Get the Books
Enjoying these notebooks and want to support the work? Check out the practical books on Data Science, Visualisation, and Evolutionary Algorithms.
Get the books
Multiple Sine Waves in the Time Domain
Preamble¶
In [14]:
# used to create block diagrams
%reload_ext xdiag_magic
%xdiag_output_format svg
import numpy as np # for multi-dimensional containers
import pandas as pd # for DataFrames
import plotly.graph_objects as go # for data visualisation
import plotly.io as pio # to set shahin plot layout
from plotly.subplots import make_subplots
pio.templates['shahin'] = pio.to_templated(go.Figure().update_layout(margin=dict(t=0,r=0,b=40,l=40))).layout.template
pio.templates.default = 'shahin'
Creating Sine Waves¶
In a previous section we looked at how to create a single Sine Wave and visualise it in the time domain.
In [11]:
sample_rate = 1000
start_time = 0
end_time = 10
time = np.arange(start_time, end_time, 1/sample_rate)
frequency = 3
amplitude = 1
theta = 0
sinewave = amplitude * np.sin(2 * np.pi * frequency * time + theta)
fig = go.Figure(layout=dict(xaxis=dict(title='Time (sec)'),yaxis=dict(title='Amplitude')))
fig.add_scatter(x=time, y=sinewave)
fig.show()
Creating Multiple Sine Wave¶
Let's create and plot five sine waves. Let's start by defining our time window and sample rate.
In [52]:
sample_rate = 1000
start_time = 0
end_time = 10
theta = 0
time = np.arange(start_time, end_time, 1/sample_rate)
Now we'll store their frequencies in a list
named frequency
, and their amplitudes in a list
named amplitude
.
In [53]:
frequency = [3, 5, 2, 1, 10]
amplitude = [1, 2, 7, 3, 0.1]
Finally, let's loop through our five frequency/amplitude values and use them to calculate and visualise the sine waves using subplots.
In [54]:
fig = make_subplots(rows=5, cols=1, shared_xaxes=True)
for i in range(5):
sinewave = amplitude[i] * np.sin(2 * np.pi * frequency[i] * time + theta)
fig.add_scatter(x=time, y=sinewave, row=i+1, col=1, name=f"wave {i+1}")
fig.show()