Plotting phase differences (circular plots)¶
We start by importing the necessary packages:
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from thebeat import Sequence
from thebeat.visualization import plot_phase_differences
from thebeat.stats import get_phase_differences
import numpy as np
import pandas as pd
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%matplotlib inline
import warnings
warnings.filterwarnings('ignore', message='Matplotlib ')
Example 1: Compare random data with isochronous sequence¶
First we take a look at what the behind-the-scenes data looks like, the phase differences themselves, here represented as degrees.
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# We create a random number generator with a seed so you get the same output as we.
rng = np.random.default_rng(seed=123)
# Create a random test sequence
test = Sequence.generate_random_normal(n_events=10, mu=500, sigma=50, rng=rng)
# Create an isochronous reference sequence
ref = Sequence.generate_isochronous(n_events=10, ioi=500)
# Get and print the phase differences
phase_diffs = get_phase_differences(test, ref, unit="degrees")
print(phase_diffs)
[ nan nan 311.15131193 357.51662134 4.49970043 37.62801282 58.4037493 35.49105803 55.00133797 43.60390173]
So what does it look like in a circular histogram?
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test_seq = Sequence.generate_random_normal(n_events=1000, mu=500, sigma=50, rng=rng)
ref_seq = Sequence.generate_isochronous(n_events=1000, ioi=500)
plot_phase_differences(test_sequence=test_seq, reference_sequence=ref_seq);
Example 2: Finger-tapping data¶
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# Change the path to the location of the dataset on your computer
df = pd.read_csv('./sampjit_sampledata.csv')
# Take a quick look at the data
print(df.head(5))
sequence_id pp_id condition length tempo variance interval_i \
0 1_13 1 jittering 25 400 0.2 4
1 1_13 1 jittering 25 400 0.2 5
2 1_13 1 jittering 25 400 0.2 6
3 1_13 1 jittering 25 400 0.2 7
4 1_13 1 jittering 25 400 0.2 8
stim_ioi resp_iti
0 488.360885 329.32
1 302.385354 384.55
2 378.198490 497.05
3 448.052241 400.91
4 418.512601 378.41
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# Load the dataset
df = pd.read_csv('./sampjit_sampledata.csv')
# Take a quick look at the data
print(df.head(5))
sequence_id pp_id condition length tempo variance interval_i \
0 1_13 1 jittering 25 400 0.2 4
1 1_13 1 jittering 25 400 0.2 5
2 1_13 1 jittering 25 400 0.2 6
3 1_13 1 jittering 25 400 0.2 7
4 1_13 1 jittering 25 400 0.2 8
stim_ioi resp_iti
0 488.360885 329.32
1 302.385354 384.55
2 378.198490 497.05
3 448.052241 400.91
4 418.512601 378.41
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stimuli = []
responses = []
# We loop over the sequence id's
for seq_id in df.sequence_id.unique():
# We get the relevant piece of the dataframe for that sequence id
df_piece = df.loc[df['sequence_id'] == seq_id]
# We create a Sequence object for the stimulus and the response
stimulus = Sequence(iois=df_piece.stim_ioi)
response = Sequence(iois=df_piece.resp_iti)
# Add them to the lists
stimuli.append(stimulus)
responses.append(response)
Now we're ready to plot.
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plot_phase_differences(stimuli, responses, dpi=300);
Say we want to change the x axis labels, we can do that as follows: