PL03-Topic02, Matplotlib

% matplotlib inline
% matplotlib qt5

import matplotlib.pyplot as plt
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])
        
plt.plot(X, Y)
plt.show()

import numpy as np
from matplotlib import pyplot as plt

X = np.linspace(-10, 10, 1024)
Y = np.sinc(X)

plt.plot(X, Y)
plt.savefig('sinc.png', c = 'k')
plt.show()

# Rendering a figure to a PNG file with a transparent background
import numpy as np
import matplotlib.pyplot as plt
X = np.linspace(-10, 10, 1024)
Y = np.sinc(X)
plt.plot(X, Y, c = 'k')
plt.savefig('sinc.png', transparent = True)

import numpy as np
import matplotlib.pyplot as plt

name_list = ('Omar', 'Serguey', 'Max', 'Zhou', 'Abidin')
value_list = np.random.randint(99, size=len(name_list))
pos_list = np.arange(len(name_list))

plt.bar(pos_list, value_list, alpha = .75, color = '.75', align ='center')
plt.xticks(pos_list, name_list)
plt.savefig('bar.png', transparent = True)

import numpy as np
from matplotlib import pyplot as plt

X = np.linspace(-10, 10, 1024)
Y = np.sinc(X)

plt.plot(X, Y)
plt.savefig('sinc.png', dpi = 300)

import numpy as np
import matplotlib.pyplot as plt

theta = np.linspace(0, 2 * np.pi, 8)
points = np.vstack((np.cos(theta), np.sin(theta))).transpose()

plt.figure(figsize=(4., 4.))
plt.gca().add_patch(plt.Polygon(points, color = '.75'))
plt.grid(True)
plt.axis('scaled')
plt.savefig('polygon.png', dpi = 128)

import numpy as np
from matplotlib import pyplot as plt

X = np.linspace(-10, 10, 1024)
Y = np.sinc(X)

plt.plot(X, Y)
plt.savefig('sinc.pdf')

import numpy as np
from matplotlib import pyplot as plt
from matplotlib.backends.backend_pdf import PdfPages

# Generate the data
data = np.random.randn(15, 1024)

# The PDF document
pdf_pages = PdfPages('barcharts.pdf')

# Generate the pages
plots_count = data.shape[0]
plots_per_page = 5
pages_count = int(np.ceil(plots_count / float(plots_per_page)))
grid_size = (plots_per_page, 1)
for i, samples in enumerate(data):
    # Create a figure instance (ie. a new page) if needed
    if i % plots_per_page == 0:
        fig = plt.figure(figsize=(8.27, 11.69), dpi=100)
    # Plot one bar chart
    plt.subplot2grid(grid_size, (i % plots_per_page, 0))
    plt.hist(samples, 32, normed=1, facecolor='.5', alpha=0.75)
    # Close the page if needed
    if (i + 1) % plots_per_page == 0 or (i + 1) == plots_count:
        plt.tight_layout()
        pdf_pages.savefig(fig)
        
# Write the PDF document to the disk
pdf_pages.close()

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

x = np.linspace(1,10,10)
y = np.linspace(2,20,10)

plt.plot(x,y, 'rs--')
plt.show()

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

x = np.linspace(1,10,100)
y = x**4 + x

plt.plot(x,y)
plt.show()

import matplotlib.pyplot as plt

plt.style.use('ggplot')

print(plt.style.available)

import platform
import sys
import matplotlib as mpl
import matplotlib.pyplot as plt

print('* OS :', platform.platform())
print('* Python version : ', sys.version_info)
print('* matplotlib version : ', mpl.__version__)
print('* matplotlib setup : ', mpl.__file__)
print('* matplotlib config : ', mpl.get_configdir())
print('* matplotlib cache : ', mpl.get_cachedir())
print('* matplotlib rc : ', mpl.matplotlib_fname())
print('  - figsize : ', plt.rcParams["figure.figsize"])
print('  - grid : ', plt.rcParams["axes.grid"])
print('  - labelsize : ', plt.rcParams['axes.labelsize'])
print('  - ticksize_x : ', plt.rcParams['xtick.labelsize'])
print('  - ticksize_y : ', plt.rcParams['ytick.labelsize'])
print('  - fontsize : ', plt.rcParams['font.size'] )
print('  - fontfamily : ', plt.rcParams['font.family'] )

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.font_manager as fm

data = np.random.randint(-100, 100, 50).cumsum()
font_path = 'C:/Windows/Fonts/EBS훈민정음R.ttf'
fontprop = fm.FontProperties(fname=font_path, size=18)

plt.ylabel('가격', fontproperties=fontprop)
plt.title('가격변동 추이', fontproperties=fontprop)
plt.plot(range(50), data, 'r')
plt.show()

import matplotlib.pyplot as plt
import matplotlib.font_manager as fm

#print([f.name for f in fm.fontManager.ttflist])
plt.rcParams["font.family"] = 'NanumBarunGothic.ttf'

import numpy as np
import matplotlib.pyplot as plt

X = np.linspace(-4, 4, 1024)
Y = .25 * (X + 4.) * (X + 1.) * (X - 2.)

plt.title('A polynomial')
plt.plot(X, Y, c = 'k')
plt.show()

import numpy as np
import matplotlib.pyplot as plt

X = np.linspace(-4, 4, 1024)
Y = .25 * (X + 4.) * (X + 1.) * (X - 2.)

plt.title('$f(x)=\\frac{1}{4}(x+4)(x+1)(x-2)$')
plt.plot(X, Y, c = 'k')
plt.show()

import numpy as np
import matplotlib.pyplot as plt

X = np.linspace(-4, 4, 1024)
Y = .25 * (X + 4.) * (X + 1.) * (X - 2.)

plt.title('Power curve for airfoil KV873')
plt.xlabel('Air speed')
plt.ylabel('Total drag')
plt.plot(X, Y, c = 'k')
plt.show()

import numpy as np
import matplotlib.pyplot as plt

X = np.linspace(-4, 4, 1024)
Y = .25 * (X + 4.) * (X + 1.) * (X - 2.)

plt.plot(X, Y, c = 'k')
plt.text(-0.5, -0.25, 'Brackmard minimum')
plt.show()

import matplotlib.pyplot as plt

fig, ax = plt.subplots(1,1)
ax.text(0.05, 0.05, 
        "hello\nhello", 
        transform=ax.transAxes, 
        fontsize=10,
        horizontalalignment='left',
        verticalalignment='bottom',
        bbox=dict(boxstyle='round',
                  facecolor='wheat',
                  alpha=0.5))

# Bounding box control
import numpy as np
import matplotlib.pyplot as plt

X = np.linspace(-4, 4, 1024)
Y = .25 * (X + 4.) * (X + 1.) * (X - 2.)

box = {
 'facecolor' : '.75',
 'edgecolor' : 'k',
 'boxstyle' : 'round'
}
plt.text(-0.5, -0.20, 'Brackmard minimum', bbox = box)
plt.plot(X, Y, c='k')
plt.show()

import matplotlib.pyplot as plt

plt.annotate(s='', xy=(1,1), xytext=(0,0), arrowprops=dict(arrowstyle='<->'))
plt.show()

import numpy as np
import matplotlib.pyplot as plt

X = np.linspace(-4, 4, 1024)
Y = .25 * (X + 4.) * (X + 1.) * (X - 2.)

plt.annotate('Brackmard minimum',
             ha = 'center', va = 'bottom',
             xytext = (-1.5, 3.),
             xy = (0.75, -2.7),
             arrowprops = { 'facecolor' : 'black', 
                            'edgecolor' : 'black',
                            'shrink' : 0.05 })
plt.plot(X, Y)
plt.show()

import numpy as np
import matplotlib.pyplot as plt

X = np.linspace(0, 6, 1024)
Y1 = np.sin(X)
Y2 = np.cos(X)

plt.xlabel('X')
plt.ylabel('Y')
plt.plot(X, Y1, c = 'k', lw = 3., label = 'sin(X)')
plt.plot(X, Y2, c = '.5', lw = 3., ls = '--', label = 'cos(X)')
plt.legend()
plt.show()

import numpy as np
import matplotlib.pyplot as plt

X = np.linspace(-4, 4, 1024)
Y = .25 * (X + 4.) * (X + 1.) * (X - 2.)

plt.plot(X, Y, c = 'k')
plt.grid(True, lw = 2, ls = '--', c = '.75')
plt.show()

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

# numpy plot
# grid 설정 : grid
x = np.linspace(-np.pi, np.pi, 50)
y = np.cos(x)

plt.title("Plot")
plt.plot(x, y, 'rs--')
plt.xticks([-np.pi, -np.pi / 2, 0, np.pi / 2, np.pi],
           [r'$-\pi$', r'$-\pi/2$', r'$0$', r'$+\pi/2$', r'$+\pni$'])
plt.yticks([-1, 0, 1], ["Low", "Zero", "High"])
plt.grid(True)

plt.show()

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

x = np.array([165., 180., 190., 188., 163., 178., 177., 172., 164., 182., 143.,
              163., 168., 160., 172., 165., 208., 175., 181., 160., 154., 169.,
              120., 184., 180., 175., 174., 175., 160., 155., 156., 161., 184.,
              171., 150., 154., 153., 177., 184., 172., 156., 153., 145., 150.,
              175., 165., 190., 156., 196., 161., 185., 159., 153., 155., 173.,
              173., 191., 162., 152., 158., 190., 136., 171., 173., 146., 158.,
              158., 159., 169., 145., 193., 178., 160., 153., 142., 143., 172.,
              170., 130., 165., 177., 190., 164., 167., 172., 160., 184., 158.,
              152., 175., 158., 156., 171., 164., 165., 160., 162., 140., 172.,
              148.])

sns.set();
plt.hist(x)
plt.axhline(y=5, ls="--", c="r", linewidth=2, label="Quartile 50%")
plt.axvline(x=165, ls="--", c="y", linewidth=2, label="sample median")
plt.legend()

import matplotlib.pyplot as plt
import numpy as np

X = np.linspace(-4, 4, 1024)
Y = .25 * (X + 4.) * (X + 1.) * (X - 2.)

plt.plot(X, Y, c = 'k')
plt.gca().add_line(plt.Line2D((0, 0), (16, 0), c='.5'))
plt.grid()

import matplotlib.pyplot as plt

N = 16
for i in range(N):
     plt.gca().add_line(plt.Line2D((0, i), (N - i, 0), color = '.75'))
     
plt.grid(True)
plt.axis('scaled')
plt.show()

import matplotlib.patches as patches
import matplotlib.pyplot as plt

# Circle
shape = patches.Circle((0, 0), radius = 1., color = '.75')
plt.gca().add_patch(shape)

# Rectangle
shape = patches.Rectangle((2.5, -.5), 2., 1., color = '.75')
plt.gca().add_patch(shape)

# Ellipse
shape = patches.Ellipse((0, -2.), 2., 1., angle = 45., color =
 '.75')
plt.gca().add_patch(shape)

# Fancy box
shape = patches.FancyBboxPatch((2.5, -2.5), 2., 1., boxstyle =
 'sawtooth', color = '.75')
plt.gca().add_patch(shape)

# Display all
plt.grid(True)
plt.axis('scaled')
plt.show()

# Working with polygons
import numpy as np
import matplotlib.patches as patches
import matplotlib.pyplot as plt

theta = np.linspace(0, 2 * np.pi, 8)
points = np.vstack((np.cos(theta), np.sin(theta))).transpose()

plt.gca().add_patch(patches.Polygon(points, color = '.75'))
plt.grid(True)
plt.axis('scaled')
plt.show()

# Working with path attributes
import numpy as np
import matplotlib.patches as patches
import matplotlib.pyplot as plt

theta = np.linspace(0, 2 * np.pi, 6)
points = np.vstack((np.cos(theta), np.sin(theta))).transpose()

plt.gca().add_patch(plt.Circle((0, 0), radius = 1., color =
 '.75'))
plt.gca().add_patch(plt.Polygon(points, closed=None, fill=None,
 lw = 3., ls = 'dashed', edgecolor = 'k'))
plt.grid(True)
plt.axis('scaled')
plt.show()

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.patches import Ellipse

delta = 45.0  # degrees

angles = np.arange(0, 360 + delta, delta)
ells = [Ellipse((1, 1), 4, 2, a) for a in angles]

a = plt.subplot(111, aspect='equal')

for e in ells:
    #e.set_clip_box(a.bbox)
    e.set_alpha(0.1)
    a.add_artist(e)

plt.xlim(-2, 4)
plt.ylim(-1, 3)

plt.show()

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

np.random.seed(0)

plt.title("Plot")
plt.plot(np.random.randn(100))
plt.show()

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

np.random.seed(0)
plt.figure(figsize=(10, 2))    # Simultaneously resize graph while defining objects

plt.title("Plot")
plt.plot(np.random.randn(100))
plt.show()

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

f1 = plt.figure(1)

plt.title("Plot")
plt.plot([1, 2, 3, 4], 'ro:')

f2 = plt.gcf()
print(f1, id(f1))             # identification1 for object directly using id
print(f2, id(f2))             # identification2 for object using gcf and id(in principle)
plt.show()

%matplotlib inline
import matplotlib.pyplot as plt

plt.plot(1,1,marker="o")
plt.show()

%matplotlib inline
import matplotlib.pyplot as plt

plt.plot([1,2,3],[10,5,8],marker="o", lw=0)
plt.show()

%matplotlib inline
import matplotlib.pyplot as plt

plt.plot([1,4,9,16])
plt.show()

%matplotlib inline
import matplotlib.pyplot as plt

plt.plot([10,20,30,40],[1,4,9,16])
plt.show()

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

x = np.linspace(-np.pi, np.pi, 50)
y = np.cos(x)

plt.plot(x, y)
plt.show()

import numpy as np
import matplotlib.pyplot as plt

data = np.random.rand(1024, 2)

plt.scatter(data[:,0], data[:,1])
plt.show()

import numpy as np
import matplotlib.pyplot as plt

A = np.random.standard_normal((100, 2))
A += np.array((-1, -1)) # Center the distrib. at <-1, -1>

B = np.random.standard_normal((100, 2))
B += np.array((1, 1)) # Center the distrib. at <1, 1>

plt.scatter(A[:,0], A[:,1], color = '.25')
plt.scatter(B[:,0], B[:,1], color = '.75')
plt.show()

import numpy as np
import matplotlib.pyplot as plt

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

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

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

color_set = ('.00', '.50', '.75')
color_list = [color_set[int(label)] for label in data[:,4]]

plt.scatter(data[:,0], data[:,1], color = color_list)
plt.show()

import numpy as np
import matplotlib.pyplot as plt

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

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

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

N = 256
angle = np.linspace(0, 8 * 2 * np.pi, N)
radius = np.linspace(.5, 1., N)

X = radius * np.cos(angle)
Y = radius * np.sin(angle)

plt.scatter(X, Y, c = angle, cmap = cm.hsv)
plt.show()

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

x = np.linspace(0.1, 2 * np.pi, 10)
plt.title("Stem Plot")
plt.stem(x, np.cos(x), '-.')
plt.show()

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

def f(x, y):
    return (1 - x / 2 + x ** 5 + y ** 3) * np.exp(-x ** 2 - y ** 2)

n = 256
x = np.linspace(-3, 3, n)
y = np.linspace(-3, 3, n)
XX, YY = np.meshgrid(x, y)
ZZ = f(XX, YY)

plt.title("Contour plots")
plt.contourf(XX, YY, ZZ, alpha=.75, cmap='jet')   # inside color
plt.contour(XX, YY, ZZ, colors='black')           # boundary line
plt.show()

%matplotlib inline
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import numpy as np

X = np.arange(-4, 4, 0.25)
Y = np.arange(-4, 4, 0.25)
XX, YY = np.meshgrid(X, Y)
RR = np.sqrt(XX**2 + YY**2)
ZZ = np.sin(RR)

fig = plt.figure()
ax = Axes3D(fig)
ax.set_title("3D Surface Plot")
ax.plot_surface(XX, YY, ZZ, rstride=1, cstride=1, cmap='hot')
plt.show()

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

np.random.seed(0)
x = np.random.randn(1000)
plt.title("Histogram")
arrays, bins, patches = plt.hist(x, bins=10)   
   # arrays is the count in each bin, 
   # bins is the lower-limit of the bin(Interval to aggregate data)
plt.show()

arrays    # arrays is the count in each bin

bins    # bins is the lower-limit of the bin

import matplotlib.pyplot as plt

data = [5., 25., 50., 20.]
plt.bar(range(len(data)), data)
plt.show()

# Horizontal bars
import matplotlib.pyplot as plt

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

plt.barh(range(len(data)), data)
plt.show()

import numpy as np
import matplotlib.pyplot as plt

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()

import matplotlib.pyplot as plt

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 = A)
plt.show()