added python tools for printing

CMakeLists.txt

@@ -34,10 +34,11 @@ find_package(LAPACK REQUIRED)
 #---------------------------------------------------------------------------------------#
 
 # Manage folder structure build folder
-file(MAKE_DIRECTORY ${PROJECT_BINARY_DIR}/data/vtk)
+file(MAKE_DIRECTORY ${PROJECT_BINARY_DIR}/data/dual)
 file(MAKE_DIRECTORY ${PROJECT_BINARY_DIR}/data/gp)
+file(MAKE_DIRECTORY ${PROJECT_BINARY_DIR}/data/py)
+file(MAKE_DIRECTORY ${PROJECT_BINARY_DIR}/data/vtk)
 file(MAKE_DIRECTORY ${PROJECT_BINARY_DIR}/log)
-file(MAKE_DIRECTORY ${PROJECT_BINARY_DIR}/data/dual)
 #---------------------------------------------------------------------------------------#
 
 # Include directories

mlmc/conf/mlmc.conf

-#Model = LinearFEM
+Model = LinearFEM
 #Model = QuadraticFEM
 #Model = MixedFEM
 #Model = HybridFEM
-Model = FVFEM
+#Model = FVFEM
 #Model = LinearDGFEM
 #Model = QuadraticDGFEM
 
@@ -16,13 +16,14 @@ StochasticField = LogNormal
 #Experiment = MLMCExperiment
 Experiment = MLMCOverEpsilon
 
-init = 3
+size_init = 3
 l_init = 3 4 5
-M_init = 8 4 2
-Lmax = 8
+M_init = 12 6 3
+Lmax = 9
 
 epsilon = 0.01
-epsilon_lst = 0.01 0.005 0.003
+size_lst = 4
+epsilon_lst = 0.01 0.005 0.003 0.001
 
 plevel = 2
 functional = L2
@@ -34,3 +35,5 @@ MCVerbose = 1
 MLMCVerbose = 2
 MCPlotting = 0
 MLMCPlotting = 0
+
+enablePython = 1
\ No newline at end of file

mlmc/src/MLMCMain.C

@@ -96,6 +96,9 @@ void MLMCMain::run() {
         mlmc->showMCTable();
         mlmc->showKurtosisWarning();
         mlmc->showExponentResults();
+        if (enablePython == 1) {
+            system("python3 ../tools/plot_statistics.py ConvergenceTest");
+        }
     } else if (experimentName == "MLMCExperiment") {
         headMLMCExperiment();
         mlmc->method(epsilon);
@@ -103,9 +106,18 @@ void MLMCMain::run() {
         mlmc->showResultsMLMCRun(epsilon);
         mlmc->showKurtosisWarning();
         mlmc->showExponentResults();
+        if (enablePython == 1 && PPM->master()) {
+            system("python3 ../tools/plot_statistics.py MLMCExperiment");
+            system("python3 ../tools/plot_mlmc.py MLMCExperiment");
+        }
     } else if (experimentName == "MLMCOverEpsilon") {
         headMLMCOverEpsilon();
         mlmc->method(epsilon_lst);
+        mlmc->showMCTable();
         mlmc->showResultsOverEpsilon(epsilon_lst);
+        if (enablePython == 1 && PPM->master()) {
+            system("python3 ../tools/plot_statistics.py MLMCOverEpsilon");
+            system("python3 ../tools/plot_mlmc.py MLMCOverEpsilon");
+        }
     }
 }
\ No newline at end of file

mlmc/src/MLMCMain.h

@@ -9,9 +9,10 @@
 
 class MLMCMain {
 public:
+    int enablePython = 1;
     double epsilon = 0.1;
-    int init = 3, plevel = 2, Lmax = 8;
-    vector<int> l_init = {3, 4, 5}, M_init = {8, 4, 2};
+    int size_init = 3, plevel = 2, Lmax = 8, size_lst = 3;
+    vector<int> l_init = {3, 4, 5}, M_init = {12, 6, 3};
     vector<double> epsilon_lst = {0.1, 0.05, 0.01};
     string problemName = "", modelName = "";
     string fieldName = "", experimentName = "";
@@ -26,13 +27,15 @@ public:
     MultilevelMonteCarlo *mlmc = nullptr;
 
     MLMCMain() {
+        ReadConfig(Settings, "enablePython", enablePython);
         ReadConfig(Settings, "epsilon", epsilon);
         ReadConfig(Settings, "plevel", plevel);
         ReadConfig(Settings, "Lmax", Lmax);
-        ReadConfig(Settings, "init", init);
-        ReadConfig(Settings, "l_init", l_init, init);
-        ReadConfig(Settings, "M_init", M_init, init);
-        ReadConfig(Settings, "epsilon_lst", epsilon_lst, init);
+        ReadConfig(Settings, "size_init", size_init);
+        ReadConfig(Settings, "l_init", l_init, size_init);
+        ReadConfig(Settings, "M_init", M_init, size_init);
+        ReadConfig(Settings, "size_lst", size_lst);
+        ReadConfig(Settings, "epsilon_lst", epsilon_lst, size_lst);
         ReadConfig(Settings, "Problem", problemName);
         ReadConfig(Settings, "Model", modelName);
         ReadConfig(Settings, "StochasticField", fieldName);

mlmc/src/Main.C

 #include "m++.h"
+#include "MLMCMain.h"
 
 #ifdef COMPLEX
 #error undef COMPLEX in src/Compiler.h
 #endif
 
-#include "MLMCMain.h"
-
 int main(int argv, char **argc) {
     SetSearchPath("../mlmc", argv, argc);
     DPO dpo(&argv, &argc);
@@ -13,5 +12,6 @@ int main(int argv, char **argc) {
     auto mlmcMain = new MLMCMain();
     mlmcMain->run();
     delete mlmcMain;
+
     return 0;
 }

mlmc/src/MultilevelMonteCarlo.C

@@ -94,7 +94,7 @@ void MultilevelMonteCarlo::method(const double eps) {
 
 void MultilevelMonteCarlo::method(const vector<double> &eps_lst) {
     Date Start;
-    vout (1) << "Start multilevel Monte Carlo method only for initial values" << endl;
+    vout (1) << "Start multilevel Monte Carlo method for several epsilon" << endl;
 
     for (auto &eps : eps_lst) {
         initializeMapMC();

mlmc/src/Utils.C

@@ -83,7 +83,7 @@ void fft2(int N2, int N1, fftw_complex *in, fftw_complex *out) {
 void fft2(const vector<vector<complex<double>>> &in, vector<vector<complex<double>>> &out) {
     int N2 = in.size();
     int N1 = in[0].size();
-    fftw_complex in_arr[N2 * N1], out_arr[N2 * N1]; //Todo: Strange crash on Level 9
+    fftw_complex in_arr[N2 * N1], out_arr[N2 * N1]; //Todo: Strange crash for N1 = N2 =1023
     for (int i = 0; i < N2; i++) {
         for (int j = 0; j < N1; j++) {
             in_arr[i * N2 + j][REAL] = in[i][j].real();

tools/plot_mlmc.py

-import re
+from datetime import datetime
 import pandas as pd
 import numpy as np
+import sys
+import re
+
+L1 = "   eps       value   MLMC cost             l                                   M\n"
+L2 = "--------------------------------------------------------------------------------\n"
 
 
 def read_log():
@@ -14,12 +19,15 @@ def read_log():
     columns = ["eps", "value", "MLMC cost", "MC cost", "l", "M"]
     df = pd.DataFrame(columns=columns)
 
-    reading = False
+    ctr = 0
     for line in content:
-        if line[0:3] == "---":
-            reading = not reading
+        if line == L1:
+            ctr += 1
             continue
-        if reading:
+        if 0 < ctr < 3:
+            if line == L2:
+                ctr += 1
+                continue
             matches = pattern.findall(line)
 
             eps = float(matches[0])
@@ -31,19 +39,23 @@ def read_log():
             l = [int(match) for match in matches[3:3 + int(rest_length / 2)]]
             M = [int(match) for match in matches[3 + int(rest_length / 2):]]
 
-            column = [eps, value, MLMC_cost, MC_cost, l, M]
-            df_to_append = pd.DataFrame([column], columns=columns)
+            df_row = [eps, value, MLMC_cost, MC_cost, l, M]
+            df_to_append = pd.DataFrame([df_row], columns=columns)
             df = df.append(df_to_append)
 
     df = df.set_index('eps')
     return df
 
 
-def plot_content(df):
+def plot_content(df, plot_name):
+    if df.empty:
+        return
+
     import matplotlib.pyplot as plt
 
-    # M vs l over epsilon
     fig = plt.figure(figsize=(13, 5))
+
+    # M vs l over epsilon
     ax1 = fig.add_subplot(1, 2, 1)
     for eps in df.index:
         plt.plot(df.loc[eps, 'l'], df.loc[eps, 'M'],
@@ -79,9 +91,13 @@ def plot_content(df):
 
     ax2.grid(which='major')
 
-    plt.show()
+    now = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
+    path = '../build/data/py/Overview{} {}.png'.format(plot_name, now)
+    plt.savefig(path)
 
 
 if __name__ == "__main__":
-    df = read_log()
-    plot_content(df)
+    argv = sys.argv
+    argv1 = argv[1]
+    results = read_log()
+    plot_content(results, argv1)

tools/plot_statistics.py

+from datetime import datetime
 import pandas as pd
+import sys
 import re
 
 L1 = " l     M    E[Qf-Qc]       E[Qf]    V[Qf-Qc]       V[Qf]    kurtosis        cost\n"
@@ -48,7 +50,7 @@ def read_log():
     return df
 
 
-def plot_content(df):
+def plot_content(df, plot_name):
     if df.empty:
         return
 
@@ -90,9 +92,13 @@ def plot_content(df):
     ax2.set_xticks(df.index)
     ax2.grid(which='major')
 
-    plt.show()
+    now = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
+    path = '../build/data/py/Statistics{} {}.png'.format(plot_name, now)
+    plt.savefig(path)
 
 
-if __name__ == "__main__":
+if __name__ == '__main__':
+    argv = sys.argv
+    argv1 = argv[1]
     statistics = read_log()
-    plot_content(statistics)
+    plot_content(statistics, argv1)