Merge pull request #785 from MolSSI/cleanup

Some small pre-3.12 cleanups

conda-build/qcportal/meta.yaml

@@ -32,6 +32,7 @@ requirements:
     - tqdm
     - pandas
     - pyjwt
+    - packaging
 
 
 test:

qcarchivetesting/conda-envs/fulltest_qcportal.yaml

@@ -15,4 +15,5 @@ dependencies:
   - tabulate
   - tqdm
   - pandas
-  - pyjwt
+  - pyjwt
+  - packaging

qcarchivetesting/conda-envs/fulltest_server.yaml

@@ -20,6 +20,7 @@ dependencies:
   - tqdm
   - pandas
   - pyjwt
+  - packaging
 
   # QCFractal dependencies
   - flask

qcarchivetesting/conda-envs/fulltest_snowflake.yaml

@@ -9,6 +9,7 @@ dependencies:
   - postgresql
 
   # QCPortal dependencies
+  # NOTE: msgpack-python in conda is msgpack in pypi (due to a rename around v0.5)
   - numpy
   - msgpack-python
   - requests
@@ -20,6 +21,7 @@ dependencies:
   - tqdm
   - pandas
   - pyjwt
+  - packaging
 
   # QCFractalCompute dependencies
   - parsl

qcarchivetesting/conda-envs/fulltest_worker.yaml

@@ -20,6 +20,7 @@ dependencies:
   - tqdm
   - pandas
   - pyjwt
+  - packaging
 
   # QCFractalCompute dependencies
   - parsl

qcarchivetesting/qcarchivetesting/helpers.py

@@ -14,6 +14,7 @@
 from qcelemental.models import Molecule
 from qcelemental.models.results import WavefunctionProperties
 
+from qcfractal.components.serverinfo.socket import geoip2_found
 from qcportal.serialization import _json_decode
 
 # Valid client encodings
@@ -26,7 +27,7 @@
 geoip_filename = "GeoLite2-City-Test.mmdb"
 ip_testdata_path = os.path.join(_my_path, "MaxMind-DB", "source-data", "GeoIP2-City-Test.json")
 
-ip_tests_enabled = os.path.exists(geoip_path) and os.path.exists(ip_testdata_path)
+ip_tests_enabled = os.path.exists(geoip_path) and os.path.exists(ip_testdata_path) and geoip2_found
 
 testconfig_path = os.path.join(_my_path, "config_files")
 migrationdata_path = os.path.join(_my_path, "migration_data")

qcfractal/qcfractal/test_periodics.py

@@ -27,15 +27,16 @@ def test_periodics_server_stats(snowflake: QCATestingSnowflake):
     sleep_time = snowflake._qcf_config.statistics_frequency
 
     snowflake.start_job_runner()
-    time.sleep(0.25)
+    time.sleep(sleep_time / 2)
 
     for i in range(5):
         time_0 = datetime.utcnow()
         time.sleep(sleep_time)
         time_1 = datetime.utcnow()
 
-        stats = storage_socket.serverinfo.query_server_stats(ServerStatsQueryFilters())
-        assert len(stats) == i + 1
+        filters = ServerStatsQueryFilters(before=time_1, after=time_0)
+        stats = storage_socket.serverinfo.query_server_stats(filters)
+        assert len(stats) == 1
         assert time_0 < stats[0]["timestamp"] < time_1
 
 

qcfractalcompute/qcfractalcompute/compute_manager.py

@@ -12,11 +12,11 @@
 
 import parsl.executors.high_throughput.interchange
 import tabulate
+from packaging.version import parse as parse_version
 from parsl.config import Config as ParslConfig
 from parsl.dataflow.dflow import DataFlowKernel
 from parsl.dataflow.futures import Future as ParslFuture
 from parsl.executors import HighThroughputExecutor, ThreadPoolExecutor
-from pkg_resources import parse_version
 from pydantic import BaseModel, Extra, Field
 from requests.exceptions import Timeout
 

qcportal/pyproject.toml

@@ -32,6 +32,7 @@ dependencies = [
     "tqdm",
     "pandas",
     "pyjwt",
+    "packaging",
 ]
 
 

qcportal/qcportal/client_base.py

@@ -17,7 +17,7 @@
 import pydantic
 import requests
 import yaml
-from pkg_resources import parse_version
+from packaging.version import parse as parse_version
 
 from . import __version__
 from .exceptions import AuthenticationFailure

qcportal/qcportal/dataset_models.py

@@ -1190,20 +1190,20 @@ def compile_values(
 
         Parameters
         -----------
-        value_call 
-            Function to call on each record to extract the desired value. Must return a scalar value or 
+        value_call
+            Function to call on each record to extract the desired value. Must return a scalar value or
             a sequence of values if 'unpack' is set to True.
 
         value_names
-            Column name(s) for the extracted value(s). If a string is provided and multiple values are 
-            returned by 'value_call', columns are named by appending an index to this string. If a list 
+            Column name(s) for the extracted value(s). If a string is provided and multiple values are
+            returned by 'value_call', columns are named by appending an index to this string. If a list
             of strings is provided, it must match the length of the sequence returned by 'value_call'.
             Default is "value".
 
-        entry_names 
+        entry_names
             Entry names to filter records. If not provided, considers all entries.
 
-        specification_names 
+        specification_names
             Specification names to filter records. If not provided, considers all specifications.
 
         unpack
@@ -1226,11 +1226,11 @@ def compile_values(
         Notes
         ------
         1. The DataFrame is structured such that the rows are entries and columns are specifications.
-        2. If 'unpack' is True, the function assumes 'value_call' returns a sequence of values that need 
+        2. If 'unpack' is True, the function assumes 'value_call' returns a sequence of values that need
         to be distributed across columns in the resulting DataFrame. 'value_call' should always return the
         same number of values for each record if unpack is True.
         """
-        
+
         def _data_generator(unpack=False):
             for entry_name, spec_name, record in self.iterate_records(
                 entry_names=entry_names,
@@ -1279,13 +1279,13 @@ def get_properties_df(self, properties_list: Sequence[str]) -> pd.DataFrame:
 
         This function uses the provided list of property names to extract corresponding
         values from each record's properties. It returns a DataFrame where rows represent
-        each record. Each column corresponds has a top level index as a specification, 
-        and a second level index as the appropriate value name. Columns with all 
+        each record. Each column corresponds has a top level index as a specification,
+        and a second level index as the appropriate value name. Columns with all
         NaN values are dropped.
 
         Parameters:
         -----------
-        properties_list 
+        properties_list
             List of property names to retrieve from the records.
 
         Returns: