236 lines
8.9 KiB
Python
236 lines
8.9 KiB
Python
import os
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from collections import Counter
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from time import sleep
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import logging
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import matplotlib.pyplot as plt
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from matplotlib import rcParams
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import numpy as np
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import pandas as pd
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logger = logging.getLogger(__name__)
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logging.basicConfig(level=logging.INFO) #change for debug prints
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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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logger.info(f"Summary Stage {stage}")
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logger.info(f"Number of files injected: {len(Counter(injections['file']))}")
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logger.info(f"Number of files with detections: {len(Counter(detections['file']))}")
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logger.info("=========================")
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logger.info(f"Number of pulses injected: {len(injections['dm'])}")
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logger.info(f"Number of pulses detected: {len(detections['dm'])}")
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logger.info("=========================")
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logger.info(f"File ratio: {round(len(Counter(detections['file']))/len(Counter(injections['file'])), 3)}")
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logger.info("=========================")
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#print initial summary
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summary(1)
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#let's track how many detections get removed by filtering
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preFilterCount = len(detections['dm'])
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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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logger.info(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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#five files have SO MANY FALSE POSITIVES so get rid of them here?
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remFiles = ["data_2025-04-30_07-53-07", "data_2025-05-01_07-47-34", "data_2025-04-24_07-36-04", "data_2025-04-29_07-50-16", "data_2025-04-30_08-18-17"]
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logger.info("Filtering out the five problem files...")
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remMask = [True] * len(detections['dm'])
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for detection in detections.itertuples():
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if detection.file in remFiles:
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remMask[detection.Index] = False
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detections = detections[remMask]
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detections = detections.reset_index(drop=True)
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summary(3)
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postFilterCount = len(detections['dm'])
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logger.info(f"Removed {preFilterCount-postFilterCount} detections by filtering the following:")
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for file in remFiles:
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logger.info(file+"_injected.fil")
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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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logger.debug(f"Detection: DM {detection.dm} and PW {detection.pulseWidth}")
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logger.debug(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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logger.debug("======")
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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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logger.debug(f"NO MATCH FOR: DM {detection.dm} and PW {detection.pulseWidth}")
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logger.debug("Injections in file:")
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logger.debug(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. Dataframe of all injected pulses. [injections]
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#2. Dataframe of detections with pulsars filtered out. [detections]
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#3. List of 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]
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logger.info(f"Successful detection ratio: {Counter(matchMaskInj)[True]/(len(injections['dm']))}")
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#Let's try to figure out if certain files are responsible for the weird amount of false
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#positives at around 10^2.7 pc/cc
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#IT WAS 5 FILES. Filtered out above.
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fpFileCounts = Counter(detections[falsePositiveMask]['file'])
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sortedFPCounts = [(k, v) for k,v in sorted(fpFileCounts.items(), key=lambda value: -value[1])]
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logger.info("False positive counts:")
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for f, c in sortedFPCounts:
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logger.info(f"{f}: {c}")
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#Let's set a matplotlib default to make figures a bit bigger cos i like them
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rcParams["figure.figsize"] = [7,5]
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plt.rcParams['figure.constrained_layout.use'] = True
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# #Injected pulses
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# plt.figure(figsize=(7,10))
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# allAx = plt.subplot(311)
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# _, bins, _ = allAx.hist(np.log10(injections['dm']), bins=15)
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# allAx.set_title("All injected pulses")
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# detAx = plt.subplot(312, sharex=allAx, sharey=allAx)
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# detAx.hist(np.log10(injections['dm'][matchMaskInj]), bins=bins)
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# detAx.set_title("Detected pulses")
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# misAx = plt.subplot(313, sharex=allAx, sharey=allAx)
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# misAx.hist(np.log10(injections['dm'][missedMask]), bins=bins)
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# misAx.set_title("Missed pulses")
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# plt.ylabel("Count")
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# plt.xlabel(r"DM (log pc cm$^3$)")
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# plt.draw()
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# #Detected pulses unstacked
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# plt.figure(figsize=(7,10))
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# allAx = plt.subplot(311)
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# _, bins, _ = allAx.hist(np.log10(detections['dm']), bins=15)
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# allAx.set_title("All detections")
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# detAx = plt.subplot(312, sharex=allAx, sharey=allAx)
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# detAx.hist(np.log10(detections['dm'][matchMaskDet]), bins=bins)
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# detAx.set_title("Detected pulses")
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# misAx = plt.subplot(313, sharex=allAx, sharey=allAx)
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# misAx.hist(np.log10(detections['dm'][falsePositiveMask]), bins=bins)
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# misAx.set_title("False positives")
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# plt.ylabel("Count")
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# plt.xlabel(r"DM (log pc cm$^3$)")
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# plt.draw()
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#Stacked histogram of injections
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plt.figure(figsize=(7,10))
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ax = plt.subplot(17,1,(1,8))
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ax.hist([np.log10(injections['dm'][matchMaskInj]), np.log10(injections['dm'][missedMask])],
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stacked=True, label=['Detected', 'Missed'], bins=15)
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ax.yaxis.get_major_locator().set_params(integer=True)
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ax.legend(loc='upper center')
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ax.set_xlabel(r"DM (log pc cm$^{-3}$)")
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ax.tick_params(labelbottom=True)
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ax2 = plt.subplot(17,1,(9,16))
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ax2.hist([np.log10(injections['pulseWidth'][matchMaskInj]), np.log10(injections['pulseWidth'][missedMask])],
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stacked=True, label=['Detected', 'Missed'], bins=15)
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ax2.yaxis.get_major_locator().set_params(integer=True)
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ax2.set_xlabel(r"Pulse width (log s)")
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plt.ylabel("Count")
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plt.title("Injected pulses")
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wordAx = plt.subplot(17,1,17)
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wordAx.text(.3,.5,f"Overall detection rate: {round(Counter(matchMaskInj)[True]/(len(injections['dm']))*100,1)}%", size=14)
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wordAx.set_axis_off()
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plt.savefig(os.path.join("out","injections.png"))
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#Stacked histogram of detections
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plt.figure(figsize=(7,10))
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ax = plt.subplot(17,1,(1,8))
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ax.hist([np.log10(detections['dm'][matchMaskDet]), np.log10(detections['dm'][falsePositiveMask])],
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stacked=True, label=['True detection', 'False positive'], bins=15)
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ax.yaxis.get_major_locator().set_params(integer=True)
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ax.set_xlabel(r"DM (log pc cm$^{-3}$)")
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ax.legend(loc='upper center')
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ax2 = plt.subplot(17,1,(9,16))
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ax2.hist([np.log10(detections['pulseWidth'][matchMaskDet]), np.log10(detections['pulseWidth'][falsePositiveMask])],
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stacked=True, label=['True detection', 'False positive'], bins=15)
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ax2.yaxis.get_major_locator().set_params(integer=True)
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ax2.set_xlabel("Pulse width (log s)")
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plt.ylabel("Count")
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plt.title("Detections")
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wordAx = plt.subplot(17,1,17)
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wordAx.text(.3,.5,f"False positive rate: {round(Counter(falsePositiveMask)[True]/(len(detections['dm']))*100,1)}%", size=14)
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wordAx.set_axis_off()
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plt.savefig(os.path.join("out","detections.png")) |