Curve fitting (curve_fit) in SciPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the curve fitting function from scipy.

SciPy

from scipy.optimize import [1]

Drag options to blanks, or click blank then click option'

Aminimize

Bcurve_fit

Clinregress

Droot

Attempts:

3 left

💡 Hint

Common Mistakes

Importing the wrong function like minimize or root.

Forgetting to import from scipy.optimize.

The curve_fit function is imported from scipy.optimize to perform curve fitting.

2fill in blank

medium

Complete the code to define a linear model function for curve fitting.

SciPy

def model(x, [1], b):
    return [1] * x + b

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Aa

Bx

Cy

Dm

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'x' as a parameter name which conflicts with input.

Using 'y' which is usually output, not parameter.

The parameter m represents the slope in the linear model m * x + b.

3fill in blank

hard

Fix the error in the code to fit the model to data using curve_fit.

SciPy

params, covariance = curve_fit(model, xdata, [1])

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Aydata

Bparams

Cmodel

Dxdata

Attempts:

3 left

💡 Hint

Common Mistakes

Passing xdata twice.

Passing the model function instead of data.

The second argument to curve_fit after the model and xdata is the ydata (observed values).

4fill in blank

hard

Fill both blanks to create a dictionary of fitted parameters with keys 'slope' and 'intercept'.

SciPy

fitted_params = {'slope': params[1] 0, 'intercept': params[2] 1}

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A[

B]

C(

D)

Attempts:

3 left

💡 Hint

Common Mistakes

Using parentheses instead of square brackets for indexing.

Using curly braces which are for dictionaries.

Parameters returned by curve_fit are in a list or array, so square brackets [] are used to access elements.

5fill in blank

hard

Fill all three blanks to plot the original data points and the fitted curve.

SciPy

import matplotlib.pyplot as plt

plt.scatter(xdata, ydata, label='Data')
plt.plot(xdata, model(xdata, [1], [2]), color='red', label='Fit')
plt.xlabel('[3]')
plt.ylabel('y')
plt.legend()
plt.show()

Drag options to blanks, or click blank then click option'

Aparams[0]

Bparams[1]

Cx

Dxdata

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing up slope and intercept order.

Using wrong variable names for x-axis label.

Use xdata as input x-values, params[0] and params[1] as slope and intercept, and label x-axis as 'x'.