{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name":...

Question

{  "nbformat": 4,  "nbformat_minor": 0,  "metadata": {    "colab": {      "name": "“Homework_11_Scheduling_with_Dependencies.ipynb”的副本",      "provenance": [],      "collapsed_sections": []    },    "kernelspec": {      "display_name": "Python 3",      "language": "python",      "name": "python3"    },    "language_info": {      "codemior_mode": {        "name": "ipython",        "version": 3      },      "file_extension": ".py",      "mimetype": "text/x-python",      "name": "python",      "nbconvert_exporter": "python",      "pygments_lexer": "ipython3",      "version": "3.7.3"    },    "test_info": {      "id": "4adb3d5055da02e7ae8251ca99f4acfa901ab256"    }  },  "cells": [    {      "cell_type": "markdown",      "metadata": {        "id": "IIqniH2KkacL"      },      "source": [        "Before you turn this problem in, make sure everything runs as expected. First, **restart the kernel** (in the menubar, select Kernel$\rightaow$Restart) and then **run all cells** (in the menubar, select Cell$\rightaow$Run All).
",        "
",        "Make sure you fill in any place that says `YOUR CODE HERE` or "YOUR ANSWER HERE", as well as your name and collaborators below:"      ]    },    {      "cell_type": "code",      "metadata": {        "id": "vZ2QllLpkacL"      },      "source": [        "NAME = "Yuxuan Guo"
",        "COLLABORATORS = """      ],      "execution_count": 1,      "outputs": []    },    {      "cell_type": "markdown",      "metadata": {        "id": "JhnyL4GOkacL"      },      "source": [        "---"      ]    },    {      "cell_type": "markdown",      "metadata": {        "deletable": false,        "editable": false,        "nbgrader": {          "checksum": "767690caa09bf309165134adec898cd2",          "grade": false,          "grade_id": "cell-fc6b5241f439f72f",          "locked": true,          "schema_version": 1,          "solution": false        },        "id": "cU91rlxQkacM"      },      "source": [        "# Homework 11: Scheduling with Dependencies
",        "
",        "Copyright Luca de Alfaro, XXXXXXXXXXn",        "License: [CC-BY-NC-ND](https:creativecommons.org/licensesy-nc-nd/4.0/)."      ]    },    {      "cell_type": "markdown",      "metadata": {        "deletable": false,        "editable": false,        "nbgrader": {          "checksum": "93460885ba39aaf770cf46acb31f4a",          "grade": false,          "grade_id": "cell-a13bd1c44a595199",          "locked": true,          "schema_version": 1,          "solution": false        },        "id": "ica_ZmxrkacM"      },      "source": [        "## Submission
",        "
",        "[Please submit to this Google Form](https:docs.google.com/forms/d/e/1FAIpQLSdVfcA_LZdtjJoLKBSkyHwbvxXvow-CX4pQEMmW3UVm7X_JrA/viewform?usp=sf_link).
",        "
",        "Deadline: Friday December 4, 11pm (check on Canvas for updated information)."      ]    },    {      "cell_type": "markdown",      "metadata": {        "deletable": false,        "editable": false,        "nbgrader": {          "checksum": "90a74a81d08e34489013a1201369ad0a",          "grade": false,          "grade_id": "cell-dcea49d1e015f68d",          "locked": true,          "schema_version": 1,          "solution": false        },        "id": "Kny6AHUmkacM"      },      "source": [        "## Test Format
",        "
",        "This test contains 4 questions, for a total of 90 points. "      ]    },    {      "cell_type": "markdown",      "metadata": {        "deletable": false,        "editable": false,        "nbgrader": {          "checksum": "637cf59cf5df5ba15cbc30a73b035287",          "grade": false,          "grade_id": "cell-7783f146ca3d6158",          "locked": true,          "schema_version": 1,          "solution": false        },        "id": "FKxK7Pb0kacM"      },      "source": [        "Assume you have to prepare Pasta Caonara.   My version of the recipe goes like this: 
",        "
",        "> Dice onions and pancetta, and fry in a mix of olive oil and butter, slowly.  Separately, put in a bowl as many eggs as there are dinner guests; you can either put in the bowls the yolks only, or you can add a few whites if you wish.  Beat the eggs.  
",        "> Bring water to a boil, and when it boils, salt it.  Put the pasta in (I like Penne Rigate).   When cooked, colander the water away, and quickly unite in the bowl the beaten eggs, the pasta, and the pancetta.  Mix well and serve immediately. 
",        "
",        "If you have to invite people over, you could do this recipe sequentially, and first woy about cooking the pasta: warming the water, putting the pasta in, then colandering it.  Then you could woy about cooking the pancetta and onions.  When that's done, you can start to beat the eggs. Finally, you could unite everything.  Technically, that would work, but there would be two problems.  The first is that, of course, the pasta would be rather cold by the time it would be served, a capital sin (pasta must be served immediately after it is cooked).  Secondly, even if you rehash the order so that you first cook the pancetta, then beat the eggs, then cook the pasta, then technically this works -- but it would take you well over one hour to have everything ready.  You want to do things in parallel, cooking the pancetta while heating up the water for the pasta, and so forth.  You want to discover what are the things that need to be done one after the other, and what are the things that can be done in parallel, and in which order to do everything.  
",        "
",        "Great cooking, by the way, is much about the perfect timing, not only the perfect preparation.  You have to have the various preparations ready at the same time, to unite them just right.  We will woy about timing in the second part of this chapter; first, we woy about what we can do and in which order.
",        "
",        "As an aside for those of you who are more interested in compiling code than in cooking, the problem of how to compile C or C++ code is very similar.  A makefile defines dependencies between tasks: you have to have compiled

Swapnil · Accepted Answer

Assign/.ipynb_checkpoints/Question File-checkpoint.ipynb{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IIqniH2KkacL"
   },
   "source": [
    "Before you turn this problem in, make sure everything runs as expected. First, **restart the kernel** (in the menubar, select Kernel$\rightarrow$Restart) and then **run all cells** (in the menubar, select Cell$\rightarrow$Run All).
",
    "
",
    "Make sure you fill in any place that says `YOUR CODE HERE` or "YOUR ANSWER HERE", as well as your name and collaborators below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "vZ2QllLpkacL"
   },
   "outputs": [],
   "source": [
    "NAME = "Yuxuan Guo"
",
    "COLLABORATORS = """
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JhnyL4GOkacL"
   },
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "id": "cU91rlxQkacM",
    "nbgrader": {
     "checksum": "767690caa09bf309165134adec898cd2",
     "grade": false,
     "grade_id": "cell-fc6b5241f439f72f",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "source": [
    "# Homework 11: Scheduling with Dependencies
",
    "
",
    "Copyright Luca de Alfaro, 2019-20. 
",
    "License: [CC-BY-NC-ND](https://creativecommons.org/licenses/by-nc-nd/4.0/)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "id": "ica_ZmxrkacM",
    "nbgrader": {
     "checksum": "93460885ba39aaf770bbcf46acb31f4a",
     "grade": false,
     "grade_id": "cell-a13bd1c44a595199",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "source": [
    "## Submission
",
    "
",
    "[Please submit to this Google Form](https://docs.google.com/forms/d/e/1FAIpQLSdVfcA_LZdtjJoLKBSkyHwbvxXvow-CX4pQEMmW3UVm7X_JrA/viewform?usp=sf_link).
",
    "
",
    "Deadline: Friday December 4, 11pm (check on Canvas for updated information)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "id": "Kny6AHUmkacM",
    "nbgrader": {
     "checksum": "90a74a81d08e34489013a1201369ad0a",
     "grade": false,
     "grade_id": "cell-dcea49d1e015f68d",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "source": [
    "## Test Format
",
    "
",
    "This test contains 4 questions, for a total of 90 points. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "id": "FKxK7Pb0kacM",
    "nbgrader": {
     "checksum": "637cf59cf5df5ba15cbc30a73b035287",
     "grade": false,
     "grade_id": "cell-7783f146ca3d6158",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "source": [
    "Assume you have to prepare Pasta Carbonara.   My version of the recipe goes like this: 
",
    "
",
    "> Dice onions and pancetta, and fry in a mix of olive oil and butter, slowly.  Separately, put in a bowl as many eggs as there are dinner guests; you can either put in the bowls the yolks only, or you can add a few whites if you wish.  Beat the eggs.  
",
    "> Bring water to a boil, and when it boils, salt it.  Put the pasta in (I like Penne Rigate).   When cooked, colander the water away, and quickly unite in the bowl the beaten eggs, the pasta, and the pancetta.  Mix well and serve immediately. 
",
    "
",
    "If you have to invite people over, you could do this recipe sequentially, and first worry about cooking the pasta: warming the water, putting the pasta in, then colandering it.  Then you could worry about cooking the pancetta and onions.  When that's done, you can start to beat the eggs. Finally, you could unite everything.  Technically, that would work, but there would be two problems.  The first is that, of course, the pasta would be rather cold by the time it would be served, a capital sin (pasta must be served immediately after it is cooked).  Secondly, even if you rehash the order so that you first cook the pancetta, then beat the eggs, then cook the pasta, then technically this works -- but it would take you well over one hour to have everything ready.  You want to do things in parallel, cooking the pancetta while heating up the water for the pasta, and so forth.  You want to discover what are the things that need to be done one after the other, and what are the things that can be done in parallel, and in which order to do everything.  
",
    "
",
    "Great cooking, by the way, is much about the perfect timing, not only the perfect preparation.  You have to have the various preparations ready at the same time, to unite them just right.  We will worry about timing in the second part of this chapter; first, we worry about what we can do and in which order.
",
    "
",
    "As an aside for those of you who are more interested in compiling code than in cooking, the problem of how to compile C or C++ code is very similar.  A makefile defines dependencies between tasks: you have to have compiled pathlib.c before you can link the result together with something else.  The task of the make program is to figure out how to parallelize the compilation, so that independent tasks can happen in different processes (possibly on different CPU cores), while respecting the precedence constraints between tasks.  We will mention this application in some of the exercises of the chapter. 
",
    "
",
    "
",
    "
"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "id": "ZDoY3oEIkacM",
    "nbgrader": {
     "checksum": "c746ef312885249b098a4a4fbf9f588c",
     "grade": false,
     "grade_id": "cell-fe45e7ce127db0d4",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "source": [
    "## Scheduling dependent tasks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "id": "cIxy_GYJkacM",
    "nbgrader": {
     "checksum": "13ab8e8184a5ce432629a168358998e9",
     "grade": false,
     "grade_id": "cell-ac839becba0a561a",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "source": [
    "We first disregard the problem of cooking (or compiling) time, and ask about the order in which we should be doing the tasks.  We want to create a _Scheduler_ object, that can tell us what to do at the same time.  What operations should this object support? 
",
    "
",
    "* **add_task:** we should be able to add a task, along with the task dependencies. 
",
    "* **reset:** indicating that we are about to run the sequences of tasks again.
",
    "* **available_tasks:** this property should return the set of things that we can do in parallel. 
",
    "* **mark_completed:** used to notify the scheduler that we have completed a task.  This should return the set of new tasks that we can do due to this task being completed; we can do these tasks in parallel alongside with the others that we are already doing. 
",
    "* **all_done:** returns True/False according to whether we have completed all tasks. 
",
    "
",
    "Choosing these operations is perhaps the most important step in the design of the scheduler.  The operations need to have a simple, clear definition, and be useful in a concrete implementation of the service which will run the tasks.  Of the above operations, they are all uncontroversial, except for the choice of behavior of _completed_.  In theory, there is no need for _completed_ to return the set of _new_ tasks that can now be undertaken.  If one remembers the set of tasks $T_1$ one can a do before a task $t \in T_1$ is completed, and marks $t$ as completed, one can simply ask the scheduler for the set of tasks $T_2$ that can now be done, and add those in $T_{21t} = T_2 \setminus (\{t\} \cup T_1)$ for execution.  However, we guess (as we have not yet written the task execution engine) that being told this set of tasks directly will simplify the design of the task execution engine. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "id": "yk-RsN-TkacM",
    "nbgrader": {
     "checksum": "79aecd3e08b0a5afe9f42889882e0bda",
     "grade": false,
     "grade_id": "cell-307dbeebce53643f",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "source": [
    "Our scheduler class will be implemented in similar fashion to our graph class, with tasks corresponding to graph vertices, and dependencies represented as edges.
",
    "The difference is that here, given a vertex (that is, a task) $v$, it will be useful to be able to access both:
",
    "
",
    "* the _predecessors_ of $v$, that is, the tasks $u$ that are declared as prerequisites of $v$, and 
",
    "* the _successors_ of $v$, that is, the tasks $u$ such that $v$ was declared as a prerequisite for $u$. 
",
    "
",
    "When we add a task, we would have to initialize its set of successors and predecessors to empty.  This is somewhat tedious, and so we resort to a defaultdict, which is a special type of dictionary such that, if the mapping for a key has not been defined, it returns a default value; in our case, an empty set.  [You can read more about defaultdict and related types here](https://docs.python.org/3.7/library/collections.html#collections.defaultdict). 
",
    "
",
    "Our first implementation of the class is as follows.  We let you complete the `available_tasks` and `mark_completed` methods. 
"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "deletable": false,
    "editable": false,
    "id": "_yCelWBbkacM",
    "nbgrader": {
     "checksum": "a3122da716c36329d70b6398881d9f6c",
     "grade": false,
     "grade_id": "cell-c1f62e6e5511e278",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "outputs": [],
   "source": [
    "from collections import defaultdict
",
    "import networkx as nx # Library for displaying graphs.
",
    "import matplotlib.pyplot as plt
",
    "
",
    "class DependencyScheduler(object):
",
    "
",
    "    def __init__(self):
",
    "        self.tasks = set()
",
    "        # The successors of a task are the tasks that depend on it, and can
",
    "        # only be done once the task is completed.
",
    "        self.successors = defaultdict(set)
",
    "        # The predecessors of a task have to be done before the task.
",
    "        self.predecessors = defaultdict(set)
",
    "        self.completed_tasks = set() # completed tasks
",
    "
",
    "    def add_task(self, t, dependencies):
",
    "        """Adds a task t with given dependencies."""
",
    "        # Makes sure we know about all tasks mentioned.
",
    "        assert t not in self.tasks or len(self.predecessors[t]) == 0, "The task was already present."
",
    "        self.tasks.add(t)
",
    "        self.tasks.update(dependencies)
",
    "        # The predecessors are the tasks that need to be done before.
",
    "        self.predecessors[t] = set(dependencies)
",
    "        # The new task is a successor of its dependencies.
",
    "        for u in dependencies:
",
    "            self.successors[u].add(t)
",
    "
",
    "    def reset(self):
",
    "        self.completed_tasks = set()
",
    "
",
    "    @property
",
    "    def done(self):
",
    "        return self.completed_tasks == self.tasks
",
    "
",
    "
",
    "    def show(self):
",
    "        """We use the nx graph to display the graph."""
",
    "        g = nx.DiGraph()
",
    "        g.add_nodes_from(self.tasks)
",
    "        g.add_edges_from([(u, v) for u in self.tasks for v in self.successors[u]])
",
    "        node_colors = ''.join([('g' if v in self.completed_tasks else 'r')
",
    "                           for v in self.tasks])
",
    "        nx.draw(g, with_labels=True, node_color=node_colors)
",
    "        plt.show()
",
    "
",
    "    @property
",
    "    def uncompleted(self):
",
    "        """Returns the tasks that have not been completed.
",
    "        This is a property, so you can say scheduler.uncompleted rather than
",
    "        scheduler.uncompleted()"""
",
    "        return self.tasks - self.completed_tasks
",
    "
",
    "    def _check(self):
",
    "        """We check that if t is a successor of u, then u is a predecessor
",
    "        of t."""
",
    "        for u in self.tasks:
",
    "            for t in self.successors[u]:
",
    "                assert u in self.predecessors[t]
"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "id": "WxGmfJPCkacM",
    "nbgrader": {
     "checksum": "f7194367628d4362f83e04121a68a6e4",
     "grade": false,
     "grade_id": "cell-1d75cf1333aa8c76",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "source": [
    "## Question 1: implement `available_tasks` and `mark_completed`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "deletable": false,
    "id": "W3lz1UGRkacM",
    "nbgrader": {
     "checksum": "f830262ac9c7f13eb8f07e85e0e1ac11",
     "grade": false,
     "grade_id": "cell-5c9e5f503f616888",
     "locked": false,
     "schema_version": 1,
     "solution": true
    }
   },
   "outputs": [],
   "source": [
    "### Implementation of `available_tasks` and `mark_completed`.
",
    "
",
    "def scheduler_available_tasks(self):
",
    "    """Returns the set of tasks that can be done in parallel.
",
    "    A task can be done if all its predecessors have been completed.
",
    "    And of course, we don't return any task that has already been
",
    "    completed."""
",
    "    # YOUR CODE HERE
",
    "    return ({t for t in self.tasks 
",
    "                 if self.predecessors[t].issubset(self.completed_tasks)}
",
    "                - self.completed_tasks)
",
    "
",
    "def scheduler_mark_completed(self, t):
",
    "    """Marks the task t as completed, and returns the additional
",
    "    set of tasks that can be done (and that could not be
",
    "    previously done) once t is completed."""
",
    "    # YOUR CODE HERE
",
    "    for p in self.predecessors[t]:
",
    "        if p in self.uncompleted:
",
    "            raise IllegalCompletion(t)
",
    "            
",
    "    self.completed_tasks.add(t)
",
    "    return {u for u in self.successors[t] 
",
    "            if self.predecessors[u].issubset(self.completed_tasks)}
",
    "
",
    "DependencyScheduler.available_tasks = property(scheduler_available_tasks)
",
    "DependencyScheduler.mark_completed = scheduler_mark_completed
"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "deletable": false,
    "id": "RnTGjkVhkacM",
    "nbgrader": {
     "checksum": "9cbfd3fceef4fc700ee6fd8765c94e66",
     "grade": false,
     "grade_id": "cell-ab6c2cd521650cd4",
     "locked": false,
     "schema_version": 1,
     "solution": true
    }
   },
   "outputs": [],
   "source": [
    "# Here is a place where you can test your code. 
",
    "
",
    "# YOUR CODE HERE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "id": "fbDAHS7IkacM",
    "nbgrader": {
     "checksum": "9abcbb7a57e188e7f19741fdef160c7f",
     "grade": false,
     "grade_id": "cell-3e19369eeb9a643d",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "source": [
    "Let us check if this works."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "deletable": false,
    "editable": false,
    "id": "5OGt-_BgkacM",
    "nbgrader": {
     "checksum": "34c28ee0d4c755735562b962526341d2",
     "grade": false,
     "grade_id": "cell-92b59a47fe7f0336",
     "locked": true,
     "schema_version": 1,
     "solution": false
    },
    "outputId": "ac6123c0-6313-4ebd-c8e5-4a85b59bc0a6"
   },
   "outputs": [],
   "source": [
    "# Let us ensure that nose is installed.
",
    "try:
",
    "    from nose.tools import assert_equal, assert_true
",
    "    from nose.tools import assert_false, assert_almost_equal
",
    "except:
",
    "    !pip install nose
",
    "    from nose.tools import assert_equal, assert_true
",
    "    from nose.tools import assert_false, assert_almost_equal
"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 353
    },
    "deletable": false,
    "editable": false,
    "id": "6vuF6du9kacM",
    "nbgrader": {
     "checksum": "af201e981d4b861bf954b7e2384b3e9a",
     "grade": false,
     "grade_id": "cell-d9857dde3e277260",
     "locked": true,
     "schema_version": 1,
     "solution": false
    },
    "outputId": "1f85633c-afa2-40fb-e026-17c3729ff9d6"
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAd0AAAE/CAYAAAADsRnnAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAIABJREFUeJzt3Xl0lPXd9/FPyAJkY98JJIAsoWJFqiwlYQnBQGbwRrmlhQcLR8Fzq1BRyqO0j8V6rJweit51Qcstp2oFSqU6E8KSGwiJgEVwQwRZyhIKsoUlK1lmnj/GGQNMQpaZ65pJ3q9zPEFyJflwPIePv+v6/b5XiNPpdAoAAPhdM7MDAADQVFC6AAAYhNIFAMAglC4AAAahdAEAMAilCwCAQShdAAAMQukCAGAQShcAAINQugAAGITSBQDAIJQuAAAGoXQBADAIpQsAgEEoXQAADELpAgBgEEoXAACDULoAABiE0gUAwCCULgAABqF0AQAwSJjZAYJCQYG0a5eUny81aya1by+NGCE1b252MgBAEKF0a/L119KyZdKqVVJ4uOR0SiEhrs85ndLs2dLjj0vx8abGBAAEhxCn0+k0O0TAqaiQHn5Y+tvfpLIyqbLS+3UREa6V74IF0uLFPxQyAABeULo3qqiQ0tOl3FypuLh2XxMZKU2fLi1fTvECAKrFRqobPfFE3QpXcl373nvSH//ov1wAgKDHSreq06elXr2ka9fq9/XR0dK5c1LLlr7NBQBoFFjpVvXGGw3/HmvXNvx7AAAaJVa6bhUVUocO0uXLXj99WtITknIkRUt6UtJcbxcOGCB9842/UgIAghgrXbdjx6Tycq+fckiySLpD0r8lbZH0sqRN3i4+eLDa7wMAaNooXbfLl6Uw78eWP5V0XtL/kxQhqZekRySt9nZxRIR05YqfQgIAghnDMdwiIlwDL7w4Idft5dZVfq9S0khvFzscru8FAMANKF23zp2r3bUcJylB0uHafJ+QECkmxofBAACNBbeX3Tp1kgYN8vqpuyXFSloiqUSuVe7Xct12vk6zZtIDDzAgAwDgFaVb1cKFXlepoZLskr6Qa8XbXtLDkm56ctuihfTUU34OCQAIVhwZqqq83HWbOT+/7l8bEiIlJrpekgAAgBesdKsKD5fWravfRKnoaAZjAABqROneKDnZ9Sq/yMjaXR8SIsXGSps2uQZjAABQDUrXm0mTpK1bpcGDXave0NCbr4mIcD3DTU6WPv1UGjbM+JwAgKDCM91b2b/f9SL7jAypsFAOSWdLStRl/nzpscd4gT0AoNYo3TpyOByKjY3V6dOnFRsba3YcAEAQ4fZyHTVr1kz9+/fXN7zUAABQR5RuPQwcOJDSBQDUGaVbD4mJidq/f7/ZMQAAQYbSrYfExERWugCAOqN062HgwIGsdAEAdcbu5XpwOByKiYnRmTNn2MEMAKg1Vrr14N7BfPDgQbOjAACCCKVbT2ymAgDUFaVbTxwbAgDUFaVbT6x0AQB1RenWEytdAEBdsXu5niorKxUTE6Nz584pOjra7DgAgCDASreeQkND1a9fPx04cMDsKACAIEHpNgBDMgAAdUHpNgDjIAEAdUHpNgArXQBAXVC6DcBKFwBQF+xebgD3Dubz588rKirK7DgAgADHSrcBQkND1bdvX3YwAwBqhdJtIG4xAwBqi9JtIDZTAQBqi9JtIFa6AIDaonQbiJUuAKC22L3cQBUVFYqJidGFCxfYwQwAqBEr3QYKCwtT3759dfDgQbOjAAACHKXrAzzXBQDUBqXrAzzXBQDUBqXrA6x0AQC1Qen6AKULAKgNdi/7gHsH88WLFxUZGWl2HABAgGKl6wNhYWHq06ePvv32W7OjAAACGKXrI2ymAgDcCqXrIzzXBQDcCqXrI6x0AQC3Qun6CCtdAMCtsHvZR8rLyxUbG6v8/Hy1bNnS7DgAgADEStdHwsPD1bt3b3YwAwCqRen6ELeYAQA1oXR9iM1UAICaULo+xEoXAFATSteHWOkCAGrC7mUfKisrU6tWrXTp0iW1aNHC7DgAgADDSteHIiIi1KtXL3YwAwC8onR9jOe6AIDqULo+lpiYyHNdAIBXlK6PDRw4kJUuAMArStfHWOkCAKrD7mUfKysrU2xsrK5cuaLmzZubHQcAEEBY6fpYRESEEhISdOjQIbOjAAACDKXrBwzJAAB4Q+n6AceGAADeULp+wGYqAIA3lK4fcGwIAOANu5f94Nq1a2rVqhU7mAEA12Gl6wfNmzdXfHy8Dh8+bHYUAEAAoXT9hOe6AIAbUbp+wnNdAMCNKF0/4dgQAOBGlK6fMCADAHAjdi/7SWlpqVq3bq2rV68qIiLC7DgAgADAStdPWrRooR49erCDGQDgQen6EZupAABVUbp+xLEhAEBVlK4fsdIFAFRF6foRx4YAAFWxe9mPSkpK1LZtW129elXh4eFmxwEAmIyVrh+1bNlS3bt315EjR8yOAgAIAJSunzEkAwDgRun6Gc91AQBulK6fcWwIAOBG6foZx4YAAG7sXvaz4uJitWvXTgUFBQoLCzM7DgDARKx0/SwyMlLdunVjBzMAgNI1ApupAAASpWsIjg0BACRK1xCsdAEAEqVrCFa6AACJ3cuGYAczAEBipWuIyMhIdenSRf/617/MjgIAMBGlaxBuMQMAKF2DsJkKAEDpGoSVLgCA0jUIK10AALuXDVJUVKQOHTqooKBAoaGhZscBAJiAla5BoqKi1KlTJ3YwA0ATRukaiFvMANC0UboGYjMVADRtlK6BWOkCQNNG6RqIlS4ANG3sXjZQYWGhOnbsyA5mAGiimL5voOjoaHXs2FHHjh1Tnz59zI4DND4XLkgrV0q7dkn5+VJ0tNS3r/Tww1JiotnpAErXaO7nupQu4ENffSX97neS3S41ayaVlPzwuU2bpOXLXaW7aJF0331SSIh5WdGk8UzXYAMHDmQzFeBLa9dKw4ZJ69ZJ165dX7iSVFHh+r29e6X/83+kRx+VKivNyYomj9I1WGJiIpupAF+x2aSHHpKKiyWH49bXFxVJ770nzZ4tsZ0FJqB0DcaxIcBH8vKkn/3s5pXtrRQXS6tXS3/9q39yATWgdA2WmJiogwcPylGb/ysHUL1XX3XdOq6P4mJp8WJWuzAcpWuwmJgYtWvXTsePHzc7ChC8yspcm6PKyrx+Ok/SZEkdJLWT9Li3i86ckXbv9ltEwBtK1wQMyQAaKCOj2lVqpaR0ST0lHZf0b0lTvV1YUiL993/7KSDgHaVrAp7rAg109Gi1z3J3Szot6Q+SoiS1kPRTbxc6HNKBA/5KCHhF6ZqAY0NAAxUUyFnN89w8uVa5tRpCUFjow1DArTEcwwSJiYl6/fXXzY4BBLTy8nKdOnVKx44d0/Hjx3Xs2DHPr1P37dNCSc29fF2cpJOSKlSLv+BiY32cGqgZpWuCqjuYmzXjZgOapsrKSp0+ffq6Uq368cyZM+rcubPi4+OVkJCg+Ph4jRs3TvHx8Rpw7Jgi5s6VCgpu+r53S+oi6f9KWiwpVNJeSSNuvDA0VPrxj/38pwSuxwsPTBIXF6ecnBwlJCSYHQXwC4fDobNnz1Zbqnl5eWrfvv11pVr1Y/fu3RUREeH9m1dWSp06SRcvev30SUlzJeVKCpH0c0k3bZmKjHTNaB40yFd/ZOCWWOmaxL2ZitJFsHI6nbpw4UK1pXrixAnFxMQoISHBU6ZDhgzRAw88oISEBPXo0UMtWrSo3w8PDZXmzZNefFEqLb3p0z0kfXir73HbbRQuDMdK1yTz589Xly5dtGDBArOjANW6dOnSTYXq/vXx48cVERHhdZUaHx+v+Ph4RUVF+S/cuXNSnz5ebzHfUmSk9P770qRJvs8F1ICVrkkSExP18ccfmx0DTVxBQYHXVar7o8PhuK5Me/furZSUFE+ptmrVyrzwHTu6zuumpbkmTNVSsaTSGTPUlsKFCShdkwwcOFBvvfWW2THQyBUXF+vEiRPVlmpJSYmnQN3l+tOf/tTz723atFFIIL8GLynJVbxWq2s6VTUTqiS5XufXsqUOpKTo/sxM5eblKS4uzrisgLi9bJrLly8rLi5OV65cYQcz6u3atWs6efLkTbd+3R8vX76sHj16XPdcterHDh06BHap1tbJk9LLL0srVrgmVVU9f+t+bjxunLRwoTRihJYtW6bly5crJydHnTp1MiczmiRK10Tdu3fXjh071LNnT7OjIEBVVFQoLy+v2lvA58+fV7du3ap9rtqlS5em9T91paXS3/8uffaZdP686xxur17StGlS587XXbp48WKtW7dO2dnZatOmjUmB0dRQuiZKTU3VL3/5S02YMMHsKDCJ+6xqdaV65swZderUqdpS7datm8LCeEpUH06nU08//bR27NihrKwsxcTEmB0JTQCla6Inn3xS3bp109NPP212FPiJ0+nUd999V22p5uXlqV27dtWWalxcXPVnVdFgTqdTs2fP1tGjR5WZmVn/I0xALVG6Jvrzn/+sXbt26e233zY7CurJ6XTq4sWLXo/UHDt2zHNWtboBED179uQvepNVVlZq+vTpKiws1Lp16xQeHm52JDRilK4JnE6nsrOz9cEHH2jVqlXq2LGjbr/9dv3tb38zOxq8uHz5crW7f48fP66wsLBqNyr17NlT0dHRZv8RcAvl5eW6//77FRUVpffee0+hoaFmR0IjRemaoLS0VO3bt1dpaakqKysVEhKi2bNna/ny5WZHa5IKCwurLdVjx46psrKy2lI1/awqfKa0tFQTJ05Ur1699NZbbzWOXd0IOJSuSdasWaNZs2apuLhY0dHR+uijjzRmzBizYzVKJSUlnlWpt1ItLi6u9vZvfHy82rZty1/ATURhYaHGjRunYcOGaenSpfx3h89RuiaaPn263n//fUVERKigoIBnSfV041nVGz9WPavqrVQ7duzIX67wuHTpkkaNGqXJkyfrueeeMzsOGhlK10RFRUWeV5ft27fP7DgBi7OqMNrZs2eVlJSkOXPmaP78+WbHQSNC6Zrp6lWdePFFRR44oA7NmkkxMVL//tKMGVL37manMwxnVRGI8vLyNHLkSC1atEiPPPKI2XHQSFC6Zjh4UFqyRFqzRmrWTCoq+uFzzZu7ZsQmJ0vPPOP6GOScTqfnvaqcVUUwOXLkiJKTk7V06VJNnTrV7DhoBChdo334oWskXVmZVFFR87WRkdKCBdJzz7mKOEB5O6t643tVo6OjOauKoPT1118rJSVFf/7zn2WxWMyOgyBH6RrJZpOmTpVKSjy/FS9phaSU6r4mMlKaP1/63e/8n68GtzqrGh4eXmOpclYVwezTTz/VxIkTtXr1ak4ZoEEoXaMcPy796EfX30pWLUpXchXv2rWSH2c0c1YVqNn27ds1ZcoU2Ww2DR061Ow4CFKUrlGefFJ6/fWb3vcZr1qUriT95CfS7t2ef62oqNDSpUs1cOBApaen3/LHc1YVaLgNGzboF7/4hTZv3qw77rjD7DgIQpSuEUpLpQ4drn/H5/fiJc2R9K6kM5Luk/SGpJuecLZsKe3dKw0YoAMHDmjKlCk6ePCgHnjgAa1evZqzqoBB1q5dq3nz5ik7O1t9+/Y1Ow6CDKVrhNWrpdmzpYKCmz4VLyla0gZJUZIskkZLeuHGC8PC5JwzR4+Wl2vlypUqLy+XJEVFRal169acVQUMtHLlSv32t79VTk4O78NGnXC40Qjffut1lev2uKS473+9SNIT8lK6FRUq3b1bf/nqKzkcDoWEhMjpdCo8PFw7duzgrCpgoJkzZ6qgoEApKSnKzc1V586dzY6EIMHSxwiXLkk13FCIq/LrnpJOV3Ndy7IylZSUKCcnR7NmzVJMTIyKiorUo0cPChcw2Ny5c/WLX/xC48aNU35+vtlxECQoXSO0bl3jOdu8Kr8+KalrdRfGxiokJETDhw/XihUrlJ+fr6+++opnsYBJnn32WU2YMEH33nuvCrw8PgJuROkaoW9fqYZzqq9JOiUpX9KLkh70ck1FSIiORkaqsMpt6rCwMPXv39/HYQHUVkhIiF566SXdddddslgsKqlyBh/whtI1wn/8R423l38uKVVSr+//+bWXa5xhYfr91avq2rWr0tLS9MYbb+jUqVP+yQug1kJCQvTaa6+pe/fueuCBB1R2w7FAoCp2Lxtl3jzpjTek73cd19ldd0l79ujq1avatGmTbDabMjMz1bNnT1ksFlmtVg0ePJhbzYBJysvLNWXKFEVERGjVqlUKDQ01OxICEKVrlGPHXBOpiovr/rWRka6XI9wwBKOiokI7d+6UzWaTzWZTcXGx0tPTZbVaNWbMGOYZAwYrLS2VxWJRXFycVqxYwTE93ITSNdI//uF62UFdnvtERkpPPCG99NItL/32229lt9tls9n05ZdfasyYMbJYLJo4caI6derUgOAAaquoqEipqakaMmSIXn75Ze4+4TqUrtH+/nfpoYeka9ekysqar3W/7OD55+v8lqGLFy8qMzNTNptNWVlZGjBggKxWq6xWqxITE/mLAPCjy5cva/To0bJYLHr++efNjoMAQumaYf9+6fe/lz74wPU+3aq3nCMiXL83YoT07LOSD95ocu3aNW3fvt2zCg4NDZXVapXFYlFSUpLCw8Mb/DMAXO/8+fNKSkrSrFmztGDBArPjIEBQuma6fFl65x1pzx4pP1+KiZH69ZNmzpT8NFrO6XRq3759stlsstvtOnTokMaPHy+r1aq0tDS1adPGLz8XaIpOnTqlpKQkLVy4UHPmzDE7DgIApdvEnTlzRhkZGbLb7crOztZdd93lWQX36dPH7HhA0Dt69KiSk5O1ZMkSTZs2zew4MBmlC4/i4mJt2bJFNptNGRkZatOmjec40tChQzkCAdTT/v37NXbsWL355puaNGmS2XFgIkoXXjkcDu3Zs8dzG/r06dOaOHGiLBaLUlNTFRMTY3ZEIKjs3btXaWlpev/995WScss3aKORonRRKydOnPBsxPrkk080YsQIWSwWz5lEALeWm5ur+++/Xx9++KGGDx9udhyYgNJFnbmnYtntdmVmZiouLs7zHHjw4MEMBABqsGnTJs2YMUMbN27UnXfeaXYcGIzSRYO4p2K5V8GFhYXXTcVq2bKl2RGBgLNu3To99thj2rp1qwYMGGB2HBiI0oVPuadi2e12ffHFFxo9erSsVitTsYAbvPPOO1q0aJFycnKUkJBgdhwYhNKF31y8eFEbNmyQzWbT5s2bNWDAAM9u6IEDBzIVC03ea6+9pj/+8Y/Kzc1V167VvkkbjQilC0OUlZVp+/btnt3QzZo18xTwyJEjFRERYXZEwBQvvfSS3n33XW3fvl3t27c3Ow78jNKF4dxTsdzPgd1TsSwWi9LS0tS2bVuzIwKGevbZZ7V582Zt2bJFrVq1MjsO/IjShenOnDmj9evXy2azKTs7W4MHD/bshr7tttvMjgf4ndPp1BNPPKEvv/xSmzZtUmRkpNmR4CeULgKKeyqWezNW69atPQU8bNgwpmKh0XI4HJo5c6a+++472Ww2NW/e3OxI8ANKFwHL4XBo7969nufA//73vzVhwgRZrVamYqFRqqio0IMPPihJWrNmjcLCwkxOBF+jdBE03FOx7Ha7du7cqREjRnhWwUzFQmNx7do1TZo0SZ06ddLKlSsZNtPIULoISlevXtXmzZtls9k8U7Hcu6GZioVgV1xcrHvvvVeDBg3Sn/70J47XNSKULoJeRUWFdu3a5dkNffXqVc9c6LFjxzIVC0HpypUrGjt2rFJTU/Xiiy+aHQc+Qumi0Tl06JCngD///HONGTNGFotF6enpTMVCULlw4YKSk5M1ffp0PfPMM2bHgQ9QumjU3FOx7Ha7Nm3apP79+8tqtTIVC0Hj9OnTSkpK0pNPPqnHHnvM7DhoIEoXTUZZWZlycnJks9lks9kUEhLi2YiVlJTEVCwErGPHjikpKUkvvPCCHnroIbPjoAEoXTRJTqdTX3/9tec40rfffqvU1FRZrVamYiEgHThwQGPGjNGrr76q+++/3+w4qCdKF5D03XffeaZibdu2jalYCEiff/65xo8fr3fffVfjx483Ow7qgdIFblBSUqItW7bIZrMpIyNDrVq18hxHYioWzLZjxw7dd999WrdunUaOHGl2HNQRpQvUwD0Vy70bmqlYCARZWVmaNm2aMjMzNWTIELPjoA4oXaAOTpw4oYyMDNlsNu3atUvDhw/3nAnu0aOH2fHQhHz44Yd69NFHtWXLFg0cONDsOKglSheoJ/dULLvdrvXr16t79+6e58B33XUXU7Hgd3/961+1cOFCbd++Xb179zY7DmqB0gV8oLKyUrt27fLshr5y5QpTsWCI5cuXa8mSJcrNzVX37t3NjoNboHQBP3BPxbLb7frss880evRoWa1WTZw4UZ07dzY7HhqZP/zhD3r77be1fft2dezY0ew4qAGlC/hZfn6+NmzYIJvNps2bN6tfv36e3dA/+tGPmIoFn/jNb36jjIwMbdu2Ta1btzY7DqpB6QIGck/Fcu+GluQpYKZioSGcTqd++ctfas+ePdq8ebOioqLMjgQvKF3AJO6pWO4Cdk/FslgsmjBhAlOxUGcOh0MPP/yw8vLyZLfb1aJFC7Mj4QaULhAg3FOx7Ha7tm7dqsGDB3tWwUzFQm1VVlbqZz/7mcrKyrR27VqFh4ebHQlVULpAACopKdHWrVs9u6FjY2M9x5GGDRumsLAwsyMigJWVlem+++5T27Zt9c4773B8LYBQukCAczgc+uyzzzwFnJeX55mKNX78eKZiwauSkhKlpaVpwIABev3119mwFyAoXSDInDx50jMVa+fOnRo2bJhnFcxULFR19epVpaSkaNSoUVqyZAnFGwAoXSCIFRQUaPPmzbLZbMrMzFS3bt08z4GZigVJunjxokaNGqWpU6dq0aJFZsdp8ihdoJFwT8Vy74a+cuWK0tPTZbVamYrVxJ05c0ZJSUl64oknNHfuXLPjNGmULtBIHT582DMVa+/evRo9erQsFovS09OZitUEnThxQklJSfrtb3+rmTNnmh2nyaJ0gSbAPRXLbrdr06ZN6tu3r+c58O23386zvibi0KFDGjVqlF555RVNmTLF7DhNEqULNDFlZWXKzc2VzWaTzWaT0+n0FHBycjJTsRq5L7/8UqmpqVq5cqUmTJhgdpwmh9IFmjCn06n9+/d7jiMdOHBAqampslqtSktLU7t27cyOCD/45JNPZLVatXbtWiUnJ5sdp0mhdAF4nD17VhkZGbLb7dq2bZt+/OMfe3ZD9+3b1+x48KGtW7dq6tSpysjI0N133212nCaD0gXglXsqlnszVkxMjKeAmYrVONjtdj3yyCPKysrS7bffbnacJoHSBXBL7qlY7uNI7qlYFotF48ePV2xsrNkRUU+rV6/WU089pezsbGZ8G4DSBVBn7qlYdrtdO3bs0NChQz2bsXr27Gl2PNTRihUr9MILLyg3N1dxcXFmx2nUKF0ADVJQUKCsrCzZbDatX79eXbt29RTwkCFDmIoVJJYtW6bly5crJydHnTp1MjtOo0XpAvCZyspKffLJJ57d0JcuXbpuKlZkZKTZEVGDxYsXa926dcrOzlabNm3MjtMoUboA/ObIkSOe58B79+7VqFGjZLVamYoVoJxOp5566int3LlTWVlZvMHKDyhdAIa4dOmSNmzYIJvN5pmK5d4NzVSswOF0OjV79mwdPXpUmZmZatGihdmRGhVKF4Dh3FOx3KvgyspKWa1WWa1WpmIFgMrKSk2bNk1FRUVat26dwsPDzY7UaFC6AEzldDr1zTffeJ4Df/PNN0pNTZXFYtGECROYimWS8vJyTZ48WdHR0XrvvfcUGhpqdqRGgdIFEFDOnj2r9evXy263a+vWrbrjjjs8u6H79etndrwmpaSkRBMnTlTv3r311ltv8QjAByhdAAGrpKRE27Zt86yCo6OjPQU8fPhwpmIZoKCgQOPGjdPw4cO1dOlSireBKF0AQcHpdF43FevkyZNKS0uT1WplKpaf5efna/To0Zo8ebKee+45s+MENUoXQFDKy8tTRkaGbDabZyqWxWKRxWJRfHy82fEanbNnz2rkyJF69NFHNX/+fLPjBC1KF0DQc0/FstvtysjIUNeuXT3HkZiK5TsnT55UUlKSFi1apEceecTsOEGJ0gXQqFRWVuqf//ynbDabbDYbU7F87PDhwxo1apSWLl2qqVOnmh0n6FC6ABo191Qsu92uPXv2aNSoUbJYLEpPT1eXLl3MjheU9u3bp5SUFP3P//yP0tPTzY4TVChdAE3GpUuXtHHjRtlsNm3cuFG33XabZygHU7HqZvfu3UpPT9eaNWs0evRos+MEDUoXQJNUXl6u3Nxcz21o91Qsi8Wi5ORkNW/e3OyIAS87O1v/+Z//KZvNpqFDh5odJyhQugCaPPdULPdxpG+++Ubjxo2T1WplKtYtZGZmaubMmcrKytKgQYPMjhPwKF0AuMG5c+e0fv162Ww2pmLVwtq1azVv3jxlZ2erb9++ZscJaJQuANSgtLRUW7du9ayCo6OjPceRmIr1g7fffluLFy9WTk6OevbsaXacgEXpAkAtOZ1Off75556xlMePH9eECRNksVh07733NvmpWK+88opeffVV5ebm8r7kalC6AFBP7qlYdrtdH3/8MVOxJL3wwgtas2aNtm/frrZt25odJ+BQugDgA4WFhcrKypLNZtP69evVuXNnz3Pgn/zkJ01mKpbT6dTChQuVnZ2tLVu2KCYmxuxIAYXSBQAfqzoVy263Kz8/XxMnTpTValVKSkqjn4rldDr1X//1Xzpw4IA2bNigli1bmh0pYFC6AOBnR48e9WzE2rNnj5KTk2W1Whv1VCyHw6EZM2bo0qVL+sc//qGIiAizIwUEShcADFR1KtamTZvUp08fz27oQYMGNaqpWOXl5ZoyZYoiIiK0atUqhYaGmh3JdJQuAJjEPRXLvQquqKjwFHBjmYpVWloqi8WiuLg4rVixosk8264OpQsAAcDpdOrAgQOesZTuqVgWi0UTJkxQ+/btzY5Yb0VFRUpNTdWQIUP08ssvN6rVfF1RugAQgNxTsex2u7az58swAAAGpElEQVRs2aJBgwZ5Xs4QjFOxLl++rNGjR8tisej55583O45pKF0ACHClpaXatm2bZzd0ZGSk5zjSiBEjgmYq1vnz55WUlKRZs2ZpwYIFZscxBaULAEHE21SstLQ0Wa1WjR8/Xq1atTI7Yo1OnTqlpKQkLVy4UHPmzDE7juEoXQAIYqdOnVJGRoZsNps+/vhj3XPPPZ5VcKBOxTp69KiSk5O1ZMkSTZs2zew4hqJ0AaCRcE/FstvtysjIUOfOnT27oQNtKtb+/fs1duxYvfnmm5o0aZLZcQxD6QJAI+SeiuU+jnTx4kWlp6fLYrEoJSVFUVFRZkfU3r17lZaWpvfff18pKSlmxzEEpQsATYB7Kpbdbtenn36q5ORkWSwWpaenq2vXrqblys3N1eTJk/XRRx9p+PDhpuUwCqULAE3M5cuXPVOxNm7cqN69e3ueA99xxx2Gn6PduHGjZsyYoU2bNunOO+809GcbjdIFgCasvLxcH3/8sWcoR3l5uec58KhRowybivXBBx/o8ccf19atWzVgwABDfqYZKF0AgKQfpmK5nwPv379fKSkpslqthkzF+stf/qJf//rXysnJUUJCgpxOp5xOZ0BtAGsoShcA4NW5c+eUmZkpm83mmYrlXgX369fPL7ehX331VS1btkwfffSRpk6dqgcffFC/+c1vfP5zzELpAgBuyT0Vy70KbtmypWcspa+nYj3zzDNaunSpHA6HEhISdPjwYZ99b7NRugCAOnE6nfriiy88z4HdU7EsFovuvffeBk3FOnXqlO655x599913cjgcat68uY4cOaLu3bv/cFFxsfTPf0oXL0rNmknt2klDh0pB8FYmShcA0CDuqVh2u125ubm65557ZLFYZLFYlJCQUKfv9cknn+i+++5TUVGRCgsLFRISomXLlmnevHnSoUPSyy9L77wj3fhuXqdTmjNHevxxqWdPH/7pfIvSBQD4TGFhof73f/9XNptN69evV8eOHT3Hke6+++5abYpyOp3asWOH3nzzTa1atUo9u3fX0YkTpZUrpYoKqbzc+xdGRLhWvk8/LT3/vBSArxCkdAEAflFZWandu3d7Xs5w4cKFaqdiFRYW6vXXX9eTTz6p8PBwz+8XFRTIOXmyonfudN1Wro3ISOnnP5feeivgipfSBQAY4l//+pdnI9ann36qpKQkWa1WpaenKzs7W9OnT9fYsWNlt9vVokUL1xc99ZS0fHntC9ctMlJ67jnpV7/y/R+kAShdAIDh3FOx7Ha7NmzYIIfDoStXrqh58+a68847lZWVpeiSEikuTrp2rX4/JCpKOnfOVcABgtIFAJiquLhYbdu21bUq5dq+fXudmTtXYb//vVRSUr9vHB0tvfKKNGuWj5I2XOMZ8wEACEr79u3TtWvXFBYWpm7dumnEiBFK/ulPFfqnP1VbuC9J6i0pRlKipH94u6iwUHrpJb/lrg9WugAAU5WXl+vIkSPq1avXD7Oe8/Kk/v2rfZa7VtIISZ2///UsSUckdbnxwtBQqaBAatnST+nrhpUuAMBU4eHhGjBgwPUvV7h8WaphytUUSV3lKrEHJd0mabe3CyMipEuXfBm3QShdAEDgCQ93DbyoxjuSfiyp9ff/fC3pgrcLHQ5X8QYI3w3LBADAVzp2rHbX8glJj0jaImmYpFC5CthrRTscUuvWfgpZd6x0AQCBp21bacgQr58qkhQiqcP3/75SrpXuTUJCpPT0Gm9TG43SBQAEpl/9SoqJuem3EyU9Jdcqt5OkfXJtqrpJZKRrJGQAYfcyACAwVVRIXbpIF7w+rb213r2lw4cDahQkK10AQGAKC5PWravfRKmoKOmDDwKqcCVKFwAQyEaOlFatqlvxRkdL69dLd9zhv1z1ROkCAAKb1Spt2ybdeadryMWN79KVXEeMWrRwvcx+1y4pOdn4nLXAM10AQPD4+mtp2TLJbndNmgoJkWJjpSlTpLlzpdtuMzthjShdAAAMwu1lAAAMQukCAGAQShcAAINQugAAGITSBQDAIJQuAAAGoXQBADAIpQsAgEEoXQAADELpAgBgEEoXAACDULoAABiE0gUAwCCULgAABqF0AQAwCKULAIBBKF0AAAxC6QIAYBBKFwAAg1C6AAAYhNIFAMAglC4AAAahdAEAMAilCwCAQShdAAAMQukCAGAQShcAAINQugAAGITSBQDAIJQuAAAGoXQBADAIpQsAgEEoXQAADELpAgBgEEoXAACDULoAABiE0gUAwCCULgAABqF0AQAwyP8HHrPAJ2O9v5cAAAAASUVORK5CYII=
",
      "text/plain": [
       ""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from nose.tools import assert_true, assert_false, assert_equal
",
    "
",
    "s = DependencyScheduler()
",
    "s.add_task('a', ['b', 'c'])
",
    "s.add_task('b', ['c', 'e'])
",
    "s._check()
",
    "s.show()
"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "id": "giXGuSh2kacM",
    "nbgrader": {
     "checksum": "b6d6104b2ca8bd21eb6fc98c96471d59",
     "grade": false,
     "grade_id": "cell-236e1fcb5f373d17",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "source": [
    "We note that in the above drawing, the edges denote temporal succession, that is, an edge from $c$ to $a$ means that $c$ must happen before $a$. 
",
    "Let us execute the schedule manually."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "id": "aMadMw9OkacM",
    "nbgrader": {
     "checksum": "ff1dbebea8f5468f341e70ee112d2541",
     "grade": false,
     "grade_id": "cell-a448f628a33f1655",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "source": [
    "Here are some tests for `available_tasks` and `mark_completed`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "deletable": false,
    "editable": false,
    "id": "DmB_OitvkacM",
    "nbgrader": {
     "checksum": "87f4aef1da9401c8b1e14ea98c65e07b",
     "grade": true,
     "grade_id": "cell-97a947c37868d065",
     "locked": true,
     "points": 5,
     "schema_version": 1,
     "solution": false
    }
   },
   "outputs": [],
   "source": [
    "### Simple tests. 5 points. 
",
    "
",
    "s = DependencyScheduler()
",
    "s.add_task('a', [])
",
    "assert_equal(s.available_tasks, {'a'})
",
    "
"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "deletable": false,
    "editable": false,
    "id": "63Y9m0-ekacM",
    "nbgrader": {
     "checksum": "1c260d93e494b94353ee43c9986a912b",
     "grade": true,
     "grade_id": "cell-e63cebb9c5161e17",
     "locked": true,
     "points": 10,
     "schema_version": 1,
     "solution": false
    }
   },
   "outputs": [],
   "source": [
    "### Slightly more complicated. 10 points. 
",
    "
",
    "s = DependencyScheduler()
",
    "s.add_task('a', ['b', 'c'])
",
    "s.add_task('b', ['c', 'e'])
",
    "assert_equal(s.available_tasks, {'e', 'c'})
",
    "
",
    "s = DependencyScheduler()
",
    "s.add_task('a', ['b'])
",
    "s.add_task('b', ['a'])
",
    "assert_equal(s.available_tasks, set())
",
    "
"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "deletable": false,
    "editable": false,
    "id": "_emz5xo7kacM",
    "nbgrader": {
     "checksum": "3d976d40ecb3e0aea04ab706c381b6a0",
     "grade": true,
     "grade_id": "cell-fdc5d30f849014af",
     "locked": true,
     "points": 5,
     "schema_version": 1,
     "solution": false
    }
   },
   "outputs": [],
   "source": [
    "### Now, let's test `mark_completed`.  Simple tests first. 5 points. 
",
    "
",
    "s = DependencyScheduler()
",
    "s.add_task('a', [])
",
    "assert_equal(s.available_tasks, {'a'})
",
    "r = s.mark_completed('a')
",
    "assert_equal(r, set())
",
    "
",
    "s = DependencyScheduler()
",
    "s.add_task('a', ['b'])
",
    "assert_equal(s.available_tasks, {'b'})
",
    "r = s.mark_completed('b')
",
    "assert_equal(r, {'a'})
",
    "
"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "deletable": false,
    "editable": false,
    "id": "iUfccczckacM",
    "nbgrader": {
     "checksum": "bc1219453bf2f8e01785141255fd7f1d",
     "grade": true,
     "grade_id": "cell-71c82e71e3d5c769",
     "locked": true,
     "points": 10,
     "schema_version": 1,
     "solution": false
    }
   },
   "outputs": [],
   "source": [
    "### Slightly more complicated. 10 points. 
",
    "
",
    "s = DependencyScheduler()
",
    "s.add_task('a', ['b', 'c'])
",
    "assert_equal(s.available_tasks, {'b', 'c'})
",
    "r = s.mark_completed('b')
",
    "assert_equal(r, set())
",
    "assert_equal(s.available_tasks, {'c'})
",
    "r = s.mark_completed('c')
",
    "assert_equal(r, {'a'})
",
    "
",
    "s = DependencyScheduler()
",
    "s.add_task('a', ['b', 'c'])
",
    "s.add_task('b', ['c', 'e'])
",
    "s.add_task('c', [])
",
    "assert_equal(s.available_tasks, {'c', 'e'})
",
    "r = s.mark_completed('e')
",
    "assert_equal(r, set())
",
    "r = s.mark_completed('c')
",
    "assert_equal(r, {'b'})
",
    "r = s.mark_completed('b')
",
    "assert_equal(r, {'a'})
",
    "r = s.mark_completed('a')
",
    "assert_equal(r, set())
",
    "assert_equal(s.available_tasks, set())
",
    "
"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "id": "4Si-0n65kacM",
    "nbgrader": {
     "checksum": "c0e617b3ab44b02e965cd7181c719847",
     "grade": false,
     "grade_id": "cell-1fdbf05f74676a81",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "source": [
    "## Executing the tasks
",
    "
",
    "Here is an execution engine for our tasks with dependencies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "deletable": false,
    "editable": false,
    "id": "03fwlwB-kacM",
    "nbgrader": {
     "checksum": "121aee66c911faf16ef686e9fcffa102",
     "grade": false,
     "grade_id": "cell-c285056bbc55c15",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "outputs": [],
   "source": [
    "import random
",
    "
",
    "def execute_schedule(s, show=False):
",
    "    s.reset()
",
    "    in_process = s.available_tasks
",
    "    print("Starting by doing:", in_process)
",
    "    while len(in_process) > 0:
",
    "        # Picks one random task to be the first to be completed.
",
    "        t = random.choice(list(in_process))
",
    "        print("Completed:", t)
",
    "        in_process = in_process - {t} | s.mark_completed(t)
",
    "        print("Now doing:", in_process)
",
    "        if show:
",
    "            s.show()
",
    "    # Have we done all?
",
    "    if not s.done:
",
    "        print("Error, there are tasks that could not be completed:", s.uncompleted)
"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "id": "JTZ1_0ffkacM",
    "nbgrader": {
     "checksum": "0b89ce00f896cfb7dd641a00c2c61d86",
     "grade": false,
     "grade_id": "cell-5c41d5760a16bd50",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "source": [
    "Let's try it on our old schedule:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 353
    },
    "deletable": false,
    "editable": false,
    "id": "fwFI8zF5kacM",
    "nbgrader": {
     "checksum": "438c73779c99e682ebebf7c068db85c2",
     "grade": false,
     "grade_id": "cell-47bed31601afff35",
     "locked": true,
     "schema_version": 1,
     "solution": false
    },
    "outputId": "5b02df8d-780a-4e28-eb73-28333d7383d1"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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
",
      "text/plain": [
       ""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "s = DependencyScheduler()
",
    "s.add_task('a', ['b', 'c'])
",
    "s.add_task('b', ['c', 'e'])
",
    "s._check()
",
    "s.show()
"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "deletable": false,
    "editable": false,
    "id": "8GLvAT5MkacM",
    "nbgrader": {
     "checksum": "49402dc6b29c411541e4c9eb0d59eaa7",
     "grade": false,
     "grade_id": "cell-cb9ee8db2218be70",
     "locked": true,
     "schema_version": 1,
     "solution": false
    },
    "outputId": "a26e52ce-2f7b-4886-d5da-f97f008d6ad0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting by doing: {'e', 'c'}
",
      "Completed: e
",
      "Now doing: {'c'}
"
     ]
    },
    {
     "data": {
      "image/png": "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
",
      "text/plain": [
       ""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Completed: c
",
      "Now doing: {'b'}
"
     ]
    },
    {
     "data": {
      "image/png": "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
",
      "text/plain": [
       ""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Completed: b
",
      "Now doing: {'a'}
"
     ]
    },
    {
     "data": {
      "image/png": "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
",
      "text/plain": [
       ""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Completed: a
",
      "Now doing: set()
"
     ]
    },
    {
     "data": {
      "image/png": "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
",
      "text/plain": [
       ""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "execute_schedule(s, show=True)
"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "id": "lAyaUHLmkacN",
    "nbgrader": {
     "checksum": "a3c215f20358f8f865a4b0090cf010ab",
     "grade": false,
     "grade_id": "cell-9c9e6ce16186ae03",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "source": [
    "What happens if there is a loop? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "deletable": false,
    "editable": false,
    "id": "BjwBKj05kacN",
    "nbgrader": {
     "checksum": "1a72092331547b48af83b369cef694b7",
     "grade": false,
     "grade_id": "cell-2dc9924f19540ab3",
     "locked": true,
     "schema_version": 1,
     "solution": false
    },
    "outputId": "97c8c6d9-520d-488d-d20b-817c552dbb08"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting by doing: set()
",
      "Error, there are tasks that could not be completed: {'b', 'c', 'a'}
"
     ]
    }
   ],
   "source": [
    "s = DependencyScheduler()
",
    "s.add_task('a', ['b'])
",
    "s.add_task('b', ['a'])
",
    "s.add_task('c', ['a'])
",
    "execute_schedule(s)
"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "id": "Y7SYMbISkacN",
    "nbgrader": {
     "checksum": "4b32a15d1debae4980463aa52d8c7d01",
     "grade": false,
     "grade_id": "cell-1d09ba345dc1de32",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "source": [
    "Ok, this is reasonable!  Let us now encode our Carbonara pasta recipe. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 353
    },
    "deletable": false,
    "editable": false,
    "id": "B4HKEQVukacN",
    "nbgrader": {
     "checksum": "a08bcaa837cddd038962a11f91fb8343",
     "grade": false,
     "grade_id": "cell-bc314c96d21a34c7",
     "locked": true,
     "schema_version": 1,
     "solution": false
    },
    "outputId": "10b6b2d6-fff5-4f5f-fbe1-fcb6b8afeaa9"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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
",
      "text/plain": [
       ""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "carbonara = DependencyScheduler()
",
    "
",
    "# First, the part about cooking the pancetta.
",
    "carbonara.add_task('dice onions', [])
",
    "carbonara.add_task('dice pancetta', [])
",
    "carbonara.add_task('put oil and butter in pan', [])
",
    "carbonara.add_task('put pancetta in pan', ['dice pancetta'])
",
    "carbonara.add_task('put onions in pan', ['dice onions'])
",
    "carbonara.add_task('cook pancetta', ['put oil and butter in pan',
",
    "                                     'put pancetta in pan',
",
    "                                     'put onions in pan'])
",
    "
",
    "# Second, the part about beating the eggs.
",
    "carbonara.add_task('put eggs in bowl', [])
",
    "carbonara.add_task('beat eggs', ['put eggs in bowl'])
",
    "
",
    "# Third, cooking the pasta.
",
    "carbonara.add_task('fill pot with water', [])
",
    "carbonara.add_task('bring pot of water to a boil', ['fill pot with water'])
",
    "carbonara.add_task('add salt to water', ['bring pot of water to a boil'])
",
    "carbonara.add_task('put pasta in water', ['bring pot of water to a boil',
",
    "                                         'add salt to water'])
",
    "carbonara.add_task('colander pasta', ['put pasta in water'])
",
    "
",
    "# And finally, we can put everything together.
",
    "carbonara.add_task('serve', ['beat eggs', 'cook pancetta', 'colander pasta'])
",
    "
",
    "# Let's look at our schedule!
",
    "carbonara.show()
"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "deletable": false,
    "editable": false,
    "id": "Ncr1M1LTkacN",
    "nbgrader": {
     "checksum": "b71b1bfa89a45be334e3eb7bb1ffe580",
     "grade": false,
     "grade_id": "cell-dde3ea491011fe74",
     "locked": true,
     "schema_version": 1,
     "solution": false
    },
    "outputId": "c2c72843-3ca6-4c6a-98ad-4daf6c5081a1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting by doing: {'dice onions', 'put eggs in bowl', 'dice pancetta', 'put oil and butter in pan', 'fill pot with water'}
",
      "Completed: put eggs in bowl
",
      "Now doing: {'dice onions', 'dice pancetta', 'put oil and butter in pan', 'fill pot with water', 'beat eggs'}
",
      "Completed: put oil and butter in pan
",
      "Now doing: {'dice onions', 'fill pot with water', 'beat eggs', 'dice pancetta'}
",
      "Completed: dice pancetta
",
      "Now doing: {'dice onions', 'fill pot with water', 'beat eggs', 'put pancetta in pan'}
",
      "Completed: beat eggs
",
      "Now doing: {'dice onions', 'fill pot with water', 'put pancetta in pan'}
",
      "Completed: put pancetta in pan
",
      "Now doing: {'dice onions', 'fill pot with water'}
",
      "Completed: dice onions
",
      "Now doing: {'put onions in pan', 'fill pot with water'}
",
      "Completed: fill pot with water
",
      "Now doing: {'put onions in pan', 'bring pot of water to a boil'}
",
      "Completed: bring pot of water to a boil
",
      "Now doing: {'put onions in pan', 'add salt to water'}
",
      "Completed: put onions in pan
",
      "Now doing: {'add salt to water', 'cook pancetta'}
",
      "Completed: add salt to water
",
      "Now doing: {'cook pancetta', 'put pasta in water'}
",
      "Completed: put pasta in water
",
      "Now doing: {'colander pasta', 'cook pancetta'}
",
      "Completed: cook pancetta
",
      "Now doing: {'colander pasta'}
",
      "Completed: colander pasta
",
      "Now doing: {'serve'}
",
      "Completed: serve
",
      "Now doing: set()
"
     ]
    }
   ],
   "source": [
    "# And let's finally prepare carbonara!
",
    "execute_schedule(carbonara)
"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "id": "lo45Mr8YkacN",
    "nbgrader": {
     "checksum": "7bade197fa4e480a09146b22ef8afb25",
     "grade": false,
     "grade_id": "cell-daa0327fa3f623ef",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "source": [
    "This is not necessarily the best order of actions to prepare pasta carbonara, but it definitely works as a schedule."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "id": "GtCxMENWkacN",
    "nbgrader": {
     "checksum": "32d632a86ab53799e90ac07e4bdbec37",
     "grade": false,
     "grade_id": "cell-62458aa5f6bd6db0",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "source": [
    "## Building a Better Execution Engine"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "id": "z0ujdvV-kacN",
    "nbgrader": {
     "checksum": "1c62725f1ed690ccb49e8526dec0f1c8",
     "grade": false,
     "grade_id": "cell-68a98344892ae6e4",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "source": [
    "Let us build a better execution engine for our schedules.  Right now, we have a function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "deletable": false,
    "editable": false,
    "id": "y9mNp7cdkacN",
    "nbgrader": {
     "checksum": "acea01d4083f8b34a8913c0d277fa2af",
     "grade": false,
     "grade_id": "cell-ee813cfbdf456ac5",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "outputs": [],
   "source": [
    "def execute_schedule(s, show=False):
",
    "    s.reset()
",
    "    in_process = s.available_tasks
",
    "    print("Starting by doing:", in_process)
",
    "    while len(in_process) > 0:
",
    "        # Picks one random task to be the first to be completed.
",
    "        t = random.choice(list(in_process))
",
    "        print("Completed:", t)
",
    "        in_process = in_process - {t} | s.mark_completed(t)
",
    "        print("Now doing:", in_process)
",
    "        if show:
",
    "            s.show()
",
    "    # Have we done all?
",
    "    if not s.done:
",
    "        print("Error, there are tasks that could not be completed:", s.uncompleted)
"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "id": "vzMENNIqkacN",
    "nbgrader": {
     "checksum": "6c135f48b406d3d04b197904ef911090",
     "grade": false,
     "grade_id": "cell-3c03e5954721ecbc",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "source": [
    "We want to wrap these methods into a class, RunSchedule.  This will allow us more flexibility in executing a schedule, as we will be able to specify parameters that guide the execution policy, interrupt and resume the execution, and so on.  
",
    "An object of class RunSchedule is initialized with a DependencyScheduler.  It then has the following methods: 
",
    "
",
    "* **reset:** mark all tasks as not completed. 
",
    "* **step:** perform one step in the schedule, completing a single task.
",
    "* **run:** performs all steps in the schedule, until completion. 
",
    "* **done:** indicates that all tasks have been done.
",
    "
",
    "What should these methods return?  _step_ will return the task executed, while _run_ will return the whole list of tasks, in the order in which they were done. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "deletable": false,
    "editable": false,
    "id": "BQWFGtRKkacN",
    "nbgrader": {
     "checksum": "baeffbbc770c2247d1b401f184c7aaf4",
     "grade": false,
     "grade_id": "cell-419c455e1fa49a35",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "outputs": [],
   "source": [
    "class RunSchedule(object):
",
    "
",
    "    def __init__(self, scheduler):
",
    "        self.scheduler = scheduler
",
    "        self.in_process = None # Indicating, we don't know yet.
",
    "
",
    "    def reset(self):
",
    "        self.scheduler.reset()
",
    "        self.in_process = None
",
    "
",
    "    def step(self):
",
    "        """Performs a step, returning the task, if any, or None,
",
    "        if there is no step that can be done."""
",
    "        # If we don't know what steps are in process, we get them.
",
    "        if self.in_process is None:
",
    "            self.in_process = self.scheduler.available_tasks
",
    "        if len(self.in_process) == 0:
",
    "            return None
",
    "        t = random.choice(list(self.in_process))
",
    "        self.in_process = self.in_process - {t} | self.scheduler.mark_completed(t)
",
    "        return t
",
    "
",
    "    @property
",
    "    def done(self):
",
    "        return self.scheduler.done
",
    "
",
    "    def run(self):
",
    "        """Runs the scheduler from the current configuration to completion.
",
    "        You must call reset() first, if you want to run the whole schedule."""
",
    "        tasks = []
",
    "        while not self.done:
",
    "            t = self.step()
",
    "            if t is not None:
",
    "                tasks.append(t)
",
    "        return tasks
"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "id": "wRSN_6JfkacN",
    "nbgrader": {
     "checksum": "d9a54aa7a503239bf81f2463cb74319f",
     "grade": false,
     "grade_id": "cell-50337ca62797b2cc",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "source": [
    "We can run our pasta carbonara with this RunSchedule class:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "deletable": false,
    "editable": false,
    "id": "_9bOnhqpkacN",
    "nbgrader": {
     "checksum": "7cd3e1103d79f0bd0bac7e4e02f77301",
     "grade": false,
     "grade_id": "cell-a0b0e7d1e74b4d33",
     "locked": true,
     "schema_version": 1,
     "solution": false
    },
    "outputId": "3ddaa8df-9d58-44ba-aaed-b8dff5cd5358"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['dice onions',
",
       " 'put onions in pan',
",
       " 'put eggs in bowl',
",
       " 'beat eggs',
",
       " 'dice pancetta',
",
       " 'fill pot with water',
",
       " 'put oil and butter in pan',
",
       " 'put pancetta in pan',
",
       " 'cook pancetta',
",
       " 'bring pot of water to a boil',
",
       " 'add salt to water',
",
       " 'put pasta in water',
",
       " 'colander pasta',
",
       " 'serve']"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "runner = RunSchedule(carbonara)
",
    "runner.reset()
",
    "runner.run()
"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "id": "gB0_0GlnkacN",
    "nbgrader": {
     "checksum": "1bc2fd9f9c1b7e56a64bb85bc56df26e",
     "grade": false,
     "grade_id": "cell-d160f03528fef7eb",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "source": [
    "Let us pause for a moment and ask: did we really need to create a new class? Could we not have done the above in the scheduler class? 
",
    "
",
    "This is debatable.  The idea in keeping the two classes separate is clarity of goals: 
",
    "
",
    "* The scheduler is concerned with what _can_ be done next. 
",
    "* The runner is concerned with any practical constraint to the execution, and with the choice of _what_, among the possible, is actually done. 
",
    "
",
    "We will have occasion below to rely on this division of concerns."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "id": "VmUnjXuCkacN",
    "nbgrader": {
     "checksum": "4bdadeaf1a6dfab0650df33f9282634c",
     "grade": false,
     "grade_id": "cell-ab56ae966c12c39d",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "source": [
    "### Code changes and rotten eggs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "id": "dYaHPtGVkacN",
    "nbgrader": {
     "checksum": "59bf5e852f155980256b6ef310bee302",
     "grade": false,
     "grade_id": "cell-10062b48fd612300",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "source": [
    "#### Code changes
",
    "
",
    "Imagine that you need to compile three programs, $a$, $c$, and then link together the results into $f.out$.   Once this is done, you compile $d$ and $e$, and link into $g.out$.  As the last step, you link the two libraries $g.out$ and $f.out$ together, and produce $h$. You do it once.  Great.  But now you realize that you need to change $b$.  Do you have to start from scratch? 
",
    "
",
    "You may think, who cares, it's the CPU doing the work, not me.  Fair enough, but there are some large systems that take minutes, dozen of minutes, to compile.  If you are compiling the linux kernel on a low power CPU, it might take hours.  Surely you don't want to redo everything from scratch!  
",
    "
",
    "So imagine you have the tasks in an intermediate state, with some being completed (possibly all of them), and some not.  You can now mark one of the tasks as incomplete, to signal you need to do it again.  What is the set of tasks that as a consequence should also be marked incomplete? 
",
    "If you have two tasks $x$ and $y$, with $y$ being a successor to $x$, if $x$ is marked as "undone" as it needs to be redone, then also $y$ will need to be redone, as it might use the results of $x$.
",
    "
",
    "To implement this, we will perform two modifications.  First, we will endow our scheduler with a _redo_ method, which marks a task and all its successors to be redone -- that is, it _unmarks_ them as done.  We let you implement this; you have already seen how to compute reachability in the graph chapter. 
"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "id": "4VKN56WzkacN",
    "nbgrader": {
     "checksum": "f4f62ac8c425709abda425a8ad8b639b",
     "grade": false,
     "grade_id": "cell-9f80ab8aa77e6100",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "source": [
    "## Question 2: redo for code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "deletable": false,
    "id": "eMfe4d1nkacN",
    "nbgrader": {
     "checksum": "dda6cc12b8a5445c115239cfadc9289d",
     "grade": false,
     "grade_id": "cell-80715adbd3dac83e",
     "locked": false,
     "schema_version": 1,
     "solution": true
    }
   },
   "outputs": [],
   "source": [
    "### Implementation of `redo`
",
    "
",
    "def dependency_scheduler_redo(self, t):
",
    "    """Mark the task t, and all its successors, as undone.
",
    "    Returns the set of successor tasks of t, with t included."""
",
    "    # YOUR CODE HERE
",
    "    tasks = set()
",
    "    tasks.add(t)
",
    "    if t in self.completed_tasks:
",
    "        self.completed_tasks.remove(t)
",
    "    for s in self.successors[t]:
",
    "        tasks.add(s)
",
    "        for x in dependency_scheduler_redo(self, s):
",
    "            tasks.add(x)
",
    "    return tasks
",
    "
",
    "DependencyScheduler.redo = dependency_scheduler_redo
"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "deletable": false,
    "id": "6CaaXn0HkacN",
    "nbgrader": {
     "checksum": "5585de9f76f2078b994256defd546daa",
     "grade": false,
     "grade_id": "cell-ffbf3c1f4fa3bdd2",
     "locked": false,
     "schema_version": 1,
     "solution": true
    }
   },
   "outputs": [],
   "source": [
    "# Here is a place where you can test your code. 
",
    "
",
    "# YOUR CODE HERE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "id": "AE0YhFAXkacN",
    "nbgrader": {
     "checksum": "65022df11bcbd0b2cfaf5c4408754571",
     "grade": false,
     "grade_id": "cell-5b62e886f0ae12ea",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "source": [
    "Let us test the implementation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "deletable": false,
    "editable": false,
    "id": "AP8H7FmhkacN",
    "nbgrader": {
     "checksum": "53c69adb193594c1059c599021d9205b",
     "grade": true,
     "grade_id": "cell-8357360c4a918edb",
     "locked": true,
     "points": 10,
     "schema_version": 1,
     "solution": false
    }
   },
   "outputs": [],
   "source": [
    "### Tests for `redo` for code. 10 points. 
",
    "
",
    "s = DependencyScheduler()
",
    "s.add_task('a', [])
",
    "s.add_task('b', ['a'])
",
    "s.add_task('c', ['a'])
",
    "s.add_task('d', ['b', 'c'])
",
    "s.add_task('e', ['a', 'd'])
",
    "
",
    "s.mark_completed('a')
",
    "s.mark_completed('b')
",
    "s.mark_completed('c')
",
    "assert_equal(s.available_tasks, {'d'})
",
    "s.redo('b')
",
    "assert_equal(s.available_tasks, {'b'})
",
    "
"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "id": "dvAbNHUukacN",
    "nbgrader": {
     "checksum": "f8f408a0c22b5baae9ebc0394d3e18f7",
     "grade": false,
     "grade_id": "cell-dc8e08c747c3f414",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "source": [
    "Next, we implement a runner that has an additional operation _redo(t)_ for a task t. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "deletable": false,
    "editable": false,
    "id": "v8Lqw1mnkacN",
    "nbgrader": {
     "checksum": "4ed044492ef70632de7970d20b137fb5",
     "grade": false,
     "grade_id": "cell-ef3550fd3be8ba89",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "outputs": [],
   "source": [
    "def run_schedule_redo(self, t):
",
    "    """Marks t as to be redone."""
",
    "    # We drop everything that was in progress.
",
    "    # This also forces us to ask the scheduler for what to redo.
",
    "    self.in_process = None
",
    "    return self.scheduler.redo(t)
",
    "
",
    "RunSchedule.redo = run_schedule_redo
"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "id": "0eQ0DQe9kacN",
    "nbgrader": {
     "checksum": "3f4ec294c647cca80d50e650f19723d8",
     "grade": false,
     "grade_id": "cell-649d49e90f721cf7",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "source": [
    "We can now play with it. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "deletable": false,
    "editable": false,
    "id": "FyhvQByTkacN",
    "nbgrader": {
     "checksum": "0b03de64cc41e4a31cc5b46ccefd49e4",
     "grade": false,
     "grade_id": "cell-e75c320e4a31c2d1",
     "locked": true,
     "schema_version": 1,
     "solution": false
    },
    "outputId": "17d36e43-85a8-4412-eaa5-b97ba6160e09"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dice onions
",
      "fill pot with water
",
      "bring pot of water to a boil
",
      "dice pancetta
",
      "add salt to water
",
      "put eggs in bowl
",
      "put oil and butter in pan
",
      "beat eggs
",
      "put onions in pan
",
      "put pasta in water
",
      "---> readd salt
",
      "marking undone: {'colander pasta', 'add salt to water', 'put pasta in water', 'serve'}
",
      "completed: {'put onions in pan', 'dice onions', 'put eggs in bowl', 'bring pot of water to a boil', 'put oil and butter in pan', 'dice pancetta', 'fill pot with water', 'beat eggs'}
",
      "put pancetta in pan
",
      "cook pancetta
",
      "add salt to water
",
      "put pasta in water
",
      "colander pasta
",
      "serve
",
      "None
",
      "None
",
      "None
",
      "None
",
      "--->redo dice pancetta
",
      "marking undone: {'put pancetta in pan', 'serve', 'cook pancetta', 'dice pancetta'}
",
      "completed: {'put onions in pan', 'dice onions', 'put eggs in bowl', 'bring pot of water to a boil', 'put oil and butter in pan', 'colander pasta', 'fill pot with water', 'add salt to water', 'beat eggs', 'put pasta in water'}
",
      "dice pancetta
",
      "put pancetta in pan
",
      "cook pancetta
",
      "serve
"
     ]
    }
   ],
   "source": [
    "runner = RunSchedule(carbonara)
",
    "runner.reset()
",
    "for _ in range(10):
",
    "    print(runner.step())
",
    "print("---> readd salt")
",
    "print("marking undone:", runner.redo("add salt to water"))
",
    "print("completed:", runner.scheduler.completed_tasks)
",
    "for _ in range(10):
",
    "    print(runner.step())
",
    "print("--->redo dice pancetta")
",
    "print("marking undone:", runner.redo("dice pancetta"))
",
    "print("completed:", runner.scheduler.completed_tasks)
",
    "for t in runner.run():
",
    "    print(t)
"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "id": "GvHfXhTrkacN",
    "nbgrader": {
     "checksum": "fefa652c64562e34bb0febc03b8e5c8c",
     "grade": false,
     "grade_id": "cell-d879d3343e6f13f3",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "source": [
    "You have learned to sequence the order in which to do tasks so as to respect their dependencies.  In the next chapter, we will learn how to also take into account the time it takes for us to do the tasks.  In the meantime, bon appetit, or rather, guten appetit, or rather, buon appetito!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "id": "YFAQC0CFkacN",
    "nbgrader": {
     "checksum": "762427c225bf731e0bb90204533754a7",
     "grade": false,
     "grade_id": "cell-d8ce6c37cba9be0",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "source": [
    "#### Redoing in cooking
",
    "
",
    "The act of redoing a cooking step is somewhat different than the act of redoing something in code.  Suppose you cook pasta, unite with it the fried bacon and onions, and then -- terrible mishap -- you unite with it the beaten egg yolks in which one of the eggs is rotten. 
",
    "
",
    "In code, when one file changes, you only need to redo the things that _depend_ on that file.  In cooking, it is different: even if nothing changed in the bacon, onions, and cooked pasta, once you add to it rotten eggs you have to redo the pasta, bacon, onions, etc, as well, as they have now been contaminated.  The root of the problem is that in a makefile, when you combine two files to compute a result, you do not destroy the original files, whereas in cooking, once you combine foods, you don't have the original foods any longer.  Cooking is like a makefile in which, once you combine files, you immediately delete them. 
",
    "
",
    "So let us come up with a precise definition of what needs to be redone in cooking, when one of the steps goes bad (the eggs are rotten, you burn the food on the stove, and so on). 
",
    "
",
    "Initially, we label _redo_ the task that needs redoing.  We then propagate the label according to these two rules: 
",
    "
",
    "* If a task $v$ is labeled _redo_, if $u$ is a successor of $v$ and $u$ is completed, then $u$ is also labeled _redo_. 
",
    "* If a task $v$ is labeled _redo_, and if $u$ is a predecessor of $v$, then $u$ is also labeled _redo_ (note that in this case, we are guaranteed that $u$ is completed). 
",
    "
",
    "The first rule corresponds to a _forward_ pass in the dependency garph; the second rule corresponds to a _backward_ pass in the dependency relation. 
",
    "Once the _redo_ label is propagated, all tasks that are marked _redo_ are changed from completed, to uncompleted. 
",
    "
",
    "We ask you to implement this in code."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "id": "t3763UgKkacN",
    "nbgrader": {
     "checksum": "bc530f1067c9f06847e1fa513a92990c",
     "grade": false,
     "grade_id": "cell-6d12164e3e76339b",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "source": [
    "## Question 3: redo for recipes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "deletable": false,
    "id": "hGpcMDickacN",
    "nbgrader": {
     "checksum": "6ccca5682cbed48c50d2a9ea4300b62c",
     "grade": false,
     "grade_id": "cell-a0618093f5920c44",
     "locked": false,
     "schema_version": 1,
     "solution": true
    }
   },
   "outputs": [],
   "source": [
    "### Implementation of `cooking_redo`
",
    "
",
    "def dependency_scheduler_cooking_redo(self, v):
",
    "    """Indicates that the task v needs to be redone, as something went bad.
",
    "    This is the "cooking" version of the redo, in which the redo propagates
",
    "    to both successors (as for code) and predecessors."""
",
    "    # YOUR CODE HERE
",
    "    redo_tasks = {t} | self.successors[t]
",
    "    self.completed_tasks = self.completed_tasks - redo_tasks
",
    "  
",
    "    return redo_tasks  
",
    "
",
    "DependencyScheduler.cooking_redo = dependency_scheduler_cooking_redo
"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "deletable": false,
    "id": "Q-CPnQRjkacN",
    "nbgrader": {
     "checksum": "fef9951e6a653a4975154941aa5e881a",
     "grade": false,
     "grade_id": "cell-aa93a1ed6a926cff",
     "locked": false,
     "schema_version": 1,
     "solution": true
    }
   },
   "outputs": [],
   "source": [
    "# Here is a place where you can test your code. 
",
    "
",
    "# YOUR CODE HERE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "id": "bGHcfSPikacN",
    "nbgrader": {
     "checksum": "27d265e4416ecc09bb1d88678feb3e3c",
     "grade": false,
     "grade_id": "cell-9a8d2fd5d7f7954d",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "source": [
    "Let us check that the code works.  First, a simple example. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 408
    },
    "deletable": false,
    "editable": false,
    "id": "jqvumL0KkacN",
    "nbgrader": {
     "checksum": "0ac9dcabcfdf363f540a35363e3c6103",
     "grade": true,
     "grade_id": "cell-c81c441e91888093",
     "locked": true,
     "points": 10,
     "schema_version": 1,
     "solution": false
    },
    "outputId": "d81d41fa-d5aa-45ee-8b9f-97430299a424"
   },
   "outputs": [
    {
     "ename": "AssertionError",
     "evalue": "Items in the second set but not the first:
'b'
'a'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m
\u001b[0;32m     17\u001b[0m \u001b[0ms\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcooking_redo\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'c'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
\u001b[0;32m     18\u001b[0m \u001b[1;31m# When we redo c, both its successor d, and predecessors a, b have to be redone.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
\u001b[1;32m---> 19\u001b[1;33m \u001b[0massert_equal\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mavailable_tasks\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m{\u001b[0m\u001b[1;34m'a'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'b'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'e'\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
\u001b[0m\u001b[0;32m     20\u001b[0m \u001b[0massert_equal\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcompleted_tasks\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
\u001b[0;32m     21\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m
",
      "\u001b[1;32m~\Anaconda3\lib\unittest\case.py\u001b[0m in \u001b[0;36massertEqual\u001b[1;34m(self, first, second, msg)\u001b[0m
\u001b[0;32m    837\u001b[0m         """
\u001b[0;32m    838\u001b[0m         \u001b[0massertion_func\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getAssertEqualityFunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfirst\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msecond\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
\u001b[1;32m--> 839\u001b[1;33m         \u001b[0massertion_func\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfirst\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msecond\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmsg\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmsg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
\u001b[0m\u001b[0;32m    840\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m
\u001b[0;32m    841\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0massertNotEqual\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfirst\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msecond\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmsg\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
",
      "\u001b[1;32m~\Anaconda3\lib\unittest\case.py\u001b[0m in \u001b[0;36massertSetEqual\u001b[1;34m(self, set1, set2, msg)\u001b[0m
\u001b[0;32m   1097\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m
\u001b[0;32m   1098\u001b[0m         \u001b[0mstandardMsg\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'\n'\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlines\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
\u001b[1;32m-> 1099\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfail\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_formatMessage\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstandardMsg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
\u001b[0m\u001b[0;32m   1100\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m
\u001b[0;32m   1101\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0massertIn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmember\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcontainer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmsg\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
",
      "\u001b[1;32m~\Anaconda3\lib\unittest\case.py\u001b[0m in \u001b[0;36mfail\u001b[1;34m(self, msg)\u001b[0m
\u001b[0;32m    678\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mfail\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmsg\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
\u001b[0;32m    679\u001b[0m         \u001b[1;34m"""Fail immediately, with the given message."""\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
\u001b[1;32m--> 680\u001b[1;33m         \u001b[1;32mraise\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfailureException\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
\u001b[0m\u001b[0;32m    681\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m
\u001b[0;32m    682\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0massertFalse\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexpr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmsg\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
",
      "\u001b[1;31mAssertionError\u001b[0m: Items in the second set but not the first:
'b'
'a'"
     ]
    }
   ],
   "source": [
    "### Basic tests for `cooking_redo`. 10 points. 
",
    "
",
    "s = DependencyScheduler()
",
    "s.add_task('a', [])
",
    "s.add_task('b', [])
",
    "s.add_task('c', ['a', 'b'])
",
    "s.add_task('d', ['c', 'a'])
",
    "s.add_task('e', [])
",
    "s.add_task('f', ['e'])
",
    "s.add_task('g', ['f', 'd'])
",
    "
",
    "s.mark_completed('a')
",
    "s.mark_completed('b')
",
    "s.mark_completed('c')
",
    "s.mark_completed('d')
",
    "assert_equal(s.available_tasks, {'e'})
",
    "s.cooking_redo('c')
",
    "# When we redo c, both its successor d, and predecessors a, b have to be redone.
",
    "assert_equal(s.available_tasks, {'a', 'b', 'e'})
",
    "assert_equal(s.completed_tasks, set())
",
    "
"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "id": "wvmDn4kkkacN",
    "nbgrader": {
     "checksum": "9837d82e5252ea166f9df307ac050674",
     "grade": false,
     "grade_id": "cell-ca557220e8080773",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "source": [
    "And now, some slightly more sophisticated tests."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "deletable": false,
    "editable": false,
    "id": "7ICulRsskacN",
    "nbgrader": {
     "checksum": "e9305566644280e83a323332ac42b1f2",
     "grade": true,
     "grade_id": "cell-4c4d6c54901c7d9",
     "locked": true,
     "points": 10,
     "schema_version": 1,
     "solution": false
    }
   },
   "outputs": [
    {
     "ename": "AssertionError",
     "evalue": "Items in the second set but not the first:
'b'
'a'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m
\u001b[0;32m     18\u001b[0m \u001b[0ms\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcooking_redo\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'c'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
\u001b[0;32m     19\u001b[0m \u001b[1;31m# When we redo c, both its successor d, and predecessors a, b have to be redone.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
\u001b[1;32m---> 20\u001b[1;33m \u001b[0massert_equal\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mavailable_tasks\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m{\u001b[0m\u001b[1;34m'a'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'b'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'f'\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
\u001b[0m\u001b[0;32m     21\u001b[0m \u001b[0massert_equal\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcompleted_tasks\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m{\u001b[0m\u001b[1;34m'e'\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
\u001b[0;32m     22\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m
",
      "\u001b[1;32m~\Anaconda3\lib\unittest\case.py\u001b[0m in \u001b[0;36massertEqual\u001b[1;34m(self, first, second, msg)\u001b[0m
\u001b[0;32m    837\u001b[0m         """
\u001b[0;32m    838\u001b[0m         \u001b[0massertion_func\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getAssertEqualityFunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfirst\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msecond\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
\u001b[1;32m--> 839\u001b[1;33m         \u001b[0massertion_func\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfirst\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msecond\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmsg\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmsg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
\u001b[0m\u001b[0;32m    840\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m
\u001b[0;32m    841\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0massertNotEqual\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfirst\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msecond\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmsg\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
",
      "\u001b[1;32m~\Anaconda3\lib\unittest\case.py\u001b[0m in \u001b[0;36massertSetEqual\u001b[1;34m(self, set1, set2, msg)\u001b[0m
\u001b[0;32m   1097\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m
\u001b[0;32m   1098\u001b[0m         \u001b[0mstandardMsg\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'\n'\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlines\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
\u001b[1;32m-> 1099\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfail\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_formatMessage\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstandardMsg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
\u001b[0m\u001b[0;32m   1100\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m
\u001b[0;32m   1101\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0massertIn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmember\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcontainer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmsg\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
",
      "\u001b[1;32m~\Anaconda3\lib\unittest\case.py\u001b[0m in \u001b[0;36mfail\u001b[1;34m(self, msg)\u001b[0m
\u001b[0;32m    678\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mfail\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmsg\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
\u001b[0;32m    679\u001b[0m         \u001b[1;34m"""Fail immediately, with the given message."""\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
\u001b[1;32m--> 680\u001b[1;33m         \u001b[1;32mraise\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfailureException\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
\u001b[0m\u001b[0;32m    681\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m
\u001b[0;32m    682\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0massertFalse\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexpr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmsg\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
",
      "\u001b[1;31mAssertionError\u001b[0m: Items in the second set but not the first:
'b'
'a'"
     ]
    }
   ],
   "source": [
    "### Advanced tests for `cooking_redo`. 10 points. 
",
    "
",
    "s = DependencyScheduler()
",
    "s.add_task('a', [])
",
    "s.add_task('b', [])
",
    "s.add_task('c', ['a', 'b'])
",
    "s.add_task('d', ['c', 'a'])
",
    "s.add_task('e', [])
",
    "s.add_task('f', ['e'])
",
    "s.add_task('g', ['f', 'd'])
",
    "
",
    "s.mark_completed('a')
",
    "s.mark_completed('b')
",
    "s.mark_completed('c')
",
    "s.mark_completed('d')
",
    "s.mark_completed('e')
",
    "assert_equal(s.available_tasks, {'f'})
",
    "s.cooking_redo('c')
",
    "# When we redo c, both its successor d, and predecessors a, b have to be redone.
",
    "assert_equal(s.available_tasks, {'a', 'b', 'f'})
",
    "assert_equal(s.completed_tasks, {'e'})
",
    "
"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "id": "k13cqyuJkacO",
    "nbgrader": {
     "checksum": "8e2d0c5ee7ca1ee6b0228ce1fcf4b129",
     "grade": false,
     "grade_id": "cell-1354768f9bcb0376",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "source": [
    "## Question 4: Implement And-Or Schedules
",
    "
",
    "In the schedules we have seen so far, the dependencies are in _and_ one with the other: if a task $a$ depends on $b, c$, then _both_ $b$ _and $c$ need to be completed before $a$ can be started. 
",
    "It is possible to consider also cases where dependencies are in an _or_ relation: if $a$ depends on $b, c$ in an _or_ way, then it suffices to complete one of $b$ _or_ $c$ before starting $a$. 
",
    "For instance, in our Carbonara Pasta example, it is possible (even though not necessarily advisable) to use shallots in place of onions. 
",
    "In that case, instead of 
",
    "
",
    "    carbonara.add_task('put onions in pan', ['dice onions'])
",
    "
",
    "we could have:
",
    "
",
    "    carbonara.add_or_task('put onions in pan', ['dice onions', 'dice shallots'])
",
    "
",
    "so that before putting the (now generally named) onions in a pan, we could choose to dice either shallots or onions. 
",
    "
",
    "Formally, the idea is to endow the Scheduler class with _two_ methods: 
",
    "
",
    "* `add_and_task(self, t, dependencies)` adds a task `t` with list of dependencies `dependencies`, so that `t` can be done when _all_ of the dependencies are done.  The task `t` is called an AND node in the dependency graph. 
",
    "
",
    "* `add_or_task(self, t, dependencies)` adds a task `t` with list of dependencies `dependencies`, so that `t` can be done when _at least one_ of the dependencies is done.  The task `t` is called an OR node in the dependency graph. 
",
    "
",
    "You need to find a way to remember which dependency graph nodes are AND or OR nodes, and you need to implement the properties `done`, `available_tasks`, `uncompleted`, and the method `mark_completed`, to make this class work. 
",
    "Implementing the `show` method is optional; do it if it helps you debug your code. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "deletable": false,
    "id": "MwsOh-7vkacO",
    "nbgrader": {
     "checksum": "b4902dcc720ffd415db259d5d7d9e70c",
     "grade": false,
     "grade_id": "cell-51732cbc3481ec67",
     "locked": false,
     "schema_version": 1,
     "solution": true
    }
   },
   "outputs": [
    {
     "ename": "IndentationError",
     "evalue": "expected an indented block (, line 9)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m""\u001b[1;36m, line \u001b[1;32m9\u001b[0m
\u001b[1;33m    def add_and_task(self, t, dependencies):\u001b[0m
\u001b[1;37m      ^\u001b[0m
\u001b[1;31mIndentationError\u001b[0m\u001b[1;31m:\u001b[0m expected an indented block
"
     ]
    }
   ],
   "source": [
    "### `AND_OR_Scheduler` implementation
",
    "
",
    "class AND_OR_Scheduler(object):
",
    "
",
    "    def __init__(self):
",
    "        # It is up to you to implement the initialization.
",
    "        # YOUR CODE HERE
",
    "
",
    "    def add_and_task(self, t, dependencies):
",
    "        """Adds an AND task t with given dependencies."""
",
    "        # YOUR CODE HERE
",
    "
",
    "    def add_or_task(self, t, dependencies):
",
    "        """Adds an OR task t with given dependencies."""
",
    "        # YOUR CODE HERE
",
    "
",
    "    @property
",
    "    def done(self):
",
    "        # YOUR CODE HERE
",
    "
",
    "    @property
",
    "    def available_tasks(self):
",
    "        """Returns the set of tasks that can be done in parallel.
",
    "        A task can be done if:
",
    "        - It is an AND task, and all its predecessors have been completed, or
",
    "        - It is an OR task, and at least one of its predecessors has been completed.
",
    "        And of course, we don't return any task that has already been
",
    "        completed."""
",
    "        # YOUR CODE HERE
",
    "
",
    "    def mark_completed(self, t):
",
    "        """Marks the task t as completed, and returns the additional
",
    "        set of tasks that can be done (and that could not be
",
    "        previously done) once t is completed."""
",
    "        # YOUR CODE HERE
",
    "
",
    "    def show(self):
",
    "        """You can use the nx graph to display the graph.  You may want to ensure
",
    "        that you display AND and OR nodes differently."""
",
    "        # YOUR CODE HERE
"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "deletable": false,
    "id": "Iol14ZOXkacO",
    "nbgrader": {
     "checksum": "27af5864ccc43f22993a72d0f647bdc4",
     "grade": false,
     "grade_id": "cell-038c005b93e6e0f1",
     "locked": false,
     "schema_version": 1,
     "solution": true
    }
   },
   "outputs": [],
   "source": [
    "# Here is a place where you can test your code. 
",
    "
",
    "# YOUR CODE HERE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "id": "CPtCKfh4kacO",
    "nbgrader": {
     "checksum": "a5b1f35544aaaa7231a3bc093db3d5b9",
     "grade": false,
     "grade_id": "cell-482edb12036108dc",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "source": [
    "Let us do some simple tests.  First, for good old AND nodes. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "deletable": false,
    "editable": false,
    "id": "uwnhgFwekacO",
    "nbgrader": {
     "checksum": "c873af11920709a2d8333932e617be8b",
     "grade": true,
     "grade_id": "cell-9a86f02cc5bca274",
     "locked": true,
     "points": 10,
     "schema_version": 1,
     "solution": false
    }
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'AND_OR_Scheduler' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m
\u001b[0;32m      1\u001b[0m \u001b[1;31m### Simple tests for AND nodes. 10 points.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m
\u001b[1;32m----> 3\u001b[1;33m \u001b[0ms\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mAND_OR_Scheduler\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
\u001b[0m\u001b[0;32m      4\u001b[0m \u001b[0ms\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd_and_task\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'a'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m'b'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'c'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
\u001b[0;32m      5\u001b[0m \u001b[0massert_equal\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mavailable_tasks\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m{\u001b[0m\u001b[1;34m'b'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'c'\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
",
      "\u001b[1;31mNameError\u001b[0m: name 'AND_OR_Scheduler' is not defined"
     ]
    }
   ],
   "source": [
    "### Simple tests for AND nodes. 10 points. 
",
    "
",
    "s = AND_OR_Scheduler()
",
    "s.add_and_task('a', ['b', 'c'])
",
    "assert_equal(s.available_tasks, {'b', 'c'})
",
    "r = s.mark_completed('b')
",
    "assert_equal(r, set())
",
    "assert_equal(s.available_tasks, {'c'})
",
    "r = s.mark_completed('c')
",
    "assert_equal(r, {'a'})
",
    "assert_equal(s.available_tasks, {'a'})
",
    "r = s.mark_completed('a')
",
    "assert_equal(r, set())
",
    "assert_equal(s.available_tasks, set())
",
    "
"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "id": "_7OpH7p-kacO",
    "nbgrader": {
     "checksum": "451c1a9c2301ae0361c28ad6e69c35da",
     "grade": false,
     "grade_id": "cell-77803bc9d9342c22",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "source": [
    "Then, some simple tests for OR nodes. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "deletable": false,
    "editable": false,
    "id": "6J8pACOTkacO",
    "nbgrader": {
     "checksum": "af51d6d7f9b58c91cc90e5e7c48a7647",
     "grade": true,
     "grade_id": "cell-19e1d2c726e4be1b",
     "locked": true,
     "points": 10,
     "schema_version": 1,
     "solution": false
    }
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'AND_OR_Scheduler' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m
\u001b[0;32m      1\u001b[0m \u001b[1;31m### Simple tests for OR nodes. 10 points.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m
\u001b[1;32m----> 3\u001b[1;33m \u001b[0ms\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mAND_OR_Scheduler\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
\u001b[0m\u001b[0;32m      4\u001b[0m \u001b[0ms\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd_or_task\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'a'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m'b'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'c'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
\u001b[0;32m      5\u001b[0m \u001b[0massert_equal\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mavailable_tasks\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m{\u001b[0m\u001b[1;34m'b'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'c'\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
",
      "\u001b[1;31mNameError\u001b[0m: name 'AND_OR_Scheduler' is not defined"
     ]
    }
   ],
   "source": [
    "### Simple tests for OR nodes. 10 points. 
",
    "
",
    "s = AND_OR_Scheduler()
",
    "s.add_or_task('a', ['b', 'c'])
",
    "assert_equal(s.available_tasks, {'b', 'c'})
",
    "r = s.mark_completed('b')
",
    "# Now 'a' becomes available.
",
    "assert_equal(r, {'a'})
",
    "# But note that 'c' is also available, even if useless.
",
    "assert_equal(s.available_tasks, {'a', 'c'})
",
    "r = s.mark_completed('a')
",
    "assert_equal(r, set())
",
    "assert_equal(s.available_tasks, {'c'})
",
    "r = s.mark_completed('c')
",
    "assert_equal(r, set())
",
    "assert_equal(s.available_tasks, set())
",
    "
"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "id": "wGCCs4GHkacO",
    "nbgrader": {
     "checksum": "587858ea7715ed0dfc82913fa3a63542",
     "grade": false,
     "grade_id": "cell-80257a5f8718f673",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "source": [
    "Note that a drawback of this simple solution, as illustrated by the above test case, is that we do not distinguish between the tasks that are useful to do the root task, and the tasks that are useless, that is, not part of a minimal solution.  We simply call them available, as they can be done, even though there is no advantage in doing them. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "deletable": false,
    "editable": false,
    "id": "yqPCp5KWkacO",
    "nbgrader": {
     "checksum": "2da9553393518e5ca462fd18da9aa002",
     "grade": true,
     "grade_id": "cell-fa32f16841d80974",
     "locked": true,
     "points": 10,
     "schema_version": 1,
     "solution": false
    }
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'AND_OR_Scheduler' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m
\u001b[0;32m      1\u001b[0m \u001b[1;31m### Tests with both AND and OR nodes. 10 points.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m
\u001b[1;32m----> 3\u001b[1;33m \u001b[0ms\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mAND_OR_Scheduler\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
\u001b[0m\u001b[0;32m      4\u001b[0m \u001b[0ms\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd_and_task\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'a'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m'b'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'c'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
\u001b[0;32m      5\u001b[0m \u001b[0ms\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd_or_task\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'b'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m'b1'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'b2'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
",
      "\u001b[1;31mNameError\u001b[0m: name 'AND_OR_Scheduler' is not defined"
     ]
    }
   ],
   "source": [
    "### Tests with both AND and OR nodes. 10 points. 
",
    "
",
    "s = AND_OR_Scheduler()
",
    "s.add_and_task('a', ['b', 'c'])
",
    "s.add_or_task('b', ['b1', 'b2'])
",
    "s.add_or_task('c', ['c1', 'c2'])
",
    "r = s.mark_completed('b1')
",
    "assert_equal(s.available_tasks, {'b', 'b2', 'c1', 'c2'})
",
    "r = s.mark_completed('b')
",
    "assert_false('a' in s.available_tasks)
",
    "r = s.mark_completed('c1')
",
    "assert_false('a' in s.available_tasks)
",
    "r = s.mark_completed('c')
",
    "assert_true('a' in s.available_tasks)
",
    "
",
    "s = AND_OR_Scheduler()
",
    "s.add_or_task('a', ['b', 'c'])
",
    "s.add_and_task('b', ['b1', 'b2'])
",
    "s.add_and_task('c', ['c1', 'c2'])
",
    "r = s.mark_completed('b1')
",
    "assert_equal(s.available_tasks, {'b2', 'c1', 'c2'})
",
    "r = s.mark_completed('c1')
",
    "assert_equal(s.available_tasks, {'b2', 'c2'})
",
    "r = s.mark_completed('c2')
",
    "assert_equal(s.available_tasks, {'b2', 'c'})
",
    "r = s.mark_completed('c')
",
    "assert_true('a' in s.available_tasks)
",
    "
"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "âHomework_11_Scheduling_with_Dependencies.ipynbâçå¯æ¬",
   "provenance": []
  },
  "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": "3.7.1"
  },
  "test_info": {
   "id": "4adb3d5055da02e7ae8251ca99f4acfa901ab256"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
Assign/.ipynb_checkpoints/Solution file-checkpoint.ipynb{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Solution File"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Homework 11: Scheduling with Dependencies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our first implementation of the class is as follows. We let you complete the available_tasks and mark_completed methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import defaultdict
",
    "import networkx as nx # Library for displaying graphs.
",
    "import matplotlib.pyplot as plt
",
    "
",
    "class DependencyScheduler(object):
",
    "
",
    "    def __init__(self):
",
    "        self.tasks = set()
",
    "        # The successors of a task are the tasks that depend on it, and can
",
    "        # only be done once the task is completed.
",
    "        self.successors = defaultdict(set)
",
    "        # The predecessors of a task have to be done before the task.
",
    "        self.predecessors = defaultdict(set)
",
    "        self.completed_tasks = set() # completed tasks
",
    "
",
    "    def add_task(self, t, dependencies):
",
    "        """Adds a task t with given dependencies."""
",
    "        # Makes sure we know about all tasks mentioned.
",
    "        assert t not in self.tasks or len(self.predecessors[t]) == 0, "The task was already present."
",
    "        self.tasks.add(t)
",
    "        self.tasks.update(dependencies)
",
    "        # The predecessors are the tasks that need to be done before.
",
    "        self.predecessors[t] = set(dependencies)
",
    "        # The new task is a successor of its dependencies.
",
    "        for u in dependencies:
",
    "            self.successors[u].add(t)
",
    "
",
    "    def reset(self):
",
    "        self.completed_tasks = set()
",
    "
",
    "    @property
",
    "    def done(self):
",
    "        return self.completed_tasks == self.tasks
",
    "
",
    "
",
    "    def show(self):
",
    "        """We use the nx graph to display the graph."""
",
    "        g = nx.DiGraph()
",
    "        g.add_nodes_from(self.tasks)
",
    "        g.add_edges_from([(u, v) for u in self.tasks for v in self.successors[u]])
",
    "        node_colors = ''.join([('g' if v in self.completed_tasks else 'r')
",
    "                           for v in self.tasks])
",
    "        nx.draw(g, with_labels=True, node_color=node_colors)
",
    "        plt.show()
",
    "
",
    "    @property
",
    "    def uncompleted(self):
",
    "        """Returns the tasks that have not been completed.
",
    "        This is a property, so you can say scheduler.uncompleted rather than
",
    "        scheduler.uncompleted()"""
",
    "        return self.tasks - self.completed_tasks
",
    "
",
    "    def _check(self):
",
    "        """We check that if t is a successor of u, then u is a predecessor
",
    "        of t."""
",
    "        for u in self.tasks:
",
    "            for t in self.successors[u]:
",
    "                assert u in self.predecessors[t]
"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 1: implement `available_tasks` and `mark_completed`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Implementation of `available_tasks` and `mark_completed`.
",
    "
",
    "def scheduler_available_tasks(self):
",
    "    """Returns the set of tasks that can be done in parallel.
",
    "    A task can be done if all its predecessors have been completed.
",
    "    And of course, we don't return any task that has already been
",
    "    completed."""
",
    "    # YOUR CODE HERE
",
    "    return ({t for t in self.tasks 
",
    "                 if self.predecessors[t].issubset(self.completed_tasks)}
",
    "                - self.completed_tasks)
",
    "
",
    "def scheduler_mark_completed(self, t):
",
    "    """Marks the task t as completed, and returns the additional
",
    "    set of tasks that can be done (and that could not be
",
    "    previously done) once t is completed."""
",
    "    # YOUR CODE HERE
",
    "    for p in self.predecessors[t]:
",
    "        if p in self.uncompleted:
",

{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "“Homework_11_Scheduling_with_Dependencies.ipynb”的副本", "provenance": [], "collapsed_sections": [] }, "kernelspec": {...

Solution

Answer To This Question Is Available To Download

Related Questions & Answers

Submit New Assignment