Test dev.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from tyssue import SheetGeometry as geom\n",
    "from tyssue.stores import stores_dir\n",
    "from tyssue.io.hdf5 import load_datasets\n",
    "from tyssue import config\n",
    "from tyssue.core.sheet import Sheet\n",
    "from tyssue.dynamics.sheet_vertex_model import SheetModel as model\n",
    "\n",
    "\n",
    "h5store = os.path.join(stores_dir, \"small_hexagonal_snaped.hf5\")\n",
    "\n",
    "datasets = load_datasets(h5store, data_names=[\"face\", \"vert\", \"edge\"])\n",
    "specs = config.geometry.cylindrical_sheet()\n",
    "sheet = Sheet(\"emin\", datasets, specs)\n",
    "geom.update_all(sheet)\n",
    "sheet.sanitize()\n",
    "#sheet.get_extra_indices()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-8.149072527885437e-09"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.compute_energy(sheet) - model.compute_energy(sheet2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "h5store = os.path.join(stores_dir, \"small_hexagonal.hf5\")\n",
    "\n",
    "datasets = load_datasets(h5store, data_names=[\"face\", \"vert\", \"edge\"])\n",
    "specs = config.geometry.cylindrical_sheet()\n",
    "sheet2 = Sheet(\"emin\", datasets, specs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "Et2, Ec2, Ev2 = model.compute_energy(sheet2, full_output=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "Et, Ec, Ev = model.compute_energy(sheet, full_output=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "face\n",
       "0     12657.983221\n",
       "1     12657.983221\n",
       "2     12657.983221\n",
       "3     12657.983221\n",
       "4     12657.983221\n",
       "5     12657.983221\n",
       "6     12657.983221\n",
       "7     12657.983221\n",
       "8     12657.983221\n",
       "9     12657.983221\n",
       "10    12657.983221\n",
       "11    12657.983221\n",
       "12    12657.983221\n",
       "13    12657.983221\n",
       "14    12657.983221\n",
       "15    12657.983221\n",
       "16    12657.983221\n",
       "17    12657.983221\n",
       "18    12657.983221\n",
       "19    12657.983221\n",
       "20    12657.983221\n",
       "21    12657.983221\n",
       "22    12657.983221\n",
       "23    12657.983221\n",
       "24    12657.983221\n",
       "25    12657.983221\n",
       "26    12657.983221\n",
       "27    12657.983221\n",
       "28    12657.983221\n",
       "29    12657.983221\n",
       "30    12657.983221\n",
       "31    12657.983221\n",
       "32    12657.983221\n",
       "33    12657.983221\n",
       "34    12657.983221\n",
       "35    12657.983221\n",
       "36    12657.983221\n",
       "37    12657.983221\n",
       "38    12657.983221\n",
       "39    12657.983246\n",
       "dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Ec[Ec<Ec.min()+1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7efd077331f0>]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.plot(Ec2[Ec<Ec.min()+1e-5]-Ec.min(),\n",
    "        Ec[Ec<Ec.min()+1e-5]-Ec.min(), 'o')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12657.983221584556"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Ec.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12657.98322159488"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Ec2.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "geom.scale(sheet, sheet.face_df.area**(-0.5), )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Reseting column contractility of the face dataset with new specs\n",
      "Reseting column vol_elasticity of the face dataset with new specs\n",
      "Reseting column prefered_height of the face dataset with new specs\n",
      "Reseting column prefered_area of the face dataset with new specs\n",
      "Reseting column prefered_vol of the face dataset with new specs\n",
      "Reseting column radial_tension of the vert dataset with new specs\n",
      "Reseting column is_active of the vert dataset with new specs\n",
      "Reseting column ux of the edge dataset with new specs\n",
      "Reseting column uy of the edge dataset with new specs\n",
      "Reseting column uz of the edge dataset with new specs\n",
      "Reseting column line_tension of the edge dataset with new specs\n",
      "Reseting column is_active of the edge dataset with new specs\n"
     ]
    }
   ],
   "source": [
    "h5store = os.path.join(stores_dir, \"small_hexagonal.hf5\")\n",
    "datasets = load_datasets(h5store, data_names=[\"face\", \"vert\", \"edge\"])\n",
    "specs = config.geometry.cylindrical_sheet()\n",
    "\n",
    "sheet = Sheet(\"emin\", datasets, specs)\n",
    "nondim_specs = config.dynamics.quasistatic_sheet_spec()\n",
    "dim_model_specs = model.dimensionalize(nondim_specs)\n",
    "sheet.update_specs(dim_model_specs, reset=True)\n",
    "\n",
    "geom.update_all(sheet)\n",
    "\n",
    "Et, Ec, Ev = model.compute_energy(sheet, full_output=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Reseting column contractility of the face dataset with new specs\n",
      "Reseting column vol_elasticity of the face dataset with new specs\n",
      "Reseting column prefered_height of the face dataset with new specs\n",
      "Reseting column prefered_area of the face dataset with new specs\n",
      "Reseting column prefered_vol of the face dataset with new specs\n",
      "Reseting column radial_tension of the vert dataset with new specs\n",
      "Reseting column is_active of the vert dataset with new specs\n",
      "Reseting column ux of the edge dataset with new specs\n",
      "Reseting column uy of the edge dataset with new specs\n",
      "Reseting column uz of the edge dataset with new specs\n",
      "Reseting column line_tension of the edge dataset with new specs\n",
      "Reseting column is_active of the edge dataset with new specs\n"
     ]
    }
   ],
   "source": [
    "h5store = os.path.join(stores_dir, \"small_hexagonal.hf5\")\n",
    "datasets = load_datasets(h5store, data_names=[\"face\", \"vert\", \"edge\"])\n",
    "specs = config.geometry.cylindrical_sheet()\n",
    "\n",
    "sheet_s = Sheet(\"emin\", datasets, specs)\n",
    "nondim_specs = config.dynamics.quasistatic_sheet_spec()\n",
    "dim_model_specs = model.dimensionalize(nondim_specs)\n",
    "sheet_s.update_specs(dim_model_specs, reset=True)\n",
    "\n",
    "geom.update_all(sheet_s)\n",
    "\n",
    "Et_s, Ec_s, Ev_s = model.compute_energy(sheet_s, full_output=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7efd07cf9550>]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.plot(Ec_s[Ec_s<Ec_s.min()+1e-5]-Ec_s.min(),\n",
    "        Ec[Ec_s<Ec_s.min()+1e-5]-Ec_s.min(), 'o')\n",
    "ax.plot(Ec_s[Ec_s<Ec_s.min()+1e-5]-Ec_s.min(),\n",
    "        Ec_s[Ec_s<Ec_s.min()+1e-5]-Ec_s.min(), 'k-')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.12093674686179141"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Ec.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.12093674686179141"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Ec_s.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.08788417666060594"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Ev.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "energy = model.compute_energy(sheet, full_output=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14.254513236339077"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "energy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tyssue.topology.sheet_topology import type1_transition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "h5store = os.path.join(stores_dir, \"small_hexagonal.hf5\")\n",
    "datasets = load_datasets(h5store, data_names=[\"face\", \"vert\", \"edge\"])\n",
    "\n",
    "sheet = Sheet(\"emin\", datasets)\n",
    "geom.update_all(sheet)\n",
    "face = sheet.edge_df.loc[84, \"face\"]\n",
    "type1_transition(sheet, 84)\n",
    "geom.update_all(sheet)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sheet.edge_df.loc[84, \"face\"] != face\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sheet.edge_df.loc[84, \"face\"] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tyssue.draw import sheet_view"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 640x480 with 1 Axes>, <AxesSubplot:>)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib qt\n",
    "\n",
    "sheet_view(sheet, list(\"zx\"), edge={\"head_width\": 0.1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sheet.validate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sheet.face_df.loc[14, \"num_sides\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7efd11520df0>]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.plot(Ec_s-Ec_s.min(),\n",
    "        Ec-Ec_s.min(), 'o')\n",
    "ax.plot(Ec_s-Ec_s.min(),\n",
    "        Ec_s-Ec_s.min(), 'k-')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "sheet.vert_df[[\"x\", \"y\", \"z\"]] += np.random.normal(scale=1e-10, size=(sheet.Nv, 3))\n",
    "geom.update_all(sheet)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "sheet.get_extra_indices()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tyssue.io.hdf5 import save_datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "save_datasets(\"../tyssue/tyssue/stores/small_hexagonal.hf5\", sheet)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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taghJXgu8h2c+EuJd061oeEk+CPwcax9Zehp4R1XdOtWihpTkZ4F/Bb7E2r9/gLdV1T9Or6rhJfkJYJG137PnALdX1R+N9JyzHuCSpN5mfgpFktSbAS5JjTLAJalRBrgkNcoAl6RGGeCS1CgDXJIa9b8ppPkEGOmxOgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "H = plt.hist(\n",
    "    np.arctan2(\n",
    "        sheet.edge_df[\"dy\"], \n",
    "        sheet.edge_df[\"dx\"]\n",
    "    ),\n",
    "    60\n",
    "\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fb2676777f0>]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(\n",
    "    sheet.edge_df[\"sz\"],\n",
    "    np.arctan2(\n",
    "        sheet.edge_df[\"dy\"], \n",
    "        sheet.edge_df[\"dx\"]+np.random.normal(scale=1e-6, size=sheet.Ne)\n",
    "    ), 'o', alpha=0.2\n",
    ")\n",
    "\n",
    "plt.plot(\n",
    "    sheet.edge_df[\"sz\"],\n",
    "    np.arctan2(\n",
    "        sheet.edge_df[\"dy\"], \n",
    "        sheet.edge_df[\"dx\"]\n",
    "        +np.random.normal(scale=1e-12, size=sheet.Ne)\n",
    "    ), 'o', alpha=0.2\n",
    ")\n",
    "\n",
    "\n",
    "plt.plot(\n",
    "    sheet.edge_df[\"sz\"],\n",
    "    np.arctan2(\n",
    "        sheet.edge_df[\"dy\"], \n",
    "        sheet.edge_df[\"dx\"]),\n",
    "    'k+', alpha=0.5\n",
    ")\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fb26e5766a0>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "xx, yy = np.meshgrid(\n",
    "    np.logspace(-12, -6, 512),\n",
    "    np.logspace(-12, -6, 512)\n",
    ")\n",
    "ax.imshow(np.arctan2(yy, xx))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax0, ax1) = plt.subplots(1, 2)\n",
    "x = np.logspace(-16, -14, 512)\n",
    "\n",
    "\n",
    "ax0.plot(x, np.arctan2(x, np.ones(512)), 'k-')\n",
    "ax1.plot(x, np.arctan2(np.ones(512), x), 'k-')\n",
    "ax0.set_xscale(\"log\")\n",
    "ax1.set_xscale(\"log\")\n",
    "ax0.set_yscale(\"log\")\n",
    "ax1.set_yscale(\"log\")\n",
    "\n",
    "fig.set_size_inches(12, 6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "H = plt.hist(\n",
    "    np.arctan2(\n",
    "        sheet.edge_df[\"dy\"], \n",
    "        sheet.edge_df[\"dx\"]+np.random.normal(scale=1e-6, size=sheet.Ne)\n",
    "    ),\n",
    "    60\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'np' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-2-4117b80141de>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m theta = np.arctan2(\n\u001b[0m\u001b[1;32m      2\u001b[0m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0medge_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdble_edges\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"dy\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0medge_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdble_edges\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"dx\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m )\n",
      "\u001b[0;31mNameError\u001b[0m: name 'np' is not defined"
     ]
    }
   ],
   "source": [
    "theta = np.arctan2(\n",
    "    self.edge_df.loc[self.dble_edges, \"dy\"],\n",
    "    self.edge_df.loc[self.dble_edges, \"dx\"]+np.,\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tempfile\n",
    "from tyssue.generation import three_faces_sheet\n",
    "from tyssue import Sheet\n",
    "from tyssue.io import csv, obj, off, ais\n",
    "\n",
    "\n",
    "sheet = Sheet('test', *three_faces_sheet())\n",
    "fh = tempfile.mktemp(suffix='.csv')\n",
    "csv.write_storm_csv(fh, sheet.vert_df[sheet.coords])\n",
    "with open(fh) as fb:\n",
    "    lines = fb.readlines()\n",
    "assert len(lines) == 14\n",
    "assert 'frame' in lines[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Faces doesn't appear to be triangular,\n",
      "                be advised the file cannot be read back in vispy\n"
     ]
    }
   ],
   "source": [
    "fh = tempfile.mktemp(suffix='.obj')\n",
    "obj.save_junction_mesh(fh, sheet)\n",
    "with open(fh) as fb:\n",
    "    lines = fb.readlines()\n",
    "assert len(lines) == 35\n",
    "assert '# 13 vertices' in lines[4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tyssue.generation import extrude\n",
    "from tyssue import Monolayer, config\n",
    "datasets, specs = three_faces_sheet()\n",
    "\n",
    "extruded = extrude(datasets, method='translation')\n",
    "mono = Monolayer('test', extruded,\n",
    "                 config.geometry.bulk_spec())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tyssue.draw.plt_draw import quick_edge_draw, sheet_view"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = quick_edge_draw(sheet)\n",
    "assert ax.lines[0].get_xydata().shape == (54, 2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "line = ax.lines[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(54, 2)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "line.get_xydata().shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}