Esempio n. 1
0
TimeCache, BeamformerTime, TimePower, BeamformerCMF, \
BeamformerCapon, BeamformerMusic, BeamformerDamas, BeamformerClean, \
BeamformerFunctional
from numpy import zeros
from os import path
from pylab import figure, subplot, imshow, show, colorbar, title

dataFile = 'example_data.h5'
calibFile = 'example_calib.xml'
micGeoFile = path.join(path.split(acoular.__file__)[0], 'xml', 'array_56.xml')

freqInt = 4000

t1 = MaskedTimeSamples(name = dataFile)
t1.start = 0
t1.stop = 16000
invalid = [1, 7]
t1.invalid_channels = invalid

t1.calib = Calib(from_file = calibFile)

m = MicGeom(from_file = micGeoFile)
m.invalid_channels = invalid

g = RectGrid(x_min = -0.6, x_max = -0.0, y_min = -0.3, y_max = 0.3,
        z = 0.68, increment = 0.05)

f = EigSpectra(time_data = t1,
        window = 'Hanning', overlap = '50%', block_size = 128,
        ind_low = 7, ind_high = 15)
Esempio n. 2
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# files
datafile = 'example_data.h5'
calibfile = 'example_calib.xml'
micgeofile = path.join( path.split(acoular.__file__)[0],'xml','array_56.xml')

#octave band of interest
cfreq = 4000

#===============================================================================
# first, we define the time samples using the MaskedTimeSamples class
# alternatively we could use the TimeSamples class that provides no masking
# of channels and samples
#===============================================================================
t1 = MaskedTimeSamples(name=datafile)
t1.start = 0 # first sample, default
t1.stop = 16000 # last valid sample = 15999
invalid = [1,7] # list of invalid channels (unwanted microphones etc.)
t1.invalid_channels = invalid 

#===============================================================================
# calibration is usually needed and can be set directly at the TimeSamples 
# object (preferred) or for frequency domain processing at the PowerSpectra 
# object (for backwards compatibility)
#===============================================================================
t1.calib = Calib(from_file=calibfile)

#===============================================================================
# the microphone geometry must have the same number of valid channels as the
# TimeSamples object has
#===============================================================================
m = MicGeom(from_file=micgeofile)
Esempio n. 3
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# files
datafile = 'example_data.h5'
calibfile = 'example_calib.xml'
micgeofile = path.join( path.split(acoular.__file__)[0],'xml','array_56.xml')

#octave band of interest
cfreq = 4000

#===============================================================================
# first, we define the time samples using the MaskedTimeSamples class
# alternatively we could use the TimeSamples class that provides no masking
# of channels and samples
#===============================================================================
t1 = MaskedTimeSamples(name=datafile)
t1.start = 0 # first sample, default
t1.stop = 16000 # last valid sample = 15999
invalid = [1,7] # list of invalid channels (unwanted microphones etc.)
t1.invalid_channels = invalid 

#===============================================================================
# calibration is usually needed and can be set directly at the TimeSamples 
# object (preferred) or for frequency domain processing at the PowerSpectra 
# object (for backwards compatibility)
#===============================================================================
t1.calib = Calib(from_file=calibfile)

#===============================================================================
# the microphone geometry must have the same number of valid channels as the
# TimeSamples object has
#===============================================================================
m = MicGeom(from_file=micgeofile)
Esempio n. 4
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# other imports
from numpy import zeros
from os import path
from pylab import figure, subplot, imshow, show, colorbar, title, tight_layout

# files
datafile = 'sammy.csv'
#calibfile = 'example_calib.xml'
micgeofile = path.join(path.split(acoular.__file__)[0], 'xml', 'array_64.xml')

#octave band of interest
cfreq = 4000

t1 = MaskedTimeSamples(name=datafile)
t1.start = 0
t1.stop = 16000
invalid = [1, 7]
t1.invalid_channels = invalid

m = MicGeom(from_file=micgeofile)
m.invalid_channels = invalid
g = RectGrid(x_min=-0.6,
             x_max=-0.0,
             y_min=-0.3,
             y_max=0.3,
             z=0.68,
             increment=0.05)
f = PowerSpectra(time_data=t1,
                 window='Hanning',
                 overlap='50%',
                 block_size=128,