resistics.regression.compute module¶
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resistics.regression.compute.evalFrequencyData(freq, evalFreq, winDataMatrix)[source]¶ Calculate spectral power data at evaluation frequencies
- Parameters
- freqnp.ndarray
Frequency array of spectra data
- evalFreqnp.ndarray
Evaluation frequencies for the decimation level
- winDataMatrixnp.ndarray
Array holding spectral power data at frequencies freq
- Returns
- outnp.ndarray
Spectral power data interpolated to evaluation frequencies
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resistics.regression.compute.remoteMatrices(ncores: int, inData: List[resistics.spectra.data.SpectrumData], outData: List[resistics.spectra.data.SpectrumData], remoteData: List[resistics.spectra.data.SpectrumData], inChannels: List[str], outChannels: List[str], remoteChannels: List[str], smoothLen: int, smoothWin: str, evalFreq: numpy.ndarray)[source]¶ Parallel calculation of spectral matrices for remote reference data
- Parameters
- ncoresint
The number of cores to run on
- inDataList[SpectrumData]
The input spectrum data
- outDataList[SpectrumData]
The output spectrum data
- remoteDataList[SpectrumData]
The remote reference spectrum data
- inChannelsList[str]
The input channels to use
- outChannelsList[str]
The output channels to use
- remoteChannelsList[str]
The remote channels to use
- smoothLenint
The smoothing length
- smoothWinstr
The smoothing window
- evalFreqnp.ndarray
The evaluation frequencies to interpolate to
- Returns
- outList[np.ndarray]
List of spectral matrices
-
resistics.regression.compute.remoteMatricesWindow(inData: resistics.spectra.data.SpectrumData, outData: resistics.spectra.data.SpectrumData, remoteData: resistics.spectra.data.SpectrumData, inChannels: List[str], outChannels: List[str], remoteChannels: List[str], smoothLen: int, smoothWin: str, evalFreq: List[float])[source]¶ Compute the spectral matrices
- Parameters
- inDataSpectrumData
The input spectrum data
- outDataSpectrumData
The output spectrum data
- remoteDataSpectrumData
The remote spectrum data
- inChannelsList[str]
The input channels to use
- outChannelsList[str]
The output channels to use
- remoteChannelsList[str]
The remote channels to use
- smoothLenint
The smoothing length
- smoothWinstr
The smoothing window
- evalFreqnp.ndarray
The evaluation frequencies to interpolate to
- Returns
- outnp.ndarray
Cross spectral matrices interpolated to evaluation frequencies
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resistics.regression.compute.spectralMatrices(ncores: int, inData: List[resistics.spectra.data.SpectrumData], outData: List[resistics.spectra.data.SpectrumData], inChannels: List[str], outChannels: List[str], smoothLen: int, smoothWin: str, evalFreq: numpy.ndarray)[source]¶ Parallel calculation of spectral matrices
- Parameters
- ncoresint
The number of cores to run on
- inDataList[SpectrumData]
The input spectrum data
- outDataList[SpectrumData]
The output spectrum data
- inChannelsList[str]
The input channels to use
- outChannelsList[str]
The output channels to use
- smoothLenint
The smoothing length
- smoothWinstr
The smoothing window
- evalFreqnp.ndarray
The evaluation frequencies to interpolate to
- Returns
- outList[np.ndarray]
List of spectral matrices
-
resistics.regression.compute.spectralMatricesWindow(inData: resistics.spectra.data.SpectrumData, outData: resistics.spectra.data.SpectrumData, inChannels: List[str], outChannels: List[str], smoothLen: int, smoothWin: str, evalFreq: List[float])[source]¶ Compute the spectral matrices
- Parameters
- inDataSpectrumData
The input spectrum data
- outDataSpectrumData
The output spectrum data
- inChannelsList[str]
The input channels to use
- outChannelsList[str]
The output channels to use
- smoothLenint
The smoothing length
- smoothWinstr
The smoothing window
- evalFreqnp.ndarray
The evaluation frequencies to interpolate to
- Returns
- outnp.ndarray
Cross spectral matrices interpolated to evaluation frequencies