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iamirmasoud/Jupyter Magic Commands.ipynb

Created April 17, 2022 10:36
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Jupyter Magic Commands
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Jupyter Magic Commands.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "62518d1a",
"metadata": {},
"outputs": [
{
"data": {
"application/json": {
"cell": {
"!": "OSMagics",
"HTML": "Other",
"SVG": "Other",
"bash": "Other",
"capture": "ExecutionMagics",
"debug": "ExecutionMagics",
"file": "Other",
"html": "DisplayMagics",
"javascript": "DisplayMagics",
"js": "DisplayMagics",
"latex": "DisplayMagics",
"markdown": "DisplayMagics",
"perl": "Other",
"prun": "ExecutionMagics",
"pypy": "Other",
"python": "Other",
"python2": "Other",
"python3": "Other",
"ruby": "Other",
"script": "ScriptMagics",
"sh": "Other",
"svg": "DisplayMagics",
"sx": "OSMagics",
"system": "OSMagics",
"time": "ExecutionMagics",
"timeit": "ExecutionMagics",
"writefile": "OSMagics"
},
"line": {
"alias": "OSMagics",
"alias_magic": "BasicMagics",
"autoawait": "AsyncMagics",
"autocall": "AutoMagics",
"automagic": "AutoMagics",
"autosave": "KernelMagics",
"bookmark": "OSMagics",
"cat": "Other",
"cd": "OSMagics",
"clear": "KernelMagics",
"colors": "BasicMagics",
"conda": "PackagingMagics",
"config": "ConfigMagics",
"connect_info": "KernelMagics",
"cp": "Other",
"debug": "ExecutionMagics",
"dhist": "OSMagics",
"dirs": "OSMagics",
"doctest_mode": "BasicMagics",
"ed": "Other",
"edit": "KernelMagics",
"env": "OSMagics",
"gui": "BasicMagics",
"hist": "Other",
"history": "HistoryMagics",
"killbgscripts": "ScriptMagics",
"ldir": "Other",
"less": "KernelMagics",
"lf": "Other",
"lk": "Other",
"ll": "Other",
"load": "CodeMagics",
"load_ext": "ExtensionMagics",
"loadpy": "CodeMagics",
"logoff": "LoggingMagics",
"logon": "LoggingMagics",
"logstart": "LoggingMagics",
"logstate": "LoggingMagics",
"logstop": "LoggingMagics",
"ls": "Other",
"lsmagic": "BasicMagics",
"lx": "Other",
"macro": "ExecutionMagics",
"magic": "BasicMagics",
"man": "KernelMagics",
"matplotlib": "PylabMagics",
"mkdir": "Other",
"more": "KernelMagics",
"mv": "Other",
"notebook": "BasicMagics",
"page": "BasicMagics",
"pastebin": "CodeMagics",
"pdb": "ExecutionMagics",
"pdef": "NamespaceMagics",
"pdoc": "NamespaceMagics",
"pfile": "NamespaceMagics",
"pinfo": "NamespaceMagics",
"pinfo2": "NamespaceMagics",
"pip": "PackagingMagics",
"popd": "OSMagics",
"pprint": "BasicMagics",
"precision": "BasicMagics",
"prun": "ExecutionMagics",
"psearch": "NamespaceMagics",
"psource": "NamespaceMagics",
"pushd": "OSMagics",
"pwd": "OSMagics",
"pycat": "OSMagics",
"pylab": "PylabMagics",
"qtconsole": "KernelMagics",
"quickref": "BasicMagics",
"recall": "HistoryMagics",
"rehashx": "OSMagics",
"reload_ext": "ExtensionMagics",
"rep": "Other",
"rerun": "HistoryMagics",
"reset": "NamespaceMagics",
"reset_selective": "NamespaceMagics",
"rm": "Other",
"rmdir": "Other",
"run": "ExecutionMagics",
"save": "CodeMagics",
"sc": "OSMagics",
"set_env": "OSMagics",
"store": "StoreMagics",
"sx": "OSMagics",
"system": "OSMagics",
"tb": "ExecutionMagics",
"time": "ExecutionMagics",
"timeit": "ExecutionMagics",
"unalias": "OSMagics",
"unload_ext": "ExtensionMagics",
"who": "NamespaceMagics",
"who_ls": "NamespaceMagics",
"whos": "NamespaceMagics",
"xdel": "NamespaceMagics",
"xmode": "BasicMagics"
}
},
"text/plain": [
"Available line magics:\n",
"%alias %alias_magic %autoawait %autocall %automagic %autosave %bookmark %cat %cd %clear %colors %conda %config %connect_info %cp %debug %dhist %dirs %doctest_mode %ed %edit %env %gui %hist %history %killbgscripts %ldir %less %lf %lk %ll %load %load_ext %loadpy %logoff %logon %logstart %logstate %logstop %ls %lsmagic %lx %macro %magic %man %matplotlib %mkdir %more %mv %notebook %page %pastebin %pdb %pdef %pdoc %pfile %pinfo %pinfo2 %pip %popd %pprint %precision %prun %psearch %psource %pushd %pwd %pycat %pylab %qtconsole %quickref %recall %rehashx %reload_ext %rep %rerun %reset %reset_selective %rm %rmdir %run %save %sc %set_env %store %sx %system %tb %time %timeit %unalias %unload_ext %who %who_ls %whos %xdel %xmode\n",
"\n",
"Available cell magics:\n",
"%%! %%HTML %%SVG %%bash %%capture %%debug %%file %%html %%javascript %%js %%latex %%markdown %%perl %%prun %%pypy %%python %%python2 %%python3 %%ruby %%script %%sh %%svg %%sx %%system %%time %%timeit %%writefile\n",
"\n",
"Automagic is ON, % prefix IS NOT needed for line magics."
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%lsmagic"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "76eb9dbf",
"metadata": {},
"outputs": [],
"source": [
"%quickref"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "a7408869",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Created `%currdir` as an alias for `%pwd`.\n"
]
}
],
"source": [
"%alias_magic currdir pwd"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "d93c3118",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'/home/masoud/Desktop'"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%currdir"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "5846714d",
"metadata": {},
"outputs": [],
"source": [
"def addition(a,b):\n",
" return a+b"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "94847e14",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Automatic calling is: Smart\n"
]
}
],
"source": [
"%autocall"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "4224f721",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"------> addition(5, 5)\n",
"------> addition(5, 5)\n"
]
},
{
"data": {
"text/plain": [
"10"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"addition 5, 5"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "2ed7c66d",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Automatic calling is: Off\n"
]
}
],
"source": [
"%autocall"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "0402db1d",
"metadata": {},
"outputs": [
{
"ename": "SyntaxError",
"evalue": "invalid syntax (645643924.py, line 1)",
"output_type": "error",
"traceback": [
"\u001b[0;36m Input \u001b[0;32mIn [9]\u001b[0;36m\u001b[0m\n\u001b[0;31m addition 5, 5\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
]
}
],
"source": [
"addition 5, 5"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "95a91696",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Automagic is OFF, % prefix IS needed for line magics.\n"
]
}
],
"source": [
"%automagic"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "6d5953fb",
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'pwd' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"Input \u001b[0;32mIn [12]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mpwd\u001b[49m\n",
"\u001b[0;31mNameError\u001b[0m: name 'pwd' is not defined"
]
}
],
"source": [
"pwd"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "df2ef7a7",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Automagic is ON, % prefix IS NOT needed for line magics.\n"
]
}
],
"source": [
"%automagic"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "ab5ba8d0",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'/home/masoud/Desktop'"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pwd"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "7c294727",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Automagic is OFF, % prefix IS needed for line magics.\n"
]
}
],
"source": [
"%automagic"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "3c8e30d6",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'/home/masoud/Desktop'"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%pwd"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "8139dbda",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['Hello World']"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%system echo 'Hello World'"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "9bbfc4c0",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['-rw-rw-r-- 1 masoud masoud 11777 Apr 17 13:24 Jupyter Magic Commands.ipynb']"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%system ls -lrt | grep Jupyter"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "d1b95abf",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['Hello World']"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%sx echo 'Hello World'"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "7bb0b44e",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 78.3 ms, sys: 15.9 ms, total: 94.2 ms\n",
"Wall time: 92.6 ms\n"
]
}
],
"source": [
"%time out = [i*i for i in range(1000000)]"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "ef765928",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"71.5 ms ± 2.78 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
]
}
],
"source": [
"%timeit out = [i*i for i in range(1000000)]"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "f30d9a81",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"68.2 ms ± 3.15 ms per loop (mean ± std. dev. of 5 runs, 5 loops each)\n"
]
}
],
"source": [
"%timeit -n 5 -r 5 out = [i*i for i in range(1000000)]"
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "3365c90a",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"e\t \n",
"d\t \n",
"b\t \n",
"c\t out\t \n"
]
}
],
"source": [
"a = 100\n",
"b = 5.5\n",
"c = [11,12,13]\n",
"d = {'key1':'val1', 'key2':'val2'}\n",
"e = \"Hello World\"\n",
"\n",
"%who str\n",
"%who dict\n",
"%who float\n",
"%who list"
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "537d40bd",
"metadata": {},
"outputs": [],
"source": [
"def addition(a,b):\n",
" return a + b\n",
"\n",
"def division(a,b):\n",
" return a / b if b!=0 else 0"
]
},
{
"cell_type": "code",
"execution_count": 30,
"id": "070f5360",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"addition\t division\t \n"
]
}
],
"source": [
"def addition(a,b):\n",
" return a + b\n",
"\n",
"def division(a,b):\n",
" return a / b if b!=0 else 0\n",
"\n",
"%who function"
]
},
{
"cell_type": "code",
"execution_count": 31,
"id": "45025364",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"addition\t c\t division\t out\t \n"
]
}
],
"source": [
"%who function list"
]
},
{
"cell_type": "code",
"execution_count": 33,
"id": "6aba2821",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['addition', 'division']"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%who_ls function"
]
},
{
"cell_type": "code",
"execution_count": 36,
"id": "f8a61f05",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Variable Type Data/Info\n",
"----------------------------\n",
"e str Hello World\n",
"Variable Type Data/Info\n",
"----------------------------\n",
"d dict n=2\n",
"Variable Type Data/Info\n",
"-----------------------------\n",
"b float 5.5\n",
"Variable Type Data/Info\n",
"----------------------------\n",
"c list n=3\n",
"out list n=1000000\n"
]
}
],
"source": [
"%whos str\n",
"%whos dict\n",
"%whos float\n",
"%whos list"
]
},
{
"cell_type": "code",
"execution_count": 37,
"id": "377086db",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Variable Type Data/Info\n",
"--------------------------------\n",
"addition function <function addition at 0x7fc345799b40>\n",
"division function <function division at 0x7fc345799990>\n"
]
}
],
"source": [
"%whos function"
]
},
{
"cell_type": "code",
"execution_count": 38,
"id": "29af16f5",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[]"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%system touch profiling_example.py"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3e01a085",
"metadata": {},
"outputs": [],
"source": [
"# %load profiling_example.py\n",
"from memory_profiler import profile\n",
"\n",
"@profile(precision=4)\n",
"def main_func():\n",
" import random\n",
" arr1 = [random.randint(1,10) for i in range(100000)]\n",
" arr2 = [random.randint(1,10) for i in range(100000)]\n",
" arr3 = [arr1[i]+arr2[i] for i in range(100000)]\n",
" tot = sum(arr3)\n",
" print(tot)\n",
"\n",
"if __name__ == \"__main__\":\n",
" main_func()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "bdb3536d",
"metadata": {},
"outputs": [],
"source": [
"# %load -r 5-10 profiling_example.py\n",
"import random\n",
"arr1 = [random.randint(1,10) for i in range(100000)]\n",
"arr2 = [random.randint(1,10) for i in range(100000)]\n",
"arr3 = [arr1[i]+arr2[i] for i in range(100000)]\n",
"tot = sum(arr3)\n",
"print(tot)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "dd6d512d",
"metadata": {},
"outputs": [],
"source": [
"# %load -s main_func profiling_example.py\n",
"def main_func():\n",
" import random\n",
" arr1 = [random.randint(1,10) for i in range(100000)]\n",
" arr2 = [random.randint(1,10) for i in range(100000)]\n",
" arr3 = [arr1[i]+arr2[i] for i in range(100000)]\n",
" tot = sum(arr3)\n",
" print(tot)\n"
]
},
{
"cell_type": "code",
"execution_count": 45,
"id": "be595aa7",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Collecting snakeviz\n",
" Downloading snakeviz-2.1.1-py2.py3-none-any.whl (282 kB)\n",
"\u001b[K |████████████████████████████████| 282 kB 153 kB/s eta 0:00:01\n",
"\u001b[?25hCollecting line_profiler\n",
" Downloading line_profiler-3.5.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (70 kB)\n",
"\u001b[K |████████████████████████████████| 70 kB 396 kB/s eta 0:00:01\n",
"\u001b[?25hCollecting memory_profiler\n",
" Downloading memory_profiler-0.60.0.tar.gz (38 kB)\n",
"Requirement already satisfied: tornado>=2.0 in /home/masoud/anaconda3/envs/music_env/lib/python3.10/site-packages (from snakeviz) (6.1)\n",
"Requirement already satisfied: psutil in /home/masoud/anaconda3/envs/music_env/lib/python3.10/site-packages (from memory_profiler) (5.9.0)\n",
"Building wheels for collected packages: memory-profiler\n",
" Building wheel for memory-profiler (setup.py) ... \u001b[?25ldone\n",
"\u001b[?25h Created wheel for memory-profiler: filename=memory_profiler-0.60.0-py3-none-any.whl size=31282 sha256=8bcd2d74a754158eddfcc0ee7d0358ac22ff14f293100e0885342d1c339a40e0\n",
" Stored in directory: /home/masoud/.cache/pip/wheels/1e/2f/ec/05ba593810ab1a8f607baaec73f43a9238627f12e7bbb0d1a7\n",
"Successfully built memory-profiler\n",
"Installing collected packages: snakeviz, memory-profiler, line-profiler\n",
"Successfully installed line-profiler-3.5.1 memory-profiler-0.60.0 snakeviz-2.1.1\n"
]
}
],
"source": [
"!pip install snakeviz line_profiler memory_profiler"
]
},
{
"cell_type": "code",
"execution_count": 46,
"id": "398211b3",
"metadata": {},
"outputs": [],
"source": [
"%load_ext snakeviz"
]
},
{
"cell_type": "code",
"execution_count": 47,
"id": "2ad9f0de",
"metadata": {},
"outputs": [],
"source": [
"def main_func():\n",
" import random\n",
" arr1 = [random.randint(1,10) for i in range(100000)]\n",
" arr2 = [random.randint(1,10) for i in range(100000)]\n",
" arr3 = [arr1[i]+arr2[i] for i in range(100000)]\n",
" tot = sum(arr3)\n",
" print(tot)"
]
},
{
"cell_type": "code",
"execution_count": 49,
"id": "038a44e1",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1098735\n",
" \n",
"*** Profile stats marshalled to file '/tmp/tmpippki01l'.\n",
"Embedding SnakeViz in this document...\n"
]
},
{
"data": {
"text/html": [
"\n",
"<iframe id='snakeviz-acd0551a-be2f-11ec-8a00-e956bae7604a' frameborder=0 seamless width='100%' height='1000'></iframe>\n",
"<script>document.getElementById(\"snakeviz-acd0551a-be2f-11ec-8a00-e956bae7604a\").setAttribute(\"src\", \"http://\" + document.location.hostname + \":8080/snakeviz/%2Ftmp%2Ftmpippki01l\")</script>\n"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%snakeviz main_func()"
]
},
{
"cell_type": "code",
"execution_count": 51,
"id": "5e487bd4",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The snakeviz extension doesn't define how to unload it.\n"
]
}
],
"source": [
"%unload_ext snakeviz"
]
},
{
"cell_type": "code",
"execution_count": 52,
"id": "bccfea9d",
"metadata": {},
"outputs": [],
"source": [
"%reload_ext snakeviz"
]
},
{
"cell_type": "code",
"execution_count": 55,
"id": "4cd4f9ac",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'ubuntu'"
]
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%env DESKTOP_SESSION"
]
},
{
"cell_type": "code",
"execution_count": 56,
"id": "0cb76f49",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'/home/masoud'"
]
},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%env HOME"
]
},
{
"cell_type": "code",
"execution_count": 57,
"id": "a4e5bcfd",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"env: TESTIT=test_value\n"
]
}
],
"source": [
"%env TESTIT=test_value"
]
},
{
"cell_type": "code",
"execution_count": 58,
"id": "4fb02bb1",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'test_value'"
]
},
"execution_count": 58,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%env TESTIT"
]
},
{
"cell_type": "code",
"execution_count": 60,
"id": "c1753d98",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"# conda environments:\n",
"#\n",
"base /home/masoud/anaconda3\n",
"ThinkBayes2 /home/masoud/anaconda3/envs/ThinkBayes2\n",
"backtest_env /home/masoud/anaconda3/envs/backtest_env\n",
"drnd_env /home/masoud/anaconda3/envs/drnd_env\n",
"fidibo /home/masoud/anaconda3/envs/fidibo\n",
"gdrl_env /home/masoud/anaconda3/envs/gdrl_env\n",
"music_env * /home/masoud/anaconda3/envs/music_env\n",
"separator_env /home/masoud/anaconda3/envs/separator_env\n",
"vocal_env /home/masoud/anaconda3/envs/vocal_env\n",
"\n",
"\n",
"Note: you may need to restart the kernel to use updated packages.\n"
]
}
],
"source": [
"%conda env list"
]
},
{
"cell_type": "code",
"execution_count": 61,
"id": "7606a360",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Requirement already satisfied: sklearn in /home/masoud/anaconda3/envs/music_env/lib/python3.10/site-packages (0.0)\n",
"Requirement already satisfied: scikit-learn in /home/masoud/anaconda3/envs/music_env/lib/python3.10/site-packages (from sklearn) (1.0.2)\n",
"Requirement already satisfied: numpy>=1.14.6 in /home/masoud/anaconda3/envs/music_env/lib/python3.10/site-packages (from scikit-learn->sklearn) (1.21.5)\n",
"Requirement already satisfied: scipy>=1.1.0 in /home/masoud/anaconda3/envs/music_env/lib/python3.10/site-packages (from scikit-learn->sklearn) (1.8.0)\n",
"Requirement already satisfied: threadpoolctl>=2.0.0 in /home/masoud/anaconda3/envs/music_env/lib/python3.10/site-packages (from scikit-learn->sklearn) (3.1.0)\n",
"Requirement already satisfied: joblib>=0.11 in /home/masoud/anaconda3/envs/music_env/lib/python3.10/site-packages (from scikit-learn->sklearn) (1.1.0)\n",
"Note: you may need to restart the kernel to use updated packages.\n"
]
}
],
"source": [
"%pip install sklearn"
]
},
{
"cell_type": "code",
"execution_count": 62,
"id": "02c7c258",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Directory history (kept in _dh)\n",
"0: /home/masoud/Desktop\n"
]
}
],
"source": [
"%dhist"
]
},
{
"cell_type": "code",
"execution_count": 64,
"id": "74129779",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" 6: %autocall\n",
" 7: addition 5, 5\n",
" 8: %autocall\n",
" 9: addition 5, 5\n"
]
}
],
"source": [
"%history -n 6-9"
]
},
{
"cell_type": "code",
"execution_count": 65,
"id": "93e5e7b5",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" 6: >>> %autocall\n",
" 7: >>> addition 5, 5\n",
" 8: >>> %autocall\n",
" 9: >>> addition 5, 5\n"
]
}
],
"source": [
"%history -n 6-9 -p"
]
},
{
"cell_type": "code",
"execution_count": 66,
"id": "a10e534f",
"metadata": {},
"outputs": [],
"source": [
"%history 6-9 -p -f magic.out"
]
},
{
"cell_type": "code",
"execution_count": 71,
"id": "955394fa",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Exception reporting mode: Plain\n",
"Doctest mode is: ON\n"
]
}
],
"source": [
"%doctest_mode"
]
},
{
"cell_type": "code",
"execution_count": 72,
"id": "0f060075",
"metadata": {},
"outputs": [
{
"ename": "ZeroDivisionError",
"evalue": "division by zero",
"output_type": "error",
"traceback": [
"Traceback \u001b[0;36m(most recent call last)\u001b[0m:\n",
"\u001b[0;36m Input \u001b[0;32mIn [72]\u001b[0;36m in \u001b[0;35m<cell line: 2>\u001b[0;36m\u001b[0m\n\u001b[0;31m a/0\u001b[0m\n",
"\u001b[0;31mZeroDivisionError\u001b[0m\u001b[0;31m:\u001b[0m division by zero\n"
]
}
],
"source": [
"a=10\n",
"a/0"
]
},
{
"cell_type": "code",
"execution_count": 73,
"id": "27b0f9a0",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Exception reporting mode: Context\n",
"Doctest mode is: OFF\n"
]
}
],
"source": [
"%doctest_mode"
]
},
{
"cell_type": "code",
"execution_count": 74,
"id": "2e00bd21",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" "
]
}
],
"source": [
"import random\n",
"%prun arr1 = [random.randint(1,10) for i in range(100000)]"
]
},
{
"cell_type": "code",
"execution_count": 75,
"id": "d0b07a3f",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" \n",
"*** Profile printout saved to text file 'prof_res.out'.\n"
]
}
],
"source": [
"%prun -l 10 -s tottime -T prof_res.out arr1 = [random.randint(1,10) for i in range(100000)]"
]
},
{
"cell_type": "code",
"execution_count": 76,
"id": "6b865379",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" 859711 function calls in 0.248 seconds\r\n",
"\r\n",
" Ordered by: internal time\r\n",
"\r\n",
" ncalls tottime percall cumtime percall filename:lineno(function)\r\n",
" 100000 0.089 0.000 0.191 0.000 random.py:292(randrange)\r\n",
" 100000 0.060 0.000 0.083 0.000 random.py:239(_randbelow_with_getrandbits)\r\n",
" 100000 0.030 0.000 0.222 0.000 random.py:366(randint)\r\n",
" 1 0.026 0.026 0.248 0.248 <string>:1(<listcomp>)\r\n",
" 300000 0.020 0.000 0.020 0.000 {built-in method _operator.index}\r\n",
" 159707 0.015 0.000 0.015 0.000 {method 'getrandbits' of '_random.Random' objects}\r\n",
" 100000 0.008 0.000 0.008 0.000 {method 'bit_length' of 'int' objects}\r\n",
" 1 0.000 0.000 0.248 0.248 {built-in method builtins.exec}\r\n",
" 1 0.000 0.000 0.248 0.248 <string>:1(<module>)\r\n",
" 1 0.000 0.000 0.000 0.000 {method 'disable' of '_lsprof.Profiler' objects}"
]
}
],
"source": [
"!cat prof_res.out"
]
},
{
"cell_type": "code",
"execution_count": 77,
"id": "ce6d98bc",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Available matplotlib backends: ['tk', 'gtk', 'gtk3', 'gtk4', 'wx', 'qt4', 'qt5', 'qt6', 'qt', 'osx', 'nbagg', 'notebook', 'agg', 'svg', 'pdf', 'ps', 'inline', 'ipympl', 'widget']\n"
]
}
],
"source": [
"%matplotlib --list"
]
},
{
"cell_type": "code",
"execution_count": 78,
"id": "54a3e329",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Using matplotlib backend: <object object at 0x7fc33416c280>\n"
]
}
],
"source": [
"%matplotlib"
]
},
{
"cell_type": "code",
"execution_count": 81,
"id": "02f2f4be",
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"plt.plot(range(10), range(10))\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 82,
"id": "84863de9",
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 83,
"id": "ac615c9b",
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.plot(range(10), range(10))\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 84,
"id": "606cfeb8",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" \u001b[0mdivision\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
" "
]
}
],
"source": [
"%pdef division"
]
},
{
"cell_type": "code",
"execution_count": 85,
"id": "e4df3c0c",
"metadata": {},
"outputs": [],
"source": [
"def division(a, b):\n",
" '''\n",
" This function divides first argument by second. \n",
" It return 0 if second argument is 0 to avoid divide by zero error\n",
" '''\n",
" return a / b if b!=0 else 0"
]
},
{
"cell_type": "code",
"execution_count": 86,
"id": "dff505cf",
"metadata": {},
"outputs": [],
"source": [
"%pdoc division"
]
},
{
"cell_type": "code",
"execution_count": 88,
"id": "77374b9b",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'%.3f'"
]
},
"execution_count": 88,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%precision 3"
]
},
{
"cell_type": "code",
"execution_count": 89,
"id": "63f8265a",
"metadata": {},
"outputs": [],
"source": [
"a = 1.23678"
]
},
{
"cell_type": "code",
"execution_count": 90,
"id": "df8f7280",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1.237"
]
},
"execution_count": 90,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a"
]
},
{
"cell_type": "code",
"execution_count": 91,
"id": "4945b620",
"metadata": {},
"outputs": [],
"source": [
"val1 = 10\n",
"val2 = 20\n",
"val3 = 50\n",
"top_val = 10000"
]
},
{
"cell_type": "code",
"execution_count": 92,
"id": "0d4dd98b",
"metadata": {},
"outputs": [],
"source": [
"%psearch val*"
]
},
{
"cell_type": "code",
"execution_count": 93,
"id": "6bed45da",
"metadata": {},
"outputs": [],
"source": [
"%psearch *val*"
]
},
{
"cell_type": "code",
"execution_count": 94,
"id": "fb3339c1",
"metadata": {},
"outputs": [],
"source": [
"%psource division"
]
},
{
"cell_type": "code",
"execution_count": 95,
"id": "56e33ac1",
"metadata": {},
"outputs": [],
"source": [
"%pycat profiling_example.py"
]
},
{
"cell_type": "code",
"execution_count": 96,
"id": "a7c9dcef",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Using matplotlib backend: TkAgg\n",
"%pylab is deprecated, use %matplotlib inline and import the required libraries.\n",
"Populating the interactive namespace from numpy and matplotlib\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/masoud/anaconda3/envs/music_env/lib/python3.10/site-packages/IPython/core/magics/pylab.py:162: UserWarning: pylab import has clobbered these variables: ['random', 'e']\n",
"`%matplotlib` prevents importing * from pylab and numpy\n",
" warn(\"pylab import has clobbered these variables: %s\" % clobbered +\n"
]
}
],
"source": [
"%pylab"
]
},
{
"cell_type": "code",
"execution_count": 97,
"id": "4328252c",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"numpy.ndarray"
]
},
"execution_count": 97,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"arr = array([1,2,3,4])\n",
"type(arr)"
]
},
{
"cell_type": "code",
"execution_count": 98,
"id": "040c08df",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0 1 2 3 4 5 6 7 8 9]\n"
]
},
{
"data": {
"text/plain": [
"numpy.ndarray"
]
},
"execution_count": 98,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rng = arange(10)\n",
"print(rng)\n",
"type(rng)"
]
},
{
"cell_type": "code",
"execution_count": 99,
"id": "212a9c40",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7fc3222b0820>]"
]
},
"execution_count": 99,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"plot(range(10), range(10))"
]
},
{
"cell_type": "code",
"execution_count": 100,
"id": "2618b0fd",
"metadata": {},
"outputs": [],
"source": [
"%recall 4"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "14313f5c",
"metadata": {},
"outputs": [],
"source": [
"%currdir"
]
},
{
"cell_type": "code",
"execution_count": 101,
"id": "7edf53fb",
"metadata": {},
"outputs": [],
"source": [
"%recall 1-4"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ca8092b2",
"metadata": {},
"outputs": [],
"source": [
"%lsmagic\n",
"%quickref\n",
"%alias_magic currdir pwd\n",
"%currdir"
]
},
{
"cell_type": "code",
"execution_count": 102,
"id": "80b18827",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"=== Executing: ===\n",
"%recall 1-4\n",
"=== Output: ===\n"
]
}
],
"source": [
"%rerun"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ddc0c93c",
"metadata": {},
"outputs": [],
"source": [
"%lsmagic\n",
"%quickref\n",
"%alias_magic currdir pwd\n",
"%currdir"
]
},
{
"cell_type": "code",
"execution_count": 103,
"id": "bebc2f32",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['a',\n",
" 'addition',\n",
" 'arr',\n",
" 'arr1',\n",
" 'autopep8',\n",
" 'b',\n",
" 'c',\n",
" 'd',\n",
" 'division',\n",
" 'json',\n",
" 'main_func',\n",
" 'out',\n",
" 'rng',\n",
" 'top_val',\n",
" 'val1',\n",
" 'val2',\n",
" 'val3']"
]
},
"execution_count": 103,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%who_ls"
]
},
{
"cell_type": "code",
"execution_count": 104,
"id": "e432d2a6",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Once deleted, variables cannot be recovered. Proceed (y/[n])? y\n"
]
}
],
"source": [
"%reset"
]
},
{
"cell_type": "code",
"execution_count": 105,
"id": "8c5fb2e1",
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'val1' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"Input \u001b[0;32mIn [105]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mval1\u001b[49m\n",
"\u001b[0;31mNameError\u001b[0m: name 'val1' is not defined"
]
}
],
"source": [
"val1"
]
},
{
"cell_type": "code",
"execution_count": 109,
"id": "12649582",
"metadata": {},
"outputs": [],
"source": [
"val1 = 10\n",
"val2 = 20\n",
"val3 = 50\n",
"top_val = 10000\n",
"a = 10\n",
"rng = range(10)"
]
},
{
"cell_type": "code",
"execution_count": 110,
"id": "6ef0b573",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['a', 'rng', 'top_val', 'val1', 'val2', 'val3']"
]
},
"execution_count": 110,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%who_ls"
]
},
{
"cell_type": "code",
"execution_count": 111,
"id": "7e6fb0c1",
"metadata": {},
"outputs": [],
"source": [
"%reset_selective -f val"
]
},
{
"cell_type": "code",
"execution_count": 112,
"id": "e0c6b511",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['a', 'rng']"
]
},
"execution_count": 112,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%who_ls"
]
},
{
"cell_type": "code",
"execution_count": 113,
"id": "37343227",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1100192\n",
"Filename: /home/masoud/Desktop/profiling_example.py\n",
"\n",
"Line # Mem usage Increment Occurrences Line Contents\n",
"=============================================================\n",
" 3 175.5625 MiB 175.5625 MiB 1 @profile(precision=4)\n",
" 4 def main_func():\n",
" 5 175.5625 MiB 0.0000 MiB 1 import random\n",
" 6 176.2617 MiB 0.6992 MiB 100003 arr1 = [random.randint(1,10) for i in range(100000)]\n",
" 7 177.0352 MiB 0.7734 MiB 100003 arr2 = [random.randint(1,10) for i in range(100000)]\n",
" 8 177.8086 MiB 0.7734 MiB 100003 arr3 = [arr1[i]+arr2[i] for i in range(100000)]\n",
" 9 177.8086 MiB 0.0000 MiB 1 tot = sum(arr3)\n",
" 10 177.8086 MiB 0.0000 MiB 1 print(tot)\n",
"\n",
"\n"
]
}
],
"source": [
"%run profiling_example.py"
]
},
{
"cell_type": "code",
"execution_count": 114,
"id": "ca0bcb77",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"-rw-rw-r-- 1 masoud masoud 52575 Apr 17 14:54 Jupyter Magic Commands.ipynb\n"
]
}
],
"source": [
"%%bash\n",
"\n",
"ls -lrt | grep \"Jupyter\""
]
},
{
"cell_type": "code",
"execution_count": 116,
"id": "7219d8e9",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"-rw-rw-r-- 1 masoud masoud 52575 Apr 17 14:54 Jupyter Magic Commands.ipynb\n"
]
}
],
"source": [
"%%script bash\n",
"ls -lrt | grep \"Jupyter\""
]
},
{
"cell_type": "code",
"execution_count": 117,
"id": "9823f4f6",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"-rw-rw-r-- 1 masoud masoud 52575 Apr 17 14:54 Jupyter Magic Commands.ipynb\n"
]
}
],
"source": [
"%%sh\n",
"ls -lrt | grep \"Jupyter\""
]
},
{
"cell_type": "code",
"execution_count": 118,
"id": "297b386a",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<h1>Heading Big</h1>\n",
"<h2>Heading Medium</h2>\n",
"<h3>Heading Small</h3>\n",
"# Comment\n"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%%html\n",
"<h1>Heading Big</h1>\n",
"<h2>Heading Medium</h2>\n",
"<h3>Heading Small</h3>\n",
"# Comment"
]
},
{
"cell_type": "code",
"execution_count": 121,
"id": "ad11a950",
"metadata": {},
"outputs": [
{
"data": {
"application/javascript": [
"// program to find the largest among three numbers\n",
"// take input from the user\n",
"const num1 = 12\n",
"const num2 = 10\n",
"const num3 = 35\n",
"let largest;\n",
"// check the condition\n",
"if(num1 >= num2 && num1 >= num3) {\n",
" largest = num1;\n",
"}\n",
"else if (num2 >= num1 && num2 >= num3) {\n",
" largest = num2;\n",
"}\n",
"else {\n",
" largest = num3;\n",
"}\n",
"// display the result\n",
"element.innerHTML = '<h1>The Largest Number is : ' + largest + '</h1>'\n"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%%javascript\n",
"// program to find the largest among three numbers\n",
"// take input from the user\n",
"const num1 = 12\n",
"const num2 = 10\n",
"const num3 = 35\n",
"let largest;\n",
"// check the condition\n",
"if(num1 >= num2 && num1 >= num3) {\n",
" largest = num1;\n",
"}\n",
"else if (num2 >= num1 && num2 >= num3) {\n",
" largest = num2;\n",
"}\n",
"else {\n",
" largest = num3;\n",
"}\n",
"// display the result\n",
"element.innerHTML = '<h1>The Largest Number is : ' + largest + '</h1>'"
]
},
{
"cell_type": "code",
"execution_count": 123,
"id": "69992911",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Hello Bill\n"
]
}
],
"source": [
"%%perl\n",
"#!/usr/bin/perl\n",
"use strict;\n",
"use warnings;\n",
"print \"Hello Bill\\n\";"
]
},
{
"cell_type": "code",
"execution_count": 124,
"id": "9a05117c",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"There are 50 Tigers and 45 Lions in the Zoo.\n"
]
}
],
"source": [
"%%ruby\n",
"tigers = 50\n",
"lions = 45\n",
"puts \"There are #{tigers} Tigers and #{lions} Lions in the Zoo.\""
]
},
{
"cell_type": "code",
"execution_count": 125,
"id": "a2c01fc6",
"metadata": {},
"outputs": [
{
"data": {
"text/latex": [
"\\begin{equation*}\n",
" e^{\\pi i} + 1 = 0\n",
"\\end{equation*}\n"
],
"text/plain": [
"<IPython.core.display.Latex object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%%latex\n",
"\\begin{equation*}\n",
" e^{\\pi i} + 1 = 0\n",
"\\end{equation*}"
]
},
{
"cell_type": "code",
"execution_count": 126,
"id": "5401fe37",
"metadata": {},
"outputs": [
{
"data": {
"text/latex": [
"$\n",
"idf(t) = {\\log_{} \\dfrac {n_d} {df(d,t)}} + 1\n",
"$\n"
],
"text/plain": [
"<IPython.core.display.Latex object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%%latex\n",
"$\n",
"idf(t) = {\\log_{} \\dfrac {n_d} {df(d,t)}} + 1\n",
"$"
]
},
{
"cell_type": "code",
"execution_count": 127,
"id": "d1a209db",
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"# H1 Heading\n",
"# H2 Heading\n",
"* List 1\n",
"* List 2\n",
"**Bold Text**\n"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%%markdown\n",
"# H1 Heading\n",
"# H2 Heading\n",
"* List 1\n",
"* List 2\n",
"**Bold Text**"
]
},
{
"cell_type": "code",
"execution_count": 128,
"id": "7e02651b",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Overwriting profiling_example.py\n"
]
}
],
"source": [
"%%writefile profiling_example.py\n",
"from memory_profiler import profile\n",
"\n",
"@profile(precision=4)\n",
"def main_func():\n",
" import random\n",
" arr1 = [random.randint(1,10) for i in range(100000)]\n",
" arr2 = [random.randint(1,10) for i in range(100000)]\n",
" arr3 = [arr1[i]+arr2[i] for i in range(100000)]\n",
" tot = sum(arr3)\n",
" print(tot)\n",
"\n",
"if __name__ == \"__main__\":\n",
" main_func()"
]
},
{
"cell_type": "code",
"execution_count": 129,
"id": "fbcefacf",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"from memory_profiler import profile\r\n",
"\r\n",
"@profile(precision=4)\r\n",
"def main_func():\r\n",
" import random\r\n",
" arr1 = [random.randint(1,10) for i in range(100000)]\r\n",
" arr2 = [random.randint(1,10) for i in range(100000)]\r\n",
" arr3 = [arr1[i]+arr2[i] for i in range(100000)]\r\n",
" tot = sum(arr3)\r\n",
" print(tot)\r\n",
"\r\n",
"if __name__ == \"__main__\":\r\n",
" main_func()\r\n"
]
}
],
"source": [
"!cat profiling_example.py"
]
},
{
"cell_type": "code",
"execution_count": 130,
"id": "cb3648eb",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1102714\n",
"CPU times: user 174 ms, sys: 2.04 ms, total: 176 ms\n",
"Wall time: 174 ms\n"
]
}
],
"source": [
"%%time\n",
"def main_func():\n",
" import random\n",
" arr1 = [random.randint(1,10) for i in range(100000)]\n",
" arr2 = [random.randint(1,10) for i in range(100000)]\n",
" arr3 = [arr1[i]+arr2[i] for i in range(100000)]\n",
" tot = sum(arr3)\n",
" print(tot)\n",
"\n",
"main_func()"
]
},
{
"cell_type": "code",
"execution_count": 131,
"id": "762df033",
"metadata": {},
"outputs": [],
"source": [
"def main_func():\n",
" import random\n",
" arr1 = [random.randint(1,10) for i in range(100000)]\n",
" arr2 = [random.randint(1,10) for i in range(100000)]\n",
" arr3 = [arr1[i]+arr2[i] for i in range(100000)]\n",
" tot = sum(arr3)\n",
"\n",
"main_func()"
]
},
{
"cell_type": "code",
"execution_count": 132,
"id": "8c74a55d",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"150 ms ± 9.78 ms per loop (mean ± std. dev. of 5 runs, 5 loops each)\n"
]
}
],
"source": [
"%%timeit -n 5 -r 5\n",
"def main_func():\n",
" import random\n",
" arr1 = [random.randint(1,10) for i in range(100000)]\n",
" arr2 = [random.randint(1,10) for i in range(100000)]\n",
" arr3 = [arr1[i]+arr2[i] for i in range(100000)]\n",
" tot = sum(arr3)\n",
"main_func()"
]
},
{
"cell_type": "code",
"execution_count": 134,
"id": "dabc4369",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"263 ms ± 2.25 ms per loop (mean ± std. dev. of 5 runs, 5 loops each)\n"
]
}
],
"source": [
"%%timeit -t -n 5 -r 5\n",
"import random\n",
"arr1 = [random.randint(1,10) for i in range(100000)]\n",
"arr2 = [random.randint(1,10) for i in range(100000)]\n",
"arr3 = [arr1[i]+arr2[i] for i in range(100000)]\n",
"tot = sum(arr3)\n",
"\n",
"main_func()"
]
},
{
"cell_type": "code",
"execution_count": 135,
"id": "35212b4e",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1100751\n",
" "
]
}
],
"source": [
"%%prun\n",
"def main_func():\n",
" import random\n",
" arr1 = [random.randint(1,10) for i in range(100000)]\n",
" arr2 = [random.randint(1,10) for i in range(100000)]\n",
" arr3 = [arr1[i]+arr2[i] for i in range(100000)]\n",
" tot = sum(arr3)\n",
" print(tot)\n",
"\n",
"main_func()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7c04e1dd",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python (music_env)",
"language": "python",
"name": "music_env"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.4"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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