Standardize Cromwell timestamps to follow analysis JSON schema (#117)

* Standardize cromwell datetime format

Because the format of Cromwell timestamps
are inconsistent, re-format them to follow
the JSON date-time format as defined in the
analysis process schema.

* Add tests for formatting timestamps

* Update docker images for testing

* Update requirements

* Update pipeline-tools version

adapter_pipelines/Optimus/adapter.wdl

@@ -126,7 +126,7 @@ workflow AdapterOptimus {
   Int max_cromwell_retries = 0
   Boolean add_md5s = false
 
-  String pipeline_tools_version = "v0.46.0"
+  String pipeline_tools_version = "v0.46.1"
 
   call GetInputs as prep {
     input:

adapter_pipelines/cellranger/adapter.wdl

@@ -150,7 +150,7 @@ workflow Adapter10xCount {
   Int max_cromwell_retries = 0
   Boolean add_md5s = false
 
-  String pipeline_tools_version = "v0.46.0"
+  String pipeline_tools_version = "v0.46.1"
 
   call GetInputs {
     input:

adapter_pipelines/ss2_single_sample/adapter.wdl

@@ -82,7 +82,7 @@ workflow AdapterSmartSeq2SingleCell{
   Int max_cromwell_retries = 0
   Boolean add_md5s = false
 
-  String pipeline_tools_version = "v0.46.0"
+  String pipeline_tools_version = "v0.46.1"
 
   call GetInputs as prep {
     input:

pipeline_tools/create_analysis_metadata.py

@@ -8,6 +8,7 @@
 from google.cloud import storage
 from typing import List
 import re
+import arrow
 
 
 def create_analysis_process(raw_schema_url,
@@ -60,8 +61,8 @@ def create_analysis_process(raw_schema_url,
         'schema_type': SCHEMA_TYPE,
         'process_core': get_analysis_process_core(analysis_workflow_id=analysis_id),
         'process_type': get_analysis_process_type(),
-        'timestamp_start_utc': workflow_metadata.get('start'),
-        'timestamp_stop_utc': workflow_metadata.get('end'),
+        'timestamp_start_utc': format_timestamp(workflow_metadata.get('start')),
+        'timestamp_stop_utc': format_timestamp(workflow_metadata.get('end')),
         'input_bundles': input_bundles_string.split(','),
         'reference_bundle': reference_bundle,
         'tasks': workflow_tasks,
@@ -352,8 +353,8 @@ def get_workflow_tasks(workflow_metadata):
                 'disk_size': runtime['disks'],
                 'docker_image': runtime['docker'],
                 'zone': runtime['zones'],
-                'start_time': task['start'],
-                'stop_time': task['end'],
+                'start_time': format_timestamp(task['start']),
+                'stop_time': format_timestamp(task['end']),
                 'log_out': task['stdout'],
                 'log_err': task['stderr']
             }
@@ -362,6 +363,21 @@ def get_workflow_tasks(workflow_metadata):
     return sorted_output_tasks
 
 
+def format_timestamp(timestamp):
+    """ Standardize Cromwell timestamps to follow the date-time JSON format required by the analysis process schema.
+
+    Args:
+        timestamp (str): A datetime string in any format
+    Returns:
+        formatted_timestamp (str): A datetime string in the format 'YYYY-MM-DDTHH:mm:ss.SSSZ'
+
+    """
+    if timestamp:
+        d = arrow.get(timestamp)
+        formatted_date = d.format('YYYY-MM-DDTHH:mm:ss.SSS')
+        return '{}Z'.format(formatted_date)
+
+
 def get_file_format(path, extension_to_format):
     """Returns the file type of the file at the given path.
 

pipeline_tools/tests/test_create_analysis_metadata.py

@@ -253,3 +253,21 @@ def verify_tasks(self, tasks):
         assert first_task['cpus'] == 1
         assert first_task['disk_size'] == 'local-disk 10 HDD'
         assert first_task['docker_image'] == 'humancellatlas/picard:2.10.10'
+
+    def test_format_timestamp_without_seconds(self):
+        timestamp = '2019-02-11T01:15Z'
+        formatted_datetime = cam.format_timestamp(timestamp)
+        expected_datetime = '2019-02-11T01:15:00.000Z'
+        assert formatted_datetime == expected_datetime
+
+    def test_format_timestamp_without_milliseconds(self):
+        timestamp = '2019-02-11T01:15:00Z'
+        formatted_timestamp = cam.format_timestamp(timestamp)
+        expected_timestamp = '2019-02-11T01:15:00.000Z'
+        assert formatted_timestamp == expected_timestamp
+
+    def test_formatting_correct_timestamp(self):
+        timestamp = '2019-02-11T01:15:00.000Z'
+        formatted_timestamp = cam.format_timestamp(timestamp)
+        expected_timestamp = '2019-02-11T01:15:00.000Z'
+        assert formatted_timestamp == expected_timestamp

requirements.txt

@@ -1,6 +1,7 @@
-requests==2.20.0
+arrow>=0.12.1
+requests>=2.20.0,<3
 google-auth>=1.6.1,<2
-google-cloud-storage==1.8.0
+google-cloud-storage>=1.10.0,<2
 tenacity==4.10.0
 PyJWT==1.6.4
 git+git://github.com/HumanCellAtlas/metadata-api@release/1.0b4#egg=hca-metadata-api[dss]

setup.py

@@ -15,16 +15,16 @@
       license='BSD 3-clause "New" or "Revised" License',
       packages=['pipeline_tools'],
       install_requires=[
+          'arrow>=0.12.1',
           'google-auth>=1.6.1,<2',
-          'google-cloud-storage>=1.8.0,<2',
+          'google-cloud-storage>=1.10.0,<2',
           'hca>=4.5.0,<5',
           'hca-metadata-api',
           'mock>=2.0.0,<3',
           'requests>=2.20.0,<3',
           'requests-mock>=1.5.2,<2',
           'setuptools_scm>=2.0.0,<3',
           'tenacity>=4.10.0,<5',
-          'google-cloud-storage>=1.10.0,<2',
           'PyJWT==1.6.4'
       ],
       entry_points={

test-requirements.txt

@@ -1,4 +1,5 @@
-requests-mock==1.5.2
+backports.tempfile==1.0
+mock>=2.0.0,<3
 pytest==3.6.3
+requests-mock>=1.5.2,<2
 tenacity==4.10.0
-backports.tempfile==1.0