# -*- coding: utf-8 -*-
"""Charts Colab

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1dvI0ZL3vntg9DbjaCHB-Y23Ic4ihzr0T

# Charting in Colaboratory

A common use for notebooks is data visualization using charts. Colaboratory makes this easy with several charting tools available as Python imports.

## Matplotlib

[Matplotlib](http://matplotlib.org/) is the most common charting package, see its [documentation](http://matplotlib.org/api/pyplot_api.html) for details, and its [examples](http://matplotlib.org/gallery.html#statistics) for inspiration.

### Line Plots
"""

import matplotlib.pyplot as plt
 
x  = [1, 2, 3, 4, 5, 6, 7, 8, 9]
y1 = [1, 3, 5, 3, 1, 3, 5, 3, 1]
y2 = [2, 4, 6, 4, 2, 4, 6, 4, 2]
plt.plot(x, y1, label="line L")
plt.plot(x, y2, label="line H")
plt.plot()

plt.xlabel("x axis")
plt.ylabel("y axis")
plt.title("Line Graph Example")
plt.legend()
plt.show()

"""### Bar Plots"""

import matplotlib.pyplot as plt

plt.style.use('default')
plt.rcParams['figure.figsize'] = (6, 5)
plt.rcParams['font.size'] = 12

x = [1, 2, 3]
y = [1, 2, 3]

fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2)

ax1.bar(x, y, color='aquamarine', edgecolor='black', hatch='/')
ax2.bar(x, y, color='salmon', edgecolor='black', hatch='\\')
ax3.bar(x, y, color='navajowhite', edgecolor='black', hatch='+')
ax4.bar(x, y, color='lightskyblue', edgecolor='black', hatch='*')

plt.tight_layout()
plt.show()

import matplotlib.pyplot as plt
from matplotlib.patches import Ellipse, Polygon

fig = plt.figure()
ax1 = fig.add_subplot(111)
# draw hatch
ax1.bar(range(1, 5), range(1, 5), color='none', edgecolor='red', hatch="/", lw=1., zorder = 0)
# draw edge
ax1.bar(range(1, 5), range(1, 5), color='none', edgecolor='k', zorder=1, lw=2.)

#ax1.set_xticks([1.5, 2.5, 3.5, 4.5])
plt.show()

import pandas as pd
import matplotlib.pyplot as plt

employees=["Rudra","Alok","Prince","Nayan","Reman"]
earnings={
    "January":[0,20,15,18,14],
    "February":[0,13,10,18,15],
    "March":[40,20,10,15,18],
}

df=pd.DataFrame(earnings,index=employees)

df.plot(kind="barh",stacked=True,figsize=(10,8))


plt.legend(loc="lower left",bbox_to_anchor=(0.8,1.0))
fig, ax = plt.subplots(1,1,figsize=(10,5))
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
#ax.spines['left'].set_visible(False)
ax.spines['bottom'].set_visible(False)
plt.show()

import numpy as np 
import matplotlib.pyplot as plt 
   
labels = ['Detail of TLSRPT using HTTPS','HTTPS','Detail of TLSRPT using Email','Email',  'Wellformed', 'Normal Response'] 
   
mine = [0,10,0,722, 730, 747] 
other_1 = [0, 0, 692, 0,0,0] 
other_2 = [0, 0, 27, 0,0,0] 
others = [0,0,720,0,0,0]
other_3 = [0, 0, 3, 0,0,0] 
others2 = [0,0,723,0,0,0]
other_4 = [3, 0, 3, 0,0,0] 
HTTPS = [8, 0, 0, 0,0,0] 

width = 0.3
   
fig, ax = plt.subplots(1,1,figsize=(10,5))
ax.xaxis.tick_top()
   
bar = ax.barh(labels, mine, width, color='none',hatch='xxxxx',edgecolor='k', lw=1, zorder = 0.3) 
for rect in bar:
    witdh = rect.get_width()
    posx = witdh * 1.01
    posy = rect.get_y() + rect.get_height() * 0.5 -0.04
    if rect.get_width() != 0:
      ax.text(posx, posy, ' %d (%.2f%%)' % (witdh,witdh/747*100), rotation=0, ha='left', va='center')

ax.barh(labels, other_1, width, left = mine, label ='Counts of using One-Email Address (Ratio): 693 (92.77%)',color='none',hatch='...',edgecolor='dodgerblue', lw=1, zorder = 0.3) 

ax.barh(labels, other_2, width, left = other_1, label ='Counts of using Two-Email Address (Ratio): 27 (3.61%)') 

ax.barh(labels, other_3, width, left = others, label ='Counts of using Three-Email Address (Ratio): 1 (0.13%)') 

ax.barh(labels, other_4, width, left = others2, label ='Counts of using Email with HTTPS (Ratio): 2 (0.27%)') 

ax.barh(labels, HTTPS, width, left = other_4, label ='Counts of using HTTPS (Ratio): 7 (1.07%)') 
 
ax.legend() 
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
#ax.spines['left'].set_visible(False)
ax.spines['bottom'].set_visible(False)
ax.set_title('Analysis the method of TLSRPT') 
box = ax.get_position() # 범례를 그래프상자 밖에 그리기위해 상자크기를 조절
ax.set_position([box.x0, box.y0, box.width, box.height])
ax.legend(frameon=False, bbox_to_anchor=(1,0), shadow=True, ncol=1)   
plt.show()

import numpy as np
import matplotlib.pyplot as plt

h = [8,2,1,27, 693, 721, 731,747]
h1 = h[:4]
h2 = h[4:]
r = ['8 (1.07%)','2 (0.27%)','1 (0.13%)', '27 (3.61%)', '693 (92.77%)', '721 (96.52%)', '731 (97.86%)','747 (100.00%)']
r1 = r[:4]
r2 = r[4:]
fig, ax = plt.subplots(1,1,figsize=(10,5))
h = np.array(h)
plt.gca().use_sticky_edges = False
plt.barh(r, width=h, left=(h.max() - h) / 2, align='center', color='white',edgecolor='black')
for ri in r2:
    plt.text(h.max() /2, ri, ri, ha='center', va='center', color='black', size=10)
for ri in r1:
    plt.text(h.max() /2, ri, ri, ha='center', va='center', color='black', size=10)
plt.yticks(np.arange(8),['HTTPS','Email-with-HTTPS','Three email address','Two email address','One email address','Email','Wellformed','Normal Response']) # optionally remove standard y ticks and their labels
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
#ax.spines['left'].set_visible(False)
ax.spines['bottom'].set_visible(False)
ax.get_xaxis().set_visible(False)
plt.show()

import numpy as np
import matplotlib.pyplot as plt

women_pop = np.array([5., 30., 45., 22.])
men_pop     = np.array( [5., 25., 50., 20.])
X = np.arange(4)

plt.barh(X, women_pop, color = 'r')
plt.barh(X, -men_pop, color = 'b')
plt.show()

import numpy as np

def compute_pos(yticks, height, i, models):
  index = np.arange(len(yticks))
  n = len(models)
  correction = i - 0.5*(n-1)
  return index + height * correction

import matplotlib.pyplot as plt
models = ['Scanned', 'model B', 'model C']
yticks = ['STARTTLS', 'DNSSEC']
data = {
    'Scanned':[0.21, 0],
    'model D':[0.65, 895540],
    'model B':[0.61, 747],
    'model C':[0.55, 894793]
    }
height = 0.15

fig, ax = plt. subplots(1,1,figsize=(10,3))
ax.xaxis.tick_top()
ax.set_yticks(range(len(yticks)))
ax.set_yticklabels(yticks, fontsize=10)	

pos0 = compute_pos(yticks, height, 0, models)
print(pos0)
bar = ax.barh(pos0, data['model D'], height=height*0.95, label='model D', color='none',hatch='xxxxx',edgecolor='dodgerblue', lw=3., zorder = 1)
present_width(ax, bar) # bar너비 출력

pos1 = compute_pos(yticks, height, 1, models)
bar = ax.barh(pos1, data['model C'], height=height*0.95, label='model D', color='none',hatch='xxxxx',edgecolor='dodgerblue', lw=3., zorder = 1)
present_width(ax, bar) # bar너비 출력

pos2 = compute_pos(yticks, height, 2, models)
bar = ax.barh(pos2, data['model B'], height=height*0.95, label='model D', color='none',hatch='xxxxx',edgecolor='dodgerblue', lw=3., zorder = 1)
present_width(ax, bar) # bar너비 출력
print(pos0,pos1,pos2)
# for i, model in enumerate(models):
#   pos = compute_pos(yticks, height, i, models)
#   bar = ax.barh(pos, data[model], height=height*0.95, label=model, color='none',hatch='xxxxx',edgecolor='dodgerblue', lw=3., zorder = 1)
#   present_width(ax, bar) # bar너비 출력

box = ax.get_position() # 범례를 그래프상자 밖에 그리기위해 상자크기를 조절
ax.set_position([box.x0, box.y0, box.width, box.height])
ax.legend(frameon=False, bbox_to_anchor=(0.65,0), shadow=True, ncol=3)
	
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.spines['left'].set_visible(False)
ax.spines['bottom'].set_visible(False)
ax.set_axisbelow(True)
ax.xaxis.grid(True, color='gray', linestyle=(0, (5, 10)), linewidth=0.5)



import numpy as np

def compute_pos(yticks, height, i, models):
  index = np.arange(len(yticks))
  n = len(models)
  correction = i - 0.5*(n-1)
  return index + height * correction

import matplotlib.pyplot as plt
yticks = ['STARTTLS', 'DNSSEC', 'Request: _smtp.tls.domain']
data = {
    'model D':[0.65, 1, 1],
    'model C':[0.55, 1, 1],
    'model B':[0.61, 2, 1]
    }
height = 2

fig, ax = plt. subplots(1,1,figsize=(10,2))
ax.xaxis.tick_top()
#ax.xaxis.set_label_position('top')
ax.set_yticks(range(len(yticks)))
ax.set_yticklabels(yticks, fontsize=10)

#pos_2 = compute_pos(yticks, height, 10, 'model D')
bar = ax.barh(5, data['model D'], height=2, label='Count of Scanned Domain', color='none',hatch='xxxxx',edgecolor='dodgerblue', lw=1, zorder = 0.3)
present_width(ax, bar) # bar너비 출력

#pos_1 = compute_pos(yticks, height, 5, 'model C')
bar = ax.barh(3, data['model C'], height=1, label='No Response (Ratio)', color='none',hatch='....',edgecolor='red', lw=1, zorder = 0.3)
present_width(ax, bar) # bar너비 출력

#pos_0 = compute_pos(yticks, height, 0, 'model B')
bar = ax.barh(1, data['model B'], height=0, label='Yes Response (Ratio)', color='none',hatch='xxxxx',edgecolor='green', lw=1., zorder = 0.3)
present_width(ax, bar) # bar너비 출력


box = ax.get_position() # 범례를 그래프상자 밖에 그리기위해 상자크기를 조절
ax.set_position([box.x0, box.y0, box.width , box.height])
ax.legend(frameon=False, bbox_to_anchor=(0.65,0), shadow=True, ncol=3)

ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.spines['left'].set_visible(False)
ax.spines['bottom'].set_visible(False)
ax.set_axisbelow(True)
ax.xaxis.grid(True, color='gray', linestyle=(0, (5, 10)), linewidth=0.5)

def compute_pos(yticks, height, i, models):
  index = np.arange(len(yticks))
  n = len(models)
  correction = i - 0.2*(n-1)
  return index + height * correction

def present_width(ax, bar):
	for rect in bar:
		witdh = rect.get_width()
		posx = witdh*1.01
		posy = rect.get_y()+rect.get_height()*0.5
		ax.text(posx, posy, '%.3d' % witdh, rotation=0, ha='left', va='center')

import matplotlib.pyplot as plt
yticks = ['STARTTLS', 'DNSSEC', 'Request: _smtp.tls.domain']
data = {
    'model D':[0.65, 0.71, 895540],
    'model C':[0.55, 0.66, 894793],
    'model B':[0.61, 0.65, 747]
    }
height = 0.15

fig, ax = plt. subplots(1,1,figsize=(10,5))
ax.xaxis.tick_top()
#ax.xaxis.set_label_position('top')
ax.set_yticks(range(len(yticks)))
ax.set_yticklabels(yticks, fontsize=10)	
#for i, model in enumerate(models):

pos_2 = compute_pos(yticks, height, 2, 'model D')
bar = ax.barh(pos_2, data['model D'], height=height*0.65, label='Count of Scanned Domain', color='none',hatch='xxxxx',edgecolor='dodgerblue', lw=1, zorder = 0.3)
present_width(ax, bar) # bar너비 출력

pos_1 = compute_pos(yticks, height, 1, 'model C')
bar = ax.barh(pos_1, data['model C'], height=height*0.65, label='No Response (Ratio)', color='none',hatch='....',edgecolor='red', lw=1, zorder = 0.3)
present_width(ax, bar) # bar너비 출력

pos_0 = compute_pos(yticks, height, 0, 'model B')
bar = ax.barh(pos_0, data['model B'], height=height*0.65, label='Yes Response (Ratio)', color='none',hatch='xxxxx',edgecolor='green', lw=1., zorder = 0.3)
present_width(ax, bar) # bar너비 출력

box = ax.get_position() # 범례를 그래프상자 밖에 그리기위해 상자크기를 조절
ax.set_position([box.x0, box.y0, box.width * 0.9, box.height])
ax.legend(frameon=False, bbox_to_anchor=(1,0), shadow=True, ncol=3)	

ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.spines['left'].set_visible(False)
ax.spines['bottom'].set_visible(False)
ax.set_axisbelow(True)
ax.xaxis.grid(True, color='gray', linestyle=(0, (5, 10)), linewidth=0.5)

import matplotlib.pyplot as plt
import numpy as np

# plt.rcParams['xtick.bottom'] = plt.rcParams['xtick.labelbottom'] = False
# plt.rcParams['xtick.top'] = plt.rcParams['xtick.labeltop'] = True

x = np.arange(10)

fig, ax = plt.subplots()

ax.plot(x)
#ax.set_xlabel('xlabel top')  # Note title moves to make room for ticks

secax = ax.secondary_xaxis('top')
secax.set_xlabel('new label top')

plt.show()

import matplotlib.pyplot as plt
import numpy as np

def main():
  
	#### 1. bar plot으로 나타낼 데이터 입력
	models = ['model A', 'model B', 'model C', 'model D']
	yticks = ['Breast', 'Liver', 'Brain']
	data = {'model A':[0.65, 0.71, 0.69],
	        'model B':[0.61, 0.65, 0.64],
			'model C':[0.55, 0.66, 0.60],
			'model D':[0.60, 0.62, 0.57]}
	
	#### 2. matplotlib의 figure 및 axis 설정
	fig, ax = plt.subplots(1,1,figsize=(7,5)) # 1x1 figure matrix 생성, 가로(7인치)x세로(5인치) 크기지정
	colors = ['salmon', 'orange', 'cadetblue', 'skyblue']
	height = 0.15
	
	#### 3. bar 그리기
	for i, model in enumerate(models):
		pos = compute_pos(yticks, height, i, models)
		bar = ax.barh(pos, data[model], height=height*0.95, label=model, color='none',hatch='xxxxx',edgecolor='dodgerblue', lw=1., zorder = 0)
		bar = ax.barh(pos, data[model], height=height*0.95, label=model, color='none', edgecolor='k', zorder=1, lw=2.)
		present_width(ax, bar) # bar너비 출력
	
	#### 4. x축 세부설정
	ax.set_xlim([0.5,0.76])
	ax.set_xticks([0.5, 0.55, 0.6, 0.65, 0.7, 0.75])
	ax.xaxis.set_tick_params(labelsize=10)
  # secax = ax.secondary_xaxis('top')
  # secax.set_xlabel('new label top')
  #ax.set_xlabel('Prediction Accuracy', fontsize=14)
	
	#### 5. y축 세부설정
	ax.set_yticks(range(len(yticks)))
	ax.set_yticklabels(yticks, fontsize=10)	
	#ax.set_ylabel('Cancer type', fontsize=14)
	
	#### 6. 범례 나타내기
	box = ax.get_position() # 범례를 그래프상자 밖에 그리기위해 상자크기를 조절
	ax.set_position([box.x0, box.y0, box.width * 0.9, box.height])
	ax.legend(loc='center left', bbox_to_anchor=(1,0.5), shadow=True, ncol=1)
	
	#### 7. 보조선(눈금선) 나타내기
	ax.set_axisbelow(True)
	ax.xaxis.grid(True, color='gray', linestyle=(0, (5, 10)), linewidth=0.5)
  
	
	#### 8. 그래프 저장하고 출력하기
	plt.savefig('ex_barhplot.png', format='png', dpi=300)
	plt.show()
	
def compute_pos(yticks, height, i, models):
  index = np.arange(len(yticks))
  n = len(models)
  correction = i - 0.5*(n-1)
  return index + height * correction


		
if __name__=='__main__':
	main()

import matplotlib.pyplot as plt

# Look at index 4 and 6, which demonstrate overlapping cases.
x1 = [1, 3, 4, 5, 6, 7, 9]
y1 = [4, 7, 2, 4, 7, 8, 3]

x2 = [2, 4, 6, 8, 10]
y2 = [5, 6, 2, 6, 2]

# Colors: https://matplotlib.org/api/colors_api.html

plt.bar(x1, y1, label="Blue Bar", color='b')
plt.bar(x2, y2, label="Green Bar", color='g')
plt.plot()

plt.xlabel("bar number")
plt.ylabel("bar height")
plt.title("Bar Chart Example")
plt.legend()
plt.show()

"""### Histograms"""

import matplotlib.pyplot as plt
import numpy as np

# Use numpy to generate a bunch of random data in a bell curve around 5.
n = 5 + np.random.randn(1000)

m = [m for m in range(len(n))]
plt.bar(m, n)
plt.title("Raw Data")
plt.show()

plt.hist(n, bins=20)
plt.title("Histogram")
plt.show()

plt.hist(n, cumulative=True, bins=20)
plt.title("Cumulative Histogram")
plt.show()

"""### Scatter Plots"""

import matplotlib.pyplot as plt

x1 = [2, 3, 4]
y1 = [5, 5, 5]

x2 = [1, 2, 3, 4, 5]
y2 = [2, 3, 2, 3, 4]
y3 = [6, 8, 7, 8, 7]

# Markers: https://matplotlib.org/api/markers_api.html

plt.scatter(x1, y1)
plt.scatter(x2, y2, marker='v', color='r')
plt.scatter(x2, y3, marker='^', color='m')
plt.title('Scatter Plot Example')
plt.show()

"""### Stack Plots"""

import matplotlib.pyplot as plt

idxes = [ 1,  2,  3,  4,  5,  6,  7,  8,  9]
arr1  = [23, 40, 28, 43,  8, 44, 43, 18, 17]
arr2  = [17, 30, 22, 14, 17, 17, 29, 22, 30]
arr3  = [15, 31, 18, 22, 18, 19, 13, 32, 39]

# Adding legend for stack plots is tricky.
plt.plot([], [], color='r', label = 'D 1')
plt.plot([], [], color='g', label = 'D 2')
plt.plot([], [], color='b', label = 'D 3')

plt.stackplot(idxes, arr1, arr2, arr3, colors= ['r', 'g', 'b'])
plt.title('Stack Plot Example')
plt.legend()
plt.show()

"""### Pie Charts"""

import matplotlib.pyplot as plt

labels = 'S1', 'S2', 'S3'
sections = [56, 66, 24]
colors = ['c', 'g', 'y']

plt.pie(sections, labels=labels, colors=colors,
        startangle=90,
        explode = (0, 0.1, 0),
        autopct = '%1.2f%%')

plt.axis('equal') # Try commenting this out.
plt.title('Pie Chart Example')
plt.show()

"""### fill_between and alpha"""

import matplotlib.pyplot as plt
import numpy as np

ys = 200 + np.random.randn(100)
x = [x for x in range(len(ys))]

plt.plot(x, ys, '-')
plt.fill_between(x, ys, 195, where=(ys > 195), facecolor='g', alpha=0.6)

plt.title("Fills and Alpha Example")
plt.show()

"""### Subplotting using Subplot2grid"""

import matplotlib.pyplot as plt
import numpy as np

def random_plots():
  xs = []
  ys = []
  
  for i in range(20):
    x = i
    y = np.random.randint(10)
    
    xs.append(x)
    ys.append(y)
aa  
  return xs, ys

fig = plt.figure()
ax1 = plt.subplot2grid((5, 2), (0, 0), rowspan=1, colspan=2)
ax2 = plt.subplot2grid((5, 2), (1, 0), rowspan=3, colspan=2)
ax3 = plt.subplot2grid((5, 2), (4, 0), rowspan=1, colspan=1)
ax4 = plt.subplot2grid((5, 2), (4, 1), rowspan=1, colspan=1)

x, y = random_plots()
ax1.plot(x, y)

x, y = random_plots()
ax2.plot(x, y)

x, y = random_plots()
ax3.plot(x, y)

x, y = random_plots()
ax4.plot(x, y)

plt.tight_layout()
plt.show()

"""## Plot styles

Colaboratory charts use [Seaborn's](https://seaborn.pydata.org) custom styling by default. To customize styling further please see the [matplotlib docs](https://matplotlib.org/users/style_sheets.html).

## 3D Graphs

### 3D Scatter Plots
"""

import matplotlib.pyplot as plt
import numpy as np
from mpl_toolkits.mplot3d import axes3d

fig = plt.figure()
ax = fig.add_subplot(111, projection = '3d')

x1 = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
y1 = np.random.randint(10, size=10)
z1 = np.random.randint(10, size=10)

x2 = [-1, -2, -3, -4, -5, -6, -7, -8, -9, -10]
y2 = np.random.randint(-10, 0, size=10)
z2 = np.random.randint(10, size=10)

ax.scatter(x1, y1, z1, c='b', marker='o', label='blue')
ax.scatter(x2, y2, z2, c='g', marker='D', label='green')

ax.set_xlabel('x axis')
ax.set_ylabel('y axis')
ax.set_zlabel('z axis')
plt.title("3D Scatter Plot Example")
plt.legend()
plt.tight_layout()
plt.show()

"""### 3D Bar Plots"""

import matplotlib.pyplot as plt
import numpy as np

fig = plt.figure()
ax = fig.add_subplot(111, projection = '3d')

x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
y = np.random.randint(10, size=10)
z = np.zeros(10)

dx = np.ones(10)
dy = np.ones(10)
dz = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]

ax.bar3d(x, y, z, dx, dy, dz, color='g')

ax.set_xlabel('x axis')
ax.set_ylabel('y axis')
ax.set_zlabel('z axis')
plt.title("3D Bar Chart Example")
plt.tight_layout()
plt.show()

"""### Wireframe Plots"""

import matplotlib.pyplot as plt

fig = plt.figure()
ax = fig.add_subplot(111, projection = '3d')

x, y, z = axes3d.get_test_data()

ax.plot_wireframe(x, y, z, rstride = 2, cstride = 2)

plt.title("Wireframe Plot Example")
plt.tight_layout()
plt.show()

"""## Seaborn

There are several libraries layered on top of Matplotlib that you can use in Colab. One that is worth highlighting is [Seaborn](http://seaborn.pydata.org):
"""

import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns

# Generate some random data
num_points = 20
# x will be 5, 6, 7... but also twiddled randomly
x = 5 + np.arange(num_points) + np.random.randn(num_points)
# y will be 10, 11, 12... but twiddled even more randomly
y = 10 + np.arange(num_points) + 5 * np.random.randn(num_points)
sns.regplot(x, y)
plt.show()

"""That's a simple scatterplot with a nice regression line fit to it, all with just one call to Seaborn's [regplot](http://seaborn.pydata.org/generated/seaborn.regplot.html#seaborn.regplot).

Here's a Seaborn [heatmap](https://seaborn.pydata.org/generated/seaborn.heatmap.html):
"""

import matplotlib.pyplot as plt
import numpy as np

# Make a 10 x 10 heatmap of some random data
side_length = 10
# Start with a 10 x 10 matrix with values randomized around 5
data = 5 + np.random.randn(side_length, side_length)
# The next two lines make the values larger as we get closer to (9, 9)
data += np.arange(side_length)
data += np.reshape(np.arange(side_length), (side_length, 1))
# Generate the heatmap
sns.heatmap(data)
plt.show()

"""## Altair

[Altair](http://altair-viz.github.io) is a declarative visualization library for creating interactive visualizations in Python, and is installed and enabled in Colab by default.

For example, here is an interactive scatter plot:
"""

import altair as alt
from vega_datasets import data
cars = data.cars()

alt.Chart(cars).mark_point().encode(
    x='Horsepower',
    y='Miles_per_Gallon',
    color='Origin',
).interactive()

"""For more examples of Altair plots, see the [Altair snippets notebook](/notebooks/snippets/altair.ipynb) or the external [Altair Example Gallery](https://altair-viz.github.io/gallery/).

## Plotly

### Sample
"""

from plotly.offline import iplot
import plotly.graph_objs as go

data = [
    go.Contour(
        z=[[10, 10.625, 12.5, 15.625, 20],
           [5.625, 6.25, 8.125, 11.25, 15.625],
           [2.5, 3.125, 5., 8.125, 12.5],
           [0.625, 1.25, 3.125, 6.25, 10.625],
           [0, 0.625, 2.5, 5.625, 10]]
    )
]
iplot(data)

"""## Bokeh

### Sample
"""

import numpy as np
from bokeh.plotting import figure, show
from bokeh.io import output_notebook

# Call once to configure Bokeh to display plots inline in the notebook.
output_notebook()

N = 4000
x = np.random.random(size=N) * 100
y = np.random.random(size=N) * 100
radii = np.random.random(size=N) * 1.5
colors = ["#%02x%02x%02x" % (r, g, 150) for r, g in zip(np.floor(50+2*x).astype(int), np.floor(30+2*y).astype(int))]

p = figure()
p.circle(x, y, radius=radii, fill_color=colors, fill_alpha=0.6, line_color=None)
p.show()