💾 Archived View for gem.sdf.org › s.kaplan › cheatsheets › libraries-and-frameworks › scipy.md captured on 2024-08-18 at 18:25:02.

-=-=-=-=-=-=-

# SciPy Cheatsheet

SciPy is a popular open-source Python library for scientific computing. It provides a wide range of tools for working with scientific data, including optimization, integration, interpolation, signal processing, and more. This cheatsheet provides a quick reference for some of SciPy's unique features, including code blocks for optimization, integration, interpolation, signal processing, and more. Additionally, it includes a list of resources for further learning.

## Optimization

import numpy as np

from scipy.optimize import minimize

Define an objective function

def rosen(x):

return sum(100.0 * (x[1:] - x[:-1] ** 2.0) ** 2.0 + (1 - x[:-1]) ** 2.0)

Minimize the objective function

res = minimize(rosen, np.zeros(10), method='nelder-mead', options={'xtol': 1e-8, 'disp': True})


## Integration

from scipy.integrate import quad

Define an integrand

def f(x):

return np.exp(-x ** 2)

Integrate the function from 0 to infinity

quad(f, 0, np.inf)


## Interpolation

from scipy.interpolate import interp1d

Define some data

x = np.linspace(0, 10, num=11, endpoint=True)

y = np.cos(-x ** 2 / 9.0)

Interpolate the data

f = interp1d(x, y, kind='cubic')

xnew = np.linspace(0, 10, num=41, endpoint=True)

ynew = f(xnew)


## Signal Processing

from scipy import signal

Define a signal

t = np.linspace(0, 1, 1000, endpoint=False)

x = signal.square(2 * np.pi * 5 * t)

Apply a lowpass filter to the signal

b, a = signal.butter(4, 0.1, 'lowpass')

y = signal.filtfilt(b, a, x)


## Other Useful Features

from scipy import stats

Generate random samples from a normal distribution

x = stats.norm.rvs(size=1000)

Compute the mean and standard deviation of the samples

stats.norm.fit(x)

Compute the correlation between two variables

x = np.random.normal(size=100)

y = np.random.normal(size=100)

stats.pearsonr(x, y)


## Resources

- [SciPy documentation](https://docs.scipy.org/doc/)
- [SciPy tutorial](https://docs.scipy.org/doc/scipy/tutorial/index.html)
- [Python Data Science Handbook](https://jakevdp.github.io/PythonDataScienceHandbook/index.html)