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The tutorial below imports Pandas, and SciPy.
import pandas as pd
from scipy.signal import find_peaksTo 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()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()