.timecorr_df["mean"]),
)
}

else:
raise ValueError("unknown mode")

df[output_name] = df[aoe_param] / np.array(
[time_dict[tstamp] for tstamp in df["run_timestamp"]]
Expand Down Expand Up @@ -1092,15 +1108,15 @@ def drift_time_correction(

hist, bins, var = pgh.get_hist(
final_df[self.dt_param],
dx=10,
dx=32,
range=(
np.nanmin(final_df[self.dt_param]),
np.nanmax(final_df[self.dt_param]),
),
)

bcs = pgh.get_bin_centers(bins)
mus = pgc.get_i_local_maxima(hist / (np.sqrt(var) + 10**-99), 2)
mus = bcs[pgc.get_i_local_maxima(hist / (np.sqrt(var) + 10**-99), 2)]
pk_pars, pk_covs = pgc.hpge_fit_energy_peak_tops(
hist,
bins,
Expand All @@ -1119,7 +1135,7 @@ def drift_time_correction(
)
else:
ids = np.full(len(mus), True, dtype=bool)
mus = [bcs[int(mu)] for mu in mus[ids]]
mus = mus[ids]
sigmas = sigmas[ids]
amps = amps[ids]

Expand Down Expand Up @@ -1156,8 +1172,8 @@ def drift_time_correction(
}

try:
self.alpha = (aoe_pars["mu"] - aoe_pars2["mu"]) / (
(mus[0] * aoe_pars2["mu"]) - (mus[1] * aoe_pars["mu"])
self.alpha = (aoe_pars2["mu"] - aoe_pars["mu"]) / (
(mus[0] * aoe_pars["mu"]) - (mus[1] * aoe_pars2["mu"])
)
except ZeroDivisionError:
self.alpha = 0
Expand Down Expand Up @@ -1743,13 +1759,16 @@ def calibrate(
dep_acc=0.9,
sf_nsamples=11,
sf_cut_range=(-5, 5),
timecorr_mode="full",
):
if peaks_of_interest is None:
peaks_of_interest = [1592.5, 1620.5, 2039, 2103.53, 2614.50]
if fit_widths is None:
fit_widths = [(40, 25), (25, 40), (0, 0), (25, 40), (50, 50)]

self.time_correction(df, initial_aoe_param, output_name="AoE_Timecorr")
self.time_correction(
df, initial_aoe_param, mode=timecorr_mode, output_name="AoE_Timecorr"
)

if self.dt_corr is True:
aoe_param = "AoE_DTcorr"
Expand Down Expand Up @@ -1966,9 +1985,12 @@ def drifttime_corr_plot(
label="data",
)
dx = np.diff(aoe_bins)
plt.plot(xs, aoe_class.pdf.get_pdf(xs, *aoe_pars) * dx[0], label="full fit")
aoe_class.pdf.components = False
plt.plot(
xs, aoe_class.pdf.get_pdf(xs, *aoe_pars.values()) * dx[0], label="full fit"
)
aoe_class.pdf.components = True
sig, bkg = aoe_class.pdf.get_pdf(xs, *aoe_pars)
sig, bkg = aoe_class.pdf.get_pdf(xs, *aoe_pars.values())
aoe_class.pdf.components = False
plt.plot(xs, sig * dx[0], label="peak fit")
plt.plot(xs, bkg * dx[0], label="bkg fit")
Expand All @@ -1988,9 +2010,11 @@ def drifttime_corr_plot(
label="Data",
)
dx = np.diff(aoe_bins2)
plt.plot(xs, aoe_class.pdf.get_pdf(xs, *aoe_pars2) * dx[0], label="full fit")
plt.plot(
xs, aoe_class.pdf.get_pdf(xs, *aoe_pars2.values()) * dx[0], label="full fit"
)
aoe_class.pdf.components = True
sig, bkg = aoe_class.pdf.get_pdf(xs, *aoe_pars2)
sig, bkg = aoe_class.pdf.get_pdf(xs, *aoe_pars2.values())
aoe_class.pdf.components = False
plt.plot(xs, sig * dx[0], label="peak fit")
plt.plot(xs, bkg * dx[0], label="bkg fit")
Expand All @@ -2017,7 +2041,7 @@ def drifttime_corr_plot(
for mu, sigma, amp in zip(mus, sigmas, amps):
plt.plot(
pgh.get_bin_centers(bins),
gaussian.get_pdf(pgh.get_bin_centers(bins), mu, sigma) * amp,
nb_gauss_amp(pgh.get_bin_centers(bins), mu, sigma, amp),
)
plt.xlabel("drift time (ns)")
plt.ylabel("Counts")
Expand Down