88 lines
2.7 KiB
Python
88 lines
2.7 KiB
Python
import os
|
|
from collections import Counter
|
|
|
|
import matplotlib.pyplot as plt
|
|
import numpy as np
|
|
import pandas as pd
|
|
|
|
def linesplit(line):
|
|
"""
|
|
Splits line of file into useful components.
|
|
|
|
Returns (dm, pulse width, originating filename).
|
|
"""
|
|
|
|
dm, line = line.split("pc/cc")
|
|
dm = int(dm)
|
|
pw, fname = line.split(" s ")
|
|
pw = float(pw)
|
|
fname = fname.strip().removesuffix("_injected")
|
|
return (dm, pw, fname)
|
|
|
|
def updateDic(lines, dic):
|
|
"""
|
|
Processes a list of lines and adds them to given dictionary in-place.
|
|
"""
|
|
|
|
for line in lines:
|
|
dm, pw, fname = linesplit(line)
|
|
dic["file"].append(fname)
|
|
dic["dm"].append(dm)
|
|
dic["pulseWidth"].append(pw)
|
|
|
|
injectedDic = {
|
|
"file" : [],
|
|
"dm" : [],
|
|
"pulseWidth" : []
|
|
}
|
|
|
|
detectedDic = {
|
|
"file" : [],
|
|
"dm" : [],
|
|
"pulseWidth" : []
|
|
}
|
|
|
|
#load injected data into dataframe
|
|
with open(os.path.join(".","out","2025-07-31T17-25-54.txt"), "r") as file:
|
|
lines = file.readlines()
|
|
updateDic(lines, injectedDic)
|
|
|
|
injections = pd.DataFrame(data=injectedDic) #this is our main object for injection data
|
|
|
|
#load detection data into dataframe
|
|
with open(os.path.join(".","out","plotOut.txt"), "r") as file:
|
|
lines = file.readlines()
|
|
updateDic(lines, detectedDic)
|
|
|
|
detections = pd.DataFrame(data=detectedDic) #this is our main object for detection data
|
|
|
|
#define summary printing for multiple steps
|
|
def summary(stage):
|
|
print(f"Summary Stage {stage}")
|
|
print(f"Number of files injected: {len(Counter(injections['file']))}")
|
|
print(f"Number of files with detections: {len(Counter(detections['file']))}")
|
|
print("=========================")
|
|
print(f"Number of pulses injected: {len(injections['dm'])}")
|
|
print(f"Number of pulses detected: {len(detections['dm'])}")
|
|
print("=========================")
|
|
print(f"File ratio: {round(len(Counter(detections['file']))/len(Counter(injections['file'])), 3)}")
|
|
print(f"Detection ratio: {round(len(detections['dm'])/len(injections['dm']), 3)}")
|
|
print("=========================")
|
|
|
|
#print initial summary
|
|
summary(1)
|
|
|
|
#many files contained pulsar bursts, so we filter those out via DM
|
|
minDM = (10**2.5) * 0.95 #as per signal generation, plus a bit of wiggle room
|
|
print(f"Filtering out pulsars (DM below {int(minDM)}...)")
|
|
detections = detections[detections['dm'] > minDM]
|
|
summary(2)
|
|
|
|
#Let's do detection matching! Yaaaay!
|
|
#What detections line up to which injections? This will determine which ones got missed entirely.
|
|
#Define some kind of epsilon for DM and pulse width; if detection is within epsilon in both DM and PW we can match it.
|
|
dmEps = 0.05
|
|
pwEps = 0.2
|
|
#and define an auxiliary array of bools for injections containing the "is matched?" information.
|
|
isMatched = [False] * len(injections['dm'])
|