import py_rootbox as rb
import numpy as np
from rb_tools import *
import matplotlib.pyplot as plt
N = 4 # layers between a and b
a = -3 # cm
b = -7 # cm
scale_elongation = rb.EquidistantGrid1D(a, b, N + 1)
scales = range(0, N)
scale_elongation.data = a2v(scales) # set proportionality factors
z_ = np.linspace(0, -10, 1000)
y_ = np.zeros((1000))
for i, z in enumerate(z_):
y_[i] = scale_elongation.getValue(rb.Vector3d(0, 0, z))
plt.plot(z_, y_)
plt.plot(a, 0, "r*") # interval borders
plt.plot(b, N - 1, "r*")
plt.xlabel("z (cm)")
plt.ylabel("y value (?)")
plt.show()
print("done.")
def call(cc):
rs = rb.RootSystem()
#name = "Triticum_aestivum_a_Bingham_2011" # is this the same as your wheat, Shehan?
name = "wheat"
rs.openFile(name)
# Pore Geometry
x_ = (-10, -5, 5, 15) # not 0, otherwise we start in crack
y_ = (0, 0, 0, 0)
#x_=(-10, -5)
#y_=(0,0)
crack = rb.SDF_PlantBox(1.0, 100, 160) # cm
cracks_ = rb.std_vector_SDF_()
py_cracks = []
for i in range(0, len(y_)):
v = rb.Vector3d(x_[i], y_[i], 0)
py_cracks.append(rb.SDF_RotateTranslate(crack, v))
cracks_.append(py_cracks[-1])
cracks = rb.SDF_Union(cracks_)
rs.setPoreGeometry(cracks)
# Increased elongation within the pores
maxS = 2 # twice the elongation rate within the pore
minS = 1 # normal elongation rate
slope = 0
soil_prop = rb.SoilLookUpSDF(cracks, maxS, minS, slope)
# Adjust Tropism
sigma = [0.4] * 10
for i in range(0, 10):
p = rs.getRootTypeParameter(i + 1)
p.dx = 0.25 # adjust resolution
p.tropismT = rb.TropismType.gravi
p.tropismN = 1 # strength of tropism
p.tropismS = sigma[i]
p.se = soil_prop
# Pore Local Axes
v1 = rb.Vector3d(0.67, 0, 0)
v2 = rb.Vector3d(0, 0.67, 0)
v3 = rb.Vector3d(0, 0, 0.67)
rs.setPoreLocalAxes(rb.Matrix3d(v1, v2, v3))
# Pore Conductivity Tensor
t1 = rb.Vector3d(1.33, 0, 0)
t2 = rb.Vector3d(0, 50.33, 0)
t3 = rb.Vector3d(0, 0, 50.33)
rs.setPoreConductivity(rb.Matrix3d(t1, t2, t3))
# Set up depth dependent elongation scaling function
scale_elongation = rb.EquidistantGrid1D(
0, -160, 17) # todo: replace this by reading in data from CSV file
scales = np.zeros(
len(scale_elongation.grid)
) + 0.1 # scales from some equation (scale = function(soil_strength) ), where scale in (0,1)
scale_elongation.data = a2v(scales) # set proportionality factors
# Proportionally scale this function
se2 = rb.ProportionalElongation()
se2.setBaseLookUp(scale_elongation)
# multiply the scale elongation functions
se3 = rb.MultiplySoilLookUps(se2, soil_prop)
# Manually set scaling function
for i in range(0, 10):
p = rs.getRootTypeParameter(i + 1)
p.se = se3
# Initialize
rs.initialize()
# Simulate
simtime = 30 * 8 # e.g. 30 or 60 days
dt = 1 #0.5 * 1./24.
N = round(simtime / dt)
for i in range(0, N):
# time-dependent and depth-dependent scaling function
scales = np.loadtxt('W2.csv', delimiter=';',
usecols=i) # reading in ith column from CSV file
scale_elongation.data = a2v(scales *
1.00) # set the data of scale elongation
rs.simulate(dt)
# Export results (as vtp)
#rs.write("../results/crack.vtp")
# Export cracks
#rs.setGeometry(cracks) # just for vizualisation
#rs.write("../results/crack.py")
z_ = np.linspace(0, -1 * 160, 160)
# Make a root length distribution
ana = rb.SegmentAnalyser(rs)
rl_ = ana.distribution(rb.ScalarType.length, 0, 160, 160, True)
np.set_printoptions(precision=4)
np.savetxt("cw_" + str(cc) + ".txt", rl_, fmt="%.2f")
import py_rootbox as rb
import math
import numpy as np
from rb_tools import *
rs = rb.RootSystem()
name = "Anagallis_femina_Leitner_2010"
rs.openFile(name)
scale_elongation = rb.EquidistantGrid1D(
0, -50, 100) # for root elongation from 0 cm to -50 cm, 100 layers
soil_strength = np.ones((99, )) * 0.5 # some data
scales = np.exp(-0.4 * soil_strength) # scales from some equation (TODO)
scale_elongation.data = a2v(scales) # set proportionality factors
print("value at -3 cm", scale_elongation.getValue(rb.Vector3d(0, 0, -3)))
# Manually set scale elongation function
for i in range(0, 10):
p = rs.getRootTypeParameter(i + 1)
p.se = scale_elongation
# Simulation
rs.initialize()
simtime = 120.
dt = 1.
N = 120 / dt
for i in range(0, round(N)):
p.se = soil_prop
# Pore Local Axes
v1 = rb.Vector3d(0, 0, -1)
v2 = rb.Vector3d(1, 0, 0)
v3 = rb.Vector3d(0, 1, 0)
rs.setPoreLocalAxes(rb.Matrix3d(v1, v2, v3))
# Pore Conductivity Tensor
t1 = rb.Vector3d(2, 0, 0)
t2 = rb.Vector3d(0, 0.5, 0)
t3 = rb.Vector3d(0, 0, 0.5)
rs.setPoreConductivity(rb.Matrix3d(t1, t2, t3))
# Set up depth dependent elongation scaling function
scale_elongation = rb.EquidistantGrid1D(
0, -100, 11) # todo: replace this by reading in data from CSV file
scales = np.zeros(
len(scale_elongation.grid)
) + 0.1 # scales from some equation (scale = function(soil_strength) ), where scale in (0,1)
scale_elongation.data = a2v(scales) # set proportionality factors
# Proportionally scale this function
se2 = rb.ProportionalElongation()
se2.setBaseLookUp(scale_elongation)
# multiply the scale elongation functions
se3 = rb.MultiplySoilLookUps(se2, soil_prop)
# Manually set scaling function
for i in range(0, 10):
p = rs.getRootTypeParameter(i + 1)