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#cython: boundscheck=False, wraparound=False
from . cimport common
from . cimport c_wt
from cpython cimport bool
import warnings
import numpy as np
cimport numpy as np
from .common cimport pywt_index_t
from ._pywt cimport c_wavelet_from_object, cdata_t, Wavelet, _check_dtype
include "config.pxi"
np.import_array()
def swt_max_level(size_t input_len):
"""
swt_max_level(input_len)
Calculates the maximum level of Stationary Wavelet Transform for data of
given length.
Parameters
----------
input_len : int
Input data length.
Returns
-------
max_level : int
Maximum level of Stationary Wavelet Transform for data of given length.
Notes
-----
For the current implementation of the stationary wavelet transform, this
corresponds to the number of times ``input_len`` is evenly divisible by
two. In other words, for an n-level transform, the signal length must be a
multiple of ``2**n``. ``numpy.pad`` can be used to pad a signal up to an
appropriate length as needed.
"""
if input_len < 1:
raise ValueError("Cannot apply swt to a size 0 signal.")
max_level = common.swt_max_level(input_len)
if max_level == 0:
warnings.warn(
"No levels of stationary wavelet decomposition are possible. The "
"signal to be transformed must have a size that is a multiple "
"of 2**n for an n-level decomposition.")
return max_level
def swt(cdata_t[::1] data, Wavelet wavelet, size_t level, size_t start_level,
bool trim_approx=False):
cdef cdata_t[::1] cA, cD
cdef Wavelet w
cdef int retval
cdef size_t end_level = start_level + level
cdef size_t data_size, output_len, i
if data.size % 2:
raise ValueError("Length of data must be even.")
if data.size < 1:
raise ValueError("Data must have non-zero size")
if level < 1:
raise ValueError("Level value must be greater than zero.")
if start_level >= common.swt_max_level(data.size):
raise ValueError("start_level must be less than %d." %
common.swt_max_level(data.size))
if end_level > common.swt_max_level(data.size):
msg = ("Level value too high (max level for current data size and "
"start_level is %d)." % (
common.swt_max_level(data.size) - start_level))
raise ValueError(msg)
output_len = common.swt_buffer_length(data.size)
if output_len < 1:
raise RuntimeError("Invalid output length.")
ret = []
for i in range(start_level+1, end_level+1):
data_size = data.size
# alloc memory, decompose D
if cdata_t is np.float64_t:
cD = np.zeros(output_len, dtype=np.float64)
with nogil:
retval = c_wt.double_swt_d(&data[0], data_size, wavelet.w,
&cD[0], output_len, i)
if retval < 0:
raise RuntimeError("C swt failed.")
elif cdata_t is np.float32_t:
cD = np.zeros(output_len, dtype=np.float32)
with nogil:
retval = c_wt.float_swt_d(&data[0], data_size, wavelet.w,
&cD[0], output_len, i)
if retval < 0:
raise RuntimeError("C swt failed.")
IF HAVE_C99_CPLX:
if cdata_t is np.complex128_t:
cD = np.zeros(output_len, dtype=np.complex128)
with nogil:
retval = c_wt.double_complex_swt_d(&data[0], data_size, wavelet.w,
&cD[0], output_len, i)
if retval < 0:
raise RuntimeError("C swt failed.")
elif cdata_t is np.complex64_t:
cD = np.zeros(output_len, dtype=np.complex64)
with nogil:
retval = c_wt.float_complex_swt_d(&data[0], data_size, wavelet.w,
&cD[0], output_len, i)
if retval < 0:
raise RuntimeError("C swt failed.")
# alloc memory, decompose A
if cdata_t is np.float64_t:
cA = np.zeros(output_len, dtype=np.float64)
with nogil:
retval = c_wt.double_swt_a(&data[0], data_size, wavelet.w,
&cA[0], output_len, i)
if retval < 0:
raise RuntimeError("C swt failed.")
elif cdata_t is np.float32_t:
cA = np.zeros(output_len, dtype=np.float32)
with nogil:
retval = c_wt.float_swt_a(&data[0], data_size, wavelet.w,
&cA[0], output_len, i)
if retval < 0:
raise RuntimeError("C swt failed.")
IF HAVE_C99_CPLX:
if cdata_t is np.complex128_t:
cA = np.zeros(output_len, dtype=np.complex128)
with nogil:
retval = c_wt.double_complex_swt_a(&data[0], data_size, wavelet.w,
&cA[0], output_len, i)
if retval < 0:
raise RuntimeError("C swt failed.")
elif cdata_t is np.complex64_t:
cA = np.zeros(output_len, dtype=np.complex64)
with nogil:
retval = c_wt.float_complex_swt_a(&data[0], data_size, wavelet.w,
&cA[0], output_len, i)
if retval < 0:
raise RuntimeError("C swt failed.")
data = cA
if not trim_approx:
ret.append((np.asarray(cA), np.asarray(cD)))
else:
ret.append(np.asarray(cD))
if trim_approx:
ret.append(np.asarray(cA))
ret.reverse()
return ret
cpdef swt_axis(np.ndarray data, Wavelet wavelet, size_t level,
size_t start_level, unsigned int axis=0,
bool trim_approx=False):
# memory-views do not support n-dimensional arrays, use np.ndarray instead
cdef common.ArrayInfo data_info, output_info
cdef np.ndarray cD, cA
cdef size_t[::1] output_shape
cdef size_t end_level = start_level + level
cdef int retval = -5
cdef size_t i
if data.shape[axis] % 2:
raise ValueError("Length of data must be even along the transform axis.")
if data.shape[axis] < 1:
raise ValueError("Data must have non-zero size along the transform axis.")
if level < 1:
raise ValueError("Level value must be greater than zero.")
if start_level >= common.swt_max_level(data.shape[axis]):
raise ValueError("start_level must be less than %d." %
common.swt_max_level(data.shape[axis]))
if end_level > common.swt_max_level(data.shape[axis]):
msg = ("Level value too high (max level for current data size and "
"start_level is %d)." % (
common.swt_max_level(data.shape[axis]) - start_level))
raise ValueError(msg)
data = data.astype(_check_dtype(data), copy=False)
# For SWT, the output matches the shape of the input
output_shape = <size_t [:data.ndim]> <size_t *> data.shape
data_info.ndim = data.ndim
data_info.strides = <pywt_index_t *> data.strides
data_info.shape = <size_t *> data.shape
output_info.ndim = data.ndim
ret = []
for i in range(start_level+1, end_level+1):
cA = np.empty(output_shape, dtype=data.dtype)
cD = np.empty(output_shape, dtype=data.dtype)
# strides won't match data_info.strides if data is not C-contiguous
output_info.strides = <pywt_index_t *> cA.strides
output_info.shape = <size_t *> cA.shape
if data.dtype == np.float64:
with nogil:
retval = c_wt.double_downcoef_axis(
<double *> data.data, data_info,
<double *> cA.data, output_info,
wavelet.w, axis,
common.COEF_APPROX, common.MODE_PERIODIZATION,
i, common.SWT_TRANSFORM)
if retval:
raise RuntimeError(
"C wavelet transform failed with error code %d" % retval)
with nogil:
retval = c_wt.double_downcoef_axis(
<double *> data.data, data_info,
<double *> cD.data, output_info,
wavelet.w, axis,
common.COEF_DETAIL, common.MODE_PERIODIZATION,
i, common.SWT_TRANSFORM)
if retval:
raise RuntimeError(
"C wavelet transform failed with error code %d" % retval)
elif data.dtype == np.float32:
with nogil:
retval = c_wt.float_downcoef_axis(
<float *> data.data, data_info,
<float *> cA.data, output_info,
wavelet.w, axis,
common.COEF_APPROX, common.MODE_PERIODIZATION,
i, common.SWT_TRANSFORM)
if retval:
raise RuntimeError(
"C wavelet transform failed with error code %d" % retval)
with nogil:
retval = c_wt.float_downcoef_axis(
<float *> data.data, data_info,
<float *> cD.data, output_info,
wavelet.w, axis,
common.COEF_DETAIL, common.MODE_PERIODIZATION,
i, common.SWT_TRANSFORM)
if retval:
raise RuntimeError(
"C wavelet transform failed with error code %d" % retval)
IF HAVE_C99_CPLX:
if data.dtype == np.complex128:
cA = np.zeros(output_shape, dtype=np.complex128)
with nogil:
retval = c_wt.double_complex_downcoef_axis(
<double complex *> data.data, data_info,
<double complex *> cA.data, output_info,
wavelet.w, axis,
common.COEF_APPROX, common.MODE_PERIODIZATION,
i, common.SWT_TRANSFORM)
if retval:
raise RuntimeError(
"C wavelet transform failed with error code %d" %
retval)
cD = np.zeros(output_shape, dtype=np.complex128)
with nogil:
retval = c_wt.double_complex_downcoef_axis(
<double complex *> data.data, data_info,
<double complex *> cD.data, output_info,
wavelet.w, axis,
common.COEF_DETAIL, common.MODE_PERIODIZATION,
i, common.SWT_TRANSFORM)
if retval:
raise RuntimeError(
"C wavelet transform failed with error code %d" %
retval)
elif data.dtype == np.complex64:
cA = np.zeros(output_shape, dtype=np.complex64)
with nogil:
retval = c_wt.float_complex_downcoef_axis(
<float complex *> data.data, data_info,
<float complex *> cA.data, output_info,
wavelet.w, axis,
common.COEF_APPROX, common.MODE_PERIODIZATION,
i, common.SWT_TRANSFORM)
if retval:
raise RuntimeError(
"C wavelet transform failed with error code %d" %
retval)
cD = np.zeros(output_shape, dtype=np.complex64)
with nogil:
retval = c_wt.float_complex_downcoef_axis(
<float complex *> data.data, data_info,
<float complex *> cD.data, output_info,
wavelet.w, axis,
common.COEF_DETAIL, common.MODE_PERIODIZATION,
i, common.SWT_TRANSFORM)
if retval:
raise RuntimeError(
"C wavelet transform failed with error code %d" %
retval)
if retval == -5:
raise TypeError("Array must be floating point, not {}"
.format(data.dtype))
if not trim_approx:
ret.append((cA, cD))
else:
ret.append(cD)
# previous approx coeffs are the data for the next level
data = cA
# update data_info to match the new data array
data_info.strides = <pywt_index_t *> data.strides
data_info.shape = <size_t *> data.shape
if trim_approx:
ret.append(cA)
ret.reverse()
return ret
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