# Path: Modules/fourier.py
# def CalcFourier(signal):
# def CalcFourierPower(signal, fs, fc):
# def CalcIFourier(signal):
#     N = transform.shape[0]
# 
# Path: Modules/SignalData.py
# class Signal(object):
#     def __init__(self, filename=None, filetype=None, filedata=None, Fsample=None, Fcentre=None):
#     def setvalues(self, filename, filetype, Fsample, Fcentre, filedata=None):
#     def setdatavalue(self, filedata):
#     def getvalues(self):


import numpy as np
import matplotlib.pyplot as plt
from scipy.signal import butter, lfilter
from Modules import fourier
from Modules import SignalData
    nhigh = int((Fhigh - (fc - fs / 2)) // (fs / length))
    if abs(Flow - freq[nlow]) > abs(Flow - freq[nlow + 1]):
        nlow = nlow + 1
    if abs(Fhigh - freq[nhigh]) > abs(Fhigh - freq[nhigh + 1]):
        nhigh = nhigh + 1

    filter_array[:nlow] = 0 + 0j
    filter_array[nhigh:] = 0 + 0j

    signal_filtered = np.zeros(
        chunknumber * (chunksize // 2), dtype=np.complex64)

    for i in range(0, chunknumber):
        startslice = i * chunksize
        endslice = startslice + chunksize

        signal_chunk = signal[startslice:endslice]
        signal_chunk_iq = np.empty(
            signal_chunk.shape[0] // 2, dtype=np.complex64)

        signal_chunk_iq.real = signal_chunk[::2] - 127.5
        signal_chunk_iq.imag = signal_chunk[1::2] - 127.5

        ''' fft start + shifting '''
        signalFFT = fourier.CalcFourier(
            signal_chunk_iq)

        # Box filter
        new_signalFFT = signalFFT * filter_array


# Path: Modules/fourier.py
# def CalcFourier(signal):
# def CalcFourierPower(signal, fs, fc):
# def CalcIFourier(signal):
#     N = transform.shape[0]
# 
# Path: Modules/SignalData.py
# class Signal(object):
#     def __init__(self, filename=None, filetype=None, filedata=None, Fsample=None, Fcentre=None):
#     def setvalues(self, filename, filetype, Fsample, Fcentre, filedata=None):
#     def setdatavalue(self, filedata):
#     def getvalues(self):


# Path: Modules/bandpass.py
import numpy as np
import matplotlib.pyplot as plt
from scipy.signal import butter, lfilter
from Modules import fourier
from Modules import SignalData

    nhigh = int((Fhigh - (fc - fs / 2)) // (fs / length))
    if abs(Flow - freq[nlow]) > abs(Flow - freq[nlow + 1]):
        nlow = nlow + 1
    if abs(Fhigh - freq[nhigh]) > abs(Fhigh - freq[nhigh + 1]):
        nhigh = nhigh + 1

    filter_array[:nlow] = 0 + 0j
    filter_array[nhigh:] = 0 + 0j

    signal_filtered = np.zeros(
        chunknumber * (chunksize // 2), dtype=np.complex64)

    for i in range(0, chunknumber):
        startslice = i * chunksize
        endslice = startslice + chunksize

        signal_chunk = signal[startslice:endslice]
        signal_chunk_iq = np.empty(
            signal_chunk.shape[0] // 2, dtype=np.complex64)

        signal_chunk_iq.real = signal_chunk[::2] - 127.5
        signal_chunk_iq.imag = signal_chunk[1::2] - 127.5

        ''' fft start + shifting '''
        signalFFT = fourier.CalcFourier(
            signal_chunk_iq)

        # Box filter
        new_signalFFT = signalFFT * filter_array


        signal_back = (fourier.CalcIFourier(new_signalFFT))