Added coherent periodic averaging methods to Measurement
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@ -21,12 +21,12 @@ this array is not found. Quantities are defaulted to 'Number / Full scale'
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- Datasets:
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'audio': 3-dimensional array of blocks of audio data. The first axis is the
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'audio': 3-dimensional array of blocks of audio data. The first axis is the
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block index, the second axis the sample number and the third axis is the channel
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number. The data type is either int16, int32 or float64 / float32. In case the
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data is stored as integers. The raw data should be scaled with the maximum value
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that can be stored for the integer bit depth to get a number between -1.0 and
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1.0.
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number. The data type is either int16, int32 or float64 / float32. If raw data
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is stored as integer values (int16, int32), the actual values should be
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pre-scaled by its maximum positive number (2**(nb-1) - 1), such that the
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corresponding 'number' lies between -1.0 and 1.0.
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'video': 4-dimensional array of video frames. The first index is the frame
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number, the second the x-value of the pixel and the third is the
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@ -44,40 +44,16 @@ import h5py as h5
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import numpy as np
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from .lasp_config import LASP_NUMPY_FLOAT_TYPE
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from scipy.io import wavfile
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import os
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import time
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import wave
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from .lasp_common import SIQtys, Qty
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import os, time, wave, logging
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from .lasp_common import SIQtys, Qty, getFreq
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from .device import DaqChannel
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import logging
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from .wrappers import AvPowerSpectra, Window, PowerSpectra
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logger = logging.Logger(__name__)
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class BlockIter:
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"""Iterate over the blocks in the audio data of a h5 file."""
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def __init__(self, f):
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"""Initialize a BlockIter object.
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Args:
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f: Audio dataset in the h5 file, accessed as f['audio']
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"""
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self.i = 0
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self.nblocks = f['audio'].shape[0]
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self.fa = f['audio']
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def __iter__(self):
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return self
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def __next__(self):
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"""Return the next block."""
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if self.i == self.nblocks:
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raise StopIteration
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self.i += 1
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return self.fa[self.i - 1][:, :]
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def getSampWidth(dtype):
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"""Returns the width of a single sample in bytes.
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"""Returns the width of a single sample in **bytes**.
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Args:
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dtype: numpy dtype
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@ -94,17 +70,14 @@ def getSampWidth(dtype):
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else:
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raise ValueError('Invalid data type: %s' % dtype)
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def scaleBlockSens(block, sens):
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"""Scale a block of raw data to return raw acoustic pressure data.
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Args:
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block: block of raw data with integer data type
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sensitivity: array of sensitivity coeficients for
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each channel
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sens: array of sensitivity coeficients for
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each channel.
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"""
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sens = np.asarray(sens)
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assert sens.ndim == 1
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assert sens.size == block.shape[1]
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if np.issubdtype(block.dtype.type, np.integer):
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sw = getSampWidth(block.dtype)
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@ -113,6 +86,89 @@ def scaleBlockSens(block, sens):
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fac = 1.
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return block.astype(LASP_NUMPY_FLOAT_TYPE) / fac / sens[np.newaxis, :]
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class IterRawData:
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"""Iterate over stored blocks if the raw measurement data of a h5 file."""
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def __init__(self, f, channels, **kwargs):
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"""Initialize a BlockIter object.
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Args:
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f: Audio dataset in the h5 file, accessed as f['audio']
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"""
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assert isinstance(channels, list)
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fa = f['audio']
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self.fa = fa
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self.i = 0
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nblocks = fa.shape[0]
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blocksize = fa.shape[1]
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self.blocksize = blocksize
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nchannels = fa.shape[2]
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self.channels = channels
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self.istart = kwargs.pop('istart', 0)
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self.istop = kwargs.pop('istop', blocksize*nblocks)
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self.firstblock = self.istart // blocksize
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self.lastblock = self.istop // blocksize
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if self.istop % blocksize == 0:
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self.lastblock -= 1
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self.firstblock_start_offset = self.istart % blocksize
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if self.istop < 0:
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self.istop += blocksize*nblocks
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if self.istop <= self.istart:
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raise ValueError('Stop index is smaller than start index')
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if self.istop != blocksize*nblocks:
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self.lastblock_stop_offset = self.istop % blocksize
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else:
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self.lastblock_stop_offset = blocksize
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def __iter__(self):
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return self
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def __next__(self):
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"""Return the next block."""
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fa = self.fa
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nblocks_to_return = self.lastblock-self.firstblock+1
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block = self.firstblock + self.i
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if block > self.lastblock:
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raise StopIteration
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if block == self.firstblock:
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start_offset = self.firstblock_start_offset
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else:
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start_offset = 0
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if block == self.lastblock:
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stop_offset = self.lastblock_stop_offset
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else:
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stop_offset = self.blocksize
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self.i +=1
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return self.fa[block,start_offset:stop_offset,self.channels]
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class IterData(IterRawData):
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"""
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Iterate over blocks of data, scaled with sensitivity and integer scaling
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between 0 and 1
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"""
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def __init__(self, fa, channels, sensitivity, **kwargs):
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super().__init__(fa, channels, **kwargs)
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self.sens = np.asarray(sensitivity)[self.channels]
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assert self.sens.ndim == 1
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def __next__(self):
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nextraw = super().__next__()
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return scaleBlockSens(nextraw, self.sens)
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def exportAsWave(fn, fs, data, force=False):
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if '.wav' not in fn[-4:]:
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@ -190,8 +246,12 @@ class Measurement:
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try:
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qtys_json = f.attrs['qtys']
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# Load quantity data
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self._qtys = [Qty.from_json(qty_json) for qty_json in qtys_json]
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except KeyError:
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# If quantity data is not available, this is an 'old'
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# measurement file.
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logger.debug('Physical quantity data not available in measurement file. Assuming {SIQtys.default}')
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self._qtys = [SIQtys.default for i in range(self.nchannels)]
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def setAttribute(self, atrname, value):
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@ -300,92 +360,164 @@ class Measurement:
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"""Returns the measurement time in seconds since the epoch."""
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return self._time
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def scaleBlock(self, block):
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"""When the data is stored as integers, we assume dB full-scale
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scaling. Hence, when we convert the data to floats, we divide by the
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maximum possible value.
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Returns:
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Block of measurement data, scaled using sensitivity values and
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retured as floating point values
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"""
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return scaleBlockSens(block, self.sensitivity)
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@property
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def prms(self):
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"""Returns the root mean square of the uncalibrated rms sound pressure
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level (equivalend SPL).
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def rms(self, channels=None):
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"""Returns the root mean square values for each channel
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Returns:
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1D array with rms values for each channel
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"""
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#
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try:
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return self._prms
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return self._rms
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except AttributeError:
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pass
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pms = 0.
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meansquare = 0.
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with self.file() as f:
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for block in self.iterBlocks(f):
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block = self.scaleBlock(block)
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pms += np.sum(block**2, axis=0) / self.N
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self._prms = np.sqrt(pms)
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return self._prms
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for block in self.iterData(channels):
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meansquare += np.sum(block**2, axis=0) / self.N
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self._rms = np.sqrt(meansquare)
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return self._rms
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def rawData(self, block=None, channel=None):
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"""Returns the raw signal, without any transformations applied
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def rawData(self, channels=None, **kwargs):
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"""Returns the raw data as stored in the measurement file,
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without any transformations applied
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args:
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block: If specified a certain block is returned
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channels: list, or tuple of channel numbers to export. If not defined, all
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channels in the measurement are returned
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returns:
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Numpy array with data. The first axis is always the time instance,
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the second axis the channel number.
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"""
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if block is not None:
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with self.file() as f:
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if channel is not None:
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blocks = f['audio'][block][:, [channel]]
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else:
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blocks = f['audio'][block]
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else:
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blocks = []
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with self.file() as f:
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for block in self.iterBlocks(f):
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if channel is not None:
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blocks.append(block[:, [channel]])
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else:
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blocks.append(block)
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if channels is None:
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channels = list(range(self.nchannels))
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blocks = np.asarray(blocks)
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rawdata = []
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if channel is None:
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blocks = blocks.reshape((self.nblocks * self.blocksize,
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self.nchannels))
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else:
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blocks = blocks.reshape((self.nblocks * self.blocksize,
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1))
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return blocks
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with self.file() as f:
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for block in IterRawData(f, channels, **kwargs):
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rawdata.append(block)
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def praw(self, block=None):
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"""Returns the uncalibrated acoustic pressure signal, but the
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sensitivity is applied, converted to floating point acoustic
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pressure values [Pa]."""
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return np.concatenate(rawdata, axis=0)
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print('TODO: THIS SHOULD BE CHANGED, VERY INCONSISTENT AND CONFUSING API')
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blocks = self.rawData(block)
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def iterData(self, channels, **kwargs):
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sensitivity = kwargs.pop('sensitivity', self.sensitivity)
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if channels is None:
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channels = list(range(self.nchannels))
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with self.file() as f:
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for block in IterData(f, channels, sensitivity, **kwargs):
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yield block
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# Apply scaling (sensitivity, integer -> float)
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blocks = self.scaleBlock(blocks)
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return blocks
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def data(self, channels=None, **kwargs):
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"""
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Returns the measurement data, scaled and sensitivity applied.
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"""
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data = []
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for d in self.iterData(channels, **kwargs):
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data.append(d)
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def iterBlocks(self, opened_file):
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"""Iterate over all the audio blocks in the opened file.
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return np.concatenate(data, axis=0)
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def CPS(self, channels=None, **kwargs):
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"""
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Compute single-sided Cross-Power-Spectrum of the measurement channels
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Args:
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opened_file: The h5File with the data
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channels: Channels to compute for (numbers)
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Optional arguments:
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nfft: FFT length
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window: Window type
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overlap: Overlap percentage (value between 0.0 and up to and
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including 100.0)
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weighting:
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Returns:
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Cross-power-spectra. C[freq, ch_i, ch_j] = C_ij
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"""
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return BlockIter(opened_file)
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nfft = kwargs.pop('nfft', 2048)
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window = kwargs.pop('window', Window.hann)
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overlap = kwargs.pop('overlap', 50.0)
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if channels is None:
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channels = list(range(self.nchannels))
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nchannels = len(channels)
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aps = AvPowerSpectra(nfft, nchannels, overlap, window)
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freq = getFreq(self.samplerate, nfft)
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for data in self.iterData(channels, **kwargs):
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CS = aps.addTimeData(data)
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return freq, CS
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def periodicAverage(self, N, channels=None, noiseCorrection=True, **kwargs):
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"""
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Return the (coherent) periodic average the measurement. This method is
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useful for a situation of periodic excitation.
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Args:
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N: The number of samples in one period. This value should
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correspond with the period of the excitation!
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noiseCorrection: whether to apply coherent averaging, according to
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the Sliding Window correlation method (SWiC): Telle et al.: A Novel
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Approach for Impulse Response Measurements with Time-Varying Noise.
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If set to False, just the arithmetic average is used.
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"""
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# Create blocks of equal length N
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Ntot = self.N
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Nblocks = Ntot//N
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# TODO: This method graps the whole measurement file into memory. Can
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# only be done with relatively small measurement files.
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signal = self.data(channels)
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# Estimate noise power in block
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blocks = [signal[i*N:(i+1)*N] for i in range(Nblocks)]
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if noiseCorrection:
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enp1 = [blocks[i+1] - blocks[i] for i in range(Nblocks-1)]
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noise_est_np1 = [np.average(enp1[i+1]*enp1[i]) for i in range(Nblocks-2)]
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# Create weighting coefficients
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sum_inverse_noise = sum([1/n for n in noise_est_np1])
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c_np1 = [1/(ni*sum_inverse_noise) for ni in noise_est_np1]
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else:
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c_np1 = [1/(Nblocks-2)]*(Nblocks-2)
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assert np.isclose(sum(c_np1), 1.0)
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# Average signal over blocks
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avg = np.zeros((blocks[0].shape), dtype=float)
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for i in range(0, Nblocks-2):
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avg += c_np1[i]*blocks[i+1]
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return avg
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def periodicCPS(self, N, channels=None, **kwargs):
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"""
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Compute Cross-Spectral Density based on periodic excitation. Uses noise
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reduction by time-averaging the data.
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"""
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if channels is None:
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channels = list(range(self.nchannels))
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nchannels = len(channels)
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window = Window.rectangular
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ps = PowerSpectra(N, window)
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avg = self.periodicAverage(N, channels, **kwargs)
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CS = ps.compute(avg)
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freq = getFreq(self.samplerate, N)
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return freq, CS
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@property
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def sensitivity(self):
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@ -492,7 +624,6 @@ class Measurement:
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wavfile.write(fn, self.samplerate, data)
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@staticmethod
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def fromtxt(fn,
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skiprows,
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