Filters - filter design and filtering

ZeroPoleGain(z::Vector, p::Vector, k::Number)

Filter representation in terms of zeros z, poles p, and gain k:

\[H(x) = k\frac{(x - \verb!z[1]!) \ldots (x - \verb!z[m]!)}{(x - \verb!p[1]!) \ldots (x - \verb!p[n]!)}\]

PolynomialRatio(b::Union{Number, Vector{<:Number}}, a::Union{Number, Vector{<:Number}})

Filter representation in terms of the coefficients of the numerator b and denominator a in the z or s domain where b and a are vectors ordered from highest power to lowest.

Inputs that are Numbers are treated as one-element Vectors.

Filter with:

• Transfer function in z domain (zero & negative z powers):

\[H(z) = \frac{\verb!b[1]! + \ldots + \verb!b[m]! z^{-m+1}}{\verb!a[1]! + \ldots + \verb!a[n]! z^{-n+1}}\]

returns PolynomialRatio object with a[1] = 1 and other specified coefficients divided by a[1].

julia> PolynomialRatio([1,1],[1,2])
PolynomialRatio{:z, Float64}(LaurentPolynomial(1.0*z⁻¹ + 1.0), LaurentPolynomial(2.0*z⁻¹ + 1.0))
julia> PolynomialRatio{:z}([1,2,3],[2,3,4])
PolynomialRatio{:z, Float64}(LaurentPolynomial(1.5*z⁻² + 1.0*z⁻¹ + 0.5), LaurentPolynomial(2.0*z⁻² + 1.5*z⁻¹ + 1.0))

• Transfer function in s domain (zero & positive s powers):

\[H(s) = \frac{\verb!b[1]! s^{m-1} + \ldots + \verb!b[m]!}{\verb!a[1]! s^{n-1} + \ldots + \verb!a[n]!}\]

returns PolynomialRatio object with specified b and a coefficients.

julia> PolynomialRatio{:s}([1,2,3],[2,3,4])
PolynomialRatio{:s, Int64}(LaurentPolynomial(3 + 2*s + s²), LaurentPolynomial(4 + 3*s + 2*s²))

Biquad(b0::T, b1::T, b2::T, a1::T, a2::T) where T <: Number

Filter representation in terms of the transfer function of a single second-order section given by:

\[H(s) = \frac{\verb!b0! s^2+\verb!b1! s+\verb!b2!}{s^2+\verb!a1! s + \verb!a2!}\]

or equivalently:

\[H(z) = \frac{\verb!b0!+\verb!b1! z^{-1}+\verb!b2! z^{-2}}{1+\verb!a1! z^{-1} + \verb!a2! z^{-2}}\]

SecondOrderSections(biquads::Vector{<:Biquad}, gain::Number)

Filter representation in terms of a cascade of second-order sections and gain. biquads must be specified as a vector of Biquads.

DF2TFilter(coef::FilterCoefficients{:z}[, si])
DF2TFilter(coef::FilterCoefficients{:z}[, sitype::Type])

Construct a stateful direct form II transposed filter with coefficients coef.

One can optionally specify as the second argument either

• si, an array representing the initial filter state, or
• sitype, the eltype of a zeroed si

The initial filter state defaults to zeros if called with one argument.

If coef is a PolynomialRatio, Biquad, or SecondOrderSections, filtering is implemented directly. If coef is a ZeroPoleGain object, it is first converted to a SecondOrderSections object.

FIRFilter(h::Vector[, ratio::Union{Integer,Rational}])

Construct a stateful FIRFilter object from the vector of filter taps h. ratio is an optional rational integer which specifies the input to output sample rate relationship (e.g. 147//160 for converting recorded audio from 48 KHz to 44.1 KHz).

FIRFilter(h::Vector, rate::AbstractFloat[, Nϕ::Integer=32])

Returns a polyphase FIRFilter object from the vector of filter taps h. rate is a floating point number that specifies the input to output sample-rate relationship $\frac{fs_{out}}{fs_{in}}$. Nϕ is an optional parameter which specifies the number of phases created from h. Nϕ defaults to 32.

filt(b::Union{AbstractVector,Number},
     a::Union{AbstractVector,Number},
     x::AbstractArray,
     [si::AbstractArray])

Apply filter described by vectors a and b to vector x, with an optional initial filter state vector si (defaults to zeros).

Inputs that are Numbers are treated as one-element Vectors.

filt(f::FilterCoefficients{:z}, x::AbstractArray[, si])

Apply filter or filter coefficients f along the first dimension of array x. If f is a filter coefficient object, si is an optional array representing the initial filter state (defaults to zeros). If f is a PolynomialRatio, Biquad, or SecondOrderSections, filtering is implemented directly. If f is a ZeroPoleGain object, it is first converted to a SecondOrderSections object. If f is a Vector, it is interpreted as an FIR filter, and a naïve or FFT-based algorithm is selected based on the data and filter length.

filt(f::DF2TFilter{<:FilterCoefficients{:z},<:Array{T}}, x::AbstractVector{V}) where {T,V}

Apply the stateful filter f on x.

filt!(out, b, a, x, [si])

Same as filt but writes the result into the out argument, which may alias the input x to modify it in-place.

filt!(out, f, x[, si])

Same as filt() but writes the result into the out argument. Output array out may not be an alias of x, i.e. filtering may not be done in place.

filtfilt(coef::FilterCoefficients, x::AbstractArray)
filtfilt(b::AbstractVector, x::AbstractArray)
filtfilt(b::AbstractVector, a::AbstractVector, x::AbstractArray)

Filter x in the forward and reverse directions using either a FilterCoefficients object coef, or the coefficients b and optionally a as in filt. The initial state of the filter is computed so that its response to a step function is steady state. Before filtering, the data is extrapolated at both ends with an odd-symmetric extension of length min(3*(max(length(b), length(a))-1), size(x, 1) - 1).

Because filtfilt applies the given filter twice, the effective filter order is twice the order of coef. The resulting signal has zero phase distortion.

fftfilt(h::AbstractVector{<:Real}, x::AbstractArray{<:Real})

Apply FIR filter taps h along the first dimension of array x using an FFT-based overlap-save algorithm.

fftfilt!(out::AbstractArray, h::AbstractVector, x::AbstractArray)

Like fftfilt but writes result into out array.

tdfilt(h::AbstractVector, x::AbstractArray)

Apply filter or filter coefficients h along the first dimension of array x using a naïve time-domain algorithm

tdfilt!(out::AbstractArray, h::AbstractVector, x::AbstractArray)

Like tdfilt, but writes the result into array out. Output array out may not be an alias of x, i.e. filtering may not be done in place.

resample(x::AbstractVector, rate::Real[, coef::Vector])

Resample x at rational or arbitrary rate. coef is an optional vector of FIR filter taps. If coef is not provided, the taps will be computed using a Kaiser window.

Internally, resample uses a polyphase FIRFilter object, but performs additional operations to make resampling a signal easier. It compensates for the FIRFilter's delay (ramp-up), and appends zeros to x. The result is that when the input and output signals are plotted on top of each other, they correlate very well, but one signal will have more samples than the other.

resample(x::AbstractArray, rate::Real, h::Vector = resample_filter(rate); dims)

Resample an array x along dimension dims.

analogfilter(responsetype::FilterType, designmethod::FilterCoefficients)

Construct an analog filter. See below for possible response and filter types.

digitalfilter(responsetype::FilterType, designmethod::FilterCoefficients[; fs::Real])

Construct a digital filter. See below for possible response and filter types.

bilinear(f::FilterCoefficients{:s}, fs::Real)

Calculate the digital filter (z-domain) ZPK representation of an analog filter defined in s-domain using bilinear transform with sampling frequency fs. The s-domain representation is first converted to a ZPK representation in s-domain and then transformed to z-domain using bilinear transform.

bilinear(f::ZeroPoleGain{:s,Z,P,K}, fs::Real) where {Z,P,K}

Calculate the digital filter (z-domain) ZPK representation of an analog filter defined as a ZPK representation in s-domain using bilinear transform with sampling frequency fs.

Input s-domain representation must be a ZeroPoleGain{:s, Z, P, K} object:

\[H(s) = f.k\frac{(s - \verb!f.z[1]!) \ldots (s - \verb!f.z[m]!)}{(s - \verb!f.p[1]!) \ldots (s - \verb!f.p[n]!)}\]

Output z-domain representation is a ZeroPoleGain{:z, Z, P, K} object:

\[H(z) = K\frac{(z - \verb!Z[1]!) \ldots (z - \verb!Z[m]!)}{(z - \verb!P[1]!) \ldots (z - \verb!P[n]!)}\]

where Z, P, K are calculated as:

\[Z[i] = \frac{(2 + \verb!f.z[i]!/\verb!fs!)}{(2 - \verb!f.z[i]!/\verb!fs!)} \quad \text{for } i = 1, \ldots, m\]

\[P[i] = \frac{(2 + \verb!f.p[i]!/\verb!fs!)}{(2 - \verb!f.p[i]!/\verb!fs!)} \quad \text{for } i = 1, \ldots, n\]

\[K = f.k \ \mathcal{Re} \left[ \frac{\prod_{i=1}^m (2*fs - f.z[i])}{\prod_{i=1}^n (2*fs - f.p[i])} \right]\]

Here, m and n are respectively the numbers of zeros and poles in the s-domain representation. If m < n, then additional n-m zeros are added at z = -1.

Lowpass(Wn::Real)

Low pass filter with cutoff frequency Wn.

Highpass(Wn::Real)

High pass filter with cutoff frequency Wn.

Bandpass(Wn1::Real, Wn2::Real)

Band pass filter with pass band frequencies (Wn1, Wn2).

ComplexBandpass(Wn1, Wn2)

Complex band pass filter with pass band frequencies (Wn1, Wn2).

Bandstop(Wn1::Real, Wn2::Real)

Band stop filter with stop band frequencies (Wn1, Wn2).

Butterworth(n::Integer)

$n$ pole Butterworth filter.

Chebyshev1(n::Integer, ripple::Real)

n pole Chebyshev type I filter with ripple dB ripple in the passband.

Chebyshev2(n::Integer, ripple::Real)

n pole Chebyshev type II filter with ripple dB ripple in the stopband.

Elliptic(n::Integer, rp::Real, rs::Real)

n pole elliptic (Cauer) filter with rp dB ripple in the passband and rs dB attentuation in the stopband.

(N, ωn) = buttord(Wp::Tuple{Real,Real}, Ws::Tuple{Real,Real}, Rp::Real, Rs::Real; domain=:z)

Butterworth order estimate for bandpass and bandstop filter types. Wp and Ws are 2-element pass and stopband frequency edges, with no more than Rp dB passband ripple and at least Rs dB stopband attenuation. Based on the ordering of passband and bandstop edges, the Bandstop or Bandpass filter type is inferred. N is an integer indicating the lowest estimated filter order, with ωn specifying the cutoff or "-3 dB" frequencies.

If a domain of :s is specified, the passband and stopband frequencies are interpreted as radians/second, giving an order and natural frequencies for an analog filter. The default domain is :z, interpreting the input frequencies as normalized from 0 to 1, where 1 corresponds to π radians/sample.

(N, ωn) = buttord(Wp::Real, Ws::Real, Rp::Real, Rs::Real; domain=:z)

LPF/HPF Butterworth filter order and -3 dB frequency approximation. Wp and Ws are the passband and stopband frequencies, whereas Rp and Rs are the passband and stopband ripple attenuations in dB. If the passband is greater than stopband, the filter type is inferred to be for estimating the order of a highpass filter. N specifies the lowest possible integer filter order, whereas ωn is the cutoff or "-3 dB" frequency.

If a domain of :s is specified, the passband and stopband edges are interpreted as radians/second, giving an order and natural frequency result for an analog filter. The default domain is :z, interpreting the input frequencies as normalized from 0 to 1, where 1 corresponds to π radians/sample.

(N, ωn) = cheb1ord(Wp::Real, Ws::Real, Rp::Real, Rs::Real; domain::Symbol=:z)

Integer and natural frequency order estimation for Chebyshev Type I Filters. Wp and Ws indicate the passband and stopband frequency edges, and Rp and Rs indicate the maximum loss in the passband and the minimum attenuation in the stopband, (in dB.)

Based on the ordering of passband and stopband edges, the Lowpass or Highpass filter type is inferred. N indicates the smallest integer filter order that achieves the desired specifications, and ωn contains the natural frequency of the filter, (in this case, simply the passband edge.)

If a domain of :s is specified, the passband and stopband edges are interpreted as radians/second, giving an order and natural frequency result for an analog filter. The default domain is :z, interpreting the input frequencies as normalized from 0 to 1, where 1 corresponds to π radians/sample.

(N, ωn) = cheb1ord(Wp::Tuple{Real, Real}, Ws::Tuple{Real, Real}, Rp::Real, Rs::Real; domain::Symbol=:z)

Integer and natural frequency order estimation for Chebyshev Type I Filters. Wp and Ws are 2-element frequency edges for Bandpass/Bandstop cases, with Rp and Rs representing the ripple maximum loss in the passband and minimum ripple attenuation in the stopband in dB. Based on the ordering of passband and bandstop edges, the Bandstop or Bandpass filter type is inferred. N is an integer indicating the lowest estimated filter order, with ωn specifying the cutoff or "-3 dB" frequencies. If a domain of :s is specified, the passband and stopband frequencies are interpreted as radians/second, giving the order and natural frequencies for an analog filter. The default domain is :z, interpreting the input frequencies as normalized from 0 to 1, where 1 corresponds to π radians/sample.

(N, ωn) = cheb2ord(Wp::Real, Ws::Real, Rp::Real, Rs::Real; domain::Symbol=:z)

Integer and natural frequency order estimation for Chebyshev Type II (inverse) Filters. Wp and Ws are the frequency edges for Bandpass/Bandstop cases, with Rp and Rs representing the ripple maximum loss in the passband and minimum ripple attenuation in the stopband in dB.

Based on the ordering of the passband and stopband edges, the Lowpass or Highpass filter type is inferred. N is an integer indicating the lowest estimated filter order, with ωn specifying the "-3 dB" cutoff frequency.

If a domain of :s is specified, the passband and stopband frequencies are interpreted as radians/second, giving the order and natural frequencies for an analog filter. The default domain is :z, interpreting the input frequencies as normalized from 0 to 1, where 1 corresponds to π radians/sample.

(N, ωn) = cheb2ord(Wp::Tuple{Real, Real}, Ws::Tuple{Real, Real}, Rp::Real, Rs::Real; domain::Symbol=:z)

Integer and natural frequency order estimation for Chebyshev Type II (inverse) Filters. Wp and Ws are 2-element frequency edges for Bandpass/Bandstop cases, with Rp and Rs representing the ripple maximum loss in the passband and minimum ripple attenuation in the stopband in dB.

Based on the ordering of the passband and bandstop edges, the Bandstop or Bandpass filter type is inferred. N is an integer indicating the lowest estimated filter order, with ωn specifying the "-3 dB" cutoff frequency.

If a domain of :s is specified, the passband and stopband frequencies are interpreted as radians/second, giving the order and natural frequencies for an analog filter. The default domain is :z, interpreting the input frequencies as normalized from 0 to 1, where 1 corresponds to π radians/sample.

(N, ωn) = ellipord(Wp::Real, Ws::Real, Rp::Real, Rs::Real; domain::Symbol=:z)

Integer and natural frequency order estimation for Elliptic (Cauer) Filters. Wp and Ws indicate the passband and stopband frequency edges, and Rp and Rs indicate the maximum loss in the passband and the minimum attenuation in the stopband, (in dB.)

Based on the ordering of passband and stopband edges, the Lowpass or Highpass filter type is inferred. N indicates the smallest integer filter order that achieves the desired specifications, and ωn contains the natural frequency of the filter, (in this case, simply the passband edge.)

If a domain of :s is specified, the passband and stopband edges are interpreted as radians/second, giving an order and natural frequency result for an analog filter. The default domain is :z, interpreting the input frequencies as normalized from 0 to 1, where 1 corresponds to π radians/sample.

(N, ωn) = ellipord(Wp::Tuple{Real, Real}, Ws::Tuple{Real, Real}, Rp::Real, Rs::Real; domain::Symbol=:z)

Integer and natural frequency order estimation for Elliptic (Cauer) Filters. Wp and Ws are 2-element frequency edges for Bandpass/Bandstop cases, with Rp and Rs representing the ripple maximum loss in the passband and minimum ripple attenuation in the stopband in dB. Based on the ordering of passband and bandstop edges, the Bandstop or Bandpass filter type is inferred. N is an integer indicating the lowest estimated filter order, with ωn specifying the cutoff or "-3 dB" frequencies. If a domain of :s is specified, the passband and stopband frequencies are interpreted as radians/second, giving the order and natural frequencies for an analog filter. The default domain is :z, interpreting the input frequencies as normalized from 0 to 1, where 1 corresponds to π radians/sample.

N = remezord(Wp::Real, Ws::Real, Rp::Real, Rs::Real)

Order estimation for lowpass digital filter cases based on the equations and coefficients in ^[Rabiner]. The original equation returned the minimum filter length, whereas this implementation returns the order (N=L-1). Wp and Ws are the normalized passband and stopband frequencies, with Rp indicating the passband ripple and Rs is the stopband attenuation (linear.)

NOTE: The value of N is an initial approximation. If under-estimated, the order should be increased until the design specifications are met.

FIRWindow(window::Vector; scale=true)

FIR filter design using window window, a vector whose length matches the number of taps in the resulting filter.

If scale is true (default), the designed FIR filter is scaled so that the following holds:

• For Lowpass and Bandstop filters, the frequency response is unity at 0 (DC).
• For Highpass filters, the frequency response is unity at the Nyquist frequency.
• For Bandpass filters, the frequency response is unity in the center of the passband.

FIRWindow(; transitionwidth::Real, attenuation::Real=60, scale::Bool=true)

Kaiser window FIR filter design. The required number of taps is calculated based on transitionwidth (in half-cycles/sample) and stopband attenuation (in dB). attenuation defaults to 60 dB.

remez(numtaps::Integer, band_defs;
      Hz::Real=1.0,
      neg::Bool=false,
      maxiter::Integer=25,
      grid_density::Integer=16)

Calculate the minimax optimal filter using the Remez exchange algorithm ^{[McClellan1973a] [McClellan1973b]}.

This is the simplified API that accepts just 2 required arguments (numtaps, band_defs). For a scipy compatible version see the 3 arguments version (numtaps, bands, desired).

Calculate the filter-coefficients for the finite impulse response (FIR) filter whose transfer function minimizes the maximum error between the desired gain and the realized gain in the specified frequency bands using the Remez exchange algorithm.

Arguments

• numtaps::Integer: The desired number of taps in the filter. The number of taps is the number of terms in the filter, or the filter order plus one.
• band_defs: A sequence of band definitions. This sequence defines the bands. Each entry is a pair. The pair's first item is a tuple of band edges (low, high). The pair's second item defines the desired response and weight in that band. The weight is optional and defaults to 1.0. Both the desired response and weight may be either scalars or functions. If a function, the function should accept a real frequency and return the real desired response or real weight. Examples:
  • LPF with unity weights. [(0, 0.475) => 1, (0.5, 1.0) => 0]
  • LPF with weight of 2 in the stop band. [(0, 0.475) => (1, 1), (0.5, 1.0) => (0, 2)]
  • BPF with unity weights. [(0, 0.375) => 0, (0.4, 0.5) => 1, (0.525, 1.0) => 0]
  • Hilbert transformer. [(0.1, 0.95) => 1]; neg=true
  • Differentiator. [(0.01, 0.99) => (f -> f/2, f -> 1/f)]; neg=true
• Hz::Real: The sampling frequency in Hz. Default is 1.
• neg::Bool: Whether the filter has negative symmetry or not. Default is false. If false, the filter is even-symmetric. If true, the filter is odd-symmetric. neg=true means that h[n]=-h[end+1-n]; neg=false means that h[n]=h[end+1-n].
• maxiter::Integer: (optional) Maximum number of iterations of the algorithm. Default is 25.
• grid_density:Integer: (optional) Grid density. The dense grid used in remez is of size (numtaps + 1) * grid_density. Default is 16.

Returns

• h::Array{Float64,1}: A rank-1 array containing the coefficients of the optimal (in a minimax sense) filter.

Examples

Construct a length 35 filter with a passband at 0.15-0.4 Hz (desired response of 1), and stop bands at 0-0.1 Hz and 0.45-0.5 Hz (desired response of 0). Note: the behavior in the frequency ranges between those bands - the transition bands - is unspecified.

julia> bpass = remez(35, [(0, 0.1)=>0, (0.15, 0.4)=>1, (0.45, 0.5)=>0]);

You can trade-off maximum error achieved for transition bandwidth. The wider the transition bands, the lower the maximum error in the bands specified. Here is a bandpass filter with the same passband, but wider transition bands.

julia> bpass2 = remez(35, [(0, 0.08)=>0, (0.15, 0.4)=>1, (0.47, 0.5)=>0]);

Here we compute the frequency responses and plot them in dB.

julia> using PyPlot
julia> b = PolynomialRatio(bpass, [1.0])
julia> b2 = PolynomialRatio(bpass2, [1.0])
julia> f = range(0, stop=0.5, length=1000)
julia> plot(f, 20*log10.(abs.(freqresp(b,f,1.0))))
julia> plot(f, 20*log10.(abs.(freqresp(b2,f,1.0))))
julia> grid()

Examples from the unittests - standard (even) symmetry.

Length 151 LPF (Low Pass Filter).

julia> h = remez(151, [(0, 0.475) => 1, (0.5, 1.0) => 0]; Hz=2.0);

Length 152 LPF. Non-default "weight" input.

julia> h = remez(152, [(0, 0.475) => (1, 1), (0.5, 1.0) => (0, 2)]; Hz=2.0);

Length 51 HPF (High Pass Filter).

julia> h = remez(51, [(0, 0.75) => 0, (0.8, 1.0) => 1]; Hz=2.0);

Length 180 BPF (Band Pass Filter).

julia> h = remez(180, [(0, 0.375) => 0, (0.4, 0.5) => 1, (0.525, 1.0) => 0]; Hz=2.0, maxiter=30);

Examples from the unittests - Odd-symmetric filters - hilbert and differentiators type.

Even length - has a much better approximation since the response is not constrained to 0 at the nyquist frequency. Length 20 Hilbert transformer.

julia> h = remez(20, [(0.1, 0.95) => 1]; neg=true, Hz=2.0);

Length 21 Hilbert transformer.

julia> h = remez(21, [(0.1, 0.95) => 1]; neg=true, Hz=2.0);

Length 200 differentiator.

julia> h = remez(200, [(0.01, 0.99) => (f -> f/2, f -> 1/f)]; neg=true, Hz=2.0);

Length 201 differentiator.

julia> h = remez(201, [(0.05, 0.95) => (f -> f/2, f -> 1/f)]; neg=true, Hz=2.0);

Inverse sinc filter - custom response function

julia> L = 64; Fs = 4800*L;
julia> passband_response_function = f -> (f==0) ? 1.0 : abs.((π*f/4800) ./ sin.(π*f/4800));
julia> h = remez(201, [(    0.0, 2880.0) => (passband_response_function, 1.0),
                (10000.0,   Fs/2) => (0.0, 100.0)]; Hz=Fs);

remez(numtaps::Integer,
      bands::Vector,
      desired::Vector;
      weight::Vector=[],
      Hz::Real=1.0,
      filter_type::RemezFilterType=filter_type_bandpass,
      maxiter::Integer=25,
      grid_density::Integer=16)

This is the scipy compatible version that requires 3 arguments (numtaps, bands, desired). For a simplified API, see the 2 argument version (numtaps, band_defs). The filters designed are equivalent, the inputs are just specified in a different way. Below the arguments and examples are described that differ from the simplified API version.

Arguments

• bands::Vector: A monotonic sequence containing the band edges in Hz. All elements must be non-negative and less than half the sampling frequency as given by Hz.
• desired::Vector:A sequence half the size of bands containing the desired gain in each of the specified bands.
• weight::Vector: (optional) A relative weighting to give to each band region. The length of weight has to be half the length of bands.
• filter_type::RemezFilterType: Default is filter_type_bandpass. The type of filter:
  • filter_type_bandpass : flat response in bands. This is the default.
  • filter_type_differentiator : frequency proportional response in bands. Odd symetric as in filter_type_hilbert case, but with a linear sloping desired response.
  • filter_type_hilbert : filter with odd symmetry, that is, type III (for even order) or type IV (for odd order) linear phase filters.

Examples

Compare the examples with the simplified API and the Scipy API. Each of the following blocks first designs a filter using the simplified (recommended) API, and then designs the same filter using the Scipy-compatible API.

julia> bpass = remez(35, [(0, 0.1)=>0, (0.15, 0.4)=>1, (0.45, 0.5)=>0]);

julia> bpass = remez(35, [0, 0.1, 0.15, 0.4, 0.45, 0.5], [0, 1, 0]);

julia> bpass2 = remez(35, [(0, 0.08)=>0, (0.15, 0.4)=>1, (0.47, 0.5)=>0]);

julia> bpass2 = remez(35, [0, 0.08, 0.15, 0.4, 0.47, 0.5], [0, 1, 0]);

julia> h = remez(20, [(0.1, 0.95) => 1]; neg=true, Hz=2.0);

julia> h = remez(20, [0.1, 0.95], [1]; filter_type=filter_type_hilbert, Hz=2.0);

julia> h = remez(200, [(0.01, 0.99) => (f -> f/2, f -> 1/f)]; neg=true, Hz=2.0);

julia> h = remez(200, [0.01, 0.99], [1]; filter_type=filter_type_differentiator, Hz=2.0);

iirnotch(Wn::Real, bandwidth::Real[; fs=2])

Second-order digital IIR notch filter ^[Orfanidis] at frequency Wn with bandwidth bandwidth. If fs is not specified, Wn is interpreted as a normalized frequency in half-cycles/sample.

H, w = freqresp(filter::FilterCoefficients)

Frequency response H of a filter at (normalized) frequencies w in radians/sample for a digital filter or radians/second for an analog filter chosen as a reasonable default.

freqresp(filter::FilterCoefficients{:z}, w)

Frequency response of digital filter at normalized frequency or frequencies w in radians/sample.

freqresp(filter::FilterCoefficients{:s}, w)

Frequency response of analog filter at frequency or frequencies w in radians/second.

phi, w = phaseresp(filter::FilterCoefficients)

Phase response phi of a filter at (normalized) frequencies w in radians/sample for a digital filter or radians/second for an analog filter chosen as a reasonable default.

phaseresp(filter::FilterCoefficients, w)

Phase response of a filter at (normalized) frequency or frequencies w in radians/sample for a digital filter or radians/second for an analog filter.

tau, w = grpdelay(filter::FilterCoefficients)

Group delay tau of a filter at (normalized) frequencies w in radians/sample for a digital filter or radians/second for an analog filter chosen as a reasonable default.

grpdelay(filter::FilterCoefficients{:z}, w)

Group delay of a digital filter at normalized frequency or frequencies w in radians/sample.

impresp(filter::FilterCoefficients{:z}, n=100)

Impulse response of a digital filter with n points.

stepresp(filter::FilterCoefficients{:z}, n=100)

Step response of a digital filter with n points.

coefb(f::PolynomialRatio)

Coefficients of the numerator of a PolynomialRatio object, highest power first, i.e., the b passed to filt()

coefa(f::PolynomialRatio)

Coefficients of the denominator of a PolynomialRatio object, highest power first, i.e., the a passed to filt()