diff --git a/script/scenarios/olivier_urdf.py b/script/scenarios/olivier_urdf.py index 34b880b318ad39bcbf5b45a5f09d4982102e0fb3..095615485f88b303d6caff463a58c05dcacb3aca 100644 --- a/script/scenarios/olivier_urdf.py +++ b/script/scenarios/olivier_urdf.py @@ -9,7 +9,7 @@ def loadRobot(packageName,meshPackageName,rootJointType,urdfName,urdfSuffix,srdf limbOffset = [0,0,0] #inutile ici limbNormal = [0,1,0] #inutile ici limbx = 0.09; limby = 0.05 #inutile ici - fullBody.addLimb(limbId,limbRoot,'',limbOffset,limbNormal, limbx, limby, 10000, "manipulability", 0.1) + fullBody.addLimb(limbId,limbRoot,'',limbOffset,limbNormal, limbx, limby, 1000, "manipulability", 0.1) return fullBody def runall(lid, valueNames): @@ -32,7 +32,7 @@ def rescaleOctreeValue(valueName, robotData): r1_max = robotData[0]["valueBounds"][valueName][1] r2_max = robotData[1]["valueBounds"][valueName][1] r_min = min(r1_min, r2_min) - r_max = min(r1_max, r2_max) + r_max = max(r1_max, r2_max) for i in range(0,len(robotData[0]["octreeValues"][valueName]['values'])): val = robotData[0]["octreeValues"][valueName]['values'][i] @@ -48,34 +48,34 @@ def rescaleOctreeValues(valueNames, robotData): #define values to analyze #EDIT valueNames = [ -"isotropy", #whole jacobian -"isotropyRot", #rotation jacobian -"isotropyTr", #translation jacobian -#~ "minimumSingularValue", -#~ "minimumSingularValueRot", -#~ "minimumSingularValueTr", -#~ "maximumSingularValue", -#~ "maximumSingularValueRot", -#~ "maximumSingularValueTr", -#~ "manipulabilityRot", -#~ "manipulabilityTr", -#~ "manipulability" +#~ "isotropy", #whole jacobian +#~ "isotropyRot", #rotation jacobian +#~ "isotropyTr", #translation jacobian +"minimumSingularValue", +"minimumSingularValueRot", +"minimumSingularValueTr", +"maximumSingularValue", +"maximumSingularValueRot", +"maximumSingularValueTr", +"manipulabilityRot", +"manipulabilityTr", +"manipulability" ] robotData = [{},{}] #first load first robot data -packageName = "hrp2_14_description" #EDIT -meshPackageName = "hrp2_14_description" #EDIT +packageName = "laas_design" #EDIT +meshPackageName = "laas_design" #EDIT rootJointType = "freeflyer" #EDIT # Information to retrieve urdf and srdf files. -urdfName = "hrp2_14" #EDIT -urdfSuffix = "_reduced" #EDIT +urdfName = "laas_arm_wrist_ZXZ" #EDIT +urdfSuffix = "" #EDIT srdfSuffix = "" #EDIT -limbId = '0rLeg' #nom que tu souhaites, peu importe #EDIT -limbRoot = 'RLEG_JOINT0' #joint racine de la chaine a analyser #EDIT +limbId = '0' #nom que tu souhaites, peu importe #EDIT +limbRoot = 'shoulder_joint' #joint racine de la chaine a analyser #EDIT limbEffector = '' # joint qui correspond a l'effecteur, laisse vide si dernier joint #EDIT fullBody = loadRobot(packageName,meshPackageName,rootJointType,urdfName,urdfSuffix,srdfSuffix, limbId, limbRoot, limbEffector) @@ -89,18 +89,18 @@ robotData[0]["name"] = urdfName + limbId #now to the second robot -packageName = "hrp2_14_description" #EDIT -meshPackageName = "hrp2_14_description" #EDIT +packageName = "laas_design" #EDIT +meshPackageName = "laas_design" #EDIT rootJointType = "freeflyer" #EDIT # Information to retrieve urdf and srdf files. -urdfName = "hrp2_14" #EDIT -urdfSuffix = "_reduced" #EDIT +urdfName = "laas_arm_wrist_ZXY" #EDIT +urdfSuffix = "" #EDIT srdfSuffix = "" #EDIT -limbId = '3Rarm' #EDIT -limbRoot = 'RARM_JOINT0' #EDIT -limbEffector = 'RARM_JOINT5' #EDIT +limbId = '1' #EDIT +limbRoot = 'shoulder_joint' #EDIT +limbEffector = '' #EDIT fullBody = loadRobot(packageName,meshPackageName,rootJointType,urdfName,urdfSuffix,srdfSuffix, limbId, limbRoot, limbEffector) # run analysis diff --git a/src/hpp/corbaserver/rbprm/tools/plot_analytics.py b/src/hpp/corbaserver/rbprm/tools/plot_analytics.py index eb8d49f799c4ec4964cc0f58e2f6660ea01ad042..2965bdaadd6eeb974c4ffdcad3e94cee243ff092 100644 --- a/src/hpp/corbaserver/rbprm/tools/plot_analytics.py +++ b/src/hpp/corbaserver/rbprm/tools/plot_analytics.py @@ -104,6 +104,7 @@ def compareOctreeValues(robotName1, robotName2, boxesValues1, boxesValues2, valu bx.set_title(robotName2) plotOctreeValuesCompare(bx, boxesValues2) #~ plt.title(valueName) + plt.savefig(valueName+'.png') plt.draw() ## Display a 3d plot of the values computed for a limb database