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]