colabseg_demo_notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e0062638",
   "metadata": {},
   "source": [
    "# ColabSeg: Interactive Membrane Segmentation and Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "67516ef3",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "from colabseg.tensorvoting_wrapper import *\n",
    "from colabseg.segmentation_gui import *\n",
    "from colabseg.membrainseg_wrapper import generate_membrainseg_gui"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "366aa3c7",
   "metadata": {},
   "source": [
    "# 1) Tensor Voting / MemBrain-seg\n",
    "\n",
    "Run one of the following cells to perform either TomoSegMemTV or MemBrain-seg to segment the membranes in your tomogram:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ee4163ad",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0cc79c755f1348fa8653ece5c45de847",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(Text(value='', description='TV Path:', placeholder='PATH/TO/TOMOSEG', style=TextStyle(descripti…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "10002d462622410d9e05060633c5e138",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Button(description='Run TV Pipeline', style=ButtonStyle())"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a0eb0176898147b88921b069213691e9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Button(description='Set optmized default', style=ButtonStyle())"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "generate_tensor_voting_gui() # run TomoSegMemTV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a322d0a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9413bb32da3546618ceb9af38209cbdf",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(Text(value='', description='MemBrain-seg model:', layout=Layout(align_items='stretch', display=…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f4ce70670b52475d8ed82bfd3821ed3e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Button(description='Run MemBrain-seg', style=ButtonStyle())"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "generate_membrainseg_gui() # run MemBrain-seg"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "450a507e",
   "metadata": {},
   "source": [
    "# 2) Load Segmented MRC and Convert to Point Cloud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a70aa255",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9346c3ad56ee41dc8b1d660705784797",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(Text(value='test_file.mrc', description='Input Filename:', placeholder='mrc or h5 file'), Butto…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bcdfd814649848a98beb823bdea11eda",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gui = JupyterFramework()\n",
    "gui.gui_elements_loading()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d844389",
   "metadata": {},
   "source": [
    "# 3) Visualize & Edit Segmentation 3D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "09bef37b",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3fab101664704e68aa86c3d9dee85b49",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Tab(children=(HBox(children=(SelectMultiple(description='Clusters:', options=(0,), rows=10, value=()), VBox(ch…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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2310.0 1550.0\\nC 1970.0 2310.0 1650.0\\nC 1970.0 2310.0 1750.0\\nC 1970.0 2310.0 1850.0\\nC 1970.0 2310.0 1950.0\\nC 1970.0 2310.0 2050.0\\nC 1970.0 2310.0 2150.0\\nC 1980.0 2310.0 0.0\\nC 1980.0 2310.0 100.0\\nC 1980.0 2310.0 200.0\\nC 1980.0 2310.0 300.0\\nC 1980.0 2310.0 400.0\\nC 1980.0 2310.0 500.0\\nC 1980.0 2310.0 600.0\\nC 1980.0 2310.0 700.0\\nC 1980.0 2310.0 800.0\\nC 1980.0 2310.0 900.0\\nC 1980.0 2310.0 1000.0\\nC 1980.0 2310.0 1100.0\\nC 1980.0 2310.0 1200.0\\nC 1980.0 2310.0 1300.0\\nC 1980.0 2310.0 1400.0\\nC 1980.0 2310.0 1500.0\\nC 1980.0 2310.0 1600.0\\nC 1980.0 2310.0 1700.0\\nC 1980.0 2310.0 1800.0\\nC 1980.0 2310.0 1900.0\\nC 1980.0 2310.0 2000.0\\nC 1980.0 2310.0 2100.0\\nC 1980.0 2310.0 2200.0\\nC 1990.0 2310.0 50.0\\nC 1990.0 2310.0 150.0\\nC 1990.0 2310.0 250.0\\nC 1990.0 2310.0 350.0\\nC 1990.0 2310.0 450.0\\nC 1990.0 2310.0 550.0\\nC 1990.0 2310.0 650.0\\nC 1990.0 2310.0 750.0\\nC 1990.0 2310.0 850.0\\nC 1990.0 2310.0 950.0\\nC 1990.0 2310.0 1050.0\\nC 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650.0\\nC 2010.0 2310.0 750.0\\nC 2010.0 2310.0 850.0\\nC 2010.0 2310.0 950.0\\nC 2010.0 2310.0 1050.0\\nC 2010.0 2310.0 1150.0\\nC 2010.0 2310.0 1250.0\\nC 2010.0 2310.0 1350.0\\nC 2010.0 2310.0 1450.0\\nC 2010.0 2310.0 1550.0\\nC 2010.0 2310.0 1650.0\\nC 2010.0 2310.0 1750.0\\nC 2010.0 2310.0 1850.0\\nC 2010.0 2310.0 1950.0\\nC 2010.0 2310.0 2050.0\\nC 2010.0 2310.0 2150.0\\nC 2020.0 2310.0 0.0\\nC 2020.0 2310.0 100.0\\nC 2020.0 2310.0 200.0\\nC 2020.0 2310.0 300.0\\nC 2020.0 2310.0 400.0\\nC 2020.0 2310.0 500.0\\nC 2020.0 2310.0 600.0\\nC 2020.0 2310.0 700.0\\nC 2020.0 2310.0 800.0\\nC 2020.0 2310.0 900.0\\nC 2020.0 2310.0 1000.0\\nC 2020.0 2310.0 1100.0\\nC 2020.0 2310.0 1200.0\\nC 2020.0 2310.0 1300.0\\nC 2020.0 2310.0 1400.0\\nC 2020.0 2310.0 1500.0\\nC 2020.0 2310.0 1600.0\\nC 2020.0 2310.0 1700.0\\nC 2020.0 2310.0 1800.0\\nC 2020.0 2310.0 1900.0\\nC 2020.0 2310.0 2000.0\\nC 2020.0 2310.0 2100.0\\nC 2020.0 2310.0 2200.0\\nC 2030.0 2310.0 50.0\\nC 2030.0 2310.0 150.0\\nC 2030.0 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2310.0 2100.0\\nC 1880.0 2310.0 2200.0\\nC 1890.0 2310.0 50.0\\nC 1890.0 2310.0 150.0\\nC 1890.0 2310.0 250.0\\nC 1890.0 2310.0 350.0\\nC 1890.0 2310.0 450.0\\nC 1890.0 2310.0 550.0\\nC 1890.0 2310.0 650.0\\nC 1890.0 2310.0 750.0\\nC 1890.0 2310.0 850.0\\nC 1890.0 2310.0 950.0\\nC 1890.0 2310.0 1050.0\\nC 1890.0 2310.0 1150.0\\nC 1890.0 2310.0 1250.0\\nC 1890.0 2310.0 1350.0\\nC 1890.0 2310.0 1450.0\\nC 1890.0 2310.0 1550.0\\nC 1890.0 2310.0 1650.0\\nC 1890.0 2310.0 1750.0\\nC 1890.0 2310.0 1850.0\\nC 1890.0 2310.0 1950.0\\nC 1890.0 2310.0 2050.0\\nC 1890.0 2310.0 2150.0\\nC 1900.0 2310.0 0.0\\nC 1900.0 2310.0 100.0\\nC 1900.0 2310.0 200.0\\nC 1900.0 2310.0 300.0\\nC 1900.0 2310.0 400.0\\nC 1900.0 2310.0 500.0\\nC 1900.0 2310.0 600.0\\nC 1900.0 2310.0 700.0\\nC 1900.0 2310.0 800.0\\nC 1900.0 2310.0 900.0\\nC 1900.0 2310.0 1000.0\\nC 1900.0 2310.0 1100.0\\nC 1900.0 2310.0 1200.0\\nC 1900.0 2310.0 1300.0\\nC 1900.0 2310.0 1400.0\\nC 1900.0 2310.0 1500.0\\nC 1900.0 2310.0 1600.0\\nC 1900.0 2310.0 1700.0\\nC 1900.0 2310.0 1800.0\\nC 1900.0 2310.0 1900.0\\nC 1900.0 2310.0 2000.0\\nC 1900.0 2310.0 2100.0\\nC 1900.0 2310.0 2200.0\\nC 1910.0 2310.0 50.0\\nC 1910.0 2310.0 150.0\\nC 1910.0 2310.0 250.0\\nC 1910.0 2310.0 350.0\\nC 1910.0 2310.0 450.0\\nC 1910.0 2310.0 550.0\\nC 1910.0 2310.0 650.0\\nC 1910.0 2310.0 750.0\\nC 1910.0 2310.0 850.0\\nC 1910.0 2310.0 950.0\\nC 1910.0 2310.0 1050.0\\nC 1910.0 2310.0 1150.0\\nC 1910.0 2310.0 1250.0\\nC 1910.0 2310.0 1350.0\\nC 1910.0 2310.0 1450.0\\nC 1910.0 2310.0 1550.0\\nC 1910.0 2310.0 1650.0\\nC 1910.0 2310.0 1750.0\\nC 1910.0 2310.0 1850.0\\nC 1910.0 2310.0 1950.0\\nC 1910.0 2310.0 2050.0\\nC 1910.0 2310.0 2150.0\\nC 1920.0 2310.0 0.0\\nC 1920.0 2310.0 100.0\\nC 1920.0 2310.0 200.0\\nC 1920.0 2310.0 300.0\\nC 1920.0 2310.0 400.0\\nC 1920.0 2310.0 500.0\\nC 1920.0 2310.0 600.0\\nC 1920.0 2310.0 700.0\\nC 1920.0 2310.0 800.0\\nC 1920.0 2310.0 900.0\\nC 1920.0 2310.0 1000.0\\nC 1920.0 2310.0 1100.0\\nC 1920.0 2310.0 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1960.0 2310.0 400.0\\nC 1960.0 2310.0 500.0\\nC 1960.0 2310.0 600.0\\nC 1960.0 2310.0 700.0\\nC 1960.0 2310.0 800.0\\nC 1960.0 2310.0 900.0\\nC 1960.0 2310.0 1000.0\\nC 1960.0 2310.0 1100.0\\nC 1960.0 2310.0 1200.0\\nC 1960.0 2310.0 1300.0\\nC 1960.0 2310.0 1400.0\\nC 1960.0 2310.0 1500.0\\nC 1960.0 2310.0 1600.0\\nC 1960.0 2310.0 1700.0\\nC 1960.0 2310.0 1800.0\\nC 1960.0 2310.0 1900.0\\nC 1960.0 2310.0 2000.0\\nC 1960.0 2310.0 2100.0\\nC 1960.0 2310.0 2200.0\\nC 1970.0 2310.0 50.0\\nC 1970.0 2310.0 150.0\\nC 1970.0 2310.0 250.0\\nC 1970.0 2310.0 350.0\\nC 1970.0 2310.0 450.0\\nC 1970.0 2310.0 550.0\\nC 1970.0 2310.0 650.0\\nC 1970.0 2310.0 750.0\\nC 1970.0 2310.0 850.0\\nC 1970.0 2310.0 950.0\\nC 1970.0 2310.0 1050.0\\nC 1970.0 2310.0 1150.0\\nC 1970.0 2310.0 1250.0\\nC 1970.0 2310.0 1350.0\\nC 1970.0 2310.0 1450.0\\nC 1970.0 2310.0 1550.0\\nC 1970.0 2310.0 1650.0\\nC 1970.0 2310.0 1750.0\\nC 1970.0 2310.0 1850.0\\nC 1970.0 2310.0 1950.0\\nC 1970.0 2310.0 2050.0\\nC 1970.0 2310.0 2150.0\\nC 1980.0 2310.0 0.0\\nC 1980.0 2310.0 100.0\\nC 1980.0 2310.0 200.0\\nC 1980.0 2310.0 300.0\\nC 1980.0 2310.0 400.0\\nC 1980.0 2310.0 500.0\\nC 1980.0 2310.0 600.0\\nC 1980.0 2310.0 700.0\\nC 1980.0 2310.0 800.0\\nC 1980.0 2310.0 900.0\\nC 1980.0 2310.0 1000.0\\nC 1980.0 2310.0 1100.0\\nC 1980.0 2310.0 1200.0\\nC 1980.0 2310.0 1300.0\\nC 1980.0 2310.0 1400.0\\nC 1980.0 2310.0 1500.0\\nC 1980.0 2310.0 1600.0\\nC 1980.0 2310.0 1700.0\\nC 1980.0 2310.0 1800.0\\nC 1980.0 2310.0 1900.0\\nC 1980.0 2310.0 2000.0\\nC 1980.0 2310.0 2100.0\\nC 1980.0 2310.0 2200.0\\nC 1990.0 2310.0 50.0\\nC 1990.0 2310.0 150.0\\nC 1990.0 2310.0 250.0\\nC 1990.0 2310.0 350.0\\nC 1990.0 2310.0 450.0\\nC 1990.0 2310.0 550.0\\nC 1990.0 2310.0 650.0\\nC 1990.0 2310.0 750.0\\nC 1990.0 2310.0 850.0\\nC 1990.0 2310.0 950.0\\nC 1990.0 2310.0 1050.0\\nC 1990.0 2310.0 1150.0\\nC 1990.0 2310.0 1250.0\\nC 1990.0 2310.0 1350.0\\nC 1990.0 2310.0 1450.0\\nC 1990.0 2310.0 1550.0\\nC 1990.0 2310.0 1650.0\\nC 1990.0 2310.0 1750.0\\nC 1990.0 2310.0 1850.0\\nC 1990.0 2310.0 1950.0\\nC 1990.0 2310.0 2050.0\\nC 1990.0 2310.0 2150.0\\nC 2000.0 2310.0 0.0\\nC 2000.0 2310.0 100.0\\nC 2000.0 2310.0 200.0\\nC 2000.0 2310.0 300.0\\nC 2000.0 2310.0 400.0\\nC 2000.0 2310.0 500.0\\nC 2000.0 2310.0 600.0\\nC 2000.0 2310.0 700.0\\nC 2000.0 2310.0 800.0\\nC 2000.0 2310.0 900.0\\nC 2000.0 2310.0 1000.0\\nC 2000.0 2310.0 1100.0\\nC 2000.0 2310.0 1200.0\\nC 2000.0 2310.0 1300.0\\nC 2000.0 2310.0 1400.0\\nC 2000.0 2310.0 1500.0\\nC 2000.0 2310.0 1600.0\\nC 2000.0 2310.0 1700.0\\nC 2000.0 2310.0 1800.0\\nC 2000.0 2310.0 1900.0\\nC 2000.0 2310.0 2000.0\\nC 2000.0 2310.0 2100.0\\nC 2000.0 2310.0 2200.0\\nC 2010.0 2310.0 50.0\\nC 2010.0 2310.0 150.0\\nC 2010.0 2310.0 250.0\\nC 2010.0 2310.0 350.0\\nC 2010.0 2310.0 450.0\\nC 2010.0 2310.0 550.0\\nC 2010.0 2310.0 650.0\\nC 2010.0 2310.0 750.0\\nC 2010.0 2310.0 850.0\\nC 2010.0 2310.0 950.0\\nC 2010.0 2310.0 1050.0\\nC 2010.0 2310.0 1150.0\\nC 2010.0 2310.0 1250.0\\nC 2010.0 2310.0 1350.0\\nC 2010.0 2310.0 1450.0\\nC 2010.0 2310.0 1550.0\\nC 2010.0 2310.0 1650.0\\nC 2010.0 2310.0 1750.0\\nC 2010.0 2310.0 1850.0\\nC 2010.0 2310.0 1950.0\\nC 2010.0 2310.0 2050.0\\nC 2010.0 2310.0 2150.0\\nC 2020.0 2310.0 0.0\\nC 2020.0 2310.0 100.0\\nC 2020.0 2310.0 200.0\\nC 2020.0 2310.0 300.0\\nC 2020.0 2310.0 400.0\\nC 2020.0 2310.0 500.0\\nC 2020.0 2310.0 600.0\\nC 2020.0 2310.0 700.0\\nC 2020.0 2310.0 800.0\\nC 2020.0 2310.0 900.0\\nC 2020.0 2310.0 1000.0\\nC 2020.0 2310.0 1100.0\\nC 2020.0 2310.0 1200.0\\nC 2020.0 2310.0 1300.0\\nC 2020.0 2310.0 1400.0\\nC 2020.0 2310.0 1500.0\\nC 2020.0 2310.0 1600.0\\nC 2020.0 2310.0 1700.0\\nC 2020.0 2310.0 1800.0\\nC 2020.0 2310.0 1900.0\\nC 2020.0 2310.0 2000.0\\nC 2020.0 2310.0 2100.0\\nC 2020.0 2310.0 2200.0\\nC 2030.0 2310.0 50.0\\nC 2030.0 2310.0 150.0\\nC 2030.0 2310.0 250.0\\nC 2030.0 2310.0 350.0\\nC 2030.0 2310.0 450.0\\nC 2030.0 2310.0 550.0\\nC 2030.0 2310.0 650.0\\nC 2030.0 2310.0 750.0\\nC 2030.0 2310.0 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2310.0 2300.0 450.0\\nC 2310.0 2300.0 550.0\\nC 2310.0 2300.0 650.0\\nC 2310.0 2300.0 750.0\\nC 2310.0 2300.0 850.0\\nC 2310.0 2300.0 950.0\\nC 2310.0 2300.0 1050.0\\nC 2310.0 2300.0 1150.0\\nC 2310.0 2300.0 1250.0\\nC 2310.0 2300.0 1350.0\\nC 2310.0 2300.0 1450.0\\nC 2310.0 2300.0 1550.0\\nC 2310.0 2300.0 1650.0\\nC 2310.0 2300.0 1750.0\\nC 2310.0 2300.0 1850.0\\nC 2310.0 2300.0 1950.0\\nC 2310.0 2300.0 2050.0\\nC 2310.0 2300.0 2150.0\\nC 2310.0 2310.0 0.0\\nC 2310.0 2310.0 100.0\\nC 2310.0 2310.0 200.0\\nC 2310.0 2310.0 300.0\\nC 2310.0 2310.0 400.0\\nC 2310.0 2310.0 500.0\\nC 2310.0 2310.0 600.0\\nC 2310.0 2310.0 700.0\\nC 2310.0 2310.0 800.0\\nC 2310.0 2310.0 900.0\\nC 2310.0 2310.0 1000.0\\nC 2310.0 2310.0 1100.0\\nC 2310.0 2310.0 1200.0\\nC 2310.0 2310.0 1300.0\\nC 2310.0 2310.0 1400.0\\nC 2310.0 2310.0 1500.0\\nC 2310.0 2310.0 1600.0\\nC 2310.0 2310.0 1700.0\\nC 2310.0 2310.0 1800.0\\nC 2310.0 2310.0 1900.0\\nC 2310.0 2310.0 2000.0\\nC 2310.0 2310.0 2100.0\\nC 2310.0 2310.0 2200.0\\n\",\"xyz\");\n",
       "\tviewer_1655642769285975.setStyle({\"model\": 0},{\"sphere\": {\"color\": \"grey\", \"radius\": \"20\", \"opacity\": \"0.6\"}});\n",
       "\tviewer_1655642769285975.zoomTo();\n",
       "\tviewer_1655642769285975.addLine({\"color\": \"red\", \"start\": {\"x\": 0, \"y\": 0, \"z\": 0}, \"end\": {\"x\": 4640.0, \"y\": 0, \"z\": 0}});\n",
       "\tviewer_1655642769285975.addLine({\"color\": \"green\", \"start\": {\"x\": 0, \"y\": 0, \"z\": 0}, \"end\": {\"x\": 0, \"y\": 4640.0, \"z\": 0}});\n",
       "\tviewer_1655642769285975.addLine({\"color\": \"blue\", \"start\": {\"x\": 0, \"y\": 0, \"z\": 0}, \"end\": {\"x\": 0, \"y\": 0, \"z\": 2250.0}});\n",
       "\tviewer_1655642769285975.addLine({\"color\": \"black\", \"start\": {\"x\": 4640.0, \"y\": 0, \"z\": 0}, \"end\": {\"x\": 4640.0, \"y\": 4640.0, \"z\": 0}});\n",
       "\tviewer_1655642769285975.addLine({\"color\": \"black\", \"start\": {\"x\": 0, \"y\": 4640.0, \"z\": 0}, \"end\": {\"x\": 4640.0, \"y\": 4640.0, \"z\": 0}});\n",
       "\tviewer_1655642769285975.addLine({\"color\": \"black\", \"start\": {\"x\": 4640.0, \"y\": 0, \"z\": 2250.0}, \"end\": {\"x\": 4640.0, \"y\": 4640.0, \"z\": 2250.0}});\n",
       "\tviewer_1655642769285975.addLine({\"color\": \"black\", \"start\": {\"x\": 0, \"y\": 4640.0, \"z\": 2250.0}, \"end\": {\"x\": 4640.0, \"y\": 4640.0, \"z\": 2250.0}});\n",
       "\tviewer_1655642769285975.addLine({\"color\": \"black\", \"start\": {\"x\": 0, \"y\": 0, \"z\": 2250.0}, \"end\": {\"x\": 4640.0, \"y\": 0, \"z\": 2250.0}});\n",
       "\tviewer_1655642769285975.addLine({\"color\": \"black\", \"start\": {\"x\": 0, \"y\": 0, \"z\": 2250.0}, \"end\": {\"x\": 0, \"y\": 4640.0, \"z\": 2250.0}});\n",
       "\tviewer_1655642769285975.addLine({\"color\": \"black\", \"start\": {\"x\": 4640.0, \"y\": 0, \"z\": 0}, \"end\": {\"x\": 4640.0, \"y\": 0, \"z\": 2250.0}});\n",
       "\tviewer_1655642769285975.addLine({\"color\": \"black\", \"start\": {\"x\": 0, \"y\": 4640.0, \"z\": 0}, \"end\": {\"x\": 0, \"y\": 4640.0, \"z\": 2250.0}});\n",
       "\tviewer_1655642769285975.addLine({\"color\": \"black\", \"start\": {\"x\": 4640.0, \"y\": 4640.0, \"z\": 0}, \"end\": {\"x\": 4640.0, \"y\": 4640.0, \"z\": 2250.0}});\n",
       "\tviewer_1655642769285975.addLabel(\"X\",{\"position\": {\"x\": 4640.0, \"y\": 0, \"z\": 0}, \"fontColor\": \"red\", \"backgroundColor\": \"white\", \"fontsize\": 8, \"backgroundOpacity\": \"0.0\"});\n",
       "\tviewer_1655642769285975.addLabel(\"Y\",{\"position\": {\"x\": 0, \"y\": 4640.0, \"z\": 0}, \"fontColor\": \"green\", \"backgroundColor\": \"white\", \"fontsize\": 8, \"backgroundOpacity\": \"0.0\"});\n",
       "\tviewer_1655642769285975.addLabel(\"Z\",{\"position\": {\"x\": 0, \"y\": 0, \"z\": 2250.0}, \"fontColor\": \"blue\", \"backgroundColor\": \"white\", \"fontsize\": 8, \"backgroundOpacity\": \"0.0\"});\n",
       "viewer_1655642769285975.render();\n",
       "});\n",
       "</script><script>\n",
       "            $3Dmolpromise.then(function() { //wrap in promise for non-interactive functionality\n",
       "                \n",
       "                viewer_1655642769285975.render();\n",
       "            });\n",
       "            </script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x800 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gui.boot_gui()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8954a1c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from colabseg.utilities import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "425413a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "positions = create_sphere_points(1,1,1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "67abe43d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def write_xyz(point_cloud, output_filename):\n",
    "    \"\"\"Write an XYZ file for viewing in VMD\"\"\"\n",
    "    xyz_file = \"{}\\n\".format(len(np.asarray(point_cloud)))\n",
    "    xyz_file += \"pointcloud as xyz positions\\n\"\n",
    "    for position in np.asarray(point_cloud):\n",
    "        xyz_file += \"C {} {} {}\\n\".format(position[0], position[1], position[2])\n",
    "    text_file = open(\"{}\".format(output_filename), \"w\")\n",
    "    text_file.write(xyz_file)\n",
    "    text_file.close()\n",
    "    return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c5763330",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'write_xyz' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Input \u001b[0;32mIn [4]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mwrite_xyz\u001b[49m(positions,\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msphere_test_position_111.txt\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "\u001b[0;31mNameError\u001b[0m: name 'write_xyz' is not defined"
     ]
    }
   ],
   "source": [
    "write_xyz(positions,\"sphere_test_position_111.txt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c96659d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import MDAnalysis as mda\n",
    "import numpy as np\n",
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "b59ed809",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def lstsq_sphere_fitting(positions):\n",
    "    \"\"\"\n",
    "    lstsq_sphere_fitting: fit a given set of 3D points (x, y, z) to a sphere.\n",
    "    Args:\n",
    "       positions: a two dimentional numpy array, of which each row represents (x, y, z) coordinates of a point\n",
    "    Returns:\n",
    "       radius, x0, y0, z0\n",
    "    Source: https://gist.github.com/WuyangLI/4bf4b067fed46789352d5019af1c11b2\n",
    "    \"\"\"\n",
    "    # add column of ones to pos_xyz to construct matrix A\n",
    "    pos_xyz = positions\n",
    "    row_num = pos_xyz.shape[0]\n",
    "    A = np.ones((row_num, 4))\n",
    "    A[:,0:3] = pos_xyz\n",
    "\n",
    "    # construct vector f\n",
    "    f = np.sum(np.multiply(pos_xyz, pos_xyz), axis=1)\n",
    "\n",
    "    sol, residules, rank, singval = np.linalg.lstsq(A,f)\n",
    "\n",
    "    # solve the radius\n",
    "    radius = math.sqrt((sol[0]*sol[0]/4.0)+(sol[1]*sol[1]/4.0)+(sol[2]*sol[2]/4.0)+sol[3])\n",
    "\n",
    "    return radius, sol[0]/2.0, sol[1]/2.0, sol[2]/2.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "c070016a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/envs/mesh/lib/python3.7/site-packages/ipykernel_launcher.py:20: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\n",
      "To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\n"
     ]
    }
   ],
   "source": [
    "pos = np.loadtxt(\"missing_wedge_sphere.txt\")\n",
    "r, x0, y0, z0 = lstsq_sphere_fitting(pos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "6907cbe4",
   "metadata": {},
   "outputs": [
    {
     "ename": "AssertionError",
     "evalue": "\nArrays are not almost equal to 7 decimals\n ACTUAL: 0.0\n DESIRED: 1",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-57-3f508ed32a1f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtesting\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0massert_almost_equal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/anaconda3/envs/mesh/lib/python3.7/site-packages/numpy/testing/_private/utils.py\u001b[0m in \u001b[0;36massert_almost_equal\u001b[0;34m(actual, desired, decimal, err_msg, verbose)\u001b[0m\n\u001b[1;32m    597\u001b[0m         \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    598\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mabs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdesired\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mactual\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0;36m1.5\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;36m10.0\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mdecimal\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[0;32m--> 599\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mAssertionError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_build_err_msg\u001b[0m\u001b[0;34m(\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[0m\u001b[1;32m    600\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    601\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAssertionError\u001b[0m: \nArrays are not almost equal to 7 decimals\n ACTUAL: 0.0\n DESIRED: 1"
     ]
    }
   ],
   "source": [
    "np.testing.assert_almost_equal(x0, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "24e1666f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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    "name": "ipython",
    "version": 3
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   "version": "3.8.0"
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