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arhamchopraNijat K
arhamchopra
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Nijat K
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Replace build with python-build, fix package order (Point72#358)
- build feedstock is deprecated: https://github.com/conda-forge/build-feedstock Signed-off-by: Arham Chopra <[email protected]>
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‎cpp/csp/adapters/websocket/rand_csp.ipynb‎

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 18,
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"metadata": {},
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"outputs": [],
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"source": [
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"import csp\n",
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"from datetime import datetime, timedelta\n",
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"\n",
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"from csp.adapters.parquet import ParquetOutputConfig, ParquetReader, ParquetWriter\n",
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"from typing import Dict, List\n",
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"\n",
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"class MySmolStruct(csp.Struct):\n",
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" v: str\n",
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" z: int = 12\n",
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"\n",
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"class MySillyStruct(csp.Struct):\n",
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" header: str = \"abad\"\n",
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" x: int\n",
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" ms: MySmolStruct\n",
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"\n",
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"\n",
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"class MyStruct(csp.Struct):\n",
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" x: int\n",
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" v: float = 1.0\n",
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" bb: MySillyStruct = MySillyStruct()\n",
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"\n",
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"\n",
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"@csp.node\n",
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"def dedup(real: csp.ts[\"T\"], hist: csp.ts[\"T\"], flag: csp.ts[bool]) -> csp.ts[\"T\"]:\n",
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" if csp.ticked(flag):\n",
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" csp.make_passive(hist)\n",
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" \n",
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" if csp.ticked(hist):\n",
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" return real\n",
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" \n",
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" if csp.ticked(real):\n",
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" return real\n",
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"\n",
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"\n",
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"# big_vals = [MyStruct(x=3) * 100]\n",
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"tup_size = 1_000_000\n",
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"# tups = [(timedelta(microseconds= i), MyStruct(x=3) ) for i in range(tup_size)]\n",
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"# tup2 = [(timedelta(microseconds = i), MyStruct(x=3) ) for i in range(tup_size)]\n",
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"\n",
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"path = \"/Users/neej/dev/csp/test_tup.pq\"\n",
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"path2 = \"/Users/neej/dev/csp/test_tup2.pq\"\n",
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"\n",
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"@csp.graph\n",
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"def write_vals():\n",
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"\n",
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" writer = ParquetWriter(\n",
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" file_name=path, timestamp_column_name=\"csp_time\", config=ParquetOutputConfig(allow_overwrite=True)\n",
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" )\n",
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" tups = [(timedelta(microseconds= i), MyStruct(x=3) ) for i in range(tup_size)]\n",
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" tups_csp = csp.curve(typ=MyStruct, data=tups)\n",
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" writer.publish_struct(tups_csp)\n",
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"\n",
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"csp.run(write_vals, starttime=datetime(2020, 1, 1), endtime=datetime(2099, 1, 1))\n",
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"\n",
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"@csp.graph\n",
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"def g_fast():\n",
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" # vals1 = csp.curve(typ=MyStruct, data=tups)\n",
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" # vals2 = csp.curve( typ=MyStruct, data=tup2 )\n",
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" struct_reader = ParquetReader(path, time_column=\"csp_time\")\n",
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" vals1 = struct_reader.subscribe_all(MyStruct)\n",
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"\n",
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" struct_reader2 = ParquetReader(path2, time_column=\"csp_time\")\n",
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" vals2 = struct_reader2.subscribe_all(MyStruct)\n",
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"\n",
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"\n",
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" flag = csp.const(True)\n",
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" filt1 = csp.filter(flag, vals1)\n",
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" res = csp.merge(filt1, vals2)\n",
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" csp.add_graph_output(\"res\", res)\n",
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"\n",
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"\n",
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"\n",
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"@csp.graph\n",
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"def g_slow():\n",
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" struct_reader = ParquetReader(path, time_column=\"csp_time\")\n",
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" vals1 = struct_reader.subscribe_all(MyStruct)\n",
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"\n",
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" struct_reader2 = ParquetReader(path2, time_column=\"csp_time\")\n",
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" vals2 = struct_reader2.subscribe_all(MyStruct)\n",
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"\n",
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" flag = csp.const(True)\n",
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" res = dedup(vals1, vals2, flag)\n",
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" csp.add_graph_output(\"res\", res)\n",
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"\n",
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"\n",
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"with csp.profiler.Profiler(cycle_file=\"cycle_data_slow.csv\", node_file=\"node_data_slow.csv\") as p:\n",
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" csp.run(g_slow, starttime=datetime(1970, 1, 1), endtime=datetime(2099, 1, 1))\n",
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"\n",
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"with csp.profiler.Profiler(cycle_file=\"cycle_data_fast.csv\", node_file=\"node_data_fast.csv\") as p:\n",
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" csp.run(g_fast, starttime=datetime(1970, 1, 1), endtime=datetime(2099, 1, 1))\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 19,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"CYCLE FAST\n",
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"\n",
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"Overall Execution Time Statistics:\n"
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]
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},
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{
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"data": {
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"text/html": [
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"<div><style>\n",
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".dataframe > thead > tr,\n",
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".dataframe > tbody > tr {\n",
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" text-align: right;\n",
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" white-space: pre-wrap;\n",
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"}\n",
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"</style>\n",
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"<small>shape: (1, 4)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>overall_mean_time_us</th><th>overall_variance_us</th><th>overall_std_dev_us</th><th>total_samples</th></tr><tr><td>f64</td><td>f64</td><td>f64</td><td>u32</td></tr></thead><tbody><tr><td>2.859916</td><td>7.21443</td><td>2.685969</td><td>1000001</td></tr></tbody></table></div>"
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],
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"text/plain": [
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"shape: (1, 4)\n",
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"┌──────────────────────┬─────────────────────┬────────────────────┬───────────────┐\n",
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"│ overall_mean_time_us ┆ overall_variance_us ┆ overall_std_dev_us ┆ total_samples │\n",
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"│ --- ┆ --- ┆ --- ┆ --- │\n",
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"│ f64 ┆ f64 ┆ f64 ┆ u32 │\n",
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"╞══════════════════════╪═════════════════════╪════════════════════╪═══════════════╡\n",
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"│ 2.859916 ┆ 7.21443 ┆ 2.685969 ┆ 1000001 │\n",
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"└──────────────────────┴─────────────────────┴────────────────────┴───────────────┘"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"import polars as pl\n",
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"\n",
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"from IPython.display import display\n",
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"\n",
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"# df = pl.read_csv(\"node_data_fast.csv\").group_by(\"Node Type\").agg(pl.col(\"Execution Time\").sum())\n",
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"# display(df)\n",
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"# df[\"Execution Time\"].sum()\n",
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"\n",
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"df = pl.read_csv(\"cycle_data_fast.csv\")\n",
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"# display(df)\n",
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"print(\"CYCLE FAST\")\n",
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"import numpy as np\n",
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"\n",
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"# Overall statistics across all node types\n",
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"overall_stats = df.select([\n",
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" (pl.col('Execution Time').mean() * 1_000_000).alias('overall_mean_time_us'),\n",
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" (pl.col('Execution Time').var() * 1_000_000 * 1_000_000).alias('overall_variance_us'), # multiply twice since variance is squared\n",
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" (pl.col('Execution Time').std() * 1_000_000).alias('overall_std_dev_us'),\n",
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" pl.col('Execution Time').count().alias('total_samples')\n",
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"]).with_columns([\n",
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" pl.col(['overall_mean_time_us', 'overall_variance_us', 'overall_std_dev_us'])\n",
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"])\n",
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"\n",
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"print(\"\\nOverall Execution Time Statistics:\")\n",
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"display(overall_stats)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 20,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"CYCLE SLOW\n",
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"\n",
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"Overall Execution Time Statistics:\n"
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]
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},
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{
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"data": {
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"text/html": [
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"<div><style>\n",
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".dataframe > thead > tr,\n",
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".dataframe > tbody > tr {\n",
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" text-align: right;\n",
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" white-space: pre-wrap;\n",
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"}\n",
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"</style>\n",
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"<small>shape: (1, 4)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>overall_mean_time_us</th><th>overall_variance_us</th><th>overall_std_dev_us</th><th>total_samples</th></tr><tr><td>f64</td><td>f64</td><td>f64</td><td>u32</td></tr></thead><tbody><tr><td>1.583237</td><td>5.8768</td><td>2.424211</td><td>1000001</td></tr></tbody></table></div>"
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],
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"text/plain": [
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"shape: (1, 4)\n",
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"┌──────────────────────┬─────────────────────┬────────────────────┬───────────────┐\n",
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"│ overall_mean_time_us ┆ overall_variance_us ┆ overall_std_dev_us ┆ total_samples │\n",
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"│ --- ┆ --- ┆ --- ┆ --- │\n",
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"│ f64 ┆ f64 ┆ f64 ┆ u32 │\n",
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"╞══════════════════════╪═════════════════════╪════════════════════╪═══════════════╡\n",
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"│ 1.583237 ┆ 5.8768 ┆ 2.424211 ┆ 1000001 │\n",
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"└──────────────────────┴─────────────────────┴────────────────────┴───────────────┘"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"import polars as pl\n",
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"\n",
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"from IPython.display import display\n",
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"\n",
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"df = pl.read_csv(\"cycle_data_slow.csv\")\n",
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"# display(df)\n",
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"print(\"CYCLE SLOW\")\n",
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"import numpy as np\n",
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"\n",
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"# Overall statistics across all node types\n",
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"overall_stats = df.select([\n",
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" (pl.col('Execution Time').mean() * 1_000_000).alias('overall_mean_time_us'),\n",
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" (pl.col('Execution Time').var() * 1_000_000 * 1_000_000).alias('overall_variance_us'), # multiply twice since variance is squared\n",
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" (pl.col('Execution Time').std() * 1_000_000).alias('overall_std_dev_us'),\n",
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" pl.col('Execution Time').count().alias('total_samples')\n",
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"]).with_columns([\n",
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" pl.col(['overall_mean_time_us', 'overall_variance_us', 'overall_std_dev_us'])\n",
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"])\n",
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"\n",
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"print(\"\\nOverall Execution Time Statistics:\")\n",
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"display(overall_stats)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.12.5"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

‎csp/adapters/testing_csp.ipynb‎

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'a': '9'}"
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]
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},
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"execution_count": 6,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"import csp\n",
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"from typing import Dict\n",
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"\n",
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"class A(csp.Struct):\n",
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" d: Dict[str, str] = {}\n",
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"\n",
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"x = A()\n",
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"x.d['a'] = '9'\n",
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"\n",
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"A().d\n",
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"x.d"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'{\"john\": 11, \"stevie\": 12, \"my_list\": [1, 2]}'"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"import json\n",
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"\n",
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"d = {'john': 11, 'stevie': 12, 'my_list': [1, 2]}\n",
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"\n",
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"x = json.dumps(d)\n",
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"\n",
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"with open('test.json', 'w') as f:\n",
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" f.write(x)\n",
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"x"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"11"
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]
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},
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"execution_count": 10,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"with open(\"test.json\", 'r') as g:\n",
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" raw_str = g.read()\n",
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"\n",
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"json.loads(raw_str)['john']"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.12.5"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

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