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akaszynskiakaszynski
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finalize public commit for public image
1 parent a6669d6 commit 4c4491a

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‎.gitignore‎

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NVIDIA-Linux*
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*.whl
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*~
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*~
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ipynb_checkpoints/

‎Dockerfile‎

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FROM jupyter/base-notebook:python-3.8.6
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FROM jupyter/base-notebook:python-3.8.8
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MAINTAINER Alex Kaszynski "[email protected]"
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USER jovyan
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# setup the itkwidgets conda enviornment
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# initial setup enviornment to install jupyterlab
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RUN conda update -n base conda
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RUN conda install mamba -n base -c conda-forge
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# install extensions
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COPY environment.yml labextensions.txt /tmp/
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RUN conda env update --name base --file /tmp/environment.yml
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RUN conda run conda install -y nodejs
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RUN mamba env update -n base --file /tmp/environment.yml
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RUN conda run jupyter labextension install $(cat /tmp/labextensions.txt)
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# machine learning and additional plotting
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RUN pip install gym==0.17.3 \
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tensorflow==2.3.1 \
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numpy==1.18.5 \
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itk-meshtopolydata==0.6.2 \
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ipycanvas==0.6.0
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# NVIDIA...
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USER root
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COPY NVIDIA-Linux-x86_64-450.51.06.run nvidia_drivers.run
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RUN ./nvidia_drivers.run -s --no-kernel-module \
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&& rm nvidia_drivers.run
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# plotting and EGL VTK
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COPY vtk-egl-9.0.1-cp38-cp38-linux_x86_64.whl vtk-egl-9.0.1-cp38-cp38-linux_x86_64.whl
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RUN pip install vtk-egl-9.0.1-cp38-cp38-linux_x86_64.whl \
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&& rm vtk-egl-9.0.1-cp38-cp38-linux_x86_64.whl \
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&& pip install pyvista==0.27.2 ipyvtk-simple==0.1.3
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COPY pyvista-0.29.1-py3-none-any.whl pyvista-0.29.1-py3-none-any.whl
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RUN pip install pyvista-0.29.1-py3-none-any.whl \
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&& rm pyvista-0.29.1-py3-none-any.whl
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# make jovyan sudo
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USER root
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RUN echo 'export PATH="/opt/conda/bin:$PATH"' >> /etc/profile
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RUN usermod -aG sudo jovyan
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RUN echo '%sudo ALL=(ALL) NOPASSWD:ALL' >> /etc/sudoers
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USER jovyan
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USER jovyan

‎IMAGE_NAME‎

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#!/bin/bash
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# contains base image name version
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VERSION=v0.5.3
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VERSION=v0.6.1
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IMAGE=mapdlhelm.azurecr.io/jupyterlab_base:$VERSION

‎README.md‎

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### Generate base jupyterhub image for pyansys.com
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This repository contains a `Dockerfile` to generate the base image
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required for upstream images for pyansys.com. Images are generated by
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running the scripts in the following order:
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- build.sh
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- push.sh
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Image is named and is built to be deployed at the MBU container
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repository, but this will be changed to be deployed via dockerhub or
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github docker
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```
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mapdlhelm.azurecr.io/jupyterlab_base:$VERSION
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```
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Obtain docker login credentials by following the steps at:
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https://portal.azure.com/#@ansys.com/resource/subscriptions/2870ae10-53f8-46b1-8971-93761377c38b/resourceGroups/PyANSYS/providers/Microsoft.ContainerRegistry/registries/mapdlhelm

‎build_image.sh‎

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set -e
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# build the ansys-base image single user image and pushes it to azure
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# build the jupyter single user image
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source IMAGE_NAME
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docker build -t $IMAGE .
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docker push $IMAGE

‎environment.yml‎

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channels:
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- conda-forge
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- defaults
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- cadquery
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- plotly
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dependencies:
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- jupyterlab=2.2.9
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- itkwidgets=0.32.0
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- ipywidgets=7.5.1
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- pandas=1.1.4
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- jupyterlab=3.0.10
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- ipywidgets=7.6.3
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- pandas=1.2.3
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- ipygany=0.5.0
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- pyvista=0.29.0
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- panel=0.11.0
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- plotly=4.14.3
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- cadquery=master
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- jupyter-dash=0.3.0
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- networkx=2.5
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- colour=0.1.5
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- cairo=1.16.0
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- ipympl=0.6.3
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- ipyevents=0.8.2
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- ipycanvas=0.8.2
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- dash-bootstrap-components=0.11.3
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- pip:
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- ipyvtk_simple==0.1.4
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- jupyter-cadquery==2.0.0
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- dash-vtk==0.0.6
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- dash-treeview-antd==0.0.1

‎labextensions.txt‎

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@jupyter-widgets/[email protected]
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[email protected]
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[email protected]
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[email protected]
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[email protected]
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[email protected]
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[email protected]
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[email protected]
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[email protected]

‎push_image.sh‎

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#!/bin/bash
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set -e
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# pushes base image to azure
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source IMAGE_NAME
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docker push $IMAGE

‎run_image.sh‎

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source IMAGE_NAME
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docker run -it --rm -v /home/$USER/python:/mnt/python -v /tmp:/mnt/tmp -p 8888:8888 --gpus all $IMAGE
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# with GPU
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# docker run -it --rm -v /home/$USER/python:/mnt/python -v /tmp:/mnt/tmp -p 8888:8888 --gpus all $IMAGE
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# w/o GPU
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docker run -it --rm \
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-v /home/$USER/python:/mnt/python \
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-v /tmp:/mnt/tmp \
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-v `pwd`/test_notebooks:/home/jovyan/test_notebooks \
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-p 8888:8888 $IMAGE

‎test_notebooks/cadquery.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": 3,
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"id": "streaming-trinity",
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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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"Overwriting auto display for cadquery Workplane and Shape\n"
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]
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}
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],
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"source": [
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"import warnings\n",
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"warnings.filterwarnings(\"ignore\")\n",
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"\n",
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"import cadquery as cq\n",
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"from jupyter_cadquery.cadquery import show\n",
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"\n",
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"from cadquery.occ_impl import exporters\n",
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"\n",
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"def make_block(lbumps, wbumps, thin):\n",
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"# lbumps = 4 # number of bumps long\n",
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"# wbumps = 2 # number of bumps wide\n",
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"# thin = False # True for thin, False for thick\n",
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"\n",
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" #\n",
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" # Lego Brick Constants-- these make a Lego brick a Lego :)\n",
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" #\n",
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" pitch = 8.0\n",
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" clearance = 0.1\n",
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" bumpDiam = 4.8\n",
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" bumpHeight = 1.8\n",
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" if thin:\n",
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" height = 3.2\n",
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" else:\n",
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" height = 9.6\n",
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"\n",
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" t = (pitch - (2 * clearance) - bumpDiam) / 2.0\n",
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" postDiam = pitch - t # works out to 6.5\n",
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" total_length = lbumps*pitch - 2.0*clearance\n",
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" total_width = wbumps*pitch - 2.0*clearance\n",
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"\n",
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" # make the base\n",
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" s = cq.Workplane(\"XY\").box(total_length, total_width, height)\n",
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"\n",
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" # shell inwards not outwards\n",
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" s = s.faces(\"<Z\").shell(-1.0 * t)\n",
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"\n",
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" # make the bumps on the top\n",
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" s = (s.faces(\">Z\").workplane().\n",
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" rarray(pitch, pitch, lbumps, wbumps, True).circle(bumpDiam / 2.0)\n",
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" .extrude(bumpHeight))\n",
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"\n",
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" # add posts on the bottom. posts are different diameter depending on geometry\n",
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" # solid studs for 1 bump, tubes for multiple, none for 1x1\n",
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" tmp = s.faces(\"<Z\").workplane(invert=True)\n",
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"\n",
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" if lbumps > 1 and wbumps > 1:\n",
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" tmp = (tmp.rarray(pitch, pitch, lbumps - 1, wbumps - 1, center=True).\n",
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" circle(postDiam / 2.0).circle(bumpDiam / 2.0).extrude(height - t))\n",
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" elif lbumps > 1:\n",
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" tmp = (tmp.rarray(pitch, pitch, lbumps - 1, 1, center=True).\n",
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" circle(t).extrude(height - t))\n",
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" elif wbumps > 1:\n",
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" tmp = (tmp.rarray(pitch, pitch, 1, wbumps - 1, center=True).\n",
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" circle(t).extrude(height - t))\n",
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" else:\n",
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" tmp = s\n",
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" \n",
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" return tmp"
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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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"id": "agricultural-blend",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "69757b311cce40d2b6bf5d02764171e5",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"HBox(children=(VBox(children=(HBox(children=(Checkbox(value=False, description='Axes', indent=False, _dom_clas…"
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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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"data": {
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"text/plain": [
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"<jupyter_cadquery.cad_display.CadqueryDisplay at 0x7fa806d74a60>"
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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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"block = make_block(2, 2, True)\n",
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"show(block)"
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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": "3.8.5"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}

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