FourierFilter¶

This module defines the FourierFilter class that performs the Fourier filter for a given range to exclude.

class pystog.fourier_filter.FourierFilter[source]¶

The FourierFilter class is used to exlude a given range in the current function by a back Fourier Transform of that section, followed by a difference from the non-excluded function, and then a forward transform of the difference function Can currently do: a real space function -> reciprocal space function -> real space function

Examples:

>>> import numpy
>>> from pystog import FourierFilter
>>> ff = FourierFilter()
>>> r, gr = numpy.loadtxt("my_gofr_file.txt",unpack=True)
>>> q = numpy.linspace(0., 25., 2500)
>>> q, sq = transformer.G_to_S(r, gr, q)
>>> q_ft, sq_ft, q, sq, r, gr = ff.G_using_F(r, gr, q, sq, 1.5)

GK_using_DCS(r, gr, q, dcs, cutoff, dgr=None, ddcs=None, **kwargs)[source]¶

Fourier filters real space $G_{Keen Version}(r)$ using the reciprocal space $\frac{d \sigma}{d \Omega}(Q)$

Parameters:

r (numpy.array or list) – $r$ -space vector
gr (numpy.array or list) – $G_{Keen Version}(r)$ vector
q (numpy.array or list) – $Q$ -space vector
dcs (numpy.array or list) – $\frac{d \sigma}{d \Omega}(Q)$ vector
cutoff (float) – The $r_{max}$ value to filter from 0. to cutoff

Returns:

A tuple of the $Q$ and $\frac{d \sigma}{d \Omega}(Q)$ for the 0. to cutoff transform, the corrected $Q$ and $\frac{d \sigma}{d \Omega}(Q)$ , and the filtered $r$ and $G_{Keen Version}(r)$ .

Thus, [ $Q_{FF}$ , $\frac{d \sigma}{d \Omega}(Q)_{FF}$ , $Q$ , $\frac{d \sigma}{d \Omega}(Q)$ , $r_{FF}$ , $G_{Keen Version}(r)_{FF}]$

Return type:

tuple of numpy.array

GK_using_F(r, gr, q, fq, cutoff, dgr=None, dfq=None, **kwargs)[source]¶

Fourier filters real space $G_{Keen Version}(r)$ using the reciprocal space $Q[S(Q)-1]$

Parameters:

r (numpy.array or list) – $r$ -space vector
gr (numpy.array or list) – $G_{Keen Version}(r)$ vector
q (numpy.array or list) – $Q$ -space vector
fq (numpy.array or list) – $Q[S(Q)-1]$ vector
cutoff (float) – The $r_{max}$ value to filter from 0. to cutoff

Returns:

A tuple of the $Q$ and $Q[S(Q)-1]$

for the 0. to cutoff transform, the corrected $Q$ and $Q[S(Q)-1]$ , and the filtered $r$ and $G_{Keen Version}(r)$ .

Thus, [

$Q_{FF}$ , $Q[S(Q)-1]_{FF}$ , $Q$ , $Q[S(Q)-1]$ , $r_{FF}$ , $G_{Keen Version}(r)_{FF}$

]

Return type:

tuple of numpy.array

GK_using_FK(r, gr, q, fq, cutoff, dgr=None, dfq=None, **kwargs)[source]¶

Fourier filters real space $G_{Keen Version}(r)$ using the reciprocal space $F(Q)$

Parameters:

r (numpy.array or list) – $r$ -space vector
gr (numpy.array or list) – $G_{Keen Version}(r)$ vector
q (numpy.array or list) – $Q$ -space vector
fq (numpy.array or list) – $F(Q)$ vector
cutoff (float) – The $r_{max}$ value to filter from 0. to cutoff

Returns:

A tuple of the $Q$ and $F(Q)$

for the 0. to cutoff transform, the corrected $Q$ and $F(Q)$ , and the filtered $r$ and $G_{Keen Version}(r)$ .

Thus, [

$Q_{FF}$ , $F(Q)_{FF}$ , $Q$ , $F(Q)$ , $r_{FF}$ , $G_{Keen Version}(r)_{FF}$

]

Return type:

tuple of numpy.array

GK_using_S(r, gr, q, sq, cutoff, dgr=None, dsq=None, **kwargs)[source]¶

Fourier filters real space $G_{Keen Version}(r)$ using the reciprocal space $S(Q)$

Parameters:

r (numpy.array or list) – $r$ -space vector
gr (numpy.array or list) – $G_{Keen Version}(r)$ vector
q (numpy.array or list) – $Q$ -space vector
fq (numpy.array or list) – $S(Q)$ vector
cutoff (float) – The $r_{max}$ value to filter from 0. to cutoff

Returns:

A tuple of the $Q$ and $S(Q)$

for the 0. to cutoff transform, the corrected $Q$ and $S(Q)$ , and the filtered $r$ and $G_{Keen Version}(r)$ .

Thus, [

$Q_{FF}$ , $S(Q)_{FF}$ , $Q$ , $S(Q)$ , $r_{FF}$ , $G_{Keen Version}(r)_{FF}$

]

Return type:

tuple of numpy.array

G_using_DCS(r, gr, q, dcs, cutoff, dgr=None, ddcs=None, **kwargs)[source]¶

Fourier filters real space $G_{PDFFIT}(r)$ using the reciprocal space $\frac{d \sigma}{d \Omega}(Q)$

Parameters:

r (numpy.array or list) – $r$ -space vector
gr (numpy.array or list) – $G_{PDFFIT}(r)$ vector
q (numpy.array or list) – $Q$ -space vector
dcs (numpy.array or list) – $\frac{d \sigma}{d \Omega}(Q)$ vector
cutoff (float) – The $r_{max}$ value to filter from 0. to cutoff

Returns:

A tuple of the $Q$ and $\frac{d \sigma}{d \Omega}(Q)$ for the 0. to cutoff transform, the corrected $Q$ and $\frac{d \sigma}{d \Omega}(Q)$ , and the filtered $r$ and $G_{PDFFIT}(r)$ .

Thus, [ $Q_{FF}$ , $\frac{d \sigma}{d \Omega}(Q)_{FF}$ , $Q$ , $\frac{d \sigma}{d \Omega}(Q)$ , $r_{FF}$ , $G_{PDFFIT}(r)_{FF}]$

Return type:

tuple of numpy.array

G_using_F(r, gr, q, fq, cutoff, dgr=None, dfq=None, **kwargs)[source]¶

Fourier filters real space $G_{PDFFIT}(r)$ using the reciprocal space $Q[S(Q)-1]$

Parameters:

r (numpy.array or list) – $r$ -space vector
gr (numpy.array or list) – $G_{PDFFIT}(r)$ vector
q (numpy.array or list) – $Q$ -space vector
fq (numpy.array or list) – $Q[S(Q)-1]$ vector
cutoff (float) – The $r_{max}$ value to filter from 0. to cutoff

Returns:

A tuple of the $Q$ and $Q[S(Q)-1]$

for the 0. to cutoff transform, the corrected $Q$ and $Q[S(Q)-1]$ , and the filtered $r$ and $G_{PDFFIT}(r)$ .

Thus, [

$Q_{FF}$ , $Q[S(Q)-1]_{FF}$ , $Q$ , $Q[S(Q)-1]$ , $r_{FF}$ , $G_{PDFFIT}(r)_{FF}$

]

Return type:

tuple of numpy.array

G_using_FK(r, gr, q, fq, cutoff, dgr=None, dfq=None, **kwargs)[source]¶

Fourier filters real space $G_{PDFFIT}(r)$ using the reciprocal space $F(Q)$

Parameters:

r (numpy.array or list) – $r$ -space vector
gr (numpy.array or list) – $G_{PDFFIT}(r)$ vector
q (numpy.array or list) – $Q$ -space vector
fq (numpy.array or list) – $F(Q)$ vector
cutoff (float) – The $r_{max}$ value to filter from 0. to cutoff

Returns:

A tuple of the $Q$ and $F(Q)$

for the 0. to cutoff transform, the corrected $Q$ and $F(Q)$ , and the filtered $r$ and $G_{PDFFIT}(r)$ .

Thus, [

$Q_{FF}$ , $F(Q)_{FF}$ , $Q$ , $F(Q)$ , $r_{FF}$ , $G_{PDFFIT}(r)_{FF}$

]

Return type:

tuple of numpy.array

G_using_S(r, gr, q, sq, cutoff, dgr=None, dsq=None, **kwargs)[source]¶

Fourier filters real space $G_{PDFFIT}(r)$ using the reciprocal space $S(Q)$

Parameters:

r (numpy.array or list) – $r$ -space vector
gr (numpy.array or list) – $G_{PDFFIT}(r)$ vector
q (numpy.array or list) – $Q$ -space vector
fq (numpy.array or list) – $S(Q)$ vector
cutoff (float) – The $r_{max}$ value to filter from 0. to cutoff

Returns:

A tuple of the $Q$ and $S(Q)$

for the 0. to cutoff transform, the corrected $Q$ and $S(Q)$ , and the filtered $r$ and $G_{PDFFIT}(r)$ .

Thus, [

$Q_{FF}$ , $S(Q)_{FF}$ , $Q$ , $S(Q)$ , $r_{FF}$ , $G_{PDFFIT}(r)_{FF}$

]

Return type:

tuple of numpy.array

g_using_DCS(r, gr, q, dcs, cutoff, dgr=None, ddcs=None, **kwargs)[source]¶

Fourier filters real space $g(r)$ using the reciprocal space $\frac{d \sigma}{d \Omega}(Q)$

Parameters:

r (numpy.array or list) – $r$ -space vector
gr (numpy.array or list) – $g(r)$ vector
q (numpy.array or list) – $Q$ -space vector
dcs (numpy.array or list) – $\frac{d \sigma}{d \Omega}(Q)$ vector
cutoff (float) – The $r_{max}$ value to filter from 0. to cutoff

Returns:

A tuple of the $Q$ and $\frac{d \sigma}{d \Omega}(Q)$ for the 0. to cutoff transform, the corrected $Q$ and $\frac{d \sigma}{d \Omega}(Q)$ , and the filtered $r$ and $g(r)$ .

Thus, [ $Q_{FF}$ , $\frac{d \sigma}{d \Omega}(Q)_{FF}$ , $Q$ , $\frac{d \sigma}{d \Omega}(Q)$ , $r_{FF}$ , $g(r)_{FF}]$

Return type:

tuple of numpy.array

g_using_F(r, gr, q, fq, cutoff, dgr=None, dfq=None, **kwargs)[source]¶

Fourier filters real space $g(r)$ using the reciprocal space $Q[S(Q)-1]$

Parameters:

r (numpy.array or list) – $r$ -space vector
gr (numpy.array or list) – $g(r)$ vector
q (numpy.array or list) – $Q$ -space vector
fq (numpy.array or list) – $Q[S(Q)-1]$ vector
cutoff (float) – The $r_{max}$ value to filter from 0. to cutoff

Returns:

A tuple of the $Q$ and $Q[S(Q)-1]$

for the 0. to cutoff transform, the corrected $Q$ and $Q[S(Q)-1]$ , and the filtered $r$ and $g(r)$ .

Thus, [

$Q_{FF}$ , $Q[S(Q)-1]_{FF}$ , $Q$ , $Q[S(Q)-1]$ , $r_{FF}$ , $g(r)_{FF}$

]

Return type:

tuple of numpy.array

g_using_FK(r, gr, q, fq, cutoff, dgr=None, dfq=None, **kwargs)[source]¶

Fourier filters real space $g(r)$ using the reciprocal space $F(Q)$

Parameters:

r (numpy.array or list) – $r$ -space vector
gr (numpy.array or list) – $g(r)$ vector
q (numpy.array or list) – $Q$ -space vector
fq (numpy.array or list) – $F(Q)$ vector
cutoff (float) – The $r_{max}$ value to filter from 0. to cutoff

Returns:

A tuple of the $Q$ and $F(Q)$ for the 0. to cutoff transform, the corrected $Q$ and $F(Q)$ , and the filtered $r$ and $g(r)$ .

Thus, [ $Q_{FF}$ , $F(Q)_{FF}$ , $Q$ , $F(Q)$ , $r_{FF}$ , $g(r)_{FF}]$

Return type:

tuple of numpy.array

g_using_S(r, gr, q, sq, cutoff, dgr=None, dsq=None, **kwargs)[source]¶

Fourier filters real space $g(r)$ using the reciprocal space $S(Q)$

Parameters:

r (numpy.array or list) – $r$ -space vector
gr (numpy.array or list) – $g(r)$ vector
q (numpy.array or list) – $Q$ -space vector
sq (numpy.array or list) – $S(Q)$ vector
cutoff (float) – The $r_{max}$ value to filter from 0. to cutoff

Returns:

A tuple of the $Q$ and $S(Q)$ for the 0. to cutoff transform, the corrected $Q$ and $S(Q)$ , and the filtered $r$ and $g(r)$ .

Thus, [ $Q_{FF}$ , $S(Q)_{FF}$ , $Q$ , $S(Q)$ , $r_{FF}$ , $g(r)_{FF}]$

Return type:

tuple of numpy.array