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
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