Merge pull request #95 from juliahuynh/master

widget border width

Author: juliahuynh

demo/census.ipynb

@@ -2,7 +2,11 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "presto": {
+     "id": "295d3ee9-1a00-46bb-a522-801bb1fb622d"
+    }
+   },
    "source": [
     "## 2010 US Census data\n",
     "\n",
@@ -21,6 +25,9 @@
     "collapsed": true,
     "jupyter": {
      "outputs_hidden": true
+    },
+    "presto": {
+     "id": "09bf1448-1e58-48a1-a857-5976e8508ca5"
     }
    },
    "outputs": [],
@@ -55,7 +62,11 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {},
+   "metadata": {
+    "presto": {
+     "id": "a0ae1770-16ec-457b-be52-56cfa21ea3b1"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -357,7 +368,11 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "metadata": {},
+   "metadata": {
+    "presto": {
+     "id": "796962e7-c06b-4513-b7e2-63461cc983a3"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -447,7 +462,11 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "presto": {
+     "id": "b30ed6fc-7a29-407b-97ca-28bd88747fdf"
+    }
+   },
    "source": [
     "(Histogram equalization also works for non-integer data, but in that case it will use a finite set of bins to divide the interval between the minimum and maximum values, and will thus not be able to normalize the histogram perfectly for highly non-uniform distributions.)  Effectively, this transformation converts the data from raw magnitudes, which can easily span a much greater range than the dynamic range visible to the eye, to a rank-order or percentile representation, which reveals density differences at all ranges but obscures the absolute magnitudes that were used to compute the ordering. In this representation, you can clearly see the effects of geography (rivers, coastlines, and mountains) on the population density, as well as history (denser near the longest-populated areas), and even infrastructure (with many small towns located at crossroads).\n",
     "\n",
@@ -3025,7 +3044,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.3"
+   "version": "3.8.5"
   },
   "presto": {
    "id": "1c9191a6-2e25-4bb0-90f0-68fe8ecbe7f5"

style/variables.css

@@ -88,7 +88,7 @@ all of MD as it is not optimized for dense, information rich UIs.
    * The following variables, specify the visual styling of borders in JupyterLab.
    */
 
-  --jp-border-width: 1px;
+  --jp-border-width: 2px;
   --jp-border-color0: var(--md-grey-700);
   --jp-border-color1: var(--md-grey-700);
   --jp-border-color2: var(--md-grey-800);