diff --git a/svo/CMakeLists.txt b/svo/CMakeLists.txt
index 16b1cba..7612a94 100644
--- a/svo/CMakeLists.txt
+++ b/svo/CMakeLists.txt
@@ -2,7 +2,7 @@
 # user build settings
 
 SET(DEBUG FALSE)
-SET(TRACE FALSE)
+SET(TRACE TRUE)
 SET(HAVE_G2O FALSE) 
 
 ################################################################################
diff --git a/svo/Doxyfile b/svo/Doxyfile
index fb60d29..b1b4256 100644
--- a/svo/Doxyfile
+++ b/svo/Doxyfile
@@ -648,7 +648,7 @@ WARN_LOGFILE           =
 # directories like "/usr/src/myproject". Separate the files or directories
 # with spaces.
 
-INPUT                  = "src", "include/nano_slam"
+INPUT                  = "src", "include/svo"
 
 # This tag can be used to specify the character encoding of the source files
 # that doxygen parses. Internally doxygen uses the UTF-8 encoding, which is
diff --git a/svo/include/svo/frame.h b/svo/include/svo/frame.h
index 68aa548..f4413e7 100644
--- a/svo/include/svo/frame.h
+++ b/svo/include/svo/frame.h
@@ -17,7 +17,6 @@
 #ifndef SVO_FRAME_H_
 #define SVO_FRAME_H_
 
-#include <algorithm>
 #include <sophus/se3.h>
 #include <vikit/math_utils.h>
 #include <vikit/abstract_camera.h>
@@ -32,7 +31,6 @@ typedef g2o::VertexSE3Expmap g2oFrameSE3;
 namespace svo {
 
 class Point;
-class Frame;
 struct Feature;
 
 typedef list<Feature*> Features;
@@ -57,7 +55,7 @@ public:
   g2oFrameSE3*                  v_kf_;                  //!< Temporary pointer to the g2o node object of the keyframe.
   int                           last_published_ts_;     //!< Timestamp of last publishing.
 
-  Frame(vk::AbstractCamera* cam, const cv::Mat& img, double timestamp=0.0);
+  Frame(vk::AbstractCamera* cam, const cv::Mat& img, double timestamp);
   ~Frame();
 
   /// Initialize new frame and create image pyramid.
diff --git a/svo/include/svo/frame_handler_base.h b/svo/include/svo/frame_handler_base.h
index 51286c7..50afc14 100644
--- a/svo/include/svo/frame_handler_base.h
+++ b/svo/include/svo/frame_handler_base.h
@@ -26,13 +26,14 @@
 #include <svo/global.h>
 #include <svo/map.h>
 
-namespace vk {
-  class AbstractCamera;
-  class PerformanceMonitor;
+namespace vk
+{
+class AbstractCamera;
+class PerformanceMonitor;
 }
 
-namespace svo {
-
+namespace svo
+{
 class Point;
 class Matcher;
 class DepthFilter;
@@ -41,9 +42,22 @@ class DepthFilter;
 class FrameHandlerBase : boost::noncopyable
 {
 public:
-  enum Stage { PAUSED, FIRST_FRAME, SECOND_FRAME, DEFAULT_FRAME };
-  enum TrackingQuality { INSUFFICIENT, BAD, GOOD };
-  enum UpdateResult { NO_KEYFRAME, IS_KEYFRAME, FAILURE };
+  enum Stage {
+    STAGE_PAUSED,
+    STAGE_FIRST_FRAME,
+    STAGE_SECOND_FRAME,
+    STAGE_DEFAULT_FRAME
+  };
+  enum TrackingQuality {
+    TRACKING_INSUFFICIENT,
+    TRACKING_BAD,
+    TRACKING_GOOD
+  };
+  enum UpdateResult {
+    RESULT_NO_KEYFRAME,
+    RESULT_IS_KEYFRAME,
+    RESULT_FAILURE
+  };
 
   FrameHandlerBase();
 
@@ -90,13 +104,13 @@ protected:
   DepthFilter* depth_filter_;                   //!< Depth estimation algorithm runs in a parallel thread and is used to initialize new 3D points.
 
   /// Before a frame is processed, this function is called.
-  bool startFrameProcessingCommon();
+  bool startFrameProcessingCommon(const double timestamp);
 
   /// When a frame is finished processing, this function is called.
   int finishFrameProcessingCommon(
-      size_t update_id,
-      UpdateResult dropout,
-      size_t num_observations);
+      const size_t update_id,
+      const UpdateResult dropout,
+      const size_t num_observations);
 
   /// Reset the map and frame handler to start from scratch.
   void resetCommon();
diff --git a/svo/include/svo/frame_handler_mono.h b/svo/include/svo/frame_handler_mono.h
index 3dc80ab..f15afaf 100644
--- a/svo/include/svo/frame_handler_mono.h
+++ b/svo/include/svo/frame_handler_mono.h
@@ -34,6 +34,9 @@ public:
   /// Provide an image.
   void addImage(const cv::Mat& img, double timestamp);
 
+  /// Set the first frame (used for synthetic datasets in benchmark node)
+  void setFirstFrame(const FramePtr& first_frame);
+
   /// Get the last frame that has been processed.
   FramePtr lastFrame() { return last_frame_; }
 
diff --git a/svo/src/frame_handler_base.cpp b/svo/src/frame_handler_base.cpp
index 3d530f7..3a8bad7 100644
--- a/svo/src/frame_handler_base.cpp
+++ b/svo/src/frame_handler_base.cpp
@@ -28,7 +28,8 @@
 #include <svo/reprojection.h>
 #include <svo/depth_filter.h>
  
-namespace svo {
+namespace svo
+{
 
 // definition of global and static variables which were declared in the header
 #ifdef SVO_TRACE
@@ -37,13 +38,13 @@ vk::PerformanceMonitor* g_permon = NULL;
 
 FrameHandlerBase::
 FrameHandlerBase() :
-  stage_(PAUSED),
+  stage_(STAGE_PAUSED),
   set_reset_(false),
   set_start_(false),
   acc_frame_timings_(10),
   acc_num_obs_(10),
   num_obs_last_(0),
-  tracking_quality_(BAD),
+  tracking_quality_(TRACKING_BAD),
   depth_filter_(NULL)
 {
 #ifdef SVO_TRACE
@@ -52,10 +53,14 @@ FrameHandlerBase() :
   g_permon->addTimer("pyramid_creation");
   g_permon->addTimer("sparse_img_align");
   g_permon->addTimer("reproject");
+  g_permon->addTimer("reproject_kfs");
+  g_permon->addTimer("reproject_candidates");
+  g_permon->addTimer("feature_align");
   g_permon->addTimer("pose_optimizer");
   g_permon->addTimer("point_optimizer");
   g_permon->addTimer("local_ba");
   g_permon->addTimer("tot_time");
+  g_permon->addLog("timestamp");
   g_permon->addLog("img_align_n_tracked");
   g_permon->addLog("repr_n_mps");
   g_permon->addLog("repr_n_new_references");
@@ -90,18 +95,18 @@ FrameHandlerBase::
 }
 
 bool FrameHandlerBase::
-startFrameProcessingCommon()
+startFrameProcessingCommon(const double timestamp)
 {
   if(set_start_)
   {
     resetAll();
-    stage_ = FIRST_FRAME;
-    set_start_ = false;
+    stage_ = STAGE_FIRST_FRAME;
   }
 
-  if(stage_ == PAUSED)
+  if(stage_ == STAGE_PAUSED)
     return false;
 
+  SVO_LOG(timestamp);
   SVO_DEBUG_STREAM("New Frame");
   SVO_START_TIMER("tot_time");
   timer_.start();
@@ -112,14 +117,17 @@ startFrameProcessingCommon()
 }
 
 int FrameHandlerBase::
-finishFrameProcessingCommon(size_t update_id, UpdateResult dropout, size_t num_observations)
+finishFrameProcessingCommon(
+    const size_t update_id,
+    const UpdateResult dropout,
+    const size_t num_observations)
 {
   SVO_DEBUG_STREAM("Frame: "<<update_id<<"\t fps-avg = "<< 1.0/acc_frame_timings_.getMean()<<"\t nObs = "<<acc_num_obs_.getMean());
   SVO_LOG(dropout);
 
   // save processing time to calculate fps
   acc_frame_timings_.push_back(timer_.stop());
-  if(stage_ == DEFAULT_FRAME)
+  if(stage_ == STAGE_DEFAULT_FRAME)
     acc_num_obs_.push_back(num_observations);
   num_obs_last_ = num_observations;
   SVO_STOP_TIMER("tot_time");
@@ -134,7 +142,7 @@ finishFrameProcessingCommon(size_t update_id, UpdateResult dropout, size_t num_o
   #endif
 
   // reset if failure or requested
-  if(dropout == FAILURE || set_reset_)
+  if(dropout == RESULT_FAILURE || set_reset_)
     resetAll();
   return 0;
 }
@@ -143,9 +151,10 @@ void FrameHandlerBase::
 resetCommon()
 {
   map_.reset();
-  stage_ = PAUSED;
+  stage_ = STAGE_PAUSED;
   set_reset_ = false;
-  tracking_quality_ = BAD;
+  set_start_ = false;
+  tracking_quality_ = TRACKING_BAD;
   depth_filter_->reset();
   num_obs_last_ = 0;
   SVO_INFO_STREAM("RESET");
@@ -154,22 +163,22 @@ resetCommon()
 void FrameHandlerBase::
 setTrackingQuality(const size_t num_observations)
 {
-  tracking_quality_ = GOOD;
+  tracking_quality_ = TRACKING_GOOD;
   if(num_observations < Config::qualityMinFts())
   {
     ROS_WARN_STREAM_THROTTLE(0.5, "Tracking less than "<< Config::qualityMinFts() <<" features!");
-    tracking_quality_ = BAD;
+    tracking_quality_ = TRACKING_BAD;
   }
   const int feature_drop = static_cast<int>(num_obs_last_) - num_observations;
   if(feature_drop > Config::qualityMaxFtsDrop())
   {
     ROS_WARN_STREAM("Lost "<< feature_drop <<" features!");
-    tracking_quality_ = BAD;
+    tracking_quality_ = TRACKING_BAD;
   }
   if(num_observations < 20)
   {
     ROS_ERROR_STREAM("Tracking less than 20 features!");
-    tracking_quality_ = INSUFFICIENT;
+    tracking_quality_ = TRACKING_INSUFFICIENT;
   }
 }
 
diff --git a/svo/src/frame_handler_mono.cpp b/svo/src/frame_handler_mono.cpp
index 95a9787..b0846c0 100644
--- a/svo/src/frame_handler_mono.cpp
+++ b/svo/src/frame_handler_mono.cpp
@@ -43,9 +43,9 @@ FrameHandlerMono::
 {}
 
 void FrameHandlerMono::
-addImage(const cv::Mat& img, double timestamp)
+addImage(const cv::Mat& img, const double timestamp)
 {
-  if(!startFrameProcessingCommon())
+  if(!startFrameProcessingCommon(timestamp))
     return;
 
   // some cleanup from last iteration, can't do before because of visualization
@@ -58,12 +58,12 @@ addImage(const cv::Mat& img, double timestamp)
   SVO_STOP_TIMER("pyramid_creation");
 
   // process frame
-  UpdateResult res = FAILURE;
-  if(stage_ == DEFAULT_FRAME)
+  UpdateResult res = RESULT_FAILURE;
+  if(stage_ == STAGE_DEFAULT_FRAME)
     res = processFrame();
-  else if(stage_ == SECOND_FRAME)
+  else if(stage_ == STAGE_SECOND_FRAME)
     res = processSecondFrame();
-  else if(stage_ == FIRST_FRAME)
+  else if(stage_ == STAGE_FIRST_FRAME)
     res = processFirstFrame();
 
   // set last frame
@@ -74,17 +74,27 @@ addImage(const cv::Mat& img, double timestamp)
   finishFrameProcessingCommon(last_frame_->id_, res, last_frame_->nObs());
 }
 
+void FrameHandlerMono::
+setFirstFrame(const FramePtr& first_frame)
+{
+  resetAll();
+  last_frame_ = first_frame;
+  last_frame_->setKeyframe();
+  map_.addKeyframe(last_frame_);
+  stage_ = STAGE_DEFAULT_FRAME;
+}
+
 FrameHandlerMono::UpdateResult FrameHandlerMono::
 processFirstFrame()
 {
   new_frame_->T_f_w_ = T_f_w_init_;
   if(klt_homography_init_.addFirstFrame(new_frame_) == initialization::FAILURE)
-    return NO_KEYFRAME;
+    return RESULT_NO_KEYFRAME;
   new_frame_->setKeyframe();
   map_.addKeyframe(new_frame_);
-  stage_ = SECOND_FRAME;
+  stage_ = STAGE_SECOND_FRAME;
   SVO_INFO_STREAM("Init: Selected first frame.");
-  return IS_KEYFRAME;
+  return RESULT_IS_KEYFRAME;
 }
 
 FrameHandlerMono::UpdateResult FrameHandlerMono::
@@ -92,9 +102,9 @@ processSecondFrame()
 {
   initialization::InitResult res = klt_homography_init_.addSecondFrame(new_frame_);
   if(res == initialization::FAILURE)
-    return FAILURE;
+    return RESULT_FAILURE;
   else if(res == initialization::NO_KEYFRAME)
-    return NO_KEYFRAME;
+    return RESULT_NO_KEYFRAME;
 
   // two-frame bundle adjustment
 #ifdef USE_BUNDLE_ADJUSTMENT
@@ -108,10 +118,10 @@ processSecondFrame()
   // add frame to map
   new_frame_->setKeyframe();
   map_.addKeyframe(new_frame_);
-  stage_ = DEFAULT_FRAME;
+  stage_ = STAGE_DEFAULT_FRAME;
   klt_homography_init_.reset();
   SVO_INFO_STREAM("Init: Selected second frame, triangulated initial map.");
-  return IS_KEYFRAME;
+  return RESULT_IS_KEYFRAME;
 }
 
 FrameHandlerMono::UpdateResult FrameHandlerMono::
@@ -140,7 +150,7 @@ processFrame()
   SVO_DEBUG_STREAM("Reprojection:\t nPoints = "<<repr_n_mps<<"\t \t nMatches = "<<repr_n_new_references);
   if(repr_n_new_references == 0) {
     SVO_ERROR_STREAM("Not enough matched features.");
-    return FAILURE;
+    return RESULT_FAILURE;
   }
 
   // pose optimization
@@ -155,7 +165,7 @@ processFrame()
   SVO_DEBUG_STREAM("PoseOptimizer:\t ErrInit = "<<sfba_error_init<<"px\t thresh = "<<sfba_thresh);
   SVO_DEBUG_STREAM("PoseOptimizer:\t ErrFin. = "<<sfba_error_final<<"px\t nObsFin. = "<<sfba_n_edges_final);
   if(sfba_n_edges_final < 10)
-    return FAILURE;
+    return RESULT_FAILURE;
 
   // structure optimization
   SVO_START_TIMER("point_optimizer");
@@ -164,14 +174,14 @@ processFrame()
 
   // select keyframe
   setTrackingQuality(sfba_n_edges_final);
-  if(tracking_quality_ == INSUFFICIENT)
-    return FAILURE;
+  if(tracking_quality_ == TRACKING_INSUFFICIENT)
+    return RESULT_FAILURE;
   double depth_mean, depth_min;
   frame_utils::getSceneDepth(*new_frame_, depth_mean, depth_min);
-  if(!needNewKf(depth_mean) || tracking_quality_ == BAD)
+  if(!needNewKf(depth_mean) || tracking_quality_ == TRACKING_BAD)
   {
     depth_filter_->addFrame(new_frame_);
-    return NO_KEYFRAME;
+    return RESULT_NO_KEYFRAME;
   }
 
   // new keyframe selected
@@ -214,7 +224,7 @@ processFrame()
   new_frame_->setKeyframe();
   map_.addKeyframe(new_frame_);
 
-  return IS_KEYFRAME;
+  return RESULT_IS_KEYFRAME;
 }
 
 void FrameHandlerMono::
diff --git a/svo/src/reprojection.cpp b/svo/src/reprojection.cpp
index 26de82c..3e27c8b 100644
--- a/svo/src/reprojection.cpp
+++ b/svo/src/reprojection.cpp
@@ -70,6 +70,7 @@ void reprojectMap(
   resetGrid(grid);
 
   // Identify those Keyframes which share a common field of view.
+  SVO_START_TIMER("reproject_kfs");
   list< pair<FramePtr,double> > close_kfs;
   map.getCloseKeyframes(frame, close_kfs);
 
@@ -103,8 +104,10 @@ void reprojectMap(
         overlap_kfs.back().second++;
     }
   }
+  SVO_STOP_TIMER("reproject_kfs");
 
   // Now project all point candidates
+  SVO_START_TIMER("reproject_candidates");
   {
     boost::unique_lock<boost::mutex> lock(candidate_points.mut_);
     auto it=candidate_points.candidates_.begin();
@@ -122,10 +125,12 @@ void reprojectMap(
       }
       ++it;
     }
-  }
+  } // unlock the mutex when out of scope
+  SVO_STOP_TIMER("reproject_candidates");
 
   // Now we go through each grid cell and select one point to match.
   // At the end, we should have at maximum one reprojected point per cell.
+  SVO_START_TIMER("feature_align");
   n_matches=0; n_trials=0;
   for(size_t i=0; i<grid.cells.size(); ++i)
   {
@@ -136,6 +141,7 @@ void reprojectMap(
     if(n_matches > (size_t) Config::maxFts())
       break;
   }
+  SVO_STOP_TIMER("feature_align");
 }
 
 bool pointQualityComparator(Candidate& lhs, Candidate& rhs)
diff --git a/svo/test/benchmark.csv b/svo/test/benchmark.csv
index e6f88a3..a3831e4 100644
--- a/svo/test/benchmark.csv
+++ b/svo/test/benchmark.csv
@@ -1,307 +1,307 @@
 local_ba,point_optimizer,pose_optimizer,pyramid_creation,reproject,sparse_img_align,tot_time,dropout,img_align_n_tracked,loba_err_fin,loba_err_init,loba_n_erredges_fin,loba_n_erredges_init,n_candidates,repr_n_mps,repr_n_new_references,sfba_error_final,sfba_error_init,sfba_n_edges_final,sfba_thresh
-0.000000000000000,0.000000000000000,0.000000000000000,0.000203000000000,0.000000000000000,0.000000000000000,0.006850000000000,1.000000000000000,0.000000000000000,0.000000000000000,0.000000000000000,0.000000000000000,0.000000000000000,0.000000000000000,0.000000000000000,0.000000000000000,0.000000000000000,0.000000000000000,0.000000000000000,0.000000000000000
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\ No newline at end of file
diff --git a/svo/test/test_depth_filter.cpp b/svo/test/test_depth_filter.cpp
index 09d0034..545d9bc 100644
--- a/svo/test/test_depth_filter.cpp
+++ b/svo/test/test_depth_filter.cpp
@@ -118,7 +118,7 @@ void DepthFilterTest::testReconstruction(
     if(i == 0)
     {
       // create reference frame and load ground truth depthmap
-      frame_ref_.reset(new svo::Frame(cam_, img));
+      frame_ref_.reset(new svo::Frame(cam_, img, 0.0));
       frame_ref_->T_f_w_ = T_w_f.inverse();
       depth_filter_->addKeyframe(frame_ref_, 3, 1);
       vk::blender_utils::loadBlenderDepthmap(dataset_dir+"/depth/" + (*it).image_name_ + "_0.depth", *cam_, depth_ref_);
@@ -126,7 +126,7 @@ void DepthFilterTest::testReconstruction(
     }
 
     n_converged_seeds_=0;
-    frame_cur_ = svo::FramePtr(new svo::Frame(cam_, img));
+    frame_cur_ = svo::FramePtr(new svo::Frame(cam_, img, 0.0));
     frame_cur_->T_f_w_ = T_w_f.inverse();
     depth_filter_->addFrame(frame_cur_);
     n_converged_per_iteration.push_back(n_converged_seeds_);
diff --git a/svo/test/test_sparse_img_align.cpp b/svo/test/test_sparse_img_align.cpp
index 1c4437c..51950a8 100644
--- a/svo/test/test_sparse_img_align.cpp
+++ b/svo/test/test_sparse_img_align.cpp
@@ -86,7 +86,7 @@ void SparseImgAlignTest::testSequence(
     if(i==0)
     {
       // set reference frame
-      frame_ref_.reset(new svo::Frame(cam_, img));
+      frame_ref_.reset(new svo::Frame(cam_, img, 0.0));
       frame_ref_->T_f_w_ = T_gt_w;
 
       // load ground-truth depth
@@ -115,7 +115,7 @@ void SparseImgAlignTest::testSequence(
       continue;
     }
 
-    frame_cur_.reset(new svo::Frame(cam_, img));
+    frame_cur_.reset(new svo::Frame(cam_, img, 0.0));
     //frame_cur_->T_f_w_ = frame_ref_->T_f_w_; // start at reference frame
     frame_cur_->T_f_w_ = T_prev_w; // start at last frame
 
diff --git a/svo_analysis/.gitignore b/svo_analysis/.gitignore
new file mode 100644
index 0000000..7e99e36
--- /dev/null
+++ b/svo_analysis/.gitignore
@@ -0,0 +1 @@
+*.pyc
\ No newline at end of file
diff --git a/svo_analysis/CMakeLists.txt b/svo_analysis/CMakeLists.txt
new file mode 100644
index 0000000..53ea7dc
--- /dev/null
+++ b/svo_analysis/CMakeLists.txt
@@ -0,0 +1,8 @@
+cmake_minimum_required(VERSION 2.8.3)
+project(svo_analysis)
+
+find_package(catkin REQUIRED COMPONENTS rospy)
+include_directories(${catkin_INCLUDE_DIRS})
+
+catkin_python_setup()
+#catkin_package(CATKIN_DEPENDS sensor_msgs)
\ No newline at end of file
diff --git a/svo_analysis/benchmark.py b/svo_analysis/benchmark.py
deleted file mode 100755
index da32e70..0000000
--- a/svo_analysis/benchmark.py
+++ /dev/null
@@ -1,67 +0,0 @@
-#!/usr/bin/python
-"""
-Created on Sat Aug 10 18:21:47 2013
-
-@author: Christian Forster
-"""
-
-import os
-import yaml
-import time
-
-class RosNode:
-  def __init__(self, package, executable):
-    self.package = package
-    self.executable = executable
-    
-  def run(self, parameter_dictionary):
-    parameter_string = ''
-    for key in parameter_dictionary.keys():
-      parameter_string = parameter_string + ' _' + key + ':=' + str(parameter_dictionary[key])
-    os.system('rosrun ' + self.package + ' ' + self.executable + ' ' + parameter_string)
-
-if __name__=="__main__":
-  
-  # config:  
-  home_dir = os.getenv("HOME")
-  dataset_dir = home_dir+'/Datasets/SlamBenchmark/'
-  algo_dir = home_dir+'/workspace/rpg_slam/svo_ros'
-  trace_dir = home_dir+'/workspace/rpg_slam/svo_analysis/results'
-  platform = 'i7'
-  
-  # load parameters
-  experiment_name = 'asl2_fast'
-  experiment_file = 'experiments/'+experiment_name+'.yaml'
-  experiment_file_stream = open(experiment_file,'r')
-  experiment_dict = yaml.load(experiment_file_stream)
-  
-  dataset_param_file = dataset_dir+experiment_dict['dataset']+'/dataset_params.yaml'
-  dataset_param_file_stream = open(dataset_param_file,'r')
-  dataset_param_dict = yaml.load(dataset_param_file_stream)
-  
-  algo_param_file = algo_dir+'/param/'+experiment_dict['param_settings']+'.param'
-  algo_param_file_stream = open(algo_param_file,'r')
-  algo_param_dict = yaml.load(algo_param_file_stream)
-  
-  params = dict(dataset_param_dict.items() + algo_param_dict.items() + experiment_dict.items())
-  localtime = time.localtime()
-  params['trace_name'] = time.strftime("%Y%m%d_%H%M", localtime)+'_nslam_'+platform+'_'+experiment_name
-  params['trace_dir'] = trace_dir+'/'+params['trace_name']
-  params['run_id'] = 0
-  
-  # create folder for experiment results
-  if not os.path.exists(params['trace_dir']):
-    os.makedirs(params['trace_dir'])
-    
-  # dump experiment params to file:
-  params_dump_file = params['trace_dir']+'/params.yaml'
-  with open(params_dump_file,'w') as outfile:
-    outfile.write( yaml.dump(params, default_flow_style=False) )
-    
-  # start ros node
-  print 'starting ros node'
-  node = RosNode('svo_ros','benchmark')
-  node.run(params)
-  print 'ros node finished processing'
-  
-  
diff --git a/svo_analysis/evaluate.py b/svo_analysis/evaluate.py
deleted file mode 100755
index 05af1ee..0000000
--- a/svo_analysis/evaluate.py
+++ /dev/null
@@ -1,47 +0,0 @@
-#!/usr/bin/python
-"""
-Created on Sat Aug 10 18:21:47 2013
-
-@author: Christian Forster
-"""
-
-import argparse
-import csv
-import numpy as np
-from matplotlib import rc
-import scripts.analyse_logs as analyse_logs
-import scripts.analyse_timing as analyse_timing
-
-if __name__=="__main__":
-  
-  # parse command line 
-  parser = argparse.ArgumentParser()
-  parser.add_argument('--trace_name', help='svo tracefile with timings and logs', default='20131121_0930_px7')
-  args = parser.parse_args()
-
-  # config
-  results_dir = 'results/'+args.trace_name
-  trace_file = results_dir+'/'+args.trace_name+'.csv'
-
-  # tracefile
-  data = csv.reader(open(trace_file))
-  fields = data.next()
-  D = dict()
-  for field in fields:
-    D[field] = list()
-
-  # fill dictionary with column values
-  for row in data:
-    for (field, value) in zip(fields, row):
-      D[field].append(float(value))
-
-  # change dictionary values from list to numpy array for easier manipulation
-  for field, value in D.items():
-    D[field] = np.array(D[field])
-
-  # tell matplotlib to use latex font
-  rc('font',**{'family':'serif','serif':['Cardo']})
-  rc('text', usetex=True)
-
-  analyse_logs.analyse_logs(D, results_dir+'/'+args.trace_name)
-  analyse_timing.analyse_timing(D, results_dir+'/'+args.trace_name)
\ No newline at end of file
diff --git a/svo_analysis/experiments/flying_room_1_fast.yaml b/svo_analysis/experiments/flying_room_1_fast.yaml
new file mode 100644
index 0000000..018e1fe
--- /dev/null
+++ b/svo_analysis/experiments/flying_room_1_fast.yaml
@@ -0,0 +1,2 @@
+dataset: flying_room_1_rig_1
+param_settings: vo_fast
\ No newline at end of file
diff --git a/svo_analysis/package.xml b/svo_analysis/package.xml
new file mode 100644
index 0000000..dcc5a62
--- /dev/null
+++ b/svo_analysis/package.xml
@@ -0,0 +1,19 @@
+<package>
+
+  <name>svo_analysis</name>
+  <version>0.0.1</version>
+  <description>
+    Python scripts to analyse performance of SVO.
+  </description>
+  <maintainer email="forster@ifi.uzh.ch">Christian Forster</maintainer>
+  <author>Christian Forster</author>
+  <license>BSD</license>
+  <buildtool_depend>catkin</buildtool_depend>
+
+  <build_depend>rospy</build_depend>
+
+  <run_depend>python-rospkg</run_depend>
+  <run_depend>python-yaml</run_depend>
+  <run_depend>rospy</run_depend>
+
+</package>
\ No newline at end of file
diff --git a/svo_analysis/scripts/analyse_logs.py b/svo_analysis/scripts/analyse_logs.py
deleted file mode 100755
index be14feb..0000000
--- a/svo_analysis/scripts/analyse_logs.py
+++ /dev/null
@@ -1,132 +0,0 @@
-#!/usr/bin/python
-
-import argparse
-import csv
-import numpy as np
-import matplotlib.pyplot as plt
-from matplotlib import rc
-
-def analyse_logs(D, trace_name):
-
-  # identify measurements which result from normal frames and which from keyframes
-  is_kf = np.argwhere( (D['dropout'] == 1) & (D['repr_n_mps'] >= 0))
-  is_frame = np.argwhere(D['repr_n_mps'] >= 0)
-  is_nokf = np.argwhere( (D['dropout'] == 0) & (D['repr_n_mps'] >= 0))
-  n_frames = len(is_frame)
-  n_kfs = len(is_kf)
-  n_nokf = len(is_nokf)
-  fps = 25.0
-  
-  # plot number of reprojected points
-  mean_n_reproj_points = np.mean(D['repr_n_mps'][is_frame]);
-  mean_n_reproj_matches = np.mean(D['repr_n_new_references'][is_frame]);
-
-  plt.figure()
-  plt.plot(np.arange(n_frames), D['repr_n_mps'][is_frame], 'r-', label='reprojected points, avg = %(i).2f'%{'i':mean_n_reproj_points})
-  plt.plot(np.arange(n_frames), D['repr_n_new_references'][is_frame], 'b-', label='reprojected matches, avg = %(i).2f'%{'i':mean_n_reproj_matches})
-  plt.legend()
-  plt.tight_layout()
-  plt.title('Reprojected points asdf')
-  plt.savefig(trace_name+'_reprojection.pdf', bbox_inches="tight")
-
-  # plot number of triangulations
-#  plt.figure(3)
-#  plt.plot(np.arange(n_kfs), D['triang_n_points'][is_kf], 'g-', label='triangulated points, avg %0.2f'%np.mean(D['triang_n_points'][is_kf]))
-#  plt.plot(np.arange(n_kfs), D['triang_n_refs'][is_kf], 'b-', label='new references, avg %0.2f'%np.mean(D['triang_n_refs'][is_kf]))
-#  plt.plot(np.arange(n_kfs), D['triang_n_fails'][is_kf], 'r-', label='failures, avg %0.2f'%np.mean(D['triang_n_fails'][is_kf]))
-#  plt.plot(np.arange(n_kfs), D['triang_n_add_refs'][is_kf], 'm-', label='additional refs, avg %0.2f'%np.mean(D['triang_n_add_refs'][is_kf]))
-#  plt.legend()
-#  plt.title('Triangulation')
-#  plt.savefig(trace_name+'_img_triangulation.png', bbox_inches="tight")
-
-  # plot number of klt tracked and matched points
-  fig = plt.figure(figsize=(8,2))
-  ax = fig.add_subplot(111, xlabel='time [s]', ylabel='no. features')
-  ax.plot(np.arange(n_frames)/fps, D['repr_n_new_references'][is_frame], 'b-', label='number of successful matches')
-  fig.tight_layout()
-  fig.savefig(trace_name+'number_of_features.pdf', bbox_inches="tight")
-
-  # plot median error before and after pose-optimzation and bundle adjustment
-  init_error_avg = np.mean(D['sfba_error_init'][is_frame])
-  opt1_avg = np.mean(D['sfba_error_final'][is_frame])
-
-  fig = plt.figure(figsize=(8,2))
-  ax = fig.add_subplot(111, xlabel='time [s]', ylabel='error [px]')
-  t_max = n_frames
-  ax.plot(np.arange(n_frames)[0:t_max]/fps, D['sfba_error_init'][is_frame][0:t_max], 'r-', label='Initial error')
-  ax.plot(np.arange(n_frames)[0:t_max]/fps, D['sfba_error_final'][is_frame][0:t_max], 'b-', label='Final error')
-  ax.legend(ncol=2)  
-  ax.set_xlim([0, t_max/fps+4])
-  #ax.set_ylim([0, 0.8])
-  fig.tight_layout()
-  fig.savefig(trace_name+'reprojection_error.pdf', bbox_inches="tight")
-  print 'average reprojection error improvement: ' + str(init_error_avg - opt1_avg)
-
-  # plot number of references
-  plt.figure()
-  plt.fill(np.arange(n_frames+1), np.append(D['repr_n_new_references'][is_frame]+D['klt_n_matches'][is_frame],0), 'g-', label='Reprojection')
-  plt.fill(np.arange(n_frames+1), np.append(D['klt_n_tracked'][is_frame],0), 'b-', label='KLT Matches')
-  plt.plot(np.arange(n_frames), D['sfba_n_edges_final'][is_frame], 'r-', label='References after Optimization', linewidth=2.0)
-  plt.legend()
-  plt.tight_layout()
-  plt.title('Pose Optimization')
-  plt.savefig(trace_name+'_img_n_references.pdf', bbox_inches="tight")
-  
-  # plot number of seeds
-  plt.figure()
-  has_seed = np.argwhere(D['seeds_n_tot'] >= 0)
-  plt.fill(np.arange(len(has_seed)+1), np.append(D['seeds_n_tot'][has_seed],0), label='nSeeds', edgecolor='none') 
-  plt.fill(np.arange(len(has_seed)+1), np.append(D['seeds_n_updates'][has_seed]+D['seeds_n_failed_matches'][has_seed],0), label='nFailedMatches', edgecolor='none')
-  plt.fill(np.arange(len(has_seed)+1), np.append(D['seeds_n_updates'][has_seed],0), label='nUpdates', edgecolor='none') 
-  plt.legend()
-  plt.tight_layout()
-  plt.title('Seeds')
-  plt.savefig(trace_name+'_seeds.pdf', bbox_inches="tight")
-
-
-  plt.figure()
-  plt.plot(D['n_candidates'])
-  plt.title('Candidates')
-  plt.show()
-  
-#  plt.figure()
-#  plt.plot())
-
-if __name__=="__main__":
-
-  dataset='20131003_1937_nslam_i7_asl2_fast'
-  n_align_frames = 10
-  save = True
-  results_dir = '../results/'+dataset
-  logfile = results_dir+'/'+dataset+'_0.csv'
-  
-  # parse command line 
-  parser = argparse.ArgumentParser(description='''
-  This script computes the absolute trajectory error from the ground truth trajectory and the estimated trajectory.
-  ''')
-  parser.add_argument('--trace_file', help='nanoslam tracefile with timings and logs', default=logfile)
-  parser.add_argument('--trace_name', help='name of the tracefile, used for saving figures etc.', default=results_dir+'/')
-  args = parser.parse_args()
-
-  # tracefile
-  data = csv.reader(open(args.trace_file))
-  fields = data.next()
-  D = dict()
-  for field in fields:
-    D[field] = list()
-
-  # fill dictionary with column values
-  for row in data:
-    for (field, value) in zip(fields, row):
-      D[field].append(float(value))
-
-  # change dictionary values from list to numpy array for easier manipulation
-  for field, value in D.items():
-    D[field] = np.array(D[field])
-
-  # tell matplotlib to use latex font
-  rc('font',**{'family':'serif','serif':['Cardo']})
-  rc('text', usetex=True)
-
-  analyse_logs(D, args.trace_name)
-
diff --git a/svo_analysis/scripts/analyse_timing.py b/svo_analysis/scripts/analyse_timing.py
deleted file mode 100755
index b0d2199..0000000
--- a/svo_analysis/scripts/analyse_timing.py
+++ /dev/null
@@ -1,97 +0,0 @@
-#!/usr/bin/python
-
-import argparse
-import csv
-import numpy as np
-import matplotlib.pyplot as plt
-from matplotlib import rc
-
-def analyse_timing(D, trace_name):
-
-  # identify measurements which result from normal frames and which from keyframes
-  is_frame = np.argwhere(D['repr_n_mps'] >= 0)
-  n_frames = len(is_frame)
-
-  # plot total time for frame processing
-  avg_time = np.mean(D['t_tot_time'][is_frame])*1000;
-
-  fig = plt.figure(figsize=(8, 3))
-  ax = fig.add_subplot(111, ylabel='processing time [ms]', xlabel='time [s]')
-  ax.plot(np.arange(n_frames), D['t_tot_time'][is_frame]*1000, 'g-', label='total time [ms]')
-  ax.plot(np.arange(n_frames), np.ones(n_frames)*avg_time, 'b--', label=str('%(time).1fms mean time' % {'time': avg_time}))
-  ax.legend()
-  fig.tight_layout()
-  fig.savefig(trace_name+'_img_tot_processing_time.pdf', bbox_inches="tight")
-  
-  fig = plt.figure(figsize=(6,2))
-  ax = fig.add_subplot(111, xlabel='Processing time [ms]')
-  ax.boxplot([ 
-                D['t_tot_time'][is_frame]*1000,
-#                D['t_local_ba'][is_kf]*1000,
-                D['t_pose_optimizer'][is_frame]*1000 + D['t_point_optimizer'][is_frame]*1000,
-                D['t_reproject'][is_frame]*1000,
-                D['t_sparse_img_align'][is_frame]*1000,
-                D['t_pyramid_creation'][is_frame]*1000
-              ], 0,'', vert=0) # 
-               
-  boxplot_labels = [
-                    r'\textbf{Total Motion Estimation: %2.2fms}' % np.median(D['t_tot_time'][is_frame]*1000),
-#                    'Local BA (KF only): %.2fms ' % np.median(D['t_local_ba'][is_kf]*1000),
-                    'Refinement: %2.2fms' % np.median(D['t_pose_optimizer'][is_frame]*1000 + D['t_point_optimizer'][is_frame]*1000),
-                    'Feature Alignment: %2.2fms' % np.median(D['t_reproject'][is_frame]*1000),
-                    'Sparse Image Alignment: %2.2fms' % np.median(D['t_sparse_img_align'][is_frame]*1000),
-                    'Pyramid Creation: %2.2fms' % np.median(D['t_pyramid_creation'][is_frame]*1000) ]
-                   
-  ax.set_yticks(np.arange(len(boxplot_labels))+1)
-  ax.set_yticklabels(boxplot_labels)
-  fig.tight_layout()
-  fig.savefig(trace_name+'_timing_boxplot.pdf', bbox_inches="tight")
-  
-  fig = plt.figure(figsize=(8, 5))
-  ax = fig.add_subplot(111, ylabel='time [ms]')
-  ax.plot(D['t_reproject']*1000, 'r', label='reproject')
-  ax.plot(D['t_reproject_find_pts']*1000, 'g', label='reproject find pts') # ok!
-  ax.plot(D['t_reproject_best']*1000, 'm', label='reproject best')
-  ax.plot(D['t_reproject_candidates']*1000, 'b', label='reproject candidates')
-  ax.legend()
-  fig.tight_layout()
-  fig.savefig(trace_name+'_reprojection.pdf', bbox_inches="tight")
-
-
-if __name__=="__main__":
-
-  # config
-  dataset='20131003_1937_nslam_i7_asl2_fast'
-  results_dir = '../results/'+dataset
-  tracefile = results_dir + '/'+dataset+'_0.csv'
-  
-  # parse command line
-  parser = argparse.ArgumentParser(description='''
-  This script computes the absolute trajectory error from the ground truth trajectory and the estimated trajectory.
-  ''')
-  parser.add_argument('--trace_file', help='nanoslam tracefile with timings and logs', default=tracefile)
-  parser.add_argument('--trace_name', help='name of the tracefile, used for saving figures etc.', default=results_dir)
-  args = parser.parse_args()
-  results_dir = args.trace_name
-  
-  # tracefile
-  data = csv.reader(open(args.trace_file))
-  fields = data.next()
-  D = dict()
-  for field in fields:
-    D[field] = list()
-
-  # fill dictionary with column values
-  for row in data:
-    for (field, value) in zip(fields, row):
-      D[field].append(float(value))
-
-  # change dictionary values from list to numpy array for easier manipulation
-  for field, value in D.items():
-    D[field] = np.array(D[field])
-
-  # tell matplotlib to use latex font
-  rc('font',**{'family':'serif','serif':['Cardo']})
-  rc('text', usetex=True)
-
-  
\ No newline at end of file
diff --git a/svo_analysis/scripts/associate.py b/svo_analysis/scripts/associate.py
deleted file mode 100644
index 8a7438c..0000000
--- a/svo_analysis/scripts/associate.py
+++ /dev/null
@@ -1,67 +0,0 @@
-#!/usr/bin/python
-#
-# Requirements:
-# sudo apt-get install python-argparse
-
-import argparse
-import sys
-import os
-import numpy
-
-
-def read_file_list(filename):
-    file = open(filename)
-    data = file.read()
-    lines = data.replace(","," ").replace("\t"," ").split("\n")
-    list = [[v.strip() for v in line.split(" ") if v.strip()!=""] for line in lines if len(line)>0 and line[0]!="#"]
-    list = [(float(l[0]),l[1:]) for l in list if len(l)>1]
-    return dict(list)
-
-def associate(first_list, second_list,offset,max_difference):
-    # first_list  = groundtruth
-    # second_list = estimate
-    first_keys = first_list.keys()
-    second_keys = second_list.keys()
-    potential_matches = [(abs(a - (b + offset)), a, b)
-                         for a in first_keys
-                         for b in second_keys
-                         if abs(a - (b + offset)) < max_difference]
-
-    potential_matches.sort()
-    matches = []
-    for diff, a, b in potential_matches:
-        if a in first_keys and b in second_keys:
-            first_keys.remove(a)
-            second_keys.remove(b)
-            matches.append((a, b))
-
-    matches.sort()
-
-    return matches
-
-if __name__ == '__main__':
-
-    # parse command line
-    parser = argparse.ArgumentParser(description='''
-    This script takes two data files with timestamps and associates them
-    ''')
-    parser.add_argument('first_file', help='first text file (format: timestamp data)')
-    parser.add_argument('second_file', help='second text file (format: timestamp data)')
-    parser.add_argument('--first_only', help='only output associated lines from first file', action='store_true')
-    parser.add_argument('--offset', help='time offset added to the timestamps of the second file (default: 0.0)',default=0.0)
-    parser.add_argument('--max_difference', help='maximally allowed time difference for matching entries (default: 0.02)',default=0.02)
-    args = parser.parse_args()
-
-    first_list = read_file_list(args.first_file)
-    second_list = read_file_list(args.second_file)
-
-    matches = associate(first_list, second_list,float(args.offset),float(args.max_difference))
-
-    if args.first_only:
-        for a,b in matches:
-            print("%f %s"%(a," ".join(first_list[a])))
-    else:
-        for a,b in matches:
-            print("%f %s %f %s"%(a," ".join(first_list[a]),b-float(args.offset)," ".join(second_list[b])))
-
-
diff --git a/svo_analysis/scripts/benchmark.py b/svo_analysis/scripts/benchmark.py
new file mode 100755
index 0000000..19688ce
--- /dev/null
+++ b/svo_analysis/scripts/benchmark.py
@@ -0,0 +1,78 @@
+#!/usr/bin/python
+"""
+Created on Sat Aug 10 18:21:47 2013
+
+@author: Christian Forster
+"""
+
+import os
+import yaml
+import rospkg
+import argparse
+import time
+import vikit_py.cpu_info as cpu_info
+import vikit_py.ros_node as ros_node
+import evaluate
+
+if __name__=="__main__":
+  
+  # parse command line
+  parser = argparse.ArgumentParser(description='''
+  Runs SVO with the dataset and parameters specified in the provided experiment file.
+  ''')
+  parser.add_argument('experiment_file', help='experiment file in svo_analysis/experiments folder')
+  parser.add_argument('--name', help='experiment name')
+  parser.add_argument('--evaluate', help='evaluate tracefile after running SVO', action='store_true')
+  parser.add_argument('--version', help='version of svo to evaluate', default='svo_ros')
+  parser.add_argument('--executable', help='the executable to be called', default='benchmark')
+  args = parser.parse_args()
+
+  # load experiment parameters
+  expParamsFile = os.path.join(rospkg.RosPack().get_path('svo_analysis'), 
+                               'experiments', args.experiment_file+'.yaml')
+  expParams = yaml.load(open(expParamsFile, 'r'))
+  expParams['time'] = time.strftime("%Y%m%d_%H%M", time.localtime())
+  expParams['platform'] = cpu_info.get_cpu_info()
+  expParams['experiment_name'] = args.name
+  if(expParams['experiment_name'] == None):
+    expParams['experiment_name'] = expParams['time']+'_'+args.version+'_' + args.experiment_file
+
+  # load dataset parameters
+  expParams['dataset_directory'] = os.path.join(rospkg.RosPack().get_path('Datasets'), expParams['dataset'])
+  if not os.path.exists(expParams['dataset_directory']):
+    raise Exception("Provided dataset folder does not exist.")
+  datasetParamsFile = os.path.join(expParams['dataset_directory'], 'dataset_params.yaml')
+  datasetParams = yaml.load(open(datasetParamsFile,'r'))
+  
+  # load algorithm parameters
+  algoParamsFile = os.path.join(rospkg.RosPack().get_path('svo_ros'), 
+                                'param', expParams['param_settings']+'.yaml')
+  algoParams = yaml.load(open(algoParamsFile,'r'))
+
+  # combine all parameters
+  params = dict(expParams.items() + datasetParams.items() + algoParams.items())
+  params['trace_name'] = 'trace'
+  params['trace_dir'] = os.path.join(rospkg.RosPack().get_path('svo_analysis'),
+                                     'results', params['experiment_name'])
+  
+  # create folder for tracing
+  if not os.path.exists(params['trace_dir']):
+    os.makedirs(params['trace_dir'])
+  
+  # dump experiment params to file:
+  params_dump_file = params['trace_dir']+'/params.yaml'
+  with open(params_dump_file,'w') as outfile:
+    outfile.write(yaml.dump(params, default_flow_style=False))
+    
+  # execute ros node
+  node = ros_node.RosNode(args.version, args.executable)
+  node.run(params)
+  
+  # TODO: check if it is a synthetic dataset 
+  evaluate.evaluate_dataset(params['trace_dir'])
+  
+  
+  
+  
+  
+  
diff --git a/svo_analysis/scripts/trajectory_comparison.py b/svo_analysis/scripts/comparison.py
similarity index 100%
rename from svo_analysis/scripts/trajectory_comparison.py
rename to svo_analysis/scripts/comparison.py
diff --git a/svo_analysis/scripts/evaluate.py b/svo_analysis/scripts/evaluate.py
new file mode 100755
index 0000000..eb9fe60
--- /dev/null
+++ b/svo_analysis/scripts/evaluate.py
@@ -0,0 +1,50 @@
+#!/usr/bin/python
+"""
+Created on Sat Aug 10 18:21:47 2013
+
+@author: Christian Forster
+"""
+
+import os
+import csv
+import numpy as np
+import rospkg
+import argparse
+import svo_analysis.analyse_logs as analyse_logs
+import svo_analysis.analyse_timing as analyse_timing
+import svo_analysis.analyse_trajectory as analyse_trajectory
+
+def evaluate_dataset(trace_dir):
+  
+  # read tracefile
+  data = csv.reader(open(os.path.join(trace_dir, 'trace.csv')))
+  fields = data.next()
+  D = dict()
+  for field in fields:
+    D[field] = list()
+
+  # fill dictionary with column values
+  for row in data:
+    for (field, value) in zip(fields, row):
+      D[field].append(float(value))
+
+  # change dictionary values from list to numpy array for easier manipulation
+  for field, value in D.items():
+    D[field] = np.array(D[field])
+
+  # generate plots
+  analyse_logs.analyse_logs(D, trace_dir)
+  analyse_timing.analyse_timing(D, trace_dir)
+  analyse_trajectory.analyse_trajectory(trace_dir)
+
+if __name__=="__main__":
+  parser = argparse.ArgumentParser(description='''
+  Evaluates tracefiles of SVO and generates the plots.
+  ''')
+  parser.add_argument('experiment_name', help='directory name of the tracefiles')
+  args = parser.parse_args()
+
+  trace_dir = os.path.join(rospkg.RosPack().get_path('svo_analysis'), 
+                           'results',
+                           args.experiment_name)
+  evaluate_dataset(trace_dir)
\ No newline at end of file
diff --git a/svo_analysis/scripts/rotation_utils.py b/svo_analysis/scripts/rotation_utils.py
deleted file mode 100644
index 4c7b711..0000000
--- a/svo_analysis/scripts/rotation_utils.py
+++ /dev/null
@@ -1,143 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-Created on Fri Jun 28 10:42:37 2013
-
-@author: cforster
-"""
-
-import math
-import numpy as np
-
-# epsilon for testing whether a number is close to zero
-_EPS = np.finfo(float).eps * 4.0
-
-# axis sequences for Euler angles
-_NEXT_AXIS = [1, 2, 0, 1]
-
-# map axes strings to/from tuples of inner axis, parity, repetition, frame
-_AXES2TUPLE = {
-    'sxyz': (0, 0, 0, 0), 'sxyx': (0, 0, 1, 0), 'sxzy': (0, 1, 0, 0),
-    'sxzx': (0, 1, 1, 0), 'syzx': (1, 0, 0, 0), 'syzy': (1, 0, 1, 0),
-    'syxz': (1, 1, 0, 0), 'syxy': (1, 1, 1, 0), 'szxy': (2, 0, 0, 0),
-    'szxz': (2, 0, 1, 0), 'szyx': (2, 1, 0, 0), 'szyz': (2, 1, 1, 0),
-    'rzyx': (0, 0, 0, 1), 'rxyx': (0, 0, 1, 1), 'ryzx': (0, 1, 0, 1),
-    'rxzx': (0, 1, 1, 1), 'rxzy': (1, 0, 0, 1), 'ryzy': (1, 0, 1, 1),
-    'rzxy': (1, 1, 0, 1), 'ryxy': (1, 1, 1, 1), 'ryxz': (2, 0, 0, 1),
-    'rzxz': (2, 0, 1, 1), 'rxyz': (2, 1, 0, 1), 'rzyz': (2, 1, 1, 1)}
-
-_TUPLE2AXES = dict((v, k) for k, v in _AXES2TUPLE.items())
-
-
-def quat2dcm(quaternion):
-    """Convert Quaternion [x y z w] to Direct Cosine Matrix representation """
-    q = np.array(quaternion[:4], dtype=np.float64, copy=True)
-    nq = np.dot(q, q)
-    if nq < _EPS:
-      return np.identity(4)
-    q *= math.sqrt(2.0 / nq)
-    q = np.outer(q, q)
-    return np.array([ [1.0-q[1, 1]-q[2, 2],     q[0, 1]-q[2, 3],     q[0, 2]+q[1, 3]],
-                      [    q[0, 1]+q[2, 3], 1.0-q[0, 0]-q[2, 2],     q[1, 2]-q[0, 3]],
-                      [    q[0, 2]-q[1, 3],     q[1, 2]+q[0, 3], 1.0-q[0, 0]-q[1, 1]]],
-                      dtype=np.float64)
-        
-def dcm2quat(matrix):
-    """Convert Direct Cosine Matrix to Quaternion
-    
-       This algorithm comes from  "Quaternion Calculus and Fast Animation",
-       Ken Shoemake, 1987 SIGGRAPH course notes
-    """
-    q = np.empty((4, ), dtype=np.float64)
-    M = np.array(matrix, dtype=np.float64, copy=False)[:3, :3]
-    t = np.trace(M)
-    if (t > 0):
-      t = math.sqrt(t + 1.0);
-      q[3] = 0.5*t;
-      t = 0.5/t;
-      q[0] = (M[2,1] - M[1,2]) * t;
-      q[1] = (M[0,2] - M[2,0]) * t;
-      q[2] = (M[1,0] - M[0,1]) * t;
-    else:
-      i = 0;
-      if (M[1,1] > M[0,0]):
-        i = 1;
-      if (M[2,2] > M[i,i]):
-        i = 2;
-      j = (i+1)%3;
-      k = (j+1)%3;
-
-      t = math.sqrt(M[i,i]-M[j,j]-M[k,k] + 1.0);
-      q[i] = 0.5 * t;
-      t = 0.5/t;
-      q[3] = (M[k,j]-M[j,k])*t;
-      q[j] = (M[j,i]+M[i,j])*t;
-      q[k] = (M[k,i]+M[i,k])*t;
-    return q
-
-def dcm2rpy(matrix, axes='sxyz'):
-    """Return Euler angles from rotation matrix for specified axis sequence.
-
-    axes : One of 24 axis sequences as string or encoded tuple
-
-    Note that many Euler angle triplets can describe one matrix.
-
-    >>> R0 = euler_matrix(1, 2, 3, 'syxz')
-    >>> al, be, ga = euler_from_matrix(R0, 'syxz')
-    >>> R1 = euler_matrix(al, be, ga, 'syxz')
-    >>> numpy.allclose(R0, R1)
-    True
-    >>> angles = (4.0*math.pi) * (numpy.random.random(3) - 0.5)
-    >>> for axes in _AXES2TUPLE.keys():
-    ...    R0 = euler_matrix(axes=axes, *angles)
-    ...    R1 = euler_matrix(axes=axes, *euler_from_matrix(R0, axes))
-    ...    if not numpy.allclose(R0, R1): print axes, "failed"
-
-    """
-    try:
-        firstaxis, parity, repetition, frame = _AXES2TUPLE[axes.lower()]
-    except (AttributeError, KeyError):
-        _ = _TUPLE2AXES[axes]
-        firstaxis, parity, repetition, frame = axes
-
-    i = firstaxis
-    j = _NEXT_AXIS[i+parity]
-    k = _NEXT_AXIS[i-parity+1]
-
-    M = np.array(matrix, dtype=np.float64, copy=False)[:3, :3]
-    if repetition:
-        sy = math.sqrt(M[i, j]*M[i, j] + M[i, k]*M[i, k])
-        if sy > _EPS:
-            ax = math.atan2( M[i, j],  M[i, k])
-            ay = math.atan2( sy,       M[i, i])
-            az = math.atan2( M[j, i], -M[k, i])
-        else:
-            ax = math.atan2(-M[j, k],  M[j, j])
-            ay = math.atan2( sy,       M[i, i])
-            az = 0.0
-    else:
-        cy = math.sqrt(M[i, i]*M[i, i] + M[j, i]*M[j, i])
-        if cy > _EPS:
-            ax = math.atan2( M[k, j],  M[k, k])
-            ay = math.atan2(-M[k, i],  cy)
-            az = math.atan2( M[j, i],  M[i, i])
-        else:
-            ax = math.atan2(-M[j, k],  M[j, j])
-            ay = math.atan2(-M[k, i],  cy)
-            az = 0.0
-
-    if parity:
-        ax, ay, az = -ax, -ay, -az
-    if frame:
-        ax, az = az, ax
-    return ax, ay, az
-    
-def quat2rpy(quaternion, axes='sxyz'):
-    """Return Euler angles from quaternion for specified axis sequence.
-
-    >>> angles = euler_from_quaternion([0.06146124, 0, 0, 0.99810947])
-    >>> numpy.allclose(angles, [0.123, 0, 0])
-    True
-
-    """
-    return dcm2rpy(quat2dcm(quaternion), axes)
-    
diff --git a/svo_analysis/scripts/transformations.py b/svo_analysis/scripts/transformations.py
deleted file mode 100644
index 6d6f19e..0000000
--- a/svo_analysis/scripts/transformations.py
+++ /dev/null
@@ -1,1705 +0,0 @@
-# -*- coding: utf-8 -*-
-# transformations.py
-
-# Copyright (c) 2006, Christoph Gohlke
-# Copyright (c) 2006-2009, The Regents of the University of California
-# All rights reserved.
-#
-# Redistribution and use in source and binary forms, with or without
-# modification, are permitted provided that the following conditions are met:
-#
-# * Redistributions of source code must retain the above copyright
-#   notice, this list of conditions and the following disclaimer.
-# * Redistributions in binary form must reproduce the above copyright
-#   notice, this list of conditions and the following disclaimer in the
-#   documentation and/or other materials provided with the distribution.
-# * Neither the name of the copyright holders nor the names of any
-#   contributors may be used to endorse or promote products derived
-#   from this software without specific prior written permission.
-#
-# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
-# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
-# ARE DISCLAIMED.  IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
-# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
-# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
-# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
-# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
-# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
-# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
-# POSSIBILITY OF SUCH DAMAGE.
-
-"""Homogeneous Transformation Matrices and Quaternions.
-
-A library for calculating 4x4 matrices for translating, rotating, reflecting,
-scaling, shearing, projecting, orthogonalizing, and superimposing arrays of
-3D homogeneous coordinates as well as for converting between rotation matrices,
-Euler angles, and quaternions. Also includes an Arcball control object and
-functions to decompose transformation matrices.
-
-:Authors:
-  `Christoph Gohlke <http://www.lfd.uci.edu/~gohlke/>`__,
-  Laboratory for Fluorescence Dynamics, University of California, Irvine
-
-:Version: 20090418
-
-Requirements
-------------
-
-* `Python 2.6 <http://www.python.org>`__
-* `Numpy 1.3 <http://numpy.scipy.org>`__
-* `transformations.c 20090418 <http://www.lfd.uci.edu/~gohlke/>`__
-  (optional implementation of some functions in C)
-
-Notes
------
-
-Matrices (M) can be inverted using numpy.linalg.inv(M), concatenated using
-numpy.dot(M0, M1), or used to transform homogeneous coordinates (v) using
-numpy.dot(M, v) for shape (4, \*) "point of arrays", respectively
-numpy.dot(v, M.T) for shape (\*, 4) "array of points".
-
-Calculations are carried out with numpy.float64 precision.
-
-This Python implementation is not optimized for speed.
-
-Vector, point, quaternion, and matrix function arguments are expected to be
-"array like", i.e. tuple, list, or numpy arrays.
-
-Return types are numpy arrays unless specified otherwise.
-
-Angles are in radians unless specified otherwise.
-
-Quaternions ix+jy+kz+w are represented as [x, y, z, w].
-
-Use the transpose of transformation matrices for OpenGL glMultMatrixd().
-
-A triple of Euler angles can be applied/interpreted in 24 ways, which can
-be specified using a 4 character string or encoded 4-tuple:
-
-  *Axes 4-string*: e.g. 'sxyz' or 'ryxy'
-
-  - first character : rotations are applied to 's'tatic or 'r'otating frame
-  - remaining characters : successive rotation axis 'x', 'y', or 'z'
-
-  *Axes 4-tuple*: e.g. (0, 0, 0, 0) or (1, 1, 1, 1)
-
-  - inner axis: code of axis ('x':0, 'y':1, 'z':2) of rightmost matrix.
-  - parity : even (0) if inner axis 'x' is followed by 'y', 'y' is followed
-    by 'z', or 'z' is followed by 'x'. Otherwise odd (1).
-  - repetition : first and last axis are same (1) or different (0).
-  - frame : rotations are applied to static (0) or rotating (1) frame.
-
-References
-----------
-
-(1)  Matrices and transformations. Ronald Goldman.
-     In "Graphics Gems I", pp 472-475. Morgan Kaufmann, 1990.
-(2)  More matrices and transformations: shear and pseudo-perspective.
-     Ronald Goldman. In "Graphics Gems II", pp 320-323. Morgan Kaufmann, 1991.
-(3)  Decomposing a matrix into simple transformations. Spencer Thomas.
-     In "Graphics Gems II", pp 320-323. Morgan Kaufmann, 1991.
-(4)  Recovering the data from the transformation matrix. Ronald Goldman.
-     In "Graphics Gems II", pp 324-331. Morgan Kaufmann, 1991.
-(5)  Euler angle conversion. Ken Shoemake.
-     In "Graphics Gems IV", pp 222-229. Morgan Kaufmann, 1994.
-(6)  Arcball rotation control. Ken Shoemake.
-     In "Graphics Gems IV", pp 175-192. Morgan Kaufmann, 1994.
-(7)  Representing attitude: Euler angles, unit quaternions, and rotation
-     vectors. James Diebel. 2006.
-(8)  A discussion of the solution for the best rotation to relate two sets
-     of vectors. W Kabsch. Acta Cryst. 1978. A34, 827-828.
-(9)  Closed-form solution of absolute orientation using unit quaternions.
-     BKP Horn. J Opt Soc Am A. 1987. 4(4), 629-642.
-(10) Quaternions. Ken Shoemake.
-     http://www.sfu.ca/~jwa3/cmpt461/files/quatut.pdf
-(11) From quaternion to matrix and back. JMP van Waveren. 2005.
-     http://www.intel.com/cd/ids/developer/asmo-na/eng/293748.htm
-(12) Uniform random rotations. Ken Shoemake.
-     In "Graphics Gems III", pp 124-132. Morgan Kaufmann, 1992.
-
-
-Examples
---------
-
->>> alpha, beta, gamma = 0.123, -1.234, 2.345
->>> origin, xaxis, yaxis, zaxis = (0, 0, 0), (1, 0, 0), (0, 1, 0), (0, 0, 1)
->>> I = identity_matrix()
->>> Rx = rotation_matrix(alpha, xaxis)
->>> Ry = rotation_matrix(beta, yaxis)
->>> Rz = rotation_matrix(gamma, zaxis)
->>> R = concatenate_matrices(Rx, Ry, Rz)
->>> euler = euler_from_matrix(R, 'rxyz')
->>> numpy.allclose([alpha, beta, gamma], euler)
-True
->>> Re = euler_matrix(alpha, beta, gamma, 'rxyz')
->>> is_same_transform(R, Re)
-True
->>> al, be, ga = euler_from_matrix(Re, 'rxyz')
->>> is_same_transform(Re, euler_matrix(al, be, ga, 'rxyz'))
-True
->>> qx = quaternion_about_axis(alpha, xaxis)
->>> qy = quaternion_about_axis(beta, yaxis)
->>> qz = quaternion_about_axis(gamma, zaxis)
->>> q = quaternion_multiply(qx, qy)
->>> q = quaternion_multiply(q, qz)
->>> Rq = quaternion_matrix(q)
->>> is_same_transform(R, Rq)
-True
->>> S = scale_matrix(1.23, origin)
->>> T = translation_matrix((1, 2, 3))
->>> Z = shear_matrix(beta, xaxis, origin, zaxis)
->>> R = random_rotation_matrix(numpy.random.rand(3))
->>> M = concatenate_matrices(T, R, Z, S)
->>> scale, shear, angles, trans, persp = decompose_matrix(M)
->>> numpy.allclose(scale, 1.23)
-True
->>> numpy.allclose(trans, (1, 2, 3))
-True
->>> numpy.allclose(shear, (0, math.tan(beta), 0))
-True
->>> is_same_transform(R, euler_matrix(axes='sxyz', *angles))
-True
->>> M1 = compose_matrix(scale, shear, angles, trans, persp)
->>> is_same_transform(M, M1)
-True
-
-"""
-
-from __future__ import division
-
-import warnings
-import math
-
-import numpy
-
-# Documentation in HTML format can be generated with Epydoc
-__docformat__ = "restructuredtext en"
-
-
-def identity_matrix():
-    """Return 4x4 identity/unit matrix.
-
-    >>> I = identity_matrix()
-    >>> numpy.allclose(I, numpy.dot(I, I))
-    True
-    >>> numpy.sum(I), numpy.trace(I)
-    (4.0, 4.0)
-    >>> numpy.allclose(I, numpy.identity(4, dtype=numpy.float64))
-    True
-
-    """
-    return numpy.identity(4, dtype=numpy.float64)
-
-
-def translation_matrix(direction):
-    """Return matrix to translate by direction vector.
-
-    >>> v = numpy.random.random(3) - 0.5
-    >>> numpy.allclose(v, translation_matrix(v)[:3, 3])
-    True
-
-    """
-    M = numpy.identity(4)
-    M[:3, 3] = direction[:3]
-    return M
-
-
-def translation_from_matrix(matrix):
-    """Return translation vector from translation matrix.
-
-    >>> v0 = numpy.random.random(3) - 0.5
-    >>> v1 = translation_from_matrix(translation_matrix(v0))
-    >>> numpy.allclose(v0, v1)
-    True
-
-    """
-    return numpy.array(matrix, copy=False)[:3, 3].copy()
-
-
-def reflection_matrix(point, normal):
-    """Return matrix to mirror at plane defined by point and normal vector.
-
-    >>> v0 = numpy.random.random(4) - 0.5
-    >>> v0[3] = 1.0
-    >>> v1 = numpy.random.random(3) - 0.5
-    >>> R = reflection_matrix(v0, v1)
-    >>> numpy.allclose(2., numpy.trace(R))
-    True
-    >>> numpy.allclose(v0, numpy.dot(R, v0))
-    True
-    >>> v2 = v0.copy()
-    >>> v2[:3] += v1
-    >>> v3 = v0.copy()
-    >>> v2[:3] -= v1
-    >>> numpy.allclose(v2, numpy.dot(R, v3))
-    True
-
-    """
-    normal = unit_vector(normal[:3])
-    M = numpy.identity(4)
-    M[:3, :3] -= 2.0 * numpy.outer(normal, normal)
-    M[:3, 3] = (2.0 * numpy.dot(point[:3], normal)) * normal
-    return M
-
-
-def reflection_from_matrix(matrix):
-    """Return mirror plane point and normal vector from reflection matrix.
-
-    >>> v0 = numpy.random.random(3) - 0.5
-    >>> v1 = numpy.random.random(3) - 0.5
-    >>> M0 = reflection_matrix(v0, v1)
-    >>> point, normal = reflection_from_matrix(M0)
-    >>> M1 = reflection_matrix(point, normal)
-    >>> is_same_transform(M0, M1)
-    True
-
-    """
-    M = numpy.array(matrix, dtype=numpy.float64, copy=False)
-    # normal: unit eigenvector corresponding to eigenvalue -1
-    l, V = numpy.linalg.eig(M[:3, :3])
-    i = numpy.where(abs(numpy.real(l) + 1.0) < 1e-8)[0]
-    if not len(i):
-        raise ValueError("no unit eigenvector corresponding to eigenvalue -1")
-    normal = numpy.real(V[:, i[0]]).squeeze()
-    # point: any unit eigenvector corresponding to eigenvalue 1
-    l, V = numpy.linalg.eig(M)
-    i = numpy.where(abs(numpy.real(l) - 1.0) < 1e-8)[0]
-    if not len(i):
-        raise ValueError("no unit eigenvector corresponding to eigenvalue 1")
-    point = numpy.real(V[:, i[-1]]).squeeze()
-    point /= point[3]
-    return point, normal
-
-
-def rotation_matrix(angle, direction, point=None):
-    """Return matrix to rotate about axis defined by point and direction.
-
-    >>> angle = (random.random() - 0.5) * (2*math.pi)
-    >>> direc = numpy.random.random(3) - 0.5
-    >>> point = numpy.random.random(3) - 0.5
-    >>> R0 = rotation_matrix(angle, direc, point)
-    >>> R1 = rotation_matrix(angle-2*math.pi, direc, point)
-    >>> is_same_transform(R0, R1)
-    True
-    >>> R0 = rotation_matrix(angle, direc, point)
-    >>> R1 = rotation_matrix(-angle, -direc, point)
-    >>> is_same_transform(R0, R1)
-    True
-    >>> I = numpy.identity(4, numpy.float64)
-    >>> numpy.allclose(I, rotation_matrix(math.pi*2, direc))
-    True
-    >>> numpy.allclose(2., numpy.trace(rotation_matrix(math.pi/2,
-    ...                                                direc, point)))
-    True
-
-    """
-    sina = math.sin(angle)
-    cosa = math.cos(angle)
-    direction = unit_vector(direction[:3])
-    # rotation matrix around unit vector
-    R = numpy.array(((cosa, 0.0,  0.0),
-                     (0.0,  cosa, 0.0),
-                     (0.0,  0.0,  cosa)), dtype=numpy.float64)
-    R += numpy.outer(direction, direction) * (1.0 - cosa)
-    direction *= sina
-    R += numpy.array((( 0.0,         -direction[2],  direction[1]),
-                      ( direction[2], 0.0,          -direction[0]),
-                      (-direction[1], direction[0],  0.0)),
-                     dtype=numpy.float64)
-    M = numpy.identity(4)
-    M[:3, :3] = R
-    if point is not None:
-        # rotation not around origin
-        point = numpy.array(point[:3], dtype=numpy.float64, copy=False)
-        M[:3, 3] = point - numpy.dot(R, point)
-    return M
-
-
-def rotation_from_matrix(matrix):
-    """Return rotation angle and axis from rotation matrix.
-
-    >>> angle = (random.random() - 0.5) * (2*math.pi)
-    >>> direc = numpy.random.random(3) - 0.5
-    >>> point = numpy.random.random(3) - 0.5
-    >>> R0 = rotation_matrix(angle, direc, point)
-    >>> angle, direc, point = rotation_from_matrix(R0)
-    >>> R1 = rotation_matrix(angle, direc, point)
-    >>> is_same_transform(R0, R1)
-    True
-
-    """
-    R = numpy.array(matrix, dtype=numpy.float64, copy=False)
-    R33 = R[:3, :3]
-    # direction: unit eigenvector of R33 corresponding to eigenvalue of 1
-    l, W = numpy.linalg.eig(R33.T)
-    i = numpy.where(abs(numpy.real(l) - 1.0) < 1e-8)[0]
-    if not len(i):
-        raise ValueError("no unit eigenvector corresponding to eigenvalue 1")
-    direction = numpy.real(W[:, i[-1]]).squeeze()
-    # point: unit eigenvector of R33 corresponding to eigenvalue of 1
-    l, Q = numpy.linalg.eig(R)
-    i = numpy.where(abs(numpy.real(l) - 1.0) < 1e-8)[0]
-    if not len(i):
-        raise ValueError("no unit eigenvector corresponding to eigenvalue 1")
-    point = numpy.real(Q[:, i[-1]]).squeeze()
-    point /= point[3]
-    # rotation angle depending on direction
-    cosa = (numpy.trace(R33) - 1.0) / 2.0
-    if abs(direction[2]) > 1e-8:
-        sina = (R[1, 0] + (cosa-1.0)*direction[0]*direction[1]) / direction[2]
-    elif abs(direction[1]) > 1e-8:
-        sina = (R[0, 2] + (cosa-1.0)*direction[0]*direction[2]) / direction[1]
-    else:
-        sina = (R[2, 1] + (cosa-1.0)*direction[1]*direction[2]) / direction[0]
-    angle = math.atan2(sina, cosa)
-    return angle, direction, point
-
-
-def scale_matrix(factor, origin=None, direction=None):
-    """Return matrix to scale by factor around origin in direction.
-
-    Use factor -1 for point symmetry.
-
-    >>> v = (numpy.random.rand(4, 5) - 0.5) * 20.0
-    >>> v[3] = 1.0
-    >>> S = scale_matrix(-1.234)
-    >>> numpy.allclose(numpy.dot(S, v)[:3], -1.234*v[:3])
-    True
-    >>> factor = random.random() * 10 - 5
-    >>> origin = numpy.random.random(3) - 0.5
-    >>> direct = numpy.random.random(3) - 0.5
-    >>> S = scale_matrix(factor, origin)
-    >>> S = scale_matrix(factor, origin, direct)
-
-    """
-    if direction is None:
-        # uniform scaling
-        M = numpy.array(((factor, 0.0,    0.0,    0.0),
-                         (0.0,    factor, 0.0,    0.0),
-                         (0.0,    0.0,    factor, 0.0),
-                         (0.0,    0.0,    0.0,    1.0)), dtype=numpy.float64)
-        if origin is not None:
-            M[:3, 3] = origin[:3]
-            M[:3, 3] *= 1.0 - factor
-    else:
-        # nonuniform scaling
-        direction = unit_vector(direction[:3])
-        factor = 1.0 - factor
-        M = numpy.identity(4)
-        M[:3, :3] -= factor * numpy.outer(direction, direction)
-        if origin is not None:
-            M[:3, 3] = (factor * numpy.dot(origin[:3], direction)) * direction
-    return M
-
-
-def scale_from_matrix(matrix):
-    """Return scaling factor, origin and direction from scaling matrix.
-
-    >>> factor = random.random() * 10 - 5
-    >>> origin = numpy.random.random(3) - 0.5
-    >>> direct = numpy.random.random(3) - 0.5
-    >>> S0 = scale_matrix(factor, origin)
-    >>> factor, origin, direction = scale_from_matrix(S0)
-    >>> S1 = scale_matrix(factor, origin, direction)
-    >>> is_same_transform(S0, S1)
-    True
-    >>> S0 = scale_matrix(factor, origin, direct)
-    >>> factor, origin, direction = scale_from_matrix(S0)
-    >>> S1 = scale_matrix(factor, origin, direction)
-    >>> is_same_transform(S0, S1)
-    True
-
-    """
-    M = numpy.array(matrix, dtype=numpy.float64, copy=False)
-    M33 = M[:3, :3]
-    factor = numpy.trace(M33) - 2.0
-    try:
-        # direction: unit eigenvector corresponding to eigenvalue factor
-        l, V = numpy.linalg.eig(M33)
-        i = numpy.where(abs(numpy.real(l) - factor) < 1e-8)[0][0]
-        direction = numpy.real(V[:, i]).squeeze()
-        direction /= vector_norm(direction)
-    except IndexError:
-        # uniform scaling
-        factor = (factor + 2.0) / 3.0
-        direction = None
-    # origin: any eigenvector corresponding to eigenvalue 1
-    l, V = numpy.linalg.eig(M)
-    i = numpy.where(abs(numpy.real(l) - 1.0) < 1e-8)[0]
-    if not len(i):
-        raise ValueError("no eigenvector corresponding to eigenvalue 1")
-    origin = numpy.real(V[:, i[-1]]).squeeze()
-    origin /= origin[3]
-    return factor, origin, direction
-
-
-def projection_matrix(point, normal, direction=None,
-                      perspective=None, pseudo=False):
-    """Return matrix to project onto plane defined by point and normal.
-
-    Using either perspective point, projection direction, or none of both.
-
-    If pseudo is True, perspective projections will preserve relative depth
-    such that Perspective = dot(Orthogonal, PseudoPerspective).
-
-    >>> P = projection_matrix((0, 0, 0), (1, 0, 0))
-    >>> numpy.allclose(P[1:, 1:], numpy.identity(4)[1:, 1:])
-    True
-    >>> point = numpy.random.random(3) - 0.5
-    >>> normal = numpy.random.random(3) - 0.5
-    >>> direct = numpy.random.random(3) - 0.5
-    >>> persp = numpy.random.random(3) - 0.5
-    >>> P0 = projection_matrix(point, normal)
-    >>> P1 = projection_matrix(point, normal, direction=direct)
-    >>> P2 = projection_matrix(point, normal, perspective=persp)
-    >>> P3 = projection_matrix(point, normal, perspective=persp, pseudo=True)
-    >>> is_same_transform(P2, numpy.dot(P0, P3))
-    True
-    >>> P = projection_matrix((3, 0, 0), (1, 1, 0), (1, 0, 0))
-    >>> v0 = (numpy.random.rand(4, 5) - 0.5) * 20.0
-    >>> v0[3] = 1.0
-    >>> v1 = numpy.dot(P, v0)
-    >>> numpy.allclose(v1[1], v0[1])
-    True
-    >>> numpy.allclose(v1[0], 3.0-v1[1])
-    True
-
-    """
-    M = numpy.identity(4)
-    point = numpy.array(point[:3], dtype=numpy.float64, copy=False)
-    normal = unit_vector(normal[:3])
-    if perspective is not None:
-        # perspective projection
-        perspective = numpy.array(perspective[:3], dtype=numpy.float64,
-                                  copy=False)
-        M[0, 0] = M[1, 1] = M[2, 2] = numpy.dot(perspective-point, normal)
-        M[:3, :3] -= numpy.outer(perspective, normal)
-        if pseudo:
-            # preserve relative depth
-            M[:3, :3] -= numpy.outer(normal, normal)
-            M[:3, 3] = numpy.dot(point, normal) * (perspective+normal)
-        else:
-            M[:3, 3] = numpy.dot(point, normal) * perspective
-        M[3, :3] = -normal
-        M[3, 3] = numpy.dot(perspective, normal)
-    elif direction is not None:
-        # parallel projection
-        direction = numpy.array(direction[:3], dtype=numpy.float64, copy=False)
-        scale = numpy.dot(direction, normal)
-        M[:3, :3] -= numpy.outer(direction, normal) / scale
-        M[:3, 3] = direction * (numpy.dot(point, normal) / scale)
-    else:
-        # orthogonal projection
-        M[:3, :3] -= numpy.outer(normal, normal)
-        M[:3, 3] = numpy.dot(point, normal) * normal
-    return M
-
-
-def projection_from_matrix(matrix, pseudo=False):
-    """Return projection plane and perspective point from projection matrix.
-
-    Return values are same as arguments for projection_matrix function:
-    point, normal, direction, perspective, and pseudo.
-
-    >>> point = numpy.random.random(3) - 0.5
-    >>> normal = numpy.random.random(3) - 0.5
-    >>> direct = numpy.random.random(3) - 0.5
-    >>> persp = numpy.random.random(3) - 0.5
-    >>> P0 = projection_matrix(point, normal)
-    >>> result = projection_from_matrix(P0)
-    >>> P1 = projection_matrix(*result)
-    >>> is_same_transform(P0, P1)
-    True
-    >>> P0 = projection_matrix(point, normal, direct)
-    >>> result = projection_from_matrix(P0)
-    >>> P1 = projection_matrix(*result)
-    >>> is_same_transform(P0, P1)
-    True
-    >>> P0 = projection_matrix(point, normal, perspective=persp, pseudo=False)
-    >>> result = projection_from_matrix(P0, pseudo=False)
-    >>> P1 = projection_matrix(*result)
-    >>> is_same_transform(P0, P1)
-    True
-    >>> P0 = projection_matrix(point, normal, perspective=persp, pseudo=True)
-    >>> result = projection_from_matrix(P0, pseudo=True)
-    >>> P1 = projection_matrix(*result)
-    >>> is_same_transform(P0, P1)
-    True
-
-    """
-    M = numpy.array(matrix, dtype=numpy.float64, copy=False)
-    M33 = M[:3, :3]
-    l, V = numpy.linalg.eig(M)
-    i = numpy.where(abs(numpy.real(l) - 1.0) < 1e-8)[0]
-    if not pseudo and len(i):
-        # point: any eigenvector corresponding to eigenvalue 1
-        point = numpy.real(V[:, i[-1]]).squeeze()
-        point /= point[3]
-        # direction: unit eigenvector corresponding to eigenvalue 0
-        l, V = numpy.linalg.eig(M33)
-        i = numpy.where(abs(numpy.real(l)) < 1e-8)[0]
-        if not len(i):
-            raise ValueError("no eigenvector corresponding to eigenvalue 0")
-        direction = numpy.real(V[:, i[0]]).squeeze()
-        direction /= vector_norm(direction)
-        # normal: unit eigenvector of M33.T corresponding to eigenvalue 0
-        l, V = numpy.linalg.eig(M33.T)
-        i = numpy.where(abs(numpy.real(l)) < 1e-8)[0]
-        if len(i):
-            # parallel projection
-            normal = numpy.real(V[:, i[0]]).squeeze()
-            normal /= vector_norm(normal)
-            return point, normal, direction, None, False
-        else:
-            # orthogonal projection, where normal equals direction vector
-            return point, direction, None, None, False
-    else:
-        # perspective projection
-        i = numpy.where(abs(numpy.real(l)) > 1e-8)[0]
-        if not len(i):
-            raise ValueError(
-                "no eigenvector not corresponding to eigenvalue 0")
-        point = numpy.real(V[:, i[-1]]).squeeze()
-        point /= point[3]
-        normal = - M[3, :3]
-        perspective = M[:3, 3] / numpy.dot(point[:3], normal)
-        if pseudo:
-            perspective -= normal
-        return point, normal, None, perspective, pseudo
-
-
-def clip_matrix(left, right, bottom, top, near, far, perspective=False):
-    """Return matrix to obtain normalized device coordinates from frustrum.
-
-    The frustrum bounds are axis-aligned along x (left, right),
-    y (bottom, top) and z (near, far).
-
-    Normalized device coordinates are in range [-1, 1] if coordinates are
-    inside the frustrum.
-
-    If perspective is True the frustrum is a truncated pyramid with the
-    perspective point at origin and direction along z axis, otherwise an
-    orthographic canonical view volume (a box).
-
-    Homogeneous coordinates transformed by the perspective clip matrix
-    need to be dehomogenized (devided by w coordinate).
-
-    >>> frustrum = numpy.random.rand(6)
-    >>> frustrum[1] += frustrum[0]
-    >>> frustrum[3] += frustrum[2]
-    >>> frustrum[5] += frustrum[4]
-    >>> M = clip_matrix(*frustrum, perspective=False)
-    >>> numpy.dot(M, [frustrum[0], frustrum[2], frustrum[4], 1.0])
-    array([-1., -1., -1.,  1.])
-    >>> numpy.dot(M, [frustrum[1], frustrum[3], frustrum[5], 1.0])
-    array([ 1.,  1.,  1.,  1.])
-    >>> M = clip_matrix(*frustrum, perspective=True)
-    >>> v = numpy.dot(M, [frustrum[0], frustrum[2], frustrum[4], 1.0])
-    >>> v / v[3]
-    array([-1., -1., -1.,  1.])
-    >>> v = numpy.dot(M, [frustrum[1], frustrum[3], frustrum[4], 1.0])
-    >>> v / v[3]
-    array([ 1.,  1., -1.,  1.])
-
-    """
-    if left >= right or bottom >= top or near >= far:
-        raise ValueError("invalid frustrum")
-    if perspective:
-        if near <= _EPS:
-            raise ValueError("invalid frustrum: near <= 0")
-        t = 2.0 * near
-        M = ((-t/(right-left), 0.0, (right+left)/(right-left), 0.0),
-             (0.0, -t/(top-bottom), (top+bottom)/(top-bottom), 0.0),
-             (0.0, 0.0, -(far+near)/(far-near), t*far/(far-near)),
-             (0.0, 0.0, -1.0, 0.0))
-    else:
-        M = ((2.0/(right-left), 0.0, 0.0, (right+left)/(left-right)),
-             (0.0, 2.0/(top-bottom), 0.0, (top+bottom)/(bottom-top)),
-             (0.0, 0.0, 2.0/(far-near), (far+near)/(near-far)),
-             (0.0, 0.0, 0.0, 1.0))
-    return numpy.array(M, dtype=numpy.float64)
-
-
-def shear_matrix(angle, direction, point, normal):
-    """Return matrix to shear by angle along direction vector on shear plane.
-
-    The shear plane is defined by a point and normal vector. The direction
-    vector must be orthogonal to the plane's normal vector.
-
-    A point P is transformed by the shear matrix into P" such that
-    the vector P-P" is parallel to the direction vector and its extent is
-    given by the angle of P-P'-P", where P' is the orthogonal projection
-    of P onto the shear plane.
-
-    >>> angle = (random.random() - 0.5) * 4*math.pi
-    >>> direct = numpy.random.random(3) - 0.5
-    >>> point = numpy.random.random(3) - 0.5
-    >>> normal = numpy.cross(direct, numpy.random.random(3))
-    >>> S = shear_matrix(angle, direct, point, normal)
-    >>> numpy.allclose(1.0, numpy.linalg.det(S))
-    True
-
-    """
-    normal = unit_vector(normal[:3])
-    direction = unit_vector(direction[:3])
-    if abs(numpy.dot(normal, direction)) > 1e-6:
-        raise ValueError("direction and normal vectors are not orthogonal")
-    angle = math.tan(angle)
-    M = numpy.identity(4)
-    M[:3, :3] += angle * numpy.outer(direction, normal)
-    M[:3, 3] = -angle * numpy.dot(point[:3], normal) * direction
-    return M
-
-
-def shear_from_matrix(matrix):
-    """Return shear angle, direction and plane from shear matrix.
-
-    >>> angle = (random.random() - 0.5) * 4*math.pi
-    >>> direct = numpy.random.random(3) - 0.5
-    >>> point = numpy.random.random(3) - 0.5
-    >>> normal = numpy.cross(direct, numpy.random.random(3))
-    >>> S0 = shear_matrix(angle, direct, point, normal)
-    >>> angle, direct, point, normal = shear_from_matrix(S0)
-    >>> S1 = shear_matrix(angle, direct, point, normal)
-    >>> is_same_transform(S0, S1)
-    True
-
-    """
-    M = numpy.array(matrix, dtype=numpy.float64, copy=False)
-    M33 = M[:3, :3]
-    # normal: cross independent eigenvectors corresponding to the eigenvalue 1
-    l, V = numpy.linalg.eig(M33)
-    i = numpy.where(abs(numpy.real(l) - 1.0) < 1e-4)[0]
-    if len(i) < 2:
-        raise ValueError("No two linear independent eigenvectors found %s" % l)
-    V = numpy.real(V[:, i]).squeeze().T
-    lenorm = -1.0
-    for i0, i1 in ((0, 1), (0, 2), (1, 2)):
-        n = numpy.cross(V[i0], V[i1])
-        l = vector_norm(n)
-        if l > lenorm:
-            lenorm = l
-            normal = n
-    normal /= lenorm
-    # direction and angle
-    direction = numpy.dot(M33 - numpy.identity(3), normal)
-    angle = vector_norm(direction)
-    direction /= angle
-    angle = math.atan(angle)
-    # point: eigenvector corresponding to eigenvalue 1
-    l, V = numpy.linalg.eig(M)
-    i = numpy.where(abs(numpy.real(l) - 1.0) < 1e-8)[0]
-    if not len(i):
-        raise ValueError("no eigenvector corresponding to eigenvalue 1")
-    point = numpy.real(V[:, i[-1]]).squeeze()
-    point /= point[3]
-    return angle, direction, point, normal
-
-
-def decompose_matrix(matrix):
-    """Return sequence of transformations from transformation matrix.
-
-    matrix : array_like
-        Non-degenerative homogeneous transformation matrix
-
-    Return tuple of:
-        scale : vector of 3 scaling factors
-        shear : list of shear factors for x-y, x-z, y-z axes
-        angles : list of Euler angles about static x, y, z axes
-        translate : translation vector along x, y, z axes
-        perspective : perspective partition of matrix
-
-    Raise ValueError if matrix is of wrong type or degenerative.
-
-    >>> T0 = translation_matrix((1, 2, 3))
-    >>> scale, shear, angles, trans, persp = decompose_matrix(T0)
-    >>> T1 = translation_matrix(trans)
-    >>> numpy.allclose(T0, T1)
-    True
-    >>> S = scale_matrix(0.123)
-    >>> scale, shear, angles, trans, persp = decompose_matrix(S)
-    >>> scale[0]
-    0.123
-    >>> R0 = euler_matrix(1, 2, 3)
-    >>> scale, shear, angles, trans, persp = decompose_matrix(R0)
-    >>> R1 = euler_matrix(*angles)
-    >>> numpy.allclose(R0, R1)
-    True
-
-    """
-    M = numpy.array(matrix, dtype=numpy.float64, copy=True).T
-    if abs(M[3, 3]) < _EPS:
-        raise ValueError("M[3, 3] is zero")
-    M /= M[3, 3]
-    P = M.copy()
-    P[:, 3] = 0, 0, 0, 1
-    if not numpy.linalg.det(P):
-        raise ValueError("Matrix is singular")
-
-    scale = numpy.zeros((3, ), dtype=numpy.float64)
-    shear = [0, 0, 0]
-    angles = [0, 0, 0]
-
-    if any(abs(M[:3, 3]) > _EPS):
-        perspective = numpy.dot(M[:, 3], numpy.linalg.inv(P.T))
-        M[:, 3] = 0, 0, 0, 1
-    else:
-        perspective = numpy.array((0, 0, 0, 1), dtype=numpy.float64)
-
-    translate = M[3, :3].copy()
-    M[3, :3] = 0
-
-    row = M[:3, :3].copy()
-    scale[0] = vector_norm(row[0])
-    row[0] /= scale[0]
-    shear[0] = numpy.dot(row[0], row[1])
-    row[1] -= row[0] * shear[0]
-    scale[1] = vector_norm(row[1])
-    row[1] /= scale[1]
-    shear[0] /= scale[1]
-    shear[1] = numpy.dot(row[0], row[2])
-    row[2] -= row[0] * shear[1]
-    shear[2] = numpy.dot(row[1], row[2])
-    row[2] -= row[1] * shear[2]
-    scale[2] = vector_norm(row[2])
-    row[2] /= scale[2]
-    shear[1:] /= scale[2]
-
-    if numpy.dot(row[0], numpy.cross(row[1], row[2])) < 0:
-        scale *= -1
-        row *= -1
-
-    angles[1] = math.asin(-row[0, 2])
-    if math.cos(angles[1]):
-        angles[0] = math.atan2(row[1, 2], row[2, 2])
-        angles[2] = math.atan2(row[0, 1], row[0, 0])
-    else:
-        #angles[0] = math.atan2(row[1, 0], row[1, 1])
-        angles[0] = math.atan2(-row[2, 1], row[1, 1])
-        angles[2] = 0.0
-
-    return scale, shear, angles, translate, perspective
-
-
-def compose_matrix(scale=None, shear=None, angles=None, translate=None,
-                   perspective=None):
-    """Return transformation matrix from sequence of transformations.
-
-    This is the inverse of the decompose_matrix function.
-
-    Sequence of transformations:
-        scale : vector of 3 scaling factors
-        shear : list of shear factors for x-y, x-z, y-z axes
-        angles : list of Euler angles about static x, y, z axes
-        translate : translation vector along x, y, z axes
-        perspective : perspective partition of matrix
-
-    >>> scale = numpy.random.random(3) - 0.5
-    >>> shear = numpy.random.random(3) - 0.5
-    >>> angles = (numpy.random.random(3) - 0.5) * (2*math.pi)
-    >>> trans = numpy.random.random(3) - 0.5
-    >>> persp = numpy.random.random(4) - 0.5
-    >>> M0 = compose_matrix(scale, shear, angles, trans, persp)
-    >>> result = decompose_matrix(M0)
-    >>> M1 = compose_matrix(*result)
-    >>> is_same_transform(M0, M1)
-    True
-
-    """
-    M = numpy.identity(4)
-    if perspective is not None:
-        P = numpy.identity(4)
-        P[3, :] = perspective[:4]
-        M = numpy.dot(M, P)
-    if translate is not None:
-        T = numpy.identity(4)
-        T[:3, 3] = translate[:3]
-        M = numpy.dot(M, T)
-    if angles is not None:
-        R = euler_matrix(angles[0], angles[1], angles[2], 'sxyz')
-        M = numpy.dot(M, R)
-    if shear is not None:
-        Z = numpy.identity(4)
-        Z[1, 2] = shear[2]
-        Z[0, 2] = shear[1]
-        Z[0, 1] = shear[0]
-        M = numpy.dot(M, Z)
-    if scale is not None:
-        S = numpy.identity(4)
-        S[0, 0] = scale[0]
-        S[1, 1] = scale[1]
-        S[2, 2] = scale[2]
-        M = numpy.dot(M, S)
-    M /= M[3, 3]
-    return M
-
-
-def orthogonalization_matrix(lengths, angles):
-    """Return orthogonalization matrix for crystallographic cell coordinates.
-
-    Angles are expected in degrees.
-
-    The de-orthogonalization matrix is the inverse.
-
-    >>> O = orthogonalization_matrix((10., 10., 10.), (90., 90., 90.))
-    >>> numpy.allclose(O[:3, :3], numpy.identity(3, float) * 10)
-    True
-    >>> O = orthogonalization_matrix([9.8, 12.0, 15.5], [87.2, 80.7, 69.7])
-    >>> numpy.allclose(numpy.sum(O), 43.063229)
-    True
-
-    """
-    a, b, c = lengths
-    angles = numpy.radians(angles)
-    sina, sinb, _ = numpy.sin(angles)
-    cosa, cosb, cosg = numpy.cos(angles)
-    co = (cosa * cosb - cosg) / (sina * sinb)
-    return numpy.array((
-        ( a*sinb*math.sqrt(1.0-co*co),  0.0,    0.0, 0.0),
-        (-a*sinb*co,                    b*sina, 0.0, 0.0),
-        ( a*cosb,                       b*cosa, c,   0.0),
-        ( 0.0,                          0.0,    0.0, 1.0)),
-        dtype=numpy.float64)
-
-
-def superimposition_matrix(v0, v1, scaling=False, usesvd=True):
-    """Return matrix to transform given vector set into second vector set.
-
-    v0 and v1 are shape (3, \*) or (4, \*) arrays of at least 3 vectors.
-
-    If usesvd is True, the weighted sum of squared deviations (RMSD) is
-    minimized according to the algorithm by W. Kabsch [8]. Otherwise the
-    quaternion based algorithm by B. Horn [9] is used (slower when using
-    this Python implementation).
-
-    The returned matrix performs rotation, translation and uniform scaling
-    (if specified).
-
-    >>> v0 = numpy.random.rand(3, 10)
-    >>> M = superimposition_matrix(v0, v0)
-    >>> numpy.allclose(M, numpy.identity(4))
-    True
-    >>> R = random_rotation_matrix(numpy.random.random(3))
-    >>> v0 = ((1,0,0), (0,1,0), (0,0,1), (1,1,1))
-    >>> v1 = numpy.dot(R, v0)
-    >>> M = superimposition_matrix(v0, v1)
-    >>> numpy.allclose(v1, numpy.dot(M, v0))
-    True
-    >>> v0 = (numpy.random.rand(4, 100) - 0.5) * 20.0
-    >>> v0[3] = 1.0
-    >>> v1 = numpy.dot(R, v0)
-    >>> M = superimposition_matrix(v0, v1)
-    >>> numpy.allclose(v1, numpy.dot(M, v0))
-    True
-    >>> S = scale_matrix(random.random())
-    >>> T = translation_matrix(numpy.random.random(3)-0.5)
-    >>> M = concatenate_matrices(T, R, S)
-    >>> v1 = numpy.dot(M, v0)
-    >>> v0[:3] += numpy.random.normal(0.0, 1e-9, 300).reshape(3, -1)
-    >>> M = superimposition_matrix(v0, v1, scaling=True)
-    >>> numpy.allclose(v1, numpy.dot(M, v0))
-    True
-    >>> M = superimposition_matrix(v0, v1, scaling=True, usesvd=False)
-    >>> numpy.allclose(v1, numpy.dot(M, v0))
-    True
-    >>> v = numpy.empty((4, 100, 3), dtype=numpy.float64)
-    >>> v[:, :, 0] = v0
-    >>> M = superimposition_matrix(v0, v1, scaling=True, usesvd=False)
-    >>> numpy.allclose(v1, numpy.dot(M, v[:, :, 0]))
-    True
-
-    """
-    v0 = numpy.array(v0, dtype=numpy.float64, copy=False)[:3]
-    v1 = numpy.array(v1, dtype=numpy.float64, copy=False)[:3]
-
-    if v0.shape != v1.shape or v0.shape[1] < 3:
-        raise ValueError("Vector sets are of wrong shape or type.")
-
-    # move centroids to origin
-    t0 = numpy.mean(v0, axis=1)
-    t1 = numpy.mean(v1, axis=1)
-    v0 = v0 - t0.reshape(3, 1)
-    v1 = v1 - t1.reshape(3, 1)
-
-    if usesvd:
-        # Singular Value Decomposition of covariance matrix
-        u, s, vh = numpy.linalg.svd(numpy.dot(v1, v0.T))
-        # rotation matrix from SVD orthonormal bases
-        R = numpy.dot(u, vh)
-        if numpy.linalg.det(R) < 0.0:
-            # R does not constitute right handed system
-            R -= numpy.outer(u[:, 2], vh[2, :]*2.0)
-            s[-1] *= -1.0
-        # homogeneous transformation matrix
-        M = numpy.identity(4)
-        M[:3, :3] = R
-    else:
-        # compute symmetric matrix N
-        xx, yy, zz = numpy.sum(v0 * v1, axis=1)
-        xy, yz, zx = numpy.sum(v0 * numpy.roll(v1, -1, axis=0), axis=1)
-        xz, yx, zy = numpy.sum(v0 * numpy.roll(v1, -2, axis=0), axis=1)
-        N = ((xx+yy+zz, yz-zy,    zx-xz,    xy-yx),
-             (yz-zy,    xx-yy-zz, xy+yx,    zx+xz),
-             (zx-xz,    xy+yx,   -xx+yy-zz, yz+zy),
-             (xy-yx,    zx+xz,    yz+zy,   -xx-yy+zz))
-        # quaternion: eigenvector corresponding to most positive eigenvalue
-        l, V = numpy.linalg.eig(N)
-        q = V[:, numpy.argmax(l)]
-        q /= vector_norm(q) # unit quaternion
-        q = numpy.roll(q, -1) # move w component to end
-        # homogeneous transformation matrix
-        M = quaternion_matrix(q)
-
-    # scale: ratio of rms deviations from centroid
-    if scaling:
-        v0 *= v0
-        v1 *= v1
-        M[:3, :3] *= math.sqrt(numpy.sum(v1) / numpy.sum(v0))
-
-    # translation
-    M[:3, 3] = t1
-    T = numpy.identity(4)
-    T[:3, 3] = -t0
-    M = numpy.dot(M, T)
-    return M
-
-
-def euler_matrix(ai, aj, ak, axes='sxyz'):
-    """Return homogeneous rotation matrix from Euler angles and axis sequence.
-
-    ai, aj, ak : Euler's roll, pitch and yaw angles
-    axes : One of 24 axis sequences as string or encoded tuple
-
-    >>> R = euler_matrix(1, 2, 3, 'syxz')
-    >>> numpy.allclose(numpy.sum(R[0]), -1.34786452)
-    True
-    >>> R = euler_matrix(1, 2, 3, (0, 1, 0, 1))
-    >>> numpy.allclose(numpy.sum(R[0]), -0.383436184)
-    True
-    >>> ai, aj, ak = (4.0*math.pi) * (numpy.random.random(3) - 0.5)
-    >>> for axes in _AXES2TUPLE.keys():
-    ...    R = euler_matrix(ai, aj, ak, axes)
-    >>> for axes in _TUPLE2AXES.keys():
-    ...    R = euler_matrix(ai, aj, ak, axes)
-
-    """
-    try:
-        firstaxis, parity, repetition, frame = _AXES2TUPLE[axes]
-    except (AttributeError, KeyError):
-        _ = _TUPLE2AXES[axes]
-        firstaxis, parity, repetition, frame = axes
-
-    i = firstaxis
-    j = _NEXT_AXIS[i+parity]
-    k = _NEXT_AXIS[i-parity+1]
-
-    if frame:
-        ai, ak = ak, ai
-    if parity:
-        ai, aj, ak = -ai, -aj, -ak
-
-    si, sj, sk = math.sin(ai), math.sin(aj), math.sin(ak)
-    ci, cj, ck = math.cos(ai), math.cos(aj), math.cos(ak)
-    cc, cs = ci*ck, ci*sk
-    sc, ss = si*ck, si*sk
-
-    M = numpy.identity(4)
-    if repetition:
-        M[i, i] = cj
-        M[i, j] = sj*si
-        M[i, k] = sj*ci
-        M[j, i] = sj*sk
-        M[j, j] = -cj*ss+cc
-        M[j, k] = -cj*cs-sc
-        M[k, i] = -sj*ck
-        M[k, j] = cj*sc+cs
-        M[k, k] = cj*cc-ss
-    else:
-        M[i, i] = cj*ck
-        M[i, j] = sj*sc-cs
-        M[i, k] = sj*cc+ss
-        M[j, i] = cj*sk
-        M[j, j] = sj*ss+cc
-        M[j, k] = sj*cs-sc
-        M[k, i] = -sj
-        M[k, j] = cj*si
-        M[k, k] = cj*ci
-    return M
-
-
-def euler_from_matrix(matrix, axes='sxyz'):
-    """Return Euler angles from rotation matrix for specified axis sequence.
-
-    axes : One of 24 axis sequences as string or encoded tuple
-
-    Note that many Euler angle triplets can describe one matrix.
-
-    >>> R0 = euler_matrix(1, 2, 3, 'syxz')
-    >>> al, be, ga = euler_from_matrix(R0, 'syxz')
-    >>> R1 = euler_matrix(al, be, ga, 'syxz')
-    >>> numpy.allclose(R0, R1)
-    True
-    >>> angles = (4.0*math.pi) * (numpy.random.random(3) - 0.5)
-    >>> for axes in _AXES2TUPLE.keys():
-    ...    R0 = euler_matrix(axes=axes, *angles)
-    ...    R1 = euler_matrix(axes=axes, *euler_from_matrix(R0, axes))
-    ...    if not numpy.allclose(R0, R1): print axes, "failed"
-
-    """
-    try:
-        firstaxis, parity, repetition, frame = _AXES2TUPLE[axes.lower()]
-    except (AttributeError, KeyError):
-        _ = _TUPLE2AXES[axes]
-        firstaxis, parity, repetition, frame = axes
-
-    i = firstaxis
-    j = _NEXT_AXIS[i+parity]
-    k = _NEXT_AXIS[i-parity+1]
-
-    M = numpy.array(matrix, dtype=numpy.float64, copy=False)[:3, :3]
-    if repetition:
-        sy = math.sqrt(M[i, j]*M[i, j] + M[i, k]*M[i, k])
-        if sy > _EPS:
-            ax = math.atan2( M[i, j],  M[i, k])
-            ay = math.atan2( sy,       M[i, i])
-            az = math.atan2( M[j, i], -M[k, i])
-        else:
-            ax = math.atan2(-M[j, k],  M[j, j])
-            ay = math.atan2( sy,       M[i, i])
-            az = 0.0
-    else:
-        cy = math.sqrt(M[i, i]*M[i, i] + M[j, i]*M[j, i])
-        if cy > _EPS:
-            ax = math.atan2( M[k, j],  M[k, k])
-            ay = math.atan2(-M[k, i],  cy)
-            az = math.atan2( M[j, i],  M[i, i])
-        else:
-            ax = math.atan2(-M[j, k],  M[j, j])
-            ay = math.atan2(-M[k, i],  cy)
-            az = 0.0
-
-    if parity:
-        ax, ay, az = -ax, -ay, -az
-    if frame:
-        ax, az = az, ax
-    return ax, ay, az
-
-
-def euler_from_quaternion(quaternion, axes='sxyz'):
-    """Return Euler angles from quaternion for specified axis sequence.
-
-    >>> angles = euler_from_quaternion([0.06146124, 0, 0, 0.99810947])
-    >>> numpy.allclose(angles, [0.123, 0, 0])
-    True
-
-    """
-    return euler_from_matrix(quaternion_matrix(quaternion), axes)
-
-
-def quaternion_from_euler(ai, aj, ak, axes='sxyz'):
-    """Return quaternion from Euler angles and axis sequence.
-
-    ai, aj, ak : Euler's roll, pitch and yaw angles
-    axes : One of 24 axis sequences as string or encoded tuple
-
-    >>> q = quaternion_from_euler(1, 2, 3, 'ryxz')
-    >>> numpy.allclose(q, [0.310622, -0.718287, 0.444435, 0.435953])
-    True
-
-    """
-    try:
-        firstaxis, parity, repetition, frame = _AXES2TUPLE[axes.lower()]
-    except (AttributeError, KeyError):
-        _ = _TUPLE2AXES[axes]
-        firstaxis, parity, repetition, frame = axes
-
-    i = firstaxis
-    j = _NEXT_AXIS[i+parity]
-    k = _NEXT_AXIS[i-parity+1]
-
-    if frame:
-        ai, ak = ak, ai
-    if parity:
-        aj = -aj
-
-    ai /= 2.0
-    aj /= 2.0
-    ak /= 2.0
-    ci = math.cos(ai)
-    si = math.sin(ai)
-    cj = math.cos(aj)
-    sj = math.sin(aj)
-    ck = math.cos(ak)
-    sk = math.sin(ak)
-    cc = ci*ck
-    cs = ci*sk
-    sc = si*ck
-    ss = si*sk
-
-    quaternion = numpy.empty((4, ), dtype=numpy.float64)
-    if repetition:
-        quaternion[i] = cj*(cs + sc)
-        quaternion[j] = sj*(cc + ss)
-        quaternion[k] = sj*(cs - sc)
-        quaternion[3] = cj*(cc - ss)
-    else:
-        quaternion[i] = cj*sc - sj*cs
-        quaternion[j] = cj*ss + sj*cc
-        quaternion[k] = cj*cs - sj*sc
-        quaternion[3] = cj*cc + sj*ss
-    if parity:
-        quaternion[j] *= -1
-
-    return quaternion
-
-
-def quaternion_about_axis(angle, axis):
-    """Return quaternion for rotation about axis.
-
-    >>> q = quaternion_about_axis(0.123, (1, 0, 0))
-    >>> numpy.allclose(q, [0.06146124, 0, 0, 0.99810947])
-    True
-
-    """
-    quaternion = numpy.zeros((4, ), dtype=numpy.float64)
-    quaternion[:3] = axis[:3]
-    qlen = vector_norm(quaternion)
-    if qlen > _EPS:
-        quaternion *= math.sin(angle/2.0) / qlen
-    quaternion[3] = math.cos(angle/2.0)
-    return quaternion
-
-
-def quaternion_matrix(quaternion):
-    """Return homogeneous rotation matrix from quaternion.
-
-    >>> R = quaternion_matrix([0.06146124, 0, 0, 0.99810947])
-    >>> numpy.allclose(R, rotation_matrix(0.123, (1, 0, 0)))
-    True
-
-    """
-    q = numpy.array(quaternion[:4], dtype=numpy.float64, copy=True)
-    nq = numpy.dot(q, q)
-    if nq < _EPS:
-        return numpy.identity(4)
-    q *= math.sqrt(2.0 / nq)
-    q = numpy.outer(q, q)
-    return numpy.array((
-        (1.0-q[1, 1]-q[2, 2],     q[0, 1]-q[2, 3],     q[0, 2]+q[1, 3], 0.0),
-        (    q[0, 1]+q[2, 3], 1.0-q[0, 0]-q[2, 2],     q[1, 2]-q[0, 3], 0.0),
-        (    q[0, 2]-q[1, 3],     q[1, 2]+q[0, 3], 1.0-q[0, 0]-q[1, 1], 0.0),
-        (                0.0,                 0.0,                 0.0, 1.0)
-        ), dtype=numpy.float64)
-
-
-def quaternion_from_matrix(matrix):
-    """Return quaternion from rotation matrix.
-
-    >>> R = rotation_matrix(0.123, (1, 2, 3))
-    >>> q = quaternion_from_matrix(R)
-    >>> numpy.allclose(q, [0.0164262, 0.0328524, 0.0492786, 0.9981095])
-    True
-
-    """
-    q = numpy.empty((4, ), dtype=numpy.float64)
-    M = numpy.array(matrix, dtype=numpy.float64, copy=False)[:4, :4]
-    t = numpy.trace(M)
-    if t > M[3, 3]:
-        q[3] = t
-        q[2] = M[1, 0] - M[0, 1]
-        q[1] = M[0, 2] - M[2, 0]
-        q[0] = M[2, 1] - M[1, 2]
-    else:
-        i, j, k = 0, 1, 2
-        if M[1, 1] > M[0, 0]:
-            i, j, k = 1, 2, 0
-        if M[2, 2] > M[i, i]:
-            i, j, k = 2, 0, 1
-        t = M[i, i] - (M[j, j] + M[k, k]) + M[3, 3]
-        q[i] = t
-        q[j] = M[i, j] + M[j, i]
-        q[k] = M[k, i] + M[i, k]
-        q[3] = M[k, j] - M[j, k]
-    q *= 0.5 / math.sqrt(t * M[3, 3])
-    return q
-
-
-def quaternion_multiply(quaternion1, quaternion0):
-    """Return multiplication of two quaternions.
-
-    >>> q = quaternion_multiply([1, -2, 3, 4], [-5, 6, 7, 8])
-    >>> numpy.allclose(q, [-44, -14, 48, 28])
-    True
-
-    """
-    x0, y0, z0, w0 = quaternion0
-    x1, y1, z1, w1 = quaternion1
-    return numpy.array((
-         x1*w0 + y1*z0 - z1*y0 + w1*x0,
-        -x1*z0 + y1*w0 + z1*x0 + w1*y0,
-         x1*y0 - y1*x0 + z1*w0 + w1*z0,
-        -x1*x0 - y1*y0 - z1*z0 + w1*w0), dtype=numpy.float64)
-
-
-def quaternion_conjugate(quaternion):
-    """Return conjugate of quaternion.
-
-    >>> q0 = random_quaternion()
-    >>> q1 = quaternion_conjugate(q0)
-    >>> q1[3] == q0[3] and all(q1[:3] == -q0[:3])
-    True
-
-    """
-    return numpy.array((-quaternion[0], -quaternion[1],
-                        -quaternion[2], quaternion[3]), dtype=numpy.float64)
-
-
-def quaternion_inverse(quaternion):
-    """Return inverse of quaternion.
-
-    >>> q0 = random_quaternion()
-    >>> q1 = quaternion_inverse(q0)
-    >>> numpy.allclose(quaternion_multiply(q0, q1), [0, 0, 0, 1])
-    True
-
-    """
-    return quaternion_conjugate(quaternion) / numpy.dot(quaternion, quaternion)
-
-
-def quaternion_slerp(quat0, quat1, fraction, spin=0, shortestpath=True):
-    """Return spherical linear interpolation between two quaternions.
-
-    >>> q0 = random_quaternion()
-    >>> q1 = random_quaternion()
-    >>> q = quaternion_slerp(q0, q1, 0.0)
-    >>> numpy.allclose(q, q0)
-    True
-    >>> q = quaternion_slerp(q0, q1, 1.0, 1)
-    >>> numpy.allclose(q, q1)
-    True
-    >>> q = quaternion_slerp(q0, q1, 0.5)
-    >>> angle = math.acos(numpy.dot(q0, q))
-    >>> numpy.allclose(2.0, math.acos(numpy.dot(q0, q1)) / angle) or \
-        numpy.allclose(2.0, math.acos(-numpy.dot(q0, q1)) / angle)
-    True
-
-    """
-    q0 = unit_vector(quat0[:4])
-    q1 = unit_vector(quat1[:4])
-    if fraction == 0.0:
-        return q0
-    elif fraction == 1.0:
-        return q1
-    d = numpy.dot(q0, q1)
-    if abs(abs(d) - 1.0) < _EPS:
-        return q0
-    if shortestpath and d < 0.0:
-        # invert rotation
-        d = -d
-        q1 *= -1.0
-    angle = math.acos(d) + spin * math.pi
-    if abs(angle) < _EPS:
-        return q0
-    isin = 1.0 / math.sin(angle)
-    q0 *= math.sin((1.0 - fraction) * angle) * isin
-    q1 *= math.sin(fraction * angle) * isin
-    q0 += q1
-    return q0
-
-
-def random_quaternion(rand=None):
-    """Return uniform random unit quaternion.
-
-    rand: array like or None
-        Three independent random variables that are uniformly distributed
-        between 0 and 1.
-
-    >>> q = random_quaternion()
-    >>> numpy.allclose(1.0, vector_norm(q))
-    True
-    >>> q = random_quaternion(numpy.random.random(3))
-    >>> q.shape
-    (4,)
-
-    """
-    if rand is None:
-        rand = numpy.random.rand(3)
-    else:
-        assert len(rand) == 3
-    r1 = numpy.sqrt(1.0 - rand[0])
-    r2 = numpy.sqrt(rand[0])
-    pi2 = math.pi * 2.0
-    t1 = pi2 * rand[1]
-    t2 = pi2 * rand[2]
-    return numpy.array((numpy.sin(t1)*r1,
-                        numpy.cos(t1)*r1,
-                        numpy.sin(t2)*r2,
-                        numpy.cos(t2)*r2), dtype=numpy.float64)
-
-
-def random_rotation_matrix(rand=None):
-    """Return uniform random rotation matrix.
-
-    rnd: array like
-        Three independent random variables that are uniformly distributed
-        between 0 and 1 for each returned quaternion.
-
-    >>> R = random_rotation_matrix()
-    >>> numpy.allclose(numpy.dot(R.T, R), numpy.identity(4))
-    True
-
-    """
-    return quaternion_matrix(random_quaternion(rand))
-
-
-class Arcball(object):
-    """Virtual Trackball Control.
-
-    >>> ball = Arcball()
-    >>> ball = Arcball(initial=numpy.identity(4))
-    >>> ball.place([320, 320], 320)
-    >>> ball.down([500, 250])
-    >>> ball.drag([475, 275])
-    >>> R = ball.matrix()
-    >>> numpy.allclose(numpy.sum(R), 3.90583455)
-    True
-    >>> ball = Arcball(initial=[0, 0, 0, 1])
-    >>> ball.place([320, 320], 320)
-    >>> ball.setaxes([1,1,0], [-1, 1, 0])
-    >>> ball.setconstrain(True)
-    >>> ball.down([400, 200])
-    >>> ball.drag([200, 400])
-    >>> R = ball.matrix()
-    >>> numpy.allclose(numpy.sum(R), 0.2055924)
-    True
-    >>> ball.next()
-
-    """
-
-    def __init__(self, initial=None):
-        """Initialize virtual trackball control.
-
-        initial : quaternion or rotation matrix
-
-        """
-        self._axis = None
-        self._axes = None
-        self._radius = 1.0
-        self._center = [0.0, 0.0]
-        self._vdown = numpy.array([0, 0, 1], dtype=numpy.float64)
-        self._constrain = False
-
-        if initial is None:
-            self._qdown = numpy.array([0, 0, 0, 1], dtype=numpy.float64)
-        else:
-            initial = numpy.array(initial, dtype=numpy.float64)
-            if initial.shape == (4, 4):
-                self._qdown = quaternion_from_matrix(initial)
-            elif initial.shape == (4, ):
-                initial /= vector_norm(initial)
-                self._qdown = initial
-            else:
-                raise ValueError("initial not a quaternion or matrix.")
-
-        self._qnow = self._qpre = self._qdown
-
-    def place(self, center, radius):
-        """Place Arcball, e.g. when window size changes.
-
-        center : sequence[2]
-            Window coordinates of trackball center.
-        radius : float
-            Radius of trackball in window coordinates.
-
-        """
-        self._radius = float(radius)
-        self._center[0] = center[0]
-        self._center[1] = center[1]
-
-    def setaxes(self, *axes):
-        """Set axes to constrain rotations."""
-        if axes is None:
-            self._axes = None
-        else:
-            self._axes = [unit_vector(axis) for axis in axes]
-
-    def setconstrain(self, constrain):
-        """Set state of constrain to axis mode."""
-        self._constrain = constrain == True
-
-    def getconstrain(self):
-        """Return state of constrain to axis mode."""
-        return self._constrain
-
-    def down(self, point):
-        """Set initial cursor window coordinates and pick constrain-axis."""
-        self._vdown = arcball_map_to_sphere(point, self._center, self._radius)
-        self._qdown = self._qpre = self._qnow
-
-        if self._constrain and self._axes is not None:
-            self._axis = arcball_nearest_axis(self._vdown, self._axes)
-            self._vdown = arcball_constrain_to_axis(self._vdown, self._axis)
-        else:
-            self._axis = None
-
-    def drag(self, point):
-        """Update current cursor window coordinates."""
-        vnow = arcball_map_to_sphere(point, self._center, self._radius)
-
-        if self._axis is not None:
-            vnow = arcball_constrain_to_axis(vnow, self._axis)
-
-        self._qpre = self._qnow
-
-        t = numpy.cross(self._vdown, vnow)
-        if numpy.dot(t, t) < _EPS:
-            self._qnow = self._qdown
-        else:
-            q = [t[0], t[1], t[2], numpy.dot(self._vdown, vnow)]
-            self._qnow = quaternion_multiply(q, self._qdown)
-
-    def next(self, acceleration=0.0):
-        """Continue rotation in direction of last drag."""
-        q = quaternion_slerp(self._qpre, self._qnow, 2.0+acceleration, False)
-        self._qpre, self._qnow = self._qnow, q
-
-    def matrix(self):
-        """Return homogeneous rotation matrix."""
-        return quaternion_matrix(self._qnow)
-
-
-def arcball_map_to_sphere(point, center, radius):
-    """Return unit sphere coordinates from window coordinates."""
-    v = numpy.array(((point[0] - center[0]) / radius,
-                     (center[1] - point[1]) / radius,
-                     0.0), dtype=numpy.float64)
-    n = v[0]*v[0] + v[1]*v[1]
-    if n > 1.0:
-        v /= math.sqrt(n) # position outside of sphere
-    else:
-        v[2] = math.sqrt(1.0 - n)
-    return v
-
-
-def arcball_constrain_to_axis(point, axis):
-    """Return sphere point perpendicular to axis."""
-    v = numpy.array(point, dtype=numpy.float64, copy=True)
-    a = numpy.array(axis, dtype=numpy.float64, copy=True)
-    v -= a * numpy.dot(a, v) # on plane
-    n = vector_norm(v)
-    if n > _EPS:
-        if v[2] < 0.0:
-            v *= -1.0
-        v /= n
-        return v
-    if a[2] == 1.0:
-        return numpy.array([1, 0, 0], dtype=numpy.float64)
-    return unit_vector([-a[1], a[0], 0])
-
-
-def arcball_nearest_axis(point, axes):
-    """Return axis, which arc is nearest to point."""
-    point = numpy.array(point, dtype=numpy.float64, copy=False)
-    nearest = None
-    mx = -1.0
-    for axis in axes:
-        t = numpy.dot(arcball_constrain_to_axis(point, axis), point)
-        if t > mx:
-            nearest = axis
-            mx = t
-    return nearest
-
-
-# epsilon for testing whether a number is close to zero
-_EPS = numpy.finfo(float).eps * 4.0
-
-# axis sequences for Euler angles
-_NEXT_AXIS = [1, 2, 0, 1]
-
-# map axes strings to/from tuples of inner axis, parity, repetition, frame
-_AXES2TUPLE = {
-    'sxyz': (0, 0, 0, 0), 'sxyx': (0, 0, 1, 0), 'sxzy': (0, 1, 0, 0),
-    'sxzx': (0, 1, 1, 0), 'syzx': (1, 0, 0, 0), 'syzy': (1, 0, 1, 0),
-    'syxz': (1, 1, 0, 0), 'syxy': (1, 1, 1, 0), 'szxy': (2, 0, 0, 0),
-    'szxz': (2, 0, 1, 0), 'szyx': (2, 1, 0, 0), 'szyz': (2, 1, 1, 0),
-    'rzyx': (0, 0, 0, 1), 'rxyx': (0, 0, 1, 1), 'ryzx': (0, 1, 0, 1),
-    'rxzx': (0, 1, 1, 1), 'rxzy': (1, 0, 0, 1), 'ryzy': (1, 0, 1, 1),
-    'rzxy': (1, 1, 0, 1), 'ryxy': (1, 1, 1, 1), 'ryxz': (2, 0, 0, 1),
-    'rzxz': (2, 0, 1, 1), 'rxyz': (2, 1, 0, 1), 'rzyz': (2, 1, 1, 1)}
-
-_TUPLE2AXES = dict((v, k) for k, v in _AXES2TUPLE.items())
-
-# helper functions
-
-def vector_norm(data, axis=None, out=None):
-    """Return length, i.e. eucledian norm, of ndarray along axis.
-
-    >>> v = numpy.random.random(3)
-    >>> n = vector_norm(v)
-    >>> numpy.allclose(n, numpy.linalg.norm(v))
-    True
-    >>> v = numpy.random.rand(6, 5, 3)
-    >>> n = vector_norm(v, axis=-1)
-    >>> numpy.allclose(n, numpy.sqrt(numpy.sum(v*v, axis=2)))
-    True
-    >>> n = vector_norm(v, axis=1)
-    >>> numpy.allclose(n, numpy.sqrt(numpy.sum(v*v, axis=1)))
-    True
-    >>> v = numpy.random.rand(5, 4, 3)
-    >>> n = numpy.empty((5, 3), dtype=numpy.float64)
-    >>> vector_norm(v, axis=1, out=n)
-    >>> numpy.allclose(n, numpy.sqrt(numpy.sum(v*v, axis=1)))
-    True
-    >>> vector_norm([])
-    0.0
-    >>> vector_norm([1.0])
-    1.0
-
-    """
-    data = numpy.array(data, dtype=numpy.float64, copy=True)
-    if out is None:
-        if data.ndim == 1:
-            return math.sqrt(numpy.dot(data, data))
-        data *= data
-        out = numpy.atleast_1d(numpy.sum(data, axis=axis))
-        numpy.sqrt(out, out)
-        return out
-    else:
-        data *= data
-        numpy.sum(data, axis=axis, out=out)
-        numpy.sqrt(out, out)
-
-
-def unit_vector(data, axis=None, out=None):
-    """Return ndarray normalized by length, i.e. eucledian norm, along axis.
-
-    >>> v0 = numpy.random.random(3)
-    >>> v1 = unit_vector(v0)
-    >>> numpy.allclose(v1, v0 / numpy.linalg.norm(v0))
-    True
-    >>> v0 = numpy.random.rand(5, 4, 3)
-    >>> v1 = unit_vector(v0, axis=-1)
-    >>> v2 = v0 / numpy.expand_dims(numpy.sqrt(numpy.sum(v0*v0, axis=2)), 2)
-    >>> numpy.allclose(v1, v2)
-    True
-    >>> v1 = unit_vector(v0, axis=1)
-    >>> v2 = v0 / numpy.expand_dims(numpy.sqrt(numpy.sum(v0*v0, axis=1)), 1)
-    >>> numpy.allclose(v1, v2)
-    True
-    >>> v1 = numpy.empty((5, 4, 3), dtype=numpy.float64)
-    >>> unit_vector(v0, axis=1, out=v1)
-    >>> numpy.allclose(v1, v2)
-    True
-    >>> list(unit_vector([]))
-    []
-    >>> list(unit_vector([1.0]))
-    [1.0]
-
-    """
-    if out is None:
-        data = numpy.array(data, dtype=numpy.float64, copy=True)
-        if data.ndim == 1:
-            data /= math.sqrt(numpy.dot(data, data))
-            return data
-    else:
-        if out is not data:
-            out[:] = numpy.array(data, copy=False)
-        data = out
-    length = numpy.atleast_1d(numpy.sum(data*data, axis))
-    numpy.sqrt(length, length)
-    if axis is not None:
-        length = numpy.expand_dims(length, axis)
-    data /= length
-    if out is None:
-        return data
-
-
-def random_vector(size):
-    """Return array of random doubles in the half-open interval [0.0, 1.0).
-
-    >>> v = random_vector(10000)
-    >>> numpy.all(v >= 0.0) and numpy.all(v < 1.0)
-    True
-    >>> v0 = random_vector(10)
-    >>> v1 = random_vector(10)
-    >>> numpy.any(v0 == v1)
-    False
-
-    """
-    return numpy.random.random(size)
-
-
-def inverse_matrix(matrix):
-    """Return inverse of square transformation matrix.
-
-    >>> M0 = random_rotation_matrix()
-    >>> M1 = inverse_matrix(M0.T)
-    >>> numpy.allclose(M1, numpy.linalg.inv(M0.T))
-    True
-    >>> for size in range(1, 7):
-    ...     M0 = numpy.random.rand(size, size)
-    ...     M1 = inverse_matrix(M0)
-    ...     if not numpy.allclose(M1, numpy.linalg.inv(M0)): print size
-
-    """
-    return numpy.linalg.inv(matrix)
-
-
-def concatenate_matrices(*matrices):
-    """Return concatenation of series of transformation matrices.
-
-    >>> M = numpy.random.rand(16).reshape((4, 4)) - 0.5
-    >>> numpy.allclose(M, concatenate_matrices(M))
-    True
-    >>> numpy.allclose(numpy.dot(M, M.T), concatenate_matrices(M, M.T))
-    True
-
-    """
-    M = numpy.identity(4)
-    for i in matrices:
-        M = numpy.dot(M, i)
-    return M
-
-
-def is_same_transform(matrix0, matrix1):
-    """Return True if two matrices perform same transformation.
-
-    >>> is_same_transform(numpy.identity(4), numpy.identity(4))
-    True
-    >>> is_same_transform(numpy.identity(4), random_rotation_matrix())
-    False
-
-    """
-    matrix0 = numpy.array(matrix0, dtype=numpy.float64, copy=True)
-    matrix0 /= matrix0[3, 3]
-    matrix1 = numpy.array(matrix1, dtype=numpy.float64, copy=True)
-    matrix1 /= matrix1[3, 3]
-    return numpy.allclose(matrix0, matrix1)
-
-
-def _import_module(module_name, warn=True, prefix='_py_', ignore='_'):
-    """Try import all public attributes from module into global namespace.
-
-    Existing attributes with name clashes are renamed with prefix.
-    Attributes starting with underscore are ignored by default.
-
-    Return True on successful import.
-
-    """
-    try:
-        module = __import__(module_name)
-    except ImportError:
-        if warn:
-            warnings.warn("Failed to import module " + module_name)
-    else:
-        for attr in dir(module):
-            if ignore and attr.startswith(ignore):
-                continue
-            if prefix:
-                if attr in globals():
-                    globals()[prefix + attr] = globals()[attr]
-                elif warn:
-                    warnings.warn("No Python implementation of " + attr)
-            globals()[attr] = getattr(module, attr)
-        return True
diff --git a/svo_analysis/setup.py b/svo_analysis/setup.py
new file mode 100644
index 0000000..0d94673
--- /dev/null
+++ b/svo_analysis/setup.py
@@ -0,0 +1,12 @@
+#!/usr/bin/env python
+
+from distutils.core import setup
+from catkin_pkg.python_setup import generate_distutils_setup
+
+d = generate_distutils_setup(
+    packages=['svo_analysis'],
+    package_dir={'': 'src'},
+    install_requires=['rospkg', 'yaml'],
+    )
+
+setup(**d)
\ No newline at end of file
diff --git a/svo_analysis/src/svo_analysis/__init__.py b/svo_analysis/src/svo_analysis/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/svo_analysis/scripts/analyse_dataset.py b/svo_analysis/src/svo_analysis/analyse_dataset.py
similarity index 98%
rename from svo_analysis/scripts/analyse_dataset.py
rename to svo_analysis/src/svo_analysis/analyse_dataset.py
index fa257b8..53a2501 100644
--- a/svo_analysis/scripts/analyse_dataset.py
+++ b/svo_analysis/src/svo_analysis/analyse_dataset.py
@@ -4,7 +4,6 @@ import associate
 import numpy as np
 import matplotlib.pyplot as plt
 import yaml
-import transformations
 
 def loadDataset(filename):
   file = open(filename)
diff --git a/svo_analysis/src/svo_analysis/analyse_logs.py b/svo_analysis/src/svo_analysis/analyse_logs.py
new file mode 100755
index 0000000..4e40bfe
--- /dev/null
+++ b/svo_analysis/src/svo_analysis/analyse_logs.py
@@ -0,0 +1,68 @@
+#!/usr/bin/python
+
+import os
+import numpy as np
+import matplotlib.pyplot as plt
+  
+def analyse_logs(D, trace_dir):
+
+  # identify measurements which result from normal frames and which from keyframes
+  is_kf = np.argwhere( (D['dropout'] == 1) & (D['repr_n_mps'] >= 0))
+  is_frame = np.argwhere(D['repr_n_mps'] >= 0)
+  is_nokf = np.argwhere( (D['dropout'] == 0) & (D['repr_n_mps'] >= 0))
+  n_frames = len(is_frame)
+  n_kfs = len(is_kf)
+  n_nokf = len(is_nokf)
+  
+  # set initial time to zero
+  D['timestamp'] = D['timestamp'] - D['timestamp'][0]
+  
+  # ----------------------------------------------------------------------------
+  # plot number of reprojected points
+  mean_n_reproj_points = np.mean(D['repr_n_mps'][is_frame]);
+  mean_n_reproj_matches = np.mean(D['repr_n_new_references'][is_frame]);
+  mean_n_edges_final = np.mean(D['sfba_n_edges_final'][is_frame]);
+
+  fig = plt.figure(figsize=(8,3))
+  ax = fig.add_subplot(111, xlabel='time [s]')
+  ax.plot(D['timestamp'][is_frame], D['repr_n_mps'][is_frame], 'r-',
+          label='Reprojected Points, avg = %.2f'%mean_n_reproj_points)
+  ax.plot(D['timestamp'][is_frame], D['repr_n_new_references'][is_frame], 'b-', 
+          label='Feature Matches, avg = %.2f'%mean_n_reproj_matches)
+  ax.plot(D['timestamp'][is_frame], D['sfba_n_edges_final'][is_frame], 'g-', 
+          label='Points after Optimization, avg = %.2f'%mean_n_edges_final)
+  ax.set_ylim(bottom=0)
+  ax.legend(loc='lower right')
+  fig.tight_layout()
+  fig.savefig(os.path.join(trace_dir,'num_reprojected.pdf'), bbox_inches="tight")
+
+  # ----------------------------------------------------------------------------
+  # plot median error before and after pose-optimzation and bundle adjustment
+  init_error_avg = np.mean(D['sfba_error_init'][is_frame])
+  opt1_avg = np.mean(D['sfba_error_final'][is_frame])
+
+  fig = plt.figure(figsize=(8,2))
+  ax = fig.add_subplot(111, xlabel='time [s]', ylabel='error [px]')
+  ax.plot(D['timestamp'][is_frame], D['sfba_error_init'][is_frame], 'r-', label='Initial error')
+  ax.plot(D['timestamp'][is_frame], D['sfba_error_final'][is_frame], 'b-', label='Final error')
+  ax.legend(ncol=2)  
+  fig.tight_layout()
+  fig.savefig(os.path.join(trace_dir,'reprojection_error.pdf'), bbox_inches="tight")
+  print 'average reprojection error improvement: ' + str(init_error_avg - opt1_avg)
+
+  # ----------------------------------------------------------------------------
+  # plot number of candidate points
+  fig = plt.figure(figsize=(8,3))
+  ax = fig.add_subplot(111, xlabel='time [s]')
+  ax.plot(D['timestamp'][is_frame], D['n_candidates'][is_frame], 'r-', label='Candidate Points')
+  fig.tight_layout()
+  fig.savefig(os.path.join(trace_dir,'candidate_points.pdf'), bbox_inches="tight")
+  
+  # ----------------------------------------------------------------------------
+  # plot number of candidate points
+  fig = plt.figure(figsize=(8,2))
+  ax = fig.add_subplot(111, xlabel='time [s]', ylabel='px')
+  ax.plot(D['timestamp'][is_frame], D['sfba_thresh'][is_frame], 'r-', label='Threshold')
+  fig.tight_layout()
+  fig.savefig(os.path.join(trace_dir,'optimization_thresh.pdf'), bbox_inches="tight")
+
diff --git a/svo_analysis/src/svo_analysis/analyse_timing.py b/svo_analysis/src/svo_analysis/analyse_timing.py
new file mode 100755
index 0000000..0dfc1bc
--- /dev/null
+++ b/svo_analysis/src/svo_analysis/analyse_timing.py
@@ -0,0 +1,68 @@
+#!/usr/bin/python
+
+import os
+import numpy as np
+import matplotlib.pyplot as plt
+
+def analyse_timing(D, trace_dir):
+
+  # identify measurements which result from normal frames and which from keyframes
+  is_frame = np.argwhere(D['repr_n_mps'] >= 0)
+  n_frames = len(is_frame)
+  
+  # set initial time to zero
+  D['timestamp'] = D['timestamp'] - D['timestamp'][0]
+
+  # ----------------------------------------------------------------------------
+  # plot total time for frame processing
+  avg_time = np.mean(D['tot_time'][is_frame])*1000;
+  fig = plt.figure(figsize=(8, 3))
+  ax = fig.add_subplot(111, ylabel='processing time [ms]', xlabel='time [s]')
+  ax.plot(D['timestamp'][is_frame], D['tot_time'][is_frame]*1000, 'g-', label='total time [ms]')
+  ax.plot(D['timestamp'][is_frame], np.ones(n_frames)*avg_time, 'b--', label=str('%(time).1fms mean time' % {'time': avg_time}))
+  ax.legend()
+  fig.tight_layout()
+  fig.savefig(os.path.join(trace_dir,'timing.pdf'), bbox_inches="tight")
+  
+  # ----------------------------------------------------------------------------
+  # plot boxplot
+  fig = plt.figure(figsize=(6,2))
+  ax = fig.add_subplot(111, xlabel='Processing time [ms]')
+  ax.boxplot([ 
+                D['tot_time'][is_frame]*1000,
+#                D['t_local_ba'][is_kf]*1000,
+                D['pose_optimizer'][is_frame]*1000 + D['point_optimizer'][is_frame]*1000,
+                D['reproject'][is_frame]*1000,
+                D['sparse_img_align'][is_frame]*1000,
+                D['pyramid_creation'][is_frame]*1000
+              ], 0,'', vert=0) 
+               
+  boxplot_labels = [
+                    r'\textbf{Total Motion Estimation: %2.2fms}' % np.median(D['tot_time'][is_frame]*1000),
+#                    'Local BA (KF only): %.2fms ' % np.median(D['local_ba'][is_kf]*1000),
+                    'Refinement: %2.2fms' % np.median(D['pose_optimizer'][is_frame]*1000 + D['point_optimizer'][is_frame]*1000),
+                    'Feature Alignment: %2.2fms' % np.median(D['reproject'][is_frame]*1000),
+                    'Sparse Image Alignment: %2.2fms' % np.median(D['sparse_img_align'][is_frame]*1000),
+                    'Pyramid Creation: %2.2fms' % np.median(D['pyramid_creation'][is_frame]*1000) ]
+                   
+  ax.set_yticks(np.arange(len(boxplot_labels))+1)
+  ax.set_yticklabels(boxplot_labels)
+  fig.tight_layout()
+  fig.savefig(os.path.join(trace_dir,'timing_boxplot.pdf'), bbox_inches="tight")
+  
+  # ----------------------------------------------------------------------------
+  # plot boxplot reprojection
+  fig = plt.figure(figsize=(6,2))
+  ax = fig.add_subplot(111, xlabel='Processing time [ms]')
+  ax.boxplot([ D['reproject'][is_frame]*1000,
+               D['feature_align'][is_frame]*1000,
+               D['reproject_candidates'][is_frame]*1000,
+               D['reproject_kfs'][is_frame]*1000 ], 0, '', vert=0) 
+  boxplot_labels = [r'\textbf{Total Reprojection: %2.2fms}' % np.median(D['reproject'][is_frame]*1000),
+                     'Feature Alignment: %2.2fms' % np.median(D['feature_align'][is_frame]*1000),
+                     'Reproject Candidates: %2.2fms' % np.median(D['reproject_candidates'][is_frame]*1000),
+                     'Reproject Keyframes: %2.2fms' % np.median(D['reproject_kfs'][is_frame]*1000) ]             
+  ax.set_yticks(np.arange(len(boxplot_labels))+1)
+  ax.set_yticklabels(boxplot_labels)
+  fig.tight_layout()
+  fig.savefig(os.path.join(trace_dir,'timing_reprojection.pdf'), bbox_inches="tight")
\ No newline at end of file
diff --git a/svo_analysis/scripts/analyse_trajectory.py b/svo_analysis/src/svo_analysis/analyse_trajectory.py
similarity index 72%
rename from svo_analysis/scripts/analyse_trajectory.py
rename to svo_analysis/src/svo_analysis/analyse_trajectory.py
index f70cd62..f2a800e 100644
--- a/svo_analysis/scripts/analyse_trajectory.py
+++ b/svo_analysis/src/svo_analysis/analyse_trajectory.py
@@ -1,43 +1,52 @@
 # -*- coding: utf-8 -*-
 
-import associate
-import numpy as np
-import matplotlib.pyplot as plt
+import os
 import yaml
-import transformations
+from .tum_benchmark_tools import associate
+import matplotlib.pyplot as plt
+import vikit_py.transformations as transformations
+import vikit_py.align_trajectory as align_trajectory
+import numpy as np
+
+# plot options
 from matplotlib import rc
+rc('font',**{'family':'serif','serif':['Cardo']})
+rc('text', usetex=True)
 
-def alignSim3(model, data, n):
+def plot_translation_error(timestamps, translation_error, results_dir):
+  fig = plt.figure(figsize=(8, 2.5))
+  ax = fig.add_subplot(111, xlabel='time [s]', ylabel='position drift [mm]', xlim=[0,timestamps[-1]-timestamps[0]+4])
+  ax.plot(timestamps-timestamps[0], translation_error[:,0]*1000, 'r-', label='x')
+  ax.plot(timestamps-timestamps[0], translation_error[:,1]*1000, 'g-', label='y')
+  ax.plot(timestamps-timestamps[0], translation_error[:,2]*1000, 'b-', label='z')
+  ax.legend()
+  fig.tight_layout()
+  fig.savefig(results_dir+'/translation_error.pdf')
 
-  # select the first n datapoints and remove its mean so their center is zero
-  M = model[0:n,:]
-  D = data[0:n,:]
+def plot_rotation_error(timestamps, rotation_error, results_dir):
+  fig = plt.figure(figsize=(8, 2.5))
+  ax = fig.add_subplot(111, xlabel='time [s]', ylabel='orientation drift [rad]', xlim=[0,timestamps[-1]-timestamps[0]+4])
+  ax.plot(timestamps-timestamps[0], rotation_error[:,0], 'r-', label='yaw')
+  ax.plot(timestamps-timestamps[0], rotation_error[:,1], 'g-', label='pitch')
+  ax.plot(timestamps-timestamps[0], rotation_error[:,2], 'b-', label='roll')
+  ax.legend()
+  fig.tight_layout()
+  fig.savefig(results_dir+'/orientation_error.pdf')
 
-  # substract mean
-  mu_M = M.mean(0)
-  mu_D = D.mean(0)
-  M_zerocentered = M - mu_M
-  D_zerocentered = D - mu_D
-
-  # correlation
-  C = 1.0/n*np.dot(M_zerocentered.transpose(), D_zerocentered)
-  sigma2 = 1.0/n*np.multiply(D_zerocentered,D_zerocentered).sum()
-  U_svd,D_svd,V_svd = np.linalg.linalg.svd(C)
-  D_svd = np.diag(D_svd)
-  V_svd = np.transpose(V_svd)
-  S = np.eye(3)
-
-  if(np.linalg.det(U_svd)*np.linalg.det(V_svd) < 0):
-    S[2,2] = -1
-
-  R = np.dot(U_svd, np.dot(S, np.transpose(V_svd)))
-  s = 1.0/sigma2*np.trace(np.dot(D_svd, S))
-  t = mu_M-s*np.dot(R,mu_D)
-
-  return s, R, t
-
-
-def getRigidBodyTrafo(quat,trans):
+def analyse_trajectory(results_dir):
+  # plot translation error
+  data = np.loadtxt(os.path.join(results_dir, 'translation_error.txt'))
+  timestamps = data[:,0]
+  translation_error = data[:,1:4]
+  plot_translation_error(timestamps, translation_error, results_dir)
+  
+  # plot orientation error
+  data = np.loadtxt(os.path.join(results_dir, 'orientation_error.txt'))
+  timestamps = data[:,0]
+  orientation_error = data[:,1:4]
+  plot_rotation_error(timestamps, orientation_error, results_dir)
+  
+def get_rigid_body_trafo(quat,trans):
   T = transformations.quaternion_matrix(quat)
   T[0:3,3] = trans
   return T
@@ -50,10 +59,6 @@ if __name__ == '__main__':
   save = True
   results_dir = '../results/'+dataset
   
-  # plot options
-  rc('font',**{'family':'serif','serif':['Cardo']})
-  rc('text', usetex=True)
-  
   # load dataset parameters
   params_stream = open(results_dir+'/params.yaml')
   params = yaml.load(params_stream)
@@ -72,7 +77,7 @@ if __name__ == '__main__':
   P = yaml.load(dataset_param_file_stream)
   T_cm_quat = np.array([P['calib_Tcm_qx'], P['calib_Tcm_qy'], P['calib_Tcm_qz'], P['calib_Tcm_qw']])
   T_cm_tran = np.array([P['calib_Tcm_tx'], P['calib_Tcm_ty'], P['calib_Tcm_tz']])
-  T_cm = getRigidBodyTrafo(T_cm_quat, T_cm_tran)
+  T_cm = get_rigid_body_trafo(T_cm_quat, T_cm_tran)
   T_mc = transformations.inverse_matrix(T_cm)
   
   # load data
@@ -94,12 +99,12 @@ if __name__ == '__main__':
   # align Sim3 to get scale
   if(n_align_frames > np.shape(matches)[0]):
     raise NameError('n_matches_for_scale is too large')
-  scale,rot,trans = alignSim3(p_gt, p_es, n_align_frames)
+  scale,rot,trans = align_trajectory.align_sim3(p_gt, p_es, n_align_frames)
   print 'scale = '+str(scale)
   
   # get trafo between (v)ision and (o)ptitrack frame
-  T_om = getRigidBodyTrafo(q_gt[0,:], p_gt[0,:])
-  T_vc = getRigidBodyTrafo(q_es[0,:], scale*p_es[0,:])
+  T_om = get_rigid_body_trafo(q_gt[0,:], p_gt[0,:])
+  T_vc = get_rigid_body_trafo(q_es[0,:], scale*p_es[0,:])
   T_cv = transformations.inverse_matrix(T_vc)
   T_ov = np.dot(T_om, np.dot(T_mc, T_cv))
   print 'T_ov = ' + str(T_ov)
@@ -111,7 +116,7 @@ if __name__ == '__main__':
   
   # rotate trajectory
   for i in range(np.shape(matches)[0]):
-    T_vc = getRigidBodyTrafo(q_es[i,:],p_es[i,:])
+    T_vc = get_rigid_body_trafo(q_es[i,:],p_es[i,:])
     T_vc[0:3,3] *= scale
     T_om = np.dot(T_ov, np.dot(T_vc, T_cm))
     p_es_aligned[i,:] = T_om[0:3,3]
@@ -136,15 +141,9 @@ if __name__ == '__main__':
           
   # plot position error (drift)
   translation_error = (p_gt-p_es_aligned)
-  fig = plt.figure(figsize=(8, 2.5))
-  ax = fig.add_subplot(111, xlabel='time [s]', ylabel='position drift [m]', xlim=[0,t_es[-1]+4])
-  ax.plot(t_es, translation_error[:,0], 'r-', label='x')
-  ax.plot(t_es, translation_error[:,1], 'g-', label='y')
-  ax.plot(t_es, translation_error[:,2], 'b-', label='z')
-  fig.tight_layout()
+  plot_translation_error(t_es, translation_error, results_dir)
   
   if save:
-    fig.savefig(results_dir+'/translation_error.pdf')
     f = open(translation_error_filename,'w')
     for i in range(translation_error.shape[0]):
       f.write('%.7f %.5f %.5f %.5f\n'%(t_es[i], translation_error[i,0],  translation_error[i,1],  translation_error[i,2]))
@@ -152,16 +151,9 @@ if __name__ == '__main__':
     
   # plot orientation error (drift)
   orientation_error = (rpy_gt - rpy_es_aligned)
-  fig = plt.figure(figsize=(8, 2.5))
-  ax = fig.add_subplot(111, xlabel='time [s]', ylabel='orientation drift [rad]', xlim=[0,t_es[-1]+4])
-  ax.plot(t_es, orientation_error[:,0], 'r--', label='yaw')
-  ax.plot(t_es, orientation_error[:,1], 'g--', label='pitch')
-  ax.plot(t_es, orientation_error[:,2], 'b--', label='roll')
-  ax.legend()
-  fig.tight_layout()
-  
-  if save:
-    fig.savefig(results_dir+'/orientation_error.pdf')
+  plot_rotation_error(t_es, orientation_error, results_dir)
+    
+  if save:  
     f = open(orientation_error_filename,'w')
     for i in range(orientation_error.shape[0]):
       f.write('%.7f %.5f %.5f %.5f\n'%(t_es[i], orientation_error[i,0],  orientation_error[i,1],  orientation_error[i,2]))
diff --git a/svo_analysis/scripts/filter_groundtruth_smooth.py b/svo_analysis/src/svo_analysis/filter_groundtruth_smooth.py
similarity index 100%
rename from svo_analysis/scripts/filter_groundtruth_smooth.py
rename to svo_analysis/src/svo_analysis/filter_groundtruth_smooth.py
diff --git a/svo_analysis/scripts/hand_eye_calib.py b/svo_analysis/src/svo_analysis/hand_eye_calib.py
similarity index 71%
rename from svo_analysis/scripts/hand_eye_calib.py
rename to svo_analysis/src/svo_analysis/hand_eye_calib.py
index 7a7bb1b..f0eae14 100644
--- a/svo_analysis/scripts/hand_eye_calib.py
+++ b/svo_analysis/src/svo_analysis/hand_eye_calib.py
@@ -11,81 +11,6 @@ import associate
 import rotation_utils as ru
 import matplotlib.pyplot as plt
 
-def alignSim3(model, data, n):
-
-  # select the first n datapoints and remove its mean so their center is zero
-  M = model[0:n,:]
-  D = data[0:n,:]
-
-  # substract mean
-  mu_M = M.mean(0)
-  mu_D = D.mean(0)
-  M_zerocentered = M - mu_M
-  D_zerocentered = D - mu_D
-
-  # correlation
-  C = 1.0/n*np.dot(M_zerocentered.transpose(), D_zerocentered)
-  sigma2 = 1.0/n*np.multiply(D_zerocentered,D_zerocentered).sum()
-  U_svd,D_svd,V_svd = np.linalg.linalg.svd(C)
-  D_svd = np.diag(D_svd)
-  V_svd = np.transpose(V_svd)
-  S = np.eye(3)
-
-  if(np.linalg.det(U_svd)*np.linalg.det(V_svd) < 0):
-    S[2,2] = -1
-
-  R = np.dot(U_svd, np.dot(S, np.transpose(V_svd)))
-  s = 1.0/sigma2*np.trace(np.dot(D_svd, S))
-  t = mu_M-s*np.dot(R,mu_D)
-
-  return s, R, t
-
-def matrixLog(A):
-  theta = np.arccos((np.trace(A)-1.0)/2.0)
-  log_theta = 0.5*theta/np.sin(theta) * (A - A.transpose())
-  x = np.array([log_theta[2,1], log_theta[0,2], log_theta[1,0]])
-  return x
-
-def handEyeCalib(q_gt, q_es, p_gt, p_es, I, delta=10, verbose=True):
-  ''' Implementation of the least squares solution described in the paper:
-  Robot Sensor Calibration: Solving AX=XB on the Euclidean Group
-  by Frank C. Park and Bryan J. Martin
-  '''
-  n = np.shape(I)[0]
-  M = np.zeros([3,3])
-  C = np.zeros([3*n, 3])
-  b_A = np.zeros([3*n,1])
-  b_B = np.zeros([3*n,1])
-  for ix, i in enumerate(I):
-    A1 = ru.quat2dcm(q_es[i,:])
-    A2 = ru.quat2dcm(q_es[i+delta,:])
-    A  = np.dot(A1.transpose(), A2)
-    B1 = ru.quat2dcm(q_gt[i,:])
-    B2 = ru.quat2dcm(q_gt[i+delta,:])
-    B  = np.dot(B1.transpose(), B2)
-    alpha = matrixLog(A)
-    beta = matrixLog(B)
-    M = M + np.dot(np.matrix(beta).transpose(), np.matrix(alpha))
-    C[3*ix:3*ix+3,:] = np.eye(3) - A
-    b_A[3*ix:3*ix+3,0] = np.dot(np.transpose(A1), p_es[i+delta,:]-p_es[i,:])
-    b_B[3*ix:3*ix+3,0] = np.dot(np.transpose(B1), p_gt[i+delta,:]-p_gt[i,:])
-
-  # compute rotation  
-  D,V = np.linalg.linalg.eig(np.dot(M.transpose(), M))
-  Lambda = np.diag([np.sqrt(1.0/D[0]), np.sqrt(1.0/D[1]), np.sqrt(1.0/D[2])])
-  Vinv = np.linalg.linalg.inv(V)
-  X = np.dot(V, np.dot(Lambda, np.dot(Vinv, M.transpose())))
-
-  # compute translation
-  d = np.zeros([3*n,1])
-  for i in range(n):
-    d[3*i:3*i+3,:] = b_A[3*i:3*i+3,:] - np.dot(X, b_B[3*i:3*i+3,:])
-
-  b = np.dot(np.linalg.inv(np.dot(np.transpose(C),C)),  np.dot(np.transpose(C),d))
-
-  return np.array(X),b
-
-
 # user config
 display = True
 dataset = '20130906_2149_ptam_i7_asl2'
@@ -121,7 +46,7 @@ q_es = np.array([[float(value) for value in data_es[b][3:7]] for a,b in matches]
 
 # --------------------------------------------------------------------------------
 # align Sim3 to get scale
-scale,rot,trans = alignSim3(p_gt, p_es, np.shape(matches)[0])
+scale,rot,trans = align_sim3(p_gt, p_es, np.shape(matches)[0])
 p_es = scale*p_es
 #p_es = np.transpose(scale*np.dot(rot,np.transpose(p_es)))+trans
 
@@ -142,7 +67,7 @@ if display:
 
 # select random measurements
 I = np.array(rand(n_measurements,1)*(np.shape(matches)[0]-delta), dtype=int)[:,0]
-R,b = handEyeCalib(q_gt, q_es, p_gt, p_es, I, delta, True)
+R,b = hand_eye_calib(q_gt, q_es, p_gt, p_es, I, delta, True)
 
 print 'quat = ' + str(ru.dcm2quat(R))
 print 'b = ' + str(b)
diff --git a/svo_analysis/scripts/__init__.py b/svo_analysis/src/svo_analysis/tum_benchmark_tools/__init__.py
similarity index 100%
rename from svo_analysis/scripts/__init__.py
rename to svo_analysis/src/svo_analysis/tum_benchmark_tools/__init__.py
diff --git a/svo_analysis/src/svo_analysis/tum_benchmark_tools/associate.py b/svo_analysis/src/svo_analysis/tum_benchmark_tools/associate.py
new file mode 100755
index 0000000..554314a
--- /dev/null
+++ b/svo_analysis/src/svo_analysis/tum_benchmark_tools/associate.py
@@ -0,0 +1,128 @@
+#!/usr/bin/python
+# Software License Agreement (BSD License)
+#
+# Copyright (c) 2013, Juergen Sturm, TUM
+# All rights reserved.
+#
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions
+# are met:
+#
+#  * Redistributions of source code must retain the above copyright
+#    notice, this list of conditions and the following disclaimer.
+#  * Redistributions in binary form must reproduce the above
+#    copyright notice, this list of conditions and the following
+#    disclaimer in the documentation and/or other materials provided
+#    with the distribution.
+#  * Neither the name of TUM nor the names of its
+#    contributors may be used to endorse or promote products derived
+#    from this software without specific prior written permission.
+#
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
+# FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
+# COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
+# INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
+# BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
+# LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
+# ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+# POSSIBILITY OF SUCH DAMAGE.
+#
+# Requirements: 
+# sudo apt-get install python-argparse
+
+"""
+The Kinect provides the color and depth images in an un-synchronized way. This means that the set of time stamps from the color images do not intersect with those of the depth images. Therefore, we need some way of associating color images to depth images.
+
+For this purpose, you can use the ''associate.py'' script. It reads the time stamps from the rgb.txt file and the depth.txt file, and joins them by finding the best matches.
+"""
+
+import argparse
+import sys
+import os
+import numpy
+
+
+def read_file_list(filename):
+    """
+    Reads a trajectory from a text file. 
+    
+    File format:
+    The file format is "stamp d1 d2 d3 ...", where stamp denotes the time stamp (to be matched)
+    and "d1 d2 d3.." is arbitary data (e.g., a 3D position and 3D orientation) associated to this timestamp. 
+    
+    Input:
+    filename -- File name
+    
+    Output:
+    dict -- dictionary of (stamp,data) tuples
+    
+    """
+    file = open(filename)
+    data = file.read()
+    lines = data.replace(","," ").replace("\t"," ").split("\n") 
+    list = [[v.strip() for v in line.split(" ") if v.strip()!=""] for line in lines if len(line)>0 and line[0]!="#"]
+    list = [(float(l[0]),l[1:]) for l in list if len(l)>1]
+    return dict(list)
+
+def associate(first_list, second_list,offset,max_difference):
+    """
+    Associate two dictionaries of (stamp,data). As the time stamps never match exactly, we aim 
+    to find the closest match for every input tuple.
+    
+    Input:
+    first_list -- first dictionary of (stamp,data) tuples
+    second_list -- second dictionary of (stamp,data) tuples
+    offset -- time offset between both dictionaries (e.g., to model the delay between the sensors)
+    max_difference -- search radius for candidate generation
+
+    Output:
+    matches -- list of matched tuples ((stamp1,data1),(stamp2,data2))
+    
+    """
+    first_keys = first_list.keys()
+    second_keys = second_list.keys()
+    potential_matches = [(abs(a - (b + offset)), a, b) 
+                         for a in first_keys 
+                         for b in second_keys 
+                         if abs(a - (b + offset)) < max_difference]
+    potential_matches.sort()
+    matches = []
+    for diff, a, b in potential_matches:
+        if a in first_keys and b in second_keys:
+            first_keys.remove(a)
+            second_keys.remove(b)
+            matches.append((a, b))
+    
+    matches.sort()
+    return matches
+
+if __name__ == '__main__':
+    
+    # parse command line
+    parser = argparse.ArgumentParser(description='''
+    This script takes two data files with timestamps and associates them   
+    ''')
+    parser.add_argument('first_file', help='first text file (format: timestamp data)')
+    parser.add_argument('second_file', help='second text file (format: timestamp data)')
+    parser.add_argument('--first_only', help='only output associated lines from first file', action='store_true')
+    parser.add_argument('--offset', help='time offset added to the timestamps of the second file (default: 0.0)',default=0.0)
+    parser.add_argument('--max_difference', help='maximally allowed time difference for matching entries (default: 0.02)',default=0.02)
+    args = parser.parse_args()
+
+    first_list = read_file_list(args.first_file)
+    second_list = read_file_list(args.second_file)
+
+    matches = associate(first_list, second_list,float(args.offset),float(args.max_difference))    
+
+    if args.first_only:
+        for a,b in matches:
+            print("%f %s"%(a," ".join(first_list[a])))
+    else:
+        for a,b in matches:
+            print("%f %s %f %s"%(a," ".join(first_list[a]),b-float(args.offset)," ".join(second_list[b])))
+            
+        
diff --git a/svo_analysis/src/svo_analysis/tum_benchmark_tools/evaluate_ate.py b/svo_analysis/src/svo_analysis/tum_benchmark_tools/evaluate_ate.py
new file mode 100755
index 0000000..683b598
--- /dev/null
+++ b/svo_analysis/src/svo_analysis/tum_benchmark_tools/evaluate_ate.py
@@ -0,0 +1,195 @@
+#!/usr/bin/python
+# Software License Agreement (BSD License)
+#
+# Copyright (c) 2013, Juergen Sturm, TUM
+# All rights reserved.
+#
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions
+# are met:
+#
+#  * Redistributions of source code must retain the above copyright
+#    notice, this list of conditions and the following disclaimer.
+#  * Redistributions in binary form must reproduce the above
+#    copyright notice, this list of conditions and the following
+#    disclaimer in the documentation and/or other materials provided
+#    with the distribution.
+#  * Neither the name of TUM nor the names of its
+#    contributors may be used to endorse or promote products derived
+#    from this software without specific prior written permission.
+#
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
+# FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
+# COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
+# INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
+# BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
+# LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
+# ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+# POSSIBILITY OF SUCH DAMAGE.
+#
+# Requirements: 
+# sudo apt-get install python-argparse
+
+"""
+This script computes the absolute trajectory error from the ground truth
+trajectory and the estimated trajectory.
+"""
+
+import sys
+import numpy
+import argparse
+import associate
+
+def align(model,data):
+    """Align two trajectories using the method of Horn (closed-form).
+    
+    Input:
+    model -- first trajectory (3xn)
+    data -- second trajectory (3xn)
+    
+    Output:
+    rot -- rotation matrix (3x3)
+    trans -- translation vector (3x1)
+    trans_error -- translational error per point (1xn)
+    
+    """
+    numpy.set_printoptions(precision=3,suppress=True)
+    model_zerocentered = model - model.mean(1)
+    data_zerocentered = data - data.mean(1)
+    
+    W = numpy.zeros( (3,3) )
+    for column in range(model.shape[1]):
+        W += numpy.outer(model_zerocentered[:,column],data_zerocentered[:,column])
+    U,d,Vh = numpy.linalg.linalg.svd(W.transpose())
+    S = numpy.matrix(numpy.identity( 3 ))
+    if(numpy.linalg.det(U) * numpy.linalg.det(Vh)<0):
+        S[2,2] = -1
+    rot = U*S*Vh
+    trans = data.mean(1) - rot * model.mean(1)
+    
+    model_aligned = rot * model + trans
+    alignment_error = model_aligned - data
+    
+    trans_error = numpy.sqrt(numpy.sum(numpy.multiply(alignment_error,alignment_error),0)).A[0]
+        
+    return rot,trans,trans_error
+
+def plot_traj(ax,stamps,traj,style,color,label):
+    """
+    Plot a trajectory using matplotlib. 
+    
+    Input:
+    ax -- the plot
+    stamps -- time stamps (1xn)
+    traj -- trajectory (3xn)
+    style -- line style
+    color -- line color
+    label -- plot legend
+    
+    """
+    stamps.sort()
+    interval = numpy.median([s-t for s,t in zip(stamps[1:],stamps[:-1])])
+    x = []
+    y = []
+    last = stamps[0]
+    for i in range(len(stamps)):
+        if stamps[i]-last < 2*interval:
+            x.append(traj[i][0])
+            y.append(traj[i][1])
+        elif len(x)>0:
+            ax.plot(x,y,style,color=color,label=label)
+            label=""
+            x=[]
+            y=[]
+        last= stamps[i]
+    if len(x)>0:
+        ax.plot(x,y,style,color=color,label=label)
+            
+
+if __name__=="__main__":
+    # parse command line
+    parser = argparse.ArgumentParser(description='''
+    This script computes the absolute trajectory error from the ground truth trajectory and the estimated trajectory. 
+    ''')
+    parser.add_argument('first_file', help='ground truth trajectory (format: timestamp tx ty tz qx qy qz qw)')
+    parser.add_argument('second_file', help='estimated trajectory (format: timestamp tx ty tz qx qy qz qw)')
+    parser.add_argument('--offset', help='time offset added to the timestamps of the second file (default: 0.0)',default=0.0)
+    parser.add_argument('--scale', help='scaling factor for the second trajectory (default: 1.0)',default=1.0)
+    parser.add_argument('--max_difference', help='maximally allowed time difference for matching entries (default: 0.02)',default=0.02)
+    parser.add_argument('--save', help='save aligned second trajectory to disk (format: stamp2 x2 y2 z2)')
+    parser.add_argument('--save_associations', help='save associated first and aligned second trajectory to disk (format: stamp1 x1 y1 z1 stamp2 x2 y2 z2)')
+    parser.add_argument('--plot', help='plot the first and the aligned second trajectory to an image (format: png)')
+    parser.add_argument('--verbose', help='print all evaluation data (otherwise, only the RMSE absolute translational error in meters after alignment will be printed)', action='store_true')
+    args = parser.parse_args()
+
+    first_list = associate.read_file_list(args.first_file)
+    second_list = associate.read_file_list(args.second_file)
+
+    matches = associate.associate(first_list, second_list,float(args.offset),float(args.max_difference))    
+    if len(matches)<2:
+        sys.exit("Couldn't find matching timestamp pairs between groundtruth and estimated trajectory! Did you choose the correct sequence?")
+
+
+    first_xyz = numpy.matrix([[float(value) for value in first_list[a][0:3]] for a,b in matches]).transpose()
+    second_xyz = numpy.matrix([[float(value)*float(args.scale) for value in second_list[b][0:3]] for a,b in matches]).transpose()
+    rot,trans,trans_error = align(second_xyz,first_xyz)
+    
+    second_xyz_aligned = rot * second_xyz + trans
+    
+    first_stamps = first_list.keys()
+    first_stamps.sort()
+    first_xyz_full = numpy.matrix([[float(value) for value in first_list[b][0:3]] for b in first_stamps]).transpose()
+    
+    second_stamps = second_list.keys()
+    second_stamps.sort()
+    second_xyz_full = numpy.matrix([[float(value)*float(args.scale) for value in second_list[b][0:3]] for b in second_stamps]).transpose()
+    second_xyz_full_aligned = rot * second_xyz_full + trans
+    
+    if args.verbose:
+        print "compared_pose_pairs %d pairs"%(len(trans_error))
+
+        print "absolute_translational_error.rmse %f m"%numpy.sqrt(numpy.dot(trans_error,trans_error) / len(trans_error))
+        print "absolute_translational_error.mean %f m"%numpy.mean(trans_error)
+        print "absolute_translational_error.median %f m"%numpy.median(trans_error)
+        print "absolute_translational_error.std %f m"%numpy.std(trans_error)
+        print "absolute_translational_error.min %f m"%numpy.min(trans_error)
+        print "absolute_translational_error.max %f m"%numpy.max(trans_error)
+    else:
+        print "%f"%numpy.sqrt(numpy.dot(trans_error,trans_error) / len(trans_error))
+        
+    if args.save_associations:
+        file = open(args.save_associations,"w")
+        file.write("\n".join(["%f %f %f %f %f %f %f %f"%(a,x1,y1,z1,b,x2,y2,z2) for (a,b),(x1,y1,z1),(x2,y2,z2) in zip(matches,first_xyz.transpose().A,second_xyz_aligned.transpose().A)]))
+        file.close()
+        
+    if args.save:
+        file = open(args.save,"w")
+        file.write("\n".join(["%f "%stamp+" ".join(["%f"%d for d in line]) for stamp,line in zip(second_stamps,second_xyz_full_aligned.transpose().A)]))
+        file.close()
+
+    if args.plot:
+        import matplotlib
+        matplotlib.use('Agg')
+        import matplotlib.pyplot as plt
+        import matplotlib.pylab as pylab
+        from matplotlib.patches import Ellipse
+        fig = plt.figure()
+        ax = fig.add_subplot(111)
+        plot_traj(ax,first_stamps,first_xyz_full.transpose().A,'-',"black","ground truth")
+        plot_traj(ax,second_stamps,second_xyz_full_aligned.transpose().A,'-',"blue","estimated")
+
+        label="difference"
+        for (a,b),(x1,y1,z1),(x2,y2,z2) in zip(matches,first_xyz.transpose().A,second_xyz_aligned.transpose().A):
+            ax.plot([x1,x2],[y1,y2],'-',color="red",label=label)
+            label=""
+            
+        ax.legend()
+            
+        ax.set_xlabel('x [m]')
+        ax.set_ylabel('y [m]')
+        plt.savefig(args.plot,dpi=90)
+        
diff --git a/svo_analysis/scripts/evaluate_rpe.py b/svo_analysis/src/svo_analysis/tum_benchmark_tools/evaluate_rpe.py
similarity index 53%
rename from svo_analysis/scripts/evaluate_rpe.py
rename to svo_analysis/src/svo_analysis/tum_benchmark_tools/evaluate_rpe.py
index 1ba24a7..fb42e4a 100755
--- a/svo_analysis/scripts/evaluate_rpe.py
+++ b/svo_analysis/src/svo_analysis/tum_benchmark_tools/evaluate_rpe.py
@@ -1,25 +1,92 @@
 #!/usr/bin/python
+# Software License Agreement (BSD License)
+#
+# Copyright (c) 2013, Juergen Sturm, TUM
+# All rights reserved.
+#
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions
+# are met:
+#
+#  * Redistributions of source code must retain the above copyright
+#    notice, this list of conditions and the following disclaimer.
+#  * Redistributions in binary form must reproduce the above
+#    copyright notice, this list of conditions and the following
+#    disclaimer in the documentation and/or other materials provided
+#    with the distribution.
+#  * Neither the name of TUM nor the names of its
+#    contributors may be used to endorse or promote products derived
+#    from this software without specific prior written permission.
+#
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
+# FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
+# COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
+# INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
+# BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
+# LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
+# ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+# POSSIBILITY OF SUCH DAMAGE.
+
+"""
+This script computes the relative pose error from the ground truth trajectory
+and the estimated trajectory.
+"""
 
-import yaml
 import argparse
 import random
 import numpy
 import sys
-import transformations
-import matplotlib
-import matplotlib.pyplot as plt
-import matplotlib.pylab as pylab
-from matplotlib import rc
+
+_EPS = numpy.finfo(float).eps * 4.0
 
 def transform44(l):
+    """
+    Generate a 4x4 homogeneous transformation matrix from a 3D point and unit quaternion.
+    
+    Input:
+    l -- tuple consisting of (stamp,tx,ty,tz,qx,qy,qz,qw) where
+         (tx,ty,tz) is the 3D position and (qx,qy,qz,qw) is the unit quaternion.
+         
+    Output:
+    matrix -- 4x4 homogeneous transformation matrix
+    """
     t = l[1:4]
-    q = l[4:8]
-    return numpy.dot(transformations.translation_matrix(t),transformations.quaternion_matrix(q))
+    q = numpy.array(l[4:8], dtype=numpy.float64, copy=True)
+    nq = numpy.dot(q, q)
+    if nq < _EPS:
+        return numpy.array((
+        (                1.0,                 0.0,                 0.0, t[0])
+        (                0.0,                 1.0,                 0.0, t[1])
+        (                0.0,                 0.0,                 1.0, t[2])
+        (                0.0,                 0.0,                 0.0, 1.0)
+        ), dtype=numpy.float64)
+    q *= numpy.sqrt(2.0 / nq)
+    q = numpy.outer(q, q)
+    return numpy.array((
+        (1.0-q[1, 1]-q[2, 2],     q[0, 1]-q[2, 3],     q[0, 2]+q[1, 3], t[0]),
+        (    q[0, 1]+q[2, 3], 1.0-q[0, 0]-q[2, 2],     q[1, 2]-q[0, 3], t[1]),
+        (    q[0, 2]-q[1, 3],     q[1, 2]+q[0, 3], 1.0-q[0, 0]-q[1, 1], t[2]),
+        (                0.0,                 0.0,                 0.0, 1.0)
+        ), dtype=numpy.float64)
 
 def read_trajectory(filename, matrix=True):
+    """
+    Read a trajectory from a text file. 
+    
+    Input:
+    filename -- file to be read
+    matrix -- convert poses to 4x4 matrices
+    
+    Output:
+    dictionary of stamped 3D poses
+    """
     file = open(filename)
     data = file.read()
-    lines = data.replace(","," ").replace("\t"," ").split("\n")
+    lines = data.replace(","," ").replace("\t"," ").split("\n") 
     list = [[float(v.strip()) for v in line.split(" ") if v.strip()!=""] for line in lines if len(line)>0 and line[0]!="#"]
     list_ok = []
     for i,l in enumerate(list):
@@ -27,7 +94,7 @@ def read_trajectory(filename, matrix=True):
             continue
         isnan = False
         for v in l:
-            if numpy.isnan(v):
+            if numpy.isnan(v): 
                 isnan = True
                 break
         if isnan:
@@ -41,6 +108,16 @@ def read_trajectory(filename, matrix=True):
     return traj
 
 def find_closest_index(L,t):
+    """
+    Find the index of the closest value in a list.
+    
+    Input:
+    L -- the list
+    t -- value to be found
+    
+    Output:
+    index of the closest element
+    """
     beginning = 0
     difference = abs(L[0] - t)
     best = 0
@@ -59,9 +136,22 @@ def find_closest_index(L,t):
     return best
 
 def ominus(a,b):
+    """
+    Compute the relative 3D transformation between a and b.
+    
+    Input:
+    a -- first pose (homogeneous 4x4 matrix)
+    b -- second pose (homogeneous 4x4 matrix)
+    
+    Output:
+    Relative 3D transformation from a to b.
+    """
     return numpy.dot(numpy.linalg.inv(a),b)
 
 def scale(a,scalar):
+    """
+    Scale the translational components of a 4x4 homogeneous matrix by a scale factor.
+    """
     return numpy.array(
         [[a[0,0], a[0,1], a[0,2], a[0,3]*scalar],
          [a[1,0], a[1,1], a[1,2], a[1,3]*scalar],
@@ -70,13 +160,22 @@ def scale(a,scalar):
                        )
 
 def compute_distance(transform):
+    """
+    Compute the distance of the translational component of a 4x4 homogeneous matrix.
+    """
     return numpy.linalg.norm(transform[0:3,3])
 
 def compute_angle(transform):
+    """
+    Compute the rotation angle from a 4x4 homogeneous matrix.
+    """
     # an invitation to 3-d vision, p 27
     return numpy.arccos( min(1,max(-1, (numpy.trace(transform[0:3,0:3]) - 1)/2) ))
 
 def distances_along_trajectory(traj):
+    """
+    Compute the translational distances along a trajectory. 
+    """
     keys = traj.keys()
     keys.sort()
     motion = [ominus(traj[keys[i+1]],traj[keys[i]]) for i in range(len(keys)-1)]
@@ -86,8 +185,11 @@ def distances_along_trajectory(traj):
         sum += compute_distance(t)
         distances.append(sum)
     return distances
-
+    
 def rotations_along_trajectory(traj,scale):
+    """
+    Compute the angular rotations along a trajectory. 
+    """
     keys = traj.keys()
     keys.sort()
     motion = [ominus(traj[keys[i+1]],traj[keys[i]]) for i in range(len(keys)-1)]
@@ -97,60 +199,36 @@ def rotations_along_trajectory(traj,scale):
         sum += compute_angle(t)*scale
         distances.append(sum)
     return distances
+    
 
-def interpolate_transformation(alpha,a,b):
-    qa,ta = transformations.quaternion_from_matrix(a),transformations.translation_from_matrix(a)
-    qb,tb = transformations.quaternion_from_matrix(b),transformations.translation_from_matrix(b)
-    qr = transformations.quaternion_slerp(qa,qb,alpha)
-    tr = (1-alpha) * ta + (alpha) * tb
-    r = numpy.dot(transformations.translation_matrix(tr),transformations.quaternion_matrix(qr))
-    return r
-
-def interpolate_and_resample(traj_gt,stamps_est,param_offset,gt_max_time_difference):
-    traj_new = {}
-
+def evaluate_trajectory(traj_gt,traj_est,param_max_pairs=10000,param_fixed_delta=False,param_delta=1.00,param_delta_unit="s",param_offset=0.00,param_scale=1.00):
+    """
+    Compute the relative pose error between two trajectories.
+    
+    Input:
+    traj_gt -- the first trajectory (ground truth)
+    traj_est -- the second trajectory (estimated trajectory)
+    param_max_pairs -- number of relative poses to be evaluated
+    param_fixed_delta -- false: evaluate over all possible pairs
+                         true: only evaluate over pairs with a given distance (delta)
+    param_delta -- distance between the evaluated pairs
+    param_delta_unit -- unit for comparison:
+                        "s": seconds
+                        "m": meters
+                        "rad": radians
+                        "deg": degrees
+                        "f": frames
+    param_offset -- time offset between two trajectories (to model the delay)
+    param_scale -- scale to be applied to the second trajectory
+    
+    Output:
+    list of compared poses and the resulting translation and rotation error
+    """
     stamps_gt = list(traj_gt.keys())
-    stamps_gt.sort()
-
-    for t_est in stamps_est:
-        t_new = t_est + param_offset
-        i_nearest = find_closest_index(stamps_gt,t_est + param_offset)
-        if stamps_gt[i_nearest] >= t_new and i_nearest>0:
-            i_low = i_nearest-1
-            i_high = i_nearest
-        else:
-            i_low = i_nearest
-            i_high = i_nearest+1
-        if i_low<0 or i_high>=len(stamps_gt):
-            continue
-
-        t_low = stamps_gt[i_low]
-        t_high = stamps_gt[i_high]
-
-        if(abs( t_new - (t_low + param_offset) ) > gt_max_time_difference or
-           abs( t_new - (t_high + param_offset) ) > gt_max_time_difference):
-            continue
-
-        alpha = (t_new - (t_low + param_offset)) / (t_high - t_low)
-        transform = interpolate_transformation(alpha,traj_gt[t_low],traj_gt[t_high])
-        traj_new[t_new] = transform
-    return traj_new
-
-
-def evaluate_trajectory(traj_gt,traj_est,param_max_pairs=10000,param_fixed_delta=False,param_delta=1.00,param_delta_unit="s",param_offset=0.00,param_scale=1.00,param_interpolation=True):
     stamps_est = list(traj_est.keys())
-    stamps_est.sort()
-    stamps_gt = list(traj_gt.keys())
     stamps_gt.sort()
-
-    gt_interval = numpy.median([s-t for s,t in zip(stamps_gt[1:],stamps_gt[:-1])])
-    gt_max_time_difference = 2*gt_interval
-
-    if param_interpolation:
-        traj_gt = interpolate_and_resample(traj_gt,stamps_est,param_offset,gt_max_time_difference)
-        stamps_gt = list(traj_gt.keys())
-        stamps_gt.sort()
-
+    stamps_est.sort()
+    
     stamps_est_return = []
     for t_est in stamps_est:
         t_gt = stamps_gt[find_closest_index(stamps_gt,t_est + param_offset)]
@@ -184,11 +262,14 @@ def evaluate_trajectory(traj_gt,traj_est,param_max_pairs=10000,param_fixed_delta
         pairs = []
         for i in range(len(traj_est)):
             j = find_closest_index(index_est,index_est[i] + param_delta)
-            if j!=len(traj_est)-1:
+            if j!=len(traj_est)-1: 
                 pairs.append((i,j))
         if(param_max_pairs!=0 and len(pairs)>param_max_pairs):
             pairs = random.sample(pairs,param_max_pairs)
-
+    
+    gt_interval = numpy.median([s-t for s,t in zip(stamps_gt[1:],stamps_gt[:-1])])
+    gt_max_time_difference = 2*gt_interval
+    
     result = []
     for i,j in pairs:
         stamp_est_0 = stamps_est[i]
@@ -196,32 +277,29 @@ def evaluate_trajectory(traj_gt,traj_est,param_max_pairs=10000,param_fixed_delta
 
         stamp_gt_0 = stamps_gt[ find_closest_index(stamps_gt,stamp_est_0 + param_offset) ]
         stamp_gt_1 = stamps_gt[ find_closest_index(stamps_gt,stamp_est_1 + param_offset) ]
-
+        
         if(abs(stamp_gt_0 - (stamp_est_0 + param_offset)) > gt_max_time_difference  or
            abs(stamp_gt_1 - (stamp_est_1 + param_offset)) > gt_max_time_difference):
             continue
-
+        
         error44 = ominus(  scale(
                            ominus( traj_est[stamp_est_1], traj_est[stamp_est_0] ),param_scale),
                            ominus( traj_gt[stamp_gt_1], traj_gt[stamp_gt_0] ) )
-
         
         trans = compute_distance(error44)
         rot = compute_angle(error44)
-
-        motion_gt = ominus( traj_gt[stamp_gt_1], traj_gt[stamp_gt_0] )
-        trans_gt = compute_distance(motion_gt)
-        rot_gt = compute_angle(motion_gt)
         
-        if(rot < 0.07):
-          result.append([stamp_est_0,stamp_est_1,stamp_gt_0,stamp_gt_1,trans,rot,trans_gt,rot_gt])
-
+        result.append([stamp_est_0,stamp_est_1,stamp_gt_0,stamp_gt_1,trans,rot])
+        
     if len(result)<2:
         raise Exception("Couldn't find matching timestamp pairs between groundtruth and estimated trajectory!")
-
+        
     return result
 
 def percentile(seq,q):
+    """
+    Return the q-percentile of a list
+    """
     seq_sorted = list(seq)
     seq_sorted.sort()
     return seq_sorted[int((len(seq_sorted)-1)*q)]
@@ -229,45 +307,28 @@ def percentile(seq,q):
 if __name__ == '__main__':
     random.seed(0)
 
-    # config -------------------------------------------------------------------------
-    
-    dataset='20131003_1937_nslam_i7_asl2_fast'
-    dataset_dir = '../results/'+dataset
-    
-    #---------------------------------------------------------------------------------
-    
-    # load dataset parameters
-    params_stream = open(dataset_dir+'/params.yaml')
-    params = yaml.load(params_stream)
-    
-    # set trajectory groundtruth and estimate file
-    traj_groundtruth = params['dataset_directory']+'/groundtruth_filtered.txt'
-    traj_estimate_rotated = dataset_dir+'/traj_estimate_rotated.txt'
-
     parser = argparse.ArgumentParser(description='''
-    This script computes the relative pose error from the ground truth trajectory and the estimated trajectory.
+    This script computes the relative pose error from the ground truth trajectory and the estimated trajectory. 
     ''')
-    parser.add_argument('--groundtruth_file', help='ground-truth trajectory file (format: "timestamp tx ty tz qx qy qz qw")', default=traj_groundtruth)
-    parser.add_argument('--estimated_file', help='estimated trajectory file (format: "timestamp tx ty tz qx qy qz qw")', default=traj_estimate_rotated)
-    parser.add_argument('--interpolate', help='interpolate and resample the ground truth trajectory', default=True)
+    parser.add_argument('groundtruth_file', help='ground-truth trajectory file (format: "timestamp tx ty tz qx qy qz qw")')
+    parser.add_argument('estimated_file', help='estimated trajectory file (format: "timestamp tx ty tz qx qy qz qw")')
     parser.add_argument('--max_pairs', help='maximum number of pose comparisons (default: 10000, set to zero to disable downsampling)', default=10000)
-    parser.add_argument('--fixed_delta', help='only consider pose pairs that have a distance of delta delta_unit (e.g., for evaluating the drift per second/meter/radian)', default=True)
+    parser.add_argument('--fixed_delta', help='only consider pose pairs that have a distance of delta delta_unit (e.g., for evaluating the drift per second/meter/radian)', action='store_true')
     parser.add_argument('--delta', help='delta for evaluation (default: 1.0)',default=1.0)
     parser.add_argument('--delta_unit', help='unit of delta (options: \'s\' for seconds, \'m\' for meters, \'rad\' for radians, \'f\' for frames; default: \'s\')',default='s')
     parser.add_argument('--offset', help='time offset between ground-truth and estimated trajectory (default: 0.0)',default=0.0)
     parser.add_argument('--scale', help='scaling factor for the estimated trajectory (default: 1.0)',default=1.0)
     parser.add_argument('--save', help='text file to which the evaluation will be saved (format: stamp_est0 stamp_est1 stamp_gt0 stamp_gt1 trans_error rot_error)')
-    parser.add_argument('--plot', help='plot the result to a file (requires --fixed_delta, output format: png)', default=True)
-    parser.add_argument('--plotname', help='plot the result to a file (requires --fixed_delta, output format: png)', default='rpe.png')
-    parser.add_argument('--verbose', help='print all evaluation data (otherwise, only the mean translational error measured in meters will be printed)', default=True)
+    parser.add_argument('--plot', help='plot the result to a file (requires --fixed_delta, output format: png)')
+    parser.add_argument('--verbose', help='print all evaluation data (otherwise, only the mean translational error measured in meters will be printed)', action='store_true')
     args = parser.parse_args()
-
+    
     if args.plot and not args.fixed_delta:
         sys.exit("The '--plot' option can only be used in combination with '--fixed_delta'")
-
+    
     traj_gt = read_trajectory(args.groundtruth_file)
     traj_est = read_trajectory(args.estimated_file)
-
+    
     result = evaluate_trajectory(traj_gt,
                                  traj_est,
                                  int(args.max_pairs),
@@ -275,81 +336,47 @@ if __name__ == '__main__':
                                  float(args.delta),
                                  args.delta_unit,
                                  float(args.offset),
-                                 float(args.scale),
-                                 args.interpolate)
-
+                                 float(args.scale))
+    
     stamps = numpy.array(result)[:,0]
     trans_error = numpy.array(result)[:,4]
     rot_error = numpy.array(result)[:,5]
     
-    
-    results = dict()
-    results['trans_rmse'] = float(numpy.sqrt(numpy.dot(trans_error,trans_error) / len(trans_error)))
-    results['trans_mean'] = float(numpy.mean(trans_error))
-    results['trans_median'] = float(numpy.median(trans_error))
-    results['trans_std'] = float(numpy.std(trans_error))
-    results['trans_min'] = float(numpy.min(trans_error))
-    results['trans_max'] = float(numpy.max(trans_error))
-    results['rot_rmse'] = float((numpy.sqrt(numpy.dot(rot_error,rot_error) / len(rot_error)) * 180.0 / numpy.pi))
-    results['rot_mean'] = float((numpy.mean(rot_error) * 180.0 / numpy.pi))
-    results['rot_median'] = float(numpy.median(rot_error) * 180.0 / numpy.pi)
-    results['rot_std'] = float((numpy.std(rot_error) * 180.0 / numpy.pi))
-    results['rot_min'] = float((numpy.min(rot_error) * 180.0 / numpy.pi))
-    results['rot_max'] = float((numpy.max(rot_error) * 180.0 / numpy.pi))
-    
-    # dump experiment results to file:
-    results_file = params['trace_dir']+'/rpe_results.yaml'
-    with open(results_file,'w') as outfile:
-      outfile.write( yaml.dump(results, default_flow_style=False) )
-    
-    if args.save: 
+    if args.save:
         f = open(args.save,"w")
         f.write("\n".join([" ".join(["%f"%v for v in line]) for line in result]))
         f.close()
-
+    
     if args.verbose:
         print "compared_pose_pairs %d pairs"%(len(trans_error))
 
-        print "translational_error.rmse %f m"%results['trans_rmse']
-        print "translational_error.mean %f m"%results['trans_mean']
-        print "translational_error.median %f m"%results['trans_median']
-        print "translational_error.std %f m"%results['trans_std']
-        print "translational_error.min %f m"%results['trans_min']
-        print "translational_error.max %f m"%results['trans_max']
+        print "translational_error.rmse %f m"%numpy.sqrt(numpy.dot(trans_error,trans_error) / len(trans_error))
+        print "translational_error.mean %f m"%numpy.mean(trans_error)
+        print "translational_error.median %f m"%numpy.median(trans_error)
+        print "translational_error.std %f m"%numpy.std(trans_error)
+        print "translational_error.min %f m"%numpy.min(trans_error)
+        print "translational_error.max %f m"%numpy.max(trans_error)
 
-        print "rotational_error.rmse %f deg"%results['rot_rmse']
-        print "rotational_error.mean %f deg"%results['rot_mean']
-        print "rotational_error.median %f deg"%results['rot_median']
-        print "rotational_error.std %f deg"%results['rot_std']
-        print "rotational_error.min %f deg"%results['rot_min']
-        print "rotational_error.max %f deg"%results['rot_max']
+        print "rotational_error.rmse %f deg"%(numpy.sqrt(numpy.dot(rot_error,rot_error) / len(rot_error)) * 180.0 / numpy.pi)
+        print "rotational_error.mean %f deg"%(numpy.mean(rot_error) * 180.0 / numpy.pi)
+        print "rotational_error.median %f deg"%numpy.median(rot_error)
+        print "rotational_error.std %f deg"%(numpy.std(rot_error) * 180.0 / numpy.pi)
+        print "rotational_error.min %f deg"%(numpy.min(rot_error) * 180.0 / numpy.pi)
+        print "rotational_error.max %f deg"%(numpy.max(rot_error) * 180.0 / numpy.pi)
     else:
         print numpy.mean(trans_error)
 
-    if args.plot:
-      
-        # tell matplotlib to use latex font
-        rc('font',**{'family':'serif','serif':['Cardo']})
-        rc('text', usetex=True)
-  
-        #matplotlib.use('Agg') # no window should appear?
-
-        fig = plt.figure(figsize=(8, 3))
-        ax = fig.add_subplot(111)
+    if args.plot:    
+        import matplotlib
+        matplotlib.use('Agg')
+        import matplotlib.pyplot as plt
+        import matplotlib.pylab as pylab
+        fig = plt.figure()
+        ax = fig.add_subplot(111)        
         ax.plot(stamps - stamps[0],trans_error,'-',color="blue")
         #ax.plot([t for t,e in err_rot],[e for t,e in err_rot],'-',color="red")
         ax.set_xlabel('time [s]')
         ax.set_ylabel('translational error [m]')
-        plt.tight_layout()
-        plt.savefig(dataset_dir+'/rpe_trans_error.pdf')
-        plt.show()
+        plt.savefig(args.plot,dpi=300)
+        
 
-        fig = plt.figure(figsize=(8, 3))
-        ax = fig.add_subplot(111)
-        ax.plot(stamps - stamps[0],rot_error* 180.0 / numpy.pi,'-',color="blue")
-        #ax.plot([t for t,e in err_rot],[e for t,e in err_rot],'-',color="red")
-        ax.set_xlabel('time [s]')
-        ax.set_ylabel('rot error [deg]')
-        plt.tight_layout()
-        plt.savefig(dataset_dir+'/rpe_rot_error.pdf')
-        plt.show()
\ No newline at end of file
diff --git a/svo_ros/include/svo_ros/dataset_img.h b/svo_ros/include/svo_ros/dataset_img.h
index 2d78ef7..4bdbc1f 100644
--- a/svo_ros/include/svo_ros/dataset_img.h
+++ b/svo_ros/include/svo_ros/dataset_img.h
@@ -33,8 +33,8 @@ public:
   DatasetImg() {}
   virtual ~DatasetImg() {}
 
-  double stamp_;
-  std::string imgname_;
+  double timestamp_;
+  std::string image_name_;
 
   friend std::ostream& operator <<(std::ostream& out, const DatasetImg& pair);
   friend std::istream& operator >>(std::istream& in, DatasetImg& pair);
@@ -43,16 +43,16 @@ public:
 std::ostream& operator <<(std::ostream& out, const DatasetImg& gt)
 {
   out
-    << gt.stamp_ << " "
-    << gt.imgname_ << " "
+    << gt.timestamp_ << " "
+    << gt.image_name_ << " "
     << std::endl;
   return out;
 }
 
 std::istream& operator >>(std::istream& in, DatasetImg& gt)
 {
-  in >> gt.stamp_;
-  in >> gt.imgname_;
+  in >> gt.timestamp_;
+  in >> gt.image_name_;
   return in;
 }
 
diff --git a/svo_ros/src/benchmark_node.cpp b/svo_ros/src/benchmark_node.cpp
index 2d0ff6e..fb121cc 100644
--- a/svo_ros/src/benchmark_node.cpp
+++ b/svo_ros/src/benchmark_node.cpp
@@ -25,11 +25,14 @@
 #include <vikit/params_helper.h>
 #include <vikit/camera_loader.h>
 #include <vikit/abstract_camera.h>
-#include <vikit/user_input_thread.h>
+#include <vikit/blender_utils.h>
 #include <svo/config.h>
 #include <svo/frame.h>
+#include <svo/feature.h>
 #include <svo/map.h>
+#include <svo/point.h>
 #include <svo/frame_handler_mono.h>
+#include <svo/feature_detection.h>
 #include <svo_ros/visualizer.h>
 #include <svo_ros/dataset_img.h>
 
@@ -39,28 +42,35 @@ class BenchmarkNode
 {
   svo::FrameHandlerMono* vo_;
   svo::Visualizer visualizer_;
-  vk::UserInputThread user_input_thread_;
   int frame_count_;
   std::ofstream trace_est_pose_;
+  std::ofstream trace_trans_error_;
+  std::ofstream trace_rot_error_;
   vk::AbstractCamera* cam_;
-  bool quit_;
 
 public:
   BenchmarkNode(ros::NodeHandle& nh);
   ~BenchmarkNode();
-  void runCamImuBenchmark();
-  void processUserActions();
+  void tracePose(const SE3& T_w_f, const double timestamp);
+  void tracePoseError(const SE3& T_f_gt, const double timestamp);
+  void runBenchmark(const std::string& dataset_dir);
+  void runBlenderBenchmark(const std::string& dataset_dir);
 };
 
 BenchmarkNode::BenchmarkNode(ros::NodeHandle& nh) :
     vo_(NULL),
-    frame_count_(0),
-    quit_(false)
+    frame_count_(0)
 {
   // Create Camera
   if(!vk::camera_loader::loadFromRosNs("svo", cam_))
     throw std::runtime_error("Camera model not correctly specified.");
 
+  // create pose tracefile
+  std::string trace_est_name(Config::traceDir() + "/traj_estimate.txt");
+  trace_est_pose_.open(trace_est_name.c_str());
+  if(trace_est_pose_.fail())
+    throw std::runtime_error("Could not create tracefile. Does folder exist?");
+
   // Initialize VO
   vo_ = new svo::FrameHandlerMono(cam_);
   vo_->start();
@@ -72,71 +82,149 @@ BenchmarkNode::~BenchmarkNode()
   delete cam_;
 }
 
-void BenchmarkNode::runCamImuBenchmark()
+void BenchmarkNode::tracePose(const SE3& T_w_f, const double timestamp)
+{
+  Quaterniond q(T_w_f.unit_quaternion());
+  Vector3d p(T_w_f.translation());
+  trace_est_pose_.precision(15);
+  trace_est_pose_.setf(std::ios::fixed, std::ios::floatfield );
+  trace_est_pose_ << timestamp << " ";
+  trace_est_pose_.precision(6);
+  trace_est_pose_ << p.x() << " " << p.y() << " " << p.z() << " "
+                  << q.x() << " " << q.y() << " " << q.z() << " " << q.w() << endl;
+}
+
+void BenchmarkNode::tracePoseError(const SE3& T_f_gt, const double timestamp)
+{
+  Vector3d et(T_f_gt.translation()); // translation error
+  trace_trans_error_.precision(15);
+  trace_trans_error_.setf(std::ios::fixed, std::ios::floatfield );
+  trace_trans_error_ << timestamp << " ";
+  trace_trans_error_.precision(6);
+  trace_trans_error_ << et.x() << " " << et.y() << " " << et.z() << " " << endl;
+  Vector3d er(vk::dcm2rpy(T_f_gt.rotation_matrix())); // rotation error in roll-pitch-yaw
+  trace_rot_error_.precision(15);
+  trace_rot_error_.setf(std::ios::fixed, std::ios::floatfield );
+  trace_rot_error_ << timestamp << " ";
+  trace_rot_error_.precision(6);
+  trace_rot_error_ << er.x() << " " << er.y() << " " << er.z() << " " << endl;
+}
+
+void BenchmarkNode::runBenchmark(const std::string& dataset_dir)
 {
   // create image reader and load dataset
-  std::string benchmark_dir(vk::getParam<std::string>("svo/dataset_directory"));
-  std::string filename_benchmark(benchmark_dir + "/images.txt");
+  std::string filename_benchmark(dataset_dir + "/images.txt");
   vk::FileReader<FileType::DatasetImg> dataset_reader(filename_benchmark);
   dataset_reader.skipComments();
-  if(!dataset_reader.next())
-    std::runtime_error("Failed to open images file");
+  if(!dataset_reader.next()) {
+    SVO_ERROR_STREAM("Failed opening dataset: "<<filename_benchmark);
+    return;
+  }
   std::vector<FileType::DatasetImg> dataset;
   dataset_reader.readAllEntries(dataset);
 
-  // create pose tracefile
-  std::string trace_est_name(Config::traceDir() + "/traj_estimate.txt");
-  trace_est_pose_.open(trace_est_name.c_str());
-  if(trace_est_pose_.fail())
-    throw std::runtime_error("ERROR:frame_count_ could not create tracefile. probably the specified folder does not exist.");
-
   // process dataset
-  for(vk::vector<FileType::DatasetImg>::iterator it = dataset.begin();
-      it != dataset.end(); ++it, ++frame_count_)
+  for(auto it = dataset.begin(); it != dataset.end() && ros::ok(); ++it, ++frame_count_)
   {
     // Read image
-    std::string img_filename(benchmark_dir + "/" + it->imgname_);
+    std::string img_filename(dataset_dir + "/" + it->image_name_);
     cv::Mat img(cv::imread(img_filename, 0));
-    if(frame_count_ == 0)
-      SVO_INFO_STREAM("Reading image "<<img_filename);
-    if(img.empty())
-      throw std::runtime_error("Image could not be loaded.");
+    if(img.empty()) {
+      SVO_ERROR_STREAM("Reading image "<<img_filename<<" failed.");
+      return;
+    }
 
     // Add image to VO
-    processUserActions();
-    vo_->addImage(img, it->stamp_);
+    vo_->addImage(img, it->timestamp_);
 
     // Visualize
-    visualizer_.publishMinimal(img, vo_->lastFrame(), *vo_, it->stamp_);
-    if(vo_->stage() != svo::FrameHandlerMono::PAUSED)
-      visualizer_.visualizeMarkers(vo_->lastFrame(), vo_->coreKeyframes(), vo_->map());
+    visualizer_.publishMinimal(img, vo_->lastFrame(), *vo_, it->timestamp_);
 
     // write pose to tracefile
-    if(vo_->stage() == svo::FrameHandlerMono::DEFAULT_FRAME)
-    {
-      Quaterniond q(vo_->lastFrame()->T_f_w_.inverse().unit_quaternion());
-      Vector3d p(vo_->lastFrame()->T_f_w_.inverse().translation());
-      trace_est_pose_.precision(15);
-      trace_est_pose_.setf(std::ios::fixed, std::ios::floatfield );
-      trace_est_pose_ << it->stamp_<< " ";
-      trace_est_pose_.precision(6);
-      trace_est_pose_ << p.x() << " " << p.y() << " " << p.z() << " "
-                      << q.x() << " " << q.y() << " " << q.z() << " " << q.w() << endl;
-    }
-    if (!ros::ok() || quit_)
-      break;
+    if(vo_->stage() == svo::FrameHandlerMono::STAGE_DEFAULT_FRAME)
+      tracePose(vo_->lastFrame()->T_f_w_.inverse(), it->timestamp_);
   }
 }
 
-void BenchmarkNode::processUserActions()
+void BenchmarkNode::runBlenderBenchmark(const std::string& dataset_dir)
 {
-  char input = user_input_thread_.getInput();
-  switch(input)
+  // create image reader and load dataset
+  std::string filename_benchmark(dataset_dir + "/trajectory.txt");
+  vk::FileReader<vk::blender_utils::file_format::ImageNameAndPose> dataset_reader(filename_benchmark);
+  dataset_reader.skipComments();
+  if(!dataset_reader.next()) {
+    SVO_ERROR_STREAM("Failed opening dataset: "<<filename_benchmark);
+    return;
+  }
+  std::vector<vk::blender_utils::file_format::ImageNameAndPose> dataset;
+  dataset_reader.readAllEntries(dataset);
+
+  trace_trans_error_.open(Config::traceDir() + "/translation_error.txt");
+  trace_rot_error_.open(Config::traceDir() + "/orientation_error.txt");
+  if(trace_trans_error_.fail() || trace_rot_error_.fail())
+    throw std::runtime_error("Could not create tracefile. Does folder exist?");
+
+  // process dataset
+  for(auto it = dataset.begin(); it != dataset.end() && ros::ok(); ++it, ++frame_count_)
   {
-    case 'q': quit_ = true;   break;
-    case 'r': vo_->reset(); break;
-    case 's': vo_->start(); break;
-    default: ;
+    // Read image
+    std::string img_filename(dataset_dir + "/img/" + it->image_name_ + "_0.png");
+    cv::Mat img(cv::imread(img_filename, 0));
+    if(img.empty()) {
+      SVO_ERROR_STREAM("Reading image "<<img_filename<<" failed.");
+      return;
+    }
+
+    // Read ground-truth pose
+    Sophus::SE3 T_w_gt(it->q_, it->t_);
+
+    // Set reference frame with depth
+    if(frame_count_ == 0)
+    {
+      // set reference frame at ground-truth pose
+      FramePtr frame_ref(new Frame(cam_, img, it->timestamp_));
+      frame_ref->T_f_w_ = T_w_gt.inverse();
+
+      // load ground-truth depth
+      cv::Mat depthmap;
+      vk::blender_utils::loadBlenderDepthmap(
+          dataset_dir+"/depth/"+it->image_name_+"_0.depth", *cam_, depthmap);
+
+      // extract features, generate features with 3D points
+      svo::feature_detection::FastDetector detector;
+      svo::feature_detection::Corners corners;
+      detector.detect(frame_ref->img_pyr_, frame_ref->fts_, svo::Config::gridSize(),
+                      svo::Config::nPyrLevels(), svo::Config::triangMinCornerScore(), &corners);
+      for(auto corner=corners.begin(); corner!=corners.end(); ++corner)
+      {
+        if(corner->score < Config::triangMinCornerScore())
+          continue;
+        svo::Feature* ftr = new svo::Feature(frame_ref.get(), Eigen::Vector2d(corner->x, corner->y), corner->level);
+        Eigen::Vector3d pt_pos_cur = ftr->f*depthmap.at<float>(corner->y, corner->x);
+        Eigen::Vector3d pt_pos_world = frame_ref->T_f_w_.inverse()*pt_pos_cur;
+        svo::Point* point = new svo::Point(pt_pos_world, svo::Point::DEPTH);
+        ftr->point = point;
+        ftr->point->addFrameRef(ftr);
+        frame_ref->addFeature(ftr);
+      }
+      SVO_INFO_STREAM("Added "<<corners.size()<<" 3d pts to the reference frame.");
+      vo_->setFirstFrame(frame_ref);
+      SVO_INFO_STREAM("Set reference frame.");
+      continue;
+    }
+
+    // Add image to VO
+    vo_->addImage(img, it->timestamp_);
+
+    if(vo_->stage() != svo::FrameHandlerMono::STAGE_DEFAULT_FRAME)
+      continue;
+
+    // Compute pose error and trace to file
+    Sophus::SE3 T_f_gt(vo_->lastFrame()->T_f_w_*T_w_gt);
+    tracePoseError(T_f_gt, it->timestamp_);
+
+    // write pose to tracefile
+    tracePose(vo_->lastFrame()->T_f_w_.inverse(), it->timestamp_);
   }
 }
 
@@ -146,10 +234,12 @@ int main(int argc, char** argv)
 {
   ros::init(argc, argv, "svo");
   ros::NodeHandle nh;
-  {
-    svo::BenchmarkNode benchmark(nh);
-    benchmark.runCamImuBenchmark();
-  }
+  svo::BenchmarkNode benchmark(nh);
+  std::string benchmark_dir(vk::getParam<std::string>("svo/dataset_directory"));
+  if(vk::getParam<bool>("svo/dataset_is_blender", false))
+    benchmark.runBlenderBenchmark(benchmark_dir);
+  else
+    benchmark.runBenchmark(benchmark_dir);
   printf("BenchmarkNode finished.\n");
   return 0;
 }
diff --git a/svo_ros/src/visualizer.cpp b/svo_ros/src/visualizer.cpp
index d8aa72f..eda01c6 100644
--- a/svo_ros/src/visualizer.cpp
+++ b/svo_ros/src/visualizer.cpp
@@ -110,7 +110,7 @@ publishMinimal(const cv::Mat& img, const FramePtr& frame, const FrameHandlerMono
     cv::cvtColor(frame->img_pyr_[img_pub_level_], img_rgb, CV_GRAY2RGB);
 
     // during initialization, draw lines
-    if(slam.stage() == FrameHandlerBase::SECOND_FRAME)
+    if(slam.stage() == FrameHandlerBase::STAGE_SECOND_FRAME)
     {
       const vector<cv::Point2f>& px_ref(slam.initFeatureTrackRefPx());
       const vector<cv::Point2f>& px_cur(slam.initFeatureTrackCurPx());
@@ -148,7 +148,7 @@ publishMinimal(const cv::Mat& img, const FramePtr& frame, const FrameHandlerMono
     img_msg.encoding = sensor_msgs::image_encodings::BGR8;
     pub_images_.publish(img_msg.toImageMsg());
   }
-  else if(frame == NULL && pub_images_.getNumSubscribers() > 0 && slam.stage() == FrameHandlerBase::PAUSED)
+  else if(frame == NULL && pub_images_.getNumSubscribers() > 0 && slam.stage() == FrameHandlerBase::STAGE_PAUSED)
   {
     // display image when slam is not running
     cv_bridge::CvImage img_msg;
diff --git a/svo_ros/src/vo_node.cpp b/svo_ros/src/vo_node.cpp
index 27b7ef0..9dc31dd 100644
--- a/svo_ros/src/vo_node.cpp
+++ b/svo_ros/src/vo_node.cpp
@@ -110,13 +110,13 @@ imgCb(const sensor_msgs::ImageConstPtr& msg)
   vo_->addImage(img, msg->header.stamp.toSec());
   visualizer_.publishMinimal(img, vo_->lastFrame(), *vo_, msg->header.stamp.toSec());
 
-  if(publish_markers_ && vo_->stage() != FrameHandlerBase::PAUSED)
+  if(publish_markers_ && vo_->stage() != FrameHandlerBase::STAGE_PAUSED)
     visualizer_.visualizeMarkers(vo_->lastFrame(), vo_->coreKeyframes(), vo_->map());
 
   if(publish_dense_input_)
     visualizer_.exportToDense(vo_->lastFrame());
 
-  if(vo_->stage() == FrameHandlerMono::PAUSED)
+  if(vo_->stage() == FrameHandlerMono::STAGE_PAUSED)
     usleep(100000); // avoid busy loop when paused
 }