testanalysis.py

@@ -1,125 +0,0 @@
-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(injections.head())
-    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]
-detections = detections.reset_index(drop=True)
-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 DM we can match it.
-dmEps = 5
-#and define an auxiliary array of 0s for injections. List of detection counts!
-matchCount = np.zeros(len(injections['dm']), dtype=int)
-#also keep track of false positives:
-falsePositiveMask = [False] * len(detections['dm'])
-#Use queries to find matches
-for detection in detections.itertuples():
-    qstring = (
-        f"(file == '{detection.file}') & "
-        f"((dm - @dmEps) < {detection.dm}) & "
-        f"((dm + @dmEps) > {detection.dm})"
-    )
-    matches = injections.query(qstring)
-    if len(matches) > 0:
-        print(f"Detection: DM {detection.dm} and PW {detection.pulseWidth}")
-        print(matches)
-        if len(matches) == 1:
-            i = matches.index[0]
-            matchCount[i] += 1
-            print("======")
-        elif len(matches) > 1:
-            raise ValueError("MULTIPLE MATCHES OHNO")
-    else: #no matching injection...
-        falsePositiveMask[detection.Index] = True
-        print(f"NO MATCH FOR: DM {detection.dm} and PW {detection.pulseWidth}")
-        print("Injections in file:")
-        print(injections.query(f"(file == '{detection.file}')"))
-matchMaskInj = [matchCount > 0]
-matchMaskDet = np.logical_not(falsePositiveMask)
-missedMask = [matchCount == 0]
-
-#So where are we?
-#We have multiple datasets.
-#1. List of all injected pulses. [injections]
-#2. List of detections with pulsars filtered out. [detections]
-#3. Number of times each injection was detected [matchCount]
-#4. A mask for only detected injections [matchMaskInj]
-#5. A mask for only true positives [matchMaskDet]
-#6. A mask for only missed injections [missedMask]
-#7. A mask for false positives [falsePositiveMask]