numpy.linspace

ToolPythonBeginner

Classification with scikit-learn: telling two populations apart

• x1g, x2g = np.meshgrid(np.linspace(-3, 5, 300), np.linspace(40, 430, 300))

RecipePythonBeginner

Draw a phase portrait of a two-variable ODE system

• X, Y = np.meshgrid(np.linspace(*xlim, 21), np.linspace(*ylim, 15))
• gx, gy = np.meshgrid(np.linspace(*xlim, 5), np.linspace(*ylim, 5))
• t = np.linspace(0, 12, 1200) # t_eval: when to report the state, in years

ToolPythonBeginner

Eigenvalues with numpy.linalg: normal modes of coupled oscillators

• t = np.linspace(0, 10, 1001) # s

RecipePythonBeginner

Fit a curve to data with error bars and draw a confidence band

• t = np.linspace(0, 10, 25) # ns
• tt = np.linspace(t.min(), t.max(), 300)

ToolPythonBeginner

Matplotlib from the ground up: a two-panel figure for one journal column

• lam = np.linspace(560, 640, 321) # wavelength / nm

ToolPythonBeginner

Numerical integration with scipy.integrate: the area under a measured peak

• lam = np.linspace(500, 540, 401) # wavelength, nm

ConceptPythonBeginner

The Fourier transform: asking a signal how much of each frequency it contains

• f = np.linspace(0.01, 0.12, 2000)
• f_zoom = np.linspace(0.07, 0.095, 800)

ToolPythonBeginner

scipy.stats from the ground up: is the difference between two samples real?

• offsets = np.linspace(-0.12, 0.12, n) # fixed spread in batch order, so no dot hides another

ToolPythonIntermediate

solve_ivp from the ground up: the pendulum beyond small angles

• t = np.linspace(0, 10, 500)
• a_fine = np.linspace(0, 179.9, 400)