{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# How to get an elevation map?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "An elevation map shows you elevations data with color. The color doesn't reflect the natural landscape (it is not a satellite map), but all the points at a certain altitude have the same color. Sometimes, contour lines are added at regular altitude interval to show the steepness: if two lines are close from each other, the slope is steep." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", " \n", " \n", " \n", " \n", "
\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this notebook, we will use data from NASA to make elevation maps like these:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# How to use this notebook?\n", "\n", "You just need to read the indications, set some variables (but you can keep the default values if you want) and execute the cells.\n", "\n", "Some cells will download data from the NASA website, some others will build LandSerf scripts. To execute these scripts, all you need to do is copy them to the [LandSerf](http://www.staff.city.ac.uk/~jwo/landserf/) interface, and then click on the button `run`. LandSerf is a program that you can freely [download](http://www.staff.city.ac.uk/~jwo/landserf/download/). It runs on Windows, Linux and Mac. All the details will be provided below.\n", "\n", "After running all the cells and excute the LandSerf scripts, you will have a nice relief map, as the one shown above!\n", "\n", "---\n", "\n", "A note about the NASA data. We will use [SRTM data](https://dds.cr.usgs.gov/srtm/version2_1/Documentation/SRTM_Topo.pdf):\n", "\n", "> The SRTM data sets result from a collaborative effort by the National Aeronautics and Space Administration (NASA) and the National Geospatial-Intelligence Agency (NGA - previously known as the National Imagery and Mapping Agency, or NIMA), as well as the participation of the German and Italian space agencies, to generate a near-global digital elevation model (DEM)\n", "\n", "---\n", "\n", "If you want to read the raw data from NASA, and get an image out of them without LandSerf, check out [this other repository of mine: `hgt2pnm`](https://github.com/boberle/hgt2pnm)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Get the tile list" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The following cells compute the list of tiles you need to download from the NASA website.\n", "\n", "First, you need to draw a rectangle on a map and find the latitude and longitude of each edge. You can use the [epsg.io](https://epsg.io/) website to find the values you need.\n", "\n", "For example, for the island of Corsica, in Southern France, you get:\n", "* North = 43N\n", "* East = 9E\n", "* West = 8E\n", "* South = 41N\n", "\n", "Then convert these into positive or negative integers:\n", "* for latitude: N is positive, S is negative (for example 45S would be -45, so Australia is all negative),\n", "* for longitude: E is positive, W is negative (so the US is all negative)." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "N = 43\n", "E = 9\n", "S = 41\n", "W = 8" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "N41E008.hgt.zip\n", "N41E009.hgt.zip\n", "N42E008.hgt.zip\n", "N42E009.hgt.zip\n", "N43E008.hgt.zip\n", "N43E009.hgt.zip\n" ] } ], "source": [ "tile_list = set()\n", "\n", "for lat in range(S, N+1) if S < N else range(N, S+1):\n", " lat = \"%s%02d\" % (\"N\" if lat >= 0 else \"S\", abs(lat))\n", " for long in range(E, W+1) if E < W else range(W, E+1):\n", " long = \"%s%03d\" % (\"E\" if long >= 0 else \"W\", abs(long))\n", " fn = \"%s%s.hgt.zip\" % (lat, long)\n", " print(fn)\n", " tile_list.add(fn)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Download data from the NASA website" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we need to download the data from the NASA website. But there is a catch: there is no tile if the tile is entirely covered by water (like seas, big lakes, etc.). So we'll have to remove these from the tile list.\n", "\n", "On the website, data are divided between SRTM1 (1 pixel is 1 meter) and SRTM3 (1 pixel is 3 meters): SRTM1 is just for US.\n", "\n", "SRTM1 are divided into 7 US regions, each in its own directory, and SRTM3 into 6 continents, each in its own diretory. We can't know in advance in which directory a tile is, so we'll test them all until we find the tile data.\n", "\n", "First, you need to choose: SRTM1 (US only) or SRTM3 (all the world):" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "TYPE = \"SRTM3\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and a destination directory:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "HGT_DIR = 'hgt_files_srtm3'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that the script doesn't download a tile if it is already here, in the `HGT_DIR`.\n", "\n", "If you now the region, you can choose in the following set. Otherwise, all regions will be tried. Here are the choices:\n", "* for SRTM1: `Region_01, Region_02, Region_03, Region_04, Region_05, Region_06, Region_07`\n", "* for SRTM3: `Eurasia, Africa, Islands, North_America, South_America`" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "#REGION = [] # if you don't know\n", "REGION = \"Eurasia\"" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Looking for 'N42E008.hgt.zip'...\n", "Trying 'https://dds.cr.usgs.gov/srtm/version2_1/SRTM3/Eurasia/N42E008.hgt.zip'... OK\n", "Saved in 'hgt_files_srtm3/N42E008.hgt'\n", "Looking for 'N41E008.hgt.zip'...\n", "Trying 'https://dds.cr.usgs.gov/srtm/version2_1/SRTM3/Eurasia/N41E008.hgt.zip'... OK\n", "Saved in 'hgt_files_srtm3/N41E008.hgt'\n", "Looking for 'N41E009.hgt.zip'...\n", "Trying 'https://dds.cr.usgs.gov/srtm/version2_1/SRTM3/Eurasia/N41E009.hgt.zip'... OK\n", "Saved in 'hgt_files_srtm3/N41E009.hgt'\n", "Looking for 'N43E009.hgt.zip'...\n", "Trying 'https://dds.cr.usgs.gov/srtm/version2_1/SRTM3/Eurasia/N43E009.hgt.zip'... OK\n", "Saved in 'hgt_files_srtm3/N43E009.hgt'\n", "Looking for 'N42E009.hgt.zip'...\n", "Trying 'https://dds.cr.usgs.gov/srtm/version2_1/SRTM3/Eurasia/N42E009.hgt.zip'... OK\n", "Saved in 'hgt_files_srtm3/N42E009.hgt'\n", "Looking for 'N43E008.hgt.zip'...\n", "Trying 'https://dds.cr.usgs.gov/srtm/version2_1/SRTM3/Eurasia/N43E008.hgt.zip'... OK\n", "Saved in 'hgt_files_srtm3/N43E008.hgt'\n" ] } ], "source": [ "import os\n", "import urllib\n", "from zipfile import ZipFile\n", "import io\n", "from io import BytesIO\n", "\n", "\n", "base_url = \"https://dds.cr.usgs.gov/srtm/version2_1/\"\n", "regions = dict(\n", " SRTM1=[\"Region_01\", \"Region_02\", \"Region_03\", \"Region_04\", \"Region_05\", \"Region_06\", \"Region_07\"],\n", " SRTM3=[\"Eurasia\", \"Africa\", \"Islands\", \"North_America\", \"South_America\"],\n", ")\n", "\n", "if REGION:\n", " regions[TYPE] = [REGION]\n", "\n", "found_tiles = set()\n", "\n", "for tile in tile_list:\n", " \n", " print(\"Looking for '%s'...\" % tile)\n", " \n", " hgt_fn = os.path.splitext(tile)[0]\n", " hgt_fpath = os.path.join(HGT_DIR, hgt_fn)\n", " if os.path.exists(hgt_fpath):\n", " print(\"'%s' already here, skipping...\" % hgt_fpath)\n", " found_tiles.add(hgt_fn)\n", " continue\n", " \n", " found = False\n", " \n", " for region in regions[TYPE]:\n", " url = os.path.join(base_url, TYPE, region, tile)\n", " print(\"Trying '%s'... \" % url, end=\"\")\n", " \n", " \"\"\"\n", " try:\n", " with ZipFile(io.BytesIO(urllib.request.urlopen(url).read())) as zf:\n", " fn = next(iter(zf.namelist()))\n", " fpath = zf.extract(fn, path=HGT_DIR) # HGT_DIR is create automatically\n", " print(\"OK\")\n", " print(\"Saved in '%s'\" % fpath)\n", " found = True\n", " break\n", " except urllib.error.URLError:\n", " print(\"not found\")\n", " \"\"\"\n", " \n", " \n", " # if the previous block doesn't work (download is hanging), try pycurl:\n", " import pycurl\n", " buffer = BytesIO()\n", " header = BytesIO()\n", " c = pycurl.Curl()\n", " c.setopt(c.URL, url)\n", " c.setopt(c.WRITEDATA, buffer)\n", " c.setopt(c.HEADERFUNCTION, header.write)\n", " c.perform()\n", " #input(len(buffer.getvalue()))\n", " #input(buffer.getvalue())\n", " if c.getinfo(pycurl.HTTP_CODE) == 404:\n", " print(\"not found\")\n", " else:\n", " with ZipFile(buffer) as zf:\n", " fn = next(iter(zf.namelist()))\n", " fpath = zf.extract(fn, path=HGT_DIR) # HGT_DIR is create automatically\n", " print(\"OK\")\n", " print(\"Saved in '%s'\" % fpath)\n", " found = True\n", " break\n", " c.close()\n", "\n", " \n", " if found:\n", " found_tiles.add(hgt_fn)\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "NOTE: if some tiles can't be found, that means that there are all sea, and so aren't present in the data (because no elevation on sea!)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Make LandSerf scripts" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, we have to make LandSerf scripts. LandSerf is a software by Jo Wood that allow you to load elevation data and add colors and contours to them.\n", "\n", "You must download it at http://www.staff.city.ac.uk/~jwo/landserf/download/. It is free for non commercial use.\n", "\n", "Assuming you have downloaded LandSerf from http://www.staff.city.ac.uk/~jwo/landserf/download/, you just have to run the `landserf.sh` script (if you are on Linux; on other platforms, just see the instructions on the web site, it's well documented):\n", "\n", " bash landserf.sh\n", "\n", "For large files, you may run out of memory, so you can add more memore by editing the `landserf.sh` script. Edit the line beginning with:\n", "\n", " java -Xmx4G...\n", "\n", "to add as many megabytes or gigabytes you want. See the java documentation (Google is fine for this search) for the `-Xmx` option.\n", "\n", "Once you have run the script, a window is displayed. In the `Edit` menu, choose `LandScript Editor` (at the bottom of the menu). This opens a new window. Copy the output of the functions below (convert to srf format, combine, find the minimum and maximum values), one at a time, and run each of them with the menu `Run > Run`.\n", "\n", "If you're in a hurry and want to execute all the scripts at once in the LandScript Editor, run all the following cells without copying anything, then run the cell under \"If you want all the scripts at once\" and copy the output of the last cell: it's just a concatenation of all the intermediate scripts." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Convert to `srf` format" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, we need to convert the `hgt` files downloaded from NASA to `srf` files used by LandSerf:\n", "* open LandSerf (in the LandSerf directory, run `bash landserf.sh`),\n", "* go the menu `Edit > LandScript Editor`\n", "* execute the following cells and copy the output of the last cell in the LandScript editor,\n", "* click on `Run` in the editor window." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "REMOVE_VOIDS = True\n", "SRF_DIR = 'srf_files'\n", "\n", "import os\n", "\n", "if not os.path.exists(SRF_DIR):\n", " print(\"Creating '%s'\" % SRF_DIR)\n", " os.mkdir(SRF_DIR)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "====== COPY THE FOLLOWING LINES IN THE LANDSCRIPT EDITOR ======\n", "\n", "version(1.0);\n", "\n", "raster = open(\"/home/bruno/prog/src/elevation_map_maker/hgt_files_srtm3/N43E009.hgt\");\n", "raster = removevoids(raster_);\n", "save(raster, \"/home/bruno/prog/src/elevation_map_maker/srf_files/N43E009.hgt.srf\");\n", "\n", "raster = open(\"/home/bruno/prog/src/elevation_map_maker/hgt_files_srtm3/N43E008.hgt\");\n", "raster = removevoids(raster_);\n", "save(raster, \"/home/bruno/prog/src/elevation_map_maker/srf_files/N43E008.hgt.srf\");\n", "\n", "raster = open(\"/home/bruno/prog/src/elevation_map_maker/hgt_files_srtm3/N42E009.hgt\");\n", "raster = removevoids(raster_);\n", "save(raster, \"/home/bruno/prog/src/elevation_map_maker/srf_files/N42E009.hgt.srf\");\n", "\n", "raster = open(\"/home/bruno/prog/src/elevation_map_maker/hgt_files_srtm3/N42E008.hgt\");\n", "raster = removevoids(raster_);\n", "save(raster, \"/home/bruno/prog/src/elevation_map_maker/srf_files/N42E008.hgt.srf\");\n", "\n", "raster = open(\"/home/bruno/prog/src/elevation_map_maker/hgt_files_srtm3/N41E008.hgt\");\n", "raster = removevoids(raster_);\n", "save(raster, \"/home/bruno/prog/src/elevation_map_maker/srf_files/N41E008.hgt.srf\");\n", "\n", "raster = open(\"/home/bruno/prog/src/elevation_map_maker/hgt_files_srtm3/N41E009.hgt\");\n", "raster = removevoids(raster_);\n", "save(raster, \"/home/bruno/prog/src/elevation_map_maker/srf_files/N41E009.hgt.srf\");\n", "\n", "\n", "===============================================================\n" ] } ], "source": [ "import os\n", "\n", "script1 = \"version(1.0);\\n\\n\"\n", "\n", "for tile in found_tiles:\n", " \n", " infpath = os.path.join(os.getcwd(), HGT_DIR, tile)\n", " outfpath = os.path.join(os.getcwd(), SRF_DIR, tile) + \".srf\"\n", " script1 += 'raster = open(\"%s\");\\n' % infpath\n", " if REMOVE_VOIDS:\n", " script1 += 'raster = removevoids(raster_);\\n'\n", " script1 += 'save(raster, \"%s\");\\n\\n' % outfpath\n", "\n", "print(\"====== COPY THE FOLLOWING LINES IN THE LANDSCRIPT EDITOR ======\\n\")\n", "print(script1)\n", "print(\"===============================================================\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Combine multiple tiles into one" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, we have different parts of the final map. It's time to combined them all in one map!\n", "\n", "Enter a name for the combined file:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "COMBINED_FILE = 'combined.srf'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As before, execute the cell, copy the output and paste it in the LandScript editor!" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "====== COPY THE FOLLOWING LINES IN THE LANDSCRIPT EDITOR ======\n", "\n", "version(1.0);\n", "\n", "raster = open(\"/home/bruno/prog/src/elevation_map_maker/srf_files/N43E009.hgt.srf\");\n", "raster = combine(raster_, open(\"/home/bruno/prog/src/elevation_map_maker/srf_files/N43E008.hgt.srf\"));\n", "raster = combine(raster_, open(\"/home/bruno/prog/src/elevation_map_maker/srf_files/N42E009.hgt.srf\"));\n", "raster = combine(raster_, open(\"/home/bruno/prog/src/elevation_map_maker/srf_files/N42E008.hgt.srf\"));\n", "raster = combine(raster_, open(\"/home/bruno/prog/src/elevation_map_maker/srf_files/N41E008.hgt.srf\"));\n", "raster = combine(raster_, open(\"/home/bruno/prog/src/elevation_map_maker/srf_files/N41E009.hgt.srf\"));\n", "save(raster, \"/home/bruno/prog/src/elevation_map_maker/srf_files/combined.srf\");\n", "\n", "\n", "===============================================================\n" ] } ], "source": [ "import os\n", "\n", "script2 = \"version(1.0);\\n\\n\"\n", "\n", "outfpath = os.path.join(os.getcwd(), SRF_DIR, COMBINED_FILE)\n", "\n", "for i, tile in enumerate(found_tiles):\n", " \n", " infpath = os.path.join(os.getcwd(), SRF_DIR, tile) + \".srf\"\n", " \n", " #if not os.path.exists(infpath):\n", " # raise FileNotFoundError(infpath)\n", " \n", " if i == 0:\n", " script2 += 'raster = open(\"%s\");\\n' % infpath\n", " else:\n", " script2 += 'raster = combine(raster_, open(\"%s\"));\\n' % infpath\n", "\n", "script2 += 'save(raster, \"%s\");\\n\\n' % outfpath\n", "\n", "print(\"====== COPY THE FOLLOWING LINES IN THE LANDSCRIPT EDITOR ======\\n\")\n", "print(script2)\n", "print(\"===============================================================\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Add colors and contours" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now you have one more step, the creative one, where you add color and, if you want, contours. Below you have a function to create a default script, but you can read the LandSerf documentation (on the web site) and change the color, change the scale, add different type of contours, etc.\n", "\n", "As before, just edit the variable values, execute the cells and copy the output in the LandScript editor." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First some options:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "CONTOURS = False # do you want contours?\n", "SMOOTH = True # do you want smooth or sharp edge between color?\n", "VERTICAL_EXAGERATION = \"3\" # a string (see LandSerf documentation)\n", "SUN_ELEVATION = \"45\" # a string, between 0 and 90 (see LandSerf documentation)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "contours = \"\"\"\n", "contours = contour(raster,min_height+(inc/2),inc,1);\n", "colouredit(contours,\"rules\",\"0 50 10 0\");\n", "vectorstyle(\"linewidth\",0.1);\n", "\"\"\" if CONTOURS else 'contours = \"null\";'" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "====== COPY THE FOLLOWING LINES IN THE LANDSCRIPT EDITOR ======\n", "\n", "version(1.0);\n", "\n", "raster = open(\"/home/bruno/prog/src/elevation_map_maker/srf_files/combined.srf\");\n", "\n", "# change the projection\n", "#raster = reproject(raster_,\"OSGB\",\"true\",90,90);\n", "\n", "min_height = info(raster,\"min\");\n", "max_height = info(raster,\"max\");\n", "range = max_height - min_height;\n", "# increment\n", "inc = range/7;\n", "\n", "colouredit(raster,\"rules\", \"0\" & \" 121 177 196 (D),\" &\n", " \"1\" & \" 141 166 141 (D),\" &\n", " min_height & \" 141 166 141 ,\" &\n", " min_height+1*inc & \" 172 194 155 ,\" &\n", " min_height+2*inc & \" 221 219 167 ,\" & \n", " min_height+3*inc & \" 254 235 181 ,\" &\n", " min_height+4*inc & \" 248 212 153 ,\" &\n", " min_height+5*inc & \" 241 170 109 ,\" &\n", " min_height+6*inc & \" 227 112 72 \");\n", "\n", "# for the blue at the sea level (0), you can also use 141 141 166\n", "\n", "contours = \"null\";\n", "\n", "edit(raster,\"sunElev\",45);\n", "edit(raster,\"vExag\",3);\n", "\n", "draw(\"/home/bruno/prog/src/elevation_map_maker/srf_files/combined.srf.png\",raster,raster,contours,\"null\",\"relief\");\n", "\n", "===============================================================\n" ] } ], "source": [ "infpath = os.path.join(os.getcwd(), SRF_DIR, COMBINED_FILE)\n", "outfpath = infpath + \".png\"\n", "\n", "script3 = \"\"\"\n", "version(1.0);\n", "\n", "raster = open(\"{infpath}\");\n", "\n", "# change the projection\n", "#raster = reproject(raster_,\"OSGB\",\"true\",90,90);\n", "\n", "min_height = info(raster,\"min\");\n", "max_height = info(raster,\"max\");\n", "range = max_height - min_height;\n", "# increment\n", "inc = range/7;\n", "\n", "colouredit(raster,\"rules\", \"0\" & \" 121 177 196 (D),\" &\n", " \"1\" & \" 141 166 141 (D),\" &\n", " min_height & \" 141 166 141 {sharp},\" &\n", " min_height+1*inc & \" 172 194 155 {sharp},\" &\n", " min_height+2*inc & \" 221 219 167 {sharp},\" & \n", " min_height+3*inc & \" 254 235 181 {sharp},\" &\n", " min_height+4*inc & \" 248 212 153 {sharp},\" &\n", " min_height+5*inc & \" 241 170 109 {sharp},\" &\n", " min_height+6*inc & \" 227 112 72 {sharp}\");\n", "\n", "# for the blue at the sea level (0), you can also use 141 141 166\n", "\n", "{contours}\n", "\n", "edit(raster,\"sunElev\",{sun_elev});\n", "edit(raster,\"vExag\",{v_exag});\n", "\n", "draw(\"{outfpath}\",raster,raster,contours,\"null\",\"relief\");\n", "\"\"\".format(**dict(\n", " infpath=infpath,\n", " outfpath=outfpath,\n", " contours=contours,\n", " sun_elev=SUN_ELEVATION,\n", " v_exag=VERTICAL_EXAGERATION,\n", " sharp=(\"\" if SMOOTH else \"(D)\"),\n", "))\n", "\n", "print(\"====== COPY THE FOLLOWING LINES IN THE LANDSCRIPT EDITOR ======\")\n", "print(script3)\n", "print(\"===============================================================\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This was the last step! Your final image is in the following file:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'/home/bruno/prog/src/elevation_map_maker/srf_files/combined.srf.png'" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "outfpath" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can open it with an external editor, or make a thumbnail and show it here (you will need `pillow`):" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from PIL import Image\n", "from IPython.display import display\n", "\n", "im = Image.open(outfpath)\n", "im.thumbnail((400, 400), Image.ANTIALIAS)\n", "\n", "display(im)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that all sea area with no elevation are transparent. This is because NASA doesn't have any file for it." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# If you want all the LandSerf scripts at once\n", "\n", "If you have not yet executed the previous script in LandSerf (but have run the cells) and want to execute them in one pass, just run the following cell and copy/paste the output in the LandScript Editor." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": false }, "outputs": [], "source": [ "print(\"====== COPY THE FOLLOWING LINES IN THE LANDSCRIPT EDITOR ======\\n\")\n", "print(script1)\n", "print(script2)\n", "print(script3)\n", "print(\"===============================================================\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "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": "3.7.5" } }, "nbformat": 4, "nbformat_minor": 2 }