Expand colour scale example

docs/tutorials/colour-scales.ipynb

@@ -154,7 +154,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Now, if we want to make the colour of the flow to be proportional to a numerical value. Use the `hue` parameter to set the name of the variable that you want to display in colour. To start off, let's use \"value\", which is the width of the lines: wider lines will be shown in a darker colour."
+    "Now, if we want to make the colour of the flow to be proportional to a numerical value."
    ]
   },
   {
@@ -290,10 +290,10 @@
     "        super().__init__(attr)\n",
     "        self.threshold = threshold\n",
     "        \n",
-    "    def get_color(self, link, value):\n",
-    "        if value < self.threshold:\n",
+    "    def get_color(self, link, normalised_value):\n",
+    "        if normalised_value < self.threshold:\n",
     "            return '#ddd'\n",
-    "        return super().get_color(link, value)"
+    "        return super().get_color(link, normalised_value)"
    ]
   },
   {
@@ -330,6 +330,87 @@
     "        .to_widget(**size_options)\n",
     "    w.links = w_new.links"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This colour scale decides whether to choose a grey colour based on the normalised value (within a range of 0 to 1) which is used to lookup a colour in the colour scale.\n",
+    "\n",
+    "Alternatively, you could intervene based on the absolute value:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class DimmingScaleAbsolute(QuantitativeScale):\n",
+    "    def __init__(self, attr, threshold, **kwargs):\n",
+    "        super().__init__(attr)\n",
+    "        self.threshold = threshold\n",
+    "        \n",
+    "    def __call__(self, link, measures):\n",
+    "        value = self.get_value(link, measures)\n",
+    "        if value < self.threshold:\n",
+    "            return '#ddd'\n",
+    "        return super().__call__(link, measures)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "my_scale3 = DimmingScaleAbsolute('Calories Burnt', threshold=2, palette='Blues_9')\n",
+    "weave(sdd, dataset, measures='Calories Burnt', link_color=my_scale3) \\\n",
+    "    .to_widget(**size_options)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A similar approach can be used with a `CategoricalScale` as well as a `QuantitativeScale`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class DimmingCategoricalScale(CategoricalScale):\n",
+    "    def __init__(self, attr, threshold_measure, threshold_value, **kwargs):\n",
+    "        \"\"\"Acts like CategoricalScale unless threshold_measure is below threshold_value.\"\"\"\n",
+    "        super().__init__(attr)\n",
+    "        self.threshold_measure = threshold_measure\n",
+    "        self.threshold_value = threshold_value\n",
+    "        \n",
+    "    def __call__(self, link, measures):\n",
+    "        value = measures[self.threshold_measure]\n",
+    "        if value < self.threshold_value:\n",
+    "            return '#ddd'\n",
+    "        return super().__call__(link, measures)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "my_scale3 = DimmingCategoricalScale(\n",
+    "    'type',\n",
+    "    threshold_measure='Calories Burnt', \n",
+    "    threshold_value=6, \n",
+    "    palette='Blues_9'\n",
+    ")\n",
+    "weave(sdd, dataset, measures='Calories Burnt', link_color=my_scale3) \\\n",
+    "    .to_widget(**size_options)"
+   ]
   }
  ],
  "metadata": {