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# SPDX-License-Identifier: GPL-2.0-or-later
# Converts a formatted string to a float tuple:
# IN - '(0.5, 0.2)' -> CONVERT -> OUT - (0.5, 0.2)
def toTuple(stringIn):
sTemp = str(stringIn)[1:len(str(stringIn)) - 1].split(', ')
fTemp = []
for i in sTemp:
fTemp.append(float(i))
return tuple(fTemp)
# Converts a formatted string to a float tuple:
# IN - '[0.5, 0.2]' -> CONVERT -> OUT - [0.5, 0.2]
def toList(stringIn):
sTemp = str(stringIn)[1:len(str(stringIn)) - 1].split(', ')
fTemp = []
for i in sTemp:
fTemp.append(float(i))
return fTemp
# Converts each item of a list into a float:
def toFloats(inList):
outList = []
for i in inList:
outList.append(float(i))
return outList
# Converts each item of a list into an integer:
def toInts(inList):
outList = []
for i in inList:
outList.append(int(i))
return outList
# Sets all faces smooth. Done this way since I can't
# find a simple way without using bpy.ops:
def smooth(mesh):
import bmesh
bm = bmesh.new()
bm.from_mesh(mesh)
for f in bm.faces:
f.smooth = True
bm.to_mesh(mesh)
return mesh
# This try block allows for the script to psudo-intelligently select the
# appropriate random to use. If Numpy's random is present it will use that.
# If Numpy's random is not present, it will through a "module not found"
# exception and instead use the slower built-in random that Python has.
try:
# from numpy.random import random_integers as randint
from numpy.random import normal as gauss
# from numpy.random import (beta,
# uniform,
# seed,
# weibull)
# print("Rock Generator: Numpy found.")
numpy = True
except:
from random import (
# randint,
gauss,
# uniform,
# seed
)
# from random import betavariate as beta
# from random import weibullvariate as weibull
print("Rock Generator: Numpy not found. Using Python's random.")
numpy = False
# Artificially skews a normal (gaussian) distribution. This will not create
# a continuous distribution curve but instead acts as a piecewise finction.
# This linearly scales the output on one side to fit the bounds.
#
# Example output histograms:
#
# Upper skewed: Lower skewed:
# | ▄ | _
# | █ | █
# | █_ | █
# | ██ | _█
# | _██ | ██
# | _▄███_ | ██ _
# | ▄██████ | ▄██▄█▄_
# | _█▄███████ | ███████
# | _██████████_ | ████████▄▄█_ _
# | _▄▄████████████ | ████████████▄█_
# | _▄_ ▄███████████████▄_ | _▄███████████████▄▄_
# ------------------------- -----------------------
# |mu |mu
# Histograms were generated in R (http://www.r-project.org/) based on the
# calculations below and manually duplicated here.
#
# param: mu - mu is the mean of the distribution.
# sigma - sigma is the standard deviation of the distribution.
# bounds - bounds[0] is the lower bound and bounds[1]
# is the upper bound.
# upperSkewed - if the distribution is upper skewed.
# return: out - Rondomly generated value from the skewed distribution.
#
# @todo: Because NumPy's random value generators are faster when called
# a bunch of times at once, maybe allow this to generate and return
# multiple values at once?
def skewedGauss(mu, sigma, bounds, upperSkewed=True):
raw = gauss(mu, sigma)
# Quicker to check an extra condition than do unnecessary math. . . .
if raw < mu and not upperSkewed:
out = ((mu - bounds[0]) / (3 * sigma)) * raw + ((mu * (bounds[0] - (mu - 3 * sigma))) / (3 * sigma))
elif raw > mu and upperSkewed:
out = ((mu - bounds[1]) / (3 * -sigma)) * raw + ((mu * (bounds[1] - (mu + 3 * sigma))) / (3 * -sigma))
else:
out = raw
return out
# @todo create a def for generating an alpha and beta for a beta distribution
# given a mu, sigma, and an upper and lower bound. This proved faster in
# profiling in addition to providing a much better distribution curve
# provided multiple iterations happen within this function; otherwise it was
# slower.
# This might be a scratch because of the bounds placed on mu and sigma:
#
# For alpha > 1 and beta > 1:
# mu^2 - mu^3 mu^3 - mu^2 + mu
# ----------- < sigma < ----------------
# 1 + mu 2 - mu
#
# def generateBeta(mu, sigma, scale, repitions=1):
# results = []
#
# return results
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