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
TensorFlow implementation of "Attention is all you need (Transformer)"

[TensorFlow 2] Attention is all you need (Transformer) TensorFlow implementation of "Attention is all you need (Transformer)" Dataset The MNIST datase

YeongHyeon Park 4 Jan 05, 2022
🐾 Semantic segmentation of paws from cute pet images (PyTorch)

🐾 paw-segmentation 🐾 Semantic segmentation of paws from cute pet images 🐾 Semantic segmentation of paws from cute pet images (PyTorch) 🐾 Paw Segme

Zabir Al Nazi Nabil 3 Feb 01, 2022
DenseNet Implementation in Keras with ImageNet Pretrained Models

DenseNet-Keras with ImageNet Pretrained Models This is an Keras implementation of DenseNet with ImageNet pretrained weights. The weights are converted

Felix Yu 568 Oct 31, 2022
Creative Applications of Deep Learning w/ Tensorflow

Creative Applications of Deep Learning w/ Tensorflow This repository contains lecture transcripts and homework assignments as Jupyter Notebooks for th

Parag K Mital 1.5k Dec 30, 2022
Code release for the ICML 2021 paper "PixelTransformer: Sample Conditioned Signal Generation".

PixelTransformer Code release for the ICML 2021 paper "PixelTransformer: Sample Conditioned Signal Generation". Project Page Installation Please insta

Shubham Tulsiani 24 Dec 17, 2022
A set of tests for evaluating large-scale algorithms for Wasserstein-2 transport maps computation.

Continuous Wasserstein-2 Benchmark This is the official Python implementation of the NeurIPS 2021 paper Do Neural Optimal Transport Solvers Work? A Co

Alexander 22 Dec 12, 2022
Implement object segmentation on images using HOG algorithm proposed in CVPR 2005

HOG Algorithm Implementation Description HOG (Histograms of Oriented Gradients) Algorithm is an algorithm aiming to realize object segmentation (edge

Leo Hsieh 2 Mar 12, 2022
Semi-Supervised Signed Clustering Graph Neural Network (and Implementation of Some Spectral Methods)

SSSNET SSSNET: Semi-Supervised Signed Network Clustering For details, please read our paper. Environment Setup Overview The project has been tested on

Yixuan He 9 Nov 24, 2022
Attentional Focus Modulates Automatic Finger‑tapping Movements

"Attentional Focus Modulates Automatic Finger‑tapping Movements", in Scientific Reports

Xingxun Jiang 1 Dec 02, 2021
An implementation of the Contrast Predictive Coding (CPC) method to train audio features in an unsupervised fashion.

CPC_audio This code implements the Contrast Predictive Coding algorithm on audio data, as described in the paper Unsupervised Pretraining Transfers we

Meta Research 283 Dec 30, 2022
Optimizing DR with hard negatives and achieving SOTA first-stage retrieval performance on TREC DL Track (SIGIR 2021 Full Paper).

Optimizing Dense Retrieval Model Training with Hard Negatives Jingtao Zhan, Jiaxin Mao, Yiqun Liu, Jiafeng Guo, Min Zhang, Shaoping Ma 🔥 News 2021-10

Jingtao Zhan 99 Dec 27, 2022
This is an official implementation for "Video Swin Transformers".

Video Swin Transformer By Ze Liu*, Jia Ning*, Yue Cao, Yixuan Wei, Zheng Zhang, Stephen Lin and Han Hu. This repo is the official implementation of "V

Swin Transformer 981 Jan 03, 2023
PyTorch implementation for STIN

STIN This repository contains PyTorch implementation for STIN. Abstract: In single-photon LiDAR, photon-efficient imaging captures the 3D structure of

Yiweins 2 Nov 22, 2022
Automatic Data-Regularized Actor-Critic (Auto-DrAC)

Auto-DrAC: Automatic Data-Regularized Actor-Critic This is a PyTorch implementation of the methods proposed in Automatic Data Augmentation for General

89 Dec 13, 2022
MLP-Numpy - A simple modular implementation of Multi Layer Perceptron in pure Numpy.

MLP-Numpy A simple modular implementation of Multi Layer Perceptron in pure Numpy. I used the Iris dataset from scikit-learn library for the experimen

Soroush Omranpour 1 Jan 01, 2022
PyTorch implementation for the visual prior component (i.e. perception module) of the Visually Grounded Physics Learner [Li et al., 2020].

VGPL-Visual-Prior PyTorch implementation for the visual prior component (i.e. perception module) of the Visually Grounded Physics Learner (VGPL). Give

Toru 8 Dec 29, 2022
Orbivator AI - To Determine which features of data (measurements) are most important for diagnosing breast cancer and find out if breast cancer occurs or not.

Orbivator_AI Breast Cancer Wisconsin (Diagnostic) GOAL To Determine which features of data (measurements) are most important for diagnosing breast can

anurag kumar singh 1 Jan 02, 2022
constructing maps of intellectual influence from publication data

Influencemap Project @ ANU Influence in the academic communities has been an area of interest for researchers. This can be seen in the popularity of a

CS Metrics 13 Jun 18, 2022
[CVPR 2019 Oral] Multi-Channel Attention Selection GAN with Cascaded Semantic Guidance for Cross-View Image Translation

SelectionGAN for Guided Image-to-Image Translation CVPR Paper | Extended Paper | Guided-I2I-Translation-Papers Citation If you use this code for your

Hao Tang 424 Dec 02, 2022
Prototypical python implementation of the trust-region algorithm presented in Sequential Linearization Method for Bound-Constrained Mathematical Programs with Complementarity Constraints by Larson, Leyffer, Kirches, and Manns.

Prototypical python implementation of the trust-region algorithm presented in Sequential Linearization Method for Bound-Constrained Mathematical Programs with Complementarity Constraints by Larson, L

3 Dec 02, 2022