Saving and loading data (scipy.io) - Step-by-Step Execution

Concept Flow - Saving and loading data (scipy.io)

Create data arrays

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Use scipy.io.savemat to save data to .mat file

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Use scipy.io.loadmat to read data from .mat file

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Access loaded data arrays

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Use data for analysis or visualization

The flow shows creating data, saving it to a file, loading it back, and then using the loaded data.

Execution Sample

SciPy

import numpy as np
from scipy.io import savemat, loadmat

# Create data
data = {'x': np.array([1,2,3]), 'y': np.array([4,5,6])}

# Save data
savemat('datafile.mat', data)

# Load data
loaded = loadmat('datafile.mat', squeeze_me=True)
print(loaded['x'])

This code saves two arrays to a .mat file and then loads them back, printing one array.

Execution Table

Step	Action	Data State	Result/Output
1	Create dictionary with arrays 'x' and 'y'	{'x': [1 2 3], 'y': [4 5 6]}	Data ready to save
2	Call savemat('datafile.mat', data)	File 'datafile.mat' created	Data saved to file
3	Call loadmat('datafile.mat', squeeze_me=True)	Read file content	Dictionary with keys including 'x', 'y', '__header__', '__version__', '__globals__'
4	Access loaded['x']	Extract array for 'x'	[1 2 3]
5	Print loaded['x']	Output to console	[1 2 3]
6	End of script	No change	Execution stops

💡 Script ends after printing loaded data array 'x'

Variable Tracker

Variable	Start	After Step 1	After Step 2	After Step 3	After Step 4	Final
data	{}	{'x': array([1, 2, 3]), 'y': array([4, 5, 6])}	Same	Same	Same	Same
loaded	N/A	N/A	N/A	{'__header__': ..., '__version__': ..., '__globals__': ..., 'x': array([1, 2, 3]), 'y': array([4, 5, 6])}	Same	Same

Key Moments - 2 Insights

Why does the loaded dictionary have extra keys like '__header__' besides 'x' and 'y'?

When loading a .mat file, scipy.io.loadmat returns a dictionary with metadata keys like '__header__', '__version__', and '__globals__' along with your saved variables. See execution_table step 3.

Can I save variables other than arrays using savemat?

savemat saves variables as arrays or compatible types. Complex Python objects may not save correctly. Use arrays or simple data structures as in execution_table step 1.

Visual Quiz - 3 Questions

Look at the execution_table at step 4, what is the value of loaded['x']?

A [4 5 6]

B [1 2 3]

C A dictionary

D An error

💡 Hint

Check the 'Result/Output' column at step 4 in execution_table.

At which step is the .mat file created?

A Step 1

B Step 3

C Step 2

D Step 5

💡 Hint

Look at the 'Action' and 'Result/Output' columns for file creation in execution_table.

If you change the data dictionary to include a string, what might happen when saving?

A It raises an error or saves incorrectly

B It saves without issues

C It converts string to array automatically

D It ignores the string variable

💡 Hint

Refer to key_moments about data types savemat can handle.

Concept Snapshot

Saving and loading data with scipy.io:
- Use savemat(filename, dict) to save arrays to .mat file
- Use loadmat(filename) to load data back as a dictionary
- Loaded dict includes metadata keys plus your variables
- Save only arrays or compatible types
- Access variables by their keys after loading

Full Transcript

This visual execution shows how to save and load data using scipy.io. First, we create a dictionary with numpy arrays. Then, we save it to a .mat file using savemat. Next, we load the file back with loadmat, which returns a dictionary including metadata and our saved arrays. We access the arrays by their keys and print them. The variable tracker shows how 'data' and 'loaded' change. Key moments clarify why extra keys appear and data type limits. The quiz tests understanding of loaded data, file creation step, and data type handling.