Note
Click here to download the full example code
The data sets imported by heliopy are returned in a
pandas.DataFrame. Most data sets have a
single variable for the DataFrame index. In this case DataFrame can be
thought of as storing multiple variables, each of which depend on the same
dependent variable. For example the x component of magnetic field depends only
on time.
Particle distribution functions have more than one dependent variable however. Typically the distribution is measured as a function of time, energy, and two angles.
import pandas as pd
import numpy as np
As a simple example, lets make a multi-index DataFrame to imitate a particle distribution function. First, set the index values
# Energy values
E = np.arange(0, 4)
# Azimuthal angles
phi = np.arange(0, 360, 90)
# Elevation angles
theta = np.arange(-90, 90, 45)
# Fake distribution function
dist = np.random.rand(E.size * phi.size * theta.size)
The index values can then be created from
pandas.MultiIndex.from_product(), and the complete DataFrame created
from pandas.DataFrame(). The distribution function is sorted first by
energy, then by theta, and then by phi.
index = pd.MultiIndex.from_product((E, theta, phi),
names=('E', 'theta', 'phi'))
data = pd.DataFrame(data=dist, index=index, columns=['df'])
print(data.head())
Out:
df
E theta phi
0 -90 0 0.639512
90 0.429693
180 0.247111
270 0.380913
-45 0 0.864078
To loop through the energy levels with the index label 'theta' do the
following. theta will give the value of the current index, and
theta_dist will give the reduced DataFrame at each value of theta.
for theta, theta_data in data.groupby(level='theta'):
print(theta)
Out:
-90
-45
0
45
Total running time of the script: ( 0 minutes 0.108 seconds)