Advanced Tricks

So you’ve tried making normal waveforms and now you need to spice up your life by making some way more weird waveforms letting the detector be whatever you want it to be? You have come to the right place!

By default fax uses some configuration file which is a huge pain to modify. So we made fax such that if you add a parameter in the instruction which corresponds to a parameter in the config it will overwrite what the value was in the config and let you deside what it should be!

This example shows how to modify the electron lifetime and the anode voltage

import numpy as np
import strax
import straxen
import wfsim

import matplotlib.pyplot as plt
from matplotlib.colors import LogNorm
from multihist import Histdd, Hist1d
from scipy import stats

st = strax.Context(
    config=dict(
        detector='XENON1T',
        fax_config='https://raw.githubusercontent.com/XENONnT/'
                   'strax_auxiliary_files/master/sim_files/fax_config_1t.json',
        fax_config_override={'field_distortion_on':True, 's2_luminescence_model':'simple'},
        **straxen.contexts.xnt_common_config),
    **straxen.contexts.common_opts)
st.register(wfsim.RawRecordsFromFax1T)

wfsim.strax_interface.RawRecordsFromFax1T

# Just some id from post-SR1, so the corrections work
run_id = '000001'

strax.Mailbox.DEFAULT_TIMEOUT=10000

dtype = wfsim.strax_interface.instruction_dtype

for new_dtype in [('electron_lifetime_liquid', np.int32),
                  ('anode_voltage', np.int32)]:
    if new_dtype not in dtype:
        dtype.append(new_dtype)

def rand_instructions(c):
    n = c['nevents'] = c['event_rate'] * c['chunk_size'] * c['nchunk']
    c['total_time'] = c['chunk_size'] * c['nchunk']

    instructions = np.zeros(2 * n, dtype=dtype)
    uniform_times = c['total_time'] * (np.arange(n) + 0.5) / n
    instructions['time'] = np.repeat(uniform_times, 2) * int(1e9)
    instructions['event_number'] = np.digitize(instructions['time'],
         1e9 * np.arange(c['nchunk']) * c['chunk_size']) - 1
    instructions['type'] = np.tile([1, 2], n)
    instructions['recoil'] = ['er' for i in range(n * 2)]

    r = np.sqrt(np.random.uniform(0, 2500, n))
    t = np.random.uniform(-np.pi, np.pi, n)
    instructions['x'] = np.repeat(r * np.cos(t), 2)
    instructions['y'] = np.repeat(r * np.sin(t), 2)
    instructions['z'] = np.repeat(np.random.uniform(-100, 0, n), 2)

    nphotons = np.random.uniform(2000, 2050, n)
    nelectrons = 10 ** (np.random.uniform(1, 4, n))
    instructions['amp'] = np.vstack([nphotons, nelectrons]).T.flatten().astype(int)
    instructions['electron_lifetime_liquid'] = np.repeat(600e10,len(instructions))
    instructions['anode_voltage'] = np.repeat(1e10,len(instructions))
    return instructions

wfsim.strax_interface.rand_instructions = rand_instructions
wfsim.strax_interface.instruction_dtype = dtype

st.set_config(dict(fax_file=None))
st.set_config(dict(nchunk=1, event_rate=1, chunk_size=100))

# Remove any previously simulated data, if such exists
# !rm -r strax_data

records = st.get_array(run_id,'raw_records', progress_bar=False)
peaks = st.get_array(run_id, ['peak_basics'], progress_bar=False)
data = st.get_df(run_id, 'event_info', progress_bar=False)
truth = st.get_df(run_id, 'truth', progress_bar=False)

truth.head()

	event_number	type	time	x	y	z	amp	recoil	electron_lifetime_liquid	anode_voltage	...	n_photon_bottom	t_first_photon	t_last_photon	t_mean_photon	t_sigma_photon	t_first_electron	t_last_electron	t_mean_electron	t_sigma_electron	endtime
0	0	1	500000026	-27.127699	-36.640770	-12.655296	2009	er	-2147483648	-2147483648	...	143.0	5.000000e+08	5.000004e+08	5.000001e+08	48.421194	NaN	NaN	NaN	NaN	500000353
1	0	2	500097027	-27.127699	-36.640770	-12.655296	39	er	-2147483648	-2147483648	...	344.0	5.000970e+08	5.000997e+08	5.000984e+08	490.645247	5.000970e+08	5.000993e+08	5.000982e+08	479.974221	500099672
2	0	4	500768320	20.171877	4.092766	-89.123016	1	er	-2147483648	-2147483648	...	10.0	5.007683e+08	5.007690e+08	5.007685e+08	167.279008	5.007683e+08	5.007683e+08	5.007683e+08	0.000000	500769026
3	0	1	1500000019	-19.476303	10.349952	-35.648590	2010	er	-2147483648	-2147483648	...	192.0	1.500000e+09	1.500000e+09	1.500000e+09	41.286365	NaN	NaN	NaN	NaN	1500000227
4	0	2	1500265504	-19.476303	10.349952	-35.648590	2817	er	-2147483648	-2147483648	...	26229.0	1.500266e+09	1.500273e+09	1.500269e+09	961.919333	1.500265e+09	1.500272e+09	1.500269e+09	955.008653	1500272834

5 rows × 22 columns