2D interpolation (interp2d, griddata) in SciPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the function used for 2D interpolation from scipy.interpolate.

SciPy

from scipy.interpolate import [1]

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

Ainterp2d

Binterp1d

Cgriddata1d

Dgriddata2d

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing interp1d which is for 1D interpolation.

Using griddata1d or griddata2d which are not valid function names.

The interp2d function is used for 2D interpolation in scipy.

2fill in blank

medium

Complete the code to create a 2D interpolation function using interp2d with linear method.

SciPy

f = interp2d(x, y, z, kind=[1])

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

A'nearest'

B'cubic'

C'linear'

D'quadratic'

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'cubic' or 'quadratic' which are more complex interpolation methods.

Using 'nearest' which picks the nearest point without interpolation.

The kind='linear' argument specifies linear interpolation.

3fill in blank

hard

Fix the error in the code to interpolate values at new points using griddata.

SciPy

from scipy.interpolate import griddata
points = np.array([[0, 0], [1, 1], [2, 2]])
values = np.array([0, 1, 4])
new_points = np.array([[1, 0], [2, 1]])
result = griddata(points, values, [1], method='linear')

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

Anew_points

Bpoints

Cvalues

Dmethod

Attempts:

3 left

💡 Hint

Common Mistakes

Passing known points or values instead of new points to interpolate.

Passing method as the third argument instead of as a keyword argument.

The third argument to griddata is the points where we want to interpolate values, here new_points.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps words to their lengths only if length is greater than 3.

SciPy

{word: [1] for word in words if [2]

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

Alen(word)

Bword

Clen(word) > 3

Dword > 3

Attempts:

3 left

💡 Hint

Common Mistakes

Using the word itself as the value instead of its length.

Using the word in the condition instead of checking its length.

The dictionary maps each word to its length, but only includes words longer than 3 characters.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps uppercase words to their values only if value is positive.

SciPy

{ [1]: [2] for ([3], [2]) in data.items() if [2] > 0 }

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

Ak.upper()

Bv

Ck

Dk.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using k.lower() instead of uppercase.

Using v in the loop variable instead of k, v.

Not filtering values greater than zero.

The dictionary comprehension converts keys to uppercase, keeps values, and filters for positive values.

Practice

1. What is the main difference between interp2d and griddata in SciPy for 2D interpolation?

easy

A. interp2d returns values directly; griddata returns a function.

B. interp2d works on regular grids; griddata works on scattered points.

C. interp2d only works for 1D data; griddata works for 2D data.

D. interp2d is faster than griddata in all cases.

Solution

1. Step 1: Understand interp2d usage

   interp2d requires data on a regular grid and returns an interpolation function.

2. Step 2: Understand griddata usage

   griddata works with scattered (irregular) points and returns interpolated values directly.

3. Final Answer:

   interp2d works on regular grids; griddata works on scattered points. -> Option B

4. Quick Check:

   Grid type difference = interp2d works on regular grids; griddata works on scattered points. [OK]

Hint: Remember: interp2d = grid, griddata = scattered points [OK]

Common Mistakes:

• Confusing which function returns a callable
• Thinking both require regular grids
• Assuming griddata returns a function

2. Which of the following is the correct way to create an interpolation function using interp2d with arrays x, y, and z?

easy

A. f = interp2d(z, x, y, kind='cubic')

B. f = interp2d(x, y)

C. f = interp2d(x, y, z, kind='linear')

D. f = interp2d(x, y, z, method='nearest')

Solution

1. Step 1: Check interp2d parameters

   interp2d requires x, y, and z arrays, plus optional kind for interpolation type.

2. Step 2: Validate option syntax

   f = interp2d(x, y, z, kind='linear') uses correct parameter order and valid kind argument. f = interp2d(z, x, y, kind='cubic') swaps parameters incorrectly. f = interp2d(x, y) misses z. f = interp2d(x, y, z, method='nearest') uses invalid method keyword.

3. Final Answer:

   f = interp2d(x, y, z, kind='linear') -> Option C

4. Quick Check:

   Correct parameters and keyword = f = interp2d(x, y, z, kind='linear') [OK]

Hint: Remember interp2d needs x, y, z arrays and 'kind' keyword [OK]

Common Mistakes:

• Swapping x, y, z order
• Using 'method' instead of 'kind'
• Omitting z array

3. What is the output of this code snippet?

import numpy as np
from scipy.interpolate import griddata
points = np.array([[0, 0], [1, 0], [0, 1]])
values = np.array([1, 2, 3])
xi = np.array([[0.5, 0.5]])
result = griddata(points, values, xi, method='linear')
print(result)

medium

A. [3.0]

B. [1.5]

C. [2.5]

D. [2.0]

Solution

1. Step 1: Understand input points and values

   Points are at (0,0)=1, (1,0)=2, (0,1)=3. We want value at (0.5,0.5).

2. Step 2: Apply linear interpolation

   Linear interpolation averages values weighted by distance. At (0.5,0.5), value is average of 1, 2, 3 weighted equally, resulting in 2.0.

3. Final Answer:

   [2.0] -> Option D

4. Quick Check:

   Linear interpolation at center = 2.0 [OK]

Hint: For triangle center, average vertex values linearly [OK]

Common Mistakes:

• Confusing method keyword
• Miscomputing weighted average
• Expecting a scalar instead of array

4. Identify the error in this code using interp2d:

import numpy as np
from scipy.interpolate import interp2d
x = np.array([0, 1, 2])
y = np.array([0, 1, 2])
z = np.array([1, 2, 3])
f = interp2d(x, y, z, kind='cubic')
print(f(1.5, 1.5))

medium

A. The shape of z does not match the grid defined by x and y.

B. The 'kind' parameter should be 'linear' instead of 'cubic'.

C. interp2d does not accept numpy arrays as input.

D. The function call f(1.5, 1.5) should be f([1.5], [1.5]).

Solution

1. Step 1: Check shapes of x, y, and z

   x and y have length 3 each, so z must be 3x3 grid (2D array), but z is 1D with length 3.

2. Step 2: Understand interp2d requirements

   interp2d expects z shape to be (len(y), len(x)) matching grid points. Mismatch causes error.

3. Final Answer:

   The shape of z does not match the grid defined by x and y. -> Option A

4. Quick Check:

   z shape must match x,y grid [OK]

Hint: z must be 2D array matching x and y grid sizes [OK]

Common Mistakes:

• Passing 1D z instead of 2D
• Ignoring shape mismatch errors
• Misusing function call syntax

5. You have scattered temperature data at points pts = np.array([[0,0],[1,0],[0,1],[1,1]]) with values temps = np.array([30, 32, 31, 29]). You want to estimate temperature on a regular 5x5 grid covering [0,1]x[0,1]. Which approach correctly achieves this?

hard

A. Use griddata with pts, temps, and the 5x5 grid points to get interpolated values.

B. Use interp2d directly with pts and temps arrays.

C. Use griddata but only with the corner points of the grid.

D. Use interp2d with a 1D array of temps and x, y coordinates.

Solution

1. Step 1: Identify data type

   Data points are scattered (not on a regular grid), so griddata is suitable.

2. Step 2: Create regular grid and interpolate

   Create 5x5 grid points with np.meshgrid and use griddata with pts and temps to get interpolated temperatures on grid.

3. Final Answer:

   Use griddata with pts, temps, and the 5x5 grid points to get interpolated values. -> Option A

4. Quick Check:

   Scattered data + regular grid = griddata [OK]

Hint: For scattered data to grid, use griddata [OK]

Common Mistakes:

• Trying interp2d on scattered points
• Using incomplete grid points
• Passing 1D temps to interp2d