Convolution in Python/v3
Learn how to perform convolution between two signals in Python.
Note: this page is part of the documentation for version 3 of Plotly.py, which is not the most recent version.
See our Version 4 Migration Guide for information about how to upgrade.
See our Version 4 Migration Guide for information about how to upgrade.
New to Plotly?¶
Plotly's Python library is free and open source! Get started by downloading the client and reading the primer.
You can set up Plotly to work in online or offline mode, or in jupyter notebooks.
We also have a quick-reference cheatsheet (new!) to help you get started!
In [1]:
import plotly.plotly as py
import plotly.graph_objs as go
import plotly.figure_factory as ff
import numpy as np
import pandas as pd
import scipy
from scipy import signal
Import Data¶
Let us import some stock data to apply convolution on.
In [2]:
stock_data = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/stockdata.csv')
df = stock_data[0:15]
table = ff.create_table(df)
py.iplot(table, filename='stockdata-peak-fitting')
Out[2]:
Convolve Two Signals¶
Convolution
is a type of transform that takes two functions f
and g
and produces another function via an integration. In particular, the convolution $(f*g)(t)$ is defined as:
We can use convolution in the discrete case between two n-dimensional arrays.
In [11]:
sample = range(15)
saw = signal.sawtooth(t=sample)
data_sample = list(stock_data['SBUX'][0:100])
data_sample2 = list(stock_data['AAPL'][0:100])
x = list(range(len(data_sample)))
y_convolve = signal.convolve(saw, data_sample2)
x_convolve = list(range(len(y_convolve)))
trace1 = go.Scatter(
x = x,
y = data_sample,
mode = 'lines',
name = 'SBUX'
)
trace2 = go.Scatter(
x = x,
y = data_sample2,
mode = 'lines',
name = 'AAPL'
)
trace3 = go.Scatter(
x = x_convolve,
y = y_convolve,
mode = 'lines',
name = 'Convolution'
)
data = [trace1, trace2, trace3]
py.iplot(data, filename='convolution-of-two-signals')
Out[11]: