def __init__(self, configFile: str = "") -> None:
"""Initialise with default configuration options
Parameters
----------
configFile : str
Path to the configuration file
"""
self.configFile: str = configFile
self.configParams = loadConfig(self.configFile)
# options dictionary
self.flags: Dict[str, bool] = {}
self.configure()
def __init__(self, decParams: DecimationParams) -> None:
"""Initialise with decParams and default window parameters
Parameters
----------
decParams : int
Decimation level
"""
self.decParams = decParams
config = loadConfig()
self.minSize = config["Window"]["minwindowsize"]
self.minOlap = config["Window"]["minoverlapsize"]
self.windowFactor = config["Window"]["windowfactor"]
self.overlapFraction = config["Window"]["overlapfraction"]
self.calcParameters(self.windowFactor, self.overlapFraction)
def __init__(self, refTime: datetime, timeData: TimeData, winSize: int,
winOlap: int):
self.timeData: TimeData = timeData
self.numSamples: int = timeData.numSamples
self.sampleFreq: float = timeData.sampleFreq
self.winSize: int = winSize
self.winDuration: float = (winSize - 1) / timeData.sampleFreq
self.winOlap: int = winOlap
self.chans: List[str] = timeData.chans
# refTime and dataTime are already datetime objects
self.refTime: datetime = refTime
self.dataTime: datetime = timeData.startTime
# min window warning setting
config = loadConfig()
self.minWindows: int = config["Window"]["minwindows"]
# initialise
self.initialiseWindows()
self.calcWindowTimes()
def __init__(self, calDirectory: str) -> None:
"""Set the calibration directory and initialise
Calibrator will automatically find calibration files in the provided directory
Parameters
----------
calDirectory : str
Path of directory containing calibration files
"""
self.calExt, self.calFormats = getKnownCalibrationFormats()
self.calFiles: List[str] = []
self.numCalFiles = 0
self.calDir: str = calDirectory
self.findCalFiles()
# set whether to use theoretical calibration functions
conf = loadConfig()
self.extend: bool = conf["Calibration"]["extend"]
self.useTheoretical: bool = conf["Calibration"]["usetheoretical"]
def __init__(self, sampleFreq: float, winSamples: int) -> None:
"""Initialise
Parameters
----------
sampleFreq : float
Sampling frequency of time data
winSamples : int
Number of samples in a window
"""
self.sampleFreq: float = sampleFreq
self.numSamples: int = winSamples
config = loadConfig()
self.window: bool = config["Spectra"]["applywindow"]
self.windowFunc: np.ndarray = signal.get_window(
config["Spectra"]["windowfunc"], winSamples)
# create an pyfftw plan
self.dataArray: np.ndarray = pyfftw.empty_aligned(self.numSamples,
dtype="float64")
self.fftObj: pyfftw.FFTW = pyfftw.builders.rfft(self.dataArray)
def __init__(self, timeData: TimeData,
decParams: DecimationParams) -> None:
"""Initialise with timeData and decimation parameters
Parameters
----------
timeData : TimeData
The time data to decimate
decParams : DecimationParams
Decimation parameters for performing the decimation
"""
self.timeData: TimeData = timeData
self.sampleFreq: float = timeData.sampleFreq * 1.0
self.chans: List = timeData.chans
self.numSamples: int = timeData.numSamples
self.decParams: DecimationParams = decParams
config = loadConfig()
self.minSamples: int = config["Decimation"]["minsamples"]
self.level: int = -1
self.maxDownsampleFactor: int = 8
def __init__(self, sampleFreq: float):
"""Initialise decimation parameters with sampling frequency
Calculates decimation factors and evaluation frequencies based on defaults
Parameters
----------
sampleFreq : float
Sampling frequency
"""
self.sampleFreq = sampleFreq
config = loadConfig()
self.divFactor: int = 2
self.numLevels: int = config["Decimation"]["numlevels"]
self.freqPerLevel: int = config["Frequencies"]["perlevel"]
self.decFactors: np.ndarray
self.decFrequencies: np.ndarray
self.evalFreq: np.ndarray
self.evalFreqPerLevel: np.ndarray
# calculate some initial values decimation parameters based on defaults
self.calcFrequencyParams(self.numLevels, self.freqPerLevel)