Using common numpy functions on DNPData

This example demonstrates how to use common numpy funtions on DNPData.

N.B.: This is still an experimental feature!

How numpy array functions are operating on DNPData

Many numpy functions can directly be used on DNPData. What the functio returns depends on the result:

  • when the result is a scalar a salar is returned

  • when the result is a ndarray a DNPData object is returned

when the axis keyword is support by the numpy function one can provide the dimension (e.g. np.sum(mydata,axis='f2')) The corresponding axis is consumed and no longer in the returned DNPData object. The following example shows how this can be conveniently used.

To get started, first, setup the python environment:

import dnplab as dnp
import numpy as np
import matplotlib.pyplot as plt

Let's load some example data the data consists of 4 fid that are phase cycled (0-90-180-270)

data = dnp.load("../../data/prospa/water_phase_cycled/data.2d")

we are interested in the spectra

data = dnp.fourier_transform(data)

since we don't know what the spectra is made of, we want to have a qick look at the magnitude:

data_magn = np.abs(data)

and lets plot the magnitude spectrum for all 4 cycles

plot 02 using numpy functions

since the spectra are phase cycled the mean of the real part of the spectrum should be 0. Let's check and plot that:

mean_real_spectrum = np.real(np.mean(data, axis="Average"))
total_mean = np.mean(mean_real_spectrum)
average_in_dims = "Average" in mean_real_spectrum.dims
print("The sum of the mean spectrum is {0} ".format(total_mean))
print("Average in mean_real_spectrum.dims: {0}".format(average_in_dims))
plot 02 using numpy functions
The sum of the mean spectrum is -1.037494719028472
Average in mean_real_spectrum.dims: False

the total mean could also be calculated directly using np.mean() without the axis keyword (up to numerical precision)

print(np.mean(np.real(data)), "~=", total_mean)
-1.037494719028473 ~= -1.037494719028472

Total running time of the script: ( 0 minutes 0.188 seconds)

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