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Nils Hölscher c6f833f513 Added requirements.txt and Dev container 2022-07-15 09:03:33 +00:00
6 changed files with 99 additions and 50 deletions

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.devcontainer/Dockerfile Normal file
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# See here for image contents: https://github.com/microsoft/vscode-dev-containers/tree/v0.241.1/containers/python-3/.devcontainer/base.Dockerfile
# [Choice] Python version (use -bullseye variants on local arm64/Apple Silicon): 3, 3.10, 3.9, 3.8, 3.7, 3.6, 3-bullseye, 3.10-bullseye, 3.9-bullseye, 3.8-bullseye, 3.7-bullseye, 3.6-bullseye, 3-buster, 3.10-buster, 3.9-buster, 3.8-buster, 3.7-buster, 3.6-buster
ARG VARIANT="3.10-bullseye"
FROM mcr.microsoft.com/vscode/devcontainers/python:0-${VARIANT}
# [Choice] Node.js version: none, lts/*, 16, 14, 12, 10
ARG NODE_VERSION="none"
RUN if [ "${NODE_VERSION}" != "none" ]; then su vscode -c "umask 0002 && . /usr/local/share/nvm/nvm.sh && nvm install ${NODE_VERSION} 2>&1"; fi
# [Optional] If your pip requirements rarely change, uncomment this section to add them to the image.
COPY requirements.txt /tmp/pip-tmp/
RUN pip3 --disable-pip-version-check --no-cache-dir install -r /tmp/pip-tmp/requirements.txt \
&& rm -rf /tmp/pip-tmp
# [Optional] Uncomment this section to install additional OS packages.
# RUN apt-get update && export DEBIAN_FRONTEND=noninteractive \
# && apt-get -y install --no-install-recommends <your-package-list-here>
# [Optional] Uncomment this line to install global node packages.
# RUN su vscode -c "source /usr/local/share/nvm/nvm.sh && npm install -g <your-package-here>" 2>&1

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// For format details, see https://aka.ms/devcontainer.json. For config options, see the README at:
// https://github.com/microsoft/vscode-dev-containers/tree/v0.241.1/containers/python-3
{
"name": "Python 3",
"build": {
"dockerfile": "Dockerfile",
"context": "..",
"args": {
// Update 'VARIANT' to pick a Python version: 3, 3.10, 3.9, 3.8, 3.7, 3.6
// Append -bullseye or -buster to pin to an OS version.
// Use -bullseye variants on local on arm64/Apple Silicon.
"VARIANT": "3.10-bullseye",
// Options
"NODE_VERSION": "lts/*"
}
},
// Configure tool-specific properties.
"customizations": {
// Configure properties specific to VS Code.
"vscode": {
// Set *default* container specific settings.json values on container create.
"settings": {
"python.defaultInterpreterPath": "/usr/local/bin/python",
"python.linting.enabled": true,
"python.linting.pylintEnabled": true,
"python.formatting.autopep8Path": "/usr/local/py-utils/bin/autopep8",
"python.formatting.blackPath": "/usr/local/py-utils/bin/black",
"python.formatting.yapfPath": "/usr/local/py-utils/bin/yapf",
"python.linting.banditPath": "/usr/local/py-utils/bin/bandit",
"python.linting.flake8Path": "/usr/local/py-utils/bin/flake8",
"python.linting.mypyPath": "/usr/local/py-utils/bin/mypy",
"python.linting.pycodestylePath": "/usr/local/py-utils/bin/pycodestyle",
"python.linting.pydocstylePath": "/usr/local/py-utils/bin/pydocstyle",
"python.linting.pylintPath": "/usr/local/py-utils/bin/pylint"
},
// Add the IDs of extensions you want installed when the container is created.
"extensions": [
"ms-python.python",
"ms-python.vscode-pylance"
]
}
},
// Use 'forwardPorts' to make a list of ports inside the container available locally.
// "forwardPorts": [],
// Use 'postCreateCommand' to run commands after the container is created.
// "postCreateCommand": "pip3 install --user -r requirements.txt",
// Comment out to connect as root instead. More info: https://aka.ms/vscode-remote/containers/non-root.
"remoteUser": "vscode",
"features": {
"git": "latest",
"fish": "latest"
}
}

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requirements.txt Normal file
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argparse
numpy
tabulate

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@ -12,15 +12,11 @@ import include.TasksHelper as TH
# C_i is accessed as: tasks[i][2] # C_i is accessed as: tasks[i][2]
# The number of tasks can be accessed as: tasks.shape[0] # The number of tasks can be accessed as: tasks.shape[0]
#The sufficient Test for the Hyperbolic bound #The sufficient Test for the Hyperbolic bound
def test(tasks): def test(tasks):
""" The Hyperbolic bound is given as Π(U + 1) of all tasks: """
# compute the hyperbolic bound as product of the U_factor of each task + 1 #####################
hb = 1 #YOUR CODE GOES HERE#
for i in range(tasks.shape[0]): # len(tasks) is 10 #####################
hb *= (TH.C_i(tasks, i) / TH.P_i(tasks, i)) + 1
# compare this computed bound to 2.0 return False
# if greater then no guaranty of schedulability
# otherwise task set is schedulable
return hb <= 2.0

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@ -14,14 +14,9 @@ import include.TasksHelper as TH
#The necessary Test for the Liu and Layland Bound #The necessary Test for the Liu and Layland Bound
def test(tasks): def test(tasks):
n = tasks.shape[0]
U = TH.getTotalUtilization(tasks=tasks, NrTasks=n)
U_lub = n * ((2 ** (1 / n)) - 1)
# for fewer tasks than 10, we use the exact computed least upper bound #####################
# if n < 10: #YOUR CODE GOES HERE#
# return U <= U_lub #####################
# from 10 tasks up unlimited, we use the limes of n(2 ** 1/n - 1)
return U <= U_lub # np.log(2)
return False

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@ -13,39 +13,15 @@ import include.TasksHelper as TH
# C_i is accessed as: tasks[i][2] # C_i is accessed as: tasks[i][2]
# The number of tasks can be accessed as: tasks.shape[0] # The number of tasks can be accessed as: tasks.shape[0]
# The Time Demand Analysis Test #The Time Demand Analysis Test
set_num = 0
def test(tasks): def test(tasks):
# Sorting Taskset by Period/Deadline #Sorting Taskset by Period/Deadline
# This makes implementing TDA a lot easier #This makes implementing TDA a lot easier
shape = tasks.shape shape = tasks.shape
sortedtasks = tasks[tasks[:, 0].argsort()] sortedtasks = tasks[tasks[:, 0].argsort()]
# For each tasks in the ordered set #####################
#YOUR CODE GOES HERE#
#####################
# calculate the time points for the demand function return False
t_old = 10**-3
i = 0
while True:
t_new = workload_func(sortedtasks, i, t_old)
# if the workload of task i exceeds the deadline
if t_new > TH.D_i(sortedtasks, i):
return False # task not schedulable
if t_new == t_old:
i += 1
t_old = 10**-3
# chech array out of bounds
if i == len(sortedtasks):
return True
t_old = t_new
def workload_func(tasks, i, t):
sum = 0
for k in range(i):
sum += math.ceil(t / TH.P_i(tasks, k)) * TH.C_i(tasks, k)
return TH.C_i(tasks, i) + sum