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Setup benchmark suite
Signed-off-by: Tim Paine <[email protected]>
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‎.gitignore

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@@ -104,6 +104,11 @@ csp/lib/
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*.so
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*.tsbuildinfo
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# Benchmarks
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.asv
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ci/benchmarks/*
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!ci/benchmarks/benchmarks.json
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# Jupyter / Editors
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.ipynb_checkpoints
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.autoversion

‎Makefile

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@@ -118,6 +118,28 @@ dockerps: ## spin up docker compose services for adapter testing
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dockerdown: ## spin up docker compose services for adapter testing
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$(DOCKER) compose -f ci/$(ADAPTER)/docker-compose.yml down
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##############
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# BENCHMARKS #
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##############
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.PHONY: benchmark benchmarks benchmark-regen benchmark-view benchmarks-regen benchmarks-view
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benchmark: ## run benchmarks
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python -m asv run --config csp/benchmarks/asv.conf.jsonc --verbose `git rev-parse --abbrev-ref HEAD`
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# https://github.com/airspeed-velocity/asv/issues/1027
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# https://github.com/airspeed-velocity/asv/issues/488
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benchmark-regen:
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python -m asv run --config csp/benchmarks/asv.conf.jsonc --verbose v0.0.4^!
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python -m asv run --config csp/benchmarks/asv.conf.jsonc --verbose v0.0.5^!
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benchmark-view: ## generate viewable website of benchmark results
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python -m asv publish --config csp/benchmarks/asv.conf.jsonc
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python -m asv preview --config csp/benchmarks/asv.conf.jsonc
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# Alias
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benchmarks: benchmark
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benchmarks-regen: benchmark-regen
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benchmarks-view: benchmark-view
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###########
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# VERSION #
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###########

‎ci/benchmarks/benchmarks.json

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{
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"stats.basic.StatsBenchmarkSuite.time_stats_qtl": {
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"code": "class StatsBenchmarkSuite:\n def time_stats_qtl(self):\n def g_qtl():\n data = csp.curve(typ=np.ndarray, data=self.DATA)\n median = csp.stats.median(data, interval=self.INTERVAL)\n csp.add_graph_output(\"final_median\", median, tick_count=1)\n \n qtl_times = []\n \n for _ in range(self.NUM_SAMPLES):\n start = time.time()\n csp.run(g_qtl, realtime=False, starttime=self.st, endtime=timedelta(seconds=self.N))\n post_qtl = time.time()\n qtl_times.append(post_qtl - start)\n \n avg_med = sum(qtl_times) / self.NUM_SAMPLES\n print(\n f\"Average time in {self.NUM_SAMPLES} tests for median with {self.N=}, {self.ARRAY_SIZE=}, {self.INTERVAL=}: {round(avg_med, 2)} s\"\n )\n return avg_med\n\n def setup(self):\n self.st = datetime(2020, 1, 1)\n self.N = 1_000\n self.ARRAY_SIZE = 100\n self.TEST_TIMES = [self.st + timedelta(seconds=i) for i in range(self.N)]\n self.RANDOM_VALUES = [np.random.normal(size=(self.ARRAY_SIZE,)) for i in range(self.N)] # 100 element np array\n self.DATA = list(zip(self.TEST_TIMES, self.RANDOM_VALUES))\n self.INTERVAL = 500\n self.NUM_SAMPLES = 100",
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"min_run_count": 2,
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"name": "stats.basic.StatsBenchmarkSuite.time_stats_qtl",
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"number": 0,
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"param_names": [],
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"params": [],
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"repeat": 0,
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"rounds": 2,
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"sample_time": 0.01,
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"type": "time",
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"unit": "seconds",
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"version": "21f280e4eeceac0ca2172bed432939c57f2b2618bd26bd27d15d4ca177e2ab26",
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"warmup_time": -1
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},
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"stats.basic.StatsBenchmarkSuite.time_stats_rank": {
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"code": "class StatsBenchmarkSuite:\n def time_stats_rank(self):\n def g_rank():\n data = csp.curve(typ=np.ndarray, data=self.DATA)\n rank = csp.stats.rank(data, interval=self.INTERVAL)\n csp.add_graph_output(\"final_rank\", rank, tick_count=1)\n \n rank_times = []\n \n for _ in range(self.NUM_SAMPLES):\n start = time.time()\n csp.run(g_rank, realtime=False, starttime=self.st, endtime=timedelta(seconds=self.N))\n post_rank = time.time()\n rank_times.append(post_rank - start)\n \n avg_rank = sum(rank_times) / self.NUM_SAMPLES\n print(\n f\"Average time in {self.NUM_SAMPLES} tests for rank with {self.N=}, {self.ARRAY_SIZE=}, {self.INTERVAL=}: {round(avg_rank, 2)} s\"\n )\n return avg_rank\n\n def setup(self):\n self.st = datetime(2020, 1, 1)\n self.N = 1_000\n self.ARRAY_SIZE = 100\n self.TEST_TIMES = [self.st + timedelta(seconds=i) for i in range(self.N)]\n self.RANDOM_VALUES = [np.random.normal(size=(self.ARRAY_SIZE,)) for i in range(self.N)] # 100 element np array\n self.DATA = list(zip(self.TEST_TIMES, self.RANDOM_VALUES))\n self.INTERVAL = 500\n self.NUM_SAMPLES = 100",
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"min_run_count": 2,
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"name": "stats.basic.StatsBenchmarkSuite.time_stats_rank",
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"number": 0,
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"param_names": [],
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"params": [],
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"repeat": 0,
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"rounds": 2,
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"sample_time": 0.01,
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"type": "time",
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"unit": "seconds",
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"version": "4c302ccf942084ac2367999fc84b2ba882c2ff74cddd80a3c27c8f8a1aee333d",
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"warmup_time": -1
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},
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"version": 2
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}

‎conda/dev-environment-unix.yml

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- conda-forge
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- nodefaults
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dependencies:
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- asv
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- bison
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- brotli
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- build

‎conda/dev-environment-win.yml

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- conda-forge
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- nodefaults
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dependencies:
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- asv
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- brotli
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- build
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- bump2version>=1

‎csp/benchmarks/__init__.py

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‎csp/benchmarks/asv.conf.jsonc

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// https://asv.readthedocs.io/en/v0.6.3/asv.conf.json.html
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{
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"version": 1,
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"project": "csp",
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"project_url": "https://github.com/Point72/csp",
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"repo": "../..",
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"branches": ["main"],
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"dvcs": "git",
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"install_command": ["in-dir={env_dir} python -mpip install {wheel_file}"],
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"uninstall_command": ["return-code=any python -mpip uninstall -y {project}"],
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"build_command": [
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"python -m pip install build",
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"python -m build --wheel -o {build_cache_dir} {build_dir}"
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],
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"environment_type": "virtualenv",
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"install_timeout": 600,
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"show_commit_url": "http://github.com/point72/csp/commit/",
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"pythons": ["3.11"],
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// "environment_type": "mamba",
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// "conda_channels": ["conda-forge"],
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// "conda_environment_file": "conda/dev-environment-unix.yml",
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"benchmark_dir": "../../csp/benchmarks",
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"env_dir": "../../.asv/env",
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"results_dir": "../../ci/benchmarks",
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"html_dir": "../../.asv/html",
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"hash_length": 8,
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"build_cache_size": 2
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}

‎csp/benchmarks/stats/__init__.py

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‎csp/benchmarks/stats/basic.py

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import numpy as np
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import time
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from datetime import datetime, timedelta
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from timeit import Timer
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import csp
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class StatsBenchmarkSuite:
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def setup(self):
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self.start_date = datetime(2020, 1, 1)
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self.num_rows = 1_000
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self.array_size = 100
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self.test_times = [self.st + timedelta(seconds=i) for i in range(self.num_rows)]
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self.random_values = [
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np.random.normal(size=(self.array_size,)) for i in range(self.num_rows)
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] # 100 element np array
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self.data = list(zip(self.test_times, self.random_values))
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self.interval = 500
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self.num_samples = 100
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def time_stats_qtl(self):
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def g_qtl():
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data = csp.curve(typ=np.ndarray, data=self.data)
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median = csp.stats.median(data, interval=self.interval)
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csp.add_graph_output("final_median", median, tick_count=1)
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timer = Timer(
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lambda: csp.run(g_qtl, realtime=False, starttime=self.st, endtime=timedelta(seconds=self.num_rows))
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)
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elapsed = timer.timeit(self.num_samples)
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avg_med = elapsed / self.num_samples
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print(
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f"Average time in {self.num_samples} tests for median with {self.num_rows=}, {self.array_size=}, {self.interval=}: {round(avg_med, 2)} s"
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)
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return avg_med
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def time_stats_rank(self):
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def g_rank():
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data = csp.curve(typ=np.ndarray, data=self.DATA)
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rank = csp.stats.rank(data, interval=self.interval)
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csp.add_graph_output("final_rank", rank, tick_count=1)
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timer = Timer(
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lambda: csp.run(g_rank, realtime=False, starttime=self.st, endtime=timedelta(seconds=self.num_rows))
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)
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elapsed = timer.timeit(self.num_samples)
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avg_rank = elapsed / self.num_samples
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print(
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f"Average time in {self.num_samples} tests for rank with {self.num_rows=}, {self.array_size=}, {self.interval=}: {round(avg_rank, 2)} s"
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)
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return avg_rank
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if __name__ == "__main__":
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sbs = StatsBenchmarkSuite()
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sbs.setup()
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sbs.time_stats_qtl()
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sbs.time_stats_rank()

‎pyproject.toml

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"sqlalchemy", # db
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"threadpoolctl", # test_random
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"tornado", # profiler, perspective, websocket
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# benchmarks
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"asv",
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]
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showgraph = [
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"graphviz",

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