Hi folks, I'm attempting to run the LandTrendr model in IDL, and have hit a snag. I have virtually no IDL experience, but was told that since the code is already written and has been tested, I should have very few hiccups. I've made it through a good number of modules, but have hit a breaking point. I don't know if there is enough background provided here for any of you to help my get past this error, but any help you may be able to provide would be greatly appreciated. Cheers. This is the error message I am getting: % OPENW: Error opening file. Unit: 100, File: E:\034032\outputs\nbr\LT_v2.00_nbr_034032_eval01_2015-02-26T20:31:39.00005310773531Z_vertyrs.bsq The filename, directory name, or volume label syntax is incorrect. And here is the code: function process_tbcd_chunks, run_params, progressbaryesno ;copy over just to make it work with historical code. image_info = run_params.image_info index = run_params.index ;string with name of index subset=run_params.subset mask_image = run_params.mask_image output_base = run_params.output_base kernelsize = run_params.kernelsize background_val = run_params.background_val skipfactor = run_params.skipfactor desawtooth_val = run_params.desawtooth_val pval = run_params.pval max_segments = run_params.max_segments fix_doy_effect = run_params.fix_doy_effect divisor =run_params.divisor minneeded = run_params.minneeded recovery_threshold=run_params.recovery_threshold distweightfactor = run_params.distweightfactor vertexcountovershoot = run_params.vertexcountovershoot bestmodelproportion = run_params.bestmodelproportion progressval = 0 ;set to record progress progressinc = 10 ;for the increment ;make a directory file_mkdir, file_dirname(output_base) ;tests if vertexcountovershoot gt 3 then begin print, 'Vertexcountovershoot cannot exceed 3' return, -1 end if bestmodelproportion gt 1 then begin print, 'bestmodelproportion cannot exceed 1. Should be in the 0.5 to 1.0 range.' return, -1 end ;first, check to see if the output image already exists. If so ; see if it has a save file to indicate that it was already in ; process, and needs to just be picked up again diagfile = output_base+'_diag.sav' if file_exists(diagfile) then begin print, 'This file has already had processing done on it' restore, diagfile image_info = diag_info.image_info index = diag_info.index mask_image = diag_info.mask_image output_image_group = diag_info.output_image_group n_chunks = diag_info.n_chunks chunks = diag_info.chunks current_chunk = diag_info.current_chunk pixels_per_chunk = diag_info.pixels_per_chunk kernelsize = diag_info.kernelsize background_val=diag_info.background_val skipfactor=diag_info.skipfactor desawtooth_val=diag_info.desawtooth_val pval=diag_info.pval max_segments=diag_info.max_segments ; normalize=diag_info.normalize fix_doy_effect=diag_info.fix_doy_effect divisor=diag_info.divisor recovery_threshold = diag_info.recovery_threshold distweightfactor = diag_info.distweightfactor vertexcountovershoot = diag_info.vertexcountovershoot bestmodelproportion = diag_info.bestmodelproportion end else begin ;if this image has not been set up before, then ;get set up to do it. ;SET UP THE OUTPUT FILE AND CHUNK INFORMATION ;First, set up the processing chunks if n_elements(max_pixels_per_chunk) eq 0 then $ max_pixels_per_chunk = 500000l ;to get the pixel size of the image, assume that all are the same if file_exists(image_info[0].image_file) eq 0 then begin print, "process_cm_biomass_image.pro: Image in print," image_list does not exist. Failing." print," Image that was not found: print, image_info[0].image_file end mastersubset = subset subset = mastersubset zot_img, image_info[0].image_file, hdr, img, subset=subset, /hdronly pixsize = hdr.pixelsize ;now get the chunks subset = mastersubset ok = define_chunks3(subset, pixsize, max_pixels_per_chunk, kernelsize) if ok.ok eq 0 then return, {ok:0} ;stop chunks = ok.subsets pixels_per_chunk = ok.pixels_per_chunk n_chunks = n_elements(chunks) current_chunk = 0 ;an index ;define the output images ;vertices: the years of the vertices ;vertvals: the values of the input band at those years ;mags: the magnitude of the segment change between two vertices ;distrec: three layer image ; 1: largest single disturbance ; 2: largest single recovery ; 3: scaled ratio between disturbance and recovery ; -1000 is all recovery ; 0 is a balance ; 1000 is all disturbance ;fitted image ; Same number of years as all of the inputs, ; but with fitted values ;Stats image for entire fit ; P of f ; f_stat ; ms_regr ; ms_resid output_image_base = {filename:'', n_layers:0, extension:'', layersize:0l, filesize:0ll, DATA:""} output_image_group = replicate(output_image_base, 10) output_image_group[0].extension = '_vertyrs.bsq' output_image_group[0].n_layers = max_segments+1 output_image_group[0].DATA = "Vertex Year" output_image_group[1].extension = '_vertvals.bsq' output_image_group[1].n_layers = max_segments+1 output_image_group[1].DATA = "Vertex Spectral Value" output_image_group[2].extension = '_mags.bsq' output_image_group[2].n_layers = max_segments output_image_group[2].DATA = "Segment Magnitude" output_image_group[3].extension = '_durs.bsq' output_image_group[3].n_layers = max_segments output_image_group[3].DATA = "Segment Duration" output_image_group[4].extension = '_distrec.bsq' output_image_group[4].n_layers = 3 output_image_group[4].DATA = "Trajectory Disturbance/Recovery" ;6/27/08 for fitted image, the output will be the ; max of 1 image per year. If multiple images ; in a given year are provided with the idea of doing ; cloud-mosaicking, then we need to compensate for ; those doubled-images in stack. years = image_info.year un_years = fast_unique(years) years = un_years[sort(un_years)] output_image_group[5].extension = '_fitted.bsq' output_image_group[5].n_layers = n_elements(years) output_image_group[5].DATA = "Fitted Spectral Stack" output_image_group[6].extension = '_stats.bsq' output_image_group[6].n_layers = 10 output_image_group[6].DATA = "Segment Statistics" output_image_group[7].extension = '_segmse.bsq' output_image_group[7].n_layers = max_segments output_image_group[7].DATA = "Segment MSE" output_image_group[8].extension = '_source.bsq' output_image_group[8].n_layers = n_elements(years) output_image_group[8].DATA = "Source Spectral Stack" output_image_group[9].extension = '_segmean.bsq' output_image_group[9].n_layers = max_segments output_image_group[9].DATA = "Segment Mean Spectral" ;this_tag = "_" + timestamp() + "_" + landtrendr_version() for i = 0, n_elements(output_image_group)-1 do begin this_file = output_base + output_image_group[i].extension output_image_group[i].filename = this_file openw, un, output_image_group[i].filename, /get_lun n_output_layers = output_image_group[i].n_layers bytes_per_pixel = 2 layersize = long(hdr.filesize[0]) * hdr.filesize[1] * bytes_per_pixel filesize = ulong64(layersize) * n_output_layers