{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Time normalization of data\n", "\n", "> Marcos Duarte \n", "> Laboratory of Biomechanics and Motor Control ([http://demotu.org/](http://demotu.org/)) \n", "> Federal University of ABC, Brazil" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Time normalization is usually employed for the temporal alignment of cyclic data obtained from different trials with different duration (number of points). The most simple and common procedure for time normalization used in Biomechanics and Motor Control is known as the normalization to percent cycle (although it might not be the most adequate procedure in certain cases ([Helwig et al., 2011](http://www.sciencedirect.com/science/article/pii/S0021929010005038)).\n", "\n", "In the percent cycle, a fixed number (typically a temporal base from 0 to 100%) of new equally spaced data is created based on the old data with a mathematical procedure known as interpolation. \n", "**Interpolation** is the estimation of new data points within the range of known data points. This is different from **extrapolation**, the estimation of data points outside the range of known data points. \n", "Time normalization of data using interpolation is a simple procedure and it doesn't matter if the original data have more or less data points than desired.\n", "\n", "The Python function tnorm.py (code at the end of this text) implements the normalization to percent cycle procedure for time normalization. The function signature is: \n", "python\n", "yn, tn, indie = tnorm(y, axis=0, step=1, k=3, smooth=0, mask=None,\n", " nan_at_ext='delete', show=False, ax=None)\n", " \n", "Let's see now how to perform interpolation and time normalization; first let's import the necessary Python libraries and configure the environment:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Import the necessary libraries\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "import sys\n", "sys.path.insert(1, r'./../functions') # add to pythonpath" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For instance, consider the data shown next. The time normalization of these data to represent a cycle from 0 to 100%, with a step of 1% (101 data points) is:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "y data:\n" ] }, { "data": { "text/plain": [ "[5, 4, 10, 8, 1, 10, 2, 7, 1, 3]" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y = [5, 4, 10, 8, 1, 10, 2, 7, 1, 3]\n", "print(\"y data:\")\n", "y" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "y data interpolated to 101 points:\n" ] }, { "data": { "text/plain": [ "array([ 5. , 4.91, 4.82, 4.73, 4.64, 4.55, 4.46, 4.37, 4.28,\n", " 4.19, 4.1 , 4.01, 4.48, 5.02, 5.56, 6.1 , 6.64, 7.18,\n", " 7.72, 8.26, 8.8 , 9.34, 9.88, 9.86, 9.68, 9.5 , 9.32,\n", " 9.14, 8.96, 8.78, 8.6 , 8.42, 8.24, 8.06, 7.58, 6.95,\n", " 6.32, 5.69, 5.06, 4.43, 3.8 , 3.17, 2.54, 1.91, 1.28,\n", " 1.45, 2.26, 3.07, 3.88, 4.69, 5.5 , 6.31, 7.12, 7.93,\n", " 8.74, 9.55, 9.68, 8.96, 8.24, 7.52, 6.8 , 6.08, 5.36,\n", " 4.64, 3.92, 3.2 , 2.48, 2.15, 2.6 , 3.05, 3.5 , 3.95,\n", " 4.4 , 4.85, 5.3 , 5.75, 6.2 , 6.65, 6.88, 6.34, 5.8 ,\n", " 5.26, 4.72, 4.18, 3.64, 3.1 , 2.56, 2.02, 1.48, 1.02,\n", " 1.2 , 1.38, 1.56, 1.74, 1.92, 2.1 , 2.28, 2.46, 2.64,\n", " 2.82, 3. ])" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "t = np.linspace(0, 100, len(y)) # time vector for the original data\n", "tn = np.linspace(0, 100, 101) # new time vector for the new time-normalized data\n", "yn = np.interp(tn, t, y) # new time-normalized data\n", "print(\"y data interpolated to 101 points:\")\n", "yn" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The key is the Numpy interp function, from its help: \n", "\n", ">interp(x, xp, fp, left=None, right=None) \n", ">One-dimensional linear interpolation. \n", ">Returns the one-dimensional piecewise linear interpolant to a function with given values at discrete data-points.\n", "\n", "A plot of the data will show what we have done:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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AUgwKoWmxAOR/Wdpj5xFC6KM703wXACWqqlapqmoBPgYm2Kcs4SwKPygAYPDl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\n1NiLouuPMiUpn2y20PjwLwI6uK58aT+f3f02dY5odox7kH7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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(10,5))\n", "plt.plot(t, y, 'bo-', lw=2, label='original data')\n", "plt.plot(tn, yn, '.-', color=[1, 0, 0, .5], lw=2, label='time normalized')\n", "plt.legend(loc='best', framealpha=.5)\n", "plt.xlabel('Cycle [%]')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The function tnorm.py implements this kind of normalization with option for a different interpolation than the linear one used, deal with missing points in the data (if these missing points are not at the extremities of the data because the interpolation function can not extrapolate data), other things. \n", "Let's see the tnorm.py examples:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from tnorm import tnorm" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(array([ 5. , 4.17809249, 3.5387693 , 3.06958033,\n", " 2.75807549, 2.59180468, 2.55831781, 2.64516477,\n", " 2.83989546, 3.13005979, 3.50320766, 3.94688897,\n", " 4.44865363, 4.99605153, 5.57663259, 6.17794669,\n", " 6.78754374, 7.39297365, 7.98178632, 8.54153165,\n", " 9.05975953, 9.52401988, 9.9218626 , 10.24155044,\n", " 10.47700754, 10.62485776, 10.68174158, 10.6442995 ,\n", " 10.509172 , 10.27299957, 9.93242271, 9.4840819 ,\n", " 8.92461763, 8.25067039, 7.46097301, 6.57858161,\n", " 5.64224479, 4.6909727 , 3.76377547, 2.89966325,\n", " 2.13764617, 1.51673437, 1.075938 , 0.85426719,\n", " 0.89073208, 1.22148967, 1.83147733, 2.66132882,\n", " 3.65021709, 4.73731509, 5.86179577, 6.96283209,\n", " 7.97959699, 8.85126343, 9.51700436, 9.91599272,\n", " 9.98916876, 9.73107126, 9.19820454, 8.45052464,\n", " 7.54798763, 6.55054957, 5.5181665 , 4.51079448,\n", " 3.58838957, 2.81090783, 2.2383053 , 1.92987303,\n", " 1.90500085, 2.12123175, 2.53078856, 3.08589411,\n", " 3.73877122, 4.44164272, 5.14673143, 5.8062602 ,\n", " 6.37245184, 6.79752918, 7.0338503 , 7.05573627,\n", " 6.88356441, 6.54351135, 6.06175374, 5.46446819,\n", " 4.77783135, 4.02801986, 3.24121034, 2.44357944,\n", " 1.66130378, 0.92056 , 0.24752473, -0.33162538,\n", " -0.79071371, -1.10356362, -1.24399848, -1.18584166,\n", " -0.90291651, -0.3690464 , 0.4419453 , 1.55623522, 3. ]),\n", " array([ 0., 1., 2., 3., 4., 5., 6., 7., 8.,\n", " 9., 10., 11., 12., 13., 14., 15., 16., 17.,\n", " 18., 19., 20., 21., 22., 23., 24., 25., 26.,\n", " 27., 28., 29., 30., 31., 32., 33., 34., 35.,\n", " 36., 37., 38., 39., 40., 41., 42., 43., 44.,\n", " 45., 46., 47., 48., 49., 50., 51., 52., 53.,\n", " 54., 55., 56., 57., 58., 59., 60., 61., 62.,\n", " 63., 64., 65., 66., 67., 68., 69., 70., 71.,\n", " 72., 73., 74., 75., 76., 77., 78., 79., 80.,\n", " 81., 82., 83., 84., 85., 86., 87., 88., 89.,\n", " 90., 91., 92., 93., 94., 95., 96., 97., 98.,\n", " 99., 100.]),\n", " [0, 9])" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ " >>> # Default options: cubic spline interpolation passing through\n", " >>> # each datum, 101 points, and no plot\n", " >>> y = [5, 4, 10, 8, 1, 10, 2, 7, 1, 3]\n", " >>> tnorm(y)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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+udCkSfSv37w57gYtsVcchU2bon99IUTU1TrBMQzjXOA+oItSqi1gBW6JVmAi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\nZkeT2I4cIWvBR4QwsNw6ssb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9zbpBHxcve0f64aSDlEhwquVywfz5GJMmYbzzDp5hI8kI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\nLJS366Yfr1gR+/sdPIhj8WxChgXLyJtjf79Edtt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IcVMNjogEzjl39ha33wWG\nBxiOiISARnBEpNDSwM5mtryrv2BmNwDnAc15i0pEQsWcc0HHICIiIpJTGsERERGR0FGCIyIiIqGj\nBEdERERCRwmOiIiIhM7/AoLLz44jHX2xAAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ " >>> # Linear interpolation passing through each datum\n", " >>> yn, tn, indie = tnorm(y, k=1, smooth=0, mask=None, show=True)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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xAjsMGiTLWbN0NpUnkJICb74p68uWseOjlHKHp5xER0te+YoVSMxKw1SK4jWo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\ngrOrSG2cSJKzcKGl6evW1fx33lv73ZAh9k8mJ8dS3iZNEh6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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ " >>> # Cubic spline interpolation with smoothing\n", " >>> yn, tn, indie = tnorm(y, k=3, smooth=1, mask=None, show=True)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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5J3ue95vneYs9z7s9wOOPeJ73Y97H757nbfJ7LMfvsQ+j2XgRib6dO20/zNat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\ngepvfA4+2EpUorFWWkXUekY8BOacdR9t22arxp9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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ " >>> # Cubic spline interpolation with smoothing and 50 points\n", " >>> x = np.linspace(-3, 3, 60)\n", " >>> y = np.exp(-x**2) + np.random.randn(60)/10\n", " >>> yn, tn, indie = tnorm(y, step=-50, k=3, smooth=1, show=True)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "run_control": { "breakpoint": false } }, "outputs": [ { "data": { "image/png": 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BvffCp59CpUrw8svw2GN5QU7BAGfPHqlTtn2rNYOjVHDTGhylvO255/IbsISE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\n7jopMA4JgUWL4KqrirxLQoJMSxUUH68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hjjsKH+/UCWbPhp4984+99pq0M6tcWVYM795d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\n7AjztYjI30RkgIgM6NixY/hHH4H9HQrYNOUtrfr3+utw+eV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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ " >>> # Deal with missing data (use NaN as mask)\n", " >>> x = np.linspace(-3, 3, 100)\n", " >>> y = np.exp(-x**2) + np.random.randn(100)/10\n", " >>> y[:10] = np.NaN # first ten points are missing\n", " >>> y[30: 41] = np.NaN # make other 10 missing points\n", " >>> yn, tn, indie = tnorm(y, step=-50, k=3, smooth=1, show=True)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": 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JW9Om7vwvv7jeG2MCn1u3zoWbj7iErvyKBfKJJIcYt4V4LZOe7obS9uyByy+H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\nr1tnjcGbNPHaGhIOBhkTQgghLiko8NfB6dLF+xouJHYocAghhKQ1Tg2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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ " >>> # Deal with missing data at the extremities replacing by first/last not-NaN\n", " >>> x = np.linspace(-3, 3, 100)\n", " >>> y = np.exp(-x**2) + np.random.randn(100)/10\n", " >>> y[0:10] = np.NaN # first ten points are missing\n", " >>> y[-10:] = np.NaN # last ten points are missing\n", " >>> yn, tn, indie = tnorm(y, step=-50, k=3, smooth=1, nan_at_ext='replace', show=True)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/png": 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mmGrNGvvO6UII/yQVHCGES/qxJUQdO4gGeP75dqsWKSlmiKmmxuxF5S/eeMP0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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ " >>> # Deal with missing data at the extremities replacing by first/last not-NaN\n", " >>> x = np.linspace(-3, 3, 100)\n", " >>> y = np.exp(-x**2) + np.random.randn(100)/10\n", " >>> y[0:10] = np.NaN # first ten points are missing\n", " >>> y[-10:] = np.NaN # last ten points are missing\n", " >>> yn, tn, indie = tnorm(y, step=-50, k=1, smooth=0, nan_at_ext='replace', show=True)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "run_control": { "breakpoint": false } }, "outputs": [ { "data": { "image/png": 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68vvJk/nbDG0zlBl3zADgz5DHWPDHtx4epSBcWYjAEYQyTE4ODIn+EaPrJB1S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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ " >>> # Deal with 2-D array\n", " >>> x = np.linspace(-3, 3, 100)\n", " >>> y = np.exp(-x**2) + np.random.randn(100)/10\n", " >>> y = np.vstack((y-1, y[::-1])).T\n", " >>> yn, tn, indie = tnorm(y, step=-50, k=3, smooth=1, show=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Function tnorm.py" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "run_control": { "breakpoint": false } }, "outputs": [], "source": [ "# %load './../functions/tnorm.py'\n", "\"\"\"Time normalization (from 0 to 100% with step interval).\"\"\"\n", "\n", "import numpy as np\n", "\n", "__author__ = 'Marcos Duarte, https://github.com/demotu/BMC'\n", "__version__ = \"1.0.6\"\n", "__license__ = \"MIT\"\n", "\n", "\n", "def tnorm(y, axis=0, step=1, k=3, smooth=0, mask=None, nan_at_ext='delete',\n", " show=False, ax=None):\n", " \"\"\"Time normalization (from 0 to 100% with step interval).\n", "\n", " Time normalization is usually employed for the temporal alignment of data\n", " obtained from different trials with different duration (number of points).\n", " This code implements a procedure knwown as the normalization to percent\n", " cycle.\n", "\n", " This code can perform simple linear interpolation passing through each\n", " datum or spline interpolation (up to quintic splines) passing through each\n", " datum (knots) or not (in case a smoothing parameter > 0 is inputted).\n", "\n", " NaNs and any value inputted as a mask parameter and that appears at the\n", " extremities might be removed or replaced by the first/last not-NaN value\n", " before the interpolation because this code does not perform extrapolation.\n", " For a 2D array, the entire row with NaN or a mask value at the extermity\n", " might be removed because of alignment issues with the data from different\n", " columns. As result, if there is a column of only NaNs in the data, the\n", " time normalization can't be performed (an empty NaNs and any value\n", " inputted as a mask parameter and that appears in the middle of the data\n", " (which may represent missing data) are ignored and the interpolation is\n", " performed through these points.\n", "\n", " See this IPython notebook [2]_.\n", "\n", " Parameters\n", " ----------\n", " y : 1-D or 2-D array_like\n", " Array of independent input data. Must be increasing.\n", " If 2-D array, the data in each axis will be interpolated.\n", " axis : int, 0 or 1, optional (default = 0)\n", " Axis along which the interpolation is performed.\n", " 0: data in each column are interpolated; 1: for row interpolation\n", " step : float or int, optional (default = 1)\n", " Interval from 0 to 100% to resample y or the number of points y\n", " should be interpolated. In the later case, the desired number of\n", " points should be expressed with step as a negative integer.\n", " For instance, step = 1 or step = -101 will result in the same\n", " number of points at the interpolation (101 points).\n", " If step == 0, the number of points will be the number of data in y.\n", " k : int, optional (default = 3)\n", " Degree of the smoothing spline. Must be 1 <= k <= 5.\n", " If 3, a cubic spline is used.\n", " The number of data points must be larger than k.\n", " smooth : float or None, optional (default = 0)\n", " Positive smoothing factor used to choose the number of knots.\n", " If 0, spline will interpolate through all data points.\n", " If None, smooth=len(y).\n", " mask : None or float, optional (default = None)\n", " Mask to identify missing values which will be ignored.\n", " It can be a list of values.\n", " NaN values will be ignored and don't need to be in the mask.\n", " nan_at_ext : string, optional (default = 'delete')\n", " Method to deal with NaNs at the extremities.\n", " 'delete' will delete any NaN at the extremities (the corresponding\n", " entire row in y for a 2-D array).\n", " 'replace' will replace any NaN at the extremities by first/last\n", " not-NaN value in y.\n", " show : bool, optional (default = False)\n", " True (1) plot data in a matplotlib figure.\n", " False (0) to not plot.\n", " ax : a matplotlib.axes.Axes instance, optional (default = None).\n", "\n", " Returns\n", " -------\n", " yn : 1-D or 2-D array\n", " Interpolated data (if axis == 0, column oriented for 2-D array).\n", " tn : 1-D array\n", " New x values (from 0 to 100) for the interpolated data.\n", " inds : list\n", " Indexes of first and last rows without NaNs at the extremities of y.\n", " If there is no NaN in the data, this list is [0, y.shape[0]-1].\n", "\n", " Notes\n", " -----\n", " This code performs interpolation to create data with the desired number of\n", " points using a one-dimensional smoothing spline fit to a given set of data\n", " points (scipy.interpolate.UnivariateSpline function).\n", "\n", " References\n", " ----------\n", " .. [1] http://www.sciencedirect.com/science/article/pii/S0021929010005038\n", " .. [2] http://nbviewer.ipython.org/github/demotu/BMC/blob/master/notebooks/TimeNormalization.ipynb\n", "\n", " See Also\n", " --------\n", " scipy.interpolate.UnivariateSpline:\n", " One-dimensional smoothing spline fit to a given set of data points.\n", "\n", " Examples\n", " --------\n", " >>> # Default options: cubic spline interpolation passing through\n", " >>> # each datum, 101 points, and no plot\n", " >>> y = [5, 4, 10, 8, 1, 10, 2, 7, 1, 3]\n", " >>> tnorm(y)\n", "\n", " >>> # Linear interpolation passing through each datum\n", " >>> y = [5, 4, 10, 8, 1, 10, 2, 7, 1, 3]\n", " >>> yn, tn, indie = tnorm(y, k=1, smooth=0, mask=None, show=True)\n", "\n", " >>> # Cubic spline interpolation with smoothing\n", " >>> y = [5, 4, 10, 8, 1, 10, 2, 7, 1, 3]\n", " >>> yn, tn, indie = tnorm(y, k=3, smooth=1, mask=None, show=True)\n", "\n", " >>> # Cubic spline interpolation with smoothing and 50 points\n", " >>> x = np.linspace(-3, 3, 100)\n", " >>> y = np.exp(-x**2) + np.random.randn(100)/10\n", " >>> yn, tn, indie = tnorm(y, step=-50, k=3, smooth=1, show=True)\n", "\n", " >>> # Deal with missing data (use NaN as mask)\n", " >>> x = np.linspace(-3, 3, 100)\n", " >>> y = np.exp(-x**2) + np.random.randn(100)/10\n", " >>> y[:10] = np.NaN # first ten points are missing\n", " >>> y[30: 41] = np.NaN # make other 10 missing points\n", " >>> yn, tn, indie = tnorm(y, step=-50, k=3, smooth=1, show=True)\n", "\n", " >>> # Deal with missing data at the extremities replacing by first/last not-NaN\n", " >>> x = np.linspace(-3, 3, 100)\n", " >>> y = np.exp(-x**2) + np.random.randn(100)/10\n", " >>> y[0:10] = np.NaN # first ten points are missing\n", " >>> y[-10:] = np.NaN # last ten points are missing\n", " >>> yn, tn, indie = tnorm(y, step=-50, k=3, smooth=1, nan_at_ext='replace', show=True)\n", "\n", " >>> # Deal with missing data at the extremities replacing by first/last not-NaN\n", " >>> x = np.linspace(-3, 3, 100)\n", " >>> y = np.exp(-x**2) + np.random.randn(100)/10\n", " >>> y[0:10] = np.NaN # first ten points are missing\n", " >>> y[-10:] = np.NaN # last ten points are missing\n", " >>> yn, tn, indie = tnorm(y, step=-50, k=1, smooth=0, nan_at_ext='replace', show=True)\n", "\n", " >>> # Deal with 2-D array\n", " >>> x = np.linspace(-3, 3, 100)\n", " >>> y = np.exp(-x**2) + np.random.randn(100)/10\n", " >>> y = np.vstack((y-1, y[::-1])).T\n", " >>> yn, tn, indie = tnorm(y, step=-50, k=3, smooth=1, show=True)\n", "\n", " Version history\n", " ---------------\n", " '1.0.6':\n", " Deleted 'from __future__ import ...'\n", " Added parameter nan_at_ext\n", " Adjusted outputs to have always the same type\n", "\n", " \"\"\"\n", "\n", " from scipy.interpolate import UnivariateSpline\n", "\n", " y = np.asarray(y)\n", " if axis:\n", " y = y.T\n", " if y.ndim == 1:\n", " y = np.reshape(y, (-1, 1))\n", " # turn mask into NaN\n", " if mask is not None:\n", " y[y == mask] = np.NaN\n", "\n", " iini = 0\n", " iend = y.shape[0]-1\n", " if nan_at_ext.lower() == 'delete':\n", " # delete rows with missing values at the extremities\n", " while y.size and np.isnan(np.sum(y[0])):\n", " y = np.delete(y, 0, axis=0)\n", " iini += 1\n", " while y.size and np.isnan(np.sum(y[-1])):\n", " y = np.delete(y, -1, axis=0)\n", " iend -= 1\n", " else:\n", " # replace NaN at the extremities by first/last not-NaN\n", " if np.any(np.isnan(y[0])):\n", " for col in range(y.shape[1]):\n", " ind_not_nan = np.nonzero(~np.isnan(y[:, col]))[0]\n", " if ind_not_nan.size:\n", " y[0, col] = y[ind_not_nan[0], col]\n", " else:\n", " y = np.empty((0, 0))\n", " break\n", " if np.any(np.isnan(y[-1])):\n", " for col in range(y.shape[1]):\n", " ind_not_nan = np.nonzero(~np.isnan(y[:, col]))[0]\n", " if ind_not_nan.size:\n", " y[-1, col] = y[ind_not_nan[-1], col]\n", " else:\n", " y = np.empty((0, 0))\n", " break\n", "\n", " # check if there are still data\n", " if not y.size:\n", " return np.empty((0, 0)), np.empty(0), []\n", " if y.size == 1:\n", " return y.flatten(), np.array(0), [0, 0]\n", "\n", " indie = [iini, iend]\n", "\n", " t = np.linspace(0, 100, y.shape[0])\n", " if step == 0:\n", " tn = t\n", " elif step > 0:\n", " tn = np.linspace(0, 100, np.round(100 / step + 1))\n", " else:\n", " tn = np.linspace(0, 100, -step)\n", " yn = np.empty([tn.size, y.shape[1]]) * np.NaN\n", " for col in np.arange(y.shape[1]):\n", " # ignore NaNs inside data for the interpolation\n", " ind = np.isfinite(y[:, col])\n", " if np.sum(ind) > 1: # at least two points for the interpolation\n", " spl = UnivariateSpline(t[ind], y[ind, col], k=k, s=smooth)\n", " yn[:, col] = spl(tn)\n", "\n", " if show:\n", " _plot(t, y, ax, tn, yn)\n", "\n", " if axis:\n", " y = y.T\n", " if yn.shape[1] == 1:\n", " yn = yn.flatten()\n", "\n", " return yn, tn, indie\n", "\n", "\n", "def _plot(t, y, ax, tn, yn):\n", " \"\"\"Plot results of the tnorm function, see its help.\"\"\"\n", " try:\n", " import matplotlib.pyplot as plt\n", " except ImportError:\n", " print('matplotlib is not available.')\n", " else:\n", " if ax is None:\n", " _, ax = plt.subplots(1, 1, figsize=(8, 5))\n", "\n", " ax.set_prop_cycle('color', ['b', 'r', 'b', 'g', 'b', 'y', 'b', 'c', 'b', 'm'])\n", " #ax.set_color_cycle(['b', 'r', 'b', 'g', 'b', 'y', 'b', 'c', 'b', 'm'])\n", " for col in np.arange(y.shape[1]):\n", " if y.shape[1] == 1:\n", " ax.plot(t, y[:, col], 'o-', lw=1, label='Original data')\n", " ax.plot(tn, yn[:, col], '.-', lw=2,\n", " label='Interpolated')\n", " else:\n", " ax.plot(t, y[:, col], 'o-', lw=1)\n", " ax.plot(tn, yn[:, col], '.-', lw=2, label='Col= %d' % col)\n", " ax.locator_params(axis='y', nbins=7)\n", " ax.legend(fontsize=12, loc='best', framealpha=.5, numpoints=1)\n", " plt.xlabel('[%]')\n", " plt.tight_layout()\n", " plt.show()\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.4" }, "varInspector": { "cols": { "lenName": 16, "lenType": 16, "lenVar": 40 }, "kernels_config": { "python": { "delete_cmd_postfix": "", "delete_cmd_prefix": "del ", "library": "var_list.py", "varRefreshCmd": "print(var_dic_list())" }, "r": { "delete_cmd_postfix": ") ", "delete_cmd_prefix": "rm(", "library": "var_list.r", "varRefreshCmd": "cat(var_dic_list()) " } }, "types_to_exclude": [ "module", "function", "builtin_function_or_method", "instance", "_Feature" ], "window_display": false } }, "nbformat": 4, "nbformat_minor": 1 }