diff --git a/dhp-workflows/dhp-dedup-openaire/src/test/java/eu/dnetlib/dhp/oa/dedup/SparkDedupTest.java b/dhp-workflows/dhp-dedup-openaire/src/test/java/eu/dnetlib/dhp/oa/dedup/SparkDedupTest.java index d0098e7e2..bcc5ddeaa 100644 --- a/dhp-workflows/dhp-dedup-openaire/src/test/java/eu/dnetlib/dhp/oa/dedup/SparkDedupTest.java +++ b/dhp-workflows/dhp-dedup-openaire/src/test/java/eu/dnetlib/dhp/oa/dedup/SparkDedupTest.java @@ -81,6 +81,10 @@ public class SparkDedupTest implements Serializable { lenient().when(isLookUpService.getResourceProfileByQuery(Mockito.contains("publication"))) .thenReturn(IOUtils.toString(SparkDedupTest.class.getResourceAsStream("/eu/dnetlib/dhp/dedup/conf/pub.curr.conf.json"))); + + lenient().when(isLookUpService.getResourceProfileByQuery(Mockito.contains("software"))) + .thenReturn(IOUtils.toString(SparkDedupTest.class.getResourceAsStream("/eu/dnetlib/dhp/dedup/conf/sw.curr.conf.json"))); + } @Test @@ -101,9 +105,11 @@ public class SparkDedupTest implements Serializable { long orgs_simrel = spark.read().load(testOutputBasePath + "/" + testActionSetId + "/organization_simrel").count(); long pubs_simrel = spark.read().load(testOutputBasePath + "/" + testActionSetId + "/publication_simrel").count(); + long sw_simrel = spark.read().load(testOutputBasePath + "/" + testActionSetId + "/software_simrel").count(); assertEquals(3288, orgs_simrel); assertEquals(7260, pubs_simrel); + assertEquals(344, sw_simrel); } @Test @@ -124,10 +130,11 @@ public class SparkDedupTest implements Serializable { long orgs_mergerel = spark.read().load(testOutputBasePath + "/" + testActionSetId + "/organization_mergerel").count(); long pubs_mergerel = spark.read().load(testOutputBasePath + "/" + testActionSetId + "/publication_mergerel").count(); + long sw_mergerel = spark.read().load(testOutputBasePath + "/" + testActionSetId + "/software_mergerel").count(); assertEquals(1244, orgs_mergerel); assertEquals(1460, pubs_mergerel); - + assertEquals(288, sw_mergerel); } @Test @@ -148,9 +155,11 @@ public class SparkDedupTest implements Serializable { long orgs_deduprecord = jsc.textFile(testOutputBasePath + "/" + testActionSetId + "/organization_deduprecord").count(); long pubs_deduprecord = jsc.textFile(testOutputBasePath + "/" + testActionSetId + "/publication_deduprecord").count(); + long sw_deduprecord = jsc.textFile(testOutputBasePath + "/" + testActionSetId + "/software_deduprecord").count(); assertEquals(82, orgs_deduprecord); assertEquals(66, pubs_deduprecord); + assertEquals(51, sw_deduprecord); } @Test @@ -171,6 +180,9 @@ public class SparkDedupTest implements Serializable { long organizations = jsc.textFile(testDedupGraphBasePath + "/organization").count(); long publications = jsc.textFile(testDedupGraphBasePath + "/publication").count(); + long projects = jsc.textFile(testDedupGraphBasePath + "/project").count(); + long datasource = jsc.textFile(testDedupGraphBasePath + "/datasource").count(); + long softwares = jsc.textFile(testDedupGraphBasePath + "/software").count(); long mergedOrgs = spark .read().load(testOutputBasePath + "/" + testActionSetId + "/organization_mergerel").as(Encoders.bean(Relation.class)) @@ -188,6 +200,9 @@ public class SparkDedupTest implements Serializable { assertEquals(897, publications); assertEquals(835, organizations); + assertEquals(100, projects); + assertEquals(100, datasource); + assertEquals(200, softwares); long deletedOrgs = jsc.textFile(testDedupGraphBasePath + "/organization") .filter(this::isDeletedByInference).count(); diff --git a/dhp-workflows/dhp-dedup-openaire/src/test/resources/eu/dnetlib/dhp/dedup/conf/ds.curr.conf.json b/dhp-workflows/dhp-dedup-openaire/src/test/resources/eu/dnetlib/dhp/dedup/conf/ds.curr.conf.json new file mode 100644 index 000000000..532900506 --- /dev/null +++ b/dhp-workflows/dhp-dedup-openaire/src/test/resources/eu/dnetlib/dhp/dedup/conf/ds.curr.conf.json @@ -0,0 +1,118 @@ +{ + "wf" : { + "threshold" : "0.99", + "dedupRun" : "001", + "entityType" : "result", + "subEntityType" : "resulttype", + "subEntityValue" : "dataset", + "orderField" : "title", + "queueMaxSize" : "2000", + "groupMaxSize" : "100", + "maxChildren" : "100", + "slidingWindowSize" : "200", + "rootBuilder" : ["result", "resultProject_outcome_isProducedBy", "resultResult_publicationDataset_isRelatedTo", "resultResult_similarity_isAmongTopNSimilarDocuments", "resultResult_similarity_hasAmongTopNSimilarDocuments", "resultOrganization_affiliation_hasAuthorInstitution", "resultResult_part_hasPart", "resultResult_part_isPartOf", "resultResult_supplement_isSupplementTo", "resultResult_supplement_isSupplementedBy", "resultResult_version_isVersionOf" ], + "includeChildren" : "true" + }, + "pace" : { + "clustering" : [ + { "name" : "ngrampairs", "fields" : [ "title" ], "params" : { "max" : "1", "ngramLen" : "3"} }, + { "name" : "suffixprefix", "fields" : [ "title" ], "params" : { "max" : "1", "len" : "3" } }, + { "name" : "lowercase", "fields" : [ "doi" ], "params" : { } } + ], + "decisionTree" : { + "start" : { + "fields": [ + { + "field": "pid", + "comparator": "jsonListMatch", + "weight": 1.0, + "countIfUndefined": "false", + "params": { + "jpath_value": "$.value", + "jpath_classid": "$.qualifier.classid" + } + } + ], + "threshold": 0.5, + "aggregation": "AVG", + "positive": "MATCH", + "negative": "layer2", + "undefined": "layer2", + "ignoreUndefined": "true" + }, + "layer2" : { + "fields": [ + { + "field": "title", + "comparator": "titleVersionMatch", + "weight": 1.0, + "countIfUndefined": "false", + "params": {} + }, + { + "field": "authors", + "comparator": "sizeMatch", + "weight": 1.0, + "countIfUndefined": "false", + "params": {} + } + ], + "threshold": 1.0, + "aggregation": "AND", + "positive": "layer3", + "negative": "NO_MATCH", + "undefined": "layer3", + "ignoreUndefined": "false" + }, + "layer3" : { + "fields": [ + { + "field": "title", + "comparator": "levensteinTitle", + "weight": 1.0, + "countIfUndefined": "true" + } + ], + "threshold": 0.99, + "aggregation": "AVG", + "positive": "MATCH", + "negative": "NO_MATCH", + "undefined": "NO_MATCH", + "ignoreUndefined": "true" + } + }, + "model" : [ + { + "name" : "doi", + "type" : "String", + "path" : "$.pid[@.qualifier.classid = 'doi'].value" + }, + { + "name" : "pid", + "type" : "JSON", + "path" : "$.pid", + "overrideMatch" : "true" + }, + { + "name" : "title", + "type" : "String", + "path" : "$.title[@.qualifier.classid = 'main title'].value", + "length" : 250, + "size" : 5 + }, + { + "name" : "authors", + "type" : "String", + "path" : "$.author[*].fullname", + "size" : 200 + }, + { + "name" : "resulttype", + "type" : "String", + "path" : "$.resulttype.classid" + } + ], + "blacklists" : {}, + "synonyms" : {} + } +} \ No newline at end of file diff --git a/dhp-workflows/dhp-dedup-openaire/src/test/resources/eu/dnetlib/dhp/dedup/conf/orp.curr.conf.json b/dhp-workflows/dhp-dedup-openaire/src/test/resources/eu/dnetlib/dhp/dedup/conf/orp.curr.conf.json new file mode 100644 index 000000000..76e656743 --- /dev/null +++ b/dhp-workflows/dhp-dedup-openaire/src/test/resources/eu/dnetlib/dhp/dedup/conf/orp.curr.conf.json @@ -0,0 +1,118 @@ +{ + "wf" : { + "threshold" : "0.99", + "dedupRun" : "001", + "entityType" : "result", + "subEntityType" : "resulttype", + "subEntityValue" : "otherresearchproduct", + "orderField" : "title", + "queueMaxSize" : "2000", + "groupMaxSize" : "100", + "maxChildren" : "100", + "slidingWindowSize" : "200", + "rootBuilder" : [ "result", "resultProject_outcome_isProducedBy", "resultResult_publicationDataset_isRelatedTo", "resultResult_similarity_isAmongTopNSimilarDocuments", "resultResult_similarity_hasAmongTopNSimilarDocuments", "resultOrganization_affiliation_hasAuthorInstitution", "resultResult_part_hasPart", "resultResult_part_isPartOf", "resultResult_supplement_isSupplementTo", "resultResult_supplement_isSupplementedBy", "resultResult_version_isVersionOf" ], + "includeChildren" : "true" + }, + "pace" : { + "clustering" : [ + { "name" : "ngrampairs", "fields" : [ "title" ], "params" : { "max" : "1", "ngramLen" : "3"} }, + { "name" : "suffixprefix", "fields" : [ "title" ], "params" : { "max" : "1", "len" : "3" } }, + { "name" : "lowercase", "fields" : [ "doi" ], "params" : { } } + ], + "decisionTree" : { + "start" : { + "fields": [ + { + "field": "pid", + "comparator": "jsonListMatch", + "weight": 1.0, + "countIfUndefined": "false", + "params": { + "jpath_value": "$.value", + "jpath_classid": "$.qualifier.classid" + } + } + ], + "threshold": 0.5, + "aggregation": "AVG", + "positive": "MATCH", + "negative": "layer2", + "undefined": "layer2", + "ignoreUndefined": "true" + }, + "layer2" : { + "fields": [ + { + "field": "title", + "comparator": "titleVersionMatch", + "weight": 1.0, + "countIfUndefined": "false", + "params": {} + }, + { + "field": "authors", + "comparator": "sizeMatch", + "weight": 1.0, + "countIfUndefined": "false", + "params": {} + } + ], + "threshold": 1.0, + "aggregation": "AND", + "positive": "layer3", + "negative": "NO_MATCH", + "undefined": "layer3", + "ignoreUndefined": "false" + }, + "layer3" : { + "fields": [ + { + "field": "title", + "comparator": "levensteinTitle", + "weight": 1.0, + "countIfUndefined": "true" + } + ], + "threshold": 0.99, + "aggregation": "AVG", + "positive": "MATCH", + "negative": "NO_MATCH", + "undefined": "NO_MATCH", + "ignoreUndefined": "true" + } + }, + "model" : [ + { + "name" : "doi", + "type" : "String", + "path" : "$.pid[@.qualifier.classid = 'doi'}].value" + }, + { + "name" : "pid", + "type" : "JSON", + "path" : "$.pid", + "overrideMatch" : "true" + }, + { + "name" : "title", + "type" : "String", + "path" : "$.title[@.qualifier.classid = 'main title'].value", + "length" : 250, + "size" : 5 + }, + { + "name" : "authors", + "type" : "String", + "path" : "$.author[*].fullname", + "size" : 200 + }, + { + "name" : "resulttype", + "type" : "String", + "path" : "$.resulttype.classid" + } + ], + "blacklists" : {}, + "synonyms" : {} + } +} \ No newline at end of file diff --git a/dhp-workflows/dhp-dedup-openaire/src/test/resources/eu/dnetlib/dhp/dedup/conf/sw.curr.conf.json b/dhp-workflows/dhp-dedup-openaire/src/test/resources/eu/dnetlib/dhp/dedup/conf/sw.curr.conf.json new file mode 100644 index 000000000..815dd7a57 --- /dev/null +++ b/dhp-workflows/dhp-dedup-openaire/src/test/resources/eu/dnetlib/dhp/dedup/conf/sw.curr.conf.json @@ -0,0 +1,92 @@ + { + "wf" : { + "threshold" : "0.99", + "dedupRun" : "001", + "entityType" : "result", + "subEntityType" : "resulttype", + "subEntityValue" : "software", + "orderField" : "title", + "queueMaxSize" : "2000", + "groupMaxSize" : "100", + "maxChildren" : "100", + "slidingWindowSize" : "200", + "rootBuilder" : [ "result", "resultProject_outcome_isProducedBy", "resultResult_publicationDataset_isRelatedTo", "resultResult_similarity_isAmongTopNSimilarDocuments", "resultResult_similarity_hasAmongTopNSimilarDocuments", "resultOrganization_affiliation_hasAuthorInstitution", "resultResult_part_hasPart", "resultResult_part_isPartOf", "resultResult_supplement_isSupplementTo", "resultResult_supplement_isSupplementedBy", "resultResult_version_isVersionOf" ], + "includeChildren" : "true" + }, + "pace" : { + "clustering" : [ + { "name" : "ngrampairs", "fields" : [ "title" ], "params" : { "max" : "1", "ngramLen" : "3"} }, + { "name" : "suffixprefix", "fields" : [ "title" ], "params" : { "max" : "1", "len" : "3" } }, + { "name" : "lowercase", "fields" : [ "doi", "url" ], "params" : { } } + ], + "decisionTree": { + "start": { + "fields": [ + { + "field": "doi", + "comparator": "exactMatch", + "weight": 1, + "countIfUndefined": "false", + "params": {} + }, + { + "field": "url", + "comparator": "exactMatch", + "weight": 1, + "countIfUndefined": "false", + "params": {} + } + ], + "threshold": 1, + "aggregation": "OR", + "positive": "MATCH", + "negative": "layer2", + "undefined": "layer2", + "ignoreUndefined": "false" + }, + "layer2": { + "fields": [ + { + "field": "title", + "comparator": "levensteinTitleIgnoreVersion", + "weight": 1, + "countIfUndefined": "false", + "params": {} + } + ], + "threshold": 0.99, + "aggregation": "AVG", + "positive": "MATCH", + "negative": "NO_MATCH", + "undefined": "NO_MATCH", + "ignoreUndefined": "false" + } + }, + "model" : [ + { + "name" : "doi", + "type" : "String", + "path" : "$.pid[?(@.qualifier.classid == 'doi')].value" + }, + { + "name" : "title", + "type" : "String", + "path" : "$.title[?(@.qualifier.classid == 'main title')].value", + "length" : 250, + "size" : 5 + }, + { + "name" : "url", + "type" : "String", + "path" : "$.instance.url" + }, + { + "name" : "resulttype", + "type" : "String", + "path" : "$.resulttype.classid" + } + ], + "blacklists" : {}, + "synonyms": {} + } + } \ No newline at end of file diff --git a/dhp-workflows/dhp-dedup-openaire/src/test/resources/eu/dnetlib/dhp/dedup/entities/datasource b/dhp-workflows/dhp-dedup-openaire/src/test/resources/eu/dnetlib/dhp/dedup/entities/datasource index e69de29bb..f54cbf071 100644 --- a/dhp-workflows/dhp-dedup-openaire/src/test/resources/eu/dnetlib/dhp/dedup/entities/datasource +++ b/dhp-workflows/dhp-dedup-openaire/src/test/resources/eu/dnetlib/dhp/dedup/entities/datasource @@ -0,0 +1,100 @@ +{"dataInfo": {"deletedbyinference": false, "provenanceaction": {"classid": "sysimport:crosswalk:entityregistry', "classname": "sysimport:crosswalk:entityregistry', "schemename": "dnet:provenance_actions', "schemeid": "dnet:provenance_actions'}, "inferred": false, "inferenceprovenance": "", "invisible": false, "trust": "0.9'}, "datauploadtype": {"dataInfo": {"deletedbyinference": false, "provenanceaction": {"classid": "", "classname": "", "schemename": "", "schemeid": ""}, "inferred": false, "inferenceprovenance": "", "invisible": false, "trust": ""}, "value": ""}, "accessinfopackage": [{"dataInfo": {"deletedbyinference": false, "provenanceaction": {"classid": "", "classname": "", "schemename": "", "schemeid": ""}, "inferred": false, "inferenceprovenance": "", "invisible": false, "trust": ""}, "value": "https://dspace-cris.4science.cloud/oai/openairecris'}], "pid": [], "websiteurl": {"dataInfo": {"deletedbyinference": false, "provenanceaction": {"classid": "", "classname": "", "schemename": "", "schemeid": ""}, "inferred": false, "inferenceprovenance": "", "invisible": false, "trust": ""}, "value": "https://www.4science.it'}, "dateofvalidation": {"dataInfo": {"deletedbyinference": false, "provenanceaction": {"classid": "", "classname": "", "schemename": "", "schemeid": ""}, "inferred": false, "inferenceprovenance": "", "invisible": false, "trust": ""}, "value": ""}, "logourl": {"dataInfo": {"deletedbyinference": false, "provenanceaction": {"classid": "", "classname": "", "schemename": "", "schemeid": ""}, "inferred": false, "inferenceprovenance": "", "invisible": false, "trust": ""}, "value": ""}, "collectedfrom": [{"dataInfo": {"deletedbyinference": false, "provenanceaction": {"classid": "", "classname": "", 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{"deletedbyinference": false, "provenanceaction": {"classid": "", "classname": "", "schemename": "", "schemeid": ""}, "inferred": false, "inferenceprovenance": "", "invisible": false, "trust": ""}, "value": false}, "namespaceprefix": {"dataInfo": {"deletedbyinference": false, "provenanceaction": {"classid": "", "classname": "", "schemename": "", "schemeid": ""}, "inferred": false, "inferenceprovenance": "", "invisible": false, "trust": ""}, "value": "4ScienceCRIS'}, "contactemail": {"dataInfo": {"deletedbyinference": false, "provenanceaction": {"classid": "", "classname": "", "schemename": "", "schemeid": ""}, "inferred": false, "inferenceprovenance": "", "invisible": false, "trust": ""}, "value": ""}, "subjects": [], "databaseaccessrestriction": {"dataInfo": {"deletedbyinference": false, "provenanceaction": {"classid": "", "classname": "", "schemename": "", "schemeid": ""}, "inferred": false, "inferenceprovenance": "", "invisible": false, "trust": ""}, "value": ""}, "latitude": 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Release to create DOI citation for Leasi et al. Biodiversity estimates and ecological interpretations of meiofaunal communities are biased by the taxonomic approach .

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We use Ideal Adsorbed Solution Theory (IAST) to predict the loading of the gas mixture on the bundle, based only on the knowledge of the pure adsorption isotherms of the individual components.

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Simulation of long polymer chains under shear flow
\\n--------------------------------------------------
\\n************* by Airidas Korolkovas *************

\\n\\n


\\n--- Overview ---
\\nThis software numerically solves the equation of motion for a network of long (entangled) polymer chains.

\\n\\n

The physics and math were originally described in this publication: (attached here)
\\nAiridas Korolkovas, Philipp Gutfreund, and Jean-Louis Barrat. "Simulation of entangled polymer solutions." The Journal of Chemical Physics 145.12 (2016): 124113.

\\n\\n

The extension for shear flow is described in this article: (also attached)
\\nAiridas Korolkovas, Philipp Gutfreund, and Maximilian Wolff. "Dynamical structure of entangled polymers simulated under shear flow." (in preparation)

\\n\\n


\\n--- System requirements ---
\\nThe code runs in Matlab, and was tested for the Matlab 2016a version. There is both a CPU and a GPU option (about 10-50x faster).
\\nTo run the GPU code effectively, a CUDA-enabled Nvidia card is required. Quadro M5000 model was tested to give acceptable performance.

\\n\\n

 

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This is a beta release of StaG-mwc v0.3.0.

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Several known issues exist, check or report issues in the issue tracker on Github. \\nKnown issues using the available rules for downloading reference databases.

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"Simulation of long polymer chains under shear flow
\\n--------------------------------------------------
\\n************* by Airidas Korolkovas *************\\n\\n
\\n--- Overview ---
\\nThis software numerically solves the equation of motion for a network of long (entangled) polymer chains.\\n\\nThe physics and math were originally described in this publication: (attached here)
\\nAiridas Korolkovas, Philipp Gutfreund, and Jean-Louis Barrat. \"Simulation of entangled polymer solutions.\" The Journal of Chemical Physics 145.12 (2016): 124113.\\n\\nThe extension for shear flow is described in this article: (also attached)
\\nAiridas Korolkovas, Philipp Gutfreund, and Maximilian Wolff. \"Dynamical structure of entangled polymers simulated under shear flow.\" (in preparation)\\n\\n
\\n--- System requirements ---
\\nThe code runs in Matlab, and was tested for the Matlab 2016a version. There is both a CPU and a GPU option (about 10-50x faster).
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Simulation of long polymer chains under shear flow
\\n--------------------------------------------------
\\n************* by Airidas Korolkovas *************

\\n\\n


\\n--- Overview ---
\\nThis software numerically solves the equation of motion for a network of long (entangled) polymer chains.

\\n\\n

The physics and math were originally described in this publication: (attached here)
\\nAiridas Korolkovas, Philipp Gutfreund, and Jean-Louis Barrat. \"Simulation of entangled polymer solutions.\" The Journal of Chemical Physics 145.12 (2016): 124113.

\\n\\n

The extension for shear flow is described in this article: (also attached)
\\nAiridas Korolkovas, Philipp Gutfreund, and Maximilian Wolff. \"Dynamical structure of entangled polymers simulated under shear flow.\" (in preparation)

\\n\\n


\\n--- System requirements ---
\\nThe code runs in Matlab, and was tested for the Matlab 2016a version. There is both a CPU and a GPU option (about 10-50x faster).
\\nTo run the GPU code effectively, a CUDA-enabled Nvidia card is required. Quadro M5000 model was tested to give acceptable performance.

\\n\\n

\\xa0

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2016.2 release of the CppTransport platform for computation of correlation functions generated during an inflationary epoch in the early universe.

\\n\\n

This is a bug-fix release that addresses a specific performance issue when integrating correlation functions with k_t exponentially different to k*. This would cause a significant slowdown and could require unnecessarily tight numerical tolerances.

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This is a python software that looks into the reduction of crime by the Los Angeles Police Department (L.A.P.D). The crimes are those that were reported to the various police divisions from the years 2010 to 2017. The software shows the most occurring crimes, the weapons used in perpetuating the crimes and the victims of the crimes. Also, a map showing the locations with most crimes and the nature of crimes that occurs most is included in the software. Bar charts were used to display various victims of crimes as well as the weapons used. Again, a pie chart was used to show the total number of crimes recorded in the various years. Further more, line graphs portraying the trend of crimes from the various years and a scatter plot displaying the various age ranges of the victims of crimes.

\\n\\n

Last but not least, a heat map of the locations of all the crimes from the years 2010 to 2017, with places that recorded the most crimes having deeper colorization of the map.

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This is a python software that looks into the reduction of crime by the Los Angeles Police Department (L.A.P.D). The crimes are those that were reported to the various police divisions from the years 2010 to 2017. The\\xa0software shows the most occurring crimes, the weapons used in perpetuating the crimes and the victims of the crimes. Also, a map showing the locations\\xa0with most crimes and the nature of crimes that occurs most is included in the\\xa0software. Bar charts\\xa0were\\xa0used to display various victims of crimes as well as the weapons used. Again, a pie chart was used to show the total number of crimes recorded in the various years. Further more, line graphs portraying the trend of crimes from the various years and a scatter plot displaying the various age ranges of the victims of\\xa0crimes.

\\n\\n

Last but not least, a heat map of the locations of all the crimes from the years 2010 to 2017, with places that recorded\\xa0the most crimes having deeper colorization of the map.

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This software is an implementation of the general error model described in the work entitled \"General error model for analysis of laser-induced incandescence
signals\" by T. A. Sipkens and coworkers, which simulates signals according to the described error model (sufficient for generating Figure 1 in the associated work) and demonstrates a fitting procedure to infer the error model parameters from the produced signals.

This software package was developed for use with MATLAB 2016a running on Windows. Users can execute the main script immediately, provided all attached files are located in the same directory and that directory is included in the MATLAB path. One must then enter \"main_simulate_C\" in the MATLAB command line.
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Added updated citations and DOI (for PSA paper in PLOS Computational Biology):

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Seyler SL, Kumar A, Thorpe MF, Beckstein O (2015) Path Similarity Analysis: a Method for Quantifying Macromolecular Pathways. PLoS Comput Biol. doi: 10.1371/journal.pcbi.1004568 (also: arXiv:1505.04807 [q-bio.QM])

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A wrapper and CLI to evaluate a TMVA Machine Learning classifier with as little boilerplate code as possible.

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