B )`#@sdZddlmZddlmZddlmZddlZddlmZ ddlm Z ddlm Z dd l mZdd l mZdd lmZdd lmZdd lmZddZedddgdejdddZedddgdejdddZdS)zDiscrete Cosine Transform ops.)absolute_import)division)print_functionN)dtypes)ops) tensor_shape) array_ops)math_ops)fft_ops)dispatch) tf_exportcCs|dkrtd||dk r,|dkr,td|dkrChecks that DCT/IDCT arguments are compatible and well formed.zaxis must be -1. Got: %sNz&n should be a positive integer or None)rz-Types I, II, III and IV (I)DCT are supported.orthoz2Normalization is not supported for the Type-I DCT.rz9Type-I DCT requires the dimension to be greater than one.)Nrz8Unknown normalization. Expected None or 'ortho', got: %s)NotImplementedError ValueErrorshape)Z input_tensorZdct_typenaxisnormrS/home/dcms/DCMS/lib/python3.7/site-packages/tensorflow/python/ops/signal/dct_ops.py_validate_dct_arguments s rz signal.dctz spectral.dct)Zv1rr c Cst|||||t|d|gt|}tjd|jd}t|jdpZt|d}|dk r||kr~|dd|f}nNt |j}dd t |D} ||| |d d <tj| t j d} tj || d }t|jdpt|d} t| |j} |d kr6tj||dd ddfgdd } tt| } | S|dkrdtt|t |  tjd| }ttj|d| gddd| f|}|dkrdt| }|td}tj t|dd| d gg|d}||9}|S|dkr|dkrBt| }|td}tj t|dd| d gg|d}||9}n|| 9}dtt|t | tjd| }ttj|t||d| gddd| f}|S|dkrt|dd| |dd}|dd ddf}|dkr|tdt| 9}|SWdQRXdS)aEComputes the 1D [Discrete Cosine Transform (DCT)][dct] of `input`. Types I, II, III and IV are supported. Type I is implemented using a length `2N` padded `tf.signal.rfft`. Type II is implemented using a length `2N` padded `tf.signal.rfft`, as described here: [Type 2 DCT using 2N FFT padded (Makhoul)] (https://dsp.stackexchange.com/a/10606). Type III is a fairly straightforward inverse of Type II (i.e. using a length `2N` padded `tf.signal.irfft`). Type IV is calculated through 2N length DCT2 of padded signal and picking the odd indices. @compatibility(scipy) Equivalent to [scipy.fftpack.dct] (https://docs.scipy.org/doc/scipy-1.4.0/reference/generated/scipy.fftpack.dct.html) for Type-I, Type-II, Type-III and Type-IV DCT. @end_compatibility Args: input: A `[..., samples]` `float32`/`float64` `Tensor` containing the signals to take the DCT of. type: The DCT type to perform. Must be 1, 2, 3 or 4. n: The length of the transform. If length is less than sequence length, only the first n elements of the sequence are considered for the DCT. If n is greater than the sequence length, zeros are padded and then the DCT is computed as usual. axis: For future expansion. The axis to compute the DCT along. Must be `-1`. norm: The normalization to apply. `None` for no normalization or `'ortho'` for orthonormal normalization. name: An optional name for the operation. Returns: A `[..., samples]` `float32`/`float64` `Tensor` containing the DCT of `input`. Raises: ValueError: If `type` is not `1`, `2`, `3` or `4`, `axis` is not `-1`, `n` is not `None` or greater than 0, or `norm` is not `None` or `'ortho'`. ValueError: If `type` is `1` and `norm` is `ortho`. [dct]: https://en.wikipedia.org/wiki/Discrete_cosine_transform dctg)dtyper N.rcSsg|] }ddgqS)rr).0_rrr pszdct..r)Zpaddings)rrg@g?)Z fft_lengthr)Zconstant_valuesrr)typerrr)r_opsZ name_scopeZconvert_to_tensorrrZdimension_valuer _array_opslenrange_dtypesint32pad _math_opscastconcatrealr Zrfftexpcomplex_mathpiZrsqrtsqrtZ expand_dimsZirfftr)inputr"rrrnameZzeroZseq_lenZrankpaddingZaxis_dimZaxis_dim_floatZ dct1_inputZdct1ZscaleZdct2Zn1Zn2weightsZdct3Zdct4rrrr5s|.      "        rz signal.idctz spectral.idctcCs6t|||||ddddd|}t||||||dS)aComputes the 1D [Inverse Discrete Cosine Transform (DCT)][idct] of `input`. Currently Types I, II, III, IV are supported. Type III is the inverse of Type II, and vice versa. Note that you must re-normalize by 1/(2n) to obtain an inverse if `norm` is not `'ortho'`. That is: `signal == idct(dct(signal)) * 0.5 / signal.shape[-1]`. When `norm='ortho'`, we have: `signal == idct(dct(signal, norm='ortho'), norm='ortho')`. @compatibility(scipy) Equivalent to [scipy.fftpack.idct] (https://docs.scipy.org/doc/scipy-1.4.0/reference/generated/scipy.fftpack.idct.html) for Type-I, Type-II, Type-III and Type-IV DCT. @end_compatibility Args: input: A `[..., samples]` `float32`/`float64` `Tensor` containing the signals to take the DCT of. type: The IDCT type to perform. Must be 1, 2, 3 or 4. n: For future expansion. The length of the transform. Must be `None`. axis: For future expansion. The axis to compute the DCT along. Must be `-1`. norm: The normalization to apply. `None` for no normalization or `'ortho'` for orthonormal normalization. name: An optional name for the operation. Returns: A `[..., samples]` `float32`/`float64` `Tensor` containing the IDCT of `input`. Raises: ValueError: If `type` is not `1`, `2` or `3`, `n` is not `None, `axis` is not `-1`, or `norm` is not `None` or `'ortho'`. [idct]: https://en.wikipedia.org/wiki/Discrete_cosine_transform#Inverse_transforms rrrr)rrrr)r"rrrr4)rr)r3r"rrrr4Z inverse_typerrridcts)r7)rNr NN)rNr NN)__doc__ __future__rrrmathr0Ztensorflow.python.frameworkrr'rr#rZtensorflow.python.opsrr$r r*Ztensorflow.python.ops.signalr Ztensorflow.python.utilr Z tensorflow.python.util.tf_exportr rZadd_dispatch_supportrr7rrrrs(