Approaches to modeling terrain and maps in python

Related tags

Deep Learningpythontopographymodeling-terrain
Overview

topography 🌎

Python 3.8 Build Status Language grade: Python Total alerts

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

image

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 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()

    image

    feature 
    opened by XDwightsBeetsX 1
  • Noise Generation

    Noise Generation

    Add Noise Generators

    This will be nice for quickly making cool topography maps

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

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

    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

    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:

    | image | image | | :-: | :-: |

    feature 
    opened by XDwightsBeetsX 1
  • IDW Improvement - Neighborhooding

    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

    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

    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

    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)

    image

    also see wikipedia

    feature tests 
    opened by XDwightsBeetsX 0
  • Bilinear Interpolation

    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)

    image

    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
John Gutierrez
GitHub Repository
PyTorch implementation of spectral graph ConvNets, NIPS’16

Graph ConvNets in PyTorch October 15, 2017 Xavier Bresson http://www.ntu.edu.sg/home/xbresson https://github.com/xbresson https://twitter.com/xbresson

Xavier Bresson 287 Jan 04, 2023
SafePicking: Learning Safe Object Extraction via Object-Level Mapping, ICRA 2022

SafePicking Learning Safe Object Extraction via Object-Level Mapping Kentaro Wad

Kentaro Wada 49 Oct 24, 2022
Evaluation and Benchmarking of Speech Super-resolution Methods

Speech Super-resolution Evaluation and Benchmarking What this repo do: A toolbox for the evaluation of speech super-resolution algorithms. Unify the e

Haohe Liu (刘濠赫) 84 Dec 20, 2022
🤗 Paper Style Guide

🤗 Paper Style Guide (Work in progress, send a PR!) Libraries to Know booktabs natbib cleveref Either seaborn, plotly or altair for graphs algorithmic

Hugging Face 66 Dec 12, 2022
Adaptive Dropblock Enhanced GenerativeAdversarial Networks for Hyperspectral Image Classification

This repo holds the codes of our paper: Adaptive Dropblock Enhanced GenerativeAdversarial Networks for Hyperspectral Image Classification, which is ac

Feng Gao 17 Dec 28, 2022
Empowering journalists and whistleblowers

Onymochat Empowering journalists and whistleblowers Onymochat is an end-to-end encrypted, decentralized, anonymous chat application. You can also host

Samrat Dutta 19 Sep 02, 2022
Flow is a computational framework for deep RL and control experiments for traffic microsimulation.

Flow Flow is a computational framework for deep RL and control experiments for traffic microsimulation. See our website for more information on the ap

867 Jan 02, 2023
An open source object detection toolbox based on PyTorch

MMDetection is an open source object detection toolbox based on PyTorch. It is a part of the OpenMMLab project.

Bo Chen 24 Dec 28, 2022
A generalized framework for prototyping full-stack cooperative driving automation applications under CARLA+SUMO.

OpenCDA OpenCDA is a SIMULATION tool integrated with a prototype cooperative driving automation (CDA; see SAE J3216) pipeline as well as regular autom

UCLA Mobility Lab 726 Dec 29, 2022
QMagFace: Simple and Accurate Quality-Aware Face Recognition

Quality-Aware Face Recognition 26.11.2021 start readme QMagFace: Simple and Accurate Quality-Aware Face Recognition Research Paper Implementation - To

Philipp Terhörst 59 Jan 04, 2023
FastReID is a research platform that implements state-of-the-art re-identification algorithms.

FastReID is a research platform that implements state-of-the-art re-identification algorithms.

JDAI-CV 2.8k Jan 07, 2023
An 16kHz implementation of HiFi-GAN for soft-vc.

HiFi-GAN An 16kHz implementation of HiFi-GAN for soft-vc. Relevant links: Official HiFi-GAN repo HiFi-GAN paper Soft-VC repo Soft-VC paper Example Usa

Benjamin van Niekerk 42 Dec 27, 2022
Code release of paper "Deep Multi-View Stereo gone wild"

Deep MVS gone wild Pytorch implementation of "Deep MVS gone wild" (Paper | website) This repository provides the code to reproduce the experiments of

François Darmon 53 Dec 24, 2022
Generalized and Efficient Blackbox Optimization System.

OpenBox Doc | OpenBox中文文档 OpenBox: Generalized and Efficient Blackbox Optimization System OpenBox is an efficient and generalized blackbox optimizatio

DAIR Lab 238 Dec 29, 2022
Human Activity Recognition example using TensorFlow on smartphone sensors dataset and an LSTM RNN. Classifying the type of movement amongst six activity categories - Guillaume Chevalier

LSTMs for Human Activity Recognition Human Activity Recognition (HAR) using smartphones dataset and an LSTM RNN. Classifying the type of movement amon

Guillaume Chevalier 3.1k Dec 30, 2022
Reaction SMILES-AA mapping via language modelling

rxn-aa-mapper Reactions SMILES-AA sequence mapping setup conda env create -f conda.yml conda activate rxn_aa_mapper In the following we consider on ex

16 Dec 13, 2022
The codes and related files to reproduce the results for Image Similarity Challenge Track 1.

ISC-Track1-Submission The codes and related files to reproduce the results for Image Similarity Challenge Track 1. Required dependencies To begin with

Wenhao Wang 115 Jan 02, 2023
Supercharging Imbalanced Data Learning WithCausal Representation Transfer

ECRT: Energy-based Causal Representation Transfer Code for Supercharging Imbalanced Data Learning With Energy-basedContrastive Representation Transfer

Zidi Xiu 11 May 02, 2022
FCN (Fully Convolutional Network) is deep fully convolutional neural network architecture for semantic pixel-wise segmentation

FCN_via_Keras FCN FCN (Fully Convolutional Network) is deep fully convolutional neural network architecture for semantic pixel-wise segmentation. This

Kento Watanabe 48 Aug 30, 2022
🔥3D-RecGAN in Tensorflow (ICCV Workshops 2017)

3D Object Reconstruction from a Single Depth View with Adversarial Learning Bo Yang, Hongkai Wen, Sen Wang, Ronald Clark, Andrew Markham, Niki Trigoni

Bo Yang 125 Nov 26, 2022