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Merge branch 'master' of ssh://git.crapts.org:10022/marco/simulation

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Marco Crapts
2024-08-31 12:07:19 +02:00
parent ed876c9f91 a4004fc6f7
commit 4a5ced1172
3 changed files with 46 additions and 124 deletions
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poetry.lock generated
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@@ -55,126 +55,47 @@ files = [
] ]
[[package]] [[package]]
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description = "Powerful data structures for data analysis, time series, and statistics" description = "Blazingly fast DataFrame library"
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[package.dependencies]
numpy = [
{version = ">=1.22.4", markers = "python_version < \"3.11\""},
{version = ">=1.23.2", markers = "python_version == \"3.11\""},
{version = ">=1.26.0", markers = "python_version >= \"3.12\""},
]
python-dateutil = ">=2.8.2"
pytz = ">=2020.1"
tzdata = ">=2022.7"
[package.extras] [package.extras]
all = ["PyQt5 (>=5.15.9)", "SQLAlchemy (>=2.0.0)", "adbc-driver-postgresql (>=0.8.0)", "adbc-driver-sqlite (>=0.8.0)", "beautifulsoup4 (>=4.11.2)", "bottleneck (>=1.3.6)", "dataframe-api-compat (>=0.1.7)", "fastparquet (>=2022.12.0)", "fsspec (>=2022.11.0)", "gcsfs (>=2022.11.0)", "html5lib (>=1.1)", "hypothesis (>=6.46.1)", "jinja2 (>=3.1.2)", "lxml (>=4.9.2)", "matplotlib (>=3.6.3)", "numba (>=0.56.4)", "numexpr (>=2.8.4)", "odfpy (>=1.4.1)", "openpyxl (>=3.1.0)", "pandas-gbq (>=0.19.0)", "psycopg2 (>=2.9.6)", "pyarrow (>=10.0.1)", "pymysql (>=1.0.2)", "pyreadstat (>=1.2.0)", "pytest (>=7.3.2)", "pytest-xdist (>=2.2.0)", "python-calamine (>=0.1.7)", "pyxlsb (>=1.0.10)", "qtpy (>=2.3.0)", "s3fs (>=2022.11.0)", "scipy (>=1.10.0)", "tables (>=3.8.0)", "tabulate (>=0.9.0)", "xarray (>=2022.12.0)", "xlrd (>=2.0.1)", "xlsxwriter (>=3.0.5)", "zstandard (>=0.19.0)"] adbc = ["adbc-driver-manager[dbapi]", "adbc-driver-sqlite[dbapi]"]
aws = ["s3fs (>=2022.11.0)"] all = ["polars[async,cloudpickle,database,deltalake,excel,fsspec,graph,iceberg,numpy,pandas,plot,pyarrow,pydantic,style,timezone]"]
clipboard = ["PyQt5 (>=5.15.9)", "qtpy (>=2.3.0)"] async = ["gevent"]
compression = ["zstandard (>=0.19.0)"] calamine = ["fastexcel (>=0.9)"]
computation = ["scipy (>=1.10.0)", "xarray (>=2022.12.0)"] cloudpickle = ["cloudpickle"]
consortium-standard = ["dataframe-api-compat (>=0.1.7)"] connectorx = ["connectorx (>=0.3.2)"]
excel = ["odfpy (>=1.4.1)", "openpyxl (>=3.1.0)", "python-calamine (>=0.1.7)", "pyxlsb (>=1.0.10)", "xlrd (>=2.0.1)", "xlsxwriter (>=3.0.5)"] database = ["nest-asyncio", "polars[adbc,connectorx,sqlalchemy]"]
feather = ["pyarrow (>=10.0.1)"] deltalake = ["deltalake (>=0.15.0)"]
fss = ["fsspec (>=2022.11.0)"] excel = ["polars[calamine,openpyxl,xlsx2csv,xlsxwriter]"]
gcp = ["gcsfs (>=2022.11.0)", "pandas-gbq (>=0.19.0)"] fsspec = ["fsspec"]
hdf5 = ["tables (>=3.8.0)"] gpu = ["cudf-polars-cu12"]
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mysql = ["SQLAlchemy (>=2.0.0)", "pymysql (>=1.0.2)"] iceberg = ["pyiceberg (>=0.5.0)"]
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pyarrow = ["pyarrow (>=10.0.1)"] pydantic = ["pydantic"]
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xlsxwriter = ["xlsxwriter"]
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six = ">=1.5"
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name = "pytz"
version = "2024.1"
description = "World timezone definitions, modern and historical"
optional = false
python-versions = "*"
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name = "tzdata"
version = "2024.1"
description = "Provider of IANA time zone data"
optional = false
python-versions = ">=2"
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[metadata] [metadata]
lock-version = "2.0" lock-version = "2.0"
python-versions = "^3.10" python-versions = "^3.10"
content-hash = "1910b9fb645e4d8afc68594cb50f5e7ca8d396392fc3fec24baad4af0eaada52" content-hash = "3b5f1c763a56f03ec547b2513ce2ef1a9935ac6df8a8caca8288795aee44cbbf"

2
pyproject.toml
View File

@@ -8,7 +8,7 @@ readme = "README.md"
[tool.poetry.dependencies] [tool.poetry.dependencies]
python = "^3.10" python = "^3.10"
numpy = "^2.0.1" numpy = "^2.0.1"
pandas = "^2.2.2" polars = "^1.4.1"
[build-system] [build-system]

19
simulation/app.py
View File

@@ -1,7 +1,7 @@
from dataclasses import dataclass from dataclasses import dataclass
import numpy as np import numpy as np
import pandas as pd import polars as pl
from simulation.lib import Process, Simulation from simulation.lib import Process, Simulation
@@ -61,14 +61,15 @@ def run_simulation(n_servers=2, lam=40):
) )
simulation.start() simulation.start()
df_customers = pd.DataFrame( df_customers = pl.DataFrame(
[customer.__dict__ for customer in customers_handled] [customer.__dict__ for customer in customers_handled]
).assign(queue_time=lambda row: row["started_at"] - row["arrived_at"]) ).with_columns(queue_time=pl.col("started_at") - pl.col("arrived_at"))
df_servers = pd.DataFrame([server.__dict__ for server in servers]).set_index("id")
print("\n") df_servers = pl.DataFrame([server.__dict__ for server in servers])
print(df_customers[["queue_time", "handling_time"]].mean())
print(f"\nservers: {len(servers)}") # print(f"\nservers: {len(servers)}")
print(df_servers[["utilization"]]) print("\ncustomers", df_customers[["queue_time", "handling_time"]].mean())
print("\nservers", df_servers[["id", "utilization"]])
run_simulation(n_servers=2, lam=30) run_simulation(n_servers=3)