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

  • 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.

Standard NMR

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

mac(dic, data[, macro, noRd, noWr, all])

Dispatcher similar to the MAC command.

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.

Linear Prediction

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

Not Implemented

ann(dic, data)

Fourier Analysis by Neural Net

ebs(dic, data)

EBS Reconstruction

mac(dic, data[, macro, noRd, noWr, all])

Dispatcher similar to the MAC command.

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