894 lines
30 KiB
Python
894 lines
30 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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from __future__ import annotations
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"""!
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Author: J.A. de Jong - ASCEE
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Description: Measurement class
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The ASCEE hdf5 measurement file format contains the following fields:
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- Attributes:
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'version': If not given, version 1 is assumed. For version 1, measurement data
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is assumed to be acoustic data.
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'samplerate': The audio data sample rate in Hz.
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'nchannels': The number of audio channels in the file
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'sensitivity': (Optionally) the stored sensitivity of the record channels.
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This can be a single value, or a list of sensitivities for
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each channel. Both representations are allowed.
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For measurement files of LASP < v1.0
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'qtys' : (Optionally): list of quantities that is recorded for each channel',
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if this array is not found. Quantities are defaulted to 'Number / Full scale'
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For measurement files of LASP >= 1.0
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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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block index, the second axis the sample number and the third axis is the
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channel number. The data type is either int16, int32 or float64 / float32. If
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raw data is stored as integer values (int16, int32), the actual values should
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be 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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y-value of the pixel. Then, the last axis is the color. This axis has
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length 3 and the colors are stored as (r,g,b). Where typically a
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color depth of 256 is used (np.uint8 data format)
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The video dataset can possibly be not present in the data.
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"""
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__all__ = ['Measurement', 'scaleBlockSens']
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from contextlib import contextmanager
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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, time, wave, logging
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from .lasp_common import SIQtys, Qty, getFreq
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from .lasp_cpp import Window, DaqChannel, LASP_VERSION_MAJOR, AvPowerSpectra
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from typing import List
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from functools import lru_cache
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def getSampWidth(dtype):
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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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Returns:
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Size of a sample in bytes (int)
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"""
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if dtype in (np.int32, np.float32):
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return 4
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elif dtype == np.int16:
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return 2
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elif dtype == np.float64:
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return 8
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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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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.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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fac = 2**(8 * sw - 1) - 1
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else:
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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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channels: list of channel indices to use
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istart: index of first sample
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istop: index of last sample (not including istop)
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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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# print(f'block: {block}, starto: {start_offset}, stopo {stop_offset}')
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self.i += 1
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return 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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class Measurement:
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"""Provides access to measurement data stored in the h5 measurement file
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format."""
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def __init__(self, fn):
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"""Initialize a Measurement object based on the filename."""
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if '.h5' not in fn:
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fn += '.h5'
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# Full filepath
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self.fn = fn
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# Base filename
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self.fn_base = os.path.split(fn)[1]
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# Open the h5 file in read-plus mode, to allow for changing the
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# measurement comment.
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with h5.File(fn, 'r') as f:
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# Check for video data
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try:
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f['video']
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self.has_video = True
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except KeyError:
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self.has_video = False
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self.nblocks, self.blocksize, self.nchannels = f['audio'].shape
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dtype = f['audio'].dtype
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self.dtype = dtype
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self.sampwidth = getSampWidth(dtype)
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self.samplerate = f.attrs['samplerate']
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self.N = (self.nblocks * self.blocksize)
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self.T = self.N / self.samplerate
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try:
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self.version_major = f.attrs['LASP_VERSION_MAJOR']
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self.version_minor = f.attrs['LASP_VERSION_MINOR']
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except KeyError:
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self.version_major = 0
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self.version_minor = 1
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# Due to a previous bug, the channel names were not stored
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# consistently, i.e. as 'channel_names' and later camelcase.
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try:
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self._channelNames = f.attrs['channelNames']
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except KeyError:
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try:
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self._channelNames = f.attrs['channel_names']
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logging.info("Measurement file obtained which stores channel names with *old* attribute 'channel_names'")
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except KeyError:
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# No channel names found in measurement file
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logging.info('No channel name data found in measurement')
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self._channelNames = [f'Unnamed {i}' for i in range(self.nchannels)]
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# comment = read-write thing
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if 'comment' in f.attrs:
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self._comment = f.attrs['comment']
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else:
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self._comment = ''
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# Sensitivity
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try:
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sens = f.attrs['sensitivity']
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self._sens = sens * \
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np.ones(self.nchannels) if isinstance(
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sens, float) else sens
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except KeyError:
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self._sens = np.ones(self.nchannels)
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# The time is cached AND ALWAYS ASSUMED TO BE AN IMMUTABLE OBJECT.
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# It is also cached. Changing the measurement timestamp should not
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# be done.
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self._time = f.attrs['time']
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# Quantity stored as channel.
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self._qtys = None
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try:
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qtys_enum_idx = f.attrs['qtys_enum_idx']
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self._qtys = [SIQtys.fromInt(idx) for idx in qtys_enum_idx]
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except KeyError:
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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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pass
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if self._qtys is None:
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self._qtys = [SIQtys.default() for i in range(self.nchannels)]
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logging.debug(f'Physical quantity data not available in measurement file. Assuming {SIQtys.default}')
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def setAttribute(self, atrname, value):
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"""
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Set an attribute in the measurement file, and keep a local copy in
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memory for efficient accessing.
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"""
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with self.file('r+') as f:
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# Update comment attribute in the file
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f.attrs[atrname] = value
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setattr(self, '_' + atrname, value)
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@property
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def name(self):
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"""Returns filename base without extension."""
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return os.path.splitext(self.fn_base)[0]
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@property
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def channelNames(self):
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return self._channelNames
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@channelNames.setter
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def channelNames(self, newchnames):
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if len(newchnames) != self.nchannels:
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raise RuntimeError('Invalid length of new channel names')
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self.setAttribute('channelNames', newchnames)
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@property
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def channelConfig(self):
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chcfg = []
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for chname, sens, qty in zip(self.channelNames, self.sensitivity,
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self.qtys):
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ch = DaqChannel()
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ch.enabled = True
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ch.name = chname
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ch.sensitivity = sens
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ch.qty = qty.cpp_enum
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chcfg.append(ch)
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return chcfg
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@channelConfig.setter
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def channelConfig(self, chcfg: List[DaqChannel]):
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chname = []
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sens = []
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qtys = []
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for ch in chcfg:
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chname.append(ch.name)
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sens.append(ch.sensitivity)
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qtys.append(SIQtys.fromCppEnum(ch.qty))
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self.channelNames = chname
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self.sensitivity = sens
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self.qtys = qtys
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@property
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def qtys(self):
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return self._qtys
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@qtys.setter
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def qtys(self, newqtys):
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if not len(newqtys) == len(self._qtys):
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raise ValueError('Invalid number of quantities')
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qtys_int = [qty.toInt() for qty in newqtys]
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# Use setAttribute here, but thos store the jsonified version as well,
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# which we have to overwrite again with the deserialized ones. This is
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# actually not a very nice way of coding.
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with self.file('r+') as f:
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# Update comment attribute in the file
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f.attrs['qtys_enum_idx'] = qtys_int
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self._qtys = newqtys
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@contextmanager
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def file(self, mode='r'):
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"""Contextmanager which opens the storage file and yields the file.
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Args:
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mode: Opening mode for the file. Should either be 'r', or 'r+'
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"""
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if mode not in ('r', 'r+'):
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raise ValueError('Invalid file opening mode.')
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with h5.File(self.fn, mode) as f:
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yield f
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@property
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def comment(self):
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"""Return the measurement comment.
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Returns:
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The measurement comment (text string)
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"""
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return self._comment
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@comment.setter
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def comment(self, cmt):
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"""Set the measurement comment.
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Args:
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cmt: Comment text string to set
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"""
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with self.file('r+') as f:
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# Update comment attribute in the file
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f.attrs['comment'] = cmt
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self._comment = cmt
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@property
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@lru_cache()
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def recTime(self):
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"""Returns the total recording time of the measurement, in float
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seconds."""
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return self.blocksize * self.nblocks / self.samplerate
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@property
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def time(self):
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"""Returns the measurement time in seconds since the epoch."""
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return self._time
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@property
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@lru_cache()
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def timestr(self):
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"""
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Return a properly formatted string of the measurement time, in order of
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year-month-day hour etc.
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"""
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time_struct = time.localtime(self.time)
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time_string = time.strftime('%Y-%m-%d %H:%M:%S', time_struct)
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return time_string
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def rms(self, channels=None, substract_average=False):
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"""Returns the root mean square values for each channel
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Args:
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channels: list of channels
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substract_average: If set to true, computes the rms of only the
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oscillating component of the signal, which is in fact the signal
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variance.
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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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meansquare = 0. # Mean square of the signal, including its average
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sum_ = 0. # Sumf of the values of the signal, used to compute average
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N = 0
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with self.file() as f:
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for block in self.iterData(channels):
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Nblock = block.shape[0]
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sum_ += np.sum(block, axis=0)
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N += Nblock
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meansquare += np.sum(block**2, axis=0) / self.N
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avg = sum_/N
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# In fact, this is not the complete RMS, as in includes the DC
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# If p = p_dc + p_osc, then rms(p_osc) = sqrt(ms(p)-ms(p_dc))
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if substract_average:
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meansquare -= avg**2
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rms = np.sqrt(meansquare)
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return rms
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def variance(self, channels=None):
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return self.rms(substract_average=True)
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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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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 channels is None:
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channels = list(range(self.nchannels))
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rawdata = []
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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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return np.concatenate(rawdata, axis=0)
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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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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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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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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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nfft = kwargs.pop('nfft', 2048)
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window = kwargs.pop('windowType', Window.WindowType.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, window, overlap)
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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.compute(data)
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return freq, aps.get_est()
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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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# The difference between the measured signal in the previous block and
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# the current block
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en = [None] + [blocks[i] - blocks[i-1] for i in range(1,Nblocks)]
|
|
|
|
noise_est = [None] + [-np.average(en[i]*en[i+1]) for i in range(1,len(en)-1)]
|
|
|
|
# Create weighting coefficients
|
|
sum_inverse_noise = sum([1/n for n in noise_est[1:]])
|
|
c_n = [1/(ni*sum_inverse_noise) for ni in noise_est[1:]]
|
|
else:
|
|
c_n = [1/(Nblocks-2)]*(Nblocks-2)
|
|
|
|
assert np.isclose(sum(c_n), 1.0)
|
|
assert Nblocks-2 == len(c_n)
|
|
|
|
# Average signal over blocks
|
|
avg = np.zeros((blocks[0].shape), dtype=float)
|
|
for n in range(0, Nblocks-2):
|
|
avg += c_n[n]*blocks[n+1]
|
|
|
|
return avg
|
|
|
|
def periodicCPS(self, N, channels=None, **kwargs):
|
|
"""
|
|
Compute Cross-Spectral Density based on periodic excitation. Uses noise
|
|
reduction by time-averaging the data.
|
|
"""
|
|
|
|
if channels is None:
|
|
channels = list(range(self.nchannels))
|
|
|
|
nchannels = len(channels)
|
|
window = Window.rectangular
|
|
ps = PowerSpectra(N, window)
|
|
|
|
avg = np.asfortranarray(self.periodicAverage(N, channels, **kwargs))
|
|
CS = ps.compute(avg)
|
|
freq = getFreq(self.samplerate, N)
|
|
|
|
return freq, CS
|
|
|
|
|
|
@property
|
|
def sensitivity(self):
|
|
"""Sensitivity of the data in U^-1, from floating point data scaled
|
|
between -1.0 and 1.0 to Units [U].
|
|
|
|
If the sensitivity is not stored in the measurement file, this
|
|
function returns 1.0 for each channel
|
|
"""
|
|
return self._sens
|
|
|
|
@sensitivity.setter
|
|
def sensitivity(self, sens):
|
|
"""Set the sensitivity of the measurement in the file.
|
|
|
|
Args:
|
|
sens: sensitivity data, should be a float, or an array of floats
|
|
equal to the number of channels.
|
|
"""
|
|
if isinstance(sens, float):
|
|
# Put all sensitivities equal
|
|
sens = sens * np.ones(self.nchannels)
|
|
elif isinstance(sens, list):
|
|
sens = np.asarray(sens)
|
|
|
|
valid = sens.ndim == 1
|
|
valid &= sens.shape[0] == self.nchannels
|
|
valid &= sens.dtype == float
|
|
if not valid:
|
|
raise ValueError('Invalid sensitivity value(s) given')
|
|
with self.file('r+') as f:
|
|
f.attrs['sensitivity'] = sens
|
|
self._sens = sens
|
|
|
|
def checkOverflow(self, channels):
|
|
"""Coarse check for overflow in measurement.
|
|
|
|
Return:
|
|
True if overflow is possible, else False
|
|
"""
|
|
|
|
for block in self.iterData(channels):
|
|
dtype = block.dtype
|
|
if dtype.kind == 'i':
|
|
# minvalue = np.iinfo(dtype).min
|
|
maxvalue = np.iinfo(dtype).max
|
|
if np.max(np.abs(block)) >= 0.9*maxvalue:
|
|
return True
|
|
else:
|
|
# Cannot check for floating point values.
|
|
return False
|
|
return False
|
|
|
|
|
|
def exportAsWave(self, fn=None, force=False, dtype=None,
|
|
normalize=False, **kwargs):
|
|
"""Export measurement file as wave. In case the measurement data is
|
|
stored as floats, the values are scaled to the proper integer (PCM)
|
|
data format.
|
|
|
|
Args:
|
|
fn: If given, this will be the filename to write to. If the
|
|
filename does not end with '.wav', this extension is added.
|
|
|
|
force: If True, overwrites any existing files with the given name
|
|
, otherwise a RuntimeError is raised.
|
|
|
|
dtype: if not None, convert data to this data type.
|
|
Options are 'int16', 'int32', 'float32'.
|
|
|
|
normalize: If set: normalize the level to something sensible.
|
|
"""
|
|
if fn is None:
|
|
fn = self.fn
|
|
fn = os.path.splitext(fn)[0]
|
|
|
|
if os.path.splitext(fn)[1] != '.wav':
|
|
fn += '.wav'
|
|
|
|
if os.path.exists(fn) and not force:
|
|
raise RuntimeError(f'File already exists: {fn}')
|
|
|
|
if not np.isclose(self.samplerate%1,0):
|
|
raise RuntimeError(f'Sample rates should be approximately integer for exporting to Wave to work')
|
|
|
|
# TODO: With VERY large measurment files, this is not possible! Is this
|
|
# a theoretical case?
|
|
# TODO: add sensitivity? Then use self.data() instead of self.rawData()
|
|
data = self.rawData(**kwargs)
|
|
|
|
if normalize:
|
|
# Scale back to maximum of absolute value
|
|
maxabs = np.max(np.abs(data))
|
|
data = data / maxabs # "data /= maxabs" fails if dtpyes differ
|
|
|
|
if dtype==None:
|
|
dtype = data.dtype # keep existing
|
|
logging.debug(f"dtype not passed as arg; using dtype = {dtype}")
|
|
|
|
# dtype conversion
|
|
if dtype=='int16':
|
|
newtype = np.int16
|
|
newsampwidth = 2
|
|
elif dtype=='int32':
|
|
newtype = np.int32
|
|
newsampwidth = 4
|
|
elif dtype=='float32':
|
|
newtype = np.float32
|
|
elif dtype=='float64':
|
|
newtype = np.float64
|
|
else:
|
|
logging.debug(f"cannot handle this dtype {dtype}")
|
|
pass
|
|
|
|
# Convert range to [-1, 1]
|
|
# TODO: this is wrong for float data where full scale > 1
|
|
sensone = np.ones_like(self.sensitivity)
|
|
data = scaleBlockSens(data, sensone)
|
|
|
|
if dtype=='int16' or dtype=='int32':
|
|
# Scale data to integer range and convert to integers
|
|
scalefac = 2**(8*newsampwidth-1)-1
|
|
data = (data*scalefac).astype(newtype)
|
|
|
|
wavfile.write(fn, int(self.samplerate), data.astype(newtype))
|
|
|
|
@staticmethod
|
|
def fromtxt(fn,
|
|
skiprows,
|
|
samplerate,
|
|
sensitivity,
|
|
mfn=None,
|
|
timestamp=None,
|
|
delimiter='\t',
|
|
firstcoltime=True):
|
|
"""Converts a txt file to a LASP Measurement file, opens the associated
|
|
Measurement object and returns it. The measurement file will have the
|
|
same file name as the txt file, except with h5 extension.
|
|
|
|
Args:
|
|
fn: Filename of text file
|
|
skiprows: Number of header rows in text file to skip
|
|
samplerate: Sampling frequency in [Hz]
|
|
sensitivity: 1D array of channel sensitivities
|
|
mfn: Filepath where measurement file is stored. If not given,
|
|
a h5 file will be created along fn, which shares its basename
|
|
timestamp: If given, a custom timestamp for the measurement
|
|
(integer containing seconds since epoch). If not given, the
|
|
timestamp is obtained from the last modification time.
|
|
delimiter: Column delimiter
|
|
firstcoltime: If true, the first column is the treated as the
|
|
sample time.
|
|
"""
|
|
if not os.path.exists(fn):
|
|
raise ValueError(f'File {fn} does not exist.')
|
|
if timestamp is None:
|
|
timestamp = os.path.getmtime(fn)
|
|
if mfn is None:
|
|
mfn = os.path.splitext(fn)[0] + '.h5'
|
|
else:
|
|
mfn = os.path.splitext(mfn)[0] + '.h5'
|
|
|
|
dat = np.loadtxt(fn, skiprows=skiprows, delimiter=delimiter)
|
|
if firstcoltime:
|
|
time = dat[:, 0]
|
|
if not np.isclose(time[1] - time[0], 1 / samplerate):
|
|
raise ValueError('Samplerate given does not agree with '
|
|
'samplerate in file')
|
|
|
|
# Chop off first column
|
|
dat = dat[:, 1:]
|
|
nchannels = dat.shape[1]
|
|
if nchannels != sensitivity.shape[0]:
|
|
raise ValueError(
|
|
f'Invalid sensitivity length given. Should be: {nchannels}')
|
|
|
|
with h5.File(mfn, 'w') as hf:
|
|
hf.attrs['samplerate'] = samplerate
|
|
hf.attrs['sensitivity'] = sensitivity
|
|
hf.attrs['time'] = timestamp
|
|
hf.attrs['blocksize'] = 1
|
|
hf.attrs['nchannels'] = nchannels
|
|
ad = hf.create_dataset('audio', (1, dat.shape[0], dat.shape[1]),
|
|
dtype=dat.dtype,
|
|
maxshape=(1, dat.shape[0], dat.shape[1]),
|
|
compression='gzip')
|
|
ad[0] = dat
|
|
return Measurement(mfn)
|
|
|
|
@staticmethod
|
|
def fromnpy(data,
|
|
samplerate,
|
|
sensitivity,
|
|
mfn,
|
|
timestamp=None,
|
|
qtys: List[SIQtys] = None,
|
|
channelNames: List[str] = None,
|
|
force=False) -> Measurement:
|
|
"""
|
|
Creates a LASP measurement file from input numpy array data.
|
|
Opens the associated Measurement object and returns it.
|
|
|
|
Args:
|
|
data: Numpy array, first column is sample, second is channel. Can
|
|
also be specified with a single column for single-channel data.
|
|
|
|
samplerate: Sampling frequency in [Hz]
|
|
|
|
sensitivity: 1D array of channel sensitivities in [U^-1], where U is
|
|
the recorded unit.
|
|
|
|
mfn: Filepath of the file where the data is stored.
|
|
|
|
timestamp: If given, a custom timestamp for the measurement
|
|
(integer containing seconds since epoch).
|
|
|
|
qtys: If a list of physical quantity data is given here
|
|
|
|
channelNames: Name of the channels
|
|
|
|
force: If True, overwrites existing files with specified `mfn`
|
|
name.
|
|
"""
|
|
if os.path.splitext(mfn)[1] != '.h5':
|
|
mfn += '.h5'
|
|
if os.path.exists(mfn) and not force:
|
|
raise ValueError(f'File {mfn} already exist.')
|
|
if timestamp is None:
|
|
timestamp = int(time.time())
|
|
|
|
if data.ndim != 2:
|
|
data = data[:, np.newaxis]
|
|
|
|
|
|
try:
|
|
len(sensitivity)
|
|
except:
|
|
raise ValueError('Sensitivity should be given as array-like data type')
|
|
sensitivity = np.asarray(sensitivity)
|
|
|
|
nchannels = data.shape[1]
|
|
if nchannels != sensitivity.shape[0]:
|
|
raise ValueError(
|
|
f'Invalid sensitivity length given. Should be: {nchannels}')
|
|
|
|
if channelNames is not None:
|
|
if len(channelNames) != nchannels:
|
|
raise RuntimeError("Illegal length of channelNames list given")
|
|
|
|
if qtys is None:
|
|
qtys = [SIQtys.AP]*nchannels
|
|
else:
|
|
if len(qtys) != nchannels:
|
|
raise RuntimeError("Illegal length of qtys list given")
|
|
|
|
qtyvals = [qty.value for qty in qtys]
|
|
|
|
with h5.File(mfn, 'w') as hf:
|
|
hf.attrs['samplerate'] = samplerate
|
|
hf.attrs['sensitivity'] = sensitivity
|
|
hf.attrs['time'] = timestamp
|
|
hf.attrs['blocksize'] = 1
|
|
hf.attrs['nchannels'] = nchannels
|
|
|
|
# Add physical quantity indices
|
|
hf.attrs['qtys_enum_idx'] = [qtyval.toInt() for qtyval in qtyvals]
|
|
|
|
# Add channel names in case given
|
|
if channelNames is not None:
|
|
hf.attrs['channelNames'] = channelNames
|
|
|
|
ad = hf.create_dataset('audio', (1, data.shape[0], data.shape[1]),
|
|
dtype=data.dtype,
|
|
maxshape=(1, data.shape[0], data.shape[1]),
|
|
compression='gzip')
|
|
ad[0] = data
|
|
return Measurement(mfn)
|
|
|
|
@staticmethod
|
|
def fromWaveFile(fn, newfn=None, force=False, timestamp=None):
|
|
"""Convert a measurement file to a wave file, and return the
|
|
measurement handle."""
|
|
if timestamp is None:
|
|
timestamp = int(time.time())
|
|
|
|
base_fn = os.path.splitext(fn)[0]
|
|
if newfn is None:
|
|
newfn = base_fn + '.h5'
|
|
if os.path.exists(newfn) and not force:
|
|
raise RuntimeError(f'Measurement file name {newfn} already exists in path, set "force" to true to overwrite')
|
|
|
|
samplerate, data = wavfile.read(fn)
|
|
if data.ndim == 2:
|
|
nframes, nchannels = data.shape
|
|
else:
|
|
nchannels = 1
|
|
nframes = len(data)
|
|
data = data[:, np.newaxis]
|
|
sensitivity = np.ones(nchannels)
|
|
|
|
with h5.File(newfn, 'w') as hf:
|
|
hf.attrs['samplerate'] = samplerate
|
|
hf.attrs['nchannels'] = nchannels
|
|
hf.attrs['time'] = timestamp
|
|
hf.attrs['blocksize'] = 1
|
|
hf.attrs['sensitivity'] = sensitivity
|
|
ad = hf.create_dataset('audio', (1, nframes, nchannels),
|
|
dtype=data.dtype,
|
|
maxshape=(1, nframes, nchannels),
|
|
compression='gzip')
|
|
ad[0] = data
|
|
|
|
return Measurement(newfn)
|