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import numpy as np
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
#----------------
#_📈_1130_12717(IR)
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z_1130 = [list(t) for t in zip(*xy_1130)]; x_1130 = z_1130[0]; y_1130 = z_1130[1]
ax.scatter(x_1130, y_1130)
ax.plot(x_1130, y_1130)
ax.annotate('ID=1130' \
, xy=(np.mean(x_1130),np.mean(y_1130)) \
, xytext=(np.mean(x_1130)+ np.std(y_1130), np.mean(y_1130) + np.std(y_1130)) \
, arrowprops=dict(arrowstyle="->"))
#----------------
plt.show()
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。
| xmin | 0 |
| xmax | 4000 |
| ymin | 0 |
| ymax | 0.05 |
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| 名称 | グラフ | 説明 |
|---|---|---|
| 指数関数 |
|
python
+matplotlib
|
| 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。 |
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
