tutorial.ipynb

{
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   "cell_type": "markdown",
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   "source": [
    "# `get_cpt`: a python utility to fetch `matplotlib` colormaps from `.cpt` files\n",
    "\n",
    "**The goal:** To provide an easy, unified interface to load external and custom colormaps in `matplotlib`. The focus is on `.cpt` files, which are a common standard in external colormaps [[1]] and are frequently featured in colormap repos such as cpt-city [[2]].\n",
    "\n",
    "**The background:** Multiple tools to load `.cpt` files already exist (e.g. see here: [[3]],[[4]]). The most encapsulated effort is `pycpt` [[5]], a well-written package that is able to load `.cpt` files from multiple sources. However, this package was originally made for python 2.x and has certain limitations and, sometimes, added complexity for many user groups. \n",
    "\n",
    "**The functionality:**\n",
    "- To create a simple, flexible interface where `.cpt` files can be loaded, building from `pycpt`.\n",
    "- To utilise the robust engine of `pycpt` for parsing `.cpt` files, after some tweaks and updates. \n",
    "- To allow multiple use cases of easy `.cpt` file loads from the user (from full paths, from a preset repo, from a url). \n",
    "- To control and simplify the complexity `matplotlib` introduces with its multiple classes of colormaps, but offer added functionality to the user as well, in case he/she wants hex color lists, fewer colors, `ListedColormap` objects instead of `LinearSegmented` ones etc.\n",
    "\n",
    "**Requirements:**\n",
    "- python `3.7` or newer\n",
    "- numpy `1.18.1` or newer\n",
    "- matplotlib `3.1.3` or newer\n",
    "\n",
    "Also uses `urllib` for the url handles.\n",
    "\n",
    "\n",
    "[1]: https://en.wikipedia.org/wiki/CPT_(file_format)\n",
    "[2]: http://soliton.vm.bytemark.co.uk/pub/cpt-city/\n",
    "[3]: https://stackoverflow.com/questions/48322741/is-there-a-way-to-save-a-custom-matplotlib-colorbar-to-use-elsewhere\n",
    "[4]: https://scipy-cookbook.readthedocs.io/items/Matplotlib_Loading_a_colormap_dynamically.html\n",
    "[5]: https://github.com/j08lue/pycpt/blob/master/pycpt/load.py\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Installation\n",
    "\n",
    "The simplest way to install the `get_ctp` module is:\n",
    "a.) Download it from github and place files in a folder `path`\n",
    "b.) Add this folder to your `PYTHON PATH`, e.g. using `sys.path.append(path)`\n",
    "c.) Use `import` to load the module. \n",
    "\n",
    "<div class=\"alert alert-block alert-warning\">\n",
    "<b>Note:</b> make sure you have admin rights to the appended path.</div>\n",
    "\n",
    "<div class=\"alert alert-block alert-info\">\n",
    "<b>Note:</b> pip installation functionality will be added in a future version.</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# example path - change directory accordingly\n",
    "cptpath = r'D:\\Users\\bouzidi\\Desktop\\getcpt-master'\n",
    "\n",
    "import os, sys\n",
    "sys.path.append(cptpath)\n",
    "\n",
    "import get_cpt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Usage\n",
    "\n",
    "## 2.1. General usage \n",
    "\n",
    "`get_cpt` is a flexible module that allows you to obtain a `LinearSegmented` matplotlib colormap [[1]] from a `cpt path`. The flexibility of the module lies in that the path can be: \n",
    "- a full directory path that points to a locally stored `.cpt` file. \n",
    "- a `.cpt` file already stored in the local repository which comes preinstalled with the module. This repository is stored in the `basedir` attribute of the module. The local repo features dozens of popular .cpt files from `cpt-city`.\n",
    "- a url that points to a `.cpt` file from a repo, e.g. a `cpt-city` link.\n",
    "\n",
    "To do this, use the `get_cmap()` function. The function has extra optional arguments, allowing you to control the `matplotlib` method used and the number of colors returned. In any case, a `matplotlib.colors.LinearSegmentedColormap` object will be returned, ready to be used in `matplotlib` plotting tasks. Use `plot_cmaps()` to view it.\n",
    "\n",
    "\n",
    "**Note:** The `basedir` attribute can be reset (by resetting it, `get_cpt.basedir = ...`) to find other local repos.\n",
    "\n",
    "[1]: https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.colors.LinearSegmentedColormap.html "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "D:\\Users\\bouzidi\\Desktop\\getcpt-master\\cpt\n",
      "<matplotlib.colors.LinearSegmentedColormap object at 0x0000020A45869908>\n",
      "mby\n"
     ]
    },
    {
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      "text/plain": [
       "<Figure size 432x144 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(get_cpt.basedir) # the local repo\n",
    "myfile1 = 'mby.cpt'\n",
    "myfile2 = 'balance.cpt'\n",
    "\n",
    "# fetch from local repo\n",
    "cmap1 = get_cpt.get_cmap(myfile1)\n",
    "cmap2 = get_cpt.get_cmap(myfile2)\n",
    "\n",
    "# check what we fetched\n",
    "print(cmap1)\n",
    "print(cmap1.name)\n",
    "\n",
    "# plot\n",
    "import matplotlib.pyplot as plt\n",
    "fig = get_cpt.plot_cmaps([cmap1, cmap2], cmap_height=1, axes_off=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.2. Controlling the number of colors\n",
    "\n",
    "By employing the method `list` in `get_cmap()`, you can control the granularity of the returned colormap and draft from the actual colors contained in the `.cpt` file. Keep in mind that this is still a (more coarse) `LinearSegmented` colormap interpolation and not a mere list of colors. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<matplotlib.colors.LinearSegmentedColormap object at 0x0000020A45AF7608>\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x216 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "myurl_1 = 'http://soliton.vm.bytemark.co.uk/pub/cpt-city/gmt/GMT_globe.cpt'\n",
    "\n",
    "mygmt_cmaps = []\n",
    "\n",
    "for N in [5,10,20,64,128,256]:\n",
    "    mygmt_cmaps.append(get_cpt.get_cmap(myurl_1, method='list', N=N))\n",
    "    \n",
    "print(mygmt_cmaps[2]) # what we fetched\n",
    "\n",
    "get_cpt.plot_cmaps(mygmt_cmaps)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.3. Getting `ListedColormaps` and lists of colors from `.cpt` files.\n",
    "\n",
    "There are cases where you might not want a `LinearSegmented` colormap, but a simpler matplotlib construct, the `ListedColormap` [[2]]. Think of it as a list of colors, instead of linearly interpolated colors. Some `matplotlib` graphs actually prefer this over the `LinearSegmented` colormap. `get_cpt` offers this functionality using the `get_listed_cmap()` function. This function actually returns two arguments: \n",
    "\n",
    "```\n",
    "pos, cmap = get_cpt.get_listed_cmap(cptpath)\n",
    "```\n",
    "\n",
    "- `cmap` is your `ListedColormap` object. \n",
    "- `pos` is a list with the relative (normalised) positions of each color, potentially useful for redefining the colormap limits in your plots.\n",
    "\n",
    "To get to the list of colors of that particular colormap, simply use the `cmap.colors` attribute. \n",
    "\n",
    "[2]: https://matplotlib.org/3.2.0/api/_as_gen/matplotlib.colors.ListedColormap.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x36 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'matplotlib.colors.ListedColormap'>\n",
      "['#7d55a5', '#9763ac', '#b074b2', '#c686b9', '#d69abf', '#e0b0c8', '#e3c7d2', '#dfdfdf', '#eed8b9', '#f7cc93', '#f9bc70', '#f6a851', '#ed9036', '#e07822', '#cd6112']\n",
      "[0.0, 0.07142857142857142, 0.14285714285714285, 0.21428571428571427, 0.2857142857142857, 0.35714285714285715, 0.42857142857142855, 0.5, 0.5714285714285714, 0.6428571428571429, 0.7142857142857143, 0.7857142857142857, 0.8571428571428571, 0.9285714285714286, 1.0]\n",
      "Colors fetched:  15\n",
      "Color positions:  15\n"
     ]
    }
   ],
   "source": [
    "myurl_2 = 'http://soliton.vm.bytemark.co.uk/pub/cpt-city/km/purple-orange-d15.cpt'\n",
    "\n",
    "pos, cmap_po = get_cpt.get_listed_cmap(myurl_2)\n",
    "\n",
    "get_cpt.plot_cmaps(cmap_po, axes_off=True)\n",
    "plt.show()\n",
    "\n",
    "print(type(cmap_po))\n",
    "po_colors = cmap_po.colors # get the hex colors of the palette\n",
    "print(po_colors) # hex colors\n",
    "print(pos) # normalised positions of colors\n",
    "print(\"Colors fetched: \", len(po_colors))\n",
    "print(\"Color positions: \", len(pos))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Getting a subset of the colormap colors for `ListedColormap` objects.\n",
    "\n",
    "You can also control the colors fetched in that case, and get only a subset of the colors included in the file. Just employ the `N` parameter of the `get_listed_cmap()` function to a number smaller than the colors contained in the `.cpt` file. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x540 with 15 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mypo_cmaps = []\n",
    "\n",
    "Nmax = len(pos)\n",
    "\n",
    "for N in range(1,Nmax+1):\n",
    "    _, cmap = get_cpt.get_listed_cmap(myurl_2, N=N)\n",
    "    mypo_cmaps.append(cmap)\n",
    "    \n",
    "get_cpt.plot_cmaps(mypo_cmaps, axes_off=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.4. Using the output\n",
    "\n",
    "The output can be used whenever a `matplotlib.colors.colormap` object of the same type would be expected. Experiment with your fetches and enjoy!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# example\n",
    "# https://seaborn.pydata.org/generated/seaborn.kdeplot.html\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "ax = plt.figure(figsize=(12,10))\n",
    "\n",
    "iris = sns.load_dataset(\"iris\")\n",
    "setosa = iris.loc[iris.species == \"setosa\"]\n",
    "virginica = iris.loc[iris.species == \"virginica\"]\n",
    "# a standard matplotlib colormap\n",
    "ax = sns.kdeplot(setosa.sepal_width, setosa.sepal_length,\n",
    "                 cmap='Reds', n_levels=12, shade=True, shade_lowest=False, cbar=True)\n",
    "# the one we fetched\n",
    "ax = sns.kdeplot(virginica.sepal_width, virginica.sepal_length,\n",
    "                 cmap=cmap_po, n_levels=12, shade=True, shade_lowest=False, cbar=True)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Epilogue\n",
    "\n",
    "Thanks go to:\n",
    "- the creator of the original `pycpt` package (https://github.com/j08lue/pycpt). \n",
    "- `dnikolop` (https://github.com/dionikolop) for his tests.\n",
    "\n",
    "Future versions of this package will:\n",
    "1. Adress unavoidable issues and bugs, as well as edge cases. \n",
    "2. Potentially offer functionality to load other colormap file templates (ideas welcome).\n",
    "3. Increase the QoL of that package, e.g. with functions to see the info contained in `.cpt` files etc. \n",
    "4. Align with changes and development in `pycpt` as well. \n",
    "\n",
    "Every modification or enhancement/redevelopment of the code is welcome. If you would like to use this module to your work, please refer to its Zenodo DOI (https://zenodo.org/record/3703160#.XmfGXqhKiUk), as well as the original pycpt package (https://github.com/j08lue/pycpt)."
   ]
  }
 ],
 "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}