/
parse_waveforms.py
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/
parse_waveforms.py
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from the_bureaucrat.bureaucrats import RunBureaucrat # https://github.com/SengerM/the_bureaucrat
from pathlib import Path
import pandas
from huge_dataframe.SQLiteDataFrame import SQLiteDataFrameDumper, load_whole_dataframe, load_only_index_without_repeated_entries # https://github.com/SengerM/huge_dataframe
import sqlite3
from signals.PeakSignal import PeakSignal, draw_in_plotly # https://github.com/SengerM/signals
import numpy
import plotly.graph_objects as go
def parse_waveform(signal:PeakSignal)->dict:
parsed = {
'Amplitude (V)': signal.amplitude,
'Noise (V)': signal.noise,
'Rise time (s)': signal.rise_time,
'Collected charge (V s)': signal.peak_integral,
'Time over noise (s)': signal.time_over_noise,
'Peak start time (s)': signal.peak_start_time,
'Whole signal integral (V s)': signal.integral_from_baseline,
'SNR': signal.SNR,
}
for threshold_percentage in [10,20,30,40,50,60,70,80,90]:
try:
time_over_threshold = signal.find_time_over_threshold(threshold_percentage)
except Exception:
time_over_threshold = float('NaN')
parsed[f'Time over {threshold_percentage}% (s)'] = time_over_threshold
for pp in [10,20,30,40,50,60,70,80,90]:
try:
time_at_this_pp = float(signal.find_time_at_rising_edge(pp))
except Exception:
time_at_this_pp = float('NaN')
parsed[f't_{pp} (s)'] = time_at_this_pp
return parsed
def plot_waveform(signal):
fig = draw_in_plotly(signal)
fig.update_layout(
xaxis_title = "Time (s)",
yaxis_title = "Amplitude (V)",
)
MARKERS = { # https://plotly.com/python/marker-style/#custom-marker-symbols
10: 'circle',
20: 'square',
30: 'diamond',
40: 'cross',
50: 'x',
60: 'star',
70: 'hexagram',
80: 'star-triangle-up',
90: 'star-triangle-down',
}
for pp in [10,20,30,40,50,60,70,80,90]:
try:
fig.add_trace(
go.Scatter(
x = [signal.find_time_at_rising_edge(pp)],
y = [signal(signal.find_time_at_rising_edge(pp))],
mode = 'markers',
name = f'Time at {pp} %',
marker=dict(
color = 'rgba(0,0,0,.5)',
size = 11,
symbol = MARKERS[pp]+'-open-dot',
line = dict(
color = 'rgba(0,0,0,.5)',
width = 2,
)
),
)
)
except Exception as e:
pass
return fig
def parse_waveforms(bureaucrat:RunBureaucrat, name_of_task_that_produced_the_waveforms_to_parse:str, continue_from_where_we_left_last_time:bool=True, silent:bool=True):
Quique = bureaucrat
Quique.check_these_tasks_were_run_successfully(name_of_task_that_produced_the_waveforms_to_parse)
with Quique.handle_task('parse_waveforms', drop_old_data=not continue_from_where_we_left_last_time) as Quiques_employee:
try:
index_of_waveforms_already_parsed_in_the_past = set(load_whole_dataframe(Quiques_employee.path_to_directory_of_my_task/'parsed_from_waveforms.sqlite').index)
except Exception:
index_of_waveforms_already_parsed_in_the_past = set()
path_to_waveforms_file = Quiques_employee.path_to_directory_of_task(name_of_task_that_produced_the_waveforms_to_parse)/'waveforms.sqlite'
sqlite_connection = sqlite3.connect(path_to_waveforms_file)
index_df = load_only_index_without_repeated_entries(path_to_waveforms_file)
index_df = index_df.set_index(list(index_df.columns))
index_of_waveforms_that_still_need_to_be_parsed = set(index_df.index) - index_of_waveforms_already_parsed_in_the_past
index_of_waveforms_that_still_need_to_be_parsed = sorted(index_of_waveforms_that_still_need_to_be_parsed)
if not silent:
print(f'{len(index_of_waveforms_that_still_need_to_be_parsed)} waveforms still need to be parsed. The others were already parsed beforehand. Will now proceed...')
with SQLiteDataFrameDumper(Quiques_employee.path_to_directory_of_my_task/Path('parsed_from_waveforms.sqlite'), dump_after_n_appends = 1111, dump_after_seconds = 60, delete_database_if_already_exists=False) as parsed_data_dumper:
for k,idx in enumerate(index_of_waveforms_that_still_need_to_be_parsed):
sqlite_query = f'SELECT * from dataframe_table where ('
index_to_select = ''
for idx_val, idx_name in zip(idx, list(index_df.index.names)):
index_to_select += f'{idx_name}=={idx_val}'
index_to_select += ' and '
index_to_select = index_to_select[:-5]
sqlite_query += index_to_select + ')'
if not silent:
print(f'Processing {index_to_select} ({int(k/len(index_of_waveforms_that_still_need_to_be_parsed)*100)} %)...')
waveform_df = pandas.read_sql_query(
sqlite_query,
sqlite_connection,
)
signal = PeakSignal(time=waveform_df['Time (s)'], samples=waveform_df['Amplitude (V)'])
parsed_from_waveform = parse_waveform(signal)
for idx_val, idx_name in zip(idx, list(index_df.index.names)):
parsed_from_waveform[idx_name] = idx_val
parsed_from_waveform_df = pandas.DataFrame(
parsed_from_waveform,
index = [0],
).set_index(list(index_df.index.names), drop=True)
parsed_data_dumper.append(parsed_from_waveform_df)
if numpy.random.rand() < 20/len(index_df):
if signal.SNR > 10 or numpy.random.rand() < .4:
if not silent:
print(f'Doing plot of this signal...')
path_for_plots = Quiques_employee.path_to_directory_of_my_task/'plots_of_some_randomly_selected_waveforms'
path_for_plots.mkdir(exist_ok=True)
fig = plot_waveform(signal)
fig.write_html(
path_for_plots/f'{index_to_select.replace("==","_")}.html',
include_plotlyjs = 'cdn',
)
def parse_waveforms_recursively(bureaucrat:RunBureaucrat, name_of_task_that_produced_the_waveforms_to_parse:str, continue_from_where_we_left_last_time:bool=True, silent:bool=True):
if bureaucrat.was_task_run_successfully(name_of_task_that_produced_the_waveforms_to_parse):
if not silent:
print(f'Going to parse {bureaucrat.run_name}...')
parse_waveforms(
bureaucrat = bureaucrat,
name_of_task_that_produced_the_waveforms_to_parse = name_of_task_that_produced_the_waveforms_to_parse,
continue_from_where_we_left_last_time = continue_from_where_we_left_last_time,
silent = silent,
)
else:
for path_to_task in bureaucrat.path_to_run_directory.iterdir():
if path_to_task.is_dir():
for subrun in bureaucrat.list_subruns_of_task(path_to_task.parts[-1]):
parse_waveforms_recursively(
bureaucrat = subrun,
name_of_task_that_produced_the_waveforms_to_parse = name_of_task_that_produced_the_waveforms_to_parse,
continue_from_where_we_left_last_time = continue_from_where_we_left_last_time,
silent = silent,
)
if __name__=='__main__':
import argparse
parser = argparse.ArgumentParser(description='Cleans a beta scan according to some criterion.')
parser.add_argument('--dir',
metavar = 'path',
help = 'Path to the base measurement directory.',
required = True,
dest = 'directory',
type = str,
)
args = parser.parse_args()
parse_waveforms_recursively(
bureaucrat = RunBureaucrat(Path(args.directory)),
name_of_task_that_produced_the_waveforms_to_parse = 'test_beam',
silent = False,
continue_from_where_we_left_last_time = True,
)