%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(0,np.pi,1000)
f = np.sin(np.exp(x))
g = np.cos(2*x)
plt.plot(x,f)
plt.plot(x,g)

plt.figure(figsize=(10,6), dpi=300) # changing figure's shape
plt.xlim(x.min()-0.1, x.max()+0.1) # adjusting horizonatal axis limits
plt.ylim(f.min()-0.1, f.max()+0.1) # adjusting vertical axis limits
plt.xticks([0, np.pi/2, np.pi],[r'$-\pi$', r'$-\pi/2$', r'$0$', r'$+\pi/2$', r'$+\pi$'],fontsize=14) # setting ticks
plt.yticks([-1, 0, +1],[r'$-1$', r'$0$', r'$+1$'],fontsize=14) # setting ticks

plt.plot(x, f, color="blue", linewidth=2.5, linestyle="-", label="$\sin(e^x)$") # changing color and thickness
plt.plot(x, g, color="red",  linewidth=2.5, linestyle="-",  label="$\cos(2x)$") # changing color and thickness
plt.legend(loc='lower left',prop={'size':16}) # placing legend on bottom left
plt.xlabel('$x$',fontsize=16) # horizontal axis name
plt.ylabel('test functions',fontsize=16) # vertical axis name
plt.title('Sample plot',fontsize=18) # title
plt.grid(True) # enabling grid

plt.savefig('trig_functions.pdf')

plt.savefig?

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

from mpl_toolkits.mplot3d.axes3d import Axes3D # imports 3D plotting
from matplotlib import cm # module for color pattern
fig = plt.figure(figsize=(14,6))

# `ax` is a 3D-aware axis instance because of the projection='3d' keyword argument to add_subplot
ax = fig.add_subplot(1, 2, 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(1, 2, 2, projection='3d')
p = ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=cm.coolwarm, linewidth=0, antialiased=False)
cb = fig.colorbar(p, shrink=0.5) # enables colorbar

# Let's generate some synthetic data
data = np.random.uniform(size=(100,2))
x, y = data[:,0], data[:,1]

# Let's create a fugure objec and an axes object
fig1 = plt.figure(figsize=(6,6))
ax1  = fig.add_axes([0.10,0.20,0.78,0.40])  # left, bottom, width, height

print dir(fig1)

print dir(plt)

# Don't forget to create figures and axes in the cell you are plotting!
fig = plt.figure(figsize=(6,6))
ax1 = fig.add_axes([0.10,0.20,0.78,0.80])  # left, bottom, width, height

# Ex: Specify a title and the axes labels

ax1.scatter(x, y, marker='o')

fig = plt.figure()

axes1 = fig.add_axes([0.1, 0.1, 0.8, 0.8]) # main axes
axes2 = fig.add_axes([0.2, 0.5, 0.4, 0.3]) # inset axes

# main figure
axes1.plot(x, y, 'r.')
axes1.set_xlabel('x')
axes1.set_ylabel('y')
axes1.set_title('title')

# insert
axes2.plot(y, x, 'g.')
axes2.set_xlabel('y')
axes2.set_ylabel('x')

# Ex: add a title to the inset with a font size of 12px


x = np.linspace(0,np.pi,1000)
f = np.sin(8*np.exp(x))
g = np.cos(2*x)

# How to enlarge a region on a plot?
fig = plt.figure(figsize=(8, 6))
ax  = fig.add_axes([0.15,0.11,0.83,0.83])
ax.set_xlim([0, 5])
ax.plot(x,f)
ax.plot(x,g)

from mpl_toolkits.axes_grid1.inset_locator import zoomed_inset_axes, mark_inset
zoomed_inset_axes?

fig = plt.figure(figsize=(8, 6))
ax  = fig.add_axes([0.15,0.11,0.83,0.83])
ax.set_xlim([0, 5])
ax.plot(x,f,linewidth=0.5)
ax.plot(x,g)

# The inset axes
axins = zoomed_inset_axes(ax, 8, loc=1)
axins.plot(x, f, linewidth=1)
axins.plot(x, g, linewidth=2)
axins.set_xlim([2.85, 3.0])
axins.set_ylim([0.9, 1.0])

# Mark inset
mark_inset(ax, axins, loc1=3, loc2=2, fc="none", ec="0")

# A very quick enhancement by just importing seaborn
import seaborn as sns
sns.set_style("whitegrid")

x = np.linspace(0,np.pi,1000)
f = np.sin(np.exp(x))
g = np.cos(2*x)
plt.plot(x,f)
plt.plot(x,g)

sns.set_style?

# Bar plots
sns.set_style("whitegrid")
data = np.random.normal(size=(20, 6)) + np.arange(6) / 2
sns.boxplot(data);

# Violing plots
f, ax = plt.subplots()
sns.violinplot(data)
sns.despine(offset=10, trim=True);

# What are the styles and how to override them?
sns.axes_style()

current_palette = sns.color_palette()
sns.palplot(current_palette)

plt.plot([0, 1], [0, 1], sns.xkcd_rgb["pale red"], lw=3)
plt.plot([0, 1], [0, 2], sns.xkcd_rgb["medium green"], lw=3)
plt.plot([0, 1], [0, 3], sns.xkcd_rgb["denim blue"], lw=3);