Fold_Origami.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/tettolizer/OrigamiAirBag/blob/main/Fold_Origami.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from math import *\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "from matplotlib import collections  as mc\n",
        "from matplotlib.patches import Ellipse, Wedge, Polygon\n",
        "from matplotlib.path import Path\n",
        "import matplotlib\n",
        "import itertools\n",
        "\n",
        "from scipy.spatial import distance\n",
        "from scipy import signal\n",
        "from google.colab import files\n"
      ],
      "metadata": {
        "id": "Zj8ZXzp-9Qug"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "The system for the curved tesselation starts with a kernel-curve. It has specific properties detailed in the book but generally can be represented with most bell-shaped curves.\n",
        "\n",
        "The rest of the system is easier if you start only with the first half of the bell.\n",
        "\n",
        "I'm using a Gaussian curve (normalized to the shape I want). I believe that generating different kernels by changing `curve_h_of_c` will lead to compatible curves for patterns such as Challenge in the book."
      ],
      "metadata": {
        "id": "5maUpJqwR3-K"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class curve_kernel(): # default is gaussian\n",
        "  def __init__(self, angle = 30.0, horiz_d = 1.0, curve_h_of_c = 0.66, N=500, std=250):\n",
        "    # angle is not used for gaussian\n",
        "    self.angle = angle;\n",
        "    self.horiz_d = horiz_d\n",
        "    self.curve_h_of_c = curve_h_of_c\n",
        "    self.a = [0,0]\n",
        "    self.mid = [horiz_d/2,0]\n",
        "    self.c = [horiz_d/2, tan(radians(angle))*horiz_d/2]\n",
        "    len = int(floor(N/2))\n",
        "\n",
        "    curve_x = np.arange(0, horiz_d,horiz_d/N)\n",
        "    curve_y = signal.gaussian(N, std)\n",
        "    curve_y -= min(curve_y)\n",
        "    curve_y *= curve_h_of_c/max(curve_y)\n",
        "    self.curve = np.array([curve_x[0:len], curve_y[0:len]])\n",
        "    self.offset = self.curve[0,-1] #+ horiz_d/N\n",
        "\n",
        "  def __repr__(self):\n",
        "    plt.plot(self.curve[0,:], self.curve[1,:])\n",
        "    print(\"Distance a to c: {:2.2}\".format(distance.euclidean(self.a, self.c)))\n",
        "    return (\"Angle {}, d {}, A {}, B {}, C {}\".format(self.angle, self.horiz_d,\n",
        "                                                    self.a, self.mid, self.c))\n",
        "class curves():\n",
        "  def __init__(self, kernel):\n",
        "    self.kernel = kernel.curve\n",
        "    self.offset = kernel.curve[0,-1]\n",
        "    self.h      = max(kernel.curve[1,:])\n",
        "    self.len    = 2*len(kernel.curve[1,:])\n",
        "\n",
        "    self.a_curve = self.concatenate(first = self.kernel,\n",
        "                                    second = self.reverse(self.kernel))\n",
        "\n",
        "    self.b_curve = self.concatenate(first = self.negative(self.reverse(self.kernel)),\n",
        "                                    second = self.kernel)\n",
        "\n",
        "    c = self.yoffset(self.kernel, -self.h)\n",
        "    self.c_curve = self.concatenate(first = c,\n",
        "                                    second = self.negative(self.reverse(c)))\n",
        "\n",
        "  def yoffset(self, curve, offset):\n",
        "    curve = np.copy(curve)\n",
        "    curve[1,:] += offset\n",
        "    return curve\n",
        "\n",
        "  def negative(self, curve):\n",
        "    curve = np.array([curve[0,:], -curve[1,:]])\n",
        "    return curve\n",
        "\n",
        "  def reverse(self, curve):\n",
        "    curve = np.array([curve[0,:], np.flip(curve[1,:])])\n",
        "    return curve\n",
        "\n",
        "  def roll(self, curve, rollby):\n",
        "    curve = np.copy(curve)\n",
        "    nanarray = np.argwhere(np.isnan(curve))\n",
        "    curve[1,:] = np.concatenate((curve[1,rollby:], curve[1,0:rollby]))\n",
        "    for x,y in nanarray: # put back in discontinuities\n",
        "      curve[x,y] = nan\n",
        "    return curve\n",
        "\n",
        "  def concatenate(self, first, second):\n",
        "    if first is None:\n",
        "      return second\n",
        "    xoffset = first[0,-1]\n",
        "    second = np.copy(second)\n",
        "    second[0,:] += xoffset\n",
        "    return(np.concatenate((first, second), axis=1))\n",
        "\n",
        "  def __repr__(self):\n",
        "    plt.plot(self.kernel[0,:], self.kernel[1,:], 'k.')\n",
        "    plt.plot(self.a_curve[0,:], self.a_curve[1,:], 'r')\n",
        "    plt.plot(self.b_curve[0,:], self.b_curve[1,:], 'b')\n",
        "    plt.plot(self.c_curve[0,:], self.c_curve[1,:], 'c')\n",
        "    return \"\"\n",
        "\n",
        "ckernel = curve_kernel(60.0, curve_h_of_c=0.2, std=400)\n",
        "cvs = curves(ckernel)\n",
        "print(cvs)"
      ],
      "metadata": {
        "id": "RDBt8hTI-Itu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "5ef6031e-edb1-44d0-9e93-6b0777370700"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from mpmath import *\n",
        "class algebraic_kernel(curve_kernel):  # Witch of Agnesi\n",
        "  def __init__(self, angle = 20.0, horiz_d = 1.0, curve_h_of_c = 0.66, N=500, std=250):\n",
        "\n",
        "    self.angle = np.radians(angle);\n",
        "    self.horiz_d = horiz_d\n",
        "    self.curve_h_of_c = curve_h_of_c\n",
        "    self.a = [0,0]\n",
        "    self.mid = [horiz_d/2,0]\n",
        "    self.c = [horiz_d/2, tan(radians(self.angle))*horiz_d/2]\n",
        "\n",
        "    len = int(floor(N/2))\n",
        "\n",
        "    curve_x = np.arange(0, horiz_d,horiz_d/N)\n",
        "    curve_x_mod = curve_x*10\n",
        "    a = self.angle\n",
        "    curve_y = 8*a**3 / (curve_x_mod*curve_x_mod + 4*a**2)\n",
        "    curve_y = curve_y[0:len]\n",
        "    curve_y -= curve_y.min()\n",
        "    curve_y /= curve_y.max()\n",
        "    curve_y *= curve_h_of_c\n",
        "\n",
        "    self.curve = np.array([curve_x[0:len], np.flip(curve_y[0:len])])\n",
        "    self.offset = self.curve[0,-1] #+ horiz_d/N\n",
        "\n",
        "  def __repr__(self):\n",
        "    plt.plot(self.curve[0,:], self.curve[1,:])\n",
        "    print(\"Distance a to c: {:2.2}\".format(distance.euclidean(self.a, self.c)))\n",
        "    return (\"Angle {}, d {}, A {}, B {}, C {}\".format(self.angle, self.horiz_d,\n",
        "                                                    self.a, self.mid, self.c))\n",
        "\n",
        "test = algebraic_kernel(angle= 270, N=200, curve_h_of_c=0.66)\n",
        "cvs = curves(test)\n",
        "print(cvs)\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "uKOfZBazv_Zm",
        "outputId": "823e6987-99cb-4130-9134-9cf9da9aab9f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class bump_kernel(curve_kernel):\n",
        "  def __init__(self, angle = 30.0, horiz_d = 1.0, curve_h_of_c = 0.66, N=500, std=250):\n",
        "    if (angle < 20.0) or (angle > 70.0):\n",
        "      print(\"WARNING: CAB angle {} should be between 20 and 70 degrees\".format(angle))\n",
        "\n",
        "    self.angle = angle;\n",
        "    self.horiz_d = horiz_d\n",
        "    self.curve_h_of_c = curve_h_of_c\n",
        "    self.a = [0,0]\n",
        "    self.mid = [horiz_d/2,0]\n",
        "    self.c = [horiz_d/2, tan(radians(angle))*horiz_d/2]\n",
        "    len = int(floor(N/2))\n",
        "\n",
        "    curve_x = np.arange(0, horiz_d,horiz_d/N)\n",
        "    curve_xsq = 4*curve_x*curve_x\n",
        "    curve_y = np.exp(-1.0/(1.0-curve_xsq))\n",
        "\n",
        "    self.curve = np.array([curve_x[0:len], np.flip(curve_y[0:len])])\n",
        "    self.offset = self.curve[0,-1] #+ horiz_d/N\n",
        "\n",
        "  def __repr__(self):\n",
        "    plt.plot(self.curve[0,:], self.curve[1,:])\n",
        "    print(\"Distance a to c: {:2.2}\".format(distance.euclidean(self.a, self.c)))\n",
        "    return (\"Angle {}, d {}, A {}, B {}, C {}\".format(self.angle, self.horiz_d,\n",
        "                                                    self.a, self.mid, self.c))\n",
        "\n",
        "bk = bump_kernel(N=200, curve_h_of_c=1.0)\n",
        "cvs_bk = curves(bk)\n",
        "print(cvs_bk)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "SlCcksc6wGdU",
        "outputId": "5c11604e-b82f-4585-90aa-0d1200862205"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-4-6b2ea276e980>:16: RuntimeWarning: divide by zero encountered in true_divide\n",
            "  curve_y = np.exp(-1.0/(1.0-curve_xsq))\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "I want to be able to use the Amanda Ghassaei's [Origami Simulator](https://origamisimulator.org/) to test the output of the script.\n",
        "\n",
        "In this next block, I'm fixing up the matplotlib output to be more compatible with the simulator."
      ],
      "metadata": {
        "id": "pX9RVVfmSrN4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "indef = False\n",
        "def rm_clippath(line):\n",
        "  global indef\n",
        "\n",
        "  if not indef:\n",
        "    f = line.find('<clipPath')\n",
        "  else:\n",
        "    f = line.find('</clipPath>')\n",
        "  if f >= 0:\n",
        "    indef = not indef\n",
        "    return \"\"\n",
        "  if indef: return \"\"\n",
        "\n",
        "  f = line.find('clip-path')\n",
        "  if (f < 0): return line\n",
        "  fc = line.find('\"', f) + 1\n",
        "  fe = line.find('\"', fc) +1\n",
        "  return line[:f]+line[fe:]\n",
        "\n",
        "def fixup_svg(infile):\n",
        "  global indef\n",
        "  indef = False\n",
        "  outfile = \"kt_\"+ infile\n",
        "  replace_list = [['fill:none;',\"\"], ['style=\"fill:#ffffff;\"/>','style=\"fill:none;stroke:#000000\"/>' ]]\n",
        "  with open(infile) as fin, open(outfile, \"w+\") as fout:\n",
        "    for line in fin:\n",
        "      line = rm_clippath(line)\n",
        "      for pair in replace_list:\n",
        "        line = line.replace(pair[0], pair[1])\n",
        "      fout.write(line)\n",
        "  return outfile\n"
      ],
      "metadata": {
        "id": "TLPpVG6BAEys"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Create a small language to interpret how to put together the curves to create a pattern."
      ],
      "metadata": {
        "id": "5eVYclo_wgYY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def curveNoneZero(curve, dcat):\n",
        "  if curve is None:\n",
        "    curve = np.zeros((2,1))\n",
        "    curve[1] = dcat[1][0]\n",
        "  return curve\n",
        "\n",
        "def A_curve(curve, cvs, mult):\n",
        "  cat = mult*np.copy(cvs.a_curve)\n",
        "  curve = curveNoneZero(curve, cat)\n",
        "  return cvs.concatenate(curve, cat)\n",
        "def a_curve(curve, cvs, mult):\n",
        "  cat = mult*cvs.yoffset(cvs.a_curve, -cvs.h)\n",
        "  curve = curveNoneZero(curve, cat)\n",
        "  return cvs.concatenate(curve, cat)\n",
        "def B_curve(curve, cvs, mult):\n",
        "  cat = mult*np.copy(cvs.b_curve)\n",
        "  curve = curveNoneZero(curve, cat)\n",
        "  return cvs.concatenate(curve, cat)\n",
        "def b_curve(curve, cvs, mult):\n",
        "  cat = mult*cvs.reverse(cvs.b_curve)\n",
        "  curve = curveNoneZero(curve, cat)\n",
        "  return cvs.concatenate(curve, cat)\n",
        "def C_curve(curve, cvs, mult):\n",
        "  cat = mult*np.copy(cvs.c_curve)\n",
        "  curve = curveNoneZero(curve, cat)\n",
        "  return cvs.concatenate(curve, cat)\n",
        "def c_curve(curve, cvs, mult):\n",
        "  cat = mult*cvs.reverse(cvs.c_curve)\n",
        "  curve = curveNoneZero(curve, cat)\n",
        "  return cvs.concatenate(curve, cat)\n",
        "def U_curve(curve, cvs, mult):\n",
        "  cat = mult*cvs.negative(np.copy(cvs.a_curve))\n",
        "  curve = curveNoneZero(curve, cat)\n",
        "  return cvs.concatenate(curve, cat)\n",
        "def u_curve(curve, cvs, mult):\n",
        "  cat = mult*cvs.negative(cvs.yoffset(cvs.a_curve, -cvs.h))\n",
        "  curve = curveNoneZero(curve, cat)\n",
        "  return cvs.concatenate(curve, cat)\n",
        "\n",
        "def horizline_curve(curve, cvs, mult):\n",
        "  xoffset = (cvs.a_curve[0,-1])\n",
        "  line = np.array([[0,xoffset],[0,0]])\n",
        "  return cvs.concatenate(curve, mult*line)\n",
        "\n",
        "def pipe_curve(curve, cvs, mult):\n",
        "  pipe = np.array([[0,0],[0,cvs.h]])\n",
        "  return cvs.concatenate(curve, mult*pipe)\n",
        "\n",
        "def blank_curve(curve, cvs, mult):\n",
        "  xoffset = (cvs.a_curve[0,-1])*mult\n",
        "  blank = np.array([[0, xoffset],[np.nan,np.nan]])\n",
        "  return cvs.concatenate(curve, blank)\n",
        "\n",
        "def parse_multi_multiplier_array(mult):\n",
        "  xmult = 1.0\n",
        "  ymult = 1.0\n",
        "  if type(mult) is list:\n",
        "    if (len(mult) == 2):\n",
        "      xmult, ymult = mult[0], mult[1]\n",
        "  else: # not a list\n",
        "    xmult = mult\n",
        "  return xmult, ymult\n",
        "\n",
        "def forward_slash(curve, cvs, mult):\n",
        "  xmult,ymult = parse_multi_multiplier_array(mult)\n",
        "  slash = np.arange(0, cvs.len-1)/(cvs.len-1)\n",
        "  line = np.array([slash, ymult*(slash)])\n",
        "  return cvs.concatenate(curve, xmult*line)\n",
        "\n",
        "def backward_slash(curve, cvs, mult):\n",
        "  xmult,ymult = parse_multi_multiplier_array(mult)\n",
        "  slash = np.arange(0, cvs.len-1)/(cvs.len-1)\n",
        "  line = np.array([slash, ymult*(np.flip(slash))])\n",
        "  return cvs.concatenate(curve, xmult*line)\n",
        "\n",
        "def paper_break(curve, cvs, mult):\n",
        "  pipe = np.array([[0,0],[0,cvs.h/20]])\n",
        "  return cvs.concatenate(curve, mult*pipe)\n",
        "\n",
        "# New and poorly tested!\n",
        "def rotate_curve(curve, cvs, degrees, origin=(0,0)):\n",
        "    angle = np.deg2rad(degrees)\n",
        "    R = np.array([[np.cos(angle), -np.sin(angle)],\n",
        "                  [np.sin(angle),  np.cos(angle)]])\n",
        "    o = np.atleast_2d(origin).T\n",
        "    npcurve = np.atleast_2d(curve)\n",
        "    npcurve = (R @ (npcurve-o) + o)\n",
        "    return npcurve\n",
        "\n",
        "\n",
        "def rollctl(cvs,e):\n",
        "  mult = 1.0\n",
        "  if len(e) > 1:\n",
        "    mult= float(e[1:])\n",
        "  return int(floor(mult*cvs.len/2))\n",
        "\n",
        "def heightctl(cvs, e):\n",
        "      return (float(e[1:]) * cvs.h)\n",
        "\n",
        "def heightctl_unmoderated(cvs, e):\n",
        "      return (float(e[1:]))\n",
        "\n",
        "def color_red():\n",
        "  return 'r-'\n",
        "def color_blue():\n",
        "  return 'b-'\n",
        "def color_black():\n",
        "  return 'k-'\n",
        "def color_green():\n",
        "  return 'g-'\n",
        "\n",
        "\n",
        "# This is the pattern creator interpretor\n",
        "# each one of these can be followed by a multiplier that indicates\n",
        "# how wide or tall it should be\n",
        "interpret_curve = {\n",
        "    \"A\"   : A_curve,             # Downward facing U-shaped curve, starts at Y midpoint\n",
        "    \"a\"   : a_curve,             # Downward facing curve, starts at min Y\n",
        "    \"B\"   : B_curve,             # Forward B (S shaped cuve)\n",
        "    \"b\"   : b_curve,             # Backward B, use with B will create a sinusoid\n",
        "    \"C\"   : C_curve,             # Forward C (often inverse S, depends on kernel)\n",
        "    \"c\"   : c_curve,             # Backward C, use with C will not create sinusoid\n",
        "    \"U\"   : U_curve,              # Upward facing U-shaped curve, starts at Y midpoint\n",
        "    \"u\"   : u_curve,              # Upward facings, starts at min Y\n",
        "    \"-\"   : horizline_curve,      # One curve's worth of horizontal line\n",
        "    \"|\"   : pipe_curve,           # A vertical line (no horizontal)\n",
        "    \"x\"   : blank_curve,          # Blank space, one curve's worth\n",
        "    \"f\"   : forward_slash,        # The multiplier controls height and width\n",
        "    \"s\"   : backward_slash,       # The multiplier controls height and width\n",
        "    \"*\"   : paper_break,          # Same as | but with a an x to break the paper\n",
        "    \"r\"   : rotate_curve,         # rotate curve from its current position: # New and poorly tested!\n",
        "}\n",
        "\n",
        "roll_control = {\n",
        "    \"o\"   : rollctl                 # move some points from the left to the right\n",
        "}\n",
        "height_control = {\n",
        "    \"h\"   : heightctl,              # height of the curve\n",
        "    \"H\"   : heightctl_unmoderated   # height of the line area\n",
        "}\n",
        "\n",
        "colors = {\n",
        "    \"red\" : color_red,\n",
        "    \"blue\": color_blue,\n",
        "    \"blk\" : color_black,\n",
        "    \"green\" : color_green\n",
        "}\n",
        "\n",
        "def interpret_line(yoffset, line, cvs, add_plot=True):\n",
        "  elements = line.split()\n",
        "  curve = None\n",
        "  color = 'k'\n",
        "  rollby = 0\n",
        "  height = cvs.h\n",
        "  for e in elements:\n",
        "    if e[0] in roll_control:\n",
        "      rollby = roll_control[e[0]](cvs, e)\n",
        "    elif e in colors:\n",
        "      color = colors[e]()\n",
        "    elif e[0] in height_control:\n",
        "      height = height_control[e[0]](cvs, e)\n",
        "    elif e[0] in interpret_curve:\n",
        "      mult = 1.0\n",
        "      if len(e) > 1:\n",
        "        split = e[1:].split(\",\",1)\n",
        "        if len(split) == 1:\n",
        "          mult = float(split[0])\n",
        "        else:\n",
        "          mult = [float(x) for x in split]\n",
        "      curve = interpret_curve[e[0]](curve, cvs, mult)\n",
        "  curve = cvs.roll(curve, rollby)\n",
        "  curve = cvs.yoffset(curve, yoffset)\n",
        "  if add_plot:\n",
        "    plt.plot(curve[0,:], curve[1,:], color)\n",
        "  return color, curve, height\n",
        "\n",
        "def minmax(minxy, maxxy, curve):\n",
        "  new_minxy = np.nanmin(curve, 1)\n",
        "  new_maxxy = np.nanmax(curve, 1)\n",
        "  minxy = np.nanmin(np.stack((minxy, new_minxy), 1),1)\n",
        "  maxxy = np.nanmax(np.stack((maxxy, new_maxxy), 1),1)\n",
        "  return minxy, maxxy\n",
        "\n",
        "def plot_outer_box(minxy, maxxy, color='k'):\n",
        "  plt.plot([minxy[0], minxy[0]], [minxy[1],maxxy[1]], color )\n",
        "  plt.plot([maxxy[0], maxxy[0]], [minxy[1],maxxy[1]], color )\n",
        "  plt.plot([minxy[0], maxxy[0]], [minxy[1],minxy[1]], color )\n",
        "  plt.plot([minxy[0], maxxy[0]], [maxxy[1],maxxy[1]], color )\n",
        "\n",
        "def interpret_tess(name, input, cvs, outputfile=True, ax=None):\n",
        "  minxy = np.ones((2)) * nan\n",
        "  maxxy = np.ones((2)) * nan\n",
        "  yoffset = 0.0\n",
        "  for line in input:\n",
        "    color, curve, height = interpret_line(yoffset, line, cvs)\n",
        "    minxy, maxxy = minmax(minxy, maxxy, curve)\n",
        "    yoffset -= height\n",
        "  if ax is not None:\n",
        "    plt.axis(ax)\n",
        "  else:\n",
        "    plt.axis('equal')\n",
        "  plt.axis('off')\n",
        "\n",
        "  plot_outer_box(minxy, maxxy, 'k')\n",
        "  plt.savefig(name + \".svg\")\n",
        "  outputfilename = fixup_svg(name + \".svg\")\n",
        "  if outputfile:\n",
        "    files.download(outputfilename)"
      ],
      "metadata": {
        "id": "Au1SfO7ZB_iB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "name = \"pretty22\" # Similar to Lukasheva's p22 in the Curved Origami patterns PDF\n",
        "pretty22 = [\n",
        "          \"red a a x x a x x a a h1\",\n",
        "          \"red  x x A2  x A2 h0\",\n",
        "          \"blue a a x x a x x a a h1\",\n",
        "          \"blue x |4 x |4 x x |4 x |4 x x |4 x |4 h0\",\n",
        "          \"blue  x x A2  x A2 h0\",\n",
        "\n",
        "          \"red  x x B b x B b x h1\",\n",
        "          \"red  o u u x x u x x u u h0\",\n",
        "\n",
        "          \"blue  x x B b x B b x h1\",\n",
        "          \"blue  o u u x x u x x u u h2\",\n",
        "\n",
        "          \"red   x0.5 |3 x |3 x3 |3 x3 |3 x |3 h-1\",\n",
        "\n",
        "          \"blue  o a a x x a x x a a h1\",\n",
        "          \"blue  x x b B x b B x h0\",\n",
        "\n",
        "          \"red  o a a x x a x x a a h1\",\n",
        "          \"red  x x b B x b B x h0\",\n",
        "\n",
        "          \"blue  x x U2  x U2 h1\",\n",
        "          \"blue u u x x u x x u u h0\",\n",
        "\n",
        "          \"red   x x U2  x U2 h1\",\n",
        "          \"red  u u x x u x x u u h2\",\n",
        "          \"blue x |4 x |4 x x |4 x |4 x x |4 x |4 h0\",\n",
        "\n",
        "]\n",
        "interpret_tess(name, pretty22, cvs)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "id": "Nzhr0Nn0aYgJ",
        "outputId": "8e01e3cd-2b33-4274-b61a-2d8857720463"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_b043ab89-d7e9-40a9-bdb0-0e497a226931\", \"kt_pretty22.svg\", 33427)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "name = \"generic_organic\" # closer Similar to Lukasheva's book p37 in the Curved Origami patterns\n",
        "organic = [\n",
        "            \"red    x0.25 |2 x x |2 x x |2 x x |2 x x |2 x0.75 h0.35\",\n",
        "\n",
        "            \"blue -10.25 h2.25\",\n",
        "            \"red    x0.25 u2 u2 u2 u2 u2 h0.5\",\n",
        "            \"green  x0.25 s f s f s f s f s x0.25 h2.75\",\n",
        "            \"red   o2.75 A2  A2 A2 A2 A2 h0\",\n",
        "\n",
        "            \"blue -10.25 h2.25\",\n",
        "            \"red    x0.25 u2 u2 u2 u2 u2 h0.5\",\n",
        "            \"green  x0.25 s f s f s f s f s x0.25 h2.75\",\n",
        "            \"red   o2.75 A2  A2 A2 A2 A2 h0\",\n",
        "\n",
        "            \"blue -10.25 h2.25\",\n",
        "            \"red    x0.25 u2 u2 u2 u2 u2 h0.5\",\n",
        "            \"green  x0.25 s f s f s f s f s x0.25 h2.75\",\n",
        "            \"red   o2.75 A2  A2 A2 A2 A2 h0\",\n",
        "\n",
        "            \"blue -10.25 h2.25\",\n",
        "            \"red    x0.25 u2 u2 u2 u2 u2 h0.5\",\n",
        "            \"green  x0.25 s f s f s f s f s x0.25 h2.75\",\n",
        "            \"red   o2.75 A2  A2 A2 A2 A2 h0\",\n",
        "\n",
        "            \"blue -10.25 h2.25\",\n",
        "            \"red    x0.25 u2 u2 u2 u2 u2 h0.5\",\n",
        "            \"green  x0.25 s f s f s f s f s x0.25 h2.75\",\n",
        "            \"red   o2.75 A2  A2 A2 A2 A2 h0\",\n",
        "\n",
        "            \"blue -10 h2.25\",\n",
        "            \"red    x0.25 u2 u2 u2 u2 u2 h0.5\",\n",
        "            \"green  x0.25 s f s f s f s f s x0.25 h2.75\",\n",
        "            \"red   o2.75 A2  A2 A2 A2 A2 h0\",\n",
        "\n",
        "            \"blue -10.25 h2.25\",\n",
        "            \"red    x0.25 u2 u2 u2 u2 u2 h0.5\",\n",
        "            \"green  x0.25 s f s f s f s f s x0.25 h2.75\",\n",
        "            \"red   o2.75 A2  A2 A2 A2 A2 h0\",\n",
        "            \"blue -10.25 h0\",\n",
        "            \"red x1.25   |-2 x x |-2 x x |-2 x x |-2 x x |-2 x0.25 h2\",\n",
        "            \"green x10 |43 h0\",\n",
        "]\n",
        "\n",
        "test = algebraic_kernel(angle= 180, N=200, curve_h_of_c=0.2)\n",
        "cvs = curves(test)\n",
        "\n",
        "interpret_tess(name, organic, cvs)"
      ],
      "metadata": {
        "id": "I5YAgZDwg6Ia",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "outputId": "7a5499ca-117b-419d-a4c5-6f56ddc7c380"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_c5dcd875-c1a4-49cb-b319-e4f23c799aa1\", \"kt_generic_organic.svg\", 36440)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "name = 'p38_etude10'\n",
        "pattern= [\n",
        "  'blue x4 |8     x4 |8        x4 |8      x4 |8     x4 |8         x4 |8  h0',\n",
        "  'red  A4        A4           A4         A4        A4      A4    A4 ',\n",
        "  'blue B2 b2     B2 b2        B2 b2      B2 b2     B2 b2   B2 b2 B2 b2',\n",
        "  'red o1 U4      U4           U4         U4        U4      U4    U4     h6',\n",
        "\n",
        "  'blue B2 b2     B2 b2        B2 b2      B2 b2     B2 b2   B2 b2 B2 b2  h6',\n",
        "\n",
        "  'red  A4        A4           A4         A4        A4      A4    A4 ',\n",
        "  'blue B2 b2     B2 b2        B2 b2      B2 b2     B2 b2   B2 b2 B2 b2',\n",
        "  'red o1 U4      U4           U4         U4        U4      U4    U4     h6',\n",
        "\n",
        "  'blue B2 b2     B2 b2        B2 b2      B2 b2     B2 b2   B2 b2 B2 b2  h6',\n",
        "\n",
        "  'red  A4        A4           A4         A4        A4      A4    A4 ',\n",
        "  'blue B2 b2     B2 b2        B2 b2      B2 b2     B2 b2   B2 b2 B2 b2',\n",
        "  'red o1 U4      U4           U4         U4        U4      U4    U4     h8',\n",
        "  'blue x2 |8     x4 |8        x4 |8     x4 |8    x4 |8  x4 |8   x4 |8 ',\n",
        "\n",
        "]\n",
        "interpret_tess(name, pattern, cvs, True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "id": "25asPB34tvXS",
        "outputId": "75188654-de2f-40d6-d047-4bcadd728ed8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_6f2d50a6-c166-4808-93c0-26907c5ee288\", \"kt_p38_etude10.svg\", 32975)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "This next one is a pattern I've been calling \"[Mitani Wedding Dress](https://www.instagram.com/p/CCLQHr6pbFf/)\". I reverse engineered the pattern from that picture. The key was making breaks in the paper where the curves could come together. The green in the below are the breaks for the top of the cones.  "
      ],
      "metadata": {
        "id": "13crZLBzKuWc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "name = 'm_ex15' # this is a pattern I've been calling Mitani wedding cake: https://www.instagram.com/p/CCLQHr6pbFf/\n",
        "pattern= [\n",
        "  'blue   x4    |8     x4    |8     x4    |8   x4    |8   x4           h0.25',\n",
        "  'red    A4           A4           A4         A4         A4           h0',\n",
        "  \"green  x2     *4    x4    *4     x4    *4   x4    *4   x4    *4     h1.5\",\n",
        "  \"red   x4           |1.5    x4   |1.5   x4   |1.5  x4   |1.5         h12\",\n",
        "\n",
        "  'red    A4           A4           A4         A4         A4           h0',\n",
        "  \"green  x2     *4    x4    *4     x4    *4   x4    *4   x4    *4     h1.5\",\n",
        "  \"red   x4           |1.5    x4   |1.5   x4   |1.5  x4   |1.5         h12\",\n",
        "\n",
        "  'red    A4           A4           A4         A4         A4           h0',\n",
        "  \"green  x2     *4    x4    *4     x4    *4   x4    *4   x4    *4     h1.5\",\n",
        "  \"red   x4           |1.5    x4   |1.5   x4   |1.5  x4   |1.5         h12\",\n",
        "\n",
        "  'red    A4           A4           A4         A4         A4           h0',\n",
        "  \"red  x2     |-8     x4    |-8   x4    |-8   x4    |-8  x4    |-8    h1.5\",\n",
        "  \"red   x4           |1.5    x4   |1.5   x4   |1.5  x4   |1.5         h12\",\n",
        "\n",
        "\n",
        "]\n",
        "test = algebraic_kernel(angle= 180, N=200, curve_h_of_c=0.40)\n",
        "cvs = curves(test)\n",
        "\n",
        "interpret_tess(name, pattern, cvs, True)\n",
        "ax = plt.axis()\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "id": "ipse159s7HNM",
        "outputId": "d090803b-d066-48fa-d2ea-fe13cd45be70"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_9d5aaeb3-c708-4cf5-a4e7-f5edae6df7b7\", \"kt_m_ex15.svg\", 13286)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Added rotation of a signal to do the leaves in Curved Origami book...\n",
        "# This rotates one section at a time (and needs an x or x0 so the lines)\n",
        "# don't try to connect\n",
        "# This is new and hasn't been well tested\n",
        "# only works with A and U inputs\n",
        "\n",
        "def rotate_snip(snip=\"A A\", angle=-45, cr = None):\n",
        "  pattern = \"\"\n",
        "  total_angle = 0\n",
        "  if cr is None:\n",
        "    cr = sum(map(str.isalpha, snip)) * np.cos(np.deg2rad(angle))\n",
        "  cr = str(round(cr, 2))\n",
        "  while (total_angle < 360):\n",
        "    pattern += snip + \" r\" + str(angle) + \" x-\" + cr + \" \"\n",
        "    total_angle += abs(angle)\n",
        "  return pattern\n",
        "\n",
        "\n",
        "name = \"rotxAAAAA8\"\n",
        "pattern =  [\n",
        "    \"red \" + rotate_snip(\"A A A A A\" , 90) + \" h0\",\n",
        "    \"blue \" + rotate_snip(\"A A A A A\", 90) + \"r45 h0\",\n",
        "]\n",
        "\n",
        "interpret_tess(name, pattern, cvs, True)\n"
      ],
      "metadata": {
        "id": "EigqWSJD-6q6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "outputId": "ea8c19c9-d7d0-4299-9076-6ecf260acb47"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_ebed52cc-618b-4c4e-8b73-5baa3218be26\", \"kt_rotxAAAAA8.svg\", 14645)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class nl_kernel(curve_kernel):\n",
        "  def __init__(self, angle = 30.0, horiz_d = 1.0, curve_h_of_c = 0.66,\n",
        "               N=500, std=250, a=0.2):\n",
        "\n",
        "    self.angle = np.deg2rad(angle);\n",
        "    self.horiz_d = horiz_d\n",
        "    self.curve_h_of_c = curve_h_of_c\n",
        "    self.a = [0,0]\n",
        "    self.mid = [horiz_d/2,0]\n",
        "    self.c = [horiz_d/2, tan(radians(angle))*horiz_d/2]\n",
        "    len = int(floor(N/2))\n",
        "\n",
        "    b = self.angle\n",
        "    theta = np.linspace(0, 10*np.pi, N)\n",
        "    curve_x = np.exp(b*theta) * a * np.cos(theta)\n",
        "    curve_y = np.exp(b*theta) * a * np.sin(theta)\n",
        "\n",
        "    self.curve = np.array([curve_x[0:len], curve_y[0:len]])\n",
        "    self.offset = self.curve[0,-1] #+ horiz_d/N\n",
        "\n",
        "  def __repr__(self):\n",
        "    plt.plot(self.curve[0,:], self.curve[1,:])\n",
        "    print(\"Distance a to c: {:2.2}\".format(distance.euclidean(self.a, self.c)))\n",
        "    return (\"Angle {}, d {}, A {}, B {}, C {}\".format(self.angle, self.horiz_d,\n",
        "                                                    self.a, self.mid, self.c))\n",
        "\n",
        "class nl_curves(curves):\n",
        "  def __init__(self, kernel):\n",
        "    self.kernel = kernel.curve\n",
        "    self.offset = kernel.curve[0,-1]\n",
        "    self.h      = max(kernel.curve[1,:])\n",
        "    self.len    = len(kernel.curve[1,:])\n",
        "\n",
        "    self.a_curve = self.kernel\n",
        "    self.b_curve = self.negative(self.kernel)\n",
        "    self.c_curve = self.yoffset(self.kernel, -self.h)\n",
        "#    self.c_curve = self.concatenate(first = c,\n",
        "#                                    second = self.negative(self.reverse(c)))\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "nl = nl_kernel(N=200, curve_h_of_c=1.0, angle = 30)\n",
        "cvs_nl = nl_curves(nl)\n",
        "print(cvs_nl)\n",
        "#plt.plot(nl.curve[0], nl.curve[1])\n"
      ],
      "metadata": {
        "id": "ho_G24-9q0bz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "eba3bae4-b3a8-4854-b98f-00a5fbcca68b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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1uxGwq8SPpVjbjn2uMUqpJKVUUnp6uiub6T6Sk+Gss0wirmHDYM0auPhiu1slhKgEcXFx/DJnDol33EGUjw89i6e0uiGXBQalVG3gO+AerXVmyce0qQZUpk9Aaz1Zax2rtY6NiIhwVTPdg9MJr78O3bqZbKhffw1ffgnh4Xa3TAhRieLi4rjx7bfxevpp838/bZrdTTohlwQGpZQvJih8obUurlyxr7iLyLpOs7anAiXLHjW2ttUM27aZsYP77oNBg0xZzWHD7G6VEKIqPfSQ6S24/XZTTMvNuGJWkgL+B6zXWr9W4qGZwGjr9mjgxxLbR1mzk/oAh0p0OVVfWpuymp06mYHmTz+FGTMgKuo0PyiEqHZ8fEwluKIis4DV6bS7RaW44ozhbGAkMEAptcK6XAy8CFyglNoEnG/dB/gF2ApsBj4EbndBG9xbaqoZO7jlFoiLM2MJo0eb1ZBCiJqpZUvTpRwfbxayupEKL3DTWv8FnOwIN/AE+2tgbEVf1yNobcYO7rjDrEt4911TBtBLymAIITAVFmfOhHHjWBEZya/bt7vF4jel3XRUvKTY2FidlJRkdzPKJj3dBIHvv4ezzzZdR1JrWQhxrH37KIyJYc2hQ5ylFMrf32UV4JRSS7XWsWX9OfnqWhlmzIAOHeDnn0092PnzJSgIIU4sKoqZF19MN625zumkoKDA9sVvEhhc6eBBU2bziitMWc2lS+HBB82SeCGEOImGY8eyUinuAvx8fW1Ppy+BwQUSEhKYfuON5LdpY8YUnnwSEhOhY0e7myaE8ABxZ51FrUceoROw5KWXbB9jkMBQQQl//82Svn0Z/sknbP33X1ZNngxPPQW+vnY3TQjhQVo99hhERNAhPt7upkhgqJDCQkLGjuUuh4O3gFilmLVvn92tEkJ4ooAAM6V95kyTEcFGEhjKKy8Phg6lw8qVPO7jw33e3mh/f9v7BoUQHuy228yY5Dvv2NoMKdRTHllZMGQIzJsH773HxV27EiSlNoUQFdWwoUmR87//wYQJULu2Lc2QwFBW+/fDRRfBsmXw2WcwYgRxIAFBCOEad90F06bx+3/+Q8j48bYcW6QrqSx274ZzzzVFdH74AUaMsLtFQohqJsHpJEkpon/6ifMHDLCl0psEhjO1bRv07Qs7dsCvv8L//Z/dLRJCVEPz5s/nfSAGaGvTYjcJDGdi3TpTh/ngQZPwSkpuCiEqSf/+/Vni5wdAT29vWya0SGA4naQk6NfPJMSbPx969bK7RUKIaiwuLo734+PJ9/PjqSFDZIzB7cyfDwMGmPrLCxfKSmYhRJWIO/ts/Hv1osEee0rVSGA4mVmzYPBgaNLEBIWWLe1ukRCiJune3RT1cjiq/KVdVdrzY6VUmlJqTYlt4Uqp2UqpTdZ1HWu7Ukq9pZTarJRapZTq7oo2uNS0aXD55eYMYf58aNTI7hYJIWqabt3g8GHYtKnKX9pVZwyfAoOP2TYOiNdatwbirfsAFwGtrcsYYJKL2uAaH3xgpqGefbYZaK5Xz+4WCSFqou7Wd+bly6v8pV0SGLTWC4ADx2weAkyxbk8BLi+xfao2FgNhSqkGrmhHhb30kimuc/HFZkpqSIjdLRJC1FTt2uH082PxpElVvpahMscYorTWxSMne4HiqveNgF0l9kuxtpWilBqjlEpSSiWlp6dXYjMxM44eeQQefhiGDzeL1wIDK/c1hRDiFBKSklheWEj2woUMHDiwSoNDlQw+W3Wey1RDVGs9WWsdq7WOjYiIqKSWAU4njB0LL7xgMht+/rmkzBZC2G7evHlkao0fVHlVt8oMDPuKu4is6zRreyrQpMR+ja1tVa+w0FRcmzQJHnrIXEu1NSGEG+jfvz9BSpEH+Pn5VelCt8oMDDOB0dbt0cCPJbaPsmYn9QEOlehyqjp5eXDVVfDFF+ZsYeJEUKrKmyGEECcSFxdHx1ataNauHfHx8VW60M0l2VWVUtOA/kA9pVQK8CTwIvC1UuomYAdwtbX7L8DFwGYgB7jBFW0ok+K02XPnwrvvwu23V3kThBDidGopRZvOnaGKVz+7JDBora89yUMDT7CvBsa64nXL5cABkzZ76VKTNvu662xrihBCnFJeni0TYWpWPYY9e2DQILNg5Pvv4bLL7G6REMKFtIaUFNiyxVTHLL5kZkJBgRlWLL4ODoaoKIiMNNdRURAdDT16QFiY3e/EkptrSn5WsZoTGLZtg/PPh3374JdfTA4kIYTH27zZ9ArPm2cuu3cffczbG5o2hfBwM9nQz898AQ8ONsHin3/MISE7u/RztmoFsbHm0rMn9O4N/v5V+a4Mx+HDLF29GkdCgueNMbi9devgggtM9I2PN79lIYTHOngQvvwSPv7Y9AoD1K8P/fubDPlt20KLFiYo+JzBUS4nB9LSYONG83xJSbBoEUyfbh4PCTEdDMOGmU6HqvgSv3jhQmJzcohftIhnBg6s0gHoah8YVn38Ma3uvBOfwED8FiyQDKlCeLDdu+Hpp2HqVNP93rkzvP66yXfZtm35JxYGBUGzZuYyaNDR7WlpsHgxzJhhLp9/bs42/u//4Oqr4dJLK2+G+8YvvqAPkKT1kXUMVRUYqnV21eXTp9PsppvYl5NDt+xsErKy7G6SEKIcsrPhscdMF88nn5jlR0lJJvnoPfdATEzlzDaPjDRnCh9/bLqcfvvNBITffjN5NmNi4KOPID/f9a99nsOBE1jg5VWt1jHY7rctW3hbKc4BNhQV2VIiTwhRMStXmnxyzz1nDsbJySbXZY8eVbv0yNcXLrzQBIK9e+G77yA0FG6+2WTlf+MNkwzVVZps2EBOTAz3Pftsla9jqNaBof+AATwXEMA+b+8qj7hCiIr79FMzJHj4sBlY/vJLM3ZgN19fuPJKWLIEfv/dnMnce6+Z1fTSS2bWU4VkZ8PixdS+7DLGjx9f5VXcqnVgiIuLIz4+nmeeeabKI64QomI++QRuuMEMJq9YAeeea3eLjqeUGZOYNw/++stU/n34YRPMVq6swBMvXGiiy8DjloJViWodGMAEBzsirhCi/GbNgptuMgfdn3+Gysyj6Spnn21mwn/3HaSmmqmuTz5p1k2U1e7PPqPI25tEmxJ6VvvAIITwLAcOmKDQubPJgG/D+q4KufJKM0N++HCYMMEEiOIptWciYdEiDk6fzt8OB+ddckmV12IACQxCCDfz5JOwfz9MmWKmkXqiunVNxp2ffjLvpXdvM2B+JlInTaK91nxK1afbLiaBQQjhNrKyzIDzf/4DXbrY3ZqKu/RSWLvWrLO49VZzBqFPVZnG6eSixES2KMWXNkxTLVbtF7gJITxHfLyZkHPjjXa3xHXCwkyX2Jgx5mxo7154++2TLIybMYNamzbhfPxxngoMpH///raMj0pgEEK4jTVrzHVsbOntCQkJTJ06lb1795baXr9+fUaNGuX2k0t8fc0iuagoU/olLc2soi41fuJ0wlNPQZs2tH7iCcafSS6PSiKBQQjhNrKzzUG0Vi1zPyEhgZcnTiRt5kzO15rzgHBMIZcNwM/A2e+/T99+/XjxxRfdOkAoBS++aILDffeZsYeffoLatc3jG154gbarV7PpqadobWNQAEBrbcsFGIz53W4Gxp1q3x49emghRPX38stag9bp6Vp/8P77eqiXl15juuV1EegNoP8GvQJ0rrV9LugI0F5eXnrRokV2v4Uz8vnnWnt5aT18uNZOp9YJCxboNUrptaBrBQS47H0ASbocx2dbBp+VUt7Au8BFQHvgWqVUezvaIoRwH337muuH7v2TwFtv5VunEw1cjzlTaAucDXS17t+CNz2BLwCn08m4ceNsaHXZjRgBzzxjsre+/TZ4jR9PB60ZD+QVFtqevseu85VewGat9VYApdR0YAiwzqb2CFGlsoqK2F1QwJ78fPYUFJDvdOKtFF5K4Q14K3Xk4lXyvnX7TPbzOuZnfJSinq8vvl7uOxmxZ09o2OAAv33elHcJ4EnyeA5wAF5eXvQ75xzCw8OZMWMGoXzJZK7lW7byDf/Fj7ksWbLE7rdwxsaNg8REuP8+J/Mcmne8vZkFbpG+x67A0AjYVeJ+ClCqSIJSagwwBqBp06ZV1zIhyklrTZbDwZ6CAnZbB/wj18dsO+x02tJGL6CRvz/RAQFEW9fNAgLM/YAAmvr7E1hZeaTPQGJiAr32PMkM/qArn7CRkSjl4PIhQ3jooYeOjCEopdiLqShcj3yGMY0CGuBdnmXGNvHyginjk4n9yZer/WYw+astPLN+rm0zkUpy28FnrfVkYDJAbGzsqWb+ClGpig/4JQ/ye/LzzbV1oC/edqIDfpCXFw39/Wng50f32rW5tG5dGvj5HdnWwM+PAC8vnIBD66MXwFnidvH28u5XqDV7CgrYnpfHjrw8/jp0iOlpaTiOaW+Ur++RQFEcQEoGj5BKHBh944UX+JDZRPAgH/IyEMGzz+7jkUf+c9y+V9OABwnkXh5hI6MAbwICbCizVl6ZmYRdfznf12lAn5x4Xn4jgj//7HNGhYUqm11NSAWalLjf2NomhG32FxbyT2YmiZmZJOfklPqmn3OKA35DPz961K5NwxIH/IbWAb+hvz/B3t6oqswPXQZFTiepBQXssILFjrw8duTnsz0vjxXZ2cz891/yj1mRFebjYwJF8ZmHddYRExRETFAQXuV8rwkJCRyeNYt6wE5eAf7Fx+c9nnoqkORkGDrUpNquVw8iI9vxdVpDvmYMcDXwJlDE2LH3VfATqRoJixZRZ8wY2m7eTOf4D/hgpxejRsGrr5okfHazKzAsAVorpZpjAsJw4PivBEJUkgKnk5XZ2Sy2AkFiVhabc3MB093SPCCARv7+xAYH06BuXXOgtw74xd/03fmAf6Z8vLyOHNxPxKk1acVnGfn5R4LH9rw8NufmEn/wINmOo+ccdX18OCc0lL5hYfQLDaVb7dr4nOGYxrx58+hmBeD5gLf3Z3zzzR38+WcPPvvMpJg4qng4Mgt4goCA17nrroeYOHFimT+DqpawcCEbzjuP6x0Oxvv4cJmfHyNHmoHol16C224zpUTtZEtg0FoXKaXuAH4HvIGPtdZr7WiLqP601mzPyysVBJZnZR35JtzQz4/eISH8t0EDegcHExscTG13OJ93A15KUd/fn/r+/vQ5weNaazKKitiel8fK7GwWHjrEgoMH+XH/fgBqeXlxVmgofa1L75CQk45h1K1bF28gH8gDHrr/fi6/vAeXX26+SScmmvQSGRkmh1LbttCvXzC1ak0AJlTOB+BqubnUveUWrnc4eAZ42ekkxCrZ+fTTZvD97bfh0UftbaZtf/1a61+AX+x6fVF9HSoqOtIllJiVRWJmJulW5ZRALy9ig4O5s3FjegcH0yckhMaelr7TjSilCPf1JdzXl+7BwdzQoAEAu/Pz+csKEgsPHeLJ7dvRgK9S9AwOpm9oKP3Cwjg7NJRQKwgvX76cAMAfqAdkZmYeeR1/f+jXz1w8VkYGXHYZrZOTuc/Xl7eczlIzkGJjTS3pV16BO+4w1eHsIl+LhEcrcjpZffhwqSCwPifnyOPtgoK4pG7dI0GgY61aZ9y1Icqvob8/V0dGcnVkJAAZhYX8feiQOaM4dIhXU1KYuGsXCuhSuzZ9Q0PZ0rgx2XXqQEYG/eG49BeeLGnmTJqOGUO9/fvxmj6dYU2aEDFv3nEzkJ5+2pQxffNNeOIJ+9qr9ClT/bmH2NhYnZSUZHczhM201qTk55cKAklZWeRa/dIRvr70Dgk5EgR6hoQc+TYq3EuOw0FiZiYLDh1i4cGDJGRmHhngb75rF21Xr+bPdeuY8cgjXHLWWTa3tmJWT55MyK23Eq411/j58bjVdXQyV1wBc+fCtm1Qp07FXlsptVRrHXv6PY/5OQkMwl1prVmSlcXcgwdJzMxkcWYme6x56n5K0T04mN7BwfQOCaFPSAjNAgI8fjC4pip0OlmWnc09H39M2uHDZHTqREZICMrp5JKICEZHRfF/9erh70lnezk58Oij6DffZKfWXAms9PbmmWeeYfz48Sf9sZUroWtXk0/vyScr1gQJDKJa0Fqz6vBhpqelMT0tje15eQC0Cgw8EgR6h4TQpXZtzzpIiDOSkJDAwHPPZU5RETktWnD1oEGoq6/mX6COjw/DIyMZXb8+vYKD3ftLwPz5pgzdli3sveIKOv/6KwcKC/Hz8zuj+vPnnWcq2VWobjQSGISHSz58mK/S05melkZyTg7ewKDwcK6JjOSS8HDq+fnZ3URRRW677Tbmvv8+CzFZVB/p14/Y559nSZ06/PDvv+Q5ncQEBTEqKoqRUVFuNXkg8c8/8Xn8cXosXgwtWsBHH8F555GQkMC8E4wpnMxzz8Fjj8G+fWAN05SLBAbhcbbn5h4JBiuys1HAuWFhXBsZyZX16kkwqKESEhIYOHAgMXl5xGtNLjDKx4fW//0vV44cyY5mzZi6bx8LDx1CAQPr1GF0VBRXRERQy650HhkZ7Hz4YYI+/JBw4D1vb3r+8Qe9Bwwo19MlJkKfPmZtwzXXlL9Z5Q0M1f5c3APiXo2yOz+fN3btIm7ZMponJjJu61YCvbx4o1UrUuLimNu1K2MaNpSgUIPFxcURHx9PxAUXcJ5SZAG/FxUR9f77XD1wIB127GBBt25s7t2bJ6Kj2ZKby8jkZOovWsSNycnMP3gQZyX/4yckJPDCCy+QNGuWyYYXHU3TDz8kEYgD7gHmJCaW+/l79DCL3OLjXdTgMqrWZwwZGabm6oQJMHBgJTRMnJH0ggK+s84MFhw6hAa61a7N8MhIro6IoFlgoN1NFG6o+MzBJzeX94DrgB3A3PPOY+/AgZw7YABxcXE4teavQ4eYsncv36Snk+Vw0CwggFFRUYyqX5+WLv77Sli0iAcGDGBEQQE3aE2Alxfq6qtZedFFxN16KwUFBWc8lnAqQ4bA6tWwdWv52ypdSSewbx9ccAFs2ABff20+aFE1DhYWMuPff5melsafGRk4gJigIK6NjOSayEjaBgXZ3UThAYpLen7yySf0LSzkRa3poTUbgKk+PsQ8/zwpRUVH+u5zHA5++Pdfpuzdy58ZGWjgnNBQHm7ShEvq1i3XgHVCQgLz5s7lkqgoOm/YQMZHH1EnI4MCYKpSFN17L7e++urRfcswlnAqb78Nd91lAkPz5uV7DgkMJ3HgAFxyCSxZAp98AiNHurhx4ojDDgc/WcHg1wMHKNCa5gEBDI+MZHhkJJ1q1XLvmSTCbRUfcHft2EHG5MmM1ZpzgCLgVyDe15frp0whNzqaefPn079/f5p068bn+/bx4Z49bM3L46yQEJ5v0YJzw8JO+Rr9+/cnrk8f2LKFjV9+yR8TJnCpw0EzwOntzaFevRiflMT3DgfZ/v4VPjM4mXXroEMH+PBD+O9/y/ccEhhOITsbLr/c9Ne98w6MHeu6ttV0eQ4Hvx04wPS0NH7av58cp5OGfn5cYwWDnu4+rVB4lOLupYKCAmKAUQ4H1wDR1uP7gL+ArT4+XPnAA7S84AISDx7klbQ05sfEkA5cWKcOw7OzSYuPZ2CnTvRo3Jj18+fz5iOP0KKoiFil6FerFj5ZWYDJ2/Qn8L1SdHrsMe6dMMGlZwYnozUEBMC995pa0eUhgeE08vJg+HD48UczFWz8eFOcW5RdodNJfEYG09PS+OHff8l0OKjn68uwiAiGR0ZyTmhouVMvC3E6xQflunXrcs8991CQn08bX1/G9+mD14IF9NKaZoDvMT+X6+fHe0OG8MKIEewPDeWqefN45uOPidl1tGZYAbAK8OrVi+4338xKX1/63norOWVYg+BKYWFw/fXwxhvl+/nyBoYaky8gIAC+/RZuvNFkLjx4ECZOlOBwppxas+DgQaanpfFtejr7i4oI8fbmyogIro2MZEBYmOQgElUiLi7uyMG5U6dOR765A0fOJgJ9fZn3xRck//IL8z7+mNpaE1BYyEUZGXwxaRIvN2jAzGHD+K5vX/pv2cI4f3/uvOUWdhUWgr8/8W+8AXFxdAF+b9Om0s8OTiYgAKxs8FWqxpwxFHM64e67TZfSzTfDpElgYyVDt+fQmm/S0nhmxw7W5eQQ5OXFZfXqMTwyksHh4bL6WLiVY7t4SnY9FX/jBxNA8gMDUdddh9cVV6CU4govL1ouXsyl55xje2nNYs2bQ9++MHVq+X5ezhjOkJcXvPWWOUV79lnIzDQfukybL63I6eSr9HSe3bGD5Jwc2gUFMTUmhivtXEQkxGmUPJsovh8fH3/cN/6S2xp168aE7dv5ZO9egvr0wbt+fdoXFblFAsbAQNMNXtUqdMaglBoGPAW0A3pprZNKPDYeuAlwAHdprX+3tg/G1OHzBj7SWp92WKWyVj6/8go8+CBcfDF8840p/lHTFTmdfJGWxnM7drApN5eOtWrxRHQ0QyMiZNxAVGsbcnJ4fNs2vklPJ9zHh/FNmzK2UaOTFhaqCt27Q6NG8NNP5ft5u1Y+rwGuBBYc05j2mHKdHYDBwHtKKW+llDfwLnAR0B641trXFg88AJMnw6+/wkUXmbOHmqrQ6eTjPXto+88/XJ+cTC1vb77v0IGVsbEMi4yUoCCqvbZBQXzdoQNLe/SgV0gID27dSuvERGYfOGBbmwID7RljqFBg0Fqv11pvOMFDQ4DpWut8rfU2YDPQy7ps1lpv1VoXANOtfW1z880wbRosWmQyGqan29maqlfgdDJ5927a/PMPN23YQB0fH37s2JFlPXpwhZwliBqoe3Awv3buzPyuXQn18WHQqlU8tGULBVa9iKrkkYHhFBoBu0rcT7G2nWy7ra65xkxjXbfOlA5MSbG7RZUv3+lkUmoqrRITuWXjRiJ9fZnVqRNLevTgsnr1ZO2BqPH6hYWxpEcPbm3YkJd37eLs5cvZXKI6YFVITYV69ar0JYEzCAxKqT+VUmtOcKnUb/pKqTFKqSSlVFJ6FXyNv/hi+P1384s45xzYvLnSX9IWeQ4Hb6ek0HLxYm7ftInG/v781rkzi7t35+JypgwQoroK8vZmUps2fNehA1tyc+m2dCmfVVHJ0exsk86nR48qeblSTjvsrrU+vxzPmwo0KXG/sbWNU2w/9nUnA5PBDD6Xow1l1q+fKak3eLAJDrNnQ6dOVfHKlS/H4WDy7t28tGsXewoK6BsaypR27RgQFibBQIjTuDIigp7BwVy3fj2jkpP5/cAB3mvThpBKnLm0cqVZ/dytW6W9xElVVlfSTGC4UspfKdUcaA38AywBWiulmiul/DAD1DMrqQ3l0qMHLFgAPj5w7rmweLHdLaqYww4Hr+zcSfPFi7l3yxZigoKY26ULC7p1Y2CdOhIUhDhDTQICmNO1KxOaNWNaWhrdkpL4pxJnrCxfbq67d6+0lzipCgUGpdQVSqkUTAryWUqp3wG01muBr4F1wG/AWK21Q2tdBNwB/A6sB7629nUr7drBX39BeDicf759OdErIquoiIk7d9Js8WIe3LqVzrVrs6BrV+Z07Ur/ilYYF6KG8laKx5s1Y0G3bhRpzdnLlzNx585Kqf+wbJmp3tawocuf+rRq3MrnstizBwYNgo0bPSdtd2ZREe+kpvLqrl0cKCpicHg4j0dHc1ZoqN1NE6JaySgs5JaNG/kmPZ2BYWFMbdeOhv7+Lnv+rl2hQQMznb68pIJbJWjQwNT07tYNhg6Fzz6zu0Und7CwkAnbtxO9eDGPbtvGWaGhJHbvzq+dO0tQEKIS1PH15av27fmwTRsWZWbSJSmJWfv3u+S58/Jg7Vp7xhdAAsNphYfDn3+a8YZRo+Ddd+1uUWkHCgt5cts2mi1ezJPbt3NuaChJPXrwU6dO9AoJsbt5QlRrSin+27AhS3v0oJGfH/+3erVLZi2tWQNFRfaML0ANzJVUHrVrw6xZJm33HXfAoUP2p+3+t6CA11NSeDs1lSyHg6H16vFYdDRdg4Pta5QQNVS7WrVY1L07l61ezejkZABG1q9f7uebPdtcx5a5E8g1JDCcoYAAk0/J7rTd+U4nz+3YwWu7dpHjdDIsIoLHoqPpVLt21TZECFFKkLc3Mzt1qnBwKCoyWZ8HDIBmzVzcyDMkgaEMfH1hyhQIDYWXXzbBoSrTdi/PymJUcjJrDh/mmogInmjWjPa1alXNiwshTssVwWHGDNi1y5QGsIsEhjLy8jJFusPCTCW4qkjbXeR08sLOnUzYsYMIK3XFxXXrVt4LCiHKrTg4/F85g8Obb5o6DJdcUlktPD0JDOWglKnlEBoKDz0EWVmVl7Z7/eHDjE5OZklWFtdGRvJO69aE+x5btFAI4U6CvL35qURwUMB1ZxAcli0za6hee83eAmIyK6kCHnzwaNrufv1g/XrXPbdTa17btYtuSUlszc3l6/bt+bJ9ewkKQniI4uBwXlgYo5KT+fwMZiu99RbUqgU33FAFDTwFCQwVdPPN8MMPsH27mXP82mumfGhFbM3N5bwVK7h/yxYuDA9nba9eDIuMdEl7hRBVpzg49D+D4JCWZkoAXH+96aq2kwQGFxgyxCxGufBCuP9+U9dh69ayP4/Wmg9276bzkiWsyM7m05gYZnTsSJTUHRXCYwV5e/PzGQSHyZOhoADuvLOKG3gCEhhcJCrKzCb49FNYsQI6d4YPPjDZEc9Ean4+F61axa0bNxIXGsqanj0ZXb++JLkTohooGRyuT04m6ZjkewUF8N57JrNz27Y2NbIECQwupBSMHm1WLcbFwa23mjoPqSdMLG5orfl87146LlnCwkOHeLd1a37v3JkmAQFV13AhRKUL8vbmuw4daODvz8jkZHIcjiOPTZxocrPde6+NDSxBAkMlaNLEFP15912TwrtjR/jii+PPHtILCrhq7VpGJifTPiiIlbGx3N6okZTTFKKaquPry6cxMSTn5DDO6m9esgSefhpGjDBJO92BBIZK4uUFt99uupXat4frroOrrjpaU3pGejodlizh5/37ealFCxZ060arypjvKoRwKwPr1OHuRo14OzWVn/YcYORIk7DTzgVtx5J1DJWsdWtz1vDaa/DYY9CuZyGdJm9mnt8+utWuzZwuXego6SyEqFFeaNGCPzIyuHZFMod39+TPH3xtn4lUUkUL9byslEpWSq1SSv2glAor8dh4pdRmpdQGpdSFJbYPtrZtVkqNq8jrewpvb7Pm4e2EA2S+sYR53vvovDya31t2l6AgRA0U6O3N2EPtOOxbSJv3NjFwoN0tKq2iXUmzgY5a687ARmA8gFKqPaZsZwdgMPCeUspbKeUNvAtcBLQHrrX2rdayi4q4beNGbslcRcsGPty8ojtrH2xOt05e/PGH3a0TQlS1Awfg+euDifilGRsbpzFt3z67m1RKhQKD1voPq1wnwGKgsXV7CDBda52vtd4GbAZ6WZfNWuutWusCYLq1b7W18OBBuiQl8cHu3TzQpAnLY3sw+f4QFi+GkBCz9uG22yA72+6WCiGqytixZkHbzyOa0CckhNs3bSIlL8/uZh3hysHnG4HiInSNgF0lHkuxtp1se7WT53DwwObNnLtiBQDzu3bl5ZYtCbASoMTGmrwoDzxg1jt06QILF9rYYCFElZg2DaZPh6eegl49vPgsJoYCp5MbN2yolNrR5XHawKCU+lMpteYElyEl9nkUKAK+cFXDlFJjlFJJSqmk9OKpPB4iKTOT7kuX8mpKCrc2bMjK2Fj6nmBkKSDApO+eP9/cP/dcEyjc6IuDEMKFUlLMbMU+feDhh822VkFBvNaqFbMzMvjWTY51pw0MWuvztdYdT3D5EUApdT1wKTBC6yPhLhVoUuJpGlvbTrb9RK87WWsdq7WOjYiIKPMbs0OB08mT27bRZ9kyMouK+L1zZ95r04baPqee/NW3L6xcaRbEvfqqKeeXlFRFjRZCVIn0dLjoIigsNPXjSx4Wbm7QgFaBgbyZkmJfA0uo6KykwcBDwGVa65wSD80Ehiul/JVSzYHWwD/AEqC1Uqq5UsoPM0A9syJtcBdrsrPps2wZE3bsYERUFGt69mRQePgZ/3zt2mZJ/O+/mxoPffrAk0+apfJCCM+Wnm4qsm3ZAj/+CK1alX7cSynubNSIRZmZx6XLsENFxxjeAYKB2UqpFUqp9wG01muBr4F1wG/AWK21wxqovgP4HVgPfG3t69HeTkmhx9KlpObn80OHDkxp146wcqbHHjTIpNQYMQImTDABYs0aFzdYCFFlSgaFn37ipFNTr69fn2Bvb946VQ6dKqK0mwx2nEpsbKxOcsO+Fa01j27bxgs7d3JZ3bp81LYtES7MhDpjBowZA4cOwTPPmMytdhbvEEKUTXFQ2LwZfv755EGh2N2bNjFp92529ulDfX//Cr++Umqp1jq2rD8nKTHKyak1Yzdt4oWdO7mlQQO+79jRpUEB4PLLTTrvSy81A1V9+8KmTS59CSFEJSlrUAC4o1EjCrXmgz17Kr+BpyCBoRwKnU5GrV/PpN27eahJEya1aYN3JSW+i4iAb7+Fzz83FeK6djXJ+SpaDEgIUXnS000gKEtQAGgdFMTF4eFMSk0l38Z/cgkMZZTncHDV2rV8kZbGC82bM7Fly0qvmaCUGXNYs8aUEL3jDjjrLFNS1AN6AoWoUYqDwqZNZQsKxe5u3Jh9hYV8nZZWOQ08AxIYyiCrqIiLV69m5v79vNu6NeOio6v09Rs1gl9+gU8+gb17Ta2HXr3MgJYECCHsVzIonGqg+VQuqFOHmKAg3kxJwa4xYAkMZ+hAYSHnr1zJgoMH+Swmhtsb2bNgWylTE3bjRvjoI9i/Hy67DHr0MIPV0sUkhD3S0koHhfPPL9/zKKW4vWFDlmZnszE317WNPEMSGM7Anvx8zl2xgpXZ2XzfsSPX1a9vd5Pw84ObboING0w50awsuOIK6NbNjElIgBCi6syZY/73KhoUivW3MiUszcqqeOPKQQLDaWzLzeWc5cvZnpfHr507c1m9enY3qRRfX1NOdP16s5oyPx+GDTM1p7/6CkpUDxRCuFhhITz6qAkEwcGwaFHFgwJAu6AgAry8WCaBwf2sO3yYc5Yv52BREfFdunBenTp2N+mkfHxMlbi1a02SLq1h+HDo1Am+/FIChBCutnWrmUL+/PNw442wdKk5a3AFHy8vOtWqxTKb0i5LYDiJpMxM+i1fjsZkRu0VEmJ3k86It7cJCKtXw9dfm/sjRpjyolOnQlHR6Z9DCHFq06aZqePJyebM/KOPoFYt175G99q1WZ6dbcsAtASGE5h/8CADVq4kxMeHhd26eWSVNS8v06W0ciV89x0EBpoup5gYM6upsNDuFgrhebKz4YYb4D//MWfjK1bA1VdXzmt1Dw7mYFER221ItyyB4Riz9u9n8KpVNPH3Z2G3brQMDLS7SRXi5QVXXgnLl5vkXWFh5rS3TRv48ENJ0ifEmVq61GQ+njoVnnjCpMtv1qzyXq+79YXUju4kCQwlTN+3j8vXrKFjrVrM79qVRi7IVeIulDLTWpcsMYtuIiNNHqbWreH9982gtRDieE6nSYcfFwe5uWYG0tNPl06bXRk61qqFN9gyAC2BwTJ5927+s349Z4eEEN+lC/VcnPfIXSgFl1wCixfDb7+ZRXO33QYtW8I770iRICFK2rfPLCR94AGTs2zlSlNQqyoEeHvTwaYBaAkMwMs7d3LLxo1cHB7Or507E1LZXwXcgFKm3vTff8Ps2dC8Odx5J7RoAW++ab4ZCVFTORxmfVDnzqbLaNIkM1ZXhhIrLlHP15dsG6YU1ujAoLXm0a1beWjrVoZHRvJDx44E1rC81kqZedcLFsDcudC2LdxzjwkUr74Khw/b3UIhqo7WJgB06mQGmaOjTTXFW281/ytVLdfpJNCr6g/TFa3g9oxSapVVpOcPpVRDa7tSSr2llNpsPd69xM+MVkptsi6jK/oGysupNXds2sTzVtrsz9u1w9eGX4C7UAr69zfBYf5884/xwAMmQLz0kpmNIUR1pbU5c+7VC666ymz77jtITIQOHexrV67TSYCnBQbgZa11Z611V+Bn4Alr+0WYcp6tgTHAJAClVDjwJNAb6AU8qZSq8lVjhU4no5OTea8K0mZ7on79zD/J33+bWRgPP2xmX0yYANu22d06IVxr8WKT42jQIJPv6JNPzDqgK6+05yyhpDxPPGPQWpcsTloLKF6JMQSYqo3FQJhSqgFwITBba31Aa50BzAYGV6QNZVWcNvvzffuqLG22pzrrLDNAvXgx9O5talC3aGFmZ7zxBuzebXcLhSi/NWtgyBDz97x2Lbz1lklOef317lMpMdfh8MgzBpRSzymldgEjOHrG0AjYVWK3FGvbybZXiayiIi6xMW22p+rdG2bNMikAXnzRzFy6915o3Nh0P73/vkk3LIQn2LoVRo48OrD87LOmHvOdd4K7zVB32zEGpdSfSqk1J7gMAdBaP6q1bgJ8AdzhqoYppcYopZKUUknpLjjqHCgs5IKVK5lvc9psT9a8uelWWr7cJO178kkzne+226BBAxg82MzkOHjQ7pYKcbw9e2DsWDPB4rvv4KGHTJB49FFw1+QGeU6nLRNiThsYtNbna607nuDy4zG7fgEMtW6nAk1KPNbY2nay7Sd63cla61itdWxERMSZvp8TKk6bvcKN0mZ7upgYExjWrTNpAR580KQAv+EGiIoy9aqnT5dZTcJ+GRkwbpxZqzN5Mtx8sym5+eKLVT/9tCycWpPjiYPPSqnWJe4OAZKt2zOBUdbspD7AIa31HuB3YJBSqo416DzI2lZptuXm0nf5crbl5vKLG6bN9nRKQZcu8MIL5ttXYqL5VrZkCVx7rVlhPXy4KSIki+dEVdHaTJ4YM8ZMnHjpJTOYnJwM770HDRva3cLTW5GdTZHWdHR1dr4zUNGVXC8qpdoCTmAHcKu1/RfgYmAzkAPcAKC1PqCUegZYYu03QWt9oIJtOKniWgq5TifxXbvS20MypHoqpcx0v1694JVX4K+/zFnDN9+YDJQhIaaY0PDhZhaIr6/dLRbVzfbtpi7J1KnmrKBWLRg61Ey97tTJ7taVzZ8ZGQAMsIr2VCVlV03RsoiNjdVJSUll/rl8p5P/btjAw02aeGSG1OqiqMjkl5k+Hb7/Hg4dgrp1zXzx4cNNTnt3mQUiPE9WlhkzmDIF5s0z2847z2QTHjrUfccPTufClStJyc9nba9e5X4OpdRSrXVsmX+uOgcG4X7y8+H3302Q+PFHyMkxA9dXX22CRO/e9s8dF+7P6TSLMadMMUEhJwdatTLBYORIs2LZk+U7ndT56y/+26ABb7VuffofOInyBobqnxRIuBV/f5Pl9bLLzMD0rFkmSLz/vsnRFB1tAsTw4WbsQoKEKGnDBhMMPv8cdu2C0FBTuXD0aLMeobr8vSQcOkSu08n5NlWNrLk5IITtatUyZwrff2+mvU6ZYirNvfqqKZHYrp2Z+bRkidSNqMkyMkwSuz59zGy4iROhY0fzhWLPHvjgA7MYs7oEBYD4gwfxAs61YXwBpCtJuKF//zXBYvp002estTnT6N7ddDX17m0OEtHR1etgII7av98sPps2DWbONF8MOnY0ZwYjRpjux+qs19KleAGLe/So0PPIGIOolvbuhYULzTTYxYtNFa3iaa+RkaUDRc+eZuaT8DwpKeb3vGCBuV671myvV8+U0Rw92pxF1oQvAvMPHqT/ihW83rIl9zRpcvofOAUJDKJGKCw0Cc6KA0Vioul3BnPQaNfuaKDo3dtkxqwB5TU8itawadPRILBw4dHkjMHBpluob19z6dMHqmnNrJPqv3w5G3Jz2dq7d4VXPUtgEDVWRoYZhygZLPbvN48FBUFsbOlgIdlQqpbDAatWHQ0CCxeaMSUwZwT9+h0NBF261OxAPjcjgwErV/Jmq1bc1bhxhZ9PAoMQFq2PrsIuDhTLl5uzDTCBoWQXVI8eZiBcuEZ+viluU9w19PffkGnlYY6OPhoE+vUzeYtqQvfQmdBa02/FCrbm5rKld28CXLC4R6arCmFRyuTFadnS9E+DOVitWHE0UCQmmgFuMIvrOnYsHSxiYqAG1206I1qbiQI7dpjLihVHx4OKx4HatTNTj4vPCpo2Ld9r7c7PZ3ZGBmsPHyajqIggLy/aBgVxUXg4zQMDXfae7BSfkcFfhw7xbuvWLgkKFSFnDKLGSk+Hf/45Giz++cesygbTnVG/vsmp06DBia8bNjRdIdU1gDidZjrojh0m1URxACi+v3OnWVhWzMvLzBwrPiM45xyoYP5LkjIzeXL7dn45YDLn+CtFHV9fDjscZFm1kK+vX593Wremlgcvn9dac/by5aTk57Opd2/8XfRHJWcMQpRRRARccom5gDkQbtxoAsXGjeaguHu36Zb666+j4xYlFQeQEwWNktsiItwvgBQWmtlAxx70iw/8u3Yd7X4rVreu6Q5q3x4uusjcjo42iepatXJd+gmtNU9u385zO3ZQ19eXJ6KjuSoigva1auGtFFprtublMXn3bl7ZtYs8p5Np7du75sVt8NKuXSRkZvJR27YuCwoVIYFBCIuXl+lCiok58eP5+Wb67O7dR4NG8fWZBJCoqBMHjQYNICDABCaHo/TFlduKikw7iwPB7t1mn5IaNDAH+V69YNiwowf96GjTDVRVeYfu2ryZd1JTGR0VxZutWxN6zIi0Uopsh4NL69alQGveTElhSkwMfm5wUC2rORkZPLJ1K1dHRHCjm5QEkMAgxBny9z/6DflUigPIiYLHnj0mgPz9t+mfrwpKmXEUb29zdhMdDQMGHH0vxQf/Jk3co4LZtH37eCc1lfsaN+aVU5Te7Wp1L9f386NZQAC+HjiKnZKXx/B162gTFMRHbdu6TZlhCQxCuNiZBpCCgqNnIAUF5sDt5XX0IF58Ke+24vtucqw5I4VOJ+O2bqVH7dq8dJp67I9HR/PMjh20DwrijVat3OageqYKnE6GrVtHrtPJ9x06EOxG83TdpyVC1DB+fqZ7prwzdaqjvw8dYmd+Pq+1aoX3aQ70E5o3Z0Lz5lXUMte7f8sWFmdm8nX79rRzs/nSLumQU0rdr5TSSql61n2llHpLKbVZKbVKKdW9xL6jlVKbrMtoV7y+EKJ6SLAWPFxgU1bRqqC15rkdO450lw2LjLS7Scep8BmDUqoJpkTnzhKbLwJaW5fewCSgt1IqHHgSiAU0sFQpNVNrnVHRdgghPN/BoiL8lCLEjbpVXMmpNfds3szbqalcFxXFiy1a2N2kE3LFGcPrwEOYA32xIcBUbSwGwpRSDYALgdla6wNWMJgNDHZBG4QQ1UC4ry8FWnPw2Hmy1UC+08m169bxdmoq9zduzJSYGHzddBZVhVqllBoCpGqtVx7zUCNgV4n7Kda2k20XQgg6W33ti4pzaFQTWUVFXLJqFV+np/Nyixa80qoVXm48WH7a8zWl1J/AiSbXPgo8gulGcjml1BhgDEBTGZ0TokYYUKcOEb6+fLhnDxfXrWt3c1xia24uw9auZWV2NlNiYhjlJmsVTuW0Zwxa6/O11h2PvQBbgebASqXUdqAxsEwpVR9IBUomEm9sbTvZ9hO97mStdazWOjaiouvqhRAewd/Li1sbNmTGv/8yN8Ozhx6dWvNWSgqdlixhU24uMzt18oigABXoStJar9ZaR2qtm2mtm2G6hbprrfcCM4FR1uykPsAhrfUe4HdgkFKqjlKqDuZs4/eKvw0hRHUxrmlTWgcGcuOGDaR7aE3XTTk5nLtiBXdv3sy5YWGs7dnTo86AKmvk4xfMGcVm4EPgdgCt9QHgGWCJdZlgbRNCCACCvL2ZGhPD3oICBq1axQEPGoh2aM2ru3bROSmJNYcP82lMDLM6daJJQIDdTSsTya4qhHBLfxw4wP+tXk2boCC+7dCBtkFBdjfppIqcTr5KT+eFHTtYm5PDZXXrMqlNGxranGOkvNlV3XOulBCixhsUHs6sTp3YW1BAj6Qkpu7di7t9kc1zOPhg927a/PMP161fj1KKr9u3Z0bHjrYHhYqQwCCEcFvnh4ezIjaWHsHBjE5O5qzly1l48KDdzSKtoIBXdu6kRWIit27cSISvLz927MjK2FiGRUZ6XN6mY1XP5YVCiGqjkb8/c7p2ZcrevTyxbRv9Vqzggjp1uLlBAy6rV6/K6hfsKyjg+/R0vk1PZ97BgziBgWFhfN6uHeeFhXl8MChJxhiEEB4jx+Hg7dRU3klNJSU/n7o+PvwnKorB4eH0DQ11aYbSXIeDldnZJGZl8UN6OgsOHUIDbQMDGRYZybCICDpXVYGKcirvGIMEBiGEx3FozZ8ZGfxvzx5+/PdfCrTGG+gRHEzf0FDaBAXRIiCAloGBNPH3x+ckZxVOrdlfWMi+ggLSCgvZlJtLUlYWSzIzWXP4MA5rv/ZBQVwVEcGwiAg61KrlMWcHUtpTCFFjeCvFheHhXBgeTo7DQUJmJvMOHmRuRgZvp6ZSUOILrzcQ4uODr1L4eXnhqxS+SnGoqIj0wkKOKWJHuI8PscHBXFK3LrHBwcQGB3vcdNOKksAghPBoQd7eDKxTh4F16kDz5ji0JjU/n625uWzNy2Nrbi6ZDgcFTicFWlOoNQVOJyE+PkT5+hLp50eUnx9Rvr5EBwTQLCDAY84IKosEBiFEteKtFE0DAmgaEEB/uxvjoWS6qhBCiFIkMAghhChFAoMQQohSJDAIIYQoRQKDEEKIUiQwCCGEKEUCgxBCiFIkMAghhCjFI3IlKaXSgR12t6OS1AP+tbsRbkg+l+PJZ3I8+UxOrPhzidZaR5T1hz0iMFRnSqmk8iS5qu7kczmefCbHk8/kxCr6uUhXkhBCiFIkMAghhChFAoP9JtvdADcln8vx5DM5nnwmJ1ahz0XGGIQQQpQiZwxCCCFKkcBQxZRSdyqlkpVSa5VSL5XYPl4ptVkptUEpdWGJ7YOtbZuVUuPsaXXlU0rdr5TSSql61n2llHrLet+rlFLdS+w7Wim1ybqMtq/VlUMp9bL1N7JKKfWDUiqsxGM1+u+kpJr4ngGUUk2UUnOVUuus48jd1vZwpdRs6/9itlKqjrX9pP9LJ6W1lksVXYDzgD8Bf+t+pHXdHlgJ+APNgS2YioTe1u0WgJ+1T3u730clfC5NgN8xa1XqWdsuBn4FFNAHSLS2hwNbres61u06dr8HF38egwAf6/ZEYKL8nRz3GdW491zivTcAulu3g4GN1t/GS8A4a/u4En83J/xfOtVFzhiq1m3Ai1rrfACtdZq1fQgwXWudr7XeBmwGelmXzVrrrVrrAmC6tW918zrwEFBywGsIMFUbi4EwpVQD4EJgttb6gNY6A5gNDK7yFlcirfUfWusi6+5ioLF1u6b/nZRUE98zAFrrPVrrZdbtLGA90Ajz/qdYu00BLrdun+x/6aQkMFStNkBfpVSiUmq+Uqqntb0RsKvEfinWtpNtrzaUUkOAVK31ymMeqrGfyTFuxHzbA/lMSqqJ7/k4SqlmQDcgEYjSWu+xHtoLRFm3y/xZSc1nF1NK/QnUP8FDj2I+73DM6VxP4GulVIsqbJ4tTvOZPILpOqlRTvWZaK1/tPZ5FCgCvqjKtgnPoJSqDXwH3KO1zlRKHXlMa62VUuWeciqBwcW01uef7DGl1G3A99p0/P2jlHJicpqkYvrZizW2tnGK7R7jZJ+JUqoTpq98pfVH3RhYppTqxck/k1QoVeO9MTDP5Y2uZKf6OwFQSl0PXAoMtP5eoJr/nZTRqT6Lak8p5YsJCl9orb+3Nu9TSjXQWu+xuoqKu6rL/lnZPZBSky7ArcAE63YbzOmdAjpQelBxK2Zwzce63ZyjA2wd7H4flfj5bOfo4PMllB4w+8faHg5swww817Fuh9vddhd/DoOBdUDEMdvl7+ToZ1Hj3nOJ966AqcAbx2x/mdKDzy9Zt0/4v3Sqi5wxVK2PgY+VUmuAAmC0Nr+5tUqprzEHgyJgrNbaAaCUugMzY8cb+Fhrvdaeple5XzCzKTYDOcANAFrrA0qpZ4Al1n4TtNYH7GlipXkHc/CfbZ1JLdZa36q1lr8Ti9a6qKa95xLOBkYCq5VSK6xtjwAvYrqnb8LM8LvaeuyE/0unIiufhRBClCKzkoQQQpQigUEIIUQpEhiEEEKUIoFBCCFEKRIYhBBClCKBQQghRCkSGIQQQpQigUEIIUQp/w9FPj8RhEG47QAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "OHfP87Ao3B_5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "BYP68qHY5OCc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "name = \"rot_e_AU12\"\n",
        "pattern =  [\n",
        "    \"red \" + rotate_snip(\"A \" , 90, 0.07) + \" h0\",\n",
        "    \"blue \" + rotate_snip(\"A \", 90, 0.07) + \"r45 h0\",\n",
        "#    \"blue A r60 x-1.0 A  r60 x-1.0 A h0\",\n",
        "#    \"green A h0\",\n",
        "]\n",
        "print(pattern)\n",
        "interpret_tess(name, pattern, cvs_nl,False)\n",
        "plt.axis('on')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "y3UJLdpovcHa",
        "outputId": "516a4f57-0fbf-4285-9d9c-273a86749558"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['red A  r90 x-0.07 A  r90 x-0.07 A  r90 x-0.07 A  r90 x-0.07  h0', 'blue A  r90 x-0.07 A  r90 x-0.07 A  r90 x-0.07 A  r90 x-0.07 r45 h0']\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(-1216.9423409737062, 1223.43826618774, -792.4676810352223, 792.4676810352223)"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "SgTN7dpt5kOu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "gtkWu7rC5ec9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "MJ_wtD1t5OzW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "NpsG5rg_4W_Z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "wlSGcsWvuOVM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "-Ld_EFZ2qoNR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "hppHQrpdqadp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "2XH_YCIPp3Pc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "TqLXaEvhpmAF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "nKXIwje-pNDP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Cs7e9t-EpJAi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "W-605XntpEWZ"
      },
      "execution_count": null,
      "outputs": []
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