{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Essential Objects: Circuits, Models, and DataSets\n",
    "Three object types form the foundational of all that pyGSTi does: [circuits](#circuits), [models](#models), and [data sets](#datasets).  This tutorial's objective is to explain what these objects are and how they relate to one another at a very high level while providing links to other notebooks that cover the details we gloss over here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pygsti\n",
    "from pygsti.objects import Circuit, Model, DataSet"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"circuits\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Circuits\n",
    "The `Circuit` object encapsulates a quantum circuit as a sequence of *layers*, each of which contains zero or more non-identity *gates*.  A `Circuit` has some number of labeled *lines* and each gate label is assigned to one or more lines. Line labels can be integers or strings.   Gate labels have two parts: a `str`-type name and a tuple of line labels.  A gate name typically begins with 'G' because this is expected when we parse circuits from text files.\n",
    "\n",
    "For example, `('Gx',0)` is a gate label that means \"do the Gx gate on qubit 0\", and `('Gcnot',(2,3))` means \"do the Gcnot gate on qubits 2 and 3\".\n",
    "\n",
    "A `Circuit` can be created from a list of gate labels:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Qubit 0 ---|Gx|-|C1|-| |-|  |---\n",
      "Qubit 1 ---|  |-|T0|-| |-|  |---\n",
      "Qubit 2 ---|  |-|  |-| |-|  |---\n",
      "Qubit 3 ---|  |-|  |-| |-|Gy|---\n",
      "\n"
     ]
    }
   ],
   "source": [
    "c = Circuit( [('Gx',0),('Gcnot',0,1),(),('Gy',3)], line_labels=[0,1,2,3])\n",
    "print(c)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you want multiple gates in a single layer, just put those gate labels in their own nested list:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Qubit 0 ---|Gx|-|C1|-| |---\n",
      "Qubit 1 ---|  |-|T0|-| |---\n",
      "Qubit 2 ---|  |-|  |-| |---\n",
      "Qubit 3 ---|  |-|Gy|-| |---\n",
      "\n"
     ]
    }
   ],
   "source": [
    "c = Circuit( [('Gx',0),[('Gcnot',0,1),('Gy',3)],()] , line_labels=[0,1,2,3])\n",
    "print(c)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We distinguish three basic types of circuit layers.  We call layers containing quantum gates *operation layers*.  All the circuits we've seen so far just have operation layers.  It's also possible to have a *preparation layer* at the beginning of a circuit and a *measurement layer* at the end of a circuit.  There can also be a fourth type of layer called an *instrument layer* which we dicuss in a separate [tutorial on Instruments](objects/advanced/Instruments.ipynb).  Assuming that `'rho'` labels a (n-qubit) state preparation and `'Mz'` labels a (n-qubit) measurement, here's a circuit with all three types of layers:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Qubit 0 ---|rho|-|  |-|Gswap:0:1|-|Mz|---\n",
      "Qubit 1 ---|rho|-|Gz|-|Gswap:0:1|-|Mz|---\n",
      "Qubit 2 ---|rho|-|  |-|   Gy    |-|Mz|---\n",
      "\n"
     ]
    }
   ],
   "source": [
    "c = Circuit( ['rho',('Gz',1),[('Gswap',0,1),('Gy',2)],'Mz'] , line_labels=[0,1,2])\n",
    "print(c)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, when dealing with small systems (e.g. 1 or 2 qubits), we typically just use a `str`-type label (without any line-labels) to denote every possible layer.  In this case, all the labels operate on the entire state space so we don't need the notion of 'lines' in a `Circuit`.  When there are no line-labels, a `Circuit` assumes a single default **'\\*'-label**, which you can usually just ignore:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Qubit * ---|Gx|-|Gy|-|Gi|---\n",
      "\n"
     ]
    }
   ],
   "source": [
    "c = Circuit( ['Gx','Gy','Gi'] )\n",
    "print(c)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pretty simple, right?  The `Circuit` object allows you to easily manipulate its labels (similar to a NumPy array) and even perform some basic operations like depth reduction and simple compiling.  For lots more details on how to create, modify, and use circuit objects see the [circuit tutorial](objects/Circuit.ipynb).\n",
    "<a id=\"models\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Models\n",
    "An instance of the `Model` class represents something that can predict the outcome probabilities of quantum circuits.  We define any such thing to be a \"QIP model\", or just a \"model\", as these probabilities define the behavior of some real or virtual QIP.  Because there are so many types of models, the `Model` class in pyGSTi is just a base class and is never instaniated directly.  Classes `ExplicitOpModel` and `ImplicitOpModel` derive from `Model` and define two broad categories of models, both of which sequentially operate on circuit *layers* (the \"Op\" in the class names is short for \"layer operation\"). \n",
    "\n",
    "#### Explicit layer-operation models\n",
    "An `ExplicitOpModel` is a container object.  Its `.preps`, `.povms`, and `.operations` members are essentially dictionaires of state preparation, measurement, and layer-operation objects, respectively.  How to create these objects and build up explicit models from scratch is a central capability of pyGSTi and a topic of the [explicit-model tutorial](objects/ExplicitModel.ipynb).  Presently, we'll create a 2-qubit model using the convenient `build_explicit_model` function: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "mdl = pygsti.construction.build_explicit_model((0,1),\n",
    "            [(),      ('Gx',0),    ('Gy',0),    ('Gx',1),    ('Gy',1),    ('Gcnot',0,1)],\n",
    "            [\"I(0,1)\",\"X(pi/2,0)\", \"Y(pi/2,0)\", \"X(pi/2,1)\", \"Y(pi/2,1)\", \"CNOT(0,1)\"]) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This creates an `ExplicitOpModel` with a default preparation (prepares all qubits in the zero-state) labeled `'rho0'`, a default measurement labeled `'Mdefault'` in the Z-basis and with 5 layer-operations given by the labels in the 2nd argument (the first argument is akin to a circuit's line labels and the third argument contains special strings that the function understands): "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Preparations:  rho0\n",
      "Measurements:  Mdefault\n",
      "Layer Ops:  [], Gx:0, Gy:0, Gx:1, Gy:1, Gcnot:0:1\n"
     ]
    }
   ],
   "source": [
    "print(\"Preparations: \", ', '.join(map(str,mdl.preps.keys())))\n",
    "print(\"Measurements: \", ', '.join(map(str,mdl.povms.keys())))\n",
    "print(\"Layer Ops: \",    ', '.join(map(str,mdl.operations.keys())))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now use this model to do what models were made to do: compute the outcome probabilities of circuits."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Qubit 0 ---|Gx|-|C1|-|  |---\n",
      "Qubit 1 ---|  |-|T0|-|Gy|---\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "OutcomeLabelDict([(('00',), 0.25000000000000033),\n",
       "                  (('01',), 0.25000000000000006),\n",
       "                  (('10',), 0.25),\n",
       "                  (('11',), 0.25000000000000006)])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = Circuit( [('Gx',0),('Gcnot',0,1),('Gy',1)] , line_labels=[0,1])\n",
    "print(c)\n",
    "mdl.probs(c) # Compute the outcome probabilities of circuit `c`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An `ExplictOpModel` only \"knows\" how to operate on circuit layers it explicitly contains in its dictionaries,\n",
    "so, for example, a circuit layer with two X gates in parallel (layer-label = `[('Gx',0),('Gx',1)]`) cannot be used with our model until we explicitly associate an operation with the layer-label `[('Gx',0),('Gx',1)]`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Qubit 0 ---|Gx|-|  |---\n",
      "Qubit 1 ---|Gx|-|Gy|---\n",
      "\n",
      "!!KeyError: missing Label[[Gx:0Gx:1]]\n",
      "Probability_of_outcome(00) =  0.2500000000000002\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "c = Circuit( [[('Gx',0),('Gx',1)],('Gy',1)] , line_labels=[0,1])\n",
    "print(c)\n",
    "\n",
    "try: \n",
    "    p = mdl.probs(c)\n",
    "except KeyError as e:\n",
    "    print(\"!!KeyError: missing\",str(e))\n",
    "    \n",
    "    #Create an operation for two parallel X-gates & rerun (now it works!)\n",
    "    mdl.operations[ [('Gx',0),('Gx',1)] ] = np.dot(mdl.operations[('Gx',0)], mdl.operations[('Gx',1)])\n",
    "    p = mdl.probs(c)\n",
    "    \n",
    "print(\"Probability_of_outcome(00) = \", p['00']) # p is like a dictionary of outcomes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OutcomeLabelDict([(('00',), 0.5000000000000004),\n",
       "                  (('01',), 0.0),\n",
       "                  (('10',), 0.0),\n",
       "                  (('11',), 0.5)])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mdl.probs((('Gx',0),('Gcnot',0,1)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Implicit layer-operation models\n",
    "In the above example, you saw how it is possible to manually add a layer-operation to an `ExplicitOpModel` based on its other, more primitive layer operations.  This often works fine for a few qubits, but can quickly become tedious as the number of qubits increases (since the number of potential layers that involve a given set of gates grows exponentially with qubit number).  This is where `ImplicitOpModel` objects come into play: these models contain rules for building up arbitrary layer-operations based on more primitive operations.  PyGSTi offers several \"built-in\" types of implicit models and a rich set of tools for building your own custom ones.  See the [tutorial on implicit models](objects/ImplicitModel.ipynb) for details. \n",
    "<a id=\"datasets\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Sets\n",
    "The `DataSet` object is a container for tabulated outcome counts.  It behaves like a dictionary whose keys are `Circuit` objects and whose values are dictionaries that associate *outcome labels* with (usually) integer counts.  There are two primary ways you go about getting a `DataSet`.  The first is by reading in a simply formatted text file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading tutorial_files/Example_Dataset.txt: 100%\n"
     ]
    }
   ],
   "source": [
    "dataset_txt = \\\n",
    "\"\"\"## Columns = 00 count, 01 count, 10 count, 11 count\n",
    "{}            100   0   0   0\n",
    "Gx:0           55   5  40   0\n",
    "Gx:0Gy:1       20  27  23  30\n",
    "Gx:0^4         85   3  10   2\n",
    "Gx:0Gcnot:0:1  45   1   4  50\n",
    "[Gx:0Gx:1]Gy:0 25  32  17  26\n",
    "\"\"\"\n",
    "with open(\"tutorial_files/Example_Dataset.txt\",\"w\") as f:\n",
    "    f.write(dataset_txt)\n",
    "ds = pygsti.io.load_dataset(\"tutorial_files/Example_Dataset.txt\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The second is by simulating a `Model` and thereby generating \"fake data\".  This essentially calls `mdl.probs(c)` for each circuit in a given list, and samples from the output probability distribution to obtain outcome counts:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "circuit_list = pygsti.construction.circuit_list([ (), \n",
    "                                                  (('Gx',0),),\n",
    "                                                  (('Gx',0),('Gy',1)),\n",
    "                                                  (('Gx',0),)*4,\n",
    "                                                  (('Gx',0),('Gcnot',0,1)),\n",
    "                                                  ((('Gx',0),('Gx',1)),('Gy',0)) ], line_labels=(0,1))\n",
    "ds_fake = pygsti.construction.generate_fake_data(mdl, circuit_list, nSamples=100,\n",
    "                                                 sampleError='multinomial', seed=8675309)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Outcome counts are accessible by indexing a `DataSet` as if it were a dictionary with `Circuit` keys:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{('00',): 20.0, ('01',): 27.0, ('10',): 23.0, ('11',): 30.0}\n",
      "{('00',): 20.0, ('01',): 27.0, ('10',): 23.0, ('11',): 30.0}\n"
     ]
    }
   ],
   "source": [
    "c = Circuit( (('Gx',0),('Gy',1)), line_labels=(0,1) )\n",
    "print(ds[c])                     # index using a Circuit\n",
    "print(ds[ [('Gx',0),('Gy',1)] ]) # or with something that can be converted to a Circuit "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because `DataSet` object can also store *timestamped* data (see the [time-dependent data tutorial](objects/advanced/TimestampedDataSets.ipynb), the values or \"rows\" of a `DataSet` aren't simple dictionary objects.  When you'd like a `dict` of counts use the `.counts` member of a data set row:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('00',) 20.0\n",
      "('01',) 27.0\n",
      "('10',) 23.0\n",
      "('11',) 30.0\n"
     ]
    }
   ],
   "source": [
    "row = ds[c]\n",
    "row['00'] # this is ok\n",
    "for outlbl, cnt in row.counts.items(): # Note: `row` doesn't have .items(), need \".counts\"\n",
    "    print(outlbl, cnt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another thing to note is that `DataSet` objects are \"sparse\" in that 0-counts are not typically stored:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No 01 or 11 outcomes here:  {('00',): 50.0, ('10',): 50.0}\n",
      "Item:  ('00',) 50.0\n",
      "Item:  ('10',) 50.0\n"
     ]
    }
   ],
   "source": [
    "c = Circuit([('Gx',0)], line_labels=(0,1))\n",
    "print(\"No 01 or 11 outcomes here: \",ds_fake[c])\n",
    "for outlbl, cnt in ds_fake[c].counts.items():\n",
    "    print(\"Item: \",outlbl, cnt) # Note: this loop never loops over 01 or 11!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can manipulate `DataSets` in a variety of ways, including:\n",
    "- adding and removing rows\n",
    "- \"trucating\" a `DataSet` to include only a subset of it's string\n",
    "- \"filtering\" a $n$-qubit `DataSet` to a $m < n$-qubit dataset\n",
    "\n",
    "To find out more about these and other operations, see our [data set tutorial](objects/DataSet.ipynb)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What's next?\n",
    "You've learned about the three main object types in pyGSTi!  The next step is to learn about how these objects are used within pyGSTi, which is the topic of the next [overview tutorial on applications](02-Applications.ipynb).  Alternatively, if you're interested in learning more about the above-described or other objects, here are some links to relevant tutorials:\n",
    "- [Circuit](objects/Circuit.ipynb) - how to build circuits ([GST circuits](objects/advanced/GSTCircuitConstruction.ipynb) in particular)\n",
    "- [ExplicitModel](objects/ExplicitModel.ipynb) - constructing explicit layer-operation models\n",
    "- [ImplicitModel](objects/ImplicitModel.ipynb) - constructing implicit layer-operation models\n",
    "- [DataSet](objects/DataSet.ipynb) - constructing data sets ([timestamped data](objects/advanced/TimestampedDataSets.ipynb) in particular)\n",
    "- [Basis](objects/advanced/MatrixBases.ipynb) - defining matrix and vector bases\n",
    "- [Results](objects/advanced/Results.ipynb) - the container object for model-based results\n",
    "- [ProcessorSpec](objects/advanced/ProcessorSpec.ipynb) - represents a QIP as a collection of models and meta information. \n",
    "- [Instrument](objects/advanced/Instruments.ipynb) - allows for circuits with intermediate measurements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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