Inkscape extensions for figure resizing and editing

Overview

Academic-Inkscape: Extensions for figure resizing and editing

This repository contains several Inkscape extensions designed for editing plots.

  1. Scale Plots: Changes the size or aspect ratio of a plot without modifying its text and ticks. Especially useful for assembling multi-panel figures.
  2. Flatten Plots: A utility that eliminates much of the structure generated by common vector graphics plotting programs. Makes editing much easier.
  3. The Homogenizer: Quickly sets uniform fonts, font sizes, and stroke widths in a selection.
  4. The Auto-Exporter: A program that will automatically export your SVG files to various formats and keep them updated.

All were written by David Burghoff at the University of Notre Dame. If you find it useful, tell your collegaues!

Installation

You must have the latest release version of Inkscape (1.0.2), and the extensions should be installed using the instructions provided here. Download all of these files, then copy them into the directory listed at Edit > Preferences > System: User extensions. After a restart of Inkscape, the group extensions will be available under Extensions > Academic.

Scale Plots

When dealing with vector graphics generated by plotting environments like Matlab and Matplotlib, resizing plots after the plot has been generated can be difficult. Generally, one wants to resize the lines and data of a plot while leaving text, ticks, and stroke widths unaffected. This is best done in the original program, but precludes quick modification.

For most plots, Scale Plots generates acceptable scalings with little effort. Lines and data are scaled while text and ticks are merely repositioned. The extension attempts to maintain the distance between axes and labels/tick labels by assigning a plot area—a bounding box that is calculated from the largest horizontal and vertical lines. Anything outside is assumed to be a label. (If your plot's axes do not have lines, temporarily add a box to define a plot area.)

Scale Plots example

To use:

  1. Run Flatten Plots on your plot to remove structure generated by the PDF/EPS/SVG exporting process.
  2. Place any objects that you wish to remain unscaled in a group.
  3. Select the elements of your plot and run Scale Plots.

Scale Plots has two modes. In Scaling Mode, the plot is scaled by a constant factor. In Matching Mode, the plot area is made to match the size of the first object you select. This can be convenient when assembling subfigures, as it allows you to match the size of one plot to another plot or to a template rectangle.

Advanced options

  1. If "Auto tick correct" is enabled, the extension assumes that any small horizontal or vertical lines near the edges of the plot area are ticks, and automatically leaves them unscaled.
  2. If a layer name is put into the "Scale-free layer" option, any elements on that layer will remain unscaled. This is basically the same thing as putting an object in a group, but can be easier if there are many such objects (e.g, if your plot has markers).

Flatten Plots

Flatten Plots is a useful utility that eliminates many of the difficulties that arise when plots are exported from common plotting programs.

  1. Deep ungroup: The Scale Plots utility uses grouping to determine when objects are to be kept together, so a deep ungroup is typically needed to remove any existing groupings initially. It also unlinks any clones.
  2. Apply text fixes: Applies a series of fixes to text described below (particularly useful for PDF/EPS text).
  3. Remove white rectangles: Removes any rectangles that have white fill and no stroke. Mostly for removing a plot's background.

Text fixes

  1. Split distant text: Depending on the renderer, it is often the case that the PDF/EPS printing process generates text implemented as a single text object. For example, all of the x-axis ticks might be one object, all of the y-axis ticks might be another, and the title and labels may be another. Internally, each letter is positioned independently. This looks fine, but causes issues when trying to scale or do anything nontrivial.

    drawing

  2. Repair shattered text: Similarly, text in PDFs is often 'shattered'—its letters are positioned individually, so if you try to edit it you will get strange results. This option reverses that, although the tradeoff is that text may be slightly repositioned.

    drawing

  3. Replace missing fonts: Useful for imported documents whose original fonts are not installed on the current machine.

The Homogenizer

The Homogenizer is a utility that does what its name implies: it will set all of the fonts, font sizes, and stroke widths in a selection to the same value. This is most useful when assembling sub-figures, as it allows you to ensure that the whole figure has a uniform look.

Auto-Exporter

The Auto-Exporter is not technically an extension, it is a Python script meant to be run in the background as a daemon. If you frequently export your figures to other formats, you know that updating them whenever you change your figure is a nuisance. This program does it automatically: you specify a directory that the program monitors, and whenever any SVGs are changed, it automatically converts them to the formats you specify. Just select (a) the location where the Inkscape binary is installed, (b) what directory you would like it to watch, and (c) where you would like it to put the exports.

It is currently implemented as a Python script and requires at least Python 3.7. If someone would like to package it into a nice GUI and create executables, let me know.

You might also like...
(ICCV 2021) Official code of
(ICCV 2021) Official code of "Dressing in Order: Recurrent Person Image Generation for Pose Transfer, Virtual Try-on and Outfit Editing."

Dressing in Order (DiOr) 👚 [Paper] 👖 [Webpage] 👗 [Running this code] The official implementation of "Dressing in Order: Recurrent Person Image Gene

Implements the training, testing and editing tools for
Implements the training, testing and editing tools for "Pluralistic Image Completion"

Pluralistic Image Completion ArXiv | Project Page | Online Demo | Video(demo) This repository implements the training, testing and editing tools for "

A large-scale face dataset for face parsing, recognition, generation and editing.
A large-scale face dataset for face parsing, recognition, generation and editing.

CelebAMask-HQ [Paper] [Demo] CelebAMask-HQ is a large-scale face image dataset that has 30,000 high-resolution face images selected from the CelebA da

Core ML tools contain supporting tools for Core ML model conversion, editing, and validation.

Core ML Tools Use coremltools to convert machine learning models from third-party libraries to the Core ML format. The Python package contains the sup

Official codebase for running the small, filtered-data GLIDE model from GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models.

GLIDE This is the official codebase for running the small, filtered-data GLIDE model from GLIDE: Towards Photorealistic Image Generation and Editing w

Colour detection is necessary to recognize objects, it is also used as a tool in various image editing and drawing apps.

Colour Detection On Image Colour detection is the process of detecting the name of any color. Simple isn’t it? Well, for humans this is an extremely e

Official implementation for
Official implementation for "Style Transformer for Image Inversion and Editing" (CVPR 2022)

Style Transformer for Image Inversion and Editing (CVPR2022) https://arxiv.org/abs/2203.07932 Existing GAN inversion methods fail to provide latent co

Editing a Conditional Radiance Field
Editing a Conditional Radiance Field

Editing Conditional Radiance Fields Project | Paper | Video | Demo Editing Conditional Radiance Fields Steven Liu, Xiuming Zhang, Zhoutong Zhang, Rich

Disentangled Face Attribute Editing via Instance-Aware Latent Space Search, accepted by IJCAI 2021.

Instance-Aware Latent-Space Search This is a PyTorch implementation of the following paper: Disentangled Face Attribute Editing via Instance-Aware Lat

Comments
  • Working with multiple subfigures in a single layer

    Working with multiple subfigures in a single layer

    Hi there! Thanks for making an amazing extension - I've just discovered it, but I'm sure it'll become a dear companion!

    For my current workflow, I prepare all figures for a paper in the same file, but on separate layers. This means that figures containing multiple subfigures have a few groups within them. Currently, it seems that the flattener flattens to the top group, even if I select only select a single subgroup (i.e. all the subfigures become a single group). Is there a way (or could there be) of only doing the deep ungrouping from the chosen group and down?

    Thanks!

    opened by roaldarbol 7
  • Points not adjusting size

    Points not adjusting size

    Hi again, sorry to pile on. Please address these at your own pace. :-)

    It seems that the Scaling doesn't work well with markers such as points. Here's a simple raw example: Screenshot 2022-12-15 at 11 32 16

    And here's the scaled version of it, tried both with Scaling mode and Correction mode: Screenshot 2022-12-15 at 11 34 21

    There also seems to be something funky happening with the header, but I think that's simply because it's not rendered well in the original (I can create a separate issue if you'd like me to dig into it a bit).

    opened by roaldarbol 3
  • Flatten Plots does not fully support differential kerning

    Flatten Plots does not fully support differential kerning

    Text that has a dx component will not always be properly de-kerned. This is not a problem for anything imported by Inkscape, but SVG files generated by other programs may cause issues.

    x_and_dx.zip

    opened by burghoff 0
Releases(v1.2.28)
Can we learn gradients by Hamiltonian Neural Networks?

Can we learn gradients by Hamiltonian Neural Networks? This project was carried out as part of the Optimization for Machine Learning course (CS-439) a

2 Aug 22, 2022
Collection of generative models in Pytorch version.

pytorch-generative-model-collections Original : [Tensorflow version] Pytorch implementation of various GANs. This repository was re-implemented with r

Hyeonwoo Kang 2.4k Dec 31, 2022
Code for ICCV2021 paper PARE: Part Attention Regressor for 3D Human Body Estimation

PARE: Part Attention Regressor for 3D Human Body Estimation [ICCV 2021] PARE: Part Attention Regressor for 3D Human Body Estimation, Muhammed Kocabas,

Muhammed Kocabas 277 Jan 03, 2023
Deep Surface Reconstruction from Point Clouds with Visibility Information

Data, code and pretrained models for the paper Deep Surface Reconstruction from Point Clouds with Visibility Information.

Raphael Sulzer 23 Jan 04, 2023
Pytorch implementation of "Geometrically Adaptive Dictionary Attack on Face Recognition" (WACV 2022)

Geometrically Adaptive Dictionary Attack on Face Recognition This is the Pytorch code of our paper "Geometrically Adaptive Dictionary Attack on Face R

6 Nov 21, 2022
PyTorch implementation of D2C: Diffuison-Decoding Models for Few-shot Conditional Generation.

D2C: Diffuison-Decoding Models for Few-shot Conditional Generation Project | Paper PyTorch implementation of D2C: Diffuison-Decoding Models for Few-sh

Jiaming Song 90 Dec 27, 2022
This is an open source library implementing hyperbox-based machine learning algorithms

hyperbox-brain is a Python open source toolbox implementing hyperbox-based machine learning algorithms built on top of scikit-learn and is distributed

Complex Adaptive Systems (CAS) Lab - University of Technology Sydney 21 Dec 14, 2022
DeepSTD: Mining Spatio-temporal Disturbances of Multiple Context Factors for Citywide Traffic Flow Prediction

DeepSTD: Mining Spatio-temporal Disturbances of Multiple Context Factors for Citywide Traffic Flow Prediction This is the implementation of DeepSTD in

5 Sep 26, 2022
Unofficial PyTorch Implementation of AHDRNet (CVPR 2019)

AHDRNet-PyTorch This is the PyTorch implementation of Attention-guided Network for Ghost-free High Dynamic Range Imaging (CVPR 2019). The official cod

Yutong Zhang 4 Sep 08, 2022
Official implementation of "Watermarking Images in Self-Supervised Latent-Spaces"

🔍 Watermarking Images in Self-Supervised Latent-Spaces PyTorch implementation and pretrained models for the paper. For details, see Watermarking Imag

Meta Research 32 Dec 13, 2022
[CVPR 2022] Back To Reality: Weak-supervised 3D Object Detection with Shape-guided Label Enhancement

Back To Reality: Weak-supervised 3D Object Detection with Shape-guided Label Enhancement Announcement 🔥 We have not tested the code yet. We will fini

Xiuwei Xu 7 Oct 30, 2022
RepVGG: Making VGG-style ConvNets Great Again

This repository is the code that needs to be submitted for OpenMMLab Algorithm Ecological Challenge,the paper is RepVGG: Making VGG-style ConvNets Great Again

Ty Feng 62 May 21, 2022
Bu repo SAHI uygulamasını mantığını öğreniyoruz.

SAHI-Learn: SAHI'den Beraber Kodlamak İster Misiniz Herkese merhabalar ben Kadir Nar. SAHI kütüphanesine gönüllü geliştiriciyim. Bu repo SAHI kütüphan

Kadir Nar 11 Aug 22, 2022
Official PyTorch Implementation of Embedding Transfer with Label Relaxation for Improved Metric Learning, CVPR 2021

Embedding Transfer with Label Relaxation for Improved Metric Learning Official PyTorch implementation of CVPR 2021 paper Embedding Transfer with Label

Sungyeon Kim 37 Dec 06, 2022
PPO Lagrangian in JAX

PPO Lagrangian in JAX This repository implements PPO in JAX. Implementation is tested on the safety-gym benchmark. Usage Install dependencies using th

Karush Suri 2 Sep 14, 2022
Code for testing convergence rates of Lipschitz learning on graphs

📈 LipschitzLearningRates The code in this repository reproduces the experimental results on convergence rates for k-nearest neighbor graph infinity L

2 Dec 20, 2021
State-Relabeling Adversarial Active Learning

State-Relabeling Adversarial Active Learning Code for SRAAL [2020 CVPR Oral] Requirements torch = 1.6.0 numpy = 1.19.1 tqdm = 4.31.1 AL Results The

10 Jul 14, 2022
Breast-Cancer-Prediction

Breast-Cancer-Prediction Trying to predict whether the cancer is benign or malignant using REGRESSION MODELS in Python. Team Members NAME ROLL-NUMBER

Shyamdev Krishnan J 3 Feb 18, 2022
2nd solution of ICDAR 2021 Competition on Scientific Literature Parsing, Task B.

TableMASTER-mmocr Contents About The Project Method Description Dependency Getting Started Prerequisites Installation Usage Data preprocess Train Infe

Jianquan Ye 298 Dec 21, 2022
The missing CMake project initializer

cmake-init - The missing CMake project initializer Opinionated CMake project initializer to generate CMake projects that are FetchContent ready, separ

1k Jan 01, 2023