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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Orientation\n",
+    "\n",
+    "## Rotation\n",
+    "\n",
+    "The world is full of animated agents, e.g. animals, aircraft, and robots. They move in the world avoiding predators, foraring and transporting back suplies, or looking for the perfect (or a suitable) mate. As observer of curiosities and strangeness of the world, we wonder where such agents look at, we wonder of their orientation. In geometry the orientation ( also: angular position, or attitude ) of an object is part of the description of how it is placed in the space it is in. Namely, it is the imaginary rotation that is needed to move the object from a reference placement to its current placement. \n",
+    "\n",
+    "To introduce the different concept related to the orientation of an agent, we will first work in a imaginary world composed of only two dimensions. The position of an agent in such world can be express as a function of two variables. Your screen is indeed a two dimentional space. The position of the mouse is express in term of pixel along the height and the length of your monitor. We could for example place the mouse at the position $(100,200)$. \n",
+    "Wait, where should I start counting? Where is the position $(0,0)$? We need to define an origin, and the direction in which we count, e.g. from left to right and from bottom to top for the first and second variable respectivly. Without knowing we are defining a reference frame. A reference frame is composed of unit vectors, i.e. the direction in which we have to count and the unit used (here the unit is the pixel), and an origin (here the bottom left corner of the monitor).\n",
+    "\n",
+    "The reference frame allows to position an agent in the world, but what about its orientation. The orientation of an agent requires another frame, one link to the agent itself. We need an origin, e.g. the center of mass, and two unit vectors (because we are in a 2D space, remember). One unit vector can be chosen along the long axis of the body, and the second one orthogonal to first one. The orthogonality will ease later the formalism. We can then place the agent in its resting position, i.e. at null orientation. When the agent will move its orientation will change, i.e. the imaginary rotation that is needed to move the reference frame at the resting position to the reference attached to the agent.\n",
+    "\n",
+    "A rotation is a circular movement of an object around a center (or point) of rotation. In linear algebra the rotation of an angle $\\alpha$ is defined by the matrix:\n",
+    "\n",
+    "$$\n",
+    "   R =\n",
+    "   \\begin{bmatrix}\n",
+    "   \\cos \\alpha & -\\sin \\alpha \\\\\n",
+    "   \\sin \\alpha & \\cos \\alpha \\\\\n",
+    "   \\end{bmatrix}\n",
+    "$$\n",
+    "\n",
+    "A vector $v_0$ can be rotated by an angle $\\alpha$ by aplying the matrix $R$ to $v_0$, i.e. $v=Rv_0$\n",
+    "\n",
+    "We will say that the agent is looking in the direction $\\alpha$ when the frame link to the agent is the rotation by the angle $\\alpha$ of the frame at the resting position, i.e. when\n",
+    "\n",
+    "$$F_a=RF_0$$\n",
+    "\n",
+    "here $F_a$ is the actual frame of the agent, $F_0$ is the frame at the resting position, and $R$ the imaginary rotation.\n",
+    "\n",
+    "\n",
+    "## Finding the orientation of an agent (2D)\n",
+    "\n",
+    "Usualy we do not know the imaginary rotation made by the agent. To find it we need to invert the linar system introduced above. \n",
+    "\n",
+    "$$R=F_a(F_0)^{-1}$$\n",
+    "\n",
+    "Once we have the orientation matrix we can find the angle $\\alpha$ by using combination of elements of the matrix. For the present case we can get $\\alpha$ by using the first column of the matrix.\n",
+    "\n",
+    "## Reference frame in the real world (3D)\n",
+    "\n",
+    "The real world do not have only two dimensions but three. The reference frames will have then three unit vectors. The first unit vector can still be choosen along the longitudinal axis of the agent. But how do we define the two other one. We can no longer determined the 2nd vector by using the 1st vector and orthogonality, because the 1st vector has an infinite amount of unit vectors. We need to introduce a convention. Scientists and ingenieurs have converged to a convention for aircraft. The first vector is along the longitidunal axis, the 2nd from left to right when seated in the aircraft, and the 3rd and last one pointing downward. The last vector is used to measure height, and it makes sens for an aircraft to measure height positivly downward. \n",
+    "\n",
+    "Once the reference frame has been introduce we need to have a look at the orientation matrix. This time it will be a 3x3 matrix, i.e. composed of 9 elements. \n",
+    "\n",
+    "A rotation in a three dimentional is made around a line, i.e. an axis or a vector. We have already defined three vectors, and you know what, the orientation can be defined by three rotations. \n",
+    "\n",
+    "**Note** The frame of the agent can be computed from three none colinear points. One will be the origin, the 1st axis can go from the origin an between the two other points. The second axis is orthogonal to the plane formed by the three points. The last vector is the cross product of the two other ones. This process assumes that the distance between any two given points of a rigid body remains constant in time regardless of external forces exerted on it, i.e. the points are placed on a rigid body\n",
+    "\n",
+    "**Note** The set of vectors is call in linear algebra, a basis, if the vectors are linearly independent and every vector in the vector space is a linear combination of this set. In physics, it is called a frame of reference, i.e. it consits of an abstract coordinate system and the set of physical reference points that uniquely fix (locate and orient) the coordinate system and standardize measurements.\n",
+    "\n",
+    "\n",
+    "## From reference points to an orientation matrix\n",
+    "\n",
+    "In geometry the orientation ( also: angular position, or attitude ) of an object is part of the description of how it is placed in the space it is in. Namely, it is the imaginary rotation that is needed to move the object from a reference placement to its current placement. For example for the head of an animal it is the description in which direction the animal is looking (foward), the direction from left to right (sideward), and the direction from top to bottom (downward).\n",
+    "Each direction is mathematically represented by a unit vector. Furthormore those vector are orthogonal to each other, and formed a directly oriented base.\n",
+    "To get an intuitive idea, use your right hand, thumbs toward a wall, the index parallel to the floor, and the major pointing downward. Your right hand form a directly orientaed orthogonal base :) .\n",
+    "    \n",
+    "The orientation of an agent is described by three vectors. Those vector can be obtained, for example, from the apexes of a triangle. Let's take an equilateral triangle for simplicity, but any triangle could work. All vectors will originate from the same points, so let choose one apex to be the origin, and mark it with a black point. The forward vector points toward the edge facing the origin, and is along the mediatrix, i.e. it crosses the the middle of the facing edge. The sideward is parrallel to the facing edge. The last vector is simply the cross product of the two other vectors. \n",
+    "\n",
+    "This example illustrated how one can define the orientation of an agent from a triangle. A triangle being described by only three points in space. To know the orinentation of the head of an animal, one only need three points on its head, neat :). \n",
+    "\n",
+    "For an aircraft, the forward, sideward, and downward vectors are nammed roll axis, pitch axis, and yaw axis, respectively. We will from now on stick to this convention. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from matplotlib import pyplot as plt\n",
+    "from navipy.trajectories.transformations import triangle2homogeous_transform\n",
+    "from navipy.trajectories.triangle import Triangle\n",
+    "from navipy.tools.plots import draw_frame\n",
+    "%matplotlib inline\n",
+    "\n",
+    "# Create some Apex \n",
+    "# The apexes comes from the 3 markers on the insect\n",
+    "apex0 = pd.Series(data=[0.,  0.25,  0.],\n",
+    "                  index=['x', 'y', 'z'])\n",
+    "apex1 = pd.Series(data=[0.5, -0.5,  0.],\n",
+    "                  index=['x', 'y', 'z'])\n",
+    "apex2 = pd.Series(data=[0.5,  0.5,  0.],\n",
+    "                  index=['x', 'y', 'z'])\n",
+    "\n",
+    "# Create a triangle with the three markers\n",
+    "mytriangle = Triangle(apex0, apex1, apex2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Isocel triangle\n",
+    "\n",
+    "Let's assume that we have one marker (mark0) centered on the center of mass \\\n",
+    "of the agent. The two others (mark1 and mark2) are placed such that\n",
+    "\n",
+    "* the markers form an isosceles triangle, here the two equal sides are \\\n",
+    "mark0-mark1 and mark0-mark2,\n",
+    "* the median of the triangle, i.e. the vector going from mark0 to the \\\n",
+    "middle of the segment between mark1 and mark2, is along the the roll axis\n",
+    "\n",
+    "The yaw,pitch,roll axis are then calculated as:\n",
+    "\n",
+    "* The roll_axis is along the median between the 2nd and 3rd apexes \\\n",
+    "  ( mark1 and mark2)\n",
+    "* The yaw_axis is the cross-product between the vector 1st appex to 2nd and \\\n",
+    "  1st appex to 3rd apexe.\n",
+    "* The pitch_axis is the cross-product between the roll_axis and the opposite \\\n",
+    "of the yaw_axis\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# calculate the frame \n",
+    "# with a mode for isocel triangle\n",
+    "isocel_frame = triangle2homogeous_transform(\n",
+    "    mytriangle, triangle_mode='x-axis=median-from-0')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Pitch align triangle\n",
+    "\n",
+    "However the markers may not be always correctly placed, therefore the \\\n",
+    "local reference frames may differ a bit from the axis convention for aircraft. To compensate this error, one may calculate the rotation between the correct local reference frame and the estimated local reference frame by identifying other reference points, when the agent is at the null orientation. This procedure is sadly rarely plausible with insects. It is rather difficult to define unambigeously the local reference frame at null orientation. The experimentalist may trust more one of the axis of the triangle. For example less error can be done while placing markers aligned with the pitch axis on the head, because the pitch axis is aligned with axis connecting the two eyes. \n",
+    "\n",
+    "When the pitch axis can be trusted, the yaw,pitch,roll axis are calculated as:\n",
+    "\n",
+    "* The pitch_axis is the vector between the 2nd and the 3rd apexe\n",
+    "* The yaw_axis is the cross-product between the vector 1st appex to 2nd and 1st appex to 3rd apexe.\n",
+    "* The roll_axis is the cross-product between the pitch_axis and yaw_axis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# calculate the frame \n",
+    "# with a mode for pitch triangle\n",
+    "pitch_frame = triangle2homogeous_transform(\n",
+    "    mytriangle, triangle_mode='y-axis=1-2')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Ploting the frames"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/bolirev/.virtualenvs/toolbox-navigation/lib/python3.6/site-packages/matplotlib-2.2.2-py3.6-linux-x86_64.egg/mpl_toolkits/mplot3d/axes3d.py:744: UserWarning: Attempting to set identical bottom==top results\n",
+      "in singular transformations; automatically expanding.\n",
+      "bottom=0.0, top=0.0\n",
+      "  'bottom=%s, top=%s') % (bottom, top))\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5,0.92,'Both frames')"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
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\n",
+      "text/plain": [
+       "<Figure size 1080x360 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# To show the results of the Triangle\n",
+    "fig = plt.figure(figsize=(15, 5))\n",
+    "ax0 = fig.add_subplot(131, projection='3d')\n",
+    "ax1 = fig.add_subplot(132, projection='3d')\n",
+    "ax2 = fig.add_subplot(133, projection='3d')\n",
+    "for ax in [ax0,ax1,ax2]:\n",
+    "    mytriangle.plot(ax=ax, apex_marker='wo')\n",
+    "    ax.set_xlim([-1, 1])\n",
+    "    ax.set_ylim([-1, 1])\n",
+    "    ax.set_zlim([-1, 1])\n",
+    "\n",
+    "draw_frame(ax=ax0, frame=isocel_frame)\n",
+    "ax0.set_title('Isocel frame')\n",
+    "draw_frame(ax=ax1, frame=pitch_frame)\n",
+    "ax1.set_title('Pitch frame')\n",
+    "draw_frame(ax=ax2, frame=isocel_frame)\n",
+    "draw_frame(ax=ax2, frame=pitch_frame)\n",
+    "ax2.set_title('Both frames')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Yaw, pitch, and roll rotations\n",
+    "A rotation is a circular movement of an object around a center (or point) of rotation . A three-dimensional object always rotates around an imaginary line called a rotation axis. In three dimensions, the orientation of an object is given by three rotations, i.e. by three rotation axis. Several conventions exist for defining the rotations. Here we will talk only about the ZYX convention, also known as yaw, pitch, and roll rotations.\n",
+    "\n",
+    "\n",
+    "(from: http://planning.cs.uiuc.edu/node102.html)\n",
+    "\n",
+    "A 3D body can be rotated about three orthogonal axes, as shown in Figure 3.8. Borrowing aviation terminology, these rotations will be referred to as yaw, pitch, and roll:\n",
+    "\n",
+    "\n",
+    "A yaw is a counterclockwise rotation of $\\alpha$ about the $z$-axis. The rotation matrix is given by\n",
+    "\n",
+    "$$ R_z(\\alpha) = \\begin{pmatrix}\\cos\\alpha & -\\sin\\alpha & 0 \\\\ \\sin\\alpha & \\cos\\alpha & 0 \\\\ 0 & 0 & 1  \\end{pmatrix}$$\n",
+    "\n",
+    "Note that the upper left entries of $R_z(\\alpha)$ form a 2D rotation applied to the $x$ and $y$ coordinates, whereas the $z$ coordinate remains constant.\n",
+    "\n",
+    "A pitch is a counterclockwise rotation of $\\beta$ about the $y$-axis. The rotation matrix is given by\n",
+    "\n",
+    "$$R_y(\\beta) = \\begin{pmatrix}\\cos\\beta & 0 & \\sin\\beta \\\\ 0 & 1 & 0 \\\\ -\\sin\\beta & 0 & \\cos\\beta  \\end{pmatrix}$$\n",
+    "\n",
+    "\n",
+    "A roll is a counterclockwise rotation of $\\gamma$ about the $x$-axis. The rotation matrix is given by\n",
+    "\n",
+    "$$R_x(\\gamma) = \\begin{pmatrix}1 & 0 & 0 \\\\ 0 & \\cos\\gamma & -\\sin\\gamma \\\\ 0 & \\sin\\gamma & \\cos\\gamma  \\end{pmatrix}$$\n",
+    "\n",
+    "Each rotation matrix is a simple extension of the 2D rotation matrix, (3.31). For example, the yaw matrix,  $R_z(\\alpha)$, essentially performs a 2D rotation with respect to the $x$ and $y$ coordinates while leaving the $z$ coordinate unchanged. Thus, the third row and third column of  $R_z(\\alpha)$ look like part of the identity matrix, while the upper right portion of  $R_z(\\alpha)$ looks like the 2D rotation matrix.\n",
+    "The yaw, pitch, and roll rotations can be used to place a 3D body in any orientation. A single rotation matrix can be formed by multiplying the yaw, pitch, and roll rotation matrices to obtain\n",
+    "\n",
+    "$$\n",
+    "   \\begin{split}\n",
+    "   R(\\alpha,& \\beta,\\gamma) = R_z(\\alpha) \\, R_y(\\beta) \\, R_x(\\gamma) = \\\\\n",
+    "   & \\begin{pmatrix}\n",
+    "   \\cos\\alpha \\cos\\beta & \n",
+    "   \\cos\\alpha \\sin\\beta \\sin\\gamma - \\sin\\alpha \\cos\\gamma &\n",
+    "   \\cos\\alpha \\sin\\beta \\cos\\gamma + \\sin\\alpha \\sin\\gamma \\\\\n",
+    "   \\sin\\alpha \\cos\\beta &\n",
+    "   \\sin\\alpha \\sin\\beta \\sin\\gamma + \\cos\\alpha \\cos\\gamma &\n",
+    "   \\sin\\alpha \\sin\\beta \\cos\\gamma - \\cos\\alpha \\sin\\gamma \\\\\n",
+    "   -\\sin\\beta & \\cos\\beta \\sin\\gamma & \\cos\\beta \\cos\\gamma \\\\\n",
+    "   \\end{pmatrix}\n",
+    "   \\end{split}\n",
+    "$$\n",
+    "\n",
+    "It is important to note that  $R(\\alpha,\\beta,\\gamma)$ performs the roll first, then the pitch, and finally the yaw. If the order of these operations is changed, a different rotation matrix would result. Be careful when interpreting the rotations. Consider the final rotation, a yaw by $\\alpha$. Imagine sitting inside of a robot ${\\cal A}$ that looks like an aircraft. If  $\\beta = \\gamma = 0$, then the yaw turns the plane in a way that feels like turning a car to the left. However, for arbitrary values of $\\beta$ and $\\gamma$, the final rotation axis will not be vertically aligned with the aircraft because the aircraft is left in an unusual orientation before $\\alpha$ is applied. The yaw rotation occurs about the $z$-axis of the world frame, not the body frame of ${\\cal A}$. Each time a new rotation matrix is introduced from the left, it has no concern for original body frame of ${\\cal A}$. It simply rotates every point in  ${\\mathbb{R}}^3$ in terms of the world frame. Note that 3D rotations depend on three parameters, $\\alpha$, $\\beta$, and $\\gamma$, whereas 2D rotations depend only on a single parameter, $\\theta $. The primitives of the model can be transformed using  $R(\\alpha,\\beta,\\gamma)$, resulting in  ${\\cal A}(\\alpha,\\beta,\\gamma)$.\n",
+    "\n",
+    "## Orientation matrix\n",
+    "\n",
+    "When introducing the body frame, we talked about an imaginary rotation of the reference frame to the animal's head frame. This is equivalent to finding the matrix $R$ so that:\n",
+    "\n",
+    "$$\n",
+    "   R.F^\\text{ref} = F^\\text{bee} \\\\\n",
+    "   F^\\text{ref} = \\begin{pmatrix}\n",
+    "   roll^\\text{ref}_x & pitch^\\text{ref}_x & yaw^\\text{ref}_z \\\\\n",
+    "   roll^\\text{ref}_y & pitch^\\text{ref}_y & yaw^\\text{ref}_y \\\\\n",
+    "   roll^\\text{ref}_z & pitch^\\text{ref}_z & yaw^\\text{ref}_z \\\\\n",
+    "   \\end{pmatrix}\n",
+    "$$\n",
+    "\n",
+    "The linear algebra tells us that if the inverse of $F^\\text{ref}$ exists; the rotation matrix $R$is equal to $F^\\text{bee}.\\left(F^\\text{ref}\\right)^{-1}$\n",
+    "\n",
+    "The rotation matrix $R$ has nine values, but we know that only three angles are necessary to know the orientation of the rigid body. So how can we have the yaw, pitch, and roll angles from the rotation matrix $R$?\n",
+    "\n",
+    "## Determining yaw, pitch, and roll from a rotation matrix\n",
+    "(adapted from http://planning.cs.uiuc.edu/node103.html)\n",
+    "\n",
+    "It is often convenient to determine the $\\alpha$, $\\beta$, and $\\gamma$ parameters directly from a given rotation matrix. Suppose an arbitrary rotation matrix\n",
+    "\n",
+    "$$\\begin{pmatrix}r_{11} & r_{12} & r_{13}\\\\ r_{21} & r_{22} & r_{23}\\\\  r_{31} & r_{32} & r_{33}  \\end{pmatrix}$$\n",
+    "\n",
+    "is given. By setting each entry equal to its corresponding entry in (3.42), equations are obtained that must be solved for $\\alpha$, $\\beta$, and $\\gamma$. Note that  $r_{21}/r_{11} = \\tan\\alpha$ and  $r_{32}/r_{33} = \\tan \\gamma$. Also,  $r_{31} = - \\sin\\beta$ and  $\\pm\\sqrt{r^2_{32}+r^2_{33}} = \\cos\\beta$. Solving for each angle yields\n",
+    "\n",
+    "$$\\displaystyle \\alpha = \\tan^{-1} (\\pm r_{21}/\\pm r_{11})$$\n",
+    "\n",
+    "$$\\beta = \\tan^{-1} \\Big(-r_{31} \\big/ \\pm\\sqrt{r^2_{32}+r^2_{33}}\\Big)$$\n",
+    "\n",
+    "$$\\gamma = \\tan^{-1} (\\pm r_{32}/\\pm r_{33})$$\n",
+    " \n",
+    "Note that the ambiguity on $\\pm$come from the sign of $\\cos\\beta$, which is a priori unknown. \n",
+    "\n",
+    "There is a choice of four quadrants for the inverse tangent functions. How can the correct quadrant be determined? Each quadrant should be chosen by using the signs of the numerator and denominator of the argument. The numerator sign selects whether the direction will be above or below the $x$ -axis, and the denominator selects whether the direction will be to the left or right of the $y$ -axis. This is the same as the $\\arctan_2$ function in the C programming language, which nicely expands the range of the arctangent to $[0,2\n",
+    "\\pi)$. This can be applied to express (3.44), (3.45), and (3.46) as\n",
+    "\n",
+    "$$\\alpha = \\arctan_2(\\pm r_{21},\\pm r_{11})$$\n",
+    "\n",
+    "$$\\beta = \\arctan_2\\Big(-r_{31},\\pm\\sqrt{r^2_{32}+r^2_{33}}\\Big)$$\n",
+    "\n",
+    "$$\\gamma = \\arctan_2(\\pm  r_{32},\\pm  r_{33})$$\n",
+    "\n",
+    "Note that this method assumes  $r_{11} \\not = 0$ and  $r_{33} \\not = 0$.\n",
+    "\n",
+    "Note that the choice of $\\pm$ can be determined by comparing the estimated orientation matrix from $\\alpha$,$\\beta$, and $\\gamma$ and the orientation matrix of the agent."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from navipy.maths.homogeneous_transformations import compose_matrix, \\\n",
+    "    decompose_matrix\n",
+    "import copy\n",
+    "\n",
+    "triangle_orig = copy.copy(mytriangle)\n",
+    "frame_orig = copy.copy(pitch_frame)\n",
+    "triangle = copy.copy(mytriangle)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's say we have the triangle `mytriangle` which is placed and orientated as follow:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pos = np.array([1.0, 1.0, 1.0])\n",
+    "yaw = +1*np.pi/3\n",
+    "pitch = +1*np.pi/6\n",
+    "roll = -1*np.pi/6"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We need to tell navipy which of the 24 Euler's convention to use.\n",
+    "\n",
+    "The Yaw-pitch-roll convention in navipy is:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "axes = 'rzyx'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The homogeneous transformation, i.e. the position-orientation matrix can then be obtained"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# The position-orientation orientation is given by\n",
+    "transform = compose_matrix(translate=pos,\n",
+    "                           angles=[yaw, pitch, roll],\n",
+    "                           axes=axes)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "and the triangle placed and orientated appropriatly"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#We can now place the triangle at the position and orientation\n",
+    "triangle.transform(transform)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**note** that the transformed triangle would be usually the one we observed, as the insect already have a given position and orientation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If we had only measured the transformed triangle, we would like to know it's position and orientation in space\n",
+    "to obtain the position and orientaiton of the insect.\n",
+    "\n",
+    "The decomposition is done in two steps:\n",
+    "1. get the frame of the triangle\n",
+    "2. decompose into euler angles and position, i.e. the translation required to reach the insect position from the center of the arena\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "frame = triangle2homogeous_transform(\n",
+    "    triangle, triangle_mode='y-axis=1-2')\n",
+    "_, _, angles, translate, _ = decompose_matrix(frame, axes=axes)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's plot the results, and check if the angles and translation have been correctly determined (in this abstract example, we know the position orientation of the triangle. In a real situation, this check can not be done.)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/bolirev/.virtualenvs/toolbox-navigation/lib/python3.6/site-packages/matplotlib-2.2.2-py3.6-linux-x86_64.egg/matplotlib/figure.py:459: UserWarning: matplotlib is currently using a non-GUI backend, so cannot show the figure\n",
+      "  \"matplotlib is currently using a non-GUI backend, \"\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure(figsize=(5, 5))\n",
+    "ax = fig.add_subplot(111, projection='3d')\n",
+    "draw_frame(ax=ax, frame=frame_orig)\n",
+    "triangle_orig.plot(ax=ax, apex_marker='wo')\n",
+    "draw_frame(ax=ax, frame=frame)\n",
+    "triangle.plot(ax=ax, apex_marker='wo')\n",
+    "\n",
+    "celltext = [['{:0.2f}'.format(np.rad2deg(yaw)),\n",
+    "             '{:0.2f}'.format(np.rad2deg(angles[0]))],\n",
+    "            ['{:0.2f}'.format(np.rad2deg(pitch)),\n",
+    "             '{:0.2f}'.format(np.rad2deg(angles[1]))],\n",
+    "            ['{:0.2f}'.format(np.rad2deg(roll)),\n",
+    "             '{:0.2f}'.format(np.rad2deg(angles[2]))]]\n",
+    "ax.table(cellText=celltext,\n",
+    "         rowLabels=['yaw', 'pitch', 'roll'],\n",
+    "         colLabels=['theoric', 'estimate'],\n",
+    "         loc='bottom')\n",
+    "\n",
+    "ax.set_xlim([-1, 1.5])\n",
+    "ax.set_ylim([-1, 1.5])\n",
+    "ax.set_zlim([-1, 1.5])\n",
+    "fig.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Axis convention for aircraft\n",
+    "\n",
+    "The local reference frames is composed of three axis.\n",
+    "\n",
+    "* X axis is the longitudinal axis pointing ahead\n",
+    "* Z axis is the vertical axis pointing downwards\n",
+    "* Y axis is the lateral one, pointing in such a way that the frame is right handed (from left to right when looking ahead)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/doc/source/tutorials/02b-orientation-3markers.ipynb b/doc/source/tutorials/02b-orientation-3markers.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..cb416a21fb5c176c79fa58f9a8c90da054748d3c
--- /dev/null
+++ b/doc/source/tutorials/02b-orientation-3markers.ipynb
@@ -0,0 +1,177 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Orientation from 3 markers\n",
+    "\n",
+    "### Example of a marked bee\n",
+    "Bumblebees are eusocial insects. The bees of the specie *bombus terrestris* have their hive underground, and can access it by a hole. When leaving the hive, they perform a learning and orientation flight to remember the location of the hive entrance and decide in which direction they should forage or explore the environment. Bees can be filmed while perforing their learning flight. As we have seen before, 3 points or markers need to be placed on the bee's thorax to determine its orientation. Bees have therefore been marked with three points, forming a triangle, on their thorax\n",
+    "\n",
+    ".. image:: media/marked_bumblebee.png\n",
+    "\n",
+    "**Note** The dataset used in this example come from recording made at the University of Bielefeld in Martin Egelhaaf's group, by Charlotte Doussot. \n",
+    "\n",
+    "### Position of the thorax's markers along time\n",
+    "\n",
+    "\n",
+    "**Note** due to occlusion during the experiment, at several time-points, the positions of the markers have not been recorded.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x216 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pkg_resources\n",
+    "%matplotlib inline\n",
+    "\n",
+    "bumblebee_flight = pkg_resources.resource_filename(\n",
+    "    'navipy', 'resources/sample_experiment/Doussot_2017/bee007.hdf')\n",
+    "markers = pd.read_hdf(bumblebee_flight, 'markers')\n",
+    "markers_thorax = [0, 1, 2]\n",
+    "\n",
+    "fig, axarr = plt.subplots(1, len(markers_thorax),\n",
+    "                          figsize=(15, 3),\n",
+    "                          sharey=True)\n",
+    "for plt_i, cmark_name in enumerate(markers_thorax):\n",
+    "    markers.loc[:, cmark_name].plot(ax=axarr[plt_i],\n",
+    "                                    linewidth=2)\n",
+    "    axarr[plt_i].set_title(cmark_name)\n",
+    "    axarr[plt_i].set_xlabel('time [frame]')\n",
+    "    axarr[plt_i].set_ylabel('position [mm]')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Yaw, pitch and roll along time\n",
+    "\n",
+    "We use the markers from above. To calculate the orientation, the euler axis convention as well as how our \n",
+    "markers are placed relative to the three rotational axis of the thorax."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rotconv = 'rzyx'\n",
+    "triangle_mode = 'y-axis=2-1'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can use get yaw-pitch-roll from markers by building the trajectory out of the markers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from navipy.trajectories import Trajectory"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mytrajectory = Trajectory(rotconv=rotconv).from_markers(markers, triangle_mode, markers_labels=markers_thorax)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The angles are in radians. Some persons prefer to read angle in degrees"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd2004cee80>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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mDc888wyLFy+224/FixfTp08fXfE7Z1KvNQNn4CszH2/F2fe/o6Y8uTbM+GhWIYfPFThc6trXkI+/yuzZs+nZsyf9+/d3qIT1xo0bdccMS1hry0rk5eVx44030q1bNx5//HH27zcPeTZl//79TJ06lU8//dRJf5Ux9VozqC3SSOQlOPGHaBOtagNz/0nnxau7svPEBQCu6CSjzKxh+/a7T1w4MoN3BYYlrMPCwhgyZIjTSlgvWbKE9PR0hgwZYrMPGRkZjB8/nvnz59OuXTsn/FXmSM1A4lPENNA7ik9dKOabzccBGNwxxlNdqrP4ymTJ0yWsL1y4wJgxY5g5c6ZO+3AFUhjYRSrKnsTZtaGEEDw6rAMAK/ed1WkGluoVSWzXJvIVPF3C+oMPPiA1NZUZM2bQq1cvevXqRWZmZq3+Jks4xUwkhJgDjAUyFUXpptkXBXwPtAHSgQmKopwXqv70HjAaKALuVBRlhzP64WxkMJF34OyfYVS3Zsz+I4VXfzvo5CvXT3y9NpGnS1g/99xzPPfcczXrfDVwlmYwFxhpsm8a8IeiKB2APzTbAKOADpp/9wEfO6kPEolDNDNxFn9yW5KHelK3kZOl+oVTNANFUdYLIdqY7L4WGKL5PA9YC0zV7J+vqPrnv0KIRkKI5oqinHFGX5yNL8x8vBoX3H/DnIIdzw+X6xhIaoUsYW2fpgYD/FmgqeZzS+CkQbsMzT6vEwZy5uMdOPt30EZ6DO4QIwWBA8jaRO7B0yWs3RJaqiiKIoSo9nMjhLgP1ZREq1atnN4vR5APe/0k5bVR+ElpXyvctSysoiiyGoAdnOHod2U00TkhRHMAzf9a9/cpwDAtOE6zzwxFUT5TFCVZUZTk2NhYF3bVMnINZM/jKmEc6O+Hv5/8fe3haTNpSEgIOTk5MqrJBoqikJOTQ0hI7RInXakZ/AJMAmZq/l9qsP9hIcRCoB+Q563+Aol3IIWyZ7E1K3f1IB0XF0dGRgZZWVku/Z66TkhICHFxcbW6hrNCS79DdRbHCCEygBdRhcAiIcTdwHFggqb5ctSw0lTU0NLJzuiDq5AzEonEMu6w3AQGBpKQUP1qw5Lq46xooputHBpmoa0CPOSM73U10kzpeaQw9izOTvqTeC8yA1ni9Uih7FlkNJFvIIWBHeTDLpFYRsro+oUUBjaQD7vnkVYizyLvv+8ghYHE65FC2bP4em0iX0EKAzvIh92zyNvvvchEsPqFFAa2kA+7xMeRkyHfQQoDidcjZ6CexVbSnwz9rT9IYWAH+ah7FjnWSCTuQQoDG8j5qHcgfwfPIZPOfAcpDCQSiW1sRRO5rxcSFyOFgR2kTdSzyJmp9yJdOfULKQxsIB92L0H+Dh7D7lxIyup6gxQGEonEJtZksSwtXr+QwkDi1UgrnUTiHqQwsIGc93gH8nfwHNJK5DtIYSCRSGxizXcmfWr1CykM7CDNFBKJxBeQwsAGsgyCdyB/Bw9iZzIkQ6/rD05Z9lIikdRfTKOG8svyqaqqkr6ceobLhYEQIh0oACqBCkVRkoUQUcD3QBsgHZigKMp5V/elJsikJ88iZ57ex8jFIykoK+Cm6O893RWJE3GXmWiooii9FEVJ1mxPA/5QFKUD8Idm2+uQMx/vQFqJPIelyVBBWQEAJ0s3y6lSPcJTPoNrgXmaz/OAcR7qh0QisYOhMC4qL9J9/qfwHfArsnCGpC7iDmGgAL8LIbYLIe7T7GuqKMoZzeezQFNLJwoh7hNCbBNCbMvKynJDV82RVgrPIm+/d7Erc5fxDv8Cz3RE4nTcIQwGKYrSBxgFPCSEuMzwoKIahS2+84qifKYoSrKiKMmxsbFu6Kox0jzhHcifwXOYToYulF4AYFpf1bKr+F10d5ckLsLlwkBRlFOa/zOBJUBf4JwQojmA5v9MV/dDIpHUDMNJkVYYJEQkqMf8pTCoL7hUGAghwoUQDbWfgauAfcAvwCRNs0nAUlf2ozZIM5FnyCvNQ1EUef+9DK0wSIxJVHcEnfNgbyTOxNWhpU2BJZqkoQDgW0VRVgohtgKLhBB3A8eBCS7uR42QVRk9w/mS81z2/WXc1e0uYLhMOvMgprL4VOEpmoQ1ITI4kob+zckLPuWRfkmcj0uFgaIox4CeFvbnAMNc+d2Susvak2sBmLNvDuMih3u2MxKjSdGZi2do2aAlAI0DWpMflOKpbkmcjCxHYQeZdOZeyirL2HRmk247v1LOPL2Ji+UXaRDYAIAg0QD8Sj3cI++gvLKcU4V1+1mVwsAW0jrhdpIWJLEibYVue0fxR/JncCPvbHuH7w/pM4tNM8BLK0oJCQgBwF8Eg1+ZW/tnSklFCedLPF+8YMa/Mxi5eCR9v+nL3qy9nu5OjZDCQOI1VClVus89YnoAEOHXylPd8Um+2v8Vr25+1WifocumpLKEYP9gAAJEMIhyj5YMmbVtFtf8fI3Hvh8goyCDn1N/BqC4ophblt/CodxDHu1TTfBZYXDu4jkWHV5kt527n/NvDn5D93ndue/3++w3rmdoI1UAWjZoSZuINpQqeR7sUd2msqqS0krHzTiODOqllaUGwiAEIZRqfYezCQ0IpaSixGPfD7D0qHkw5Btb3vBAT2qHzwqDJ9Y9wSv/vsLZi2ettnG3eeLsxbPM3DITgE1nNpFTnOPmHniWtLw03edLW15Kh8YdyK7YD8J8kKqoqnBn1+ocJ/JP0OvrXiQvSOZE/gmHzjEUxlpM73xphbEwAHU27ClCAkIoqSwxE2RF5UVkFrknfclQo9US6BfIr8d+dVsfnIHPCoO80jyj/63hTsXA0FYOsC97nxu/3fMsSVkCwJWtrmRMwhi6RHWhkjLUgrd61mesp/fXvVmZvtIDvXQNJRUl7M/e77Trvbn1Td3nMUvGODTr35utt3VbGuBAoxkEqMIgUKj/e1IYaAWTVjvZdHoT3ed1p9+3/Rj2g+sDFquUKv46+ZfZ/rNFZ5m+YTrDfhjG+KXjXd4PZ+CTwmBp6lKO5x8H4OfUnymvLLfYzt3h7eVVxv3YnrndvR3wMJlFmXSJ6sK7Q98l0D+Q0IBQ9YBJxMrc/XMBeGrdU1Z/O0usSl/Fr8d+dVZ3ncr8A/OZ+NtEpq6f6pTrhQWGGW2vObHG7jmv/qv3FfSc39NMa65SqiirKiPEX9UI/EQg4FktTfuMaJ3IpuYZbYVVV1FQVkDK+RSC/IJ0+xoGNjTSclMvpNJ9XneX9sMZ+KQweO7v53SfFxxcwIRfJ7DmuP2XxdWUVaqRGa0jWtMrtheLjyyu1mBX1ympLCEiKEK3rRMGwvgeaO8TwCv/vuLw9aesm8L0DdO9bo2Ebw9+y/s73wdgedpyHv7j4VoPYtoZs5ZNpzdZaannzMUzRtsLDy008pkVlhcCEB4YDoCfJk3JdBLjTrRayZT1UwCoUIwF05R1U1z6/VrT2gsDXtDtKyi3/Nt523Nnis8Jg5P5J832pV5I5fG1j1s+wY2/n/Zl+37s90zsPJH8snx2ZO6o1TUVRWH+/vkcvXCUqeunGpUgdpTj+cc5duFYrfphD0VR2Jm5kyB//QxLKwwUYRy+aGjCWJK6pNrf5W3awcZTG42212Ws49Pdn9bqmtroFi0/HPnB5gy+skpvipuSrA6gWjOlNgNcO/uOCokCwF/4A54VBvml+QDsydqDoihmk6d/Tv/jUu1AKwwahzRmZJuRvHXZW1bb9pjfg+zibJf1pbb4nDAYvWS01WOmA6W7y1EUlBXQPLw54YHhxIaqVVrv+f2eGl9PURRG/TSKt7a9xbil41iettzML+EIY5eM5dql19a4H47wzcFvANh8ZrNun04zCNS/QLsydxnZtsF4IHOEZzY+w9Prnmb7Oe8wwwX4mRcCmHdgHgdyDtToetYG5/yyfKvnpF5I1X2elDgJf+HP5rObyQ/4R7c/tyQXgOiQaAD8hEYz8KD22iikke7zjctutBjZNGffHJd9v9bn2Ci4EW9d/hYjE0bqhKUlhi4aytazW13Wn9rgU8LAdLA3NEmA3hbtKQrLCmkQpGZ3RgZH2mx7LO8YwxYN4+zFs1ad4CcKTjg9K3Jn5k6dv8WZLDy8EICyKr0W0KJBCwCqQvSO1dtX3G6xT9VlRfoK7lx5Z7XPcwUNgxpa3L8na0+Nrrft7DaL+y1FC2m5YdkNAIxOUCdLlYoqYAsD9ANXbrEqDKJC1cHOG8xEt3fVPw+Hzx8mp8Q8Aq+wrLBa19yTtYfkBck8//fzdttq3z3D9/XT4cZaXUJkgtH2Xavu4nThaa/TEnxKGJgOmtd3vN5o25I66c5yFAXlBTQMVAeGZuHN9H0wsTUqisKsrbPILM5k+I/DGbRwEKnnUzFFO5MzpArLUSLWuFiuL1E8ful47lhxB2OXjOW9He9ZPadKqWJl+spqzdgt2VM7RXVSrxdyxOzY5ls2c2PHGwHIKnZs4SNbMzZPckvnWxjQfABLxy2lW3Q33f6aPnvahKdm4c24qdNNfDjsQwBOF562e+417dQELr224q/Tj7UDrd5MpLax9Jy5i0C/QCOBYAmtYHOUW5ffSmllKT+n/kz/b/tz47Ibrc7mtQK2UbBeQwkQxpqeVss3ZMTiEVyzxLPJcqb4lDAwdezc0vkW3hisjz5YcHCB0Y/u7mii5/s/z7P9nwXUmcbUS9TIkr9P/23U7re039hwaoPRvuVpy82uZ0m4fbHni2qFMM7bP0/32dCU8MXeL6ye89ux33hq3VO62b4jlFSqiUO9m/Q2O6YE5FBUXmRkjggLDOO+HmpinqN+EEshkBdKrM+W3UViTCKfXfUZbSPbMn/UfN1+a+Gd9sgvy8df+PP79b/zXP/nSG6ajL/wt6oxGKLVTOeMUE0rJf6HdcfWZ6wHoHFwY0CvGVj1t7mJDo06mO0b1HKQ7rMtjcgeF8svcij3EHetusvicW0Ir6F21yqiFT1je/LJlZ9wd7e7+WDYBzqNy5CC8gK6z+vOjnO18ws6C98SBgaD45NJT9IsvBmj245m5+07GdN2DFC96BRnkxCZQMfGHXXb2oHxgTUPUFReRH5ZPmWVZRZtyZZUTsNZvZbTF08z8beJDseGmzo3HeFckVrj3lZCn+l3ZBZlMrz1cD6+8mOLbV7e9LJuBjq+vRq3rfUprM9Yb9du/eORH43+Zm3lTUdMAe4k0D9QN5DN3DKzRrkm+aX5RARF6By/YYFhNApuRF6Z/WxurWbau0lv2kW2QxHlVKA+R1rzYKC/GlLqp3Ege5pLml1itB0VEsWQuCGAqjnYyyVyFFsOeD+hH0qD/INYMHoBA1sO5P+S/o/QgFBevvRlukV30/XLkEkrJ5Gel86bW980ipRzNz4lDA7mHNR97tBYP5sI8Atg5uCZBPgFkJaXxi9Hf9EdUxSgsgL2/gg/PwhVNZut1YTEmEQmd5sMqFEzA78bSNKCJL4+8LVZW0tagHbfAz0f4POrPjc6du/v9zrUh9ySXEa1GWXxmKXILICfUn4CVKfwoIWDbGoRoPfV3N3tbl3YoinL05brhMzl8ZcDEBagxtL/efJPFh3RlxYxFUKVVZX8b8f/1Otct5xfx/+qM8N5m90W0Jl1AB758xGjY2tPrrUbJrroyCKdpqUlIjjC6qCofd5bNmhJ20Ztdfvv7HYnABXiPHmleaTnp3Ndh+t0x/2Fq5dDcYy4hnGsvmG1bvvlS19mQqcJLL9uOQ2DGrLl7BbztZut8MORH6wes3T/IoIiuLnzzXavGxIQwndjv+PpS562eHzyqsl8feBrpm2Y5lA/XYFPCYMtZ7fQNKwpv4z7hYEtB5od10r+93a8x/+2/498v11MKPwaXomGxXfDrm9g/09u7fMTSU+QEJlgMQpozx17+H7s9/SK7WU2qGUXZ+u0nHt73Ev/5v2Nju/O2u3Q9+eW5BIbFqsLI+wR20N3zDDLVcsbW97gZIEqJMqryskrzWPOXtvRHFlFWQxvPVy/epYVbl1+K6CPZtHOUAFd6Y71GesZ/uNwnUkDVFt3Xmkez/d/nviG8bSOaK1LnDKNS/cGDGeZ2cXZRrb+R/58hPtWW69bpTUttYloY7Q/MiiSnZk7WXdyndk5s3fMpkloE74eZTzJaNVQLRJ4wO8Ep6TRAAAgAElEQVRFPt6tamyG0V6KHf9TaWWp28o6G/rYhsQPQQhBfMN4nTY5498Zdq+hKAqvbLJuGTCd1FQpVRSUFZgFotjCNBlQi/b9XX18tcfyEXxGGBSWFbLx1Eb6NO1j5t3Xoq3TnlmUyZf7vuR4wIdMuPitvkFQA+g6zh3dNWJCxwkWB28hBF2ju1JWVcaurF0cOa93tN6+XHWqBfoFEuinDppaE4CjzN4xm+KKYhqHNOafm/9hw00b+Gb0N+ydtJc+TfpwtsjcDLTg4AKzfX5+5o+ZoT08vyy/Wi9UXMM4s31abUAbWXQw56Aad15VrqvNY+jIaxWhDnSmzj5vZMTiEQ6XRdZqg1e3u9pof/Pw5mQXZ/Pwnw/rZrhllWXM3DKTc0XnuKHjDcSGGTs6Dbe1dvfn+usTNm0Kg1kd+fj7sYxcPJJ/Tv9jvZ2bSDlvfxGe/LJ8FBRGJYzijcFvEOQXxJi2Y3Q+kgUHFxg9t/uy96GgVOvZ1UYdhfiH8PKlL3N719uNhD94rryHzwiDU4WnKK8qZ2j8UKttvhzxpe2L3L8e/N0/eIzvYLu2iVZNNbQvZxRmAHBTp5t0+5Zcu8Roxqid7Vnj872qaalBYAPV7mwQ090msg2ZRZlWTQ+PJ+mditr1jLUUlRcxful4us/rTvd53ckuzrY6Y+od/BiBeWN1232a9CEmNMas3dqTa9mfs18nEIsqivhkzyf0+bqPzgke3zBe1/6JpCcA6Bzd2eY98Ba2nN3iUDttEpZpaPJd3e/SCb7iimKO5x8naUGSLr8jIth8QDMUnqcK1Bn+gOYDdPuiA1STUrvIdvqTCjPhpUgoPMdRTaLitwcNJlRuxjAz2B7aezewxUBGtx3N9tu3M3PwTNZPXK8LVtC2Ab2mauneWSPAL4CFYxeycOxCrutwHU9f8jQTOhqv+jtk0RCHr+dMPCYMhBAjhRCHhRCpQgiXG8q+O/QdoM6QrNE1uqvZvuMBmsH/hfMQ3c7suDsIDww3GwBHthmp+3xlqysB4wfVEk3Dm7Js/DJmXT4LUJ2qjmApxDEqJIrcklwGLRykc3oZhpLqEsY0GGZS/5b2G8fyjDOaTc0aWpoH9CWw8Ar9dQNDLbYrKC9g4q8TdeahOfvm8NGujwDYk72H0IBQ2jdur2uvXaDF0XvgbjpHGQupIP8g/jllf4atncFHBhkLg85RnXllkGoCSctLY+ySsUbHLc1utfcIYFfWLiKDI/H30zuNhfCjsqgNR/OO8sgfGt9Glhp9dMHPj1OB6rvjycqdl8ddrvtsb8adX66+P5byPtpGqoLPUgKb1nflKInRibRrpB9LplwyhZYNWurecZ/SDIQQ/sCHwCigK3CzEMJ8JHYSs3fMZnHKYgAS1rwOxQahZtmpsPheKFcdbk3Dmhqd+0HjSOh2PVgwdbiTOxPvBFQtYN7Iefx30H91x8IDw/EX/roMU8NZuKWBfESbEUzuNpnzJedRFIXDuYdZfGSxURvDRKLGIY3NrmEYs68tU6Bduxjg+g7Xs3y8PtzVMErCUmluW9qPMHhMR7QeYXTs2nb2M6NzS3KtmgbBO2vG/HD1D8wdOVe3/e3Bb7l/zf26bWthp9qIIUtJi1o/iaHPQRvdYuk3BmhRqJ9ZD2452OiYEICi/jZrM9Ya3cfBreNICVJLi+RaSARzBWtuWMOK64x9a4YCbeW2D2DXd1bP/z39d8CyYNSaJr/a/xXFFcVGE5/q5u6YEuwfzIrrVjCtr35OXFJR4vbIIk8ZTPsCqYqiHAMQQiwErgVqln9vhbLKMpakLNGZOwAij6yEN1rrG4VGQXEu9L0X4vvyy7hfGLWgH7ma8ee8vz+M/8yZ3aoR2sJjFVUV9Gnax+iYEIKwwDA+3/s5d3e/28jhaG2giw6JpryqnB7z9Q7h8R3G6+yX5y6qkTvxDePNBmAwFgY5JTk0DW+qc4J9O/pbgvyDiI/Qm2VKKkooqSghJCDELCM0LjCSwNQ/oaP592iF2bbbtlFZVWlmTnp10KvMGDiDnvN7Wvw7ddexcB8eT3qcd7e/S3FFsVUzlSdJappEYnQi+3P268x+WgrLCy0OWpYyYrWYFq/rHNWZp/s+TY/YHvRr3s9iH4Kq9Jp0p8adLLTQC+oLpRdovPRBsxY5RVkUVxSbaYvOpml4U7N9WgEIoGx8FwovglIFHUdCeLTu2PmS87pZv6E5UUvP2J5cHnc56zLWcSL/hC5IAiAhwvpEw1GEEEZO8Eu+UcNl905y3xKanprutgQM4xIzNPucioJitoSfGZoUe/b+CIdXEnb+BHFl+vomm0NDqPKwVgB6NdVQvTREO2vLKMjgRIH9xUwMo4K0XCi9oBtMtBUsp/WdpotXN8RQg1p2dBkAuaXqvbRkh3/0r0e55JtLGLl4JPMOzDM69tKJI/DtBLNztAihDmTWBmw/4WdXQ7BUl0ebNWqpxIW3sHCs5cQ9a8XXdGYiC8LA3yQvILMok/iG8dzb415dkIEprSvSSSzvy9C4IWYZ+wCKor9mZs4RuGD87A25WEQFit3wYldh+Hfla9/jpQ/Cj3catTtfqmq3rw581aJQAbiho1qy44ZlN+hKVC8cu1CXKV9besb2NNIOALrP6+7S2kqGeH6Us4EQ4j4hxDYhxLasLMdKDhgS7B/MzMEzddtzzpyz3njLp/DdTfDDJN7OzOax3AsklKmmEm8oaNa3eV8Wjl1oNaZZW5ohtyTXqDyAtZIGlmZ5l39/OYMWDuJi+UVdxmV0aLRZO1BnrR9c8QGgr7aaU5xDZHCk0Qu4cIzxYGYaarj0Ygj9SjTCt7D6v7GWJ5KfYFSbUXw23LIWZxqxAXr/kWEUljdiWKJCy+d7PrdoKtL6jSxpDaY1hOyWkUhbzwd5D7Ew40dmKzFmtnQBOjMRQEmOeh8Niz+8nZnNE7nnGRfWGk8ghNCFzC6MMOh/bppRO622as1cBsZ+nLS8NASCLlFdnNhby1qJrdIvzsRTwuAUYPhXx2n2GaEoymeKoiQripIcG2te38MRxpTBjrQTLD57nktKHFirtfg8zSoruScvn/ka4eEtK44lRidaHNRAb7Z55d9XdCuGgXl2phZbZhFDm742pt8UIQSXx19Op8addINpbkmuWfvEmES+Gf2N2fkDonuw2D+BtoY+mlntIcNY8Dpqzo8KieLNy99kQIsBFo9bqkDbr3k/YkJjLAYOeBOWNLPFKYstTlLyyvJoGBBGwNyrIctYyJkKjyXX2Cn/fdogUWvjO1BmntFuOIRUXlSF+bkAvbYQBEzOKyA+z8ZEzMX00pSrOBUYoJ8aFamCMLckl/9u/q8uMsha0UBQcxnev0Jdd2LZsWUE+gVafR9riqWw6ZqWJakunhIGW4EOQogEIUQQMBH4xc45NSNjK4FAx//8C4OegMf2wDXvG7e5ejbEaqR+of6hfb7pfFCEy1dLcgbxEfFc1foqThacZG/2Xvo168f6m9YzvPVwh85Pbpqs+2xY7sKaMNASGRzJgZwDLD6ymJziHIvF4CzNUhMunKFj6jpIN66xxHzz4l3VLRFlWJdGi6WQYj/hR4+YHlRUVZBdnK3LVbhQcsGrnMrtG+mjoPbcsYefr1XXKrDkiL9QeoGIKgVO/ANH/zQ6NihuELd2uZWPhn3Ejtt3GEVXWcS0btO/5qHIioFmUFmg+qreSFaDAZJje0EzzQpfNVhHwylkHoT/tmBajjr452hNReUXIf1vLv/+cl2kIdjPxdHmIoFxhV1noTUHewKPCANFUSqAh4FVwEFgkaIozlsAVktJHmx8V/0cGQdXvgiNW0OMgYkkPBaSJsGdv5mdfiGgCX6EkV+Wz7az2zh64ajTu+gsAv0CdSGjANe2v9amyguw5dYt7Lh9B3sn7eWWLrfo9j+1/in9df0t25K1TO87HVBDR3NLci2alUy1kLaRbXnQz6BdlIEfpKwQdn9v8zuNKDCfcX407COj7dU3rOb/kv7P4unBAcEcOX+EoYuGMvzH4eTmnWTw94P5dE/tFpdxJtP7TeeTKz9h76S9CCF0s1fTwouVVZUcOX+EaO1rnWNcyTbQL5BpfacxOG6wVR+Bjn0/wYa3jff9+QoUGCQaquFEus17z6wip0lnIhuq7r9Hkh6H/2yEXrdBtHkxObdwRk3WjK5UZ9cXWquO8ipAmasvHnd/j/uZeslUqz45LYaFFO/tbqWki6JANdfYMKRrdFcjoWCY2+FKPOYzUBRluaIoHRVFaacoymsu+RJtMkioyWy1VT+Ykgo3fQNPaOoVhcfAE4fMLuGnhLI4ZTGTV01m3NJxXlnLRouhOcHejB7UXADtoKAtPWDIF1fZd/q1b9yeLlFdyCvNI6ckh6jAhmq4rgFNwpoYhcLO7PEwkUd+1zfo/wAYqudL7oN0tVKrzfn5qR3wdkc1yemlSHgjAU7vQhxZxb0hbfjiys/YO2kvzUKiCfhvS9hlnvxkquaf+FldTGiuhxyelggNCDUqn6KdnR7PM15XYm/2XlLOp3Bdrsb3svVzWGZZCNokOxV+nGz52GnjtSOE0JfzqAT+bRRLlVJF07Cm+qi3cR9CZ+uLSrmUc6qJt7EmWOD1mGguTJjP8PgW9EjQP/OjEkZxW9fbLJrkDPH389f52wzLVhvx2xPwalPHbZxFuUZtF45ZyNJxS3Xbn0UmWzrL6Xi1A7nWCAFTj8PDFmqRN4iFLmPBcOYb0RyuUZ2iXPY0QggCq2KMqhU6WtPHIxRmMqh5f/yEn824ekuYRkQE+AVYDTc0paSyhHUZ6ygoKyB67xL4IAkqjP0zhuURGh000MI6jlLzOLTmBC3n9Iqi1Rf0nIkvpzgXPrscvruJRw+up1+YJizy5BaoKIGfH1CFxvtJME/tj2n569tRTR3XRDrXMehMtCGaplFZBRrncYcig8ip7V9BaTUWd9nzg/r7WeOHO402/cOMkweP5h5h6dGlVgMP3IqiqFGCHUYQdZs6uG7J3sMa/1IyA4yj6g3NP/bQvheW7PucOwDb5kBVOeTaWSq2qhLeT4Y3E9RzNGif9//1epzFGWdgxVNuKZBZv4UBQGgjddbvKH1uh5fy4Ap1XYGwSv0g9Z+e/2FYq2HO7qHzmNWBt/f9w69jFtG8gfVMawB+ul8dGAssl5m+LMIBtV4zmzGM5W6drzHbZFpPGYncNlf9MPFbuGUhhEXBxG9g7P/0jdbNtHhutcjRmPXmmsxKc1IhTc1UtpbtOS7cc7ZbewghjO65lqJdal2o0CoFuhjUJnq9GlHbP5kvs7ow6gGYpFk3uqJEF/UlAEUxHlQ/b6jmMlR3KVKXUJQDBWeg3RW0b9xBF4lXbLI0ZiP/UKvhpJZ46pKnWHn9SobED9HvPLwCVkyFjw1MOguuMztXx/l0mBEFOZqaSUdWmTUZtmkOHcvLod8Dam6Ei6n/wqCWBGqSbhoFN+KhXg95uDcmKIr6EFZVwpqXAQjLzyD+y1Fw0YY5KzcN9mhCPt/upIusGN56OAn+4byUlcP0PWtg+zzIy7B8jc+GwOdqmYiPrvyINzKzeTMzm6suambaFmZFTyU/xbXtriVUqxLHGUQ6hUVB8mR4ViOcinIg56htTdveC1KYCWttCJWKMu4/Y3kJz06Kd9Tqt8Z1Ha4zc3YWn1BLW4cqVdDqUuMT0kwc9fZIUMs4bAtIYk3kDRBloGka+CKKjj/A470eMT1bt0iOR9mk0fIbNkUIwVPJqi/MtNrusPPVDGkuL6FlYKSxifG7ibD5E+N259ONTUWV5fDbk7D4HvV/QyxFJZ3VJJwNmeaWmmhSGNhAACGV7QkNCOXJ5Cfttq81JzbDzw85bmvc8736EP6vuxr6p6UoB9bPsn7e7F7G2wdUFfqdtV/yS+pBri+8SLPKSlj2KLybCKUWoqlO74TTO+DgMmJS/mT0xSJGXSzSP1A/3gVvtjP6W+5IvINXB72KCAiF1gOhQRPz6xrWHtI6/w05s0d9ydI36pyDVrmYCWtft37840tJTtts8ZB/ie06T54mMjiSgvICFhxYwOHcw5RXlVMco0YHhXabAC1NTD0XHFi32tC0F9EC/m8frzVQAwRo2AKaa7K8DWpgKWWxXL7PPPjCkubidrTPT7galm4tGMIPxfqkxxKfDFS1raoq1Sxk4kfhycPQRlO6oygHjm+Cb26E1D9g6xew9wdIXWN8TkCQ/nNRLuzRrM8R0VK1brgB76/f62H8CGLLrY5VjKw188ZCZRkk3wVxNuy2WrQmnnwLNeNPWh7ktDWYjPj1/6BxG+vf83ocvHjB8jqg399m/byibLWPEQYmq+LzUFEM7a+0ft7gKbBhFuz8mriEwXxd+gqkfwJtBsGng62fZ4jwg9V2KlbmWC5rPPdCBTTw/HKYttAmJ72xVV229Y6udxClyT4OHfMOBITCyJmq1vj7s2pknTUytquD/zsGmeMjZ0JoI0pEurrt5wfXfwkfJINGUGofh4C0jRDfwuiSri494RANmkJAiDrxsEBSkyS2Z26nCqFOeqamQ6jtCDyyU/Wa0ZL71IG9jcEzecXz0LAZ9LtfDZt+yyA6KeV342sNeFjN5Ti+EcqLIe+UmsG9arpewFh6t12E1Azs4c54c21hqi+uUGcH2XZqsPsHWd4f2lidtR/53fyYYez5IIO1a782Waeh40jodat+W1tPKGMbvF2Nss+mlVRXqOs6Y6sWUP8HdB8fT7uPtspJmDsG1r9lub3wh8TxcN86SJoMty2GDuZ1jnTcZ7zAy8dn9VU1J3e+haR+j0HnMdbP9wKubG0sTHed3canlerfERIQoo7U/R+AARrT5qpnLD/L5SXq82YoCBq1MpqN6uYA2ui8ncaL4PhbiPma2ndq9f4gZ3N6l5oz1OVqq4uZa0Ov/bT9z7KTiZ7+t7Fzfa9mVTRtrswVz+nfqSYOJDJ2HguTf4P4fqqgeLcrfDXSWNO4uRph1rVECgMb2Ikycz4hBvVkVk5XZ2HnLDhiqyrVl9iS2jt5pTr7BrXEhikLNeUsrnwZrnzJel9u+R6u/VAf5fN6HHx3C3wxTHXKWcPUVlxkUvJAO0O1NdiGx0BcX/P9f1qpM/ViLtw4F1r0gqv/p2odraxEQt32kzpzM2BQcQm7007wTKM+PJD0f9D/P6pwcRd/z1ad+Z8NVU0KDhAaEGpU2GxP7gGK/fwYGBhlbMs2fIgtlTi3FEIaYMXEE6IRBmnrjARLgIksGJ0w2qhvHuEzTenqEOMaTbGhsQT6BdIjpgdJTdWBXZuDgI01jjmzxzwQwZTku0Fb4juqLTQ3MMd2NigZ/lIePLQVWmuczda0+G43QKeRlo+5ACkMvIkpBvH5WgdvnoV1hhfdAa81NX94A8OglcHyllqhYAlrM/Ox78LzGuezEDDcYLnAw+a2YSOePAyP74NpJ6HDVeo+0/DP0gJoNQAamddgMeKOn20ft0czk0J8geHwf/ug/TCdDRlQM9Kveg2/O3/j5mvmesa8sUMTInp6hzozdBDtClyGfDhqnoWWGv55X/UlZR2GzZ+qvpfDy43bBIbBCH1OiFEmdmCofoCb1UFX4sNUM7CUhe4xTCZMf074kx237+CbMd9wc+ebmd53OvfkaSYoprZ/LSV5jpknDRe5EQLuN9BAh8+Algb5ArEd9Z/7WwlMGWW+rKwrkcLADm4tShAQBDd8ZbzP0mzlkCbML/UP9f/gCPVhezpNfQi11ygzKQFgGKucrBai4w6DKiD+Qep+wxeopZWEl85j1bIeY96GHjepM6GGzVQTVUiEXr1dPsVYKOUcVdvaIygc2l1hvr/1IFWYADy6y1iAGhLfDyLjoefNcNWr8Mg2vQAyWKCFxq3h0odVf4TbVUENg6cYbztomrSUB+LfwMKM/CbNUqTr31KziD/sCyuehvcslP1+9gx0MC5hYnRXBqsrxHFRH4Hjr2j/V7in+z3eEXWn1VCT7rTaxN/Pn1u63EKw9nZvn2u54dcOaInDX7Ec8dNbUxE3oiVMXg7TLTiqR/5X9cmZFl4Jd2+uhnQg28AjQ0PXcYCB6m5QK4lz+9UoHS2pq9XM3ekm2kO361RV/pDJTF5brrtlsv7BbatfCYrnLYTYhUTAvX/B5ya1fa77TB2wAS4xj003Wgxo74/qehFHVkHhWWjR27y9JYa9QHHaZkKrNAXSOoyAWxc5dm5wAzXZUGs/N2XyCtXB6A30uhl+/o9++8fJqtnLDo/1eYy5+03aBVow8cQ6mEBno0ibDgONa2jqTN7lOrSiNVhR++RxKspU/9ulj9p3CAM8+C98OUINh76Yrc9LqqqENS/BKQtVi0e8rk7eIuMtrsOhY+y7qi9B+7sEBFtuJwQ8sh1+mKSGlF72lOV2LkRqBt6Gnx/cvwGu05RD+PVx3VKCfHwpZJmUzJhovgA9AGEx6uwtzyAaoVDjKB1gsgDJHb/A3Sahboa07KPO0q/S2OyHvaAXBLbQXnP5FNUkoV2zoI15ITmLtOjNM52Wc1poBu2RNsJELREYan223/pSjy1japF4A/Pe/iUOaQcBfg7O5aLbGdusrXHVK/bbGESd9Tj7I3EiE63+7DWZGVkHVWHQopf9tgBNusCNc0CpNE6WzNgK/8zWb7caoPraLntafYcuuce2IABVy27ooP8kup1ay+mlPFWAuBmpGdjBI8Urm/dQ/2mzQVNWq85FS7ToY2W/5kVI3wA9J6ox+Z9epu4zNSUYagfWuF1T7vhS8wQjq7Q06JuhScJWGKsFXgh+mi86bK72eXWKWxaqZSP+p1m7YN7VcOev1ttXVUJRLl+fPsuXkRE0VgR33rzCclsh1Azv1S/C3/8zPz4lRdVAmyRaPd1o46U81eEN3O2/grAq9SW5q9CBEvHuQFt6u7mDwgD0xRK3fA4JmvfE0DYQHgu3/2xZ86onSM3ABvaKVrmN1NVqyV1T2gzWR3iY0l5j9105XVWbtYIAjDN/XYmfvxqvbsg9fxonljnAEb92cP3nxrb++kZoY2OnujZcMTvF8oxk9Qswqz29Sst4PzObGaIJbaPslBCxZM7re5+a/Nese/XW+R7wMACTA1YRCOxNO8E9ReW2z3EFWUdgv0GwQWmBmiwJjvmmtDRuozrPz6fr983RBEE06w5PpdZrQQBSGNjF2kphbuXYWvN9Tx4xdv6aEhii2suLc+HUNnVf3CXqrC7ASn6CK4gwSEbqPsGxZDoDvGldAbfQySB8MW29Gl686xv4/nbjjGzTCqyODOSN4uFe4zUOzIS1CVZv//BXyA6zsx6CO9jyqVqAsFxTY+qEQZhmdSZzQkCfSfp6VoZc+2Ht+lhHkMLABh7XCyJMqiL2vl11SN37FzRsan8AGKMpUfHVKPV/DzilCDZwSiZbKYtsB29R0NzChPlwtWaZw7VqdjFLH4KDv6jOzI3vqlFhxSb5G9d97tj1W/RRF3N6aAvcudwhbcvSKnH4+bG1lQVNw910HKkunKOpy2Qx98ZRwmNUDby8RK0jpMXe2g/1BOkz8GbuX6faMLUVPK9+r3qmElNzkCNOX2djuDB760utt5Oo+Afqo62ObzQ/vuYl48WAAJ4957gJQwh1MSeA2Not5F7pDYNkfD+19MjxTWqQw4Gl9s+xRpgmlDMnBT4xCHJw1AFcx5HCwA4etVKEx8DQ6eo/Ran+FDm2oxpWmKVZwMd0EHEHWuHV1HxRd0fwMSORSqSdhLxFtxtvu9CWbctMWulnEiZ5vfsXBKoIbEBRZGcapq5GFJyGnZrouuEOREaZog0pPbhMv++po2pFXR9ACgMbeJV5oqad+c8GtbBWEw8t1qL1GfS6xXY7G3jTz+AWjAYfoZaPzj2mmglN6gJZTQp0ItYePSPNYMgztosPuoj3/kghNrsldwSsNs4grsnzpi1ot18TOTfmneqthVLHkcKgvuMf6DlBAGqkyjNnqh1B5PPc9buan3HbT+qqfAVnIbyJsTBIvtt2fSkXowgDk6WHIr32nsqjqLK/Kgy0XHJvzQbxsCj1HmdrCtYZLhDkA7jMgSyEeEkIcUoIsUvzb7TBselCiFQhxGEhhJ2sDc/ia8EsLiEorMaajc/e/1b9VK2ugaaOUsNm5gEDVzxnPbTYSdi6/0Y/aW0ct7Ugr7icXYpJVFMNAxUA4/IvjmQv1yNcHU30rqIovTT/lgMIIboCE4FEYCTwkRDCSwPI3WugUBSFsgrXL29X1/CafA9v4IF/wC9Aze52ky3bodvvoVd45nU9KCOQn0b8q65f3vs2iKmFY7ynpqrv4CkeE3CewhNmomuBhYqilAJpQohUoC/gWO3eeoqiKFz9wUbOXCjh32eGEegvo34lFmiaCC/keLoXACiGc8nGrT3Sh2aRqvM8pzwYBtyurmFeGy6bolYpHfioE3pXt3D1iPOwEGKPEGKOEEKrc7UEDCurZWj2mSGEuE8IsU0IsS0rq5rrlDoJd1kphBAIBDkXyzh/scxN3+r9+KqVyFuwdf/PRnTXbzhS+8gFRIQE0DA4gIzzRfYbO0JYFIz70GwdBF+gVsJACLFGCLHPwr9rgY+BdkAv4AzwdnWvryjKZ4qiJCuKkhwbG2v/BCfjbuvEg0PU0M9DZy2sOezDSCORZ7GYdAYg/Ohe8gUlD+/2WOidEILWMWGk5zhJGPgwtTITKYriUCyZEOJzQFt16xRgGEgdp9nn80Q3UOO275izhWP/HY2fnxwGJd6LEFBAmP28CBfTOjqczcdyUBRF+pdqgSujiQxWQWc8oF3y6hdgohAiWAiRAHQA3LTifPVxZ22cpNb66IXFOywsguGD+FxtIi+jLtz/xBYRZBeWseDf457uSp3GlT6DN4UQe4UQe4ChwOMAiqLsBxYBB4CVwEOKolS6sB81xt1zDH8DTeCpH/e4+du9GDnZ8yxefv/vGaRWJ03Llqai2uAyYaAoyu2Kol4v26kAAB5RSURBVHRXFKWHoijXKIpyxuDYa4qitFMUpZOiKFaKsPsmz43xYIKYRFINrPoS3ExQgB/xUaHM+TuNZbtPe7o7dRYZv+hl3DUwQfc5zxP14b0M7zdS1G/qyv0/mauWsH7kOyuL2kvsIoWBDTzhizJ0Gvec8TupmYXu74SX4R3zT9/F3v33NrdCXfBzeCNSGHghb9+oXyLy3TVHuFhaYaO1ROIZvClw55qe+kWUnlmy14M9qbtIYWAHT0wyrk+KI+U1dUGa3/ac4Y2Vh9zfCW9BTvI8Sx25/7Nu7EnvVo0A+G7LSTutJZaQwsAGnnSQGZajmL/pOIU+rB3U99jxqirFq7PO7d1/b1gaNijAj6/v7gdAH41QkFQPKQzqCL/sklES9ZXbvtzMA99s93Q3qo23iegGwQH0S4giwJH1oCVmyLtmB0/PesKC1GqQc/5O486vtnDqQrFH++NuPH3/3UHnZhH8eyyXhVtOOHxOZZXCgn+PU1TmWo2xrt39iNBAtqTnkl8iI/GqixQGNvC0deLAjBHseH44r4zrRmpmIWsPZzF7TYpnO+UBvG0G6mwmD2wDwMvLDrB4ewZHsyxHkJWUV9Jm2m/MXHGIaz7YyHM/7+P5n/e7vH91KZpo36k8AGYsO+DhntQ9pDDwYsKCAggJ9Oe63vqirt9vO8mP22WpivpEfFQYY7o3p7i8kid/2M2wt9cZHS8oKeeX3adZslMt4fXJuqPsP50PwC+7T1FSbpzAvzElm87PryAt+6JL++3pyZIl/DSdyiuWmkF1kcLADt4w6wkPDqBFpH7R8yk/7OYXH8m09Ib77w5MAwSeXLSbg2fUAf/Fpft59LudTP/JOGTyyi5NKK9UuHTmnyS9spq3VqlRZ7d9uZmS8ioeWLCdf4/Vbu2DuhazH6oxq3qhnPJ6pDCwgTfNfH5+aKDR9qPf7eRcfgmlFV5Z1smpeNPv4CqqTAbdxTsyGPXeBt5dfYRjFmb4zSNDeHx4RwByL5aRc7GMD/86ysCZf+raHDpbwMTP/q113+zdf28SF3GN1bW2K6u8qVd1AykM6ghNIvSaQaC/+nb2++8fdHpuJZtrOfvzZurYxLTG/Hd8dx4d1sFs/3t/pLDr5AWz/asev4zEFpFMHdnZaL82wKBpRLBuX1ZBqZN7q+IttYkM0SZsNm8UYqelxBQpDOzgTWPRzw8NZMuzw0h5bTTJBuWuN6flerBXrscbBx1nEx8VxhPDO7L1WYeWCKFBkLoUSUyDIIvHF90/gPdv7g3A/E3pNe6XNz3/jhDdIJhuLSM4luVaf0l9RAoDG3jbINQrvhFNGqoznp7x+sSaren1Wxj4ErENg3n5mkSLx+4YoF9nWFvDamjnJmbtFt0/gNbR4Yzp3pyo8CB+2lG7taPsRxN5l8jo1DSCf47myLpe1aRWK51JPEfnZg11nzekZFNVpRgVuTt4Jp9R723gm3v6MbB9jCe66BR8Ic/AlNv6t6ZpRAgRIQFMnruV0ooqBrSNZsa13SguqzRa9yKmQTDpM8ew+VgOzSJDaBUVpssY9vMTdGzagH+P5XLwTD5dmkdUuy+2xnlv9eXkXlTNYle+s470mWM83Ju6g9QM7OBtsx4tNyTFsezhQbrtJxbt4kKRvqTBP0dVP8KtX2xmyg+73d4/Z+Ktg46r8PcTjOzWjEvbxzC4g7r29/VJcQC8dWNPZl7fw+ycfm2jaR0dblY6Qrvwy4lc31n4pV1sA93nbVJrdhgpDGzhxYOQEILucZE0CFaVu593nabXjNUAHDidzyu/6pNuftyewWkfy1yuL4zq1gyAyzrUTLvrER8JqLkHNcV+bSLvYsqITrrPO0+YO98tUVFZRc+Xf/fp9RCkMKjjbHh6qNH29R//w+jZG8zaDX7zL3d1yal4qWLmNq5PiiP1tVFG0WTVISZcjSo6fK6gRufXRTNdSKA/HZqo2sFryw/abV9RWcX1H/9DXnE5y3af9lprgKuplTAQQtwohNgvhKgSQiSbHJsuhEgVQhwWQoww2D9Ssy9VCDGtNt/vDrz9sWgcHmSUg7D9+HmL7TwVd52aWchTP+zmn6M1n5n6OgH+NX9N/fwEw7s2Jb8WGblerCBb5dt7++s+rz5wzmIbRVFYue8MfV5Zze6MPN3+g2dqJjjrOrXVDPYB1wHrDXcKIboCE4FEYCTwkRDCXwjhD3wIjAK6Ajdr2noldeUl6BkXySe3JTF1ZGeC/P2IaRDMNT1b0DzSeDb55spDfLz2qNv6pSgKV76zjh+2Z3DL55t5/ud9bvtuiZ7I0ECXlmfwxol0bMNgrtBEWt07f5tZyQ6A3/ae4T8LdpBfomZ/v3F9dwAyzvuOf8WQWkUTKYpyECzaFK8FFiqKUgqkCSFSgb6aY6mKohzTnLdQ09Z7q0p54YNuihCqwxHggSHtdPtLyiu5a+5WnTP5I40gMGzjKhRFYdR7xuaqr/89zsvXJBpFPdm9jrM75oNEhgZyoagcRVGqvTaE7Wgi754uDe0Uy5+HMgF49bcDvDquu+7YX4czefhbvX/gjgGtGaRx1p/LL3FvR70EV/kMWgKGyw1laPZZ2++VePvDbo+QQH++vbc/g02cj1VuMBl9tPYoh86aq9tPLNpV7WvV9d/B07Rv0oDi8sqamz/q6O0f2a05jcMCAVi45aSRL+C7zfpy4bNu7MmMa7vRTOOXOWjhufUF7AoDIcQaIcQ+C/+udXXnhBD3CSG2CSG2ZWVlufrr6i2f35FMfFSobrvtM8trFV1ii78OZdJm2m+8teqwbp9hTsTPu0779KptnkCbrZ6S6aJBzkvVt9iGwex84SqCAvxoF9uAhOnLWb73DPtO5fG7xo8wd/Il3KAJ29Xmb3y7+QR3z93qc45ku8JAUZQrFUXpZuHfUhunnQLiDbbjNPus7bf23Z8pipKsKEpybGysva66hPrwOIQE+vPIFcZ1b277cjP7T+dZOaNmHMsqZPLcrbrtuwcl8N7EXrwzoZeRdnKmGmGuPvY+uoSWmuJt0xbvrXYggU0zUW065UYu6xCri6Z6a9Vhxn/0NwDvTOjJkE7GGdxBAeqQ+MehTF5f4fm1x9ccOMeeDMfCY2uLq8xEvwAThRDBQogEoAOwBdgKdBBCJAghglCdzL+4qA+1pq487I5wQ584fnl4oFEZizGzN7L9uHOScsorq7jCpA7/HQNac22vlnRtEcEntyUxoG00AGfyqmeTrU+/gycICwpgUPsYissrrS6cYwtvK8tSXXrGReo+p2VfpLxSoVlECNf1iTNru/elq3SfP1t/zC0mVVvcM38b13zwt1u+q7ahpeOFEBnAAOA3IcQqAEVR9gOLUB3DK4GHFEWpVBSlAngYWAUcBBZp2kpcjJ+foEdcIxb/ZwBf391Xt//6jzc55fp/pxqbncb2aE7r6HDddnhwAK+M6wbA+SLvXfy9vvLsmC4AvLv6iNPLnnt7LsINyeaDvvZZNCU4wN9o+70/PLey4FmDSZM7QsNrJQwURVmiKEqcoijBiqI0VRRlhMGx1xRFaacoSidFUVYY7F+uKEpHzbHXavP97qC+2Q0D/P0Y3CGWh4bqI4qcsV7sDk2m538ub8eRV0fxwS19zNponXnnL1ZHGNSv++8ptHX+V+w7y5srD9tp7Rh1xa/fPDKUOy9to9s+OGMkw7s2dejcdUc856t8Y6Vqplr+6GCjelSuQmYg26CuPOw14Ynhnbiso+qH2XCk9s7k/afy6Ni0AdNGddbZXU1pFBaEEJBxvnqlMerz7+AuGoYE6j6nVLOap93FbeqAvH7pmkT+ePJyvr23n241NGt8NfkSndN918kLzPsn3Q09NKaiskq3zGnXFtUvMFgTpDDwUfz9BF9OSibI3491RzJrfb19p/Po1iLSZht/P8Gg9jGs3H+23mlcdQHt2geWErBqQl2T0e1iG3BpO/s1noZ2asIP/xmg237xl/1sSHGvhqBdv/r+y9u67TulMLBDfR6yAv39aBsbzqJtGSzadtL+CVbILCjhXH6pQzOYqxKbkXG+2OJSjpaQMsN5RIWrwqCgxPHQXl8V2kIIwg00iNu/3OLW788qVMtwD+lovl6Fq5DCwAZ1beZTE7Qzj3d+P8Ivu09TUVlldDzlXIFuYXZrbE1T6yElGay+Zo0hGtPU2sOOz7Skmcg5fHa7Wj4su7DU7He2hd3FbWrRJ2/mrylDSIgJt9/QBfy+X82DsLaSnSuQwsDHGderJcmtG3M2v4RHv9vJS8vU4K6UcwWMfm8Dw99dz6j3NjBnYxpFZZZnlJvTcggP8qdbS9tmIlCXd2waEWxXwEicT5uYcO4dnEBWQSm/7jlT6+vV98zwJhEh/Pnk5VzaTg2JPpHjnppFFZVVzNX4KbQrG7oDKQzsUN+1ZCEEX0xKJkyjEn+7+QQHTudz/4LtHDAYsGf8eoBbPt9s8RrL956hfdOGBDpYXbNlo1BOOehErue33+1oa/3/3/e7WLTVvmnQGff/x+0ZnKqj62kIIZh1Y08ALnvrL47VIE+juqQbCJ3IsEAbLZ2LFAY2qO8zHy2NwoLY+cJw2saEU6XA6NkbLC4ovuvkBbIKSo327T+dR3ZhGbtPOp4l2bJxGKfzHB8c6nrSkzdhGEf/9OI9Dp1jP5rIusjIOF/EUz/uNqoFVNdo0UhfykUb4eNK7tJk8f/y8EA7LZ2LFAYSQB0kck2SwRqFBZL2+mgeHaYvZTH2/Q1UVikUl6kRKd9tUV/yyQPbOPxdLRqFcOZCicezO30VrdnDsGZUTXBkrrTpaA6KAtf2alGr7/IW3v8z1aXXr6is0i1R2rFp7X6f6iKFgR28PbvSmbxhsrbu/27qhRCCJ4Z35OGh7QE4l19Ku2eW0+WFlVwoKmOVxtE1bVRnh78nrlEoZZVVuogJW/hqNIsrmTu5L2O6NyfHgeS/2t7+A2fyCQ305//bO/voqqorgf92EhMgEJJACIHwkSAfiihgLFCRukAFwYK2TqtlRtQq07ocq3bWFMWurmrtojrT6TDLEV1MHZ0FFStgLZZSUZZ2YQflQyF8BAIIJMhHJCQgIYTkzB/3vHDz9d7Lx3v33mT/1rqLe8+5L5z9zn1n37PPPnvnu/ISB5HX519KlhNLU9H63c7vqUdyIt0uC78foqNRZRCGrmacmD66P4vvHseM0f0pfvbWBkG8Hrt5BJf3a/iDHvv0u5w8U80Dk/OabOMPR2jaHa0duYtY6+JGclIC44dkcPJMNUdORV4UjWSmC6cvdh6tZGT/XnHZQRtLJub34XsTBgPw2IrWh2GPlpCC/vfvjo3Z/9ESqgyUBsy+ZgBL/uHaJqkWExOEdx6Z3OxnHrKzhmgJRdGMdhFZ6XiuG+q4Ae882navrkjDe12dYffRSkbHaQdtrHl69miABikyO5rS8iqSEoRpo+K3vyCEKoMIqJXiEilJiex79tb6TGlPzxnNmn+aXL+ZKVpCM4PDUbyV6tcfG0Jmm0hRTNtjJt16uJwz1RcZHWFnelBISkzgtqtzALjvlY9btVcjWkpPV9G/d7d25b1uK6oMwhHsmW1MuCwxgZ/MGMXni2Zxz6ShUe0taEyajZPz/LoiiqNIuKLd0PH0TEmif1q3qEJatzU20TPv7AZg/JD05m8IID++xXHN3VB0ku+81DERf93s+eIMgzN7dPjfjQZVBoonpFv/6UVrw0fQ1JlZ7Lg6tzcbi8vavkgfRksYYzhw4izfGJHFqP6dw0wEkNc3lbl27WDr4dMs/euBDgsvXXm+hqLjZ7j+8sjxk2KBKoMI6GAUGzb8+EYADpadpSbSdFtXkGPChPw+HK+s5vS5lkOYt/X533/yLGeqL3LjSG8yFMaSZ+8Yw3PW8+4X7+yud68GZy/Omu1HWbbpUKv/7olKx7suFG483qgyCINudoodGanJ3Dgyi/0nv+Jnb2t+Iy8ImSNe2Xgw7H0RzUTNrCv87YCTQW/aqOjyBgSN28cNrD9/6q1CAN7aVsrtL2zk4eXbWLi6sNUzrlCwyHiGoHCjyiBK9hyr5Pl1e9TvvQPJ6e28AS3fdJhVW0uavUe/7dgxyW4+W/x+cZsSHIXTET+1A+RAj95yY03jnB0LVm7n0UYup9Hs4wjx63f38vKHBwDol5bS/ga2AVUGUfLPv/+MFzbsZ8+xyAueSnT8ZMbI+vPH3/isxft0fhYbeqYk1Z8XtfBct0UZu3eWB31/QTjmT7mUa+D1ZuI8tSYs/GJXes3sNJ0Z+A739DjZunq1Jha8Ep70Hsls++nN9ddtSdautI9QSIr1u46HuSuinagBZXZn+dNzRrejZf7nyZlX8OdHb2ixfl3hsYh/wxjDjN982KDMraTjSbuUgYj8nYjsFJE6ESlwlQ8VkSoR+dQeS1x114rIDhEpFpHFEpBocLV1hhuG9+VreZleN6VTkZGazF8emwLAt/7royZZuNQsF1v+/OgUBmV258SZyKFBGtPSLzcU7XZ4v/jG1vECtxvoxgVT+XzRLFbMn8g9k4bwWUlF2KxyFy7WMe+VT+qtDc99+2r2PDMj5m1uifbODAqBbwEfNlO33xgz1h4/cJW/CDwIDLeHd9JHgTEGYwyHT50jN8Mb/9/OTiggV0VVDb/80+4m9cF4XQgumakpLc7K2qKLd9gdulcN7DwupS3RIzmJtT+6gfWPf4OBdjPlhPw+jBvs7K14cvUOgCYecx/tL2PEU2v5cK+T5Ck/K5XZYwfEPR6Rm3YpA2PMbmNMeEdxFyKSA6QZY/7POK98rwG3t6cNsSQ0Bu0oraD8XA1j2rDBSomOn33zSgBe+9uhuCURURwGZXRne0kFG4vLmq2P7E0EJyrP1w94O0oryO+bSq9u8YvF7yVX5KQ1ids1ZXgWgzK7s2prKQtX72D4wrV8+8WP6pXuB3sbZvp74x8neaoIILZrBnkisk1EPhCRkGFtIOB2GymxZb7mqA2oNnZQ59lJ6Tfuuz6PqTYey5TnN3C2Wtdm4sUzc64CYO7STRyvPB/150Ku11UXavnaL9/jqdWFXKyt4y+7jjMmt2u/OPXpmcKqHzr5CJbZXA5bDpUz7d8+YMHK7ew6WklqciKbn7qJj5+cRt+e3ngQuYmoDERkvYgUNnPMCfOxL4DBxphxwOPAchFp9ZxRROaLyGYR2XzyZPQ5czsSA5TbTTnpccw61BX5TkFu/fkHrhzJaiWKLRmu2FIPvLq5UW1kO1H1RWdGsLbwi3qvmCF9vMkd7CeyejU/wL/+yRH+uq+M/Kye9O2ZQj+PvIcaE1EZGGNuMsZc1czxhzCfqTbGfGnPtwD7gRFAKZDrujXXlrX0d142xhQYYwqysuK/kzE0PS63SV8yesQvOXVXpGBoJinWf3tfFDGLlI5nR2lFEzNdJGU83XrDGHNp8fi71w2KRfMCR2gMeXHu+CZ1+Vn+Upgx8WESkSzglDGmVkTycRaKDxhjTolIpYhMBDYB9wD/GYs2dCSnz9WQkpRA92RvbXqdnb49Uyj6xa1cv+h9DtkBSZ2J4sNv7y3g/v9xZgVPr9nJ0nnXRfxM47WEOmOoqKphQl5m/WJqV+dPj9zAxVrDmNzefL5oFsYYLtTWsXJLKbNsBFS/0F7X0jtEpASYBLwjIuts1RRgu4h8CrwJ/MAYc8rWPQQsBYpxZgxr29OGWGMMlH91QWcFcWRInx4cLLuUgzkg3seBZuqobJ69w1k7WL/7BO/afQetUcYGOFZ5nv69/WH28ANX5KQ1WD8REVKSEvnehMH07u4vs3N7vYlWG2NyjTEpxphsY8x0W77SGDPaupWON8b80fWZzdbMNMwY87DxsSN5aIHsdFWNrhfEkVH909h1tJLyVmznV9rP14ddipb54GuX1g6i1cXnLtRy5FQV+X2DneKyq6I7kCNgMHx5tlpnBnFkYn4mF2rrKCmv6lI5qL2mX6MFz4qq8PGKWtIRcycO7qAWKfFElUEYRKC2DvYcO8OIbH3biRfpVvEeOuWYitRIFB9SU5IaeHRtPVzeJlXsBzdJpfWoMohA2dlqzl2obVNGL6VthGypDy/f5nFLuh7P3XkNhT+fToLAxn3OJjQN5d418CYiUgC5Iqfzb633C+71Gf+uKHVeeqYkMXNMDis+OUJCmKijza0lTNDYXYFFZwZhCD3siQnSZLu5Ejuy07ox0sYrKjtbrbGJPGDSsD6cqb4Ydt3geGXT4HaP3jQils1SYogqgygYlpXqedyQrkYo/PHe4xrW2gsGuPYJtKSMjzUKXbHk76+tT5ijBA9VBlEweoCuF8SbwX00QqyXDOgdedNYYwuSVxm6lI5BlUEYtteH4lVlEG+ye3UjrZuzpKULmPFnQHrkjWPnay6FZR6Z3Ythur8g0KgyCMPOo06clVlj/LVtvCuQkCD0SHaUQUm5hrSON726XUZ+Xyd2TkuL+FU2ccv91+ex7rEp9NaNmYFGlUEYQl4t2Tr99YSQTfpoRfRhlZWO45vXDABg/e7mU2JWXXCUwdd1naBToMogDH98eDIr5k/U2Dge8cjUywF48IY8j1vSNXnAfu9fnm0+LEh361SR5rMYO0rbEB+HBmpAQUGB2by5cax1pbNzvqaW5MSEsP7uSux49aPPuWZQerOJncq/usDKrSV8f3KevjD5FBHZYowpiHynKgNFUZROS2uUgZqJFEVRFFUGiqIoiioDRVEUBVUGiqIoCqoMFEVRFFQZKIqiKKgyUBRFUVBloCiKohCQTGciMh8oE5FDXrelg+gLlHndiA5E5fE3nU0e6HwyxUqeIdHeGIgdyCKyOdpddEFA5fE3Ko//6Wwy+UEeNRMpiqIoqgwURVGU4CiDl71uQAej8vgblcf/dDaZPJcnEGsGiqIoSmwJysxAURRFiSG+VwYiMkNEikSkWEQWeN2eaBCRQSKyQUR2ichOEfmRLc8UkXdFZJ/9N8OWi4gstjJuF5Hx3krQFBFJFJFtIrLGXueJyCbb5hUikmzLU+x1sa0f6mW7W0JE0kXkTRHZIyK7RWRSwPvnMfusFYrI70SkW5D6SER+KyInRKTQVdbq/hCRefb+fSIyzwtZbDuak+d5+7xtF5HVIpLuqnvCylMkItNd5fEb/4wxvj2ARGA/kA8kA58BV3rdrijanQOMt+e9gL3AlcBzwAJbvgD4lT2fCawFBJgIbPJahmZkehxYDqyx128Ad9nzJcAP7flDwBJ7fhewwuu2tyDPq8AD9jwZSA9q/wADgYNAd1ff3BukPgKmAOOBQldZq/oDyAQO2H8z7HmGj+S5BUiy579yyXOlHdtSgDw75iXGe/zz/EGO8IVOAta5rp8AnvC6XW2Q4w/AzUARkGPLcoAie/4ScLfr/vr7/HAAucB7wFRgjf0Rlrke7Pp+AtYBk+x5kr1PvJahkTy97eApjcqD2j8DgSN2EEyyfTQ9aH0EDG00eLaqP4C7gZdc5Q3u81qeRnV3AMvseYNxLdQ/8R7//G4mCj3kIUpsWWCwU/BxwCYg2xjzha06BmTbc7/L+RvgX4A6e90HOG2MuWiv3e2tl8XWV9j7/UQecBJ4xZq+lopIKgHtH2NMKfCvwGHgC5zvfAvB7iNofX/4up8acT/O7AZ8Io/flUGgEZGewErgUWNMpbvOOKre965cInIbcMIYs8XrtnQgSThT+BeNMeOAr3DMEPUEpX8ArC19Do6SGwCkAjM8bVQHE6T+iISILAQuAsu8bosbvyuDUmCQ6zrXlvkeEbkMRxEsM8asssXHRSTH1ucAJ2y5n+W8HpgtIp8Dr+OYiv4DSBeRUGwrd3vrZbH1vYEv49ngKCgBSowxm+z1mzjKIYj9A3ATcNAYc9IYUwOswum3IPcRtL4//N5PiMi9wG3AXKvgwCfy+F0ZfAIMt14RyTiLXW973KaIiIgA/w3sNsb82lX1NhDycJiHs5YQKr/HeklMBCpc02NPMcY8YYzJNcYMxfn+3zfGzAU2AHfa2xrLEpLxTnu/r97ojDHHgCMiMtIWTQN2EcD+sRwGJopID/vsheQJbB9ZWtsf64BbRCTDzpZusWW+QERm4JhbZxtjzrmq3gbusl5eecBw4GPiPf55tbjSikWYmTjeOPuBhV63J8o2T8aZ0m4HPrXHTBy77HvAPmA9kGnvF+AFK+MOoMBrGVqQ60YueRPl2we2GPg9kGLLu9nrYluf73W7W5BlLLDZ9tFbON4nge0f4OfAHqAQ+F8cz5TA9BHwO5z1jhqcmdv329IfOLb4Ynvc5zN5inHWAEJjwhLX/QutPEXAra7yuI1/ugNZURRF8b2ZSFEURYkDqgwURVEUVQaKoiiKKgNFURQFVQaKoigKqgwURVEUVBkoiqIoqDJQFEVRgP8Hfz5X9hYq7fgAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "np.rad2deg(mytrajectory.loc[:, rotconv].astype(float)).plot()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/doc/source/tutorials/02c-orientation-2markers.ipynb b/doc/source/tutorials/02c-orientation-2markers.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..54f8e020d823aadde592ca19c012e2352e30c8fc
--- /dev/null
+++ b/doc/source/tutorials/02c-orientation-2markers.ipynb
@@ -0,0 +1,347 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Orientation from two markers\n",
+    "\n",
+    "We have seen in the previous section, how to describe the orientation of a rigid body marked by three markers, i.e. forming a local reference frame (X,Y,Z). Having three markers is not always possible, due to physical constrains such as space and camera resolutions. The task is then to find the solutions (they are not unique) to the system:\n",
+    "\n",
+    "$$\n",
+    "  \\begin{equation}\n",
+    "   R.v^{ref}=v^{bee}\n",
+    "   \\end{equation}\n",
+    "$$\n",
+    "   \n",
+    "here $v^{bee}$ is the vector given by the 3D coordinates of the two markers, $v^{ref}$ is the vector given by the 3D coordinates of the two markers when the orientation of the agent is null, i.e. the orientation of the matrix is equal to the identity matrix. \n",
+    "\n",
+    "The system has 3 equations and 9 unknown variables: the elements of the orientation matrix.\n",
+    "\n",
+    "**Note** Why 9 unknown variables if we have three rotation angles? The rotation angles appear in the orientation matrix in cosine and sine function, both nonlinear function. Therefore the sine and the cosine of the rotation angle have to be treated separatly. Moreover the multiplication of two cosines, two sine, or one cosine and one sine has to be treated has variables, because multiplication is... nonlinear.\n",
+    "\n",
+    "$$\\begin{align}\n",
+    "   v_x^{bee} & = +v_x^{ref} \\cos\\alpha \\cos\\beta + v_y^{ref}(\\cos\\alpha \\sin\\beta \\sin\\gamma - \\sin\\alpha \\cos\\gamma) + v_z^{ref} (\\cos\\alpha \\sin\\beta \\cos\\gamma + \\sin\\alpha \\sin\\gamma) \\\\\n",
+    "   v_y^{bee} & = +v_x^{ref} \\sin\\alpha \\cos\\beta + v_y^{ref}(\\sin\\alpha \\sin\\beta \\sin\\gamma + \\cos\\alpha \\cos\\gamma) + v_z^{ref} ( \\sin\\alpha \\sin\\beta \\cos\\gamma - \\cos\\alpha \\sin\\gamma )\\\\\n",
+    "   v_z^{bee} & = -v_x^{ref}\\sin\\beta + v_y^{ref} \\cos\\beta \\sin\\gamma + v_z^{ref} \\cos\\beta \\cos\\gamma\n",
+    "\\end{align}$$\n",
+    "\n",
+    "or equivalently we can look at $v^{ref}=R^Tv^{bee}$, because $R^T=R^{-1}$\n",
+    "\n",
+    "$$\\begin{align}\n",
+    "   v_x^{ref} & = +v_x^{bee} \\cos\\alpha \\cos\\beta +v_y^{bee} \\sin\\alpha \\cos\\beta -v_z^{bee}\\sin\\beta \\\\\n",
+    "   v_y^{ref} & = +v_x^{bee}(\\cos\\alpha \\sin\\beta \\sin\\gamma - \\sin\\alpha \\cos\\gamma) +v_y^{bee}(\\sin\\alpha \\sin\\beta \\sin\\gamma + \\cos\\alpha \\cos\\gamma)+ v_z^{bee} \\cos\\beta \\sin\\gamma \\\\\n",
+    "   v_z^{ref} & = +v_x^{bee}(\\cos\\alpha \\sin\\beta \\cos\\gamma + \\sin\\alpha \\sin\\gamma) + v_y^{bee} ( \\sin\\alpha \\sin\\beta \\cos\\gamma - \\cos\\alpha \\sin\\gamma )+v_z^{bee} \\cos\\beta \\cos\\gamma\n",
+    "   \\end{align}$$\n",
+    "\n",
+    "To simplify the problem, we need to remove terms in the system of equations. Removing terms is easily done by letting certain variables to be zero. We want to determine $\\alpha$, $\\beta$, and $\\gamma$. Thus we can only set to zeros $v_x^{ref}$, $v_y^{ref}$, or $v_z^{ref}$.\n",
+    "\n",
+    "### A simple case\n",
+    "\n",
+    "If we assume that the two markers are aligned with the roll axis,\n",
+    "i.e. $v^{ref}=(1,0,0)^T$, the problem can easily be solve as follow:\n",
+    "\n",
+    "\n",
+    "$$\n",
+    "   \\begin{align}\n",
+    "   v_x^{bee} & = +\\cos\\alpha \\cos\\beta \\\\\n",
+    "   v_y^{bee} & = +\\sin\\alpha \\cos\\beta \\\\\n",
+    "   v_z^{bee} & = -\\sin\\beta\n",
+    "   \\end{align}\n",
+    "$$\n",
+    "\n",
+    "\n",
+    "$$\n",
+    "   \\begin{align}\n",
+    "   \\tan\\alpha & = \\frac{\\pm v_y^{bee}}{\\pm v_x^{bee}}  &\\quad \\text{from L1 and L2} \\\\\n",
+    "   \\tan\\beta & =  \\frac{-v_z^{bee}}{\\pm\\sqrt{v_y^{bee}+v_x^{bee} }} &\\quad\\text{from all}\n",
+    "   \\end{align}\n",
+    "$$\n",
+    "\n",
+    "We remark that the solution does not depend of the angle $\\gamma$, \n",
+    "the roll angle. Thus, when we do not care about the full orientation of \n",
+    "a body but simply care about pitch and yaw, two markers are sufficients.\n",
+    "\n",
+    "### Other cases\n",
+    "\n",
+    "The two markers may be aligned with the pitch (or yaw axis) of the body. \n",
+    "In such situation, the problem is slightly more complex. We can, still \n",
+    "solve the system of equation, but can not find a solution independent of \n",
+    "the pitch (or yaw angle), we need to guess the pitch (or yaw angle)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Angles from two markers\n",
+    "\n",
+    "Here we illustrate, how the solution of the system in three cases: \n",
+    "roll-aligned, pitch-aligned, and yaw-aligned, varies as a function of \n",
+    "the a priori known angle.\n",
+    "\n",
+    "Note that the a priori known angle is the third angle of a convention internally \n",
+    "used by the orientation estimater, namely:\n",
+    "\n",
+    "* The rotation around x, with convention $R_zR_yR_x$, for x-aligned markers\n",
+    "* The rotation around y, with convention $R_zR_xR_y$, for y-aligned markers\n",
+    "* The rotation around z, with convention $R_yR_xR_z$, for x-aligned markers\n",
+    "\n",
+    "Thus the none of the yaw pitch roll angles may match of the a priori \n",
+    "known angle (except for x-aligned markers, because the internal convention is the yaw pitch roll convention)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from navipy.maths.homogeneous_transformations import compose_matrix\n",
+    "import navipy.trajectories.transformations as mtf\n",
+    "from navipy.trajectories.triangle import Triangle"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Similar to the notebook about the background of the orientation, we place a triangle at a known position orientation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "yaw_t = +3*np.pi/4\n",
+    "pitch_t = -1*np.pi/6\n",
+    "roll_t = -1*np.pi/12\n",
+    "angles = [yaw_t, pitch_t, roll_t]\n",
+    "euler_axes = 'rzyx'\n",
+    "# Create a triangle in a given orientation\n",
+    "# and get the x,y,z axis used as our two markers\n",
+    "triangle_mode = 'x-axis=median-from-0'\n",
+    "transform = compose_matrix(angles=angles,\n",
+    "                           axes=euler_axes)\n",
+    "markers = pd.Series(data=0,\n",
+    "                    index=pd.MultiIndex.from_product(\n",
+    "                        [[0, 1, 2], ['x', 'y', 'z']]))\n",
+    "markers.loc[(0, 'x')] = -1\n",
+    "markers.loc[(2, 'y')] = np.sin(np.pi / 3)\n",
+    "markers.loc[(1, 'y')] = -np.sin(np.pi / 3)\n",
+    "markers.loc[(1, 'x')] = np.cos(np.pi / 3)\n",
+    "markers.loc[(2, 'x')] = np.cos(np.pi / 3)\n",
+    "equilateral = Triangle(markers.loc[0],\n",
+    "                       markers.loc[1],\n",
+    "                       markers.loc[2])\n",
+    "equilateral.transform(transform)\n",
+    "_, x_axis, y_axis, z_axis = mtf.triangle2bodyaxis(\n",
+    "    equilateral, triangle_mode)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If can only can get the position of two markers out of three, we have only access to a vector in 3D space and not a triangle. \n",
+    "\n",
+    "We need to assume that this vector is align to one axis, and that one of the three Euler angles is known. When we do not know the angle, we can look at all possible angles, for our known angles."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "known_angles = np.linspace(-np.pi, np.pi, 180)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's assume that the two markers are aligned with one the axes\n",
+    "1. along the x-axis,\n",
+    "2. along the y-axis, or\n",
+    "3. along the z-axis."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "axis_alignement = 'x-axis'\n",
+    "mark0 = pd.Series(data=0, index=['x', 'y', 'z'])\n",
+    "mark1 = pd.Series(x_axis, index=['x', 'y', 'z'])\n",
+    "solution_x_axis = pd.DataFrame(index=known_angles, columns=['yaw',\n",
+    "                                                            'pitch',\n",
+    "                                                            'roll'])\n",
+    "for known_angle in known_angles:\n",
+    "    angles_estimate = mtf.twomarkers2euler(\n",
+    "        mark0, mark1, axis_alignement, known_angle, euler_axes)\n",
+    "    solution_x_axis.loc[known_angle, :] = angles_estimate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "axis_alignement = 'y-axis'\n",
+    "mark0 = pd.Series(data=0, index=['x', 'y', 'z'])\n",
+    "mark1 = pd.Series(y_axis, index=['x', 'y', 'z'])\n",
+    "solution_y_axis = pd.DataFrame(index=known_angles, columns=['yaw',\n",
+    "                                                            'pitch',\n",
+    "                                                            'roll'])\n",
+    "for known_angle in known_angles:\n",
+    "    angles_estimate = mtf.twomarkers2euler(\n",
+    "        mark0, mark1, axis_alignement, known_angle, euler_axes)\n",
+    "    solution_y_axis.loc[known_angle, :] = angles_estimate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "axis_alignement = 'z-axis'\n",
+    "mark0 = pd.Series(data=0, index=['x', 'y', 'z'])\n",
+    "mark1 = pd.Series(z_axis, index=['x', 'y', 'z'])\n",
+    "solution_z_axis = pd.DataFrame(index=known_angles, columns=['yaw',\n",
+    "                                                            'pitch',\n",
+    "                                                            'roll'])\n",
+    "for known_angle in known_angles:\n",
+    "    angles_estimate = mtf.twomarkers2euler(\n",
+    "        mark0, mark1, axis_alignement, known_angle, euler_axes)\n",
+    "    solution_z_axis.loc[known_angle, :] = angles_estimate"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The possible two other angles are shown below, as a function of the known angle"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x288 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axarr = plt.subplots(1, 3, figsize=(15, 4),\n",
+    "                          sharey=True)\n",
+    "ax = axarr[0]\n",
+    "solution_x_axis.plot(ax=ax)\n",
+    "ax.set_title('x-aligned')\n",
+    "ax = axarr[1]\n",
+    "solution_y_axis.plot(ax=ax)\n",
+    "ax.set_title('y-aligned')\n",
+    "ax = axarr[2]\n",
+    "solution_z_axis.plot(ax=ax)\n",
+    "ax.set_title('z-aligned')\n",
+    "\n",
+    "for ax in axarr:\n",
+    "    ax.set_xlabel('input angle [rad]')\n",
+    "    ax.set_ylabel('euler angle [rad]')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We observe that when the markers are aligned with the x-axis, the yaw and pitch can be determined (without having to guess the roll), because yaw and pitch are constant. However we can not obtain the roll angle. \n",
+    "\n",
+    "When the two markers are aligned with the y-axis or z-axis, the pitch or the yaw needs to be apriori known to get the two other angles, repectively. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Solving pitch aligned markers with yaw, pitch, roll convention\n",
+    "\n",
+    "$v^{ref}=(0,-1,0)^T$\n",
+    "\n",
+    "$$\\begin{align}\n",
+    "   v_x^{bee} & = -\\cos\\alpha \\sin\\beta \\sin\\gamma + \\sin\\alpha \\cos\\gamma &\\quad \\text{from L1}\\\\\n",
+    "   v_y^{bee} & = -\\sin\\alpha \\sin\\beta \\sin\\gamma - \\cos\\alpha \\cos\\gamma &\\quad \\text{from L2}\\\\\n",
+    "   v_z^{bee} & = -\\cos\\beta \\sin\\gamma &\\quad \\text{from L3}\n",
+    "   \\end{align}$$\n",
+    "\n",
+    "### for $v_z^{bee}\\neq0 \\Rightarrow \\sin\\gamma\\neq0$\n",
+    "\n",
+    "$$\\begin{align}\n",
+    "   \\cos\\gamma & = v_x^{bee}\\sin\\alpha -v_y^{bee}\\cos\\alpha  &\\quad \\text{from L1 and L2} \\\\\n",
+    "   \\cos\\beta & =  -\\frac{v_z^{bee}}{\\sin\\gamma } &\\quad\\text{from L3}\n",
+    "   \\end{align}$$\n",
+    "\n",
+    "From the two last equation $\\gamma$ and $\\beta$ can be found as a function of $\\alpha$ and $v^{bee}$\n",
+    "\n",
+    "### for $v_z^{bee}=0$\n",
+    "$$\\begin{align}\n",
+    "   \\tan\\alpha & = -\\frac{v_x^{bee}}{v_y^{bee}} &\\quad \\text{from L1 and L2}\\\\\n",
+    "   \\cos\\gamma & =\\pm\\sqrt{ \\left(v_x^{bee}\\right)^2 + \\left(v_y^{bee}\\right)^2 } &\\quad \\text{from L1 and L2}\\\\\n",
+    "   \\end{align}$$\n",
+    "\n",
+    "for $\\beta$ known, and $\\beta\\neq\\pm\\pi/2$:\n",
+    "\n",
+    "$$\\begin{align}\n",
+    "   \\sin\\gamma & = \\frac{v_z^{bee}}{-\\cos\\beta} \\\\\n",
+    "   \\tan\\left(\\alpha+\\theta\\right) & =  \\frac{v_x^{bee}}{-v_y^{bee}} \\\\\n",
+    "   \\tan\\theta&=\\frac{v_z^{bee}\\tan\\beta}{\\pm\\sqrt{1-\\sin^2\\gamma}}\n",
+    "   \\end{align}$$\n",
+    "\n",
+    "for $\\gamma$ known, and $\\gamma\\neq 0 + k2pi$:\n",
+    "\n",
+    "$$\\begin{align}\n",
+    "   \\sin\\left(\\alpha+\\theta\\right) & = \\frac{\\cos\\gamma}{\\sqrt{ \\left(v_x^{bee}\\right)^2+\\left(v_y^{bee}\\right)^2}} \\\\\n",
+    "   \\cos\\beta & =  -\\frac{v_z^{bee}}{\\sin\\gamma } \\\\\n",
+    "   \\tan\\theta&=\\frac{v_y^{bee}}{v_x^{bee}}\n",
+    "   \\end{align}$$"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/navipy/trajectories/__init__.py b/navipy/trajectories/__init__.py
index 5bc744a34d49d83e710b062c1bb6252abfc1f569..17ce2196e96d1ce8cf0abc53d280957bd8e16b8e 100644
--- a/navipy/trajectories/__init__.py
+++ b/navipy/trajectories/__init__.py
@@ -382,18 +382,26 @@ class Trajectory(pd.DataFrame):
                 raise KeyError('df should contains q_2 or alpha_2')
         return self
 
-    def from_markers(self, markers, triangle_mode, error=None):
+    def from_markers(self, markers, triangle_mode,
+                     error=None, markers_labels=[0, 1, 2]):
+        indeces = markers.index
+        # Reinit the pandas dataframe super class
+        # because we now know the indeces
+        super().__init__(index=indeces, columns=self.columns, dtype=float)
+        # If error is provided, we can propagate the error
         if error is not None:
             self.trajectory_error = pd.DataFrame(data=np.nan,
                                                  index=self.index,
                                                  columns=self.columns)
-        mark0 = markers.loc[:, 0]
-        mark1 = markers.loc[:, 1]
-        mark2 = markers.loc[:, 2]
+        markers2use = markers.loc[:, markers_labels]
+        markers2use = markers2use.dropna()
+        mark0 = markers2use.loc[:, markers_labels[0]]
+        mark1 = markers2use.loc[:, markers_labels[1]]
+        mark2 = markers2use.loc[:, markers_labels[2]]
         x = np.zeros(9)  # 3points with x,y,z
         kwargs = {'triangle_mode': triangle_mode,
                   'euler_axes': self.rotation_mode}
-        for index_i in self.index:
+        for index_i in markers2use.index:
             # Assign mark to pos
             x[0:3] = mark0.loc[index_i, ['x', 'y', 'z']].values
             x[3:6] = mark1.loc[index_i, ['x', 'y', 'z']].values
@@ -420,6 +428,7 @@ class Trajectory(pd.DataFrame):
 
             self.loc[index_i, 'location'] = position
             self.loc[index_i, self.rotation_mode] = orientation
+        return self
 
     def world2body(self, markers):
         """ Transform markers in world coordinate to body coordinate