diff --git a/.gitmodules b/.gitmodules
new file mode 100644
index 0000000..5f4ec26
--- /dev/null
+++ b/.gitmodules
@@ -0,0 +1,3 @@
+[submodule "utils/orbslam_evaluation/library/rpg_trajectory_evaluation"]
+	path = utils/orbslam_evaluation/library/rpg_trajectory_evaluation
+	url = https://github.com/Mayankm96/rpg_trajectory_evaluation.git
diff --git a/utils/orbslam_evaluation/.gitignore b/utils/orbslam_evaluation/.gitignore
new file mode 100644
index 0000000..917a680
--- /dev/null
+++ b/utils/orbslam_evaluation/.gitignore
@@ -0,0 +1,2 @@
+**/eval/**
+**/.ipynb_checkpoints/**
\ No newline at end of file
diff --git a/utils/orbslam_evaluation/KITTI_parameters/2011_09_30_drive_0034/camera.yaml b/utils/orbslam_evaluation/KITTI_parameters/2011_09_30_drive_0034/camera.yaml
new file mode 100755
index 0000000..74e2a32
--- /dev/null
+++ b/utils/orbslam_evaluation/KITTI_parameters/2011_09_30_drive_0034/camera.yaml
@@ -0,0 +1,3 @@
+K: [ 9.786977e+02, 0.000000e+00, 6.900000e+02, 0.000000e+00, 9.717435e+02, 2.497222e+02, 0.000000e+00, 0.000000e+00, 1.000000e+00]
+D: [-3.792567e-01, 2.121203e-01, 9.182571e-04, 1.911304e-03, -7.605535e-02]
+
diff --git a/utils/orbslam_evaluation/KITTI_parameters/2011_09_30_drive_0034/gt_kitti.txt b/utils/orbslam_evaluation/KITTI_parameters/2011_09_30_drive_0034/gt_kitti.txt
new file mode 100755
index 0000000..047e494
--- /dev/null
+++ b/utils/orbslam_evaluation/KITTI_parameters/2011_09_30_drive_0034/gt_kitti.txt
@@ -0,0 +1,1201 @@
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diff --git a/utils/orbslam_evaluation/KITTI_parameters/2011_09_30_drive_0034/stamped_groundtruth.txt b/utils/orbslam_evaluation/KITTI_parameters/2011_09_30_drive_0034/stamped_groundtruth.txt
new file mode 100755
index 0000000..0139a1d
--- /dev/null
+++ b/utils/orbslam_evaluation/KITTI_parameters/2011_09_30_drive_0034/stamped_groundtruth.txt
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diff --git a/utils/orbslam_evaluation/KITTI_parameters/2011_09_30_drive_0034/times.txt b/utils/orbslam_evaluation/KITTI_parameters/2011_09_30_drive_0034/times.txt
new file mode 100755
index 0000000..1845f3c
--- /dev/null
+++ b/utils/orbslam_evaluation/KITTI_parameters/2011_09_30_drive_0034/times.txt
@@ -0,0 +1,1201 @@
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diff --git a/utils/orbslam_evaluation/README.md b/utils/orbslam_evaluation/README.md
new file mode 100644
index 0000000..1a9dbc6
--- /dev/null
+++ b/utils/orbslam_evaluation/README.md
@@ -0,0 +1,135 @@
+# VO-Pipeline Evaluation
+
+In this experiment, we try to evaluate how knowing incorrect calibration parameters affect the accuracy of Visual-Odometry 
+pipelines. In particular, we evaluate against monocular [ORBSLAM](http://webdiis.unizar.es/~raulmur/MurMontielTardosTRO15.pdf).
+
+The evaluation is done in the following procedure:
+1. Perturb the known camera parameters
+2. Generate the configuration file which is used to run OrbSLAM
+3. Perform VO-evaluation and plots results
+
+## Dataset 
+
+We use KITTI dataset in this experiment. Any of the datasets with odometry groundtruth (i.e. sequence numbers 00-10) can be used. To save some time downloading,
+following is the mapping between sequence number and name:
+
+| __Nr.__ | __Sequence Name__ | __Start Frame__ | __End Frame__ |   
+|---------|-------------------|-----------------|---------------|   
+| 00: | 2011_10_03_drive_0027 | 000000 | 004540 |
+| 01: | 2011_10_03_drive_0042 | 000000 | 001100 |
+| 02: | 2011_10_03_drive_0034 | 000000 | 004660 |
+| 03: | 2011_09_26_drive_0067 | 000000 | 000800 |
+| 04: | 2011_09_30_drive_0016 | 000000 | 000270 |
+| 05: | 2011_09_30_drive_0018 | 000000 | 002760 |
+| 06: | 2011_09_30_drive_0020 | 000000 | 001100 | 
+| 07: | 2011_09_30_drive_0027 | 000000 | 001100 |
+| 08: | 2011_09_30_drive_0028 | 001100 | 005170 |
+| 09: | 2011_09_30_drive_0033 | 000000 | 001590 |
+| 10: | 2011_09_30_drive_0034 | 000000 | 001200 |
+
+
+1. Download the corresponding sequence: http://www.cvlibs.net/datasets/kitti/raw_data.php
+2. Download the groundtruth and timestamp information from: http://www.cvlibs.net/datasets/kitti/eval_odometry.php 
+3. Paste the following files into "KITTI_parameters/<seq-name>" directory:  
+    a. The calibration file in  as `camera.yaml`  
+    b. `gt_kitti.txt` and `times.txt` files corresponding to the sequence   
+    
+## Trajectory Evaluation
+
+Since monocular odometry works well up to a scale, we consider relative errors in sub-trajectories as the metric.
+
+Following the paper: ["A Tutorial on Quantitative Trajectory Evaluation"](http://rpg.ifi.uzh.ch/docs/IROS18_Zhang.pdf) by Zhang
+_et. al_, 
+the algorithm used is described briefly as follows:
+
+1. Select all sub-trajectory of length d.
+2. Align each sub-trajectory using the first state.
+3. Calculate the error of the end state of each sub-trajectory.
+4. Collect the errors for all the sub-trajectories.
+5. For different lengths d, repeat step 1-4.
+
+## ORB-SLAM
+
+Released in 2016, ORB-SLAM is one of the most popular algorithms for monocular SLAM.  It is able to close large loops and
+perform global re-localisation in real-time and from wide baselines. Due to the popularity of the algorithm, we use it to 
+check how robust the algorithm is to miscalibration in camera parameters. 
+
+1. Clone the fork of the repository:    
+        
+        git clone -b feature/projects https://github.com/Mayankm96/ORB_SLAM2.git
+         
+2. Build the fork by following the instructions present in the ORB-SLAM's repository README:        
+        
+        cd ORB_SLAM2    
+        chmod +x build.sh   
+        ./build.sh  
+
+This will create `libORB_SLAM2.so` at _lib_ folder and the executables `mono_plr` in _Projects/PLR_Evaluation_ folder.
+
+## Running the ORBSLAM-Evaluation script
+
+First install the python dependencies required:
+```bash
+# UZH Trajectory Evaluation toolbox (present as git submodule)
+git submodule init && git submodule update
+# EVO: https://github.com/MichaelGrupp/evo
+pip install evo --upgrade --no-binary evo
+# CarnivalMirror: https://github.com/AleksandarPetrov/carnivalmirror.git
+pip install carnivalmirror
+# other standard python
+pip install matplotlib numpy pandas opencv-python
+```
+
+To run the script `run_orbslam.py`, care needs to be taken in the parameters specified. The script requires the following:
+* `--orbslam_root` , `-b`: path to ORBSLAM2 directory
+* `--dataset_folder` , `-k`: path to a directory containing KITTI sequence dataset
+* `--sequence_name` , `-e`: name of the KITTI sequence
+* `--sampling_settings` , `-s`: path to a file with the sampling settings (default: `config/sampling_settings.yaml`)
+* `--num_of_runs` , `-n`: number of runs to perform (default: `6`)
+* `--experiment_type`, `-t`: type of experiment to perform (range/random) (default: `range`)
+* `--perturb_focal`, `-f`: perturb focal length parameters of the camera (y/n) (default: `y`)
+* `--perturb_principle_point`, `-c`: perturb principle point parameters of the camera (y/n) (default: `y`)
+* `--perturb_distortion`, `-k`: perturb distortion parameters of the camera (y/n) (default: `n`)
+* `--output_dir` , `-o`: path of the directory to save experiment resutls in (default: `eval`)
+
+### Example:
+```bash
+# to perturb only intrinsic calibration matrix 
+python run_orbslam.py -b ~/my_projects/ORB_SLAM2 \ 
+                      -k ~/my_projects/data/kitti/2011_09_30_drive_0034 \
+                      -e 2011_09_30_drive_0034 \
+                      -t range \
+                      -f y -c y -d n \
+                      -n 20             
+```
+
+For each run in an experiment, the following information are stored:
+1. an example raw image from the dataset
+2. rectified image from the perturbed calibration parameters
+3. diagnostoc image with useful information about the perturbation performed
+4. configuration file for orbslam with the perturbed calibration settings
+5. keyframe trajectory outputted from running OrbSLAM on that sequence
+6. evaluation statistics on the estimated trajectory and plots
+
+A `csv` file is also generated which contains all the summary information about the experiment. These include the camera 
+parameters, average reprojection error, relative errors in evaluated trajectory etc. The file can be loaded as a panda dataframe
+and be used to plot the data collected to observe trends. The jupyter notebook `correlation_plotting.ipynb` is an example 
+on how this can be done.
+
+
+### Results:
+
+
+On x-axis, the % change in the the parameter is plotted.
+
+#### Perturbing intrinsic parameters only:
+
+Reprojection Error (in pixels)           |  Trajectory Evaluation (in m)
+:-------------------------:|:-------------------------:
+![](img/intrinsic_reproj_10.png)  |  ![](img/intrinsic_trans_10.png)
+
+#### Perturbing distortion parameters only:
+
+Reprojection Error (in pixels)             |  Trajectory Evaluation (in m)
+:-------------------------:|:-------------------------:
+![](img/distortion_reproj_10.png)  |  ![](img/distortion_trans_10.png)
\ No newline at end of file
diff --git a/utils/orbslam_evaluation/config/ORBSLAM_TUM1.yaml b/utils/orbslam_evaluation/config/ORBSLAM_TUM1.yaml
new file mode 100755
index 0000000..546b334
--- /dev/null
+++ b/utils/orbslam_evaluation/config/ORBSLAM_TUM1.yaml
@@ -0,0 +1,54 @@
+#--------------------------------------------------------------------------------------------
+# Camera Parameters. Adjust them!
+#--------------------------------------------------------------------------------------------
+
+# Camera calibration and distortion parameters (OpenCV)
+Camera.fx: 718.856
+Camera.fy: 718.856
+Camera.cx: 607.1928
+Camera.cy: 185.2157
+
+Camera.k1: 0.0
+Camera.k2: 0.0
+Camera.p1: 0.0
+Camera.p2: 0.0
+
+# Camera frames per second
+Camera.fps: 10.0
+
+# Color order of the images (0: BGR, 1: RGB. It is ignored if images are grayscale)
+Camera.RGB: 1
+
+#--------------------------------------------------------------------------------------------
+# ORB Parameters
+#--------------------------------------------------------------------------------------------
+
+# ORB Extractor: Number of features per image
+ORBextractor.nFeatures: 2000
+
+# ORB Extractor: Scale factor between levels in the scale pyramid
+ORBextractor.scaleFactor: 1.2
+
+# ORB Extractor: Number of levels in the scale pyramid
+ORBextractor.nLevels: 8
+
+# ORB Extractor: Fast threshold
+# Image is divided in a grid. At each cell FAST are extracted imposing a minimum response.
+# Firstly we impose iniThFAST. If no corners are detected we impose a lower value minThFAST
+# You can lower these values if your images have low contrast
+ORBextractor.iniThFAST: 20
+ORBextractor.minThFAST: 7
+
+#--------------------------------------------------------------------------------------------
+# Viewer Parameters
+#--------------------------------------------------------------------------------------------
+Viewer.KeyFrameSize: 0.1
+Viewer.KeyFrameLineWidth: 1
+Viewer.GraphLineWidth: 1
+Viewer.PointSize: 2
+Viewer.CameraSize: 0.15
+Viewer.CameraLineWidth: 2
+Viewer.ViewpointX: 0
+Viewer.ViewpointY: -10
+Viewer.ViewpointZ: -0.1
+Viewer.ViewpointF: 2000
diff --git a/utils/orbslam_evaluation/config/sampling_settings.yaml b/utils/orbslam_evaluation/config/sampling_settings.yaml
new file mode 100755
index 0000000..08304f9
--- /dev/null
+++ b/utils/orbslam_evaluation/config/sampling_settings.yaml
@@ -0,0 +1,11 @@
+# Establish the perturbation ranges
+# [!!!] This needs to be fine tuned depending on the dataset used
+fx_range: [0.95, 1.2]
+fy_range: [0.95, 1.2]
+cx_range: [0.95, 1.05]
+cy_range: [0.95, 1.05]
+k1_range: [0.85, 1.15]
+k2_range: [0.85, 1.15]
+p1_range: [0.85, 1.15]
+p2_range: [0.85, 1.15]
+k3_range: [0.85, 1.15]
diff --git a/utils/orbslam_evaluation/correlation_plotter.ipynb b/utils/orbslam_evaluation/correlation_plotter.ipynb
new file mode 100644
index 0000000..ae25823
--- /dev/null
+++ b/utils/orbslam_evaluation/correlation_plotter.ipynb
@@ -0,0 +1,864 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import os\n",
+    "import yaml\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_scatter_with_line(df, x, y, color='red'):\n",
+    "    ax = df.plot.scatter(x=x, y=y, color=color)\n",
+    "    df.plot.line(x=x, y=y, ax=ax, color=color)\n",
+    "    ax.grid(True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Directory to read data from\n",
+    "\n",
+    "Since this script complements the `run_orbslam.py`. The results are typically stored in the following fashion:\n",
+    "\n",
+    "```\n",
+    "eval\n",
+    "└── 2011_09_30_drive_0034\n",
+    "    └── range_perturbations_fc\n",
+    "        ├── run_0\n",
+    "        ├── run_1\n",
+    "        └── run_2\n",
+    "        └── summary.csv\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# specify path to the output directory\n",
+    "output_folder = os.path.abspath('eval')\n",
+    "# specify the name of the KITTI sequence\n",
+    "KITTI_sequence = '2011_09_30_drive_0034'\n",
+    "# specify the experiment type: 'random_perturbations' or 'range_perturbations'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# path to camera calibration file\n",
+    "camera_config_file =  os.path.abspath('KITTI_parameters/' + KITTI_sequence + '/camera.yaml')\n",
+    "with open(camera_config_file, 'r') as stream:\n",
+    "    camera = yaml.safe_load(stream)\n",
+    "# retrieve camera calibration matrices\n",
+    "K = np.array(camera[\"K\"]).reshape((3, 3))\n",
+    "D = np.array(camera[\"D\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "Exception",
+     "evalue": "File not found: /media/storage/my_projects/semantics_miscalib_PLR/plr_miscalib_detection/utils/orbslam_evaluation/eval/2011_09_30_drive_0034/range_perturbations_f/summary.csv",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mException\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-6-d377bfc453e6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     14\u001b[0m     \u001b[0msummary_df_f\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msummary_filename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m     \u001b[0;32mraise\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'File not found: %s'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0msummary_filename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     17\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[0;31m# process data frame to get % deviation\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mException\u001b[0m: File not found: /media/storage/my_projects/semantics_miscalib_PLR/plr_miscalib_detection/utils/orbslam_evaluation/eval/2011_09_30_drive_0034/range_perturbations_f/summary.csv"
+     ]
+    }
+   ],
+   "source": [
+    "experiment_type = 'range_perturbations'\n",
+    "# sepcify which parameters to perturb: 'f' for focal length, 'c' for principle point, 'd' for distortion parameters\n",
+    "# perturb_parameters = ['f', 'c']\n",
+    "perturb_parameters = ['f']\n",
+    "# construct experiment directory name\n",
+    "experiment_name = experiment_type + '_'\n",
+    "for param in perturb_parameters:\n",
+    "    experiment_name = experiment_name + param\n",
+    "output_folder_f = os.path.join(output_folder, KITTI_sequence, experiment_name)\n",
+    "\n",
+    "# read panda dataframe\n",
+    "summary_filename = os.path.join(output_folder_f, 'summary.csv')\n",
+    "if os.path.exists(summary_filename):\n",
+    "    summary_df_f = pd.read_csv(summary_filename)\n",
+    "else:\n",
+    "    raise Exception('File not found: %s' % summary_filename)\n",
+    "    \n",
+    "# process data frame to get % deviation\n",
+    "summary_df_f['delta_fx'] =  - (K[0, 0] - summary_df_f['fx']) / K[0, 0] * 100\n",
+    "summary_df_f['delta_cx'] =  - (K[0, 2] - summary_df_f['cx']) / K[0, 2] * 100\n",
+    "summary_df_f['delta_fy'] =  - (K[1, 1] - summary_df_f['fy']) / K[1, 1] * 100\n",
+    "summary_df_f['delta_cy'] =  - (K[1, 2] - summary_df_f['cy']) / K[1, 2] * 100\n",
+    "summary_df_f['delta_k1'] =  - (D[0] - summary_df_f['k1']) / D[0] * 100\n",
+    "summary_df_f['delta_k2'] =  - (D[1] - summary_df_f['k2']) / D[1] * 100\n",
+    "summary_df_f['delta_k3'] =  - (D[4] - summary_df_f['k3']) / D[4] * 100\n",
+    "summary_df_f['delta_p1'] =  - (D[2] - summary_df_f['p1']) / D[2] * 100\n",
+    "summary_df_f['delta_p2'] =  - (D[3] - summary_df_f['p2']) / D[3] * 100"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "experiment_type = 'range_perturbations'\n",
+    "# sepcify which parameters to perturb: 'f' for focal length, 'c' for principle point, 'd' for distortion parameters\n",
+    "# perturb_parameters = ['f', 'c']\n",
+    "perturb_parameters = ['d']\n",
+    "# construct experiment directory name\n",
+    "experiment_name = experiment_type + '_'\n",
+    "for param in perturb_parameters:\n",
+    "    experiment_name = experiment_name + param\n",
+    "output_folder_d = os.path.join(output_folder, KITTI_sequence, experiment_name)\n",
+    "\n",
+    "# read panda dataframe\n",
+    "summary_filename = os.path.join(output_folder_d, 'summary.csv')\n",
+    "if os.path.exists(summary_filename):\n",
+    "    summary_df_d = pd.read_csv(summary_filename)\n",
+    "else:\n",
+    "    raise Exception('File not found: %s' % summary_filename)\n",
+    "    \n",
+    "# process data frame to get % deviation\n",
+    "summary_df_d['delta_fx'] =  - (K[0, 0] - summary_df_d['fx']) / K[0, 0] * 100\n",
+    "summary_df_d['delta_cx'] =  - (K[0, 2] - summary_df_d['cx']) / K[0, 2] * 100\n",
+    "summary_df_d['delta_fy'] =  - (K[1, 1] - summary_df_d['fy']) / K[1, 1] * 100\n",
+    "summary_df_d['delta_cy'] =  - (K[1, 2] - summary_df_d['cy']) / K[1, 2] * 100\n",
+    "summary_df_d['delta_k1'] =  - (D[0] - summary_df_d['k1']) / D[0] * 100\n",
+    "summary_df_d['delta_k2'] =  - (D[1] - summary_df_d['k2']) / D[1] * 100\n",
+    "summary_df_d['delta_k3'] =  - (D[4] - summary_df_d['k3']) / D[4] * 100\n",
+    "summary_df_d['delta_p1'] =  - (D[2] - summary_df_d['p1']) / D[2] * 100\n",
+    "summary_df_d['delta_p2'] =  - (D[3] - summary_df_d['p2']) / D[3] * 100"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "experiment_type = 'random_perturbations'\n",
+    "# sepcify which parameters to perturb: 'f' for focal length, 'c' for principle point, 'd' for distortion parameters\n",
+    "# perturb_parameters = ['f', 'c']\n",
+    "perturb_parameters = ['f', 'd']\n",
+    "# construct experiment directory name\n",
+    "experiment_name = experiment_type + '_'\n",
+    "for param in perturb_parameters:\n",
+    "    experiment_name = experiment_name + param\n",
+    "output_folder_fd = os.path.join(output_folder, KITTI_sequence, experiment_name)\n",
+    "\n",
+    "# read panda dataframe\n",
+    "summary_filename = os.path.join(output_folder_f, 'summary.csv')\n",
+    "if os.path.exists(summary_filename):\n",
+    "    summary_df_fd = pd.read_csv(summary_filename)\n",
+    "else:\n",
+    "    raise Exception('File not found: %s' % summary_filename)\n",
+    "    \n",
+    "# process data frame to get % deviation\n",
+    "summary_df_fd['delta_fx'] =  - (K[0, 0] - summary_df_fd['fx']) / K[0, 0] * 100\n",
+    "summary_df_fd['delta_cx'] =  - (K[0, 2] - summary_df_fd['cx']) / K[0, 2] * 100\n",
+    "summary_df_fd['delta_fy'] =  - (K[1, 1] - summary_df_fd['fy']) / K[1, 1] * 100\n",
+    "summary_df_fd['delta_cy'] =  - (K[1, 2] - summary_df_fd['cy']) / K[1, 2] * 100\n",
+    "summary_df_fd['delta_k1'] =  - (D[0] - summary_df_fd['k1']) / D[0] * 100\n",
+    "summary_df_fd['delta_k2'] =  - (D[1] - summary_df_fd['k2']) / D[1] * 100\n",
+    "summary_df_fd['delta_k3'] =  - (D[4] - summary_df_fd['k3']) / D[4] * 100\n",
+    "summary_df_fd['delta_p1'] =  - (D[2] - summary_df_fd['p1']) / D[2] * 100\n",
+    "summary_df_fd['delta_p2'] =  - (D[3] - summary_df_fd['p2']) / D[3] * 100"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plotting evaluated trajectory statistics against the perturbed camera parameters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stats = ['rel_trans_stats_rmse_trajlen_100.0', 'rel_rot_stats_rmse_trajlen_100.0']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/mayankm/.virtualenvs/plr_pytorch/lib/python3.5/site-packages/matplotlib/pyplot.py:514: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  max_open_warning, RuntimeWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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rVUyDcEznfUYp9TtF4yAuBX4E+ngtOiGEEOexHDtG+IoVZPfpg61ZM69dx5VxEEeAm5VSl+PoxXRuHITW+jevRSaEEKJMEbNmgclExujRXr2Oy0OPncnAJxJCXM+epM6bh61xY6NDEUKIGmU5eJCwNWvIevRRCi+6yKvXqtbYeqVUoFLqc08F46rgXbuITkio6csKIYTh6s2ciT0oiMxRo7x+repOvmIGDFmaypKcbMRlhRDCMAG//ELYRx+RNXgwhfXre/96lb3A2TBdHsNm97LFxhp1aSGEMES9//s/CiMjyRwxokau50obRCzwDHCojH1BQI3PW2298EJS582r6csKIYRhAn/4gdB//pP0Z5/FHl0zS/C4kiB+AHK01ltL71BKBePm5E+eYI+KkgZqIUSdEjljBrbYWLKGDKmxa7pSRTQV2F/OvnygxudiCty7l4ADB2r6skIIYYigHTsI/uorMhMSsEdE1Nh1PTrdt1LqYa31Ko+dsGx2u9lMxtNPkzlmjJcv5dvqwlTGrpJ7UUTuRZFacS/sduLvuw/LsWMkfv01hIZW6TRVme7b043MCzx8vjLl33ADoQYt2SmEEDXFcuQI9Tt1ImjnTgqDgrCcPl2j1/f0Gp1lZiel1CXAe0BDwA4s1FrPVkpNBoYCZ//qCVrrLZVdJKdHD6InTiRg/36sLVp4JnIhhPAx0cOHE+isTg88epTohASSNmyoset7ugRRXn2VFXhaa30VcCMwUil1lXPfLK31dc6fSpMDQO7dd2M3mQjduNEDIQshhO8x5eQQ9OuvJbbV9PivGhnHoLU+obX+wfk4A9gLVHmMeGHDhuTfeCMhGzeC/y2ZKoQQFbPZHLNFlFpbvKbHf9X4QDelVFOgNUXThCcopX5SSi1VSsW4ep6c7t0J/O03AkplWCGE8Gt2O5GTJxP6ySdkjBlDXtu2WJs1I69t2xof/+XpNoijFe1USkUAa4EntdbpSqm3cKwpYXf+ngk8VsZxw4BhAFpr4uPjYcAA7C+8QNzWrdg6dPDwn+EfAgICHPdCyL0oRu5FkWrdi0OHCHj0UUxnzmCPj8e6bBl4cWrts8yzZxOwdCm20aMJefVVAApxNPC6/A3aQ9zq5qqUigKuwDHl9zla60on7FNKBeIYdf2p1vqNMvY3BTZpra+p5FT248ePAxCnFOaTJzm9bRuYany8nuFqRRc+D5F7UUTuRZHq3Iu4e+4h+Mcfzz3Pa9vW6w3EIZs2ETN8OLndupGyYAGYPVfJ49VurkqpR4HjwEZgSbGfxS4ca3K+dm/x5KCUuqDYy3oBP7saDzh6MwUePEjAL7+4c5gQQlTMaiWwhhuIg77/npgnnqCgbVtS5szxaHKoKneqmKYBvbXWH1fhOu2BAcAepdTZlDwBeFgpdR2OKqbDwOPunDT37ruxP/88oRs3knH11VUISwghSrHbiZowAXNOTsnNXryk5eBBYh99FNuFF5L8zjtVHgznae4kiADgn1W5iNb6a8ou2rjUrbU8hXFx5LVv70gQ48bVyWomIYRnRcyZQ/jKlWQNGEDAL79gOX0ac3IygYcOEfnyy6Q/9xxYLB67nvnMGeIGDMBusZC0YgWFPjRTtTtlmNeAiUop48s9xeT26EHA4cME/O9/RocihPBzoVoTOWMG2fffT9orr5C0YQOnvvmGk3v2kDVwIBFvvUVs//6YUlI8cj1TTg6xjz6KOTGR5GXLsDVt6pHzeoo7JYgxQCNgrFIqqfgOrbVhU6vmdO1K1PjxjlLENZW1bwshRNmCt20j+tlnyevQgdSZM0vWSAQFkfbKKxRcey1Rzz9P/XvuIXnJEqwtW1b9gjYb0SNHEvjjj6QsXkxBmzbV/yM8zJ3SQH/gDuBuHO0JxX8MY4+NJe+WWwjdsEEGzQkhqiTg55+JGToU6+WXk7xoEQQFlfm67L59OfPhh5hyc4nv0cMxWLcq7HYiX3yR0E8/Je2ll8jt2rUa0XuPyyUIrfU2bwZSHTk9ehDz9NME/vQTBa1aGR2OEMKPWP74g7gBAyiMiiJpxQrskZEVvr6gbVtOf/wxsUOHEjt8OBk//0zG2LFutUuEL1xIxDvvkPn442QPGlTdP8Fr3OnmGqyUmqaU+l0plebcdqdSKsF74bkmt2tX7IGBMjeTEMItppQUR5tCXh7JK1ZQ2KiRS8cVNmzImTVryOrXj3rz5hH7yCOYUlNdOjZk40aipk4l5557SJ84sTrhe507VUyzgGuAfhT1+PofUDOLo1bAHh1N3i23yNxMQgjX5eYS+9hjBBw96mhPuOIK944PDiZtxgxSX32V4K+/pv4991Q69U/Qd98RM3o0eddf7zNjHSriTnS9gL5a629wjPxGa/0n1Zh0z5NyevQg4NgxAouNfBRCiDLZbMSMGkXwd9+RMns2+TfdVOVTZQ8YQNKaNZiysx3tElvK7r1vOXCA2EGDHGMdli6FkJAqX7OmuJMg8inVZqGUqg8klf3ympV7112OaqYanCtdCOGH7HYip0whdMsW0l58kdyePat9yvzrr+f0li1YW7QgduhQ6s2YAYWF5/abT58uMdbB7kNjHSriToJYA7yrlGoG56bJmAd84I3A3GWPiiKvY0dCNm0q8Q8jhBDFhS9YQMSSJWQOGULWsGEeO2/hBRdwZu1asvr0od7s2cQOGoQpPR1TdrZjrMOpUyS/+67PjXWoiDsJYgJwCNgDRAO/4ZibaYoX4qqSnJ49CTh+nMAffjA6FCGEDwr56COiXnqJnO7dSX/xRc9fIDiYtNdfJ3XaNIK/+IKG111Hw7/8hcAffyR96lQKWrf2/DW9yK3ZXM9yVi2d0Vob0SJ8bjbX0kwZGTRq1YqsAQNIn+IzectrZNbOInIvisi9KFL8XgTt2EFcv37kt2lD0sqVXm8DiO/cmaD9+889r4nZYCtSldlcKxwHoZS6tILd9ZRSAGitf3fnot5ir1eP3E6dCN20yfHtwMd7CAghakbAvn3EDh6MtUkTkpcsqZEGYnNBQYnnNb1cqCdUNlDuAI4urRVlHTvguZmrqim3Rw9CP/2UoJ07yb/hBqPDEUIYzHz8OHH9+2MPDSV55Urs0dE1cl1bbCwBhw6VeO5vKkwQWmu/+wqe26UL9pAQQjZulAQhRF2XlkbcwIGYMjI4s24dtotqrld+6rx5RCckYElOxhYbW+PLhXqCp5ccNZw9IoLc224jdPNm0idP9ui0vEII/2A5csQxEd6+fZCTQ+rs2VhreM0YW+PGhrY5eEJlbRCfaK27Oh9/RflrZuTj6OE0U2u9z7Mhui+ne3dCt2wh6Pvvyb/xRqPDEULUJLudmMGDCdq799ymsPfeI6d3bwOD8k+VlSDeK/a4oqVFzcDVOMZKXFvdoKor7447KAwJIXTDBkkQQtQR5j//JGz9ekLXriWwWO8h8M8GYl9QWRvE+8Uev1vZyZRSl3kiqOqyh4eTd/vthGzeTNpLL0k1kxC1lCkjg5DNmwn78EOC/vMfTHY7eddfT0HjxgQePXrudf7YQOwL3GqDUEo1BG4A4inWs0lrvdT5u5dHo6uGnJ49Cd28maD//If89u2NDkcI4SkFBQR/+SVh69YR8s9/YsrNxdqsGRlPP03O/fdja9IEy9GjRCckEJSWRn5UlF82EPsClxOEUuo+YAWOEdRX45jJ9Rrga2CpV6Krhrzbb6cwNJTQjRslQQjh7+x2AnfvJnTtWkI/+ghLUhK2mBiy+/Qh+4EHHCOUi60Ad7aBOD4+niQZNFhl7pQgXgYGaa3XKKVStNatlVKDcCQLn2MPDSWvSxdCtmwh7eWXIaDWddgSolayHDlC9KhRju6h4eEU3HwzwVu3EnjwIPbgYHK7dCH7gQfI69wZAgONDrdWc+dTs7HWek2pbe8CJ4FnPBeS5+T06EHohg0E7dhB/q23Gh2OEMIF0aNGEbxrF+D4gAr++WfybrqJ1BEjyLn7buxRUcYGWIe4MxDulLMNAuCwUuom4DJ8aBR1abmdO1MYHk7opk1GhyKEcJHl1KkSz60XX0zShx+S/fDDkhxqmDsJYhHQwfl4FvAFsBt409NBeUxoKLl33ulYwKPUvChCCB9kt2NOSyuxydawYTkvFt7mToL4P631WgCt9XtAC6Ct1voFr0TmIbk9emBJSSF4xw6jQxFCVCLsgw8wp6dTcMklWJs1I69tW+mBZCCX2iCUUhYgUykVrbXOA9BaH63kMJ+Q27EjhRERhGzcSF7HjkaHI4Qoh/nPP4mcMoW8m24iSWuZjdkHuPQvoLW2AfuBOO+G4wUhIeTeeSehH38M+flGRyOEKIvdTvTYsWCzkTpzpiQHH+FOL6aVwCal1GzgGMXmZdJaf+7pwDwpp0cPwtatI/jrr8m77TajwxFClBL2wQeEfPklqdOmYWvSxOhwhJM7CWKE8/fkUtvtQEULCxkur2NHCiMjCd24URKEED6meNVS9sCBRocjinE5QWitm3kzEK8KDib3rrsI+eQTeO01CAoyOiIhBEjVko9z+V9DKfVROdvXeS4c78np0QNzejrB27cbHYoQwils1SpCvvyS9Oefl6olH+ROuu5czvZOHojD6/JuuYXC6GhC/XwBDyFqC4tULfm8SquYlFJTnQ+Dij0+61LgiMej8oagIPI6dCB0/XqCdu7EFh9P6rx52Bo3NjoyIeoeu52oZ56BwkJS33hDqpZ8lCv/Kpc4f8zFHl8CXAz8ATzoteg8zLJvH6bCQgKOHCF41y6iExKMDkmIOils1SpCtm93VC3JlzSfVWkJQms9CEAptUNrvaii1yqlHtZar/JUcJ5mtlpLPJdVpoSoeVK15D9cLtdVlhycFlQjFq+zxZUc5yerTAlRw6Rqya94+l/HVPlLjJM6bx55rVphN5nOtUEIIWpO2PvvS9WSH/F0grBX/hLj2Bo3JmnLFrL79sWcmYk9IsLokISoMyx//knk1Knk3XyzVC35iRpZZk0pdQnwHtAQRxJZqLWerZSKBVYDTYHDgNJap3g7nqzBgwlfuZKwFSvIfOIJb19OCFG8akkGxPmNmvpXsgJPa62vAm4ERiqlrgLGA1u11pcDW53PvR/MFVeQ27Ej4cuWyQR+QtSAc1VLEydK1ZIf8XSCKHMKcK31Ca31D87HGcBe4CLgXhzLluL8fZ+H4ylX1pAhWBITZbU5IbysRNXSgAFGhyPc4FYVk1LqShzjHhpprUc6nwdprX8C0Fpf48I5mgKtgW+BhlrrE85dJ3FUQdWIvE6dKGjenPBFi8jp1QtMPt2+LoR/kqolv+ZyglBKPQjMB9YBfYGRQATwKnCHi+eIANYCT2qt05VS5/Zpre1KqTIbuZVSw4BhztcRHx/vatgVMo0eTeCoUdT/9VfsHTpUfoCPCQgI8Ni98HdyL4r40r0wL15MwPbtWOfMIaZNmxq/vi/dC3/kTgliKtBFa71bKfWQc9tuoJUrByulAnEkh5Va67MT/CUqpS7QWp9QSl0AnCrrWK31QmCh86n9zJkzboRdPlO3bjSMjsY6cyYpV17pkXPWpPj4eDx1L/yd3IsivnIvLMeOUX/sWPLatyepVy8wICZfuRe+4MILL3T7GHfKew2An5yP7cV+V9q1VSllApYAe7XWbxTbtQF4xPn4EaDMGWO9xR4aSlb//oR88gmWo36xgqoQ/sFuJ/qZZ8Bul6olP+ZOCWIXMABHd9Wz+gDfuXBse+exe5RSPzq3TcBRPaWVUoNxTPqnyjnea7IeeYSIt98mfOlS0idPrunLC1GrWI4cIXrUKAIOH8aSlET6s89iu+QSo8MSVWSy210b2+ZskP4ncAhHV9UvgRbAnVrr37wVYBnsx48f9+gJo0eOJORf/yJx507s9ep59NzeJMXnInIvihh5L+J69iR4165zz/PatCFp40ZDYgF5XxTnrGJyqzeOO3Mx7QOuxNFQPRF4B7i2hpODV2QNGYI5M5Ow1auNDkUIv2Y5fbrk8xSvj3sVXuRWN1etdTagAZRSlwLxQKYX4qpRBa1bk//XvxK+ZAlZgwaBxWJ0SEL4n9xczKVmSJYJMf2bO0uOrlJK3ex8PAj4H/A/Z/uB38scOpSAo0cJ+ewzo0MRwv/YbMSMGoWor2LoAAAgAElEQVQ5M5OCZs2wNmtGXtu2MiGmn3OnBHE7RT2OnsIx9iEV+AeOHkp+LbdrV6wXX0z4okXkdu1qdDhC+A+7nahJkwjdsoW0yZPJGjrU6IiEh7jT9yxIa52vlLoIiNVa/1tr/T9qcPSzVwUEkDVoEMH/+Q+Be/YYHY0QfiNi7lzCly0jc8QISQ61jDsJ4kel1HPAC8BmAGeySPdGYEbI7tuXwvBwwhe5sjaSECJ09WoiX3uN7PvvJ33CBKPDER7mToIYDFwLhOLoxQRwE7DS00EZxR4ZSfZDDxG6YQPmxESjwxHCpwX/619EP/ssuR07ymC4WsrlcRA+xOPjIIqzHDpEg1tuIfOJJ8gYO9Zr1/EE6eNdRO5FkZq4F4E//ECcUlibNyfpww99dvEteV8Uqco4CHdnc70Fx0ysJd4NWuvp7pzHl9maNSO3SxfCli8nY9QoCA01OiQhfIrl4EFiBw6ksEEDkpcv99nkIKrPnW6uc4EPgVuBlsV+/G+Wu0pkDR2KJTmZsPXrjQ5FCJ9iTkwkrl8/MJtJWrmSwvr1jQ5JeJE7JYh+wDVaa+/V7/iI/JtuouCqqwhfvJjshx+WtSKEAEzp6cT17485KYmkDz/E1qyZ0SEJL3OnVekPIM9bgfgUk4nMoUMJ/PVXgr/6yuhohDBeXh6xQ4YQsH8/KQsXUtDKpVn+hZ9zpwQxGFiklFoFlOjio7Xe7tGofEDOvfcSOX064YsWkXfrrUaHI4RxCguJHjOG4H//m5TZs8nr3NnoiEQNcSdBtAW64WiDyCm23Q7UvlXIg4PJeuQRIl9/nYADB7A2b250RELUPLudyClTCPvoI9Kff56c3r2NjkjUIHcSxHSgh9b6X94KxtdkDxhAvblzCV+8mLRXXzU6HCFqXPiCBUQsXkzm4MFkjhhhdDiihrnTBpEF1LqqpIoUxseT3asXoWvWYJJpi0UdE7p2LVEvvUROjx6OxbSks0ad404JYhLwd6XUVEqtHa21LvRoVD4ka8gQwj/4gPCVK8lMSDA6HCFqRPC2bUQ/9RR5N91EyuzZMkq6jnLnX30pMBz4Eyhw/lidv2sta8uW5HXoQPg770BBrf5ThcBy5Ajxt99ObL9+2AMDSZs6FYKDjQ5LGMSdBNHM+XNpsZ+zz2u1zKFDsZw8SejmzUaHIoRXRQ8dStC+fZjsdsw5OUSNH290SMJALlUxKaUswLvAXVrrujEWopi8227DeumlhC9aRM6990pdrKiVzCdOEPTrryW2WUqtECfqFpdKEFprG47SQt2siDSbyRw8mKAffyRw506joxHC40zJycT17QuFJZsTZcnQus2dRuopwFtKqReBYzjGPwC1u5H6rJwHHyRyxgwiFi0i5frrjQ5HCI8xZWURN3AgAUeOkDJnDuHvvIMlORlbbKwsGVrHuZMgFjt/Dyi2zYQjUVg8FpGPsoeHk9WvHxFvv43l2DFsF19sdEhCVF9eHrGDBxP400+kLFpE7l13kdurl9FRCR8hjdRuyO3SBQoLqd+lC3E9e2I5etTokISoOpuNmIQEgr/6itTXXyf3rruMjkj4GJdLEFrrI5W9Rim1R2t9bfVC8l2RL7+MCceslsG7dhGdkEDShg1GhyWE++x2osaNI3TLFtImTyZHKaMjEj7I043OTT18Pp9SukeH9PAQ/ipy2jTCV60iY/RosoYONToc4aM8nSD8bv1Sd5Tu0VEoq80JPxQxfz4Rb71F1iOPkPHss0aHI3xY3ey2WkWp8+aR17Yt1saNsVssEBQE/remt6jDwlasIHL6dLLvu4+0l1+WMT2iQpIg3GBr3JikDRs49c03pL3yCkE//kjohx8aHZYQLgnZuJGo8ePJve02Uv/+d5lfSVTK0++QOvN1JPvhh8lv3drRcJ2WZnQ4QlQoeNs2YkaNIv/660lZuBACA40OSfiBKicIpVSoUqr0LF6PVzMe/2E2k/rqq5iTk4mcMcPoaIQoV+DOncQMHoz18stJXrYMu7SdCRe5nCCUUq8rpW5wPr4HSAZSlFI9zr5Ga/2+50P0XdZrriHrkUcIe+89AvfsMTocIc4TsHcvcY88QmHDhiS9/z72qCijQxJ+xJ0SRD/gZ+fjSUB/oCeOlebqrIxnn6UwLo6o5547bx4bIYxkOXKEuL59sYeEkPTBBxTWr290SMLPuJMgwrTW2UqpOOBSrfVa5/KjTbwUm1+wR0WRPnEiQf/9L2GrVhkdjhAAmBMTiXv4YUz5+SStWoXtkkuMDkn4IXcSxH6lVD8gAfgMQCkVD+R4IzB/kvPAA+TdeCOR06djlsFzwmgpKcT17Yv59GmSVqzA2qKF0REJP+VOgvgbMBLoDLzg3HYX8E9PB+V3TCbSpk3DlJFBvVdeMToaUUdZjhwh7p57CGzalIBffyXt1VcpaN3a6LCEH3NnNtc/tNY3F9+gtV6plNrq4Zj8kvXKK8kaMoSIBQvI7tOHgrZtjQ5J1DExjz1G0L59556HvfsuOQ88YGBEwt+5VcVUzvZfPBFIbZDx1FPYGjUi+rnnwGo1OhxRF9jtBO3YQezDD5dIDiBzhYnqc6cEcd4gOKVUJFBp1x2l1FKgO3BKa32Nc9tkYChw2vmyCVrrLW7E43PsERGkvfgisSNGEP7ee2Q99pjRIYnaym4n+PPPqTdnDkE7d2KrXx/rRRcR8Oef514iq8GJ6qo0QSil/sAxCV+oUqr0AghxgCtdd5YB84D3Sm2fpbV+3YXj/UZujx7kvf8+9WbMIKd7dwobNDA6JFGb2GyEfPwxEXPnEvTzz1gvuojUadPIfughLKdPE52QQFBaGvlRUbIanKg2V0oQ/XGUHrZQcjU5O5Cotf61zKOK0VpvV0o1rVKE/sZkIvXll2lwxx1EvvQSqXPnGh2RqA0KCghdv56I+fMJPHAA66WXkvLGG+Tcf/+5aTPOzhUWHx9P0pkzBgcsaoNKE4TWehs4urRqrbM9fP0EpdRAYCfwtNY6xcPnN4SteXMyhw+n3ty5ZPftS/5NNxkdkvBXubmErV5NxFtvEfDHHxRcdRXJb71F7j33gKXWr/QrDGayuzFdtVLqOuAWIJ5ibRJa60kuHNsU2FSsDaIhcAZHSeQl4AKtdZmV9kqpYcAw57Xa5ufnuxyzYbKzCbzuOuzh4Vi/+84rk6MFBARglcZwoBbei8xMzAsXYpk9G9PJkxS2a4dt3Djsd99d6RTdte5eVIPciyJBQUHg5oSqLjdSOz+kZ+EY99AN+Bi4E/jInQuepbVOLHbuRcCmCl67EFjofGo/4yfF5+DJk4kbNIic114ja/hwj58/Pj4ef7kX3ubv98Jy5AjRo0ZhOX0aCgowZ2VhTk8nr0MHMubMIf/mmx2JISmp0nP5+73wJLkXRS688EK3j3Gnm+tYoKvWuheQ4/zdGyhw+6qAUuqCYk97UTTPU62Rd+ed5N5xB/VmzsR8/LjR4QgfFj1qFMG7dhFw9CgBJ05gN5k4vXEjSatXk9++vSzsIwzhToJooLX+yvm4UCll1lp/DPSo6CAApdQq4BvgCqXUMaXUYGCGUmqPUuonHKOzx7gbvD9Ie+klTIWFRE2ZYnQowlfZ7QQePFhyU2wsBW3aGBSQEA7ujIM4ppRqqrU+jGPQ3L1KqTNApQ0CWuuHy9i8xI1r+y1b48ZkJCQQ+frrZG/bRl7HjkaHJHyJ3U7klCmYU1NLbJYxDMIXuFOCmAG0dD6eCqwAPnc+FhXIHDECa9OmRD3/POTlGR2O8BU2G1HjxhGxaBFZSpHXpg3WZs3Ia9tWxjAIn+BWL6bilFJBQJDWOtOzIVXKftwP6/ODv/ySuH79SB87lszRoz1yTmmAK+J396KggOgxYwhbv56M0aPJePZZj7Uz+N298CK5F0WcjdRuvcncWVHuv8Wfa63ztdaZSqmd7lywrsrr1Imcu+8mYs4cLH/8YXQ4wkh5ecQ8/jhh69eTPmECGWPHSiO08EnuVDE1L71BKWUCLvVcOLVb2uTJYDYTOanSYSOiljJlZxP76KOEfvopqdOmkTlypNEhCVEuV+ZiOjt/UlCxx2c1Bf7n6aBqq8KLLiLr0Uep9+abBLVpg/Xii0mdNw9b48ZGhyZqgCk9ndhHHiFo507HNBkPPWR0SEJUyJVeTAfLeWwH/g2s8WhEtVzQN98AYElMxJKYSHRCAkkbNhgclfA2U3Iycf36EfjLL6S8+Sa5PSrtHS6E4VyZi2kKgFLqP1rrT70fUu1mKdWdUebsr/3Org8dcPgwyUuWkHfHHUaHJIRL3BkHka+Uaqa1PqSUagS8hmMtiOe01ie9E17tY4uNJeDQoaLnMTEGRiO8zXLsGHEPPYT51CmSli93jIoWwk+400j9JmBzPn4DCMSRIBaWe4Q4T+q8eeS1bYstLg6ArGHDDI5IeIvl99+J69ULc3IySatWSXIQfsedEsRFWuujSqkA4C6gCY5R1P43KMFAZ+fsN+Xk0PCvfyX0H/+Q+uhaKGDvXuIefhhsNs6sWYP1mmuMDkkIt7lTgkh3TtHdEfil2AA5z89jXQfYQ0PJGjiQkE8/xfL770aHIzwocPdu4nv3BouFpHXrJDkIv+VOgpgLfA+sBOY7t7UH9pV7hKhQ1qBBEBhIxKJFRociPCTo22+JU4rCevU4s24d1ssvNzokIarM5QShtX4NuANor7X+wLn5T2DI2dcopS72bHi1W2GDBuT06kWo1pikN5Pfshw5QlzPnjRs04a4Bx6gMDaWM+vWYWvSxOjQhKgWd9og0Frvr+g58AsQWd2g6pLMYcMIW72a8PfeI/PJJ40OR1TB2bUczrJFR1NYhcVZhPA17lQxuUImlHGT9corye3cmfBlyyA31+hwRBVYSk0GZ8nIMCgSITzL0wmialPD1nGZw4ZhOX2a0H/8w+hQRFWUWiNd1nIQtYWnE4SogvxbbqGgZUsiFi6EKk6/LoxhOXAAy5kz2GJjZS0HUeu41QYhvMRkIvPxx4l58kmCv/ySvM6djY5IuMJuJ3r8eOyhoZz+/HMK69c3OiIhPEraIHxEzr33YmvUiIgFC4wORbgodO1agr/5hvTnnpPkIGqlKicIpVRnpVTpBZavqmY8dVdQEFmDBhH81VcE/E9mUPd1ppQUIqdOJb9NG7L79zc6HCG8wp0V5bYppdo7H48DPgDeV0pNOPsarbUslVYNWf37UxgW5miLED4tcvp0zKmppL72GpilKU/UTu68s68B/uN8PBToDNwIDPd0UHWVPTqa7D59CP3oI8wnThgdjihH0PffE/7++2QNHYr1Kik0i9rLnQRhBuxKqcsAk9b6F2eJQear9qCsIUPAZiP8nXeMDkWUpaCAqPHjsV54IRlPPWV0NEJ4lTsJ4mtgHvA6sB7AmSzOVHSQcI+tSRNyu3UjfMUKTFlZRocjSolYuJDAfftImzYNe3i40eEI4VXuJIhHgVTgJ2Cyc9uVwGzPhiQyH38cc1oaYR98UPmLRY2x/PEHEW+8QU7XruTdeafR4QjhdSa7/w3Msh8/XvuXoIi/917Mp05x6uuvwWIp+zXx8Zw5IwU4qIF7YbcT+8gjBH3zDae+/JLCiy7y3rWqSd4XReReFLnQMT+YW0MRXB4op5QKwlGKuA6IKL5Paz3QnYuKymU+/jixQ4cS8vHH5HbvbnQ4dV7Ixx8TsnUraZMm+XRyEMKT3Kliehd4EsgADpb6ER6We9ddWJs2lYFzPsCUmUnUCy9QcNVVZA0ebHQ4QtQYd6ba6Ao001qneisYUYzFQuaQIURPnEjg999TcP31RkdUZ9WbMQNzYiLJixZBgMxOI+oOd0oQR4FgbwUizpfz0EMURkfLwDkDBe7ZQ/g775A9YAAFbdoYHY4QNcqdr0PvAR8ppWYDicV3aK0/92hUAgB7WBhZ/fsTMX8+lsOHsTVtanRIdYvNRtS4cRTGxZE+frzR0QhR49wpQSQADYHpwJJiP4u9EJdwyho0CAICCF8st7mmhb33HkG7d5M+eTL2qCijwxGixrlcgtBaN/NmIKJshY0akdOrF2EffEDG009jj5GB6zXBfPIkka++Su6tt5Jz771GhyOEIWSWMT+QOWwY5pwcwlesMDqUOiNq8mRMBQWkTZ8OJpnFXtRN7oyDiMQxgrojEE+xARda68Yej0ycY23ZktyOHQlfupTMYcMgWPoKeFPwF18QunEj6c88g62ZFJxF3eVOCeJNoA0wFYgFRuHo2TTLC3GJUrIefxzLqVOybrW35eQQNWECBZddRubf/mZ0NEIYyp0EcSfwgNb6I8Dm/P0QMMArkYkS8m691bFu9aJFsm61F9WbPZuAo0dJe/VVKamJOs/d6b7TnI8zlVJRwAmgucejEuczmcgcOpTAvXsJ3r7d6GhqpYD9+4l4+22ye/cm/+abjQ5HCMO5Mw5iN472h63AVziqnDKB/ZUdqJRaCnQHTmmtr3FuiwVWA02Bw4DSWqe4EU+dk3PffUS++irhCxaQ17H0aq+iWux2osaPxx4eTvqkSUZHI4RPcKcEMRTHBznAaCAHiAZcmahvGY6pOoobD2zVWl+OI+nISKTKBAeTNWgQIdu2EbB3r9HR1BqWI0eo3749wd9+iy0qStbhEMLJpQShlLLgmMn1GIDW+pTWeojW+iGt9S+VHa+13g4kl9p8L44JAHH+vs/VoOuyrAEDKAwNlek3PChm4EACjxwBIPDIEaITEgyOSAjf4FKC0FrbgL8BBR68dkOt9dmFl0/iGKUtKmGPiSH37rsJXbOGwCuuIK5nTyxHjxodll+y/P67Y42HAwdKbk8u/V1GiLrJ3bmYhuNoe/AorbVdKVVu1xyl1DBgmPO1xMfHezoEvxJw8CAmux0OHyb48GHqjxmDdds2o8MyVEBAgOvvi/R0LK+8gnnuXAgOprBxY8zFkqy5YUO/fo+5dS9qObkX1eNOgrgBGKWUGgv8AZz7QNda31qFaycqpS7QWp9QSl0AnCrvhVrrhcDZOhV7XV8hqkFaWomin/3AAc6cPl2nR/y6tHJYYSGha9YQ+corWE6fJlsp0p97DlNuLtEJCViSk7HFxpI6axY2P36PySpqReReFHGuKOcWdxLEIuePp2wAHgFedf7+yIPnrtVssbEEHDp07rnlzBnievUi7eWXsV5zjYGR+a7AXbuImjSJoB9/JL9NG5KXLaPguuvO7U/asMHA6ITwTe4kiH1a629Lb1RK3VDZgUqpVUAnIF4pdQx4EUdi0EqpwcARQLkRS52WOm8e0QkJBKWlkR8ZSW7XrkQsWED9bt3IHjiQ9GeekUn9nMwnThA5fTph69Zha9SIlDlzyOnVC8wyDZkQlXEnQXwGRJax/RMcU2+US2v9cDm7bnfj+sLJ1rgxSRs2EB8fT5Kz+Jzdrx/1Zs4kfNkyQjZsIOO558ju06fufhDm5hKxcCERc+dislrJGDWKzFGjsIeHGx2ZEH6j0gShlDLjmJjPpJQyUWySPuAywOql2IQb7NHRpL/0Etl9+hA1cSLRzz5L2MqVpL38MgWtWxsdXs2x2wn55BMip04l4OhRcrp2JX3SJGxNmhgdmRB+x5Wvl1YgHwhzPi4o9vMLXujVJKrOevXVJK1bR8rcuViOH6d+9+5EPfMM5qQko0PzuoB9+4jr04fYIUOwh4ZyZtUqUpYskeQgRBW5UsXUDEepYRtQvLeSHTittc7xRmCiGkwmcu6/n9wuXag3axbhS5YQumUL6WPHkt2/PwS4U7Po2yxHjhA9YgSBhw9TPy0Ne716pL78MtkDBtSqv1MII5jsHpwZVCm1R2t9rcdOWDb78ePHvXwJ/+BqF76A/fuJmjiR4H//m4KrriJt2jTyb6i0b4HPMqWkEPT99wR/+y1h772HOTv73L68Vq1I2rLFwOiMJ107i8i9KOLs5upWX3hPf8Vq6uHzCQ+wtmhB0urVhGzeTOSUKcT36kXOXXdhPnECS0aGo+//vHnYGvvmuk/mEycI+u47gr/9lqBvvyVw3z4A7EFB5439sKSnGxGiELWSpxOELFTgq0wmcrt3J++224iYM4eIefMco7GBgEOHiE5I8I2xAHY7lkOHSiSEAOc8SYXh4eT/9a/k9OxJfrt25F93HXFKEbxr17nDbbEVdqgTQrhBKmnrGHtYGBnjxxO6fj0Bx46d2x7w5581HovFOTGeJTERu9mMrUULAvfswXLKMajeFhNDfrt2ZD36KPnt2lFw9dXntSuUGBMSFUXqvHk1/ncIUVtJgqijbA0blkgQlpMniRk6lPQXX8R28cXeD8BqJbZvXwIPHz63KeDkSXK6d3eUDtq1w9q8eaXjOMoaEyKE8AxPJ4i6OxmQnzn7zduSnIwtOpqCG24g7N13afD552QkJJA5fDiEhnr+wvn5hH34IRHz5p2rOjrLdtFFUgIQwod4OkE87uHzCS85+827uKzBg4mcOpXI118nbPVq0qdMIffOOz0zCWBuLmEffEDE/PkEHD9OfqtWFAQEEHjwYFFMcXHVv44QwmMq7OaqlFqOCw3PWmtXVpXzFOnm6uStLnxBX39N1AsvELh/P7mdOpE2ZQq25lVbetyUnU3Y8uVEvP02llOnyP/rX8l48knyOnXC8scfJWdRrUZPKunOWETuRRG5F0W80c31QCX7RS2U36EDp//5T8KXLaPezJk0uOMOMocOJXP0aOwRES6dw5SRQfiyZYQvXIglOZm89u1JmT+f/JtuOlciKasUI4TwHR4dKFdDpAThVBPfjsynTztmQ9UaW8OGpE+c6JgNtZxqJ1NKChFLlxK+ZAnmtDRyb7uNjCeeoOD6670ap3xTLCL3oojciyJeHyinlOoC9AEaaK17KKX+CkRqrT935zzCfxTWr0/qrFlk9e9P1AsvEDNqFGErVpD20ktYr7763OvMZ84QvnAh4cuWYc7KIqdbNzKfeIKCv/zFwOiFENXhcoJQSo0CRgOLgd7OzTnAHOBmz4cmfElB27ac2bSJsA8+oN4rr1C/a1cK4+Ico5kLCrCkpUF+Pjk9e5I5ahTWli2NDlkIUU3uLBbwJHCH1vpVoNC5bR9whcejEr7JbCa7b19OffUVhXFxWE6fJuDPPwk4dYrCiAhOffklqW++KclBiFrCnQRRD8da1FDUsykQx1Tgog6xR0ef11htj4ysck8nIYRvcidBfAWML7XtCeALz4Uj/EXpOY9kDiQhah93GqmfBNYrpYYC9ZRSvwIZQHevRCZ8WomR2M4xDEKI2sWlBKGUsgD7caw9/RegMY7qpu+01oUVHStqJxnDIETt51KC0FrblFL7gRit9bfAt94NSwghhNHcqWJaCWxSSs0GjlFsCg4ZByGEELWPOwlihPP35FLb7cClHolGCCGEz3A5QWitm3kzECGEEL7FnW6uQggh6hBJEEIIIcrkl7O5Gh2AEEL4Kbdmc/W7EoRSaheOP7LO/8i9kHsh90LuhZv3wi1+lyCEEELUDEkQQgghyuSPCWKh0QH4ELkXReReFJF7UUTuRRG374U/NlILIYSoAf5YghBCCFED3FqT2lcopSYDQ4HTzk0TtNZbjIuoZimlugKzAQuw2LnKX52klDqMY9p5G2DVWv/V2IhqllJqKY4p909pra9xbosFVgNNgcOA0lqnGBVjTSnnXkymjn1WKKUuAd4DGuIYFrBQaz27Ku8Lfy5BzNJaX+f8qdX/4MU5p16fD3QDrgIeVkpdZWxUhuvsfB/UqeTgtAzoWmrbeGCr1vpyYCvnL/RVWy3j/HsBde+zwgo8rbW+CrgRGOn8jHD7feHPCaKuugE4oLX+XWudD3wA3GtwTMIgWuvtQHKpzfcC7zofvwvcV6NBGaSce1HnaK1PaK1/cD7OAPYCF1GF94VfVjE5JSilBgI7cWTLWl+EdrqIorXBwTH1ejuDYvEFduCfSik7sEBrLb1WoKHW+oTz8UkcVQ11WV39rEAp1RRojWMNH7ffFz5bglBK/Usp9XMZP/cCbwGXAdcBJ4CZhgYrjNRBa90GR5XbSKXUrUYH5Eu01nbq9vQ0dfazQikVAawFntRapxff5+r7wmdLEFrrO1x5nVJqEbDJy+H4kj+BS4o9v9i5rU7SWv/p/H1KKbUeRxXcdmOjMlyiUuoCrfUJpdQFwCmjAzKK1jrx7OO69FmhlArEkRxWaq3XOTe7/b7w2RJERZx/3Fm9gJ+NisUA3wOXK6WaKaWCgD5AnVwcWikVrpSqd/YxcCd1671Qng3AI87HjwAfGRiLoeriZ4VSygQsAfZqrd8otsvt94VfDpRTSi3HUWS04+iu9XixurVaTyl1N/B3HN1cl2qtpxkckiGUUpcC651PA4D369q9UEqtAjoB8UAi8CLwD0ADjYEjOLoz1vrG23LuRSfq2GeFUqoD8BWwByh0bp6Aox3CrfeFXyYIIYQQ3ueXVUxCCCG8TxKEEEKIMkmCEEIIUSZJEEIIIcokCUIIIUSZfHagnBA1RSm1DDimtZ5YyesOA0O01v/y8PVHAJOBcKCJ1jrJk+cXoqokQQhRBc5ppJtrrftX8zyBwBvAjVrr3Z6ITQhPkSomIYzVEAgB/md0IEKUJgPlRJ2jlGqNYyqCy4EtOEbZHtBaT1RKdQdexrGoyi/AcK31T87jDgNDcJS8NwAmIA84qLVupZQaBIzFMT/WaeA1rfWCCuJoAfwXCAOygO9wTM2cq7V+utjrNgBfaK1neeoeCOEKKUGIOsU5f9U/gOVALLAGeMC5rzWwFHgciAMWABuUUsHFz6G1/gSYDqzWWkdorVs5d53CsaJZJDAImKWUalNeLFrr/cDVzqfRWuvbcMzT/7BSyuyMKR64A3i/mn+6EG6TNghR19wIBAJ/d055/KFS6innvmE41pT41vn8XaXUBOcx2yo7sdZ6c7Gn25RS/wRuAX5wNTit9XdKqTTgduAzHJMxfll8VlIhahnqKQQAAAF5SURBVIokCFHXXAj86UwOZx1x/m4CPKKUGlVsX5DzmEoppbrhmCCuBY7SeRiOCdPc9S7QH0eC6I9j/XEhapwkCFHXnAAuUkqZiiWJxsBBHCv1TXNxRtgSjXfOaqi1wEDgI611gVLqHzjaKdy1AvhZKdUKaImjSkyIGicJQtQ13+BY1P0JpdSbQA8ciwx9ASwC1iul/oWjwTgMx3TR251r+xaXCHRRSpm11oU4ShrBOBqnrc7SRJXWp9BaH1NKfY+jnWSt1jrH/T9TiOqTRmpRp2it84H7gUdxLHD/ELDOuW8nMBSYB6QAB5yvK8sa5+8kpdQPzgTyBI759lOAvlRvIad3gWtxJAkhDCHdXIXwQc61tVfgGFkt/0mFIaQEIYSPcY6uHg0sluQgjCRtEEJ4mbOr7IQydn2lte5W6rUtgZ3AbhxjKYQwjFQxCSGEKJNUMQkhhCiTJAghhBBlkgQhhBCiTJIghBBClEkShBBCiDJJghBCCFGm/we3kGDZIlVgRwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for stat in stats:\n",
+    "    for param in perturb_parameters:\n",
+    "        if summary_df_f is not None:\n",
+    "            plot_scatter_with_line(summary_df_f, x='delta_fx',y=stat,color='red')\n",
+    "            plot_scatter_with_line(summary_df_f, x='delta_fy',y=stat,color='red')\n",
+    "        if summary_df_d is not None:\n",
+    "            plot_scatter_with_line(summary_df_d, x='delta_k1',y=stat,color='green')\n",
+    "            plot_scatter_with_line(summary_df_d, x='delta_k2',y=stat,color='green')\n",
+    "            plot_scatter_with_line(summary_df_d, x='delta_p1',y=stat,color='green')\n",
+    "            plot_scatter_with_line(summary_df_d, x='delta_p2',y=stat,color='green')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plotting rectification map statistics against the perturbed camera parameters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# name of columns to plot\n",
+    "stats = ['d_avg']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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f3/yL9rXa80rLV4yO41ROGaxPKfUp0AuI11o3tq6rDqwA6gKnAKW1TnJGntK6mHOx0HJ0SrRBSYQQjhZ9MZox28bw04Wf8PXy5aUWL+HjVa7GN3XaGcQCoNtV6yYD27TWtwLbrMsu7eoZoRKzElnw0wJjwgghHOrJHU/y4/kfKTAXkJ2fzYvfvGh0JKdzSoHQWu8Grp6Q9UFgofXrhcBDzshSFlfOJ3FPjXtoX6s9L337EnN+nGN0NCGEHRWYC/gt6bdC69xxTumyMvJ8KVRrfdb69Tkg1MAsJXL1fBK5Bbk8tf0p/vP9f8jJz2Hc3dInIYQnmHZgGhl5GYXWueOc0mXlEh+oaa3NSilzcY8rpUYDo63bEhIS4rRs16OVZsT6Eby9/218A3x5sc2LZRoL3sfHx6XaZ2+e3D5PbhuUn/at+GkFMw/NpG/DvpxJOcP5zPOEVAhhQZ8FhFT13PYXxcgCEaeUqqm1PquUqgnEF7eh1noOcOlzHHNiYqJTApbUu63fpSCvgCl7ppCUmsQLzV4odZEICQnB1dpnT57cPk9uG5SP9kUdj2LUhlG0vLEl0+6bVnhuhzzcuv1hYWE2P8fIArEOiACmWv9fa2CWMvH28mZ6++n4evny4eEPycnP4ZWWr3jMrFJClAcxqTGMiBrBDYE3eNzEP6XlrMtclwEdgRCl1BngVSyFQSulRgDRgHJGFkfxMnnxTtt38PPyY/aPs8ktyGVK6ylSJIRwA5l5maj1irTcNNb1WedxE/+UllMKhNZ6YDEPPeCM4zuLyWTi9ftex8/bUiSy87OZ2naqR4/2KIS7KzAX8MzOZzh07hDzu86nYfWGRkdyGS7RSe1JTCYTr7R8BT9vPz48/CG5BblMazcNby9vo6MJIYow4+AMvjz5JW91eosuN3cxOo5LkQLhACaTiReavYC/tz/TDkwjNz+XGR1nlLu7MIVwdev/XM/0g9Ppd2s/nmv5HOfPnzc6kkuRv1gOYjKZePbeZ/H18uXt/W+TW5BL5P2R+Hr5Gh1NCAEcTTjKMzufoVloM95p9470FxZBCoSDjbt7HH7efvzn+/+QuzWXjx74CH9vf6NjCVGunUs/x7AtwwgODOaTLp/I72QxpPfUCUbfOZo373uTzdGbGRk1ksy8TKMjCVFuZeZlMiJqBCk5KSzouoCQwPJ185st5AzCSYY2Goqvty8v7HmBe5bcQ/WA6oQEhhDZKZI6lesYHU+IcsFsNjNh9wSOJBzhky6fcEfwHUZHcmlyBuFEgxsO5uZKN5Oam0p0arTMKSGEk808NJO1f6xlcvPJhNcNNzqOy5MC4WRXd4QlZrrvrftCuJMNJzfw3oH3eKT+Izx111NGx3ELUiCcrKg5JVJzUg1KI0T5cCzxGE/vfJp7b7iX99q9J1cslZAUCCe7ck6JWyrfQlZeFoM3DSYlJ8XoaEJ4pPiMeIZuGUo1/2p80uUTAnwCjI7kNqST2smunlNi08lNjN02lkGbBvFZ988IQa6oEMJesvKyGB41nOTsZL7o/QU3VLjB6EhuRc4gDNa9XnfmdJ7DscRjDNw4kOSsZKMjCeH2oi9G0/uL3jRZ0oRD8Yf4d8t/0ziksdGx3I4UCBcQXjecOZ3ncPz8cbov605SVpLRkYRwa+N3judgwkHSc9MBWPX7KoMTuScpEC6i681dmddlHscSjjFg44ByOf+tEPbyV+pfhZbl96l0pEC4kAfqPMCqvqv4Pfl3+m/oLz/UQpTCscRjf7t8vDzOJ20PJe6kVkrtAYqaNzobOAOs0Vqvt1ew8ir8H+HM7zqf4VuGozYoVvRYIZOXCFFC59LPEbElghoVahAaGEpqbirVA6oT2SnS6GhuyZYziJ1AXWAXsMT6/83AD0Ac8KlSapKd85VLHWp3YH74fE5ePEm/Df1IyEgwOpIQLi8zL5PhW4aTkp3C4m6L2fTIJr7u/zXrHlwnw9mUki0FoisQrrV+RWs9R2v9ChAO3K+1ngz0BMY4ImR51L5WexaGLyQ6JZp+G/oRnxFvdCQhXNalWeGOJh5l1v2zaBTcyOhIHsGW+yAaAn9etS4auA1Aa71PKRVqawCl1LPASCwfX/0IDNNaZ9m6H0/UtlZblnRfwuNfPU6/Df3QPTWhFWz+Fgvh8aYfmM6XJ7/k5RYv0/XmrkbH8Ri2nEHsBuYrpeorpQKUUvWBucDXAEqpO4GzthxcKVUL+CfQTGvdGPAGBtiyD0/XumZrlnZbSmxaLI9++Shn0236Fgvh8T4/8TkzDs2gf4P+jG0y1ug4HsWWAhFh3f44kA78hOUP+lDr4znAwFJk8AEClVI+QAUgthT78Ggta7bks+6fEZcRx6NfPkpsmnyLhAD4Ie4HJuyeQKsbWzG17VQZY8nOTGZzURcmFU8p5QXUABK01gVlDaCUehp4E8gEtmitB1/nKebYWM/9AxkSEkJiYtEjvO6P289jmx6jin8VqvtXJy037fIVGu7SCXet9rk7T24buF77zqSeoefanlT0qciXD31Z5ktZXa199hYWFgZgUwUtcYFQSn0BLAXWaa2zbU5X9D6rAauB/kAysBJYpbVectV2o4HRAFrrpjk5OfY4vEvy8fEhLy+v2Mf3xuyl0+JO5JvzL69rVasVu4bscka8Mrte+9yZJ7cNXKt9qdmpdFzckb9S/mLXkF3cHnJ7mffpSu1zBD8/P7CxQNjSSb0LmAjMsxaLz4CoMp5FdAZOaq0TAJRSa4D7sFxGe5nWeg4wx7po9uQqf713Mf/w/wehFUKJTf//s6i41Di3eefjye/SPLlt4Drtyy/IZ3jUcH5O/JnF3RZTgxp2yeUq7XMU6xmETUrcB6G1fl9r3QJohuVqphlArFLqA5uP+v9OA62UUhWUUibgAeDnMuyvXKhZsWah5SDfIIOSCOF8b+57k62ntzKl9RQ61O5gdByPZvNQG1rr37XW/8FytdFRoNRTM2mt9wKrgINYLnH14v/PFEQxLs0pEVYxDC+TFxeyLhCTFmN0LCEcbtkvy5j942yG3jGUoY2GGh3H49nUSa2U+geWK5UGYumoXgks01p/7Zh4RSq3ndRFORh/kEEbBxEcGMzKnisJC7L9NNKZPPk03pPbBsa379vYbxm4cSBtwtqwqNsifLzsO52N0e1ztNJ0Upf4DEIptR/LO/0GwPNAmNb6KScXB3GVe2+4l6Xdl5KYmUi/Df3kPgnhkU5ePMmoraOoW6UuHz3wkd2LgyiaLd/l94D1WutMR4URpdM0tCmfdf+MQZsG8eiXj7Kq16q/9VMI4a6Ss5OJ2ByBCRMLwxdSxb+K0ZHKjRIXCK21vvS1tUPZdMVjZb4fQpRN09CmLO2+lEGbBtHvy36s6rWKGyveaHQsIcoktyCXsdvGcjr1NMt7LKdu5bpGRypXbBnuOwyYBbQHql71sLc9Q4nSaRbajKXdlzJ402D6bejHyp4rpUgIt2U2m/n3t/9mT8we/tv+v7Sq2croSOWOLVcxzcYynMYDQBpwL7AOkMFPXEjz0OYs7baUuIw41AZFXEac0ZGEsEn0xWj6rO1DkyVNWPTzIgY3HEz/2/obHatcsqVA3AcM11ofBsxa6yPACGCCQ5KJUmt+Y3OWdFvC2fSzqA1KhgoXbmX8zvEciD9weUbFn8/LrVFGsaVA5AOX7kNPVkrVwDJoXy27pxJl1uLGFizptoTYtFiZdEi4lXPp5wotJ2UnGZRE2FIg9gI9rF9vBlYAa7DMKCdcUMuaLVncbTExaTFSJIRbiM+IJyGz8M+pzCdtHFsKxONYxmMCeAbYDhwDBtk7lLCfVjVbsbjbYs6knUFtUH+bzF0IV3FpylAvkxcNqzWkXuV6NL2hqcwnbSCbh/u+FqXUBq11T7vtsGhyJ3UpfBv7LUM2D6FOpTronpqQwBC7H6MkPPluVU9uGzi2fQXmAsZuG8vGkxuZ12Ue3ep2c8hxrsXTXz+H3kldQu3svD9hJ/eF3Xd5juv+G/pzPvO80ZGEuOydH95hw8kNvNzyZUOKgyia3K9ejrQJa8PC8IVEbI7g4fUPU9G3Iqk5qW436ZDwLCt+XUHk4Ugea/gYY+4cY3QccQV7n0EIF9e2VlsWhC/gz4t/cjTxKCdTTnIg/gDjdowzOpooh76J/YZJeybRvlZ73mjzhkwZ6mKkQJRD7Wq1I7RCaKF1l645F8JZTiSfYFTUKG6pcguzO8/G18vX6EjiKvYuEFL+3UStoMK3r1T2q2xQElEeXci6QMTmCHy9fVnUbZH8/Lmoa/ZBKKWmlGQnWut/W798q8yJhFNEdopk3I5xnEk9Q3xmPPnmfFJzUqnkV8noaMLDZednM3zLcM6ln0P31NxU6SajI4liXK+T+spXLgDoC+wHooE6QAtg9aUNtNZv2zugcIw6leuw7sF1AGw+tZnRW0cz5KshLOm+hIq+FQ1OJzyV2Wxmwq4J7I/bz0f3f0TT0KZGRxLXcM0CobUedulrpdRyYKDWevUV6x4B+jkunnCG8LrhzLp/Fk9uf5KIzREs7raYQJ9Ao2MJD/T+wff5/I/PeaHZC/T5Rx+j44jrsOUy1+7A4KvWrQPmlyWAUqoqMA9oDJixDAj4XVn2KWzX65Ze5Bbk8s+d/2TYlmEs6LqAAJ8Ao2MJD7LmxBqmH5yOaqAYf/d4o+OIErClk/oE8NRV654A/ihjhpnAV1rrhsBdgAzdaJCH6z/M9PbT+Trma0ZtHUV2frbRkYSH2HduHxN2TaB1zda80/YduZzVTdhyBjES+FwpNQmIwTKKax7wSGkPrpSqgmUCoqEAWuscLHNOCIOoBoq8gjwm7pnImK1jmNN5Dn7efkbHEm7sVMophm8ZTu1KtZnbea78PLkRW6YcPaSUuhVoBYQBZ4HvtNa5ZTh+PSABmK+Uugs4ADyttU4vwz5FGQ1qOIjcglxe/OZFntz+JB898JFcoy5KJTk7mSFfDcGMmUXhi6gWUM3oSMIGdh2sz1ZKqWbA90AbrfVepdRMIEVr/cpV240GRgNorZvm5HjuSYaPjw95eXnX39AJIvdHMmHrBPrd3o8FfRbg41X2kVlcqX325sltA9vbl5OfQ+8Vvfn2zLdsGriJtje1dWC6svP018/Pzw9svFfN6LGYzgBntNZ7rcurgMlXb6S1ngPMsS6aPXnERVcaUXJAvQEkt0zm9b2vk5+bz4wOM/D2Ktv0467UPnvz5LaBbe0zm808v/t5dkbv5IOOH9AwsKHLf288/fWzjuZqE0OH2tBanwP+UkrdZl31AHDcwEjiKmObjGVy88msObGGiXsmUmAuMDqScGGX5pO+c/GdLP9tOcMbDafvrX2NjiVKyegzCIDxwFKllB/wJzDsOtsLJxt/93hy83OZfnA6Pl4+chWKKNal+aQvORx/2MA0oqwMLxBa68NAM6NziGt79t5nySnI4cPDH+Lr5csb98nIm+LvYtMKT+Yl80m7N8MLhHAPJpOJF5q9QG5BLh8f/RgfLx9ea/WaFAlx2amUUzKftIeRAiFKzGQy8XKLl8nNz2XesXms/3M9gd6BBAcGy4RD5VxSVhJDvhpCBd8K3BR0Exl5GZcnohLuSwqEsInJZOI/rf/Duj/XEZcRB8Cp1FOM2zHu8uB/onzJzs9mZNRI/kr9ixU9V9DixhZGRxJ2IhMGCZuZTCYq+hQe8VUmHCqfLl3O+v2573m/w/tSHDyMFAhRKsGBwYWWcwvKckO9cFfTD05nzYk1TGo2iYfqP2R0HGFnUiBEqUR2iqTpDU2pW6kuVf2rcibtDAuPLzQ6lnAi/Zvm/YPvM6DBAP559z+NjiMcQPogRKlcOeFQTn4Oo7aO4sVvXiTQJxDVQBmcTjja1zFfM3H3RNqGtWVqu6lyNZuHkjMIUWZ+3n7MfmA27Wu1Z8LuCaz9Y63RkYQD/Zb0G6O2juKWKrcwp/McGcjRg0mBEHYR4BPAp10/pUVoC8bvGM9Xp74yOpJwgISMBIZ8NQR/b38Wd1tMFf8qRkcSDiQFQthNoE8gC8MX0qRGE57Y9gQ7/tphdCRhRxm5GQzdMpTErEQWhi+kdqXaRkcSDiYFQthVkF8QS7ot4dZqtzIyaiTfxH5jdCRhB/kF+QxdN5QjCUcPI62sAAAUb0lEQVSY1WkWd9W4y+hIwgmkQAi7q+pfleU9llOnUh2Gbh7K/rj9RkcSZfTGvjdY+9taXmv9GuF1w42OI5xECoRwiOoB1VneczmhFUJ5fNPjHEk4YnQkUUoLji9gzo9zeLLpk4xsPNLoOMKJpEAIhwmtEMqKniuo6l+VQZsGcfy8TPXhbrae3sor375ClzpdmNZ5mtFxhJNJgRAOVSuoFit6riDAJ4ABGwfwy/lfjI4kSuhY4jGe2PYEjYIbMev+WWWeTVC4HykQwuFurnwzK3qswGQy0f2z7pxKOWV0JHEdMWkxRGyOoKp/VRaGL6Sib8XrP0l4HCkQwinqV63P8h7LycrLov+G/sSkxRgdSRQh+mI0PT/vSZsVbYjPjOedtu8QWiHU6FjCIFIghNPcXv12Ng7cSEpOCmqD4lz6OaMjiauM2zGOw4mHyS3IpcBcwIxDM4yOJAzkEgVCKeWtlDqklPrS6CzCse658R4Wd1tMfEY8AzYO4HzmeaMjCSuz2cxvyb8VWifDuJdvLlEggKeBn40OIZyjWWgzFoYvJDo1mpbLW9J6eWv6rO3D6ZTTRkcr1z44/AFpuWmF1smUoeWb4QVCKVUb6AnMMzqLcJ77wu6jTlAdMvMyOZ16mgPxBxi3Y5zRscqt1b+v5t0f3qXbzd24t8a91Ktcj6Y3NJUpQ8s5VxjuewYwCahU3AZKqdHAaACtNSEhIU6K5nw+Pj7lp31XvT1Jykly67a762u349QOJuyeQMebO7Ky/0r8vP2K3M5d21dSnt6+0jC0QCilegHxWusDSqmOxW2ntZ4DzLEumhMTE50RzxAhISGUl/ZV8S08EmhiRiKxcbHF/oFyde742v1y4Rf6revHLVVu4X8d/kdKUkqx27pj+2zh6e0LCwuz+TlGf8TUBuijlDoFLAfuV0otMTaScJZLs9LVq1yPOpXqkJKTwrgd48gryDM6WrlwNv0sj331GBV9K8rQ3aJIJrPZbHQGAKxnEM9rrXtdZ1NzbGysExIZw9PfxVyrfbOPzmbK3imoBorp7afjZTL6/Ytt3Om1S8tJ4+H1DxOdGs2a3mtoHNz4us9xp/aVhqe3z3oGYdPUf67QByEEAGOajCEtN43/HvwvQb5BTGk9RaaydIDcglxGbx3Nr0m/sih8UYmKgyifXKZAaK13AjsNjiEM9ty9z5Gak8rcY3MJ8g3iheYvGB3Jo5jNZibvmcyumF1Mbz+djjd1NDqScGEuUyCEADCZTLza6lXSc9P54PAHBPkG8dTdTxkdy2PMODSD5b8t59l7n2XAbQOMjiNcnBQI4XJMJhNT204lPS+dt/a/RUW/igy9Y6jRsdye/k0z7cA0Hr31USbcO8HoOMINSIEQLsnby5uZHWeSkZvBS9+8REWfivRr0M/oWG5rd8xuJu6eSLta7Xiv3XvStyNKxL0uExHliq+XLx8/8DFtwtrw3O7n2Hhyo9GR3NLx88cZFTWKW6vdypzOc9z2PhPhfFIghEsL8Algftf53FPjHp7c/iQ7/9ppdCS3EpsWy+ObHyfIL4hF4Yuo7FfZ6EjCjUiBEC7v0o1cDao1YETUCPae3Wt0JLeQmpPKkM1DSMtJY3H4YsKCbL+TVpRvUiCEW6jiX4XPun9GraBaDNk8hCMJR4yO5NJyC3IZtXUUvyf9ztzOc7kj+A6jIwk3JAVCuI2QwBCW91hONf9qDN40mF8v/Gp0JJdkNpuZtGcSe2L28G77d2lfu73RkYSbkgIh3EpYUBjLey7Hz9uPARsHcPLiSaMjuYzoi9H0WduHxosbo3/TjGw0kv4N+hsdS7gxKRDC7dStXJdl3ZeRW5DLgI0DiE3z3LG5bDF+53gOxB8gOTsZgIPxBw1OJNydFAjhlm6rfhufdf+MpOwk2uq2tF4ms9KdSTtTaDkpO8mgJMJTSIEQbqtJjSbUrlib7PxsTqeV71npjiYcJSEzodA6mS5UlJXcSS3cWk5BTqHlxEzPHa65ONEp0Ty++XFuCLyBGwJvIDU3leoB1WW6UFFmUiCEW6seUJ2TKf/fUZ2UnUROfk65uVv4QtYFBm8aTF5BHqv7rKZ+1fpGRxIeRD5iEm6tqFnpntn1DPkF+UZHc7jMvEyGbB7C2fSzLAhfIMVB2J2cQQi3VqdyHdY9uO7y8v+O/I83971JZb/KvN3mbY8dlC6vII8ntj3BkYQjzO08l+ahzY2OJDyQFAjhUZ6860mSs5OZdWQWVf2rMrn5ZKMj2Z3ZbObFb14k6nQUb7V5i251uxkdSXgoQwuEUuomYBEQCpiBOVrrmUZmEu7vX83/RXJ2Mh8e/pCq/lUZ22Ss0ZHsauahmSz9ZSnj7h5HxB0RRscRHszoPog8YILW+g6gFfCUUkoGjRFlYjKZeLvN2/S+pTev732dZb8sMzqS3az4dQXvHXiPR299lMnNPO/sSLgWQ88gtNZngbPWr1OVUj8DtYDjRuYS7s/by5sPOn5AWk4ak76eRCW/SvS6pZfRscpk+1/bmbhnIu1rtZdJf4RTGH0GcZlSqi5wDyBjOQu78PP2Y07nOTS9oSnjdoxj95ndRkcqtSMJRxizdQy3V7+duZ3nlpvLeIWxTGaz2egMKKWCgF3Am1rrNUU8PhoYDaC1bpqTk3P1Jh7Dx8eHvLw8o2M4jBHtS85KpsvSLpxIOsGmAZtoVbuVQ47jqLb9kfQHHRd1JNA3kN1DdnNj0I12P0ZJyM+me/Pz8wOw6bTT8AKhlPIFvgQ2a63/W4KnmGNjPXdwtpCQEBITPfduYKPal5CRwEPrHyI5O5lVvVZxe/Xb7X4MR7TtfOZ5+qzrQ3J2Mmv7rDX0Xgf52XRvYWFhYGOBMPQjJqWUCfgE+LmExUGIUqlRoQbLeywnwDuAQRsHcSrllNGRrisjN4OIzRGcSz8nN8IJQxjdB9EGeBy4Xyl12Pqvh8GZhIe6qdJNLOthGSZ84MaBnEs/Z3SkYuUV5PHE9ic4kniEWffPkhvhhCGMvorpa2w85RGiLBpUa8CS7ktQGxSDNg1ida/VVAuoZnSsQi7dCLf19Fa5EU4YyugzCCGc7u4adzO/63xOpZzi8c2Pk56bbnSkQmYcmsHSX5Yy/u7xciOcMJQUCFEutQlrw0f3f8TRhKOMiBpBdn62oXkuTRd61+K7mHZgGt3rdueFZi8YmkkIKRCi3AqvG8709tPZE7OHe5feS5vlbQyble7SdKGJWZaraOLS4+RGOGE4KRCiXOvXoB+1g2qTnJ3MqdRThs1Kd/W82jJdqHAFUiBEuefr5Vto+ULWBace/0TyCeIz4wutk+lChSuQ4b5FuXf1rHS5BblOO/a59HMM3jSYyn6VCasYRkZehkwXKlyGFAhR7kV2imTcjnGczzxPck4yZ9LOsPK3lfRr0M+hx72YfZHHvnqMpOwkVvVcRZMaTRx6PCFsJQVClHtXzkqXnZ9NxOYIJuyeQFX/qnS5uYtDjpmVl8XwLcM5kXyCReGLpDgIlyR9EEJcwd/bn3md59E4uDFjt41l37l9dj9GfkE+43eM5/tz3zOjwwza125v92MIYQ9SIIS4SpBfEIu7LaZWUC0iNkdw/Lz9picxm828/O3LbDy1kVdbvcpD9R+y276FsDcpEEIUITgwmGU9llHBtwKPffWY3e6NmHFoBot+XsQTTZ5g9J2j7bJPIRxFCoQQxagVVItl3ZeRnZ/NwE0DSchIKNP+lv6ylGkHptG3fl9ebPGinVIK4ThSIIS4hgbVGrAofBFxGXE89tVjpOSklGo/m09tZvLXk+lUuxPTO0zHyyS/esL1yU+pENfRNLQpczvP5ZcLvzB8y3Cy8rJsev7+c/t5cvuTNAlpwuzOs/92Y54QrkoKhBAl0OmmTszoOIPvzn7HU9ufIq+gZFNT/nrhVyI2R1CzYk0WhS+iom9FBycVwn6kQAhRQg/Xf5jXW7/OV9FfMfnryVxvut6YtBgGfzUYf29/Puv+GcGBwU5KKoR9yI1yQthgeOPhnM86z4xDMwgOCOZfLf5V5HZJWUkM3jSYtJw0VvdeTZ3KdZycVIiykwIhhI2eb/o8iZmJRB6JpHpAdcY0GVPo8cy8TIZuGUp0SjRLuy+lUXAjg5IKUTaGFwilVDdgJuANzNNaTzU4khDXZDKZeKvNWyRlJzFl7xSqB1S/PG5TXkEeT2x7ggNxB/j4gY+5L+w+g9MKUXqG9kEopbyBWUB34A5goFLqDiMzCVES3l7efNjpQ9qGtWXC7glERUdhNpuZ/PVkok5H8cZ9b9Drll5GxxSiTIzupG4BnNBa/6m1zgGWAw8anEmIEvH39ueTLp9wa9VbGbZlGCH/DWHZr8sYesdQhjYaanQ8IcrM6AJRC/jriuUz1nVCuIUgvyD8vP0wYyYtJw2AowlHDU4lhH0Y3gdREkqp0cBoAK01ISEhBidyHB8fH2mfm8nIzyi0nJKX4nFtBM987a7k6e0rDaMLRAxw0xXLta3rCtFazwHmWBfNiYmJTohmjJCQEKR97qWKb5W/LXtaG8EzX7sreXr7wsLCbH6O0QViP3CrUqoelsIwABhkbCQhbHNpRrqLuRep4ltFpgsVHsPQAqG1zlNKjQM2Y7nM9VOt9U9GZhLCVpdmpPP0d6Ci/DH6DAKt9UZgo9E5hBBCFGb0VUxCCCFclBQIIYQQRZICIYQQokhSIIQQQhRJCoQQQogima436YkLcrvAQgjhIky2bOx2ZxBKqQNYGumR/6R97vvPk9sm7XP/f9b22cTtCoQQQgjnkAIhhBCiSO5YIOZcfxO3Ju1zX57cNpD2uTub2+eOndRCCCGcwB3PIIQQQjiB4YP1lYRSqh/wGnA70EJr/YN1fV3gZ+BX66bfa63HGpGxLIprn/WxfwEjgHzgn1rrzYaEtBOl1GvAKCDBuupF64CNbk0p1Q2YiWVU4nla66kGR7IrpdQpIBXLz2Ge1rqZsYnKRin1KdALiNdaN7auqw6sAOoCpwCltU4yKmNpFdO21yjF751bFAjgGPAIMLuIx/7QWt/t5Dz2VmT7lFJ3YJkjoxEQBmxVSjXQWuc7P6Jdva+1nmZ0CHtRSnkDs4AuWKbN3a+UWqe1Pm5sMrvrpLX2lPHMFwCRwKIr1k0GtmmtpyqlJluXXzAgW1kt4O9tg1L83rnFR0xa65+11r9ef0v3dI32PQgs11pna61PAieAFs5NJ0qgBXBCa/2n1joHWI7ltRMuSmu9G7hw1eoHgYXWrxcCDzk1lJ0U07ZScZcziGupp5Q6BKQAL2ut9xgdyI5qAd9fsXzGus7djVNKDQF+ACa442n8VWoBf12xfAZoaVAWRzEDW5RSZmC2dRpgTxOqtT5r/focEGpkGAew+ffOZQqEUmorcGMRD72ktV5bzNPOAnW01ueVUk2BL5RSjbTWKQ4LWkqlbJ9bulZbgY+A17H8wXkdmA4Md146UUpttdYxSqkbgCil1C/Wd6oeSWttthZDT1Gq3zuXKRBa686leE42kG39+oBS6g+gAZYK6VJK0z4s83TfdMVybes6l1bStiql5gJfOjiOM7jl62QLrXWM9f94pdTnWD5W87QCEaeUqqm1PquUqgnEGx3IXrTWcZe+tuX3zi36IIqjlKph7SBEKXULcCvwp7Gp7GodMEAp5a+UqoelffsMzlQm1l+8Sx7G0kHv7vYDtyql6iml/LBcWLDO4Ex2o5SqqJSqdOlroCue8bpdbR0QYf06AvCYM/vS/t65xY1ySqmHgQ+BGkAycFhrHa6U6gtMAXKBAuBVrfV645KWTnHtsz72EpZTwTzgGa31JsOC2oFSajFwN5ZT3VPAmCs+93VbSqkewAwsl7l+qrV+0+BIdmN98/W5ddEH+Mzd26eUWgZ0BEKAOOBV4AtAA3WAaCyXudqls9eZimlbR0rxe+cWBUIIIYTzufVHTEIIIRxHCoQQQogiSYEQQghRJCkQQgghiiQFQgghRJGkQIhySym1QCn1Rgm2O6WUKs2NjkK4NZe5k1oId2AdNrm+1voxBx9nIpabtW4GEoH/aa3fc+QxhbianEEI4ZpMwBCgGtANy0BrA4yNJMobuVFOlBtKqXuAT7AMWbIRy12lJ7TWLyulegFvYJks5jgwVmt91Pq8U8BILGfc67D88c7GMhfJXUqpYcAkLGMwJQDvaK2LmrvkyiwdgSXA/4DngDQsAzcuLWb7DwCT1np8adsvhK3kDEKUC9Yxkr4AFgPVgZVAX+tj9wCfAmOAYCwTN61TSvlfuQ+t9VfAW8AKrXWQ1vou60PxWGbwqgwMA95XSt1bglg3YhkOoRaWj5PmKKVuKyK7CWgH/GRLm4UoK+mDEOVFK8AXmKG1NgOrlFLPWR8bjWWOg73W5YVKqRetz9l1vR1rrTdcsbhLKbUFyx/0gyXI9Yp1VOJdSqkNgMIyHPOVXsPyZm5+CfYnhN1IgRDlRRgQYy0Ol0Rb/78ZiFBKXfnxjZ/1OdellOqOZUC0Blj+kFcAfizBU5O01ulX5Sl0TKXUOCx9Ee2shUQIp5ECIcqLs0AtpZTpiiJRB/gDy2xwb5ZwhNJCnXbWj6FWY/kjvlZrnauU+gJLP8X1VFNKVbyiSNThimGYlVLDscyL3F5rfaYE+xPCrqRAiPLiOyxDpv9TKfU/oDeWSW92AHOBz60z4e3DcgbQEdittU69aj9xQBellJfWugDLmYY/ls7pPOvZhC3zJfzH+nFWSyz9GK8CKKUGY+nv6KS19qQ5ToQbkU5qUS5orXOAR4ChWCZ07w+ssT72AzAKiASSgBPW7Yqy0vr/eaXUQWsB+SeWeQSSgEGUfLKgc9bnxAJLsVw59Yv1sTewdJjvV0qlWf99XML9CmEXcpmrEAa4dJmr1rq20VmEKI6cQQghhCiS9EEI4SDWvoUXi3hoD/COk+MIYTP5iEkIIUSR5CMmIYQQRZICIYQQokhSIIQQQhRJCoQQQogiSYEQQghRJCkQQgghivR/WZb8jQUnpXoAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for stat in stats:\n",
+    "    for param in perturb_parameters:\n",
+    "        if summary_df_f is not None:\n",
+    "            plot_scatter_with_line(summary_df_f, x='delta_fx',y=stat,color='red')\n",
+    "            plot_scatter_with_line(summary_df_f, x='delta_fy',y=stat,color='red')\n",
+    "        if summary_df_d is not None:\n",
+    "            plot_scatter_with_line(summary_df_d, x='delta_k1',y=stat,color='green')\n",
+    "            plot_scatter_with_line(summary_df_d, x='delta_k2',y=stat,color='green')\n",
+    "            plot_scatter_with_line(summary_df_d, x='delta_p1',y=stat,color='green')\n",
+    "            plot_scatter_with_line(summary_df_d, x='delta_p2',y=stat,color='green')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plotting trajectory evaluation statistics against reprojection error"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# name of axes to plot\n",
+    "y_stats = ['abs_e_trans_stats_mean', 'abs_e_rot_stats_mean', 'rel_trans_stats_rmse_trajlen_100.0', 'rel_rot_stats_rmse_trajlen_100.0']\n",
+    "x_stats = ['d_avg']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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diFuQlJ7EmzvfpFX1VjzZ/Em9yxECkEAX4pZM3DmRDEsGM7vMxMPooXc5QgAS6ELctNUnV7Pm1BpeafUKDao20LscIXJJoAtxEy5lXmLizoncGXgnz975rN7lCJGPnPovxE2YtHMSV7OusqjfIkxG+e8jnIv00IUopvWn17PixApeavkSTao10bscIQqQQBeiGK5kXWHC9gk0qdaE0ZGj9S5HiELJZ0YhimHy7slcyLjAvN7z8PLw0rscIQolPXQhirA5djPqMZV/Rv6TOwLv0LscIa5LAl2IG0jJTmHctnE0rNKQMa3G6F2OEDckQy5C3MC7e94lIT2BWffNwtvDW+9yhLgh6aELcR3b4rax4MgCnr7jaVpVb6V3OUIUSQJdiEKkmdMYt3UcdSvX5dW7XtW7HCGKRYZchCjE+7++z9nUs6wYuAJfk6/e5QhRLNJDF+Iae87t4evDX/N4s8dpE9JG73KEKDYJdCHyyLBk8MrWV4ioGMGENhP0LkeImyJDLkLk8dHvHxGTHIPaX8XP00/vcoS4KdJDF8Lhj4Q/mH1wNiObjKRjWEe9yxHipkmgCwH8nvA7ozeNJrRCKG+2fVPvcoS4JRLoolxLN6czadckBv04CBs2vuz5Jf5e/nqXJcQtkTF0UW5ti9vG+G3jOZNyhlFNR/FGmzckzIVLk0AX5U5ydjLv7XmPBUcWUKdSHZYNWEb70PZ6lyXEbZNAF+XKhtMbmLBjAonpiTx353OMvWusnDgk3IYEuigXLmVeYtLOSaw4sYLGVRsz9565tAhqoXdZQpQoCXTh1jRN48eTP/LWzrdIzk5mbKuxPN/ieblIhXBLEujCbZ1PO88bO97g59M/0yKoBTO6zKBxtcZ6lyVEqZFAF25H0zQWH1vM5N2TybZm81a7t3iy+ZOYjLK7C/cme7hwK7EpsYzfNp6tcVtpH9Kej7p8RN3KdfUuS4gyUaxAVxSlCjAHaA5owOPAUWAxUBuIARRVVS+XSpVCFMGm2fjm0De8/9v7GAwGpnWcxsgmIzEa5Nw5UX4Ud2//F/CTqqqNgUggCpgAbFRVtQGw0fGzEGUu+ko0D656kLd2vUW7kHZsHrKZUU1HSZiLcqfIHrqiKJWBLsA/AFRVzQayFUW5H+jm2GwesAV4rTSKFKIwFpuF/+7/LzP2zsDX5MunXT9lSIMhGAwGvUsTQhfFGXKpAyQBXyuKEgn8AbwEBKuqes6xzXkguHRKFKKgQxcPMXbrWA5cOEC/Ov2Y2mEq1f2q612WELoqTqCbgFbAC6qq7lEU5V9cM7yiqqqmKIpW2JMVRXkaeNqxHYGBgbdZsvsymUzSPkUwmUzMOTqHd7e/SzWfaix8YCGDGw/WuyynIftQ0dy5jYoT6GeBs6qq7nH8vBR7oCcoihKqquo5RVFCgcTCnqyq6ixgluNH7cKFC7dbs9sKDAxE2ufGtl/czttb3+a+uvcxteNUqvlUkzbLQ/ahorliG4WFhRVruyKPGqmqeh6IVRSlkeOunsBh4EdglOO+UcDKmy9TiOK7knWFl35+iWYBzfis+2dU86mmd0lCOJXizkN/AVigKIoXcBJ4DPsfA1VRlCeA04BSOiUKYffenvdISk/im3u/wdPoqXc5QjidYgW6qqp/Aq0LeahnyZYjROG2x21n4dGFjG0/ljsC79C7HCGckkzUFU4vw5LB+G3jqV2pNm91ekvvcoRwWnLqv3B6H//xMadTTrOk/xJ8PX1JI03vkoRwStJDF07tr6S/mHVgFsMbD6dDWAe9yxHCqUmgC6dltpkZu3UsQb5BTGw7Ue9yhHB6MuQinNaX+78k6lIUc++ZS2XvynqXI4TTkx66cErRV6L5ZO8n9K/Tnz61++hdjhAuQQJdOB2bZmP8tvH4mnx5r8N7epcjhMuQIRfhdL6L+o495/cws8tMWXBLiJsgPXThVOJT45n661Q61+iM0lBOPhbiZkigC6ehaRpv7HgDq2Zleqfpsq65EDdJAl04jR9P/siGMxsYd9c4alWqpXc5QrgcCXThFC5lXuKtnW/RIqgFTzZ/Uu9yhHBJclBUOIXJuydzNesqi/otwsPooXc5Qrgk6aEL3W2J3cLS40sZ3WI0TQOa6l2OEC5LAl3oKs2cxmvbX6Ne5Xq82OJFvcsRwqXJkIvQ1fTfp3M29SwrBq7Ax+SjdzlCuDTpoQvd/JHwB18d/IpRTUfRNqSt3uUI4fIk0IUusq3ZjNs2jpAKIbze5nW9yxHCLciQi9DFf/78D0cvH2Ve73lU9KqodzlCuAXpoYsyd+zyMT778zMG1RtEr4heepcjhNuQQBdlymqz8urWV/H39GfK3VP0LkcItyJDLqJMzTs8jz8S/+Czbp8R4BugdzlCuBXpoYsyczblLO//9j49wnswuP5gvcsRwu1IoIsyoWkaE7ZPAOD9ju/LSopClAIJdFEmlkcvZ/PZzbze5nVqVqypdzlCuCUJdFHqLmZc5O1db3NX9bsY1XSU3uUI4bYk0EWpm7RrEmnmND7u8rGspChEKZJAF6XqlzO/8MOJH3ix5Ys0rNpQ73KEcGsS6KLUpGSnMGH7BBpXbczoyNF6lyOE25N56KJUZFmzeGPHG5xPO8+sXrPw8vDSuyQh3J4EuihxexP3MvZ/Yzl25RhjWo2hVfVWepckRLkggS5KTIYlg49+/4jZB2cT7BfM/D7z6RHeQ++yhCg3JNBFidh1bhevbn2VmOQYRjYZycS2E2UVRSHKmAS6uC2p2alM+20a8w7Po1bFWizpv4QOYR30LkuIckkCXdyyLbFbGL99PPGp8TzV/CnGtx6Pn6ef3mUJUW4VO9AVRfEAfgfiVFUdoChKHWAREAD8AYxUVTW7dMoUzuRK1hWm7J7C4mOLaVClAT/c9wOtg1vrXZYQ5d7NzEN/CYjK8/N04BNVVesDl4EnSrIw4Zx+jvmZ7ku6s/T4Ul5o8QI/PfCThLkQTqJYga4oSk2gPzDH8bMB6AEsdWwyDxhUGgUK53Ax4yLPbXyOxzc8TqBvIGsHrWVCmwn4mHz0Lk0I4VDcIZdPgfFAzrSFAOCKqqoWx89ngRolXJtwApqm8ePJH3lz55ukZqcyvvV4/hn5TzyNnnqXJoS4RpGBrijKACBRVdU/FEXpdrNvoCjK08DTAKqqEhgYeNNFlhcmk8mp2ic+JZ4Xfn6B1cdX0ya0DbP6z6JpUFNda3K2NnI20j5Fc+c2MmiadsMNFEV5HxgJWAAfoBKwAugNhKiqalEU5W7gHVVVexfxflp8fPztV+2mAgMDuXDhgt5loGkai48tZvLuyWRbsxnfejxPNn/SKVZKdJY2clbSPkVzxTYKCwsDKPKqMEWOoauq+rqqqjVVVa0NPARsUlV1OLAZGOLYbBSw8parFU4jNiWWR9Y9wtitY2kW0IxfHvyFZ+58xinCXAhxY7czD/01YJGiKO8B+4C5JVOS0INNs/Ht4W+Z+utUDAYD0zpOY2STkRgNsiCnEK6iyCGXEiZDLjeg10fBsylneXHLi+w5v4duNbsxvdN0p71MnCt+XC5L0j5Fc8U2Ku6Qi5wpWs4duniIEetGkGnNZGbXmSgNFLmAsxAuSgK9HNsRv4Mn1j+Bv5c/P/T7gUbVGuldkhDiNsgAaTm16uQqRqwbQWiFUFbet1LCXAg3IIFeDn196Gue2/gcLYJasHzgcmr4yzlhQrgDGXIpRzRNY/rv0/n3n/+md63efN7jc3xNvnqXJYQoIRLo5YTZZmb8tvGox1SGNx7OtI7TMBnln18IdyL/o8uBdHM6z2x8hk2xmxjbaixjWo2RmSxCuCEJdDd3KfMSj/78KH8l/cUHnT5gZJORepckhCglEuhuLDYlluHrhnM29Syze82mT+0+epckhChFEuhu6vDFw4z4aQSZlkwW9VtE25C2epckhChlMm3RDe2M38ngVYMxGoysGLhCwlyIckIC3c2sPrma4euGE1IhRE4YEqKckUB3I98c+oZnNz5LZFAkKwaukBOGhChnZAzdDeQ9YejeWvfyfz3+T04YEqIckkB3cRabhfHbxrP42GI5YUiIck7+57uwDEsGz/zyDBtjN/JKq1d4pdUrcsKQEOWYBLqLupR5iVE/j+LPpD95v+P7PNr0Ub1LEkLoTALdBZ1NOcsj6x7hbOpZZvWcRd86ffUuSQjhBCTQXUzUpSiGrxtOpiWThX0X0i60nd4lCSGchAS6C4m6FMWQ1UPwMfmwfOByGldrrHdJQggnIoHuIo5dPsawNcPwMfmwbMAyaleqrXdJ4hqapslBaaErObHIBZy4coJha4bhYfBA7adKmDsRTdPYfW43I38ayfyo+XqXI8o56aE7uZjkGJS1ClbNytIBS6lXpZ7eJQnAptlYf3o9n//1OXsT9xLgE0DvWr31LkuUcxLoTuxsylmUNQpZliyWDFhCw6oN9S6p3MuyZrH8+HK+2P8FJ66eIKJiBFM7TmVYw2Fydq7QnQS6k4pPjWfomqGkZqei9ldpUq2J3iWVaynZKXwX9R1zDs7hfPp5mgU04/96/B/96/SXM3OF05A90QklpCegrFG4nHmZRf0X0Tywud4llVuJ6YnMPTiXb6O+JTk7mY5hHZnZdSZdanSRA6Ci9GkaPmvXwlNPFWtzCXQnk5SehLJGITEjkQV9F9AiqIXeJZVLp66e4ov9X7D0+FKyrdn0q9OPf0b+U/49RJnxiI2l8uuv47N5swS6K7qQfoGH1j5EXGoc3/X5jjbBbfQuqdz5K+kvPv/rc9aeWouXhxdDGgzh2TufpW7lunqXJsoLi4UKc+dS8aOPwGDg6pQpVC7mUyXQncTlzMsM/3E4MckxzOs9j/ah7fUuqdzQNI2tcVv5/K/P2RG/g0pelRgdOZonmj9Bdb/qepcnyhHPAweoPG4cXgcOkNmrF1enTcNao4YEuitJzk5m+LrhRF2O4ut7vqZTjU56l1QuJKYnsiJ6BUuOLyHqUhTBfsG82fZNRjQZQUWvinqXJ8oRQ3o6FWfMoMLs2diqVePSl1+SOWAA3ORxGgl0naVmpzJ83XAOXzrM4sGLaVdV1mYpTZmWTNafXs+S40v439n/YdWstAhqwcedP2Zwg8F4e3jrXaIoZ7y3bKHyhAmYYmNJGz6c5DfeQKtS5ZZeSwJdR+nmdEb+NJK/kv7ivz3/S/8G/blw4YLeZbkdTdP4PeF3lhxfwqqTq0jOTiakQgjP3fkcQxoMoUHVBnqXKMoh44ULVJo8Gb/lyzHXq8eFZcvIbn97Q60S6DrJsGQw6udR/J74O593/1yWwC0FZ5LPsCx6GUuPLyUmOQZfky/9avdjSMMhdAztiIfRQ+8SRXmkafiqKpWnTMGQlkbKK6+Q8vzz4H37nw4l0HWQacnkifVPsOvcLj7r/hn31btP75LcRkp2CqtPrmbp8aXsPr8bgA6hHXip5Uv0q90Pfy9/nSsU5ZnHqVNUee01vHfsIKtNG65++CGWhiV3BrgEehnLtmbz9C9P87+4/zGzy0wG1x+sd0kuz2qzsi1uG0uOL+GnmJ/ItGZSt3Jdxrcez4P1H6RmxZp6lyiciaZhOnQI7+3bMTdvbh/m8CjdT2umgwfxW7SICgsXonl6cuX990kfMQKMJbs+YpGBrihKOPAtEAxowCxVVf+lKEo1YDFQG4gBFFVVL5dodW7GbDPz3Mbn2Bi7kemdpjOs0TC9S3IpVpuVc6nniEqK4nz6eRLSE4i+Es2qk6tISE+gincVlIYKQxoMoVX1VnImp/ibpuF54AA+a9bguW4d1U+cyH3IGhxMxoABZAwahLlly5ueWXI9xkuX8F2+HL/Fi/E8fBjN25uMAQNIfuMNbCEhJfIe1ypOD90CjFVVda+iKBWBPxRF2QD8A9ioquoHiqJMACYAr5VKlW7AYrPw/Kbn+en0T7zX4T1GNBmhd0lOw6bZuJR5yR7SaQkkpNu/zqedz72dkJ5AUkYSNs2W77kmg4nu4d0Z2nAovSJ6ySwV8TdNw3PfPnzXrMFnzRpMsbFoHh5o3btz9ZlnyOraFc+9e/FduZIK8+fjP3culogIMu67j4xBg7A0uYX1kywWvLdswW/xYnw2bMBgNpMdGcmVqVPJGDTolmci8vVzAAAcU0lEQVSvFJdB07SbeoKiKCuB/zi+uqmqek5RlFBgi6qqjYp4uhYfH39rlbowq83KS1teYsWJFUxqN4ln7nym0O0CAwPdfpaLpmnsS9rHwiMLibocRUJaAonpiVg0S4FtA3wCqO5XnRC/EIL9ggmuEEy96vXwt/kTXCGYYL9gAn0D8TR66vCbOKfysA/dkM2G1x9/4LN6NT5r12KKj0fz9CSrc2cyBgwg8557CGjYsEAbGa5exeenn/BduRLv7dsxWK2YGzWyh/v992OtU+eGb+sRHY2fquK3dCkeCQlYAwLIGDyY9GHDbu0PwzXCwsIAivzocFOBrihKbWAr0Bw4o6pqFcf9BuByzs/XPOdp4GkAVVXvys7OLvb7uQObZuOZNc/w7YFvea/be4y7e9x1tzWZTFgsBYPNHaRmp7Lo0CJm75vNnwl/UsGzAu1rtCesYhih/qGE+ofmux3iH4KXh1eB13HnNioJ5bJ9rFYMO3ZgXL4c48qVGOLj0by90e65B9vgwdj694c8PeMi2ygx0f5aqopxxw4AbK1bYxs6FNvQoVCjhn27lBSMS5dinDcP465d9t5/nz5YR41C69sXvAruv7fKy/5aJRfoiqL4A/8DpqqqulxRlCt5A1xRlMuqqlYt4mXKVQ/dptmYsH0CC44s4NW7XmVMqzE33N4de1dRl6KYHzWfZceXkWpOpUm1Jjza5FEeqP/ALZ2N6Y5tVJJctn00DbKzMWRkYMjMtH+/9nYhj3nExeGzfj0eSUloPj5k9uhBZv/+ZPbsiVax8P3rZtrIGBeH76pV+K5cidf+/WgGA9nt22MNDcVn3TqMGRmYGzQgfdgwMh58EFv10lkqorg99GLNclEUxRNYBixQVXW54+4ERVFC8wy5JN5qse7oQsYF3tn1DitOrOCFFi/wcsuX9S6pzGRaMllzag3fRn3L7wm/4+3hzcC6AxnZZCR3Vb9LDlaWd5qG559/4rdgAT4bN2JITbUHtc1W9HOvYfPzI6tHDzL69yerZ0+0ChVKtFRbjRqkPfssac8+i8eJE/j++CO+K1fieehQ7pCKuVWrEjuQeruKM8vFAMwFolRVnZnnoR+BUcAHju8rS6VCF2OxWZh3eB4f//Ex6eZ0Xr3rVV5u+XK5CLGTV0/yXdR3LD62mCtZV6hTqQ6T2k1iaMOhVPOppnd5QmeG5GR8ly+nwoIFeB4+jM3Pj8zevbEFBaH5+KD5+tq/rne7kMfw9CyzMLXWq0fqmDGkjrnxJ209FaeH3hEYCRxQFOVPx31vYA9yVVGUJ4DTgFI6JbqOXed28dbOt4i6FEXnGp159+533f60crPNzPrT65kfNZ9tcdswGUz0rt2bkU1G0imsU7n4QyZuQNPw3LuXCgsW4LNyJcbMTLKbN+fKBx/YZ31cZ1hE3JoiA11V1e1cf+ymZ8mW45riU+N5d8+7/HjyR2r612R2r9n0rd3XrcMsLjWO7498z8KjC0lITyCsQhjj7hrHw40fJtgvWO/yhM4MV6/+3RuPisJWoQIZDz5I+ogRmO+8U+/y3JacKXobsqxZzDowi3/t+xc2zcaYVmMYHTnabS8WbLaZ2RK7hQVHFrAxdiOaptE9vDvTO02nR3gPWRulvNM0vH7/Hb/vvsN39WoMmZn2OdgffkjG/fej+cuyC6VNAv0WbTyzkUm7JhGTHEOfWn14u/3bRFSK0LusEqdpGocuHmLJ8SX8cOIHLmRcINA3kNGRoxneeDjhFcP1LlHozHD5Mn7LluG3YAGex45h8/cnfehQ0ocPx3zHHXqXV65IoN+kmOQY3t71Nr+c+YW6leuyoM8CuoV307usEncu7Rwrolew7Pgyjlw+gpfRi161ejG0wVC61exW6BxxUU7YbBiTkjAdO4bfkiX4rllj7423bMmVjz8m4777Sny2iSgeCfRiSjen8+8//82X+7/E08OTN9u+yRPNn3CrYEs3p7MuZh1Ljy9lW9w2NDTuqn4X73d8n4F1B1LVp6jTDIRbyMjAIz4eU1wcHnm/zp7FIz4ej/h4DI4TBG0VK5I+bBhpjzyCpXlznQsXEuhF0DSN1adWM2X3FOLT4hlcfzAT204kpELpLK5T1qw2KzvP7WTZ8WWsObWGdEs64f7hvNzqZQbXHywXR3ZDhpQUTCdPFgzqnNsXL+bbXjMasQUHY61Rg+zISKz9+2MNC8NasybZHTqg+fnp9JuIa0mg38DRS0d5c+eb7Dy3k6bVmvKf7v+hXah7XCLu+OXjLD2+lGXRyziXdo6KnhUZVG8QQxoMoU1IG4yGkl3WU+ShaRhSU/FISMB4/jweCQn5bhsTEkieMqXEZoMYrl7Fa88evHftwmv3bjwPHsx3Eo/N1xdrzZpYa9TA3Lw51ho17F+O+6whIfb53sLpSaAX4mrWVWbsncE3h76holdFpnacyojGIzAZXbu5LmZcZOWJlSw9vpS/LvyFh8GDbjW7MandJO6pdY/bzs4pS4aMDIwJCXicP5/7PSekPfLcb0xPL/Bcm78/1uBgbMHBcBvrsRiuXMHr11/x3rkTr1278Dx0CIOmoXl7k92qFakvv4y5WTMsNWtiDQtDq1rVac50FLfHtROqhNk0G0uOLWHab9O4mHGR4Y2H81qb11z6LEebZmPr2a18G/UtG89sxKJZaB7QnHfav8OgeoMI8gvSu0TXkJ2NR2Ji/h51IYFtvHq1wFM1Hx+sISFYg4Mx33EH1l69sIaE2IcxHF+24OBbntZnuHwZ7z178Nq5E9PvvxOyf789wH18yG7VipSxY8lu357sli3Bx+d2W0I4MQl07OPk2+K28eEfH7IvcR93Vb+L+b3nc2eQ654AcTXrKuoxlXmH53Eq+RSBvoE8dcdTPNjgQZpUu/3lPN2GxYLxwoXCA/r8+b972pcuFXiq5umJtXp1bMHBWOrXJ6tjR3tIO8Lb5viuVapUoj1gw6VL9gDftQvvnTsxHTmCQdOw+fjA3XeT8uqrZN99N9ktWpTIdSqF6yjXgZ5hyWBF9ArmHJzD0ctHqe5bnU+7fsqDDR502THkwxcP883hb1gevZwMSwatg1sz9q6x9KvTr3xd/MFmw3j5MsZz527co05KKrAolGY0YqteHWtwMJbwcGytW+cL6JzbtqpVS/wSYn8XoWG8dMlef3w8HufOYTp+HO/du/GMirL/ir6+mFu3JmXcOLI7dCA7MpLAsDBSXXG1RVEiymWgJ6Qn8M2hb5gfNZ/LWZdpFtCMT7p+wv317nfJ0DPbzKw7tY5vDn/DnvN78PHw4YH6D/CPpv+geaCbTSXTNAxXrhR6IDHvGLVHYiIGs7nA060BAbm9aHOzZn8Pe+QMgYSEYAsMLN1rTFqtGBMT7X9czp3L/TLmue1x/nzu1MAcNl9fstu2JeP++8lq3x5zZGSJrrktXF+5CvT9SfuZfXA2q06uwmKzcG+te3nqjqdoH9LeJdddSUhPYEHUAr478h0J6QlEVIzgrXZvMazhMNecM24226fOxccXPuzh+ArNzCzwVFvlyrlDHZZ69fKHdE7vOiio9AMwIwMPR1gbrwns3OBOTMRgteZ7mubtjTU0FGtoKNl33YU1NBSb4+ecr1L/QyNcntsHutVm5efTPzPn4Bz2nN9DBc8KPNr0UR5v9ji1K9XWu7ybpmkavyX8xteHvmbtqbVYNAs9wnvwYecP6V6zu3Ovp2KzYUxMxHTmDB5nzuARG2u/HRtr//ncuQLDHzY/v9yhjuxWrfCuXZvUSpXyDYHYgoPRfEt5ho7VijEp6e9PAY4/NPmmHp4/j/HKlYK/tp9fbkBndeqUL6StoaHYwsLswzcu2KkQzsVtAz05O5lFRxfx1cGviE2NJdw/nLfbv81DjR6iklclvcu7aenmdFacWMHXh74m6lIUlb0q83jzx3m0yaPUqXzj6x2WGcdwiCknoK8JbNPZsxiysvI9xRoSgiU8nOx27bBGRGCJiMid+2wLCSkw8yMwMJC0kh4jttkwRUfbe885QZ33U8H58/Ze9fXG2kNCsNSqZf8dcj4Z5Iy3h4XZl4iVsBZlwO0CPSY5hq8OfcXio4tJNafSLqQdk9pPonet3s7de72OU1dPMe/wPBYfW0xydjJNqzXlw84f8kC9B/DzLPsz9AwZGX/3qPMEdk6v25iSkm97W5UqWMLDsTRqRNa992IJD7cHd3g41po1dZtGZ7h6Fe8tW/DZtAnvzZsLnB1pq1Ll7yGcRo3yB3XOJ4OgIBkCEU7FLQJd0zR2n9/N7AOzWX96PSajiYF1B/JU86dcauqhTbNx/PJx9ibt5c/EP9mXtI8DFw5gMpjoX7c//2j6D9oEtynd8X6LxT6GfeZMwZ72mTN4JCXlr9nHB2t4ONbwcLLatrXfzgnsiAj7lD1noGmYjhyxB/jGjXj9/jsGqxVblSpkdu9OVufOWGvXzp3FQmkP4QhRClw60LOsWaw8sZI5B+dw6OIhqvlU48WWL/Jok0ddYq2V82nn+TPJHtz7Evdx4OIBkrOSAajoWZHIoEjGtx7PQ40eKrmLRmiafSz42sA+fdre846Pz3fATvPwsK/bERFBZq9eBQLbFhTktMMJhvR0vLZvx2fjRrw3bcLkuEC5uVkzUkePJrNHD/v1IKWXLdyESwZ6XGoci44uYn7UfJIykmhUtREfdf6IB+o/4LSnr6dmp7L/wn72Je7LDfFzaecAMBlMNA1oysPNHqZxxca0qt6KupXr3vJceENycr7AznsQ0iM2FuM1s0SsQUFYIyLIbt26QGBbw8LA5Dq7icepU7m9cO9duzBkZ2OrUIGsLl1IHTOGzO7dsYWG6l2mEKXCZf6nmm1mfjn9C98f/Z7NsZsB6B7enaeaP0XnGp2datqhxWbhyOUj9vB2DJ0cu3wMDQ2A2pVq0z6kPS2qt6BlUEuaBTTDx+RDYGAgF4o64JedbT+z8cIFe087NjZ/Tzs2tsBMC1vFivaQrl+frO7d7QcecwI7PLz0Z4iUpqwsvPfswXvjRnw2bcJ08iQA5nr1SPvHP8js0YPstm3ljElRLjh9oMckx7DwyELUYyqJGYmE+IXwYssXebjRw053tZx9ifv46tBXrD21lkyrvRdc1bsqLau3ZECdAbSo3oIWQS0KrA1jyMjAGH8Gw8mTeEdH45GUhDEpyR7cju/GpCR7iBe2Voi3t32hpYgIMlq2zBfYlvBwtCpVnHZY5FYY4+Px2bzZ3gvftg1jejqatzdZHTqQ9thjZPbogbV2bb3LFKLMOWWgZ1mz+CnmJxYcWcCO+B0YDUZ6hvfkkcaP0CO8h1Otemi2mVl7ai1zDs5hb+Je/D39GdpgKO2rRtLaEE6dVE88Ll7EeDAJjwv7MCatLxDUxrS03NcLyPPatsqVsQUEYA0KwtK4MdlBQVgDA7EFBWFz3LaGhdlX5yutU9D1oGn2YaOced45p++fP49p3z5C9u8HwFKjBhkPPkhmz55kd+wo63KLcs+gaVpZvp8W7zgwVZjoK9EsOLKAJceWcDnrMjX9a/Jwo4cZ1mgYoRV0HvfMOeXcEcKXE04x/+LPzLXuJt6YRr0MX/4ZXY1/7NWoeu4ShkLOZtQMBmxVq9oDOTAQa1AQtoAAezgHBeFfty5XvL3toR0Y6J7DBNeeSVnYyTnnzxcY5wf7VELuuIPULl3I7NkTS8OGbvXJoyQUa9iunHPFNgoLCwMocmfXvaubYclg9cnVfH/ke35N+BWTwUTv2r0Z3ng4nWt0Lv1FsqxW++nmp08XHOq4eNE+1OG4bTCb2R8Mn7WDBXdApif0ioH/i6pMn7QwtKDq2FoFkuYI6NywzulVBwTc8ABjhcBAzC62o+Uym+3tVlRYF3ImZe7ysiEhZEdGYuvdu+AJOo6phIGBgbL4lBDXoVugH7p4iO+PfM/y6OUkZydTp1IdJradyNAGQ0tljW7DpUuYTp7EdOKE/XvO7ZiYAmcval5euUMdtqAgspo2ZnWNNP6v0mG2E4OvwQslpDePN3uMBrXagNHIxeu8r0vLyMj9RJJvmKiwsf3CgtrDw/4HLSQES+3a9jMpc07KCQ3NDe2SXl5WiPKqzAN9wZEFfH/ke/5M+hNvD2/61e7HI40f4e7Qu29/pkpWFqbTp+1BnRPcJ07gceIEHpcv526mmUxYatfGUrcuWT16YKlXD0utWva1rYOCcgPmatZVFh1dxDeHv+FMyhnCKoQxsdlEHm70sGsufuW49FluCDs+kXg4QvpGY/t52SpVyh0ysjRsSHbHjvY/foGB+ddYkcWkhChTZR7o47eNp2GVhky+ezIP1n/w5oNR0zCeO/d3D/vECUynTtmDOzY233ob1uBgLHXrktmvnz2069a1r8QXEXHDoY/oK9F8fehr1GMq6ZZ02ga3ZWLbifSp3cepDsgWkJmJx9mzmM6etU9jPHvWPgf97Fn7cq0XLxZrbD87MhKbY5goJ6hzfw4IkKveCOGkdEmnS1mXOHTxECF+IXSt2ZWKXhULbGNITc0f2idO4OEYKsl7PUabry/WunUxR0aSMXhwbmhb6ta1L4pUTDbNxv/O/o+vDn3FpthNeBm9uK/efTzZ/EnuCLyjRH7v22Y2/31afk5o58xDj43FIyEh3+aap2fuxX6z27fPH9COcC7O2L4QwjWU+f/if3f/N7+c+YWfY35GPabiaTDR3rshfdJr0u+MN02OXsB08mS+cNIMBqzh4Vjq1SO9Xbu/Q7tePWwhIbc0ZU/TNOLT4jlw4QAHLhxg9anVRF+JprpvdV6961VGNB5R9tfbzDlAe21Q53xds7ysZjTaT8sPDyera9e/55+Hh9uvtBMcLEMeQpQjZT5tMfWf/8TjxAk4dYJfrTGsrWtlTUM4VN2+Qf1kT/qk1+Seyq1pW68HxvqNsNSqdVsf822ajZNXT3Lo4iEOXDjAwYsHOXDhAFey7AfyjAYjkUGRPNb0MQbWHYiXx21cBEHTwGLBYLFAdrb9u9lsv3qO47sxOfnvFQvzDIt4xMXZt895KYPBfr3KiAisjhOHLI7AtoaHYw0NBU/PW6/VBbnilLOyJO1TNFdso+JOWyzzQL/ebIaYKrCmAaxuCJvrQJYJKmbBvScN9D1lol+sD9WzvcDTE83TM/d73tuYTGR7m4iqlMW+yun85Z/KnxWSOeB7lVSjPSi9NCPNLAHcaQmkRVY17syowh1p/lQw83fwWiz2y3/l/V7YfTkhnSe8C7vs2Y1Yg4Kw1qyJJSIC74YNSQkMzO1hW2vUcM+56LfBFf8zliVpn6K5Yhs5baBfWLw4X2jm68k6vqeZ09hqPc56QzQ/eZzknId9tsVdmQH0SQ2j75XqRKZUINuSxUGvy/zpe5m//JL5s2IKB/0zyPaw/04VzAYiL3nRMtGDlolGWp6DpucseGdbwWhEM5kK/mEwmcDLy/7dZELz8sr/Pc8fj7z35f2jUuAPzrXP9/e397Br1sy3joor7mhlTdroxqR9iuaKbeS0gX6jM0ULfYKmcejSIX45/QsbYzeyL3EfGhpVvKuQnJ2MTbOPKVfxrkLzgObcEXgHzQOa0zywOXUq1XGpi1q44o5W1qSNbkzap2iu2EYuc6ZoUQwGgz2gA5rzcquXuZhxkU2xm9h9bjchFUJyA7yGfw2nWnFRCCHKmtMH+rUCfAMY2nAoQxsO1bsUIYRwKrcV6Iqi9AH+BXgAc1RV/aBEqhJCCHHTbnnlK0VRPIDPgb5AU+BhRVGallRhQgghbs7tLGXYFohWVfWkqqrZwCLg/pIpSwghxM26nUCvAcTm+fms4z4hhBA6KPWDooqiPA08DaCqKoGBgaX9li7LZDJJ+xRB2ujGpH2K5s5tdDuBHgfkvahnTcd9+aiqOguY5fhRc7X5n2XJFefHljVpoxuT9imaK7aRYx56kW4n0H8DGiiKUgd7kD8EPHIbryeEEOI23PIYuqqqFuB54Gcgyn6XeqikChNCCHFzyn5xLiGEELeiyFPhS/kKzPkpivIH9qLkq5AvaR9pI2kfaaMbfBWpTANdCCFE6ZFAF0IIN1HWgT6r6E3KNWmfokkb3Zi0T9Hcto3K+qCoEEKIUiJDLkII4SbKZD10WWa3aIqixAApgBWwqKraWt+K9KcoylfAACBRVdXmjvuqAYuB2kAMoKiqelmvGvV0nfZ5B3gKSHJs9oaqqmv1qVBfiqKEA98CwdinTM9SVfVf7rwPlXoPXZbZvSndVVVtIWGe6xugzzX3TQA2qqraANjo+Lm8+oaC7QPwiWM/alFew9zBAoxVVbUp0B4Y7cget92HymLIRZbZFbdEVdWtwKVr7r4fmOe4PQ8YVKZFOZHrtI9wUFX1nKqqex23U7Cf0V4DN96HymLIpbBldtuVwfu6Gg1YryiKBvzXsaiZKChYVdVzjtvnsX+cFvk9ryjKo8Dv2HuobjGccDsURakNtAT24Mb7kBwUdR6dVFVthX1oarSiKF30LsjZqaqqIctJXOsLoB7QAjgHzNC3HP0piuIPLANeVlU1Oe9j7rYPlUWgF2uZ3fJOVdU4x/dEYAX2oSpRUIKiKKEAju+JOtfjVFRVTVBV1aqqqg2YTTnfjxRF8cQe5gtUVV3uuNtt96GyCPTcZXYVRfHCvszuj2Xwvi5DUZQKiqJUzLkN3Asc1Lcqp/UjMMpxexSwUsdanE5OUDk8QDnejxRFMQBzgShVVWfmecht96EyObFIUZR+wKfYpy1+parq1FJ/UxeiKEpd7L1ysB/X+F7aCBRFWQh0AwKBBOBt4AdABSKA09innJXLA4PXaZ9u2IdbNOxT8p7JM15criiK0gnYBhwAbI6738A+ju6W+5CcKSqEEG5CDooKIYSbkEAXQgg3IYEuhBBuQgJdCCHchAS6EEK4CQl04ZYURflGUZT39K5DiLIkgS6EEG5CAl0IIdxEmVzgQojSpihKS+yneTcA1lLEgkuKolQF5mNf+dME7ACeVVX1rKIow4BxedelVxRlDPb16u9TFCUA+1rkXYGjwM9AN1VVO5X4LybETZAeunB5jjWCfsAe0NWAJcCDRTzNCHwN1MJ+CngG8B/HY6uARoqiNMiz/SPA947bnwNpQAj2tUBGIYQTkB66cAftAU/gU8dyqEsVRXnlRk9QVfUi9lX4AFAUZSqw2fFYuqIoK4GHgSmOYG8M/Oi4AteDQHNVVdOBw4qizMO+hooQupJAF+4gDIhzhHmO0zd6gqIofsAn2C/hVtVxd0VFUTxUVbVi743PAKZg753/4Aj6EOz/b/JetCXvbSF0I0Muwh2cA2o4lkvNEVHEc8YCjYB2qqpWAnIuKJLzGhuAIEVRWmDvqecMtyRhv1ZlzTyvlXe9fyF0I4Eu3MEu7CH7oqIonoqiDKboCztUxD5ufsVxFfi38z6oqqoZ+1j8R9jH5Tc47rcCy4F3FEXxUxSlMfBoSf4yQtwqCXTh8hwXHx8M/AP7RZOHYQ/dG/kU8AUuALuBnwrZ5nugF7BEVVVLnvufBypjvx7lfGAhkHXrv4EQJUPWQxfiNimKMh0IUVVVZrsIXclBUSFukmOYxQv7lXDaAE8AT+palBBIoAs3pijKG9gvOXatbaqq9r2Nl66IfZglDPul32bgRtelFK5LhlyEEMJNyEFRIYRwExLoQgjhJiTQhRDCTUigCyGEm5BAF0IINyGBLoQQbuL/AeysBP+L1mThAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for y_stat in y_stats:\n",
+    "    for x_stat in x_stats:\n",
+    "        ax = summary_df_f.plot(kind='line', x=x_stat,y=y_stat,color='red')\n",
+    "        summary_df_d.plot(kind='line', ax=ax, x=x_stat,y=y_stat,color='green')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.style.use('ggplot')\n",
+    "# name of axes to plot\n",
+    "y_stats = ['abs_e_trans_stats_mean', 'abs_e_rot_stats_mean']\n",
+    "y_stats_label = ['Abs. Trans. Error Mean (in m)', 'Abs. Rot. Error Mean (in deg)']\n",
+    "\n",
+    "labels = dict(zip(y_stats, y_stats_label))\n",
+    "\n",
+    "x_stats = ['d_avg']\n",
+    "\n",
+    "for y_stat in y_stats:\n",
+    "    for x_stat in x_stats:\n",
+    "        ax = summary_df_fd.plot(kind='scatter', x=x_stat,y=y_stat,color='blue')\n",
+    "        ax.set_xlabel(\"APPD (in pixels)\")\n",
+    "        ax.set_ylabel(labels[y_stat])\n",
+    "        ax.get_figure().savefig(y_stat + '.pdf')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "plr_pytorch",
+   "language": "python",
+   "name": "plr_pytorch"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/utils/orbslam_evaluation/img/distortion_reproj_10.png b/utils/orbslam_evaluation/img/distortion_reproj_10.png
new file mode 100755
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diff --git a/utils/orbslam_evaluation/img/distortion_trans_10.png b/utils/orbslam_evaluation/img/distortion_trans_10.png
new file mode 100755
index 0000000..1d9897c
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diff --git a/utils/orbslam_evaluation/img/intrinsic_reproj_10.png b/utils/orbslam_evaluation/img/intrinsic_reproj_10.png
new file mode 100755
index 0000000..d90b93d
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diff --git a/utils/orbslam_evaluation/img/intrinsic_trans_10.png b/utils/orbslam_evaluation/img/intrinsic_trans_10.png
new file mode 100755
index 0000000..5cefe9c
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diff --git a/utils/orbslam_evaluation/library/__init__.py b/utils/orbslam_evaluation/library/__init__.py
new file mode 100755
index 0000000..e69de29
diff --git a/utils/orbslam_evaluation/library/helper.py b/utils/orbslam_evaluation/library/helper.py
new file mode 100644
index 0000000..5ba66c7
--- /dev/null
+++ b/utils/orbslam_evaluation/library/helper.py
@@ -0,0 +1,144 @@
+import numpy as np
+import pandas as pd
+import argparse
+
+# evo toolbox
+from evo.core.trajectory import PoseTrajectory3D
+from evo.tools import file_interface
+
+
+def str2bool(v):
+    if v.lower() in ('yes', 'true', 't', 'y', '1'):
+        return True
+    elif v.lower() in ('no', 'false', 'f', 'n', '0'):
+        return False
+    else:
+        raise argparse.ArgumentTypeError('Boolean value expected.')
+
+
+def create_summary_dataframe(num_of_runs, boxplot_distances):
+    """
+    Creates a panda dataframe with column names corresponding to the run number, camera parameters, the appd metric
+    and trajectory evaluations
+    :param num_of_runs (int): Number of runs for the experiments
+    :param boxplot_distances (list): the relative-distance ranges for trajectory evaluation
+    :return:
+    """
+    indices = ["run_%d" % i for i in range(num_of_runs)]
+    columns = ["run_name", "fx", "fy", "cx", "cy", "k1", "k2", "k3", "p1", "p2", "appd", 'abs_e_rot_stats_mean',
+               'abs_e_rot_stats_median',
+               'abs_e_rot_stats_rmse', 'abs_e_scale_stats_mean', 'abs_e_scale_stats_median', 'abs_e_scale_stats_rmse',
+               'abs_e_trans_stats_mean', 'abs_e_trans_stats_median', 'abs_e_trans_stats_rmse']
+    for dist in boxplot_distances:
+        dist = float(dist)
+        relative_columns = ['rel_rot_stats_mean_trajlen_' + str(dist),
+                            'rel_rot_stats_median_trajlen_' + str(dist),
+                            'rel_rot_stats_rmse_trajlen_' + str(dist),
+                            'rel_trans_perc_stats_mean_trajlen_' + str(dist),
+                            'rel_trans_perc_stats_median_trajlen_' + str(dist),
+                            'rel_trans_perc_stats_rmse_trajlen_' + str(dist),
+                            'rel_trans_stats_mean_trajlen_' + str(dist),
+                            'rel_trans_stats_median_trajlen_' + str(dist),
+                            'rel_trans_stats_rmse_trajlen_' + str(dist)
+                            ]
+        columns = columns + relative_columns
+
+    dataframe = pd.DataFrame(index=indices, columns=columns)
+    dataframe["run_name"] = ["Run %d" % i for i in range(num_of_runs)]
+    return dataframe
+
+
+def update_dataframe_with_rectification_stats(df, row_name, K, D, appd=None):
+    """
+    Update panda dataframe with current information
+    :param df: panda dataframe
+    :param row_name: row name in the dataframe
+    :param K: Intrinsic camera matrix (3, 3)
+    :param D: Distortion parameters (1, 5)
+    :param appd: Statistic on new perturbed image
+    :return: updated panda dataframe
+    """
+    # store camera intrinsic matrix
+    df.at[row_name, "fx"] = K[0, 0]
+    df.at[row_name, "fy"] = K[1, 1]
+    df.at[row_name, "cx"] = K[0, 2]
+    df.at[row_name, "cy"] = K[1, 2]
+    # store camera distortion parameters
+    df.at[row_name, "k1"] = D[0]
+    df.at[row_name, "k2"] = D[1]
+    df.at[row_name, "k3"] = D[4]
+    df.at[row_name, "p1"] = D[2]
+    df.at[row_name, "p2"] = D[3]
+    # store perturbed image statistics
+    df.at[row_name, "appd"] = appd
+
+    return df
+
+
+def get_range_perturbed_camera_parameters(K, D, settings, perturb_parameters):
+    # default ranges without any perturbation
+    ranges = {'fx': (K[0, 0], K[0, 0]),
+              'fy': (K[1, 1], K[1, 1]),
+              'cx': (K[0, 2], K[0, 2]),
+              'cy': (K[1, 2], K[1, 2]),
+              'k1': (D[0], D[0]),
+              'k2': (D[1], D[1]),
+              'p1': (D[2], D[2]),
+              'p2': (D[3], D[3]),
+              'k3': (D[4], D[4])}
+
+    for param in perturb_parameters:
+        # focal length parameters
+        if param == 'f':
+            ranges['fx'] = (K[0, 0] * settings["fx_range"][0], K[0, 0] * settings["fx_range"][1])
+            ranges['fy'] = (K[1, 1] * settings["fy_range"][0], K[1, 1] * settings["fy_range"][1])
+        # principle point parameters
+        if param == 'c':
+            ranges['cx'] = (K[0, 2] * settings["cx_range"][0], K[0, 2] * settings["cx_range"][1])
+            ranges['cy'] = (K[1, 2] * settings["cy_range"][0], K[1, 2] * settings["cy_range"][1])
+        # distortion parameters
+        if param == 'd':
+            ranges['k1'] = (D[0] * settings["k1_range"][0], D[0] * settings["k1_range"][1])
+            ranges['k2'] = (D[1] * settings["k2_range"][0], D[1] * settings["k2_range"][1])
+            ranges['p1'] = (D[2] * settings["p1_range"][0], D[2] * settings["p1_range"][1])
+            ranges['p2'] = (D[3] * settings["p2_range"][0], D[3] * settings["p2_range"][1])
+            ranges['k3'] = (D[4] * settings["k3_range"][0], D[4] * settings["k3_range"][1])
+
+    return ranges
+
+
+def kitti_poses_and_timestamps_to_trajectory(poses_file, timestamp_file):
+    pose_path = file_interface.read_kitti_poses_file(poses_file)
+    raw_timestamps_mat = file_interface.csv_read_matrix(timestamp_file)
+    error_msg = "timestamp file must have one column of timestamps and same number of rows as the KITTI poses file"
+    if len(raw_timestamps_mat) > 0 and len(raw_timestamps_mat[0]) != 1 or \
+            len(raw_timestamps_mat) != pose_path.num_poses:
+        raise file_interface.FileInterfaceException(error_msg)
+    try:
+        timestamps_mat = np.array(raw_timestamps_mat).astype(float)
+    except ValueError:
+        raise file_interface.FileInterfaceException(error_msg)
+    return PoseTrajectory3D(poses_se3=pose_path.poses_se3, timestamps=timestamps_mat)
+
+
+def modify_orbslam_settings(orbslam_settings, K, D):
+    """
+    Modify the orbslam configuration settings with the perturbed parameters
+    :param settings: (dict) original orbslam settings
+    :param K: perturbed camera intrinsic matrix
+    :param D: perturbed camera distortion parameters
+    :return:  (dict) modified orbslam settings
+    """
+    orbslam_settings['Camera.fx'] = float(K[0, 0])
+    orbslam_settings['Camera.fy'] = float(K[1, 1])
+    orbslam_settings['Camera.cx'] = float(K[0, 2])
+    orbslam_settings['Camera.cy'] = float(K[1, 2])
+    orbslam_settings['Camera.k1'] = float(D[0])
+    orbslam_settings['Camera.k2'] = float(D[1])
+    orbslam_settings['Camera.k3'] = float(D[4])
+    orbslam_settings['Camera.p1'] = float(D[2])
+    orbslam_settings['Camera.p2'] = float(D[3])
+
+    return orbslam_settings
+
+# EOF
diff --git a/utils/orbslam_evaluation/library/rangesampler.py b/utils/orbslam_evaluation/library/rangesampler.py
new file mode 100644
index 0000000..26cb7d2
--- /dev/null
+++ b/utils/orbslam_evaluation/library/rangesampler.py
@@ -0,0 +1,67 @@
+import numpy as np
+
+from carnivalmirror import Sampler
+from carnivalmirror import Calibration
+
+class RangeSampler(Sampler):
+    """RangeSampler provides ranged-based sampling within the specified range of calibration parameters.
+
+    Attributes:
+        width (:obj:`int`): The width of the image(s) for which the calibrations are
+        height (:obj:`int`): The height of the image(s) for which the calibrations are
+        ranges (:obj:`dict`): The provided sampling ranges for the calibration parameters
+        num_of_runs (:obj:`int`): The number of runs for evaluation
+    """
+
+    def __init__(self, ranges, cal_width, cal_height, num_of_runs):
+        """Initializes a ParameterSampler object
+        Args:
+            ranges (:obj:`dict`): A dictionary with keys `[fx, fy, cx, cy, k1, k2, p1, p2, k3]` and elements tuples
+                describing the sampling range for each parameter. All intrinsic parameters must be provided.
+                Missing distortion parameters will be sampled as 0
+            cal_width (:obj:`int`): The width of the image(s) for which the calibrations are
+            cal_height (:obj:`int`): The height of the image(s) for which the calibrations are
+            num_of_runs (:obj:`int`): The number of runs for evaluation
+        Raises:
+            ValueError: If one of `[fx, fy, cx, cy]` is missing from `ranges`
+        """
+
+        super(RangeSampler, self).__init__(cal_width=cal_width, cal_height=cal_height)
+
+        # Validate the ranges
+        for key in ['fx', 'fy', 'cx', 'cy']:
+            if key not in ranges: raise ValueError("Key %s missing in ranges" % key)
+        for key in ['k1', 'k2', 'p1', 'p2', 'k3']:
+            if key not in ranges: ranges[key] = (0, 0)
+        self.ranges = ranges
+        self.num_of_runs = num_of_runs
+        self.index = 0
+
+
+    def next(self):
+        """Generator method providing a randomly sampled :obj:`Calibration`
+        Returns:
+            :obj:`Calibration`: A :obj:`Calibration` object
+        """
+
+        # Sample the values
+        sample = dict()
+        for key in self.ranges:
+            sample[key] = self.ranges[key][0] + self.index / self.num_of_runs * (self.ranges[key][1] - self.ranges[key][0])
+
+
+        # Construct a Calibration object
+        K = np.array([[sample['fx'], 0.0, sample['cx']],
+                      [0.0, sample['fy'], sample['cy']],
+                      [0.0, 0.0, 1.0]])
+        D = np.array([sample['k1'], sample['k2'], sample['p1'], sample['p2'], sample['k3']])
+
+        # Check if index is correct
+        if self.index > self.num_of_runs:
+            raise Exception("Number of times next() is called should not exceed the number of runs!")
+        else:
+            self.index = self.index + 1
+
+        return Calibration(K=K, D=D, width=self.cal_width, height=self.cal_height)
+
+# EOF
diff --git a/utils/orbslam_evaluation/library/rpg_trajectory_evaluation b/utils/orbslam_evaluation/library/rpg_trajectory_evaluation
new file mode 160000
index 0000000..40ca5cb
--- /dev/null
+++ b/utils/orbslam_evaluation/library/rpg_trajectory_evaluation
@@ -0,0 +1 @@
+Subproject commit 40ca5cbd42cc1af484da0451ea32913112997e9b
diff --git a/utils/orbslam_evaluation/library/trajectory.py b/utils/orbslam_evaluation/library/trajectory.py
new file mode 100755
index 0000000..d6c47b9
--- /dev/null
+++ b/utils/orbslam_evaluation/library/trajectory.py
@@ -0,0 +1,424 @@
+#!/usr/bin/env python
+
+import os
+import pickle
+import sys
+import numpy as np
+
+# uzh trajectory evaluation toolbox
+sys.path.append(os.path.abspath(os.path.dirname(os.path.abspath(__file__)) \
+                                + '/rpg_trajectory_evaluation/src/rpg_trajectory_evaluation'))
+
+import trajectory_utils as traj_utils
+import associate_timestamps as associ
+import results_writer as res_writer
+import compute_trajectory_errors as traj_err
+import align_utils as au
+import transformations as tf
+import plot_utils as pu
+
+import matplotlib.pyplot as plt
+from matplotlib import rc
+
+rc('font', **{'family': 'serif', 'serif': ['Cardo']})
+rc('text', usetex=True)
+
+class Trajectory:
+    """
+    Class to perform trajectory evaluation given the input groundtruth and estimated pose.
+
+    Source: https://github.com/uzh-rpg/rpg_trajectory_evaluation/blob/master/src/rpg_trajectory_evaluation/trajectory.py
+
+    """
+    rel_error_cached_nm = 'cached_rel_err.pickle'
+    rel_error_prefix = 'relative_error_statistics_'
+    saved_res_dir_nm = 'saved_results'
+
+    def __init__(self, results_dir, run_name='', gt_traj_file=None, estimated_traj_file=None, align_type='sim3',
+                 align_num_frames=-1, preset_boxplot_distances=[]):
+
+        assert os.path.exists(results_dir), \
+            "Specified directory {0} does not exist.".format(results_dir)
+        assert align_type in ['posyaw', 'sim3', 'se3', 'none']
+
+        # information of the results, useful as labels
+        self.alg = run_name
+        self.uid = self.alg
+        self.data_dir = results_dir
+        self.plots_dir = os.path.join(results_dir, 'plots')
+        self.data_loaded = False
+        self.data_aligned = False
+        self.saved_results_dir = os.path.join(self.data_dir, Trajectory.saved_res_dir_nm)
+
+        if not os.path.exists(self.saved_results_dir):
+            os.makedirs(self.saved_results_dir)
+        if not os.path.exists(self.plots_dir):
+            os.makedirs(self.plots_dir)
+
+        self.align_type = align_type
+        self.align_num_frames = int(align_num_frames)
+
+        self.align_str = self.align_type + '_' + str(self.align_num_frames)
+
+        self.abs_errors = {}
+
+        # we cache relative error since it is time-comsuming to compute
+        self.rel_errors = {}
+        self.cached_rel_err_fn = os.path.join(self.saved_results_dir,
+                                              self.rel_error_cached_nm)
+
+        self.load_data(gt_traj_file, estimated_traj_file)
+
+        if len(preset_boxplot_distances) != 0:
+            self.preset_boxplot_distances = preset_boxplot_distances
+        else:
+            self.compute_boxplot_distances()
+
+        self.align_trajectory()
+
+    def load_data(self, gt_traj_file=None, estimated_traj_file=None):
+        """
+        Loads the trajectory data. The resuls {p_es, q_es, p_gt, q_gt} is
+        synchronized and has the same length.
+        """
+        print('Loading trajectory data...')
+
+        # only timestamped pose series is supported
+        self.t_es, self.p_es, self.q_es, self.t_gt, self.p_gt, self.q_gt = self.load_stamped_dataset(self.data_dir,
+                                                                                                     fn_gt=gt_traj_file,
+                                                                                                     fn_es=estimated_traj_file)
+        self.accum_distances = traj_utils.get_distance_from_start(self.p_gt)
+        self.traj_length = self.accum_distances[-1]
+
+        if os.path.isfile(self.cached_rel_err_fn):
+            print('Loading cached relative (odometry) errors from ' +
+                  self.cached_rel_err_fn)
+            with open(self.cached_rel_err_fn, 'rb') as f:
+                self.rel_errors = pickle.load(f)
+            print("Loaded odometry error calcualted at {0}".format(
+                self.rel_errors.keys()))
+
+        self.data_loaded = True
+        print('...done.')
+
+    def cache_current_error(self):
+        if self.rel_errors:
+            with open(self.cached_rel_err_fn, 'wb') as f:
+                pickle.dump(self.rel_errors, f)
+
+    def compute_boxplot_distances(self):
+        pcts = [0.1, 0.2, 0.3, 0.4, 0.5]
+        print("Computing preset subtrajectory lengths for relative errors...")
+        print("Use percentage {0} of trajectory length.".format(pcts))
+        self.preset_boxplot_distances = [np.floor(pct * self.traj_length)
+                                         for pct in pcts]
+
+        print("...done. Computed preset subtrajecory lengths:"
+              " {0}".format(self.preset_boxplot_distances))
+
+    def align_trajectory(self):
+        if self.data_aligned:
+            print("Trajectory already aligned")
+            return
+        print("Aliging the trajectory estimate to the groundtruth...")
+
+        print("Alignment type is {0}.".format(self.align_type))
+        n = int(self.align_num_frames)
+        if n < 0.0:
+            print('To align all frames.')
+            n = len(self.p_es)
+        else:
+            print('To align trajectory using ' + str(n) + ' frames.')
+
+        self.trans = np.zeros((3,))
+        self.rot = np.eye(3)
+        self.scale = 1.0
+        if self.align_type == 'none':
+            pass
+        else:
+            self.scale, self.rot, self.trans = au.alignTrajectory(
+                self.p_es, self.p_gt, self.q_es, self.q_gt,
+                self.align_type, self.align_num_frames)
+
+        self.p_es_aligned = np.zeros(np.shape(self.p_es))
+        self.q_es_aligned = np.zeros(np.shape(self.q_es))
+        for i in range(np.shape(self.p_es)[0]):
+            self.p_es_aligned[i, :] = self.scale * \
+                                      self.rot.dot(self.p_es[i, :]) + self.trans
+            q_es_R = self.rot.dot(
+                tf.quaternion_matrix(self.q_es[i, :])[0:3, 0:3])
+            q_es_T = np.identity(4)
+            q_es_T[0:3, 0:3] = q_es_R
+            self.q_es_aligned[i, :] = tf.quaternion_from_matrix(q_es_T)
+
+        self.data_aligned = True
+        print("... trajectory alignment done.")
+
+    def compute_absolute_error(self):
+        if self.abs_errors:
+            print("Absolute errors already calculated")
+        else:
+            print('Calculating RMSE...')
+            # align trajectory if necessary
+            self.align_trajectory()
+            e_trans, e_trans_vec, e_rot, e_ypr, e_scale_perc = \
+                traj_err.compute_absolute_error(self.p_es_aligned,
+                                                self.q_es_aligned,
+                                                self.p_gt,
+                                                self.q_gt)
+            stats_trans = res_writer.compute_statistics(e_trans)
+            stats_rot = res_writer.compute_statistics(e_rot)
+            stats_scale = res_writer.compute_statistics(e_scale_perc)
+
+            self.abs_errors['abs_e_trans'] = e_trans
+            self.abs_errors['abs_e_trans_stats'] = stats_trans
+
+            self.abs_errors['abs_e_trans_vec'] = e_trans_vec
+
+            self.abs_errors['abs_e_rot'] = e_rot
+            self.abs_errors['abs_e_rot_stats'] = stats_rot
+
+            self.abs_errors['abs_e_ypr'] = e_ypr
+
+            self.abs_errors['abs_e_scale_perc'] = e_scale_perc
+            self.abs_errors['abs_e_scale_stats'] = stats_scale
+            print('...RMSE calculated.')
+        return
+
+    def get_abs_errors_stats(self):
+        errors_dict = dict()
+
+        labels = ['abs_e_trans_stats', 'abs_e_rot_stats', 'abs_e_scale_stats']
+        stats = ['rmse', 'mean', 'median']
+
+        for label in labels:
+            for stat in stats:
+                errors_dict[label + '_' + stat] = self.abs_errors[label][stat]
+
+        return errors_dict
+
+    def get_rel_errors_stats(self):
+        errors_dict = dict()
+
+        labels = ['rel_trans_stats', 'rel_trans_perc_stats', 'rel_rot_stats']
+        stats = ['rmse', 'mean', 'median']
+
+        for dist in self.rel_errors:
+            for label in labels:
+                for stat in stats:
+                    errors_dict[label + '_' + stat + '_trajlen_'+ str(dist)] = self.rel_errors[dist][label][stat]
+
+        return errors_dict
+
+    def write_errors_to_yaml(self):
+        self.abs_err_stats_fn = os.path.join(
+            self.saved_results_dir, 'absolute_err_statistics' + '_' +
+                                    self.align_str + '.yaml')
+        res_writer.update_and_save_stats(
+            self.abs_errors['abs_e_trans_stats'], 'trans',
+            self.abs_err_stats_fn)
+        res_writer.update_and_save_stats(
+            self.abs_errors['abs_e_rot_stats'], 'rot',
+            self.abs_err_stats_fn)
+        res_writer.update_and_save_stats(
+            self.abs_errors['abs_e_scale_stats'], 'scale',
+            self.abs_err_stats_fn)
+
+        self.rel_error_stats_fns = []
+        for dist in self.rel_errors:
+            cur_err = self.rel_errors[dist]
+            dist_str = "{:3.1f}".format(dist).replace('.', '_')
+            dist_fn = os.path.join(
+                self.saved_results_dir,
+                Trajectory.rel_error_prefix + dist_str + '.yaml')
+            res_writer.update_and_save_stats(
+                cur_err['rel_trans_stats'], 'trans', dist_fn)
+            res_writer.update_and_save_stats(
+                cur_err['rel_rot_stats'], 'rot', dist_fn)
+            res_writer.update_and_save_stats(
+                cur_err['rel_trans_perc_stats'], 'trans_perc', dist_fn)
+            res_writer.update_and_save_stats(
+                cur_err['rel_yaw_stats'], 'yaw', dist_fn)
+            res_writer.update_and_save_stats(
+                cur_err['rel_gravity_stats'], 'gravity', dist_fn)
+            self.rel_error_stats_fns.append(dist_fn)
+
+    def compute_relative_error_at_subtraj_len(self, subtraj_len,
+                                              max_dist_diff=-1):
+        if max_dist_diff < 0:
+            max_dist_diff = 0.2 * subtraj_len
+
+        if self.rel_errors and (subtraj_len in self.rel_errors):
+            print("Relative error at sub-trajectory length {0} is already "
+                  "computed or loaded from cache.".format(subtraj_len))
+        else:
+            print("Computing relative error at sub-trajectory "
+                  "length {0}".format(subtraj_len))
+            Tcm = np.identity(4)
+            _, e_trans, e_trans_perc, e_yaw, e_gravity, e_rot = \
+                traj_err.compute_relative_error(
+                    self.p_es, self.q_es, self.p_gt, self.q_gt, Tcm,
+                    subtraj_len, max_dist_diff, self.accum_distances,
+                    self.scale)
+            dist_rel_err = {'rel_trans': e_trans,
+                            'rel_trans_stats':
+                                res_writer.compute_statistics(e_trans),
+                            'rel_trans_perc': e_trans_perc,
+                            'rel_trans_perc_stats':
+                                res_writer.compute_statistics(e_trans_perc),
+                            'rel_rot': e_rot,
+                            'rel_rot_stats':
+                                res_writer.compute_statistics(e_rot),
+                            'rel_yaw': e_yaw,
+                            'rel_yaw_stats':
+                                res_writer.compute_statistics(e_yaw),
+                            'rel_gravity': e_gravity,
+                            'rel_gravity_stats':
+                                res_writer.compute_statistics(e_gravity)}
+            self.rel_errors[subtraj_len] = dist_rel_err
+
+    def compute_relative_errors(self, subtraj_lengths=[]):
+        if subtraj_lengths:
+            for l in subtraj_lengths:
+                self.compute_relative_error_at_subtraj_len(l)
+        else:
+            print("Computing the relative errors based on preset"
+                  " subtrajectory lengths...")
+            for l in self.preset_boxplot_distances:
+                self.compute_relative_error_at_subtraj_len(l)
+
+    def plot_trajectory(self, FORMAT='.png'):
+        print(">>> Plotting trajectory...")
+        # trajectory view from top
+        fig = plt.figure(figsize=(6, 5.5))
+        ax = fig.add_subplot(111, aspect='equal', xlabel='x [m]', ylabel='y [m]')
+        pu.plot_trajectory_top(ax, self.p_es_aligned, 'b', 'Estimate')
+        pu.plot_trajectory_top(ax, self.p_gt, 'm', 'Groundtruth')
+        pu.plot_aligned_top(ax, self.p_es_aligned, self.p_gt, self.align_num_frames)
+        plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
+        fig.tight_layout()
+        fig.savefig(self.plots_dir + '/trajectory_top' + '_' + self.align_str + FORMAT,
+                    bbox_inches="tight")
+
+        # trajectory view from side
+        fig = plt.figure(figsize=(6, 5.5))
+        ax = fig.add_subplot(111, aspect='equal', xlabel='x [m]', ylabel='y [m]')
+        pu.plot_trajectory_side(ax, self.p_es_aligned, 'b', 'Estimate')
+        pu.plot_trajectory_side(ax, self.p_gt, 'm', 'Groundtruth')
+        plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
+        fig.tight_layout()
+        fig.savefig(self.plots_dir + '/trajectory_side' + '_' + self.align_str + FORMAT,
+                    bbox_inches="tight")
+
+    def plot_rel_traj_error(self, FORMAT='.png'):
+        print(">>> Plotting relative (odometry) error...")
+
+        distances = self.preset_boxplot_distances
+        rel_trans_err = [[self.rel_errors[d]['rel_trans'] for d in distances]]
+        rel_trans_err_perc = [[self.rel_errors[d]['rel_trans_perc']
+                               for d in distances]]
+        rel_yaw_err = [[self.rel_errors[d]['rel_yaw'] for d in distances]]
+        labels = ['Estimate']
+        colors = ['k', 'b']
+
+        # plot relative translation error (in meters)
+        fig = plt.figure(figsize=(6, 2.5))
+        ax = fig.add_subplot(111, xlabel='Distance traveled [m]', ylabel='Translation error [m]')
+        pu.boxplot_compare(ax, distances, rel_trans_err, labels, colors)
+        fig.tight_layout()
+        fig.savefig(self.plots_dir + '/rel_translation_error' + FORMAT, bbox_inches="tight")
+        plt.close(fig)
+
+        # plot relative translation error (in %)
+        fig = plt.figure(figsize=(6, 2.5))
+        ax = fig.add_subplot( 111, xlabel='Distance traveled [m]',ylabel='Translation error [\%]')
+        pu.boxplot_compare(ax, distances, rel_trans_err_perc, labels, colors)
+        fig.tight_layout()
+        fig.savefig(self.plots_dir + '/rel_translation_error_perc' + FORMAT,
+                    bbox_inches="tight")
+        plt.close(fig)
+
+        # plot relative rotation error (in degrees)
+        fig = plt.figure(figsize=(6, 2.5))
+        ax = fig.add_subplot(111, xlabel='Distance traveled [m]', ylabel='Yaw error [deg]')
+        pu.boxplot_compare(ax, distances, rel_yaw_err, labels, colors)
+        fig.tight_layout()
+        fig.savefig(self.plots_dir + '/rel_yaw_error' + FORMAT, bbox_inches="tight")
+        plt.close(fig)
+
+    @staticmethod
+    def remove_cached_error(data_dir):
+        print("Remove cached error in {0}".format(data_dir))
+        rel_error_fn = os.path.join(data_dir, Trajectory.saved_res_dir_nm,
+                                    Trajectory.rel_error_cached_nm)
+        if os.path.exists(rel_error_fn):
+            os.remove(rel_error_fn)
+
+    @staticmethod
+    def load_stamped_dataset(results_dir, fn_es=None, fn_gt=None, max_diff=0.02):
+        '''
+        read synchronized estimation and groundtruth and associate the timestamps
+        '''
+        print('loading dataset in ' + results_dir)
+
+        if fn_es == None:
+            fn_es = os.path.join(results_dir, 'stamped_traj_estimate.txt')
+        if fn_gt == None:
+            fn_gt = os.path.join(results_dir, 'stamped_groundtruth.txt')
+        fn_matches = os.path.join(results_dir, 'stamped_est_gt_matches.txt')
+
+        matches = associ.read_files_and_associate(fn_es, fn_gt, 0.0, max_diff)
+        dict_matches = dict(matches)
+        np.savetxt(fn_matches, np.array(matches, dtype=int), fmt='%d')
+
+        data_es = np.loadtxt(fn_es)
+        data_gt = np.loadtxt(fn_gt)
+
+        p_es = []
+        p_gt = []
+        q_es = []
+        q_gt = []
+        t_gt = []
+        for es_id, es in enumerate(data_es):
+            if es_id in dict_matches:
+                gt = data_gt[dict_matches[es_id]]
+                p_es.append(es[1:4])
+                p_gt.append(gt[1:4])
+                q_es.append(es[4:8])
+                q_gt.append(gt[4:8])
+                t_gt.append(gt[0])
+        p_es = np.array(p_es)
+        p_gt = np.array(p_gt)
+        q_es = np.array(q_es)
+        q_gt = np.array(q_gt)
+        t_gt = np.array(t_gt)
+
+        return t_gt, p_es, q_es, t_gt, p_gt, q_gt
+
+if __name__ == '__main__':
+    # example usage
+
+    # define parameters
+    results_dir = 'eval/2011_09_30_drive_0034/random_perturbations/run_0'
+    run_name = 'run_0'
+    gt_traj_file = 'KITTI_parameters/2011_09_30_drive_0034/stamped_groundtruth.txt'
+    estimated_traj_file = results_dir + '/stamped_traj_estimate.txt'
+    align_type = 'sim3'
+    output_dir = os.path.join(results_dir, 'evaluation')
+
+    # create folder if it does not exist
+    os.makedirs(output_dir, exist_ok=True)
+
+    # create instance of the class
+    traj = Trajectory(output_dir, run_name, gt_traj_file, estimated_traj_file, align_type=align_type)
+
+    # compute errors
+    traj.compute_absolute_error()
+    traj.compute_relative_errors()
+    traj.cache_current_error()
+
+    # write/plot the computed results
+    traj.write_errors_to_yaml()
+    traj.plot_trajectory('.png')
+
+# EOF
diff --git a/utils/orbslam_evaluation/run_orbslam.py b/utils/orbslam_evaluation/run_orbslam.py
new file mode 100755
index 0000000..ae3d540
--- /dev/null
+++ b/utils/orbslam_evaluation/run_orbslam.py
@@ -0,0 +1,350 @@
+#!/usr/bin/env python
+
+import os
+import ruamel
+from ruamel import yaml
+import subprocess
+import numpy as np
+import cv2
+import argparse
+
+# Evo's Trajectory Toolbox
+from evo.tools import file_interface
+
+# Aleks's image rectification toolbox
+import carnivalmirror as cm
+
+# customized library toolboxes
+from library.trajectory import Trajectory
+from library.rangesampler import RangeSampler
+from library.helper import str2bool, create_summary_dataframe, update_dataframe_with_rectification_stats, \
+    kitti_poses_and_timestamps_to_trajectory, get_range_perturbed_camera_parameters, modify_orbslam_settings
+
+
+def run_orbslam(orbslam_applet, vocab_path, orbslam_settings_path, dataset_folder, output_file):
+    # define the command to run
+    command = [orbslam_applet,
+               '--sequenceType', 'kitti',
+               '--vocabularyPath', vocab_path,
+               '--settingsPath', orbslam_settings_path,
+               '--sequencePath', dataset_folder,
+               '--outputFilePath', output_file]
+    # execute the command
+    process = subprocess.Popen(command, stdout=subprocess.PIPE, shell=False)
+    if process.wait():
+        print("Releasing command!")
+
+
+def perform_traj_evaluation(results_dir, run_name, gt_traj_file, estimated_traj_file, align_type='sim3',
+                            align_num_frames=-1, plot_results=True, boxplot_distances=[]):
+    """
+    Uses UZH Trajectory Evaluation toolbox to evaluate accuracy of the algorithm
+    :param results_dir: directory to save the results in
+    :param run_name: name of the experiment run
+    :param gt_traj_file: Stamped groundtruth poses in TUM format
+    :param estimated_traj_file: Stamped estimated poses in TUM format
+    :param align_type: alignment type to perform
+    :param align_num_frames: number of frames to align
+    :return: dictionary with the evaluated statistics
+    """
+    # create directory to save evaluation results
+    os.makedirs(results_dir, exist_ok=True)
+    # compute trajectory errors
+    traj = Trajectory(results_dir, run_name, gt_traj_file, estimated_traj_file, align_type=align_type,
+                      align_num_frames=align_num_frames, preset_boxplot_distances=boxplot_distances)
+    traj.compute_absolute_error()
+    traj.compute_relative_errors()
+    traj.cache_current_error()
+    traj.write_errors_to_yaml()
+    if plot_results:
+        traj.plot_trajectory('.png')
+        traj.plot_rel_traj_error('.png')
+
+    error_dict = traj.get_abs_errors_stats()
+    error_dict.update(traj.get_rel_errors_stats())
+
+    return error_dict
+
+
+def parse_args():
+    """
+    Parses CLI arguments applicable for this helper script
+    """
+    # Create parser instance
+    parser = argparse.ArgumentParser(description='Evaluate ORBSLAM with different camera parameters')
+    # Define arguments
+    parser.add_argument('--orbslam_root', '-b', metavar='', dest='orbslam_root', help="path to ORBSLAM2 directory")
+    parser.add_argument('--dataset_folder', '-k', metavar='', dest='dataset_folder',
+                        help='path to a directory containing KITTI sequence dataset')
+    parser.add_argument('--sequence_name', '-e', metavar='', dest='sequence_name', help='name of the KITTI sequence')
+    parser.add_argument('--sampling_settings', '-s', metavar='', dest='sampling_settings',
+                        help='path to a file with the sampling settings',
+                        default="config/sampling_settings.yaml")
+    parser.add_argument('--num_of_runs', '-n', metavar='', type=int, dest='num_of_runs',
+                        help='number of runs to perform', default=6)
+    parser.add_argument('--output_dir', '-o', metavar='', dest='output_dir',
+                        help='path of the directory to save experiment resutls in',
+                        default='eval')
+    parser.add_argument('--experiment_type', '-t', metavar='', dest='experiment_type',
+                        help='type of experiment to perform: random/range', default='range')
+    parser.add_argument('--perturb_focal', '-f', metavar='', type=str2bool,
+                        help='set to true to perturb focal length (y/n)',
+                        default='y')
+    parser.add_argument('--perturb_principle_point', '-c', metavar='', type=str2bool,
+                        help='set to true to perturb principle point (y/n)',
+                        default='y')
+    parser.add_argument('--perturb_distortion', '-d', metavar='', type=str2bool,
+                        help='set to true to perturb distortion parameters (y/n)',
+                        default='n')
+
+    # Retrieve arguments
+    args = parser.parse_args()
+    return args
+
+
+def main(args):
+    """
+    Script to evaluate performance of visual odometry algorithms against miscalibrations.
+    The script does the following:
+    1. Perturb the known camera parameters
+    2. Generate the configuration file which is used to run OrbSLAM
+    3. Perform VO-evaluation and plots results
+
+    All outputs are stored in the "eval" directory.
+    """
+
+    ################################################
+    ################## PARAMETERS ##################
+    ################################################
+
+    """
+    Define parameters to run the OrbSLAM executable.
+    1. Clone the repository: https://github.com/Mayankm96/ORB_SLAM2/tree/feature/projects 
+    2. Build it by following the instructions present in the README 
+    """
+    # specify the root path to the OrbSLAM directory
+    ORBSLAM_ROOT = args.orbslam_root
+    # path to the vocabulary used in OrbSLAM
+    vocab_path = os.path.join(ORBSLAM_ROOT, 'Vocabulary/ORBvoc.txt')
+    # path to the executable file which is used to run the experiments
+    orbslam_applet = os.path.join(ORBSLAM_ROOT, 'Projects/PLR_Evaluation/mono_plr')
+    # sample ORBSLAM settings file which is used as a template to auto-generate settings for the expriments
+    orbslam_config_file = 'config/ORBSLAM_TUM1.yaml'
+
+    """
+    We use KITTI dataset in the experiment.
+    1. Download the sequence: http://www.cvlibs.net/datasets/kitti/eval_odometry.php
+    2. Paste the following files into "KITTI_parameters/<seq-name>" directory 
+        a. Paste the calibration file in  as 'camera.yaml'
+        b. 'gt_kitti.txt' and 'times.txt' files corresponding to the sequence
+    """
+    # specify the name of the KITTI sequence
+    KITTI_sequence = args.sequence_name
+    # specify the directory containing the raw data
+    dataset_folder = args.dataset_folder
+    # path to the directory with the images
+    image_dir = os.path.join(dataset_folder, 'image_00', 'data')
+    # path to camera calibration file
+    camera_config_file = 'KITTI_parameters/' + KITTI_sequence + '/camera.yaml'
+    # path to times.txt file
+    timestamps_file = 'KITTI_parameters/' + KITTI_sequence + '/times.txt'
+    # path to groundtruth file
+    traj_gt_file = 'KITTI_parameters/' + KITTI_sequence + '/gt_kitti.txt'
+
+    """
+    The perturbation settings for camera calibration
+    """
+    # specify path to dataset generation configuration
+    sampling_config_file = args.sampling_settings
+    # specify the number of runs to perform
+    num_of_runs = args.num_of_runs
+    # specify the experiment type: 'random_perturbations' or 'range_perturbations'
+    assert args.experiment_type in ['random', 'range']
+    experiment_type = args.experiment_type + '_perturbations'
+    # sepcify which parameters to perturb: 'f' for focal length, 'c' for principle point, 'd' for distortion parameters
+    perturb_parameters = []
+    if args.perturb_focal:
+        perturb_parameters = perturb_parameters + ['f']
+    if args.perturb_principle_point:
+        perturb_parameters = perturb_parameters + ['c']
+    if args.perturb_distortion:
+        perturb_parameters = perturb_parameters + ['d']
+
+    """
+    Output directory to save all data related to the experiment
+    """
+    # path to the output directory
+    output_folder = args.output_dir
+
+    # trajectory evaluation
+    boxplot_distances = [100, 200, 300, 400, 500]  # preset relative error trajectory distances in meters
+    align_type = 'sim3'  # choose from: ['posyaw', 'sim3', 'se3', 'none']
+    align_num_frames = -1  # all (note: this is only used for calculating absolute trajectory errors)
+
+    ################################################
+    ##################### DISP #####################
+    ################################################
+
+    print('-- Dataset Folder: ', dataset_folder)
+    print('-- Output Folder: ', output_folder)
+    print('-- Number of Runs: ', num_of_runs)
+    print('-- Experiment | Type: ', experiment_type)
+    print('-- Experiment | Perturbing Parameters: ', perturb_parameters)
+    print('-- Trajectory Evaluation | Align Type: ', align_type)
+    print('-- Trajectory Evaluation | Align Number of Frames: ', align_num_frames)
+    print('----------------------------------------')
+
+    ##########################################################
+    ##################### INITIALIZATION #####################
+    ##########################################################
+
+    # convert boxplot distances list to float
+    boxplot_distances = [float(i) for i in boxplot_distances]
+    ## construct output directory name
+    experiment_name = experiment_type + '_'
+    for param in perturb_parameters:
+        experiment_name = experiment_name + param
+    output_folder = os.path.join(output_folder, KITTI_sequence, experiment_name)
+
+    ## combine KITTI poses and timestamps files to a TUM trajectory file
+    # path to stamped trajectory groundtruth file
+    stamped_traj_gt_file = 'KITTI_parameters/' + KITTI_sequence + '/stamped_groundtruth.txt'
+    # create stamped trajectory groundtruth file if it does not exist
+    if not os.path.exists(stamped_traj_gt_file):
+        # create concatenated matrix with timestamps and pose details
+        trajectory = kitti_poses_and_timestamps_to_trajectory(traj_gt_file, timestamps_file)
+        # write the trajectory into output file
+        file_interface.write_tum_trajectory_file(stamped_traj_gt_file, trajectory)
+
+    ## read data from the configuration files
+    with open(camera_config_file, 'r') as stream:
+        camera = yaml.safe_load(stream)
+
+    with open(orbslam_config_file, 'r') as stream:
+        orbslam_settings = yaml.safe_load(stream)
+
+    with open(sampling_config_file, 'r') as stream:
+        sampling_settings = yaml.safe_load(stream)
+
+    ## create pandas dataframe to save all the results
+    dataframe = create_summary_dataframe(num_of_runs, boxplot_distances)
+
+    # retrieve the correct camera calibration parameters
+    K = np.array(camera["K"]).reshape((3, 3))
+    D = np.array(camera["D"])
+
+    # define the sampling ranges
+    sampling_ranges = get_range_perturbed_camera_parameters(K, D, sampling_settings, perturb_parameters)
+
+    # read the first raw image in the sequence (to get the shape)
+    seq_first_im_file = sorted(os.listdir(image_dir))[0]
+    seq_first_image = cv2.imread(os.path.join(image_dir, seq_first_im_file))
+    # extract the size of the image
+    height, width = seq_first_image.shape[:2]
+
+    # Create a Calibration object for the correct calibration. That will be used
+    # as a reference calibration when calculating APPD values
+    cam_reference = cm.Calibration(K=K, D=D, width=width, height=height)
+
+    # Create a Sampler to sample parameters from it
+    if experiment_type == 'random_perturbations':
+        sampler = cm.UniformAPPDSampler(ranges=sampling_ranges, cal_height=height, cal_width=width, reference=cam_reference, width=width, height=height)
+    else:
+        sampler = RangeSampler(ranges=sampling_ranges, cal_height=height, cal_width=width, num_of_runs=num_of_runs)
+
+    ################################################
+    ##################### MAIN #####################
+    ################################################
+
+    ## perform runs for the given experiment type
+    for i in range(num_of_runs):
+
+        # create directory to save data from the run
+        run_name = 'run_%d' % i
+        run_output_folder = os.path.join(output_folder, run_name)
+        os.makedirs(run_output_folder, exist_ok=True)
+
+        """
+        The following information is stored from each run:
+        1. an example raw image from the dataset
+        2. rectified image from the perturbed calibration parameters
+        3. diagnostoc image with useful information about the perturbation
+        4. configuration file for orbslam with the perturbed calibration settings
+        5. keyframe trajectory outputted from running OrbSLAM on that sequence
+        6. evaluation statistics on the estimated trajectory
+        """
+        raw_im_file = os.path.join(run_output_folder, 'raw_' + seq_first_im_file)
+        perturbed_im_file = os.path.join(run_output_folder, 'perturbed_' + seq_first_im_file)
+        diagostic_im_file = os.path.join(run_output_folder, 'diagnostic_' + seq_first_im_file)
+        output_config_file = os.path.join(run_output_folder, 'config.yaml')
+        traj_output_file = os.path.join(run_output_folder, 'stamped_traj_estimate.txt')
+
+        # first run is done with correct calibration parameters for
+        if i == 0:
+            cam_run = cam_reference
+        else:
+            # get parameters from the sampler
+            cam_run = sampler.next()
+
+        print("Running number: ", i + 1)
+        print("Intrinsic Matrix: ", cam_run.get_K(height))
+        print("Distortion Coefficients: ", cam_run.get_D())
+
+        K = cam_run.get_K(height)
+        D = cam_run.get_D()
+        # Calculate the APPD value for these parameters
+        while True:
+            try:
+                appd, diff_map = cam_run.appd(reference=cam_reference, width=width, height=height,
+                                              return_diff_map=True, normalized=True)
+                break
+            except RuntimeError as e:
+                continue
+
+        # (Mis)rectify the image
+        misrect_image = cam_run.rectify(seq_first_image, result_width=width, result_height=height, mode='preserving')
+
+        # Save reference image ang with the rectified image
+        cv2.imwrite(raw_im_file, seq_first_image)
+        cv2.imwrite(perturbed_im_file, misrect_image)
+        cv2.imwrite(diagostic_im_file, diff_map)
+
+        # update dataframe with generated statistics on rectification
+        dataframe = update_dataframe_with_rectification_stats(dataframe, run_name, K, D, appd)
+
+        # generate configuration file for the perturbed camera settings
+        orbslam_settings = modify_orbslam_settings(orbslam_settings, K, D)
+        with open(output_config_file, 'w') as outfile:
+            outfile.write('%YAML:1.0\n')
+            yaml.dump(orbslam_settings, outfile, Dumper=ruamel.yaml.RoundTripDumper)
+
+        # run OrbSLAM
+        if not os.path.exists(traj_output_file):
+            run_orbslam(orbslam_applet=orbslam_applet, vocab_path=vocab_path,
+                        orbslam_settings_path=output_config_file,
+                        output_file=traj_output_file,
+                        dataset_folder=dataset_folder)
+
+        # evaluate run
+        error_stats_dict = perform_traj_evaluation(os.path.join(run_output_folder, 'evaluation'), run_name,
+                                                   stamped_traj_gt_file, traj_output_file, align_type=align_type,
+                                                   align_num_frames=align_num_frames,
+                                                   boxplot_distances=boxplot_distances)
+        for key, value in error_stats_dict.items():
+            dataframe.at[run_name, key] = value
+
+        print('-----------------------------------')
+
+        # save the created data frame after every finite number of runs (checkpoints)
+        if i % 5 == 0:
+            dataframe.to_csv(os.path.join(output_folder, 'summary.csv'))
+
+    # save the created dataframe
+    dataframe.to_csv(os.path.join(output_folder, 'summary.csv'))
+
+
+if __name__ == '__main__':
+    # Read CLI Arguments
+    ARGS = parse_args()
+    # Run main()
+    main(ARGS)