humble/notebooks/PlotGithubStats.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "path = \"./data/git_metrics/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "fast_commit = pd.read_csv(path+\"fast_commit.csv\")\n",
    "fast_issue = pd.read_csv(path+\"fast_issue.csv\")\n",
    "fast_pr = pd.read_csv(path+\"fast_pr.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24\n"
     ]
    }
   ],
   "source": [
    "fast_rmw_commit = pd.read_csv(path+\"fast_rmw_commit.csv\")\n",
    "fast_rmw_issue = pd.read_csv(path+\"fast_rmw_issue.csv\")\n",
    "fast_rmw_pr = pd.read_csv(path+\"fast_rmw_pr.csv\")\n",
    "print(len(fast_rmw_pr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "cyclone_commit = pd.read_csv(path+\"cyclone_commit.csv\")\n",
    "cyclone_issue = pd.read_csv(path+\"cyclone_issue.csv\")\n",
    "cyclone_pr = pd.read_csv(path+\"cyclone_pr.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "cyclone_rmw_commit = pd.read_csv(path+\"cyclone_rmw_commit.csv\")\n",
    "cyclone_rmw_issue = pd.read_csv(path+\"cyclone_rmw_issue.csv\")\n",
    "cyclone_rmw_pr = pd.read_csv(path+\"cyclone_rmw_pr.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[38, 2, 17, 2] [181, 22, 125, 24]\n",
      "[105, 14, 61, 8] [236, 29, 159, 32]\n"
     ]
    }
   ],
   "source": [
    "# Count the total open and closed PR and issues\n",
    "\n",
    "pr_set = [fast_pr,fast_rmw_pr,cyclone_pr,cyclone_rmw_pr]\n",
    "issue_set = [fast_issue,fast_rmw_issue,cyclone_issue,cyclone_rmw_issue]\n",
    "names = [\"eProsima/Fast-RTPS\", \"ros2/rmw_fastrtps\", \"eclipse-cyclonedds/cyclonedds\", \"ros2/rmw_cyclonedds\"]\n",
    "\n",
    "\n",
    "# Open and Closed PRs\n",
    "closed_pr = []\n",
    "still_open_pr = []\n",
    "for df in pr_set:\n",
    "    vals = df[\"state\"].value_counts()\n",
    "    if \"open\" in vals.keys():\n",
    "        still_open_pr.append(vals[\"open\"])\n",
    "    else: # no open PRS!!! \n",
    "        still_open_pr.append(0)\n",
    "\n",
    "    if \"closed\" in vals.keys():\n",
    "        closed_pr.append(vals[\"closed\"])\n",
    "    else: # no open PRS!!! \n",
    "        closed_pr.append(0)\n",
    "        \n",
    "        \n",
    "closed_issues = []\n",
    "still_open_issues = []\n",
    "for df in issue_set:\n",
    "    vals = df[\"state\"].value_counts()\n",
    "    if \"open\" in vals.keys():\n",
    "        still_open_issues.append(vals[\"open\"])\n",
    "    else: # no open PRS!!! \n",
    "        still_open_issue.append(0)\n",
    "\n",
    "    if \"closed\" in vals.keys():\n",
    "        closed_issues.append(vals[\"closed\"])\n",
    "    else: # no open PRS!!! \n",
    "        closed_issues.append(0)        \n",
    "               \n",
    "print(still_open_pr,closed_pr)\n",
    "print(still_open_issues,closed_issues)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 0, 0, 0]\n",
      "[0, 0, 0, 0]\n",
      "[0, 0, 0, 0]\n",
      "[0, 0, 0, 0]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1188x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def autolabel(rects,offs=None):\n",
    "    \"\"\"Attach a text label above each bar in *rects*, displaying its height.\"\"\"\n",
    "    if offs is None:\n",
    "        offs = [0 for i in range(0,len(rects))]\n",
    "    print(offs)\n",
    "    for rect,off in zip(rects,offs):\n",
    "        height = rect.get_height()\n",
    "        ax.annotate('{}'.format(height),\n",
    "                    xy=(rect.get_x() + rect.get_width() / 2, height+off),\n",
    "                    xytext=(0, 0),  # 3 points vertical offset\n",
    "                    textcoords=\"offset points\",\n",
    "                    fontsize =14,\n",
    "                    ha='center', va='center',rotation=0)\n",
    "\n",
    "N = len(closed_pr)\n",
    "\n",
    "ind = np.arange(N)    # the x locations for the groups\n",
    "width = 0.35       # the width of the bars: can also be len(x) sequence\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "fig.set_size_inches(16.5,12) \n",
    "\n",
    "p1 = plt.bar(ind, still_open_pr, width)\n",
    "\n",
    "p2 = plt.bar(ind, closed_pr, width,bottom=still_open_pr)\n",
    "\n",
    "p3 = plt.bar(ind+width, still_open_issues, width)\n",
    "\n",
    "p4 = plt.bar(ind+width, closed_issues, width,\n",
    "             bottom=still_open_issues,)\n",
    "\n",
    "autolabel(p1)\n",
    "autolabel(p2, offs=[0,0,0,0])\n",
    "autolabel(p3)\n",
    "autolabel(p4, offs=[0,0,0,0])\n",
    "\n",
    "\n",
    "plt.ylabel('Count')\n",
    "plt.title('Open and Closed Issues and Pull Requests in the Previous Six Months')\n",
    "plt.xticks(ind+(width/2), names)\n",
    "plt.legend(('Open Pull Requests','Closed Pull Requests', 'Open Issues','Closed Issues'))\n",
    "plt.grid()\n",
    "plt.savefig(\"./plots/PullRequestsAndIssues.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1152 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Now do PR / Issue Burn Downs\n",
    "fig, ax = plt.subplots(2,figsize=(16, 16))\n",
    "n_bins = 30*6 # bin per day\n",
    "# convert Six months to seconds \n",
    "total_period = 6*30*24*60*60\n",
    "\n",
    "for name,df in zip(names,pr_set):\n",
    "    ax[0].set_xlim((-5,80)) # cap at 100 days \n",
    "    x = np.array(df[\"time_to_close\"].fillna(total_period).tolist()) / ((60.0**2)*24) # convert to days\n",
    "    # plot the cumulative histogram\n",
    "    n, bins, patches = ax[0].hist(x, n_bins, density=True, histtype='step',\n",
    "                               cumulative=True, label=name)\n",
    "\n",
    "\n",
    "# tidy up the figure\n",
    "ax[0].grid(True,which=\"both\")\n",
    "ax[0].legend(loc='lower right')\n",
    "ax[0].set_title('Cumulative Time to Close a Pull Request')\n",
    "ax[0].set_xlabel('Days')\n",
    "ax[0].set_ylabel('Likelihood of occurrence')\n",
    "\n",
    "\n",
    "for name,df in zip(names,issue_set):\n",
    "    ax[1].set_xlim((-5,80)) # cap at 100 days \n",
    "\n",
    "    x = np.array(df[\"turn_round\"].fillna(total_period).tolist()) / ((60.0**2)*24) # convert to days\n",
    "# plot the cumulative histogram\n",
    "    n, bins, patches = ax[1].hist(x, n_bins, density=True, histtype='step',\n",
    "                               cumulative=True, label=name)\n",
    "\n",
    "\n",
    "# tidy up the figure\n",
    "ax[1].grid(True,which=\"both\")\n",
    "ax[1].legend(loc='lower right')\n",
    "ax[1].set_title('Cumulative Time to Close an Issue')\n",
    "ax[1].set_xlabel('Days')\n",
    "ax[1].set_ylabel('Likelihood of occurrence')\n",
    "\n",
    "\n",
    "plt.savefig(\"./plots/IssueAndPRTurnAround.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fast_issue.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}