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src/s/c/scipy-0.13.3/scipy/signal/_peak_finding.py   scipy(Download)
 
from scipy.lib.six.moves import xrange
from scipy.signal.wavelets import cwt, ricker
from scipy.stats import scoreatpercentile
 
        wavelet = ricker
 
    cwt_dat = cwt(vector, wavelet, widths)
    ridge_lines = _identify_ridge_lines(cwt_dat, max_distances, gap_thresh)
    filtered = _filter_ridge_lines(cwt_dat, ridge_lines, min_length=min_length,

src/s/c/scipy-HEAD/scipy/signal/_peak_finding.py   scipy(Download)
import numpy as np
 
from scipy.signal.wavelets import cwt, ricker
from scipy.stats import scoreatpercentile
 
        wavelet = ricker
 
    cwt_dat = cwt(vector, wavelet, widths)
    ridge_lines = _identify_ridge_lines(cwt_dat, max_distances, gap_thresh)
    filtered = _filter_ridge_lines(cwt_dat, ridge_lines, min_length=min_length,

src/s/c/scipy-0.13.3/scipy/signal/tests/test_wavelets.py   scipy(Download)
 
        #Test delta function input gives same data as output
        cwt_dat = wavelets.cwt(test_data, delta_wavelet, widths)
        assert_(cwt_dat.shape == (len(widths), len_data))
        assert_array_almost_equal(test_data, cwt_dat.flatten())
 
        #Check proper shape on output
        widths = [1, 3, 4, 5, 10]
        cwt_dat = wavelets.cwt(test_data, wavelets.ricker, widths)
        #Note: this wavelet isn't defined quite right, but is fine for this test
        flat_wavelet = lambda l, w: np.ones(w) / w
        cwt_dat = wavelets.cwt(test_data, flat_wavelet, widths)
        assert_array_almost_equal(cwt_dat, np.mean(test_data))
 

src/s/c/scipy-HEAD/scipy/signal/tests/test_wavelets.py   scipy(Download)
 
        #Test delta function input gives same data as output
        cwt_dat = wavelets.cwt(test_data, delta_wavelet, widths)
        assert_(cwt_dat.shape == (len(widths), len_data))
        assert_array_almost_equal(test_data, cwt_dat.flatten())
 
        #Check proper shape on output
        widths = [1, 3, 4, 5, 10]
        cwt_dat = wavelets.cwt(test_data, wavelets.ricker, widths)
        #Note: this wavelet isn't defined quite right, but is fine for this test
        flat_wavelet = lambda l, w: np.ones(w) / w
        cwt_dat = wavelets.cwt(test_data, flat_wavelet, widths)
        assert_array_almost_equal(cwt_dat, np.mean(test_data))