samila/examples/demo.ipynb

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   "source": [
    "# Samila Demo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Version : 1.4\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "try:\n",
    "  import google.colab\n",
    "  !{sys.executable} -m pip -q -q install samila\n",
    "except:\n",
    "  pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import random\n",
    "import math\n",
    "from samila import GenerativeImage, Projection, Marker, GenerateMode"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Magic\n",
    "If we create a `GenerativeImage` instance with no input parameters, we will get a plot generated from two random equations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "g1 = GenerativeImage()\n",
    "g1.generate()\n",
    "g1.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Assigning Functions\n",
    "By defining `f1` and `f2`, we can control the general shape of the plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def f1(x, y):\n",
    "    result = random.uniform(-1,1) * x**2  - math.sin(y**2) + abs(y-x)\n",
    "    return result\n",
    "\n",
    "def f2(x, y):\n",
    "    result = random.uniform(-1,1) * y**3 - math.cos(x**2) + 2*x\n",
    "    return result\n",
    "\n",
    "g2 = GenerativeImage(f1, f2)\n",
    "g2.generate()\n",
    "g2.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generation Mode"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have the option to select from various generation modes.\n",
    "\n",
    "ℹ️ The avaliable modes are `F1_VS_F2`, `F2_VS_F1`, `F1_VS_INDEX`, `F2_VS_INDEX`, `INDEX_VS_F1`, `INDEX_VS_F2`, `F1_VS_X1`, `F1_VS_X2`, `F2_VS_X1`, `F2_VS_X2`, `X1_VS_F1`, `X1_VS_F2`, `X2_VS_F1` and `X2_VS_F2`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ℹ️ Default mode is `F1_VS_F2`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "g2 = GenerativeImage(f1, f2)\n",
    "\n",
    "for mode in list(GenerateMode):\n",
    "    print(mode)\n",
    "    g2.generate(mode=mode)\n",
    "    g2.plot()\n",
    "    plt.show()\n",
    "    plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Projection\n",
    "We can use the `projection` attribute to define the coordinate system to transform our functions.\n",
    "\n",
    "ℹ️ The avaliable projections are `RECTILINEAR`, `POLAR`, `AITOFF`, `HAMMER`, `LAMBERT`, `MOLLWEIDE` and `RANDOM`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ℹ️ Default projection is `RECTILINEAR`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "g2 = GenerativeImage(f1, f2)\n",
    "g2.generate()\n",
    "\n",
    "for projection in list(Projection):\n",
    "    print(projection)\n",
    "    g2.plot(projection=projection)\n",
    "    plt.show()\n",
    "    plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Marker\n",
    "We can use the `marker` attribute to change the plotting marker.\n",
    "\n",
    "ℹ️ The available markers are `POINT`, `PIXEL`, `CIRCLE`, `TRIANGLE_DOWN`, `TRIANGLE_UP`, `TRIANGLE_LEFT`, `TRIANGLE_RIGHT`, `TRI_DOWN`, `TRI_UP`, `TRI_LEFT`, `TRI_RIGHT`, `OCTAGON`, `SQUARE`, `PENTAGON`, `PLUS`, `PLUS_FILLED`, `STAR`, `HEXAGON_VERTICAL`, `HEXAGON_HORIZONTAL`, `X`, `X_FILLED`, `DIAMOND`, `DIAMON_THIN`, `VLINE`, `HLINE` and `RANDOM`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ℹ️ Default marker is `POINT`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "g2 = GenerativeImage(f1, f2)\n",
    "g2.generate()\n",
    "\n",
    "for m in list(Marker):\n",
    "    print(m)\n",
    "    g2.plot(marker=m, spot_size=100)\n",
    "    plt.show()\n",
    "    plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Rotation\n",
    "You can even rotate your art by using `rotation` parameter. Enter your desired rotation for the image in degrees and you will have it.\n",
    "\n",
    "ℹ️ Default rotation is `0`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "g = GenerativeImage(f1, f2)\n",
    "g.generate()\n",
    "g.plot(rotation=45)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Range\n",
    "\n",
    "Control the range over which the input values span."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ℹ️ Default range is $(-\\pi, \\pi)$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "g2 = GenerativeImage(f1, f2)\n",
    "g2.generate(start=-1.5*math.pi, step=0.007, stop=0)\n",
    "g2.plot(projection=Projection.POLAR)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Color\n",
    "\n",
    "We can assign colors for both the background as well as the line.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ℹ️ Default color is `black`\n",
    "\n",
    "ℹ️ Default background-color is `white`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "g2 = GenerativeImage(f1, f2)\n",
    "g2.generate()\n",
    "g2.plot(color=\"maroon\", bgcolor=\"antiquewhite\", projection=Projection.POLAR)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ℹ️ Supported colors are available in `VALID_COLORS` list\n",
    "\n",
    "ℹ️ `color` and `bgcolor` parameters supported formats:\n",
    "\n",
    "    1. Color name (example: `color=\"yellow\"`)\n",
    "    2. RGB/RGBA (example: `color=(0.1,0.1,0.1)`, `color=(0.1,0.1,0.1,0.1)`)\n",
    "    3. Hex (example: `color=\"#eeefff\"`)\n",
    "    4. Random (example: `color=\"random\"`)\n",
    "    5. Complement (example: `color=\"complement\", bgcolor=\"blue\"`)\n",
    "    6. Transparent (example: `bgcolor=\"transparent\"`)\n",
    "    7. List (example: `color=[\"black\", \"#fffeef\",...]`)\n",
    "\n",
    "⚠️ **Transparent** mode is only available for background\n",
    "\n",
    "⚠️ **List** mode is only available for color\n",
    "\n",
    "⚠️ In **List** mode, the length of this list must be equal to the lengths of data1 and data2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Point Color\n",
    "\n",
    "You can make your custom color map and use it in Samila."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "colorarray = [\n",
    "    [0.7, 0.2, 0.2, 1],\n",
    "    [0.6, 0.3, 0.2, 1],\n",
    "    \"black\",\n",
    "    [0.4, 0.4, 0.3, 1],\n",
    "    [0.3, 0.4, 0.4, 1],\n",
    "    \"#ff2561\"]\n",
    "g2.generate()\n",
    "g2.plot(cmap=colorarray, color=g2.data2, projection=Projection.POLAR)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Spot Size\n",
    "\n",
    "We can also change the size of the plot marker."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ℹ️ Default spot-size is `0.01`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "g2 = GenerativeImage(f1, f2)\n",
    "g2.generate()\n",
    "g2.plot(color=\"yellow\", bgcolor=\"darkslategray\", spot_size=0.25)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Seeds\n",
    "If we do not pass in a seed when calling `generate`, a random seed will be created for us.\n",
    "\n",
    "Passing this seed into `generate` will guarantee plot reproducibility."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "seed = g2.seed\n",
    "print(seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "g2 = GenerativeImage(f1, f2)\n",
    "g2.generate(seed)\n",
    "g2.plot(color=\"yellow\", bgcolor=\"darkslategray\", spot_size=0.05)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Saving Images\n",
    "\n",
    "Save generated images.\n",
    "\n",
    "Use the `depth` attribute to increase the resolution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "g2.save_image(file_adr=\"test.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "g2.save_image(file_adr=\"test_high_res.png\", depth=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Saving Data\n",
    "\n",
    "Save generated image data.\n",
    "\n",
    "It can then be loaded into a `GenerativeImage` instance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "g2.save_data(file_adr=\"data.json\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "g2 = GenerativeImage(data=open('data.json', 'r'))\n",
    "g2.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Saving Config\n",
    "\n",
    "Save generated image config. It contains string formats of functions which is also human readable.\n",
    "\n",
    "It can then be loaded into a `GenerativeImage` instance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "g1.save_config(file_adr=\"config.json\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "g1 = GenerativeImage(config=open('config.json', 'r'))\n",
    "g1.generate()\n",
    "g1.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## NFT.storage"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Upload generated image directly to [NFT.storage](https://nft.storage)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "g1.nft_storage(api_key=\"YOUR_API_KEY\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "g1.nft_storage(api_key=\"YOUR_API_KEY\", timeout=5000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also upload your config/data to nft storage as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "g1.nft_storage(api_key=\"YOUR_API_KEY\", upload_config=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "g1.nft_storage(api_key=\"YOUR_API_KEY\", upload_data=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "⚠️ This method is deprecated and may be removed in future releases\n",
    "\n",
    "ℹ️ Default timeout is `3000` seconds\n",
    "\n",
    "ℹ️ Default gateway is `IPFS_IO`"
   ]
  }
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