Code, final versions, and information on the Sparkfun Graphical Datasheets

Overview

Graphical Datasheets

Code, final versions, and information on the SparkFun Graphical Datasheets.

Generated Cells Completed Graphical Datasheet
Generated Cells After Running Script Example Completed Graphical Datasheet

This repo includes the Python script used to help generate the graphical datasheets. It also includes the final .svg, and .pdf files as well as the .csv files use for development boards. The .csv files were used as a starting point and some text did change between the file and the final version. There is also a User Submitted folder for external contributions.

Setting Up and Running the Script via Notepad++

One method is to use Notepad++ to edit and a plug-in to run the script. Download and install Notepadd++ v7.7.1 on your computer. From Notepad++'s Plugins > Plugins Admin... menu, search for PyNPP plug-in and install. We used PyNPP v1.0.0. You may need to search online, download the plug-in, and manually install on Notepad++ from the Settings > Import > Import plug-in(s)... menu. This plug-in is optional if you want to run the script from Notepad++.

We'll assume that you have Python 2.7 installed. If you have not already, open up the command prompt. To check the version of Python, type the following to see if you are using Python 2 or Python 3. If you do not see Python 2, you will need to adjust your environment variables [i.e. System Properties > Environment Variables..., then System Variables > Path > Edit..., and add the location of your installed Python (in this case it was C:\Python27) to a field] to be able to use that specific version.

python --version

To manually install, download and unzip the svgwrite module (v1.2.0). In a command line, change the path to where ...\svgwrite folder is located and use the following command to install.

python setup.py install

Create a CSV of the pinout for your development board. You can also edit the CSV from any of the examples. For simplicity, copy the Pro Mini's file (...Graphical_Datasheets\Datasheets\ProMini\ProMini.csv ) and paste it in the same folder as the python script (...\Graphical_Datasheets). Open one of the tagscript.py scripts in Notepad++ and run the script from the menu: Plugins > PyNPP > Run File in Python.

A window will pop up requesting for the CSV file name. Enter the file name (like ProMini), it will output the SVG with the same name.

After running the script, open the SVG file in Inkscape (or Illustrator) with an image of your development board to align or adjust the pinouts! Feel free to adjust the script to format your cells based on your personal preferences. Have fun!

Setting Up and Running the Script via Command Line

You can use any text editor to edit the script. The following instructions do not require PyNPP. Additionally, it is an alternative method to install the svgwrite module and run the Python script via command line.

Again, we'll assume that you have Python 2.7 installed. If you have not already, open up the command prompt. To check the version of Python, type the following to see if you are using Python 2 or Python 3. If you do not see Python 2, you will need to adjust your environment variables [i.e. System Properties > Environment Variables..., then System Variables > Path > Edit..., and add the location of your installed Python (in this case it was C:\Python27) to a field] to be able to use that specific version.

python --version

Open a command prompt and use the following command to install the older version of svgwrite.

python -m pip install svgwrite==1.2.1

Create a CSV of the pinout for your development board. You can also edit the CSV from any of the examples. For simplicity, copy the Pro Mini's file (...Graphical_Datasheets\Datasheets\ProMini\ProMini.csv ) and paste it in the same folder as the python script (...\Graphical_Datasheets). Use the following command to execute the script.

python tagscript.py

A window will pop up requesting for the CSV file name. Enter the file name (like ProMini), it will output the SVG with the same name.

After running the script, open the SVG file in Inkscape (or Illustrator) with an image of your development board to align or adjust the pinouts! Feel free to adjust the script to format your cells based on your personal preferences. Have fun!

Required Software

Some software used to create graphical datasheets. At the time of writing, Python 2 was used to generate the cells. Note that support Python 2 has ended but the tools should still work if you are using archived versions of the plug-in and module. You may need to adjust the script to work with the latest NotePad++, NyPP plug-in, Python 3, and svgwrite versions.

  • Notepad++ v7.7.1 - Text editor to modify the Python script
    • PyNPP v1.0.0 - Optional plug-in to run Python Scripts
  • Python v2.7.13
    • svgwrite v1.2.0 - The script uses this version of svgwrite which is compatible with Python 2
  • Inkscape v0.92.4

Repository Contents

  • /Datasheets - CSV of pinouts and graphical datasheets for development boards
  • tagscript.py - Script to generate cells for graphical datasheets
  • tagscript_original-mshorter.py - Original script to individually modify each column attribute if necessary

Documentation

Owner
SparkFun Electronics
Building opensource widgets to make prototyping hardware easier since 2002.
SparkFun Electronics
A pytorch implementation of faster RCNN detection framework (Use detectron2, it's a masterpiece)

Notice(2019.11.2) This repo was built back two years ago when there were no pytorch detection implementation that can achieve reasonable performance.

Ruotian(RT) Luo 1.8k Jan 01, 2023
Barlow Twins and HSIC

Barlow Twins and HSIC Unofficial Pytorch implementation for Barlow Twins and HSIC_SSL on small datasets (CIFAR10, STL10, and Tiny ImageNet). Correspon

Yao-Hung Hubert Tsai 49 Nov 24, 2022
Code repo for realtime multi-person pose estimation in CVPR'17 (Oral)

Realtime Multi-Person Pose Estimation By Zhe Cao, Tomas Simon, Shih-En Wei, Yaser Sheikh. Introduction Code repo for winning 2016 MSCOCO Keypoints Cha

Zhe Cao 4.9k Dec 31, 2022
Trading and Backtesting environment for training reinforcement learning agent or simple rule base algo.

TradingGym TradingGym is a toolkit for training and backtesting the reinforcement learning algorithms. This was inspired by OpenAI Gym and imitated th

Yvictor 1.1k Jan 02, 2023
Histocartography is a framework bringing together AI and Digital Pathology

Documentation | Paper Welcome to the histocartography repository! histocartography is a python-based library designed to facilitate the development of

155 Nov 23, 2022
A tensorflow model that predicts if the image is of a cat or of a dog.

Quick intro Hello and thank you for your interest in my project! This is the backend part of a two-repo application. The other part can be found here

Tudor Matei 0 Mar 08, 2022
Learning Spatio-Temporal Transformer for Visual Tracking

STARK The official implementation of the paper Learning Spatio-Temporal Transformer for Visual Tracking Hiring research interns for visual transformer

Multimedia Research 484 Dec 29, 2022
This is Unofficial Repo. Lips Don't Lie: A Generalisable and Robust Approach to Face Forgery Detection (CVPR 2021)

Lips Don't Lie: A Generalisable and Robust Approach to Face Forgery Detection This is a PyTorch implementation of the LipForensics paper. This is an U

Minha Kim 2 May 11, 2022
Official implementation of CVPR2020 paper "Deep Generative Model for Robust Imbalance Classification"

Deep Generative Model for Robust Imbalance Classification Deep Generative Model for Robust Imbalance Classification Xinyue Wang, Yilin Lyu, Liping Jin

9 Nov 01, 2022
Heart Arrhythmia Classification

This program takes and input of an ECG in European Data Format (EDF) and outputs the classification for heartbeats into normal vs different types of arrhythmia . It uses a deep learning model for cla

4 Nov 02, 2022
Building a real-time environment using webcam frame division in OpenCV and classify cropped images using a fine-tuned vision transformers on hybryd datasets samples for facial emotion recognition.

Visual Transformer for Facial Emotion Recognition (FER) This project has the aim to build an efficient Visual Transformer for the Facial Emotion Recog

Mario Sessa 8 Dec 12, 2022
A repo for Causal Imitation Learning under Temporally Correlated Noise

CausIL A repo for Causal Imitation Learning under Temporally Correlated Noise. Running Experiments To re-train an expert, run: python experts/train_ex

Gokul Swamy 5 Nov 01, 2022
Bio-Computing Platform Featuring Large-Scale Representation Learning and Multi-Task Deep Learning “螺旋桨”生物计算工具集

English | 简体中文 Latest News 2021.10.25 Paper "Docking-based Virtual Screening with Multi-Task Learning" is accepted by BIBM 2021. 2021.07.29 PaddleHeli

633 Jan 04, 2023
NeurIPS'21 Tractable Density Estimation on Learned Manifolds with Conformal Embedding Flows

NeurIPS'21 Tractable Density Estimation on Learned Manifolds with Conformal Embedding Flows This repo contains the code for the paper Tractable Densit

Layer6 Labs 4 Dec 12, 2022
Small-bets - Ergodic Experiment With Python

Ergodic Experiment Based on this video. Run this experiment with this command: p

Michael Brant 3 Jan 11, 2022
Source code of our BMVC 2021 paper: AniFormer: Data-driven 3D Animation with Transformer

AniFormer This is the PyTorch implementation of our BMVC 2021 paper AniFormer: Data-driven 3D Animation with Transformer. Haoyu Chen, Hao Tang, Nicu S

24 Nov 02, 2022
Generating Radiology Reports via Memory-driven Transformer

R2Gen This is the implementation of Generating Radiology Reports via Memory-driven Transformer at EMNLP-2020. Citations If you use or extend our work,

CUHK-SZ NLP Group 101 Dec 13, 2022
N-gram models- Unsmoothed, Laplace, Deleted Interpolation

N-gram models- Unsmoothed, Laplace, Deleted Interpolation

Ravika Nagpal 1 Jan 04, 2022
Use stochastic processes to generate samples and use them to train a fully-connected neural network based on Keras

Use stochastic processes to generate samples and use them to train a fully-connected neural network based on Keras which will then be used to generate residuals

Federico Lopez 2 Jan 14, 2022
Human segmentation models, training/inference code, and trained weights, implemented in PyTorch

Human-Segmentation-PyTorch Human segmentation models, training/inference code, and trained weights, implemented in PyTorch. Supported networks UNet: b

Thuy Ng 474 Dec 19, 2022