examples/03_group_features.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Group feature extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import movekit as mkit\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Extracting all absolute features: 100%|██████████| 100.0/100 [00:01<00:00, 76.96it/s]\n"
     ]
    },
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>animal_id</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>distance</th>\n",
       "      <th>direction</th>\n",
       "      <th>turning</th>\n",
       "      <th>average_speed</th>\n",
       "      <th>average_acceleration</th>\n",
       "      <th>stopped</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>312</td>\n",
       "      <td>405.29</td>\n",
       "      <td>417.76</td>\n",
       "      <td>0.0</td>\n",
       "      <td>(0.0, 0.0)</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.115306</td>\n",
       "      <td>0.005298</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>511</td>\n",
       "      <td>369.99</td>\n",
       "      <td>428.78</td>\n",
       "      <td>0.0</td>\n",
       "      <td>(0.0, 0.0)</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.012708</td>\n",
       "      <td>0.002823</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>607</td>\n",
       "      <td>390.33</td>\n",
       "      <td>405.89</td>\n",
       "      <td>0.0</td>\n",
       "      <td>(0.0, 0.0)</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.045118</td>\n",
       "      <td>0.003392</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>811</td>\n",
       "      <td>445.15</td>\n",
       "      <td>411.94</td>\n",
       "      <td>0.0</td>\n",
       "      <td>(0.0, 0.0)</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.232790</td>\n",
       "      <td>0.026729</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>905</td>\n",
       "      <td>366.06</td>\n",
       "      <td>451.76</td>\n",
       "      <td>0.0</td>\n",
       "      <td>(0.0, 0.0)</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.067000</td>\n",
       "      <td>0.001639</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   time  animal_id       x       y  distance   direction  turning  \\\n",
       "0     1        312  405.29  417.76       0.0  (0.0, 0.0)      0.0   \n",
       "1     1        511  369.99  428.78       0.0  (0.0, 0.0)      0.0   \n",
       "2     1        607  390.33  405.89       0.0  (0.0, 0.0)      0.0   \n",
       "3     1        811  445.15  411.94       0.0  (0.0, 0.0)      0.0   \n",
       "4     1        905  366.06  451.76       0.0  (0.0, 0.0)      0.0   \n",
       "\n",
       "   average_speed  average_acceleration  stopped  \n",
       "0       0.115306              0.005298        1  \n",
       "1       0.012708              0.002823        1  \n",
       "2       0.045118              0.003392        1  \n",
       "3       0.232790              0.026729        1  \n",
       "4       0.067000              0.001639        1  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path = \"./datasets/fish-5-cleaned.csv\"\n",
    "data = mkit.read_data(path)\n",
    "data = mkit.extract_features(data)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Detecting outliers\n",
    "Function performs detection of outliers, based on the KNN algorithm: user can define the regarding features for the detection, the number of the nearest neighbors taken into account for the outlier classification, the metric to calculate the distance, the method to aggregate the different distances, and the share of outliers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>animal_id</th>\n",
       "      <th>outlier</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>distance</th>\n",
       "      <th>direction</th>\n",
       "      <th>turning</th>\n",
       "      <th>average_speed</th>\n",
       "      <th>average_acceleration</th>\n",
       "      <th>stopped</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>312</td>\n",
       "      <td>1</td>\n",
       "      <td>405.31</td>\n",
       "      <td>417.37</td>\n",
       "      <td>0.390512</td>\n",
       "      <td>(0.02, -0.39)</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.122306</td>\n",
       "      <td>0.003547</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2</td>\n",
       "      <td>811</td>\n",
       "      <td>1</td>\n",
       "      <td>445.48</td>\n",
       "      <td>412.26</td>\n",
       "      <td>0.459674</td>\n",
       "      <td>(0.33, 0.32)</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.282267</td>\n",
       "      <td>0.028174</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2</td>\n",
       "      <td>905</td>\n",
       "      <td>1</td>\n",
       "      <td>365.86</td>\n",
       "      <td>451.76</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>(-0.2, 0.0)</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.071000</td>\n",
       "      <td>0.000271</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1098</th>\n",
       "      <td>220</td>\n",
       "      <td>811</td>\n",
       "      <td>1</td>\n",
       "      <td>408.85</td>\n",
       "      <td>468.18</td>\n",
       "      <td>0.325576</td>\n",
       "      <td>(-0.06, -0.32)</td>\n",
       "      <td>0.707107</td>\n",
       "      <td>0.276132</td>\n",
       "      <td>0.014037</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1263</th>\n",
       "      <td>253</td>\n",
       "      <td>811</td>\n",
       "      <td>1</td>\n",
       "      <td>404.67</td>\n",
       "      <td>460.60</td>\n",
       "      <td>0.014142</td>\n",
       "      <td>(0.01, -0.01)</td>\n",
       "      <td>-0.514496</td>\n",
       "      <td>0.154391</td>\n",
       "      <td>0.025176</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      time  animal_id  outlier       x       y  distance       direction  \\\n",
       "5        2        312        1  405.31  417.37  0.390512   (0.02, -0.39)   \n",
       "8        2        811        1  445.48  412.26  0.459674    (0.33, 0.32)   \n",
       "9        2        905        1  365.86  451.76  0.200000     (-0.2, 0.0)   \n",
       "1098   220        811        1  408.85  468.18  0.325576  (-0.06, -0.32)   \n",
       "1263   253        811        1  404.67  460.60  0.014142   (0.01, -0.01)   \n",
       "\n",
       "       turning  average_speed  average_acceleration  stopped  \n",
       "5     0.000000       0.122306              0.003547        1  \n",
       "8     0.000000       0.282267              0.028174        1  \n",
       "9     0.000000       0.071000              0.000271        1  \n",
       "1098  0.707107       0.276132              0.014037        1  \n",
       "1263 -0.514496       0.154391              0.025176        1  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Detect outliers based on KNN.\n",
    "# mkit.outlier_detection(dataset, features=[\"distance\", \"average_speed\", \"average_acceleration\",\n",
    "# \"stopped\", \"turning\"], contamination=0.01, n_neighbors=5, method=\"mean\", metric=\"minkowski\")\n",
    "outs = mkit.outlier_detection(data)\n",
    "# printing all rows where outliers are present\n",
    "outs[outs.loc[:,\"outlier\"] == 1].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>animal_id</th>\n",
       "      <th>outlier</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>distance</th>\n",
       "      <th>direction</th>\n",
       "      <th>turning</th>\n",
       "      <th>average_speed</th>\n",
       "      <th>average_acceleration</th>\n",
       "      <th>stopped</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1522</th>\n",
       "      <td>305</td>\n",
       "      <td>607</td>\n",
       "      <td>1</td>\n",
       "      <td>179.53</td>\n",
       "      <td>417.02</td>\n",
       "      <td>3.268103</td>\n",
       "      <td>(-3.26, -0.23)</td>\n",
       "      <td>0.999998</td>\n",
       "      <td>3.195286</td>\n",
       "      <td>-0.068100</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1602</th>\n",
       "      <td>321</td>\n",
       "      <td>607</td>\n",
       "      <td>1</td>\n",
       "      <td>132.58</td>\n",
       "      <td>411.00</td>\n",
       "      <td>2.211990</td>\n",
       "      <td>(-2.2, -0.23)</td>\n",
       "      <td>0.999645</td>\n",
       "      <td>2.088872</td>\n",
       "      <td>-0.217909</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1607</th>\n",
       "      <td>322</td>\n",
       "      <td>607</td>\n",
       "      <td>1</td>\n",
       "      <td>130.81</td>\n",
       "      <td>410.81</td>\n",
       "      <td>1.780169</td>\n",
       "      <td>(-1.77, -0.19)</td>\n",
       "      <td>0.999996</td>\n",
       "      <td>1.824672</td>\n",
       "      <td>-0.218531</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1612</th>\n",
       "      <td>323</td>\n",
       "      <td>607</td>\n",
       "      <td>1</td>\n",
       "      <td>129.26</td>\n",
       "      <td>410.63</td>\n",
       "      <td>1.560417</td>\n",
       "      <td>(-1.55, -0.18)</td>\n",
       "      <td>0.999962</td>\n",
       "      <td>1.561242</td>\n",
       "      <td>-0.214846</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1617</th>\n",
       "      <td>324</td>\n",
       "      <td>607</td>\n",
       "      <td>1</td>\n",
       "      <td>127.95</td>\n",
       "      <td>410.58</td>\n",
       "      <td>1.310954</td>\n",
       "      <td>(-1.31, -0.05)</td>\n",
       "      <td>0.997001</td>\n",
       "      <td>1.296308</td>\n",
       "      <td>-0.205183</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      time  animal_id  outlier       x       y  distance       direction  \\\n",
       "1522   305        607        1  179.53  417.02  3.268103  (-3.26, -0.23)   \n",
       "1602   321        607        1  132.58  411.00  2.211990   (-2.2, -0.23)   \n",
       "1607   322        607        1  130.81  410.81  1.780169  (-1.77, -0.19)   \n",
       "1612   323        607        1  129.26  410.63  1.560417  (-1.55, -0.18)   \n",
       "1617   324        607        1  127.95  410.58  1.310954  (-1.31, -0.05)   \n",
       "\n",
       "       turning  average_speed  average_acceleration  stopped  \n",
       "1522  0.999998       3.195286             -0.068100        0  \n",
       "1602  0.999645       2.088872             -0.217909        0  \n",
       "1607  0.999996       1.824672             -0.218531        0  \n",
       "1612  0.999962       1.561242             -0.214846        0  \n",
       "1617  0.997001       1.296308             -0.205183        0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# same function, different parameters\n",
    "other_outs = mkit.outlier_detection(dataset = data, features = [\"average_speed\", \"average_acceleration\"], contamination = 0.05, n_neighbors = 8, method = \"median\", metric = \"euclidean\")\n",
    "\n",
    "# printing all rows where outliers are present\n",
    "other_outs[other_outs.loc[:,\"outlier\"] == 1].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Group-level Analysis\n",
    "\n",
    "Below we perform Analysis on Group-Level. This consists of:\n",
    "- Group-Level averages,\n",
    "- Centroid Medoid computation\n",
    "- A dynamic time warping matrix, \n",
    "- A clustering over time based on absolute features,\n",
    "- The centroid direction,\n",
    "- The heading difference of each animal with respect to the current centroid\n",
    "- The group - polarization for each timestep. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Obtain group-level records for each point in time\n",
    "Records consist of total group-distance covered, mean speed, mean acceleration and mean distance from centroid for each timestamp. If input doesn't contain centroid or feature data, it is calculated, showing a warning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/timkleinlein/DbvisHiwi/movekit/g/lib/python3.9/site-packages/movekit-0.0.0-py3.9.egg/movekit/feature_extraction.py:550: UserWarning: Recalculating centroid-distances, since not found in input dataset!\n",
      "  warnings.warn(\n",
      "Calculating centroid distances: 100%|██████████| 1000/1000 [00:05<00:00, 170.11it/s]\n"
     ]
    },
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_dist</th>\n",
       "      <th>mean_speed</th>\n",
       "      <th>mean_acceleration</th>\n",
       "      <th>mean_distance_centroid</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.094584</td>\n",
       "      <td>0.007976</td>\n",
       "      <td>29.4616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.174908</td>\n",
       "      <td>0.108927</td>\n",
       "      <td>0.007736</td>\n",
       "      <td>29.5850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.025155</td>\n",
       "      <td>0.122863</td>\n",
       "      <td>0.007782</td>\n",
       "      <td>29.6914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.918960</td>\n",
       "      <td>0.138007</td>\n",
       "      <td>0.007898</td>\n",
       "      <td>29.7782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.830461</td>\n",
       "      <td>0.155004</td>\n",
       "      <td>0.008365</td>\n",
       "      <td>29.8518</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      total_dist  mean_speed  mean_acceleration  mean_distance_centroid\n",
       "time                                                                   \n",
       "1       0.000000    0.094584           0.007976                 29.4616\n",
       "2       1.174908    0.108927           0.007736                 29.5850\n",
       "3       1.025155    0.122863           0.007782                 29.6914\n",
       "4       0.918960    0.138007           0.007898                 29.7782\n",
       "5       0.830461    0.155004           0.008365                 29.8518"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group_data = mkit.group_movement(data)\n",
    "group_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Obtain centroid, medoid and distance to centroid for each movement record"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Calculating centroid distances: 100%|██████████| 1000/1000 [00:04<00:00, 232.12it/s]\n"
     ]
    },
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      "text/plain": [
       "   time  animal_id  outlier       x       y  distance   direction  turning  \\\n",
       "0     1        312        0  405.29  417.76       0.0  (0.0, 0.0)      0.0   \n",
       "1     1        511        0  369.99  428.78       0.0  (0.0, 0.0)      0.0   \n",
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       "3     1        811        0  445.15  411.94       0.0  (0.0, 0.0)      0.0   \n",
       "4     1        905        0  366.06  451.76       0.0  (0.0, 0.0)      0.0   \n",
       "\n",
       "   average_speed  average_acceleration  stopped  x_centroid  y_centroid  \\\n",
       "0       0.115306              0.005298        1     395.364     423.226   \n",
       "1       0.012708              0.002823        1     395.364     423.226   \n",
       "2       0.045118              0.003392        1     395.364     423.226   \n",
       "3       0.232790              0.026729        1     395.364     423.226   \n",
       "4       0.067000              0.001639        1     395.364     423.226   \n",
       "\n",
       "   medoid  distance_to_centroid  \n",
       "0     312                11.331  \n",
       "1     312                25.975  \n",
       "2     312                18.052  \n",
       "3     312                51.049  \n",
       "4     312                40.901  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movement = mkit.centroid_medoid_computation(data, object_output = False)\n",
    "movement.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Get the heading difference between centroids and animal's direction\n",
    "Heading difference is computed with the cosine similarity of the two direction vectors, thus ranges from -1 to 1. While 1 indicates the animal and the centroid have the same direction, -1 indicates they move in different directions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/timkleinlein/DbvisHiwi/movekit/g/lib/python3.9/site-packages/movekit-0.0.0-py3.9.egg/movekit/clustering.py:87: UserWarning: x_centroid or y_centroid not found in data. Calculating centroid...\n",
      "  warnings.warn(\n",
      "Calculating centroid distances: 100%|██████████| 1000/1000 [00:02<00:00, 444.64it/s]\n",
      "Computing centroid direction: 100%|██████████| 100.0/100 [00:00<00:00, 846.21it/s]\n",
      "Calculating centroid distances: 100%|██████████| 1000/1000 [00:04<00:00, 237.34it/s]\n",
      "Calculating heading difference: 100%|██████████| 100.0/100 [00:01<00:00, 86.41it/s] \n"
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       "   time  animal_id  outlier       x       y  distance   direction  turning  \\\n",
       "0     1        312        0  405.29  417.76       0.0  (0.0, 0.0)      0.0   \n",
       "1     1        511        0  369.99  428.78       0.0  (0.0, 0.0)      0.0   \n",
       "2     1        607        0  390.33  405.89       0.0  (0.0, 0.0)      0.0   \n",
       "3     1        811        0  445.15  411.94       0.0  (0.0, 0.0)      0.0   \n",
       "4     1        905        0  366.06  451.76       0.0  (0.0, 0.0)      0.0   \n",
       "\n",
       "   average_speed  average_acceleration  stopped  x_centroid  y_centroid  \\\n",
       "0       0.115306              0.005298        1     395.364     423.226   \n",
       "1       0.012708              0.002823        1     395.364     423.226   \n",
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       "4       0.067000              0.001639        1     395.364     423.226   \n",
       "\n",
       "   medoid  distance_to_centroid centroid_direction  heading_difference  \n",
       "0     312                11.331         (0.0, 0.0)                 0.0  \n",
       "1     312                25.975         (0.0, 0.0)                 0.0  \n",
       "2     312                18.052         (0.0, 0.0)                 0.0  \n",
       "3     312                51.049         (0.0, 0.0)                 0.0  \n",
       "4     312                40.901         (0.0, 0.0)                 0.0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
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   "source": [
    "centroid_dir = mkit.compute_centroid_direction(data).sort_values(['time','animal_id'])\n",
    "heading_diff = mkit.get_heading_difference(data)\n",
    "heading_diff.head()\n"
   ]
  },
  {
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   "metadata": {
    "tags": []
   },
   "source": [
    "#### Obtain a matrix, based on dynamic time warping\n",
    "Each Animal-ID is displayed in the indices, the entries reflect the similarity of the animal's trajectories based on the DTW algorithm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
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     "text": [
      "Calculating dynamic time warping: 100%|██████████| 5/5 [00:05<00:00,  1.09s/it]\n"
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   "source": [
    "#Obtain dynamic time warping amongst all trajectories from the animals. The lower the value for two animals is, the more similar their trajectories are based on the DTW algorithm.\n",
    "#mkit.dtw_matrix(preprocessed_data, path=False, distance=euclidean)\n",
    "#preprocessed_data: DataFrame containing the movement data.\n",
    "#path: Boolean to specify if matrix of dtw-path gets returned as well. (the warping path for all the sequence pairs which are examined)\n",
    "#distance: Specify with distance measure to use. Default: \"euclidean\". Other example alternatives are pdist or minkowski. (all distances defined by fastdtw package are possible.\n",
    "\n",
    "mkit.dtw_matrix(data)"
   ]
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