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scientific-computing-2/auto_examples_jupyter_2/exercises/plot_exercise_1.ipynb Normal file
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n# Exercise 1\n\nSolution of the exercise 1 with matplotlib.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np\nimport matplotlib.pyplot as plt\n\nn = 256\nX = np.linspace(-np.pi, np.pi, 256)\nC, S = np.cos(X), np.sin(X)\nplt.plot(X, C)\nplt.plot(X, S)\n\nplt.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.11"
}
},
"nbformat": 4,
"nbformat_minor": 0
}

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scientific-computing-2/auto_examples_jupyter_2/exercises/plot_exercise_10.ipynb Normal file
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n# Exercise\n\nExercises with matplotlib.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np\nimport matplotlib.pyplot as plt\n\nplt.figure(figsize=(8, 5), dpi=80)\nplt.subplot(111)\n\nX = np.linspace(-np.pi, np.pi, 256)\nC, S = np.cos(X), np.sin(X)\n\nplt.plot(X, C, color=\"blue\", linewidth=2.5, linestyle=\"-\", label=\"cosine\")\nplt.plot(X, S, color=\"red\", linewidth=2.5, linestyle=\"-\", label=\"sine\")\n\nax = plt.gca()\nax.spines[\"right\"].set_color(\"none\")\nax.spines[\"top\"].set_color(\"none\")\nax.xaxis.set_ticks_position(\"bottom\")\nax.spines[\"bottom\"].set_position((\"data\", 0))\nax.yaxis.set_ticks_position(\"left\")\nax.spines[\"left\"].set_position((\"data\", 0))\n\nplt.xlim(X.min() * 1.1, X.max() * 1.1)\nplt.xticks(\n [-np.pi, -np.pi / 2, 0, np.pi / 2, np.pi],\n [r\"$-\\pi$\", r\"$-\\pi/2$\", r\"$0$\", r\"$+\\pi/2$\", r\"$+\\pi$\"],\n)\n\nplt.ylim(C.min() * 1.1, C.max() * 1.1)\nplt.yticks([-1, 1], [r\"$-1$\", r\"$+1$\"])\n\nplt.legend(loc=\"upper left\")\n\nt = 2 * np.pi / 3\nplt.plot([t, t], [0, np.cos(t)], color=\"blue\", linewidth=1.5, linestyle=\"--\")\nplt.scatter(\n [\n t,\n ],\n [\n np.cos(t),\n ],\n 50,\n color=\"blue\",\n)\nplt.annotate(\n r\"$sin(\\frac{2\\pi}{3})=\\frac{\\sqrt{3}}{2}$\",\n xy=(t, np.sin(t)),\n xycoords=\"data\",\n xytext=(10, 30),\n textcoords=\"offset points\",\n fontsize=16,\n arrowprops={\"arrowstyle\": \"->\", \"connectionstyle\": \"arc3,rad=.2\"},\n)\n\nplt.plot([t, t], [0, np.sin(t)], color=\"red\", linewidth=1.5, linestyle=\"--\")\nplt.scatter(\n [\n t,\n ],\n [\n np.sin(t),\n ],\n 50,\n color=\"red\",\n)\nplt.annotate(\n r\"$cos(\\frac{2\\pi}{3})=-\\frac{1}{2}$\",\n xy=(t, np.cos(t)),\n xycoords=\"data\",\n xytext=(-90, -50),\n textcoords=\"offset points\",\n fontsize=16,\n arrowprops={\"arrowstyle\": \"->\", \"connectionstyle\": \"arc3,rad=.2\"},\n)\n\nfor label in ax.get_xticklabels() + ax.get_yticklabels():\n label.set_fontsize(16)\n label.set_bbox({\"facecolor\": \"white\", \"edgecolor\": \"None\", \"alpha\": 0.65})\n\nplt.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.11"
}
},
"nbformat": 4,
"nbformat_minor": 0
}

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scientific-computing-2/auto_examples_jupyter_2/exercises/plot_exercise_2.ipynb Normal file
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n# Exercise 2\n\nExercise 2 with matplotlib.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np\nimport matplotlib.pyplot as plt\n\n# Create a new figure of size 8x6 points, using 100 dots per inch\nplt.figure(figsize=(8, 6), dpi=80)\n\n# Create a new subplot from a grid of 1x1\nplt.subplot(111)\n\nX = np.linspace(-np.pi, np.pi, 256)\nC, S = np.cos(X), np.sin(X)\n\n# Plot cosine using blue color with a continuous line of width 1 (pixels)\nplt.plot(X, C, color=\"blue\", linewidth=1.0, linestyle=\"-\")\n\n# Plot sine using green color with a continuous line of width 1 (pixels)\nplt.plot(X, S, color=\"green\", linewidth=1.0, linestyle=\"-\")\n\n# Set x limits\nplt.xlim(-4.0, 4.0)\n\n# Set x ticks\nplt.xticks(np.linspace(-4, 4, 9))\n\n# Set y limits\nplt.ylim(-1.0, 1.0)\n\n# Set y ticks\nplt.yticks(np.linspace(-1, 1, 5))\n\n# Show result on screen\nplt.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.11"
}
},
"nbformat": 4,
"nbformat_minor": 0
}

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scientific-computing-2/auto_examples_jupyter_2/exercises/plot_exercise_3.ipynb Normal file
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n# Exercise 3\n\nExercise 3 with matplotlib.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np\nimport matplotlib.pyplot as plt\n\nplt.figure(figsize=(8, 5), dpi=80)\nplt.subplot(111)\n\nX = np.linspace(-np.pi, np.pi, 256)\nC, S = np.cos(X), np.sin(X)\n\nplt.plot(X, C, color=\"blue\", linewidth=2.5, linestyle=\"-\")\nplt.plot(X, S, color=\"red\", linewidth=2.5, linestyle=\"-\")\n\nplt.xlim(-4.0, 4.0)\nplt.xticks(np.linspace(-4, 4, 9))\n\nplt.ylim(-1.0, 1.0)\nplt.yticks(np.linspace(-1, 1, 5))\n\nplt.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.11"
}
},
"nbformat": 4,
"nbformat_minor": 0
}

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scientific-computing-2/auto_examples_jupyter_2/exercises/plot_exercise_4.ipynb Normal file
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n# Exercise 4\n\nExercise 4 with matplotlib.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np\nimport matplotlib.pyplot as plt\n\nplt.figure(figsize=(8, 5), dpi=80)\nplt.subplot(111)\n\nX = np.linspace(-np.pi, np.pi, 256)\nS = np.sin(X)\nC = np.cos(X)\n\nplt.plot(X, C, color=\"blue\", linewidth=2.5, linestyle=\"-\")\nplt.plot(X, S, color=\"red\", linewidth=2.5, linestyle=\"-\")\n\nplt.xlim(X.min() * 1.1, X.max() * 1.1)\nplt.ylim(C.min() * 1.1, C.max() * 1.1)\n\nplt.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.11"
}
},
"nbformat": 4,
"nbformat_minor": 0
}

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scientific-computing-2/auto_examples_jupyter_2/exercises/plot_exercise_5.ipynb Normal file
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n# Exercise 5\n\nExercise 5 with matplotlib.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np\nimport matplotlib.pyplot as plt\n\nplt.figure(figsize=(8, 5), dpi=80)\nplt.subplot(111)\n\nX = np.linspace(-np.pi, np.pi, 256)\nS = np.sin(X)\nC = np.cos(X)\n\nplt.plot(X, C, color=\"blue\", linewidth=2.5, linestyle=\"-\")\nplt.plot(X, S, color=\"red\", linewidth=2.5, linestyle=\"-\")\n\nplt.xlim(X.min() * 1.1, X.max() * 1.1)\nplt.xticks([-np.pi, -np.pi / 2, 0, np.pi / 2, np.pi])\n\nplt.ylim(C.min() * 1.1, C.max() * 1.1)\nplt.yticks([-1, 0, +1])\n\nplt.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.11"
}
},
"nbformat": 4,
"nbformat_minor": 0
}

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scientific-computing-2/auto_examples_jupyter_2/exercises/plot_exercise_6.ipynb Normal file
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n# Exercise 6\n\nExercise 6 with matplotlib.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np\nimport matplotlib.pyplot as plt\n\nplt.figure(figsize=(8, 5), dpi=80)\nplt.subplot(111)\n\nX = np.linspace(-np.pi, np.pi, 256)\nC = np.cos(X)\nS = np.sin(X)\n\nplt.plot(X, C, color=\"blue\", linewidth=2.5, linestyle=\"-\")\nplt.plot(X, S, color=\"red\", linewidth=2.5, linestyle=\"-\")\n\nplt.xlim(X.min() * 1.1, X.max() * 1.1)\nplt.xticks(\n [-np.pi, -np.pi / 2, 0, np.pi / 2, np.pi],\n [r\"$-\\pi$\", r\"$-\\pi/2$\", r\"$0$\", r\"$+\\pi/2$\", r\"$+\\pi$\"],\n)\n\nplt.ylim(C.min() * 1.1, C.max() * 1.1)\nplt.yticks([-1, 0, +1], [r\"$-1$\", r\"$0$\", r\"$+1$\"])\n\nplt.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.11"
}
},
"nbformat": 4,
"nbformat_minor": 0
}

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scientific-computing-2/auto_examples_jupyter_2/exercises/plot_exercise_7.ipynb Normal file
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n# Exercise 7\n\nExercise 7 with matplotlib\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np\nimport matplotlib.pyplot as plt\n\nplt.figure(figsize=(8, 5), dpi=80)\nplt.subplot(111)\n\nX = np.linspace(-np.pi, np.pi, 256, endpoint=True)\nC = np.cos(X)\nS = np.sin(X)\n\nplt.plot(X, C, color=\"blue\", linewidth=2.5, linestyle=\"-\")\nplt.plot(X, S, color=\"red\", linewidth=2.5, linestyle=\"-\")\n\nax = plt.gca()\nax.spines[\"right\"].set_color(\"none\")\nax.spines[\"top\"].set_color(\"none\")\nax.xaxis.set_ticks_position(\"bottom\")\nax.spines[\"bottom\"].set_position((\"data\", 0))\nax.yaxis.set_ticks_position(\"left\")\nax.spines[\"left\"].set_position((\"data\", 0))\n\nplt.xlim(X.min() * 1.1, X.max() * 1.1)\nplt.xticks(\n [-np.pi, -np.pi / 2, 0, np.pi / 2, np.pi],\n [r\"$-\\pi$\", r\"$-\\pi/2$\", r\"$0$\", r\"$+\\pi/2$\", r\"$+\\pi$\"],\n)\n\nplt.ylim(C.min() * 1.1, C.max() * 1.1)\nplt.yticks([-1, 0, +1], [r\"$-1$\", r\"$0$\", r\"$+1$\"])\n\nplt.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.11"
}
},
"nbformat": 4,
"nbformat_minor": 0
}

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scientific-computing-2/auto_examples_jupyter_2/exercises/plot_exercise_8.ipynb Normal file
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n# Exercise 8\n\nExercise 8 with matplotlib.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np\nimport matplotlib.pyplot as plt\n\nplt.figure(figsize=(8, 5), dpi=80)\nplt.subplot(111)\n\nX = np.linspace(-np.pi, np.pi, 256, endpoint=True)\nC = np.cos(X)\nS = np.sin(X)\n\nplt.plot(X, C, color=\"blue\", linewidth=2.5, linestyle=\"-\", label=\"cosine\")\nplt.plot(X, S, color=\"red\", linewidth=2.5, linestyle=\"-\", label=\"sine\")\n\nax = plt.gca()\nax.spines[\"right\"].set_color(\"none\")\nax.spines[\"top\"].set_color(\"none\")\nax.xaxis.set_ticks_position(\"bottom\")\nax.spines[\"bottom\"].set_position((\"data\", 0))\nax.yaxis.set_ticks_position(\"left\")\nax.spines[\"left\"].set_position((\"data\", 0))\n\nplt.xlim(X.min() * 1.1, X.max() * 1.1)\nplt.xticks(\n [-np.pi, -np.pi / 2, 0, np.pi / 2, np.pi],\n [r\"$-\\pi$\", r\"$-\\pi/2$\", r\"$0$\", r\"$+\\pi/2$\", r\"$+\\pi$\"],\n)\n\nplt.ylim(C.min() * 1.1, C.max() * 1.1)\nplt.yticks([-1, +1], [r\"$-1$\", r\"$+1$\"])\n\nplt.legend(loc=\"upper left\")\n\nplt.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.11"
}
},
"nbformat": 4,
"nbformat_minor": 0
}

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scientific-computing-2/auto_examples_jupyter_2/exercises/plot_exercise_9.ipynb Normal file
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n# Exercise 9\n\nExercise 9 with matplotlib.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np\nimport matplotlib.pyplot as plt\n\nplt.figure(figsize=(8, 5), dpi=80)\nplt.subplot(111)\n\nX = np.linspace(-np.pi, np.pi, 256, endpoint=True)\nC = np.cos(X)\nS = np.sin(X)\n\nplt.plot(X, C, color=\"blue\", linewidth=2.5, linestyle=\"-\", label=\"cosine\")\nplt.plot(X, S, color=\"red\", linewidth=2.5, linestyle=\"-\", label=\"sine\")\n\nax = plt.gca()\nax.spines[\"right\"].set_color(\"none\")\nax.spines[\"top\"].set_color(\"none\")\nax.xaxis.set_ticks_position(\"bottom\")\nax.spines[\"bottom\"].set_position((\"data\", 0))\nax.yaxis.set_ticks_position(\"left\")\nax.spines[\"left\"].set_position((\"data\", 0))\n\nplt.xlim(X.min() * 1.1, X.max() * 1.1)\nplt.xticks(\n [-np.pi, -np.pi / 2, 0, np.pi / 2, np.pi],\n [r\"$-\\pi$\", r\"$-\\pi/2$\", r\"$0$\", r\"$+\\pi/2$\", r\"$+\\pi$\"],\n)\n\nplt.ylim(C.min() * 1.1, C.max() * 1.1)\nplt.yticks([-1, +1], [r\"$-1$\", r\"$+1$\"])\n\nt = 2 * np.pi / 3\nplt.plot([t, t], [0, np.cos(t)], color=\"blue\", linewidth=1.5, linestyle=\"--\")\nplt.scatter(\n [\n t,\n ],\n [\n np.cos(t),\n ],\n 50,\n color=\"blue\",\n)\nplt.annotate(\n r\"$sin(\\frac{2\\pi}{3})=\\frac{\\sqrt{3}}{2}$\",\n xy=(t, np.sin(t)),\n xycoords=\"data\",\n xytext=(+10, +30),\n textcoords=\"offset points\",\n fontsize=16,\n arrowprops={\"arrowstyle\": \"->\", \"connectionstyle\": \"arc3,rad=.2\"},\n)\n\nplt.plot([t, t], [0, np.sin(t)], color=\"red\", linewidth=1.5, linestyle=\"--\")\nplt.scatter(\n [\n t,\n ],\n [\n np.sin(t),\n ],\n 50,\n color=\"red\",\n)\nplt.annotate(\n r\"$cos(\\frac{2\\pi}{3})=-\\frac{1}{2}$\",\n xy=(t, np.cos(t)),\n xycoords=\"data\",\n xytext=(-90, -50),\n textcoords=\"offset points\",\n fontsize=16,\n arrowprops={\"arrowstyle\": \"->\", \"connectionstyle\": \"arc3,rad=.2\"},\n)\n\nplt.legend(loc=\"upper left\")\n\nplt.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.11"
}
},
"nbformat": 4,
"nbformat_minor": 0
}

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scientific-computing-2/auto_examples_jupyter_2/options/plot_aliased.ipynb Normal file
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n# Aliased versus anti-aliased\n\nThis example demonstrates aliased versus anti-aliased text.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n\nsize = 128, 16\ndpi = 72.0\nfigsize = size[0] / float(dpi), size[1] / float(dpi)\nfig = plt.figure(figsize=figsize, dpi=dpi)\nfig.patch.set_alpha(0)\n\nplt.axes((0, 0, 1, 1), frameon=False)\n\nplt.rcParams[\"text.antialiased\"] = False\nplt.text(0.5, 0.5, \"Aliased\", ha=\"center\", va=\"center\")\n\nplt.xlim(0, 1)\nplt.ylim(0, 1)\nplt.xticks([])\nplt.yticks([])\n\nplt.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.11"
}
},
"nbformat": 4,
"nbformat_minor": 0
}

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scientific-computing-2/auto_examples_jupyter_2/options/plot_alpha.ipynb Normal file
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n# Alpha: transparency\n\nThis example demonstrates using alpha for transparency.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n\nsize = 256, 16\ndpi = 72.0\nfigsize = size[0] / float(dpi), size[1] / float(dpi)\nfig = plt.figure(figsize=figsize, dpi=dpi)\nfig.patch.set_alpha(0)\nplt.axes((0, 0.1, 1, 0.8), frameon=False)\n\nfor i in range(1, 11):\n plt.axvline(i, linewidth=1, color=\"blue\", alpha=0.25 + 0.75 * i / 10.0)\n\nplt.xlim(0, 11)\nplt.xticks([])\nplt.yticks([])\nplt.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.11"
}
},
"nbformat": 4,
"nbformat_minor": 0
}

43
scientific-computing-2/auto_examples_jupyter_2/options/plot_antialiased.ipynb Normal file
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n# Aliased versus anti-aliased\n\nThe example shows aliased versus anti-aliased text.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n\nsize = 128, 16\ndpi = 72.0\nfigsize = size[0] / float(dpi), size[1] / float(dpi)\nfig = plt.figure(figsize=figsize, dpi=dpi)\nfig.patch.set_alpha(0)\nplt.axes((0, 0, 1, 1), frameon=False)\n\nplt.rcParams[\"text.antialiased\"] = True\nplt.text(0.5, 0.5, \"Anti-aliased\", ha=\"center\", va=\"center\")\n\nplt.xlim(0, 1)\nplt.ylim(0, 1)\nplt.xticks([])\nplt.yticks([])\n\nplt.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.11"
}
},
"nbformat": 4,
"nbformat_minor": 0
}

43
scientific-computing-2/auto_examples_jupyter_2/options/plot_color.ipynb Normal file
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n# The colors matplotlib line plots\n\nAn example demoing the various colors taken by matplotlib's plot.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n\nsize = 256, 16\ndpi = 72.0\nfigsize = size[0] / float(dpi), size[1] / float(dpi)\nfig = plt.figure(figsize=figsize, dpi=dpi)\nfig.patch.set_alpha(0)\nplt.axes((0, 0.1, 1, 0.8), frameon=False)\n\nfor i in range(1, 11):\n plt.plot([i, i], [0, 1], lw=1.5)\n\nplt.xlim(0, 11)\nplt.xticks([])\nplt.yticks([])\nplt.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.11"
}
},
"nbformat": 4,
"nbformat_minor": 0
}

43
scientific-computing-2/auto_examples_jupyter_2/options/plot_colormaps.ipynb Normal file
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n# Colormaps\n\nAn example plotting the matplotlib colormaps.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np\n\nimport matplotlib\nimport matplotlib.pyplot as plt\n\n\nplt.rc(\"text\", usetex=False)\na = np.outer(np.arange(0, 1, 0.01), np.ones(10))\n\nplt.figure(figsize=(10, 5))\nplt.subplots_adjust(top=0.8, bottom=0.05, left=0.01, right=0.99)\nmaps = [m for m in matplotlib.colormaps if not m.endswith(\"_r\")]\nmaps.sort()\nl = len(maps) + 1\n\nfor i, m in enumerate(maps):\n plt.subplot(1, l, i + 1)\n plt.axis(\"off\")\n plt.imshow(a, aspect=\"auto\", cmap=plt.get_cmap(m), origin=\"lower\")\n plt.title(m, rotation=90, fontsize=10, va=\"bottom\")\n\nplt.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.11"
}
},
"nbformat": 4,
"nbformat_minor": 0
}

43
scientific-computing-2/auto_examples_jupyter_2/options/plot_dash_capstyle.ipynb Normal file
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n# Dash capstyle\n\nAn example demoing the dash capstyle.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np\nimport matplotlib.pyplot as plt\n\nsize = 256, 16\ndpi = 72.0\nfigsize = size[0] / float(dpi), size[1] / float(dpi)\nfig = plt.figure(figsize=figsize, dpi=dpi)\nfig.patch.set_alpha(0)\nplt.axes((0, 0, 1, 1), frameon=False)\n\nplt.plot(\n np.arange(4),\n np.ones(4),\n color=\"blue\",\n dashes=[15, 15],\n linewidth=8,\n dash_capstyle=\"butt\",\n)\n\nplt.plot(\n 5 + np.arange(4),\n np.ones(4),\n color=\"blue\",\n dashes=[15, 15],\n linewidth=8,\n dash_capstyle=\"round\",\n)\n\nplt.plot(\n 10 + np.arange(4),\n np.ones(4),\n color=\"blue\",\n dashes=[15, 15],\n linewidth=8,\n dash_capstyle=\"projecting\",\n)\n\nplt.xlim(0, 14)\nplt.xticks([])\nplt.yticks([])\n\nplt.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.11"
}
},
"nbformat": 4,
"nbformat_minor": 0
}

43
scientific-computing-2/auto_examples_jupyter_2/options/plot_dash_joinstyle.ipynb Normal file
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n# Dash join style\n\nExample demoing the dash join style.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np\nimport matplotlib.pyplot as plt\n\nsize = 256, 16\ndpi = 72.0\nfigsize = size[0] / float(dpi), size[1] / float(dpi)\nfig = plt.figure(figsize=figsize, dpi=dpi)\nfig.patch.set_alpha(0)\nplt.axes((0, 0, 1, 1), frameon=False)\n\nplt.plot(\n np.arange(3),\n [0, 1, 0],\n color=\"blue\",\n dashes=[12, 5],\n linewidth=8,\n dash_joinstyle=\"miter\",\n)\nplt.plot(\n 4 + np.arange(3),\n [0, 1, 0],\n color=\"blue\",\n dashes=[12, 5],\n linewidth=8,\n dash_joinstyle=\"bevel\",\n)\nplt.plot(\n 8 + np.arange(3),\n [0, 1, 0],\n color=\"blue\",\n dashes=[12, 5],\n linewidth=8,\n dash_joinstyle=\"round\",\n)\n\nplt.xlim(0, 12)\nplt.ylim(-1, 2)\nplt.xticks([])\nplt.yticks([])\n\nplt.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.11"
}
},
"nbformat": 4,
"nbformat_minor": 0
}

43
scientific-computing-2/auto_examples_jupyter_2/options/plot_linestyles.ipynb Normal file
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n# Linestyles\n\nPlot the different line styles.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np\nimport matplotlib.pyplot as plt\n\n\ndef linestyle(ls, i):\n X = i * 0.5 * np.ones(11)\n Y = np.arange(11)\n plt.plot(\n X,\n Y,\n ls,\n color=(0.0, 0.0, 1, 1),\n lw=3,\n ms=8,\n mfc=(0.75, 0.75, 1, 1),\n mec=(0, 0, 1, 1),\n )\n plt.text(0.5 * i, 10.25, ls, rotation=90, fontsize=15, va=\"bottom\")\n\n\nlinestyles = [\n \"-\",\n \"--\",\n \":\",\n \"-.\",\n \".\",\n \",\",\n \"o\",\n \"^\",\n \"v\",\n \"<\",\n \">\",\n \"s\",\n \"+\",\n \"x\",\n \"d\",\n \"1\",\n \"2\",\n \"3\",\n \"4\",\n \"h\",\n \"p\",\n \"|\",\n \"_\",\n \"D\",\n \"H\",\n]\nn_lines = len(linestyles)\n\nsize = 20 * n_lines, 300\ndpi = 72.0\nfigsize = size[0] / float(dpi), size[1] / float(dpi)\nfig = plt.figure(figsize=figsize, dpi=dpi)\nplt.axes((0, 0.01, 1, 0.9), frameon=False)\n\nfor i, ls in enumerate(linestyles):\n linestyle(ls, i)\n\nplt.xlim(-0.2, 0.2 + 0.5 * n_lines)\nplt.xticks([])\nplt.yticks([])\n\nplt.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.11"
}
},
"nbformat": 4,
"nbformat_minor": 0
}

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scientific-computing-2/auto_examples_jupyter_2/options/plot_linewidth.ipynb Normal file
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n# Linewidth\n\nPlot various linewidth with matplotlib.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n\nsize = 256, 16\ndpi = 72.0\nfigsize = size[0] / float(dpi), size[1] / float(dpi)\nfig = plt.figure(figsize=figsize, dpi=dpi)\nfig.patch.set_alpha(0)\nplt.axes((0, 0.1, 1, 0.8), frameon=False)\n\nfor i in range(1, 11):\n plt.plot([i, i], [0, 1], color=\"b\", lw=i / 2.0)\n\nplt.xlim(0, 11)\nplt.ylim(0, 1)\nplt.xticks([])\nplt.yticks([])\n\nplt.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.11"
}
},
"nbformat": 4,
"nbformat_minor": 0
}

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scientific-computing-2/auto_examples_jupyter_2/options/plot_markers.ipynb Normal file
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n# Markers\n\nShow the different markers of matplotlib.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np\nimport matplotlib.pyplot as plt\n\n\ndef marker(m, i):\n X = i * 0.5 * np.ones(11)\n Y = np.arange(11)\n\n plt.plot(X, Y, lw=1, marker=m, ms=10, mfc=(0.75, 0.75, 1, 1), mec=(0, 0, 1, 1))\n plt.text(0.5 * i, 10.25, repr(m), rotation=90, fontsize=15, va=\"bottom\")\n\n\nmarkers = [\n 0,\n 1,\n 2,\n 3,\n 4,\n 5,\n 6,\n 7,\n \"o\",\n \"h\",\n \"_\",\n \"1\",\n \"2\",\n \"3\",\n \"4\",\n \"8\",\n \"p\",\n \"^\",\n \"v\",\n \"<\",\n \">\",\n \"|\",\n \"d\",\n \",\",\n \"+\",\n \"s\",\n \"*\",\n \"|\",\n \"x\",\n \"D\",\n \"H\",\n \".\",\n]\n\nn_markers = len(markers)\n\nsize = 20 * n_markers, 300\ndpi = 72.0\nfigsize = size[0] / float(dpi), size[1] / float(dpi)\nfig = plt.figure(figsize=figsize, dpi=dpi)\nplt.axes((0, 0.01, 1, 0.9), frameon=False)\n\nfor i, m in enumerate(markers):\n marker(m, i)\n\nplt.xlim(-0.2, 0.2 + 0.5 * n_markers)\nplt.xticks([])\nplt.yticks([])\n\nplt.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.11"
}
},
"nbformat": 4,
"nbformat_minor": 0
}

43
scientific-computing-2/auto_examples_jupyter_2/options/plot_mec.ipynb Normal file
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n# Marker edge color\n\nDemo the marker edge color of matplotlib's markers.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np\nimport matplotlib.pyplot as plt\n\nsize = 256, 16\ndpi = 72.0\nfigsize = size[0] / float(dpi), size[1] / float(dpi)\nfig = plt.figure(figsize=figsize, dpi=dpi)\nfig.patch.set_alpha(0)\nplt.axes((0, 0, 1, 1), frameon=False)\n\nrng = np.random.default_rng()\n\nfor i in range(1, 11):\n r, g, b = np.random.uniform(0, 1, 3)\n plt.plot(\n [\n i,\n ],\n [\n 1,\n ],\n \"s\",\n markersize=5,\n markerfacecolor=\"w\",\n markeredgewidth=1.5,\n markeredgecolor=(r, g, b, 1),\n )\n\nplt.xlim(0, 11)\nplt.xticks([])\nplt.yticks([])\n\nplt.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.11"
}
},
"nbformat": 4,
"nbformat_minor": 0
}

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scientific-computing-2/auto_examples_jupyter_2/options/plot_mew.ipynb Normal file
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@@ -0,0 +1,43 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n# Marker edge width\n\nDemo the marker edge widths of matplotlib's markers.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n\nsize = 256, 16\ndpi = 72.0\nfigsize = size[0] / float(dpi), size[1] / float(dpi)\nfig = plt.figure(figsize=figsize, dpi=dpi)\nfig.patch.set_alpha(0)\nplt.axes((0, 0, 1, 1), frameon=False)\n\nfor i in range(1, 11):\n plt.plot(\n [\n i,\n ],\n [\n 1,\n ],\n \"s\",\n markersize=5,\n markeredgewidth=1 + i / 10.0,\n markeredgecolor=\"k\",\n markerfacecolor=\"w\",\n )\nplt.xlim(0, 11)\nplt.xticks([])\nplt.yticks([])\n\nplt.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.11"
}
},
"nbformat": 4,
"nbformat_minor": 0
}

43
scientific-computing-2/auto_examples_jupyter_2/options/plot_mfc.ipynb Normal file
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@@ -0,0 +1,43 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n# Marker face color\n\nDemo the marker face color of matplotlib's markers.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np\nimport matplotlib.pyplot as plt\n\nsize = 256, 16\ndpi = 72.0\nfigsize = size[0] / float(dpi), size[1] / float(dpi)\nfig = plt.figure(figsize=figsize, dpi=dpi)\nfig.patch.set_alpha(0)\nplt.axes((0, 0, 1, 1), frameon=False)\n\nrng = np.random.default_rng()\n\nfor i in range(1, 11):\n r, g, b = np.random.uniform(0, 1, 3)\n plt.plot(\n [\n i,\n ],\n [\n 1,\n ],\n \"s\",\n markersize=8,\n markerfacecolor=(r, g, b, 1),\n markeredgewidth=0.1,\n markeredgecolor=(0, 0, 0, 0.5),\n )\nplt.xlim(0, 11)\nplt.xticks([])\nplt.yticks([])\nplt.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.11"
}
},
"nbformat": 4,
"nbformat_minor": 0
}

43
scientific-computing-2/auto_examples_jupyter_2/options/plot_ms.ipynb Normal file
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@@ -0,0 +1,43 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n# Marker size\n\nDemo the marker size control in matplotlib.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n\nsize = 256, 16\ndpi = 72.0\nfigsize = size[0] / float(dpi), size[1] / float(dpi)\nfig = plt.figure(figsize=figsize, dpi=dpi)\nfig.patch.set_alpha(0)\nplt.axes((0, 0, 1, 1), frameon=False)\n\nfor i in range(1, 11):\n plt.plot(\n [\n i,\n ],\n [\n 1,\n ],\n \"s\",\n markersize=i,\n markerfacecolor=\"w\",\n markeredgewidth=0.5,\n markeredgecolor=\"k\",\n )\n\nplt.xlim(0, 11)\nplt.xticks([])\nplt.yticks([])\n\nplt.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.11"
}
},
"nbformat": 4,
"nbformat_minor": 0
}

43
scientific-computing-2/auto_examples_jupyter_2/options/plot_solid_capstyle.ipynb Normal file
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n# Solid cap style\n\nAn example demoing the solide cap style in matplotlib.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np\nimport matplotlib.pyplot as plt\n\nsize = 256, 16\ndpi = 72.0\nfigsize = size[0] / float(dpi), size[1] / float(dpi)\nfig = plt.figure(figsize=figsize, dpi=dpi)\nfig.patch.set_alpha(0)\nplt.axes((0, 0, 1, 1), frameon=False)\n\nplt.plot(np.arange(4), np.ones(4), color=\"blue\", linewidth=8, solid_capstyle=\"butt\")\n\nplt.plot(\n 5 + np.arange(4), np.ones(4), color=\"blue\", linewidth=8, solid_capstyle=\"round\"\n)\n\nplt.plot(\n 10 + np.arange(4),\n np.ones(4),\n color=\"blue\",\n linewidth=8,\n solid_capstyle=\"projecting\",\n)\n\nplt.xlim(0, 14)\nplt.xticks([])\nplt.yticks([])\n\nplt.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.11"
}
},
"nbformat": 4,
"nbformat_minor": 0
}

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n# Solid joint style\n\nAn example showing the different solid joint styles in matplotlib.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np\nimport matplotlib.pyplot as plt\n\nsize = 256, 16\ndpi = 72.0\nfigsize = size[0] / float(dpi), size[1] / float(dpi)\nfig = plt.figure(figsize=figsize, dpi=dpi)\nfig.patch.set_alpha(0)\nplt.axes((0, 0, 1, 1), frameon=False)\n\nplt.plot(np.arange(3), [0, 1, 0], color=\"blue\", linewidth=8, solid_joinstyle=\"miter\")\nplt.plot(\n 4 + np.arange(3), [0, 1, 0], color=\"blue\", linewidth=8, solid_joinstyle=\"bevel\"\n)\nplt.plot(\n 8 + np.arange(3), [0, 1, 0], color=\"blue\", linewidth=8, solid_joinstyle=\"round\"\n)\n\nplt.xlim(0, 12)\nplt.ylim(-1, 2)\nplt.xticks([])\nplt.yticks([])\n\nplt.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.11"
}
},
"nbformat": 4,
"nbformat_minor": 0
}

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n# Locators for tick on axis\n\nAn example demoing different locators to position ticks on axis for\nmatplotlib.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np\n\nfrom matplotlib import ticker\nimport matplotlib.pyplot as plt\n\n\ndef tickline():\n plt.xlim(0, 10), plt.ylim(-1, 1), plt.yticks([])\n ax = plt.gca()\n ax.spines[\"right\"].set_color(\"none\")\n ax.spines[\"left\"].set_color(\"none\")\n ax.spines[\"top\"].set_color(\"none\")\n ax.xaxis.set_ticks_position(\"bottom\")\n ax.spines[\"bottom\"].set_position((\"data\", 0))\n ax.yaxis.set_ticks_position(\"none\")\n ax.xaxis.set_minor_locator(ticker.MultipleLocator(0.1))\n ax.plot(np.arange(11), np.zeros(11))\n return ax\n\n\nlocators = [\n \"ticker.NullLocator()\",\n \"ticker.MultipleLocator(1.0)\",\n \"ticker.FixedLocator([0, 2, 8, 9, 10])\",\n \"ticker.IndexLocator(3, 1)\",\n \"ticker.LinearLocator(5)\",\n \"ticker.LogLocator(2, [1.0])\",\n \"ticker.AutoLocator()\",\n]\n\nn_locators = len(locators)\n\nsize = 512, 40 * n_locators\ndpi = 72.0\nfigsize = size[0] / float(dpi), size[1] / float(dpi)\nfig = plt.figure(figsize=figsize, dpi=dpi)\nfig.patch.set_alpha(0)\n\n\nfor i, locator in enumerate(locators):\n plt.subplot(n_locators, 1, i + 1)\n ax = tickline()\n ax.xaxis.set_major_locator(eval(locator))\n plt.text(5, 0.3, locator[7:], ha=\"center\")\n\nplt.subplots_adjust(bottom=0.01, top=0.99, left=0.01, right=0.99)\nplt.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.11"
}
},
"nbformat": 4,
"nbformat_minor": 0
}

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"# Subplots\n",
"\n",
"Show multiple subplots in matplotlib.\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"outputs": [
{
"data": {
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"text/plain": [
"<Figure size 640x480 with 4 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"fig = plt.figure()\n",
"fig.subplots_adjust(bottom=0.025, left=0.025, top=0.975, right=0.975)\n",
"\n",
"plt.subplot(2, 1, 1)\n",
"plt.xticks([]), plt.yticks([])\n",
"\n",
"plt.subplot(2, 3, 4)\n",
"plt.xticks([])\n",
"plt.yticks([])\n",
"\n",
"plt.subplot(2, 3, 5)\n",
"plt.xticks([])\n",
"plt.yticks([])\n",
"\n",
"plt.subplot(2, 3, 6)\n",
"plt.xticks([])\n",
"plt.yticks([])\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.11"
}
},
"nbformat": 4,
"nbformat_minor": 4
}

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n# Plotting a vector field: quiver\n\nA simple example showing how to plot a vector field (quiver) with\nmatplotlib.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np\nimport matplotlib.pyplot as plt\n\nn = 8\nX, Y = np.mgrid[0:n, 0:n]\nT = np.arctan2(Y - n / 2.0, X - n / 2.0)\nR = 10 + np.sqrt((Y - n / 2.0) ** 2 + (X - n / 2.0) ** 2)\nU, V = R * np.cos(T), R * np.sin(T)\n\nplt.axes((0.025, 0.025, 0.95, 0.95))\nplt.quiver(X, Y, U, V, R, alpha=0.5)\nplt.quiver(X, Y, U, V, edgecolor=\"k\", facecolor=\"None\", linewidth=0.5)\n\nplt.xlim(-1, n)\nplt.xticks([])\nplt.ylim(-1, n)\nplt.yticks([])\n\nplt.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.11"
}
},
"nbformat": 4,
"nbformat_minor": 0
}

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n# Subplot grid\n\nAn example showing the subplot grid in matplotlib.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n\nplt.figure(figsize=(6, 4))\nplt.subplot(2, 2, 1)\nplt.xticks([])\nplt.yticks([])\nplt.text(0.5, 0.5, \"subplot(2,2,1)\", ha=\"center\", va=\"center\", size=20, alpha=0.5)\n\nplt.subplot(2, 2, 2)\nplt.xticks([])\nplt.yticks([])\nplt.text(0.5, 0.5, \"subplot(2,2,2)\", ha=\"center\", va=\"center\", size=20, alpha=0.5)\n\nplt.subplot(2, 2, 3)\nplt.xticks([])\nplt.yticks([])\n\nplt.text(0.5, 0.5, \"subplot(2,2,3)\", ha=\"center\", va=\"center\", size=20, alpha=0.5)\n\nplt.subplot(2, 2, 4)\nplt.xticks([])\nplt.yticks([])\nplt.text(0.5, 0.5, \"subplot(2,2,4)\", ha=\"center\", va=\"center\", size=20, alpha=0.5)\n\nplt.tight_layout()\nplt.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.11"
}
},
"nbformat": 4,
"nbformat_minor": 0
}

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n# Horizontal arrangement of subplots\n\nAn example showing horizontal arrangement of subplots with matplotlib.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n\nplt.figure(figsize=(6, 4))\nplt.subplot(2, 1, 1)\nplt.xticks([])\nplt.yticks([])\nplt.text(0.5, 0.5, \"subplot(2,1,1)\", ha=\"center\", va=\"center\", size=24, alpha=0.5)\n\nplt.subplot(2, 1, 2)\nplt.xticks([])\nplt.yticks([])\nplt.text(0.5, 0.5, \"subplot(2,1,2)\", ha=\"center\", va=\"center\", size=24, alpha=0.5)\n\nplt.tight_layout()\nplt.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.11"
}
},
"nbformat": 4,
"nbformat_minor": 0
}

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n# Subplot plot arrangement vertical\n\nAn example showing vertical arrangement of subplots with matplotlib.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n\n\nplt.figure(figsize=(6, 4))\nplt.subplot(1, 2, 1)\nplt.xticks([])\nplt.yticks([])\nplt.text(0.5, 0.5, \"subplot(1,2,1)\", ha=\"center\", va=\"center\", size=24, alpha=0.5)\n\nplt.subplot(1, 2, 2)\nplt.xticks([])\nplt.yticks([])\nplt.text(0.5, 0.5, \"subplot(1,2,2)\", ha=\"center\", va=\"center\", size=24, alpha=0.5)\n\nplt.tight_layout()\nplt.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.11"
}
},
"nbformat": 4,
"nbformat_minor": 0
}

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n# Demo text printing\n\nA example showing off elaborate text printing with matplotlib.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np\nimport matplotlib.pyplot as plt\n\n\neqs = []\neqs.append(\n r\"$W^{3\\beta}_{\\delta_1 \\rho_1 \\sigma_2} = U^{3\\beta}_{\\delta_1 \\rho_1} + \\frac{1}{8 \\pi 2} \\int^{\\alpha_2}_{\\alpha_2} d \\alpha^\\prime_2 \\left[\\frac{ U^{2\\beta}_{\\delta_1 \\rho_1} - \\alpha^\\prime_2U^{1\\beta}_{\\rho_1 \\sigma_2} }{U^{0\\beta}_{\\rho_1 \\sigma_2}}\\right]$\"\n)\neqs.append(\n r\"$\\frac{d\\rho}{d t} + \\rho \\vec{v}\\cdot\\nabla\\vec{v} = -\\nabla p + \\mu\\nabla^2 \\vec{v} + \\rho \\vec{g}$\"\n)\neqs.append(r\"$\\int_{-\\infty}^\\infty e^{-x^2}dx=\\sqrt{\\pi}$\")\neqs.append(r\"$E = mc^2 = \\sqrt{{m_0}^2c^4 + p^2c^2}$\")\neqs.append(r\"$F_G = G\\frac{m_1m_2}{r^2}$\")\n\nplt.axes((0.025, 0.025, 0.95, 0.95))\n\nrng = np.random.default_rng()\n\nfor i in range(24):\n index = rng.integers(0, len(eqs))\n eq = eqs[index]\n size = np.random.uniform(12, 32)\n x, y = np.random.uniform(0, 1, 2)\n alpha = np.random.uniform(0.25, 0.75)\n plt.text(\n x,\n y,\n eq,\n ha=\"center\",\n va=\"center\",\n color=\"#11557c\",\n alpha=alpha,\n transform=plt.gca().transAxes,\n fontsize=size,\n clip_on=True,\n )\nplt.xticks([])\nplt.yticks([])\n\nplt.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.11"
}
},
"nbformat": 4,
"nbformat_minor": 0
}

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n# Bar plot advanced\n\nAn more elaborate bar plot example\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np\nimport matplotlib.pyplot as plt\n\nn = 16\nX = np.arange(n)\nY1 = (1 - X / float(n)) * np.random.uniform(0.5, 1.0, n)\nY2 = (1 - X / float(n)) * np.random.uniform(0.5, 1.0, n)\nplt.bar(X, Y1, facecolor=\"#9999ff\", edgecolor=\"white\")\nplt.bar(X, -Y2, facecolor=\"#ff9999\", edgecolor=\"white\")\nplt.xlim(-0.5, n)\nplt.xticks([])\nplt.ylim(-1, 1)\nplt.yticks([])\n\n\n# Add a title and a box around it\nfrom matplotlib.patches import FancyBboxPatch\n\nax = plt.gca()\nax.add_patch(\n FancyBboxPatch(\n (-0.05, 0.87),\n width=0.66,\n height=0.165,\n clip_on=False,\n boxstyle=\"square,pad=0\",\n zorder=3,\n facecolor=\"white\",\n alpha=1.0,\n transform=plt.gca().transAxes,\n )\n)\n\nplt.text(\n -0.05,\n 1.02,\n \" Bar Plot: plt.bar(...)\\n\",\n horizontalalignment=\"left\",\n verticalalignment=\"top\",\n size=\"xx-large\",\n transform=plt.gca().transAxes,\n)\n\nplt.text(\n -0.05,\n 1.01,\n \"\\n\\n Make a bar plot with rectangles \",\n horizontalalignment=\"left\",\n verticalalignment=\"top\",\n size=\"large\",\n transform=plt.gca().transAxes,\n)\n\nplt.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.11"
}
},
"nbformat": 4,
"nbformat_minor": 0
}

43
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n# Boxplot with matplotlib\n\nAn example of doing box plots with matplotlib\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np\nimport matplotlib.pyplot as plt\n\n\nfig = plt.figure(figsize=(8, 5))\naxes = plt.subplot(111)\n\nn = 5\nZ = np.zeros((n, 4))\nX = np.linspace(0, 2, n)\nrng = np.random.default_rng()\nY = rng.random((n, 4))\nplt.boxplot(Y)\n\nplt.xticks([])\nplt.yticks([])\n\n\n# Add a title and a box around it\nfrom matplotlib.patches import FancyBboxPatch\n\nax = plt.gca()\nax.add_patch(\n FancyBboxPatch(\n (-0.05, 0.87),\n width=0.66,\n height=0.165,\n clip_on=False,\n boxstyle=\"square,pad=0\",\n zorder=3,\n facecolor=\"white\",\n alpha=1.0,\n transform=plt.gca().transAxes,\n )\n)\n\nplt.text(\n -0.05,\n 1.02,\n \" Box Plot: plt.boxplot(...)\\n \",\n horizontalalignment=\"left\",\n verticalalignment=\"top\",\n size=\"xx-large\",\n transform=axes.transAxes,\n)\n\nplt.text(\n -0.04,\n 0.98,\n \"\\n Make a box and whisker plot \",\n horizontalalignment=\"left\",\n verticalalignment=\"top\",\n size=\"large\",\n transform=axes.transAxes,\n)\n\nplt.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.11"
}
},
"nbformat": 4,
"nbformat_minor": 0
}

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n# Display the contours of a function\n\nAn example demoing how to plot the contours of a function, with\nadditional layout tweaks.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np\nimport matplotlib.pyplot as plt\n\n\ndef f(x, y):\n return (1 - x / 2 + x**5 + y**3) * np.exp(-(x**2) - y**2)\n\n\nn = 256\nx = np.linspace(-3, 3, n)\ny = np.linspace(-3, 3, n)\nX, Y = np.meshgrid(x, y)\n\nplt.contourf(X, Y, f(X, Y), 8, alpha=0.75, cmap=\"hot\")\nC = plt.contour(X, Y, f(X, Y), 8, colors=\"black\", linewidth=0.5)\nplt.clabel(C, inline=1, fontsize=10)\nplt.xticks([])\nplt.yticks([])\n\n\n# Add a title and a box around it\nfrom matplotlib.patches import FancyBboxPatch\n\nax = plt.gca()\nax.add_patch(\n FancyBboxPatch(\n (-0.05, 0.87),\n width=0.66,\n height=0.165,\n clip_on=False,\n boxstyle=\"square,pad=0\",\n zorder=3,\n facecolor=\"white\",\n alpha=1.0,\n transform=plt.gca().transAxes,\n )\n)\n\nplt.text(\n -0.05,\n 1.02,\n \" Contour Plot: plt.contour(..)\\n\",\n horizontalalignment=\"left\",\n verticalalignment=\"top\",\n size=\"xx-large\",\n transform=plt.gca().transAxes,\n)\n\nplt.text(\n -0.05,\n 1.01,\n \"\\n\\n Draw contour lines and filled contours \",\n horizontalalignment=\"left\",\n verticalalignment=\"top\",\n size=\"large\",\n transform=plt.gca().transAxes,\n)\n\nplt.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.11"
}
},
"nbformat": 4,
"nbformat_minor": 0
}

43
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@@ -0,0 +1,43 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n# Grid elaborate\n\nAn example displaying a grid on the axes and tweaking the layout.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\nfrom matplotlib.ticker import MultipleLocator\n\nfig = plt.figure(figsize=(8, 6), dpi=72, facecolor=\"white\")\naxes = plt.subplot(111)\naxes.set_xlim(0, 4)\naxes.set_ylim(0, 3)\n\naxes.xaxis.set_major_locator(MultipleLocator(1.0))\naxes.xaxis.set_minor_locator(MultipleLocator(0.1))\naxes.yaxis.set_major_locator(MultipleLocator(1.0))\naxes.yaxis.set_minor_locator(MultipleLocator(0.1))\naxes.grid(which=\"major\", axis=\"x\", linewidth=0.75, linestyle=\"-\", color=\"0.75\")\naxes.grid(which=\"minor\", axis=\"x\", linewidth=0.25, linestyle=\"-\", color=\"0.75\")\naxes.grid(which=\"major\", axis=\"y\", linewidth=0.75, linestyle=\"-\", color=\"0.75\")\naxes.grid(which=\"minor\", axis=\"y\", linewidth=0.25, linestyle=\"-\", color=\"0.75\")\naxes.set_xticklabels([])\naxes.set_yticklabels([])\n\n\n# Add a title and a box around it\nfrom matplotlib.patches import FancyBboxPatch\n\nax = plt.gca()\nax.add_patch(\n FancyBboxPatch(\n (-0.05, 0.87),\n width=0.66,\n height=0.165,\n clip_on=False,\n boxstyle=\"square,pad=0\",\n zorder=3,\n facecolor=\"white\",\n alpha=1.0,\n transform=plt.gca().transAxes,\n )\n)\n\nplt.text(\n -0.05,\n 1.02,\n \" Grid: plt.grid(...)\\n\",\n horizontalalignment=\"left\",\n verticalalignment=\"top\",\n size=\"xx-large\",\n transform=axes.transAxes,\n)\n\nplt.text(\n -0.05,\n 1.01,\n \"\\n\\n Draw ticks and grid \",\n horizontalalignment=\"left\",\n verticalalignment=\"top\",\n size=\"large\",\n transform=axes.transAxes,\n)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.11"
}
},
"nbformat": 4,
"nbformat_minor": 0
}

43
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@@ -0,0 +1,43 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n# Imshow demo\n\nDemoing imshow\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np\nimport matplotlib.pyplot as plt\n\n\ndef f(x, y):\n return (1 - x / 2 + x**5 + y**3) * np.exp(-(x**2) - y**2)\n\n\nn = 10\nx = np.linspace(-3, 3, 8 * n)\ny = np.linspace(-3, 3, 6 * n)\nX, Y = np.meshgrid(x, y)\nZ = f(X, Y)\nplt.imshow(Z, interpolation=\"nearest\", cmap=\"bone\", origin=\"lower\")\nplt.xticks([])\nplt.yticks([])\n\n\n# Add a title and a box around it\nfrom matplotlib.patches import FancyBboxPatch\n\nax = plt.gca()\nax.add_patch(\n FancyBboxPatch(\n (-0.05, 0.87),\n width=0.66,\n height=0.165,\n clip_on=False,\n boxstyle=\"square,pad=0\",\n zorder=3,\n facecolor=\"white\",\n alpha=1.0,\n transform=plt.gca().transAxes,\n )\n)\n\nplt.text(\n -0.05,\n 1.02,\n \" Imshow: plt.imshow(...)\\n\",\n horizontalalignment=\"left\",\n verticalalignment=\"top\",\n size=\"xx-large\",\n transform=plt.gca().transAxes,\n)\n\nplt.text(\n -0.05,\n 1.01,\n \"\\n\\n Display an image to current axes \",\n horizontalalignment=\"left\",\n verticalalignment=\"top\",\n family=\"DejaVu Sans\",\n size=\"large\",\n transform=plt.gca().transAxes,\n)\n\nplt.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.11"
}
},
"nbformat": 4,
"nbformat_minor": 0
}

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@@ -0,0 +1,43 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n# Multiple plots vignette\n\nDemo multiple plots and style the figure.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n\nax = plt.subplot(2, 1, 1)\nax.set_xticklabels([])\nax.set_yticklabels([])\n\n\n# Add a title and a box around it\nfrom matplotlib.patches import FancyBboxPatch\n\nax = plt.gca()\nax.add_patch(\n FancyBboxPatch(\n (-0.05, 0.72),\n width=0.66,\n height=0.34,\n clip_on=False,\n boxstyle=\"square,pad=0\",\n zorder=3,\n facecolor=\"white\",\n alpha=1.0,\n transform=plt.gca().transAxes,\n )\n)\n\nplt.text(\n -0.05,\n 1.02,\n \" Multiplot: plt.subplot(...)\\n\",\n horizontalalignment=\"left\",\n verticalalignment=\"top\",\n size=\"xx-large\",\n transform=ax.transAxes,\n)\nplt.text(\n -0.05,\n 1.01,\n \"\\n\\n Plot several plots at once \",\n horizontalalignment=\"left\",\n verticalalignment=\"top\",\n size=\"large\",\n transform=ax.transAxes,\n)\n\nax = plt.subplot(2, 2, 3)\nax.set_xticklabels([])\nax.set_yticklabels([])\n\nax = plt.subplot(2, 2, 4)\nax.set_xticklabels([])\nax.set_yticklabels([])\n\nplt.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.11"
}
},
"nbformat": 4,
"nbformat_minor": 0
}

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@@ -0,0 +1,43 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n# Pie chart vignette\n\nDemo pie chart with matplotlib and style the figure.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np\nimport matplotlib.pyplot as plt\n\nn = 20\nX = np.ones(n)\nX[-1] *= 2\nplt.pie(X, explode=X * 0.05, colors=[f\"{i / float(n):f}\" for i in range(n)])\n\nfig = plt.gcf()\nw, h = fig.get_figwidth(), fig.get_figheight()\nr = h / float(w)\n\nplt.xlim(-1.5, 1.5)\nplt.ylim(-1.5 * r, 1.5 * r)\nplt.xticks([])\nplt.yticks([])\n\n\n# Add a title and a box around it\nfrom matplotlib.patches import FancyBboxPatch\n\nax = plt.gca()\nax.add_patch(\n FancyBboxPatch(\n (-0.05, 0.87),\n width=0.66,\n height=0.165,\n clip_on=False,\n boxstyle=\"square,pad=0\",\n zorder=3,\n facecolor=\"white\",\n alpha=1.0,\n transform=plt.gca().transAxes,\n )\n)\n\nplt.text(\n -0.05,\n 1.02,\n \" Pie Chart: plt.pie(...)\\n\",\n horizontalalignment=\"left\",\n verticalalignment=\"top\",\n size=\"xx-large\",\n transform=plt.gca().transAxes,\n)\n\nplt.text(\n -0.05,\n 1.01,\n \"\\n\\n Make a pie chart of an array \",\n horizontalalignment=\"left\",\n verticalalignment=\"top\",\n size=\"large\",\n transform=plt.gca().transAxes,\n)\n\nplt.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.11"
}
},
"nbformat": 4,
"nbformat_minor": 0
}

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@@ -0,0 +1,43 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n# 3D plotting vignette\n\nDemo 3D plotting with matplotlib and decorate the figure.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.mplot3d import Axes3D\n\nx = np.arange(-4, 4, 0.25)\ny = np.arange(-4, 4, 0.25)\nX, Y = np.meshgrid(x, y)\nR = np.sqrt(X**2 + Y**2)\nZ = np.sin(R)\n\nfig = plt.figure()\nax: Axes3D = fig.add_subplot(111, projection=\"3d\")\n\nax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=\"hot\")\nax.contourf(X, Y, Z, zdir=\"z\", offset=-2, cmap=\"hot\")\n\nax.set_zlim(-2, 2)\nplt.xticks([])\nplt.yticks([])\nax.set_zticks([])\n\nax.text2D(\n 0.05,\n 0.93,\n \" 3D plots \\n\",\n horizontalalignment=\"left\",\n verticalalignment=\"top\",\n size=\"xx-large\",\n bbox={\"facecolor\": \"white\", \"alpha\": 1.0},\n transform=plt.gca().transAxes,\n)\n\nax.text2D(\n 0.05,\n 0.87,\n \" Plot 2D or 3D data\",\n horizontalalignment=\"left\",\n verticalalignment=\"top\",\n size=\"large\",\n transform=plt.gca().transAxes,\n)\n\nplt.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.11"
}
},
"nbformat": 4,
"nbformat_minor": 0
}

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@@ -0,0 +1,43 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n# Plot example vignette\n\nAn example of plots with matplotlib, and added annotations.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np\nimport matplotlib.pyplot as plt\n\nn = 256\nX = np.linspace(0, 2, n)\nY = np.sin(2 * np.pi * X)\n\nplt.plot(X, Y, lw=2, color=\"violet\")\nplt.xlim(-0.2, 2.2)\nplt.xticks([])\nplt.ylim(-1.2, 1.2)\nplt.yticks([])\n\n\n# Add a title and a box around it\nfrom matplotlib.patches import FancyBboxPatch\n\nax = plt.gca()\nax.add_patch(\n FancyBboxPatch(\n (-0.05, 0.87),\n width=0.66,\n height=0.165,\n clip_on=False,\n boxstyle=\"square,pad=0\",\n zorder=3,\n facecolor=\"white\",\n alpha=1.0,\n transform=plt.gca().transAxes,\n )\n)\n\nplt.text(\n -0.05,\n 1.02,\n \" Regular Plot: plt.plot(...)\\n\",\n horizontalalignment=\"left\",\n verticalalignment=\"top\",\n size=\"xx-large\",\n transform=plt.gca().transAxes,\n)\n\nplt.text(\n -0.05,\n 1.01,\n \"\\n\\n Plot lines and/or markers \",\n horizontalalignment=\"left\",\n verticalalignment=\"top\",\n size=\"large\",\n transform=plt.gca().transAxes,\n)\n\nplt.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
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"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.11"
}
},
"nbformat": 4,
"nbformat_minor": 0
}

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n# Plotting in polar, decorated\n\nAn example showing how to plot in polar coordinate, and some\ndecorations.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np\n\nimport matplotlib\nimport matplotlib.pyplot as plt\n\n\nplt.subplot(1, 1, 1, polar=True)\n\nN = 20\ntheta = np.arange(0.0, 2 * np.pi, 2 * np.pi / N)\nrng = np.random.default_rng()\nradii = 10 * rng.random(N)\nwidth = np.pi / 4 * rng.random(N)\nbars = plt.bar(theta, radii, width=width, bottom=0.0)\njet = matplotlib.colormaps[\"jet\"]\n\nfor r, bar in zip(radii, bars, strict=True):\n bar.set_facecolor(jet(r / 10.0))\n bar.set_alpha(0.5)\nplt.gca().set_xticklabels([])\nplt.gca().set_yticklabels([])\n\n\nplt.text(\n -0.2,\n 1.02,\n \" Polar Axis \\n\",\n horizontalalignment=\"left\",\n verticalalignment=\"top\",\n size=\"xx-large\",\n bbox={\"facecolor\": \"white\", \"alpha\": 1.0},\n transform=plt.gca().transAxes,\n)\n\nplt.text(\n -0.2,\n 1.01,\n \"\\n\\n Plot anything using polar axis \",\n horizontalalignment=\"left\",\n verticalalignment=\"top\",\n size=\"large\",\n transform=plt.gca().transAxes,\n)\n\nplt.show()"
]
}
],
"metadata": {
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"display_name": "Python 3",
"language": "python",
"name": "python3"
},
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"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.11"
}
},
"nbformat": 4,
"nbformat_minor": 0
}

43
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n# Plotting quiver decorated\n\nAn example showing quiver with decorations.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np\nimport matplotlib.pyplot as plt\n\nn = 8\nX, Y = np.mgrid[0:n, 0:n]\nT = np.arctan2(Y - n / 2.0, X - n / 2.0)\nR = 10 + np.sqrt((Y - n / 2.0) ** 2 + (X - n / 2.0) ** 2)\nU, V = R * np.cos(T), R * np.sin(T)\n\nplt.quiver(X, Y, U, V, R, alpha=0.5)\nplt.quiver(X, Y, U, V, edgecolor=\"k\", facecolor=\"None\", linewidth=0.5)\n\nplt.xlim(-1, n)\nplt.xticks([])\nplt.ylim(-1, n)\nplt.yticks([])\n\n\n# Add a title and a box around it\nfrom matplotlib.patches import FancyBboxPatch\n\nax = plt.gca()\nax.add_patch(\n FancyBboxPatch(\n (-0.05, 0.87),\n width=0.66,\n height=0.165,\n clip_on=False,\n boxstyle=\"square,pad=0\",\n zorder=3,\n facecolor=\"white\",\n alpha=1.0,\n transform=plt.gca().transAxes,\n )\n)\n\nplt.text(\n -0.05,\n 1.02,\n \" Quiver Plot: plt.quiver(...)\\n\",\n horizontalalignment=\"left\",\n verticalalignment=\"top\",\n size=\"xx-large\",\n transform=plt.gca().transAxes,\n)\n\nplt.text(\n -0.05,\n 1.01,\n \"\\n\\n Plot a 2-D field of arrows \",\n horizontalalignment=\"left\",\n verticalalignment=\"top\",\n size=\"large\",\n transform=plt.gca().transAxes,\n)\n\n\nplt.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.11"
}
},
"nbformat": 4,
"nbformat_minor": 0
}

43
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n# Plot scatter decorated\n\nAn example showing the scatter function, with decorations.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np\nimport matplotlib.pyplot as plt\n\nn = 1024\nrng = np.random.default_rng()\nX = rng.normal(0, 1, n)\nY = rng.normal(0, 1, n)\n\nT = np.arctan2(Y, X)\n\nplt.scatter(X, Y, s=75, c=T, alpha=0.5)\nplt.xlim(-1.5, 1.5)\nplt.xticks([])\nplt.ylim(-1.5, 1.5)\nplt.yticks([])\n\n\n# Add a title and a box around it\nfrom matplotlib.patches import FancyBboxPatch\n\nax = plt.gca()\nax.add_patch(\n FancyBboxPatch(\n (-0.05, 0.87),\n width=0.66,\n height=0.165,\n clip_on=False,\n boxstyle=\"square,pad=0\",\n zorder=3,\n facecolor=\"white\",\n alpha=1.0,\n transform=plt.gca().transAxes,\n )\n)\n\nplt.text(\n -0.05,\n 1.02,\n \" Scatter Plot: plt.scatter(...)\\n\",\n horizontalalignment=\"left\",\n verticalalignment=\"top\",\n size=\"xx-large\",\n transform=plt.gca().transAxes,\n)\n\nplt.text(\n -0.05,\n 1.01,\n \"\\n\\n Make a scatter plot of x versus y \",\n horizontalalignment=\"left\",\n verticalalignment=\"top\",\n size=\"large\",\n transform=plt.gca().transAxes,\n)\n\nplt.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
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}
},
"nbformat": 4,
"nbformat_minor": 0
}

43
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@@ -0,0 +1,43 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n# Text printing decorated\n\nAn example showing text printing and decorating the resulting figure.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np\nimport matplotlib.pyplot as plt\n\nfig = plt.figure()\nplt.xticks([])\nplt.yticks([])\n\neqs = []\neqs.append(\n r\"$W^{3\\beta}_{\\delta_1 \\rho_1 \\sigma_2} = U^{3\\beta}_{\\delta_1 \\rho_1} + \\frac{1}{8 \\pi 2} \\int^{\\alpha_2}_{\\alpha_2} d \\alpha^\\prime_2 \\left[\\frac{ U^{2\\beta}_{\\delta_1 \\rho_1} - \\alpha^\\prime_2U^{1\\beta}_{\\rho_1 \\sigma_2} }{U^{0\\beta}_{\\rho_1 \\sigma_2}}\\right]$\"\n)\neqs.append(\n r\"$\\frac{d\\rho}{d t} + \\rho \\vec{v}\\cdot\\nabla\\vec{v} = -\\nabla p + \\mu\\nabla^2 \\vec{v} + \\rho \\vec{g}$\"\n)\neqs.append(r\"$\\int_{-\\infty}^\\infty e^{-x^2}dx=\\sqrt{\\pi}$\")\neqs.append(r\"$E = mc^2 = \\sqrt{{m_0}^2c^4 + p^2c^2}$\")\neqs.append(r\"$F_G = G\\frac{m_1m_2}{r^2}$\")\n\nrng = np.random.default_rng()\n\nfor i in range(24):\n index = rng.integers(0, len(eqs))\n eq = eqs[index]\n size = rng.uniform(12, 32)\n x, y = rng.uniform(0, 1, 2)\n alpha = rng.uniform(0.25, 0.75)\n plt.text(\n x,\n y,\n eq,\n ha=\"center\",\n va=\"center\",\n color=\"#11557c\",\n alpha=alpha,\n transform=plt.gca().transAxes,\n fontsize=size,\n clip_on=True,\n )\n\n\n# Add a title and a box around it\nfrom matplotlib.patches import FancyBboxPatch\n\nax = plt.gca()\nax.add_patch(\n FancyBboxPatch(\n (-0.05, 0.87),\n width=0.66,\n height=0.165,\n clip_on=False,\n boxstyle=\"square,pad=0\",\n zorder=3,\n facecolor=\"white\",\n alpha=1.0,\n transform=plt.gca().transAxes,\n )\n)\n\nplt.text(\n -0.05,\n 1.02,\n \" Text: plt.text(...)\\n\",\n horizontalalignment=\"left\",\n verticalalignment=\"top\",\n size=\"xx-large\",\n transform=plt.gca().transAxes,\n)\n\nplt.text(\n -0.05,\n 1.01,\n \"\\n\\n Draw any kind of text \",\n horizontalalignment=\"left\",\n verticalalignment=\"top\",\n size=\"large\",\n transform=plt.gca().transAxes,\n)\n\nplt.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.11"
}
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"nbformat_minor": 0
}