diff --git a/Microfaune_Local_Score_Package_Tutorial.ipynb b/Microfaune_Local_Score_Package_Tutorial.ipynb
index 022eeda..8dfb99a 100644
--- a/Microfaune_Local_Score_Package_Tutorial.ipynb
+++ b/Microfaune_Local_Score_Package_Tutorial.ipynb
@@ -2357,534 +2357,6 @@
    "source": [
     "manual_df_with_IoU"
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Function that determines the overlap between human and automated labels with respect to the number of samples in the human label. Referred to as \"Catch\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[1.     0.7344 1.     1.     0.7588]\n"
-     ]
-    }
-   ],
-   "source": [
-    "Catch_Array = clip_catch(automated_piha_df,manual_piha_df)\n",
-    "print(Catch_Array)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "###  Function that determines the label-by-label \"Catch\" across multiple clips"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "20190622_210000.WAV\n",
-      "20190623_222000.WAV\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/jacob/Desktop/EngineersForExploration/Bioacoustics/Automated_Audio_Labelling_System_AID/microfaune_local_score.py:855: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  clip_manual_df[\"Catch\"] = Catch_Array\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "BlackFacedAntbird1.wav\n",
-      "HowlerMonkey1.WAV\n",
-      "20190624_152000.WAV\n",
-      "ScreamingPiha2.wav\n"
-     ]
-    }
-   ],
-   "source": [
-    "manual_df_with_catch = dataset_Catch(automated_df,manual_df)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>FOLDER</th>\n",
-       "      <th>IN FILE</th>\n",
-       "      <th>CHANNEL</th>\n",
-       "      <th>CLIP LENGTH</th>\n",
-       "      <th>OFFSET</th>\n",
-       "      <th>DURATION</th>\n",
-       "      <th>MANUAL ID</th>\n",
-       "      <th>Catch</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>/home/jacob/Acoustic-Species-Identification/pa...</td>\n",
-       "      <td>20190622_210000.WAV</td>\n",
-       "      <td>0</td>\n",
-       "      <td>60.000000</td>\n",
-       "      <td>0.000</td>\n",
-       "      <td>9.700</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1.0000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>/home/jacob/Acoustic-Species-Identification/pa...</td>\n",
-       "      <td>20190622_210000.WAV</td>\n",
-       "      <td>0</td>\n",
-       "      <td>60.000000</td>\n",
-       "      <td>11.500</td>\n",
-       "      <td>16.675</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1.0000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>/home/jacob/Acoustic-Species-Identification/pa...</td>\n",
-       "      <td>20190622_210000.WAV</td>\n",
-       "      <td>0</td>\n",
-       "      <td>60.000000</td>\n",
-       "      <td>29.500</td>\n",
-       "      <td>15.000</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.7885</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>/home/jacob/Acoustic-Species-Identification/pa...</td>\n",
-       "      <td>20190622_210000.WAV</td>\n",
-       "      <td>0</td>\n",
-       "      <td>60.000000</td>\n",
-       "      <td>48.525</td>\n",
-       "      <td>1.000</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.0000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>/home/jacob/Acoustic-Species-Identification/pa...</td>\n",
-       "      <td>20190622_210000.WAV</td>\n",
-       "      <td>0</td>\n",
-       "      <td>60.000000</td>\n",
-       "      <td>50.500</td>\n",
-       "      <td>8.000</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.8444</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>/home/jacob/Acoustic-Species-Identification/pa...</td>\n",
-       "      <td>20190623_222000.WAV</td>\n",
-       "      <td>0</td>\n",
-       "      <td>60.000000</td>\n",
-       "      <td>14.500</td>\n",
-       "      <td>12.500</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.6778</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>/home/jacob/Acoustic-Species-Identification/pa...</td>\n",
-       "      <td>BlackFacedAntbird1.wav</td>\n",
-       "      <td>0</td>\n",
-       "      <td>31.200000</td>\n",
-       "      <td>0.000</td>\n",
-       "      <td>4.100</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.6448</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>/home/jacob/Acoustic-Species-Identification/pa...</td>\n",
-       "      <td>BlackFacedAntbird1.wav</td>\n",
-       "      <td>0</td>\n",
-       "      <td>31.200000</td>\n",
-       "      <td>5.000</td>\n",
-       "      <td>1.500</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.8951</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>/home/jacob/Acoustic-Species-Identification/pa...</td>\n",
-       "      <td>BlackFacedAntbird1.wav</td>\n",
-       "      <td>0</td>\n",
-       "      <td>31.200000</td>\n",
-       "      <td>9.800</td>\n",
-       "      <td>5.200</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.4523</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>/home/jacob/Acoustic-Species-Identification/pa...</td>\n",
-       "      <td>BlackFacedAntbird1.wav</td>\n",
-       "      <td>0</td>\n",
-       "      <td>31.200000</td>\n",
-       "      <td>21.500</td>\n",
-       "      <td>5.500</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.4737</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>/home/jacob/Acoustic-Species-Identification/pa...</td>\n",
-       "      <td>HowlerMonkey1.WAV</td>\n",
-       "      <td>0</td>\n",
-       "      <td>60.000000</td>\n",
-       "      <td>0.000</td>\n",
-       "      <td>10.900</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.8562</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>/home/jacob/Acoustic-Species-Identification/pa...</td>\n",
-       "      <td>HowlerMonkey1.WAV</td>\n",
-       "      <td>0</td>\n",
-       "      <td>60.000000</td>\n",
-       "      <td>11.500</td>\n",
-       "      <td>13.000</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.4616</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>/home/jacob/Acoustic-Species-Identification/pa...</td>\n",
-       "      <td>HowlerMonkey1.WAV</td>\n",
-       "      <td>0</td>\n",
-       "      <td>60.000000</td>\n",
-       "      <td>25.000</td>\n",
-       "      <td>34.900</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.6903</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>/home/jacob/Acoustic-Species-Identification/pa...</td>\n",
-       "      <td>20190624_152000.WAV</td>\n",
-       "      <td>0</td>\n",
-       "      <td>60.000000</td>\n",
-       "      <td>1.200</td>\n",
-       "      <td>0.450</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.0000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>/home/jacob/Acoustic-Species-Identification/pa...</td>\n",
-       "      <td>20190624_152000.WAV</td>\n",
-       "      <td>0</td>\n",
-       "      <td>60.000000</td>\n",
-       "      <td>4.000</td>\n",
-       "      <td>0.300</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.0000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>/home/jacob/Acoustic-Species-Identification/pa...</td>\n",
-       "      <td>20190624_152000.WAV</td>\n",
-       "      <td>0</td>\n",
-       "      <td>60.000000</td>\n",
-       "      <td>7.400</td>\n",
-       "      <td>0.600</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.4630</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>/home/jacob/Acoustic-Species-Identification/pa...</td>\n",
-       "      <td>20190624_152000.WAV</td>\n",
-       "      <td>0</td>\n",
-       "      <td>60.000000</td>\n",
-       "      <td>10.500</td>\n",
-       "      <td>0.800</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1.0000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>/home/jacob/Acoustic-Species-Identification/pa...</td>\n",
-       "      <td>20190624_152000.WAV</td>\n",
-       "      <td>0</td>\n",
-       "      <td>60.000000</td>\n",
-       "      <td>20.800</td>\n",
-       "      <td>0.500</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.0000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>/home/jacob/Acoustic-Species-Identification/pa...</td>\n",
-       "      <td>20190624_152000.WAV</td>\n",
-       "      <td>0</td>\n",
-       "      <td>60.000000</td>\n",
-       "      <td>28.000</td>\n",
-       "      <td>0.400</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.0000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>/home/jacob/Acoustic-Species-Identification/pa...</td>\n",
-       "      <td>20190624_152000.WAV</td>\n",
-       "      <td>0</td>\n",
-       "      <td>60.000000</td>\n",
-       "      <td>34.000</td>\n",
-       "      <td>1.000</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.0000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>/home/jacob/Acoustic-Species-Identification/pa...</td>\n",
-       "      <td>ScreamingPiha2.wav</td>\n",
-       "      <td>0</td>\n",
-       "      <td>33.933061</td>\n",
-       "      <td>0.000</td>\n",
-       "      <td>5.373</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1.0000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>/home/jacob/Acoustic-Species-Identification/pa...</td>\n",
-       "      <td>ScreamingPiha2.wav</td>\n",
-       "      <td>0</td>\n",
-       "      <td>33.933061</td>\n",
-       "      <td>10.590</td>\n",
-       "      <td>5.585</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.7344</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>/home/jacob/Acoustic-Species-Identification/pa...</td>\n",
-       "      <td>ScreamingPiha2.wav</td>\n",
-       "      <td>0</td>\n",
-       "      <td>33.933061</td>\n",
-       "      <td>22.000</td>\n",
-       "      <td>4.000</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1.0000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>23</th>\n",
-       "      <td>/home/jacob/Acoustic-Species-Identification/pa...</td>\n",
-       "      <td>ScreamingPiha2.wav</td>\n",
-       "      <td>0</td>\n",
-       "      <td>33.933061</td>\n",
-       "      <td>27.200</td>\n",
-       "      <td>0.900</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1.0000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24</th>\n",
-       "      <td>/home/jacob/Acoustic-Species-Identification/pa...</td>\n",
-       "      <td>ScreamingPiha2.wav</td>\n",
-       "      <td>0</td>\n",
-       "      <td>33.933061</td>\n",
-       "      <td>29.000</td>\n",
-       "      <td>4.500</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.7588</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                               FOLDER                 IN FILE  \\\n",
-       "0   /home/jacob/Acoustic-Species-Identification/pa...     20190622_210000.WAV   \n",
-       "1   /home/jacob/Acoustic-Species-Identification/pa...     20190622_210000.WAV   \n",
-       "2   /home/jacob/Acoustic-Species-Identification/pa...     20190622_210000.WAV   \n",
-       "3   /home/jacob/Acoustic-Species-Identification/pa...     20190622_210000.WAV   \n",
-       "4   /home/jacob/Acoustic-Species-Identification/pa...     20190622_210000.WAV   \n",
-       "5   /home/jacob/Acoustic-Species-Identification/pa...     20190623_222000.WAV   \n",
-       "6   /home/jacob/Acoustic-Species-Identification/pa...  BlackFacedAntbird1.wav   \n",
-       "7   /home/jacob/Acoustic-Species-Identification/pa...  BlackFacedAntbird1.wav   \n",
-       "8   /home/jacob/Acoustic-Species-Identification/pa...  BlackFacedAntbird1.wav   \n",
-       "9   /home/jacob/Acoustic-Species-Identification/pa...  BlackFacedAntbird1.wav   \n",
-       "10  /home/jacob/Acoustic-Species-Identification/pa...       HowlerMonkey1.WAV   \n",
-       "11  /home/jacob/Acoustic-Species-Identification/pa...       HowlerMonkey1.WAV   \n",
-       "12  /home/jacob/Acoustic-Species-Identification/pa...       HowlerMonkey1.WAV   \n",
-       "13  /home/jacob/Acoustic-Species-Identification/pa...     20190624_152000.WAV   \n",
-       "14  /home/jacob/Acoustic-Species-Identification/pa...     20190624_152000.WAV   \n",
-       "15  /home/jacob/Acoustic-Species-Identification/pa...     20190624_152000.WAV   \n",
-       "16  /home/jacob/Acoustic-Species-Identification/pa...     20190624_152000.WAV   \n",
-       "17  /home/jacob/Acoustic-Species-Identification/pa...     20190624_152000.WAV   \n",
-       "18  /home/jacob/Acoustic-Species-Identification/pa...     20190624_152000.WAV   \n",
-       "19  /home/jacob/Acoustic-Species-Identification/pa...     20190624_152000.WAV   \n",
-       "20  /home/jacob/Acoustic-Species-Identification/pa...      ScreamingPiha2.wav   \n",
-       "21  /home/jacob/Acoustic-Species-Identification/pa...      ScreamingPiha2.wav   \n",
-       "22  /home/jacob/Acoustic-Species-Identification/pa...      ScreamingPiha2.wav   \n",
-       "23  /home/jacob/Acoustic-Species-Identification/pa...      ScreamingPiha2.wav   \n",
-       "24  /home/jacob/Acoustic-Species-Identification/pa...      ScreamingPiha2.wav   \n",
-       "\n",
-       "    CHANNEL  CLIP LENGTH  OFFSET  DURATION  MANUAL ID   Catch  \n",
-       "0         0    60.000000   0.000     9.700          1  1.0000  \n",
-       "1         0    60.000000  11.500    16.675          1  1.0000  \n",
-       "2         0    60.000000  29.500    15.000          1  0.7885  \n",
-       "3         0    60.000000  48.525     1.000          1  0.0000  \n",
-       "4         0    60.000000  50.500     8.000          1  0.8444  \n",
-       "5         0    60.000000  14.500    12.500          1  0.6778  \n",
-       "6         0    31.200000   0.000     4.100          1  0.6448  \n",
-       "7         0    31.200000   5.000     1.500          1  0.8951  \n",
-       "8         0    31.200000   9.800     5.200          1  0.4523  \n",
-       "9         0    31.200000  21.500     5.500          1  0.4737  \n",
-       "10        0    60.000000   0.000    10.900          1  0.8562  \n",
-       "11        0    60.000000  11.500    13.000          1  0.4616  \n",
-       "12        0    60.000000  25.000    34.900          1  0.6903  \n",
-       "13        0    60.000000   1.200     0.450          1  0.0000  \n",
-       "14        0    60.000000   4.000     0.300          1  0.0000  \n",
-       "15        0    60.000000   7.400     0.600          1  0.4630  \n",
-       "16        0    60.000000  10.500     0.800          1  1.0000  \n",
-       "17        0    60.000000  20.800     0.500          1  0.0000  \n",
-       "18        0    60.000000  28.000     0.400          1  0.0000  \n",
-       "19        0    60.000000  34.000     1.000          1  0.0000  \n",
-       "20        0    33.933061   0.000     5.373          1  1.0000  \n",
-       "21        0    33.933061  10.590     5.585          1  0.7344  \n",
-       "22        0    33.933061  22.000     4.000          1  1.0000  \n",
-       "23        0    33.933061  27.200     0.900          1  1.0000  \n",
-       "24        0    33.933061  29.000     4.500          1  0.7588  "
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "manual_df_with_catch"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'calc_local_scores_simpler' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-27-0ca4828555e3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnew_automated_df\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcalc_local_scores_simpler\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m: name 'calc_local_scores_simpler' is not defined"
-     ]
-    }
-   ],
-   "source": [
-    "new_automated_df = calc_local_scores_simpler(path)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "new_automated_df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "local_score_visualization2(\"./TEST/ScreamingPiha2.wav\",automated_df = True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "stats_df2 = dataset_IoU_Statistics(new_automated_df,manual_df,threshold = 0.5)\n",
-    "stats_df2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "global_stats_df = global_IoU_Statistics(stats_df2)\n",
-    "global_stats_df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "annotation_duration_statistics(new_automated_df)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
diff --git a/ScreamingPiha2_Manual_Labels.csv b/ScreamingPiha2_Manual_Labels.csv
deleted file mode 100644
index 93804de..0000000
--- a/ScreamingPiha2_Manual_Labels.csv
+++ /dev/null
@@ -1,6 +0,0 @@
-FOLDER,IN FILE,CHANNEL,CLIP LENGTH,OFFSET,DURATION,MANUAL ID
-/home/jacob/Acoustic-Species-Identification/passive-acoustic-biodiversity/BinaryBirdDet/TEST/,ScreamingPiha2.wav,0,33.9330612244898,0,5.373,1
-/home/jacob/Acoustic-Species-Identification/passive-acoustic-biodiversity/BinaryBirdDet/TEST/,ScreamingPiha2.wav,1,34.9330612244898,10.59,5.585,1
-/home/jacob/Acoustic-Species-Identification/passive-acoustic-biodiversity/BinaryBirdDet/TEST/,ScreamingPiha2.wav,2,35.9330612244898,22,4,1
-/home/jacob/Acoustic-Species-Identification/passive-acoustic-biodiversity/BinaryBirdDet/TEST/,ScreamingPiha2.wav,3,36.9330612244898,27.2,0.9,1
-/home/jacob/Acoustic-Species-Identification/passive-acoustic-biodiversity/BinaryBirdDet/TEST/,ScreamingPiha2.wav,4,37.9330612244898,29,4.5,1
diff --git a/microfaune_local_score.py b/microfaune_local_score.py
index 0de553b..025d122 100644
--- a/microfaune_local_score.py
+++ b/microfaune_local_score.py
@@ -18,10 +18,6 @@
 import math
 
 
-# Another option would be to allow for curve smoothing on the local score array that is being passed in. This could come in
-# the form of a high order polynomial fit or possibly testing out my curve smoothing algorithm that uses a bell-curved distribution to
-# loop around and average each sample with its surrounding samples over many iterations. We could also play around with filtering.
-
 # function that encapsulates many different isolation techniques to the dictionary isolation_parameters
 def isolate(local_scores, SIGNAL, SAMPLE_RATE, audio_dir, filename,isolation_parameters,manual_id = "bird"):
 
@@ -35,12 +31,25 @@ def isolate(local_scores, SIGNAL, SAMPLE_RATE, audio_dir, filename,isolation_par
         isolation_df = steinberg_isolate(local_scores, SIGNAL, SAMPLE_RATE, audio_dir, filename,isolation_parameters, manual_id = "bird")
     elif isolation_parameters["technique"] == "stack":
         isolation_df = stack_isolate(local_scores, SIGNAL, SAMPLE_RATE, audio_dir, filename, isolation_parameters, manual_id = "bird")
-        # stack_isolate(local_scores, SIGNAL, SAMPLE_RATE, audio_dir, filename, isolation_parameters, manual_id = "bird"):
 
     return isolation_df
 
 
 def steinberg_isolate(local_scores, SIGNAL, SAMPLE_RATE, audio_dir, filename,isolation_parameters,manual_id = "bird"):
+    """
+    Returns a dataframe of automated labels for the given audio clip. The automated labels determine intervals of bird noise as
+    determined by the local scores given by an RNNDetector.
+
+    Args:
+        scores (list of floats) - Local scores of the audio clip as determined by RNNDetector.
+        SIGNAL (list of ints) - Samples from the audio clip.
+        SAMPLE_RATE (int) - Sampling rate of the audio clip, usually 44100.
+        audio_dir (string) - Directory of the audio clip.
+        filename (string) - Name of the audio clip file.
+
+    Returns:
+        Dataframe of automated labels for the audio clip.
+    """
     # calculate original duration
     old_duration = len(SIGNAL) / SAMPLE_RATE
 
@@ -270,8 +279,21 @@ def stack_isolate(local_scores, SIGNAL, SAMPLE_RATE, audio_dir, filename, isolat
     # returning pandas dataframe from dictionary constructed with all of the annotations
     return pd.DataFrame.from_dict(entry)
 
+
 ## Function that applies the moment to moment labeling system to a directory full of wav files.
 def generate_automated_labels(bird_dir, isolation_parameters, weight_path=None, Normalized_Sample_Rate = 44100):
+    """
+    Function that applies the moment to moment labeling system to a directory full of wav files.
+
+    Args:
+        bird_dir (string) - Directory with wav audio files.
+        weight_path (string) - File path of weights to be used by the RNNDetector for determining presence of bird sounds.
+        Normalized_Sample_Rate (int) - Sampling rate that the audio files should all be normalized to.
+
+    Returns:
+        Dataframe of automated labels for the audio clips in bird_dir.
+    """
+
     # init detector
     # Use Default Microfaune Detector
     if weight_path is None:
@@ -352,8 +374,24 @@ def annotation_duration_statistics(df):
     # returning the dictionary as a pandas dataframe
     return pd.DataFrame.from_dict([entry])
 
-# Function that produces graphs with the local score plot and spectrogram of an audio clip. Now integrated with Pandas so you can visualize human and automated annotations.
+
 def local_line_graph(local_scores,clip_name, sample_rate,samples, automated_df=None, human_df=None,log_scale = False, save_fig = False):
+    """
+    Function that produces graphs with the local score plot and spectrogram of an audio clip. Now integrated with Pandas so you can visualize human and automated annotations.
+
+    Args:
+        local_scores (list of floats) - Local scores for the clip determined by the RNN.
+        clip_name (string) - Directory of the clip.
+        sample_rate (int) - Sample rate of the audio clip, usually 44100.
+        samples (list of ints) - Each of the samples from the audio clip.
+        automated_df (Dataframe) - Dataframe of automated labelling of the clip.
+        human_df (Dataframe) - Dataframe of human labelling of the clip.
+        log_scale (boolean) - Whether the axis for local scores should be logarithmically scaled on the plot.
+        save_fig (boolean) - Whether the clip should be saved in a directory as a png file.
+
+    Returns:
+        None
+    """
     # Calculating the length of the audio clip
     duration = samples.shape[0]/sample_rate
     # Calculating the number of local scores outputted by Microfaune
@@ -412,11 +450,27 @@ def local_line_graph(local_scores,clip_name, sample_rate,samples, automated_df=N
     if save_fig:
         plt.savefig(clip_name + "_Local_Score_Graph.png")
 
-# Wrapper function for the local_line_graph function for ease of use.
 # TODO rework function so that instead of generating the automated labels, it takes the automated_df as input
 # same as it does with the manual dataframe.
+
 def local_score_visualization(clip_path,weight_path = None, human_df = None,automated_df = False, isolation_parameters = None,log_scale = False, save_fig = False):
 
+    """
+    Wrapper function for the local_line_graph function for ease of use. Processes clip for local scores to be used for
+    the local_line_graph function.
+
+    Args:
+        clip_path (string) - Directory of the clip.
+        weight_path (string) - Weights to be used for RNNDetector.
+        human_df (Dataframe) - Dataframe of human labels for the audio clip.
+        automated_df (Dataframe) - Whether the audio clip should be labelled by the isolate function and subsequently plotted.
+        log_scale (boolean) - Whether the axis for local scores should be logarithmically scaled on the plot.
+        save_fig (boolean) - Whether the plots should be saved in a directory as a png file.
+
+    Returns:
+        None
+    """
+
     # Loading in the clip with Microfaune's built-in loading function
     SAMPLE_RATE, SIGNAL = audio.load_wav(clip_path)
     # downsample the audio if the sample rate > 44.1 kHz
@@ -455,7 +509,19 @@ def local_score_visualization(clip_path,weight_path = None, human_df = None,auto
 
 
 def bird_label_scores(automated_df,human_df,plot_fig = False, save_fig = False):
-
+    """
+    Function to generate a dataframe with statistics relating to the efficiency of the automated label compared to the human label.
+    These statistics include true positive, false positive, false negative, true negative, union, precision, recall, F1, and Global IoU.
+
+    Args:
+        automated_df (Dataframe) - Dataframe of automated labels for one clip
+        human_df (Dataframe) - Dataframe of human labels for one clip.
+        plot_fig (boolean) - Whether or not the efficiency statistics should be displayed.
+        save_fig (boolean) - Whether or not the plot should be saved within a file.
+
+    Returns:
+        Dataframe with statistics comparing the automated and human labeling.
+    """
     duration = automated_df["CLIP LENGTH"].to_list()[0]
     SAMPLE_RATE = automated_df["SAMPLE RATE"].to_list()[0]
     # Initializing two arrays that will represent the human labels and automated labels with respect to
@@ -565,9 +631,23 @@ def bird_label_scores(automated_df,human_df,plot_fig = False, save_fig = False):
 
     return pd.DataFrame(entry,index=[0])
 
-# Function that will allow users to easily pass in two dataframes, and it will output statistics on them
 # Will have to adjust the isolate function so that it adds a sampling rate onto the dataframes.
 def automated_labeling_statistics(automated_df,manual_df):
+    """
+    Function that will allow users to easily pass in two dataframes of manual labels and automated labels,
+    and a dataframe is returned with statistics examining the efficiency of the automated labelling system compared
+    to the human labels for multiple clips.
+
+    Calls bird_local_scores on corresponding audio clips to generate the efficiency statistics for one specific clip which is then all put into one
+    dataframe of statistics for multiple audio clips.
+
+    Args:
+        automated_df (Dataframe) - Dataframe of automated labels of multiple clips.
+        manual_df (Dataframe) - Dataframe of human labels of multiple clips.
+
+    Returns:
+        Dataframe of statistics comparing automated labels and human labels for multiple clips.
+    """
     # Getting a list of clips
     clips = automated_df["IN FILE"].to_list()
     # Removing duplicates
@@ -590,8 +670,16 @@ def automated_labeling_statistics(automated_df,manual_df):
         statistics_df.reset_index(inplace = True, drop = True)
     return statistics_df
 
-# Small function that takes in the statistics and outputs their global values
 def global_dataset_statistics(statistics_df):
+    """
+    Function that takes in a dataframe of efficiency statistics for multiple clips and outputs their global values.
+
+    Args:
+        statistics_df (Dataframe) - Dataframe of statistics value for multiple audio clips as returned by the function automated_labelling_statistics.
+
+    Returns:
+        Dataframe of global statistics for the multiple audio clips' labelling.
+    """
     tp_sum = statistics_df["TRUE POSITIVE"].sum()
     fp_sum = statistics_df["FALSE POSITIVE"].sum()
     fn_sum = statistics_df["FALSE NEGATIVE"].sum()
@@ -610,6 +698,17 @@ def global_dataset_statistics(statistics_df):
 # TODO rework this function to implement some linear algebra, right now the nested for loop won't handle larger loads well
 # To make a global matrix, find the clip with the most amount of automated labels and set that to the number of columns
 def clip_IoU(automated_df,manual_df):
+    """
+    Function that takes in the manual and automated labels for a clip and outputs human label-by-label IoU Scores.
+
+    Args:
+        automated_df (Dataframe) - Dataframe of automated labels for an audio clip.
+        manual_df (Dataframe) - Dataframe of human labels for an audio clip.
+
+    Returns:
+        Numpy Array of human label IoU scores.
+    """
+
     automated_df.reset_index(inplace = True, drop = True)
     manual_df.reset_index(inplace = True, drop = True)
     # Determining the number of rows in the output numpy array
@@ -664,11 +763,21 @@ def clip_IoU(automated_df,manual_df):
         human_arr[human_arr == 1] = 0
 
     return IoU_Matrix
-# Function that takes in the IoU Matrix from the clip_IoU function and ouputs the number of true positives and false positives
-# It also calculates the precision.
+
 def matrix_IoU_Scores(IoU_Matrix,manual_df,threshold):
-    # This might not work in a situation where there is only one human label and multiple automated labels.
-    #IoU_Matrix_size = IoU_Matrix.shape[0] * IoU_Matrix.shape[1]
+    """
+    Function that takes in the IoU Matrix from the clip_IoU function and ouputs the number of true positives and false positives,
+    as well as calculating the precision.
+
+    Args:
+        IoU_Matrix (Numpy Array) - Matrix of human label IoU scores.
+        manual_df (Dataframe) - Dataframe of human labels for an audio clip.
+        threshold (float) - Threshold for determining true positives and false negatives.
+
+    Returns:
+        Dataframe of clip statistics such as True Positive, False Negative, Precision, Recall, and F1 value.
+    """
+
     audio_dir = manual_df["FOLDER"][0]
     filename = manual_df["IN FILE"][0]
 
@@ -708,8 +817,18 @@ def matrix_IoU_Scores(IoU_Matrix,manual_df,threshold):
 
     return pd.DataFrame.from_dict([entry])
 
-# Function that can help us determine whether or not a call was detected.
 def clip_catch(automated_df,manual_df):
+    """
+    Function that determines the overlap between human and automated labels with respect to the number of samples in the human label.
+
+    Args:
+        automated_df (Dataframe) - Dataframe of automated labels for an audio clip.
+        manual_df (Dataframe) - Dataframe of human labels for an audio clip.
+
+    Returns:
+        Numpy Array of statistics regarding the amount of overlap between the manual and automated labels relative to the number of
+        samples.
+    """
     # resetting the indices to make this function work
     automated_df.reset_index(inplace = True, drop = True)
     manual_df.reset_index(inplace = True, drop = True)
@@ -755,10 +874,18 @@ def clip_catch(automated_df,manual_df):
 
 
 
-# Function that takes in two Pandas dataframes that represent human labels and automated labels.
-# It then runs the clip_IoU function across each clip and appends the best fit IoU score to each labels
-# on the manual dataframe as its output.
 def dataset_IoU(automated_df,manual_df):
+    """
+    Function that takes in two Pandas dataframes that represent human labels and automated labels.
+    It then runs the clip_IoU function across each clip and appends the best fit IoU score to each labels on the manual dataframe as its output.
+
+    Args:
+        automated_df (Dataframe) - Dataframe of automated labels for multiple audio clips.
+        manual_df (Dataframe) - Dataframe of human labels for multiple audio clips.
+
+    Returns:
+        Dataframe of manual labels with the best fit IoU score as a column.
+    """
     # Getting a list of clips
     clips = automated_df["IN FILE"].to_list()
     # Removing duplicates
@@ -786,6 +913,18 @@ def dataset_IoU(automated_df,manual_df):
 
 
 def dataset_IoU_Statistics(automated_df,manual_df,threshold = 0.5):
+    """
+    Wrapper function that takes matrix_IoU_Scores across multiple clips.
+    Allows user to modify the threshold that determines whether or not a label is a true positive.
+
+    Args:
+        automated_df (Dataframe) - Dataframe of automated labels for multiple audio clips.
+        manual_df (Dataframe) - Dataframe of human labels for multiple audio clips.
+        threshold (float) - Threshold for determining true positives.
+
+    Returns:
+        Dataframe of IoU statistics for multiple audio clips.
+    """
     # isolating the names of the clips that have been labelled into an array.
     clips = automated_df["IN FILE"].to_list()
     clips = list(dict.fromkeys(clips))
@@ -808,8 +947,18 @@ def dataset_IoU_Statistics(automated_df,manual_df,threshold = 0.5):
             IoU_Statistics = IoU_Statistics.append(clip_stats_df)
     IoU_Statistics.reset_index(inplace = True, drop = True)
     return IoU_Statistics
-# Function that takes the output of dataset_IoU_Statistics and computes a global precision score.
+
 def global_IoU_Statistics(statistics_df):
+    """
+    Function that takes the output of dataset_IoU Statistics and outputs a global count of true positives and false positives,
+    as well as computing the precision across the dataset.
+
+    Args:
+        statistics_df (Dataframe) - Dataframe of matrix IoU scores for multiple clips.
+
+    Returns:
+        Dataframe of global IoU statistics.
+    """
     # taking the sum of the number of true positives and false positives.
     tp_sum = statistics_df["TRUE POSITIVE"].sum()
     fn_sum = statistics_df["FALSE NEGATIVE"].sum()
@@ -837,6 +986,16 @@ def global_IoU_Statistics(statistics_df):
     return pd.DataFrame.from_dict([entry])
 
 def dataset_Catch(automated_df,manual_df):
+    """
+    Function that determines the label-by-label "Catch" across multiple clips.
+
+    Args:
+        automated_df (Dataframe) - Dataframe of automated labels for multiple audio clips.
+        manual_df (Dataframe) - Dataframe of human labels for multiple audio clips.
+
+    Returns:
+        Dataframe of human labels with a column for the catch values of each label.
+    """
     # Getting a list of clips
     clips = automated_df["IN FILE"].to_list()
     # Removing duplicates