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 Dataset for Direct Quotation Extraction and Attribution in News Articles.

DirectQuote - A Dataset for Direct Quotation Extraction and Attribution in News Articles DirectQuote is a corpus containing 19,760 paragraphs and 10,3

THUNLP-MT 9 Sep 23, 2022
Torch implementation of various types of GAN (e.g. DCGAN, ALI, Context-encoder, DiscoGAN, CycleGAN, EBGAN, LSGAN)

gans-collection.torch Torch implementation of various types of GANs (e.g. DCGAN, ALI, Context-encoder, DiscoGAN, CycleGAN, EBGAN). Note that EBGAN and

Minchul Shin 53 Jan 22, 2022
Individual Treatment Effect Estimation

CAPE Individual Treatment Effect Estimation Run CAPE python train_causal.py --loop 10 -m cape_cau -d NI --i_t 1 Run a baseline model python train_cau

S. Deng 4 Sep 02, 2022
Adversarial Attacks on Probabilistic Autoregressive Forecasting Models.

Attack-Probabilistic-Models This is the source code for Adversarial Attacks on Probabilistic Autoregressive Forecasting Models. This repository contai

SRI Lab, ETH Zurich 25 Sep 14, 2022
TreeSubstitutionCipher - Encryption system based on trees and substitution

Tree Substitution Cipher Generation Algorithm: Generate random tree. Tree nodes

stepa 1 Jan 08, 2022
The 7th edition of NTIRE: New Trends in Image Restoration and Enhancement workshop will be held on June 2022 in conjunction with CVPR 2022.

NTIRE 2022 - Image Inpainting Challenge Important dates 2022.02.01: Release of train data (input and output images) and validation data (only input) 2

Andrés Romero 37 Nov 27, 2022
A privacy-focused, intelligent security camera system.

Self-Hosted Home Security Camera System A privacy-focused, intelligent security camera system. Features: Multi-camera support w/ minimal configuration

Scott Barnes 175 Jan 01, 2023
Weakly Supervised Scene Text Detection using Deep Reinforcement Learning

Weakly Supervised Scene Text Detection using Deep Reinforcement Learning This repository contains the setup for all experiments performed in our Paper

Emanuel Metzenthin 3 Dec 16, 2022
A PyTorch implementation of the WaveGlow: A Flow-based Generative Network for Speech Synthesis

WaveGlow A PyTorch implementation of the WaveGlow: A Flow-based Generative Network for Speech Synthesis Quick Start: Install requirements: pip install

Yuchao Zhang 204 Jul 14, 2022
Introducing neural networks to predict stock prices

IntroNeuralNetworks in Python: A Template Project IntroNeuralNetworks is a project that introduces neural networks and illustrates an example of how o

Vivek Palaniappan 637 Jan 04, 2023
Code for the paper "Benchmarking and Analyzing Point Cloud Classification under Corruptions"

ModelNet-C Code for the paper "Benchmarking and Analyzing Point Cloud Classification under Corruptions". For the latest updates, see: sites.google.com

Jiawei Ren 45 Dec 28, 2022
High-performance moving least squares material point method (MLS-MPM) solver.

High-Performance MLS-MPM Solver with Cutting and Coupling (CPIC) (MIT License) A Moving Least Squares Material Point Method with Displacement Disconti

Yuanming Hu 2.2k Dec 31, 2022
Normalization Calibration (NorCal) for Long-Tailed Object Detection and Instance Segmentation

NorCal Normalization Calibration (NorCal) for Long-Tailed Object Detection and Instance Segmentation On Model Calibration for Long-Tailed Object Detec

Tai-Yu (Daniel) Pan 24 Dec 25, 2022
⚡ H2G-Net for Semantic Segmentation of Histopathological Images

H2G-Net This repository contains the code relevant for the proposed design H2G-Net, which was introduced in the manuscript "Hybrid guiding: A multi-re

André Pedersen 8 Nov 24, 2022
Localized representation learning from Vision and Text (LoVT)

Localized Vision-Text Pre-Training Contrastive learning has proven effective for pre- training image models on unlabeled data and achieved great resul

Philip Müller 10 Dec 07, 2022
A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning

CLEVR Dataset Generation This is the code used to generate the CLEVR dataset as described in the paper: CLEVR: A Diagnostic Dataset for Compositional

Facebook Research 503 Jan 04, 2023
a reimplementation of Holistically-Nested Edge Detection in PyTorch

pytorch-hed This is a personal reimplementation of Holistically-Nested Edge Detection [1] using PyTorch. Should you be making use of this work, please

Simon Niklaus 375 Dec 06, 2022
CTRL-C: Camera calibration TRansformer with Line-Classification

CTRL-C: Camera calibration TRansformer with Line-Classification This repository contains the official code and pretrained models for CTRL-C (Camera ca

57 Nov 14, 2022
Implementation of "StrengthNet: Deep Learning-based Emotion Strength Assessment for Emotional Speech Synthesis"

StrengthNet Implementation of "StrengthNet: Deep Learning-based Emotion Strength Assessment for Emotional Speech Synthesis" https://arxiv.org/abs/2110

RuiLiu 65 Dec 20, 2022
A real-time approach for mapping all human pixels of 2D RGB images to a 3D surface-based model of the body

DensePose: Dense Human Pose Estimation In The Wild Rıza Alp Güler, Natalia Neverova, Iasonas Kokkinos [densepose.org] [arXiv] [BibTeX] Dense human pos

Meta Research 6.4k Jan 01, 2023