Add HexTiles element

examples/gallery/demos/bokeh/hextile_movie_ratings.ipynb

@@ -0,0 +1,64 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import holoviews as hv\n",
+    "hv.extension('bokeh')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Define data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sqlite3\n",
+    "from bokeh.sampledata import movies_data\n",
+    "\n",
+    "# Load data\n",
+    "conn = sqlite3.connect(movies_data.movie_path)\n",
+    "movies = pd.read_sql('SELECT userRating, imdbRating FROM omdb, tomatoes WHERE omdb.ID = tomatoes.ID', conn)\n",
+    "\n",
+    "# Declare element\n",
+    "hextiles = hv.HexTiles(movies, [('userRating', 'Tomato User Rating'), ('imdbRating', 'IMDb Rating')], [])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plot data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "hextiles.options(width=500, height=500, min_count=0) * hv.Bivariate(hextiles).options(show_legend=False)"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python",
+   "pygments_lexer": "ipython3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

examples/gallery/demos/matplotlib/hextile_movie_ratings.ipynb

@@ -0,0 +1,65 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import holoviews as hv\n",
+    "hv.extension('matplotlib')\n",
+    "%output fig='svg' size=200"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Define data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sqlite3\n",
+    "from bokeh.sampledata import movies_data\n",
+    "\n",
+    "# Load data\n",
+    "conn = sqlite3.connect(movies_data.movie_path)\n",
+    "movies = pd.read_sql('SELECT userRating, imdbRating FROM omdb, tomatoes WHERE omdb.ID = tomatoes.ID', conn)\n",
+    "\n",
+    "# Declare element\n",
+    "hextiles = hv.HexTiles(movies, [('userRating', 'Tomato User Rating'), ('imdbRating', 'IMDb Rating')], [])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plot data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "hextiles.options(min_count=0) * hv.Bivariate(hextiles)"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python",
+   "pygments_lexer": "ipython3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

examples/reference/elements/bokeh/HexTiles.ipynb

@@ -0,0 +1,116 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### **Title**: HexTiles Element\n",
+    "\n",
+    "**Dependencies** Bokeh\n",
+    "\n",
+    "**Backends** [Bokeh](./HexTiles.ipynb), [Matplotlib]('../matplotlib/HexTiles.ipynb')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import holoviews as hv\n",
+    "hv.extension('bokeh')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "``HexTiles`` provide a representation of the data as a bivariate histogram useful for visualizing the structure in larger datasets. The ``HexTiles`` are computed by tesselating the x-, y-ranges of the data, storing the number of points falling in each hexagonal bin. By default ``HexTiles`` simply counts the data in each bin but it also supports weighted aggregations. Currently only linearly spaced bins are supported when using the bokeh backend.\n",
+    "\n",
+    "As a simple example we will generate 100,000 normally distributed points and plot them using ``HexTiles``:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.random.seed(44)\n",
+    "hex_tiles = hv.HexTiles(np.random.randn(100000, 2))\n",
+    "hex_tiles.options(width=500, height=400, tools=['hover'],\n",
+    "                  colorbar=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that the hover shows a ``Count`` by default, representing the number of points falling within each bin.\n",
+    "\n",
+    "It is also possible to provide additional value dimensions and define an ``aggregator`` to aggregate by value. If multiple value dimensions are defined the dimension to be colormapped may be defined using the ``color_index``. Note however that multiple value dimensions are allowed and will be displayed in the hover tooltip."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "xs, ys = np.random.randn(2, 1000)\n",
+    "hex_with_values = hv.HexTiles((xs, ys, xs*(ys/2.), (xs**2)*ys), vdims=['Values', 'Hover Values'])\n",
+    "\n",
+    "hex_with_values.options(width=400, height=400, aggregator=np.sum, tools=['hover']) *\\\n",
+    "hv.Points(hex_with_values).options(size=1, color='black')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The bokeh backend also supports scaling the size of each hexagon by a value, which can be enabled using the ``size_index`` option. The minimum and maximum scaling can be set using the ``min_scale`` and ``max_scale`` options, defining how large the bins are relative to the size of a hexagon under uniform tiling.\n",
+    "\n",
+    "Here we will generate two separate distributions and visualize them by scaling each bin by the 'Count'."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = np.hstack([np.random.randn(2, 10000), np.random.randn(2, 10000)*0.8+2])\n",
+    "hex_two_distributions = hv.HexTiles((x, y))\n",
+    "hex_two_distributions.options(size_index='Count', width=500, height=500, min_scale=0.3, max_scale=0.85)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "By default ``HexTiles`` do not plot bins that do not contain any data but using the ``min_count`` option it is possible to plot bins with zero values as well or alternatively set a higher cutoff. Using this options can result in a more visually appealing plot particularly when overlaid with other elements.\n",
+    "\n",
+    "Here we will plot the same distributions as above and overlay a ``Bivariate`` plot of the same data:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "hex_two_distributions.options(min_count=0, width=500, height=500, tools=['hover']) *\\\n",
+    "hv.Bivariate(hex_two_distributions).options(show_legend=False, line_width=3)"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python",
+   "pygments_lexer": "ipython3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

examples/reference/elements/matplotlib/HexTiles.ipynb

@@ -0,0 +1,98 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### **Title**: HexTiles Element\n",
+    "\n",
+    "**Dependencies** Matplotlib\n",
+    "\n",
+    "**Backends** [Matplotlib](./HexTiles.ipynb), [Bokeh]('../bokeh/HexTiles.ipynb')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import holoviews as hv\n",
+    "hv.extension('matplotlib')\n",
+    "%output fig='svg' size=200"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "``HexTiles`` provide a representation of the data as a bivariate histogram useful for visualizing the structure in larger datasets. The ``HexTiles`` are computed by tesselating the x-, y-ranges of the data, storing the number of points falling in each hexagonal bin. By default ``HexTiles`` simply counts the data in each bin but it also supports weighted aggregations. Currently only linearly spaced bins are supported when using the bokeh backend.\n",
+    "\n",
+    "As a simple example we will generate 100,000 normally distributed points and plot them using ``HexTiles``:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.random.seed(44)\n",
+    "hex_tiles = hv.HexTiles(np.random.randn(100000, 2))\n",
+    "hex_tiles.options(colorbar=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that the hover shows a ``Count`` by default, representing the number of points falling within each bin.\n",
+    "\n",
+    "It is also possible to provide additional value dimensions and define an ``aggregator`` to aggregate by value. If multiple value dimensions are defined the dimension to be colormapped may be defined using the ``color_index``. Note however that multiple value dimensions are allowed and will be displayed in the hover tooltip."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "xs, ys = np.random.randn(2, 1000)\n",
+    "hex_with_values = hv.HexTiles((xs, ys, xs*(ys/2.), (xs**2)*ys), vdims=['Values', 'Hover Values'])\n",
+    "\n",
+    "hex_with_values.options(aggregator=np.sum) *\\\n",
+    "hv.Points(hex_with_values).options(s=3, color='black')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "By default ``HexTiles`` do not plot bins that do not contain any data but using the ``min_count`` option it is possible to plot bins with zero values as well or alternatively set a higher cutoff. Using this options can result in a more visually appealing plot particularly when overlaid with other elements.\n",
+    "\n",
+    "Here we will plot the same distributions as above and overlay a ``Bivariate`` plot of the same data:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = np.hstack([np.random.randn(2, 10000), np.random.randn(2, 10000)*0.8+2])\n",
+    "hex_two_distributions = hv.HexTiles((x, y))\n",
+    "hex_two_distributions.options(min_count=0) *\\\n",
+    "hv.Bivariate(hex_two_distributions).options(show_legend=False, linewidth=3)"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python",
+   "pygments_lexer": "ipython3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}