diff --git a/run_workflow_phaeoexplorer.py b/run_workflow_phaeoexplorer.py
index 78856471f248463241e8d5e809527513709cdd37..30ad7e5dc3753da8ca26db2d1a822e0c56a565fb 100755
--- a/run_workflow_phaeoexplorer.py
+++ b/run_workflow_phaeoexplorer.py
@@ -575,11 +575,13 @@ class RunWorkflow(speciesData.SpeciesData):
genome_hda_id, gff_hda_id, transcripts_hda_id, proteins_hda_id, blast_diamond_hda_id, interproscan_hda_id = None, None, None, None, None, None
# Finding datasets in history (matches datasets name)
+ # TODO: match version as well
+ # TODO: match whoole dataset name (not just prefix and affix)
for dataset in history_datasets_li:
dataset_name = dataset["name"]
if dataset_shortname in dataset_name:
dataset_id = dataset["id"]
- if dataset_name.endswith(".fasta"):
+ if dataset_name.endswith("%s.fasta" % self.genome_version):
genome_hda_id = dataset_id
if dataset_name.endswith(".gff"):
gff_hda_id = dataset_id
@@ -961,7 +963,7 @@ if __name__ == "__main__":
logging.info("Input organism %s: 1 species detected in input dictionary" % k)
# Set workflow path (1 organism)
- workflow_path = os.path.join(os.path.abspath(script_dir), "workflows/Galaxy-Workflow-chado_load_tripal_synchronize_jbrowse_1org_v2.ga")
+ workflow_path = os.path.join(os.path.abspath(script_dir), "workflows_phaeoexplorer/Galaxy-Workflow-chado_load_tripal_synchronize_jbrowse_1org_v2.ga")
# Set the galaxy instance variables
for k2, v2 in v.items():
@@ -986,7 +988,7 @@ if __name__ == "__main__":
logging.info("Input organism %s: 2 species detected in input dictionary" % k)
# Set workflow path (2 organisms)
- workflow_path = os.path.join(os.path.abspath(script_dir), "workflows/Galaxy-Workflow-chado_load_tripal_synchronize_jbrowse_2org_v3.ga")
+ workflow_path = os.path.join(os.path.abspath(script_dir), "workflows_phaeoexplorer/Galaxy-Workflow-chado_load_tripal_synchronize_jbrowse_2org_v3.ga")
# Instance object required variables
instance_url, email, password = None, None, None
@@ -1160,48 +1162,51 @@ if __name__ == "__main__":
datamap[GFF_FILE_ORG2] = {"src": "hda", "id": org2_gff_hda_id}
datamap[PROTEINS_FASTA_FILE_ORG2] = {"src": "hda", "id": org2_proteins_hda_id}
- # with open(workflow_path, 'r') as ga_in_file:
-
- # # Store the decoded json dictionary
- # workflow_dict = json.load(ga_in_file)
- # workflow_name = workflow_dict["name"]
-
- # # For the Jbrowse tool, we unfortunately have to manually edit the parameters instead of setting them
- # # as runtime values, using runtime parameters makes the tool throw an internal critical error ("replace not found" error)
- # # Scratchgmod test: need "http" (or "https"), the hostname (+ port)
- # if "menu_url" not in config.keys():
- # jbrowse_menu_url_org1 = "https://{hostname}/sp/{genus_sp}/feature/{Genus}/{species}/mRNA/{id}".format(hostname=config["hostname"], genus_sp=org1_genus_species, Genus=org1_genus[0].upper() + org1_genus[1:], species=org1_species, id="\{id\}")
- # jbrowse_menu_url_org2 = "https://{hostname}/sp/{genus_sp}/feature/{Genus}/{species}/mRNA/{id}".format(hostname=config["hostname"], genus_sp=org2_genus_species, Genus=org2_genus[0].upper() + org2_genus[1:], species=org2_species, id="\{id\}")
- # else:
- # jbrowse_menu_url_org1 = config["menu_url"]
- # jbrowse_menu_url_org2 = jbrowse_menu_url_org1
-
- # # Replace values in the workflow dictionary
- # workflow_dict["steps"]["7"]["tool_state"] = workflow_dict["steps"]["7"]["tool_state"].replace("__MENU_URL_ORG1__", jbrowse_menu_url_org1)
- # workflow_dict["steps"]["8"]["tool_state"] = workflow_dict["steps"]["8"]["tool_state"].replace("__MENU_URL_ORG2__", jbrowse_menu_url_org2)
- # # The UNIQUE_ID is specific to a combination genus_species_strain_sex so every combination should have its unique workflow
- # # in galaxy --> define a naming method for these workflows
- # workflow_dict["steps"]["10"]["tool_state"] = workflow_dict["steps"]["10"]["tool_state"].replace("__FULL_NAME_ORG1__", org1_full_name).replace("__UNIQUE_ID_ORG1__", org1_species_folder_name)
- # workflow_dict["steps"]["10"]["tool_state"] = workflow_dict["steps"]["10"]["tool_state"].replace("__FULL_NAME_ORG2__", org2_full_name).replace("__UNIQUE_ID_ORG2__", org2_species_folder_name)
-
- # # Import the workflow in galaxy as a dict
- # instance.workflows.import_workflow_dict(workflow_dict=workflow_dict)
-
- # # Get its attributes
- # workflow_attributes = instance.workflows.get_workflows(name=workflow_name)
- # # Then get its ID (required to invoke the workflow)
- # workflow_id = workflow_attributes[0]["id"] # Index 0 is the most recently imported workflow (the one we want)
- # show_workflow = instance.workflows.show_workflow(workflow_id=workflow_id)
- # # Check if the workflow is found
- # try:
- # logging.debug("Workflow ID: %s" % workflow_id)
- # except bioblend.ConnectionError:
- # logging.warning("Error finding workflow %s" % workflow_name)
-
- # # Finally, invoke the workflow alogn with its datamap, parameters and the history in which to invoke it
- # instance.workflows.invoke_workflow(workflow_id=workflow_id, history_id=history_id, params=workflow_parameters, inputs=datamap, allow_tool_state_corrections=True)
-
- # logging.info("Successfully imported and invoked workflow {0}, check the galaxy instance ({1}) for the jobs state".format(workflow_name, instance_url))
+ with open(workflow_path, 'r') as ga_in_file:
+
+ # Store the decoded json dictionary
+ workflow_dict = json.load(ga_in_file)
+ workflow_name = workflow_dict["name"]
+
+ # For the Jbrowse tool, we unfortunately have to manually edit the parameters instead of setting them
+ # as runtime values, using runtime parameters makes the tool throw an internal critical error ("replace not found" error)
+ # Scratchgmod test: need "http" (or "https"), the hostname (+ port)
+ jbrowse_menu_url_org1 = "https://{hostname}/sp/{genus_sp}/feature/{Genus}/{species}/mRNA/{id}".format(hostname=config["hostname"], genus_sp=org1_genus_species, Genus=org1_genus[0].upper() + org1_genus[1:], species=org1_species, id="\{id\}")
+ jbrowse_menu_url_org2 = "https://{hostname}/sp/{genus_sp}/feature/{Genus}/{species}/mRNA/{id}".format(hostname=config["hostname"], genus_sp=org2_genus_species, Genus=org2_genus[0].upper() + org2_genus[1:], species=org2_species, id="\{id\}")
+ if "menu_url" not in config.keys():
+ jbrowse_menu_url_org1 = "https://{hostname}/sp/{genus_sp}/feature/{Genus}/{species}/mRNA/{id}".format(hostname=config["hostname"], genus_sp=org1_genus_species, Genus=org1_genus[0].upper() + org1_genus[1:], species=org1_species, id="\{id\}")
+ jbrowse_menu_url_org2 = "https://{hostname}/sp/{genus_sp}/feature/{Genus}/{species}/mRNA/{id}".format(hostname=config["hostname"], genus_sp=org2_genus_species, Genus=org2_genus[0].upper() + org2_genus[1:], species=org2_species, id="\{id\}")
+ else:
+ jbrowse_menu_url_org1 = config["menu_url"]
+ jbrowse_menu_url_org2 = "https://{hostname}/sp/{genus_sp}/feature/{Genus}/{species}/mRNA/{id}".format(hostname=config["hostname"], genus_sp=org2_genus_species, Genus=org2_genus[0].upper() + org2_genus[1:], species=org2_species, id="\{id\}")
+
+
+ # Replace values in the workflow dictionary
+ workflow_dict["steps"]["7"]["tool_state"] = workflow_dict["steps"]["7"]["tool_state"].replace("__MENU_URL_ORG1__", jbrowse_menu_url_org1)
+ workflow_dict["steps"]["8"]["tool_state"] = workflow_dict["steps"]["8"]["tool_state"].replace("__MENU_URL_ORG2__", jbrowse_menu_url_org2)
+ # The UNIQUE_ID is specific to a combination genus_species_strain_sex so every combination should have its unique workflow
+ # in galaxy --> define a naming method for these workflows
+ workflow_dict["steps"]["10"]["tool_state"] = workflow_dict["steps"]["10"]["tool_state"].replace("__FULL_NAME_ORG1__", org1_full_name).replace("__UNIQUE_ID_ORG1__", org1_species_folder_name)
+ workflow_dict["steps"]["10"]["tool_state"] = workflow_dict["steps"]["10"]["tool_state"].replace("__FULL_NAME_ORG2__", org2_full_name).replace("__UNIQUE_ID_ORG2__", org2_species_folder_name)
+
+ # Import the workflow in galaxy as a dict
+ instance.workflows.import_workflow_dict(workflow_dict=workflow_dict)
+
+ # Get its attributes
+ workflow_attributes = instance.workflows.get_workflows(name=workflow_name)
+ # Then get its ID (required to invoke the workflow)
+ workflow_id = workflow_attributes[0]["id"] # Index 0 is the most recently imported workflow (the one we want)
+ show_workflow = instance.workflows.show_workflow(workflow_id=workflow_id)
+ # Check if the workflow is found
+ try:
+ logging.debug("Workflow ID: %s" % workflow_id)
+ except bioblend.ConnectionError:
+ logging.warning("Error finding workflow %s" % workflow_name)
+
+ # Finally, invoke the workflow alogn with its datamap, parameters and the history in which to invoke it
+ instance.workflows.invoke_workflow(workflow_id=workflow_id, history_id=history_id, params=workflow_parameters, inputs=datamap, allow_tool_state_corrections=True)
+
+ logging.info("Successfully imported and invoked workflow {0}, check the galaxy instance ({1}) for the jobs state".format(workflow_name, instance_url))