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

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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

A curated list of resources for Image and Video Deblurring

Intrusion Test Tool with Python

4th place solution for the SIGIR 2021 challenge.

Hierarchical Motion Encoder-Decoder Network for Trajectory Forecasting (HMNet)

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Code for ICCV 2021 paper Graph-to-3D: End-to-End Generation and Manipulation of 3D Scenes using Scene Graphs

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This repo uses a combination of logits and feature distillation method to teach the PSPNet model of ResNet18 backbone with the PSPNet model of ResNet50 backbone. All the models are trained and tested on the PASCAL-VOC2012 dataset.

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