import numpy as np import matplotlib.pyplot as plt

L1 = list(range(9)) L2 = list(range(10,19)) L1, L2

D = dict( L1=L1, L2=L2) D

D['L1']

plt.rcParams['figure.figsize'] = (10.0, 6.0) # set figures display bigger x = np.linspace(- 5*np.pi,5*np.pi,200) plt.plot(x,np.sin(x)/x);

plt.plot(x,np.sin(x)/x,x,np.sin(2*x)/x);

plt.plot(np.c_[x,x],np.c_[np.sin(x)/x,np.sin(2*x)/x]);

%config InlineBackend.figure_format = 'retina'

# red, dot-dash, triangles and blue, dot-dash, bullet plt.plot(x,np.sin(x)/x, 'r-^',x,np.sin(2*x)/x, 'b-o');

x = np.linspace(-1,1,50) y = np.sqrt(1-x**2) plt.scatter(x,y);

theta = np.linspace(0,6*np.pi,50) # 50 steps from 0 to 6 PI size = 30*np.ones(50) # array with 50 values set to 30 z = np.random.rand(50) # array with 50 random values in [0,1] x = theta*np.cos(theta) y = theta*np.sin(theta) plt.scatter(x,y,size,z) plt.colorbar();

n = 2000 threshold = 0.25 rng = np.random.RandomState(1234) X1 = rng.rand(n) X2 = rng.rand(n) U = rng.rand(n) Y = np.zeros(n, dtype=np.uint8) Y[(X1 <= 0.25) & (U <= threshold)] = 1 Y[(X1 > 0.25) & (X2 >= 0.75) & (U <= threshold)] = 1 Y[(X1 > 0.25) & (X2 < 0.75) & (U > threshold)] = 1 plt.axes().set_aspect("equal") plt.scatter(X1, X2, c=Y, s=5)

fig = plt.figure() # create a figure ax = fig.add_subplot(1, 1, 1) # add a single plot ax.scatter(x,y,size,z,cmap='jet'); ax.set_aspect('equal', 'datalim')

plt.figure() plt.plot(x) plt.figure() plt.plot(z,'ro');

plt.subplot(1,2,1) # 1 row 1, 2 columns, active plot number 1 plt.plot(x,'b-*') plt.subplot(1,2,2) # 1 row 1, 2 columns, active plot number 2 plt.plot(z,'ro');

theta =np.linspace(0,4*np.pi,200) plt.plot(np.sin(theta), label='sin') plt.plot(np.cos(theta), label='cos') plt.legend();

plt.plot(np.sin(theta)) plt.plot(np.cos(theta)**2) plt.legend(['sin','$\cos^2$']);

plt.plot(theta,np.sin(theta)) plt.xlabel('radians from 0 to $4\pi$') plt.ylabel('amplitude');

t = np.arange(0.01, 20.0, 0.01) plt.subplot(121) plt.semilogy(t, np.exp(-t/5.0)) plt.title('semilogy') plt.grid(True) plt.subplot(122,fc='y') plt.semilogx(t, np.sin(2*np.pi*t)) plt.title('semilogx') plt.grid(True)

theta = np.linspace(0,2*np.pi,100) plt.plot(np.cos(theta),np.sin(theta)) plt.grid();

plt.savefig('circle.png'); %ls *.png

from numpy.random import randn plt.hist(randn(1000));

plt.hist(randn(1000), 30, ec='w');

x = y = np.arange(-2.0*np.pi, 2.0*np.pi+0.01, 0.01) X, Y = np.meshgrid(x, y) Z = np.sin(X)*np.cos(Y) plt.contourf(X, Y, Z,cmap=plt.cm.hot);

img = plt.imread("https://hackage.haskell.org/package/JuicyPixels-extra-0.1.0/src/data-examples/lenna.png") plt.imshow(img)

fig = plt.figure() axis = fig.add_subplot(111, aspect='equal', xlim=(-2, 2), ylim=(-2, 2)) state = -0.5 + np.random.random((50, 4)) state[:, :2] *= 3.9 bounds = [-1, 1, -1, 1] particles = axis.plot(state[:,0], state[:,1], 'bo', ms=6) rect = plt.Rectangle(bounds[::2], bounds[1] - bounds[0], bounds[3] - bounds[2], ec='r', lw=2, fc='none') axis.grid() axis.add_patch(rect) axis.text(-0.5,1.1,"BOX")

import numpy as np import matplotlib.pyplot as plt from matplotlib import animation, rc from IPython.display import HTML from matplotlib.animation import FuncAnimation

fig, ax = plt.subplots() ax.set_xlim(( 0, 2)) ax.set_ylim((-2, 2)) line, = ax.plot([], [], lw=2)

def init(): line.set_data([], []) return (line,)

def animate(i): x = np.linspace(0, 2, 1000) y = np.sin(2 * np.pi * (x - 0.01 * i)) line.set_data(x, y) return (line,)

anim = animation.FuncAnimation(fig, animate, init_func=init, frames=100, interval=20, blit=True) HTML(anim.to_jshtml())

fig = plt.figure() ax1 = fig.add_subplot(1,1,1) xdata, ydata = [], [] line1, = plt.plot([], [], 'r-', animated=True) def init(): ax1.set_xlim((0,1)) ax1.set_ylim((-1,1)) return line1, def update(frame): xdata.append(frame) ydata.append(np.sin(8*np.pi*frame)) line1.set_data(xdata, ydata) return line1, anim = FuncAnimation(fig, update, frames=np.linspace(0, 1.0, 100), init_func=init, blit=True) HTML(anim.to_jshtml())

#example from Filipe Fernandes #http://nbviewer.jupyter.org/gist/ocefpaf/9730c697819e91b99f1d694983e39a8f import numpy as np import matplotlib.pyplot as plt from matplotlib import animation g = 9.81 denw = 1025.0 # Seawater density [kg/m**3]. sig = 7.3e-2 # Surface tension [N/m]. a = 1.0 # Wave amplitude [m]. L, h = 100.0, 50.0 # Wave height and water column depth. k = 2 * np.pi / L omega = np.sqrt((g * k + (sig / denw) * (k**3)) * np.tanh(k * h)) T = 2 * np.pi / omega c = np.sqrt((g / k + (sig / denw) * k) * np.tanh(k * h)) # We'll solve the wave velocities in the `x` and `z` directions. x, z = np.meshgrid(np.arange(0, 160, 10), np.arange(0, -80, -10),) u, w = np.zeros_like(x), np.zeros_like(z) def compute_vel(phase): u = a * omega * (np.cosh(k * (z+h)) / np.sinh(k*h)) * np.cos(k * x - phase) w = a * omega * (np.sinh(k * (z+h)) / np.sinh(k*h)) * np.sin(k * x - phase) mask = -z > h u[mask] = 0.0 w[mask] = 0.0 return u, w def basic_animation(frames=91, interval=30, dt=0.3): fig = plt.figure(figsize=(8, 6)) ax = plt.axes(xlim=(0, 150), ylim=(-70, 10)) # Animated. quiver = ax.quiver(x, z, u, w, units='inches', scale=2) ax.quiverkey(quiver, 120, -60, 1, label=r'1 m s$^{-1}$', coordinates='data') line, = ax.plot([], [], 'b') # Non-animated. ax.plot([0, 150], [0, 0], 'k:') ax.set_ylabel('Depth [m]') ax.set_xlabel('Distance [m]') text = (r'$\lambda$ = %s m; h = %s m; kh = %2.3f; h/L = %s' % (L, h, k * h, h/L)) ax.text(10, -65, text) time_step = ax.text(10, -58, '') line.set_data([], []) def init(): return line, quiver, time_step def animate(i): time = i * dt phase = omega * time eta = a * np.cos(x[0] * k - phase) u, w = compute_vel(phase) quiver.set_UVC(u, w) line.set_data(x[0], 5 * eta) time_step.set_text('Time = {:.2f} s'.format(time)) return line, quiver, time_step return animation.FuncAnimation(fig, animate, init_func=init, frames=frames, interval=interval)

from IPython.display import HTML HTML(basic_animation(dt=0.3).to_jshtml())