├── .gitignore ├── www ├── logo-wur.png ├── logo-tribes.png ├── logo-ms-research.png ├── styles.css ├── logo-fapesp.svg ├── logo-unicamp.svg └── logo-cnpq.svg ├── md ├── images │ ├── tutorial-01.jpg │ ├── tutorial-02.jpg │ ├── tutorial-03.jpg │ ├── tutorial-04.jpg │ ├── tutorial-05.jpg │ ├── tutorial-06.jpg │ ├── tutorial-07.jpg │ ├── tutorial-08.jpg │ ├── tutorial-09.jpg │ ├── tutorial-10.jpg │ ├── tutorial-11.jpg │ ├── tutorial-12.jpg │ ├── tutorial-13.jpg │ └── tutorial-14.jpg ├── tutorial.md └── tutorial.Rmd ├── bfast-explorer.Rproj ├── .gitattributes ├── README.md ├── test.R ├── python ├── gee-px-ls-new.py └── gee-px-ls.py ├── sandbox.R ├── global.R ├── ui.R ├── server.R └── LICENSE /.gitignore: -------------------------------------------------------------------------------- 1 | .Rproj.user 2 | .Rhistory 3 | .RData 4 | .Ruserdata 5 | .DS_Store 6 | -------------------------------------------------------------------------------- /www/logo-wur.png: 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-------------------------------------------------------------------------------- 1 | Version: 1.0 2 | 3 | RestoreWorkspace: Yes 4 | SaveWorkspace: No 5 | AlwaysSaveHistory: Yes 6 | 7 | EnableCodeIndexing: Yes 8 | UseSpacesForTab: Yes 9 | NumSpacesForTab: 3 10 | Encoding: UTF-8 11 | 12 | RnwWeave: Sweave 13 | LaTeX: pdfLaTeX 14 | 15 | AutoAppendNewline: Yes 16 | StripTrailingWhitespace: Yes 17 | -------------------------------------------------------------------------------- /.gitattributes: -------------------------------------------------------------------------------- 1 | # Auto detect text files and perform LF normalization 2 | * text=auto 3 | 4 | # Custom for Visual Studio 5 | *.cs diff=csharp 6 | 7 | # Standard to msysgit 8 | *.doc diff=astextplain 9 | *.DOC diff=astextplain 10 | *.docx diff=astextplain 11 | *.DOCX diff=astextplain 12 | *.dot diff=astextplain 13 | *.DOT diff=astextplain 14 | *.pdf diff=astextplain 15 | *.PDF diff=astextplain 16 | *.rtf diff=astextplain 17 | *.RTF diff=astextplain 18 | -------------------------------------------------------------------------------- /www/styles.css: -------------------------------------------------------------------------------- 1 | /* customize map toolbar style */ 2 | #map-toolbar { 3 | padding: 15px 15px 15px 15px; 4 | cursor: move; 5 | /* fade out while not hovering */ 6 | opacity: 0.7; 7 | /* make sure that the toolbar is high enough to be above the Leaflet map */ 8 | z-index: 500; 9 | transition: opacity 500ms 0s; 10 | } 11 | #map-toolbar:hover { 12 | /* fade in while hovering */ 13 | opacity: 0.9; 14 | transition-delay: 0; 15 | } 16 | 17 | /* customize leaflet map */ 18 | #map-leaflet { 19 | position: fixed; 20 | top: 51px; 21 | bottom: 0; 22 | left: 0; 23 | right: 0; 24 | padding: 0; 25 | overflow: hidden; 26 | background-color: rgb(0,0,0); 27 | } 28 | 29 | /* change leaflet container background color */ 30 | .leaflet-container { 31 | background-color: rgb(0,0,0); 32 | } 33 | 34 | /* define a custom color to highlight text */ 35 | .text-highlight { 36 | color: rgb(86,175,56); 37 | } 38 | 39 | .text-heading { 40 | margin-top: 0px; 41 | } 42 | 43 | /* highlight images in About tab when hovered */ 44 | .image-highlight { 45 | border: 0; 46 | } 47 | .image-highlight img:hover { 48 | opacity: .7; 49 | } 50 | 51 | /* change Shiny error text color to blue and center it */ 52 | .shiny-output-error-validation { 53 | color: blue; 54 | text-align: center; 55 | margin-left: auto; 56 | margin-right: auto; 57 | } 58 | 59 | .hidden-n { 60 | /* fade out while not hovering */ 61 | opacity: 0.0; 62 | transition: opacity 500ms 0s; 63 | cursor: default; 64 | } 65 | .hidden-n:hover { 66 | /* fade in while hovering */ 67 | opacity: 1.0; 68 | transition-delay: 0; 69 | } 70 | 71 | /* tutorial tab images */ 72 | img[alt="md"] { 73 | max-width: 100%; 74 | width: auto; 75 | height: auto; 76 | } 77 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # BFAST Explorer v0.0.3 2 | 3 | ## Description 4 | 5 | **BFAST Explorer** is a [Shiny](https://shiny.rstudio.com/) app, developed using R and Python, designed for the analysis of *Landsat Surface Reflectance* time series pixel data. 6 | 7 | Three change detection algorithms - **bfastmonitor**, **bfast01** and **bfast** - are used in order to investigate temporal changes in trend and seasonal components, via breakpoint detection. 8 | 9 | If you encounter any bugs, please create an [issue](https://github.com/almeidaxan/bfast-explorer/issues). 10 | 11 | ## Usage 12 | 13 | Currently, this tool only supports UNIX-like systems (no Windows OS) due to the integration between R and Python. 14 | 15 | In order to successfully run this tool, apart from having R and Python installed, you need the following: 16 | 17 | * Request access to use GEE (https://earthengine.google.com/signup/) 18 | * Install and set up GEE Python API (https://developers.google.com/earth-engine/python_install) 19 | 20 | ## Citation 21 | 22 | To cite BFAST Explorer in publications, please use 23 | 24 | > Alexandre Almeida, Nathalia Menini, Jan Verbesselt, Ricardo Torres (2018). BFAST Explorer: An Effective Tool for Time Series Analysis. In: 2018 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). 22-27 July 2018. Valencia, Spain. DOI: 10.1109/IGARSS.2018.8517877. 25 | 26 | or, alternatively, the corresponding BibTeX entry 27 | 28 | ``` 29 | @inproceedings{, 30 | author = {Alexandre Almeida and Nathalia Menini and Jan Verbesselt and Ricardo Torres}, 31 | title = {BFAST Explorer: An Effective Tool for Time Series Analysis}, 32 | booktitle = {2018 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)}, 33 | location = {Valencia, Spain}, 34 | eventdate = {2018-07-22/2018-07-27}, 35 | doi = {10.1109/IGARSS.2018.8517877} 36 | } 37 | ``` 38 | -------------------------------------------------------------------------------- /test.R: -------------------------------------------------------------------------------- 1 | # BFAST01 2 | 3 | z <- rbind(serie[[1]], serie[[2]], serie[[3]]) # serie[, matchCol] 4 | z <- z[order(z$date), ] 5 | date <- z$date # serie$date 6 | z <- z$ndvi 7 | 8 | res <- ppBfast(x = z, date = date, h = 0.15) 9 | 10 | plot( 11 | res, 12 | regular = F 13 | ) 14 | 15 | # ----- 16 | 17 | ### nao incluir pois ja existe 18 | ylimCustom <- c(0,1) 19 | xAxisCustom <- seq(as.numeric(substr(range(date), 1, 4))[1] - 1, 20 | as.numeric(substr(range(date), 1, 4))[2] + 1, 21 | 1) 22 | yAxisStep <- 0.1 23 | 24 | # ----- 25 | 26 | par(mar = c(4, 4, 0, 0) + 0.1) 27 | plot(y = z, 28 | x = date, 29 | col = "white", 30 | axes = F, 31 | ylim = ylimCustom, # ylimCustom 32 | ylab = "NDVI", # ylab 33 | xlab = "Time") 34 | 35 | # redraw axis 36 | axis(side = 1, 37 | at = date(paste0(xAxisCustom, "-01-01")), 38 | labels = xAxisCustom) 39 | axis(side = 2, 40 | at = seq(0, 1, 0.1)) 41 | for(i in xAxisCustom) { 42 | abline(v = date(paste0(i, "-01-01")), 43 | col = rgb(.8, .8, .8), 44 | lty = 2) 45 | } 46 | for(i in seq(ylimCustom[1], ylimCustom[2], yAxisStep)) { 47 | if(i == 0) { 48 | # highlight a guide line at "0" 49 | abline(h = i, 50 | col = "black", 51 | lty = 3, 52 | lwd = 1.3) 53 | } else { 54 | abline(h = i, 55 | col = rgb(.8, .8, .8), 56 | lty = 2) 57 | } 58 | } 59 | box() 60 | 61 | lines(y = res$Yt, x = as.Date(time(res$Yt)), col = "gray", lwd = 2) 62 | lines(y = res$output[[1]]$Tt + res$output[[1]]$St, x = as.Date(time(res$Yt)), col = "blue", lwd = 2) 63 | 64 | if(!res$nobp$Vt & !is.na(res$output[[1]]$Vt.bp)[1]) { 65 | for(i in 1:length(res$output[[1]]$Vt.bp)) { 66 | abline( 67 | v = as.Date(time(res$Yt))[res$output[[1]]$Vt.bp[i]], 68 | lty = 2, 69 | lwd = 1.5, 70 | col = "red" 71 | ) 72 | } 73 | } 74 | if(!res$nobp$Wt & !is.na(res$output[[1]]$Wt.bp)[1]) { 75 | for(i in 1:length(res$output[[1]]$Wt.bp)) { 76 | abline( 77 | v = as.Date(time(res$Yt))[res$output[[1]]$Wt.bp[i]], 78 | lty = 2, 79 | lwd = 1.5, 80 | col = "darkgreen" 81 | ) 82 | } 83 | } 84 | -------------------------------------------------------------------------------- /www/logo-fapesp.svg: -------------------------------------------------------------------------------- 1 | 2 | image/svg+xml -------------------------------------------------------------------------------- /python/gee-px-ls-new.py: -------------------------------------------------------------------------------- 1 | import ee 2 | import numpy 3 | import ssl 4 | from datetime import datetime 5 | 6 | # Earth Engine API init 7 | ee.Initialize() 8 | 9 | # Convert YYYYMMDD format to YYYY-MM-DD 10 | def convertDate(s): 11 | return datetime.strptime(s, '%Y%m%d').strftime('%Y_%m_%d') 12 | 13 | # Function to calculate and add some spectral-band-based indexes (LS 4,5,7) 14 | def getVi(image): 15 | scalingFactor = 0.0001 16 | b1 = image.select(['B1']).multiply(scalingFactor) 17 | b2 = image.select(['B2']).multiply(scalingFactor) 18 | b3 = image.select(['B3']).multiply(scalingFactor) 19 | b4 = image.select(['B4']).multiply(scalingFactor) 20 | b5 = image.select(['B5']).multiply(scalingFactor) 21 | b7 = image.select(['B7']).multiply(scalingFactor) 22 | evi = image.expression('2.5*(NIR - R)/(NIR + 6.0*R - 7.5*B + 1)', {'R': b3, 'NIR': b4, 'B': b1}) 23 | evi2 = image.expression('2.5*(NIR - R)/(NIR + 2.4*R + 1)', {'R': b3, 'NIR': b4}) 24 | ndvi = image.normalizedDifference(['B4','B3']) 25 | ndmi = image.normalizedDifference(['B4','B5']) 26 | image2 = ( 27 | image.select(['pixel_qa']) 28 | .addBands(b1.select([0],['b1'])) 29 | .addBands(b2.select([0],['b2'])) 30 | .addBands(b3.select([0],['b3'])) 31 | .addBands(b4.select([0],['b4'])) 32 | .addBands(b5.select([0],['b5'])) 33 | .addBands(b7.select([0],['b7'])) 34 | .addBands(evi.select([0],['evi'])) 35 | .addBands(evi2.select([0],['evi2'])) 36 | .addBands(ndvi.select([0],['ndvi'])) 37 | .addBands(ndmi.select([0],['ndmi'])) 38 | ) 39 | return(image2) 40 | 41 | # Function to calculate and add some spectral-band-based indexes (LS 8) 42 | def getVi8(image): 43 | scalingFactor = 0.0001 44 | b1 = image.select(['B1']).multiply(scalingFactor) 45 | b2 = image.select(['B2']).multiply(scalingFactor) 46 | b3 = image.select(['B3']).multiply(scalingFactor) 47 | b4 = image.select(['B4']).multiply(scalingFactor) 48 | b5 = image.select(['B5']).multiply(scalingFactor) 49 | b7 = image.select(['B7']).multiply(scalingFactor) 50 | evi = image.expression('2.5*(NIR - R)/(NIR + 6.0*R - 7.5*B + 1)', {'R': b4, 'NIR': b5, 'B': b2}) 51 | evi2 = image.expression('2.5*(NIR - R)/(NIR + 2.4*R + 1)', {'R': b4, 'NIR': b5}) 52 | ndvi = image.normalizedDifference(['B5','B4']) 53 | ndmi = image.normalizedDifference(['B5','B6']) 54 | image2 = ( 55 | image.select(['pixel_qa']) 56 | .addBands(b1.select([0],['b1'])) 57 | .addBands(b2.select([0],['b2'])) 58 | .addBands(b3.select([0],['b3'])) 59 | .addBands(b4.select([0],['b4'])) 60 | .addBands(b5.select([0],['b5'])) 61 | .addBands(b7.select([0],['b7'])) 62 | .addBands(evi.select([0],['evi'])) 63 | .addBands(evi2.select([0],['evi2'])) 64 | .addBands(ndvi.select([0],['ndvi'])) 65 | .addBands(ndmi.select([0],['ndmi'])) 66 | ) 67 | return(image2) 68 | 69 | # Create a geometry point with provided coordinates 70 | longCen = float(coords[0]) 71 | latCen = float(coords[1]) 72 | bounds = ee.Geometry.Point([longCen, latCen]) 73 | 74 | # Define the image collection 75 | imgCol = ee.ImageCollection('LANDSAT/' + satChoice) 76 | 77 | # Get time series from GEE 78 | if satChoice == 'LC08/C01/T1_SR': 79 | values = imgCol.filterBounds(bounds).map(getVi8).getRegion(bounds, 30) 80 | else: 81 | values = imgCol.filterBounds(bounds).map(getVi).getRegion(bounds, 30) 82 | 83 | # Get time series info and apply cfmask filter 84 | try: 85 | aux = values.getInfo() 86 | serie = [] 87 | # for i in range(1, len(aux)): 88 | # print aux[i][4] == None 89 | for i in range(1, len(aux)): 90 | serie += [convertDate(str(aux[i][0])[12:20])] + \ 91 | ['NA' if v is None else v for v in aux[i][5:15]] 92 | #numpy.round(aux[i][5:15], 4).tolist() 93 | 94 | # Additional variables to be used in R 95 | if len(aux) > 0: 96 | colNames = [u'date'] + aux[0][5:15] 97 | numCol = len(colNames) 98 | numRow = len(aux) - 1 99 | except ee.ee_exception.EEException: 100 | # No data available 101 | serie = [] 102 | -------------------------------------------------------------------------------- /python/gee-px-ls.py: -------------------------------------------------------------------------------- 1 | import ee 2 | import numpy 3 | import ssl 4 | from datetime import datetime 5 | 6 | # earth engine API init 7 | ee.Initialize() 8 | 9 | # convert Julian day to YYYY-MM-DD date 10 | def julianDayToDate(s): 11 | return datetime.strptime(s, '%Y%j').strftime('%Y_%m_%d') 12 | 13 | # function to calculate and add some spectral-bands-based indexes (LS 4,5,7) 14 | def getVi(image): 15 | scalingFactor = 0.0001 16 | b1 = image.select(['B1']).multiply(scalingFactor) 17 | b2 = image.select(['B2']).multiply(scalingFactor) 18 | b3 = image.select(['B3']).multiply(scalingFactor) 19 | b4 = image.select(['B4']).multiply(scalingFactor) 20 | b5 = image.select(['B5']).multiply(scalingFactor) 21 | b7 = image.select(['B7']).multiply(scalingFactor) 22 | evi = image.expression('2.5 * (NIR - R) / (NIR + 6.0*R - 7.5*B + 1)', {'R': b3, 'NIR': b4, 'B': b1}) 23 | evi2 = image.expression('2.5 * (NIR - R) / (NIR + 2.4*R + 1)', {'R': b3, 'NIR': b4}) 24 | ndvi = image.normalizedDifference(['B4','B3']) 25 | ndmi = image.normalizedDifference(['B4','B5']) 26 | image2 = ( 27 | image.select(['cfmask']) 28 | .addBands(b1.select([0],['b1'])) 29 | .addBands(b2.select([0],['b2'])) 30 | .addBands(b3.select([0],['b3'])) 31 | .addBands(b4.select([0],['b4'])) 32 | .addBands(b5.select([0],['b5'])) 33 | .addBands(b7.select([0],['b7'])) 34 | .addBands(evi.select([0],['evi'])) 35 | .addBands(evi2.select([0],['evi2'])) 36 | .addBands(ndvi.select([0],['ndvi'])) 37 | .addBands(ndmi.select([0],['ndmi'])) 38 | ) 39 | return(image2) 40 | 41 | # function to calculate and add some spectral-bands-based indexes (LS 8) 42 | def getVi8(image): 43 | scalingFactor = 0.0001 44 | b1 = image.select(['B1']).multiply(scalingFactor) 45 | b2 = image.select(['B2']).multiply(scalingFactor) 46 | b3 = image.select(['B3']).multiply(scalingFactor) 47 | b4 = image.select(['B4']).multiply(scalingFactor) 48 | b5 = image.select(['B5']).multiply(scalingFactor) 49 | # b6 = image.select(['B6']).multiply(scalingFactor) 50 | b7 = image.select(['B7']).multiply(scalingFactor) 51 | evi = image.expression('2.5 * (NIR - R) / (NIR + 6.0*R - 7.5*B + 1)', {'R': b4, 'NIR': b5, 'B': b2}) 52 | evi2 = image.expression('2.5 * (NIR - R) / (NIR + 2.4*R + 1)', {'R': b4, 'NIR': b5}) 53 | ndvi = image.normalizedDifference(['B5','B4']) 54 | ndmi = image.normalizedDifference(['B5','B6']) 55 | image2 = ( 56 | image.select(['cfmask']) 57 | .addBands(b1.select([0],['b1'])) 58 | .addBands(b2.select([0],['b2'])) 59 | .addBands(b3.select([0],['b3'])) 60 | .addBands(b4.select([0],['b4'])) 61 | .addBands(b5.select([0],['b5'])) 62 | # .addBands(b6.select([0],['b6'])) 63 | .addBands(b7.select([0],['b7'])) 64 | .addBands(evi.select([0],['evi'])) 65 | .addBands(evi2.select([0],['evi2'])) 66 | .addBands(ndvi.select([0],['ndvi'])) 67 | .addBands(ndmi.select([0],['ndmi'])) 68 | ) 69 | return(image2) 70 | 71 | # create a geometry point with provided coordinates 72 | longCen = float(coords[0]) 73 | latCen = float(coords[1]) 74 | bounds = ee.Geometry.Point([longCen, latCen]) 75 | 76 | # define image collection 77 | imgCol = ee.ImageCollection('LANDSAT/' + satChoice) 78 | 79 | # get time series from GEE 80 | if satChoice == 'LC8_SR': 81 | values = imgCol.filterBounds(bounds).map(getVi8).getRegion(bounds, 30) 82 | else: 83 | values = imgCol.filterBounds(bounds).map(getVi).getRegion(bounds, 30) 84 | 85 | # get time series info and apply cfmask filter 86 | try: 87 | aux = values.getInfo() 88 | serie = [] 89 | for i in range(1, len(aux)): 90 | # filtering using cfmask (only clear pixels are kept [value = 0]) 91 | if aux[i][4] == 0: 92 | serie += [julianDayToDate(str(aux[i][0])[9:16])] + \ 93 | numpy.round(aux[i][5:15], 4).tolist() 94 | 95 | # additional variables to be used in R 96 | if len(aux) > 0: 97 | colNames = [u'date'] + aux[0][5:15] 98 | numCol = len(colNames) 99 | numRow = len(aux) - 1 100 | except ee.ee_exception.EEException: 101 | # no data available 102 | serie = [] 103 | -------------------------------------------------------------------------------- /sandbox.R: -------------------------------------------------------------------------------- 1 | # ------------------------------------------------------ TO-DO ADVANCES ---- 2 | 3 | ## Dynamically set 'harmonic order' parameter for bfastmonitor 4 | 5 | # currently, error occurs when: 6 | # condition: ht = "ROC"; order > 9 7 | # error msg: history_roc > Error in if: missing value where TRUE/FALSE needed 8 | # 9 | # condition: ht = "BP"; order > 1 10 | # error msg: history_break -> Error in breakpoints.formula: minimum segment size must be smaller than half the number of observations 11 | # 12 | # condition: ht = "all"; order > 18 13 | # error msg: monitor -> Error in if: missing value where TRUE/FALSE needed 14 | 15 | library(strucchange) 16 | load("test.RData") 17 | 18 | x <- rbind(serie[[1]]) 19 | x <- x[order(x$date), ] 20 | 21 | if(input$select_bfm_formula != "trend") { 22 | if(input$select_bfm_history == "ROC") { 23 | orderMaxBfm <- 0 24 | cond4 <- F 25 | while(!cond4) { 26 | orderMaxBfm <- orderMaxBfm + 1 27 | data <- bfastts(data = serieSel()[, matchCol], 28 | dates = serieSel()$date, 29 | type = "irregular") 30 | data_tspp <- bfastpp(data, order = orderMaxBfm, lag = NULL, slag = NULL) 31 | history_tspp <- subset(data_tspp, time < decimal_date(input$select_bfm_monitor)) 32 | data_rev <- history_tspp[nrow(history_tspp):1,] 33 | data_rev$response <- ts(data_rev$response) 34 | suppressWarnings( 35 | y_rcus <- efp(bfm_formula, data = data_rev, type = "Rec-CUSUM") 36 | ) 37 | cond4 <- is.na(y_rcus$process[1]) 38 | } 39 | orderMaxBfm <- orderMaxBfm - 1 40 | updateSliderInput( 41 | session = session, 42 | inputId = "select_bfm_order", 43 | max = orderMaxBfm 44 | ) 45 | } 46 | } 47 | 48 | # ----------------------------------------------------- DISCARDED CODE ---- 49 | 50 | # discarded code which my be useful in the future 51 | 52 | ## map draw toolbar observer 53 | coordsDf <- reactive({ 54 | # check if no markers are drawn on the map 55 | validate( 56 | need(!is.null(input$leaf_drawLayer_created$geometry$coordinates[[1]]), 57 | "Place at least one marker on the map") 58 | ) 59 | 60 | # create a data.frame with marker's lng-lat coordinates 61 | coords <- input$leaf_drawLayer_features$features %>% 62 | sapply(function(x) { 63 | c(x$geometry$coordinates[[1]], 64 | x$geometry$coordinates[[2]]) 65 | }) %>% 66 | t() %>% 67 | data.frame() 68 | 69 | # add names to dataframe columns 70 | if(ncol(coords) > 0) coords <- coords %>% setNames(c("lon", "lat")) 71 | 72 | # if at least one point is drawn and shown on map, show output list 73 | output$mapDraw <- renderPrint(coords) 74 | 75 | return(coords) 76 | }) 77 | 78 | ## coords data.frame observer to update input$select_coords 79 | observe({ 80 | coords <- coordsDf() 81 | 82 | if(ncol(coords) > 0) { 83 | # change the available choices of input$select_coords to 1:nrow(z) 84 | updateSelectInput(session, 85 | inputId = "select_coords", 86 | label = "Now, select a marker from the list below", 87 | choices = 1:nrow(coords)) 88 | } else { 89 | # if all points are deleted after drawn, reset input$select_coords 90 | updateSelectInput(session, 91 | inputId = "select_coords", 92 | label = "Place at least one marker on the map", 93 | choices = "") 94 | } 95 | }) 96 | 97 | ## overlaying custom Tiles on the map 98 | addTiles( 99 | map = m, 100 | urlTemplate = "https://earthengine.googleapis.com/map/47fdcba98ca0ee3bdd8c25059f8403f3/{z}/{x}/{y}?token=18335d5369c9a0ccebcd45458d1a1d79", 101 | options = list( 102 | minZoom = 3, 103 | maxZoom = 19, 104 | noWrap = T, 105 | unloadInvisibleTiles = T, 106 | updateWhenIdle = T, 107 | reuseTiles = T, 108 | opacity = 0.5 109 | ) 110 | ) 111 | 112 | ## save a fixed time series to easily debug the Analysis tab 113 | 114 | k <- c(-47.109375, -4.56547355071028) 115 | python.assign("coords", k) 116 | 117 | serieList <- list() 118 | for (i in 1:3) { 119 | # assign Python variables using Shiny inputs 120 | python.assign("satChoice", c("LT5_SR", "LE7_SR", "LC8_SR")[i]) 121 | 122 | # execute Python download script 123 | python.load(paste0(getwd(), "/python/gee-px-ls.py")) 124 | 125 | # get Python output and show download message 126 | if (is.null(unlist(python.get("serie")))) { 127 | serieList[[i]] <- NA 128 | } else { 129 | serieList[[i]] <- unlist(python.get("serie")) 130 | } 131 | } 132 | 133 | # remove from serieList those satellites with no data 134 | # also keep track of the satellites names in satOrder 135 | satOrder <- c("LT5_SR", "LE7_SR", "LC8_SR") 136 | serieList[is.na(serieList)] <- NULL 137 | satOrder[-is.na(serieList)] 138 | 139 | # arrange each list element form serieList as a df 140 | serie <- lapply(serieList, function(x) { 141 | tmp <- data.frame(matrix(x, 142 | ncol = python.get("numCol"), 143 | byrow = T)) 144 | 145 | # format data type and columns names 146 | tmp[, 1] <- as.Date(tmp[, 1], format = "%Y_%m_%d") 147 | tmp[, 2:ncol(tmp)] <- apply(tmp[, 2:ncol(tmp)], 148 | MARGIN = 2, 149 | as.numeric) 150 | colnames(tmp) <- python.get("colNames") 151 | 152 | # exclude saturated data 153 | filterWhich <- which(rowSums(tmp[, 2:ncol(tmp)] == 2) > 0) 154 | if (length(filterWhich) > 0) { 155 | tmp <- tmp[-filterWhich, ] 156 | } 157 | 158 | return(tmp) 159 | }) 160 | 161 | # create new column for each list element with corresponding satellite 162 | # name, also include this info as an attribute of the list 163 | for (i in 1:length(serie)) { 164 | serie[[i]]$sat <- satOrder[i] 165 | } 166 | attributes(serie) <- list(satOrder = satOrder) 167 | 168 | save(file = "test.RData", 169 | list = "serie", 170 | envir = .GlobalEnv) 171 | 172 | # load the previously saved data 173 | load("test.RData") 174 | -------------------------------------------------------------------------------- /www/logo-unicamp.svg: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 7 | 8 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 45 | 46 | -------------------------------------------------------------------------------- /md/tutorial.md: -------------------------------------------------------------------------------- 1 | 2 | 3 | *This tutorial comprehends the BFAST Explorer v0.0.1. Notice that if you are using a newer version, some features might be different.* 4 | 5 | *** 6 | 7 | ## Description 8 | 9 | **BFAST Explorer** is a [Shiny](https://shiny.rstudio.com/) app, developed using R and Python, designed for the analysis of *Landsat Surface Reflectance* time series pixel data. 10 | 11 | Three change detection algorithms - **bfastmonitor**, **bfast01** and **bfast** - are used in order to investigate temporal changes in trend and seasonal components, via breakpoint detection. 12 | 13 | If you encounter any bugs, please send a message to almeida.xan@gmail.com, or create an issue on the [GitHub page](https://github.com/almeidaxan/bfast-explorer/). 14 | 15 | ## Tutorial 16 | 17 | Albeit very simple, please follow this short usage guide to learn how to 18 | properly use the tool. 19 | 20 | ### Map Tab 21 | 22 | This is the starting tab, which we first see when we run the tool. The 23 | tab is composed of an interactive map (rendered using Google Maps engine) 24 | and a navigation toolbar. Feel free to zoom and pan the map. 25 | 26 | ![md](./images/tutorial-01.jpg) 27 | 28 | If we wish, we can also use the *search field* located on the top of the 29 | toolbar to search for a location. Then, the map 30 | automatically zooms to the desired location, similar to how Google Maps 31 | works. In the example, we searched for `unicamp campinas`, which is the 32 | University of Campinas. 33 | 34 | ![md](./images/tutorial-02.jpg) 35 | 36 | Now, let's zoom out all the way back and place a marker at the north of 37 | Brazil, as shown. To *place* a marker, simply click on the map. If we want 38 | to, we can also place multiple markers. 39 | 40 | We may also wish to clear all the placed markers. To do that, click on the 41 | **[ Clear Markers]** red button on the left side 42 | of the toolbar. 43 | 44 | ![md](./images/tutorial-03.jpg) 45 | 46 | After that, we need to *select* one of the markers in order download its 47 | Landsat pixel data. To do that, simply click on an already placer marker, 48 | and it will be highlighted. Only one marker may be selected at a time. 49 | 50 | By selecting a marker, we can now choose a combination of which satellites 51 | to download from using the drop-down menu, located on the bottom of the 52 | toolbar. For instance, let's choose all the available satellites products: 53 | Landsat 5, 7 and 8 SR. 54 | 55 | ![md](./images/tutorial-04.jpg) 56 | 57 | Then, we press the **[ Get Data]** blue 58 | button, located on the right side of the toolbar. By pressing that button, 59 | the download will start. We can keep track of the download progress by 60 | looking to the lower right corner. All the historical data available are 61 | downloaded, which should take less than 10 seconds for the three products 62 | selected. 63 | 64 | Note: as of the writing of this guide, not all Surface Reflectance 65 | data are availble from GEE. So, depending on where we place our markers, 66 | we may face a message indicating that *'No data available for the chosen* 67 | *satellite(s) and/or region... Please change your query and try again.'*. 68 | Since we rely heavily on GEE to download the data, there's nothing we can 69 | do yet. We're sorry for that. 70 | 71 | ![md](./images/tutorial-05.jpg) 72 | 73 | If the download is successful, we'll receive a message directing to the 74 | ** Analysis** tab. 75 | 76 | ### Analysis Tab 77 | 78 | In this tab, we can analyze the downloaded data and, then, 79 | locally save the results as files. 80 | 81 | ![md](./images/tutorial-06.jpg) 82 | 83 | First, let's choose which satellite time series date to visualize. Note 84 | that, even though we downloaded data from Landsat 5, 7 and 8 SR, we're 85 | can still analyze them separately. However, let's proceed by choosing 86 | all of them. 87 | 88 | As we can see, the time series of the first spectral band 89 | (`b1`) is plotted for all satellites. A colored legend distinguishes the 90 | different sources. 91 | 92 | Note: be careful when comparing *spectral bands* data from 93 | different satellites, as they may not correspond to the same wavelength range! Read more about this [here](https://landsat.usgs.gov/what-are-band-designations-landsat-satellites). 94 | 95 | ![md](./images/tutorial-07.jpg) 96 | 97 | Apart from the spectral bands, there are also four spectral-bands-derived 98 | indexes available: NDVI, NDMI, EVI and EVI2. Let's check, for example, the 99 | NDVI time series. 100 | 101 | ![md](./images/tutorial-08.jpg) 102 | 103 | If we want to, we can also download *all* the time series data as a file. 104 | To do that, press the **[ Data]** blue 105 | button. All the data will be downloaded as a .CSV, ordered by the acquisiton 106 | date. Also, an additional column is included, in order to distinguish the 107 | satellite sources. 108 | 109 | ![md](./images/tutorial-09.jpg) 110 | 111 | We may download the time series plot as an image, by pressing the 112 | **[ Plot]** blue button. A window will appear 113 | offering some raster (.JPEG, .PNG) and a vectorial (.SVG) image output 114 | formats. 115 | 116 | ![md](./images/tutorial-10.jpg) 117 | 118 | Next, we select the *change detection algorithm*. Three options are available: **bfastmonitor**, **bfast01** and **bfast**. More information 119 | about these algorithms can be found [here](http://bfast.r-forge.r-project.org/). 120 | 121 | ![md](./images/tutorial-11.jpg) 122 | 123 | By selecting **bfastmonitor**, we are able to tweak four parameters on the 124 | left side-bar: `formula`, `history period type`, `harmonic order`, and 125 | `start of monitoring`. These parameters have different impacts on the 126 | results, which can be verified on the right side plot. Here, we set the 127 | maximum value of the `harmonic order` to 9 to avoid some problems. 128 | 129 | Similar to the time series, we can also download the *results* of the change detection algorithms as .RDS data files, by clicking on the **[ Results]** blue button. If we wish to download the plot, we can press the **[ Plot]** blue button. 130 | 131 | For more information on how to load .RDS files on R, please check this [link](http://www.fromthebottomoftheheap.net/2012/04/01/saving-and-loading-r-objects/). 132 | 133 | ![md](./images/tutorial-12.jpg) 134 | 135 | By selecting **bfast01**, we can tweak two parameters: `formula`, and `harmonic order`. 136 | Here, the maximum value of the `harmonic order` is dynamically set 137 | depending on the time series data length and the choice of the `formula` 138 | parameter. 139 | 140 | ![md](./images/tutorial-13.jpg) 141 | 142 | Finally, by selecting **bfast**, we may tweak two parameters: `h` (minimal 143 | segment size), and `season type`. Please note that, since **bfast** can 144 | detect multiple breakpoints, it may take a couple of seconds to process, 145 | in comparison to the previous two algorithms. 146 | 147 | ![md](./images/tutorial-14.jpg) 148 | -------------------------------------------------------------------------------- /md/tutorial.Rmd: -------------------------------------------------------------------------------- 1 | ```{r setup, include = F, echo = F} 2 | htmltools::tagList(rmarkdown::html_dependency_font_awesome()) 3 | ``` 4 | 5 | *This tutorial comprehends the BFAST Explorer v0.0.1. Notice that if you are using a newer version, some features might be different.* 6 | 7 | *** 8 | 9 | ## Description 10 | 11 | **BFAST Explorer** is a [Shiny](https://shiny.rstudio.com/) app, developed using R and Python, designed for the analysis of *Landsat Surface Reflectance* time series pixel data. 12 | 13 | Three change detection algorithms - **bfastmonitor**, **bfast01** and **bfast** - are used in order to investigate temporal changes in trend and seasonal components, via breakpoint detection. 14 | 15 | If you encounter any bugs, please send a message to almeida.xan@gmail.com, or create an issue on the [GitHub page](https://github.com/almeidaxan/bfast-explorer/). 16 | 17 | ## Tutorial 18 | 19 | Albeit very simple, please follow this short usage guide to learn how to 20 | properly use the tool. 21 | 22 | ### Map Tab 23 | 24 | This is the starting tab, which we first see when we run the tool. The 25 | tab is composed of an interactive map (rendered using Google Maps engine) 26 | and a navigation toolbar. Feel free to zoom and pan the map. 27 | 28 | ![md](./images/tutorial-01.jpg) 29 | 30 | If we wish, we can also use the *search field* located on the top of the 31 | toolbar to search for a location. Then, the map 32 | automatically zooms to the desired location, similar to how Google Maps 33 | works. In the example, we searched for `unicamp campinas`, which is the 34 | University of Campinas. 35 | 36 | ![md](./images/tutorial-02.jpg) 37 | 38 | Now, let's zoom out all the way back and place a marker at the north of 39 | Brazil, as shown. To *place* a marker, simply click on the map. If we want 40 | to, we can also place multiple markers. 41 | 42 | We may also wish to clear all the placed markers. To do that, click on the 43 | **[ Clear Markers]** red button on the left side 44 | of the toolbar. 45 | 46 | ![md](./images/tutorial-03.jpg) 47 | 48 | After that, we need to *select* one of the markers in order download its 49 | Landsat pixel data. To do that, simply click on an already placer marker, 50 | and it will be highlighted. Only one marker may be selected at a time. 51 | 52 | By selecting a marker, we can now choose a combination of which satellites 53 | to download from using the drop-down menu, located on the bottom of the 54 | toolbar. For instance, let's choose all the available satellites products: 55 | Landsat 5, 7 and 8 SR. 56 | 57 | ![md](./images/tutorial-04.jpg) 58 | 59 | Then, we press the **[ Get Data]** blue 60 | button, located on the right side of the toolbar. By pressing that button, 61 | the download will start. We can keep track of the download progress by 62 | looking to the lower right corner. All the historical data available are 63 | downloaded, which should take less than 10 seconds for the three products 64 | selected. 65 | 66 | Note: as of the writing of this guide, not all Surface Reflectance 67 | data are availble from GEE. So, depending on where we place our markers, 68 | we may face a message indicating that *'No data available for the chosen* 69 | *satellite(s) and/or region... Please change your query and try again.'*. 70 | Since we rely heavily on GEE to download the data, there's nothing we can 71 | do yet. We're sorry for that. 72 | 73 | ![md](./images/tutorial-05.jpg) 74 | 75 | If the download is successful, we'll receive a message directing to the 76 | ** Analysis** tab. 77 | 78 | ### Analysis Tab 79 | 80 | In this tab, we can analyze the downloaded data and, then, 81 | locally save the results as files. 82 | 83 | ![md](./images/tutorial-06.jpg) 84 | 85 | First, let's choose which satellite time series date to visualize. Note 86 | that, even though we downloaded data from Landsat 5, 7 and 8 SR, we're 87 | can still analyze them separately. However, let's proceed by choosing 88 | all of them. 89 | 90 | As we can see, the time series of the first spectral band 91 | (`b1`) is plotted for all satellites. A colored legend distinguishes the 92 | different sources. 93 | 94 | Note: be careful when comparing *spectral bands* data from 95 | different satellites, as they may not correspond to the same wavelength range! Read more about this [here](https://landsat.usgs.gov/what-are-band-designations-landsat-satellites). 96 | 97 | ![md](./images/tutorial-07.jpg) 98 | 99 | Apart from the spectral bands, there are also four spectral-bands-derived 100 | indexes available: NDVI, NDMI, EVI and EVI2. Let's check, for example, the 101 | NDVI time series. 102 | 103 | ![md](./images/tutorial-08.jpg) 104 | 105 | If we want to, we can also download *all* the time series data as a file. 106 | To do that, press the **[ Data]** blue 107 | button. All the data will be downloaded as a .CSV, ordered by the acquisiton 108 | date. Also, an additional column is included, in order to distinguish the 109 | satellite sources. 110 | 111 | ![md](./images/tutorial-09.jpg) 112 | 113 | We may download the time series plot as an image, by pressing the 114 | **[ Plot]** blue button. A window will appear 115 | offering some raster (.JPEG, .PNG) and a vectorial (.SVG) image output 116 | formats. 117 | 118 | ![md](./images/tutorial-10.jpg) 119 | 120 | Next, we select the *change detection algorithm*. Three options are available: **bfastmonitor**, **bfast01** and **bfast**. More information 121 | about these algorithms can be found [here](http://bfast.r-forge.r-project.org/). 122 | 123 | ![md](./images/tutorial-11.jpg) 124 | 125 | By selecting **bfastmonitor**, we are able to tweak four parameters on the 126 | left side-bar: `formula`, `history period type`, `harmonic order`, and 127 | `start of monitoring`. These parameters have different impacts on the 128 | results, which can be verified on the right side plot. Here, we set the 129 | maximum value of the `harmonic order` to 9 to avoid some problems. 130 | 131 | Similar to the time series, we can also download the *results* of the change detection algorithms as .RDS data files, by clicking on the **[ Results]** blue button. If we wish to download the plot, we can press the **[ Plot]** blue button. 132 | 133 | For more information on how to load .RDS files on R, please check this [link](http://www.fromthebottomoftheheap.net/2012/04/01/saving-and-loading-r-objects/). 134 | 135 | ![md](./images/tutorial-12.jpg) 136 | 137 | By selecting **bfast01**, we can tweak two parameters: `formula`, and `harmonic order`. 138 | Here, the maximum value of the `harmonic order` is dynamically set 139 | depending on the time series data length and the choice of the `formula` 140 | parameter. 141 | 142 | ![md](./images/tutorial-13.jpg) 143 | 144 | Finally, by selecting **bfast**, we may tweak two parameters: `h` (minimal 145 | segment size), and `season type`. Please note that, since **bfast** can 146 | detect multiple breakpoints, it may take a couple of seconds to process, 147 | in comparison to the previous two algorithms. 148 | 149 | ![md](./images/tutorial-14.jpg) 150 | -------------------------------------------------------------------------------- /global.R: -------------------------------------------------------------------------------- 1 | # --------------------------------------------------------------- LIBS ---- 2 | 3 | options(shiny.sanitize.errors = FALSE) 4 | 5 | # this tool is still not compatible with Windows OS 6 | if(Sys.info()[["sysname"]] == "Windows") { 7 | stop("This tool is not yet compatible with Windows.\nPlease use a UNIX-like system.") 8 | } 9 | 10 | # load and install packages if needed 11 | if(!require("pacman")) { 12 | install.packages("pacman") 13 | require("pacman") 14 | } 15 | 16 | # CRAN 17 | packs <- c( 18 | "bfast", 19 | "devtools", 20 | "dplyr", 21 | "ggmap", 22 | "knitr", 23 | "leaflet", 24 | "lubridate", 25 | "markdown", 26 | "magrittr", 27 | "RColorBrewer", 28 | "raster", 29 | "rPython", 30 | "shiny", 31 | "shinyBS", 32 | "shinyjs", 33 | "strucchange", 34 | "tools", 35 | "zoo" 36 | ) 37 | 38 | p_load(char = packs) 39 | 40 | # ---------------------------------------------------------- FUNCTIONS ---- 41 | 42 | # preprocessing 43 | ppBfastmonitor <- function(x, date, ...) { 44 | tmpTs <- bfastts(data = x, 45 | dates = date, 46 | type = "irregular") 47 | tmp <- bfastmonitor(tmpTs, ...) 48 | return(tmp) 49 | } 50 | 51 | ppBfast01 <- function(x, date, ...) { 52 | tmpTs <- bfastts(data = x, 53 | dates = date, 54 | type = "irregular") 55 | tmp <- bfast01(tmpTs, ...) 56 | return(tmp) 57 | } 58 | 59 | ppBfast <- function(x, date, ...) { 60 | # bfast needs regularly spaced data. Here, we force the data regularity 61 | # with a monthly aggregation. Temporal gaps are linearly filled. 62 | timeYM <- strftime(date, "%Y-%m") 63 | x <- data.frame(time = timeYM, x) 64 | x <- aggregate(x = x$x, 65 | by = list(x$time), 66 | FUN = median) 67 | names(x)[1] <- "time" 68 | 69 | # create an empty continuous year-month df 70 | mYear <- as.numeric(substr(head(x$time, 1), 1, 4)) 71 | MYear <- as.numeric(substr(tail(x$time, 1), 1, 4)) 72 | mMonth <- as.numeric(substr(head(x$time, 1), 6, 7)) 73 | MMonth <- as.numeric(substr(tail(x$time, 1), 6, 7)) 74 | fullYear <- rep(mYear:MYear, each = 12) 75 | fullMonth <- rep(1:12, times = length(mYear:MYear)) 76 | 77 | # correcting the first year 78 | if(mMonth > 1) { 79 | fullYear <- fullYear[-(1:(mMonth - 1))] 80 | fullMonth <- fullMonth[-(1:(mMonth - 1))] 81 | } 82 | 83 | # correcting the last year 84 | if (MMonth < 12) { 85 | fullYear <- fullYear[-((length(fullYear) - 12 + MMonth + 1):length(fullYear))] 86 | fullMonth <- fullMonth[-((length(fullMonth) - 12 + MMonth + 1):length(fullMonth))] 87 | } 88 | dfull <- data.frame(time = paste0(fullYear, "-", sprintf("%02d", fullMonth))) 89 | 90 | # merging x df with the full year-month df 91 | d <- merge(dfull, x, all = T) 92 | d$time <- NULL 93 | 94 | dts <- na.approx( 95 | ts(data = c(d)$x, 96 | start = c(mYear, mMonth), 97 | end = c(MYear, MMonth), 98 | frequency = 12), 99 | rule = 2 100 | ) 101 | 102 | tmp <- bfast(dts, max.iter = 1, ...) 103 | return(tmp) 104 | } 105 | 106 | # plotting 107 | plotRaw <- function(serie, matchCol, xAxisCustom, ylimCustom, ylab, seriePar, coords) { 108 | # placeholder blank plot 109 | par(mar = c(4, 4, 0, 0) + 0.1) 110 | plot(y = serie[, matchCol], 111 | x = serie$date, 112 | axes = F, 113 | xlab = "Time", 114 | ylab = toupper(colnames(serie)[matchCol]), 115 | ylim = ylimCustom, 116 | col = "white") 117 | 118 | # redraw axis 119 | axis(side = 1, 120 | at = date(paste0(xAxisCustom, "-01-01")), 121 | labels = xAxisCustom) 122 | if(ylimCustom[1] == -1) { 123 | yAxisStep <- 0.2 124 | } else { 125 | yAxisStep <- 0.1 126 | } 127 | axis(side = 2, 128 | at = seq(ylimCustom[1], ylimCustom[2], yAxisStep)) 129 | 130 | # draw a grid on both axis directions 131 | for(i in xAxisCustom) { 132 | abline(v = date(paste0(i, "-01-01")), 133 | col = rgb(.8, .8, .8), 134 | lty = 2) 135 | } 136 | for(i in seq(ylimCustom[1], ylimCustom[2], yAxisStep)) { 137 | if(i == 0) { 138 | # highlight a guide line at "0" 139 | abline(h = i, 140 | col = "black", 141 | lty = 3, 142 | lwd = 1.3) 143 | } else { 144 | abline(h = i, 145 | col = rgb(.8, .8, .8), 146 | lty = 2) 147 | } 148 | } 149 | box() 150 | 151 | # draw time series as points and lines 152 | lines( 153 | y = serie[, matchCol], 154 | x = serie$date, 155 | col = rgb(.1, .1, .1) 156 | ) 157 | points( 158 | y = serie[, matchCol], 159 | x = serie$date, 160 | col = seriePar[, 1], 161 | pch = as.numeric(seriePar[, 2]), 162 | cex = 0.6 163 | ) 164 | 165 | # if(mean(serie[, matchCol]) > (ylimCustom[1] + 0.5 * (diff(ylimCustom)))) 166 | if(mean(serie[, matchCol], na.rm = TRUE) > (ylimCustom[1] + 0.5 * (diff(ylimCustom)))) { 167 | posText <- 0.1 168 | } else { 169 | posText <- 0.9 170 | } 171 | 172 | if(coords$show) { 173 | text( 174 | x = serie$date %>% year() %>% unique() %>% range() %>% mean() %>% sum(0.5) %>% date_decimal() %>% format("%Y-%m-%d") %>% as.Date(), 175 | y = ylimCustom[1] + posText * (diff(ylimCustom)), 176 | labels = paste0("LatLong = (", sprintf("%.5f", round(coords$lat, 5)), ", ", sprintf("%.5f", round(coords$lon, 5)), ")"), 177 | cex = 1.3 178 | ) 179 | } 180 | } 181 | plotRawLegend <- function(satOrder, seriePar) { 182 | # placeholder blank plot 183 | par(mar = c(4, 0, 0, 0) + 0.1) 184 | plot(0, 185 | col = "white", 186 | axes = F, 187 | xlab = "", 188 | ylab = "") 189 | 190 | # first, draw lines 191 | legend(x = "center", 192 | lty = 1, 193 | x.intersp = 2, 194 | y.intersp = 1.5, 195 | legend = names(satOrder), 196 | text.col = "white", 197 | bty = "n", 198 | col = rgb(.1, .1, .1)) 199 | 200 | serieParOrder <- unique(seriePar) 201 | serieParOrder <- rbind(serieParOrder[order(serieParOrder[, 2]), ]) 202 | 203 | # then, draw points 204 | legend(x = "center", 205 | x.intersp = 2, 206 | y.intersp = 1.5, 207 | legend = names(satOrder), 208 | bty = "n", 209 | col = serieParOrder[, 1], 210 | pch = as.numeric(serieParOrder[, 2]) 211 | ) 212 | } 213 | 214 | plotBfm <- function(serie, matchCol, bfmOut, xAxisCustom, ylimCustom, ylab) { 215 | # define a vector with rounded dates to avoid strange rounding errors 216 | # to interfere with the conditions below 217 | dates <- round(decimal_date(serie$date), digits = 3) 218 | 219 | # conditions 220 | condHist <- dates >= round(bfmOut$history[1], digits = 3) & dates <= round(bfmOut$history[2], digits = 3) 221 | condMoni <- dates >= round(bfmOut$monitor[1], digits = 3) 222 | condStart <- dates <= round(bfmOut$history[2], digits = 3) 223 | condPred <- dates >= round(bfmOut$history[1], digits = 3) 224 | 225 | # background blank plot 226 | par(mar = c(4, 4, 0, 0) + 0.1) 227 | plot(y = serie[, matchCol], 228 | x = serie$date, 229 | col = "white", 230 | axes = F, 231 | ylim = ylimCustom, 232 | ylab = ylab, 233 | xlab = "Time") 234 | 235 | # redraw axis 236 | axis(side = 1, 237 | at = date(paste0(xAxisCustom, "-01-01")), 238 | labels = xAxisCustom) 239 | if(ylimCustom[1] == -1) { 240 | yAxisStep <- 0.2 241 | } else { 242 | yAxisStep <- 0.1 243 | } 244 | axis(side = 2, 245 | at = seq(ylimCustom[1], ylimCustom[2], yAxisStep)) 246 | for(i in xAxisCustom) { 247 | abline(v = date(paste0(i, "-01-01")), 248 | col = rgb(.8, .8, .8), 249 | lty = 2) 250 | } 251 | for(i in seq(ylimCustom[1], ylimCustom[2], yAxisStep)) { 252 | if(i == 0) { 253 | # highlight a guide line at "0" 254 | abline(h = i, 255 | col = "black", 256 | lty = 3, 257 | lwd = 1.3) 258 | } else { 259 | abline(h = i, 260 | col = rgb(.8, .8, .8), 261 | lty = 2) 262 | } 263 | } 264 | box() 265 | 266 | # draw a guide line at "0" 267 | abline(h = 0, 268 | lty = 3, 269 | lwd = 1.3, 270 | col = "black") 271 | 272 | # draw historical data 273 | lines(y = serie[, matchCol][which(condStart)], 274 | x = serie$date[which(condStart)]) 275 | 276 | # draw data of monitoring period 277 | lines(y = serie[, matchCol][which(condMoni)], 278 | x = serie$date[which(condMoni)], 279 | col = "red", 280 | type = "o", 281 | pch = 20) 282 | 283 | # draw stable history points 284 | points(y = serie[, matchCol][which(condHist)], 285 | x = serie$date[which(condHist)], 286 | pch = 20, 287 | col = "darkgreen") 288 | 289 | # draw fit based on stable history 290 | lines(y = bfmOut$tspp$prediction, 291 | x = serie$date[which(condPred)], 292 | col = "blue", 293 | lwd = 1.5) 294 | 295 | # draw line of start of the monitoring period 296 | abline(v = as.Date(date_decimal(bfmOut$monitor[1]), format = "YYYY-MM-DD"), 297 | lty = 2, 298 | lwd = 1.2) 299 | 300 | # draw line of time of detected break (if it exists) 301 | if(is.na(!bfmOut$mefp$breakpoint)) { 302 | abline(v = serie$date[c(which(condHist), which(condMoni))[bfmOut$mefp$breakpoint]-1], 303 | lty = 2, 304 | lwd = 2, 305 | col = "red") 306 | } 307 | } 308 | plotBfmLegend <- function() { 309 | # placeholder blank plot 310 | par(mar = c(4, 0, 0, 0) + 0.1) 311 | plot(0, 312 | col = "white", 313 | axes = F, 314 | xlab = "", 315 | ylab = "") 316 | 317 | legendText <- c( 318 | "Historical data", 319 | "Stable history", 320 | "New data", 321 | "Fit using stable history", 322 | "Start of monitoring", 323 | "Detected break" 324 | ) 325 | 326 | # first, draw the lines 327 | legend(x = "center", 328 | x.intersp = 2, 329 | y.intersp = 1.5, 330 | legend = legendText, 331 | text.col = "white", 332 | bty = "n", 333 | lty = c(rep(1, 4), rep(2, 2)), 334 | lwd = c(rep(1, 2), 1.2, 1.5, .9, 2), 335 | col = c(rep("black", 2), "red", "blue", "black", "red")) 336 | 337 | # then, draw the points 338 | legend(x = "center", 339 | x.intersp = 2, 340 | y.intersp = 1.5, 341 | legend = legendText, 342 | bty = "n", 343 | col = c(rgb(0,0,0,0), "darkgreen", "red", rep(rgb(0,0,0,0), 3)), 344 | pch = 20) 345 | } 346 | 347 | plotBf01 <- function(serie, matchCol, bf01Out, xAxisCustom, ylimCustom, ylab) { 348 | # background blank plot 349 | par(mar = c(4, 4, 0, 0) + 0.1) 350 | plot(y = serie[, matchCol], 351 | x = serie$date, 352 | col = "white", 353 | axes = F, 354 | ylim = ylimCustom, 355 | ylab = ylab, 356 | xlab = "Time") 357 | 358 | # redraw axis 359 | axis(side = 1, 360 | at = date(paste0(xAxisCustom, "-01-01")), 361 | labels = xAxisCustom) 362 | if(ylimCustom[1] == -1) { 363 | yAxisStep <- 0.2 364 | } else { 365 | yAxisStep <- 0.1 366 | } 367 | axis(side = 2, 368 | at = seq(ylimCustom[1], ylimCustom[2], yAxisStep)) 369 | for(i in xAxisCustom) { 370 | abline(v = date(paste0(i, "-01-01")), 371 | col = rgb(.8, .8, .8), 372 | lty = 2) 373 | } 374 | for(i in seq(ylimCustom[1], ylimCustom[2], yAxisStep)) { 375 | if(i == 0) { 376 | # highlight a guide line at "0" 377 | abline(h = i, 378 | col = "black", 379 | lty = 3, 380 | lwd = 1.3) 381 | } else { 382 | abline(h = i, 383 | col = rgb(.8, .8, .8), 384 | lty = 2) 385 | } 386 | } 387 | box() 388 | 389 | # draw a guide line at "0" 390 | abline(h = 0, 391 | lty = 3, 392 | lwd = 1.3, 393 | col = "black") 394 | 395 | lines( 396 | y = as.zoo(bf01Out)$response, 397 | x = serie$date, 398 | col = "black", 399 | lwd = 1 400 | ) 401 | lines( 402 | y = as.zoo(bf01Out)$fitted, 403 | x = serie$date, 404 | col = "blue", 405 | lwd = 2 406 | ) 407 | 408 | if(bf01Out$breaks == 1) { 409 | # bp 410 | abline( 411 | v = serie$date[bf01Out$confint[2]], 412 | lty = 2, 413 | lwd = 2, 414 | col = "red" 415 | ) 416 | } 417 | } 418 | plotBf01Legend <- function() { 419 | # placeholder blank plot 420 | par(mar = c(4, 0, 0, 0) + 0.1) 421 | plot(0, 422 | col = "white", 423 | axes = F, 424 | xlab = "", 425 | ylab = "") 426 | 427 | legendText <- c( 428 | "Data", 429 | "Fitted model", 430 | "Detected break" 431 | ) 432 | 433 | # draw the lines 434 | legend(x = "center", 435 | x.intersp = 2, 436 | y.intersp = 1.5, 437 | legend = legendText, 438 | bty = "n", 439 | lty = c(1, 1, 2), 440 | lwd = c(1, 1, 2), 441 | col = c("black", "blue", "red")) 442 | } 443 | 444 | plotBfast <- function(serie, matchCol, bfastOut, xAxisCustom, ylimCustom, ylab) { 445 | # background blank plot 446 | par(mar = c(4, 4, 0, 0) + 0.1) 447 | plot(y = serie[, matchCol], 448 | x = serie$date, 449 | col = "white", 450 | axes = F, 451 | ylim = ylimCustom, 452 | ylab = ylab, 453 | xlab = "Time") 454 | 455 | # redraw axis 456 | axis(side = 1, 457 | at = date(paste0(xAxisCustom, "-01-01")), 458 | labels = xAxisCustom) 459 | if(ylimCustom[1] == -1) { 460 | yAxisStep <- 0.2 461 | } else { 462 | yAxisStep <- 0.1 463 | } 464 | axis(side = 2, 465 | at = seq(ylimCustom[1], ylimCustom[2], yAxisStep)) 466 | for(i in xAxisCustom) { 467 | abline(v = date(paste0(i, "-01-01")), 468 | col = rgb(.8, .8, .8), 469 | lty = 2) 470 | } 471 | for(i in seq(ylimCustom[1], ylimCustom[2], yAxisStep)) { 472 | if(i == 0) { 473 | # highlight a guide line at "0" 474 | abline(h = i, 475 | col = "black", 476 | lty = 3, 477 | lwd = 1.3) 478 | } else { 479 | abline(h = i, 480 | col = rgb(.8, .8, .8), 481 | lty = 2) 482 | } 483 | } 484 | box() 485 | 486 | # draw a guide line at "0" 487 | abline(h = 0, 488 | lty = 3, 489 | lwd = 1.3, 490 | col = "black") 491 | 492 | lines(y = bfastOut$Yt, x = as.Date(time(bfastOut$Yt)), col = "gray", lwd = 2) 493 | lines(y = bfastOut$output[[1]]$Tt + bfastOut$output[[1]]$St, x = as.Date(time(bfastOut$Yt)), col = "blue", lwd = 2) 494 | 495 | if(!bfastOut$nobp$Vt & !is.na(bfastOut$output[[1]]$Vt.bp)[1]) { 496 | for(i in 1:length(bfastOut$output[[1]]$Vt.bp)) { 497 | abline( 498 | v = as.Date(time(bfastOut$Yt))[bfastOut$output[[1]]$Vt.bp[i]], 499 | lty = 2, 500 | lwd = 1.5, 501 | col = "red" 502 | ) 503 | } 504 | } 505 | if(!bfastOut$nobp$Wt & !is.na(bfastOut$output[[1]]$Wt.bp)[1]) { 506 | for(i in 1:length(bfastOut$output[[1]]$Wt.bp)) { 507 | abline( 508 | v = as.Date(time(bfastOut$Yt))[bfastOut$output[[1]]$Wt.bp[i]], 509 | lty = 2, 510 | lwd = 1.5, 511 | col = "darkgreen" 512 | ) 513 | } 514 | } 515 | } 516 | plotBfastLegend <- function() { 517 | # placeholder blank plot 518 | par(mar = c(4, 0, 0, 0) + 0.1) 519 | plot(0, 520 | col = "white", 521 | axes = F, 522 | xlab = "", 523 | ylab = "") 524 | 525 | legendText <- c( 526 | "Data", 527 | "Fitted model", 528 | "Detected trend break", 529 | "Detected season break" 530 | ) 531 | 532 | # draw the lines 533 | legend(x = "center", 534 | x.intersp = 2, 535 | y.intersp = 1.5, 536 | legend = legendText, 537 | bty = "n", 538 | lty = c(1, 1, 2, 2), 539 | lwd = c(1, 2, 2, 2), 540 | col = c("black", "blue", "red", "darkgreen")) 541 | } 542 | 543 | # --------------------------------------------------------------- DEFS ---- 544 | 545 | # define custom AwesomeIcons markers 546 | iconSet <- awesomeIconList( 547 | # unselected marker 548 | marker = makeAwesomeIcon( 549 | icon = "circle", 550 | markerColor = "lightgray", 551 | iconColor = "white", 552 | library = "fa" 553 | ), 554 | # selected marker 555 | markerSel = makeAwesomeIcon( 556 | icon = "circle", 557 | markerColor = "blue", 558 | iconColor = "white", 559 | library = "fa" 560 | ) 561 | ) 562 | 563 | # define all available satellites the user can choose from: 564 | # LHS is the alias name; RHS is the name that GEE understands 565 | # satChoices <- c( 566 | # "Landsat 5 SR" = "LT5_SR", 567 | # "Landsat 7 SR" = "LE7_SR", 568 | # "Landsat 8 SR" = "LC8_SR" 569 | # ) 570 | satChoices <- c( 571 | "Landsat 5 SR" = "LT05/C01/T1_SR", 572 | "Landsat 7 SR" = "LE07/C01/T1_SR", 573 | "Landsat 8 SR" = "LC08/C01/T1_SR" 574 | ) 575 | 576 | # coordinates (long, lat, zoom) of the default fixed center 577 | viewCenter <- c(-5.98, 24.69, 3) 578 | 579 | # defining colors & pch for points from differente satellite sources 580 | # satPar <- function(x) { 581 | # customPalette <- brewer.pal(length(satChoices) + 1, "Dark2") 582 | # switch( 583 | # x, 584 | # "LT5_SR" = c(customPalette[1], 15), 585 | # "LE7_SR" = c(customPalette[2], 16), 586 | # "LC8_SR" = c(customPalette[3], 17), 587 | # "Mixed" = c(customPalette[4], 18) 588 | # ) 589 | # } 590 | satPar <- function(x) { 591 | customPalette <- brewer.pal(length(satChoices) + 1, "Dark2") 592 | switch( 593 | x, 594 | "LT05/C01/T1_SR" = c(customPalette[1], 15), 595 | "LE07/C01/T1_SR" = c(customPalette[2], 16), 596 | "LC08/C01/T1_SR" = c(customPalette[3], 17), 597 | "Mixed" = c(customPalette[4], 18) 598 | ) 599 | } 600 | 601 | # create a .md file from the following .Rmd, in order to be embedded into 602 | # the Shiny app 603 | knit(input = "./md/tutorial.Rmd", output = "md/tutorial.md", quiet = T) 604 | 605 | # longlat projection default CRS 606 | proj_ll <- "+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0" 607 | -------------------------------------------------------------------------------- /ui.R: -------------------------------------------------------------------------------- 1 | library(shiny) 2 | 3 | shinyUI( 4 | navbarPage( 5 | title = div( 6 | em(strong("BFAST Explorer")), 7 | HTML("by Alexandre E. Almeida") 8 | ), 9 | windowTitle = "BFAST Explorer", 10 | id = "navbar", 11 | position = "fixed-top", 12 | collapsible = T, 13 | header = { 14 | # load styles.css file with custom styles 15 | tags$head(includeCSS("www/styles.css")) 16 | }, 17 | 18 | # ---------------------------------------------------- MAP TAB ---- 19 | 20 | tabPanel( 21 | title = "Map", 22 | icon = icon(name = "map-marker", lib = "font-awesome", class = "fa-lg"), 23 | # set up shinyjs 24 | useShinyjs(), 25 | div( 26 | id = "map-leaflet", 27 | # render a leaflet map on the background 28 | leafletOutput( 29 | outputId = "leaf", 30 | height = "100%" 31 | ), 32 | # create a toolbar on top of the map 33 | fixedPanel( 34 | id = "map-toolbar", 35 | class = "panel panel-default", 36 | top = 60, 37 | bottom = "auto", 38 | right = 250, 39 | left = 250, 40 | # map search query 41 | fluidRow( 42 | column( 43 | 4, 44 | HTML(' 45 |
46 |
47 | 52 | 53 |
54 |
55 | '), 56 | # enable "Enter" key press on textInput to activate the search button 57 | tags$script(' 58 | $(document).on("keyup", function (e) { 59 | if (e.keyCode == 13) { 60 | $("#action_search").click(); 61 | } 62 | }); 63 | ') 64 | ), 65 | # 'insert shape' button 66 | column( 67 | 4, 68 | div( 69 | HTML(' 70 |
71 |
72 | 78 | 79 |
80 |
81 |
82 |
83 |
84 | '), 85 | bsTooltip( 86 | id = "help_insertShape", 87 | title = '

The shape must be compressed into a zip with, at least, the .shp, .shx, .dbf, and .prj files.

The zip file must have the same name as its contents.
', 88 | placement = "bottom", 89 | trigger = "hover", 90 | options = list(container = "body") 91 | ) 92 | ) 93 | ), 94 | # 'clear all markers' button 95 | column( 96 | 4, 97 | bsButton( 98 | inputId = "action_clearMarkers", 99 | label = "Clear Markers", 100 | style = "danger", 101 | icon = icon(name = "trash", lib = "font-awesome"), 102 | width = "100%" 103 | ) 104 | ) 105 | ), 106 | fluidRow( 107 | # select which satellite products to download 108 | column( 109 | 6, 110 | selectInput( 111 | inputId = "select_satGet", 112 | label = NULL, 113 | width = "100%", 114 | choices = c(""), 115 | multiple = T 116 | ) 117 | ), 118 | # 'get data' button 119 | column( 120 | 6, 121 | bsButton( 122 | inputId = "action_getTs", 123 | label = "Get Data", 124 | style = "success", 125 | icon = icon(name = "download", lib = "font-awesome"), 126 | width = "100%" 127 | ), 128 | # tooltip with data preprocessing description 129 | bsTooltip( 130 | id = "action_getTs", 131 | title = '

The Landsat "Surface Refle- ctance" (SR) product is, by construction, already atmosphe- rically corrected.

In addition, the downloaded data is also filtered for cloud effects by using the CFMask algorithm.
', 132 | placement = "bottom", 133 | trigger = "hover", 134 | options = list(container = "body") 135 | ) 136 | ) 137 | ), 138 | # output text with download results 139 | htmlOutput(outputId = "text_getTs") 140 | ) 141 | ) 142 | ), 143 | 144 | # ----------------------------------------------- ANALYSIS TAB ---- 145 | 146 | tabPanel( 147 | title = "Analysis", 148 | icon = icon(name = "bar-chart", lib = "font-awesome", class = "fa-lg"), 149 | fluidPage( 150 | # ----------------------------------------- TIME SERIES --- 151 | 152 | fixedRow( 153 | style = "margin-top: 71px;", 154 | column( 155 | 3, 156 | h3(class = "text-heading", "Visualization"), 157 | # select which satellite products to plot 158 | selectInput( 159 | inputId = "select_satPlot", 160 | label = "Satellite product", 161 | width = "100%", 162 | choices = NULL, 163 | multiple = T 164 | ), 165 | # select which band/index to plot 166 | selectInput( 167 | inputId = "select_index", 168 | label = "Data", 169 | width = "100%", 170 | choices = NULL 171 | ), 172 | # add a tooltip to select_index warning about mixing bands from different satellites 173 | bsTooltip( 174 | id = "select_index", 175 | title = '
Be careful when comparing bands from different satellites.

For more information about Landsat bands, please visit this link.
', 176 | placement = "right", 177 | trigger = "focus", 178 | options = list(container = "body") 179 | ), 180 | # select which change detection algorithm to run 181 | selectInput( 182 | inputId = "select_chgDet", 183 | label = "Change detection algorithm", 184 | width = "100%", 185 | choices = c("Select the change detection algorithm..." = "", "bfastmonitor", "bfast01", "bfast") 186 | ), 187 | # tooltip with brief explanations about the change detection algorithms 188 | bsTooltip( 189 | id = "select_chgDet", 190 | title = '
bfastmonitor
Monitoring for the first break at the end of the time series.

bfast01
Checking for one major break in the time series.

bfast
Time series decomposition and multiple breakpoint detection in trend and seasonal components.

For more information about these algorithms, please visit this link.
', 191 | placement = "right", 192 | trigger = "focus", 193 | options = list(container = "body") 194 | ), 195 | # buttons to download the data and the plot figures 196 | h5(style = "cursor:default;", strong("Download")), 197 | downloadButton( 198 | outputId = "action_downloadDataRaw", 199 | label = "Data", 200 | class = "btn-primary" 201 | ), 202 | bsButton( 203 | inputId = "action_downloadPlotRaw", 204 | label = "Plot", 205 | style = "primary", 206 | icon = icon(name = "download", lib = "font-awesome") 207 | ), 208 | # checkbox to show/hide latlong coordinates 209 | checkboxInput( 210 | inputId = "select_showCoords", 211 | label = "Show coordinates?", 212 | value = T, 213 | width = "100%" 214 | ) 215 | ), 216 | # time series plot and its legend 217 | column( 218 | 9, 219 | splitLayout( 220 | cellWidths = c("75%", "25%"), 221 | plotOutput(outputId = "plot_raw", height = 300), 222 | plotOutput(outputId = "plot_raw_legend", height = 300) 223 | ) 224 | ) 225 | ), 226 | 227 | # ---------------------------------------- BFASTMONITOR --- 228 | 229 | conditionalPanel( 230 | condition = "input.select_chgDet == 'bfastmonitor' && input.select_satPlot != null", 231 | hr(), 232 | fluidRow( 233 | column( 234 | 3, 235 | h3(class = "text-heading", "bfastMonitor"), 236 | helpText("Based on", a("bfmApp", href = "https://github.com/loicdtx/bfmApp", target = "_blank"), "by Loïc Dutrieux"), 237 | fluidRow( 238 | # formula parameter 239 | column( 240 | 6, 241 | selectInput( 242 | inputId = "select_bfm_formula", 243 | label = "Formula", 244 | width = "100%", 245 | choices = c("trend + harmon", "trend", "harmon"), 246 | selected = "trend + harmon" 247 | ) 248 | ), 249 | # history period type parameter 250 | column( 251 | 6, 252 | selectInput( 253 | inputId = "select_bfm_history", 254 | label = "History period type", 255 | width = "100%", 256 | choices = c("ROC", "BP", "all"), 257 | selected = "ROC" 258 | ), 259 | # tooltip with brief explanations about the history period type parameter 260 | bsTooltip( 261 | id = "select_bfm_history", 262 | title = '
ROC
Reverse-ordered CUSUM.

BP
Bai & Perron
breakpoint estimation.

all
All available observations.
', 263 | placement = "right", 264 | trigger = "focus", 265 | options = list(container = "body") 266 | ) 267 | ) 268 | ), 269 | fluidRow( 270 | # harmonic order parameter (only show if applicable) 271 | column( 272 | 6, 273 | conditionalPanel( 274 | condition = "input.select_bfm_formula == 'harmon' | input.select_bfm_formula == 'trend + harmon'", 275 | sliderInput( 276 | inputId = "select_bfm_order", 277 | label = "Harmonic order", 278 | width = "100%", 279 | value = 1, 280 | min = 1, 281 | max = 9, 282 | step = 1 283 | ) 284 | ) 285 | ), 286 | # start of monitoring period parameter 287 | column( 288 | 6, 289 | dateInput( 290 | inputId = "select_bfm_monitor", 291 | label = "Start of monitoring", 292 | width = "100%", 293 | format = "yyyy-mm", 294 | startview = "decade" 295 | ), 296 | # tooltip with brief explanations about the monitoring period default 297 | bsTooltip( 298 | id = "select_bfm_monitor", 299 | title = '
Defaults to the last
30% of the data.
', 300 | placement = "bottom", 301 | options = list(container = "body") 302 | ) 303 | ) 304 | ), 305 | # buttons to download the data and the plot figures 306 | h5(style = "cursor:default;", strong("Download")), 307 | downloadButton( 308 | outputId = "action_downloadDataBfm", 309 | label = "Results", 310 | class = "btn-primary" 311 | ), 312 | bsButton( 313 | inputId = "action_downloadPlotBfm", 314 | label = "Plot", 315 | style = "primary", 316 | icon = icon(name = "download", lib = "font-awesome") 317 | ) 318 | ), 319 | # bfastmonitor plot and its legend 320 | column( 321 | 9, 322 | splitLayout( 323 | cellWidths = c("75%", "25%"), 324 | plotOutput(outputId = "plot_bfm", height = 300), 325 | plotOutput(outputId = "plot_bfm_legend", height = 300) 326 | ) 327 | ) 328 | ) 329 | ), 330 | 331 | # --------------------------------------------- BFAST01 --- 332 | 333 | conditionalPanel( 334 | condition = "input.select_chgDet == 'bfast01' && input.select_satPlot != null", 335 | hr(), 336 | fluidRow( 337 | column( 338 | 3, 339 | h3(class = "text-heading", "bfast01"), 340 | fluidRow( 341 | # formula parameter 342 | column( 343 | 6, 344 | selectInput( 345 | inputId = "select_bf01_formula", 346 | label = "Formula", 347 | width = "100%", 348 | choices = c("trend + harmon", "trend", "harmon"), 349 | selected = "trend + harmon" 350 | ) 351 | ), 352 | # harmonic order parameter (only show if applicable) 353 | column( 354 | 6, 355 | conditionalPanel( 356 | condition = "input.select_bf01_formula == 'harmon' | input.select_bf01_formula == 'trend + harmon'", 357 | sliderInput( 358 | inputId = "select_bf01_order", 359 | label = "Harmonic order", 360 | width = "100%", 361 | value = 1, 362 | min = 1, 363 | max = 1, 364 | step = 1 365 | ) 366 | ) 367 | ) 368 | ), 369 | # buttons to download the data and the plot figures 370 | h5(style = "cursor:default;", strong("Download")), 371 | downloadButton( 372 | outputId = "action_downloadDataBf01", 373 | label = "Results", 374 | class = "btn-primary" 375 | ), 376 | bsButton( 377 | inputId = "action_downloadPlotBf01", 378 | label = "Plot", 379 | style = "primary", 380 | icon = icon(name = "download", lib = "font-awesome") 381 | ) 382 | ), 383 | # bfast01 plot and its legend 384 | column( 385 | 9, 386 | splitLayout( 387 | cellWidths = c("75%", "25%"), 388 | plotOutput(outputId = "plot_bf01", height = 300), 389 | plotOutput(outputId = "plot_bf01_legend", height = 300) 390 | ) 391 | ) 392 | ) 393 | ), 394 | 395 | # ----------------------------------------------- BFAST --- 396 | 397 | conditionalPanel( 398 | condition = "input.select_chgDet == 'bfast' && input.select_satPlot != null", 399 | hr(), 400 | fluidRow( 401 | column( 402 | 3, 403 | h3(class = "text-heading", "bfast"), 404 | fluidRow( 405 | # h parameter (minimal segment size) 406 | column( 407 | 6, 408 | sliderInput( 409 | inputId = "select_bfast_h", 410 | label = "h", 411 | width = "100%", 412 | value = 0.15, 413 | step = 0.01, 414 | min = 0.00, 415 | max = 0.50 416 | ), 417 | # explanation about what the h parameter means 418 | bsTooltip( 419 | id = "select_bfast_h", 420 | title = '
Minimal segment size between potentially detected breaks.
', 421 | placement = "right", 422 | options = list(container = "body") 423 | ) 424 | ), 425 | # season type parameter 426 | column( 427 | 6, 428 | selectInput( 429 | inputId = "select_bfast_season", 430 | label = "Season type", 431 | width = "100%", 432 | choices = c("dummy", "harmonic", "none"), 433 | selected = "dummy" 434 | ) 435 | ) 436 | ), 437 | # buttons to download the data and the plot figures 438 | h5(style = "cursor:default;", strong("Download")), 439 | downloadButton(outputId = "action_downloadDataBfast", 440 | label = "Results", 441 | class = "btn-primary"), 442 | bsButton( 443 | inputId = "action_downloadPlotBfast", 444 | label = "Plot", 445 | style = "primary", 446 | icon = icon(name = "download", lib = "font-awesome") 447 | ) 448 | ), 449 | # bfast plot and its legend 450 | column( 451 | 9, 452 | splitLayout( 453 | cellWidths = c("75%", "25%"), 454 | plotOutput(outputId = "plot_bfast", height = 300), 455 | plotOutput(outputId = "plot_bfast_legend", height = 300) 456 | ) 457 | ) 458 | ) 459 | ) 460 | ) 461 | ), 462 | 463 | # ----------------------------------------------- TUTORIAL TAB ---- 464 | 465 | tabPanel( 466 | title = "Tutorial", 467 | icon = icon(name = "question-circle", lib = "font-awesome", class = "fa-lg"), 468 | fluidPage( 469 | div(style = "max-width: 70%; margin-left: auto; margin-right: auto; margin-top: 71px;", 470 | align = "justify", 471 | includeMarkdown("./md/tutorial.md") 472 | ) 473 | ) 474 | ), 475 | 476 | # -------------------------------------------------- ABOUT TAB ---- 477 | 478 | tabPanel( 479 | title = "About", 480 | icon = icon(name = "info-circle", lib = "font-awesome", class = "fa-lg"), 481 | fluidPage( 482 | div( 483 | style = "margin-left: auto; margin-right: auto; margin-top: 71px; width: 600px;", 484 | div( 485 | align = "center", 486 | HTML('

BFAST Explorer
v0.0.3

') 487 | ), 488 | hr(), 489 | HTML('

Authors

'), 490 | p(), 491 | div("MSc. Alexandre Esteves Almeida", a(icon("user", lib = "font-awesome"), href = "http://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4672382A6", target = "_blank"), a(icon("envelope", lib = "font-awesome"), href = "mailto:almeida.xan@gmail.com")), 492 | div("Nathalia Menini, Master's Student", a(icon("user", lib = "font-awesome"), href = "http://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K8200531Y0", target = "_blank"), a(icon("envelope", lib = "font-awesome"), href = "mailto:nathmenini@gmail.com")), 493 | div("Prof. Dr. Jan Verbesselt", a(icon("user", lib = "font-awesome"), href = "https://www.wur.nl/en/Persons/Jan-Verbesselt.htm", target = "_blank")), 494 | div("Prof. Dr. Ricardo da Silva Torres", a(icon("user", lib = "font-awesome"), href = "http://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4769295A9", target = "_blank")), 495 | hr(), 496 | HTML('

Citation

'), 497 | p(), 498 | "To cite BFAST Explorer in publications, please use", 499 | pre('Alexandre Almeida, Nathalia Menini, Jan Verbesselt, Ricardo Torres (2018). BFAST Explorer: An Effective Tool for Time Series Analysis. In: 2018 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). 22-27 July 2018. Valencia, Spain. DOI: 10.1109/IGARSS.2018.8517877.'), 500 | "or, alternatively, the corresponding BibTeX entry described", 501 | a("here", href = "https://github.com/almeidaxan/bfast-explorer#citation", target = "_blank"), 502 | hr(), 503 | HTML('

Development

'), 504 | p(), 505 | div( 506 | align = "center", 507 | div(class = "image-highlight", a(img(src = "logo-tribes.png", height = "80px"), href = "http://www.e-tribes.com.br", target = "_blank")), 508 | p(), 509 | div(class = "image-highlight", a(img(src = "logo-unicamp.svg", height = "80px"), href = "http://www.unicamp.br/unicamp/english/", target = "_blank")), 510 | div(class = "image-highlight", a(img(src = "logo-wur.png", height = "120px"), href = "http://www.wur.nl/en.htm", target = "_blank")) 511 | ), 512 | p(), 513 | HTML('This work is a product of the Tribes project, developed within the University of Campinas (UNICAMP) Institute of Computing and the Wageningen University & Research (WUR) Laboratory of Geo-information Science and Remote Sensing.'), 514 | hr(), 515 | HTML('

Supporters

'), 516 | p(), 517 | div(align = "center", 518 | div(class = "image-highlight", a(img(src = "logo-fapesp.svg", height = "40px"), href = "http://www.fapesp.br/en/", target = "_blank")), 519 | div(style = "display:inline;", HTML("  ")), 520 | div(class = "image-highlight", a(img(src = "logo-ms-research.png", height = "40px"), href = "http://www.fapesp.br/en/5392", target = "_blank")), 521 | div(style = "display:inline;", HTML("  ")), 522 | div(class = "image-highlight", a(img(src = "logo-cnpq.svg", height = "60px"), href = "http://cnpq.br/", target = "_blank")) 523 | ) 524 | ), 525 | HTML(' 526 | 527 | 528 | N 529 | 530 | ') 531 | ) 532 | ) 533 | ) 534 | ) 535 | -------------------------------------------------------------------------------- /server.R: -------------------------------------------------------------------------------- 1 | library(shiny) 2 | source("global.R") 3 | 4 | shinyServer(function(input, output, session) { 5 | # ------------------------------------------------------------- SESSION ---- 6 | 7 | # allow reconnection by a certain grace period 8 | session$allowReconnect(T) 9 | 10 | # ------------------------------------------------------------- SHARED ---- 11 | 12 | # reactive values 13 | v <- reactiveValues(markerId = 1, 14 | # selected marker info 15 | markerSel = NULL, 16 | # downloaded marker info 17 | markerDown = NULL, 18 | # defaults view to the center of the atlantic ocean 19 | searchLoc = data.frame(lon = viewCenter[1], 20 | lat = viewCenter[2], 21 | zoom = viewCenter[3])) 22 | 23 | # ---------------------------------------------------------------- MAP ---- 24 | 25 | # render leaflet map 26 | output$leaf <- renderLeaflet({ 27 | m <- leaflet(options = list(attributionControl = F)) %>% 28 | addTiles( 29 | urlTemplate = "http://{s}.google.com/vt/lyrs=s,h&x={x}&y={y}&z={z}", 30 | attribution = paste( 31 | "Map data ©", 32 | substr(Sys.Date(), 1, 4), 33 | " Google, Imagery ©", 34 | substr(Sys.Date(), 1, 4), 35 | " TerraMetrics" 36 | ), 37 | options = list( 38 | minZoom = 3, 39 | maxZoom = 19, 40 | noWrap = T, 41 | subdomains = c("mt0", "mt1", "mt2", "mt3") 42 | ) 43 | ) %>% 44 | setMaxBounds(-180, -90, 180, 90) 45 | m 46 | }) 47 | 48 | # update map view with v$searchLoc coordinates 49 | observeEvent(v$searchLoc, { 50 | leafletProxy("leaf") %>% 51 | setView( 52 | lng = v$searchLoc$lon, 53 | lat = v$searchLoc$lat, 54 | zoom = v$searchLoc$zoom 55 | ) 56 | }) 57 | 58 | # update map with input shapefile 59 | observeEvent(input$file_insertShape, { 60 | inFile <- input$file_insertShape 61 | inFolder <- substr(inFile$datapath, 1, nchar(inFile$datapath) - 5) 62 | unzip(inFile$datapath, exdir = inFolder) 63 | shpPath <- paste0(substr(inFile$name, 1, tools::file_path_sans_ext(inFile$name) %>% nchar), ".shp") 64 | shp <- shapefile(file.path(inFolder, shpPath)) 65 | shp <- spTransform(shp, proj_ll) 66 | leafletProxy("leaf") %>% 67 | clearShapes() %>% 68 | addPolylines( 69 | data = shp, 70 | weight = 4, 71 | opacity = 0.9, 72 | color = rgb(1, 1, 0) 73 | ) %>% 74 | fitBounds( 75 | lng1 = extent(shp)[1], 76 | lng2 = extent(shp)[2], 77 | lat1 = extent(shp)[3], 78 | lat2 = extent(shp)[4] 79 | ) 80 | }) 81 | 82 | # draw markers by clicking on the map 83 | observeEvent(input$leaf_click, { 84 | id <- as.character(v$markerId) 85 | tmp <- input$leaf_click 86 | v$markerId <- v$markerId + 1 87 | leafletProxy("leaf") %>% 88 | addAwesomeMarkers( 89 | lng = tmp$lng, 90 | lat = tmp$lat, 91 | layerId = id, 92 | icon = iconSet$marker 93 | ) 94 | }) 95 | 96 | # select markers by clicking and show a 'Selected' label next to them 97 | observeEvent(input$leaf_marker_click, { 98 | tmp <- input$leaf_marker_click 99 | 100 | # if user clicks on an already 'Selected' marker, deselect it 101 | if (grepl("selected", tmp$id)) { 102 | leafletProxy("leaf") %>% 103 | addAwesomeMarkers( 104 | lng = tmp$lng, 105 | lat = tmp$lat, 106 | layerId = as.character(v$markerSel[3]), 107 | icon = iconSet$marker 108 | ) %>% 109 | removeMarker("selectedLabel") %>% 110 | removeMarker("selectedHighlight") 111 | updateSelectInput( 112 | session, 113 | inputId = "select_satGet", 114 | choices = c("") 115 | ) 116 | v$markerSel <- NULL 117 | } else { 118 | # redraw selected marker without highlighting it 119 | if (length(v$markerSel) > 0) { 120 | leafletProxy("leaf") %>% 121 | addAwesomeMarkers( 122 | lng = as.numeric(v$markerSel[1]), 123 | lat = as.numeric(v$markerSel[2]), 124 | layerId = v$markerSel[3], 125 | icon = iconSet$marker 126 | ) 127 | } 128 | leafletProxy("leaf") %>% 129 | removeMarker(tmp$id) %>% 130 | removeMarker("selectedLabel") %>% 131 | removeMarker("selectedHighlight") %>% 132 | addAwesomeMarkers( 133 | lng = tmp$lng, 134 | lat = tmp$lat, 135 | layerId = "selectedHighlight", 136 | icon = iconSet$markerSel 137 | ) %>% 138 | addLabelOnlyMarkers( 139 | lng = tmp$lng, 140 | lat = tmp$lat, 141 | layerId = "selectedLabel", 142 | label = "Selected", 143 | labelOptions = list( 144 | textsize = "12px", 145 | direction = "auto", 146 | noHide = T, 147 | offset = c(18,-40) 148 | ), 149 | options = list(keyboard = F) 150 | ) 151 | v$markerSel <- c(tmp$lng, tmp$lat, tmp$id) 152 | } 153 | }) 154 | 155 | observeEvent(input$select_showCoords, { 156 | if(input$select_showCoords) { 157 | v$markerDown$show <- T 158 | } else { 159 | v$markerDown$show <- F 160 | } 161 | }) 162 | 163 | # ----------------------------------------------------- ACTION BUTTONS ---- 164 | 165 | # update v$searchLoc with map search query 166 | observeEvent(input$action_search, { 167 | # evaluate geocode, but suppress messages and warnings 168 | options(warn = -1) 169 | gc <- suppressMessages(geocode(input$select_search)) 170 | options(warn = 0) 171 | if (is.na(gc$lon)) { 172 | # defaults view to the center of the atlantic ocean 173 | v$searchLoc$lon <- viewCenter[1] 174 | v$searchLoc$lat <- viewCenter[2] 175 | v$searchLoc$zoom <- viewCenter[3] 176 | } else { 177 | # if user-selected region exists, center on it 178 | v$searchLoc$lon <- gc$lon 179 | v$searchLoc$lat <- gc$lat 180 | v$searchLoc$zoom <- 16 181 | } 182 | }) 183 | 184 | # change the state of clearMarkers ab if at least a marker is drawn 185 | observeEvent(v$markerId, { 186 | shinyjs::toggleState(id = "action_clearMarkers", 187 | condition = v$markerId > 1, 188 | selector = NULL) 189 | }) 190 | 191 | # select_satPlot observer 192 | observeEvent(input$select_satPlot, ignoreNULL = F, handlerExpr = { 193 | # change the state of time series download buttons 194 | shinyjs::toggleState(id = "action_downloadDataRaw", 195 | condition = !is.null(input$select_satPlot), 196 | selector = NULL) 197 | shinyjs::toggleState(id = "action_downloadPlotRaw", 198 | condition = !is.null(input$select_satPlot), 199 | selector = NULL) 200 | }) 201 | 202 | # change of state of getTs ab if a marker is selected 203 | # also update select_satGet values 204 | observeEvent(length(v$markerSel), { 205 | shinyjs::toggleState(id = "action_getTs", 206 | condition = length(v$markerSel) > 0, 207 | selector = NULL) 208 | if (length(v$markerSel) > 0) { 209 | updateSelectInput( 210 | session = session, 211 | inputId = "select_satGet", 212 | choices = c("Select the satellite product(s)..." = "", 213 | satChoices) 214 | ) 215 | } 216 | }) 217 | 218 | # clears all markers currently drawn on the map 219 | observeEvent(input$action_clearMarkers, { 220 | leafletProxy("leaf") %>% 221 | clearMarkers() 222 | updateSelectInput( 223 | session, 224 | inputId = "select_satGet", 225 | choices = c("") 226 | ) 227 | v$markerSel <- NULL 228 | v$markerId <- 1 229 | }) 230 | 231 | # calling gee-px-ls.py to download satellite data 232 | event_getTs <- eventReactive(input$action_getTs, { 233 | withProgress(message = "Downloading satellite data...", 234 | value = 0, 235 | expr = { 236 | validate(need( 237 | input$select_satGet != "", 238 | "Please select a satellite from the list above." 239 | )) 240 | 241 | # store info about the downloaded marker 242 | v$markerDown$lat <- as.numeric(v$markerSel[2]) 243 | v$markerDown$lon <- as.numeric(v$markerSel[1]) 244 | 245 | # assign variable in Python with selected coordinates (lng,lat order) 246 | python.assign("coords", as.numeric(v$markerSel[1:2])) 247 | 248 | # call Python download script for each selected satellite 249 | serieList <- list() 250 | for(i in 1:length(input$select_satGet)) { 251 | # assign Python variables using Shiny inputs 252 | python.assign("satChoice", input$select_satGet[i]) 253 | 254 | # execute Python download script 255 | # python.load(paste0(getwd(), "/python/gee-px-ls.py")) 256 | python.load(paste0(getwd(), "/python/gee-px-ls-new.py")) 257 | 258 | # update progress 259 | incProgress(amount = 1 / length(input$select_satGet)) 260 | 261 | # get Python output and show download message 262 | if (is.null(unlist(python.get("serie")))) { 263 | showNotification( 264 | ui = paste(names(satChoices[satChoices == input$select_satGet[i]]), 265 | "- no data available."), 266 | type = "error", 267 | duration = 4, 268 | closeButton = F 269 | ) 270 | serieList[[i]] <- NA 271 | } else { 272 | showNotification( 273 | ui = paste(names(satChoices[satChoices == input$select_satGet[i]]), 274 | "- data downloaded."), 275 | type = "message", 276 | duration = 4, 277 | closeButton = F 278 | ) 279 | serieList[[i]] <- unlist(python.get("serie")) 280 | } 281 | } 282 | 283 | # check for data availability 284 | validate( 285 | need( 286 | prod(is.na(serieList)) == 0, 287 | "No data available for the chosen satellite(s) and/or region... Please change your query and try again." 288 | ) 289 | ) 290 | 291 | # remove from serieList those satellites with no data 292 | # also keep track of the satellites names in satOrder 293 | satOrder <- input$select_satGet[!is.na(serieList)] 294 | serieList[is.na(serieList)] <- NULL 295 | 296 | # arrange each list element form serieList as a df 297 | serie <- lapply(serieList, function(x) { 298 | tmp <- data.frame(matrix(x, 299 | ncol = python.get("numCol"), 300 | byrow = T)) 301 | 302 | # format data type and columns names 303 | tmp[, 1] <- as.Date(tmp[, 1], format = "%Y_%m_%d") 304 | tmp[, 2:ncol(tmp)] <- apply(tmp[, 2:ncol(tmp)], 305 | MARGIN = 2, 306 | as.numeric) 307 | colnames(tmp) <- python.get("colNames") 308 | 309 | # exclude saturated data 310 | # filterWhich <- which(rowSums(tmp[, 2:ncol(tmp)] == 2) > 0) 311 | filterWhich <- which(rowSums(tmp[, 2:ncol(tmp)] == 2, na.rm = TRUE) > 0) 312 | if (length(filterWhich) > 0) { 313 | tmp <- tmp[-filterWhich, ] 314 | } 315 | 316 | return(tmp) 317 | }) 318 | 319 | # create new column for each list element with corresponding satellite 320 | # name, also include this info as an attribute of the list 321 | for (i in 1:length(serie)) { 322 | serie[[i]]$sat <- satOrder[i] 323 | } 324 | attributes(serie) <- list(satOrder = satOrder) 325 | 326 | # update select_satPlot with all possible satellite choices to be 327 | # visualized 328 | updateSelectInput( 329 | session, 330 | inputId = "select_satPlot", 331 | choices = satChoices[which(satChoices %in% satOrder)] 332 | ) 333 | 334 | # update select_index 335 | updateSelectInput( 336 | session = session, 337 | inputId = "select_index", 338 | choices = python.get("colNames")[-1] 339 | ) 340 | 341 | python.assign("aux", NULL) 342 | python.assign("serie", NULL) 343 | python.assign("values", NULL) 344 | python.assign("numCol", NULL) 345 | python.assign("colNames", NULL) 346 | 347 | return(serie) 348 | }) 349 | }) 350 | 351 | # download plot modal 352 | observeEvent(input$action_downloadPlotRaw, { 353 | showModal(modalDownloadPlot(type = "Raw")) 354 | }) 355 | 356 | observeEvent(input$action_downloadPlotBfm, { 357 | showModal(modalDownloadPlot(type = "Bfm")) 358 | }) 359 | 360 | observeEvent(input$action_downloadPlotBf01, { 361 | showModal(modalDownloadPlot(type = "Bf01")) 362 | }) 363 | 364 | observeEvent(input$action_downloadPlotBfast, { 365 | showModal(modalDownloadPlot(type = "Bfast")) 366 | }) 367 | 368 | # ------------------------------------------------------------- MODALS ---- 369 | 370 | modalDownloadPlot <- function(type = c("Raw", "Bfm", "Bf01", "Bfast")) { 371 | type = match.arg(type) 372 | 373 | modalDialog( 374 | title = "Please choose the output format", 375 | easyClose = T, 376 | 377 | div(align = "center", 378 | downloadButton(outputId = paste0("action_downloadPlot", type, "_jpg"), 379 | label = "JPEG", 380 | class = "btn-primary"), 381 | downloadButton(outputId = paste0("action_downloadPlot", type, "_png"), 382 | label = "PNG", 383 | class = "btn-primary"), 384 | downloadButton(outputId = paste0("action_downloadPlot", type, "_svg"), 385 | label = "SVG", 386 | class = "btn-primary")), 387 | 388 | footer = tagList( 389 | modalButton("Cancel") 390 | ) 391 | ) 392 | } 393 | 394 | # ----------------------------------------------------------- ANALYSIS ---- 395 | 396 | # call to event_getTs in Map tab 397 | # display a message if the download is successful 398 | output$text_getTs <- renderText({ 399 | event_getTs() 400 | return( 401 | as.character(div( 402 | align = "center", 403 | strong("Data succesfully downloaded!"), 404 | HTML('Please head to the Analysis tab.') 405 | )) 406 | ) 407 | }) 408 | 409 | # subset from serie using select_satPlot, and merge data in a single df 410 | serieSel <- eventReactive(input$select_satPlot, valueExpr = { 411 | serie <- event_getTs() 412 | 413 | # only show satellites selected in select_satPlot 414 | satOrder <- attr(serie, "satOrder") 415 | whichSel <- which(satOrder %in% input$select_satPlot) 416 | serie <- serie[whichSel] 417 | 418 | # join all data in a single df 419 | tmp <- NULL 420 | for (i in 1:length(whichSel)) { 421 | tmp <- rbind(tmp, serie[[i]]) 422 | } 423 | serie <- tmp 424 | 425 | # sort data by date 426 | serie <- serie[order(serie$date), ] 427 | 428 | # removendo NAs 429 | serie <- na.omit(serie) 430 | 431 | # remove leap year additional day (29th Feb), if it exists 432 | leapDay <- grep("-02-29", serie$date) 433 | if(length(leapDay) > 0) { 434 | serie <- serie[-leapDay, ] 435 | } 436 | 437 | # group by date and satellite using median 438 | serie <- serie %>% 439 | group_by(date) %>% 440 | summarise_all(function(x) { 441 | if (typeof(x) == "character") { 442 | if (x %>% unique() %>% length() > 1) { 443 | "Mixed" 444 | } else { 445 | x[1] 446 | } 447 | } else { 448 | median(x) 449 | } 450 | }) %>% 451 | data.frame() 452 | 453 | return(serie) 454 | }) 455 | 456 | observe({ 457 | # subset the chosen index from the data 458 | matchCol <- which(input$select_index == colnames(serieSel())) 459 | satOrder <- satChoices[which(satChoices %in% input$select_satPlot)] 460 | 461 | # add "Mixed" to satOrder, if it exists in the data 462 | if("Mixed" %in% serieSel()$sat) { 463 | satOrder <- c(satOrder, "Mixed" = "Mixed") 464 | } 465 | 466 | # define a vector of graphical parameters (color and pch), 467 | # per satellite 468 | seriePar <- matrix(sapply(serieSel()$sat, satPar), 469 | ncol = 2, 470 | byrow = T) 471 | 472 | # custom ylim parameter 473 | ylimCustom <- c(0, 1) 474 | # if (sum(serieSel()[, matchCol] < 0) > 0) { 475 | # ylimCustom[1] <- -1 476 | # } 477 | # if (sum(serieSel()[, matchCol] > 1) > 0) { 478 | # ylimCustom[2] <- 1.5 479 | # } 480 | 481 | if (sum(serieSel()[, matchCol] < 0, na.rm = TRUE) > 0) { 482 | ylimCustom[1] <- -1 483 | } 484 | if (sum(serieSel()[, matchCol] > 1, na.rm = TRUE) > 0) { 485 | ylimCustom[2] <- 1.5 486 | } 487 | 488 | # custom x axis 489 | xAxisCustom <- seq(as.numeric(substr(range(serieSel()$date), 1, 4))[1] - 1, 490 | as.numeric(substr(range(serieSel()$date), 1, 4))[2] + 1, 491 | 1) 492 | 493 | # bfastmonitor line segment parameter 494 | h <- 0.25 495 | 496 | bfm_formula <- switch( 497 | input$select_bfm_formula, 498 | "trend + harmon" = response ~ trend + harmon, 499 | "harmon" = response ~ harmon, 500 | "trend" = response ~ trend 501 | ) 502 | 503 | # conditions to check if few data is available for bfastmonitor 504 | cond1 <- switch( 505 | input$select_bfm_formula, 506 | "trend + harmon" = length(serieSel()[, matchCol]) > 4, 507 | "harmon" = length(serieSel()[, matchCol]) > 3, 508 | "trend" = length(serieSel()[, matchCol]) > 2 509 | ) 510 | cond2 <- floor(h * length(serieSel()[, matchCol])) > 1 511 | 512 | # update input$select_bfm_monitor 513 | updateDateInput( 514 | session = session, 515 | inputId = "select_bfm_monitor", 516 | min = head(serieSel()$date, 1), 517 | max = tail(serieSel()$date, 1), 518 | value = tail(serieSel()$date, 519 | floor(length(serieSel()$date)*0.3))[1] 520 | ) 521 | 522 | # update input$select_bf01_order 523 | if(input$select_bf01_formula != "trend") { 524 | if(input$select_bf01_formula == "harmon") { 525 | pars <- 1 526 | } else { # "trend + harmon" 527 | pars <- 2 528 | } 529 | orderMaxBf01 <- 0 530 | cond3 <- T 531 | while(cond3) { 532 | orderMaxBf01 <- orderMaxBf01 + 1 533 | cond3 <- (5 * (pars + 2 * orderMaxBf01)) < length(serieSel()[, matchCol]) / 2 534 | } 535 | orderMaxBf01 <- orderMaxBf01 - 1 536 | updateSliderInput( 537 | session = session, 538 | inputId = "select_bf01_order", 539 | max = orderMaxBf01 540 | ) 541 | } 542 | 543 | # update select_bfm_order (WIP) 544 | # if(input$select_bfm_formula != "trend") { 545 | # if(input$select_bfm_history == "ROC") { 546 | # orderMaxBfm <- 0 547 | # cond4 <- F 548 | # while(!cond4) { 549 | # orderMaxBfm <- orderMaxBfm + 1 550 | # data <- bfastts(data = serieSel()[, matchCol], 551 | # dates = serieSel()$date, 552 | # type = "irregular") 553 | # data_tspp <- bfastpp(data, order = orderMaxBfm, lag = NULL, slag = NULL) 554 | # history_tspp <- subset(data_tspp, time < decimal_date(input$select_bfm_monitor)) 555 | # data_rev <- history_tspp[nrow(history_tspp):1,] 556 | # data_rev$response <- ts(data_rev$response) 557 | # suppressWarnings( 558 | # y_rcus <- efp(bfm_formula, data = data_rev, type = "Rec-CUSUM") 559 | # ) 560 | # cond4 <- is.na(y_rcus$process[1]) 561 | # } 562 | # orderMaxBfm <- orderMaxBfm - 1 563 | # updateSliderInput( 564 | # session = session, 565 | # inputId = "select_bfm_order", 566 | # max = orderMaxBfm 567 | # ) 568 | # } 569 | # } 570 | 571 | # raw time series plot 572 | output$plot_raw <- renderPlot({ 573 | output$action_downloadDataRaw <- downloadHandler( 574 | filename = paste0("be-data-ts", ".csv"), 575 | content = {function(file) { 576 | if(v$markerDown$show) { 577 | header <- rep("", ncol(serieSel())) 578 | names(header) <- c("LatLong Coordinates", as.character(c(v$markerDown$lat, v$markerDown$lon)), rep("", ncol(serieSel()) - 3)) 579 | write.table(x = t(header), file = file, sep = ",", row.names = F) 580 | options(warn = -1) 581 | write.table(x = serieSel(), file = file, sep = ",", row.names = F, append = T) 582 | options(warn = 0) 583 | } else { 584 | write.table(x = serieSel(), file = file, sep = ",", row.names = F) 585 | } 586 | }} 587 | ) 588 | 589 | plotRawComb <- function() { 590 | plotRaw( 591 | serie = serieSel(), 592 | matchCol = matchCol, 593 | xAxisCustom = xAxisCustom, 594 | ylimCustom = ylimCustom, 595 | ylab = toupper(colnames(serieSel())[matchCol]), 596 | seriePar = seriePar, 597 | coords = v$markerDown 598 | ) 599 | plotRawLegend( 600 | satOrder = satOrder, 601 | seriePar = seriePar 602 | ) 603 | } 604 | 605 | output$action_downloadPlotRaw_jpg <- downloadHandler( 606 | filename = paste0("be-", colnames(serieSel())[matchCol], "-plot-ts", ".jpg"), 607 | content = {function(file) { 608 | jpeg(file, width = 1080, height = 600) 609 | layout(mat = matrix(c(1, 2), ncol = 2), 610 | widths = c(1.5, 0.5)) 611 | plotRawComb() 612 | dev.off() 613 | }} 614 | ) 615 | 616 | output$action_downloadPlotRaw_png <- downloadHandler( 617 | filename = paste0("be-", colnames(serieSel())[matchCol], "-plot-ts", ".png"), 618 | content = {function(file) { 619 | png(file, width = 1080, height = 600) 620 | layout(mat = matrix(c(1, 2), ncol = 2), 621 | widths = c(1.5, 0.5)) 622 | plotRawComb() 623 | dev.off() 624 | }} 625 | ) 626 | 627 | output$action_downloadPlotRaw_svg <- downloadHandler( 628 | filename = paste0("be-", colnames(serieSel())[matchCol], "-plot-ts", ".svg"), 629 | content = {function(file) { 630 | svg(file, width = 16, height = 8) 631 | layout(mat = matrix(c(1, 2), ncol = 2), 632 | widths = c(1.5, 0.5)) 633 | plotRawComb() 634 | dev.off() 635 | }} 636 | ) 637 | 638 | plotRaw( 639 | serie = serieSel(), 640 | matchCol = matchCol, 641 | xAxisCustom = xAxisCustom, 642 | ylimCustom = ylimCustom, 643 | ylab = toupper(colnames(serieSel())[matchCol]), 644 | seriePar = seriePar, 645 | coords = v$markerDown 646 | ) 647 | }) 648 | 649 | # bfastmonitor results plot 650 | output$plot_bfm <- renderPlot({ 651 | # if(input$select_bfm_formula != "trend") { 652 | # validate( 653 | # need( 654 | # input$select_bfm_order <= orderMaxBfm, 655 | # FALSE 656 | # ) 657 | # ) 658 | # } 659 | validate( 660 | need( 661 | cond1 & cond2, 662 | "The selected history period hasn't enough observations." 663 | ) 664 | ) 665 | 666 | # run bfastmonitor 667 | res <- ppBfastmonitor( 668 | x = serieSel()[, matchCol], 669 | date = serieSel()$date, 670 | formula = bfm_formula, 671 | order = input$select_bfm_order, 672 | start = decimal_date(input$select_bfm_monitor), 673 | history = input$select_bfm_history, 674 | h = h 675 | ) 676 | 677 | output$action_downloadDataBfm <- downloadHandler( 678 | filename = paste0("be-", colnames(serieSel())[matchCol], "-results-bfastmonitor", ".rds"), 679 | content = {function(file) { 680 | saveRDS(object = res, 681 | file = file) 682 | }} 683 | ) 684 | 685 | plotBfmComb <- function() { 686 | plotBfm( 687 | serie = serieSel(), 688 | matchCol = matchCol, 689 | bfmOut = res, 690 | xAxisCustom = xAxisCustom, 691 | ylimCustom = ylimCustom, 692 | ylab = toupper(colnames(serieSel())[matchCol]) 693 | ) 694 | plotBfmLegend() 695 | } 696 | 697 | output$action_downloadPlotBfm_jpg <- downloadHandler( 698 | filename = paste0("be-", colnames(serieSel())[matchCol], "-plot-bfastmonitor", ".jpg"), 699 | content = {function(file) { 700 | jpeg(file, width = 1080, height = 600) 701 | layout(mat = matrix(c(1, 2), ncol = 2), 702 | widths = c(1.5, 0.5)) 703 | plotBfmComb() 704 | dev.off() 705 | }} 706 | ) 707 | 708 | output$action_downloadPlotBfm_png <- downloadHandler( 709 | filename = paste0("be-", colnames(serieSel())[matchCol], "-plot-bfastmonitor", ".png"), 710 | content = {function(file) { 711 | png(file, width = 1080, height = 600) 712 | layout(mat = matrix(c(1, 2), ncol = 2), 713 | widths = c(1.5, 0.5)) 714 | plotBfmComb() 715 | dev.off() 716 | }} 717 | ) 718 | 719 | output$action_downloadPlotBfm_svg <- downloadHandler( 720 | filename = paste0("be-", colnames(serieSel())[matchCol], "-plot-bfastmonitor", ".svg"), 721 | content = {function(file) { 722 | svg(file, width = 16, height = 8) 723 | layout(mat = matrix(c(1, 2), ncol = 2), 724 | widths = c(1.5, 0.5)) 725 | plotBfmComb() 726 | dev.off() 727 | }} 728 | ) 729 | 730 | # plot bfastmonitor results 731 | plotBfm( 732 | serie = serieSel(), 733 | matchCol = matchCol, 734 | bfmOut = res, 735 | xAxisCustom = xAxisCustom, 736 | ylimCustom = ylimCustom, 737 | ylab = toupper(colnames(serieSel())[matchCol]) 738 | ) 739 | }) 740 | 741 | # bfast01 results plot 742 | output$plot_bf01 <- renderPlot({ 743 | if(input$select_bf01_formula != "trend") { 744 | validate( 745 | need( 746 | input$select_bf01_order <= orderMaxBf01, 747 | FALSE 748 | ) 749 | ) 750 | } 751 | 752 | bf01_formula <- switch( 753 | input$select_bf01_formula, 754 | "trend + harmon" = response ~ trend + harmon, 755 | "harmon" = response ~ harmon, 756 | "trend" = response ~ trend 757 | ) 758 | 759 | res <- ppBfast01( 760 | x = serieSel()[, matchCol], 761 | date = serieSel()$date, 762 | formula = bf01_formula, 763 | order = input$select_bf01_order 764 | ) 765 | 766 | output$action_downloadDataBf01 <- downloadHandler( 767 | filename = paste0("be-", colnames(serieSel())[matchCol], "-results-bfast01", ".rds"), 768 | content = {function(file) { 769 | saveRDS(object = res, 770 | file = file) 771 | }} 772 | ) 773 | 774 | plotBf01Comb <- function() { 775 | plotBf01( 776 | serie = serieSel(), 777 | matchCol = matchCol, 778 | bf01Out = res, 779 | xAxisCustom = xAxisCustom, 780 | ylimCustom = ylimCustom, 781 | ylab = toupper(colnames(serieSel())[matchCol]) 782 | ) 783 | plotBf01Legend() 784 | } 785 | 786 | output$action_downloadPlotBf01_jpg <- downloadHandler( 787 | filename = paste0("be-", colnames(serieSel())[matchCol], "-plot-bfast01", ".jpg"), 788 | content = {function(file) { 789 | jpeg(file, width = 1080, height = 600) 790 | layout(mat = matrix(c(1, 2), ncol = 2), 791 | widths = c(1.5, 0.5)) 792 | plotBf01Comb() 793 | dev.off() 794 | }} 795 | ) 796 | 797 | output$action_downloadPlotBf01_png <- downloadHandler( 798 | filename = paste0("be-", colnames(serieSel())[matchCol], "-plot-bfast01", ".png"), 799 | content = {function(file) { 800 | png(file, width = 1080, height = 600) 801 | layout(mat = matrix(c(1, 2), ncol = 2), 802 | widths = c(1.5, 0.5)) 803 | plotBf01Comb() 804 | dev.off() 805 | }} 806 | ) 807 | 808 | output$action_downloadPlotBf01_svg <- downloadHandler( 809 | filename = paste0("be-", colnames(serieSel())[matchCol], "-plot-bfast01", ".svg"), 810 | content = {function(file) { 811 | svg(file, width = 16, height = 8) 812 | layout(mat = matrix(c(1, 2), ncol = 2), 813 | widths = c(1.5, 0.5)) 814 | plotBf01Comb() 815 | dev.off() 816 | }} 817 | ) 818 | 819 | plotBf01( 820 | serie = serieSel(), 821 | matchCol = matchCol, 822 | bf01Out = res, 823 | xAxisCustom = xAxisCustom, 824 | ylimCustom = ylimCustom, 825 | ylab = toupper(colnames(serieSel())[matchCol]) 826 | ) 827 | }) 828 | 829 | # bfast results plot 830 | output$plot_bfast <- renderPlot({ 831 | res <- ppBfast( 832 | x = serieSel()[, matchCol], 833 | date = serieSel()$date, 834 | h = input$select_bfast_h, 835 | season = input$select_bfast_season 836 | ) 837 | 838 | output$action_downloadDataBfast <- downloadHandler( 839 | filename = paste0("be-", colnames(serieSel())[matchCol], "-results-bfast", ".rds"), 840 | content = {function(file) { 841 | saveRDS(object = res, 842 | file = file) 843 | }} 844 | ) 845 | 846 | plotBfastComb <- function() { 847 | plotBfast( 848 | serie = serieSel(), 849 | matchCol = matchCol, 850 | bfastOut = res, 851 | xAxisCustom = xAxisCustom, 852 | ylimCustom = ylimCustom, 853 | ylab = toupper(colnames(serieSel())[matchCol]) 854 | ) 855 | plotBfastLegend() 856 | } 857 | 858 | output$action_downloadPlotBfast_jpg <- downloadHandler( 859 | filename = paste0("be-", colnames(serieSel())[matchCol], "-plot-bfast", ".jpg"), 860 | content = {function(file) { 861 | jpeg(file, width = 1080, height = 600) 862 | layout(mat = matrix(c(1, 2), ncol = 2), 863 | widths = c(1.5, 0.5)) 864 | plotBfastComb() 865 | dev.off() 866 | }} 867 | ) 868 | 869 | output$action_downloadPlotBfast_png <- downloadHandler( 870 | filename = paste0("be-", colnames(serieSel())[matchCol], "-plot-bfast", ".png"), 871 | content = {function(file) { 872 | png(file, width = 1080, height = 600) 873 | layout(mat = matrix(c(1, 2), ncol = 2), 874 | widths = c(1.5, 0.5)) 875 | plotBfastComb() 876 | dev.off() 877 | }} 878 | ) 879 | 880 | output$action_downloadPlotBfast_svg <- downloadHandler( 881 | filename = paste0("be-", colnames(serieSel())[matchCol], "-plot-bfast", ".svg"), 882 | content = {function(file) { 883 | svg(file, width = 16, height = 8) 884 | layout(mat = matrix(c(1, 2), ncol = 2), 885 | widths = c(1.5, 0.5)) 886 | plotBfastComb() 887 | dev.off() 888 | }} 889 | ) 890 | 891 | plotBfast( 892 | serie = serieSel(), 893 | matchCol = matchCol, 894 | bfastOut = res, 895 | xAxisCustom = xAxisCustom, 896 | ylimCustom = ylimCustom, 897 | ylab = toupper(colnames(serieSel())[matchCol]) 898 | ) 899 | }) 900 | 901 | # raw time series legend 902 | output$plot_raw_legend <- renderPlot({ 903 | plotRawLegend( 904 | satOrder = satOrder, 905 | seriePar = seriePar 906 | ) 907 | }) 908 | 909 | # bfastmonitor legend 910 | output$plot_bfm_legend <- renderPlot({ 911 | plotBfmLegend() 912 | }) 913 | 914 | # bfast01 legend 915 | output$plot_bf01_legend <- renderPlot({ 916 | plotBf01Legend() 917 | }) 918 | 919 | # bfast legend 920 | output$plot_bfast_legend <- renderPlot({ 921 | plotBfastLegend() 922 | }) 923 | }) 924 | }) 925 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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Patents. 472 | 473 | A "contributor" is a copyright holder who authorizes use under this 474 | License of the Program or a work on which the Program is based. The 475 | work thus licensed is called the contributor's "contributor version". 476 | 477 | A contributor's "essential patent claims" are all patent claims 478 | owned or controlled by the contributor, whether already acquired or 479 | hereafter acquired, that would be infringed by some manner, permitted 480 | by this License, of making, using, or selling its contributor version, 481 | but do not include claims that would be infringed only as a 482 | consequence of further modification of the contributor version. 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"Knowingly relying" means you have 508 | actual knowledge that, but for the patent license, your conveying the 509 | covered work in a country, or your recipient's use of the covered work 510 | in a country, would infringe one or more identifiable patents in that 511 | country that you have reason to believe are valid. 512 | 513 | If, pursuant to or in connection with a single transaction or 514 | arrangement, you convey, or propagate by procuring conveyance of, a 515 | covered work, and grant a patent license to some of the parties 516 | receiving the covered work authorizing them to use, propagate, modify 517 | or convey a specific copy of the covered work, then the patent license 518 | you grant is automatically extended to all recipients of the covered 519 | work and works based on it. 520 | 521 | A patent license is "discriminatory" if it does not include within 522 | the scope of its coverage, prohibits the exercise of, or is 523 | conditioned on the non-exercise of one or more of the rights that are 524 | specifically granted under this License. You may not convey a covered 525 | work if you are a party to an arrangement with a third party that is 526 | in the business of distributing software, under which you make payment 527 | to the third party based on the extent of your activity of conveying 528 | the work, and under which the third party grants, to any of the 529 | parties who would receive the covered work from you, a discriminatory 530 | patent license (a) in connection with copies of the covered work 531 | conveyed by you (or copies made from those copies), or (b) primarily 532 | for and in connection with specific products or compilations that 533 | contain the covered work, unless you entered into that arrangement, 534 | or that patent license was granted, prior to 28 March 2007. 535 | 536 | Nothing in this License shall be construed as excluding or limiting 537 | any implied license or other defenses to infringement that may 538 | otherwise be available to you under applicable patent law. 539 | 540 | 12. No Surrender of Others' Freedom. 541 | 542 | If conditions are imposed on you (whether by court order, agreement or 543 | otherwise) that contradict the conditions of this License, they do not 544 | excuse you from the conditions of this License. If you cannot convey a 545 | covered work so as to satisfy simultaneously your obligations under this 546 | License and any other pertinent obligations, then as a consequence you may 547 | not convey it at all. For example, if you agree to terms that obligate you 548 | to collect a royalty for further conveying from those to whom you convey 549 | the Program, the only way you could satisfy both those terms and this 550 | License would be to refrain entirely from conveying the Program. 551 | 552 | 13. Use with the GNU Affero General Public License. 553 | 554 | Notwithstanding any other provision of this License, you have 555 | permission to link or combine any covered work with a work licensed 556 | under version 3 of the GNU Affero General Public License into a single 557 | combined work, and to convey the resulting work. The terms of this 558 | License will continue to apply to the part which is the covered work, 559 | but the special requirements of the GNU Affero General Public License, 560 | section 13, concerning interaction through a network will apply to the 561 | combination as such. 562 | 563 | 14. Revised Versions of this License. 564 | 565 | The Free Software Foundation may publish revised and/or new versions of 566 | the GNU General Public License from time to time. Such new versions will 567 | be similar in spirit to the present version, but may differ in detail to 568 | address new problems or concerns. 569 | 570 | Each version is given a distinguishing version number. If the 571 | Program specifies that a certain numbered version of the GNU General 572 | Public License "or any later version" applies to it, you have the 573 | option of following the terms and conditions either of that numbered 574 | version or of any later version published by the Free Software 575 | Foundation. If the Program does not specify a version number of the 576 | GNU General Public License, you may choose any version ever published 577 | by the Free Software Foundation. 578 | 579 | If the Program specifies that a proxy can decide which future 580 | versions of the GNU General Public License can be used, that proxy's 581 | public statement of acceptance of a version permanently authorizes you 582 | to choose that version for the Program. 583 | 584 | Later license versions may give you additional or different 585 | permissions. However, no additional obligations are imposed on any 586 | author or copyright holder as a result of your choosing to follow a 587 | later version. 588 | 589 | 15. Disclaimer of Warranty. 590 | 591 | THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY 592 | APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT 593 | HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY 594 | OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, 595 | THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR 596 | PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM 597 | IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF 598 | ALL NECESSARY SERVICING, REPAIR OR CORRECTION. 599 | 600 | 16. Limitation of Liability. 601 | 602 | IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING 603 | WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS 604 | THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY 605 | GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE 606 | USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF 607 | DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD 608 | PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS), 609 | EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF 610 | SUCH DAMAGES. 611 | 612 | 17. Interpretation of Sections 15 and 16. 613 | 614 | If the disclaimer of warranty and limitation of liability provided 615 | above cannot be given local legal effect according to their terms, 616 | reviewing courts shall apply local law that most closely approximates 617 | an absolute waiver of all civil liability in connection with the 618 | Program, unless a warranty or assumption of liability accompanies a 619 | copy of the Program in return for a fee. 620 | 621 | END OF TERMS AND CONDITIONS 622 | 623 | How to Apply These Terms to Your New Programs 624 | 625 | If you develop a new program, and you want it to be of the greatest 626 | possible use to the public, the best way to achieve this is to make it 627 | free software which everyone can redistribute and change under these terms. 628 | 629 | To do so, attach the following notices to the program. It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | {one line to give the program's name and a brief idea of what it does.} 635 | Copyright (C) {year} {name of author} 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | {project} Copyright (C) {year} {fullname} 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /www/logo-cnpq.svg: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 6 | 12 | 17 | 19 | 173 | 344 | 480 | 517 | 518 | --------------------------------------------------------------------------------