advanced-python-homework-2023/interpreters/analyse.ipynb

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2023-10-10 18:42:30 +03:00
{
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"source": [
"import os, sys\n",
"import numpy as np\n",
"import pandas as pd\n",
"import json\n",
"pd.options.mode.chained_assignment = None\n",
"\n",
"import plotly.graph_objects as go\n",
"import plotly.io as pio\n",
"import plotly.express as px\n",
"from plotly.subplots import make_subplots\n",
"from unicodeit import replace as tex_to_unis\n",
"# sys.path.append(\"C:/Users/rurur/Desktop/p/python/plotly\")\n",
"# import plotly_setup"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
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"name": "stdout",
"output_type": "stream",
"text": [
"CPython-3.11.txt CPython-3.11_full.txt CPython-3.9.txt CPython-3.9_full.txt Pyodide.txt PyPy-3.9-v7.3.13.txt PyPy-3.9-v7.3.13_full.txt Xeus.txt\n",
"PyPy-3.9-v7.3.13 CPython-3.9 CPython-3.11 Pyodide Xeus\n"
]
}
],
"source": [
"result_files = os.listdir(\"./results\")\n",
"result_titles = sorted(set([f.rstrip(\"full_.txt\") for f in result_files]))\n",
"result_titles[0], result_titles[2] = result_titles[2], result_titles[0]\n",
"print(*result_files)\n",
"print(*result_titles)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"df = pd.DataFrame()\n",
"funcs = [\"time_with_types\", \"time_no_types\", \"time_np\"]\n",
"funcs_titles = [f.lstrip(\"time_\") for f in funcs]\n",
"funcs_titles[-1] = \"numpy\"\n",
"for rtitle in result_titles:\n",
" with open(f\"results/{rtitle}.txt\", \"r\", encoding=\"utf-8\") as f:\n",
" result = json.load(f)\n",
" result_df = pd.DataFrame(dict(\n",
" interpreter=result[\"interpreter\"],\n",
" time=[result[func] for func in funcs],\n",
" funcs=funcs_titles,\n",
" test_mode=\"func\"\n",
" ))\n",
" df = pd.concat([df, result_df], ignore_index=True)\n",
"\n",
" fname_full_time = f\"{rtitle}_full.txt\"\n",
" if fname_full_time in result_files:\n",
" with open(f\"results/{fname_full_time}\", \"r\", encoding=\"utf-8\") as f:\n",
" times = [float(line) for line in f.readlines()]\n",
" result_df = pd.DataFrame(dict(\n",
" interpreter=result[\"interpreter\"],\n",
" time=times,\n",
" funcs=funcs_titles,\n",
" test_mode=\"full\"\n",
" ))\n",
" df = pd.concat([df, pd.DataFrame(result_df)], ignore_index=True)\n"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
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" interpreter time funcs test_mode\n",
"0 PyPy-3.9-v7.3.13 0.099364 with_types func\n",
"1 PyPy-3.9-v7.3.13 0.109924 no_types func\n",
"2 PyPy-3.9-v7.3.13 3.676712 numpy func\n",
"3 PyPy-3.9-v7.3.13 5.319000 with_types full\n",
"4 PyPy-3.9-v7.3.13 3.638000 no_types full\n",
"5 PyPy-3.9-v7.3.13 14.639000 numpy full\n",
"6 CPython-3.9 1.445288 with_types func\n",
"7 CPython-3.9 1.476920 no_types func\n",
"8 CPython-3.9 1.407270 numpy func\n",
"9 CPython-3.9 2.242000 with_types full\n",
"10 CPython-3.9 1.502000 no_types full\n",
"11 CPython-3.9 1.986000 numpy full\n",
"12 CPython-3.11 0.820678 with_types func\n",
"13 CPython-3.11 0.856216 no_types func\n",
"14 CPython-3.11 1.202004 numpy func\n",
"15 CPython-3.11 1.630000 with_types full\n",
"16 CPython-3.11 1.166000 no_types full\n",
"17 CPython-3.11 1.675000 numpy full\n",
"18 piodide 1.785000 with_types func\n",
"19 piodide 1.681000 no_types func\n",
"20 piodide 2.833000 numpy func\n",
"21 Xeus 1.155519 with_types func\n",
"22 Xeus 1.038669 no_types func\n",
"23 Xeus 1.272793 numpy func"
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"title": {}
},
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"showticklabels": true,
"title": {}
},
"xaxis3": {
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"showticklabels": true,
"title": {}
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"xaxis4": {
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"domain": [
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"showticklabels": true,
"title": {}
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"domain": [
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"showticklabels": true,
"title": {}
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"xaxis6": {
"anchor": "y6",
"domain": [
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"showticklabels": true,
"title": {}
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"yaxis": {
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"range": [
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"title": {
"text": "Time, s"
}
},
"yaxis2": {
"anchor": "x2",
"domain": [
0,
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"range": [
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"showticklabels": false
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"yaxis3": {
"anchor": "x3",
"domain": [
0,
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"range": [
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"showticklabels": false
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"yaxis4": {
"anchor": "x4",
"domain": [
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"range": [
0,
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"title": {
"text": "Time, s"
}
},
"yaxis5": {
"anchor": "x5",
"domain": [
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"range": [
0,
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"showticklabels": false
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"domain": [
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"range": [
0,
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],
"showticklabels": false
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}
},
"metadata": {},
"output_type": "display_data"
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],
"source": [
"fig = px.bar(df,\n",
" x=\"interpreter\",\n",
" y=\"time\",\n",
" facet_col=\"funcs\",\n",
" facet_row=\"test_mode\",\n",
" facet_row_spacing=0.15,\n",
" facet_col_spacing=0.03,\n",
" labels=dict(time=\"Time, s\")\n",
").update_layout(\n",
" width=800, height=620,\n",
" margin=dict(b=10, t=20),\n",
" # title=dict(text=\"Results of Testing\"),\n",
" font=dict(size=13)\n",
").update_yaxes(\n",
" matches=None,\n",
").update_yaxes(\n",
" # showticklabels=True,\n",
" row=1,\n",
" range=(0, 16)\n",
").update_yaxes(\n",
" # showticklabels=True,\n",
" row=2,\n",
" range=(0, 4)\n",
").update_xaxes(\n",
" matches=None,\n",
" showticklabels=True,\n",
" title=None\n",
").for_each_annotation(\n",
" lambda a: a.update(text=a.text.split(\"=\")[-1])\n",
")\n",
"fig.write_image(\"result.png\", scale=2.5)\n",
"# fig.layout.yaxis.matches = 'y'\n",
"# fig.layout.yaxis1.matches = 'y1'\n",
"fig.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"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": "3.10.10"
}
},
"nbformat": 4,
"nbformat_minor": 2
}