Created
May 16, 2019 10:20
-
-
Save holgi/b7f3f77427368ab7f90948d14b5a1cf6 to your computer and use it in GitHub Desktop.
Fix for peakutils.plot.peakplot
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| { | |
| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "# fixing peakutils.plot for pandas data series" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 1, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "# basic imports\n", | |
| "%matplotlib inline\n", | |
| "import numpy as np\n", | |
| "import matplotlib.pyplot as plt\n", | |
| "import pandas as pd\n", | |
| "import peakutils\n", | |
| "from peakutils.plot import plot as peakplot" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "The peakplot function works well with numpy data arrays. Here is a simple sine curve with peak detection as example:" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 2, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": "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\n", | |
| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "x = np.arange(0,2*np.pi,0.1) # start,stop,step\n", | |
| "y = np.sin(x)\n", | |
| "indexes_np = peakutils.indexes(y)\n", | |
| "peakplot(x, y, indexes_np)\n", | |
| "plt.show()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Even if this is converted to a panda dataframe consisting of two series it works well:" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 3, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
| " vertical-align: middle;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe tbody tr th {\n", | |
| " vertical-align: top;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe thead th {\n", | |
| " text-align: right;\n", | |
| " }\n", | |
| "</style>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>x</th>\n", | |
| " <th>y</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>0.0</td>\n", | |
| " <td>0.000000</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>0.1</td>\n", | |
| " <td>0.099833</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>0.2</td>\n", | |
| " <td>0.198669</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>3</th>\n", | |
| " <td>0.3</td>\n", | |
| " <td>0.295520</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4</th>\n", | |
| " <td>0.4</td>\n", | |
| " <td>0.389418</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " x y\n", | |
| "0 0.0 0.000000\n", | |
| "1 0.1 0.099833\n", | |
| "2 0.2 0.198669\n", | |
| "3 0.3 0.295520\n", | |
| "4 0.4 0.389418" | |
| ] | |
| }, | |
| "execution_count": 3, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "df_all = pd.DataFrame({'x':x, 'y':y})\n", | |
| "df_all.head()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 4, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": "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\n", | |
| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "indexes_all = peakutils.indexes(df_all['y'])\n", | |
| "peakplot(df_all['x'], df_all['y'], indexes_all)\n", | |
| "plt.show()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "But if some data is removed, e.g. by filtering outliers, the peakplot funtion shows not the right peak" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 5, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
| " vertical-align: middle;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe tbody tr th {\n", | |
| " vertical-align: top;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe thead th {\n", | |
| " text-align: right;\n", | |
| " }\n", | |
| "</style>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>x</th>\n", | |
| " <th>y</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>0.0</td>\n", | |
| " <td>0.000000</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>0.2</td>\n", | |
| " <td>0.198669</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4</th>\n", | |
| " <td>0.4</td>\n", | |
| " <td>0.389418</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>6</th>\n", | |
| " <td>0.6</td>\n", | |
| " <td>0.564642</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>8</th>\n", | |
| " <td>0.8</td>\n", | |
| " <td>0.717356</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " x y\n", | |
| "0 0.0 0.000000\n", | |
| "2 0.2 0.198669\n", | |
| "4 0.4 0.389418\n", | |
| "6 0.6 0.564642\n", | |
| "8 0.8 0.717356" | |
| ] | |
| }, | |
| "execution_count": 5, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "df_selection = df_all.iloc[::2].copy()\n", | |
| "df_selection.head()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 6, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAD8CAYAAABzTgP2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAIABJREFUeJzt3Xl8VNX5x/HPk8kkQxZCSMKahLDvyBJZFUEQXMEF9yq4lKp1b221trW1tUXbitZal6qIdcHtp1CtIDvKIgmI7CQQgkSWhAAhBLLNnN8fGWyCCVlmMneW5/16zSszd+6d+51A5plz77nniDEGpZRS6pQwqwMopZTyL1oYlFJK1aCFQSmlVA1aGJRSStWghUEppVQNWhiUUkrVoIVBKaVUDVoYlFJK1aCFQSmlVA3hVgdoisTERJOWlmZ1DKWUCijr1q07ZIxJqm+9gCwMaWlpZGZmWh1DKaUCiojsach6eihJKaVUDVoYlFJK1aCFQSmlVA0BeY5BKRWaKioqyMvLo7S01Ooofs3hcJCcnIzdbm/S9loYlFIBIy8vj9jYWNLS0hARq+P4JWMMhYWF5OXl0blz5ya9hlcOJYnIayKSLyKb63heROTvIrJTRDaKyOBqz00VkWz3bao38iilglNpaSkJCQlaFM5AREhISPCoVeWtcwyvAxee4fmLgO7u23TgBQARaQ08BgwDhgKPiUi8lzIppYKQFoX6efo78sqhJGPMChFJO8Mqk4E3TNU8omtEpJWItAfGAAuNMYcBRGQhVQXmHW/kUk1njOHIiQpyC0vIPVTC/qJSerWLZXSPJOw27bOgVDDz1TmGjsDeao/z3MvqWv4DIjKdqtYGqampzZMyBB0rrSD74HFyD5Wwp7CEe8Z1x24L44lPt/HKl7trrBsTGc6G314AwOJtB3HYbQzpFI/DbrMiulKqmfiqMNTWrjFnWP7Dhca8DLwMkJ6eXus6quGKTlbwwrJdzFq5m7JKFwBhAlenp5DSOorze7ehXZyDtIRo0hKjadsykrwjJwl3txb++nkW2/YfIyI8jCGp8YzqlsB5PdrQPznOyrellPICXxWGPCCl2uNkYJ97+ZjTli/zUaaQtuW7Il5asYvJZ3XgsrM6kJYYTXJ8CyLDq779j+yayMiuiTW26d3+f13f3r9jBGt3F7JqZyErdxXy18+z2HHwOM9dP8in70MpX/rNb35DYmIi9913HwCPPvoobdu25d5777U4mXf5qjDMA+4WkTlUnWguMsbsF5EFwJ+qnXCeADzio0whxekyfPT1d+QXl3LXmG6M7JbIkp+NoXNidJNeLyYynPN7teX8Xm0BOFxSTklZJQDbDxzjT//dzh8m96VTQtNeX6mGuPal1T9YdumA9tw0Io2T5U6mzVr7g+enDEnm6vQUDpeUc+eb62o89+5PRpxxf7fddhtXXnkl9913Hy6Xizlz5rB27Q/3Eei8UhhE5B2qvvknikgeVT2N7ADGmBeB/wIXAzuBE8At7ucOi8gfgAz3Sz1+6kS08g5jDEt35PPkZzvYcbCY9E7x/GR0V2xh0uSiUJvW0RG0jo4AYE/hCdbvOcLEZ1bw8wk9uWVUZ2xh2pNEBb60tDQSEhL4+uuvOXjwIIMGDSIhIcHqWF4nVR2FAkt6errR0VXrl3WwmF9/vJm1uw+TlhDFQxN7cXH/dj7p7re/6CSPfrSZJdvzGZTaiqeuGkD3trHNvl8V3LZt20bv3r0tzfDuu++yatUqDhw4wNSpU7n44ostzVOX2n5XIrLOGJNe37ba7zCI2cKEbwtP8IfL+7HwwfO4ZEB7n/UBbx/XglenpvPsdQPJPVTCxxu+88l+lWpuV1xxBfPnzycjI4OJEydaHadZ6JAYQeZ4WSUfrsvj5hGd6JoUw5e/HPt9TyJfExEmD+zIqG6JxERW/VfLyD2MI9ymvZdUwIqIiGDs2LG0atUKmy04u2prYQgihcfLmDYrg637j5GeFk/fDnGWFYXqEmMiv78/47PtbNh7lLvGdOWB8T0I03MPKsC4XC7WrFnD+++/b3WUZmP9p4byir2HTzDlxdVk5xfzr5uH0LeDf34jf23a2VwxqCPPLdnJY/O2EIjnuFTo2rp1K926dWPcuHF0797d6jjNRlsMQWD7gWPc/OpaSiucvHnbMNLTWlsdqU5xLez8ZcoAEmIieGl5DrYw4bHL+uj4Nyog9OnTh5ycHKtjNDstDEFg/9FS7LYw/n3bMHq28/+ePyLCwxf2wuk05B05idNlCLdpYVDKX2hhCGAHikppF+dgbK82LP7ZeQE1ZpGI8Oglvd1FIYzjZZVER9i05aCUH9BzDAHqg3V5jH5qKV9kFwAEVFE4RUQIt4VRdLKCK55fyV8W7NBzDkr5AS0MAejlFbv4+fvfMLRzawalBv70FbGR4aSnteafy3Yxc1G21XGUCnlaGALMc4uz+dN/t3NJ//a8Oi39++sDAllYmPDE5f24Jj2Zvy/O5u+LtTgo/3XrrbfSpk0b+vXr1+z7ys3N9cl+TqeFIYB8s/cof1uYxRWDOvL36wd9PxJqMAgLE2ZcOYCrBifz9MIs3lyzx+pIStVq2rRpzJ8/3+oYzUoLQwAZkBzHM9cO5M9X9g/KQenCwoSnpgzgp2O7MqFPW6vjqGAhUnXzktGjR9O69Zm7hE+bNo077riDc889lx49evDJJ58A4HQ6eeihhzj77LMZMGAAL730EgDHjx9n3LhxDB48mP79+zN37twfvGZOTg6DBg0iIyODLVu2MHToUAYOHMiAAQPIzvZuKzvwj0OEgGOlFRQUl9E1KYbLB9U6wV3QsIUJD03sBUCl08XKXYWc1yPJ4lQqYFUvCKfu+6iDQ25uLsuXL2fXrl2MHTuWnTt38sYbbxAXF0dGRgZlZWWMGjWKCRMmkJKSwkcffUTLli05dOgQw4cPZ9KkSd+/1o4dO7juuuuYNWsWAwcO5J577uG+++7jxhtvpLy8HKfT6dXsWhj8nMtleGDOBtZ/e4QVvxhLrMNe/0ZBYtbKXP782TbmTB/B0M7+e9GeUrW55pprCAsLo3v37nTp0oXt27fz+eefs3HjRj744AMAioqKyM7OJjk5mV/96lesWLGCsLAwvvvuOw4ePAhAQUEBkydP5sMPP6Rv374AjBgxgieeeIK8vDyuvPJKr1+FrYeS/Nyzi7NZvD2f+8f3CKmiAHDd0BRSW0dx7ztfc7ik3Oo4KhBVbx0Y47PWAvCDa3JEBGMMzz33HBs2bGDDhg3s3r2bCRMm8NZbb1FQUMC6devYsGEDbdu2pbS0FIC4uDhSUlJYuXLl9691ww03MG/ePFq0aMHEiRNZsmSJV7N7pTCIyIUiskNEdorIw7U8P1NENrhvWSJytNpzzmrPzfNGnmCxcOtBnl2czZQhydw8opPVcXwu1mHnHzcM5nBJOQ++twGXS69xUIHj/fffx+VysWvXLnJycujZsycTJ07khRdeoKKiAoCsrCxKSkooKiqiTZs22O12li5dyp49/+t8ERERwccff8wbb7zB22+/DVSdb+jSpQv33nsvkyZNYuPGjV7N7vGhJBGxAc8DF1A1h3OGiMwzxmw9tY4x5oFq698DVJ8Y+KQxZqCnOYLN7kMlPPDuBgYkx/HHy/uF7BXB/TrG8ZtLe/ObuVv41xc5/OS8rlZHUoHGy62E66+/nmXLlnHo0CGSk5P5/e9/z2233faD9Xr27Ml5553HwYMHefHFF3E4HNx+++3k5uYyePBgjDEkJSXx8ccfc+ONN3LZZZeRnp7OwIED6dWrV43Xio6O5pNPPuGCCy4gOjqarVu38uabb2K322nXrh2//e1vvfoePZ7BTURGAL8zxkx0P34EwBjz5zrWXwU8ZoxZ6H583BgT05h9hsIMbqUVTv6yYAe3ndOZDq1aWB3HUsYY/vjpNiYP7MCA5FZWx1EW8ocZ3Bpi2rRpXHrppUyZMsWyDJ7M4OaNk88dgb3VHucBw2pbUUQ6AZ2B6gfEHCKSCVQCM4wxH3shU8ByuQwl5ZXEOuz85tI+VsfxCyJS43dR6XT5xTwTSgUrbxSG2o5x1NUMuQ74wBhTvW9VqjFmn4h0AZaIyCZjzK4f7ERkOjAdIDU11dPMfuu5JTv56Os8PrprFPHREVbH8Tt//GQruYUn+NfNQ0L28Jryf6+//rrVETzija9deUBKtcfJwL461r0OeKf6AmPMPvfPHGAZNc8/VF/vZWNMujEmPSkpCPu1uy/Cmbkoi8Gd4mkVFVo9kBqqY3wLFm07yKtf7rY6irKIDrRYP09/R94oDBlAdxHpLCIRVH34/6B3kYj0BOKB1dWWxYtIpPt+IjAK2Hr6tkGv2jff3Ccv5elrB+m34TpMG5nGhD5teXJ+1RShKrQ4HA4KCwu1OJyBMYbCwkIcDkeTX8PjQ0nGmEoRuRtYANiA14wxW0TkcSDTGHOqSFwPzDE1/0V7Ay+JiIuqIjWjem8mpU4nIvxlyllc/PcvuPvt9Xx677nEtdDWVahITk4mLy+PgoICq6P4NYfDQXJycpO397hXkhWCrVfSkZJy4mMiqx4E4L+HFdZ/e4Rpr63lxZuGMLJrotVxlAoIvuyVpDykJ5kbb3BqPCsfPj/krgZXyhe0z5+FjDE8tzib/GOlPr9cPxjEOuwYY3g341s2f1dkdRylgoYWBgt9sC6Pvy3MYsHWg1ZHCVgl5U5mLszmZ+99Q4XTZXUcpYKCFgaL5BeX8odPtnJ2Wjw3Dg3e6zKaW0xkOI9P7suOg8W8pl1YlfIKLQwWeWzuFkorXcy4agBhQTjpji9N6NuO8b3b8syibPKOnLA6jlIBTwuDBeZvPsBnmw9w//judE1q1DBRqg6/n1w1Tv1jc7dYnESpwKe9kiwwtHNr7h7bjR+f28XqKEGjY6sW/H5SXxJjtYeXUp7S6xh8zBijVzUrpSzR0OsY9FCSD63IKuDyf65if9FJq6MELWMMzyzK4sn5262OolTA0sLgIyVllTzyf5soLq0gPkoPdzQXEaGguIyXlu/SaxuUaiItDD7ylwU72Fd0kqeuGoDDbrM6TlD7xcRetI6O4NGPNuHU6UCVajQtDD6wbs9hZq/O5ebhnUhPa211nKAXF1U1ydE3eUW89dWe+jdQStWghcEHXlyeQ4e4Fjx0Ya/6V1ZeMemsDpzTLZGnF2ZxorzS6jhKBRTtruoDz10/iLwjJ4iJ1F+3r4gIT1zRj5IyJ1ER+ntXqjH0L6YZFZ2sIDI8DIfdRrc2sVbHCTmdEqK/v19cWqEjsSrVQHooqRnN+GwbE2auoKzSWf/Kqtk8/fkOLn3uS0or9N9BqYbwSmEQkQtFZIeI7BSRh2t5fpqIFIjIBvft9mrPTRWRbPdtqjfy+INt+4/xbsZexvduS2S49kKy0vCuCewpPMHzS3daHUWpgOBxYRARG/A8cBHQB7heRPrUsuq7xpiB7tsr7m1bA48Bw4ChwGMiEu9pJqsZY3ji023EOuzcO66b1XFC3siuiVw5qCMvLt/Fzvxiq+Mo5fe80WIYCuw0xuQYY8qBOcDkBm47EVhojDlsjDkCLAQu9EImSy3Zns+XOw9x//jutNKL2fzCry7pjcNu44lPt1kdRSm/543C0BHYW+1xnnvZ6a4SkY0i8oGIpDRy24Ayf/MBuiRG86PhnayOotwSYyL56dhuZOQeYd9RHZJEqTPxRq+k2kaEO/1y0/8A7xhjykTkDmA2cH4Dt63aich0YDpAaqp/T2zz1JQBFBwvw27Tc/v+ZNrINKYMSSYxJtLqKEr5NW98cuUBKdUeJwP7qq9gjCk0xpS5H/4LGNLQbau9xsvGmHRjTHpSUpIXYntf0ckK8o+VIiK0iXVYHUedxmG3kRgTiTFGWw1KnYE3CkMG0F1EOotIBHAdMK/6CiLSvtrDScCpA70LgAkiEu8+6TzBvSwgPbsom3F/W07RiQqro6gzePjDTVz94mrtRqxUHTwuDMaYSuBuqj7QtwHvGWO2iMjjIjLJvdq9IrJFRL4B7gWmubc9DPyBquKSATzuXhZwcgqO88bqXC49qz1xUXohlT+bNLAD3x09yRurdBwlpWqjE/V4ye2zM1mTU8jSn48hKVaPYfu7abPWsn7PEVb8Yqz2HFMhQyfq8aFVOw+xaNtB7hrbVYtCgHj4ol4cL6vUi96UqoUWBi/IyD1CSusW3Dqqs9VRVAP1ateSKUOSWbwtn/JKl9VxlPIreijJS46XVeroqQGm6EQFkfYwnThJhQw9lOQDx8sq2brvGIAWhQAUF2XHYbdRWuHUebiVqkYLgwdeWLaTy/7xJXsPn7A6imoiYwxXv7iaB97dQCC2npVqDloYmmh/0Ule+WI3lw1oT0rrKKvjqCYSEaYMSWZNzmGWbM+3Oo5SfkELQxP9ffFOXMbwswk9rY6iPHTDsFQ6J0Yz47PtVDr1RLRSWhiaYE9hCe9n7uX6oanaWggCdlsYv7ywJ9n5x3l/XZ7VcZSynBaGJtiy7xgtW9i5e6zOtRAsJvZtx5BO8SzfUWB1FKUsp91Vm6i0wqndHIPM0RPlxLWwI1LboL9KBT7trtpMNuUVYYzRohCEWkVFICIUFJdxpKTc6jhKWUYLQyNsyivisn98yTtr99a/sgpIx8sqGfe3ZTy3RIfKUKFLC0Mj/PXzHbSKsnPZWe3rX1kFpJjIcCb2bcebX+3hQFGp1XGUsoQWhgbKyD3M8qwC7jivK7EOHVY7mN07rjsul9EB9lTI0sLQAMYY/rJgB0mxkUwdkWZ1HNXMUlpHcc3ZKczJ+Ja8I3pVuwo9WhgaoKC4jD2FJdw9thstIvSkcyi4e2w3wkRYpt1XVQjySndVEbkQeBawAa8YY2ac9vyDwO1AJVAA3GqM2eN+zglscq/6rTFmEvWwortqaYWTMBEiwrWWhor84lKdu1sFFZ91VxURG/A8cBHQB7heRPqcttrXQLoxZgDwAfBUtedOGmMGum/1FgVf23v4BOWVLhx2mxaFEHOqKOQX60loFVq88Uk3FNhpjMkxxpQDc4DJ1Vcwxiw1xpw6WLsGSPbCfpud02W4fXYmP37Dv+Z+UL7z8dffMWrGEnYVHLc6ilI+443C0BGo3rE/z72sLrcBn1V77BCRTBFZIyKX17WRiEx3r5dZUOCb476fbNzHjoPFTBkSEHVMNYNzuidit4Xx7KJsq6Mo5TPeKAy1jR9Q64kLEfkRkA78pdriVPcxrxuAZ0Ska23bGmNeNsakG2PSk5KSPM1crwqni5kLs+jdviWX9NfrFkJVYkwkU0em8Z+N+9hxoNjqOEr5hDcKQx6QUu1xMrDv9JVEZDzwKDDJGFN2arkxZp/7Zw6wDBjkhUwe+3BdHrmFJ/jZBT0IC9Oxc0LZ9HO7EB0RzjOLsqyOopRPeKMwZADdRaSziEQA1wHzqq8gIoOAl6gqCvnVlseLSKT7fiIwCtjqhUweW7w9n4EprRjXu43VUZTF4qMjuPWczizdkU9BcVn9GygV4LzVXfVi4Bmququ+Zox5QkQeBzKNMfNEZBHQH9jv3uRbY8wkERlJVcFwUVWknjHGvFrf/nzRXdXlMhw5UU5CTGSz7kcFhuLSCk6WO2nTUruvqsDV0O6qOuz2aUornJRVuIiL0mEv1A8ZYygpdxITGW51FKUaTYfdbqI31+zhnCeX6ABqqlZ3vrmeu95ab3UMpZqVFoZqTpY7eXF5DgNS4mgXp4cM1A8NSm3FiqwCMnMPWx1FqWajhaGat77aw6HjZdw3rofVUZSfumlEJxJjIvjb59pDSQUvLQxupRVOXlqRw8iuCQzt3NrqOMpPRUWEc+eYbqzOKWTVrkNWx1GqWWhhcFu2o4CC4jLuG9fd6ijKz904LJW2LSOZtTLX6ihKNQvtlVTNzvxiurWJ9frrquCz+bsiuibF6DDsKqBor6RGKK1wAmhRUA3Wr2McLSJsVDpdBOKXK6XOJOQLQ2mFk/P/uoxXvsixOooKMDvzjzP+6eWs2lVodRSlvCrkC8PbX33LvqJS+nWMszqKCjDJ8S04WeHk2UXZ2mpQQSWkC0NphZMXlu9iRJcEhndJsDqOCjAOu427xnRjbe5hVudoq0EFj5AuDG9/9W1VT6Tx2hNJNc21Z6fQtmUkz+h8DSqIhGxhcLkMr365W1sLyiMOu407zuvK2t2H9WpoFTRCdiSwsDDhvTtGcLK80uooKsBdPzSVdi0dDE6NtzqKUl4RkoXBGIOI0LFVC6ujqCDgsNu4SGf5U0EkJA8lzV6Vy49e+YqSMm0tKO95Y3Uud721zuoYSnks5ApDaYWTfy7bRYXTRbSOqa+8qLzSxX83HWDtbj3XoAKbVwqDiFwoIjtEZKeIPFzL85Ei8q77+a9EJK3ac4+4l+8QkYneyHMmc9Z+S35xGfeP1xFUlXfdOKwTiTGRPLtYR15Vgc3jwiAiNuB54CKgD3C9iPQ5bbXbgCPGmG7ATOBJ97Z9qJojui9wIfBP9+s1i1OthaGdWzOiq/ZEUt7VIsLGT0Z3YeXOQu2hpAKaN1oMQ4GdxpgcY0w5MAeYfNo6k4HZ7vsfAONERNzL5xhjyowxu4Gd7tdrFu9l7nW3FvS6BdU8bhyeSkJ0BM8u1usaVODyxkH2jsDeao/zgGF1rWOMqRSRIiDBvXzNadt2rG0nIjIdmA6QmprapKBXDOpIC7uNEXrdgmomURHhPHFFP5JiI62OolSTeaMwSC3LTh84pq51GrJt1UJjXgZehqphtxsT8JRYh52r01OasqlSDXZhP+26qgKbNw4l5QHVP22TgX11rSMi4UAccLiB2yoVcI6UlPOrjzax/tsjVkdRqtG8URgygO4i0llEIqg6mTzvtHXmAVPd96cAS0zVcJTzgOvcvZY6A92BtV7IpJSlIsLDmL/5AM/qGEoqAHlcGIwxlcDdwAJgG/CeMWaLiDwuIpPcq70KJIjITuBB4GH3tluA94CtwHzgp8YYp6eZlLJadGQ4t5/bmeVZBWzYe9TqOEo1ik7tqVQzOV5WyblPLmFgSitm3dJsne2UajCd2lMpi8VEhvPj0V1YukNbDSqw6JgQSjWjm0ekcaCoVLuvqoCihUGpZhQTGc7jk/tZHUMFgbe/+pbDJWXcNaYbYWG19fT3Hj2UpJQPbMorYuZCHUNJNY0xhtdW7mZNzuFmLwqgLQalfGLlrkM8uzib0T2SGNJJJ/RRjSMifHTXSI6eqPDJ/rTFoJQP3DS8E62jI3hmkbYaVOOUV7pwugyxDjspraN8sk8tDEr5QHRkOD8Z3YUvsg+xbo+OvKoa7pUvc7jo2RUUl/qmtQBaGJTymZtGdCIxJoKZC/VqaNUwx8sqeXlFDh1atSDWYffZfvUcg1I+EhURzv3je5BfXIbTZbD54CSiCmyzV+Vy9ESFzycW08KglA/9aHgnqyOoAFFcWsHLK3I4v1cbBqa08um+9VCSUj7mchnmbz7AN3o1tDqD9zPzKDpZYcnEYtpiUMrHyipd/PrjzXRvE8M704dbHUf5qZtHdKJH21gGJPu2tQDaYlDK51pE2LjjvC6szilkTU6h1XGUH3K5DOG2MM7pnmjJ/rUwKGWBHw3vRFJspF7XoH6g6GQF459ezqKtBy3LoIVBKQs47DbuPK8ra3IOs3qXthrU/7z65W5yDpXQoVULyzJ4VBhEpLWILBSRbPfPH1zrLyIDRWS1iGwRkY0icm21514Xkd0issF9G+hJHqUCyQ3DUhnSKZ7SCp2bSlU5eqKcWV/u5qJ+7ejToaVlOTxtMTwMLDbGdAcWux+f7gRwszGmL3Ah8IyIVD+b8pAxZqD7tsHDPEoFDIfdxod3jmRsrzZWR1F+4pUvdlNcVsl9FvREqs7TwjAZmO2+Pxu4/PQVjDFZxphs9/19QD6Q5OF+lQoapRVO5n2zj0CcTVF5z7HSCmat3M0lA9rTq511rQXwvDC0NcbsB3D/PONXHxEZCkQAu6otfsJ9iGmmiOhsJirkfLg+j3vf+VrPNYS42Mhw/nVzOj+7wLdXOdem3sIgIotEZHMtt8mN2ZGItAf+DdxijHG5Fz8C9ALOBloDvzzD9tNFJFNEMgsKChqza6X82lWDk2nX0sHfFmZpqyGEiQgjuyXSJSnG6ij1FwZjzHhjTL9abnOBg+4P/FMf/Pm1vYaItAQ+BX5tjFlT7bX3myplwCygzhnTjTEvG2PSjTHpSUl6JEoFD4fdxj3jurFuzxGWbK/1T0gFuacXZvGHT7b6zRcDTw8lzQOmuu9PBeaevoKIRAAfAW8YY94/7blTRUWoOj+x2cM8SgWka9JTSEuI4qn5O3C6/OPDQflGQXEZ/1qRQ+HxMqo+Cq3naWGYAVwgItnABe7HiEi6iLziXucaYDQwrZZuqW+JyCZgE5AI/NHDPEoFJLstjAcn9KRFhI3CkjKr4ygf+seSbMqdLu4dZ21PpOrEX5oujZGenm4yMzOtjqGUV536W/SXb42q+X1beIJxTy/j6vQU/nRF/2bfn4isM8ak17eeXvmslJ8QEUSEQ8fLWLtbZ3kLBTMXZWELE+7zo9YC6OiqSvmdh97/hk3fHWPFL8YQFaF/osHs7vO7MbZXG9q2dFgdpQZtMSjlZ+4+vzuHjpcxa2Wu1VFUM+uaFMOkszpYHeMHtDAo5WeGdIrngj5teXHZLo6UlFsdRzWD1bsKmf5GJgXF/tnRQAuDUn7o5xN6cry8kheX76p/ZRVQjDHMmL+dTd8VEevwz0OFWhiU8kM928Vy5aBkCkvK/eaiJ+UdC7ZUTev6wPgeOOw2q+PUyj/LlVKKp6YMwBamXVeDSaXTxVMLdtCtTQxXDu5odZw6aYtBKT91qihkHSxm7+ETFqdR3vDh+jxyCkp4aGJPwm3++/GrLQal/FhJWSVX/XMVo3sm8fwNg62Oozw0oU87SsqcTOjT1uooZ+S/JUspRXRkOLeMSuPTjfvZlFdkdRzlofjoCG49p7PfX92uhUEpP3f76C60irLz1ILtVkdRTVR0ooIb/rWGb/YetTpKg2hhUMrPtXTY+emYbnyRfYhVuw5ZHUc1wQvLd7E6p5CI8MD4yA2MlEqFuJtGdKJLYjS5h/QkdKA5UFQOdd02AAAQsUlEQVTKrJW7uXxgR3q3t3bKzobSk89KBQCH3cbnD4z2654sqnbPLs7CZQwP+sGUnQ2l/8uUChDhtjCMMSzbkU9phdPqOKoBdhUc573MPG4c1omU1lFWx2kwLQxKBZBN3xUxbVYGr3652+ooqgFS4qN47LI+3H1+N6ujNIpHhUFEWovIQhHJdv+Mr2M9Z7XZ2+ZVW95ZRL5yb/+uexpQpVQdBiS3YkKftjy/dCcHikqtjqPqEREexs0j0kiMibQ6SqN42mJ4GFhsjOkOLHY/rs1JY8xA921SteVPAjPd2x8BbvMwj1JB79eX9KHSZZjx2Taro6g6VDhd3PjKGuZv3m91lCbxtDBMBma7788GLm/ohlJ1hcf5wAdN2V6pUJWaEMVPRnfh4w37yMzVmd780b9X72HlzkK/v5CtLp4WhrbGmP0A7p9t6ljPISKZIrJGRE59+CcAR40xle7HeYD/jiqllB+5c0xXBiTHcfREhdVR1GkKisuYuTCL0T2S/H7oi7rU211VRBYB7Wp56tFG7CfVGLNPRLoAS0RkE3CslvXqHF9YRKYD0wFSU1MbsWulgk9URDhzfzoqYL+RBrMn52+ntNLJY5f1Cdh/n3pbDMaY8caYfrXc5gIHRaQ9gPtnfh2vsc/9MwdYBgwCDgGtRORUcUoG9p0hx8vGmHRjTHpSUlIj3qJSwUlEqHS6+PfqXIq05eAXsg4W88G6PG47pwtdk2KsjtNknh5KmgdMdd+fCsw9fQURiReRSPf9RGAUsNVUzT6yFJhypu2VUnXbVVDCY/O2MHNRltVRFNC9TQyvTUvnngDrnno6TwvDDOACEckGLnA/RkTSReQV9zq9gUwR+YaqQjDDGLPV/dwvgQdFZCdV5xxe9TCPUiGlZ7tYbhiWyr/X7GHHgWKr44S00gonIsL5vdoSHRnYg0pIIE4bmJ6ebjIzM62OoZRfOFJSzpi/LqNvh5a8dfuwgD2uHciOlJRz4bMr+OWFvbhycLLVceokIuuMMen1radXPisV4OKjI/jZhB6s2lXIgi0HrI4Tkp5asINDx8vp2yHO6iheEdjtHaUUADcMTSUj9wgJAXaFbTDYmHeUORnfcsvIzvRsF2t1HK/QwqBUEAi3hfHc9YOsjhFyXC7Db+duISE6kvsv6G51HK/RQ0lKBZFjpRU88elW8o7ovA2+8PXeo2zMO8ojF/WipcNudRyv0RaDUkGkuLSSf6/Zw76jpTx/42Cr4wS9IZ3i+fyB0QF9zUJttMWgVBDp2KoFd57XjU837eeL7AKr4wS1vYerWmXd2sQGXU8wLQxKBZmfnNeFLknR/PKDjRwr1Suim8PWfccY89dlfLguz+oozUILg1JBxmG38fQ1AzlYXMaf/7vd6jhBx+ky/PrjTcS1sDOud13jhgY2PcegVBAamNKKGVf2Z2jn1lZHCTovLt/F+m+P8sy1A2kVFZxzi2lhUCpIXZ2eAoAxhrJKFw67zeJEgW9TXhEzF2Zx6YD2TB7Yweo4zUYPJSkVxIwx3P3O19w/ZwOBOPyNv9l75ASpCVE8cXn/oDvhXJ0WBqWCmIjQv2Mc87cc4OMN31kdJ+Bd3L89n98/mrio4LlmoTZaGJQKcj8+twvpneL57dwt7C86aXWcgLQ8q4D3MvdijCHcFvwfm8H/DpUKcbYw4W/XnIXTZfjFBxv1kFIjHS4p5+fvf8MrX+RQ7nRZHccntDAoFQI6JUTzq4t7s21/MXlHtNXQUMYYHvm/jRSdqOCZawcRGR4aJ/C1V5JSIeLGYalcNqBD0B8f96b31+WxYMtBHrmoF306tLQ6js941GIQkdYislBEst0/42tZZ6yIbKh2KxWRy93PvS4iu6s9N9CTPEqpuokIcVF2Kp0u3svYi9Olh5TO5EhJOY//ZyvDOrfm9nO7WB3HpzxtMTwMLDbGzBCRh92Pf1l9BWPMUmAgVBUSYCfwebVVHjLGfOBhDqVUAy3PKuAXH26ksKScO8d0tTqO34qPjuCZawfSq30strDg7ZpaG0/PMUwGZrvvzwYur2f9KcBnxhgdE1gpi5zfqw0X9WvHzIVZbD9wzOo4fulwSTkA4/u0JTk+yuI0vudpYWhrjNkP4P5Z38Ah1wHvnLbsCRHZKCIzRUSnn1KqmYkIf7y8Hy1bhPPAu99QWuG0OpJf2Zh3lFEzlvB5CE+TWm9hEJFFIrK5ltvkxuxIRNoD/YEF1RY/AvQCzgZac9phqNO2ny4imSKSWVCgwwkr5YmEmEhmXDmAbfuP8du5m62O4zdOlju5/90NtIqyM6xzgtVxLFPvOQZjzPi6nhORgyLS3hiz3/3Bn3+Gl7oG+MgY8/04wKdaG0CZiMwCfn6GHC8DLwOkp6frWTOlPDS+T1t+P6kvg1N/0GckJBlj+PXHm8kpKOGt24eFdO8tTw8lzQOmuu9PBeaeYd3rOe0wkruYIFWDjlwO6FcXpXxo6sg0+ifHAZBTcNziNNZ6emEWH67P475x3RnVLdHqOJbytDDMAC4QkWzgAvdjRCRdRF45tZKIpAEpwPLTtn9LRDYBm4BE4I8e5lFKNcF7GXuZMHMFX2YfsjqKZaIiwrl+aAr3j+9udRTLSSBeHp+enm4yMzOtjqFU0DhWWsHVL6xm39GTfHjXSHq0jbU6ks+UVji/H5LcGBPUo6aKyDpjTHp96+mQGEopWjrsvHbL2TgibNwyK4P84lKrI/lERu5hzn1qKRv2HgUI6qLQGFoYlFIAdGzVgtemns3hknJ+/Ma6oL8yOvtgMbe9nkFsZDiprUPvWoUz0bGSlFLf658cx3PXD6KkvDKor/bdX3SSqa+tJdJuY/atQ2kdHZxTdDaVFgalVA3j+7T9/v6ewhI6JURbmMb7ik5WMO21DI6VVvLuT4aToq2FH9BDSUqpWn2z9yjjn17O7FW5VkfxKoc9jL4dWvLSTUPo2yHO6jh+SVsMSqla9esYx5iebfj9f7bQsVWLGi2JQOR0GUrKK2npsPP0tTqQ85loi0EpVStbmPDsdQPp1zGOu95ez/+tz7M6UpMZY3j8P1u44vmVFJdW1L9BiNPCoJSqU1REOK/fMpRBKa148L1vWL2r0OpIjVZW6eSxeVuYvXoP5/dqQ6wjdIe6aCg9lKSUOqPW0RG8efsw5m3Yx/AurYHAuRAs78gJfvrWer7JK+L2czrzyEW9rY4UELTFoJSql90WxlVDkhERcgqOc9k/vgyIuRx+N28LOQUlvPijwfz60j6EBXEXXG/SFoNSqlFKypzkHyvjyn+u4q9Xn8XF/dtbHamGSqeLExVOWjrs/PHy/pRWOElLDK4ut81NWwxKqUbpnxzHJ/ecQ892sdz11nr+smA7Lj+5Sjr/WCk3vPIVd7/9NcYY2sU5tCg0gRYGpVSjtWnpYM704VybnsLzS3fxuh9c67Bq5yEu/vsXbMor4opBHQLiHIi/0kNJSqkmiQy3MeOq/ozqnsgE9zUOJ8oriYrw7ceKy2V4fulOZi7KonNiNG//eHhIjQ7bHLQwKKWaTESYdFYHACqcLsb9bTl9O7Tk5hFpnNMt0Scne4+VVvDWV99y2Vkd+NMV/YmO1I81T+lvUCnlFeWVLqYMSeadtd+yaNtauiRFc/PwTlw1JNmr1w7kF5cy9+t9fL71AP++bRitoiKYd88okmIi9fCRl3h0jkFErhaRLSLiEpE6J38QkQtFZIeI7BSRh6st7ywiX4lItoi8KyI6xKFSASo6MpyfTejJyofPZ+a1ZxHrsPO7/2z9fq4DT05Ql1U6+WzTfm57PYMRf17CE//dRqXLkH+sDIA2sQ4tCl7kaYthM3Al8FJdK4iIDXieqqk/84AMEZlnjNkKPAnMNMbMEZEXgduAFzzMpJSyUGS4jSsGJXPFoGQ25RXRt0NLAGbM3862/ceYOiKNs9NaExEeht0mhNtq/35qjKGs0oXDbmPLvmPc+dZ62raMZProLlw1OJlubWJ8+bZCikeFwRizDeqd9WgosNMYk+Nedw4wWUS2AecDN7jXmw38Di0MSgWN/sn/G720XUsHczd8x+1v/G9a3pFdE3j7x8MBuOTvX3CgqBS7LYyI8DCcLsN5PZP40xX9GZTSirdvH8awLglBPU+Ev/DFOYaOwN5qj/OAYUACcNQYU1ltece6XkREpgPTAVJTU5snqVKq2dx6TmduGtGJJdvzyTtykgqni/Zxju+fv6BPWwqKy6hwuqhwGipdhiGp8UDVl8+R3RKtih5y6i0MIrIIaFfLU48aY+Y2YB+1lXdzhuW1Msa8DLwMkJ6e7h9X0yilGsVuC2Ni39o+TuD+8T18nEbVpd7CYIwZ7+E+8oCUao+TgX3AIaCViIS7Ww2nliullLKQL658zgC6u3sgRQDXAfOMMQZYCkxxrzcVaEgLRCmlVDPytLvqFSKSB4wAPhWRBe7lHUTkvwDu1sDdwAJgG/CeMWaL+yV+CTwoIjupOufwqid5lFJKeU6qvrgHlvT0dJOZmVn/ikoppb4nIuuMMXVec3aKDqKnlFKqBi0MSimlatDCoJRSqgYtDEoppWoIyJPPIlIA7Gni5olUXUMRyPQ9+Ad9D/4hGN4D+OZ9dDLGJNW3UkAWBk+ISGZDzsr7M30P/kHfg38IhvcA/vU+9FCSUkqpGrQwKKWUqiEUC8PLVgfwAn0P/kHfg38IhvcAfvQ+Qu4cg1JKqTMLxRaDUkqpMwipwlDX3NOBQkReE5F8EdlsdZamEpEUEVkqItvc84XfZ3WmxhIRh4isFZFv3O/h91ZnaioRsYnI1yLyidVZmkJEckVkk4hsEJGAHEBNRFqJyAcist39dzHC8kyhcijJPfd0FtXmngaud889HRBEZDRwHHjDGNPP6jxNISLtgfbGmPUiEgusAy4PsH8HAaKNMcdFxA58CdxnjFljcbRGE5EHgXSgpTHmUqvzNJaI5ALpxpiAvY5BRGYDXxhjXnFPTRBljDlqZaZQajF8P/e0MaYcmANMtjhToxhjVgCHrc7hCWPMfmPMevf9YqqGYq9zSld/ZKocdz+0u28B9w1LRJKBS4BXrM4SqkSkJTAa95QDxphyq4sChFZhqG3u6YD6QAo2IpIGDAK+sjZJ47kPwWwA8oGFxpiAew/AM8AvAJfVQTxggM9FZJ17XvhA0wUoAGa5D+m9IiLRVocKpcLQqDmmVfMSkRjgQ+B+Y8wxq/M0ljHGaYwZSNWUtENFJKAO7YnIpUC+MWad1Vk8NMoYMxi4CPip+3BrIAkHBgMvGGMGASWA5ec/Q6kw1DX3tPIx93H5D4G3jDH/Z3UeT7ib/cuACy2O0lijgEnuY/RzgPNF5E1rIzWeMWaf+2c+8BFVh4wDSR6QV63F+QFVhcJSoVQYap172uJMIcd94vZVYJsx5mmr8zSFiCSJSCv3/RbAeGC7takaxxjziDEm2RiTRtXfwhJjzI8sjtUoIhLt7sCA+/DLBCCgeuwZYw4Ae0Wkp3vROMDyjhjhVgfwFWNMpYicmnvaBrxWbe7pgCAi7wBjgET3XNuPGWMCbZ7sUcBNwCb3MXqAXxlj/mthpsZqD8x293QLo2oe84Ds7hng2gIfVX3XIBx42xgz39pITXIP8Jb7C2sOcIvFeUKnu6pSSqmGCaVDSUoppRpAC4NSSqkatDAopZSqQQuDUkqpGrQwKKWUqkELg1JKqRq0MCillKpBC4NSSqka/h+V2zpMj90B2AAAAABJRU5ErkJggg==\n", | |
| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "indexes_selection = peakutils.indexes(df_selection['y'])\n", | |
| "peakplot(df_selection['x'], df_selection['y'], indexes_selection)\n", | |
| "plt.show()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "This is problem arises, because the `indexes_selection` does not contain the data frame index but the numeric array position:" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 7, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "indexes list: [8]\n", | |
| "\n", | |
| "values used by peakplot:\n", | |
| " x y\n", | |
| "8 0.8 0.717356\n", | |
| "\n", | |
| "values that should be used:\n", | |
| " x y\n", | |
| "16 1.6 0.999574\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "print('indexes list:', indexes_selection)\n", | |
| "print('')\n", | |
| "print('values used by peakplot:')\n", | |
| "print(df_selection.loc[indexes_selection])\n", | |
| "print('')\n", | |
| "print('values that should be used:')\n", | |
| "print(df_selection.iloc[indexes_selection])" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Since numpy does not have an .iloc method / accessor, two conditionals were added in the peakplot function:" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 8, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "def peakplot_fixed(x, y, ind):\n", | |
| " \"\"\"\n", | |
| " Plots the original data with the peaks that were identified\n", | |
| "\n", | |
| " Parameters\n", | |
| " ----------\n", | |
| " x : array-like\n", | |
| " Data on the x-axis\n", | |
| " y : array-like\n", | |
| " Data on the y-axis\n", | |
| " ind : array-like\n", | |
| " Indexes of the identified peaks\n", | |
| " \"\"\"\n", | |
| " plt.plot(x, y, '--')\n", | |
| " \n", | |
| " marker_x = x.iloc[ind] if hasattr(x, 'iloc') else x[ind]\n", | |
| " marker_y = y.iloc[ind] if hasattr(y, 'iloc') else y[ind]\n", | |
| " \n", | |
| " plt.plot(marker_x, marker_y, 'r+', ms=5, mew=2, label='{} peaks'.format(len(ind)))\n", | |
| " plt.legend()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "The updated function works with pandas data frames and numpy arrays:" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 9, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": "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\n", | |
| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
| { | |
| "data": { | |
| "image/png": "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\n", | |
| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "peakplot_fixed(df_selection['x'], df_selection['y'], indexes_selection)\n", | |
| "plt.show()\n", | |
| "peakplot_fixed(x, y, indexes_np)\n", | |
| "plt.show()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Also in the documentation of the peakutils.indexes() documentation there should be a note, that the returned indexes are the value positions, not the dataframe indexes and therefore `iloc` should be used to access the values." | |
| ] | |
| } | |
| ], | |
| "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": "3.6.6" | |
| } | |
| }, | |
| "nbformat": 4, | |
| "nbformat_minor": 2 | |
| } |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment