import numpy as np
import matplotlib.pyplot as plt

x = np.linspace(0, 4 * np.pi, 200)
fig = plt.figure(figsize=(6,3), dpi=100)
ax = fig.add_subplot(111)
ax.plot(x, np.sin(x), 'r-', label='sin(x)')
ax.plot(x, np.cos(x), 'b--', label='cos(x)')
ax.set_xlim(0, 4 * np.pi)
ax.set_title('harmooniline võnkumine')
ax.set_xlabel('aeg')
ax.set_ylabel('signaal')
ax.legend(loc='lower left')
ax.grid(color='gray', linestyle=':')
plt.show()

from matplotlib.pyplot import *

figure(figsize=(6,3), dpi=100)
subplot(111) # võib ära jätta, meil on ainult üks teljestik
plot(x, np.sin(x), 'r-', label='sin(x)')
plot(x, np.cos(x), 'b--', label='cos(x)')
xlim(0, 4 * np.pi)
title('harmooniline võnkumine')
xlabel('aeg')
ylabel('signaal')
legend(loc='lower left')
grid(color='gray', ls=':')
show()

import locale
locale.setlocale(locale.LC_NUMERIC, 'et-EE') # eestikeelsel graafikul pigem koma punkti asemel

style.use({
    #'figure.figsize': (6,4), # joonise suurus tollides
    'figure.dpi': 120, # lahutusvõime (mitu punkti tolli kohta)
    #'savefig.dpi': 300, # lahutusvõime joonise salvestamisel
    #'font.size': 12, # teksti suurus punktides
    'figure.titlesize': 'medium', # siin ja edaspidi suhteline suurus
    #'axes.titlesize': 'medium',
    'legend.fontsize': 'small',
    #'figure.facecolor': 'white', # joonise tausta värv
    #'lines.linewidth': 2, # graafiku joone jämedus
    #'lines.markeredgewidth': 0,
    #'lines.markersize': 5, # sümboli suurus
    #'axes.prop_cycle': cycler('color', 'rbgm'), # andmeseeriate värvused
    #'patch.facecolor': 'black', # noolte jms värvus
    #'patch.edgecolor': 'black',
    #'patch.linewidth': 0.8, # noolte jms joone jämedus
    #'mathtext.fontset': 'cm', # matemaatikašrift
    'axes.formatter.use_locale': True,
    'axes.formatter.use_mathtext': True
})

θ = np.linspace(0, 2 * np.pi, 300)
r1 = np.cos(3 * θ)**2
r2 = 2 - np.sin(6 * θ) - 0.5 * np.cos(30 * θ)

figure(figsize=(7,3))
subplot(121, polar=True)
plot(θ, r1, 'b-')
ylim(0, 1)
subplot(122, polar=True)
plot(θ, r2, 'b-')
ylim(0, 3.5)
subplots_adjust(wspace=0.3)
show()

fig, (ax1, ax2) = subplots(1, 2, figsize=(7,3), subplot_kw=dict(polar=True))
ax1.plot(θ, r1, 'b-')
ax1.set_ylim(0, 1)
ax2.plot(θ, r2, 'b-')
ax2.set_ylim(0, 3.5)
subplots_adjust(wspace=0.3)
show()

figure(figsize=(4,4))
polar(θ, r2, 'b-')
ylim(0, 3.5)
show()

# "eksperimentaalsed" andmepunktid
x1 = [1, 2, 3, 4, 5]
y1 = [2.5, 8, 20, 56, 173]

# teoreetiline sõltvus (pidevjoon) peenema sammuga
x2 = np.arange(0, 5.5, 0.1)
y2 = np.exp(x2)

figure(figsize=(7,3))
subplot(121)
plot(x1, y1, "b.", label="katse")
plot(x2, y2, "r-", label="teooria")
grid()
legend()
subplot(122)
plot(x1, y1, "b.")
plot(x2, y2, "r-")
yscale('log')
grid()
show()

figure(figsize=(7,3))
subplot(121)
plot(x1, y1, 'b.')
plot(x2, y2, 'r-')
gca().xaxis.set_major_locator( MultipleLocator(1) )
gca().xaxis.set_minor_locator( MultipleLocator(0.5) )
gca().yaxis.set_major_locator( MultipleLocator(50) )
gca().yaxis.set_minor_locator( MultipleLocator(25) )
grid(which='both', ls=':')

subplot(122)
plot(x1, y1, 'b.')
plot(x2, y2, 'r-')
yscale('log')
xticks( np.arange(0, 6, 1) )
xticks( np.arange(0.5, 6.5, 1), minor=True )
asukohad = 1, 3, 10, 30, 100
yticks( asukohad, asukohad )  # kriipsud ja nende tähised
grid(which='both', ls=':')
show()

from scipy.constants import micro, c, h, k

# x on lainepikkus või lainepikkuste vektor [µm], T on temperatuur [K]
def planck(x, T):
    x = x * micro
    a = np.exp(h * c / (x * k * T)) - 1
    return 2 * np.pi * h * c**2 / x**5 / a * micro

x = np.geomspace(0.1, 100, 200) # 0.1 kuni 100 mikromeetrit, 200 punkti

gca().set_prop_cycle( cycler('color', 'bgr') )
for T in (400, 1000, 3000):
    plot(x, planck(x, T), '-', label='T=%.0f K' % T)

xlabel('lainepikkus (µm)')
ylabel('kiirguse intensiivsus\nW m$^{-2}$ µm$^{-1}$')
ylim(ymin=1)
xscale('log')
yscale('log')
legend()
grid(which='both', ls=':')
show()

doppler = lambda x: np.exp(-4 * np.log(2) * x**2 )
lorentz = lambda x: 1 / (4 * x**2 + 1.0)
x = np.linspace(-2, 2, 200)

plot(x, doppler(x), 'r-', label='doppler')
plot(x, lorentz(x), 'b-', label='lorentz')
xlim(-2, 2)
ylim(0, 1)
xlabel('sagedus $\omega$')
ylabel('signaal')
axhline( 0.5, color='black', linewidth=0.8, linestyle='--')
axvline(-0.5, color='black', linewidth=0.8, linestyle='--')
axvline( 0.5, color='black', linewidth=0.8, linestyle='--')

text(-0.55, 0.51, 'poolkõrgus', fontsize='small', va='bottom', ha='right')
annotate('', xy=(0.5, 0.4), xytext=(-0.5, 0.4), arrowprops=dict(arrowstyle='<|-|>'))
text(0, 0.38, 'FWHM=1', fontsize='small', va='top', ha='center')
annotate('$(4+\omega^2)^{-1}$', xy=(0.69, 0.34), xytext=(1, 0.63),
             va='center', ha='center', arrowprops=dict(arrowstyle='-|>'))
annotate('$e^{-4\ln(2)\omega^2}$', xy=(0.94, 0.082), xytext=(1.25, 0.4),
             va='center', ha='center', arrowprops=dict(arrowstyle='-|>'))
legend()
savefig('test.pdf', bbox_inches='tight')
show()

def axis_text(x, y, s, bs='round', fc=None, ec='none', alpha=None, **kwargs):
    annotate(s, (x, y), xycoords='axes fraction',
             bbox=dict(boxstyle=bs, fc=fc, ec=ec, alpha=alpha), **kwargs)

n = 20
x = np.linspace(0, 5, n)

np.random.seed(42)
y = 3 * x + 7 + 2 * np.random.randn(n)
a, b = np.polyfit(x, y, 1)
plot(x, y, 'b.')
plot(x, a * x + b, 'r-')
axis_text(0.05, 0.95, f'$y={a:.3f}x+{b:.3f}$', fc='white', alpha=0.5, va='top')
grid()
show()

from scipy.special import erf

x = np.linspace(-6, 6, 200)
D = np.exp(-x**2/2) / np.sqrt(2 * np.pi)
F = (1 + erf(x / np.sqrt(2))) / 2

figure(figsize=(6,3))
axes( (0.15, 0.10, 0.80, 0.85) )  # left, bottom, width, height
plot(x, F, 'r-')
xlim(-6, 4)
ylim(ymin=0)
gca().xaxis.set_major_locator( MultipleLocator(2) )
gca().xaxis.set_minor_locator( MultipleLocator(0.5) )
gca().yaxis.set_major_locator( MultipleLocator(0.4) )
gca().yaxis.set_minor_locator( MultipleLocator(0.1) )
xlabel('juhuslik muutuja')
ylabel('kumulatiivne tõenäosus')
grid(which='both', ls=':')
title('normaaljaotus')
axes( (0.27, 0.48, 0.32, 0.43) )  # left, bottom, width, height
plot(x, D, 'b-')
xlim(-3, 3)
ylim(ymin=0)
gca().xaxis.set_major_locator( MultipleLocator(1) )
gca().xaxis.set_minor_locator( MultipleLocator(0.5) )
gca().yaxis.set_major_locator( MultipleLocator(0.2) )
gca().yaxis.set_minor_locator( MultipleLocator(0.05) )
ylabel('tõenäosustihedus')
grid(which='both', ls=':')
show()

def harrow(x, y, p): # horis. nool pikkusega p alates punktist (x,y)
    annotate('', xy=(x + p, y), xytext=(x, y), arrowprops=dict(arrowstyle='-|>') )

figure(figsize=(5, 3.5))
plot(x, D, 'b-')
fill_between(x, D, 0, where=abs(x)<=1, color='green', alpha=0.5)
xlim(-4, 4)
ylim(0, 0.45)
gca().xaxis.set_major_locator( MultipleLocator(1) )
gca().xaxis.set_minor_locator( MultipleLocator(0.5) )
gca().yaxis.set_major_locator( MultipleLocator(0.1) )
gca().yaxis.set_minor_locator( MultipleLocator(0.02) )
xlabel('juhuslik muutuja')
ylabel('tõenäosustihedus')
harrow(-1.2, 0.2, -1)
grid(which='both', ls=':')
twinx()
plot(x, F, 'r-')
ylim(0, 1.125)
gca().yaxis.set_major_locator( MultipleLocator(0.25) )
gca().yaxis.set_minor_locator( MultipleLocator(0.05) )
ylabel('kumulatiivne tõenäosus')
title('normaaljaotus')
harrow(1.1, 0.85, 1)
show()

figure(figsize=(5,6))
ax = axes( (0.15, 0.5, 0.80, 0.40) )  # left, bottom, width, height
setp(ax.get_xticklabels(), visible=False)
plot(x, D, 'b-')
ylim(ymin=0)
ylabel('tõenäosustihedus')
grid()
axes( (0.15, 0.1, 0.80, 0.40), sharex=ax)
plot(x, F, 'r-')
xlabel('juhuslik muutuja')
ylabel('kumulatiivne tõenäosus')
xlim(-4, 4)
ylim(0, 1.1)
grid()
show()

# juhuslike arvude järjendid
np.random.seed(42)
n = 10000 # elementide arv
a = np.random.uniform(-0.5, 0.5, n)
b = np.random.randn(n)

# teoreetilise kõvera jaoks 400 punkti
x = np.linspace(-3, 3, 400)

figure(figsize=(8,3))
subplot(121)
hist(a, bins=20, range=(-0.5,0.5), facecolor='g', edgecolor='k', alpha=0.7)
y = np.where(abs(x) <= 0.5, n * 0.05, 0.0)  # vahemik 1 ühikut jagatud 20 osaks = 0.05
plot(x, y, 'r-')
xlim(-0.7,0.7)
xlabel('juhusliku suuruse väärtus')
ylabel('sündmuste arv')
title('ühtlane jaotus $-0.5\ldots 0.5$')

subplot(122)
hist(b, 30, range=(-3,3), facecolor='g', edgecolor='k', alpha=0.7)
y = n * 0.2 * np.exp(-x**2 / 2) / np.sqrt(2 * np.pi)  # vahemik 6 ühikut jagatud 30 osaks = 0.2
plot(x, y, 'r-')
xlabel('juhusliku suuruse väärtus')
ylabel('sündmuste arv')
title('normaaljaotus $\mu=0$, $\sigma=1$')
show()

from mpl_toolkits.mplot3d import Axes3D
from matplotlib import cm

m, δ = 2.5, 0.1
n = int(round(2 * m/δ)) + 1
x = np.linspace(-m, m, n)
y = np.linspace(-m, m, n)
X, Y = np.meshgrid(x, y)
Z = np.sin(X + Y**2)

fig = figure(figsize=(7,4))
ax = fig.add_subplot(111, projection='3d')
pind = ax.plot_surface(X, Y, Z, cmap=cm.rainbow, rstride=1, cstride=1,
                       linewidth=0.5, edgecolors='black')

ax.set_xlim(-m, m)
ax.set_ylim(-m, m)
ax.set_zlim(-2, 2)

ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_zlabel('Z')

ax.view_init(40, 70)
fig.colorbar(pind, aspect=10)
show()

%matplotlib notebook

fig = figure(figsize=(5,4))
ax = fig.add_subplot(111, projection='3d')
ax.plot_surface(X, Y, Z, cmap=cm.rainbow, rstride=1, cstride=1, linewidth=0.5, edgecolors='black')
ax.set_xlim(-m, m)
ax.set_ylim(-m, m)
ax.set_zlim(-2, 2)
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_zlabel('Z')
show()

import matplotlib.animation

fig = figure(figsize=(5,4))
ax = fig.add_subplot(111, projection='3d')
ax.plot_surface(X, Y, Z, cmap=cm.rainbow, rstride=1, cstride=1, linewidth=0.5, edgecolors='black')
ax.set_xlim(-m, m)
ax.set_ylim(-m, m)
ax.set_zlim(-2, 2)
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_zlabel('Z')

def animate(i):
    ax.view_init(40, 2 * i)

ani = matplotlib.animation.FuncAnimation(fig, animate, frames=180, interval=100)
show()

kelluke = lambda x, y: np.exp(-0.5 * (x**2 + y**2))

xmin, xmax = -3, 1.5    # neid parameetreid läheb ka hiljem tarvis
ymin, ymax = -1.5, 3.5  # pildi x-y skaala seadistamiseks
Δx, Δy = 0.1, 0.1
nx = int(round((xmax - xmin) / Δx))
ny = int(round((ymax - ymin) / Δy))
x = np.linspace(xmin + Δx, xmax - Δx, nx)
y = np.linspace(ymax - Δy, ymin + Δy, ny)
X, Y = np.meshgrid(x, y)
Z = kelluke(X, Y - 2.0) + 0.55 * kelluke(X, Y + 0.5) + 0.7 * kelluke(X + 2.0, Y - 0.5)

fig = figure(figsize=(6,4))
pind = imshow(Z, cmap=cm.rainbow, interpolation = 'bicubic', vmin=0)
xlabel('X')
ylabel('Y')
värviskaala = fig.colorbar(pind)
värviskaala.set_label('Z')
show()

fig = figure(figsize=(5,4))
pind = pcolormesh(Z, cmap=cm.rainbow, shading='gouraud', vmin=0)
xlabel('X')
ylabel('Y')
värviskaala = fig.colorbar(pind)
värviskaala.set_label('Z')
show()

fig = figure(figsize=(6,4))
pind = imshow(Z, cmap=cm.gnuplot2, interpolation = 'bicubic',
              extent=(xmin, xmax, ymin, ymax) )  # left, right, bottom, top
fig.colorbar(pind)
show()

figure(figsize=(3,3))
imshow(rand(4,4), cmap=cm.rainbow, extent=(0, 4, 2, 6) )  # left, right, bottom, top
show()

fig=figure(figsize=(9,4))
subplot(121)
kont = contour(X, Y, Z, colors='blue')
clabel(kont, inline=1, fontsize=10, fmt='%.2f')
subplot(122)
contourf(X, Y, Z, cmap=cm.gnuplot2)
xlabel('X')
ylabel('Y')
show()

pilt = imread('cat.jpg')
print('mõõdud:', pilt.shape)
print('andmetüüp:', pilt.dtype)

imshow(pilt)
gca().axis('off')
show()

mõõdud: (1080, 1920, 3)
andmetüüp: uint8

Graafikute kujundamine Pythonis paketiga Matplotlib¶