{"cells":[{"metadata":{},"cell_type":"markdown","source":"\n    Title: Spatio Temporal Clustering\n    Author: Benseguane Abderrahim\n    Date: 23/03/2020\n    Code version: 0\n    Kaggle team: Data Solution\n    Company: ENGIE IT"},{"metadata":{},"cell_type":"markdown","source":"### Spatio-temporal feature extraction\n\n#### Unsuppervised learning approach"},{"metadata":{},"cell_type":"markdown","source":"Note: It was hard for me to install gdal and arcpy in this kernel, inside Kaggle, so please take time to install these packages like you can.\nAlso be careful about paths to files, and all should work fine."},{"metadata":{},"cell_type":"markdown","source":"Intro:\n\nThis challenge introduced me to geoprocessing, and Raster file format. Now it became apparent to me that Raster files are really to be treated so! It is not just a 2D image. \n\nFor example:\nA raster image is an image file format that is defined by a pixel that has one or more numbers associated with it.\nIt has environment variables, like Extent, Origin, .. \n(see https://desktop.arcgis.com/fr/arcmap/10.3/manage-data/raster-and-images/what-is-raster-data.htm)\n\nAs a result, clustering analysis, missing values imputations, and many other analysis, need to take these into consideration. \nHopefully, there are people specilized in these, and the open source community if full of easy and sophisticated tools.\n\nHere I tried to use QGis and ArcGis Pro (see http://arcgis.com) to ease exploration, but most importantly to export commands and run then from Python on all files (batch mode like). \nMost QGis processing routine call GDAL python library (see https://www.osgeo.org/) and also some arcgis tools are also documented giving there equivalent arcpy code (see https://pro.arcgis.com/en/pro-app/arcpy/get-started/what-is-arcpy-.htm)\n"},{"metadata":{"trusted":false},"cell_type":"code","source":"import glob, os\nimport gdal\nfrom arcpy import env\nfrom arcpy.sa import *\nfrom arcpy import RasterToNumPyArray\nimport numpy as np\n\nfor file in glob.glob(\"*.tif\"):\n    print(file)\n    break","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Lets count nan values in our fils"},{"metadata":{"trusted":false},"cell_type":"code","source":"np.count_nonzero(np.isnan(RasterToNumPyArray(Raster('s5p_no2_20180917T164548_20180923T182508.tif'))))","execution_count":null,"outputs":[]},{"metadata":{"trusted":false},"cell_type":"code","source":"np.count_nonzero(np.isnan(RasterToNumPyArray(Raster('20180917T164548_20180923T182508.tif.tiff'))))","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Lets see if there are different extent coordinates"},{"metadata":{"trusted":false},"cell_type":"code","source":"\n\nfull_files = []\nextents = []\n\none_extent = '{\"xmin\":-67.324313912888414,\"ymin\":17.90015055109518,\"xmax\":-65.190815113104549,\"ymax\":18.564903861343627,\"spatialReference\":{\"wkid\":4326,\"latestWkid\":4326}}'\nfor file in glob.glob(\"NODATA/*.tiff\"):  \n    outraster0 = Raster(file)\n    arr0 = RasterToNumPyArray(outraster0)\n    extents.append(outraster0.extent.JSON)\n    if(np.count_nonzero(np.isnan(arr0))==0):\n        full_files.append(file[7:])","execution_count":null,"outputs":[]},{"metadata":{"trusted":false},"cell_type":"code","source":"list(set(extents))","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Let's unify extents"},{"metadata":{"trusted":false},"cell_type":"code","source":"one_extent = '{\"xmin\":-67.324313912888414,\"ymin\":17.90015055109518,\"xmax\":-65.190815113104549,\"ymax\":18.564903861343627,\"spatialReference\":{\"wkid\":4326,\"latestWkid\":4326}}'\nfor file in glob.glob(\"NODATA/*.tiff\"):\n    filename = os.path.basename(file)[8:]\n    ds = gdal.Translate(\"NODATA_NORM/\"+filename, file, projWin = [-67.32, 17.90, -65.19, 18.56])\n    if ds is None:\n        print(file + ' failed')","execution_count":null,"outputs":[]},{"metadata":{"trusted":false},"cell_type":"code","source":"outraster0.extent.JSON","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"#### Translate .tif to .vrt, extracting bands to different folders \n##### With gdal.Translate"},{"metadata":{},"cell_type":"markdown","source":"We can export files to there specific separate bands in different files"},{"metadata":{"trusted":false},"cell_type":"code","source":"# AttributeError: 'NoneType' object has no attribute 'GetMaskBand'","execution_count":null,"outputs":[]},{"metadata":{"trusted":false},"cell_type":"code","source":"# This saves bands to individual files\n\n\nfrom osgeo import gdal\n\n\nfor i in range(12):\n    os.mkdir(\"output_multibands/band_\"+str(i+1))\n\nfor file in glob.glob(\"*.tif\"):\n    in_path = file  #input composite raster\n    out_path = 'output_multibands/' #output directory for individual bands as files\n\n    #Open existing raster ds\n    src_ds = gdal.Open(in_path)\n\n    for i in range(1,src_ds.RasterCount +1): #Save bands as individual files\n        out_ds = gdal.Translate(out_path + '/band_' + str(i) + \"/\" + file.split('.')[0]+\".vrt\", src_ds, format='VRT', bandList=[i])\n        out_ds=None\n        \n","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"I will be running gdal routines using subprocess#check_output"},{"metadata":{"trusted":false},"cell_type":"code","source":"[\"\\\"C:/Program Files/QGIS 3.12/bin/python3.exe\\\"\",\"\\\"C:/Program Files/QGIS 3.12/apps/Python37/Scripts/gdal_fillnodata.py\\\"\", \"-md \", \" 10 \", \" -b \", \" 4 \", \"-of\", \"GTiff\", \"C:/Users/HR5537/Downloads/ds4g-environmental-insights-explorer/eie_data/s5p_no2/s5p_no2_20190629T174803_20190705T194117.tif\", \"C:/Users/HR5537/Downloads/ds4g-environmental-insights-explorer/eie_data/s5p_no2/OUTPUT4.tif\"]","execution_count":null,"outputs":[]},{"metadata":{"trusted":false},"cell_type":"code","source":"\"\\\"C:/Program Files/QGIS 3.12/bin/python3.exe\\\" \\\"C:/Program Files/QGIS 3.12/apps/Python37/Scripts/gdal_fillnodata.py\\\" -md 10 -b 4 -of GTiff  C:/Users/HR5537/Downloads/ds4g-environmental-insights-explorer/eie_data/s5p_no2/s5p_no2_20190629T174803_20190705T194117.tif OUTPUT10.tif\"","execution_count":null,"outputs":[]},{"metadata":{"trusted":false},"cell_type":"code","source":"# \"\\\"C:\\Program Files\\QGIS 3.12\\bin\\python3.exe\\\" \\\"C:\\Program Files\\QGIS 3.12\\apps\\Python37\\Scripts\\gdal_fillnodata.py\\\" -md 10 -b 4 -of GTiff  s5p_no2_20180705T163817_20180712T105412.tif OUTPUT.tif","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"#### Add nodata value to rasters tif \n##### with gdalwarp"},{"metadata":{"trusted":false},"cell_type":"code","source":"# Risky: shell commands from python.\nfrom subprocess import check_output\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":false},"cell_type":"code","source":"# let's analyse by one band at a time,\n# taking first band here\n# Note even when not tif original file is not specified in input, \n# it must be there with the vrt file (for some reason. I don't know why)\n\n\nfor file in glob.glob(\"*.vrt\"):\n    check_output(\"gdalwarp \"+file+\" \"+file.split('.')[0]+\"_warp.tif -dstnodata -9999\", shell=True)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Separatly, we can approximate NODATA points using gdal_fillnodata "},{"metadata":{},"cell_type":"markdown","source":"The following command is to be run in Windows Bash, I could not use subprocess#check_output to run it for some reason. "},{"metadata":{},"cell_type":"markdown","source":"FOR %i IN (C:\\Users\\HR5537\\Downloads\\ds4g-environmental-insights-explorer\\eie_data\\s5p_no2\\NODATA\\\\*.tif) DO \"C:/Program Files/QGIS 3.12/bin/python3.exe\" \"C:/Program Files/QGIS 3.12/apps/Python37/Scripts/gdal_fillnodata.py\" -md 10 -b 4 -of GTiff  \"%i\" \"%i.tif2\""},{"metadata":{},"cell_type":"markdown","source":"FOR %i IN (C:\\Users\\HR5537\\Downloads\\ds4g-environmental-insights-explorer\\eie_data\\s5p_no2\\NODATA\\\\*.tif) DO \"C:/Program Files/QGIS 3.12/bin/python3.exe\" \"C:/Program Files/QGIS 3.12/apps/Python37/Scripts/gdal_fillnodata.py\" -md 10 -b 4 -of GTiff  \"%i\" \"%i.tiff\""},{"metadata":{},"cell_type":"markdown","source":"### Calculate spatial signature on out time-serie satellite images\n##### Using: gpat_gridts "},{"metadata":{},"cell_type":"markdown","source":"Let's build gpat_gridts commands\npart of GeoPAT2 (see https://usermanual.wiki/Pdf/GeoPAT2Manual.703444930/)"},{"metadata":{"trusted":false},"cell_type":"code","source":"import glob\n\ninput_builder = []\nos.chdir(\"NODATA_NORM\")\n\nfor file in glob.glob(\"*.tiff\"): #NODATA_NORM/*.tiff\n    input_builder.append(os.path.basename(file))\ninputs = np.array_split(input_builder, 12)\ninputs = [\" -i \" + \" -i \".join(input) for input in inputs]\ncommands = [\"gpat_gridts \"+input+\" -o spatiotemporalgrid -n\" for input in inputs]\n# check_output(\"gpat_gridts \"+input_files+\" -o spatiotemporalgrid -n\", shell=True)\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":false},"cell_type":"code","source":"check_output(\"gpat_gridts -i s5p_no2_20190612T162549_20190618T180057_warp.tif -i s5p_no2_20190613T160647_20190619T174418_warp.tif -i s5p_no2_20190613T174817_20190619T193125_warp.tif -i s5p_no2_20190614T172915_20190620T191204_warp.tif -i s5p_no2_20190616T165111_20190622T183247_warp.tif -i s5p_no2_20190617T163209_20190623T182116_warp.tif -i s5p_no2_20190618T161307_20190624T180620_warp.tif -i s5p_no2_20190619T173534_20190625T192848_warp.tif -i s5p_no2_20190620T171632_20190626T190833_warp.tif -i s5p_no2_20190621T165729_20190627T185343_warp.tif -i s5p_no2_20190622T163826_20190628T181049_warp.tif -i s5p_no2_20190623T161923_20190629T180700_warp.tif -i s5p_no2_20190624T160021_20190630T174143_warp.tif -i s5p_no2_20190624T174150_20190630T192108_warp.tif -i s5p_no2_20190625T172247_20190701T190612_warp.tif -i s5p_no2_20190626T170344_20190703T114437_warp.tif -i s5p_no2_20190627T164441_20190703T183146_warp.tif -i s5p_no2_20190628T162537_20190704T180917_warp.tif -i s5p_no2_20190629T160634_20190705T174521_warp.tif -i s5p_no2_20190629T174803_20190705T194117_warp.tif -o spatiotemporalgrid -n\", shell=True)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"The gotcha here, is that gpat_gridts is very sensible to missing values, and different environment values in input files, so \nto make it work, we better unify files (impute NODATA, unify extents...)\nAlso, I think gpat_gridts cannot catch long list of files for some reason. so we can iterately run on time oredered batch of files and get clustering results from each, then finally average clustering results. "},{"metadata":{"trusted":false},"cell_type":"code","source":"commands[1]","execution_count":null,"outputs":[]},{"metadata":{"trusted":false},"cell_type":"code","source":"check_output(commands[1], shell=True)\n","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"#### latlon.csv is the list of geo-points to find very similar temporal patterns of a NO2 (NO2_column_number_density).    "},{"metadata":{"trusted":false},"cell_type":"code","source":"# df_power_plants = pd.read_csv('gppd_120_pr.csv')\n# import json\n\n# def string_to_dict(dict_string):\n#     # Convert to proper json format (from here: https://stackoverflow.com/questions/39169718/convert-string-to-dict-then-access-keyvalues-how-to-access-data-in-a-class)\n#     dict_string = dict_string.replace(\"'\", '\"').replace('u\"', '\"')\n#     return json.loads(dict_string)['coordinates']\n\n# df_power_plants['coord'] = df_power_plants['.geo'].apply(string_to_dict)\n# pd.DataFrame(df_power_plants['coord'].values.tolist()).to_csv('latlon.csv', index=False)","execution_count":null,"outputs":[]},{"metadata":{"trusted":false},"cell_type":"code","source":"os.chdir(\"NODATA_NORM\")","execution_count":null,"outputs":[]},{"metadata":{"trusted":false},"cell_type":"code","source":"check_output(\"gpat_pointsts -i spatiotemporalgrid -o stpoints --xy_file=latlon.csv\", shell=True)","execution_count":null,"outputs":[]},{"metadata":{"scrolled":true,"trusted":false},"cell_type":"code","source":"check_output(\"gpat_distmtx -i stpoints -o result.csv -m tsEUC\", shell=True)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"#### Plot!"},{"metadata":{"trusted":false},"cell_type":"code","source":"# install.packages(\"mapproj\")\n# library(sf)\n# library(tmap)\n# library(tigris)\n# library(rgeopat2)\n# library(mapproj)\n\n# distmatrix = gpat_read_distmtx(\"result.csv\")\n# hclust_result = hclust(d = distmatrix, method = \"ward.D2\")\n# plot(hclust_result, labels = FALSE)\n# sel_points = read.csv(\"latlon.csv\", header = FALSE)\n# sel_points$class = as.factor(cutree(hclust_result, k = 4))\n# sel_points_st = st_as_sf(sel_points, coords = c(\"V1\", \"V2\"))\n\n\n# me = counties(\"PR\", cb = TRUE).\n# tm_shape(me) + tm_borders(alpha=0.5) + tm_shape(sel_points_st) + tm_dots(col = \"class\", size = 0.25, title = \"Class: \")","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"![plot.png](attachment:plot.png)","attachments":{"plot.png":{"image/png":"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"}}},{"metadata":{},"cell_type":"markdown","source":"##### Build a z-axis and visualize"},{"metadata":{"trusted":false},"cell_type":"code","source":"# > library(raster)\n# > library(rasterVis)\n# > listFich <- dir(pattern='\\\\.tiff')\n# > stackSIS <- stack(listFich)\n# > idx <- seq(as.Date('2008-07-14'), as.Date('2008-07-17'), 'day')\n# > SISmm <- setZ(stackSIS, idx)\n# > names(SISmm) <- c('07/14','07/15','07/16','07/18')\n# > levelplot(SISmm)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"![plot3.PNG](attachment:plot3.PNG)","attachments":{"plot3.PNG":{"image/png":"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"}}},{"metadata":{},"cell_type":"markdown","source":"Alternatives !!\nApparently, there are many studies in the academia, about spatio-temporal clustering, in most cases I found, it is about clustering a set of datapoints dispersed on a single map area, and polygons need to be present in the map and not for clustering all values without boundaries. Nevertheless, here I share all, to gain perspectives and hopefully other perspectives.\n\nhttps://github.com/Kersauson/ST-DBSCAN\nhttps://tslearn.readthedocs.io/en/latest/gen_modules/clustering/tslearn.clustering.TimeSeriesKMeans.html\n\nz-axis (time):\nhttps://en.wikipedia.org/wiki/Hovm%C3%B6ller_diagram\nhttps://informatique-mia.inra.fr/resste/sites/informatique-mia.inra.fr.resste/files/analyzing-spatio-temporal.pdf\nhttps://oscarperpinan.github.io/rastervis/\n"}],"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":"3.7.1"}},"nbformat":4,"nbformat_minor":4}