Scale jobtime

Resolves #2 (closed)

src/fsl_pipe/job.py

@@ -338,6 +338,16 @@ class JobList:
                 raise ValueError("One or more of the jobs have failed.")
             logger.info("Successfully finished running all jobs using Dask.")
 
+    def scale_jobtime(self, scaling):
+        """
+        Scale the submit job times of all jobs by `scaling`.
+        
+        This will only affect jobs submitted to the cluster.
+        """
+        for job in self.jobs:
+            job.scale_jobtime(scaling)
+        return self
+
 
 class JobParent:
     """Parent for `SingleJob` and `BatchJob`.
@@ -520,7 +530,7 @@ class SingleJob(JobParent):
         self.input_targets = input_targets
         self.output_targets = output_targets
         self.optional_targets = set(optionals)
-        self.submit_params = submit_params
+        self.submit_params = dict(submit_params)
         self.batch = batch
         for target in self.input_targets:
             target.required_by.add(self)
@@ -573,6 +583,15 @@ class SingleJob(JobParent):
         result = JobList(tree, [self], {}).copy()
         return self.batch, self.submit_params, result
 
+    def scale_jobtime(self, scaling):
+        """
+        Scale the submit job time in place by `scaling`.
+        
+        This will only affect jobs submitted to the cluster.
+        """
+        if "jobtime" in self.submit_params.keys():
+            self.submit_params["jobtime"] *= scaling
+
 
 def get_target(filename: Path, all_targets, from_template=None) -> "FileTarget":
     """
@@ -780,6 +799,15 @@ class BatchJob(JobParent):
         as_tuples = set.intersection(*[{(key, value) for key, value in job.set_parameters.items()} for job in self.torun])
         self.set_parameters = {key: value for key, value in as_tuples}
 
+    def copy(self, targets: Dict[str, "FileTarget"]):
+        """
+        Create a copy of the `BatchJob` to be included in a new :class:`JobList`.
+
+        `targets` contain the set of FileTargets recognised by this new `JobList`.
+        This will be updated based on the input/output targets of each job within this batch.
+        """
+        return BatchJob(*[job.copy(targets) for job in self.torun])
+
     @property
     def batch(self, ):
         b = self.torun[0].batch
@@ -797,7 +825,7 @@ class BatchJob(JobParent):
     @property
     def submit_params(self, ):
         def sum_value(name, *times):
-            total = sum(int(t) for t in times if t is not None)
+            total = sum(t for t in times if t is not None)
             return None if total == 0 else total
 
         def max_value(name, *rams):
@@ -852,6 +880,15 @@ class BatchJob(JobParent):
         value = re.sub(r'[^\w\s-]', '', name).strip().lower()
         return base + re.sub(r'[-\s]+', '-', value)
 
+    def scale_jobtime(self, scaling):
+        """
+        Scale the submit job time in place by `scaling`.
+        
+        This will only affect jobs submitted to the cluster.
+        """
+        for job in self.torun:
+            job.scale_jobtime(scaling)
+
     def __repr__(self, ):
         """Print job as a function call."""
         return f"Batch({self.torun})"