AENC/switchchain Changeset - 717d9916f17e · Centrum Wiskunde & Informatica (CWI)

Changeset - 717d9916f17e

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[Not reviewed]

0 4 0

Tom Bannink - 8 years ago 2017-08-25 14:32:48
tom.bannink@cwi.nl

Add improved ccm-initial-tris-distribution plots

4 files changed with 120 insertions and 1990 deletions:

cpp/switchchain_ccm_initialtris.cpp

data/graphdata_ccm_initialtris.m

1808

plots/ccm_initialtris.pdf

bin+mod

triangle_gcm_initial_analysis.m

104

0 comments (0 inline, 0 general)

cpp/switchchain_ccm_initialtris.cpp

➞

Show inline comments

 #include "switchchain.hpp"
 #include "exports.hpp"
 #include "graph.hpp"
 #include "graph_ccm.hpp"
 #include "graph_powerlaw.hpp"
 #include "switchchain.hpp"
 #include <algorithm>
 #include <fstream>
 #include <iostream>
@@ @@ -10,28 +10,27 @@ @@
 #include <random>
 #include <vector>
-int main(int argc, char* argv[]) {
+int main(int argc, char *argv[]) {
     // Simulation parameters
     const int numVerticesMin = 1000;
     const int numVerticesMax = 1000;
     const int numVerticesStep = 500;
     //float tauValues[] = {2.1f, 2.2f, 2.3f, 2.4f, 2.5f, 2.6f, 2.7f, 2.8f, 2.9f};
     // float tauValues[] = {2.1f, 2.2f, 2.3f, 2.4f, 2.5f, 2.6f, 2.7f, 2.8f,
     // 2.9f};
     float tauValues[] = {2.1f, 2.5f, 2.9f};
     const int totalDegreeSamples = 200;
     auto getMixingTime = [](int n, float tau) {
-        return int(50.0f * (50.0f - 30.0f * (tau - 2.0f)) * n);
+        return int(50.0f * (50.0f - 5.0f * (tau - 2.0f)) * n);
     };
     auto getMeasurements = [](int n, float tau) {
         (void)n;
         (void)tau;
-        return 100;
+        return 5000;
     };
     auto getMeasureSkip = [](int n, float tau) {
         (void)tau;
-        return 10 * n; // Take a sample every ... steps
+        return 30 * n; // Take a sample every ... steps
     };
     // Output file
@@ @@ -50,17 +49,17 @@ int main(int argc, char* argv[]) { @@
     outfile << "n from " << numVerticesMin << " to " << numVerticesMax
             << " step " << numVerticesStep << std::endl;
     outfile << "tauValues: " << tauValues << std::endl;
     outfile << "degreeSamples: " << totalDegreeSamples << std::endl;
     outfile << "mixingTime: 50 * (50 - 30 (tau - 2)) n\n";
     outfile << "measurements: 100\n";
     outfile << "measureSkip: 10 n\n";
     outfile << "Canonical degree sequence.\n";
     outfile << "mixingTime: 50 * (50 - 5 (tau - 2)) n\n";
     outfile << "measurements: 5000\n";
     outfile << "measureSkip: 30 n\n";
     outfile << "data:\n";
     outfile << "1: {n,tau}\n";
     outfile << "2: avgTriangles\n";
     outfile << "3: {ccmTris1, ccmsrate1} \n";
     outfile << "4: {ccmTris2, ccmsrate2} \n";
     outfile << "2: {uniform tri samples}\n";
     outfile << "3: {CCMdu initial tri samples} \n";
     outfile << "4: {CCMd initial tri samples} \n";
     outfile << "*)" << std::endl;
     // Mathematica does not accept normal scientific notation
     outfile << std::fixed;
     outfile << '{';
@@ @@ -71,80 +70,71 @@ int main(int argc, char* argv[]) { @@
     for (int numVertices = numVerticesMin; numVertices <= numVerticesMax;
          numVertices += numVerticesStep) {
         for (float tau : tauValues) {
             int mixingTime = getMixingTime(numVertices, tau);
             int measurements = getMeasurements(numVertices, tau);
             int measureSkip = getMeasureSkip(numVertices, tau);
             // For a single n,tau take samples over several instances of
             // the degree distribution.
             for (int degreeSample = 0; degreeSample < totalDegreeSamples;
                  ++degreeSample) {
                 DegreeSequence ds;
                 generatePowerlawGraph(numVertices, tau, g, ds, rng);
                 std::cout << "Running (n,tau) = (" << numVertices << ',' << tau
                           << "). " << std::flush;
                 //
                 // Test the GCM1 and GCM2 success rate
                 //
                 long long gcmTris1tot = 0;
                 long long gcmTris2tot = 0;
                 int successrate1 = 0;
                 int successrate2 = 0;
                 for (int i = 0; i < 100; ++i) {
                     Graph gtemp;
                     // Take new highest degree every time
                     if (constrainedConfigurationModel(ds, gtemp, rng, false)) {
                         ++successrate1;
                         gcmTris1tot += gtemp.countTriangles();
+                    }
                     // Finish all pairings of highest degree first
                     if (constrainedConfigurationModel(ds, gtemp, rng, true)) {
                         ++successrate2;
                         gcmTris2tot += gtemp.countTriangles();
+                    }
             DegreeSequence ds;
             generateCanonicalPowerlawGraph(numVertices, tau, g, ds);
             std::cout << "Running (n,tau) = (" << numVertices << ',' << tau
                       << "). " << std::flush;
             //
             // CCM triangles
             //
             std::vector<int> CCMduTris;
             std::vector<int> CCMdTris;
             for (int i = 0; i < measurements; ++i) {
                 Graph gtemp;
                 // Take new highest degree every time
                 if (constrainedConfigurationModel(ds, gtemp, rng, false)) {
                     CCMduTris.push_back(gtemp.countTriangles());
+                }
                 float gcmTris1 = float(gcmTris1tot) / float(successrate1);
                 float gcmTris2 = float(gcmTris2tot) / float(successrate2);
                 SwitchChain chain;
                 if (!chain.initialize(g)) {
                     std::cerr << "Could not initialize Markov chain.\n";
                     return 1;
                 // Finish all pairings of highest degree first
                 if (constrainedConfigurationModel(ds, gtemp, rng, true)) {
                     CCMdTris.push_back(gtemp.countTriangles());
+                }
+            }
             std::cout << " Finished CCM samples." << std::flush;
                 long long trianglesTotal = 0;
             // Uniform triangles
             std::vector<int> uniformTris;
                 std::cout << " Finished CCM generation." << std::flush;
             SwitchChain chain;
             if (!chain.initialize(g)) {
                 std::cerr << "Could not initialize Markov chain.\n";
                 return 1;
+            }
                 int mixingTime = getMixingTime(numVertices, tau);
                 for (int i = 0; i < mixingTime; ++i) {
                     chain.doMove();
+                }
                 chain.g.getTrackedTriangles() = chain.g.countTriangles();
                 int measurements = getMeasurements(numVertices, tau);
                 int measureSkip = getMeasureSkip(numVertices, tau);
                 for (int i = 0; i < measurements; ++i) {
                     for (int j = 0; j < measureSkip; ++j)
                         chain.doMove(true);
                     trianglesTotal += chain.g.getTrackedTriangles();
+                }
             for (int i = 0; i < mixingTime; ++i) {
                 chain.doMove();
+            }
             chain.g.getTrackedTriangles() = chain.g.countTriangles();
             for (int i = 0; i < measurements; ++i) {
                 for (int j = 0; j < measureSkip; ++j)
                     chain.doMove(true);
                 uniformTris.push_back(chain.g.getTrackedTriangles());
+            }
-                std::cout << " Finished mixing and measurements." << std::flush;
             std::cout << " Finished mixing and measurements." << std::flush;
                 if (outputComma)
                     outfile << ',' << '\n';
                 outputComma = true;
             if (outputComma)
                 outfile << ',' << '\n';
             outputComma = true;
                 float avgTriangles =
                     float(trianglesTotal) / float(measurements);
                 outfile << '{';
                 outfile << '{' << numVertices << ',' << tau << '}';
                 outfile << ',' << avgTriangles;
                 outfile << ',' << '{' << gcmTris1 << ',' << successrate1 << '}';
                 outfile << ',' << '{' << gcmTris2 << ',' << successrate2 << '}';
                 outfile << '}' << std::flush;
             outfile << '{';
             outfile << '{' << numVertices << ',' << tau << '}';
             outfile << ',' << uniformTris;
             outfile << ',' << CCMduTris;
             outfile << ',' << CCMdTris;
             outfile << '}' << std::flush;
                 std::cout << std::endl;
+            }
             std::cout << std::endl;
+        }
+    }
     outfile << '}';

data/graphdata_ccm_initialtris.m

➞

Show inline comments

 (*
 n from 1000 to 1000 step 500
 tauValues: {2.1,2.2,2.3,2.4,2.5,2.6,2.7,2.8,2.9}
 degreeSamples: 200
 mixingTime: 50 * (50 - 30 (tau - 2)) n
 measurements: 100
 measureSkip: 10 n
 tauValues: {2.1,2.5,2.9}
 Canonical degree sequence.
 mixingTime: 50 * (50 - 5 (tau - 2)) n
 measurements: 5000
 measureSkip: 30 n
 data:
 : {n,tau}
 : avgTriangles
 : {ccmTris1, ccmsrate1}
 : {ccmTris2, ccmsrate2}
 : {uniform tri samples}
 : {CCMdu initial tri samples}
 : {CCMd initial tri samples}
 *)
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 {{1000,2.100000},4996.430176,{-nan,0},{5295.109863,100}},
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4,8,8,8,13,5,9,3,4,5,4,8,8,8,4,4,4,6,2,3,6,6,8,8,8,6,13,8,7,10,7,5,7,7,3,7,4,9,14,3,7,1,7,2,7,5,6,5,8,4,8,9,10,10,5,9,3,9,3,4,4,8,5,2,9,2,8,10,4,13,6,5,9,8,5,5,6,8,3,10,4,2,6,5,9,2,7,4,6,8,13,5,5,8,6,7,6,4,3,5,10,8,5,3,2,6,4,6,2,3,9,4,5,7,7,8,1,10,7,2,7,5,4,6,10,6,7,6,3,3,1,7,4,6,3,6,6,11,8,5,7,14,3,6,4,11,4,8,5,12,5,6,3,9,13,5,9,6,14,6,5,5,7,9,12,10,6,3,6,6,6,10,10,3,3,2,7,4,4,4,6,12,2,2,11,5,3,3,6,4,4,8,1,2,6,3,8,9,5,5,7,7,12,6,8,6,4,4,6,4,8,5,12,7,5,11,10,3,8,0,4,6,14,4,8,9,7,8,5,5,4,7,7,5,2,3,4,10,3,11,9,4,6,11,13,10,4,4,6,7,10,1,9,6,9,6,8,6,10,9,3,6,4,7,6,3,5,6,5,6,5,9,6,6,4,4,5,4,7,10,1,9,9,5,5,4,9,3,8,8,5,3,3,14,6,5,5,3,5,9,7,5,5,10,12,5,4,9,7,3,4,6,7,8,5,5,7,4,4,9,3,6,5,4,3,6,7,9,10,7,7,7,8,4,5,7,8,1,7,9,6,6,4,9,10,9,3,8,5,7,9,6,6,6,6,5,8,2,5,12,4,9,9,7,6,9,16,11,6,4,6,8,10,6,8,9,2,6,6,5,7,9,3,6,8,14,2,7,16,4,9,6,7,7,4,6,7,4,6,4,12,4,4,9,10,8,3,4,9,5,8,6,6,4,9,8,6,8,4,3,6,10,11,8,4,2,7,3,11,5,6,9,4,5,5,9,8,4,13,7,6,6,4,10,9,7,6,4,2,3,7,5,6,7,3,5,5,5,6,7,4,7,10,7,15,7,9,4,5,9,9,7,7,7,5,4,4,8,4,5,7,8,8,12,8,2,2,9,6,13,17,12,15,6,8,5,4,5,6,5,7,10,11,3,5,5,6,6,6,5,7,13,9,5,8,6,12,6,5,7,11,7,11,8,4,8,7,3,4,11,4,6,8,9,9,2,8,6,10,8,3,4,8,10,5,5,3,10,7,6,9,6,3,6,7,7,13,9,5,6,6,5,4,8,8,6,4,9,3,6,11,1,8,7,7,7,3,8,5,10,7,7,6,9,7,8,6,8,5,4,9,8,3,5,1,4,14,5,4,4,1,6,3,4,7,8,3,12,3,5,9,5,8,11,2,7,3,14,4,13,1,9,8,7,3,6,2,8,2,11,9,6,3,5,11,5,5,8,1,4,7,2,8,16,10,8,2,5,6,6,7,5,2,5,1,5,7,7,8,7,5,8,6,3,8,8,11,5,3,6,6,7,10,4,6,9,3,4,5,5,5,5,9,7,7,1,5,12,7,2,7,9,7,2,4,5,6,4,10,5,6,4,1,9,3,4,7,2,10,6,4,5,4,11,3,4,6,2,7,3,7,8,5,6,6,7,6,8,6,5,5,15,7,7,9,17,3,1,5,5,5,7,6,9,16,8,10,3,8,6,7,5,4,6,5,9,7,9,5,4,7,1,4,9,6,7,5,3,3,7,4,9,5,7,5,9,4,8,9,3,2,2,4,2,5,3,5,12,4,13,6,3,5,7,4,5,4,8,4,4,9,9,12,2,8,9,10,3,5,7,3,11,3,3,2,7,8,11,5,7,7,5,9,6,6,4,7,4,5,4,5,8,7,3,14,5,8,4,8,5,11,5,7,5,4,7,13,7,9,6,7,5,8,9,11,2,12,3,4,9,4,14,2,5,3,7,5,4,6,3,5,5,1,6,3,6,2,11,6,8,5,4,7,5,2,7,8,7,4,2,8,10,6,9,4,7,6,7,7,7,2,11,5,5,5,6,8,3,7,6,8,10,7,3,8,3,8,9,3,5,6,11,6,2,8,6,8,4,5,9,5,3,8,6,5,8,4,12,6,2,8,5,20,4,5,6,10,6,10,8,5,6,5,7,7,4,7,4,6,5,4,9,3,5,8,8,10,4,6,8,11,8,4,11,6,4,6,5,6,7,8,3,9,1,11,7,9,8,10,7,6,6,8,2,9,8,7,4,5,8,4,5,6,6,10,7,13,6,6,4,1,3,8,5,4,7,5,2,8,3,2,4,12,4,6,3,5,7,5,10,6,8,8,5,5,8,4,8,7,8,5,4,6,8,8,5,13,14,7,6,7,4,3,9,4,7,2,8,2,6,6,10,4,8,8,6,1,8,6,7,1,6,3,2,6,8,7,5,5,7,4,3,10,7,5,7,4,5,1,8,6,4,5,3,7,6,10,3,5,7,3,5,5,10,9,6,4,9,13,9,7,8,6,1,7,2,4,6,13,7,6,8,10,5,9,10,8,7,7,6,9,4,8,5,10,2,3,1,5,9,16,7,9,8,5,3,2,4,10,15,3,6,2,5,3,7,5,10,8,5,5,5,5,10,7,2,5,6,12,6,5,9,8,1,7,7,1,9,8,9,4,10,7,7,5,3,5,3,7,5,3,4,1,7,5,7,10,5,4,10,13,3,3,9,9,10,4,8,7,5,6,3,3,12,9,5,9,5,7,0,10,5,3,5,3,5,6,5,7,5,5,12,5,6,12,6,8,14,9,14,8,8,5,5,3,8,10,9,6,3,4,7,4,8,7,6,4,3,7,4,2,5,5,3,4,5,3,9,4,3,10,4,6,5,7,8,3,8,10,6,3,15,5,4,8,6,2,6,4,3,9,7,7,3,9,7,2,8,5,15,9,7,10,1,8,6,5,4,5,11,6,9,10,1,3,7,2,10,6,7,8,3,4,7,7,3,9,3,8,6,6,4,8,5,3,4,5,8,5,13,5,4,5,4,5,7,5,4,11,7,11,9,3,9,8,9,6,5,14,8,6,4,9,5,2,7,7,8,4,8,9,11,2,9,4,5,6,9,9,8,7,4,4,7,8,7,6,8,7,5,8,6,14,6,5,3,3,7,6,5,4,6,4,8,4,7,3,6,6,3,6,7,3,7,7,1,7,3,7,3,8,10,7,7,4,7,8,1,1,3,2,11,8,6,4,8,3,2,4,4,6,5,4,8,3,7,7,8,8,3,9,5,4,3,5,6,3,7,5,6,4,1,6,5,12,13,7,5,8,6,6,6,8,9,7,6,7,3,7,7,8,5,11,5,5,11,5,13,7,9,6,3,1,5,8,6,3,7,9,4,7,4,2,9,3,11,5,6,7,5,13,14,5,6,9,3,6,1,3,6,7,8,6,5,7,2,2,0,3,10,4,5,6,6,6,7,8,9,4,6,7,6,9,4,11,6,2,12,6,10,3,8,3,10,5,10,8,5,9,3,4,3,8,6,8,5,4,7,4,7,0,4,4,5,9,12,4,3,6,4,5,9,5,11,5,8,6,7,8,5,6,5,5,8,8,1,5,6,7,8,6,4,5,8,8,1,5,7,3,3,8,10,6,3,7,2,0,7,5,8,8,8,7,3,8,7,3,3,7,10,9,13,5,5,5,5,5,8,7,9,9,7,9,5,2,7,8,4,11,5,3,5,9,8,4,12,6,5,4,9,4,4,7,7,3,13,7,8,6,14,7,8,7,8,3,3,6,7,8,8,9,5,5,4,8,2,9,7,7,7,6,7,8,7,6,3,12,5,6,5,6,2,9,7,5,9,8,2,6,5,3,8,6,6,8,8,6,4,6,7,5,8,3,4,5,4,7,8,4,6,12,6,5,7,1,3,6,7,3,3,8,11,5,8,7,3,8,4,2,6,4,5,10,8,2,3,5,5,7,4,3,6,6,6,10,5,5,2,6,5,8,9,7,8,5,1,10,8,4,11,4,5,6,6,5,3,5,5,7,7,6,8,6,8,5,4,7,4,6,3,5,6,6,6,11,4,5,7,2,12,9,5,1,8,7,6,4,4,9,6,5,7,7,6,4,9,10,8,10,10,4,5,5,6,7,11,3,5,6,6,12,5,5,3,4,8,9,7,11,4,6,9,3,7,8,8,4,9,5,6,9,5,9,1,5,4,11,7,3,2,7,10,8,3,11,7,8,5,15,5,10,4,5,7,5,9,7,2,6,4,9,7,1,5,7,7,3,5,4,3,5,8,12,4,6,5,5,8,6,5,2,6,8,11,8,7,6,3,8,6,8,7,5,5,4,9,6,7,8,6,10,3,12,6,4,3,7,7,5,4,6,11,5,5,11,6,6,4,7,9,6,10,4,8,6,8,6,5,7,6,2,10,7,4,9,5,8,7,11,6,6,6,7,6,5,7,12,2,7,10,6,5,8,5,5,6,10,9,8,5,7,7,3,8,11,3,7,3,3,9,7,8,5,6,7,2,4,4,5,1,5,4,5,6,2,9,8,11,1,3,2,2,0,3,5,3,6,6,3,13,3,3,7,15,8,6,4,4,9,3,5,1,6,9,5,3,11,2,16,13,6,7,5,5,8,10,7,13,3,4,5,6,6,7,6,5,6,5,7,2,3,2,2,5,4,7,7,5,11,4,8,8,6,8,10,9,8,8,7,8,11,9,12,9,8,8,8,5,7,7,6,5,6,3,2,11,3,7,9,7,11,6,9,2,7,9,7,6,2,6,5,5,6,6,5,7,8,11,8,9,9,7,7,4,8,4,9,4,4,1,9,5,7,13,3,9,10,7,10,5,7,4,2,8,5,7,4,5,6,6,5,11,7,8,5,4,6,8,8,7,7,6,6,8,5,5,3,4,8,7,4,7,4,5,8,8,4,3,8,7,7,7,3,7,5,10,4,2,5,7,4,9,8,3,9,8,5,2,9,6,5,3,4,4,3,5,4,4,9,5,5,4,3,4,15,7,10,5,4,3,6,7,8,8,4,6,4,8,3,6,12,6,4,4,8,7,2,5,2,5,8,8,3,8,2,4,8,3,8,6,5,10,7,9,3,5,2,6,11,5,7,6,8,10,9,12,5,7,4,5,2,8,10,7,5,5,5,5,3,4,9,5,5,11,9,8,2,4,6,3,3,4,8,4,5,8,4,7,6,10,5,6,5,11,6,7,9,4,7,7,6,9,3,5,8,2,9,5,6,7,10,6,7,8,7,6,7,7,11,9,8,4,5,10,7,9,8,4,3,11,6,7,12,8,4,8,2,4,6,9,3,5,7,5,7,3,11,6,6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@@ \ No newline at end of file @@

plots/ccm_initialtris.pdf

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binary diff not shown

triangle_gcm_initial_analysis.m

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 (* gsraw=SortBy[gsraw,{#[[1,1]]&,#[[1,2]]&}]; (* Sort by n and then by tau. The {} forces a *stable* sort because otherwise Mathematica sorts also on triangle count and other things. *) *)
-gdata=GatherBy[gsraw,{#[[1,2]]&,#[[1,1]]&}];
 gdata=gsraw;
 (* Data format: *)
 (* gdata[[ tau index, n index, run index , datatype index ]] *)
 (* datatype index:
 : {n,tau}
 : avgtriangles when mixed
 : {GCM1 starting triangles average, GCM1 number of successes} <-- CCMu: get new highest degree vertex every time
 : {GCM2 starting triangles average, GCM2 number of successes} <-- CCMb: finish vertex completely
 *)
 nlabels=Map["n = "<>ToString[#]&,gdata[[1,All,1,1,1]]];
 taulabels=Map["\[Tau] = "<>ToString[#]&,gdata[[All,1,1,1,2]]];
 (* ::Subsection:: *)
 (*New data format import*)
 gsraw=Import[NotebookDirectory[]<>"data/graphdata_ccm_constructionrate.m"];
 (* gsraw=SortBy[gsraw,{#[[1,1]]&,#[[1,2]]&}]; (* Sort by n and then by tau. The {} forces a *stable* sort because otherwise Mathematica sorts also on triangle count and other things. *) *)
 gdata2=GatherBy[gsraw,{#[[1,2]]&,#[[1,1]]&}];
 (* Data format: *)
 (* gdata[[ tau index, n index, run index , datatype index ]] *)
 (* gdata[[ tau index , datatype index ]] *)
 (* datatype index:
 : {n,tau}
 : CCMdu construction rate <-- CCMdu: get new highest degree vertex every time
 : CCMd  construction rate <-- CCMd: finish vertex completely
 : {uniform triangle samples}
 : {CCMdu initial triangle samples} <-- CCMdu: get new highest degree vertex every time
 : {CCMd initial triangle samples} <-- CCMd: finish vertex completely
 *)
@@ @@ -47,103 +27,60 @@ gdata2=GatherBy[gsraw,{#[[1,2]]&,#[[1,1]]&}]; @@
 (* ::Subsection:: *)
 (*Distribution of initial #triangles for GCM1,GCM2,EG compared to average #triangles.*)
  (* Consider all runs *)
 getIt[x_,avg_]:=If[x[[2]]>=5, x[[1]]/avg,0]
 getAverage[run_]:=Module[{avg},
     avg=run[[2]];
     If[avg>0,
     { getIt[run[[3]],avg], getIt[run[[4]],avg] }
     , {3,3}]
+]
 getTotalStats[runs_]:=Transpose[Map[getAverage,runs]];
 totalStats=Map[getTotalStats,gdata,{2}];
 (* Yellow: CCMu (take new highest everytime *)
 (* Blue: CCMb (finish highest first, more similar to EG) *)
 histogramsTotal=Map[Histogram[#,{0.05},PlotRange->{{-0.5,2},Automatic},ImageSize->300,AxesOrigin->{0,0}]&,totalStats,{2}];
 (*Distribution of initial #triangles for CCMd(u) compared to uniform triangle distribution*)
 TableForm[histogramsTotal,TableHeadings->{taulabels,nlabels}]
 getHistogram[run_]:=Histogram[{run[[2]],run[[3]],run[[4]]},Automatic,"Probability",
 ChartLegends->Placed[{"Uniform","CCMdu","CCMd"},Bottom],
 ImageSize->250,
 Frame->True,
 FrameLabel->{"Triangles","Probability"},
 PlotLabel->("n = "<>ToString[run[[1,1]]]<>", \[Tau] = "<>ToString[run[[1,2]]])
 ];
 histograms=Map[getHistogram,gdata]
 (* ::Subsubsection:: *)
 (*Exporting plots*)
 makeHistogram[datasets_,n_,tau_]:=Histogram[datasets,{0.05},"Probability",
 PlotRange->{{0,1.3},{0,0.5}},
 ImageSize->300,AxesOrigin->{0,0},
 AspectRatio->3/5,
 getHistogram[run_,bins_,plotrange_,tickDelta_,textpos_,bottomLegends_,bottomLabel_]:=Histogram[{run[[2]],run[[3]],run[[4]]},bins,"Probability",
 ImageSize->250,
 AspectRatio->4/14,
 PlotRange->plotrange,
 Frame->True,
 FrameLabel->{"fraction of average #triangles at CCM start","frequency"},
 ChartLegends->Placed[{"CCMu   avg = "<>ToString[NumberForm[N[Mean[datasets[[1]]]],2]],"CCMb   avg = "<>ToString[NumberForm[N[Mean[datasets[[2]]]],2]]},Center],
 (*LabelingFunction->(Placed[NumberForm[#,{2,3}],Above]&),*)
 (*LabelingFunction\[Rule](Placed[If[#2[[2]]\[Equal]1,NumberForm[#1,{2,3}],""],Above]&),*)
 PlotLabel->n<>", "<>tau];
 histogramsTotal=MapIndexed[makeHistogram[#1,nlabels[[#2[[2]]]],taulabels[[#2[[1]]]]]&,totalStats,{2}];
 tauIndices={1,5,9};
 nIndices={1};
 (* TableForm[histogramsTotal[[tauIndices,nIndices]],TableHeadings->{taulabels[[tauIndices]],nlabels[[nIndices]]}]*)
 plotgrid1=GraphicsGrid[histogramsTotal[[tauIndices,nIndices]],ImageSize->350,ItemAspectRatio->3/5]
 FrameLabel->{If[bottomLabel,"Triangles",None],"Probability"},
 FrameTicks->{{{#,NumberForm[#,{2,2}]}&/@Range[0,0.30,tickDelta],Automatic},{Automatic,Automatic}},
 Epilog->Text["n = "<>ToString[run[[1,1]]]<>", \[Tau] = "<>ToString[run[[1,2]]],textpos],
 ChartLegends->If[bottomLegends,Placed[{"Uniform","CCMdu","CCMd"},Top],None]
 ];
 {h1,h2,h3}={
 getHistogram[gdata[[1]],Automatic,{0,0.30} ,0.1 ,Scaled[{0.82,0.86}],True ,False],
 getHistogram[gdata[[2]],      {5},{0,0.15} ,0.05,Scaled[{0.82,0.86}],False,False],
 getHistogram[gdata[[3]],Automatic,Automatic,0.05,Scaled[{0.82,0.86}],False,True]
 };
 plotgrid1=Column[{h1,h2,h3}]
 Export[NotebookDirectory[]<>"plots/ccm_initialtris.pdf",plotgrid1]
 (* ::Subsection:: *)
 (*GCM1 vs GCM2 success rates*)
 successrates=Map[{#[[3,2]]/100,#[[4,2]]/100}&,gdata,{3}];
 successrates=Map[Transpose,successrates,{2}];
 successratesDelta=Map[#[[3,2]]-#[[4,2]]&,gdata,{3}];
 successratesAvg=Map[{Mean[#[[1]]],Mean[#[[2]]]}&,successrates,{2}];
 rateHistograms=Map[Histogram[#,{0.1},"Probability",PlotRange->{{0,1},Automatic}]&,successrates,{2}];
 rateDeltaHistograms=Map[Histogram[#,{10},"Probability",PlotRange->{{-100,100},Automatic}]&,successratesDelta,{2}];
 TableForm[rateHistograms,TableHeadings->{taulabels,nlabels}]
 TableForm[rateDeltaHistograms,TableHeadings->{taulabels,nlabels}]
 (*TableForm[Transpose[rateHistograms],TableHeadings->{nlabels,taulabels}]*)
 (* For export *)
 makeHistogram2[datasets_,label_]:=Histogram[datasets,{0.1},"Probability",
 PlotRange->{{0,1},{0,1}},
 ImageSize->300,AxesOrigin->{0,0},
 Frame->True,
 FrameLabel->{"successrate of CCM construction","frequency"},
 ChartLegends->Placed[{"CCMu   avg = "<>ToString[NumberForm[N[Mean[datasets[[1]]]],2]],"CCMb   avg = "<>ToString[NumberForm[N[Mean[datasets[[2]]]],2]]},Center],(*LabelingFunction->(Placed[NumberForm[#,{2,3}],Above]&),*)
 (*LabelingFunction\[Rule](Placed[If[#2[[2]]\[Equal]1,NumberForm[#1,{2,3}],""],Above]&),*)
 PlotLabel->label];
 plot1=makeHistogram2[successrates[[1,1]],"n = 1000, \[Tau] = 2.1"]
 plot2=makeHistogram2[successrates[[5,1]],"n = 1000, \[Tau] = 2.5"]
 plot3=makeHistogram2[successrates[[9,1]],"n = 1000, \[Tau] = 2.9"]
 (* columnplot1=GraphicsColumn[{plot1,plot2,plot3}] *)
 Export[NotebookDirectory[]<>"plots/ccm_construction_successrate.pdf",columnplot1]
 Export[NotebookDirectory[]<>"plots/ccm_construction_successrate1.pdf",plot1]
 Export[NotebookDirectory[]<>"plots/ccm_construction_successrate5.pdf",plot2]
 Export[NotebookDirectory[]<>"plots/ccm_construction_successrate9.pdf",plot3]
 (* ::Section:: *)
 (*CCMu rates only*)
 successrates=Map[#[[3,2]]/100&,gdata,{3}]; (* Old datafile *)
 legends=Map["\[Tau] = "<>ToString[#[[1,1,2]]]<>" ; avg = "<>ToString[NumberForm[N[Mean[#[[All,3,2]]/100]],3]]&,gdata,{2}];
 gsraw2=Import[NotebookDirectory[]<>"data/graphdata_ccm_constructionrate.m"];
 (* gsraw=SortBy[gsraw,{#[[1,1]]&,#[[1,2]]&}]; (* Sort by n and then by tau. The {} forces a *stable* sort because otherwise Mathematica sorts also on triangle count and other things. *) *)
 gdata2=GatherBy[gsraw2,{#[[1,2]]&,#[[1,1]]&}];
 (* Data format: *)
 (* gdata[[ tau index, n index, run index , datatype index ]] *)
 (* datatype index:
 : {n,tau}
 : CCMdu construction rate <-- CCMdu: get new highest degree vertex every time
 : CCMd  construction rate <-- CCMd: finish vertex completely
 *)
 successrates=gdata2[[All,All,All,2]]; (* New datafile *)

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