userStudt.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               0      1                                                  2\n",
      "0  Very Negative  State                 Pirate Position, Treasure Position\n",
      "1  Very Negative  State  Pirate Position, Pirate Orientation, Treasure ...\n",
      "2            NaN    NaN                                                NaN\n",
      "3            NaN    NaN                                                NaN\n",
      "4            NaN    NaN                                                NaN\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "# Load the CSV file\n",
    "\n",
    "responsesdf = pd.read_csv(\"Responses.csv\",header=None)\n",
    "answersdf=pd.read_csv('Answers.csv',header=None)\n",
    "\n",
    "\n",
    "# Calculate the number of 3-column DataFrames\n",
    "num_sub_dfs = responsesdf.shape[1] // 3\n",
    "\n",
    "# Dictionary to hold the smaller DataFrames\n",
    "responses_dfs = {}\n",
    "answers_dfs={}\n",
    "\n",
    "# Loop through and create each smaller DataFrame\n",
    "for i in range(num_sub_dfs):\n",
    "    start_col = i * 3\n",
    "    end_col = start_col + 3\n",
    "    response_df_name = f\"response_{i+1}\"  # Naming each smaller DataFrame\n",
    "    answer_df_name=f\"answer_{i+1}\"\n",
    "    responses_dfs[response_df_name] = responsesdf.iloc[:, start_col:end_col]\n",
    "    answers_dfs[answer_df_name] = answersdf.iloc[:,start_col:end_col]\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# Now 'sub_dfs' dictionary contains each smaller DataFrame\n",
    "# You can access them like sub_dfs['sub_df_1'], sub_dfs['sub_df_2'], etc.\n",
    "\n",
    "# Example to print the first small DataFrame\n",
    "# print(responses_dfs['response_1'])\n",
    "\n",
    "print(answers_dfs['answer_1'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Perfect Trajectories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd  # Make sure pandas is imported\n",
    "\n",
    "matching_counts = {}\n",
    "\n",
    "# Loop through each response sub-DataFrame\n",
    "for i,(name, response_df) in enumerate(responses_dfs.items()):\n",
    "    # Initialize the count for this sub-DataFrame\n",
    "    matching_count = 0\n",
    "    \n",
    "    # Get the corresponding answer DataFrame\n",
    "    answer_df = answers_dfs[f\"answer_{i+1}\"]\n",
    "\n",
    "    # Iterate through the rows of the response DataFrame (assuming first 12 rows are respondent answers)\n",
    "    for i in range(12):  # Adjust this if the number of respondents changes\n",
    "        response_row = response_df.iloc[i]\n",
    "        row_matched = False\n",
    "\n",
    "        # Iterate through each row in the answer DataFrame, which contains possible correct answers\n",
    "        for j in range(len(answer_df)):\n",
    "            correct_answers = answer_df.iloc[j]\n",
    "            match = True\n",
    "\n",
    "            # Check each cell in the row for a match\n",
    "            for col_index, (response_val, correct_ans) in enumerate(zip(response_row, correct_answers)):\n",
    "                # Check if we are in the third column and handle NaN values\n",
    "                if col_index == 2:  # Assuming third column is indexed as 2\n",
    "                    if pd.isna(correct_ans) or pd.isna(response_val):\n",
    "                        # If correct_ans is NaN, any response_val is considered a match, same if response_val is NaN\n",
    "                        continue\n",
    "                else:\n",
    "                    # For the first two columns, treat NaN in response as no match\n",
    "                    if pd.isna(correct_ans):\n",
    "                        match = False\n",
    "                        break\n",
    "\n",
    "                # For non-NaN values, check if they match\n",
    "                if not pd.isna(correct_ans) and response_val != correct_ans:\n",
    "                    match = False\n",
    "                    break\n",
    "\n",
    "            # If the row matches any one of the correct answer rows, mark it as matched\n",
    "            if match:\n",
    "                row_matched = True\n",
    "                break  # Break out of the loop through answer_df rows as we found a match\n",
    "\n",
    "        # If the row matched any of the correct answer rows, increment the count\n",
    "        if row_matched:\n",
    "            matching_count += 1\n",
    "\n",
    "    # Store the count of matching rows for this sub-DataFrame\n",
    "    matching_counts[name] = matching_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = [f\"Traj_{i+1}\" for i in range(len(matching_counts))]\n",
    "\n",
    "# Bar chart\n",
    "plt.bar(labels, matching_counts.values())\n",
    "\n",
    "plt.xlabel('Trajectory')\n",
    "plt.ylabel('Correct Responses')\n",
    "plt.title('Number Of People Who Gave Perfect Feedback in Each Trajectory')\n",
    "\n",
    "# Adding the count on each bar\n",
    "for i, value in enumerate(matching_counts.values()):\n",
    "    plt.text(i, value + 0.1, str(value), ha='center')\n",
    "\n",
    "plt.xticks(rotation=45)  # Rotate the x-axis labels for better readability\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Perfect Signals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "matching_counts = {}\n",
    "\n",
    "# Loop through each response sub-DataFrame\n",
    "for i, (name, response_df) in enumerate(responses_dfs.items()):\n",
    "    # Initialize the count for this sub-DataFrame\n",
    "    matching_count = 0\n",
    "    \n",
    "    # Get the corresponding answer DataFrame\n",
    "    answer_df = answers_dfs[f\"answer_{i+1}\"]\n",
    "\n",
    "    # Assuming first 12 rows are respondent answers\n",
    "    for idx in range(12):\n",
    "        # Get the respondent's answer for the first column\n",
    "        response_val = response_df.iloc[idx, 0]\n",
    "        \n",
    "        # Assuming correct answers are in the first row of the answer DataFrame\n",
    "        correct_answers = answer_df.iloc[:, 0]\n",
    "\n",
    "        # Check if the response matches any of the correct answers for the first column\n",
    "        if response_val in correct_answers.values and not pd.isna(response_val):\n",
    "            matching_count += 1\n",
    "\n",
    "    # Store the count of matching rows for this sub-DataFrame\n",
    "    matching_counts[name] = matching_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = [f\"Traj_{i+1}\" for i in range(len(matching_counts))]\n",
    "plt.bar(labels, matching_counts.values())\n",
    "plt.xlabel('Trajectory')\n",
    "plt.ylabel('Number of Correct Responses')\n",
    "plt.title('Signal Correctly Identified')\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}