From b7ef48caf5bd0bbaa4ff77ef887f8ff71b5e3d1f Mon Sep 17 00:00:00 2001
From: aho <an.ho@bsc.es>
Date: Wed, 29 Jan 2020 12:56:57 +0100
Subject: [PATCH] Remove 'stats' dim in output

---
 R/Eno.R                   | 15 ++++++++-------
 man/Eno.Rd                | 10 +++++-----
 tests/testthat/test-Eno.R |  7 ++-----
 3 files changed, 15 insertions(+), 17 deletions(-)

diff --git a/R/Eno.R b/R/Eno.R
index aac2197c..6c787743 100644
--- a/R/Eno.R
+++ b/R/Eno.R
@@ -1,8 +1,8 @@
 #'Compute effective sample size with classical method
 #'
 #'Compute the number of effective samples along one dimension of an array. This
-#'effective number of independent observations can be used in statistical/inference 
-#'tests.\cr
+#'effective number of independent observations can be used in
+#'statistical/inference tests.\cr
 #'The calculation is based on eno function from Caio Coelho from rclim.txt.
 #'
 #'@param data A numeric array with named dimensions.
@@ -14,9 +14,9 @@
 #'@param ncores An integer indicating the number of cores to use for parallel 
 #'  computation. The default value is NULL.
 #'
-#'@return An array with the first dimension 'stats', which is the number of 
-#'  effective samples, and the following dimensions are same as parameter 'data'
-#'  except the time_dim dimension, which is removed after the computation.
+#'@return An array with the same dimension as parameter 'data' except the 
+#'  time_dim dimension, which is removed after the computation. The array  
+#'  indicates the number of effective sample along time_dim.
 #'
 #'@keywords datagen
 #'@author History:\cr
@@ -82,7 +82,7 @@ Eno <- function(data, time_dim = 'sdate', na.action = na.pass, ncores = NULL) {
 
   eno <- Apply(data = list(data),
                target_dims = time_dim,
-               output_dims = 'stats',
+               output_dims = NULL, 
                fun = .Eno,
                na.action = na.action,
                ncores = ncores)$output1
@@ -103,5 +103,6 @@ Eno <- function(data, time_dim = 'sdate', na.action = na.pass, ncores = NULL) {
   } else {
     eno <- NA
   }
-  return(as.array(eno))
+
+  return(eno)
 }
diff --git a/man/Eno.Rd b/man/Eno.Rd
index 02506785..53f28131 100644
--- a/man/Eno.Rd
+++ b/man/Eno.Rd
@@ -20,14 +20,14 @@ The default value is na.pass.}
 computation. The default value is NULL.}
 }
 \value{
-An array with the first dimension 'stats', which is the number of 
- effective samples, and the following dimensions are same as parameter 'data'
- except the time_dim dimension, which is removed after the computation.
+An array with the same dimension as parameter 'data' except the 
+ time_dim dimension, which is removed after the computation. The array  
+ indicates the number of effective sample along time_dim.
 }
 \description{
 Compute the number of effective samples along one dimension of an array. This
-effective number of independent observations can be used in statistical/inference 
-tests.\cr
+effective number of independent observations can be used in
+statistical/inference tests.\cr
 The calculation is based on eno function from Caio Coelho from rclim.txt.
 }
 \examples{
diff --git a/tests/testthat/test-Eno.R b/tests/testthat/test-Eno.R
index 5c11dee9..1b85092c 100644
--- a/tests/testthat/test-Eno.R
+++ b/tests/testthat/test-Eno.R
@@ -58,19 +58,16 @@ test_that("2. Output checks: dat1", {
 
   expect_equal(
     dim(res),
-    dim(array(dim = c(stats = 1, dataset = 1, member = 2, ftime = 4, lat = 10, lon = 10)))
+    dim(array(dim = c(dataset = 1, member = 2, ftime = 4, lat = 10, lon = 10)))
   )
-
   expect_equal(
     length(res[which(is.na(res))]),
     1
   )
-
   expect_equal(
     length(res[which(res != 4)]),
     37
   )
-
   expect_equal(
     mean(res, na.rm = T),
     2.768103,
@@ -85,7 +82,7 @@ test_that("3. Output checks: dat2", {
 
   expect_equal(
     Eno(dat2, time_dim = 'date'),
-    array(c(6.237689, 5.683186), dim = c(stats = 1, ftime = 2)),
+    array(c(6.237689, 5.683186), dim = c(ftime = 2)),
     tolerance = 0.0001
   )
 
-- 
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