{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# pip install colorcet"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import folium\n",
"import matplotlib\n",
"import colorcet\n",
"from matplotlib.pyplot import imread\n",
"from matplotlib.colors import Normalize\n",
"from folium import raster_layers"
]
},
{
"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",
"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"
]
}
],
"source": [
"!gdalinfo '/notebooks/resources/gpm/gpm_1d.20190531.tif'"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/bin/sh: 1: gdal_edit: not found\r\n"
]
}
],
"source": [
"!gdal_edit -colorinterp_1 alpha /notebooks/resources/gpm/gpm_1d.20190531.tif"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"# 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)"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"execution_count": 34,
"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.bmw\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": null,
"metadata": {},
"outputs": [],
"source": [
"vars(m)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(data)\n",
"print(data.shape)\n",
"# data = data.transpose()\n",
"# print(data)\n",
"# print(data.shape)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"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": 18,
"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": 21,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(0.72312, 0.11873, 1.0, 1.0)\n",
"(0.72312, 0.11873, 1.0)\n",
"\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": 27,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 27,
"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": 33,
"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": 33,
"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": null,
"metadata": {},
"outputs": [],
"source": []
}
],
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