In [35]:
%matplotlib inline
from sklearn import datasets
from sklearn.cross_validation import cross_val_predict
from sklearn import linear_model
import matplotlib.pyplot as plt
lr = linear_model.LinearRegression()
boston = datasets.load_boston()
y = boston.target
# cross_val_predict returns an array of the same size as `y` where each entry
# is a prediction obtained by cross validated:
predicted = cross_val_predict(lr, boston.data, y, cv=10)
fig, ax = plt.subplots()
ax.scatter(y, predicted)
ax.plot([y.min(), y.max()], [y.min(), y.max()], 'k--', lw=4)
ax.set_xlabel('Measured', fontsize=20)
ax.set_ylabel('Predicted', fontsize=20)
plt.show()
In [12]:
%matplotlib inline
# import numpy as np
import matplotlib.pyplot as plt
N = 5
menMeans = (20, 35, 30, 35, 27)
menStd = (2, 3, 4, 1, 2)
ind = np.arange(N) # the x locations for the groups
width = 0.35 # the width of the bars
fig, ax = plt.subplots()
rects1 = ax.bar(ind, menMeans, width, color='r', yerr=menStd)
womenMeans = (25, 32, 34, 20, 25)
womenStd = (3, 5, 2, 3, 3)
rects2 = ax.bar(ind+width, womenMeans, width, color='y', yerr=womenStd)
# add some
ax.set_ylabel('Scores')
ax.set_title('Scores by group and gender')
ax.set_xticks(ind+width)
ax.set_xticklabels( ('G1', 'G2', 'G3', 'G4', 'G5') )
ax.legend( (rects1[0], rects2[0]), ('Men', 'Women') )
def autolabel(rects):
# attach some text labels
for rect in rects:
height = rect.get_height()
ax.text(rect.get_x()+rect.get_width()/2., 1.05*height, '%d'%int(height),
ha='center', va='bottom')
autolabel(rects1)
autolabel(rects2)
plt.show()
In [6]:
%matplotlib inline
from pylab import *
x = np.linspace(0, 5, 10)
y = x ** 2
figure()
subplot(1,2,1)
plot(x, y, 'r--')
xlabel('x')
ylabel('y')
title('title')
subplot(1,2,2)
plot(y, x, 'g*-')
xlabel('y')
ylabel('x')
title('title')
show()
In [29]:
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(0, 5, 10)
y = x ** 2
y2 = x**3
fig = plt.figure(figsize=(10,8), dpi=100)
axes = fig.add_axes([0.1, 0.1, 0.8, 0.8]) # left, bottom, width, height (range 0 to 1)
axes.plot(x, y, 'r', label="test1")
axes.plot(x, y2, 'g-*', label="test2")
axes.legend()
axes.set_xlabel('x')
axes.set_ylabel('y')
axes.set_title('title');
axes.grid(color='b', alpha=0.5, linestyle='dashed', linewidth=0.5)
#Scientific notation
from matplotlib import ticker
formatter = ticker.ScalarFormatter(useMathText=True)
formatter.set_scientific(True)
formatter.set_powerlimits((-1,1))
axes.yaxis.set_major_formatter(formatter)
plt.show()
In [31]:
fig = plt.figure()
ax = fig.add_axes([0.0, 0.0, .6, .6], polar=True)
t = np.linspace(0, 2 * np.pi, 100)
ax.plot(t, t, color='blue', lw=3);
plt.show()
In [38]:
n = np.random.randn(100000)
fig, axes = plt.subplots(2, 1, figsize=(8,6))
axes[0].hist(n)
axes[0].set_title("Default histogram", fontsize=20)
axes[0].set_xlim((min(n), max(n)))
axes[1].hist(n, cumulative=True, bins=50)
axes[1].set_title("Cumulative detailed histogram", fontsize=20)
axes[1].set_xlim((min(n), max(n)));
In [46]:
%matplotlib inline
from mpl_toolkits.mplot3d.axes3d import Axes3D
alpha = 0.7
phi_ext = 2 * np.pi * 0.5
def flux_qubit_potential(phi_m, phi_p):
return 2 + alpha - 2 * np.cos(phi_p) * np.cos(phi_m) - alpha * np.cos(phi_ext - 2*phi_p)
phi_m = np.linspace(0, 2*np.pi, 100)
phi_p = np.linspace(0, 2*np.pi, 100)
X,Y = np.meshgrid(phi_p, phi_m)
Z = flux_qubit_potential(X, Y).T
fig = plt.figure(figsize=(14,6))
fig = plt.figure(figsize=(20, 32), dpi=200)
# `ax` is a 3D-aware axis instance because of the projection='3d' keyword argument to add_subplot
ax = fig.add_subplot(2, 1, 1, projection='3d')
p = ax.plot_surface(X, Y, Z, rstride=4, cstride=4, linewidth=0)
# surface_plot with color grading and color bar
ax = fig.add_subplot(2, 1, 2, projection='3d')
p = ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=matplotlib.cm.coolwarm, linewidth=0, antialiased=False)
cb = fig.colorbar(p, shrink=0.5)
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