Peak Finding in Python
Learn how to find peaks and valleys on datasets in Python
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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:
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.
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.
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
