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update notebooks (#133)
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@kylebarron
kylebarron committed Oct 17, 2023
1 parent c363fcb commit 920eeb7
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17 changes: 10 additions & 7 deletions examples/internet-speeds.ipynb
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Expand Up @@ -365,20 +365,22 @@
"cell_type": "code",
"execution_count": 10,
"id": "326f5f4e-a8f7-425b-8e9f-6744ba65cf62",
"metadata": {},
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "30ba30dd2b9f4d47b307fa98ea9bf896",
"model_id": "cd6f767934bd451bb8f89088039e7af5",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"ScatterplotLayer(table=pyarrow.Table\n",
"avg_d_kbps: int64\n",
"__index_level_0__: int64\n",
"geometry: fixed_size_list<item…"
"geometry: fixed_size_list<item: double>[2]\n",
" chi…"
]
},
"execution_count": 10,
Expand All @@ -387,7 +389,7 @@
}
],
"source": [
"map_ = viz(gdf)\n",
"map_ = viz(gdf.geometry)\n",
"map_"
]
},
Expand All @@ -396,7 +398,8 @@
"id": "91af2a88-9363-4c00-8e54-f06a635ff991",
"metadata": {},
"source": [
"This map object is a `ScatterplotLayer` type. You could have created the same map by using\n",
"This map object is a [`ScatterplotLayer`](https://developmentseed.org/lonboard/api/layers/scatterplot-layer/) type. You could have created the same map by using\n",
"\n",
"```py\n",
"map_ = lonboard.ScatterplotLayer.from_geopandas(gdf)\n",
"```"
Expand Down Expand Up @@ -531,7 +534,7 @@
"<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>"
],
"text/plain": [
"<matplotlib.colors.LinearSegmentedColormap at 0x1105c2950>"
"<matplotlib.colors.LinearSegmentedColormap at 0x2d8cd3590>"
]
},
"execution_count": 15,
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141 changes: 86 additions & 55 deletions examples/north-america-roads.ipynb
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Expand Up @@ -39,7 +39,7 @@
"import geopandas as gpd\n",
"import palettable.colorbrewer.diverging\n",
"\n",
"from lonboard import PathLayer\n",
"from lonboard import PathLayer, viz\n",
"from lonboard.colormap import apply_continuous_cmap"
]
},
Expand Down Expand Up @@ -101,7 +101,9 @@
"cell_type": "code",
"execution_count": 4,
"id": "14d60ee4-4006-48b3-8ddb-52e3ae6002da",
"metadata": {},
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
Expand Down Expand Up @@ -277,7 +279,7 @@
"metadata": {},
"outputs": [],
"source": [
"gdf = gdf[(gdf['country'] == 'United States') & (~gdf['state'].isin(['Hawaii', 'Alaska']))]"
"gdf = gdf[gdf[\"state\"] == \"California\"]"
]
},
{
Expand All @@ -297,17 +299,14 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "ab1597e0dde74743a143d1b4f35247a7",
"model_id": "8b473d8dd0c640d09b2f7b1163785f8d",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"PathLayer(get_color=[0, 0, 0], table=pyarrow.Table\n",
"prefix: string\n",
"number: string\n",
"class: string\n",
"type: string\n",
"di…"
"PathLayer(table=pyarrow.Table\n",
"__index_level_0__: int64\n",
"geometry: list<item: list<item: fixed_size_list<item: d…"
]
},
"execution_count": 6,
Expand All @@ -316,11 +315,61 @@
}
],
"source": [
"black = [0, 0, 0]\n",
"map_ = PathLayer.from_geopandas(gdf, get_color=black, width_min_pixels=0.8)\n",
"map_ = viz(gdf.geometry, width_min_pixels=0.8)\n",
"map_"
]
},
{
"cell_type": "markdown",
"id": "42b7892e-b0b6-4fa8-87a5-937e363c6e34",
"metadata": {},
"source": [
"This map object is a [`PathLayer`](https://developmentseed.org/lonboard/api/layers/path-layer/) type. You could have created the same map by using\n",
"\n",
"```py\n",
"map_ = lonboard.PathLayer.from_geopandas(gdf)\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "8ff0879a-ae90-4302-91df-7c381a0a1f09",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"lonboard.layer.PathLayer"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(map_)"
]
},
{
"cell_type": "markdown",
"id": "a6bdfd6d-2689-42f6-a538-8f833c6d539a",
"metadata": {},
"source": [
"We can look at the [documentation for `PathLayer`](https://developmentseed.org/lonboard/api/layers/path-layer/) to see what other rendering options it allows. Let's set the path color to something other than black:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "75f67a77-062a-4043-9454-5b2b97b52288",
"metadata": {},
"outputs": [],
"source": [
"map_.get_color = [200, 0, 200]"
]
},
{
"cell_type": "markdown",
"id": "9bac0f0b-7129-476c-8ea0-51c1bede0f4d",
Expand All @@ -333,7 +382,7 @@
},
{
"cell_type": "code",
"execution_count": 7,
"execution_count": 9,
"id": "5c33098b-0348-4b07-9ae4-5c56d5d50723",
"metadata": {
"scrolled": true
Expand All @@ -343,20 +392,20 @@
"data": {
"text/plain": [
"scalerank\n",
"3 255\n",
"4 320\n",
"5 317\n",
"6 700\n",
"7 3332\n",
"8 4339\n",
"9 1228\n",
"10 5902\n",
"11 9859\n",
"12 10179\n",
"3 19\n",
"4 93\n",
"5 35\n",
"6 94\n",
"7 178\n",
"8 122\n",
"9 1\n",
"10 237\n",
"11 541\n",
"12 1562\n",
"Name: count, dtype: int64"
]
},
"execution_count": 7,
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
Expand All @@ -375,7 +424,7 @@
},
{
"cell_type": "code",
"execution_count": 8,
"execution_count": 10,
"id": "c7d26678-418e-48f8-827d-050d9f2df1dd",
"metadata": {},
"outputs": [],
Expand All @@ -393,7 +442,7 @@
},
{
"cell_type": "code",
"execution_count": 9,
"execution_count": 11,
"id": "12be09c8-0857-44d8-ad14-69b4c7277fbc",
"metadata": {},
"outputs": [
Expand All @@ -403,7 +452,7 @@
"(0.0, 1.0)"
]
},
"execution_count": 9,
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
Expand All @@ -422,7 +471,7 @@
},
{
"cell_type": "code",
"execution_count": 10,
"execution_count": 12,
"id": "1df9d2c9-6f10-49e6-aa4f-6718f3d2f765",
"metadata": {},
"outputs": [],
Expand All @@ -432,7 +481,7 @@
},
{
"cell_type": "code",
"execution_count": 11,
"execution_count": 13,
"id": "6e0f48ab-0cbe-4ce9-b804-9a1a3330ff9f",
"metadata": {},
"outputs": [
Expand All @@ -443,10 +492,10 @@
"<div style=\"vertical-align: middle;\"><strong>PuOr</strong> </div><div class=\"cmap\"><img alt=\"PuOr colormap\" title=\"PuOr\" 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=\"#7f3b08ff\" style=\"display: inline-block; width: 1em; height: 1em; margin: 0; vertical-align: middle; border: 1px solid #555; background-color: #7f3b08ff;\"></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=\"#2d004bff\" style=\"display: inline-block; width: 1em; height: 1em; margin: 0; vertical-align: middle; border: 1px solid #555; background-color: #2d004bff;\"></div></div>"
],
"text/plain": [
"<matplotlib.colors.LinearSegmentedColormap at 0x125fcbcd0>"
"<matplotlib.colors.LinearSegmentedColormap at 0x2959c6b50>"
]
},
"execution_count": 11,
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
Expand All @@ -460,51 +509,33 @@
"id": "74d9d749-4222-4aa8-bf12-ded79187a616",
"metadata": {},
"source": [
"Now we'll use `apply_continuous_cmap` onto this array to generate colors for our data:"
"Now we'll use `apply_continuous_cmap` onto this array to generate colors for our data. Just set this new array onto the existing map, and you'll see the map update with the new colors!"
]
},
{
"cell_type": "code",
"execution_count": 12,
"execution_count": 14,
"id": "7288a2d7-3190-4f9e-b29e-bbba32109024",
"metadata": {},
"outputs": [],
"source": [
"get_color = apply_continuous_cmap(normalized_scale_rank, palettable.colorbrewer.diverging.PuOr_10, alpha=0.8)"
]
},
{
"cell_type": "markdown",
"id": "efbe28aa-c004-41cd-90c9-d12c310b5d95",
"metadata": {},
"source": [
"Just set this new array onto the existing map, and you'll see the map update with the new colors!"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "0173cced-68fa-4966-9bb8-42f44f35e74b",
"metadata": {},
"outputs": [],
"source": [
"map_.get_color = get_color"
"map_.get_color = apply_continuous_cmap(normalized_scale_rank, palettable.colorbrewer.diverging.PuOr_10, alpha=0.8)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "82e50385-137d-46e5-91cc-558b252fd757",
"id": "72807632-f5de-4dcc-9c8e-f4e0c8495e2b",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "lonboard",
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "lonboard"
"name": "python3"
},
"language_info": {
"codemirror_mode": {
Expand Down

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