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Update internet speeds example (#129)
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‎examples/internet-speeds.ipynb

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"import shapely\n",
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"from palettable.colorbrewer.diverging import BrBG_10\n",
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"\n",
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"from lonboard import ScatterplotLayer\n",
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"from lonboard import ScatterplotLayer, viz\n",
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"from lonboard.colormap import apply_continuous_cmap"
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]
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},
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},
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{
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"cell_type": "markdown",
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"id": "63537833-887d-4d40-acd2-8e212dc0412c",
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"id": "65436a4a-c498-4f40-ba79-1082062376bf",
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"metadata": {},
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"source": [
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"This is all we need to get data onto the map! Let's render a simple `ScatterplotLayer`, drawing each point as blue:"
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"To ensure that this demo is snappy on most computers, we'll filter to a bounding box over Europe."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"id": "498d466e-0f9f-497a-b4b7-8e274dffd9cf",
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"id": "80326895-70ba-4f4b-a7b3-106b4bbd36d9",
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"metadata": {},
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"outputs": [],
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"source": [
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"gdf = gdf.cx[-11.83:25.5, 34.9:59]"
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]
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},
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{
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"cell_type": "markdown",
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"id": "3cc2215e-7706-4ab3-b674-3de4ca41899c",
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"metadata": {},
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"source": [
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"Even this filtered data frame still has 800,000 rows, so it's still a lot of data to explore:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"id": "a34a6a27-0259-4da9-94c4-923466da05fb",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>avg_d_kbps</th>\n",
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" <th>geometry</th>\n",
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" </tr>\n",
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" <tbody>\n",
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" <th>383429</th>\n",
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" <td>13570</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>...</th>\n",
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" <td>POINT (25.45807 34.99175)</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"<p>807221 rows × 2 columns</p>\n",
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"</div>"
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],
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"text/plain": [
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" avg_d_kbps geometry\n",
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"... ... ...\n",
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"\n",
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"[807221 rows x 2 columns]"
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]
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},
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"execution_count": 9,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"gdf"
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]
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},
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{
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"cell_type": "markdown",
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"id": "81a61ec4-2a09-40c0-aa92-7dca570bbd49",
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"metadata": {},
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"source": [
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"The simplest way to get data on the map is to use `viz`:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"id": "326f5f4e-a8f7-425b-8e9f-6744ba65cf62",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "bcbf7e3fa1bc4dc3ac46bbf3f97d48da",
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"model_id": "30ba30dd2b9f4d47b307fa98ea9bf896",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"ScatterplotLayer(get_fill_color=[0, 0, 200, 30], table=pyarrow.Table\n",
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"geometry: fixed_size_list<item: double>[2…"
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"ScatterplotLayer(table=pyarrow.Table\n",
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"avg_d_kbps: int64\n",
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"__index_level_0__: int64\n",
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"geometry: fixed_size_list<item…"
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]
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},
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"execution_count": 10,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"map_ = viz(gdf)\n",
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"map_"
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]
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},
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{
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"cell_type": "markdown",
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"id": "91af2a88-9363-4c00-8e54-f06a635ff991",
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"metadata": {},
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"source": [
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"This map object is a `ScatterplotLayer` type. You could have created the same map by using\n",
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"```py\n",
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"map_ = lonboard.ScatterplotLayer.from_geopandas(gdf)\n",
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"```"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"id": "7d0f6f03-ca11-4842-8e53-4a619ad7d3dd",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"lonboard.layer.ScatterplotLayer"
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]
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},
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"execution_count": 8,
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"execution_count": 11,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"layer = ScatterplotLayer.from_geopandas(gdf[[\"geometry\"]], get_fill_color=[0, 0, 200, 30])\n",
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"layer"
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"type(map_)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "6f4d89c3-282a-4beb-9f35-68eb9645e8c0",
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"metadata": {},
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"source": [
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"We can look at the [documentation for `ScatterplotLayer`](https://developmentseed.org/lonboard/api/layers/scatterplot-layer/) to see what other rendering options it allows. Let's set the fill color to something other than black:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"id": "3912b241-577f-4ac3-b78f-2702e89d6010",
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"metadata": {},
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"outputs": [],
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"source": [
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"map_.get_fill_color = [0, 0, 200, 200]"
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]
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},
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{
@@ -258,18 +454,18 @@
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"id": "ce630455-3e19-47f1-bb69-81fdcd99b126",
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"metadata": {},
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"source": [
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"Here we compute a linear statistic for the download speed. Given a minimum bound of `1000` and a maximum bound of `30,000` the normalized speed is linearly scaled to between 0 and 1."
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"Here we compute a linear statistic for the download speed. Given a minimum bound of `5000` and a maximum bound of `50,000` the normalized speed is linearly scaled to between 0 and 1."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"execution_count": 13,
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"id": "179071b3",
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"metadata": {},
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"outputs": [],
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"source": [
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"min_bound = 1000\n",
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"max_bound = 30000\n",
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"min_bound = 5000\n",
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"max_bound = 50000\n",
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"download_speed = gdf['avg_d_kbps']\n",
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"normalized_download_speed = (download_speed - min_bound) / (max_bound - min_bound)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"execution_count": 14,
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"id": "a8df3963-2bc2-4f89-8a38-20e232a13932",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"0 0.171828\n",
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"1 0.094759\n",
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"383433 0.138133\n",
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" ... \n",
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"3231242 0.887828\n",
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"3231243 2.310172\n",
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"Name: avg_d_kbps, Length: 3231245, dtype: float64"
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"execution_count": 14,
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"metadata": {},
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"output_type": "execute_result"
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{
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"cell_type": "code",
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"execution_count": 11,
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"execution_count": 15,
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"id": "9d5347e2-84c7-40bc-af45-c8638188709e",
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"metadata": {},
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"outputs": [
@@ -335,10 +531,10 @@
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"<div style=\"vertical-align: middle;\"><strong>BrBG</strong> </div><div class=\"cmap\"><img alt=\"BrBG colormap\" title=\"BrBG\" style=\"border: 1px solid #555;\" src=\"data:image/png;base64,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\"></div><div style=\"vertical-align: middle; max-width: 514px; display: flex; justify-content: space-between;\"><div style=\"float: left;\"><div title=\"#543005ff\" style=\"display: inline-block; width: 1em; height: 1em; margin: 0; vertical-align: middle; border: 1px solid #555; background-color: #543005ff;\"></div> under</div><div style=\"margin: 0 auto; display: inline-block;\">bad <div title=\"#00000000\" style=\"display: inline-block; width: 1em; height: 1em; margin: 0; vertical-align: middle; border: 1px solid #555; background-color: #00000000;\"></div></div><div style=\"float: right;\">over <div title=\"#003c30ff\" style=\"display: inline-block; width: 1em; height: 1em; margin: 0; vertical-align: middle; border: 1px solid #555; background-color: #003c30ff;\"></div></div>"
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],
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"text/plain": [
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"<matplotlib.colors.LinearSegmentedColormap at 0x156515290>"
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"<matplotlib.colors.LinearSegmentedColormap at 0x1105c2950>"
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]
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},
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"execution_count": 11,
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"execution_count": 15,
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"metadata": {},
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"output_type": "execute_result"
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}
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},
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{
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"cell_type": "code",
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"execution_count": 19,
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"execution_count": 16,
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"id": "5a77f728-9cbe-4372-9bfd-d6dee4b93a01",
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"metadata": {},
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"outputs": [],
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"source": [
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"layer.get_fill_color = apply_continuous_cmap(normalized_download_speed, BrBG_10, alpha=0.3)"
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"map_.get_fill_color = apply_continuous_cmap(normalized_download_speed, BrBG_10)"
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]
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},
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{
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"execution_count": 17,
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"id": "579233ef-e077-4c8f-a111-f33d44f30a0d",
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"metadata": {},
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"outputs": [],
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"source": [
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"# for now, cast to a numpy array until the layer is updated to support pandas series\n",
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"layer.get_radius = np.array(normalized_download_speed) * 200\n",
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"layer.radius_units = \"meters\"\n",
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"layer.radius_min_pixels = 0.5"
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"map_.get_radius = np.array(normalized_download_speed) * 200\n",
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"map_.radius_units = \"meters\"\n",
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"map_.radius_min_pixels = 0.5"
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]
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},
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{
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],
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"metadata": {
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"kernelspec": {
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"display_name": "lonboard",
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "lonboard"
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {

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