def pixel_rating(image_ws, ini_path, stats_flag=False, overwrite_flag=None): """Calculate pixel rating Args: image_ws (str): Image folder path ini_path (str): Pixel regions config file path stats_flag (bool): if True, compute raster statistics ovewrite_flag (bool): if True, overwrite existing files Returns: None """ logging.info('Generating suggested hot/cold pixel regions') log_fmt = ' {:<18s} {}' env = gdc.env image = et_image.Image(image_ws, env) np.seterr(invalid='ignore') # # Check that image_ws is valid # image_re = re.compile( # '^(LT04|LT05|LE07|LC08)_(\d{3})(\d{3})_(\d{4})(\d{2})(\d{2})') # if not os.path.isdir(image_ws) or not image_re.match(scene_id): # logging.error('\nERROR: Image folder is invalid or does not exist\n') # return False # Folder Paths region_ws = os.path.join(image_ws, 'PIXEL_REGIONS') # Open config file config = open_ini(ini_path) # Get input parameters logging.debug(' Reading Input File') # Arrays are processed by block bs = read_param('block_size', 1024, config) logging.info(' {:<18s} {}'.format('Block Size:', bs)) # Raster pyramids/statistics pyramids_flag = read_param('pyramids_flag', False, config) if pyramids_flag: gdal.SetConfigOption('HFA_USE_RRD', 'YES') if stats_flag is None: stats_flag = read_param('statistics_flag', False, config) # Overwrite if overwrite_flag is None: overwrite_flag = read_param('overwrite_flag', True, config) # Check that common_area raster exists if not os.path.isfile(image.common_area_raster): logging.error( '\nERROR: A common area raster was not found.' + '\nERROR: Please rerun prep tool to build these files.\n' + ' {}\n'.format(image.common_area_raster)) sys.exit() # Use common_area to set mask parameters common_ds = gdal.Open(image.common_area_raster) # env.mask_proj = raster_ds_proj(common_ds) env.mask_geo = gdc.raster_ds_geo(common_ds) env.mask_rows, env.mask_cols = gdc.raster_ds_shape(common_ds) env.mask_extent = gdc.geo_extent(env.mask_geo, env.mask_rows, env.mask_cols) env.mask_array = gdc.raster_ds_to_array(common_ds)[0] env.mask_path = image.common_area_raster env.snap_osr = gdc.raster_path_osr(image.common_area_raster) env.snap_proj = env.snap_osr.ExportToWkt() env.cellsize = gdc.raster_path_cellsize(image.common_area_raster)[0] common_ds = None logging.debug(' {:<18s} {}'.format('Mask Extent:', env.mask_extent)) # Read Pixel Regions config file # Currently there is no code to support applying an NLCD mask apply_nlcd_mask = False # apply_nlcd_mask = read_param('apply_nlcd_mask', False, config) apply_cdl_ag_mask = read_param('apply_cdl_ag_mask', False, config) apply_field_mask = read_param('apply_field_mask', False, config) apply_ndwi_mask = read_param('apply_ndwi_mask', True, config) apply_ndvi_mask = read_param('apply_ndvi_mask', True, config) # Currently the code to apply a study area mask is commented out # apply_study_area_mask = read_param( # 'apply_study_area_mask', False, config) albedo_rating_flag = read_param('albedo_rating_flag', True, config) nlcd_rating_flag = read_param('nlcd_rating_flag', True, config) ndvi_rating_flag = read_param('ndvi_rating_flag', True, config) ts_rating_flag = read_param('ts_rating_flag', True, config) ke_rating_flag = read_param('ke_rating_flag', False, config) # if apply_study_area_mask: # study_area_path = config.get('INPUTS', 'study_area_path') if apply_nlcd_mask or nlcd_rating_flag: nlcd_raster = config.get('INPUTS', 'landuse_raster') if apply_cdl_ag_mask: cdl_ag_raster = config.get('INPUTS', 'cdl_ag_raster') cdl_buffer_cells = read_param('cdl_buffer_cells', 0, config) cdl_ag_eroded_name = read_param('cdl_ag_eroded_name', 'cdl_ag_eroded_{}.img', config) if apply_field_mask: field_raster = config.get('INPUTS', 'fields_raster') cold_rating_pct = read_param('cold_percentile', 99, config) hot_rating_pct = read_param('hot_percentile', 99, config) # min_cold_rating_score = read_param('min_cold_rating_score', 0.3, config) # min_hot_rating_score = read_param('min_hot_rating_score', 0.3, config) ts_bin_count = int(read_param('ts_bin_count', 10, config)) if 100 % ts_bin_count != 0: logging.warning( 'WARNING: ts_bins_count of {} is not a divisor ' + 'of 100. Using default ts_bins_count = 4'.format(ts_bin_count)) ts_bin_count = 10 bin_size = 1. / (ts_bin_count - 1) hot_rating_values = np.arange(0., 1. + bin_size, step=bin_size) cold_rating_values = hot_rating_values[::-1] # Input raster paths r_fmt = '.img' if 'Landsat' in image.type: albedo_raster = image.albedo_sur_raster ndvi_raster = image.ndvi_toa_raster ndwi_raster = image.ndwi_toa_raster ts_raster = image.ts_raster ke_raster = image.ke_raster # Check config file input paths # if apply_study_area_mask and not os.path.isfile(study_area_path): # logging.error( # ('\nERROR: The study area shapefile {} does ' + # 'not exist\n').format(study_area_path)) # sys.exit() if ((apply_nlcd_mask or nlcd_rating_flag) and not os.path.isfile(nlcd_raster)): logging.error(('\nERROR: The NLCD raster {} does ' + 'not exist\n').format(nlcd_raster)) sys.exit() if apply_cdl_ag_mask and not os.path.isfile(cdl_ag_raster): logging.error(('\nERROR: The CDL Ag raster {} does ' + 'not exist\n').format(cdl_ag_raster)) sys.exit() if apply_field_mask and not os.path.isfile(field_raster): logging.error(('\nERROR: The field raster {} does ' + 'not exist\n').format(field_raster)) sys.exit() if (not (isinstance(cold_rating_pct, (int, float)) and (0 <= cold_rating_pct <= 100))): logging.error( '\nERROR: cold_percentile must be a value between 0 and 100\n') sys.exit() if (not (isinstance(hot_rating_pct, (int, float)) and (0 <= hot_rating_pct <= 100))): logging.error( '\nERROR: hot_percentile must be a value between 0 and 100\n') sys.exit() # Set raster names raster_dict = dict() # Output Rasters raster_dict['region_mask'] = os.path.join(region_ws, 'region_mask' + r_fmt) raster_dict['cold_rating'] = os.path.join(region_ws, 'cold_pixel_rating' + r_fmt) raster_dict['hot_rating'] = os.path.join(region_ws, 'hot_pixel_rating' + r_fmt) raster_dict['cold_sugg'] = os.path.join(region_ws, 'cold_pixel_suggestion' + r_fmt) raster_dict['hot_sugg'] = os.path.join(region_ws, 'hot_pixel_suggestion' + r_fmt) # Read pixel region raster flags save_dict = dict() save_dict['region_mask'] = read_param('save_region_mask_flag', False, config) save_dict['cold_rating'] = read_param('save_rating_rasters_flag', False, config) save_dict['hot_rating'] = read_param('save_rating_rasters_flag', False, config) save_dict['cold_sugg'] = read_param('save_suggestion_rasters_flag', True, config) save_dict['hot_sugg'] = read_param('save_suggestion_rasters_flag', True, config) # Output folder if not os.path.isdir(region_ws): os.mkdir(region_ws) # Remove existing files if necessary region_ws_file_list = [ os.path.join(region_ws, item) for item in os.listdir(region_ws) ] if overwrite_flag and region_ws_file_list: for raster_path in raster_dict.values(): if raster_path in region_ws_file_list: remove_file(raster_path) # Check scene specific input paths if apply_ndwi_mask and not os.path.isfile(ndwi_raster): logging.error( 'ERROR: NDWI raster does not exist\n {}'.format(ndwi_raster)) sys.exit() elif apply_ndvi_mask and not os.path.isfile(ndvi_raster): logging.error( 'ERROR: NDVI raster does not exist\n {}'.format(ndvi_raster)) sys.exit() elif ke_rating_flag and not os.path.isfile(ke_raster): logging.error( ('ERROR: The Ke raster does not exist\n {}').format(ke_raster)) sys.exit() # Remove existing and build new empty rasters if necessary # If processing by block, rating rasters must be built logging.debug('\nBuilding empty rasters') for name, save_flag in sorted(save_dict.items()): if save_flag and 'rating' in name: gdc.build_empty_raster(raster_dict[name], 1, np.float32) elif save_flag: gdc.build_empty_raster(raster_dict[name], 1, np.uint8, output_nodata=0) if apply_cdl_ag_mask: logging.info('Building CDL ag mask') cdl_array = gdc.raster_to_array(cdl_ag_raster, mask_extent=env.mask_extent, return_nodata=False) if cdl_buffer_cells > 0: logging.info(' Eroding CDL by {} cells'.format(cdl_buffer_cells)) structure_array = np.ones((cdl_buffer_cells, cdl_buffer_cells), dtype=np.int) # Deadbeef - This could blow up in memory on bigger rasters cdl_array = ndimage.binary_erosion( cdl_array, structure_array).astype(structure_array.dtype) cdl_ag_eroded_raster = os.path.join( image.support_ws, cdl_ag_eroded_name.format(cdl_buffer_cells)) gdc.array_to_raster(cdl_array, cdl_ag_eroded_raster, output_geo=env.mask_geo, output_proj=env.snap_proj, mask_array=env.mask_array, output_nodata=0, stats_flag=False) cdl_array = None del cdl_array # Build region mask logging.debug('Building region mask') region_mask = np.copy(env.mask_array).astype(np.bool) if apply_field_mask: field_mask, field_nodata = gdc.raster_to_array( field_raster, mask_extent=env.mask_extent, return_nodata=True) region_mask &= field_mask != field_nodata del field_mask, field_nodata if apply_ndwi_mask: ndwi_array = gdc.raster_to_array(ndwi_raster, 1, mask_extent=env.mask_extent, return_nodata=False) region_mask &= ndwi_array > 0.0 del ndwi_array if apply_ndvi_mask: ndvi_array = gdc.raster_to_array(ndvi_raster, 1, mask_extent=env.mask_extent, return_nodata=False) region_mask &= ndvi_array > 0.12 del ndvi_array if apply_cdl_ag_mask: cdl_array, cdl_nodata = gdc.raster_to_array( cdl_ag_eroded_raster, mask_extent=env.mask_extent, return_nodata=True) region_mask &= cdl_array != cdl_nodata del cdl_array, cdl_nodata if save_dict['region_mask']: gdc.array_to_raster(region_mask, raster_dict['region_mask'], stats_flag=False) # Initialize rating arrays # This needs to be done before the ts_rating if block cold_rating_array = np.ones(env.mask_array.shape, dtype=np.float32) hot_rating_array = np.ones(env.mask_array.shape, dtype=np.float32) cold_rating_array[~region_mask] = np.nan hot_rating_array[~region_mask] = np.nan # Temperature pixel rating - grab the max and min value for the entire # Ts image in a memory safe way by using gdal_common blocks # The following is a percentile based approach if ts_rating_flag: logging.debug('Computing Ts percentile rating') ts_array = gdc.raster_to_array(ts_raster, mask_extent=env.mask_extent, return_nodata=False) ts_array[~region_mask] = np.nan percentiles = range(0, (100 + ts_bin_count), int(100 / ts_bin_count)) ts_score_value = 1. / (ts_bin_count - 1) hot_rating_values = np.arange(0, (1. + ts_score_value), step=ts_score_value)[:ts_bin_count] cold_rating_values = hot_rating_values[::-1] ts_percentile_array = stats.scoreatpercentile( ts_array[np.isfinite(ts_array)], percentiles) for bins_i in range(len(ts_percentile_array))[:-1]: bool_array = ((ts_array > ts_percentile_array[bins_i]) & (ts_array <= ts_percentile_array[bins_i + 1])) cold_rating_array[bool_array] = cold_rating_values[bins_i] hot_rating_array[bool_array] = hot_rating_values[bins_i] # gdc.array_to_raster(cold_rating_array, raster_dict['cold_rating']) # gdc.array_to_raster(hot_rating_array, raster_dict['hot_rating']) # Cleanup del ts_array, ts_percentile_array del cold_rating_values, hot_rating_values del ts_score_value, percentiles # Process by block logging.info('\nProcessing by block') logging.debug(' Mask cols/rows: {}/{}'.format(env.mask_cols, env.mask_rows)) for b_i, b_j in gdc.block_gen(env.mask_rows, env.mask_cols, bs): logging.debug(' Block y: {:5d} x: {:5d}'.format(b_i, b_j)) block_data_mask = gdc.array_to_block(env.mask_array, b_i, b_j, bs).astype(np.bool) # block_nodata_mask = ~block_data_mask block_rows, block_cols = block_data_mask.shape block_geo = gdc.array_offset_geo(env.mask_geo, b_j, b_i) block_extent = gdc.geo_extent(block_geo, block_rows, block_cols) logging.debug(' Block rows: {} cols: {}'.format( block_rows, block_cols)) # logging.debug(' Block extent: {}'.format(block_extent)) # logging.debug(' Block geo: {}'.format(block_geo)) # Don't skip empty blocks since block rating needs to be written # back to the array at the end of the block loop block_region_mask = gdc.array_to_block(region_mask, b_i, b_j, bs) if not np.any(block_region_mask): logging.debug(' Empty block') block_empty_flag = True else: block_empty_flag = False # New style continuous pixel weighting cold_rating_block = gdc.array_to_block(cold_rating_array, b_i, b_j, bs) hot_rating_block = gdc.array_to_block(hot_rating_array, b_i, b_j, bs) # Rating arrays already have region_mask set # cold_rating_block = np.ones(block_region_mask.shape, dtype=np.float32) # hot_rating_block = np.ones(block_region_mask.shape, dtype=np.float32) # cold_rating_block[~block_region_mask] = np.nan # hot_rating_block[~block_region_mask] = np.nan # del block_region_mask if ndvi_rating_flag and not block_empty_flag: # NDVI based rating ndvi_array = gdc.raster_to_array(ndvi_raster, 1, mask_extent=block_extent, return_nodata=False) # Don't let NDVI be negative ndvi_array.clip(0., 0.833, out=ndvi_array) # ndvi_array.clip(0.001, 0.833, out=ndvi_array) cold_rating_block *= ndvi_array cold_rating_block *= 1.20 ndvi_mask = (ndvi_array > 0) # DEADBEEF - Can this calculation be masked to only NDVI > 0? ndvi_mask = ndvi_array > 0 hot_rating_block[ndvi_mask] *= stats.norm.pdf( np.log(ndvi_array[ndvi_mask]), math.log(0.15), 0.5) hot_rating_block[ndvi_mask] *= 1.25 del ndvi_mask # hot_rating_block *= stats.norm.pdf( # np.log(ndvi_array), math.log(0.15), 0.5) # hot_rating_block *= 1.25 # cold_rating_block.clip(0., 1., out=cold_rating_block) # hot_rating_block.clip(0., 1., out=hot_rating_block) del ndvi_array if albedo_rating_flag and not block_empty_flag: # Albdo based rating albedo_array = gdc.raster_to_array(albedo_raster, 1, mask_extent=block_extent, return_nodata=False) albedo_cold_pdf = stats.norm.pdf(albedo_array, 0.21, 0.03) albedo_hot_pdf = stats.norm.pdf(albedo_array, 0.21, 0.06) del albedo_array cold_rating_block *= albedo_cold_pdf cold_rating_block *= 0.07 hot_rating_block *= albedo_hot_pdf hot_rating_block *= 0.15 # cold_rating_block.clip(0., 1., out=cold_rating_block) # hot_rating_block.clip(0., 1., out=hot_rating_block) del albedo_cold_pdf, albedo_hot_pdf if nlcd_rating_flag and not block_empty_flag: # NLCD based weighting, this could be CDL instead? nlcd_array = nlcd_rating( gdc.raster_to_array(nlcd_raster, 1, mask_extent=block_extent, return_nodata=False)) cold_rating_block *= nlcd_array hot_rating_block *= nlcd_array del nlcd_array if ke_rating_flag and not block_empty_flag: # SWB Ke based rating ke_array = gdc.raster_to_array(ke_raster, 1, mask_extent=block_extent, return_nodata=False) # Don't let NDVI be negative ke_array.clip(0., 1., out=ke_array) # Assumption, lower Ke is better for selecting the hot pixel # As the power (2) decreases and approaches 1, # the relationship gets more linear # cold_rating_block *= (1 - ke_array ** 2) # hot_rating_block *= (1 - ke_array ** 1.5) # Linear inverse # cold_rating_block *= (1. - ke_array) hot_rating_block *= (1. - ke_array) # cold_rating_block.clip(0., 1., out=cold_rating_block) # hot_rating_block.clip(0., 1., out=hot_rating_block) del ke_array # Clearness # clearness = 1.0 # cold_rating *= clearness # hot_rating *= clearness # Reset nan values # cold_rating_block[~region_mask] = np.nan # hot_rating_block[~region_mask] = np.nan # Save rating values cold_rating_array = gdc.block_to_array(cold_rating_block, cold_rating_array, b_i, b_j, bs) hot_rating_array = gdc.block_to_array(hot_rating_block, hot_rating_array, b_i, b_j, bs) # Save rating rasters if save_dict['cold_rating']: gdc.block_to_raster(cold_rating_block, raster_dict['cold_rating'], b_i, b_j, bs) if save_dict['hot_rating']: gdc.block_to_raster(hot_rating_block, raster_dict['hot_rating'], b_i, b_j, bs) # Save rating values cold_rating_array = gdc.block_to_array(cold_rating_block, cold_rating_array, b_i, b_j, bs) hot_rating_array = gdc.block_to_array(hot_rating_block, hot_rating_array, b_i, b_j, bs) del cold_rating_block, hot_rating_block # Select pixels above target percentile # Only build suggestion arrays if saving logging.debug('Building suggested pixel rasters') if save_dict['cold_sugg']: cold_rating_score = float( stats.scoreatpercentile( cold_rating_array[np.isfinite(cold_rating_array)], cold_rating_pct)) # cold_rating_array, cold_rating_nodata = gdc.raster_to_array( # raster_dict['cold_rating'], 1, mask_extent=env.mask_extent) # if cold_rating_score < float(min_cold_rating_score): # logging.error(('ERROR: The cold_rating_score ({}) is less ' + # 'than the min_cold_rating_score ({})').format( # cold_rating_score, min_cold_rating_score)) # sys.exit() cold_sugg_mask = cold_rating_array >= cold_rating_score gdc.array_to_raster(cold_sugg_mask, raster_dict['cold_sugg'], stats_flag=stats_flag) logging.debug(' Cold Percentile: {}'.format(cold_rating_pct)) logging.debug(' Cold Score: {:.6f}'.format(cold_rating_score)) logging.debug(' Cold Pixels: {}'.format(np.sum(cold_sugg_mask))) del cold_sugg_mask, cold_rating_array if save_dict['hot_sugg']: hot_rating_score = float( stats.scoreatpercentile( hot_rating_array[np.isfinite(hot_rating_array)], hot_rating_pct)) # hot_rating_array, hot_rating_nodata = gdc.raster_to_array( # raster_dict['hot_rating'], 1, mask_extent=env.mask_extent) # if hot_rating_score < float(min_hot_rating_score): # logging.error(('ERROR: The hot_rating_array ({}) is less ' + # 'than the min_hot_rating_score ({})').format( # hot_rating_array, min_hot_rating_score)) # sys.exit() hot_sugg_mask = hot_rating_array >= hot_rating_score gdc.array_to_raster(hot_sugg_mask, raster_dict['hot_sugg'], stats_flag=stats_flag) logging.debug(' Hot Percentile: {}'.format(hot_rating_pct)) logging.debug(' Hot Score: {:.6f}'.format(hot_rating_score)) logging.debug(' Hot Pixels: {}'.format(np.sum(hot_sugg_mask))) del hot_sugg_mask, hot_rating_array # Raster Statistics if stats_flag: logging.info('Calculating Statistics') for name, save_flag in save_dict.items(): if save_flag: gdc.raster_statistics(raster_dict[name]) # Raster Pyramids if pyramids_flag: logging.info('Building Pyramids') for name, save_flag in save_dict.items(): if save_flag: gdc.raster_pyramids(raster_dict[name])
def main(grb_ws=os.getcwd(), ancillary_ws=os.getcwd(), output_ws=os.getcwd(), variables=['pr'], landsat_ws=None, start_date=None, end_date=None, times_str='', extent_path=None, output_extent=None, stats_flag=True, overwrite_flag=False): """Extract NLDAS target variable(s) Args: grb_ws (str): folder of NLDAS GRB files ancillary_ws (str): folder of ancillary rasters output_ws (str): folder of output rasters variable (list): NLDAS variables to download ('ppt', 'srad', 'sph', 'tair', tmmn', 'tmmx', 'vs') landsat_ws (str): folder of Landsat scenes or tar.gz files start_date (str): ISO format date (YYYY-MM-DD) end_date (str): ISO format date (YYYY-MM-DD) times (str): comma separated values and/or ranges of UTC hours (i.e. "1, 2, 5-8") Parsed with python_common.parse_int_set() extent_path (str): file path defining the output extent output_extent (list): decimal degrees values defining output extent stats_flag (bool): if True, compute raster statistics. Default is True. overwrite_flag (bool): if True, overwrite existing files Returns: None """ logging.info('\nExtract NLDAS target variable(s)') # input_fmt = 'NLDAS_FORA0125_H.A{:04d}{:02d}{:02d}.{}.002.grb' input_re = re.compile( 'NLDAS_FORA0125_H.A(?P<YEAR>\d{4})(?P<MONTH>\d{2})' + '(?P<DAY>\d{2}).(?P<TIME>\d{4}).002.grb$') output_fmt = '{}_{:04d}{:02d}{:02d}_hourly_nldas.img' # output_fmt = '{}_{:04d}{:02d}{:02d}_{:04d}_nldas.img' # If a date is not set, process 2017 try: start_dt = dt.datetime.strptime(start_date, '%Y-%m-%d') logging.debug(' Start date: {}'.format(start_dt)) except: start_dt = dt.datetime(2017, 1, 1) logging.info(' Start date: {}'.format(start_dt)) try: end_dt = dt.datetime.strptime(end_date, '%Y-%m-%d') logging.debug(' End date: {}'.format(end_dt)) except: end_dt = dt.datetime(2017, 12, 31) logging.info(' End date: {}'.format(end_dt)) # Only process a specific hours if not times_str: time_list = range(0, 24, 1) else: time_list = list(parse_int_set(times_str)) time_list = ['{:02d}00'.format(t) for t in time_list] # Assume NLDAS is NAD83 # input_epsg = 'EPSG:4269' # NLDAS rasters to extract data_full_list = ['pr', 'srad', 'sph', 'tair', 'tmmn', 'tmmx', 'vs'] if not variables: logging.error('\nERROR: variables parameter is empty\n') sys.exit() elif type(variables) is not list: # DEADBEEF - I could try converting comma separated strings to lists? logging.warning('\nERROR: variables parameter must be a list\n') sys.exit() elif not set(variables).issubset(set(data_full_list)): logging.error('\nERROR: variables parameter is invalid\n {}'.format( variables)) sys.exit() # Ancillary raster paths mask_path = os.path.join(ancillary_ws, 'nldas_mask.img') # Build a date list from landsat_ws scene folders or tar.gz files date_list = [] if landsat_ws is not None and os.path.isdir(landsat_ws): logging.info('\nReading dates from Landsat IDs') logging.info(' {}'.format(landsat_ws)) landsat_re = re.compile( '^(?:LT04|LT05|LE07|LC08)_(?:\d{3})(?:\d{3})_' + '(?P<year>\d{4})(?P<month>\d{2})(?P<day>\d{2})') for root, dirs, files in os.walk(landsat_ws, topdown=True): # If root matches, don't explore subfolders try: landsat_match = landsat_re.match(os.path.basename(root)) date_list.append(dt.datetime.strptime( '_'.join(landsat_match.groups()), '%Y_%m_%d').date().isoformat()) dirs[:] = [] except: pass for file in files: try: landsat_match = landsat_re.match(file) date_list.append(dt.datetime.strptime( '_'.join(landsat_match.groups()), '%Y_%m_%d').date().isoformat()) except: pass date_list = sorted(list(set(date_list))) # elif landsat_ws is not None and os.path.isfile(landsat_ws): # with open(landsat_ws) as landsat_f: # This allows GDAL to throw Python Exceptions # gdal.UseExceptions() # mem_driver = gdal.GetDriverByName('MEM') # Get the NLDAS spatial reference from the mask raster nldas_ds = gdal.Open(mask_path) nldas_osr = gdc.raster_ds_osr(nldas_ds) nldas_proj = gdc.osr_proj(nldas_osr) nldas_cs = gdc.raster_ds_cellsize(nldas_ds, x_only=True) nldas_extent = gdc.raster_ds_extent(nldas_ds) nldas_geo = nldas_extent.geo(nldas_cs) nldas_x, nldas_y = nldas_extent.origin() nldas_ds = None logging.debug(' Projection: {}'.format(nldas_proj)) logging.debug(' Cellsize: {}'.format(nldas_cs)) logging.debug(' Geo: {}'.format(nldas_geo)) logging.debug(' Extent: {}'.format(nldas_extent)) # Subset data to a smaller extent if output_extent is not None: logging.info('\nComputing subset extent & geo') logging.debug(' Extent: {}'.format(output_extent)) nldas_extent = gdc.Extent(output_extent) nldas_extent.adjust_to_snap('EXPAND', nldas_x, nldas_y, nldas_cs) nldas_geo = nldas_extent.geo(nldas_cs) logging.debug(' Geo: {}'.format(nldas_geo)) logging.debug(' Extent: {}'.format(output_extent)) elif extent_path is not None: logging.info('\nComputing subset extent & geo') if extent_path.lower().endswith('.shp'): nldas_extent = gdc.feature_path_extent(extent_path) extent_osr = gdc.feature_path_osr(extent_path) extent_cs = None else: nldas_extent = gdc.raster_path_extent(extent_path) extent_osr = gdc.raster_path_osr(extent_path) extent_cs = gdc.raster_path_cellsize(extent_path, x_only=True) nldas_extent = gdc.project_extent( nldas_extent, extent_osr, nldas_osr, extent_cs) nldas_extent.adjust_to_snap('EXPAND', nldas_x, nldas_y, nldas_cs) nldas_geo = nldas_extent.geo(nldas_cs) logging.debug(' Geo: {}'.format(nldas_geo)) logging.debug(' Extent: {}'.format(nldas_extent)) logging.debug('') # Read the NLDAS mask array if present if mask_path and os.path.isfile(mask_path): mask_array, mask_nodata = gdc.raster_to_array( mask_path, mask_extent=nldas_extent, fill_value=0, return_nodata=True) mask_array = mask_array != mask_nodata else: mask_array = None # NLDAS band name dictionary nldas_band_dict = dict() nldas_band_dict['pr'] = 'Total precipitation [kg/m^2]' nldas_band_dict['srad'] = 'Downward shortwave radiation flux [W/m^2]' nldas_band_dict['sph'] = 'Specific humidity [kg/kg]' nldas_band_dict['tair'] = 'Temperature [C]' nldas_band_dict['tmmn'] = 'Temperature [C]' nldas_band_dict['tmmx'] = 'Temperature [C]' nldas_band_dict['vs'] = [ 'u-component of wind [m/s]', 'v-component of wind [m/s]'] # NLDAS band name dictionary # nldas_band_dict = dict() # nldas_band_dict['pr'] = 'precipitation_amount' # nldas_band_dict['srad'] = 'surface_downwelling_shortwave_flux_in_air' # nldas_band_dict['sph'] = 'specific_humidity' # nldas_band_dict['tmmn'] = 'air_temperature' # nldas_band_dict['tmmx'] = 'air_temperature' # nldas_band_dict['vs'] = 'wind_speed' # NLDAS band name dictionary (EarthEngine keys, GRID_ELEMENT values) # nldas_band_dict = dict() # nldas_band_dict['total_precipitation'] = 'Total precipitation [kg/m^2]' # nldas_band_dict['shortwave_radiation'] = 'Downward shortwave radiation flux [W/m^2]' # nldas_band_dict['specific_humidity'] = 'Specific humidity [kg/kg]' # nldas_band_dict['pressure'] = 'Pressure [Pa]' # nldas_band_dict['temperature'] = 'Temperature [C]' # nldas_band_dict['wind_u'] = 'u-component of wind [m/s]' # nldas_band_dict['wind_v'] = 'v-component of wind [m/s]' # Process each variable logging.info('\nReading NLDAS GRIBs') for input_var in variables: logging.info("Variable: {}".format(input_var)) # Build output folder var_ws = os.path.join(output_ws, input_var) if not os.path.isdir(var_ws): os.makedirs(var_ws) # Each sub folder in the main folde has all imagery for 1 day # The path for each subfolder is the /YYYY/DOY # This approach will process files for target dates # for input_dt in date_range(start_dt, end_dt + dt.timedelta(1)): # logging.info(input_dt.date()) # Iterate all available files and check dates if necessary for root, folders, files in os.walk(grb_ws): root_split = os.path.normpath(root).split(os.sep) # If the year/doy is outside the range, skip if (re.match('\d{4}', root_split[-2]) and re.match('\d{3}', root_split[-1])): root_dt = dt.datetime.strptime('{}_{}'.format( root_split[-2], root_split[-1]), '%Y_%j') logging.info('{}-{:02d}-{:02d}'.format( root_dt.year, root_dt.month, root_dt.day)) if ((start_dt is not None and root_dt < start_dt) or (end_dt is not None and root_dt > end_dt)): continue elif date_list and root_dt.date().isoformat() not in date_list: continue # If the year is outside the range, don't search subfolders elif re.match('\d{4}', root_split[-1]): root_year = int(root_split[-1]) logging.info('Year: {}'.format(root_year)) if ((start_dt is not None and root_year < start_dt.year) or (end_dt is not None and root_year > end_dt.year)): folders[:] = [] else: folders[:] = sorted(folders) continue else: continue # Create a single raster for each day with 24 bands # Each time step will be stored in a separate band output_name = output_fmt.format( input_var, root_dt.year, root_dt.month, root_dt.day) output_path = os.path.join( var_ws, str(root_dt.year), output_name) logging.debug(' {}'.format(output_path)) if os.path.isfile(output_path): if not overwrite_flag: logging.debug(' File already exists, skipping') continue else: logging.debug(' File already exists, removing existing') os.remove(output_path) logging.debug(' {}'.format(root)) if not os.path.isdir(os.path.dirname(output_path)): os.makedirs(os.path.dirname(output_path)) gdc.build_empty_raster( output_path, band_cnt=24, output_dtype=np.float32, output_proj=nldas_proj, output_cs=nldas_cs, output_extent=nldas_extent, output_fill_flag=True) # Iterate through hourly files for input_name in sorted(files): logging.info(' {}'.format(input_name)) input_path = os.path.join(root, input_name) input_match = input_re.match(input_name) if input_match is None: logging.debug( ' Regular expression didn\'t match, skipping') continue input_dt = dt.datetime( int(input_match.group('YEAR')), int(input_match.group('MONTH')), int(input_match.group('DAY'))) time_str = input_match.group('TIME') band_num = int(time_str[:2]) + 1 # if start_dt is not None and input_dt < start_dt: # continue # elif end_dt is not None and input_dt > end_dt: # continue # elif date_list and input_dt.date().isoformat() not in date_list: # continue if time_str not in time_list: logging.debug(' Time not in list, skipping') continue logging.debug(' Time: {} {}'.format( input_dt.date(), time_str)) logging.debug(' Band: {}'.format(band_num)) # Determine band numbering/naming input_band_dict = grib_band_names(input_path) # Extract array and save input_ds = gdal.Open(input_path) # Convert Kelvin to Celsius (old NLDAS files were in K i think) if input_var in ['tair', 'tmmx', 'tmmn']: # Temperature should be in C for et_common.refet_hourly_func() if 'Temperature [K]' in input_band_dict.keys(): temp_band_units = 'K' output_array = gdc.raster_ds_to_array( input_ds, band=input_band_dict['Temperature [K]'], mask_extent=nldas_extent, return_nodata=False) elif 'Temperature [C]' in input_band_dict.keys(): temp_band_units = 'C' output_array = gdc.raster_ds_to_array( input_ds, band=input_band_dict['Temperature [C]'], mask_extent=nldas_extent, return_nodata=False) else: logging.error('Unknown Temperature units, skipping') logging.error(' {}'.format(input_band_dict.keys())) continue # DEADBEEF - Having issue with T appearing to be C but labeled as K # Try to determine temperature units from values temp_mean = float(np.nanmean(output_array)) temp_units_dict = {20: 'C', 293: 'K'} temp_array_units = temp_units_dict[ min(temp_units_dict, key=lambda x:abs(x - temp_mean))] if temp_array_units == 'K' and temp_band_units == 'K': logging.debug(' Converting temperature from K to C') output_array -= 273.15 elif temp_array_units == 'C' and temp_band_units == 'C': pass elif temp_array_units == 'C' and temp_band_units == 'K': logging.debug( (' Temperature units are K in the GRB band name, ' + 'but values appear to be C\n Mean temperature: {:.2f}\n' + ' Values will NOT be adjusted').format(temp_mean)) elif temp_array_units == 'K' and temp_band_units == 'C': logging.debug( (' Temperature units are C in the GRB band name, ' + 'but values appear to be K\n Mean temperature: {:.2f}\n' + ' Values will be adjusted from K to C').format(temp_mean)) output_array -= 273.15 # Compute wind speed from vectors elif input_var == 'vs': wind_u_array = gdc.raster_ds_to_array( input_ds, band=input_band_dict['u-component of wind [m/s]'], mask_extent=nldas_extent, return_nodata=False) wind_v_array = gdc.raster_ds_to_array( input_ds, band=input_band_dict['v-component of wind [m/s]'], mask_extent=nldas_extent, return_nodata=False) output_array = np.sqrt( wind_u_array ** 2 + wind_v_array ** 2) # Read all other variables directly else: output_array = gdc.raster_ds_to_array( input_ds, band=input_band_dict[nldas_band_dict[input_var]], mask_extent=nldas_extent, return_nodata=False) # Save the projected array as 32-bit floats gdc.array_to_comp_raster( output_array.astype(np.float32), output_path, band=band_num) # gdc.block_to_raster( # ea_array.astype(np.float32), output_path, band=band) # gdc.array_to_raster( # output_array.astype(np.float32), output_path, # output_geo=nldas_geo, output_proj=nldas_proj, # stats_flag=stats_flag) del output_array input_ds = None if stats_flag: gdc.raster_statistics(output_path) logging.debug('\nScript Complete')
def main(gis_ws, cdl_year='', block_size=16384, mask_flag=False, overwrite_flag=False, pyramids_flag=False, stats_flag=False, agland_nodata=0, agmask_nodata=0): """Mask CDL values for non-agricultural pixels Use CDL derived agmask (in CDL workspace) to define agricultural pixels Args: gis_ws (str): Folder/workspace path of the GIS data for the project cdl_year (str): Comma separated list and/or range of years block_size (int): Maximum block size to use for raster processing mask_flag (bool): If True, mask pixels outside extent shapefile overwrite_flag (bool): If True, overwrite output rasters pyramids_flag (bool): If True, build pyramids/overviews for the output rasters stats_flag (bool): If True, compute statistics for the output rasters agland_nodata: Integer of the nodata value in the agland raster agmask_nodata: Integer of the nodata value in the agmask raster Returns: None """ logging.info('\nExtracting Agriculatural CDL Values') cdl_format = '{0}_30m_cdls.img' cdl_ws = os.path.join(gis_ws, 'cdl') scratch_ws = os.path.join(gis_ws, 'scratch') zone_raster_path = os.path.join(scratch_ws, 'zone_raster.img') # Ag landuses are 1, all others in state are 0, outside state is nodata # Crop 61 is fallow/idle and was excluded from analysis # Crops 176 is Grassland/Pasture in the new national CDL rasters # Crops 181 was Pasture/Hay in the old state CDL rasters # Crops 182 was Cultivated Crop in the old state CDL rasters agmask_remap = [ [1, 60, 1], [61, 65, 0], # [1, 61, 1], [62, 65, 0], [66, 80, 1], [81, 92, 0], # [93, 180, 0], [181, 182, 1], [183, 203, 0], [204, 254, 1]] # [93, 180, 0], [176, 1], [183, 203, 0], [204, 254, 1]] [93, 203, 0], [204, 254, 1]] # Check input folders if not os.path.isdir(gis_ws): logging.error(('\nERROR: The GIS workspace {} ' + 'does not exist\n').format(gis_ws)) sys.exit() elif not os.path.isdir(cdl_ws): logging.error(('\nERROR: The CDL workspace {} ' + 'does not exist\n').format(cdl_ws)) sys.exit() elif mask_flag and not os.path.isfile(zone_raster_path): logging.error( ('\nERROR: The zone raster {} does not exist\n' + ' Try re-running "clip_cdl_raster.py"').format(zone_raster_path)) sys.exit() logging.info('\nGIS Workspace: {}'.format(gis_ws)) logging.info('CDL Workspace: {}'.format(cdl_ws)) if pyramids_flag: levels = '2 4 8 16 32 64 128' # gdal.SetConfigOption('USE_RRD', 'YES') # gdal.SetConfigOption('HFA_USE_RRD', 'YES') # Process each CDL year separately for cdl_year in list(util.parse_int_set(cdl_year)): logging.info('\n{0}'.format(cdl_year)) cdl_path = os.path.join(cdl_ws, cdl_format.format(cdl_year)) agmask_path = os.path.join( cdl_ws, 'agmask_{}_30m_cdls.img'.format(cdl_year)) agland_path = os.path.join( cdl_ws, 'agland_{}_30m_cdls.img'.format(cdl_year)) if not os.path.isfile(cdl_path): logging.error(('\nERROR: The CDL raster {} ' + 'does not exist\n').format(cdl_path)) continue # Get color table and spatial reference from CDL raster logging.info('Reading CDL color table') cdl_raster_ds = gdal.Open(cdl_path, 0) cdl_geo = gdc.raster_ds_geo(cdl_raster_ds) cdl_rows, cdl_cols = gdc.raster_ds_shape(cdl_raster_ds) cdl_extent = gdc.geo_extent(cdl_geo, cdl_rows, cdl_cols) cdl_proj = gdc.raster_ds_proj(cdl_raster_ds) # DEADBEEF - Why is this hardcoded? cdl_cellsize = 30 # cdl_cellsize = gdc.raster_ds_cellsize(cdl_raster_ds)[0] cdl_band = cdl_raster_ds.GetRasterBand(1) cdl_rat = cdl_band.GetDefaultRAT() cdl_classname_dict = dict() for row_i in range(cdl_rat.GetRowCount()): cdl_classname_dict[row_i] = cdl_rat.GetValueAsString( row_i, cdl_rat.GetColOfUsage(2)) cdl_raster_ds = None del cdl_raster_ds, cdl_band, cdl_rat # Copy the input raster to hold the ag data logging.debug('{}'.format(agland_path)) if os.path.isfile(agland_path) and overwrite_flag: subprocess.call(['gdalmanage', 'delete', agland_path]) if not os.path.isfile(agland_path): logging.info('Copying CDL raster') logging.debug('{}'.format(cdl_path)) subprocess.call( ['gdal_translate', '-of', 'HFA', '-co', 'COMPRESSED=YES', cdl_path, agland_path]) # '-a_nodata', agland_nodata # Set the nodata value after copying agland_ds = gdal.Open(agland_path, 1) agland_band = agland_ds.GetRasterBand(1) agland_band.SetNoDataValue(agland_nodata) agland_ds = None # Get the colormap from the input CDL raster logging.debug('Re-building raster attribute tables') agland_ds = gdal.Open(agland_path, 1) agland_band = agland_ds.GetRasterBand(1) agland_rat = agland_band.GetDefaultRAT() agland_usage_list = [ agland_rat.GetUsageOfCol(col_i) for col_i in range(agland_rat.GetColumnCount())] # if 1 not in agland_usage_list: # _rat.CreateColumn('Count', 1, 1) if 2 not in agland_usage_list: agland_rat.CreateColumn('Class_Name', 2, 2) for row_i in range(agland_rat.GetRowCount()): agland_rat.SetValueAsString( row_i, agland_rat.GetColOfUsage(2), cdl_classname_dict[row_i]) agland_band.SetDefaultRAT(agland_rat) agland_ds = None # Build an empty output raster to hold the ag mask logging.info('\nBuilding empty ag mask raster') logging.debug('{}'.format(agmask_path)) if os.path.isfile(agmask_path) and overwrite_flag: subprocess.call(['gdalmanage', 'delete', agmask_path]) if not os.path.isfile(agmask_path): gdc.build_empty_raster( agmask_path, band_cnt=1, output_dtype=np.uint8, output_nodata=agmask_nodata, output_proj=cdl_proj, output_cs=cdl_cellsize, output_extent=cdl_extent) # Set the nodata value after initializing agmask_ds = gdal.Open(agmask_path, 1) agmask_band = agmask_ds.GetRasterBand(1) agmask_band.SetNoDataValue(agmask_nodata) agmask_ds = None # Set non-ag areas to nodata value logging.info('\nProcessing by block') logging.debug(' Input cols/rows: {0}/{1}'.format(cdl_cols, cdl_rows)) for b_i, b_j in gdc.block_gen(cdl_rows, cdl_cols, block_size): logging.info(' Block y: {0:5d} x: {1:5d}'.format(b_i, b_j)) # Read in data for block cdl_array = gdc.raster_to_block( cdl_path, b_i, b_j, block_size, fill_value=0, return_nodata=False) cdl_mask = np.zeros(cdl_array.shape, dtype=np.bool) remap_mask = np.zeros(cdl_array.shape, dtype=np.bool) # Mask CDL values outside extent shapefile if mask_flag and os.path.isfile(zone_raster_path): zone_array = gdc.raster_to_block( zone_raster_path, b_i, b_j, block_size) cdl_array[zone_array == 0] = 0 # Reclassify to 1 for ag and 0 for non-ag for [start, end, value] in agmask_remap: if value == 0: continue logging.debug([start, end, value]) remap_mask |= (cdl_array >= start) & (cdl_array <= end) cdl_mask[remap_mask] = True del remap_mask # Set non-ag areas in agmask to nodata cdl_mask[~cdl_mask] = agmask_nodata # Set non-ag areas in aglands to nodata cdl_array[~cdl_mask] = agland_nodata gdc.block_to_raster(cdl_array, agland_path, b_i, b_j, block_size) gdc.block_to_raster(cdl_mask.astype(np.uint8), agmask_path, b_i, b_j, block_size) del cdl_array, cdl_mask if stats_flag: logging.info('Computing statistics') if os.path.isfile(agland_path): logging.debug('{}'.format(agland_path)) subprocess.call( ['gdalinfo', '-stats', '-nomd', '-noct', '-norat', agland_path]) if os.path.isfile(agmask_path): logging.debug('{}'.format(agmask_path)) subprocess.call( ['gdalinfo', '-stats', '-nomd', '-noct', '-norat', agmask_path]) if pyramids_flag: logging.info('Building pyramids') if os.path.isfile(agland_path): logging.debug('{}'.format(agland_path)) subprocess.call( ['gdaladdo', '-ro', agland_path] + levels.split()) if os.path.isfile(agmask_path): logging.debug('{}'.format(agmask_path)) subprocess.call( ['gdaladdo', '-ro', agmask_path] + levels.split())
def main(netcdf_ws=os.getcwd(), ancillary_ws=os.getcwd(), output_ws=os.getcwd(), start_date=None, end_date=None, extent_path=None, output_extent=None, stats_flag=True, overwrite_flag=False): """Extract GRIDMET temperature Args: netcdf_ws (str): folder of GRIDMET netcdf files ancillary_ws (str): folder of ancillary rasters output_ws (str): folder of output rasters start_date (str): ISO format date (YYYY-MM-DD) end_date (str): ISO format date (YYYY-MM-DD) extent_path (str): filepath a raster defining the output extent output_extent (list): decimal degrees values defining output extent stats_flag (bool): if True, compute raster statistics. Default is True. overwrite_flag (bool): if True, overwrite existing files Returns: None """ logging.info('\nExtracting GRIDMET vapor pressure') # If a date is not set, process 2017 try: start_dt = dt.datetime.strptime(start_date, '%Y-%m-%d') logging.debug(' Start date: {}'.format(start_dt)) except: start_dt = dt.datetime(2017, 1, 1) logging.info(' Start date: {}'.format(start_dt)) try: end_dt = dt.datetime.strptime(end_date, '%Y-%m-%d') logging.debug(' End date: {}'.format(end_dt)) except: end_dt = dt.datetime(2017, 12, 31) logging.info(' End date: {}'.format(end_dt)) # Save GRIDMET lat, lon, and elevation arrays elev_raster = os.path.join(ancillary_ws, 'gridmet_elev.img') output_fmt = '{}_{}_daily_gridmet.img' gridmet_re = re.compile('(?P<VAR>\w+)_(?P<YEAR>\d{4}).nc$') # GRIDMET band name dictionary gridmet_band_dict = dict() gridmet_band_dict['pr'] = 'precipitation_amount' gridmet_band_dict['srad'] = 'surface_downwelling_shortwave_flux_in_air' gridmet_band_dict['sph'] = 'specific_humidity' gridmet_band_dict['tmmn'] = 'air_temperature' gridmet_band_dict['tmmx'] = 'air_temperature' gridmet_band_dict['vs'] = 'wind_speed' # Get extent/geo from elevation raster gridmet_ds = gdal.Open(elev_raster) gridmet_osr = gdc.raster_ds_osr(gridmet_ds) gridmet_proj = gdc.osr_proj(gridmet_osr) gridmet_cs = gdc.raster_ds_cellsize(gridmet_ds, x_only=True) gridmet_extent = gdc.raster_ds_extent(gridmet_ds) gridmet_full_geo = gridmet_extent.geo(gridmet_cs) gridmet_x, gridmet_y = gridmet_extent.origin() gridmet_ds = None logging.debug(' Projection: {}'.format(gridmet_proj)) logging.debug(' Cellsize: {}'.format(gridmet_cs)) logging.debug(' Geo: {}'.format(gridmet_full_geo)) logging.debug(' Extent: {}'.format(gridmet_extent)) # Subset data to a smaller extent if output_extent is not None: logging.info('\nComputing subset extent & geo') logging.debug(' Extent: {}'.format(output_extent)) gridmet_extent = gdc.Extent(output_extent) gridmet_extent.adjust_to_snap('EXPAND', gridmet_x, gridmet_y, gridmet_cs) gridmet_geo = gridmet_extent.geo(gridmet_cs) logging.debug(' Geo: {}'.format(gridmet_geo)) logging.debug(' Extent: {}'.format(gridmet_extent)) elif extent_path is not None: logging.info('\nComputing subset extent & geo') gridmet_extent = gdc.raster_path_extent(extent_path) extent_osr = gdc.raster_path_osr(extent_path) extent_cs = gdc.raster_path_cellsize(extent_path, x_only=True) gridmet_extent = gdc.project_extent(gridmet_extent, extent_osr, gridmet_osr, extent_cs) gridmet_extent.adjust_to_snap('EXPAND', gridmet_x, gridmet_y, gridmet_cs) gridmet_geo = gridmet_extent.geo(gridmet_cs) logging.debug(' Geo: {}'.format(gridmet_geo)) logging.debug(' Extent: {}'.format(gridmet_extent)) else: gridmet_geo = gridmet_full_geo # Get indices for slicing/clipping input arrays g_i, g_j = gdc.array_geo_offsets(gridmet_full_geo, gridmet_geo, cs=gridmet_cs) g_rows, g_cols = gridmet_extent.shape(cs=gridmet_cs) # Read the elevation array elev_array = gdc.raster_to_array(elev_raster, mask_extent=gridmet_extent, return_nodata=False) pair_array = et_common.air_pressure_func(elev_array) del elev_array # Process each variable input_var = 'sph' output_var = 'ea' logging.info("\nVariable: {}".format(input_var)) # Build output folder var_ws = os.path.join(output_ws, output_var) if not os.path.isdir(var_ws): os.makedirs(var_ws) # Process each file in the input workspace for input_name in sorted(os.listdir(netcdf_ws)): input_match = gridmet_re.match(input_name) if not input_match: logging.debug("{}".format(input_name)) logging.debug(' Regular expression didn\'t match, skipping') continue elif input_match.group('VAR') != input_var: logging.debug("{}".format(input_name)) logging.debug(' Variable didn\'t match, skipping') continue else: logging.info("{}".format(input_name)) year_str = input_match.group('YEAR') logging.info(" {}".format(year_str)) year_int = int(year_str) year_days = int(dt.datetime(year_int, 12, 31).strftime('%j')) if start_dt is not None and year_int < start_dt.year: logging.debug(' Before start date, skipping') continue elif end_dt is not None and year_int > end_dt.year: logging.debug(' After end date, skipping') continue # Build input file path input_raster = os.path.join(netcdf_ws, input_name) # if not os.path.isfile(input_raster): # logging.debug( # ' Input NetCDF doesn\'t exist, skipping {}'.format( # input_raster)) # continue # Create a single raster for each year with 365 bands # Each day will be stored in a separate band output_path = os.path.join(var_ws, output_fmt.format(output_var, year_str)) logging.debug(' {}'.format(output_path)) if os.path.isfile(output_path): if not overwrite_flag: logging.debug(' File already exists, skipping') continue else: logging.debug(' File already exists, removing existing') os.remove(output_path) gdc.build_empty_raster(output_path, band_cnt=366, output_dtype=np.float32, output_proj=gridmet_proj, output_cs=gridmet_cs, output_extent=gridmet_extent, output_fill_flag=True) # Read in the GRIDMET NetCDF file # Immediatly clip input array to save memory input_nc_f = netCDF4.Dataset(input_raster, 'r') input_nc = input_nc_f.variables[ gridmet_band_dict[input_var]][:, g_i:g_i + g_cols, g_j:g_j + g_rows].copy() input_nc = np.transpose(input_nc, (0, 2, 1)) # A numpy array is returned when slicing a masked array # if there are no masked pixels # This is a hack to force the numpy array back to a masked array if type(input_nc) != np.ma.core.MaskedArray: input_nc = np.ma.core.MaskedArray( input_nc, np.zeros(input_nc.shape, dtype=bool)) # Check all valid dates in the year year_dates = date_range(dt.datetime(year_int, 1, 1), dt.datetime(year_int + 1, 1, 1)) for date_dt in year_dates: if start_dt is not None and date_dt < start_dt: # logging.debug(' before start date, skipping') continue elif end_dt is not None and date_dt > end_dt: # logging.debug(' after end date, skipping') continue logging.info(' {}'.format(date_dt.strftime('%Y_%m_%d'))) doy = int(date_dt.strftime('%j')) doy_i = range(1, year_days + 1).index(doy) # Arrays are being read as masked array with a fill value of -9999 # Convert to basic numpy array arrays with nan values try: input_full_ma = input_nc[doy_i, :, :] except IndexError: logging.info(' date not in netcdf, skipping') continue input_full_array = input_full_ma.data.astype(np.float32) input_full_nodata = float(input_full_ma.fill_value) input_full_array[input_full_array == input_full_nodata] = np.nan # Since inputs are netcdf, need to create GDAL raster # datasets in order to use gdal_common functions # Create an in memory dataset of the full ETo array input_full_ds = gdc.array_to_mem_ds(input_full_array, output_geo=gridmet_full_geo, output_proj=gridmet_proj) # Then extract the subset from the in memory dataset sph_array = gdc.raster_ds_to_array(input_full_ds, 1, mask_extent=gridmet_extent, return_nodata=False) # Compute ea [kPa] from specific humidity [kg/kg] ea_array = (sph_array * pair_array) / (0.622 + 0.378 * sph_array) # Save the projected array as 32-bit floats gdc.array_to_comp_raster(ea_array.astype(np.float32), output_path, band=doy, stats_flag=False) # gdc.array_to_raster( # ea_array.astype(np.float32), output_path, # output_geo=gridmet_geo, output_proj=gridmet_proj, # stats_flag=False) del sph_array, ea_array input_nc_f.close() del input_nc_f if stats_flag: gdc.raster_statistics(output_path) logging.debug('\nScript Complete')
def main(grb_ws=os.getcwd(), ancillary_ws=os.getcwd(), output_ws=os.getcwd(), etr_flag=False, eto_flag=False, landsat_ws=None, start_date=None, end_date=None, times_str='', extent_path=None, output_extent=None, daily_flag=True, stats_flag=True, overwrite_flag=False): """Compute hourly ETr/ETo from NLDAS data Args: grb_ws (str): folder of NLDAS GRB files ancillary_ws (str): folder of ancillary rasters output_ws (str): folder of output rasters etr_flag (bool): if True, compute alfalfa reference ET (ETr) eto_flag (bool): if True, compute grass reference ET (ETo) landsat_ws (str): folder of Landsat scenes or tar.gz files start_date (str): ISO format date (YYYY-MM-DD) end_date (str): ISO format date (YYYY-MM-DD) times (str): comma separated values and/or ranges of UTC hours (i.e. "1, 2, 5-8") Parsed with python_common.parse_int_set() extent_path (str): file path defining the output extent output_extent (list): decimal degrees values defining output extent daily_flag (bool): if True, save daily ETr/ETo sum raster. Default is True stats_flag (bool): if True, compute raster statistics. Default is True. overwrite_flag (bool): if True, overwrite existing files Returns: None """ logging.info('\nComputing NLDAS hourly ETr/ETo') np.seterr(invalid='ignore') # Compute ETr and/or ETo if not etr_flag and not eto_flag: logging.info(' ETo/ETr flag(s) not set, defaulting to ETr') etr_flag = True # If a date is not set, process 2017 try: start_dt = dt.datetime.strptime(start_date, '%Y-%m-%d') logging.debug(' Start date: {}'.format(start_dt)) except: start_dt = dt.datetime(2017, 1, 1) logging.info(' Start date: {}'.format(start_dt)) try: end_dt = dt.datetime.strptime(end_date, '%Y-%m-%d') logging.debug(' End date: {}'.format(end_dt)) except: end_dt = dt.datetime(2017, 12, 31) logging.info(' End date: {}'.format(end_dt)) # Only process a specific hours if not times_str: time_list = range(0, 24, 1) else: time_list = list(parse_int_set(times_str)) time_list = ['{:02d}00'.format(t) for t in time_list] etr_folder = 'etr' eto_folder = 'eto' hour_fmt = '{}_{:04d}{:02d}{:02d}_hourly_nldas.img' # hour_fmt = '{}_{:04d}{:02d}{:02d}_{4:04d}_nldas.img' day_fmt = '{}_{:04d}{:02d}{:02d}_nldas.img' # input_fmt = 'NLDAS_FORA0125_H.A{:04d}{:02d}{:02d}.{}.002.grb' input_re = re.compile('NLDAS_FORA0125_H.A(?P<YEAR>\d{4})(?P<MONTH>\d{2})' + '(?P<DAY>\d{2}).(?P<TIME>\d{4}).002.grb$') # Assume NLDAS is NAD83 # input_epsg = 'EPSG:4269' # Ancillary raster paths mask_path = os.path.join(ancillary_ws, 'nldas_mask.img') elev_path = os.path.join(ancillary_ws, 'nldas_elev.img') lat_path = os.path.join(ancillary_ws, 'nldas_lat.img') lon_path = os.path.join(ancillary_ws, 'nldas_lon.img') # Build a date list from landsat_ws scene folders or tar.gz files date_list = [] if landsat_ws is not None and os.path.isdir(landsat_ws): logging.info('\nReading dates from Landsat IDs') logging.info(' {}'.format(landsat_ws)) landsat_re = re.compile( '^(?:LT04|LT05|LE07|LC08)_(?:\d{3})(?:\d{3})_' + '(?P<year>\d{4})(?P<month>\d{2})(?P<day>\d{2})') for root, dirs, files in os.walk(landsat_ws, topdown=True): # If root matches, don't explore subfolders try: landsat_match = landsat_re.match(os.path.basename(root)) date_list.append( dt.datetime.strptime('_'.join(landsat_match.groups()), '%Y_%m_%d').date().isoformat()) dirs[:] = [] except: pass for file in files: try: landsat_match = landsat_re.match(file) date_list.append( dt.datetime.strptime('_'.join(landsat_match.groups()), '%Y_%m_%d').date().isoformat()) except: pass date_list = sorted(list(set(date_list))) # elif landsat_ws is not None and os.path.isfile(landsat_ws): # with open(landsat_ws) as landsat_f: # This allows GDAL to throw Python Exceptions # gdal.UseExceptions() # mem_driver = gdal.GetDriverByName('MEM') # Get the NLDAS spatial reference from the mask raster nldas_ds = gdal.Open(mask_path) nldas_osr = gdc.raster_ds_osr(nldas_ds) nldas_proj = gdc.osr_proj(nldas_osr) nldas_cs = gdc.raster_ds_cellsize(nldas_ds, x_only=True) nldas_extent = gdc.raster_ds_extent(nldas_ds) nldas_geo = nldas_extent.geo(nldas_cs) nldas_x, nldas_y = nldas_extent.origin() nldas_ds = None logging.debug(' Projection: {}'.format(nldas_proj)) logging.debug(' Cellsize: {}'.format(nldas_cs)) logging.debug(' Geo: {}'.format(nldas_geo)) logging.debug(' Extent: {}'.format(nldas_extent)) # Subset data to a smaller extent if output_extent is not None: logging.info('\nComputing subset extent & geo') logging.debug(' Extent: {}'.format(output_extent)) nldas_extent = gdc.Extent(output_extent) nldas_extent.adjust_to_snap('EXPAND', nldas_x, nldas_y, nldas_cs) nldas_geo = nldas_extent.geo(nldas_cs) logging.debug(' Geo: {}'.format(nldas_geo)) logging.debug(' Extent: {}'.format(output_extent)) elif extent_path is not None: logging.info('\nComputing subset extent & geo') if extent_path.lower().endswith('.shp'): nldas_extent = gdc.feature_path_extent(extent_path) extent_osr = gdc.feature_path_osr(extent_path) extent_cs = None else: nldas_extent = gdc.raster_path_extent(extent_path) extent_osr = gdc.raster_path_osr(extent_path) extent_cs = gdc.raster_path_cellsize(extent_path, x_only=True) nldas_extent = gdc.project_extent(nldas_extent, extent_osr, nldas_osr, extent_cs) nldas_extent.adjust_to_snap('EXPAND', nldas_x, nldas_y, nldas_cs) nldas_geo = nldas_extent.geo(nldas_cs) logging.debug(' Geo: {}'.format(nldas_geo)) logging.debug(' Extent: {}'.format(nldas_extent)) logging.debug('') # Read the NLDAS mask array if present if mask_path and os.path.isfile(mask_path): mask_array, mask_nodata = gdc.raster_to_array(mask_path, mask_extent=nldas_extent, fill_value=0, return_nodata=True) mask_array = mask_array != mask_nodata else: mask_array = None # Read ancillary arrays (or subsets?) elev_array = gdc.raster_to_array(elev_path, mask_extent=nldas_extent, return_nodata=False) # pair_array = et_common.air_pressure_func(elev_array) lat_array = gdc.raster_to_array(lat_path, mask_extent=nldas_extent, return_nodata=False) lon_array = gdc.raster_to_array(lon_path, mask_extent=nldas_extent, return_nodata=False) # Hourly RefET functions expects lat/lon in radians lat_array *= (math.pi / 180) lon_array *= (math.pi / 180) # Build output folder etr_ws = os.path.join(output_ws, etr_folder) eto_ws = os.path.join(output_ws, eto_folder) if etr_flag and not os.path.isdir(etr_ws): os.makedirs(etr_ws) if eto_flag and not os.path.isdir(eto_ws): os.makedirs(eto_ws) # DEADBEEF - Instead of processing all available files, the following # code will process files for target dates # for input_dt in date_range(start_dt, end_dt + dt.timedelta(1)): # logging.info(input_dt.date()) # Iterate all available files and check dates if necessary # Each sub folder in the main folder has all imagery for 1 day # (in UTC time) # The path for each subfolder is the /YYYY/DOY errors = defaultdict(list) for root, folders, files in os.walk(grb_ws): root_split = os.path.normpath(root).split(os.sep) # If the year/doy is outside the range, skip if (re.match('\d{4}', root_split[-2]) and re.match('\d{3}', root_split[-1])): root_dt = dt.datetime.strptime( '{}_{}'.format(root_split[-2], root_split[-1]), '%Y_%j') logging.info('{}'.format(root_dt.date())) if ((start_dt is not None and root_dt < start_dt) or (end_dt is not None and root_dt > end_dt)): continue elif date_list and root_dt.date().isoformat() not in date_list: continue # If the year is outside the range, don't search subfolders elif re.match('\d{4}', root_split[-1]): root_year = int(root_split[-1]) logging.info('Year: {}'.format(root_year)) if ((start_dt is not None and root_year < start_dt.year) or (end_dt is not None and root_year > end_dt.year)): folders[:] = [] else: folders[:] = sorted(folders) continue else: continue logging.debug(' {}'.format(root)) # Start off assuming every file needs to be processed day_skip_flag = False # Build output folders if necessary etr_year_ws = os.path.join(etr_ws, str(root_dt.year)) eto_year_ws = os.path.join(eto_ws, str(root_dt.year)) if etr_flag and not os.path.isdir(etr_year_ws): os.makedirs(etr_year_ws) if eto_flag and not os.path.isdir(eto_year_ws): os.makedirs(eto_year_ws) # Build daily total paths etr_day_path = os.path.join( etr_year_ws, day_fmt.format('etr', root_dt.year, root_dt.month, root_dt.day)) eto_day_path = os.path.join( eto_year_ws, day_fmt.format('eto', root_dt.year, root_dt.month, root_dt.day)) etr_hour_path = os.path.join( etr_year_ws, hour_fmt.format('etr', root_dt.year, root_dt.month, root_dt.day)) eto_hour_path = os.path.join( eto_year_ws, hour_fmt.format('eto', root_dt.year, root_dt.month, root_dt.day)) # logging.debug(' {}'.format(etr_hour_path)) # If daily ETr/ETo files are present, day can be skipped if not overwrite_flag and daily_flag: if etr_flag and not os.path.isfile(etr_day_path): pass elif eto_flag and not os.path.isfile(eto_day_path): pass else: day_skip_flag = True # If the hour and daily files don't need to be made, skip the day if not overwrite_flag: if etr_flag and not os.path.isfile(etr_hour_path): pass elif eto_flag and not os.path.isfile(eto_hour_path): pass elif day_skip_flag: logging.debug(' File(s) already exist, skipping') continue # Create a single raster for each day with 24 bands # Each time step will be stored in a separate band if etr_flag: logging.debug(' {}'.format(etr_day_path)) gdc.build_empty_raster(etr_hour_path, band_cnt=24, output_dtype=np.float32, output_proj=nldas_proj, output_cs=nldas_cs, output_extent=nldas_extent, output_fill_flag=True) if eto_flag: logging.debug(' {}'.format(eto_day_path)) gdc.build_empty_raster(eto_hour_path, band_cnt=24, output_dtype=np.float32, output_proj=nldas_proj, output_cs=nldas_cs, output_extent=nldas_extent, output_fill_flag=True) # Sum all ETr/ETo images in each folder to generate a UTC day total etr_day_array = 0 eto_day_array = 0 # Process each hour file for input_name in sorted(files): logging.info(' {}'.format(input_name)) input_match = input_re.match(input_name) if input_match is None: logging.debug(' Regular expression didn\'t match, skipping') continue input_dt = dt.datetime(int(input_match.group('YEAR')), int(input_match.group('MONTH')), int(input_match.group('DAY'))) input_doy = int(input_dt.strftime('%j')) time_str = input_match.group('TIME') band_num = int(time_str[:2]) + 1 # if start_dt is not None and input_dt < start_dt: # continue # elif end_dt is not None and input_dt > end_dt: # continue # elif date_list and input_dt.date().isoformat() not in date_list: # continue if not daily_flag and time_str not in time_list: logging.debug(' Time not in list and not daily, skipping') continue input_path = os.path.join(root, input_name) logging.debug(' Time: {} {}'.format(input_dt.date(), time_str)) logging.debug(' Band: {}'.format(band_num)) # Determine band numbering/naming try: input_band_dict = grib_band_names(input_path) except RuntimeError as e: errors[input_path].append(e) logging.error(' RuntimeError: {} Skipping: {}'.format( e, input_path)) continue # Read input bands input_ds = gdal.Open(input_path) # Temperature should be in C for et_common.refet_hourly_func() if 'Temperature [K]' in input_band_dict.keys(): temp_band_units = 'K' temp_array = gdc.raster_ds_to_array( input_ds, band=input_band_dict['Temperature [K]'], mask_extent=nldas_extent, return_nodata=False) elif 'Temperature [C]' in input_band_dict.keys(): temp_band_units = 'C' temp_array = gdc.raster_ds_to_array( input_ds, band=input_band_dict['Temperature [C]'], mask_extent=nldas_extent, return_nodata=False) else: logging.error('Unknown Temperature units, skipping') logging.error(' {}'.format(input_band_dict.keys())) continue # DEADBEEF - Having issue with T appearing to be C but labeled as K # Try to determine temperature units from values temp_mean = float(np.nanmean(temp_array)) temp_units_dict = {20: 'C', 293: 'K'} temp_array_units = temp_units_dict[min( temp_units_dict, key=lambda x: abs(x - temp_mean))] if temp_array_units == 'K' and temp_band_units == 'K': logging.debug(' Converting temperature from K to C') temp_array -= 273.15 elif temp_array_units == 'C' and temp_band_units == 'C': pass elif temp_array_units == 'C' and temp_band_units == 'K': logging.debug(( ' Temperature units are K in the GRB band name, ' + 'but values appear to be C\n Mean temperature: {:.2f}\n' + ' Values will NOT be adjusted').format(temp_mean)) elif temp_array_units == 'K' and temp_band_units == 'C': logging.debug(( ' Temperature units are C in the GRB band name, ' + 'but values appear to be K\n Mean temperature: {:.2f}\n' + ' Values will be adjusted from K to C').format(temp_mean)) temp_array -= 273.15 try: sph_array = gdc.raster_ds_to_array( input_ds, band=input_band_dict['Specific humidity [kg/kg]'], mask_extent=nldas_extent, return_nodata=False) rs_array = gdc.raster_ds_to_array( input_ds, band=input_band_dict[ 'Downward shortwave radiation flux [W/m^2]'], mask_extent=nldas_extent, return_nodata=False) wind_u_array = gdc.raster_ds_to_array( input_ds, band=input_band_dict['u-component of wind [m/s]'], mask_extent=nldas_extent, return_nodata=False) wind_v_array = gdc.raster_ds_to_array( input_ds, band=input_band_dict['v-component of wind [m/s]'], mask_extent=nldas_extent, return_nodata=False) input_ds = None except KeyError as e: errors[input_path].append(e) logging.error(' KeyError: {} Skipping: {}'.format( e, input_ds.GetDescription())) continue rs_array *= 0.0036 # W m-2 to MJ m-2 hr-1 wind_array = np.sqrt(wind_u_array**2 + wind_v_array**2) del wind_u_array, wind_v_array # ETr if etr_flag: etr_array = et_common.refet_hourly_func(temp_array, sph_array, rs_array, wind_array, zw=10, elev=elev_array, lat=lat_array, lon=lon_array, doy=input_doy, time=int(time_str) / 100, ref_type='ETR') if daily_flag: etr_day_array += etr_array if time_str in time_list: gdc.array_to_comp_raster(etr_array.astype(np.float32), etr_hour_path, band=band_num, stats_flag=False) del etr_array # ETo if eto_flag: eto_array = et_common.refet_hourly_func(temp_array, sph_array, rs_array, wind_array, zw=10, elev=elev_array, lat=lat_array, lon=lon_array, doy=input_doy, time=int(time_str) / 100, ref_type='ETO') if eto_flag and daily_flag: eto_day_array += eto_array if eto_flag and time_str in time_list: gdc.array_to_comp_raster(eto_array.astype(np.float32), eto_hour_path, band=band_num, stats_flag=False) del eto_array del temp_array, sph_array, rs_array, wind_array if stats_flag and etr_flag: gdc.raster_statistics(etr_hour_path) if stats_flag and eto_flag: gdc.raster_statistics(eto_hour_path) # Save the projected ETr/ETo as 32-bit floats if not day_skip_flag and daily_flag: if etr_flag: try: gdc.array_to_raster(etr_day_array.astype(np.float32), etr_day_path, output_geo=nldas_geo, output_proj=nldas_proj, stats_flag=stats_flag) except AttributeError: pass if eto_flag: try: gdc.array_to_raster(eto_day_array.astype(np.float32), eto_day_path, output_geo=nldas_geo, output_proj=nldas_proj, stats_flag=stats_flag) except AttributeError: pass del etr_day_array, eto_day_array if len(errors) > 0: logging.info('\nThe following errors were encountered:') for key, value in errors.items(): logging.error(' Filepath: {}, error: {}'.format(key, value)) logging.debug('\nScript Complete')
def main(grb_ws=os.getcwd(), ancillary_ws=os.getcwd(), output_ws=os.getcwd(), landsat_ws=None, start_date=None, end_date=None, times_str='', extent_path=None, output_extent=None, stats_flag=True, overwrite_flag=False): """Extract hourly NLDAS vapour pressure rasters Args: grb_ws (str): folder of NLDAS GRB files ancillary_ws (str): folder of ancillary rasters output_ws (str): folder of output rasters landsat_ws (str): folder of Landsat scenes or tar.gz files start_date (str): ISO format date (YYYY-MM-DD) end_date (str): ISO format date (YYYY-MM-DD) times (str): comma separated values and/or ranges of UTC hours (i.e. "1, 2, 5-8") Parsed with python_common.parse_int_set() extent_path (str): file path defining the output extent output_extent (list): decimal degrees values defining output extent stats_flag (bool): if True, compute raster statistics. Default is True. overwrite_flag (bool): if True, overwrite existing files Returns: None """ logging.info('\nExtracting NLDAS vapour pressure rasters') # input_fmt = 'NLDAS_FORA0125_H.A{:04d}{:02d}{:02d}.{}.002.grb' input_re = re.compile('NLDAS_FORA0125_H.A(?P<YEAR>\d{4})(?P<MONTH>\d{2})' + '(?P<DAY>\d{2}).(?P<TIME>\d{4}).002.grb$') output_folder = 'ea' output_fmt = 'ea_{:04d}{:02d}{:02d}_hourly_nldas.img' # output_fmt = 'ea_{:04d}{:02d}{:02d}_{:04d}_nldas.img' # If a date is not set, process 2017 try: start_dt = dt.datetime.strptime(start_date, '%Y-%m-%d') logging.debug(' Start date: {}'.format(start_dt)) except: start_dt = dt.datetime(2017, 1, 1) logging.info(' Start date: {}'.format(start_dt)) try: end_dt = dt.datetime.strptime(end_date, '%Y-%m-%d') logging.debug(' End date: {}'.format(end_dt)) except: end_dt = dt.datetime(2017, 12, 31) logging.info(' End date: {}'.format(end_dt)) # Only process a specific hours if not times_str: time_list = range(0, 24, 1) else: time_list = list(parse_int_set(times_str)) time_list = ['{:02d}00'.format(t) for t in time_list] # Assume NLDAS is NAD83 # input_epsg = 'EPSG:4269' # Ancillary raster paths mask_path = os.path.join(ancillary_ws, 'nldas_mask.img') elev_path = os.path.join(ancillary_ws, 'nldas_elev.img') # Build a date list from landsat_ws scene folders or tar.gz files date_list = [] if landsat_ws is not None and os.path.isdir(landsat_ws): logging.info('\nReading dates from Landsat IDs') logging.info(' {}'.format(landsat_ws)) landsat_re = re.compile( '^(?:LT04|LT05|LE07|LC08)_(?:\d{3})(?:\d{3})_' + '(?P<year>\d{4})(?P<month>\d{2})(?P<day>\d{2})') for root, dirs, files in os.walk(landsat_ws, topdown=True): # If root matches, don't explore subfolders try: landsat_match = landsat_re.match(os.path.basename(root)) date_list.append( dt.datetime.strptime('_'.join(landsat_match.groups()), '%Y_%m_%d').date().isoformat()) dirs[:] = [] except: pass for file in files: try: landsat_match = landsat_re.match(file) date_list.append( dt.datetime.strptime('_'.join(landsat_match.groups()), '%Y_%m_%d').date().isoformat()) except: pass date_list = sorted(list(set(date_list))) # elif landsat_ws is not None and os.path.isfile(landsat_ws): # with open(landsat_ws) as landsat_f: # This allows GDAL to throw Python Exceptions # gdal.UseExceptions() # mem_driver = gdal.GetDriverByName('MEM') # Get the NLDAS spatial reference from the mask raster nldas_ds = gdal.Open(mask_path) nldas_osr = gdc.raster_ds_osr(nldas_ds) nldas_proj = gdc.osr_proj(nldas_osr) nldas_cs = gdc.raster_ds_cellsize(nldas_ds, x_only=True) nldas_extent = gdc.raster_ds_extent(nldas_ds) nldas_geo = nldas_extent.geo(nldas_cs) nldas_x, nldas_y = nldas_extent.origin() nldas_ds = None logging.debug(' Projection: {}'.format(nldas_proj)) logging.debug(' Cellsize: {}'.format(nldas_cs)) logging.debug(' Geo: {}'.format(nldas_geo)) logging.debug(' Extent: {}'.format(nldas_extent)) # Subset data to a smaller extent if output_extent is not None: logging.info('\nComputing subset extent & geo') logging.debug(' Extent: {}'.format(output_extent)) nldas_extent = gdc.Extent(output_extent) nldas_extent.adjust_to_snap('EXPAND', nldas_x, nldas_y, nldas_cs) nldas_geo = nldas_extent.geo(nldas_cs) logging.debug(' Geo: {}'.format(nldas_geo)) logging.debug(' Extent: {}'.format(output_extent)) elif extent_path is not None: logging.info('\nComputing subset extent & geo') if extent_path.lower().endswith('.shp'): nldas_extent = gdc.feature_path_extent(extent_path) extent_osr = gdc.feature_path_osr(extent_path) extent_cs = None else: nldas_extent = gdc.raster_path_extent(extent_path) extent_osr = gdc.raster_path_osr(extent_path) extent_cs = gdc.raster_path_cellsize(extent_path, x_only=True) nldas_extent = gdc.project_extent(nldas_extent, extent_osr, nldas_osr, extent_cs) nldas_extent.adjust_to_snap('EXPAND', nldas_x, nldas_y, nldas_cs) nldas_geo = nldas_extent.geo(nldas_cs) logging.debug(' Geo: {}'.format(nldas_geo)) logging.debug(' Extent: {}'.format(nldas_extent)) logging.debug('') # Read the NLDAS mask array if present if mask_path and os.path.isfile(mask_path): mask_array, mask_nodata = gdc.raster_to_array(mask_path, mask_extent=nldas_extent, fill_value=0, return_nodata=True) mask_array = mask_array != mask_nodata else: mask_array = None # Read elevation arrays (or subsets?) elev_array = gdc.raster_to_array(elev_path, mask_extent=nldas_extent, return_nodata=False) pair_array = et_common.air_pressure_func(elev_array) # Build output folder var_ws = os.path.join(output_ws, output_folder) if not os.path.isdir(var_ws): os.makedirs(var_ws) # Each sub folder in the main folder has all imagery for 1 day # The path for each subfolder is the /YYYY/DOY # This approach will process files for target dates # for input_dt in date_range(start_dt, end_dt + dt.timedelta(1)): # logging.info(input_dt.date()) # Iterate all available files and check dates if necessary for root, folders, files in os.walk(grb_ws): root_split = os.path.normpath(root).split(os.sep) # If the year/doy is outside the range, skip if (re.match('\d{4}', root_split[-2]) and re.match('\d{3}', root_split[-1])): root_dt = dt.datetime.strptime( '{}_{}'.format(root_split[-2], root_split[-1]), '%Y_%j') logging.info('{}'.format(root_dt.date())) if ((start_dt is not None and root_dt < start_dt) or (end_dt is not None and root_dt > end_dt)): continue elif date_list and root_dt.date().isoformat() not in date_list: continue # If the year is outside the range, don't search subfolders elif re.match('\d{4}', root_split[-1]): root_year = int(root_split[-1]) logging.info('Year: {}'.format(root_year)) if ((start_dt is not None and root_year < start_dt.year) or (end_dt is not None and root_year > end_dt.year)): folders[:] = [] else: folders[:] = sorted(folders) continue else: continue # Create a single raster for each day with 24 bands # Each time step will be stored in a separate band output_name = output_fmt.format(root_dt.year, root_dt.month, root_dt.day) output_path = os.path.join(var_ws, str(root_dt.year), output_name) logging.debug(' {}'.format(output_path)) if os.path.isfile(output_path): if not overwrite_flag: logging.debug(' File already exists, skipping') continue else: logging.debug(' File already exists, removing existing') os.remove(output_path) logging.debug(' {}'.format(root)) if not os.path.isdir(os.path.dirname(output_path)): os.makedirs(os.path.dirname(output_path)) gdc.build_empty_raster(output_path, band_cnt=24, output_dtype=np.float32, output_proj=nldas_proj, output_cs=nldas_cs, output_extent=nldas_extent, output_fill_flag=True) # Iterate through hourly files for input_name in sorted(files): logging.info(' {}'.format(input_name)) input_path = os.path.join(root, input_name) input_match = input_re.match(input_name) if input_match is None: logging.debug(' Regular expression didn\'t match, skipping') continue input_dt = dt.datetime(int(input_match.group('YEAR')), int(input_match.group('MONTH')), int(input_match.group('DAY'))) time_str = input_match.group('TIME') band_num = int(time_str[:2]) + 1 # if start_dt is not None and input_dt < start_dt: # continue # elif end_dt is not None and input_dt > end_dt: # continue # elif date_list and input_dt.date().isoformat() not in date_list: # continue if time_str not in time_list: logging.debug(' Time not in list, skipping') continue logging.debug(' Time: {} {}'.format(input_dt.date(), time_str)) logging.debug(' Band: {}'.format(band_num)) # Determine band numbering/naming input_band_dict = grib_band_names(input_path) # Compute vapour pressure from specific humidity input_ds = gdal.Open(input_path) sph_array = gdc.raster_ds_to_array( input_ds, band=input_band_dict['Specific humidity [kg/kg]'], mask_extent=nldas_extent, return_nodata=False) ea_array = (sph_array * pair_array) / (0.622 + 0.378 * sph_array) # Save the projected array as 32-bit floats gdc.array_to_comp_raster(ea_array.astype(np.float32), output_path, band=band_num) # gdc.block_to_raster( # ea_array.astype(np.float32), output_path, band=band) # gdc.array_to_raster( # ea_array.astype(np.float32), output_path, # output_geo=nldas_geo, output_proj=nldas_proj, # stats_flag=stats_flag) del sph_array input_ds = None if stats_flag: gdc.raster_statistics(output_path) logging.debug('\nScript Complete')
def main(netcdf_ws=os.getcwd(), ancillary_ws=os.getcwd(), output_ws=os.getcwd(), etr_flag=False, eto_flag=False, start_date=None, end_date=None, extent_path=None, output_extent=None, stats_flag=True, overwrite_flag=False): """Compute daily ETr/ETo from GRIDMET data Args: netcdf_ws (str): folder of GRIDMET netcdf files ancillary_ws (str): folder of ancillary rasters output_ws (str): folder of output rasters etr_flag (bool): if True, compute alfalfa reference ET (ETr) eto_flag (bool): if True, compute grass reference ET (ETo) start_date (str): ISO format date (YYYY-MM-DD) end_date (str): ISO format date (YYYY-MM-DD) extent_path (str): file path defining the output extent output_extent (list): decimal degrees values defining output extent stats_flag (bool): if True, compute raster statistics. Default is True. overwrite_flag (bool): if True, overwrite existing files Returns: None """ logging.info('\nComputing GRIDMET ETo/ETr') np.seterr(invalid='ignore') # Compute ETr and/or ETo if not etr_flag and not eto_flag: logging.info(' ETo/ETr flag(s) not set, defaulting to ETr') etr_flag = True # If a date is not set, process 2017 try: start_dt = dt.datetime.strptime(start_date, '%Y-%m-%d') logging.debug(' Start date: {}'.format(start_dt)) except: start_dt = dt.datetime(2017, 1, 1) logging.info(' Start date: {}'.format(start_dt)) try: end_dt = dt.datetime.strptime(end_date, '%Y-%m-%d') logging.debug(' End date: {}'.format(end_dt)) except: end_dt = dt.datetime(2017, 12, 31) logging.info(' End date: {}'.format(end_dt)) # Save GRIDMET lat, lon, and elevation arrays elev_raster = os.path.join(ancillary_ws, 'gridmet_elev.img') lat_raster = os.path.join(ancillary_ws, 'gridmet_lat.img') # Wind speed is measured at 2m zw = 10 etr_fmt = 'etr_{}_daily_gridmet.img' eto_fmt = 'eto_{}_daily_gridmet.img' # gridmet_re = re.compile('(?P<VAR>\w+)_(?P<YEAR>\d{4}).nc') # GRIDMET band name dictionary gridmet_band_dict = dict() gridmet_band_dict['pr'] = 'precipitation_amount' gridmet_band_dict['srad'] = 'surface_downwelling_shortwave_flux_in_air' gridmet_band_dict['sph'] = 'specific_humidity' gridmet_band_dict['tmmn'] = 'air_temperature' gridmet_band_dict['tmmx'] = 'air_temperature' gridmet_band_dict['vs'] = 'wind_speed' # Get extent/geo from elevation raster gridmet_ds = gdal.Open(elev_raster) gridmet_osr = gdc.raster_ds_osr(gridmet_ds) gridmet_proj = gdc.osr_proj(gridmet_osr) gridmet_cs = gdc.raster_ds_cellsize(gridmet_ds, x_only=True) gridmet_extent = gdc.raster_ds_extent(gridmet_ds) gridmet_full_geo = gridmet_extent.geo(gridmet_cs) gridmet_x, gridmet_y = gridmet_extent.origin() gridmet_ds = None logging.debug(' Projection: {}'.format(gridmet_proj)) logging.debug(' Cellsize: {}'.format(gridmet_cs)) logging.debug(' Geo: {}'.format(gridmet_full_geo)) logging.debug(' Extent: {}'.format(gridmet_extent)) # Subset data to a smaller extent if output_extent is not None: logging.info('\nComputing subset extent & geo') logging.debug(' Extent: {}'.format(output_extent)) gridmet_extent = gdc.Extent(output_extent) gridmet_extent.adjust_to_snap('EXPAND', gridmet_x, gridmet_y, gridmet_cs) gridmet_geo = gridmet_extent.geo(gridmet_cs) logging.debug(' Geo: {}'.format(gridmet_geo)) logging.debug(' Extent: {}'.format(output_extent)) elif extent_path is not None: logging.info('\nComputing subset extent & geo') if extent_path.lower().endswith('.shp'): gridmet_extent = gdc.feature_path_extent(extent_path) extent_osr = gdc.feature_path_osr(extent_path) extent_cs = None else: gridmet_extent = gdc.raster_path_extent(extent_path) extent_osr = gdc.raster_path_osr(extent_path) extent_cs = gdc.raster_path_cellsize(extent_path, x_only=True) gridmet_extent = gdc.project_extent(gridmet_extent, extent_osr, gridmet_osr, extent_cs) gridmet_extent.adjust_to_snap('EXPAND', gridmet_x, gridmet_y, gridmet_cs) gridmet_geo = gridmet_extent.geo(gridmet_cs) logging.debug(' Geo: {}'.format(gridmet_geo)) logging.debug(' Extent: {}'.format(gridmet_extent)) else: gridmet_geo = gridmet_full_geo # Get indices for slicing/clipping input arrays g_i, g_j = gdc.array_geo_offsets(gridmet_full_geo, gridmet_geo, cs=gridmet_cs) g_rows, g_cols = gridmet_extent.shape(cs=gridmet_cs) # Read the elevation and latitude arrays elev_array = gdc.raster_to_array(elev_raster, mask_extent=gridmet_extent, return_nodata=False) lat_array = gdc.raster_to_array(lat_raster, mask_extent=gridmet_extent, return_nodata=False) lat_array *= math.pi / 180 # Check elevation and latitude arrays if np.all(np.isnan(elev_array)): logging.error('\nERROR: The elevation array is all nodata, exiting\n') sys.exit() elif np.all(np.isnan(lat_array)): logging.error('\nERROR: The latitude array is all nodata, exiting\n') sys.exit() # Build output folder etr_ws = os.path.join(output_ws, 'etr') eto_ws = os.path.join(output_ws, 'eto') if etr_flag and not os.path.isdir(etr_ws): os.makedirs(etr_ws) if eto_flag and not os.path.isdir(eto_ws): os.makedirs(eto_ws) # By default, try to process all possible years if start_dt.year == end_dt.year: year_list = [str(start_dt.year)] year_list = sorted(map(str, range((start_dt.year), (end_dt.year + 1)))) # Process each year separately for year_str in year_list: logging.info("\nYear: {}".format(year_str)) year_int = int(year_str) year_days = int(dt.datetime(year_int, 12, 31).strftime('%j')) if start_dt is not None and year_int < start_dt.year: logging.debug(' Before start date, skipping') continue elif end_dt is not None and year_int > end_dt.year: logging.debug(' After end date, skipping') continue # Build input file path tmin_path = os.path.join(netcdf_ws, 'tmmn_{}.nc'.format(year_str)) tmax_path = os.path.join(netcdf_ws, 'tmmx_{}.nc'.format(year_str)) sph_path = os.path.join(netcdf_ws, 'sph_{}.nc'.format(year_str)) rs_path = os.path.join(netcdf_ws, 'srad_{}.nc'.format(year_str)) wind_path = os.path.join(netcdf_ws, 'vs_{}.nc'.format(year_str)) # Check that all input files are present missing_flag = False for input_path in [tmin_path, tmax_path, sph_path, rs_path, wind_path]: if not os.path.isfile(input_path): logging.debug( ' Input NetCDF doesn\'t exist\n {}'.format(input_path)) missing_flag = True if missing_flag: logging.debug(' skipping') continue logging.debug(" {}".format(tmin_path)) logging.debug(" {}".format(tmax_path)) logging.debug(" {}".format(sph_path)) logging.debug(" {}".format(rs_path)) logging.debug(" {}".format(wind_path)) # Create a single raster for each year with 365 bands # Each day will be stored in a separate band etr_raster = os.path.join(etr_ws, etr_fmt.format(year_str)) eto_raster = os.path.join(eto_ws, eto_fmt.format(year_str)) if etr_flag and (overwrite_flag or not os.path.isfile(etr_raster)): logging.debug(' {}'.format(etr_raster)) gdc.build_empty_raster(etr_raster, band_cnt=366, output_dtype=np.float32, output_proj=gridmet_proj, output_cs=gridmet_cs, output_extent=gridmet_extent, output_fill_flag=True) if eto_flag and (overwrite_flag or not os.path.isfile(eto_raster)): logging.debug(' {}'.format(eto_raster)) gdc.build_empty_raster(eto_raster, band_cnt=366, output_dtype=np.float32, output_proj=gridmet_proj, output_cs=gridmet_cs, output_extent=gridmet_extent, output_fill_flag=True) # DEADBEEF - Need to find a way to test if both of these conditionals # did not pass and pass logging debug message to user # Read in the GRIDMET NetCDF file tmin_nc_f = netCDF4.Dataset(tmin_path, 'r') tmax_nc_f = netCDF4.Dataset(tmax_path, 'r') sph_nc_f = netCDF4.Dataset(sph_path, 'r') rs_nc_f = netCDF4.Dataset(rs_path, 'r') wind_nc_f = netCDF4.Dataset(wind_path, 'r') logging.info(' Reading NetCDFs into memory') # Immediatly clip input arrays to save memory tmin_nc = tmin_nc_f.variables[ gridmet_band_dict['tmmn']][:, g_i:g_i + g_cols, g_j:g_j + g_rows].copy() tmax_nc = tmax_nc_f.variables[ gridmet_band_dict['tmmx']][:, g_i:g_i + g_cols, g_j:g_j + g_rows].copy() sph_nc = sph_nc_f.variables[gridmet_band_dict['sph']][:, g_i:g_i + g_cols, g_j:g_j + g_rows].copy() rs_nc = rs_nc_f.variables[gridmet_band_dict['srad']][:, g_i:g_i + g_cols, g_j:g_j + g_rows].copy() wind_nc = wind_nc_f.variables[gridmet_band_dict['vs']][:, g_i:g_i + g_cols, g_j:g_j + g_rows].copy() # tmin_nc = tmin_nc_f.variables[gridmet_band_dict['tmmn']][:] # tmax_nc = tmax_nc_f.variables[gridmet_band_dict['tmmx']][:] # sph_nc = sph_nc_f.variables[gridmet_band_dict['sph']][:] # rs_nc = rs_nc_f.variables[gridmet_band_dict['srad']][:] # wind_nc = wind_nc_f.variables[gridmet_band_dict['vs']][:] # Transpose arrays back to row x col tmin_nc = np.transpose(tmin_nc, (0, 2, 1)) tmax_nc = np.transpose(tmax_nc, (0, 2, 1)) sph_nc = np.transpose(sph_nc, (0, 2, 1)) rs_nc = np.transpose(rs_nc, (0, 2, 1)) wind_nc = np.transpose(wind_nc, (0, 2, 1)) # A numpy array is returned when slicing a masked array # if there are no masked pixels # This is a hack to force the numpy array back to a masked array # For now assume all arrays need to be converted if type(tmin_nc) != np.ma.core.MaskedArray: tmin_nc = np.ma.core.MaskedArray( tmin_nc, np.zeros(tmin_nc.shape, dtype=bool)) if type(tmax_nc) != np.ma.core.MaskedArray: tmax_nc = np.ma.core.MaskedArray( tmax_nc, np.zeros(tmax_nc.shape, dtype=bool)) if type(sph_nc) != np.ma.core.MaskedArray: sph_nc = np.ma.core.MaskedArray(sph_nc, np.zeros(sph_nc.shape, dtype=bool)) if type(rs_nc) != np.ma.core.MaskedArray: rs_nc = np.ma.core.MaskedArray(rs_nc, np.zeros(rs_nc.shape, dtype=bool)) if type(wind_nc) != np.ma.core.MaskedArray: wind_nc = np.ma.core.MaskedArray( wind_nc, np.zeros(wind_nc.shape, dtype=bool)) # Check all valid dates in the year year_dates = date_range(dt.datetime(year_int, 1, 1), dt.datetime(year_int + 1, 1, 1)) for date_dt in year_dates: if start_dt is not None and date_dt < start_dt: logging.debug(' {} - before start date, skipping'.format( date_dt.date())) continue elif end_dt is not None and date_dt > end_dt: logging.debug(' {} - after end date, skipping'.format( date_dt.date())) continue else: logging.info(' {}'.format(date_dt.date())) doy = int(date_dt.strftime('%j')) doy_i = range(1, year_days + 1).index(doy) # Arrays are being read as masked array with a fill value of -9999 # Convert to basic numpy array arrays with nan values try: tmin_ma = tmin_nc[doy_i, :, :] except IndexError: logging.info(' date not in netcdf, skipping') continue tmin_array = tmin_ma.data.astype(np.float32) tmin_nodata = float(tmin_ma.fill_value) tmin_array[tmin_array == tmin_nodata] = np.nan try: tmax_ma = tmax_nc[doy_i, :, :] except IndexError: logging.info(' date not in netcdf, skipping') continue tmax_array = tmax_ma.data.astype(np.float32) tmax_nodata = float(tmax_ma.fill_value) tmax_array[tmax_array == tmax_nodata] = np.nan try: sph_ma = sph_nc[doy_i, :, :] except IndexError: logging.info(' date not in netcdf, skipping') continue sph_array = sph_ma.data.astype(np.float32) sph_nodata = float(sph_ma.fill_value) sph_array[sph_array == sph_nodata] = np.nan try: rs_ma = rs_nc[doy_i, :, :] except IndexError: logging.info(' date not in netcdf, skipping') continue rs_array = rs_ma.data.astype(np.float32) rs_nodata = float(rs_ma.fill_value) rs_array[rs_array == rs_nodata] = np.nan try: wind_ma = wind_nc[doy_i, :, :] except IndexError: logging.info(' date not in netcdf, skipping') continue wind_array = wind_ma.data.astype(np.float32) wind_nodata = float(wind_ma.fill_value) wind_array[wind_array == wind_nodata] = np.nan del tmin_ma, tmax_ma, sph_ma, rs_ma, wind_ma # Since inputs are netcdf, need to create GDAL raster # datasets in order to use gdal_common functions # Create an in memory dataset of the full ETo array tmin_ds = gdc.array_to_mem_ds( tmin_array, output_geo=gridmet_geo, # tmin_array, output_geo=gridmet_full_geo, output_proj=gridmet_proj) tmax_ds = gdc.array_to_mem_ds( tmax_array, output_geo=gridmet_geo, # tmax_array, output_geo=gridmet_full_geo, output_proj=gridmet_proj) sph_ds = gdc.array_to_mem_ds( sph_array, output_geo=gridmet_geo, # sph_array, output_geo=gridmet_full_geo, output_proj=gridmet_proj) rs_ds = gdc.array_to_mem_ds( rs_array, output_geo=gridmet_geo, # rs_array, output_geo=gridmet_full_geo, output_proj=gridmet_proj) wind_ds = gdc.array_to_mem_ds( wind_array, output_geo=gridmet_geo, # wind_array, output_geo=gridmet_full_geo, output_proj=gridmet_proj) # Then extract the subset from the in memory dataset tmin_array = gdc.raster_ds_to_array(tmin_ds, 1, mask_extent=gridmet_extent, return_nodata=False) tmax_array = gdc.raster_ds_to_array(tmax_ds, 1, mask_extent=gridmet_extent, return_nodata=False) sph_array = gdc.raster_ds_to_array(sph_ds, 1, mask_extent=gridmet_extent, return_nodata=False) rs_array = gdc.raster_ds_to_array(rs_ds, 1, mask_extent=gridmet_extent, return_nodata=False) wind_array = gdc.raster_ds_to_array(wind_ds, 1, mask_extent=gridmet_extent, return_nodata=False) del tmin_ds, tmax_ds, sph_ds, rs_ds, wind_ds # Adjust units tmin_array -= 273.15 tmax_array -= 273.15 rs_array *= 0.0864 # ETr/ETo if etr_flag: etr_array = et_common.refet_daily_func(tmin_array, tmax_array, sph_array, rs_array, wind_array, zw, elev_array, lat_array, doy, 'ETR') if eto_flag: eto_array = et_common.refet_daily_func(tmin_array, tmax_array, sph_array, rs_array, wind_array, zw, elev_array, lat_array, doy, 'ETO') # del tmin_array, tmax_array, sph_array, rs_array, wind_array # Save the projected array as 32-bit floats if etr_flag: gdc.array_to_comp_raster(etr_array.astype(np.float32), etr_raster, band=doy, stats_flag=False) # gdc.array_to_raster( # etr_array.astype(np.float32), etr_raster, # output_geo=gridmet_geo, output_proj=gridmet_proj, # stats_flag=stats_flag) del etr_array if eto_flag: gdc.array_to_comp_raster(eto_array.astype(np.float32), eto_raster, band=doy, stats_flag=False) # gdc.array_to_raster( # eto_array.astype(np.float32), eto_raster, # output_geo=gridmet_geo, output_proj=gridmet_proj, # stats_flag=stats_flag) del eto_array del tmin_nc del tmax_nc del sph_nc del rs_nc del wind_nc tmin_nc_f.close() tmax_nc_f.close() sph_nc_f.close() rs_nc_f.close() wind_nc_f.close() del tmin_nc_f, tmax_nc_f, sph_nc_f, rs_nc_f, wind_nc_f if stats_flag and etr_flag: gdc.raster_statistics(etr_raster) if stats_flag and eto_flag: gdc.raster_statistics(eto_raster) logging.debug('\nScript Complete')
def main(image_ws, ini_path, blocksize=2048, smooth_flag=False, stats_flag=False, overwrite_flag=False): """Prep a Landsat scene for METRIC Args: image_ws (str): the landsat scene folder that will be prepped ini_path (str): file path of the input parameters file blocksize (int): defines the size of blocks to process smooth_flag (bool): If True, dilate/erode image to remove fringe/edge pixels stats_flag (bool): if True, compute raster statistics overwrite_flag (bool): If True, overwrite existing files Returns: True is successful """ # Open config file config = python_common.open_ini(ini_path) # Get input parameters logging.debug(' Reading Input File') calc_refl_toa_flag = python_common.read_param( 'calc_refl_toa_flag', True, config, 'INPUTS') calc_refl_toa_qa_flag = python_common.read_param( 'calc_refl_toa_qa_flag', True, config, 'INPUTS') # calc_refl_sur_ledaps_flag = python_common.read_param( # 'calc_refl_sur_ledaps_flag', False, config, 'INPUTS') # calc_refl_sur_qa_flag = python_common.read_param( # 'calc_refl_sur_qa_flag', False, config, 'INPUTS') calc_ts_bt_flag = python_common.read_param( 'calc_ts_bt_flag', True, config, 'INPUTS') # Use QA band to set common area # Fmask cloud, shadow, & snow pixels will be removed from common area calc_fmask_common_flag = python_common.read_param( 'calc_fmask_common_flag', True, config, 'INPUTS') fmask_buffer_flag = python_common.read_param( 'fmask_buffer_flag', False, config, 'INPUTS') fmask_erode_flag = python_common.read_param( 'fmask_erode_flag', False, config, 'INPUTS') if fmask_erode_flag: fmask_erode_cells = int(python_common.read_param( 'fmask_erode_cells', 10, config, 'INPUTS')) if fmask_erode_cells == 0 and fmask_erode_flag: fmask_erode_flag = False # Number of cells to buffer Fmask clouds # For now use the same buffer radius and apply to if fmask_buffer_flag: fmask_buffer_cells = int(python_common.read_param( 'fmask_buffer_cells', 25, config, 'INPUTS')) if fmask_buffer_cells == 0 and fmask_buffer_flag: fmask_buffer_flag = False # Include hand made cloud masks cloud_mask_flag = python_common.read_param( 'cloud_mask_flag', False, config, 'INPUTS') cloud_mask_ws = "" if cloud_mask_flag: cloud_mask_ws = config.get('INPUTS', 'cloud_mask_ws') # Extract separate Fmask rasters calc_fmask_flag = python_common.read_param( 'calc_fmask_flag', True, config, 'INPUTS') calc_fmask_cloud_flag = python_common.read_param( 'calc_fmask_cloud_flag', True, config, 'INPUTS') calc_fmask_snow_flag = python_common.read_param( 'calc_fmask_snow_flag', True, config, 'INPUTS') calc_fmask_water_flag = python_common.read_param( 'calc_fmask_water_flag', True, config, 'INPUTS') # Keep Landsat DN, LEDAPS, and Fmask rasters keep_dn_flag = python_common.read_param( 'keep_dn_flag', True, config, 'INPUTS') # keep_sr_flag = python_common.read_param( # 'keep_sr_flag', True, config, 'INPUTS') # For this to work I would need to pass in the metric input file # calc_elev_flag = python_common.read_param( # 'calc_elev_flag', False, config, 'INPUTS') # calc_landuse_flag = python_common.read_param( # 'calc_landuse_flag', False, config, 'INPUTS') # calc_acca_cloud_flag = python_common.read_param( # 'calc_acca_cloud_flag', True, config, 'INPUTS') # calc_acca_snow_flag = python_common.read_param( # 'calc_acca_snow_flag', True, config, 'INPUTS') # calc_ledaps_dem_land_flag = python_common.read_param( # 'calc_ledaps_dem_land_flag', False, config, 'INPUTS') # calc_ledaps_veg_flag = python_common.read_param( # 'calc_ledaps_veg_flag', False, config, 'INPUTS') # calc_ledaps_snow_flag = python_common.read_param( # 'calc_ledaps_snow_flag', False, config, 'INPUTS') # calc_ledaps_land_flag = python_common.read_param( # 'calc_ledaps_land_flag', False, config, 'INPUTS') # calc_ledaps_cloud_flag = python_common.read_param( # 'calc_ledaps_cloud_flag', False, config, 'INPUTS') # Interpolate/clip/project hourly rasters for each Landsat scene # calc_metric_flag = python_common.read_param( # 'calc_metric_flag', False, config, 'INPUTS') calc_metric_ea_flag = python_common.read_param( 'calc_metric_ea_flag', False, config, 'INPUTS') calc_metric_wind_flag = python_common.read_param( 'calc_metric_wind_flag', False, config, 'INPUTS') calc_metric_etr_flag = python_common.read_param( 'calc_metric_etr_flag', False, config, 'INPUTS') calc_metric_tair_flag = python_common.read_param( 'calc_metric_tair_flag', False, config, 'INPUTS') # Interpolate/clip/project AWC and daily ETr/PPT rasters # to compute SWB Ke for each Landsat scene calc_swb_ke_flag = python_common.read_param( 'calc_swb_ke_flag', False, config, 'INPUTS') if cloud_mask_flag: spinup_days = python_common.read_param( 'swb_spinup_days', 30, config, 'INPUTS') min_spinup_days = python_common.read_param( 'swb_min_spinup_days', 5, config, 'INPUTS') # Round ea raster to N digits to save space rounding_digits = python_common.read_param( 'rounding_digits', 3, config, 'INPUTS') env = gdc.env image = et_image.Image(image_ws, env) np.seterr(invalid='ignore', divide='ignore') gdal.UseExceptions() # Input file paths dn_image_dict = et_common.landsat_band_image_dict( image.orig_data_ws, image.image_re) # # Open METRIC config file # if config_file: # logging.info( # log_f.format('METRIC INI File:', os.path.basename(config_file))) # config = configparser.ConfigParser() # try: # config.read(config_file) # except: # logging.error('\nERROR: Config file could not be read, ' + # 'is not an input file, or does not exist\n' + # 'ERROR: config_file = {}\n').format(config_file) # sys.exit() # # Overwrite # overwrite_flag = read_param('overwrite_flag', True, config) # # # Elevation and landuse parameters/flags from METRIC input file # calc_elev_flag = read_param('save_dem_raster_flag', True, config) # calc_landuse_flag = read_param( # 'save_landuse_raster_flag', True, config) # if calc_elev_flag: # elev_pr_path = config.get('INPUTS','dem_raster') # if calc_landuse_flag: # landuse_pr_path = config.get('INPUTS', 'landuse_raster') # else: # overwrite_flag = False # calc_elev_flag = False # calc_landuse_flag = False # # Elev raster must exist # if calc_elev_flag and not os.path.isfile(elev_pr_path): # logging.error('\nERROR: Elevation raster {} does not exist\n'.format( # elev_pr_path)) # return False # Landuse raster must exist # if calc_landuse_flag and not os.path.isfile(landuse_pr_path): # logging.error('\nERROR: Landuse raster {} does not exist\n'.format( # landuse_pr_path)) # return False # Removing ancillary files before checking for inputs if os.path.isdir(os.path.join(image.orig_data_ws, 'gap_mask')): shutil.rmtree(os.path.join(image.orig_data_ws, 'gap_mask')) for item in os.listdir(image.orig_data_ws): if (image.type == 'Landsat7' and (item.endswith('_B8.TIF') or item.endswith('_B6_VCID_2.TIF'))): os.remove(os.path.join(image.orig_data_ws, item)) elif (image.type == 'Landsat8' and (item.endswith('_B1.TIF') or item.endswith('_B8.TIF') or item.endswith('_B9.TIF') or item.endswith('_B11.TIF'))): os.remove(os.path.join(image.orig_data_ws, item)) elif (item.endswith('_VER.jpg') or item.endswith('_VER.txt') or item.endswith('_GCP.txt') or item == 'README.GTF'): os.remove(os.path.join(image.orig_data_ws, item)) # Check correction level (image must be L1T to process) if image.correction != 'L1TP': logging.debug(' Image is not L1TP corrected, skipping') return False # calc_fmask_common_flag = False # calc_refl_toa_flag = False # calc_ts_bt_flag = False # calc_metric_ea_flag = False # calc_metric_wind_flag = False # calc_metric_etr_flag = False # overwrite_flag = False # QA band must exist if (calc_fmask_common_flag and image.qa_band not in dn_image_dict.keys()): logging.warning( ('\nQA band does not exist but calc_fmask_common_flag=True' + '\n Setting calc_fmask_common_flag=False\n {}').format( os.path.basename(image.qa_input_raster))) calc_fmask_common_flag = False if cloud_mask_flag and not os.path.isdir(cloud_mask_ws): logging.warning( ('\ncloud_mask_ws is not a directory but cloud_mask_flag=True.' + '\n Setting cloud_mask_flag=False\n {}').format(cloud_mask_ws)) cloud_mask_flag = False # Check for Landsat TOA images if (calc_refl_toa_flag and (set(list(image.band_toa_dict.keys()) + [image.thermal_band, image.qa_band]) != set(dn_image_dict.keys()))): logging.warning( '\nMissing Landsat images but calc_refl_toa_flag=True' + '\n Setting calc_refl_toa_flag=False') calc_refl_toa_flag = False # Check for Landsat brightness temperature image if calc_ts_bt_flag and image.thermal_band not in dn_image_dict.keys(): logging.warning( '\nThermal band image does not exist but calc_ts_bt_flag=True' + '\n Setting calc_ts_bt_flag=False') calc_ts_bt_flag = False # DEADBEEF - Should the function return False if Ts doesn't exist? # return False # Check for METRIC hourly/daily input folders if calc_metric_ea_flag: metric_ea_input_ws = config.get('INPUTS', 'metric_ea_input_folder') if not os.path.isdir(metric_ea_input_ws): logging.warning( ('\nHourly Ea folder does not exist but calc_metric_ea_flag=True' + '\n Setting calc_metric_ea_flag=False\n {}').format( metric_ea_input_ws)) calc_metric_ea_flag = False if calc_metric_wind_flag: metric_wind_input_ws = config.get('INPUTS', 'metric_wind_input_folder') if not os.path.isdir(metric_wind_input_ws): logging.warning( ('\nHourly wind folder does not exist but calc_metric_wind_flag=True' + '\n Setting calc_metric_wind_flag=False\n {}').format( metric_wind_input_ws)) calc_metric_wind_flag = False if calc_metric_etr_flag: metric_etr_input_ws = config.get('INPUTS', 'metric_etr_input_folder') if not os.path.isdir(metric_etr_input_ws): logging.warning( ('\nHourly ETr folder does not exist but calc_metric_etr_flag=True' + '\n Setting calc_metric_etr_flag=False\n {}').format( metric_etr_input_ws)) calc_metric_etr_flag = False if calc_metric_tair_flag: metric_tair_input_ws = config.get('INPUTS', 'metric_tair_input_folder') if not os.path.isdir(metric_tair_input_ws): logging.warning( ('\nHourly Tair folder does not exist but calc_metric_tair_flag=True' + '\n Setting calc_metric_tair_flag=False\n {}').format( metric_tair_input_ws)) calc_metric_tair_flag = False if (calc_metric_ea_flag or calc_metric_wind_flag or calc_metric_etr_flag or calc_metric_tair_flag): metric_hourly_re = re.compile(config.get('INPUTS', 'metric_hourly_re')) metric_daily_re = re.compile(config.get('INPUTS', 'metric_daily_re')) if calc_swb_ke_flag: awc_input_path = config.get('INPUTS', 'awc_input_path') etr_input_ws = config.get('INPUTS', 'etr_input_folder') ppt_input_ws = config.get('INPUTS', 'ppt_input_folder') etr_input_re = re.compile(config.get('INPUTS', 'etr_input_re')) ppt_input_re = re.compile(config.get('INPUTS', 'ppt_input_re')) if not os.path.isfile(awc_input_path): logging.warning( ('\nAWC raster does not exist but calc_swb_ke_flag=True' + '\n Setting calc_swb_ke_flag=False\n {}').format( awc_input_path)) calc_swb_ke_flag = False if not os.path.isdir(etr_input_ws): logging.warning( ('\nDaily ETr folder does not exist but calc_swb_ke_flag=True' + '\n Setting calc_swb_ke_flag=False\n {}').format( etr_input_ws)) calc_swb_ke_flag = False if not os.path.isdir(ppt_input_ws): logging.warning( ('\nDaily PPT folder does not exist but calc_swb_ke_flag=True' + '\n Setting calc_swb_ke_flag=False\n {}').format( ppt_input_ws)) calc_swb_ke_flag = False # Build folders for support rasters if ((calc_fmask_common_flag or calc_refl_toa_flag or # calc_refl_sur_ledaps_flag or calc_ts_bt_flag or calc_metric_ea_flag or calc_metric_wind_flag or calc_metric_etr_flag or calc_metric_tair_flag or calc_swb_ke_flag) and not os.path.isdir(image.support_ws)): os.makedirs(image.support_ws) if calc_refl_toa_flag and not os.path.isdir(image.refl_toa_ws): os.makedirs(image.refl_toa_ws) # if calc_refl_sur_ledaps_flag and not os.path.isdir(image.refl_sur_ws): # os.makedirs(image.refl_sur_ws) # Apply overwrite flag if overwrite_flag: overwrite_list = [ image.fmask_cloud_raster, image.fmask_snow_raster, image.fmask_water_raster # image.elev_raster, image.landuse_raster # image.common_area_raster ] for overwrite_path in overwrite_list: try: python_common.remove_file(image.fmask_cloud_raster) except: pass # Use QA band to build common area rasters logging.info('\nCommon Area Raster') qa_ds = gdal.Open(dn_image_dict[image.qa_band], 0) common_geo = gdc.raster_ds_geo(qa_ds) common_extent = gdc.raster_ds_extent(qa_ds) common_proj = gdc.raster_ds_proj(qa_ds) common_osr = gdc.raster_ds_osr(qa_ds) # Initialize common_area as all non-fill QA values qa_array = gdc.raster_ds_to_array(qa_ds, return_nodata=False) common_array = qa_array != 1 common_rows, common_cols = common_array.shape del qa_ds # First try applying user defined cloud masks cloud_mask_path = os.path.join( cloud_mask_ws, image.folder_id + '_mask.shp') if cloud_mask_flag and os.path.isfile(cloud_mask_path): logging.info(' Applying cloud mask shapefile') feature_path = os.path.join( cloud_mask_ws, (image.folder_id + '_mask.shp')) logging.info(' {}'.format(feature_path)) cloud_mask_memory_ds = gdc.polygon_to_raster_ds( feature_path, nodata_value=0, burn_value=1, output_osr=common_osr, output_cs=30, output_extent=common_extent) cloud_array = gdc.raster_ds_to_array( cloud_mask_memory_ds, return_nodata=False) # DEADBEEF - If user sets a cloud mask, # it is probably better than Fmask # Eventually change "if" calc_fmask_common_flag: to "elif" common_array[cloud_array == 1] = 0 del cloud_mask_memory_ds, cloud_array if calc_fmask_common_flag: fmask_array = et_numpy.bqa_fmask_func(qa_array) fmask_mask = (fmask_array >= 2) & (fmask_array <= 4) if fmask_erode_flag: logging.info( (' Eroding and dilating Fmask clouds, shadows, and snow ' + '{} cells\n to remove errantly masked pixels.').format( fmask_erode_cells)) fmask_mask = ndimage.binary_erosion( fmask_mask, iterations=fmask_erode_cells, structure=ndimage.generate_binary_structure(2, 2)) fmask_mask = ndimage.binary_dilation( fmask_mask, iterations=fmask_erode_cells, structure=ndimage.generate_binary_structure(2, 2)) if fmask_buffer_flag: logging.info( (' Buffering Fmask clouds, shadows, and snow ' + '{} cells').format(fmask_buffer_cells)) # Only buffer clouds, shadow, and snow (not water or nodata) if fmask_mask is None: fmask_mask = (fmask_array >= 2) & (fmask_array <= 4) fmask_mask = ndimage.binary_dilation( fmask_mask, iterations=fmask_buffer_cells, structure=ndimage.generate_binary_structure(2, 2)) # Reset common_array for buffered cells common_array[fmask_mask] = 0 del fmask_array, fmask_mask if common_array is not None: # Erode and dilate to remove fringe on edge # Default is to not smooth, but user can force smoothing if smooth_flag: common_array = smooth_func(common_array) # Check that there are some cloud free pixels if not np.any(common_array): logging.error(' ERROR: There are no cloud/snow free pixels') return False # Always overwrite common area raster # if not os.path.isfile(image.common_area_raster): gdc.array_to_raster( common_array, image.common_area_raster, output_geo=common_geo, output_proj=common_proj, stats_flag=stats_flag) # Print common geo/extent logging.debug(' Common geo: {}'.format(common_geo)) logging.debug(' Common extent: {}'.format(common_extent)) # Extract Fmask components as separate rasters if (calc_fmask_flag or calc_fmask_cloud_flag or calc_fmask_snow_flag or calc_fmask_water_flag): logging.info('\nFmask') fmask_array = et_numpy.bqa_fmask_func(qa_array) # Save Fmask data as separate rasters if (calc_fmask_flag and not os.path.isfile(image.fmask_output_raster)): gdc.array_to_raster( fmask_array.astype(np.uint8), image.fmask_output_raster, output_geo=common_geo, output_proj=common_proj, mask_array=None, output_nodata=255, stats_flag=stats_flag) if (calc_fmask_cloud_flag and not os.path.isfile(image.fmask_cloud_raster)): fmask_cloud_array = (fmask_array == 2) | (fmask_array == 4) gdc.array_to_raster( fmask_cloud_array.astype(np.uint8), image.fmask_cloud_raster, output_geo=common_geo, output_proj=common_proj, mask_array=None, output_nodata=255, stats_flag=stats_flag) del fmask_cloud_array if (calc_fmask_snow_flag and not os.path.isfile(image.fmask_snow_raster)): fmask_snow_array = (fmask_array == 3) gdc.array_to_raster( fmask_snow_array.astype(np.uint8), image.fmask_snow_raster, output_geo=common_geo, output_proj=common_proj, mask_array=None, output_nodata=255, stats_flag=stats_flag) del fmask_snow_array if (calc_fmask_water_flag and not os.path.isfile(image.fmask_water_raster)): fmask_water_array = (fmask_array == 1) gdc.array_to_raster( fmask_water_array.astype(np.uint8), image.fmask_water_raster, output_geo=common_geo, output_proj=common_proj, mask_array=None, output_nodata=255, stats_flag=stats_flag) del fmask_water_array del fmask_array # # Calculate elevation # if calc_elev_flag and not os.path.isfile(elev_path): # logging.info('Elevation') # elev_array, elev_nodata = gdc.raster_to_array( # elev_pr_path, 1, common_extent) # gdc.array_to_raster( # elev_array, elev_raster, # output_geo=common_geo, output_proj=env.snap_proj, # mask_array=common_array, stats_flag=stats_flag) # del elev_array, elev_nodata, elev_path # # # Calculate landuse # if calc_landuse_flag and not os.path.isfile(landuse_raster): # logging.info('Landuse') # landuse_array, landuse_nodata = gdc.raster_to_array( # landuse_pr_path, 1, common_extent) # gdc.array_to_raster( # landuse_array, landuse_raster, # output_geo=common_geo, output_proj=env.snap_proj, # mask_array=common_array, stats_flag=stats_flag) # del landuse_array, landuse_nodata, landuse_raster # Calculate toa reflectance # f32_gtype, f32_nodata = numpy_to_gdal_type(np.float32) if calc_refl_toa_flag: logging.info('Top-of-Atmosphere Reflectance') if os.path.isfile(image.refl_toa_raster) and overwrite_flag: logging.debug(' Overwriting: {}'.format( image.refl_toa_raster)) python_common.remove_file(image.refl_toa_raster) if not os.path.isfile(image.refl_toa_raster): # First build empty composite raster gdc.build_empty_raster( image.refl_toa_raster, image.band_toa_cnt, np.float32, None, env.snap_proj, env.cellsize, common_extent) # cos_theta_solar_flt = et_common.cos_theta_solar_func( # image.sun_elevation) # Process by block logging.info('Processing by block') logging.debug(' Mask cols/rows: {}/{}'.format( common_cols, common_rows)) for b_i, b_j in gdc.block_gen(common_rows, common_cols, blocksize): logging.debug(' Block y: {:5d} x: {:5d}'.format(b_i, b_j)) block_data_mask = gdc.array_to_block( common_array, b_i, b_j, blocksize).astype(np.bool) block_rows, block_cols = block_data_mask.shape block_geo = gdc.array_offset_geo(common_geo, b_j, b_i) block_extent = gdc.geo_extent( block_geo, block_rows, block_cols) logging.debug(' Block rows: {} cols: {}'.format( block_rows, block_cols)) logging.debug(' Block extent: {}'.format(block_extent)) logging.debug(' Block geo: {}'.format(block_geo)) # Process each TOA band # for band, band_i in sorted(image.band_toa_dict.items()): for band, dn_image in sorted(dn_image_dict.items()): if band not in image.band_toa_dict.keys(): continue # thermal_band_flag = (band == image.thermal_band) # Set 0 as nodata value gdc.raster_path_set_nodata(dn_image, 0) # Calculate TOA reflectance dn_array, dn_nodata = gdc.raster_to_array( dn_image, 1, block_extent) dn_array = dn_array.astype(np.float64) # dn_array = dn_array.astype(np.float32) dn_array[dn_array == 0] = np.nan # if image.type in ['Landsat4', 'Landsat5', 'Landsat7']: refl_toa_array = et_numpy.l457_refl_toa_band_func( dn_array, image.cos_theta_solar, image.dr, image.esun_dict[band], image.lmin_dict[band], image.lmax_dict[band], image.qcalmin_dict[band], image.qcalmax_dict[band]) elif image.type in ['Landsat8']: refl_toa_array = et_numpy.l8_refl_toa_band_func( dn_array, image.cos_theta_solar, image.refl_mult_dict[band], image.refl_add_dict[band]) # if (image.type in ['Landsat4', 'Landsat5', 'Landsat7'] and # not thermal_band_flag): # refl_toa_array = et_numpy.l457_refl_toa_band_func( # dn_array, image.cos_theta_solar, # image.dr, image.esun_dict[band], # image.lmin_dict[band], image.lmax_dict[band], # image.qcalmin_dict[band], # image.qcalmax_dict[band]) # # image.rad_mult_dict[band], # # image.rad_add_dict[band]) # elif (image.type in ['Landsat8'] and # not thermal_band_flag): # refl_toa_array = et_numpy.l8_refl_toa_band_func( # dn_array, image.cos_theta_solar, # image.refl_mult_dict[band], # image.refl_add_dict[band]) # elif (image.type in ['Landsat4', 'Landsat5', 'Landsat7'] and # thermal_band_flag): # refl_toa_array = et_numpy.l457_ts_bt_band_func( # dn_array, # image.lmin_dict[band], image.lmax_dict[band], # image.qcalmin_dict[band], # image.qcalmax_dict[band], # # image.rad_mult_dict[band], # # image.rad_add_dict[band], # image.k1_dict[band], image.k2_dict[band]) # elif (image.type in ['Landsat8'] and # thermal_band_flag): # refl_toa_array = et_numpy.l8_ts_bt_band_func( # dn_array, # image.rad_mult_dict[band], # image.rad_add_dict[band], # image.k1_dict[band], image.k2_dict[band]) # refl_toa_array = et_numpy.refl_toa_band_func( # dn_array, cos_theta_solar_flt, # image.dr, image.esun_dict[band], # image.lmin_dict[band], image.lmax_dict[band], # image.qcalmin_dict[band], image.qcalmax_dict[band], # thermal_band_flag) gdc.block_to_raster( refl_toa_array.astype(np.float32), image.refl_toa_raster, b_i, b_j, band=image.band_toa_dict[band]) # gdc.array_to_comp_raster( # refl_toa_array.astype(np.float32), # image.refl_toa_raster, # image.band_toa_dict[band], common_array) del refl_toa_array, dn_array if stats_flag: gdc.raster_statistics(image.refl_toa_raster) # # Process each TOA band # # for band, band_i in sorted(image.band_toa_dict.items()): # for band, dn_image in sorted(dn_image_dict.items()): # thermal_band_flag = (band == image.thermal_band) # # Set 0 as nodata value # gdc.raster_path_set_nodata(dn_image, 0) # # Calculate TOA reflectance # dn_array, dn_nodata = gdc.raster_to_array( # dn_image, 1, common_extent) # dn_array = dn_array.astype(np.float64) # # dn_array = dn_array.astype(np.float32) # dn_array[dn_array == 0] = np.nan # # # if (image.type in ['Landsat4', 'Landsat5', 'Landsat7'] and # not thermal_band_flag): # refl_toa_array = et_numpy.l457_refl_toa_band_func( # dn_array, image.cos_theta_solar, # image.dr, image.esun_dict[band], # image.lmin_dict[band], image.lmax_dict[band], # image.qcalmin_dict[band], image.qcalmax_dict[band]) # # image.rad_mult_dict[band], image.rad_add_dict[band]) # elif (image.type in ['Landsat4', 'Landsat5', 'Landsat7'] and # thermal_band_flag): # refl_toa_array = et_numpy.l457_ts_bt_band_func( # dn_array, image.lmin_dict[band], image.lmax_dict[band], # image.qcalmin_dict[band], image.qcalmax_dict[band], # # image.rad_mult_dict[band], image.rad_add_dict[band], # image.k1_dict[band], image.k2_dict[band]) # elif (image.type in ['Landsat8'] and # not thermal_band_flag): # refl_toa_array = et_numpy.l8_refl_toa_band_func( # dn_array, image.cos_theta_solar, # image.refl_mult_dict[band], image.refl_add_dict[band]) # elif (image.type in ['Landsat8'] and # thermal_band_flag): # refl_toa_array = et_numpy.l8_ts_bt_band_func( # dn_array, # image.rad_mult_dict[band], image.rad_add_dict[band], # image.k1_dict[band], image.k2_dict[band]) # # refl_toa_array = et_numpy.refl_toa_band_func( # # dn_array, cos_theta_solar_flt, # # image.dr, image.esun_dict[band], # # image.lmin_dict[band], image.lmax_dict[band], # # image.qcalmin_dict[band], image.qcalmax_dict[band], # # thermal_band_flag) # gdc.array_to_comp_raster( # refl_toa_array.astype(np.float32), image.refl_toa_raster, # image.band_toa_dict[band], common_array) # del refl_toa_array, dn_array # Calculate brightness temperature if calc_ts_bt_flag: logging.info('Brightness Temperature') if os.path.isfile(image.ts_bt_raster) and overwrite_flag: logging.debug(' Overwriting: {}'.format(image.ts_bt_raster)) python_common.remove_file(image.ts_bt_raster) if not os.path.isfile(image.ts_bt_raster): band = image.thermal_band thermal_dn_path = dn_image_dict[band] gdc.raster_path_set_nodata(thermal_dn_path, 0) thermal_dn_array, thermal_dn_nodata = gdc.raster_to_array( thermal_dn_path, 1, common_extent, return_nodata=True) thermal_dn_mask = thermal_dn_array != thermal_dn_nodata if image.type in ['Landsat4', 'Landsat5', 'Landsat7']: ts_bt_array = et_numpy.l457_ts_bt_band_func( thermal_dn_array, image.lmin_dict[band], image.lmax_dict[band], image.qcalmin_dict[band], image.qcalmax_dict[band], # image.rad_mult_dict[band], image.rad_add_dict[band], image.k1_dict[band], image.k2_dict[band]) elif image.type in ['Landsat8']: ts_bt_array = et_numpy.l8_ts_bt_band_func( thermal_dn_array, image.rad_mult_dict[band], image.rad_add_dict[band], image.k1_dict[band], image.k2_dict[band]) # thermal_rad_array = et_numpy.refl_toa_band_func( # thermal_dn_array, image.cos_theta_solar, # image.dr, image.esun_dict[band], # image.lmin_dict[band], image.lmax_dict[band], # image.qcalmin_dict[band], image.qcalmax_dict[band], # thermal_band_flag=True) # ts_bt_array = et_numpy.ts_bt_func( # thermal_rad_array, image.k1_dict[image.thermal_band], # image.k2_dict[image.thermal_band]) ts_bt_array[~thermal_dn_mask] = np.nan gdc.array_to_raster( ts_bt_array, image.ts_bt_raster, output_geo=common_geo, output_proj=env.snap_proj, # mask_array=common_array, stats_flag=stats_flag) # del thermal_dn_array, thermal_rad_array del thermal_dn_path, thermal_dn_array, ts_bt_array # Interpolate/project/clip METRIC hourly/daily rasters if (calc_metric_ea_flag or calc_metric_wind_flag or calc_metric_etr_flag): logging.info('METRIC hourly/daily rasters') # Get bracketing hours from image acquisition time image_prev_dt = image.acq_datetime.replace( minute=0, second=0, microsecond=0) image_next_dt = image_prev_dt + timedelta(seconds=3600) # Get NLDAS properties from one of the images input_ws = os.path.join( metric_etr_input_ws, str(image_prev_dt.year)) try: input_path = [ os.path.join(input_ws, file_name) for file_name in os.listdir(input_ws) for match in [metric_hourly_re.match(file_name)] if (match and (image_prev_dt.strftime('%Y%m%d') == match.group('YYYYMMDD')))][0] except IndexError: logging.error(' No hourly file for {}'.format( image_prev_dt.strftime('%Y-%m-%d %H00'))) return False try: input_ds = gdal.Open(input_path) input_osr = gdc.raster_ds_osr(input_ds) # input_proj = gdc.osr_proj(input_osr) input_extent = gdc.raster_ds_extent(input_ds) input_cs = gdc.raster_ds_cellsize(input_ds, x_only=True) # input_geo = input_extent.geo(input_cs) input_x, input_y = input_extent.origin() input_ds = None except: logging.error(' Could not get default input image properties') logging.error(' {}'.format(input_path)) return False # Project Landsat scene extent to NLDAS GCS common_gcs_osr = common_osr.CloneGeogCS() common_gcs_extent = gdc.project_extent( common_extent, common_osr, common_gcs_osr, cellsize=env.cellsize) common_gcs_extent.buffer_extent(0.1) common_gcs_extent.adjust_to_snap( 'EXPAND', input_x, input_y, input_cs) # common_gcs_geo = common_gcs_extent.geo(input_cs) def metric_weather_func(output_raster, input_ws, input_re, prev_dt, next_dt, resample_method=gdal.GRA_NearestNeighbour, rounding_flag=False): """Interpolate/project/clip METRIC hourly rasters""" logging.debug(' Output: {}'.format(output_raster)) if os.path.isfile(output_raster): if overwrite_flag: logging.debug(' Overwriting output') python_common.remove_file(output_raster) else: logging.debug(' Skipping, file already exists ' + 'and overwrite is False') return False prev_ws = os.path.join(input_ws, str(prev_dt.year)) next_ws = os.path.join(input_ws, str(next_dt.year)) # Technically previous and next could come from different days # or even years, although this won't happen in the U.S. try: prev_path = [ os.path.join(prev_ws, input_name) for input_name in os.listdir(prev_ws) for input_match in [input_re.match(input_name)] if (input_match and (prev_dt.strftime('%Y%m%d') == input_match.group('YYYYMMDD')))][0] logging.debug(' Input prev: {}'.format(prev_path)) except IndexError: logging.error(' No previous hourly file') logging.error(' {}'.format(prev_dt)) return False try: next_path = [ os.path.join(next_ws, input_name) for input_name in os.listdir(next_ws) for input_match in [input_re.match(input_name)] if (input_match and (next_dt.strftime('%Y%m%d') == input_match.group('YYYYMMDD')))][0] logging.debug(' Input next: {}'.format(next_path)) except IndexError: logging.error(' No next hourly file') logging.error(' {}'.format(next_dt)) return False # Band numbers are 1's based prev_band = int(prev_dt.strftime('%H')) + 1 next_band = int(next_dt.strftime('%H')) + 1 logging.debug(' Input prev band: {}'.format(prev_band)) logging.debug(' Input next band: {}'.format(next_band)) # Read arrays prev_array = gdc.raster_to_array( prev_path, band=prev_band, mask_extent=common_gcs_extent, return_nodata=False) next_array = gdc.raster_to_array( next_path, band=next_band, mask_extent=common_gcs_extent, return_nodata=False) if not np.any(prev_array) or not np.any(next_array): logging.warning('\nWARNING: Input NLDAS array is all nodata\n') return None output_array = hourly_interpolate_func( prev_array, next_array, prev_dt, next_dt, image.acq_datetime) output_array = gdc.project_array( output_array, resample_method, input_osr, input_cs, common_gcs_extent, common_osr, env.cellsize, common_extent, output_nodata=None) # Apply common area mask output_array[~common_array] = np.nan # Reduce the file size by rounding to the nearest n digits if rounding_flag: output_array = np.around(output_array, rounding_digits) # Force output to 32-bit float gdc.array_to_raster( output_array.astype(np.float32), output_raster, output_geo=common_geo, output_proj=common_proj, stats_flag=stats_flag) del output_array return True # Ea - Project to Landsat scene after clipping if calc_metric_ea_flag: logging.info(' Hourly vapor pressure (Ea)') metric_weather_func( image.metric_ea_raster, metric_ea_input_ws, metric_hourly_re, image_prev_dt, image_next_dt, gdal.GRA_Bilinear, rounding_flag=True) # Wind - Project to Landsat scene after clipping if calc_metric_wind_flag: logging.info(' Hourly windspeed') metric_weather_func( image.metric_wind_raster, metric_wind_input_ws, metric_hourly_re, image_prev_dt, image_next_dt, gdal.GRA_NearestNeighbour, rounding_flag=False) # ETr - Project to Landsat scene after clipping if calc_metric_etr_flag: logging.info(' Hourly reference ET (ETr)') metric_weather_func( image.metric_etr_raster, metric_etr_input_ws, metric_hourly_re, image_prev_dt, image_next_dt, gdal.GRA_NearestNeighbour, rounding_flag=False) # ETr 24hr - Project to Landsat scene after clipping if calc_metric_etr_flag: logging.info(' Daily reference ET (ETr)') logging.debug(' Output: {}'.format( image.metric_etr_24hr_raster)) if (os.path.isfile(image.metric_etr_24hr_raster) and overwrite_flag): logging.debug(' Overwriting output') os.remove(image.metric_etr_24hr_raster) if not os.path.isfile(image.metric_etr_24hr_raster): etr_prev_ws = os.path.join( metric_etr_input_ws, str(image_prev_dt.year)) try: input_path = [ os.path.join(etr_prev_ws, file_name) for file_name in os.listdir(etr_prev_ws) for match in [metric_daily_re.match(file_name)] if (match and (image_prev_dt.strftime('%Y%m%d') == match.group('YYYYMMDD')))][0] logging.debug(' Input: {}'.format(input_path)) except IndexError: logging.error(' No daily file for {}'.format( image_prev_dt.strftime('%Y-%m-%d'))) return False output_array = gdc.raster_to_array( input_path, mask_extent=common_gcs_extent, return_nodata=False) output_array = gdc.project_array( output_array, gdal.GRA_NearestNeighbour, input_osr, input_cs, common_gcs_extent, common_osr, env.cellsize, common_extent, output_nodata=None) # Apply common area mask output_array[~common_array] = np.nan # Reduce the file size by rounding to the nearest n digits # output_array = np.around(output_array, rounding_digits) gdc.array_to_raster( output_array, image.metric_etr_24hr_raster, output_geo=common_geo, output_proj=common_proj, stats_flag=stats_flag) del output_array del input_path # Tair - Project to Landsat scene after clipping if calc_metric_tair_flag: logging.info(' Hourly air temperature (Tair)') metric_weather_func( image.metric_tair_raster, metric_tair_input_ws, metric_hourly_re, image_prev_dt, image_next_dt, gdal.GRA_NearestNeighbour, rounding_flag=False) # Cleanup del image_prev_dt, image_next_dt # Soil Water Balance if calc_swb_ke_flag: logging.info('Daily soil water balance') # Check if output file already exists logging.debug(' Ke: {}'.format(image.ke_raster)) if os.path.isfile(image.ke_raster): if overwrite_flag: logging.debug(' Overwriting output') python_common.remove_file(image.ke_raster) else: logging.debug(' Skipping, file already ' + 'exists and overwrite is False') return False ke_array = et_common.raster_swb_func( image.acq_datetime, common_osr, env.cellsize, common_extent, awc_input_path, etr_input_ws, etr_input_re, ppt_input_ws, ppt_input_re, spinup_days=spinup_days, min_spinup_days=min_spinup_days) # Apply common area mask ke_array[~common_array] = np.nan # Reduce the file size by rounding to the nearest 2 digits np.around(ke_array, 2, out=ke_array) # Force output to 32-bit float gdc.array_to_raster( ke_array.astype(np.float32), image.ke_raster, output_geo=common_geo, output_proj=common_proj, stats_flag=stats_flag) # Remove Landsat TOA rasters if not keep_dn_flag: for landsat_item in python_common.build_file_list( image.orig_data_ws, image.image_re): os.remove(os.path.join(image.orig_data_ws, landsat_item)) return True
def main(img_ws=os.getcwd(), ancillary_ws=os.getcwd(), output_ws=os.getcwd(), etr_flag=False, eto_flag=False, start_date=None, end_date=None, extent_path=None, output_extent=None, stats_flag=True, overwrite_flag=False, use_cimis_eto_flag=False): """Compute daily ETr/ETo from CIMIS data Args: img_ws (str): root folder of GRIDMET data ancillary_ws (str): folder of ancillary rasters output_ws (str): folder of output rasters etr_flag (bool): if True, compute alfalfa reference ET (ETr) eto_flag (bool): if True, compute grass reference ET (ETo) start_date (str): ISO format date (YYYY-MM-DD) end_date (str): ISO format date (YYYY-MM-DD) extent_path (str): file path defining the output extent output_extent (list): decimal degrees values defining output extent stats_flag (bool): if True, compute raster statistics. Default is True. overwrite_flag (bool): If True, overwrite existing files use_cimis_eto_flag (bool): if True, use CIMIS ETo raster if one of the component rasters is missing and ETo/ETr cannot be computed Returns: None """ logging.info('\nComputing CIMIS ETo/ETr') np.seterr(invalid='ignore') # Use CIMIS ETo raster directly instead of computing from components # Currently this will only be applied if one of the inputs is missing use_cimis_eto_flag = True # Compute ETr and/or ETo if not etr_flag and not eto_flag: logging.info(' ETo/ETr flag(s) not set, defaulting to ETr') etr_flag = True # If a date is not set, process 2017 try: start_dt = dt.datetime.strptime(start_date, '%Y-%m-%d') logging.debug(' Start date: {}'.format(start_dt)) except: start_dt = dt.datetime(2017, 1, 1) logging.info(' Start date: {}'.format(start_dt)) try: end_dt = dt.datetime.strptime(end_date, '%Y-%m-%d') logging.debug(' End date: {}'.format(end_dt)) except: end_dt = dt.datetime(2017, 12, 31) logging.info(' End date: {}'.format(end_dt)) etr_folder = 'etr' eto_folder = 'eto' etr_fmt = 'etr_{}_daily_cimis.img' eto_fmt = 'eto_{}_daily_cimis.img' # DEM for air pressure calculation mask_raster = os.path.join(ancillary_ws, 'cimis_mask.img') dem_raster = os.path.join(ancillary_ws, 'cimis_elev.img') lat_raster = os.path.join(ancillary_ws, 'cimis_lat.img') # lon_raster = os.path.join(ancillary_ws, 'cimis_lon.img') # Interpolate zero windspeed pixels # interpolate_zero_u2_flag = False # Interpolate edge and coastal cells # interpolate_edge_flag = False # Resample type # 0 = GRA_NearestNeighbour, Nearest neighbour (select on one input pixel) # 1 = GRA_Bilinear,Bilinear (2x2 kernel) # 2 = GRA_Cubic, Cubic Convolution Approximation (4x4 kernel) # 3 = GRA_CubicSpline, Cubic B-Spline Approximation (4x4 kernel) # 4 = GRA_Lanczos, Lanczos windowed sinc interpolation (6x6 kernel) # 5 = GRA_Average, Average (computes the average of all non-NODATA contributing pixels) # 6 = GRA_Mode, Mode (selects the value which appears most often of all the sampled points) resample_type = gdal.GRA_CubicSpline # Wind speed is measured at 2m zw = 2 # Output workspaces etr_ws = os.path.join(output_ws, etr_folder) eto_ws = os.path.join(output_ws, eto_folder) if etr_flag and not os.path.isdir(etr_ws): os.makedirs(etr_ws) if eto_flag and not os.path.isdir(eto_ws): os.makedirs(eto_ws) # Check ETr/ETo functions test_flag = False # Check that the daily_refet_func produces the correct values if test_flag: doy_test = 245 elev_test = 1050.0 lat_test = 39.9396 * math.pi / 180 tmin_test = 11.07 tmax_test = 34.69 rs_test = 22.38 u2_test = 1.94 zw_test = 2.5 tdew_test = -3.22 ea_test = et_common.saturation_vapor_pressure_func(tdew_test) pair_test = 101.3 * np.power((285 - 0.0065 * elev_test) / 285, 5.26) q_test = 0.622 * ea_test / (pair_test - (0.378 * ea_test)) etr = float( et_common.daily_refet_func(tmin_test, tmax_test, q_test, rs_test, u2_test, zw_test, elev_test, doy_test, lat_test, 'ETR')) eto = float( et_common.daily_refet_func(tmin_test, tmax_test, q_test, rs_test, u2_test, zw_test, elev_test, doy_test, lat_test, 'ETO')) print('ETr: 8.89', etr) print('ETo: 6.16', eto) sys.exit() # Get CIMIS grid properties from mask cimis_mask_ds = gdal.Open(mask_raster) cimis_osr = gdc.raster_ds_osr(cimis_mask_ds) cimis_proj = gdc.osr_proj(cimis_osr) cimis_cs = gdc.raster_ds_cellsize(cimis_mask_ds, x_only=True) cimis_extent = gdc.raster_ds_extent(cimis_mask_ds) cimis_full_geo = cimis_extent.geo(cimis_cs) cimis_x, cimis_y = cimis_extent.origin() cimis_mask_ds = None logging.debug(' Projection: {}'.format(cimis_proj)) logging.debug(' Cellsize: {}'.format(cimis_cs)) logging.debug(' Geo: {}'.format(cimis_full_geo)) logging.debug(' Extent: {}'.format(cimis_extent)) # Manually set CIMIS grid properties # cimis_extent = gdc.Extent((-400000, -650000, 600000, 454000)) # cimis_cs = 2000 # cimis_geo = gdc.extent_geo(cimis_extent, cellsize) # cimis_epsg = 3310 # NAD_1983_California_Teale_Albers # cimis_x, cimis_y = (0,0) # Subset data to a smaller extent if output_extent is not None: logging.info('\nComputing subset extent & geo') logging.debug(' Extent: {}'.format(output_extent)) cimis_extent = gdc.Extent(output_extent) cimis_extent.adjust_to_snap('EXPAND', cimis_x, cimis_y, cimis_cs) cimis_geo = cimis_extent.geo(cimis_cs) logging.debug(' Geo: {}'.format(cimis_geo)) logging.debug(' Extent: {}'.format(output_extent)) elif extent_path is not None: logging.info('\nComputing subset extent & geo') if extent_path.lower().endswith('.shp'): cimis_extent = gdc.feature_path_extent(extent_path) extent_osr = gdc.feature_path_osr(extent_path) extent_cs = None else: cimis_extent = gdc.raster_path_extent(extent_path) extent_osr = gdc.raster_path_osr(extent_path) extent_cs = gdc.raster_path_cellsize(extent_path, x_only=True) cimis_extent = gdc.project_extent(cimis_extent, extent_osr, cimis_osr, extent_cs) cimis_extent.adjust_to_snap('EXPAND', cimis_x, cimis_y, cimis_cs) cimis_geo = cimis_extent.geo(cimis_cs) logging.debug(' Geo: {}'.format(cimis_geo)) logging.debug(' Extent: {}'.format(cimis_extent)) else: cimis_geo = cimis_full_geo # Latitude lat_array = gdc.raster_to_array(lat_raster, mask_extent=cimis_extent, return_nodata=False) lat_array = lat_array.astype(np.float32) lat_array *= math.pi / 180 # Elevation data elev_array = gdc.raster_to_array(dem_raster, mask_extent=cimis_extent, return_nodata=False) elev_array = elev_array.astype(np.float32) # Process each year in the input workspace logging.info("") for year_str in sorted(os.listdir(img_ws)): logging.debug('{}'.format(year_str)) if not re.match('^\d{4}$', year_str): logging.debug(' Not a 4 digit year folder, skipping') continue year_ws = os.path.join(img_ws, year_str) year_int = int(year_str) # year_days = int(dt.datetime(year_int, 12, 31).strftime('%j')) if start_dt is not None and year_int < start_dt.year: logging.debug(' Before start date, skipping') continue elif end_dt is not None and year_int > end_dt.year: logging.debug(' After end date, skipping') continue logging.info('{}'.format(year_str)) # Output paths etr_raster = os.path.join(etr_ws, etr_fmt.format(year_str)) eto_raster = os.path.join(eto_ws, eto_fmt.format(year_str)) if etr_flag and (overwrite_flag or not os.path.isfile(etr_raster)): logging.debug(' {}'.format(etr_raster)) gdc.build_empty_raster(etr_raster, band_cnt=366, output_dtype=np.float32, output_proj=cimis_proj, output_cs=cimis_cs, output_extent=cimis_extent, output_fill_flag=True) if eto_flag and (overwrite_flag or not os.path.isfile(eto_raster)): logging.debug(' {}'.format(eto_raster)) gdc.build_empty_raster(eto_raster, band_cnt=366, output_dtype=np.float32, output_proj=cimis_proj, output_cs=cimis_cs, output_extent=cimis_extent, output_fill_flag=True) # Process each date in the year for date_str in sorted(os.listdir(year_ws)): logging.debug('{}'.format(date_str)) try: date_dt = dt.datetime.strptime(date_str, '%Y_%m_%d') except ValueError: logging.debug( ' Invalid folder date format (YYYY_MM_DD), skipping') continue if start_dt is not None and date_dt < start_dt: logging.debug(' Before start date, skipping') continue elif end_dt is not None and date_dt > end_dt: logging.debug(' After end date, skipping') continue logging.info(date_str) date_ws = os.path.join(year_ws, date_str) doy = int(date_dt.strftime('%j')) # Set file paths tmax_path = os.path.join(date_ws, 'Tx.img') tmin_path = os.path.join(date_ws, 'Tn.img') tdew_path = os.path.join(date_ws, 'Tdew.img') rso_path = os.path.join(date_ws, 'Rso.img') rs_path = os.path.join(date_ws, 'Rs.img') u2_path = os.path.join(date_ws, 'U2.img') eto_path = os.path.join(date_ws, 'ETo.img') # k_path = os.path.join(date_ws, 'K.img') # rnl_path = os.path.join(date_ws, 'Rnl.img') input_list = [ tmin_path, tmax_path, tdew_path, u2_path, rs_path, rso_path ] # If any input raster is missing, skip the day # Unless ETo is present (and use_cimis_eto_flag is True) day_skip_flag = False for t_path in input_list: if not os.path.isfile(t_path): logging.info(' {} is missing'.format(t_path)) day_skip_flag = True if (day_skip_flag and use_cimis_eto_flag and os.path.isfile(eto_path)): logging.info(' Using CIMIS ETo directly') eto_array = gdc.raster_to_array(eto_path, 1, cimis_extent, return_nodata=False) eto_array = eto_array.astype(np.float32) if not np.any(eto_array): logging.info(' {} is empty or missing'.format(eto_path)) logging.info(' Skipping date') continue # ETr if etr_flag: gdc.array_to_comp_raster(1.2 * eto_array, etr_raster, band=doy, stats_flag=False) # gdc.array_to_raster( # 1.2 * eto_array, etr_raster, # output_geo=cimis_geo, output_proj=cimis_proj, # stats_flag=stats_flag) # ETo if eto_flag: gdc.array_to_comp_raster(eto_array, eto_raster, band=doy, stats_flag=False) # gdc.array_to_raster( # eto_array, eto_raster, # output_geo=cimis_geo, output_proj=cimis_proj, # stats_flag=stats_flag) del eto_array continue elif not day_skip_flag: # Read in rasters # DEADBEEF - Read with extent since some arrays are too big # i.e. 2012-03-21, 2013-03-20, 2014-02-27 tmin_array = gdc.raster_to_array(tmin_path, 1, cimis_extent, return_nodata=False) tmax_array = gdc.raster_to_array(tmax_path, 1, cimis_extent, return_nodata=False) tdew_array = gdc.raster_to_array(tdew_path, 1, cimis_extent, return_nodata=False) rso_array = gdc.raster_to_array(rso_path, 1, cimis_extent, return_nodata=False) rs_array = gdc.raster_to_array(rs_path, 1, cimis_extent, return_nodata=False) u2_array = gdc.raster_to_array(u2_path, 1, cimis_extent, return_nodata=False) # k_array = gdc.raster_to_array( # k_path, 1, cimis_extent, return_nodata=False) # rnl_array = gdc.raster_to_array( # rnl_path, 1, cimis_extent, return_nodata=False) # Check that all input arrays have data for t_name, t_array in [[tmin_path, tmin_array], [tmax_path, tmax_array], [tdew_path, tdew_array], [u2_path, u2_array], [rs_path, rs_array]]: if not np.any(t_array): logging.warning( ' {} is empty or missing'.format(t_name)) day_skip_flag = True if day_skip_flag: logging.warning(' Skipping date') continue # DEADBEEF - Some arrays have a 500m cellsize # i.e. 2011-07-25, 2010-01-01 -> 2010-07-27 tmin_array = rescale_array_func(tmin_array, elev_array, 'tmin') tmax_array = rescale_array_func(tmax_array, elev_array, 'tmax') tdew_array = rescale_array_func(tdew_array, elev_array, 'tdew') rso_array = rescale_array_func(rso_array, elev_array, 'rso') rs_array = rescale_array_func(rs_array, elev_array, 'rs') u2_array = rescale_array_func(u2_array, elev_array, 'u2') # k_array = rescale_array_func(k_array, elev_array, 'k') # rnl_array = rescale_array_func(rnl_array, elev_array, 'rnl') # Back calculate q from tdew by first calculating ea from tdew es_array = et_common.saturation_vapor_pressure_func(tdew_array) pair_array = et_common.air_pressure_func(elev_array) q_array = 0.622 * es_array / (pair_array - (0.378 * es_array)) del es_array, pair_array, tdew_array # Back calculate rhmin/rhmax from tdew # ea_tmax = et_common.saturation_vapor_pressure_func(tmax_array) # ea_tmin = et_common.saturation_vapor_pressure_func(tmin_array) # rhmin = ea_tdew * 2 / (ea_tmax + ea_tmin); # rhmax = ea_tdew * 2 / (ea_tmax + ea_tmin); # del ea_tmax, ea_tmin # ETr if etr_flag: etr_array = et_common.refet_daily_func(tmin_array, tmax_array, q_array, rs_array, u2_array, zw, elev_array, lat_array, doy, ref_type='ETR', rso_type='ARRAY', rso=rso_array) gdc.array_to_comp_raster(etr_array.astype(np.float32), etr_raster, band=doy, stats_flag=False) # gdc.array_to_raster( # etr_array.astype(np.float32), etr_raster, # output_geo=cimis_geo, output_proj=cimis_proj, # stats_flag=stats_flag) del etr_array # ETo if eto_flag: eto_array = et_common.refet_daily_func(tmin_array, tmax_array, q_array, rs_array, u2_array, zw, elev_array, lat_array, doy, ref_type='ETO', rso_type='ARRAY', rso=rso_array) gdc.array_to_comp_raster(eto_array.astype(np.float32), eto_raster, band=doy, stats_flag=False) # gdc.array_to_raster( # eto_array.astype(np.float32), eto_raster, # output_geo=cimis_geo, output_proj=cimis_proj, # stats_flag=stats_flag) del eto_array # Cleanup del tmin_array, tmax_array, u2_array, rs_array, q_array # del rnl, rs, rso else: logging.info(' Skipping date') continue if stats_flag and etr_flag: gdc.raster_statistics(etr_raster) if stats_flag and eto_flag: gdc.raster_statistics(eto_raster) logging.debug('\nScript Complete')