🏠

令和8年2月24日（火）

⇒#1019@グラフ;

📈 オリビン

1019_オリビン

👨‍🏫 0

import numpy as np
import matplotlib.pyplot as plt

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

#----------------
#_📈_1019_オリビン
xy_1019 = [(3.365,0.0025) \
, (3.365,0.0023) \
, (3.37,0.0063) \
, (3.379,0.0104) \
, (3.389,0.0143) \
, (3.399,0.0176) \
, (3.409,0.0206) \
, (3.419,0.0236) \
, (3.429,0.0265) \
, (3.439,0.0294) \
, (3.449,0.0324) \
, (3.459,0.0356) \
, (3.469,0.0393) \
, (3.479,0.0431) \
, (3.489,0.0472) \
, (3.498,0.0517) \
, (3.508,0.0567) \
, (3.519,0.0623) \
, (3.529,0.0684) \
, (3.539,0.075) \
, (3.549,0.0821) \
, (3.559,0.0897) \
, (3.569,0.0976) \
, (3.579,0.1061) \
, (3.589,0.1148) \
, (3.599,0.1237) \
, (3.609,0.1329) \
, (3.619,0.1422) \
, (3.629,0.1517) \
, (3.639,0.1612) \
, (3.649,0.1708) \
, (3.659,0.1804) \
, (3.669,0.1902) \
, (3.679,0.1999) \
, (3.689,0.2094) \
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, (3.709,0.2283) \
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, (3.739,0.2564) \
, (3.749,0.2655) \
, (3.759,0.2745) \
, (3.769,0.2835) \
, (3.779,0.2924) \
, (3.79,0.3011) \
, (3.8,0.3097) \
, (3.81,0.3183) \
, (3.82,0.3266) \
, (3.829,0.335) \
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, (3.987,0.441) \
, (3.98,0.4301) \
, (3.971,0.4181) \
, (3.961,0.4061) \
, (3.951,0.3941) \
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, (3.931,0.3707) \
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, (3.911,0.3484) \
, (3.901,0.3375) \
, (3.891,0.3268) \
, (3.881,0.3164) \
, (3.871,0.3063) \
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, (3.851,0.2863) \
, (3.841,0.2766) \
, (3.831,0.2671) \
, (3.821,0.2578) \
, (3.811,0.2486) \
, (3.801,0.2394) \
, (3.791,0.2304) \
, (3.781,0.2215) \
, (3.771,0.2129) \
, (3.761,0.2043) \
, (3.751,0.1958) \
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, (3.731,0.1788) \
, (3.72,0.1707) \
, (3.711,0.1626) \
, (3.701,0.1546) \
, (3.691,0.1466) \
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, (3.671,0.1309) \
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, (3.651,0.1155) \
, (3.641,0.1078) \
, (3.63,0.1002) \
, (3.621,0.0928) \
, (3.61,0.0854) \
, (3.601,0.078) \
, (3.59,0.0705) \
, (3.58,0.0631) \
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, (3.55,0.0412) \
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, (3.53,0.0266) \
, (3.52,0.0193) \
, (3.51,0.012) \
, (3.5,0.0048) \
, (3.49,-0.0027) \
, (3.481,-0.01) \
, (3.47,-0.0172) \
, (3.46,-0.0245) \
, (3.45,-0.032) \
, (3.44,-0.0393) \
, (3.43,-0.0467) \
, (3.42,-0.0541) \
, (3.41,-0.0615) \
, (3.4,-0.0691) \
, (3.39,-0.0767) \
, (3.38,-0.0842) \
, (3.37,-0.0918) \
, (3.36,-0.0995) \
, (3.35,-0.1073) \
, (3.34,-0.1151) \
, (3.33,-0.1229) \
, (3.32,-0.1308) \
, (3.31,-0.1387) \
, (3.3,-0.1468) \
, (3.29,-0.1551) \
, (3.28,-0.1632) \
, (3.27,-0.1715) \
, (3.26,-0.1797) \
, (3.25,-0.1881) \
, (3.24,-0.1965) \
, (3.23,-0.2049) \
, (3.22,-0.213) \
, (3.21,-0.2213) \
, (3.2,-0.2295) \
, (3.19,-0.2378) \
, (3.18,-0.2459) \
, (3.17,-0.2539) \
, (3.16,-0.2615) \
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, (3.14,-0.277) \
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, (3.1,-0.3054) \
, (3.09,-0.3118) \
, (3.079,-0.3182) \
, (3.069,-0.3241) \
, (3.059,-0.3296) \
, (3.05,-0.3347) \
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, (3.02,-0.3477) \
, (3.009,-0.3508) \
, (3,-0.3531) \
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, (3.288,-0.0456) \
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, (3.307,-0.036) \
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, (3.328,-0.0267) \
, (3.338,-0.0223) \
, (3.348,-0.0179) \
, (3.358,-0.0135) \
, (3.368,-0.0089) \
, (3.378,-0.0044) \
, (3.388,0.0002) \
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, (3.408,0.0096) \
, (3.418,0.0147) \
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, (3.438,0.0253) \
, (3.448,0.0309) \
, (3.458,0.037) \
, (3.468,0.0432) \
, (3.478,0.0498) \
, (3.488,0.0565) \
, (3.498,0.0636) \
, (3.508,0.0712) \
, (3.518,0.079) \
, (3.528,0.0872) \
, (3.538,0.0956) \
, (3.548,0.104) \
, (3.558,0.1127) \
, (3.568,0.1219) \
, (3.578,0.1311) \
, (3.588,0.1403) \
, (3.599,0.1496) \
, (3.608,0.159) \
, (3.619,0.1684) \
, (3.629,0.1778) \
, (3.639,0.1872) \
, (3.649,0.1964) \
, (3.659,0.2055) \
, (3.669,0.2146) \
, (3.679,0.2236) \
, (3.689,0.2322) \
, (3.699,0.2408) \
, (3.709,0.2491) \
, (3.719,0.2575) \
, (3.729,0.2657) \
, (3.739,0.2735) \
, (3.749,0.2812) \
, (3.759,0.2888) \
, (3.769,0.2962) \
, (3.779,0.3036) \
, (3.789,0.3105) \
, (3.799,0.3173) \
, (3.809,0.3239) \
, (3.819,0.3304) \
, (3.829,0.3366) \
, (3.839,0.3426) \
, (3.849,0.3484) \
, (3.859,0.3541) \
, (3.869,0.3596) \
, (3.88,0.365) \
, (3.89,0.3702) \
, (3.9,0.3751) \
]
z_1019 = [list(t) for t in zip(*xy_1019)]; x_1019 = z_1019[0]; y_1019 = z_1019[1]

ax.scatter(x_1019, y_1019)
ax.plot(x_1019, y_1019)
ax.annotate('ID=1019' \
, xy=(np.mean(x_1019),np.mean(y_1019)) \
, xytext=(np.mean(x_1019)+ np.std(y_1019), np.mean(y_1019) + np.std(y_1019)) \
, arrowprops=dict(arrowstyle="->"))
#----------------

plt.show()

図 1 . python コード

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

図 3 . canvas オリビン

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 4 . google chart APIを使った描画

xmin	3.5
xmax	4
ymin	-0.2
ymax	0.5

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図形と関数

数式の例

表 2 . 数式の例

数式	意味	説明

$y = a x + b$	一次関数直線	数に量の意味はありません。変数には、`x`,`y`,`z`のようにアルファベットの後ろの方を使い定数には、`a`,`b`,`c`のようにアルファベットの前の方を使います。デカルト座標系では、図形を表します。座標の数に量を割り当てたものをグラフやチャートと呼びます。
$p V = n R T$	気体の状態方程式 1662～1802	左辺 `pV` が仕事、右辺`nRT`が熱量で、エネルギー収支を表す量方程式です。量方程式なので量を単位で割った数値を代入したり求めたりします。
$E = E^{0} - \frac{R T}{n F} \ln K$	ネルンストの式 1889
$S = k_{B} \ln W$	ボルツマンの式 1877

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。

👨‍🏫 数式の表現、量の表現 👨‍🏫 ウルフラムアルファ（WolframAlpha） 👨‍🏫 計算式のページ（フォーム）

<figure>
<img src="https://a.yamagata-u.ac.jp/amenity/Laboratory/xyGraphImage.aspx?id=1019" />
<figcaption>
<a href="https://edu.yz.yamagata-u.ac.jp/developer/Asp/Youzan/Laboratory/Plot/Plot_Index.asp">Fig</a> <a href="https://edu.yz.yamagata-u.ac.jp/developer/Asp/Youzan/Laboratory/Plot/@Plot.asp?nxyGraphID=1019"> オリビン </a>
<div> </div>
</figcaption>
</figure>

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管理運用：山形大学データベースアメニティ研究会

〒992-8510 山形県米沢市城南4丁目3-16

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。