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   "source": [
    "> # Select data vectors by similarity using a metric score\n",
    "\n",
    "> Marcos Duarte  \n",
    "> [Laboratory of Biomechanics and Motor Control](https://bmclab.pesquisa.ufabc.edu.br/)  \n",
    "> Federal University of ABC, Brazil"
   ]
  },
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     "text": [
      "Last updated: 2023-08-08 18:09:03\n",
      "\n",
      "Python implementation: CPython\n",
      "Python version       : 3.11.4\n",
      "IPython version      : 8.14.0\n",
      "\n",
      "Compiler    : GCC 12.2.0\n",
      "OS          : Linux\n",
      "Release     : 6.2.0-26-generic\n",
      "Machine     : x86_64\n",
      "Processor   : x86_64\n",
      "CPU cores   : 16\n",
      "Architecture: 64bit\n",
      "\n",
      "numpy     : 1.25.2\n",
      "sys       : 3.11.4 | packaged by conda-forge | (main, Jun 10 2023, 18:08:17) [GCC 12.2.0]\n",
      "matplotlib: 3.7.2\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import sys\n",
    "sys.path.insert(1, r'./../functions')\n",
    "from simila import similarity, mse\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "%load_ext line_profiler\n",
    "%load_ext watermark\n",
    "%watermark -u -t -d -m -v --iversions\n",
    "\n",
    "np.set_printoptions(precision=3)"
   ]
  },
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     "text": [
      "Help on function similarity in module simila:\n",
      "\n",
      "similarity(y: numpy.ndarray, axis1: int = 0, axis2: int = 1, threshold: float = 0, nmin: int = 3, repeat: bool = True, metric: Callable = <function mse at 0x7f982168dbc0>, drop=True, msg: bool = True, **kwargs: Callable) -> tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray, numpy.ndarray]\n",
      "    Select vectors in array by their similarity using a metric score.\n",
      "    \n",
      "    For example, if `y` is a 2-D numpy.ndarray, with shape (n, m), `axis1`=0\n",
      "    (n is the number of rows) and `axis2`=1 (m is the number of columns), this\n",
      "    function will select the vectors along the columns, that are more similar\n",
      "    to a `central` statistics of `y` or to a `target`, using a `metric` score.\n",
      "    The metric score can be calculated repeatedly until all selected vectors\n",
      "    have a `metric` score not greater than a `threshold`, but the minimum\n",
      "    number of vectors to keep or the maximum number of vectors to discard\n",
      "    can be specified with parameter `nmin`.\n",
      "    \n",
      "    The default `metric` and target are the mean squared error (`mse`) of `y`\n",
      "    w.r.t. the median of `y` along `axis2`. The `mse` metric is equivalent\n",
      "    to the squared Euclidean distance and it is prefered because it\n",
      "    penalizes largest differences more than the Euclidian distance. But any\n",
      "    other `metric` that can be calculated with a function can be used.\n",
      "    \n",
      "    A possible use of this function is to discard the time-series data from bad\n",
      "    trials (treated as outliers) stored in a 2-D array (each trial as a column\n",
      "    and instants as rows) where the criterion is the similarity of the trial\n",
      "    w.r.t. the median trial (the median statistics is more robust to remove\n",
      "    outliers than the mean in case there are very bad trials). After the bad\n",
      "    trials are discarded, the mean of all trials could then be calculated more\n",
      "    reliablly.\n",
      "    \n",
      "    Parameters\n",
      "    ----------\n",
      "    y : numpy.ndarray\n",
      "        Array for the calculation of similarity (defined by a `metric`) of its\n",
      "        vectors w.r.t. a `target` or a `central` statistics of `y`.\n",
      "    axis1 : integer, optional, default = 0\n",
      "        Axis of `y` for which the `metric` will be calculated at each value and\n",
      "        possibly averaged in the `metric` calculation.\n",
      "    axis2 : integer, optional, default = 1\n",
      "        Axis of `y` along which the different vectors are going to be compared\n",
      "        with the `threshold` for their similarity (using their `metric` score).\n",
      "    threshold : float, optional, default = 0\n",
      "        If greater than 0, vector with `metric` score above this value will be\n",
      "        discarded. If 0, threshold will be automatically calculated as the\n",
      "        minimum of [qs[2] + 1.5*(qs[2]-qs[0]), score[-2], 3], where qs are the\n",
      "        quantiles and score[-2] is the before-last largest score of `metric`\n",
      "        among vectors calculated at the first time, not updated by the `repeat`\n",
      "        option.\n",
      "    nmin : integer, optional, default = 3\n",
      "        If greater than 0, minumum number of vectors to keep.\n",
      "        If lower than 0, maximum number of vectors to discard.\n",
      "    repeat :bool, optional, default = True\n",
      "        Whether to calculate similarity `metric` repeatedly, updating the\n",
      "        score calculation each time a vector is discarded.\n",
      "        With `repeat` True, the output `scores` will contain at each row\n",
      "        the updated score values for the used `metric` for each data vector.\n",
      "        The first row will contain the calculated original scores before any\n",
      "        vector was discarded. On the next equent rows, the vectors discarded\n",
      "        are represented by NaN values and the kept vectors by their updated\n",
      "        scores.\n",
      "        The last row will contain the updated scores of the final vectors kept.\n",
      "        With the `repeat` False, the comparison of score values with\n",
      "        `threshold` are made only once and vectors are discarded accordingly at\n",
      "        once. In this case, the output `scores` will contain only two rows, the\n",
      "        first row will contain the calculated original scores before any\n",
      "        vectors were discarded. At the second row, the vectors discarded are\n",
      "        represented with NaN values and the kept vectors by their updated\n",
      "        scores.\n",
      "    metric : optional, default=mse\n",
      "        Function to use as metric to compute similarity.\n",
      "    drop : bool, optional, default = True\n",
      "        Whether to drop (delete) the discarded vectors from `y` in the output.\n",
      "        If False, the values of the vectors discarded will be replaced by nans\n",
      "        and the returned array will have the same dimensions as the original\n",
      "        array.\n",
      "    msg : bool, optional, default = True\n",
      "        Whether to print some messages.\n",
      "    kwargs : optional\n",
      "        Options for the metric function (e.g., see `mse` function).\n",
      "    \n",
      "    Returns\n",
      "    -------\n",
      "    y : numpy.ndarray\n",
      "        Array similar to input `y` but with vectors discarded (deleted) if\n",
      "        option `drop` is True or with all values of vectors discarded replaced\n",
      "        by nans if option `drop` is False.\n",
      "    ikept : numpy.ndarray\n",
      "        Indexes of kept vectors.\n",
      "    inotkept : numpy.ndarray\n",
      "        Indexes of not kept (discarded or replaced by nan) vectors.\n",
      "    scores : 2-D numpy.ndarray\n",
      "        Metric score values of each vector (as columns) for each round of\n",
      "        vector selection (one row per round plus the final values).\n",
      "    \n",
      "    References\n",
      "    ----------\n",
      "    .. [1] https://nbviewer.org/github/BMClab/BMC/blob/master/notebooks/Similarity.ipynb\n",
      "    \n",
      "    Examples\n",
      "    --------\n",
      "    >>> import matplotlib.pyplot as plt\n",
      "    >>> rng = np.random.default_rng()\n",
      "    >>> t, n = 100, 10\n",
      "    >>> y = rng.random((t, n)) / 2\n",
      "    >>> y = y + np.atleast_2d(2*np.sin(2*np.pi*np.linspace(0, 1, t))).T\n",
      "    >>> for i in range(0, n, 2):\n",
      "    >>>    j = rng.integers(t-20)\n",
      "    >>>    p = rng.integers(20)\n",
      "    >>>    y[j:j+p, i] = y[j:j+p, i] + rng.integers(10) - 5\n",
      "    >>>    y[:, i] += rng.integers(4) - 2\n",
      "    >>> ysr, ikeptr, inotkeptr, scoresr = similarity(y)\n",
      "    >>> ysn, ikeptn, inotkeptn, scoresn = similarity(y, repeat=False)\n",
      "    >>> fig, axs = plt.subplots(3, 1, sharex=True, figsize=(8, 8))\n",
      "    >>> axs[0].plot(y, label=list(range(n)))\n",
      "    >>> axs[0].legend(loc=(1.01, 0))\n",
      "    >>> axs[0].set_title(f'Original vectors (n={n})')\n",
      "    >>> axs[1].plot(ysr, label= ikeptr.tolist())\n",
      "    >>> axs[1].set_title(f'Vectors maintained with repeat selection (n={len(ikeptr)})')\n",
      "    >>> axs[1].legend(loc=(1.01, 0))\n",
      "    >>> axs[2].plot(ysn, label= ikeptn.tolist())\n",
      "    >>> axs[2].set_title(f'Vectors maintained without repeat selection (n={len(ikeptn)})')\n",
      "    >>> axs[2].legend(loc=(1.01, 0))\n",
      "    >>> plt.show()\n",
      "    \n",
      "    Version history\n",
      "    ---------------\n",
      "    '1.0.0':\n",
      "        First release version\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(similarity)"
   ]
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    {
     "name": "stdout",
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     "text": [
      "Help on function mse in module simila:\n",
      "\n",
      "mse(y: numpy.ndarray, target: numpy.ndarray | None = None, axis1: int = 0, axis2: int = 1, central: Callable = <function nanmedian at 0x7f98cc1059b0>, normalization: Callable = <function nanmedian at 0x7f98cc1059b0>) -> numpy.ndarray\n",
      "    Mean Squared Error of `y` w.r.t. `target` or `central` along `axis2` at `axis1`.\n",
      "    \n",
      "    Parameters\n",
      "    ----------\n",
      "    y : numpy.ndarray\n",
      "        At least a 2-D numpy.ndarray of data for the calculation of mean squared\n",
      "        error w.r.t. a `target` or a `central` statistics of the data.\n",
      "    target : 1-D numpy.ndarray of length `axis1`, optional, default = None\n",
      "        Reference value to calculate the mean squared error of `y` w.r.t. this\n",
      "        vector. If it is None, the mse value will be calculated w.r.t. a `central`\n",
      "        calculated along `axis2` of `y`.\n",
      "    axis1 : integer, optional, default = 0\n",
      "        Axis of `y` for which the mse will be calculated at each value.\n",
      "    axis2 : integer, optional, default = 1\n",
      "        Axis of `y` along which the `central` statistics might be calculated or\n",
      "        along which the target will be subtracted.\n",
      "    central : Python function, optional, default = np.nanmedian\n",
      "        Function to calculate statistics on `y` w.r.t. mse is computed.\n",
      "    normalization : Python function, optional, default = np.nanmedian\n",
      "        Function to normalize the calculated mse values\n",
      "    \n",
      "    Returns\n",
      "    -------\n",
      "    score : numpy.ndarray\n",
      "        Mean Squared Error values\n",
      "    \n",
      "    References\n",
      "    ----------\n",
      "    .. [1] https://nbviewer.org/github/BMClab/BMC/blob/master/notebooks/Similarity.ipynb\n",
      "    \n",
      "    Examples\n",
      "    --------\n",
      "    >>> import numpy as np\n",
      "    >>> rng = np.random.default_rng()\n",
      "    >>> y = rng.random((100, 10))\n",
      "    >>> y = y + np.atleast_2d(np.sin(2*np.pi*np.linspace(0, 1, 100))).T\n",
      "    >>> mse(y, axis1=0, axis2=1, central=np.nanmedian, normalization=np.nanmedian)\n",
      "    \n",
      "    Version history\n",
      "    ---------------\n",
      "    '1.0.0':\n",
      "        First release version\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(mse)"
   ]
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   "source": [
    "### Example"
   ]
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculated threshold: 3.0\n",
      "Vectors discarded (dimension 1, n=5): [8 4 2 0 6]\n",
      "Calculated threshold: 3.0\n",
      "Vectors discarded (dimension 1, n=2): [8 0]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    ">>> import matplotlib.pyplot as plt\n",
    ">>> rng = np.random.default_rng()\n",
    ">>> t, n = 100, 10\n",
    ">>> y = rng.random((t, n)) / 2\n",
    ">>> y +=  np.atleast_2d(2*np.sin(2*np.pi*np.linspace(0, 1, t))).T\n",
    ">>> for i in range(0, n, 2):\n",
    ">>>    j = rng.integers(t-20)\n",
    ">>>    p = rng.integers(20)\n",
    ">>>    y[j:j+p, i] = y[j:j+p, i] + rng.integers(10) - 5\n",
    ">>>    y[:, i] += rng.integers(4) - 2\n",
    ">>> ysr, ikeptr, inotkeptr, scoresr = similarity(y)\n",
    ">>> ysn, ikeptn, inotkeptn, scoresn = similarity(y, repeat=False)\n",
    ">>> fig, axs = plt.subplots(3, 1, sharex=True, figsize=(8, 8))\n",
    ">>> axs[0].plot(y, label=list(range(n)))\n",
    ">>> axs[0].legend(loc=(1.01, 0))\n",
    ">>> axs[0].set_title(f'Original vectors (n={n})')\n",
    ">>> axs[1].plot(ysr, label= ikeptr.tolist())\n",
    ">>> axs[1].set_title(f'Vectors maintained with repeat selection (n={len(ikeptr)})')\n",
    ">>> axs[1].legend(loc=(1.01, 0))\n",
    ">>> axs[2].plot(ysn, label= ikeptn.tolist())\n",
    ">>> axs[2].set_title(f'Vectors maintained without repeat selection (n={len(ikeptn)})')\n",
    ">>> axs[2].legend(loc=(1.01, 0))\n",
    ">>> plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculated threshold: 3.0\n",
      "Vectors discarded (dimension 1, n=5): [8 4 2 0 6]\n"
     ]
    }
   ],
   "source": [
    "ys, ikept, inotkept, scores = similarity(y, threshold=0, nmin=3, repeat=True, metric=mse,\n",
    "                                         msg=1, central=np.nanmedian, normalization=np.nanmedian)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100, 5)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ys.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 3, 5, 7, 9])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ikept"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([8, 4, 2, 0, 6])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inotkept"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3.064e+00, 3.714e-02, 2.942e+00, 4.471e-02, 2.952e+00, 4.792e-02,\n",
       "        1.952e+00, 4.131e-02, 3.177e+00, 4.547e-02],\n",
       "       [5.671e+01, 7.606e-01, 5.623e+01, 8.209e-01, 5.938e+01, 1.000e+00,\n",
       "        4.111e+01, 8.213e-01,       nan, 9.374e-01],\n",
       "       [6.461e+01, 8.569e-01, 6.616e+01, 8.682e-01,       nan, 9.941e-01,\n",
       "        4.917e+01, 1.006e+00,       nan, 9.440e-01],\n",
       "       [6.780e+01, 8.077e-01,       nan, 9.623e-01,       nan, 8.091e-01,\n",
       "        4.691e+01, 1.070e+00,       nan, 1.000e+00],\n",
       "       [      nan, 9.317e-01,       nan, 9.755e-01,       nan, 8.965e-01,\n",
       "        4.548e+01, 1.025e+00,       nan, 1.126e+00],\n",
       "       [      nan, 8.394e-01,       nan, 1.000e+00,       nan, 8.408e-01,\n",
       "              nan, 1.112e+00,       nan, 1.039e+00]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculated threshold: 3.0\n",
      "Vectors discarded (dimension 1, n=5): [8 4 2 0 6]\n"
     ]
    }
   ],
   "source": [
    "ys, ikept, inotkept, scores = similarity(y, threshold=0, nmin=3, repeat=True, metric=mse,\n",
    "                                         drop=False, msg=True, central=np.nanmedian, normalization=np.nanmedian)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100, 10)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ys.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculated threshold: 3.0\n",
      "Vectors discarded (dimension 1, n=2): [8 0]\n"
     ]
    }
   ],
   "source": [
    "ys, ikept, inotkept, scores = similarity(y, threshold=0, nmin=3, repeat=False, metric=mse,\n",
    "                                         msg=True, central=np.nanmedian, normalization=np.nanmedian)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Function profiling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.31 ms ± 20 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "ys, ikept, inotkept, score_all = similarity(y, repeat=True, msg=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "887 µs ± 21 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "ys, ikept, inotkept, score_all = similarity(y, repeat=False, msg=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The computation of the metric score (in the example above, `mse`) takes 86% of the funtion time. Let's look on that."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "328 µs ± 3.24 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "score = mse(y, central=np.nanmedian, normalization=np.nanmedian)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "77.4 µs ± 4.13 µs per loop (mean ± std. dev. of 7 runs, 10,000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "score = mse(y, central=np.nanmean, normalization=np.nanmean)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using `median` to calculate statistics slows down the code by about 4 times compared to using `mean`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timer unit: 1e-09 s\n",
       "\n",
       "Total time: 0.00471052 s\n",
       "File: /home/marcos/adrive/Python/BMC/notebooks/./../functions/simila.py\n",
       "Function: similarity at line 87\n",
       "\n",
       "Line #      Hits         Time  Per Hit   % Time  Line Contents\n",
       "==============================================================\n",
       "    87                                           def similarity(y: np.ndarray, axis1: int = 0, axis2: int = 1, threshold: float = 0,\n",
       "    88                                                          nmin: int = 3, repeat: bool = True, metric: Callable = mse,\n",
       "    89                                                          drop=True, msg: bool = True, **kwargs: Callable\n",
       "    90                                                          ) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:\n",
       "    91                                           \n",
       "    92                                               \"\"\"Select vectors in array by their similarity using a metric score.\n",
       "    93                                           \n",
       "    94                                               For example, if `y` is a 2-D numpy.ndarray, with shape (n, m), `axis1`=0\n",
       "    95                                               (n is the number of rows) and `axis2`=1 (m is the number of columns), this\n",
       "    96                                               function will select the vectors along the columns, that are more similar\n",
       "    97                                               to a `central` statistics of `y` or to a `target`, using a `metric` score.\n",
       "    98                                               The metric score can be calculated repeatedly until all selected vectors\n",
       "    99                                               have a `metric` score not greater than a `threshold`, but the minimum\n",
       "   100                                               number of vectors to keep or the maximum number of vectors to discard\n",
       "   101                                               can be specified with parameter `nmin`.\n",
       "   102                                           \n",
       "   103                                               The default `metric` and target are the mean squared error (`mse`) of `y`\n",
       "   104                                               w.r.t. the median of `y` along `axis2`. The `mse` metric is equivalent\n",
       "   105                                               to the squared Euclidean distance and it is prefered because it\n",
       "   106                                               penalizes largest differences more than the Euclidian distance. But any\n",
       "   107                                               other `metric` that can be calculated with a function can be used.\n",
       "   108                                           \n",
       "   109                                               A possible use of this function is to discard the time-series data from bad\n",
       "   110                                               trials (treated as outliers) stored in a 2-D array (each trial as a column\n",
       "   111                                               and instants as rows) where the criterion is the similarity of the trial\n",
       "   112                                               w.r.t. the median trial (the median statistics is more robust to remove\n",
       "   113                                               outliers than the mean in case there are very bad trials). After the bad\n",
       "   114                                               trials are discarded, the mean of all trials could then be calculated more\n",
       "   115                                               reliablly.\n",
       "   116                                           \n",
       "   117                                               Parameters\n",
       "   118                                               ----------\n",
       "   119                                               y : numpy.ndarray\n",
       "   120                                                   Array for the calculation of similarity (defined by a `metric`) of its\n",
       "   121                                                   vectors w.r.t. a `target` or a `central` statistics of `y`.\n",
       "   122                                               axis1 : integer, optional, default = 0\n",
       "   123                                                   Axis of `y` for which the `metric` will be calculated at each value and\n",
       "   124                                                   possibly averaged in the `metric` calculation.\n",
       "   125                                               axis2 : integer, optional, default = 1\n",
       "   126                                                   Axis of `y` along which the different vectors are going to be compared\n",
       "   127                                                   with the `threshold` for their similarity (using their `metric` score).\n",
       "   128                                               threshold : float, optional, default = 0\n",
       "   129                                                   If greater than 0, vector with `metric` score above this value will be\n",
       "   130                                                   discarded. If 0, threshold will be automatically calculated as the\n",
       "   131                                                   minimum of [qs[2] + 1.5*(qs[2]-qs[0]), score[-2], 3], where qs are the\n",
       "   132                                                   quantiles and score[-2] is the before-last largest score of `metric`\n",
       "   133                                                   among vectors calculated at the first time, not updated by the `repeat`\n",
       "   134                                                   option.\n",
       "   135                                               nmin : integer, optional, default = 3\n",
       "   136                                                   If greater than 0, minumum number of vectors to keep.\n",
       "   137                                                   If lower than 0, maximum number of vectors to discard.\n",
       "   138                                               repeat :bool, optional, default = True\n",
       "   139                                                   Whether to calculate similarity `metric` repeatedly, updating the\n",
       "   140                                                   score calculation each time a vector is discarded.\n",
       "   141                                                   With `repeat` True, the output `scores` will contain at each row\n",
       "   142                                                   the updated score values for the used `metric` for each data vector.\n",
       "   143                                                   The first row will contain the calculated original scores before any\n",
       "   144                                                   vector was discarded. On the next equent rows, the vectors discarded\n",
       "   145                                                   are represented by NaN values and the kept vectors by their updated\n",
       "   146                                                   scores.\n",
       "   147                                                   The last row will contain the updated scores of the final vectors kept.\n",
       "   148                                                   With the `repeat` False, the comparison of score values with\n",
       "   149                                                   `threshold` are made only once and vectors are discarded accordingly at\n",
       "   150                                                   once. In this case, the output `scores` will contain only two rows, the\n",
       "   151                                                   first row will contain the calculated original scores before any\n",
       "   152                                                   vectors were discarded. At the second row, the vectors discarded are\n",
       "   153                                                   represented with NaN values and the kept vectors by their updated\n",
       "   154                                                   scores.\n",
       "   155                                               metric : optional, default=mse\n",
       "   156                                                   Function to use as metric to compute similarity.\n",
       "   157                                               drop : bool, optional, default = True\n",
       "   158                                                   Whether to drop (delete) the discarded vectors from `y` in the output.\n",
       "   159                                                   If False, the values of the vectors discarded will be replaced by nans\n",
       "   160                                                   and the returned array will have the same dimensions as the original\n",
       "   161                                                   array.\n",
       "   162                                               msg : bool, optional, default = True\n",
       "   163                                                   Whether to print some messages.\n",
       "   164                                               kwargs : optional\n",
       "   165                                                   Options for the metric function (e.g., see `mse` function).\n",
       "   166                                           \n",
       "   167                                               Returns\n",
       "   168                                               -------\n",
       "   169                                               y : numpy.ndarray\n",
       "   170                                                   Array similar to input `y` but with vectors discarded (deleted) if\n",
       "   171                                                   option `drop` is True or with all values of vectors discarded replaced\n",
       "   172                                                   by nans if option `drop` is False.\n",
       "   173                                               ikept : numpy.ndarray\n",
       "   174                                                   Indexes of kept vectors.\n",
       "   175                                               inotkept : numpy.ndarray\n",
       "   176                                                   Indexes of not kept (discarded or replaced by nan) vectors.\n",
       "   177                                               scores : 2-D numpy.ndarray\n",
       "   178                                                   Metric score values of each vector (as columns) for each round of\n",
       "   179                                                   vector selection (one row per round plus the final values).\n",
       "   180                                           \n",
       "   181                                               References\n",
       "   182                                               ----------\n",
       "   183                                               .. [1] https://nbviewer.org/github/BMClab/BMC/blob/master/notebooks/Similarity.ipynb\n",
       "   184                                           \n",
       "   185                                               Examples\n",
       "   186                                               --------\n",
       "   187                                               >>> import matplotlib.pyplot as plt\n",
       "   188                                               >>> rng = np.random.default_rng()\n",
       "   189                                               >>> t, n = 100, 10\n",
       "   190                                               >>> y = rng.random((t, n)) / 2\n",
       "   191                                               >>> y = y + np.atleast_2d(2*np.sin(2*np.pi*np.linspace(0, 1, t))).T\n",
       "   192                                               >>> for i in range(0, n, 2):\n",
       "   193                                               >>>    j = rng.integers(t-20)\n",
       "   194                                               >>>    p = rng.integers(20)\n",
       "   195                                               >>>    y[j:j+p, i] = y[j:j+p, i] + rng.integers(10) - 5\n",
       "   196                                               >>>    y[:, i] += rng.integers(4) - 2\n",
       "   197                                               >>> ysr, ikeptr, inotkeptr, scoresr = similarity(y)\n",
       "   198                                               >>> ysn, ikeptn, inotkeptn, scoresn = similarity(y, repeat=False)\n",
       "   199                                               >>> fig, axs = plt.subplots(3, 1, sharex=True, figsize=(8, 8))\n",
       "   200                                               >>> axs[0].plot(y, label=list(range(n)))\n",
       "   201                                               >>> axs[0].legend(loc=(1.01, 0))\n",
       "   202                                               >>> axs[0].set_title(f'Original vectors (n={n})')\n",
       "   203                                               >>> axs[1].plot(ysr, label= ikeptr.tolist())\n",
       "   204                                               >>> axs[1].set_title(f'Vectors maintained with repeat selection (n={len(ikeptr)})')\n",
       "   205                                               >>> axs[1].legend(loc=(1.01, 0))\n",
       "   206                                               >>> axs[2].plot(ysn, label= ikeptn.tolist())\n",
       "   207                                               >>> axs[2].set_title(f'Vectors maintained without repeat selection (n={len(ikeptn)})')\n",
       "   208                                               >>> axs[2].legend(loc=(1.01, 0))\n",
       "   209                                               >>> plt.show()\n",
       "   210                                           \n",
       "   211                                               Version history\n",
       "   212                                               ---------------\n",
       "   213                                               '1.0.0':\n",
       "   214                                                   First release version\n",
       "   215                                               \"\"\"\n",
       "   216                                           \n",
       "   217         1       9085.0   9085.0      0.2      logger.debug('Similarity...')\n",
       "   218                                           \n",
       "   219         1       1620.0   1620.0      0.0      if y.ndim < 2:\n",
       "   220                                                   raise ValueError('The input array must be at least a 2-D array.')\n",
       "   221         1      10706.0  10706.0      0.2      y = y.copy()\n",
       "   222         1    1153430.0 1153430.0     24.5      score: np.ndarray = metric(y, axis1=axis1, axis2=axis2, **kwargs)\n",
       "   223         1       8608.0   8608.0      0.2      scores: np.ndarray = np.atleast_2d(score)\n",
       "   224         1       4393.0   4393.0      0.1      ikept: np.ndarray = np.where(~np.isnan(score))[0]  # indexes of kept vectors\n",
       "   225         1       1715.0   1715.0      0.0      inotkept: np.ndarray = np.where(np.isnan(score))[0]  # indexes of discarded vectors\n",
       "   226         1       7560.0   7560.0      0.2      idx: np.ndarray = np.argsort(score)\n",
       "   227         1        791.0    791.0      0.0      score = score[idx]\n",
       "   228         1       3164.0   3164.0      0.1      nkept: int = np.count_nonzero(~np.isnan(score))  # number of kept vectors\n",
       "   229         1        368.0    368.0      0.0      if nkept < 3:\n",
       "   230                                                   logger.debug('nkept: %s', nkept)\n",
       "   231                                                   raise ValueError('The input array must have at least 3 valid vectors.')\n",
       "   232         1        222.0    222.0      0.0      if nmin < 0:\n",
       "   233                                                   nmin = np.max([3, nkept + nmin])\n",
       "   234         1        167.0    167.0      0.0      if threshold == 0:  # automatic threshold calculation\n",
       "   235         1     186565.0 186565.0      4.0          qs: np.ndarray = np.nanquantile(a=score, q=[.25, .50, .75])\n",
       "   236         1      16580.0  16580.0      0.4          threshold = np.min([qs[2] + 1.5*(qs[2]-qs[0]), score[-2], 3.])\n",
       "   237         1        223.0    223.0      0.0          if msg:\n",
       "   238                                                       print(f'Calculated threshold: {threshold}')\n",
       "   239                                           \n",
       "   240         1        332.0    332.0      0.0      if not repeat:  # discard all vectors at once\n",
       "   241                                                   idx2: np.ndarray = np.nonzero(score > threshold)[0]  # vectors to discard\n",
       "   242                                                   if len(idx2) > 0:\n",
       "   243                                                       if nkept > nmin:  # keep at least nmin vectors\n",
       "   244                                                           inotkept = np.r_[inotkept, idx[idx2[-(y.shape[axis2] - nmin):]][::-1]]\n",
       "   245                                                           y.swapaxes(0, axis2)[inotkept, ...] = np.nan\n",
       "   246                                                           score = metric(y, axis1=axis1, axis2=axis2, **kwargs)\n",
       "   247                                                           scores = np.vstack((scores, score))\n",
       "   248                                                           logger.debug('not repeat - score: %s', score)\n",
       "   249                                               else:  # discard vector with largest updated score one by one\n",
       "   250         5       5300.0   1060.0      0.1          while nkept > nmin and score[nkept-1] > threshold:\n",
       "   251         5     135611.0  27122.2      2.9              inotkept = np.r_[inotkept, idx[nkept-1]]\n",
       "   252         5      14350.0   2870.0      0.3              y.swapaxes(0, axis2)[inotkept[-1], ...] = np.nan\n",
       "   253         5    2891765.0 578353.0     61.4              score = metric(y, axis1=axis1, axis2=axis2, **kwargs)\n",
       "   254         5      67722.0  13544.4      1.4              scores = np.vstack((scores, score))\n",
       "   255         5      29507.0   5901.4      0.6              idx = np.argsort(score)\n",
       "   256         5       3727.0    745.4      0.1              score = score[idx]\n",
       "   257         5       1410.0    282.0      0.0              nkept = nkept - 1\n",
       "   258         5       7003.0   1400.6      0.1              logger.debug('repeat - nkept: %s, score: %s', nkept, score)\n",
       "   259                                           \n",
       "   260         1        528.0    528.0      0.0      if len(inotkept):\n",
       "   261         1     140764.0 140764.0      3.0          ikept = np.setdiff1d(ikept, inotkept)\n",
       "   262         1        211.0    211.0      0.0          if drop:\n",
       "   263         1       6709.0   6709.0      0.1              y = y.swapaxes(0, axis2)[ikept, ...].swapaxes(0, axis2)\n",
       "   264         1        155.0    155.0      0.0          if msg:\n",
       "   265                                                       print(\n",
       "   266                                                             f'Vectors discarded (dimension {axis2}, n={len(inotkept)}): {inotkept}')\n",
       "   267                                           \n",
       "   268         1        232.0    232.0      0.0      return y, ikept, inotkept, scores"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%lprun -f similarity similarity(y, msg=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Function `similarity`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# %load ./../functions/simila.py\n",
    "\"\"\"Select vectors in array by their similarity using a metric score.\n",
    "\"\"\"\n",
    "\n",
    "import logging\n",
    "from typing import Callable\n",
    "import numpy as np\n",
    "\n",
    "__author__ = 'Marcos Duarte, https://github.com/demotu/BMC'\n",
    "__version__ = 'simila.py v.1.0.0 20123/07/31'\n",
    "__license__ = \"MIT\"\n",
    "\n",
    "# set logger and configure it not to confuse with matplotlib debug messages\n",
    "logger = logging.getLogger(__name__)\n",
    "logger.setLevel(20)  # 0: NOTSET, 10: DEBUG, 20: INFO, 30: WARNING, 40: ERROR, 50: CRITICAL\n",
    "ch = logging.StreamHandler()\n",
    "formatter = logging.Formatter('{name}: {levelname}: {message}', style='{')\n",
    "ch.setFormatter(formatter)\n",
    "logger.addHandler(ch)\n",
    "\n",
    "\n",
    "def mse(y: np.ndarray, target: np.ndarray | None = None, axis1: int = 0, axis2: int = 1,\n",
    "        central: Callable = np.nanmedian, normalization: Callable = np.nanmedian\n",
    "        ) -> np.ndarray:\n",
    "    \"\"\"Mean Squared Error of `y` w.r.t. `target` or `central` along `axis2` at `axis1`.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    y : numpy.ndarray\n",
    "        At least a 2-D numpy.ndarray of data for the calculation of mean squared\n",
    "        error w.r.t. a `target` or a `central` statistics of the data.\n",
    "    target : 1-D numpy.ndarray of length `axis1`, optional, default = None\n",
    "        Reference value to calculate the mean squared error of `y` w.r.t. this\n",
    "        vector. If it is None, the mse value will be calculated w.r.t. a `central`\n",
    "        calculated along `axis2` of `y`.\n",
    "    axis1 : integer, optional, default = 0\n",
    "        Axis of `y` for which the mse will be calculated at each value.\n",
    "    axis2 : integer, optional, default = 1\n",
    "        Axis of `y` along which the `central` statistics might be calculated or\n",
    "        along which the target will be subtracted.\n",
    "    central : Python function, optional, default = np.nanmedian\n",
    "        Function to calculate statistics on `y` w.r.t. mse is computed.\n",
    "    normalization : Python function, optional, default = np.nanmedian\n",
    "        Function to normalize the calculated mse values\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    score : numpy.ndarray\n",
    "        Mean Squared Error values\n",
    "\n",
    "    References\n",
    "    ----------\n",
    "    .. [1] https://nbviewer.org/github/BMClab/BMC/blob/master/notebooks/Similarity.ipynb\n",
    "\n",
    "    Examples\n",
    "    --------\n",
    "    >>> import numpy as np\n",
    "    >>> rng = np.random.default_rng()\n",
    "    >>> y = rng.random((100, 10))\n",
    "    >>> y = y + np.atleast_2d(np.sin(2*np.pi*np.linspace(0, 1, 100))).T\n",
    "    >>> mse(y, axis1=0, axis2=1, central=np.nanmedian, normalization=np.nanmedian)\n",
    "\n",
    "    Version history\n",
    "    ---------------\n",
    "    '1.0.0':\n",
    "        First release version\n",
    "    \"\"\"\n",
    "\n",
    "    logger.debug('mse...')\n",
    "\n",
    "    score: np.ndarray = np.empty((y.shape[axis2]), dtype=float)\n",
    "    score.fill(np.nan)\n",
    "    idx: np.ndarray = np.where(~np.all(np.isnan(y), axis=axis1))[0]\n",
    "    y = y.swapaxes(0, axis2)[idx, ...].swapaxes(0, axis2)  # faster than .take\n",
    "    if target is not None:\n",
    "        logger.debug('target shape: %s', target.shape)\n",
    "        score[idx] = np.nanmean((y - target)**2, axis=axis1)\n",
    "    else:\n",
    "        score[idx] = np.nanmean((y - central(y, axis=axis2, keepdims=True))**2, axis=axis1)\n",
    "\n",
    "    if normalization is not None:\n",
    "        score = score/normalization(score)\n",
    "    logger.debug('idx: %s, score: %s', idx, score)\n",
    "\n",
    "    return score\n",
    "\n",
    "\n",
    "def similarity(y: np.ndarray, axis1: int = 0, axis2: int = 1, threshold: float = 0,\n",
    "               nmin: int = 3, repeat: bool = True, metric: Callable = mse,\n",
    "               drop=True, msg: bool = True, **kwargs: Callable\n",
    "               ) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:\n",
    "\n",
    "    \"\"\"Select vectors in array by their similarity using a metric score.\n",
    "\n",
    "    For example, if `y` is a 2-D numpy.ndarray, with shape (n, m), `axis1`=0\n",
    "    (n is the number of rows) and `axis2`=1 (m is the number of columns), this\n",
    "    function will select the vectors along the columns, that are more similar\n",
    "    to a `central` statistics of `y` or to a `target`, using a `metric` score.\n",
    "    The metric score can be calculated repeatedly until all selected vectors\n",
    "    have a `metric` score not greater than a `threshold`, but the minimum\n",
    "    number of vectors to keep or the maximum number of vectors to discard\n",
    "    can be specified with parameter `nmin`.\n",
    "\n",
    "    The default `metric` and target are the mean squared error (`mse`) of `y`\n",
    "    w.r.t. the median of `y` along `axis2`. The `mse` metric is equivalent\n",
    "    to the squared Euclidean distance and it is prefered because it\n",
    "    penalizes largest differences more than the Euclidian distance. But any\n",
    "    other `metric` that can be calculated with a function can be used.\n",
    "\n",
    "    A possible use of this function is to discard the time-series data from bad\n",
    "    trials (treated as outliers) stored in a 2-D array (each trial as a column\n",
    "    and instants as rows) where the criterion is the similarity of the trial\n",
    "    w.r.t. the median trial (the median statistics is more robust to remove\n",
    "    outliers than the mean in case there are very bad trials). After the bad\n",
    "    trials are discarded, the mean of all trials could then be calculated more\n",
    "    reliablly.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    y : numpy.ndarray\n",
    "        Array for the calculation of similarity (defined by a `metric`) of its\n",
    "        vectors w.r.t. a `target` or a `central` statistics of `y`.\n",
    "    axis1 : integer, optional, default = 0\n",
    "        Axis of `y` for which the `metric` will be calculated at each value and\n",
    "        possibly averaged in the `metric` calculation.\n",
    "    axis2 : integer, optional, default = 1\n",
    "        Axis of `y` along which the different vectors are going to be compared\n",
    "        with the `threshold` for their similarity (using their `metric` score).\n",
    "    threshold : float, optional, default = 0\n",
    "        If greater than 0, vector with `metric` score above this value will be\n",
    "        discarded. If 0, threshold will be automatically calculated as the\n",
    "        minimum of [qs[2] + 1.5*(qs[2]-qs[0]), score[-2], 3], where qs are the\n",
    "        quantiles and score[-2] is the before-last largest score of `metric`\n",
    "        among vectors calculated at the first time, not updated by the `repeat`\n",
    "        option.\n",
    "    nmin : integer, optional, default = 3\n",
    "        If greater than 0, minumum number of vectors to keep.\n",
    "        If lower than 0, maximum number of vectors to discard.\n",
    "    repeat :bool, optional, default = True\n",
    "        Whether to calculate similarity `metric` repeatedly, updating the\n",
    "        score calculation each time a vector is discarded.\n",
    "        With `repeat` True, the output `scores` will contain at each row\n",
    "        the updated score values for the used `metric` for each data vector.\n",
    "        The first row will contain the calculated original scores before any\n",
    "        vector was discarded. On the next equent rows, the vectors discarded\n",
    "        are represented by NaN values and the kept vectors by their updated\n",
    "        scores.\n",
    "        The last row will contain the updated scores of the final vectors kept.\n",
    "        With the `repeat` False, the comparison of score values with\n",
    "        `threshold` are made only once and vectors are discarded accordingly at\n",
    "        once. In this case, the output `scores` will contain only two rows, the\n",
    "        first row will contain the calculated original scores before any\n",
    "        vectors were discarded. At the second row, the vectors discarded are\n",
    "        represented with NaN values and the kept vectors by their updated\n",
    "        scores.\n",
    "    metric : optional, default=mse\n",
    "        Function to use as metric to compute similarity.\n",
    "    drop : bool, optional, default = True\n",
    "        Whether to drop (delete) the discarded vectors from `y` in the output.\n",
    "        If False, the values of the vectors discarded will be replaced by nans\n",
    "        and the returned array will have the same dimensions as the original\n",
    "        array.\n",
    "    msg : bool, optional, default = True\n",
    "        Whether to print some messages.\n",
    "    kwargs : optional\n",
    "        Options for the metric function (e.g., see `mse` function).\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    y : numpy.ndarray\n",
    "        Array similar to input `y` but with vectors discarded (deleted) if\n",
    "        option `drop` is True or with all values of vectors discarded replaced\n",
    "        by nans if option `drop` is False.\n",
    "    ikept : numpy.ndarray\n",
    "        Indexes of kept vectors.\n",
    "    inotkept : numpy.ndarray\n",
    "        Indexes of not kept (discarded or replaced by nan) vectors.\n",
    "    scores : 2-D numpy.ndarray\n",
    "        Metric score values of each vector (as columns) for each round of\n",
    "        vector selection (one row per round plus the final values).\n",
    "\n",
    "    References\n",
    "    ----------\n",
    "    .. [1] https://nbviewer.org/github/BMClab/BMC/blob/master/notebooks/Similarity.ipynb\n",
    "\n",
    "    Examples\n",
    "    --------\n",
    "    >>> import matplotlib.pyplot as plt\n",
    "    >>> rng = np.random.default_rng()\n",
    "    >>> t, n = 100, 10\n",
    "    >>> y = rng.random((t, n)) / 2\n",
    "    >>> y = y + np.atleast_2d(2*np.sin(2*np.pi*np.linspace(0, 1, t))).T\n",
    "    >>> for i in range(0, n, 2):\n",
    "    >>>    j = rng.integers(t-20)\n",
    "    >>>    p = rng.integers(20)\n",
    "    >>>    y[j:j+p, i] = y[j:j+p, i] + rng.integers(10) - 5\n",
    "    >>>    y[:, i] += rng.integers(4) - 2\n",
    "    >>> ysr, ikeptr, inotkeptr, scoresr = similarity(y)\n",
    "    >>> ysn, ikeptn, inotkeptn, scoresn = similarity(y, repeat=False)\n",
    "    >>> fig, axs = plt.subplots(3, 1, sharex=True, figsize=(8, 8))\n",
    "    >>> axs[0].plot(y, label=list(range(n)))\n",
    "    >>> axs[0].legend(loc=(1.01, 0))\n",
    "    >>> axs[0].set_title(f'Original vectors (n={n})')\n",
    "    >>> axs[1].plot(ysr, label= ikeptr.tolist())\n",
    "    >>> axs[1].set_title(f'Vectors maintained with repeat selection (n={len(ikeptr)})')\n",
    "    >>> axs[1].legend(loc=(1.01, 0))\n",
    "    >>> axs[2].plot(ysn, label= ikeptn.tolist())\n",
    "    >>> axs[2].set_title(f'Vectors maintained without repeat selection (n={len(ikeptn)})')\n",
    "    >>> axs[2].legend(loc=(1.01, 0))\n",
    "    >>> plt.show()\n",
    "\n",
    "    Version history\n",
    "    ---------------\n",
    "    '1.0.0':\n",
    "        First release version\n",
    "    \"\"\"\n",
    "\n",
    "    logger.debug('Similarity...')\n",
    "\n",
    "    if y.ndim < 2:\n",
    "        raise ValueError('The input array must be at least a 2-D array.')\n",
    "    y = y.copy()\n",
    "    score: np.ndarray = metric(y, axis1=axis1, axis2=axis2, **kwargs)\n",
    "    scores: np.ndarray = np.atleast_2d(score)\n",
    "    ikept: np.ndarray = np.where(~np.isnan(score))[0]  # indexes of kept vectors\n",
    "    inotkept: np.ndarray = np.where(np.isnan(score))[0]  # indexes of discarded vectors\n",
    "    idx: np.ndarray = np.argsort(score)\n",
    "    score = score[idx]\n",
    "    nkept: int = np.count_nonzero(~np.isnan(score))  # number of kept vectors\n",
    "    if nkept < 3:\n",
    "        logger.debug('nkept: %s', nkept)\n",
    "        raise ValueError('The input array must have at least 3 valid vectors.')\n",
    "    if nmin < 0:\n",
    "        nmin = np.max([3, nkept + nmin])\n",
    "    if threshold == 0:  # automatic threshold calculation\n",
    "        qs: np.ndarray = np.nanquantile(a=score, q=[.25, .50, .75])\n",
    "        threshold = np.min([qs[2] + 1.5*(qs[2]-qs[0]), score[-2], 3.])\n",
    "        if msg:\n",
    "            print(f'Calculated threshold: {threshold}')\n",
    "\n",
    "    if not repeat:  # discard all vectors at once\n",
    "        idx2: np.ndarray = np.nonzero(score > threshold)[0]  # vectors to discard\n",
    "        if len(idx2) > 0:\n",
    "            if nkept > nmin:  # keep at least nmin vectors\n",
    "                inotkept = np.r_[inotkept, idx[idx2[-(y.shape[axis2] - nmin):]][::-1]]\n",
    "                y.swapaxes(0, axis2)[inotkept, ...] = np.nan\n",
    "                score = metric(y, axis1=axis1, axis2=axis2, **kwargs)\n",
    "                scores = np.vstack((scores, score))\n",
    "                logger.debug('not repeat - score: %s', score)\n",
    "    else:  # discard vector with largest updated score one by one\n",
    "        while nkept > nmin and score[nkept-1] > threshold:\n",
    "            inotkept = np.r_[inotkept, idx[nkept-1]]\n",
    "            y.swapaxes(0, axis2)[inotkept[-1], ...] = np.nan\n",
    "            score = metric(y, axis1=axis1, axis2=axis2, **kwargs)\n",
    "            scores = np.vstack((scores, score))\n",
    "            idx = np.argsort(score)\n",
    "            score = score[idx]\n",
    "            nkept = nkept - 1\n",
    "            logger.debug('repeat - nkept: %s, score: %s', nkept, score)\n",
    "\n",
    "    if len(inotkept):\n",
    "        ikept = np.setdiff1d(ikept, inotkept)\n",
    "        if drop:\n",
    "            y = y.swapaxes(0, axis2)[ikept, ...].swapaxes(0, axis2)\n",
    "        if msg:\n",
    "            print(\n",
    "                  f'Vectors discarded (dimension {axis2}, n={len(inotkept)}): {inotkept}')\n",
    "\n",
    "    return y, ikept, inotkept, scores\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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