notebook.community

Edit and run



In [2]:

    
import matplotlib.pyplot as plt

X = range(100)
Y = [value**2 for value in X]

plt.plot(X, Y)
plt.show()
Y



In [3]:

    
import math

T = range(100)
X = [(2 * math.pi * t) / len(T) for t in T]
Y = [math.sin(value) for value in X]

plt.plot(X, Y)
plt.show()



In [4]:

    
import numpy as np

X = np.linspace(0, 2 * np.pi, 100)
Y = np.sin(X)

plt.plot(X, Y)
plt.show()



In [23]:

    
X = np.linspace(0, 2 * np.pi, 100)
Ya = np.sin(X)
Yb = np.cos(X)
plt.plot(X, Ya)
plt.plot(X, Yb)
plt.show()



In [33]:

    
def plot_slope(X, Y):
    Xs = X[1:] - X[:-1]
    Ys = Y[1:] - Y[:-1]
    plt.plot(X[1:], Ys / Xs)
   
X = np.linspace(-3,3,100)
Y = np.exp(-X **2)
print(X)
plt.plot(X,Y)
plot_slope(X,Y)
plt.show()









    



[-3.         -2.93939394 -2.87878788 -2.81818182 -2.75757576 -2.6969697
 -2.63636364 -2.57575758 -2.51515152 -2.45454545 -2.39393939 -2.33333333
 -2.27272727 -2.21212121 -2.15151515 -2.09090909 -2.03030303 -1.96969697
 -1.90909091 -1.84848485 -1.78787879 -1.72727273 -1.66666667 -1.60606061
 -1.54545455 -1.48484848 -1.42424242 -1.36363636 -1.3030303  -1.24242424
 -1.18181818 -1.12121212 -1.06060606 -1.         -0.93939394 -0.87878788
 -0.81818182 -0.75757576 -0.6969697  -0.63636364 -0.57575758 -0.51515152
 -0.45454545 -0.39393939 -0.33333333 -0.27272727 -0.21212121 -0.15151515
 -0.09090909 -0.03030303  0.03030303  0.09090909  0.15151515  0.21212121
  0.27272727  0.33333333  0.39393939  0.45454545  0.51515152  0.57575758
  0.63636364  0.6969697   0.75757576  0.81818182  0.87878788  0.93939394
  1.          1.06060606  1.12121212  1.18181818  1.24242424  1.3030303
  1.36363636  1.42424242  1.48484848  1.54545455  1.60606061  1.66666667
  1.72727273  1.78787879  1.84848485  1.90909091  1.96969697  2.03030303
  2.09090909  2.15151515  2.21212121  2.27272727  2.33333333  2.39393939
  2.45454545  2.51515152  2.57575758  2.63636364  2.6969697   2.75757576
  2.81818182  2.87878788  2.93939394  3.        ]



In [98]:

    
li = np.array([1,2,3,4,5])
ll = np.array([1,2,3,4,5])
li[1:]









    Out[98]:





array([2, 3, 4, 5])



In [45]:

    
X, Y = [], []
for line in open('my_data.txt', 'r'):
    values = [float(s) for s in line.split()]
    X.append(values[0])
    Y.append(values[1])
    print(values)
plt.plot(X, Y)
plt.show()









    



[0.0, 0.0]
[1.0, 1.0]
[2.0, 4.0]
[4.0, 16.0]
[5.0, 25.0]
[6.0, 36.0]



In [51]:

    
with open('my_data.txt', 'r') as f:
    X,Y = zip(*[[float(s) for s in line.split()] for line in f])
plt.plot(X, Y)
plt.show()









    












    



(0.0, 1.0, 2.0, 4.0, 5.0, 6.0) (0.0, 1.0, 4.0, 16.0, 25.0, 36.0)



In [62]:

    
data = np.loadtxt('my_data.txt')
for i in range(len(data[0])):
    plt.plot(data[:,0],data[:, i])
plt.show()



In [106]:

    
data = np.random.rand(1024, 2)
print(data)
plt.scatter(data[:,0], data[:,1])
plt.show()









    



[[ 0.39221706  0.77280779]
 [ 0.73985205  0.03350308]
 [ 0.10030575  0.2527359 ]
 ..., 
 [ 0.22957173  0.74367686]
 [ 0.37502038  0.9934316 ]
 [ 0.52586905  0.70218786]]






    












    Out[106]:





1024



In [100]:

    
data = [5., 25., 50., 20.]

plt.bar(range(len(data)), data, width=0.5)
plt.show()



In [95]:

    
data = [[5., 25., 50., 20.],
        [4., 23., 51., 17],
        [6., 22., 52., 19]]

X = np.arange(4)
plt.bar(X + 0.00, data[0], color = 'b', width = 0.25)
plt.bar(X + 0.25, data[1], color = 'g', width = 0.25)
plt.bar(X + 0.50, data[2], color = 'r', width = 0.25)
plt.show()
print(X)



In [108]:

    
data = [[5., 25., 50., 20.],
        [4., 23., 51., 17],
        [6., 22., 52., 19]]

color_list = ['b', 'g', 'r']
gap = .8/len(data)
for i, row in enumerate(data):
    X = np.arange(len(row))
    plt.bar(X + i * gap, row, width = gap, color = color_list[i % len(color_list)])
    print(len(color_list))
plt.show()



In [114]:

    
data = np.array([[5., 25., 50., 20.],
        [4., 23., 51., 17],
        [6., 22., 52., 19]])

color_list = ['b', 'g', 'r']
X = np.arange(data.shape[1])
for i in range(data.shape[0]):
    plt.bar(X, data[i], bottom = np.sum(data[:i], axis=0), color = color_list[i])
    print( np.sum(data[:i], axis=0))
plt.show()









    



[ 0.  0.  0.  0.]
[  5.  25.  50.  20.]
[   9.   48.  101.   37.]



In [107]:

    
A = [5., 30., 45., 22.]
B = [5, 25, 50, 20]

X = range(4)

plt.bar(X, A, color = 'b')
plt.bar(X, B, color = 'r', bottom = 60)
plt.show()



In [119]:

    
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()



In [241]:

    
data = [5, 25, 50, 20,15, 27]
color_list = ['b', 'r', 'g', 'y']
plt.pie(data, colors= color_list)
plt.show()



In [147]:

    
X = np.random.randn(1000)

plt.hist(X)
plt.show()
len(X)



In [150]:

    
data = np.random.randn(100,5)

plt.boxplot(data)
plt.show()



In [153]:

    
import matplotlib.tri as tri

data = np.random.rand(100, 2)
triangles = tri.Triangulation(data[:,0], data[:,1])
plt.triplot(triangles)
plt.show()









    



<matplotlib.tri.triangulation.Triangulation object at 0x0000014E65535780>



In [156]:

    
def pdf(X, mu, sigma):
	a = 1. / (sigma * np.sqrt(2. * np.pi))
	b = -1. / (2. * sigma ** 2)
	return a * np.exp(b * (X - mu) ** 2)

X = np.linspace(-6, 6, 1024)

for i in range(5):
	samples = np.random.standard_normal(50)
	mu, sigma = np.mean(samples), np.std(samples)
	plt.plot(X, pdf(X, mu, sigma), color = '.75')

plt.plot(X, pdf(X, 0., 1.), color = 'k')

plt.show()



In [169]:

    
import numpy
import matplotlib.pyplot as plot

A = numpy.random.standard_normal((100, 2))
A += numpy.array((-1, -1))
A
B = numpy.random.standard_normal((100, 2))
B += numpy.array((1, 1))

plot.scatter(A[:,0], A[:,1], color = '.25')
plot.scatter(B[:,0], B[:,1], color = '.7')

plot.show()



In [107]:

    
import numpy
import matplotlib.pyplot as plot

label_list = (
	b'Iris-setosa',
	b'Iris-versicolor',
	b'Iris-virginica',
)

def read_label(label):
	return label_list.index(label)

data = numpy.loadtxt('iris.data.txt',
                     delimiter = ',',
                     converters = {4: read_label})
df = open('iris.data.txt', 'r')
# color_set = ('.00', '.50', '.25')
# color_list = [color_set[int(label)] for label in data[:,4]]

# plot.scatter(data[:,0], data[:,1], color = color_list)
# plot.show()
ff=[]
for line in df:
    ff.append(line.split())
ff[:,4]









    



---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-107-f830bbf1831b> in <module>()
     23 for line in df:
     24     ff.append(line.split())
---> 25 ff[:,4]

TypeError: list indices must be integers or slices, not tuple



In [211]:

    
import numpy
import matplotlib.pyplot as plot

data = numpy.random.standard_normal((100, 2))

plot.scatter(data[:,0], data[:,1], color = '1.0', edgecolor='0.0')
plot.show()



In [105]:

    
import random
import matplotlib.pyplot as plot

values = [random.randint(0, 99) for i in range(50)]
values.sort()
color_set = ['.00', '.25', '.50', '.75']
color_list = [color_set[(len(color_set) * val) // 100] for val in values]

plot.bar(range(len(values)), values, color = color_list)
plot.show()
len(values)



In [102]:

    
import numpy
import matplotlib.cm as cm
import matplotlib.pyplot as plot

N = 256
angle  = numpy.linspace(0, 8 * 2 * numpy.pi, N)

radius = numpy.linspace(.5, 1., N)

X = radius * numpy.cos(angle)

Y = radius * numpy.sin(angle)
# plot.scatter(X, Y, c = angle, cmap = cm.hsv)
# plot.scatter(X, Y, c = angle, cmap = cm.PuOr)
plot.plot(X,Y)
plot.show()



In [263]:

    
import random
import matplotlib.cm as cm
import matplotlib.colors as col
import matplotlib.pyplot as plot

values = [random.randint(0, 99) for i in range(50)]
values.sort()
print(values)
cmap = cm.ScalarMappable(col.Normalize(0, 99))

plot.bar(range(len(values)), values, color = cmap.to_rgba(values))
plot.show()









    



[2, 3, 17, 17, 21, 23, 24, 24, 25, 27, 33, 36, 37, 37, 38, 42, 42, 42, 43, 43, 44, 48, 51, 52, 53, 54, 58, 59, 61, 64, 65, 65, 66, 66, 67, 68, 68, 74, 76, 76, 77, 79, 81, 84, 90, 90, 95, 98, 98, 99]



In [269]:

    
import numpy
import matplotlib.pyplot as plot


def pdf(X, mu, sigma):
	a = 1. / (sigma * numpy.sqrt(2. * numpy.pi))
	b = -1. / (2. * sigma ** 2)
	return a * numpy.exp(b * (X - mu) ** 2)

X = numpy.linspace(-6, 6, 1024)

plot.plot(X, pdf(X, 0., 1.),   color = 'k' , linestyle = 'solid')
plot.plot(X, pdf(X, 0.,  .5),  color = 'k', linestyle = 'dashed')
plot.plot(X, pdf(X, 0.,  .25), color = 'k', linestyle = 'dashdot')

plot.show()



In [276]:

    
import numpy
import matplotlib.pyplot as plot

N = 8
A = numpy.random.random(N)
B = numpy.random.random(N)

plot.bar(range(N), A, color = '.75')
plot.bar(range(N), A + B, bottom = A, color = 'w', edgecolor='k', linestyle = 'dashdot')

plot.show()



In [274]:

    
import numpy
import matplotlib.pyplot as plot


def pdf(X, mu, sigma):
	a = 1. / (sigma * numpy.sqrt(2. * numpy.pi))
	b = -1. / (2. * sigma ** 2)
	return a * numpy.exp(b * (X - mu) ** 2)

X = numpy.linspace(-6, 6, 1024)

for i in range(64):
	samples = numpy.random.standard_normal(50)
	mu, sigma = numpy.mean(samples), numpy.std(samples)
	plot.plot(X, pdf(X, mu, sigma), color = '.75', linewidth = .5)

plot.plot(X, pdf(X, 0., 1.), color = 'k', linewidth = 3.)

plot.show()



In [281]:

    
import numpy
import matplotlib.pyplot as plot

N = 8
A = numpy.random.random(N)
B = numpy.random.random(N)

plot.bar(range(N), A, color = 'w',edgecolor = 'k', hatch = 'x')
plot.bar(range(N), A + B, bottom = A, color = 'w',edgecolor= 'k', hatch = '*')

plot.show()



In [282]:

    
import numpy
import matplotlib.pyplot as plot

X = numpy.linspace(0, 6, 1024)
Y1 = numpy.sin(X)
Y2 = numpy.cos(2 * X)

plot.fill_between(X, Y1, Y2, color = '.75')

plot.show()



In [283]:

    
import numpy
import matplotlib.pyplot as plot

A = numpy.random.standard_normal((100, 2))
A += numpy.array((-1, -1))

B = numpy.random.standard_normal((100, 2))
B += numpy.array((1, 1))

plot.scatter(A[:,0], A[:,1], color = 'k', marker = 'x')
plot.scatter(B[:,0], B[:,1], color = 'k', marker = '^')

plot.show()



In [6]:

    
# label_list = (
# 	b'Iris-setosa',
# 	b'Iris-versicolor',
# 	b'Iris-virginica',
# )

# def read_label(label):
# 	return label_list.index(label)

# data = np.loadtxt('iris.data.txt',
#                      delimiter = ',',
#                      converters = {4: read_label})

label_list = (b'Iris-setosa',b'Iris-versicolor',b'Iris-virginica',)

def read_label(label):
    return label_list.index(label)

data = np.loadtxt('iris.data.txt', delimiter = ',', converters = {4: read_label})

marker_set=('^', 'x', '.')
for i, marker in enumerate(marker_set):
    data_subset = np.asarray([x for x in data if x[4] == i])
    plt.scatter(data_subset[:,0], data_subset[:,1], color ='k', marker = marker)
plt.show()
data









    












    Out[6]:





array([[ 5.1,  3.5,  1.4,  0.2,  0. ],
       [ 4.9,  3. ,  1.4,  0.2,  0. ],
       [ 4.7,  3.2,  1.3,  0.2,  0. ],
       [ 4.6,  3.1,  1.5,  0.2,  0. ],
       [ 5. ,  3.6,  1.4,  0.2,  0. ],
       [ 5.4,  3.9,  1.7,  0.4,  0. ],
       [ 4.6,  3.4,  1.4,  0.3,  0. ],
       [ 5. ,  3.4,  1.5,  0.2,  0. ],
       [ 4.4,  2.9,  1.4,  0.2,  0. ],
       [ 4.9,  3.1,  1.5,  0.1,  0. ],
       [ 5.4,  3.7,  1.5,  0.2,  0. ],
       [ 4.8,  3.4,  1.6,  0.2,  0. ],
       [ 4.8,  3. ,  1.4,  0.1,  0. ],
       [ 4.3,  3. ,  1.1,  0.1,  0. ],
       [ 5.8,  4. ,  1.2,  0.2,  0. ],
       [ 5.7,  4.4,  1.5,  0.4,  0. ],
       [ 5.4,  3.9,  1.3,  0.4,  0. ],
       [ 5.1,  3.5,  1.4,  0.3,  0. ],
       [ 5.7,  3.8,  1.7,  0.3,  0. ],
       [ 5.1,  3.8,  1.5,  0.3,  0. ],
       [ 5.4,  3.4,  1.7,  0.2,  0. ],
       [ 5.1,  3.7,  1.5,  0.4,  0. ],
       [ 4.6,  3.6,  1. ,  0.2,  0. ],
       [ 5.1,  3.3,  1.7,  0.5,  0. ],
       [ 4.8,  3.4,  1.9,  0.2,  0. ],
       [ 5. ,  3. ,  1.6,  0.2,  0. ],
       [ 5. ,  3.4,  1.6,  0.4,  0. ],
       [ 5.2,  3.5,  1.5,  0.2,  0. ],
       [ 5.2,  3.4,  1.4,  0.2,  0. ],
       [ 4.7,  3.2,  1.6,  0.2,  0. ],
       [ 4.8,  3.1,  1.6,  0.2,  0. ],
       [ 5.4,  3.4,  1.5,  0.4,  0. ],
       [ 5.2,  4.1,  1.5,  0.1,  0. ],
       [ 5.5,  4.2,  1.4,  0.2,  0. ],
       [ 4.9,  3.1,  1.5,  0.1,  0. ],
       [ 5. ,  3.2,  1.2,  0.2,  0. ],
       [ 5.5,  3.5,  1.3,  0.2,  0. ],
       [ 4.9,  3.1,  1.5,  0.1,  0. ],
       [ 4.4,  3. ,  1.3,  0.2,  0. ],
       [ 5.1,  3.4,  1.5,  0.2,  0. ],
       [ 5. ,  3.5,  1.3,  0.3,  0. ],
       [ 4.5,  2.3,  1.3,  0.3,  0. ],
       [ 4.4,  3.2,  1.3,  0.2,  0. ],
       [ 5. ,  3.5,  1.6,  0.6,  0. ],
       [ 5.1,  3.8,  1.9,  0.4,  0. ],
       [ 4.8,  3. ,  1.4,  0.3,  0. ],
       [ 5.1,  3.8,  1.6,  0.2,  0. ],
       [ 4.6,  3.2,  1.4,  0.2,  0. ],
       [ 5.3,  3.7,  1.5,  0.2,  0. ],
       [ 5. ,  3.3,  1.4,  0.2,  0. ],
       [ 7. ,  3.2,  4.7,  1.4,  1. ],
       [ 6.4,  3.2,  4.5,  1.5,  1. ],
       [ 6.9,  3.1,  4.9,  1.5,  1. ],
       [ 5.5,  2.3,  4. ,  1.3,  1. ],
       [ 6.5,  2.8,  4.6,  1.5,  1. ],
       [ 5.7,  2.8,  4.5,  1.3,  1. ],
       [ 6.3,  3.3,  4.7,  1.6,  1. ],
       [ 4.9,  2.4,  3.3,  1. ,  1. ],
       [ 6.6,  2.9,  4.6,  1.3,  1. ],
       [ 5.2,  2.7,  3.9,  1.4,  1. ],
       [ 5. ,  2. ,  3.5,  1. ,  1. ],
       [ 5.9,  3. ,  4.2,  1.5,  1. ],
       [ 6. ,  2.2,  4. ,  1. ,  1. ],
       [ 6.1,  2.9,  4.7,  1.4,  1. ],
       [ 5.6,  2.9,  3.6,  1.3,  1. ],
       [ 6.7,  3.1,  4.4,  1.4,  1. ],
       [ 5.6,  3. ,  4.5,  1.5,  1. ],
       [ 5.8,  2.7,  4.1,  1. ,  1. ],
       [ 6.2,  2.2,  4.5,  1.5,  1. ],
       [ 5.6,  2.5,  3.9,  1.1,  1. ],
       [ 5.9,  3.2,  4.8,  1.8,  1. ],
       [ 6.1,  2.8,  4. ,  1.3,  1. ],
       [ 6.3,  2.5,  4.9,  1.5,  1. ],
       [ 6.1,  2.8,  4.7,  1.2,  1. ],
       [ 6.4,  2.9,  4.3,  1.3,  1. ],
       [ 6.6,  3. ,  4.4,  1.4,  1. ],
       [ 6.8,  2.8,  4.8,  1.4,  1. ],
       [ 6.7,  3. ,  5. ,  1.7,  1. ],
       [ 6. ,  2.9,  4.5,  1.5,  1. ],
       [ 5.7,  2.6,  3.5,  1. ,  1. ],
       [ 5.5,  2.4,  3.8,  1.1,  1. ],
       [ 5.5,  2.4,  3.7,  1. ,  1. ],
       [ 5.8,  2.7,  3.9,  1.2,  1. ],
       [ 6. ,  2.7,  5.1,  1.6,  1. ],
       [ 5.4,  3. ,  4.5,  1.5,  1. ],
       [ 6. ,  3.4,  4.5,  1.6,  1. ],
       [ 6.7,  3.1,  4.7,  1.5,  1. ],
       [ 6.3,  2.3,  4.4,  1.3,  1. ],
       [ 5.6,  3. ,  4.1,  1.3,  1. ],
       [ 5.5,  2.5,  4. ,  1.3,  1. ],
       [ 5.5,  2.6,  4.4,  1.2,  1. ],
       [ 6.1,  3. ,  4.6,  1.4,  1. ],
       [ 5.8,  2.6,  4. ,  1.2,  1. ],
       [ 5. ,  2.3,  3.3,  1. ,  1. ],
       [ 5.6,  2.7,  4.2,  1.3,  1. ],
       [ 5.7,  3. ,  4.2,  1.2,  1. ],
       [ 5.7,  2.9,  4.2,  1.3,  1. ],
       [ 6.2,  2.9,  4.3,  1.3,  1. ],
       [ 5.1,  2.5,  3. ,  1.1,  1. ],
       [ 5.7,  2.8,  4.1,  1.3,  1. ],
       [ 6.3,  3.3,  6. ,  2.5,  2. ],
       [ 5.8,  2.7,  5.1,  1.9,  2. ],
       [ 7.1,  3. ,  5.9,  2.1,  2. ],
       [ 6.3,  2.9,  5.6,  1.8,  2. ],
       [ 6.5,  3. ,  5.8,  2.2,  2. ],
       [ 7.6,  3. ,  6.6,  2.1,  2. ],
       [ 4.9,  2.5,  4.5,  1.7,  2. ],
       [ 7.3,  2.9,  6.3,  1.8,  2. ],
       [ 6.7,  2.5,  5.8,  1.8,  2. ],
       [ 7.2,  3.6,  6.1,  2.5,  2. ],
       [ 6.5,  3.2,  5.1,  2. ,  2. ],
       [ 6.4,  2.7,  5.3,  1.9,  2. ],
       [ 6.8,  3. ,  5.5,  2.1,  2. ],
       [ 5.7,  2.5,  5. ,  2. ,  2. ],
       [ 5.8,  2.8,  5.1,  2.4,  2. ],
       [ 6.4,  3.2,  5.3,  2.3,  2. ],
       [ 6.5,  3. ,  5.5,  1.8,  2. ],
       [ 7.7,  3.8,  6.7,  2.2,  2. ],
       [ 7.7,  2.6,  6.9,  2.3,  2. ],
       [ 6. ,  2.2,  5. ,  1.5,  2. ],
       [ 6.9,  3.2,  5.7,  2.3,  2. ],
       [ 5.6,  2.8,  4.9,  2. ,  2. ],
       [ 7.7,  2.8,  6.7,  2. ,  2. ],
       [ 6.3,  2.7,  4.9,  1.8,  2. ],
       [ 6.7,  3.3,  5.7,  2.1,  2. ],
       [ 7.2,  3.2,  6. ,  1.8,  2. ],
       [ 6.2,  2.8,  4.8,  1.8,  2. ],
       [ 6.1,  3. ,  4.9,  1.8,  2. ],
       [ 6.4,  2.8,  5.6,  2.1,  2. ],
       [ 7.2,  3. ,  5.8,  1.6,  2. ],
       [ 7.4,  2.8,  6.1,  1.9,  2. ],
       [ 7.9,  3.8,  6.4,  2. ,  2. ],
       [ 6.4,  2.8,  5.6,  2.2,  2. ],
       [ 6.3,  2.8,  5.1,  1.5,  2. ],
       [ 6.1,  2.6,  5.6,  1.4,  2. ],
       [ 7.7,  3. ,  6.1,  2.3,  2. ],
       [ 6.3,  3.4,  5.6,  2.4,  2. ],
       [ 6.4,  3.1,  5.5,  1.8,  2. ],
       [ 6. ,  3. ,  4.8,  1.8,  2. ],
       [ 6.9,  3.1,  5.4,  2.1,  2. ],
       [ 6.7,  3.1,  5.6,  2.4,  2. ],
       [ 6.9,  3.1,  5.1,  2.3,  2. ],
       [ 5.8,  2.7,  5.1,  1.9,  2. ],
       [ 6.8,  3.2,  5.9,  2.3,  2. ],
       [ 6.7,  3.3,  5.7,  2.5,  2. ],
       [ 6.7,  3. ,  5.2,  2.3,  2. ],
       [ 6.3,  2.5,  5. ,  1.9,  2. ],
       [ 6.5,  3. ,  5.2,  2. ,  2. ],
       [ 6.2,  3.4,  5.4,  2.3,  2. ],
       [ 5.9,  3. ,  5.1,  1.8,  2. ]])



In [12]:

    
import numpy
import matplotlib.pyplot as plot

X = numpy.linspace(-6, 6, 1024)
Y1 = numpy.sinc(X)
Y2 = numpy.sinc(X) + 1

plot.plot(X, Y1, marker = 'o', color = '.75')
plot.plot(X, Y2, marker = 'o', color = 'k', markevery = 130)

plot.show()
len(X)



In [19]:

    
import numpy
import matplotlib.pyplot as plot

X = numpy.linspace(-6, 6, 1024)
Y = numpy.sinc(X)

plot.plot(X, Y,
          linewidth = 9.,
          color = 'k',
          markersize = 14,
          markeredgewidth = 2.5,
          markerfacecolor = '.88',
          markeredgecolor = 'k',
          marker = 'o',
          markevery = 150)

plot.show()



In [22]:

    
import numpy
import matplotlib.pyplot as plot

A = numpy.random.standard_normal((100, 2))
A += numpy.array((-1, -1))

B = numpy.random.standard_normal((100, 2))
B += numpy.array((1, 1))

plot.scatter(B[:,0], B[:,1], c = 'k', s = 200.)
plot.scatter(A[:,0], A[:,1], c = 'y', s = 25.)

plot.show()



In [28]:

    
import numpy
import matplotlib.pyplot as plot

M = numpy.random.standard_normal((1000, 2))
R = numpy.sum(M ** 2, axis = 1)

plot.scatter(M[:, 0], M[:, 1], c = 'b', marker = 's', s = 8. * R)
plot.show()
8*R









    












    Out[28]:





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         1.07651843e+01])



In [29]:

    
import numpy
import matplotlib as mpl
from matplotlib import pyplot as plot
#matplotlib 모듈은 중앙집중식 구성으로 동작하는 rc객체를 갖는다.모든 맷플롯립 객체는 rc 객체의 기본 설정을 가져온다.
#rc객체는 속성 집합과 관련된 값을 담는다.
mpl.rc('lines', linewidth = 2.)#lines.linewidth속성의 기본값을 2로 설정
mpl.rc('axes', facecolor = 'k', edgecolor = 'w')
mpl.rc('xtick', color = 'w')
mpl.rc('ytick', color = 'w')
mpl.rc('text', color = 'w')
mpl.rc('figure', facecolor = 'k', edgecolor ='w')
mpl.rc('axes', color_cycle = ('w', '.5', '.75'))
mpl.rc('savefig', facecolor = 'k', edgecolor = 'w')
X = numpy.linspace(0, 7, 1024)

plot.plot(X, numpy.sin(X))
plot.plot(X, numpy.cos(X))
plot.show()









    



c:\users\gin22\miniconda3\envs\ml_python\lib\site-packages\matplotlib\__init__.py:913: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.
  warnings.warn(self.msg_depr % (key, alt_key))



In [51]:

    
import numpy
import matplotlib.path as mpath
from matplotlib import pyplot as plot

shape_description = [
	( 1.,  2., mpath.Path.MOVETO),
	( 1.,  1., mpath.Path.LINETO),
	( 2.,  1., mpath.Path.LINETO),
	( 2., -1., mpath.Path.LINETO),
	( 1., -1., mpath.Path.LINETO),
  ( 1., -2., mpath.Path.LINETO),
	(-1., -2., mpath.Path.LINETO),
	(-1., -1., mpath.Path.LINETO),
	(-2., -1., mpath.Path.LINETO),
	(-2.,  1., mpath.Path.LINETO),
	(-1.,  1., mpath.Path.LINETO),
	(-1.,  2., mpath.Path.LINETO),
	( 0.,  0., mpath.Path.CLOSEPOLY),
]

u, v, codes = zip(*shape_description)
my_marker = mpath.Path(numpy.asarray((u, v)).T, codes)

data = numpy.random.rand(8, 8)
plot.scatter(data[:,0], data[:, 2], c = '.75', marker = my_marker, s = 400)
plot.show()
np.asarray((u,v)).T
data[:,1]









    












    Out[51]:





array([  8.34333802e-02,   5.78733248e-01,   1.34170012e-01,
         9.82457948e-01,   3.03953586e-01,   7.99542393e-01,
         3.20158919e-01,   3.90449919e-04])



In [94]:

    
import matplotlib.pyplot as plt
plt.rcdefaults()

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.path as mpath
import matplotlib.lines as mlines
import matplotlib.patches as mpatches
from matplotlib.collections import PatchCollection


def label(xy, text):
    y = xy[1] - 0.15  # shift y-value for label so that it's below the artist
    plt.text(xy[0], y, text, ha="center", family='sans-serif', size=14)


fig, ax = plt.subplots()
# create 3x3 grid to plot the artists
grid = np.mgrid[0.2:0.8:3j, 0.2:0.8:3j].reshape(2, -1).T

patches = []

# add a circle
circle = mpatches.Circle(grid[0], 0.1, ec="none")
patches.append(circle)
label(grid[0], "Circle")

# add a rectangle
rect = mpatches.Rectangle(grid[1] - [0.025, 0.05], 0.05, 0.1, ec="none")
patches.append(rect)
label(grid[1], "Rectangle")

# add a wedge
wedge = mpatches.Wedge(grid[2], 0.1, 30, 270, ec="none")
patches.append(wedge)
label(grid[2], "Wedge")

# add a Polygon
polygon = mpatches.RegularPolygon(grid[3], 5, 0.1)
patches.append(polygon)
label(grid[3], "Polygon")

# add an ellipse
ellipse = mpatches.Ellipse(grid[4], 0.2, 0.1)
patches.append(ellipse)
label(grid[4], "Ellipse")

# add an arrow
arrow = mpatches.Arrow(grid[5, 0] - 0.05, grid[5, 1] - 0.05, 0.1, 0.1, width=0.1)
patches.append(arrow)
label(grid[5], "Arrow")

# add a path patch
Path = mpath.Path
path_data = [
    (Path.MOVETO, [0.018, -0.11]),
    (Path.CURVE4, [-0.031, -0.051]),
    (Path.CURVE4, [-0.115,  0.073]),
    (Path.CURVE4, [-0.03 ,  0.073]),
    (Path.LINETO, [-0.011,  0.039]),
    (Path.CURVE4, [0.043,  0.121]),
    (Path.CURVE4, [0.075, -0.005]),
    (Path.CURVE4, [0.035, -0.027]),
    (Path.CLOSEPOLY, [0.018, -0.11])
    ]
codes, verts = zip(*path_data)
path = mpath.Path(verts + grid[6], codes)
patch = mpatches.PathPatch(path)
patches.append(patch)
label(grid[6], "PathPatch")

# add a fancy box
fancybox = mpatches.FancyBboxPatch(
    grid[7] - [0.025, 0.05], 0.05, 0.1,
    boxstyle=mpatches.BoxStyle("Round", pad=0.02))
patches.append(fancybox)
label(grid[7], "FancyBboxPatch")

# add a line
x, y = np.array([[-0.06, 0.0, 0.1], [0.05, -0.05, 0.05]])
line = mlines.Line2D(x + grid[8, 0], y + grid[8, 1], lw=5., alpha=0.3)
label(grid[8], "Line2D")

colors = np.linspace(0, 1, len(patches))
collection = PatchCollection(patches, cmap=plt.cm.hsv, alpha=0.3)
collection.set_array(np.array(colors))
ax.add_collection(collection)
ax.add_line(line)

plt.subplots_adjust(left=0, right=1, bottom=0, top=1)
plt.axis('equal')
plt.axis('off')

plt.show()



In [ ]: