Merge pull request #879 from e-sensing/dev

Pre-release 1.2.0

Author: rolfsimoes

.drone.yml

@@ -4,7 +4,7 @@ name: default
 steps:
 - name: test
   pull: always
-  image: oldlipe/sits-dev:0.16.3
+  image: oldlipe/sits-dev:1.1.0
   commands:
   - R -e 'pacman::p_load(desc::desc_get_deps()[desc::desc_get_deps()[["type"]] != "Depends",][["package"]], install = T, update = F, character.only = T)'
   - R -e 'v<-gsub("(>) |(>=) ", "", desc::desc_get_deps()[desc::desc_get_deps()[["type"]] != "Depends" & desc::desc_get_deps()[["version"]] != "*", ][["version"]]); pacman::p_install_version(package = desc::desc_get_deps()[desc::desc_get_deps()[["type"]] != "Depends" & desc::desc_get_deps()[["version"]] != "*", ][["package"]], version = v)'

.gitignore

@@ -24,10 +24,11 @@ Meta
 *.bkp
 Makevars
 Makevars.*
+.sits/
 *.gcda
 *.gcno
-^_snaps
+_snaps/
 tags
 R/tags
 *.swp
-
+RtmpJFrMnd.log

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: sits
 Type: Package
-Version: 1.1.0-8
+Version: 1.2.0
 Title: Satellite Image Time Series Analysis for Earth Observation Data Cubes
 Authors@R: c(person('Rolf', 'Simoes', role = c('aut'), email = '[email protected]'),
              person('Gilberto', 'Camara', role = c('aut', 'cre'), email = '[email protected]'),
@@ -26,7 +26,7 @@ Description: An end-to-end toolkit for land use and land cover classification
     random forests, extreme gradient boosting, multi-layer perceptrons,
     temporal convolutional neural networks <doi:10.3390/rs11050523>, 
     residual networks <arxiv:1809.04356>, and temporal attention encoders
-    <arXiv:2007.00586>.
+    <arxiv:2007.00586>.
     Performs efficient classification of big Earth observation data cubes and includes 
     functions for post-classification smoothing based on Bayesian inference, and 
     methods for uncertainty assessment. Enables best
@@ -48,7 +48,6 @@ Imports:
     dplyr (>= 1.0.0),
     gdalUtilities,
     grDevices,
-    ggplot2,
     graphics,
     lubridate,
     parallel (>= 4.0.5),
@@ -67,13 +66,13 @@ Suggests:
     caret,
     dendextend,
     dtwclust,
-    dtwSat (>= 0.2.7),
     DiagrammeR,
     digest,
     e1071,
     FNN,
     gdalcubes (>= 0.6.0),
     geojsonsf,
+    ggplot2,
     httr,
     jsonlite,
     kohonen(>= 3.0.11),
@@ -85,10 +84,12 @@ Suggests:
     openxlsx,
     randomForest,
     randomForestExplainer,
+    RColorBrewer,
     RcppArmadillo (>= 0.11),
     scales,
     stars (>= 0.5),
     testthat (>= 3.1.3),
+    tmap,
     torchopt(>= 0.1.2),
     xgboost,
     zoo
@@ -109,40 +110,51 @@ Collate:
     'sits_active_learning.R'
     'sits_bands.R'
     'sits_bbox.R'
+    'sits_block.R'
+    'sits_chunks.R'
     'sits_classification.R'
     'sits_classify_ts.R'
     'sits_classify_cube.R'
+    'sits_colors.R'
+    'sits_combine_predictions.R'
     'sits_compare.R'
     'sits_config.R'
     'sits_csv.R'
     'sits_cube.R'
     'sits_cube_aux_functions.R'
+    'sits_cube_copy.R'
     'sits_check.R'
     'sits_cluster.R'
     'sits_debug.R'
     'sits_distances.R'
     'sits_dt_reference.R'
     'sits_factory.R'
+    'sits_file.R'
     'sits_file_info.R'
     'sits_filters.R'
     'sits_gdalcubes.R'
     'sits_geo_dist.R'
     'sits_get_data.R'
     'sits_imputation.R'
+    'sits_internals.R'
+    'sits_jobs.R'
     'sits_labels.R'
     'sits_label_classification.R'
     'sits_lighttae.R'
     'sits_machine_learning.R'
     'sits_merge.R'
     'sits_mixture_model.R'
     'sits_mlp.R'
+    'sits_mosaic.R'
+    'sits_model_export.R'
     'sits_parallel.R'
     'sits_patterns.R'
+    'sits_period.R'
     'sits_plot.R'
+    'sits_reclassify.R'
     'sits_raster_api.R'
-    'sits_raster_api_terra.R'
-    'sits_raster_blocks.R'
     'sits_raster_data.R'
+    'sits_raster_api_terra.R'
     'sits_raster_sub_image.R'
     'sits_regularize.R'
     'sits_resnet.R'
@@ -152,7 +164,6 @@ Collate:
     'sits_sf.R'
     'sits_shp.R'
     'sits_smooth.R'
-    'sits_smooth_aux_functions.R'
     'sits_som.R'
     'sits_source_api.R'
     'sits_source_api_aws.R'
@@ -172,10 +183,10 @@ Collate:
     'sits_torch_spatial_encoder.R'
     'sits_torch_temporal_attention_encoder.R'
     'sits_tibble.R'
+    'sits_tile_api.R'
     'sits_timeline.R'
     'sits_train.R'
     'sits_tuning.R'
-    'sits_twdtw.R'
     'sits_utils.R'
     'sits_uncertainty.R'
     'sits_validate.R'