DLC_process_position.ipynb

{
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  {
   "cell_type": "markdown",
   "id": "aa21cf58",
   "metadata": {},
   "source": [
    "In this notebook, we will learn how to analyze pmosition data from a given sub-region of your environment. In particular, this example deals with a mouse running on the radial arm maze, but the idea can be generalized to the analysis of a given sub-region of any environment. \n",
    "\n",
    "\n",
    "Let's get right into it! First, import the necessary libraries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ee9c3d76",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pynapple as nap\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy.io\n",
    "import os, sys\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from pylab import *\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1440e2c9",
   "metadata": {},
   "source": [
    "Next, load the data from your directory. My data is being read from an h5 file, but this can be replaced to read whatever format you are working with (csv, MAT file etc)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "39db3389",
   "metadata": {},
   "outputs": [
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       "scorer    DLC_mobnet_100_unimplantedJan4shuffle1_200000                        \\\n",
       "bodyparts                                          nose                         \n",
       "coords                                                x          y likelihood   \n",
       "0                                            324.503784  84.962143   0.026416   \n",
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       "\n",
       "scorer                                                                \\\n",
       "bodyparts     leftear                           rightear               \n",
       "coords              x           y likelihood           x           y   \n",
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     "execution_count": 2,
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   ],
   "source": [
    "data_directory = '/media/DataDhruv/Recordings/unimplanted/211231'\n",
    "tracking_data =  pd.read_hdf(data_directory + '/' + '1637-211231_3.h5', mode = 'r')\n",
    "\n",
    "tracking_data\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3cdd606",
   "metadata": {},
   "source": [
    "Here, we see that tracking_data has 9 columns. Namely, the x and y positions of the 3 bodyparts I labelled (nose, left ear and right ear), as well as the likelihood. We are only interested in the x and y positions, so we will extract these, and create a new DataFrame, with just the relevant data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e8dba974",
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "scorer    DLC_mobnet_100_unimplantedJan4shuffle1_200000             \\\n",
       "bodyparts                                          nose              \n",
       "coords                                                x          y   \n",
       "0                                            324.503784  84.962143   \n",
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       "...                                                 ...        ...   \n",
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       "146204                                       324.914368  85.998718   \n",
       "146205                                       324.977417  85.712440   \n",
       "\n",
       "scorer                                                     \n",
       "bodyparts     leftear                rightear              \n",
       "coords              x           y           x           y  \n",
       "0          326.843628   90.024574  381.853790  158.946304  \n",
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       "\n",
       "[146206 rows x 6 columns]"
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     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hd_cols = [0,1,3,4,6,7]\n",
    "hd_data = tracking_data.iloc[:,hd_cols]\n",
    "\n",
    "hd_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f97b3f34",
   "metadata": {},
   "source": [
    "Now, from this data shall compute the centroid of these 3 body parts. This will result in a proxy for head position.\n",
    "The centroid or geometric center is the arithmetic mean position of all the points (in our case, the ears and the nose).\n",
    "\n",
    "First, we select the columns containing the x-and y-coordinates of all parts. We will store these in x_cols and y_cols, respectively. Then create a new DataFrame with just the x-coordinate and y-coordinates, which we call \"all_x_coords\" and \"all_y_coords\" respectively. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2f51bf19",
   "metadata": {},
   "outputs": [
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>324.503784</td>\n",
       "      <td>326.843628</td>\n",
       "      <td>381.853790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>328.924713</td>\n",
       "      <td>78.792007</td>\n",
       "      <td>381.974884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>325.112549</td>\n",
       "      <td>329.019196</td>\n",
       "      <td>382.148254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>324.465240</td>\n",
       "      <td>327.157867</td>\n",
       "      <td>382.320770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>325.845581</td>\n",
       "      <td>994.155090</td>\n",
       "      <td>987.756836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146201</th>\n",
       "      <td>324.558075</td>\n",
       "      <td>328.169495</td>\n",
       "      <td>385.295135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146202</th>\n",
       "      <td>324.929230</td>\n",
       "      <td>328.647644</td>\n",
       "      <td>382.822784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146203</th>\n",
       "      <td>330.673004</td>\n",
       "      <td>329.564850</td>\n",
       "      <td>386.001648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146204</th>\n",
       "      <td>324.914368</td>\n",
       "      <td>327.542847</td>\n",
       "      <td>384.997955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146205</th>\n",
       "      <td>324.977417</td>\n",
       "      <td>415.963928</td>\n",
       "      <td>384.394562</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>146206 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "scorer    DLC_mobnet_100_unimplantedJan4shuffle1_200000              \\\n",
       "bodyparts                                          nose     leftear   \n",
       "coords                                                x           x   \n",
       "0                                            324.503784  326.843628   \n",
       "1                                            328.924713   78.792007   \n",
       "2                                            325.112549  329.019196   \n",
       "3                                            324.465240  327.157867   \n",
       "4                                            325.845581  994.155090   \n",
       "...                                                 ...         ...   \n",
       "146201                                       324.558075  328.169495   \n",
       "146202                                       324.929230  328.647644   \n",
       "146203                                       330.673004  329.564850   \n",
       "146204                                       324.914368  327.542847   \n",
       "146205                                       324.977417  415.963928   \n",
       "\n",
       "scorer                 \n",
       "bodyparts    rightear  \n",
       "coords              x  \n",
       "0          381.853790  \n",
       "1          381.974884  \n",
       "2          382.148254  \n",
       "3          382.320770  \n",
       "4          987.756836  \n",
       "...               ...  \n",
       "146201     385.295135  \n",
       "146202     382.822784  \n",
       "146203     386.001648  \n",
       "146204     384.997955  \n",
       "146205     384.394562  \n",
       "\n",
       "[146206 rows x 3 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_cols = [0,2,4]\n",
    "y_cols = [1,3,5]\n",
    "all_x_coords = hd_data.iloc[:,x_cols]\n",
    "all_y_coords = hd_data.iloc[:,y_cols]\n",
    "all_x_coords"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55350cd0",
   "metadata": {},
   "source": [
    "Wonderful! Now to compute the centroid. Remember, it is a mean. Therefore, we need the sum of the observations for each time point. We compute the sums for each coordinate separately, below:  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5deb543f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         1033.201202\n",
       "1          789.691605\n",
       "2         1036.279999\n",
       "3         1033.943878\n",
       "4         2307.757507\n",
       "             ...     \n",
       "146201    1038.022705\n",
       "146202    1036.399658\n",
       "146203    1046.239502\n",
       "146204    1037.455170\n",
       "146205    1125.335907\n",
       "Length: 146206, dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_sum = all_x_coords.sum(axis = 1)\n",
    "y_sum = all_y_coords.sum(axis = 1)\n",
    "\n",
    "x_sum"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80633a53",
   "metadata": {},
   "source": [
    "Now, we need to divide the sum by the number of body parts. In our case this is 3, but we will express this in more general terms so that we do not hard-code our variables. Remember, it is good practice to write the most generalizable code. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c4408a51",
   "metadata": {},
   "outputs": [],
   "source": [
    "length = all_x_coords.iloc[0,:].shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94ba2ab9",
   "metadata": {},
   "source": [
    "Now, we will compute the centroid, and store it in a DataFrame called hd_centroid. From here, we will extract the X and Y positions of the head, stored in the variables x and y, respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ad98bac4",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_cent = x_sum/length\n",
    "y_cent = y_sum/length\n",
    "\n",
    "hd_centroid = np.zeros((len(x_cent),2))\n",
    "hd_centroid[:,0] = x_cent \n",
    "hd_centroid[:,1] = y_cent\n",
    "\n",
    "x = hd_centroid[:,0]\n",
    "y = hd_centroid[:,1]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c13006fd",
   "metadata": {},
   "source": [
    "Now, let us create a DataFrame for position. Time to use Pynapple! This recording was acquired at 120Hz, so we will make the timestamps first, as below: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0c1f62ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "fs = 120\n",
    "timestamps = x_cent.index.values/fs\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3c79d4a",
   "metadata": {},
   "source": [
    "Now, we create the position DataFrame as below: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5bbdb539",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Time (s)</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.000000</th>\n",
       "      <td>344.400401</td>\n",
       "      <td>111.311007</td>\n",
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       "    <tr>\n",
       "      <th>0.008333</th>\n",
       "      <td>263.230535</td>\n",
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       "    <tr>\n",
       "      <th>0.016667</th>\n",
       "      <td>345.426666</td>\n",
       "      <td>112.536496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.025000</th>\n",
       "      <td>344.647959</td>\n",
       "      <td>111.654310</td>\n",
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       "    <tr>\n",
       "      <th>0.033333</th>\n",
       "      <td>769.252502</td>\n",
       "      <td>411.222036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1218.341667</th>\n",
       "      <td>346.007568</td>\n",
       "      <td>111.750453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1218.350000</th>\n",
       "      <td>345.466553</td>\n",
       "      <td>112.780380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1218.358333</th>\n",
       "      <td>348.746501</td>\n",
       "      <td>115.356644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1218.366667</th>\n",
       "      <td>345.818390</td>\n",
       "      <td>111.690325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1218.375000</th>\n",
       "      <td>375.111969</td>\n",
       "      <td>144.834963</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>146206 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      x           y\n",
       "Time (s)                           \n",
       "0.000000     344.400401  111.311007\n",
       "0.008333     263.230535  374.801732\n",
       "0.016667     345.426666  112.536496\n",
       "0.025000     344.647959  111.654310\n",
       "0.033333     769.252502  411.222036\n",
       "...                 ...         ...\n",
       "1218.341667  346.007568  111.750453\n",
       "1218.350000  345.466553  112.780380\n",
       "1218.358333  348.746501  115.356644\n",
       "1218.366667  345.818390  111.690325\n",
       "1218.375000  375.111969  144.834963\n",
       "\n",
       "[146206 rows x 2 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "position = np.vstack([x, y]).T\n",
    "position = nap.TsdFrame(t = timestamps, d = position, columns = ['x', 'y'], time_units = 's')\n",
    "\n",
    "position\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "873c444a",
   "metadata": {},
   "source": [
    "This looks good, but does not give us an idea of what the data really represents. Let's plot this"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "59aba406",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(data = position, x = x, y = y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c30bd4c7",
   "metadata": {},
   "source": [
    "Aha! What we said before is now evident. The position plot reveals the radial arm maze outline. There are some points outside the bounds of the mze, due to DeepLabCut detecting the mouse erroneously. But these point are irrelevant to us, since we only care about points in the centre of the maze. Now, I will define the centre point of the maze, using the coordinates (xth, yth) and a radius of the circle given by rth.\n",
    "\n",
    "(xth, yth) are approximate values for the centre of this maze. Feel free to pick these values as per your convenience and application. rth is also modifiable as per your application.\n",
    "\n",
    "We will go with the following parameter values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "731765a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "xth = 683.4\n",
    "yth = 484.4\n",
    "rth = 200"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c26a321",
   "metadata": {},
   "source": [
    "Let us visualize this; we will plot the position with the circle around it. And then we will only consider those trajectories that lie within the circle."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4afcc25d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.patches.Circle at 0x7ff2670ee6d0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "circle1 = plt.Circle((xth, yth), rth, color='k', fill = False)\n",
    "ax = sns.scatterplot(data = position, x = x, y = y)\n",
    "ax.add_patch(circle1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a80fdfe",
   "metadata": {},
   "source": [
    "Great! So now we can restrict our position data to the centre of the maze. \n",
    "\n",
    "So, to do this, firstly, we will restrict our position to points within the centre. This can be done by selecting points whose distance from the centre is smaller than the radius of our circle. Time to put Pynapple to use!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9e9bf7c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Time (s)\n",
       "0.000000       504.099320\n",
       "0.008333       434.228235\n",
       "0.016667       502.502179\n",
       "0.025000       503.678761\n",
       "0.033333       112.808096\n",
       "                  ...    \n",
       "1218.341667    502.694080\n",
       "1218.350000    502.294890\n",
       "1218.358333    498.182661\n",
       "1218.366667    502.865633\n",
       "1218.375000    458.634848\n",
       "Length: 146206, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = np.sqrt((x - xth)**2 + (y - yth)**2)\n",
    "dist_center = nap.Tsd(t = timestamps, d = d, time_units = 's')\n",
    "\n",
    "dist_center"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "708d2218",
   "metadata": {},
   "source": [
    "Pynapple time!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "78c87b40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Time (s)\n",
       "0.033333       112.808096\n",
       "0.241667       111.840542\n",
       "0.408333       112.876936\n",
       "0.566667       114.982099\n",
       "0.591667       112.613405\n",
       "                  ...    \n",
       "1215.033333    112.091947\n",
       "1215.166667    196.402651\n",
       "1215.191667     95.696973\n",
       "1215.591667    112.072994\n",
       "1216.241667    110.800920\n",
       "Length: 40298, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "within_center = dist_center.threshold(rth, 'below')\n",
    "ep = within_center.time_support\n",
    "\n",
    "within_center"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bacf588d",
   "metadata": {},
   "source": [
    "As you can see, all values in within_center are now less than our threshold value (rth). \n",
    "\n",
    "What does the time support of this look like?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "907437d8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           start          end\n",
      "0       6.354166    13.087500\n",
      "1      21.037500    35.754166\n",
      "2      39.112500    50.070834\n",
      "3      58.004166    63.720834\n",
      "4      74.437500    76.504166\n",
      "..           ...          ...\n",
      "122  1185.837500  1187.370834\n",
      "123  1190.195834  1191.662500\n",
      "124  1193.479166  1194.620834\n",
      "125  1195.587500  1197.129166\n",
      "126  1207.512500  1209.729166\n",
      "\n",
      "[127 rows x 2 columns]\n"
     ]
    }
   ],
   "source": [
    "print(ep)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2566a658",
   "metadata": {},
   "source": [
    "So now we have a set of points for when the animal is within the radius of the maze. We will now seaparate these points into trials. For our purposes, we will say that trials that are only greater than 0.7s be considered. Additionally, each trial can have a maximal duration of 20s. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "51fea121",
   "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>start</th>\n",
       "      <th>end</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.354166</td>\n",
       "      <td>13.087500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21.037500</td>\n",
       "      <td>35.754166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>39.112500</td>\n",
       "      <td>50.070834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>58.004166</td>\n",
       "      <td>63.720834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>74.437500</td>\n",
       "      <td>76.504166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1185.837500</td>\n",
       "      <td>1187.370834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1190.195834</td>\n",
       "      <td>1191.662500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1193.479166</td>\n",
       "      <td>1194.620834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1195.587500</td>\n",
       "      <td>1197.129166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1207.512500</td>\n",
       "      <td>1209.729166</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>127 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           start          end\n",
       "0       6.354166    13.087500\n",
       "1      21.037500    35.754166\n",
       "2      39.112500    50.070834\n",
       "3      58.004166    63.720834\n",
       "4      74.437500    76.504166\n",
       "..           ...          ...\n",
       "122  1185.837500  1187.370834\n",
       "123  1190.195834  1191.662500\n",
       "124  1193.479166  1194.620834\n",
       "125  1195.587500  1197.129166\n",
       "126  1207.512500  1209.729166\n",
       "\n",
       "[127 rows x 2 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ep = ep.drop_short_intervals(0.7, time_units = 's')\n",
    "\n",
    "ep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "336a76b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "ep = ep.drop_long_intervals(20, time_units = 's')\n",
    "ep = ep.reset_index(drop=True)\n",
    "ep"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c16d1d5e",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "61819f01",
   "metadata": {},
   "outputs": [],
   "source": [
    "   \n",
    "    \n",
    "\n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "       \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    ep = ep.drop_short_intervals(0.7, time_units = 's')\n",
    "    \n",
    "    dy = nap.Tsd(t = timestamps, d = y-yth, time_units = 's')    \n",
    "    \n",
    "    \n",
    "    ## COMMENT OUT FOR RANDOM EXPLORATION\n",
    "##########################################################################    \n",
    "    diffy = []\n",
    "    for i in ep.index.values:\n",
    "        tmp = dy.restrict(ep.loc[[i]])\n",
    "        diffy.append(tmp.iloc[-1] - tmp.iloc[0])\n",
    "    diffy = pd.Series(data = diffy)\n",
    "    diffy = diffy[diffy > rth/2]\n",
    "    \n",
    "    ep = ep.loc[diffy.index]\n",
    "###########################################################################    \n",
    "    # plt.figure()\n",
    "    # plt.plot(dy.restrict(ep), 'o')\n",
    "    \n",
    "    circle1 = plt.Circle((xth, yth), rth, color='k', fill = False)\n",
    "    \n",
    "    plt.figure()\n",
    "    plt.plot(position['x'].restrict(ep),position['y'].restrict(ep),'silver', zorder = 1)\n",
    "    plt.scatter(position['x'].restrict(ep), position['y'].restrict(ep), zorder = 2)\n",
    "    # plt.scatter(position['x'].restrict(rev), position['z'].restrict(rev), zorder = 2)\n",
    "    plt.gca().add_patch(circle1)\n",
    "        \n",
    "    # position.as_units('s').to_csv(data_directory + '/' + name + '_posMATLAB.csv', index=True)\n",
    "    # ep.as_units('s').to_csv(data_directory + '/' + name + '_fwd.csv', index=False)\n",
    "    \n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4e0b3952",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "25a0ca9d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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