2D interpolation (interp2d, griddata) in SciPy - Mini Project: Build & Apply

2D Interpolation with interp2d and griddata

📖 Scenario: You have temperature readings from a few weather stations scattered in a small area. You want to estimate the temperature at other points between these stations to create a smooth temperature map.

🎯 Goal: Build a Python program that uses scipy.interpolate.interp2d and scipy.interpolate.griddata to estimate temperatures at new points based on given scattered data.

📋 What You'll Learn

Create arrays for known x and y coordinates and their temperature values

Set up a grid of new points where temperature will be estimated

Use interp2d to interpolate temperature on the grid

Use griddata to interpolate temperature on the grid

Print the interpolated temperature arrays

💡 Why This Matters

🌍 Real World

Weather forecasting and environmental monitoring often require estimating values at locations where no direct measurements exist. 2D interpolation helps create smooth maps from scattered sensor data.

💼 Career

Data scientists and analysts use interpolation techniques to fill missing data, create heatmaps, and prepare data for machine learning models in fields like meteorology, agriculture, and urban planning.

1

Create known data points

Create three NumPy arrays called x, y, and temp with these exact values: x = [0, 1, 2, 0, 1, 2], y = [0, 0, 0, 1, 1, 1], and temp = [30, 32, 34, 28, 29, 31].

SciPy

# Create arrays x, y, and temp with given values
# Your code here

Need a hint?

Use np.array to create arrays for x, y, and temp.

2

Create grid points for interpolation

Create two 1D NumPy arrays called xi and yi using np.linspace to generate 5 points each from 0 to 2 for xi and from 0 to 1 for yi.

SciPy

import numpy as np

x = np.array([0, 1, 2, 0, 1, 2])
y = np.array([0, 0, 0, 1, 1, 1])
temp = np.array([30, 32, 34, 28, 29, 31])

# Create xi and yi arrays with 5 points each
# Your code here

Need a hint?

Use np.linspace(start, stop, num_points) to create evenly spaced points.

3

Interpolate temperatures using interp2d and griddata

Import interp2d and griddata from scipy.interpolate. Use interp2d with x, y, and temp to create a function f_interp2d. Then use f_interp2d with xi and yi to get temp_interp2d. Also, create a 2D grid with np.meshgrid from xi and yi. Use griddata with points from x and y, values temp, and the grid points to get temp_griddata.

SciPy

import numpy as np

x = np.array([0, 1, 2, 0, 1, 2])
y = np.array([0, 0, 0, 1, 1, 1])
temp = np.array([30, 32, 34, 28, 29, 31])

xi = np.linspace(0, 2, 5)
yi = np.linspace(0, 1, 5)

# Import interp2d and griddata
# Create f_interp2d function
# Use f_interp2d with xi and yi to get temp_interp2d
# Create grid with np.meshgrid
# Use griddata to get temp_griddata
# Your code here

Need a hint?

Use interp2d to create an interpolation function and call it with xi and yi. Use np.meshgrid to create grid points for griddata.

4

Print the interpolated temperature arrays

Print the arrays temp_interp2d and temp_griddata using two separate print statements.

SciPy

import numpy as np
from scipy.interpolate import interp2d, griddata

x = np.array([0, 1, 2, 0, 1, 2])
y = np.array([0, 0, 0, 1, 1, 1])
temp = np.array([30, 32, 34, 28, 29, 31])

xi = np.linspace(0, 2, 5)
yi = np.linspace(0, 1, 5)

f_interp2d = interp2d(x, y, temp, kind='linear')
temp_interp2d = f_interp2d(xi, yi)

X_grid, Y_grid = np.meshgrid(xi, yi)
points = np.column_stack((x, y))
temp_griddata = griddata(points, temp, (X_grid, Y_grid), method='linear')

# Print temp_interp2d and temp_griddata
# Your code here

Need a hint?

Use print(temp_interp2d) and print(temp_griddata) to show the results.