{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. How to Build a Model\n",
    "\n",
    "`Model` is composed of a set of `Species` and `ReactionRule`s.\n",
    "\n",
    "* `Species` describes a molecule entitie (e.g. a type or state of a protein) in the model. `Species` also has its attributes like the size.  \n",
    "* `ReactionRule` describes the interactions between `Species` (e.g. binding and unbinding)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from ecell4 import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.1. Species\n",
    "\n",
    "`Species` can be generated with its name:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A\n"
     ]
    }
   ],
   "source": [
    "sp1 = Species(\"A\")\n",
    "print(sp1.serial())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A name of `Species` (called `serial`) has a number of naming rules.\n",
    "It requires attention to use special symbols (e.g. parenthesis `()`, dot `.`, underbar `_`), numbers and brank.\n",
    "\n",
    "`Species` has a set of APIs for handling its attributes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.005\n",
      "False\n"
     ]
    }
   ],
   "source": [
    "sp1.set_attribute(\"radius\", \"0.005\")\n",
    "sp1.set_attribute(\"D\", \"1\")\n",
    "sp1.set_attribute(\"location\", \"cytoplasm\")\n",
    "print(sp1.get_attribute(\"radius\"))\n",
    "sp1.remove_attribute(\"radius\")\n",
    "print(sp1.has_attribute(\"radius\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The arguments in `set_attribute` is the name of attribute and its value. Both of them have to be string. There is a shortcut to set the attributes above at once because `radius`, `D` (a diffusion coefficient) and `location` are frequently used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sp1 = Species(\"A\", \"0.005\", \"1\", \"cytoplasm\")  # serial, radius, D, location"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The equality between `Species` is just evaluated based on their serial:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False True\n"
     ]
    }
   ],
   "source": [
    "print(Species(\"A\") == Species(\"B\"), Species(\"A\") == Species(\"A\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A Species consists of one or more UnitSpecies:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C\n",
      "C.A.B 3\n"
     ]
    }
   ],
   "source": [
    "sp1 = Species()\n",
    "usp1 = UnitSpecies(\"C\")\n",
    "print(usp1.serial())\n",
    "sp1.add_unit(usp1)\n",
    "sp1.add_unit(UnitSpecies(\"A\"))\n",
    "sp1.add_unit(UnitSpecies(\"B\"))\n",
    "print(sp1.serial(), sp1.num_units())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A Species can be reproduced from a serial. In a serial, all serials of UnitSpecies are joined with the separator, dot `.`. The comparison between Species **matters** the oder of UnitSpecies in each Species."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C.A.B\n",
      "False\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "sp1 = Species(\"C.A.B\")\n",
    "print(sp1.serial())\n",
    "print(Species(\"A.B.C\") == Species(\"C.A.B\"))\n",
    "print(Species(\"A.B.C\") == Species(\"A.B.C\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`UnitSpecies` can have sites. Sites consists of a `name`, `state` and `bond`, and are sorted automatically in `UnitSpecies`. `name` must be unique in a `UnitSpecies`. All the value have to be string. Do not include parenthesis, dot and blank, and not start from numbers except for `bond`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A(bs^_,ps=p^_,us=u)\n"
     ]
    }
   ],
   "source": [
    "usp1 = UnitSpecies(\"A\")\n",
    "usp1.add_site(\"us\", \"u\", \"\")\n",
    "usp1.add_site(\"ps\", \"p\", \"_\")\n",
    "usp1.add_site(\"bs\", \"\", \"_\")\n",
    "print(usp1.serial())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`UnitSpecies` can be also reproduced from its serial. Please be careful with the order of sites where a site with a state must be placed after sites with no state specification:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A(bs^_,ps=p^_,us=u)\n"
     ]
    }
   ],
   "source": [
    "usp1 = UnitSpecies()\n",
    "usp1.deserialize(\"A(bs^_, us=u, ps=p^_)\")\n",
    "print(usp1.serial())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Of course, a site of `UnitSpecies` is available even in `Species`' serial."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A(bs^1,ps=u).A(bs,ps=p^1)\n",
      "2\n"
     ]
    }
   ],
   "source": [
    "sp1 = Species(\"A(bs^1, ps=u).A(bs, ps=p^1)\")\n",
    "print(sp1.serial())\n",
    "print(sp1.num_units())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The information (`UnitSpecies` and its `site`) is used for rule-based modeling. The way of rule-based modeling in E-Cell4 will be explained in **7. Introduction of Rule-based Modeling** [local ipynb](tutorial7.ipynb) [readthedocs](http://ecell4.readthedocs.io/en/latest/tutorials/tutorial7.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.2. ReactionRule\n",
    "\n",
    "`ReactionRule` consists of `reactants`, `products` and `k`. `reactants` and `products` are a list of `Species`, and `k` is a kinetic rate given as a floating number."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "rr1 = ReactionRule()\n",
    "rr1.add_reactant(Species(\"A\"))\n",
    "rr1.add_reactant(Species(\"B\"))\n",
    "rr1.add_product(Species(\"C\"))\n",
    "rr1.set_k(1.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is a binding reaction from `A` and `B` to `C`. In this reaction definition, you don't need to set attributes to `Species`. This is equivalent to call an utility function as follows: `create_binding_reaction_rule(Species(\"A\"), Species(\"B\"), Species(\"C\"), 1.0)`.\n",
    "\n",
    "To inspect `ReactionRule`, `as_string` function is available:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A+B>C|1\n"
     ]
    }
   ],
   "source": [
    "rr1 = create_binding_reaction_rule(Species(\"A\"), Species(\"B\"), Species(\"C\"), 1.0)\n",
    "print(rr1.as_string())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also provide components to the constructor:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A+B>C|1\n"
     ]
    }
   ],
   "source": [
    "rr1 = ReactionRule([Species(\"A\"), Species(\"B\")], [Species(\"C\")], 1.0)\n",
    "print(rr1.as_string())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In general, `ReactionRule` suggests a mass action reaction with rate `k`. `ode` solver also supports rate laws thought it's under development yet. `ode.ODERatelaw` is explained in **6. How to Solve ODEs with Rate Law Functions** [local ipynb](tutorial6.ipynb) [readthedocs](http://ecell4.readthedocs.io/en/latest/tutorials/tutorial6.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.3. NetworkModel\n",
    "\n",
    "Now you have known how to create components of `Model`. Next let's register these components to `Model`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sp1 = Species(\"A\", \"0.005\", \"1\")\n",
    "sp2 = Species(\"B\", \"0.005\", \"1\")\n",
    "sp3 = Species(\"C\", \"0.01\", \"0.5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rr1 = create_binding_reaction_rule(Species(\"A\"), Species(\"B\"), Species(\"C\"), 0.01)\n",
    "rr2 = create_unbinding_reaction_rule(Species(\"C\"), Species(\"A\"), Species(\"B\"), 0.3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`NetworkModel` has interfaces to register `Species` and `ReactionRule` named `add_species_attribute` and `add_reaction_rule`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "m1 = NetworkModel()\n",
    "m1.add_species_attribute(sp1)\n",
    "m1.add_species_attribute(sp2)\n",
    "m1.add_species_attribute(sp3)\n",
    "m1.add_reaction_rule(rr1)\n",
    "m1.add_reaction_rule(rr2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is a simple model with binding and unbinding reactions. To inspect `Model`, `species_attributes` and `reaction_rules` is available:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['A', 'B', 'C']\n",
      "['A+B>C|0.01', 'C>A+B|0.3']\n"
     ]
    }
   ],
   "source": [
    "print([sp.serial() for sp in m1.species_attributes()])\n",
    "print([rr.as_string() for rr in m1.reaction_rules()])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`Species`' attributes in `Model` are indispensable for spatial simulations, but not necessarily needed for non-spatial algorithms, i.e. `gillespie` and `ode`. The attribute pushed first has higher priority than one pushed later. You can also attribute a `Species` based on the attributes in a `Model`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n",
      "True\n",
      "0.005\n"
     ]
    }
   ],
   "source": [
    "sp1 = Species(\"A\")\n",
    "print(sp1.has_attribute(\"radius\"))\n",
    "sp2 = m1.apply_species_attributes(sp1)\n",
    "print(sp2.has_attribute(\"radius\"))\n",
    "print(sp2.get_attribute(\"radius\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All functions related to `Species`, `ReactionRule` and `NetworkModel` above are available even on C++ in the same way.\n",
    "\n",
    "You can solve this model with `run_simulation` as explained in **1. Brief Tour of E-Cell4 Simulations** [local ipynb](tutorial1.ipynb) [readthedocs](http://ecell4.readthedocs.io/en/latest/tutorials/tutorial1.html) :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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VZtYo9P4FM8uMbiyRKNtnHxgwACZNglWrYMIEr3fj00+98R0NG8LBB3srk77z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\nNcZBw8zMzBrjoGFmZmaN+Tee1Ql9JJ1GNgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a11e6cba20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "run_simulation(10.0, model=m1, y0={'C': 60})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.4. Python Utilities to Build a Model\n",
    "\n",
    "As shown in **1. Brief Tour of E-Cell4 Simulations** [local ipynb](tutorial1.ipynb) [readthedocs](http://ecell4.readthedocs.io/en/latest/tutorials/tutorial1.html), E-Cell4 also provides the easier way to build a model using `with` statement:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with species_attributes():\n",
    "    A | B | {'radius': '0.005', 'D': '1'}\n",
    "    C | {'radius': '0.01', 'D': '0.5'}\n",
    "\n",
    "with reaction_rules():\n",
    "    A + B == C | (0.01, 0.3)\n",
    "\n",
    "m1 = get_model()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For reversible reactions, `<>` is also available instead of `==` on Python 2, but deprecated on Python3. In the `with` statement, undeclared variables are automaticaly assumed to be a `Species`. Any Python variables, functions and statement are available even in the `with` block."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from math import log\n",
    "\n",
    "ka, kd, kf = 0.01, 0.3, 0.1\n",
    "tau = 10.0\n",
    "\n",
    "with reaction_rules():\n",
    "    E0 + S == ES | (ka, kd)\n",
    "\n",
    "    if tau > 0:\n",
    "        ES > E1 + P | kf\n",
    "        E1 > E0 | log(2) / tau\n",
    "    else:\n",
    "        ES > E0 + P | kf\n",
    "\n",
    "m1 = get_model()\n",
    "del ka, kd, kf, tau"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Meanwhile, once some variable is declared even outside the block, you can not use its name as a `Species` as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RuntimeError('invalid expression; \"10\" given',)\n"
     ]
    }
   ],
   "source": [
    "A = 10\n",
    "\n",
    "try:\n",
    "    with reaction_rules():\n",
    "        A + B == C | (0.01, 0.3)\n",
    "except Exception as e:\n",
    "    print(repr(e))\n",
    "\n",
    "del A"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "where `A + B == C` exactly means `10 + B == C`.\n",
    "\n",
    "In the absence of left or right hand side of `ReactionRule` like synthesis or degradation, you may want to describe like:\n",
    "\n",
    "\n",
    "```python\n",
    "with reaction_rules():\n",
    "    A > | 1.0  # XXX: will raise SyntaxError\n",
    "    > A | 1.0  # XXX: will raise SyntaxError\n",
    "```\n",
    "\n",
    "However, they are not accepted because of `SyntaxError` on Python. To describe these cases, a special operator, tilde `~`, is available. `~` sets a stoichiometric coefficient of the following `Species` as zero, which means the `Species` is just ignored in the `ReactionRule`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['>A|2', 'A>|1']\n"
     ]
    }
   ],
   "source": [
    "with reaction_rules():\n",
    "    ~A > A | 2.0  # equivalent to `create_synthesis_reaction_rule`\n",
    "    A > ~A | 1.0  # equivalent to `create_degradation_reaction_rule`\n",
    "\n",
    "m1 = get_model()\n",
    "print([rr.as_string() for rr in m1.reaction_rules()])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following `Species`' name is not necessarily needed to be the same as others. The model above describes $[A]'=2-[A]$:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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aigZ3iIhI6RfIXS23AfMLaJ8H9C5ZHCmOW0cMhuwEXr9Vt7KIiEjZEEjhUQvY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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1242a4f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from math import exp\n",
    "run_simulation(10.0, model=m1, opt_args=['-', lambda t: 2.0 * (1 - exp(-t)), '--'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A chain of serial reactions can be described in one line. To split a line into two or more physical lines, wrap lines in a parenthesis:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['E+S>ES|0.5', 'ES>E+S|1', 'ES>E+P|1.5']\n"
     ]
    }
   ],
   "source": [
    "with reaction_rules():\n",
    "    (E + S == ES | (0.5, 1.0)\n",
    "         > E + P | 1.5)\n",
    "\n",
    "m1 = get_model()\n",
    "print([rr.as_string() for rr in m1.reaction_rules()])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The method uses global variables in `ecell4.util.decorator` (e.g. `REACTION_RULES`) to cache objects created in the `with` statement:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[<ecell4.core.ReactionRule object at 0x000001A123D02BA0>, <ecell4.core.ReactionRule object at 0x000001A123D02CD8>]\n",
      "[]\n"
     ]
    }
   ],
   "source": [
    "import ecell4.util.decorator\n",
    "\n",
    "with reaction_rules():\n",
    "    A + B == C | (0.01, 0.3)\n",
    "\n",
    "print(ecell4.util.decorator.REACTION_RULES)  #XXX: Only for debugging\n",
    "get_model()\n",
    "print(ecell4.util.decorator.REACTION_RULES)  #XXX: Only for debugging"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the modularity in building `Model`, decorator functions are also usefull. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[<ecell4.core.Species object at 0x000001A123D02CD8>, <ecell4.core.Species object at 0x000001A123D02BA0>, <ecell4.core.Species object at 0x000001A123D02DC8>]\n",
      "[<ecell4.core.ReactionRule object at 0x000001A123D02D20>, <ecell4.core.ReactionRule object at 0x000001A123D02DE0>]\n"
     ]
    }
   ],
   "source": [
    "@species_attributes\n",
    "def attrgen1(radius, D):\n",
    "    A | B | {'radius': str(radius), 'D': str(D)}\n",
    "    C | {'radius': str(radius * 2), 'D': str(D * 0.5)}\n",
    "\n",
    "@reaction_rules\n",
    "def rrgen1(kon, koff):\n",
    "    A + B == C | (kon, koff)\n",
    "\n",
    "attrs1 = attrgen1(0.005, 1)\n",
    "rrs1 = rrgen1(0.01, 0.3)\n",
    "print(attrs1)\n",
    "print(rrs1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Do **not** add parenthesis after decorators in contrast to the case of the `with` statement. The functions decorated by `reaction_rules` and `species_attributes` return a list of `ReactionRule`s and `Species` respectively. The list can be registered to `Model` at once by using `add_reaction_rules` and `add_species_attributes`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n"
     ]
    }
   ],
   "source": [
    "m1 = NetworkModel()\n",
    "m1.add_species_attributes(attrs1)\n",
    "m1.add_reaction_rules(rrs1)\n",
    "print(m1.num_reaction_rules())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This method is modular and reusable relative to the way using `with` statement."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
   "name": "conda-root-py"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
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 },
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