Synth-data-analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "productive-honor",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import colors\n",
    "import scipy\n",
    "import pandas as pd\n",
    "from sklearn import cluster, metrics\n",
    "from sklearn.manifold import MDS\n",
    "from scipy.cluster import hierarchy\n",
    "from scipy.spatial.distance import squareform\n",
    "from tslearn.metrics import cdist_dtw, dtw\n",
    "\n",
    "from numpy.random import default_rng\n",
    "rng = default_rng(8888)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "focused-exercise",
   "metadata": {},
   "source": [
    "Generate data from $\\mathbf{X}_t=\\varepsilon_t$, where $\\varepsilon_t\\sim \\mathcal{N}(\\mu,\\Sigma)$. Assume normalised. \n",
    "\n",
    "The original data has positive covariance between .19 and .76 after normalisation, so $\\Sigma = \\mathbf{a}^\\mathrm{T}\\mathbf{a}$ where $\\mathbf{a}=[1, c]$ with $c\\sim\\mathcal{U}_{[0.15, 0.8]}$.\n",
    "\n",
    "Q: apply autocorrelation as a convolution filter to data? (or add periodicity?)  \n",
    "Q: this has been generated assuming that it's already scaled. How to add a perturbation so that it's also appropriately scaled (similar to original data)?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "featured-occupation",
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_data(n_patients=10, \n",
    "                  pct_abnormal=0.2, \n",
    "                  perturb_method=['gaussian', 'gradual rise', 'log', 'opposite'], \n",
    "                  verbose=False):\n",
    "    \"\"\"\n",
    "    pct_abnormal: percentage of patients for which to add a perturbation, float between 0 and 1\n",
    "    perturb_method: ['top hat', 'gaussian', 'gradual rise', 'log', 'opposite']\n",
    "        constant: adds a flat value to existing series, basically increasing the means by amount\n",
    "            set in perturb_constant\n",
    "        gaussian: adds gaussian noise with a positive mean, set in perturb_mean\n",
    "    \"\"\"\n",
    "    \n",
    "    dfs_raw = []\n",
    "    patient_list = list(range(1, n_patients+1)) #start at patient no. 1\n",
    "\n",
    "    time_range = pd.date_range(start='1/1/2021', end='1/9/2021', freq='min') #starts and stops at midnight - this is 8 days\n",
    "    \n",
    "    for idx, df in enumerate(range(n_patients)):\n",
    "        df = pd.DataFrame(time_range, columns=['Timestamp'])\n",
    "        labels = np.zeros(time_range.shape[0])\n",
    "\n",
    "        # generate mean, covariance matrices \n",
    "        # going to normalize them later anyway, so precise values don't matter\n",
    "        mean = np.array([0,0])\n",
    "        cov_r = rng.uniform(low=0.15, high=0.6, size=1)[0]\n",
    "        cov = np.array([[1., cov_r],[cov_r, 1.]])\n",
    "        vitals = rng.multivariate_normal(mean, cov, time_range.shape[0])\n",
    "        \n",
    "        # add some sinusoidal variation (an abomination of a function, see graph below)\n",
    "        freq = 1/(24*60) # 1 day, Hz*60 (minutes)\n",
    "        ph = rng.integers(0, 60*12) # also in minutes, up to 12h lag\n",
    "        amp = 0.25 # in std. devs \n",
    "        rhythm = np.fromfunction(lambda i, j: \\\n",
    "            -0.3*j*(amp*np.sin(2*np.pi*freq*i + ph)+0.15*np.sin(2*np.pi/240*i+10)) + \\\n",
    "            amp*(np.sin(2*np.pi*freq*i + ph)+0.05*np.sin(2*np.pi/240*i)), \\\n",
    "            vitals.shape, dtype=float)     \n",
    "        vitals += rhythm\n",
    "        \n",
    "        # add some nonstationary elements? let's try some top hat functions\n",
    "        # always add at the end of the time series to make my life easier, say the last 10% \n",
    "        # (approx 21.5 hours if using 9 days e.g. start='1/1/2021', end='1/8/2021')     \n",
    "        # series of top hats with gaussian noise\n",
    "        if 'gaussian' in perturb_method:\n",
    "            if idx+1 > n_patients*pct_abnormal and idx+1 <= n_patients*pct_abnormal*2:\n",
    "                cov_r = rng.uniform(low=0.25, high=0.5, size=(1,2)) # increase variance a bit\n",
    "                cov = cov_r.T*cov_r\n",
    "                perturb_mean=[1.5, 0] # 1.5 std devs on ONE variable (can change this...)\n",
    "                perturb = rng.multivariate_normal(perturb_mean, cov, time_range.shape[0])\n",
    "                perturb_idx = int(time_range.shape[0]*0.9) # change vals AFTER this\n",
    "                perturb[:perturb_idx] = 0\n",
    "                vitals += perturb\n",
    "                labels[perturb_idx+1:] = 1\n",
    "                \n",
    "                if verbose:\n",
    "                    print(f\"perturbed (gaussian). Label 1\")\n",
    "            \n",
    "        if 'top hat' in perturb_method and 'gaussian' not in perturb_method:\n",
    "            # use either this or gaussian, not both\n",
    "            if idx+1 > n_patients*pct_abnormal and idx+1 <= n_patients*pct_abnormal*2:\n",
    "                var = rng.uniform(low=0.5, high=1.5, size=1) # increase variance a bit\n",
    "                perturb_var = rng.normal(0, var, time_range.shape[0]) # add some variance to both\n",
    "                t = np.arange(time_range.shape[0])\n",
    "                perturb_mean = np.where(t%600 <=120, 1.5, 0) # bunch of little top hats\n",
    "                \n",
    "                perturb_idx = int(time_range.shape[0]*0.9) # change vals AFTER this\n",
    "                perturb[:perturb_idx] = 0\n",
    "                vitals[:,0] += perturb_mean # NOTE: changed here to only change ONE vital sign\n",
    "                vitals[:,0] += perturb_var\n",
    "                labels[perturb_idx+1:] = 1\n",
    "                \n",
    "                if verbose:\n",
    "                    print(f\"perturbed (top hat). Label 1\")\n",
    "                \n",
    "        # increase over the period of a day\n",
    "        if 'gradual rise' in perturb_method:\n",
    "            if idx+1 > n_patients*pct_abnormal*2 and idx+1 <= n_patients*pct_abnormal*3:\n",
    "                # linear increase in 2D, from 24 hours before end of recording\n",
    "                perturb_idx = int(24*60) # 24 hours of indices - take LAST \n",
    "                perturb = np.zeros_like(vitals)\n",
    "                # 2 standard deviations - big increase!\n",
    "                perturb[-perturb_idx:] = np.fromfunction(lambda i, j: i, (perturb_idx, 2), dtype=float)*(1.5/perturb_idx) \n",
    "                vitals += perturb\n",
    "                # how early should we be able to detect this? There should be a trend, so instantly\n",
    "                labels[-perturb_idx+1:] = 2\n",
    "                \n",
    "                if verbose:\n",
    "                    print(f\"perturbed (gradual rise). Label 2\")\n",
    "        \n",
    "        # increase quickly and stay high\n",
    "        if 'log' in perturb_method:\n",
    "            if idx+1 > n_patients*pct_abnormal*3 and idx+1 <= n_patients*pct_abnormal*4:\n",
    "                perturb = np.zeros_like(vitals)\n",
    "                perturb_idx = int(time_range.shape[0]*0.1) # take from end to this idx, so -ve\n",
    "                perturb[-perturb_idx:] = np.fromfunction(lambda i, j: np.log(i+1), (perturb_idx, 2), dtype=float)*(1/7)\n",
    "                vitals += perturb\n",
    "                labels[-perturb_idx+1:] = 3\n",
    "                \n",
    "                if verbose:\n",
    "                    print(f\"perturbed (logarithmic increase). Label 3\")\n",
    "            \n",
    "        # vitals go in opposite directions\n",
    "        if 'opposite' in perturb_method:\n",
    "            if idx+1 > n_patients*pct_abnormal*4 and idx+1 <= n_patients*pct_abnormal*5:\n",
    "                perturb = np.zeros_like(vitals)\n",
    "                perturb_idx = int(time_range.shape[0]*0.1)\n",
    "                perturb[-perturb_idx:] =  np.fromfunction(lambda i, j: j*2*np.log(i+1)-np.log(i+1), \\\n",
    "                                                          (perturb_idx, 2), dtype=float)*(1/7)\n",
    "                vitals += perturb\n",
    "                labels[-perturb_idx+1:] = 4\n",
    "                \n",
    "                if verbose:\n",
    "                    print(f\"perturbed (opposite directions). Label 4\")\n",
    "        \n",
    "        if verbose:\n",
    "            print(f\"mean {np.mean(vitals)}\")\n",
    "            print(f\"var {np.var(vitals)}\\n\")\n",
    "\n",
    "        df['Lifetouch Heart Rate'] = vitals[:,0]\n",
    "        df['Lifetouch Respiration Rate'] = vitals[:,1]\n",
    "        df['Labels'] = labels\n",
    "        dfs_raw.append(df)\n",
    "        \n",
    "    return dfs_raw, patient_list"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "clinical-closer",
   "metadata": {},
   "source": [
    "## Smooth, split, cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "alleged-campus",
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocessing(dfs_raw, length=180, window=25):\n",
    "    # length parameter: 3 hours or 180 minutes\n",
    "    # normalize then apply rolling window\n",
    "    use_center=False\n",
    "    smoothed_dfs = []\n",
    "    labels = []\n",
    "\n",
    "    # places in rightmost position in window, alternately can set center=True\n",
    "    for df in dfs_raw:\n",
    "        data = df[[\"Lifetouch Heart Rate\", \"Lifetouch Respiration Rate\"]]\n",
    "        ndf = (data - data.mean(skipna=True))/data.std(skipna=True)\n",
    "        smoothed_dfs.append(ndf.rolling(window=window, center=use_center).median())\n",
    "        labels.append(df[\"Labels\"])\n",
    "    \n",
    "    return smoothed_dfs, labels\n",
    "\n",
    "\n",
    "# length parameter is length of interval in minutes\n",
    "def split_into_intervals(smoothed_dfs, labels, patient_list, length=180):\n",
    "    data = []\n",
    "    metadata = []\n",
    "    source_file_list = []\n",
    "    targets = []\n",
    "    \n",
    "    for idx, df in enumerate(smoothed_dfs):\n",
    "        # initialize list into which we'll put the split arrays\n",
    "        segments = [] \n",
    "        # and maybe a good idea to return the file index, start and stop indices\n",
    "        metadata_pts = []\n",
    "        source_list = []\n",
    "        seg_labels = []\n",
    "\n",
    "        # Find rows with any NaN values, create as mask\n",
    "        # add leading and trailing buffer so we can compare to the next row sequentially\n",
    "        mask = np.concatenate(( [True], df.isna().any(axis=1).to_numpy(), [True] ))\n",
    "\n",
    "        # get start and stop limits, reshape them into array of (start, stop) pairs\n",
    "        ss = np.flatnonzero(mask[1:] != mask[:-1]).reshape(-1,2)\n",
    "        # mask to get contiguous data only greater in length than length parameter\n",
    "        ss = ss[(ss[:,1] - ss[:,0]) > length]\n",
    "        # find how many len(length) segments are in each contiguous block of data\n",
    "        nb_splits = (ss[:,1] - ss[:,0])//length\n",
    "\n",
    "        # finally, we can get our start, stop, nb_splits tuples and use np.split to\n",
    "        # return a list of equal size arrays of our desired length\n",
    "        ends = ss[:,1]\n",
    "        starts = ends - nb_splits*length\n",
    "        for start, end, nb_splits in zip(starts,ends,nb_splits):\n",
    "            # Difference between append and extend here is how they're added to the \n",
    "            # list. Extend \"flattens\" the resulting list so we don't end up with extra\n",
    "            # dimentions, but also we can't easily tell which original file the data \n",
    "            # belonged to, which is why I've included metadata. \n",
    "            segments.extend( np.split(df.to_numpy()[start:end],nb_splits) )\n",
    "            seg_label = np.split(labels[idx].to_numpy()[start:end],nb_splits)\n",
    "            for split in range(nb_splits):\n",
    "                metadata_pts.append( (\"Patient \" + str(patient_list[idx]), start+split*length, start+(split+1)*length) )\n",
    "                source_list.append(patient_list[idx])\n",
    "                # find the modal label for this segment\n",
    "                seg_labels.extend(scipy.stats.mode(seg_label[split])[0])\n",
    "        \n",
    "        if metadata_pts != []: # if no contiguous samples long enough, both lists are empty\n",
    "            data.extend(segments)\n",
    "            metadata.extend(metadata_pts)\n",
    "            source_file_list.extend(source_list)\n",
    "            targets.extend(seg_labels)\n",
    "    \n",
    "    return data, metadata, source_file_list, targets\n",
    "\n",
    "\n",
    "def dtw_calc(data):    \n",
    "    # take all the processed data, flatten each array, and add to a numpy array \n",
    "    # (n_epochs, epoch_length, n_variables)\n",
    "    X_ts = np.empty((len(data), len(data[0]), smoothed_dfs[0].shape[1]))\n",
    "    for i, seg in enumerate(data):\n",
    "        X_ts[i] = seg\n",
    "    # get the dtw distance matrix\n",
    "    dtw_dist_matrix = cdist_dtw(X_ts)\n",
    "\n",
    "    # Silhouette score\n",
    "#     score = metrics.silhouette_score(dtw_dist_matrix, targets, metric=\"precomputed\")\n",
    "    \n",
    "    return dtw_dist_matrix\n",
    "\n",
    "\n",
    "def cluster_data(dtw_dist_matrix, targets):\n",
    "    #hierarchical clustering using DTW distance matrix\n",
    "    hierarchical_dtw = hierarchy.linkage(squareform(dtw_dist_matrix), method=\"average\")\n",
    "\n",
    "    # Silhouette score\n",
    "    score = metrics.silhouette_score(dtw_dist_matrix, targets, metric=\"precomputed\")\n",
    "\n",
    "    return hierarchical_dtw, score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "textile-jacob",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean 0.006587367682026591\n",
      "var 1.0384080024827171\n",
      "\n",
      "mean 0.012619650979548327\n",
      "var 1.0242033579931051\n",
      "\n",
      "perturbed (gaussian). Label 1\n",
      "mean 0.08162930026319287\n",
      "var 1.1433657151279495\n",
      "\n",
      "perturbed (gaussian). Label 1\n",
      "mean 0.0836485286069894\n",
      "var 1.1514125286360999\n",
      "\n",
      "mean -5.8272299070410456e-06\n",
      "var 1.013437679858378\n",
      "\n",
      "mean 0.013341038603687952\n",
      "var 1.0143052275700493\n",
      "\n",
      "perturbed (logarithmic increase). Label 3\n",
      "mean 0.08181748426393173\n",
      "var 1.1073424849239284\n",
      "\n",
      "perturbed (logarithmic increase). Label 3\n",
      "mean 0.0910693809113253\n",
      "var 1.1043226299619238\n",
      "\n",
      "perturbed (opposite directions). Label 4\n",
      "mean 0.00027887151479162034\n",
      "var 1.09201911026754\n",
      "\n",
      "perturbed (opposite directions). Label 4\n",
      "mean 0.007747245778259306\n",
      "var 1.1255751445894222\n",
      "\n",
      "mean 0.009877509101456343\n",
      "var 1.0152283352768943\n",
      "\n",
      "mean -0.0022577622096471373\n",
      "var 1.0260373114140022\n",
      "\n",
      "mean -0.005325050889790382\n",
      "var 1.028956523871008\n",
      "\n",
      "mean -0.005674004538279446\n",
      "var 1.0191451739329591\n",
      "\n",
      "mean 0.004760524271589522\n",
      "var 1.0306214462184553\n",
      "\n",
      "mean -0.008266768600000078\n",
      "var 1.0219849855291523\n",
      "\n",
      "mean 0.013191934289352636\n",
      "var 0.9896165186205409\n",
      "\n",
      "mean 0.015753337598513144\n",
      "var 1.0292227489604422\n",
      "\n",
      "mean -0.009698352434306192\n",
      "var 1.0335983703316112\n",
      "\n",
      "mean -0.013850954531899948\n",
      "var 1.0227674611602413\n",
      "\n"
     ]
    }
   ],
   "source": [
    "dfs_raw, patient_list = generate_data(n_patients=20, \n",
    "                  pct_abnormal=0.1, \n",
    "                  perturb_method=['gaussian', 'log', 'opposite'], \n",
    "                  verbose=True)\n",
    "\n",
    "smoothed_dfs, labels = preprocessing(dfs_raw)\n",
    "\n",
    "data, metadata, source_file_list, targets = \\\n",
    "    split_into_intervals(smoothed_dfs, labels, length=180, patient_list=patient_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bce3a99b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# time_range = pd.date_range(start='1/1/2021', end='1/9/2021', freq='min')\n",
    "# mean = np.array([0,0])\n",
    "# cov_r = rng.uniform(low=0.15, high=0.6, size=1)[0]\n",
    "# cov = np.array([[1., cov_r],[cov_r, 1.]])\n",
    "# vitals = rng.multivariate_normal(mean, cov, time_range.shape[0])\n",
    "\n",
    "# cov_r = rng.uniform(low=0.25, high=0.5, size=(1,2)) # increase variance a bit\n",
    "# cov = cov_r.T*cov_r\n",
    "# perturb_mean=[1.5, 1] # 1.5 std devs (can change this...)\n",
    "# perturb = rng.multivariate_normal(perturb_mean, cov, time_range.shape[0])\n",
    "# perturb_idx = int(time_range.shape[0]*0.9) # change vals AFTER this\n",
    "# perturb[:perturb_idx] = 0\n",
    "# perturb[:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4d8f34ae",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "types = ['no permutation', 'top hat + noise', 'no permutation', 'log', 'opposite']\n",
    "\n",
    "n = 2 # n_patients*pct_abnormal\n",
    "\n",
    "for i in range(5):\n",
    "#     label = int(scipy.stats.mode(labels[i*n][-1100:], keepdims=True)[0][0])\n",
    "    label = int(scipy.stats.mode(labels[i*n][-1100:])[0][0])\n",
    "#     print(label)\n",
    "    smoothed_dfs[i*n].iloc[-2400:].plot(subplots=True, style='.-', figsize=(8,4), title=types[label])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d50c315e",
   "metadata": {},
   "source": [
    "`hierarchical_dtw`:  A $(n-1)$ by 4 matrix `z` is returned. At the $i$-th iteration, clusters with indices `Z[i, 0]` and `Z[i, 1]` are combined to form cluster $n+i$. A cluster with an index less than $n$ corresponds to one of the $n$ original observations. The distance between clusters `Z[i, 0]` and `Z[i, 1]` is given by `Z[i, 2]`. The fourth value `Z[i, 3]` represents the number of original observations in the newly formed cluster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "neither-brooklyn",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "64.00555555555556"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time_range = pd.date_range(start='1/1/2021', end='1/9/2021', freq='min')\n",
    "len(time_range)/180\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7f4b570f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# this is the time consuming bit\n",
    "dtw_dist_matrix = dtw_calc(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "identical-services",
   "metadata": {},
   "outputs": [],
   "source": [
    "# MDS embedding \n",
    "embedding = MDS(n_components=2, dissimilarity='precomputed', random_state=326)\n",
    "X_dtw_transformed = embedding.fit_transform(dtw_dist_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "respected-chart",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 2.75422745,  2.11669213],\n",
       "       [ 2.56503427,  0.20333513],\n",
       "       [ 2.69010313, -3.7926146 ],\n",
       "       ...,\n",
       "       [ 0.23521306, -0.27322742],\n",
       "       [ 4.3490534 ,  2.80801532],\n",
       "       [-0.54212986, -3.5354982 ]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pat_number = 1\n",
    "\n",
    "sfl = np.asarray(source_file_list)\n",
    "pat_mask = np.where(sfl==pat_number)\n",
    "X_dtw_transformed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b4866d40",
   "metadata": {},
   "outputs": [],
   "source": [
    "hierarchical_dtw = hierarchy.linkage(squareform(dtw_dist_matrix), method=\"average\")\n",
    "\n",
    "# Silhouette score\n",
    "# want predicted membership, not targets\n",
    "# score = metrics.silhouette_score(dtw_dist_matrix, targets, metric=\"precomputed\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a605f2ee",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "xs = np.arange(hierarchical_dtw[:,2].shape[0])\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(xs[-10:], hierarchical_dtw[:,2][-10:])\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "411cca0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9.581534605073193"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "0.6*max(hierarchical_dtw[:,2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e9dc416e",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "t : scalar\n",
    "    For criteria ‘inconsistent’, ‘distance’ or ‘monocrit’,\n",
    "        this is the threshold to apply when forming flat clusters.\n",
    "    For ‘maxclust’ or ‘maxclust_monocrit’ criteria,\n",
    "        this would be max number of clusters requested.\n",
    "citerion: str\n",
    "    distance :\n",
    "        Forms flat clusters so that the original observations in \n",
    "        each flat cluster have no greater a cophenetic distance than t.\n",
    "\n",
    "        The cophenetic distance between two objects is the height \n",
    "        of the dendrogram where the two branches that include the \n",
    "        two objects merge into a single branch.\n",
    "\"\"\"\n",
    "# this should be the same for colors on dendrograms below as well as \n",
    "# flat cluster membership.\n",
    "thresh=10 # 0.6*max(hierarchical_dtw[:,2])\n",
    "\n",
    "y_pred = hierarchy.fcluster(hierarchical_dtw, thresh, criterion='distance')\n",
    "y_pred -= np.ones_like(y_pred) # because y_pred starts at 1 for some reason"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "legal-intranet",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5066523836436703"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Silhouette score\n",
    "# want predicted membership, not targets\n",
    "score = metrics.silhouette_score(dtw_dist_matrix, y_pred, metric=\"precomputed\")\n",
    "score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "desirable-weapon",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 2 3 4]\n",
      "1260\n",
      "1260\n"
     ]
    }
   ],
   "source": [
    "print(np.unique(y_pred))\n",
    "print(len(y_pred))\n",
    "print(len(targets))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "358e155c",
   "metadata": {},
   "source": [
    "**CAREFUL!** `y_pred` is not the same indices as `targets`! y_pred assigns arbitrary labels!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "36dea62d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['Patient 7', '10441', '10621', '3.0'],\n",
       "       ['Patient 7', '10621', '10801', '3.0'],\n",
       "       ['Patient 7', '10801', '10981', '3.0'],\n",
       "       ['Patient 7', '10981', '11161', '3.0'],\n",
       "       ['Patient 7', '11161', '11341', '3.0'],\n",
       "       ['Patient 7', '11341', '11521', '3.0'],\n",
       "       ['Patient 8', '10441', '10621', '3.0'],\n",
       "       ['Patient 8', '10621', '10801', '3.0'],\n",
       "       ['Patient 8', '10801', '10981', '3.0'],\n",
       "       ['Patient 8', '10981', '11161', '3.0'],\n",
       "       ['Patient 8', '11161', '11341', '3.0'],\n",
       "       ['Patient 8', '11341', '11521', '3.0']], dtype='<U32')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mdata1 = np.asarray(metadata)[np.where(y_pred==1)[0]]\n",
    "targs = np.asarray(targets)[np.where(y_pred==1)[0]].reshape((-1,1))\n",
    "mdata1 = np.hstack((mdata1, targs))\n",
    "mdata1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "celtic-portfolio",
   "metadata": {},
   "source": [
    "##  DTW distance matrix and clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "processed-affair",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25.60222222222223"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# approx how many epochs should be outliers?\n",
    "# n_timesteps per patient/epoch size * n_patients * pct_abnormal * pct_perturbed\n",
    "dfs_raw[0].shape[0]/180*10*0.4*0.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "hundred-sullivan",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 7))\n",
    "hierarchy.dendrogram(\n",
    "    hierarchical_dtw,\n",
    "    orientation='right',\n",
    "    truncate_mode='lastp',  # show only the last p merged clusters\n",
    "    p=30,\n",
    "    labels=metadata,\n",
    "    distance_sort='descending',\n",
    "    show_leaf_counts=False,\n",
    "    leaf_rotation=0.,\n",
    "    color_threshold=thresh,\n",
    "    )\n",
    "# plt.title(\"Trucated dendrogram of epoch clusters\\n using DTW distance measure\")\n",
    "plt.xticks([])\n",
    "plt.rcParams.update({'font.size': 15, \"axes.facecolor\": (1.0, 1.0, 1.0, 0.0), 'text.color': 'k', \n",
    "                     'axes.labelcolor' : 'k'})\n",
    "# plt.savefig('figures/al-dendrogram', format='pdf', bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "increasing-ceremony",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_single_trajectory(labels=source_file_list, \n",
    "                           metric='euclidean', \n",
    "                           color_file=1, \n",
    "                           seed=0, # actually not used\n",
    "                           ax=None,\n",
    "                          ): \n",
    "    \"\"\"    \n",
    "    metric: str, default='euclidean'\n",
    "        The distance matrix to use for the embedding, can be euclidean or dtw\n",
    "        \n",
    "    labels: 1D array or list of len(embedding.shape[0])\n",
    "        The labels corresponding to the files in the embedding\n",
    "    \n",
    "    color_file: int, default=1\n",
    "        index of the file to appear in red; all others will be grey\n",
    "        \n",
    "    \"\"\"\n",
    "\n",
    "    sfl_greyscale = [1 if i==color_file+1 else 0 for i in labels] # +1 because I can't count\n",
    "    sfl_greyscale = np.asarray(sfl_greyscale)\n",
    "    \n",
    "    # make a color map of fixed colors\n",
    "    cmap = colors.ListedColormap(['grey', 'blue'])\n",
    "\n",
    "    # hackery to get the index of the last 1 value in the array (and first)\n",
    "    start = sfl_greyscale.argmax()\n",
    "    end = sfl_greyscale.shape[0] - sfl_greyscale[::-1].argmax()\n",
    "    \n",
    "    if metric=='euclidean':\n",
    "        X_embed = X_transformed\n",
    "        title = f'MDS embedding using Euclidean distance matrix'\n",
    "    elif metric=='dtw':\n",
    "        X_embed = X_dtw_transformed\n",
    "        title = f'MDS embedding using DTW distance matrix'\n",
    "    else:\n",
    "        print('Distance metric not recognized.')\n",
    "        return\n",
    "        \n",
    "    \n",
    "    # plot the things\n",
    "    plt.figure(figsize=(6.5, 4.5))\n",
    "    plt.scatter(X_embed[:,0], X_embed[:,1],\n",
    "               c=sfl_greyscale,\n",
    "               cmap=cmap)\n",
    "\n",
    "    if sfl_greyscale.max() == 1:\n",
    "        plt.plot(X_embed[start:end,0], X_embed[start:end,1],  '.b-', \n",
    "                 label=f\"Generated file {color_file}\")\n",
    "\n",
    "        plt.annotate(f\"Start\", \n",
    "                     (X_embed[start,0], X_embed[start,1]),\n",
    "                     xytext=(1,5),\n",
    "                     textcoords=\"offset points\",\n",
    "                    )\n",
    "        plt.annotate(f\"End\", \n",
    "                     (X_embed[end-1,0], X_embed[end-1,1]), #end-1 for correct indexing\n",
    "                     xytext=(5,0),\n",
    "                     textcoords=\"offset points\",\n",
    "                    )\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    plt.legend(loc=\"upper right\")\n",
    "    plt.title('D', y=0.95, x=0.05, pad=-10)\n",
    "#     plt.title(title)\n",
    "    plt.tight_layout()\n",
    "#     plt.savefig(f'figures/mds-pt{color_file}', format='pdf')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "early-quantum",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'MDS embedding using DTW distance matrix')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 7))\n",
    "\n",
    "cmap = plt.cm.rainbow\n",
    "# extract all colors from the map \n",
    "# from https://stackoverflow.com/questions/14777066/matplotlib-discrete-colorbar\n",
    "cmaplist = [cmap(i) for i in range(cmap.N)]\n",
    "# force the first color entry to be grey\n",
    "cmaplist[0] = (.5, .5, .5, 1.0)\n",
    "# create the new map\n",
    "cmap = colors.LinearSegmentedColormap.from_list(\n",
    "    'Custom cmap', cmaplist, cmap.N)\n",
    "# define the bins and normalize\n",
    "n_clust = int(np.max(targets)+1)\n",
    "bounds = np.linspace(0, n_clust, n_clust+1)\n",
    "norm = colors.BoundaryNorm(bounds, cmap.N)\n",
    "\n",
    "plt.scatter(X_dtw_transformed[:,0], X_dtw_transformed[:,1],\n",
    "           c=targets,\n",
    "           cmap=cmap, norm=norm)\n",
    "plt.colorbar()\n",
    "plt.title('MDS embedding using DTW distance matrix')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "27873882",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 468x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6.5, 5))\n",
    "cmap = plt.cm.nipy_spectral\n",
    "# # extract all colors from the map \n",
    "# # from https://stackoverflow.com/questions/14777066/matplotlib-discrete-colorbar\n",
    "# cmaplist = [cmap(i) for i in range(cmap.N)]\n",
    "# # force the first color entry to be grey\n",
    "# cmaplist[0] = (.5, .5, .5, 1.0)\n",
    "# # create the new map\n",
    "# cmap = colors.LinearSegmentedColormap.from_list(\n",
    "#     'Custom cmap', cmaplist, cmap.N)\n",
    "# # define the bins and normalize\n",
    "# n_clust = int(np.max(y_pred)+1)\n",
    "# bounds = np.linspace(0, n_clust, n_clust+1)\n",
    "# norm = colors.BoundaryNorm(bounds, cmap.N)\n",
    "\n",
    "plt.scatter(X_dtw_transformed[:,0], X_dtw_transformed[:,1],\n",
    "           c=y_pred,\n",
    "#            marker=targets,\n",
    "           cmap=cmap)\n",
    "# plt.colorbar()\n",
    "plt.title('MDS visualisation using DTW distance matrix')\n",
    "plt.xticks([])\n",
    "plt.yticks([])\n",
    "plt.tight_layout()\n",
    "# plt.title('C', y=0.95, x=0.05, pad=-10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "aging-stamp",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 468x324 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams.update({'font.size': 15, \"axes.facecolor\": (1.0, 1.0, 1.0, 0.0), 'text.color': 'k'}) # last entry is transparency\n",
    "plot_single_trajectory(metric='dtw', color_file=7)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "7e93acc6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(targets[500:1000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a6e98f3f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(y_pred[500:1000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "094cc183",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4], dtype=int32)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "casual-logic",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "central cluster\n",
      "0.9983660130718954\n",
      "2\n",
      "step\n",
      "1.0\n",
      "log\n",
      "1.0\n",
      "12\n",
      "inverted\n",
      "1.0\n"
     ]
    }
   ],
   "source": [
    "targets = np.array(targets)\n",
    "mask0 = np.where(targets==0)\n",
    "print(\"central cluster\")\n",
    "print(np.where(y_pred[mask0]==4, 1, 0).mean())\n",
    "print(np.where(y_pred[mask0]!=4, 1, 0).sum())\n",
    "\n",
    "mask1 = np.where(targets==1)\n",
    "print(\"step\")\n",
    "print(np.where(y_pred[mask1]==0, 1, 0).mean())\n",
    "\n",
    "mask2 = np.where(targets==3)\n",
    "print(\"log\")\n",
    "print(np.where(y_pred[mask2]==1, 1, 0).mean())\n",
    "print(np.where(y_pred[mask2]==1, 1, 0).sum())\n",
    "# print(np.where(y_pred[mask3]==0, 1, 0).mean())\n",
    "\n",
    "mask3 = np.where(targets==4)\n",
    "print(\"inverted\")\n",
    "print(np.where(y_pred[mask3]==2, 1, 0).mean())\n",
    "# print(np.where(y_pred[mask2]==2, 1, 0).mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "individual-nevada",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "448.0388888888889"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time_range = pd.date_range(start='1/1/2021', end='1/9/2021', freq='min')\n",
    "len(time_range)/180*7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "chief-uzbekistan",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9.0"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(mask2[0])*.75"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "japanese-ordering",
   "metadata": {},
   "source": [
    "The Silhouette Coefficient for a point $i$ is defined as\n",
    "$$S(i) = \\frac{b(i)-a(i)}{\\max\\{a(i), b(i)\\}}$$\n",
    "where $b(i)$ is the smallest average distance of point $i$ to all points in any other cluster and $a(i)$ is the average distance of $i$ from all other points in its cluster ([Source](https://towardsdatascience.com/cheat-sheet-to-implementing-7-methods-for-selecting-optimal-number-of-clusters-in-python-898241e1d6ad)). Since this is agglomerative clustering, $b(i)$ is trivial, but $a(i)$ would have to be computed manually? Need to have a think about this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "executed-gravity",
   "metadata": {},
   "outputs": [],
   "source": [
    "# from tslearn.clustering import TimeSeriesKMeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "passive-north",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# X_ts = np.empty((len(data), len(data[0]), smoothed_dfs[0].shape[1]))\n",
    "# for i, seg in enumerate(data):\n",
    "#     X_ts[i] = seg\n",
    "# print(X_ts.shape)\n",
    "\n",
    "# n_clust = int(np.max(targets)+1)\n",
    "\n",
    "# dba_km = TimeSeriesKMeans(n_clusters=n_clust,\n",
    "#                           n_init=2,\n",
    "#                           metric=\"dtw\",\n",
    "#                           verbose=True,\n",
    "#                           max_iter_barycenter=10,\n",
    "#                           random_state=8888)\n",
    "# y_pred = dba_km.fit_predict(X_ts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "676208cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# n_clust"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "encouraging-mining",
   "metadata": {},
   "outputs": [],
   "source": [
    "# for yi in range(n_clust):\n",
    "#     plt.figure(figsize=(10, 7))\n",
    "#     plt.subplot(3, 2, yi+1)\n",
    "#     for xx in X_ts[y_pred == yi]:\n",
    "#         plt.plot(xx.ravel(), \"k-\", alpha=.2)\n",
    "#     plt.plot(dba_km.cluster_centers_[yi].ravel(), \"r-\")\n",
    "#     plt.xlim(0, 180) #epoch length\n",
    "#     plt.ylim(-2, 2)\n",
    "#     plt.text(0.55, 0.85,'Cluster %d' % (yi + 1),\n",
    "#              transform=plt.gca().transAxes)\n",
    "#     if yi == 1:\n",
    "#         plt.title(\"DBA $k$-means\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "impaired-feeling",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5066523836436703"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score = metrics.silhouette_score(dtw_dist_matrix, y_pred, metric=\"precomputed\")\n",
    "score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "german-vessel",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "interstate-pilot",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "christian-quilt",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAESCAYAAAD67L7dAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAejElEQVR4nO3de7gcVZ3u8e+PQBJCkk5CEu6kkYsxCAoDKBwUEBVwO4IC4gWPoJiD4ujjeGbcDKjt5ZnZg3hjUJFRJ6MexAEVBlrkkZswCmKQi1zCvSO3JASSTnZCyG2dP9Zqdu1K9+7una6u2tXv53nWU93V1atWL8JbtatWVZlzDhER6R3bpN0AERHpLgW/iEiPUfCLiPQYBb+ISI9R8IuI9BgFv4hIj0kk+M3sVDP7bzN7xswGzewuM3t/EusSEZH2bJtQvX8PPAl8BlgOvAO4zMxmOuf+baQvmtnzwOKE2iUikldznHOzWlnQkriAKwT88ti8y4DDnXN7NfnuQufcIR1vlIhIjrWTnYkc6omHfnA3sGsS6xMRkdZ18+Tu4cAjXVyfiIjUkdQx/mHM7FjgJOAj3VifiIg0lnjwm1kRuAy42jm3oMEy84H54e3MpNskItLLEjm5+0rlZjOA3wOrgaOdc2tb+I5O7oqItCn1k7uhEZOAa4HxwDtbCX0REUleIod6zGxb4ApgX+AI59yyJNYjIiLtS+oY/3fxF219GtjRzHaMfHa3c+7lhNYrIpIdpYIBE4DJoezQ5PVSStVLk25WUsH/9jD9dp3P9gIqCa1XRKR9PqDH03pAt7NcO4fU7wDGZvA754pJ1Csi8opSYTwwBZgaplPqvB/ps3hAj2tj7euBNcBgKLXXzzSYX+99/PUaStUN7XbDaHRlHL+ICFAL60Io7YR0vc/Gt7jWdcAq/OjCWlnC8EBuFsrxgF4/ug7IBgW/iLSmVNgOH9jTGArvemWkzye2uLZBhkK6FtqV2Pv463qfDXZrL3osUfCL9Aq/tz09UmZEXk+jeWhv38Ja1gDVSHkRf6feap3SKLQHKVU3b92PlZEo+EXGEr/XPb1OmdHC/ElNal/L8GBeid/Lrhfa9YNce9djgoJfJA1+FMkU/C1KdmxhWgvvyU1qXgOswO9prwAeC9MVsfnxslKh3TsU/CJbayjEZ9N6kO8IbNegxs34gF4OvIDf6/4z9QM7GuQrx/pJR+kOBb9IPf6Qyix8mO8UptESnzehQU2b8OG9PJRHGAr0RtOVOsYtSVLwS+8oFbbFB/auwC6h7Ez9QJ/eoJb1wFJgWSj3R14vY8sQr1KqJncnRJFRUPDL2FcqTGAoyOMlGvKzAKtTwwsMBfd9YRoN92hZpSCXsU7BL9lWKkwG9gB2D9Po693xgT6jzjc34cP7OeAp4I/hdbQ8CyzTSU3pNQp+SU+pMBGYQ/1Qr72eVuebS/Fh/ihwKz7A44G+nFJ1U7I/QGRsUvBLcvwJ0j3xN+Yr1pnuUudby4CngceBW8Lrp0J5GnhGI1dEto6CX7ZOqbAj/rkL+wJ7Mzzcd2P4nQk3AX/FX8l5XZguDvNqob6uW00X6VUKfmmuVCgwFO61sl+YRke/OPzdCZ8EbsaPP38yMn2GUnVjt5otIvUp+MXzFyHtBsyLlf3wo2FqHEPH138eprXyJKWqHrIjknEK/l7jA34PYH+2DPmpkSWXAw8CVzE83B/X4RiRsU3Bn2f+bozzgNeH8rownRZZaik+4H8CPBBeP0ip+nz3Gioi3aTgz4tSYXvgYOBQhoJ+HkP3g1mLvzjpcuBe/BWnD1GqvtDtpopIuhT8Y1GpsA3wauANoRwGHMjQf88lwD34kTP3hPKYxrWLCCj4xwa/N/8G4M3Am/BBXzsevwr4E3ABcCdwJ6Xqc2k0U0TGBgV/FpUKU4Aj8EH/ZnzQj8ePqLkPuAx/C4I/Ag/rTo4i0g4FfxaUCuPwx+ePC+VwYBywEbgL+Bb+1gS/p1RdmU4jRSQvFPxpKRVmAX3A8cBb8Q/mcPgHbvwr/gKoOyhVB1Nro4jkkoK/m0qFInAS8G7gSPztDJYA1wLXAzdoGKWIJE3BnzQf9h8ATsUPsQT4C/BV/MVR9+j+7iLSTQr+JJQK0/FBfzp+FA7A7cA/AFdRqj6WVtNERBT8neJvhXAk8HHgZPwonEXAecBllKqV9BonIjJEwb+1SoWpwIfwgb8/UAUuAf4TuFuHcUQkaxT8o1UqzAY+DZwDFICFwEeByylV16bZNBGRkSj421Uq7A58DjgLmAD8AvgapeqdqbZLRKRFCv5W+RO2/cCn8BdX/Rgf+A+n2i4RkTYp+Jvxz439JPB5/O2MfwJ8gVJ1cZrNEhEZLQX/SEqFNwLfx9/58nrgc5Sq96bbKBGRraPgr8ffDXMA+DvgWeA9+PH3GqEjImOegj+uVDgA+Bl+aObFwD9Rqq5Ot1EiIp2j4I8qFc4EvgesBI6nVL0+3QaJiHSegh9qt0W+APh74Abgg5Sqy9JtlIhIMhT8pcIE4OfAicBFwGcpVTem2ygRkeT0dvCXCpOAXwFvBz5FqfpvKbdIRCRx2yRVsZntY2bfN7P7zGyTmd2S1LpGpVQYD1wNvA34qEJfRHpFknv8+wPvAO4AtktwPe0rFbYBfoR/8tWZlKoL0m2QiEj3JLbHD1zjnNvDOXcq8ECC6xmNEvBB4DyFvoj0msSC3zm3Oam6t0qpcBxwPrAA+Jd0GyMi0n1J7vFnT6mwC/BT/F8g5+hKXBHpRb02quciYArwJt0zX0R6VSaC38zmA/PD25mJrKRU+FvgFPxx/UWJrENEZAzIxKEe59ylzrlDnHOHAMs7vgI/dPNb+EM8F3a8fhGRMSQTe/xd8BHgVUAfper6tBsjIpKmTOzxJ6pUmIh/iMofgOtSbo2ISOoS2+M3s0n4C7gAdgOmmtkp4f2vnXPdOrn6PmBX4MMaxSMikuyhntnAFbF5tfd7AZUE1+2VCoZ/bOKDwI2Jr09EZAxILPidcxXAkqq/RYcCf4PG7IuIvCLvx/hPB17GX7QlIiLkOfj9jdhOBq6jVF2VdnNERLIiv8EPR+BP6sbPM4iI9LQ8B38fsBG4Nu2GiIhkSZ6D/xjgTh3mEREZLp/BXypMBQ4Bbkq7KSIiWZPP4IcjgXHAzWk3REQka/Ia/IcCDrgz7YaIiGRNXoP/IOARStXBtBsiIpI1eQ7+u9NuhIhIFuUv+EuF6cCeKPhFROrKX/DDfmH6UKqtEBHJqDwG/z5h+niqrRARyag8Bv/e+BE9T6TdEBGRLMpj8O8DPEOpui7thoiIZFEeg39PuvGQFxGRMSqPwb8zsCTtRoiIZFUeg38nFPwiIg3lK/hLhYnANGBpyi0REcmsfAW/f8A7KPhFRBrKW/DvHKY61CMi0kDegn+nMNUev4hIA3kL/mlhuiLNRoiIZFnegn9KmK5OtRUiIhmm4BcR6TF5DP7NwNq0GyIiklV5DP5BSlWXdkNERLIqj8GvwzwiIiNQ8IuI9BgFv4hIj8lb8E8GBtNuhIhIluUt+CcCL6XdCBGRLMtb8G8HbEi7ESIiWZa34B8PrE+7ESIiWZa34Ncev4hIE3kLfu3xi4g0kbfg1x6/iEgTeQx+7fGLiIwgb8E/Hu3xi4iMKLHgN7N5Znajma01s2fN7MtmNi6p9QXa4xcRaWLbJCo1s+nADcCDwInA3sDX8Rua85NYZ6A9fhGRJhIJfuBsYHvgPc65VcBvzWwqUDKzC8K8zioVxuE3LNrjFxEZQVKHek4Aro8F/OX4jcFRCa1zuzDVHr+IyAiSCv65wKLoDOfcX/FPxpqb0DoV/CIiLUgq+KcDK+vMXxE+67gfbjx+JsBtmw7YO4n6RUTyIhPDOc1svpktNLOFwMzR1PESE8Yt2rwHD7vdt+9w80REciWpk7srgEKd+dPDZ8M45y4FLgUI4d+2CzeetuxCTgP4y1mjqUBEpEcktce/iNixfDPbA5hE7Nh/B9WO7W834lIiIj0uqeC/DjjOzKZE5p2Gf0jK7xJaZ20Y5/iE6hcRyYWkgv8S4GXgl2b2VjObD5SAbyQyhh+oDPRtAhza4xcRGVEix/idcyvM7FjgYuAa/Aifb+LDP0nr0R6/iMiIkjq5i3PuQeAtSdXfwAa0xy8iMqJMDOfsoPUo+EVERpS34N+ADvWIiIwob8GvPX4RkSbyFvza4xcRaSKPwa89fhGREeQt+DWcU0SkibwFv/b4RUSayFvwvwxMTLsRIiJZlrfgHwQmp90IEZEsy1vwrwamNF1KRKSHKfhFRHqMgl9EpMfkMviL/WVLuyEiIlmVx+Afh0b2iIg0lMfgBx3uERFpSMEvItJj8hb81TCdlmYjRESyLG/BvyxMZ6faChGRDMtb8C8J051TbYWISIblLfiXhulOqbZCRCTDchX8lYG+Nfj79WiPX0SkgVwFf7AU7fGLiDSUx+Bfgvb4RUQaymPwPw3smXYjRESyKo/B/xgwp9hf1pO4RETqyGPwP46/X8+ctBsiIpJFeQz+x8J071RbISKSUXkM/kfDdG6qrRARyag8Bv9S/K0bXp9yO0REMil3wV8Z6HPA3cBBabdFRCSLchf8wd3A/sX+8oS0GyIikjV5Df67gG3RXr+IyBbyGvy3hukxqbZCRCSDchn8lYG+ZcD9KPhFRLaQy+APbgKOLPaX9eB1EZGIPAf/b4Dtgbel3RARkSzJc/DfCKwETk25HSIimZLb4K8M9K0HrgJO1LBOEZEhuQ3+4GfAVOA9aTdERCQrEgl+MzvNzH5pZs+ZmTOzM5JYTwtuwN+07ZMprV9EJHOS2uM/BSgC1yZUf0sqA32bge8CRxT7ywen2RYRkaxIKvhPc84dDHwmofrb8R/AKuDzaTdERCQLEgl+59zmJOodjcpA30rgQuCkYn/5sJSbIyKSuryf3K35FrAcuKDYX7aU2yIikqqeCP7KQN9q4DzgKODDKTdHRCRVLQW/mRXMbG6zMtpGmNl8M1toZguBmaOtp4kfAL8Hvl7sL++U0DpERDLPnHPNFzI7C/j3Zss554YdRjGzycBq4Ezn3IKWGmS20Dl3SCvLtqvYX34N8Gfgf4DjKwN9m5JYj4hIt7WTnS3t8TvnfuCcs2Zl65qdvMpA30P4Mf1vBc5PuTkiIqnoiWP8MT8CfgKUiv3l09JujIhItyV15e48MzsFOCnMOsTMTjGzo5JYXzvCM3nnA7cBPy72l49Ot0UiIt2V1B7/e4Er8HvWAOeE919KaH1tqQz0rQNOxN/O4dpif1kPbBGRnpHUBVylBucBjk5ifaNRGehbARwLVIBfF/vLJ6TbIhGR7ujFY/yvqAz0LQGOBh4Crin2lz+RbotERJLX08EPUBnoW44P/+uA7xT7y9/T/ftFJM96PvgBKgN9q/Anor8GnA3cUewvj/qCNBGRLGvpAq5uSvICrlYU+8snAj8EJgH/F7gk3N5ZRCSzOn4BVy+pDPRdDRyIv7r3O8Dvi/3lA9JtlYhI5yj466gM9D0LHAf8b2Af4O5if/mbxf7yjum2TERk6+lQTxMh7P8F+CgwCAwA364M9K1NtWEiIhHtZKeCv0XF/vL+wD8D7wKeA74BfD/c8llEJFUK/gQV+8tHAl/E3+htJXAxcFFloO/5NNslIr1Nwd8Fxf7yoUA/8G5gPf6WFN8Dbg/3AxIR6RoFfxeF8f7n4E8ETwXuAy4BLg+3hRARSZyCPwXF/vJk4P3AJ4DX4/8KKAM/BcqVgb6X02udiOSdgj9F4WHuBwGnAx8AdsKfC/gl8CvghnB3UBGRjlHwZ0Sxv7wt8BbgQ/jRQFPxQ0Kvw28Efl0Z6Kum10IRyQsFfwYV+8vj8RuBk/DPAtgZ2IB/APz1odyr20OIyGgo+DOu2F/eBngjfgNwHPC68NEy4Lf4jcDNlYG+p9NpoYiMNQr+MabYX94ZeBt+I/B2YFb46Engd8CtoTyhoaIiUo+CfwwLfw0cCBwFvDmUmeHjZ/EbgDuAPwL36ESxiICCP1fCKKG5DN8Q7BY+3gDci98I3Bmmj+o8gUjvUfDnXLG/vBtwGPCGUA4BJoePVwL3xMpDlYG+9d1tpYh0k4K/xxT7y+OA1+A3AofiLyA7ENg+LLIBeIChDcG9wAO6v5BIfij4pbYx2Be/EaiV1+GHkdY8DzxYpyzVSWSRsUXBLw2FEUSvA+ZFyv5AIbLYCvwG4AHgEeDRUJ7QrSdEsknBL20JJ5B3xm8A5sVK9Kljm4HFDG0IoqVSGejb0MVmi0iEgl86pthfnoE/ZFQr+0VeT40suhG/UXgyUiqRqQ4fiSRIwS+JC38lzGL4RmFvYC+gCMyOfWUdQxuC+EbhKeB5DUMVGT0Fv6Su2F/eAZiD3xDUNgbR6fTYV9YDz+A3Ak+HafT10/iNQ7b+wYpkhIJfMq/YXy7gNwJFYHdgj1B2j0y3i33tZfwGILoxeA5/RfNztVIZ6Hsp8R8gkjEKfhnzwq0rZrHlBiH6ejdg2zpfX0lkQ0CdjQN+A7E60R8h0kUKfukJYeOwI7ALsGuYxktt/oQ6VQwCS4Cl+Dujxkt0/gqdg5AsU/CLRIQT0dPYcmOwC34Y6+xImQlsU6eaTfgL3uIbhHhZDrwArNL5COkmBb/IKIUrnndk+MZgp9j76LwdGlS1Eb8BWB6ZLq8zLzqt6q8KGS0Fv0iXhNFLsxjaGOwYyswRpuMaVLcJeJEtNwgvhPkr6pQX8X9daIPR4xT8IhkVDjtNZeQNQ73p+BGqdfgT2vENQr2NRHyeDknlhIJfJEfCxmJ7/LUPM8I0XhrNn86Ww2KjNjO00ag2KCtH+KwKrNPGI30KfhEBXtlo7EDzjcU0/I36atNamRqvs44NtL6RiJZVwOpQBnW4auso+EWkI8KQ2SkM3xhMi72Pl/jnU1pc3SBDG4JVDV639FlloG/jaH/zWKXgF5HMCCOl4huP2gZhapjGXzf6bKRzHVHraL7BWIPf2NTKmhFer836XyQKfhHJpWJ/eTyj22DUez+pzdWvYeSNQ7PXjTYoHQnhVIPfzKYCnwVOAF4NvATcDnzOOfdIC99X8ItI4sJhrEn451VPxp8Laed1o8+2p3WOoQ3KGuBPlYG+943m97STnfXuc7K19gQ+BvwQOA/fsecCfzSzA51zTyWwThGRtoRDN7W9744Jh7ba3aDU3i/uZFsaSWKPfwdgs3Pupci8GcBfga85577U5Pva4xcRaVOqe/zOuTV15r1oZovx90gREZEU1bsZVceZ2SxgH/yDu0VEJEVdCX7g6/jjaAu6tD4REWmgpUM9ZlbA38J2RM65RXW++3HgdOBk59wLDeqfD8wPb2e20iYRERmdlk7umtlZwL83W845Z7HvvQv4JXCuc+5rLTVIJ3dFRNrWTna2dKjHOfcD55w1K7FG/C/gcuCSVkNfRESSl8gxfjPbH7gG+A3wqSTWISIio9Px4ZxmNhsf+IPARcBhZq/8MbDKOfdgp9cpIiKtS+ICrqOBmxt8/Dvn3NFNvv88W3f12kz8k4ukNeqv9qi/2qP+as/W9Ncc59ysVhbM3E3atpZODrdH/dUe9Vd71F/t6VZ/dWscv4iIZISCX0Skx+Qx+C9NuwFjjPqrPeqv9qi/2tOV/srdMX4RERlZHvf4RURkBLkIfjObZ2Y3mtlaM3vWzL5sZuPSble3mdkZZubqlLMjy5iZ/ZOZPWVmL5nZrWb2+jp15a5PzWwfM/u+md1nZpvM7JY6y3Ssf1qtK6ta7K9KnX9vS+osl+v+MrNTzey/zewZMxs0s7vM7P11lvuYmT1qZuvCMsfWWWY3M/uVma02s+VmdrGZbfGYyFbqasg5N6YLMB14FrgBeBtwNv4RZl9Nu20p9MUZ+Ee5HQO8MVJmR5Y5F/84zE8CbwV+jR83vHPe+xQ4EXgKuAJ4CLilzjId659W6spyabG/KsD/i/17Ozi2TO77C/942cuA9wJvAS4M/y/+XWSZ9wObgM+H/0d/HH7vayPLbAfcD/wZ6AM+CCwFfhpbX9O6Rmxv2h3WgQ4/F1gBTI3M+0dgbXReL5RI8E9u8PlEoAp8ITJvB+D56P+Eee1TYJvI6yvjQdbJ/mm1riyXZv0V5leAC5vUk/v+AmbWmXcZ8GTk/cPAj6L9C/wlGuqRQN8rMu+9wGZg33bqGqnk4VDPCcD1zrlVkXmX4x94fFQ6TcqsI4CpwH/VZjj/xLRr8P1Yk8s+dc5tbrJIJ/un1boyq4X+alXu+8s5V+9q27sJTx00s1cB+zH8923G/zUV/7f1J+fck5F5VwHrgePbrKuhPAT/XGDYcwCcc3/F703MTaVF6XvczDaa2cNm9n8i8+fi9yYejS3/EMP7qlf7tJP902pdefBRM1tvZlUzu9LM5sQ+79X+Opyhpw7WfkP8mSUPATPMP6Wwtly8r9YDjzO8r1qpq6GO36QtBdOBlXXmrwif9ZLn8Mf87gTGAe8DLjGzSc65b+L7Y9A5tyn2vRXAJDMbH/6R9WqfdrJ/Wq1rrLsauAN4GngN8EXgNjM7wDlXDcv0XH+FE60nAR8Js2q/c2Vs0RWRz5+n9b5qpa6G8hD8Ejjnrgeuj8y6zswmAueb2bdTapbkmHPu05G3t5nZH4B7gDOBb6XRprSZWRF/fP9q59yCdFtTXx4O9awACnXmT2doC9jLrgRmAEV8f0yuMyxzOrA2skfVq33ayf5pta5ccc7djz/xeHBkds/0l5nNAK7D32H4g5GPar8z3g/TY5+32let1NVQHoJ/EbFjgGa2BzCJLY+B9SIXmS7CHwLaJ7ZM/Lhir/ZpJ/un1bryyDH07w56pL/CWPtrgfHAO51zayMf135D/HzFXOBF59zzkeXifTUeeBXD+6qVuhrKQ/BfBxxnZlMi807Dj2n9XTpNypRT8GOhFwN/AFYBp9Y+DP9Y/xbfjzW92qed7J9W68oVM3stPoDuiszOfX+Z2bb4UTX7Asc755ZFP3fOPYE/0Rv9fduE9/F/W4fGTpC/C5iAf8BVO3U1lvb41w6Mn52OP6n5W/xFH/PxT//K/NjfBPriF8Dn8EO63gn8hC0vIjkXP5riHOBYoIzfMOyU9z7F72GeEsrtwAOR95M63T+t1JXl0qy/8BcY/Qx/SOMY4OPAM8ATDB+zn/v+wt9czeEfNfvGWJkQlqmN0T8/9NcCGl/AdRfwjvCdJTS+gKthXSO2N+0O61CnzwNuCj/8OeArwLi025VCP/wz/vjq2tAXdwEfii1jwHn4URgvAbcBB/VCn+LPc7gGpdjp/mm1rqyWZv0FHAjciB9BsiEE1AJg117rL/yFbCP+2wrLfQx4DHgZf3XusXXq2h0/dn8QeAH4DmHHJLZc07oaFd2dU0Skx+ThGL+IiLRBwS8i0mMU/CIiPUbBLyLSYxT8IiI9RsEvItJjFPwyppjZe83sjNi8W8zsypSatAUzm29mJ6XdDpFGNI5fxpQQ8DOdc0dH5s0DNjjn4vdyT4WZLQTud86dkXZbROrRbZllzHPOPZh2G0TGEh3qkTHDzBYAJwNHmZkLpRQ/1BPmLTezN5jZQjN7ycz+x8z2MrPZZnaVmQ2a2UNm9pY66znLzB4ws5fNbLGZ/WPs8/3N7Ddm9qKZrQn1nBM+uwX4G+DDkTae0UbdC0KbTzKzRWa2LrR9Xge7Unqc9vhlLPkKsCcwDfhEmPc0cHSdZSfhb5x1AbAGuAh/07qX8Xcw/C7+gd9XmNkeLtxC18z+AX/PowuAW/Ah/hUzW+ucuzjUfQ3+MXenh/pejX9eLKFdv8DfqOwrYd7jbdQNMAf4Bv5pai8BXwKuN7N9nXPrWuwrkcbSvrmRiko7Bf9gmVti824Broy8L+FvjnVUZN4nwrwvRObNC/NOCO+n4m+M9cVY/V/G34BsHDAzfOeAEdq4EFgQm9e07vB+Qaj/iMgyc4CNwNlp979KPooO9Uhercff3bHmsTC9qc683cL0cGAH/F8B29ZK+M5O+Lsmvgg8hX+W8WlmNrvF9rRSd80y59wfam+cc4vxd1o9rMV1iYxIwS95tdo5tznyvvbovpW1GW7ocX4Tw3RmmD6Av81wrdwc5u8R6nw7fi/9R8ASM7vNzA5q0p6mdUeWHfYQj8i8XZqsQ6QlOsYvMuTFMH0nsLTO5w8DOOcWASeb2XbAm4B/BcpmtntsY9N23UG9vyJm4zcaIltNwS9jzXqG9tA77Xb8ydRdnXPlZgs75zYAN5nZN4DL8CedX2zQxnbqnm1mR9QO95jZnviHl/9HG79FpCEFv4w1i4ATw5WxTwPPdqpi59xKMysB3w7PPL0Vfzh0P+AY59y7zexA4ELg5/iRO9Pxj7u81zlX26tfhH/G7HH4Jyg96Zx7oVndkaYsB35qZuczNKpnGf7Er8hWU/DLWPNd4CD88fXp+FDsGOfcBWb2LPAZ4LPAOvyDrX8eFlmCP1RzHrAr/pzBzfjwr/kqftjpf+FH85yJH+XTrO6axfhhnwP4ET0LgQ84DeWUDtEtG0QyJFyk9lrn3CFpt0XyS6N6RER6jIJfRKTH6FCPiEiP0R6/iEiPUfCLiPQYBb+ISI9R8IuI9BgFv4hIj1Hwi4j0mP8P1fenFZ0hwwoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "t = np.arange(0, 2000, 1)\n",
    "# a = np.fromfunction(lambda i, j: np.log(i+1), (perturb_idx, 2), dtype=float)*(1/3)\n",
    "a = np.fromfunction(lambda i, j: j*2*np.log(i+1)-np.log(i+1), (2000, 2), dtype=float)*(1/4)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "# ax.plot(t, perturb[-2000:])\n",
    "ax.plot(t, a)\n",
    "\n",
    "ax.set(xlabel='timestep')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "d5931859",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.004166666666666667"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1/(60*4)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "experienced-adult",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "t_idx = 2880 # exactly 2 days\n",
    "time_range = pd.date_range(start='1/1/2021', end='1/9/2021', freq='min')\n",
    "perturb_idx = int(time_range.shape[0]*0.1) # take from end to this idx, so -ve\n",
    "\n",
    "t = np.arange(0, t_idx, 1) \n",
    "# a = np.fromfunction(lambda i, j: np.log(i+1), (perturb_idx, 2), dtype=float)*(1/3)\n",
    "freq = 1/(24*60) # 1 day, Hz*60 (minutes)\n",
    "ph = 0 #rng.integers(0, 60*12) # also in minutes, up to 12h lag\n",
    "a = np.fromfunction(lambda i, j: -0.3*j*(0.5*np.sin(2*np.pi*freq*i + ph)+\n",
    "                                         0.2*np.sin(2*np.pi/240*i+10)) + \n",
    "                    0.5*(np.sin(2*np.pi*freq*i + ph)+0.1*np.sin(2*np.pi/240*i)), \n",
    "                    (t_idx, 2), dtype=float)\n",
    "\n",
    "fig, axs = plt.subplots(4, 1, sharey=True)\n",
    "fig.set_figheight(5)\n",
    "fig.set_figwidth(7)\n",
    "# ax.plot(t, perturb[-2000:])\n",
    "axs[0].plot(t, a)\n",
    "# axs[0].set(xlabel='timestep')\n",
    "\n",
    "# cov_r = rng.uniform(low=0.25, high=0.5, size=(1,2)) # increase variance a bit\n",
    "# cov = cov_r.T*cov_r\n",
    "cov = np.zeros((2,2))\n",
    "perturb_mean=[1.5, 0] # 1.5 std devs on ONE variable (can change this...)\n",
    "b = rng.multivariate_normal(perturb_mean, cov, time_range.shape[0])\n",
    "b[:-perturb_idx] = 0\n",
    "# b[-t_idx:]+=a\n",
    "axs[1].plot(t, b[-t_idx:])\n",
    "\n",
    "c = np.zeros((time_range.shape[0], 2))\n",
    "c[-perturb_idx:] = np.fromfunction(lambda i, j: np.log(i+1), (perturb_idx, 2), \n",
    "                                   dtype=float)*(1/7)\n",
    "# c[-t_idx:]+=a\n",
    "axs[2].plot(t, c[-t_idx:])\n",
    "\n",
    "d = np.zeros((time_range.shape[0], 2))\n",
    "d[-perturb_idx:] = np.fromfunction(lambda i, j: j*2*np.log(i+1)-np.log(i+1), \\\n",
    "                                        (perturb_idx, 2), dtype=float)*(1/7)\n",
    "# d[-t_idx:]+=a\n",
    "axs[3].plot(t, d[-t_idx:])\n",
    "for ax in axs:\n",
    "    ax.set_xticks([])\n",
    "    ax.set_yticks([])\n",
    "    ax.axis('off')\n",
    "plt.savefig(f'figures/perturbs', format='pdf', bbox_inches='tight', \n",
    "               transparent=True,\n",
    "               pad_inches=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "770e384a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.4"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "int(time_range.shape[0]*0.1)/180"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "d3fbe751",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x252 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "t_idx = 2880 # exactly 2 days\n",
    "time_range = pd.date_range(start='1/1/2021', end='1/9/2021', freq='min')\n",
    "perturb_idx = int(time_range.shape[0]*0.1) # take from end to this idx, so -ve\n",
    "\n",
    "t = np.arange(0, t_idx, 1) \n",
    "# a = np.fromfunction(lambda i, j: np.log(i+1), (perturb_idx, 2), dtype=float)*(1/3)\n",
    "freq = 1/(24*60) # 1 day, Hz*60 (minutes)\n",
    "ph = 0 #rng.integers(0, 60*12) # also in minutes, up to 12h lag\n",
    "a = np.fromfunction(lambda i, j: -0.3*j*(0.5*np.sin(2*np.pi*freq*i + ph)+\n",
    "                                         0.2*np.sin(2*np.pi/240*i+10)) + \n",
    "                    0.5*(np.sin(2*np.pi*freq*i + ph)+0.1*np.sin(2*np.pi/240*i)), \n",
    "                    (t_idx, 2), dtype=float)\n",
    "\n",
    "fig, axs = plt.subplots(3, 1, sharey=True)\n",
    "fig.set_figheight(3.5)\n",
    "fig.set_figwidth(7)\n",
    "# ax.plot(t, perturb[-2000:])\n",
    "# axs[0].plot(t, a)\n",
    "# axs[0].set(xlabel='timestep')\n",
    "\n",
    "# cov_r = rng.uniform(low=0.25, high=0.5, size=(1,2)) # increase variance a bit\n",
    "# cov = cov_r.T*cov_r\n",
    "cov = np.zeros((2,2))\n",
    "perturb_mean=[1.5, 0] # 1.5 std devs on ONE variable (can change this...)\n",
    "b = rng.multivariate_normal(perturb_mean, cov, time_range.shape[0])\n",
    "b[:-perturb_idx] = 0\n",
    "# b[-t_idx:]+=a\n",
    "axs[0].plot(t, b[-t_idx:])\n",
    "\n",
    "c = np.zeros((time_range.shape[0], 2))\n",
    "c[-perturb_idx:] = np.fromfunction(lambda i, j: np.log(i+1), (perturb_idx, 2), \n",
    "                                   dtype=float)*(1/7)\n",
    "# c[-t_idx:]+=a\n",
    "axs[1].plot(t, c[-t_idx:])\n",
    "\n",
    "d = np.zeros((time_range.shape[0], 2))\n",
    "d[-perturb_idx:] = np.fromfunction(lambda i, j: j*2*np.log(i+1)-np.log(i+1), \\\n",
    "                                        (perturb_idx, 2), dtype=float)*(1/7)\n",
    "# d[-t_idx:]+=a\n",
    "axs[2].plot(t, d[-t_idx:])\n",
    "for ax in axs:\n",
    "    ax.set_xticks([])\n",
    "    ax.set_yticks([])\n",
    "    ax.axis('off')\n",
    "plt.savefig(f'figures/perturbs-only', format='pdf', bbox_inches='tight', \n",
    "               transparent=True,\n",
    "               pad_inches=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "surface-bikini",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "360"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "6*60\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "necessary-objective",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "t = np.arange(0, 2880, 1) # exactly 2 days\n",
    "\n",
    "a = np.where(t%600 <=120, 2, 0)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "# ax.plot(t, perturb[-2000:])\n",
    "ax.plot(t, a)\n",
    "\n",
    "ax.set(xlabel='timestep')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "f0db0cb9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[                Timestamp  Lifetouch Heart Rate  Lifetouch Respiration Rate  \\\n",
       " 0     2021-01-01 00:00:00             -0.736969                   -0.573997   \n",
       " 1     2021-01-01 00:01:00              0.660630                   -0.207843   \n",
       " 2     2021-01-01 00:02:00             -1.993921                   -0.914951   \n",
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       " 0     2021-01-01 00:00:00             -0.418917                    0.022631   \n",
       " 1     2021-01-01 00:01:00             -1.666132                    0.878165   \n",
       " 2     2021-01-01 00:02:00             -0.557983                    0.036473   \n",
       " 3     2021-01-01 00:03:00              0.195097                   -0.722582   \n",
       " 4     2021-01-01 00:04:00             -1.094727                   -0.810873   \n",
       " ...                   ...                   ...                         ...   \n",
       " 11516 2021-01-08 23:56:00             -0.599941                    0.272085   \n",
       " 11517 2021-01-08 23:57:00             -0.520489                    0.420079   \n",
       " 11518 2021-01-08 23:58:00              1.665173                    0.792172   \n",
       " 11519 2021-01-08 23:59:00              1.136200                    1.096143   \n",
       " 11520 2021-01-09 00:00:00              1.283111                    0.105195   \n",
       " \n",
       "        Labels  \n",
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       " \n",
       " [11521 rows x 4 columns],\n",
       "                 Timestamp  Lifetouch Heart Rate  Lifetouch Respiration Rate  \\\n",
       " 0     2021-01-01 00:00:00              1.135468                   -0.707286   \n",
       " 1     2021-01-01 00:01:00              1.290853                   -0.108421   \n",
       " 2     2021-01-01 00:02:00              1.364889                    0.317928   \n",
       " 3     2021-01-01 00:03:00              0.610457                    0.982972   \n",
       " 4     2021-01-01 00:04:00              0.648215                    0.617063   \n",
       " ...                   ...                   ...                         ...   \n",
       " 11516 2021-01-08 23:56:00              0.436918                   -0.774174   \n",
       " 11517 2021-01-08 23:57:00              0.091721                   -0.028038   \n",
       " 11518 2021-01-08 23:58:00             -0.858530                   -0.512520   \n",
       " 11519 2021-01-08 23:59:00             -0.213129                    0.315953   \n",
       " 11520 2021-01-09 00:00:00              0.835755                    0.782137   \n",
       " \n",
       "        Labels  \n",
       " 0         0.0  \n",
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       " 2         0.0  \n",
       " 3         0.0  \n",
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       " \n",
       " [11521 rows x 4 columns],\n",
       "                 Timestamp  Lifetouch Heart Rate  Lifetouch Respiration Rate  \\\n",
       " 0     2021-01-01 00:00:00              0.078223                   -0.500245   \n",
       " 1     2021-01-01 00:01:00              0.600687                    1.118607   \n",
       " 2     2021-01-01 00:02:00             -1.276472                   -1.430159   \n",
       " 3     2021-01-01 00:03:00              0.212430                    0.725789   \n",
       " 4     2021-01-01 00:04:00              2.737297                    1.763952   \n",
       " ...                   ...                   ...                         ...   \n",
       " 11516 2021-01-08 23:56:00             -2.694428                   -2.221884   \n",
       " 11517 2021-01-08 23:57:00              0.969149                    0.028527   \n",
       " 11518 2021-01-08 23:58:00              0.676024                   -1.455599   \n",
       " 11519 2021-01-08 23:59:00             -0.280219                   -0.696451   \n",
       " 11520 2021-01-09 00:00:00             -0.797819                   -2.711932   \n",
       " \n",
       "        Labels  \n",
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       " \n",
       " [11521 rows x 4 columns],\n",
       "                 Timestamp  Lifetouch Heart Rate  Lifetouch Respiration Rate  \\\n",
       " 0     2021-01-01 00:00:00              0.773308                    2.704285   \n",
       " 1     2021-01-01 00:01:00             -0.693728                   -0.512154   \n",
       " 2     2021-01-01 00:02:00              0.238264                   -0.013927   \n",
       " 3     2021-01-01 00:03:00             -0.195905                   -0.676320   \n",
       " 4     2021-01-01 00:04:00             -0.065410                   -0.168564   \n",
       " ...                   ...                   ...                         ...   \n",
       " 11516 2021-01-08 23:56:00             -2.121551                   -0.357296   \n",
       " 11517 2021-01-08 23:57:00              1.039911                    2.116208   \n",
       " 11518 2021-01-08 23:58:00              0.278594                    0.916896   \n",
       " 11519 2021-01-08 23:59:00             -1.756791                   -1.655438   \n",
       " 11520 2021-01-09 00:00:00             -0.829676                   -1.458711   \n",
       " \n",
       "        Labels  \n",
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       " \n",
       " [11521 rows x 4 columns],\n",
       "                 Timestamp  Lifetouch Heart Rate  Lifetouch Respiration Rate  \\\n",
       " 0     2021-01-01 00:00:00              0.030250                    0.002654   \n",
       " 1     2021-01-01 00:01:00             -0.546289                   -0.279020   \n",
       " 2     2021-01-01 00:02:00              0.719112                    1.288242   \n",
       " 3     2021-01-01 00:03:00             -0.577306                   -0.346520   \n",
       " 4     2021-01-01 00:04:00              0.445666                    0.928894   \n",
       " ...                   ...                   ...                         ...   \n",
       " 11516 2021-01-08 23:56:00             -1.215906                   -0.294527   \n",
       " 11517 2021-01-08 23:57:00             -0.441097                   -0.348676   \n",
       " 11518 2021-01-08 23:58:00              0.311424                   -1.922286   \n",
       " 11519 2021-01-08 23:59:00              1.110180                    0.641512   \n",
       " 11520 2021-01-09 00:00:00             -1.286066                   -0.835200   \n",
       " \n",
       "        Labels  \n",
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       " 2         0.0  \n",
       " 3         0.0  \n",
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       " \n",
       " [11521 rows x 4 columns]]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfs_raw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "71a2c7dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 2, 2, ..., 0, 0, 0])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "annoying-kansas",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.74448838  2.449803  ]\n",
      " [-0.17090371 -1.15151501]\n",
      " [ 0.81524853 -0.93187938]\n",
      " [ 0.1203901  -0.92232376]\n",
      " [-1.04078886 -1.65286375]]\n",
      "[[ 0.          0.        ]\n",
      " [ 0.          0.        ]\n",
      " [ 0.          0.        ]\n",
      " [ 0.          0.        ]\n",
      " [-1.23694165 -2.36202726]]\n",
      "[[ 1.74448838  2.449803  ]\n",
      " [-0.17090371 -1.15151501]\n",
      " [ 0.81524853 -0.93187938]\n",
      " [ 0.1203901  -0.92232376]\n",
      " [-2.27773051 -4.01489101]]\n"
     ]
    }
   ],
   "source": [
    "l = 5\n",
    "\n",
    "# generate mean, covariance matrices (assume normalized)\n",
    "mean = np.array([0,0])\n",
    "cov_r = rng.uniform(low=0.15, high=0.8, size=1)[0]\n",
    "cov = np.array([[1., cov_r],[cov_r, 1.]])\n",
    "vitals = rng.multivariate_normal(mean, cov, 5)\n",
    "print(vitals)\n",
    "\n",
    "perturb = rng.multivariate_normal(mean, cov, l)\n",
    "cutoff = int(l*0.8)\n",
    "perturb[:cutoff] = 0\n",
    "print(perturb)\n",
    "\n",
    "vitals += perturb\n",
    "\n",
    "print(vitals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "dying-trainer",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-0.24199296  0.87050251]\n",
      " [-1.3665058  -1.82977253]\n",
      " [-0.25370431 -1.16770847]\n",
      " [ 1.0190162   0.76351255]\n",
      " [-0.79426215 -1.6184054 ]]\n",
      "[[0.2 0.1]\n",
      " [0.2 0.1]\n",
      " [0.2 0.1]\n",
      " [0.2 0.1]\n",
      " [0.2 0.1]]\n",
      "[[-0.24199296  0.87050251]\n",
      " [-1.3665058  -1.82977253]\n",
      " [-0.25370431 -1.16770847]\n",
      " [ 1.0190162   0.76351255]\n",
      " [-0.59426215 -1.5184054 ]]\n"
     ]
    }
   ],
   "source": [
    "l = 5\n",
    "perturb_constant = [0.2, 0.1]\n",
    "\n",
    "# generate mean, covariance matrices (assume normalized)\n",
    "mean = np.array([0,0])\n",
    "cov_r = rng.uniform(low=0.15, high=0.8, size=1)[0]\n",
    "cov = np.array([[1., cov_r],[cov_r, 1.]])\n",
    "vitals = rng.multivariate_normal(mean, cov, 5)\n",
    "print(vitals)\n",
    "\n",
    "perturb = np.ones_like(vitals)*perturb_constant\n",
    "print(perturb)\n",
    "perturb_idx = int(l*0.9) # values AFTER this index will be altered\n",
    "perturb[:perturb_idx] = 0\n",
    "vitals += perturb\n",
    "\n",
    "print(vitals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "exempt-facing",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.1773497  0.71932355]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[0.03145291, 0.12757181],\n",
       "       [0.12757181, 0.51742636]])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cov_r = rng.uniform(low=0.15, high=0.8, size=(1,2))\n",
    "print(cov_r)\n",
    "cov_r.T*cov_r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "pregnant-following",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "a 2\n",
      "a 3\n"
     ]
    }
   ],
   "source": [
    "n_patients = 10\n",
    "pct_abnormal = 0.2\n",
    "\n",
    "for idx in range(n_patients):\n",
    "    if idx+1 <= n_patients*pct_abnormal:\n",
    "        print(idx)\n",
    "    if idx+1 > n_patients*pct_abnormal and idx+1 <= n_patients*pct_abnormal*2:\n",
    "        print(f\"a {idx}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "43f0b29f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "61796494",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5e88f755",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "parallel-psychology",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 60 patients. Will take a while to run.\n",
    "\n",
    "# dfs_raw_60, patient_list = generate_data(n_patients=60, \n",
    "#                   pct_abnormal=0.1, \n",
    "#                   perturb_method=['gaussian', 'gradual rise', 'log', 'opposite'], \n",
    "#                   verbose=True)\n",
    "\n",
    "# smoothed_dfs_60, labels_60 = preprocessing(dfs_raw)\n",
    "\n",
    "# data_60, metadata_60, source_file_list_60, targets_60 = \\\n",
    "#     split_into_intervals(smoothed_dfs_60, labels_60, length=180, patient_list=patient_list)\n",
    "\n",
    "# # this is the time consuming bit\n",
    "# dtw_dist_matrix_60 = dtw_calc(data_60)\n",
    "\n",
    "# hierarchical_dtw_60, score_60 = cluster_data(dtw_dist_matrix_60, targets_60)\n",
    "# print(f\"Score: {score_60}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "acceptable-organization",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(y_pred).shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "relevant-economics",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'hierarchical_dtw_60' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-50-3358ddf7d01d>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mpct\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mthresholds\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m     \u001b[0mthresh\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mpct\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mmax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhierarchical_dtw_60\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      6\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m     \u001b[0my_pred_60\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mhierarchy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfcluster\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhierarchical_dtw_60\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mthresh\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcriterion\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'distance'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'hierarchical_dtw_60' is not defined"
     ]
    }
   ],
   "source": [
    "sscores = []\n",
    "thresholds = np.arange(0.6, 1, 0.05)\n",
    "\n",
    "for pct in thresholds:\n",
    "    thresh=pct*max(hierarchical_dtw_60[:,2])\n",
    "\n",
    "    y_pred_60 = hierarchy.fcluster(hierarchical_dtw_60, thresh, criterion='distance')\n",
    "\n",
    "    # Silhouette score\n",
    "    # want predicted membership, not targets\n",
    "    score = metrics.silhouette_score(dtw_dist_matrix, y_pred_60, metric=\"precomputed\")\n",
    "    sscores.append(score)\n",
    "    print(f\"unique clusters: {np.unique(y_pred_60).shape[0]}, threshold: {pct:0.2f}, n score: {score:0.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "electronic-arcade",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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