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   "source": [
    "%reload_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import excolor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Show Colormap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# List qualitative colormaps\n",
    "excolor.list_qualitative_cmaps()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# Show \"rtd\" colormap\n",
    "excolor.show_cmap(\"rtd\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Show Colors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get colors from colormap\n",
    "colors = excolor.get_colors(\"rtd\", n=24)\n",
    "\n",
    "# Show colors\n",
    "excolor.show_colors(colors)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generate palette from a seed color\n",
    "\n",
    "Mode \"superellipse\" defines the path from black to white. Higher degree produces more vibrant colors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Seed color\n",
    "color = '#70B3E0'\n",
    "\n",
    "# Generate palette\n",
    "colors = excolor.generate_palette(color, mode='superellipse', power=3)\n",
    "\n",
    "# Show colors\n",
    "excolor.show_colors(colors)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generate primary colors palette from the same color"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Seed color\n",
    "color = '#70B3E0'\n",
    "\n",
    "# Generate a palette for primary CSS colors\n",
    "primary_colors = excolor.generate_primary_palette(color)\n",
    "\n",
    "# Show primary colors\n",
    "excolor.show_colors(primary_colors)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generate background colors palette from the same color"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Seed color\n",
    "color = '#70B3E0'\n",
    "\n",
    "# Generate a palette for background CSS colors\n",
    "background_colors = excolor.generate_background_palette(color)\n",
    "\n",
    "# Show background colors\n",
    "excolor.show_colors(background_colors)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generate foreground colors palette from the same color"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Seed color\n",
    "color = '#70B3E0'\n",
    "\n",
    "# Generate a palette for foreground CSS colors\n",
    "foreground_colors = excolor.generate_foreground_palette(color)\n",
    "\n",
    "# Show foreground colors\n",
    "excolor.show_colors(foreground_colors)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set colorcycler for a plot\n",
    "\n",
    "By default sets cycler for the current matplotlib ax. To set for current python session use parameter: `globally=True`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate sample data\n",
    "x = np.arange(7)\n",
    "y1 = [1, 4, 9, 5, 2, 1, 1]\n",
    "y2 = [2, 3, 4, 3, 4, 5, 3]\n",
    "y3 = [4, 5, 5, 7, 9, 8, 6]\n",
    "\n",
    "# List of data and labels\n",
    "labels = ['Series A', 'Series B', 'Series C']\n",
    "data = [y1, y2, y3]\n",
    "\n",
    "# Show data\n",
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "# SET COLOR CYCLER HERE\n",
    "excolor.set_color_cycler(\"rtd\")\n",
    "\n",
    "# Plot data\n",
    "for i, y in enumerate(data):\n",
    "    plt.plot(x, y, label=labels[i], linewidth=3)\n",
    "    plt.scatter(x, y, s = 100)\n",
    "\n",
    "# Add labels and legend\n",
    "plt.title(\"Combined Line, Scatter, and Fill Plot\")\n",
    "plt.xlabel(\"X Axis\")\n",
    "plt.ylabel(\"Y Axis\")\n",
    "plt.grid(True, linestyle='--', alpha=0.3)\n",
    "plt.legend()\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Log-scaled colormap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import and generate perlin noise array\n",
    "from pythonperlin import perlin\n",
    "\n",
    "dens = 32\n",
    "shape = (8,8)\n",
    "x = perlin(shape, dens=dens)\n",
    "\n",
    "# Log-scaled colormap\n",
    "log_cmap = excolor.logscale_cmap(\"rtd\")\n",
    "\n",
    "# Apply log-scaled colormap\n",
    "plt.figure(figsize=(6,6), facecolor=\"#00000000\")\n",
    "plt.imshow(np.abs(x), cmap=log_cmap)\n",
    "plt.axis(\"off\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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