125 lines
4.2 KiB
Python
125 lines
4.2 KiB
Python
import os
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from collections import Counter
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import matplotlib.pyplot as plt
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import numpy as np
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import pandas as pd
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def linesplit(line):
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"""
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Splits line of file into useful components.
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Returns (dm, pulse width, originating filename).
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"""
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dm, line = line.split("pc/cc")
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dm = int(dm)
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pw, fname = line.split(" s ")
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pw = float(pw)
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fname = fname.strip().removesuffix("_injected")
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return (dm, pw, fname)
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def updateDic(lines, dic):
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"""
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Processes a list of lines and adds them to given dictionary in-place.
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"""
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for line in lines:
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dm, pw, fname = linesplit(line)
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dic["file"].append(fname)
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dic["dm"].append(dm)
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dic["pulseWidth"].append(pw)
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injectedDic = {
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"file" : [],
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"dm" : [],
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"pulseWidth" : []
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}
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detectedDic = {
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"file" : [],
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"dm" : [],
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"pulseWidth" : []
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}
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#load injected data into dataframe
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with open(os.path.join(".","out","2025-07-31T17-25-54.txt"), "r") as file:
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lines = file.readlines()
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updateDic(lines, injectedDic)
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injections = pd.DataFrame(data=injectedDic) #this is our main object for injection data
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#load detection data into dataframe
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with open(os.path.join(".","out","plotOut.txt"), "r") as file:
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lines = file.readlines()
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updateDic(lines, detectedDic)
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detections = pd.DataFrame(data=detectedDic) #this is our main object for detection data
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#define summary printing for multiple steps
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def summary(stage):
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print(f"Summary Stage {stage}")
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print(injections.head())
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print(f"Number of files injected: {len(Counter(injections['file']))}")
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print(f"Number of files with detections: {len(Counter(detections['file']))}")
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print("=========================")
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print(f"Number of pulses injected: {len(injections['dm'])}")
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print(f"Number of pulses detected: {len(detections['dm'])}")
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print("=========================")
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print(f"File ratio: {round(len(Counter(detections['file']))/len(Counter(injections['file'])), 3)}")
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print(f"Detection ratio: {round(len(detections['dm'])/len(injections['dm']), 3)}")
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print("=========================")
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#print initial summary
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summary(1)
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#many files contained pulsar bursts, so we filter those out via DM
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minDM = (10**2.5) * 0.95 #as per signal generation, plus a bit of wiggle room
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print(f"Filtering out pulsars (DM below {int(minDM)}...)")
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detections = detections[detections['dm'] > minDM]
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detections = detections.reset_index(drop=True)
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summary(2)
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#Let's do detection matching! Yaaaay!
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#What detections line up to which injections? This will determine which ones got missed entirely.
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#Define some kind of epsilon for DM and pulse width; if detection is within epsilon in DM we can match it.
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dmEps = 5
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#and define an auxiliary array of 0s for injections. List of detection counts!
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matchCount = np.zeros(len(injections['dm']), dtype=int)
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#also keep track of false positives:
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falsePositiveMask = [False] * len(detections['dm'])
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#Use queries to find matches
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for detection in detections.itertuples():
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qstring = (
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f"(file == '{detection.file}') & "
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f"((dm - @dmEps) < {detection.dm}) & "
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f"((dm + @dmEps) > {detection.dm})"
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)
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matches = injections.query(qstring)
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if len(matches) > 0:
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print(f"Detection: DM {detection.dm} and PW {detection.pulseWidth}")
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print(matches)
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if len(matches) == 1:
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i = matches.index[0]
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matchCount[i] += 1
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print("======")
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elif len(matches) > 1:
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raise ValueError("MULTIPLE MATCHES OHNO")
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else: #no matching injection...
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falsePositiveMask[detection.Index] = True
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print(f"NO MATCH FOR: DM {detection.dm} and PW {detection.pulseWidth}")
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print("Injections in file:")
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print(injections.query(f"(file == '{detection.file}')"))
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matchMaskInj = [matchCount > 0]
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matchMaskDet = np.logical_not(falsePositiveMask)
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missedMask = [matchCount == 0]
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#So where are we?
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#We have multiple datasets.
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#1. List of all injected pulses. [injections]
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#2. List of detections with pulsars filtered out. [detections]
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#3. Number of times each injection was detected [matchCount]
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#4. A mask for only detected injections [matchMaskInj]
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#5. A mask for only true positives [matchMaskDet]
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#6. A mask for only missed injections [missedMask]
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#7. A mask for false positives [falsePositiveMask] |