sandp package

Submodules

sandp.event module

get event property

class sandp.event.Event(event_number, file_name, config)

Bases: object

Event class stores all the properties of event

baseline()

get information for baseline

boundary()

get S1, S2 information, including

• S1: list of list of left and right boundaries of S1
• S2: list of list of left and right boundaries of S2
• NbS1Peaks: number of S1 peaks
• NbS2Peaks: number of S2 peaks

coincidence()

S1s and S2s coincidence levels

get_config(cfg)

help __init__ get properties from configuration file

get_positions()

get xy position for event

main_s2_in_pmt()

get main s2 size in each PMT

peak_width()

get peak width and rise/drop time information

prepare_entropy()

get entropy for s1 and s2 peaks

size()

get s1 s2 size

• S1sTot: list of S1 size, indexed by size
• S2sTot: list of S2 size, indexed by size
• S1s_Key: Keys for S1 to be ordered by size
• S2s_Key: Keys for S2 to be ordered by size

uniformity()

sandp.Alls2 module

For single electron analysis

sandp.Alls2.get_all_scalar(arr1, ndarr2)

vectorize a scalar based on another vector

sandp.Alls2.get_all_vector(ndarr)

sandp.Alls2.get_all_vector_as_scalar(ndarr)

only select the first entry of a vector if it’s non-empty

sandp.Alls2.get_all_vector_by_other_vector(ndarr1, ndarr2)

get vector as scalar based on another vector, and assign nan if that vector is empty

sandp.Alls2.get_max(ndarr)

sandp.Alls2.load(input, processor='sandp_test')

load data into all s2 format. Input can be either folder name or run number

sandp.Alls2.load_data(file)

sandp.Alls2.to_new_df(data, amplifier=True)

Make dataframe for all S2s. It provides:

• run_number: number of the run
• event_id: event number in the run
• event_time: when event happened in unix time in seconds
• x: x position of event in arbitrary unit (a.u.)
• y: y position of event in a.u.
• s2: s2 size, PE
• main_s2: size of main s2 for the event where the s2 is, PE
• s2_width_50: s2 width of 50 percent area in us
• s2_width_90: s2 width of 90 percent area in us
• s2_rise_time: s2 rise time in us
• s2_drop_time: s2 drop time in us
• s1: s1 size in PE
• s1_time: center time of main s1 in us
• s2_time: center time of s2 in us
• main_s2_time: center time of main s2 in us
• s2_delay_time: how long s2 is delayed after main s2, us

sandp.utils module

Convert root into pandas dataframe

sandp.utils.code_hider()

Stolen from hax Make a button in the jupyter notebook to hide all code

sandp.utils.doc_s_to_run_info(doc_s, version_name)

get info of run (file location, amplifier_on, run_number) based on doc after selection and processor version name

sandp.utils.folders_to_file_s(folder, processor)

find files and amplifier conditions based on folder name(s). Return dictionary with keys of file_location and amplifier_on

sandp.utils.folders_to_path(folder, processor)

find absolute path of each folder based on name of folder and processor version

sandp.utils.get_coll()

get data collection info from mongodb

sandp.utils.get_datasets()

get run info as pd dataframe

sandp.utils.get_file_from_path(path)

get absolute path for files under certain path(s)

sandp.utils.get_processor_version_name(processor)

get processor name from processor name. You may find it stupid, but ‘.’ is not supported in BSON for mongodb.. Plus we would like to send a reminder if name goes wrong… limited choices!

sandp.utils.judge_str(input)

sandp.utils.load(input, processor='sandix_v1.1')

load data into pd dataframe by run numbers, or folder name

sandp.utils.load_dataframe(filename, amplifier=10)

convert ROOT file into dataframe format.

Parameters:filename – file name including the path Return data:data in pandas dataframe format

sandp.utils.load_path(path, amplifier=10)

Load all root files from specific path

sandp.utils.run_number_to_file_s(run_numbers, processor)

find file path(s) and amplifier condition based on run numbers