fah-api-tracker.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "30fb6049-1b72-4441-9143-ac88c6c94b79",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import requests\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import subprocess"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8ff794dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams.update({'font.size': 22})\n",
    "# Set rcParams for matplotlib so all plots have opaque background\n",
    "plt.rcParams['figure.facecolor'] = 'white'"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "61f1aebd-8e6f-4ef6-8018-6d83dbb2b093",
   "metadata": {},
   "source": [
    "# Checking Progress of FAH Project\n",
    "\n",
    "This notebook uses API calls to pull down project data about how much data a FAH project\\\n",
    "has collected so far. With this you can check for the progress of projects. "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "96bc41b5-6bd0-4e7d-a42e-f3d1a8e817bc",
   "metadata": {},
   "source": [
    "# Define key variables\n",
    "\n",
    "Also define paths to your license and key files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2d2b416c-cd5d-4180-befa-8b7707033f28",
   "metadata": {},
   "outputs": [],
   "source": [
    "project_number = 17636\n",
    "num_ns_per_wu = 10 # 10 ns per work unit - this could change depending on the project\n",
    "license_path = \"/home/sukrit/licenses/fah-cert-sukritsingh.pem\"\n",
    "key_path = \"/home/sukrit/licenses/private-sukritsingh.pem\"\n",
    "work_server_url = \"https://pllwskifah2.mskcc.org\" #Ensure the WS URL is correct for your project"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "e8aa9097",
   "metadata": {},
   "source": [
    "## Pull down summary information from the API"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "9aedfce2",
   "metadata": {},
   "source": [
    "Now use requests and json to pull down the JSON file returned by the API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4e1b0016",
   "metadata": {},
   "outputs": [],
   "source": [
    "# use requests to get the project data\n",
    "r = requests.get(\"%s/api/projects/%s\" % (work_server_url, project_number), \n",
    "                 cert=(license_path, key_path))\n",
    "project_data = r.json()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "db4978e8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Project data received successfully\n"
     ]
    }
   ],
   "source": [
    "# assert that len(project_data) = 20 to ensure a correct response was received from the WS\n",
    "try: \n",
    "    assert len(project_data) == 20\n",
    "    print(\"Project data received successfully\")\n",
    "except AssertionError as e:\n",
    "    print(\"Error: %s\" % e)\n",
    "    print(\"Ensure the WS URL is correct for your project\")\n",
    "    print(\"Ensure the license and key paths are correct\")\n",
    "    print(\"Ensure the project number is correct\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "78ea291e-056c-44d5-9ea4-4ac3272db631",
   "metadata": {},
   "source": [
    "## Process summary API JSON file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3a2bda5b-2d5a-496f-af33-1d7abfbd5375",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of max possible gens: 1000\n",
      "Total dataset so far: 1878810 nanoseconds\n"
     ]
    }
   ],
   "source": [
    "wu_hist = np.asarray(project_data['histogram'])\n",
    "wu_gens = int(project_data['gens']) # the max number of possible generations being histogrammed\n",
    "wu_total = int(project_data['gens_completed'])\n",
    "print(\"Number of max possible gens: %s\" % wu_gens)\n",
    "print(\"Total dataset so far: %s nanoseconds\" % (wu_total*num_ns_per_wu))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e008286d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Condense the histogram to stop at the last generation with work units\n",
    "# find where the last work unit was generated\n",
    "trunc_ind = np.where(wu_hist > 0)[0][-1] # trunc_ind indicates the bin that contains the longest trajectory\n",
    "wu_hist_trunc = wu_hist[:trunc_ind+1] # truncate the histogram to only include the bins with work units\n",
    "\n",
    "# Convert trunc_ind also to the max possible generation run\n",
    "trunc_gen = (trunc_ind*wu_gens)/1000 # we divide by number of buckets\n",
    "gen_vals = np.linspace(0, int(trunc_gen)+1, num=trunc_ind+1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "61e13771",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the histogram\n",
    "ax = plt.figure(figsize=(20,10))\n",
    "plt.bar(gen_vals, wu_hist_trunc)    \n",
    "plt.xlabel(\"Number of generations\")\n",
    "plt.ylabel(\"Number of Trajectories\")\n",
    "plt.title(\"Project %s: %s ns Total\" % (project_number, wu_total*num_ns_per_wu))\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "aadf5af1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the histogram but for trajectory length\n",
    "plt.figure(figsize=(20,10))\n",
    "plt.bar(gen_vals*num_ns_per_wu,wu_hist_trunc, width=num_ns_per_wu)\n",
    "plt.xlabel(\"Trajectory length (ns)\")\n",
    "plt.ylabel(\"Number of Trajectories\")\n",
    "plt.title(\"Project %s: %s ns Total\" % (project_number, wu_total*num_ns_per_wu))\n",
    "plt.savefig(\"/home/sukrit/downloads/progress-17638.png\", dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "afdeafb1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the cumulative histogram along trajectory length\n",
    "plt.figure(figsize=(20,10))\n",
    "\n",
    "# Get the cumulative histogram\n",
    "cum_hist = np.cumsum(wu_hist_trunc)/np.sum(wu_hist_trunc)\n",
    "plt.bar(gen_vals*num_ns_per_wu,cum_hist*100, width=num_ns_per_wu)\n",
    "plt.xlabel(\"Trajectory length (ns)\")\n",
    "plt.ylabel(\"Cumulative % of Trajectories\")\n",
    "plt.title(\"Project %s: %s ns Total\" % (project_number, wu_total*num_ns_per_wu))\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "c3df0411",
   "metadata": {},
   "source": [
    "# Requesting job-level information for detailed parsing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "2ae78c37",
   "metadata": {},
   "outputs": [],
   "source": [
    "# use requests to get the project data\n",
    "r = requests.get(\"%s/api/projects/%s/jobs\" % (work_server_url, project_number), \n",
    "                 cert=(license_path, key_path))\n",
    "job_data = r.json()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3d757458",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Job data received successfully\n"
     ]
    }
   ],
   "source": [
    "# confirm everything returned okay\n",
    "if 'error' in job_data.keys(): \n",
    "    print('Issue')\n",
    "else:\n",
    "    print(\"Job data received successfully\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "973ab461",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of runs: 8\n"
     ]
    }
   ],
   "source": [
    "wu_data = job_data['jobs']\n",
    "num_runs = project_data['runs']\n",
    "print(\"Number of runs: %s\" % num_runs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "cbcefbd3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a list of lists whose length is defined by num_runs\n",
    "wu_data_runs = [[] for i in range(num_runs)]\n",
    "\n",
    "# Iterate through each element of wu_data, and add wu_data['gen'] to the appropriate index\n",
    "# The appropriate index is defined by wu_data['run']\n",
    "for wu in wu_data:\n",
    "    wu_data_runs[wu['run']].append(wu['gen'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c7db57e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the histogram but for trajectory length for each run\n",
    "plt.figure(figsize=(20,10))\n",
    "for i in range(num_runs):\n",
    "    plt.hist(np.array(wu_data_runs[i])*num_ns_per_wu, bins=50, histtype='bar', label=\"Run %s\" % i, alpha=0.5)\n",
    "plt.xlabel(\"Trajectory length (ns)\")\n",
    "plt.ylabel(\"Number of Trajectories\")\n",
    "plt.title(\"Project %s: %s ns Total\" % (project_number, wu_total*num_ns_per_wu))\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "5e5a59d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the histogram but for trajectory length for each run\n",
    "plt.figure(figsize=(20,10))\n",
    "hist_vals = np.concatenate(wu_data_runs)*num_ns_per_wu\n",
    "# for i in range(num_runs):\n",
    "plt.hist(hist_vals, bins=50, histtype='bar')\n",
    "plt.xlabel(\"Trajectory length (ns)\")\n",
    "plt.ylabel(\"Number of Trajectories\")\n",
    "plt.title(\"Project %s: %s ns Total\" % (project_number, wu_total*num_ns_per_wu))\n",
    "# plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e068b79a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b2ace2fb",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.8.5"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {},
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}