Wavelets¶
Wavelet families()
¶

pywt.
families
()¶ Returns a list of available builtin wavelet families. Currently the builtin families are:
 Haar (
haar
)  Daubechies (
db
)  Symlets (
sym
)  Coiflets (
coif
)  Biorthogonal (
bior
)  Reverse biorthogonal (
rbio
)  “Discrete” FIR approximation of Meyer wavelet (
dmey
)
Example:
>>> import pywt >>> print pywt.families() ['haar', 'db', 'sym', 'coif', 'bior', 'rbio', 'dmey']
 Haar (
Builtin wavelets  wavelist()
¶

pywt.
wavelist
([family])¶ The
wavelist()
function returns a list of names of the builtin wavelets.If the family name is
None
then names of all the builtin wavelets are returned. Otherwise the function returns names of wavelets that belong to the given family.Example:
>>> import pywt >>> print pywt.wavelist('coif') ['coif1', 'coif2', 'coif3', 'coif4', 'coif5']
Custom user wavelets are also supported through the
Wavelet
object constructor as described below.
Wavelet
object¶

class
pywt.
Wavelet
(name[, filter_bank=None])¶ Describes properties of a wavelet identified by the specified wavelet name. In order to use a builtin wavelet the name parameter must be a valid wavelet name from the
pywt.wavelist()
list.Custom Wavelet objects can be created by passing a userdefined filters set with the filter_bank parameter.
Parameters:  name – Wavelet name
 filter_bank – Use a user supplied filter bank instead of a builtin
Wavelet
.
The filter bank object can be a list of four filters coefficients or an object with
filter_bank
attribute, which returns a list of such filters in the following order:[dec_lo, dec_hi, rec_lo, rec_hi]
Wavelet objects can also be used as a base filter banks. See section on using custom wavelets for more information.
Example:
>>> import pywt >>> wavelet = pywt.Wavelet('db1')

name
¶ Wavelet name.

short_name
¶ Short wavelet name.

dec_lo
¶ Decomposition filter values.

dec_hi
¶ Decomposition filter values.

rec_lo
¶ Reconstruction filter values.

rec_hi
¶ Reconstruction filter values.

dec_len
¶ Decomposition filter length.

rec_len
¶ Reconstruction filter length.

filter_bank
¶ Returns filters list for the current wavelet in the following order:
[dec_lo, dec_hi, rec_lo, rec_hi]

inverse_filter_bank
¶ Returns list of reverse wavelet filters coefficients. The mapping from the filter_coeffs list is as follows:
[rec_lo[::1], rec_hi[::1], dec_lo[::1], dec_hi[::1]]

short_family_name
¶ Wavelet short family name

family_name
¶ Wavelet family name

orthogonal
¶ Set if wavelet is orthogonal

biorthogonal
¶ Set if wavelet is biorthogonal

symmetry
¶ asymmetric
,near symmetric
,symmetric

vanishing_moments_psi
¶ Number of vanishing moments for the wavelet function

vanishing_moments_phi
¶ Number of vanishing moments for the scaling function
Example:
>>> def format_array(arr): ... return "[%s]" % ", ".join(["%.14f" % x for x in arr]) >>> import pywt >>> wavelet = pywt.Wavelet('db1') >>> print wavelet Wavelet db1 Family name: Daubechies Short name: db Filters length: 2 Orthogonal: True Biorthogonal: True Symmetry: asymmetric >>> print format_array(wavelet.dec_lo), format_array(wavelet.dec_hi) [0.70710678118655, 0.70710678118655] [0.70710678118655, 0.70710678118655] >>> print format_array(wavelet.rec_lo), format_array(wavelet.rec_hi) [0.70710678118655, 0.70710678118655] [0.70710678118655, 0.70710678118655]
Approximating wavelet and scaling functions  Wavelet.wavefun()
¶

Wavelet.
wavefun
(level)¶ Changed in version 0.2: The time (space) localisation of approximation function points was added.
The
wavefun()
method can be used to calculate approximations of scaling function (phi) and wavelet function (psi) at the given level of refinement.For
orthogonal
wavelets returns approximations of scaling function and wavelet function with corresponding xgrid coordinates:[phi, psi, x] = wavelet.wavefun(level)
Example:
>>> import pywt >>> wavelet = pywt.Wavelet('db2') >>> phi, psi, x = wavelet.wavefun(level=5)
For other (
biorthogonal
but notorthogonal
) wavelets returns approximations of scaling and wavelet function both for decomposition and reconstruction and corresponding xgrid coordinates:[phi_d, psi_d, phi_r, psi_r, x] = wavelet.wavefun(level)
Example:
>>> import pywt >>> wavelet = pywt.Wavelet('bior3.5') >>> phi_d, psi_d, phi_r, psi_r, x = wavelet.wavefun(level=5)
See also
You can find live examples of
wavefun()
usage and images of all the builtin wavelets on the Wavelet Properties Browser page.
Using custom wavelets¶
PyWavelets comes with a long list
of the most popular
wavelets builtin and ready to use. If you need to use a specific wavelet which
is not included in the list it is very easy to do so. Just pass a list of four
filters or an object with a filter_bank
attribute as a
filter_bank argument to the Wavelet
constructor.
The filters list, either in a form of a simple Python list or returned via
the filter_bank
attribute, must be in the following order:
 lowpass decomposition filter
 highpass decomposition filter
 lowpass reconstruction filter
 highpass reconstruction filter
just as for the filter_bank
attribute of the
Wavelet
class.
The Wavelet object created in this way is a standard Wavelet
instance.
The following example illustrates the way of creating custom Wavelet objects from plain Python lists of filter coefficients and a filter banklike objects.
Example:
>>> import pywt, math >>> c = math.sqrt(2)/2 >>> dec_lo, dec_hi, rec_lo, rec_hi = [c, c], [c, c], [c, c], [c, c] >>> filter_bank = [dec_lo, dec_hi, rec_lo, rec_hi] >>> myWavelet = pywt.Wavelet(name="myHaarWavelet", filter_bank=filter_bank) >>> >>> class HaarFilterBank(object): ... @property ... def filter_bank(self): ... c = math.sqrt(2)/2 ... dec_lo, dec_hi, rec_lo, rec_hi = [c, c], [c, c], [c, c], [c, c] ... return [dec_lo, dec_hi, rec_lo, rec_hi] >>> filter_bank = HaarFilterBank() >>> myOtherWavelet = pywt.Wavelet(name="myHaarWavelet", filter_bank=filter_bank)