📖 Scenario: You have recorded a simple signal from a sensor over time. The signal has some peaks that represent important events. You want to find where these peaks occur.
🎯 Goal: Build a small program to find the peaks in a signal using the scipy.signal.find_peaks function.
📋 What You'll Learn
Create a list called signal with exact values representing the sensor data.
Create a variable called height_threshold to filter peaks by minimum height.
Use scipy.signal.find_peaks with the height parameter set to height_threshold to find peaks.
Print the indices of the peaks found.
💡 Why This Matters
🌍 Real World
Peak finding is used in many fields like analyzing heartbeats in medical data, detecting events in sensor data, or finding important points in stock price movements.
💼 Career
Data scientists and analysts often need to extract meaningful features from time series or signal data, and peak detection is a common technique for this.
Progress0 / 4 steps
1
Create the signal data
Create a list called signal with these exact values: [0, 2, 1, 3, 7, 1, 2, 6, 0, 1].
SciPy
Hint
Use square brackets to create a list and separate numbers with commas.
2
Set the height threshold for peaks
Create a variable called height_threshold and set it to 4 to filter peaks by minimum height.
SciPy
Hint
Just assign the number 4 to the variable height_threshold.
3
Find peaks using scipy.signal.find_peaks
Import find_peaks from scipy.signal. Use find_peaks on signal with the parameter height=height_threshold. Store the result indices in a variable called peaks.
SciPy
Hint
Use from scipy.signal import find_peaks to import. Then call find_peaks(signal, height=height_threshold) and unpack the first result as peaks.
4
Print the indices of the peaks
Print the variable peaks to display the indices of the peaks found in the signal.
SciPy
Hint
Use print(peaks) to show the peak indices.
Practice
(1/5)
1. What does the find_peaks function in scipy do?
easy
A. It finds local maxima (peaks) in a 1D array of data.
B. It sorts the data in ascending order.
C. It calculates the average value of the data.
D. It removes all negative values from the data.
Solution
Step 1: Understand the purpose of find_peaks
The function is designed to identify points in data where values are higher than their neighbors, called peaks.
Step 2: Compare options with function behavior
Only It finds local maxima (peaks) in a 1D array of data. correctly describes this behavior; others describe unrelated operations.
Final Answer:
It finds local maxima (peaks) in a 1D array of data. -> Option A
Quick Check:
find_peaks locates peaks = D [OK]
Hint: Remember: find_peaks finds highs, not sorting or averaging [OK]
Common Mistakes:
Confusing peak finding with sorting data
Thinking it calculates statistics like mean
Assuming it filters values instead of locating peaks
2. Which of the following is the correct way to import and use find_peaks from scipy.signal?
easy
A. import find_peaks from scipy.signal
peaks = find_peaks(data)
B. from scipy.signal import find_peaks
peaks = find_peaks(data)
C. from scipy import find_peaks
peaks = find_peaks(data)
D. import scipy.signal.find_peaks
peaks = find_peaks(data)
Solution
Step 1: Recall correct import syntax in Python
Python uses 'from module import function' to import specific functions.
Step 2: Match syntax with options
from scipy.signal import find_peaks
peaks = find_peaks(data) uses correct syntax. Options A, C, and D have invalid import statements.
Final Answer:
from scipy.signal import find_peaks\npeaks = find_peaks(data) -> Option B
Quick Check:
Correct import syntax = B [OK]
Hint: Use 'from module import function' for specific imports [OK]
Common Mistakes:
Using 'import function from module' which is invalid
Trying to import from wrong module path
Using dot notation in import statement incorrectly
3. Given the code below, what will be the output of peaks?
import numpy as np
from scipy.signal import find_peaks
data = np.array([0, 2, 1, 3, 0, 1, 2, 1])
peaks, _ = find_peaks(data, height=2)
print(peaks)
medium
A. [3]
B. [1 3]
C. [1 3 6]
D. [2 5]
Solution
Step 1: Identify peaks in the data array
Data values: [0, 2, 1, 3, 0, 1, 2, 1]. Peaks are points higher than neighbors: indices 1 (2), 3 (3), and 6 (2).
Step 2: Apply height=2 filter
Peaks with height >= 2 are kept. Index 1 (2 >= 2), 3 (3 >= 2), 6 (2 >= 2), so indices 1, 3, 6 remain.
Final Answer:
[1 3 6] -> Option C
Quick Check:
Peaks with height >= 2 = [1 3 6] [OK]
Hint: Check peak values against height filter carefully [OK]
Common Mistakes:
Thinking height filter requires strictly greater than threshold (it's >=)
Misidentifying peak positions
Ignoring the height parameter
4. The following code is intended to find peaks at least 3 units apart, but it returns more peaks than expected. What is the error?
import numpy as np
from scipy.signal import find_peaks
data = np.array([1, 3, 2, 4, 3, 5, 2])
peaks = find_peaks(data, 3)
print(peaks)
medium
A. The distance parameter is not applied because it should be passed as a keyword argument.
B. The distance parameter is applied correctly; the issue is with the data array.
C. The code is missing parentheses around the print argument.
D. The find_peaks function does not support the distance parameter.
Solution
Step 1: Check how distance parameter is passed
In find_peaks, distance must be passed as a keyword argument, e.g., distance=3.
Step 2: Analyze the code
The code passes 3 positionally after data, which sets height=3 instead of distance=3. Thus distance is not applied, returning all local peaks [1,3,5] instead of filtered [1,5].
Step 3: Verify output type
find_peaks returns a tuple (indices, properties). Printing peaks directly prints the tuple, which may confuse.
Final Answer:
The distance parameter is not applied because it should be passed as a keyword argument. -> Option A
Quick Check:
distance must be keyword argument = C [OK]
Hint: Always pass distance as keyword argument: distance=number [OK]
Common Mistakes:
Passing distance as positional argument
Expecting find_peaks to return only indices
Misunderstanding output format of find_peaks
5. You have noisy sensor data and want to find peaks that are at least 5 units high and at least 10 samples apart. Which find_peaks call correctly applies these conditions?
import numpy as np
from scipy.signal import find_peaks
sensor_data = np.random.rand(100) * 10
# Which call is correct?
hard
A. peaks = find_peaks(sensor_data, height=(5, None), distance=10)
B. peaks = find_peaks(sensor_data, height=5, distance=10)
C. peaks, _ = find_peaks(sensor_data, min_height=5, min_distance=10)
D. peaks, _ = find_peaks(sensor_data, height=5, distance=10)
Solution
Step 1: Understand parameter usage in find_peaks
Height is specified with 'height=5' to find peaks at least 5 units high. Distance is specified with 'distance=10' to ensure peaks are at least 10 samples apart.
Step 2: Evaluate each option
peaks, _ = find_peaks(sensor_data, height=5, distance=10) correctly uses height and distance parameters and unpacks the tuple output. peaks = find_peaks(sensor_data, height=5, distance=10) misses unpacking. peaks, _ = find_peaks(sensor_data, min_height=5, min_distance=10) uses invalid parameter names. peaks = find_peaks(sensor_data, height=(5, None), distance=10) uses a tuple for height which is valid but unnecessary here.
Final Answer:
peaks, _ = find_peaks(sensor_data, height=5, distance=10) -> Option D
Quick Check:
Use height and distance keywords, unpack output = A [OK]
Hint: Use height= and distance= with unpacking for correct results [OK]
Common Mistakes:
Using wrong parameter names like min_height
Not unpacking the tuple returned by find_peaks
Passing height as a tuple unnecessarily
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