between_the_lines/user_study_analysis/viz_draw.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import json\n",
    "import copy\n",
    "import json\n",
    "from tqdm import tqdm\n",
    "import pickle\n",
    "import logging\n",
    "import math\n",
    "import datetime as dt\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "from matplotlib.collections import PolyCollection\n",
    "# import patch\n",
    "from matplotlib.patches import Polygon\n",
    "from matplotlib.colors import colorConverter\n",
    "from matplotlib.dates import date2num\n",
    "from matplotlib.dates import num2date\n",
    "from matplotlib.dates import DateFormatter\n",
    "# impor Line2d\n",
    "from matplotlib.lines import Line2D\n",
    "from matplotlib.patches import Patch\n",
    "from collections import namedtuple\n",
    "import datetime\n",
    "\n",
    "# open pickle logs_by_user_session_labeled\n",
    "with open('../data/data_labeled_study.pkl', 'rb') as f:\n",
    "    logs_by_user_session_labeled = pickle.load(f)\n",
    "import matplotlib\n",
    "matplotlib.rcParams['pdf.fonttype'] = 42\n",
    "matplotlib.rcParams['ps.fonttype'] = 42\n",
    "plt.rc('text', usetex=True)\n",
    "plt.rc('font', family='sans-serif')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Draw Timelines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2880x1080 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "###################################\n",
    "# To change which users and time window, change the following \n",
    "initial_index = 0\n",
    "user_ids = [4,5,8,6,7]\n",
    "\n",
    "time_window = 3*60 # in seconds \n",
    "max_lenghts = [50] * len(logs_by_user_session_labeled)\n",
    "###################################\n",
    "for i in range(len(logs_by_user_session_labeled)):\n",
    "    times = logs_by_user_session_labeled[i].TimeSpentInState.to_numpy()\n",
    "    # get cummulative times\n",
    "    times = np.cumsum(times)\n",
    "    first_index = len(times) - 1\n",
    "    for j in range(len(times)):\n",
    "        if times[j] > time_window:\n",
    "            first_index = j\n",
    "            break\n",
    "    max_lenghts[i] = first_index\n",
    "#print(max_lenghts)\n",
    "max_lenghts = np.array(max_lenghts)\n",
    "max_lenghts = max_lenghts[user_ids]\n",
    "\n",
    "# enumerate user_ids\n",
    "fig, ax = plt.subplots(len(user_ids))\n",
    "# for each user_id get state and concatenate\n",
    "all_states = []\n",
    "for i, user_id in enumerate(user_ids):\n",
    "    states = logs_by_user_session_labeled[user_id].LabeledState.to_numpy()[initial_index:max_lenghts[i]]\n",
    "    all_states.extend(states)\n",
    "\n",
    "states_unique = np.unique(all_states)\n",
    "states_unique_dict = {states_unique[i]:i for i in range(len(states_unique))}\n",
    "# color mapping more distinct colors\n",
    "# make colors very distinct\n",
    "\n",
    "\n",
    "cset = namedtuple('Mcset',\n",
    "            'rose indigo sand green cyan wine teal olive purple pale_grey black white yellow')\n",
    "colors = cset('#CC6677', '#332288', '#DDCC77', '#117733', '#88CCEE',\n",
    "            '#882255', '#44AA99', '#999933', '#AA4499', '#DDDDDD',\n",
    "            '#000000', '#FFFFFF', '#DDAA33')\n",
    "\n",
    "color_mapping = {'Debugging/Testing Code (H)': colors.yellow, 'Deferring Thought For Later (D)': colors.cyan, 'Editing Last Suggestion (X)': colors.rose, 'Editing Written Code(C)': colors.purple, 'Looking up Documentation (N)': colors.olive, 'Not Thinking': colors.white,  'Prompt Crafting (V)': colors.indigo, 'Thinking About New Code To Write (F)': colors.teal, 'Thinking/Verifying Suggestion (A)': colors.green, 'Waiting For Suggestion (G)': colors.pale_grey, 'Writing Documentation (B)': colors.sand, 'Writing New Functionality (Z)': colors.wine}\n",
    "\n",
    "\n",
    "colormapping = {states_unique[i]:color_mapping[states_unique[i]] for i in range(len(states_unique))}\n",
    "\n",
    "\n",
    "for id_plot, user_id in enumerate(user_ids):\n",
    "    states = logs_by_user_session_labeled[user_id].LabeledState.to_numpy()[initial_index:max_lenghts[id_plot]]\n",
    "    times = logs_by_user_session_labeled[user_id].TimeGenerated.to_numpy()[initial_index:max_lenghts[id_plot]]\n",
    "    times = [pd.to_datetime(t) for t in times]\n",
    "    times = [times[i]-times[0] for i in range(len(times)) ]\n",
    "    times = [t.total_seconds() for t in times]\n",
    "    actions = logs_by_user_session_labeled[user_id].StateName.to_numpy()[initial_index:max_lenghts[id_plot]]\n",
    "\n",
    "    data_states = [ (times[i-1], times[i], states[i]) for i in range(1,len(states)) ]\n",
    "    # states dict to index\n",
    "    # get all unique values of states\n",
    "\n",
    "    # color mapping for actions\n",
    "    colormapping_actions = {'Shown':'black', 'Replay':'black', 'Rejected':'red', 'Accepted': 'green'}\n",
    "    linestyles = {'Shown':'-', 'Replay':'-', 'Rejected':'-', 'Accepted': '--'}\n",
    "    # plot data on a timeline\n",
    "    min_d = 0\n",
    "    max_d = 0\n",
    "    for d in data_states:\n",
    "        if d[0] <= min_d:\n",
    "            min_d = d[0]\n",
    "        if d[1] >= max_d:\n",
    "            max_d = d[1]\n",
    "        ax[id_plot].axvspan(d[0], d[1], ymin=0.1, ymax= 0.9, facecolor=colormapping[d[2]],  alpha=1)\n",
    "    ax[id_plot].axvspan(min_d, max_d, ymin=0.1, ymax= 0.9, facecolor = 'None', edgecolor = 'black',  alpha=1)\n",
    "    # plot actions on a timeline\n",
    "    # add events for actions\n",
    "    for i in range(len(actions)):\n",
    "        if actions[i] in ['Shown', 'Rejected', 'Accepted']:\n",
    "            ax[id_plot].axvline(times[i], ymin=0, ymax=1, color=colormapping_actions[actions[i]], linestyle=linestyles[actions[i]])\n",
    "        #ax.text(times[i], 0.8, actions[i], ha='center', va='center')\n",
    "    #ax.set_xlim(dt.datetime(times[0]), dt.datetime(time[-1]))\n",
    "    ax[id_plot].set_ylim(0, 1)\n",
    "    ax[id_plot].set_yticks([])\n",
    "    # add legend with colormapping\n",
    "\n",
    "    # add legend for actions with are lines\n",
    "    if id_plot == len(user_ids)-1:\n",
    "        ax[id_plot].set_xlabel('Time (s)', fontsize=50)\n",
    "    #ax[id_plot].set_ylabel('State')\n",
    "    # convert time to relative time\n",
    "    # formate times to minute:seconds format\n",
    "    times_formated = [str(datetime.timedelta(seconds=int(t)))  for t in times]\n",
    "    # remove first two characters from string\n",
    "    times_formated = [t[2:] for t in times_formated]\n",
    "    # add time to plot\n",
    "    # set xticks to times\n",
    "    # keep only times for actions\n",
    "    times_actions = [t for i,t in enumerate(times) if actions[i] in ['Rejected', 'Accepted']]\n",
    "    ax[id_plot].set_xticks(times_actions)\n",
    "    #times_formated = [str(datetime.timedelta(seconds=int(t)))  for t in times_actions]    \n",
    "    #times_formated = [t[2:] for t in times_formated]\n",
    "    times_formated = [  str(int(t)) for t in times_actions]\n",
    "    # only keep times_formatted that are atleast 3 apart\n",
    "    times_x_ticks_labels = []\n",
    "    for i in range(len(times_formated)):\n",
    "        if i == 0:\n",
    "            times_x_ticks_labels.append(times_formated[i])\n",
    "\n",
    "        elif int(times_formated[i]) - int(times_formated[i-1]) > 3:\n",
    "            times_x_ticks_labels.append(times_formated[i])\n",
    "        else:\n",
    "            times_x_ticks_labels.append('')\n",
    "    ax[id_plot].set_xticklabels(times_x_ticks_labels)\n",
    "    # change font size of xticks\n",
    "    for label in ax[id_plot].xaxis.get_ticklabels():\n",
    "        label.set_fontsize(30)\n",
    "    # make x axis labels rotated\n",
    "    plt.setp(ax[id_plot].get_xticklabels(), rotation=90, ha=\"right\", rotation_mode=\"anchor\")\n",
    "    # # # show on large figure horizontally\n",
    "    ax[id_plot].spines['top'].set_visible(False)\n",
    "    ax[id_plot].spines['right'].set_visible(False)\n",
    "    ax[id_plot].spines['bottom'].set_visible(False)\n",
    "    ax[id_plot].spines['left'].set_visible(False)\n",
    "legend_elements = []\n",
    "legend_elements = [Patch(facecolor=colormapping[states_unique[i]], label=states_unique[i]) for i in range(len(states_unique))]\n",
    "# append to legend with actions with vertical dotted line\n",
    "legend_elements.append(Line2D([0], [0], color='red', linestyle='-', label='Suggestion Rejected'))\n",
    "legend_elements.append(Line2D([0], [0], color='green', linestyle='--', label='Suggestion Accepted'))\n",
    "fig.legend(handles=legend_elements, loc = 'lower center', ncol=3, fontsize=50, bbox_to_anchor=(0.5, -0.35))\n",
    "fig.set_size_inches(40, 3*len(user_ids))\n",
    "    # flip x axis labels\n",
    "    #fig.autofmt_xdate()\n",
    "\n",
    "    # save figure\n",
    "fig.tight_layout()#rect=[1, 1, 1, 1])\n",
    "# show legend\n",
    "#fig.show()\n",
    "\n",
    "fig.savefig('user_timelines'+'.pdf', dpi=1000 , bbox_inches=\"tight\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_states = []\n",
    "all_actions = []\n",
    "all_times = []\n",
    "all_states_continuous = []\n",
    "all_states_list = []\n",
    "all_times_list = []\n",
    "\n",
    "all_times_continuous = []\n",
    "for i in range(len(logs_by_user_session_labeled)):\n",
    "    states = logs_by_user_session_labeled[i].LabeledState.to_numpy()\n",
    "    actions = logs_by_user_session_labeled[i].StateName.to_numpy()\n",
    "    times = logs_by_user_session_labeled[i].TimeSpentInState.to_numpy()\n",
    "    # clean up states\n",
    "    for j in range(len(states)):\n",
    "        if states[j] == 'IDK':\n",
    "            states[j] = 'IDK (I)'\n",
    "        if states[j] == 'Looking up documentation' or states[j] == 'Looking up documentation (N)':\n",
    "            states[j] = 'Looking up Documentation (N)'\n",
    "        if states[j] == 'h':\n",
    "            states[j] = 'Thinking About New Code To Write (F)'\n",
    "        if states[j] == 'Thinking about new code to write (F)':\n",
    "            states[j] = 'Thinking About New Code To Write (F)'\n",
    "\n",
    "    # get indices where state is IDK\n",
    "    idk_indices = [i for i in range(len(states)) if states[i] == 'IDK (I)']\n",
    "    states = [states[i] for i in range(len(states)) if i not in idk_indices]\n",
    "    actions = [actions[i] for i in range(len(actions)) if i not in idk_indices]\n",
    "    times = [times[i] for i in range(len(times)) if i not in idk_indices]\n",
    "\n",
    "    states_continuous = []\n",
    "    times_continuous = []\n",
    "    # merge states with same name\n",
    "    last_state = states[0]\n",
    "    times_cumalitive = 0\n",
    "    for i in range(len(states)):\n",
    "        if states[i] != last_state:\n",
    "            states_continuous.append(last_state)\n",
    "            last_state = states[i]\n",
    "            times_continuous.append(times_cumalitive)\n",
    "            times_cumalitive = times[i]\n",
    "        else:\n",
    "            times_cumalitive += times[i]\n",
    "    states_continuous.append(last_state)\n",
    "    times_continuous.append(times_cumalitive)\n",
    "    \n",
    "    all_states_list.extend(states)\n",
    "    all_times_list.extend(times)\n",
    "    all_states_continuous.extend(states_continuous)\n",
    "    all_times_continuous.extend(times_continuous)\n",
    "    all_states.append(states)\n",
    "    all_actions.append(actions)\n",
    "    all_times.append(times)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from hmmviz import TransGraph\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "T = pd.crosstab(\n",
    "    pd.Series(all_states_continuous[:-1], name='Today'),\n",
    "    pd.Series(all_states_continuous[1:], name='Tomorrow'),\n",
    "    normalize=0\n",
    ")\n",
    "'''\n",
    "T = pd.crosstab(\n",
    "    pd.Series(all_states_continuous[:-1], name='Today'),\n",
    "    pd.Series(all_states_continuous[1:], name='Tomorrow'),\n",
    "    normalize=0\n",
    ")\n",
    "'''\n",
    "graph = TransGraph(T)\n",
    "# for each element of graph dataframe, if less than 0.1, make it\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "transition_matrix = np.array(graph.dataframe)\n",
    "nodes = list(graph.dataframe.index)\n",
    "# take each string in nodes and add new line if more than 2 words in string\n",
    "nodes_new = []\n",
    "for i in range(len(nodes)):\n",
    "    if len(nodes[i].split(' ')) > 2:\n",
    "        words = nodes[i].split(' ')\n",
    "        temp_str = ''\n",
    "        added_line = False\n",
    "        for j in range(len(words)-1):\n",
    "            temp_str += words[j] +' ' \n",
    "            if (j == 1 or len(temp_str)> 15) and not added_line:\n",
    "                temp_str += '\\n'\n",
    "                added_line = True\n",
    "        nodes_new.append(temp_str)\n",
    "    else:\n",
    "        nodes_new.append(nodes[i])\n",
    "nodes = nodes_new\n",
    "nodes[3] = 'Editing Written \\n Code'\n",
    "nodes[-2] = 'Writing \\n Documentation '\n",
    "labels_nodes = {}\n",
    "# color nodes\n",
    "frequencies = [11.31, 1.39, 11.9,4.28,7.45,0.01,11.56,10.91,22.4,4.2,0.53,14.05]\n",
    "avg_times = [29.24,  7.34, 24.22, 14.08, 106.51, 11.08, 10.67, 19.67, 10.58, 10.54,  9.12, 13.60]\n",
    "for i in range(len(nodes)):\n",
    "    #nodes[i] = nodes[i] + f' \\n {avg_times[i]:.2f}s , {frequencies[i]:.2f}per ' \n",
    "    labels_nodes[nodes[i]] = f\"{nodes[i]}\"#  \\n {avg_times[i]:.2f}s, {frequencies[i]:.2f}/100 \" \n",
    "# permute array nodes given new indices\n",
    "new_permutation = [0,4,1,8,7,5,9,11,3,2,10,6] # list(range(12))#\n",
    "nodes = [nodes[i] for i in new_permutation]\n",
    "# new permutation is the new order for nodes\n",
    "# make new transition matrix that is consistent with new permutation\n",
    "transition_matrix_new = np.zeros((len(nodes),len(nodes)))\n",
    "for i in range(len(nodes)):\n",
    "    for j in range(len(nodes)):\n",
    "        transition_matrix_new[i][j] = transition_matrix[new_permutation[i]][new_permutation[j]]\n",
    "transition_matrix = transition_matrix_new\n",
    "\n",
    "from collections import namedtuple\n",
    "\n",
    "cset = namedtuple('Mcset',\n",
    "            'rose indigo sand green cyan wine teal olive purple pale_grey black white yellow')\n",
    "colors = cset('#CC6677', '#332288', '#DDCC77', '#117733', '#88CCEE',\n",
    "            '#882255', '#44AA99', '#999933', '#AA4499', '#DDDDDD',\n",
    "            '#000000', '#FFFFFF', '#DDAA33')\n",
    "\n",
    "color_mapping = {'Debugging/Testing Code (H)': colors.yellow, 'Deferring Thought For Later (D)': colors.cyan, 'Editing Last Suggestion (X)': colors.rose, 'Editing Written Code(C)': colors.purple, 'Looking up Documentation (N)': colors.olive, 'Not Thinking': colors.white,  'Prompt Crafting (V)': colors.indigo, 'Thinking About New Code To Write (F)': colors.teal, 'Thinking/Verifying Suggestion (A)': colors.green, 'Waiting For Suggestion (G)': colors.pale_grey, 'Writing Documentation (B)': colors.sand, 'Writing New Functionality (Z)': colors.wine}\n",
    "nodes_colors = list(color_mapping.values())\n",
    "edgecolors  = {k: colors.black for k in color_mapping.keys()}\n",
    "nodes_colors = [nodes_colors[i] for i in new_permutation]\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyvis.network import Network\n",
    "import networkx as nx\n",
    "nx_graph = nx.DiGraph()\n",
    "# label all nodes with nodes\n",
    "nx_graph.add_nodes_from(nodes)\n",
    "# add edges to graph\n",
    "for i in range(len(nodes)):\n",
    "    for j in range( len(nodes)):\n",
    "        if transition_matrix[i][j] > 0.2 and transition_matrix[i][j] < 1:\n",
    "            nx_graph.add_edge(nodes[i], nodes[j], weight=transition_matrix[i][j]*15)\n",
    "            # # draw graph\n",
    "# draw graph with edge weights\n",
    "# make arrows more intense by multiplying by weight\n",
    "# print all edges in graph\n",
    "\n",
    "\n",
    "weights = nx.get_edge_attributes(nx_graph,'weight').values()\n",
    "\n",
    "# get fig and ax\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "\n",
    "nx.draw(nx_graph, with_labels = True, alpha=0.8,arrows= True, node_size=21000, font_size = 18.5, \n",
    "arrowsize=30, arrowstyle='-|>',  width = list(weights), node_color=nodes_colors, edge_color='black',\n",
    "ax = ax,     connectionstyle=\"arc3,rad=0.1\"  ,  labels = labels_nodes, font_family='sans-serif', font_weight='bold',\n",
    "pos = nx.circular_layout(nx_graph))\n",
    "ax.collections[0].set_edgecolor(\"#000000\") # edge color\n",
    "\n",
    "#plt.show()\n",
    "# change figure size\n",
    "fig.set_size_inches(16, 16)\n",
    "# save graph as pdf\n",
    "plt.savefig('graph_full.pdf')\n",
    "\n",
    "\n",
    "\n",
    "# populates the nodes and edges data structures\n",
    "# show the graph\n",
    "\n",
    "\n",
    "#nt.from_nx(nx_graph)\n",
    "#nt.show_buttons(filter_=['physics'])\n",
    "#nt.show('nx.html')"
   ]
  }
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