def _apply(self, rhs, out):
""" Inverse wavelet transform: Reconstruct output vector from vector of wavelet coefficients.
Parameters
----------
rhs : RnVector
RnVector of dimension n to be transformed.
out : RnVector
RnVector of dimension n the result should be written to.
Returns
-------
RnVector of dimension n of inverse wavelet transformed input RnVector of dimension n.
Examples
--------
>>> n = 10
>>> rn = Rn(n)
>>> data = rn.element(range(1, n + 1, 1))
>>> decompose = WaveletDecompose(n)
>>> decomposedData = decompose(data)
>>> reconstruct = WaveletReconstruct(n)
>>> result = reconstruct(decomposedData)
>>> print(result)
[ 1. 2. 3. 4. 5. 6. 7. 8. 9. 10.]
"""
tmp = rhs.copy()
wl.invwavelet_transform1D(tmp.data.ctypes.data, self.range.n, out.data.ctypes.data)
def _apply(self, rhs, out):
""" Inverse wavelet transform: Reconstruct output vector from vector of wavelet coefficients.
Parameters
----------
rhs : RnVector
RnVector of dimension n to be transformed.
out : RnVector
RnVector of dimension n the result should be written to.
Returns
-------
RnVector of dimension n of inverse wavelet transformed input RnVector of dimension n.
Examples
--------
>>> n = 10
>>> rn = Rn(n)
>>> data = rn.element(range(1, n + 1, 1))
>>> decompose = WaveletDecompose(n)
>>> decomposedData = decompose(data)
>>> reconstruct = WaveletReconstruct(n)
>>> result = reconstruct(decomposedData)
>>> print(result)
[ 1. 2. 3. 4. 5. 6. 7. 8. 9. 10.]
"""
tmp = rhs.copy()
wl.invwavelet_transform1D(tmp.data.ctypes.data, self.range.n,
out.data.ctypes.data)
import numpy as np import libwaveletspy as lw import matplotlib.pyplot as plt n = 1000 x = np.linspace(0, 3.14, n, dtype=np.float64) data = np.sin(3 * x) + np.exp(x / 5) + np.cos(1 / (0.01 + x)) wavelets = np.zeros_like(x) result = np.zeros_like(x) dataCpy = data.copy() lw.wavelet_transform1D(dataCpy.ctypes.data, n, wavelets.ctypes.data) lw.invwavelet_transform1D(wavelets.ctypes.data, n, result.ctypes.data) plt.plot(data, label="data") plt.plot(result, label="result") plt.legend() plt.show()
import numpy as np import libwaveletspy as lw import matplotlib.pyplot as plt n=1000 x = np.linspace(0, 3.14, n, dtype=np.float64) data = np.sin(3*x) + np.exp(x/5) + np.cos(1/(0.01+x)) wavelets = np.zeros_like(x) result = np.zeros_like(x) dataCpy = data.copy() lw.wavelet_transform1D(dataCpy.ctypes.data, n, wavelets.ctypes.data) lw.invwavelet_transform1D(wavelets.ctypes.data, n, result.ctypes.data) plt.plot(data, label="data") plt.plot(result, label="result") plt.legend() plt.show()
