mag.py

import pandas as pd
import numpy as np
import scipy as sp
import glob
import matplotlib.pyplot as plt
import matplotlib as mpl
import os
import scipy.signal as sps
from datetime import datetime, timedelta
import time
import math

def mag(filepath, day, start_dt=None, end_dt=None, plot = False):
    
    if day == 1:
        origin = datetime(2019,6,21, hour = 8, minute = 57, second = 4)
    elif day == 2:
        origin = datetime(2019,6,24, hour = 7, minute = 48, second = 19)

    if start_dt == None:
        start_dt = origin
        skiprows = 0
        dtime = start_dt - origin
    else:
        assert start_dt >= origin
        dtime = start_dt - origin
        skiprows = int(dtime.total_seconds()*128 - 0.518/(1/128))
    
    if end_dt != None:
        assert (end_dt-origin).total_seconds()*128 <= 3332096 #making sure the end_dt time is within the file
        des_time = end_dt - start_dt
        nrows = int(des_time.total_seconds()*128 - 0.518/(1/128))
    else:
        nrows = int(3332096 - int(dtime.total_seconds()*128 - 0.518/(1/128)))


    df = pd.read_csv(filepath, header = None, skiprows = skiprows, nrows = nrows)

    df.columns = ['time','X','Y','Z']
    df['time'] = df['time'] + 0.518


    #df.index = df.index*(1/128) #hardcoding 128 vectors/second
    df.index = pd.to_datetime(df['time'], unit = 's', origin = origin) #microsecond not added because when to_datetime unit must be 's' (due to data format of csv)
    df = df.loc[:, 'X':]
    #print(df.head())
    #print(df.tail())

    if plot:
        plt.figure()
        cols = df.columns.tolist()
        for col in cols:
            plt.plot(df.index.time, df[col], label =f'{col}')
        plt.xlabel('Time [H:M:S]')
        plt.ylabel('B [nT]')
        plt.legend(loc="best")
        plt.title(f'MAG Powered Day {day}')
        
        #finding the calibration spikes - only appropriate if not in the current time span when the spikes occur (at beginning of data - but first 3 spikes seen are MAG changing measurement ranges)
        time_list = []
        df2 = df.abs()
        for col in cols:
            time_list.append(df2[col].idxmax())
        print(time_list)
        print(time_list[2]- time_list[0], time_list[1]-time_list[2])

        plt.show()
        print (type(df))
    return df 
    
if __name__ == "__main__":
    day = 1
    windows = False
    

    if day == 1:
        if windows:
            filepath = r'C:\Users\jonas\MSci-Data\PoweredDay1.csv'
        else:
            filepath = os.path.expanduser("~/Documents/MSciProject/Data/mag/Day1MAGBurst.csv")
        
    if day == 2:
        if windows:
            filepath = r'C:\Users\jonas\MSci-Data\PoweredDay2.csv.txt'
        else:
            filepath = os.path.expanduser("~/Documents/MSciProject/Data/mag/PoweredDay2.csv.txt")
        
    start_dt = datetime(2019,6,21,8,58)# this is the start of the time we want to look at, #datetime(2019,6,21,10,57,50)
    end_dt = datetime(2019,6,21,12,30)# this is the end

    df = mag(filepath, day, start_dt=start_dt, end_dt=end_dt, plot = True)