Add support for batch priorities

* Add support for batch priorities

* Include priorities in documentation

* Update README.md

Author: ardcore

README.md

@@ -18,7 +18,6 @@ m1wrapper = MoleculeOneWrapper(api_token, 'https://app.molecule.one')
   server, but you will need to provide custom value if you're using a dedicated solution.
 
 ### Running batch scoring request:
-
 ```py
 search = m1wrapper.run_batch_search(
     targets=['cc', 'O=C(Nc1cc(Nc2nc(-c3cnccc3)ccn2)c(cc1)C)c3ccc(cc3)CN3CCN(CC3)C'],
@@ -28,7 +27,28 @@ search = m1wrapper.run_batch_search(
 - *targets*: list of target compounds in SMILES format
 - *parameters* (optional): additional configuration for your batch
   scoring request. See [Batch Scoring API](https://github.com/molecule-one/api/blob/master/batch-scoring.md) for more information.
+- *priority* (optional): priority of the batch request.
 - *starting_materials* (optional): list of available compounds in SMILES format
+
+### Batch scoring priorities:
+Priorities are defined as integers in a range of 1 to 10. Requests with higher priority will be processed before those with lower priority.
+For convenience, we also define a `Priority` enum with the following variants:
+- `Priority.LOWEST` (1)
+- `Priority.LOW` (3)
+- `Priority.NORMAL` (5, default)
+- `Priority.HIGH` (8)
+- `Priority.HIGHEST` (10)
+
+#### Example:
+```py
+from m1wrapper import MoleculeOneWrapper, Priority
+m1wrapper = MoleculeOneWrapper(api_token, 'https://app.molecule.one')
+search = m1wrapper.run_batch_search(
+    targets=['cc', 'O=C(Nc1cc(Nc2nc(-c3cnccc3)ccn2)c(cc1)C)c3ccc(cc3)CN3CCN(CC3)C'],
+    parameters={'exploratory_search': False, 'detail_level': 'score'},
+    priority=Priority.HIGH)
+```
+
 ### Getting exisiting scoring request by id:
 ```py
 search = m1wrapper.get_batch_search(id)

m1wrapper/__init__.py

@@ -1 +1 @@
-from .m1wrapper import MoleculeOneWrapper
+from .m1wrapper import MoleculeOneWrapper, Priority

m1wrapper/m1wrapper.py

@@ -1,8 +1,15 @@
 from typing import List, Dict
+from enum import IntEnum
 
 from .search import BatchSearch
 from .config import api_token_version, wrapper_version, api_base_url
 
+class Priority(IntEnum):
+    LOWEST = 1,
+    LOW = 3,
+    NORMAL = 5,
+    HIGH = 8,
+    HIGHEST = 10
 
 class MoleculeOneWrapper:
     """
@@ -29,13 +36,15 @@ def run_batch_search(
             self,
             targets: List[str],
             parameters: Dict = None,
+            priority = Priority.NORMAL,
             starting_materials: List[str] = None,
     ) -> BatchSearch:
         return BatchSearch(
                 self.api_base_url,
                 self.request_headers,
                 targets=targets,
                 parameters=parameters,
+                priority=int(priority),
                 starting_materials=starting_materials
             )
 

m1wrapper/search.py

@@ -43,6 +43,7 @@ def __init__(
         search_id=None,
         targets=None,
         parameters=None,
+        priority=None,
         starting_materials=None,
     ):
         self.search_id = search_id
@@ -53,14 +54,16 @@ def __init__(
             new_search = self.__run(
                     targets=targets,
                     parameters=parameters,
+                    priority=priority,
                     starting_materials=starting_materials
             )
             self.search_id = new_search['id']
 
-    def __prepare_payload(self, targets, parameters, starting_materials) -> dict:
+    def __prepare_payload(self, targets, parameters, priority, starting_materials ) -> dict:
         payload = {
             'targets': targets,
             'params': parameters or {},
+            'priority': priority
         }
         if starting_materials is not None:
             payload["startingMaterials"] = starting_materials
@@ -81,8 +84,8 @@ def __prepare_http(self):
         http.mount("http://", adapter)
         return http
 
-    def __run(self, targets, parameters, starting_materials):
-        payload = self.__prepare_payload(targets, parameters, starting_materials)
+    def __run(self, targets, parameters, priority, starting_materials):
+        payload = self.__prepare_payload(targets, parameters, priority, starting_materials)
         response = self.http.post(
             urljoin(self.base_url, api_search_endpoint),
             data=json.dumps(payload),