Approaches to modeling terrain and maps in python

Last update: Aug 10, 2022

Overview

topography 🌎

Contains different approaches to modeling terrain and topographic-style maps in python

Features

Inverse Distance Weighting (IDW)

A given point P(x, y) is determined by the values of its neighbors, inversely proportional to the distance of each neighbor.

P is more heavily influenced by nearer points via a weighting function w(x, y).

Steps

The value of P(x, y) is determined only by the closest raw data point.

This approach works best to get a "feel" for larger datasets. With few input points, the resulting map has little detail.

In the case of multiple equidistant points being closest, point values are stored, and averaged.

Bilinear

in progress 👷 🛠️

Bicubic

in progress 👷 🛠️

Install

pip install topography

Requirements

numpy
matplotlib

see the requirements.txt

Example

from topography.Map import Map
from topography.utils.io import getPointValuesFromCsv

# # make map from noise data
# noiseMaker = Noise((0, 50), (0, 50))
# noiseData = noiseMaker.getRandom(scaleFactor=1)
# M = Map(noiseData)

# make map from recorded data
rawData = getPointValuesFromCsv("tests/data/20x20.csv")
M = Map(rawData)

# # Display the inputted raw data values
M.showRawPointValues()

# interpolate the Map
M.idw(showWhenDone=True)

# Display the interpolated data values
M.showFilledPointValues()

# Save the data to a .csv file
# optionally, write to file as a matrix
# default is x, y, z
M.writeLastToCsv("idw_20x20", writeAsMatrix=True)

Comments

NN - Improvements and Possible Design Changes
NN Improvements and Design Changes

Consider breaking up the current implementation of NN

[x] current NN ➡️ Map.steps()

[ ] new NN via voroni tesselation ➡️ Map.voroni() or Map.nn()

feature
opened by XDwightsBeetsX 1
Noise Generation
Add Noise Generators

This will be nice for quickly making cool topography maps

start with random noise, but ideas for later...

perlin noise

simplex noise

feature
opened by XDwightsBeetsX 1
allows for user to input map size

Custom Map Dimensions, closes #5

Can now customize views of the Map by specifying a custom Map(rawData, xRange=(lower, upper), yRange=(lower, upper))

This does not impact the determination of points by interpolation, but does give a "sliced" view of the Map
feature

opened by XDwightsBeetsX 1
Add Surface Plotting
New Surface Plot

In addition to the heatmap-style plot, add a surface representation plot of the Map

It should be displayed alongside the 2D Heatmap in a horizontal subplot

This may require some refactoring of the Map PointValue storage so that it can be used as a series of X, Y, Z lists

See this documentation on matplotlib

Something Like This:

| | | | :-: | :-: |
feature
opened by XDwightsBeetsX 1
IDW Improvement - Neighborhooding
Add Neighborhooding to IDW

only apply IDW to a minimum number of nearby neighbors

the point of interest is more likely to be similar to nearby points

feature
opened by XDwightsBeetsX 0
Added NN Interpolation
New NN Interpolation

This is going to work better with larger data sets to get a "feel" for the Map.

Should add some noise generator to see how this looks with larger data sets.

Also add some docs, mentioning above

can add sophistication by grouping within a nearby region

feature
opened by XDwightsBeetsX 0
Allow User to Input Map Size
Currently

The size of the Map is determined by the user input RawData:

width = self.xMax - self.xMin + 1 height = self.yMax - self.yMin + 1

Desired

This should be changed to allow for the Instantiation of a Map's size to be set in the constructor.

Something like Map(rawData, xRange=(lower, upper), yRange=(lower, upper)) where lower and upper are inclusive

This change will have to be accounted for when finding max values

Undecided on if interpolation approaches should still consider these points

feature
opened by XDwightsBeetsX 0
Bicubic Interpolation
Add Bicubic Interpolation Scheme

[ ] in interpolaion.py add bicubic(thisPt, rawPts)

[ ] in tests/test_interpolate add test_bicubic.py

[ ] in tests/visual/1d add test_visual_bicubic.py

[ ] in Map.py add Map.bicubic(showWhenDone=True)

also see wikipedia
feature tests
opened by XDwightsBeetsX 0
Bilinear Interpolation
Add Bilinear Interpolation Scheme

[ ] in interpolaion.py add bilinear(thisPt, rawPts)

[ ] in tests/test_interpolate add test_bilinear.py

[ ] in tests/visual/1d add test_visual_bilinear.py

[ ] in Map.py add Map.bilinear(showWhenDone=True)

also see wikipedia
feature tests
opened by XDwightsBeetsX 3

Releases(1.0.0)

1.0.0(Jun 27, 2021)
check out the new topography package on pypi 🌎

This package provides some visualization and interpolation for topography data using the Map data structure

read data from file into PointValues using topography.utils.io.getPointValuesFromCsv(filename)

make a map with M = Map(rawData) and perform some interpolation like Map.idw(showWhenDone=True)

write the results to a data file with M.writeLastToCsv("cool_idw_interpolation", writeAsMatrix=True)

Current interpolation schemes:

inverse distance weighting

step function

Source code(tar.gz)
Source code(zip)

Owner

John Gutierrez

Texas A&M MEEN '22. CS minor. Texas Water Safari Finisher '19 '21

GitHub Repository

Official PyTorch implementation of the ICRA 2021 paper: Adversarial Differentiable Data Augmentation for Autonomous Systems.

Adversarial Differentiable Data Augmentation This repository provides the official PyTorch implementation of the ICRA 2021 paper: Adversarial Differen

3 Oct 15, 2022

PyGCL: Graph Contrastive Learning Library for PyTorch

PyGCL: Graph Contrastive Learning for PyTorch PyGCL is an open-source library for graph contrastive learning (GCL), which features modularized GCL com

594 Jan 08, 2023

A graph neural network (GNN) model to predict protein-protein interactions (PPI) with no sample features

2 Jul 25, 2022

This repo is developed for Strong Baseline For Vehicle Re-Identification in Track 2 Ai-City-2021 Challenges

A STRONG BASELINE FOR VEHICLE RE-IDENTIFICATION This paper is accepted to the IEEE Conference on Computer Vision and Pattern Recognition Workshop(CVPR

78 Dec 29, 2022

Official repository of IMPROVING DEEP IMAGE MATTING VIA LOCAL SMOOTHNESS ASSUMPTION.

IMPROVING DEEP IMAGE MATTING VIA LOCAL SMOOTHNESS ASSUMPTION This is the official repository of IMPROVING DEEP IMAGE MATTING VIA LOCAL SMOOTHNESS ASSU

14 Dec 15, 2022

Diffgram - Supervised Learning Data Platform

Data Annotation, Data Labeling, Annotation Tooling, Training Data for Machine Learning

1.6k Jan 07, 2023

Code for paper "Extract, Denoise and Enforce: Evaluating and Improving Concept Preservation for Text-to-Text Generation" EMNLP 2021

The repo provides the code for paper "Extract, Denoise and Enforce: Evaluating and Improving Concept Preservation for Text-to-Text Generation" EMNLP 2

18 May 24, 2022

Home repository for the Regularized Greedy Forest (RGF) library. It includes original implementation from the paper and multithreaded one written in C++, along with various language-specific wrappers.

Regularized Greedy Forest Regularized Greedy Forest (RGF) is a tree ensemble machine learning method described in this paper. RGF can deliver better r

364 Dec 28, 2022

SenseNet is a sensorimotor and touch simulator for deep reinforcement learning research

59 Feb 25, 2022

[CVPR 2021] NormalFusion: Real-Time Acquisition of Surface Normals for High-Resolution RGB-D Scanning

NormalFusion: Real-Time Acquisition of Surface Normals for High-Resolution RGB-D Scanning Project Page | Paper | Supplemental material #1 | Supplement

49 Nov 24, 2022

Related resources for our EMNLP 2021 paper

Plan-then-Generate: Controlled Data-to-Text Generation via Planning Authors: Yixuan Su, David Vandyke, Sihui Wang, Yimai Fang, and Nigel Collier Code

61 Jan 03, 2023

Does Pretraining for Summarization Reuqire Knowledge Transfer?

Pretraining summarization models using a corpus of nonsense

12 Dec 19, 2022

Open source code for the paper of Neural Sparse Voxel Fields.

Neural Sparse Voxel Fields (NSVF) Project Page | Video | Paper | Data Photo-realistic free-viewpoint rendering of real-world scenes using classical co

647 Dec 27, 2022

This repo contains the official code and pre-trained models for the Dynamic Vision Transformer (DVT).

Dynamic-Vision-Transformer (Pytorch) This repo contains the official code and pre-trained models for the Dynamic Vision Transformer (DVT). Not All Ima

210 Dec 18, 2022

PyTorch version of the paper 'Enhanced Deep Residual Networks for Single Image Super-Resolution' (CVPRW 2017)

About PyTorch 1.2.0 Now the master branch supports PyTorch 1.2.0 by default. Due to the serious version problem (especially torch.utils.data.dataloade

2.1k Jan 01, 2023

AttGAN: Facial Attribute Editing by Only Changing What You Want (IEEE TIP 2019)

News 11 Jan 2020: We clean up the code to make it more readable! The old version is here: v1. AttGAN TIP Nov. 2019, arXiv Nov. 2017 TensorFlow impleme

568 Dec 14, 2022

Flexible Networks for Learning Physical Dynamics of Deformable Objects (2021)

Flexible Networks for Learning Physical Dynamics of Deformable Objects (2021) By Jinhyung Park, Dohae Lee, In-Kwon Lee from Yonsei University (Seoul,

0 Jan 09, 2022

using STGCN to achieve egg classification task

EEG Classification The task requires us to classify electroencephalography(EEG) into six categories, including human body, human face, animal body,

4 Jun 13, 2022

an implementation of 3D Ken Burns Effect from a Single Image using PyTorch

3d-ken-burns This is a reference implementation of 3D Ken Burns Effect from a Single Image [1] using PyTorch. Given a single input image, it animates

1.4k Dec 28, 2022

Gym environment for FLIPIT: The Game of "Stealthy Takeover"

gym-flipit Gym environment for FLIPIT: The Game of "Stealthy Takeover" invented by Marten van Dijk, Ari Juels, Alina Oprea, and Ronald L. Rivest. Desi

2 Dec 15, 2021