From 4b260850a2a4e406687f561dd8c57eff782f0a2f Mon Sep 17 00:00:00 2001 From: lgperez Date: Tue, 17 Jul 2018 15:33:12 -0700 Subject: [PATCH] included the r scripts included the r scripts in github to have a better versioning control. --- ArcGIS/##Arcpy on Python Console ArcMAP.py | 89 ++ ArcGIS/Batch_zonal_stats.py | 193 +++++ ...CalculateNDVIandNiblackFromHyperIterate.py | 104 +++ .../ChooseFieldValueToolbox.tbx | Bin 0 -> 182272 bytes .../Scratch.gdb/a00000001.TablesByName.atx | Bin 0 -> 4118 bytes .../Scratch/Scratch.gdb/a00000001.freelist | Bin 0 -> 4440 bytes .../Scratch/Scratch.gdb/a00000001.gdbindexes | Bin 0 -> 110 bytes .../Scratch/Scratch.gdb/a00000001.gdbtable | Bin 0 -> 326 bytes .../Scratch/Scratch.gdb/a00000001.gdbtablx | Bin 0 -> 5152 bytes .../Scratch/Scratch.gdb/a00000002.gdbtable | Bin 0 -> 1456 bytes .../Scratch/Scratch.gdb/a00000002.gdbtablx | Bin 0 -> 5152 bytes .../Scratch/Scratch.gdb/a00000003.gdbindexes | Bin 0 -> 42 bytes .../Scratch/Scratch.gdb/a00000003.gdbtable | Bin 0 -> 1057 bytes .../Scratch/Scratch.gdb/a00000003.gdbtablx | Bin 0 -> 5152 bytes .../a00000004.CatItemsByPhysicalName.atx | Bin 0 -> 4118 bytes .../Scratch.gdb/a00000004.CatItemsByType.atx | Bin 0 -> 4118 bytes .../Scratch.gdb/a00000004.FDO_UUID.atx | Bin 0 -> 4118 bytes .../Scratch/Scratch.gdb/a00000004.freelist | Bin 0 -> 37208 bytes .../Scratch/Scratch.gdb/a00000004.gdbindexes | Bin 0 -> 310 bytes .../Scratch/Scratch.gdb/a00000004.gdbtable | Bin 0 -> 325048 bytes .../Scratch/Scratch.gdb/a00000004.gdbtablx | Bin 0 -> 5152 bytes .../Scratch/Scratch.gdb/a00000004.spx | Bin 0 -> 4118 bytes .../a00000005.CatItemTypesByName.atx | Bin 0 -> 12310 bytes .../a00000005.CatItemTypesByParentTypeID.atx | Bin 0 -> 4118 bytes .../a00000005.CatItemTypesByUUID.atx | Bin 0 -> 4118 bytes .../Scratch/Scratch.gdb/a00000005.gdbindexes | Bin 0 -> 296 bytes .../Scratch/Scratch.gdb/a00000005.gdbtable | Bin 0 -> 1803 bytes .../Scratch/Scratch.gdb/a00000005.gdbtablx | Bin 0 -> 5152 bytes .../a00000006.CatRelsByDestinationID.atx | Bin 0 -> 4118 bytes .../a00000006.CatRelsByOriginID.atx | Bin 0 -> 4118 bytes .../Scratch.gdb/a00000006.CatRelsByType.atx | Bin 0 -> 4118 bytes .../Scratch.gdb/a00000006.FDO_UUID.atx | Bin 0 -> 4118 bytes .../Scratch/Scratch.gdb/a00000006.freelist | Bin 0 -> 4440 bytes .../Scratch/Scratch.gdb/a00000006.gdbindexes | Bin 0 -> 318 bytes .../Scratch/Scratch.gdb/a00000006.gdbtable | Bin 0 -> 263 bytes .../Scratch/Scratch.gdb/a00000006.gdbtablx | Bin 0 -> 5152 bytes .../a00000007.CatRelTypesByBackwardLabel.atx | Bin 0 -> 4118 bytes .../a00000007.CatRelTypesByDestItemTypeID.atx | Bin 0 -> 4118 bytes .../a00000007.CatRelTypesByForwardLabel.atx | Bin 0 -> 4118 bytes .../a00000007.CatRelTypesByName.atx | Bin 0 -> 4118 bytes ...00000007.CatRelTypesByOriginItemTypeID.atx | Bin 0 -> 4118 bytes .../a00000007.CatRelTypesByUUID.atx | Bin 0 -> 4118 bytes .../Scratch/Scratch.gdb/a00000007.gdbindexes | Bin 0 -> 602 bytes .../Scratch/Scratch.gdb/a00000007.gdbtable | Bin 0 -> 2504 bytes .../Scratch/Scratch.gdb/a00000007.gdbtablx | Bin 0 -> 5152 bytes .../ChooseFieldValue/Scratch/Scratch.gdb/gdb | Bin 0 -> 4 bytes .../Scratch/Scratch.gdb/timestamps | Bin 0 -> 400 bytes .../Script/DynamicValueList.py | 9 + .../ToolData.gdb/a00000001.TablesByName.atx | Bin 0 -> 4118 bytes .../ToolData.gdb/a00000001.gdbindexes | Bin 0 -> 110 bytes .../ToolData/ToolData.gdb/a00000001.gdbtable | Bin 0 -> 324 bytes .../ToolData/ToolData.gdb/a00000001.gdbtablx | Bin 0 -> 5152 bytes .../ToolData/ToolData.gdb/a00000002.gdbtable | Bin 0 -> 1456 bytes .../ToolData/ToolData.gdb/a00000002.gdbtablx | Bin 0 -> 5152 bytes .../ToolData.gdb/a00000003.gdbindexes | Bin 0 -> 42 bytes .../ToolData/ToolData.gdb/a00000003.gdbtable | Bin 0 -> 1057 bytes .../ToolData/ToolData.gdb/a00000003.gdbtablx | Bin 0 -> 5152 bytes .../a00000004.CatItemsByPhysicalName.atx | Bin 0 -> 4118 bytes .../ToolData.gdb/a00000004.CatItemsByType.atx | Bin 0 -> 4118 bytes .../ToolData.gdb/a00000004.FDO_UUID.atx | Bin 0 -> 4118 bytes .../ToolData.gdb/a00000004.gdbindexes | Bin 0 -> 310 bytes .../ToolData/ToolData.gdb/a00000004.gdbtable | Bin 0 -> 49826 bytes .../ToolData/ToolData.gdb/a00000004.gdbtablx | Bin 0 -> 5152 bytes .../ToolData/ToolData.gdb/a00000004.spx | Bin 0 -> 4118 bytes .../a00000005.CatItemTypesByName.atx | Bin 0 -> 12310 bytes .../a00000005.CatItemTypesByParentTypeID.atx | Bin 0 -> 4118 bytes .../a00000005.CatItemTypesByUUID.atx | Bin 0 -> 4118 bytes .../ToolData.gdb/a00000005.gdbindexes | Bin 0 -> 296 bytes .../ToolData/ToolData.gdb/a00000005.gdbtable | Bin 0 -> 1803 bytes .../ToolData/ToolData.gdb/a00000005.gdbtablx | Bin 0 -> 5152 bytes .../a00000006.CatRelsByDestinationID.atx | Bin 0 -> 4118 bytes .../a00000006.CatRelsByOriginID.atx | Bin 0 -> 4118 bytes .../ToolData.gdb/a00000006.CatRelsByType.atx | Bin 0 -> 4118 bytes .../ToolData.gdb/a00000006.FDO_UUID.atx | Bin 0 -> 4118 bytes .../ToolData.gdb/a00000006.gdbindexes | Bin 0 -> 318 bytes .../ToolData/ToolData.gdb/a00000006.gdbtable | Bin 0 -> 263 bytes .../ToolData/ToolData.gdb/a00000006.gdbtablx | Bin 0 -> 5152 bytes .../a00000007.CatRelTypesByBackwardLabel.atx | Bin 0 -> 4118 bytes .../a00000007.CatRelTypesByDestItemTypeID.atx | Bin 0 -> 4118 bytes .../a00000007.CatRelTypesByForwardLabel.atx | Bin 0 -> 4118 bytes .../a00000007.CatRelTypesByName.atx | Bin 0 -> 4118 bytes ...00000007.CatRelTypesByOriginItemTypeID.atx | Bin 0 -> 4118 bytes .../a00000007.CatRelTypesByUUID.atx | Bin 0 -> 4118 bytes .../ToolData.gdb/a00000007.gdbindexes | Bin 0 -> 602 bytes .../ToolData/ToolData.gdb/a00000007.gdbtable | Bin 0 -> 2504 bytes .../ToolData/ToolData.gdb/a00000007.gdbtablx | Bin 0 -> 5152 bytes .../ToolData.gdb/a00000009.gdbindexes | Bin 0 -> 124 bytes .../ToolData/ToolData.gdb/a00000009.gdbtable | Bin 0 -> 182011 bytes .../ToolData/ToolData.gdb/a00000009.gdbtablx | Bin 0 -> 25632 bytes .../ToolData/ToolData.gdb/a00000009.spx | Bin 0 -> 45078 bytes .../ToolData/ToolData.gdb/gdb | Bin 0 -> 4 bytes .../ToolData/ToolData.gdb/timestamps | Bin 0 -> 400 bytes ArcGIS/ENVI.tbx | Bin 0 -> 6144 bytes ...ssion_gsp_vs_GS_on the go_displacement.cal | Bin 0 -> 166 bytes ...ion_gsp_vs_GS_on the go_displacement_Y.cal | Bin 0 -> 166 bytes ArcGIS/FocalStatisticsWeighted.py | 20 + ArcGIS/Herramientas.tbx | Bin 0 -> 1350656 bytes ArcGIS/NDVI.cal | Bin 0 -> 68 bytes ArcGIS/NDVI_NIR_RED.cal | Bin 0 -> 60 bytes ArcGIS/NDVI_float.exp | 2 + ArcGIS/Replace.cal | Bin 0 -> 86 bytes ArcGIS/Split_delimited_field.cal | Bin 0 -> 44 bytes ArcGIS/ZonalStats_hyper - bandas auto.py | 327 ++++++++ ArcGIS/addFieldb1-b250.py | 29 + ArcGIS/addLayerToView.txt | 9 + ArcGIS/celsius.cal | Bin 0 -> 56 bytes ArcGIS/createFolderIfNotExists.py | 17 + ArcGIS/data_handler.py | 764 ++++++++++++++++++ ArcGIS/deleteExnameRellenoOrEmpty.exp | Bin 0 -> 70 bytes ...leteExnameRellenoVorobeyBorlaugOrEmpty.exp | Bin 0 -> 156 bytes ArcGIS/descriptiveStatistics_manyColumns.R | 137 ++++ ArcGIS/descriptiveStatistics_protein.R | 92 +++ ArcGIS/extract-hyper-masking.py | 325 ++++++++ ArcGIS/extractOneBandToGDBFeature.py | 122 +++ ArcGIS/extract_data_multi6bandas.py | 180 +++++ ArcGIS/extract_multiband_raster.py | 325 ++++++++ ArcGIS/field_check.py | 73 ++ ArcGIS/getLayerPath.py | 8 + ArcGIS/in not null.exp | Bin 0 -> 50 bytes ArcGIS/iterate_raster_band_list.py | 44 + ArcGIS/joinZonalStats_hyper_BW - fijo.py | 134 +++ ArcGIS/joinZonalStats_hyper_BW - fijo1.py | 167 ++++ ArcGIS/joinZonalStats_hyper_BW - fijo2.py | 167 ++++ ArcGIS/joinZonalStats_hyper_BW.py | 193 +++++ ArcGIS/joinZonalStats_hyper_BWspr.py | 193 +++++ ...nalStats_hyper_PA - bandas auto - copia.py | 262 ++++++ .../joinZonalStats_hyper_PA - bandas auto.py | 325 ++++++++ ...A - bandas auto_NameFieldwithbuffername.py | 333 ++++++++ ...yper_PA - bandas auto_createSaveFeature.py | 327 ++++++++ ArcGIS/joinZonalStats_hyper_PA.py | 215 +++++ ArcGIS/joinZonalStats_hyper_unaTirada_BW.py | 80 ++ ...oinZonalStats_thermal_createSaveFeature.py | 204 +++++ ArcGIS/kelvin x 100 a celsius.exp | 2 + ArcGIS/legacy.tbx | Bin 0 -> 11810304 bytes ArcGIS/ndvi_hyper_spr.cal | Bin 0 -> 54 bytes ArcGIS/paste_plot_and_exname_AF.cal | Bin 0 -> 46 bytes ArcGIS/plots.pdf | Bin 0 -> 3448 bytes ArcGIS/raster symbology min-max.py | 26 + ArcGIS/read_band_names_from_header.py | 72 ++ ArcGIS/read_band_names_from_header_1.py | 115 +++ ArcGIS/replace_py.cal | Bin 0 -> 62 bytes ArcGIS/script_pruebas.py | 82 ++ ArcGIS/select no relleno y calor.exp | Bin 0 -> 360 bytes ArcGIS/select relleno and demo name.exp | Bin 0 -> 302 bytes ArcGIS/select relleno id.exp | Bin 0 -> 42 bytes ArcGIS/select relleno name.exp | Bin 0 -> 254 bytes ArcGIS/select relleno y calor.exp | Bin 0 -> 348 bytes ArcGIS/select relleno.exp | Bin 0 -> 82 bytes ArcGIS/select_slashes_in_name_field.exp | Bin 0 -> 38 bytes ArcGIS/sobelHorizontalKernel-9x9.txt | 10 + ArcGIS/sobelHorizontalKernel-modif.txt | 4 + ArcGIS/sobelHorizontalKernel.txt | 4 + ArcGIS/split python text to float.exp | 12 + ArcGIS/split python.cal.exp | Bin 0 -> 42 bytes ArcGIS/split_field_python.cal | Bin 0 -> 42 bytes ArcGIS/sselect like.exp | Bin 0 -> 38 bytes ArcGIS/stackCVStoXLSX.R | 24 + ArcGIS/timeSeries_joinData.R | 79 ++ ArcGIS/truncateName.py | 15 + ...zonalStatsMultibandCSVInputsSelectFiles.py | 441 ++++++++++ ArcGIS/zonalStatsMultiband_inFolder.py | 443 ++++++++++ ArcGIS/zonalStatsTablesAutoInputsCSV.py | 402 +++++++++ .../zonalStatsTablesAutoInputsCSV_AguaFria.py | 410 ++++++++++ ArcGIS/zonalStatsTablesAutoInputsCSV_GS.py | 413 ++++++++++ .../zonalStatsTablesAutoInputsCSV_sam_ad.py | 413 ++++++++++ .../zonalStatsTablesAutoInputsCSV_stars_ad.py | 412 ++++++++++ ArcGIS/zonalStatsTables_MultibandImage .py | 351 ++++++++ ArcGIS/zonalStats_bsqMultibandImage.py | 475 +++++++++++ ENVI/ENVIProgramGenerator.sav | Bin 0 -> 455320 bytes ENVI/G.exp | 4 + ENVI/GM1.exp | 3 + ENVI/MCARI1.exp | 10 + ENVI/MCARI2.exp | 11 + ENVI/MSR.exp | 9 + ENVI/MTVI1.exp | 12 + ENVI/MTVI2.exp | 13 + ENVI/NDVI.exp | 6 + ENVI/OSAVI.exp | 8 + ENVI/PSSRa.exp | 5 + ENVI/RDVI.exp | 7 + ENVI/TVI.exp | 2 + ENVI/batch_subset.pro | 14 + ENVI/kelvin2celsius.pro | 35 + ENVI/ndvi_float.exp | 2 + ENVIMaskRasters.pro | 72 ++ Excel_VBAmodule_getAllSheetNames.txt | 15 + Pix4D/Pix4d_ComandLine.bat | 19 + TileLayerPugin_GoogleTiles_test.tsv | 1 + WindowsShell/Replace-0By-1.cmd | 3 + WindowsShell/StackMCAAutopanoOutput.cmd | 4 + WindowsShell/ToASCII.cmd | 3 + .../makeHardlinkOfFileInCompass710folder.bat | 11 + WindowsShell/makeJunctionInDropbox.bat | 6 + WindowsShell/remove_thisJunction.bat | 7 + batch_subset.pro | 14 + batch_subset.pro.crdownload | 0 excel insert rows macro.txt | 19 + excel table to one row formula.txt | 3 + kelvin2celsius.pro | 35 + python/autopanoFile_replace-0By-1frames.py | 32 + .../createFolderIfDoesntExists_hardcoded.py | 26 + python/csv2txt_space_delimited_HEADERSCMD.py | 33 + python/polygonbisector2wayfc.py | 264 ++++++ python/pycharm/.idea/misc.xml | 14 + python/pycharm/.idea/modules.xml | 8 + python/pycharm/.idea/pycharm.iml | 11 + python/pycharm/.idea/workspace.xml | 389 +++++++++ python/pycharm/calendario.html | 0 python/pycharm/dataManager.xml | 212 +++++ python/pycharm/dataManager.xsl | 34 + python/pycharm/loadCalendar.php | 24 + python/replaceCerosByOneMultispecPano.py | 33 + python/stackMCAPairImagesFolder.py | 47 ++ r/.Rhistory | 512 ++++++++++++ r/EM38cleaning.R | 118 +++ ...ct_pixel_values_from_raster_firstEdition.R | 80 ++ ...bands_separateBandsIntoIndividualRasters.R | 61 ++ r/NDVIFromMCA-6Images.R | 42 + r/Plant_height_code.R | 143 ++++ r/Plant_height_code_Obregon.R | 152 ++++ r/ScatterPlotsGS_Y16-17.R | 86 ++ r/VIs_Calculation.R | 313 +++++++ r/VIs_Calculation_Multispec4C.R | 134 +++ r/applyNiblackFilteronImage.R | 11 + r/arrangeAFextractedData2017DBF.R | 94 +++ r/arrangeBWtables.R | 94 +++ r/arrangeBWtables2017.R | 94 +++ r/arrangeSTARStables.R | 101 +++ r/arrangeVItables.R | 73 ++ r/commandine_test.R | 17 + r/cropTiffsInsideFolder.R | 53 ++ r/descriptiveStatistics_manyColumns.R | 137 ++++ r/descriptiveStatistics_protein.R | 92 +++ r/excel_to_row_column_CSV.R | 25 + r/exercises_data_frames.R | 230 ++++++ r/extract/AF_extract_with_mask2016.R | 155 ++++ .../AF_extract_with_mask2016_test_Dataframe.R | 230 ++++++ r/extract/extract_data2017.R | 153 ++++ r/formulas_maiz_sas.txt | 52 ++ r/functions_ImgProcessing.R | 26 + r/getCeptometerDataFromExcel.R | 104 +++ r/getDataFromDB.R | 25 + r/global.R | 2 + r/greenseeker/2_juntar_lecturas_v0.R | 32 + .../AF_230_mapa_norte_arriba.csv | 12 + .../orden_de_muestreo_GS_230.csv | 12 + r/greenseeker/methods_greenseeker.R | 86 ++ r/greenseeker/ordenar_lecturas_v0.R | 56 ++ r/greenseeker/ordenar_lecturas_v0.Rmd | 18 + r/histogramPlots.R | 86 ++ r/hyper_divedeBy10000.R | 64 ++ r/io_methods.R | 58 ++ r/kelvin100toCelsiusInsideFolder.R | 74 ++ r/kelvinx100toCelsius.R | 82 ++ r/learning R spatial analysis.R | 14 + r/list_files.R | 6 + ...lStatisticsOneBandOneExperimentManyDates.R | 74 ++ r/moisture/.Rhistory | 512 ++++++++++++ .../171020_getMoistureSamplingDataFromExcel.R | 101 +++ ...stureSamplingDataFromExcel_710comparison.R | 115 +++ ...ngDataFromExcel_trial521_graficasHumedad.R | 219 +++++ ...etMoistureSamplingDataFromExcel_trial200.R | 166 ++++ ...etMoistureSamplingDataFromExcel_trial521.R | 109 +++ ...DataFromExcel_trialAE521_graficasHumedad.R | 270 +++++++ r/moisture/methods_formating.R | 9 + r/moisture/methods_moisture.R | 198 +++++ r/ongoning.R | 24 + r/outliers_multiple_files_dbf_files.R | 19 + r/readExcel.R | 13 + r/server.R | 101 +++ r/shiny_exampleApp/data/counties.rds | Bin 0 -> 60810 bytes r/shiny_exampleApp/helpers.R | 42 + .../shinyapps.io/nutrs/shiny_exampleapp.dcf | 8 + r/shiny_exampleApp/server.R | 31 + r/shiny_exampleApp/ui.R | 53 ++ .../www/circles in a circle Kandinsky.jpg | Bin 0 -> 2887 bytes r/shiny_extractApp/data/AE_vuelos.csv | 211 +++++ r/shiny_extractApp/helpers.R | 35 + .../shinyapps.io/nutrs/shiny_exampleapp.dcf | 8 + r/shiny_extractApp/server.R | 75 ++ r/shiny_extractApp/ui.R | 31 + .../www/circles in a circle Kandinsky.jpg | Bin 0 -> 2887 bytes r/shiny_importData/global.R | 3 + r/shiny_importData/notas.txt | 1 + r/shiny_importData/server.R | 101 +++ r/shiny_importData/ui.R | 70 ++ r/shiny_listFiles/data/AE_vuelos.csv | 211 +++++ r/shiny_listFiles/helpers.R | 35 + .../shinyapps.io/nutrs/shiny_exampleapp.dcf | 8 + r/shiny_listFiles/server.R | 75 ++ r/shiny_listFiles/ui.R | 0 .../www/circles in a circle Kandinsky.jpg | Bin 0 -> 2887 bytes r/shiny_output_test.csv | 1 + r/stackBandsSequoia.R | 67 ++ r/stackCVStoXLSX.R | 24 + r/stackMCAbandsAsExportedFromAutopano.R | 39 + r/stackMCAbandsAsExportedFromAutopanoFolder.R | 47 ++ r/stackMCAbandsAsExportedFromAutopano_cmd.R | 53 ++ r/substract_raster.R | 58 ++ r/summarizeGreenseekrReadings.R | 51 ++ r/tests_generate_matix_agreggate.R | 9 + r/timeSeries_joinData.R | 79 ++ r/ui.R | 70 ++ regex_example.txt | 2 + share/Toolbox.tbx | Bin 0 -> 132608 bytes ...03\261o de la muestra representativa.xlsx" | Bin 0 -> 14969 bytes test.pro | 24 + 307 files changed, 18615 insertions(+) create mode 100644 ArcGIS/##Arcpy on Python Console ArcMAP.py create mode 100644 ArcGIS/Batch_zonal_stats.py create mode 100644 ArcGIS/CalculateNDVIandNiblackFromHyperIterate.py create mode 100644 ArcGIS/ChooseFieldValue/ChooseFieldValueToolbox.tbx create mode 100644 ArcGIS/ChooseFieldValue/Scratch/Scratch.gdb/a00000001.TablesByName.atx create mode 100644 ArcGIS/ChooseFieldValue/Scratch/Scratch.gdb/a00000001.freelist create mode 100644 ArcGIS/ChooseFieldValue/Scratch/Scratch.gdb/a00000001.gdbindexes create mode 100644 ArcGIS/ChooseFieldValue/Scratch/Scratch.gdb/a00000001.gdbtable create mode 100644 ArcGIS/ChooseFieldValue/Scratch/Scratch.gdb/a00000001.gdbtablx create mode 100644 ArcGIS/ChooseFieldValue/Scratch/Scratch.gdb/a00000002.gdbtable create mode 100644 ArcGIS/ChooseFieldValue/Scratch/Scratch.gdb/a00000002.gdbtablx create mode 100644 ArcGIS/ChooseFieldValue/Scratch/Scratch.gdb/a00000003.gdbindexes create mode 100644 ArcGIS/ChooseFieldValue/Scratch/Scratch.gdb/a00000003.gdbtable create mode 100644 ArcGIS/ChooseFieldValue/Scratch/Scratch.gdb/a00000003.gdbtablx create mode 100644 ArcGIS/ChooseFieldValue/Scratch/Scratch.gdb/a00000004.CatItemsByPhysicalName.atx create mode 100644 ArcGIS/ChooseFieldValue/Scratch/Scratch.gdb/a00000004.CatItemsByType.atx create mode 100644 ArcGIS/ChooseFieldValue/Scratch/Scratch.gdb/a00000004.FDO_UUID.atx create mode 100644 ArcGIS/ChooseFieldValue/Scratch/Scratch.gdb/a00000004.freelist create mode 100644 ArcGIS/ChooseFieldValue/Scratch/Scratch.gdb/a00000004.gdbindexes create mode 100644 ArcGIS/ChooseFieldValue/Scratch/Scratch.gdb/a00000004.gdbtable create mode 100644 ArcGIS/ChooseFieldValue/Scratch/Scratch.gdb/a00000004.gdbtablx create mode 100644 ArcGIS/ChooseFieldValue/Scratch/Scratch.gdb/a00000004.spx create mode 100644 ArcGIS/ChooseFieldValue/Scratch/Scratch.gdb/a00000005.CatItemTypesByName.atx create mode 100644 ArcGIS/ChooseFieldValue/Scratch/Scratch.gdb/a00000005.CatItemTypesByParentTypeID.atx create mode 100644 ArcGIS/ChooseFieldValue/Scratch/Scratch.gdb/a00000005.CatItemTypesByUUID.atx create mode 100644 ArcGIS/ChooseFieldValue/Scratch/Scratch.gdb/a00000005.gdbindexes create mode 100644 ArcGIS/ChooseFieldValue/Scratch/Scratch.gdb/a00000005.gdbtable create mode 100644 ArcGIS/ChooseFieldValue/Scratch/Scratch.gdb/a00000005.gdbtablx create mode 100644 ArcGIS/ChooseFieldValue/Scratch/Scratch.gdb/a00000006.CatRelsByDestinationID.atx create mode 100644 ArcGIS/ChooseFieldValue/Scratch/Scratch.gdb/a00000006.CatRelsByOriginID.atx create mode 100644 ArcGIS/ChooseFieldValue/Scratch/Scratch.gdb/a00000006.CatRelsByType.atx create mode 100644 ArcGIS/ChooseFieldValue/Scratch/Scratch.gdb/a00000006.FDO_UUID.atx create mode 100644 ArcGIS/ChooseFieldValue/Scratch/Scratch.gdb/a00000006.freelist create mode 100644 ArcGIS/ChooseFieldValue/Scratch/Scratch.gdb/a00000006.gdbindexes create mode 100644 ArcGIS/ChooseFieldValue/Scratch/Scratch.gdb/a00000006.gdbtable create mode 100644 ArcGIS/ChooseFieldValue/Scratch/Scratch.gdb/a00000006.gdbtablx create mode 100644 ArcGIS/ChooseFieldValue/Scratch/Scratch.gdb/a00000007.CatRelTypesByBackwardLabel.atx create mode 100644 ArcGIS/ChooseFieldValue/Scratch/Scratch.gdb/a00000007.CatRelTypesByDestItemTypeID.atx create mode 100644 ArcGIS/ChooseFieldValue/Scratch/Scratch.gdb/a00000007.CatRelTypesByForwardLabel.atx create mode 100644 ArcGIS/ChooseFieldValue/Scratch/Scratch.gdb/a00000007.CatRelTypesByName.atx create mode 100644 ArcGIS/ChooseFieldValue/Scratch/Scratch.gdb/a00000007.CatRelTypesByOriginItemTypeID.atx create mode 100644 ArcGIS/ChooseFieldValue/Scratch/Scratch.gdb/a00000007.CatRelTypesByUUID.atx create mode 100644 ArcGIS/ChooseFieldValue/Scratch/Scratch.gdb/a00000007.gdbindexes create mode 100644 ArcGIS/ChooseFieldValue/Scratch/Scratch.gdb/a00000007.gdbtable create mode 100644 ArcGIS/ChooseFieldValue/Scratch/Scratch.gdb/a00000007.gdbtablx create mode 100644 ArcGIS/ChooseFieldValue/Scratch/Scratch.gdb/gdb create mode 100644 ArcGIS/ChooseFieldValue/Scratch/Scratch.gdb/timestamps create mode 100644 ArcGIS/ChooseFieldValue/Script/DynamicValueList.py create mode 100644 ArcGIS/ChooseFieldValue/ToolData/ToolData.gdb/a00000001.TablesByName.atx create mode 100644 ArcGIS/ChooseFieldValue/ToolData/ToolData.gdb/a00000001.gdbindexes create mode 100644 ArcGIS/ChooseFieldValue/ToolData/ToolData.gdb/a00000001.gdbtable create mode 100644 ArcGIS/ChooseFieldValue/ToolData/ToolData.gdb/a00000001.gdbtablx create mode 100644 ArcGIS/ChooseFieldValue/ToolData/ToolData.gdb/a00000002.gdbtable create mode 100644 ArcGIS/ChooseFieldValue/ToolData/ToolData.gdb/a00000002.gdbtablx create mode 100644 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Console ArcMAP +#################### CHANGE SYMBOLOGY############### +import arcpy, sys + +try: + symbologyLayer = arcpy.mapping.Layer(sys.argv[1]) +except: + arcpy.AddError("Input parameters could not be resolved") + sys.exit(-1) + +arcpy.AddMessage("Source Layer is %s" % symbologyLayer.symbologyType) + +# Exit if the source layer is broken +if symbologyLayer.isBroken: + arcpy.AddError("Source layer is broken") + sys.exit(-2) + +# Exit if the source layer is not a raster layer +if not symbologyLayer.isRasterLayer: + arcpy.AddError("Source layer is not a raster layer") + sys.exit(-3) +mxd = arcpy.mapping.MapDocument("Current") # This MXD + +#df = arcpy.mapping.ListDataFrames(mxd,"Georgia")[0] # the first data frame called Georgia +df = arcpy.mapping.ListDataFrames(mxd)[0] # Just the first data frame in the MXD +rasters = arcpy.mapping.ListLayers(mxd,"*",df) # all the layers + +for ThisLayer in rasters: + arcpy.AddMessage( "Working on " + ThisLayer.name) + if not ThisLayer.isBroken: # only try to work with layers that aren't broken + arcpy.AddMessage( "-not broken") + if not ThisLayer.name.upper() == symbologyLayer.name.upper(): + arcpy.AddMessage( "-not the source layer") + # not the source layer + if ThisLayer.isRasterLayer: + arcpy.AddMessage( "-is a raster layer") + # only applies to raster layers + arcpy.CalculateStatistics_management(ThisLayer.dataSource) + arcpy.AddMessage( "--Statistics calculated") + arcpy.AddMessage( "--Raster symbology is %s" % ThisLayer.symbologyType) + arcpy.ApplySymbologyFromLayer_management(ThisLayer,symbologyLayer) + arcpy.AddMessage( "--Symbology Applied") + +#mxd.save() # I'm not saving, uncomment this to save +del mxd + + +############################ TURN ON AND OFF########################## + +l1 = arcpy.mapping.Layer(r'h160303\h160303bgsort.bsq') # Get layer by its name (belongs to group) +#l1.longName # This will give the name including the group + +#A list of layer names that you want to be turned off. +names = [x,y,z,etc] + +mxd = arcpy.mapping.MapDocument("current") +df = arcpy.mapping.ListDataFrames(, mxd, "Layers")[0] #Get the 1st dataframe called "Layers" +layers = arcpy.mapping.ListLayers(mxd, "*", df) + +for layer in layers: + if layer.name in names: + layer.visible = False + +arcpy.RefreshTOC() +arcpy.RefreshActiveView() + +###################turn off layer in group layer h160303? +#lyr.isGroupLayer == 1 +mxd = arcpy.mapping.MapDocument("current") +df = arcpy.mapping.ListDataFrames(mxd, "Layers")[0] #Get the 1st dataframe called "Layers" +groupLayer = arcpy.mapping.Layer(r'h160303') # fixed the name of the group layer +for i in arcpy.mapping.ListLayers(groupLayer): print (i) ## Print layers in group layer, INCLUDES THE layer group itself +for i in arcpy.mapping.ListLayers(groupLayer): i.visible = True ## Turn Off layers in group layer +>>> arcpy.RefreshTOC() # Refresh Layers Panel + +## Calsulate statistics and apply symbology from layer on all layer of group layer + +layerRef = arcpy.mapping.Layer("h160303bgsort.bsq") #Layer to use as symbology, hardcoded +groupLayer = arcpy.mapping.Layer(r'h160303') # fixed the name of the group layer +for i in arcpy.mapping.ListLayers(groupLayer): + if not i.isGroupLayer: + if not i.name.upper() == layerRef.name.upper(): #If its not the reference layer + if i.isRasterLayer: #apply only to rasters + arcpy.CalculateStatistics_management(i.dataSource,"1","1","0") + arcpy.ApplySymbologyFromLayer_management(i,layerRef) + i.visible = False #Uncheck visibility +arcpy.RefreshTOC() #Apply the layer state update: visibility +arcpy.RefreshActiveView() + diff --git a/ArcGIS/Batch_zonal_stats.py b/ArcGIS/Batch_zonal_stats.py new file mode 100644 index 0000000..8e0d421 --- /dev/null +++ b/ArcGIS/Batch_zonal_stats.py @@ -0,0 +1,193 @@ +# -*- coding: utf-8 -*- +# --------------------------------------------------------------------------- +# joinZonalStats_hyper_BW_pruebas.py +# Created on: 2014-06-07 11:06:16.00000 +# (generated by ArcGIS/ModelBuilder) +# Usage: joinZonalStats_hyper_BW_pruebas +# Description: +# --------------------------------------------------------------------------- + +# Import arcpy module +import os +import arcpy + +try: + # Check out any necessary licenses + arcpy.CheckOutExtension("spatial") + + #workspace + + bwPath = "D:\\AB2013-2014\\Bread_wheat" + bwVectoresPath = bwPath + "\\" + "vectores" + bwDataPath = bwPath + "\\" + "data" + bwGdbPath = bwPath + "\\" + "AB_BreadWheat.gdb" + tempGdb = "C:\\vuelos\\temp.gdb" + arcpy.env.workspace = bwPath + + #List of band names + bandNames = 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+ #Short list of band names for test + #bandNames = ["392.03027","393.87228"] #Testing purpose + + + # Load required toolboxes + print "Start!" + #Type: Feature layer + bf_Lx_1 = arcpy.GetParameterAsText(0) + arcpy.AddMessage("read b1 " + bf_Lx_1) + print bf_Lx_1 + + + #Type: Feature layer + # Script arguments + bf_Lx_2 = arcpy.GetParameterAsText(1) + #bf_Lx_2 = bwVectoresPath + "\\" + "EYTBWMEL\\H\\H140117_EYTBWMEL_bf_L3.shp" # provide a default value if unspecified + arcpy.AddMessage("read b2 " + bf_Lx_2) + print bf_Lx_2 + + #Type: Feature layer + # Script arguments + bf_Lx_3 = arcpy.GetParameterAsText(2) + arcpy.AddMessage("read b3 " + bf_Lx_2) + print bf_Lx_3 + + #type: Feature layer + Grid = arcpy.GetParameterAsText(3) + #gridName = "H140117_BW" + #Grid = bwGdbPath + "\\" + gridName + arcpy.AddMessage("read " + Grid) + desc = arcpy.Describe(Grid) + gPath = desc.path + gridSource = str(gPath) + "\\" + Grid + arcpy.AddMessage("Grid source: " + gridSource) + gridName = desc.baseName + arcpy.AddMessage("Grid basename: " + gridName) + + print Grid + + #type: Raster layer + Raster = arcpy.GetParameterAsText(4) + #Raster = "E:\\140117\\140117H\\ortho\\140117_3_810.bsq" # provide a default value if unspecified + #Raster = "c:\\vuelos\\140117_3_810.bsq" + arcpy.AddMessage("read " + Raster) + desc = arcpy.Describe(Raster) + rPath = desc.path + Raster = str(rPath) + "\\" + desc.baseName+ "." +desc.extension + arcpy.AddMessage("Raster source: " + Raster) + print Raster + + #Folder to store the tables with the bands + #type: Geodatabase + #tablesPath = arcpy.GetParameterAsText(5) + tablesPath = tempGdb + print tablesPath + + # Local variables: + arcpy.AddMessage("Local variables") + print "Local variables" + Zone_field_and_join_Field = "Name" + gridLayer = "gridLayer" + #Line + line = bf_Lx_1[-2:] + + + ## Make a layer + print "Make layer" + arcpy.MakeFeatureLayer_management(Grid, "gridLayer") + + #Where to store the merge feature + print "Where to store the merge feature" + L_merge = tempGdb + "\\l_merge" + arcpy.AddMessage("merge path: " + L_merge) + if arcpy.Exists(L_merge): + arcpy.Delete_management(L_merge) + + #List of features to merge + mergeList = "" + + # Process: Merge + arcpy.AddMessage("Merging") + print "merging..." + if bf_Lx_1 != '#': + mergeList += bf_Lx_1 + if bf_Lx_2 != '#': + mergeList += ";"+bf_Lx_2 + if bf_Lx_3 != '#': + mergeList += ";"+bf_Lx_3 + arcpy.AddMessage("merge List: " + str(mergeList)) + m = arcpy.Merge_management(mergeList, L_merge) + arcpy.AddMessage(m) + + i=1 + for b in bandNames: + band = Raster + "\\" + b + bandField = "B" + str(i) + print bandField + #Set table name + tableName = gridName + "_" + line + "_" + bandField #+ ".dbf" + L_Stats_table = tablesPath + "\\" + tableName + print L_Stats_table + Field_Name_Calculate_field = gridName + "." + bandField + print Field_Name_Calculate_field + Expression_Select_layer = tableName+".Mean IS NOT NULL" + # Use this if the stats were saved to a dbf file "\""+tableName+".Mean\" IS NOT NULL" + # Use this if the stats were saved to a table "\""+tableName+":Mean\" IS NOT NULL" + # Use this if the stats were saved to a database table tableName+".Mean IS NOT NULL" + print Expression_Select_layer + Expression_Calculate_field = "round(!"+tableName+".Mean!,0)" + #the "!"+tableName+":Mean!" notation (:)is used when the stats are saved to a table + #the "!"+tableName+".Mean!" notation (.)is used when the stats are saved to a dbf file + print Expression_Calculate_field + + + + # Process: Zonal Statistics as Table + print "Zonal statistics..." + zs = arcpy.gp.ZonalStatisticsAsTable_sa(L_merge, Zone_field_and_join_Field, band, L_Stats_table, "DATA", "MEAN") + arcpy.AddMessage("stats table: " + str(zs)) + +## # Process: Join Field +## print "Joining Field..." +## arcpy.AddMessage("Joining Field...") +## arcpy.JoinField_management(gridLayer, Zone_field_and_join_Field, L_Stats_table, Zone_field_and_join_Field, "MEAN") + + + # Process: Add Join + print "Joining..." + arcpy.AddMessage("grid..."+ gridLayer) + arcpy.AddMessage("field..."+Zone_field_and_join_Field) + arcpy.AddJoin_management(gridLayer, Zone_field_and_join_Field, zs, Zone_field_and_join_Field, "KEEP_ALL") + + # Process: Select Layer By Attribute + if i==1: + print "Selecting Layer..." + gridLayerSelection = arcpy.SelectLayerByAttribute_management(gridLayer, "NEW_SELECTION", Expression_Select_layer) + + # Process: Calculate Field + print "Calculating field" + arcpy.AddMessage("Calculating field...") + arcpy.CalculateField_management(gridLayerSelection, Field_Name_Calculate_field, Expression_Calculate_field, "PYTHON") + + # Process: Remove Join + arcpy.AddMessage("remove Join") + arcpy.RemoveJoin_management(gridLayer, "") + #Erase stats table + #arcpy.Delete_management(zs) + i+=1 + + #Erase merge file + arcpy.AddMessage("delete merge temp") + arcpy.Delete_management(L_merge) + print "Finish" + +except Exception, e: + # If an error occurred, print line number and error message + import traceback, sys + tb = sys.exc_info()[2] + arcpy.AddMessage("Line:" + str(tb.tb_lineno)) + print "Line %i" % tb.tb_lineno + arcpy.AddMessage("Error " + e.message) + print e.message + print "OMG!" + + diff --git a/ArcGIS/CalculateNDVIandNiblackFromHyperIterate.py b/ArcGIS/CalculateNDVIandNiblackFromHyperIterate.py new file mode 100644 index 0000000..786568a --- /dev/null +++ b/ArcGIS/CalculateNDVIandNiblackFromHyperIterate.py @@ -0,0 +1,104 @@ +# -*- coding: utf-8 -*- +# --------------------------------------------------------------------------- +# CalculateNDVIandNiblackFromHyperIterate.py +# Created on: 2015-08-31 23:45:21.00000 +# (generated by ArcGIS/ModelBuilder) +# Usage: CalculateNDVIandNiblackFromHyperIterate +# Description: +# --------------------------------------------------------------------------- + +# Import arcpy module +import arcpy, os + +# Check out any necessary licenses +arcpy.CheckOutExtension("spatial") + +#Method to truncate string +def getBasename(inputName, fromChar, toChar): + subsetName = inputName[fromChar:int(toChar)] + arcpy.AddMessage("Truncated name: "+subsetName) + return subsetName + +def upLevel(locationpath): + #Up one level the path + folderUp = "\\".join(locationpath.split("\\")[0:-1]) + folderUp.replace("\\", "\\\\") + return folderUp + +def checkFolder(folderPath): +#Create folder if it doesnt exists + folderPath.replace("\\", "\\\\") + if not os.path.exists(folderPath): + os.makedirs(folderPath) + +# Script arguments +raster = arcpy.GetParameterAsText(0) + +truncate_charsEnd = arcpy.GetParameterAsText(1) +if truncate_charsEnd == '#' or not truncate_charsEnd: + truncate_charsEnd = "-3" # provide a default value if unspecified + +# Process: Parse Path +desc = arcpy.Describe(raster) +name = desc.name +arcpy.AddMessage("name: "+name) +path = desc.path +arcpy.AddMessage("path: "+path) +folderUp = upLevel(path) +saveFolder = folderUp+"\\mask" +checkFolder(saveFolder) #Check if exists + +# Process: truncateString to get basename +basename = getBasename(name,0,truncate_charsEnd) + +ndvi = arcpy.GetParameterAsText(7) +if ndvi == '#' or not ndvi: + ndvi = saveFolder+"\\"+basename+"nvi.tif" # provide the automatic value unless other is indicated + +NIR_wl = arcpy.GetParameterAsText(2) +if NIR_wl == '#' or not NIR_wl: + NIR_wl = "802.79626" # provide a default value if unspecified + +RED_wl = arcpy.GetParameterAsText(3) +if RED_wl == '#' or not RED_wl: + RED_wl = "670.17230" # provide a default value if unspecified + +niblack = arcpy.GetParameterAsText(4) +if niblack == '#' or not niblack: + niblack = saveFolder+"\\"+basename+"msk.tif" # provide the automatic value unless other is indicated + +Dark_background = arcpy.GetParameterAsText(5) +if Dark_background == '#' or not Dark_background: + Dark_background = "true" # provide a default value if unspecified + +Neighborhood = arcpy.GetParameterAsText(6) +if Neighborhood == '#' or not Neighborhood: + Neighborhood = "Rectangle 3 3 CELL" # provide a default value if unspecified + + + +# Local variables: +focalSTD = "C:\Users\usuario\Documents\ArcGIS\Default.gdb\std" +focalMEAN = "C:\Users\usuario\Documents\ArcGIS\Default.gdb\mean" + +#Assign names + +NIR_path = raster+"\\"+NIR_wl +RED_path = raster+"\\"+RED_wl + + +# Process: Raster Calculator +arcpy.gp.RasterCalculator_sa("Float((Float(\""+NIR_path+"\") - Float(\""+RED_path+"\"))/(Float(\""+NIR_path+"\") + Float(\""+RED_path+"\")))", ndvi) + +# Process: Focal MEAN 3x3 +arcpy.gp.FocalStatistics_sa(ndvi, focalMEAN, Neighborhood, "MEAN", "DATA") + +# Process: Focal STD 3x3 +arcpy.gp.FocalStatistics_sa(ndvi, focalSTD, Neighborhood, "STD", "DATA") + +# Process: calculate Nib +#######REEEROOR +arcpy.gp.RasterCalculator_sa("Con("+Dark_background+" == true,Con(\"Float("+ndvi+")\">(\"Float("+focalMEAN+")\" +(0.2 * \"Float("+focalSTD+")\")),1,0),Con(\"Float("+ndvi+")\">(\"Float("+focalMEAN+")\" +(Float(-0.2) * \"Float("+focalSTD+")\")),1,0))", niblack) +#Delete temporals 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+neighborhood = NbrWeight(inkarnelfile) + +# Check out any necessary licenses +arcpy.CheckOutExtension("spatial") + +# Execute FocalStatistics +outFocalStatistics = FocalStatistics(inRaster, neighborhood, "MEAN") + +# Save the output +saveLocation = arcpy.GetParameterAsText(2) +outFocalStatistics.save(saveLocation) \ No newline at end of file diff --git a/ArcGIS/Herramientas.tbx b/ArcGIS/Herramientas.tbx new file mode 100644 index 0000000000000000000000000000000000000000..815763054cf75c7f67e280e411e4deb8effcd3b2 GIT binary patch literal 1350656 zcmeEP2Y6h?wO(P=nVr4&&b?bCci7SQYww+2&zw1P zX72hwXaD1-Ump6pOEaTfgIr%ncX#b#vOf^dJE`}5U9O7VT&^s~j4Xcpr&;0DK5kBhDIN!R{{CLI6=`%oePHuMjue zFJr!)HPGbh!C%DXc2&E&@f&h2#OO`meAl(JWsIg1QqhZa{HEi_+;Zvo-`sw7J7w(!Kb)7?4k^3~ z=Mngy6Z2Rp(v2V&?ET?%f?NFThxGf~{$uxWxf(c`!nBC-*Xhcaul@eN*8tK#9rJ4F zB236ID({2w%((#5v6%BYM$t?zcy%GWt~~+P^WFfP?mGZj09Q7!AFw}g0Kj5>7dQww z7&rtt6gUhx95@0v68IkA295$ez|p`l!1sY;f#ZN7z);|L-~@neHVnuG@&MLZ0Z<4O 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0000000000000000000000000000000000000000..994d1f664afd9dc05b66839ab0d4e933a05220d9 GIT binary patch literal 44 tcmWG$C}7B8$YdyC&|pwth-UC(NMy)mNM(ox@^l!K81#Wy6DVf@Bmtkw1@`~| literal 0 HcmV?d00001 diff --git a/ArcGIS/ZonalStats_hyper - bandas auto.py b/ArcGIS/ZonalStats_hyper - bandas auto.py new file mode 100644 index 0000000..33b769f --- /dev/null +++ b/ArcGIS/ZonalStats_hyper - bandas auto.py @@ -0,0 +1,327 @@ +# -*- coding: utf-8 -*- +# --------------------------------------------------------------------------- +# joinZonalStats_hyper_BW_pruebas.py +# Created on: 2014-06-07 11:06:16.00000 +# (generated by ArcGIS/ModelBuilder) +# Usage: joinZonalStats_hyper_BW_pruebas +# Description: +#This script takes 3 inputs: +#Buffer: zone feature to use as input for zonal statistic tool +#Grid: feature whose table will be used to save the mean values of the zonal statistic tool. +# This should contain the same features ID field than the "Buffer" input. +# A field named B1, B2... will be created to store the mean values for each zone for each band. +#Raster: Raster to use as input for zonal statistic tool, along with the "Buffer". +###The temporal stats tables are created in the default ArcGIS GDB +# --------------------------------------------------------------------------- + +# Import arcpy module +import os +import arcpy, arcgisscripting +import itertools +import re +import traceback, sys + +def print_kwinfo(): + if deb==1: + print "kwinfo se guarda:" + print kwinfo + +def create_if_not_exists(path): + try: + if not os.path.exists(path): + os.makedirs(path) + except Exception, e: + # If an error occurred, print line number and error message + tb = sys.exc_info()[2] + arcpy.AddMessage("Line:" + str(tb.tb_lineno)) + arcpy.AddMessage("Error " + e.message) + +def exists(path): + exists = True if os.path.exists(path)else False + return exists + +def replace_in_list(regex,lst): + return [re.sub(regex, '', x).strip() for x in lst] #List comprehension + +def print_dict(dictio): + #The next line iterates, formats and prints the dictionary, key:value + #print "\n".join('{}={}'.format(k,v) for k,v in dictio.items()) print in console + arcpy.AddMessage("\n".join('{}={}'.format(k,v) for k,v in dictio.items()))#Print to arcmap results window + +def letsgo(header_file): + arcpy.AddMessage("started reading header file:" + str(header_file)) + envi_header_keywords={"acquisition time","band names","bands", + "bbl","byte order","class lookup","class names", + "classes","cloud cover","complex function", + "coordinate system string","data gain values", + "data ignore value","data offset values", + "data reflectance gain values","data reflectance offset values", + "data type","default bands","default stretch","dem band", + "dem file","description","file type","fwhm","geo points", + "header offset","interleave","lines","map info", + "major frame offsets","minor frame offsets","pixel size", + "product type","projection info","read procedures", + "reflectance scale factor","rpc info","samples","security tag", + "sensor type","solar irradiance","spectra names","sun azimuth", + "sun elevation","wavelength","wavelength units","x start", + "y start","z plot average","z plot range","z plot titles"} + envi_header_keywords_or="|".join(envi_header_keywords) + # Read and Iterate over the lines of the file + with open(header_file, 'rt') as f: + data = f.read()[5:] + lines = re.split(r"[\n]", data)#the info corresponding to one line + arcpy.AddMessage("Finished reading header lines") + dictio = {} + global deb + deb=0 + wl=[]#List to save wavelengths + bn=[]#List to save band names + clines=[]#complete lines with all the info corresponding to one header keyword + ongoing=0 #Variable para marcar si se está buscando el resto de la línea de info + #to one header keyword is not in one single line + kwinfo="" + for i,l in enumerate(lines): + if deb==1: print str(i)+"-l- "+str(l) + kwinfo+=l + if deb==1: print str(i)+"-kwinfo- "+str(kwinfo) + if ongoing==1: + if "}" in l: + if deb==1: print "ongoing==se cierra por fin el parentesis" + print_kwinfo() + clines.append(kwinfo) + kwinfo="" + ongoing=0 + continue + else: + if deb==1: print "ongoing==1 else" + if ongoing==0: + ongoing=1 + for keyw in envi_header_keywords: + if keyw in l: + if deb==1: print keyw+" attribute found" + if "{" in l: + if "}" in l: + if deb==1: print "ongoing==parentesis cerrando en linea" + print_kwinfo() + clines.append(kwinfo) + kwinfo="" + ongoing=0 + else: + if deb==1: print "if bracket in l: --- else" + else: + if deb==1: print "ongoing==sin paretesis" + print_kwinfo() + clines.append(kwinfo) + kwinfo="" + ongoing=0 + if deb==1: print "fin de ronda de for" + for cl in clines: + #expresion to filter how to sepatate the string. + regexpresion = re.compile(r"""(.+?)\s*=\s*(.+)""") + dictio.update(dict(regexpresion.findall(cl))) + if deb==1: print regexpresion.findall(cl) + for key in dictio: dictio[key]=dictio[key].strip() + print "Header attributes:" + arcpy.AddMessage("Header attributes read") + print_dict(dictio) + expr_replace = re.compile(r"{|}") #Expression to remove brackets below + #wavelength + if "wavelength" in dictio: + wl=re.split(r"[,]", dictio["wavelength"]) + wl =replace_in_list(expr_replace,wl) + arcpy.AddMessage(wl) + arcpy.AddMessage("saved wl") + #Band names + if "band names" in dictio: + arcpy.AddMessage("band names attribute found") + bn=re.split(r"[,]", dictio["band names"]) + arcpy.AddMessage("split competed") + bn=replace_in_list(expr_replace,bn) + arcpy.AddMessage("saved bn") + #Next we add the wl units only if they are defined + arcpy.AddMessage("Format composed band name") + if "wavelength units" in dictio: + if "band names" in dictio and "wavelength" in dictio: + join_str = '{bandn} ({waveln} '+dictio["wavelength units"]+')' if dictio["wavelength units"]!="Unknown" else '{bandn} ({waveln})' + arcpy.AddMessage("bn and wl found") + full_bn= '\n'.join(join_str.format(bandn=b, waveln=w) for b,w in itertools.izip(bn, wl)).split('\n') + if not "band names" in dictio and "wavelength" in dictio: + join_str = '{waveln} '+dictio["wavelength units"] if dictio["wavelength units"]!="Unknown" else '{waveln}' + arcpy.AddMessage("wl found+units") + full_bn= '\n'.join(join_str.format(waveln=w) for w in wl).split('\n') + else: + if "band names" in dictio and "wavelength" in dictio: + join_str = '{bandn} ({waveln})' + arcpy.AddMessage("bn and wl found") + full_bn= '\n'.join(join_str.format(bandn=b, waveln=w) for b,w in itertools.izip(bn, wl)).split('\n') + if not "band names" in dictio and "wavelength" in dictio: + arcpy.AddMessage("wl found") + full_bn= wl + arcpy.AddMessage(full_bn) + if deb==1: + print full_bn + print "Bands: "+len(full_bn) + arcpy.AddMessage("Bands: "+len(full_bn)) + #Output parameter + arcpy.SetParameterAsText(4, str(len(full_bn))) # Say how may bands are in the raster + arcpy.AddMessage("Finished reading header") + return full_bn + +##def save_bn_wl(): +## full_bn= '\n'.join(join_str.format(bandn=b, waveln=w) for b,w in itertools.izip(bn, wl)).split('\n') +## +##def save_wl(): +## full_bn= '\n'.join(join_str.format(waveln=w) for w in itertools.izip(wl)).split('\n') + +## Starts the script +try: + # Check out any necessary licenses + arcpy.CheckOutExtension("spatial") + + # Create the Geoprocessor object + gp = arcgisscripting.create() + + # Load required toolboxes + print "Start!" + #Type: Feature layer -buffer for the zonalStats + bf_Lx_1 = arcpy.GetParameterAsText(0) + L_merge = bf_Lx_1 + arcpy.AddMessage("read b1 " + bf_Lx_1) + print bf_Lx_1 + + #Type: Field -ID field + f = arcpy.GetParameterAsText(0) + L_merge = bf_Lx_1 + arcpy.AddMessage("read b1 " + bf_Lx_1) + print bf_Lx_1 + + #type: Feature layer - Feature in whose table the stats will be stored + Grid = arcpy.GetParameterAsText(1) + arcpy.AddMessage("read " + Grid) + desc = arcpy.Describe(Grid) + gPath = desc.path + gridSource = str(gPath) + "\\" + Grid + arcpy.AddMessage("Grid source: " + gridSource) + gridName = desc.baseName + arcpy.AddMessage("Grid basename: " + gridName) + + print Grid + #type: Raster layer + Raster = arcpy.GetParameterAsText(2) + arcpy.AddMessage("read " + Raster) + desc = arcpy.Describe(Raster) + rPath = desc.path + Raster = str(rPath) + "\\" + desc.baseName+ "." +desc.extension + arcpy.AddMessage("Raster source: " + Raster) + print Raster + + #type: Field - Field for join and statistics + id_field = arcpy.GetParameterAsText(3) + arcpy.AddMessage("read " + id_field) + + #List of band names + hdr_file = os.path.join(str(rPath) + "\\" + desc.baseName+".hdr") + bandNames = letsgo(hdr_file) + #Next I was try ing to print the band number. Not yet + if type(bandNames).__name__ != 'int': + print r"# of bands in raster: "+str(len(bandNames)) + + #Folder to store the tables with the stats + tempGdb_path = r"C:\Users\usuario\Documents\ArcGIS" + tempGdb = r"Default.gdb" + #This is the path + tablesPath = os.path.join(tempGdb_path, tempGdb) + #Keep the individual stats tables? + #Either way the MEAN is stored to the table of the grid feature selected + keep_tables=False + # Execute CreateFileGDB + create_if_not_exists(tempGdb_path) + if not exists(tablesPath): + arcpy.CreateFileGDB_management(tempGdb_path, tempGdb) + print tablesPath + + # Local variables: + arcpy.AddMessage("Local variables") + print "Local variables" + Zone_field_and_join_Field = id_field + gridLayer = "gridLayer" + + + ## Make a layer + print "Make layer" + arcpy.MakeFeatureLayer_management(Grid, "gridLayer") + + i=1 + for b in bandNames: + arcpy.AddMessage("b: " + b) + band = Raster + "\\" + b + arcpy.AddMessage("band: " + band) + bandField = "B" + str(i) + print bandField + #Set table name + tableName = gridName + "_" + bandField #+ ".dbf" + L_Stats_table = tablesPath + "\\" + tableName + print L_Stats_table + Field_Name_Calculate_field = gridName + "." + bandField + print Field_Name_Calculate_field + Expression_Select_layer = tableName+".Mean IS NOT NULL" + # Use this if the stats were saved to a dbf file "\""+tableName+".Mean\" IS NOT NULL" + # Use this if the stats were saved to a table "\""+tableName+":Mean\" IS NOT NULL" + # Use this if the stats were saved to a database table tableName+".Mean IS NOT NULL" + print Expression_Select_layer + Expression_Calculate_field = "round(!"+tableName+".Mean!,0)" + #the "!"+tableName+":Mean!" notation (:)is used when the stats are saved to a table + #the "!"+tableName+".Mean!" notation (.)is used when the stats are saved to a dbf file + print Expression_Calculate_field + + # Process: Zonal Statistics as Table + print "Zonal statistics..." + #Perform the zonalstatistics getting only the "MEAN" value of the cells + #"MEAN" can be changed to get different statistics: + #ALL,MEAN,MAJORITY,MAXIMUM,MEDIAN,MINIMUM,MINORITY,RANGE,STD,SUM,VARIETY,MIN_MAX,MEAN_STD,MIN_MAX_MEAN  + #arcpy.AddMessage("zs exists? : " + str(exists(path))) + zs = arcpy.gp.ZonalStatisticsAsTable_sa(L_merge, Zone_field_and_join_Field, band, L_Stats_table, "DATA", "MEAN") + arcpy.AddMessage("stats table: " + str(zs)) + fields = gp.ListFields(gridLayer, bandField) + field_found = fields.Next() + if (not field_found): + arcpy.AddField_management(gridLayer, bandField, "FLOAT", 10, 3, "", "", "NULLABLE", "NON_REQUIRED", "") + arcpy.AddMessage("Added. Before field not found " + str(bandField)) + else: + arcpy.AddMessage("field_found " + str(field_found.name)) + arcpy.AddMessage("field found " + str(bandField)) + + # Process: Add Join + print "Joining..." + arcpy.AddMessage("grid..."+ gridLayer) + arcpy.AddMessage("field..."+Zone_field_and_join_Field) + arcpy.AddJoin_management(gridLayer, Zone_field_and_join_Field, zs, Zone_field_and_join_Field, "KEEP_ALL") + + # Process: Select Layer By Attribute + if i==1: + print "Selecting Layer..." + gridLayerSelection = arcpy.SelectLayerByAttribute_management(gridLayer, "NEW_SELECTION", Expression_Select_layer) + + # Process: Calculate Field + print "Calculating field" + arcpy.AddMessage("Calculating field...") + arcpy.CalculateField_management(gridLayerSelection, Field_Name_Calculate_field, Expression_Calculate_field, "PYTHON") + + # Process: Remove Join + arcpy.AddMessage("remove Join") + arcpy.RemoveJoin_management(gridLayer, "") + #Erase stats table + if not keep_tables: arcpy.Delete_management(zs) + i+=1 + + print "Finish" + +except Exception, e: + # If an error occurred, print line number and error message + import traceback, sys + tb = sys.exc_info()[2] + arcpy.AddMessage("Line:" + str(tb.tb_lineno)) + arcpy.AddMessage("Error " + e.message) + print "OMG!" + + diff --git a/ArcGIS/addFieldb1-b250.py b/ArcGIS/addFieldb1-b250.py new file mode 100644 index 0000000..a6185e0 --- /dev/null +++ b/ArcGIS/addFieldb1-b250.py @@ -0,0 +1,29 @@ +# -*- coding: utf-8 -*- +# --------------------------------------------------------------------------- +# addFieldb1-b250.py +# Created on: 2014-06-07 12:40:25.00000 +# (generated by ArcGIS/ModelBuilder) +# Usage: addFieldb1-b250 +# Description: +# --------------------------------------------------------------------------- + +# Import arcpy module +import arcpy + +# Load required toolboxes +arcpy.ImportToolbox("Model Functions") + +# Script arguments +Feature_to_add_250_Fields = arcpy.GetParameterAsText(0) +if Feature_to_add_250_Fields == '#' or not Feature_to_add_250_Fields: + Feature_to_add_250_Fields = "H140117_BW" # provide a default value if unspecified + +# Local variables: +Feature_with_250_fields = Feature_to_add_250_Fields + +# Process: For +arcpy.IterateCount_mb("1", "250", "1") + +# Process: Add Field +arcpy.AddField_management(Feature_to_add_250_Fields, "B%band_value%", "DOUBLE", "", "", "", "", "NULLABLE", "NON_REQUIRED", "") + diff --git a/ArcGIS/addLayerToView.txt b/ArcGIS/addLayerToView.txt new file mode 100644 index 0000000..36064ed --- /dev/null +++ b/ArcGIS/addLayerToView.txt @@ -0,0 +1,9 @@ +import arcpy +import os +import os.path +from arcpy import env + +lyr = arcpy.GetParameterAsText(0) +mxd = arcpy.mapping.MapDocument("CURRENT") +dataframe = arcpy.mapping.ListDataFrames(mxd)[0] +arcpy.mapping.AddLayer(dataframe,lyr,"AUTO_ARRANGE") \ No newline at end of file diff --git a/ArcGIS/celsius.cal b/ArcGIS/celsius.cal new file mode 100644 index 0000000000000000000000000000000000000000..2bf7cea9b27d4a7b8ea5c53e5f1757b7a374fd35 GIT binary patch literal 56 zcmXS7$Y&^J$YV%h&;Y_{244nO21f=zhFAuD215n|Ak<{gWiVneXE0{a1B#e3=m2?| F3;^%g2Oj_c literal 0 HcmV?d00001 diff --git a/ArcGIS/createFolderIfNotExists.py b/ArcGIS/createFolderIfNotExists.py new file mode 100644 index 0000000..31571ff --- /dev/null +++ b/ArcGIS/createFolderIfNotExists.py @@ -0,0 +1,17 @@ +from os.path import exists +from os import makedirs + +#Folder which will be checked if exists +folder = arcpy.GetParameter(0) +folder = str(folder) +arcpy.AddMessage("path to check: " + str(folder)) +folder.replace("\\", "\\\\") + +#Create folder if it doesn't exist +try: + os.makedirs(folder) + os.path.exists(folder) +except: + os.makedirs(folder) +ready = folder +arcpy.SetParameterAsText(1,ready) \ No newline at end of file diff --git a/ArcGIS/data_handler.py b/ArcGIS/data_handler.py new file mode 100644 index 0000000..ff83e97 --- /dev/null +++ b/ArcGIS/data_handler.py @@ -0,0 +1,764 @@ +############################################################################## +# Python library to deal with BIL (Band Interleaved by Line) and BSQ (Band Sequential) files +# +# Author: Ben Taylor +# +# History: +# 12th Feb. 2009: (benj) Created +# 9th Jun. 2009: (benj) Added writeData function +# 20th Aug. 2009: (benj) Added append option to writeDataFile +# 3rd Dec. 2009: (benj) Added findHdrFile function +# +# Available functions: +# readxy: Wrapper function for readBil/readBsq that allows you to omit the number of bands in the file (works it out from the file size) +# readxb: Wrapper function for readBil/readBsq that allows you to omit the number of lines in the file (works it out from the file size) +# readyb: Wrapper function for readBil/readBsq that allows you to omit the number of pixels per line (works it out from the file size) +# readBil: Reads a BIL file and returns a list containing the data from the file +# readBilLine: Reads a line from an open BIL file and returns a list containing the data that was read +# readBsq: Reads a BSQ file and returns a list containing the data from the file +# readBsqBand: Reads a band from an open BSQ file and returns a list containing the data that was read +# writeData: Writes data to an output file straight from an input list +# writeDataFile: Writes a BIL or BSQ file from a 1D list containing data already in the right file order +# write3DData: Writes a BIL or BSQ file from a 3D list with 1st dimension band number, 2nd dimension line number and 3rd dimension pixel number (unfolds to a 1D array then calls writeDataFile) +# writeHdrFile: Writes an ENVI .hdr file to be associated with a BIL or BSQ file +# readHdrFile: Reads data from a given ENVI-style header file +# getEnviType: Gets the ENVI type code equivalent to a particular Python struct format string +# getStructType: Gets the Python struct format string equivalent to a particular ENVI type code +# findHdrFile: Tries to find an associated hdr file for the given filename +# +# You may use or alter this script as you wish, but no warranty of any kind is offered, nor is it guaranteed +# not to cause security holes in an unsafe environment. +############################################################################## + +import os +import os.path +import stat +import struct +import re +import sys + +defformat = "h" # Default data format (2-byte signed short int) + +# Function readxy +# Wrapper function for readBil/readBsq that allows you to omit the number of bands in the file (works it out from the file size) +# See readBil/readBsq description for arguments and return value +# filetype: "bil" or "bsq" appropriately +def readxy(filename, numlines, pixperline, dataformat=defformat, filetype="bil"): + fileinfo = os.stat(filename) + filesize = fileinfo[stat.ST_SIZE] + + # Check given format string is valid + try: + bytesperpix = struct.calcsize(dataformat) + except: + raise ValueError, "Supplied data format " + str(dataformat) + " is invalid" + # end try + + numbands = ((filesize / float(numlines)) / float(pixperline)) / float(bytesperpix) + + # Should be an integer, if it's not then one of the given attributes is wrong or the file is corrupt + if (numbands == int(numbands)): + if (filetype == "bil"): + return readBil(filename, int(numlines), int(pixperline), int(numbands), dataformat) + else: + if (filetype == "bsq"): + return readBsq(filename, int(numlines), int(pixperline), int(numbands), dataformat) + else: + raise ValueError, "File type argument must be either 'bil' or 'bsq', got: " + filetype + # end if + # end if + else: + raise ValueError, "File size and supplied attributes do not match" + # end if +# end function + +# Function readxb +# Wrapper function for readBil/readBsq that allows you to omit the number of lines in the file (works it out from the file size) +# See readBil/readBsq description for arguments and return value +# filetype: "bil" or "bsq" appropriately +def readxb(filename, pixperline, numbands, dataformat=defformat, filetype="bil"): + fileinfo = os.stat(filename) + filesize = fileinfo[stat.ST_SIZE] + + # Check given format string is valid + try: + bytesperpix = struct.calcsize(dataformat) + except: + raise ValueError, "Supplied data format " + str(dataformat) + " is invalid" + # end try + + numlines = ((filesize / float(numbands)) / float(pixperline)) / float(bytesperpix) + + # Should be an integer, if it's not then one of the given attributes is wrong or the file is corrupt + if (numlines == int(numlines)): + if (filetype == "bil"): + return readBil(filename, int(numlines), int(pixperline), int(numbands), dataformat) + else: + if (filetype == "bsq"): + return readBsq(filename, int(numlines), int(pixperline), int(numbands), dataformat) + else: + raise ValueError, "File type argument must be either 'bil' or 'bsq', got: " + filetype + # end if + # end if + else: + raise ValueError, "File size and supplied attributes do not match" + # end if +# end function + +# Function readyb +# Wrapper function for readBil/readBsq that allows you to omit the number of pixels per line (works it out from the file size) +# See readBil/readBsq description for arguments and return value +# filetype: "bil" or "bsq" appropriately +def readyb(filename, numlines, numbands, dataformat=defformat, filetype="bil"): + fileinfo = os.stat(filename) + filesize = fileinfo[stat.ST_SIZE] + + # Check given format string is valid + try: + bytesperpix = struct.calcsize(dataformat) + except: + raise ValueError, "Supplied data format " + str(dataformat) + " is invalid" + # end try + + pixperline = ((filesize / float(numbands)) / float(numlines)) / float(bytesperpix) + + # Should be an integer, if it's not then one of the given attributes is wrong or the file is corrupt + if (numlines == int(numlines)): + if (filetype == "bil"): + return readBil(filename, int(numlines), int(pixperline), int(numbands), dataformat) + else: + if (filetype == "bsq"): + return readBsq(filename, int(numlines), int(pixperline), int(numbands), dataformat) + else: + raise ValueError, "File type argument must be either 'bil' or 'bsq', got: " + filetype + # end if + # end if + else: + raise ValueError, "File size and supplied attributes do not match" + # end if +# end function + +# Function readBil +# Reads a BIL file and returns a list containing the data from the file +# +# Arguments: +# filename: Name of file to read +# numlines: Number of lines of data in the file +# pixperline: Number of pixels on a line +# numbands: Number of bands in the file +# dataformat: Format string for data, as Python struct definition +# +# Returns: A list containing the data from filename formatted as a list of bands +# containing a list of lines, each containing a list of pixel values +def readBil(filename, numlines, pixperline, numbands, dataformat=defformat): + + # Check file exists and is a file + if (not os.path.isfile(filename)): + raise ValueError, "Supplied filename " + str(filename) + " does not exist" + # end if + + # Check given format string is valid + try: + bytesperpix = struct.calcsize(dataformat) + except: + raise ValueError, "Supplied data format " + str(dataformat) + " is invalid" + # end try + + # Check file size matches with size attributes + fileinfo = os.stat(filename) + filesize = fileinfo[stat.ST_SIZE] + checknum = (((filesize / float(numbands)) / float(numlines)) / float(bytesperpix)) / pixperline + if (checknum != 1): + raise ValueError, "File size and supplied attributes do not match" + # end if + + # Open the file for reading in binary mode + try: + bilfile = open(filename, "rb") + except: + print "Failed to open BIL file " + filename + raise + # end try + + # Create a list of bands containing an empty list for each band + bands = [[] for i in range(0, numbands)] + + # BIL format so have to cycle through lines at top level rather than bands + for linenum in range(0, numlines): + for bandnum in range(0, numbands): + + if (linenum == 0): + # For each band create an empty list of lines in the band, but only the first time + bands[bandnum] = [[] for i in range(0, numlines)] + # end if + + for pixnum in range(0, pixperline): + + # Read one data item (pixel) from the data file. No error checking because we want this to fall over + # if it fails. + dataitem = bilfile.read(bytesperpix) + + # If we get a blank string then we hit EOF early, raise an error + if (dataitem == ""): + raise EOFError, "Ran out of data to read before we should have" + # end if + + # If everything worked, unpack the binary value and store it in the appropriate pixel value + bands[bandnum][linenum].append(struct.unpack(dataformat, dataitem)[0]) + # end for + # end for + # end for + bilfile.close() + + return bands +# end function + +# Function readBilLine +# Reads a line of data from an open BIL file +# +# Arguments: +# bilfile: Open BIL file object +# pixperline: Number of pixels on a line +# numbands: Number of bands in the file +# dataformat: Format string for data, as Python struct definition +# +# Returns: A 2D list with the band number in the first dimension and the pixel number in the second, containing the data values +# for the line that was read +def readBilLine(bilfile, pixperline, numbands, dataformat=defformat): + line = [] + + # Get the size in bytes for the given data format + itemsize = struct.calcsize(dataformat) + + # For each pixel in each band, read a data item, unpack it and store it in the output list + for bandnum in range(0, numbands): + line.append([]) + for pixnum in range(0, pixperline): + dataitem = bilfile.read(itemsize) + + if ((dataitem == "") or (len(dataitem) < itemsize)): + raise EOFError, "Ran out of data to read before we should have" + # end if + + line[bandnum].append(struct.unpack(dataformat, dataitem)[0]) + # end for + # end for + + return line +# end function + +# Function readBsq +# Reads a BSQ file and returns a list containing the data from the file +# +# Arguments: +# filename: Name of file to read +# numlines: Number of lines of data in the file +# pixperline: Number of pixels on a line +# numbands: Number of bands in the file +# dataformat: Format string for data, as Python struct definition +# +# Returns: A list containing the data from filename formatted as a list of bands +# containing a list of lines, each containing a list of pixel values +def readBsq(filename, numlines, pixperline, numbands, dataformat=defformat): + + # Check file exists and is a file + if (not os.path.isfile(filename)): + raise ValueError, "Supplied filename " + str(filename) + " does not exist" + # end if + + # Check given format string is valid + try: + bytesperpix = struct.calcsize(dataformat) + except: + raise ValueError, "Supplied data format " + str(dataformat) + " is invalid" + # end try + + # Check file size matches with size attributes + fileinfo = os.stat(filename) + filesize = fileinfo[stat.ST_SIZE] + checknum = (((filesize / float(numbands)) / float(numlines)) / float(bytesperpix)) / pixperline + if (checknum != 1): + raise ValueError, "File size and supplied attributes do not match" + # end if + + # Open the file for reading in binary mode + try: + bsqfile = open(filename, "rb") + except: + print "Failed to open BSQ file " + filename + raise + # end try + + # Create a list of bands containing an empty list for each band + bands = [] + + # Read data for each band at a time + for bandnum in range(0, numbands): + bands.append([]) + + for linenum in range(0, numlines): + + bands[bandnum].append([]) + + for pixnum in range(0, pixperline): + + # Read one data item (pixel) from the data file. No error checking because we want this to fall over + # if it fails. + dataitem = bsqfile.read(bytesperpix) + + # If we get a blank string then we hit EOF early, raise an error + if (dataitem == ""): + raise EOFError, "Ran out of data to read before we should have" + # end if + + # If everything worked, unpack the binary value and store it in the appropriate pixel value + bands[bandnum][linenum].append(struct.unpack(dataformat, dataitem)[0]) + # end for + # end for + # end for + + bsqfile.close() + + return bands +# end function + +# Function readBsqBand +# Reads a band of data from an open BSQ file +# +# Arguments: +# bsqfile: Open BSQ file object +# pixperline: Number of pixels on a line +# numlines: Number of lines in the file +# dataformat: Format string for data, as Python struct definition +# +# Returns: A 2D list with the band number in the first dimension and the pixel number in the second, containing the data values +# for the line that was read +def readBsqBand(bsqfile, pixperline, numlines, dataformat=defformat): + band = [] + + # Get the size in bytes for the given data format + itemsize = struct.calcsize(dataformat) + + # For each pixel in each band, read a data item, unpack it and store it in the output list + for linenum in range(0, numlines): + band.append([]) + for pixnum in range(0, pixperline): + dataitem = bsqfile.read(itemsize) + + if ((dataitem == "") or (len(dataitem) < itemsize)): + raise EOFError, "Ran out of data to read before we should have" + # end if + + band[linenum].append(struct.unpack(dataformat, dataitem)[0]) + # end for + # end for + + return band +# end function + +# Function writeData +# Writes data to an output file straight from an input list +# +# Arguments: +# data: List containing data to be written +# datafile: Open (binary) data file to write to +# dataformat: Format string for data, as Python struct definition +def writeData(data, datafile, dataformat=defformat): + + # Check given format string is valid + try: + bytesperpix = struct.calcsize(dataformat) + except: + raise ValueError, "Supplied data format " + str(dataformat) + " is invalid" + # end try + + # Get the size in bytes for the given data format + itemsize = struct.calcsize(dataformat) + + writestr = "" + + for i in range(0, len(data)): + try: + packeditem = struct.pack(dataformat, data[i]) + except: + datafile.close() + raise IOError, "Could not pack " + str(dataitem) + " into " + str(bytesperpix) + " bytes. Reason: " + str(sys.exc_info()[1]) + # end try + + if ((i % 1000 != 999) and (i != len(data) - 1)): + writestr = writestr + packeditem + else: + writestr = writestr + packeditem + try: + datafile.write(writestr) + except: + datafile.close() + raise IOError, "Failed to write to data file. Reason: " + str(sys.exc_info()[1]) + # end try + writestr = "" + # end if + + # end if + + # Write data to file in order + #for dataitem in data: + # try: + # packeditem = struct.pack(dataformat, dataitem) + # except: + # datafile.close() + # raise IOError, "Could not pack " + str(dataitem) + " into " + str(bytesperpix) + " bytes. Reason: " + str(sys.exc_info()[1]) + # # end try + # + # if (len(writestr) < 1000): + # writestr = writestr + packeditem + # else: + # writestr = packeditem + # # end try + # + # try: + # datafile.write(packeditem) + # except: + # datafile.close() + # raise IOError, "Failed to write to data file. Reason: " + str(sys.exc_info()[1]) + # # end try + ## end for +# end function + +# Function writeDataFile +# Writes a data file (BIL or BSQ) from a 1D list containing data already in the right file order +# (ie data are written to the file in the order that they're in the list) +# +# Arguments: +# data: List containing data to write to the file +# filename: Name of file to be written to +# dataformat: Format string for data, as Python struct definition +# append: If True then appends the data to the end of the file rather than writing a new blank file. Default False +def writeDataFile(data, filename, dataformat=defformat, append=False): + + # Get correct format string to open the file with + if (append): + writeformat = "ab" + else: + writeformat = "wb" + # end if + + # Open the data file for writing in binary mode + try: + datafile = open(filename, writeformat) + except: + print "Could not open data file " + str(filename) + " for writing" + raise + # end try + + # Check given format string is valid + try: + bytesperpix = struct.calcsize(dataformat) + except: + raise ValueError, "Supplied data format " + str(dataformat) + " is invalid" + # end try + + # Pack each data item into binary data and write it to the output file + for dataitem in data: + try: + packeditem = struct.pack(dataformat, dataitem) + except: + datafile.close() + print "Could not pack " + str(dataitem) + " into " + str(bytesperpix) + " bytes" + raise + # end try + + try: + datafile.write(packeditem) + except: + print "Failed to write to data file. Reason: " + str(sys.exc_info()[1]) + datafile.close() + raise + # end try + # end for + + datafile.close() +# end function + +# Function write3DData +# Writes a BIL or BSQ file from a 3D list with 1st dimension band number, 2nd dimension line number +# and 3rd dimension pixel number (unfolds to a 1D array then calls writeDataFile) +# +# Arguments: +# data: List containing data to write to the file +# filename: Name of file to be written to +# writehdr: Flag denoting whether to write an ENVI header file (default true) +# dataformat: Format string for data, as Python struct definition +# interleave: "bil" or "bsq" appropriately +def write3DData(data, filename, writehdr=True, dataformat=defformat, interleave="bil"): + + # Store numbers of bands, lines and pixels per line for convenience + numbands = len(data) + numlines = len(data[0]) + pixperline = len(data[0][0]) + + # Check given format string is valid + try: + bytesperpix = struct.calcsize(dataformat) + except: + raise ValueError, "Supplied data format " + str(dataformat) + " is invalid" + # end try + + # Create list for unfolding to + outdata = [0.0 for i in range(0, numbands * numlines * pixperline)] + + # Run through the data array and put all the data in the right place in the unfolded list + for bandnum in range(0, numbands): + for linenum in range(0, numlines): + for pixnum in range(0, pixperline): + # Work out appropriate index within BIL file format for next pixel and store in 1D data array + if (interleave == "bil"): + pixindex = (pixperline * numbands * linenum) + (pixperline * bandnum) + pixnum + else: + if (interleave == "bsq"): + pixindex = (pixperline * numbands * linenum) + (pixperline * linenum) + pixnum + else: + raise ValueError, "Interleave argument to write3DData must be either 'bil' or 'bsq', got: " + interleave + # end if + # end if + outdata[pixindex] = data[bandnum][linenum][pixnum] + # end for + # end for + # end for + + # Write output file (or throw an error if there's no data to write) + if (len(outdata) > 0): + writeDataFile(outdata, filename, dataformat) + else: + raise ValueError, "One or more dimensions of the data array were 0" + # end if + + # Write header file if requested + if (writehdr): + try: + # Check ENVI data type + datatype = getEnviType(dataformat) + except: + datafile.close() + print "Unable to generate header for type " + dataformat + ", data type is not valid for ENVI" + # end try + + writeHdrFile(filename + ".hdr", pixperline, numlines, numbands, datatype, interleave) + # end if +# end function + +# Function writeHdrFile +# Writes an ENVI .hdr file to be associated with a data file +# +# Arguments: +# filename: Name of .hdr file to be written +# samples: Number of pixels per line (samples) +# lines: Number of lines +# bands: Number of bands +# datatype: Numeric code for relevant data type +def writeHdrFile(filename, samples, lines, bands, datatype, interleave="bil"): + try: + hdrfile = open(filename, "w") + except: + print "Could not open header file " + str(filename) + " for writing" + raise + # end try + + hdrfile.write("ENVI\n") + hdrfile.write("description = { Created by bil_handler.py }\n") + hdrfile.write("samples = " + str(samples) + "\n") + hdrfile.write("lines = " + str(lines) + "\n") + hdrfile.write("bands = " + str(bands) + "\n") + hdrfile.write("header offset = 0\n") + hdrfile.write("file type = ENVI Standard\n") + hdrfile.write("data type = " + str(datatype) + "\n") + hdrfile.write("interleave = " + interleave + "\n") + hdrfile.write("byte order = 0\n") + + hdrfile.flush() + hdrfile.close() +# end function + +# Function readHdrFile +# Reads data from a given ENVI-style header file +# +# Arguments +# hdrfilename: Name of header file to be read +# +# Returns: Dictionary containing keys/values from header file +def readHdrFile(hdrfilename): + output = {} + inblock = False + + try: + hdrfile = open(hdrfilename, "r") + except: + print "Could not open hdr file '" + str(hdrfilename) + "'" + raise + # end try + + # Read line, split it on equals, strip whitespace from resulting strings and add key/value pair to output + currentline = hdrfile.readline() + while (currentline != ""): + # ENVI headers accept blocks bracketed by curly braces - check for these + if (not inblock): + # Split line on first equals sign + if (re.search("=", currentline) != None): + linesplit = re.split("=", currentline, 1) + key = linesplit[0].strip() + value = linesplit[1].strip() + + # If value starts with an open brace, it's the start of a block - strip the brace off and read the rest of the block + if (re.match("{", value) != None): + inblock = True + value = re.sub("^{", "", value, 1) + + # If value ends with a close brace it's the end of the block as well - strip the brace off + if (re.search("}$", value)): + inblock = False + value = re.sub("}$", "", value, 1) + # end if + # end if + value = value.strip() + output[key] = value + # end if + else: + # If we're in a block, just read the line, strip whitespace (and any closing brace ending the block) and add the whole thing + value = currentline.strip() + if (re.search("}$", value)): + inblock = False + value = re.sub("}$", "", value, 1) + value = value.strip() + # end if + output[key] = output[key] + value + # end if + + currentline = hdrfile.readline() + # end while + + hdrfile.close() + + return output +# end function + +# Function getEnviType +# Gets the ENVI type code equivalent to a particular Python struct format string +# +# Arguments +# formatstr: Struct format string to get ENVI type code for +# +# Returns: ENVI numeric type code for supplied format string +def getEnviType(formatstr): + + dtype = -1 + + # Check the given format string is valid + try: + struct.calcsize(formatstr) + except: + raise ValueError, formatstr + " is not a valid format string" + # end try + + # Do the conversion + if (formatstr == "b"): + dtype = 1 # Signed (?) byte + elif (formatstr == "h"): + dtype = 2 # 2-byte signed short int (ENVI calls it an int) + elif (formatstr == "H"): + dtype = 12 # 2-byte unsigned int (ENVI calls it an int) + elif (formatstr == "i"): + dtype = 3 # 4-byte signed int (ENVI calls it a Long) + elif (formatstr == "I"): + dtype = 13 # 4-byte unsigned int (ENVI calls it a Long) + elif (formatstr == "f"): + dtype = 4 # 4-byte float + elif (formatstr == "d"): + dtype = 5 # 8-byte double precision + elif (formatstr == "l"): + dtype = 14 # 8-byte long int (ENVI 64-bit int) + elif (formatstr == "L"): + dtype = 15 # 8-byte unsigned long int (ENVI 64-bit int) + else: + # If we get here then the format string is valid for Python but not for ENVI, raise an error + raise ValueError, formatstr + " is a valid Python format string but does not have an ENVI equivalent" + # end if + + return dtype +# end function + +# Function getStructType +# Gets the Python struct format string equivalent to a particular ENVI type code +# +# Arguments +# typecode: ENVI type code to get Python format string for +# +# Returns: Single-character Python struct format string +def getStructType(typecode): + + try: + inttype = int(typecode) + except: + raise ValueError, str(typecode) + " is not a valid ENVI type for conversion" + # end try + + # Do the conversion + if (inttype == 1): + formatstr = "b" # Signed (?) byte + elif (inttype == 2): + formatstr = "h" # 2-byte signed short int (ENVI calls it an int) + elif (inttype == 12): + formatstr = "H" # 2-byte unsigned int (ENVI calls it an int) + elif (inttype == 3): + formatstr = "i" # 4-byte signed int (ENVI calls it a Long) + elif (inttype == 13): + formatstr = "I" # 4-byte unsigned int (ENVI calls it a Long) + elif (inttype == 4): + formatstr = "f" # 4-byte float + elif (inttype == 5): + formatstr = "d" # 8-byte double precision + elif (inttype == 14): + formatstr = "l" # 8-byte long int (ENVI 64-bit int) + elif (inttype == 15): + formatstr = "L" # 8-byte unsigned long int (ENVI 64-bit int) + else: + # If we get here then the type code doesn't have a Python equivalent, raise an error + raise ValueError, str(typecode) + " does not have an equivalent Python format string" + # end if + + return formatstr +# end function + +# Function findHdrFile +# Tries to find an associated hdr file for the given filename +# +# Arguments +# rawfilename: File name to try and match to a header file +# +# Returns: The name of the header file found, or None if no header was found +# +# Known issues: Only works by filename matching and file extension, will break if the files in the directory +# are named such that this doesn't work - will either not find a header that exists (ie if the header file isn't +# just the raw file name with ".hdr" either on the end or replacing the existing suffix) or potentially may return the +# wrong header file if for some reason the header for a different file is named to match the file given. +def findHdrFile(rawfilename): + # Check the file exists + if (not os.path.isfile(rawfilename)): + raise IOError, "Could not find file " + rawfilename + # end if + + # Get the filename without path or extension + filename = os.path.basename(rawfilename) + filesplit = os.path.splitext(filename) + filebase = filesplit[0] + dirname = os.path.dirname(rawfilename) + + # See if we can find the header file to use + if (os.path.isfile(os.path.join(dirname, filebase + ".hdr"))): + hdrfile = os.path.join(dirname, filebase + ".hdr") + elif (os.path.isfile(os.path.join(dirname, filename + ".hdr"))): + hdrfile = os.path.join(dirname, filename + ".hdr") + else: + hdrfile = None + # end if + + return hdrfile +# end function diff --git a/ArcGIS/deleteExnameRellenoOrEmpty.exp b/ArcGIS/deleteExnameRellenoOrEmpty.exp new file mode 100644 index 0000000000000000000000000000000000000000..cc0c413f65b30786b0c1774d797b41814c3c863e GIT binary patch literal 70 vcmYdgs9?xrNMy)mNM%r9umxguDE4Ov0 +# Description: +#This script takes 3 inputs: +#Buffer: zone feature to use as input for zonal statistic tool +#Grid: feature whose table will be used to save the mean values of the zonal statistic tool. +# This should contain the same features ID field than the "Buffer" input. +# A field named B1, B2... will be created to store the mean values for each zone for each band. +#Raster: Raster to use as input for zonal statistic tool, along with the "Buffer". +# The band information is read from the envi HDR file +###The temporal stats tables are created in the default ArcGIS GDB +# --------------------------------------------------------------------------- + +# Import arcpy module +import os +import arcpy, arcgisscripting +import itertools +import re +import traceback, sys + +def print_kwinfo(): + if deb==1: + print "kwinfo se guarda:" + print kwinfo + +def create_if_not_exists(path): + try: + if not os.path.exists(path): + os.makedirs(path) + except Exception, e: + # If an error occurred, print line number and error message + tb = sys.exc_info()[2] + arcpy.AddMessage("Line:" + str(tb.tb_lineno)) + arcpy.AddMessage("Error " + e.message) + +def exists(path): + exists = True if os.path.exists(path)else False + return exists + +def replace_in_list(regex,lst): + return [re.sub(regex, '', x).strip() for x in lst] #List comprehension + +def print_dict(dictio): + #The next line iterates, formats and prints the dictionary, key:value + #print "\n".join('{}={}'.format(k,v) for k,v in dictio.items()) print in console + arcpy.AddMessage("\n".join('{}={}'.format(k,v) for k,v in dictio.items()))#Print to arcmap results window + +def letsgo(header_file): + arcpy.AddMessage("started reading header file:" + str(header_file)) + envi_header_keywords={"acquisition time","band names","bands", + "bbl","byte order","class lookup","class names", + "classes","cloud cover","complex function", + "coordinate system string","data gain values", + "data ignore value","data offset values", + "data reflectance gain values","data reflectance offset values", + "data type","default bands","default stretch","dem band", + "dem file","description","file type","fwhm","geo points", + "header offset","interleave","lines","map info", + "major frame offsets","minor frame offsets","pixel size", + "product type","projection info","read procedures", + "reflectance scale factor","rpc info","samples","security tag", + "sensor type","solar irradiance","spectra names","sun azimuth", + "sun elevation","wavelength","wavelength units","x start", + "y start","z plot average","z plot range","z plot titles"} + envi_header_keywords_or="|".join(envi_header_keywords) + # Read and Iterate over the lines of the file + with open(header_file, 'rt') as f: + data = f.read()[5:] + lines = re.split(r"[\n]", data)#the info corresponding to one line + arcpy.AddMessage("Finished reading header lines") + dictio = {} + global deb + deb=0 + wl=[]#List to save wavelengths + bn=[]#List to save band names + clines=[]#complete lines with all the info corresponding to one header keyword + ongoing=0 #Variable para marcar si se está buscando el resto de la línea de info + #to one header keyword is not in one single line + kwinfo="" + for i,l in enumerate(lines): + if deb==1: print str(i)+"-l- "+str(l) + kwinfo+=l + if deb==1: print str(i)+"-kwinfo- "+str(kwinfo) + if ongoing==1: + if "}" in l: + if deb==1: print "ongoing==se cierra por fin el parentesis" + print_kwinfo() + clines.append(kwinfo) + kwinfo="" + ongoing=0 + continue + else: + if deb==1: print "ongoing==1 else" + if ongoing==0: + ongoing=1 + for keyw in envi_header_keywords: + if keyw in l: + if deb==1: print keyw+" attribute found" + if "{" in l: + if "}" in l: + if deb==1: print "ongoing==parentesis cerrando en linea" + print_kwinfo() + clines.append(kwinfo) + kwinfo="" + ongoing=0 + else: + if deb==1: print "if bracket in l: --- else" + else: + if deb==1: print "ongoing==sin paretesis" + print_kwinfo() + clines.append(kwinfo) + kwinfo="" + ongoing=0 + if deb==1: print "fin de ronda de for" + for cl in clines: + #expresion to filter how to sepatate the string. + regexpresion = re.compile(r"""(.+?)\s*=\s*(.+)""") + dictio.update(dict(regexpresion.findall(cl))) + if deb==1: print regexpresion.findall(cl) + for key in dictio: dictio[key]=dictio[key].strip() + print "Header attributes:" + arcpy.AddMessage("Header attributes read") + print_dict(dictio) + expr_replace = re.compile(r"{|}") #Expression to remove brackets below + #wavelength + if "wavelength" in dictio: + wl=re.split(r"[,]", dictio["wavelength"]) + wl =replace_in_list(expr_replace,wl) + arcpy.AddMessage(wl) + arcpy.AddMessage("saved wl") + #Band names + if "band names" in dictio: + arcpy.AddMessage("band names attribute found") + bn=re.split(r"[,]", dictio["band names"]) + arcpy.AddMessage("split competed") + bn=replace_in_list(expr_replace,bn) + arcpy.AddMessage("saved bn") + #Next we add the wl units only if they are defined + arcpy.AddMessage("Format composed band name") + if "wavelength units" in dictio: + if "band names" in dictio and "wavelength" in dictio: + join_str = '{bandn} ({waveln} '+dictio["wavelength units"]+')' if dictio["wavelength units"]!="Unknown" else '{bandn} ({waveln})' + arcpy.AddMessage("bn and wl found") + full_bn= '\n'.join(join_str.format(bandn=b, waveln=w) for b,w in itertools.izip(bn, wl)).split('\n') + if not "band names" in dictio and "wavelength" in dictio: + join_str = '{waveln} '+dictio["wavelength units"] if dictio["wavelength units"]!="Unknown" else '{waveln}' + arcpy.AddMessage("wl found+units") + full_bn= '\n'.join(join_str.format(waveln=w) for w in wl).split('\n') + else: + if "band names" in dictio and "wavelength" in dictio: + join_str = '{bandn} ({waveln})' + arcpy.AddMessage("bn and wl found") + full_bn= '\n'.join(join_str.format(bandn=b, waveln=w) for b,w in itertools.izip(bn, wl)).split('\n') + if not "band names" in dictio and "wavelength" in dictio: + arcpy.AddMessage("wl found") + full_bn= wl + arcpy.AddMessage(full_bn) + if deb==1: + print full_bn + print "Bands: "+len(full_bn) + arcpy.AddMessage("Finished reading header") + return full_bn + +##def save_bn_wl(): +## full_bn= '\n'.join(join_str.format(bandn=b, waveln=w) for b,w in itertools.izip(bn, wl)).split('\n') +## +##def save_wl(): +## full_bn= '\n'.join(join_str.format(waveln=w) for w in itertools.izip(wl)).split('\n') + +## Starts the script +try: + # Check out any necessary licenses + arcpy.CheckOutExtension("spatial") + + # Create the Geoprocessor object + gp = arcgisscripting.create() + + # Load required toolboxes + print "Start!" + #Type: Feature layer -buffer for the zonalStats + bf_Lx_1 = arcpy.GetParameterAsText(0) + L_merge = bf_Lx_1 + arcpy.AddMessage("read b1 " + bf_Lx_1) + print bf_Lx_1 + + #Type: Field -ID field + f = arcpy.GetParameterAsText(0) + L_merge = bf_Lx_1 + arcpy.AddMessage("read b1 " + bf_Lx_1) + print bf_Lx_1 + + #type: Feature layer - Feature in whose table the stats will be stored + Grid = arcpy.GetParameterAsText(1) + arcpy.AddMessage("read " + Grid) + desc = arcpy.Describe(Grid) + gPath = desc.path + gridSource = str(gPath) + "\\" + Grid + arcpy.AddMessage("Grid source: " + gridSource) + gridName = desc.baseName + arcpy.AddMessage("Grid basename: " + gridName) + + print Grid + #type: Raster layer + Raster = arcpy.GetParameterAsText(2) + arcpy.AddMessage("read " + Raster) + desc = arcpy.Describe(Raster) + rPath = desc.path + Raster = str(rPath) + "\\" + desc.baseName+ "." +desc.extension + arcpy.AddMessage("Raster source: " + Raster) + print Raster + + #type: Field - Field for join and statistics + id_field = arcpy.GetParameterAsText(3) + arcpy.AddMessage("read " + id_field) + + #List of band names + hdr_file = os.path.join(str(rPath) + "\\" + desc.baseName+".hdr") + bandNames = letsgo(hdr_file) + #Next I was try ing to print the band number. Not yet + if type(bandNames).__name__ != 'int': + print r"# of bands in raster: "+str(len(bandNames)) + + #Folder to store the tables with the stats + tempGdb_path = r"C:\Users\usuario\Documents\ArcGIS" + tempGdb = r"Default.gdb" + #This is the path + tablesPath = os.path.join(tempGdb_path, tempGdb) + #Keep the individual stats tables? + #Either way the MEAN is stored to the table of the grid feature selected + keep_tables=False + # Execute CreateFileGDB + create_if_not_exists(tempGdb_path) + if not exists(tablesPath): + arcpy.CreateFileGDB_management(tempGdb_path, tempGdb) + print tablesPath + + # Local variables: + arcpy.AddMessage("Local variables") + print "Local variables" + Zone_field_and_join_Field = id_field + gridLayer = "gridLayer" + + + ## Make a layer + print "Make layer" + arcpy.MakeFeatureLayer_management(Grid, "gridLayer") + + i=1 + for b in bandNames: + arcpy.AddMessage("b: " + b) + band = Raster + "\\" + b + arcpy.AddMessage("band: " + band) + bandField = "B" + str(i) + print bandField + #Set table name + tableName = gridName + "_" + bandField #+ ".dbf" + L_Stats_table = tablesPath + "\\" + tableName + print L_Stats_table + Field_Name_Calculate_field = gridName + "." + bandField + print Field_Name_Calculate_field + Expression_Select_layer = tableName+".Mean IS NOT NULL" + # Use this if the stats were saved to a dbf file "\""+tableName+".Mean\" IS NOT NULL" + # Use this if the stats were saved to a table "\""+tableName+":Mean\" IS NOT NULL" + # Use this if the stats were saved to a database table tableName+".Mean IS NOT NULL" + print Expression_Select_layer + Expression_Calculate_field = "round(!"+tableName+".Mean!,0)" + #the "!"+tableName+":Mean!" notation (:)is used when the stats are saved to a table + #the "!"+tableName+".Mean!" notation (.)is used when the stats are saved to a dbf file + print Expression_Calculate_field + + # Process: Zonal Statistics as Table + print "Zonal statistics..." + #Perform the zonalstatistics getting only the "MEAN" value of the cells + #"MEAN" can be changed to get different statistics: + #ALL,MEAN,MAJORITY,MAXIMUM,MEDIAN,MINIMUM,MINORITY,RANGE,STD,SUM,VARIETY,MIN_MAX,MEAN_STD,MIN_MAX_MEAN  + #arcpy.AddMessage("zs exists? : " + str(exists(path))) + zs = arcpy.gp.ZonalStatisticsAsTable_sa(L_merge, Zone_field_and_join_Field, band, L_Stats_table, "DATA", "MEAN") + arcpy.AddMessage("stats table: " + str(zs)) + fields = gp.ListFields(gridLayer, bandField) + field_found = fields.Next() + if (not field_found): + arcpy.AddField_management(gridLayer, bandField, "FLOAT", 10, 3, "", "", "NULLABLE", "NON_REQUIRED", "") + arcpy.AddMessage("Added. Before field not found " + str(bandField)) + else: + arcpy.AddMessage("field_found " + str(field_found.name)) + arcpy.AddMessage("field found " + str(bandField)) + + # Process: Add Join + print "Joining..." + arcpy.AddMessage("grid..."+ gridLayer) + arcpy.AddMessage("field..."+Zone_field_and_join_Field) + arcpy.AddJoin_management(gridLayer, Zone_field_and_join_Field, zs, Zone_field_and_join_Field, "KEEP_ALL") + + # Process: Select Layer By Attribute + if i==1: + print "Selecting Layer..." + gridLayerSelection = arcpy.SelectLayerByAttribute_management(gridLayer, "NEW_SELECTION", Expression_Select_layer) + + # Process: Calculate Field + print "Calculating field" + arcpy.AddMessage("Calculating field...") + arcpy.CalculateField_management(gridLayerSelection, Field_Name_Calculate_field, Expression_Calculate_field, "PYTHON") + + # Process: Remove Join + arcpy.AddMessage("remove Join") + arcpy.RemoveJoin_management(gridLayer, "") + #Erase stats table + if not keep_tables: arcpy.Delete_management(zs) + i+=1 + + print "Finish" + +except Exception, e: + # If an error occurred, print line number and error message + import traceback, sys + tb = sys.exc_info()[2] + arcpy.AddMessage("Line:" + str(tb.tb_lineno)) + arcpy.AddMessage("Error " + e.message) + print "OMG!" + + diff --git a/ArcGIS/extractOneBandToGDBFeature.py b/ArcGIS/extractOneBandToGDBFeature.py new file mode 100644 index 0000000..c2e6cc7 --- /dev/null +++ b/ArcGIS/extractOneBandToGDBFeature.py @@ -0,0 +1,122 @@ +# -*- coding: utf-8 -*- +# --------------------------------------------------------------------------- +# extractOneBandToGDBFeature.py +# Created on: 2016-06-30 11:31:06.00000 +# (generated by ArcGIS/ModelBuilder) +# Usage: extractOneBandToGDBFeature +# Description: +# --------------------------------------------------------------------------- + +# Import arcpy module +import arcpy + +# Check out any necessary licenses +arcpy.CheckOutExtension("spatial") + +# Load required toolboxes +arcpy.ImportToolbox("Model Functions") +arcpy.ImportToolbox("C:/Dropbox/Software/Scripts/ArcGIS/Herramientas.tbx") + +# Set Geoprocessing environments +arcpy.env.snapRaster = "C:\\Dropbox\\data\\AD\\AD_nut\\ndvi_geo\\c160216jlonvi.tif" +arcpy.env.extent = "606004.483 3029183.7528 607206.2229 3030188.6423" + +# Script arguments +Buffer = arcpy.GetParameterAsText(0) +if Buffer == '#' or not Buffer: + Buffer = "C:\\Dropbox\\data\\AD\\AD_nut\\shp\\ad_GS.gdb\\areas\\areas_jlo" # provide a default value if unspecified + +Raster = arcpy.GetParameterAsText(1) +if Raster == '#' or not Raster: + Raster = "C:\\Dropbox\\data\\AD\\AD_nut\\ndvi_geo\\c160216jlonvi.tif" # provide a default value if unspecified + +Grid = arcpy.GetParameterAsText(2) +if Grid == '#' or not Grid: + Grid = "C:\\Dropbox\\data\\AD\\AD_nut\\shp\\ad_GS.gdb\\areas\\areas_jlo" # provide a default value if unspecified + +Folder_to_save_stats = arcpy.GetParameterAsText(3) +if Folder_to_save_stats == '#' or not Folder_to_save_stats: + Folder_to_save_stats = "C:\\Users\\usuario\\Documents\\ArcGIS\\Default.gdb" # provide a default value if unspecified + +Stats_table = arcpy.GetParameterAsText(4) +if Stats_table == '#' or not Stats_table: + Stats_table = "%Folder to save stats%\\%cam_date%_%trial%" # provide a default value if unspecified + +Zone_field_and_join_Field = arcpy.GetParameterAsText(5) +if Zone_field_and_join_Field == '#' or not Zone_field_and_join_Field: + Zone_field_and_join_Field = "Id" # provide a default value if unspecified + +Create_field_to_save_data = arcpy.GetParameterAsText(6) +if Create_field_to_save_data == '#' or not Create_field_to_save_data: + Create_field_to_save_data = "%cam_date%" # provide a default value if unspecified + +# Local variables: +areas_jlo_Layer__2_ = Stats_table +acbwypt_bf = areas_jlo_Layer__2_ +mean_value = acbwypt_bf +EYTBW__3_ = mean_value +stat_table = Stats_table +raster_name = Raster +date = raster_name +acsamtplotsbf__2_ = date +areas_jlo_Layer = acsamtplotsbf__2_ +trial = raster_name +cam = raster_name +cam_date = cam +grid_name = Grid +Expression = "%stat_table%.Mean IS NOT NULL" + +# Process: Parse Path (4) +arcpy.ParsePath_mb(Raster, "NAME") + +# Process: truncateString +arcpy.gp.toolbox = "C:/Dropbox/Software/Scripts/ArcGIS/Herramientas.tbx"; +# Warning: the toolbox C:/Dropbox/Software/Scripts/ArcGIS/Herramientas.tbx DOES NOT have an alias. +# Please assign this toolbox an alias to avoid tool name collisions +# And replace arcpy.gp.truncateString(...) with arcpy.truncateString_ALIAS(...) +arcpy.gp.truncateString("1", "7", raster_name) + +# Process: truncateString (3) +arcpy.gp.toolbox = "C:/Dropbox/Software/Scripts/ArcGIS/Herramientas.tbx"; +# Warning: the toolbox C:/Dropbox/Software/Scripts/ArcGIS/Herramientas.tbx DOES NOT have an alias. +# Please assign this toolbox an alias to avoid tool name collisions +# And replace arcpy.gp.truncateString(...) with arcpy.truncateString_ALIAS(...) +arcpy.gp.truncateString("0", "1", raster_name) + +# Process: Calculate Value +arcpy.CalculateValue_management("paste(\"%cam%\",\"%date%\")", "def paste(a,y):\\n return str(a)+str(y)", "String") + +# Process: Add Field +arcpy.AddField_management(Grid, Create_field_to_save_data, "FLOAT", "8", "3", "", "", "NULLABLE", "NON_REQUIRED", "") + +# Process: Make Feature Layer +arcpy.MakeFeatureLayer_management(acsamtplotsbf__2_, areas_jlo_Layer, "", "", "OBJECTID OBJECTID VISIBLE NONE;Shape Shape VISIBLE NONE;Id Id VISIBLE NONE;Shape_Length Shape_Length VISIBLE NONE;Shape_Area Shape_Area VISIBLE NONE;area area VISIBLE NONE;%cam_date% %cam_date% VISIBLE NONE") + +# Process: truncateString (2) +arcpy.gp.toolbox = "C:/Dropbox/Software/Scripts/ArcGIS/Herramientas.tbx"; +# Warning: the toolbox C:/Dropbox/Software/Scripts/ArcGIS/Herramientas.tbx DOES NOT have an alias. +# Please assign this toolbox an alias to avoid tool name collisions +# And replace arcpy.gp.truncateString(...) with arcpy.truncateString_ALIAS(...) +arcpy.gp.truncateString("7", "10", raster_name) + +# Process: Zonal Statistics as Table +arcpy.gp.ZonalStatisticsAsTable_sa(Buffer, Zone_field_and_join_Field, Raster, Stats_table, "DATA", "MEAN") + +# Process: Add Join +arcpy.AddJoin_management(areas_jlo_Layer, Zone_field_and_join_Field, Stats_table, Zone_field_and_join_Field, "KEEP_ALL") + +# Process: Parse Path (3) +arcpy.ParsePath_mb(Stats_table, "NAME") + +# Process: Select Layer By Attribute +arcpy.SelectLayerByAttribute_management(areas_jlo_Layer__2_, "NEW_SELECTION", Expression) + +# Process: Calculate Field +arcpy.CalculateField_management(acbwypt_bf, "%grid_name%.%Create field to save data%", "round(!%stat_table%.Mean!,3)", "PYTHON", "") + +# Process: Remove Join +arcpy.RemoveJoin_management(mean_value, "") + +# Process: Parse Path (2) +arcpy.ParsePath_mb(Grid, "NAME") + diff --git a/ArcGIS/extract_data_multi6bandas.py b/ArcGIS/extract_data_multi6bandas.py new file mode 100644 index 0000000..ca84756 --- /dev/null +++ b/ArcGIS/extract_data_multi6bandas.py @@ -0,0 +1,180 @@ +# -*- coding: utf-8 -*- +# --------------------------------------------------------------------------- +# extract_data_multi6bandas.py +# Created on: 2015-07-10 11:06:16.00000 +# Description: +#This script takes 3 inputs: +#Buffer: zone feature to use as input for zonal statistic tool +#Grid: feature whose table will be used to save the mean values of the zonal statistic tool. +# This should contain the same features ID field than the "Buffer" input. +# A field named B1, B2... will be created to store the mean values for each zone for each band. +#Raster: Raster to use as input for zonal statistic tool (stats table stored in the feature location), along with the "Buffer". +# The script will iterate 6 bands +###The temporal stats tables are created in the default ArcGIS GDB +# --------------------------------------------------------------------------- + +# Import arcpy module +import os +import arcpy, arcgisscripting +import itertools +import traceback, sys + +def create_if_not_exists(path): + try: + if not os.path.exists(path): + os.makedirs(path) + except Exception, e: + # If an error occurred, print line number and error message + tb = sys.exc_info()[2] + arcpy.AddMessage("Line:" + str(tb.tb_lineno)) + arcpy.AddMessage("Error " + e.message) + +def exists(path): + exists = True if os.path.exists(path)else False + return exists + +## starts the script +try: + # Check out any necessary licenses + arcpy.CheckOutExtension("spatial") + + # Create the Geoprocessor object + gp = arcgisscripting.create() + + # Load required toolboxes + print "Start!" + #Type: Feature layer of plots areas to extract data from the raster + bf_Lx_1 = arcpy.GetParameterAsText(0) + L_merge = bf_Lx_1 + arcpy.AddMessage("read buffer1 " + bf_Lx_1) + print bf_Lx_1 + + #type: Feature layer - Feature in whose table the stats will be stored. + #Needs to have a field matching the ID field, because a Join is made ith it + Grid = arcpy.GetParameterAsText(1) + arcpy.AddMessage("read " + Grid) + desc = arcpy.Describe(Grid) + gPath = desc.path + gridSource = str(gPath) + "\\" + Grid + arcpy.AddMessage("Grid source: " + gridSource) + gridName = desc.baseName + arcpy.AddMessage("Grid basename: " + gridName) + + print Grid + #type: Raster layer from which data will be extracted + Raster = arcpy.GetParameterAsText(2) + arcpy.AddMessage("read " + Raster) + desc = arcpy.Describe(Raster) + rPath = desc.path + Raster = str(rPath) + "\\" + desc.baseName+ "." +desc.extension + arcpy.AddMessage("Raster source: " + Raster) + print Raster + + #type: Field - Field for join and statistics + id_field = arcpy.GetParameterAsText(3) + arcpy.AddMessage("read " + id_field) + + #Number of bands + bandNumber = desc.bandCount + arcpy.AddMessage("Number of bands: "+str(bandNumber)) + + #Folder to store the tables with the stats + tempGdb_path = r"C:\Users\usuario\Documents\ArcGIS" + tempGdb = r"Default.gdb" + #This is the path + tablesPath = os.path.join(tempGdb_path, tempGdb) + + #Keep the individual stats tables? + #Either way the MEAN is stored to the table of the grid feature selected + keep_tables=False + # Execute CreateFileGDB + create_if_not_exists(tempGdb_path) + if not exists(tablesPath): + arcpy.CreateFileGDB_management(tempGdb_path, tempGdb) + print tablesPath + + # Local variables: + arcpy.AddMessage("Local variables") + print "Local variables" + Zone_field_and_join_Field = id_field + gridLayer = "gridLayer" + + + ## Make a layer + print "Make layer" + arcpy.MakeFeatureLayer_management(Grid, "gridLayer") + + #It is assumed that the Band names are something like "Band_1" + for b in range(1, bandNumber+1): + arcpy.AddMessage("band: " + str(b)) + band = Raster + "\\Band_" + str(b) + arcpy.AddMessage("band: " + band) + bandField = "B" + str(b) + print bandField + #Set table name + tableName = gridName + "_" + bandField #+ ".dbf" + L_Stats_table = tablesPath + "\\" + tableName + print L_Stats_table + Field_Name_Calculate_field = gridName + "." + bandField + print Field_Name_Calculate_field + + #Expression_Select_layer = tableName+".Mean IS NOT NULL" + # Use this if the stats were saved to a dbf file "\""+tableName+".Mean\" IS NOT NULL" + # Use this if the stats were saved to a table "\""+tableName+":Mean\" IS NOT NULL" + # Use this if the stats were saved to a database table tableName+".Mean IS NOT NULL" + #print Expression_Select_layer + + #Round values + Expression_Calculate_field = "round(!"+tableName+".Mean!,2)" + #the "!"+tableName+":Mean!" notation (:)is used when the stats are saved to a table + #the "!"+tableName+".Mean!" notation (.)is used when the stats are saved to a dbf file + print Expression_Calculate_field + + # Process: Zonal Statistics as Table + print "Zonal statistics..." + #Perform the zonalstatistics getting only the "MEAN" value of the cells + #"MEAN" can be changed to get different statistics: + #ALL,MEAN,MAJORITY,MAXIMUM,MEDIAN,MINIMUM,MINORITY,RANGE,STD,SUM,VARIETY,MIN_MAX,MEAN_STD,MIN_MAX_MEAN  + #arcpy.AddMessage("zs exists? : " + str(exists(path))) + zs = arcpy.gp.ZonalStatisticsAsTable_sa(L_merge, Zone_field_and_join_Field, band, L_Stats_table, "DATA", "MEAN") + arcpy.AddMessage("stats table: " + str(zs)) + + ##Chech if the field where the value will be store exists + ##Otherwise, create it + fields = gp.ListFields(gridLayer, bandField) + field_found = fields.Next() + if (not field_found): + arcpy.AddField_management(gridLayer, bandField, "FLOAT", 10, 3, "", "", "NULLABLE", "NON_REQUIRED", "") + arcpy.AddMessage("Field not found, added: " + str(bandField)) + else: + arcpy.AddMessage("field_found " + str(field_found.name)) + arcpy.AddMessage("field found " + str(bandField)) + + # Process: Add Join + print "Joining..." + arcpy.AddMessage("grid..."+ gridLayer) + arcpy.AddMessage("field..."+Zone_field_and_join_Field) + arcpy.AddJoin_management(gridLayer, Zone_field_and_join_Field, zs, Zone_field_and_join_Field, "KEEP_ALL") + + # Process: Calculate Field + print "Calculating field" + arcpy.AddMessage("Calculating field...") + arcpy.CalculateField_management(gridLayer, Field_Name_Calculate_field, Expression_Calculate_field, "PYTHON") + + # Process: Remove Join + arcpy.AddMessage("remove Join") + arcpy.RemoveJoin_management(gridLayer, "") + #Erase stats table + if not keep_tables: arcpy.Delete_management(zs) + + print "Finish" + +except Exception, e: + # If an error occurred, print line number and error message + import traceback, sys + tb = sys.exc_info()[2] + arcpy.AddMessage("Line:" + str(tb.tb_lineno)) + arcpy.AddMessage("Error " + e.message) + print "OMG!" + + diff --git a/ArcGIS/extract_multiband_raster.py b/ArcGIS/extract_multiband_raster.py new file mode 100644 index 0000000..3538d5c --- /dev/null +++ b/ArcGIS/extract_multiband_raster.py @@ -0,0 +1,325 @@ +# -*- coding: utf-8 -*- +# --------------------------------------------------------------------------- +# joinZonalStats_hyper_BW_pruebas.py +# Created on: 2014-06-07 11:06:16.00000 +# (generated by ArcGIS/ModelBuilder) +# Usage: joinZonalStats_hyper_BW_pruebas +# Description:Gets zonal statistics from multiple bands of a Raster stores them in a feature (can extract from one feature and store statistics in another if they have the same ID or "Name" field). Gets band names from *.hdr file (needed). Can save the resulting table of the statistics +#This script takes 3 inputs: +#Buffer: zone feature to use as input for zonal statistic tool +#Grid: feature whose table will be used to save the mean values of the zonal statistic tool. +# This should contain the same features ID field than the "Buffer" input. +# A field named B1, B2... will be created to store the mean values for each zone for each band. +#Raster: Raster to use as input for zonal statistic tool, along with the "Buffer". +# The band information is read from the envi HDR file +###The temporal stats tables are created in the default ArcGIS GDB +# --------------------------------------------------------------------------- + +# Import arcpy module +import os +import arcpy, arcgisscripting +import itertools +import re +import traceback, sys + +def print_kwinfo(): + if deb==1: + print "kwinfo se guarda:" + print kwinfo + +def create_if_not_exists(path): + try: + if not os.path.exists(path): + os.makedirs(path) + except Exception, e: + # If an error occurred, print line number and error message + tb = sys.exc_info()[2] + arcpy.AddMessage("Line:" + str(tb.tb_lineno)) + arcpy.AddMessage("Error " + e.message) + +def exists(path): + exists = True if os.path.exists(path)else False + return exists + +def replace_in_list(regex,lst): + return [re.sub(regex, '', x).strip() for x in lst] #List comprehension + +def print_dict(dictio): + #The next line iterates, formats and prints the dictionary, key:value + #print "\n".join('{}={}'.format(k,v) for k,v in dictio.items()) print in console + arcpy.AddMessage("\n".join('{}={}'.format(k,v) for k,v in dictio.items()))#Print to arcmap results window + +def letsgo(header_file): + arcpy.AddMessage("started reading header file:" + str(header_file)) + envi_header_keywords={"acquisition time","band names","bands", + "bbl","byte order","class lookup","class names", + "classes","cloud cover","complex function", + "coordinate system string","data gain values", + "data ignore value","data offset values", + "data reflectance gain values","data reflectance offset values", + "data type","default bands","default stretch","dem band", + "dem file","description","file type","fwhm","geo points", + "header offset","interleave","lines","map info", + "major frame offsets","minor frame offsets","pixel size", + "product type","projection info","read procedures", + "reflectance scale factor","rpc info","samples","security tag", + "sensor type","solar irradiance","spectra names","sun azimuth", + "sun elevation","wavelength","wavelength units","x start", + "y start","z plot average","z plot range","z plot titles"} + envi_header_keywords_or="|".join(envi_header_keywords) + # Read and Iterate over the lines of the file + with open(header_file, 'rt') as f: + data = f.read()[5:] + lines = re.split(r"[\n]", data)#the info corresponding to one line + arcpy.AddMessage("Finished reading header lines") + dictio = {} + global deb + deb=0 + wl=[]#List to save wavelengths + bn=[]#List to save band names + clines=[]#complete lines with all the info corresponding to one header keyword + ongoing=0 #Variable para marcar si se está buscando el resto de la línea de info + #to one header keyword is not in one single line + kwinfo="" + for i,l in enumerate(lines): + if deb==1: print str(i)+"-l- "+str(l) + kwinfo+=l + if deb==1: print str(i)+"-kwinfo- "+str(kwinfo) + if ongoing==1: + if "}" in l: + if deb==1: print "ongoing==se cierra por fin el parentesis" + print_kwinfo() + clines.append(kwinfo) + kwinfo="" + ongoing=0 + continue + else: + if deb==1: print "ongoing==1 else" + if ongoing==0: + ongoing=1 + for keyw in envi_header_keywords: + if keyw in l: + if deb==1: print keyw+" attribute found" + if "{" in l: + if "}" in l: + if deb==1: print "ongoing==parentesis cerrando en linea" + print_kwinfo() + clines.append(kwinfo) + kwinfo="" + ongoing=0 + else: + if deb==1: print "if bracket in l: --- else" + else: + if deb==1: print "ongoing==sin paretesis" + print_kwinfo() + clines.append(kwinfo) + kwinfo="" + ongoing=0 + if deb==1: print "fin de ronda de for" + for cl in clines: + #expresion to filter how to sepatate the string. + regexpresion = re.compile(r"""(.+?)\s*=\s*(.+)""") + dictio.update(dict(regexpresion.findall(cl))) + if deb==1: print regexpresion.findall(cl) + for key in dictio: dictio[key]=dictio[key].strip() + print "Header attributes:" + arcpy.AddMessage("Header attributes read") + print_dict(dictio) + expr_replace = re.compile(r"{|}") #Expression to remove brackets below + #wavelength + if "wavelength" in dictio: + wl=re.split(r"[,]", dictio["wavelength"]) + wl =replace_in_list(expr_replace,wl) + arcpy.AddMessage(wl) + arcpy.AddMessage("saved wl") + #Band names + if "band names" in dictio: + arcpy.AddMessage("band names attribute found") + bn=re.split(r"[,]", dictio["band names"]) + arcpy.AddMessage("split competed") + bn=replace_in_list(expr_replace,bn) + arcpy.AddMessage("saved bn") + #Next we add the wl units only if they are defined + arcpy.AddMessage("Format composed band name") + if "wavelength units" in dictio: + if "band names" in dictio and "wavelength" in dictio: + join_str = '{bandn} ({waveln} '+dictio["wavelength units"]+')' if dictio["wavelength units"]!="Unknown" else '{bandn} ({waveln})' + arcpy.AddMessage("bn and wl found") + full_bn= '\n'.join(join_str.format(bandn=b, waveln=w) for b,w in itertools.izip(bn, wl)).split('\n') + if not "band names" in dictio and "wavelength" in dictio: + join_str = '{waveln} '+dictio["wavelength units"] if dictio["wavelength units"]!="Unknown" else '{waveln}' + arcpy.AddMessage("wl found+units") + full_bn= '\n'.join(join_str.format(waveln=w) for w in wl).split('\n') + else: + if "band names" in dictio and "wavelength" in dictio: + join_str = '{bandn} ({waveln})' + arcpy.AddMessage("bn and wl found") + full_bn= '\n'.join(join_str.format(bandn=b, waveln=w) for b,w in itertools.izip(bn, wl)).split('\n') + if not "band names" in dictio and "wavelength" in dictio: + arcpy.AddMessage("wl found") + full_bn= wl + arcpy.AddMessage(full_bn) + if deb==1: + print full_bn + print "Bands: "+len(full_bn) + arcpy.AddMessage("Finished reading header") + return full_bn + +##def save_bn_wl(): +## full_bn= '\n'.join(join_str.format(bandn=b, waveln=w) for b,w in itertools.izip(bn, wl)).split('\n') +## +##def save_wl(): +## full_bn= '\n'.join(join_str.format(waveln=w) for w in itertools.izip(wl)).split('\n') + +## Starts the script +try: + # Check out any necessary licenses + arcpy.CheckOutExtension("spatial") + + # Create the Geoprocessor object + gp = arcgisscripting.create() + + # Load required toolboxes + print "Start!" + #Type: Feature layer -buffer for the zonalStats + bf_Lx_1 = arcpy.GetParameterAsText(0) + L_merge = bf_Lx_1 + arcpy.AddMessage("read b1 " + bf_Lx_1) + print bf_Lx_1 + + #Type: Field -ID field + f = arcpy.GetParameterAsText(0) + L_merge = bf_Lx_1 + arcpy.AddMessage("read b1 " + bf_Lx_1) + print bf_Lx_1 + + #type: Feature layer - Feature in whose table the stats will be stored + Grid = arcpy.GetParameterAsText(1) + arcpy.AddMessage("read " + Grid) + desc = arcpy.Describe(Grid) + gPath = desc.path + gridSource = str(gPath) + "\\" + Grid + arcpy.AddMessage("Grid source: " + gridSource) + gridName = desc.baseName + arcpy.AddMessage("Grid basename: " + gridName) + + print Grid + #type: Raster layer + Raster = arcpy.GetParameterAsText(2) + arcpy.AddMessage("read " + Raster) + desc = arcpy.Describe(Raster) + rPath = desc.path + Raster = str(rPath) + "\\" + desc.baseName+ "." +desc.extension + arcpy.AddMessage("Raster source: " + Raster) + print Raster + + #type: Field - Field for join and statistics + id_field = arcpy.GetParameterAsText(3) + arcpy.AddMessage("read " + id_field) + + #List of band names + hdr_file = os.path.join(str(rPath) + "\\" + desc.baseName+".hdr") + bandNames = letsgo(hdr_file) + #Next I was try ing to print the band number. Not yet + if type(bandNames).__name__ != 'int': + print r"# of bands in raster: "+str(len(bandNames)) + + #Folder to store the tables with the stats + tempGdb_path = r"C:\Users\usuario\Documents\ArcGIS" + tempGdb = r"Default.gdb" + #This is the path + tablesPath = os.path.join(tempGdb_path, tempGdb) + #Keep the individual stats tables? + #Either way the MEAN is stored to the table of the grid feature selected + keep_tables=False + # Execute CreateFileGDB + create_if_not_exists(tempGdb_path) + if not exists(tablesPath): + arcpy.CreateFileGDB_management(tempGdb_path, tempGdb) + print tablesPath + + # Local variables: + arcpy.AddMessage("Local variables") + print "Local variables" + Zone_field_and_join_Field = id_field + gridLayer = "gridLayer" + + + ## Make a layer + print "Make layer" + arcpy.MakeFeatureLayer_management(Grid, "gridLayer") + + i=1 + for b in bandNames: + arcpy.AddMessage("b: " + b) + band = Raster + "\\" + b + arcpy.AddMessage("band: " + band) + bandField = "B" + str(i) + print bandField + #Set table name + tableName = gridName + "_" + bandField #+ ".dbf" + L_Stats_table = tablesPath + "\\" + tableName + print L_Stats_table + Field_Name_Calculate_field = gridName + "." + bandField + print Field_Name_Calculate_field + Expression_Select_layer = tableName+".Mean IS NOT NULL" + # Use this if the stats were saved to a dbf file "\""+tableName+".Mean\" IS NOT NULL" + # Use this if the stats were saved to a table "\""+tableName+":Mean\" IS NOT NULL" + # Use this if the stats were saved to a database table tableName+".Mean IS NOT NULL" + print Expression_Select_layer + Expression_Calculate_field = "round(!"+tableName+".Mean!,0)" + #the "!"+tableName+":Mean!" notation (:)is used when the stats are saved to a table + #the "!"+tableName+".Mean!" notation (.)is used when the stats are saved to a dbf file + print Expression_Calculate_field + + # Process: Zonal Statistics as Table + print "Zonal statistics..." + #Perform the zonalstatistics getting only the "MEAN" value of the cells + #"MEAN" can be changed to get different statistics: + #ALL,MEAN,MAJORITY,MAXIMUM,MEDIAN,MINIMUM,MINORITY,RANGE,STD,SUM,VARIETY,MIN_MAX,MEAN_STD,MIN_MAX_MEAN  + #arcpy.AddMessage("zs exists? : " + str(exists(path))) + zs = arcpy.gp.ZonalStatisticsAsTable_sa(L_merge, Zone_field_and_join_Field, band, L_Stats_table, "DATA", "MEAN") + arcpy.AddMessage("stats table: " + str(zs)) + fields = gp.ListFields(gridLayer, bandField) + field_found = fields.Next() + if (not field_found): + arcpy.AddField_management(gridLayer, bandField, "FLOAT", 10, 3, "", "", "NULLABLE", "NON_REQUIRED", "") + arcpy.AddMessage("Added. Before field not found " + str(bandField)) + else: + arcpy.AddMessage("field_found " + str(field_found.name)) + arcpy.AddMessage("field found " + str(bandField)) + + # Process: Add Join + print "Joining..." + arcpy.AddMessage("grid..."+ gridLayer) + arcpy.AddMessage("field..."+Zone_field_and_join_Field) + arcpy.AddJoin_management(gridLayer, Zone_field_and_join_Field, zs, Zone_field_and_join_Field, "KEEP_ALL") + + # Process: Select Layer By Attribute + if i==1: + print "Selecting Layer..." + gridLayerSelection = arcpy.SelectLayerByAttribute_management(gridLayer, "NEW_SELECTION", Expression_Select_layer) + + # Process: Calculate Field + print "Calculating field" + arcpy.AddMessage("Calculating field...") + arcpy.CalculateField_management(gridLayerSelection, Field_Name_Calculate_field, Expression_Calculate_field, "PYTHON") + + # Process: Remove Join + arcpy.AddMessage("remove Join") + arcpy.RemoveJoin_management(gridLayer, "") + #Erase stats table + if not keep_tables: arcpy.Delete_management(zs) + i+=1 + + print "Finish" + +except Exception, e: + # If an error occurred, print line number and error message + import traceback, sys + tb = sys.exc_info()[2] + arcpy.AddMessage("Line:" + str(tb.tb_lineno)) + arcpy.AddMessage("Error " + e.message) + print "OMG!" + + diff --git a/ArcGIS/field_check.py b/ArcGIS/field_check.py new file mode 100644 index 0000000..cc13e0b --- /dev/null +++ b/ArcGIS/field_check.py @@ -0,0 +1,73 @@ +#********************************************************************** +# Description: +# Tests if a field exists and outputs two booleans: +# Exists - true if the field exists, false if it doesn't exist +# Not_Exists - true if the field doesn't exist, false if it does exist +# (the logical NOT of the first output). +# +# Arguments: +# 0 - Table name +# 1 - Field name +# 2 - Exists (boolean - see above) +# 3 - Not_Exists (boolean - see above) +# +# Created by: ESRI +#********************************************************************** + +# Standard error handling - put everything in a try/except block +# +try: + + # Import system modules + import sys, string, os, arcgisscripting + + # Create the Geoprocessor object + gp = arcgisscripting.create() + + # Get input arguments - table name, field name + # + in_Table = gp.GetParameterAsText(0) + in_Field = gp.GetParameterAsText(1) + + # First check that the table exists + # + if not gp.Exists(in_Table): + raise Exception, "Input table does not exist" + + # Use the ListFields function to return a list of fields that matches + # the name of in_Field. This is a wildcard match. Since in_Field is an + # exact string (no wildcards like "*"), only one field should be returned, + # exactly matching the input field name. + # + fields = gp.ListFields(in_Table, in_Field) + + # If ListFields returned anything, the Next operator will fetch the + # field. We can use this as a Boolean condition. + # + field_found = fields.Next() + + # Branch depending on whether field found or not. Issue a + # message, and then set our two output variables accordingly + # + if field_found: + gp.AddMessage("Field %s found in %s" % (in_Field, in_Table)) + gp.SetParameterAsText(2, "True") + gp.SetParameterAsText(3, "False") + else: + gp.AddMessage("Field %s not found in %s" % (in_Field, in_Table)) + gp.SetParameterAsText(2, "False") + gp.SetParameterAsText(3, "True") + + +# Handle script errors +# +except Exception, errMsg: + + # If we have messages of severity error (2), we assume a GP tool raised it, + # so we'll output that. Otherwise, we assume we raised the error and the + # information is in errMsg. + # + if gp.GetMessages(2): + gp.AddError(GP.GetMessages(2)) + else: + gp.AddError(str(errMsg)) \ No newline at end of file diff --git a/ArcGIS/getLayerPath.py b/ArcGIS/getLayerPath.py new file mode 100644 index 0000000..b0384cd --- /dev/null +++ b/ArcGIS/getLayerPath.py @@ -0,0 +1,8 @@ +from os.path import exists + +#Get layer to obtain absolute path +layer = arcpy.GetParameterAsText(0) +arcpy.AddMessage("Layer is: " + layer) +desc = arcpy.Describe(layer) +path = desc.path +arcpy.SetParameterAsText(1, str(path)) diff --git a/ArcGIS/in not null.exp b/ArcGIS/in not null.exp new file mode 100644 index 0000000000000000000000000000000000000000..0d8a485aec9b44222f325dd9e84d5a43335c673e GIT binary patch literal 50 wcmY#lC}A*UFkvtNl7#%B?bisPas=?!H>b8Ap}f^GWY-?0HKHmbN~PV literal 0 HcmV?d00001 diff --git a/ArcGIS/iterate_raster_band_list.py b/ArcGIS/iterate_raster_band_list.py new file mode 100644 index 0000000..4e3ddbf --- /dev/null +++ b/ArcGIS/iterate_raster_band_list.py @@ -0,0 +1,44 @@ +import os +import arcpy +##No he podido obtener los nombres de las bandas en model builder... +workspace = "E:\\140117\\140117H\\ortho\\910\\140117_10_910.bsq" + +#List of band names hardcoded +#bands = ["392.03027","393.87228","395.71426","397.55627","399.39830","401.24026","403.08228","404.92430","406.76627","408.60828","410.45030","412.29227","414.13428","415.97630","417.81827","419.66028","421.50230","423.34427","425.18628","427.02830","428.87027","430.71228","432.55430","434.39627","436.23828","438.08030","439.92227","441.76428","443.60630","445.44827","447.29028","449.13230","450.97427","452.81628","454.65826","456.50027","458.34230","460.18427","462.02628","463.86830","465.71027","467.55228","469.39430","471.23627","473.07828","474.92030","476.76227","478.60428","480.44630","482.28827","484.13028","485.97230","487.81427","489.65628","491.49830","493.34027","495.18228","497.02430","498.86627","500.70828","502.55030","504.39227","506.23428","508.07626","509.91827","511.76028","513.60230","515.44430","517.28625","519.12830","520.97030","522.81226","524.65430","526.49630","528.33826","530.18030","532.02230","533.86426","535.70630","537.54830","539.39026","541.23230","543.07430","544.91626","546.75830","548.60030","550.44226","552.28430","554.12630","555.96826","557.81030","559.65230","561.49426","563.33630","565.17830","567.02026","568.86230","570.70430","572.54626","574.38830","576.23030","578.07227","579.91430","581.75630","583.59827","585.44030","587.28230","589.12427","590.96630","592.80830","594.65027","596.49225","598.33430","600.17630","602.01825","603.86030","605.70230","607.54425","609.38630","611.22830","613.07025","614.91230","616.75430","618.59625","620.43830","622.28030","624.12225","625.96430","627.80630","629.64825","631.49030","633.33230","635.17426","637.01630","638.85830","640.70026","642.54230","644.38430","646.22626","648.06830","649.91030","651.75226","653.59430","655.43630","657.27826","659.12030","660.96230","662.80426","664.64630","666.48830","668.33026","670.17230","672.01430","673.85626","675.69830","677.54030","679.38226","681.22430","683.06630","684.90826","686.75030","688.59230","690.43427","692.27630","694.11830","695.96027","697.80230","699.64430","701.48627","703.32830","705.17030","707.01227","708.85430","710.69630","712.53827","714.38025","716.22230","718.06430","719.90625","721.74830","723.59030","725.43225","727.27430","729.11630","730.95825","732.80030","734.64230","736.48425","738.32630","740.16830","742.01025","743.85230","745.69430","747.53625","749.37830","751.22030","753.06226","754.90430","756.74630","758.58826","760.43030","762.27230","764.11426","765.95630","767.79830","769.64026","771.48230","773.32430","775.16626","777.00830","778.85030","780.69226","782.53430","784.37630","786.21826","788.06030","789.90230","791.74426","793.58630","795.42830","797.27026","799.11230","800.95430","802.79626","804.63830","806.48030","808.32227","810.16430","812.00630","813.84827","815.69030","817.53230","819.37427","821.21630","823.05830","824.90027","826.74225","828.58430","830.42630","832.26825","834.11030","835.95230","837.79425","839.63630","841.47830","843.32025","845.16230","847.00430","848.84625","850.68830"] + +#rasterList = arcpy.ListRasters("140117_1", "BSQ") + +##for r in rasterList: +## rasterPath = filePath + "\\" + r +## desc = arcpy.Describe(rasterPath) +## desc.bandCount +## if desc.bandCount == 250: +## print r +## for band in bands: +## bandPath = rasterPath + "\\" + band +## desc = arcpy.Describe(bandPath) +## print band + " es tipo : " +desc.pixelType + +##path = "E:\\140117\\140117H\\ortho" +##raster = "" +##workspace = os.path.join(path, raster) +##print workspace +##bands = [] +##for dirpath, dirnames, filenames in arcpy.da.Walk(workspace): +## for filename in filenames: +## print "filename : "+filename +## for dirname in dirnames: +## print "dirname : "+dirname +## for dirp in dirpath: +## print "dirpath : "+dirp + +for root, dirs, files in os.walk(workspace): + for name in files: + bands = os.path.join(root,name) + print bands + print root + print dirs + + + + diff --git a/ArcGIS/joinZonalStats_hyper_BW - fijo.py b/ArcGIS/joinZonalStats_hyper_BW - fijo.py new file mode 100644 index 0000000..84c44b6 --- /dev/null +++ b/ArcGIS/joinZonalStats_hyper_BW - fijo.py @@ -0,0 +1,134 @@ +# -*- coding: utf-8 -*- +# --------------------------------------------------------------------------- +# joinZonalStats_hyper_BW_pruebas.py +# Created on: 2014-06-07 11:06:16.00000 +# (generated by ArcGIS/ModelBuilder) +# Usage: joinZonalStats_hyper_BW_pruebas +# Description: +# --------------------------------------------------------------------------- + +# Import arcpy module +import os +import arcpy + +try: + # Check out any necessary licenses + arcpy.CheckOutExtension("spatial") + + #workspace + + bwPath = "D:\\AB2013-2014\\Bread_wheat" + bwVectoresPath = bwPath + "\\" + "vectores" + bwDataPath = bwPath + "\\" + "data" + bwGdbPath = bwPath + "\\" + "AB_BreadWheat.gdb" + arcpy.env.workspace = bwPath + + #List of band names + #bandNames = ["392.03027","393.87228","395.71426","397.55627","399.39830","401.24026","403.08228","404.92430","406.76627","408.60828","410.45030","412.29227","414.13428","415.97630","417.81827","419.66028","421.50230","423.34427","425.18628","427.02830","428.87027","430.71228","432.55430","434.39627","436.23828","438.08030","439.92227","441.76428","443.60630","445.44827","447.29028","449.13230","450.97427","452.81628","454.65826","456.50027","458.34230","460.18427","462.02628","463.86830","465.71027","467.55228","469.39430","471.23627","473.07828","474.92030","476.76227","478.60428","480.44630","482.28827","484.13028","485.97230","487.81427","489.65628","491.49830","493.34027","495.18228","497.02430","498.86627","500.70828","502.55030","504.39227","506.23428","508.07626","509.91827","511.76028","513.60230","515.44430","517.28625","519.12830","520.97030","522.81226","524.65430","526.49630","528.33826","530.18030","532.02230","533.86426","535.70630","537.54830","539.39026","541.23230","543.07430","544.91626","546.75830","548.60030","550.44226","552.28430","554.12630","555.96826","557.81030","559.65230","561.49426","563.33630","565.17830","567.02026","568.86230","570.70430","572.54626","574.38830","576.23030","578.07227","579.91430","581.75630","583.59827","585.44030","587.28230","589.12427","590.96630","592.80830","594.65027","596.49225","598.33430","600.17630","602.01825","603.86030","605.70230","607.54425","609.38630","611.22830","613.07025","614.91230","616.75430","618.59625","620.43830","622.28030","624.12225","625.96430","627.80630","629.64825","631.49030","633.33230","635.17426","637.01630","638.85830","640.70026","642.54230","644.38430","646.22626","648.06830","649.91030","651.75226","653.59430","655.43630","657.27826","659.12030","660.96230","662.80426","664.64630","666.48830","668.33026","670.17230","672.01430","673.85626","675.69830","677.54030","679.38226","681.22430","683.06630","684.90826","686.75030","688.59230","690.43427","692.27630","694.11830","695.96027","697.80230","699.64430","701.48627","703.32830","705.17030","707.01227","708.85430","710.69630","712.53827","714.38025","716.22230","718.06430","719.90625","721.74830","723.59030","725.43225","727.27430","729.11630","730.95825","732.80030","734.64230","736.48425","738.32630","740.16830","742.01025","743.85230","745.69430","747.53625","749.37830","751.22030","753.06226","754.90430","756.74630","758.58826","760.43030","762.27230","764.11426","765.95630","767.79830","769.64026","771.48230","773.32430","775.16626","777.00830","778.85030","780.69226","782.53430","784.37630","786.21826","788.06030","789.90230","791.74426","793.58630","795.42830","797.27026","799.11230","800.95430","802.79626","804.63830","806.48030","808.32227","810.16430","812.00630","813.84827","815.69030","817.53230","819.37427","821.21630","823.05830","824.90027","826.74225","828.58430","830.42630","832.26825","834.11030","835.95230","837.79425","839.63630","841.47830","843.32025","845.16230","847.00430","848.84625","850.68830"] + #Short list of band names for test + bandNames = ["392.03027","393.87228"] + + + # Load required toolboxes + #arcpy.ImportToolbox("Model Functions") + + # Script arguments + #bf_Lx_2 = arcpy.GetParameterAsText(0) + #if bf_Lx_2 == '#' or not bf_Lx_2: + bf_Lx_2 = bwVectoresPath + "\\" + "EYTBWMEL\\H\\H140117_EYTBWMEL_bf_L3.shp" # provide a default value if unspecified + + #bf_Lx_1 = arcpy.GetParameterAsText(1) + #if bf_Lx_1 == '#' or not bf_Lx_1: + bf_Lx_1 = bwVectoresPath + "\\" + "EYTBWH_early\\H\\H140117_EYTBWEH_bf_L3.shp" # provide a default value if unspecified + + #Grid = arcpy.GetParameterAsText(2) + #if Grid == '#' or not Grid: + gridName = "H140117_BW" + Grid = bwGdbPath + "\\" + gridName # provide a default value if unspecified + + #L_Stats_table = arcpy.GetParameterAsText(3) + #if L_Stats_table == '#' or not L_Stats_table: + # L_Stats_table = "D:\\AB2013-2014\\Bread_wheat\\data\\140117\\L3\\b1" # provide a default value if unspecified + + #Raster = arcpy.GetParameterAsText(4) + #if Raster == '#' or not Raster: + Raster = "E:\\140117\\140117H\\ortho\\140117_3_810.bsq" # provide a default value if unspecified + + #Folder to store the tables with the bands + #tablesPath = arcpy.GetParameterAsText(5) + #if tablesPath == '#' or not tablesPath: + # tablesPath = bwDataPath # provide a default value if unspecified + tablesPath = bwDataPath + "\\" + "140117\\L3" + + # Local variables: + Zone_field_and_join_Field = "Name" + gridLayer = "gridLayer" + #Field_Name_Calculate_field = "%grid_name%.%B%" + #Expression_Calculate_field = "[%B%:Mean]" + #Expression_Select_layer = "\"%B%:Mean\" IS NOT NULL" + + #Check fields + #fields = arcpy.ListFields(Grid) + #for fld in fields: + # print fld.name + + ## Make a layer + arcpy.MakeFeatureLayer_management(Grid, "gridLayer") + + L_merge = bwVectoresPath + "\\" + "merge_temp" + "\\" + "l_merge.shp" + + # Process: Merge + arcpy.Merge_management(bf_Lx_1+";"+bf_Lx_2, L_merge) + print "merging " + bf_Lx_1 + ";" + bf_Lx_2 + + i=1 + for b in bandNames: + band = Raster + "\\" + b + bandField = "B" + str(i) + print bandField + L_Stats_table = tablesPath + "\\" + bandField + ".dbf" + print L_Stats_table + Field_Name_Calculate_field = gridName + "." + bandField + print Field_Name_Calculate_field + Expression_Calculate_field = "!"+bandField+".Mean!" + print Expression_Calculate_field + Expression_Select_layer = "\""+bandField+".Mean\" IS NOT NULL" + print Expression_Select_layer + + # Process: Zonal Statistics as Table + print "Zonal statistics..." + arcpy.gp.ZonalStatisticsAsTable_sa(L_merge, Zone_field_and_join_Field, band, L_Stats_table, "DATA", "MEAN") + + # Process: Add Join + print "Joining..." + arcpy.AddJoin_management(gridLayer, Zone_field_and_join_Field, L_Stats_table, Zone_field_and_join_Field, "KEEP_ALL") + + # Process: Select Layer By Attribute + print "Selecting Layer..." + arcpy.SelectLayerByAttribute_management(gridLayer, "NEW_SELECTION", Expression_Select_layer) + + # Process: Calculate Field + print "Calculating field" + arcpy.CalculateField_management(gridLayer, Field_Name_Calculate_field, Expression_Calculate_field, "PYTHON") + + # Process: Remove Join + arcpy.RemoveJoin_management(gridLayer, "") + + i+=1 + + + # Process: parse_table_name + #arcpy.ParsePath_mb(L_Stats_table, "NAME") + + # Process: Parse_grid_name + #arcpy.ParsePath_mb(Grid, "NAME") + arcpy.Delete_management(L_merge) + print "Finish" + +except Exception, e: + # If an error occurred, print line number and error message + import traceback, sys + tb = sys.exc_info()[2] + print "Line %i" % tb.tb_lineno + print e.message + diff --git a/ArcGIS/joinZonalStats_hyper_BW - fijo1.py b/ArcGIS/joinZonalStats_hyper_BW - fijo1.py new file mode 100644 index 0000000..fbd912e --- /dev/null +++ b/ArcGIS/joinZonalStats_hyper_BW - fijo1.py @@ -0,0 +1,167 @@ +# -*- coding: utf-8 -*- +# --------------------------------------------------------------------------- +# joinZonalStats_hyper_BW_pruebas.py +# Created on: 2014-06-07 11:06:16.00000 +# (generated by ArcGIS/ModelBuilder) +# Usage: joinZonalStats_hyper_BW_pruebas +# Description: +# --------------------------------------------------------------------------- + +# Import arcpy module +import os +import arcpy + +try: + # Check out any necessary licenses + arcpy.CheckOutExtension("spatial") + + #workspace + + bwPath = "D:\\AB2013-2014\\Bread_wheat" + bwVectoresPath = bwPath + "\\" + "vectores" + bwDataPath = bwPath + "\\" + "data" + bwGdbPath = bwPath + "\\" + "AB_BreadWheat.gdb" + tempGdb = "C:\\vuelos\\temp.gdb" + arcpy.env.workspace = tempGdb + + #List of band names + bandNames = ["392.03027","393.87228","395.71426","397.55627","399.39830","401.24026","403.08228","404.92430","406.76627","408.60828","410.45030","412.29227","414.13428","415.97630","417.81827","419.66028","421.50230","423.34427","425.18628","427.02830","428.87027","430.71228","432.55430","434.39627","436.23828","438.08030","439.92227","441.76428","443.60630","445.44827","447.29028","449.13230","450.97427","452.81628","454.65826","456.50027","458.34230","460.18427","462.02628","463.86830","465.71027","467.55228","469.39430","471.23627","473.07828","474.92030","476.76227","478.60428","480.44630","482.28827","484.13028","485.97230","487.81427","489.65628","491.49830","493.34027","495.18228","497.02430","498.86627","500.70828","502.55030","504.39227","506.23428","508.07626","509.91827","511.76028","513.60230","515.44430","517.28625","519.12830","520.97030","522.81226","524.65430","526.49630","528.33826","530.18030","532.02230","533.86426","535.70630","537.54830","539.39026","541.23230","543.07430","544.91626","546.75830","548.60030","550.44226","552.28430","554.12630","555.96826","557.81030","559.65230","561.49426","563.33630","565.17830","567.02026","568.86230","570.70430","572.54626","574.38830","576.23030","578.07227","579.91430","581.75630","583.59827","585.44030","587.28230","589.12427","590.96630","592.80830","594.65027","596.49225","598.33430","600.17630","602.01825","603.86030","605.70230","607.54425","609.38630","611.22830","613.07025","614.91230","616.75430","618.59625","620.43830","622.28030","624.12225","625.96430","627.80630","629.64825","631.49030","633.33230","635.17426","637.01630","638.85830","640.70026","642.54230","644.38430","646.22626","648.06830","649.91030","651.75226","653.59430","655.43630","657.27826","659.12030","660.96230","662.80426","664.64630","666.48830","668.33026","670.17230","672.01430","673.85626","675.69830","677.54030","679.38226","681.22430","683.06630","684.90826","686.75030","688.59230","690.43427","692.27630","694.11830","695.96027","697.80230","699.64430","701.48627","703.32830","705.17030","707.01227","708.85430","710.69630","712.53827","714.38025","716.22230","718.06430","719.90625","721.74830","723.59030","725.43225","727.27430","729.11630","730.95825","732.80030","734.64230","736.48425","738.32630","740.16830","742.01025","743.85230","745.69430","747.53625","749.37830","751.22030","753.06226","754.90430","756.74630","758.58826","760.43030","762.27230","764.11426","765.95630","767.79830","769.64026","771.48230","773.32430","775.16626","777.00830","778.85030","780.69226","782.53430","784.37630","786.21826","788.06030","789.90230","791.74426","793.58630","795.42830","797.27026","799.11230","800.95430","802.79626","804.63830","806.48030","808.32227","810.16430","812.00630","813.84827","815.69030","817.53230","819.37427","821.21630","823.05830","824.90027","826.74225","828.58430","830.42630","832.26825","834.11030","835.95230","837.79425","839.63630","841.47830","843.32025","845.16230","847.00430","848.84625","850.68830"] + #Short list of band names for test + #bandNames = ["392.03027","393.87228"] + + + # Load required toolboxes + #arcpy.ImportToolbox("Model Functions") + print "Start!" + #Type: shapefile + #bf_Lx_1 = arcpy.GetParameterAsText(0) + #if bf_Lx_1 == '#' or not bf_Lx_1: + bf_Lx_1 = bwVectoresPath + "\\" + "EYTBWH_early\\H\\H140117_EYTBWEH_bf_L3.shp" # provide a default value if unspecified + + # Script arguments + #bf_Lx_2 = arcpy.GetParameterAsText(1) + #if bf_Lx_2 == '#' or not bf_Lx_2: + bf_Lx_2 = bwVectoresPath + "\\" + "EYTBWMEL\\H\\H140117_EYTBWMEL_bf_L3.shp" # provide a default value if unspecified + + # Script arguments + #bf_Lx_3 = arcpy.GetParameterAsText(2) + #if bf_Lx_3 == '#' or not bf_Lx_3: + bf_Lx_3 = '#' + #bf_Lx_3 = bwVectoresPath + "\\" + "EYTBWMEL\\H\\H140117_EYTBWMEL_bf_L3.shp" # provide a default value if unspecified + + #Grid = arcpy.GetParameterAsText(3) + #if Grid == '#' or not Grid: + #Grid = "H140117_BW" # provide a default value if unspecified + gridName = "H140117_BW" + Grid = bwGdbPath + "\\" + gridName + + #L_Stats_table = arcpy.GetParameterAsText(4) + #if L_Stats_table == '#' or not L_Stats_table: + # L_Stats_table = "D:\\AB2013-2014\\Bread_wheat\\data\\140117\\L3\\b1" # provide a default value if unspecified + + #Raster = arcpy.GetParameterAsText(5) + #if Raster == '#' or not Raster: + #Raster = "E:\\140117\\140117H\\ortho\\140117_3_810.bsq" # provide a default value if unspecified + Raster = "c:\\vuelos\\140117_3_810.bsq" + + #Folder to store the tables with the bands + #tablesPath = arcpy.GetParameterAsText(6) + #if tablesPath == '#' or not tablesPath: + # tablesPath = bwDataPath # provide a default value if unspecified + tablesPath = tempGdb #+ "\\" + "140117" + + # Local variables: + Zone_field_and_join_Field = "Name" + gridLayer = "gridLayer" + #Field_Name_Calculate_field = "%grid_name%.%B%" + #Expression_Calculate_field = "[%B%:Mean]" + #Expression_Select_layer = "\"%B%:Mean\" IS NOT NULL" + + #Check fields + #fields = arcpy.ListFields(Grid) + #for fld in fields: + # print fld.name + + ## Make a layer + arcpy.MakeFeatureLayer_management(Grid, "gridLayer") + + L_merge = tempGdb + "\\l_merge" + + # Process: Merge + print "merging..." + if bf_Lx_3 == '#' or not bf_Lx_3: + if bf_Lx_2 == '#' or not bf_Lx_2: + arcpy.CopyFeatures_management(bf_Lx_1, L_merge) + print "One input buffer" + else: + arcpy.Merge_management(bf_Lx_1+";"+bf_Lx_2, L_merge) + print "Two input buffer" + else: + arcpy.Merge_management(bf_Lx_1+";"+bf_Lx_2+";"+bf_Lx_3, L_merge) + print "Tree input buffer" + + #Line + line = bf_Lx_1[-6:-4] + + i=1 + for b in bandNames: + band = Raster + "\\" + b + bandField = "B" + str(i) + print bandField + #Set table name + tableName = gridName + "_" + line + "_" + bandField #+ ".dbf" + L_Stats_table = tablesPath + "\\" + tableName + print L_Stats_table + Field_Name_Calculate_field = gridName + "." + bandField + print Field_Name_Calculate_field + Expression_Select_layer = tableName+".Mean IS NOT NULL" + # Use this if the stats were saved to a dbf file "\""+tableName+".Mean\" IS NOT NULL" + # Use this if the stats were saved to a table "\""+tableName+":Mean\" IS NOT NULL" + # Use this if the stats were saved to a database table tableName+".Mean IS NOT NULL" + Expression_Calculate_field = "round(!"+tableName+".Mean!,0)" + #the "!"+tableName+":Mean!" notation (:)is used when the stats are saved to a table + #the "!"+tableName+".Mean!" notation (.)is used when the stats are saved to a dbf file + print Expression_Calculate_field + + print Expression_Select_layer + + # Process: Zonal Statistics as Table + print "Zonal statistics..." + arcpy.gp.ZonalStatisticsAsTable_sa(L_merge, Zone_field_and_join_Field, band, L_Stats_table, "DATA", "MEAN") + + # Process: Add Join + print "Joining..." + arcpy.AddJoin_management(gridLayer, Zone_field_and_join_Field, L_Stats_table, Zone_field_and_join_Field, "KEEP_ALL") + + # Process: Select Layer By Attribute + if i==1: + print "Selecting Layer..." + gridLayerSelection = arcpy.SelectLayerByAttribute_management(gridLayer, "NEW_SELECTION", Expression_Select_layer) + + # Process: Calculate Field + print "Calculating field" + arcpy.CalculateField_management(gridLayerSelection, Field_Name_Calculate_field, Expression_Calculate_field, "PYTHON") + + # Process: Remove Join + arcpy.RemoveJoin_management(gridLayer, "") + #Erase stats table + arcpy.Delete_management(L_Stats_table) + i+=1 + + # Process: parse_table_name + #arcpy.ParsePath_mb(L_Stats_table, "NAME") + + # Process: Parse_grid_name + #arcpy.ParsePath_mb(Grid, "NAME") + #Erase merge file + arcpy.Delete_management(L_merge) + print "Finish" + +except Exception, e: + # If an error occurred, print line number and error message + import traceback, sys + tb = sys.exc_info()[2] + print "Line %i" % tb.tb_lineno + print e.message + print "OMG!" + diff --git a/ArcGIS/joinZonalStats_hyper_BW - fijo2.py b/ArcGIS/joinZonalStats_hyper_BW - fijo2.py new file mode 100644 index 0000000..c9b0542 --- /dev/null +++ b/ArcGIS/joinZonalStats_hyper_BW - fijo2.py @@ -0,0 +1,167 @@ +# -*- coding: utf-8 -*- +# --------------------------------------------------------------------------- +# joinZonalStats_hyper_BW_pruebas.py +# Created on: 2014-06-07 11:06:16.00000 +# (generated by ArcGIS/ModelBuilder) +# Usage: joinZonalStats_hyper_BW_pruebas +# Description: +# --------------------------------------------------------------------------- + +# Import arcpy module +import os +import arcpy + +try: + # Check out any necessary licenses + arcpy.CheckOutExtension("spatial") + + #workspace + + bwPath = "D:\\AB2013-2014\\Bread_wheat" + bwVectoresPath = bwPath + "\\" + "vectores" + bwDataPath = bwPath + "\\" + "data" + bwGdbPath = bwPath + "\\" + "AB_BreadWheat.gdb" + tempGdb = "C:\\vuelos\\temp.gdb" + arcpy.env.workspace = tempGdb + + #List of band names + bandNames = ["392.03027","393.87228","395.71426","397.55627","399.39830","401.24026","403.08228","404.92430","406.76627","408.60828","410.45030","412.29227","414.13428","415.97630","417.81827","419.66028","421.50230","423.34427","425.18628","427.02830","428.87027","430.71228","432.55430","434.39627","436.23828","438.08030","439.92227","441.76428","443.60630","445.44827","447.29028","449.13230","450.97427","452.81628","454.65826","456.50027","458.34230","460.18427","462.02628","463.86830","465.71027","467.55228","469.39430","471.23627","473.07828","474.92030","476.76227","478.60428","480.44630","482.28827","484.13028","485.97230","487.81427","489.65628","491.49830","493.34027","495.18228","497.02430","498.86627","500.70828","502.55030","504.39227","506.23428","508.07626","509.91827","511.76028","513.60230","515.44430","517.28625","519.12830","520.97030","522.81226","524.65430","526.49630","528.33826","530.18030","532.02230","533.86426","535.70630","537.54830","539.39026","541.23230","543.07430","544.91626","546.75830","548.60030","550.44226","552.28430","554.12630","555.96826","557.81030","559.65230","561.49426","563.33630","565.17830","567.02026","568.86230","570.70430","572.54626","574.38830","576.23030","578.07227","579.91430","581.75630","583.59827","585.44030","587.28230","589.12427","590.96630","592.80830","594.65027","596.49225","598.33430","600.17630","602.01825","603.86030","605.70230","607.54425","609.38630","611.22830","613.07025","614.91230","616.75430","618.59625","620.43830","622.28030","624.12225","625.96430","627.80630","629.64825","631.49030","633.33230","635.17426","637.01630","638.85830","640.70026","642.54230","644.38430","646.22626","648.06830","649.91030","651.75226","653.59430","655.43630","657.27826","659.12030","660.96230","662.80426","664.64630","666.48830","668.33026","670.17230","672.01430","673.85626","675.69830","677.54030","679.38226","681.22430","683.06630","684.90826","686.75030","688.59230","690.43427","692.27630","694.11830","695.96027","697.80230","699.64430","701.48627","703.32830","705.17030","707.01227","708.85430","710.69630","712.53827","714.38025","716.22230","718.06430","719.90625","721.74830","723.59030","725.43225","727.27430","729.11630","730.95825","732.80030","734.64230","736.48425","738.32630","740.16830","742.01025","743.85230","745.69430","747.53625","749.37830","751.22030","753.06226","754.90430","756.74630","758.58826","760.43030","762.27230","764.11426","765.95630","767.79830","769.64026","771.48230","773.32430","775.16626","777.00830","778.85030","780.69226","782.53430","784.37630","786.21826","788.06030","789.90230","791.74426","793.58630","795.42830","797.27026","799.11230","800.95430","802.79626","804.63830","806.48030","808.32227","810.16430","812.00630","813.84827","815.69030","817.53230","819.37427","821.21630","823.05830","824.90027","826.74225","828.58430","830.42630","832.26825","834.11030","835.95230","837.79425","839.63630","841.47830","843.32025","845.16230","847.00430","848.84625","850.68830"] + #Short list of band names for test + #bandNames = ["392.03027","393.87228"] + + + # Load required toolboxes + #arcpy.ImportToolbox("Model Functions") + print "Start!" + #Type: shapefile + #bf_Lx_1 = arcpy.GetParameterAsText(0) + #if bf_Lx_1 == '#' or not bf_Lx_1: + bf_Lx_1 = bwVectoresPath + "\\" + "EYTBWH_early\\H\\H140117_EYTBWEH_bf_L3.shp" # provide a default value if unspecified + + # Script arguments + #bf_Lx_2 = arcpy.GetParameterAsText(1) + #if bf_Lx_2 == '#' or not bf_Lx_2: + bf_Lx_2 = bwVectoresPath + "\\" + "EYTBWMEL\\H\\H140117_EYTBWMEL_bf_L3.shp" # provide a default value if unspecified + + # Script arguments + #bf_Lx_3 = arcpy.GetParameterAsText(2) + #if bf_Lx_3 == '#' or not bf_Lx_3: + bf_Lx_3 = '#' + #bf_Lx_3 = bwVectoresPath + "\\" + "EYTBWMEL\\H\\H140117_EYTBWMEL_bf_L3.shp" # provide a default value if unspecified + + #Grid = arcpy.GetParameterAsText(3) + #if Grid == '#' or not Grid: + #Grid = "H140117_BW" # provide a default value if unspecified + gridName = "H140117_BW" + Grid = bwGdbPath + "\\" + gridName + + #L_Stats_table = arcpy.GetParameterAsText(4) + #if L_Stats_table == '#' or not L_Stats_table: + # L_Stats_table = "D:\\AB2013-2014\\Bread_wheat\\data\\140117\\L3\\b1" # provide a default value if unspecified + + #Raster = arcpy.GetParameterAsText(5) + #if Raster == '#' or not Raster: + #Raster = "E:\\140117\\140117H\\ortho\\140117_3_810.bsq" # provide a default value if unspecified + Raster = "c:\\vuelos\\140117_3_810.bsq" + + #Folder to store the tables with the bands + #tablesPath = arcpy.GetParameterAsText(6) + #if tablesPath == '#' or not tablesPath: + # tablesPath = bwDataPath # provide a default value if unspecified + tablesPath = tempGdb #+ "\\" + "140117" + + # Local variables: + Zone_field_and_join_Field = "Name" + gridLayer = "gridLayer" + + ## Make a layer + arcpy.MakeFeatureLayer_management(Grid, "gridLayer") + + #Where to store the merge feature + L_merge = tempGdb + "\\l_merge" + + #List of features to merge + mergeList = "" + + # Process: Merge + print "merging..." + if bf_Lx_3 == '#' or not bf_Lx_3: + if bf_Lx_2 == '#' or not bf_Lx_2: + #arcpy.CopyFeatures_management(bf_Lx_1, L_merge) + mergeList = bf_Lx_1 + print "One input buffer" + else: + #arcpy.Merge_management(bf_Lx_1+";"+bf_Lx_2, L_merge) + mergeList += ";"+bf_Lx_2 + print "Two input buffer" + else: + #arcpy.Merge_management(bf_Lx_1+";"+bf_Lx_2+";"+bf_Lx_3, L_merge) + mergeList += ";"+bf_Lx_3 + print "Tree input buffer" + arcpy.Merge_management(mergeList, L_merge) + #Line + line = bf_Lx_1[-6:-4] + + i=1 + for b in bandNames: + band = Raster + "\\" + b + bandField = "B" + str(i) + print bandField + #Set table name + tableName = gridName + "_" + line + "_" + bandField #+ ".dbf" + L_Stats_table = tablesPath + "\\" + tableName + print L_Stats_table + Field_Name_Calculate_field = gridName + "." + bandField + print Field_Name_Calculate_field + Expression_Select_layer = tableName+".Mean IS NOT NULL" + # Use this if the stats were saved to a dbf file "\""+tableName+".Mean\" IS NOT NULL" + # Use this if the stats were saved to a table "\""+tableName+":Mean\" IS NOT NULL" + # Use this if the stats were saved to a database table tableName+".Mean IS NOT NULL" + print Expression_Select_layer + Expression_Calculate_field = "round(!"+tableName+".Mean!,0)" + #the "!"+tableName+":Mean!" notation (:)is used when the stats are saved to a table + #the "!"+tableName+".Mean!" notation (.)is used when the stats are saved to a dbf file + print Expression_Calculate_field + + + + # Process: Zonal Statistics as Table + print "Zonal statistics..." + arcpy.gp.ZonalStatisticsAsTable_sa(L_merge, Zone_field_and_join_Field, band, L_Stats_table, "DATA", "MEAN") + + # Process: Add Join + print "Joining..." + arcpy.JoinField_management(gridLayer, Zone_field_and_join_Field, L_Stats_table, Zone_field_and_join_Field, "KEEP_ALL") + + # Process: Select Layer By Attribute + if i==1: + print "Selecting Layer..." + gridLayerSelection = arcpy.SelectLayerByAttribute_management(gridLayer, "NEW_SELECTION", Expression_Select_layer) + + # Process: Calculate Field + print "Calculating field" + arcpy.CalculateField_management(gridLayerSelection, Field_Name_Calculate_field, Expression_Calculate_field, "PYTHON") + + # Process: Remove Join + arcpy.RemoveJoin_management(gridLayer, "") + #Erase stats table + arcpy.Delete_management(L_Stats_table) + i+=1 + + # Process: parse_table_name + #arcpy.ParsePath_mb(L_Stats_table, "NAME") + + # Process: Parse_grid_name + #arcpy.ParsePath_mb(Grid, "NAME") + #Erase merge file + arcpy.Delete_management(L_merge) + print "Finish" + +except Exception, e: + # If an error occurred, print line number and error message + import traceback, sys + tb = sys.exc_info()[2] + print "Line %i" % tb.tb_lineno + print e.message + print "OMG!" + diff --git a/ArcGIS/joinZonalStats_hyper_BW.py b/ArcGIS/joinZonalStats_hyper_BW.py new file mode 100644 index 0000000..8e0d421 --- /dev/null +++ b/ArcGIS/joinZonalStats_hyper_BW.py @@ -0,0 +1,193 @@ +# -*- coding: utf-8 -*- +# --------------------------------------------------------------------------- +# joinZonalStats_hyper_BW_pruebas.py +# Created on: 2014-06-07 11:06:16.00000 +# (generated by ArcGIS/ModelBuilder) +# Usage: joinZonalStats_hyper_BW_pruebas +# Description: +# --------------------------------------------------------------------------- + +# Import arcpy module +import os +import arcpy + +try: + # Check out any necessary licenses + arcpy.CheckOutExtension("spatial") + + #workspace + + bwPath = "D:\\AB2013-2014\\Bread_wheat" + bwVectoresPath = bwPath + "\\" + "vectores" + bwDataPath = bwPath + "\\" + "data" + bwGdbPath = bwPath + "\\" + "AB_BreadWheat.gdb" + tempGdb = "C:\\vuelos\\temp.gdb" + arcpy.env.workspace = bwPath + + #List of band names + bandNames = ["392.03027","393.87228","395.71426","397.55627","399.39830","401.24026","403.08228","404.92430","406.76627","408.60828","410.45030","412.29227","414.13428","415.97630","417.81827","419.66028","421.50230","423.34427","425.18628","427.02830","428.87027","430.71228","432.55430","434.39627","436.23828","438.08030","439.92227","441.76428","443.60630","445.44827","447.29028","449.13230","450.97427","452.81628","454.65826","456.50027","458.34230","460.18427","462.02628","463.86830","465.71027","467.55228","469.39430","471.23627","473.07828","474.92030","476.76227","478.60428","480.44630","482.28827","484.13028","485.97230","487.81427","489.65628","491.49830","493.34027","495.18228","497.02430","498.86627","500.70828","502.55030","504.39227","506.23428","508.07626","509.91827","511.76028","513.60230","515.44430","517.28625","519.12830","520.97030","522.81226","524.65430","526.49630","528.33826","530.18030","532.02230","533.86426","535.70630","537.54830","539.39026","541.23230","543.07430","544.91626","546.75830","548.60030","550.44226","552.28430","554.12630","555.96826","557.81030","559.65230","561.49426","563.33630","565.17830","567.02026","568.86230","570.70430","572.54626","574.38830","576.23030","578.07227","579.91430","581.75630","583.59827","585.44030","587.28230","589.12427","590.96630","592.80830","594.65027","596.49225","598.33430","600.17630","602.01825","603.86030","605.70230","607.54425","609.38630","611.22830","613.07025","614.91230","616.75430","618.59625","620.43830","622.28030","624.12225","625.96430","627.80630","629.64825","631.49030","633.33230","635.17426","637.01630","638.85830","640.70026","642.54230","644.38430","646.22626","648.06830","649.91030","651.75226","653.59430","655.43630","657.27826","659.12030","660.96230","662.80426","664.64630","666.48830","668.33026","670.17230","672.01430","673.85626","675.69830","677.54030","679.38226","681.22430","683.06630","684.90826","686.75030","688.59230","690.43427","692.27630","694.11830","695.96027","697.80230","699.64430","701.48627","703.32830","705.17030","707.01227","708.85430","710.69630","712.53827","714.38025","716.22230","718.06430","719.90625","721.74830","723.59030","725.43225","727.27430","729.11630","730.95825","732.80030","734.64230","736.48425","738.32630","740.16830","742.01025","743.85230","745.69430","747.53625","749.37830","751.22030","753.06226","754.90430","756.74630","758.58826","760.43030","762.27230","764.11426","765.95630","767.79830","769.64026","771.48230","773.32430","775.16626","777.00830","778.85030","780.69226","782.53430","784.37630","786.21826","788.06030","789.90230","791.74426","793.58630","795.42830","797.27026","799.11230","800.95430","802.79626","804.63830","806.48030","808.32227","810.16430","812.00630","813.84827","815.69030","817.53230","819.37427","821.21630","823.05830","824.90027","826.74225","828.58430","830.42630","832.26825","834.11030","835.95230","837.79425","839.63630","841.47830","843.32025","845.16230","847.00430","848.84625","850.68830"] + #Short list of band names for test + #bandNames = ["392.03027","393.87228"] #Testing purpose + + + # Load required toolboxes + print "Start!" + #Type: Feature layer + bf_Lx_1 = arcpy.GetParameterAsText(0) + arcpy.AddMessage("read b1 " + bf_Lx_1) + print bf_Lx_1 + + + #Type: Feature layer + # Script arguments + bf_Lx_2 = arcpy.GetParameterAsText(1) + #bf_Lx_2 = bwVectoresPath + "\\" + "EYTBWMEL\\H\\H140117_EYTBWMEL_bf_L3.shp" # provide a default value if unspecified + arcpy.AddMessage("read b2 " + bf_Lx_2) + print bf_Lx_2 + + #Type: Feature layer + # Script arguments + bf_Lx_3 = arcpy.GetParameterAsText(2) + arcpy.AddMessage("read b3 " + bf_Lx_2) + print bf_Lx_3 + + #type: Feature layer + Grid = arcpy.GetParameterAsText(3) + #gridName = "H140117_BW" + #Grid = bwGdbPath + "\\" + gridName + arcpy.AddMessage("read " + Grid) + desc = arcpy.Describe(Grid) + gPath = desc.path + gridSource = str(gPath) + "\\" + Grid + arcpy.AddMessage("Grid source: " + gridSource) + gridName = desc.baseName + arcpy.AddMessage("Grid basename: " + gridName) + + print Grid + + #type: Raster layer + Raster = arcpy.GetParameterAsText(4) + #Raster = "E:\\140117\\140117H\\ortho\\140117_3_810.bsq" # provide a default value if unspecified + #Raster = "c:\\vuelos\\140117_3_810.bsq" + arcpy.AddMessage("read " + Raster) + desc = arcpy.Describe(Raster) + rPath = desc.path + Raster = str(rPath) + "\\" + desc.baseName+ "." +desc.extension + arcpy.AddMessage("Raster source: " + Raster) + print Raster + + #Folder to store the tables with the bands + #type: Geodatabase + #tablesPath = arcpy.GetParameterAsText(5) + tablesPath = tempGdb + print tablesPath + + # Local variables: + arcpy.AddMessage("Local variables") + print "Local variables" + Zone_field_and_join_Field = "Name" + gridLayer = "gridLayer" + #Line + line = bf_Lx_1[-2:] + + + ## Make a layer + print "Make layer" + arcpy.MakeFeatureLayer_management(Grid, "gridLayer") + + #Where to store the merge feature + print "Where to store the merge feature" + L_merge = tempGdb + "\\l_merge" + arcpy.AddMessage("merge path: " + L_merge) + if arcpy.Exists(L_merge): + arcpy.Delete_management(L_merge) + + #List of features to merge + mergeList = "" + + # Process: Merge + arcpy.AddMessage("Merging") + print "merging..." + if bf_Lx_1 != '#': + mergeList += bf_Lx_1 + if bf_Lx_2 != '#': + mergeList += ";"+bf_Lx_2 + if bf_Lx_3 != '#': + mergeList += ";"+bf_Lx_3 + arcpy.AddMessage("merge List: " + str(mergeList)) + m = arcpy.Merge_management(mergeList, L_merge) + arcpy.AddMessage(m) + + i=1 + for b in bandNames: + band = Raster + "\\" + b + bandField = "B" + str(i) + print bandField + #Set table name + tableName = gridName + "_" + line + "_" + bandField #+ ".dbf" + L_Stats_table = tablesPath + "\\" + tableName + print L_Stats_table + Field_Name_Calculate_field = gridName + "." + bandField + print Field_Name_Calculate_field + Expression_Select_layer = tableName+".Mean IS NOT NULL" + # Use this if the stats were saved to a dbf file "\""+tableName+".Mean\" IS NOT NULL" + # Use this if the stats were saved to a table "\""+tableName+":Mean\" IS NOT NULL" + # Use this if the stats were saved to a database table tableName+".Mean IS NOT NULL" + print Expression_Select_layer + Expression_Calculate_field = "round(!"+tableName+".Mean!,0)" + #the "!"+tableName+":Mean!" notation (:)is used when the stats are saved to a table + #the "!"+tableName+".Mean!" notation (.)is used when the stats are saved to a dbf file + print Expression_Calculate_field + + + + # Process: Zonal Statistics as Table + print "Zonal statistics..." + zs = arcpy.gp.ZonalStatisticsAsTable_sa(L_merge, Zone_field_and_join_Field, band, L_Stats_table, "DATA", "MEAN") + arcpy.AddMessage("stats table: " + str(zs)) + +## # Process: Join Field +## print "Joining Field..." +## arcpy.AddMessage("Joining Field...") +## arcpy.JoinField_management(gridLayer, Zone_field_and_join_Field, L_Stats_table, Zone_field_and_join_Field, "MEAN") + + + # Process: Add Join + print "Joining..." + arcpy.AddMessage("grid..."+ gridLayer) + arcpy.AddMessage("field..."+Zone_field_and_join_Field) + arcpy.AddJoin_management(gridLayer, Zone_field_and_join_Field, zs, Zone_field_and_join_Field, "KEEP_ALL") + + # Process: Select Layer By Attribute + if i==1: + print "Selecting Layer..." + gridLayerSelection = arcpy.SelectLayerByAttribute_management(gridLayer, "NEW_SELECTION", Expression_Select_layer) + + # Process: Calculate Field + print "Calculating field" + arcpy.AddMessage("Calculating field...") + arcpy.CalculateField_management(gridLayerSelection, Field_Name_Calculate_field, Expression_Calculate_field, "PYTHON") + + # Process: Remove Join + arcpy.AddMessage("remove Join") + arcpy.RemoveJoin_management(gridLayer, "") + #Erase stats table + #arcpy.Delete_management(zs) + i+=1 + + #Erase merge file + arcpy.AddMessage("delete merge temp") + arcpy.Delete_management(L_merge) + print "Finish" + +except Exception, e: + # If an error occurred, print line number and error message + import traceback, sys + tb = sys.exc_info()[2] + arcpy.AddMessage("Line:" + str(tb.tb_lineno)) + print "Line %i" % tb.tb_lineno + arcpy.AddMessage("Error " + e.message) + print e.message + print "OMG!" + + diff --git a/ArcGIS/joinZonalStats_hyper_BWspr.py b/ArcGIS/joinZonalStats_hyper_BWspr.py new file mode 100644 index 0000000..8e0d421 --- /dev/null +++ b/ArcGIS/joinZonalStats_hyper_BWspr.py @@ -0,0 +1,193 @@ +# -*- coding: utf-8 -*- +# --------------------------------------------------------------------------- +# joinZonalStats_hyper_BW_pruebas.py +# Created on: 2014-06-07 11:06:16.00000 +# (generated by ArcGIS/ModelBuilder) +# Usage: joinZonalStats_hyper_BW_pruebas +# Description: +# --------------------------------------------------------------------------- + +# Import arcpy module +import os +import arcpy + +try: + # Check out any necessary licenses + arcpy.CheckOutExtension("spatial") + + #workspace + + bwPath = "D:\\AB2013-2014\\Bread_wheat" + bwVectoresPath = bwPath + "\\" + "vectores" + bwDataPath = bwPath + "\\" + "data" + bwGdbPath = bwPath + "\\" + "AB_BreadWheat.gdb" + tempGdb = "C:\\vuelos\\temp.gdb" + arcpy.env.workspace = bwPath + + #List of band names + bandNames = ["392.03027","393.87228","395.71426","397.55627","399.39830","401.24026","403.08228","404.92430","406.76627","408.60828","410.45030","412.29227","414.13428","415.97630","417.81827","419.66028","421.50230","423.34427","425.18628","427.02830","428.87027","430.71228","432.55430","434.39627","436.23828","438.08030","439.92227","441.76428","443.60630","445.44827","447.29028","449.13230","450.97427","452.81628","454.65826","456.50027","458.34230","460.18427","462.02628","463.86830","465.71027","467.55228","469.39430","471.23627","473.07828","474.92030","476.76227","478.60428","480.44630","482.28827","484.13028","485.97230","487.81427","489.65628","491.49830","493.34027","495.18228","497.02430","498.86627","500.70828","502.55030","504.39227","506.23428","508.07626","509.91827","511.76028","513.60230","515.44430","517.28625","519.12830","520.97030","522.81226","524.65430","526.49630","528.33826","530.18030","532.02230","533.86426","535.70630","537.54830","539.39026","541.23230","543.07430","544.91626","546.75830","548.60030","550.44226","552.28430","554.12630","555.96826","557.81030","559.65230","561.49426","563.33630","565.17830","567.02026","568.86230","570.70430","572.54626","574.38830","576.23030","578.07227","579.91430","581.75630","583.59827","585.44030","587.28230","589.12427","590.96630","592.80830","594.65027","596.49225","598.33430","600.17630","602.01825","603.86030","605.70230","607.54425","609.38630","611.22830","613.07025","614.91230","616.75430","618.59625","620.43830","622.28030","624.12225","625.96430","627.80630","629.64825","631.49030","633.33230","635.17426","637.01630","638.85830","640.70026","642.54230","644.38430","646.22626","648.06830","649.91030","651.75226","653.59430","655.43630","657.27826","659.12030","660.96230","662.80426","664.64630","666.48830","668.33026","670.17230","672.01430","673.85626","675.69830","677.54030","679.38226","681.22430","683.06630","684.90826","686.75030","688.59230","690.43427","692.27630","694.11830","695.96027","697.80230","699.64430","701.48627","703.32830","705.17030","707.01227","708.85430","710.69630","712.53827","714.38025","716.22230","718.06430","719.90625","721.74830","723.59030","725.43225","727.27430","729.11630","730.95825","732.80030","734.64230","736.48425","738.32630","740.16830","742.01025","743.85230","745.69430","747.53625","749.37830","751.22030","753.06226","754.90430","756.74630","758.58826","760.43030","762.27230","764.11426","765.95630","767.79830","769.64026","771.48230","773.32430","775.16626","777.00830","778.85030","780.69226","782.53430","784.37630","786.21826","788.06030","789.90230","791.74426","793.58630","795.42830","797.27026","799.11230","800.95430","802.79626","804.63830","806.48030","808.32227","810.16430","812.00630","813.84827","815.69030","817.53230","819.37427","821.21630","823.05830","824.90027","826.74225","828.58430","830.42630","832.26825","834.11030","835.95230","837.79425","839.63630","841.47830","843.32025","845.16230","847.00430","848.84625","850.68830"] + #Short list of band names for test + #bandNames = ["392.03027","393.87228"] #Testing purpose + + + # Load required toolboxes + print "Start!" + #Type: Feature layer + bf_Lx_1 = arcpy.GetParameterAsText(0) + arcpy.AddMessage("read b1 " + bf_Lx_1) + print bf_Lx_1 + + + #Type: Feature layer + # Script arguments + bf_Lx_2 = arcpy.GetParameterAsText(1) + #bf_Lx_2 = bwVectoresPath + "\\" + "EYTBWMEL\\H\\H140117_EYTBWMEL_bf_L3.shp" # provide a default value if unspecified + arcpy.AddMessage("read b2 " + bf_Lx_2) + print bf_Lx_2 + + #Type: Feature layer + # Script arguments + bf_Lx_3 = arcpy.GetParameterAsText(2) + arcpy.AddMessage("read b3 " + bf_Lx_2) + print bf_Lx_3 + + #type: Feature layer + Grid = arcpy.GetParameterAsText(3) + #gridName = "H140117_BW" + #Grid = bwGdbPath + "\\" + gridName + arcpy.AddMessage("read " + Grid) + desc = arcpy.Describe(Grid) + gPath = desc.path + gridSource = str(gPath) + "\\" + Grid + arcpy.AddMessage("Grid source: " + gridSource) + gridName = desc.baseName + arcpy.AddMessage("Grid basename: " + gridName) + + print Grid + + #type: Raster layer + Raster = arcpy.GetParameterAsText(4) + #Raster = "E:\\140117\\140117H\\ortho\\140117_3_810.bsq" # provide a default value if unspecified + #Raster = "c:\\vuelos\\140117_3_810.bsq" + arcpy.AddMessage("read " + Raster) + desc = arcpy.Describe(Raster) + rPath = desc.path + Raster = str(rPath) + "\\" + desc.baseName+ "." +desc.extension + arcpy.AddMessage("Raster source: " + Raster) + print Raster + + #Folder to store the tables with the bands + #type: Geodatabase + #tablesPath = arcpy.GetParameterAsText(5) + tablesPath = tempGdb + print tablesPath + + # Local variables: + arcpy.AddMessage("Local variables") + print "Local variables" + Zone_field_and_join_Field = "Name" + gridLayer = "gridLayer" + #Line + line = bf_Lx_1[-2:] + + + ## Make a layer + print "Make layer" + arcpy.MakeFeatureLayer_management(Grid, "gridLayer") + + #Where to store the merge feature + print "Where to store the merge feature" + L_merge = tempGdb + "\\l_merge" + arcpy.AddMessage("merge path: " + L_merge) + if arcpy.Exists(L_merge): + arcpy.Delete_management(L_merge) + + #List of features to merge + mergeList = "" + + # Process: Merge + arcpy.AddMessage("Merging") + print "merging..." + if bf_Lx_1 != '#': + mergeList += bf_Lx_1 + if bf_Lx_2 != '#': + mergeList += ";"+bf_Lx_2 + if bf_Lx_3 != '#': + mergeList += ";"+bf_Lx_3 + arcpy.AddMessage("merge List: " + str(mergeList)) + m = arcpy.Merge_management(mergeList, L_merge) + arcpy.AddMessage(m) + + i=1 + for b in bandNames: + band = Raster + "\\" + b + bandField = "B" + str(i) + print bandField + #Set table name + tableName = gridName + "_" + line + "_" + bandField #+ ".dbf" + L_Stats_table = tablesPath + "\\" + tableName + print L_Stats_table + Field_Name_Calculate_field = gridName + "." + bandField + print Field_Name_Calculate_field + Expression_Select_layer = tableName+".Mean IS NOT NULL" + # Use this if the stats were saved to a dbf file "\""+tableName+".Mean\" IS NOT NULL" + # Use this if the stats were saved to a table "\""+tableName+":Mean\" IS NOT NULL" + # Use this if the stats were saved to a database table tableName+".Mean IS NOT NULL" + print Expression_Select_layer + Expression_Calculate_field = "round(!"+tableName+".Mean!,0)" + #the "!"+tableName+":Mean!" notation (:)is used when the stats are saved to a table + #the "!"+tableName+".Mean!" notation (.)is used when the stats are saved to a dbf file + print Expression_Calculate_field + + + + # Process: Zonal Statistics as Table + print "Zonal statistics..." + zs = arcpy.gp.ZonalStatisticsAsTable_sa(L_merge, Zone_field_and_join_Field, band, L_Stats_table, "DATA", "MEAN") + arcpy.AddMessage("stats table: " + str(zs)) + +## # Process: Join Field +## print "Joining Field..." +## arcpy.AddMessage("Joining Field...") +## arcpy.JoinField_management(gridLayer, Zone_field_and_join_Field, L_Stats_table, Zone_field_and_join_Field, "MEAN") + + + # Process: Add Join + print "Joining..." + arcpy.AddMessage("grid..."+ gridLayer) + arcpy.AddMessage("field..."+Zone_field_and_join_Field) + arcpy.AddJoin_management(gridLayer, Zone_field_and_join_Field, zs, Zone_field_and_join_Field, "KEEP_ALL") + + # Process: Select Layer By Attribute + if i==1: + print "Selecting Layer..." + gridLayerSelection = arcpy.SelectLayerByAttribute_management(gridLayer, "NEW_SELECTION", Expression_Select_layer) + + # Process: Calculate Field + print "Calculating field" + arcpy.AddMessage("Calculating field...") + arcpy.CalculateField_management(gridLayerSelection, Field_Name_Calculate_field, Expression_Calculate_field, "PYTHON") + + # Process: Remove Join + arcpy.AddMessage("remove Join") + arcpy.RemoveJoin_management(gridLayer, "") + #Erase stats table + #arcpy.Delete_management(zs) + i+=1 + + #Erase merge file + arcpy.AddMessage("delete merge temp") + arcpy.Delete_management(L_merge) + print "Finish" + +except Exception, e: + # If an error occurred, print line number and error message + import traceback, sys + tb = sys.exc_info()[2] + arcpy.AddMessage("Line:" + str(tb.tb_lineno)) + print "Line %i" % tb.tb_lineno + arcpy.AddMessage("Error " + e.message) + print e.message + print "OMG!" + + diff --git a/ArcGIS/joinZonalStats_hyper_PA - bandas auto - copia.py b/ArcGIS/joinZonalStats_hyper_PA - bandas auto - copia.py new file mode 100644 index 0000000..417d207 --- /dev/null +++ b/ArcGIS/joinZonalStats_hyper_PA - bandas auto - copia.py @@ -0,0 +1,262 @@ +# -*- coding: utf-8 -*- +# --------------------------------------------------------------------------- +# joinZonalStats_hyper_BW_pruebas.py +# Created on: 2014-06-07 11:06:16.00000 +# (generated by ArcGIS/ModelBuilder) +# Usage: joinZonalStats_hyper_BW_pruebas +# Description: +# --------------------------------------------------------------------------- + +# Import arcpy module +import os +import arcpy, arcgisscripting +import itertools +import re + +def print_kwinfo(): + if deb==1: + print "kwinfo se guarda:" + print kwinfo + +def replace_in_list(regex,lst): + return [re.sub(regex, '', x).strip() for x in lst] #List comprehension + +def print_dict(dictio): + #The next line iterates, formats and prints the dictionary, key:value + print "\n".join('{}={}'.format(k,v) for k,v in dictio.items()) + +def letsgo(header_file): + envi_header_keywords={"acquisition time","band names","bands", + "bbl","byte order","class lookup","class names", + "classes","cloud cover","complex function", + "coordinate system string","data gain values", + "data ignore value","data offset values", + "data reflectance gain values","data reflectance offset values", + "data type","default bands","default stretch","dem band", + "dem file","description","file type","fwhm","geo points", + "header offset","interleave","lines","map info", + "major frame offsets","minor frame offsets","pixel size", + "product type","projection info","read procedures", + "reflectance scale factor","rpc info","samples","security tag", + "sensor type","solar irradiance","spectra names","sun azimuth", + "sun elevation","wavelength","wavelength units","x start", + "y start","z plot average","z plot range","z plot titles"} + envi_header_keywords_or="|".join(envi_header_keywords) + + # Read and Iterate over the lines of the file + with open(header_file, 'rt') as f: + data = f.read()[5:] + lines = re.split(r"[\n]", data)#the info corresponding to one line + dictio = {} + global deb + deb=0 + wl=[]#List to save wavelengths + bn=[]#List to save band names + clines=[]#complete lines with all the info corresponding to one header keyword + ongoing=0 #Variable para marcar si se está buscando el resto de la línea de info + #to one header keyword is not in one single line + kwinfo="" + for i,l in enumerate(lines): + if deb==1: print str(i)+"-l- "+str(l) + kwinfo+=l + if deb==1: print str(i)+"-kwinfo- "+str(kwinfo) + if ongoing==1: + if "}" in l: + if deb==1: print "ongoing==se cierra por fin el parentesis" + print_kwinfo() + clines.append(kwinfo) + kwinfo="" + ongoing=0 + continue + else: + if deb==1: print "ongoing==1 else" + if ongoing==0: + ongoing=1 + for keyw in envi_header_keywords: + if keyw in l: + if deb==1: print keyw+" attribute found" + if "{" in l: + if "}" in l: + if deb==1: print "ongoing==parentesis cerrando en linea" + print_kwinfo() + clines.append(kwinfo) + kwinfo="" + ongoing=0 + else: + if deb==1: print "if bracket in l: --- else" + else: + if deb==1: print "ongoing==sin paretesis" + print_kwinfo() + clines.append(kwinfo) + kwinfo="" + ongoing=0 + if deb==1: print "fin de ronda de for" + for cl in clines: + #expresion to filter how to sepatate the string. + regexpresion = re.compile(r"""(.+?)\s*=\s*(.+)""") + dictio.update(dict(regexpresion.findall(cl))) + if deb==1: print regexpresion.findall(cl) + for key in dictio: dictio[key]=dictio[key].strip() + print "Header attributes:" + print_dict(dictio) + #Now we save to a list what the wavelengt name info + expr_replace = re.compile(r"{|}") #Expression to remove brackets below + #wavelength + wl=re.split(r"[,]", dictio["wavelength"]) + wl =replace_in_list(expr_replace,wl) + #Band names + bn=re.split(r"[,]", dictio["band names"]) + bn=replace_in_list(expr_replace,bn) + #Next we add the wl units only if they are defined + join_str = '{bandn} ({waveln} '+dictio["wavelength units"]+')' if dictio["wavelength units"]!="Unknown" else '{bandn} ({waveln})' + full_bn= '\n'.join(join_str.format(bandn=b, waveln=w) for b,w in itertools.izip(bn, wl)).split('\n') + if deb==1: + print full_bn + print "Bands: "+len(full_bn) + return full_bn + +## Starts the script +try: + # Check out any necessary licenses + arcpy.CheckOutExtension("spatial") + + # Create the Geoprocessor object + gp = arcgisscripting.create() + + #workspace + + bwPath = "D:\\AB2013-2014\\PA" + bwVectoresPath = bwPath + "\\" + "vectoriales" + bwDataPath = bwPath + "\\" + "data" + bwGdbPath = bwVectoresPath + "\\" + "EM38.gdb" + tempGdb = "C:\\vuelos\\temp.gdb" + arcpy.env.workspace = bwPath + + # Load required toolboxes + print "Start!" + #Type: Feature layer + bf_Lx_1 = arcpy.GetParameterAsText(0) + L_merge = bf_Lx_1 + arcpy.AddMessage("read b1 " + bf_Lx_1) + print bf_Lx_1 + + #type: Feature layer + Grid = arcpy.GetParameterAsText(1) + #gridName = "H140117_BW" + #Grid = bwGdbPath + "\\" + gridName + arcpy.AddMessage("read " + Grid) + desc = arcpy.Describe(Grid) + gPath = desc.path + gridSource = str(gPath) + "\\" + Grid + arcpy.AddMessage("Grid source: " + gridSource) + gridName = desc.baseName + arcpy.AddMessage("Grid basename: " + gridName) + + print Grid + #type: Raster layer + Raster = arcpy.GetParameterAsText(2) + #Raster = "E:\\140117\\140117H\\ortho\\140117_3_810.bsq" # provide a default value if unspecified + #Raster = "c:\\vuelos\\140117_3_810.bsq" + arcpy.AddMessage("read " + Raster) + desc = arcpy.Describe(Raster) + rPath = desc.path + Raster = str(rPath) + "\\" + desc.baseName+ "." +desc.extension + arcpy.AddMessage("Raster source: " + Raster) + print Raster + + #List of band names + hdr_file = os.path.join(str(rPath) + "\\" + desc.baseName+".hdr") + bandNames = letsgo(hdr_file) + + #Folder to store the tables with the bands + #type: Geodatabase + #tablesPath = arcpy.GetParameterAsText(5) + tablesPath = tempGdb + print tablesPath + + # Local variables: + arcpy.AddMessage("Local variables") + print "Local variables" + Zone_field_and_join_Field = "PointID" + gridLayer = "gridLayer" + + + ## Make a layer + print "Make layer" + arcpy.MakeFeatureLayer_management(Grid, "gridLayer") + + i=1 + for b in bandNames: + arcpy.AddMessage("b: " + b) + band = Raster + "\\" + b + arcpy.AddMessage("band: " + band) + bandField = "B" + str(i) + print bandField + #Set table name + tableName = gridName + "_" + bandField #+ ".dbf" + L_Stats_table = tablesPath + "\\" + tableName + print L_Stats_table + Field_Name_Calculate_field = gridName + "." + bandField + print Field_Name_Calculate_field + Expression_Select_layer = tableName+".Mean IS NOT NULL" + # Use this if the stats were saved to a dbf file "\""+tableName+".Mean\" IS NOT NULL" + # Use this if the stats were saved to a table "\""+tableName+":Mean\" IS NOT NULL" + # Use this if the stats were saved to a database table tableName+".Mean IS NOT NULL" + print Expression_Select_layer + Expression_Calculate_field = "round(!"+tableName+".Mean!,0)" + #the "!"+tableName+":Mean!" notation (:)is used when the stats are saved to a table + #the "!"+tableName+".Mean!" notation (.)is used when the stats are saved to a dbf file + print Expression_Calculate_field + + + + # Process: Zonal Statistics as Table + print "Zonal statistics..." + zs = arcpy.gp.ZonalStatisticsAsTable_sa(L_merge, Zone_field_and_join_Field, band, L_Stats_table, "DATA", "MEAN") + arcpy.AddMessage("stats table: " + str(zs)) + + fields = gp.ListFields(gridLayer, bandField) + field_found = fields.Next() + arcpy.AddMessage("field_found " + str(field_found)) + if (not field_found): + arcpy.AddField_management(gridLayer, bandField, "FLOAT", 10, 3, "", "", "NULLABLE", "NON_REQUIRED", "") + arcpy.AddMessage("Added. Before field not found " + str(bandField)) + else: + arcpy.AddMessage("field found " + str(bandField)) + + # Process: Add Join + print "Joining..." + arcpy.AddMessage("grid..."+ gridLayer) + arcpy.AddMessage("field..."+Zone_field_and_join_Field) + arcpy.AddJoin_management(gridLayer, Zone_field_and_join_Field, zs, Zone_field_and_join_Field, "KEEP_ALL") + + # Process: Select Layer By Attribute + if i==1: + print "Selecting Layer..." + gridLayerSelection = arcpy.SelectLayerByAttribute_management(gridLayer, "NEW_SELECTION", Expression_Select_layer) + + # Process: Calculate Field + print "Calculating field" + arcpy.AddMessage("Calculating field...") + arcpy.CalculateField_management(gridLayerSelection, Field_Name_Calculate_field, Expression_Calculate_field, "PYTHON") + + # Process: Remove Join + arcpy.AddMessage("remove Join") + arcpy.RemoveJoin_management(gridLayer, "") + #Erase stats table + arcpy.Delete_management(zs) + i+=1 + + print "Finish" + +except Exception, e: + # If an error occurred, print line number and error message + import traceback, sys + tb = sys.exc_info()[2] + arcpy.AddMessage("Line:" + str(tb.tb_lineno)) + print "Line %i" % tb.tb_lineno + arcpy.AddMessage("Error " + e.message) + print e.message + print "OMG!" + + diff --git a/ArcGIS/joinZonalStats_hyper_PA - bandas auto.py b/ArcGIS/joinZonalStats_hyper_PA - bandas auto.py new file mode 100644 index 0000000..250282a --- /dev/null +++ b/ArcGIS/joinZonalStats_hyper_PA - bandas auto.py @@ -0,0 +1,325 @@ +# -*- coding: utf-8 -*- +# --------------------------------------------------------------------------- +# joinZonalStats_hyper_BW_pruebas.py +# Created on: 2014-06-07 11:06:16.00000 +# (generated by ArcGIS/ModelBuilder) +# Usage: joinZonalStats_hyper_BW_pruebas +# Description: +#This script takes 3 inputs: +#Buffer: zone feature to use as input for zonal statistic tool +#Grid: feature whose table will be used to save the mean values of the zonal statistic tool. +# This should contain the same features ID field than the "Buffer" input. +# A field named B1, B2... will be created to store the mean values for each zone for each band. +#Raster: Raster to use as input for zonal statistic tool, along with the "Buffer". +# The band information is read from the envi HDR file +###The temporal stats tables are created in the default ArcGIS GDB +# --------------------------------------------------------------------------- + +# Import arcpy module +import os +import arcpy, arcgisscripting +import itertools +import re +import traceback, sys + +def print_kwinfo(): + if deb==1: + print "kwinfo se guarda:" + print kwinfo + +def create_if_not_exists(path): + try: + if not os.path.exists(path): + os.makedirs(path) + except Exception, e: + # If an error occurred, print line number and error message + tb = sys.exc_info()[2] + arcpy.AddMessage("Line:" + str(tb.tb_lineno)) + arcpy.AddMessage("Error " + e.message) + +def exists(path): + exists = True if os.path.exists(path)else False + return exists + +def replace_in_list(regex,lst): + return [re.sub(regex, '', x).strip() for x in lst] #List comprehension + +def print_dict(dictio): + #The next line iterates, formats and prints the dictionary, key:value + #print "\n".join('{}={}'.format(k,v) for k,v in dictio.items()) print in console + arcpy.AddMessage("\n".join('{}={}'.format(k,v) for k,v in dictio.items()))#Print to arcmap results window + +def letsgo(header_file): + arcpy.AddMessage("started reading header file:" + str(header_file)) + envi_header_keywords={"acquisition time","band names","bands", + "bbl","byte order","class lookup","class names", + "classes","cloud cover","complex function", + "coordinate system string","data gain values", + "data ignore value","data offset values", + "data reflectance gain values","data reflectance offset values", + "data type","default bands","default stretch","dem band", + "dem file","description","file type","fwhm","geo points", + "header offset","interleave","lines","map info", + "major frame offsets","minor frame offsets","pixel size", + "product type","projection info","read procedures", + "reflectance scale factor","rpc info","samples","security tag", + "sensor type","solar irradiance","spectra names","sun azimuth", + "sun elevation","wavelength","wavelength units","x start", + "y start","z plot average","z plot range","z plot titles"} + envi_header_keywords_or="|".join(envi_header_keywords) + # Read and Iterate over the lines of the file + with open(header_file, 'rt') as f: + data = f.read()[5:] + lines = re.split(r"[\n]", data)#the info corresponding to one line + arcpy.AddMessage("Finished reading header lines") + dictio = {} + global deb + deb=0 + wl=[]#List to save wavelengths + bn=[]#List to save band names + clines=[]#complete lines with all the info corresponding to one header keyword + ongoing=0 #Variable para marcar si se está buscando el resto de la línea de info + #to one header keyword is not in one single line + kwinfo="" + for i,l in enumerate(lines): + if deb==1: print str(i)+"-l- "+str(l) + kwinfo+=l + if deb==1: print str(i)+"-kwinfo- "+str(kwinfo) + if ongoing==1: + if "}" in l: + if deb==1: print "ongoing==se cierra por fin el parentesis" + print_kwinfo() + clines.append(kwinfo) + kwinfo="" + ongoing=0 + continue + else: + if deb==1: print "ongoing==1 else" + if ongoing==0: + ongoing=1 + for keyw in envi_header_keywords: + if keyw in l: + if deb==1: print keyw+" attribute found" + if "{" in l: + if "}" in l: + if deb==1: print "ongoing==parentesis cerrando en linea" + print_kwinfo() + clines.append(kwinfo) + kwinfo="" + ongoing=0 + else: + if deb==1: print "if bracket in l: --- else" + else: + if deb==1: print "ongoing==sin paretesis" + print_kwinfo() + clines.append(kwinfo) + kwinfo="" + ongoing=0 + if deb==1: print "fin de ronda de for" + for cl in clines: + #expresion to filter how to sepatate the string. + regexpresion = re.compile(r"""(.+?)\s*=\s*(.+)""") + dictio.update(dict(regexpresion.findall(cl))) + if deb==1: print regexpresion.findall(cl) + for key in dictio: dictio[key]=dictio[key].strip() + print "Header attributes:" + arcpy.AddMessage("Header attributes read") + print_dict(dictio) + expr_replace = re.compile(r"{|}") #Expression to remove brackets below + #wavelength + if "wavelength" in dictio: + wl=re.split(r"[,]", dictio["wavelength"]) + wl =replace_in_list(expr_replace,wl) + arcpy.AddMessage(wl) + arcpy.AddMessage("saved wl") + #Band names + if "band names" in dictio: + arcpy.AddMessage("band names attribute found") + bn=re.split(r"[,]", dictio["band names"]) + arcpy.AddMessage("split competed") + bn=replace_in_list(expr_replace,bn) + arcpy.AddMessage("saved bn") + #Next we add the wl units only if they are defined + arcpy.AddMessage("Format composed band name") + if "wavelength units" in dictio: + if "band names" in dictio and "wavelength" in dictio: + join_str = '{bandn} ({waveln} '+dictio["wavelength units"]+')' if dictio["wavelength units"]!="Unknown" else '{bandn} ({waveln})' + arcpy.AddMessage("bn and wl found") + full_bn= '\n'.join(join_str.format(bandn=b, waveln=w) for b,w in itertools.izip(bn, wl)).split('\n') + if not "band names" in dictio and "wavelength" in dictio: + join_str = '{waveln} '+dictio["wavelength units"] if dictio["wavelength units"]!="Unknown" else '{waveln}' + arcpy.AddMessage("wl found+units") + full_bn= '\n'.join(join_str.format(waveln=w) for w in wl).split('\n') + else: + if "band names" in dictio and "wavelength" in dictio: + join_str = '{bandn} ({waveln})' + arcpy.AddMessage("bn and wl found") + full_bn= '\n'.join(join_str.format(bandn=b, waveln=w) for b,w in itertools.izip(bn, wl)).split('\n') + if not "band names" in dictio and "wavelength" in dictio: + arcpy.AddMessage("wl found") + full_bn= wl + arcpy.AddMessage(full_bn) + if deb==1: + print full_bn + print "Bands: "+len(full_bn) + arcpy.AddMessage("Finished reading header") + return full_bn + +##def save_bn_wl(): +## full_bn= '\n'.join(join_str.format(bandn=b, waveln=w) for b,w in itertools.izip(bn, wl)).split('\n') +## +##def save_wl(): +## full_bn= '\n'.join(join_str.format(waveln=w) for w in itertools.izip(wl)).split('\n') + +## Starts the script +try: + # Check out any necessary licenses + arcpy.CheckOutExtension("spatial") + + # Create the Geoprocessor object + gp = arcgisscripting.create() + + # Load required toolboxes + print "Start!" + #Type: Feature layer -buffer for the zonalStats + bf_Lx_1 = arcpy.GetParameterAsText(0) + L_merge = bf_Lx_1 + arcpy.AddMessage("read b1 " + bf_Lx_1) + print bf_Lx_1 + + #Type: Field -ID field + f = arcpy.GetParameterAsText(0) + L_merge = bf_Lx_1 + arcpy.AddMessage("read b1 " + bf_Lx_1) + print bf_Lx_1 + + #type: Feature layer - Feature in whose table the stats will be stored + Grid = arcpy.GetParameterAsText(1) + arcpy.AddMessage("read " + Grid) + desc = arcpy.Describe(Grid) + gPath = desc.path + gridSource = str(gPath) + "\\" + Grid + arcpy.AddMessage("Grid source: " + gridSource) + gridName = desc.baseName + arcpy.AddMessage("Grid basename: " + gridName) + + print Grid + #type: Raster layer + Raster = arcpy.GetParameterAsText(2) + arcpy.AddMessage("read " + Raster) + desc = arcpy.Describe(Raster) + rPath = desc.path + Raster = str(rPath) + "\\" + desc.baseName+ "." +desc.extension + arcpy.AddMessage("Raster source: " + Raster) + print Raster + + #type: Field - Field for join and statistics + id_field = arcpy.GetParameterAsText(3) + arcpy.AddMessage("read " + id_field) + + #List of band names + hdr_file = os.path.join(str(rPath) + "\\" + desc.baseName+".hdr") + bandNames = letsgo(hdr_file) + #Next I was try ing to print the band number. Not yet + if type(bandNames).__name__ != 'int': + print r"# of bands in raster: "+str(len(bandNames)) + + #Folder to store the tables with the stats + tempGdb_path = r"C:\Users\usuario\Documents\ArcGIS" + tempGdb = r"Default.gdb" + #This is the path + tablesPath = os.path.join(tempGdb_path, tempGdb) + #Keep the individual stats tables? + #Either way the MEAN is stored to the table of the grid feature selected + keep_tables=False + # Execute CreateFileGDB + create_if_not_exists(tempGdb_path) + if not exists(tablesPath): + arcpy.CreateFileGDB_management(tempGdb_path, tempGdb) + print tablesPath + + # Local variables: + arcpy.AddMessage("Local variables") + print "Local variables" + Zone_field_and_join_Field = id_field + gridLayer = "gridLayer" + + + ## Make a layer + print "Make layer" + arcpy.MakeFeatureLayer_management(Grid, "gridLayer") + + i=1 + for b in bandNames: + arcpy.AddMessage("b: " + b) + band = Raster + "\\" + b + arcpy.AddMessage("band: " + band) + bandField = "B" + str(i) + print bandField + #Set table name + tableName = gridName + "_" + bandField #+ ".dbf" + L_Stats_table = tablesPath + "\\" + tableName + print L_Stats_table + Field_Name_Calculate_field = gridName + "." + bandField + print Field_Name_Calculate_field + Expression_Select_layer = tableName+".Mean IS NOT NULL" + # Use this if the stats were saved to a dbf file "\""+tableName+".Mean\" IS NOT NULL" + # Use this if the stats were saved to a table "\""+tableName+":Mean\" IS NOT NULL" + # Use this if the stats were saved to a database table tableName+".Mean IS NOT NULL" + print Expression_Select_layer + Expression_Calculate_field = "round(!"+tableName+".Mean!,0)" + #the "!"+tableName+":Mean!" notation (:)is used when the stats are saved to a table + #the "!"+tableName+".Mean!" notation (.)is used when the stats are saved to a dbf file + print Expression_Calculate_field + + # Process: Zonal Statistics as Table + print "Zonal statistics..." + #Perform the zonalstatistics getting only the "MEAN" value of the cells + #"MEAN" can be changed to get different statistics: + #ALL,MEAN,MAJORITY,MAXIMUM,MEDIAN,MINIMUM,MINORITY,RANGE,STD,SUM,VARIETY,MIN_MAX,MEAN_STD,MIN_MAX_MEAN  + #arcpy.AddMessage("zs exists? : " + str(exists(path))) + zs = arcpy.gp.ZonalStatisticsAsTable_sa(L_merge, Zone_field_and_join_Field, band, L_Stats_table, "DATA", "MEAN") + arcpy.AddMessage("stats table: " + str(zs)) + fields = gp.ListFields(gridLayer, bandField) + field_found = fields.Next() + if (not field_found): + arcpy.AddField_management(gridLayer, bandField, "FLOAT", 10, 3, "", "", "NULLABLE", "NON_REQUIRED", "") + arcpy.AddMessage("Added. Before field not found " + str(bandField)) + else: + arcpy.AddMessage("field_found " + str(field_found.name)) + arcpy.AddMessage("field found " + str(bandField)) + + # Process: Add Join + print "Joining..." + arcpy.AddMessage("grid..."+ gridLayer) + arcpy.AddMessage("field..."+Zone_field_and_join_Field) + arcpy.AddJoin_management(gridLayer, Zone_field_and_join_Field, zs, Zone_field_and_join_Field, "KEEP_ALL") + + # Process: Select Layer By Attribute + if i==1: + print "Selecting Layer..." + gridLayerSelection = arcpy.SelectLayerByAttribute_management(gridLayer, "NEW_SELECTION", Expression_Select_layer) + + # Process: Calculate Field + print "Calculating field" + arcpy.AddMessage("Calculating field...") + arcpy.CalculateField_management(gridLayerSelection, Field_Name_Calculate_field, Expression_Calculate_field, "PYTHON") + + # Process: Remove Join + arcpy.AddMessage("remove Join") + arcpy.RemoveJoin_management(gridLayer, "") + #Erase stats table + if not keep_tables: arcpy.Delete_management(zs) + i+=1 + + print "Finish" + +except Exception, e: + # If an error occurred, print line number and error message + import traceback, sys + tb = sys.exc_info()[2] + arcpy.AddMessage("Line:" + str(tb.tb_lineno)) + arcpy.AddMessage("Error " + e.message) + print "OMG!" + + diff --git a/ArcGIS/joinZonalStats_hyper_PA - bandas auto_NameFieldwithbuffername.py b/ArcGIS/joinZonalStats_hyper_PA - bandas auto_NameFieldwithbuffername.py new file mode 100644 index 0000000..9862415 --- /dev/null +++ b/ArcGIS/joinZonalStats_hyper_PA - bandas auto_NameFieldwithbuffername.py @@ -0,0 +1,333 @@ +# -*- coding: utf-8 -*- +# --------------------------------------------------------------------------- +# joinZonalStats_hyper_BW_pruebas.py +# Created on: 2014-06-07 11:06:16.00000 +# (generated by ArcGIS/ModelBuilder) +# Usage: joinZonalStats_hyper_BW_pruebas +# Description: +#This script takes 3 inputs: +#Buffer: zone feature to use as input for zonal statistic tool +#Grid: feature whose table will be used to save the mean values of the zonal statistic tool. +# This should contain the same features ID field than the "Buffer" input. +# A field named B1, B2... will be created to store the mean values for each zone for each band. +#Raster: Raster to use as input for zonal statistic tool, along with the "Buffer". +# The band information is read from the envi HDR file +###The temporal stats tables are created in the default ArcGIS GDB +# --------------------------------------------------------------------------- + +# Import arcpy module +import os +import arcpy, arcgisscripting +import itertools +import re +import traceback, sys + +def print_kwinfo(): + if deb==1: + print "kwinfo se guarda:" + print kwinfo + +def create_if_not_exists(path): + try: + if not os.path.exists(path): + os.makedirs(path) + except Exception, e: + # If an error occurred, print line number and error message + tb = sys.exc_info()[2] + arcpy.AddMessage("Line:" + str(tb.tb_lineno)) + arcpy.AddMessage("Error " + e.message) + +def exists(path): + exists = True if os.path.exists(path)else False + return exists + +def replace_in_list(regex,lst): + return [re.sub(regex, '', x).strip() for x in lst] #List comprehension + +def print_dict(dictio): + #The next line iterates, formats and prints the dictionary, key:value + #print "\n".join('{}={}'.format(k,v) for k,v in dictio.items()) print in console + arcpy.AddMessage("\n".join('{}={}'.format(k,v) for k,v in dictio.items()))#Print to arcmap results window + +def letsgo(header_file): + arcpy.AddMessage("started reading header file:" + str(header_file)) + envi_header_keywords={"acquisition time","band names","bands", + "bbl","byte order","class lookup","class names", + "classes","cloud cover","complex function", + "coordinate system string","data gain values", + "data ignore value","data offset values", + "data reflectance gain values","data reflectance offset values", + "data type","default bands","default stretch","dem band", + "dem file","description","file type","fwhm","geo points", + "header offset","interleave","lines","map info", + "major frame offsets","minor frame offsets","pixel size", + "product type","projection info","read procedures", + "reflectance scale factor","rpc info","samples","security tag", + "sensor type","solar irradiance","spectra names","sun azimuth", + "sun elevation","wavelength","wavelength units","x start", + "y start","z plot average","z plot range","z plot titles"} + envi_header_keywords_or="|".join(envi_header_keywords) + # Read and Iterate over the lines of the file + with open(header_file, 'rt') as f: + data = f.read()[5:] + lines = re.split(r"[\n]", data)#the info corresponding to one line + arcpy.AddMessage("Finished reading header lines") + dictio = {} + global deb + deb=0 + wl=[]#List to save wavelengths + bn=[]#List to save band names + clines=[]#complete lines with all the info corresponding to one header keyword + ongoing=0 #Variable para marcar si se está buscando el resto de la línea de info + #to one header keyword is not in one single line + kwinfo="" + for i,l in enumerate(lines): + if deb==1: print str(i)+"-l- "+str(l) + kwinfo+=l + if deb==1: print str(i)+"-kwinfo- "+str(kwinfo) + if ongoing==1: + if "}" in l: + if deb==1: print "ongoing==se cierra por fin el parentesis" + print_kwinfo() + clines.append(kwinfo) + kwinfo="" + ongoing=0 + continue + else: + if deb==1: print "ongoing==1 else" + if ongoing==0: + ongoing=1 + for keyw in envi_header_keywords: + if keyw in l: + if deb==1: print keyw+" attribute found" + if "{" in l: + if "}" in l: + if deb==1: print "ongoing==parentesis cerrando en linea" + print_kwinfo() + clines.append(kwinfo) + kwinfo="" + ongoing=0 + else: + if deb==1: print "if bracket in l: --- else" + else: + if deb==1: print "ongoing==sin paretesis" + print_kwinfo() + clines.append(kwinfo) + kwinfo="" + ongoing=0 + if deb==1: print "fin de ronda de for" + for cl in clines: + #expresion to filter how to sepatate the string. + regexpresion = re.compile(r"""(.+?)\s*=\s*(.+)""") + dictio.update(dict(regexpresion.findall(cl))) + if deb==1: print regexpresion.findall(cl) + for key in dictio: dictio[key]=dictio[key].strip() + print "Header attributes:" + arcpy.AddMessage("Header attributes read") + print_dict(dictio) + expr_replace = re.compile(r"{|}") #Expression to remove brackets below + #wavelength + if "wavelength" in dictio: + wl=re.split(r"[,]", dictio["wavelength"]) + wl =replace_in_list(expr_replace,wl) + arcpy.AddMessage(wl) + arcpy.AddMessage("saved wl") + #Band names + if "band names" in dictio: + arcpy.AddMessage("band names attribute found") + bn=re.split(r"[,]", dictio["band names"]) + arcpy.AddMessage("split competed") + bn=replace_in_list(expr_replace,bn) + arcpy.AddMessage("saved bn") + #Next we add the wl units only if they are defined + arcpy.AddMessage("Format composed band name") + if "wavelength units" in dictio: + if "band names" in dictio and "wavelength" in dictio: + join_str = '{bandn} ({waveln} '+dictio["wavelength units"]+')' if dictio["wavelength units"]!="Unknown" else '{bandn} ({waveln})' + arcpy.AddMessage("bn and wl found") + full_bn= '\n'.join(join_str.format(bandn=b, waveln=w) for b,w in itertools.izip(bn, wl)).split('\n') + if not "band names" in dictio and "wavelength" in dictio: + join_str = '{waveln} '+dictio["wavelength units"] if dictio["wavelength units"]!="Unknown" else '{waveln}' + arcpy.AddMessage("wl found+units") + full_bn= '\n'.join(join_str.format(waveln=w) for w in wl).split('\n') + else: + if "band names" in dictio and "wavelength" in dictio: + join_str = '{bandn} ({waveln})' + arcpy.AddMessage("bn and wl found") + full_bn= '\n'.join(join_str.format(bandn=b, waveln=w) for b,w in itertools.izip(bn, wl)).split('\n') + if not "band names" in dictio and "wavelength" in dictio: + arcpy.AddMessage("wl found") + full_bn= wl + arcpy.AddMessage(full_bn) + if deb==1: + print full_bn + print "Bands: "+len(full_bn) + arcpy.AddMessage("Finished reading header") + return full_bn + +##def save_bn_wl(): +## full_bn= '\n'.join(join_str.format(bandn=b, waveln=w) for b,w in itertools.izip(bn, wl)).split('\n') +## +##def save_wl(): +## full_bn= '\n'.join(join_str.format(waveln=w) for w in itertools.izip(wl)).split('\n') + +## Starts the script +try: + # Check out any necessary licenses + arcpy.CheckOutExtension("spatial") + + # Create the Geoprocessor object + gp = arcgisscripting.create() + + # Load required toolboxes + print "Start!" + #Type: Feature layer -buffer for the zonalStats + bf_Lx_1 = arcpy.GetParameterAsText(0) + L_merge = bf_Lx_1 + arcpy.AddMessage("read b1 " + bf_Lx_1) + desc = arcpy.Describe(L_merge) + gPath = desc.path + buffSource = str(gPath) + "\\" + L_merge + arcpy.AddMessage("Grid source: " + buffSource) + buffName = desc.baseName + arcpy.AddMessage("Grid basename: " + buffName) + #This is to match the name that the field "Name" has, because it go the name of the feature I joined" + buff_no_null=buffName[0:-4] + print bf_Lx_1 + + #Type: Field -ID field + f = arcpy.GetParameterAsText(0) + L_merge = bf_Lx_1 + arcpy.AddMessage("read b1 " + bf_Lx_1) + print bf_Lx_1 + + #type: Feature layer - Feature in whose table the stats will be stored + Grid = arcpy.GetParameterAsText(1) + arcpy.AddMessage("read " + Grid) + desc = arcpy.Describe(Grid) + gPath = desc.path + gridSource = str(gPath) + "\\" + Grid + arcpy.AddMessage("Grid source: " + gridSource) + gridName = desc.baseName + arcpy.AddMessage("Grid basename: " + gridName) + + print Grid + #type: Raster layer + Raster = arcpy.GetParameterAsText(2) + arcpy.AddMessage("read " + Raster) + desc = arcpy.Describe(Raster) + rPath = desc.path + Raster = str(rPath) + "\\" + desc.baseName+ "." +desc.extension + arcpy.AddMessage("Raster source: " + Raster) + print Raster + + #type: Field - Field for join and statistics + id_field = arcpy.GetParameterAsText(3) + arcpy.AddMessage("read " + id_field) + + #List of band names + hdr_file = os.path.join(str(rPath) + "\\" + desc.baseName+".hdr") + bandNames = letsgo(hdr_file) + #Next I was try ing to print the band number. Not yet + if type(bandNames).__name__ != 'int': + print r"# of bands in raster: "+str(len(bandNames)) + + #Folder to store the tables with the stats + tempGdb_path = r"C:\Users\usuario\Documents\ArcGIS" + tempGdb = r"Default.gdb" + #This is the path + tablesPath = os.path.join(tempGdb_path, tempGdb) + #Keep the individual stats tables? + #Either way the MEAN is stored to the table of the grid feature selected + keep_tables=False + # Execute CreateFileGDB + create_if_not_exists(tempGdb_path) + if not exists(tablesPath): + arcpy.CreateFileGDB_management(tempGdb_path, tempGdb) + print tablesPath + + # Local variables: + arcpy.AddMessage("Local variables") + print "Local variables" + Zone_field_and_join_Field = buff_no_null+"_"+id_field + gridLayer = "gridLayer" + + + ## Make a layer + print "Make layer" + arcpy.MakeFeatureLayer_management(Grid, "gridLayer") + + i=1 + for b in bandNames: + arcpy.AddMessage("b: " + b) + band = Raster + "\\" + b + arcpy.AddMessage("band: " + band) + bandField = "B" + str(i) + print bandField + #Set table name + tableName = gridName + "_" + bandField #+ ".dbf" + L_Stats_table = tablesPath + "\\" + tableName + print L_Stats_table + Field_Name_Calculate_field = gridName + "." + bandField + print Field_Name_Calculate_field + Expression_Select_layer = tableName+".Mean IS NOT NULL" + # Use this if the stats were saved to a dbf file "\""+tableName+".Mean\" IS NOT NULL" + # Use this if the stats were saved to a table "\""+tableName+":Mean\" IS NOT NULL" + # Use this if the stats were saved to a database table tableName+".Mean IS NOT NULL" + print Expression_Select_layer + Expression_Calculate_field = "round(!"+tableName+".Mean!,0)" + #the "!"+tableName+":Mean!" notation (:)is used when the stats are saved to a table + #the "!"+tableName+".Mean!" notation (.)is used when the stats are saved to a dbf file + print Expression_Calculate_field + + # Process: Zonal Statistics as Table + print "Zonal statistics..." + #Perform the zonalstatistics getting only the "MEAN" value of the cells + #"MEAN" can be changed to get different statistics: + #ALL,MEAN,MAJORITY,MAXIMUM,MEDIAN,MINIMUM,MINORITY,RANGE,STD,SUM,VARIETY,MIN_MAX,MEAN_STD,MIN_MAX_MEAN  + #arcpy.AddMessage("zs exists? : " + str(exists(path))) + zs = arcpy.gp.ZonalStatisticsAsTable_sa(L_merge, Zone_field_and_join_Field, band, L_Stats_table, "DATA", "MEAN") + arcpy.AddMessage("stats table: " + str(zs)) + fields = gp.ListFields(gridLayer, bandField) + field_found = fields.Next() + if (not field_found): + arcpy.AddField_management(gridLayer, bandField, "FLOAT", 10, 3, "", "", "NULLABLE", "NON_REQUIRED", "") + arcpy.AddMessage("Added. Before field not found " + str(bandField)) + else: + arcpy.AddMessage("field_found " + str(field_found.name)) + arcpy.AddMessage("field found " + str(bandField)) + + # Process: Add Join + print "Joining..." + arcpy.AddMessage("grid..."+ gridLayer) + arcpy.AddMessage("field..."+Zone_field_and_join_Field) + arcpy.AddJoin_management(gridLayer, id_field, zs, Zone_field_and_join_Field, "KEEP_ALL") + + # Process: Select Layer By Attribute + if i==1: + print "Selecting Layer..." + gridLayerSelection = arcpy.SelectLayerByAttribute_management(gridLayer, "NEW_SELECTION", Expression_Select_layer) + + # Process: Calculate Field + print "Calculating field" + arcpy.AddMessage("Calculating field...") + arcpy.CalculateField_management(gridLayerSelection, Field_Name_Calculate_field, Expression_Calculate_field, "PYTHON") + + # Process: Remove Join + arcpy.AddMessage("remove Join") + arcpy.RemoveJoin_management(gridLayer, "") + #Erase stats table + if not keep_tables: arcpy.Delete_management(zs) + i+=1 + + print "Finish" + +except Exception, e: + # If an error occurred, print line number and error message + import traceback, sys + tb = sys.exc_info()[2] + arcpy.AddMessage("Line:" + str(tb.tb_lineno)) + arcpy.AddMessage("Error " + e.message) + print "OMG!" + + diff --git a/ArcGIS/joinZonalStats_hyper_PA - bandas auto_createSaveFeature.py b/ArcGIS/joinZonalStats_hyper_PA - bandas auto_createSaveFeature.py new file mode 100644 index 0000000..4d09a33 --- /dev/null +++ b/ArcGIS/joinZonalStats_hyper_PA - bandas auto_createSaveFeature.py @@ -0,0 +1,327 @@ +# -*- coding: utf-8 -*- +# --------------------------------------------------------------------------- +# joinZonalStats_hyper_BW_pruebas.py +# Created on: 2014-06-07 11:06:16.00000 +# (generated by ArcGIS/ModelBuilder) +# Usage: joinZonalStats_hyper_BW_pruebas +# Description: +#This script takes 3 inputs: +#Buffer: zone feature to use as input for zonal statistic tool +#Grid: feature whose table will be used to save the mean values of the zonal statistic tool. +# This should contain the same features ID field than the "Buffer" input. +# A field named B1, B2... will be created to store the mean values for each zone for each band. +#Raster: Raster to use as input for zonal statistic tool, along with the "Buffer". +# The band information is read from the envi HDR file +###The temporal stats tables are created in the default ArcGIS GDB +# --------------------------------------------------------------------------- + +# Import arcpy module +import os +import arcpy, arcgisscripting +import itertools +import re +import traceback, sys + +def print_kwinfo(): + if deb==1: + print "kwinfo se guarda:" + print kwinfo + +def create_if_not_exists(path): + try: + if not os.path.exists(path): + os.makedirs(path) + except Exception, e: + # If an error occurred, print line number and error message + tb = sys.exc_info()[2] + arcpy.AddMessage("Line:" + str(tb.tb_lineno)) + arcpy.AddMessage("Error " + e.message) + +def exists(path): + exists = True if os.path.exists(path)else False + return exists + +def replace_in_list(regex,lst): + return [re.sub(regex, '', x).strip() for x in lst] #List comprehension + +def print_dict(dictio): + #The next line iterates, formats and prints the dictionary, key:value + #print "\n".join('{}={}'.format(k,v) for k,v in dictio.items()) print in console + arcpy.AddMessage("\n".join('{}={}'.format(k,v) for k,v in dictio.items()))#Print to arcmap results window + +def letsgo(header_file): + arcpy.AddMessage("started reading header file:" + str(header_file)) + envi_header_keywords={"acquisition time","band names","bands", + "bbl","byte order","class lookup","class names", + "classes","cloud cover","complex function", + "coordinate system string","data gain values", + "data ignore value","data offset values", + "data reflectance gain values","data reflectance offset values", + "data type","default bands","default stretch","dem band", + "dem file","description","file type","fwhm","geo points", + "header offset","interleave","lines","map info", + "major frame offsets","minor frame offsets","pixel size", + "product type","projection info","read procedures", + "reflectance scale factor","rpc info","samples","security tag", + "sensor type","solar irradiance","spectra names","sun azimuth", + "sun elevation","wavelength","wavelength units","x start", + "y start","z plot average","z plot range","z plot titles"} + envi_header_keywords_or="|".join(envi_header_keywords) + # Read and Iterate over the lines of the file + with open(header_file, 'rt') as f: + data = f.read()[5:] + lines = re.split(r"[\n]", data)#the info corresponding to one line + arcpy.AddMessage("Finished reading header lines") + dictio = {} + global deb + deb=0 + wl=[]#List to save wavelengths + bn=[]#List to save band names + clines=[]#complete lines with all the info corresponding to one header keyword + ongoing=0 #Variable para marcar si se está buscando el resto de la línea de info + #to one header keyword is not in one single line + kwinfo="" + for i,l in enumerate(lines): + if deb==1: print str(i)+"-l- "+str(l) + kwinfo+=l + if deb==1: print str(i)+"-kwinfo- "+str(kwinfo) + if ongoing==1: + if "}" in l: + if deb==1: print "ongoing==se cierra por fin el parentesis" + print_kwinfo() + clines.append(kwinfo) + kwinfo="" + ongoing=0 + continue + else: + if deb==1: print "ongoing==1 else" + if ongoing==0: + ongoing=1 + for keyw in envi_header_keywords: + if keyw in l: + if deb==1: print keyw+" attribute found" + if "{" in l: + if "}" in l: + if deb==1: print "ongoing==parentesis cerrando en linea" + print_kwinfo() + clines.append(kwinfo) + kwinfo="" + ongoing=0 + else: + if deb==1: print "if bracket in l: --- else" + else: + if deb==1: print "ongoing==sin paretesis" + print_kwinfo() + clines.append(kwinfo) + kwinfo="" + ongoing=0 + if deb==1: print "fin de ronda de for" + for cl in clines: + #expresion to filter how to sepatate the string. + regexpresion = re.compile(r"""(.+?)\s*=\s*(.+)""") + dictio.update(dict(regexpresion.findall(cl))) + if deb==1: print regexpresion.findall(cl) + for key in dictio: dictio[key]=dictio[key].strip() + print "Header attributes:" + arcpy.AddMessage("Header attributes read") + print_dict(dictio) + expr_replace = re.compile(r"{|}") #Expression to remove brackets below + #wavelength + if "wavelength" in dictio: + wl=re.split(r"[,]", dictio["wavelength"]) + wl =replace_in_list(expr_replace,wl) + arcpy.AddMessage(wl) + arcpy.AddMessage("saved wl") + #Band names + if "band names" in dictio: + arcpy.AddMessage("band names attribute found") + bn=re.split(r"[,]", dictio["band names"]) + arcpy.AddMessage("split competed") + bn=replace_in_list(expr_replace,bn) + arcpy.AddMessage("saved bn") + #Next we add the wl units only if they are defined + arcpy.AddMessage("Format composed band name") + full_bn=1 + if "wavelength units" in dictio: + if "band names" in dictio and "wavelength" in dictio: + join_str = '{bandn} ({waveln} '+dictio["wavelength units"]+')' if dictio["wavelength units"]!="Unknown" else '{bandn} ({waveln})' + arcpy.AddMessage("bn and wl found") + full_bn= '\n'.join(join_str.format(bandn=b, waveln=w) for b,w in itertools.izip(bn, wl)).split('\n') + if not "band names" in dictio and "wavelength" in dictio: + join_str = '{waveln} '+dictio["wavelength units"] if dictio["wavelength units"]!="Unknown" else '{waveln}' + arcpy.AddMessage("wl found+units") + full_bn= '\n'.join(join_str.format(waveln=w) for w in wl).split('\n') + else: + if "band names" in dictio and "wavelength" in dictio: + join_str = '{bandn} ({waveln})' + arcpy.AddMessage("bn and wl found") + full_bn= '\n'.join(join_str.format(bandn=b, waveln=w) for b,w in itertools.izip(bn, wl)).split('\n') + if not "band names" in dictio and "wavelength" in dictio: + arcpy.AddMessage("wl found") + full_bn= wl + arcpy.AddMessage(full_bn) + if deb==1: + print full_bn + print "Bands: "+len(full_bn) + arcpy.AddMessage("Finished reading header") + return full_bn + +##def save_bn_wl(): +## full_bn= '\n'.join(join_str.format(bandn=b, waveln=w) for b,w in itertools.izip(bn, wl)).split('\n') +## +##def save_wl(): +## full_bn= '\n'.join(join_str.format(waveln=w) for w in itertools.izip(wl)).split('\n') + +## Starts the script +try: + # Check out any necessary licenses + arcpy.CheckOutExtension("spatial") + + # Create the Geoprocessor object + gp = arcgisscripting.create() + + # Load required toolboxes + print "Start!" + + #Type: Feature layer -buffer for the zonalStats + bf_Lx_1 = arcpy.GetParameterAsText(0) + L_merge = bf_Lx_1 + arcpy.AddMessage("read b1 " + bf_Lx_1) + print bf_Lx_1 + + #type: Raster layer + Raster = arcpy.GetParameterAsText(2) + arcpy.AddMessage("read " + Raster) + desc = arcpy.Describe(Raster) + rPath = desc.path + rdate = desc.baseName[1:7] #Get the date from the raster to create the save feature + Raster = str(rPath) + "\\" + desc.baseName+ "." +desc.extension + rname = desc.baseName + arcpy.AddMessage("Raster source: " + Raster) + print Raster + + #type: Field - Field for join and statistics + id_field = arcpy.GetParameterAsText(3) + arcpy.AddMessage("read " + id_field) + + #type: Feature layer - Feature in whose table the stats will be stored + folder_storing = arcpy.GetParameterAsText(1) + Grid = str(folder_storing)+ "\\h" + rdate + bf_Lx_1 + #create the feature to extract to + if not exists(Grid): + arcpy.CopyFeatures_management(bf_Lx_1, Grid) + arcpy.AddMessage("read " + Grid) + desc = arcpy.Describe(Grid) + gPath = desc.path + gridSource = str(gPath) + arcpy.AddMessage("Grid source: " + gridSource) + gridName = desc.baseName + arcpy.AddMessage("Grid basename: " + gridName) + print Grid + + #List of band names + hdr_file = os.path.join(str(rPath) + "\\" + rname+".hdr") + bandNames = letsgo(hdr_file) + #Next I was try ing to print the band number. Not yet + if type(bandNames).__name__ != 'int': + print r"# of bands in raster: "+str(len(bandNames)) + + #Folder to store the tables with the stats + tempGdb_path = r"C:\Users\usuario\Documents\ArcGIS" + tempGdb = r"Default.gdb" + #This is the path + tablesPath = os.path.join(tempGdb_path, tempGdb) + #Keep the individual stats tables? + #Either way the MEAN is stored to the table of the grid feature selected + keep_tables=True + # Execute CreateFileGDB + create_if_not_exists(tempGdb_path) + if not exists(tablesPath): + arcpy.CreateFileGDB_management(tempGdb_path, tempGdb) + print tablesPath + + # Local variables: + arcpy.AddMessage("Local variables") + print "Local variables" + Zone_field_and_join_Field = id_field + gridLayer = "gridLayer" + + + ## Make a layer + print "Make layer" + arcpy.MakeFeatureLayer_management(Grid, "gridLayer") + + i=1 + for b in bandNames: + arcpy.AddMessage("b: " + b) + band = Raster + "\\" + b + arcpy.AddMessage("band: " + band) + bandField = "B" + str(i) + print bandField + #Set table name + tableName = gridName + "_" + bandField #+ ".dbf" + L_Stats_table = tablesPath + "\\" + tableName + print L_Stats_table + Field_Name_Calculate_field = gridName + "." + bandField + print Field_Name_Calculate_field + Expression_Select_layer = tableName+".Mean IS NOT NULL" + # Use this if the stats were saved to a dbf file "\""+tableName+".Mean\" IS NOT NULL" + # Use this if the stats were saved to a table "\""+tableName+":Mean\" IS NOT NULL" + # Use this if the stats were saved to a database table tableName+".Mean IS NOT NULL" + print Expression_Select_layer + Expression_Calculate_field = "round(!"+tableName+".Mean!,0)" + #the "!"+tableName+":Mean!" notation (:)is used when the stats are saved to a table + #the "!"+tableName+".Mean!" notation (.)is used when the stats are saved to a dbf file + print Expression_Calculate_field + + # Process: Zonal Statistics as Table + print "Zonal statistics..." + #Perform the zonalstatistics getting only the "MEAN" value of the cells + #"MEAN" can be changed to get different statistics: + #ALL,MEAN,MAJORITY,MAXIMUM,MEDIAN,MINIMUM,MINORITY,RANGE,STD,SUM,VARIETY,MIN_MAX,MEAN_STD,MIN_MAX_MEAN  + #arcpy.AddMessage("zs exists? : " + str(exists(path))) + zs = arcpy.gp.ZonalStatisticsAsTable_sa(L_merge, Zone_field_and_join_Field, band, L_Stats_table, "DATA", "MEAN") + arcpy.AddMessage("stats table: " + str(zs)) + fields = gp.ListFields(gridLayer, bandField) + field_found = fields.Next() + if (not field_found): + arcpy.AddField_management(gridLayer, bandField, "FLOAT", 10, 3, "", "", "NULLABLE", "NON_REQUIRED", "") + arcpy.AddMessage("Added. Before field not found " + str(bandField)) + else: + arcpy.AddMessage("field_found " + str(field_found.name)) + arcpy.AddMessage("field found " + str(bandField)) + + # Process: Add Join + print "Joining..." + arcpy.AddMessage("grid..."+ gridLayer) + arcpy.AddMessage("field..."+Zone_field_and_join_Field) + arcpy.AddJoin_management(gridLayer, Zone_field_and_join_Field, zs, Zone_field_and_join_Field, "KEEP_ALL") + + # Process: Select Layer By Attribute + if i==1: + print "Selecting Layer..." + gridLayerSelection = arcpy.SelectLayerByAttribute_management(gridLayer, "NEW_SELECTION", Expression_Select_layer) + + # Process: Calculate Field + print "Calculating field" + arcpy.AddMessage("Calculating field...") + arcpy.CalculateField_management(gridLayerSelection, Field_Name_Calculate_field, Expression_Calculate_field, "PYTHON") + + # Process: Remove Join + arcpy.AddMessage("remove Join") + arcpy.RemoveJoin_management(gridLayer, "") + #Erase stats table + if not keep_tables: arcpy.Delete_management(zs) + i+=1 + + print "Finish" + +except Exception, e: + # If an error occurred, print line number and error message + import traceback, sys + tb = sys.exc_info()[2] + arcpy.AddMessage("Line:" + str(tb.tb_lineno)) + arcpy.AddMessage("Error " + e.message) + print "OMG!" + + diff --git a/ArcGIS/joinZonalStats_hyper_PA.py b/ArcGIS/joinZonalStats_hyper_PA.py new file mode 100644 index 0000000..d303236 --- /dev/null +++ b/ArcGIS/joinZonalStats_hyper_PA.py @@ -0,0 +1,215 @@ +# -*- coding: utf-8 -*- +# --------------------------------------------------------------------------- +# joinZonalStats_hyper_BW_pruebas.py +# Created on: 2014-06-07 11:06:16.00000 +# (generated by ArcGIS/ModelBuilder) +# Usage: joinZonalStats_hyper_BW_pruebas +# Description: +# --------------------------------------------------------------------------- + +# Import arcpy module +import os +import arcpy + +try: + # Check out any necessary licenses + arcpy.CheckOutExtension("spatial") + + #workspace + + bwPath = "D:\\AB2013-2014\\PA" + bwVectoresPath = bwPath + "\\" + "vectoriales" + bwDataPath = bwPath + "\\" + "data" + bwGdbPath = bwVectoresPath + "\\" + "EM38.gdb" + tempGdb = "C:\\vuelos\\temp.gdb" + arcpy.env.workspace = bwPath + + #List of band names + #bandNames = ["392.03027","393.87228","395.71426","397.55627","399.39830","401.24026","403.08228","404.92430","406.76627","408.60828","410.45030","412.29227","414.13428","415.97630","417.81827","419.66028","421.50230","423.34427","425.18628","427.02830","428.87027","430.71228","432.55430","434.39627","436.23828","438.08030","439.92227","441.76428","443.60630","445.44827","447.29028","449.13230","450.97427","452.81628","454.65826","456.50027","458.34230","460.18427","462.02628","463.86830","465.71027","467.55228","469.39430","471.23627","473.07828","474.92030","476.76227","478.60428","480.44630","482.28827","484.13028","485.97230","487.81427","489.65628","491.49830","493.34027","495.18228","497.02430","498.86627","500.70828","502.55030","504.39227","506.23428","508.07626","509.91827","511.76028","513.60230","515.44430","517.28625","519.12830","520.97030","522.81226","524.65430","526.49630","528.33826","530.18030","532.02230","533.86426","535.70630","537.54830","539.39026","541.23230","543.07430","544.91626","546.75830","548.60030","550.44226","552.28430","554.12630","555.96826","557.81030","559.65230","561.49426","563.33630","565.17830","567.02026","568.86230","570.70430","572.54626","574.38830","576.23030","578.07227","579.91430","581.75630","583.59827","585.44030","587.28230","589.12427","590.96630","592.80830","594.65027","596.49225","598.33430","600.17630","602.01825","603.86030","605.70230","607.54425","609.38630","611.22830","613.07025","614.91230","616.75430","618.59625","620.43830","622.28030","624.12225","625.96430","627.80630","629.64825","631.49030","633.33230","635.17426","637.01630","638.85830","640.70026","642.54230","644.38430","646.22626","648.06830","649.91030","651.75226","653.59430","655.43630","657.27826","659.12030","660.96230","662.80426","664.64630","666.48830","668.33026","670.17230","672.01430","673.85626","675.69830","677.54030","679.38226","681.22430","683.06630","684.90826","686.75030","688.59230","690.43427","692.27630","694.11830","695.96027","697.80230","699.64430","701.48627","703.32830","705.17030","707.01227","708.85430","710.69630","712.53827","714.38025","716.22230","718.06430","719.90625","721.74830","723.59030","725.43225","727.27430","729.11630","730.95825","732.80030","734.64230","736.48425","738.32630","740.16830","742.01025","743.85230","745.69430","747.53625","749.37830","751.22030","753.06226","754.90430","756.74630","758.58826","760.43030","762.27230","764.11426","765.95630","767.79830","769.64026","771.48230","773.32430","775.16626","777.00830","778.85030","780.69226","782.53430","784.37630","786.21826","788.06030","789.90230","791.74426","793.58630","795.42830","797.27026","799.11230","800.95430","802.79626","804.63830","806.48030","808.32227","810.16430","812.00630","813.84827","815.69030","817.53230","819.37427","821.21630","823.05830","824.90027","826.74225","828.58430","830.42630","832.26825","834.11030","835.95230","837.79425","839.63630","841.47830","843.32025","845.16230","847.00430","848.84625","850.68830"] + #Short list of band names for test + #bandNames = ["392.03027","393.87228"] #Testing purpose + bandNames = [ + "Resampled Band 1 (H140507_PA_1m.dat) (397.556274 Nanometers)", + "Resampled Band 2 (H140507_PA_1m.dat) (404.924286 Nanometers)", + "Resampled Band 3 (H140507_PA_1m.dat) (412.292267 Nanometers)", + "Resampled Band 4 (H140507_PA_1m.dat) (419.660278 Nanometers)", + "Resampled Band 5 (H140507_PA_1m.dat) (427.028290 Nanometers)", + "Resampled Band 6 (H140507_PA_1m.dat) (434.396271 Nanometers)", + "Resampled Band 7 (H140507_PA_1m.dat) (441.764282 Nanometers)", + "Resampled Band 8 (H140507_PA_1m.dat) (449.132294 Nanometers)", + "Resampled Band 9 (H140507_PA_1m.dat) (456.500275 Nanometers)", + "Resampled Band 10 (H140507_PA_1m.dat) (463.868286 Nanometers)", + "Resampled Band 11 (H140507_PA_1m.dat) (471.236267 Nanometers)", + "Resampled Band 12 (H140507_PA_1m.dat) (478.604279 Nanometers)", + "Resampled Band 13 (H140507_PA_1m.dat) (485.972290 Nanometers)", + "Resampled Band 14 (H140507_PA_1m.dat) (493.340271 Nanometers)", + "Resampled Band 15 (H140507_PA_1m.dat) (500.708282 Nanometers)", + "Resampled Band 16 (H140507_PA_1m.dat) (508.076263 Nanometers)", + "Resampled Band 17 (H140507_PA_1m.dat) (515.444275 Nanometers)", + "Resampled Band 18 (H140507_PA_1m.dat) (522.812256 Nanometers)", + "Resampled Band 19 (H140507_PA_1m.dat) (530.180298 Nanometers)", + "Resampled Band 20 (H140507_PA_1m.dat) (537.548279 Nanometers)", + "Resampled Band 21 (H140507_PA_1m.dat) (544.916260 Nanometers)", + "Resampled Band 22 (H140507_PA_1m.dat) (552.284302 Nanometers)", + "Resampled Band 23 (H140507_PA_1m.dat) (559.652283 Nanometers)", + "Resampled Band 24 (H140507_PA_1m.dat) (567.020264 Nanometers)", + "Resampled Band 25 (H140507_PA_1m.dat) (574.388306 Nanometers)", + "Resampled Band 26 (H140507_PA_1m.dat) (581.756287 Nanometers)", + "Resampled Band 27 (H140507_PA_1m.dat) (589.124268 Nanometers)", + "Resampled Band 28 (H140507_PA_1m.dat) (596.492249 Nanometers)", + "Resampled Band 29 (H140507_PA_1m.dat) (603.860291 Nanometers)", + "Resampled Band 30 (H140507_PA_1m.dat) (611.228271 Nanometers)", + "Resampled Band 31 (H140507_PA_1m.dat) (618.596252 Nanometers)", + "Resampled Band 32 (H140507_PA_1m.dat) (625.964294 Nanometers)", + "Resampled Band 33 (H140507_PA_1m.dat) (633.332275 Nanometers)", + "Resampled Band 34 (H140507_PA_1m.dat) (640.700256 Nanometers)", + "Resampled Band 35 (H140507_PA_1m.dat) (648.068298 Nanometers)", + "Resampled Band 36 (H140507_PA_1m.dat) (655.436279 Nanometers)", + "Resampled Band 37 (H140507_PA_1m.dat) (662.804260 Nanometers)", + "Resampled Band 38 (H140507_PA_1m.dat) (670.172302 Nanometers)", + "Resampled Band 39 (H140507_PA_1m.dat) (677.540283 Nanometers)", + "Resampled Band 40 (H140507_PA_1m.dat) (684.908264 Nanometers)", + "Resampled Band 41 (H140507_PA_1m.dat) (692.276306 Nanometers)", + "Resampled Band 42 (H140507_PA_1m.dat) (699.644287 Nanometers)", + "Resampled Band 43 (H140507_PA_1m.dat) (707.012268 Nanometers)", + "Resampled Band 44 (H140507_PA_1m.dat) (714.380249 Nanometers)", + "Resampled Band 45 (H140507_PA_1m.dat) (721.748291 Nanometers)", + "Resampled Band 46 (H140507_PA_1m.dat) (729.116272 Nanometers)", + "Resampled Band 47 (H140507_PA_1m.dat) (736.484253 Nanometers)", + "Resampled Band 48 (H140507_PA_1m.dat) (743.852295 Nanometers)", + "Resampled Band 49 (H140507_PA_1m.dat) (751.220276 Nanometers)", + "Resampled Band 50 (H140507_PA_1m.dat) (758.588257 Nanometers)", + "Resampled Band 51 (H140507_PA_1m.dat) (765.956299 Nanometers)", + "Resampled Band 52 (H140507_PA_1m.dat) (773.324280 Nanometers)", + "Resampled Band 53 (H140507_PA_1m.dat) (780.692261 Nanometers)", + "Resampled Band 54 (H140507_PA_1m.dat) (788.060303 Nanometers)", + "Resampled Band 55 (H140507_PA_1m.dat) (795.428284 Nanometers)", + "Resampled Band 56 (H140507_PA_1m.dat) (802.796265 Nanometers)", + "Resampled Band 57 (H140507_PA_1m.dat) (810.164307 Nanometers)", + "Resampled Band 58 (H140507_PA_1m.dat) (817.532288 Nanometers)", + "Resampled Band 59 (H140507_PA_1m.dat) (824.900269 Nanometers)", + "Resampled Band 60 (H140507_PA_1m.dat) (832.268250 Nanometers)", + "Resampled Band 61 (H140507_PA_1m.dat) (839.636292 Nanometers)", + "Resampled Band 62 (H140507_PA_1m.dat) (847.004272 Nanometers)"] + + + # Load required toolboxes + print "Start!" + #Type: Feature layer + bf_Lx_1 = arcpy.GetParameterAsText(0) + L_merge = bf_Lx_1 + arcpy.AddMessage("read b1 " + bf_Lx_1) + print bf_Lx_1 + + #type: Feature layer + Grid = arcpy.GetParameterAsText(1) + #gridName = "H140117_BW" + #Grid = bwGdbPath + "\\" + gridName + arcpy.AddMessage("read " + Grid) + desc = arcpy.Describe(Grid) + gPath = desc.path + gridSource = str(gPath) + "\\" + Grid + arcpy.AddMessage("Grid source: " + gridSource) + gridName = desc.baseName + arcpy.AddMessage("Grid basename: " + gridName) + + print Grid + + #type: Raster layer + Raster = arcpy.GetParameterAsText(2) + #Raster = "E:\\140117\\140117H\\ortho\\140117_3_810.bsq" # provide a default value if unspecified + #Raster = "c:\\vuelos\\140117_3_810.bsq" + arcpy.AddMessage("read " + Raster) + desc = arcpy.Describe(Raster) + rPath = desc.path + Raster = str(rPath) + "\\" + desc.baseName+ "." +desc.extension + arcpy.AddMessage("Raster source: " + Raster) + print Raster + + #Folder to store the tables with the bands + #type: Geodatabase + #tablesPath = arcpy.GetParameterAsText(5) + tablesPath = tempGdb + print tablesPath + + # Local variables: + arcpy.AddMessage("Local variables") + print "Local variables" + Zone_field_and_join_Field = "PointID" + gridLayer = "gridLayer" + + + ## Make a layer + print "Make layer" + arcpy.MakeFeatureLayer_management(Grid, "gridLayer") + + i=1 + for b in bandNames: + arcpy.AddMessage("b: " + b) + band = Raster + "\\" + b + arcpy.AddMessage("band: " + band) + bandField = "B" + str(i) + print bandField + #Set table name + tableName = gridName + "_" + str(i) + "_" + bandField #+ ".dbf" + L_Stats_table = tablesPath + "\\" + tableName + print L_Stats_table + Field_Name_Calculate_field = gridName + "." + bandField + print Field_Name_Calculate_field + Expression_Select_layer = tableName+".Mean IS NOT NULL" + # Use this if the stats were saved to a dbf file "\""+tableName+".Mean\" IS NOT NULL" + # Use this if the stats were saved to a table "\""+tableName+":Mean\" IS NOT NULL" + # Use this if the stats were saved to a database table tableName+".Mean IS NOT NULL" + print Expression_Select_layer + Expression_Calculate_field = "round(!"+tableName+".Mean!,0)" + #the "!"+tableName+":Mean!" notation (:)is used when the stats are saved to a table + #the "!"+tableName+".Mean!" notation (.)is used when the stats are saved to a dbf file + print Expression_Calculate_field + + + + # Process: Zonal Statistics as Table + print "Zonal statistics..." + zs = arcpy.gp.ZonalStatisticsAsTable_sa(L_merge, Zone_field_and_join_Field, band, L_Stats_table, "DATA", "MEAN") + arcpy.AddMessage("stats table: " + str(zs)) + + + # Process: Add Join + print "Joining..." + arcpy.AddMessage("grid..."+ gridLayer) + arcpy.AddMessage("field..."+Zone_field_and_join_Field) + arcpy.AddJoin_management(gridLayer, Zone_field_and_join_Field, zs, Zone_field_and_join_Field, "KEEP_ALL") + + # Process: Select Layer By Attribute + if i==1: + print "Selecting Layer..." + gridLayerSelection = arcpy.SelectLayerByAttribute_management(gridLayer, "NEW_SELECTION", Expression_Select_layer) + + # Process: Calculate Field + print "Calculating field" + arcpy.AddMessage("Calculating field...") + arcpy.CalculateField_management(gridLayerSelection, Field_Name_Calculate_field, Expression_Calculate_field, "PYTHON") + + # Process: Remove Join + arcpy.AddMessage("remove Join") + arcpy.RemoveJoin_management(gridLayer, "") + #Erase stats table + arcpy.Delete_management(zs) + i+=1 + + #Erase merge file + arcpy.AddMessage("delete merge temp") + arcpy.Delete_management(L_merge) + print "Finish" + +except Exception, e: + # If an error occurred, print line number and error message + import traceback, sys + tb = sys.exc_info()[2] + arcpy.AddMessage("Line:" + str(tb.tb_lineno)) + print "Line %i" % tb.tb_lineno + arcpy.AddMessage("Error " + e.message) + print e.message + print "OMG!" + + diff --git a/ArcGIS/joinZonalStats_hyper_unaTirada_BW.py b/ArcGIS/joinZonalStats_hyper_unaTirada_BW.py new file mode 100644 index 0000000..cc5b211 --- /dev/null +++ b/ArcGIS/joinZonalStats_hyper_unaTirada_BW.py @@ -0,0 +1,80 @@ +# -*- coding: utf-8 -*- +# --------------------------------------------------------------------------- +# joinZonalStats_hyper_unaTirada_BW.py +# Created on: 2014-06-07 16:08:27.00000 +# (generated by ArcGIS/ModelBuilder) +# Usage: joinZonalStats_hyper_unaTirada_BW +# Description: +# --------------------------------------------------------------------------- + +# Import arcpy module +import arcpy + +# Check out any necessary licenses +arcpy.CheckOutExtension("spatial") + +# Load required toolboxes +arcpy.ImportToolbox("Model Functions") + +# Script arguments +bf_Lx_2 = arcpy.GetParameterAsText(0) +if bf_Lx_2 == '#' or not bf_Lx_2: + bf_Lx_2 = "140117\\H140117_EYTBWMEL_bf_L3" # provide a default value if unspecified + +bf_Lx_1 = arcpy.GetParameterAsText(1) +if bf_Lx_1 == '#' or not bf_Lx_1: + bf_Lx_1 = "140117\\H140117_EYTBWEH_bf_L3" # provide a default value if unspecified + +Grid = arcpy.GetParameterAsText(2) +if Grid == '#' or not Grid: + Grid = "H140117_BW" # provide a default value if unspecified + +L_Stats_table = arcpy.GetParameterAsText(3) +if L_Stats_table == '#' or not L_Stats_table: + L_Stats_table = "D:\\AB2013-2014\\Bread_wheat\\data\\140117\\L3\\b1" # provide a default value if unspecified + +Raster = arcpy.GetParameterAsText(4) +if Raster == '#' or not Raster: + Raster = "E:\\140117\\140117H\\ortho\\140117_3_810.bsq" # provide a default value if unspecified + +# Local variables: +BW_AB_join_result = L_Stats_table +BW_AB_selection_result = BW_AB_join_result +BW_AB_calculateField_result = BW_AB_selection_result +BW_AB_remove_join_result = BW_AB_calculateField_result +B = L_Stats_table +path_table = L_Stats_table +L_merge = bf_Lx_2 +Zone_field_and_join_Field = "Name" +grid_name = Grid +Field_Name_Calculate_field = "%grid_name%.%B%" +Expression_Calculate_field = "[%B%:Mean]" +Expression_Select_layer = "\"%B%:Mean\" IS NOT NULL" + +# Process: Merge +arcpy.Merge_management("140117\\H140117_EYTBWMEL_bf_L3;140117\\H140117_EYTBWEH_bf_L3", L_merge, "Name \"Name\" true true false 254 Text 0 0 ,First,#,140117\\H140117_EYTBWMEL_bf_L3,Name,-1,-1,140117\\H140117_EYTBWEH_bf_L3,Name,-1,-1;environm \"environm\" true true false 20 Text 0 0 ,First,#,140117\\H140117_EYTBWMEL_bf_L3,environm,-1,-1") + +# Process: Zonal Statistics as Table +arcpy.gp.ZonalStatisticsAsTable_sa(L_merge, Zone_field_and_join_Field, Raster, L_Stats_table, "DATA", "MEAN") + +# Process: Add Join +arcpy.AddJoin_management(Grid, Zone_field_and_join_Field, L_Stats_table, Zone_field_and_join_Field, "KEEP_ALL") + +# Process: parse_table_name +arcpy.ParsePath_mb(L_Stats_table, "NAME") + +# Process: Parse_grid_name +arcpy.ParsePath_mb(Grid, "NAME") + +# Process: Select Layer By Attribute +arcpy.SelectLayerByAttribute_management(BW_AB_join_result, "NEW_SELECTION", Expression_Select_layer) + +# Process: Calculate Field +arcpy.CalculateField_management(BW_AB_selection_result, Field_Name_Calculate_field, Expression_Calculate_field, "VB", "") + +# Process: Remove Join +arcpy.RemoveJoin_management(BW_AB_calculateField_result, "") + +# Process: parse_table_path +arcpy.ParsePath_mb(L_Stats_table, "PATH") + diff --git a/ArcGIS/joinZonalStats_thermal_createSaveFeature.py b/ArcGIS/joinZonalStats_thermal_createSaveFeature.py new file mode 100644 index 0000000..997a6ed --- /dev/null +++ b/ArcGIS/joinZonalStats_thermal_createSaveFeature.py @@ -0,0 +1,204 @@ +# -*- coding: utf-8 -*- +# --------------------------------------------------------------------------- +# joinZonalStats_hyper_BW_pruebas.py +# Created on: 2014-06-07 11:06:16.00000 +# (generated by ArcGIS/ModelBuilder) +# Usage: joinZonalStats_hyper_BW_pruebas +# Description: +#This script takes 3 inputs: +#Buffer: zone feature to use as input for zonal statistic tool +#Grid: feature whose table will be used to save the mean values of the zonal statistic tool. +# This should contain the same features ID field than the "Buffer" input. +# A field named B1, B2... will be created to store the mean values for each zone for each band. +#Raster: Raster to use as input for zonal statistic tool, along with the "Buffer". +# The band information is read from the envi HDR file +###The temporal stats tables are created in the default ArcGIS GDB +# --------------------------------------------------------------------------- + +# Import arcpy module +import os +import arcpy, arcgisscripting +import itertools +import re +import traceback, sys + +def print_kwinfo(): + if deb==1: + print "kwinfo se guarda:" + print kwinfo + +def create_if_not_exists(path): + try: + if not os.path.exists(path): + os.makedirs(path) + except Exception, e: + # If an error occurred, print line number and error message + tb = sys.exc_info()[2] + arcpy.AddMessage("Line:" + str(tb.tb_lineno)) + arcpy.AddMessage("Error " + e.message) + +def exists(path): + exists = True if os.path.exists(path)else False + return exists + +def replace_in_list(regex,lst): + return [re.sub(regex, '', x).strip() for x in lst] #List comprehension + +def print_dict(dictio): + #The next line iterates, formats and prints the dictionary, key:value + #print "\n".join('{}={}'.format(k,v) for k,v in dictio.items()) print in console + arcpy.AddMessage("\n".join('{}={}'.format(k,v) for k,v in dictio.items()))#Print to arcmap results window + +##def save_bn_wl(): +## full_bn= '\n'.join(join_str.format(bandn=b, waveln=w) for b,w in itertools.izip(bn, wl)).split('\n') +## +##def save_wl(): +## full_bn= '\n'.join(join_str.format(waveln=w) for w in itertools.izip(wl)).split('\n') + +## Starts the script +try: + # Check out any necessary licenses + arcpy.CheckOutExtension("spatial") + + # Create the Geoprocessor object + gp = arcgisscripting.create() + + # Load required toolboxes + print "Start!" + + #Type: Feature layer -buffer for the zonalStats + bf_Lx_1 = arcpy.GetParameterAsText(0) + L_merge = bf_Lx_1 + arcpy.AddMessage("read b1 " + bf_Lx_1) + print bf_Lx_1 + + #type: Raster layer + Raster = arcpy.GetParameterAsText(2) + arcpy.AddMessage("read " + Raster) + desc = arcpy.Describe(Raster) + rPath = desc.path + rdate = desc.baseName[1:7] #Get the date from the raster to create the save feature + Raster = str(rPath) + "\\" + desc.baseName+ "." +desc.extension + rname = desc.baseName + arcpy.AddMessage("Raster source: " + Raster) + print Raster + + #type: Field - Field for join and statistics + id_field = arcpy.GetParameterAsText(3) + arcpy.AddMessage("read " + id_field) + + #type: Feature layer - Feature in whose table the stats will be stored + folder_storing = arcpy.GetParameterAsText(1) + Grid = str(folder_storing)+ "\\t" + rdate + bf_Lx_1[0:3] + #create the feature to extract to + if not exists(Grid): + arcpy.CopyFeatures_management(bf_Lx_1, Grid) + arcpy.AddMessage("read " + Grid) + desc = arcpy.Describe(Grid) + gPath = desc.path + gridSource = str(gPath) + arcpy.AddMessage("Grid source: " + gridSource) + gridName = desc.baseName + arcpy.AddMessage("Grid basename: " + gridName) + print Grid + + #List of band names + hdr_file = os.path.join(str(rPath) + "\\" + rname+".hdr") + + +#Folder to store the tables with the stats + tempGdb_path = r"C:\Users\usuario\Documents\ArcGIS" + tempGdb = r"Default.gdb" + #This is the path + tablesPath = os.path.join(tempGdb_path, tempGdb) + #Keep the individual stats tables? + #Either way the MEAN is stored to the table of the grid feature selected + keep_tables=True +# Execute CreateFileGDB + create_if_not_exists(tempGdb_path) + if not exists(tablesPath): + arcpy.CreateFileGDB_management(tempGdb_path, tempGdb) + print tablesPath + +# Local variables: + arcpy.AddMessage("Local variables") + print "Local variables" + Zone_field_and_join_Field = id_field + gridLayer = "gridLayer" + + +## Make a layer + print "Make layer" + arcpy.MakeFeatureLayer_management(Grid, "gridLayer") + + ############################################################################ + i=1 + bandField = "B1" + band = Raster + print bandField + #Set table name + tableName = gridName + "_" + bandField #+ ".dbf" + L_Stats_table = tablesPath + "\\" + tableName + print L_Stats_table + Field_Name_Calculate_field = gridName + "." + bandField + print Field_Name_Calculate_field + Expression_Select_layer = tableName+".Mean IS NOT NULL" + # Use this if the stats were saved to a dbf file "\""+tableName+".Mean\" IS NOT NULL" + # Use this if the stats were saved to a table "\""+tableName+":Mean\" IS NOT NULL" + # Use this if the stats were saved to a database table tableName+".Mean IS NOT NULL" + print Expression_Select_layer + Expression_Calculate_field = "round(!"+tableName+".Mean!,2)" + #the "!"+tableName+":Mean!" notation (:)is used when the stats are saved to a table + #the "!"+tableName+".Mean!" notation (.)is used when the stats are saved to a dbf file + print Expression_Calculate_field + + # Process: Zonal Statistics as Table + print "Zonal statistics..." + #Perform the zonalstatistics getting only the "MEAN" value of the cells + #"MEAN" can be changed to get different statistics: + #ALL,MEAN,MAJORITY,MAXIMUM,MEDIAN,MINIMUM,MINORITY,RANGE,STD,SUM,VARIETY,MIN_MAX,MEAN_STD,MIN_MAX_MEAN  + #arcpy.AddMessage("zs exists? : " + str(exists(path))) + zs = arcpy.gp.ZonalStatisticsAsTable_sa(L_merge, Zone_field_and_join_Field, band, L_Stats_table, "DATA", "MEAN") + arcpy.AddMessage("stats table: " + str(zs)) + fields = gp.ListFields(gridLayer, bandField) + field_found = fields.Next() + if (not field_found): + arcpy.AddField_management(gridLayer, bandField, "FLOAT", 10, 3, "", "", "NULLABLE", "NON_REQUIRED", "") + arcpy.AddMessage("Added. Before field not found " + str(bandField)) + else: + arcpy.AddMessage("field_found " + str(field_found.name)) + arcpy.AddMessage("field found " + str(bandField)) + + # Process: Add Join + print "Joining..." + arcpy.AddMessage("grid..."+ gridLayer) + arcpy.AddMessage("field..."+Zone_field_and_join_Field) + arcpy.AddJoin_management(gridLayer, Zone_field_and_join_Field, zs, Zone_field_and_join_Field, "KEEP_ALL") + + # Process: Select Layer By Attribute + if i==1: + print "Selecting Layer..." + gridLayerSelection = arcpy.SelectLayerByAttribute_management(gridLayer, "NEW_SELECTION", Expression_Select_layer) + + # Process: Calculate Field + print "Calculating field" + arcpy.AddMessage("Calculating field...") + arcpy.CalculateField_management(gridLayerSelection, Field_Name_Calculate_field, Expression_Calculate_field, "PYTHON") + + # Process: Remove Join + arcpy.AddMessage("remove Join") + arcpy.RemoveJoin_management(gridLayer, "") + #Erase stats table + if not keep_tables: arcpy.Delete_management(zs) +############################################################################### + print "Finish" + +except Exception, e: + # If an error occurred, print line number and error message + import traceback, sys + tb = sys.exc_info()[2] + arcpy.AddMessage("Line:" + str(tb.tb_lineno)) + arcpy.AddMessage("Error " + e.message) + print "OMG!" + + diff --git a/ArcGIS/kelvin x 100 a celsius.exp b/ArcGIS/kelvin x 100 a celsius.exp new file mode 100644 index 0000000..d7dad66 --- /dev/null +++ b/ArcGIS/kelvin x 100 a celsius.exp @@ -0,0 +1,2 @@ +ENVI EXPRESSIONS +(float(B1)/100)-273.15 diff --git a/ArcGIS/legacy.tbx b/ArcGIS/legacy.tbx new file mode 100644 index 0000000000000000000000000000000000000000..8bac2e1ef388669346f015516e3884a7e8ee7a55 GIT binary patch literal 11810304 zcmeEv31DMKdA~LZ2`mYaa0V!SD7#@dvDcPw?|L_@wPddn+cJ{8M|PJNS(0tBvScKA zz0M|uhAZVPlp9*E0--J{ 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Match and capture a single word (1+ alnum characters) +##\s*:\s* # Match a colon, optionally surrounded by whitespace +##([^:]*) # Match any number of non-colon characters +##(?= # Make sure that we stop when the following can be matched: +## \s+\w+\s*: # the next dictionary key +##| # or +## $ # the end of the string +##) # End of lookahead + diff --git a/ArcGIS/read_band_names_from_header_1.py b/ArcGIS/read_band_names_from_header_1.py new file mode 100644 index 0000000..bbd6fce --- /dev/null +++ b/ArcGIS/read_band_names_from_header_1.py @@ -0,0 +1,115 @@ +import re +import arcpy +import itertools +import os + +def print_kwinfo(): + if deb==1: + print "kwinfo se guarda:" + print kwinfo + +def replace_in_list(regex,lst): + return [re.sub(regex, '', x).strip() for x in lst] #List comprehension + +def print_dict(dictio): + #The next line iterates, formats and prints the dictionary, key:value + print "\n".join('{}={}'.format(k,v) for k,v in dictio.items()) + +def letsgo(header_file): + envi_header_keywords={"acquisition time","band names","bands", + "bbl","byte order","class lookup","class names", + "classes","cloud cover","complex function", + "coordinate system string","data gain values", + "data ignore value","data offset values", + "data reflectance gain values","data reflectance offset values", + "data type","default bands","default stretch","dem band", + "dem file","description","file type","fwhm","geo points", + "header offset","interleave","lines","map info", + "major frame offsets","minor frame offsets","pixel size", + "product type","projection info","read procedures", + "reflectance scale factor","rpc info","samples","security tag", + "sensor type","solar irradiance","spectra names","sun azimuth", + "sun elevation","wavelength","wavelength units","x start", + "y start","z plot average","z plot range","z plot titles"} + envi_header_keywords_or="|".join(envi_header_keywords) + + # Read and Iterate over the lines of the file + with open(header_file, 'rt') as f: + data = f.read()[5:] + lines = re.split(r"[\n]", data)#the info corresponding to one line + dictio = {} + global deb + deb=0 + wl=[]#List to save wavelengths + bn=[]#List to save band names + clines=[]#complete lines with all the info corresponding to one header keyword + ongoing=0 #Variable para marcar si se está buscando el resto de la línea de info + #to one header keyword is not in one single line + kwinfo="" + for i,l in enumerate(lines): + if deb==1: print str(i)+"-l- "+str(l) + kwinfo+=l + if deb==1: print str(i)+"-kwinfo- "+str(kwinfo) + if ongoing==1: + if "}" in l: + if deb==1: print "ongoing==se cierra por fin el parentesis" + print_kwinfo() + clines.append(kwinfo) + kwinfo="" + ongoing=0 + continue + else: + if deb==1: print "ongoing==1 else" + if ongoing==0: + ongoing=1 + for keyw in envi_header_keywords: + if keyw in l: + if deb==1: print keyw+" attribute found" + if "{" in l: + if "}" in l: + if deb==1: print "ongoing==parentesis cerrando en linea" + print_kwinfo() + clines.append(kwinfo) + kwinfo="" + ongoing=0 + else: + if deb==1: print "if bracket in l: --- else" + else: + if deb==1: print "ongoing==sin paretesis" + print_kwinfo() + clines.append(kwinfo) + kwinfo="" + ongoing=0 + if deb==1: print "fin de ronda de for" + for cl in clines: + #expresion to filter how to sepatate the string. + regexpresion = re.compile(r"""(.+?)\s*=\s*(.+)""") + dictio.update(dict(regexpresion.findall(cl))) + if deb==1: print regexpresion.findall(cl) + for key in dictio: dictio[key]=dictio[key].strip() + print "Header attributes:" + print_dict(dictio) + #Now we save to a list what the wavelengt name info + expr_replace = re.compile(r"{|}") #Expression to remove brackets below + #wavelength + wl=re.split(r"[,]", dictio["wavelength"]) + wl =replace_in_list(expr_replace,wl) + #Band names + bn=re.split(r"[,]", dictio["band names"]) + bn=replace_in_list(expr_replace,bn) + #Next we add the wl units only if they are defined + join_str = '"{bandn} ({waveln} '+dictio["wavelength units"]+')"' if dictio["wavelength units"]!="Unknown" else '"{bandn} ({waveln})"' + full_bn= '\n'.join(join_str.format(bandn=b, waveln=w) for b,w in itertools.izip(bn, wl)).split('\n') + if deb==1: + print full_bn + print "Bands: "+len(full_bn) + return full_bn + +## Starts the script +try: + workspace = "E:\\140328\\140328H\\mosaico" + nombre_raster = "H140328_PA_1m_60bands" + hdr_file = os.path.join(workspace, nombre_raster+".hdr") + print letsgo(hdr_file) +except ValueError: + print ValueError diff --git a/ArcGIS/replace_py.cal b/ArcGIS/replace_py.cal new file mode 100644 index 0000000000000000000000000000000000000000..54778c3364d2a1f3f7aaa5f04f2612986005214b GIT binary patch literal 62 zcmY#n@MB11$Yn@nP-M_!C<4+23^_oNWFSj}p_D<1p@boWA(NpP$kKuH6M>>i44Mo8 DN81V} literal 0 HcmV?d00001 diff --git a/ArcGIS/script_pruebas.py b/ArcGIS/script_pruebas.py new file mode 100644 index 0000000..8db773c --- /dev/null +++ b/ArcGIS/script_pruebas.py @@ -0,0 +1,82 @@ +# -*- coding: utf-8 -*- +# --------------------------------------------------------------------------- +# script_pruebas.py +# Created on: 2014-06-07 16:59:26.00000 +# (generated by ArcGIS/ModelBuilder) +# Usage: script_pruebas +# Description: +# --------------------------------------------------------------------------- + +# Import arcpy module +import arcpy + +# Check out any necessary licenses +arcpy.CheckOutExtension("spatial") + +# Load required toolboxes +arcpy.ImportToolbox("Model Functions") + +# Script arguments +bf_Lx_2 = arcpy.GetParameterAsText(0) +if bf_Lx_2 == '#' or not bf_Lx_2: + bf_Lx_2 = "140117\\H140117_EYTBWMEL_bf_L3" # provide a default value if unspecified + +bf_Lx_1 = arcpy.GetParameterAsText(1) +if bf_Lx_1 == '#' or not bf_Lx_1: + bf_Lx_1 = "140117\\H140117_EYTBWEH_bf_L3" # provide a default value if unspecified + +Grid = arcpy.GetParameterAsText(2) +if Grid == '#' or not Grid: + Grid = "H140117_BW" # provide a default value if unspecified + +L_Stats_table = arcpy.GetParameterAsText(3) +if L_Stats_table == '#' or not L_Stats_table: + L_Stats_table = "D:\\AB2013-2014\\Bread_wheat\\data\\140117\\L3\\b1" # provide a default value if unspecified + +Raster = arcpy.GetParameterAsText(4) +if Raster == '#' or not Raster: + Raster = "E:\\140117\\140117H\\ortho\\140117_3_810.bsq" # provide a default value if unspecified + +Folder_tablas = arcpy.GetParameterAsText(5) + +# Local variables: +BW_AB_join_result = L_Stats_table +BW_AB_selection_result = BW_AB_join_result +BW_AB_calculateField_result = BW_AB_selection_result +BW_AB_remove_join_result = BW_AB_calculateField_result +B = L_Stats_table +path_table = L_Stats_table +L_merge = bf_Lx_2 +Zone_field_and_join_Field = "Name" +grid_name = Grid +Field_Name_Calculate_field = "%grid_name%.%B%" +Expression_Calculate_field = "[%B%:Mean]" +Expression_Select_layer = "\"%B%:Mean\" IS NOT NULL" + +# Process: Merge +arcpy.Merge_management("140117\\H140117_EYTBWMEL_bf_L3;140117\\H140117_EYTBWEH_bf_L3", L_merge, "Name \"Name\" true true false 254 Text 0 0 ,First,#,140117\\H140117_EYTBWMEL_bf_L3,Name,-1,-1,140117\\H140117_EYTBWEH_bf_L3,Name,-1,-1;environm \"environm\" true true false 20 Text 0 0 ,First,#,140117\\H140117_EYTBWMEL_bf_L3,environm,-1,-1") + +# Process: Zonal Statistics as Table +arcpy.gp.ZonalStatisticsAsTable_sa(L_merge, Zone_field_and_join_Field, Raster, L_Stats_table, "DATA", "MEAN") + +# Process: Add Join +arcpy.AddJoin_management(Grid, Zone_field_and_join_Field, L_Stats_table, Zone_field_and_join_Field, "KEEP_ALL") + +# Process: parse_table_name +arcpy.ParsePath_mb(L_Stats_table, "NAME") + +# Process: Parse_grid_name +arcpy.ParsePath_mb(Grid, "NAME") + +# Process: Select Layer By Attribute +arcpy.SelectLayerByAttribute_management(BW_AB_join_result, "NEW_SELECTION", Expression_Select_layer) + +# Process: Calculate Field +arcpy.CalculateField_management(BW_AB_selection_result, Field_Name_Calculate_field, Expression_Calculate_field, "VB", "") + +# Process: Remove Join +arcpy.RemoveJoin_management(BW_AB_calculateField_result, "") + +# Process: parse_table_path +arcpy.ParsePath_mb(L_Stats_table, "PATH") + diff --git a/ArcGIS/select no relleno y calor.exp b/ArcGIS/select no relleno y calor.exp new file mode 100644 index 0000000000000000000000000000000000000000..1e8f545d1df350ef753fe5e17eb1ac94cf46471e GIT binary patch literal 360 zcma)&!3u*g5Jcan1VK_i!h>S4rJ6t~h2HBys1-!}{f@a7N?2HCmziaDJb^og6B(Y0 z9w%hzxNsFVSaNPcoyv(5G+XV1w&t(u5jlZcx@WSgXXDM9%s;$2n&{ZmChKZu7GCP9 d|1=CtKm36YUq;=Ug{GKZ8FM?!SEY0<1v`x#EHVH9 literal 0 HcmV?d00001 diff --git a/ArcGIS/select relleno and demo name.exp b/ArcGIS/select relleno and demo name.exp new file mode 100644 index 0000000000000000000000000000000000000000..5366544e03252b58aec8a738881b04e1ec6693da GIT binary patch literal 302 zcma)%OA3H65JTTliXc6QrA5R~!G+*ncZwi-e`9eglz|}=ULaF2P|*`8FmyDu(gsV( zDAj3#n5f&zk8+LJ|J0P2h*NIPPW9YuT-p5FE#On*fjT?qI5=6fbN}(I>>vLCS$QN- literal 0 HcmV?d00001 diff --git a/ArcGIS/select relleno id.exp b/ArcGIS/select relleno id.exp new file mode 100644 index 0000000000000000000000000000000000000000..a05d3335e8517ce5e4a6d0ddb0895465a0c8f491 GIT binary patch literal 42 rcmY#l$Ye-iP-0ME@L}*|@MdrYveX$=8G?W;A0TvP@MG`?^3@ptaxw)V literal 0 HcmV?d00001 diff --git a/ArcGIS/select relleno name.exp b/ArcGIS/select relleno name.exp new file mode 100644 index 0000000000000000000000000000000000000000..ed3229b865a3474050988a9d6b04e1f071a0d32d GIT binary patch literal 254 zcma)%OA3H65JTTlNOrUt P1Izg{I002h0@VTl+cOIi literal 0 HcmV?d00001 diff --git a/ArcGIS/select_slashes_in_name_field.exp b/ArcGIS/select_slashes_in_name_field.exp new file mode 100644 index 0000000000000000000000000000000000000000..c5253a36192bfc335bbbfa0fe9b24c48d2530b81 GIT binary patch literal 38 ncmY#l@MB11$Yn@nP-0ME@L}*|@MdrYveX$=8T7$W703nvV7>%U literal 0 HcmV?d00001 diff --git a/ArcGIS/sobelHorizontalKernel-9x9.txt b/ArcGIS/sobelHorizontalKernel-9x9.txt new file mode 100644 index 0000000..8bbdbb5 --- /dev/null +++ b/ArcGIS/sobelHorizontalKernel-9x9.txt @@ -0,0 +1,10 @@ +9 9 +4 5 6 7 8 7 6 5 4 +3 4 5 6 7 6 5 4 3 +2 3 4 5 6 5 4 3 2 +1 2 3 4 5 4 3 2 1 +0 0 0 0 0 0 0 0 0 +1 2 3 4 5 4 3 2 1 +2 3 4 5 6 5 4 3 2 +3 4 5 6 7 6 5 4 3 +4 5 6 7 8 7 6 5 4 \ No newline at end of file diff --git a/ArcGIS/sobelHorizontalKernel-modif.txt b/ArcGIS/sobelHorizontalKernel-modif.txt new file mode 100644 index 0000000..03ce6b8 --- /dev/null +++ b/ArcGIS/sobelHorizontalKernel-modif.txt @@ -0,0 +1,4 @@ +3 3 +1 2 1 +0 0 0 +1 2 1 \ No newline at end of file diff --git a/ArcGIS/sobelHorizontalKernel.txt b/ArcGIS/sobelHorizontalKernel.txt new file mode 100644 index 0000000..dab7c34 --- /dev/null +++ b/ArcGIS/sobelHorizontalKernel.txt @@ -0,0 +1,4 @@ +3 3 +-1 -2 -1 +0 0 0 +1 2 1 \ No newline at end of file diff --git a/ArcGIS/split python text to float.exp b/ArcGIS/split python text to float.exp new file mode 100644 index 0000000..8a3b0f4 --- /dev/null +++ b/ArcGIS/split python text to float.exp @@ -0,0 +1,12 @@ +prelogic block + +def toNum(inValue): + try: + outValue = float(inValue) + return outValue + except: + doNothing = 4 + + +field= + toNum(!Name!.split(u'/')[2]) \ No newline at end of file diff --git a/ArcGIS/split python.cal.exp b/ArcGIS/split python.cal.exp new file mode 100644 index 0000000000000000000000000000000000000000..c40441eae5b5045557b9eea654dd52ef0cf3ede9 GIT binary patch literal 42 vcmY#n@MB11$Yn@nP-M_!C}t>N$YIE2C}GfGC}mJ*&N$YIE2C}GfGC}mJ$&~7m0RUM{1h@bI literal 0 HcmV?d00001 diff --git a/ArcGIS/stackCVStoXLSX.R b/ArcGIS/stackCVStoXLSX.R new file mode 100644 index 0000000..89a6f18 --- /dev/null +++ b/ArcGIS/stackCVStoXLSX.R @@ -0,0 +1,24 @@ +####### +##script to save to Excel lots of CVS in a folder +####### +require(xlsx) +##Set working drectory +setwd("C:/vuelos/ac2015/r") + +##Something like this to read all the cvs files +##Another example of use: list.files("C:/temp",full=TRUE) +csv_files = list.files(pattern="*.csv$") #List of the files +DATA <- do.call("rbind", lapply(csv_files, read.csv, header=TRUE)) #Read them all, like a loop + +#Create file +library(xlsx) #load the package +write.xlsx(x = sample.dataframe, file = "test.excelfile.xlsx", + sheetName = "TestSheet", row.names = FALSE) + +#Write to existing file +workbook.sheets +workbook.test +addDataFrame(x = sample.dataframe, sheet = workbook.test, +row.names = FALSE, startColumn = 4) # write data to sheet starting on line 1, column 4 +saveWorkbook(workbook.sheets, "test.excelfile.xlsx") # and of course you need to save it. + diff --git a/ArcGIS/timeSeries_joinData.R b/ArcGIS/timeSeries_joinData.R new file mode 100644 index 0000000..fe62c1c --- /dev/null +++ b/ArcGIS/timeSeries_joinData.R @@ -0,0 +1,79 @@ +##Script to get the descriptive statistics of a time series data +##Rearrange the data to have it grouped by VIs +##Reads may csv in working directory with cerain pattern +## Saves the statistics in a TXT in the same location as the data file +##July 2015 + +################# Check these inputs######################### +##Set working directory to avoid giving the absolute path of files +##IMPORTANT: use the / slash instead of the backslash \ in the paths +##Or use double backslash \\ +wd<-("C:/vuelos/ab2014/Marco/") +setwd(wd) +## Sets the pattern of name in CSV files to be read, save names without extension +data_set = gsub(".csv", "", list.files(pattern="vis[0-9]*.csv")) +#Get dates of the measuremnts +dates<-sub("vis","",x=data_set) + +#Load to a dataframe each CSV file with all the VIs per date +for (i in 1:length(data_set)) { + #assigns to variables each of the CSV + #Select the columns after the 6th one, because the firsts have ancillary data + assign(data_set[i], read.csv(paste(data_set[i],".csv",sep=""), + header = TRUE, row.names = 1, + colClasses = c("character",rep("NULL",5),rep("numeric",41)))) +} +#Get names of vis +vis_list<-sub("[A-z]*[0-9]*_?","",x=colnames(get(data_set[i]))) + +#Create empty dataframe to store re organized data by VI, separately +#vis_data<-data.frame(data.frame(matrix(nrow = 114, ncol = 1))) +#create an array to store all the vis by date +vis_data <- array(0, dim=c(114,10,41), dimnames=c("points","dates","vis")) + +date<-1 #Initialize to use it before the loop +##Loop trough the VIS +for (vi in 1:length(vis_list)) { + #Name each row as the measurement point + rownames(vis_data)<-rownames(get(data_set[date])) + + ##Loop trough the dates of one VI to store it together + for (date in 1:length(dates)) { + + ##Solution with merge function. Had troubles making it doing the ""join"" + ##withe the row names that start with number but have a letter also, like 5R or 7S + #vis_data<-merge(vis_data,get(data_set[i])[,j],by="row.names", all = TRUE) + ##Solution assigning the values to the corresponding column + vis_data[,date,vi]<-get(data_set[date])[,vi] + } + colnames(vis_data)<-dates #Name each column with the date + + #################Managed separately + ##Use this to save results to files (41 VI = 41 files) + #dir.create(paste(wd,"vis_by_date/",sep="")) #Creates a directory to store vis by date + #write.csv(vis_data, file = paste(wd,"vis_by_date/", "VIalldates.csv", sep="")) + ##Use this to save to a variable + #assign(paste("VIalldates_",vis[j], sep=""),vis_data) + + ##Create empty dataframe to store re organized data by VI (to reset this) + ##Used with the MERGE alternative + #vis_data<-data.frame(data.frame(matrix(NA, nrow = 114, ncol = 1))) + #####################Managed separately +} +##To find the index which corresponds to a VI in the vis_array: +#match("BGI2", vis) + +#### Plot average values of VIS during Cycle to PDF +pdf(file=paste("vis_by_date/"," VIS along cycle.pdf", sep="")) +#par(mfrow=c(7,6)) +for (vi in 1:length(vis_list)) { + #Gets the average of all points by date, this means the meand is applied across the COLUMNS -> MARGIN=2 + plot(1:10, apply(vis_data[,,vis=vi], MARGIN=2, FUN=mean), + main=vis_list[vi], + xlab="All 10 image dates") + + ##Gets the average of a point "5R" across all dates + #points(1, mean(vis_data[match("5R", rownames(vis_data)),,vis=vi]), xlab="5R",main=vis[vi]) +} +dev.off()#Finish saving to PDF + diff --git a/ArcGIS/truncateName.py b/ArcGIS/truncateName.py new file mode 100644 index 0000000..e55cfcd --- /dev/null +++ b/ArcGIS/truncateName.py @@ -0,0 +1,15 @@ +#Get subset of string based on a beginning char position and end position +import arcpy, os +def GetName(inputName): + subsetName = inputName[fromChar:toChar] + return subsetName + +fromChar = arcpy.GetParameter(0) +toChar = arcpy.GetParameter(1) +inputName = arcpy.GetParameter(2) +resultString = GetName(inputName) + +arcpy.SetParameterAsText(3, resultString ) +arcpy.AddMessage("Original string: "+inputName ) +arcpy.AddMessage("Truncated from: "+str(fromChar)+", to: "+str(toChar)) +arcpy.AddMessage("Result string: "+ resultString ) \ No newline at end of file diff --git a/ArcGIS/zonalStatsMultibandCSVInputsSelectFiles.py b/ArcGIS/zonalStatsMultibandCSVInputsSelectFiles.py new file mode 100644 index 0000000..b2a8687 --- /dev/null +++ b/ArcGIS/zonalStatsMultibandCSVInputsSelectFiles.py @@ -0,0 +1,441 @@ +#Perform zonal statistics tool based on a list of on a specified list of dates and names. +#The inputs are specified as para meters in the tool in ArcMap +#Automatically calls the appropriate raster and zonal data from the specified locations. +#Assumes the default "Greenplane" naming standards + +#This script is to execute the "Zonal statistics" tool in a multiband raster. It saves the resulting table to a specified >workspace<. +# One table per band is generated, they are named based on the raster and feature zone names + the extracted band number. +# To iterate along the bands, the ENVI HDR file is atempted to be read to get the band names. +# If header doesn't exist, a known number of bands can be specified to iterate. +#This script takes 3 important inputs and outputs: +#(input) +# Buffer: zone feature to use as input for zonal statistic tool. +# Standard file name system on the features is espected: tttYYMMDDfff +# Being: +# YYMMDD= date in a YYMMDD format. E.g. 160311 for march 11 of 2016. +# fff = 3 char indicating the features that are represented in the vector shapes. E.g. "gss" for greenseeker sampling, "plt" for field plots. +# ttt = 3 char representing the trial or area covered in the image. E.g. "bwh" for bread wheat trials, "sam" for maize summer trials + +# ID field: Field of the input feature to use as identifier of the different zones for generating the statistics + +# image: data image to use as input for zonal statistic tool. +# Standard file name system on the raster is espected: xYYMMDDtttsss +# Being: +# x = [h,m,t,c,a,r,n] one char representing the camera used to acquire the imagery + # sss = 3 char representing the processing stage of the imagery. E.g. "geo" for georeferenced, "pno" for the multi mosaic +# The band information is read from the envi HDR file + +# Workspace: where to save the statistics tables and where to create the features to save the mean values per plot + +# Known bands: [Optional] Number of bands of the input raster. Necessary if +# the header file doesn't exist -> default is 1 if not specified. + +#(ouput) +# Tables: The full default statistics table +# Table name structure: xYYMMDDtttfffBi +# Being: +# i: the band number from which the statistics was calculated based on the zones of the input feature +# --------------------------------------------------------------------------- + +#################################WARNING +#crashes if the csv has just one row + +import numpy +import os +import arcpy, arcgisscripting +import itertools +import re +import traceback, sys +import time + +#Read the file to know which feature-raster to extract +def readWhatToExtract(csvWhatToExtract): + arcpy.AddMessage("will read file: "+csvWhatToExtract) + extractMatrix=numpy.genfromtxt(open(csvWhatToExtract,"rb"), + delimiter=',', + dtype=None, + names=True) + dates = list(extractMatrix['date']) #get the dates + arcpy.AddMessage("Dates: "+str(dates)) + trials=extractMatrix.dtype.names[1:] + arcpy.AddMessage("Trials: "+str(trials)) + extractMatrix = extractMatrix[list(extractMatrix.dtype.names)[1:]]#remove "date" column (1st) + arcpy.AddMessage("What to extract: "+str(extractMatrix)) + return extractMatrix,dates,trials + #Return the matrix of the kind of images acquired on each day on each trial [h m t], + #the list of dates and the list of trials (removing the "date" item) +def readDbf():# read dbf file + print("Algo haremos aquĂ­")# zsFields= ["id","COUNT","MEAN","STD"] + # table="C:\\Users\\usuario\\Documents\\ArcGIS\\m150326pcwbufB1.dbf" + # with arcpy.da.SearchCursor(table, zsFields) as cursor: + # for row in cursor: + # print(('Feature {0} has an area of {1}'.format(row[0], row[1])) +def zonalStatsMultiband(tableBaseName, bandNames, image, plot_polygons,id_field): + try: + tables=list() + #zonalStats iterated in all bands of the raster Multiband + #Return list of the name of the generated tables + arcpy.AddMessage("Iterating the zonal statistics across all bands...") + #Get bands + #Returns: [0]numberOfBands, [1]isBandNamesInteger? + numOfBands = getNumberOfBands(bandNames) + arcpy.AddMessage("Number of bands: "+str(numOfBands[0])) + #If the image has more than one band... + if numOfBands[0] != 1: + #If we only have the number of bands, but no band names (because there was no hdr)... + #We will call the bands by the default way ArcMap does: RasterName + Band_1, Band_2, etc... + #we create the list to iterate it + if numOfBands[1]: bandNames = list('Band_%s' % b for b in range(1, numOfBands[0]+1)) + #Here iterates through the bands and runs the zonal statistics tool + #Get the count with the function "enumerate" + for i, b in enumerate(bandNames, start=1): + #Next we add the band name to the raster basename + band = image + "\\" + b + arcpy.AddMessage("Iterating band # "+ str(i)+" - " + band) + #Set table name + statsTable = tableBaseName+"B" + str(i)+ ".dbf" #We add the band id to the StatsTable name + #arcpy.AddMessage("Willbe stored in table "+str(statsTable)) + #Here we call the method to do the zonal statistics + zs = arcpy.gp.ZonalStatisticsAsTable_sa(plot_polygons, id_field, band, statsTable, "DATA", "ALL") + arcpy.AddMessage("resulting stats table: " + str(zs)) + tables.append(plot_polygons+","+band+","+str(zs)) + else: + #If we have just one band we call the raster without specifying any band + zs = arcpy.gp.ZonalStatisticsAsTable_sa(plot_polygons, id_field, image, tableBaseName+"B1.dbf", "DATA", "ALL") + tables.append(plot_polygons+","+image+","+str(zs)) + arcpy.AddMessage("||| resulting zs table: " + str(zs)) + return tables + except Exception as e: + # If an error occurred, print( line number and error message + import traceback, sys + tb = sys.exc_info()[2] + arcpy.AddMessage("Line:" + str(tb.tb_lineno)) + arcpy.AddMessage("Error " + e.message) +def print_kwinfo(): + if deb==1: + print( "kwinfo se guarda:") + print( kwinfo) + +def create_if_not_exists(path): + try: + if not os.path.exists(path): + os.makedirs(path) + except Exception as e: + # If an error occurred, print( line number and error message + tb = sys.exc_info()[2] + arcpy.AddMessage("Line:" + str(tb.tb_lineno)) + arcpy.AddMessage("Error " + e.message) + +def exists(path): + exists = True if os.path.exists(path) else False + return exists + +def write_hdr_file(hdr_filename,assign_bands=1,mode="w"): + try: + hdr_file = open(hdr_filename, mode) + hdr_file.write("HEAD\nbands = "+ str(assign_bands)+"\n") + arcpy.AddMessage("writing in hdr file") + hdr_file.close() + except Exception as e: + # If an error occurred, print( line number and error message + import traceback, sys + tb = sys.exc_info()[2] + arcpy.AddMessage("Line:" + str(tb.tb_lineno)) + arcpy.AddMessage("Error " + e.message) + print( "OMG! Error while in: write_hdr_file") + raise e +def appendListToFile(txt_filename,thelist,mode="w"): + try: + file = open(txt_filename, mode) + #arcpy.AddMessage("writing in "+txt_filename+" file") + for item in thelist: + file.write("%s\n" % item) + file.close() + except Exception as e: + # If an error occurred, print( line number and error message + import traceback, sys + tb = sys.exc_info()[2] + arcpy.AddMessage("Line:" + str(tb.tb_lineno)) + arcpy.AddMessage("Error " + e.message) + print( "OMG! Error while in: appendListToFile") + raise e + +def replace_in_list(regex,lst): + return [re.sub(regex, '', x).strip() for x in lst] #List comprehension +#Method to concatenate the band name to the raster basename +#BandNames has or all the list of band names or the number of bands +def getNumberOfBands(bandNames): + try: + #If there was no bandnames or header, a band number was assigned + #This would be an integer + numOfBands = int(bandNames) + isANumber = True + return numOfBands, isANumber + except ValueError: + #If there is a band name as a string we will say it's one band + numOfBands = 1 + isANumber = False + return numOfBands, isANumber + except TypeError: + #If there are band names as list or array we will count them here + numOfBands = len(bandNames) + isANumber = False + return numOfBands, isANumber + except Exception as e: + import traceback, sys + tb = sys.exc_info()[2] + arcpy.AddMessage("Line:" + str(tb.tb_lineno)) + arcpy.AddMessage("Error " + e.message) + print( "OMG!") + raise e +def print_dict(dictio): + #The next line iterates, formats and prints the dictionary, key:value + #print( "\n".join('{}={}'.format(k,v) for k,v in dictio.items()) print( in console + arcpy.AddMessage("\n".join('{}={}'.format(k,v) for k,v in dictio.items()))#Print to arcmap results window +def readHeaderFile(header_file,assign_bands=1): + try: + if not exists(header_file): + write_hdr_file(header_file,assign_bands) + arcpy.AddMessage("Header not found, it was created a basic one. Number of bands assigned: "+str(assign_bands)) + arcpy.AddMessage("started reading header file:" + str(header_file)) + envi_header_keywords={"acquisition time","band names","bands", + "bbl","byte order","class lookup","class names", + "classes","cloud cover","complex function", + "coordinate system string","data gain values", + "data ignore value","data offset values", + "data reflectance gain values","data reflectance offset values", + "data type","default bands","default stretch","dem band", + "dem file","description","file type","fwhm","geo points", + "header offset","interleave","lines","map info", + "major frame offsets","minor frame offsets","pixel size", + "product type","projection info","read procedures", + "reflectance scale factor","rpc info","samples","security tag", + "sensor type","solar irradiance","spectra names","sun azimuth", + "sun elevation","wavelength","wavelength units","x start", + "y start","z plot average","z plot range","z plot titles"} + envi_header_keywords_or="|".join(envi_header_keywords) + # Read and Iterate over the lines of the file + with open(header_file, 'rt') as f: + data = f.read()[5:] #Skip the first "ENVI" letters of the header + lines = re.split(r"[\n]", data)#the info corresponding to one line + arcpy.AddMessage("reading header lines...") + dictio = {} + global deb + deb=0 + wl=[]#List to save wavelengths + bn=[]#List to save band names + clines=[]#complete lines with all the info corresponding to one header keyword + ongoing=0 #Variable para marcar si se está buscando el resto de la lĂ­nea de info + #to one header keyword is not in one single line + kwinfo="" + for i,l in enumerate(lines): + if deb==1: print( str(i)+"-l- "+str(l)) + kwinfo+=l + if deb==1: print( str(i)+"-kwinfo- "+str(kwinfo)) + if ongoing==1: + if "}" in l: + if deb==1: print( "ongoing==se cierra por fin el parentesis") + print_kwinfo() + clines.append(kwinfo) + kwinfo="" + ongoing=0 + continue + else: + if deb==1: print( "ongoing==1 else") + if ongoing==0: + ongoing=1 + for keyw in envi_header_keywords: + if keyw in l: + if deb==1: print( keyw+" attribute found") + if "{" in l: + if "}" in l: + if deb==1: print( "ongoing==parentesis cerrando en linea") + print_kwinfo() + clines.append(kwinfo) + kwinfo="" + ongoing=0 + else: + if deb==1: print( "if bracket in l: --- else") + else: + if deb==1: print( "ongoing==sin paretesis") + print_kwinfo() + clines.append(kwinfo) + kwinfo="" + ongoing=0 + if deb==1: print( "fin de ronda de for") + for cl in clines: + #expresion to filter how to sepatate the string. + regexpresion = re.compile(r"""(.+?)\s*=\s*(.+)""") + dictio.update(dict(regexpresion.findall(cl))) + if deb==1: print( regexpresion.findall(cl)) + for key in dictio: dictio[key]=dictio[key].strip() + print( "Header attributes:") + #arcpy.AddMessage("Header attributes read") + #print_dict(dictio) + expr_replace = re.compile(r"{|}") #Expression to remove brackets below + #wavelength + if "wavelength" in dictio: + wl=re.split(r"[,]", dictio["wavelength"]) + wl =replace_in_list(expr_replace,wl) + #arcpy.AddMessage(wl) + #arcpy.AddMessage("saved wl") + #Number of Bands + if "bands" in dictio: + hdr_bands=dictio["bands"] + #arcpy.AddMessage(hdr_bands) + #arcpy.AddMessage("saved Number of bands from header") + #Band names + if "band names" in dictio: + #arcpy.AddMessage("band names attribute found") + bn=re.split(r"[,]", dictio["band names"]) + #arcpy.AddMessage("split completed") + bn=replace_in_list(expr_replace,bn) + #arcpy.AddMessage("saved bn") + #Next we add the wl units only if they are defined + #arcpy.AddMessage("Format composed band name") + full_bn=hdr_bands #We assign by default the number of bands in the raster + if "wavelength units" in dictio: + if "band names" in dictio and "wavelength" in dictio: + join_str = '{bandn} ({waveln} '+dictio["wavelength units"]+')' if dictio["wavelength units"]!="Unknown" else '{bandn} ({waveln})' + #arcpy.AddMessage("bn and wl found") + full_bn= '\n'.join(join_str.format(bandn=b, waveln=w) for b,w in itertools.izip(bn, wl)).split('\n') + if not "band names" in dictio and "wavelength" in dictio: + join_str = '{waveln} '+dictio["wavelength units"] if dictio["wavelength units"]!="Unknown" else '{waveln}' + #arcpy.AddMessage("wl found+units") + full_bn= '\n'.join(join_str.format(waveln=w) for w in wl).split('\n') + else: + if "band names" in dictio and "wavelength" in dictio: + join_str = '{bandn} ({waveln})' + #arcpy.AddMessage("bn and wl found") + full_bn= '\n'.join(join_str.format(bandn=b, waveln=w) for b,w in itertools.izip(bn, wl)).split('\n') + if not "band names" in dictio and "wavelength" in dictio: + #arcpy.AddMessage("wl found") + full_bn= wl + #arcpy.AddMessage(full_bn) + if deb==1: + arcpy.AddMessage( full_bn) + arcpy.AddMessage( "Bands descripted in header: "+len(full_bn)) + #CHECK IF the "data ignore value is set", otherwise, set it to 0. + if not "data ignore value" in dictio: + arcpy.AddMessage("Set -data ignore value to 0-") + add=["data ignore value = 0"] + appendListToFile(header_file,add,mode="a") + #arcpy.AddMessage("Finished reading header") + #arcpy.AddMessage(type(full_bn)) #to know the type of variable it was created + return full_bn + except Exception as e: + # If an error occurred, print( line number and error message + import traceback, sys + tb = sys.exc_info()[2] + arcpy.AddMessage("Line:" + str(tb.tb_lineno)) + arcpy.AddMessage("Error " + e.message) + print( "OMG!") + raise e +#^^^^^^^^^^^^^^^^^^^^^^^ Methods ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +try: + # Check out any necessary licenses and Create the Geoprocessor object + arcpy.CheckOutExtension("spatial") + gp = arcgisscripting.create() + ######################################################################################### + ################################# START:INPUTS ########################################## + #Hard coded this + csvExtractThis = arcpy.GetParameterAsText(0) + #imgWs="G:\\AD15_16\\" #imageBaseLocation + imgWs = arcpy.GetParameterAsText(6) #test + #GDB whare the feature classes are stored and where the tables will be written: + gdb = arcpy.GetParameterAsText(1) #BW + #gdb = "C:\\Users\\usuario\\Documents\\ArcGIS\\Default.gdb\\" #Default + #tws = where to save tables inside the workspace + tws =arcpy.GetParameterAsText(9)######Not worinkg... it saves a folder up####### + ds=arcpy.GetParameterAsText(3) #Use this ds="" fro m and t + #ds="h" #Use this for hyper + arcpy.env.workspace = gdb + #arcpy.AddMessage("wspace: "+str(arcpy.env.workspace)) + #knownBands="62" # ##################### hyper resampled + knownBands=arcpy.GetParameterAsText(8) # ####################### thermal + id_field = arcpy.GetParameterAsText(5) #Table field that has the unique plot identifier + log= arcpy.GetParameterAsText(10) + ## END:INPUTS ## + + ## START:VARIABLES ## + #Get all feature classes gdb/extract dataset + listFc = arcpy.ListFeatureClasses( + feature_type="Polygon", + feature_dataset=ds) + mosaicFolder = { + #Get the folder and suffix for the images according to the camera + #"m": ("msk","mkd"), #When you will use the masked rasters + "m": ("geo","geo"), #When you will use the original multispectral rasters + "h": ("rfl_spr_ort","ort"), + "t": ("cel","cel"), #This to extract from the full mosaic + #"t": ("msk","mkd"), #This to extract from the masked mosaic + } + arcpy.env.workspace = gdb + ext=arcpy.GetParameterAsText(7) #extension of the rasters + buf=arcpy.GetParameterAsText(2) #we use the buffered plot features -buf- to extract the stats or the full plots -plt- + bw8=arcpy.GetParameterAsText(4) #default trial eg. bw8 if there is a raster that includes all the zonal features + + #################################### + ############################ END:VARIABLES ############################################## + ######################################################################################### + ex, dates,trials = readWhatToExtract(csvExtractThis) + #find the image paths and features to perform the zonal statistics + for f, flightdate in enumerate(ex): + for t,trial in enumerate(flightdate): #skip the date column + for cam in trial.split(): + if cam !="-": + #QuĂ© hacer si está vacĂ­o? + #ZONES FEATURE + #Search for the corresponding grids for this date/trial/cam + #if date is __ad__ the grid is the general for the cycle e.g. ypt__ad__buf + #if date is YYMMDD the grid was adjusted, and needs to have indicated also the camera + # this way we can know at which image the adjustment corresponds. + # grids adjusted will be like bw1160215bufh -> with the date and camera indicated + pattern = str(trials[t])+"("+str(dates[f])+'|__ae__)'+buf+'['+cam+']*$'#################################################this is hardcoded year code cycle + arcpy.AddMessage("pattern: "+str(pattern)) + #arcpy.AddMessage("FList: "+str(listFc)) #print List of features selected + plotsF = [ft for ft in listFc if re.search(pattern, ft)] + #Will throw error if list empty? + if plotsF: + if len(plotsF)>1: plotsF=plotsF[0] #get just the edited feature if the general exists + plotsF=os.path.join(arcpy.env.workspace, ds, ''.join(plotsF)) + arcpy.AddMessage("Zones: "+plotsF) + #RASTER + #search for the images that match them + # hyper mosaics folder is rfl_spr_ort, for multi is geo, for thermal is cel + mF, sx = mosaicFolder.get(cam,("geo","geo")) #corresponding folder and sfx, geo default + imgLocation = imgWs+"\\"+str(dates[f])+"\\"+cam+"\\"+mF+"\\" + imgName =cam+str(dates[f])+str(trials[t])+sx+ext + img=imgLocation+imgName + arcpy.AddMessage("looking for raster for area: "+str(img)) #print to check + if not exists(img): #Use the general mosaic + imgName =cam+str(dates[f])+bw8+sx+ext + img=imgLocation+imgName + arcpy.AddMessage("looking for general mosaic: "+str(img)) #print to check + if exists(img): + arcpy.AddMessage("Raster: "+img) + #PREPARE FOR THE ZS + #Reading header to get Bands + #We assume header file is there and named as the raster + hdr_file = img[:-4]+".hdr" + #Read header to know number and name of bands or set them a priori + #knownBands is optional + bandNames = readHeaderFile(hdr_file, knownBands) + tableBaseName = arcpy.ValidateTableName(cam+str(dates[f])+str(trials[t])+buf,tws) #stats table base name in ws + #arcpy.AddMessage("zs") + #arcpy.AddMessage("tb:"+tableBaseName) + zsm = zonalStatsMultiband(tableBaseName, bandNames, img, plotsF,id_field) + appendListToFile(log,zsm,mode="a") + else: + arcpy.AddMessage("|||No raster to use |||") + appendListToFile(log,str(img),mode="a") + else: + arcpy.AddMessage("|||No features to use|||") + appendListToFile(log,str(plotsF),mode="a") + else: arcpy.AddMessage("No data extracting for this trial on this date") + appendListToFile(log,("Finished",time.strftime("%Y/%m/%d %H:%M:%S")),mode="a") #Write finish time in log +except Exception as e: + # If an error occurred, print( line number and error message + import traceback, sys + tb = sys.exc_info()[2] + arcpy.AddMessage("Line:" + str(tb.tb_lineno)+ " Error " + e.message) \ No newline at end of file diff --git a/ArcGIS/zonalStatsMultiband_inFolder.py b/ArcGIS/zonalStatsMultiband_inFolder.py new file mode 100644 index 0000000..3635a72 --- /dev/null +++ b/ArcGIS/zonalStatsMultiband_inFolder.py @@ -0,0 +1,443 @@ +#Perform zonal statistics tool in all the imagery on a folder. +#The inputs are specified as para meters in the tool in ArcMap +#Automatically calls the appropriate raster and zonal data from the specified locations. +#Assumes the default "Greenplane" naming standards + +#This script is to execute the "Zonal statistics" tool in a multiband raster. It saves the resulting table to a specified >workspace<. +# One table per band is generated, they are named based on the raster and feature zone names + the extracted band number. +# To iterate along the bands, the ENVI HDR file is atempted to be read to get the band names. +# If header doesn't exist, a known number of bands can be specified to iterate. +#This script takes 3 important inputs and outputs: +#(input) +# Buffer: zone feature to use as input for zonal statistic tool. +# Standard file name system on the features is espected: tttYYMMDDfff +# Being: +# YYMMDD= date in a YYMMDD format. E.g. 160311 for march 11 of 2016. +# fff = 3 char indicating the features that are represented in the vector shapes. E.g. "gss" for greenseeker sampling, "plt" for field plots. +# ttt = 3 char representing the trial or area covered in the image. E.g. "bwh" for bread wheat trials, "sam" for maize summer trials + +# ID field: Field of the input feature to use as identifier of the different zones for generating the statistics + +# image: data image to use as input for zonal statistic tool. +# Standard file name system on the raster is espected: xYYMMDDtttsss +# Being: +# x = [h,m,t,c,a,r,n] one char representing the camera used to acquire the imagery + # sss = 3 char representing the processing stage of the imagery. E.g. "geo" for georeferenced, "pno" for the multi mosaic +# The band information is read from the envi HDR file + +# Workspace: where to save the statistics tables and where to create the features to save the mean values per plot + +# Known bands: [Optional] Number of bands of the input raster. Necessary if +# the header file doesn't exist -> default is 1 if not specified. + +#(ouput) +# Tables: The full default statistics table +# Table name structure: xYYMMDDtttfffBi +# Being: +# i: the band number from which the statistics was calculated based on the zones of the input feature +# --------------------------------------------------------------------------- + +#################################WARNING +#crashes if the csv has just one row + +import numpy +import os +import arcpy, arcgisscripting +import itertools +import re +import traceback, sys +import time + +#Read the file to know which feature-raster to extract +def readWhatToExtract(csvWhatToExtract): + arcpy.AddMessage("will read file: "+csvWhatToExtract) + extractMatrix=numpy.genfromtxt(open(csvWhatToExtract,"rb"), + delimiter=',', + dtype=None, + names=True) + dates = list(extractMatrix['date']) #get the dates + arcpy.AddMessage("Dates: "+str(dates)) + trials=extractMatrix.dtype.names[1:] + arcpy.AddMessage("Trials: "+str(trials)) + extractMatrix = extractMatrix[list(extractMatrix.dtype.names)[1:]]#remove "date" column (1st) + arcpy.AddMessage("What to extract: "+str(extractMatrix)) + return extractMatrix,dates,trials + #Return the matrix of the kind of images acquired on each day on each trial [h m t], + #the list of dates and the list of trials (removing the "date" item) +def readDbf():# read dbf file + print("Algo haremos aquĂ­")# zsFields= ["id","COUNT","MEAN","STD"] + # table="C:\\Users\\usuario\\Documents\\ArcGIS\\m150326pcwbufB1.dbf" + # with arcpy.da.SearchCursor(table, zsFields) as cursor: + # for row in cursor: + # print(('Feature {0} has an area of {1}'.format(row[0], row[1])) +def zonalStatsMultiband(tableBaseName, bandNames, image, plot_polygons,id_field): + try: + tables=list() + #zonalStats iterated in all bands of the raster Multiband + #Return list of the name of the generated tables + arcpy.AddMessage("Iterating the zonal statistics across all bands...") + #Get bands + #Returns: [0]numberOfBands, [1]isBandNamesInteger? + numOfBands = getNumberOfBands(bandNames) + arcpy.AddMessage("Number of bands: "+str(numOfBands[0])) + #If the image has more than one band... + if numOfBands[0] != 1: + #If we only have the number of bands, but no band names (because there was no hdr)... + #We will call the bands by the default way ArcMap does: RasterName + Band_1, Band_2, etc... + #we create the list to iterate it + if numOfBands[1]: bandNames = list('Band_%s' % b for b in range(1, numOfBands[0]+1)) + #Here iterates through the bands and runs the zonal statistics tool + #Get the count with the function "enumerate" + for i, b in enumerate(bandNames, start=1): + #Next we add the band name to the raster basename + band = image + "\\" + b + arcpy.AddMessage("Iterating band # "+ str(i)+" - " + band) + #Set table name + statsTable = tableBaseName+"B" + str(i)+ ".dbf" #We add the band id to the StatsTable name + #arcpy.AddMessage("Willbe stored in table "+str(statsTable)) + #Here we call the method to do the zonal statistics + zs = arcpy.gp.ZonalStatisticsAsTable_sa(plot_polygons, id_field, band, statsTable, "DATA", "ALL") + arcpy.AddMessage("resulting stats table: " + str(zs)) + tables.append(plot_polygons+","+band+","+str(zs)) + else: + #If we have just one band we call the raster without specifying any band + zs = arcpy.gp.ZonalStatisticsAsTable_sa(plot_polygons, id_field, image, tableBaseName+"B1.dbf", "DATA", "ALL") + tables.append(plot_polygons+","+image+","+str(zs)) + arcpy.AddMessage("||| resulting zs table: " + str(zs)) + return tables + except Exception as e: + # If an error occurred, print( line number and error message + import traceback, sys + tb = sys.exc_info()[2] + arcpy.AddMessage("Line:" + str(tb.tb_lineno)) + arcpy.AddMessage("Error " + e.message) +def print_kwinfo(): + if deb==1: + print( "kwinfo se guarda:") + print( kwinfo) + +def create_if_not_exists(path): + try: + if not os.path.exists(path): + os.makedirs(path) + except Exception as e: + # If an error occurred, print( line number and error message + tb = sys.exc_info()[2] + arcpy.AddMessage("Line:" + str(tb.tb_lineno)) + arcpy.AddMessage("Error " + e.message) + +def exists(path): + exists = True if os.path.exists(path) else False + return exists + +def write_hdr_file(hdr_filename,assign_bands=1,mode="w"): + try: + hdr_file = open(hdr_filename, mode) + hdr_file.write("HEAD\nbands = "+ str(assign_bands)+"\n") + arcpy.AddMessage("writing in hdr file") + hdr_file.close() + except Exception as e: + # If an error occurred, print( line number and error message + import traceback, sys + tb = sys.exc_info()[2] + arcpy.AddMessage("Line:" + str(tb.tb_lineno)) + arcpy.AddMessage("Error " + e.message) + print( "OMG! Error while in: write_hdr_file") + raise e +def appendListToFile(txt_filename,thelist,mode="w"): + try: + file = open(txt_filename, mode) + #arcpy.AddMessage("writing in "+txt_filename+" file") + for item in thelist: + file.write("%s\n" % item) + file.close() + except Exception as e: + # If an error occurred, print( line number and error message + import traceback, sys + tb = sys.exc_info()[2] + arcpy.AddMessage("Line:" + str(tb.tb_lineno)) + arcpy.AddMessage("Error " + e.message) + print( "OMG! Error while in: appendListToFile") + raise e + +def replace_in_list(regex,lst): + return [re.sub(regex, '', x).strip() for x in lst] #List comprehension +#Method to concatenate the band name to the raster basename +#BandNames has or all the list of band names or the number of bands +def getNumberOfBands(bandNames): + try: + #If there was no bandnames or header, a band number was assigned + #This would be an integer + numOfBands = int(bandNames) + isANumber = True + return numOfBands, isANumber + except ValueError: + #If there is a band name as a string we will say it's one band + numOfBands = 1 + isANumber = False + return numOfBands, isANumber + except TypeError: + #If there are band names as list or array we will count them here + numOfBands = len(bandNames) + isANumber = False + return numOfBands, isANumber + except Exception as e: + import traceback, sys + tb = sys.exc_info()[2] + arcpy.AddMessage("Line:" + str(tb.tb_lineno)) + arcpy.AddMessage("Error " + e.message) + print( "OMG!") + raise e +def print_dict(dictio): + #The next line iterates, formats and prints the dictionary, key:value + #print( "\n".join('{}={}'.format(k,v) for k,v in dictio.items()) print( in console + arcpy.AddMessage("\n".join('{}={}'.format(k,v) for k,v in dictio.items()))#Print to arcmap results window +def readHeaderFile(header_file,assign_bands=1): + try: + if not exists(header_file): + write_hdr_file(header_file,assign_bands) + arcpy.AddMessage("Header not found, it was created a basic one. Number of bands assigned: "+str(assign_bands)) + arcpy.AddMessage("started reading header file:" + str(header_file)) + envi_header_keywords={"acquisition time","band names","bands", + "bbl","byte order","class lookup","class names", + "classes","cloud cover","complex function", + "coordinate system string","data gain values", + "data ignore value","data offset values", + "data reflectance gain values","data reflectance offset values", + "data type","default bands","default stretch","dem band", + "dem file","description","file type","fwhm","geo points", + "header offset","interleave","lines","map info", + "major frame offsets","minor frame offsets","pixel size", + "product type","projection info","read procedures", + "reflectance scale factor","rpc info","samples","security tag", + "sensor type","solar irradiance","spectra names","sun azimuth", + "sun elevation","wavelength","wavelength units","x start", + "y start","z plot average","z plot range","z plot titles"} + envi_header_keywords_or="|".join(envi_header_keywords) + # Read and Iterate over the lines of the file + with open(header_file, 'rt') as f: + data = f.read()[5:] #Skip the first "ENVI" letters of the header + lines = re.split(r"[\n]", data)#the info corresponding to one line + arcpy.AddMessage("reading header lines...") + dictio = {} + global deb + deb=0 + wl=[]#List to save wavelengths + bn=[]#List to save band names + clines=[]#complete lines with all the info corresponding to one header keyword + ongoing=0 #Variable para marcar si se está buscando el resto de la lĂ­nea de info + #to one header keyword is not in one single line + kwinfo="" + for i,l in enumerate(lines): + if deb==1: print( str(i)+"-l- "+str(l)) + kwinfo+=l + if deb==1: print( str(i)+"-kwinfo- "+str(kwinfo)) + if ongoing==1: + if "}" in l: + if deb==1: print( "ongoing==se cierra por fin el parentesis") + print_kwinfo() + clines.append(kwinfo) + kwinfo="" + ongoing=0 + continue + else: + if deb==1: print( "ongoing==1 else") + if ongoing==0: + ongoing=1 + for keyw in envi_header_keywords: + if keyw in l: + if deb==1: print( keyw+" attribute found") + if "{" in l: + if "}" in l: + if deb==1: print( "ongoing==parentesis cerrando en linea") + print_kwinfo() + clines.append(kwinfo) + kwinfo="" + ongoing=0 + else: + if deb==1: print( "if bracket in l: --- else") + else: + if deb==1: print( "ongoing==sin paretesis") + print_kwinfo() + clines.append(kwinfo) + kwinfo="" + ongoing=0 + if deb==1: print( "fin de ronda de for") + for cl in clines: + #expresion to filter how to sepatate the string. + regexpresion = re.compile(r"""(.+?)\s*=\s*(.+)""") + dictio.update(dict(regexpresion.findall(cl))) + if deb==1: print( regexpresion.findall(cl)) + for key in dictio: dictio[key]=dictio[key].strip() + print( "Header attributes:") + #arcpy.AddMessage("Header attributes read") + #print_dict(dictio) + expr_replace = re.compile(r"{|}") #Expression to remove brackets below + #wavelength + if "wavelength" in dictio: + wl=re.split(r"[,]", dictio["wavelength"]) + wl =replace_in_list(expr_replace,wl) + #arcpy.AddMessage(wl) + #arcpy.AddMessage("saved wl") + #Number of Bands + if "bands" in dictio: + hdr_bands=dictio["bands"] + #arcpy.AddMessage(hdr_bands) + #arcpy.AddMessage("saved Number of bands from header") + #Band names + if "band names" in dictio: + #arcpy.AddMessage("band names attribute found") + bn=re.split(r"[,]", dictio["band names"]) + #arcpy.AddMessage("split completed") + bn=replace_in_list(expr_replace,bn) + #arcpy.AddMessage("saved bn") + #Next we add the wl units only if they are defined + #arcpy.AddMessage("Format composed band name") + full_bn=hdr_bands #We assign by default the number of bands in the raster + if "wavelength units" in dictio: + if "band names" in dictio and "wavelength" in dictio: + join_str = '{bandn} ({waveln} '+dictio["wavelength units"]+')' if dictio["wavelength units"]!="Unknown" else '{bandn} ({waveln})' + #arcpy.AddMessage("bn and wl found") + full_bn= '\n'.join(join_str.format(bandn=b, waveln=w) for b,w in itertools.izip(bn, wl)).split('\n') + if not "band names" in dictio and "wavelength" in dictio: + join_str = '{waveln} '+dictio["wavelength units"] if dictio["wavelength units"]!="Unknown" else '{waveln}' + #arcpy.AddMessage("wl found+units") + full_bn= '\n'.join(join_str.format(waveln=w) for w in wl).split('\n') + else: + if "band names" in dictio and "wavelength" in dictio: + join_str = '{bandn} ({waveln})' + #arcpy.AddMessage("bn and wl found") + full_bn= '\n'.join(join_str.format(bandn=b, waveln=w) for b,w in itertools.izip(bn, wl)).split('\n') + if not "band names" in dictio and "wavelength" in dictio: + #arcpy.AddMessage("wl found") + full_bn= wl + #arcpy.AddMessage(full_bn) + if deb==1: + arcpy.AddMessage( full_bn) + arcpy.AddMessage( "Bands descripted in header: "+len(full_bn)) + #CHECK IF the "data ignore value is set", otherwise, set it to 0. + if not "data ignore value" in dictio: + arcpy.AddMessage("Set -data ignore value to 0-") + add=["data ignore value = 0"] + appendListToFile(header_file,add,mode="a") + #arcpy.AddMessage("Finished reading header") + #arcpy.AddMessage(type(full_bn)) #to know the type of variable it was created + return full_bn + except Exception as e: + # If an error occurred, print( line number and error message + import traceback, sys + tb = sys.exc_info()[2] + arcpy.AddMessage("Line:" + str(tb.tb_lineno)) + arcpy.AddMessage("Error " + e.message) + print( "OMG!") + raise e +#^^^^^^^^^^^^^^^^^^^^^^^ Methods ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +try: + # Check out any necessary licenses and Create the Geoprocessor object + arcpy.CheckOutExtension("spatial") + gp = arcgisscripting.create() + ######################################################################################### + ################################# START:INPUTS ########################################## + #Hard coded this + csvExtractThis = arcpy.GetParameterAsText(0) + #imgWs="G:\\AD15_16\\" #imageBaseLocation + imgWs = arcpy.GetParameterAsText(6) #test + #GDB whare the feature classes are stored and where the tables will be written: + gdb = arcpy.GetParameterAsText(1) #BW + #gdb = "C:\\Users\\usuario\\Documents\\ArcGIS\\Default.gdb\\" #Default + #tws = where to save tables inside the workspace + tws =arcpy.GetParameterAsText(9)######Not worinkg... it saves a folder up####### + ds=arcpy.GetParameterAsText(3) #Use this ds="" fro m and t + #ds="h" #Use this for hyper + arcpy.env.workspace = gdb + #arcpy.AddMessage("wspace: "+str(arcpy.env.workspace)) + #knownBands="62" # ##################### hyper resampled + knownBands=arcpy.GetParameterAsText(8) # ####################### thermal + id_field = arcpy.GetParameterAsText(5) #Table field that has the unique plot identifier + log= arcpy.GetParameterAsText(10) + ## END:INPUTS ## + + ## START:VARIABLES ## + #Get all feature classes gdb/extract dataset + listFc = arcpy.ListFeatureClasses( + feature_type="Polygon", + feature_dataset=ds) + mosaicFolder = { + #Get the folder and suffix for the images according to the camera + #"m": ("msk","mkd"), #When you will use the masked rasters + "m": ("geo","geo"), #When you will use the original multispectral rasters + #"h": ("rfl_spr_ort","ort"), + "h": ("rfl_spr_ort","rfl"), #Specific for this test + "t": ("cel","cel"), #This to extract from the full mosaic + #"t": ("msk","mkd"), #This to extract from the masked mosaic + } + arcpy.env.workspace = gdb + ext=arcpy.GetParameterAsText(7) #extension of the rasters + buf=arcpy.GetParameterAsText(2) #we use the buffered plot features -buf- to extract the stats or the full plots -plt- + bw8=arcpy.GetParameterAsText(4) #default trial eg. bw8 if there is a raster that includes all the zonal features + + #################################### + ############################ END:VARIABLES ############################################## + ######################################################################################### + ex, dates,trials = readWhatToExtract(csvExtractThis) + #find the image paths and features to perform the zonal statistics + for f, flightdate in enumerate(ex): + for t,trial in enumerate(flightdate): #skip the date column + for cam in trial.split(): + if cam !="-": + #QuĂ© hacer si está vacĂ­o? + #ZONES FEATURE + #Search for the corresponding grids for this date/trial/cam + #if date is __ad__ the grid is the general for the cycle e.g. ypt__ad__buf + #if date is YYMMDD the grid was adjusted, and needs to have indicated also the camera + # this way we can know at which image the adjustment corresponds. + # grids adjusted will be like bw1160215bufh -> with the date and camera indicated + pattern = str(trials[t])+"("+str(dates[f])+'|__ae__)'+buf+'['+cam+']*$'#################################################this is hardcoded year code cycle + arcpy.AddMessage("pattern: "+str(pattern)) + #arcpy.AddMessage("FList: "+str(listFc)) #print List of features selected + plotsF = [ft for ft in listFc if re.search(pattern, ft)] + #Will throw error if list empty? + if plotsF: + if len(plotsF)>1: plotsF=plotsF[0] #get just the edited feature if the general exists + plotsF=os.path.join(arcpy.env.workspace, ds, ''.join(plotsF)) + arcpy.AddMessage("Zones: "+plotsF) + #RASTER + #search for the images that match them + # hyper mosaics folder is rfl_spr_ort, for multi is geo, for thermal is cel + mF, sx = mosaicFolder.get(cam,("geo","geo")) #corresponding folder and sfx, geo default ##Use this to search in dates folders + #imgLocation = imgWs+"\\"+str(dates[f])+"\\"+cam+"\\"+mF+"\\" ##Use this to search in dates folders + imgLocation = imgWs+"\\"+cam+"\\" #This to search in the base folder + cam + imgName =cam+str(dates[f])+str(trials[t])+sx+ext + img=imgLocation+imgName + arcpy.AddMessage("looking for raster for area: "+str(img)) #print to check + if not exists(img): #Use the general mosaic + imgName =cam+str(dates[f])+bw8+sx+ext + img=imgLocation+imgName + arcpy.AddMessage("looking for general mosaic: "+str(img)) #print to check + if exists(img): + arcpy.AddMessage("Raster: "+img) + #PREPARE FOR THE ZS + #Reading header to get Bands + #We assume header file is there and named as the raster + hdr_file = img[:-4]+".hdr" + #Read header to know number and name of bands or set them a priori + #knownBands is optional + bandNames = readHeaderFile(hdr_file, knownBands) + tableBaseName = arcpy.ValidateTableName(cam+str(dates[f])+str(trials[t])+buf,tws) #stats table base name in ws + #arcpy.AddMessage("zs") + #arcpy.AddMessage("tb:"+tableBaseName) + zsm = zonalStatsMultiband(tableBaseName, bandNames, img, plotsF,id_field) + appendListToFile(log,zsm,mode="a") + else: + arcpy.AddMessage("|||No raster to use |||") + appendListToFile(log,str(img),mode="a") + else: + arcpy.AddMessage("|||No features to use|||") + appendListToFile(log,str(plotsF),mode="a") + else: arcpy.AddMessage("No data extracting for this trial on this date") + appendListToFile(log,("Finished",time.strftime("%Y/%m/%d %H:%M:%S")),mode="a") #Write finish time in log +except Exception as e: + # If an error occurred, print( line number and error message + import traceback, sys + tb = sys.exc_info()[2] + arcpy.AddMessage("Line:" + str(tb.tb_lineno)+ " Error " + e.message) \ No newline at end of file diff --git a/ArcGIS/zonalStatsTablesAutoInputsCSV.py b/ArcGIS/zonalStatsTablesAutoInputsCSV.py new file mode 100644 index 0000000..7dec3f6 --- /dev/null +++ b/ArcGIS/zonalStatsTablesAutoInputsCSV.py @@ -0,0 +1,402 @@ +#Perform zonal statistics tool based on a list of on a specified list of dates and names. +#Automatically calls the appropriate raster and zonal data from the default locations. + +#################################WARNING +#crashes if the csv has just one row + +import numpy +import os +import arcpy, arcgisscripting +import itertools +import re +import traceback, sys +import time + +#Read the file to know which feature-raster to extract +def readWhatToExtract(csvWhatToExtract): + arcpy.AddMessage("will read file: "+csvWhatToExtract) + extractMatrix=numpy.genfromtxt(open(csvWhatToExtract,"rb"), + delimiter=',', + dtype=None, + names=True) + dates = list(extractMatrix['date']) #get the dates + arcpy.AddMessage("Dates: "+str(dates)) + trials=extractMatrix.dtype.names[1:] + arcpy.AddMessage("Trials: "+str(trials)) + extractMatrix = extractMatrix[list(extractMatrix.dtype.names)[1:]]#remove "date" column (1st) + arcpy.AddMessage("What to extract: "+str(extractMatrix)) + return extractMatrix,dates,trials + #Return the matrix of the kind of images acquired on each day on each trial [h m t], + #the list of dates and the list of trials (removing the "date" item) +def readDbf():# read dbf file + print("Algo haremos aquĂ­")# zsFields= ["id","COUNT","MEAN","STD"] + # table="C:\\Users\\usuario\\Documents\\ArcGIS\\m150326pcwbufB1.dbf" + # with arcpy.da.SearchCursor(table, zsFields) as cursor: + # for row in cursor: + # print(('Feature {0} has an area of {1}'.format(row[0], row[1])) +def zonalStatsMultiband(tableBaseName, bandNames, image, plot_polygons,id_field): + try: + tables=list() + #zonalStats iterated in all bands of the raster Multiband + #Return list of the name of the generated tables + arcpy.AddMessage("Iterating the zonal statistics across all bands...") + #Get bands + #Returns: [0]numberOfBands, [1]isBandNamesInteger? + numOfBands = getNumberOfBands(bandNames) + arcpy.AddMessage("Number of bands: "+str(numOfBands[0])) + #If the image has more than one band... + if numOfBands[0] != 1: + #If we only have the number of bands, but no band names (because there was no hdr)... + #We will call the bands by the default way ArcMap does: RasterName + Band_1, Band_2, etc... + #we create the list to iterate it + if numOfBands[1]: bandNames = list('Band_%s' % b for b in range(1, numOfBands[0]+1)) + #Here iterates through the bands and runs the zonal statistics tool + #Get the count with the function "enumerate" + for i, b in enumerate(bandNames, start=1): + #Next we add the band name to the raster basename + band = image + "\\" + b + arcpy.AddMessage("Iterating band # "+ str(i)+" - " + band) + #Set table name + statsTable = tableBaseName+"B" + str(i)+ ".dbf" #We add the band id to the StatsTable name + #arcpy.AddMessage("Willbe stored in table "+str(statsTable)) + #Here we call the method to do the zonal statistics + zs = arcpy.gp.ZonalStatisticsAsTable_sa(plot_polygons, id_field, band, statsTable, "DATA", "ALL") + arcpy.AddMessage("resulting stats table: " + str(zs)) + tables.append(plot_polygons+","+band+","+str(zs)) + else: + #If we have just one band we call the raster without specifying any band + zs = arcpy.gp.ZonalStatisticsAsTable_sa(plot_polygons, id_field, image, tableBaseName+"B1.dbf", "DATA", "ALL") + tables.append(plot_polygons+","+image+","+str(zs)) + arcpy.AddMessage("||| resulting zs table: " + str(zs)) + return tables + except Exception as e: + # If an error occurred, print( line number and error message + import traceback, sys + tb = sys.exc_info()[2] + arcpy.AddMessage("Line:" + str(tb.tb_lineno)) + arcpy.AddMessage("Error " + e.message) +def print_kwinfo(): + if deb==1: + print( "kwinfo se guarda:") + print( kwinfo) + +def create_if_not_exists(path): + try: + if not os.path.exists(path): + os.makedirs(path) + except Exception as e: + # If an error occurred, print( line number and error message + tb = sys.exc_info()[2] + arcpy.AddMessage("Line:" + str(tb.tb_lineno)) + arcpy.AddMessage("Error " + e.message) + +def exists(path): + exists = True if os.path.exists(path) else False + return exists + +def write_hdr_file(hdr_filename,assign_bands=1,mode="w"): + try: + hdr_file = open(hdr_filename, mode) + hdr_file.write("HEAD\nbands = "+ str(assign_bands)) + arcpy.AddMessage("writing in hdr file") + hdr_file.close() + except Exception as e: + # If an error occurred, print( line number and error message + import traceback, sys + tb = sys.exc_info()[2] + arcpy.AddMessage("Line:" + str(tb.tb_lineno)) + arcpy.AddMessage("Error " + e.message) + print( "OMG! Error while in: write_hdr_file") + raise e +def appendListToFile(txt_filename,thelist,mode="w"): + try: + file = open(txt_filename, mode) + #arcpy.AddMessage("writing in "+txt_filename+" file") + for item in thelist: + file.write("%s\n" % item) + file.close() + except Exception as e: + # If an error occurred, print( line number and error message + import traceback, sys + tb = sys.exc_info()[2] + arcpy.AddMessage("Line:" + str(tb.tb_lineno)) + arcpy.AddMessage("Error " + e.message) + print( "OMG! Error while in: appendListToFile") + raise e + +def replace_in_list(regex,lst): + return [re.sub(regex, '', x).strip() for x in lst] #List comprehension +#Method to concatenate the band name to the raster basename +#BandNames has or all the list of band names or the number of bands +def getNumberOfBands(bandNames): + try: + #If there was no bandnames or header, a band number was assigned + #This would be an integer + numOfBands = int(bandNames) + isANumber = True + return numOfBands, isANumber + except ValueError: + #If there is a band name as a string we will say it's one band + numOfBands = 1 + isANumber = False + return numOfBands, isANumber + except TypeError: + #If there are band names as list or array we will count them here + numOfBands = len(bandNames) + isANumber = False + return numOfBands, isANumber + except Exception as e: + import traceback, sys + tb = sys.exc_info()[2] + arcpy.AddMessage("Line:" + str(tb.tb_lineno)) + arcpy.AddMessage("Error " + e.message) + print( "OMG!") + raise e +def print_dict(dictio): + #The next line iterates, formats and prints the dictionary, key:value + #print( "\n".join('{}={}'.format(k,v) for k,v in dictio.items()) print( in console + arcpy.AddMessage("\n".join('{}={}'.format(k,v) for k,v in dictio.items()))#Print to arcmap results window +def readHeaderFile(header_file,assign_bands=1): + try: + if not exists(header_file): + write_hdr_file(header_file,assign_bands) + arcpy.AddMessage("Header not found, it was created a basic one. Number of bands assigned: "+str(assign_bands)) + arcpy.AddMessage("started reading header file:" + str(header_file)) + envi_header_keywords={"acquisition time","band names","bands", + "bbl","byte order","class lookup","class names", + "classes","cloud cover","complex function", + "coordinate system string","data gain values", + "data ignore value","data offset values", + "data reflectance gain values","data reflectance offset values", + "data type","default bands","default stretch","dem band", + "dem file","description","file type","fwhm","geo points", + "header offset","interleave","lines","map info", + "major frame offsets","minor frame offsets","pixel size", + "product type","projection info","read procedures", + "reflectance scale factor","rpc info","samples","security tag", + "sensor type","solar irradiance","spectra names","sun azimuth", + "sun elevation","wavelength","wavelength units","x start", + "y start","z plot average","z plot range","z plot titles"} + envi_header_keywords_or="|".join(envi_header_keywords) + # Read and Iterate over the lines of the file + with open(header_file, 'rt') as f: + data = f.read()[5:] #Skip the first "ENVI" letters of the header + lines = re.split(r"[\n]", data)#the info corresponding to one line + arcpy.AddMessage("reading header lines...") + dictio = {} + global deb + deb=0 + wl=[]#List to save wavelengths + bn=[]#List to save band names + clines=[]#complete lines with all the info corresponding to one header keyword + ongoing=0 #Variable para marcar si se está buscando el resto de la lĂ­nea de info + #to one header keyword is not in one single line + kwinfo="" + for i,l in enumerate(lines): + if deb==1: print( str(i)+"-l- "+str(l)) + kwinfo+=l + if deb==1: print( str(i)+"-kwinfo- "+str(kwinfo)) + if ongoing==1: + if "}" in l: + if deb==1: print( "ongoing==se cierra por fin el parentesis") + print_kwinfo() + clines.append(kwinfo) + kwinfo="" + ongoing=0 + continue + else: + if deb==1: print( "ongoing==1 else") + if ongoing==0: + ongoing=1 + for keyw in envi_header_keywords: + if keyw in l: + if deb==1: print( keyw+" attribute found") + if "{" in l: + if "}" in l: + if deb==1: print( "ongoing==parentesis cerrando en linea") + print_kwinfo() + clines.append(kwinfo) + kwinfo="" + ongoing=0 + else: + if deb==1: print( "if bracket in l: --- else") + else: + if deb==1: print( "ongoing==sin paretesis") + print_kwinfo() + clines.append(kwinfo) + kwinfo="" + ongoing=0 + if deb==1: print( "fin de ronda de for") + for cl in clines: + #expresion to filter how to sepatate the string. + regexpresion = re.compile(r"""(.+?)\s*=\s*(.+)""") + dictio.update(dict(regexpresion.findall(cl))) + if deb==1: print( regexpresion.findall(cl)) + for key in dictio: dictio[key]=dictio[key].strip() + print( "Header attributes:") + #arcpy.AddMessage("Header attributes read") + #print_dict(dictio) + expr_replace = re.compile(r"{|}") #Expression to remove brackets below + #wavelength + if "wavelength" in dictio: + wl=re.split(r"[,]", dictio["wavelength"]) + wl =replace_in_list(expr_replace,wl) + #arcpy.AddMessage(wl) + #arcpy.AddMessage("saved wl") + #Number of Bands + if "bands" in dictio: + hdr_bands=dictio["bands"] + #arcpy.AddMessage(hdr_bands) + #arcpy.AddMessage("saved Number of bands from header") + #Band names + if "band names" in dictio: + #arcpy.AddMessage("band names attribute found") + bn=re.split(r"[,]", dictio["band names"]) + #arcpy.AddMessage("split completed") + bn=replace_in_list(expr_replace,bn) + #arcpy.AddMessage("saved bn") + #Next we add the wl units only if they are defined + #arcpy.AddMessage("Format composed band name") + full_bn=hdr_bands #We assign by default the number of bands in the raster + if "wavelength units" in dictio: + if "band names" in dictio and "wavelength" in dictio: + join_str = '{bandn} ({waveln} '+dictio["wavelength units"]+')' if dictio["wavelength units"]!="Unknown" else '{bandn} ({waveln})' + #arcpy.AddMessage("bn and wl found") + full_bn= '\n'.join(join_str.format(bandn=b, waveln=w) for b,w in itertools.izip(bn, wl)).split('\n') + if not "band names" in dictio and "wavelength" in dictio: + join_str = '{waveln} '+dictio["wavelength units"] if dictio["wavelength units"]!="Unknown" else '{waveln}' + #arcpy.AddMessage("wl found+units") + full_bn= '\n'.join(join_str.format(waveln=w) for w in wl).split('\n') + else: + if "band names" in dictio and "wavelength" in dictio: + join_str = '{bandn} ({waveln})' + #arcpy.AddMessage("bn and wl found") + full_bn= '\n'.join(join_str.format(bandn=b, waveln=w) for b,w in itertools.izip(bn, wl)).split('\n') + if not "band names" in dictio and "wavelength" in dictio: + #arcpy.AddMessage("wl found") + full_bn= wl + #arcpy.AddMessage(full_bn) + if deb==1: + arcpy.AddMessage( full_bn) + arcpy.AddMessage( "Bands descripted in header: "+len(full_bn)) + #CHECK IF the "data ignore value is set", otherwise, set it to 0. + if not "data ignore value" in dictio: + arcpy.AddMessage("Set -data ignore value to 0-") + add=["data ignore value = 0"] + appendListToFile(header_file,add,mode="a") + #arcpy.AddMessage("Finished reading header") + #arcpy.AddMessage(type(full_bn)) #to know the type of variable it was created + return full_bn + except Exception as e: + # If an error occurred, print( line number and error message + import traceback, sys + tb = sys.exc_info()[2] + arcpy.AddMessage("Line:" + str(tb.tb_lineno)) + arcpy.AddMessage("Error " + e.message) + print( "OMG!") + raise e +#^^^^^^^^^^^^^^^^^^^^^^^ Methods ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +try: + # Check out any necessary licenses and Create the Geoprocessor object + arcpy.CheckOutExtension("spatial") + gp = arcgisscripting.create() + ######################################################################################### + ################################# START:INPUTS ########################################## + #Hard coded this + csvExtractThis = "C:\\Dropbox\\data\\AE\\bw\\ae_bw_extraction_hyper_heat.csv" + #imgWs="G:\\AD15_16\\" #imageBaseLocation + imgWs="G:\\AE\\" #test + #GDB whare the feature classes are stored and where the tables will be written: + gdb = "C:\\Dropbox\\data\\AE\\\\bw\\ae_bw.gdb\\" #BW + #gdb = "C:\\Users\\usuario\\Documents\\ArcGIS\\Default.gdb\\" #Default + #tws = where to save tables inside the workspace + tws ="C:\\Dropbox\\data\\AE\\bw\\predata\\tables"######Not worinkg... it save in shp folder####### + ds="h" #Use this ds="" fro m and t + #ds="h" #Use this for hyper + arcpy.env.workspace = gdb + #arcpy.AddMessage("wspace: "+str(arcpy.env.workspace)) + #knownBands="62" # ##################### hyper resampled + knownBands="62" # ####################### thermal + id_field = "Name" #Table field that has the unique plot identifier + log= "C:\\Dropbox\\data\\AE\\bw\\log.txt" + ## END:INPUTS ## + + ## START:VARIABLES ## + #Get all feature classes gdb/extract dataset + listFc = arcpy.ListFeatureClasses( + feature_type="Polygon", + feature_dataset=ds) + mosaicFolder = { + #Get the folder and suffix for the images according to the camera + "m": ("msk","mkd"), + "h": ("rfl_spr_ort","ort"), + #"t": ("cel","cel"), #This to extract from the full mosaic + "t": ("msk","mkd"), #This to extract from the masked mosaic + } + arcpy.env.workspace = gdb + ext=".bsq" #extension of the rasters + buf="buf" #we use the buffered plot features -buf- to extract the stats or the full plots -plt- + bw8="xxx" #default trial eg. bw8 if there is a raster that includes all the zonal features + + #################################### + ############################ END:VARIABLES ############################################## + ######################################################################################### + ex, dates,trials = readWhatToExtract(csvExtractThis) + #find the image paths and features to perform the zonal statistics + for f, flightdate in enumerate(ex): + for t,trial in enumerate(flightdate): #skip the date column + for cam in trial.split(): + if cam !="-": + #QuĂ© hacer si está vacĂ­o? + #ZONES FEATURE + #Search for the corresponding grids for this date/trial/cam + #if date is __ad__ the grid is the general for the cycle e.g. ypt__ad__buf + #if date is YYMMDD the grid was adjusted, and needs to have indicated also the camera + # this way we can know at which image the adjustment corresponds. + # grids adjusted will be like bw1160215bufh -> with the date and camera indicated + pattern = str(trials[t])+"("+str(dates[f])+'|__ad__)'+buf+'['+cam+']*$'##################################################include this ifthere are different grids by camera + arcpy.AddMessage("pattern: "+str(pattern)) + #arcpy.AddMessage("FList: "+str(listFc)) #print List of features selected + plotsF = [ft for ft in listFc if re.search(pattern, ft)] + #Will throw error if list empty? + if plotsF: + if len(plotsF)>1: plotsF=plotsF[0] #get just the edited feature if the general exists + plotsF=os.path.join(arcpy.env.workspace, ds, ''.join(plotsF)) + arcpy.AddMessage("Zones: "+plotsF) + #RASTER + #search for the images that match them + # hyper mosaics folder is rfl_spr_ort, for multi is geo, for thermal is cel + mF, sx = mosaicFolder.get(cam,("geo","geo")) #corresponding folder and sfx, geo default + imgLocation = imgWs+str(dates[f])+"\\"+cam+"\\"+mF+"\\" + imgName =cam+str(dates[f])+str(trials[t])+sx+ext + img=imgLocation+imgName + if not exists(img): #Use the general mosaic + imgName =cam+str(dates[f])+bw8+sx+ext + img=imgLocation+imgName + if exists(img): + arcpy.AddMessage("Raster: "+img) + #PREPARE FOR THE ZS + #Reading header to get Bands + #We assume header file is there and named as the raster + hdr_file = img[:-4]+".hdr" + #Read header to know number and name of bands or set them a priori + #knownBands is optional + bandNames = readHeaderFile(hdr_file, knownBands) + tableBaseName = arcpy.ValidateTableName(cam+str(dates[f])+str(trials[t])+buf,tws) #stats table base name in ws + #arcpy.AddMessage("zs") + #arcpy.AddMessage("tb:"+tableBaseName) + zsm = zonalStatsMultiband(tableBaseName, bandNames, img, plotsF,id_field) + appendListToFile(log,zsm,mode="a") + else: + arcpy.AddMessage("|||No raster to use |||") + appendListToFile(log,str(img),mode="a") + else: + arcpy.AddMessage("|||No features to use|||") + appendListToFile(log,str(plotsF),mode="a") + else: arcpy.AddMessage("No data extracting for this trial on this date") + appendListToFile(log,("Finished",time.strftime("%Y/%m/%d %H:%M:%S")),mode="a") #Write finish time in log +except Exception as e: + # If an error occurred, print( line number and error message + import traceback, sys + tb = sys.exc_info()[2] + arcpy.AddMessage("Line:" + str(tb.tb_lineno)+ " Error " + e.message) \ No newline at end of file diff --git a/ArcGIS/zonalStatsTablesAutoInputsCSV_AguaFria.py b/ArcGIS/zonalStatsTablesAutoInputsCSV_AguaFria.py new file mode 100644 index 0000000..de019ca --- /dev/null +++ b/ArcGIS/zonalStatsTablesAutoInputsCSV_AguaFria.py @@ -0,0 +1,410 @@ +#Perform zonal statistics tool based on a list of on a specified list of dates and names. +#Automatically calls the appropriate raster and zonal data from within the selected folder. + +#################################WARNING +#crashes if the csv has just one row + +import numpy +import os +import arcpy, arcgisscripting +import itertools +import re +import traceback, sys +import time + +#Read the file to know which feature-raster to extract +def readWhatToExtract(csvWhatToExtract): + try: + arcpy.AddMessage("will read file: "+csvWhatToExtract) + extractMatrix=numpy.genfromtxt(open(csvWhatToExtract,"rb"), + delimiter=',', + dtype=None, + names=True) + dates = list(extractMatrix['date']) #get the dates + arcpy.AddMessage("Dates: "+str(dates)) + trials=extractMatrix.dtype.names[1:] + arcpy.AddMessage("Trials: "+str(trials)) + extractMatrix = extractMatrix[list(extractMatrix.dtype.names)[1:]]#remove "date" column (1st) + arcpy.AddMessage("What to extract: "+str(extractMatrix)) + return extractMatrix,dates,trials + except Exception as e: + # If an error occurred, print( line number and error message + import traceback, sys + tb = sys.exc_info()[2] + arcpy.AddMessage("Line:" + str(tb.tb_lineno)) + arcpy.AddMessage("Error " + e.message) + + #Return the matrix of the kind of images acquired on each day on each trial [h m t], + #the list of dates and the list of trials (removing the "date" item) +def readDbf():# read dbf file + print("Algo haremos aquĂ­")# zsFields= ["id","COUNT","MEAN","STD"] + # table="C:\\Users\\usuario\\Documents\\ArcGIS\\m150326pcwbufB1.dbf" + # with arcpy.da.SearchCursor(table, zsFields) as cursor: + # for row in cursor: + # print(('Feature {0} has an area of {1}'.format(row[0], row[1])) +def zonalStatsMultiband(tableBaseName, bandNames, image, plot_polygons,id_field): + try: + tables=list() + #zonalStats iterated in all bands of the raster Multiband + #Return list of the name of the generated tables + arcpy.AddMessage("Iterating the zonal statistics across all bands...") + #Get bands + #Returns: [0]numberOfBands, [1]isBandNamesInteger? + numOfBands = getNumberOfBands(bandNames) + arcpy.AddMessage("Number of bands: "+str(numOfBands[0])) + #If the image has more than one band... + if numOfBands[0] != 1: + #If we only have the number of bands, but no band names (because there was no hdr)... + #We will call the bands by the default way ArcMap does: RasterName + Band_1, Band_2, etc... + #we create the list to iterate it + if numOfBands[1]: bandNames = list('Band_%s' % b for b in range(1, numOfBands[0]+1)) + #Here iterates through the bands and runs the zonal statistics tool + #Get the count with the function "enumerate" + for i, b in enumerate(bandNames, start=1): + #Next we add the band name to the raster basename + band = image + "\\" + b + arcpy.AddMessage("Iterating band # "+ str(i)+" - " + band) + #Set table name + statsTable = tableBaseName+"B" + str(i)+ ".dbf" #We add the band id to the StatsTable name + #arcpy.AddMessage("Willbe stored in table "+str(statsTable)) + #Here we call the method to do the zonal statistics + zs = arcpy.gp.ZonalStatisticsAsTable_sa(plot_polygons, id_field, band, statsTable, "DATA", "ALL") + arcpy.AddMessage("resulting stats table: " + str(zs)) + tables.append(plot_polygons+","+band+","+str(zs)) + else: + #If we have just one band we call the raster without specifying any band + zs = arcpy.gp.ZonalStatisticsAsTable_sa(plot_polygons, id_field, image, tableBaseName+"B1.dbf", "DATA", "ALL") + tables.append(plot_polygons+","+image+","+str(zs)) + arcpy.AddMessage("resulting zs table: " + str(zs)) + return tables + except Exception as e: + # If an error occurred, print( line number and error message + import traceback, sys + tb = sys.exc_info()[2] + arcpy.AddMessage("Line:" + str(tb.tb_lineno)) + arcpy.AddMessage("Error " + e.message) +def print_kwinfo(): + if deb==1: + print( "kwinfo se guarda:") + print( kwinfo) + +def create_if_not_exists(path): + try: + if not os.path.exists(path): + os.makedirs(path) + except Exception as e: + # If an error occurred, print( line number and error message + tb = sys.exc_info()[2] + arcpy.AddMessage("Line:" + str(tb.tb_lineno)) + arcpy.AddMessage("Error " + e.message) + +def exists(path): + exists = True if os.path.exists(path) else False + return exists + +def write_hdr_file(hdr_filename,assign_bands=1,mode="w"): + try: + hdr_file = open(hdr_filename, mode) + hdr_file.write("HEAD\nbands = "+ str(assign_bands)) + arcpy.AddMessage("writing in hdr file") + hdr_file.close() + except Exception as e: + # If an error occurred, print( line number and error message + import traceback, sys + tb = sys.exc_info()[2] + arcpy.AddMessage("Line:" + str(tb.tb_lineno)) + arcpy.AddMessage("Error " + e.message) + print( "OMG! Error while in: write_hdr_file") + raise e +def appendListToFile(txt_filename,thelist,mode="w"): + try: + file = open(txt_filename, mode) + #arcpy.AddMessage("writing in "+txt_filename+" file") + for item in thelist: + file.write("%s\n" % item) + file.close() + except Exception as e: + # If an error occurred, print( line number and error message + import traceback, sys + tb = sys.exc_info()[2] + arcpy.AddMessage("Line:" + str(tb.tb_lineno)) + arcpy.AddMessage("Error " + e.message) + print( "OMG! Error while in: appendListToFile") + raise e + +def replace_in_list(regex,lst): + return [re.sub(regex, '', x).strip() for x in lst] #List comprehension +#Method to concatenate the band name to the raster basename +#BandNames has or all the list of band names or the number of bands +def getNumberOfBands(bandNames): + try: + #If there was no bandnames or header, a band number was assigned + #This would be an integer + numOfBands = int(bandNames) + isANumber = True + return numOfBands, isANumber + except ValueError: + #If there is a band name as a string we will say it's one band + numOfBands = 1 + isANumber = False + return numOfBands, isANumber + except TypeError: + #If there are band names as list or array we will count them here + numOfBands = len(bandNames) + isANumber = False + return numOfBands, isANumber + except Exception as e: + import traceback, sys + tb = sys.exc_info()[2] + arcpy.AddMessage("Line:" + str(tb.tb_lineno)) + arcpy.AddMessage("Error " + e.message) + print( "OMG!") + raise e +def print_dict(dictio): + #The next line iterates, formats and prints the dictionary, key:value + #print( "\n".join('{}={}'.format(k,v) for k,v in dictio.items()) print( in console + arcpy.AddMessage("\n".join('{}={}'.format(k,v) for k,v in dictio.items()))#Print to arcmap results window +def readHeaderFile(header_file,assign_bands=1): + try: + if not exists(header_file): + write_hdr_file(header_file,assign_bands) + arcpy.AddMessage("Header not found, it was created a basic one. Number of bands assigned: "+str(assign_bands)) + arcpy.AddMessage("started reading header file:" + str(header_file)) + envi_header_keywords={"acquisition time","band names","bands", + "bbl","byte order","class lookup","class names", + "classes","cloud cover","complex function", + "coordinate system string","data gain values", + "data ignore value","data offset values", + "data reflectance gain values","data reflectance offset values", + "data type","default bands","default stretch","dem band", + "dem file","description","file type","fwhm","geo points", + "header offset","interleave","lines","map info", + "major frame offsets","minor frame offsets","pixel size", + "product type","projection info","read procedures", + "reflectance scale factor","rpc info","samples","security tag", + "sensor type","solar irradiance","spectra names","sun azimuth", + "sun elevation","wavelength","wavelength units","x start", + "y start","z plot average","z plot range","z plot titles"} + envi_header_keywords_or="|".join(envi_header_keywords) + # Read and Iterate over the lines of the file + with open(header_file, 'rt') as f: + data = f.read()[5:] #Skip the first "ENVI" letters of the header + lines = re.split(r"[\n]", data)#the info corresponding to one line + arcpy.AddMessage("reading header lines...") + dictio = {} + global deb + deb=0 + wl=[]#List to save wavelengths + bn=[]#List to save band names + clines=[]#complete lines with all the info corresponding to one header keyword + ongoing=0 #Variable para marcar si se está buscando el resto de la lĂ­nea de info + #to one header keyword is not in one single line + kwinfo="" + for i,l in enumerate(lines): + if deb==1: print( str(i)+"-l- "+str(l)) + kwinfo+=l + if deb==1: print( str(i)+"-kwinfo- "+str(kwinfo)) + if ongoing==1: + if "}" in l: + if deb==1: print( "ongoing==se cierra por fin el parentesis") + print_kwinfo() + clines.append(kwinfo) + kwinfo="" + ongoing=0 + continue + else: + if deb==1: print( "ongoing==1 else") + if ongoing==0: + ongoing=1 + for keyw in envi_header_keywords: + if keyw in l: + if deb==1: print( keyw+" attribute found") + if "{" in l: + if "}" in l: + if deb==1: print( "ongoing==parentesis cerrando en linea") + print_kwinfo() + clines.append(kwinfo) + kwinfo="" + ongoing=0 + else: + if deb==1: print( "if bracket in l: --- else") + else: + if deb==1: print( "ongoing==sin paretesis") + print_kwinfo() + clines.append(kwinfo) + kwinfo="" + ongoing=0 + if deb==1: print( "fin de ronda de for") + for cl in clines: + #expresion to filter how to sepatate the string. + regexpresion = re.compile(r"""(.+?)\s*=\s*(.+)""") + dictio.update(dict(regexpresion.findall(cl))) + if deb==1: print( regexpresion.findall(cl)) + for key in dictio: dictio[key]=dictio[key].strip() + print( "Header attributes:") + #arcpy.AddMessage("Header attributes read") + #print_dict(dictio) + expr_replace = re.compile(r"{|}") #Expression to remove brackets below + #wavelength + if "wavelength" in dictio: + wl=re.split(r"[,]", dictio["wavelength"]) + wl =replace_in_list(expr_replace,wl) + #arcpy.AddMessage(wl) + #arcpy.AddMessage("saved wl") + #Number of Bands + if "bands" in dictio: + hdr_bands=dictio["bands"] + #arcpy.AddMessage(hdr_bands) + #arcpy.AddMessage("saved Number of bands from header") + #Band names + if "band names" in dictio: + #arcpy.AddMessage("band names attribute found") + bn=re.split(r"[,]", dictio["band names"]) + #arcpy.AddMessage("split completed") + bn=replace_in_list(expr_replace,bn) + #arcpy.AddMessage("saved bn") + #Next we add the wl units only if they are defined + #arcpy.AddMessage("Format composed band name") + full_bn=hdr_bands #We assign by default the number of bands in the raster + if "wavelength units" in dictio: + if "band names" in dictio and "wavelength" in dictio: + join_str = '{bandn} ({waveln} '+dictio["wavelength units"]+')' if dictio["wavelength units"]!="Unknown" else '{bandn} ({waveln})' + #arcpy.AddMessage("bn and wl found") + full_bn= '\n'.join(join_str.format(bandn=b, waveln=w) for b,w in itertools.izip(bn, wl)).split('\n') + if not "band names" in dictio and "wavelength" in dictio: + join_str = '{waveln} '+dictio["wavelength units"] if dictio["wavelength units"]!="Unknown" else '{waveln}' + #arcpy.AddMessage("wl found+units") + full_bn= '\n'.join(join_str.format(waveln=w) for w in wl).split('\n') + else: + if "band names" in dictio and "wavelength" in dictio: + join_str = '{bandn} ({waveln})' + #arcpy.AddMessage("bn and wl found") + full_bn= '\n'.join(join_str.format(bandn=b, waveln=w) for b,w in itertools.izip(bn, wl)).split('\n') + if not "band names" in dictio and "wavelength" in dictio: + #arcpy.AddMessage("wl found") + full_bn= wl + #arcpy.AddMessage(full_bn) + if deb==1: + arcpy.AddMessage( full_bn) + arcpy.AddMessage( "Bands descripted in header: "+len(full_bn)) + #CHECK IF the "data ignore value is set", otherwise, set it to 0. + if not "data ignore value" in dictio: + arcpy.AddMessage("Set -data ignore value to 0-") + add=["data ignore value = 0"] + appendListToFile(header_file,add,mode="a") + #arcpy.AddMessage("Finished reading header") + #arcpy.AddMessage(type(full_bn)) #to know the type of variable it was created + return full_bn + except Exception as e: + # If an error occurred, print( line number and error message + import traceback, sys + tb = sys.exc_info()[2] + arcpy.AddMessage("Line:" + str(tb.tb_lineno)) + arcpy.AddMessage("Error " + e.message) + print( "OMG!") + raise e +#^^^^^^^^^^^^^^^^^^^^^^^ Methods ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +try: + # Check out any necessary licenses and Create the Geoprocessor object + arcpy.CheckOutExtension("spatial") + gp = arcgisscripting.create() + ## START:INPUTS ## + ##Hard coded this + #csvExtractThis = "C:\\Dropbox\\data\\AD\\AD_BW\\pre_data\\bw_dates_areas_bw_EYT_bwh.csv" ########################## + csvExtractThis = "C:\\Dropbox\\data\\AD\\AD-AF\\data\\ad_aff_extract.csv" ########################## + #imgWs="G:\\AD15_16\\" #imageBaseLocation + imgWs="G:\\AD15_16\\AF\\Processed\\CompB_geo\\" #test ################################### + ##GDB whare the feature classes are stored and where the tables will be written: + #gdb = "C:\\Dropbox\\data\\AD\\AD_BW\\shp\\ad_bw.gdb\\" #BW + #gdb = "C:\\Users\\usuario\\Documents\\ArcGIS\\Default.gdb\\" #Default + gdb = "C:\\Dropbox\\data\\AD\\AD-AF\\shp\\ad_af.gdb\\" + ##tws = where to save tables inside the workspace + #tws ="C:\\Dropbox\\data\\AD\\AD_BW\\shp\\pre_data\\extract\\" For BW ######Not worinkg... it save in shp folder####### + tws ="C:\\Dropbox\\data\\AD\\AD-AF\\shp\\ad_af.gdb\\" #For AF ######Not worinkg... it save in shp folder####### + #ds="extract" #Use this fro m and t ############################### + #ds="h" #Use this for hyper + ds = "" #no ds + arcpy.env.workspace = gdb + #knownBands="62" + knownBands="4" # ####################### + #id_field = "id" ##################### + id_field = "plot_trial" ##################### + log= "C:\\Dropbox\\data\\AD\\AD-AF\\data\\ad_aff_log.txt" ############################ + ## END:INPUTS ## + + ## START:VARIABLES ## + #Get all feature classes gdb/extract dataset + listFc = arcpy.ListFeatureClasses( + feature_type="Polygon", + feature_dataset=ds) + mosaicFolder = { + #Get the folder name used and suffix for the images according to the camera + "m": ("geo","geo"), + "h": ("rfl_spr_ort","ort"), + "t": ("cel","cel"), + "a": ("cel","cel"), + "c": ("geo","geo"), + } + arcpy.env.workspace = gdb + ext=".bsq" #extension of the rasters + buf="bfs" #we use the buffered plot features to extract the stats ############# + bw8="_af" #default trial #################################### + ## END:VARIABLES ## + ex, dates,trials = readWhatToExtract(csvExtractThis) + #find the image paths and features to perform the zonal statistics + for f, flightdate in enumerate(ex): + for t,trial in enumerate(flightdate): #skip the date column + for cam in trial.split(): + if cam !="-": + #QuĂ© hacer si está vacĂ­o? + #ZONES FEATURE + #Search for the corresponding grids for this date/trial/cam + #if date is __ad__ the grid is the general for the cycle e.g. ypt__ad__buf + #if date is YYMMDD the grid was adjusted, and needs to have indicated also the camera + # this way we can know at which image the adjustment corresponds. + # grids adjusted will be like bw1160215bufh -> with the date and camera indicated + pattern = str(trials[t])+"("+str(dates[f])+'|__ad__)'+buf+'['+cam+']*$' + arcpy.AddMessage("pattern: "+str(pattern)) + #arcpy.AddMessage("FList: "+str(listFc)) + plotsF = [ft for ft in listFc if re.search(pattern, ft)] + if plotsF: + if len(plotsF)>1: plotsF=plotsF[0] #get just the edited feature if the general exists + plotsF=os.path.join(arcpy.env.workspace, ds, ''.join(plotsF)) + arcpy.AddMessage("Zones: "+plotsF) + #RASTER + #search for the images that match them + # hyper mosaics folder is rfl_spr_ort, for multi is geo, for thermal is cel + mF, sx = mosaicFolder.get(cam,("geo","geo")) #corresponding folder and sfx + imgLocation = imgWs ###################################### + imgName =cam+str(dates[f])+str(trials[t])+sx+ext + img=imgLocation+imgName + if not exists(img): #Use the general mosaic + imgName =cam+str(dates[f])+bw8+sx+ext + img=imgLocation+imgName + if exists(img): + arcpy.AddMessage("Raster: "+img) + #PREPARE FOR THE ZS + #Reading header to get Bands + #We assume header file is there and named as the raster + hdr_file = img[:-4]+".hdr" + #Read header to know number and name of bands or set them a priori + #knownBands is optional + bandNames = readHeaderFile(hdr_file, knownBands) + tableBaseName = arcpy.ValidateTableName(cam+str(dates[f])+str(trials[t])+buf,tws) #stats table base name in ws + #arcpy.AddMessage("zs") + #arcpy.AddMessage("tb:"+tableBaseName) + zsm = zonalStatsMultiband(tableBaseName, bandNames, img, plotsF,id_field) + appendListToFile(log,zsm,mode="a") + else: + arcpy.AddMessage("|||No raster to use |||") + appendListToFile(log,str(img),mode="a") + else: + arcpy.AddMessage("|||No features to use|||") + appendListToFile(log,str(plotsF),mode="a") + else: arcpy.AddMessage("No data extracting for this trial on this date") + appendListToFile(log,("Finished",time.strftime("%Y/%m/%d %H:%M:%S")),mode="a") #Write finish time in log +except Exception as e: + # If an error occurred, print( line number and error message + import traceback, sys + tb = sys.exc_info()[2] + arcpy.AddMessage("Line:" + str(tb.tb_lineno)+ " Error " + e.message) \ No newline at end of file diff --git a/ArcGIS/zonalStatsTablesAutoInputsCSV_GS.py b/ArcGIS/zonalStatsTablesAutoInputsCSV_GS.py new file mode 100644 index 0000000..996325b --- /dev/null +++ b/ArcGIS/zonalStatsTablesAutoInputsCSV_GS.py @@ -0,0 +1,413 @@ +#Perform zonal statistics tool based on a list of on a specified list of dates and names. +#Automatically calls the appropriate raster and zonal data from within the selected folder. + +#Tuned for extracting data to match with the GreenSeeker +#################################WARNING +#crashes if the csv has just one row + +import numpy +import os +import arcpy, arcgisscripting +import itertools +import re +import traceback, sys +import time + +#Read the file to know which feature-raster to extract +def readWhatToExtract(csvWhatToExtract): + try: + arcpy.AddMessage("will read file: "+csvWhatToExtract) + extractMatrix=numpy.genfromtxt(open(csvWhatToExtract,"rb"), + delimiter=',', + dtype=None, + names=True) + dates = list(extractMatrix['date']) #get the dates + arcpy.AddMessage("Dates: "+str(dates)) + trials=extractMatrix.dtype.names[1:] + arcpy.AddMessage("Trials: "+str(trials)) + extractMatrix = extractMatrix[list(extractMatrix.dtype.names)[1:]]#remove "date" column (1st) + arcpy.AddMessage("What to extract: "+str(extractMatrix)) + return extractMatrix,dates,trials + except Exception as e: + # If an error occurred, print( line number and error message + import traceback, sys + tb = sys.exc_info()[2] + arcpy.AddMessage("Line:" + str(tb.tb_lineno)) + arcpy.AddMessage("Error " + e.message) + + #Return the matrix of the kind of images acquired on each day on each trial [h m t], + #the list of dates and the list of trials (removing the "date" item) +def readDbf():# read dbf file + print("Algo haremos aquĂ­")# zsFields= ["id","COUNT","MEAN","STD"] + # table="C:\\Users\\usuario\\Documents\\ArcGIS\\m150326pcwbufB1.dbf" + # with arcpy.da.SearchCursor(table, zsFields) as cursor: + # for row in cursor: + # print(('Feature {0} has an area of {1}'.format(row[0], row[1])) +def zonalStatsMultiband(tableBaseName, bandNames, image, plot_polygons,id_field): + try: + tables=list() + #zonalStats iterated in all bands of the raster Multiband + #Return list of the name of the generated tables + arcpy.AddMessage("Iterating the zonal statistics across all bands...") + #Get bands + #Returns: [0]numberOfBands, [1]isBandNamesInteger? + numOfBands = getNumberOfBands(bandNames) + arcpy.AddMessage("Number of bands: "+str(numOfBands[0])) + #If the image has more than one band... + if numOfBands[0] != 1: + #If we only have the number of bands, but no band names (because there was no hdr)... + #We will call the bands by the default way ArcMap does: RasterName + Band_1, Band_2, etc... + #we create the list to iterate it + if numOfBands[1]: bandNames = list('Band_%s' % b for b in range(1, numOfBands[0]+1)) + #Here iterates through the bands and runs the zonal statistics tool + #Get the count with the function "enumerate" + for i, b in enumerate(bandNames, start=1): + #Next we add the band name to the raster basename + band = image + "\\" + b + arcpy.AddMessage("Iterating band # "+ str(i)+" - " + band) + #Set table name + statsTable = tableBaseName+"B" + str(i)+ ".dbf" #We add the band id to the StatsTable name + #arcpy.AddMessage("Willbe stored in table "+str(statsTable)) + #Here we call the method to do the zonal statistics + zs = arcpy.gp.ZonalStatisticsAsTable_sa(plot_polygons, id_field, band, statsTable, "DATA", "ALL") + arcpy.AddMessage("resulting stats table: " + str(zs)) + tables.append(plot_polygons+","+band+","+str(zs)) + else: + #If we have just one band we call the raster without specifying any band + zs = arcpy.gp.ZonalStatisticsAsTable_sa(plot_polygons, id_field, image, tableBaseName+"B1.dbf", "DATA", "ALL") + tables.append(plot_polygons+","+image+","+str(zs)) + arcpy.AddMessage("||| resulting zs table: " + str(zs)) + return tables + except Exception as e: + # If an error occurred, print( line number and error message + import traceback, sys + tb = sys.exc_info()[2] + arcpy.AddMessage("Line:" + str(tb.tb_lineno)) + arcpy.AddMessage("Error " + e.message) +def print_kwinfo(): + if deb==1: + print( "kwinfo se guarda:") + print( kwinfo) + +def create_if_not_exists(path): + try: + if not os.path.exists(path): + os.makedirs(path) + except Exception as e: + # If an error occurred, print( line number and error message + tb = sys.exc_info()[2] + arcpy.AddMessage("Line:" + str(tb.tb_lineno)) + arcpy.AddMessage("Error " + e.message) + +def exists(path): + exists = True if os.path.exists(path) else False + return exists + +def write_hdr_file(hdr_filename,assign_bands=1,mode="w"): + try: + hdr_file = open(hdr_filename, mode) + hdr_file.write("HEAD\nbands = "+ str(assign_bands)) + arcpy.AddMessage("writing in hdr file") + hdr_file.close() + except Exception as e: + # If an error occurred, print( line number and error message + import traceback, sys + tb = sys.exc_info()[2] + arcpy.AddMessage("Line:" + str(tb.tb_lineno)) + arcpy.AddMessage("Error " + e.message) + print( "OMG! Error while in: write_hdr_file") + raise e +def appendListToFile(txt_filename,thelist,mode="w"): + try: + file = open(txt_filename, mode) + #arcpy.AddMessage("writing in "+txt_filename+" file") + for item in thelist: + file.write("%s" % item) #################### + file.close() + except Exception as e: + # If an error occurred, print( line number and error message + import traceback, sys + tb = sys.exc_info()[2] + arcpy.AddMessage("Line:" + str(tb.tb_lineno)) + arcpy.AddMessage("Error " + e.message) + print( "OMG! Error while in: appendListToFile") + raise e + +def replace_in_list(regex,lst): + return [re.sub(regex, '', x).strip() for x in lst] #List comprehension +#Method to concatenate the band name to the raster basename +#BandNames has or all the list of band names or the number of bands +def getNumberOfBands(bandNames): + try: + #If there was no bandnames or header, a band number was assigned + #This would be an integer + numOfBands = int(bandNames) + isANumber = True + return numOfBands, isANumber + except ValueError: + #If there is a band name as a string we will say it's one band + numOfBands = 1 + isANumber = False + return numOfBands, isANumber + except TypeError: + #If there are band names as list or array we will count them here + numOfBands = len(bandNames) + isANumber = False + return numOfBands, isANumber + except Exception as e: + import traceback, sys + tb = sys.exc_info()[2] + arcpy.AddMessage("Line:" + str(tb.tb_lineno)) + arcpy.AddMessage("Error " + e.message) + print( "OMG!") + raise e +def print_dict(dictio): + #The next line iterates, formats and prints the dictionary, key:value + #print( "\n".join('{}={}'.format(k,v) for k,v in dictio.items()) print( in console + arcpy.AddMessage("\n".join('{}={}'.format(k,v) for k,v in dictio.items()))#Print to arcmap results window +def readHeaderFile(header_file,assign_bands=1): + try: + if not exists(header_file): + write_hdr_file(header_file,assign_bands) + arcpy.AddMessage("Header not found, it was created a basic one. Number of bands assigned: "+str(assign_bands)) + arcpy.AddMessage("started reading header file:" + str(header_file)) + envi_header_keywords={"acquisition time","band names","bands", + "bbl","byte order","class lookup","class names", + "classes","cloud cover","complex function", + "coordinate system string","data gain values", + "data ignore value","data offset values", + "data reflectance gain values","data reflectance offset values", + "data type","default bands","default stretch","dem band", + "dem file","description","file type","fwhm","geo points", + "header offset","interleave","lines","map info", + "major frame offsets","minor frame offsets","pixel size", + "product type","projection info","read procedures", + "reflectance scale factor","rpc info","samples","security tag", + "sensor type","solar irradiance","spectra names","sun azimuth", + "sun elevation","wavelength","wavelength units","x start", + "y start","z plot average","z plot range","z plot titles"} + envi_header_keywords_or="|".join(envi_header_keywords) + # Read and Iterate over the lines of the file + with open(header_file, 'rt') as f: + data = f.read()[5:] #Skip the first "ENVI" letters of the header + lines = re.split(r"[\n]", data)#the info corresponding to one line + arcpy.AddMessage("reading header lines...") + dictio = {} + global deb + deb=0 + wl=[]#List to save wavelengths + bn=[]#List to save band names + clines=[]#complete lines with all the info corresponding to one header keyword + ongoing=0 #Variable para marcar si se está buscando el resto de la lĂ­nea de info + #to one header keyword is not in one single line + kwinfo="" + for i,l in enumerate(lines): + if deb==1: print( str(i)+"-l- "+str(l)) + kwinfo+=l + if deb==1: print( str(i)+"-kwinfo- "+str(kwinfo)) + if ongoing==1: + if "}" in l: + if deb==1: print( "ongoing==se cierra por fin el parentesis") + print_kwinfo() + clines.append(kwinfo) + kwinfo="" + ongoing=0 + continue + else: + if deb==1: print( "ongoing==1 else") + if ongoing==0: + ongoing=1 + for keyw in envi_header_keywords: + if keyw in l: + if deb==1: print( keyw+" attribute found") + if "{" in l: + if "}" in l: + if deb==1: print( "ongoing==parentesis cerrando en linea") + print_kwinfo() + clines.append(kwinfo) + kwinfo="" + ongoing=0 + else: + if deb==1: print( "if bracket in l: --- else") + else: + if deb==1: print( "ongoing==sin paretesis") + print_kwinfo() + clines.append(kwinfo) + kwinfo="" + ongoing=0 + if deb==1: print( "fin de ronda de for") + for cl in clines: + #expresion to filter how to sepatate the string. + regexpresion = re.compile(r"""(.+?)\s*=\s*(.+)""") + dictio.update(dict(regexpresion.findall(cl))) + if deb==1: print( regexpresion.findall(cl)) + for key in dictio: dictio[key]=dictio[key].strip() + print( "Header attributes:") + arcpy.AddMessage("Header attributes read:") + print_dict(dictio) + expr_replace = re.compile(r"{|}") #Expression to remove brackets below + #wavelength + if "wavelength" in dictio: + wl=re.split(r"[,]", dictio["wavelength"]) + wl =replace_in_list(expr_replace,wl) + #arcpy.AddMessage(wl) + #arcpy.AddMessage("saved wl") + #Number of Bands + if "bands" in dictio: + hdr_bands=dictio["bands"] + #arcpy.AddMessage(hdr_bands) + #arcpy.AddMessage("saved Number of bands from header") + #Band names + if "band names" in dictio: + #arcpy.AddMessage("band names attribute found") + bn=re.split(r"[,]", dictio["band names"]) + #arcpy.AddMessage("split completed") + bn=replace_in_list(expr_replace,bn) + #arcpy.AddMessage("saved bn") + #Next we add the wl units only if they are defined + #arcpy.AddMessage("Format composed band name") + full_bn=hdr_bands #We assign by default the number of bands in the raster + if "wavelength units" in dictio: + if "band names" in dictio and "wavelength" in dictio: + join_str = '{bandn} ({waveln} '+dictio["wavelength units"]+')' if dictio["wavelength units"]!="Unknown" else '{bandn} ({waveln})' + arcpy.AddMessage("bn and wl found") + full_bn= '\n'.join(join_str.format(bandn=b, waveln=w) for b,w in itertools.izip(bn, wl)).split('\n') + if not "band names" in dictio and "wavelength" in dictio: + join_str = '{waveln} '+dictio["wavelength units"] if dictio["wavelength units"]!="Unknown" else '{waveln}' + arcpy.AddMessage("wl found+units") + full_bn= '\n'.join(join_str.format(waveln=w) for w in wl).split('\n') + else: + if "band names" in dictio and "wavelength" in dictio: + join_str = '{bandn} ({waveln})' + arcpy.AddMessage("bn and wl found") + full_bn= '\n'.join(join_str.format(bandn=b, waveln=w) for b,w in itertools.izip(bn, wl)).split('\n') + if not "band names" in dictio and "wavelength" in dictio: + arcpy.AddMessage("wl found") + full_bn= wl + arcpy.AddMessage("Data got from the Hdr"+str(full_bn)) + + if deb==1: + arcpy.AddMessage( full_bn) + arcpy.AddMessage( "Bands descripted in header: "+len(full_bn)) + #CHECK IF the "data ignore value is set", otherwise, set it to 0. + if not "data ignore value" in dictio: + arcpy.AddMessage("Set -data ignore value to 0-") + add=["data ignore value = 0"] + appendListToFile(header_file,add,mode="a") + #arcpy.AddMessage("Finished reading header") + #arcpy.AddMessage(type(full_bn)) #to know the type of variable it was created + return full_bn + except Exception as e: + # If an error occurred, print( line number and error message + import traceback, sys + tb = sys.exc_info()[2] + arcpy.AddMessage("Line:" + str(tb.tb_lineno)) + arcpy.AddMessage("Error " + e.message) + print( "OMG!") + raise e +#^^^^^^^^^^^^^^^^^^^^^^^ Methods ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +try: + # Check out any necessary licenses and Create the Geoprocessor object + arcpy.CheckOutExtension("spatial") + gp = arcgisscripting.create() + ## START:INPUTS ## + ##Hard coded this + #csvExtractThis = "C:\\Dropbox\\data\\AD\\AD_BW\\pre_data\\bw_dates_areas_bw_EYT_bwh.csv" ########################## + csvExtractThis = "C:\\Dropbox\\data\\AD\\AD_seed\\pre-data\\ad_extract_m_sed.csv" ########################## + #imgWs="G:\\AD15_16\\" #imageBaseLocation + imgWs="G:\\AD15_16\\" #test ################################### + ##GDB whare the feature classes are stored and where the tables will be written: + #gdb = "C:\\Dropbox\\data\\AD\\AD_BW\\shp\\ad_bw.gdb\\" #BW + #gdb = "C:\\Users\\usuario\\Documents\\ArcGIS\\Default.gdb\\" #Default + gdb = "C:\\Dropbox\\data\\AD\\AD_seed\\shp\\ad_sed.gdb\\" + ##tws = where to save tables inside the workspace + #tws ="C:\\Dropbox\\data\\AD\\AD_BW\\shp\\pre_data\\extract\\" For BW ######Not worinkg... it save in shp folder####### + tws ="C:\\Dropbox\\data\\AD\\AD_seed\\shp\\ad_sed.gdb\\" #For AF ######Not worinkg... it save in shp folder####### + #ds="extract" #Use this fro m and t ############################### + #ds="h" #Use this for hyper + ds = "" #no ds + arcpy.env.workspace = gdb + #knownBands="62" + knownBands="6" # ####################### + #id_field = "id" ##################### + id_field = "id" ##################### + log= "C:\\Dropbox\\data\\AD\\AD_seed\\pre-data\\ad_sed_extract_log.txt" ############################ + ## END:INPUTS ## + + ## START:VARIABLES ## + #Get all feature classes gdb/extract dataset + listFc = arcpy.ListFeatureClasses( + feature_type="Polygon", + feature_dataset=ds) + mosaicFolder = { + #Get the folder name used and suffix for the images according to the camera + #camera: folder, suffix + "m": ("msk","mkd"), + "h": ("rfl_spr_ort","ort"), + "t": ("cel","cel"), + "a": ("cel","cel"), + "c": ("ndvi_geo","nvi"), #specific dor this time + } + arcpy.env.workspace = gdb + ext=".bsq" #extension of the rasters + buf="plt" #we use the buffered plot features to extract the stats ############# + bw8="sed" #default trial #################################### + ## END:VARIABLES ## + ex, dates,trials = readWhatToExtract(csvExtractThis) + #find the image paths and features to perform the zonal statistics + for f, flightdate in enumerate(ex): + for t,trial in enumerate(flightdate): #skip the date column + for cam in trial.split(): + if cam !="-": + #QuĂ© hacer si está vacĂ­o? + #ZONES FEATURE + #Search for the corresponding grids for this date/trial/cam + #if date is __ad__ the grid is the general for the cycle e.g. ypt__ad__buf + #if date is YYMMDD the grid was adjusted, and needs to have indicated also the camera + # this way we can know at which image the adjustment corresponds. + # grids adjusted will be like bw1160215bufh -> with the date and camera indicated + pattern = str(trials[t])+"("+str(dates[f])+'|__ad__)'+buf+'['+cam+']*$' + arcpy.AddMessage("pattern: "+str(pattern)) + #arcpy.AddMessage("FList: "+str(listFc)) + plotsF = [ft for ft in listFc if re.search(pattern, ft)] + if plotsF: + if len(plotsF)>1: plotsF=plotsF[0] #get just the edited feature if the general exists + plotsF=os.path.join(arcpy.env.workspace, ds, ''.join(plotsF)) + arcpy.AddMessage("Zones: "+plotsF) + #RASTER + #search for the images that match them + # hyper mosaics folder is rfl_spr_ort, for multi is geo, for thermal is cel + mF, sx = mosaicFolder.get(cam,("geo","geo")) #corresponding folder and sfx + imgLocation = imgLocation = imgWs+str(dates[f])+"\\"+cam+"\\"+mF+"\\" + imgName =cam+str(dates[f])+str(trials[t])+sx+ext + img=imgLocation+imgName + if not exists(img): #Use the general mosaic + imgName =cam+str(dates[f])+bw8+sx+ext + img=imgLocation+imgName + arcpy.AddMessage("Raster to use: "+img) + if exists(img): + #PREPARE FOR THE ZS + #Reading header to get Bands + #We assume header file is there and named as the raster + hdr_file = img[:-4]+".hdr" + #Read header to know number and name of bands or set them a priori + #knownBands is optional + bandNames = readHeaderFile(hdr_file, knownBands) + tableBaseName = arcpy.ValidateTableName(cam+str(dates[f])+str(trials[t])+buf,tws) #stats table base name in ws + #arcpy.AddMessage("zs") + #arcpy.AddMessage("tb:"+tableBaseName) + zsm = zonalStatsMultiband(tableBaseName, bandNames, img, plotsF,id_field) + appendListToFile(log,zsm,mode="a") + else: + arcpy.AddMessage("|||No raster to use |||") + appendListToFile(log,str(img),mode="a") + else: + arcpy.AddMessage("|||No features to use|||") + appendListToFile(log,str(plotsF),mode="a") + else: arcpy.AddMessage("No data extracting for this trial on this date") + appendListToFile(log,("Finished",time.strftime("%Y/%m/%d %H:%M:%S")),mode="a") #Write finish time in log +except Exception as e: + # If an error occurred, print( line number and error message + import traceback, sys + tb = sys.exc_info()[2] + arcpy.AddMessage("Line:" + str(tb.tb_lineno)+ " Error " + e.message) \ No newline at end of file diff --git a/ArcGIS/zonalStatsTablesAutoInputsCSV_sam_ad.py b/ArcGIS/zonalStatsTablesAutoInputsCSV_sam_ad.py new file mode 100644 index 0000000..c9c409c --- /dev/null +++ b/ArcGIS/zonalStatsTablesAutoInputsCSV_sam_ad.py @@ -0,0 +1,413 @@ +#Perform zonal statistics tool based on a list of on a specified list of dates and names. +#Automatically calls the appropriate raster and zonal data from within the selected folder. + +#Tuned for extracting data to match with the GreenSeeker +#################################WARNING +#crashes if the csv has just one row + +import numpy +import os +import arcpy, arcgisscripting +import itertools +import re +import traceback, sys +import time + +#Read the file to know which feature-raster to extract +def readWhatToExtract(csvWhatToExtract): + try: + arcpy.AddMessage("will read file: "+csvWhatToExtract) + extractMatrix=numpy.genfromtxt(open(csvWhatToExtract,"rb"), + delimiter=',', + dtype=None, + names=True) + dates = list(extractMatrix['date']) #get the dates + arcpy.AddMessage("Dates: "+str(dates)) + trials=extractMatrix.dtype.names[1:] + arcpy.AddMessage("Trials: "+str(trials)) + extractMatrix = extractMatrix[list(extractMatrix.dtype.names)[1:]]#remove "date" column (1st) + arcpy.AddMessage("What to extract: "+str(extractMatrix)) + return extractMatrix,dates,trials + except Exception as e: + # If an error occurred, print( line number and error message + import traceback, sys + tb = sys.exc_info()[2] + arcpy.AddMessage("Line:" + str(tb.tb_lineno)) + arcpy.AddMessage("Error " + e.message) + + #Return the matrix of the kind of images acquired on each day on each trial [h m t], + #the list of dates and the list of trials (removing the "date" item) +def readDbf():# read dbf file + print("Algo haremos aquĂ­")# zsFields= ["id","COUNT","MEAN","STD"] + # table="C:\\Users\\usuario\\Documents\\ArcGIS\\m150326pcwbufB1.dbf" + # with arcpy.da.SearchCursor(table, zsFields) as cursor: + # for row in cursor: + # print(('Feature {0} has an area of {1}'.format(row[0], row[1])) +def zonalStatsMultiband(tableBaseName, bandNames, image, plot_polygons,id_field): + try: + tables=list() + #zonalStats iterated in all bands of the raster Multiband + #Return list of the name of the generated tables + arcpy.AddMessage("Iterating the zonal statistics across all bands...") + #Get bands + #Returns: [0]numberOfBands, [1]isBandNamesInteger? + numOfBands = getNumberOfBands(bandNames) + arcpy.AddMessage("Number of bands: "+str(numOfBands[0])) + #If the image has more than one band... + if numOfBands[0] != 1: + #If we only have the number of bands, but no band names (because there was no hdr)... + #We will call the bands by the default way ArcMap does: RasterName + Band_1, Band_2, etc... + #we create the list to iterate it + if numOfBands[1]: bandNames = list('Band_%s' % b for b in range(1, numOfBands[0]+1)) + #Here iterates through the bands and runs the zonal statistics tool + #Get the count with the function "enumerate" + for i, b in enumerate(bandNames, start=1): + #Next we add the band name to the raster basename + band = image + "\\" + b + arcpy.AddMessage("Iterating band # "+ str(i)+" - " + band) + #Set table name + statsTable = tableBaseName+"B" + str(i)+ ".dbf" #We add the band id to the StatsTable name + #arcpy.AddMessage("Willbe stored in table "+str(statsTable)) + #Here we call the method to do the zonal statistics + zs = arcpy.gp.ZonalStatisticsAsTable_sa(plot_polygons, id_field, band, statsTable, "DATA", "ALL") + arcpy.AddMessage("resulting stats table: " + str(zs)) + tables.append(plot_polygons+","+band+","+str(zs)) + else: + #If we have just one band we call the raster without specifying any band + zs = arcpy.gp.ZonalStatisticsAsTable_sa(plot_polygons, id_field, image, tableBaseName+"B1.dbf", "DATA", "ALL") + tables.append(plot_polygons+","+image+","+str(zs)) + arcpy.AddMessage("||| resulting zs table: " + str(zs)) + return tables + except Exception as e: + # If an error occurred, print( line number and error message + import traceback, sys + tb = sys.exc_info()[2] + arcpy.AddMessage("Line:" + str(tb.tb_lineno)) + arcpy.AddMessage("Error " + e.message) +def print_kwinfo(): + if deb==1: + print( "kwinfo se guarda:") + print( kwinfo) + +def create_if_not_exists(path): + try: + if not os.path.exists(path): + os.makedirs(path) + except Exception as e: + # If an error occurred, print( line number and error message + tb = sys.exc_info()[2] + arcpy.AddMessage("Line:" + str(tb.tb_lineno)) + arcpy.AddMessage("Error " + e.message) + +def exists(path): + exists = True if os.path.exists(path) else False + return exists + +def write_hdr_file(hdr_filename,assign_bands=1,mode="w"): + try: + hdr_file = open(hdr_filename, mode) + hdr_file.write("HEAD\nbands = "+ str(assign_bands)) + arcpy.AddMessage("writing in hdr file") + hdr_file.close() + except Exception as e: + # If an error occurred, print( line number and error message + import traceback, sys + tb = sys.exc_info()[2] + arcpy.AddMessage("Line:" + str(tb.tb_lineno)) + arcpy.AddMessage("Error " + e.message) + print( "OMG! Error while in: write_hdr_file") + raise e +def appendListToFile(txt_filename,thelist,mode="w"): + try: + file = open(txt_filename, mode) + #arcpy.AddMessage("writing in "+txt_filename+" file") + for item in thelist: + file.write("%s" % item) #################### + file.close() + except Exception as e: + # If an error occurred, print( line number and error message + import traceback, sys + tb = sys.exc_info()[2] + arcpy.AddMessage("Line:" + str(tb.tb_lineno)) + arcpy.AddMessage("Error " + e.message) + print( "OMG! Error while in: appendListToFile") + raise e + +def replace_in_list(regex,lst): + return [re.sub(regex, '', x).strip() for x in lst] #List comprehension +#Method to concatenate the band name to the raster basename +#BandNames has or all the list of band names or the number of bands +def getNumberOfBands(bandNames): + try: + #If there was no bandnames or header, a band number was assigned + #This would be an integer + numOfBands = int(bandNames) + isANumber = True + return numOfBands, isANumber + except ValueError: + #If there is a band name as a string we will say it's one band + numOfBands = 1 + isANumber = False + return numOfBands, isANumber + except TypeError: + #If there are band names as list or array we will count them here + numOfBands = len(bandNames) + isANumber = False + return numOfBands, isANumber + except Exception as e: + import traceback, sys + tb = sys.exc_info()[2] + arcpy.AddMessage("Line:" + str(tb.tb_lineno)) + arcpy.AddMessage("Error " + e.message) + print( "OMG!") + raise e +def print_dict(dictio): + #The next line iterates, formats and prints the dictionary, key:value + #print( "\n".join('{}={}'.format(k,v) for k,v in dictio.items()) print( in console + arcpy.AddMessage("\n".join('{}={}'.format(k,v) for k,v in dictio.items()))#Print to arcmap results window +def readHeaderFile(header_file,assign_bands=1): + try: + if not exists(header_file): + write_hdr_file(header_file,assign_bands) + arcpy.AddMessage("Header not found, it was created a basic one. Number of bands assigned: "+str(assign_bands)) + arcpy.AddMessage("started reading header file:" + str(header_file)) + envi_header_keywords={"acquisition time","band names","bands", + "bbl","byte order","class lookup","class names", + "classes","cloud cover","complex function", + "coordinate system string","data gain values", + "data ignore value","data offset values", + "data reflectance gain values","data reflectance offset values", + "data type","default bands","default stretch","dem band", + "dem file","description","file type","fwhm","geo points", + "header offset","interleave","lines","map info", + "major frame offsets","minor frame offsets","pixel size", + "product type","projection info","read procedures", + "reflectance scale factor","rpc info","samples","security tag", + "sensor type","solar irradiance","spectra names","sun azimuth", + "sun elevation","wavelength","wavelength units","x start", + "y start","z plot average","z plot range","z plot titles"} + envi_header_keywords_or="|".join(envi_header_keywords) + # Read and Iterate over the lines of the file + with open(header_file, 'rt') as f: + data = f.read()[5:] #Skip the first "ENVI" letters of the header + lines = re.split(r"[\n]", data)#the info corresponding to one line + arcpy.AddMessage("reading header lines...") + dictio = {} + global deb + deb=0 + wl=[]#List to save wavelengths + bn=[]#List to save band names + clines=[]#complete lines with all the info corresponding to one header keyword + ongoing=0 #Variable para marcar si se está buscando el resto de la lĂ­nea de info + #to one header keyword is not in one single line + kwinfo="" + for i,l in enumerate(lines): + if deb==1: print( str(i)+"-l- "+str(l)) + kwinfo+=l + if deb==1: print( str(i)+"-kwinfo- "+str(kwinfo)) + if ongoing==1: + if "}" in l: + if deb==1: print( "ongoing==se cierra por fin el parentesis") + print_kwinfo() + clines.append(kwinfo) + kwinfo="" + ongoing=0 + continue + else: + if deb==1: print( "ongoing==1 else") + if ongoing==0: + ongoing=1 + for keyw in envi_header_keywords: + if keyw in l: + if deb==1: print( keyw+" attribute found") + if "{" in l: + if "}" in l: + if deb==1: print( "ongoing==parentesis cerrando en linea") + print_kwinfo() + clines.append(kwinfo) + kwinfo="" + ongoing=0 + else: + if deb==1: print( "if bracket in l: --- else") + else: + if deb==1: print( "ongoing==sin paretesis") + print_kwinfo() + clines.append(kwinfo) + kwinfo="" + ongoing=0 + if deb==1: print( "fin de ronda de for") + for cl in clines: + #expresion to filter how to sepatate the string. + regexpresion = re.compile(r"""(.+?)\s*=\s*(.+)""") + dictio.update(dict(regexpresion.findall(cl))) + if deb==1: print( regexpresion.findall(cl)) + for key in dictio: dictio[key]=dictio[key].strip() + print( "Header attributes:") + arcpy.AddMessage("Header attributes read:") + print_dict(dictio) + expr_replace = re.compile(r"{|}") #Expression to remove brackets below + #wavelength + if "wavelength" in dictio: + wl=re.split(r"[,]", dictio["wavelength"]) + wl =replace_in_list(expr_replace,wl) + #arcpy.AddMessage(wl) + #arcpy.AddMessage("saved wl") + #Number of Bands + if "bands" in dictio: + hdr_bands=dictio["bands"] + #arcpy.AddMessage(hdr_bands) + #arcpy.AddMessage("saved Number of bands from header") + #Band names + if "band names" in dictio: + #arcpy.AddMessage("band names attribute found") + bn=re.split(r"[,]", dictio["band names"]) + #arcpy.AddMessage("split completed") + bn=replace_in_list(expr_replace,bn) + #arcpy.AddMessage("saved bn") + #Next we add the wl units only if they are defined + #arcpy.AddMessage("Format composed band name") + full_bn=hdr_bands #We assign by default the number of bands in the raster + if "wavelength units" in dictio: + if "band names" in dictio and "wavelength" in dictio: + join_str = '{bandn} ({waveln} '+dictio["wavelength units"]+')' if dictio["wavelength units"]!="Unknown" else '{bandn} ({waveln})' + arcpy.AddMessage("bn and wl found") + full_bn= '\n'.join(join_str.format(bandn=b, waveln=w) for b,w in itertools.izip(bn, wl)).split('\n') + if not "band names" in dictio and "wavelength" in dictio: + join_str = '{waveln} '+dictio["wavelength units"] if dictio["wavelength units"]!="Unknown" else '{waveln}' + arcpy.AddMessage("wl found+units") + full_bn= '\n'.join(join_str.format(waveln=w) for w in wl).split('\n') + else: + if "band names" in dictio and "wavelength" in dictio: + join_str = '{bandn} ({waveln})' + arcpy.AddMessage("bn and wl found") + full_bn= '\n'.join(join_str.format(bandn=b, waveln=w) for b,w in itertools.izip(bn, wl)).split('\n') + if not "band names" in dictio and "wavelength" in dictio: + arcpy.AddMessage("wl found") + full_bn= wl + arcpy.AddMessage("Data got from the Hdr"+str(full_bn)) + + if deb==1: + arcpy.AddMessage( full_bn) + arcpy.AddMessage( "Bands descripted in header: "+len(full_bn)) + #CHECK IF the "data ignore value is set", otherwise, set it to 0. + if not "data ignore value" in dictio: + arcpy.AddMessage("Set -data ignore value to 0-") + add=["data ignore value = 0"] + appendListToFile(header_file,add,mode="a") + #arcpy.AddMessage("Finished reading header") + #arcpy.AddMessage(type(full_bn)) #to know the type of variable it was created + return full_bn + except Exception as e: + # If an error occurred, print( line number and error message + import traceback, sys + tb = sys.exc_info()[2] + arcpy.AddMessage("Line:" + str(tb.tb_lineno)) + arcpy.AddMessage("Error " + e.message) + print( "OMG!") + raise e +#^^^^^^^^^^^^^^^^^^^^^^^ Methods ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +try: + # Check out any necessary licenses and Create the Geoprocessor object + arcpy.CheckOutExtension("spatial") + gp = arcgisscripting.create() + ## START:INPUTS ## + ##Hard coded this + csvExtractThis = "C:\\Dropbox\\data\\AD\\ad_sam\\extract_sam_ad_h.csv" ########################## + imgWs="G:\\AD15_16\\" #imageBaseLocation + ##GDB whare the feature classes are stored and where the tables will be written: + #gdb = "C:\\Users\\usuario\\Documents\\ArcGIS\\Default.gdb\\" #Default + gdb = "C:\\Dropbox\\data\\AD\\ad_sam\\shp\\adsam.gdb\\" + ##tws = where to save tables inside the workspace + tws ="C:\\Users\\usuario\\Documents\\ArcGIS\\Default.gdb\\" + #ds="extract" #Use this fro m and t ################ dataset inside GDB ############### + #ds="h" #Use this for hyper + ds = "" #no ds + arcpy.env.workspace = gdb + bandsPerImage = { + #Get the KNOWN NUMBER OF BANDS for the images according to the camera + #camera: NUMBER OF BANDS + "t": ("1"), + "h": ("62"), + "m": ("6"), + } + id_field = "id" ##################### + log= "C:\\Dropbox\\data\\AD\\AD-STARS\\extract_sam_ad_log.txt" ############################ + ## END:INPUTS ## + + ## START:VARIABLES ## + #Get all feature classes gdb/extract dataset + listFc = arcpy.ListFeatureClasses( + feature_type="Polygon", + feature_dataset=ds) + mosaicFolder = { + #Get the folder name used and suffix for the images according to the camera + #camera: folder, suffix + "m": ("geo","geo"), + "t": ("cel","cel"), + "h": ("rfl_spr_ort","ort"), + } + arcpy.env.workspace = gdb + ext=".bsq" #extension of the rasters BSQ | TIF + buf="buf" #we use the buffered plot features to extract the stats ############# + bw8="sam" #default trial #################################### + ## END:VARIABLES ## + ex, dates,trials = readWhatToExtract(csvExtractThis) + #find the image paths and features to perform the zonal statistics + for f, flightdate in enumerate(ex): + for t,trial in enumerate(flightdate): #skip the date column + for cam in trial.split(): + if cam !="-": + #QuĂ© hacer si está vacĂ­o? + #ASSIGN Known number of bands + knownBands = bandsPerImage.get(cam,("1")) + #ZONES FEATURE + #Search for the corresponding grids for this date/trial/cam + #if date is __ad__ the grid is the general for the cycle e.g. ypt__ad__buf + #if date is YYMMDD the grid was adjusted, and needs to have indicated also the camera + # this way we can know at which image the adjustment corresponds. + # grids adjusted will be like bw1160215bufh -> with the date and camera indicated + pattern = str(trials[t])+"("+str(dates[f])+'|__ad__)'+buf+'['+cam+']*$' + arcpy.AddMessage("pattern: "+str(pattern)) + #arcpy.AddMessage("FList: "+str(listFc)) + plotsF = [ft for ft in listFc if re.search(pattern, ft)] + if plotsF: + if len(plotsF)>1: plotsF=plotsF[0] #get just the edited feature if the general exists + plotsF=os.path.join(arcpy.env.workspace, ds, ''.join(plotsF)) + arcpy.AddMessage("Zones: "+plotsF) + #RASTER + #search for the images that match them + # hyper mosaics folder is rfl_spr_ort, for multi is geo, for thermal is cel + mF, sx = mosaicFolder.get(cam,("geo","geo")) #corresponding folder and sfx + imgLocation = imgLocation = imgWs+str(dates[f])+"\\"+cam+"\\"+mF+"\\" + imgName =cam+str(dates[f])+str(trials[t])+sx+ext + img=imgLocation+imgName + if not exists(img): #Use the general mosaic + imgName =cam+str(dates[f])+bw8+sx+ext + img=imgLocation+imgName + arcpy.AddMessage("Raster to use: "+img) + if exists(img): + #PREPARE FOR THE ZS + #Reading header to get Bands + #We assume header file is there and named as the raster + hdr_file = img[:-4]+".hdr" + #Read header to know number and name of bands or set them a priori + #knownBands is optional + bandNames = readHeaderFile(hdr_file, knownBands) + tableBaseName = arcpy.ValidateTableName(cam+str(dates[f])+str(trials[t])+buf,tws) #stats table base name in ws + #arcpy.AddMessage("zs") + #arcpy.AddMessage("tb:"+tableBaseName) + zsm = zonalStatsMultiband(tableBaseName, bandNames, img, plotsF,id_field) + appendListToFile(log,zsm,mode="a") + else: + arcpy.AddMessage("|||No raster to use |||") + appendListToFile(log,(str(img),"\n"),mode="a") + else: + arcpy.AddMessage("|||No features to use|||") + appendListToFile(log,(str(plotsF),"\n"),mode="a") + else: arcpy.AddMessage("No data extracting for this trial on this date") + appendListToFile(log,("Finished",time.strftime("%Y/%m/%d %H:%M:%S"),"\n"),mode="a") #Write finish time in log +except Exception as e: + # If an error occurred, print( line number and error message + import traceback, sys + tb = sys.exc_info()[2] + arcpy.AddMessage("Line:" + str(tb.tb_lineno)+ " Error " + e.message) \ No newline at end of file diff --git a/ArcGIS/zonalStatsTablesAutoInputsCSV_stars_ad.py b/ArcGIS/zonalStatsTablesAutoInputsCSV_stars_ad.py new file mode 100644 index 0000000..6d2fb44 --- /dev/null +++ b/ArcGIS/zonalStatsTablesAutoInputsCSV_stars_ad.py @@ -0,0 +1,412 @@ +#Perform zonal statistics tool based on a list of on a specified list of dates and names. +#Automatically calls the appropriate raster and zonal data from within the selected folder. + +#Tuned for extracting data to match with the GreenSeeker +#################################WARNING +#crashes if the csv has just one row + +import numpy +import os +import arcpy, arcgisscripting +import itertools +import re +import traceback, sys +import time + +#Read the file to know which feature-raster to extract +def readWhatToExtract(csvWhatToExtract): + try: + arcpy.AddMessage("will read file: "+csvWhatToExtract) + extractMatrix=numpy.genfromtxt(open(csvWhatToExtract,"rb"), + delimiter=',', + dtype=None, + names=True) + dates = list(extractMatrix['date']) #get the dates + arcpy.AddMessage("Dates: "+str(dates)) + trials=extractMatrix.dtype.names[1:] + arcpy.AddMessage("Trials: "+str(trials)) + extractMatrix = extractMatrix[list(extractMatrix.dtype.names)[1:]]#remove "date" column (1st) + arcpy.AddMessage("What to extract: "+str(extractMatrix)) + return extractMatrix,dates,trials + except Exception as e: + # If an error occurred, print( line number and error message + import traceback, sys + tb = sys.exc_info()[2] + arcpy.AddMessage("Line:" + str(tb.tb_lineno)) + arcpy.AddMessage("Error " + e.message) + + #Return the matrix of the kind of images acquired on each day on each trial [h m t], + #the list of dates and the list of trials (removing the "date" item) +def readDbf():# read dbf file + print("Algo haremos aquĂ­")# zsFields= ["id","COUNT","MEAN","STD"] + # table="C:\\Users\\usuario\\Documents\\ArcGIS\\m150326pcwbufB1.dbf" + # with arcpy.da.SearchCursor(table, zsFields) as cursor: + # for row in cursor: + # print(('Feature {0} has an area of {1}'.format(row[0], row[1])) +def zonalStatsMultiband(tableBaseName, bandNames, image, plot_polygons,id_field): + try: + tables=list() + #zonalStats iterated in all bands of the raster Multiband + #Return list of the name of the generated tables + arcpy.AddMessage("Iterating the zonal statistics across all bands...") + #Get bands + #Returns: [0]numberOfBands, [1]isBandNamesInteger? + numOfBands = getNumberOfBands(bandNames) + arcpy.AddMessage("Number of bands: "+str(numOfBands[0])) + #If the image has more than one band... + if numOfBands[0] != 1: + #If we only have the number of bands, but no band names (because there was no hdr)... + #We will call the bands by the default way ArcMap does: RasterName + Band_1, Band_2, etc... + #we create the list to iterate it + if numOfBands[1]: bandNames = list('Band_%s' % b for b in range(1, numOfBands[0]+1)) + #Here iterates through the bands and runs the zonal statistics tool + #Get the count with the function "enumerate" + for i, b in enumerate(bandNames, start=1): + #Next we add the band name to the raster basename + band = image + "\\" + b + arcpy.AddMessage("Iterating band # "+ str(i)+" - " + band) + #Set table name + statsTable = tableBaseName+"B" + str(i)+ ".dbf" #We add the band id to the StatsTable name + #arcpy.AddMessage("Willbe stored in table "+str(statsTable)) + #Here we call the method to do the zonal statistics + zs = arcpy.gp.ZonalStatisticsAsTable_sa(plot_polygons, id_field, band, statsTable, "DATA", "ALL") + arcpy.AddMessage("resulting stats table: " + str(zs)) + tables.append(plot_polygons+","+band+","+str(zs)) + else: + #If we have just one band we call the raster without specifying any band + zs = arcpy.gp.ZonalStatisticsAsTable_sa(plot_polygons, id_field, image, tableBaseName+"B1.dbf", "DATA", "ALL") + tables.append(plot_polygons+","+image+","+str(zs)) + arcpy.AddMessage("||| resulting zs table: " + str(zs)) + return tables + except Exception as e: + # If an error occurred, print( line number and error message + import traceback, sys + tb = sys.exc_info()[2] + arcpy.AddMessage("Line:" + str(tb.tb_lineno)) + arcpy.AddMessage("Error " + e.message) +def print_kwinfo(): + if deb==1: + print( "kwinfo se guarda:") + print( kwinfo) + +def create_if_not_exists(path): + try: + if not os.path.exists(path): + os.makedirs(path) + except Exception as e: + # If an error occurred, print( line number and error message + tb = sys.exc_info()[2] + arcpy.AddMessage("Line:" + str(tb.tb_lineno)) + arcpy.AddMessage("Error " + e.message) + +def exists(path): + exists = True if os.path.exists(path) else False + return exists + +def write_hdr_file(hdr_filename,assign_bands=1,mode="w"): + try: + hdr_file = open(hdr_filename, mode) + hdr_file.write("HEAD\nbands = "+ str(assign_bands)) + arcpy.AddMessage("writing in hdr file") + hdr_file.close() + except Exception as e: + # If an error occurred, print( line number and error message + import traceback, sys + tb = sys.exc_info()[2] + arcpy.AddMessage("Line:" + str(tb.tb_lineno)) + arcpy.AddMessage("Error " + e.message) + print( "OMG! Error while in: write_hdr_file") + raise e +def appendListToFile(txt_filename,thelist,mode="w"): + try: + file = open(txt_filename, mode) + #arcpy.AddMessage("writing in "+txt_filename+" file") + for item in thelist: + file.write("%s" % item) #################### + file.close() + except Exception as e: + # If an error occurred, print( line number and error message + import traceback, sys + tb = sys.exc_info()[2] + arcpy.AddMessage("Line:" + str(tb.tb_lineno)) + arcpy.AddMessage("Error " + e.message) + print( "OMG! Error while in: appendListToFile") + raise e + +def replace_in_list(regex,lst): + return [re.sub(regex, '', x).strip() for x in lst] #List comprehension +#Method to concatenate the band name to the raster basename +#BandNames has or all the list of band names or the number of bands +def getNumberOfBands(bandNames): + try: + #If there was no bandnames or header, a band number was assigned + #This would be an integer + numOfBands = int(bandNames) + isANumber = True + return numOfBands, isANumber + except ValueError: + #If there is a band name as a string we will say it's one band + numOfBands = 1 + isANumber = False + return numOfBands, isANumber + except TypeError: + #If there are band names as list or array we will count them here + numOfBands = len(bandNames) + isANumber = False + return numOfBands, isANumber + except Exception as e: + import traceback, sys + tb = sys.exc_info()[2] + arcpy.AddMessage("Line:" + str(tb.tb_lineno)) + arcpy.AddMessage("Error " + e.message) + print( "OMG!") + raise e +def print_dict(dictio): + #The next line iterates, formats and prints the dictionary, key:value + #print( "\n".join('{}={}'.format(k,v) for k,v in dictio.items()) print( in console + arcpy.AddMessage("\n".join('{}={}'.format(k,v) for k,v in dictio.items()))#Print to arcmap results window +def readHeaderFile(header_file,assign_bands=1): + try: + if not exists(header_file): + write_hdr_file(header_file,assign_bands) + arcpy.AddMessage("Header not found, it was created a basic one. Number of bands assigned: "+str(assign_bands)) + arcpy.AddMessage("started reading header file:" + str(header_file)) + envi_header_keywords={"acquisition time","band names","bands", + "bbl","byte order","class lookup","class names", + "classes","cloud cover","complex function", + "coordinate system string","data gain values", + "data ignore value","data offset values", + "data reflectance gain values","data reflectance offset values", + "data type","default bands","default stretch","dem band", + "dem file","description","file type","fwhm","geo points", + "header offset","interleave","lines","map info", + "major frame offsets","minor frame offsets","pixel size", + "product type","projection info","read procedures", + "reflectance scale factor","rpc info","samples","security tag", + "sensor type","solar irradiance","spectra names","sun azimuth", + "sun elevation","wavelength","wavelength units","x start", + "y start","z plot average","z plot range","z plot titles"} + envi_header_keywords_or="|".join(envi_header_keywords) + # Read and Iterate over the lines of the file + with open(header_file, 'rt') as f: + data = f.read()[5:] #Skip the first "ENVI" letters of the header + lines = re.split(r"[\n]", data)#the info corresponding to one line + arcpy.AddMessage("reading header lines...") + dictio = {} + global deb + deb=0 + wl=[]#List to save wavelengths + bn=[]#List to save band names + clines=[]#complete lines with all the info corresponding to one header keyword + ongoing=0 #Variable para marcar si se está buscando el resto de la lĂ­nea de info + #to one header keyword is not in one single line + kwinfo="" + for i,l in enumerate(lines): + if deb==1: print( str(i)+"-l- "+str(l)) + kwinfo+=l + if deb==1: print( str(i)+"-kwinfo- "+str(kwinfo)) + if ongoing==1: + if "}" in l: + if deb==1: print( "ongoing==se cierra por fin el parentesis") + print_kwinfo() + clines.append(kwinfo) + kwinfo="" + ongoing=0 + continue + else: + if deb==1: print( "ongoing==1 else") + if ongoing==0: + ongoing=1 + for keyw in envi_header_keywords: + if keyw in l: + if deb==1: print( keyw+" attribute found") + if "{" in l: + if "}" in l: + if deb==1: print( "ongoing==parentesis cerrando en linea") + print_kwinfo() + clines.append(kwinfo) + kwinfo="" + ongoing=0 + else: + if deb==1: print( "if bracket in l: --- else") + else: + if deb==1: print( "ongoing==sin paretesis") + print_kwinfo() + clines.append(kwinfo) + kwinfo="" + ongoing=0 + if deb==1: print( "fin de ronda de for") + for cl in clines: + #expresion to filter how to sepatate the string. + regexpresion = re.compile(r"""(.+?)\s*=\s*(.+)""") + dictio.update(dict(regexpresion.findall(cl))) + if deb==1: print( regexpresion.findall(cl)) + for key in dictio: dictio[key]=dictio[key].strip() + print( "Header attributes:") + arcpy.AddMessage("Header attributes read:") + print_dict(dictio) + expr_replace = re.compile(r"{|}") #Expression to remove brackets below + #wavelength + if "wavelength" in dictio: + wl=re.split(r"[,]", dictio["wavelength"]) + wl =replace_in_list(expr_replace,wl) + #arcpy.AddMessage(wl) + #arcpy.AddMessage("saved wl") + #Number of Bands + if "bands" in dictio: + hdr_bands=dictio["bands"] + #arcpy.AddMessage(hdr_bands) + #arcpy.AddMessage("saved Number of bands from header") + #Band names + if "band names" in dictio: + #arcpy.AddMessage("band names attribute found") + bn=re.split(r"[,]", dictio["band names"]) + #arcpy.AddMessage("split completed") + bn=replace_in_list(expr_replace,bn) + #arcpy.AddMessage("saved bn") + #Next we add the wl units only if they are defined + #arcpy.AddMessage("Format composed band name") + full_bn=hdr_bands #We assign by default the number of bands in the raster + if "wavelength units" in dictio: + if "band names" in dictio and "wavelength" in dictio: + join_str = '{bandn} ({waveln} '+dictio["wavelength units"]+')' if dictio["wavelength units"]!="Unknown" else '{bandn} ({waveln})' + arcpy.AddMessage("bn and wl found") + full_bn= '\n'.join(join_str.format(bandn=b, waveln=w) for b,w in itertools.izip(bn, wl)).split('\n') + if not "band names" in dictio and "wavelength" in dictio: + join_str = '{waveln} '+dictio["wavelength units"] if dictio["wavelength units"]!="Unknown" else '{waveln}' + arcpy.AddMessage("wl found+units") + full_bn= '\n'.join(join_str.format(waveln=w) for w in wl).split('\n') + else: + if "band names" in dictio and "wavelength" in dictio: + join_str = '{bandn} ({waveln})' + arcpy.AddMessage("bn and wl found") + full_bn= '\n'.join(join_str.format(bandn=b, waveln=w) for b,w in itertools.izip(bn, wl)).split('\n') + if not "band names" in dictio and "wavelength" in dictio: + arcpy.AddMessage("wl found") + full_bn= wl + arcpy.AddMessage("Data got from the Hdr"+str(full_bn)) + + if deb==1: + arcpy.AddMessage( full_bn) + arcpy.AddMessage( "Bands descripted in header: "+len(full_bn)) + #CHECK IF the "data ignore value is set", otherwise, set it to 0. + if not "data ignore value" in dictio: + arcpy.AddMessage("Set -data ignore value to 0-") + add=["data ignore value = 0"] + appendListToFile(header_file,add,mode="a") + #arcpy.AddMessage("Finished reading header") + #arcpy.AddMessage(type(full_bn)) #to know the type of variable it was created + return full_bn + except Exception as e: + # If an error occurred, print( line number and error message + import traceback, sys + tb = sys.exc_info()[2] + arcpy.AddMessage("Line:" + str(tb.tb_lineno)) + arcpy.AddMessage("Error " + e.message) + print( "OMG!") + raise e +#^^^^^^^^^^^^^^^^^^^^^^^ Methods ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +try: + # Check out any necessary licenses and Create the Geoprocessor object + arcpy.CheckOutExtension("spatial") + gp = arcgisscripting.create() + ## START:INPUTS ## + ##Hard coded this + csvExtractThis = "C:\\Dropbox\\data\\AD\\AD-STARS\\extract_stars_ad.csv" ########################## + imgWs="G:\\AD15_16\\" #imageBaseLocation + ##GDB whare the feature classes are stored and where the tables will be written: + #gdb = "C:\\Users\\usuario\\Documents\\ArcGIS\\Default.gdb\\" #Default + gdb = "C:\\Dropbox\\data\\AD\\AD-STARS\\stars_ad.gdb\\" + ##tws = where to save tables inside the workspace + tws ="C:\\Users\\usuario\\Documents\\ArcGIS\\Default.gdb\\" + #ds="extract" #Use this fro m and t ################ dataset inside GDB ############### + #ds="h" #Use this for hyper + ds = "" #no ds + arcpy.env.workspace = gdb + bandsPerImage = { + #Get the KNOWN NUMBER OF BANDS for the images according to the camera + #camera: NUMBER OF BANDS + "t": ("1"), + "h": ("62"), + "m": ("6"), + } + id_field = "id" ##################### + log= "C:\\Dropbox\\data\\AD\\AD-STARS\\extract_stars_ad_log.txt" ############################ + ## END:INPUTS ## + + ## START:VARIABLES ## + #Get all feature classes gdb/extract dataset + listFc = arcpy.ListFeatureClasses( + feature_type="Polygon", + feature_dataset=ds) + mosaicFolder = { + #Get the folder name used and suffix for the images according to the camera + #camera: folder, suffix + "m": ("geo","geo"), + "t": ("cel","cel"), + } + arcpy.env.workspace = gdb + ext=".tif" #extension of the rasters BSQ | TIF + buf="buf" #we use the buffered plot features to extract the stats ############# + bw8="xxx" #default trial #################################### + ## END:VARIABLES ## + ex, dates,trials = readWhatToExtract(csvExtractThis) + #find the image paths and features to perform the zonal statistics + for f, flightdate in enumerate(ex): + for t,trial in enumerate(flightdate): #skip the date column + for cam in trial.split(): + if cam !="-": + #QuĂ© hacer si está vacĂ­o? + #ASSIGN Known number of bands + knownBands = bandsPerImage.get(cam,("1")) + #ZONES FEATURE + #Search for the corresponding grids for this date/trial/cam + #if date is __ad__ the grid is the general for the cycle e.g. ypt__ad__buf + #if date is YYMMDD the grid was adjusted, and needs to have indicated also the camera + # this way we can know at which image the adjustment corresponds. + # grids adjusted will be like bw1160215bufh -> with the date and camera indicated + pattern = str(trials[t])+"("+str(dates[f])+'|__ad__)'+buf+'['+cam+']*$' + arcpy.AddMessage("pattern: "+str(pattern)) + #arcpy.AddMessage("FList: "+str(listFc)) + plotsF = [ft for ft in listFc if re.search(pattern, ft)] + if plotsF: + if len(plotsF)>1: plotsF=plotsF[0] #get just the edited feature if the general exists + plotsF=os.path.join(arcpy.env.workspace, ds, ''.join(plotsF)) + arcpy.AddMessage("Zones: "+plotsF) + #RASTER + #search for the images that match them + # hyper mosaics folder is rfl_spr_ort, for multi is geo, for thermal is cel + mF, sx = mosaicFolder.get(cam,("geo","geo")) #corresponding folder and sfx + imgLocation = imgLocation = imgWs+str(dates[f])+"\\"+cam+"\\"+mF+"\\" + imgName =cam+str(dates[f])+str(trials[t])+sx+ext + img=imgLocation+imgName + if not exists(img): #Use the general mosaic + imgName =cam+str(dates[f])+bw8+sx+ext + img=imgLocation+imgName + arcpy.AddMessage("Raster to use: "+img) + if exists(img): + #PREPARE FOR THE ZS + #Reading header to get Bands + #We assume header file is there and named as the raster + hdr_file = img[:-4]+".hdr" + #Read header to know number and name of bands or set them a priori + #knownBands is optional + bandNames = readHeaderFile(hdr_file, knownBands) + tableBaseName = arcpy.ValidateTableName(cam+str(dates[f])+str(trials[t])+buf,tws) #stats table base name in ws + #arcpy.AddMessage("zs") + #arcpy.AddMessage("tb:"+tableBaseName) + zsm = zonalStatsMultiband(tableBaseName, bandNames, img, plotsF,id_field) + appendListToFile(log,zsm,mode="a") + else: + arcpy.AddMessage("|||No raster to use |||") + appendListToFile(log,(str(img),"\n"),mode="a") + else: + arcpy.AddMessage("|||No features to use|||") + appendListToFile(log,(str(plotsF),"\n"),mode="a") + else: arcpy.AddMessage("No data extracting for this trial on this date") + appendListToFile(log,("Finished",time.strftime("%Y/%m/%d %H:%M:%S"),"\n"),mode="a") #Write finish time in log +except Exception as e: + # If an error occurred, print( line number and error message + import traceback, sys + tb = sys.exc_info()[2] + arcpy.AddMessage("Line:" + str(tb.tb_lineno)+ " Error " + e.message) \ No newline at end of file diff --git a/ArcGIS/zonalStatsTables_MultibandImage .py b/ArcGIS/zonalStatsTables_MultibandImage .py new file mode 100644 index 0000000..d730039 --- /dev/null +++ b/ArcGIS/zonalStatsTables_MultibandImage .py @@ -0,0 +1,351 @@ +# -*- coding: utf-8 -*- +# Description: +#This script is to execute the "Zonal statistics" tool in a multiband raster. It saves the resulting table to a specified >workspace<. +# One table per band is generated, they are named based on the raster and feature zone names + the extracted band number. +# To iterate along the bands, the ENVI HDR file is atempted to be read to get the band names. +# If header doesn't exist, a known number of bands can be specified to iterate. +#This script takes 3 important inputs and outputs: +#(input) +# Buffer: zone feature to use as input for zonal statistic tool. +# Standard file name system on the features is espected: tttYYMMDDfff +# Being: +# YYMMDD= date in a YYMMDD format. E.g. 160311 for march 11 of 2016. +# fff = 3 char indicating the features that are represented in the vector shapes. E.g. "gss" for greenseeker sampling, "plt" for field plots. +# ttt = 3 char representing the trial or area covered in the image. E.g. "bwh" for bread wheat trials, "sam" for maize summer trials + +# ID field: Field of the input feature to use as identifier of the different zones for generating the statistics + +# image: data image to use as input for zonal statistic tool. +# Standard file name system on the raster is espected: xYYMMDDtttsss +# Being: +# x = [h,m,t,c,a,r,n] one char representing the camera used to acquire the imagery + # sss = 3 char representing the processing stage of the imagery. E.g. "geo" for georeferenced, "pno" for the multi mosaic +# The band information is read from the envi HDR file + +# Workspace: where to save the statistics tables and where to create the features to save the mean values per plot + +# Known bands: [Optional] Number of bands of the input raster. Necessary if +# the header file doesn't exist -> default is 1 if not specified. + +#(ouput) +# Tables: The full default statistics table +# Table name structure: xYYMMDDtttfffBi +# Being: +# i: the band number from which the statistics was calculated based on the zones of the input feature +# --------------------------------------------------------------------------- + +# Import arcpy module +import os +import arcpy, arcgisscripting +import itertools +import re +import traceback, sys + + def zonalStatsMultiband(tableBaseName, bandNames, image, plot_polygons,id_field): + #zonalStats iterated in all bands of the raster Multiband + #Return list of the name of the generated tables + tables=list() + arcpy.AddMessage("Iterating the zonal statistics across all bands...") + #Get bands + #Returns: [0]numberOfBands, [1]isBandNamesInteger? + numOfBands = getNumberOfBands(bandNames) + arcpy.AddMessage("Number of bands: "+str(numOfBands[0])) + #If the image has more than one band... + if numOfBands[0] != 1: + #If we only have the number of bands, but no band names (because there was no hdr)... + #We will call the bands by the default way ArcMap does: RasterName + Band_1, Band_2, etc... + #we create the list to iterate it + if numOfBands[1]: bandNames = list('Band_%s' % b for b in range(1, numOfBands[0]+1)) + #Here iterates through the bands and runs the zonal statistics tool + #Get the count with the function "enumerate" + for i, b in enumerate(bandNames, start=1): + #Next we add the band name to the raster basename + band = image + "\\" + b + arcpy.AddMessage("Iterating band # "+ str(i)+" - " + band) + #Set table name + statsTable = tableBaseName+"B" + str(i)#+ ".dbf" #We add the band id to the StatsTable name + #arcpy.AddMessage("Willbe stored in table "+str(statsTable)) + #Here we call the method to do the zonal statistics + zs = arcpy.gp.ZonalStatisticsAsTable_sa(plot_polygons, id_field, band, statsTable, "DATA", "ALL") + arcpy.AddMessage("resulting stats table: " + str(zs)) + tables.append(zs) + else: + #If we have just one band we call the raster without specifying any band + zs = arcpy.gp.ZonalStatisticsAsTable_sa(plot_polygons, id_field, image, tableBaseName+"B1.dbf", "DATA", "ALL") + tables.append(zs) + arcpy.AddMessage("resulting stats table: " + str(zs)) + return tables +def print_kwinfo(): + if deb==1: + print "kwinfo se guarda:" + print kwinfo + +def create_if_not_exists(path): + try: + if not os.path.exists(path): + os.makedirs(path) + except Exception, e: + # If an error occurred, print line number and error message + tb = sys.exc_info()[2] + arcpy.AddMessage("Line:" + str(tb.tb_lineno)) + arcpy.AddMessage("Error " + e.message) + +def exists(path): + exists = True if os.path.exists(path)else False + return exists + +def write_hdr_file(hdr_filename,assign_bands=1,mode="w"): + try: + hdr_file = open(hdr_filename, mode) + hdr_file.write("HEAD\nbands = "+ str(assign_bands)) + arcpy.AddMessage("writing in hdr file") + hdr_file.close() + except Exception, e: + # If an error occurred, print line number and error message + import traceback, sys + tb = sys.exc_info()[2] + arcpy.AddMessage("Line:" + str(tb.tb_lineno)) + arcpy.AddMessage("Error " + e.message) + print "OMG! Error while in: write_hdr_file" + raise e +def write_txt_file(hdr_filename,mode="w",txt=""): + try: + hdr_file = open(hdr_filename, mode) + hdr_file.write(txt) + arcpy.AddMessage("writing in "+hdr_filename+" file") + hdr_file.close() + except Exception, e: + # If an error occurred, print line number and error message + import traceback, sys + tb = sys.exc_info()[2] + arcpy.AddMessage("Line:" + str(tb.tb_lineno)) + arcpy.AddMessage("Error " + e.message) + print "OMG! Error while in: write_txt_file" + raise e + +def replace_in_list(regex,lst): + return [re.sub(regex, '', x).strip() for x in lst] #List comprehension +#Method to concatenate the band name to the raster basename +#BandNames has or all the list of band names or the number of bands +def getNumberOfBands(bandNames): + try: + #If there was no bandnames or header, a band number was assigned + #This would be an integer + numOfBands = int(bandNames) + isANumber = True + return numOfBands, isANumber + except ValueError: + #If there is a band name as a string we will say it's one band + numOfBands = 1 + isANumber = False + return numOfBands, isANumber + except TypeError: + #If there are band names as list or array we will count them here + numOfBands = len(bandNames) + isANumber = False + return numOfBands, isANumber + except Exception, e: + import traceback, sys + tb = sys.exc_info()[2] + arcpy.AddMessage("Line:" + str(tb.tb_lineno)) + arcpy.AddMessage("Error " + e.message) + print "OMG!" + raise e +def print_dict(dictio): + #The next line iterates, formats and prints the dictionary, key:value + #print "\n".join('{}={}'.format(k,v) for k,v in dictio.items()) print in console + arcpy.AddMessage("\n".join('{}={}'.format(k,v) for k,v in dictio.items()))#Print to arcmap results window +def readHeaderFile(header_file,assign_bands=1): + try: + if not exists(header_file): + write_hdr_file(header_file,assign_bands) + arcpy.AddMessage("Header not found, it was created a basic one. Number of bands assigned: "+str(assign_bands)) + arcpy.AddMessage("started reading header file:" + str(header_file)) + envi_header_keywords={"acquisition time","band names","bands", + "bbl","byte order","class lookup","class names", + "classes","cloud cover","complex function", + "coordinate system string","data gain values", + "data ignore value","data offset values", + "data reflectance gain values","data reflectance offset values", + "data type","default bands","default stretch","dem band", + "dem file","description","file type","fwhm","geo points", + "header offset","interleave","lines","map info", + "major frame offsets","minor frame offsets","pixel size", + "product type","projection info","read procedures", + "reflectance scale factor","rpc info","samples","security tag", + "sensor type","solar irradiance","spectra names","sun azimuth", + "sun elevation","wavelength","wavelength units","x start", + "y start","z plot average","z plot range","z plot titles"} + envi_header_keywords_or="|".join(envi_header_keywords) + # Read and Iterate over the lines of the file + with open(header_file, 'rt') as f: + data = f.read()[5:] #Skip the first "ENVI" letters of the header + lines = re.split(r"[\n]", data)#the info corresponding to one line + arcpy.AddMessage("reading header lines...") + dictio = {} + global deb + deb=0 + wl=[]#List to save wavelengths + bn=[]#List to save band names + clines=[]#complete lines with all the info corresponding to one header keyword + ongoing=0 #Variable para marcar si se está buscando el resto de la lĂ­nea de info + #to one header keyword is not in one single line + kwinfo="" + for i,l in enumerate(lines): + if deb==1: print str(i)+"-l- "+str(l) + kwinfo+=l + if deb==1: print str(i)+"-kwinfo- "+str(kwinfo) + if ongoing==1: + if "}" in l: + if deb==1: print "ongoing==se cierra por fin el parentesis" + print_kwinfo() + clines.append(kwinfo) + kwinfo="" + ongoing=0 + continue + else: + if deb==1: print "ongoing==1 else" + if ongoing==0: + ongoing=1 + for keyw in envi_header_keywords: + if keyw in l: + if deb==1: print keyw+" attribute found" + if "{" in l: + if "}" in l: + if deb==1: print "ongoing==parentesis cerrando en linea" + print_kwinfo() + clines.append(kwinfo) + kwinfo="" + ongoing=0 + else: + if deb==1: print "if bracket in l: --- else" + else: + if deb==1: print "ongoing==sin paretesis" + print_kwinfo() + clines.append(kwinfo) + kwinfo="" + ongoing=0 + if deb==1: print "fin de ronda de for" + for cl in clines: + #expresion to filter how to sepatate the string. + regexpresion = re.compile(r"""(.+?)\s*=\s*(.+)""") + dictio.update(dict(regexpresion.findall(cl))) + if deb==1: print regexpresion.findall(cl) + for key in dictio: dictio[key]=dictio[key].strip() + print "Header attributes:" + arcpy.AddMessage("Header attributes read") + print_dict(dictio) + expr_replace = re.compile(r"{|}") #Expression to remove brackets below + #wavelength + if "wavelength" in dictio: + wl=re.split(r"[,]", dictio["wavelength"]) + wl =replace_in_list(expr_replace,wl) + arcpy.AddMessage(wl) + arcpy.AddMessage("saved wl") + #Number of Bands + if "bands" in dictio: + hdr_bands=dictio["bands"] + arcpy.AddMessage(hdr_bands) + arcpy.AddMessage("saved Number of bands from header") + #Band names + if "band names" in dictio: + arcpy.AddMessage("band names attribute found") + bn=re.split(r"[,]", dictio["band names"]) + arcpy.AddMessage("split competed") + bn=replace_in_list(expr_replace,bn) + arcpy.AddMessage("saved bn") + #Next we add the wl units only if they are defined + arcpy.AddMessage("Format composed band name") + full_bn=hdr_bands #We assign by default the number of bands in the raster + if "wavelength units" in dictio: + if "band names" in dictio and "wavelength" in dictio: + join_str = '{bandn} ({waveln} '+dictio["wavelength units"]+')' if dictio["wavelength units"]!="Unknown" else '{bandn} ({waveln})' + arcpy.AddMessage("bn and wl found") + full_bn= '\n'.join(join_str.format(bandn=b, waveln=w) for b,w in itertools.izip(bn, wl)).split('\n') + if not "band names" in dictio and "wavelength" in dictio: + join_str = '{waveln} '+dictio["wavelength units"] if dictio["wavelength units"]!="Unknown" else '{waveln}' + arcpy.AddMessage("wl found+units") + full_bn= '\n'.join(join_str.format(waveln=w) for w in wl).split('\n') + else: + if "band names" in dictio and "wavelength" in dictio: + join_str = '{bandn} ({waveln})' + arcpy.AddMessage("bn and wl found") + full_bn= '\n'.join(join_str.format(bandn=b, waveln=w) for b,w in itertools.izip(bn, wl)).split('\n') + if not "band names" in dictio and "wavelength" in dictio: + arcpy.AddMessage("wl found") + full_bn= wl + arcpy.AddMessage(full_bn) + if deb==1: + print full_bn + print "Bands descripted in header: "+len(full_bn) + arcpy.AddMessage("Finished reading header") + #arcpy.AddMessage(type(full_bn)) #to know the type of variable it was created + return full_bn + except Exception, e: + # If an error occurred, print line number and error message + import traceback, sys + tb = sys.exc_info()[2] + arcpy.AddMessage("Line:" + str(tb.tb_lineno)) + arcpy.AddMessage("Error " + e.message) + print "OMG!" + raise e + +##--------------------------------- Starts the script +try: + # Check out any necessary licenses and Create the Geoprocessor object + arcpy.CheckOutExtension("spatial") + gp = arcgisscripting.create() + #START----------------Get Input parameters + #Type: Feature layer -buffer for the zonalStats + plot_polygons = arcpy.GetParameterAsText(0) + arcpy.AddMessage("read b1 " + plot_polygons) + print plot_polygons + arcpy.AddMessage("read " + plot_polygons) + desc = arcpy.Describe(plot_polygons) + gPath = desc.path + gridSource = str(gPath) + arcpy.AddMessage("Grid source: " + gridSource) + gridName = desc.baseName + gridTrial = desc.baseName[0:3]#Framer's Field or trial that this buffer is representing + gridFeatures = desc.baseName[9:12]#What feature of that trial field is the grid representing: plots, buffered plots, dissolved plots, etc + arcpy.AddMessage("Grid basename: " + gridName) + print plot_polygons + #type: image layer + image = arcpy.GetParameterAsText(1) + arcpy.AddMessage("read " + image) + desc = arcpy.Describe(image) + rPath = desc.path + rdate = desc.baseName[1:7] #Get the date from the raster to create the save feature + r_img = desc.baseName[0:1] #Get the image id from the raster to create the save feature + trialName = desc.baseName[7:10] #Get the trial id from the raster to create the save feature + image = str(rPath) + "\\" + desc.baseName+ "." +desc.extension + rname = desc.baseName + arcpy.AddMessage("image source: " + image) + print image + #type: Field - Field for statistics + id_field = arcpy.GetParameterAsText(2) + arcpy.AddMessage("read " + id_field) + #tablesPath: tablesPath - GDB where to save the statistics tables + tablesPath = arcpy.GetParameterAsText(3) + arcpy.AddMessage("GDB where to save the statistics tables: " + tablesPath) + #KnownBands: Known number of bands in case there is no header file + #Optional, but set to default as 1 if there is no header file + knownBands = arcpy.GetParameterAsText(4) + arcpy.AddMessage("known number of Bands: " + str(knownBands)) + #ENDS---------------------Input parameters + #Reading header to get Bands + #We assume header file is there and named as the raster + hdr_file = os.path.join(str(rPath) + "\\" + rname+".hdr") + #Read header to know number and name of bands or set them a priori + #knownBands is optional + bandNames = readHeaderFile(hdr_file, knownBands) + tableBaseName = tablesPath + "\\"+r_img+rdate+gridTrial+gridFeatures #stats table base name + zonalStatsMultiband(tableBaseName, bandNames, image, plot_polygons,id_field) #do the zs + f="Finish" + print f + arcpy.SetParameterAsText(5,f) +except Exception, e: + # If an error occurred, print line number and error message + import traceback, sys + tb = sys.exc_info()[2] + arcpy.AddMessage("Line:" + str(tb.tb_lineno) + "Error " + e.message) \ No newline at end of file diff --git a/ArcGIS/zonalStats_bsqMultibandImage.py b/ArcGIS/zonalStats_bsqMultibandImage.py new file mode 100644 index 0000000..bde7584 --- /dev/null +++ b/ArcGIS/zonalStats_bsqMultibandImage.py @@ -0,0 +1,475 @@ +# -*- coding: utf-8 -*- +# --------------------------------------------------------------------------- +# joinZonalStats_hyper_BW_pruebas.py +# Created on: 2014-06-07 11:06:16.00000 +# (generated by ArcGIS/ModelBuilder) +# Usage: joinZonalStats_hyper_BW_pruebas +# Description: +# The script is used to perform the zonals statistics tool on a multiband raster. +# The mean values are stored in the input feature (or a copy of it) +#This script takes 3 important inputs: +#Buffer: zone feature to use as input for zonal statistic tool +#Grid: feature whose table will be used to save the mean values of the zonal statistic tool. +# This should contain the same features ID field than the "Buffer" input. +# A field named B1, B2... will be created to store the mean values for each zone for each band. +#image: image to use as input for zonal statistic tool, along with the "Buffer". +# The band information is read from the envi HDR file +#Also +# +#Workspace: where to save the statistics tables and where to create the features to save the mean values per plot +#Decimals: round figure to save the mean on the features +# --------------------------------------------------------------------------- + +# Import arcpy module +import os +import arcpy, arcgisscripting +import itertools +import re +import traceback, sys + +def print_kwinfo(): + if deb==1: + print "kwinfo se guarda:" + print kwinfo + +def create_if_not_exists(path): + try: + if not os.path.exists(path): + os.makedirs(path) + except Exception, e: + # If an error occurred, print line number and error message + tb = sys.exc_info()[2] + arcpy.AddMessage("Line:" + str(tb.tb_lineno)) + arcpy.AddMessage("Error " + e.message) + +def exists(path): + exists = True if os.path.exists(path)else False + return exists + +def write_hdr_file(hdr_filename,assign_bands=1,mode="w"): + try: + hdr_file = open(hdr_filename, mode) + hdr_file.write("HEAD\nbands = "+ str(assign_bands)) + arcpy.AddMessage("writing in hdr file") + hdr_file.close() + except Exception, e: + # If an error occurred, print line number and error message + import traceback, sys + tb = sys.exc_info()[2] + arcpy.AddMessage("Line:" + str(tb.tb_lineno)) + arcpy.AddMessage("Error " + e.message) + print "OMG! Error while in: write_hdr_file" + raise e +def write_txt_file(hdr_filename,mode="w",txt=""): + try: + hdr_file = open(hdr_filename, mode) + hdr_file.write(txt) + arcpy.AddMessage("writing in "+hdr_filename+" file") + hdr_file.close() + except Exception, e: + # If an error occurred, print line number and error message + import traceback, sys + tb = sys.exc_info()[2] + arcpy.AddMessage("Line:" + str(tb.tb_lineno)) + arcpy.AddMessage("Error " + e.message) + print "OMG! Error while in: write_txt_file" + raise e + +def replace_in_list(regex,lst): + return [re.sub(regex, '', x).strip() for x in lst] #List comprehension +#Method to concatenate the band name to the raster basename +#BandNames has or all the list of band names or the number of bands +def getNumberOfBands(bandNames): + try: + #If there was no bandnames or header, a band number was assigned + #This would be an integer + numOfBands = int(bandNames) + isANumber = True + return numOfBands, isANumber + except ValueError: + #If there is a band name as a string we will say it's one band + numOfBands = 1 + isANumber = False + return numOfBands, isANumber + except TypeError: + #If there are band names as list or array we will count them here + numOfBands = len(bandNames) + isANumber = False + return numOfBands, isANumber + except Exception, e: + import traceback, sys + tb = sys.exc_info()[2] + arcpy.AddMessage("Line:" + str(tb.tb_lineno)) + arcpy.AddMessage("Error " + e.message) + print "OMG!" + raise e +def print_dict(dictio): + #The next line iterates, formats and prints the dictionary, key:value + #print "\n".join('{}={}'.format(k,v) for k,v in dictio.items()) print in console + arcpy.AddMessage("\n".join('{}={}'.format(k,v) for k,v in dictio.items()))#Print to arcmap results window +def joinZonalStatsToTable (band, tableName, bandField): + statsTable = tablesPath + "\\" + tableName + arcpy.AddMessage("Stats_table: " + statsTable) + print statsTable + Field_Name_Calculate_field = gridName + "." + bandField + print Field_Name_Calculate_field + #Expression_Select_layer = tableName+".Mean IS NOT NULL" + # Use this if the stats were saved to a dbf file "\""+tableName+".Mean\" IS NOT NULL" + # Use this if the stats were saved to a table "\""+tableName+":Mean\" IS NOT NULL" + # Use this if the stats were saved to a database table tableName+".Mean IS NOT NULL" + + #We will save the field Mean values of the table into the feature attributes + #The round decimals are an input from the User + Expression_Calculate_field = "!"+tableName+".Mean!" #Not to round + #Expression_Calculate_field = "round(!"+tableName+".Mean!,"+ str(roundDecimals) +")" + #the "!"+tableName+":Mean!" notation (:)is used when the stats are saved to a table + #the "!"+tableName+".Mean!" notation (.)is used when the stats are saved to a dbf file + print Expression_Calculate_field + + # Process: Zonal Statistics as Table + arcpy.AddMessage("Zonal statistics...") + print "Zonal statistics..." + #Perform the zonalstatistics getting only the "MEAN" value of the cells + #Save ALL statistics on table + #"ALL" can be changed to get different statistics: + #ALL,MEAN,MAJORITY,MAXIMUM,MEDIAN,MINIMUM,MINORITY,RANGE,STD,SUM,VARIETY,MIN_MAX,MEAN_STD,MIN_MAX_MEAN  + zs = arcpy.gp.ZonalStatisticsAsTable_sa(plot_polygons, Zone_field_and_join_Field, band, statsTable, "DATA", stats) + arcpy.AddMessage("stats table: " + str(zs)) + fields = gp.ListFields(gridLayer, bandField) + field_found = fields.Next() + if (not field_found): + arcpy.AddField_management(gridLayer, bandField, "FLOAT", 10, 3, "", "", "NULLABLE", "NON_REQUIRED", "") + arcpy.AddMessage("Added field " + str(bandField)) + else: + arcpy.AddMessage("Field found " + str(field_found.name)) + + # Process: Add Join + print "Joining..." + arcpy.AddMessage("grid..."+ gridLayer) + arcpy.AddMessage("field..."+Zone_field_and_join_Field) + arcpy.AddJoin_management(gridLayer, Zone_field_and_join_Field, zs, Zone_field_and_join_Field, "KEEP_ALL") + + # Process: Select Layer By Attribute +## if i==1: +## print "Selecting Layer..." +## gridLayerSelection = arcpy.SelectLayerByAttribute_management(gridLayer, "NEW_SELECTION", Expression_Select_layer) + + # Process: Calculate Field + print "Calculating field" + arcpy.AddMessage("Calculating field...") + arcpy.CalculateField_management(gridLayer, Field_Name_Calculate_field, Expression_Calculate_field, "PYTHON") + + # Process: Remove Join + arcpy.AddMessage("remove Join") + arcpy.RemoveJoin_management(gridLayer, "") + #Erase stats table + if not keep_tables: arcpy.Delete_management(zs) + +def letsgo(header_file,assign_bands=1): + try: + if not exists(header_file): + write_hdr_file(header_file,assign_bands) + arcpy.AddMessage("Header not found, it was created a basic one. Number of bands assigned: "+str(assign_bands)) + arcpy.AddMessage("started reading header file:" + str(header_file)) + envi_header_keywords={"acquisition time","band names","bands", + "bbl","byte order","class lookup","class names", + "classes","cloud cover","complex function", + "coordinate system string","data gain values", + "data ignore value","data offset values", + "data reflectance gain values","data reflectance offset values", + "data type","default bands","default stretch","dem band", + "dem file","description","file type","fwhm","geo points", + "header offset","interleave","lines","map info", + "major frame offsets","minor frame offsets","pixel size", + "product type","projection info","read procedures", + "reflectance scale factor","rpc info","samples","security tag", + "sensor type","solar irradiance","spectra names","sun azimuth", + "sun elevation","wavelength","wavelength units","x start", + "y start","z plot average","z plot range","z plot titles"} + envi_header_keywords_or="|".join(envi_header_keywords) + # Read and Iterate over the lines of the file + with open(header_file, 'rt') as f: + data = f.read()[5:] #Skip the first "ENVI" letters of the header + lines = re.split(r"[\n]", data)#the info corresponding to one line + arcpy.AddMessage("reading header lines...") + dictio = {} + global deb + deb=0 + wl=[]#List to save wavelengths + bn=[]#List to save band names + clines=[]#complete lines with all the info corresponding to one header keyword + ongoing=0 #Variable para marcar si se está buscando el resto de la lĂ­nea de info + #to one header keyword is not in one single line + kwinfo="" + for i,l in enumerate(lines): + if deb==1: print str(i)+"-l- "+str(l) + kwinfo+=l + if deb==1: print str(i)+"-kwinfo- "+str(kwinfo) + if ongoing==1: + if "}" in l: + if deb==1: print "ongoing==se cierra por fin el parentesis" + print_kwinfo() + clines.append(kwinfo) + kwinfo="" + ongoing=0 + continue + else: + if deb==1: print "ongoing==1 else" + if ongoing==0: + ongoing=1 + for keyw in envi_header_keywords: + if keyw in l: + if deb==1: print keyw+" attribute found" + if "{" in l: + if "}" in l: + if deb==1: print "ongoing==parentesis cerrando en linea" + print_kwinfo() + clines.append(kwinfo) + kwinfo="" + ongoing=0 + else: + if deb==1: print "if bracket in l: --- else" + else: + if deb==1: print "ongoing==sin paretesis" + print_kwinfo() + clines.append(kwinfo) + kwinfo="" + ongoing=0 + if deb==1: print "fin de ronda de for" + for cl in clines: + #expresion to filter how to sepatate the string. + regexpresion = re.compile(r"""(.+?)\s*=\s*(.+)""") + dictio.update(dict(regexpresion.findall(cl))) + if deb==1: print regexpresion.findall(cl) + for key in dictio: dictio[key]=dictio[key].strip() + print "Header attributes:" + arcpy.AddMessage("Header attributes read") + print_dict(dictio) + expr_replace = re.compile(r"{|}") #Expression to remove brackets below + #wavelength + if "wavelength" in dictio: + wl=re.split(r"[,]", dictio["wavelength"]) + wl =replace_in_list(expr_replace,wl) + arcpy.AddMessage(wl) + arcpy.AddMessage("saved wl") + #Number of Bands + if "bands" in dictio: + hdr_bands=dictio["bands"] + arcpy.AddMessage(hdr_bands) + arcpy.AddMessage("saved Number of bands from header") + #Band names + if "band names" in dictio: + arcpy.AddMessage("band names attribute found") + bn=re.split(r"[,]", dictio["band names"]) + arcpy.AddMessage("split competed") + bn=replace_in_list(expr_replace,bn) + arcpy.AddMessage("saved bn") + #Next we add the wl units only if they are defined + arcpy.AddMessage("Format composed band name") + full_bn=hdr_bands #We assign by default the number of bands in the raster + if "wavelength units" in dictio: + if "band names" in dictio and "wavelength" in dictio: + join_str = '{bandn} ({waveln} '+dictio["wavelength units"]+')' if dictio["wavelength units"]!="Unknown" else '{bandn} ({waveln})' + arcpy.AddMessage("bn and wl found") + full_bn= '\n'.join(join_str.format(bandn=b, waveln=w) for b,w in itertools.izip(bn, wl)).split('\n') + if not "band names" in dictio and "wavelength" in dictio: + join_str = '{waveln} '+dictio["wavelength units"] if dictio["wavelength units"]!="Unknown" else '{waveln}' + arcpy.AddMessage("wl found+units") + full_bn= '\n'.join(join_str.format(waveln=w) for w in wl).split('\n') + else: + if "band names" in dictio and "wavelength" in dictio: + join_str = '{bandn} ({waveln})' + arcpy.AddMessage("bn and wl found") + full_bn= '\n'.join(join_str.format(bandn=b, waveln=w) for b,w in itertools.izip(bn, wl)).split('\n') + if not "band names" in dictio and "wavelength" in dictio: + arcpy.AddMessage("wl found") + full_bn= wl + arcpy.AddMessage(full_bn) + if deb==1: + print full_bn + print "Bands descripted in header: "+len(full_bn) + arcpy.AddMessage("Finished reading header") + arcpy.AddMessage(type(full_bn)) #to know the type of variable it was created + return full_bn + except Exception, e: + # If an error occurred, print line number and error message + import traceback, sys + tb = sys.exc_info()[2] + arcpy.AddMessage("Line:" + str(tb.tb_lineno)) + arcpy.AddMessage("Error " + e.message) + print "OMG!" + raise e +##def save_bn_wl(): +## full_bn= '\n'.join(join_str.format(bandn=b, waveln=w) for b,w in itertools.izip(bn, wl)).split('\n') +## +##def save_wl(): +## full_bn= '\n'.join(join_str.format(waveln=w) for w in itertools.izip(wl)).split('\n') + +##--------------------------------- Starts the script +try: + # Check out any necessary licenses + arcpy.CheckOutExtension("spatial") + + # Create the Geoprocessor object + gp = arcgisscripting.create() + + # Load required toolboxes + print "Start!" + #--------------------------------------- + #start----------------Input parameters + #--------------------------------------- + #Type: Feature layer -buffer for the zonalStats + plot_polygons = arcpy.GetParameterAsText(0) + arcpy.AddMessage("read b1 " + plot_polygons) + print plot_polygons + + #type: image layer + image = arcpy.GetParameterAsText(2) + arcpy.AddMessage("read " + image) + desc = arcpy.Describe(image) + rPath = desc.path + rdate = desc.baseName[1:7] #Get the date from the raster to create the save feature + r_img = desc.baseName[0:1] #Get the image id from the raster to create the save feature + trialName = desc.baseName[7:10] #Get the trial id from the raster to create the save feature + image = str(rPath) + "\\" + desc.baseName+ "." +desc.extension + rname = desc.baseName + arcpy.AddMessage("image source: " + image) + print image + + ####create the feature to extract to + ##type: Feature layer - Feature in whose table the copy of the features will be created to store the Mean stats + wantTocopyInputFeatures = arcpy.GetParameter(10) #Choose if the input feature will be used to sava the stats or a new will be created + if wantTocopyInputFeatures: + folder_storing = arcpy.GetParameterAsText(1) #Where to create the copies + Grid = str(folder_storing)+ "\\"+ r_img + rdate + plot_polygons #location and name of copy + if not exists(Grid): + arcpy.CopyFeatures_management(plot_polygons, Grid)#Copy feature + else: + Grid = plot_polygons ###Save in the input feature when not creting new feature + arcpy.AddMessage("read " + Grid) + desc = arcpy.Describe(Grid) + gPath = desc.path + gridSource = str(gPath) + arcpy.AddMessage("Grid source: " + gridSource) + gridName = desc.baseName + arcpy.AddMessage("Grid basename: " + gridName) + print Grid + + #type: Field - Field for join and statistics + id_field = arcpy.GetParameterAsText(3) + arcpy.AddMessage("read " + id_field) + + #tablesPath: tablesPath - GDB where to save the statistics tables + tablesPath = arcpy.GetParameterAsText(4) + arcpy.AddMessage("tablesPath " + tablesPath) + + #roundDecimals: roundDecimals - number of decimals to round the "Mean" statistics that are joined to the feature's table + roundDecimals = arcpy.GetParameterAsText(5) + arcpy.AddMessage("roundDecimals: " + roundDecimals) + + #SaveStats: SaveStats - wether to save the statistics table or not + keep_tables = arcpy.GetParameter(7) + arcpy.AddMessage("keep_tables: " + str(keep_tables)) + + #stats: stats - a parameter for the "zonal statistics as table" tool in UPPERCASE PLEASE + #TODO: make a selectable list of the options + #ALL,MEAN,MAJORITY,MAXIMUM,MEDIAN,MINIMUM,MINORITY,RANGE,STD,SUM,VARIETY,MIN_MAX,MEAN_STD,MIN_MAX_MEAN + stats = arcpy.GetParameterAsText(8) + arcpy.AddMessage("stats to be computed: " + stats) + + #KnownBands: Known number of bands in case there is no header file + #Optional, but set to default as 1 if there is no header file + knownBands = arcpy.GetParameterAsText(9) + arcpy.AddMessage("known number of Bands: " + str(knownBands)) + #----------------------------------------- + #ENDS---------------------Input parameters + #----------------------------------------- + + #List of band names + # ---------------------Reading header to get Bands + #We assume header file is there and named as the raster + hdr_file = os.path.join(str(rPath) + "\\" + rname+".hdr") + #Read header to know number and name of bands or set them a priori + #knownBands is optional + bandNames = letsgo(hdr_file, knownBands) + #Default GDB to store the tables with the stats if not User input + #tempGdb_path = r"C:\Users\usuario\Documents\ArcGIS" + #tempGdb = r"Default.gdb" + #This is the path + #tablesPath = os.path.join(tempGdb_path, tempGdb) + #Keep the individual stats tables? + #Either way the MEAN is stored to the table of the grid feature selected + #keep_tables=True # Input as user selects + # Execute CreateFileGDB + #create_if_not_exists(tempGdb_path) + #if not exists(tablesPath): + # arcpy.CreateFileGDB_management(tempGdb_path, tempGdb) + print tablesPath + + # Local variables: + arcpy.AddMessage("Local variables") + print "Local variables" + Zone_field_and_join_Field = id_field + gridLayer = "gridLayer" + + + ## Make a layer + print "Make layer" + arcpy.MakeFeatureLayer_management(Grid, "gridLayer") + + i=1 + try: + arcpy.AddMessage("Iterating the zonal statistics across all bands...") + #Returns: [0]numberOfBands, [1]isBandNamesInteger? + numOfBands = getNumberOfBands(bandNames) + arcpy.AddMessage("Number of bands: "+str(numOfBands[0])) + #If the image has more than one band... + if numOfBands[0] != 1: + #If we only have the number of bands, but no band names (because there was no hdr)... + #We will call the bands by the default way ArcMap does: RasterName + Band_1, Band_2, etc... + #we create the list to iterate it + if numOfBands[1]: bandNames = list('Band_%s' % b for b in range(1, numOfBands[0]+1)) + #Here iterates through the bands and runs the zonal statistics tool + for b in bandNames: + #Next we add the band name to the raster basename + band = image + "\\" + b + arcpy.AddMessage("Iterating band # " + band) + bandField = "B" + str(i) + print bandField + #Set table name + tableName = gridName+ trialName + bandField #+ ".dbf" + #Here we call the method to do the zonal statistics and join the mean to the feature table + joinZonalStatsToTable (band, tableName, bandField) + i+=1 + else: + #If we have just one band we call the raster without specifying any band + band = image + bandField = "B1" + tableName = gridName+ trialName + #Here we call the method to do the zonal statistics and join the mean to the feature table + joinZonalStatsToTable (band, tableName, bandField) + + ###Experiment to see which ended well + check=r'C:\\vuelos\\temp\\g.txt' + write= "Finished "+str(band)+" with "+ str(tableName)+"\n" + write_txt_file(check,"a",write) + ##Experiment + except Exception, e: + # If an error occurred, print line number and error message + import traceback, sys + tb = sys.exc_info()[2] + arcpy.AddMessage("Line:" + str(tb.tb_lineno)) + arcpy.AddMessage("Error " + e.message) + print "OMG!" + raise e + f="Finish" + print f + arcpy.SetParameterAsText(6,f) + +except Exception, e: + # 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z4GzN%4B!Tn;Reg$2AOb!1h~OaxWRF_!92LZI{EH!Yq-H1xPccErF-m{F It;FyD0kq}Y3IG5A literal 0 HcmV?d00001 diff --git a/ENVI/G.exp b/ENVI/G.exp new file mode 100644 index 0000000..487c44b --- /dev/null +++ b/ENVI/G.exp @@ -0,0 +1,4 @@ +ENVI EXPRESSIONS +B550/B670 +B750/B550 +0.5*(120*(B750-B550)-200*(B670-B550)) diff --git a/ENVI/GM1.exp b/ENVI/GM1.exp new file mode 100644 index 0000000..d03c711 --- /dev/null +++ b/ENVI/GM1.exp @@ -0,0 +1,3 @@ +ENVI EXPRESSIONS +B750/B550 +0.5*(120*(B750-B550)-200*(B670-B550)) diff --git a/ENVI/MCARI1.exp b/ENVI/MCARI1.exp new file mode 100644 index 0000000..2a8f8e1 --- /dev/null +++ b/ENVI/MCARI1.exp @@ -0,0 +1,10 @@ +ENVI EXPRESSIONS +1.2*(1.2*(B800-B550)-2.5*(B670-B550)) +(B800/B670-1)/((B800/B670)^0.5)+1 +(1+0.16)*(B800-B670)/(B800+B670+0.16) +(B800-B670)/sqrt(B800+B670) +(B800-B670)/(B800+B670) +B800/B680 +B550/B670 +B750/B550 +0.5*(120*(B750-B550)-200*(B670-B550)) diff --git a/ENVI/MCARI2.exp b/ENVI/MCARI2.exp new file mode 100644 index 0000000..c96fed6 --- /dev/null +++ b/ENVI/MCARI2.exp @@ -0,0 +1,11 @@ +ENVI EXPRESSIONS +1.5*(2.5*(B800-B670)-1.3*(B800-B550))/sqrt((2*B800+1)^2-(6*B800-5*sqrt(B670))-0.5) +1.2*(1.2*(B800-B550)-2.5*(B670-B550)) +(B800/B670-1)/((B800/B670)^0.5)+1 +(1+0.16)*(B800-B670)/(B800+B670+0.16) +(B800-B670)/sqrt(B800+B670) +(B800-B670)/(B800+B670) +B800/B680 +B550/B670 +B750/B550 +0.5*(120*(B750-B550)-200*(B670-B550)) diff --git a/ENVI/MSR.exp b/ENVI/MSR.exp new file mode 100644 index 0000000..e375a2d --- /dev/null +++ b/ENVI/MSR.exp @@ -0,0 +1,9 @@ +ENVI EXPRESSIONS +(B800/B670-1)/((B800/B670)^0.5)+1 +(1+0.16)*(B800-B670)/(B800+B670+0.16) +(B800-B670)/sqrt(B800+B670) +(B800-B670)/(B800+B670) +B800/B680 +B550/B670 +B750/B550 +0.5*(120*(B750-B550)-200*(B670-B550)) diff --git a/ENVI/MTVI1.exp b/ENVI/MTVI1.exp new file mode 100644 index 0000000..2d7a55d --- /dev/null +++ b/ENVI/MTVI1.exp @@ -0,0 +1,12 @@ +ENVI EXPRESSIONS +1.2*(2.5*(B800-B670)-1.3*(B800-B550)) +1.5*(2.5*(B800-B670)-1.3*(B800-B550))/sqrt((2*B800+1)^2-(6*B800-5*sqrt(B670))-0.5) +1.2*(1.2*(B800-B550)-2.5*(B670-B550)) +(B800/B670-1)/((B800/B670)^0.5)+1 +(1+0.16)*(B800-B670)/(B800+B670+0.16) +(B800-B670)/sqrt(B800+B670) +(B800-B670)/(B800+B670) +B800/B680 +B550/B670 +B750/B550 +0.5*(120*(B750-B550)-200*(B670-B550)) diff --git a/ENVI/MTVI2.exp b/ENVI/MTVI2.exp new file mode 100644 index 0000000..6f48c77 --- /dev/null +++ b/ENVI/MTVI2.exp @@ -0,0 +1,13 @@ +ENVI EXPRESSIONS +1.5*(1.2*(B800-B550)-2.5*(B670-B550))/sqrt((2*B800+1)^2-(6*B800-5*sqrt(B670))-0.5) +1.2*(2.5*(B800-B670)-1.3*(B800-B550)) +1.5*(2.5*(B800-B670)-1.3*(B800-B550))/sqrt((2*B800+1)^2-(6*B800-5*sqrt(B670))-0.5) +1.2*(1.2*(B800-B550)-2.5*(B670-B550)) +(B800/B670-1)/((B800/B670)^0.5)+1 +(1+0.16)*(B800-B670)/(B800+B670+0.16) +(B800-B670)/sqrt(B800+B670) +(B800-B670)/(B800+B670) +B800/B680 +B550/B670 +B750/B550 +0.5*(120*(B750-B550)-200*(B670-B550)) diff --git a/ENVI/NDVI.exp b/ENVI/NDVI.exp new file mode 100644 index 0000000..2c58135 --- /dev/null +++ b/ENVI/NDVI.exp @@ -0,0 +1,6 @@ +ENVI EXPRESSIONS +(B800-B670)/(B800+B670) +B800/B680 +B550/B670 +B750/B550 +0.5*(120*(B750-B550)-200*(B670-B550)) diff --git a/ENVI/OSAVI.exp b/ENVI/OSAVI.exp new file mode 100644 index 0000000..236f0f2 --- /dev/null +++ b/ENVI/OSAVI.exp @@ -0,0 +1,8 @@ +ENVI EXPRESSIONS +(1+0.16)*(B800-B670)/(B800+B670+0.16) +(B800-B670)/sqrt(B800+B670) +(B800-B670)/(B800+B670) +B800/B680 +B550/B670 +B750/B550 +0.5*(120*(B750-B550)-200*(B670-B550)) diff --git a/ENVI/PSSRa.exp b/ENVI/PSSRa.exp new file mode 100644 index 0000000..1c028a0 --- /dev/null +++ b/ENVI/PSSRa.exp @@ -0,0 +1,5 @@ +ENVI EXPRESSIONS +B800/B680 +B550/B670 +B750/B550 +0.5*(120*(B750-B550)-200*(B670-B550)) diff --git a/ENVI/RDVI.exp b/ENVI/RDVI.exp new file mode 100644 index 0000000..aba203d --- /dev/null +++ b/ENVI/RDVI.exp @@ -0,0 +1,7 @@ +ENVI EXPRESSIONS +(B800-B670)/sqrt(B800+B670) +(B800-B670)/(B800+B670) +B800/B680 +B550/B670 +B750/B550 +0.5*(120*(B750-B550)-200*(B670-B550)) diff --git a/ENVI/TVI.exp b/ENVI/TVI.exp new file mode 100644 index 0000000..477b129 --- /dev/null +++ b/ENVI/TVI.exp @@ -0,0 +1,2 @@ +ENVI EXPRESSIONS +0.5*(120*(B750-B550)-200*(B670-B550)) diff --git a/ENVI/batch_subset.pro b/ENVI/batch_subset.pro new file mode 100644 index 0000000..123e2d3 --- /dev/null +++ b/ENVI/batch_subset.pro @@ -0,0 +1,14 @@ +; Launch the application +e = ENVI() + +; Open a file +File = FILEPATH('qb_boulder_msi', ROOT_DIR=e.ROOT_DIR, $ + SUBDIRECTORY = ['data']) +Raster = e.OpenRaster(file) + +;coordinates of roi +;MapX, MapY2, MapX2, MapY + +; Define the geographic subset +Subset = ENVISubsetRaster(Raster, SPATIALREF=SpatialRef, $ + SUB_RECT=[MapX, MapY2, MapX2, MapY]) diff --git a/ENVI/kelvin2celsius.pro b/ENVI/kelvin2celsius.pro new file mode 100644 index 0000000..b628ad4 --- /dev/null +++ b/ENVI/kelvin2celsius.pro @@ -0,0 +1,35 @@ +pro kelvin2celsius + compile_opt idl2 + + e = ENVI(); Start ENVI. ENVI help code + + CATCH, theError; Establish error handler. + IF theError NE 0 THEN BEGIN;This statement begins the error handler: + Catch, /Cancel + Help, /Last_Message, Output=theErrorMessage + FOR j=0,N_Elements(theErrorMessage)-1 DO BEGIN + Print, theErrorMessage[j] + ENDFOR + RETURN + ENDIF + + ;Pick file dialog: + fileKelvin = DIALOG_PICKFILE(PATH='E:', $ + TITLE='Select image in kelvin units to convert to celsius', FILTER='*.tif') + print,'File: ' + fileKelvin + rasterKelvin = e.OpenRaster(fileKelvin) + ; Convert from kelvin x 100 to celsius + thermal = ((rasterKelvin.GetData(BANDS=0))/100)-273.15 + + ; Determine an output file + outFile = file_dirname(fileKelvin, /MARK_DIRECTORY)+file_basename(fileKelvin, '.tif') + '_c.bsq' + fileThermal = ENVIRaster(thermal, URI=outFile, NBANDS=1) + ;Save file in celsius degrees + ;The header needs to be created + ;We want to use a ROI to crop the image + fileThermal.Save + ;Save to TIF + ;The header needs to be created + outFileTif = file_dirname(fileKelvin, /MARK_DIRECTORY)+file_basename(fileKelvin, '.tif') + '_c.tif' + WRITE_TIFF, outFileTif,fileThermal,/float +end \ No newline at end of file diff --git a/ENVI/ndvi_float.exp b/ENVI/ndvi_float.exp new file mode 100644 index 0000000..e72c5af --- /dev/null +++ b/ENVI/ndvi_float.exp @@ -0,0 +1,2 @@ +ENVI EXPRESSIONS +float(float(float(B6)-float(B1))/(float(float(B6)+float(B1)))) diff --git a/ENVIMaskRasters.pro b/ENVIMaskRasters.pro new file mode 100644 index 0000000..454ef22 --- /dev/null +++ b/ENVIMaskRasters.pro @@ -0,0 +1,72 @@ + ;+ + ; :Author: Lorena 4/09/2015 + ;- + ;+ + ; :Description: + ; + ; Mask and subset hyper raster +pro ENVIMaskRasters + compile_opt idl2 + ; Start the application + e = ENVI() + ;-------------Inputs and mask---------------- + ;test location + test5 = FILEPATH('test5.bsq', ROOT_DIR='c:\vuelos\', $ + SUBDIRECTORY = ['temp']) + + test4 = FILEPATH('test14.bsq', ROOT_DIR='c:\vuelos\', $ + SUBDIRECTORY = ['temp']) + + test6 = FILEPATH('test10.bsq', ROOT_DIR='c:\vuelos\', $ + SUBDIRECTORY = ['temp']) + + ; Open an input file + PRINT, 'Open the input file.' + filei = FILEPATH('150110_bw2_spr.bsq', ROOT_DIR='E:\150110\h150110\', $ + SUBDIRECTORY = ['ortho_res']) + raster = e.OpenRaster(filei) + + ; Open the mask file + PRINT, 'Open the mask file.' + filem = FILEPATH('150110_bw2msk.tif', ROOT_DIR='E:\150110\h150110\', $ + SUBDIRECTORY = ['mask']) + maskFile = e.OpenRaster(filem) + ;maskRas = (maskFile EQ 1 ) + + +; ; create a masked raster +; PRINT, 'Mask raster' +; rasterWithMask = ENVIMaskRaster(raster, maskFile) +;; +;; PRINT, 'Save masked raster.' +;; rasterWithMask.Export, test5, 'envi' +; +;;-------------Spatial subset---------------- +;; This is the area of interest: +;UpperLeftLat = 27.386274 +;UpperLeftLon = -109.922458 +;LowerRightLat = 27.381419 +;LowerRightLon = -109.915562 +; +;; Get the spatial reference of the raster +;SpatialRef = raster.SPATIALREF +; +;; Convert from Lon/Lat to MapX/MayY +;SpatialRef.ConvertLonLatToMap, UpperLeftLon, UpperLeftLat, MapX, MapY +;SpatialRef.ConvertLonLatToMap, LowerRightLon, LowerRightLat, MapX2, MapY2 +; +;; Define the geographic subset +;subset = ENVISubsetRaster(rasterWithMask, SPATIALREF= SpatialRef, $ +; SUB_RECT=[MapX, MapY2, MapX2, MapY]) +; +;;Save the masked subset of the raster +;;mkdfile = FILEPATH('h150110bw2mkd.bsq', ROOT_DIR='E:\150110\h150110\', $ +; ;SUBDIRECTORY = ['mask']) +; subset.Export, test6, 'envi' + ;------------DISLAY----------------- + ; display the new raster, the masked areas are transparent + viewss = e.GetView() + viewss.Zoom, /FULL_EXTENT + layer1 = viewss.CreateLayer(maskFile) + ; +end \ No newline at end of file diff --git a/Excel_VBAmodule_getAllSheetNames.txt b/Excel_VBAmodule_getAllSheetNames.txt new file mode 100644 index 0000000..7d4c525 --- /dev/null +++ b/Excel_VBAmodule_getAllSheetNames.txt @@ -0,0 +1,15 @@ +Sub ListWorkSheetNamesNewWs() +'Updateby20140624 +Dim xWs As Worksheet +On Error Resume Next +Application.DisplayAlerts = False +xTitleId = "KutoolsforExcel" +Application.Sheets(xTitleId).Delete +Application.Sheets.Add Application.Sheets(1) +Set xWs = Application.ActiveSheet +xWs.Name = xTitleId +For i = 2 To Application.Sheets.Count + xWs.Range("A" & (i - 1)) = Application.Sheets(i).Name +Next +Application.DisplayAlerts = True +End Sub \ No newline at end of file diff --git a/Pix4D/Pix4d_ComandLine.bat b/Pix4D/Pix4d_ComandLine.bat new file mode 100644 index 0000000..e4a90bd --- /dev/null +++ b/Pix4D/Pix4d_ComandLine.bat @@ -0,0 +1,19 @@ + +:: Autor: Gil Thompson +:: Fecha: 09/11/17 +:: Descr: TEST Generador de proyectos en Pix4D desde bash win +:: Falta test Ag Multispectral 3d-maps + +:: Direccion de la carpeta de imgs +set path= "E:\MEDICIONES PHY MATRICE_2017\PHY_SQ_DRIP_REDEDGE_170323\0001SET\000" + +@echo %time% +"C:\Program Files\Pix4dmapper\Pix4dmapper" -c -n --image-dir %path% --template ag-multispectral "E:\MEDICIONES PHY MATRICE_2017\PHY_SQ_DRIP_REDEDGE_170323\PHY_SQ_DRIP_REDEDGE_170323.p4d" +@echo %time% + + +pause + +"C:\Program Files\Pix4dmapper\Pix4dmapper" -c -i "E:\MEDICIONES PHY MATRICE_2017\PHY_SQ_DRIP_REDEDGE_170323\PHY_SQ_DRIP_REDEDGE_170323.p4d" + +exit b \ No newline at end of file diff --git a/TileLayerPugin_GoogleTiles_test.tsv b/TileLayerPugin_GoogleTiles_test.tsv new file mode 100644 index 0000000..213d23c --- /dev/null +++ b/TileLayerPugin_GoogleTiles_test.tsv @@ -0,0 +1 @@ +google local file:///d:/tilemaps/slope/{z}/{x}/{y}.png 0 6 13 130.5 33.6 135.0 36.0 \ No newline at end of file diff --git a/WindowsShell/Replace-0By-1.cmd b/WindowsShell/Replace-0By-1.cmd new file mode 100644 index 0000000..7b18237 --- /dev/null +++ b/WindowsShell/Replace-0By-1.cmd @@ -0,0 +1,3 @@ +@echo off +cls +python C:\Dropbox\Software\Scripts\python\autopanoFile_replace-0By-1frames.py %* \ No newline at end of file diff --git a/WindowsShell/StackMCAAutopanoOutput.cmd b/WindowsShell/StackMCAAutopanoOutput.cmd new file mode 100644 index 0000000..6586c88 --- /dev/null +++ b/WindowsShell/StackMCAAutopanoOutput.cmd @@ -0,0 +1,4 @@ +@echo off +cls +Rscript C:\Dropbox\Software\Scripts\r\stackMCAbandsAsExportedFromAutopano_cmd.R %* +pause \ No newline at end of file diff --git a/WindowsShell/ToASCII.cmd b/WindowsShell/ToASCII.cmd new file mode 100644 index 0000000..e065e06 --- /dev/null +++ b/WindowsShell/ToASCII.cmd @@ -0,0 +1,3 @@ +@echo off +cls +python C:\Dropbox\Software\Scripts\python\csv2pnr.py %* \ No newline at end of file diff --git a/WindowsShell/makeHardlinkOfFileInCompass710folder.bat b/WindowsShell/makeHardlinkOfFileInCompass710folder.bat new file mode 100644 index 0000000..fa4c381 --- /dev/null +++ b/WindowsShell/makeHardlinkOfFileInCompass710folder.bat @@ -0,0 +1,11 @@ +@echo off +cls +REM The selected inputs names will be used to generate the hardlink on the compass 710 AF Folder +REM +for %%f in (%*) do ( +mklink /H C:\Dropbox\AFebee\compass\710\%%~nxf %%f +echo C:\Dropbox\AFebee\compass\710\%%~nxf +echo %%f +) +echo Finished +pause \ No newline at end of file diff --git a/WindowsShell/makeJunctionInDropbox.bat b/WindowsShell/makeJunctionInDropbox.bat new file mode 100644 index 0000000..13c0e79 --- /dev/null +++ b/WindowsShell/makeJunctionInDropbox.bat @@ -0,0 +1,6 @@ +@echo off +cls +set /p var=Name of the Junction folder to be created in Dropbox/NAS/AF Folder +mklink /J c:\Dropbox\NAS\AF\%var% %1 +echo Finished +pause \ No newline at end of file diff --git a/WindowsShell/remove_thisJunction.bat b/WindowsShell/remove_thisJunction.bat new file mode 100644 index 0000000..2a4d3e8 --- /dev/null +++ b/WindowsShell/remove_thisJunction.bat @@ -0,0 +1,7 @@ +@echo off +cls +echo this folder will be removed if it is empty (Junction) +pause +rmdir %1 +echo Finished +pause \ No newline at end of file diff --git a/batch_subset.pro b/batch_subset.pro new file mode 100644 index 0000000..123e2d3 --- /dev/null +++ b/batch_subset.pro @@ -0,0 +1,14 @@ +; Launch the application +e = ENVI() + +; Open a file +File = FILEPATH('qb_boulder_msi', ROOT_DIR=e.ROOT_DIR, $ + SUBDIRECTORY = ['data']) +Raster = e.OpenRaster(file) + +;coordinates of roi +;MapX, MapY2, MapX2, MapY + +; Define the geographic subset +Subset = ENVISubsetRaster(Raster, SPATIALREF=SpatialRef, $ + SUB_RECT=[MapX, MapY2, MapX2, MapY]) diff --git a/batch_subset.pro.crdownload b/batch_subset.pro.crdownload new file mode 100644 index 0000000..e69de29 diff --git a/excel insert rows macro.txt b/excel insert rows macro.txt new file mode 100644 index 0000000..5043c12 --- /dev/null +++ b/excel insert rows macro.txt @@ -0,0 +1,19 @@ +Sub insertrow() +' insertrow Macro + +Application.ScreenUpdating = True +Dim count As Integer +Dim X As Integer + +For count = 1 To 44 +If ActiveCell.Value <> "" Then +ActiveCell.Offset(1, 0).Select +Range(ActiveCell, ActiveCell.Offset(0, 0)).EntireRow.Insert +ActiveCell.Offset(1, 0).Select +For X = 1 To 1 +Next X +Else +ActiveCell.Offset(1, 0).Range("a1").Select +End If +Next count +End Sub \ No newline at end of file diff --git a/excel table to one row formula.txt b/excel table to one row formula.txt new file mode 100644 index 0000000..10939c1 --- /dev/null +++ b/excel table to one row formula.txt @@ -0,0 +1,3 @@ +=INDEX(MyData,1+INT((ROW(A1)-1)/COLUMNS(MyData)),MOD(ROW(A1)-1+COLUMNS(MyData),COLUMNS(MyData))+1) + +the table name here is MyData \ No newline at end of file diff --git a/kelvin2celsius.pro b/kelvin2celsius.pro new file mode 100644 index 0000000..b628ad4 --- /dev/null +++ b/kelvin2celsius.pro @@ -0,0 +1,35 @@ +pro kelvin2celsius + compile_opt idl2 + + e = ENVI(); Start ENVI. ENVI help code + + CATCH, theError; Establish error handler. + IF theError NE 0 THEN BEGIN;This statement begins the error handler: + Catch, /Cancel + Help, /Last_Message, Output=theErrorMessage + FOR j=0,N_Elements(theErrorMessage)-1 DO BEGIN + Print, theErrorMessage[j] + ENDFOR + RETURN + ENDIF + + ;Pick file dialog: + fileKelvin = DIALOG_PICKFILE(PATH='E:', $ + TITLE='Select image in kelvin units to convert to celsius', FILTER='*.tif') + print,'File: ' + fileKelvin + rasterKelvin = e.OpenRaster(fileKelvin) + ; Convert from kelvin x 100 to celsius + thermal = ((rasterKelvin.GetData(BANDS=0))/100)-273.15 + + ; Determine an output file + outFile = file_dirname(fileKelvin, /MARK_DIRECTORY)+file_basename(fileKelvin, '.tif') + '_c.bsq' + fileThermal = ENVIRaster(thermal, URI=outFile, NBANDS=1) + ;Save file in celsius degrees + ;The header needs to be created + ;We want to use a ROI to crop the image + fileThermal.Save + ;Save to TIF + ;The header needs to be created + outFileTif = file_dirname(fileKelvin, /MARK_DIRECTORY)+file_basename(fileKelvin, '.tif') + '_c.tif' + WRITE_TIFF, outFileTif,fileThermal,/float +end \ No newline at end of file diff --git a/python/autopanoFile_replace-0By-1frames.py b/python/autopanoFile_replace-0By-1frames.py new file mode 100644 index 0000000..a01f85c --- /dev/null +++ b/python/autopanoFile_replace-0By-1frames.py @@ -0,0 +1,32 @@ +##Script para separar el archivo CSV en el formato separado por espacio y con los headers por separado. +import sys, os +import numpy +import re +import fileinput +import shutil +##Leer archivo y remplaza los Frames que terminan en -0.tif con -1.tif +##Para tener el archivo de mosaico autopano con las Ăşltimas 3 bandas +def replace(thefile): + src=thefile #Get the input file name + dst=thefile.replace("-0", "-1") #Copy fila and rename + thefile1=shutil.copy(src, dst) #File to replace + with fileinput.FileInput(thefile1, inplace=True) as file: + for line in file: + print(line.replace("-0.tif", "-1.tif"), end='') + +#Script +for f in sys.argv[1:]: replace(f) + +#######Opening XML files +# from xml.dom import minidom +# Test_file = open('C:/test_file.xml','r') +# xmldoc = minidom.parse(Test_file) + +# Test_file.close() + +# def printNode(node): +# print node +# for child in node.childNodes: +# printNode(child) + +# printNode(xmldoc.documentElement) \ No newline at end of file diff --git a/python/createFolderIfDoesntExists_hardcoded.py b/python/createFolderIfDoesntExists_hardcoded.py new file mode 100644 index 0000000..4d165e1 --- /dev/null +++ b/python/createFolderIfDoesntExists_hardcoded.py @@ -0,0 +1,26 @@ +import arcpy +from os.path import exists, join +from os import makedirs +#Folder which will be checked if exists +dates= ['160505','160509','160514','160520','160525'] +img= 'm' +d= ['msk','ndvi'] +drive_path= 'G:\\AD15_16' + +def checkCreateFolder(f): + print("path to check: " + str(f)) + #Method to Create folder if it doesn't exist + try: + if arcpy.os.path.exists(f): + print('exists') + else: + arcpy.os.makedirs(f) + print('created') + except: + print('error') + +#Iterate method to several dates +for date in dates: + for i in d: + folder = arcpy.os.path.join(drive_path,date,img,i) + checkCreateFolder(folder) \ No newline at end of file diff --git a/python/csv2txt_space_delimited_HEADERSCMD.py b/python/csv2txt_space_delimited_HEADERSCMD.py new file mode 100644 index 0000000..90ae12c --- /dev/null +++ b/python/csv2txt_space_delimited_HEADERSCMD.py @@ -0,0 +1,33 @@ +##Script para separar el archivo CSV en el formato separado por espacio y con los headers por separado. +import sys, os +import numpy +import re +##Leer archivo +def toPNR(thefile): + with open(thefile, 'rb') as f: + contents=numpy.genfromtxt(f,delimiter=',',dtype=None,names=True) + headers = contents.dtype.names + print ('headers: {} '.format(headers)) + #print ('input file {}'.format(thefile)) + #print ('file {}'.format(str(contents))) + basename = os.path.basename(thefile) + print ('input basename {}'.format(basename)) + name =os.path.splitext(basename)[0] + #Salvar datos con la extension . txt + saveFile = os.path.join(os.getcwd(),name+'.txt') + expr = re.compile("^b'|'$") #expresion + with open(saveFile, 'w') as spaceFile: + for i,line in enumerate(contents): #archivos con 1 solo registro? + for item in line: + #escribir linea y quitar los indicadores de tipo de dato "byte" + spaceFile.write('%-25s' % (re.sub(expr, "", str(item)))) + #escribir nueva linea excepto al final + if i!=(len(contents)-1): spaceFile.write('\n') + #Salvar headers con la extension . pnr + saveHeaders = os.path.join(os.getcwd(),name+'_HEADERS.txt') + with open(saveHeaders, 'w') as spaceFile: + for item in headers: + spaceFile.write('%-25s' % (item)) + +#Script +for f in sys.argv[1:]: toPNR(f) diff --git a/python/polygonbisector2wayfc.py b/python/polygonbisector2wayfc.py new file mode 100644 index 0000000..022e2bd --- /dev/null +++ b/python/polygonbisector2wayfc.py @@ -0,0 +1,264 @@ +'''------------------------------------------------------------------------------------------- + Tool Name: Polygon Bisector + Source Name: polygonbisector.py + Version: ArcGIS 10.0 + Author: ESRI, Inc. + Required Arguments: + Input Features (Feature Layer) + Output Feature Class (Feature Class) + Optional Arguments: + Axis (X|Y) + Group Field(s) (Field) + Acceptable Error Percent (Double) + + Description: Computes a line that bisects, or divides in half, a polygon area along a line + of constant latitude or longitude. Half of each input polygon's area will be + on either side of the bisecting line. +----------------------------------------------------------------------------------------------''' + +# Import system modules +import arcpy +import os +import sys +import math + +# Main function, all functions run in GravityModel +def PolygonBisector(in_features, out_fc, axis="x", groupfields=[], error=0.001): + # Error if sufficient license is not available + if arcpy.ProductInfo().lower() not in ['arcinfo']: + arcpy.AddError("An ArcInfo/Advanced license is required.") + sys.exit() + + # Set geoprocessing environments + arcpy.env.overwriteOutput = True + arcpy.env.qualifiedFieldNames = False + + shapefield = arcpy.Describe(in_features).shapeFieldName + rounder = GetRounder(in_features) + + # If group fields are specified, dissolve by them + if groupfields: + in_features = arcpy.management.Dissolve(in_features, "in_memory/grouped", groupfields) + else: + groupfields = [arcpy.Describe(in_features).OIDFieldName] + fields = [shapefield] + groupfields + + # Create output feature class and set up cursor + icur = irow = scur = None + arcpy.management.CreateFeatureclass(os.path.dirname(out_fc), os.path.basename(out_fc), "POLYLINE", "", "", "", arcpy.Describe(in_features).spatialReference) + arcpy.management.AddField(out_fc, "Group_", "TEXT", "", "", "", "Group: {0}".format(", ".join(groupfields))) + icur = arcpy.InsertCursor(out_fc) + scur = arcpy.SearchCursor(in_features, "", "", ";".join(fields)) + count = int(arcpy.management.GetCount(in_features).getOutput(0)) + arcpy.SetProgressor("step", "Processing polygons...", 0, count, 1) + bigi = 1 + + # Begin processing + try: + for row in scur: + minx = miny = float("inf") + maxx = maxy = float("-inf") + totalarea = 0 + feat = row.getValue(shapefield) + totalarea = row.getValue(shapefield).area + group = [] + for field in groupfields: + group.append(str(row.getValue(field))) + partnum = 0 + # Get the min and max X and Y + for part in feat: + for point in feat.getPart(partnum): + if point: + minx = point.X if point.X < minx else minx + miny = point.Y if point.Y < miny else miny + maxx = point.X if point.X > maxx else maxx + maxy = point.Y if point.Y > maxy else maxy + partnum += 1 + + # Process the polygon + # Some variables + conditionmet = False + difference = 0 + lastdifference = float("inf") + differences = {} + itys = {} + i = 1 + strike = 0 + + # The starting bisector (half the distance from min to max) + if axis == "x": + ity = (miny + maxy)/2.0 + else: + ity = (minx + maxx)/2.0 + + while not conditionmet: + # Construct a line through the middle + if axis == "x": + line = MakeBisector(minx, maxx, ity, in_features, axis) + else: + line = MakeBisector(miny, maxy, ity, in_features, axis) + # The FeatureToPolygon function does not except a geometry object, so make a temporary feature class + templine = arcpy.management.CopyFeatures(line, "in_memory/templine") + temppoly = arcpy.management.CopyFeatures(feat, "in_memory/temppoly") + # Intersect then Feature To Polygon + bisected = arcpy.management.FeatureToPolygon([temppoly, templine], "in_memory/bisected") + clip = arcpy.analysis.Clip(bisected, in_features, "in_memory/clip") + + # Group bisected polygons according to above or below the bisector + arcpy.management.AddField(clip, "FLAG", "SHORT") + ucur = arcpy.UpdateCursor(clip, "", "") + flag = 0 + try: + for urow in ucur: + ufeat = urow.getValue(arcpy.Describe(clip).shapeFieldName) + partnum = 0 + for upart in ufeat: + for upoint in ufeat.getPart(partnum): + if upoint: + if axis == "x": + if round(upoint.Y, rounder) > round(ity, rounder): + flag = 1 + break + elif round(upoint.Y, rounder) < round(ity, rounder): + flag = -1 + break + else: + if round(upoint.X, rounder) > round(ity, rounder): + flag = 1 + break + elif round(upoint.X, rounder) < round(ity, rounder): + flag = -1 + break + partnum += 1 + urow.setValue("FLAG", flag) + ucur.updateRow(urow) + except: + raise + finally: + if ucur: + del ucur + + # Check if the areas are halved + dissolve = arcpy.management.Dissolve(clip, "in_memory/dissolve", "FLAG") + scur2 = arcpy.SearchCursor(dissolve) + try: + for row2 in scur2: + firstarea = row2.getValue(arcpy.Describe(dissolve).shapeFieldName).area + firstflag = row2.getValue("FLAG") + break + except: + raise + finally: + if scur2: + del scur2 + + # ################################################################# + # Modify the Y of the line (move it up or down) to resize the split + # ################################################################# + difference = abs(firstarea - (totalarea/2.0)) + differences[i] = difference + itys[i] = ity + print round(100*(difference/(totalarea/2.0)),5) + #arcpy.AddWarning(round(100*(difference/(totalarea/2.0)),5)) + # Stop if tolerance is achieved + if (difference/(totalarea/2.0))*100 <= error: + conditionmet = True + break + # Moving the line in the wrong direction? due to coordinate system origins or over-compensation + if difference > lastdifference: + firstflag = firstflag*-1.0 + # If we're not improving + if abs(difference) > min(differences.values()): + strike+=1 + # Or if the same values keep appearing + if differences.values().count(difference) > 3 or strike >=3: + arcpy.AddWarning("Tolerance could not be achieved. Output will be the closest possible.") + # Reconstruct the best line + if axis == "x": + line = MakeBisector(minx, maxx, itys[min(differences,key = lambda a: differences.get(a))], in_features, axis) + else: + line = MakeBisector(miny, maxy, itys[min(differences,key = lambda a: differences.get(a))], in_features, axis) + break + # Otherwise move the bisector so that the areas will be more evenly split + else: + if firstflag == 1: + if axis == "x": + ity = ((ity-miny)/((totalarea/2.0)/firstarea)) + miny + else: + ity = ((ity-minx)/((totalarea/2.0)/firstarea)) + minx + elif firstflag == -1: + if axis == "x": + ity = ((ity-miny)*math.sqrt((totalarea/2.0)/firstarea)) + miny + else: + ity = ((ity-minx)*math.sqrt((totalarea/2.0)/firstarea)) + minx + lastdifference = difference + i +=1 + irow = icur.newRow() + irow.setValue(arcpy.Describe(out_fc).shapeFieldName, line) + irow.setValue("Group_", ", ".join(group)) + icur.insertRow(irow) + arcpy.SetProgressorPosition() + arcpy.AddMessage("{0}/{1}".format(bigi, count)) + bigi +=1 + + except: + if arcpy.Exists(out_fc): + arcpy.management.Delete(out_fc) + raise + finally: + if scur: + del scur + if icur: + del icur + if irow: + del irow + for data in ["in_memory/grouped", temppoly, templine, clip, bisected, dissolve]: + if data: + try: + arcpy.management.Delete(data) + except: + "" + +def MakeBisector(min,max,constant, templatefc, axis): + if axis == "x": + array = arcpy.Array() + array.add(arcpy.Point(min, constant)) + array.add(arcpy.Point(max, constant)) + else: + array = arcpy.Array() + array.add(arcpy.Point(constant, min)) + array.add(arcpy.Point(constant, max)) + line = arcpy.Polyline(array, arcpy.Describe(templatefc).spatialReference) + return line + +def GetRounder(in_features): + try: + unit = arcpy.Describe(in_features).spatialReference.linearUnitName.lower() + except: + unit = "dd" + if unit.find("foot") > -1: + rounder = 1 + elif unit.find("kilo") > -1: + rounder = 3 + elif unit.find("meter") > -1: + rounder = 1 + elif unit.find("mile") > -1: + rounder = 3 + elif unit.find("dd") > -1: + rounder = 5 + else: + rounder = 3 + return rounder + +# Run the script +if __name__ == '__main__': + # Get Parameters + in_features = arcpy.GetParameterAsText(0) or r"C:\PolygonBisector\Data\Sample.gdb\Poly" + out_fc = arcpy.GetParameterAsText(1) or r"C:\PolygonBisector\Data\Sample.gdb\Poly_xbisector2" + axis = arcpy.GetParameterAsText(2).lower() or "x" + groupfields = arcpy.GetParameterAsText(3).split(";") if arcpy.GetParameterAsText(3) else [] + error = float(arcpy.GetParameter(4)) if arcpy.GetParameter(4) else 0.001 + + # Run the main script + PolygonBisector(in_features, out_fc, axis, groupfields, error) + print "finished" diff --git a/python/pycharm/.idea/misc.xml b/python/pycharm/.idea/misc.xml new file mode 100644 index 0000000..3c79a6a --- /dev/null +++ b/python/pycharm/.idea/misc.xml @@ -0,0 +1,14 @@ + + + + + + + + + + + + + + \ No newline at end of file diff --git a/python/pycharm/.idea/modules.xml b/python/pycharm/.idea/modules.xml new file mode 100644 index 0000000..24b1219 --- /dev/null +++ b/python/pycharm/.idea/modules.xml @@ -0,0 +1,8 @@ + + + + + + + + \ No newline at end of file diff --git a/python/pycharm/.idea/pycharm.iml b/python/pycharm/.idea/pycharm.iml new file mode 100644 index 0000000..6711606 --- /dev/null +++ b/python/pycharm/.idea/pycharm.iml @@ -0,0 +1,11 @@ + + + + + + + + + + \ No newline at end of file diff --git a/python/pycharm/.idea/workspace.xml b/python/pycharm/.idea/workspace.xml new file mode 100644 index 0000000..6fe3853 --- /dev/null +++ b/python/pycharm/.idea/workspace.xml @@ -0,0 +1,389 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 1477349674863 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/python/pycharm/calendario.html b/python/pycharm/calendario.html new file mode 100644 index 0000000..e69de29 diff --git a/python/pycharm/dataManager.xml b/python/pycharm/dataManager.xml new file mode 100644 index 0000000..3d36c49 --- /dev/null +++ b/python/pycharm/dataManager.xml @@ -0,0 +1,212 @@ + + + + +Empire Burlesque +Bob Dylan +USA +Columbia +10.90 +1985 + + +Hide your heart +Bonnie Tyler +UK +CBS Records +9.90 +1988 + + +Greatest Hits +Dolly Parton +USA +RCA +9.90 +1982 + + +Still got the blues +Gary Moore +UK +Virgin records +10.20 +1990 + + +Eros +Eros Ramazzotti +EU +BMG +9.90 +1997 + + +One night only +Bee Gees +UK +Polydor +10.90 +1998 + + +Sylvias Mother +Dr.Hook +UK +CBS +8.10 +1973 + + +Maggie May +Rod Stewart +UK +Pickwick +8.50 +1990 + + +Romanza +Andrea Bocelli +EU +Polydor +10.80 +1996 + + +When a man loves a woman +Percy Sledge +USA +Atlantic +8.70 +1987 + + +Black angel +Savage Rose +EU +Mega +10.90 +1995 + + +1999 Grammy Nominees +Many +USA +Grammy +10.20 +1999 + + +For the good times +Kenny Rogers +UK +Mucik Master +8.70 +1995 + + +Big Willie style +Will Smith +USA +Columbia +9.90 +1997 + + +Tupelo Honey +Van Morrison +UK +Polydor +8.20 +1971 + + +Soulsville +Jorn Hoel +Norway +WEA +7.90 +1996 + + +The very best of +Cat Stevens +UK +Island +8.90 +1990 + + +Stop +Sam Brown +UK +A and M +8.90 +1988 + + +Bridge of Spies +T`Pau +UK +Siren +7.90 +1987 + + +Private Dancer +Tina Turner +UK +Capitol +8.90 +1983 + + +Midt om natten +Kim Larsen +EU +Medley +7.80 +1983 + + +Pavarotti Gala Concert +Luciano Pavarotti +UK +DECCA +9.90 +1991 + + +The dock of the bay +Otis Redding +USA +Atlantic +7.90 +1987 + + +Picture book +Simply Red +EU +Elektra +7.20 +1985 + + +Red +The Communards +UK +London +7.80 +1987 + + +Unchain my heart +Joe Cocker +USA +EMI +8.20 +1987 + + \ No newline at end of file diff --git a/python/pycharm/dataManager.xsl b/python/pycharm/dataManager.xsl new file mode 100644 index 0000000..024b500 --- /dev/null +++ b/python/pycharm/dataManager.xsl @@ -0,0 +1,34 @@ + + + + + + + +

    My CD Collection

    + + + + + + +

    + + +

    +     + + + Title: + +
    +     + + + Artist: + +
    +     + + \ No newline at end of file diff --git a/python/pycharm/loadCalendar.php b/python/pycharm/loadCalendar.php new file mode 100644 index 0000000..4f0c4aa --- /dev/null +++ b/python/pycharm/loadCalendar.php @@ -0,0 +1,24 @@ + + + + +load('dataManager.xml'); + +// Load XSL file +$xsl = new DOMDocument; +$xsl->load('dataManager.xsl'); + +// Configure the transformer +$proc = new XSLTProcessor; + +// Attach the xsl rules +$proc->importStyleSheet($xsl); + +echo $proc->transformToXML($xml); +?> + + + \ No newline at end of file diff --git a/python/replaceCerosByOneMultispecPano.py b/python/replaceCerosByOneMultispecPano.py new file mode 100644 index 0000000..210e724 --- /dev/null +++ b/python/replaceCerosByOneMultispecPano.py @@ -0,0 +1,33 @@ +import fileinput, sys, os + +#Replace text in file +def replaceInFile(file,searchText,replaceText): + for line in fileinput.input(file, inplace=True): + line = line.replace(searchText, replaceText) + # sys.stdout is redirected to the file + sys.stdout.write(line) +#Copy the file +def copyFile(inputFile, copy): + #Copy the file + with open(inputFile) as f: + with open(copy, "w") as f1: + for line in f: + f1.write(line) + +################ INPUTS ###################### +############################################## +date="160204" +file = "m160204bw8-0.pano" # make if finish in "-0.pano" + +folder=os.path.normpath(r"G:/AD15_16/"+date+"/m/pno/") +############################################## +fileToSearch=os.path.join(folder,file) +textToSearch="-0.tif" +textToReplace="-1.tif" +copiedfile=fileToSearch.replace("-0.pano", "-1.pano") + +print ("start") +#Copy the file +copyFile(fileToSearch, copiedfile) +#Replace +replaceInFile(copiedfile,textToSearch,textToReplace) diff --git a/python/stackMCAPairImagesFolder.py b/python/stackMCAPairImagesFolder.py new file mode 100644 index 0000000..de6df83 --- /dev/null +++ b/python/stackMCAPairImagesFolder.py @@ -0,0 +1,47 @@ +#Script to stack the Multispectra bands from the Autopano mosaic +#Names should be like: +# m[date][trial]pno-0.tif +# m[date][trial]pno-1.tif +# e.g. m160204bw8pno-1.tif +# The required input is: date and trial and the working directory +#This is assuming the name formatting of the greenplane +#0. First step : load packages +#install.packages(c("rgdal","raster")) +require(raster) +require(rgdal) +library(stringr) + +#Function multibandStack +multibandStack <- function(imgBasename, wd) +{ + tryCatch({ + setwd(wd) + #Input file with bands 1,2,3 + img_basename<-imgBasename + img_0<-paste(img_basename,'-0.tif',sep = "") + imported_raster_0=raster(img_0) + #Input file with bands 4,5,6 + img_1<-paste(img_basename,'-1.tif',sep = "") + imported_raster_1=raster(img_1) + #plot(imported_raster_0) #see image just to test + #stack 6 bands in one pile + img=stack(list(img_0,img_1)) + #Write the tif to disk + writeRaster(img, file=paste(imgBasename,".tif",sep = ""),datatype='INT2U',format="GTiff",overwrite=FALSE) + }) +} +########### START ########################## +#Working directory +wd="G:\\AD15_16\\160525\\m\\rfl" +setwd(wd) + +imgFilesList <-list.files(pattern="\\.tif$") +##Next the names of the images, collapse to get one entry per pair(-0.tif and -1.tif) +#imgsPairNames <- unique(str_extract(imgFilesList, "TTC[:digit:]{5}")) #For individual frames +imgsPairNames <- unique(str_extract(imgFilesList, ".{0,8}")) #For mosaics {0,13} +## Sets the pattern of name in files to be stacked +for (i in imgsPairNames){ + print(paste("Image:",i)) + multibandStack(i, wd) +} +paste(print("Finished merging",toString(length(imgsPairNames)),"images")) \ No newline at end of file diff --git a/r/.Rhistory b/r/.Rhistory new file mode 100644 index 0000000..2aaacaf --- /dev/null +++ b/r/.Rhistory @@ -0,0 +1,512 @@ +#add date info in column +#For every sampling date +for (s in 1:samplings) { +df.list[[s]]<-df.list[[s]][df.list[[s]]$id<=maxsp,] #Exclude points above 40 FOR ESCAMILLA etc +sPoints = length(df.list[[s]]$id) #total samples per date +for (i in 1:sPoints) { +print(paste("sampling",sheets[s],"sample",df.list[[s]]$id[i])) +df.list[[s]]$date[i] <- as.character(sheets[s]) #add date info in column +} +} +return (list(df.list,sheets)) +} +##Replace something in a column of a df list +replaceInCol <- function(df_list,col,txt_orig,txt_replace){ +r_df_list<-df_list +for(i in 1:length(df_list)){ +print(i) +l <- length (r_df_list[[i]][,col]) +for(j in 1:l){ +print(j) +print(df_list[[i]][j,col]) +r_df_list[[i]][j,col] <- gsub(txt_orig, txt_replace, df_list[[i]][j,col]) +} +} +return(r_df_list) +} +################################# EXECUTE SCRIPT +############# INPUTS +file="C://Dropbox//data//AD//AD-STARS//ros//ad_ros_ceptometer.xlsx" +trial <- "ros" +whatData <- "par" #What does this data represent +col_names<-c('id', +'par', +'par_time', +'par_ref', +'ref_time') +colTypes <- c('numeric', 'numeric', 'character','numeric','character') +maxsp<-25 +##Edit next +##getMoistreFromExcel <- function (file, startSheet,endCol,endRow,colTypes,col_names,maxsamplepointID) +mData <- getCeptometerFromExcel(file, 2,5,26,colTypes,col_names,maxsp) #get the moisture data[[1]] and sheetnames[[2]] +############# END INPUTS +##Resulting data +dates<-mData[[2]] +mData <-mData[[1]] +##Replace the data "wrong date" in column 3 to keep the time only +#mData <- replaceInCol(mData,5,"1899-12-31 ","") +##Connecto to local Database +mydb = dbConnect(MySQL(), user='root', password='cimmyt', dbname='ac_stars', host='localhost') +#dbListFields(mydb, 'ros') #List fields on table +#Save moisture data to DB +for(i in 1:length(dates)){ +##Check if table already exists... +tExists = dbSendQuery(mydb, paste("SHOW TABLES LIKE '", dates[[i]],"'",sep="")) ##ask if the table exists +data = dbFetch(tExists, n=-1) #get results. The n=-1 is to get all the results, can be used to set a MAX +if(length(row.names(data))==0){ #If the table doesnt exists +print(dates[[i]]) +#withe df to DB +dbWriteTable(mydb, value = mData[[i]], name = paste(trial,dates[[i]],whatData,sep=""), row.names = FALSE) #THere is an issue vs append and autoincrement index +} +} +require(rgdal) +require(raster) +get_roi <- function(trial_roi) {readOGR(dsn=fgdb,layer=trial_roi)} +fgdb = "C:\\Dropbox\\data\\AE\\physio\\ae_fisio.gdb" +roi <- get_roi("hap__ae__bry") +dsm <- raster("G:/AE/161026/r161026fis/r161026hap/3_dsm_ortho/1_dsm/r161026hap_dsm.tif") +r_crop<-crop(dsm, extent(roi), snap="out") +roi +dsm +plot(dsm) +install.packages("shiny") +require(shiny) +runExample("01_hello") +setwd("C:/Dropbox/Software/Scripts/r") +runApp("shiny_extractPhysio") +runApp('shiny_extractPhysio') +runApp('shiny_extractPhysio',display.mode = "showcase") +runApp('shiny_extractPhysio') +runApp('shiny_extractPhysio') +runApp('shiny_extractPhysio') +runApp('shiny_extractPhysio') +runApp('shiny_extractPhysio') +runApp('shiny_extractPhysio') +runApp('shiny_extractPhysio') +runApp('shiny_extractPhysio') +runApp('shiny_extractPhysio') +runApp('shiny_extractPhysio') +runApp('shiny_extractPhysio') +runApp('shiny_extractPhysio') +runApp('shiny_extractPhysio') +runApp('shiny_extractPhysio') +runApp('shiny_extractPhysio') +runApp('shiny_extractPhysio') +runApp('shiny_extractPhysio') +runApp('shiny_extractPhysio') +install.packages(c("maps", "mapproj")) +percent_map <- function(var, color, legend.title, min = 0, max = 100) { +# generate vector of fill colors for map +shades <- colorRampPalette(c("white", color))(100) +# constrain gradient to percents that occur between min and max +var <- pmax(var, min) +var <- pmin(var, max) +percents <- as.integer(cut(var, 100, +include.lowest = TRUE, ordered = TRUE)) +fills <- shades[percents] +# plot choropleth map +map("county", fill = TRUE, col = fills, +resolution = 0, lty = 0, projection = "polyconic", +myborder = 0, mar = c(0,0,0,0)) +# overlay state borders +map("state", col = "white", fill = FALSE, add = TRUE, +lty = 1, lwd = 1, projection = "polyconic", +myborder = 0, mar = c(0,0,0,0)) +# add a legend +inc <- (max - min) / 4 +legend.text <- c(paste0(min, " % or less"), +paste0(min + inc, " %"), +paste0(min + 2 * inc, " %"), +paste0(min + 3 * inc, " %"), +paste0(max, " % or more")) +legend("bottomleft", +legend = legend.text, +fill = shades[c(1, 25, 50, 75, 100)], +title = legend.title) +} +runApp('shiny_extractPhysio') +runApp('shiny_extractPhysio') +runApp('shiny_extractPhysio') +library(reshape2) +library(plyr) +df +myFile = file.path("C:","Dropbox", "data","AE","ae_stars","ae_data_extract.csv", fsep = .Platform$file.sep) +dat = read.csv(myFile, stringsAsFactors = FALSE) +date <- dat$date +#Formatear como fechas las fechas +date <- as.Date(as.character(date),format = "%y%m%d") +# range_date <- range(date) +# all_dates <- seq(range_date[1],range_date[2],1) +# length(all_dates) +# length(date) +# which(all_dates %in% date) +#Vector lĂłgico checando cuales fechas son en enero +if_enero <- date >= as.Date("2017-01-01") & date <= as.Date("2017-1-31") +#porcentaje de las mediciones que fueron hechas en enero +#Todas / las de enero +sum(if_enero)/length(if_enero) +#subset las fechas que pertenecen a enero en base al vector lĂłgico +mediciones_enero <- date [if_enero] +#plot(date, 1:13, type = "l") ## ejemplo de plot lĂ­nea con fecha de mediciones +#### Otro tema +## Data frames +##CREate one +df = data.frame( +num = 1:4, +lower = c("a","b","c","d"), +upper = c("A","B","C","D"), stringsAsFactors = FALSE) +##Examine the object +str(df) +##Subset a column of the data Frame, but always keep the data frame class +df[ ,3, drop = FALSE] +#Subset with another method, including a conditional, and the names of columns +df[df$lower %in% c("a","d"), c("lower","upper")] ##x %in% table +# Exclude rows that are not complete +df[complete.cases(df),] +##Create a column based on a specific value of a cell (row) +df$word[df$num == 2] <- "two" +## create another column mixing the others +df$mix <- paste(df$num,df$lower,df$upper, sep="") +##Use of ifElse +ifelse(df >= 2, ">2", "menor") +"a" > 2 +as.numeric("a") +#####Another subject +###Apply family +#tapply : apply a function across subsets +#data +df$categories <- c("1","1","2","2") +#Get the mean vakue of a column, by groups +#An array is returned +df$mean <- tapply(df$num,df$categories, mean) ##tapply(values, categories, function) +#Eliminate that just created column from the df "mean" +df <- df[, which(!(colnames(df) %in% "mean"))] #From the logical vector we get the indexes of the columns not named "mean" +#Check for missing values AKA NA's, an array is returned with its names +na_values <- tapply(df$word, df$upper, function (x) any(is.na(x))) +#columns with NA values +names(na_values[na_values]) +##use the function apply. It can apply across rows or columns the functions +# parameter with value 1 or 2 refers if it should aggregate rows or columns +#Get the max from num and categories column +apply( df[, c("num","categories")], 2, max, na.rm =TRUE) +#paste values in the row +apply(df, 1, paste) +df +long_table <- melt(df, id.vars ="num") +long_table +dcast(long_table, ... ~ variable) +df +dcast(long_table, var1 ~ variable) +dcast(long_table, num+value ~ variable) +dcast(long_table, value ~ variable) +dcast(long_table, value+num ~ variable) +dcast(long_table, num ~ variable) +df = dcast(long_table, num ~ variable) +df +melt(df, id.vars =c("num","categories") +) +df <- dcast(long_table, num ~ variable) +df +long_table <- melt(df, id.vars =c("num","categories")) +df <- dcast(long_table, num ~ variable) +df +df <- dcast(long_table, ... ~ variable) +df +means_df <- dcast(long_table, categories ~ variable, mean) +means_df <- dcast(long_table, categories ~ variable, mean, na.rm = TRUE) +df_summ <- ddply(df, .(categories), summarize, +nums_mean = mean(num), +nums_max = max(num)) +df_summ +df_summ <- ddply(df, .(categories), summarize, +nums_mean = mean(num), +nums_max = max(num), +mix_nonums = gsub("","",mix,fixed = TRUE)) +df_summ <- ddply(df, .(categories), summarize, +nums_mean = mean(num), +nums_max = max(num), +mix_nonums = gsub("[:alpha:]","",mix,fixed = FALSE)) +df_summ <- ddply(df, .(categories), summarize, +nums_mean = mean(num), +nums_max = max(num), +mix_nonums = gsub("[a-z]","",mix,fixed = FALSE)) +df_summ <- ddply(df, .(categories), summarize, +nums_mean = mean(num), +nums_max = max(num), +mix_nonums = gsub("a","",mix,fixed = FALSE)) +df_summ <- ddply(df, .(categories), summarize, +nums_mean = mean(num), +nums_max = max(num)) +df_summ +df_summ <- ddply(df, .(categories), summarize, +nums_mean = mean(num), +nums_max = max(num*num)) +df_summ +df_summ <- ddply(df, .(categories), summarize, +nums_mean = mean(num), +nums_max = max(num*num), +l = length(mix)) +df_summ +df_summ <- ddply(df, .(categories), summarize, +nums_mean = mean(num), +nums_max = max(num*num), +l = length(mix), +other = paste(mix)) +df_summ <- ddply(df, .(categories), summarize, +nums_mean = mean(num), +nums_max = max(num*num), +l = length(mix), +other = c(mix)) +df_summ <- ddply(df, .(categories), summarize, +nums_mean = mean(num), +nums_max = max(num*num), +l = length(mix), +other = mix) +df_summ <- ddply(df, .(categories), summarize, +nums_mean = mean(num), +nums_max = max(num*num), +l = length(mix), +other = mix[which.max(mix)]) +df_summ <- ddply(df, .(categories), summarize, +nums_mean = mean(num), +nums_max = max(num*num), +l = length(mix), +NAs_index_word = which(is.na(word))) +df_summ <- ddply(df, .(categories), summarize, +nums_mean = mean(num), +nums_max = max(num*num), +l = length(mix), +NAs_index_word = which(word[is.na(word)])) +df_summ <- ddply(df, .(categories), summarize, +nums_mean = mean(num), +nums_max = max(num*num), +l = length(mix), +NAs_index_word = which(is.na(word))) +is.na(df$word) +df_summ <- ddply(df, .(categories), summarize, +nums_mean = mean(num), +nums_max = max(num*num), +l = length(mix), +NAs_index_word = which(is.na(df$word))) +is.na(df$word) +df_summ <- ddply(df, .(categories), summarize, +nums_mean = mean(num), +nums_max = max(num*num), +l = length(mix), +NAs_index_word = which(!(is.na(word))) +df_summ +df_summ <- ddply(df, .(categories), summarize, +nums_mean = mean(num), +nums_max = max(num*num), +l = length(mix), +NAs_index_word = which(!(is.na(word)))) +!(is.na(word)) +df_summ <- ddply(df, .(categories), summarize, +nums_mean = mean(num), +nums_max = max(num*num), +l = length(mix)) +df_summ +df_summ <- ddply(df, .(categories), summarize, +nums_mean = mean(num), +nums_max = max(num*num), +l = length(mix), +word_something = word[1]) +df_summ +df_summ <- ddply(df, .(categories), summarize, +nums_mean = mean(num), +nums_max = max(num*num), +l = length(mix), +word_something = word[2]) +df_summ +df_summ <- ddply(df, .(categories), summarize, +nums_mean = mean(num), +nums_max = max(num*num), +l = length(mix), +word_something = word[2], +sum_or_mean <- ifelse(categories == 1, sum(num),mean(num))) +df_summ <- ddply(df, .(categories), summarize, +nums_mean = mean(num), +nums_max = max(num*num), +l = length(mix), +word_something = word[2], +sum_or_mean <- ifelse(categories[1] == 1, sum(num),mean(num))) +df_summ +df_summ <- ddply(df, .(categories), summarize, +nums_mean = mean(num), +nums_max = max(num*num), +l = length(mix), +word_ish = word[2], +sum_or_mean <- ifelse(categories[1] == 1, sum(num),mean(num))) +df_summ +df_summ <- ddply(df, .(categories), summarize, +nums_mean = mean(num), +nums_max = max(num*num), +l = length(mix), +word_ish = word[2], +sum_mean <- ifelse(categories[1] == 1, sum(num),mean(num))) +df_summ +df_summ <- ddply(df, .(categories), summarize, +nums_max = max(num*num), +l = length(mix), +word_ish = word[2], +sum_mean <- ifelse(categories[1] == 1, sum(num),mean(num))) +df_summ +season = rep(c("winter","spring","summer","fall"), each = 3) +season +combined = join(df, +df, +by = "num", type = "left", match="all") +combined +combined = join(df, +df_summ, +by = "categories", type = "left", match="all") +combined +combined = join(df, +df_summ, +by = "categories", type = "right", match="all") +combined +combined = join(df, +df_summ, +by = "categories", type = "left", match="all") +matrix(1:9,ncol=3) +matrix(1:9,ncol=3, byrow=TRUE) +m <- matrix(1:9,ncol=3, byrow=TRUE) +m +matrix(1:8,ncol=3, byrow=TRUE) +m <- matrix(1:8,ncol=4, byrow=TRUE) +m +matrix(12:1, ncol = 4, nrow = 2) +matrix(12:1, ncol = 4) +matrix(12:1, ncol = 8) +matrix(12:1, ncol = 8, nrow = 5) +mx <- matrix(12:1, ncol = 8, nrow = 5) +mx +ghdfg +length(m) +dim(m) #get dimensions +m +m <- matrix(1:8,ncol=4, byrow=TRUE) +length(m) #Get lenght +nrow(m) #Get num of rows +ncol(m) #Get num of columns +dim(m) #get dimensions +as.vector(x) +as.vector(m) +as.vector(m) # get as linear vector ordered by columns +m[2, , drop = FALSE] +class(m) +matrix(1:4, nrow = 2) +m[2:3, 1:2] +m <- matrix(1:9,ncol=3, byrow=TRUE) +m +m[2:3, 1:2] +m[2:3, 1:2] = matrix(22:19, nrow = 2) +m +m[2:3, 1:2] = matrix(-1:-4, nrow = 2) +m +colMeans(m) +apply(m,2,mean) +a = array(1:24, c(2,2,3)) +a +a = array(1:24, c(2,2,3,2)) +a +a = array(1:24, c(2,2,3,2,3)) +a +a = array(1:24, c(2,2,3,2)) +a +a = array(1:24, c(2,2,3)) +a +time_array = array(NA, c(12,4,7,24))##Array that can store months of year, weeks, days, and hours of each day. Idea taken from Quora ask. +time_array +time_array = array(NA, c(4,7,12,24))##Array that can store months of year, weeks, days, and hours of each day. Idea taken from Quora ask. +time_array +time_array = array(NA, c(4,12,7,24))##Array that can store months of year, weeks, days, and hours of each day. Idea taken from Quora ask. +time_array +time_array = array(NA, c(12,4,7,24))##Array that can store months of year, weeks, days, and hours of each day. Idea taken from Quora ask. +time_array +colnames(time_array) <- c(rep("a",each = 12)) +dimnames(time_array) +rep("a",each = 12) +colnames(time_array) <- c(rep("a",each = 4)) +time_array +colnames(time_array) <- c("semana1","semana2", "semana3","semana4") +time_array +rownames(time_array) <- c(1:12) +time_array +lapply(1:12,paste, sep="_") +lapply(1:12,paste, "mes") +lapply(1:12,paste, "mes", sep="") +rownames(time_array) <- lapply(1:12,paste, "mes", sep="") +time_array +rownames(time_array) <- lapply("mes",paste, 1:12, sep="") +lapply("mes",paste, 1:12, sep="") +lapply("mes",paste, 1:12, sep=" ") +time_array +dimnames(time_array)[[3]] +dimnames(time_array) +dimnames(time_array)[[3]] <- c("Lun", "Mar", "Mier","Jue","sab","dom") +dimnames(time_array)[[3]] <- c("Lun", "Mar", "Mier","Jue","vier", "sab","dom") +dimnames(time_array)[[3]] <- c("Lun", "Mar", "Mier","Jue","vier", "sab","dom") #Set names for the 3 dimension +time_array +dimnames(time_array)[[4]] <- lapply(1:12,paste, "hora", sep="") #Set names for the 4th dimension +dimnames(time_array)[[4]] <- lapply(1:24,paste, "hora", sep="") #Set names for the 4th dimension +time_array +time_array = array(NA, c(7,24,4,12))##Array that can store values for months of year, weeks, days, and hours of each day. Idea taken from Quora ask. +time_array +time_array = array(NA, c(24,7,4,12))##Array that can store values for months of year, weeks, days, and hours of each day. Idea taken from Quora ask. +time_array +rownames(time_array) <- lapply(1:24,paste, "hora", sep="") #set rownames +colnames(time_array) <- c("Lun", "Mar", "Mier","Jue","vier", "sab","dom") #set column names +dimnames(time_array)[[3]] <- c("semana1","semana2", "semana3","semana4") #Set names for the 3 dimension +dimnames(time_array)[[4]] <- lapply(1:12,paste, "mes", sep="") +time_array +r1 = raster(m) +library(raster) +r1 = raster(m) +r1 +plot(m) +plot(r1) +stack(r1, r1) +stack(r1, r1) +s <- stack(r1, r1) +s +s[[1:2]] +writeRaster(s,"C:\\vuelos\\temp\\img.tif", +format = "Gtiff", +overwrite = FALSE) +writeRaster(s,"C:\\vuelos\\temp\\img.tif", +format = "tiff", +overwrite = FALSE) +writeRaster(s,"C:\\vuelos\\temp\\img.tif", +format = "Gtif", +overwrite = FALSE) +nrows(s) +nrow(s) +nlayers(l_00) ## Get number of bands +nlayers(s) ## Get number of bands +dims(s) ##get dimensions +dim(s) ##get dimensions +res(s) ## get the spatial resolution +extent(s) ## get the bounds +proj4string(s) +CRS(proj4string(s)) ## Get the spatial reference, as a Coordinate Reference System Class CRS +names(s) #get the band names +rownames(time_array) <- paste("hora", 1:24, sep = "_") #set rownames +time_array +rownames(time_array) <- paste(1:24,":00", sep = "") +time_array +dimnames(time_array)[[4]] <- paste("mes",1:12, sep = "_") #Set names for the 4th dimension +time_array +names (s) <- c("band1","band2") +names (s) +writeRaster(s,"C:\\vuelos\\temp\\img.tif", +format = "GTiff", +overwrite = FALSE) +proj4string(s) <- CRS("+proj=utm +zone=36 +ellps=WGS84 +units=m +no_defs") +writeRaster(s,"C:\\vuelos\\temp\\img.tif", +format = "GTiff", +overwrite = FALSE) +writeRaster(s,"C:\\vuelos\\temp\\img.tif", +format = "GTiff", +overwrite = FALSE) +hist(s) diff --git a/r/EM38cleaning.R b/r/EM38cleaning.R new file mode 100644 index 0000000..239b3d4 --- /dev/null +++ b/r/EM38cleaning.R @@ -0,0 +1,118 @@ +####### +##Clean EM38 data: normalize and Standardize +## Input format: CSV file with columns: "x_utm" "y_utm" "elev" "CV_1" "CV_05" "IV_1" "IV_05" +####### + +#Libraries used +library(pastecs) +library(tools) + +##INPUTS########################################################### +##Set working drectory +setwd("C:\\Dropbox\\data\\AF\\bw\\em38\\blockD171121\\raw") +##Input file CSV +name_file_data<- "bw_d_171121_withoutTurns_cleaned.csv" + +##Script########################################################### +basename_file_data <- strsplit(name_file_data, "\\.")[[1]]#name without extension +basename_file_data<-basename_file_data[1] +##Name to store the cleaned data CSV +output_name<- paste(basename_file_data,"_cleaned.csv", sep="") +data<-read.csv(name_file_data, header = TRUE) +#standardize column name +cols <- c('x_utm', 'y_utm', 'CV_1', 'CV_05', 'IV_1', 'IV_05') #without elev +#cols <- c('x_utm', 'y_utm','elev', 'CV_1', 'CV_05', 'IV_1', 'IV_05') #with elev +#cols <- c('x_utm', 'y_utm','CV_1', 'CV_05') #without elev IV nor elev +colnames(data) <- cols +#get statistics for all data +data_stats<-stat.desc(data,basic=TRUE, desc=TRUE, norm=FALSE, p=0.95) +# Values it returns: +#1 nbr.val (the count of values) +#2 nbr.null (the count of null values) +#3 nbr.na (the count of missing values) +#4 min +#5 max +#6 range +#7 sum +#8 median +#9 mean +#10 SE.mean +#11 CI.mean +#12 var +#13 std.dev +# coef.var +# skewness ##need to enable norm = TRUE +# skew.2SE +# kurtosis +# kurt.2SE +# normtest.W +# normtest.p + +### clean CV_1m ### +cv1m_mean<-data_stats$CV_1[9] #Get the mean for the CV at 1m +cv1m_min<-data_stats$CV_1[4] #Get the min for the CV at 1m +cv1m_max<-data_stats$CV_1[5] #Get the max for the CV at 1m +cv1m_std<-data_stats$CV_1[13] #Get the std.dev for the CV at 1m +#Normalize formula:reading - mean / std +for (i in 1:length(data$CV_1)) { + #Normalize CV_1m + data$CV_1_norm[i] <- (data$CV_1[i]-cv1m_mean)/cv1m_std +} + +### clean CV_05m ### +cv05m_mean<-data_stats$CV_05[9] #Get the mean for the CV at 1m +cv05m_min<-data_stats$CV_05[4] #Get the min for the CV at 1m +cv05m_max<-data_stats$CV_05[5] #Get the max for the CV at 1m +cv05m_std<-data_stats$CV_05[13] #Get the std.dev for the CV at 1m +#Normalize formula:reading - mean / std +for (i in 1:length(data$CV_05)) { + #Normalize CV_05m + data$CV_05_norm[i] <- (data$CV_05[i]-cv05m_mean)/cv05m_std +} + +### clean IV_1m ### +iv1m_mean<-data_stats$IV_1[9] #Get the mean for the CV at 1m +iv1m_min<-data_stats$IV_1[4] #Get the min for the CV at 1m +iv1m_max<-data_stats$IV_1[5] #Get the max for the CV at 1m +iv1m_std<-data_stats$IV_1[13] #Get the std.dev for the CV at 1m +#Normalize formula:reading - mean / std +for (i in 1:length(data$IV_1)) { + #Normalize CV_1m + data$IV_1_norm[i] <- (data$IV_1[i]-iv1m_mean)/iv1m_std +} + +### clean IV_05m ### +iv05m_mean<-data_stats$IV_05[9] #Get the mean for the CV at 1m +iv05m_min<-data_stats$IV_05[4] #Get the min for the CV at 1m +iv05m_max<-data_stats$IV_05[5] #Get the max for the CV at 1m +iv05m_std<-data_stats$IV_05[13] #Get the std.dev for the CV at 1m +#Normalize formula:reading - mean / std +for (i in 1:length(data$IV_05)) { + #Normalize CV_05m + data$IV_05_norm[i] <- (data$IV_05[i]-iv05m_mean)/iv05m_std +} + +#keep values of 1m normalization beteen 3 and -3 +data_cleaned1<-subset(data, data$CV_05_norm<=3 & data$CV_05_norm>=-3) +data_cleaned1<-subset(data, data$CV_1_norm<=3 & data$CV_1_norm>=-3) +data_cleaned1<-subset(data, data$IV_05_norm<=3 & data$IV_05_norm>=-3) +data_cleaned1<-subset(data, data$IV_1_norm<=3 & data$IV_1_norm>=-3) + +### Graphics ### +#Graphics to save to PDF +pdf(file=paste("hist",basename_file_data,".pdf", sep="")) +par(mfrow=c(2,1)) +#hist(data$CV_05_norm, main =paste("Histogram data$CV_05_norm"),xlab = "data$CV_05_norm", nclass = 20) +hist(data$CV_05, main =paste("Histogram data$CV_05"),xlab = "data$CV_05", nclass = 20) +hist(data$CV_1, main =paste("Histogram data$CV_1"),xlab = "data$CV_1", nclass = 20) +#hist(data$CV_1_norm, main =paste("Histogram data$CV_1_norm"),xlab = "data$CV_1_norm", nclass = 20) + +#now the normalized ones +hist(data_cleaned1$CV_05, main =paste("Histogram data$CV_05"),xlab = "data_cleaned1$CV_05", nclass = 20) +hist(data_cleaned1$CV_1, main =paste("Histogram data$CV_1"),xlab = "data_cleaned1$CV_1", nclass = 20) +dev.off() +#Finish saving to PDF + +##Write result to text file +data_cleaned1 <- subset(data_cleaned1, select=cols) +write.csv(data_cleaned1, file = output_name,row.names=FALSE) diff --git a/r/JM_Extract_pixel_values_from_raster_firstEdition.R b/r/JM_Extract_pixel_values_from_raster_firstEdition.R new file mode 100644 index 0000000..38a6c7e --- /dev/null +++ b/r/JM_Extract_pixel_values_from_raster_firstEdition.R @@ -0,0 +1,80 @@ +#This procedure use the raster file and shapefile_buffer file to extract the mean pixeles values without outliers into a CSV file +#Author: Jose Manuel Mendoza Reyes +library (sp) +library (raster) +library(rgdal) + +#Select the work directory wich are the raster file and shp +setwd(choose.dir(getwd(), "Selecciona tu archivo")) + +#select the raster file (NDVI or thermal mosaic) +raster <-raster(list.files(pattern = "\\.tif$")) +#select the shapefile (a shp with buffer genereted in Arcgis) +shp <- shapefile(list.files(pattern = "\\.shp$")) + +#plot(raster) +#plot(shp, add= TRUE) + +#Set the name of the outut csv table +name<-list.files(pattern = "\\.shp$") +name<-gsub(".shp", "", name) + + +#Overlap and extract the pixel value in each polygon +pixelval <- extract(raster,shp) + + +#Add the plot name to the pixel value list +names(pixelval)<- shp$Name + +#check the list +#str(pixelval) + +#function to eliminate soil pixels or high temperature values +#NDVI +soil.mask <- function(p){ + p.1 = subset(p,p >0.39) + return(p.1) +} +#TEMPERATURA +# soil.mask <- function(p){ +# p.1 = subset(p,p <6691) +# return(p.1) +# } + +#Apply the mask to the list +massk <- lapply(pixelval,soil.mask) + + +#function to calculate and eliminate outliers +outliers <- function(dat){ +n=2 +a=median(dat) +b=sd(dat) +limitesuperior=(a+n*b) +limiteinferior=(a-n*b) +dat1= subset(dat,dat <= limitesuperior & dat >= limiteinferior) +return(dat1) +} + +#extract the outliers +f.t = lapply(massk,outliers) + + +str(f.t) +#Calculate the mean of the new list without outliers and convert it in a data base +output.table = lapply(f.t,mean) +output.table<-data.frame(names(output.table), unlist(output.table)) + +#add the name of the columns +colnames(output.table)<-c("Plot", "mean_") + +#this step is to average rows with the same name (double plots and bordos) +csv=aggregate(mean_ ~ Plot, output.table, mean) + + +#write the final table +write.csv(csv,paste(name,"_final.csv", sep=""), row.names=FALSE) + + + diff --git a/r/MCAbands_separateBandsIntoIndividualRasters.R b/r/MCAbands_separateBandsIntoIndividualRasters.R new file mode 100644 index 0000000..8393806 --- /dev/null +++ b/r/MCAbands_separateBandsIntoIndividualRasters.R @@ -0,0 +1,61 @@ +#Script to separate the Multispectra bands from the iMapQ processing result that stacks them in 2 rasters of 3 bands. +#Names should be like: +# TTC[name]-0.tif +# TTC[name]-1.tif +# e.g. TTC07016-1.tif +# The required input is: the working directory +#The output is written in the same folder + +#0. First step : load packages +#install.packages(c("rgdal","raster")) +require(raster) +require(rgdal) +library(stringr) + +########### Functions ########################## +#Function multibandUnStack +multibandUnStack <- function(img3Band) +{ + tryCatch({ + ##img3Band <- "TTC07016-0.tif" ## %%%% ! HARDCODED TO TEST + + #Truncate string to Get base name of frame + basename <- substr(toString(img3Band), 1, 8) + #Truncate string to Get if the sufix of the filename is 0 or 1 + sx <- substr(toString(img3Band), 10, 10) + # If it is 0 we will name the bands 1, 2, 3 otherwise 4, 5, 6 + # So fix will be used to use the index to asign the correct bandnumber + fix <- if(sx==0) 0 else 3 + #separate Input file into 3 separate rasters + for (i in 1:3){ + b <- raster(img3Band, band=i) + rSave(b, paste(basename,"-b",i+fix,sep = "")) + } + + ##plot(b1) #see image just to test + + }) +} + +#function to plot save image to disk +rSave <- function(img, name) +{ + tryCatch({ + #Write the tif to disk + writeRaster(img, file=paste(name,".tif",sep = ""),datatype='INT2U',format="GTiff",overwrite=FALSE) + }) +} + +########### START ########################## +#Working directory +wd="G:\\AE\\170531\\m\\rfl" +setwd(wd) +## Sets the pattern of name files to be processed (-0.tif and -1.tif) and process each one +imgFilesList <-list.files(wd, pattern="-{1}0*1*.tif$") +for (i in seq_along(imgFilesList)){ + print(paste("Image:",i,"of",toString(length(imgFilesList)),":",imgFilesList[i])) + multibandUnStack(imgFilesList[i]) +} + +#Notify that it finished +print(paste("Finished merging",toString(length(imgFilesList)),"images")) \ No newline at end of file diff --git a/r/NDVIFromMCA-6Images.R b/r/NDVIFromMCA-6Images.R new file mode 100644 index 0000000..a75307a --- /dev/null +++ b/r/NDVIFromMCA-6Images.R @@ -0,0 +1,42 @@ +#Script to calculate the NDVI from a folder of MCA-6 images BSQ format +#Files should be named as follows: +# [camera(1 char)]yymmdd[AOI(3 char)][imageProcessingStage(3 char)].bsq +#Example: m151216jfxgeo.bsq +# The required input is: the working directory +#0. First step : load packages +#install.packages(c("rgdal","raster")) +require(raster) +require(rgdal) +#NDVI function on a 6 band multispectral image +f_NDVI <- function(r,n) { + (n-r)/(n+r) +} + +#Function multibandStack (folder, nband_red, nband_nir) +nDVIFromMCA6 <- function(wd,red_band=2,nir_band=6) +{ + tryCatch({ + setwd(wd) + #Make a list of all files in the wd + im <- list.files(wd, full.names=TRUE) + #Select the *geo.bsq files + im.bsq <- im[grep("geo.bsq$", im)] + #generate NDVI of all rasters + for (img in im.bsq) { + img="C:/vuelos/temp/ad_nut_MCA/m151216jfxgeo.bsq" #Fixed testing + r_red=raster(img, band=red_band) + r_nir=raster(img, band=nir_band) + NDVI <- f_NDVI(r_red,r_nir) + #plot(NDVI) plot if we want to see it + #Save the NDVI replacing the suffix of the images + writeRaster(NDVI, file=gsub("geo.bsq$", "nvi.tif$", img),datatype='FLT4S', format="GTiff",overwrite=FALSE) + } + }) + +} + +#Working directory +wd = "C:/vuelos/temp/ad_nut_MCA" +#Call NDV maker +n = nDVIFromMCA6(wd,2,6) +print ("Finished generating the NDVI for all the images") diff --git a/r/Plant_height_code.R b/r/Plant_height_code.R new file mode 100644 index 0000000..738b8c1 --- /dev/null +++ b/r/Plant_height_code.R @@ -0,0 +1,143 @@ +#Protocol to evaluate Plant height +#Authors: Jose Manuel Mendoza Reyes and Lorena Gonzalez Perez + +#Coments specific to make it work in obregón marked as #L# + +library (sp) +library (raster) +library(rgdal) +library(data.table) +library(plyr) +library(dplyr) + + +# Load the raster base (DTM) +rasterBase<- raster("G:/AE/161026/r161026fis/r161026hap/3_dsm_ortho/1_dsm/r161026hap_dsm.tif") + +#Select the work directory wich are the raster file and shp +setwd(choose.dir(getwd(), "Selecciona tu archivo")) + +#select the raster file (DSM) +raster <-raster(list.files(pattern = "\\.tif$")) + +#L# raster<- raster("G:/AE/161026/r161026fis/r161026hap/3_dsm_ortho/1_dsm/r161026hap_dsm.tif") + +#Cut the DSM base on the DTM extent +r_crop<-crop(raster, extent(rasterBase), snap="out") +#L# r_crop<-raster + +#Change the spatial resolution of DTM (if it is necessary) +rasrecla <- resample(rasterBase, r_crop, resample='ngb') +#L# rasrecla <- r_crop + +#Geoidal height for the area of interest (checked it on INEGI web) +n=-32.37 + +#Get Geoidal height base on the ellipsoidal height +r_croph <- (r_crop-n) + +#Check that the two raster have the same attributes + +#DTM +show(rasrecla) +#DSM +show(r_croph) + +rest <-function(r1,base){(r1-base)} +plant.height <- rest(r_croph, rasrecla) +#L# plant.height <- rasterBase + +# show(plant.height) +# plot(plant.height) + +#select the shapefile (a shp with buffer genereted in Arcgis) +shp <- shapefile(list.files(pattern = "\\.shp$")) +#L# Select one file interactively +#L# shp <-shapefile(file.choose(new = FALSE)) + +#plot(plant.height) +#plot(shp, add= TRUE) + +#Set the name of the outut csv table +name<-list.files(pattern = "\\.shp$") +name<-gsub(".shp", "", name) + + +#Write the new plant height raster (DSM) +writeRaster(plant.height, filename= paste(name, "heightRaster_2.tif"), format = "GTiff") + +#Overlap and extract the pixel value in each polygon +pixelval <- extract(plant.height,shp) + +#Add the plot name to the pixel value list +names(pixelval)<- shp$Name + + +#check the list +#str(pixelval) + +#function to eliminate soil pixels +soil.mask <- function(p){ + p.1 = subset(p,p >0.05) + return(p.1) +} + +#Apply the mask to the list +PlantsValue <- lapply(pixelval,soil.mask) + + +#function to calculate and eliminate outliers +outliers <- function(dat){ +n=2 +a=median(dat) +b=sd(dat) +limitesuperior=(a+n*b) +limiteinferior=(a-n*b) +dat1= subset(dat,dat <= limitesuperior & dat >= limiteinferior) +return(dat1) +} + +#extract the outliers +Tableclean = lapply(PlantsValue,outliers) + + +str(Tableclean) + + +#Calculate the descriptive statistics of the new list without outliers and convert it in a data base + +allstats = lapply(Tableclean,summary) +sd = lapply(Tableclean, sd) +n = lapply(Tableclean, length) + +#Join all the statistics calculated in one table +desc.stats.t = mapply(c,allstats,sd, n) + +# Transpose the matrix +desc.stats <- t(desc.stats.t) + + +#Convert to data frame +df <- as.data.frame(desc.stats) + +# Add the rownames as a new column +setDT(df, keep.rownames = TRUE)[] + +#Rename the columns +colnames(df)[1:9]<-c("Plot", "Min", "Q1", "Median", "Mean", "Q3", "Max", "SD", "n") + +#Rewrite again the data frame +df<-data.frame(df) + +# Average the values using the unique ID (Plot names) +final.df=aggregate(cbind(Min, Q1, Median, Mean, Q3, Max, SD, n)~Plot,df, mean) + +#write the final table +write.csv(final.df,paste(name,"_plant_Height.csv", sep=""), row.names=FALSE) + + + + + + + diff --git a/r/Plant_height_code_Obregon.R b/r/Plant_height_code_Obregon.R new file mode 100644 index 0000000..3306ce2 --- /dev/null +++ b/r/Plant_height_code_Obregon.R @@ -0,0 +1,152 @@ +#Protocol to evaluate Plant height +#Authors: Jose Manuel Mendoza Reyes and Lorena Gonzalez Perez + +#Coments specific to make it work in obregón marked as #L# + +library (sp) +library (raster) +library(rgdal) +library(data.table) +library(plyr) +library(dplyr) + +#L# +date <- "170127" + +note <- "check treshhold" + +# Load the raster base (DTM) +rasterBase<- raster("G:/AE/161026/r161026fis/r161026hap/3_dsm_ortho/1_dsm/r161026hap_dsm.tif") + +#Select the work directory wich are the raster file and shp +#setwd(choose.dir(getwd(), "Selecciona tu archivo")) +#L# +setwd("C:/Dropbox/data/AE/physio/data") + +#select the raster file (DSM) +#raster <-raster(list.files(pattern = "\\.tif$")) +#L# +raster<- raster(file.choose(new = FALSE)) #interactive +print(raster) + +#Cut the DSM base on the DTM extent +r_crop<-crop(raster, extent(rasterBase), snap="out") +#plot(r_crop) + +#Change the spatial resolution of DTM (if it is necessary) +rasrecla <- resample(rasterBase, r_crop, resample='ngb') + +#Geoidal height for the area of interest (checked it on INEGI web) +#n=-32.37 +#L# we have the units in mamsl + +#Get Geoidal height base on the ellipsoidal height +#r_croph <- (r_crop-n) +#L# that is why we dont perform this operation + +#Check that the two raster have the same attributes + +#DTM +#show(rasrecla) +#DSM +#show(r_croph) + +rest <-function(r1,base){(r1-base)} +#plant.height <- rest(r_croph, rasrecla) +#L# +plant.height <- rest(r_crop, rasrecla) #because didnt convert to Geoidal + +# show(plant.height) +# plot(plant.height) +#hist(plant.height) + +#select the shapefile (a shp with buffer genereted in Arcgis) +#shp <- shapefile(list.files(pattern = "\\.shp$")) +#L# Select one file interactively +#shpName <- file.choose(new = FALSE) +shpName <- "C:/Dropbox/data/AE/physio/shp/Hibap_RGB_170308.shp" +shp <-shapefile(shpName) + +#plot(plant.height) +#plot(shp, add= TRUE) + +#Set the name of the outut csv table +name<-shpName +name<-gsub(".shp", "", name) + + +#Write the new plant height raster (DSM) +writeRaster(plant.height, filename= paste(date, "_heightRaster.tif",sep=""), format = "GTiff") + +#Overlap and extract the pixel value in each polygon +#pixelval <- extract(plant.height,shp) +#L# To re-do with the fixed shp +pixelval <- extract(plant.height,shp) #L# Temporal + +#Add the plot name to the pixel value list +names(pixelval)<- shp$Name + + +#check the list +#str(pixelval) +#min(pixelval) + +#Just to tremember to check threshold +#L# if you want to create a "Popup" on windows, as a notification +system('CMD /C "ECHO Just to remember to check threshold"', + invisible=FALSE, wait=FALSE) +print("ll") +#Write the threshold used +x = "Expression used in the soil Masking: pixel > 0.0 = plant" +write(x, file = paste("Read me - ",date,".txt",sep=""), + append = FALSE, sep = ",") + +#function to eliminate soil pixels +soil.mask <- function(p){ + p.1 = subset(p,p >0.0) + return(p.1) +} + +#Apply the mask to the list +PlantsValue <- lapply(pixelval,soil.mask) + +#function to calculate and eliminate outliers +outliers <- function(dat){ +n=2 +a=median(dat) +b=sd(dat) +limitesuperior=(a+n*b) +limiteinferior=(a-n*b) +dat1= subset(dat,dat <= limitesuperior & dat >= limiteinferior) +return(dat1) +} + +#extract the outliers +Tableclean = lapply(PlantsValue,outliers) + + +str(Tableclean) + + +#Calculate the descriptive statistics of the new list without outliers and convert it in a data base + +allstats = lapply(Tableclean,summary) +sd = lapply(Tableclean, sd) +n = lapply(Tableclean, length) +#Join all the statistics calculated in one table +desc.stats.t = mapply(c,allstats,sd, n) +# Transpose the matrix +desc.stats <- t(desc.stats.t) +#Convert to data frame +df <- as.data.frame(desc.stats) +# Add the rownames as a new column +setDT(df, keep.rownames = TRUE)[] +#Rename the columns +colnames(df)[1:9]<-c("Plot", "Min", "Q1", "Median", "Mean", "Q3", "Max", "SD", "n") +#Rewrite again the data frame +df<-data.frame(df) +# Average the values using the unique ID (Plot names) +final.df=aggregate(cbind(Min, Q1, Median, Mean, Q3, Max, SD, n)~Plot,df, mean) + +#write the final table +write.csv(final.df,paste(date,"_plant_Height.csv", sep=""), row.names=FALSE) diff --git a/r/ScatterPlotsGS_Y16-17.R b/r/ScatterPlotsGS_Y16-17.R new file mode 100644 index 0000000..fc23c7b --- /dev/null +++ b/r/ScatterPlotsGS_Y16-17.R @@ -0,0 +1,86 @@ +## To show the histogram of selected plots in shapefile +## ONGOING WORK!!!! + +require(raster) +require(rgdal) + +###################### DEFINED FUNCTIONS (no need to edit) +# Read the feature class +get_roi <- function(trial_roi) {readOGR(dsn=fgdb,layer=trial_roi)} + + +######################## DEFINE VARIABLES (Enter your variables) +# #Working directory +wd="G:\\AD15_16\\" +setwd(wd) +ext<-".tif" +shp_name<- "__ad__buf" +sx<-"cel" #input sufix +fgdb = "C:\\Dropbox\\data\\AD\\ad_sam\\shp\\adsam.gdb" + +#Select individual image instead of loop all + cam<-"t" + d<-"160804" + t<-"sam" + +######################### STARTs SCRIPT +tryCatch({ + + + + imgLocation = paste(wd,d,"\\",cam,"\\",sx,"\\",sep="") #Look into cel folder + name = paste(cam,d,t,sx,ext,sep="") + rasterName= paste(imgLocation,name,sep="") + r<-raster(rasterName) + plt <- get_roi(paste(t,shp_name,sep="")) + ids <- data.frame(plt$id) + + ##Graphics to save to PDF + pdf(file=paste(name," plots.pdf", sep="")) + par(mar = rep(2, 4),mfrow=c(4,4)) + + #Iterate the plots + for(i in 1:3){ ###length(ids[,1]) %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + ##Select every plot polygon + roi <- plt[plt@data["id"]==ids[i,1]] #%%%%%%%%%%%%%%%%%% WTFFF %%%%%%%%%%%%%%%% + print(ids[i,1]) + ##roi <- plt[plt$id=="thanda 264 rows /131",] + ##Crop the image to get what is inside the plot + r_selected <-crop(r, extent(roi), snap="out") + plot(r_selected,col=heat.colors(8,0.8)) # Plot the image cropped + #Get histogram of that piece of image + hist(r_selected,main=paste(t,cam,d,"plot= [",ids[i,1],"]"),breaks=20) + + # KMEANS + #assume bimodal distribution in clipped raster + #Use K means to filter Soil (hotter pixels) + #The group 1 is the cooler (vegetation?) and the 2 is the hotter (Soil?) + v<-data.frame(getValues(r_selected)) + km <- kmeans(v,centers=2) + v$clust <- as.factor(km$cluster) + hist(v[v$clust==1,1]) + #Store mean plot value in table + ids$d[i] <- mean(v[v$clust==1,1]) + + #NIBLACK???? + #Filter soil with this filter + #pixel = ( pixel > mean + k * standard_deviation - c) ? object : background +# sd(r_selected) +# r_mean <- focal(r_selected, w=matrix(1/9,nrow=5,ncol=5)) + ##How to calculate focal std ? +# sd(x, na.rm = FALSE) +# zonal(x, z, stat='mean', digits=0, na.rm=TRUE, ...) +# focal(r_selected, w=3, fun=, na.rm=TRUE) +# sd2 <- sqrt(sum((x - mean(x))^2) / (n - 1)) +# +# #r_sd <- cellStats(r_selected,'sd') # Returns a value, not raster +# add=TRUE +# plot(r_mean) +# plot(r_sd) + } + + dev.off() + ##Finish saving to PDF + +}) + diff --git a/r/VIs_Calculation.R b/r/VIs_Calculation.R new file mode 100644 index 0000000..f78fdd2 --- /dev/null +++ b/r/VIs_Calculation.R @@ -0,0 +1,313 @@ +##################################################################### +# Title: ENVI_Indices +# Name: Francisco Manuel Rodriguez Huerta +# Date: 9 Abril 2014 +# Description: Compute Indices +##################################################################### + +#Load required libraries +require(raster) +require(rgdal) + +### nos posicionamos en el directorio de trabajo ### +rm(list = ls()) + +work.dir <- "I:\\CIMMYT\\YQ_Variability\\Data\\Images_PAexp\\1200m\\Resampled" ### Change the input directory + +setwd(work.dir) +getwd() + +#set filename +filename<-"H140507_PA_1m_62bands.dat" ### check file name + + +#create rasterstack object +r1 <- raster(filename, band=1) +rs <- stack(r1) +index <- r1@file@nbands +rm(r1) +for (i in 2:index){ + r1 <- raster(filename, band=i) + rs <- stack(rs,r1) + rm(r1) +} +show(rs) + +#numbers of rows and columns +nr <- nrow(rs) +nc <- ncol(rs) +nlayers(rs) + +#get values + +R <- getValuesBlock(rs, row=1, nrows=nr, col=1, ncols=nc)/10000 +dim(R) + +#read identifier of bands and wavelengths + +#setwd("H:\\CIMMYT\\YQ_Variability\\Data\\Images_PAexp\\") +#getwd() + + +bands <- read.table("bands62.csv",sep=",",header=TRUE) + +setwd("I:\\CIMMYT\\YQ_Variability\\Data\\Images_PAexp\\1200m\\VIs\\140507") ### Output folder to VI maps +getwd() + + +#Important bands +R400 <- R[,bands[which.min(abs(bands[,2]-400)),1]] +R445 <- R[,bands[which.min(abs(bands[,2]-445)),1]] +R450 <- R[,bands[which.min(abs(bands[,2]-450)),1]] +R470 <- R[,bands[which.min(abs(bands[,2]-470)),1]] +R500 <- R[,bands[which.min(abs(bands[,2]-500)),1]] +R510 <- R[,bands[which.min(abs(bands[,2]-510)),1]] +R513 <- R[,bands[which.min(abs(bands[,2]-513)),1]] +R515 <- R[,bands[which.min(abs(bands[,2]-515)),1]] +R530 <- R[,bands[which.min(abs(bands[,2]-530)),1]] +R550 <- R[,bands[which.min(abs(bands[,2]-550)),1]] +R570 <- R[,bands[which.min(abs(bands[,2]-570)),1]] +R635 <- R[,bands[which.min(abs(bands[,2]-635)),1]] +R670 <- R[,bands[which.min(abs(bands[,2]-670)),1]] +R675 <- R[,bands[which.min(abs(bands[,2]-675)),1]] +R680 <- R[,bands[which.min(abs(bands[,2]-680)),1]] +R700 <- R[,bands[which.min(abs(bands[,2]-700)),1]] +R710 <- R[,bands[which.min(abs(bands[,2]-710)),1]] +R720 <- R[,bands[which.min(abs(bands[,2]-720)),1]] +R740 <- R[,bands[which.min(abs(bands[,2]-740)),1]] +R746 <- R[,bands[which.min(abs(bands[,2]-746)),1]] +R750 <- R[,bands[which.min(abs(bands[,2]-750)),1]] +R760 <- R[,bands[which.min(abs(bands[,2]-760)),1]] +R770 <- R[,bands[which.min(abs(bands[,2]-770)),1]] +R800 <- R[,bands[which.min(abs(bands[,2]-800)),1]] + +hist(R500) +hist(R800) +################### +### Get indices ### +################### +date <- strsplit(filename,split="_")[[1]][1] + +#Structural Indices +NDVI <- (R800-R670)/(R800+R670) +Rastout <- raster(ncol=nc,nr=nr) +Rastout[] <- NDVI +Rastout@extent <- rs@extent +proj4string(Rastout) <- CRS("+proj=utm +zone=12 +datum=WGS84 +units=m +no_defs +ellps=WGS84 +towgs84=0,0,0") +writeRaster(Rastout, filename=paste(date,"NDVI.tif",sep="_"), format="GTiff") +hist(NDVI) + + +RDVI <- (R800-R670)/sqrt(R800+R670) +Rastout[] <- RDVI +proj4string(Rastout) <- CRS("+proj=utm +zone=12 +datum=WGS84 +units=m +no_defs +ellps=WGS84 +towgs84=0,0,0") +writeRaster(Rastout,filename=paste(date,"RDVI.tif",sep="_"),format="GTiff") + +OSAVI <- (1+0.16)*(R800-R670)/(R800+R670+0.16) +Rastout[] <- OSAVI +proj4string(Rastout) <- CRS("+proj=utm +zone=12 +datum=WGS84 +units=m +no_defs +ellps=WGS84 +towgs84=0,0,0") +writeRaster(Rastout,filename=paste(date,"OSAVI.tif",sep="_"),format="GTiff") + +SR <- R800/R670 +Rastout[] <- SR +proj4string(Rastout) <- CRS("+proj=utm +zone=12 +datum=WGS84 +units=m +no_defs +ellps=WGS84 +towgs84=0,0,0") +writeRaster(Rastout,filename=paste(date,"SR.tif",sep="_"),format="GTiff") + +MSR <- (SR-1)/sqrt(SR)+1 +Rastout[] <- MSR +proj4string(Rastout) <- CRS("+proj=utm +zone=12 +datum=WGS84 +units=m +no_defs +ellps=WGS84 +towgs84=0,0,0") +writeRaster(Rastout,filename=paste(date,"MSR.tif",sep="_"),format="GTiff") + +MTVI1 <- 1.2*(1.2*(R800-R550)-2.5*(R670-R550)) +Rastout[] <- MTVI1 +proj4string(Rastout) <- CRS("+proj=utm +zone=12 +datum=WGS84 +units=m +no_defs +ellps=WGS84 +towgs84=0,0,0") +writeRaster(Rastout,filename=paste(date,"MTVI1.tif",sep="_"),format="GTiff") + +MCARI1 <- 1.2*(2.5*(R800-R670)-1.3*(R800-R550)) +Rastout[] <- MCARI1 +proj4string(Rastout) <- CRS("+proj=utm +zone=12 +datum=WGS84 +units=m +no_defs +ellps=WGS84 +towgs84=0,0,0") +writeRaster(Rastout,filename=paste(date,"MCARI1.tif",sep="_"),format="GTiff") + +deno <- sqrt((2*R800+1)^2-(6*R800-5*sqrt(R670))-0.5) +MCARI2 <- (1.5/1.2)*MCARI1/deno +Rastout[] <- MCARI2 +proj4string(Rastout) <- CRS("+proj=utm +zone=12 +datum=WGS84 +units=m +no_defs +ellps=WGS84 +towgs84=0,0,0") +writeRaster(Rastout,filename=paste(date,"MCARI2.tif",sep="_"),format="GTiff") + +MTVI2 <- (1.5/1.2)*MTVI1/deno +Rastout[] <- MTVI2 +proj4string(Rastout) <- CRS("+proj=utm +zone=12 +datum=WGS84 +units=m +no_defs +ellps=WGS84 +towgs84=0,0,0") +writeRaster(Rastout,filename=paste(date,"MTVI2.tif",sep="_"),format="GTiff") + +#Chlorophyll indices +TVI <- 0.5*(120*(R750-R550)-200*(R670-R550)) +Rastout[] <- TVI +proj4string(Rastout) <- CRS("+proj=utm +zone=12 +datum=WGS84 +units=m +no_defs +ellps=WGS84 +towgs84=0,0,0") +writeRaster(Rastout,filename=paste(date,"TVI.tif",sep="_"),format="GTiff") + +MCARI <- ((R700-R670)-0.2*(R700-R550))*(R700/R670) +Rastout[] <- MCARI +proj4string(Rastout) <- CRS("+proj=utm +zone=12 +datum=WGS84 +units=m +no_defs +ellps=WGS84 +towgs84=0,0,0") +writeRaster(Rastout,filename=paste(date,"MCARI.tif",sep="_"),format="GTiff") + +TCARI <- 3*MCARI +Rastout[] <- TCARI +proj4string(Rastout) <- CRS("+proj=utm +zone=12 +datum=WGS84 +units=m +no_defs +ellps=WGS84 +towgs84=0,0,0") +writeRaster(Rastout,filename=paste(date,"TCARI.tif",sep="_"),format="GTiff") + +TCARI_OSAVI <- TCARI/OSAVI +Rastout[] <- TCARI_OSAVI +proj4string(Rastout) <- CRS("+proj=utm +zone=12 +datum=WGS84 +units=m +no_defs +ellps=WGS84 +towgs84=0,0,0") +writeRaster(Rastout,filename=paste(date,"TCARI_OSAVI.tif",sep="_"),format="GTiff") + +MCARI_OSAVI <- MCARI/OSAVI +Rastout[] <- MCARI_OSAVI +proj4string(Rastout) <- CRS("+proj=utm +zone=12 +datum=WGS84 +units=m +no_defs +ellps=WGS84 +towgs84=0,0,0") +writeRaster(Rastout,filename=paste(date,"MCARI_OSAVI.tif",sep="_"),format="GTiff") + +GM1 <- R750/R550 +Rastout[] <- GM1 +proj4string(Rastout) <- CRS("+proj=utm +zone=12 +datum=WGS84 +units=m +no_defs +ellps=WGS84 +towgs84=0,0,0") +writeRaster(Rastout,filename=paste(date,"GM1.tif",sep="_"),format="GTiff") + +GM2 <- R750/R700 +Rastout[] <- GM2 +proj4string(Rastout) <- CRS("+proj=utm +zone=12 +datum=WGS84 +units=m +no_defs +ellps=WGS84 +towgs84=0,0,0") +writeRaster(Rastout,filename=paste(date,"GM2.tif",sep="_"),format="GTiff") + + +#Red edge ratios +#ZM +CI <- R750/R710 +Rastout[] <- CI +proj4string(Rastout) <- CRS("+proj=utm +zone=12 +datum=WGS84 +units=m +no_defs +ellps=WGS84 +towgs84=0,0,0") +writeRaster(Rastout,filename=paste(date,"CI.tif",sep="_"),format="GTiff") + +R750_R700 <- R750/R700 +Rastout[] <- R750_R700 +proj4string(Rastout) <- CRS("+proj=utm +zone=12 +datum=WGS84 +units=m +no_defs +ellps=WGS84 +towgs84=0,0,0") +writeRaster(Rastout,filename=paste(date,"R750_R700.tif",sep="_"),format="GTiff") + +R750_R670 <- R750/R670 +Rastout[] <- R750_R670 +proj4string(Rastout) <- CRS("+proj=utm +zone=12 +datum=WGS84 +units=m +no_defs +ellps=WGS84 +towgs84=0,0,0") +writeRaster(Rastout,filename=paste(date,"R750_R670.tif",sep="_"),format="GTiff") + +R710_R700 <- R710/R700 +Rastout[] <- R710_R700 +proj4string(Rastout) <- CRS("+proj=utm +zone=12 +datum=WGS84 +units=m +no_defs +ellps=WGS84 +towgs84=0,0,0") +writeRaster(Rastout,filename=paste(date,"R710_R700.tif",sep="_"),format="GTiff") + +R710_R670 <- R710/R670 +Rastout[] <- R710_R670 +proj4string(Rastout) <- CRS("+proj=utm +zone=12 +datum=WGS84 +units=m +no_defs +ellps=WGS84 +towgs84=0,0,0") +writeRaster(Rastout,filename=paste(date,"R710_R670.tif",sep="_"),format="GTiff") + +#RGB Indices +R700_R670 <- R700/R670 +Rastout[] <- R700_R670 +proj4string(Rastout) <- CRS("+proj=utm +zone=12 +datum=WGS84 +units=m +no_defs +ellps=WGS84 +towgs84=0,0,0") +writeRaster(Rastout,filename=paste(date,"R700_R670.tif",sep="_"),format="GTiff") + +G <- R550/R670 +Rastout[] <- G +proj4string(Rastout) <- CRS("+proj=utm +zone=12 +datum=WGS84 +units=m +no_defs +ellps=WGS84 +towgs84=0,0,0") +writeRaster(Rastout,filename=paste(date,"G.tif",sep="_"),format="GTiff") + +#Indices PDF +CAR <- R515/R570 +Rastout[] <- CAR +proj4string(Rastout) <- CRS("+proj=utm +zone=12 +datum=WGS84 +units=m +no_defs +ellps=WGS84 +towgs84=0,0,0") +writeRaster(Rastout,filename=paste(date,"CAR.tif",sep="_"),format="GTiff") + +PRI <- (R570-R530)/(R570+R530) +Rastout[] <- PRI +proj4string(Rastout) <- CRS("+proj=utm +zone=12 +datum=WGS84 +units=m +no_defs +ellps=WGS84 +towgs84=0,0,0") +writeRaster(Rastout,filename=paste(date,"PRI.tif",sep="_"),format="GTiff") + +PRIn <- PRI*R670/(RDVI*R700) +Rastout[] <- PRIn +proj4string(Rastout) <- CRS("+proj=utm +zone=12 +datum=WGS84 +units=m +no_defs +ellps=WGS84 +towgs84=0,0,0") +writeRaster(Rastout,filename=paste(date,"PRIn.tif",sep="_"),format="GTiff") + +SIPI <- (R800-R445)/(R800+R680) +Rastout[] <- SIPI +proj4string(Rastout) <- CRS("+proj=utm +zone=12 +datum=WGS84 +units=m +no_defs +ellps=WGS84 +towgs84=0,0,0") +writeRaster(Rastout,filename=paste(date,"SIPI.tif",sep="_"),format="GTiff") + +RARS <- R746/R513 +Rastout[] <- RARS +proj4string(Rastout) <- CRS("+proj=utm +zone=12 +datum=WGS84 +units=m +no_defs +ellps=WGS84 +towgs84=0,0,0") +writeRaster(Rastout,filename=paste(date,"RARS.tif",sep="_"),format="GTiff") + +PSSRa <- R800/R680 +Rastout[] <- PSSRa +proj4string(Rastout) <- CRS("+proj=utm +zone=12 +datum=WGS84 +units=m +no_defs +ellps=WGS84 +towgs84=0,0,0") +writeRaster(Rastout,filename=paste(date,"PSSRa.tif",sep="_"),format="GTiff") + +PSSRb <- R800/R635 +Rastout[] <- PSSRb +proj4string(Rastout) <- CRS("+proj=utm +zone=12 +datum=WGS84 +units=m +no_defs +ellps=WGS84 +towgs84=0,0,0") +writeRaster(Rastout,filename=paste(date,"PSSRb.tif",sep="_"),format="GTiff") + +PSSRc <- R800/R470 +Rastout[] <- PSSRc +proj4string(Rastout) <- CRS("+proj=utm +zone=12 +datum=WGS84 +units=m +no_defs +ellps=WGS84 +towgs84=0,0,0") +writeRaster(Rastout,filename=paste(date,"PSSRc.tif",sep="_"),format="GTiff") + +PSNDc <- (R800-R470)/(R800+R470) +Rastout[] <- PSNDc +proj4string(Rastout) <- CRS("+proj=utm +zone=12 +datum=WGS84 +units=m +no_defs +ellps=WGS84 +towgs84=0,0,0") +writeRaster(Rastout,filename=paste(date,"PSNDc.tif",sep="_"),format="GTiff") + +RNIRxCRI550 <- (1/R510)-(1/R550)*R770 +Rastout[] <- RNIRxCRI550 +proj4string(Rastout) <- CRS("+proj=utm +zone=12 +datum=WGS84 +units=m +no_defs +ellps=WGS84 +towgs84=0,0,0") +writeRaster(Rastout,filename=paste(date,"RNIRxCRI550.tif",sep="_"),format="GTiff") + +RNIRxCRI700 <- (1/R510)-(1/R700)*R770 +Rastout[] <- RNIRxCRI700 +proj4string(Rastout) <- CRS("+proj=utm +zone=12 +datum=WGS84 +units=m +no_defs +ellps=WGS84 +towgs84=0,0,0") +writeRaster(Rastout,filename=paste(date,"RNIRxCRI700.tif",sep="_"),format="GTiff") + +PRI515 <- (R515-R530)/(R515+R530) +Rastout[] <- PRI515 +proj4string(Rastout) <- CRS("+proj=utm +zone=12 +datum=WGS84 +units=m +no_defs +ellps=WGS84 +towgs84=0,0,0") +writeRaster(Rastout,filename=paste(date,"PRI515.tif",sep="_"),format="GTiff") + +PRIxCI <- PRI*((R760/R700)-1) +Rastout[] <- PRIxCI +proj4string(Rastout) <- CRS("+proj=utm +zone=12 +datum=WGS84 +units=m +no_defs +ellps=WGS84 +towgs84=0,0,0") +writeRaster(Rastout,filename=paste(date,"PRIxCI.tif",sep="_"),format="GTiff") + +PSRI <- (R680-R500)/R750 +Rastout[] <- PSRI +proj4string(Rastout) <- CRS("+proj=utm +zone=12 +datum=WGS84 +units=m +no_defs +ellps=WGS84 +towgs84=0,0,0") +writeRaster(Rastout,filename=paste(date,"PSRI.tif",sep="_"),format="GTiff") + +VOG <- R740/R720 +Rastout[] <- PSRI +proj4string(Rastout) <- CRS("+proj=utm +zone=12 +datum=WGS84 +units=m +no_defs +ellps=WGS84 +towgs84=0,0,0") +writeRaster(Rastout,filename=paste(date,"VOG.tif",sep="_"),format="GTiff") + +BGI1 <- R400/R550 +Rastout[] <- BGI1 +proj4string(Rastout) <- CRS("+proj=utm +zone=12 +datum=WGS84 +units=m +no_defs +ellps=WGS84 +towgs84=0,0,0") +writeRaster(Rastout,filename=paste(date,"BGI1.tif",sep="_"),format="GTiff") + +BGI2 <- R450/R550 +Rastout[] <- BGI2 +proj4string(Rastout) <- CRS("+proj=utm +zone=12 +datum=WGS84 +units=m +no_defs +ellps=WGS84 +towgs84=0,0,0") +writeRaster(Rastout,filename=paste(date,"BGI2.tif",sep="_"),format="GTiff") + +EVI <- 2.5*(R800-R670)/(R800+6*R670-7.5*R400+1) +Rastout[] <- EVI +proj4string(Rastout) <- CRS("+proj=utm +zone=12 +datum=WGS84 +units=m +no_defs +ellps=WGS84 +towgs84=0,0,0") +writeRaster(Rastout,filename=paste(date,"EVI.tif",sep="_"),format="GTiff") + + + + + + + diff --git a/r/VIs_Calculation_Multispec4C.R b/r/VIs_Calculation_Multispec4C.R new file mode 100644 index 0000000..28840f5 --- /dev/null +++ b/r/VIs_Calculation_Multispec4C.R @@ -0,0 +1,134 @@ +##################################################################### +# Title: ENVI_Indices for Multispec 4C images +# Name: Francisco Manuel Rodriguez Huerta, edited by Lorena GP +# Date: 9 Abril 2014. Sep 13 2016 +# Description: Compute Indices +##################################################################### + +#Load required libraries +require(raster) +require(rgdal) + +### nos posicionamos en el directorio de trabajo ### +rm(list = ls()) +work.dir <- "G:\\AD15_16\\AF\\Processed\\CompB_geo\\" ### Change the input directory +setwd(work.dir) +getwd() + +#set filename +filename<-"c160407_afgeo.bsq" ### check file name + +#create rasterstack object +r1 <- raster(filename, band=1) +rs <- stack(r1) +index <- r1@file@nbands +rm(r1) +for (i in 2:index){ + r1 <- raster(filename, band=i) + rs <- stack(rs,r1) + rm(r1) +} +show(rs) + +#numbers of rows and columns +nr <- nrow(rs) +nc <- ncol(rs) +nlayers(rs) + +#get values +R <- getValuesBlock(rs, row=1, nrows=nr, col=1, ncols=nc) +dim(R) + +#read identifier of bands and wavelengths +bands <- read.table("bands_multispec4c.csv",sep=",",header=TRUE) #### Bands + +setwd("C:\\Dropbox\\data\\AD\\AD-AF\\img\\vi") ### Output folder to VI maps +getwd() + +#Important bands +R550 <- R[,1] +R670 <- R[,2] +R680 <- R[,2] +R750 <- R[,3] +R800 <- R[,4] + +################### +### Calculate indices ### +################### +date <- strsplit(filename,split="_")[[1]][1] +#Define coordinate system to be applied +coord_sys = CRS("+proj=utm +zone=14 +datum=WGS84 +units=m +no_defs +ellps=WGS84 +towgs84=0,0,0") + +#Structural Indices +NDVI <- (R800-R670)/(R800+R670) +Rastout <- raster(ncol=nc,nr=nr) +Rastout[] <- NDVI +Rastout@extent <- rs@extent +proj4string(Rastout) <- coord_sys +writeRaster(Rastout, filename=paste(date,"NDVI.tif",sep="_"), format="GTiff") +#hist(NDVI) + +RDVI <- (R800-R670)/sqrt(R800+R670) +Rastout[] <- RDVI +proj4string(Rastout) <- coord_sys +writeRaster(Rastout,filename=paste(date,"RDVI.tif",sep="_"),format="GTiff") + +OSAVI <- (1+0.16)*(R800-R670)/(R800+R670+0.16) +Rastout[] <- OSAVI +proj4string(Rastout) <- coord_sys +writeRaster(Rastout,filename=paste(date,"OSAVI.tif",sep="_"),format="GTiff") + + SR <- R800/R670 +# Rastout[] <- SR +# proj4string(Rastout) <- coord_sys +# writeRaster(Rastout,filename=paste(date,"SR.tif",sep="_"),format="GTiff") + +MSR <- (SR-1)/sqrt(SR)+1 +Rastout[] <- MSR +proj4string(Rastout) <- coord_sys +writeRaster(Rastout,filename=paste(date,"MSR.tif",sep="_"),format="GTiff") + +MTVI1 <- 1.2*(1.2*(R800-R550)-2.5*(R670-R550)) +Rastout[] <- MTVI1 +proj4string(Rastout) <- coord_sys +writeRaster(Rastout,filename=paste(date,"MTVI1.tif",sep="_"),format="GTiff") + +MCARI1 <- 1.2*(2.5*(R800-R670)-1.3*(R800-R550)) +Rastout[] <- MCARI1 +proj4string(Rastout) <- coord_sys +writeRaster(Rastout,filename=paste(date,"MCARI1.tif",sep="_"),format="GTiff") + +deno <- sqrt((2*R800+1)^2-(6*R800-5*sqrt(R670))-0.5) +MCARI2 <- (1.5/1.2)*MCARI1/deno +Rastout[] <- MCARI2 +proj4string(Rastout) <- coord_sys +writeRaster(Rastout,filename=paste(date,"MCARI2.tif",sep="_"),format="GTiff") + +MTVI2 <- (1.5/1.2)*MTVI1/deno +Rastout[] <- MTVI2 +proj4string(Rastout) <- coord_sys +writeRaster(Rastout,filename=paste(date,"MTVI2.tif",sep="_"),format="GTiff") + +#Chlorophyll indices +TVI <- 0.5*(120*(R750-R550)-200*(R670-R550)) +Rastout[] <- TVI +proj4string(Rastout) <- coord_sys +writeRaster(Rastout,filename=paste(date,"TVI.tif",sep="_"),format="GTiff") + +GM1 <- R750/R550 +Rastout[] <- GM1 +proj4string(Rastout) <- coord_sys +writeRaster(Rastout,filename=paste(date,"GM1.tif",sep="_"),format="GTiff") + + +G <- R550/R670 +Rastout[] <- G +proj4string(Rastout) <- coord_sys +writeRaster(Rastout,filename=paste(date,"G.tif",sep="_"),format="GTiff") + +#Indices PDF + +PSSRa <- R800/R680 +Rastout[] <- PSSRa +proj4string(Rastout) <- coord_sys +writeRaster(Rastout,filename=paste(date,"PSSRa.tif",sep="_"),format="GTiff") \ No newline at end of file diff --git a/r/applyNiblackFilteronImage.R b/r/applyNiblackFilteronImage.R new file mode 100644 index 0000000..76043a9 --- /dev/null +++ b/r/applyNiblackFilteronImage.R @@ -0,0 +1,11 @@ +#Apply the niblack filter on an image +# february 2018 +# !!!!!!!!!!!!!!! !# ongoing work + +library (raster) + +getTop5percInsideROIandRemoveFR <- function (i,fgdb,roi_name){ + + meanImg <- cellStats(img95, stat='mean', na.rm=TRUE) + + focal(x, w, fun, filename='', na.rm=FALSE, pad=FALSE, padValue=NA, NAonly=FALSE, ...) diff --git a/r/arrangeAFextractedData2017DBF.R b/r/arrangeAFextractedData2017DBF.R new file mode 100644 index 0000000..73c261a --- /dev/null +++ b/r/arrangeAFextractedData2017DBF.R @@ -0,0 +1,94 @@ +#Arrange tables from zonalStatistics Extraction +#Take all the tables from a date-cam and put in one Excel table +# Used Packages +options(java.parameters = "-Xmx30000m") #available RAM for JAVA +library(foreign) +library(xlsx) +library(stringr) +library(plyr) +library(gtools) + +### FUNCTIONS +###############InputInputInputInputInputInputInputInputInputInputInput +#InputInputInputInputInputInputInputInput +#Input should be a dbf file + +id = "Name" # For most of the cases +feature = "buf" ##buf for the BW trials + +####################################################### + +addBandToColumn <- function (f){ + t <- read.dbf(f) + t <- subset(t, select=c(id,"MEAN")) + band <- str_extract(f, "B[0-9][0-9]*[0-9]*") #Get the band # + origFields <- colnames(t) + colnamesB <- origFields + #Next put the band number in the column but not in the id column + for (i in 1:length(colnamesB) ){ + if (colnamesB[i] != id) + colnamesB[i]<- paste(colnamesB[i],"_",band,feature, sep="") + } + colnames(t)<- colnamesB + return (t) +} +#Merge to use with reduce sucessively by "id" +merge.all <- function(x, y) { + merge(x, y, all=TRUE, by=id) +} +#stack by-band-dbf statistics in excel +arrangeDbf <- function(pattern) { + #Name of the workbook will be the camera+date + cam_date <- substr(pattern, 1, 7) + wbookName <- paste(cam_date,".xlsx",sep="") + wbook <- createWorkbook() + wbSheet <- createSheet(wb = wbook, sheetName = toString(cam_date)) + file_list = list.files(pattern=pattern)#list and filter dbf from ws + #get mentioned trial names to group them + trials <- unique(lapply(file_list, function(x) substr(x,8,10))) + t1=TRUE + for (tr in trials){ + patt_tr = paste(cam_date,tr,feature,".*",sep="") + fileListTr <- grep(patt_tr, file_list, value=TRUE) + fileListTr <- mixedsort(fileListTr)#Natural sorting + # batch read all into a list + t.list = lapply(fileListTr, addBandToColumn) + #merge them into a single data frame + tables <- Reduce(merge.all, t.list) + #Next remove the duplicate "id" column + tables <- tables[,!duplicated(colnames(tables), fromLast = TRUE)] + if (t1==TRUE){ + startRow<-1 + print(paste("Escribiendo",nrow(tables),"registros en",tr)) + addDataFrame(x=tables, sheet=wbSheet,row.names=FALSE,startRow = startRow) + t1<-FALSE + startRow = startRow+nrow(tables) + 1 #We count that we printed the header line + }else{ + print(paste("Escribiendo",nrow(tables),"registros en",tr)) + addDataFrame(x=tables,sheet=wbSheet, + row.names=FALSE,col.names=FALSE,startRow=startRow) + startRow = startRow+nrow(tables) + } + } + saveWorkbook(wbook, wbookName) + print(paste("Se guardó el archivo",wbookName)) + return (wbook) +} +#read tables +################# Check these inputs######################### +wd<-("C:\\Users\\usuario\\Documents\\ArcGIS\\") +setwd(wd) +getwd() + +#change inputs on top + +#Get the unique date and camera to stack the tables +allDbfFiles <-list.files(pattern="\\.dbf$") +date_and_cam <- unique(str_extract(allDbfFiles, "[:alpha:]{1}[:digit:]{6}")) +## Sets the pattern of name in files to be stacked +for (i in date_and_cam){ + print(paste("Fecha y cámara:",i)) + pattern= paste(i,".*dbf$",sep="") + xlsx <-arrangeDbf(pattern) +} + diff --git a/r/arrangeBWtables.R b/r/arrangeBWtables.R new file mode 100644 index 0000000..70c260d --- /dev/null +++ b/r/arrangeBWtables.R @@ -0,0 +1,94 @@ +#Arrange tables from zonalStatistics Extraction +#Take all the tables from a date-cam and put in one Excel table +# Used Packages +options(java.parameters = "-Xmx30000m") #available RAM for JAVA +library(foreign) +library(xlsx) +library(stringr) +library(plyr) +library(gtools) + +### FUNCTIONS +###############InputInputInputInputInputInputInputInputInputInputInput +#InputInputInputInputInputInputInputInput +#Input should be a dbf file + +id = "id" # For most of the cases +feature = "buf" ##buf for the BW trials + +####################################################### + +addBandToColumn <- function (f){ + t <- read.dbf(f) + t <- subset(t, select=c("id","COUNT","MEAN","STD")) + band <- str_extract(f, "B[0-9][0-9]*[0-9]*") #Get the band # + origFields <- colnames(t) + colnamesB <- origFields + #Next put the band number in the column but not in the id column + for (i in 1:length(colnamesB) ){ + if (colnamesB[i] != id) + colnamesB[i]<- paste(colnamesB[i], band, sep="") + } + colnames(t)<- colnamesB + return (t) +} +#Merge to use with reduce sucessively by "id" +merge.all <- function(x, y) { + merge(x, y, all=TRUE, by=id) +} +#stack by-band-dbf statistics in excel +arrangeDbf <- function(pattern) { + #Name of the workbook will be the camera+date + cam_date <- substr(pattern, 1, 7) + wbookName <- paste(cam_date,".xlsx",sep="") + wbook <- createWorkbook() + wbSheet <- createSheet(wb = wbook, sheetName = toString(cam_date)) + file_list = list.files(pattern=pattern)#list and filter dbf from ws + #get mentioned trial names to group them + trials <- unique(lapply(file_list, function(x) substr(x,8,10))) + t1=TRUE + for (tr in trials){ + patt_tr = paste(cam_date,tr,feature,".*",sep="") + fileListTr <- grep(patt_tr, file_list, value=TRUE) + fileListTr <- mixedsort(fileListTr)#Natural sorting + # batch read all into a list + t.list = lapply(fileListTr, addBandToColumn) + #merge them into a single data frame + tables <- Reduce(merge.all, t.list) + #Next remove the duplicate "id" column + tables <- tables[,!duplicated(colnames(tables), fromLast = TRUE)] + if (t1==TRUE){ + startRow<-1 + print(paste("Escribiendo",nrow(tables),"registros en",tr)) + addDataFrame(x=tables, sheet=wbSheet,row.names=FALSE,startRow = startRow) + t1<-FALSE + startRow = startRow+nrow(tables) + 1 #We count that we printed the header line + }else{ + print(paste("Escribiendo",nrow(tables),"registros en",tr)) + addDataFrame(x=tables,sheet=wbSheet, + row.names=FALSE,col.names=FALSE,startRow=startRow) + startRow = startRow+nrow(tables) + } + } + saveWorkbook(wbook, wbookName) + print(paste("Se guardó el archivo",wbookName)) + return (wbook) +} +#read tables +################# Check these inputs######################### +wd<-("C:\\Users\\usuario\\Documents\\ArcGIS\\") +setwd(wd) +getwd() + +#change inputs on top + +#Get the unique date and camera to stack the tables +allDbfFiles <-list.files(pattern="\\.dbf$") +date_and_cam <- unique(str_extract(allDbfFiles, "[:alpha:]{1}[:digit:]{6}")) +## Sets the pattern of name in files to be stacked +for (i in date_and_cam){ + print(paste("Fecha y cámara:",i)) + pattern= paste(i,".*dbf$",sep="") + xlsx <-arrangeDbf(pattern) +} + diff --git a/r/arrangeBWtables2017.R b/r/arrangeBWtables2017.R new file mode 100644 index 0000000..dc7f2bd --- /dev/null +++ b/r/arrangeBWtables2017.R @@ -0,0 +1,94 @@ +#Arrange tables from zonalStatistics Extraction +#Take all the tables from a date-cam and put in one Excel table +# Used Packages +options(java.parameters = "-Xmx30000m") #available RAM for JAVA +library(foreign) +library(xlsx) +library(stringr) +library(plyr) +library(gtools) + +### FUNCTIONS +###############InputInputInputInputInputInputInputInputInputInputInput +#InputInputInputInputInputInputInputInput +#Input should be a dbf file + +id = "Name" # For most of the cases +feature = "buf" ##buf for the BW trials + +####################################################### + +addBandToColumn <- function (f){ + t <- read.dbf(f) + t <- subset(t, select=c(id,"MEAN")) + band <- str_extract(f, "B[0-9][0-9]*[0-9]*") #Get the band # + origFields <- colnames(t) + colnamesB <- origFields + #Next put the band number in the column but not in the id column + for (i in 1:length(colnamesB) ){ + if (colnamesB[i] != id) + colnamesB[i]<- paste(colnamesB[i], band, sep="") + } + colnames(t)<- colnamesB + return (t) +} +#Merge to use with reduce sucessively by "id" +merge.all <- function(x, y) { + merge(x, y, all=TRUE, by=id) +} +#stack by-band-dbf statistics in excel +arrangeDbf <- function(pattern) { + #Name of the workbook will be the camera+date + cam_date <- substr(pattern, 1, 7) + wbookName <- paste(cam_date,".xlsx",sep="") + wbook <- createWorkbook() + wbSheet <- createSheet(wb = wbook, sheetName = toString(cam_date)) + file_list = list.files(pattern=pattern)#list and filter dbf from ws + #get mentioned trial names to group them + trials <- unique(lapply(file_list, function(x) substr(x,8,10))) + t1=TRUE + for (tr in trials){ + patt_tr = paste(cam_date,tr,feature,".*",sep="") + fileListTr <- grep(patt_tr, file_list, value=TRUE) + fileListTr <- mixedsort(fileListTr)#Natural sorting + # batch read all into a list + t.list = lapply(fileListTr, addBandToColumn) + #merge them into a single data frame + tables <- Reduce(merge.all, t.list) + #Next remove the duplicate "id" column + tables <- tables[,!duplicated(colnames(tables), fromLast = TRUE)] + if (t1==TRUE){ + startRow<-1 + print(paste("Escribiendo",nrow(tables),"registros en",tr)) + addDataFrame(x=tables, sheet=wbSheet,row.names=FALSE,startRow = startRow) + t1<-FALSE + startRow = startRow+nrow(tables) + 1 #We count that we printed the header line + }else{ + print(paste("Escribiendo",nrow(tables),"registros en",tr)) + addDataFrame(x=tables,sheet=wbSheet, + row.names=FALSE,col.names=FALSE,startRow=startRow) + startRow = startRow+nrow(tables) + } + } + saveWorkbook(wbook, wbookName) + print(paste("Se guardó el archivo",wbookName)) + return (wbook) +} +#read tables +################# Check these inputs######################### +wd<-("C:\\Users\\usuario\\Documents\\ArcGIS\\") +setwd(wd) +getwd() + +#change inputs on top + +#Get the unique date and camera to stack the tables +allDbfFiles <-list.files(pattern="\\.dbf$") +date_and_cam <- unique(str_extract(allDbfFiles, "[:alpha:]{1}[:digit:]{6}")) +## Sets the pattern of name in files to be stacked +for (i in date_and_cam){ + print(paste("Fecha y cámara:",i)) + pattern= paste(i,".*dbf$",sep="") + xlsx <-arrangeDbf(pattern) +} + diff --git a/r/arrangeSTARStables.R b/r/arrangeSTARStables.R new file mode 100644 index 0000000..7b427a7 --- /dev/null +++ b/r/arrangeSTARStables.R @@ -0,0 +1,101 @@ +#Arrange tables from zonalStatistics Extraction +#Take all the tables from a date-cam and put in one Excel table +# Used Packages +options(java.parameters = "-Xmx30000m") #available RAM for JAVA +library(foreign) +library(xlsx) +library(stringr) +library(plyr) +library(gtools) + +### FUNCTIONS +###############InputInputInputInputInputInputInputInputInputInputInput +#InputInputInputInputInputInputInputInput +#Input should be a dbf file + +id = "Id" # For most of the cases +feature = "buf" ##buf for the BW trials + +####################################################### + +addBandToColumn <- function (f){ + ##read dbFfile + t <- read.dbf(f) + ##know date and cam + idate <- str_extract(f, "[:alpha:]{1}[:digit:]{6}") + ##t <- subset(t, select=c("Id","COUNT","MEAN","STD")) + t <- subset(t, select=c("Id","MEAN")) ##Keep only mean column + band <- str_extract(f, "B[0-9][0-9]*[0-9]*") #Get the band # + origFields <- colnames(t) + colnamesB <- origFields + #Next put the band number in the column but not in the id column + for (i in 1:length(colnamesB) ){ + if (colnamesB[i] != id) + #here we did not include the word "mean" in the field name, to make it shorter. + colnamesB[i]<- paste(toString(idate), band, sep="") + } + colnames(t)<- colnamesB + return (t) +} +#Merge to use with reduce sucessively by "id" +merge.all <- function(x, y) { + merge(x, y, all=TRUE, by=id) +} +#stack by-band-dbf statistics in excel +arrangeDbf <- function(pattern) { + #Name of the workbook will be the camera+date+trial + cam_date_trial <- substr(pattern, 1, 10) + wbookName <- paste(cam_date_trial,".xlsx",sep="") + wbook <- createWorkbook() + wbSheet <- createSheet(wb = wbook, sheetName = toString(cam_date_trial)) + file_list = list.files(pattern=pattern)#list and filter dbf from ws + #get mentioned trial names to group them + trials <- unique(lapply(file_list, function(x) substr(x,8,10))) + t1=TRUE + for (tr in trials){ + patt_tr = paste(cam_date_trial,feature,".*",sep="") + fileListTr <- grep(patt_tr, file_list, value=TRUE) + fileListTr <- mixedsort(fileListTr)#Natural sorting + # batch read all into a list + t.list = lapply(fileListTr, addBandToColumn) + #merge them into a single data frame + tables <- Reduce(merge.all, t.list) + #Next remove the duplicate "id" column + tables <- tables[,!duplicated(colnames(tables), fromLast = TRUE)] + ##remove decimal values from reflectance values * 100 000 + #tables <- round(tables[,],0) + if (t1==TRUE){ + startRow<-1 + print(paste("Escribiendo",nrow(tables),"registros en",tr)) + addDataFrame(x=tables, sheet=wbSheet,row.names=FALSE,startRow = startRow) + t1<-FALSE + startRow = startRow+nrow(tables) + 1 #We count that we printed the header line + }else{ + print(paste("Escribiendo",nrow(tables),"registros en",tr)) + addDataFrame(x=tables,sheet=wbSheet, + row.names=FALSE,col.names=FALSE,startRow=startRow) + startRow = startRow+nrow(tables) + } + } + saveWorkbook(wbook, wbookName) + print(paste("Se guardó el archivo",wbookName)) + return (wbook) +} +#read tables +################# Check these inputs######################### +wd<-("C:\\Users\\usuario\\Documents\\ArcGIS\\") +setwd(wd) +getwd() + +#change inputs on top + +#Get the unique date and camera to stack the tables +allDbfFiles <-list.files(pattern="\\.dbf$") +date_and_cam_trial <- unique(str_extract(allDbfFiles, "[:alpha:]{1}[:digit:]{6}[:alpha:]{3}")) +## Sets the pattern of name in files to be stacked +for (i in date_and_cam_trial){ + print(paste("Fecha y cámara:",i)) + pattern= paste(i,".*dbf$",sep="") + xlsx <-arrangeDbf(pattern) +} + diff --git a/r/arrangeVItables.R b/r/arrangeVItables.R new file mode 100644 index 0000000..2d24fd0 --- /dev/null +++ b/r/arrangeVItables.R @@ -0,0 +1,73 @@ +#Arrange tables from zonalStatistics Extraction +#Take all the tables from a date-cam and put in one Excel table + +# NOT FINISHEDDDDDDDDDDDD + +# Used Packages +options(java.parameters = "-Xmx30000m") #available RAM for JAVA +library(foreign) +library(xlsx) +library(stringr) +library(plyr) +library(gtools) + +### FUNCTIONS +#Input should be a dbf file + +id="Name" #for AF trials +#id = "id" # For most of the cases +#feature = "buf" ##buf for the BW trials +#feature= "(bfs|b2s)" # for AF trials +feature= "buf" # for AF trials + +addFileNameToColumn <- function (f){ + t <- read.dbf(f) + t <- subset(t, select=c(id,"MEAN")) #select only the MEAN column of the table + vi <- strsplit(f,split="_")[[1]][2] #Get the VI name from the table name# + date <- strsplit(f,split="_")[[1]][1] #date + feature <- strsplit(f,split="_")[[1]][3] #feature + vi_datefeature <- paste(vi,"_",date,feature, sep="") + origFields <- colnames(t) + colnamesB <- origFields + #Next put the VI name and date in the column but not in the id column + for (i in 1:length(colnamesB) ){ + if (colnamesB[i] != id) + colnamesB[i]<- paste(colnamesB[i], vi_datefeature, sep="") + } + colnames(t)<- colnamesB + return (t) +} +#Merge to use with reduce sucessively by "id" +merge.all <- function(x, y) { + merge(x, y, all=TRUE, by=id) +} +#stack by-band-dbf statistics in excel +arrangeDbf <- function(pattern) { + wbookName <- paste(feature,".xlsx",sep="") + wbook <- createWorkbook() + wbSheet <- createSheet(wb = wbook, sheetName = toString(feature)) + file_list = list.files(pattern=pattern)#list and filter dbf from ws + fileList <- mixedsort(file_list)#Natural sorting + # batch read all into a list + t.list = lapply(fileList, addFileNameToColumn) + #merge them into a single data frame + tables <- Reduce(merge.all, t.list) + #Next remove the duplicate "id" column + tables <- tables[,!duplicated(colnames(tables), fromLast = TRUE)] + startRow<-1 + print(paste("Escribiendo",nrow(tables),"registros")) + addDataFrame(x=tables, sheet=wbSheet,row.names=FALSE,startRow = startRow) + + saveWorkbook(wbook, wbookName) + print(paste("Se guardó el archivo",wbookName)) + return (wbook) +} +#read tables +################# Check these inputs######################### +wd<-("C:\\Users\\usuario\\Documents\\ArcGIS\\") +setwd(wd) +getwd() + +## Sets the pattern of name in files to be stacked +pattern= ".*dbf$" #it will iterate all the tables +xlsx <-arrangeDbf(pattern) \ No newline at end of file diff --git a/r/commandine_test.R b/r/commandine_test.R new file mode 100644 index 0000000..02aa53e --- /dev/null +++ b/r/commandine_test.R @@ -0,0 +1,17 @@ +#Function test +#To be run in CMD: +#example +#Rscript commandine_test.R "hi lola" 456 + +#Function +printer <- function(input1,input2=2) +{ + tryCatch({ + print(input1) + print(input2) + }) +} + +#Call function +args <- commandArgs(TRUE) +printer(args[1],args[2]) \ No newline at end of file diff --git a/r/cropTiffsInsideFolder.R b/r/cropTiffsInsideFolder.R new file mode 100644 index 0000000..bb46452 --- /dev/null +++ b/r/cropTiffsInsideFolder.R @@ -0,0 +1,53 @@ +######### Crop images (Tif) in a folder based on a ROI +######## the roi is read from a GDB + +#0. First step : load packages +#install.packages(c("rgdal","raster")) +require(raster) +require(rgdal) + +###################### DEFINED FUNCTIONS (no need to edit) + +#Function to select the apropriate roi for each trial +# Read the feature class +get_roi <- function(trial_roi) {readOGR(dsn=fgdb,layer=trial_roi)} + +######################## DEFINE VARIABLES (Enter your variables) +# #Working directory +wd="C:\\Dropbox\\New folder\\temp\\Franz\\AE\\" #For images, the root folder +setwd(wd) +## What kind of files to seach +pattern="\\.tif$" +roi_name<- "d30__ae__roi" +#Feature geodatabase to get ROIS +fgdb = "C:\\Dropbox\\data\\AE\\nut\\ae_nut.gdb" +#prefix to add to theOutput name of the crop file +pfx <- "crp_" + +######################### STARTs SCRIPT (No need to edit) + +#Error handling try +tryCatch({ + #list all imagery in the folder + imgs <-list.files(pattern=pattern) + print(imgs) #for debugging + #get roi + roi <- get_roi(roi_name) + print(paste("roi")) #for debugging + + #loop for the images to crop + for (i in imgs){ + #Read raster + r <- raster(i) + #crop + r_cr<-crop(r, extent(roi), snap="out") #execute script, crop by roi + print(paste("cropped")) #for debugging + #name for outpul file + #outFile <- paste0("t",i,"_q21cel") + outFile <- paste0(pfx,i) + #Save file + writeRaster(r_cr, file=outFile,datatype='FLT4S',format="GTiff",overwrite=FALSE) + #inform about saving + print(paste("saved raster:",outFile," Succesfully")) + } +}) \ No newline at end of file diff --git a/r/descriptiveStatistics_manyColumns.R b/r/descriptiveStatistics_manyColumns.R new file mode 100644 index 0000000..da2cf72 --- /dev/null +++ b/r/descriptiveStatistics_manyColumns.R @@ -0,0 +1,137 @@ +##Script to get the descriptive statistics of a table of data +##Reads may csv in working directory with cerain pattern +## Saves the statistics in a TXT in the same location as the data file +##July 2015 + +################# Check these inputs######################### +##Set working directory to avoid giving the absolute path of files +##IMPORTANT: use the / slash instead of the backslash \ in the paths +##Or use double backslash \\ +wd<-("C:/vuelos/ab2014/Marco/") +setwd(wd) +## Sets the pattern of name in CSV files to be read +data_set = list.files(pattern="vis[0-9]*.csv") +dir.create(paste(wd,"stats/",sep="")) #Creates a directory to store statistics + +#NOTE:#In this script the columns used to get the statistics go from[,6:46] +## because the first 5 have other data like coordinates and lenght +################# END Check these inputs######################### +#dir() # Lists files in the working directory +#dir.create("C:/test") #Creates a directory + +#Libraries used +library(pastecs) +library(tools) +library(Hmisc) +library(PerformanceAnalytics) + +##Load data in CSV format +# name_file_data<- file.choose() #Choose file, save name +# path_wo_extension<-file_path_sans_ext(name_file_data) #save path without extension +# data_basename<-basename(path_wo_extension) #save filename + +######### Load protein data ############# +##Fixed way, writing the name of file +protein_data_name<- "AB-Marco-protein.csv" +#Next read the data from the protein results file +##the parameter colClasses specifies to skip the second column of this data +protein_data<-read.csv(protein_data_name, header = TRUE, row.names = 1,colClasses = c("character","NULL","numeric")) +######### END Load protein data ############# + +### READ VIS data +##Here read for a CSV with headers and the ID of the entry on the first column +for (i in 1:length(data_set)) { + #Get names of vis + vis<-sub("[A-z]*[0-9]*_?","",x=colnames(get(data_set[i]))) + #assigns to variables each of the CSV + #Select the columns after the 6th one, because the firsts have ancillary data + assign(data_set[i], read.csv(data_set[i], + header = TRUE, row.names = 1, + colClasses = c("character",rep("NULL",5),rep("numeric",41)))) + ##Descriptive statistics on the data + data_stats<-stat.desc(get(data_set[i]),basic=TRUE, desc=TRUE, norm=TRUE, p=0.95) + # Values it returns: + # nbr.val (the count of values) + # nbr.null (the count of null values) + # nbr.na (the count of missing values) + # min + # max + # range + # sum + # median + # mean + # SE.mean + # CI.mean + # var + # std.dev + # coef.var + # skewness + # skew.2SE + # kurtosis + # kurt.2SE + # normtest.W + # normtest.p + + file_name<-file_path_sans_ext(data_set[i]) + ########### Graphs ############## + pdf(file=paste("stats/",file_name," plots.pdf", sep="")) + + #Correlation from Hmisc package: protein vs all vis + #merge protein & vis tables + col_to_compare<-merge(data.matrix(protein_data),data.matrix(get(data_set[i])),by="row.names") + rownames(col_to_compare)<-t(col_to_compare[1])#Set the rownames. traspose the column of rownames to make it match + col_to_compare<-col_to_compare[,2:length(col_to_compare)]#remove the first column with the rownames + data_corr<-rcorr(data.matrix(col_to_compare), type="pearson") + #Chart from PerformanceAnalytics Package + chart.Correlation(data_corr$r, histogram=FALSE,method = c("pearson")) + for (j in 1:length(vis)) { + + currcolumn_name<-colnames(get(data_set[i])[j]) + currcolumn <-get(data_set[i])[,j] + par(mfrow=c(2,1)) + #Boxplot + boxplot(currcolumn, main =paste("Boxplot ",currcolumn_name)) + #histogram + hist(currcolumn, main =paste("Histogram ",currcolumn_name),xlab = paste(currcolumn_name), nclass = 10) + #Get median and mean from calculated stats to draw in histogram + median<-data_stats[8,j] #median. + abline(v=median,lty=2,lwd=2,col="blue") + mean<-data_stats[9,j] #median. + abline(v=mean,lty=3,lwd=2, col="red") + legend("topleft", legend=c("median","mean"), col=c("blue","red"), lty=c(2,3),lwd=c(2,1)) + + } + dev.off();#Finish saving to PDF + ########### END Graphs ############## + + ##Write stats and correlation result to text file + output_name<- paste(wd, "stats/",file_name, "stats.csv", sep="") + write.csv(data_stats, file = output_name) + + write.csv(data_corr$r, file = paste(wd,"stats/", file_name, "corr.csv", sep="")) + write.csv(data_corr$r, file = paste(wd,"stats/", file_name, "corr_p.csv", sep="")) +} + +# #Example of a function +# f1 <- function(param1,param2){ +# param1*param1+param2 +# } +# a<-f1(param2=2,param1=3) + +##Add Quantile data +#data_quantile<-quantile(data$protein) +#write.table(data_quantile, file = output_name, sep = ",", +# col.names = FALSE, append=TRUE) + +#histogram +#hist(data$protein,xlab = "protein (%)", nclass = 18) + +##This is to create an histogram with normal curve from random data +# x <- rnorm(100) +# hist(x, freq=F) +# curve(dnorm(x), add=T) +# +# h <- hist(x, plot=F) +# ylim <- range(0, h$density, dnorm(0)) +# hist(x, freq=F, ylim=ylim) +# curve(dnorm(x), add=T) diff --git a/r/descriptiveStatistics_protein.R b/r/descriptiveStatistics_protein.R new file mode 100644 index 0000000..9b1cad3 --- /dev/null +++ b/r/descriptiveStatistics_protein.R @@ -0,0 +1,92 @@ +##Script to get the descriptive statistics of a table of data +##the column used to get the statistics is called "protein" +##You can replace "protein" to get the statistics from a different value in the table +## Saves the statistics in a TXT in the same location as the data file +##July 2015 + +##Set working directory to avoid giving the absolute path of files +##IMPORTANT: use the / slash instead of the backslash \ in the paths +##Or use double backslash \\ +setwd("C:/vuelos/ab2014/Marco") +#dir() # Lists files in the working directory +#dir.create("C:/test") #Creates a directory + +#Libraries used +library(pastecs) +library(tools) + +##Load data in CSV format +#name_file_data<- file.choose()#Interactive choose file +data_basename<-basename(file_path_sans_ext(name_file_data)) +##Fixed way, writing the name of file +name_file_data<- "AB-Marco-protein.csv" +##Here is for a CSV with headers and the ID of the entry on the first column +#data<-read.csv(file = name_file_data, header = TRUE, row.names = 1) +##Interactive way where you can select the file +data<-read.csv(name_file_data, header = TRUE, row.names = 1,colClasses = c("character","NULL","numeric")) + + +##Descriptive statistics on the data +data_stats<-stat.desc(data,basic=TRUE, desc=TRUE, norm=TRUE, p=0.95) +data_stats +# Values it returns: +# nbr.val (the count of values) +# nbr.null (the count of null values) +# nbr.na (the count of missing values) +# min +# max +# range +# sum +# median +# mean +# SE.mean +# CI.mean +# var +# std.dev +# coef.var +# skewness +# skew.2SE +# kurtosis +# kurt.2SE +# normtest.W +# normtest.p + +##Quantile +data_quantile<-quantile(data$protein) + +#Graphics to save to PDF +pdf(file=paste("stats/",file_name," plots.pdf", sep="")) +par(mfrow=c(2,1)) +#Boxplot +boxplot(data, main =paste("Boxplot ","protein")) +#histogram +hist(data, main =paste("Histogram protein"),xlab = "protein (%)", nclass = 20) +#Get median and mean from calculated stats to draw in histogram +median<-data_stats[8,1] #median. +abline(v=median,lty=2,lwd=2,col="blue") +mean<-data_stats[9,1] #median. +abline(v=mean,lty=3,lwd=2, col="red") +legend("topleft", legend=c("median","mean"), col=c("blue","red"), lty=c(2,3),lwd=c(2,1)) +dev.off(); +#Finish saving to PDF + +##Write result to text file +output_name<- paste(data_basename, "stats.csv", collapse = NULL) +write.csv(data_stats, file = output_name) +#Add quartile data to previous file +q<-c("value") +names(q)<-c("Quantiles_perc") +write.table(q, file = output_name, sep = ",", + col.names = FALSE, append=TRUE) +write.table(data_quantile, file = output_name, sep = ",", + col.names = FALSE, append=TRUE) + +##This is to create an histogram with normal curve from random data +# x <- rnorm(100) +# hist(x, freq=F) +# curve(dnorm(x), add=T) +# +# h <- hist(x, plot=F) +# ylim <- range(0, h$density, dnorm(0)) +# hist(x, freq=F, ylim=ylim) +# curve(dnorm(x), add=T) diff --git a/r/excel_to_row_column_CSV.R b/r/excel_to_row_column_CSV.R new file mode 100644 index 0000000..340aa85 --- /dev/null +++ b/r/excel_to_row_column_CSV.R @@ -0,0 +1,25 @@ +# Get field maps by trial in excel format and converto to the format to input in athena's script csv to kml. +##################################### WORK IN PROGRESSSSS + +#Libraries +#library("XLConnect") #For the excels +#library(reshape2) #for the melt + +source(file.path("C:","Dropbox","Software","Scripts","r","io_methods.R", fsep = .Platform$file.sep)) + +####### INPUTS + +#Field maps Excels location +fm_folder <- file.path("C:","Dropbox","data","AF","bw","BWOBREGON Y17-18","MAPAS DE SIEMBRA Y17-18", fsep = .Platform$file.sep) +setwd(fm_folder) +#trial to get all sections. Starts with... +trial <- "EYTBWBLHT" + +####### SCRIPT + +#Get all the excels in the wd that starts with the trial name +xlsx_list <-list.files(pattern= paste0("^",trial,".*\\.xlsx$")) # The \\. is escaping the .'s special char +#Get all the Excel fieldmaps in a table in the "row | col | plot" format +fieldmap_rowcol <- readFieldMapsExcel(xlsx_list) + +write.csv(fieldmap_rowcol,file.path("C:","vuelos","temp","fieldmap_rowcol.csv", fsep = .Platform$file.sep), row.names=FALSE) diff --git a/r/exercises_data_frames.R b/r/exercises_data_frames.R new file mode 100644 index 0000000..255d643 --- /dev/null +++ b/r/exercises_data_frames.R @@ -0,0 +1,230 @@ +####### Michael%20Dorman-Learning%20R%20for%20Geospatial%20Analysis +setwd("C:\\vuelos\\temp\\") #working directory for tests +myFile = file.path("C:","Dropbox", "data","AE","ae_stars","ae_data_extract.csv", fsep = .Platform$file.sep) +#Get only the file name from a path (with extension) +basename(myFile) +#without extension +library(tools) +tools::file_path_sans_ext("ABCD.csv") +dat = read.csv(myFile, stringsAsFactors = FALSE) +date <- dat$date +#Formatear como fechas las fechas +date <- as.Date(as.character(date),format = "%y%m%d") + # range_date <- range(date) + # all_dates <- seq(range_date[1],range_date[2],1) + # length(all_dates) + # length(date) + # which(all_dates %in% date) +#Vector lĂłgico checando cuales fechas son en enero +if_enero <- date >= as.Date("2017-01-01") & date <= as.Date("2017-1-31") +#porcentaje de las mediciones que fueron hechas en enero +#Todas / las de enero +sum(if_enero)/length(if_enero) +#subset las fechas que pertenecen a enero en base al vector lĂłgico +mediciones_enero <- date [if_enero] +#plot(date, 1:13, type = "l") ## ejemplo de plot lĂ­nea con fecha de mediciones + +#### Otro tema +## Data frames +##CREate one +df = data.frame( + num = 1:4, + lower = c("a","b","c","d"), + upper = c("A","B","C","D"), stringsAsFactors = FALSE) +##Examine the object +str(df) +##Subset a column of the data Frame, but always keep the data frame class +df[ ,3, drop = FALSE] +#Subset with another method, including a conditional, and the names of columns +df[df$lower %in% c("a","d"), c("lower","upper")] ##x %in% table +# Exclude rows that are not complete +df[complete.cases(df),] +##Create a column based on a specific value of a cell (row) +df$word[df$num == 2] <- "two" +## create another column mixing the others +df$mix <- paste(df$num,df$lower,df$upper, sep="") +##Use of ifElse +ifelse(df >= 2, ">2", "menor") +"a" > 2 +as.numeric("a") +#get the duplicated values of the vector +u[duplicated(u)] + +#####Another subject +###Apply family +#tapply : apply a function across subsets +#data +df$categories <- c("1","1","2","2") +#Get the mean vakue of a column, by groups +#An array is returned +df$mean <- tapply(df$num,df$categories, mean) ##tapply(values, categories, function) +#Eliminate column named "mean" +df <- df[, which(!(colnames(df) %in% "mean"))] #From the logical vector we get the indexes of the columns not named "mean" +#Check for missing values AKA NA's, an array is returned with its names, by group +na_values <- tapply(df$word, df$upper, function (x) any(is.na(x))) +#columns with NA values +names(na_values[na_values]) +##use the function apply. It can apply across rows or columns the functions +# parameter with value 1 or 2 refers if it should aggregate rows or columns +#Get the max from num and categories column +apply( df[, c("num","categories")], 2, max, na.rm =TRUE) +#paste values in the row +apply(df, 1, paste) + +####Another topic +##Changing between wide and long tables +##Wide tables are the ones which store in columns the different traits measured +## Long tables have format like: a ID / variables / value +library(reshape2) +#Change from wide to long table +long_table <- melt(df, id.vars =c("num","categories")) +#Change back from long to wide table +# a "formula object" is used in the form: varDependent ~ varIndependent +df <- dcast(long_table, ... ~ variable) # ... means all other variables + +#stack dataframes from a list +#Now put all the different fieldmaps in one table +length(do.call("rbind", list.of.df)) +rowcol_xlsxs <- mapply(rbind, list.of.df, SIMPLIFY=FALSE) #este creo que no hace eso... + +##### CHECK OUT dplyr and tidyr !!!!! ##### + +library(plyr) +##Summarize +## Create new columns by group and making operations +## Transform would append to the same table +df_summ <- ddply(df, .(categories), summarize, + nums_max = max(num*num), + l = length(mix), + word_ish = word[2], + sum_mean <- ifelse(categories[1] == 1, sum(num),mean(num))) +##repeat something several times +season = rep(c("winter","spring","summer","fall"), each = 3) +##MAKE A DATA JOIN !!!!!! +combined = join(df, + df_summ, + by = "categories", type = "left", match="all") +#Summarize the reps 1,2,3 to get just the mean value +ts <- aggregate(wlayer ~ noRep+depth+date, data=wLTable, FUN=mean) +##MATRIX +################################# +##create a matrix (2 dimensions) +m <- matrix(1:9,ncol=3, byrow=TRUE) +length(m) #Get lenght +nrow(m) #Get num of rows +ncol(m) #Get num of columns +dim(m) #get dimensions +as.vector(m) # get as linear vector ordered by columns +m[2, , drop = FALSE] #subset second row of the matix and keep the class Matrix +m[2:3, 1:2] = matrix(-1:-4, nrow = 2) #REplace values inside matrix +colMeans(m) # Get the mean values of each column of the matix +apply(m,2,mean) # Get the mean values of each column of the matix, again + +##ARRAY N -dimensions!! +##################################### +##Create an array (3 dimensions) +a = array(1:24, c(2,2,3)) +##Create Array that can store values for months of year, weeks, days, and hours of each day. Idea taken from Quora ask. +time_array = array(NA, c(24,7,4,12)) +rownames(time_array) <- paste(1:24,":00", sep = "") #set rownames +colnames(time_array) <- c("Lun", "Mar", "Mier","Jue","vier", "sab","dom") #set column names +dimnames(time_array)[[3]] <- c("semana1","semana2", "semana3","semana4") #Set names for the 3 dimension +dimnames(time_array)[[4]] <- paste("mes",1:12, sep = "_") #Set names for the 4th dimension + +##Raster!! +###################### +library(raster) +#Create a raster +r1 = raster(m) ## the input can be the filepath to an image, the parameter band= n selects a band other than the first +##the function brick can read directly a multiband raster +#Create a raster stack (multispectral raster) +s <- stack(r1, r1) +#Select a susbet of the bands from the stack +s[[1:2]] +nrow(s) ##Get number of rows from layer +nlayers(s) ## Get number of bands +dim(s) ##get dimensions +res(s) ## get the spatial resolution on X and Y +extent(s) ## get the bounds +CRS(proj4string(s)) ## Get the spatial reference, as a Coordinate Reference System Class CRS +##assign Reference system +proj4string(s) <- CRS("+proj=utm +zone=36 +ellps=WGS84 +units=m +no_defs") +names(s) #get the band names +#change band names +names (s) <- c("band1","band2") +hist(s) ## Get the histogram of the bands +writeRaster(s,"C:\\vuelos\\temp\\img.tif", + format = "GTiff", + overwrite = FALSE) +## VISUALIZE RASTER +library(rasterVis) +levelplot(s) #plot raster +library(plotKML) +plotKML(s[[1]]) #Export to KML from R +shp <- shapefile("C:\\Dropbox\\data\\AE\\nut\\shp\\521__ae__plt.shp") +plotKML(shp) #Export shapefile to KML from R +## Access raster values as a matrix +s[[1]][1, 1:3] ## raster[[band]][row,column] +## do something with the values +mean(s[[1]][]) ##get the mean of all layer 1 +sd(s[[1:2]][]) ##get the sd of all the 2 layers +s[2,2] ##Get all values of a pixel along the bands (matrix) +s[2,2][1,] ## ...in a vector format +## TRansform Raster to matrix or vector +as.matrix(s[[1]]) +as.array(s[[1:2]]) +## Operations +min(s, na.rm = TRUE) ## Raster of the min values along the bands +min(s[[1]][], na.rm = TRUE) ## value with the min value of first band + +######### CHECK CALC(operations on bands) and OVERLAY(between different rasters) functions +ndvi = function(x) (x[2] - x[1]) / (x[2] + x[1]) ##Formula to get NDVI +ndvi_00 = calc(s, fun = ndvi) ## get NDVI from multiband raster with CALC + +##Get number of null values in raster band +sum(is.na(s[[1]])[]) #thevalues are transformed to a vector with the [] +# Logical rasters can be used as masks and to apply values selectively +temp <- s[[1]] +temp[is.na(temp)] = mean(temp[], na.rm = TRUE) ##Replace NA with the mean value of all +temp[temp < 2] <- 2.5 ## USe a treshold to reassign a value in a raster +#Reclassify values on raster +##Directtly +temp1 <- temp +temp1[temp1 < 5] = -9 +temp1[temp1 >= 5] = 1 +#plot(temp1) +#hist(temp1) +##Using the reclassify function +temp2 = reclassify(temp, c(-Inf, 4.999, -9, 5, Inf, 1)) ## the ranges are passed as triplets: from, to , newValue + +## READ ENVI file as array +library("caTools") +envi <- read.ENVI("h170110escgeo.bsq", headerfile="h170110escgeo.hdr") +asRaster <- brick(envi) +levelplot(asRaster) + +#Order the data +dataFrame <- dataFrame[order(dataFrame$column),] + +###EGRAPH +# ggplot(data=tableSum, aes(x=date, y=wlayer,group=interaction(id, depth))) + +# geom_line(aes(color=depth))+ +# geom_point(aes(color=depth))+ +# labs(title = "Soil water content", x = "Date", y = "water layer (mm)")+ +# geom_col(aes(x=date, y=moisture,group=interaction(id, depth)))+ +# scale_y_continuous(sec.axis = sec_axis(~.*2, name = "Moisture (%)")) + +###SPRINTF +#thanks https://www.rdocumentation.org/packages/base/versions/3.4.1/topics/sprintf +## re-cycle arguments +sprintf("%s %d", "test", 1:3) + +## Using arguments out of order +sprintf("second %2$1.0f, first %1$1.2f, third %s", pi, 2, "3rd") #2f indica los decimales mostrados + +sprintf("min 10-char string '%10s'", + c("this", "that", "and an even longer one")) + +########## +#ggplot +# to match the secondary axis, the data is "transformed" and then adapted (divide and *) diff --git a/r/extract/AF_extract_with_mask2016.R b/r/extract/AF_extract_with_mask2016.R new file mode 100644 index 0000000..e34c69c --- /dev/null +++ b/r/extract/AF_extract_with_mask2016.R @@ -0,0 +1,155 @@ +#This script is a copy of the Protocol to evaluate Plant height +#Authors: Jose Manuel Mendoza Reyes and Lorena Gonzalez Perez +# this vas adapted to extract multispectral data from Aguafria 2016 +#Coments specific to make it work in obregón marked as #L# + +library (sp) +library (raster) +library(rgdal) +library(data.table) +library(plyr) +library(dplyr) + + +#L# Fixed parameters that should be edited each time +date<-"160511" + +############################## Funciones definidas + +### zonalStatsByPlot (Raster extracted by shapefile) and return a raster +zonalStatsByPlot <- function(shp,raster,fun){ +#Calculate Function by plot on raster, we get a data frame +shp_fun <- extract(raster,shp,df=TRUE, fun = fun, sp=TRUE) #sp=TRUE appends results to the shp table +#Put the Function label on the generated column +#toString and substitute are nedded to convert from "function" to "character" class +setnames(shp_fun@data, old="layer",new= toString(substitute(fun))) +#Rasterize Function +r_fun <- raster +r_fun <- rasterize(shp_fun, r_fun, field= toString(substitute(fun))) + +return(r_fun) +} +############################ Funciones definidas (END) + +#Select the work directory wich are the raster file and shp +setwd(choose.dir(getwd(), "Selecciona tu archivo")) + +#select the raster file +#L# +rfile<- file.choose(new = FALSE) +raster<- stack(rfile) +#Count bands +numBands <- dim(raster)[3] + +#select band X +#raster <- raster [[4]] +#plot(raster) + +#Calculate NDVI +NIR <- raster [[4]] +RED <- raster [[2]] +ndvi = (NIR - RED)/(NIR + RED) +plot(ndvi) +#Manipulate values below x ndvi value +#this may replaace the UDF function "soil.mask" directly on a raster +#ndvi_trshld <- ndvi +#ndvi_trshld[ndvi_trshld<=0.65] = NA +#plot(ndvi_trshld) + +#select the shapefile (a shp with buffer genereted in Arcgis) +#shp <- shapefile(list.files(pattern = "\\.shp$")) +#L# Select one file interactively +#shpName <- file.choose(new = FALSE) +shpName <- "C:/Dropbox/data/AD/AD-AF/shp/ad_af_bfs_UTM14.shp" +shp <-shapefile(shpName) +#Add colimn with simple numeric ID +shp$ID<-1:nrow(shp) +#plot(shp, add= TRUE) + +#Mask ndvi raster against the plots to get histogram +r_ndviPlots <- mask(ndvi, shp) +#plot(r_ndviPlots) +#hist(r_ndviPlots) + +#-----------STV filter +#Calculate STANDARD deviation by plot, return as raster +r_sd <- zonalStatsByPlot(shp,r_ndviPlots,fun=sd) +#plot(r_sd) +#Calculate MEAN by plot, return as raster +r_mean <- zonalStatsByPlot(shp,r_ndviPlots,fun=mean) +#plot(r_mean) +#Apply conditional funtion to exclude what is outside 2-SD by plot +lim_sup <- r_mean + r_sd*2 +lim_inf <- r_mean - r_sd*2 +r_ndviMasked <- overlay(r_ndviPlots, lim_sup, lim_inf, + fun=function(r,lim_sup,lim_inf){ + ifelse(r< lim_sup & r> lim_inf, r, NA)}) +#-----------STV filter (END) + +#-----------quantiles filter +# #Calculate Qs by plot +# shp_Q <- extract(r_ndviPlots,shp,df=TRUE, fun = quantile, sp=TRUE) #sp=TRUE appends results to the shp table +# #Put the Function label on the generated column +# #Rename the column names +# colnames(shp_Q@data)[5:9] <- c("Q0", "Q1", "Q2","Q3","Q4") +# #Rasterize Function and get the interquantile range and limits that define outliers +# r_Q1 <- r_ndviPlots +# r_Q3 <- r_ndviPlots +# r_Q1 <- rasterize(shp_Q, r_Q1, field= "Q1") +# r_Q3 <- rasterize(shp_Q, r_Q3, field= "Q3") +# r_RIC <- r_Q3 - r_Q1 +# r_lim_supQ <- r_Q3 + r_RIC * 1.5 +# r_lim_infQ <- r_Q1 - r_RIC * 1.5 +# +# maskSupQ <- (r_ndviPlots < r_lim_supQ) +# maskInfQ <- (r_ndviPlots > r_lim_infQ) +# maskQ <- maskSupQ * maskInfQ +# +# r_ndviPlotsMaskedQ <- mask(r_ndviPlots, maskQ, maskvalue=0) + +#-----------quantiles filter (END) + +#Save view to pdf +pdf(file=paste("masked ndvi stdv",date," plots.pdf", sep="")) +par(mfrow=c(1,1)) +plot(r_ndviMasked) +hist(r_ndviMasked) +dev.off(); + +#Save to WD +writeRaster(r_ndviMasked, filename= paste(date, "ndvi_maskSTDV.tif"), format = "GTiff") + +#Overlap and extract the pixel value in each polygon +Tableclean <- extract(r_ndviPlotsMaskedQ,shp, na.rm=TRUE) + + +#Calculate the descriptive statistics of the new list without outliers and convert it in a data base + +allstats = lapply(Tableclean,summary) +sd = lapply(Tableclean, sd) +n = lapply(Tableclean, length) + +#Join all the statistics calculated in one table +desc.stats.t = mapply(c,allstats,sd, n) + +# Transpose the matrix +desc.stats <- t(desc.stats.t) + + +#Convert to data frame +df <- as.data.frame(desc.stats) + +# Add the rownames as a new column +setDT(df, keep.rownames = TRUE)[] + +#Rename the columns +colnames(df)[1:9]<-c("Plot", "Min", "Q1", "Median", "Mean", "Q3", "Max", "SD", "n") + +#Rewrite again the data frame +df<-data.frame(df) + +# Average the values using the unique ID (Plot names) +#final.df=aggregate(cbind(Min, Q1, Median, Mean, Q3, Max, SD, n)~Plot,df, mean) + +#write the final table +write.csv(df,paste(date,"_ndviMaskedQ.csv", sep="")) diff --git a/r/extract/AF_extract_with_mask2016_test_Dataframe.R b/r/extract/AF_extract_with_mask2016_test_Dataframe.R new file mode 100644 index 0000000..239a08f --- /dev/null +++ b/r/extract/AF_extract_with_mask2016_test_Dataframe.R @@ -0,0 +1,230 @@ +#This script is a copy of the Protocol to evaluate Plant height +#Authors: Jose Manuel Mendoza Reyes and Lorena Gonzalez Perez +# this vas adapted to extract multispectral data from Aguafria 2016 +#Coments specific to make it work in obregón marked as #L# + +library (sp) +library (raster) +library(rgdal) +library(data.table) +library(plyr) +library(dplyr) + + +#L# Fixed parameters that should be edited each time +date<-"160407" +band <- "B1" + +############################## Funciones definidas + +### zonalStatsByPlot (Raster extracted by shapefile) and return a raster +zonalStatsByPlot <- function(shp,raster,fun){ +#Calculate Function by plot on raster, we get a data frame +shp_fun <- extract(raster,shp,df=TRUE, fun = fun, sp=TRUE) #sp=TRUE appends results to the shp table +#Put the Function label on the generated column +#toString and substitute are nedded to convert from "function" to "character" class +setnames(shp_fun@data, old="layer",new= toString(substitute(fun))) +#Rasterize Function +r_fun <- raster +r_fun <- rasterize(shp_fun, r_fun, field= toString(substitute(fun))) + +return(r_fun) +} +############################ Funciones definidas (END) + +#Select the work directory wich are the raster file and shp +setwd(choose.dir(getwd(), "Selecciona tu archivo")) + +#select the raster file +#L# +rfile<- file.choose(new = FALSE) +raster<- stack(rfile) +#Count bands +numBands <- dim(raster)[3] + +#select band X +#raster <- raster [[4]] +#plot(raster) + +#Calculate NDVI +NIR <- raster [[4]] +RED <- raster [[2]] +ndvi = (NIR - RED)/(NIR + RED) +plot(ndvi) +#Manipulate values below x ndvi value +#this may replaace the UDF function "soil.mask" directly on a raster +#ndvi_trshld <- ndvi +#ndvi_trshld[ndvi_trshld<=0.65] = NA +#plot(ndvi_trshld) + +#select the shapefile (a shp with buffer genereted in Arcgis) +#shp <- shapefile(list.files(pattern = "\\.shp$")) +#L# Select one file interactively +#shpName <- file.choose(new = FALSE) +shpName <- "C:/Dropbox/data/AD/AD-AF/shp/ad_af_bfs_UTM14.shp" +shp <-shapefile(shpName) +#Add colimn with simple numeric ID +shp$ID<-1:nrow(shp) +#plot(shp, add= TRUE) + +#Mask ndvi raster against the plots to get histogram +r_ndviPlots <- mask(ndvi, shp) +#plot(r_ndviPlots) +#hist(r_ndviPlots) + +#-----------STV filter +#Calculate STANDARD deviation by plot, return as raster +# r_sd <- zonalStatsByPlot(shp,r_ndviPlots,fun=sd) +# #plot(r_sd) +# #Calculate MEAN by plot, return as raster +# r_mean <- zonalStatsByPlot(shp,r_ndviPlots,fun=mean) +# #plot(r_mean) +# #Apply conditional funtion to exclude what is outside 2-SD by plot +# lim_sup <- r_mean + r_sd*2 +# lim_inf <- r_mean - r_sd*2 +# r_ndviMasked <- overlay(r_ndviPlots, lim_sup, lim_inf, +# fun=function(r,lim_sup,lim_inf){ +# ifelse(r< lim_sup & r> lim_inf, r, NA)}) +#-----------STV filter (END) + +#-----------quantiles filter +#Calculate Qs by plot +shp_Q <- extract(r_ndviPlots,shp,df=TRUE, fun = quantile, sp=TRUE) #sp=TRUE appends results to the shp table +#Put the Function label on the generated column +#Rename the column names +colnames(shp_Q@data)[5:9] <- c("Q0", "Q1", "Q2","Q3","Q4") +#Rasterize Function and get the interquantile range and limits that define outliers +r_Q1 <- r_ndviPlots +r_Q3 <- r_ndviPlots +r_Q1 <- rasterize(shp_Q, r_Q1, field= "Q1") +r_Q3 <- rasterize(shp_Q, r_Q3, field= "Q3") +r_RIC <- r_Q3 - r_Q1 +r_lim_supQ <- r_Q3 + r_RIC * 1.5 +r_lim_infQ <- r_Q1 - r_RIC * 1.5 + +maskSupQ <- (r_ndviPlots < r_lim_supQ) +maskInfQ <- (r_ndviPlots > r_lim_infQ) +maskQ <- maskSupQ * maskInfQ + +r_ndviPlotsMaskedQ <- mask(r_ndviPlots, maskQ, maskvalue=0) + +#-----------quantiles filter (END) + +#Save view to pdf +pdf(file=paste("masked ndvi quantiles",date," plots.pdf", sep="")) +par(mfrow=c(1,1)) +plot(r_ndviPlotsMaskedQ) +hist(r_ndviPlotsMaskedQ) +dev.off(); + +#Save to WD +writeRaster(r_ndviPlotsMaskedQ, filename= paste(date, "ndvi_maskQuantiles.tif"), format = "GTiff") + + +#Overlap and extract the pixel value in each polygon +#pixelval <- extract(plant.height,shp) +#L save as date.frame +#pixelval <- extract(raster,shp,df=TRUE) +pixelval <- extract(ndvi,shp,df=TRUE) # when extracting directly from the input raster + +#L# if you want to create a "Popup" on windows, as a notification +#system('CMD /C "ECHO The R process has finished running && PAUSE"', +# invisible=FALSE, wait=FALSE) + +#L# Save to PDF the histograms of each plot +# pdf(file=paste("histograms ndvi",date," plots.pdf", sep="")) +# par(mfrow=c(4,4)) +# +# for( i in unique(pixelval[,1])){ +# hist(pixelval[pixelval$ID == i,2], +# main = paste("Hist Plot",i), +# nclass = 15, +# xlab = "rfl") +# } +# +# +# dev.off(); +#L# Finish Save to PDF + +#Add the plot name to the pixel value list +#names(pixelval)<- shp$plot_trial + +#L# assign plot rownames based on the ID column of the DF generated by the extract function +#save the shapefile dataframe as an object +shpDF <- as.data.frame(shp$plot_trial,stringsAsFactors=TRUE) +#Rename the column to ID +setnames(shpDF,"shp$plot_trial","plot") +#create column with the rownames and rename to ID +shpDF1 <- cbind(rownames(shpDF),shpDF) +colnames(shpDF1)[1] <- "ID" +#Merge both tables based on the ID as key +pixelval = merge(pixelval, shpDF, by="ID") + + +#function to eliminate soil pixels +# soil.mask <- function(p,threshold){ +# #Keep all the values greater than the threshold +# p.1 = subset(p,p >threshold) +# return(p.1) +# } +# +# #Apply the mask to the list +#PlantsValue <- lapply(pixelval,threshold=t,soil.mask) +#L# applied filter without custom function, using conditional indexing +#this keeps only the rows which are bigger than the treshold on the column 2 (pixel value) +PlantsValue <- pixelval[pixelval[,2]>t, ] + +#function to calculate and eliminate outliers +outliers <- function(dat){ +n=2 +a=median(dat) +b=sd(dat) +limitesuperior=(a+n*b) +limiteinferior=(a-n*b) +dat1= subset(dat,dat <= limitesuperior & dat >= limiteinferior) +return(dat1) +} + +#extract the outliers +#Tableclean = lapply(pixelval,outliers) +#L +#L# if you want to create a "Popup" on windows, as a notification +system('CMD /C "ECHO Work in progress && PAUSE"', + invisible=FALSE, wait=FALSE) + +Tableclean2 <- PlantsValue +Tableclean2[2] <- lapply(Tableclean2[2], outliers) #SOMETHING IS WRONG HERE +class (Tableclean2) +#str(Tableclean) + +#Calculate the descriptive statistics of the new list without outliers and convert it in a data base + +allstats = lapply(Tableclean,summary) +sd = lapply(Tableclean, sd) +n = lapply(Tableclean, length) + +#Join all the statistics calculated in one table +desc.stats.t = mapply(c,allstats,sd, n) + +# Transpose the matrix +desc.stats <- t(desc.stats.t) + + +#Convert to data frame +df <- as.data.frame(desc.stats) + +# Add the rownames as a new column +setDT(df, keep.rownames = TRUE)[] + +#Rename the columns +#pops up a warning, check +colnames(df)[1:9]<-c("Plot", "Min", "Q1", "Median", "Mean", "Q3", "Max", "SD", "n") + +#Rewrite again the data frame +df<-data.frame(df) + +# Average the values using the unique ID (Plot names) +final.df=aggregate(cbind(Min, Q1, Median, Mean, Q3, Max, SD, n)~Plot,df, mean) + +#write the final table +write.csv(final.df,paste(date,band,"_rfl.csv", sep=""), row.names=FALSE) diff --git a/r/extract/extract_data2017.R b/r/extract/extract_data2017.R new file mode 100644 index 0000000..e423234 --- /dev/null +++ b/r/extract/extract_data2017.R @@ -0,0 +1,153 @@ +## Script to extract values from imagery +## Input files are indicated in a Postgres table +## Lorena 2017 +########################################## WORK in progressssssssssssssssssssssssssssssssssssss !"#$&$%/&(/)&/()&" + +#Special comments +#.# this is very specific to this data +#!# Error code or work in progress + +############################LIBRARIES +library(plyr) +library (raster) +library(rgdal) +library(reshape2) +require("RPostgreSQL") +library("caroline") + +####Fixed things} +##The drive where the imagery is located +baseDir <- file.path("G:","AE", fsep = .Platform$file.sep) +#setwd(baseDir) + +############################FUNCTIONS + +#Function to check if file exists +#!# + +##############################SCRIPT + +##<<<<<<<<<<<<<<<<# Connect to database { +##based on r-bloggers.com getting-started-with-postgresql-in-r +# install.packages("RPostgreSQL") +#require("RPostgreSQL") + +# create a connection to a DB in postres +# save the password that we can "hide" it as best as we can by collapsing it +pw <- { + "cimmyt" +} + +# loads the PostgreSQL driver +drv <- dbDriver("PostgreSQL") +# creates a connection to the postgres database +# note that "con" will be used later in each connection to the database +# here the connection values should be specified +connection <- dbConnect(drv, dbname = "ceneb_nut", + host = "localhost", port = 5432, + user = "postgres", password = pw) +rm(pw) # removes the password + +# check for the table "capture" to test connection +dbExistsTable(connection, "capture") +# TRUE means there is connection + +# fetch the data from postgreSQL #capture" table +df_capture <- dbGetQuery(connection, "SELECT * from capture") #Returns a dataframe type + +# close the connection when finished +dbDisconnect(connection) +dbUnloadDriver(drv) +##>>>>>>>>>>>>># } connection to DB losed + +##Select values of interest to extract +#only the ones that meet the required criteria +#.# get the rows which "extract_now" == 1, keep all columns (but "extract_now") +log <- df_capture[df_capture$extract_now %in% 1 & df_capture$sensor %in% c("t","h"), 1:5] +#this just to get the date format I want +log$date <- gsub("-", "", log$cap_date, fixed = TRUE) #.# +log$date <- gsub("^2017", "17", log$date) #.# ^ is to represent the begining of the string + +#This df will be to query the extension & suffix of the raster depending on the camera +#Cam values could be factors... +###############!########## thisshould be imported from the Database as well +img_notes <- data.frame("sensor"=c("t","h"), + "ext"=c(".tif",".bsq"), + "sfx" =c("geo","geo"), + "folder" =c("geo","rfl_spr_ort_geo"), + "nBands" = c(1,62), + stringsAsFactors = FALSE) + +#Now we need to be able to form the location of the imagery +#we add the img_notes to the log, based on the sensor +log <- join(log, + img_notes, + by = "sensor", type = "left", match="all") +#Put the info from ("sensor","date","area","processing stage suffix","extention") together +img_names <- apply(log[,c("sensor","date","area","sfx","ext")],1,paste,collapse="") #one way to paste all the values by row +img_paths <- paste(log$date,log$sensor,log$folder,sep=.Platform$file.sep) #another way to paste values in the same row +img_lists <- paste(baseDir,img_paths,img_names,sep=.Platform$file.sep) + +#Check files that exist and not +# Yes exist +files_exist <- img_lists[file.exists(img_lists)] +#length(files_exist) +# Does not exist, allows to check what happened +files_not_exist <- img_lists[!file.exists(img_lists)] + +############### #!# EXTRACT (still work in progress) + +#To extract the data we need the location of the plot polygons +#!# This should be taken from the spatial database +#.# For now this is hardcoded + +##import the shapefile of sampling points of the experiments +shp_esc <- shapefile("C:\\Dropbox\\data\\AE\\ae_stars\\esc\\shp\\esc__ae__pts.shp") +shp_ser <- shapefile("C:\\Dropbox\\data\\AE\\ae_stars\\ser\\shp\\ser__ae__pts.shp") +shp_ros <- shapefile("C:\\Dropbox\\data\\AE\\ae_stars\\ros\\shp\\ros__ae__pts.shp") + +## if you wanted to save a shapefile: +#writeOGR(obj=file_to_be_saved, dsn="algo", layer="file_to_be_saved", driver="ESRI Shapefile") + +#Next extract the mean values of the raster at the point locations, using a buffer, ignore noData +#!# +##the parameter "layer" is potentially useful to append the result to the shapefile table +buff = 1.5 ##Seleccionar tamaño de buffer. Units are the projection units -> meters +######## HARDCODED FOR TESTS#.# +img <- stack(img_lists[15])## create the raster stack from the image name(stack because its mutispectral) #!# +#Extraction execution +table <-extract(img,shp_ser, buffer=buff,fun = mean, df =TRUE, na.rm = TRUE) #this takes around 5 min to run each image +rm(img) # Remove the raster stack from memory after use to avoid overload + +#################################### data manupulation +#!# # # # # # Missing to implement quality checks here of the data +## Rename column names (with the band information) to simplify them +nBands <- img_notes$nBands[img_notes$sensor == "h"] ###################!!!!!!!!!!!#!#!!######.###### HARDCODED h +names(table) <- c("ID", seq(1,nBands)) ###################!!!!!!!!!!!#!#!!######.###### HARDCODED. Here I should know how many band there are depending on the sensor +#Change from wide to long table to manage better the different number of bands +table1 <- melt(table, id.vars = "ID") #####!### where did ID come from??? +table1$trial <- "esc" ###################!!!!!!!!!!!#!#!!######.###### HARDCODED +table1$date <- "2017-01-23" #############!!!!!!!!!!!#!#!!######.###### HARDCODED +table1$sensor <- "h" ####################!!!!!!!!!!!#!#!!######.###### HARDCODED + + +#!# # # # ########## SAVE DATA to DB (still work in progress) + + +# add rows to the PostgreSQL database , table "capture" +#library("caroline") #!# NEED to CHECK THIS, cant make it insert values iin a table with a serial type +#!# NEED to CHECK THIS +# +#max_id <- dbGetQuery(connection, "select max(id) from measurement") #Returns a last position for the serial +#max_id <- max_id +1 +#class(max_id) +#rows <- nrow(table1) +#table1$serial_test <- seq(max_id+1,rows)################!!!!!!!!!!!#!#!!######.###### HARDCODED. serial test +# +#df <- table1[,c("serial_test","ID","trial","date","sensor","value","variable")] +#names(df) <- c("id", "plot_msure","trial_msure","date_msure","sensor_msure","value_msure","variable_msure") + +#dbWriteTable(connection, "measurement", value = df, append=TRUE,row.names=FALSE) + + +#!# # # # ########## EDN SAVE DATA to DB (still work in progress) diff --git a/r/formulas_maiz_sas.txt b/r/formulas_maiz_sas.txt new file mode 100644 index 0000000..f3a9a9e --- /dev/null +++ b/r/formulas_maiz_sas.txt @@ -0,0 +1,52 @@ +ATA AB250C1; +INFILE 'Z:\ALLSASFILES\AB250C1.DAT'; +INPUT REP NTRT HA HEIGHT NPLANT GRANO WM DM WG DG DW5P DW5M DW5G; + + +YIELD0=(((GRANO)+DW5G)/HA)*10; +*HI=(DW5GRAIN /((TW5P*(DSTRAWSUB5P/WSTRAWSUB5P)) + DW5MAZ)); +HI=(DW5G / ((DW5P))); +BIOMASS2=YIELD0/HI; +MOIST=WG-DG; +*W1000=W400*1000/400; +*GRAINSM2=(YIELD0*100)/W1000; +YIELD14=(((GRANO+DW5G)*10)/HA)*((100-(((WG-DG)/WG)*100))/(100-14)); + +*YIELD12=((GRANO*10)/HA)*((100-(((WG-DG)/WG)*100))/(100-12)); +*NGBIOMAS=BIOMASS-YIELD0; +*NTOT=(YIELD0*(NGRANO/100))+(NGBIOMAS*(NSTRAW/100)); +*PROT=NGRANO*5.83; +*LABEL BIOMASS='DRY BIOMASS (Kg/ha)'; +*LABEL YIELD0='YIELD Kg/ha at 0% moisture'; +*LABEL HI='HARVEST INDEX'; +*LABEL W1000='1000 KERNEL WT. gms/1000 KERNELS'; +LABEL YIELD14='YIELD KG/HA AT 14% MOISTURE'; +*LABEL NGBIOMAS='NON GRAIN BIOMASS (KG/HA)'; +PROC SORT; +BY NTRT REP; +PROC PRINT; +*VAR REP NTRT HIGHT PLANTS BIOMASS WMAZTOT TW6P WW6MAZ DW6MAZ DW6GRAIN WSTRAWSUB6P DSTRAWSUB6P GRANO WG DG; +VAR REP NTRT YIELD14 YIELD0 BIOMASS2 HI MOIST DW5G; +PROC GLM; +CLASS REP NTRT; +MODEL YIELD14 YIELD0 BIOMASS2 HI DW5G + = REP NTRT; +MEANS NTRT/ LSD; +MEANS NTRT; +PROC GLM; +CLASS REP NTRT; +MODEL YIELD14 YIELD0 BIOMASS2 HI + = REP NTRT NPLANT; +LSMEANS NTRT; +PROC CORR NOSIMPLE RANK; + VAR YIELD14 YIELD0 BIOMASS2 HI DW5G; +RUN; +*PROC SORT; + *BY NTRT; +*PROC MEANS NOPRINT; + *VAR YIELD12 BIOMASS HI W1000 CHLH NGRANO NHB NSTRAW NTOT; + *BY NTRT; +*OUTPUT OUT=K801 MEAN= YIELD12M BIOMASSM HIM W1000M CHLHM NGRANOM NHBM + NSTRAWM NTOTM; + + diff --git a/r/functions_ImgProcessing.R b/r/functions_ImgProcessing.R new file mode 100644 index 0000000..24ff712 --- /dev/null +++ b/r/functions_ImgProcessing.R @@ -0,0 +1,26 @@ +#functions for image processinh + +require(raster) + +#Para agarrar las 4 bandas de reflectancia de la sequoia y unirlas en un archivo +stackSeq <- function(name,path){ + r_green <- file.path(path,paste0(name,"_transparent_reflectance_","green",ext), fsep = .Platform$file.sep) + r_red <- file.path(path,paste0(name,"_transparent_reflectance_","red",ext), fsep = .Platform$file.sep) + r_rededge <- file.path(path,paste0(name,"_transparent_reflectance_","red edge",ext), fsep = .Platform$file.sep) + r_nir <- file.path(path,paste0(name,"_transparent_reflectance_","nir",ext), fsep = .Platform$file.sep) + #print(r_nir) + s <- stack(r_green, + r_red, + r_rededge, + r_nir) + return(s) +} + + + +require(rgdal) + +# Read the feature class +get_feature <- function(feature,database) {readOGR(dsn=database,layer=feature)} +# Read a shapefile +get_shape <- function(shape,path){readOGR(dsn = path, layer = shape)} \ No newline at end of file diff --git a/r/getCeptometerDataFromExcel.R b/r/getCeptometerDataFromExcel.R new file mode 100644 index 0000000..9605631 --- /dev/null +++ b/r/getCeptometerDataFromExcel.R @@ -0,0 +1,104 @@ +#### +# ceptimeter sampling data FROM THE EXCEL FIle indicated.fixed format +# STORE TO local DATABASE by par date in a table +#### + +#library(xlsx) +library(RMySQL) +library(XLConnect) + +############################ FUNCTIONS DEFINITION + + +#########Read Excel sheets +getCeptometerFromExcel <- function (file, startSheet,endCol,endRow,colTypes,col_names,maxsp){ + xlxsF =file + wb <- loadWorkbook(xlxsF) + sheets <- getSheets(wb) + sheets <- lapply(sheets,function(x) tolower(x)) + setMissingValue(wb, value = "NA") + sheetsIndex <- startSheet:length(sheets)#Avoid the first x sheets of this file because they dont have data + df.list <- readWorksheetFromFile(xlxsF, #Get the data of th xlsx file + sheet = sheetsIndex, + header=TRUE, + colTypes = colTypes, + endCol = endCol, + endRow=endRow) + #Next just rename the columns + for (i in 1:length(df.list)){ + colnames(df.list[[i]])<-col_names + } + + sheets <- sheets[-c(1:(startSheet-1))] #Remove the sheet names that were not used + + samplings = length(sheets) # Total sampling dates + #add date info in column + #For every sampling date + for (s in 1:samplings) { + df.list[[s]]<-df.list[[s]][df.list[[s]]$id<=maxsp,] #Exclude points above 40 FOR ESCAMILLA etc + sPoints = length(df.list[[s]]$id) #total samples per date + for (i in 1:sPoints) { + print(paste("sampling",sheets[s],"sample",df.list[[s]]$id[i])) + df.list[[s]]$date[i] <- as.character(sheets[s]) #add date info in column + } + } + + return (list(df.list,sheets)) +} + +##Replace something in a column of a df list +replaceInCol <- function(df_list,col,txt_orig,txt_replace){ + r_df_list<-df_list + for(i in 1:length(df_list)){ + print(i) + l <- length (r_df_list[[i]][,col]) + for(j in 1:l){ + print(j) + print(df_list[[i]][j,col]) + r_df_list[[i]][j,col] <- gsub(txt_orig, txt_replace, df_list[[i]][j,col]) + } + + } + return(r_df_list) +} + +################################# EXECUTE SCRIPT +############# INPUTS +file="C://Dropbox//data//AD//AD-STARS//ros//ad_ros_ceptometer.xlsx" +trial <- "ros" +whatData <- "par" #What does this data represent +col_names<-c('id', + 'par', + 'par_time', + 'par_ref', + 'ref_time') +colTypes <- c('numeric', 'numeric', 'character','numeric','character') +maxsp<-25 +##Edit next +##getMoistreFromExcel <- function (file, startSheet,endCol,endRow,colTypes,col_names,maxsamplepointID) +mData <- getCeptometerFromExcel(file, 2,5,26,colTypes,col_names,maxsp) #get the moisture data[[1]] and sheetnames[[2]] +############# END INPUTS +##Resulting data +dates<-mData[[2]] +mData <-mData[[1]] + +##Replace the data "wrong date" in column 3 to keep the time only +#mData <- replaceInCol(mData,5,"1899-12-31 ","") + + +##Connecto to local Database +mydb = dbConnect(MySQL(), user='root', password='cimmyt', dbname='ac_stars', host='localhost') +#dbListFields(mydb, 'ros') #List fields on table + +#Save moisture data to DB +for(i in 1:length(dates)){ + ##Check if table already exists... + tExists = dbSendQuery(mydb, paste("SHOW TABLES LIKE '", dates[[i]],"'",sep="")) ##ask if the table exists + data = dbFetch(tExists, n=-1) #get results. The n=-1 is to get all the results, can be used to set a MAX + if(length(row.names(data))==0){ #If the table doesnt exists + print(dates[[i]]) + #withe df to DB + dbWriteTable(mydb, value = mData[[i]], name = paste(trial,dates[[i]],whatData,sep=""), row.names = FALSE) #THere is an issue vs append and autoincrement index + } +} + diff --git a/r/getDataFromDB.R b/r/getDataFromDB.R new file mode 100644 index 0000000..7a6e2cb --- /dev/null +++ b/r/getDataFromDB.R @@ -0,0 +1,25 @@ +#### +# GET local DATABASE +#### + +#library(xlsx) +library(RMySQL) +library(XLConnect) + +############################ FUNCTIONS DEFINITION + + +##Connecto to local Database +mydb = dbConnect(MySQL(), user='root', password='cimmyt', dbname='ac_stars', host='localhost') +tables <- dbListTables(mydb) #List tables on DB +t<- "esc151111ar1" +for (t in tables) { + ##Make a query + rs = dbSendQuery(mydb, paste("select * from",t)) + #fetch function to get the data + data = fetch(rs, n=-1) +} + +new <- merge(table,data, by = "id") + +table$(paste(toString(t),"moisture")) <-data[,c("moisture")] diff --git a/r/global.R b/r/global.R new file mode 100644 index 0000000..b0368f0 --- /dev/null +++ b/r/global.R @@ -0,0 +1,2 @@ +library("shiny") +library("foreign") diff --git a/r/greenseeker/2_juntar_lecturas_v0.R b/r/greenseeker/2_juntar_lecturas_v0.R new file mode 100644 index 0000000..884e1ab --- /dev/null +++ b/r/greenseeker/2_juntar_lecturas_v0.R @@ -0,0 +1,32 @@ +# Aggregate all the ndvi readings ordered from their CSV files +#The files are the ones generated by the "ordenar_lecturas_V0.R" script + +############## Functions source +source(file.path("C:","Dropbox","Software","Scripts","r","greenseeker","methods_greenseeker.R", fsep = .Platform$file.sep)) + +##Get all the CSV files from the corresponding trial,They are separated by folders +#Set the WD +path <- setwd(choose.dir(default = file.path("C:","Dropbox","data","AF", fsep = .Platform$file.sep), caption = "Select folder to work on")) +#Get the name of theworking directory +basenameF <- basename(path) +## Get the names of the files. Expected something like : eg. AF_230_020418_ndvi.csv +ndvisF <- list.files(path = path ,pattern="[0123456789]{6}(_ndvi){1}\\.csv$") +# GET THE DATA +ndvis = lapply(ndvisF, read.table,header = TRUE,sep = ",") #We get a list of dataframes +##Add a column for the date to each dataaframe +for (day in 1:length(ndvisF)) { + #Traverse the list of dataframes and Get the date from the initial list of files + ndvis[[day]]$samp_date <- toString(get6DigitDate(ndvisF[day])) + #We also add a column for the tratment of each plot (AKA ID will disregard the repetition) + ndvis[[day]]$treatment <- substr(ndvis[[day]]$Plot,2,4) #not include the rep ID in the plot +} +#merge them all in one table, stacking the rows +ndvi_all1 <- Reduce(rbind,ndvis) +#Average by the treatment +ndvi_by_treatment <- averageNDVI_byTreatment(ndvi_all1) +#ordered by date +ndvi_by_treatment <- ndvi_by_treatment[order(ndvi_by_treatment$samp_date),] +#Make the date format as dd-mm-yy +ndvi_by_treatment$samp_date <- paste(substr(ndvi_by_treatment$samp_date,1,2),substr(ndvi_by_treatment$samp_date,3,4),substr(ndvi_by_treatment$samp_date,5,6),sep="-") +#Save to file, add the prefix of the working directory +write.csv(ndvi_by_treatment,paste0(basenameF,"_ndvi_avg_by_treatment.csv"), row.names=FALSE) diff --git a/r/greenseeker/ejemplos_archivos_entrada/AF_230_mapa_norte_arriba.csv b/r/greenseeker/ejemplos_archivos_entrada/AF_230_mapa_norte_arriba.csv new file mode 100644 index 0000000..107f26d --- /dev/null +++ b/r/greenseeker/ejemplos_archivos_entrada/AF_230_mapa_norte_arriba.csv @@ -0,0 +1,12 @@ +3322,3323,3321,3324,3325,3113,3114,3111,3115,3112,3223,3221,3224,3225,3222 +3342,3344,3345,3341,3343,3125,3122,3124,3121,3123,3212,3215,3213,3211,3214 +3333,3334,3331,3332,3335,3144,3141,3143,3145,3142,3245,3241,3242,3244,3243 +3312,3315,3311,3314,3313,3133,3134,3132,3135,3131,3233,3234,3235,3232,3231 +2243,2244,2241,2242,2245,2334,2331,2335,2333,2332,2134,2133,2132,2131,2135 +2224,2221,2222,2225,2223,2315,2312,2311,2314,2313,2145,2144,2141,2142,2143 +2212,2214,2213,2211,2215,2345,2342,2343,2344,2341,2123,2125,2122,2121,2124 +2232,2233,2231,2234,2235,2324,2325,2322,2323,2321,2112,2114,2115,2111,2113 +1145,1144,1143,1142,1141,1213,1212,1211,1214,1215,1345,1341,1343,1342,1344 +1131,1132,1133,1134,1135,1222,1224,1225,1223,1221,1333,1332,1334,1335,1331 +1125,1124,1123,1122,1121,1234,1231,1232,1233,1235,1312,1314,1311,1315,1313 +1111,1112,1113,1114,1115,1244,1241,1242,1245,1243,1324,1325,1322,1321,1323 diff --git a/r/greenseeker/ejemplos_archivos_entrada/orden_de_muestreo_GS_230.csv b/r/greenseeker/ejemplos_archivos_entrada/orden_de_muestreo_GS_230.csv new file mode 100644 index 0000000..fb3e15e --- /dev/null +++ b/r/greenseeker/ejemplos_archivos_entrada/orden_de_muestreo_GS_230.csv @@ -0,0 +1,12 @@ +12,13,36,37,60,61,84,85,108,109,132,133,156,157,180 +11,14,35,38,59,62,83,86,107,110,131,134,155,158,179 +10,15,34,39,58,63,82,87,106,111,130,135,154,159,178 +9,16,33,40,57,64,81,88,105,112,129,136,153,160,177 +8,17,32,41,56,65,80,89,104,113,128,137,152,161,176 +7,18,31,42,55,66,79,90,103,114,127,138,151,162,175 +6,19,30,43,54,67,78,91,102,115,126,139,150,163,174 +5,20,29,44,53,68,77,92,101,116,125,140,149,164,173 +4,21,28,45,52,69,76,93,100,117,124,141,148,165,172 +3,22,27,46,51,70,75,94,99,118,123,142,147,166,171 +2,23,26,47,50,71,74,95,98,119,122,143,146,167,170 +1,24,25,48,49,72,73,96,97,120,121,144,145,168,169 diff --git a/r/greenseeker/methods_greenseeker.R b/r/greenseeker/methods_greenseeker.R new file mode 100644 index 0000000..effb076 --- /dev/null +++ b/r/greenseeker/methods_greenseeker.R @@ -0,0 +1,86 @@ +#General functions to process Files, dataframes + +#Librearies +library(stringr) #To use str_extract +library(plyr)#Used in: ddply + +### GREENSEEKER(GS) ### +#This method will go trhought the input file of readings returning the reading and the corresponding plot: +#Matching the trial map and the order file +s_order <- function(map, ord, dataNDVI){ + #Allocate the size of the vector to save the order of the plots based on the + #way the sampling was done + #Get the NDVI data + id_s <- dataNDVI + #Add a column to set the ID + id_s$Plot <- "" + + #Check if the map has the same dimensions of the "order" + if( dim(map)[1] == dim(ord)[1] && dim(map)[2] == dim(ord)[2]) { + #TODO: make this more R-like way + #Assign the trial plot that corresponds to the reading + #Go trhough the ndvi samples + for (s in dataNDVI$Sample_No){ + #Get the array position of the current sample # from the order matrix + #TODO: Validate unique numbers + index <- which(ord == s, arr.ind = TRUE) #We get row and column location of the "plot" (thst is the incremental number) from this sample + #Assign the corresponding ID of the plot from the map to the sample + id_s[id_s$Sample_No==s,"Plot"] <- map[index[1,"row"],index[1,"col"] ] + } + return(id_s) + } + else{ + #Return empty if sizes dont match + print("the size of the map and the order file don't match. Check.") + return(NULL) + } +} + +### GREENSEEKER (GS) ### +# USES: library(stringr) +# the file containing the order of the sampling automatically based on the date of the input NDVI Txt file +#The txt file is expected to have a standard name as: eg. AF_521_080318.txt ~ the date in ddmmyy format +#The expected format of the ord files is: eg. exp021118ord.csv + # exp == 3 digits for the trial code + # 021118 == 6 digits for the date in ddmmyy format + # ord == identifier for the sampling Order file fro the greenseeker sampling +#indicate the directory to look for the file +#Here I will use the DATE as ddmmyy format because that is how the field guys write the names of the files +get_ordFile <- function(NDVI_txt,path_ord){ + # Get the 6 digit "date". + date1 <- str_extract(NDVI_txt,"[0123456789]{6}") #Return from the filename what matches the pattern of 6 digits + #Get the files with CSV extention and that contain the word "ord" somewhere after the date + ordF <-list.files(path = path_ord,pattern=paste0(date1,"{1}","(ord){1}[[:alnum:]_]*\\.csv$")) + + #Return the path + the file location + return(file.path(path_ord, ordF, fsep = .Platform$file.sep)) +} + +### Get 6 digit date from filename, based on pattern. very wide search pattern +## Have in mind that it doesnt take into account if the format is yymmdd or ddmmyy +get6DigitDate <- function(string1){ + date1 <- str_extract(string1,"[0123456789]{6}") + return(date1) +} + +### GREENSEEKER (GS) ### +#Average the Greenseeker readings (*.txt not the averaged file), get SD, and CV. Group by "Sample_no" +#This are the expected headers: +# Time.ms. Sample_No Count NDVI VI_2 +# NDVI AND Sample_No are NECESSARY +averageNDVI <- function(NDVIreadings){ + ddply(NDVIreadings,~ Sample_No, summarize, + Count = length(NDVI), + Avg_NDVI = mean(NDVI), + Stdev = sd(NDVI), + CV = sd(NDVI)/mean(NDVI)*100) +} + +#Summarize the averaged ndvi readings by treatment +#Several dates are in the table now +averageNDVI_byTreatment <- function(NDVIreadings){ + ddply(NDVIreadings,~ treatment+samp_date, summarize, + Avg_NDVI = mean(Avg_NDVI), + Avg_Stdev = mean(Stdev), + Avg_CV = mean(CV)) +} \ No newline at end of file diff --git a/r/greenseeker/ordenar_lecturas_v0.R b/r/greenseeker/ordenar_lecturas_v0.R new file mode 100644 index 0000000..b33ff70 --- /dev/null +++ b/r/greenseeker/ordenar_lecturas_v0.R @@ -0,0 +1,56 @@ +#Format the Greenseeker readings done in the experimental station to match the plot number ID +#Because they have an automatic incremental id +library(tools) #to use file_path_sans_ext + + +###############Functions +source(file.path("C:","Dropbox","Software","Scripts","r","greenseeker","methods_greenseeker.R", fsep = .Platform$file.sep)) + + +################ INPUTS +#Set the WD +setwd(choose.dir(default = file.path("C:","Dropbox","data","AF", fsep = .Platform$file.sep), caption = "Select folder to work on")) + +#Choose NDVIfile +#The txt file is expected to have a standard name as: eg. AF_521_080318.txt ~ the date in ddmmyy format +ndviF <- choose.files(default = "", caption = "Selecciona el archivo a procesar", + multi = FALSE, filters = matrix(c("txt", "*txt")), + index = nrow(Filters)) +filename <- tools::file_path_sans_ext(basename(ndviF)) + +#Choose Trial map in csv format, north is up. +mapF <- choose.files(default = "", caption = "Selecciona el archivo mapa del experimento", + multi = FALSE, filters = matrix(c("csv", "*csv")), + index = nrow(Filters)) + +#Choose measuring order +#### interactively +ordF <- choose.files(default = "", caption = "Selecciona el archivo que indica el orden de las lecturas (# filas y columnas = al mapa)", + multi = FALSE, filters = matrix(c("csv", "*csv")), + index = nrow(Filters)) +#### OR Use this to get the order file automatically based on the date in ddmmyy in the NDVI txt file +#ord_Folder_location <- file.path("C:","Dropbox","data","AF","nut","csv_maps", fsep = .Platform$file.sep) #Location of all the ord files +#ordF <- get_ordFile(ndviF,ord_Folder_location) +#### OR hardcoded... +#ordF <- file.path(ord_Folder_location,"q21__af__ord_se.csv", fsep = .Platform$file.sep) + +#"extra" Readings we dont need +no_go <- c("") #HardCoded !!!!!!!!!!!! eg. c("5","46") + + +############### SCRIPT +tryCatch({ + + dataNDVI<-read.table(ndviF, header = TRUE,sep = ",") #get the data in the csv File + #Clean extra readings from the NDVI file, that are by mistake. Indicated in the "no_go" variable. Cleaning the order file should be done separately, its not done here. + ndviCl <- dataNDVI[which(!(dataNDVI$Plot %in% no_go)),] + #Replace the "Plot" header by "Sample_No", because in the NDVI file + colnames(ndviCl)[colnames(ndviCl) == "Plot"] <- "Sample_No" + #Average the readings, get SD, and CV. Group by "Sample_no" + ndviAvg <-averageNDVI(ndviCl) + map<-read.table(mapF, header = FALSE,sep = ",") #get the data in the csv "map" + ord<-read.table(ordF, header = FALSE,sep = ",") #get the sampling order from file + #print(ord) + orddata <- s_order(map,ord,ndviAvg) #match the readings and the plot names + write.csv(orddata,paste0(filename,"_ndvi.csv"), row.names=FALSE) #write the data in a text file +}) diff --git a/r/greenseeker/ordenar_lecturas_v0.Rmd b/r/greenseeker/ordenar_lecturas_v0.Rmd new file mode 100644 index 0000000..aee8e4b --- /dev/null +++ b/r/greenseeker/ordenar_lecturas_v0.Rmd @@ -0,0 +1,18 @@ +--- +title: "R Notebook" +output: html_notebook +--- + +This is an [R Markdown](http://rmarkdown.rstudio.com) Notebook. When you execute code within the notebook, the results appear beneath the code. + +Try executing this chunk by clicking the *Run* button within the chunk or by placing your cursor inside it and pressing *Ctrl+Shift+Enter*. + +```{r} +plot(cars) +``` + +Add a new chunk by clicking the *Insert Chunk* button on the toolbar or by pressing *Ctrl+Alt+I*. + +When you save the notebook, an HTML file containing the code and output will be saved alongside it (click the *Preview* button or press *Ctrl+Shift+K* to preview the HTML file). + +The preview shows you a rendered HTML copy of the contents of the editor. Consequently, unlike *Knit*, *Preview* does not run any R code chunks. Instead, the output of the chunk when it was last run in the editor is displayed. diff --git a/r/histogramPlots.R b/r/histogramPlots.R new file mode 100644 index 0000000..fc23c7b --- /dev/null +++ b/r/histogramPlots.R @@ -0,0 +1,86 @@ +## To show the histogram of selected plots in shapefile +## ONGOING WORK!!!! + +require(raster) +require(rgdal) + +###################### DEFINED FUNCTIONS (no need to edit) +# Read the feature class +get_roi <- function(trial_roi) {readOGR(dsn=fgdb,layer=trial_roi)} + + +######################## DEFINE VARIABLES (Enter your variables) +# #Working directory +wd="G:\\AD15_16\\" +setwd(wd) +ext<-".tif" +shp_name<- "__ad__buf" +sx<-"cel" #input sufix +fgdb = "C:\\Dropbox\\data\\AD\\ad_sam\\shp\\adsam.gdb" + +#Select individual image instead of loop all + cam<-"t" + d<-"160804" + t<-"sam" + +######################### STARTs SCRIPT +tryCatch({ + + + + imgLocation = paste(wd,d,"\\",cam,"\\",sx,"\\",sep="") #Look into cel folder + name = paste(cam,d,t,sx,ext,sep="") + rasterName= paste(imgLocation,name,sep="") + r<-raster(rasterName) + plt <- get_roi(paste(t,shp_name,sep="")) + ids <- data.frame(plt$id) + + ##Graphics to save to PDF + pdf(file=paste(name," plots.pdf", sep="")) + par(mar = rep(2, 4),mfrow=c(4,4)) + + #Iterate the plots + for(i in 1:3){ ###length(ids[,1]) %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + ##Select every plot polygon + roi <- plt[plt@data["id"]==ids[i,1]] #%%%%%%%%%%%%%%%%%% WTFFF %%%%%%%%%%%%%%%% + print(ids[i,1]) + ##roi <- plt[plt$id=="thanda 264 rows /131",] + ##Crop the image to get what is inside the plot + r_selected <-crop(r, extent(roi), snap="out") + plot(r_selected,col=heat.colors(8,0.8)) # Plot the image cropped + #Get histogram of that piece of image + hist(r_selected,main=paste(t,cam,d,"plot= [",ids[i,1],"]"),breaks=20) + + # KMEANS + #assume bimodal distribution in clipped raster + #Use K means to filter Soil (hotter pixels) + #The group 1 is the cooler (vegetation?) and the 2 is the hotter (Soil?) + v<-data.frame(getValues(r_selected)) + km <- kmeans(v,centers=2) + v$clust <- as.factor(km$cluster) + hist(v[v$clust==1,1]) + #Store mean plot value in table + ids$d[i] <- mean(v[v$clust==1,1]) + + #NIBLACK???? + #Filter soil with this filter + #pixel = ( pixel > mean + k * standard_deviation - c) ? object : background +# sd(r_selected) +# r_mean <- focal(r_selected, w=matrix(1/9,nrow=5,ncol=5)) + ##How to calculate focal std ? +# sd(x, na.rm = FALSE) +# zonal(x, z, stat='mean', digits=0, na.rm=TRUE, ...) +# focal(r_selected, w=3, fun=, na.rm=TRUE) +# sd2 <- sqrt(sum((x - mean(x))^2) / (n - 1)) +# +# #r_sd <- cellStats(r_selected,'sd') # Returns a value, not raster +# add=TRUE +# plot(r_mean) +# plot(r_sd) + } + + dev.off() + ##Finish saving to PDF + +}) + diff --git a/r/hyper_divedeBy10000.R b/r/hyper_divedeBy10000.R new file mode 100644 index 0000000..e21c9cd --- /dev/null +++ b/r/hyper_divedeBy10000.R @@ -0,0 +1,64 @@ +## Script to extract values from NDVI imagery inside a (GSA: greenseeker area) ROI, +## get the mean value of the top 5%. after removing the values < 0.3 +## The ROI is read from a GDB, and any feature with attribute area = "FR" is ignored. +## +# +## No data quality check is implemented yet +## Lorena 2018 + +#Special comments +#.# this is very specific to this data +#!# Error code or work in progress + +#################################################################################### +################################## Libraries +#################################################################################### +library(plyr) +library (raster) +library(rgdal) +library(reshape2) + +#################################################################################### +################################## Functions +#################################################################################### + +###########Function to execute the top 5% mean value. Saves PDF with plots on default working directory + +#Function divide by 10000 +fun <-function(x){(x/10000)} + +# divide by 10000 +divide10000 <- function(img) { + img2 <- celsius<-calc(img, fun) + return(img2) +} +# Choose file interactively | *tif file +chooseTif <- function() { + choose.files(default = "", caption = "Selecciona la imagen", + multi = FALSE, filters = matrix(c("tif", "*.tif")), + index = nrow(Filters))} + + +#################################################################################### +################################## SCRIPT +#################################################################################### + +#Base Working directory +baseDir <- file.path("C:","Dropbox (RSG)","data","AF","compass", fsep = .Platform$file.sep) +setwd(baseDir) + +##Image to work ok +i <- chooseTif() #Opens select file dialog +img <- stack(i) + +#Call function to execute task +imgH<- divide10000(img) + +#Output filename, same as input, add a "_" +out_File <- paste0(tools::file_path_sans_ext(i),"_.tif") + +#Write file , same folder as input +writeRaster(imgH, file=out_File,datatype='FLT4S',format="GTiff",overwrite=FALSE) + + + diff --git a/r/io_methods.R b/r/io_methods.R new file mode 100644 index 0000000..75e7b58 --- /dev/null +++ b/r/io_methods.R @@ -0,0 +1,58 @@ +#General methods to read and import csv and Excel files + +library("XLConnect") +library(reshape2) #for the melt in readFieldMapsExcel + +readExcel <- function(xlsxFile){ + xlxsF =file + wb <- loadWorkbook(xlxsF) + sheets <- getSheets(wb) + sheets <- lapply(sheets,function(x) tolower(x)) + setMissingValue(wb, value = "NA") + sheetsIndex <- startSheet:endSheet#Avoid the first 3 sheets of this file because they dont have data + df.list <- readWorksheetFromFile(xlxsF, #Get the data of th xlsx file + sheet = sheetsIndex, + header=TRUE, + colTypes = colTypes, + endCol = endCol, + endRow=endRow, + startRow = startRow) +} + +#Function readFieldMapsExcel to read the field maps that are done in the BW program in MS Excel. A list of their names is passed. +#Usually it is a small number of fieldmap files corresponding to one section, because they need to have unique column numbers. +#It is oriented to format the field map to input in the script Athena did CSVtoKML in python. +#The files need to have all the same size and start and end row and columns of the data in the first sheet of the Excel file. +#the start and end column and row to read from the Excel are defaulted, but can be changed. +#the trial name is expected to be in the last column of the excel file +readFieldMapsExcel <- function(xlsx_list, endCol = 19, endRow=46,startRow = 2,startCol=3){ + xlsxs <- list(0) + for (i in seq_along(xlsx_list)) { + mapi <- readWorksheetFromFile(xlsx_list[1], sheet = 1,header=TRUE, #we assume all have the same fixed size + endCol = endCol, endRow=endRow,startRow = startRow,startCol=startCol) + mapi$row <- rownames(mapi) ## Add the row number to a column + + #Get all values listed with the column and row corresponding + trial_column <- paste0("Col",endCol-startCol+1) #number of the last column corresponding to the trial + #TODO: fix the row |col|plot order from here + mapi <- melt(mapi, id.vars =c("row",trial_column)) + #rename the columnName column and keep just the number in the values + colnames(mapi)[2:4] <- c("trial","col","plot") + mapi$col <- gsub('Col', '', long_table$col) + #check for NA's in rows and remove those rows (they belong to an empty column in the field map) + na_inrow <- apply(mapi, 1, function(x){any(is.na(x))}) + #Save the formated fieldmap in the list + xlsxs[[i]] <- mapi[!na_inrow, ] + } + + #Now put all the different fieldmaps in one table + rowcol_xlsxs <- do.call("rbind", xlsxs) + #Merge the plot and trial name + rowcol_xlsxs$plot <- paste(rowcol_xlsxs$plot,rowcol_xlsxs$trial, sep="_") + #just order the columns as row |col| plot + rowcol_xlsxs <- rowcol_xlsxs[,c(1,3,4)] + + #Return value + return(rowcol_xlsxs) +} + diff --git a/r/kelvin100toCelsiusInsideFolder.R b/r/kelvin100toCelsiusInsideFolder.R new file mode 100644 index 0000000..677d8c3 --- /dev/null +++ b/r/kelvin100toCelsiusInsideFolder.R @@ -0,0 +1,74 @@ +######### Crop images (Tif) in a folder based on a ROI +######## the roi is read from a GDB + +############################ Work in progresssssssssssssssssssssssssssssssssssssssssss + +#0. First step : load packages +#install.packages(c("rgdal","raster")) +require(raster) +require(rgdal) +library("caTools") + +###################### DEFINED FUNCTIONS (no need to edit) + +#Function to select the apropriate roi for each trial +# Read the feature class +get_roi <- function(trial_roi) {readOGR(dsn=fgdb,layer=trial_roi)} + +#Function Kelvin 100 to raster +fun <-function(x){(x/100)-273.15} + +#Function kelvinx100toCelsius +kelvinx100toCelsius <- function(imgK,roi) +{ + tryCatch({ + celsius<-calc(imgK, fun) + #celsius <- mask(celsius, roi) + celsius<-crop(celsius, extent(roi), snap="out") + return(celsius) + }) +} + +######################## DEFINE VARIABLES (Enter your variables) +# #Working directory +wd="C:\\vuelos\\temp\\marcothermal\\150\\" #For images, the root folder +setwd(wd) +## What kind of files to seach +pattern="\\margeo.bsq$" +roi_name<- "mar__ab__roi" +#Feature geodatabase to get ROIS +fgdb = "C:\\Users\\usuario\\Documents\\ArcGIS\\Default.gdb" + +#prefix to add to theOutput name of the crop file +pfx <- "cel" + +######################### STARTs SCRIPT (No need to edit) + +#Error handling try +tryCatch({ + #list all imagery in the folder + imgs <-list.files(pattern=pattern) + print(imgs) #for debugging + #get roi + #roi <- get_roi(roi_name) + roi <- readOGR("C:\\vuelos\\temp\\marcothermal\\150", "mar__ab__roi") #Read SHP + print(paste("roi")) #for debugging + + #loop for the images to crop + for (i in imgs){ + #Read raster + r <- raster(i) + #crop + r_cr<-kelvinx100toCelsius(r,roi) #execute script, crop by roi + print(paste("cropped")) #for debugging + #name for outpul file + #outFile <- paste0("t",i,"_q21cel") + outFile <- paste0(pfx,i) + print("after outfile") + #Save file + writeRaster(r_cr, file=outFile,datatype='FLT4S',format="GTiff",overwrite=FALSE) + #write.ENVI (r_cr, outFile, interleave = "bsq") + #inform about saving + print(paste("saved raster:",outFile," Succesfully")) + } +}) \ No newline at end of file diff --git a/r/kelvinx100toCelsius.R b/r/kelvinx100toCelsius.R new file mode 100644 index 0000000..a37cf7a --- /dev/null +++ b/r/kelvinx100toCelsius.R @@ -0,0 +1,82 @@ +######### Raster calculation to convert the units from the thermal FLir to celsius +######## Because they come in Kelvin multiplied by 100 +####### CROPS the rasters to ROIS read from the GDB to crop image +######## Reads from standard formating of files, from GEO folder to CEL + +#0. First step : load packages +#install.packages(c("rgdal","raster")) +require(raster) +require(rgdal) + +###################### DEFINED FUNCTIONS (no need to edit) +#Function Kelvin 100 to raster +fun <-function(x){(x/100)-273.15} + +#Function to select the apropriate roi for each trial +# Read the feature class +get_roi <- function(trial_roi) {readOGR(dsn=fgdb,layer=trial_roi)} + +#Function kelvinx100toCelsius +kelvinx100toCelsius <- function(imgK,roi) +{ + tryCatch({ + celsius<-calc(imgK, fun) + #celsius <- mask(celsius, roi) + celsius<-crop(celsius, extent(roi), snap="out") + #Write the tif to disk + #replacing the suffix of the raster geo -> cel + out_File= gsub("geo", "cel", rasterName) + #out_FileCopy= gsub("geo", "cel", rasterCopy) + writeRaster(celsius, file=out_File,datatype='FLT4S',format="GTiff",overwrite=FALSE) + if (copy) writeRaster(celsius, file=out_FileCopy,datatype='FLT4S',format="GTiff",overwrite=FALSE) + print(paste("saved raster: <<",out_File,">> Succesfully")) + }) +} + +######################## DEFINE VARIABLES (Enter your variables) +# #Working directory +wd="G:\\AF\\" #For images, the root folder +setwd(wd) + +ext<-".tif" +roi_name<- "__af__roi" +sx<-"geo" #input sufix +#Feature geodatabase to get ROIS +fgdb = "C:\\Dropbox\\data\\AF\\compass\\af_compass\\af_compass.gdb" +#MAtrix of images dates to process +#Beware if there is only one trial, fake another column filling with "-" in the cameras ??NOTE!! +name_file_data <- "C:\\Dropbox\\data\\AF\\nut\\q21__af__dayt.csv" +##Next the location if you want to copy the data somewhere else +copy <- FALSE #check the function if you want to copy !!!!!!!!!!!F#FFFFF##### +copyLocation <- "C:\\Dropbox\\New folder\\temp\\521\\AD\\" + +######################### STARTs SCRIPT (No need to edit) +tryCatch({ + # rasterName<-args[1] # to read from cmd + data<-read.table(name_file_data, header = TRUE,sep = ",") + row.names(data)<-data[,1] # name the rows as the date + dates<- row.names(data) + #Get the trial names + data<- data[,2:length(colnames(data))] #Remove 1st column that had the dates + trials<- colnames(data) #assign them as column names of the dataframe + #loop for the images to extract + for (d in dates){ + #TODO: put the trial loop outside the date, to be able to call the get_roi just once per trial. + for (t in trials){ + cam<-data[d,t] + if (cam != "-"){ + imgLocation = paste(wd,d,"\\",cam,"\\geo\\",sep="") #Look into geo folder + name = paste(cam,d,t,sx,ext,sep="") + rasterName= paste(imgLocation,name,sep="") + if (copy) {rasterCopy= paste(copyLocation,name,sep="")}#location to copy if needed + if(file.exists(rasterName)){#if the raster exists + print(rasterName) #image name + dir.create(paste(wd,d,"\\",cam,"\\cel\\",sep="")) #create folder for celsius + r<-raster(rasterName) + kelvinx100toCelsius(r,get_roi(paste(t,roi_name,sep=""))) #execute script, crop by roi + } + else{print(paste(rasterName," Doesn't exists--"))} + } + } + } +}) \ No newline at end of file diff --git a/r/learning R spatial analysis.R b/r/learning R spatial analysis.R new file mode 100644 index 0000000..6aa9e59 --- /dev/null +++ b/r/learning R spatial analysis.R @@ -0,0 +1,14 @@ +c(1,2,3,4) +n <- c(4:1) +as.character(n) +f <- factor (n) +ls() +mean(n) +min(n) +range(n) +length(n) +any(n) +all(n) +l = c(TRUE, FALSE, FALSE, TRUE) +which(l) # get position of TRUE values on vector +which.min(n)# get position of the minimun value \ No newline at end of file diff --git a/r/list_files.R b/r/list_files.R new file mode 100644 index 0000000..96c3923 --- /dev/null +++ b/r/list_files.R @@ -0,0 +1,6 @@ +setwd("C:/Dropbox/data/AE/nut/img") + +files <- list.files(path = ".", full.names = FALSE, pattern = ".tif$",recursive = FALSE) +#file.path("folder","file") #function to concatenate folders to create a filepath +#write the final table +write.csv(files,paste("Files_img.csv", sep="")) diff --git a/r/mergeZonalStatisticsOneBandOneExperimentManyDates.R b/r/mergeZonalStatisticsOneBandOneExperimentManyDates.R new file mode 100644 index 0000000..6b4e82d --- /dev/null +++ b/r/mergeZonalStatisticsOneBandOneExperimentManyDates.R @@ -0,0 +1,74 @@ +##Script para unir las tablas de NDVI al extraer de las imágenes de varias fechas de una misma area +#Tables are the regular zonal statistics output from arcGIS +# It MUST include the MEAN statistic and an ID field. + +#++++++Methods########################################################## +#Libraries +options(java.parameters = "-Xmx30000m") #available RAM for JAVA +library(foreign) +library(xlsx) +library(stringr) +library(plyr) +library(gtools) + +#Add the filename to the column name, because all the zonal stats tables have the same column names when from ArcGIS +addFileNameToColumn <- function (f){ + t <- read.dbf(f) + #filename without extension + name <- substr(f, 1, nchar(f)-4) + t <- subset(t, select=c(id,"MEAN")) + origFields <- colnames(t) + colnamesB <- origFields + #Next put the filename column but not in the id column + for (i in 1:length(colnamesB) ){ + if (colnamesB[i] != id) + colnamesB[i]<- paste(toString(name), sep="") + } + colnames(t)<- colnamesB + return (t) +} + +#Merge to use with reduce sucessively by "id" +merge.all <- function(x, y) { + merge(x, y, all=TRUE, by=id) +} + +#Merge all "MEAN" columns into one Excel file +arrangeDbf <- function(file_list) { + #Workbook to save MEAN values + wbookName <- paste("Data",".xlsx",sep="") + wbook <- createWorkbook() + wbSheet <- createSheet(wb = wbook, sheetName = "data") + + # batch read all into a addFileNameToColumn + m.list = lapply(file_list, addFileNameToColumn) + #merge them into a single data frame + tables <- Reduce(merge.all, m.list) + #Next remove the duplicate "id" column + tables <- tables[,!duplicated(colnames(tables), fromLast = TRUE)] + + #Write to Excel file + startRow<-1 + print(paste("Escribiendo",nrow(tables),"registros")) + addDataFrame(x=tables, sheet=wbSheet,row.names=FALSE,startRow = startRow) + + saveWorkbook(wbook, wbookName) + print(paste("Se guardĂł el archivo",wbookName)) + return (wbook) +} + + +#++++++Methods-End########################################################## + +#Working Directory +wd<-("C:\\Users\\usuario\\Documents\\ArcGIS\\") +setwd(wd) +getwd() +#Id Field to join the tables +id = "Name" + +#Get tables +zsTables <-list.files(pattern="\\.dbf$") +#Merge the MEAN column of the tables +xlsx <-arrangeDbf(zsTables) + diff --git a/r/moisture/.Rhistory b/r/moisture/.Rhistory new file mode 100644 index 0000000..ea51ff9 --- /dev/null +++ b/r/moisture/.Rhistory @@ -0,0 +1,512 @@ +wLTable$irrnum <- gsub('-', '',regmatches(wLTable$sheetName, regexpr("-[0-9A-Za-z]+$", wLTable$sheetName))) +#check for NA's in rows +na_inrow <- apply(wLTable, 1, function(x){any(is.na(x))}) # logical vector of the rows with any NA's +#wLTable <- wLTable[complete.cases(wLTable), ] #This iis to remove any row that has a NA on it. +#keep only the rows that dont have NA's and their treatment correspond to the performed sampling +wLTable <- wLTable[!na_inrow, ] +#Formate the date column to format the axis labels in ggplot +wLTable$date <- as.POSIXct(wLTable$date) +#write the final table to CSV if you want to save to text file +write.csv(wLTable,csvfile, row.names=FALSE) +mData +wLTable +na_inrow +wLTable +wLTable$irrtype <- regmatches(wLTable$sheetName, regexpr("bi|ai|ah|m1", wLTable$sheetName)) #español ar|dr|mv|dt|m1" +wLTable +write.csv(wLTable,csvfile, row.names=FALSE) +write.csv(wLTable,csvfile, row.names=FALSE) +rm(list=ls() +) +mData <- getMoistreFromExcel(file, startSheet,endSheet,endCol,endRow,colTypes,col_names,startRow) #get the raw moisture data[[1]] and sheetnames[[2]] +##Resulting data +dates<-unlist(mData[[2]]) +moistData <-mData[[1]] +#Calculate water table based on the moisture frome each measurement +samplings = length(dates) +waterLayerTable <- calculateWLayer(moistData,dates) +##Bind all samplings in one table (stack the rows, keep the columns) +wLTable <- Reduce(rbind,waterLayerTable) +##FORMATTING +#Format the headers (use if needed) +# wLTable$depth <- gsub('0-15', '1w', wLTable$depth) #replace the depth by an ordered index +# wLTable$depth <- gsub('15-30', '2w', wLTable$depth) #replace the depth by an ordered index +# wLTable$depth <- gsub('30-60', '3w', wLTable$depth) #replace the depth by an ordered index +# wLTable$depth <- gsub('60-90', '4w', wLTable$depth) #replace the depth by an ordered index +wLTable$depth <- gsub('0-15', 'depth1', wLTable$depth) #replace the depth by an ordered index +wLTable$depth <- gsub('15-30', 'depth2', wLTable$depth) #replace the depth by an ordered index +wLTable$depth <- gsub('30-60', 'depth3', wLTable$depth) #replace the depth by an ordered index +wLTable$depth <- gsub('60-90', 'depth4', wLTable$depth) #replace the depth by an ordered index +###########Separate the sheets name into columns (maybe change after the filtering thing?) +#The format should be something like: dr30-11-2015_pre-conv +#sheetname should be in a format dd-mm-yyyy and will have a prefix of 2 char: AR or DR or DT or MV (indicating the kind of sampling: after/before iirgation, etc) +#and a suffix indicating _[pre]/[2R]/[4R]-[1/2/3/4/5]/[pb]/[conv]) +#date column - get what has the format of a date dd-mm-yyyy +wLTable$date <- as.Date(regmatches(wLTable$sheetName, regexpr("[0-9]{2}-[0-9]{2}-[0-9]{4}", wLTable$sheetName)),"%d-%m-%Y") +#sampling type column - what looks like [dr]/[ar]/[mv]/[dt] ... (antes de riego, despuĂ©s, verano, trilla...) +wLTable$irrtype <- regmatches(wLTable$sheetName, regexpr("bi|ai|ah|m1", wLTable$sheetName)) #español ar|dr|mv|dt|m1" +#irrarea sampled column - the sampling corresponded to which irrigation instance: pre(siembra), 4R(4 riegos) or 2R(2 riegos) +wLTable$irrarea <- gsub('_', '',regmatches(wLTable$sheetName, regexpr("_[0-9A-Za-z]+", wLTable$sheetName))) +#irrigation sampled column - weather its the first, second.. irrigation etc +wLTable$irrnum <- gsub('-', '',regmatches(wLTable$sheetName, regexpr("-[0-9A-Za-z]+$", wLTable$sheetName))) +#check for NA's in rows +na_inrow <- apply(wLTable, 1, function(x){any(is.na(x))}) # logical vector of the rows with any NA's +#wLTable <- wLTable[complete.cases(wLTable), ] #This iis to remove any row that has a NA on it. +#keep only the rows that dont have NA's and their treatment correspond to the performed sampling +wLTable <- wLTable[!na_inrow, ] +#Formate the date column to format the axis labels in ggplot +wLTable$date <- as.POSIXct(wLTable$date) +#write the final table to CSV if you want to save to text file +write.csv(wLTable,csvfile, row.names=FALSE) +source(file.path("C:","Dropbox","Software","Scripts","r","moisture","methods_moisture.R", fsep = .Platform$file.sep)) +###################################################### EXECUTE SCRIPT +############################### INPUTS +#set WDir +setwd("C://Dropbox//Software//Scripts//r//moisture") +file="C://Dropbox//data//AE//ae_stars//ser//AE-851 ANTILLON humedad.xlsx" +trial <- "ros" +col_names<-c('id', +'depth', +'canType', +'can', +'wSoilw', +'dSoilw', +'moisture') +colTypes <- c('numeric', 'character','numeric', 'numeric','numeric','numeric','numeric') +##Check this for every excel read +startRow<- 1 #Skip the fisrt 5 rows because it has other ancillary data +startSheet <- 2 +endSheet <- 9 +endCol <- 7 +endRow <- 101 +csvfile <- "C://Dropbox//data//AE//ae_stars//ser//waterLayer_humedad_AEser.csv" +################################# END INPUTS +##Edit next +##getMoistreFromExcel <- function (file, startSheet,endCol,endRow,colTypes,col_names,maxsamplepointID) +mData <- getMoistreFromExcel(file, startSheet,endSheet,endCol,endRow,colTypes,col_names,startRow) #get the raw moisture data[[1]] and sheetnames[[2]] +##Resulting data +dates<-unlist(mData[[2]]) +moistData <-mData[[1]] +#Calculate water table based on the moisture frome each measurement +samplings = length(dates) +waterLayerTable <- calculateWLayer(moistData,dates) +##Bind all samplings in one table (stack the rows, keep the columns) +wLTable <- Reduce(rbind,waterLayerTable) +##FORMATTING +#Format the headers (use if needed) +# wLTable$depth <- gsub('0-15', '1w', wLTable$depth) #replace the depth by an ordered index +# wLTable$depth <- gsub('15-30', '2w', wLTable$depth) #replace the depth by an ordered index +# wLTable$depth <- gsub('30-60', '3w', wLTable$depth) #replace the depth by an ordered index +# wLTable$depth <- gsub('60-90', '4w', wLTable$depth) #replace the depth by an ordered index +wLTable$depth <- gsub('0-15', 'depth1', wLTable$depth) #replace the depth by an ordered index +wLTable$depth <- gsub('15-30', 'depth2', wLTable$depth) #replace the depth by an ordered index +wLTable$depth <- gsub('30-60', 'depth3', wLTable$depth) #replace the depth by an ordered index +wLTable$depth <- gsub('60-90', 'depth4', wLTable$depth) #replace the depth by an ordered index +###########Separate the sheets name into columns (maybe change after the filtering thing?) +#The format should be something like: dr30-11-2015_pre-conv +#sheetname should be in a format dd-mm-yyyy and will have a prefix of 2 char: AR or DR or DT or MV (indicating the kind of sampling: after/before iirgation, etc) +#and a suffix indicating _[pre]/[2R]/[4R]-[1/2/3/4/5]/[pb]/[conv]) +#date column - get what has the format of a date dd-mm-yyyy +wLTable$date <- as.Date(regmatches(wLTable$sheetName, regexpr("[0-9]{2}-[0-9]{2}-[0-9]{4}", wLTable$sheetName)),"%d-%m-%Y") +#sampling type column - what looks like [dr]/[ar]/[mv]/[dt] ... (antes de riego, despuĂ©s, verano, trilla...) +wLTable$irrtype <- regmatches(wLTable$sheetName, regexpr("bi|ai|ah|m1", wLTable$sheetName)) #español ar|dr|mv|dt|m1" +#irrarea sampled column - the sampling corresponded to which irrigation instance: pre(siembra), 4R(4 riegos) or 2R(2 riegos) +wLTable$irrarea <- gsub('_', '',regmatches(wLTable$sheetName, regexpr("_[0-9A-Za-z]+", wLTable$sheetName))) +#irrigation sampled column - weather its the first, second.. irrigation etc +wLTable$irrnum <- gsub('-', '',regmatches(wLTable$sheetName, regexpr("-[0-9A-Za-z]+$", wLTable$sheetName))) +#check for NA's in rows +na_inrow <- apply(wLTable, 1, function(x){any(is.na(x))}) # logical vector of the rows with any NA's +#wLTable <- wLTable[complete.cases(wLTable), ] #This iis to remove any row that has a NA on it. +#keep only the rows that dont have NA's and their treatment correspond to the performed sampling +wLTable <- wLTable[!na_inrow, ] +#Formate the date column to format the axis labels in ggplot +wLTable$date <- as.POSIXct(wLTable$date) +#write the final table to CSV if you want to save to text file +write.csv(wLTable,csvfile, row.names=FALSE) +moistData +rm(list=ls()) +source(file.path("C:","Dropbox","Software","Scripts","r","moisture","methods_moisture.R", fsep = .Platform$file.sep)) +###################################################### EXECUTE SCRIPT +############################### INPUTS +#set WDir +setwd("C://Dropbox//Software//Scripts//r//moisture") +file="C://Dropbox//data//AE//ae_stars//ser//AE-851 ANTILLON humedad.xlsx" +trial <- "ros" +col_names<-c('id', +'depth', +'canType', +'can', +'wSoilw', +'dSoilw', +'moisture') +colTypes <- c('numeric', 'character','numeric', 'numeric','numeric','numeric','numeric') +##Check this for every excel read +startRow<- 1 #Skip the fisrt 5 rows because it has other ancillary data +startSheet <- 2 +endSheet <- 9 +endCol <- 7 +endRow <- 101 +csvfile <- "C://Dropbox//data//AE//ae_stars//ser//waterLayer_humedad_AEser.csv" +################################# END INPUTS +##Edit next +##getMoistreFromExcel <- function (file, startSheet,endCol,endRow,colTypes,col_names,maxsamplepointID) +mData <- getMoistreFromExcel(file, startSheet,endSheet,endCol,endRow,colTypes,col_names,startRow) #get the raw moisture data[[1]] and sheetnames[[2]] +##Resulting data +dates<-unlist(mData[[2]]) +moistData <-mData[[1]] +#Calculate water table based on the moisture frome each measurement +samplings = length(dates) +waterLayerTable <- calculateWLayer(moistData,dates) +##Bind all samplings in one table (stack the rows, keep the columns) +wLTable <- Reduce(rbind,waterLayerTable) +##FORMATTING +#Format the headers (use if needed) +# wLTable$depth <- gsub('0-15', '1w', wLTable$depth) #replace the depth by an ordered index +# wLTable$depth <- gsub('15-30', '2w', wLTable$depth) #replace the depth by an ordered index +# wLTable$depth <- gsub('30-60', '3w', wLTable$depth) #replace the depth by an ordered index +# wLTable$depth <- gsub('60-90', '4w', wLTable$depth) #replace the depth by an ordered index +wLTable$depth <- gsub('0-15', 'depth1', wLTable$depth) #replace the depth by an ordered index +wLTable$depth <- gsub('15-30', 'depth2', wLTable$depth) #replace the depth by an ordered index +wLTable$depth <- gsub('30-60', 'depth3', wLTable$depth) #replace the depth by an ordered index +wLTable$depth <- gsub('60-90', 'depth4', wLTable$depth) #replace the depth by an ordered index +###########Separate the sheets name into columns (maybe change after the filtering thing?) +#The format should be something like: dr30-11-2015_pre-conv +#sheetname should be in a format dd-mm-yyyy and will have a prefix of 2 char: AR or DR or DT or MV (indicating the kind of sampling: after/before iirgation, etc) +#and a suffix indicating _[pre]/[2R]/[4R]-[1/2/3/4/5]/[pb]/[conv]) +#date column - get what has the format of a date dd-mm-yyyy +wLTable$date <- as.Date(regmatches(wLTable$sheetName, regexpr("[0-9]{2}-[0-9]{2}-[0-9]{4}", wLTable$sheetName)),"%d-%m-%Y") +#sampling type column - what looks like [dr]/[ar]/[mv]/[dt] ... (antes de riego, despuĂ©s, verano, trilla...) +wLTable$irrtype <- regmatches(wLTable$sheetName, regexpr("bi|ai|ah|m1", wLTable$sheetName)) #español ar|dr|mv|dt|m1" +#irrarea sampled column - the sampling corresponded to which irrigation instance: pre(siembra), 4R(4 riegos) or 2R(2 riegos) +wLTable$irrarea <- gsub('_', '',regmatches(wLTable$sheetName, regexpr("_[0-9A-Za-z]+", wLTable$sheetName))) +#irrigation sampled column - weather its the first, second.. irrigation etc +wLTable$irrnum <- gsub('-', '',regmatches(wLTable$sheetName, regexpr("-[0-9A-Za-z]+$", wLTable$sheetName))) +#check for NA's in rows +na_inrow <- apply(wLTable, 1, function(x){any(is.na(x))}) # logical vector of the rows with any NA's +#wLTable <- wLTable[complete.cases(wLTable), ] #This iis to remove any row that has a NA on it. +#keep only the rows that dont have NA's and their treatment correspond to the performed sampling +wLTable <- wLTable[!na_inrow, ] +#Formate the date column to format the axis labels in ggplot +wLTable$date <- as.POSIXct(wLTable$date) +#write the final table to CSV if you want to save to text file +write.csv(wLTable,csvfile, row.names=FALSE) +mData <- getMoistreFromExcel(file, startSheet,endSheet,endCol,endRow,colTypes,col_names,startRow) #get the raw moisture data[[1]] and sheetnames[[2]] +##Resulting data +dates<-unlist(mData[[2]]) +moistData <-mData[[1]] +#Calculate water table based on the moisture frome each measurement +samplings = length(dates) +waterLayerTable <- calculateWLayer(moistData,dates) +##Bind all samplings in one table (stack the rows, keep the columns) +wLTable <- Reduce(rbind,waterLayerTable) +##FORMATTING +#Format the headers (use if needed) +# wLTable$depth <- gsub('0-15', '1w', wLTable$depth) #replace the depth by an ordered index +# wLTable$depth <- gsub('15-30', '2w', wLTable$depth) #replace the depth by an ordered index +# wLTable$depth <- gsub('30-60', '3w', wLTable$depth) #replace the depth by an ordered index +# wLTable$depth <- gsub('60-90', '4w', wLTable$depth) #replace the depth by an ordered index +wLTable$depth <- gsub('0-15', 'depth1', wLTable$depth) #replace the depth by an ordered index +wLTable$depth <- gsub('15-30', 'depth2', wLTable$depth) #replace the depth by an ordered index +wLTable$depth <- gsub('30-60', 'depth3', wLTable$depth) #replace the depth by an ordered index +wLTable$depth <- gsub('60-90', 'depth4', wLTable$depth) #replace the depth by an ordered index +###########Separate the sheets name into columns (maybe change after the filtering thing?) +#The format should be something like: dr30-11-2015_pre-conv +#sheetname should be in a format dd-mm-yyyy and will have a prefix of 2 char: AR or DR or DT or MV (indicating the kind of sampling: after/before iirgation, etc) +#and a suffix indicating _[pre]/[2R]/[4R]-[1/2/3/4/5]/[pb]/[conv]) +#date column - get what has the format of a date dd-mm-yyyy +wLTable$date <- as.Date(regmatches(wLTable$sheetName, regexpr("[0-9]{2}-[0-9]{2}-[0-9]{4}", wLTable$sheetName)),"%d-%m-%Y") +#sampling type column - what looks like [dr]/[ar]/[mv]/[dt] ... (antes de riego, despuĂ©s, verano, trilla...) +wLTable$irrtype <- regmatches(wLTable$sheetName, regexpr("bi|ai|ah|m1", wLTable$sheetName)) #español ar|dr|mv|dt|m1" +#irrarea sampled column - the sampling corresponded to which irrigation instance: pre(siembra), 4R(4 riegos) or 2R(2 riegos) +wLTable$irrarea <- gsub('_', '',regmatches(wLTable$sheetName, regexpr("_[0-9A-Za-z]+", wLTable$sheetName))) +#irrigation sampled column - weather its the first, second.. irrigation etc +wLTable$irrnum <- gsub('-', '',regmatches(wLTable$sheetName, regexpr("-[0-9A-Za-z]+$", wLTable$sheetName))) +#check for NA's in rows +na_inrow <- apply(wLTable, 1, function(x){any(is.na(x))}) # logical vector of the rows with any NA's +#wLTable <- wLTable[complete.cases(wLTable), ] #This iis to remove any row that has a NA on it. +#keep only the rows that dont have NA's and their treatment correspond to the performed sampling +wLTable <- wLTable[!na_inrow, ] +#Formate the date column to format the axis labels in ggplot +wLTable$date <- as.POSIXct(wLTable$date) +#write the final table to CSV if you want to save to text file +write.csv(wLTable,csvfile, row.names=FALSE) +dates +mData <- getMoistreFromExcel(file, startSheet,endSheet,endCol,endRow,colTypes,col_names,startRow) #get the raw moisture data[[1]] and sheetnames[[2]] +##Resulting data +dates<-unlist(mData[[2]]) +moistData <-mData[[1]] +#Calculate water table based on the moisture frome each measurement +samplings = length(dates) +waterLayerTable <- calculateWLayer(moistData,dates) +##Bind all samplings in one table (stack the rows, keep the columns) +wLTable <- Reduce(rbind,waterLayerTable) +##FORMATTING +#Format the headers (use if needed) +# wLTable$depth <- gsub('0-15', '1w', wLTable$depth) #replace the depth by an ordered index +# wLTable$depth <- gsub('15-30', '2w', wLTable$depth) #replace the depth by an ordered index +# wLTable$depth <- gsub('30-60', '3w', wLTable$depth) #replace the depth by an ordered index +# wLTable$depth <- gsub('60-90', '4w', wLTable$depth) #replace the depth by an ordered index +wLTable$depth <- gsub('0-15', 'depth1', wLTable$depth) #replace the depth by an ordered index +wLTable$depth <- gsub('15-30', 'depth2', wLTable$depth) #replace the depth by an ordered index +wLTable$depth <- gsub('30-60', 'depth3', wLTable$depth) #replace the depth by an ordered index +wLTable$depth <- gsub('60-90', 'depth4', wLTable$depth) #replace the depth by an ordered index +###########Separate the sheets name into columns (maybe change after the filtering thing?) +#The format should be something like: dr30-11-2015_pre-conv +#sheetname should be in a format dd-mm-yyyy and will have a prefix of 2 char: AR or DR or DT or MV (indicating the kind of sampling: after/before iirgation, etc) +#and a suffix indicating _[pre]/[2R]/[4R]-[1/2/3/4/5]/[pb]/[conv]) +#date column - get what has the format of a date dd-mm-yyyy +wLTable$date <- as.Date(regmatches(wLTable$sheetName, regexpr("[0-9]{2}-[0-9]{2}-[0-9]{4}", wLTable$sheetName)),"%d-%m-%Y") +#sampling type column - what looks like [dr]/[ar]/[mv]/[dt] ... (antes de riego, despuĂ©s, verano, trilla...) +wLTable$irrtype <- regmatches(wLTable$sheetName, regexpr("bi|ai|ah|m1", wLTable$sheetName)) #español ar|dr|mv|dt|m1" +#irrarea sampled column - the sampling corresponded to which irrigation instance: pre(siembra), 4R(4 riegos) or 2R(2 riegos) +wLTable$irrarea <- gsub('_', '',regmatches(wLTable$sheetName, regexpr("_[0-9A-Za-z]+", wLTable$sheetName))) +#irrigation sampled column - weather its the first, second.. irrigation etc +wLTable$irrnum <- gsub('-', '',regmatches(wLTable$sheetName, regexpr("-[0-9A-Za-z]+$", wLTable$sheetName))) +#check for NA's in rows +na_inrow <- apply(wLTable, 1, function(x){any(is.na(x))}) # logical vector of the rows with any NA's +#wLTable <- wLTable[complete.cases(wLTable), ] #This iis to remove any row that has a NA on it. +#keep only the rows that dont have NA's and their treatment correspond to the performed sampling +wLTable <- wLTable[!na_inrow, ] +#Formate the date column to format the axis labels in ggplot +wLTable$date <- as.POSIXct(wLTable$date) +#write the final table to CSV if you want to save to text file +write.csv(wLTable,csvfile, row.names=FALSE) +dates +file="C://Dropbox//data//AE//ae_stars//ser//AE-851 ANTILLON humedad.xlsx" +mData <- getMoistreFromExcel(file, startSheet,endSheet,endCol,endRow,colTypes,col_names,startRow) #get the raw moisture data[[1]] and sheetnames[[2]] +##Resulting data +dates<-unlist(mData[[2]]) +moistData <-mData[[1]] +#Calculate water table based on the moisture frome each measurement +samplings = length(dates) +waterLayerTable <- calculateWLayer(moistData,dates) +##Bind all samplings in one table (stack the rows, keep the columns) +wLTable <- Reduce(rbind,waterLayerTable) +##FORMATTING +#Format the headers (use if needed) +# wLTable$depth <- gsub('0-15', '1w', wLTable$depth) #replace the depth by an ordered index +# wLTable$depth <- gsub('15-30', '2w', wLTable$depth) #replace the depth by an ordered index +# wLTable$depth <- gsub('30-60', '3w', wLTable$depth) #replace the depth by an ordered index +# wLTable$depth <- gsub('60-90', '4w', wLTable$depth) #replace the depth by an ordered index +wLTable$depth <- gsub('0-15', 'depth1', wLTable$depth) #replace the depth by an ordered index +wLTable$depth <- gsub('15-30', 'depth2', wLTable$depth) #replace the depth by an ordered index +wLTable$depth <- gsub('30-60', 'depth3', wLTable$depth) #replace the depth by an ordered index +wLTable$depth <- gsub('60-90', 'depth4', wLTable$depth) #replace the depth by an ordered index +###########Separate the sheets name into columns (maybe change after the filtering thing?) +#The format should be something like: dr30-11-2015_pre-conv +#sheetname should be in a format dd-mm-yyyy and will have a prefix of 2 char: AR or DR or DT or MV (indicating the kind of sampling: after/before iirgation, etc) +#and a suffix indicating _[pre]/[2R]/[4R]-[1/2/3/4/5]/[pb]/[conv]) +#date column - get what has the format of a date dd-mm-yyyy +wLTable$date <- as.Date(regmatches(wLTable$sheetName, regexpr("[0-9]{2}-[0-9]{2}-[0-9]{4}", wLTable$sheetName)),"%d-%m-%Y") +#sampling type column - what looks like [dr]/[ar]/[mv]/[dt] ... (antes de riego, despuĂ©s, verano, trilla...) +wLTable$irrtype <- regmatches(wLTable$sheetName, regexpr("bi|ai|ah|m1", wLTable$sheetName)) #español ar|dr|mv|dt|m1" +#irrarea sampled column - the sampling corresponded to which irrigation instance: pre(siembra), 4R(4 riegos) or 2R(2 riegos) +wLTable$irrarea <- gsub('_', '',regmatches(wLTable$sheetName, regexpr("_[0-9A-Za-z]+", wLTable$sheetName))) +#irrigation sampled column - weather its the first, second.. irrigation etc +wLTable$irrnum <- gsub('-', '',regmatches(wLTable$sheetName, regexpr("-[0-9A-Za-z]+$", wLTable$sheetName))) +#check for NA's in rows +na_inrow <- apply(wLTable, 1, function(x){any(is.na(x))}) # logical vector of the rows with any NA's +#wLTable <- wLTable[complete.cases(wLTable), ] #This iis to remove any row that has a NA on it. +#keep only the rows that dont have NA's and their treatment correspond to the performed sampling +wLTable <- wLTable[!na_inrow, ] +#Formate the date column to format the axis labels in ggplot +wLTable$date <- as.POSIXct(wLTable$date) +#write the final table to CSV if you want to save to text file +write.csv(wLTable,csvfile, row.names=FALSE) +dates +endSheet <- 10 +mData <- getMoistreFromExcel(file, startSheet,endSheet,endCol,endRow,colTypes,col_names,startRow) #get the raw moisture data[[1]] and sheetnames[[2]] +##Resulting data +dates<-unlist(mData[[2]]) +moistData <-mData[[1]] +#Calculate water table based on the moisture frome each measurement +samplings = length(dates) +waterLayerTable <- calculateWLayer(moistData,dates) +##Bind all samplings in one table (stack the rows, keep the columns) +wLTable <- Reduce(rbind,waterLayerTable) +##FORMATTING +#Format the headers (use if needed) +# wLTable$depth <- gsub('0-15', '1w', wLTable$depth) #replace the depth by an ordered index +# wLTable$depth <- gsub('15-30', '2w', wLTable$depth) #replace the depth by an ordered index +# wLTable$depth <- gsub('30-60', '3w', wLTable$depth) #replace the depth by an ordered index +# wLTable$depth <- gsub('60-90', '4w', wLTable$depth) #replace the depth by an ordered index +wLTable$depth <- gsub('0-15', 'depth1', wLTable$depth) #replace the depth by an ordered index +wLTable$depth <- gsub('15-30', 'depth2', wLTable$depth) #replace the depth by an ordered index +wLTable$depth <- gsub('30-60', 'depth3', wLTable$depth) #replace the depth by an ordered index +wLTable$depth <- gsub('60-90', 'depth4', wLTable$depth) #replace the depth by an ordered index +###########Separate the sheets name into columns (maybe change after the filtering thing?) +#The format should be something like: dr30-11-2015_pre-conv +#sheetname should be in a format dd-mm-yyyy and will have a prefix of 2 char: AR or DR or DT or MV (indicating the kind of sampling: after/before iirgation, etc) +#and a suffix indicating _[pre]/[2R]/[4R]-[1/2/3/4/5]/[pb]/[conv]) +#date column - get what has the format of a date dd-mm-yyyy +wLTable$date <- as.Date(regmatches(wLTable$sheetName, regexpr("[0-9]{2}-[0-9]{2}-[0-9]{4}", wLTable$sheetName)),"%d-%m-%Y") +#sampling type column - what looks like [dr]/[ar]/[mv]/[dt] ... (antes de riego, despuĂ©s, verano, trilla...) +wLTable$irrtype <- regmatches(wLTable$sheetName, regexpr("bi|ai|ah|m1", wLTable$sheetName)) #español ar|dr|mv|dt|m1" +#irrarea sampled column - the sampling corresponded to which irrigation instance: pre(siembra), 4R(4 riegos) or 2R(2 riegos) +wLTable$irrarea <- gsub('_', '',regmatches(wLTable$sheetName, regexpr("_[0-9A-Za-z]+", wLTable$sheetName))) +#irrigation sampled column - weather its the first, second.. irrigation etc +wLTable$irrnum <- gsub('-', '',regmatches(wLTable$sheetName, regexpr("-[0-9A-Za-z]+$", wLTable$sheetName))) +#check for NA's in rows +na_inrow <- apply(wLTable, 1, function(x){any(is.na(x))}) # logical vector of the rows with any NA's +#wLTable <- wLTable[complete.cases(wLTable), ] #This iis to remove any row that has a NA on it. +#keep only the rows that dont have NA's and their treatment correspond to the performed sampling +wLTable <- wLTable[!na_inrow, ] +#Formate the date column to format the axis labels in ggplot +wLTable$date <- as.POSIXct(wLTable$date) +#write the final table to CSV if you want to save to text file +write.csv(wLTable,csvfile, row.names=FALSE) +dates +library("XLConnect") +library(reshape2) +source(file.path("C:","Dropbox","Software","Scripts","r","moisture","methods_moisture.R", fsep = .Platform$file.sep)) +setwd("C://Dropbox//Software//Scripts//r//moisture") +setwd("C://Dropbox//Software//Scripts//r//moisture") +file="C://Dropbox//Bascula (1)//Muestras suelo//AF//AF-710COMPASS.xlsx" +trial <- "710" +setwd("C://Dropbox//Software//Scripts//r//moisture") +file="C://Dropbox//Bascula (1)//Muestras suelo//AF//AF-710COMPASS.xlsx" +trial <- "710" +col_names<-c('id', +'depth', +'canType', +'can', +'wSoilw', +'dSoilw', +'moisture') +colTypes <- c('numeric', 'character','numeric', 'numeric','numeric','numeric','numeric') +##Check this for every excel read +startRow<- 6 #Skip the fisrt 5 rows because it has other ancillary data +startSheet <- 2 +endSheet <- 10 +endCol <- 7 +endRow <- 66 +csvfile <- "C://Dropbox//Bascula (1)//Muestras suelo//AF//AF-710COMPASS.csv" +##Edit next +##getMoistreFromExcel <- function (file, startSheet,endCol,endRow,colTypes,col_names,maxsamplepointID) +mData <- getMoistreFromExcel(file, startSheet,endSheet,endCol,endRow,colTypes,col_names,startRow) #get the raw moisture data[[1]] and sheetnames[[2]] +dates<-unlist(mData[[2]]) +moistData <-mData[[1]] +dates +moistData +mData <- getMoistreFromExcel(file, startSheet,endSheet,endCol,endRow,colTypes,col_names,startRow) #get the raw moisture data[[1]] and sheetnames[[2]] +##Resulting data +dates<-unlist(mData[[2]]) +moistData <-mData[[1]] +dates +moistData +samplings = length(dates) +waterLayerTable <- calculateWLayer(moistData,dates) +samplings +moistData +##Edit next +##getMoistreFromExcel <- function (file, startSheet,endCol,endRow,colTypes,col_names,maxsamplepointID) +mData <- getMoistreFromExcel(file, startSheet,endSheet,endCol,endRow,colTypes,col_names,startRow) #get the raw moisture data[[1]] and sheetnames[[2]] +dates<-unlist(mData[[2]]) +moistData <-mData[[1]] +dates +moistData +mData <- getMoistreFromExcel(file, startSheet,endSheet,endCol,endRow,colTypes,col_names,startRow) #get the raw moisture data[[1]] and sheetnames[[2]] +##Resulting data +dates<-unlist(mData[[2]]) +moistData <-mData[[1]] +moistData +mData <- getMoistreFromExcel(file, startSheet,endSheet,endCol,endRow,colTypes,col_names,startRow) #get the raw moisture data[[1]] and sheetnames[[2]] +##Resulting data +dates<-unlist(mData[[2]]) +moistData <-mData[[1]] +moistData +samplings = length(dates) +waterLayerTable <- calculateWLayer(moistTable1,dates) +waterLayerTable <- calculateWLayer(moistData,dates) +samplings +waterLayerTable <- calculateWLayer(moistData,dates) +moistData +waterLayerTable <- calculateWLayer(moistData,dates) +for (s in 1:length(dates)) { +#Calculate the water layer for each sample +sPoints = length(moistData[[s]]$moisture) #total samples per date +for (i in 1:sPoints) { +moistData[[s]]$sheetName[i] <- as.character(sheets[s]) #add date info in column +} +} +for (s in 1:length(dates)) { +#Calculate the water layer for each sample +sPoints = length(moistData[[s]]$moisture) #total samples per date +for (i in 1:sPoints) { +moistData[[s]]$sheetName[i] <- as.character(dates[s]) #add date info in column +} +} +moistData +mData <- getMoistreFromExcel(file, startSheet,endSheet,endCol,endRow,colTypes,col_names,startRow) #get the raw moisture data[[1]] and sheetnames[[2]] +##Resulting data +dates<-unlist(mData[[2]]) +moistData <-mData[[1]] +moistData +mTable <- moistData +for (s in 1:samplings) { +#Calculate the water layer for each sample +sPoints = length(mTable[[s]]$moisture) #total samples per date +for (i in 1:sPoints) { +mTable[[s]]$sheetName[i] <- as.character(dates[s]) #add date info in column +} +} +mTable +mTable <- Reduce(rbind,mTable) +mTable +###########Separate the sheets name into columns (maybe change after the filtering thing?) +#The format should be something like: dr30-11-2015_pre-conv +#sheetname should be in a format dd-mm-yyyy and will have a prefix of 2 char: AR or DR or DT or MV (indicating the kind of sampling: after/before iirgation, etc) +#and a suffix indicating _[pre]/[2R]/[4R]-[1/2/3/4/5]/[pb]/[conv]) +#date column - get what has the format of a date dd-mm-yyyy +mTable$date <- as.Date(regmatches(mTable$sheetName, regexpr("[0-9]{2}-[0-9]{2}-[0-9]{4}", mTable$sheetName)),"%d-%m-%Y") +#sampling type column - what looks like [dr]/[ar]/[mv]/[dt] ... (antes de riego, despu?s, verano, trilla...) +mTable$irrtype <- regmatches(mTable$sheetName, regexpr("bi|ai|ah|m1", mTable$sheetName)) #espa?ol ar|dr|mv|dt|m1" +mTable +#sampling type column - what looks like [dr]/[ar]/[mv]/[dt] ... (antes de riego, despu?s, verano, trilla...) +mTable$irrtype <- regmatches(mTable$sheetName, regexpr("dr", mTable$sheetName)) #espa?ol ar|dr|mv|dt|m1" +#irrarea sampled column - the sampling corresponded to which irrigation instance: pre(siembra), 4R(4 riegos) or 2R(2 riegos) +mTable$irrarea <- gsub('_', '',regmatches(mTable$sheetName, regexpr("_[0-9A-Za-z]+", mTable$sheetName))) +#irrigation sampled column - weather its the first, second.. irrigation etc +mTable$irrnum <- gsub('-', '',regmatches(mTable$sheetName, regexpr("-[0-9A-Za-z]+$", mTable$sheetName))) +#check for NA's in rows +na_inrow <- apply(mTable, 1, function(x){any(is.na(x))}) # logical vector of the rows with any NA's +#wLTable <- wLTable[complete.cases(wLTable), ] #This iis to remove any row that has a NA on it. +#keep only the rows that dont have NA's and their treatment correspond to the performed sampling +mTable <- mTable[!na_inrow, ] +#Formate the date column to format the axis labels in ggplot +mTable$date <- as.POSIXct(mTable$date) +mTable +write.csv(mTable,csvfile, row.names=FALSE) +mData <- getMoistreFromExcel(file, startSheet,endSheet,endCol,endRow,colTypes,col_names,startRow) #get the raw moisture data[[1]] and sheetnames[[2]] +##Resulting data +dates<-unlist(mData[[2]]) +moistData <-mData[[1]] +mData +dates<-unlist(mData[[2]]) +moistData <-mData[[1]] +dates +moistData +samplings = length(dates) +mTable <- moistData +for (s in 1:samplings) { +#Calculate the water layer for each sample +sPoints = length(mTable[[s]]$moisture) #total samples per date +for (i in 1:sPoints) { +mTable[[s]]$sheetName[i] <- as.character(dates[s]) #add date info in column +} +} +mTable <- Reduce(rbind,mTable) +mTable$date <- as.Date(regmatches(mTable$sheetName, regexpr("[0-9]{2}-[0-9]{2}-[0-9]{4}", mTable$sheetName)),"%d-%m-%Y") +#sampling type column - what looks like [dr]/[ar]/[mv]/[dt] ... (antes de riego, despu?s, verano, trilla...) +mTable$irrtype <- regmatches(mTable$sheetName, regexpr("dr", mTable$sheetName)) #espa?ol ar|dr|mv|dt|m1" +#irrarea sampled column - the sampling corresponded to which irrigation instance: pre(siembra), 4R(4 riegos) or 2R(2 riegos) +mTable$irrarea <- gsub('_', '',regmatches(mTable$sheetName, regexpr("_[0-9A-Za-z]+", mTable$sheetName))) +#irrigation sampled column - weather its the first, second.. irrigation etc +mTable$irrnum <- gsub('-', '',regmatches(mTable$sheetName, regexpr("-[0-9A-Za-z]+$", mTable$sheetName))) +#check for NA's in rows +na_inrow <- apply(mTable, 1, function(x){any(is.na(x))}) # logical vector of the rows with any NA's +#wLTable <- wLTable[complete.cases(wLTable), ] #This iis to remove any row that has a NA on it. +#keep only the rows that dont have NA's and their treatment correspond to the performed sampling +mTable <- mTable[!na_inrow, ] +mTable$date <- as.POSIXct(mTable$date) +write.csv(mTable,csvfile, row.names=FALSE) diff --git a/r/moisture/171020_getMoistureSamplingDataFromExcel.R b/r/moisture/171020_getMoistureSamplingDataFromExcel.R new file mode 100644 index 0000000..8d1347c --- /dev/null +++ b/r/moisture/171020_getMoistureSamplingDataFromExcel.R @@ -0,0 +1,101 @@ +#### +# GET MOISTURE SAMPLING FROM THE EXCEL FIle indicated (names of the sheets should be in eg. AR20-11-2015_conv-pre format). used to save the wet and dry weight of the sampling cans +#Calculates the water depth in mm based on the % moisture and using a given formula +# +# Execute a new instance of R to every new file to be processed, to avoid re-using variables +# +# STORE TO local file +# +# # # # #Check conditions and quality check needed to be implemented +# +# # # # # check Harcoded inputs every time. +# +#### + +#library(xlsx) #preferred to read XLSX? +library("XLConnect") +library(reshape2) + + + +####################################################### FUNCTIONS DEFINITION +source(file.path("C:","Dropbox","Software","Scripts","r","moisture","methods_moisture.R", fsep = .Platform$file.sep)) + +###################################################### EXECUTE SCRIPT +############################### INPUTS +#set WDir +setwd("C://Dropbox//Software//Scripts//r//moisture") + +file="C://Dropbox//Bascula (1)//Muestras suelo//AF//AF-710COMPASS.xlsx" +trial <- "710" +col_names<-c('id', + 'depth', + 'canType', + 'can', + 'wSoilw', #wet soil weight + 'dSoilw', #dry soil weight + 'moisture') +colTypes <- c('numeric', 'character','numeric', 'numeric','numeric','numeric','numeric') +##Check this for every excel read +startRow<- 6 #Skip the fisrt 5 rows because it has other ancillary data +startSheet <- 2 +endSheet <- 10 +endCol <- 7 +endRow <- 66 + +csvfile <- "C://Dropbox//Bascula (1)//Muestras suelo//AF//AF-710COMPASS.csv" + +################################# END INPUTS + +##Edit next +##getMoistreFromExcel <- function (file, startSheet,endCol,endRow,colTypes,col_names,maxsamplepointID) +mData <- getMoistreFromExcel(file, startSheet,endSheet,endCol,endRow,colTypes,col_names,startRow) #get the raw moisture data[[1]] and sheetnames[[2]] + +##Resulting data +dates<-unlist(mData[[2]]) +moistData <-mData[[1]] + +#Calculate water table based on the moisture frome each measurement +samplings = length(dates) +#Use next line to calculate moisture if it is not in the tables +#moistTable1 <- calculateMoist(moistData) +waterLayerTable <- calculateWLayer(moistTable1,dates) + +##Bind all samplings in one table (stack the rows, keep the columns) +wLTable <- Reduce(rbind,waterLayerTable) + +##FORMATTING +#Format the headers (use if needed) +# wLTable$depth <- gsub('0-15', '1w', wLTable$depth) #replace the depth by an ordered index +# wLTable$depth <- gsub('15-30', '2w', wLTable$depth) #replace the depth by an ordered index +# wLTable$depth <- gsub('30-60', '3w', wLTable$depth) #replace the depth by an ordered index +# wLTable$depth <- gsub('60-90', '4w', wLTable$depth) #replace the depth by an ordered index +wLTable$depth <- gsub('0-15', 'depth1', wLTable$depth) #replace the depth by an ordered index +wLTable$depth <- gsub('15-30', 'depth2', wLTable$depth) #replace the depth by an ordered index +wLTable$depth <- gsub('30-60', 'depth3', wLTable$depth) #replace the depth by an ordered index +wLTable$depth <- gsub('60-90', 'depth4', wLTable$depth) #replace the depth by an ordered index + +###########Separate the sheets name into columns (maybe change after the filtering thing?) +#The format should be something like: dr30-11-2015_pre-conv +#sheetname should be in a format dd-mm-yyyy and will have a prefix of 2 char: AR or DR or DT or MV (indicating the kind of sampling: after/before iirgation, etc) +#and a suffix indicating _[pre]/[2R]/[4R]-[1/2/3/4/5]/[pb]/[conv]) +#date column - get what has the format of a date dd-mm-yyyy +wLTable$date <- as.Date(regmatches(wLTable$sheetName, regexpr("[0-9]{2}-[0-9]{2}-[0-9]{4}", wLTable$sheetName)),"%d-%m-%Y") +#sampling type column - what looks like [dr]/[ar]/[mv]/[dt] ... (antes de riego, despu?s, verano, trilla...) +wLTable$irrtype <- regmatches(wLTable$sheetName, regexpr("bi|ai|ah|m1", wLTable$sheetName)) #espa?ol ar|dr|mv|dt|m1" +#irrarea sampled column - the sampling corresponded to which irrigation instance: pre(siembra), 4R(4 riegos) or 2R(2 riegos) +wLTable$irrarea <- gsub('_', '',regmatches(wLTable$sheetName, regexpr("_[0-9A-Za-z]+", wLTable$sheetName))) +#irrigation sampled column - weather its the first, second.. irrigation etc +wLTable$irrnum <- gsub('-', '',regmatches(wLTable$sheetName, regexpr("-[0-9A-Za-z]+$", wLTable$sheetName))) + +#check for NA's in rows +na_inrow <- apply(wLTable, 1, function(x){any(is.na(x))}) # logical vector of the rows with any NA's +#wLTable <- wLTable[complete.cases(wLTable), ] #This iis to remove any row that has a NA on it. +#keep only the rows that dont have NA's and their treatment correspond to the performed sampling +wLTable <- wLTable[!na_inrow, ] + +#Formate the date column to format the axis labels in ggplot +wLTable$date <- as.POSIXct(wLTable$date) + +#write the final table to CSV if you want to save to text file +write.csv(wLTable,csvfile, row.names=FALSE) diff --git a/r/moisture/171020_getMoistureSamplingDataFromExcel_710comparison.R b/r/moisture/171020_getMoistureSamplingDataFromExcel_710comparison.R new file mode 100644 index 0000000..82b9ef8 --- /dev/null +++ b/r/moisture/171020_getMoistureSamplingDataFromExcel_710comparison.R @@ -0,0 +1,115 @@ +#### +# GET MOISTURE SAMPLING FROM THE EXCEL FIle indicated (names of the sheets should be in eg. AR20-11-2015_conv-pre format). used to save the wet and dry weight of the sampling cans +#Calculates the water depth in mm based on the % moisture and using a given formula +#check that the excel sheets's names are formated as: +# DR16-08-2017_all-4 +# DR o AR > is after or before irrigation +# dd-mm-yyyy +# which plots were sampled: all, conv, 2aux, 4aux, pb +# the # of current irrigation, after or before the sampling was performed (or the preplanting irrigation): 1 2 3 4 pre +# +# +# Execute a new instance of R to every new file to be processed, to avoid re-using variables +# +# STORE TO local file +# +# # # # #Check conditions and quality check needed to be implemented +# +# # # # # check Harcoded inputs every time. +# +#### + +#library(xlsx) #preferred to read XLSX? +library("XLConnect") +library(reshape2) + + + +####################################################### FUNCTIONS DEFINITION +source(file.path("C:","Dropbox","Software","Scripts","r","moisture","methods_moisture.R", fsep = .Platform$file.sep)) + +###################################################### EXECUTE SCRIPT +############################### INPUTS +#set WDir +setwd("C://Dropbox//Software//Scripts//r//moisture") + +file="C://Dropbox//Bascula (1)//Muestras suelo//AF//AF-710COMPASS.xlsx" +trial <- "710" +col_names<-c('id', + 'depth', + 'canType', + 'can', + 'wSoilw', #wet soil weight + 'dSoilw', #dry soil weight + 'moisture') +colTypes <- c('numeric', 'character','numeric', 'numeric','numeric','numeric','numeric') +##Check this for every excel read +startRow<- 6 #Skip the fisrt 5 rows because it has other ancillary data +startSheet <- 2 +endSheet <- 10 +endCol <- 7 +endRow <- 66 + +csvfile <- "C://Dropbox//Bascula (1)//Muestras suelo//AF//AF-710COMPASS.csv" + +################################# END INPUTS + +##Edit next +##getMoistreFromExcel <- function (file, startSheet,endCol,endRow,colTypes,col_names,maxsamplepointID) +mData <- getMoistreFromExcel(file, startSheet,endSheet,endCol,endRow,colTypes,col_names,startRow) #get the raw moisture data[[1]] and sheetnames[[2]] + +##Resulting data +dates<-unlist(mData[[2]]) +moistData <-mData[[1]] + +#Calculate water table based on the moisture frome each measurement +samplings = length(dates) + +## Function to get the date in the column +mTable <- moistData +for (s in 1:samplings) { + #Calculate the water layer for each sample + sPoints = length(mTable[[s]]$moisture) #total samples per date + for (i in 1:sPoints) { + mTable[[s]]$sheetName[i] <- as.character(dates[s]) #add date info in column + } +} + +##Bind all samplings in one table (stack the rows, keep the columns) +mTable <- Reduce(rbind,mTable) + +##FORMATTING +#Format the headers (use if needed) +# wLTable$depth <- gsub('0-15', '1w', wLTable$depth) #replace the depth by an ordered index +# wLTable$depth <- gsub('15-30', '2w', wLTable$depth) #replace the depth by an ordered index +# wLTable$depth <- gsub('30-60', '3w', wLTable$depth) #replace the depth by an ordered index +# wLTable$depth <- gsub('60-90', '4w', wLTable$depth) #replace the depth by an ordered index +# wLTable$depth <- gsub('0-15', 'depth1', wLTable$depth) #replace the depth by an ordered index +# wLTable$depth <- gsub('15-30', 'depth2', wLTable$depth) #replace the depth by an ordered index +# wLTable$depth <- gsub('30-60', 'depth3', wLTable$depth) #replace the depth by an ordered index +# wLTable$depth <- gsub('60-90', 'depth4', wLTable$depth) #replace the depth by an ordered index + +###########Separate the sheets name into columns (maybe change after the filtering thing?) +#The format should be something like: dr30-11-2015_pre-conv +#sheetname should be in a format dd-mm-yyyy and will have a prefix of 2 char: AR or DR or DT or MV (indicating the kind of sampling: after/before iirgation, etc) +#and a suffix indicating _[pre]/[2R]/[4R]-[1/2/3/4/5]/[pb]/[conv]) +#date column - get what has the format of a date dd-mm-yyyy +mTable$date <- as.Date(regmatches(mTable$sheetName, regexpr("[0-9]{2}-[0-9]{2}-[0-9]{4}", mTable$sheetName)),"%d-%m-%Y") +#sampling type column - what looks like [dr]/[ar]/[mv]/[dt] ... (antes de riego, despu?s, verano, trilla...) +mTable$irrtype <- regmatches(mTable$sheetName, regexpr("dr", mTable$sheetName)) #espa?ol ar|dr|mv|dt|m1" +#irrarea sampled column - the sampling corresponded to which irrigation instance: pre(siembra), 4R(4 riegos) or 2R(2 riegos) +mTable$irrarea <- gsub('_', '',regmatches(mTable$sheetName, regexpr("_[0-9A-Za-z]+", mTable$sheetName))) +#irrigation sampled column - weather its the first, second.. irrigation etc +mTable$irrnum <- gsub('-', '',regmatches(mTable$sheetName, regexpr("-[0-9A-Za-z]+$", mTable$sheetName))) + +#check for NA's in rows +na_inrow <- apply(mTable, 1, function(x){any(is.na(x))}) # logical vector of the rows with any NA's +#wLTable <- wLTable[complete.cases(wLTable), ] #This iis to remove any row that has a NA on it. +#keep only the rows that dont have NA's and their treatment correspond to the performed sampling +mTable <- mTable[!na_inrow, ] + +#Formate the date column to format the axis labels in ggplot +mTable$date <- as.POSIXct(mTable$date) + +#write the final table to CSV if you want to save to text file +write.csv(mTable,csvfile, row.names=FALSE) diff --git a/r/moisture/171020_getMoistureSamplingDataFromExcel_trial521_graficasHumedad.R b/r/moisture/171020_getMoistureSamplingDataFromExcel_trial521_graficasHumedad.R new file mode 100644 index 0000000..b3c1849 --- /dev/null +++ b/r/moisture/171020_getMoistureSamplingDataFromExcel_trial521_graficasHumedad.R @@ -0,0 +1,219 @@ +#### +# GET MOISTURE SAMPLING FROM THE EXCEL FIle indicated. used to save the wet and dry weight of the sampling cans +#Calculates the water depth in mm based on the % moisture and using a given formula +#This is intended includes summer samplings +#and arrange them to plot the moisture levels, and mm of infiltrated water, across time, by depths +# +# regex to split the columns are not robust and the input format should be the same as AD-521 file +# regex to split the columns are not robust and the input format should be the same as AD-521 file +#check that the excel sheets's names are formated as: +# DR16-08-2017_all-4 +# DR o AR > is after or before irrigation +# dd-mm-yyyy +# which plots were sampled: all, conv, 2aux, 4aux, pb +# the # of current irrigation, after or before the sampling was performed (or the preplanting irrigation): 1 2 3 4 pre +# +#There should be a sheet in the Excel that contains the irrigationdates events standard format (see prev years), +#this should not be included in the sheets that are read to get the data +# +# Execute a new instance of R to every new file to be processed, to avoid re-using variables +# +# STORE TO local file +# +# # # # #Check conditions and quality check needed to be implemented +# +# # # # # check Harcoded inputs every time. +# +# +#### + +#library(xlsx) #preferred to read XLSX? +library("XLConnect") +library(reshape2) +library(ggplot2) +library("dplyr") +library(raster) + + +####################################################### FUNCTIONS DEFINITION +source(file.path("C:","Dropbox","Software","Scripts","r","moisture","methods_moisture.R", fsep = .Platform$file.sep)) + +###################################################### EXECUTE SCRIPT +############################### INPUTS + +# EDIT NOW +setwd("C://Dropbox//data//AF//nut//data") #("C://Dropbox//data//AE//nut//521_humedad") +file="C://Dropbox//Bascula (1)//Muestras suelo//AF//AF-521.xlsx" #//AE//AE-521.xlsx + +#AD521 endSheet <- 19 leaves out the summer samplings +trial <- "AF-521" #"AD521" +year <- "2017-2018" #"2016-2017" + +#This is #Fixed for the standard format. Edit otherwise. +col_names<-c('id','depth','canType','can','wSoilw','dSoilw','moisture') +colTypes <- c('numeric', 'character','numeric', 'numeric','numeric','numeric','numeric') +startRow<- 6 #Skip the fisrt 5 rows because it has other ancillary data +endRow <- 102 #Fixed for the standard format for the trial 521 +endCol <- 7 #Fixed for the standard format +startSheet <- 2 # CHECK THIS EACH TIME. numbar of sheet of the excel file that has sampling data!! ! ! ! ! ! !! !!! !! ! !! !!! !! ! !! ! ! ! ! ! ! +endSheet <- 17 # number of sheets to read starting from the startSheet +#info about the "dates" sheet +datesSheetPosition = 18 +endColdates=4 +endRowdates=24 +startRowdates=13 + +#Info about the graph +#I couldnt iterate the variable because of ggplot, so, set manually: +# REVISE WHEN MAKING A NEW PLOT. This is for labels +variable <- "Moisture" #Or: +#variable <- "Water layer" +varUnit <- "%" #"for the moisture #Or: +#varUnit <- "mm"# for the water layer +#after changing there 2 values you actually only need to re-run the ggplot in lines 147 to 204 +######################## # IMPORTANTE # ############ "#$%#$&%(%&/(/)/()) +# Y HAY QUE EDITAR LA LINEA QUE DICE: +# --->>> ggplot(data=tableSum, aes(x=date, y=wlayer,group=interaction(idSum,depth))) +#PARA SELECCIONAR y=wlayer O y=moisture +######################################################## # IMPORTANTE # ############ "#$%#$&%(%&/(/)/()) + + +csvfile <- sprintf("%s_%s.csv",trial,variable) #for the output table with the summary +################################# END INPUTS + +##getMoistreFromExcel <- function (file, startSheet,endCol,endRow,colTypes,col_names,maxsamplepointID) +mData <- getMoistreFromExcel(file, startSheet,endSheet,endCol,endRow,colTypes,col_names,startRow) #get the raw moisture data[[1]] and sheetnames[[2]] +#Get irrigation dates from the excel from the "fechas" spread sheet +ev <- readWorksheetFromFile(file, sheet = datesSheetPosition,header=TRUE,colTypes=rep("character",3),endCol = endColdates, + endRow=endRowdates,startRow = startRowdates) +ev[,2] <- as.POSIXct(ev[,2]) #Give the format of date to the column + +##Resulting data +sheetNames<-unlist(mData[[2]]) +moistData <-mData[[1]] + +#Calculate water table based on the moisture frome each measurement +#length(sheetNames) +waterLayerTable <- calculateWLayer(moistData,sheetNames) + +##Bind all samplings in one table (stack the rows, keep the columns) +wLTable <- Reduce(rbind,waterLayerTable) + +##FORMATTING +#Format the headers (use if needed) +wLTable$depth <- gsub('0-15', 'depth1', wLTable$depth) #replace the depth by an ordered index +wLTable$depth <- gsub('15-30', 'depth2', wLTable$depth) #replace the depth by an ordered index +wLTable$depth <- gsub('30-60', 'depth3', wLTable$depth) #replace the depth by an ordered index +wLTable$depth <- gsub('60-90', 'depth4', wLTable$depth) #replace the depth by an ordered index + +###########Separate the sheets name into columns (maybe change after the filtering thing?) +#The format should be something like: dr30-11-2015_pre-conv +#sheetname should be in a format dd-mm-yyyy and will have a prefix of 2 char: AR or DR or DT or MV (indicating the kind of sampling: after/before iirgation, etc) +#and a suffix indicating _[pre]/[2R]/[4R]-[1/2/3/4/5]/[pb]/[conv]) +#date column - get what has the format of a date dd-mm-yyyy +wLTable$date <- as.Date(regmatches(wLTable$sheetName, regexpr("[0-9]{2}-[0-9]{2}-[0-9]{4}", wLTable$sheetName)),"%d-%m-%Y") +#sampling type column - what looks like [dr]/[ar]/[mv]/[dt] ... (antes de riego, despu?s, verano, trilla...) +wLTable$irrtype <- regmatches(wLTable$sheetName, regexpr("ar|dr|mv|dt", wLTable$sheetName)) +#irrarea sampled column - the sampling corresponded to which irrigation instance: pre(siembra), 4R(4 riegos) or 2R(2 riegos) +wLTable$irrarea <- gsub('_', '',regmatches(wLTable$sheetName, regexpr("_[0-9A-Za-z]+", wLTable$sheetName))) +#irrigation sampled column - weather its the first, second.. irrigation etc +wLTable$irrnum <- gsub('-', '',regmatches(wLTable$sheetName, regexpr("-[0-9A-Za-z]+$", wLTable$sheetName))) +############# Separate the ID to know the treatments +# Get the number of the repetition from the ID +wLTable$rep <- substr(sapply(wLTable$id,toString), 1,1) +# Get the irrigation scheme from the 2rd digit of the ID, number of irrigation treatment 1 = 2AUX, 2 = 4AUX +wLTable$irr <- substr(sapply(wLTable$id,toString), 2,2) +#use the names of the tillage codes +wLTable$irr[wLTable$irr == 1] <- "2aux" +wLTable$irr[wLTable$irr == 2] <- "4aux" +# Get the type of tillage from the 3rd digit of the ID, Type of tillage 1 = conv, 2 = PB +wLTable$till <- substr(sapply(wLTable$id,toString), 3,3) +#use the names of the tillage codes +wLTable$till[wLTable$till == 1] <- "conv" +wLTable$till[wLTable$till == 2] <- "pb" +# Get the type of residue treatment from the 4rd digit of the ID, 1= 100%, 2=40% +wLTable$res <- substr(sapply(wLTable$id,toString), 4,4) +wLTable$res[wLTable$res == 1] <- "100%" +wLTable$res[wLTable$res == 2] <- "40%" + +# column to summarize the reps, we eliminate the rep ID which is the first char, and also the N level (5th digit) +wLTable$idSum <- substr(sapply(wLTable$id,toString), 2,4) # Use for the graphs, dont summarize on the tables + +#check for NA's in rows +na_inrow <- apply(wLTable, 1, function(x){any(is.na(x))}) # logical vector of the rows with any NA's +#wLTable <- wLTable[complete.cases(wLTable), ] #This iis to remove any row that has a NA on it. +#check if the plot corresponds to the performed sampling +irr_corresponds <- wLTable$irrarea == wLTable$irr | wLTable$irrarea == wLTable$till | wLTable$irrarea =="all" +#keep only the rows that dont have NA's and their treatment correspond to the performed sampling +wLTable <- wLTable[!na_inrow & irr_corresponds, ] #the summer samplings are removed innecesarily + +## Plot +## library(ggplot2) +auxs <-c("2","4") +residues <-c("100","40") +#variables <- setNames(c("moisture","wlayer"),c("Moisture","Water layer"))#create a vector with names to have the variable and ith units +#variablesString <- setNames(c("%","mm"),c("Moisture","Water layer"))# first are the variables, the second are the names +#!# For future development: make a list of all the parameters to make it more manageable. +#ls <- list(auxs,residues,variables,variablesString) + +#Make the graphs of the data making the next combinations + for(res in residues){ + for(aux in auxs){ + wLFiltered <- wLTable[wLTable$irr %in% sprintf("%saux",aux) & wLTable$res %in% sprintf("%s%%",res),] #one way to filter, just keeping the plots/samplings of the 2 auxiliary irrigation scheme + #Summarize the reps 1,2,3 to get just the mean value, of columns wlayer and mositure (put together with the cbind function) + # Use for the graphs, dont summarize the final tables + tableSum <- aggregate(cbind(wlayer, moisture) ~ idSum+depth+date+irr+till+res, data=wLFiltered, FUN=mean) + #Formate the date column to format the axis labels in ggplot + tableSum$date <- as.POSIXct(tableSum$date) + + #plot the wlayer, group by 2 different variables with "interaction" function + ggplot(data=tableSum, aes(x=date, y=moisture,group=interaction(idSum,depth))) + #### EDIT HERE !! wlayer/moisture + geom_line(aes(color=till),size=1)+ #,linetype=till + facet_grid(depth~.,labeller=labeller(depth = labelsDepth))+ #Use this to divide into "panels" + #geom_point(aes(fill=depthCol,shape="a"),colour="black",pch=21, size=2)+ + #scale_fill_manual(values=depthColors3,labels=depthLabel)+ + scale_color_manual(values=tillColors,labels=tillTypeLabel)+ + scale_linetype_manual(values=tillLType,labels=tillTypeLabel)+ + ###Vertical lines to indicate the irrigation, harvest, trilla + #geom_vline(data = ev, aes(xintercept = as.numeric(fecha)),linetype="dotted")+ + #geom_text(data = ev, mapping = aes(label = info, y = 100), angle = 60, hjust = 0)+ + annotate("segment",x = ev[ev$event=="riego","date"],y = 2, xend = ev[ev$event=="riego","date"], + yend = 4, color="#0052a5", size=1)+ #yend = 8 for wlayer, 4 for moisture + annotate("segment",x = ev[ev$event=="siembra","date"],y = 2, xend = ev[ev$event=="siembra","date"], + yend = 4, color="#63a500", size=1)+ + annotate("segment",x = ev[ev$event=="cosecha","date"],y = 2, xend = ev[ev$event=="cosecha","date"], + yend = 4, color="orange", size=1)+ + + ##LABELS + labs(title=sprintf("Trial %s, Soil %s (%s)", trial, variable,year), + subtitle = sprintf("%s auxiliary irrigations treatment, %s%% residue",aux,res), + x = "Soil sampling date", y = sprintf("%s (%s)",variable,varUnit)) + + labs(fill = "Depth (cm)",color="Tillage")+ #format the legend #,linetype="Tillage" + #TEMA DE LA GRAFICA + theme(plot.title = element_text(family = "Arial", color="black", face="bold", size=18))+ + theme(plot.subtitle = element_text(family = "Arial", color="black", size=16))+ + theme(axis.title = element_text(family = "Arial", color="black", size=14))+ + theme(panel.background = element_rect(fill = "white", colour = "black"))+ + theme(panel.border = element_rect(linetype = "solid", fill = NA))+ + theme(panel.grid.major.y = element_line(colour = "#cccccc"))+ + theme(panel.grid.minor.y = element_line(colour = "#cccccc",linetype="dotted"))+ + scale_y_continuous(minor_breaks = seq(0 , 160, 10))+ + theme(panel.grid.major.x = element_blank())+ + scale_x_datetime(date_breaks = "30 day",date_labels= "%d-%b-%Y")+ #poner la escala en cada 15 d?as + theme(axis.text.x = element_text(angle = 25, vjust = 1.0, hjust = 1.0))+ + theme(axis.text = element_text(colour = "black", size=12)) + #save image + #output name + namePlot<- paste(trial,"_",aux,"aux_",res,"perc_",variable,".jpg",sep="") + #print(namePlot) + ggsave(namePlot,width = 7, height = 5,dpi=150,units = "in") + } + } + +#Graphs #TODO +#this would send all the code to the +#iterateGraph(auxs,residues,variables,variablesString, +# makeGraphAndSave,tableSum,tableSum$date,tableSum$idSum,tableSum$depth,trial,year) + +#write the final table to CSV if you want to save to text file +write.csv(wLTable,csvfile, row.names=FALSE) diff --git a/r/moisture/180528_getMoistureSamplingDataFromExcel_trial200.R b/r/moisture/180528_getMoistureSamplingDataFromExcel_trial200.R new file mode 100644 index 0000000..6351a91 --- /dev/null +++ b/r/moisture/180528_getMoistureSamplingDataFromExcel_trial200.R @@ -0,0 +1,166 @@ +#### +# GET MOISTURE SAMPLING FROM THE EXCEL FIle indicated. used to save the wet and dry weight of the sampling cans +#Calculates the water depth in mm based on the % moisture and using a given formula +# +#There should be a sheet in the Excel that contains the irrigationdates events standard format (see prev years), +#this should not be included in the sheets that are read to get the data +# +# Execute a new instance of R to every new file to be processed, to avoid re-using variables +# +# STORE TO local file +# +# # # # #Check conditions and quality check needed to be implemented +# +# # # # # check Harcoded inputs every time. +# +# +#### + +#library(xlsx) #preferred to read XLSX? +library("XLConnect") +library(reshape2) +library(ggplot2) +library("dplyr") +library(raster) + + +####################################################### FUNCTIONS DEFINITION +source(file.path("C:","Dropbox","Software","Scripts","r","moisture","methods_moisture.R", fsep = .Platform$file.sep)) + +###################################################### EXECUTE SCRIPT +############################### INPUTS + +# EDIT NOW +setwd("C://Dropbox//data//AF//nut//data") #("C://Dropbox//data//AE//nut//521_humedad") +file="C://Dropbox//Bascula (1)//Muestras suelo//AF//AF-200.xlsx" #//AE//AE-521.xlsx + +#AD521 endSheet <- 19 leaves out the summer samplings +trial <- "AF-200" #"AD521" +year <- "2017-2018" #"2016-2017" + +#This is #Fixed for the standard format. Edit otherwise. +col_names<-c('id','type','canType','can','wSoilw','dSoilw','moisture') +colTypes <- c('numeric', 'character','numeric', 'numeric','numeric','numeric','numeric') +startRow<-4 #Skip the fisrt 5 rows because it has other ancillary data +endRow <- 34 #Fixed for the standard format for the trial 521 +endCol <- 7 #Fixed for the standard format +startSheet <- 3 # CHECK THIS EACH TIME. numbar of sheet of the excel file that has sampling data!! ! ! ! ! ! !! !!! !! ! !! !!! !! ! !! ! ! ! ! ! ! +endSheet <- 39 # number of sheets to read starting from the startSheet +#info about the "dates" sheet +datesSheetPosition = 40 +endColdates=2 +endRowdates=38 +startRowdates=1 + + +csvfile <- sprintf("%s_%s.csv",trial,variable) #for the output table with the summary + +#Info about the graph +#I couldnt iterate the variable because of ggplot, so, set manually: +# REVISE WHEN MAKING A NEW PLOT. This is for labels +variable <- "Moisture" #Or: +#variable <- "Water layer" +varUnit <- "%" #"for the moisture #Or: +#varUnit <- "mm"# for the water layer +#after changing there 2 values you actually only need to re-run the ggplot in lines 147 to 204 +######################## # IMPORTANTE # ############ "#$%#$&%(%&/(/)/()) +# Y HAY QUE EDITAR LA LINEA QUE DICE: +# --->>> ggplot(data=tableSum, aes(x=date, y=wlayer,group=interaction(idSum,depth))) +#PARA SELECCIONAR y=wlayer O y=moisture +################################# END INPUTS + +##getMoistreFromExcel <- function (file, startSheet,endCol,endRow,colTypes,col_names,maxsamplepointID) +mData <- getMoistreFromExcel(file, startSheet,endSheet,endCol,endRow,colTypes,col_names,startRow) #get the raw moisture data[[1]] and sheetnames[[2]] + +##Resulting data +sheetNames<-unlist(mData[[2]]) #Names from the excel sheets, but they are not standardized for this trial +moistData <-mData[[1]] + +#Get the date from Excel sheet list +dates1 <- readWorksheetFromFile(file, sheet = datesSheetPosition,header=TRUE,colTypes="character",endCol = endColdates, + endRow=endRowdates,startRow = startRowdates) + +##Obtener el nĂşmero de muestreo que le corresponde a cada fecha +s <- getMoistreFromExcel(file, startSheet,endSheet,6,3,"character","sampling",2) +s <- s[[1]] #Get only the data matrix (because the function returns you also the sheetnames) +s <- Reduce(rbind,s)[,6] #merge them all in one table, stacking the rows +s <- regmatches(s, regexpr("[0-9]{1,2}$", s)) #Return only what looks like 1 or 2 digits (this is our sampling number) +#Add the sampling data to the list of samplings +moistData_n <- mapply(cbind, moistData, "sampling"=s, SIMPLIFY=F) +#Add also the date +moistData_s <- mapply(cbind, moistData_n, "s_date"=as.vector(dates1), SIMPLIFY=F) + +#Calculate water table based on the moisture frome each measurement +#the dates1 is used to "count" th eiterate times, but also adds the date of the sampling to the tables +waterLayerTable <- calculateWLayer0_15(moistData_n,dates1[["dates"]]) + +##Bind all samplings in one table (stack the rows, keep the columns) +wLTable <- Reduce(rbind,waterLayerTable) + +############# Separate the ID to know the treatments +# Get the number of the repetition from the ID +wLTable$rep <- substr(sapply(wLTable$id,toString), 1,1) +# Get the irrigation scheme from the 2rd digit of the ID +wLTable$irr <- substr(sapply(wLTable$id,toString), 2,2) +# Get the nitrogen level fertilization from the 3rd digit of the ID +wLTable$nlevel <- substr(sapply(wLTable$id,toString), 3,3) + + +# column to summarize the reps, we eliminate the rep ID which is the first char, and also the N level (5th digit) +wLTable$idNoRep <- substr(sapply(wLTable$id,toString), 1,2) # Use for the graphs, dont summarize on the tables + +##########Generate and Save graph +tableSum <- aggregate(cbind(wlayer, moisture) ~ irr+nlevel+dates, data=wLTable, FUN=mean) +#Formate the date column to format the axis labels in ggplot +tableSum$dates <- as.POSIXct(tableSum$date) + +#plot the wlayer, group by 2 different variables with "interaction" function +ggplot(data=tableSum, aes(x=dates, y=moisture,group=nlevel)) + #### EDIT HERE !! wlayer/moisture + geom_line(aes(color=nlevel),size=1)+ #,linetype=till + facet_grid(irr~.,labeller=labeller(irr = c("1" = "Furrow", "2" = "Drip")))+ #Use this to divide into "panels" + #geom_point(aes(fill=depthCol,shape="a"),colour="black",pch=21, size=2)+ + #scale_fill_manual(values=depthColors3,labels=depthLabel)+ + #scale_color_manual(values=tillColors,labels=tillTypeLabel)+ + #scale_linetype_manual(values=tillLType,labels=tillTypeLabel)+ + ###Vertical lines to indicate the irrigation, harvest, trilla + #geom_vline(data = ev, aes(xintercept = as.numeric(fecha)),linetype="dotted")+ + #geom_text(data = ev, mapping = aes(label = info, y = 100), angle = 60, hjust = 0)+ + #Anotate to mark the irrigations and planting and harvesting + #annotate("segment",x = ev[ev$event=="riego","date"],y = 2, xend = ev[ev$event=="riego","date"], + #yend = 4, color="#0052a5", size=1)+ #yend = 8 for wlayer, 4 for moisture + #annotate("segment",x = ev[ev$event=="siembra","date"],y = 2, xend = ev[ev$event=="siembra","date"], + #yend = 4, color="#63a500", size=1)+ + #annotate("segment",x = ev[ev$event=="cosecha","date"],y = 2, xend = ev[ev$event=="cosecha","date"], + #yend = 4, color="orange", size=1)+ + + ##LABELS + labs(title=sprintf("Trial %s, Soil %s (%s)", trial, variable,year), + x = "Soil sampling date", y = sprintf("%s (%s)",variable,varUnit)) + + labs(color="N level")+ #format the legend #,linetype="Tillage" + # #TEMA DE LA GRAFICA + theme(plot.title = element_text(family = "Arial", color="black", face="bold", size=18))+ + theme(plot.subtitle = element_text(family = "Arial", color="black", size=16))+ + theme(axis.title = element_text(family = "Arial", color="black", size=14))+ + theme(panel.background = element_rect(fill = "white", colour = "black"))+ + theme(panel.border = element_rect(linetype = "solid", fill = NA))+ + theme(panel.grid.major.y = element_line(colour = "#cccccc"))+ + theme(panel.grid.minor.y = element_line(colour = "#cccccc",linetype="dotted"))+ + scale_y_continuous(minor_breaks = seq(0 , 160, 10))+ + theme(panel.grid.major.x = element_blank())+ + scale_x_datetime(date_breaks = "15 day",date_labels= "%d-%b-%Y")+ #poner la escala en cada 15 d?as + theme(axis.text.x = element_text(angle = 25, vjust = 1.0, hjust = 1.0))+ + theme(axis.text = element_text(colour = "black", size=12)) +#save image +#output name +namePlot<- paste(trial,variable,".jpg",sep="") +#print(namePlot) +ggsave(namePlot,width = 7, height = 5,dpi=150,units = "in") +#######End of graph + +#check for NA's in rows +na_inrow <- apply(wLTable, 1, function(x){any(is.na(x))}) # logical vector of the rows with any NA's +#keep only the rows that dont have NA's +wLTable <- wLTable[!na_inrow, ] #the summer samplings are removed innecesarily + +#write the final table to CSV if you want to save to text file +write.csv(wLTable,csvfile, row.names=FALSE) diff --git a/r/moisture/archive/171020_getMoistureSamplingDataFromExcel_trial521.R b/r/moisture/archive/171020_getMoistureSamplingDataFromExcel_trial521.R new file mode 100644 index 0000000..81d0d71 --- /dev/null +++ b/r/moisture/archive/171020_getMoistureSamplingDataFromExcel_trial521.R @@ -0,0 +1,109 @@ +#### +# GET MOISTURE SAMPLING FROM THE EXCEL FIle indicated. used to save the wet and dry weight of the sampling cans +#Calculates the water depth in mm based on the % moisture and using a given formula + +#check that the excel sheets's names are formated as: +# DR16-08-2017_all-4 +# DR o AR > is after or before irrigation +# dd-mm-yyyy +# which plots were sampled: all, conv, 2aux, 4aux, pb +# the # of current irrigation, after or before the sampling was performed (or the preplanting irrigation): 1 2 3 4 pre +# +# Execute a new instance of R to every new file to be processed, to avoid re-using variables +# +# STORE TO local file +# +# # # # #Check conditions and quality check needed to be implemented +# +# # # # # check Harcoded inputs every time. +# +#### + +#library(xlsx) #preferred to read XLSX? +library("XLConnect") +library(reshape2) + +####################################################### FUNCTIONS DEFINITION +source(file.path("C:","Dropbox","Software","Scripts","r","moisture","methods_moisture.R", fsep = .Platform$file.sep)) + + +###################################################### EXECUTE SCRIPT +############################### INPUTS +setwd("C://Dropbox//data//AF//nut//data") + +file="C://Dropbox//Bascula (1)//Muestras suelo//AF//AF-521.xlsx" +trial <- "521" +col_names<-c('id', + 'depth', + 'canType', + 'can', + 'wSoilw', + 'dSoilw', + 'moisture') +colTypes <- c('numeric', 'character','numeric', 'numeric','numeric','numeric','numeric') +##Check this for every excel read +startRow<- 6 #Skip the fisrt 5 rows because it has other ancillary data +startSheet <- 2 +endCol <- 7 +endRow <- 102 +endSheet <- 17 + +csvfile <- "waterLayer_humedad_AE521.csv" +pnrfile <- "waterLayer_humedad_AE521" +################################# END INPUTS + +##Edit next +##getMoistreFromExcel <- function (file, startSheet,endCol,endRow,colTypes,col_names,maxsamplepointID) +mData <- getMoistreFromExcel(file, startSheet,endSheet,endCol,endRow,colTypes,col_names,startRow) #get the raw moisture data[[1]] and sheetnames[[2]] +##Resulting data +dates<-unlist(mData[[2]]) +moistData <-mData[[1]] + +#Calculate water table based on the moisture frome each measurement +samplings = length(dates) +waterLayerTable <- calculateWLayer(moistData,dates) + +##Bind all samplings in one table (stack the rows, keep the columns) +waterLayerTableBind <- Reduce(rbind,waterLayerTable) + +##FORMATTING +#Format the headers +waterLayerTableBind$depth <- gsub('0-15', '1w', waterLayerTableBind$depth) #replace the depth by an ordered index +waterLayerTableBind$depth <- gsub('15-30', '2w', waterLayerTableBind$depth) #replace the depth by an ordered index +waterLayerTableBind$depth <- gsub('30-60', '3w', waterLayerTableBind$depth) #replace the depth by an ordered index +waterLayerTableBind$depth <- gsub('60-90', '4w', waterLayerTableBind$depth) #replace the depth by an ordered index +#date should be in a format dd-mm-yyyy and will have a prefix of 2 char: AR or DR or DT or MV +waterLayerTableBind$date <- substr(waterLayerTableBind$sheetName,3,12) #Keep only date +waterLayerTableBind$date <- gsub('201', '1', waterLayerTableBind$date) #Replace 4 digits of the year by 2 digit +#format the decimal places of the water layer column to 2 digits, to make cells of same length +#Only format the cells that do have a value, not a NA +waterLayerTableBind$wlayer[!is.na(waterLayerTableBind$wlayer)] <- format(round(waterLayerTableBind$wlayer[!is.na(waterLayerTableBind$wlayer)], 2), nsmall = 2) +#replace Na by a point +waterLayerTableBind[is.na(waterLayerTableBind)] <- "." #use a logical conditional + +#convert table to wide format +#Keep the water layer infomation and the IDs +# a "formula object" is used in the form: varInDependent ~ vardependent +#This fails with duplicate or missing rows +wTable_depth_date <- dcast(waterLayerTableBind, id ~ depth + date, value.var = "wlayer") ## ... means "all other variables" + +##FORMATTING +#split the plot wID into its meaning +wTable_depth_date$rep <- substr(wTable_depth_date$id,1,1) #Repetition +wTable_depth_date$irr <- substr(wTable_depth_date$id,2,2) # irrigation +wTable_depth_date$till <- substr(wTable_depth_date$id,3,3) # till +wTable_depth_date$residue <- substr(wTable_depth_date$id,4,4) #residue +wTable_depth_date$nlevel <- substr(wTable_depth_date$id,5,5) # nitrogen level +#And reorder the columns +n= length(colnames(wTable_depth_date)) #Count the columns +wTable_depth_date <- wTable_depth_date[,c((n-4):n,2:(n-5))] #didnt include the first one +#Replace the scores in the headers +colnames(wTable_depth_date) <- gsub('-', '', colnames(wTable_depth_date)) +colnames(wTable_depth_date) <- gsub('_', '', colnames(wTable_depth_date)) + +#write the final table to CSV if you want to save to text file +write.csv(wTable_depth_date,csvfile, row.names=FALSE) +##write the space delimited file +#How to make the columns stay aligned? +write.table(wTable_depth_date,file = paste(pnrfile,".prn",sep=""),sep = " ",row.names=FALSE,col.names=FALSE,quote = FALSE)#data +# how to treaspose?# write.table(colnames(wTable_depth_date),file = paste(pnrfile,"header.prn",sep="_"),sep = " ",row.names=FALSE,col.names=FALSE,quote = FALSE)#header diff --git a/r/moisture/archive/171031_getMoistureSamplingDataFromExcel_trialAE521_graficasHumedad.R b/r/moisture/archive/171031_getMoistureSamplingDataFromExcel_trialAE521_graficasHumedad.R new file mode 100644 index 0000000..a6c42d8 --- /dev/null +++ b/r/moisture/archive/171031_getMoistureSamplingDataFromExcel_trialAE521_graficasHumedad.R @@ -0,0 +1,270 @@ +#### +# GET MOISTURE SAMPLING FROM THE EXCEL FIle indicated. used to save the wet and dry weight of the sampling cans +#Calculates the water depth in mm based on the % moisture and using a given formula +#This is intended to keep inly the sampling corresponding to the irrigations (no summer samplings) +#and arrange them to plot the moisture levels, and mm of infiltrated water, across time +# +# regex to split the columns are not robust and the input format should be the same as AD-521 file +#check that the excel sheets's names are formated as: +# DR16-08-2017_all-4 +# DR o AR > is after or before irrigation +# dd-mm-yyyy +# which plots were sampled: all, conv, 2aux, 4aux, pb +# the # of current irrigation, after or before the sampling was performed (or the preplanting irrigation): 1 2 3 4 pre +# +# +# Execute a new instance of R to every new file to be processed, to avoid re-using variables +# +# STORE TO local file +# +# # # # #Check conditions and quality check needed to be implemented +# +# # # # # check Harcoded inputs every time. +# +#Ediciones para leed el AE521 +#### + +#library(xlsx) #preferred to read XLSX? +library("XLConnect") +library(reshape2) +library(ggplot2) +library("dplyr") + + +####################################################### FUNCTIONS DEFINITION +source(file.path("C:","Dropbox","Software","Scripts","r","moisture","methods_moisture.R", fsep = .Platform$file.sep)) + +###################################################### EXECUTE SCRIPT +############################### INPUTS + +# EDIT NOW +setwd("C://Dropbox//data//AD//AD_nut//521_humedad") +file="C://Dropbox//Bascula (1)//Muestras suelo//AD//AD-521.xlsx" +startSheet <- 5 # CHECK THIS EACH TIME. numbar of sheet of the excel file that has sampling data!! ! ! ! ! ! !! !!! !! ! !! !!! !! ! !! ! ! ! ! ! ! +endSheet <- 23#16AE# number of sheets to read starting from the startSheet #!# WTF not working +trial <- "521" +csvfile <- "humedad_AD521.csv" +pnrfile <- "humedad_AD521" + +#This is #Fixed for the standard format. Edit otherwise. +col_names<-c('id', + 'depth', + 'canType', + 'can', + 'wSoilw', + 'dSoilw', + 'moisture') +colTypes <- c('numeric', 'character','numeric', 'numeric','numeric','numeric','numeric') +startRow<- 6 #Skip the fisrt 5 rows because it has other ancillary data +endCol <- 7 #Fixed for the standard format +endRow <- 103 #Fixed for the standard format for the trial 521 +################################# END INPUTS + +##getMoistreFromExcel <- function (file, startSheet,endCol,endRow,colTypes,col_names,maxsamplepointID) +mData <- getMoistreFromExcel(file, startSheet,endSheet,endCol,endRow,colTypes,col_names,startRow) #get the raw moisture data[[1]] and sheetnames[[2]] + +##Resulting data +sheetNames<-unlist(mData[[2]]) +moistData <-mData[[1]] + +#Calculate water table based on the moisture frome each measurement +#length(sheetNames) +waterLayerTable <- calculateWLayer(moistData,sheetNames) + +##Bind all samplings in one table (stack the rows, keep the columns) +wLTable <- Reduce(rbind,waterLayerTable) + +##FORMATTING +#Format the headers (use if needed) +# wLTable$depth <- gsub('0-15', '1w', wLTable$depth) #replace the depth by an ordered index +# wLTable$depth <- gsub('15-30', '2w', wLTable$depth) #replace the depth by an ordered index +# wLTable$depth <- gsub('30-60', '3w', wLTable$depth) #replace the depth by an ordered index +# wLTable$depth <- gsub('60-90', '4w', wLTable$depth) #replace the depth by an ordered index +wLTable$depth <- gsub('0-15', 'depth1', wLTable$depth) #replace the depth by an ordered index +wLTable$depth <- gsub('15-30', 'depth2', wLTable$depth) #replace the depth by an ordered index +wLTable$depth <- gsub('30-60', 'depth3', wLTable$depth) #replace the depth by an ordered index +wLTable$depth <- gsub('60-90', 'depth4', wLTable$depth) #replace the depth by an ordered index + +###########Separate the sheets name into columns (maybe change after the filtering thing?) +#The format should be something like: dr30-11-2015_pre-conv +#sheetname should be in a format dd-mm-yyyy and will have a prefix of 2 char: AR or DR or DT or MV (indicating the kind of sampling: after/before iirgation, etc) +#and a suffix indicating _[pre]/[2R]/[4R]-[1/2/3/4/5]/[pb]/[conv]) +#date column - get what has the format of a date dd-mm-yyyy +wLTable$date <- as.Date(regmatches(wLTable$sheetName, regexpr("[0-9]{2}-[0-9]{2}-[0-9]{4}", wLTable$sheetName)),"%d-%m-%Y") +#sampling type column - what looks like [dr]/[ar]/[mv]/[dt] ... (antes de riego, despu?s, verano, trilla...) +wLTable$irrtype <- regmatches(wLTable$sheetName, regexpr("ar|dr|mv|dt", wLTable$sheetName)) +#irrarea sampled column - the sampling corresponded to which irrigation instance: pre(siembra), 4R(4 riegos) or 2R(2 riegos) +wLTable$irrarea <- gsub('_', '',regmatches(wLTable$sheetName, regexpr("_[0-9A-Za-z]+", wLTable$sheetName))) +#irrigation sampled column - weather its the first, second.. irrigation etc +wLTable$irrnum <- gsub('-', '',regmatches(wLTable$sheetName, regexpr("-[0-9A-Za-z]+$", wLTable$sheetName))) +############# Separate the ID to know the treatments +# Get the number of the repetition from the ID +wLTable$rep <- substr(sapply(wLTable$id,toString), 1,1) +# Get the irrigation scheme from the 2rd digit of the ID, number of irrigation treatment 1 = 2AUX, 2 = 4AUX +wLTable$irr <- substr(sapply(wLTable$id,toString), 2,2) +#use the names of the tillage codes +wLTable$irr[wLTable$irr == 1] <- "2aux" +wLTable$irr[wLTable$irr == 2] <- "4aux" +# Get the type of tillage from the 3rd digit of the ID, Type of tillage 1 = conv, 2 = PB +wLTable$till <- substr(sapply(wLTable$id,toString), 3,3) +#use the names of the tillage codes +wLTable$till[wLTable$till == 1] <- "conv" +wLTable$till[wLTable$till == 2] <- "pb" +# Get the type of residue treatment from the 4rd digit of the ID, 1= 100%, 2=40% +wLTable$res <- substr(sapply(wLTable$id,toString), 4,4) +wLTable$res[wLTable$res == 1] <- "100%" +wLTable$res[wLTable$res == 2] <- "40%" +# column to summarize the reps, we eliminate the rep ID which is the first char, and also the N level (5th digit) +wLTable$idSum <- substr(sapply(wLTable$id,toString), 2,4) + +#check for NA's in rows +na_inrow <- apply(wLTable, 1, function(x){any(is.na(x))}) # logical vector of the rows with any NA's +#wLTable <- wLTable[complete.cases(wLTable), ] #This iis to remove any row that has a NA on it. +#check if the plot corresponds to the performed sampling +irr_corresponds <- wLTable$irrarea == wLTable$irr | wLTable$irrarea == wLTable$till +#keep only the rows that dont have NA's and their treatment correspond to the performed sampling +wLTable <- wLTable[!na_inrow, ] +##Filter the table as DESIRED +#The following is a harcoded way to generate different filterd tables, +#Fro future development this can be converted to a loop or a swith menu. +#wLFiltered <- wLTable[wLTable$irr %in% c("2aux") & wLTable$till %in% c("pb")& wLTable$res %in% c("40%"),] #one way to filter +#wLFiltered <- wLTable[wLTable$irr %in% c("2aux") & wLTable$till %in% c("conv")& wLTable$res %in% c("40%"),] #one way to filter +wLFiltered <- wLTable[wLTable$irr %in% c("4aux")& wLTable$res %in% c("40%"),] #one way to filter, just keeping the plots/samplings of the 2 auxiliary irrigation scheme + +#Summarize the reps 1,2,3 to get just the mean value, of columns wlayer and mositure (put together with the cbind function) +tableSum <- aggregate(cbind(wlayer, moisture) ~ idSum+depth+date+irr+till+res, data=wLFiltered, FUN=mean) + +#Formate the date column to format the axis labels in ggplot +tableSum$date <- as.POSIXct(tableSum$date) + +### FORMATIING headers ### +#wLTable$date <- substr(wLTable$sheetNames,3,12) #Keep only the date in the column date #!# we actually need the AR/DR +#wLTable$date <- gsub('201', '1', wLTable$sheetNames) #Replace 4 digits of the year by 2 digit +##format the decimal places of the water layer column to 2 digits, to make cells of same length +##Only format the cells that do have a value, not a NA +#wLTable$wlayer[!is.na(wLTable$wlayer)] <- format(round(wLTable$wlayer[!is.na(wLTable$wlayer)], 2), nsmall = 2) +##replace Na by a point +#wLTable[is.na(wLTable)] <- "." #use a logical conditional + + + +#Order the data +#wLFiltered <- wLFiltered[order(wLFiltered$depth),] by depth + + +########convert table to wide format +##Keep the water layer infomation and the IDs +## a "formula object" is used in the form: varInDependent ~ vardependent +##This fails with duplicate or missing rows +####Here the order of the columns will arrange the table as DESIRED to get a useful format table################################### +#wTable_depth_date <- dcast(wLTable, id + depth ~ irr +area + type, value.var = "wlayer") ##arrange rows by id and columns by depth + date +#wTable_depth_date <- dcast(wLFiltered, id ~ irr +area + type +depth, value.var = "wlayer") ## Arrange for the ASCIIs +#wTable_depth_date <- dcast(wLFiltered, id+date+rep ~ depth , value.var = "wlayer") ## Arrange for the plots + +#30251f,#715A4B,#A78772,#E1B79B + +## Plot +## library(ggplot2) + +###Some tricks to format the graph +depthColors <- c(depth1 = "#E1B79B", depth2 = "#A78772", depth3 ="#6d5749", depth4 = "#30251f") +depthColors2 <- c(depth1 = "#8c1a1a", depth2 = "#ed9e36", depth3 ="#008706", depth4 = "#8dc159") +depthColors3 <- c(depth1 = "#ffece0", depth2 = "#ffa474", depth3 ="#db4551", depth4 = "#8b0000") +tillLType <- c(conv = "solid", pb = "longdash") +tillTypeLabel <-c("Conventional","PB") +depthLabel <- c("0-15","15-30","30-60","60-90") +depthPontShape <- c("0-15","15-30","30-60","60-90") +breaks =tableSum$date + +#plot the wlayer, group by 2 different variables with "interaction" function +ggplot(data=tableSum, aes(x=date, y=wlayer,group=interaction(idSum,depth))) + + #geom_line(aes(color="black",linetype=till))+ + geom_line(aes(linetype=till))+ + geom_point(aes(fill=depth,shape="a"),colour="black",pch=21, size=3)+ + scale_fill_manual(values=depthColors3,labels=depthLabel)+ + scale_linetype_manual(values=tillLType,labels=tillTypeLabel)+ + #scale_linetype_manual(values=tillLType,labels=tillTypeLabel)+ #set the linetype according to the till, list defined + # to match the secondary axis, the data is "transformed" and then adapted (divide and *) + ##LABELS + labs(title="Trial AE-521, Soil water content (2016-2017)", + subtitle = "4 auxiliary irrigations treatment, 40% residue", + x = "Soil sampling date", y = "Water content (mm)") + + labs(fill = "Depth (cm)",linetype="Tillage")+ #format the legend + theme(plot.title = element_text(family = "Arial", color="black", face="bold", size=18))+ + theme(plot.subtitle = element_text(family = "Arial", color="black", size=16))+ + theme(axis.title = element_text(family = "Arial", color="black", size=14))+ + #TEMA DE LA GRAFICA + theme(panel.background = element_rect(fill = "white", colour = "black"))+ + theme(panel.border = element_rect(linetype = "solid", fill = NA))+ + theme(panel.grid.major.y = element_line(colour = "#cccccc"))+ + theme(panel.grid.minor.y = element_line(colour = "#cccccc",linetype="dotted"))+ + scale_y_continuous(minor_breaks = seq(0 , 160, 10))+ + theme(panel.grid.major.x = element_blank())+ + scale_x_datetime(date_breaks = "15 day",date_labels= "%d-%b-%Y")+ #poner la escala en cada 15 d?as + theme(axis.text.x = element_text(angle = 25, vjust = 1.0, hjust = 1.0))+ + theme(axis.text = element_text(colour = "black", size=12)) + +#plot the moisture, group by 2 different variables with "interaction" function +ggplot(data=tableSum, aes(x=date, y=moisture,group=interaction(idSum,depth))) + + geom_line(aes(linetype=till))+ #optional color=depth,,size=0.8 + geom_point(aes(fill=depth,shape="a"),colour="black",pch=21, size=3)+ + scale_fill_manual(values=depthColors3,labels=depthLabel)+ + scale_color_manual(values=depthColors3,labels=depthLabel)+ + scale_linetype_manual(values=tillLType,labels=tillTypeLabel)+ #set the linetype according to the till, list defined + # to match the secondary axis, the data is "transformed" and then adapted (divide and *) + ##LABELS + labs(title="Trial AE-521, Soil moisture content (2016-2017)", + subtitle = "4 auxiliary irrigations treatment, 40% residue", + x = "Soil sampling date", y = "moisture (%)") + + labs(fill = "Depth (cm)",linetype="Tillage")+ #optional ,color = "Depth (cm) + theme(plot.title = element_text(family = "Arial", color="black", face="bold", size=18))+ + theme(plot.subtitle = element_text(family = "Arial", color="black", size=16))+ + theme(axis.title = element_text(family = "Arial", color="black", size=14))+ + #TEMA DE LA GRAFICA + theme(panel.background = element_rect(fill = "white", colour = "black"))+ + theme(panel.border = element_rect(linetype = "solid", fill = NA))+ + theme(panel.grid.major.y = element_line(colour = "#cccccc"))+ + theme(panel.grid.minor.y = element_line(colour = "#cccccc",linetype="dotted"))+ + scale_y_continuous(minor_breaks = seq(0 , 160, 2))+ + theme(panel.grid.major.x = element_blank())+ + scale_x_datetime(date_breaks = "15 day",date_labels= "%d-%b-%Y")+ + #limits = as.Date(c('28-11-2015','12-03-2016')))+ #poner la escala en cada 15 d?as + theme(axis.text.x = element_text(angle = 25, vjust = 1.0, hjust = 1.0))+ + theme(axis.text = element_text(colour = "black", size=12)) +#save image +namePlot<- "AE521_4aux_40perc_mmWater_PByConv.jpeg" +namePlot<- "AE521_4aux_40perc_moisture_PByConv.jpeg" +ggsave(namePlot) + + + ###Example +# ggplot(data=tableSum, aes(x=date, y=wlayer,group=interaction(id, depth))) + +# geom_line(aes(color=depth))+ +# geom_point(aes(color=depth))+ +# labs(title = "Soil water content", x = "Date", y = "water layer (mm)")+ +# geom_col(aes(x=date, y=moisture,group=interaction(id, depth)))+ +# scale_y_continuous(sec.axis = sec_axis(~.*2, name = "Moisture (%)")) + + +##FORMATTING +##split the plot wID into its meaning +#wTable_depth_date$rep <- substr(wTable_depth_date$id,1,1) #Repetition +#wTable_depth_date$irr <- substr(wTable_depth_date$id,2,2) # irrigation +#wTable_depth_date$till <- substr(wTable_depth_date$id,3,3) # till +#wTable_depth_date$residue <- substr(wTable_depth_date$id,4,4) #residue +#wTable_depth_date$nlevel <- substr(wTable_depth_date$id,5,5) # nitrogen level + +#And reorder the columns to put the id columns at the begining +#n= length(colnames(wTable_depth_date)) #Count the columns +#wTable_depth_date <- wTable_depth_date[,c((n-4):n,2:(n-5))] #didnt include the first one to omit the full plot name +#FORMATTING: Replace the scores in the headers +#colnames(wTable_depth_date) <- gsub('-', '', colnames(wTable_depth_date)) +#colnames(wTable_depth_date) <- gsub('_', '', colnames(wTable_depth_date)) + +#write the final table to CSV if you want to save to text file +write.csv(wLTable,csvfile, row.names=FALSE) +##write the space delimited file +#How to make the columns stay aligned? +write.table(wTable_depth_date,file = paste(pnrfile,".prn",sep=""),sep = " ",row.names=FALSE,col.names=FALSE,quote = FALSE)#data +# how to treaspose?# write.table(colnames(wTable_depth_date),file = paste(pnrfile,"header.prn",sep="_"),sep = " ",row.names=FALSE,col.names=FALSE,quote = FALSE)#header + diff --git a/r/moisture/methods_formating.R b/r/moisture/methods_formating.R new file mode 100644 index 0000000..77de8b9 --- /dev/null +++ b/r/moisture/methods_formating.R @@ -0,0 +1,9 @@ +###Some tricks to format the graph from the 521 moisture graphs +depthColors <- c(depth1 = "#E1B79B", depth2 = "#A78772", depth3 ="#6d5749", depth4 = "#30251f") +depthColors2 <- c(depth1 = "#8c1a1a", depth2 = "#ed9e36", depth3 ="#008706", depth4 = "#8dc159") +depthColors3 <- c(depth1 = "#ffece0", depth2 = "#ffa474", depth3 ="#db4551", depth4 = "#8b0000") +tillLType <- c(conv = "solid", pb = "longdash") +tillTypeLabel <-c("Conventional","PB") +depthLabel <- c("0-15","15-30","30-60","60-90") +depthPontShape <- c("0-15","15-30","30-60","60-90") +breaks =tableSum$date \ No newline at end of file diff --git a/r/moisture/methods_moisture.R b/r/moisture/methods_moisture.R new file mode 100644 index 0000000..6fa8231 --- /dev/null +++ b/r/moisture/methods_moisture.R @@ -0,0 +1,198 @@ +#Methods for the moisture sampling calculations +#last edited: 27/10/2017 + +#Library +#library(xlsx) #preferred to read XLSX? +library("XLConnect") +library(reshape2) +library(ggplot2) +library("dplyr") + +####################################################### FUNCTIONS DEFINITION +#########Water table calculation +#0-15 cm depth with current sampling settings +#x is the moisture soil percent al 0-100 scale +getwLayer <- function(mperc, depth) { + switch(depth, + "0-15" = round((mperc/100)*1.21*15*10, digits = 3), + "15-30" = round((mperc/100)*1.25*15*10, digits = 3), + "30-60" = round((mperc/100)*1.44*30*10, digits = 3), + "60-90" = round((mperc/100)*1.49*30*10, digits = 3)) +} + +## Function to calculate the Wlayer +calculateWLayer <- function(df.list,sheets) { + for (s in 1:length(sheets)) { + #Calculate the water layer for each sample + sPoints = length(df.list[[s]]$moisture) #total samples per date + for (i in 1:sPoints) { + wl <- getwLayer(df.list[[s]]$moisture[i], df.list[[s]]$depth[i]) + #print result + #print(paste0("Date ",sheets[s],", plot ",df.list[[s]]$id[i],": wLayer:",wl)) + df.list[[s]]$wlayer[i] <- wl #add water layer info in column + df.list[[s]]$sheetName[i] <- as.character(sheets[s]) #add date info in column + } + } + return (df.list) #data with wLayer info +} + +## Function to calculate the Wlayer only at 0-15 depth +calculateWLayer0_15 <- function(df.list,dates1) { + for (s in 1:length(df.list)) { + #Calculate the water layer for each sample + sPoints = length(df.list[[s]]$moisture) #total samples per date + for (i in 1:sPoints) { + wl <- getwLayer(df.list[[s]]$moisture[i], "0-15") + #print result + #print(paste0("Date ",sheets[s],", plot ",df.list[[s]]$id[i],": wLayer:",wl)) + df.list[[s]]$wlayer[i] <- wl #add water layer info in column + df.list[[s]]$dates[i] <- as.character(dates1[s]) #add date info in column + } + } + return (df.list) #data with wLayer info +} + + +#########Read Excel sheets +#The sheet names should be in the format "AR20-11-2015_conv-pre" +# after or before irrigation, after harvest or summer sampling: AR | DR | DT | MV +# date dd-mm-yyyy +# what trial is measured: conv | pv | 4AUX | 2AUX | all +# number of irrigation: pre | 1 | 2 | 3 | 4 +getMoistreFromExcel <- function (file, startSheet,endSheet,endCol,endRow,colTypes,col_names,startRow){ + xlxsF =file + wb <- loadWorkbook(xlxsF) + sheets <- getSheets(wb) + sheets <- lapply(sheets,function(x) tolower(x)) + setMissingValue(wb, value = "NA") + sheetsIndex <- startSheet:endSheet#Avoid the first 3 sheets of this file because they dont have data + df.list <- readWorksheetFromFile(xlxsF, #Get the data of th xlsx file + sheet = sheetsIndex, + header=TRUE, + colTypes = colTypes, + endCol = endCol, + endRow=endRow, + startRow = startRow) + #Next just rename the columns WITH the given input names + for (i in 1:length(df.list)){ + colnames(df.list[[i]])<-col_names + } + + sheets <- sheets[sheetsIndex] #Remove the sheet names that were not used + + ##Calculate water table according to the moisture + #For every sampling date + #df.list <- calculateWLayer(df.list,sheets) + + return (list(df.list,sheets)) +} + +###Some settings to format the graphs +depthColors <- c(depth1 = "#E1B79B", depth2 = "#A78772", depth3 ="#6d5749", depth4 = "#30251f") +depthColors2 <- c(depth1 = "#8c1a1a", depth2 = "#ed9e36", depth3 ="#008706", depth4 = "#8dc159") +depthColors3 <- c(depth1 = "#ffece0", depth2 = "#ffa474", depth3 ="#db4551", depth4 = "#8b0000") +tillLType <- c(conv = "solid", pb = "longdash") +tillColors <- c(conv = "black", pb = "brown") +tillTypeLabel <-c("Conventional","PB") +depthLabel <- c("0-15","15-30","30-60","60-90") +depthPontShape <- c("0-15","15-30","30-60","60-90") +labelsDepth <- c(depth1 = "0-15", depth2 = "15-30", depth3 ="30-60", depth4 = "60-90") +labelsN200 <- c("1" = "0 N", "2" = "150 N", "3" ="300 N", "4" = "150 N", "5" = "300 N") +#sp + facet_grid(. ~ sex, labeller=labeller(sex = labels)) + +library(ggplot2) +#Make moisture Graph And Save JPG to working directoy +#Graphs the date vs a variable of the moisture sampling (eg. moisture or water layer mm) +#INPUTS: +# df dataframe +# varCol data of variable to plot on the Y axis +# dateCol data of variable to plot on the X axis +# idCol column that has the identifier of the data (data or name?) +# depthCol column that has the depth of each sample (data or name?) +# aux String of the auxiliary irrigation scheme of the data (for labels) +# res String of the residue percent treatment of the data (for labels) +# trial String to which trial the data corresponds +# variable String of the name of the variable in the Y axis (varCol) +# year String of the year cycle +# varUnit Sting indicating the units of the variable +makeGraphAndSave <- function(df,varCol,dateCol,idCol,depthCol,aux,res,trial,variable,year,varUnit){ + #plot the wlayer, group by 2 different variables with "interaction" function + ggplot(data=df, aes(x=dateCol, y=varCol,group=interaction(idCol,depthCol))) + + #geom_line(aes(color="black",linetype=till))+ + geom_line(aes(linetype=till))+ + geom_point(aes(fill=depthCol,shape="a"),colour="black",pch=21, size=2)+ + scale_fill_manual(values=depthColors3,labels=depthLabel)+ + scale_linetype_manual(values=tillLType,labels=tillTypeLabel)+ + #scale_linetype_manual(values=tillLType,labels=tillTypeLabel)+ #set the linetype according to the till, list defined + # to match the secondary axis, the data is "transformed" and then adapted (divide and *) + ##LABELS + labs(title=sprintf("Trial %s, Soil %s (%s)", trial, variable,year), + subtitle = sprintf("%s auxiliary irrigations treatment, %s%% residue",aux,res), + x = "Soil sampling date", y = sprintf("%s (%s)",variable,varUnit)) + + labs(fill = "Depth (cm)",linetype="Tillage")+ #format the legend + theme(plot.title = element_text(family = "Arial", color="black", face="bold", size=18))+ + theme(plot.subtitle = element_text(family = "Arial", color="black", size=16))+ + theme(axis.title = element_text(family = "Arial", color="black", size=14))+ + #TEMA DE LA GRAFICA + theme(panel.background = element_rect(fill = "white", colour = "black"))+ + theme(panel.border = element_rect(linetype = "solid", fill = NA))+ + theme(panel.grid.major.y = element_line(colour = "#cccccc"))+ + theme(panel.grid.minor.y = element_line(colour = "#cccccc",linetype="dotted"))+ + scale_y_continuous(minor_breaks = seq(0 , 160, 5))+ + theme(panel.grid.major.x = element_blank())+ + scale_x_datetime(date_breaks = "30 day",date_labels= "%d-%b-%Y")+ #poner la escala en cada 15 d?as + theme(axis.text.x = element_text(angle = 25, vjust = 1.0, hjust = 1.0))+ + theme(axis.text = element_text(colour = "black", size=12)) + + #save image + #output name + namePlot<- paste(trial,"_",aux,"aux_",res,"perc_",variable,".jpg",sep="") + #print(namePlot) + ggsave(namePlot,width = 7, height = 5,dpi=150,units = "in") +} + +#######Iterate graph, to use with the "graph Function" makeGraphAndSave +#This can be further improved +#The first are to know how to make the loop +# auxs string vector of auxiliary irrigations (just the number) +# residues string vector of residues treatments (just the number) +# variables named string vector of the variables to be plotted on the Y axis And units to use +# variablesString named string vector of the variables as "human readable version" +#Examples: +# auxs <-c("2","4") +# residues <-c("100","40") +# variables <- setNames(c("moisture","wlayer"),c("Moisture","Water layer"))#create a vector with names to have the variable (column name in the df) and its units +# variablesString <- setNames(c("%","mm"),c("Moisture","Water layer")) +#######These are just to feed the graph method: +# (df,varCol,dateCol,idCol,depthCol,aux,res,trial,variable,year,varUnit) +#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!needs improvement +iterateGraph <- function(auxs,residues,variables,variablesString, #paramenters to iterate + graphFunc,df,dateCol,idCol,depthCol,trial,year){ #parameters to graph (some are already implicit in the iterate parameters) + #Make the graphs of the data making the next combinations + for(var in names(variablesString)){ + #indicate the units of the variable + varUnit <-variablesString[var] #get the units from the names vector + for(res in residues){ + for(aux in auxs){ + ##Filter the table as DESIRED + #The following is a harcoded way to generate different filterd tables, + #Fro future development this can be converted to a loop or a swith menu. + wLFiltered <- wLTable[wLTable$irr %in% sprintf("%saux",aux) & wLTable$res %in% sprintf("%s%%",res),] #one way to filter, just keeping the plots/samplings of the 2 auxiliary irrigation scheme + #Summarize the reps 1,2,3 to get just the mean value, of columns wlayer and mositure (put together with the cbind function) + # Use for the graphs, dont summarize the final tables + tableSum <- aggregate(cbind(wlayer, moisture) ~ idSum+depth+date+irr+till+res, data=wLFiltered, FUN=mean) + #Formate the date column to format the axis labels in ggplot + tableSum$date <- as.POSIXct(tableSum$date) + + #Select variable name + variableCol <-variables[var] + + #Graph the data + graphFunc(df,df[variableCol],dateCol,idCol,depthCol,aux,res,trial,var,year,varUnit) + #Next just to check the loop + #print(sprintf("Variable is %s in %s, %s%% residue and %saux - varCol %s",var,varUnit,res,aux,variableCol)) + #print(paste(aux,res,trial,var,year,varUnit)) + } + } + } +} diff --git a/r/ongoning.R b/r/ongoning.R new file mode 100644 index 0000000..d91d478 --- /dev/null +++ b/r/ongoning.R @@ -0,0 +1,24 @@ + +### GREENSEEKSER(GS) ### +#This method will generate how the data will be ordered based on a matrix size and direction. +#way the sampling was done +#Skips the unwanted readings #: no_go +#Serpentine (serp): Indicate starting corner and it will be generated acordingly. It always runs in the North -south direction: + #Possible values: + #sw,se,no,ne +##################################################################################ONGOING +make_serpentine <- function(rows,cols,serp,no_go){ + #Number of total samples + n <- 1:(rows*cols) + length(no_go) + ord <- integer(0) + #complete the order of the sampling + for (cl in 1:cols){ + #cREATE the sequence based on the first and last readings in the bed + ordColumn <- ordi[1,cl]:ordi[2,cl] + print(ordColumn) + #Remove numbers that correspond to the unwanted readings + ordColumn[which(!(ordColumn %in% no_go))] + ord <- cbind(ord,ordColumn) + } + return(ord) +} \ No newline at end of file diff --git a/r/outliers_multiple_files_dbf_files.R b/r/outliers_multiple_files_dbf_files.R new file mode 100644 index 0000000..6af2978 --- /dev/null +++ b/r/outliers_multiple_files_dbf_files.R @@ -0,0 +1,19 @@ +library(RODBC) +library(xlsx) +library(foreign) +################# EDIT######################### +setwd("C:/Dropbox/data/AD/AD_nut/data/extracted1.5mbuffervsGS") +path = "C:/Dropbox/data/AD/AD_nut/data/extracted1.5mbuffervsGS" +path +dir() +file.names <- dir(path, pattern =".dbf") +for(i in 1:length(file.names)){ + dat<- read.dbf(file.names[i]) + dat<- as.data.frame(dat) + a<-unname(quantile(dat$id, c(0.975))) ##ID field + b<-unname(quantile(dat$id, c(0.025))) + dat1<-subset(dat,dat$id<= a & dat$id >= b) + archivo <- paste("C:/Dropbox/data/AD/AD_nut/data/extracted1.5mbuffervsGS/no_outliers/",file.names[i],sep = "") + write.dbf(dat1,file=archivo) + print(i) +} \ No newline at end of file diff --git a/r/readExcel.R b/r/readExcel.R new file mode 100644 index 0000000..5398873 --- /dev/null +++ b/r/readExcel.R @@ -0,0 +1,13 @@ +#General file to read exel with data from #Bascula + +source(file.path("C:","Dropbox","Software","Scripts","r","io_methods.R", fsep = .Platform$file.sep)) + +#Set the WD +setwd(choose.dir(default = file.path("C:","Dropbox", fsep = .Platform$file.sep), caption = "Select folder")) + +#Choose the file interactively +file_options <-matrix(c("Excel","Excel","*.xls","*.xlsx"), nrow = 2,ncol = ) +f <- choose.files(filters = file_options) + +wb <- loadWorkbook(f) +alfo <- readWorksheetFromFile(wb) diff --git a/r/server.R b/r/server.R new file mode 100644 index 0000000..d3e3c4a --- /dev/null +++ b/r/server.R @@ -0,0 +1,101 @@ +shinyServer(function(input, output) { + + ### Argument names: + ArgNames <- reactive({ + Names <- names(formals(input$readFunction)[-1]) + Names <- Names[Names!="..."] + return(Names) + }) + + # Argument selector: + output$ArgSelect <- renderUI({ + if (length(ArgNames())==0) return(NULL) + + selectInput("arg","Argument:",ArgNames()) + }) + + ## Arg text field: + output$ArgText <- renderUI({ + fun__arg <- paste0(input$readFunction,"__",input$arg) + + if (is.null(input$arg)) return(NULL) + + Defaults <- formals(input$readFunction) + + if (is.null(input[[fun__arg]])) + { + textInput(fun__arg, label = "Enter value:", value = deparse(Defaults[[input$arg]])) + } else { + textInput(fun__arg, label = "Enter value:", value = input[[fun__arg]]) + } + }) + + + ### Data import: + Dataset <- reactive({ + if (is.null(input$file)) { + # User has not uploaded a file yet + return(data.frame()) + } + + args <- grep(paste0("^",input$readFunction,"__"), names(input), value = TRUE) + + argList <- list() + for (i in seq_along(args)) + { + argList[[i]] <- eval(parse(text=input[[args[i]]])) + } + names(argList) <- gsub(paste0("^",input$readFunction,"__"),"",args) + + argList <- argList[names(argList) %in% ArgNames()] + + Dataset <- as.data.frame(do.call(input$readFunction,c(list(input$file$datapath),argList))) + return(Dataset) + }) + + # Select variables: + output$varselect <- renderUI({ + + if (identical(Dataset(), '') || identical(Dataset(),data.frame())) return(NULL) + + # Variable selection: + selectInput("vars", "Variables to use:", + names(Dataset()), names(Dataset()), multiple =TRUE) + }) + + # Show table: + output$table <- renderTable({ + + if (is.null(input$vars) || length(input$vars)==0) return(NULL) + + return(Dataset()[,input$vars,drop=FALSE]) + }) + + + ### Download dump: + + output$downloadDump <- downloadHandler( + filename = "Rdata.R", + content = function(con) { + + assign(input$name, Dataset()[,input$vars,drop=FALSE]) + + dump(input$name, con) + } + ) + + ### Download save: + + output$downloadSave <- downloadHandler( + filename = "Rdata.RData", + content = function(con) { + + assign(input$name, Dataset()[,input$vars,drop=FALSE]) + + save(list=input$name, file=con) + } + ) + +}) + + diff --git a/r/shiny_exampleApp/data/counties.rds b/r/shiny_exampleApp/data/counties.rds new file mode 100644 index 0000000000000000000000000000000000000000..8ac7abe3181fb9225858f105af93f6185b45466b GIT binary patch literal 60810 zcmV)PK()UgiwFP!000001B|^1oLp6vKi);y$F{7f6yN)Oz1`tD710f_NECH0xLLd+zB!Fz{|Gsy= z-*fLhuj=QY50%&N-0j?R&pG$pbI-l+iv7E~y7uTA*OlwqW1RbQ+*J3^-tO1k)ir+q 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generate vector of fill colors for map + shades <- colorRampPalette(c("white", color))(100) + + # constrain gradient to percents that occur between min and max + var <- pmax(var, min) + var <- pmin(var, max) + percents <- as.integer(cut(var, 100, + include.lowest = TRUE, ordered = TRUE)) + fills <- shades[percents] + + # plot choropleth map + map("county", fill = TRUE, col = fills, + resolution = 0, lty = 0, projection = "polyconic", + myborder = 0, mar = c(0,0,0,0)) + + # overlay state borders + map("state", col = "white", fill = FALSE, add = TRUE, + lty = 1, lwd = 1, projection = "polyconic", + myborder = 0, mar = c(0,0,0,0)) + + # add a legend + inc <- (max - min) / 4 + legend.text <- c(paste0(min, " % or less"), + paste0(min + inc, " %"), + paste0(min + 2 * inc, " %"), + paste0(min + 3 * inc, " %"), + paste0(max, " % or more")) + + legend("bottomleft", + legend = legend.text, + fill = shades[c(1, 25, 50, 75, 100)], + title = legend.title) +} diff --git a/r/shiny_exampleApp/rsconnect/shinyapps.io/nutrs/shiny_exampleapp.dcf b/r/shiny_exampleApp/rsconnect/shinyapps.io/nutrs/shiny_exampleapp.dcf new file mode 100644 index 0000000..70762ef --- /dev/null +++ b/r/shiny_exampleApp/rsconnect/shinyapps.io/nutrs/shiny_exampleapp.dcf @@ -0,0 +1,8 @@ +name: shiny_exampleapp +title: +account: nutrs +server: shinyapps.io +appId: 201629 +bundleId: 925472 +url: https://nutrs.shinyapps.io/shiny_exampleapp/ +when: 1501711471.56152 diff --git a/r/shiny_exampleApp/server.R b/r/shiny_exampleApp/server.R new file mode 100644 index 0000000..eaf9d8a --- /dev/null +++ b/r/shiny_exampleApp/server.R @@ -0,0 +1,31 @@ +library(shiny) +library(maps) +library(mapproj) +source("helpers.R") +counties <- readRDS("data/counties.rds") + +shinyServer(function(input, output) { + + output$text1 <- renderText({ + paste("You have selected", input$select) + }) + + output$text2 <- renderText({ + paste("You have selected", input$slider2[1],"and", input$slider2[2]) + }) + + output$map <- renderPlot({ + args <- switch(input$var, + "Percent White" = list(counties$white, "darkgreen", "% White"), + "Percent Black" = list(counties$black, "black", "% Black"), + "Percent Hispanic" = list(counties$hispanic, "darkorange", "% Hispanic"), + "Percent Asian" = list(counties$asian, "darkviolet", "% Asian")) + + args$min <- input$range[1] + args$max <- input$range[2] + + do.call(percent_map, args) + }) + + } +) \ No newline at end of file diff --git a/r/shiny_exampleApp/ui.R b/r/shiny_exampleApp/ui.R new file mode 100644 index 0000000..0632188 --- /dev/null +++ b/r/shiny_exampleApp/ui.R @@ -0,0 +1,53 @@ +library(shiny) + + +shinyUI(fluidPage( + titlePanel("Calculate plant height"), + sidebarLayout( + sidebarPanel( + h3("Involved images"), + + fluidRow( + column(3, + h3("Help text"), + helpText("Tests: ")), + column(3, + fileInput("file", label = h3("File input"))), + column(3, + numericInput("num", + label = h3("Numeric input"), + value = 1)) + + ), + + fluidRow( + helpText("Create demographic maps with + information from the 2010 US Census."), + + selectInput("var", + label = "Choose a variable to display", + choices = c("Percent White", "Percent Black", + "Percent Hispanic", "Percent Asian"), + selected = "Percent White"), + + sliderInput("range", + label = "Range of interest:", + min = 0, max = 100, value = c(0, 100)) + ) + + ), + mainPanel( + textOutput("text1"), + textOutput("text2"), + #plotOutput(), + #tableOutput(), + + p("Got to the CIMMYT ", + a("web page", + href = "http://www.cimmyt.org/es/")), + img(src="circles in a circle Kandinsky.jpg", height = 400, width = 400), + + plotOutput("map") + ) + ) +)) \ No newline at end of file diff --git a/r/shiny_exampleApp/www/circles in a circle Kandinsky.jpg b/r/shiny_exampleApp/www/circles in a circle Kandinsky.jpg new file mode 100644 index 0000000000000000000000000000000000000000..e9b6c4aa148fa5052fa6c6b50931f7dd0af95446 GIT binary patch literal 2887 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a/r/shiny_extractApp/data/AE_vuelos.csv b/r/shiny_extractApp/data/AE_vuelos.csv new file mode 100644 index 0000000..dce1746 --- /dev/null +++ b/r/shiny_extractApp/data/AE_vuelos.csv @@ -0,0 +1,211 @@ +Date,Date_format,Rol,Area,Sensor,Downloaded,Backup stage,Notes,plan,stage,shared_Mrgt/involved,Julia,GCP,GS_grande,L1,temp_download,check,extract?,,nu7 +161026,26/10/2016,fis,710,RGB,,auto (DS),mosaico Fis necesita ajuste por desfase. Hap ya Ajustado,,dsm & mosaic,1,,1,,,,q161026710,,, +161128,28/11/2016,bw_,oct,miniMCA,1,auto (DS),"microtops 5 h vuelo quedan, light clouds",ae_bw_oct_M,1,,,,,,,q161128oct,,, +161215,15/12/2016,fis,710,RGB,,dropbox/on the laptop,ajusté desface en el mosaico que está en Dropbox. La copia local en PC ajusté directo a la imagen enero 27. Sumé 0.35m porque salía debajo del DTM,,dsm & mosaic,1,,1,,,1,q161215710,,, +170107,07/01/2017,fis,hap,sequoia,1,,,,reflectance & NDVI,,,1,,,,q170107hap,,, +170107,07/01/2017,fis,hap,RGB,1,auto (DS),,,dsm & mosaic,1,,1,,,1,q170107hap,,, +170104,04/01/2017,nut,fx1,no,,,,,,,,,1,,,q170104fx1,,, +170109,09/01/2017,nut,fx2,sequoia,1,auto (DS),,ae_nut_jf2_Q,ndvi,,,1,1,,,q170109fx2,,, +170109,09/01/2017,nut,po5,sequoia,1,dropbox/AE,,ae_nut_po5_Q,ndvi,,,1,1,,,q170109po5,,, +170110,10/01/2017,bw_,810,FLIR,1,,,ae_bw_810_T,pix?,1,,,,,,q170110810,,, +170110,10/01/2017,frz,esc,FLIR,1,,,ae_frz_esc_T,celsius mosaic,1,,1,,,,q170110esc,1,, +170110,10/01/2017,frz,ros,FLIR,1,,,,celsius mosaic,,,1,,,,q170110ros,1,, +170110,10/01/2017,frz,ser,FLIR,1,,,,celsius mosaic,,,1,,,,q170110ser,1,, +170110,10/01/2017,bw_,810,HYPEr,1,auto (DS),irradiance from microtops,ae_bw_810_H,rfl_spr_ort,,,,,SOME DRIp PLOTS NOT COVERED,,q170110810,,, +170110,10/01/2017,frz,esc,HYPEr,1,auto (DS),irradiance from microtops,ae_frz_esc_H,rfl_spr_ort_geo,,,1,,,,q170110esc,1,, +170110,10/01/2017,frz,ros,HYPEr,1,auto (DS),irradiance from microtops,,rfl_spr_ort_geo,,,1,,,,q170110ros,1,, +170110,10/01/2017,frz,ser,HYPEr,1,auto (DS),irradiance from microtops,,rfl_spr_ort_geo,,,1,,,,q170110ser,1,, +170110,10/01/2017,nut,poc,miniMCA,1,auto (DS),,ae_nut_poc_M,ndvi,,,1,,,,m170110poc,,, +170110,10/01/2017,nut,fx2,miniMCA,1,auto (DS),,ae_nut_jf2_M,ndvi,,,1,,,,m170110fx2,,, +170110,10/01/2017,nut,po5,miniMCA,1,auto (DS),,ae_nut_po5_M,ndvi,,,1,,,,m170110po5,,, +170110,10/01/2017,nut,po4,miniMCA,1,auto (DS),,ae_nut_po4_M,ndvi,,,1,,,,m170110po4,,, +170110,10/01/2017,nut,poc,sequoia,1,dropbox/AE,,ae_nut_poc_Q,ndvi,,,1,1,,,q170110poc,,, +170112,12/01/2017,nut,val,sequoia,1,on the laptop,on the laptop & Dropbox AE,ae_nut_val_Q,ndvi,,,1,0,,,q170112val,,, +170112,12/01/2017,nut,tyc,sequoia,1,on the laptop,on the laptop & Dropbox AE,ae_nut_tyc_Q,ndvi,,,1,0,,,q170112tyc,,, +170113,13/01/2017,nut,ae1,sequoia,1,,"too small weat. We repeated later. bat11, 65% 11.5v 11min",,,,,0,0,,,q170113ae1,,, +170117,17/01/2017,frz,esc,FLIR,1,auto (DS),clouds on the image,ae_frz_esc_T,celsius mosaic,,,1,,,,q170117esc,1,, +170117,17/01/2017,frz,ros,FLIR,1,auto (DS),clouds on the image,,pno,,,,,,,q170117ros,0,, +170117,17/01/2017,frz,ser,FLIR,1,auto (DS),,,celsius mosaic,,,1,,,,q170117ser,1,, +170117,17/01/2017,bw_,810,FLIR,1,auto (DS),,ae_bw_810_T,pix?,1,,,,,,q170117810,,, +170117,17/01/2017,frz,esc,HYPEr,1,auto (DS),clouds on the image,ae_frz_esc_H,bsq,,,-,,,,q170117esc,0,, +170117,17/01/2017,frz,ros,HYPEr,1,auto (DS),clouds on the image,,bsq,,,-,,,,q170117ros,0,, +170117,17/01/2017,frz,ser,HYPEr,1,auto (DS),,,rfl_spr_ort_geo,,,1,,,,q170117ser,1,, +170117,17/01/2017,bw_,810,HYPEr,1,auto (DS),,ae_bw_810_H,rfl_spr_ort,,,,,SOME DRIp PLOTS NOT COVERED,,q170117810,,, +170119,19/01/2017,nut,ae1,sequoia,1,dropbox/AE,"clouds, wind to W 3.5m/s, batt12 61% 11.3v 11.5min",ae_nut_ae1_Q,ndvi,,,1,1,,,q170119ae1,,, +170123,23/01/2017,bw_,810,FLIR,1,auto (DS),,ae_bw_810_T,pix?,1,,,,,,q170123810,,, +170123,23/01/2017,frz,esc,FLIR,1,auto (DS),,ae_frz_esc_T,celsius mosaic,,,1,,,,q170123esc,1,, +170123,23/01/2017,frz,ros,FLIR,1,auto (DS),,,celsius mosaic,,,1,,,,q170123ros,1,, +170123,23/01/2017,frz,ser,FLIR,1,auto (DS),,ae_fis_710_R,celsius mosaic,,,1,,,,q170123ser,1,, +170123,23/01/2017,bw_,810,HYPEr,1,auto (DS),,ae_bw_810_H,rfl_spr_ort,,,,,SOME DRIp PLOTS NOT COVERED,,q170123810,,, +170123,23/01/2017,frz,esc,HYPEr,1,auto (DS),,ae_frz_esc_H,rfl_spr_ort_geo,,,1,,,,q170123esc,1,, +170123,23/01/2017,frz,ros,HYPEr,1,auto (DS),,ae_fis_hap_Q,rfl_spr_ort_geo,,,1,,,,q170123ros,1,, +170123,23/01/2017,frz,ser,HYPEr,1,auto (DS),,,rfl_spr_ort_geo,,,1,,,,q170123ser,1,, +170123,23/01/2017,fis,hap,RGB,1,on the laptop,"clear, low wind N",ae_fis_710_R,dsm & mosaic,1,,1,,,1,q170123hap,,, +170123,23/01/2017,fis,hap,sequoia,1,,"clear, low wind N",ae_fis_hap_Q,reflectance & NDVI,,,1,,,,q170123hap,,, +170123,23/01/2017,nut,ae2,miniMCA,1,dropbox/AE,"clouds around and wind, inverted drives 2 and 4 so they have wrong exposition time",,ndvi,,,1,,,,m170123ae2,,, +170125,25/01/2017,fis,hap,RGB,1,on the laptop,,,dsm & mosaic,1,,1,,,1,q170125hap,,, +170125,25/01/2017,fis,hap,sequoia,1,,,,reflectance & NDVI,,,1,,,,q170125hap,,, +170125,25/01/2017,nut,ae2,sequoia,1,,,,ndvi,,,1,1,,,q170125ae2,,, +170126,26/01/2017,nut,sa1,sequoia,1,dropbox/AE,"clouds, wind to W 5m/s",,ndvi,,,1,1,,,q170126sa1,,, +170127,27/01/2017,fis,hap,RGB,1,on the laptop,DSM has bad artifacts. Due to low overlapp? Was very windy,ae_fis_710_R,dsm & mosaic,1,,1,,,1,q170127hap,,, +170127,27/01/2017,fis,hap,sequoia,1,,,ae_fis_hap_Q,reflectance & NDVI,,,1,,,,q170127hap,,, +170130,30/01/2017,ceb,ofi,RGB,1,,"Respaldo raw data en disco externo ADATA ""A 2017"". oficinas cebeb",,mosaico,,,,,,,q170130ofi,,, +170131,31/01/2017,nut,ma1,sequoia,1,on the laptop,"proyecto en pc, nubes",,ndvi,,,0,1,,,q170131ma1,,, +170201,01/02/2017,nut,coc,sequoia,1,on the laptop,"mosaico en PC, claro",,ndvi,,,1,1,,,q170201coc,,, +170201,01/02/2017,nut,d16,sequoia,1,on the laptop,"Block1216, mosaico en PC, claro",,ndvi,,,1,1,,,q170201d16,,, +170201,01/02/2017,nut,po5,sequoia,1,on the laptop,"mosaico en PC, claro",,ndvi,,,1,1,,,q170201po5,,, +170202,02/02/2017,bw_,810,FLIR,1,auto (DS),,ae_bw_810_T,pix?,1,,,,,,q170202810,,, +170202,02/02/2017,frz,esc,FLIR,1,auto (DS),,ae_frz_esc_T,celsius mosaic,,,1,,,,q170202esc,1,, +170202,02/02/2017,frz,ros,FLIR,1,auto (DS),,,celsius mosaic,,,1,,,,q170202ros,1,, +170202,02/02/2017,frz,ser,FLIR,1,auto (DS),,ae_fis_710_R,celsius mosaic,,,1,,,,q170202ser,1,, +170202,02/02/2017,bw_,810,HYPEr,1,auto (DS),,ae_bw_810_H,rfl_spr_ort,,,,,SOME DRIp PLOTS NOT COVERED,,q170202810,,, +170202,02/02/2017,frz,esc,HYPEr,1,auto (DS),,ae_frz_esc_H,rfl_spr_ort_geo,,1,1,,,,q170202esc,1,, +170202,02/02/2017,frz,ros,HYPEr,1,auto (DS),,ae_fis_hap_Q,rfl_spr_ort_geo,,1,1,,,,q170202ros,1,, +170202,02/02/2017,frz,ser,HYPEr,1,auto (DS),,,rfl_spr_ort_geo,,1,1,,,,q170202ser,1,, +170202,02/02/2017,nut,ole,miniMCA,1,auto (DS),,,ndvi,,,1,,,,m170202ole,,, +170202,02/02/2017,nut,521,miniMCA,1,auto (DS),,,ndvi,,,1,,,,m170202521,,, +170202,02/02/2017,nut,pad,miniMCA,1,auto (DS),,,ndvi,,,1,,,,m170202pad,,, +170203,03/02/2017,nut,ole,sequoia,1,on the laptop/dropboxAE,junto ole y pad,,ndvi,,,1,1,,,q170203ole,,, +170203,03/02/2017,nut,pad,sequoia,1,on the laptop/dropboxAE,junto ole y pad,,ndvi,,,1,1,,,q170203pad,,, +170203,03/02/2017,nut,111,sequoia,1,auto (DS),,,ndvi,,,0,0,,,q170203111,,, +170202,02/02/2017,nut,710,sequoia,1,,,,,,,0,,,,q170202710,,, +170207,07/02/2017,ceb,ce1,NIR,1,,"Respaldo en disco externo ADATA ""A 2017""",,,,,,,,,q170207ce1,,, +170207,07/02/2017,ceb,ce2,NIR,1,,"Respaldo en disco externo ADATA ""A 2017""",,,,,,,,,q170207ce2,,, +170207,07/02/2017,nut,fx1,sequoia,1,auto (DS),,,ndvi,,,1,0,,,q170207fx1,,, +170207,07/02/2017,nut,fx2,sequoia,1,on the laptop/dropboxAE,,,ndvi,,,1,1,,,q170207fx2,,, +170210,10/02/2017,bw_,810,FLIR,1,auto (DS),,,pix?,1,,,,,,q170210810,,,pedacito sur +170210,10/02/2017,frz,esc,FLIR,1,auto (DS),,,celsius mosaic,,,1,,,,q170210esc,1,, +170210,10/02/2017,frz,ros,FLIR,1,auto (DS),,,celsius mosaic,,,1,,,,q170210ros,1,, +170210,10/02/2017,frz,ser,FLIR,1,auto (DS),,,celsius mosaic,,,1,,,,q170210ser,1,, +170210,10/02/2017,bw_,810,HYPEr,1,auto (DS),paneles referencia,,rfl_spr_ort,,,,,SOME DRIp PLOTS NOT COVERED,,q170210810,,, +170210,10/02/2017,frz,esc,HYPEr,1,auto (DS),,,rfl_spr_ort_geo,,1,1,,,,q170210esc,1,, +170210,10/02/2017,frz,ros,HYPEr,1,auto (DS),,,rfl_spr_ort_geo,,1,1,,,,q170210ros,1,, +170210,10/02/2017,frz,ser,HYPEr,1,auto (DS),,,rfl_spr_ort_geo,,,1,,,,q170210ser,1,, +170210,10/02/2017,nut,fx2,miniMCA,1,auto (DS),,,ndvi,,,1,,,,m170210fx2,,, +170210,10/02/2017,fis,710,RGB,1,on the laptop,,,dsm & mosaic,1,,1,,,,q170210710,,, +170210,10/02/2017,fis,hap,RGB,1,on the laptop,,,dsm & mosaic,1,,1,,,1,q170210hap,,, +170211,11/02/2017,nut,po5,sequoia,1,on the laptop,copiado al PC proyecto. sin biomasa?,,ndvi,,,1,0,,,q170211po5,,, +170211,11/02/2017,nut,coc,sequoia,1,on the laptop,copiado al PC proyecto.,,ndvi,,,1,1,,,q170211coc,,, +170211,11/02/2017,nut,d16,sequoia,1,on the laptop,"Block1216,copiado al PC proyecto. sin biomasa?",,ndvi,,,1,0,,,q170211d16,,, +170213,13/02/2017,fis,hap,RGB,1,on the laptop/ADATA A2017,,,dsm & mosaic,1,,1,,,1,q170213hap,,, +170213,13/02/2017,fis,hap,sequoia,1,,,,reflectance & NDVI,,,1,,,,q170213hap,,, +170215,15/02/2017,fis,hap,RGB,1,on the laptop/ADATA A2017,,,dsm & mosaic,1,,1,,,1,q170215hap,,, +170215,15/02/2017,frz,ser,RGB,1,,,,mosaico,,,1,,,,q170215ser,1,, +170215,15/02/2017,fis,hap,sequoia,1,,,,reflectance & NDVI,,,1,,,,q170215hap,,, +170216,16/02/2017,bw_,810,FLIR,1,auto (DS),,ae_bw_810_T,pix?,1,,,,,,q170216810,,,pedacitito al este +170216,16/02/2017,frz,esc,FLIR,1,auto (DS),,ae_frz_esc_T,celsius mosaic,,,1,,,,q170216esc,1,,pedacitito al este +170216,16/02/2017,frz,ser,FLIR,1,auto (DS),,,celsius mosaic,,,1,,,,q170216ser,1,,pedacitito al este +170216,16/02/2017,bw_,810,HYPEr,1,auto (DS),"nubes alrededor, jaz_bw",ae_bw_810_H,rfl_spr_ort,,,,,SOME DRIp PLOTS NOT COVERED,,q170216810,,,pedacitito al este +170216,16/02/2017,frz,esc,HYPEr,1,auto (DS),"nubes cerca,jaz_stars",ae_frz_esc_H,rfl_spr_ort_geo,,,1,,,,q170216esc,1,,pedacitito al este +170216,16/02/2017,frz,ser,HYPEr,1,auto (DS),"nubes alrededor,jaz_stars",,rfl_spr_ort_geo,,,1,,,,q170216ser,1,,pedacitito al este +170220,20/02/2017,frz,ros,NIR,1,,no RTK,,mosaico,,,…,,,,q170220ros,1,,pedacitito al este +170220,20/02/2017,fis,hap,RGB,1,on the laptop/ADATA A2017,,,dsm & mosaic,1,,1,,,1,q170220hap,,,pedacitito al este +170220,20/02/2017,fis,710,RGB,1,on the laptop/ADATA A2017,,,dsm & mosaic,1,,1,,,,q170220710,,,pedacitito al este +170220,20/02/2017,fis,hap,sequoia,1,,,,reflectance & NDVI,,,1,,,,q170220hap,,,pedacitito al este +170221,21/02/2017,nut,po5,sequoia,1,on the laptop,copiado al PC,,ndvi,,,1,1,,,q170221po5,,,pedacitito al este +170221,21/02/2017,nut,coc,sequoia,1,on the laptop,copiado al PC,,ndvi,,,1,1,,,q170221coc,,,pedacitito al este +170221,21/02/2017,nut,d16,sequoia,1,on the laptop,"Block1216,copiado al PC",,ndvi,,,1,1,,,q170221d16,,,pedacitito al este +170222,22/02/2017,bw_,810,FLIR,1,auto (DS),,,pix?,1,,,,,,q170222810,,,pedacitito al este +170222,22/02/2017,frz,esc,FLIR,1,auto (DS),,,celsius mosaic,,,1,,,,q170222esc,1,, +170222,22/02/2017,frz,ros,FLIR,1,auto (DS),,,celsius mosaic,,,1,,,,q170222ros,1,, +170222,22/02/2017,frz,ser,FLIR,1,auto (DS),,,celsius mosaic,,,1,,,,q170222ser,1,, +170222,22/02/2017,bw_,810,HYPEr,1,auto (DS),"despejado, jaz_170222",,rfl_spr_ort,0,,,,SOME DRIp PLOTS NOT COVERED,,q170222810,,, +170222,22/02/2017,frz,esc,HYPEr,1,auto (DS),"despejado, jaz_170222",,rfl_spr_ort_geo,,1,1,,,,q170222esc,1,, +170222,22/02/2017,frz,ros,HYPEr,1,auto (DS),"despejado, jaz_170222",,rfl_spr_ort_geo,,,1,,,,q170222ros,1,, +170222,22/02/2017,frz,ser,HYPEr,1,auto (DS),"despejado, jaz_170222",,rfl_spr_ort_geo,,1,1,,,,q170222ser,1,, +170222,22/02/2017,nut,521&230,miniMCA,1,auto (DS),,,ndvi,,,1,,,,m170222521&230,,, +170222,22/02/2017,fis,hap,RGB,1,on the laptop/ADATA A2017,,,dsm & mosaic,1,,1,,,1,q170222hap,,, +170227,27/02/2017,frz,esc,RGB,1,,no RTK,,mosaico,,,…,,,,q170227esc,1,, +170301,01/03/2017,nut,sa1,sequoia,1,on the laptop,copiado al pc el mosaico. cirrus,,ndvi,,,1,1,,,q170301sa1,,, +170301,01/03/2017,nut,sa2,sequoia,1,on the laptop,copiado proyecto al pc. cirrus,,ndvi,,,1,1,,,q170301sa2,,, +170302,02/03/2017,nut,cas,sequoia,1,dropbox/AE,,,ndvi,,,1,1,,,q170302cas,,, +170302,02/03/2017,nut,521&230,sequoia,1,dropbox/AE,,,ndvi,,,1,,,,q170302521&230,,, +170302,02/03/2017,nut,521&230,miniMCA,1,auto (DS),,,ndvi,,,1,,,,m170302521&230,,, +170308,08/03/2017,fis,hap,RGB,1,on the laptop/ADATA A2017,clouds,,dsm & mosaic,1,,1,,,1,q170308hap,,, +170310,10/03/2017,nut,521&230,miniMCA,1,auto (DS),,,ndvi,,,1,,,,m170310521&230,,, +170310,10/03/2017,nut,521&230,sequoia,1,dropbox/AE,,,ndvi,,,1,,,,q170310521&230,,, +170310,10/03/2017,bw_,810,FLIR,1,,,,pix?,1,,,,,,q170310810,,, +170310,10/03/2017,frz,ros,FLIR,1,,,,celsius mosaic,,,1,,,,q170310ros,1,, +170310,10/03/2017,frz,ser,FLIR,1,,,,celsius mosaic,,,1,,,,q170310ser,1,, +170310,10/03/2017,frz,ros,HYPEr,1,,,,rfl_spr_ort_geo,,,1,,,,q170310ros,1,, +170310,10/03/2017,frz,ser,HYPEr,1,,,,rfl_spr_ort_geo,,,1,,,,q170310ser,1,, +170313,13/03/2017,nut,po5,sequoia,1,on the laptop,,,ndvi,,,1,1,,,q170313po5,,, +170313,13/03/2017,nut,coc,sequoia,1,on the laptop,copiado mosaico al pc,,ndvi,,,1,1,,,q170313coc,,, +170313,13/03/2017,nut,d16,sequoia,1,on the laptop,"Block1216,copiado mosaico al pc",,ndvi,,,1,1,,,q170313d16,,, +170315,15/03/2017,bw_,810,FLIR,1,,,,pix?,1,,,,SOME DRIp PLOTS NOT COVERED,,q170315810,,, +170315,15/03/2017,frz,esc,FLIR,1,,,,celsius mosaic,,,1,,,,q170315esc,1,, +170315,15/03/2017,frz,ros,FLIR,1,,,,celsius mosaic,,,1,,,,q170315ros,1,, +170315,15/03/2017,frz,ser,FLIR,1,,,,celsius mosaic,,,1,,,,q170315ser,1,, +170315,15/03/2017,bw_,810,HYPEr,1,,,,rfl_spr_ort,0,,,,,,q170315810,,, +170315,15/03/2017,frz,esc,HYPEr,1,,,,rfl_spr_ort_geo,,,1,,,,q170315esc,1,, +170315,15/03/2017,frz,ros,HYPEr,1,,,,rfl_spr_ort_geo,,,1,,,,q170315ros,1,, +170315,15/03/2017,frz,ser,HYPEr,1,,,,rfl_spr_ort_geo,,,1,,,,q170315ser,1,, +170315,15/03/2017,nut,521&230,miniMCA,1,auto (DS),,,ndvi,,,1,,,,m170315521&230,,, +170315,15/03/2017,nut,521&230,sequoia,1,dropbox/AE,,,ndvi,-,,1,,,,q170315521&230,,, +170324,24/03/2017,est,bw3,RGB,1,,"Respaldo raw data en disco externo ADATA ""A 2017"". despejado",,pix,-,,rtk 521,,,,q170324bw3,,, +170324,24/03/2017,est,bw4,RGB,1,,"Respaldo raw data en disco externo ADATA ""A 2017"". despejado",,pix,-,,rtk 521,,,,q170324bw4,,, +170329,29/03/2017,bw_,810,FLIR,1,,,,pix?,1,,,,,,q170329810,,,parte superior +170329,29/03/2017,frz,esc,FLIR,1,,,,celsius mosaic,-,,1,,,,q170329esc,1,, +170329,29/03/2017,frz,ros,FLIR,1,,,,celsius mosaic,-,,1,,,,q170329ros,1,, +170329,29/03/2017,frz,ser,FLIR,1,,,,celsius mosaic,-,,1,,,,q170329ser,1,, +170329,29/03/2017,sed,sed,FLIR,1,,,,celsius mosaic,,,1,,,,q170329sed,,, +170329,29/03/2017,bw_,810,HYPEr,1,,,,rfl_spr_ort,0,,,,"SOME DRIp PLOTS NOT COVERED, drip not adjusted",,q170329810,,, +170329,29/03/2017,frz,esc,HYPEr,1,,,,rfl_spr_ort_geo,-,,1,,,,q170329esc,1,, +170329,29/03/2017,frz,ros,HYPEr,1,,,,rfl_spr_ort_geo,-,,1,,,,q170329ros,1,, +170329,29/03/2017,frz,ser,HYPEr,1,,,,rfl_spr_ort_geo,-,,1,,,,q170329ser,1,, +170329,29/03/2017,nut,521&230,miniMCA,1,auto (DS),,,ndvi,-,,1,,,,m170329521&230,,, +170329,29/03/2017,sed,sed,miniMCA,1,,,,mosaico,-,,1,,,,q170329sed,,, +170330,30/03/2017,fis,hap,RGB,1,on the laptop/ADATA A2017,"there was an issue with the datum in pix 4D. I rested 32.316 m to get the plant height, but it looks some cm lower than the previous date.",,dsm & mosaic,1,,1,,,1,q170330hap,,, +170330,30/03/2017,fis,hap,sequoia,1,,,,reflectance & NDVI,1,,1,,,,q170330hap,,, +170403,03/04/2017,nut,po5,sequoia,1,on the laptop,proyecto copiado a PC,,pix…,,,0,1,,,q170403po5,,, +170403,03/04/2017,nut,coc,sequoia,1,on the laptop,proyecto copiado a PC,,ndvi,,,1,1,,,q170403coc,,, +170403,03/04/2017,nut,d16,sequoia,1,on the laptop,"Block1216,mosaico copiado a PC",,ndvi,,,1,1,,,q170403d16,,, +170405,05/04/2017,bw_,810,FLIR,1,,,,pix?,1,,,,,,q170405810,,, +170405,05/04/2017,frz,esc,FLIR,1,,,,celsius mosaic,,,1,,,,q170405esc,1,, +170405,05/04/2017,frz,ros,FLIR,1,,,,celsius mosaic,,,1,,,,q170405ros,1,, +170405,05/04/2017,frz,ser,FLIR,1,,,,celsius mosaic,,,1,,,,q170405ser,1,, +170405,05/04/2017,bw_,810,HYPEr,1,,checar pasadas extras,,rfl_spr_ort,0,,,,"SOME DRIp PLOTS NOT COVERED, drip not adjusted nor october area",,q170405810,,, +170405,05/04/2017,frz,esc,HYPEr,1,,,,rfl_spr_ort_geo,,,1,,,,q170405esc,1,, +170405,05/04/2017,frz,ros,HYPEr,1,,,,rfl_spr_ort_geo,,,1,,,,q170405ros,1,, +170405,05/04/2017,frz,ser,HYPEr,1,,,,rfl_spr_ort_geo,,,1,,,,q170405ser,1,, +170405,05/04/2017,nut,521&230,miniMCA,1,auto (DS),,,ndvi,,,1,,,,m170405521&230,,, +170406,06/04/2017,nut,521&230,sequoia,1,dropbox/AE,,,ndvi,,,1,,,,q170406521&230,,, +170411,11/04/2017,bw_,810,FLIR,1,,out of focus,,tif,-,,,,,,q170411810,,, +170411,11/04/2017,frz,esc,FLIR,1,,,,celsius mosaic,-,,1,,,,q170411esc,1,, +170411,11/04/2017,bw_,810,HYPEr,1,,,,rfl_spr_ort,0,,,,"SOME DRIp PLOTS NOT COVERED, drip not adjusted nor october area, nor 2 IR",,q170411810,,, +170411,11/04/2017,frz,esc,HYPEr,1,,,,rfl_spr_ort_geo,-,,1,,,,q170411esc,1,, +170411,11/04/2017,sed,sed,miniMCA,1,,,,mosaico,-,,1,,,,q170411sed,,, +170411,11/04/2017,sed,sed,FLIR,1,,"out of focus, 20cm",,tif,-,,,,,,q170411sed,,, +170411,11/04/2017,bw_,hea,miniMCA,1,,,,ndvi,0,,1,,,,q170411hea,,, +170411,11/04/2017,bw_,hea,FLIR,1,,"out of focus, 20cm",,tif,0,,,,,,q170411hea,,, +170418,18/04/2017,frz,esc,FLIR,1,,,,celsius mosaic,,,1,,,,q170418esc,1,, +170418,18/04/2017,frz,ros,FLIR,1,,,,celsius mosaic,,,1,,,,q170418ros,1,, +170418,18/04/2017,frz,ser,FLIR,1,,,,celsius mosaic,,,1,,,,q170418ser,1,, +170418,18/04/2017,sed,sed,FLIR,1,,,,celsius mosaic,,,1,,,,q170418sed,,, +170418,18/04/2017,frz,esc,HYPEr,1,,,,rfl_spr_ort_geo,,,1,,,,q170418esc,1,, +170418,18/04/2017,frz,ros,HYPEr,1,,,,rfl_spr_ort_geo,,,1,,,,q170418ros,1,, +170418,18/04/2017,frz,ser,HYPEr,1,,,,rfl_spr_ort_geo,,,1,,,,q170418ser,1,, +170426,26/04/2017,sed,sed,FLIR,1,,,,celsius mosaic,,,1,,,,q170426sed,,, +170426,26/04/2017,sed,sed,miniMCA,1,,,,mosaico,,,1,,,,q170426sed,,, +170426,26/04/2017,bw_,sed,FLIR,1,,también bw heat salió en la foto,,pno…,,,,,,,q170426sed,,, +170502,02/05/2017,bw_,hea,FLIR,1,,,,cel,,,,,ok,,q170502hea,,, +170502,02/05/2017,bw_,hea,HYPEr,1,,,,rfl_spr_ort,,,,,ok,,q170502hea,,, +170511,11/05/2017,sed,sed,FLIR,,,,,celsius mosaic,,,1,,,,q170511sed,,, +170511,11/05/2017,sed,sed,miniMCA,1,,,,mosaico,,,1,,,,q170511sed,,, +170511,11/05/2017,bw_,hea,FLIR,,,,,cel,,,,,ok,,q170511hea,,, +170511,11/05/2017,bw_,hea,HYPEr,,,,,capture error,,,,,issues,,q170511hea,,, +170512,12/05/2017,bw_,sed,RGB,1,auto (DS),,,mosaico,,,1,,,,q170512sed,,, +170512,12/05/2017,bw_,hea,RGB,1,auto (DS),,,mosaico,,,1,,,,q170512hea,,, +170522,22/05/2017,bw_,hea,FLIR,1,auto (DS),,,cel,,,,,ok,,q170522hea,,, +170522,22/05/2017,bw_,hea,HYPEr,1,auto (DS),"cirrus, Jaz 80 81 82 83",,rfl_spr_ort,,,,,issues,,q170522hea,,, +170531,31/05/2017,bw_,hea,FLIR,,,,,cel,,,,,ok,,q170531hea,,, +170531,31/05/2017,bw_,hea,HYPEr,1,,very windy,,rfl_spr_ort,,,,,issues,,q170531hea,,, +170531,31/05/2017,sed,sed,FLIR,1,,"clear sky, windy",,celsius mosaic,,,1,,,,q170531sed,,, +170531,31/05/2017,sed,sed,miniMCA,1,,"very windy, weeds all around",,mosaico,,,1,,,,q170531sed,,, +,,,,,,,,,,,,,,,,,,, +,,,,,,,,,,,,,,,,,,, +,,,,,,,,,,,,,,,,,,, +,,,,,,,,,,,,,,,,,,, +,,,,,,,,,,,,,,,,,,, +,,,,,,,,,,,,,,,,,,, +,,,,,,,,,,,,,,,,,,, diff --git a/r/shiny_extractApp/helpers.R b/r/shiny_extractApp/helpers.R new file mode 100644 index 0000000..548fbe2 --- /dev/null +++ b/r/shiny_extractApp/helpers.R @@ -0,0 +1,35 @@ +saveData <- function(data) { + #data <- t(data) + + #test to save to file + write.csv(data, file="C://Dropbox//data//shiny_output_test.csv") +} + +#Load data function... +loadDBData <- function() { + # create a connection to a DB in postres + # save the password that we can "hide" it as best as we can by collapsing it + pw <- { + "cimmyt" + } + + # loads the PostgreSQL driver + drv <- dbDriver("PostgreSQL") + # creates a connection to the postgres database + # note that "con" will be used later in each connection to the database + # here the connection values should be specified + connection <- dbConnect(drv, dbname = "ceneb_nut", + host = "localhost", port = 5432, + user = "postgres", password = pw) + rm(pw) # removes the password + + # fetch the data from postgreSQL #capture" table + df_log <- dbGetQuery(connection, "SELECT * from capture") #Returns a dataframe type + + # close the connection when finished + dbDisconnect(connection) + dbUnloadDriver(drv) + ##>>>>>>>>>>>>># } connection to DB losed + + df_log +} \ No newline at end of file diff --git a/r/shiny_extractApp/rsconnect/shinyapps.io/nutrs/shiny_exampleapp.dcf b/r/shiny_extractApp/rsconnect/shinyapps.io/nutrs/shiny_exampleapp.dcf new file mode 100644 index 0000000..70762ef --- /dev/null +++ b/r/shiny_extractApp/rsconnect/shinyapps.io/nutrs/shiny_exampleapp.dcf @@ -0,0 +1,8 @@ +name: shiny_exampleapp +title: +account: nutrs +server: shinyapps.io +appId: 201629 +bundleId: 925472 +url: https://nutrs.shinyapps.io/shiny_exampleapp/ +when: 1501711471.56152 diff --git a/r/shiny_extractApp/server.R b/r/shiny_extractApp/server.R new file mode 100644 index 0000000..39be958 --- /dev/null +++ b/r/shiny_extractApp/server.R @@ -0,0 +1,75 @@ +#This procedure loads the log DB of the flights +#thanks https://ipub.com/shiny-crud-app/ +# https://rstudio.github.io/DT/options.html +# Thanks to https://shiny.rstudio.com/articles/persistent-data-storage.html +#thanks to R documentation +#Edited: Lorena + +library (raster) +library (sp) +library(rgdal) +require("RPostgreSQL") +library(rgdal) +source("helpers.R") + +shinyServer(function(input, output,session) { + + #############Get the data from the DB + df_log <- loadDBData() + + #Get the available unique data from the log table + sensors <- unique(df_log$sensor) #sensors + #date <- unique(df_log$cap_date) #dates + trials <- unique(df_log$area) #trials + + + #get the sensors as the options of a check box + output$choose_cam <- renderUI({ + # If missing input, return to avoid error later in function + #if(is.null(sensors)) + # return() + + # Create the checkboxes and select them all by default + checkboxGroupInput("chk_cam", "Choose sensors", + choices = sensors, + selected = NULL) + }) + + #get the available trials as the options of a check box + output$choose_trial <- renderUI({ + # If missing input, return to avoid error later in function + #if(is.null(sensors)) + # return() + + # Create the checkboxes and select them all by default + checkboxGroupInput("chk_trial", "Choose trial", + choices = trials, + selected = NULL) + }) + + + #Render the table to display in the GUI + output$logTable <- DT::renderDataTable({ + + #update after submit is clicked + input$submit + #Filter the data as needed + df_log <- df_log[df_log$area %in% c(input$chk_trial) & df_log$sensor %in% c(input$chk_cam), ] + #save to csv the data + #saveData(df_log) # This works! + + #The data retrieved from the DB, filtered + df_log #This will be showed in the table UI + + + },options = list(pageLength = 15)) #show 15 rows by default + + #React when a row is selected + # (Not working yet) # ! # + # observeEvent(input$logTable_rows_selected, { + # output$txtOut <- renderText({ + # data <- loadData()[input$logTable_rows_selected,] ### loadData() function???? + # sprintf("You have selected: %s",data)}) #!# this doesnt work, how get the value selected I just get the index within the filtered table + # }) + } +) \ No newline at end of file diff --git a/r/shiny_extractApp/ui.R b/r/shiny_extractApp/ui.R new file mode 100644 index 0000000..7a02017 --- /dev/null +++ b/r/shiny_extractApp/ui.R @@ -0,0 +1,31 @@ +#show the table of the flights info + +# thanks to https://shiny.rstudio.com/articles/datatables.html +# thanks to https://gist.github.com/wch/4211337 + + +library("shiny") +library(DT) + + +shinyUI(fluidPage( + titlePanel("Log control"), + + #For the inputs + sidebarLayout( + sidebarPanel( + h3("Select options"), + uiOutput("choose_cam"), #check options cam populated by the input table + uiOutput("choose_trial") #check options trial populated by the input table + + ), + + #For the outputs + mainPanel( + h3("Log"), + DT::dataTableOutput("logTable"), + textOutput("txtOut") #here I want to show the selected row + + ) + ) +)) \ No newline at end of file diff --git a/r/shiny_extractApp/www/circles in a circle Kandinsky.jpg b/r/shiny_extractApp/www/circles in a circle Kandinsky.jpg new file mode 100644 index 0000000000000000000000000000000000000000..e9b6c4aa148fa5052fa6c6b50931f7dd0af95446 GIT binary patch literal 2887 zcmbW!c{J4T9tZI6m|79rymu@1Ar2y7#`H=Q-y&&vrhq^Kd?MW`KQ`W)@}u2m}J$ zxeef~0m3GMNS{yu1V8`)FyJmt0_Oo95NNmm6)-P&H+&EXm>0?ih3*a*d=DH3@47efJi(3&5bT-4p*?IPU;q7@z^DgF*5Dk1z-<4B~VF zvH$?$+{J8xoYW`4oK(*BZz zqm%RHE6D3Nyl&p|zWpE|Feo@A^kGbF+~fF!#3yO#8JStx&vSBL7MGNkl~+_&)xCLJ z-+*mwYJT6@)!ozE_o07uY#cv9_)H|t&do0@eqCCotkA!0e*eMPV*dQK>jDAbf3g0S z{fA4K>*C@50R+110`UZM2Ur-wD|d=d#Ml<JRQ8k#VW*d9>+Rg!&>DaGbr zoNot*%Q0gLIeod33a%dtGo1Ir~Gh^nolTWPUj?W}Z>?M<~v=Rmc7yAb9C-o5z%T&c>^uda^RoPux zq4=V3bo7})f0)xqU25SovByu#fzEiDz67CUR5Ap1ZjuQ&h^%jW+% z>qq=L=E(jujySQSB9OQKHY`(Y%Svv@y5)L}WY1-I5Bx4%hl(DsCnq;GS09gX+vLso zsvCfmHVLh4Jy5tX9yIFYdY0eBJZO!N6XSqOX7O^}L$hosvf#WFGG zdMk&7>3gEmYTb@Z3dFkq^nXn{)jE=3PZe|0>+R+MBCJ2DV*xH&gZ+R&=cjyXSdeSm z(tH6ihVg_B-4PA$ImiKGQOY5~oe#0m^&eF+c0)s|sE_3)byd7dC0?6+G{+mN^8F=A zHy3``d_HrP(2n#YnO|aMhWk`qT_xEt!kF>1P6;)U+h&!eaz;Tp8iEJIE+#oeCCsH$ zdlwGV?l&IHTxqtyn)(MiAnjXNb)egW^|c2E$1&AmM&zXh4p7bzFal_-S5_j+zg$1R zvu&P|tkqd~W?{6Hdo;+Z)XBY%IIQWgt>nE~l z+OxhJPtk`J)B>-aquhCeZP^o)nDIj(M>@*&2Q80*rHQZapD&OOk+s3$8Yghz5K=^f zhl{s@i+7f{Cu{uGslyiNujgibh2UrI6`vWNR%*(dmE94yy^E`$66TgVT=HbZBoQBT zr*G#5&NTUWoP_kUQ~Vkmp|`SibUH>@H7NCvm|1dEJ!nQo#9GCb?thw`7?`XsSMlKh zy#)DT(?NUiTy`?T8Q~~zVw(7nEOSNkWxW%&Ij?@YhCspCXT=s4>wYvmUPYAP-D)095ljilwi=tuxs3RvnvZI!tQn-k>f_noidipdp)FVw2H)YCI zQ!=70Yq#Us{K@k#lxGsIY68h9kndhKIMhY6-xN@Gh=j4ehN21}} zPJz{(jbiKi@B1riQ~k>_OVED!(C?2%-q6a~aAR7}6hK+305t0CUIr3tD0XO;iU z{tH1sUgC$Kpuh{CXGF?J+ho|ohX7Xoox2A5GprM5$2J@x>2^LUNw?qhVtHJi1sg=> zo0|@Jh(3r}WVAPItc%j(eY3kZh!I_`^0(ObH9H8r9O2l;eSC3UX4ahWvQl_1Ms~d( zvudO|e%c0#>TQ4PSTx7_ov`LVFn3LFpi?ZpH}1XRAi+)^{2JsQJ1edWIE`N#K-y*F zR}^RQTDd5!!AefDCB8HfqoS5ljiN5uADk6`*>A;m;>-*-(k~29~L=& zsjgbNETkUI=Nb8YNc36PK<-vb68Q}xb#8-)(!drS{l0!^v(wT0Mwv75ebxlpTjD)2 z?xLt@L`I>UmpSjTRgU(&eKI;XH+X->96q4nKpllbDL<+Yh3?GyoA zz}~SRFY6Ge@wJU4 zlao>jTRW4A$8brk-}MDgCGj)LUSwGp1A@epiQXQfo>x2{Jnbn2&OK6yzvI+QYse0K hpqNH!P`6MJ3l0nL^8ejJMLK=e^nl~zs}&sLKLBl#d-DJQ literal 0 HcmV?d00001 diff --git a/r/shiny_importData/global.R b/r/shiny_importData/global.R new file mode 100644 index 0000000..1120d6f --- /dev/null +++ b/r/shiny_importData/global.R @@ -0,0 +1,3 @@ +library("shiny") +library("foreign") +library (DT) \ No newline at end of file diff --git a/r/shiny_importData/notas.txt b/r/shiny_importData/notas.txt new file mode 100644 index 0000000..e183b1a --- /dev/null +++ b/r/shiny_importData/notas.txt @@ -0,0 +1 @@ +Gracias a https://gist.github.com/SachaEpskamp/5796467 \ No newline at end of file diff --git a/r/shiny_importData/server.R b/r/shiny_importData/server.R new file mode 100644 index 0000000..3d72cb1 --- /dev/null +++ b/r/shiny_importData/server.R @@ -0,0 +1,101 @@ +shinyServer(function(input, output) { + + ### Argument names: + ArgNames <- reactive({ + Names <- names(formals(input$readFunction)[-1]) + Names <- Names[Names!="..."] + return(Names) + }) + + # Argument selector: + output$ArgSelect <- renderUI({ + if (length(ArgNames())==0) return(NULL) + + selectInput("arg","Argument:",ArgNames()) + }) + + ## Arg text field: + output$ArgText <- renderUI({ + fun__arg <- paste0(input$readFunction,"__",input$arg) + + if (is.null(input$arg)) return(NULL) + + Defaults <- formals(input$readFunction) + + if (is.null(input[[fun__arg]])) + { + textInput(fun__arg, label = "Enter value:", value = deparse(Defaults[[input$arg]])) + } else { + textInput(fun__arg, label = "Enter value:", value = input[[fun__arg]]) + } + }) + + + ### Data import: + Dataset <- reactive({ + if (is.null(input$file)) { + # User has not uploaded a file yet + return(data.frame()) + } + + args <- grep(paste0("^",input$readFunction,"__"), names(input), value = TRUE) + + argList <- list() + for (i in seq_along(args)) + { + argList[[i]] <- eval(parse(text=input[[args[i]]])) + } + names(argList) <- gsub(paste0("^",input$readFunction,"__"),"",args) + + argList <- argList[names(argList) %in% ArgNames()] + + Dataset <- as.data.frame(do.call(input$readFunction,c(list(input$file$datapath),argList))) + return(Dataset) + }) + + # Select variables: + output$varselect <- renderUI({ + + if (identical(Dataset(), '') || identical(Dataset(),data.frame())) return(NULL) + + # Variable selection: + selectInput("vars", "Variables to use:", + names(Dataset()), names(Dataset()), multiple =TRUE) + }) + + # Show table: + output$table <- DT::renderDataTable({ + + if (is.null(input$vars) || length(input$vars)==0) return(NULL) + + return(Dataset()[,input$vars,drop=FALSE]) + }) + + + ### Download dump: + + output$downloadDump <- downloadHandler( + filename = "Rdata.R", + content = function(con) { + + assign(input$name, Dataset()[,input$vars,drop=FALSE]) + + dump(input$name, con) + } + ) + + ### Download save: + + output$downloadSave <- downloadHandler( + filename = "Rdata.RData", + content = function(con) { + + assign(input$name, Dataset()[,input$vars,drop=FALSE]) + + save(list=input$name, file=con) + } + ) + +}) + + diff --git a/r/shiny_importData/ui.R b/r/shiny_importData/ui.R new file mode 100644 index 0000000..90e377e --- /dev/null +++ b/r/shiny_importData/ui.R @@ -0,0 +1,70 @@ + +shinyUI(pageWithSidebar( + + # Header: + headerPanel("R data reader"), + + # Input in sidepanel: + sidebarPanel( + tags$style(type='text/css', ".well { max-width: 20em; }"), + # Tags: + tags$head( + tags$style(type="text/css", "select[multiple] { width: 100%; height:10em}"), + tags$style(type="text/css", "select { width: 100%}"), + tags$style(type="text/css", "input { width: 19em; max-width:100%}") + ), + + # Select filetype: + selectInput("readFunction", "Function to read data:", c( + # Base R: + "read.table", + "read.csv", + "read.csv2", + "read.delim", + "read.delim2", + + # foreign functions: + "read.spss", + "read.arff", + "read.dbf", + "read.dta", + "read.epiiinfo", + "read.mtp", + "read.octave", + "read.ssd", + "read.systat", + "read.xport", + + # Advanced functions: + "scan", + "readLines" + )), + + # Argument selecter: + htmlOutput("ArgSelect"), + + # Argument field: + htmlOutput("ArgText"), + + # Upload data: + fileInput("file", "Upload data-file:"), + + # Variable selection: + htmlOutput("varselect"), + + br(), + + textInput("name","Dataset name:","Data"), + + downloadLink('downloadDump', 'Download source'), + downloadLink('downloadSave', 'Download binary') + + ), + + # Main: + mainPanel( + + DT::dataTableOutput("table") + + ) +)) \ No newline at end of file diff --git a/r/shiny_listFiles/data/AE_vuelos.csv b/r/shiny_listFiles/data/AE_vuelos.csv new file mode 100644 index 0000000..dce1746 --- /dev/null +++ b/r/shiny_listFiles/data/AE_vuelos.csv @@ -0,0 +1,211 @@ +Date,Date_format,Rol,Area,Sensor,Downloaded,Backup stage,Notes,plan,stage,shared_Mrgt/involved,Julia,GCP,GS_grande,L1,temp_download,check,extract?,,nu7 +161026,26/10/2016,fis,710,RGB,,auto (DS),mosaico Fis necesita ajuste por desfase. Hap ya Ajustado,,dsm & mosaic,1,,1,,,,q161026710,,, +161128,28/11/2016,bw_,oct,miniMCA,1,auto (DS),"microtops 5 h vuelo quedan, light clouds",ae_bw_oct_M,1,,,,,,,q161128oct,,, +161215,15/12/2016,fis,710,RGB,,dropbox/on the laptop,ajusté desface en el mosaico que está en Dropbox. La copia local en PC ajusté directo a la imagen enero 27. Sumé 0.35m porque salía debajo del DTM,,dsm & mosaic,1,,1,,,1,q161215710,,, +170107,07/01/2017,fis,hap,sequoia,1,,,,reflectance & NDVI,,,1,,,,q170107hap,,, +170107,07/01/2017,fis,hap,RGB,1,auto (DS),,,dsm & mosaic,1,,1,,,1,q170107hap,,, +170104,04/01/2017,nut,fx1,no,,,,,,,,,1,,,q170104fx1,,, +170109,09/01/2017,nut,fx2,sequoia,1,auto (DS),,ae_nut_jf2_Q,ndvi,,,1,1,,,q170109fx2,,, +170109,09/01/2017,nut,po5,sequoia,1,dropbox/AE,,ae_nut_po5_Q,ndvi,,,1,1,,,q170109po5,,, +170110,10/01/2017,bw_,810,FLIR,1,,,ae_bw_810_T,pix?,1,,,,,,q170110810,,, +170110,10/01/2017,frz,esc,FLIR,1,,,ae_frz_esc_T,celsius mosaic,1,,1,,,,q170110esc,1,, +170110,10/01/2017,frz,ros,FLIR,1,,,,celsius mosaic,,,1,,,,q170110ros,1,, +170110,10/01/2017,frz,ser,FLIR,1,,,,celsius mosaic,,,1,,,,q170110ser,1,, +170110,10/01/2017,bw_,810,HYPEr,1,auto (DS),irradiance from microtops,ae_bw_810_H,rfl_spr_ort,,,,,SOME DRIp PLOTS NOT COVERED,,q170110810,,, +170110,10/01/2017,frz,esc,HYPEr,1,auto (DS),irradiance from microtops,ae_frz_esc_H,rfl_spr_ort_geo,,,1,,,,q170110esc,1,, +170110,10/01/2017,frz,ros,HYPEr,1,auto (DS),irradiance from microtops,,rfl_spr_ort_geo,,,1,,,,q170110ros,1,, +170110,10/01/2017,frz,ser,HYPEr,1,auto (DS),irradiance from microtops,,rfl_spr_ort_geo,,,1,,,,q170110ser,1,, +170110,10/01/2017,nut,poc,miniMCA,1,auto (DS),,ae_nut_poc_M,ndvi,,,1,,,,m170110poc,,, +170110,10/01/2017,nut,fx2,miniMCA,1,auto (DS),,ae_nut_jf2_M,ndvi,,,1,,,,m170110fx2,,, +170110,10/01/2017,nut,po5,miniMCA,1,auto (DS),,ae_nut_po5_M,ndvi,,,1,,,,m170110po5,,, +170110,10/01/2017,nut,po4,miniMCA,1,auto (DS),,ae_nut_po4_M,ndvi,,,1,,,,m170110po4,,, +170110,10/01/2017,nut,poc,sequoia,1,dropbox/AE,,ae_nut_poc_Q,ndvi,,,1,1,,,q170110poc,,, +170112,12/01/2017,nut,val,sequoia,1,on the laptop,on the laptop & Dropbox AE,ae_nut_val_Q,ndvi,,,1,0,,,q170112val,,, +170112,12/01/2017,nut,tyc,sequoia,1,on the laptop,on the laptop & Dropbox AE,ae_nut_tyc_Q,ndvi,,,1,0,,,q170112tyc,,, +170113,13/01/2017,nut,ae1,sequoia,1,,"too small weat. We repeated later. bat11, 65% 11.5v 11min",,,,,0,0,,,q170113ae1,,, +170117,17/01/2017,frz,esc,FLIR,1,auto (DS),clouds on the image,ae_frz_esc_T,celsius mosaic,,,1,,,,q170117esc,1,, +170117,17/01/2017,frz,ros,FLIR,1,auto (DS),clouds on the image,,pno,,,,,,,q170117ros,0,, +170117,17/01/2017,frz,ser,FLIR,1,auto (DS),,,celsius mosaic,,,1,,,,q170117ser,1,, +170117,17/01/2017,bw_,810,FLIR,1,auto (DS),,ae_bw_810_T,pix?,1,,,,,,q170117810,,, +170117,17/01/2017,frz,esc,HYPEr,1,auto (DS),clouds on the image,ae_frz_esc_H,bsq,,,-,,,,q170117esc,0,, +170117,17/01/2017,frz,ros,HYPEr,1,auto (DS),clouds on the image,,bsq,,,-,,,,q170117ros,0,, +170117,17/01/2017,frz,ser,HYPEr,1,auto (DS),,,rfl_spr_ort_geo,,,1,,,,q170117ser,1,, +170117,17/01/2017,bw_,810,HYPEr,1,auto (DS),,ae_bw_810_H,rfl_spr_ort,,,,,SOME DRIp PLOTS NOT COVERED,,q170117810,,, +170119,19/01/2017,nut,ae1,sequoia,1,dropbox/AE,"clouds, wind to W 3.5m/s, batt12 61% 11.3v 11.5min",ae_nut_ae1_Q,ndvi,,,1,1,,,q170119ae1,,, +170123,23/01/2017,bw_,810,FLIR,1,auto (DS),,ae_bw_810_T,pix?,1,,,,,,q170123810,,, +170123,23/01/2017,frz,esc,FLIR,1,auto (DS),,ae_frz_esc_T,celsius mosaic,,,1,,,,q170123esc,1,, +170123,23/01/2017,frz,ros,FLIR,1,auto (DS),,,celsius mosaic,,,1,,,,q170123ros,1,, +170123,23/01/2017,frz,ser,FLIR,1,auto (DS),,ae_fis_710_R,celsius mosaic,,,1,,,,q170123ser,1,, +170123,23/01/2017,bw_,810,HYPEr,1,auto (DS),,ae_bw_810_H,rfl_spr_ort,,,,,SOME DRIp PLOTS NOT COVERED,,q170123810,,, +170123,23/01/2017,frz,esc,HYPEr,1,auto (DS),,ae_frz_esc_H,rfl_spr_ort_geo,,,1,,,,q170123esc,1,, +170123,23/01/2017,frz,ros,HYPEr,1,auto (DS),,ae_fis_hap_Q,rfl_spr_ort_geo,,,1,,,,q170123ros,1,, +170123,23/01/2017,frz,ser,HYPEr,1,auto (DS),,,rfl_spr_ort_geo,,,1,,,,q170123ser,1,, +170123,23/01/2017,fis,hap,RGB,1,on the laptop,"clear, low wind N",ae_fis_710_R,dsm & mosaic,1,,1,,,1,q170123hap,,, +170123,23/01/2017,fis,hap,sequoia,1,,"clear, low wind N",ae_fis_hap_Q,reflectance & NDVI,,,1,,,,q170123hap,,, +170123,23/01/2017,nut,ae2,miniMCA,1,dropbox/AE,"clouds around and wind, inverted drives 2 and 4 so they have wrong exposition time",,ndvi,,,1,,,,m170123ae2,,, +170125,25/01/2017,fis,hap,RGB,1,on the laptop,,,dsm & mosaic,1,,1,,,1,q170125hap,,, +170125,25/01/2017,fis,hap,sequoia,1,,,,reflectance & NDVI,,,1,,,,q170125hap,,, +170125,25/01/2017,nut,ae2,sequoia,1,,,,ndvi,,,1,1,,,q170125ae2,,, +170126,26/01/2017,nut,sa1,sequoia,1,dropbox/AE,"clouds, wind to W 5m/s",,ndvi,,,1,1,,,q170126sa1,,, +170127,27/01/2017,fis,hap,RGB,1,on the laptop,DSM has bad artifacts. Due to low overlapp? Was very windy,ae_fis_710_R,dsm & mosaic,1,,1,,,1,q170127hap,,, +170127,27/01/2017,fis,hap,sequoia,1,,,ae_fis_hap_Q,reflectance & NDVI,,,1,,,,q170127hap,,, +170130,30/01/2017,ceb,ofi,RGB,1,,"Respaldo raw data en disco externo ADATA ""A 2017"". oficinas cebeb",,mosaico,,,,,,,q170130ofi,,, +170131,31/01/2017,nut,ma1,sequoia,1,on the laptop,"proyecto en pc, nubes",,ndvi,,,0,1,,,q170131ma1,,, +170201,01/02/2017,nut,coc,sequoia,1,on the laptop,"mosaico en PC, claro",,ndvi,,,1,1,,,q170201coc,,, +170201,01/02/2017,nut,d16,sequoia,1,on the laptop,"Block1216, mosaico en PC, claro",,ndvi,,,1,1,,,q170201d16,,, +170201,01/02/2017,nut,po5,sequoia,1,on the laptop,"mosaico en PC, claro",,ndvi,,,1,1,,,q170201po5,,, +170202,02/02/2017,bw_,810,FLIR,1,auto (DS),,ae_bw_810_T,pix?,1,,,,,,q170202810,,, +170202,02/02/2017,frz,esc,FLIR,1,auto (DS),,ae_frz_esc_T,celsius mosaic,,,1,,,,q170202esc,1,, +170202,02/02/2017,frz,ros,FLIR,1,auto (DS),,,celsius mosaic,,,1,,,,q170202ros,1,, +170202,02/02/2017,frz,ser,FLIR,1,auto (DS),,ae_fis_710_R,celsius mosaic,,,1,,,,q170202ser,1,, +170202,02/02/2017,bw_,810,HYPEr,1,auto (DS),,ae_bw_810_H,rfl_spr_ort,,,,,SOME DRIp PLOTS NOT COVERED,,q170202810,,, +170202,02/02/2017,frz,esc,HYPEr,1,auto (DS),,ae_frz_esc_H,rfl_spr_ort_geo,,1,1,,,,q170202esc,1,, +170202,02/02/2017,frz,ros,HYPEr,1,auto (DS),,ae_fis_hap_Q,rfl_spr_ort_geo,,1,1,,,,q170202ros,1,, +170202,02/02/2017,frz,ser,HYPEr,1,auto (DS),,,rfl_spr_ort_geo,,1,1,,,,q170202ser,1,, +170202,02/02/2017,nut,ole,miniMCA,1,auto (DS),,,ndvi,,,1,,,,m170202ole,,, +170202,02/02/2017,nut,521,miniMCA,1,auto (DS),,,ndvi,,,1,,,,m170202521,,, +170202,02/02/2017,nut,pad,miniMCA,1,auto (DS),,,ndvi,,,1,,,,m170202pad,,, +170203,03/02/2017,nut,ole,sequoia,1,on the laptop/dropboxAE,junto ole y pad,,ndvi,,,1,1,,,q170203ole,,, +170203,03/02/2017,nut,pad,sequoia,1,on the laptop/dropboxAE,junto ole y pad,,ndvi,,,1,1,,,q170203pad,,, +170203,03/02/2017,nut,111,sequoia,1,auto (DS),,,ndvi,,,0,0,,,q170203111,,, +170202,02/02/2017,nut,710,sequoia,1,,,,,,,0,,,,q170202710,,, +170207,07/02/2017,ceb,ce1,NIR,1,,"Respaldo en disco externo ADATA ""A 2017""",,,,,,,,,q170207ce1,,, +170207,07/02/2017,ceb,ce2,NIR,1,,"Respaldo en disco externo ADATA ""A 2017""",,,,,,,,,q170207ce2,,, +170207,07/02/2017,nut,fx1,sequoia,1,auto (DS),,,ndvi,,,1,0,,,q170207fx1,,, +170207,07/02/2017,nut,fx2,sequoia,1,on the laptop/dropboxAE,,,ndvi,,,1,1,,,q170207fx2,,, +170210,10/02/2017,bw_,810,FLIR,1,auto (DS),,,pix?,1,,,,,,q170210810,,,pedacito sur +170210,10/02/2017,frz,esc,FLIR,1,auto (DS),,,celsius mosaic,,,1,,,,q170210esc,1,, +170210,10/02/2017,frz,ros,FLIR,1,auto (DS),,,celsius mosaic,,,1,,,,q170210ros,1,, +170210,10/02/2017,frz,ser,FLIR,1,auto (DS),,,celsius mosaic,,,1,,,,q170210ser,1,, +170210,10/02/2017,bw_,810,HYPEr,1,auto (DS),paneles referencia,,rfl_spr_ort,,,,,SOME DRIp PLOTS NOT COVERED,,q170210810,,, +170210,10/02/2017,frz,esc,HYPEr,1,auto (DS),,,rfl_spr_ort_geo,,1,1,,,,q170210esc,1,, +170210,10/02/2017,frz,ros,HYPEr,1,auto (DS),,,rfl_spr_ort_geo,,1,1,,,,q170210ros,1,, +170210,10/02/2017,frz,ser,HYPEr,1,auto (DS),,,rfl_spr_ort_geo,,,1,,,,q170210ser,1,, +170210,10/02/2017,nut,fx2,miniMCA,1,auto (DS),,,ndvi,,,1,,,,m170210fx2,,, +170210,10/02/2017,fis,710,RGB,1,on the laptop,,,dsm & mosaic,1,,1,,,,q170210710,,, +170210,10/02/2017,fis,hap,RGB,1,on the laptop,,,dsm & mosaic,1,,1,,,1,q170210hap,,, +170211,11/02/2017,nut,po5,sequoia,1,on the laptop,copiado al PC proyecto. sin biomasa?,,ndvi,,,1,0,,,q170211po5,,, +170211,11/02/2017,nut,coc,sequoia,1,on the laptop,copiado al PC proyecto.,,ndvi,,,1,1,,,q170211coc,,, +170211,11/02/2017,nut,d16,sequoia,1,on the laptop,"Block1216,copiado al PC proyecto. sin biomasa?",,ndvi,,,1,0,,,q170211d16,,, +170213,13/02/2017,fis,hap,RGB,1,on the laptop/ADATA A2017,,,dsm & mosaic,1,,1,,,1,q170213hap,,, +170213,13/02/2017,fis,hap,sequoia,1,,,,reflectance & NDVI,,,1,,,,q170213hap,,, +170215,15/02/2017,fis,hap,RGB,1,on the laptop/ADATA A2017,,,dsm & mosaic,1,,1,,,1,q170215hap,,, +170215,15/02/2017,frz,ser,RGB,1,,,,mosaico,,,1,,,,q170215ser,1,, +170215,15/02/2017,fis,hap,sequoia,1,,,,reflectance & NDVI,,,1,,,,q170215hap,,, +170216,16/02/2017,bw_,810,FLIR,1,auto (DS),,ae_bw_810_T,pix?,1,,,,,,q170216810,,,pedacitito al este +170216,16/02/2017,frz,esc,FLIR,1,auto (DS),,ae_frz_esc_T,celsius mosaic,,,1,,,,q170216esc,1,,pedacitito al este +170216,16/02/2017,frz,ser,FLIR,1,auto (DS),,,celsius mosaic,,,1,,,,q170216ser,1,,pedacitito al este +170216,16/02/2017,bw_,810,HYPEr,1,auto (DS),"nubes alrededor, jaz_bw",ae_bw_810_H,rfl_spr_ort,,,,,SOME DRIp PLOTS NOT COVERED,,q170216810,,,pedacitito al este +170216,16/02/2017,frz,esc,HYPEr,1,auto (DS),"nubes cerca,jaz_stars",ae_frz_esc_H,rfl_spr_ort_geo,,,1,,,,q170216esc,1,,pedacitito al este +170216,16/02/2017,frz,ser,HYPEr,1,auto (DS),"nubes alrededor,jaz_stars",,rfl_spr_ort_geo,,,1,,,,q170216ser,1,,pedacitito al este +170220,20/02/2017,frz,ros,NIR,1,,no RTK,,mosaico,,,…,,,,q170220ros,1,,pedacitito al este +170220,20/02/2017,fis,hap,RGB,1,on the laptop/ADATA A2017,,,dsm & mosaic,1,,1,,,1,q170220hap,,,pedacitito al este +170220,20/02/2017,fis,710,RGB,1,on the laptop/ADATA A2017,,,dsm & mosaic,1,,1,,,,q170220710,,,pedacitito al este +170220,20/02/2017,fis,hap,sequoia,1,,,,reflectance & NDVI,,,1,,,,q170220hap,,,pedacitito al este +170221,21/02/2017,nut,po5,sequoia,1,on the laptop,copiado al PC,,ndvi,,,1,1,,,q170221po5,,,pedacitito al este +170221,21/02/2017,nut,coc,sequoia,1,on the laptop,copiado al PC,,ndvi,,,1,1,,,q170221coc,,,pedacitito al este +170221,21/02/2017,nut,d16,sequoia,1,on the laptop,"Block1216,copiado al PC",,ndvi,,,1,1,,,q170221d16,,,pedacitito al este +170222,22/02/2017,bw_,810,FLIR,1,auto (DS),,,pix?,1,,,,,,q170222810,,,pedacitito al este +170222,22/02/2017,frz,esc,FLIR,1,auto (DS),,,celsius mosaic,,,1,,,,q170222esc,1,, +170222,22/02/2017,frz,ros,FLIR,1,auto (DS),,,celsius mosaic,,,1,,,,q170222ros,1,, +170222,22/02/2017,frz,ser,FLIR,1,auto (DS),,,celsius mosaic,,,1,,,,q170222ser,1,, +170222,22/02/2017,bw_,810,HYPEr,1,auto (DS),"despejado, jaz_170222",,rfl_spr_ort,0,,,,SOME DRIp PLOTS NOT COVERED,,q170222810,,, +170222,22/02/2017,frz,esc,HYPEr,1,auto (DS),"despejado, jaz_170222",,rfl_spr_ort_geo,,1,1,,,,q170222esc,1,, +170222,22/02/2017,frz,ros,HYPEr,1,auto (DS),"despejado, jaz_170222",,rfl_spr_ort_geo,,,1,,,,q170222ros,1,, +170222,22/02/2017,frz,ser,HYPEr,1,auto (DS),"despejado, jaz_170222",,rfl_spr_ort_geo,,1,1,,,,q170222ser,1,, +170222,22/02/2017,nut,521&230,miniMCA,1,auto (DS),,,ndvi,,,1,,,,m170222521&230,,, +170222,22/02/2017,fis,hap,RGB,1,on the laptop/ADATA A2017,,,dsm & mosaic,1,,1,,,1,q170222hap,,, +170227,27/02/2017,frz,esc,RGB,1,,no RTK,,mosaico,,,…,,,,q170227esc,1,, +170301,01/03/2017,nut,sa1,sequoia,1,on the laptop,copiado al pc el mosaico. cirrus,,ndvi,,,1,1,,,q170301sa1,,, +170301,01/03/2017,nut,sa2,sequoia,1,on the laptop,copiado proyecto al pc. cirrus,,ndvi,,,1,1,,,q170301sa2,,, +170302,02/03/2017,nut,cas,sequoia,1,dropbox/AE,,,ndvi,,,1,1,,,q170302cas,,, +170302,02/03/2017,nut,521&230,sequoia,1,dropbox/AE,,,ndvi,,,1,,,,q170302521&230,,, +170302,02/03/2017,nut,521&230,miniMCA,1,auto (DS),,,ndvi,,,1,,,,m170302521&230,,, +170308,08/03/2017,fis,hap,RGB,1,on the laptop/ADATA A2017,clouds,,dsm & mosaic,1,,1,,,1,q170308hap,,, +170310,10/03/2017,nut,521&230,miniMCA,1,auto (DS),,,ndvi,,,1,,,,m170310521&230,,, +170310,10/03/2017,nut,521&230,sequoia,1,dropbox/AE,,,ndvi,,,1,,,,q170310521&230,,, +170310,10/03/2017,bw_,810,FLIR,1,,,,pix?,1,,,,,,q170310810,,, +170310,10/03/2017,frz,ros,FLIR,1,,,,celsius mosaic,,,1,,,,q170310ros,1,, +170310,10/03/2017,frz,ser,FLIR,1,,,,celsius mosaic,,,1,,,,q170310ser,1,, +170310,10/03/2017,frz,ros,HYPEr,1,,,,rfl_spr_ort_geo,,,1,,,,q170310ros,1,, +170310,10/03/2017,frz,ser,HYPEr,1,,,,rfl_spr_ort_geo,,,1,,,,q170310ser,1,, +170313,13/03/2017,nut,po5,sequoia,1,on the laptop,,,ndvi,,,1,1,,,q170313po5,,, +170313,13/03/2017,nut,coc,sequoia,1,on the laptop,copiado mosaico al pc,,ndvi,,,1,1,,,q170313coc,,, +170313,13/03/2017,nut,d16,sequoia,1,on the laptop,"Block1216,copiado mosaico al pc",,ndvi,,,1,1,,,q170313d16,,, +170315,15/03/2017,bw_,810,FLIR,1,,,,pix?,1,,,,SOME DRIp PLOTS NOT COVERED,,q170315810,,, +170315,15/03/2017,frz,esc,FLIR,1,,,,celsius mosaic,,,1,,,,q170315esc,1,, +170315,15/03/2017,frz,ros,FLIR,1,,,,celsius mosaic,,,1,,,,q170315ros,1,, +170315,15/03/2017,frz,ser,FLIR,1,,,,celsius mosaic,,,1,,,,q170315ser,1,, +170315,15/03/2017,bw_,810,HYPEr,1,,,,rfl_spr_ort,0,,,,,,q170315810,,, +170315,15/03/2017,frz,esc,HYPEr,1,,,,rfl_spr_ort_geo,,,1,,,,q170315esc,1,, +170315,15/03/2017,frz,ros,HYPEr,1,,,,rfl_spr_ort_geo,,,1,,,,q170315ros,1,, +170315,15/03/2017,frz,ser,HYPEr,1,,,,rfl_spr_ort_geo,,,1,,,,q170315ser,1,, +170315,15/03/2017,nut,521&230,miniMCA,1,auto (DS),,,ndvi,,,1,,,,m170315521&230,,, +170315,15/03/2017,nut,521&230,sequoia,1,dropbox/AE,,,ndvi,-,,1,,,,q170315521&230,,, +170324,24/03/2017,est,bw3,RGB,1,,"Respaldo raw data en disco externo ADATA ""A 2017"". despejado",,pix,-,,rtk 521,,,,q170324bw3,,, +170324,24/03/2017,est,bw4,RGB,1,,"Respaldo raw data en disco externo ADATA ""A 2017"". despejado",,pix,-,,rtk 521,,,,q170324bw4,,, +170329,29/03/2017,bw_,810,FLIR,1,,,,pix?,1,,,,,,q170329810,,,parte superior +170329,29/03/2017,frz,esc,FLIR,1,,,,celsius mosaic,-,,1,,,,q170329esc,1,, +170329,29/03/2017,frz,ros,FLIR,1,,,,celsius mosaic,-,,1,,,,q170329ros,1,, +170329,29/03/2017,frz,ser,FLIR,1,,,,celsius mosaic,-,,1,,,,q170329ser,1,, +170329,29/03/2017,sed,sed,FLIR,1,,,,celsius mosaic,,,1,,,,q170329sed,,, +170329,29/03/2017,bw_,810,HYPEr,1,,,,rfl_spr_ort,0,,,,"SOME DRIp PLOTS NOT COVERED, drip not adjusted",,q170329810,,, +170329,29/03/2017,frz,esc,HYPEr,1,,,,rfl_spr_ort_geo,-,,1,,,,q170329esc,1,, +170329,29/03/2017,frz,ros,HYPEr,1,,,,rfl_spr_ort_geo,-,,1,,,,q170329ros,1,, +170329,29/03/2017,frz,ser,HYPEr,1,,,,rfl_spr_ort_geo,-,,1,,,,q170329ser,1,, +170329,29/03/2017,nut,521&230,miniMCA,1,auto (DS),,,ndvi,-,,1,,,,m170329521&230,,, +170329,29/03/2017,sed,sed,miniMCA,1,,,,mosaico,-,,1,,,,q170329sed,,, +170330,30/03/2017,fis,hap,RGB,1,on the laptop/ADATA A2017,"there was an issue with the datum in pix 4D. I rested 32.316 m to get the plant height, but it looks some cm lower than the previous date.",,dsm & mosaic,1,,1,,,1,q170330hap,,, +170330,30/03/2017,fis,hap,sequoia,1,,,,reflectance & NDVI,1,,1,,,,q170330hap,,, +170403,03/04/2017,nut,po5,sequoia,1,on the laptop,proyecto copiado a PC,,pix…,,,0,1,,,q170403po5,,, +170403,03/04/2017,nut,coc,sequoia,1,on the laptop,proyecto copiado a PC,,ndvi,,,1,1,,,q170403coc,,, +170403,03/04/2017,nut,d16,sequoia,1,on the laptop,"Block1216,mosaico copiado a PC",,ndvi,,,1,1,,,q170403d16,,, +170405,05/04/2017,bw_,810,FLIR,1,,,,pix?,1,,,,,,q170405810,,, +170405,05/04/2017,frz,esc,FLIR,1,,,,celsius mosaic,,,1,,,,q170405esc,1,, +170405,05/04/2017,frz,ros,FLIR,1,,,,celsius mosaic,,,1,,,,q170405ros,1,, +170405,05/04/2017,frz,ser,FLIR,1,,,,celsius mosaic,,,1,,,,q170405ser,1,, +170405,05/04/2017,bw_,810,HYPEr,1,,checar pasadas extras,,rfl_spr_ort,0,,,,"SOME DRIp PLOTS NOT COVERED, drip not adjusted nor october area",,q170405810,,, +170405,05/04/2017,frz,esc,HYPEr,1,,,,rfl_spr_ort_geo,,,1,,,,q170405esc,1,, +170405,05/04/2017,frz,ros,HYPEr,1,,,,rfl_spr_ort_geo,,,1,,,,q170405ros,1,, +170405,05/04/2017,frz,ser,HYPEr,1,,,,rfl_spr_ort_geo,,,1,,,,q170405ser,1,, +170405,05/04/2017,nut,521&230,miniMCA,1,auto (DS),,,ndvi,,,1,,,,m170405521&230,,, +170406,06/04/2017,nut,521&230,sequoia,1,dropbox/AE,,,ndvi,,,1,,,,q170406521&230,,, +170411,11/04/2017,bw_,810,FLIR,1,,out of focus,,tif,-,,,,,,q170411810,,, +170411,11/04/2017,frz,esc,FLIR,1,,,,celsius mosaic,-,,1,,,,q170411esc,1,, +170411,11/04/2017,bw_,810,HYPEr,1,,,,rfl_spr_ort,0,,,,"SOME DRIp PLOTS NOT COVERED, drip not adjusted nor october area, nor 2 IR",,q170411810,,, +170411,11/04/2017,frz,esc,HYPEr,1,,,,rfl_spr_ort_geo,-,,1,,,,q170411esc,1,, +170411,11/04/2017,sed,sed,miniMCA,1,,,,mosaico,-,,1,,,,q170411sed,,, +170411,11/04/2017,sed,sed,FLIR,1,,"out of focus, 20cm",,tif,-,,,,,,q170411sed,,, +170411,11/04/2017,bw_,hea,miniMCA,1,,,,ndvi,0,,1,,,,q170411hea,,, +170411,11/04/2017,bw_,hea,FLIR,1,,"out of focus, 20cm",,tif,0,,,,,,q170411hea,,, +170418,18/04/2017,frz,esc,FLIR,1,,,,celsius mosaic,,,1,,,,q170418esc,1,, +170418,18/04/2017,frz,ros,FLIR,1,,,,celsius mosaic,,,1,,,,q170418ros,1,, +170418,18/04/2017,frz,ser,FLIR,1,,,,celsius mosaic,,,1,,,,q170418ser,1,, +170418,18/04/2017,sed,sed,FLIR,1,,,,celsius mosaic,,,1,,,,q170418sed,,, +170418,18/04/2017,frz,esc,HYPEr,1,,,,rfl_spr_ort_geo,,,1,,,,q170418esc,1,, +170418,18/04/2017,frz,ros,HYPEr,1,,,,rfl_spr_ort_geo,,,1,,,,q170418ros,1,, +170418,18/04/2017,frz,ser,HYPEr,1,,,,rfl_spr_ort_geo,,,1,,,,q170418ser,1,, +170426,26/04/2017,sed,sed,FLIR,1,,,,celsius mosaic,,,1,,,,q170426sed,,, +170426,26/04/2017,sed,sed,miniMCA,1,,,,mosaico,,,1,,,,q170426sed,,, +170426,26/04/2017,bw_,sed,FLIR,1,,también bw heat salió en la foto,,pno…,,,,,,,q170426sed,,, +170502,02/05/2017,bw_,hea,FLIR,1,,,,cel,,,,,ok,,q170502hea,,, +170502,02/05/2017,bw_,hea,HYPEr,1,,,,rfl_spr_ort,,,,,ok,,q170502hea,,, +170511,11/05/2017,sed,sed,FLIR,,,,,celsius mosaic,,,1,,,,q170511sed,,, +170511,11/05/2017,sed,sed,miniMCA,1,,,,mosaico,,,1,,,,q170511sed,,, +170511,11/05/2017,bw_,hea,FLIR,,,,,cel,,,,,ok,,q170511hea,,, +170511,11/05/2017,bw_,hea,HYPEr,,,,,capture error,,,,,issues,,q170511hea,,, +170512,12/05/2017,bw_,sed,RGB,1,auto (DS),,,mosaico,,,1,,,,q170512sed,,, +170512,12/05/2017,bw_,hea,RGB,1,auto (DS),,,mosaico,,,1,,,,q170512hea,,, +170522,22/05/2017,bw_,hea,FLIR,1,auto (DS),,,cel,,,,,ok,,q170522hea,,, +170522,22/05/2017,bw_,hea,HYPEr,1,auto (DS),"cirrus, Jaz 80 81 82 83",,rfl_spr_ort,,,,,issues,,q170522hea,,, +170531,31/05/2017,bw_,hea,FLIR,,,,,cel,,,,,ok,,q170531hea,,, +170531,31/05/2017,bw_,hea,HYPEr,1,,very windy,,rfl_spr_ort,,,,,issues,,q170531hea,,, +170531,31/05/2017,sed,sed,FLIR,1,,"clear sky, windy",,celsius mosaic,,,1,,,,q170531sed,,, +170531,31/05/2017,sed,sed,miniMCA,1,,"very windy, weeds all around",,mosaico,,,1,,,,q170531sed,,, +,,,,,,,,,,,,,,,,,,, +,,,,,,,,,,,,,,,,,,, +,,,,,,,,,,,,,,,,,,, +,,,,,,,,,,,,,,,,,,, +,,,,,,,,,,,,,,,,,,, +,,,,,,,,,,,,,,,,,,, +,,,,,,,,,,,,,,,,,,, diff --git a/r/shiny_listFiles/helpers.R b/r/shiny_listFiles/helpers.R new file mode 100644 index 0000000..548fbe2 --- /dev/null +++ b/r/shiny_listFiles/helpers.R @@ -0,0 +1,35 @@ +saveData <- function(data) { + #data <- t(data) + + #test to save to file + write.csv(data, file="C://Dropbox//data//shiny_output_test.csv") +} + +#Load data function... +loadDBData <- function() { + # create a connection to a DB in postres + # save the password that we can "hide" it as best as we can by collapsing it + pw <- { + "cimmyt" + } + + # loads the PostgreSQL driver + drv <- dbDriver("PostgreSQL") + # creates a connection to the postgres database + # note that "con" will be used later in each connection to the database + # here the connection values should be specified + connection <- dbConnect(drv, dbname = "ceneb_nut", + host = "localhost", port = 5432, + user = "postgres", password = pw) + rm(pw) # removes the password + + # fetch the data from postgreSQL #capture" table + df_log <- dbGetQuery(connection, "SELECT * from capture") #Returns a dataframe type + + # close the connection when finished + dbDisconnect(connection) + dbUnloadDriver(drv) + ##>>>>>>>>>>>>># } connection to DB losed + + df_log +} \ No newline at end of file diff --git a/r/shiny_listFiles/rsconnect/shinyapps.io/nutrs/shiny_exampleapp.dcf b/r/shiny_listFiles/rsconnect/shinyapps.io/nutrs/shiny_exampleapp.dcf new file mode 100644 index 0000000..70762ef --- /dev/null +++ b/r/shiny_listFiles/rsconnect/shinyapps.io/nutrs/shiny_exampleapp.dcf @@ -0,0 +1,8 @@ +name: shiny_exampleapp +title: +account: nutrs +server: shinyapps.io +appId: 201629 +bundleId: 925472 +url: https://nutrs.shinyapps.io/shiny_exampleapp/ +when: 1501711471.56152 diff --git a/r/shiny_listFiles/server.R b/r/shiny_listFiles/server.R new file mode 100644 index 0000000..a9c0ca3 --- /dev/null +++ b/r/shiny_listFiles/server.R @@ -0,0 +1,75 @@ +#This procedure loads the log DB of the flights +#thanks https://ipub.com/shiny-crud-app/ +# https://rstudio.github.io/DT/options.html +# Thanks to https://shiny.rstudio.com/articles/persistent-data-storage.html +#thanks to R documentation +#Edited: Lorena + +library (raster) +library (sp) +library(rgdal) +require("RPostgreSQL") +library(rgdal) +source("helpers.R") + +shinyServer(function(input, output,session) { + + #############Get the data from the DB + df_log <- loadDBData() + + #Get the available unique data from the log table + sensors <- unique(df_log$sensor) #sensors + #date <- unique(df_log$cap_date) #dates + trials <- unique(df_log$area) #trials + + + #get the sensors as the options of a check box + output$choose_cam <- renderUI({ + # If missing input, return to avoid error later in function + #if(is.null(sensors)) + # return() + + # Create the checkboxes and select them all by default + checkboxGroupInput("chk_cam", "Choose sensors", + choices = sensors, + selected = NULL) + }) + + #get the available trials as the options of a check box + output$choose_trial <- renderUI({ + # If missing input, return to avoid error later in function + #if(is.null(sensors)) + # return() + + # Create the checkboxes and select them all by default + checkboxGroupInput("chk_trial", "Choose trial", + choices = trials, + selected = NULL) + }) + + + #Render the table to display in the GUI + output$logTable <- DT::renderDataTable({ + + #update after submit is clicked + input$submit + #Filter the data as needed + df_log <- df_log[df_log$area %in% c(input$chk_trial) & df_log$sensor %in% c(input$chk_cam), ] + #save to csv the data + #saveData(df_log) # This works! + + #The data retrieved from the DB, filtered + df_log #This will be showed in the table UI + + + },options = list(pageLength = 15)) #show 15 rows by default + + #React when a row is selected + # (Not working yet) # ! # + # observeEvent(input$logTable_rows_selected, { + # output$txtOut <- renderText({ + # data <- loadData()[input$logTable_rows_selected,] ### loadData() function???? + # sprintf("You have selected: %s",data)}) #!# this doesnt work, how get the value selected I just get the index within the filtered table + # }) + } +) \ No newline at end of file diff --git a/r/shiny_listFiles/ui.R b/r/shiny_listFiles/ui.R new file mode 100644 index 0000000..e69de29 diff --git a/r/shiny_listFiles/www/circles in a circle Kandinsky.jpg b/r/shiny_listFiles/www/circles in a circle Kandinsky.jpg new file mode 100644 index 0000000000000000000000000000000000000000..e9b6c4aa148fa5052fa6c6b50931f7dd0af95446 GIT binary patch literal 2887 zcmbW!c{J4T9tZI6m|79rymu@1Ar2y7#`H=Q-y&&vrhq^Kd?MW`KQ`W)@}u2m}J$ zxeef~0m3GMNS{yu1V8`)FyJmt0_Oo95NNmm6)-P&H+&EXm>0?ih3*a*d=DH3@47efJi(3&5bT-4p*?IPU;q7@z^DgF*5Dk1z-<4B~VF zvH$?$+{J8xoYW`4oK(*BZz zqm%RHE6D3Nyl&p|zWpE|Feo@A^kGbF+~fF!#3yO#8JStx&vSBL7MGNkl~+_&)xCLJ z-+*mwYJT6@)!ozE_o07uY#cv9_)H|t&do0@eqCCotkA!0e*eMPV*dQK>jDAbf3g0S z{fA4K>*C@50R+110`UZM2Ur-wD|d=d#Ml<JRQ8k#VW*d9>+Rg!&>DaGbr zoNot*%Q0gLIeod33a%dtGo1Ir~Gh^nolTWPUj?W}Z>?M<~v=Rmc7yAb9C-o5z%T&c>^uda^RoPux zq4=V3bo7})f0)xqU25SovByu#fzEiDz67CUR5Ap1ZjuQ&h^%jW+% z>qq=L=E(jujySQSB9OQKHY`(Y%Svv@y5)L}WY1-I5Bx4%hl(DsCnq;GS09gX+vLso zsvCfmHVLh4Jy5tX9yIFYdY0eBJZO!N6XSqOX7O^}L$hosvf#WFGG zdMk&7>3gEmYTb@Z3dFkq^nXn{)jE=3PZe|0>+R+MBCJ2DV*xH&gZ+R&=cjyXSdeSm z(tH6ihVg_B-4PA$ImiKGQOY5~oe#0m^&eF+c0)s|sE_3)byd7dC0?6+G{+mN^8F=A zHy3``d_HrP(2n#YnO|aMhWk`qT_xEt!kF>1P6;)U+h&!eaz;Tp8iEJIE+#oeCCsH$ zdlwGV?l&IHTxqtyn)(MiAnjXNb)egW^|c2E$1&AmM&zXh4p7bzFal_-S5_j+zg$1R zvu&P|tkqd~W?{6Hdo;+Z)XBY%IIQWgt>nE~l z+OxhJPtk`J)B>-aquhCeZP^o)nDIj(M>@*&2Q80*rHQZapD&OOk+s3$8Yghz5K=^f zhl{s@i+7f{Cu{uGslyiNujgibh2UrI6`vWNR%*(dmE94yy^E`$66TgVT=HbZBoQBT zr*G#5&NTUWoP_kUQ~Vkmp|`SibUH>@H7NCvm|1dEJ!nQo#9GCb?thw`7?`XsSMlKh zy#)DT(?NUiTy`?T8Q~~zVw(7nEOSNkWxW%&Ij?@YhCspCXT=s4>wYvmUPYAP-D)095ljilwi=tuxs3RvnvZI!tQn-k>f_noidipdp)FVw2H)YCI zQ!=70Yq#Us{K@k#lxGsIY68h9kndhKIMhY6-xN@Gh=j4ehN21}} zPJz{(jbiKi@B1riQ~k>_OVED!(C?2%-q6a~aAR7}6hK+305t0CUIr3tD0XO;iU z{tH1sUgC$Kpuh{CXGF?J+ho|ohX7Xoox2A5GprM5$2J@x>2^LUNw?qhVtHJi1sg=> zo0|@Jh(3r}WVAPItc%j(eY3kZh!I_`^0(ObH9H8r9O2l;eSC3UX4ahWvQl_1Ms~d( zvudO|e%c0#>TQ4PSTx7_ov`LVFn3LFpi?ZpH}1XRAi+)^{2JsQJ1edWIE`N#K-y*F zR}^RQTDd5!!AefDCB8HfqoS5ljiN5uADk6`*>A;m;>-*-(k~29~L=& zsjgbNETkUI=Nb8YNc36PK<-vb68Q}xb#8-)(!drS{l0!^v(wT0Mwv75ebxlpTjD)2 z?xLt@L`I>UmpSjTRgU(&eKI;XH+X->96q4nKpllbDL<+Yh3?GyoA zz}~SRFY6Ge@wJU4 zlao>jTRW4A$8brk-}MDgCGj)LUSwGp1A@epiQXQfo>x2{Jnbn2&OK6yzvI+QYse0K hpqNH!P`6MJ3l0nL^8ejJMLK=e^nl~zs}&sLKLBl#d-DJQ literal 0 HcmV?d00001 diff --git a/r/shiny_output_test.csv b/r/shiny_output_test.csv new file mode 100644 index 0000000..05f8550 --- /dev/null +++ b/r/shiny_output_test.csv @@ -0,0 +1 @@ +"","id_capture","cap_date","cap_project","area","sensor" diff --git a/r/stackBandsSequoia.R b/r/stackBandsSequoia.R new file mode 100644 index 0000000..0197fda --- /dev/null +++ b/r/stackBandsSequoia.R @@ -0,0 +1,67 @@ +#### +# it is used to stack in one file the individual bands from the sequoia ( for COMPASS) +# It searches in the structure of the Pix4D results folders OR you can set to work on the base directory + +#0. First step : load packages +#install.packages(c("rgdal","raster")) +#require(raster) +#require(rgdal) #it is called in the functions file + +######################## DEFINE VARIABLES (Enter your variables) +# #Working directory +wd= choose.dir(default = "", caption = "Select base working directory for the imagery") +setwd(wd) +sprintf("Wd: %s",wd) + +#Choose file list as date | trial1 | trial2...(our standard format) +listDates <- choose.files(default = "", caption = "Selecciona la matriz de nombres y fechas de archivos a procesar", + multi = FALSE, filters = matrix(c("csv", "*.csv")), + index = nrow(Filters)) + +#choose shapefile directory. The name of the ROI is expected to be like: q21__af__roi where q21 is the 3 char id for the trial, af is the cycle id +shpFolder= choose.dir(default = "", caption = "Select the folder where the shapefiles are (Polygon ROIs to crop imagery)") +sprintf("shpF: %s",shpFolder) + +#trial <-"coc" #this will be selected from the matrix +#cam <- "q" this will be selected from the matrix +ext <- ".tif" #This is assumed +cycle <- "af" #code for the cycle. af is 2017-2018 +if_crop <- TRUE #tell if the imagery should be cropped + +################### Functions +source(file.path("C:","Dropbox","Software","Scripts","r","functions_ImgProcessing.R", fsep = .Platform$file.sep)) + +data############### Script +tryCatch({ + data<-read.table(listDates, header = TRUE,sep = ",") #get the data in the csv + row.names(data)<-data[,1] # name the rows as the date + dates<- row.names(data) + #Get the trial names + data<- data[,2:length(colnames(data))] #Remove 1st column that had the dates + trials<- colnames(data) #assign them as column names of the dataframe. This could be used to + + for (trial in trials){ + #read the roi boundary + roi <- get_shape(paste0(trial,"__",cycle,"__roi"),shpFolder) + for(date in dates){ + for(cam in data[date,trial]){ + if (cam != "-"){ + #construct the codename of the image + name <- paste0(cam,date,trial) + ##path of the standard Pix4D folder structure + path <- file.path(wd,name,name,"4_index","reflectance", fsep = "\\") + ##Use the working directory path if all the imagery bands were copied to 1 same location + #path <- wd + print(sprintf("processing bands... %s\\%s",path,name)) + #stack the bands and save in the WD + s <- stackSeq(name,path) + #cut the excess outside the ROI boundary + s_crop<-crop(s, extent(roi), snap="out") #require(raster) + outFile <- file.path(wd,paste0(name,"rfl.tif"), fsep = .Platform$file.sep) + print(paste("will write this to disk: ",outFile)) + writeRaster(s_crop, file=outFile, datatype='FLT4S',format="GTiff",overwrite=FALSE) + } + } + } + } +}) diff --git a/r/stackCVStoXLSX.R b/r/stackCVStoXLSX.R new file mode 100644 index 0000000..e933577 --- /dev/null +++ b/r/stackCVStoXLSX.R @@ -0,0 +1,24 @@ +####### +##script to save to Excel lots of CVS in a folder +####### +require(xlsx) +##Set working drectory +setwd("C:\Dropbox\data\AC\AC_SAM\data\m") + +##Something like this to read all the cvs files +##Another example of use: list.files("C:/temp",full=TRUE) +csv_files = list.files(pattern="*.csv$") #List of the files +data <- do.call("rbind", lapply(csv_files, read.csv, header=TRUE)) #Read them all, like a loop + +#Create file +library(xlsx) #load the package +write.xlsx(x = data, file = "test.excelfile.xlsx", + sheetName = "TestSheet", row.names = FALSE) + +#Write to existing file +workbook.sheets +workbook.test +addDataFrame(x = sample.dataframe, sheet = workbook.test, +row.names = FALSE, startColumn = 1) # write data to sheet starting on line 1, column 4 +saveWorkbook(workbook.sheets, "test.excelfile.xlsx") # and of course you need to save it. + diff --git a/r/stackMCAbandsAsExportedFromAutopano.R b/r/stackMCAbandsAsExportedFromAutopano.R new file mode 100644 index 0000000..0cbfab8 --- /dev/null +++ b/r/stackMCAbandsAsExportedFromAutopano.R @@ -0,0 +1,39 @@ +#Script to stack the Multispectra bands from the Autopano mosaic +#Names should be like: +# m[date][trial]pno-0.tif +# m[date][trial]pno-1.tif +# e.g. m160204bw8pno-1.tif +# The required input is: date and trial and the working directory +#This is assuming the name formatting of the greenplane +#0. First step : load packages +#install.packages(c("rgdal","raster")) +require(raster) +require(rgdal) + +#Function multibandStack +multibandStack <- function(imgBasename,wd) +{ + tryCatch({ + setwd(wd) + #Input file with bands 1,2,3 + img_basename<-imgBasename + img_0<-paste(img_basename,'-0.tif',sep = "") + imported_raster_0=raster(img_0) + #Input file with bands 4,5,6 + img_1<-paste(img_basename,'-1.tif',sep = "") + imported_raster_1=raster(img_1) + #plot(imported_raster_0) #see image just to test + #stack 6 bands in one pile + img=stack(list(img_0,img_1)) + #Write the tif to disk + writeRaster(img, file=paste(imgBasename,".tif",sep = ""),datatype='INT2U',format="GTiff",overwrite=FALSE) + }) +} + +#Working directory +date ="160129" +trial = "ros" +wd = paste('G:\\AD15_16\\',date,'\\m\\pno',sep="") +img_b<-paste('m',date,trial,'pno',sep="") +multibandStack(img_b,wd) + diff --git a/r/stackMCAbandsAsExportedFromAutopanoFolder.R b/r/stackMCAbandsAsExportedFromAutopanoFolder.R new file mode 100644 index 0000000..ebdf2c9 --- /dev/null +++ b/r/stackMCAbandsAsExportedFromAutopanoFolder.R @@ -0,0 +1,47 @@ +#Script to stack the Multispectra bands from the Autopano mosaic +#Names should be like: +# m[date][trial]pno-0.tif +# m[date][trial]pno-1.tif +# e.g. m160204bw8pno-1.tif +# The required input is: date and trial and the working directory +#This is assuming the name formatting of the greenplane +#0. First step : load packages +#install.packages(c("rgdal","raster")) +require(raster) +require(rgdal) +library(stringr) + +#Function multibandStack +multibandStack <- function(imgBasename, wd) +{ + tryCatch({ + setwd(wd) + #Input file with bands 1,2,3 + img_basename<-imgBasename + img_0<-paste(img_basename,'-0.tif',sep = "") + imported_raster_0=raster(img_0) + #Input file with bands 4,5,6 + img_1<-paste(img_basename,'-1.tif',sep = "") + imported_raster_1=raster(img_1) + #plot(imported_raster_0) #see image just to test + #stack 6 bands in one pile + img=stack(list(img_0,img_1)) + #Write the tif to disk + writeRaster(img, file=paste(imgBasename,".tif",sep = ""),datatype='INT2U',format="GTiff",overwrite=FALSE) + }) +} +########### START ########################## +#Working directory +wd="G:\\AD15_16\\160525\\m\\rfl" +setwd(wd) + +imgFilesList <-list.files(pattern="\\.tif$") +##Next the names of the images, collapse to get one entry per pair(-0.tif and -1.tif) +#imgsPairNames <- unique(str_extract(imgFilesList, "TTC[:digit:]{5}")) #For individual frames +imgsPairNames <- unique(str_extract(imgFilesList, ".{0,8}")) #For mosaics {0,13} +## Sets the pattern of name in files to be stacked +for (i in imgsPairNames){ + print(paste("Image:",i)) + multibandStack(i, wd) +} +paste(print("Finished merging",toString(length(imgsPairNames)),"images")) diff --git a/r/stackMCAbandsAsExportedFromAutopano_cmd.R b/r/stackMCAbandsAsExportedFromAutopano_cmd.R new file mode 100644 index 0000000..a607c8b --- /dev/null +++ b/r/stackMCAbandsAsExportedFromAutopano_cmd.R @@ -0,0 +1,53 @@ +#Script to stack the Multispectra bands from the Autopano mosaic +#Selecting them in the windows explorer and executing this file +#Names should be like: +# m[date][trial]pno-0.tif +# m[date][trial]pno-1.tif +# e.g. m160204bw8pno-1.tif +#This is assuming the name formatting of the greenplane +#TODO: +######filter to accept the input files in any order +#######(so far cant stack if the files are not ordered by name) + + +#0. First step : load packages +#install.packages(c("rgdal","raster")) +require(rgdal) + +require(raster) + +#Function multibandStack +multibandStack <- function(img_0,img_1) +{ + tryCatch({ + print(paste("primeras 3 bandas",img_0)) + print(paste("últimas 3 bandas",img_1)) + wd<-dirname(normalizePath(img_0)) + #print(wd) + setwd(wd) + #Img basename + imgBasename<-gsub("-0.tif", "", img_0) + #print(imgBasename) + #stack 6 bands in one pile + img=stack(list(img_0=img_0,img_1=img_1)) + #Write the tif to disk + writeRaster(img, file=paste(imgBasename,".tif",sep = ""),datatype='INT2U',format="GTiff",overwrite=FALSE) + print(paste("Imagen escrita: ",imgBasename,".tif",sep = "")) + }) +} + +#Call function with the images as parameters +print ("################# Inicio de script ##################") +args <- commandArgs(TRUE) +args<- sort(args) +#See the inputs +## Loop the args and call the function in pairs +nargs <- length(args)/2 +print (paste("# of images to create:",nargs)) +for (i in 1:nargs){ + #print(args[2*i-1]) + #print(args[2*i]) + multibandStack(args[2*i-1],args[i*2]) + print("Success in round!") +} + diff --git a/r/substract_raster.R b/r/substract_raster.R new file mode 100644 index 0000000..4dc1b0f --- /dev/null +++ b/r/substract_raster.R @@ -0,0 +1,58 @@ +require(rgdal) +require(raster) + + +###################### DEFINED FUNCTIONS (no need to edit) +#Function to substract the baseline to the DSM +rest <-function(r1,base){(r1-base)} + +#Function to select the apropriate roi for the trial +# Read the feature class +get_roi <- function(trial_roi) {readOGR(dsn=fgdb,layer=trial_roi)} + + +######################## DEFINE VARIABLES (Enter your variables) +## WD +setwd("C:/Dropbox/Lectura y cursos/R/Cimmyt_Spatial Analysis in R/") + +#Feature geodatabase to get ROI +fgdb = "C:\\Dropbox\\data\\AE\\physio\\ae_fisio.gdb" + +imgLocation = "C:\\AE\\" +out_raster = "C:\\Dropbox\\data\\AE\\physio\\ae_fisio.gdb" + +######################## Execute the thing +rasterBase <- raster("Data/Modis2008-2012/MYD13Q1.MRTWEB.A2012361.005.250m_16_days_NDVI.tif") +raster1 <- raster("Data/Modis2008-2012/MYD13Q1.MRTWEB.A2012265.005.250m_16_days_NDVI.tif") +#Select one file interactively +#raster <-raster(choose.files(default = getwd(), caption = "Select XX files", +# multi = FALSE, filter = Filters <- matrix(c("TIF", ".tif"), +# 1, 2, byrow = TRUE), index = 1)) + + +#plot (raster1) + +roi <- get_roi("hap__ae__bry") +#plot (roi) + +##Crop raster +#r_crop<-crop(raster, extent(roi), snap="out") + +#Substract rasters +height <- rest(raster1, rasterBase) +#plot(height) + +##Save ROI to shp +#writeOGR(roi, "C:\\Dropbox\\data\\AE\\physio\\shp\\hap__ae__bry.shp", layer="hap__ae__bry.shp", driver="ESRI Shapefile") + +##Name for resulting raster +out_raster <- "outputR" + +#save raster +writeRaster(height, file= paste(imgLocation,out_raster,sep=""),datatype='FLT4S',format="GTiff",overwrite=FALSE) + + +#TESTS--------------------------------------------------------- +dsm <- raster("G:/AE/161026/r161026fis/r161026hap/3_dsm_ortho/1_dsm/r161026hap_dsm.tif") +r_crop<-crop(dsm, extent(roi), snap="out") +plot(r_crop) diff --git a/r/summarizeGreenseekrReadings.R b/r/summarizeGreenseekrReadings.R new file mode 100644 index 0000000..3dac99c --- /dev/null +++ b/r/summarizeGreenseekrReadings.R @@ -0,0 +1,51 @@ +# get Greenseekr measurements + + +library(plyr) + + + + + +# ongoing + + + + + + + + + + + + + +# #Working directory +wd= choose.dir(default = "", caption = "Select working directory") +setwd(wd) +## What kind of files to seach +pattern="\\Avg.txt$" +#list all imagery in the folder +files <-list.files(pattern=pattern) + +#Here we will save the summary of the readings +gs <- data.frame(Plot="0", + NDVI=0.5, + stringsAsFactors=FALSE) +cols <- c("Plot", "NDVI") + +for(f in files) { + # read text + d<-read.table(f, header = TRUE,sep = ",") + #save just the coulumns I need + d <- d[,c("Plot", "NDVI")] + #Name the plot as the input filename + d$Plot <- f + gs <- rbind(gs,d) + +} + +#AVG +#avg_gs <- ddply(gs, .("Plot"), summarize,x = max(NDVI)) + diff --git a/r/tests_generate_matix_agreggate.R b/r/tests_generate_matix_agreggate.R new file mode 100644 index 0000000..e4a05ac --- /dev/null +++ b/r/tests_generate_matix_agreggate.R @@ -0,0 +1,9 @@ +library(data.table) +x <- matrix(c("TIF", 0.9,1,"ZIP", 0.5,2,"ZIP", 0.7,2), + 3, 3, byrow = TRUE) + +rownames(x) <- c("a","b","b") +colnames(x) <- c("name","val2","val1") +x <- as.data.frame(x) +x1 <-aggregate(. ~ name, x, mean, na.rm = TRUE) +df2 <- setDT(x)[, lapply(mean), by=.(name)] diff --git a/r/timeSeries_joinData.R b/r/timeSeries_joinData.R new file mode 100644 index 0000000..fe62c1c --- /dev/null +++ b/r/timeSeries_joinData.R @@ -0,0 +1,79 @@ +##Script to get the descriptive statistics of a time series data +##Rearrange the data to have it grouped by VIs +##Reads may csv in working directory with cerain pattern +## Saves the statistics in a TXT in the same location as the data file +##July 2015 + +################# Check these inputs######################### +##Set working directory to avoid giving the absolute path of files +##IMPORTANT: use the / slash instead of the backslash \ in the paths +##Or use double backslash \\ +wd<-("C:/vuelos/ab2014/Marco/") +setwd(wd) +## Sets the pattern of name in CSV files to be read, save names without extension +data_set = gsub(".csv", "", list.files(pattern="vis[0-9]*.csv")) +#Get dates of the measuremnts +dates<-sub("vis","",x=data_set) + +#Load to a dataframe each CSV file with all the VIs per date +for (i in 1:length(data_set)) { + #assigns to variables each of the CSV + #Select the columns after the 6th one, because the firsts have ancillary data + assign(data_set[i], read.csv(paste(data_set[i],".csv",sep=""), + header = TRUE, row.names = 1, + colClasses = c("character",rep("NULL",5),rep("numeric",41)))) +} +#Get names of vis +vis_list<-sub("[A-z]*[0-9]*_?","",x=colnames(get(data_set[i]))) + +#Create empty dataframe to store re organized data by VI, separately +#vis_data<-data.frame(data.frame(matrix(nrow = 114, ncol = 1))) +#create an array to store all the vis by date +vis_data <- array(0, dim=c(114,10,41), dimnames=c("points","dates","vis")) + +date<-1 #Initialize to use it before the loop +##Loop trough the VIS +for (vi in 1:length(vis_list)) { + #Name each row as the measurement point + rownames(vis_data)<-rownames(get(data_set[date])) + + ##Loop trough the dates of one VI to store it together + for (date in 1:length(dates)) { + + ##Solution with merge function. Had troubles making it doing the ""join"" + ##withe the row names that start with number but have a letter also, like 5R or 7S + #vis_data<-merge(vis_data,get(data_set[i])[,j],by="row.names", all = TRUE) + ##Solution assigning the values to the corresponding column + vis_data[,date,vi]<-get(data_set[date])[,vi] + } + colnames(vis_data)<-dates #Name each column with the date + + #################Managed separately + ##Use this to save results to files (41 VI = 41 files) + #dir.create(paste(wd,"vis_by_date/",sep="")) #Creates a directory to store vis by date + #write.csv(vis_data, file = paste(wd,"vis_by_date/", "VIalldates.csv", sep="")) + ##Use this to save to a variable + #assign(paste("VIalldates_",vis[j], sep=""),vis_data) + + ##Create empty dataframe to store re organized data by VI (to reset this) + ##Used with the MERGE alternative + #vis_data<-data.frame(data.frame(matrix(NA, nrow = 114, ncol = 1))) + #####################Managed separately +} +##To find the index which corresponds to a VI in the vis_array: +#match("BGI2", vis) + +#### Plot average values of VIS during Cycle to PDF +pdf(file=paste("vis_by_date/"," VIS along cycle.pdf", sep="")) +#par(mfrow=c(7,6)) +for (vi in 1:length(vis_list)) { + #Gets the average of all points by date, this means the meand is applied across the COLUMNS -> MARGIN=2 + plot(1:10, apply(vis_data[,,vis=vi], MARGIN=2, FUN=mean), + main=vis_list[vi], + xlab="All 10 image dates") + + ##Gets the average of a point "5R" across all dates + #points(1, mean(vis_data[match("5R", rownames(vis_data)),,vis=vi]), xlab="5R",main=vis[vi]) +} +dev.off()#Finish saving to PDF + diff --git a/r/ui.R b/r/ui.R new file mode 100644 index 0000000..d3af3a5 --- /dev/null +++ b/r/ui.R @@ -0,0 +1,70 @@ + +shinyUI(pageWithSidebar( + + # Header: + headerPanel("R data reader"), + + # Input in sidepanel: + sidebarPanel( + tags$style(type='text/css', ".well { max-width: 20em; }"), + # Tags: + tags$head( + tags$style(type="text/css", "select[multiple] { width: 100%; height:10em}"), + tags$style(type="text/css", "select { width: 100%}"), + tags$style(type="text/css", "input { width: 19em; max-width:100%}") + ), + + # Select filetype: + selectInput("readFunction", "Function to read data:", c( + # Base R: + "read.table", + "read.csv", + "read.csv2", + "read.delim", + "read.delim2", + + # foreign functions: + "read.spss", + "read.arff", + "read.dbf", + "read.dta", + "read.epiiinfo", + "read.mtp", + "read.octave", + "read.ssd", + "read.systat", + "read.xport", + + # Advanced functions: + "scan", + "readLines" + )), + + # Argument selecter: + htmlOutput("ArgSelect"), + + # Argument field: + htmlOutput("ArgText"), + + # Upload data: + fileInput("file", "Upload data-file:"), + + # Variable selection: + htmlOutput("varselect"), + + br(), + + textInput("name","Dataset name:","Data"), + + downloadLink('downloadDump', 'Download source'), + downloadLink('downloadSave', 'Download binary') + + ), + + # Main: + mainPanel( + + tableOutput("table") + 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z|F_1+`+)a52Y&;i0H*PGz~6m@_fhUQV*N&e2JUPFDEHg4?gQMv3H}?Pm*5}6_<2kG zKFa;o^ly|drk^PPv9`XC@b_in@9`iIGYbfGf6;h<^nJ_yJ)WHH=lB23fbWmK?{B|H w@BbQo&)xpf?e2sA-4cF70u6KQe_YUC2B9Dg3)BG!ga-VT0HxHxarfze0GYxwwg3PC literal 0 HcmV?d00001 diff --git a/test.pro b/test.pro new file mode 100644 index 0000000..419e6d5 --- /dev/null +++ b/test.pro @@ -0,0 +1,24 @@ +PRO test; Start the application + e = ENVI() + + ; Open an input file + file = FILEPATH('150110_bw2_spr.bsq', ROOT_DIR='E:\150110\h150110\', $ + SUBDIRECTORY = ['ortho_res']) + raster = e.OpenRaster(file) + + ; generate a mask + mask = (raster.GetData(BAND=0) ge 220) + + ; write out the mask to a file + file = FILEPATH('test3.bsq', ROOT_DIR='c:\vuelos\', $ + SUBDIRECTORY = ['temp']) + maskRaster = ENVIRaster(mask, URI=file) + maskRaster.Save + + ; create a masked raster + rasterWithMask = ENVIMaskRaster(raster, MaskRaster) + + ; display the new raster, the masked areas are transparent + view = e.GetView() + layer = view.CreateLayer(rasterWithMask) +END \ No newline at end of file