<reponame>Justgo13/sensor_library
# This script is used to read the binary file produced by the DCA1000 and Mmwave Studio
# Command to run in Matlab GUI - readDCA1000('<ADC capture bin file>') 
import numpy as np
import csv


def readTIdata(filename,csvname):
    """
    Takes a .bin binary file and outputs the iq data to a csv file specified by csvname.

    :parameter:

    filename: str
        file name of binary file.

    csvname: str
        csv file name that stores the iq data from binary file.

    :example:

    >>> readTIdata('TIdata.bin','TIdata')
    >>> 'converted'

    :return:

    A csv file with the iq data taken from the binary file.
    """
    # global variables
    # change based on sensor config
    numADCSamples = 256; # number of ADC samples per chirp
    numADCBits = 16; # number of ADC bits per sample
    numRX = 4; # number of receivers
    numLanes = 2; # do not change. number of lanes is always 2
    isReal = 0; # set to True if real only data, False if complex data
    # read file
    # read .bin file
    with open(filename,'rb') as f:
        adcData = np.fromfile(filename, dtype = 'int16', count = -1)
        adcData = np.transpose([adcData])
        # if 12 or 14 bits ADC per sample compensate for sign extension
        if numADCBits != 16:
            l_max = 2**(numADCBits-1)-1
            # If value greater than l_max, this loop prevents it
            for index,val in enumerate(adcData):
                if adcData[index] > l_max:
                    adcData[index] -= 2**(numADCBits)
        fileSize = len(adcData)
        # real data reshape, filesize = numADCSamples*numChirps

        if isReal:
            numChirps = int(fileSize/numADCSamples/numRX)
            LVDS = np.zeros((1,fileSize),dtype = 'int16')
            # each row is data from one chirp
            LVDS = np.reshape(adcData,(numChirps,numADCSamples*numRX))
        else:
            # for complex data
            # filesize = 2 * numADCSamples*numChirps
            numChirps = int(fileSize/2/numADCSamples/numRX)
            LVDS = np.zeros((1,int(fileSize/2)),dtype = 'complex')
            # combine real and imaginary part into complex data
            # read in file: 2I is followed by 2Q
            counter = 0
            for i in range(0,fileSize,4):
                LVDS[0,counter] = complex(adcData[i,0],adcData[i+2,0])
                LVDS[0,counter+1] = complex(adcData[i+1,0],adcData[i+3,0])
                counter += 2
            # each row is data from one chirp
            LVDS = np.reshape(LVDS,(numChirps,numADCSamples*numRX))
        #organize data per RX
        adcData = np.zeros((numRX,numChirps*numADCSamples), dtype = 'complex')
        for row in range(0,numRX):
            for i in range(0,numChirps):
                adcData[row,i*numADCSamples:(i+1)*numADCSamples] = LVDS[i,row*numADCSamples:(row+1)*numADCSamples]

        for row in range(0,numRX):
            for col in range(0,numChirps*numADCSamples):

                with open(csvname+'.csv','a', newline="") as csvFile:
                    writer = csv.writer(csvFile)
                    writer.writerow(adcData[row, col])
    f.close()
    csvFile.close()
    print('converted')


# This script is used to read the binary file produced by the DCA1000 and Mmwave Studio
# Command to run in Matlab GUI - readDCA1000('<ADC capture bin file>') 
import numpy as np
import csv


def readTIdata(filename,csvname):
    """
    Takes a .bin binary file and outputs the iq data to a csv file specified by csvname.

    :parameter:

    filename: str
        file name of binary file.

    csvname: str
        csv file name that stores the iq data from binary file.

    :example:

    >>> readTIdata('TIdata.bin','TIdata')
    >>> 'converted'

    :return:

    A csv file with the iq data taken from the binary file.
    """
    # global variables
    # change based on sensor config
    numADCSamples = 256; # number of ADC samples per chirp
    numADCBits = 16; # number of ADC bits per sample
    numRX = 4; # number of receivers
    numLanes = 2; # do not change. number of lanes is always 2
    isReal = 0; # set to True if real only data, False if complex data
    # read file
    # read .bin file
    with open(filename,'rb') as f:
        adcData = np.fromfile(filename, dtype = 'int16', count = -1)
        adcData = np.transpose([adcData])
        # if 12 or 14 bits ADC per sample compensate for sign extension
        if numADCBits != 16:
            l_max = 2**(numADCBits-1)-1
            # If value greater than l_max, this loop prevents it
            for index,val in enumerate(adcData):
                if adcData[index] > l_max:
                    adcData[index] -= 2**(numADCBits)
        fileSize = len(adcData)
        # real data reshape, filesize = numADCSamples*numChirps

        if isReal:
            numChirps = int(fileSize/numADCSamples/numRX)
            LVDS = np.zeros((1,fileSize),dtype = 'int16')
            # each row is data from one chirp
            LVDS = np.reshape(adcData,(numChirps,numADCSamples*numRX))
        else:
            # for complex data
            # filesize = 2 * numADCSamples*numChirps
            numChirps = int(fileSize/2/numADCSamples/numRX)
            LVDS = np.zeros((1,int(fileSize/2)),dtype = 'complex')
            # combine real and imaginary part into complex data
            # read in file: 2I is followed by 2Q
            counter = 0
            for i in range(0,fileSize,4):
                LVDS[0,counter] = complex(adcData[i,0],adcData[i+2,0])
                LVDS[0,counter+1] = complex(adcData[i+1,0],adcData[i+3,0])
                counter += 2
            # each row is data from one chirp
            LVDS = np.reshape(LVDS,(numChirps,numADCSamples*numRX))
        #organize data per RX
        adcData = np.zeros((numRX,numChirps*numADCSamples), dtype = 'complex')
        for row in range(0,numRX):
            for i in range(0,numChirps):
                adcData[row,i*numADCSamples:(i+1)*numADCSamples] = LVDS[i,row*numADCSamples:(row+1)*numADCSamples]

        for row in range(0,numRX):
            for col in range(0,numChirps*numADCSamples):

                with open(csvname+'.csv','a', newline="") as csvFile:
                    writer = csv.writer(csvFile)
                    writer.writerow(adcData[row, col])
    f.close()
    csvFile.close()
    print('converted')
# This script is used to read the binary file produced by the DCA1000 and Mmwave Studio # Command to run in Matlab GUI - readDCA1000('<ADC capture bin file>') Takes a .bin binary file and outputs the iq data to a csv file specified by csvname.      :parameter:      filename: str         file name of binary file.      csvname: str         csv file name that stores the iq data from binary file.      :example:      >>> readTIdata('TIdata.bin','TIdata')     >>> 'converted'      :return:      A csv file with the iq data taken from the binary file. # global variables # change based on sensor config # number of ADC samples per chirp # number of ADC bits per sample # number of receivers # do not change. number of lanes is always 2 # set to True if real only data, False if complex data # read file # read .bin file # if 12 or 14 bits ADC per sample compensate for sign extension # If value greater than l_max, this loop prevents it # real data reshape, filesize = numADCSamples*numChirps # each row is data from one chirp # for complex data # filesize = 2 * numADCSamples*numChirps # combine real and imaginary part into complex data # read in file: 2I is followed by 2Q # each row is data from one chirp #organize data per RX