# -*- coding: utf-8 -*- """ Created on Fri Nov 18 10:39:10 2022 @author: ppantina """ import numpy as np import datetime from matplotlib import pyplot as plt import glob def L0_parseTelemetry_TESTARENA (files, key): ##Define some variables filenames = np.sort(glob.glob(files)) cat = 0 for i, j in enumerate(filenames): ##for all files print (j) data = {} if key == 'hkp': raw = np.genfromtxt(j, dtype = ['S3', 'S19', 'i','i','i','i','S1','S6','i',\ 'i','i','i','i','S6','S6','f','f','f', 'f',\ 'f','f','f','f','f','f','f','f','f'], delimiter = ',', skip_footer = 1,invalid_raise=False) data['instrumentName'] = raw['f0'] data['time'] = raw['f1'] data['instrumentStatus'] = raw['f2'] data['arenaState'] = raw['f3'] data['arenaTargetState'] = raw['f4'] data['acqAutoModeState'] = raw['f5'] data['acqAutoModeCompliance'] = raw['f6'] data['acqAutoModeTransition'] = raw['f7'] data['arenaControlTimeoutCount']= raw['f8'] data['numGPS'] = raw['f9'] data['numNAV'] = raw['f10'] data['fault1'] = raw['f11'] data['fault2'] = raw['f12'] data['ttlOutputs'] = raw['f13'] data['ttlInputs'] = raw['f14'] data['tempCTU'] = raw['f15'] data['tempRfElectronics'] = raw['f16'] data['tempRfBase'] = raw['f17'] data['voltagePowerDectector'] = raw['f18'] data['pressureRf'] = raw['f19'] data['tempIFLO'] = raw['f20'] data['tempSSPA'] = raw['f21'] data['currentDC1'] = raw['f22'] data['currentDC2'] = raw['f23'] data['currentDC3'] = raw['f24'] data['tempPDU'] = raw['f25'] data['pressurePDU'] = raw['f26'] data['tempPC104'] = raw['f27'] seconds= np.zeros(len(data['time'])) for i, j in enumerate(data['time']): datestr = str(j, 'utf-8') yy = int(datestr[0 :4] ) mm = int(datestr[4 :6] ) dd = int(datestr[6 :8] ) HH = int(datestr[9 :11]) MM = int(datestr[11:13]) SS = int(datestr[13:15]) UU = int(datestr[16:19]) seconds[i] = datetime.datetime(yy,mm, dd, HH, MM, SS).timestamp() + UU/1000 data['seconds'] = seconds data['cpusec'] =np.floor(data['seconds'][:]/1000.) data['cpunsec'] = 1e9*(data['seconds'][:]/1000. - np.floor(data['seconds'][:]/1000.)) elif key == 'nav': raw = np.genfromtxt(j, dtype = ['i', 'S19', 'i8', 'i', 'S9', 'S23',\ 'f', 'f', 'f', 'f', 'f', 'f', 'f', \ 'f', 'f', 'f', 'f', 'f', 'f', 'f', 'f', 'f', 'f',\ 'f', 'f', 'f', 'f', 'f', 'f', 'f', 'f', 'f', 'f',\ 'f', 'f', 'f', 'f'], delimiter = ',', skip_footer = 1, invalid_raise=False) data['numRecord'] = raw['f0'] data['time'] = raw['f1'] data['seconds'] = raw['f2'] data['cpusec'] =np.floor(data['seconds']/1000.) data['cpunsec'] = 1e9*(data['seconds']/1000. - np.floor(data['seconds']/1000.)) data['unknown1'] = raw['f3'] data['navType'] = raw['f4'] data['datetime'] = raw['f5'] data['IWG_lat'] = raw['f6'] data['IWG_lon'] = raw['f7'] data['gpsMSLalt'] = raw['f8'] data['wgs84alt'] = raw['f9'] data['pressureAlt'] = raw['f10'] data['radarAlt'] = raw['f11'] data['IWG_gspeed'] = raw['f12'] data['IWG_aspeed'] = raw['f13'] data['indicatedAirSpeed'] = raw['f14'] data['machNumber'] = raw['f15'] data['IWG_upspeed'] = raw['f16'] data['IWG_head'] = raw['f17'] data['IWG_track'] = raw['f18'] data['IWG_drift'] = raw['f19'] data['IWG_pitch'] = raw['f20'] data['IWG_roll'] = raw['f21'] data['sideSlip'] = raw['f22'] data['angleOfAttack'] = raw['f23'] data['ambientTemp'] = raw['f24'] data['dewPoint'] = raw['f25'] data['totalTemp'] = raw['f26'] data['staticPressure'] = raw['f27'] data['dynamicPressure'] = raw['f28'] data['cabinPressure'] = raw['f29'] data['IWG_windspeed'] = raw['f30'] data['IWG_winddirection'] = raw['f31'] data['IWG_vwind'] = raw['f32'] data['solarZenith'] = raw['f33'] data['sunElevAC'] = raw['f34'] data['sunAzGrd'] = raw['f35'] data['sunAzAC'] = raw['f36'] elif key == 'gps': raw = np.genfromtxt(j, dtype = ['i', 'S19', 'i8', 'i', 'S9', 'i',\ 'S', 'f', 'f', 'f', 'f', 'f', 'f', \ 'f', 'f','f','f', 'S'], delimiter = ',', skip_footer = 1, invalid_raise=False) data['numRecord'] = raw['f0'] data['time'] = raw['f1'] data['seconds'] = raw['f2'] data['cpusec'] =np.floor(data['seconds']/1000.) data['cpunsec'] = 1e9*(data['seconds']/1000. - np.floor(data['seconds']/1000.)) data['unknown1'] = raw['f3'] data['navType'] = raw['f4'] data['INSPVA_week'] = raw['f5'] data['unknown3'] = raw['f6'] data['INSPVA_sec'] = raw['f7'] data['INSPVA_lat'] = raw['f8'] data['INSPVA_lon'] = raw['f9'] data['INSPVA_height'] = raw['f10'] data['INSPVA_nvel'] = raw['f11'] data['INSPVA_evel'] = raw['f12'] data['INSPVA_upvel'] = raw['f13'] data['INSPVA_roll'] = raw['f14'] data['INSPVA_pitch'] = raw['f15'] data['INSPVA_head'] = raw['f16'] data['unknown5'] = raw['f17'] ##Create or concatenate the output dict if cat == 0: dataOut = data.copy() cat = 1 else: ##for each key, cat the values together dataOut = {key:np.concatenate([dataOut[key],data[key]], axis = 0) for key in dataOut.keys()} print ('catting') #endif ''' ##Convert time string to array time= np.zeros(len(data['time']), dtype = 'float64') for i, j in enumerate(data['time']): datestr = str(j, 'utf-8') yy = int(datestr[0 :4] ) mm = int(datestr[4 :6] ) dd = int(datestr[6 :8] ) HH = int(datestr[9 :11]) MM = int(datestr[11:13]) SS = int(datestr[13:15]) sdate_day = datetime.datetime.toordinal(datetime.date(yy, mm, dd)) sdate_frac = HH/24. + MM/1440. + SS/86400. time[i] = sdate_day + sdate_frac keys = data.keys() for i, j in enumerate(keys): if ((data[j].dtype == 'int32') | (data[j].dtype == 'float32')): fig = plt.figure() ax = fig.add_subplot(111) ax.plot(time, data[j]) ax.set_xlabel('Serial Date') ax.set_ylabel('Parameter') ax.set_title(j) plt.show() ''' return(dataOut)