Peak finding (find_peaks) in SciPy - Step-by-Step Execution

Concept Flow - Peak finding (find_peaks)

Input: 1D data array

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Call find_peaks function

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Analyze data points

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Identify local maxima

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Return indices of peaks

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Use peaks for analysis or plotting

The function takes a list of numbers, checks each point to see if it is higher than its neighbors, and returns the positions of these peaks.

Execution Sample

SciPy

import numpy as np
from scipy.signal import find_peaks

data = np.array([1, 3, 2, 5, 4, 6, 1])
peaks, _ = find_peaks(data)
print(peaks)

This code finds the positions of peaks in a simple numeric array.

Execution Table

Step	Index	Data Value	Neighbors	Is Peak?	Action	Peaks Found So Far
1	0	1	N/A (start)	No	Skip (no left neighbor)	[]
2	1	3	1 (left), 2 (right)	Yes	Add index 1 to peaks	[1]
3	2	2	3 (left), 5 (right)	No	Skip	[1]
4	3	5	2 (left), 4 (right)	Yes	Add index 3 to peaks	[1, 3]
5	4	4	5 (left), 6 (right)	No	Skip	[1, 3]
6	5	6	4 (left), 1 (right)	Yes	Add index 5 to peaks	[1, 3, 5]
7	6	1	N/A (end)	No	Skip (no right neighbor)	[1, 3, 5]

💡 Reached end of data array; all peaks identified.

Variable Tracker

Variable	Start	After Step 2	After Step 4	After Step 6	Final
peaks	[]	[1]	[1, 3]	[1, 3, 5]	[1, 3, 5]

Key Moments - 3 Insights

Why is the first data point not considered a peak?

Because it has no left neighbor to compare with, so it cannot be a local maximum. See execution_table step 1.

Why does the function skip the last data point?

The last point has no right neighbor, so it cannot be a peak by definition. See execution_table step 7.

How does find_peaks decide if a point is a peak?

It checks if the point is greater than its immediate neighbors. If yes, it adds the index to peaks. See steps 2, 4, and 6.

Visual Quiz - 3 Questions

Look at the execution_table, what is the value of 'peaks' after step 4?

A [1, 3, 5]

B [1, 3]

C [1]

D []

💡 Hint

Check the 'Peaks Found So Far' column at step 4.

At which index does the condition 'Is Peak?' become false after being true at index 3?

A Index 5

B Index 2

C Index 4

D Index 6

💡 Hint

Look at the 'Is Peak?' column in execution_table rows after step 4.

If the data array ended at index 5 instead of 6, how would the peaks list change?

A [1, 3]

B [1, 3, 5]

C [1, 3, 4]

D [3, 5]

💡 Hint

Check variable_tracker and note that index 5 is a peak before the last point.

Concept Snapshot

find_peaks(data)
- Input: 1D numeric array
- Output: indices of local maxima
- Checks if each point > neighbors
- Skips first and last points if no neighbors
- Useful for signal or data analysis

Full Transcript

This visual execution shows how the find_peaks function from scipy.signal works. It takes a list of numbers and checks each point to see if it is higher than its neighbors. The first and last points are skipped because they don't have two neighbors. When a point is higher than both neighbors, its index is added to the peaks list. The example data array is [1, 3, 2, 5, 4, 6, 1]. The function finds peaks at indices 1, 3, and 5. The execution table tracks each step, showing the data value, neighbors, if it is a peak, and the peaks found so far. The variable tracker shows how the peaks list grows. Key moments clarify why edges are skipped and how peaks are identified. The quiz tests understanding of the steps and results. This helps beginners see exactly how peak finding works step-by-step.