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 :construction_worker: :hammer_and_wrench:
Bicubic
in progress :construction_worker: :hammer_and_wrench:
Install
pip install topography
Requirements
numpymatplotlib
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)
