%matplotlib inline

import matplotlib # only need to get matplotlib version
import numpy as np
import sys # only need to get python version

print("Python version: " + sys.version)
print("Matplotlib version: " + matplotlib.__version__)
print("Numpy version: " + np.__version__)

import matplotlib.pyplot as plt

plt.plot([1,2,3,4])
plt.ylabel('some numbers')
plt.show()

plt.plot([1,2,3,4], [1,4,9,16], 'ro')
plt.axis([0, 6, 0, 20])
plt.show()

plt.plot([1,2,3,4], [1,4,9,16], 'ro-', label = 'data 1')
plt.plot([1,2,3,4], [1,2,3,4], 'bo', label='data 2')
plt.axis([0, 6, 0, 20])
plt.legend()
plt.show()

def f(t):
    return np.exp(-t) * np.cos(2*np.pi*t)

t1 = np.arange(0.0, 5.0, 0.1)
t2 = np.arange(0.0, 5.0, 0.02)

plt.figure(1)
plt.subplot(211)
plt.plot(t1, f(t1), 'bo', t2, f(t2), 'k')

plt.subplot(212)
plt.plot(t2, np.cos(2*np.pi*t2), 'r--')
plt.show()

plt.figure(1)
plt.subplot(411)
plt.plot(t1, f(t1), 'bo', t2, f(t2), 'k')

plt.subplot(412)
plt.plot(t2, np.cos(2*np.pi*t2), 'r--')
plt.show()

plt.figure(1)
plt.subplot(421)
plt.plot(t1, f(t1), 'bo', t2, f(t2), 'k')

plt.subplot(422)
plt.plot(t2, np.cos(2*np.pi*t2), 'r--')
plt.show()

mu, sigma = 100, 15
x = mu + sigma * np.random.randn(10000)

# the histogram of the data
n, bins, patches = plt.hist(x, 50, normed=1, facecolor='g', alpha=0.75)


plt.xlabel('Smarts')
plt.ylabel('Probability')
plt.title('Histogram of IQ')
plt.text(60, .025, r'$\mu=100,\ \sigma=15$')
plt.axis([40, 160, 0, 0.03])
plt.grid(True)
plt.show()

import datetime
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import matplotlib.cbook as cbook

years    = mdates.YearLocator()   # every year
months   = mdates.MonthLocator()  # every month
yearsFmt = mdates.DateFormatter('%Y')

# load a numpy record array from yahoo csv data with fields date,
# open, close, volume, adj_close from the mpl-data/example directory.
# The record array stores python datetime.date as an object array in
# the date column
datafile = cbook.get_sample_data('goog.npy')
r = np.load(datafile).view(np.recarray)

fig, ax = plt.subplots()
ax.plot(r.date, r.adj_close, label='goog')

# format the ticks
ax.xaxis.set_major_locator(years)
ax.xaxis.set_major_formatter(yearsFmt)
ax.xaxis.set_minor_locator(months)

datemin = datetime.date(r.date.min().year, 1, 1)
datemax = datetime.date(r.date.max().year+1, 1, 1)
ax.set_xlim(datemin, datemax)
plt.legend(loc='upper left')

ax.grid(True)

# rotates and right aligns the x labels, and moves the bottom of the
# axes up to make room for them
fig.autofmt_xdate()

plt.show()

from matplotlib import cm
from numpy.random import randn

# Make plot with vertical (default) colorbar
fig, ax = plt.subplots()

data = np.clip(randn(250, 250), -1, 1)

cax = ax.imshow(data, interpolation='nearest', cmap=cm.coolwarm)
ax.set_title('Gaussian noise with vertical colorbar')

# Add colorbar, make sure to specify tick locations to match desired ticklabels
cbar = fig.colorbar(cax, ticks=[-1, 0, 1])
cbar.ax.set_yticklabels(['< -1', '0', '> 1'])# vertically oriented colorbar

plt.show()

# Make plot with horizontal colorbar
fig, ax = plt.subplots()

data = np.clip(randn(250, 250), -1, 1)

cax = ax.imshow(data, interpolation='nearest', cmap=cm.coolwarm)
ax.set_title('Gaussian noise with horizontal colorbar')

cbar = fig.colorbar(cax, ticks=[-1, 0, 1], orientation='horizontal')
cbar.ax.set_xticklabels(['Low', 'Medium', 'High'])# horizontal colorbar

plt.show()

import matplotlib.mlab as mlab
delta = 0.025
x = np.arange(-3.0, 3.0, delta)
y = np.arange(-2.0, 2.0, delta)
X, Y = np.meshgrid(x, y)
Z1 = mlab.bivariate_normal(X, Y, 1.0, 1.0, 0.0, 0.0)
Z2 = mlab.bivariate_normal(X, Y, 1.5, 0.5, 1, 1)
# difference of Gaussians
Z = 10.0 * (Z2 - Z1)

plt.figure()
CS = plt.contour(X, Y, Z)
plt.clabel(CS, inline=1, fontsize=10)
plt.title('Simplest default with labels')
plt.show()

# Or you can use a colormap to specify the colors; the default
# colormap will be used for the contour lines
plt.figure()

# plot the image
im = plt.imshow(Z, interpolation='bilinear', origin='lower', cmap=cm.gray, extent=(-3,3,-2,2))

# now overplot the contours
levels = np.arange(-3.0, 3.0, 0.5)
CS = plt.contour(Z, levels, origin='lower', linewidths=2, extent=(-3,3,-2,2))

plt.clabel(CS, levels ,inline=1, fmt='%1.1f', fontsize=14)

# make a colorbar for the contour lines
CB = plt.colorbar(CS, shrink=0.8, extend='both')

plt.title('Lines with colorbar')

# We can still add a colorbar for the image, too.
CBI = plt.colorbar(im, orientation='horizontal', shrink=0.8)

# This makes the original colorbar look a bit out of place,
# so let's improve its position.
#l,b,w,h = plt.gca().get_position().bounds
#ll,bb,ww,hh = CB.ax.get_position().bounds
#CB.ax.set_position([ll, b+0.1*h, ww, h*0.8])
plt.show()

from matplotlib.colors import LogNorm
from numpy.random import normal

#normal distribution center at x=0 and y=5
x = normal(0,1,100000)
y = normal(0,1,100000)+5

plt.hist2d(x, y, bins=[40,20])
plt.colorbar()
x1,x2,y1,y2 = plt.axis()
plt.show()

heatmap, xedges, yedges = np.histogram2d(x, y, bins=[20,40], range=[[x1,x2],[y1,y2]])
print(x1, x2, y1, y2)
print(xedges[0], xedges[-1], yedges[0],yedges[-1])
extent = [x1, x2, y1, y2]

plt.imshow(heatmap, extent=extent)
plt.colorbar()
plt.show()

from mpl_toolkits.mplot3d.axes3d import Axes3D

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)

fig = plt.figure(figsize=(8,6))

ax = fig.add_subplot(1,1,1, projection='3d')

ax.plot_surface(X, Y, Z, rstride=4, cstride=4, alpha=0.25)
cset = ax.contour(X, Y, Z, zdir='z', offset=-3.5, cmap=cm.coolwarm)
cset = ax.contour(X, Y, Z, zdir='x', offset=-3.5, cmap=cm.coolwarm)
cset = ax.contour(X, Y, Z, zdir='y', offset=2, cmap=cm.coolwarm)

ax.set_xlim3d(-3.5, 3.5);
ax.set_ylim3d(-2.5, 2.5);
ax.set_zlim3d(-3, 2);
ax.set_xlabel('x')
ax.set_ylabel('y')
ax.set_zlabel('z')
plt.show()

fig = plt.figure(figsize=(12,6))

ax = fig.add_subplot(1,2,1, projection='3d')
ax.plot_surface(X, Y, Z, rstride=4, cstride=4, alpha=0.25)
ax.view_init(30, 45)

ax = fig.add_subplot(1,2,2, projection='3d')
ax.plot_surface(X, Y, Z, rstride=4, cstride=4, alpha=0.25)
ax.view_init(70, 30)

fig.tight_layout()

fig = plt.figure(figsize=(8,6))

ax = fig.add_subplot(1,1,1, projection='3d')

ax.plot_surface(X, Y, Z, rstride=4, cstride=4, alpha=0.1)
cset = ax.contour(X, Y, Z, zdir='z', offset=-3.5, cmap=cm.coolwarm)
cset = ax.contour(X, Y, Z, zdir='x', offset=-3.0, cmap=cm.coolwarm)
cset = ax.contour(X, Y, Z, zdir='y', offset=-2.5, cmap=cm.coolwarm)

ax.set_xlim3d(-3.5, 3.5);
ax.set_ylim3d(-2.5, 2.5);
ax.set_zlim3d(-3, 2);
ax.set_xlabel('x')
ax.set_ylabel('y')
ax.set_zlabel('z')
ax.view_init(40, 45)
plt.show()