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misc python code

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ys
2024-12-20 21:50:09 +00:00
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"\n",
"from pylatex import Document, Section, Subsection, Math, Matrix, VectorName\n",
"\n",
"if __name__ == '__main__':\n",
" a = np.array([[100, 10, 20]]).T\n",
"\n",
" doc = Document()\n",
" section = Section('Numpy tests')\n",
" subsection = Subsection('Array')\n",
"\n",
" vec = Matrix(a)\n",
" vec_name = VectorName('a')\n",
" math = Math(data=[vec_name, '=', vec])\n",
"\n",
" subsection.append(math)\n",
" section.append(subsection)\n",
"\n",
" subsection = Subsection('Matrix')\n",
" M = np.matrix([[2, 3, 4],\n",
" [0, 0, 1],\n",
" [0, 0, 2]])\n",
" matrix = Matrix(M, mtype='b')\n",
" math = Math(data=['M=', matrix])\n",
"\n",
" subsection.append(math)\n",
" section.append(subsection)\n",
"\n",
" subsection = Subsection('Product')\n",
"\n",
" math = Math(data=['M', vec_name, '=', Matrix(M * a)])\n",
" subsection.append(math)\n",
"\n",
" section.append(subsection)\n",
"\n",
" doc.append(section)\n",
" doc.generate_pdf('numpy_ex', clean_tex=False)"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/home/ys/.pyenv/versions/3.12.0/bin/pip\n",
"asttokens==3.0.0\n",
"comm==0.2.2\n",
"debugpy==1.8.9\n",
"decorator==5.1.1\n",
"executing==2.1.0\n",
"ipykernel==6.29.5\n",
"ipython==8.30.0\n",
"jax==0.4.35\n",
"jax-cuda12-pjrt==0.4.35\n",
"jax-cuda12-plugin==0.4.35\n",
"jaxlib==0.4.34\n",
"jedi==0.19.2\n",
"jupyter_client==8.6.3\n",
"jupyter_core==5.7.2\n",
"matplotlib-inline==0.1.7\n",
"ml_dtypes==0.5.0\n",
"nest-asyncio==1.6.0\n",
"numpy==2.1.3\n",
"nvidia-cublas-cu12==12.6.4.1\n",
"nvidia-cuda-cupti-cu12==12.6.80\n",
"nvidia-cuda-nvcc-cu12==12.6.85\n",
"nvidia-cuda-runtime-cu12==12.6.77\n",
"nvidia-cudnn-cu12==9.5.1.17\n",
"nvidia-cufft-cu12==11.3.0.4\n",
"nvidia-cusolver-cu12==11.7.1.2\n",
"nvidia-cusparse-cu12==12.5.4.2\n",
"nvidia-nccl-cu12==2.23.4\n",
"nvidia-nvjitlink-cu12==12.6.85\n",
"opt_einsum==3.4.0\n",
"packaging==24.2\n",
"parso==0.8.4\n",
"pexpect==4.9.0\n",
"platformdirs==4.3.6\n",
"prompt_toolkit==3.0.48\n",
"psutil==6.1.0\n",
"ptyprocess==0.7.0\n",
"pure_eval==0.2.3\n",
"Pygments==2.18.0\n",
"python-dateutil==2.9.0.post0\n",
"pyzmq==26.2.0\n",
"scipy==1.14.1\n",
"six==1.16.0\n",
"stack-data==0.6.3\n",
"tornado==6.4.2\n",
"traitlets==5.14.3\n",
"wcwidth==0.2.13\n"
]
}
],
"source": [
"!pyenv which pip\n",
"!pip freeze\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import jax\n",
"import jax.numpy as jnp\n",
"\n",
"x = jnp.arange(5)\n",
"isinstance(x, jax.Array)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{CpuDevice(id=0)}"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x.devices()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0. 1.05 2.1 3.1499999 4.2 ]\n"
]
}
],
"source": [
"import jax.numpy as jnp\n",
"def selu(x, alpha=1.67, lmbda=1.05):\n",
" return lmbda * jnp.where(x > 0, x, alpha * jnp.exp(x) - alpha)\n",
"\n",
"x = jnp.arange(5.0)\n",
"print(selu(x))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"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": "3.12.0"
}
},
"nbformat": 4,
"nbformat_minor": 4
}