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chenyc

2025-02-12 ea42ff3ebee1eeb3fb29423aa848a249441db81c

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/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * 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 { assert } from '../../util';
import { complex } from '../complex';
import { concat } from '../concat';
import { imag } from '../imag';
import { op } from '../operation';
import { real } from '../real';
import { reshape } from '../reshape';
import { slice } from '../slice';
import { split } from '../split';
import { zeros } from '../zeros';
import { zerosLike } from '../zeros_like';
import { fft } from './fft';
/**
 * Real value input fast Fourier transform.
 *
 * Computes the 1-dimensional discrete Fourier transform over the
 * inner-most dimension of the real input.
 *
 * ```js
 * const real = tf.tensor1d([1, 2, 3]);
 *
 * real.rfft().print();
 * ```
 * @param input The real value input to compute an rfft over.
 *
 * @doc {heading: 'Operations', subheading: 'Spectral', namespace: 'spectral'}
 */
function rfft_(input, fftLength) {
    assert(input.dtype === 'float32', () => `The dtype for rfft() must be real value but got ${input.dtype}`);
    let innerDimensionSize = input.shape[input.shape.length - 1];
    const batch = input.size / innerDimensionSize;
    let adjustedInput;
    if (fftLength != null && fftLength < innerDimensionSize) {
        // Need to crop
        const begin = input.shape.map(v => 0);
        const size = input.shape.map(v => v);
        size[input.shape.length - 1] = fftLength;
        adjustedInput = slice(input, begin, size);
        innerDimensionSize = fftLength;
    }
    else if (fftLength != null && fftLength > innerDimensionSize) {
        // Need to pad with zeros
        const zerosShape = input.shape.map(v => v);
        zerosShape[input.shape.length - 1] = fftLength - innerDimensionSize;
        adjustedInput = concat([input, zeros(zerosShape)], input.shape.length - 1);
        innerDimensionSize = fftLength;
    }
    else {
        adjustedInput = input;
    }
    // Complement the input with zero imaginary numbers.
    const zerosInput = zerosLike(adjustedInput);
    const complexInput = reshape(complex(adjustedInput, zerosInput), [batch, innerDimensionSize]);
    const ret = fft(complexInput);
    // Exclude complex conjugations. These conjugations are put symmetrically.
    const half = Math.floor(innerDimensionSize / 2) + 1;
    const realValues = real(ret);
    const imagValues = imag(ret);
    const realComplexConjugate = split(realValues, [half, innerDimensionSize - half], realValues.shape.length - 1);
    const imagComplexConjugate = split(imagValues, [half, innerDimensionSize - half], imagValues.shape.length - 1);
    const outputShape = adjustedInput.shape.slice();
    outputShape[adjustedInput.shape.length - 1] = half;
    return reshape(complex(realComplexConjugate[0], imagComplexConjugate[0]), outputShape);
}
export const rfft = /* @__PURE__ */ op({ rfft_ });
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All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor} from '../../tensor';\nimport {assert} from '../../util';\nimport {complex} from '../complex';\nimport {concat} from '../concat';\nimport {imag} from '../imag';\nimport {op} from '../operation';\nimport {real} from '../real';\nimport {reshape} from '../reshape';\nimport {slice} from '../slice';\nimport {split} from '../split';\nimport {zeros} from '../zeros';\nimport {zerosLike} from '../zeros_like';\n\nimport {fft} from './fft';\n\n/**\n * Real value input fast Fourier transform.\n *\n * Computes the 1-dimensional discrete Fourier transform over the\n * inner-most dimension of the real input.\n *\n * ```js\n * const real = tf.tensor1d([1, 2, 3]);\n *\n * real.rfft().print();\n * ```\n * @param input The real value input to compute an rfft over.\n *\n * @doc {heading: 'Operations', subheading: 'Spectral', namespace: 'spectral'}\n */\nfunction rfft_(input: Tensor, fftLength?: number): Tensor {\n  assert(\n      input.dtype === 'float32',\n      () => `The dtype for rfft() must be real value but got ${input.dtype}`);\n\n  let innerDimensionSize = input.shape[input.shape.length - 1];\n  const batch = input.size / innerDimensionSize;\n\n  let adjustedInput: Tensor;\n  if (fftLength != null && fftLength < innerDimensionSize) {\n    // Need to crop\n    const begin = input.shape.map(v => 0);\n    const size = input.shape.map(v => v);\n    size[input.shape.length - 1] = fftLength;\n    adjustedInput = slice(input, begin, size);\n    innerDimensionSize = fftLength;\n  } else if (fftLength != null && fftLength > innerDimensionSize) {\n    // Need to pad with zeros\n    const zerosShape = input.shape.map(v => v);\n    zerosShape[input.shape.length - 1] = fftLength - innerDimensionSize;\n    adjustedInput = concat([input, zeros(zerosShape)], input.shape.length - 1);\n    innerDimensionSize = fftLength;\n  } else {\n    adjustedInput = input;\n  }\n\n  // Complement the input with zero imaginary numbers.\n  const zerosInput = zerosLike(adjustedInput);\n  const complexInput =\n      reshape(complex(adjustedInput, zerosInput), [batch, innerDimensionSize]);\n\n  const ret = fft(complexInput);\n\n  // Exclude complex conjugations. These conjugations are put symmetrically.\n  const half = Math.floor(innerDimensionSize / 2) + 1;\n  const realValues = real(ret);\n  const imagValues = imag(ret);\n  const realComplexConjugate = split(\n      realValues, [half, innerDimensionSize - half],\n      realValues.shape.length - 1);\n  const imagComplexConjugate = split(\n      imagValues, [half, innerDimensionSize - half],\n      imagValues.shape.length - 1);\n\n  const outputShape = adjustedInput.shape.slice();\n  outputShape[adjustedInput.shape.length - 1] = half;\n\n  return reshape(\n      complex(realComplexConjugate[0], imagComplexConjugate[0]), outputShape);\n}\n\nexport const rfft = /* @__PURE__ */ op({rfft_});\n"]}