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

ROMP: Monocular, One-stage, Regression of Multiple 3D People, ICCV21

Monocular, One-stage, Regression of Multiple 3D People ROMP, accepted by ICCV 2021, is a concise one-stage network for multi-person 3D mesh recovery f

937 Jan 04, 2023

StyleGAN2-ADA-training-jupyter - Training custom datasets in styleGAN2-ADA by NVIDIA using Jupyter

styleGAN2-ADA-training-jupyter Training custom datasets in styleGAN2-ADA on Jupyter Official StyleGAN2-ADA by NIVIDIA Paper Training Generative Advers

2 Feb 24, 2022

Predicting 10 different clothing types using Xception pre-trained model.

Predicting-Clothing-Types Predicting 10 different clothing types using Xception pre-trained model from Keras library. It is reimplemented version from

3 Dec 29, 2021

Official repository of the paper "A Variational Approximation for Analyzing the Dynamics of Panel Data". Mixed Effect Neural ODE. UAI 2021.

Official repository of the paper (UAI 2021) "A Variational Approximation for Analyzing the Dynamics of Panel Data", Mixed Effect Neural ODE. Panel dat

7 Nov 26, 2022

Code for the ICASSP-2021 paper: Continuous Speech Separation with Conformer.

Continuous Speech Separation with Conformer Introduction We examine the use of the Conformer architecture for continuous speech separation. Conformer

81 Nov 28, 2022

《Geo Word Clouds》paper implementation

2 Jan 28, 2022

Find-Lane-Line - Use openCV library and Python to detect the road-lane-line

Find-Lane-Line This project is to use openCV library and Python to detect the road-lane-line. Data Pipeline Step one : Color Selection Step two : Cann

3 Aug 17, 2022

Code for the preprint "Well-classified Examples are Underestimated in Classification with Deep Neural Networks"

This is a repository for the paper of "Well-classified Examples are Underestimated in Classification with Deep Neural Networks" The implementation and

25 Dec 11, 2022

This is the repository for the AAAI 21 paper [Contrastive and Generative Graph Convolutional Networks for Graph-based Semi-Supervised Learning].

CG3 This is the repository for the AAAI 21 paper [Contrastive and Generative Graph Convolutional Networks for Graph-based Semi-Supervised Learning]. R

12 Oct 28, 2022

Approaches to modeling terrain and maps in python

Related tags

Overview

topography 🌎

Features

Steps

Bilinear

Bicubic

Install

Requirements

Example

Comments

NN Improvements and Design Changes

Add Noise Generators

Custom Map Dimensions, closes #5

New Surface Plot

Something Like This:

Add Neighborhooding to IDW

New NN Interpolation

Currently

Desired

Add Bicubic Interpolation Scheme

Add Bilinear Interpolation Scheme

Releases(1.0.0)

1.0.0(Jun 27, 2021)

check out the new topography package on pypi 🌎

Owner

John Gutierrez

ROMP: Monocular, One-stage, Regression of Multiple 3D People, ICCV21

StyleGAN2-ADA-training-jupyter - Training custom datasets in styleGAN2-ADA by NVIDIA using Jupyter

Predicting 10 different clothing types using Xception pre-trained model.

Official repository of the paper "A Variational Approximation for Analyzing the Dynamics of Panel Data". Mixed Effect Neural ODE. UAI 2021.

Code for the ICASSP-2021 paper: Continuous Speech Separation with Conformer.

《Geo Word Clouds》paper implementation

Find-Lane-Line - Use openCV library and Python to detect the road-lane-line

Code for the preprint "Well-classified Examples are Underestimated in Classification with Deep Neural Networks"

This is the repository for the AAAI 21 paper [Contrastive and Generative Graph Convolutional Networks for Graph-based Semi-Supervised Learning].

CN24 is a complete semantic segmentation framework using fully convolutional networks

use tensorflow 2.0 to tell a dog and cat from a specified picture

Unsupervised Learning of Probably Symmetric Deformable 3D Objects from Images in the Wild

[NeurIPS-2020] Self-paced Contrastive Learning with Hybrid Memory for Domain Adaptive Object Re-ID.

A minimal solution to hand motion capture from a single color camera at over 100fps. Easy to use, plug to run.

3rd Place Solution of the Traffic4Cast Core Challenge @ NeurIPS 2021

The Python ensemble sampling toolkit for affine-invariant MCMC

Experiments with differentiable stacks and queues in PyTorch

Multi-Joint dynamics with Contact. A general purpose physics simulator.

Official repository of the paper Privacy-friendly Synthetic Data for the Development of Face Morphing Attack Detectors

Semi-supervised Domain Adaptation via Minimax Entropy