How to use complexToFloat method in Airtest

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...72 if _data == 0:73 return "false"74 return "true"75 elif _type == tc.TYPE_DIMENSION:76 return "%f%s" % (self.complexToFloat(_data), tc.DIMENSION_UNITS[_data & tc.COMPLEX_UNIT_MASK])77 elif _type == tc.TYPE_FRACTION:78 return "%f%s" % (self.complexToFloat(_data), tc.FRACTION_UNITS[_data & tc.COMPLEX_UNIT_MASK])79 elif _type >= tc.TYPE_FIRST_COLOR_INT and _type <= tc.TYPE_LAST_COLOR_INT:80 return "#%08X" % _data81 elif _type >= tc.TYPE_FIRST_INT and _type <= tc.TYPE_LAST_INT:82 if _data > 0x7fffffff:83 _data = (0x7fffffff & _data) - 0x8000000084 return "%d" % _data85 elif _type == tc.TYPE_INT_DEC:86 return "%d" % _data87 # raise exception here?88 return "<0x%X, type 0x%02X>" % (_data, _type)89 def complexToFloat(self, xcomplex):90 return (float)(xcomplex & 0xFFFFFF00) * tc.RADIX_MULTS[(xcomplex>>4) & 3];91 def getPackage(self, id):92 if id >> 24 == 1:93 return "android:"...

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1import tensorflow as tf2import keras.backend as K3from keras.models import Model4from keras.layers import Input, Dense, BatchNormalization, ReLU, Lambda, Concatenate, Flatten, Add5def IFFT(sig, name=None):6 N = int(sig.shape[-1])7 return Lambda(tf.ifft, name=name, output_shape=(1,N))(sig)8def FloatToComplex(sig, name=None):9 N = int(sig.shape[-1])10 return Lambda(lambda x : tf.complex(x[:, 0:1, :], x[:, 1:2, :]), name=name, output_shape=(1,N))(sig)11def ComplexToFloat(sig, name=None):12 N = int(sig.shape[-1])13 return Concatenate(name=name, axis=-2)([Lambda(tf.real, output_shape=(1, N), name=name+'_Re')(sig), Lambda(tf.imag, output_shape=(1, N), name=name+'_Im')(sig)])14def PAPRnetEncoder(N):15 enc_in = Input((2, N), name="encoder_input")16 h1 = Dense(N*2, activation='relu', name='Dense1')(enc_in)17 h1 = BatchNormalization(name='DenseBN1')(h1)18 h2 = Dense(N*2, activation='relu', name='Dense2')(h1)19 h2 = BatchNormalization(name='DenseBN2')(h2)20 h3 = Dense(N, activation='relu', name='Dense3')(h2)21 h3 = BatchNormalization(name='DenseBN3')(h3)22 enc_out = Lambda(lambda x: x, name='encoder_output')(h3)23 return Model(inputs=[enc_in], outputs=[enc_out], name="PAPRnet_Encoder")24def PAPRnetDecoder(N):25 dec_in = Input((2,N), name="decoder_input")26 h4 = Dense(N*2, activation='relu', name='Dense4')(dec_in)27 h4 = BatchNormalization(name='DenseBN4')(h4)28 h5 = Dense(N*2, activation='relu', name='Dense5')(h4)29 h5 = BatchNormalization(name='DenseBN5')(h5)30 dec_out = Dense(N, activation='linear', name='decoder_output')(h5)31 return Model(inputs=[dec_in], outputs=[dec_out], name="PAPRnet_Decoder")32def PAPRnetAutoEncoder(N, enc, dec):33 34 # auto encoder35 enc_in = enc.input36 enc_out = enc(enc_in)37 dec_out = dec(enc_out)38 # taking ifft of encoder output - used to minimize PAPR39 cmplx = FloatToComplex(enc_out, name='EncoderOut-FloatToComplex')40 ifft = IFFT(cmplx, name='%d-IFFT' % N)41 ifft = ComplexToFloat(ifft, name='%d-IFFT-ComplexToFloat' % N)...

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