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Add function to preprocess shadow related features #52

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Apr 29, 2024
Merged

Add function to preprocess shadow related features #52

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48 changes: 20 additions & 28 deletions axidence/plugins/pairing/peaks_paired.py
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Original file line number Diff line number Diff line change
Expand Up @@ -200,6 +200,18 @@ def digitize2d(data_sample, bin_edges, n_bins):
)
return digit

@staticmethod
def preprocess_shadow(data, shadow_deltatime_exponent, delta_t=0, prefix=""):
dt_s2_time_shadow = np.clip(data[f"{prefix}dt_s2_time_shadow"] - delta_t, 1, np.inf)
pre_s2_area = (
data[f"{prefix}shadow_s2_time_shadow"]
* data[f"{prefix}dt_s2_time_shadow"] ** -shadow_deltatime_exponent
)
x = np.log10(pre_s2_area * dt_s2_time_shadow**shadow_deltatime_exponent)
y = np.sqrt(np.log10(pre_s2_area) ** 2 + np.log10(dt_s2_time_shadow) ** 2)
sample = np.stack([x, y]).T
return sample

@staticmethod
def shadow_matching(
s1,
Expand All @@ -223,26 +235,10 @@ def shadow_matching(
# 2D equal binning
# prepare the 2D space, x is log(S2/dt), y is (log(S2)**2+log(dt)**2)**0.5
# because these 2 dimension is orthogonal
x = np.log10(shadow_reference["shadow_s2_time_shadow"])
y = np.sqrt(
np.log10(
shadow_reference["shadow_s2_time_shadow"]
* shadow_reference["dt_s2_time_shadow"] ** -shadow_deltatime_exponent
)
** 2
+ np.log10(shadow_reference["dt_s2_time_shadow"]) ** 2
)
sampled_correlation = np.stack([x, y]).T

x = np.log10(s1["shadow_s2_time_shadow"])
y = np.sqrt(
np.log10(
s1["shadow_s2_time_shadow"] * s1["dt_s2_time_shadow"] ** -shadow_deltatime_exponent
)
** 2
+ np.log10(s1["dt_s2_time_shadow"]) ** 2
sampled_correlation = PeaksPaired.preprocess_shadow(
shadow_reference, shadow_deltatime_exponent
)
s1_sample = np.stack([x, y]).T
s1_sample = PeaksPaired.preprocess_shadow(s1, shadow_deltatime_exponent)

# use (x, y) distribution of isolated S1 as reference
# because it is more intense when shadow(S2/dt) is large
Expand All @@ -251,7 +247,7 @@ def shadow_matching(
# largest bin number in x & y dimension
n_shadow_bins = max_n_shadow_bins
# find suitable bin number
# we do not allow binning which provides empty bin(count = 0) for Shadow's (x,y)
# we do not allow binning which provides empty bin(count = 0) for Shadow's (x, y)
# because it is denominator
bins_find = False
while not bins_find:
Expand Down Expand Up @@ -307,19 +303,15 @@ def shadow_matching(
delta_t = drift_time_bin_center[i]
else:
delta_t = 0
s2_shadow_dt_request = np.clip(s2["dt_s2_time_shadow"] - delta_t, 1, np.inf)
pre_area_s2 = (
s2["shadow_s2_time_shadow"] * s2["dt_s2_time_shadow"] ** -shadow_deltatime_exponent
data_sample = PeaksPaired.preprocess_shadow(
s2, shadow_deltatime_exponent, delta_t=delta_t
)
x = np.log10(pre_area_s2 * s2_shadow_dt_request**shadow_deltatime_exponent)
y = np.sqrt(np.log10(pre_area_s2) ** 2 + np.log10(s2_shadow_dt_request) ** 2)
data_sample = np.stack([x, y]).T
ge.check_sample_sanity(data_sample)
# apply binning to (x,y)
# apply binning to (x, y)
s2_shadow_count = ge.apply_irregular_binning(
data_sample=data_sample, bin_edges=bin_edges
)
# conditional rate is AC rate in each (x,y) bin
# conditional rate is AC rate in each (x, y) bin
ac_rate_conditional = (
s1_shadow_count / shadow_run_time * s2_shadow_count / shadow_run_time
)
Expand Down
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