#!/usr/bin/env python # -*- coding: utf-8 -*- # Copyright 1999-2020 Alibaba Group Holding Ltd. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import numpy as np from ... import opcodes as OperandDef from ...tiles import NotSupportTile from ...serialize import Int32Field, Float64Field, KeyField from ..operands import TensorOperand, TensorHasInput, TensorOperandMixin from ..datasource import arange from ..core import TensorOrder class TensorFFTFreq(TensorOperand, TensorOperandMixin): _op_type_ = OperandDef.FFTFREQ _n = Int32Field('n') _d = Float64Field('d') def __init__(self, n=None, d=None, dtype=None, gpu=False, **kw): super().__init__(_n=n, _d=d, _dtype=dtype, _gpu=gpu, **kw) @property def n(self): return self._n @property def d(self): return self._d def __call__(self, chunk_size=None): shape = (self.n,) return self.new_tensor(None, shape, raw_chunk_size=chunk_size, order=TensorOrder.C_ORDER) @classmethod def tile(cls, op): tensor = op.outputs[0] in_tensor = arange(op.n, gpu=op.gpu, dtype=op.dtype, chunks=tensor.extra_params.raw_chunk_size)._inplace_tile() out_chunks = [] for c in in_tensor.chunks: chunk_op = TensorFFTFreqChunk(n=op.n, d=op.d, dtype=op.dtype) out_chunk = chunk_op.new_chunk([c], shape=c.shape, index=c.index, order=tensor.order) out_chunks.append(out_chunk) new_op = op.copy() return new_op.new_tensors(op.inputs, tensor.shape, order=tensor.order, chunks=out_chunks, nsplits=in_tensor.nsplits, **tensor.extra_params) class TensorFFTFreqChunk(TensorHasInput, TensorOperandMixin): _op_type_ = OperandDef.FFTFREQ_CHUNK _input = KeyField('input') _n = Int32Field('n') _d = Float64Field('d') def __init__(self, n=None, d=None, dtype=None, **kw): super().__init__(_n=n, _d=d, _dtype=dtype, **kw) @property def n(self): return self._n @property def d(self): return self._d def _set_inputs(self, inputs): super()._set_inputs(inputs) self._input = self._inputs[0] @classmethod def tile(cls, op): raise NotSupportTile('FFTFreqChunk is a chunk operand which does not support tile') @classmethod def execute(cls, ctx, op): n, d = op.n, op.d x = ctx[op.inputs[0].key].copy() x[x >= (n + 1) // 2] -= n x /= n * d ctx[op.outputs[0].key] = x [docs]def fftfreq(n, d=1.0, gpu=False, chunk_size=None): """ Return the Discrete Fourier Transform sample frequencies. The returned float tensor `f` contains the frequency bin centers in cycles per unit of the sample spacing (with zero at the start). For instance, if the sample spacing is in seconds, then the frequency unit is cycles/second. Given a window length `n` and a sample spacing `d`:: f = [0, 1, ..., n/2-1, -n/2, ..., -1] / (d*n) if n is even f = [0, 1, ..., (n-1)/2, -(n-1)/2, ..., -1] / (d*n) if n is odd Parameters ---------- n : int Window length. d : scalar, optional Sample spacing (inverse of the sampling rate). Defaults to 1. gpu : bool, optional Allocate the tensor on GPU if True, False as default chunk_size : int or tuple of int or tuple of ints, optional Desired chunk size on each dimension Returns ------- f : Tensor Array of length `n` containing the sample frequencies. Examples -------- >>> import mars.tensor as mt >>> signal = mt.array([-2, 8, 6, 4, 1, 0, 3, 5], dtype=float) >>> fourier = mt.fft.fft(signal) >>> n = signal.size >>> timestep = 0.1 >>> freq = mt.fft.fftfreq(n, d=timestep) >>> freq.execute() array([ 0. , 1.25, 2.5 , 3.75, -5. , -3.75, -2.5 , -1.25]) """ n, d = int(n), float(d) op = TensorFFTFreq(n=n, d=d, dtype=np.dtype(float), gpu=gpu) return op(chunk_size)