NMRPipe like processing functions for use with the nmrglue.fileio.pipe module.
These functions attempt to mimic NMRPipe’s processing functions but small differences exist between to two implementations. In particular when using this module:
- hdr=True overrides all values in the calling function.
- A di flag is not used, rather the di() function should be used to delete the imaginary portion of a spectra.
- x1, xn and other limits must be expressed in points. A unit conversion object function should be used before calling the processing function to calculate these values.
- No functions implement the dmx or nodmx flags.
Additional differences from NMRPipe’s functions are documented in the individual processing functions.
The following functions have not been implemented and will raise a NotImplemented exception:
- ann Fourier Analysis by Neural Net
- ebs EBS Reconstruction
- mac Macro Language Interpreter
- mem Maximum Entropy
- ml Maximum likelyhood frequency
- poly Polynomail baseline correction
- xyz2zyx 3D matrix transpose
- ztp 3D matrix transpose
This module is imported as nmrglue.pipe_proc and can be called as such.
apod(dic, data[, qName, q1, q2, q3, c, ...]) | Generic apodization. |
em(dic, data[, lb, c, start, size, inv, ...]) | Exponential apodization. |
gm(dic, data[, g1, g2, g3, c, start, size, ...]) | Lorentz-to-Gauss apodization |
gmb(dic, data[, lb, gb, c, start, size, ...]) | Modified Gaussian Apodization |
jmod(dic, data[, off, j, lb, sin, cos, c, ...]) | Exponentially Damped J-Modulation Apodization |
sp(dic, data[, off, end, pow, c, start, ...]) | Sine bell apodization. |
sine(dic, data[, off, end, pow, c, start, ...]) | Sine bell apodization. |
tm(dic, data[, t1, t2, c, start, size, inv, ...]) | Trapezoid apodization. |
tri(dic, data[, loc, lHi, rHi, c, start, ...]) | Triangular apodization |
rs(dic, data[, rs, sw]) | Right shift and zero pad. |
ls(dic, data[, ls, sw]) | Left Shift and Zero Pad |
cs(dic, data, dir[, pts, neg, sw]) | Circular shift |
fsh(dic, data, dir, pts[, sw]) | Frequency shift. |
ft(dic, data[, auto, real, inv, alt, neg, ...]) | Complex Fourier transform. |
rft(dic, data[, inv]) | Real Fourier transform. |
ha(dic, data[, inv]) | Hadamard transform. |
ht(dic, data[, mode, zf, td, auto]) | Hilbert transform. |
di(dic, data) | Delete imaginaries |
ps(dic, data[, p0, p1, inv, hdr, noup, ht, ...]) | Phase shift |
tp(dic, data[, hyper, nohyper, auto, nohdr]) | Transpose data (2D). |
zf(dic, data[, zf, pad, size, mid, inter, ...]) | Zero fill |
base(dic, data[, nl, nw, first, last]) | Linear baseline correction. |
cbf(dic, data[, last, reg, slice]) | Constant baseline correction. |
med(dic, data[, nw, sf, sigma]) | Median baseline correction |
sol(dic, data[, mode, fl, fs, head]) | Solvent filter |
add(dic, data[, r, i, c, ri, x1, xn]) | Add a constant |
dx(dic, data) | Derivative by central difference. |
ext(dic, data[, x1, xn, y1, yn, round, ...]) | Extract a region. |
integ(dic, data) | Integral by simple sum |
mc(dic, data[, mode]) | Modules or magnitude calculation. |
mir(dic, data[, mode, invl, invr, sw]) | Append mirror image. |
mult(dic, data[, r, i, c, inv, hdr, x1, xn]) | Multiple by a constant. |
rev(dic, data[, sw]) | Reverse data. |
set(dic, data[, r, i, c, x1, xn]) | Set data to a constant. |
shuf(dic, data[, mode]) | Shuffle Utilities |
sign(dic, data[, ri, r, i, left, right, ...]) | Sign manipulation utilities |
coadd(dic, data[, cList, axis, time]) | Co-addition of data |
coad(dic, data[, cList, axis, time]) | Co-addition of data |
dev(dic, data) | Development function (does nothing) |
img(dic, data, filter[, dx, dy, kern, conv, ...]) | Image processing utilities |
null(dic, data) | Null function |
qart(dic, data[, a, f, auto]) | Scale Quad Artifacts |
qmix(dic, data[, ic, oc, cList, time]) | Complex mixing of input to outputs |
save(dic, data, name[, overwrite]) | Save the current vector. |
smo(dic, data[, n, center]) | Smooth data. |
zd(dic, data[, wide, x0, slope, func, g]) | Zero diagonal band. |
lp(dic, data[, pred, x1, xn, ord, mode, ...]) | Linear Prediction |
lpc(dic, data[, pred, x1, xn, ord, mode, ...]) | Linear Prediction |
lp2d(dic, data[, xOrd, yOrd, xSize, ySize, ...]) | 2D Linear Prediction using LP2D procedure |
ann(dic, data) | Fourier Analysis by Neural Net |
ebs(dic, data) | EBS Reconstruction |
mac(dic, data) | Macro Language Interpreter |
mem(dic, data) | Maximum Entropy Reconstruction |
ml(dic, data) | Maximum Likelihood Frequency Map |
poly(dic, data) | Polynomial Baseline Correction |
xyz2zyx(dic, data) | 3D Matrix transpose |
ztp(dic, data) | 3D Matrix Transpose |