🏠

令和8年3月6日 （金）

📈 Al|NMC,AB,PVDF|TEMA BF4 PC|Pt

import numpy as np
import matplotlib.pyplot as plt

#fig, ax = plt.subplots(figsize=(2.9, 2.1)) 
fig, ax = plt.subplots()

#----------------
#_📈_1118_Al|NMC,AB,PVDF|TEMA BF4 PC|Pt  
xy_1118 = [(0,0) \
, (0.025,0.02575) \
, (0.05,0.034376712) \
, (0.075,0.037608416) \
, (0.1,0.039140323) \
, (0.125,0.040136677) \
, (0.15,0.040964296) \
, (0.175,0.041738751) \
, (0.2,0.042496456) \
, (0.225,0.043248883) \
, (0.25,0.043999648) \
, (0.275,0.044749889) \
, (0.3,0.045499965) \
, (0.325,0.046249989) \
, (0.35,0.046999997) \
, (0.375,0.047749999) \
, (0.4,0.0485) \
, (0.425,0.04925) \
, (0.45,0.05) \
, (0.475,0.05075) \
, (0.5,0.0515) \
, (0.475,0.00075) \
, (0.45,-0.015753425) \
, (0.425,-0.021466832) \
, (0.4,-0.023780646) \
, (0.375,-0.025023354) \
, (0.35,-0.025928591) \
, (0.325,-0.026727501) \
, (0.3,-0.027492911) \
, (0.275,-0.028247767) \
, (0.25,-0.028999296) \
, (0.225,-0.029749778) \
, (0.2,-0.03049993) \
, (0.175,-0.031249978) \
, (0.15,-0.031999993) \
, (0.125,-0.032749998) \
, (0.1,-0.033499999) \
, (0.075,-0.03425) \
, (0.05,-0.035) \
, (0.025,-0.03575) \
, (-2.22045E-16,-0.0365) \
, (-0.025,-0.03725) \
, (-0.05,-0.038) \
, (-0.075,-0.03875) \
, (-0.1,-0.0395) \
, (-0.125,-0.04025) \
, (-0.15,-0.041) \
, (-0.175,-0.04175) \
, (-0.2,-0.0425) \
, (-0.225,-0.04325) \
, (-0.25,-0.044) \
, (-0.275,-0.04475) \
, (-0.3,-0.0455) \
, (-0.325,-0.04625) \
, (-0.35,-0.047) \
, (-0.375,-0.04775) \
, (-0.4,-0.0485) \
, (-0.425,-0.04925) \
, (-0.45,-0.05) \
, (-0.475,-0.05075) \
, (-0.5,-0.0515) \
, (-0.475,-0.00075) \
, (-0.45,0.015753425) \
, (-0.425,0.021466832) \
, (-0.4,0.023780646) \
, (-0.375,0.025023354) \
, (-0.35,0.025928591) \
, (-0.325,0.026727501) \
, (-0.3,0.027492911) \
, (-0.275,0.028247767) \
, (-0.25,0.028999296) \
, (-0.225,0.029749778) \
, (-0.2,0.03049993) \
, (-0.175,0.031249978) \
, (-0.15,0.031999993) \
, (-0.125,0.032749998) \
, (-0.1,0.033499999) \
, (-0.075,0.03425) \
, (-0.05,0.035) \
, (-0.025,0.03575) \
, (-2.22045E-16,0.0365) \
, (0.025,0.03725) \
, (0.05,0.038) \
, (0.075,0.03875) \
, (0.1,0.0395) \
, (0.125,0.04025) \
, (0.15,0.041) \
, (0.175,0.04175) \
, (0.2,0.0425) \
, (0.225,0.04325) \
, (0.25,0.044) \
, (0.275,0.04475) \
, (0.3,0.0455) \
, (0.325,0.04625) \
, (0.35,0.047) \
, (0.375,0.04775) \
, (0.4,0.0485) \
, (0.425,0.04925) \
, (0.45,0.05) \
, (0.475,0.05075) \
, (0.5,0.0515) \
, (0.475,0.00075) \
, (0.45,-0.015753425) \
, (0.425,-0.021466832) \
, (0.4,-0.023780646) \
, (0.375,-0.025023354) \
, (0.35,-0.025928591) \
, (0.325,-0.026727501) \
, (0.3,-0.027492911) \
, (0.275,-0.028247767) \
, (0.25,-0.028999296) \
, (0.225,-0.029749778) \
, (0.2,-0.03049993) \
, (0.175,-0.031249978) \
, (0.15,-0.031999993) \
, (0.125,-0.032749998) \
, (0.1,-0.033499999) \
, (0.075,-0.03425) \
, (0.05,-0.035) \
, (0.025,-0.03575) \
, (-2.22045E-16,-0.0365) \
, (-0.025,-0.03725) \
, (-0.05,-0.038) \
, (-0.075,-0.03875) \
, (-0.1,-0.0395) \
, (-0.125,-0.04025) \
, (-0.15,-0.041) \
, (-0.175,-0.04175) \
, (-0.2,-0.0425) \
, (-0.225,-0.04325) \
, (-0.25,-0.044) \
, (-0.275,-0.04475) \
, (-0.3,-0.0455) \
, (-0.325,-0.04625) \
, (-0.35,-0.047) \
, (-0.375,-0.04775) \
, (-0.4,-0.0485) \
, (-0.425,-0.04925) \
, (-0.45,-0.05) \
, (-0.475,-0.05075) \
, (-0.5,-0.0515) \
, (-0.475,-0.00075) \
, (-0.45,0.015753425) \
, (-0.425,0.021466832) \
, (-0.4,0.023780646) \
, (-0.375,0.025023354) \
, (-0.35,0.025928591) \
, (-0.325,0.026727501) \
, (-0.3,0.027492911) \
, (-0.275,0.028247767) \
, (-0.25,0.028999296) \
, (-0.225,0.029749778) \
, (-0.2,0.03049993) \
, (-0.175,0.031249978) \
, (-0.15,0.031999993) \
, (-0.125,0.032749998) \
, (-0.1,0.033499999) \
, (-0.075,0.03425) \
, (-0.05,0.035) \
, (-0.025,0.03575) \
, (-2.22045E-16,0.0365) \
, (0.025,0.03725) \
, (0.05,0.038) \
, (0.075,0.03875) \
, (0.1,0.0395) \
, (0.125,0.04025) \
, (0.15,0.041) \
, (0.175,0.04175) \
, (0.2,0.0425) \
, (0.225,0.04325) \
, (0.25,0.044) \
, (0.275,0.04475) \
, (0.3,0.0455) \
, (0.325,0.04625) \
, (0.35,0.047) \
, (0.375,0.04775) \
, (0.4,0.0485) \
, (0.425,0.04925) \
, (0.45,0.05) \
, (0.475,0.05075) \
, (0.5,0.0515) \
, (0.475,0.00075) \
, (0.45,-0.015753425) \
, (0.425,-0.021466832) \
, (0.4,-0.023780646) \
, (0.375,-0.025023354) \
, (0.35,-0.025928591) \
, (0.325,-0.026727501) \
, (0.3,-0.027492911) \
, (0.275,-0.028247767) \
, (0.25,-0.028999296) \
, (0.225,-0.029749778) \
, (0.2,-0.03049993) \
, (0.175,-0.031249978) \
, (0.15,-0.031999993) \
, (0.125,-0.032749998) \
, (0.1,-0.033499999) \
, (0.075,-0.03425) \
, (0.05,-0.035) \
, (0.025,-0.03575) \
, (-2.22045E-16,-0.0365) \
, (-0.025,-0.03725) \
, (-0.05,-0.038) \
, (-0.075,-0.03875) \
, (-0.1,-0.0395) \
, (-0.125,-0.04025) \
, (-0.15,-0.041) \
, (-0.175,-0.04175) \
, (-0.2,-0.0425) \
, (-0.225,-0.04325) \
, (-0.25,-0.044) \
, (-0.275,-0.04475) \
, (-0.3,-0.0455) \
, (-0.325,-0.04625) \
, (-0.35,-0.047) \
, (-0.375,-0.04775) \
, (-0.4,-0.0485) \
, (-0.425,-0.04925) \
, (-0.45,-0.05) \
, (-0.475,-0.05075) \
, (-0.5,-0.0515) \
, (-0.475,-0.00075) \
, (-0.45,0.015753425) \
, (-0.425,0.021466832) \
, (-0.4,0.023780646) \
, (-0.375,0.025023354) \
, (-0.35,0.025928591) \
, (-0.325,0.026727501) \
, (-0.3,0.027492911) \
, (-0.275,0.028247767) \
, (-0.25,0.028999296) \
, (-0.225,0.029749778) \
, (-0.2,0.03049993) \
, (-0.175,0.031249978) \
, (-0.15,0.031999993) \
, (-0.125,0.032749998) \
, (-0.1,0.033499999) \
, (-0.075,0.03425) \
, (-0.05,0.035) \
, (-0.025,0.03575) \
, (-2.22045E-16,0.0365) \
, (0.025,0.03725) \
, (0.05,0.038) \
, (0.075,0.03875) \
, (0.1,0.0395) \
, (0.125,0.04025) \
, (0.15,0.041) \
, (0.175,0.04175) \
, (0.2,0.0425) \
, (0.225,0.04325) \
, (0.25,0.044) \
, (0.275,0.04475) \
, (0.3,0.0455) \
, (0.325,0.04625) \
, (0.35,0.047) \
, (0.375,0.04775) \
]
z_1118 = [list(t) for t in zip(*xy_1118)]; x_1118 = z_1118[0]; y_1118 = z_1118[1]

ax.scatter(x_1118, y_1118)
ax.plot(x_1118, y_1118)
ax.annotate('ID=1118' \
, xy=(np.mean(x_1118),np.mean(y_1118)) \
, xytext=(np.mean(x_1118)+ np.std(y_1118), np.mean(y_1118) + np.std(y_1118)) \
, arrowprops=dict(arrowstyle="->"))
#----------------

plt.show()

A4 （210 × 297mm）あるいは少し大きめのレターサイズ（215.9 × 279.4ミリ）が一般的です。 2 カラムとすると 3.34645669291339インチ程度。 アスペクトを 4:3にすれば、2.9インチ×2.1インチぐらいの図が論文投稿の図として適切です。

サーバーサイドスクリプト

サーバーサイドでラスタライズ（bmp,jpg,png）しているので、レスポンシブな表示が可能です。 サーバーサイドでダイナミックに生成している画像なので、ダウンロードだけでなく、リンクもできます。

クライアントサイドスクリプト

クライアントサイドでラスタライズ（bmp,jpg,png）しているので、レスポンシブな表示が可能です。 クライアントサイドでダイナミックに生成している画像なので、ダウンロードはできますが、リンクはできません。

図形と関数

名称	グラフ	説明
指数関数		python + matplotlib import numpy as np import math import matplotlib.pyplot as plt xy = [(p, math.exp(p)) for p in \ np.arange(start = - 2, stop = 2, step = 0.1)] z = [list(t) for t in zip(*xy)]; x = z[0]; y = z[1] fig, ax = plt.subplots() ax.plot(x, y) plt.show()
逆ネルンスト		電池の充放電曲線で現れます。
確率曲線
正規分布関数		確率統計で多用されます。 品質管理 でも大切です。

数式の例

数式には、インドアラビア数字、 ラテン文字、 ギリシャ文字、記号など多くの文字が現れます。 文字の多くは、数を表現します。量を数で表現している場合もあります。

数式は、量との量の関係を表現しているので、グラフにできます。

数式で数値を求めるときは、量を単位で割ってから代入します。このような数式を量方程式あるいは 量式*と言います。 単位が指定された数式を 数値方程式 と言います。単位の定義が変わると 数値方程式 の係数も変わります。 文献に記載された 数値方程式 を使う場合は、単位の定義がいつのものなのかを確認する必要があります。

コンピュータ上では直接数式を表現できないため、 TeXを使います。 MathMLを使います。

動画、音声及び写真を含む図表等を転載する場合には転載許諾書による同意があった方が無難です。 動画、音声及び写真を含む図表等の転載許諾書

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https://edu.yz.yamagata-u.ac.jp/developer/Asp/Youzan/Laboratory/Plot/@Plot.asp?nxyGraphID=1118

名称： 教育用公開ウェブサービス

URL： 🔗 https://edu.yz.yamagata-u.ac.jp/

管理運用 ： 山形大学 学術情報基盤センター

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🎄🎂🌃🕯🎉

名称： サイバーキャンパス「鷹山」

URL: 🔗 http://amenity.yz.yamagata-u.ac.jp/

管理運用 ： 山形大学 データベースアメニティ研究会

代表： 多田隈　理一郎

〒992-8510 山形県米沢市城南4丁目3-16