<jupyter_start><jupyter_text>Explanation of the data format The raw data underlying the figures is stored in the file `data/eigenmode_info_data_frame.csv`. In this notebook we explain what this file contains and the data format.The data is stored in CSV (= comma-separated values) format. In principle, it is human-readable. The first few lines look as follows.<jupyter_code>!head -n 5 ../data/eigenmode_info_data_frame.csv<jupyter_output>has_particle,x,y,d,d_particle,Ms_particle,Hx,Hy,Hz,N,freq,freq_diff
False,,,,,,0,0,80000.0,1,4.44366221658866,
False,,,,,,0,0,80000.0,2,5.916577999212971,
False,,,,,,0,0,80000.0,3,7.917764571095144,
False,,,,,,0,0,80000.0,4,8.415238223354994,<jupyter_text>However, it is much more convenient to read and explore the data using [pandas](http://pandas.pydata.org/). To do this, we first load the CSV file into a `pandas.DataFrame`.<jupyter_code>import pandas as pd
df = pd.read_csv('../data/eigenmode_info_data_frame.csv')<jupyter_output><empty_output><jupyter_text>Using `pandas` we can display the data in a nice table format. The first few entries of the data frame (which correspond to the lines listed above) look as follows.<jupyter_code>df.head(4)<jupyter_output><empty_output><jupyter_text>Each row contains the simulation parameters and computed eigenfrequency for one of the five eigenmodes computed in a particular simulation run. The rows shown above all correspond to a simulation with no particle present, hence the missing values (marked by `NaN` = "not a number") for some of the parameters. For illustration, we also show a few rows corresponding to simulations which included a particle.<jupyter_code>df.iloc[103:107]<jupyter_output><empty_output>
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