diff --git a/dhp-workflows/dhp-enrichment/src/main/java/eu/dnetlib/dhp/bulktag/SparkEoscTag.java b/dhp-workflows/dhp-enrichment/src/main/java/eu/dnetlib/dhp/bulktag/SparkEoscTag.java index e8c79e11d..b9de5dd11 100644 --- a/dhp-workflows/dhp-enrichment/src/main/java/eu/dnetlib/dhp/bulktag/SparkEoscTag.java +++ b/dhp-workflows/dhp-enrichment/src/main/java/eu/dnetlib/dhp/bulktag/SparkEoscTag.java @@ -95,13 +95,14 @@ public class SparkEoscTag { if (containsCriteriaNotebook(s)) { sbject.add(EOSC_NOTEBOOK); - if (sbject.stream().anyMatch(sb -> sb.getValue().equals("EOSC Jupyter Notebook"))){ + if (sbject.stream().anyMatch(sb -> sb.getValue().equals("EOSC Jupyter Notebook"))) { sbject = sbject.stream().map(sb -> { - if (sb.getValue().equals("EOSC Jupyter Notebook")){ + if (sb.getValue().equals("EOSC Jupyter Notebook")) { return null; } return sb; }).filter(Objects::nonNull).collect(Collectors.toList()); + s.setSubject(sbject); } } if (containsCriteriaGalaxy(s)) { diff --git a/dhp-workflows/dhp-enrichment/src/test/java/eu/dnetlib/dhp/bulktag/EOSCTagJobTest.java b/dhp-workflows/dhp-enrichment/src/test/java/eu/dnetlib/dhp/bulktag/EOSCTagJobTest.java index 55d3939e1..1ea254157 100644 --- a/dhp-workflows/dhp-enrichment/src/test/java/eu/dnetlib/dhp/bulktag/EOSCTagJobTest.java +++ b/dhp-workflows/dhp-enrichment/src/test/java/eu/dnetlib/dhp/bulktag/EOSCTagJobTest.java @@ -132,7 +132,7 @@ public class EOSCTagJobTest { Assertions .assertEquals( - 2, tmp + 1, tmp .filter(sw -> sw.getId().equals("50|od______1582::4132f5ec9496f0d6adc7b00a50a56ff4")) .collect() .get(0) @@ -326,7 +326,7 @@ public class EOSCTagJobTest { Assertions .assertEquals( - 2, + 1, tmp .filter( s -> s.getSubject().stream().anyMatch(sbj -> sbj.getValue().equals("EOSC::Galaxy Workflow"))) @@ -352,21 +352,12 @@ public class EOSCTagJobTest { Assertions .assertEquals( - 6, tmp + 5, tmp .filter(sw -> sw.getId().equals("50|od______1582::501b25d420f808c8eddcd9b16e917f11")) .collect() .get(0) .getSubject() .size()); - Assertions - .assertTrue( - tmp - .filter(sw -> sw.getId().equals("50|od______1582::501b25d420f808c8eddcd9b16e917f11")) - .collect() - .get(0) - .getSubject() - .stream() - .anyMatch(s -> s.getValue().equals("EOSC::Galaxy Workflow"))); Assertions .assertEquals( @@ -394,7 +385,7 @@ public class EOSCTagJobTest { Assertions .assertEquals( - 2, + 1, orp .filter( s -> s.getSubject().stream().anyMatch(sbj -> sbj.getValue().equals("EOSC::Galaxy Workflow"))) @@ -438,14 +429,14 @@ public class EOSCTagJobTest { Assertions .assertEquals( - 3, orp + 2, orp .filter(sw -> sw.getId().equals("50|od______2017::1e400f1747487fd15998735c41a55c72")) .collect() .get(0) .getSubject() .size()); Assertions - .assertTrue( + .assertFalse( orp .filter(sw -> sw.getId().equals("50|od______2017::1e400f1747487fd15998735c41a55c72")) .collect() diff --git a/dhp-workflows/dhp-enrichment/src/test/resources/eu/dnetlib/dhp/eosctag/jupyter/software/software_10.json b/dhp-workflows/dhp-enrichment/src/test/resources/eu/dnetlib/dhp/eosctag/jupyter/software/software_10.json index 2439dc1b6..2acc856a4 100644 --- a/dhp-workflows/dhp-enrichment/src/test/resources/eu/dnetlib/dhp/eosctag/jupyter/software/software_10.json +++ b/dhp-workflows/dhp-enrichment/src/test/resources/eu/dnetlib/dhp/eosctag/jupyter/software/software_10.json @@ -1,4 +1,4 @@ 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expected heterozygosity, or by R, the allelic richness). This contribution is splitted in two components: one due to the diversity of the population, the other due to its differentiation from the remaining populations. The program may also be used to obtain allelic richness after rarefaction (to a sample size chosen by the investigator) for a set of populations. It can be used in conjunction with the program haplodiv, based on the paper by Pons & Petit 1995, TAG 90, 462-470, which will provide standard errors for the diversity and differentiation parameters. The input file is a text file (see example: rartest.txt), where the first line indicates the number of haplotypes (here it is 18), the number of populations (here it is 4), and the rarefaction size (it should not be larger than the smallest population sample size; here the rarefaction size is 10, and the smallest sample size is 20). Then follows the data for each population (line), with the number of each haplotype in each population (don't use relative frequencies): 18 4 10 1 0 1 0 0 0 1 1 ...(18 columns) 0 1 2 1 1 0 13 0 ... 0 0 8 0 0 3 6 0 ... 1 0 9 0 0 3 7 1 Results can be seen in the output file (rartest.out here; to be printed horizontally). General measures are given first: within population diversity (Hs), total diversity (Ht), and Gst are given, followed by similar measures based on allelic richness. Then you get the results for each population : H, its standard error, allelic richness after rarefaction, the divergence from the other populations (DHs, DHt, DGst, see the paper in Conservation Biology), and the contributions Ct, Cs, Cd followed by the contributions for allelic richness measures. The program is written for an haploid gene but may be used for nuclear genes, assuming Hardy-Weinberg equilibrium. How to proceed when there are several loci? Do not take the mean across Gst or across Contributions. They are ratios, so you should take the mean of the numerator and the mean of the denominator separately. For the denominator: take the mean of hT and Rt-1 across loci. For the numerator: multiply the contributions by hT or Rt-1 (respectively for contributions to diversity or to allelic richness) and take the mean of these products across loci. 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into account the distances between haplotypes (ie, that consider all haplotypes equally divergent).[br/] The source file should be an ASCII file (its name should have 8 characters maximum: 12345678.txt) and should include the following information:[br/] First line :[br/] Number of cytotypes Number of populations Number of characters distinguishing the variants (for instance number of polymorphic fragments, or of polymorphic nucleotide sites). The program asks for the number of permutations to be made.[br/] see the example (\\ExamplePermut\\input.txt and \\ExamplePermut\\output.out).[br/] Then follows the number of individuals having a given cytotype (column) in a given population (row). Finally, and without interruption, provide the table of character states for all haplotypes, where each line corresponds to one haplotype, and each column to a character. No column should be empty (no missing haplotype) and each population (row) should be composed of AT LEAST 3 individuals![br/] The output file provides permutated values of Nst in a single row, and the value of the last 5% and last 1%. The mean of the permutated values is also given and should be close to the Gst value (by construction). To test if the observed Nst value is larger than the Gst, we count how many permutated values are larger than the observed Nst. If you have 5% of the permutated values greater than the observed value of Nst, then your test is not significant, otherwise it is and you know the P-value. This is akin to testing if Gst = Nst.[br/] [br/] README CpSSR :[br/] It computes measures of diversity and differenciation from haploid population genetic data, when the difference in number of repeats between alleles is available, and tests whether the differentiation and diversity measures differ from the equivalent measures when the distances between haplotypes is not considered (ie, when all haplotypes are considered equally divergent). The source file should be an ASCII file (its name should have 8 characters maximum: 12345678.txt) and should include the following information:[br/] First line :[br/] Number of cytotypes Number of populations Number of cpSSR loci. The program asks for the number of permutations to be made. See the example (\\ExampleCpSSR\\input.txt and \\ExamplePermut\\CpSSR.out).[br/] Then follows the number of individuals having a given haplotype (column) in a given population (row). Finally, and without interruption, provide the table of length variant states for all haplotypes, where each line corresponds to one haplotype, and each column to a character. No column should be empty (no missing haplotype) and each population (row) should be composed of AT LEAST 3 individuals![br/] The output file provides permutated values of Rst in a single row, and the value of the last 5% and last 1%. The mean of the permutated values is also given and should be close to the Gst value (by construction). To test if the observed Rst value is larger than the Gst, you count how many permutated values are larger than the observed Rst. If you have 5% of the permutated values greater than the observed value of Rst, then your test is not significant, otherwise it is and you know the P-value. This is akin to testing if Gst = Rst. I usually go for a one-sided test (i.e. I test if Rst>Gst, and not 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