def test_specgram_band_pass(self):
time_points = 10000
sampling_frequency = 100.0
data = numpy.zeros((time_points,1))
for time_index in range(time_points):
data[time_index,0] = numpy.sin(2.0 * numpy.pi * 5.0 * (time_index / sampling_frequency))
data[time_index,0] += numpy.sin(2.0 * numpy.pi * 15.0 * (time_index / sampling_frequency))
data[time_index,0] += numpy.sin(2.0 * numpy.pi * 25.0 * (time_index / sampling_frequency))
data[time_index,0] += numpy.sin(2.0 * numpy.pi * 35.0 * (time_index / sampling_frequency))
data[time_index,0] += numpy.sin(2.0 * numpy.pi * 45.0 * (time_index / sampling_frequency))
pass_band=(20.,30.)
#Generate a time series build out of the data
from pySPACE.resources.data_types.time_series import TimeSeries
test_data = TimeSeries(input_array = data,
channel_names = ["test_channel_1"],
sampling_frequency = sampling_frequency,
start_time = 0,
end_time = float(time_points) / sampling_frequency)
lpf_node = filtering.FFTBandPassFilterNode(pass_band=pass_band)
filtered_time_series = lpf_node.execute(test_data)
lpf_node_fir = filtering.FIRFilterNode(pass_band=pass_band)
filtered_time_series_fir = lpf_node_fir.execute(test_data)
lpf_node_fir2 = filtering.FIRFilterNode(pass_band=pass_band,window='hann')
filtered_time_series_fir2 = lpf_node_fir2.execute(test_data)
lpf_node_iir = filtering.IIRFilterNode(pass_band=pass_band,stop_band_rifle=90)
filtered_time_series_iir = lpf_node_iir.execute(test_data)
def test_specgram_low_pass(self):
time_points = 10000
sampling_frequency = 100.0
data = numpy.zeros((time_points, 1))
for time_index in range(time_points):
data[time_index, 0] = numpy.sin(2.0 * numpy.pi * 5.0 *
(time_index / sampling_frequency))
data[time_index, 0] += numpy.sin(2.0 * numpy.pi * 15.0 *
(time_index / sampling_frequency))
data[time_index, 0] += numpy.sin(2.0 * numpy.pi * 25.0 *
(time_index / sampling_frequency))
data[time_index, 0] += numpy.sin(2.0 * numpy.pi * 35.0 *
(time_index / sampling_frequency))
data[time_index, 0] += numpy.sin(2.0 * numpy.pi * 45.0 *
(time_index / sampling_frequency))
#Generate a time series build out of the data
from pySPACE.resources.data_types.time_series import TimeSeries
test_data = TimeSeries(input_array=data,
channel_names=["test_channel_1"],
sampling_frequency=sampling_frequency,
start_time=0,
end_time=float(time_points) /
sampling_frequency)
lpf_node = filtering.SimpleLowPassFilterNode(cutoff_frequency=20.0)
filtered_time_series = lpf_node.execute(test_data)
lpf_node_fir = filtering.FIRFilterNode([20.0])
filtered_time_series_fir = lpf_node_fir.execute(test_data)
def testTimeShifting(self):
time_points = 10
channels = 1
import pySPACE.tests.utils.data.test_data_generation as tdg
counter = tdg.Counter()
test_data = test_ts_generator.generate_test_data(channels=channels,
time_points=time_points,
function=counter)
filter_node = filtering.FIRFilterNode([10,20],time_shift="middle",taps=3)
filter_node.initialize_data_dependencies(test_data)
filter_node.filter_kernel = numpy.array([1,1,1])/3.
filtered_data = filter_node.execute(test_data)
# the result should be
#[[ 0.33333333]
# [ 1. ]
# [ 2. ]
# [ 3. ]
# [ 4. ]
# [ 5. ]
# [ 6. ]
# [ 8. ]
# [ 7. ]
# [ 5.66666667]]
desired_result=numpy.array([1.0/3,1,2,3,4,5,6,7,8,17./3])
desired_result.shape=(10,1)
#print filtered_data
#print desired_result
self.assertTrue(numpy.allclose(filtered_data,desired_result))
def setUp(self):
self.logger = logging.getLogger("TestLogger")
self.pass_band = [10,20]
self.filter_node = filtering.FIRFilterNode(self.pass_band)
time_points = 1000
channels = 10
self.data = test_ts_generator.generate_test_data(channels,time_points,function=test_data_generation.Delta())
def create_filter(self,target_frequency,downsampling_factor,transition_region_width):
skip = 0
if self.skipping:
skip = int(round(downsampling_factor-1))
return filtering.FIRFilterNode([target_frequency],
comp_type=self.comp_type,
width=transition_region_width,
skip=skip,
time_shift = self.time_shift)