381 lines
12 KiB
Python
381 lines
12 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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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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'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 an array of sensitivities for
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each channel. Both representations are allowed.
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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 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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'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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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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import wave
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import os
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class BlockIter:
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"""
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Iterate over the blocks in the audio data of a h5 file
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"""
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def __init__(self, faudio):
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"""
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Initialize a BlockIter object
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Args:
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faudio: 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 = faudio.shape[0]
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self.fa = faudio
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def __iter__(self):
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return self
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def __next__(self):
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"""
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Return the next block
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"""
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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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"""
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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 == np.int32:
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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 exportAsWave(fn, fs, data, force=False):
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if not '.wav' in fn[-4:]:
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fn += '.wav'
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nchannels = data.shape[1]
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sampwidth = getSampWidth(data.dtype)
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if os.path.exists(fn) and not force:
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raise RuntimeError('File already exists: %s', fn)
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with wave.open(fn, 'w') as wf:
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wf.setparams((nchannels, sampwidth, fs, 0, 'NONE', 'NONE'))
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wf.writeframes(np.asfortranarray(data).tobytes())
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class Measurement:
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"""
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Provides access to measurement data stored in the h5 measurement file
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format.
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"""
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def __init__(self, fn):
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"""
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Initialize a Measurement object based on the filename
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"""
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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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f = h5.File(fn, 'r+')
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self.f = 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.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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# comment = read-write thing
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try:
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self._comment = self.f.attrs['comment']
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except KeyError:
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self.f.attrs['comment'] = ''
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self._comment = ''
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@property
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def comment(self):
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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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self.f.attrs['comment'] = cmt
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self._comment = cmt
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@property
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def recTime(self):
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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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"""
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Returns the measurement time in seconds since the epoch.
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"""
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return self.f.attrs['time']
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@property
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def prms(self):
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"""
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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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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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except AttributeError:
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pass
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sens = self.sensitivity
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pms = 0.
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for block in self.iterBlocks():
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pms += np.sum(block/sens[np.newaxis, :], axis=0)**2/self.N
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self._prms = np.sqrt(pms)
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return self._prms
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def praw(self, block=None):
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"""
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Returns the raw uncalibrated data, converted to floating point format.
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"""
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if block is not None:
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blocks = self.f['audio'][block]
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else:
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blocks = []
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for block in self.iterBlocks():
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blocks.append(block)
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blocks = np.asarray(blocks)
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blocks = blocks.reshape(self.nblocks*self.blocksize,
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self.nchannels)
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# When the data is stored as integers, we assume dB full-scale scaling.
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# Hence, when we convert the data to floats, we divide by the maximum
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# possible value.
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if blocks.dtype == np.int32:
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fac = 2**31
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elif blocks.dtype == np.int16:
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fac = 2**15
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elif blocks.dtype == np.float64:
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fac = 1.0
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else:
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raise RuntimeError(
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f'Unimplemented data type from recording: {blocks.dtype}.')
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sens = self.sensitivity
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blocks = blocks.astype(LASP_NUMPY_FLOAT_TYPE)/fac/sens[np.newaxis, :]
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return blocks
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def iterBlocks(self):
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return BlockIter(self.f['audio'])
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@property
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def sensitivity(self):
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"""
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Sensitivity of the data in Pa^-1, from floating point data scaled
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between -1.0 and 1.0 to Pascal. If the sensitivity is not stored in
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the measurement file, this function returns 1.0
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"""
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try:
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return self.f.attrs['sensitivity']
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except KeyError:
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return np.ones(self.nchannels)
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@sensitivity.setter
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def sensitivity(self, sens):
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"""
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Set the sensitivity of the measurement in the file
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Args:
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sens: sensitivity data, should be a float, or an array of floats
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equal to the number of channels.
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"""
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if isinstance(sens, float):
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sens = sens*np.ones(self.nchannels)
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valid = sens.ndim == 1
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valid &= sens.shape[0] == self.nchannels
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valid &= isinstance(sens.dtype, float)
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if not valid:
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raise ValueError('Invalid sensitivity value(s) given')
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self.f.attrs['sensitivity'] = sens
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def exportAsWave(self, fn=None, force=False, sampwidth=None):
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"""
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Export measurement file as wave. In case the measurement data is stored
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as floats, the values are scaled between 0 and 1
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Args:
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fn: If given, this will be the filename to write to. If the
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filename does not end with '.wav', this extension is added.
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force: If True, overwrites any existing files with the given name
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, otherwise a RuntimeError is raised.
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sampwidth: sample width in bytes with which to export the data.
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This should only be given in case the measurement data is stored as
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floating point values, otherwise an
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"""
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if fn is None:
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fn = self.fn
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fn = os.path.splitext(fn)[0]
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if '.wav' not in fn[-4:]:
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fn += '.wav'
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if os.path.exists(fn) and not force:
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raise RuntimeError(f'File already exists: {fn}')
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audio = self.f['audio']
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if isinstance(audio.dtype, float):
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if sampwidth is None:
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raise ValueError('sampwidth parameter should be given '
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'for float data in measurement file')
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elif sampwidth == 2:
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itype = np.int16
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elif sampwidth == 4:
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itype = np.int32
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else:
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raise ValueError('Invalid sample width, should be 2 or 4')
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# Find maximum
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max = 0.
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for block in self.iterBlocks():
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blockmax = np.max(np.abs(block))
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if blockmax > max:
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max = blockmax
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# Scale with maximum value only if we have a nonzero maximum value.
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if max == 0.:
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max = 1.
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scalefac = 2**(8*sampwidth)/max
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with wave.open(fn, 'w') as wf:
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wf.setparams((self.nchannels, self.sampwidth,
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self.samplerate, 0, 'NONE', 'NONE'))
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for block in self.iterBlocks():
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if isinstance(block.dtype, float):
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# Convert block to integral data type
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block = (block*scalefac).astype(itype)
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wf.writeframes(np.asfortranarray(block).tobytes())
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@staticmethod
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def fromtxt(fn, skiprows, samplerate, sensitivity, mfn=None,
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timestamp=None,
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delimiter='\t', firstcoltime=True):
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"""
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Converts a txt file to a LASP Measurement object and returns the
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measurement.
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Args:
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fn: Filename of text file
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skiprows: Number of header rows in text file to skip
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samplerate: Sampling frequency in [Hz]
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sensitivity: 1D array of channel sensitivities
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mfn: Filepath where measurement file is stored. If not given,
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a h5 file will be created along fn, which shares its basename
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timestamp: If given, a custom timestamp for the measurement
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(integer containing seconds since epoch). If not given, the
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timestamp is obtained from the last modification time.
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delimiter: Column delimiter
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firstcoltime: If true, the first column is the treated as the
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sample time.
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"""
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if not os.path.exists(fn):
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raise ValueError(f'File {fn} does not exist.')
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if timestamp is None:
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timestamp = os.path.getmtime(fn)
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if mfn is None:
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mfn = os.path.splitext(fn)[0] + '.h5'
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dat = np.loadtxt(fn, skiprows=skiprows, delimiter=delimiter)
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if firstcoltime:
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time = dat[:, 0]
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if not np.isclose(time[1] - time[0], 1/samplerate):
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raise ValueError('Samplerate given does not agree with '
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'samplerate in file')
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dat = dat[:, 1:]
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nchannels = dat.shape[1]
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with h5.File(mfn, 'w') as hf:
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hf.attrs['samplerate'] = samplerate
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hf.attrs['sensitivity'] = sensitivity
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hf.attrs['time'] = timestamp
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hf.attrs['blocksize'] = 1
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hf.attrs['nchannels'] = nchannels
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ad = hf.create_dataset('audio',
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(1, dat.shape[0], dat.shape[1]),
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dtype=dat.dtype,
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maxshape=(1, dat.shape[0], dat.shape[1]),
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compression='gzip')
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ad[0] = dat
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return Measurement(mfn)
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def __del__(self):
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try:
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self.f.close()
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except AttributeError:
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pass
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