inject study_type in EBI and improvements to current automatic proces…

…sing pipeline (#3023)

* inject study_type in ebi and improvements to current automatic proecssing pipeline

* addressing @ElDeveloper comments

qiita_ware/ebi.py

@@ -356,6 +356,11 @@ def generate_study_xml(self):
         study_title = ET.SubElement(descriptor, 'STUDY_TITLE')
         study_title.text = escape(clean_whitespace(self.study_title))
 
+        # study type is deprecated and not displayed anywhere on EBI-ENA;
+        # however it's required for submission so just injecting with Other
+        ET.SubElement(
+            descriptor, 'STUDY_TYPE', {'existing_study_type': 'Other'})
+
         study_abstract = ET.SubElement(descriptor, 'STUDY_ABSTRACT')
         study_abstract.text = clean_whitespace(escape(self.study_abstract))
 

qiita_ware/test/test_ebi.py

@@ -1314,6 +1314,7 @@ def test_parse_EBI_reply(self):
       <STUDY_TITLE>
         Identification of the Microbiomes for Cannabis Soils
       </STUDY_TITLE>
+      <STUDY_TYPE existing_study_type="Other" />
       <STUDY_ABSTRACT>
         This is a preliminary study to examine the microbiota associated with \
 the Cannabis plant. Soils samples from the bulk soil, soil associated with \

scripts/qiita-auto-processing

@@ -33,6 +33,7 @@ user = User('[email protected]')
 #   'version': the version of the plugin,
 #   'cmd_name': the command we want to run,
 #   'input_name': the name of the input parameter of that command
+#   'ignore_parameters': list of parameters to ignore, for example: threads
 #   'parent_artifact_name': name of the parent output, input for this command
 #   'parameters_names': list of the names of the parameter sets we want to run
 # }
@@ -41,21 +42,24 @@ full_pipelines = [
      'data_type': ['Metagenomic'],
      'artifact_type': 'per_sample_FASTQ',
      'previous-step': None,
+     'requirements': dict(),
      'steps': [
          {'previous-step': None,
           'plugin': 'qp-shogun',
           'version': '012020',
           'cmd_name': 'Atropos v1.1.24',
           'input_name': 'input',
+          'ignore_parameters': ['Number of threads used'],
           'parent_artifact_name': None,
           'parameters_names': ['KAPA HyperPlus with iTru']},
          {'previous-step': 'Atropos v1.1.24',
           'plugin': 'qp-shogun',
-          'version': '012020',
-          'cmd_name': 'Shogun v1.0.7',
+          'version': '072020',
+          'cmd_name': 'Shogun v1.0.8',
           'input_name': 'input',
+          'ignore_parameters': ['Number of threads'],
           'parent_artifact_name': 'Adapter trimmed files',
-          'parameters_names': ['wol_bowtie2', 'rep94_bowtie2']}
+          'parameters_names': ['wol_bowtie2', 'rep200_bowtie2']}
       ]},
     {'name': 'Target Gene Processing',
      'data_type': ['16S', '18S', 'ITS'],
@@ -73,6 +77,7 @@ full_pipelines = [
          'version': '1.9.1',
          'cmd_name': 'Trimming',
          'input_name': 'input_data',
+         'ignore_parameters': [],
          'parent_artifact_name': None,
          'parameters_names': ['90 base pairs',
                               '100 base pairs',
@@ -83,13 +88,15 @@ full_pipelines = [
          'version': '1.9.1',
          'cmd_name': 'Pick closed-reference OTUs',
          'input_name': 'input_data',
+         'ignore_parameters': [],
          'parent_artifact_name': 'Trimmed Demultiplexed',
          'parameters_names': ['Defaults - parallel']},
         {'previous-step': 'Trimming',
          'plugin': 'deblur',
          'version': '1.1.0',
          'cmd_name': 'Deblur',
          'input_name': 'Demultiplexed sequences',
+         'ignore_parameters': [],
          'parent_artifact_name': 'Trimmed Demultiplexed',
          'parameters_names': ['Defaults']}
         ]},
@@ -122,6 +129,22 @@ def _check_requirements(requirements, template):
     return satisfied
 
 
+def _check_parameters(jobs, cmd):
+    params = [{k: str(v) for k, v in j.parameters.values.items()
+              if k not in cmd['ignore_parameters']} for j in jobs]
+    return params
+
+
+def _submit_workflows(artifact_process):
+    for artifact in artifact_process:
+        if artifact['workflow'] is None:
+            continue
+        # nodes will return in position [0] the first job created
+        first_job = list(artifact['workflow'].graph.nodes())[0]
+        if first_job.status == 'in_construction':
+            artifact['workflow'].submit()
+
+
 # Step 1. Loop over the full_pipelines to process each step
 for pipeline in full_pipelines:
     # Step 2. From the steps generate the list of commands to add to the
@@ -149,6 +172,7 @@ for pipeline in full_pipelines:
             'previous-step': step['previous-step'],
             'parent_artifact_name': step['parent_artifact_name'],
             'input_name': step['input_name'],
+            'ignore_parameters': step['ignore_parameters'],
             'parameters': parameters})
 
     # Step 2. - for children. Get their commands. We currently only support
@@ -161,7 +185,9 @@ for pipeline in full_pipelines:
                      if c['previous-step'] == commands[0]['command-name']]
 
     # Step 3. Find all preparations/artifacts that we can add the pipeline
-    artifacts_all = [a for study in Study.iter()
+    #         ... as a first pass we will only process study 10317 (AGP) ...
+    # artifacts_all = [a for study in Study.iter()
+    artifacts_all = [a for study in [Study(10317)]
                      # loop over all artifacts of artifact_type with in study
                      for a in study.artifacts(
                          artifact_type=pipeline['artifact_type'])
@@ -172,7 +198,10 @@ for pipeline in full_pipelines:
     artifacts_compliant = []
     for a in artifacts_all:
         st = a.study.sample_template
-        pt = a.prep_templates[0]
+        pts = a.prep_templates
+        if not pts:
+            continue
+        pt = pts[0]
 
         # {'sandbox', 'awaiting_approval', 'private', 'public'}
         if a.visibility in ('sandbox', 'awaiting_approval'):
@@ -194,23 +223,29 @@ for pipeline in full_pipelines:
     #          of Step 4 but for debugging it makes sense to separate
     artifact_process = []
     children_compliant = []
+    cmd = commands[0]
     for a in artifacts_compliant:
-        cmd = commands[0]
         # getting all jobs, includen hiddens, in case the job failed
         jobs = a.jobs(cmd=cmd['command'], show_hidden=True)
-        params = [j.parameters.values for j in jobs]
+        params = _check_parameters(jobs, cmd)
 
         # checking that all required parameters of this command exist
         missing_parameters = []
         for p in cmd['parameters']:
             p = p['values']
             p.update({cmd['input_name']: str(a.id)})
-            if p not in params:
+            p_to_compare = p.copy()
+            for k in cmd['ignore_parameters']:
+                del p_to_compare[k]
+            if p_to_compare not in params:
                 missing_parameters.append(p)
             else:
                 for c in a.children:
-                    if c.processing_parameters.values == p:
-                        children_compliant.append(c)
+                    cpp = c.processing_parameters
+                    if cpp.command.name == cmd['command-name']:
+                        cparams = _check_parameters([cpp], cmd)
+                        if cparams == p_to_compare:
+                            children_compliant.append(c)
         if missing_parameters:
             # note that we are building a dict for each artifact so we can
             # save the workflow id, useful for when we run this in a terminal
@@ -224,14 +259,18 @@ for pipeline in full_pipelines:
         for cmd_id, cmd in enumerate(children_cmds):
             # getting all jobs, includen hiddens, in case the job failed
             jobs = a.jobs(cmd=cmd['command'], show_hidden=True)
-            params = [j.parameters.values for j in jobs]
+            params = _check_parameters(jobs, cmd)
 
             # checking that all required parameters of this command exist
             missing_parameters = []
             for p in cmd['parameters']:
                 p = p['values']
-                p.update({cmd['input_name']: str(c.id)})
-                if p not in params:
+                p.update({cmd['input_name']: str(a.id)})
+                p_to_compare = p.copy()
+                for k in cmd['ignore_parameters']:
+                    del p_to_compare[k]
+
+                if p_to_compare not in params:
                     missing_parameters.append(p)
             if missing_parameters:
                 artifact_process.append(
@@ -266,9 +305,9 @@ for pipeline in full_pipelines:
         # now we can add the rest of the parameters to the workflow for
         # the first command
         for params in artifact['missing_parameters'][1:]:
-            params.update({cmd['input_name']: str(a.id)})
             job_params = Parameters.load(cmd['command'], values_dict=params)
-            artifact['workflow'].add(job_params)
+            artifact['workflow'].add(
+                job_params, req_params={cmd['input_name']: str(a.id)})
 
         for cmd in commands[cmd_id + 1:]:
             # get jobs from the workflow to which we can add this new command
@@ -286,10 +325,4 @@ for pipeline in full_pipelines:
                         cmd['parent_artifact_name']: cmd['input_name']}})
 
     # Step 7. submit the workflows!
-    for artifact in artifact_process:
-        if artifact['workflow'] is None:
-            continue
-        # nodes will return in position [0] the first job created
-        first_job = list(artifact['workflow'].graph.nodes())[0]
-        if first_job.status == 'in_construction':
-            artifact['workflow'].submit()
+    _submit_workflows(artifact_process)