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bradyrx/shpfile_demo.ipynb

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shpfile_demo.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"import cartopy.crs as ccrs\n",
"import cartopy.feature as cfeature\n",
"import cartopy.mpl.ticker as cticker\n",
"import geopandas as gpd\n",
"from descartes import PolygonPatch"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"def load_shape_file(filepath):\n",
" \"\"\"Loads the shape file desired to mask a grid.\n",
" Args:\n",
" filepath: Path to *.shp file\n",
" \"\"\"\n",
" shpfile = gpd.read_file(filepath)\n",
" return shpfile\n",
"\n",
"\n",
"def select_shape(shpfile, category, name):\n",
" \"\"\"Select the submask of interest from the shapefile.\n",
" Args:\n",
" shpfile: (*.shp) loaded through `load_shape_file`\n",
" category: (str) header of shape file from which to filter shape.\n",
" (Run print(shpfile) to see options)\n",
" name: (str) name of shape relative to category.\n",
" plot: (optional bool) if True, plot the polygon that will be masking.\n",
" Returns:\n",
" shapely polygon\n",
" Example:\n",
" from esmask.mask import load_shape_file, select_shape\n",
" LME = load_shape_file('LMEs.shp')\n",
" CalCS = select_shape(LME, 'LME_NAME', 'California Current')\n",
" \"\"\"\n",
" s = shpfile\n",
" polygon = s[s[category] == name]\n",
" polygon = polygon.geometry[:].unary_union\n",
" return polygon"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>OBJECTID</th>\n",
" <th>LME_NUMBER</th>\n",
" <th>LME_NAME</th>\n",
" <th>GROUPING</th>\n",
" <th>ARCTIC</th>\n",
" <th>USLMES</th>\n",
" <th>Shape_Leng</th>\n",
" <th>Shape_Area</th>\n",
" <th>SUM_GIS_KM</th>\n",
" <th>geometry</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>23.0</td>\n",
" <td>Baltic Sea</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>219.326402</td>\n",
" <td>61.950021</td>\n",
" <td>3.951472e+05</td>\n",
" <td>POLYGON ((10.97943973554271 54.38055038418429,...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>52.0</td>\n",
" <td>Sea of Okhotsk</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>145.932989</td>\n",
" <td>213.796584</td>\n",
" <td>1.561297e+06</td>\n",
" <td>POLYGON ((156.7070450503869 51.18857905056069,...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>22.0</td>\n",
" <td>North Sea</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>209.486403</td>\n",
" <td>103.965247</td>\n",
" <td>6.938472e+05</td>\n",
" <td>POLYGON ((5.278888225314518 61.98027038609212,...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>2.0</td>\n",
" <td>Gulf of Alaska</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>Yes</td>\n",
" <td>495.473780</td>\n",
" <td>211.045622</td>\n",
" <td>1.530668e+06</td>\n",
" <td>POLYGON ((-149.4114074707736 61.49637985210421...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>9.0</td>\n",
" <td>Labrador - Newfoundland</td>\n",
" <td>Arctic</td>\n",
" <td>Arctic</td>\n",
" <td>None</td>\n",
" <td>270.847479</td>\n",
" <td>114.597691</td>\n",
" <td>9.077923e+05</td>\n",
" <td>(POLYGON ((-64.56793350674803 60.4550934642860...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" OBJECTID LME_NUMBER LME_NAME GROUPING ARCTIC USLMES \\\n",
"0 1 23.0 Baltic Sea None None None \n",
"1 2 52.0 Sea of Okhotsk None None None \n",
"2 3 22.0 North Sea None None None \n",
"3 4 2.0 Gulf of Alaska None None Yes \n",
"4 5 9.0 Labrador - Newfoundland Arctic Arctic None \n",
"\n",
" Shape_Leng Shape_Area SUM_GIS_KM \\\n",
"0 219.326402 61.950021 3.951472e+05 \n",
"1 145.932989 213.796584 1.561297e+06 \n",
"2 209.486403 103.965247 6.938472e+05 \n",
"3 495.473780 211.045622 1.530668e+06 \n",
"4 270.847479 114.597691 9.077923e+05 \n",
"\n",
" geometry \n",
"0 POLYGON ((10.97943973554271 54.38055038418429,... \n",
"1 POLYGON ((156.7070450503869 51.18857905056069,... \n",
"2 POLYGON ((5.278888225314518 61.98027038609212,... \n",
"3 POLYGON ((-149.4114074707736 61.49637985210421... \n",
"4 (POLYGON ((-64.56793350674803 60.4550934642860... "
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"shp = load_shape_file('LMEs/LMEs66.shp')\n",
"shp.head()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
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],
"text/plain": [
"<shapely.geometry.polygon.Polygon at 0xb1ef5add8>"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"CalCS_shp = select_shape(shp, 'LME_NAME', 'California Current')\n",
"CalCS_shp"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"def lat_lon_formatter(ax):\n",
" \"\"\"\n",
" Creates nice latitude/longitude labels\n",
" for maps\n",
" \"\"\"\n",
" lon_formatter = cticker.LongitudeFormatter()\n",
" lat_formatter = cticker.LatitudeFormatter()\n",
" ax.xaxis.set_major_formatter(lon_formatter)\n",
" ax.yaxis.set_major_formatter(lat_formatter)\n",
" ax.tick_params(labelsize=16)\n",
" \n",
"\n",
"def set_up_map(ax, x0, x1, y0, y1):\n",
" \"\"\"\n",
" Adds coastline, etc.\n",
" \n",
" x0, x1: longitude bounds to zoom into\n",
" y0, y1: latitude bounds to zoom into\n",
" \"\"\"\n",
" # set up land overlay\n",
" ax.add_feature(cfeature.LAND, facecolor='k')\n",
" \n",
" # zoom in on region of interest\n",
" ax.set_extent([x0, x1, y0, y1])\n",
" \n",
" # set nicer looking ticks\n",
" ax.set_xticks(np.arange(x0, x1, 10), crs=ccrs.PlateCarree())\n",
" ax.set_yticks(np.arange(y0, y1, 10), crs=ccrs.PlateCarree())\n",
" lat_lon_formatter(ax)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## California Current Demo"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.patches.PathPatch at 0xb1eefa630>"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 720x360 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"f, ax = plt.subplots(ncols=2, figsize=(10,5),\n",
" subplot_kw=dict(projection=ccrs.PlateCarree()))\n",
"set_up_map(ax[0], -140, -107, 20, 50)\n",
"set_up_map(ax[1], -140, -107, 20, 50)\n",
"\n",
"\n",
"\n",
"# add shapefile to map\n",
"ax[0].add_patch(PolygonPatch(CalCS_shp, fc='#add8e6', zorder=4))\n",
"# some other attributes to play around with\n",
"ax[1].add_patch(PolygonPatch(CalCS_shp, fc='None', ec='r', linewidth=2,\n",
" linestyle=':'))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Color Masks by Some Data\n",
"\n",
"---\n",
"\n",
"Can we color our shape files based on some quantitative data? Similar to a choropleth in GIS."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib\n",
"import cartopy.crs as ccrs\n",
"import geoplot.crs as gcrs"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>OBJECTID</th>\n",
" <th>LME_NUMBER</th>\n",
" <th>LME_NAME</th>\n",
" <th>GROUPING</th>\n",
" <th>ARCTIC</th>\n",
" <th>USLMES</th>\n",
" <th>Shape_Leng</th>\n",
" <th>Shape_Area</th>\n",
" <th>SUM_GIS_KM</th>\n",
" <th>geometry</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>23.0</td>\n",
" <td>Baltic Sea</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>219.326402</td>\n",
" <td>61.950021</td>\n",
" <td>3.951472e+05</td>\n",
" <td>POLYGON ((10.97943973554271 54.38055038418429,...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>52.0</td>\n",
" <td>Sea of Okhotsk</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>145.932989</td>\n",
" <td>213.796584</td>\n",
" <td>1.561297e+06</td>\n",
" <td>POLYGON ((156.7070450503869 51.18857905056069,...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>22.0</td>\n",
" <td>North Sea</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>209.486403</td>\n",
" <td>103.965247</td>\n",
" <td>6.938472e+05</td>\n",
" <td>POLYGON ((5.278888225314518 61.98027038609212,...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>2.0</td>\n",
" <td>Gulf of Alaska</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>Yes</td>\n",
" <td>495.473780</td>\n",
" <td>211.045622</td>\n",
" <td>1.530668e+06</td>\n",
" <td>POLYGON ((-149.4114074707736 61.49637985210421...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>9.0</td>\n",
" <td>Labrador - Newfoundland</td>\n",
" <td>Arctic</td>\n",
" <td>Arctic</td>\n",
" <td>None</td>\n",
" <td>270.847479</td>\n",
" <td>114.597691</td>\n",
" <td>9.077923e+05</td>\n",
" <td>(POLYGON ((-64.56793350674803 60.4550934642860...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" OBJECTID LME_NUMBER LME_NAME GROUPING ARCTIC USLMES \\\n",
"0 1 23.0 Baltic Sea None None None \n",
"1 2 52.0 Sea of Okhotsk None None None \n",
"2 3 22.0 North Sea None None None \n",
"3 4 2.0 Gulf of Alaska None None Yes \n",
"4 5 9.0 Labrador - Newfoundland Arctic Arctic None \n",
"\n",
" Shape_Leng Shape_Area SUM_GIS_KM \\\n",
"0 219.326402 61.950021 3.951472e+05 \n",
"1 145.932989 213.796584 1.561297e+06 \n",
"2 209.486403 103.965247 6.938472e+05 \n",
"3 495.473780 211.045622 1.530668e+06 \n",
"4 270.847479 114.597691 9.077923e+05 \n",
"\n",
" geometry \n",
"0 POLYGON ((10.97943973554271 54.38055038418429,... \n",
"1 POLYGON ((156.7070450503869 51.18857905056069,... \n",
"2 POLYGON ((5.278888225314518 61.98027038609212,... \n",
"3 POLYGON ((-149.4114074707736 61.49637985210421... \n",
"4 (POLYGON ((-64.56793350674803 60.4550934642860... "
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"shp = load_shape_file('LMEs/LMEs66.shp')\n",
"shp.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Solution 1: Just use GeoPandas native plot ability.\n",
"\n",
"---\n",
"\n",
"Easiest way but doesn't let you tweak your map as much. There's a lot in the below reference that could be used much better than I used it.\n",
"\n",
"Reference: http://geopandas.org/gallery/plotting_with_geoplot.html"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Define the CartoPy CRS object.\n",
"crs = ccrs.Orthographic()\n",
"\n",
"# This can be converted into a `proj4` string/dict compatible with GeoPandas\n",
"crs_proj4 = crs.proj4_init\n",
"df = shp.to_crs(crs_proj4)\n",
"\n",
"# Here's what the plot looks like in GeoPandas\n",
"ax = df.plot(column='Shape_Area', cmap='Greens')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Solution 2: Manually create colormap\n",
"\n",
"---\n",
"\n",
"This seems preferred since we can leverage all of cartopy's features and our method of adding shapefiles.\n",
"\n",
"\n",
"Reference: https://gis.stackexchange.com/questions/199288/python-color-canadian-provinces-in-a-shapefile-based-on-given-values"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 61.95002083, 213.79658433, 103.9652466 , 211.04562178,\n",
" 114.59769105, 98.76234894, 106.4723238 , 61.2565279 ,\n",
" 32.82897845, 215.80474439, 51.53428045, 34.46993029,\n",
" 256.74516848, 32.64916394, 125.30692921, 44.31324943,\n",
" 100.88447633, 72.65292052, 28.3566505 , 20.08767998,\n",
" 137.23483119, 333.7834462 , 40.03099102, 87.00886967,\n",
" 276.60375166, 264.39735731, 304.14862517, 165.39739227,\n",
" 82.97198949, 31.86466527, 86.10546653, 156.53424018,\n",
" 68.5710742 , 184.89796521, 89.28660914, 253.06909724,\n",
" 128.34895907, 64.92069895, 109.05087309, 232.56965593,\n",
" 77.34806371, 60.6370155 , 51.87599868, 103.1440359 ,\n",
" 137.36485309, 127.89582666, 1341.22332028, 100.61149023,\n",
" 53.20192097, 17.61440056, 97.1401216 , 347.56076119,\n",
" 232.30978104, 678.86112464, 353.2011834 , 303.97769292,\n",
" 187.92018455, 111.53247693, 32.31120891, 190.73872065,\n",
" 331.64437588, 209.46431332, 292.45094913, 297.73226526,\n",
" 3181.18885474, 332.63134626])"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# The data of interest should map to the same indices as your shape files,\n",
"# such as LMEs.\n",
"total_area = shp['Shape_Area'].values\n",
"total_area"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"def make_colormap(data, cmap_name, norm='linear'):\n",
" \"\"\"\n",
" Make colormap for mapping shapefiles to.\n",
" \n",
" data: an array of data that the colormap will map to.\n",
" cmap_name: string of a matplotlib colormap.\n",
" norm: defaults to 'linear'. Pass 'log' for logarithmic colorbar.\n",
" \"\"\"\n",
" cmap = matplotlib.cm.get_cmap(cmap_name)\n",
" if norm == 'linear':\n",
" norm = matplotlib.colors.Normalize(min(data), max(data))\n",
" elif norm == 'log':\n",
" norm = matplotlib.colors.LogNorm(min(total_area), max(total_area))\n",
" else:\n",
" raise ValueError(\"Supply 'linear' or 'log' for norm.\")\n",
" sm = matplotlib.cm.ScalarMappable(norm=norm, cmap=cmap)\n",
" sm._A = [] # some weird bug to make sure we can plot our colorbar later.\n",
" return sm"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x216 with 2 Axes>"
]
},
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"source": [
"f, axes = plt.subplots(figsize=(8,3),\n",
" subplot_kw=dict(projection=ccrs.PlateCarree()))\n",
"\n",
"# Learning how to iterate cleanly is a good skill in python.\n",
"# Here, enumerate returns a tuple of values. First a counter\n",
"# (just like if you did i += 1 at the end of every loop) and second,\n",
"# the value fhmom the list you're iterating through.\n",
"#\n",
"# Here, I am just iterating through the LME names to plot them\n",
"# one by one. The i will be used to reference our total_area array\n",
"# and color it.\n",
"sm = make_colormap(total_area, 'YlGnBu')\n",
"for i, LME in enumerate(shp['LME_NAME'].values):\n",
" poly = select_shape(shp, 'LME_NAME', LME)\n",
" color = sm.to_rgba(total_area[i])\n",
" test = axes.add_patch(PolygonPatch(poly, fc=color, zorder=4))\n",
"axes.axis('scaled')\n",
"axes.add_feature(cfeature.LAND, color='k')\n",
"\n",
"# add colorbar.\n",
"# reference: https://stackoverflow.com/questions/35873209/matplotlib-add-colorbar-to-cartopy-image\n",
"cb = plt.colorbar(sm, orientation='horizontal', pad=0.05, fraction=0.09)\n",
"cb.set_label('LME Area')"
]
}
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