{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# pip install colorcet"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import folium\n",
"import matplotlib\n",
"import numpy as np\n",
"import colorcet\n",
"from matplotlib.pyplot import imread\n",
"from matplotlib.colors import Normalize\n",
"from matplotlib.colors import ListedColormap\n",
"from folium import raster_layers\n",
"from folium import plugins\n",
"from folium import branca"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"image = '/notebooks/resources/gpm/gpm_1d.20190531.tif'"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"10.0 -180.0\n",
"-60.0 -180.0\n",
"-60.0 -30.0\n",
"10.0 -30.0\n",
"ext = [[10.0, -180.0], [-60.0, -180.0], [-60.0, -30.0], [10.0, -30.0]]\n",
"geo_ext = [[10.0, -180.0], [-60.0, -180.0], [-60.0, -30.0], [10.0, -30.0]]\n"
]
}
],
"source": [
"from osgeo import gdal,ogr,osr\n",
"\n",
"def GetExtent(gt,cols,rows):\n",
" ''' Return list of corner coordinates from a geotransform\n",
"\n",
" @type gt: C{tuple/list}\n",
" @param gt: geotransform\n",
" @type cols: C{int}\n",
" @param cols: number of columns in the dataset\n",
" @type rows: C{int}\n",
" @param rows: number of rows in the dataset\n",
" @rtype: C{[float,...,float]}\n",
" @return: coordinates of each corner\n",
" '''\n",
" ext=[]\n",
" xarr=[0,cols]\n",
" yarr=[0,rows]\n",
"\n",
" for px in xarr:\n",
" for py in yarr:\n",
" x=gt[0]+(px*gt[1])+(py*gt[2])\n",
" y=gt[3]+(px*gt[4])+(py*gt[5])\n",
" ext.append([y,x])\n",
" print (y,x)\n",
" yarr.reverse()\n",
" return ext\n",
"\n",
"def ReprojectCoords(coords,src_srs,tgt_srs):\n",
" ''' Reproject a list of x,y coordinates.\n",
"\n",
" @type geom: C{tuple/list}\n",
" @param geom: List of [[x,y],...[x,y]] coordinates\n",
" @type src_srs: C{osr.SpatialReference}\n",
" @param src_srs: OSR SpatialReference object\n",
" @type tgt_srs: C{osr.SpatialReference}\n",
" @param tgt_srs: OSR SpatialReference object\n",
" @rtype: C{tuple/list}\n",
" @return: List of transformed [[x,y],...[x,y]] coordinates\n",
" '''\n",
" trans_coords=[]\n",
" transform = osr.CoordinateTransformation( src_srs, tgt_srs)\n",
" for x,y in coords:\n",
" x,y,z = transform.TransformPoint(x,y)\n",
" trans_coords.append([x,y])\n",
" return trans_coords\n",
"\n",
"raster=image\n",
"ds=gdal.Open(raster)\n",
"\n",
"gt=ds.GetGeoTransform()\n",
"cols = ds.RasterXSize\n",
"rows = ds.RasterYSize\n",
"\n",
"ext=GetExtent(gt,cols,rows)\n",
"print(\"ext = \" + str(ext))\n",
"\n",
"src_srs=osr.SpatialReference()\n",
"src_srs.ImportFromWkt(ds.GetProjection())\n",
"# tgt_srs=osr.SpatialReference()\n",
"# tgt_srs.ImportFromEPSG(3857)\n",
"tgt_srs = src_srs.CloneGeogCS()\n",
"\n",
"geo_ext=ReprojectCoords(ext,src_srs,tgt_srs)\n",
"print(\"geo_ext = \" + str(geo_ext))"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Driver: GTiff/GeoTIFF\r\n",
"Files: /notebooks/resources/gpm/gpm_1d.20190531.tif\r\n",
" /notebooks/resources/gpm/gpm_1d.20190531.tif.aux.xml\r\n",
"Size is 1500, 700\r\n",
"Coordinate System is:\r\n",
"GEOGCS[\"WGS 84\",\r\n",
" DATUM[\"WGS_1984\",\r\n",
" SPHEROID[\"WGS 84\",6378137,298.2572326660159,\r\n",
" AUTHORITY[\"EPSG\",\"7030\"]],\r\n",
" AUTHORITY[\"EPSG\",\"6326\"]],\r\n",
" PRIMEM[\"Greenwich\",0],\r\n",
" UNIT[\"degree\",0.0174532925199433],\r\n",
" AUTHORITY[\"EPSG\",\"4326\"]]\r\n",
"Origin = (-180.000000000000000,10.000000000000000)\r\n",
"Pixel Size = (0.100000000000000,-0.100000000000000)\r\n",
"Metadata:\r\n",
" AREA_OR_POINT=Area\r\n",
" TIFFTAG_DATETIME=2019:06:01 13:35:13\r\n",
" TIFFTAG_DOCUMENTNAME=/NRTPUB/imerg/gis/2019/05/3B-HHR-L.MS.MRG.3IMERG.20190531-S233000-E235959.1410.V06B.1day.tif\r\n",
" TIFFTAG_IMAGEDESCRIPTION=DOI=10.5067/GPM/IMERG/3B-HH-L/06 DOIauthority=http://dx.doi.org/ DOIshortName=3IMERGHH_LATE Unit=0.1(mm) ScaleFactor=10\r\n",
" TIFFTAG_RESOLUTIONUNIT=2 (pixels/inch)\r\n",
" TIFFTAG_SOFTWARE=IDL 8.7.2, Harris Geospatial Solutions, Inc.\r\n",
" TIFFTAG_XRESOLUTION=100\r\n",
" TIFFTAG_YRESOLUTION=100\r\n",
"Image Structure Metadata:\r\n",
" COMPRESSION=LZW\r\n",
" INTERLEAVE=BAND\r\n",
"Corner Coordinates:\r\n",
"Upper Left (-180.0000000, 10.0000000) (180d 0' 0.00\"W, 10d 0' 0.00\"N)\r\n",
"Lower Left (-180.0000000, -60.0000000) (180d 0' 0.00\"W, 60d 0' 0.00\"S)\r\n",
"Upper Right ( -30.0000000, 10.0000000) ( 30d 0' 0.00\"W, 10d 0' 0.00\"N)\r\n",
"Lower Right ( -30.0000000, -60.0000000) ( 30d 0' 0.00\"W, 60d 0' 0.00\"S)\r\n",
"Center (-105.0000000, -25.0000000) (105d 0' 0.00\"W, 25d 0' 0.00\"S)\r\n",
"Band 1 Block=1500x2 Type=UInt16, ColorInterp=Gray\r\n",
" Min=0.000 Max=2237.000 \r\n",
" Minimum=0.000, Maximum=2237.000, Mean=34.214, StdDev=111.334\r\n",
" Metadata:\r\n",
" STATISTICS_MAXIMUM=2237\r\n",
" STATISTICS_MEAN=34.214233333333\r\n",
" STATISTICS_MINIMUM=0\r\n",
" STATISTICS_STDDEV=111.33382267193\r\n"
]
}
],
"source": [
"!gdalinfo '/notebooks/resources/gpm/gpm_1d.20190531.tif'"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"# !gdal_edit -colorinterp_1 alpha /notebooks/resources/gpm/gpm_1d.20190531.tif"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"# Choose colormap\n",
"cmap = colorcet.cm.fire\n",
"\n",
"# Get the colormap colors\n",
"my_cmap = cmap(np.arange(cmap.N))\n",
"\n",
"\n",
"# Set alpha\n",
"my_cmap[:,-1] = np.linspace(0, 1, cmap.N)\n",
"\n",
"# Create new colormap\n",
"my_cmap = ListedColormap(my_cmap)\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"m = folium.Map(\n",
" location = [-22, -114]\n",
" , zoom_start = 2\n",
" , control_scale = True \n",
" , tiles = 'Stamen Terrain'\n",
")\n",
"\n",
"data = matplotlib.pyplot.imread(image)\n",
"\n",
"# Image bounds on the map in the form\n",
"# [[lat_min, lon_min], [lat_max, lon_max]]\n",
"m.add_child(raster_layers.ImageOverlay(\n",
" data\n",
" , opacity = 0.7\n",
" , bounds = [ext[2], ext[0]]\n",
" , mercator_project = True\n",
"# , colormap = lambda x: (1, 0, 0, x)\n",
" , colormap = colorcet.cm.fire\n",
"# , colormap = branca.colormap.linear.PuBuGn_07.scale(0,700)\n",
"# , colormap = my_cmap\n",
")\n",
" )\n",
"\n",
"folium.Marker(\n",
" ext[2]\n",
" , popup = str(ext[2])\n",
" , tooltip = str(ext[2])\n",
").add_to(m)\n",
"\n",
"folium.Marker(\n",
" ext[0]\n",
" , popup = str(ext[0])\n",
" , tooltip = str(ext[0])\n",
").add_to(m)\n",
"\n",
"m"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'_children': OrderedDict([('stamenterrain',\n",
" ),\n",
" ('image_overlay_92155413a6b84d5387a381702772f37a',\n",
" ),\n",
" ('marker_ac7d26db7daf4a5789ade1e0b5287481',\n",
" ),\n",
" ('marker_29b98858dab84a42ba7fd08322bdfefe',\n",
" )]),\n",
" '_env': ,\n",
" '_id': '5302fb64bbe34b86b83a04708129f218',\n",
" '_name': 'Map',\n",
" '_parent': ,\n",
" '_png_image': None,\n",
" 'control_scale': True,\n",
" 'crs': 'EPSG3857',\n",
" 'global_switches': ,\n",
" 'height': (100.0, '%'),\n",
" 'left': (0.0, '%'),\n",
" 'location': [-22, -114],\n",
" 'max_bounds': False,\n",
" 'max_lat': 90,\n",
" 'max_lon': 180,\n",
" 'min_lat': -90,\n",
" 'min_lon': -180,\n",
" 'no_wrap': False,\n",
" 'objects_to_stay_in_front': [],\n",
" 'png_enabled': False,\n",
" 'position': 'relative',\n",
" 'top': (0.0, '%'),\n",
" 'width': (100.0, '%'),\n",
" 'world_copy_jump': False,\n",
" 'zoom_control': True,\n",
" 'zoom_start': 2}"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"vars(m)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ 2 3 3 ... 0 0 0]\n",
" [ 4 6 3 ... 0 0 0]\n",
" [10 9 3 ... 0 0 0]\n",
" ...\n",
" [ 0 0 0 ... 0 0 0]\n",
" [ 0 0 0 ... 0 0 0]\n",
" [ 0 0 0 ... 1 0 0]]\n",
"(700, 1500)\n"
]
}
],
"source": [
"print(data)\n",
"print(data.shape)\n",
"# data = data.transpose()\n",
"# print(data)\n",
"# print(data.shape)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"## ds ##:\n",
"\n",
" >\n",
"\n",
"\n",
"## data ##:\n",
"\n",
"[[ 2 3 3 ... 0 0 0]\n",
" [ 4 6 3 ... 0 0 0]\n",
" [10 9 3 ... 0 0 0]\n",
" ...\n",
" [ 0 0 0 ... 0 0 0]\n",
" [ 0 0 0 ... 0 0 0]\n",
" [ 0 0 0 ... 1 0 0]]\n",
"\n",
"\n",
"## gt ##:\n",
"\n",
"(-180.0, 0.1, 0.0, 10.0, 0.0, -0.1)\n",
"\n",
"\n",
"## proj ##:\n",
"\n",
"GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.2572326660159,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0],UNIT[\"degree\",0.0174532925199433],AUTHORITY[\"EPSG\",\"4326\"]]\n",
"\n",
"\n",
"## inproj ##:\n",
"\n",
"GEOGCS[\"WGS 84\",\n",
" DATUM[\"WGS_1984\",\n",
" SPHEROID[\"WGS 84\",6378137,298.2572326660159,\n",
" AUTHORITY[\"EPSG\",\"7030\"]],\n",
" AUTHORITY[\"EPSG\",\"6326\"]],\n",
" PRIMEM[\"Greenwich\",0],\n",
" UNIT[\"degree\",0.0174532925199433],\n",
" AUTHORITY[\"EPSG\",\"4326\"]]\n"
]
}
],
"source": [
"# First: read the geotiff image with GDAL.\n",
"from osgeo import gdal, osr\n",
"\n",
"gdal.UseExceptions()\n",
"\n",
"ds = gdal.Open(image)\n",
"data = ds.ReadAsArray()\n",
"gt = ds.GetGeoTransform()\n",
"proj = ds.GetProjection()\n",
"\n",
"inproj = osr.SpatialReference()\n",
"inproj.ImportFromWkt(proj)\n",
"\n",
"print('\\n\\n## ds ##:\\n\\n' + str(ds))\n",
"print('\\n\\n## data ##:\\n\\n' + str(data))\n",
"print('\\n\\n## gt ##:\\n\\n' + str(gt))\n",
"print('\\n\\n## proj ##:\\n\\n' + str(proj))\n",
"print('\\n\\n## inproj ##:\\n\\n' + str(inproj))"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(0, 0, 0, 0)\n"
]
}
],
"source": [
"q = lambda x: (0, 0, 0, 0)\n",
"print(q(0))"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(0.72312, 0.11873, 1.0, 1.0)\n",
"(0.72312, 0.11873, 1.0)\n"
]
}
],
"source": [
"cmap = colorcet.cm.bmw\n",
"\n",
"rgba = cmap(0.5)\n",
"print(rgba) # (0.99807766255210428, 0.99923106502084169, 0.74602077638401709, 1.0\n",
"print(rgba[:-1])\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import numpy as np\n",
"import matplotlib.pylab as pl\n",
"from matplotlib.colors import ListedColormap\n",
"\n",
"# Random data\n",
"data1 = np.random.random((4,4))\n",
"\n",
"# Choose colormap\n",
"cmap = pl.cm.RdBu\n",
"\n",
"# Get the colormap colors\n",
"my_cmap = cmap(np.arange(cmap.N))\n",
"\n",
"# Set alpha\n",
"my_cmap[:,-1] = np.linspace(0, 1, cmap.N)\n",
"\n",
"# Create new colormap\n",
"my_cmap = ListedColormap(my_cmap)\n",
"\n",
"pl.figure()\n",
"pl.subplot(121)\n",
"pl.pcolormesh(data1, cmap=pl.cm.RdBu)\n",
"pl.colorbar()\n",
"\n",
"pl.subplot(122)\n",
"pl.pcolormesh(data1, cmap=my_cmap)\n",
"pl.colorbar()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[0.0000e+00 0.0000e+00 0.0000e+00 1.0000e+00]\n",
" [2.7065e-02 2.1430e-05 0.0000e+00 1.0000e+00]\n",
" [5.2054e-02 7.4728e-05 0.0000e+00 1.0000e+00]\n",
" ...\n",
" [1.0000e+00 9.9953e-01 8.7115e-01 1.0000e+00]\n",
" [1.0000e+00 9.9989e-01 9.3683e-01 1.0000e+00]\n",
" [1.0000e+00 1.0000e+00 1.0000e+00 1.0000e+00]]\n"
]
},
{
"data": {
"text/plain": [
""
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Choose colormap\n",
"cmap = colorcet.cm.fire\n",
"\n",
"# Get the colormap colors\n",
"my_cmap = cmap(np.arange(cmap.N))\n",
"\n",
"print(my_cmap)\n",
"\n",
"# Set alpha\n",
"my_cmap[:,-1] = np.linspace(0, 1, cmap.N)\n",
"\n",
"# Create new colormap\n",
"my_cmap = ListedColormap(my_cmap)\n",
"\n",
"pl.figure()\n",
"pl.subplot(121)\n",
"pl.pcolormesh(data1, cmap=colorcet.cm.fire)\n",
"pl.colorbar()\n",
"\n",
"pl.subplot(122)\n",
"pl.pcolormesh(data1, cmap=my_cmap)\n",
"pl.colorbar()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import branca\n",
"# branca.colormap.linear.Spectral_04\n",
"colormap = branca.colormap.linear.Spectral_04.scale(0, 10000)\n",
"colormap = colormap.to_step(index=[0, 25, 50, 100, 250, 500, 1000, 2500, 5000, 10000])\n",
"colormap.caption = 'mm'\n",
"colormap"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import branca\n",
"\n",
"\n",
"colormap = branca.colormap.StepColormap(\n",
" ['#64abb0','#9dd3a7', '#c7e9ad', '#edf8b9', '#ffedaa', '#fec980', '#f99e59', '#e85b3a', '#d7191c'],\n",
" vmin=0, vmax=10000,\n",
" index=[0, 25, 50, 100, 250, 500, 1000, 2500, 5000, 10000],\n",
" caption='step'\n",
")\n",
"\n",
"colormap"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"scrolled": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'cm' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mStepColormap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'#64abb0'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mNameError\u001b[0m: name 'cm' is not defined"
]
}
],
"source": [
"cm.StepColormap(['#64abb0'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ftp://jsimpson.pps.eosdis.nasa.gov/NRTPUB/imerg/gis/README.GIS.pdf"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}