{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Jones_vector class\n",
    "\n",
    "Jones_Vector is a class that manages Jones vectors. It allows the user to create and manipulate them.\n",
    "\n",
    "## Creating an instance\n",
    "\n",
    "An instance must be created before starting to operate with the Jones vector. The initialization accepts one argument: the name of the vector. This name will be used for printing:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from py_pol.jones_vector import Jones_vector, create_Jones_vectors\n",
    "from py_pol.stokes import Stokes\n",
    "from py_pol.utils import degrees\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Source 1 = \n",
      "[+0.000]   \n",
      "[+0.000]   \n",
      "\n"
     ]
    }
   ],
   "source": [
    "E = Jones_vector(\"Source 1\")\n",
    "print(E)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Several Jones_vector objects can be created at the same time using the function *create_Jones_vectors*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Source 2 = \n",
      "[+0.000]   \n",
      "[+0.000]   \n",
      " Source 3 = \n",
      "[+0.000]   \n",
      "[+0.000]   \n",
      "\n",
      "[E = \n",
      "[+0.000]   \n",
      "[+0.000]   \n",
      ", E = \n",
      "[+0.000]   \n",
      "[+0.000]   \n",
      ", E = \n",
      "[+0.000]   \n",
      "[+0.000]   \n",
      "]\n"
     ]
    }
   ],
   "source": [
    "E1, E2 = create_Jones_vectors(name=('Source 2', 'Source 3'))\n",
    "print(E1, E2)\n",
    "\n",
    "list_of_E = create_Jones_vectors(N=3)\n",
    "print(list_of_E)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Jones_vector class fields\n",
    "\n",
    "Jones_vector class objects have some fields where some methods and information  stored:\n",
    "\n",
    "* **M**: 2xN array containing all the Jones vectors.\n",
    "* **name**: Name of the object for print purposes.\n",
    "* **size**: Number of stored Jones vectors.\n",
    "* **shape**: Shape desired for the outputs.\n",
    "* **ndim**: Number of dimensions for representation purposes.\n",
    "* **type**: Type of the object ('Jones_vector'). This is used for determining the object class as using isinstance may throw unexpected results in .ipynb files.\n",
    "\n",
    "Jones_vector class has two classes which groups different methods to extract information:\n",
    "\n",
    "* **parameters**: Object of class *Parameters_Jones_vector*.\n",
    "* **checks**: Object of class *Checks_Jones_vector*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Array:  [[0.61501703+0.j 0.42104134+0.j 0.0344764 +0.j 0.86957058+0.j\n",
      "  0.00166809+0.j 0.11932857+0.j 0.26234312+0.j 0.21023932+0.j\n",
      "  0.98768422+0.j]\n",
      " [0.        +0.j 0.        +0.j 0.        +0.j 0.        +0.j\n",
      "  0.        +0.j 0.        +0.j 0.        +0.j 0.        +0.j\n",
      "  0.        +0.j]]\n",
      "Name:  Source 1\n",
      "Size:  9\n",
      "Shape:  [3, 3]\n",
      "Number of dimensions:  2\n",
      "Type:  Jones_vector \n",
      "\n",
      "Parameters (intensity): \n",
      " [[3.78245944e-01 1.77275808e-01 1.18862244e-03]\n",
      " [7.56152990e-01 2.78252862e-06 1.42393071e-02]\n",
      " [6.88239100e-02 4.42005707e-02 9.75520120e-01]] \n",
      "\n",
      "Checks (circularly polarized): \n",
      " [[False False False]\n",
      " [False False False]\n",
      " [False False False]] \n",
      "\n"
     ]
    }
   ],
   "source": [
    "E = Jones_vector(\"Source 1\")\n",
    "amp = np.random.rand(3,3)\n",
    "E.linear_light(azimuth = 0, amplitude=amp)\n",
    "\n",
    "print('Array: ', E.M)\n",
    "print('Name: ', E.name)\n",
    "print('Size: ', E.size)\n",
    "print('Shape: ', E.shape)\n",
    "print('Number of dimensions: ', E.ndim)\n",
    "print('Type: ', E._type, '\\n')\n",
    "\n",
    "print('Parameters (intensity): \\n', E.parameters.intensity(), '\\n')\n",
    "print('Checks (circularly polarized): \\n', E.checks.is_circular(), '\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generating polarization states\n",
    "\n",
    "As shown in the previous example, the Jones vector is initialized with all elements equal to zero. There are many methods that can be used to generate a more desirable vector:\n",
    "\n",
    "* **from_components**: Creates a Jones_vector object directly from the electric field components $E_x$ and $E_y$.\n",
    "* **from_matrix**: Creates a Jones_vector object directly from a 2xN numpy array.\n",
    "* **from_list**: Creates a Jones_vector object directly from a list of 2 or 2x1 numpy arrays.\n",
    "* **from_distribution**: Creates a Jones vector from the temporal evolution of the electric field components.\n",
    "* **from_Stokes**: Creates a Jones vector from a Stokes object. Take into account that only pure (totally polarized) Stokes vectors must be transformed to Jones vectors, and thet even for them, the global phase is unknown.\n",
    "* **linear_light**: Creates a state of linear polarization with the desired angle.\n",
    "* **circular_light**: Creates a state of circular polarization.\n",
    "* **eliptical_light**: Creates a state of eliptical polarization.\n",
    "* **general**: Creates the most general Jones vector.\n",
    "* **general_azimuth_ellipticity**: Creates a Jones vector from the azimuth, ellipticity and amplitude parameters.\n",
    "* **general_charac_angles**: Creates a Jones vector from the characteristic angles and amplitude parameters.\n",
    "\n",
    "\n",
    "For a more detailed description of each method, refer to the individual documentation of each one.\n",
    "\n",
    "Example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Source 1 = \n",
      "[+0.866]   \n",
      "[+0.500]   \n",
      "\n"
     ]
    }
   ],
   "source": [
    "E = Jones_vector(\"Source 1\")\n",
    "E.linear_light(azimuth = 30*degrees, amplitude=1)\n",
    "print(E)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The previous example only stores one Jones vector. It is possible to store many Jones vectors in the same object. This is useful specially when the same operation is performed upon all of them, as rotation. In this way, it is not required to use for loops, reducing significantly the computation time.\n",
    "\n",
    "There are many ways of creating several Jones vectors in the same object. The first way is creating an object with several identical vectors. This is performed using the *length* argument:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Source 1 = \n",
      "[+1.414]   [+1.414]   [+1.414]   [+1.414]   [+1.414]   \n",
      "[+1.414]   [+1.414]   [+1.414]   [+1.414]   [+1.414]   \n",
      "\n"
     ]
    }
   ],
   "source": [
    "E = Jones_vector(\"Source 1\")\n",
    "E.linear_light(azimuth = 45*degrees, amplitude=2, length = 5)\n",
    "print(E)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A second way of creating several vectors at the same time is using an array as one of the parameters. Take into account that, if you use this option, all parameters must have the same number of elements or just one element. Otherwise, the program will throw an exception."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Source 1 = \n",
      "[+2.000]   [+1.848]   [+1.414]   [+0.765]   [+0.000]   \n",
      "[+0.000]   [+0.765]   [+1.414]   [+1.848]   [+2.000]   \n",
      "\n"
     ]
    }
   ],
   "source": [
    "E = Jones_vector(\"Source 1\")\n",
    "angles = np.linspace(0, 90*degrees, 5)\n",
    "E.linear_light(azimuth = angles, amplitude=2)\n",
    "print(E)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If the parameters have dimension higher than 1, the program will store that information in order to make prints and plots. In that case, the *print* function separates the two components Jones vectors:\n",
    "\n",
    "$$E=\\left[\\begin{array}{c}\n",
    "E_{x}\\\\\n",
    "E_y\n",
    "\\end{array}\\right]$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Source 1 Ex = \n",
      "[[0.74801925+0.j 0.06778686+0.j 0.84726696+0.j]\n",
      " [0.62279092+0.j 0.24990154+0.j 0.20596498+0.j]\n",
      " [0.10693462+0.j 0.31805699+0.j 0.87936437+0.j]]\n",
      "Source 1 Ey = \n",
      "[[0.+0.j 0.+0.j 0.+0.j]\n",
      " [0.+0.j 0.+0.j 0.+0.j]\n",
      " [0.+0.j 0.+0.j 0.+0.j]]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "E = Jones_vector(\"Source 1\")\n",
    "amp = np.random.rand(3,3)\n",
    "E.linear_light(azimuth = 0, amplitude=amp)\n",
    "print(E)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is possible to transfor Stokes vectors into Jones vectors. This process will remove any depolarization present in the Stokes vector."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "E = \n",
      "[+1.000]   \n",
      "[+0.000]   \n",
      "\n"
     ]
    }
   ],
   "source": [
    "S = Stokes().linear_light(azimuth=0)\n",
    "E = Jones_vector().from_Stokes(S)\n",
    "print(E)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Features of creation methods"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*py_pol* model sets the origin of phase in $E_x$. It means that the parameter *global_phase* of most creation methods and the methods *remove_global_phase* and *set_global_phase* (see below) will take it as the reference."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Source 1 = \n",
      "[+0.000 + +0.707j]   \n",
      "[+0.000 + +0.707j]   \n",
      "\n",
      "Source 1 = \n",
      "[+0.707]   \n",
      "[+0.707]   \n",
      "\n"
     ]
    }
   ],
   "source": [
    "E = Jones_vector(\"Source 1\")\n",
    "E.linear_light(azimuth = 45*degrees, global_phase=90*degrees)\n",
    "print(E)\n",
    "\n",
    "E.remove_global_phase()\n",
    "print(E)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All creation methods that accepts an *amplitude* or an *intensity* parameter ($a$ and $b$ for *elliptical_light*) in order to set the electric field amplitude (the norm of the electric field vector) or the intensity. If both of them are given together to the method, it will use the *amplitude*:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Source 1 = \n",
      "[+3.536]   \n",
      "[+3.536]   \n",
      "\n",
      "The intensity of Source 1 is (a.u.):\n",
      "25.0\n",
      "\n",
      "Source 2 = \n",
      "[+1.000]   \n",
      "[+1.000]   \n",
      "\n",
      "The intensity of Source 2 is (a.u.):\n",
      "2.000000000000001\n",
      "\n",
      "Source 3 = \n",
      "[+3.536]   \n",
      "[+3.536]   \n",
      "\n",
      "The intensity of Source 3 is (a.u.):\n",
      "25.0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "E = Jones_vector(\"Source 1\")\n",
    "E.linear_light(azimuth = 45*degrees, amplitude=5)\n",
    "print(E)\n",
    "_ = E.parameters.intensity(verbose=True)\n",
    "\n",
    "E = Jones_vector(\"Source 2\")\n",
    "E.linear_light(azimuth = 45*degrees, intensity=2)\n",
    "print(E)\n",
    "_ = E.parameters.intensity(verbose=True)\n",
    "\n",
    "E = Jones_vector(\"Source 3\")\n",
    "E.linear_light(azimuth = 45*degrees, intensity=2, amplitude=5)\n",
    "print(E)\n",
    "_ = E.parameters.intensity(verbose=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic operations\n",
    "\n",
    "Some physical phenomena that affects polarized light is described by simple operations performed to their Jones vectors.\n",
    "\n",
    "### Addition of two Jones vectors\n",
    "\n",
    "The interference of two light waves can be represented by the sum of their Jones vectors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Source 1 = \n",
      "[+1.000]   \n",
      "[+0.000]   \n",
      "\n",
      "Source 2 = \n",
      "[+0.500 + +0.500j]   \n",
      "[+0.500 + +0.500j]   \n",
      "\n",
      "Source 1 + Source 2 = \n",
      "[+1.500 + +0.500j]   \n",
      "[+0.500 + +0.500j]   \n",
      "\n"
     ]
    }
   ],
   "source": [
    "E1 = Jones_vector(\"Source 1\")\n",
    "E1.linear_light(azimuth = 0*degrees, amplitude=1)\n",
    "print(E1)\n",
    "E2 = Jones_vector(\"Source 2\")\n",
    "E2.linear_light(azimuth = 45*degrees, amplitude=1, global_phase = 45*degrees)\n",
    "print(E2)\n",
    "E3 = E1 + E2\n",
    "print(E3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Multiply by a constant\n",
    "\n",
    "The absorption and gain experienced by a light wave is described by multiplying its Jones vector by a real positive number $c$. The light wave will experience absorption if $c<0$ and gain if $c>0$:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Source 1 = \n",
      "[+0.500]   \n",
      "[+0.000]   \n",
      "\n",
      "Source 1 * 2 = \n",
      "[+1.000]   \n",
      "[+0.000]   \n",
      "\n",
      "Source 1 * 3 = \n",
      "[+1.500]   \n",
      "[+0.000]   \n",
      "\n",
      "Source 1 / 3 = \n",
      "[+0.167]   \n",
      "[+0.000]   \n",
      "\n"
     ]
    }
   ],
   "source": [
    "E1 = Jones_vector(\"Source 1\")\n",
    "E1.linear_light(azimuth = 0*degrees, amplitude = 0.5)\n",
    "print(E1)\n",
    "\n",
    "E2 = 2 * E1\n",
    "print(E2)\n",
    "\n",
    "E2 =  E1*3\n",
    "print(E2)\n",
    "\n",
    "E2 =  E1/3\n",
    "print(E2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If the constant is complex, the constant phase will be added to the global phase of the light, while its absolute value will increase or decrease the electric field amplitude. \n",
    "\n",
    "Take into account that real negative values are a special case of complex numbers whose phase is 180º."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Source 1 = \n",
      "[+0.500]   \n",
      "[+0.000]   \n",
      "\n",
      "Source 1 * (0.5-0.5j) = \n",
      "[+0.250 + -0.250j]   \n",
      "[+0.000 + +0.000j]   \n",
      "\n",
      "Source 1 * 1j = \n",
      "[+0.000 + +0.500j]   \n",
      "[+0.000 + +0.000j]   \n",
      "\n"
     ]
    }
   ],
   "source": [
    "E1 = Jones_vector(\"Source 1\")\n",
    "E1.linear_light(azimuth = 0*degrees, amplitude = 0.5)\n",
    "print(E1)\n",
    "\n",
    "c = 0.5-0.5j\n",
    "E2 = c * E1\n",
    "print(E2)\n",
    "\n",
    "c = 1j\n",
    "E2 = c * E1\n",
    "print(E2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Equality\n",
    "\n",
    "It is possible to compare two *Jones_vector* objects and tell if they are the same. It just compares electric field, not the rest of object fields."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Source 1 = \n",
      "[+1.000]   \n",
      "[+0.000]   \n",
      "\n",
      "Source 2 = \n",
      "[+1.000]   \n",
      "[+0.000]   \n",
      "\n",
      "Comparison:  False\n"
     ]
    }
   ],
   "source": [
    "E1 = Jones_vector(\"Source 1\")\n",
    "E1.linear_light(azimuth = 0*degrees)\n",
    "print(E1)\n",
    "E2 = Jones_vector(\"Source 2\")\n",
    "E2.linear_light(azimuth = 0.01*degrees)\n",
    "print(E2)\n",
    "\n",
    "print('Comparison: ', E1==E2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Operations and multidimensionality\n",
    "\n",
    "The basic operations of *Jones_vector* objects are subject to the same casting rules as numpy arrays. This means that they can be easily used even if one or both elements of the operation have more than one element.\n",
    "\n",
    "Here are some examples:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Source 1 = \n",
      "[+1.000]   \n",
      "[+0.000]   \n",
      "\n",
      "Source 2 = \n",
      "[+0.707 + +0.707j]   [+0.653 + +0.653j]   [+0.500 + +0.500j]   [+0.271 + +0.271j]   [+0.000 + +0.000j]   \n",
      "[+0.000 + +0.000j]   [+0.271 + +0.271j]   [+0.500 + +0.500j]   [+0.653 + +0.653j]   [+0.707 + +0.707j]   \n",
      "\n",
      "Source 1 + Source 2 = \n",
      "[+1.707 + +0.707j]   [+1.653 + +0.653j]   [+1.500 + +0.500j]   [+1.271 + +0.271j]   [+1.000 + +0.000j]   \n",
      "[+0.000 + +0.000j]   [+0.271 + +0.271j]   [+0.500 + +0.500j]   [+0.653 + +0.653j]   [+0.707 + +0.707j]   \n",
      "\n"
     ]
    }
   ],
   "source": [
    "E1 = Jones_vector(\"Source 1\")\n",
    "E1.linear_light(azimuth = 0*degrees, amplitude=1)\n",
    "print(E1)\n",
    "E2 = Jones_vector(\"Source 2\")\n",
    "angles = np.linspace(0, 90*degrees, 5)\n",
    "E2.linear_light(azimuth = angles, amplitude=1, global_phase = 45*degrees)\n",
    "print(E2)\n",
    "\n",
    "E3 = E1 + E2\n",
    "print(E3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Source 1 = \n",
      "[+0.866]   \n",
      "[+0.500]   \n",
      "\n",
      "E = \n",
      "[+0.087]   [+0.563]   [+1.039]   [+1.516]   [+1.992]   \n",
      "[+0.050]   [+0.325]   [+0.600]   [+0.875]   [+1.150]   \n",
      "\n"
     ]
    }
   ],
   "source": [
    "E1 = Jones_vector(\"Source 1\")\n",
    "E1.linear_light(azimuth = 30*degrees, amplitude = 1)\n",
    "print(E1)\n",
    "\n",
    "c = np.linspace(0.1, 2.3, 5)\n",
    "E2 = c * E1\n",
    "print(E2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Source 1 = \n",
      "[+0.000]   [-0.383]   [-0.707]   [-0.924]   [-1.000]   \n",
      "[+1.000]   [+0.924]   [+0.707]   [+0.383]   [+0.000]   \n",
      "\n",
      "Source 2 = \n",
      "[+0.707 + +0.707j]   [+0.653 + +0.653j]   [+0.500 + +0.500j]   [+0.271 + +0.271j]   [+0.000 + +0.000j]   \n",
      "[+0.000 + +0.000j]   [+0.271 + +0.271j]   [+0.500 + +0.500j]   [+0.653 + +0.653j]   [+0.707 + +0.707j]   \n",
      "\n",
      "Source 1 - Source 2 = \n",
      "[-0.707 + -0.707j]   [-1.036 + -0.653j]   [-1.207 + -0.500j]   [-1.194 + -0.271j]   [-1.000 + -0.000j]   \n",
      "[+1.000 + +0.000j]   [+0.653 + -0.271j]   [+0.207 + -0.500j]   [-0.271 + -0.653j]   [-0.707 + -0.707j]   \n",
      "\n"
     ]
    }
   ],
   "source": [
    "E1 = Jones_vector(\"Source 1\")\n",
    "angles = np.linspace(0, 90*degrees, 5)\n",
    "E1.linear_light(azimuth = 90*degrees + angles, amplitude=1)\n",
    "print(E1)\n",
    "E2 = Jones_vector(\"Source 2\")\n",
    "E2.linear_light(azimuth = angles, amplitude=1, global_phase = 45*degrees)\n",
    "print(E2)\n",
    "\n",
    "E3 = E1 - E2\n",
    "print(E3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Jones vector manipulation\n",
    "\n",
    "There are several operations that can be applied to a Jones vector. Some of them are common to all py_pol objects and are inherited from their parent *Py_pol* class:\n",
    "\n",
    "* **clear**:  Removes data and name form Jones vector.\n",
    "* **copy**:  Creates a copy of the Jones_vector object.\n",
    "* **stretch**:  Stretches a Jones vector of size 1.\n",
    "* **shape_like**:  Takes the shape of another object to use as its own.\n",
    "* **reshape**: Changes the shape of the object.\n",
    "* **flatten**:  Transforms N-D objects into 1-D objects (0-D if only 1 element).\n",
    "* **flip**: Flips the object along some dimensions.\n",
    "* **get_list**: Creates a list with single elements.\n",
    "* **from_list**: Creates the object from a list of single elements.\n",
    "* **concatenate**: Canocatenates several objects into a single one.\n",
    "* **draw**: Draws the components of the object.\n",
    "* **clear**: Clears the information of the object.\n",
    "\n",
    "The rest of the manipulation methods are:\n",
    "\n",
    "* **simplify**:  Simplifies the Jones vector in several ways.\n",
    "* **rotate**: Rotates the Jones vector.\n",
    "* **rotate_to_azimuth**: Rotates the Jones vector to have a certain azimuth.\n",
    "* **sum**: Calculates the summatory of the Jones vectors in the object.\n",
    "* **reciprocal**: Calculates the Jones vector that propagates backwards.\n",
    "* **orthogonal**: Calculates the orthogonal Jones vector.\n",
    "* **normalize**: Normalize the electric field to be normalized in electric field amplitude or intensity.\n",
    "* **remove_global_phase**: Calculates the global phase of the electric field (respect to the X component) and removes it.\n",
    "* **add_global_phase**: Adds a global phase to the Jones vector.\n",
    "* **set_global_phase**: Sets the global phase of the Jones vector.\n",
    "* **add_delay**: Adds a phase to the Y component of the electric field of the Jones vector.\n",
    "\n",
    "All the functions return the *Jones_vector* object. For a more detailed description of each method, refer to the individual documentation of each one.\n",
    "\n",
    "Example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Source 1 = \n",
      "[+1.000]   \n",
      "[+0.000]   \n",
      "\n",
      "Source 1 @ 45.00 deg = \n",
      "[+0.707]   \n",
      "[+0.707]   \n",
      "\n"
     ]
    }
   ],
   "source": [
    "E1 = Jones_vector('Source 1')\n",
    "E1.linear_light(azimuth=0*degrees)\n",
    "print(E1)\n",
    "\n",
    "E1.rotate(angle=45*degrees)\n",
    "print(E1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All manipulation methods have the *keep* argument that specifies if the originial object must be preserved or transformed. If *keep* is *True* (default is *False*), a new object is created:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Source 1 = \n",
      "[+1.000]   \n",
      "[+0.000]   \n",
      " Source 2 = \n",
      "[+0.707]   \n",
      "[+0.707]   \n",
      "\n",
      "Source 2 = \n",
      "[+0.707]   \n",
      "[+0.707]   \n",
      " Source 2 = \n",
      "[+0.707]   \n",
      "[+0.707]   \n",
      "\n"
     ]
    }
   ],
   "source": [
    "E1 = Jones_vector('Source 1')\n",
    "E1.linear_light(azimuth=0*degrees)\n",
    "\n",
    "E2 = E1.rotate(angle=45*degrees, keep=True)\n",
    "E2.name = 'Source 2'\n",
    "print(E1, E2)\n",
    "\n",
    "E2 = E1.rotate(angle=45*degrees, keep=False)\n",
    "E2.name = 'Source 2'\n",
    "print(E1, E2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Jones_vector* objects allow taking elements and changing them through indices like a *numpy.ndarray*.\n",
    "\n",
    "Examples:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original Ex = \n",
      "[[0.35500101+0.j 0.94538106+0.j 0.65144759+0.j 0.63379627+0.j\n",
      "  0.98255865+0.j]\n",
      " [0.43714343+0.j 0.85102872+0.j 0.4445816 +0.j 0.98904345+0.j\n",
      "  0.56117136+0.j]\n",
      " [0.95505127+0.j 0.47750025+0.j 0.55089862+0.j 0.47048076+0.j\n",
      "  0.05289377+0.j]]\n",
      "Original Ey = \n",
      "[[0.30705444+0.j 0.52885621+0.j 0.62037901+0.j 0.37755201+0.j\n",
      "  0.46620769+0.j]\n",
      " [0.62435427+0.j 0.42716733+0.j 0.58935287+0.j 0.86122974+0.j\n",
      "  0.02752495+0.j]\n",
      " [0.06751956+0.j 0.12168219+0.j 0.29304389+0.j 0.05574455+0.j\n",
      "  0.99074919+0.j]]\n",
      "\n",
      "Original_picked = \n",
      "[+0.355]   [+0.945]   [+0.651]   \n",
      "[+0.307]   [+0.529]   [+0.620]   \n",
      "\n"
     ]
    }
   ],
   "source": [
    "M = np.random.rand(2, 3, 5)\n",
    "E = Jones_vector('Original')\n",
    "E.from_matrix(M)\n",
    "print(E)\n",
    "\n",
    "E2 = E[0:3]\n",
    "print(E2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original Ex = \n",
      "[[0.96241651+0.j 0.87062664+0.j 0.91045247+0.j 0.9812124 +0.j\n",
      "  0.99605732+0.j]\n",
      " [0.85265436+0.j 0.97903424+0.j 0.70090319+0.j 0.88598524+0.j\n",
      "  0.84667768+0.j]\n",
      " [0.75594403+0.j 0.67035388+0.j 0.96930049+0.j 0.6361048 +0.j\n",
      "  0.94094799+0.j]\n",
      " [0.89500501+0.j 0.98413128+0.j 0.72509852+0.j 0.96560159+0.j\n",
      "  0.77291154+0.j]]\n",
      "Original Ey = \n",
      "[[0.27157771+0.j 0.49194435+0.j 0.4136137 +0.j 0.19293064+0.j\n",
      "  0.08871204+0.j]\n",
      " [0.5224754 +0.j 0.20369577+0.j 0.71325642+0.j 0.46371345+0.j\n",
      "  0.53210611+0.j]\n",
      " [0.65463626+0.j 0.74204156+0.j 0.24587915+0.j 0.77160267+0.j\n",
      "  0.33855116+0.j]\n",
      " [0.44605609+0.j 0.1774419 +0.j 0.68864515+0.j 0.2600261 +0.j\n",
      "  0.63451379+0.j]]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "E2 = Jones_vector(\"New data\").linear_light(azimuth=np.random.rand(3, 2))\n",
    "E3 = Jones_vector(\"Original\").linear_light(azimuth=np.random.rand(4, 5))\n",
    "E3[0:3,0:2] = E2\n",
    "print(E3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Picking and setting\n",
    "\n",
    "*Py_pol* objects allow taking elements and changing them through indices like a *numpy.ndarray*.\n",
    "\n",
    "Examples:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original = \n",
      "[+0.925]   [+0.872]   [+0.980]   [+0.096]   [+0.788]   \n",
      "[+0.777]   [+0.798]   [+0.066]   [+0.912]   [+0.579]   \n",
      "\n",
      "Original_picked = \n",
      "[+0.872]   [+0.980]   \n",
      "[+0.798]   [+0.066]   \n",
      "\n"
     ]
    }
   ],
   "source": [
    "M = np.random.rand(2, 5)\n",
    "J1 = Jones_vector('Original')\n",
    "J1.from_matrix(M)\n",
    "print(J1)\n",
    "\n",
    "J2 = J1[1:3]\n",
    "print(J2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original Ex = \n",
      "[[ 1.00000000e+00+0.j  9.91444861e-01+0.j  9.65925826e-01+0.j\n",
      "   9.23879533e-01+0.j  8.66025404e-01+0.j]\n",
      " [ 7.93353340e-01+0.j  7.07106781e-01+0.j  6.08761429e-01+0.j\n",
      "   5.00000000e-01+0.j  3.82683432e-01+0.j]\n",
      " [ 2.58819045e-01+0.j  1.30526192e-01+0.j  6.12323400e-17+0.j\n",
      "  -1.30526192e-01+0.j -2.58819045e-01+0.j]\n",
      " [-3.82683432e-01+0.j -5.00000000e-01+0.j -6.08761429e-01+0.j\n",
      "  -7.07106781e-01+0.j -7.93353340e-01+0.j]\n",
      " [-8.66025404e-01+0.j -9.23879533e-01+0.j -9.65925826e-01+0.j\n",
      "  -9.91444861e-01+0.j -1.00000000e+00+0.j]]\n",
      "Original Ey = \n",
      "[[0.00000000e+00+0.j 1.30526192e-01+0.j 2.58819045e-01+0.j\n",
      "  3.82683432e-01+0.j 5.00000000e-01+0.j]\n",
      " [6.08761429e-01+0.j 7.07106781e-01+0.j 7.93353340e-01+0.j\n",
      "  8.66025404e-01+0.j 9.23879533e-01+0.j]\n",
      " [9.65925826e-01+0.j 9.91444861e-01+0.j 1.00000000e+00+0.j\n",
      "  9.91444861e-01+0.j 9.65925826e-01+0.j]\n",
      " [9.23879533e-01+0.j 8.66025404e-01+0.j 7.93353340e-01+0.j\n",
      "  7.07106781e-01+0.j 6.08761429e-01+0.j]\n",
      " [5.00000000e-01+0.j 3.82683432e-01+0.j 2.58819045e-01+0.j\n",
      "  1.30526192e-01+0.j 1.22464680e-16+0.j]]\n",
      "\n",
      "Original_picked Ex = \n",
      "[[ 6.08761429e-01+0.j  5.00000000e-01+0.j]\n",
      " [ 6.12323400e-17+0.j -1.30526192e-01+0.j]]\n",
      "Original_picked Ey = \n",
      "[[0.79335334+0.j 0.8660254 +0.j]\n",
      " [1.        +0.j 0.99144486+0.j]]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "J1 = Jones_vector('Original')\n",
    "angles = np.linspace(0,180*degrees, 25)\n",
    "J1.linear_light(azimuth=angles, shape=[5,5])\n",
    "print(J1)\n",
    "\n",
    "J2 = J1[1:3,2:4]\n",
    "print(J2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Iterating\n",
    "\n",
    "*Py_pol* objects are iterable. When introduced in a for loop, a new object picking in the first dimension is returned."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "E_picked = \n",
      "[+0.985 + +0.000j]   \n",
      "[+0.000 + +0.174j]   \n",
      "\n",
      "E_picked = \n",
      "[+0.696 + -0.123j]   \n",
      "[+0.696 + +0.123j]   \n",
      "\n",
      "E_picked = \n",
      "[+0.000 + -0.174j]   \n",
      "[+0.985 + +0.000j]   \n",
      "\n",
      "E_picked = \n",
      "[-0.696 + -0.123j]   \n",
      "[+0.696 + -0.123j]   \n",
      "\n",
      "E_picked = \n",
      "[-0.985 + -0.000j]   \n",
      "[+0.000 + -0.174j]   \n",
      "\n"
     ]
    }
   ],
   "source": [
    "angles = np.linspace(0, 180*degrees, 5)\n",
    "Jtotal = Jones_vector().general_azimuth_ellipticity(azimuth=angles, ellipticity=10*degrees)\n",
    "for J in Jtotal:\n",
    "    print(J)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parameters of Jones_vector\n",
    "\n",
    "Several parameters can be measured from a Jones vector. They are implemented in the independent class *Parameters_Jones_vector*, which is stored in the *parameters* field of *Jones_vector* class.\n",
    "\n",
    "* **matrix**:  Gets a numpy array with all the vectors (similat to object.M).\n",
    "* **components**: Calculates the electric field components of the Jones vector.\n",
    "* **amplitudes**: Calculates the electric field amplitudes of the Jones vector.\n",
    "* **intensity**: Calculates the intensity of the Jones vector.\n",
    "* **irradiance**: Calculates the irradiance of the Jones vector.\n",
    "* **alpha**: Calculates the ratio between electric field amplitude angle ($|E_x|$/$|E_y|$).\n",
    "* **delay / delta**: Calculates the delay (phase shift) between Ex and Ey components of the electric field.\n",
    "* **charac_angles**: Calculates both alpha and delay, the characteristic angles of the Jones vector.\n",
    "* **azimuth**: Calculates azimuth, that is, the orientation angle of the major axis.\n",
    "* **ellipticity_angle**: Calculates the ellipticity angle.\n",
    "* **azimuth_ellipticity**: Calculates both azimuth and ellipticity angles.\n",
    "* **ellipse_axes**: Calculates the length of major and minor axis (a,b).\n",
    "* **ellipticity_param**: Calculates the ellipticity parameter, b/a.\n",
    "* **eccentricity**: Calculates the eccentricity, the complementary of the ellipticity parameter.\n",
    "* **global_phase**: Calculates the global phase of the Jones vector (respect to the X component of the electric field).\n",
    "* **degree_linear_polarization**: Calculates the degree of linear polarization of the Jones vector.\n",
    "* **degree_circular_polarization**: Calculates the degree of circular polarization of the Jones vector.\n",
    "* **norm**: Calculates the norm of the Jones vector (same as intensity for Jones vectors).\n",
    "\n",
    "\n",
    "* **get_all**: Returns a dictionary with all the parameters of Jones vector.\n",
    "\n",
    "For a more detailed description of each method, refer to the individual documentation of each one.\n",
    "\n",
    "Example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n"
     ]
    }
   ],
   "source": [
    "E = Jones_vector(\"Source 1\")\n",
    "E.linear_light(azimuth = 45*degrees)\n",
    "I0 = E.parameters.intensity()\n",
    "print(I0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When several Jones vectors are stored in the object, setting *verbose* argument to *True* makes the method print the values in screen. Also, 1D or 2D figures can be shown if the *draw* argument is set to *True*:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "azimuth (12,)\n",
      "The azimuth of Source 1 is (deg.):\n",
      "[  0.          16.36272727  32.72545455  49.08818182  65.45090909\n",
      "  81.81363636  98.17636364 114.53909091 130.90181818 147.26454545\n",
      " 163.62727273 179.99      ]\n",
      "The mean value is 89.995 +- 56.484994071904\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "az = np.linspace(0, 179.99*degrees, 12)\n",
    "E = Jones_vector(\"Source 1\")\n",
    "E.general_azimuth_ellipticity(azimuth=az)\n",
    "az = E.parameters.azimuth(draw=True, verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The azimuth and ellipticity angles of Source 1 are (deg.):\n",
      "The mean value of param Azimuth (deg.) is 91.39034179687499 +- 52.24055378247462\n",
      "The mean value of param Ellipticity angle (deg.) is 3.552713678800501e-15 +- 26.184535918359188\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "az = np.linspace(0, 179.99*degrees, 128)\n",
    "el = np.linspace(-45*degrees, 45*degrees, 128)\n",
    "AZ, EL = np.meshgrid(az, el)\n",
    "\n",
    "E = Jones_vector(\"Source 1\")\n",
    "E.general_azimuth_ellipticity(azimuth=AZ, ellipticity=EL)\n",
    "AZ, EL = E.parameters.azimuth_ellipticity(draw=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is a method in *Parameters_Jones_vector* class, *get_all* that computes all the parameters available and stores in a dictionary *.dict_params()*. Using the *print* function upon the *Parameters_Jones_vector* class invokes the method *get_all*.\n",
    "\n",
    "Example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Source 1 = \n",
      "[+1.000]   [+0.831]   [+0.383]   [-0.195]   [-0.707]   \n",
      "[+0.000]   [+0.556]   [+0.924]   [+0.981]   [+0.707]   \n",
      " \n",
      "\n",
      "The elctric field components of Source 1 are (V/m):\n",
      "  Ex (V/m)\n",
      "[ 1.        +0.j  0.83146961+0.j  0.38268343+0.j -0.19509032+0.j\n",
      " -0.70710678+0.j]\n",
      "  Ey (V/m)\n",
      "[0.        +0.j 0.55557023+0.j 0.92387953+0.j 0.98078528+0.j\n",
      " 0.70710678+0.j]\n",
      "The mean value of param Ex (V/m) is (0.2623911882929919+0j) +- 0.6374328012998054\n",
      "The mean value of param Ey (V/m) is (0.6334683654241334+0j) +- 0.3514941224830809\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "The elctric field amplitudes of Source 1 are (V/m):\n",
      "  Ex (V/m)\n",
      "[1.         0.83146961 0.38268343 0.19509032 0.70710678]\n",
      "  Ey (V/m)\n",
      "[0.         0.55557023 0.92387953 0.98078528 0.70710678]\n",
      "The mean value of param Ex (V/m) is 0.6232700295740622 +- 0.294455738781694\n",
      "The mean value of param Ey (V/m) is 0.6334683654241334 +- 0.3514941224830809\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "The intensity of Source 1 is (a.u.):\n",
      "[1. 1. 1. 1. 1.]\n",
      "The mean value is 1.0 +- 0.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "The norm of Source 1 is (a.u.):\n",
      "[1. 1. 1. 1. 1.]\n",
      "The mean value is 1.0 +- 0.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "The irradiance of Source 1 is (W/m^2):\n",
      "[0.00132721 0.00132721 0.00132721 0.00132721 0.00132721]\n",
      "The mean value is 0.0013272104690361798 +- 0.0\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "The ratio angle between electric field amplitudes of Source 1 is (deg.):\n",
      "[ 0.   33.75 67.5  78.75 45.  ]\n",
      "The mean value is 45.0 +- 27.55675960631076\n"
     ]
    },
    {
     "data": {
      "image/png": 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t8nU5jcKanPG5J1bmsKewhHuTBxARbj+S3nbxsHiGdovhwfcyKTwW/CGE/UQZn9pccITnPs7lp8PiA+pWgIEsLEy4N3kg+48e52/LN/m6HK+zJmd8pipsaBppYUNjG9ilNVeO7M7Ln20lY3eRr8vxKmtyxmfe37CXj7P38ccJvYltZWFDY7v1/D7ENI9izsINqAZvCGFNzvhE8fFy7l2cQd9OrbhqVHdflxOSWjeP5PZJffhi60HeCeIQwpqc8YknPsphd2EJc5MHWtjgQ5cM78qQrjE8sDSLopLgDCHsp8s0utyCIzz7cS4XDe0S1LcIDATfhxClQRtCWJMzjUpVuWdRBk0jwpk1xcIGfzAovjU/G9GNlz/bRuae4AshrMmZRrUsPY/Vmwq4eUJvOrRq6utyjONPE/sQ3TSC2QuCL4SwJmcazbHjFf8LG64ebWGDP4lpHsXtk/ryxdaDvPt1cIUQ1uRMo3lyZQ67Dh0jZZqd2eCPLk3qymAnhDgcRCGE/aSZRrFl31Hmrc7l/4Z2YWRPu3WgP3KFEAPYd6SUxz7M9nU5HmNNznhd1ZkNURFh3GFnNvi10+NjuPyMbrz06Vay9gZHCOFWkxORGBF5S0SyRCRTREaLSFsRWS4i2c5jG28XawLTBxl5rNpUwE3jE+kQbWGDv7ttYh9aNY1g9oL0oAgh3N2T+zvwvqr2BQYDmcAsYIWqJgIrnGVjfuDY8QrmLsqgd8eWzDwzwdflGDe0aRHFbRP7sm7LARZ+s9vX5TRYrU1ORKKBc4DnAVT1uKoeApKB+c5m84Hp3inRBLKnU11hw9zkgURa2BAwLjujK6fHt+a+JZkBH0K481PXEygAXhSRr0TkORFpAXRU1T0AzmMHL9ZpAtDWfUd5ZlUuyUM6M8rChoAS7pwJse9IKX8P8BDCnSYXAQwDnlbVocBR6nBoKiLXiUiaiKQVFBTUs0wTaFSVlEXpRIYLd07p5+tyTD0M7hrD5Wd05cVPt7Ip77Cvy6k3d5rcTmCnqq51lt/C1fTyRCQOwHnMr+nNqjpPVZNUNSk2NtYTNZsA8GFmPis3us5s6GhhQ8D608S+TggRuGdC1NrkVHUvsENE+jgvjQMygIXATOe1mcACr1RoAk5JWQUpi9ItbAgCbVtEcev5ffg8N3BDCHe/Cf4D8IqIfAsMAR4AHgQmiEg2MMFZNoanUjez8+AxUqZZ2BAMrhjRjUFdWvPA0kyOlJb7upw6c+snUFW/dg45T1fV6ap6UFX3q+o4VU10Hg94u1jj/7btP8ozqzZz4eDOjD7NwoZgEB4mzE0eQF5RKY+vCLwQwv43azxq7qIMIsOEuyxsCCpDu7XhsqSuvLBmC9kBFkJYkzMe82FGHiuy8rlxfCKdWlvYEGxum9SHFk0C70wIa3LGI0rKKkhZnE6vDi25ZkwPX5djvKBdyybcOrEPn+XuZ/G3e3xdjtusyRmPeGbVZnYcOMbcaQMsbAhiPxvRjYFdorlvSUbAhBD202gabPv+Yp5K3cwFp8dxZq/2vi7HeFF4mJAybSB5RaX8I0BCCGtypsHmLk4nIky4a6qFDaFgePc2XJoUz/NrtpCT7/8hhDU50yAfZeXxYWY+N45LJK51M1+XYxrJ7ZP60jwqnDkL/T+EsCZn6q2krIJ7FmZwWmwLCxtCTFUI8UnOfpZ8598hhDU5U2//XJXL9gPFzE0eSFSE/SiFmhkjuzOgczT3Lc7kqB+HEPaTaeplx4FinkrNYeqgOMZY2BCSXGdCDGRvUQn/+CjH1+WclDU5Uy9zF2cQHib8+QILG0LZ8O5tuHh4PM+vySUn/4ivy6mRNTlTZyuz8lmekccfzrOwwcCsyX1pGhnOPX4aQliTM3VSUlbBPYvS6RnbgmvPsrDBQPuWTbj1/D6sydnHexv2+rqcH7EmZ+rk2dW5bNtfTMq0ARY2mP+ZMbIb/eKiuXdxBsXH/SuEsJ9S47YdB4p5MjWHKYM6cXaiXeXZfC8iPIx7kwewp9D/QghrcsZt9y7OQBD+PLW/r0sxfigpoS0/HRbPcx/nsrnAf0IIa3LGLakb8/kgI48/jOtF5xgLG0zN/DGEsCZnalVaXsE9C9Pp2b4Fvzyrp6/LMX4stlUT/jihNx9n72NZun+EENbkTK2eXZ3L1v3F3GNhg3HDVaO607dTK+Yu8o8Qwn5izSntPFjMEytzmDSgE+f0trDB1C4iPIy5yQPZXVjCkyt9H0JYkzOndN/iTADuvtDCBuO+ET3actHQLjy7egu5Pg4hrMmZk1q1qYD30/fyh/MS6WJhg6mjWVP60iQijHsWZfg0hLAmZ2pUFTb0aN+CX55tZzaYuuvQqik3T+jN6k0FLEvP81kdbjU5EdkqIt+JyNcikua81lZElotItvPYxrulmsb03Mdb2LLvKHMu7E+TiHBfl2MC1NWjXSHEvYszOHa8wic11GVP7ieqOkRVk5zlWcAKVU0EVjjLJgjsOnSMJz7KYeKAjozt08HX5ZgAFhEeRsq0Aew6dMxnIURDDleTgfnO8/nA9AZXY/zCfYszUJS7L7CwwTTcyJ7tmD6kM/NW57Jl39FGH9/dJqfAByKyXkSuc17rqKp7AJxH+19+EPg4u4D3Nuzl9z/pRXyb5r4uxwSJO6f0IyoijJRFjX8mhLtNboyqDgMmA9eLyDnuDiAi14lImoikFRQU1KtI0ziOl1cyZ2E6Ce2a86tz7MwG4zkdopty0/hEUjcWsDyjcUMIt5qcqu52HvOBd4ARQJ6IxAE4j/knee88VU1S1aTYWPtlUn/2/Jot5BYcZc60ARY2GI+beWYCvTu2JGVR44YQtTY5EWkhIq2qngPnAxuAhcBMZ7OZwAJvFWm8b/ehYzy+IpsJ/TvyEwsbjBdEOmdC7Dp0jKdTGy+EcGdPriOwRkS+AdYBS1T1feBBYIKIZAMTnGUToO5fkkmlKrMtbDBeNKpnO5KHdOaZ1blsbaQQotYmp6q5qjrY+TNAVe93Xt+vquNUNdF5POD9co03rMnex5Lv9nD9T3rRta2FDca77pzSj8gwabQQws54CHHHyyuZvXAD3ds15zoLG0wj6BjtOhNi5cYCPsys8at8j7ImF+Je+MQJGy7sT9NICxtM4/g+hEinpMy7IYQ1uRC2p9AVNozv15Hz+nb0dTkmhESGh5EybSA7Dx7j6dTNXh3LmlwIu39JJhWVyhy7jJLxgdGntePCwZ15etVmtu33XghhTS5EfZKzj8Xf7uG3Y0+zsMH4zF1OCDF3UYbXxrAmF4Kqzmzo1rY5vzn3NF+XY0JYp9ZNuXF8Iiuy8vnQS2dCWJMLQS99uoWc/CMWNhi/cM2YHvTq0JKUxd4JIazJhZi9hSU89mE24/p2YFw/CxuM70WGhzF32gB2HDjGM6s8H0JYkwsx9y/NpLxSmXPhAF+XYsz/nNmrPRecHsfTqZvZcaDYo59tTS6EfLp5H4u+2c1vzz2Nbu0sbDD+5a6p/QgPE1I8HEJYkwsRZRWVzFmQTte2zfjtWAsbjP+Ja92MG8Yl8uX2g+QVlXjscyM89knGr730yVay84/w3NVJFjYYv/WLMT24/IyuxDSP8thnWpMLAXlFJTz24SbO69uB8f0tbDD+KyoijKgIzzU4sMPVkPDA0kzK7MwGE6KsyQW5zzbvZ8HXu/nNOT3p3q6Fr8sxptFZkwtiZRWVzFm4gfg2zfjt2F6+LscYn7AmF8Tmf7qVTXlHmH1Bf5pFWdhgQpM1uSCVX+Q6s2Fsn1gmWNhgQpg1uSD1wNJMjpdXcs+FAxARX5djjM9YkwtCa3P38+7Xu/n1uT1JaG9hgwlt1uSCTFlFJbMXpNMlphm/s7DBGGtyweblz7axMe8wsy+0sMEYqEOTE5FwEflKRBY7y21FZLmIZDuPbbxXpnFH/uESHlu+iXN7x3K+hQ3GAHXbk7sRyKy2PAtYoaqJwApn2fjQg0uzKC2v5J5pFjYYU8WtJici8cBU4LlqLycD853n84HpHq3M1Mm6LQd4+6td/OqcHvSwsMGY/3F3T+4x4DagstprHVV1D4Dz2MGzpRl3lVdUMnvBBrrENOP6n1jYYEx1tTY5EbkAyFfV9fUZQESuE5E0EUkrKCioz0eYWvzr821k7T3M3Rf0o3mUXVjGmOrc2ZMbA0wTka3A68B5IvJvIE9E4gCcx/ya3qyq81Q1SVWTYmNjPVS2qVJwuJRHP9jE2YntmTigk6/LMcbv1NrkVPUOVY1X1QTgcuAjVb0SWAjMdDabCSzwWpXmpP7fe5mUlFeQYmGDMTVqyO/JPQhMEJFsYIKzbBpR2tYDvP3lLn51dk96xrb0dTnG+KU6fYGjqqlAqvN8PzDO8yUZd5RXVHL3gnQ6t27K78+zsMGYk7EzHgLUK2u3k7mniLsv6G9hgzGnYE0uABUcLuWRDzZydmJ7Jg20sMGYU7EmF4Aeej+LkrIKO7PBGDdYkwsw67cd4K31O7n2rJ6cZmGDMbWyJhdAKiqVu99NJ651U/5gYYMxbrEmF0BeWbuNjD1F/Hlqf1o0sbDBGHdYkwsQ+4+U8siyjYzp1Y4pgyxsMMZd1uQCxEPvZ3GsrIKUaQMtbDCmDqzJBYD12w7yZtpOfnFWD3p1sLDBmLqwJufnKiqVOQs30Cm6KTecl+jrcowJONbk/Nyr67azYVcRd03tZ2GDMfVgTc6PVYUNZ57WjgtOj/N1OcYEJGtyfuwv72/kaGk5c5PtzAZj6suanJ/6avtB3kjb4YQNrXxdjjEBy5qcH6qoVGYvSKdjdBNuGGdhgzENYU3OD722bjvf7Srkrqn9aWlhgzENYk3Ozxw4epyHl21kdM92XGhhgzENZk3Ozzy8LIujpeWkWNhgjEdYk/MjX+84xOtf7OCaMQn07mhhgzGeYE3OT7jChg3EtmzCjeN7+7ocY4KGNTk/8cYXO/h2ZyF3Te1nYYMxHmRNzg8cPHqcvyzLYmSPtkwb3NnX5RgTVKzJ+YG/LNvI4ZJy5ibbZZSM8bRam5yINBWRdSLyjYiki0iK83pbEVkuItnOYxvvlxt8vt15iNe/2M7Pz0ygTycLG4zxNHf25EqB81R1MDAEmCQio4BZwApVTQRWOMumDiorlbvf3UD7lk24abyd2WCMN9Ta5NTliLMY6fxRIBmY77w+H5jujQKD2RtpO/hmZyF3TelHq6aRvi7HmKDk1ndyIhIuIl8D+cByVV0LdFTVPQDOY4eTvPc6EUkTkbSCggIPlR34DhUf5y/vZzGiR1uSh1jYYIy3uNXkVLVCVYcA8cAIERno7gCqOk9Vk1Q1KTY2tp5lBp+Hl22kqMQuo2SMt9UpXVXVQ0AqMAnIE5E4AOcx39PFBavvdhby6rrtzBydQN9O0b4ux5ig5k66GisiMc7zZsB4IAtYCMx0NpsJLPBSjUGlslK5e8EG2rVowk0TLGwwxtvc+dX6OGC+iITjaopvqupiEfkMeFNErgW2A5d4sc6g8Z/1O/h6xyEevXQw0RY2GON1tTY5Vf0WGFrD6/uBcd4oKlgdKj7OQ+9v5IyENvzf0C6+LseYkGBnPDSiRz7YSOGxMjuzwZhGZE2ukWzYVcgra7dz1aju9IuzsMGYxmJNrhF8HzZEcfMEu4ySMY3JmlwjeGv9Tr7afog7JvejdTMLG4xpTNbkvKywuIwH388iqXsbLhpmYYMxjc2anJf9dflGDhUft7DBGB+xJudFG3YV8u/Pt3H16AT6d7awwRhfsCbnJZXOPRvaWthgjE9Zk/OS/365ky+3H+L2SX0tbDDGh6zJeUHhsTIefC+LYd1i+OmweF+XY0xIs9tCecGjH2zkYPFx5v9iBGFhFjYY40u2J+dh6bsL+dfn27hyVHcGdmnt63KMCXnW5DyoslKZsyCdNs2juGVCH1+XY4zBmpxHvf3VLtK2HeT2yX1p3dzCBmP8gTU5D3GFDZkM7RbDxRY2GOM3LHjwkL8t38T+o8d56RoLG4zxJ7Yn5wEZu4t4+bOtzBjZzcIGY/yMNbkGUlXmLNxATPMobj3fwgZj/I01uQZ656tdfLH1ILdP6kNM8yhfl2OMOYE1uQYoKinjgaVZDOkawyXDu/q6HGNMDSx4aABX2FDKiz8/w8IGY/yU7cnVU9beIl7+bBs/G9GNQfEWNhjjr9y5uXRXEVkpIpkiki4iNzqvtxWR5SKS7Ty28X65/kFVmf1uOtFNI/jTRAsbjPFn7uzJlQO3qGo/YBRwvYj0B2YBK1Q1EVjhLIeEBV/vZt3WA9w2qa+FDcb4uVqbnKruUdUvneeHgUygC5AMzHc2mw9M91KNfuVwSRn3L81kcNcYLkuysMEYf1en7+REJAEYCqwFOqrqHnA1QqCDx6vzQ499mM2+I6XcmzzAwgZjAoDbTU5EWgL/BW5S1aI6vO86EUkTkbSCgoL61Og3Nu49zEufbuXyM7pxenyMr8sxxrjBrSYnIpG4Gtwrqvq283KeiMQ56+OA/Jreq6rzVDVJVZNiY2M9UbNPqLpuEN2qaQS3WdhgTMBwJ10V4HkgU1UfrbZqITDTeT4TWOD58vzHwm92s27LAW6b2Jc2LSxsMCZQuPPLwGOAq4DvRORr57U7gQeBN0XkWmA7cIlXKvQDh0vKuH9JJqfHt+ayMyxsMCaQ1NrkVHUNcLJv2Md5thz/9PcPsyk4Usq8q5MIt7DBmIBiZzzUYlPeYV78dCuXn9GVIV1jfF2OMaaOrMmdgqrrBtGtmkbwp4l9fV2OMaYerMmdwqJv9/B57gFuPb8PbS1sMCYgWZM7iSOl5dy/JINBXVpzxYhuvi7HGFNPdqmlk3h8RTZ5RaU8c+VwCxuMCWC2J1eD7LzDvLBmC5cldWVot5C5uIoxQcma3AlcYUM6zaPCuW2SndlgTKCzJneCxd/u4bPc/fxpUl/atWzi63KMMQ1kTa6aI6Xl3Lckg4FdovmZhQ3GBAULHqr5hxM2PDXDwgZjgoXtyTly8g/z/JotXJoUz/DuFjYYEyysyVF1g2hX2HD7JDuzwZhgYk0OWPrdXj7J2c+tE/tY2GBMkAn5Jne0tJx7F2fQPy6aGSO7+7ocY4yHhXzw8I+PcthbVMKTM4Za2GBMEArpPbmc/CM8vyaXi4fHM7x7W1+XY4zxgpBtcqrKPQvTaRoZzqzJFjYYE6xCtsm9t2Eva3L2cev5fWhvYYMxQSskm1zx8XLuW5xBv7hoZoy0MxuMCWYh2eSe+CiH3YUl3Js8gIjwkPxPYEzICLl/4ZsLjvDsx7lcNKwLSQkWNhgT7EKqyf0vbIgI547J/XxdjjGmEbhzc+kXRCRfRDZUe62tiCwXkWznMSBO9lyWvpePs/fxx/N7E9vKwgZjQoE7e3IvAZNOeG0WsEJVE4EVzrJfKz5eztxFGfTt1IqrRtmZDcaEilqbnKquBg6c8HIyMN95Ph+Y7tmyPO/Jla6wYW7yQAsbjAkh9f3X3lFV9wA4jx08V5Ln5RYc4dnVW7hoaBdG9LCwwZhQ4vVdGhG5TkTSRCStoKDA28P9iKpyz6IMmkSEMWuKndlgTKipb5PLE5E4AOcx/2Qbquo8VU1S1aTY2Nh6Dld/y9LzWL2pgJsm9KZDq6aNPr4xxrfq2+QWAjOd5zOBBZ4px7OOHa/g3sWusGHmaAsbjAlF7vwKyWvAZ0AfEdkpItcCDwITRCQbmOAs+52nUnPYdegYKdPszAZjQlWt15NT1StOsmqch2vxqC37jvLPVblMH9KZkT3b+bocY4yPBOXujaqSsiidqIgw7pxiZzYYE8qCssktz8gjdWMBN41PpEO0hQ3GhLKga3LHjleQsiiD3h1bMvPMBF+XY4zxsaC7x8PTTtjw+nWjiLSwwZiQF1RdYOu+ozyzOpfkIZ0ZZWGDMYYganJVYUNkmFjYYIz5n6Bpch9m5rNyYwE3je9NRwsbjDGOoGhyJWUVpCxKJ7FDS34+JsHX5Rhj/EhQBA9Pp25m58FjvPYrCxuMMT8U8B1h2/6jPL1qMxcO7szo0yxsMMb8UMA3ubmLMogME+6ysMEYU4OAbnIfZuSxIiufG8cn0qm1hQ3GmB8L2CZXUlZByuJ0enVoyTVjevi6HGOMnwrY4OGZVZvZceAYr/5ypIUNxpiTCsjusONAMU+nbuaC0+M4s1d7X5djjPFjAdnkUhZlEB4m3DXVwgZjzKkFXJP7KCuPDzPzuGFcInGtm/m6HGOMnwuoJldSVsE9CzM4LbYFv7CwwRjjhoAKHuatzmX7gWL+fe1IoiICqj8bY3wkYDrFjgPFPLkyh6mD4jgr0cIGY4x7AqbJzV2cQZhY2GCMqZuAaHIrs/JZnuEKGzrHWNhgjHGf3ze5krIK7lmUTs/YFlx7loUNxpi6aVCTE5FJIrJRRHJEZJaniqru2dW5bNtfTMq0ARY2GGPqrN5dQ0TCgSeByUB/4AoR6e+pwgB2HizmydQcJg/sxNmJsZ78aGNMiGjIrtEIIEdVc1X1OPA6kOyZslzuXZyBIPz5Ao/2TmNMCGlIk+sC7Ki2vNN57QdE5DoRSRORtIKCArc/PGN3EcvS8/j9eb3oYmGDMaaeGvLLwFLDa/qjF1TnAfMAkpKSfrT+ZPp3juat34xmUHzr+ldojAl5DWlyO4Gu1Zbjgd0NK+eHkhLaevLjjDEhqCGHq18AiSLSQ0SigMuBhZ4pyxhjPKPee3KqWi4ivweWAeHAC6qa7rHKjDHGAxp0gr6qLgWWeqgWY4zxOPvtWmNMULMmZ4wJatbkjDFBzZqcMSaoWZMzxgQ1a3LGmKBmTc4YE9RE1e3TSRs+mEgBsK2Ob2sP7PNCOf4+dqiPH8pzD/Xx6zN2d1Wt8Xpsjdrk6kNE0lQ1KdTGDvXxQ3nuoT6+p8e2w1VjTFCzJmeMCWqB0OTmhejYoT5+KM891Mf36Nh+/52cMcY0RCDsyRljTL35RZOr7daG4vK4s/5bERnWyOOPFZFCEfna+TPbg2O/ICL5IrLhJOu9Pffaxvfm3LuKyEoRyRSRdBG5sYZtvDZ/N8f35vybisg6EfnGGT+lhm28Mn83x/ba3KuNES4iX4nI4hrWeWbuqurTP7guuLkZ6AlEAd8A/U/YZgrwHq77SowC1jby+GOBxV6a/znAMGDDSdZ7be5uju/NuccBw5znrYBNjfx378743py/AC2d55HAWmBUY8zfzbG9NvdqY/wReLWmcTw1d3/Yk3Pn1obJwMvq8jkQIyJxjTi+16jqauDAKTbx5tzdGd9rVHWPqn7pPD8MZPLjO755bf5uju81zpyOOIuRzp8TvyT3yvzdHNurRCQemAo8d5JNPDJ3f2hy7tza0K3bH3pxfIDRzq79eyIywENju8Obc3eX1+cuIgnAUFx7FNU1yvxPMT54cf7O4drXQD6wXFUbbf5ujA3e/bt/DLgNqDzJeo/M3R+anDu3NnTr9odeHP9LXKeNDAb+AbzrobHd4c25u8PrcxeRlsB/gZtUtejE1TW8xaPzr2V8r85fVStUdQiuu92NEJGBJ5ZX09saaWyvzV1ELgDyVXX9qTar4bU6z90fmpw7tzb05u0Pa/1sVS2q2rVX130tIkWkvYfGb3B93uTtuYtIJK4G84qqvl3DJl6df23jN9bfvaoeAlKBSSes8vrf/8nG9vLcxwDTRGQrrq+IzhORf5+wjUfm7g9Nzp1bGy4ErnbSllFAoaruaazxRaSTiIjzfASu/277PTR+bbw591p5c+7O5z4PZKrqoyfZzGvzd2d8L88/VkRinOfNgPFA1gmbeWX+7oztzbmr6h2qGq+qCbj+zX2kqleesJlH5t6gu3V5gp7k1oYi8htn/TO47gg2BcgBioFrGnn8i4Hfikg5cAy4XJ34p6FE5DVcKVZ7EdkJzMH1JbDX5+7m+F6bO67/m18FfOd8NwRwJ9Ct2vjenL8743tz/nHAfBEJx9VA3lTVxY30s+/O2N6ce428MXc748EYE9T84XDVGGO8xpqcMSaoWZMzxgQ1a3LGmKBmTc4YE9SsyRljgpo1OWNMULMmZ4wJav8f+RVAICJxCH8AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Delay between electric field components of Source 1 is (deg.):\n",
      "[  0.   0.   0. 180. 180.]\n",
      "The mean value is 72.0 +- 88.18163074019441\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "azimuth (5,)\n",
      "The azimuth of Source 1 is (deg.):\n",
      "[  0.    33.75  67.5  101.25 135.  ]\n",
      "The mean value is 67.49999999999999 +- 47.72970773009196\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "The ellipticity angle of Source 1 is (deg.):\n",
      "[0.00000000e+00 0.00000000e+00 0.00000000e+00 1.34258920e-15\n",
      " 3.50835465e-15]\n",
      "The mean value is 9.70188769680624e-16 +- 1.3714785349104249e-15\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "The global phase of Source 1 is (deg.):\n",
      "[  0.   0.   0. 180. 180.]\n",
      "The mean value is 72.0 +- 88.18163074019441\n"
     ]
    },
    {
     "data": {
      "image/png": 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amtk9wFVAh5n1Ap8jd3a3BXjQzAC2Rmd23wb8kZkNAyPAR929v+CGRerc1p6QK7rbaGrUeF81mjT83P3GApO/NcGy9wP3l1qUSK3bO3CcXX3HuOmKlUmXIhPQf0kiMchGt6jUyY7qpfATiUEmCFk4ewYXLJ6XdCkyAYWfSJm5O1t7QjZ2t9PQYEmXIxNQ+ImU2cv9Q7w6cFzf31flFH4iZZYJxq7v03hfNVP4iZRZNgjpnNfC6s45SZciZ6DwEykjdycThGxe3U50DaxUKYWfSBntPHCUvqMnNd5XAxR+ImWU7dH1fbVC4SdSRpmdIctaZ7GibXbSpcgkFH4iZTI66mR7Qh3y1giFn0iZPLdvkMPHT7P5HIVfLVD4iZTJ69/f163xvlqg8BMpk0wQ0t0xh8ULZiZdihRB4SdSBsMjozyyq1/f2lxDFH4iZfD0q4c5enJY4VdDFH4iZTD2ed6N3Qq/WqHwEymDbBByweJ5dMxtSboUKZLCT6REJ4dH2L5H4321RuEnUqIdLw9w4vQom3TIW1MUfiIlygQhDQZXKPxqisJPpETZIOSiZQtYMGtG0qXIFCj8REpw/NQIT7xySIe8NUjhJ1KC7Xv6OT3iOtlRgxR+IiXIBCFNDcb6rrakS5EpmjT8zOwOMztgZs/kTWszswfN7OfRz4V58z5jZjvN7EUze0dchYtUg2wQ8uYVrcxpaUq6FJmiYvb87gSuHTft08BD7n4u8FD0GjNbA9wAXBit8zUzayxbtSJVZPDEaZ7qHdD399WoScPP3R8G+sdNvh64K3p+F/CevOn3uvtJd98F7AQ2lKdUkery6K5+Rh2N99Wo6Y75neXu+wCin4ui6cuAV/KW642midSdbBDS3NTAZSsXTr6wVJ1yn/AodK8+L7ig2RYz225m2w8ePFjmMkTilwlCLl+5kJkzNLJTi6YbfvvNbAlA9PNANL0XWJG33HJgb6ENuPvt7r7O3dd1dnZOswyRZBw6dorn9g1qvK+GTTf8HgBujp7fDHw/b/oNZtZiZquAc4FHSitRpPps2xXdolL366hZk56fN7N7gKuADjPrBT4H/Clwn5l9CHgZeB+Auz9rZvcBzwHDwO+4+0hMtYskJhOEzG5u5JLlrUmXItM0afi5+40TzLp6guVvBW4tpSiRapcJQtZ3tTGjUZ8TqFV650Sm6MDgCXYeOKrxvhqn8BOZouzYLSoVfjVN4ScyRdkgZN7MJi5cuiDpUqQECj+RKcoEIRu722lsKHRZq9QKhZ/IFPQeGuLl/iF9f18dUPiJTEE20PV99ULhJzIF2SCkfU4z5y2al3QpUiKFn0iR3J1sT8jG1e00aLyv5in8RIq0Oxxi3+ETGu+rEwo/kSJlgj4AXdxcJxR+IkXKBCGL589kVcecpEuRMlD4iRTB3dkahGxa3Y6ZxvvqgcJPpAgv7T9KeOyUPtJWRxR+IkXQeF/9UfiJFCEbhKxom8XyhbOTLkXKROEnMomRUWdrT8jm7o6kS5EyUviJTOK5vYMMnhjWR9rqjMJPZBLZntx4ny5uri8KP5FJZIKQ1Z1zWDR/ZtKlSBkp/ETO4PTIKI/s6mfzao331RuFn8gZPNU7wNCpEV3iUocUfiJnMPb9fVdovK/uKPxEziAThLxpyXza5jQnXYqUmcJPZAInTo+wfc8hHfLWKYWfyASeeHmAU8OjusSlTin8RCaQDfpoMNjQ3ZZ0KRKDpumuaGbnA9/Jm9QN/AHQCnwYOBhN/6y7/3C67YgkJROEXLy8lfkzZyRdisRg2nt+7v6iu69197XA5cAQ8L1o9lfG5in4pBYNnRpmxysDOuStY+U67L0aCNx9T5m2J5KoR3cfYnjUdbKjjpUr/G4A7sl7fYuZPWVmd5jZwkIrmNkWM9tuZtsPHjxYaBGRxGSCPmY0Guu6Cv75Sh0oOfzMrBl4N/A/o0lfB1YDa4F9wG2F1nP32919nbuv6+zsLLUMkbLaGoRcumIhs5unPSwuVa4ce37vBB539/0A7r7f3UfcfRT4JrChDG2IVMzh46d5+tXDbNQhb10rR/jdSN4hr5ktyZv3XuCZMrQhUjGP7Opn1PWV9fWupH16M5sN/GvgI3mTv2RmawEHdo+bJ1L1MkEfLU0NXLqyNelSJEYlhZ+7DwHt46Z9oKSKRBKWDULWdS2kpakx6VIkRvqEh0ie8OhJXnjtiL6/LwUUfiJ5tvb0A+j+vCmg8BPJk+3pY05zIxcvW5B0KRIzhZ9InkwQsmFVGzMa9U+j3ukdFonsHzxBz8FjGu9LCYWfSGTsK+s13pcOCj+RSCboY8GsGaxZMj/pUqQCFH4ikUwQsrG7jYYGS7oUqQCFnwjwSv8QvYeOa7wvRRR+Imi8L40UfiLkxvs65jZz7qK5SZciFaLwk9RzdzJByKbVHZhpvC8tFH6Sej19xzhw5KTu15EyCj9JvUw03qfv70sXhZ+kXjboY+mCmZzdPjvpUqSCFH6SaqOjztaefo33pZDCT1Ltxf1H6D92Spe4pJDCT1Ito+v7UkvhJ6mWDUK62mezrHVW0qVIhSn8JLWGR0bZ1hNqry+lFH6SWs/uHeTIyWE26fO8qaTwk9R6fbxPFzenksJPUivbE3Luorl0zmtJuhRJgMJPUunU8CiP7urXpzpSTOEnqfRk7wDHT49ovC/FmkpZ2cx2A0eAEWDY3deZWRvwHaAL2A28390PlVamSHllgxAz2NjdlnQpkpBy7Pn9K3df6+7rotefBh5y93OBh6LXIlUlE/SxZsl8Wmc3J12KJCSOw97rgbui53cB74mhDZFpO3F6hMf3DGi8L+VKDT8Hfmxmj5nZlmjaWe6+DyD6uajENkTK6vE9hzg1Mqr7daRcSWN+wJXuvtfMFgEPmtkLxa4YheUWgJUrV5ZYhkjxMkFIY4OxfpXG+9KspD0/d98b/TwAfA/YAOw3syUA0c8DE6x7u7uvc/d1nZ2dpZQhMiWZoI9Lli9gbkup//dLLZt2+JnZHDObN/Yc+GXgGeAB4OZosZuB75dapEi5HD05zJO9hzXeJyUd9p4FfC/6Asgm4G53/wczexS4z8w+BLwMvK/0MkXK49Hd/YyMOpu6Nd6XdtMOP3fvAd5cYHoIXF1KUSJxyQYhzY0NXH72wqRLkYTpEx6SKpmgj0tXtjKruTHpUiRhCj9JjcNDp3l276C+v08AhZ+kyNZdIe7o+j4BFH6SItkgZOaMBtauaE26FKkCCj9JjWwQsr6rjeYm/dmLwk9S4uCRk7y4/4jG++R1Cj9Jha09ua+s13ifjFH4SSpkgpB5LU1ctHR+0qVIlVD4SSps7QnZsKqNpkb9yUuO/hKk7u0dOM6uvmMa75NfoPCTupcNNN4nb6Twk7qX7QlZOHsGFyyel3QpUkUUflLX3J1sELKxu52GBku6HKkiCj+pay/3D/HqwHF9f5+8gcJP6trYeJ9Odsh4Cj+pa5kgpHNeC6s75yZdilQZhZ/ULXcnE4RsXt1O9I3jIq9T+EndCg4epe/oSY33SUEKP6lbmbHxPt2vQwpQ+EndyuwMWdY6ixVts5IuRaqQwk/q0uios3WXxvtkYgo/qUvPvzbIwNBpXeIiE1L4SV3S9X0yGYWf1KVMENLdMYclCzTeJ4Up/KTuDI+M8siufjZqr0/OYNrhZ2YrzOyfzOx5M3vWzD4eTf+8mb1qZjuix3XlK1dkck+/epijJ4d1fZ+cUVMJ6w4Dv+vuj5vZPOAxM3swmvcVd/+z0ssTmbqx6/s2div8ZGLTDj933wfsi54fMbPngWXlKkxkurb2hFyweB4dc1uSLkWqWFnG/MysC7gU2BZNusXMnjKzO8xsYTnaECnGyeERHt3dr70+mVTJ4Wdmc4H7gU+4+yDwdWA1sJbcnuFtE6y3xcy2m9n2gwcPllqGCAA7Xh7gxOlRjffJpEoKPzObQS74vu3u3wVw9/3uPuLuo8A3gQ2F1nX32919nbuv6+zsLKUMkddlgpAGgyu05yeTKOVsrwHfAp539z/Pm74kb7H3As9MvzyRqcn2hFy4dAELZs1IuhSpcqWc7b0S+ADwtJntiKZ9FrjRzNYCDuwGPlJCGyJFO35qhCdePsQHr1yVdClSA0o52/szoNAnxn84/XJEpm/7nn5Oj7g+0iZF0Sc8pG5kg5CmBmN9V1vSpUgNUPhJ3cgEIW9e0cqcllJGcyQtFH5SF46cOM3Trx7WJS5SNIWf1IVHd/czMqrxPimewk/qQmZnSHNTA5et1AeKpDgKP6kLmSDk8pULmTmjMelSpEYo/KTmHTp2iudfG9R4n0yJwk9q3rZdIe76ynqZGoWf1LxMEDK7uZFLlrcmXYrUEIWf1LxMELK+q43mJv05S/H01yI17cCRE+w8cFSHvDJlCj+paWO3qNTJDpkqhZ/UtGwQMm9mExcuXZB0KVJjFH5S07I9IVesaqexodAXDIlMTOEnNav30BB7wiEd8sq0KPykZr0+3neOwk+mTuEnNSvbE9I+p5nzFs1LuhSpQQo/qUnuTjYI2djdToPG+2QaFH5Sk3aHQ+w7fELX98m0KfykJmWCPkDX98n0KfykJmWDkLPmt7CqY07SpUiNUvhJzRkb79u8uoPc7aNFpk7hJzXnpf1HCY+d0niflEThJzUnG433bepW+Mn0Kfyk5mSCkBVts1jRNjvpUqSGKfykpoyMOlt7QjZ3dyRditS42MLPzK41sxfNbKeZfTqudiRdnt83yOCJYX2kTUoWS/iZWSPwVeCdwBrgRjNbE0dbki4ZjfdJmTTFtN0NwE537wEws3uB64HnyrHxx/b003/sdDk2JTXmH555jdWdc1g0f2bSpUiNiyv8lgGv5L3uBa7IX8DMtgBbAFauXDmljf/ZP75EticssUSpVR+8clXSJUgdiCv8Cl156r/wwv124HaAdevWeYHlJ3Trey9i6NTI9KuTmnbuWXOTLkHqQFzh1wusyHu9HNhbro13d+qPX0RKE9fZ3keBc81slZk1AzcAD8TUlojIlMWy5+fuw2Z2C/CPQCNwh7s/G0dbIiLTEddhL+7+Q+CHcW1fRKQU+oSHiKSSwk9EUknhJyKppPATkVRS+IlIKin8RCSVFH4ikkrmPqWP1cZThNlBYM8UV+sA+mIopxbaT3Pf095+mvs+nfbPdvfOQjOqIvymw8y2u/u6NLaf5r6nvf00973c7euwV0RSSeEnIqlUy+F3e4rbT3Pf095+mvte1vZrdsxPRKQUtbznJyIybVUffpPdAtNy/jKa/5SZXVbBtq8ys8NmtiN6/EG52o62f4eZHTCzZyaYH2ffJ2s77r6vMLN/MrPnzexZM/t4gWVi6X+RbcfWfzObaWaPmNmTUft/WGCZON/7YtqP+/1vNLMnzOwHBeaVp+/uXrUPcl+EGgDdQDPwJLBm3DLXAT8id9+QjcC2CrZ9FfCDGPv/NuAy4JkJ5sfS9yLbjrvvS4DLoufzgJcq+N4X03Zs/Y/6Mzd6PgPYBmys4HtfTPtxv/+fBO4u1Ea5+l7te36v3wLT3U8BY7fAzHc98NeesxVoNbMlFWo7Vu7+MNB/hkXi6nsxbcfK3fe5++PR8yPA8+TuCpgvlv4X2XZsov4cjV7OiB7jB+fjfO+LaT82ZrYc+DfAf59gkbL0vdrDr9AtMMf/ERazTFxtA2yKDg9+ZGYXlqHdqYir78WqSN/NrAu4lNweSL7Y+3+GtiHG/keHfTuAA8CD7l7RvhfRPsTX//8C/D4wOsH8svS92sNv0ltgFrlMXG0/Tu7jM28G/ivw92Vodyri6nsxKtJ3M5sL3A98wt0Hx88usErZ+j9J27H2391H3H0tuTsfbjCzi8aXV2i1CrYfS//N7F3AAXd/7EyLFZg25b5Xe/gVcwvMuG6TOel23X1w7PDAc/csmWFmHWVou1ix3iL0TCrRdzObQS58vu3u3y2wSGz9n6ztSr337j4A/DNw7bhZFXnvJ2o/xv5fCbzbzHaTG2p6u5n97bhlytL3ag+/Ym6B+QDwG9EZoI3AYXffV4m2zWyxmVn0fAO532dYhraLFVffJxV336Ntfwt43t3/fILFYul/MW3H2X8z6zSz1uj5LOAa4IVxi8X23hfTflz9d/fPuPtyd+8i92/uJ+7+6+MWK0vfY7t7Wzn4BLfANLOPRvO/Qe4OcdcBO4Eh4Lcq2PavAr9tZsPAceAGj05HlYOZ3UPurFqHmfUCnyM3+Bxr34tsO9a+k9sD+ADwdDT2BPBZYGVeDXH1v5i24+z/EuAuM2skFyr3ufsPKvF3P4X2437/f0EcfdcnPEQklar9sFdEJBYKPxFJJYWfiKSSwk9EUknhJyKppPATkVRS+IlIKin8RCSV/j+XXgGUj+/f3QAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "azimuth (5,)\n",
      "The ellipse axes of Source 1 are (V/m):\n",
      "  a (V/m)\n",
      "[1. 1. 1. 1. 1.]\n",
      "  b (V/m)\n",
      "[0.0000000e+00 7.4505806e-09 0.0000000e+00 0.0000000e+00 0.0000000e+00]\n",
      "The mean value of param a (V/m) is 1.0 +- 7.021666937153402e-17\n",
      "The mean value of param b (V/m) is 1.4901161193847657e-09 +- 2.980232238769532e-09\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "azimuth (5,)\n",
      "The ellipticity parameter of Source 1 is:\n",
      "[0.0000000e+00 7.4505806e-09 0.0000000e+00 0.0000000e+00 0.0000000e+00]\n",
      "The mean value is 1.490116119384766e-09 +- 2.9802322387695322e-09\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "azimuth (5,)\n",
      "The eccentricity of Source 1 is:\n",
      "[1. 1. 1. 1. 1.]\n",
      "The mean value is 1.0 +- 4.965068306494546e-17\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "The degree of circular polarization of Source 1 is:\n",
      "[-0. -0. -0. -0. -0.]\n",
      "The mean value is 0.0 +- 0.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "The degree of linear polarization of Source 1 is:\n",
      "[1. 1. 1. 1. 1.]\n",
      "The mean value is 1.0 +- 0.0\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "E = Jones_vector(\"Source 1\")\n",
    "E.linear_light(azimuth = np.linspace(0, 135*degrees, 5))\n",
    "print(E,'\\n')      \n",
    "print(E.parameters)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Checks of Jones_vector\n",
    "\n",
    "There are several checks that can be performed upon a Jones vector. They are implemented in the independent class *Checks_Jones_vector*, which is stored in the *checks* field of *Jones_vector* class.\n",
    "\n",
    "* **is_linear**: Checks if the Jones vector is lienarly polarized.\n",
    "* **is_circular**: Checks if the Jones vector is circularly polarized.\n",
    "* **is_right_handed**: Checks if the Jones vector rotation direction is right handed.\n",
    "* **is_left_handed**: Checks if the Jones vector rotation direction is left handed.\n",
    "\n",
    "\n",
    "* **get_all**: Returns a dictionary with all the checks of Jones vector.\n",
    "\n",
    "For a more detailed description of each method, refer to the individual documentation of each one.\n",
    "\n",
    "Example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "E = Jones_vector(\"Source 1\")\n",
    "E.linear_light(azimuth = 45*degrees)\n",
    "cond = E.checks.is_linear()\n",
    "print(cond)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1D and 2D plot draws are also implemented for this class:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3249,) (3249,)\n",
      "Source 1 is right handed:\n",
      "The mean value is nan +- nan\n"
     ]
    },
    {
     "data": {
      "image/png": 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ZEdEYSWYR0QhJZhFRdyLNzIhoipoks7yaERHVhjQ5o6Q1kh6UtFtS5SzVkn5J0rSk3+pUZpJZRLQ34PnMykkrrgMuAU4DrpR0WsV5HwZu7ybMJLOIaG/wkzOeDey2vcf2c8CNFNPwz/Xvgc9TMVfiXElmEdHWEJqZy4FHW7b3lvv+/z2l5cBvABu7jTMdABExDIslTbVsb7K9qVyf7yMBc1PgR4E/tD0tdfdNgSSziGivv97Mg7ZXVxzbC5zcsr0C2DfnnNXAjWUiWwxcKumQ7b+uumGSWURUG84HSnYAqySdAnwfuAJ42wtua58yuy7pBuDWdokMkswiooNBvzRr+5Ckayl6KRcA19veJema8njXz8laJZlFRHtDeGnW9lZg65x98yYx27/bTZlJZhHRVoYzRUQzJJlFRO2N8QvlvUoyi4hKYv6XwiZRkllEtJeaWUQ0QToAIqIZkswiohGSzCKi9vJ1pohojCSziGiCutTMup6cUdICSf8k6dZy+0RJ2yQ9VP4uGl6YETE2g59pdih6mWn23cADLdsbgO22VwHby+2IaJhhfNBkGLpKZpJWAG8CPtGyey2wuVzfDFw20MgiInrQbc3so8B7gZmWfUtt7wcof5cMNrSIGLt+mpiTWjOT9GbggO2d/dxA0npJU5KmnufZfoqIiHGqSTLrpjfzPODXJV0KHAu8QtJfAo9LWmZ7v6RlVHwOqvyIwSaAV+jEmvSLRASUA81r8r+2Y83M9vtsr7C9kmKu7r+3/TvAFmBdedo64JahRRkR49OgmlmVDwE3SboaeAS4fDAhRcQkketRNespmdm+A7ijXH8SuHDwIUXExMjkjBHRFHV5ZpZkFhHtJZlFRBOkZhYRzZBkFhG1l/nMIqIxkswiou7qNAIgySwi2mviS7MR8eKTmllE1F+NRgD0MtNsRMTESs0sItrSTOdzJkGSWUS0V5NmZpJZRLSVDoCIqD+TVzMiohlSM4uIZkgyi4i6y3CmiGgGO8/MIqIZUjOLiGZIMouIJkjNLCLqz8BMPbJZkllEtFePXJZkFhHtpZkZEc1Qk1czMp9ZRLQl9750LFNaI+lBSbslbZjn+G9Lurdc/lHSGZ3KTDKLiJGStAC4DrgEOA24UtJpc077LvArtk8H/hjY1KncJLOIqOY+l/bOBnbb3mP7OeBGYO0Lbmv/o+0flJt3ASs6FZpnZhFRqRib2dczs8WSplq2N9merV0tBx5tObYXOKdNWVcDf9vphl0lM0kPA08D08Ah26slnQj8FbASeBh4S0smjYim6G/a7IO2V1cc0zz75s2Ykt5Akcz+Tacb9tLMfIPtM1sC3ABst70K2F5uR0TDyO556WAvcHLL9gpg32H3lU4HPgGstf1kp0KP5JnZWmBzub4ZuOwIyoqISTScZ2Y7gFWSTpF0NHAFsKX1BEmvAm4G3m77292E2u0zMwNfkmTgf5Rt36W29wPY3i9pSZdlRURtDH4KINuHJF0L3A4sAK63vUvSNeXxjcD7gZ8CPi4Jysdb7crtNpmdZ3tfmbC2SfpWt4FLWg+sBziWl3V7WURMiGGMALC9Fdg6Z9/GlvV3Au/spcyumpm295W/B4AvUHStPi5pGUD5e6Di2k22V9tevZBjeoktIibB7ASNvSxj0DGZSXq5pONn14GLgfso2rjrytPWAbcMK8iIGBMXHwHudRmHbpqZS4EvlO3WlwCftX2bpB3ATZKuBh4BLh9emBExNjUZm9kxmdneAxw2LqrsKr1wGEFFxASpRy7LCICIaK/PEQAjl2QWEe0lmUVE7Zl+hzONXJJZRFQSXQ1PmghJZhHRXk2SWeYzi4hGSM0sItqrSc0sySwiqqUDICKaIh0AEdEMSWYRUX/jmwWjV0lmEVHNJJlFREOkAyAimiAdABHRDElmEVF7BmaSzCKi9tKbOa/XnPWzbJv6X6O8ZUQAknb2fXGSWUQ0QpJZRNRenplFRDMYXI8XzUaazHbu3HlQ0veAxcDBUd67B4mtP5McG0x2fKOI7Wf6vjLNzMPZPglA0pTt1aO8d7cSW38mOTaY7PgmObY6STMzIqrlmVlENEaamW1tGtN9u5HY+jPJscFkxzfJsdUmmck1CTQiRu+VRy/xvz7prT1fd9u+P9856ueAaWZGRDUDM/V4NWOkn5qTtEbSg5J2S9owyntXxHO9pAOS7mvZd6KkbZIeKn8XjSm2kyV9WdIDknZJevekxCfpWElfk/SNMrYPTkpsLTEukPRPkm6dpNgkPSzpm5LukTQ1SbFVsntfxmBkyUzSAuA64BLgNOBKSaeN6v4VbgDWzNm3AdhuexWwvdweh0PAe2yfCpwLvKv8+5qE+J4FLrB9BnAmsEbSuRMS26x3Aw+0bE9SbG+wfWZLM2ySYjtcktlhzgZ2295j+zngRmDtCO9/GNt3Ak/N2b0W2FyubwYuG2VMs2zvt/31cv1piv+YyychPheeKTcXlosnITYASSuANwGfaNk9EbFVmODYXLya0esyBqNMZsuBR1u295b7Js1S2/uhSCjAkjHHg6SVwOuBu5mQ+Mpm3D3AAWCb7YmJDfgo8F5eOOHzpMRm4EuSdkpaP2GxHc5gz/S8jMMoOwA0z750pXYg6Tjg88Af2P6RNN9f4+jZngbOlHQC8AVJrxtzSABIejNwwPZOSeePOZz5nGd7n6QlwDZJ3xp3QB3V5KXZUdbM9gInt2yvAPaN8P7delzSMoDy98C4ApG0kCKRfcb2zZMWH4DtHwJ3UDx7nITYzgN+XdLDFI8yLpD0lxMSG7b3lb8HgC9QPH6ZiNgq5ZnZYXYAqySdIulo4Apgywjv360twLpyfR1wyziCUFEF+yTwgO0/bTk09vgknVTWyJD0UuCNwLcmITbb77O9wvZKin9jf2/7dyYhNkkvl3T87DpwMXDfJMRWyS5ezeh1GYORNTNtH5J0LXA7sAC43vauUd1/PpI+B5wPLJa0F/gA8CHgJklXA48Al48pvPOAtwPfLJ9NAfwnJiO+ZcDmsof6KOAm27dK+uoExFZlEv7ellI0yaH4v/dZ27dJ2jEBsVWryYv1GQEQEZVeuWCxz33pm3q+7ks//nRGAETEJMkHTSKiCTIFUEQ0Rk2mzR7p2MyIiGFJMouISgY8456XTjpNOqHCx8rj90r6xU5lppkZEdU8+K8ztUw6cRHFy/Q7JG2xfX/LaZcAq8rlHOAvyt9KqZlFRFtDqJl1M+nEWuDT5aQGdwEnzI6SqJJkFhHteab3pb1uJp3oeWKKNDMjotLT/OD2v/P/XtzHpcfOTj5Z2mR79lsH3Uw60fPEFElmEVHJ9tzJSwehm0knep6YIs3MiBi1biad2AK8o+zVPBf459k536qkZhYRI1U16YSka8rjG4GtwKXAbuAnwFWdys1A84hohDQzI6IRkswiohGSzCKiEZLMIqIRkswiohGSzCKiEZLMIqIRkswiohH+L5LcwzLcBpmnAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "alpha = np.linspace(45*degrees, 45*degrees, 57)\n",
    "delay = np.linspace(0, 360*degrees, 57)\n",
    "Alpha, Delay = np.meshgrid(alpha, delay)\n",
    "\n",
    "E = Jones_vector(\"Source 1\")\n",
    "E.general_charac_angles(alpha=Alpha, delay=Delay)\n",
    "_ = E.checks.is_right_handed(draw=True)"
   ]
  }
 ],
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