{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e3190b5e",
   "metadata": {},
   "source": [
    "# Interpolating hexadecimal colour values\n",
    "\n",
    "_by A. Maurits van der Veen_  \n",
    "_2022-01-28_  \n",
    "\n",
    "In visualizations, it is often desirable to be able to display gradations of colour, or to interpolate from one colour to another. \n",
    "\n",
    "When plotting data from a dataframe, we would like to be able to do this automatically from any one colour to any other colour. For instance, if we want to plot a number of distinct colours but want the transition from one to the next to be gradual.\n",
    "\n",
    "This notebook shows how to do this easily and neatly."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7f58f9c",
   "metadata": {},
   "source": [
    "### 1. Import modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "876a74ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4d633b5",
   "metadata": {},
   "source": [
    "### 2. Interpolation functions\n",
    "\n",
    "Two functions handle the interpolation; both are built on/inspired by very helpful Stack Overflow answers:\n",
    "- `colorFader_array` is an array-based version of Marcus Dutschke's answer here: https://stackoverflow.com/questions/25668828/how-to-create-colour-gradient-in-python  \n",
    "\n",
    "- `interpolate_hexcolor_df` is an adaptation of jdehesa's answer here: https://stackoverflow.com/questions/41895857/creating-a-custom-interpolation-function-for-pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "28a6e36d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def colorFader_array(c1, c2, mix=0):\n",
    "    \"\"\"Fade (linear interpolate) from color c1 (at mix=0) to c2 (mix=1).\n",
    "    \n",
    "    Array-based version of Marcus Dutschke answer here:\n",
    "    https://stackoverflow.com/questions/25668828/how-to-create-colour-gradient-in-python\n",
    "    \"\"\"\n",
    "    return np.array([mpl.colors.to_hex((1-mix_i) * np.array(mpl.colors.to_rgb(c1_i)) \\\n",
    "                                       + mix_i * np.array(mpl.colors.to_rgb(c2_i))) \\\n",
    "                     for c1_i, c2_i, mix_i in zip(c1, c2, mix)])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1e63c8b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def interpolate_hexcolor_df(df):\n",
    "    \"\"\"Take a dataframe with columns that are color values, \n",
    "       with missing values (NaN) inbetween that are to be filled by interpolation.\n",
    "    \n",
    "    Interpolate by \"averaging\" the colors over the missing range.\n",
    "    \n",
    "    Inspired by the answer by jdehesa at\n",
    "    https://stackoverflow.com/questions/41895857/creating-a-custom-interpolation-function-for-pandas\n",
    "    \"\"\"\n",
    "    # Extract into numpy array\n",
    "    vals = df.values.copy()\n",
    "\n",
    "    # Produce a mask of the elements that are NaN\n",
    "    empty = np.any(pd.isnull(vals), axis=1)\n",
    "\n",
    "    # Positions of the valid values\n",
    "    valid_loc = np.argwhere(~empty).squeeze(axis=-1)\n",
    "\n",
    "    # Indices (e.g. time) of the valid values\n",
    "    valid_index = df.index[valid_loc].values\n",
    "\n",
    "    # Positions of the missing values\n",
    "    empty_loc = np.argwhere(empty).squeeze(axis=-1)\n",
    "\n",
    "    # Discard missing values before first or after last valid\n",
    "    empty_loc = empty_loc[(empty_loc > valid_loc.min()) & (empty_loc < valid_loc.max())]\n",
    "\n",
    "    # Index value for missing values\n",
    "    empty_index = df.index[empty_loc].values\n",
    "\n",
    "    # Get valid values to use as interpolation ends for each missing value\n",
    "    interp_loc_end = np.searchsorted(valid_loc, empty_loc)\n",
    "    interp_loc_start = interp_loc_end - 1\n",
    "\n",
    "    # The indices (e.g. time) of the interpolation endpoints\n",
    "    interp_t_start = valid_index[interp_loc_start]\n",
    "    interp_t_end = valid_index[interp_loc_end]\n",
    "\n",
    "    # The share of the distance between the two endpoints represented by each index location\n",
    "    share_of_distance = (empty_index - interp_t_start)/(interp_t_end - interp_t_start)\n",
    "\n",
    "    # Now apply to values, 1 column at a time\n",
    "    newcolors = []\n",
    "    valsT = vals.transpose()\n",
    "    for column in valsT:\n",
    "\n",
    "        # Select the valid values\n",
    "        valid_vals = column[valid_loc]\n",
    "\n",
    "        # These are the actual values of the interpolation ends\n",
    "        interp_q_start = valid_vals[interp_loc_start]\n",
    "        interp_q_end = valid_vals[interp_loc_end]\n",
    "\n",
    "        newcolors.append(colorFader_array(interp_q_start, interp_q_end, mix=share_of_distance))\n",
    "\n",
    "    newcolors = np.array(newcolors)\n",
    "    newvals = newcolors.transpose()\n",
    "\n",
    "    # Put the interpolated values into place\n",
    "    interpolated_df = df.copy()\n",
    "    interpolated_df.iloc[empty_loc] = newvals\n",
    "\n",
    "    return interpolated_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad23b4eb",
   "metadata": {},
   "source": [
    "### 3. Application\n",
    "\n",
    "To illustrate the process, we'll create a df with some arbitrary colors, and create room to interpolate by adding blank intermediate rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3d25a9ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pick some arbitrary colours\n",
    "setofcolours1 = ['red', 'blue', 'yellow', 'gray', 'green']\n",
    "setofcolours2 = ['teal', 'darkviolet', 'orange', 'brown', 'ivory']\n",
    "\n",
    "# Set up the data frame\n",
    "df = pd.DataFrame(zip(setofcolours1, setofcolours2), columns=['colour1', 'colour2'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b5bcddf8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create blank rows inbetween\n",
    "emptyrowstoadd = 12\n",
    "df.index = df.index * (emptyrowstoadd + 1)\n",
    "new_index = range(df.index[-1] + 1)\n",
    "df = df.reindex(new_index)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "69ca9a1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replace the NaN values with interpolated colours\n",
    "newdf = interpolate_hexcolor_df(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27a4dc92",
   "metadata": {},
   "source": [
    "### 4. Test\n",
    "\n",
    "Draw simple plots to check that the interpolation worked."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e40b46d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 5))\n",
    "for x in newdf['colour1']:\n",
    "    ax.axvline(x, color=x, linewidth=6) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6023a33a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 5))\n",
    "for x in newdf['colour2']:\n",
    "    ax.axvline(x, color=x, linewidth=6) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f31335d1",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}