Converter

This module defines the Converter class that converts functions in the same space

class pystog.converter.Converter[source]

Bases: object

The Converter class is used to convert between either different reciprocal space functions or different real space functions

Examples:

>>> import numpy
>>> from pystog import Converter
>>> converter = Converter()
>>> q, sq = numpy.loadtxt("my_sofq_file.txt",unpack=True)
>>> fq, dfq = converter.S_to_F(q, sq)
>>> r, gr = numpy.loadtxt("my_gofr_file.txt",unpack=True)
>>> kwargs = {'rho' : 1.0}
>>> gr_keen, dgr_keen = converter.g_to_GK(r, gr, **kwargs)

DCS_to_F(q, dcs, ddcs=None, **kwargs)[source]

Convert $\frac{d \sigma}{d \Omega}(Q)$ to $Q[S(Q)-1]$

Parameters:

q (numpy.array or list) – Q-space vector
dcs (numpy.array or list) – $\frac{d \sigma}{d \Omega}(Q)$ vector
ddcs (numpy.array or list) – uncertainty vector

Returns:

( $Q[S(Q)-1]$ vector, uncertainty vector)

Return type:

(numpy.array, numpy.array)

DCS_to_FK(q, dcs, ddcs=None, **kwargs)[source]

Convert $\frac{d \sigma}{d \Omega}(Q)$ to $F(Q)$

Parameters:

q (numpy.array or list) – $Q$ -space vector
fq (numpy.array or list) – $\frac{d \sigma}{d \Omega}(Q)$ vector
ddcs (numpy.array or list) – uncertainty vector

Returns:

( $F(Q)$ vector, uncertainty vector)

Return type:

(numpy.array, numpy.array)

DCS_to_S(q, dcs, ddcs=None, **kwargs)[source]

Convert $\frac{d \sigma}{d \Omega}(Q)$ to $S(Q)$

Parameters:

q (numpy.array or list) – $Q$ -space vector
dcs (numpy.array or list) – $\frac{d \sigma}{d \Omega}(Q)$ vector
ddcs (numpy.array or list) – uncertainty vector

Returns:

( $S(Q)$ vector, uncertainty vector)

Return type:

(numpy.array, numpy.array)

FK_to_DCS(q, fq, dfq=None, **kwargs)[source]

Convert $F(Q)$ to $\frac{d \sigma}{d \Omega}(Q)$

Parameters:

q (numpy.array or list) – $Q$ -space vector
fq (numpy.array or list) – $F(Q)$ vector
dfq (numpy.array or list) – uncertainty vector

Returns:

( $\frac{d \sigma}{d \Omega}(Q)$ vector, uncertainty vector)

Return type:

(numpy.array, numpy.array)

FK_to_F(q, fq_keen, dfq_keen=None, **kwargs)[source]

Convert $F(Q)$ to $Q[S(Q)-1]$

Parameters:

q (numpy.array or list) – $Q$ -space vector
fq_keen (numpy.array or list) – $F(Q)$ vector
dfq_keen (numpy.array or list) – uncertainty vector

Returns:

( $Q[S(Q)-1]$ vector, uncertainty vector)

Return type:

(numpy.array, numpy.array)

FK_to_S(q, fq_keen, dfq_keen=None, **kwargs)[source]

Convert $F(Q)$ to $S(Q)$

Parameters:

q (numpy.array or list) – $Q$ -space vector
fq_keen (numpy.array or list) – $F(Q)$ vector
dfq_keen (numpy.array or list) – uncertainty vector

Returns:

( $S(Q)$ vector, uncertainty vector)

Return type:

(numpy.array, numpy.array)

F_to_DCS(q, fq, dfq=None, **kwargs)[source]

Converts from $Q[S(Q)-1]$ to $\frac{d \sigma}{d \Omega}(Q)$

Parameters:

q (numpy.array or list) – $Q$ -space vector
fq (numpy.array or list) – $Q[S(Q)-1]$ vector
dfq (numpy.array or list) – uncertainty vector

Returns:

( $\frac{d \sigma}{d \Omega}(Q)$ vector, uncertainty vector)

Return type:

(numpy.array, numpy.array)

F_to_FK(q, fq, dfq=None, **kwargs)[source]

Converts from $Q[S(Q)-1]$ to $F(Q)$

Parameters:

q (numpy.array or list) – $Q$ -space vector
fq (numpy.array or list) – $Q[S(Q)-1]$ vector
dfq (numpy.array or list) – uncertainty vector

Returns:

( $F(Q)$ vector, uncertainty vector)

Return type:

(numpy.array, numpy.array)

F_to_S(q, fq, dfq=None, **kwargs)[source]

Converts from $Q[S(Q)-1]$ to $S(Q)$

Parameters:

q (numpy.array or list) – $Q$ -space vector
fq (numpy.array or list) – $Q[S(Q)-1]$ vector
dfq (numpy.array or list) – uncertainty vector

Returns:

( $S(Q)$ vector, uncertainty vector)

Return type:

(numpy.array, numpy.array)

GK_to_G(r, gr, dgr=None, **kwargs)[source]

Convert $G_{Keen Version}(r)$ to $G_{PDFFIT}(r)$

Parameters:

r (numpy.array or list) – r-space vector
gr (numpy.array or list) – $G_{Keen Version}(r)$ vector
dgr (numpy.array or list) – uncertainty vector $\Delta g(r)$

Returns:

$G_{PDFFIT}(r)$ vector, uncertainty vector

Return type:

(numpy.array, numpy.array)

GK_to_g(r, gr, dgr=None, **kwargs)[source]

Convert $G_{Keen Version}(r)$ to $g(r)$

Parameters:

r (numpy.array or list) – r-space vector
gr (numpy.array or list) – $G_{Keen Version}(r)$ vector
dgr (numpy.array or list) – uncertainty vector $\Delta g(r)$

Returns:

$g(r)$ vector, uncertainty vector

Return type:

(numpy.array, numpy.array)

G_to_GK(r, gr, dgr=None, **kwargs)[source]

Convert $G_{PDFFIT}(r)$ to $G_{Keen Version}(r)$

Parameters:

r (numpy.array or list) – r-space vector
gr (numpy.array or list) – $G_{PDFFIT}(r)$ vector
dgr (numpy.array or list) – uncertainty vector $\Delta g(r)$

Returns:

$G_{Keen Version}(r)$ vector, uncertainty vector

Return type:

(numpy.array, numpy.array)

G_to_g(r, gr, dgr=None, **kwargs)[source]

Convert $G_{PDFFIT}(r)$ to $g(r)$

Parameters:

r (numpy.array or list) – r-space vector
gr (numpy.array or list) – $G_{PDFFIT}(r)$ vector
dgr (numpy.array or list) – uncertainty vector $\Delta g(r)$

Returns:

$g(r)$ vector, uncertainty vector

Return type:

(numpy.array, numpy.array)

S_to_DCS(q, sq, dsq=None, **kwargs)[source]

Convert $S(Q)$ to $\frac{d \sigma}{d \Omega}(Q)$

Parameters:

q (numpy.array or list) – $Q$ -space vector
sq (numpy.array or list) – $S(Q)$ vector
dsq (numpy.array or list) – uncertainty vector

Returns:

( $\frac{d \sigma}{d \Omega}(Q)$ vector, uncertainty vector)

Return type:

(numpy.array, numpy.array)

S_to_F(q, sq, dsq=None, **kwargs)[source]

Convert $S(Q)$ to $Q[S(Q)-1]$

Parameters:

q (numpy.array or list) – $Q$ -space vector
sq (numpy.array or list) – $S(Q)$ vector
dfq (numpy.array or list) – uncertainty vector
dsq (numpy.array or list) – uncertainty vector

Returns:

( $Q[S(Q)-1]$ vector, uncertainty vector)

Return type:

(numpy.array, numpy.array)

S_to_FK(q, sq, dsq=None, **kwargs)[source]

Convert $S(Q)$ to $F(Q)$

Parameters:

q (numpy.array or list) – $Q$ -space vector
sq (numpy.array or list) – $S(Q)$ vector
dsq (numpy.array or list) – uncertainty vector

Returns:

( $F(Q)$ vector, uncertainty vector)

Return type:

(numpy.array, numpy.array)

g_to_G(r, gr, dgr=None, **kwargs)[source]

Convert $g(r)$ to $G_{PDFFIT}(r)$

Parameters:

r (numpy.array or list) – r-space vector
gr (numpy.array or list) – $g(r)$ vector
dgr (numpy.array or list) – uncertainty vector $\Delta g(r)$

Returns:

$G_{PDFFIT}(r)$ vector, uncertainty vector

Return type:

(numpy.array, numpy.array)

g_to_GK(r, gr, dgr=None, **kwargs)[source]

Convert $g(r)$ to $G_{Keen Version}(r)$

Parameters:

r (numpy.array or list) – r-space vector
gr (numpy.array or list) – $g(r)$ vector
dgr (numpy.array or list) – uncertainty vector $\Delta g(r)$

Returns:

$G_{Keen Version}(r)$ vector, uncertainty vector

Return type:

(numpy.array, numpy.array)