Peak Finding in Python

Learn how to find peaks and valleys on datasets in Python

Plotly is known for its graphing libraries, but it's also a broader ecosystem for building interactive data apps, AI data analysis tools, and deploying and hosting Dash apps. Explore the full set of tools and platforms designed to help turn data into action. Discover Plotly.

Imports¶

The tutorial below imports Pandas, and SciPy.

In [1]:

import pandas as pd
from scipy.signal import find_peaks

Import Data¶

To start detecting peaks, we will import some data on milk production by month:

In [2]:

import plotly.graph_objects as go
import pandas as pd

milk_data = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/monthly-milk-production-pounds.csv')
time_series = milk_data['Monthly milk production (pounds per cow)']

fig = go.Figure(data=go.Scatter(
    y = time_series,
    mode = 'lines'
))

fig.show()

Peak Detection¶

We need to find the x-axis indices for the peaks in order to determine where the peaks are located.

In [3]:

import plotly.graph_objects as go
import pandas as pd
from scipy.signal import find_peaks

milk_data = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/monthly-milk-production-pounds.csv')
time_series = milk_data['Monthly milk production (pounds per cow)']

indices = find_peaks(time_series)[0]

fig = go.Figure()
fig.add_trace(go.Scatter(
    y=time_series,
    mode='lines+markers',
    name='Original Plot'
))

fig.add_trace(go.Scatter(
    x=indices,
    y=[time_series[j] for j in indices],
    mode='markers',
    marker=dict(
        size=8,
        color='red',
        symbol='cross'
    ),
    name='Detected Peaks'
))

fig.show()

Only Highest Peaks¶

We can attempt to set our threshold so that we identify as many of the highest peaks that we can.

In [4]:

import plotly.graph_objects as go
import numpy as np
import pandas as pd
from scipy.signal import find_peaks

milk_data = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/monthly-milk-production-pounds.csv')
time_series = milk_data['Monthly milk production (pounds per cow)']

indices = find_peaks(time_series, threshold=20)[0]

fig = go.Figure()
fig.add_trace(go.Scatter(
    y=time_series,
    mode='lines+markers',
    name='Original Plot'
))

fig.add_trace(go.Scatter(
    x=indices,
    y=[time_series[j] for j in indices],
    mode='markers',
    marker=dict(
        size=8,
        color='red',
        symbol='cross'
    ),
    name='Detected Peaks'
))

fig.show()

What About Dash?¶

Dash is an open-source framework for building analytical applications, with no Javascript required, and it is tightly integrated with the Plotly graphing library.

Learn about how to install Dash at https://dash.plot.ly/installation.

Everywhere in this page that you see fig.show(), you can display the same figure in a Dash application by passing it to the figure argument of the Graph component from the built-in dash_core_components package like this:

import plotly.graph_objects as go # or plotly.express as px
fig = go.Figure() # or any Plotly Express function e.g. px.bar(...)
# fig.add_trace( ... )
# fig.update_layout( ... )

from dash import Dash, dcc, html

app = Dash()
app.layout = html.Div([
    dcc.Graph(figure=fig)
])

app.run(debug=True, use_reloader=False)  # Turn off reloader if inside Jupyter