Matplotlib散布図 (scatter)

# -*- coding:utf-8 -*-
import numpy as np
import matplotlib.pyplot as plt

# ----- 条件設定 -----
N = 1000                # サンプル数

# ----- 散布図用のデータの作成 -----
x0 = np.random.normal(0, 1, N)
y0 = np.random.normal(0, 1, N)-x0**2+7
x =  x0*np.cos(np.pi/4) + y0*np.sin(np.pi/4)
y = -x0*np.sin(np.pi/4) + y0*np.cos(np.pi/4)

# ----- プロット(散布図)作成 -----
plt.rcParams["font.size"] = 18
fig    = plt.figure()
fig.subplots_adjust(bottom=0.12)
ax = fig.add_subplot(1,1,1)
ax.set_xlabel("$x$", fontsize=24, fontname='serif')
ax.set_ylabel("$y$", fontsize=24, fontname='serif')
ax.tick_params(axis='both', length=10,  which='major')
ax.tick_params(axis='both', length=5,  which='minor')
ax.set_xlim(-10, 10)
ax.set_ylim(-10, 10)
ax.minorticks_on()
ax.scatter(x, y, s=10, marker='x', edgecolor="b")

plt.show()
fig.savefig('scatter.pdf', orientation='portrait',
            transparent=False, bbox_inches=None,
            frameon=None)
fig.savefig('scatter.svg', orientation='portrait',
            transparent=False, bbox_inches=None,
            frameon=None)
fig.clf()

# -*- coding:utf-8 -*-
import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import gaussian_kde

# ----- 条件設定 -----
N = 10000                # サンプル数

# ----- 散布図用のデータの作成 -----
x0 = np.random.normal(0, 1, N)
y0 = np.random.normal(0, 1, N)-x0**2+7
x =  x0*np.cos(np.pi/4) + y0*np.sin(np.pi/4)
y = -x0*np.sin(np.pi/4) + y0*np.cos(np.pi/4)

# ----- データ点の密度の計算 ----
xy = np.vstack([x,y])
z = gaussian_kde(xy)(xy)

# ----- 密度の高い点を最後にプロットするように密度でソート ----
idx = z.argsort()
x, y, z = x[idx], y[idx], z[idx]

# ----- プロット(散布図)作成 -----
plt.rcParams["font.size"] = 18
fig    = plt.figure()
fig.subplots_adjust(bottom=0.12)
ax = fig.add_subplot(1,1,1)
ax.set_xlabel("$x$", fontsize=24, fontname='serif')
ax.set_ylabel("$y$", fontsize=24, fontname='serif')
ax.tick_params(axis='both', length=10,  which='major')
ax.tick_params(axis='both', length=5,  which='minor')
ax.set_xlim(-10, 10)
ax.set_ylim(-10, 10)
ax.minorticks_on()
ax.scatter(x, y, c=z, marker='o', s=10, edgecolor='')

plt.show()
fig.savefig('scatter.pdf', orientation='portrait',
            transparent=False, bbox_inches=None,
            frameon=None)
fig.savefig('scatter.svg', orientation='portrait',
            transparent=False, bbox_inches=None,
            frameon=None)
fig.clf()

# -*- coding:utf-8 -*-
import numpy as np
from numpy.random import multivariate_normal as normal
import matplotlib.pyplot as plt
from matplotlib.patches import Ellipse
import matplotlib.transforms as transforms

# -------------------------------------------------------------
# https://matplotlib.org/devdocs/gallery/statistics/
#         confidence_ellipse.html
# 入力引数
#    x, y:  入力データ 配列, shape (n, )
#    ax:    matplotlib.axes.Axes
#    n_std: float  標準偏差の n_std 倍のプロットを作成
#    **kwargs: `~matplotlib.patches.Ellipse` が引き継ぐ
# 戻り値
#    matplotlib.patches.Ellipse
# -------------------------------------------------------------
def confidence_ellipse(x, y, ax, n_std=3.0, facecolor='none',
                       **kwargs):
    ''' 分布の楕円を作成する． '''
    if x.size != y.size:
        raise ValueError("x and y must be the same size")

    cov = np.cov(x, y)
    mean_x, mean_y  = np.mean(x), np.mean(y)
    pearson = cov[0, 1]/np.sqrt(cov[0, 0] * cov[1, 1])

    ell_radius_x = np.sqrt(1 + pearson)
    ell_radius_y = np.sqrt(1 - pearson)
    ellipse = Ellipse((0.0, 0.0),
                      width=ell_radius_x*2,
                      height=ell_radius_y*2,
                      facecolor=facecolor, **kwargs)

    scale_x = np.sqrt(cov[0, 0]) * n_std
    scale_y = np.sqrt(cov[1, 1]) * n_std

    transf = transforms.Affine2D() \
                       .rotate_deg(45) \
                       .scale(scale_x, scale_y) \
                       .translate(mean_x, mean_y)
    ellipse.set_transform(transf + ax.transData)
    return ax.add_patch(ellipse)


if __name__ == "__main__":
    # --- プロットデータの作成 ---
    xlim, ylim = [-10, 10], [-10, 10]
    mean = [-1, -2]
    cov  = [[8, -3], [-3, 4]]
    x, y = normal(mean, cov, 1000).T

    #  --- プロットの作成 OO-API ---
    fig = plt.figure()
    ax  = fig.add_subplot(1,1,1)
    ax.set_title("2D normal distribution",
                 fontsize=20, fontname='serif')
    ax.set_xlabel("x data", fontsize=20, fontname='serif')
    ax.set_ylabel("y data", fontsize=20, fontname='serif')
    ax.tick_params(axis='both', length=10, which='major',
                   labelsize=16)
    ax.tick_params(axis='both', length=5,  which='minor')
    ax.set_xlim(xlim)
    ax.set_ylim(ylim)
    ax.minorticks_on()
    ax.grid(b=True, which='major', axis='both')
    ax.scatter(x, y, s=5, marker='o', edgecolor="b",
               label='scatter data')
    confidence_ellipse(x, y, ax, n_std=1,
                       edgecolor='red', linewidth=2)
    confidence_ellipse(x, y, ax, n_std=2,
                       edgecolor='green', linewidth=2)
    confidence_ellipse(x, y, ax, n_std=3,
                       edgecolor='blue', linewidth=2)

    # ----- プロットの表示 -----
    plt.show()

    # ----- プロットファイルの作成 -----
    fig.savefig('normal_dist.pdf', orientation='portrait')
    fig.savefig('normal_dist.svg', orientation='portrait')

    fig.clf()