dataset poking complete, time for stats

testanalysis.py

@@ -59,6 +59,7 @@ detections = pd.DataFrame(data=detectedDic) #this is our main object for detecti
 #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("=========================")
@@ -76,12 +77,49 @@ summary(1)
 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 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'])
+#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]