Learning Scientific Programming with Python (2nd edition)
Examples
- E6.1: A "comb" Function
- E6.2: Creating a Magic Square
- E6.3: vstack and hstack
- E6.4: Checking a Sudoku grid for validity
- E6.5: argmax and argmin
- E6.6: Using NumPy's loadtxt method
- E6.7: The Stroop effect
- E6.8: Simulating radioactive decay
- E6.9: Covariance with np.cov
- E6.10: The correlation between air pressure and temperature
- E6.11: The height of liquid in a spherical tank
- E6.12: Finding a best-fit straight line
- E6.13: Creating a rotation matrix in NumPy
- E6.14: Mesh analysis of a electrical network
- E6.15: Matrix operations
- E6.16: Visualizing linear transformations
- E6.17: Fitting the Beer-Lambert law with NumPy
- E6.18: Random sampling of evenly-spaced real numbers
- E6.19: Simulating coin-tosses
- E6.20: The normal distribution
- E6.21: Modelling the distribution of $\mathrm{^{13}C}$ atoms in $\mathrm{C_{60}}$
- E6.22: The probability of cleaving DNA with EcoRI
- E6.23: Blurring an image with a two-dimensional FFT