plot_data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting matplotlib-scalebar\n",
      "  Downloading matplotlib_scalebar-0.8.1-py2.py3-none-any.whl.metadata (13 kB)\n",
      "Requirement already satisfied: matplotlib in /home/tobi/.local/lib/python3.9/site-packages (from matplotlib-scalebar) (3.5.1)\n",
      "Requirement already satisfied: cycler>=0.10 in /home/tobi/.local/lib/python3.9/site-packages (from matplotlib->matplotlib-scalebar) (0.11.0)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in /home/tobi/.local/lib/python3.9/site-packages (from matplotlib->matplotlib-scalebar) (4.33.3)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /home/tobi/.local/lib/python3.9/site-packages (from matplotlib->matplotlib-scalebar) (1.4.2)\n",
      "Requirement already satisfied: numpy>=1.17 in ./.conda/lib/python3.9/site-packages (from matplotlib->matplotlib-scalebar) (1.19.5)\n",
      "Requirement already satisfied: packaging>=20.0 in /home/tobi/.local/lib/python3.9/site-packages (from matplotlib->matplotlib-scalebar) (21.3)\n",
      "Requirement already satisfied: pillow>=6.2.0 in /home/tobi/.local/lib/python3.9/site-packages (from matplotlib->matplotlib-scalebar) (9.1.0)\n",
      "Requirement already satisfied: pyparsing>=2.2.1 in /home/tobi/.local/lib/python3.9/site-packages (from matplotlib->matplotlib-scalebar) (3.0.8)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in /home/tobi/.local/lib/python3.9/site-packages (from matplotlib->matplotlib-scalebar) (2.8.2)\n",
      "Requirement already satisfied: six>=1.5 in ./.conda/lib/python3.9/site-packages (from python-dateutil>=2.7->matplotlib->matplotlib-scalebar) (1.15.0)\n",
      "Downloading matplotlib_scalebar-0.8.1-py2.py3-none-any.whl (17 kB)\n",
      "Installing collected packages: matplotlib-scalebar\n",
      "Successfully installed matplotlib-scalebar-0.8.1\n"
     ]
    }
   ],
   "source": [
    "!pip install matplotlib-scalebar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "file logging/roshambo-log-20241016-1700.csv:\n",
      "file logging/roshambo-log-20241016-1555.csv:\n",
      "file logging/actions-log-2024-10-24-0208.csv:\n",
      "file logging/actions-log-2024-10-22-1808.csv:\n",
      "logging/actions-log-2024-10-24-0608.csv is empty, skipping\n",
      "file logging/roshambo-log-20241014-1336.csv:\n",
      "file logging/actions-log-2024-10-22-1002.csv:\n",
      "file logging/actions-log-2024-10-21-1802.csv:\n",
      "file logging/actions-log-2024-10-20-1821.csv:\n",
      "file logging/actions-log-2024-10-23-2208.csv:\n",
      "file logging/actions-log-2024-10-23-1408.csv:\n",
      "file logging/actions-log-2024-10-23-0608.csv:\n",
      "file logging/actions-log-2024-10-21-0621.csv:\n",
      "file logging/actions-log-2024-10-22-0602.csv:\n",
      "file logging/actions-log-2024-10-23-1008.csv:\n",
      "file logging/actions-log-2024-10-20-2221.csv:\n",
      "file logging/actions-log-2024-10-23-0208.csv:\n",
      "file logging/actions-log-2024-10-23-1808.csv:\n",
      "file logging/roshambo-log-20241011-1802.csv:\n",
      "file logging/actions-log-2024-10-22-1408.csv:\n",
      "file logging/actions-log-2024-10-21-1402.csv:\n",
      "file logging/actions-log-2024-10-22-2208.csv:\n",
      "file logging/actions-log-2024-10-22-1008.csv:\n",
      "file logging/actions-log-2024-10-21-2202.csv:\n",
      "file logging/actions-log-2024-10-22-0202.csv:\n",
      "file logging/actions-log-2024-10-21-0221.csv:\n",
      "file logging/actions-log-2024-10-21-1002.csv:\n",
      "file logging/roshambo-log-20241011-1512.csv:\n",
      "2024-Oct-11 to 2024-Oct-24\n",
      "12.6d: 4,188 movements (332.0 moves/d)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.1, 40.5, '2024-Oct-11 to 2024-Oct-24\\n12.6d: 4,188 movements (332.0 moves/d)')"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import os\n",
    "from typing import Union\n",
    "import matplotlib.pyplot as plt\n",
    "import csv\n",
    "from glob import glob\n",
    "from numpy import genfromtxt\n",
    "import numpy as np\n",
    "import matplotlib.ticker as plticker\n",
    "from matplotlib_scalebar.scalebar import ScaleBar\n",
    "import datetime\n",
    "\n",
    "dir='logging'\n",
    "data=np.empty((0,6),dtype=int) # year,day_of_year,weekday,hour,minute,museum_movements_this_hour\n",
    "elapsed_minutes=[] # populated with correct data depending on CSV format (old with only 6 columns or new with elapsed minutes column)\n",
    "minutes=[]\n",
    "for f in glob(os.path.join(dir,'*.csv')):\n",
    "    if os.path.getsize(f)==0:\n",
    "        print(f'{f} is empty, skipping')\n",
    "        continue   \n",
    "    print(f'file {f}:')\n",
    "    rows = genfromtxt(f, delimiter=',', encoding=\"utf8\", dtype=int, skip_header=1)\n",
    "    # print(row)\n",
    "    if len(rows.shape)==2 and rows.shape[0]>1: # has more than 1 row of data (first row is header that is int-parsed as vector of -1 values)\n",
    "        if rows.shape[1]==6: # old format, does not have minutes_since_last\n",
    "            data=np.append(data,rows[:,:],axis=0)\n",
    "            elapsed_minutes.append(data)\n",
    "        elif rows.shape[1]==7: # new format\n",
    "            idx=[0,1,2,3,4,6]\n",
    "            data=np.append(data,rows[:,idx],axis=0)\n",
    "            \n",
    "years=data[:,0]\n",
    "days=data[:,1]\n",
    "hours=data[:,3]\n",
    "minutes=data[:,4]\n",
    "year_frac_days=(days+hours/24.0+minutes/(24.*60)) # fraction of year in days\n",
    "frac_days=year_frac_days%7 # fraction of week\n",
    "int_weeks=np.floor(year_frac_days/7) # floored year weeks\n",
    "start_week=np.min(int_weeks)\n",
    "moves=data[:,5]\n",
    "moves_nonzero=moves.astype(np.float32)\n",
    "moves_nonzero[moves_nonzero==0]=np.nan\n",
    "max_moves=np.max(moves)\n",
    "norm_moves=(moves_nonzero/max_moves)+int_weeks # shift weeks vertically\n",
    "datetimes=[]\n",
    "for i in range(len(years)):\n",
    "    datetimes.append(datetime.datetime(years[i], 1, 1) + datetime.timedelta(float(days[i]) - 1))\n",
    "datetimes.sort()\n",
    "# start/end date\n",
    "date_start= datetimes[0]\n",
    "date_end  = datetimes[-1]\n",
    "\n",
    "\n",
    "fig=plt.figure(figsize=(10,4))\n",
    "x=frac_days\n",
    "y=norm_moves\n",
    "plt.plot(x,y,'o')\n",
    "plt.xlabel('days of week')\n",
    "plt.ylabel('week of year')\n",
    "plt.xlim([0,7])\n",
    "plt.title(f'Dextra robot hand movements (max {max_moves})')\n",
    "xticks=plt.gca().get_xticks()\n",
    "weekdays=['Su','M','Tu','W','Th','Fr','Sa','']\n",
    "# with_noons=[]\n",
    "# for w in weekdays:\n",
    "#     with_noons.append(w)\n",
    "#     with_noons.append('12')\n",
    "# plt.xticks(range(len(with_noons)),with_noons)\n",
    "plt.xticks(range(len(weekdays)),weekdays)\n",
    "loc = plticker.MultipleLocator(base=1.0)\n",
    "plt.gca().yaxis.set_major_locator(loc)\n",
    "plt.grid(True)\n",
    "# for w in int_weeks:\n",
    "#     plt.plot([0,8],[w,w],linewidth=0.05,color='k')\n",
    "\n",
    "total_moves=np.sum(moves)\n",
    "total_days=np.max(year_frac_days)-np.min(year_frac_days)\n",
    "moves_per_day=total_moves/total_days\n",
    "t=f'{date_start.strftime(\"%Y-%b-%d\")} to {date_end.strftime(\"%Y-%b-%d\")}\\n{total_days:.1f}d: {total_moves:,} movements ({moves_per_day:.1f} moves/d)'\n",
    "print(t)\n",
    "plt.text(.1,np.min(int_weeks)+.5,t, color='b', fontsize=12)\n",
    "\n",
    "\n",
    "# scalebar = ScaleBar(max_moves, \"cm\", length_fraction=0.25, rotation='vertical')\n",
    "# plt.gca().add_artist(scalebar)"
   ]
  }
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