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Commit 0f72fceb authored by steffen.schotthoefer's avatar steffen.schotthoefer
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removed unneccesary python files, added fct to mn solver to extract NN training data

parent 796bfdd0
Pipeline #98358 passed with stages
in 38 minutes and 46 seconds
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code/include/solvers/mnsolver.h
View file @ 0f72fceb
......@@ -28,22 +28,35 @@ class MNSolver : public Solver
unsigned _nTotalEntries; /*! @brief: Total number of equations in the system */
unsigned short _nMaxMomentsOrder; /*! @brief: Max Order of Moments */
VectorVector _sigmaA; /*! @brief: Absorption coefficient for all energies*/
// Solver specific physics
VectorVector _sigmaA; /*! @brief: Absorption coefficient for all energies*/
// Moment basis
SphericalHarmonics* _basis; /*! @brief: Class to compute and store current spherical harmonics basis */
VectorVector _moments; /*! @brief: Moment Vector pre-computed at each quadrature point: dim= _nq x _nTotalEntries */
// Right hand side members
Vector _scatterMatDiag; /*! @brief: Diagonal of the scattering matrix (its a diagonal matrix by construction) */
// TODO: Source
// quadrature related numbers
// quadrature related members
VectorVector _quadPoints; /*! @brief quadrature points, dim(_quadPoints) = (_nq,spatialDim) */
Vector _weights; /*! @brief quadrature weights, dim(_weights) = (_nq) */
VectorVector _quadPointsSphere; /*! @brief (my,phi), dim(_quadPoints) = (_nq,2) */
EntropyBase* _entropy; /*! @brief: Class to handle entropy functionals */
VectorVector _alpha; /*! @brief: Lagrange Multipliers for Minimal Entropy problem for each gridCell
Layout: _nCells x _nTotalEntries*/
// Entropy Optimization related members
EntropyBase* _entropy; /*! @brief: Class to handle entropy functionals */
VectorVector _alpha; /*! @brief: Lagrange Multipliers for Minimal Entropy problem for each gridCell
Layout: _nCells x _nTotalEntries*/
OptimizerBase* _optimizer; /*! @brief: Class to solve minimal entropy problem */
// Output related members
std::vector<std::vector<double>> _outputFields; /*! @brief: Protoype output */
/*! @brief Function that writes NN Training Data in a .csv file */
void WriteNNTrainingData( unsigned idx_pseudoTime );
// Member functions
/*! @brief : computes the global index of the moment corresponding to basis function (l,k)
* @param : degree l, it must hold: 0 <= l <=_nq
* @param : order k, it must hold: -l <=k <= l
......
code/python/AssLegendrePlotter.m deleted 100644 → 0
View file @ 796bfdd0
clear;
clc;
filename = 'assLegendre.csv';
M = csvread(filename);
[nPts, nMom] =size(M);
for i =2: nMom
plot(M(:,1),M(:,i))
hold on;
end
legend("0,0" , "1,0" , "1,1", "2,0" , "2,1" , "2,2" )
\ No newline at end of file
code/python/assLegendre.csv deleted 100644 → 0
View file @ 796bfdd0
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0.85,0.398942,0.58734,-0.257387,0.520741,-0.489205,0.151591
0.86,0.398942,0.59425,-0.249331,0.543623,-0.479468,0.14225
0.87,0.398942,0.60116,-0.240906,0.566772,-0.468654,0.132799
0.88,0.398942,0.60807,-0.232073,0.590188,-0.45666,0.123239
0.89,0.398942,0.61498,-0.222783,0.613873,-0.443361,0.11357
0.9,0.398942,0.621889,-0.212977,0.637824,-0.428608,0.103792
0.91,0.398942,0.628799,-0.202579,0.662044,-0.412211,0.0939045
0.92,0.398942,0.635709,-0.191492,0.686531,-0.393935,0.0839077
0.93,0.398942,0.642619,-0.179591,0.711286,-0.373466,0.0738016
0.94,0.398942,0.649529,-0.166699,0.736308,-0.350385,0.0635863
0.95,0.398942,0.656439,-0.152566,0.761598,-0.324091,0.0532617
0.96,0.398942,0.663349,-0.136809,0.787156,-0.293677,0.0428279
0.97,0.398942,0.670259,-0.118782,0.812981,-0.257636,0.0322848
0.98,0.398942,0.677169,-0.0972307,0.839074,-0.213066,0.0216325
0.99,0.398942,0.684078,-0.0689259,0.865434,-0.152582,0.0108709
code/python/examples.py deleted 100644 → 0
View file @ 796bfdd0
from mpl_toolkits import mplot3d
import numpy as np
import matplotlib.pyplot as plt
#fig = plt.figure(figsize=plt.figaspect(1.))
#ax = fig.add_subplot(111, projection='3d')
fig = plt.figure()
ax = plt.axes(projection='3d')
# Data for a three-dimensional line
zline = np.linspace(0, 15, 1000)
xline = np.sin(zline)
yline = np.cos(zline)
ax.plot3D(xline, yline, zline, 'gray')
# Data for three-dimensional scattered points
zdata = 15 * np.random.random(100)
xdata = np.sin(zdata) + 0.1 * np.random.randn(100)
ydata = np.cos(zdata) + 0.1 * np.random.randn(100)
ax.scatter3D(xdata, ydata, zdata, c=zdata, cmap='Greens');
#plt.show()
def f(x, y):
return np.sin(np.sqrt(x ** 2 + y ** 2))
x = np.linspace(-6, 6, 30)
y = np.linspace(-6, 6, 30)
X, Y = np.meshgrid(x, y)
Z = f(X, Y)
fig = plt.figure()
ax = plt.axes(projection='3d')
ax.plot_wireframe(X, Y, Z, color='black')
ax.set_title('wireframe');
plt.show()
x = [1, 2, 3]
y = [4, 5, 6]
x1 = np.linspace(0, 10, 1000)
figure, axes = plt.subplots(nrows=2, ncols=2)
axes[0, 0].plot(x, y)
#create specific subplots
axes[0, 1].plot(x1, np.sin(x1))
axes[1, 0].plot(x1, np.cos(x1))
axes[1, 1].plot(range(10))
figure.tight_layout()
plt.show()
code/python/harmonicTest.csv deleted 100644 → 0
View file @ 796bfdd0
This source diff could not be displayed because it is too large. You can view the blob instead.
code/python/plotAssLegendre.py deleted 100644 → 0
View file @ 796bfdd0
# importing the required module
import matplotlib.pyplot as plt
import csv
import numpy as np
'''
# x axis values
x = [1, 2, 3]
# corresponding y axis values
y = [2, 4, 1]
# plotting the points
plt.plot(x, y)
# naming the x axis
plt.xlabel('x - axis')
# naming the y axis
plt.ylabel('y - axis')
# giving a title to my graph
plt.title('My first graph!')
# function to show the plot
plt.show()
'''
# Read csv data
my = []
moments = list(list())
with open('assLegendre.csv') as csv_file:
csv_reader = csv.reader(csv_file, delimiter=',')
line_count = 0
for row in csv_reader:
my.append( row[0])
moments.append( row[1:])
#transform to array
momentsArr = np.array( moments)
myArr = np.array(my)
# plot stuff
print(len(momentsArr[0,:]))
#for idx in range (0, len(momentsArr[0,:])):
# print(idx)
# print("\n")
print(momentsArr[:, 2])
print(myArr)
plt.plot(myArr, momentsArr[:,2], label=1 )
print("------------------\n")
axes = plt.gca()
#axes.set_xlim([-1, 1])
#axes.set_ylim([-2, 2])
#plt.legend()
plt.show()
code/python/plotBasis.py deleted 100644 → 0
View file @ 796bfdd0
import matplotlib.pyplot as plt
from matplotlib import cm, colors
from mpl_toolkits.mplot3d import Axes3D
import numpy as np
from scipy.special import sph_harm
# This import registers the 3D projection, but is otherwise unused.
from mpl_toolkits.mplot3d import Axes3D # noqa: F401 unused import
#--------------------------#
# Own Values #
#--------------------------#
# read in own coordinates
import csv
ndimGrid = 51
X = np.zeros((ndimGrid, ndimGrid))
Y = np.zeros((ndimGrid, ndimGrid))
Z = np.zeros((ndimGrid, ndimGrid))
X0 = np.zeros((ndimGrid, ndimGrid))
Y0 = np.zeros((ndimGrid, ndimGrid))
Z0 = np.zeros((ndimGrid, ndimGrid))
X1 = np.zeros((ndimGrid, ndimGrid))
Y1 = np.zeros((ndimGrid, ndimGrid))
Z1 = np.zeros((ndimGrid, ndimGrid))
X2 = np.zeros((ndimGrid, ndimGrid))
Y2 = np.zeros((ndimGrid, ndimGrid))
Z2 = np.zeros((ndimGrid, ndimGrid))
Harm0 = np.zeros((ndimGrid, ndimGrid))
Harm1 = np.zeros((ndimGrid, ndimGrid))
Harm2 = np.zeros((ndimGrid, ndimGrid))
Harm3 = np.zeros((ndimGrid, ndimGrid))
Harm4 = np.zeros((ndimGrid, ndimGrid))
Harm5 = np.zeros((ndimGrid, ndimGrid))
Harm6 = np.zeros((ndimGrid, ndimGrid))
Harm7 = np.zeros((ndimGrid, ndimGrid))
Harm8 = np.zeros((ndimGrid, ndimGrid))
idx_row = 0
idx_col = 0
#fill grid_data with values
with open('harmonicTest.csv') as csv_file:
csv_reader = csv.reader(csv_file, delimiter=',')
line_count = 0
for row in csv_reader:
if line_count == 0:
print(f'Column names are {", ".join(row)}')
line_count += 1
else:
#fill grid data: row[0]
idx_col = (line_count-1)%ndimGrid
idx_row = (line_count-1)//ndimGrid
print ( str(line_count) + ' ' + str(idx_row) + ' ' + str(idx_col) )
my = float( row[0])
phi = float(row[1])
X[idx_row,idx_col] = np.sqrt(1-my*my)*np.cos(phi)
Y[idx_row, idx_col] = np.sqrt(1 - my * my) * np.sin(phi)
Z[idx_row, idx_col] = my
Harm0[idx_row, idx_col] = row[2]
Harm1[idx_row, idx_col] = row[3]
Harm2[idx_row, idx_col] = row[4]
Harm3[idx_row, idx_col] = row[5]
Harm4[idx_row, idx_col] = row[6]
Harm5[idx_row, idx_col] = row[7]
Harm6[idx_row, idx_col] = row[8]
Harm7[idx_row, idx_col] = row[9]
Harm8[idx_row, idx_col] = row[10]
line_count += 1
# radius version
X0[idx_row, idx_col] = X[idx_row, idx_col] * Harm0[idx_row, idx_col]
Y0[idx_row, idx_col] = Y[idx_row, idx_col] * Harm0[idx_row, idx_col]
Z0[idx_row, idx_col] = Z[idx_row, idx_col] * Harm0[idx_row, idx_col]
X1[idx_row, idx_col] = X[idx_row, idx_col] * Harm1[idx_row, idx_col]
Y1[idx_row, idx_col] = Y[idx_row, idx_col] * Harm1[idx_row, idx_col]
Z1[idx_row, idx_col] = Z[idx_row, idx_col] * Harm1[idx_row, idx_col]
X2[idx_row, idx_col] = X[idx_row, idx_col] * Harm2[idx_row, idx_col]
Y2[idx_row, idx_col] = Y[idx_row, idx_col] * Harm2[idx_row, idx_col]
Z2[idx_row, idx_col] = Z[idx_row, idx_col] * Harm2[idx_row, idx_col]
print(f'Processed {line_count} lines.')
# setup colors
fmax, fmin = Harm0.max(), Harm0.min()
fcolors0 = (Harm0) # constan function - fmin)/(fmax - fmin)
fmax, fmin = Harm1.max(), Harm1.min()
fcolors1 = (Harm1 - fmin)/(fmax - fmin)
fmax, fmin = Harm2.max(), Harm2.min()
fcolors2 = (Harm2 - fmin)/(fmax - fmin)
fmax, fmin = Harm3.max(), Harm3.min()
fcolors3 = (Harm3 - fmin)/(fmax - fmin)
fmax, fmin = Harm4.max(), Harm4.min()
fcolors4 = (Harm4 - fmin)/(fmax - fmin)
fmax, fmin = Harm5.max(), Harm5.min()
fcolors5 = (Harm5 - fmin)/(fmax - fmin)
fmax, fmin = Harm6.max(), Harm6.min()
fcolors6 = (Harm6 - fmin)/(fmax - fmin)
fmax, fmin = Harm7.max(), Harm7.min()
fcolors7 = (Harm7 - fmin)/(fmax - fmin)
fmax, fmin = Harm8.max(), Harm8.min()
fcolors8 = (Harm8 - fmin)/(fmax - fmin)
# set up a figure twice as wide as it is tall
fig = plt.figure(figsize=plt.figaspect(1))
# first plot
ax = fig.add_subplot(3, 3, 1, projection='3d')
surf = ax.plot_surface(X, Y, Z, rstride=1, cstride=1, facecolors=cm.seismic(fcolors0))
ax.set_axis_off()
#second plot
ax = fig.add_subplot(3, 3, 2, projection='3d')
ax.plot_surface(X, Y, Z, rstride=1, cstride=1, facecolors=cm.seismic(fcolors1))
ax.set_axis_off()
#third plot
ax = fig.add_subplot(3, 3, 3, projection='3d')
ax.plot_surface(X, Y, Z, rstride=1, cstride=1, facecolors=cm.seismic(fcolors2))
ax.set_axis_off()
#fourth plot
ax = fig.add_subplot(3, 3, 4, projection='3d')
ax.plot_surface(X, Y, Z, rstride=1, cstride=1, facecolors=cm.seismic(fcolors3))
ax.set_axis_off()
ax = fig.add_subplot(3, 3, 5, projection='3d')
ax.plot_surface(X, Y, Z, rstride=1, cstride=1, facecolors=cm.seismic(fcolors4))
ax.set_axis_off()
ax = fig.add_subplot(3, 3, 6, projection='3d')
ax.plot_surface(X, Y, Z, rstride=1, cstride=1, facecolors=cm.seismic(fcolors5))
ax.set_axis_off()
ax = fig.add_subplot(3, 3, 7, projection='3d')
ax.plot_surface(X, Y, Z, rstride=1, cstride=1, facecolors=cm.seismic(fcolors6))
ax.set_axis_off()
ax = fig.add_subplot(3, 3, 8, projection='3d')
ax.plot_surface(X, Y, Z, rstride=1, cstride=1, facecolors=cm.seismic(fcolors7))
ax.set_axis_off()
ax = fig.add_subplot(3, 3, 9, projection='3d')
ax.plot_surface(X, Y, Z, rstride=1, cstride=1, facecolors=cm.seismic(fcolors8))
ax.set_axis_off()
plt.show()
#--------------------------#
# Reference Values #
#--------------------------#
phi = np.linspace(0, np.pi, 100)
theta = np.linspace(0, 2*np.pi, 100)
phi, theta = np.meshgrid(phi, theta)
# The Cartesian coordinates of the unit sphere
x = np.sin(phi) * np.cos(theta)
y = np.sin(phi) * np.sin(theta)
z = np.cos(phi)
m, l = 0, 1
# Calculate the spherical harmonic Y(l,m) and normalize to [0,1]
fcolors = sph_harm(m, l, theta, phi).real
fmax, fmin = fcolors.max(), fcolors.min()
fcolors = (fcolors - fmin)/(fmax - fmin)
# Set the aspect ratio to 1 so our sphere looks spherical
fig = plt.figure(figsize=plt.figaspect(1.))
ax = fig.add_subplot(111, projection='3d')
ax.plot_surface(x, y, z, rstride=1, cstride=1, facecolors=cm.seismic(fcolors))
# Turn off the axis planes
ax.set_axis_off()
plt.show()
# setup colors
fcolors00 = sph_harm(0, 0 , theta, phi).real
fmax, fmin = fcolors00.max(), fcolors00.min()
#fcolors00 = (fcolors00 - fmin)/(fmax - fmin)
fcolors01 = sph_harm(-1, 1 , theta, phi).real
fmax, fmin = fcolors01.max(), fcolors01.min()
fcolors01 = (fcolors01 - fmin)/(fmax - fmin)
fcolors02 = sph_harm(0, 1 , theta, phi).real
fmax, fmin = fcolors02.max(), fcolors02.min()
fcolors02 = (fcolors02 - fmin)/(fmax - fmin)
fcolors03 = sph_harm(1, 1 , theta, phi).real
fmax, fmin = fcolors03.max(), fcolors03.min()
fcolors03 = (fcolors03 - fmin)/(fmax - fmin)
fcolors04 = sph_harm(-2, 2 , theta, phi).real
fmax, fmin = fcolors04.max(), fcolors04.min()
fcolors04 = (fcolors04 - fmin)/(fmax - fmin)