Merge remote-tracking branch 'origin/clusters' into cluster_update_normal

scontact/scontact.c

@@ -546,7 +546,7 @@ static void contact_apply(const Contact *c, ParticleProblem *p) {
   result[0] = (c->dv[0]/wnn)*c->basis[0][0] + (c->dv[1]/wtt)*c->basis[1][0];
   result[1] = (c->dv[0]/wnn)*c->basis[0][1] + (c->dv[1]/wtt)*c->basis[1][1];
   if(omega0)
-    omega0[0] += c->iinertmom0*(result[0]*c->r0[1] - result[1]*c->r0[0]);
+    omega0[0] -= c->iinertmom0*(result[0]*c->r0[1] - result[1]*c->r0[0]);
   omega1[0] -= c->iinertmom1*(result[0]*c->r1[1] - result[1]*c->r1[0]);
 #else
 //TODO

testcases/hopper-3d/plot.py

@@ -5,27 +5,32 @@ import shutil
 import random
 import csv 
 import matplotlib.pyplot as plt
-filename = "fric/1output005r=6e-3mu=3171/Drag.csv"
+filename = "../../../../../media/coppinn/Maxtor/Hopper/hopper04/2/Drag.csv"
+filename = "../../../Latex/ArticleContacts/Drag/noRelaxStressDrag64/2/Drag.csv"
 with open(filename) as file1:
       reader = csv.reader(file1,delimiter = ";",quoting=csv.QUOTE_NONNUMERIC)
       data = [r for r in reader]  
 data = np.array(data);
 react = data[:,1]
 prod = np.zeros_like(react)
-ranges = 25
-startMean=6000
+ranges = 500
+startMean=7000
+endMean = 20000
+endMean2 = 4500
+print(react)
 for i in range(len(react)):
   if i <ranges:
     prod[i] = np.sum(react[0:i+ranges])/(i+ranges)
-  elif len(react)-i<ranges:
-    prod[i] = np.sum(react[i-ranges:])/((len (react)-i)+ranges)
   else:
-    prod[i] = np.sum(react[i-ranges:i+ranges])/(2*ranges)
-print("Mean :",np.mean(prod[startMean:]),"\nVelocity :",np.mean(data[startMean:, 4])*1000, "mm/s")
-mean = np.ones_like(prod[startMean:])*np.mean(prod[startMean:])
-plt.plot(data[:, 3],-prod,color='black')
-plt.plot(data[startMean:, 3],-mean,color = 'red')
+    prod[i] = np.sum(react[i-ranges:i])/(ranges)
+#print("Mean :",np.mean(prod[startMean:]),"\nVelocity :",np.mean(data[startMean:, 4])*1000, "mm/s")
+mean = np.ones_like(react[startMean:endMean])*np.mean(react[startMean:endMean])
+plt.plot(data[:, 3],-prod,color='black',linewidth=2)
+plt.plot(data[:, 3],-react,'.',color='black',alpha=0.03,markersize=2.5)
+plt.plot(data[startMean:endMean, 3],-mean,color = 'darkorange',linewidth=2.5)
+mean2 = np.ones_like(react[:endMean2])*np.mean(react[:endMean2])
+plt.plot(data[:endMean2, 3],-mean2,color = 'darkturquoise',linewidth=2.5)
 plt.xlabel("Time [s]",size=15)
-plt.ylabel("Drag force [N]",size=15)
-plt.title("v = %.1f mm/s"%(np.mean(data[startMean:, 4])*1000),size=19)
+plt.ylabel("F [N]",size=15,rotation=0)
+plt.ylim([0,25])
 plt.show()