SciPy — Differential Equations

MATLAB files

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

# Save to .mat file
data = {'array': np.array([1, 2, 3])}
savemat('data.mat', data)

# Load from .mat file
loaded = loadmat('data.mat')
print(f"Array: {loaded['array']}")

NetCDF files

from scipy.io import netcdf_file
import numpy as np

# Write NetCDF
with netcdf_file('data.nc', 'w') as f:
    f.dimensions['time'] = 10
    var = f.createVariable('temperature', 'f4', ('time',))
    var[:] = np.random.rand(10)

# Read NetCDF
with netcdf_file('data.nc', 'r') as f:
    print(f"Temperature: {f.variables['temperature'][:]}")

WAV files

from scipy.io import wavfile
import numpy as np

# Generate audio
sample_rate = 44100
duration = 1.0
t = np.linspace(0, duration, int(sample_rate * duration))
audio = np.sin(2 * np.pi * 440 * t)

# Save
wavfile.write('tone.wav', sample_rate, audio.astype(np.float32))

# Read
rate, data = wavfile.read('tone.wav')
print(f"Sample rate: {rate}, Duration: {len(data)/rate:.2f}s")

ARFF files

from scipy.io import arff
import pandas as pd

# Read ARFF
data, meta = arff.loadarff('data.arff')
df = pd.DataFrame(data)
print(df.head())

Image files

from scipy import ndimage
from scipy.misc import face

# Load image
image = face()

# Save
ndimage.imwrite('face.png', image)

Mini Practice

1. Save and load MATLAB files
2. Work with WAV files
3. Read NetCDF data
4. Process image files

Up Next

Continue with Performance - Optimization tips.