How to use complexToFloat method in Airtest

Best Python code snippet using Airtest

axmlprinter.py

Source:axmlprinter.py

...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:"...

PAPRnet.py

Source:PAPRnet.py

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)...

Automation Testing Tutorials

Learn to execute automation testing from scratch with LambdaTest Learning Hub. Right from setting up the prerequisites to run your first automation test, to following best practices and diving deeper into advanced test scenarios. LambdaTest Learning Hubs compile a list of step-by-step guides to help you be proficient with different test automation frameworks i.e. Selenium, Cypress, TestNG etc.