{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/usnistgov/AFL-agent/blob/main/docs/source/how-to/saving_pipelines.ipynb)\n",
    "\n",
    "# Reading and Writing Pipelines \n",
    "\n",
    "Its"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Google Colab Setup\n",
    "\n",
    "Only uncomment and run the next cell if you are running this notebook in Google Colab or if don't already have the AFL-agent package installed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install git+https://github.com/usnistgov/AFL-agent.git"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Writing A Pipeline\n",
    "\n",
    "To begin, let's load the necessary libraries and define a short pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PipelineOp                               input_variable ---> output_variable\n",
      "----------                               -----------------------------------\n",
      "0  ) <SavgolFilter>                      measurement ---> derivative\n",
      "1  ) <SimilarityMetric>                  derivative ---> similarity\n",
      "2  ) <SpectralClustering>                similarity ---> labels\n",
      "\n",
      "Input Variables\n",
      "---------------\n",
      "0) measurement\n",
      "\n",
      "Output Variables\n",
      "----------------\n",
      "0) labels\n"
     ]
    }
   ],
   "source": [
    "from AFL.double_agent import *\n",
    "\n",
    "with Pipeline('MyPipeline') as my_important_pipeline:\n",
    "\n",
    "        SavgolFilter(\n",
    "            input_variable='measurement', \n",
    "            output_variable='derivative', \n",
    "            dim='x', \n",
    "            derivative=1\n",
    "            )\n",
    "\n",
    "        Similarity(\n",
    "            input_variable='derivative', \n",
    "            output_variable='similarity', \n",
    "            sample_dim='sample',\n",
    "            params={'metric': 'laplacian','gamma':1e-4}\n",
    "            )\n",
    "    \n",
    "        SpectralClustering(\n",
    "            input_variable='similarity',\n",
    "            output_variable='labels',\n",
    "            dim='sample',\n",
    "            params={'n_phases': 2}\n",
    "            )\n",
    "\n",
    "my_important_pipeline.print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we can write the pipeline by simply calling the `.write_json()` method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline successfully written to pipeline.json.\n"
     ]
    }
   ],
   "source": [
    "my_important_pipeline.write_json('pipeline.json')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Excellent! Let's take a look at the json file that was written. We can inspect it using the json module"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'name': 'MyPipeline',\n",
       " 'date': '03/04/25 19:59:19-576491',\n",
       " 'ops': [{'class': 'AFL.double_agent.Preprocessor.SavgolFilter',\n",
       "   'args': {'input_variable': 'measurement',\n",
       "    'output_variable': 'derivative',\n",
       "    'dim': 'x',\n",
       "    'xlo': None,\n",
       "    'xhi': None,\n",
       "    'xlo_isel': None,\n",
       "    'xhi_isel': None,\n",
       "    'pedestal': None,\n",
       "    'npts': 250,\n",
       "    'derivative': 1,\n",
       "    'window_length': 31,\n",
       "    'polyorder': 2,\n",
       "    'apply_log_scale': True,\n",
       "    'name': 'SavgolFilter'}},\n",
       "  {'class': 'AFL.double_agent.PairMetric.Similarity',\n",
       "   'args': {'input_variable': 'derivative',\n",
       "    'output_variable': 'similarity',\n",
       "    'sample_dim': 'sample',\n",
       "    'params': {'metric': 'laplacian', 'gamma': 0.0001},\n",
       "    'constrain_same': [],\n",
       "    'constrain_different': [],\n",
       "    'name': 'SimilarityMetric'}},\n",
       "  {'class': 'AFL.double_agent.Labeler.SpectralClustering',\n",
       "   'args': {'input_variable': 'similarity',\n",
       "    'output_variable': 'labels',\n",
       "    'dim': 'sample',\n",
       "    'params': {'n_phases': 2},\n",
       "    'name': 'SpectralClustering',\n",
       "    'use_silhouette': False}}]}"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import json\n",
    "\n",
    "with open('pipeline.json','r') as f:\n",
    "    display(json.load(f))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With this, we can see that all of the `PipelineOps` are stored in the `ops` keyword with the keyword arguments we specified above. Also included are any default arguments that we didn't explicitly specify. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading a Pipeline\n",
    "\n",
    "So the next and final step is to load the pipeline from disk."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PipelineOp                               input_variable ---> output_variable\n",
      "----------                               -----------------------------------\n",
      "0  ) <SavgolFilter>                      measurement ---> derivative\n",
      "1  ) <SimilarityMetric>                  derivative ---> similarity\n",
      "2  ) <SpectralClustering>                similarity ---> labels\n",
      "\n",
      "Input Variables\n",
      "---------------\n",
      "0) measurement\n",
      "\n",
      "Output Variables\n",
      "----------------\n",
      "0) labels\n"
     ]
    }
   ],
   "source": [
    "loaded_pipeline = Pipeline.read_json('pipeline.json')\n",
    "loaded_pipeline.print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Success!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion"
   ]
  }
 ],
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