drop python 3.9 support (#91)

Category: CI
JIRA issue: https://jira.ihme.washington.edu/browse/MIC-5524

Changes and notes
Remove python 3.9 from build matrices.
Modernize type hinting.

Testing
Built repo using cookiecutter.

{{cookiecutter.package_name}}/README.rst

@@ -27,7 +27,7 @@ all necessary requirements as follows::
   ({{ cookiecutter.package_name }}) :~$ pip install -e .
   ...pip will install vivarium and other requirements...
 
-Supported Python versions: 3.9, 3.10, 3.11
+Supported Python versions: 3.10, 3.11
 
 Note the ``-e`` flag that follows pip install. This will install the python
 package in-place, which is important for making the model specifications later.

{{cookiecutter.package_name}}/python_versions.json

@@ -1 +1 @@
-["3.9", "3.10", "3.11"]
+["3.10", "3.11"]

{{cookiecutter.package_name}}/src/{{cookiecutter.package_name}}/data/builder.py

@@ -10,7 +10,6 @@
 
 """
 from pathlib import Path
-from typing import Optional
 
 import pandas as pd
 from loguru import logger
@@ -50,7 +49,7 @@ def open_artifact(output_path: Path, location: str) -> Artifact:
 
 
 def load_and_write_data(
-    artifact: Artifact, key: str, location: str, years: Optional[str], replace: bool
+    artifact: Artifact, key: str, location: str, years: str | None, replace: bool
 ):
     """Loads data and writes it to the artifact if not already present.
 

{{cookiecutter.package_name}}/src/{{cookiecutter.package_name}}/data/loader.py

@@ -12,8 +12,6 @@
 
    No logging is done here. Logging is done in vivarium inputs itself and forwarded.
 """
-from typing import List, Optional, Union
-
 import numpy as np
 import pandas as pd
 from gbd_mapping import causes, covariates, risk_factors
@@ -29,7 +27,7 @@
 
 
 def get_data(
-    lookup_key: str, location: str, years: Optional[Union[int, str, List[int]]] = None
+    lookup_key: str, location: str, years: int | str | list[int] | None = None
 ) -> pd.DataFrame:
     """Retrieves data from an appropriate source.
 
@@ -66,7 +64,7 @@ def get_data(
 
 
 def load_population_location(
-    key: str, location: str, years: Optional[Union[int, str, List[int]]] = None
+    key: str, location: str, years: int | str | list[int] | None = None
 ) -> str:
     if key != data_keys.POPULATION.LOCATION:
         raise ValueError(f"Unrecognized key {key}")
@@ -75,38 +73,38 @@ def load_population_location(
 
 
 def load_population_structure(
-    key: str, location: str, years: Optional[Union[int, str, List[int]]] = None
+    key: str, location: str, years: int | str | list[int] | None = None
 ) -> pd.DataFrame:
     return interface.get_population_structure(location, years)
 
 
 def load_age_bins(
-    key: str, location: str, years: Optional[Union[int, str, List[int]]] = None
+    key: str, location: str, years: int | str | list[int] | None = None
 ) -> pd.DataFrame:
     return interface.get_age_bins()
 
 
 def load_demographic_dimensions(
-    key: str, location: str, years: Optional[Union[int, str, List[int]]] = None
+    key: str, location: str, years: int | str | list[int] | None = None
 ) -> pd.DataFrame:
     return interface.get_demographic_dimensions(location, years)
 
 
 def load_theoretical_minimum_risk_life_expectancy(
-    key: str, location: str, years: Optional[Union[int, str, List[int]]] = None
+    key: str, location: str, years: int | str | list[int] | None = None
 ) -> pd.DataFrame:
     return interface.get_theoretical_minimum_risk_life_expectancy()
 
 
 def load_standard_data(
-    key: str, location: str, years: Optional[Union[int, str, List[int]]] = None
+    key: str, location: str, years: int | str | list[int] | None = None
 ) -> pd.DataFrame:
     key = EntityKey(key)
     entity = get_entity(key)
     return interface.get_measure(entity, key.measure, location, years).droplevel("location")
 
 
-def load_metadata(key: str, location: str, years: Optional[Union[int, str, List[int]]] = None):
+def load_metadata(key: str, location: str, years: int | str | list[int] | None = None):
     key = EntityKey(key)
     entity = get_entity(key)
     entity_metadata = entity[key.measure]
@@ -115,7 +113,7 @@ def load_metadata(key: str, location: str, years: Optional[Union[int, str, List[
     return entity_metadata
 
 
-def load_categorical_paf(key: str, location: str, years: Optional[Union[int, str, List[int]]] = None) -> pd.DataFrame:
+def load_categorical_paf(key: str, location: str, years: int | str | list[int] | None = None) -> pd.DataFrame:
     try:
         risk = {
             # todo add keys as needed
@@ -163,7 +161,7 @@ def _load_em_from_meid(location, meid, measure):
 # TODO - add project-specific data functions here
 
 
-def get_entity(key: Union[str, EntityKey]):
+def get_entity(key: str | EntityKey):
     # Map of entity types to their gbd mappings.
     type_map = {
         "cause": causes,

{{cookiecutter.package_name}}/src/{{cookiecutter.package_name}}/tools/cli.py

@@ -1,5 +1,3 @@
-from typing import Optional, Tuple
-
 import click
 from loguru import logger
 from vivarium.framework.utilities import handle_exceptions
@@ -52,10 +50,10 @@
 )
 def make_artifacts(
     location: str,
-    years: Optional[str],
+    years: str | None,
     output_dir: str,
     append: bool,
-    replace_keys: Tuple[str, ...],
+    replace_keys: tuple[str, ...],
     verbose: int,
     with_debugger: bool,
 ) -> None:

{{cookiecutter.package_name}}/src/{{cookiecutter.package_name}}/tools/make_artifacts.py

@@ -10,7 +10,6 @@
 import sys
 import time
 from pathlib import Path
-from typing import Optional, Tuple, Union
 
 import click
 from loguru import logger
@@ -30,7 +29,7 @@ def running_from_cluster() -> bool:
 
 
 def check_for_existing(
-    output_dir: Path, location: str, append: bool, replace_keys: Tuple
+    output_dir: Path, location: str, append: bool, replace_keys: tuple
 ) -> None:
     existing_artifacts = set(
         [
@@ -66,18 +65,18 @@ def check_for_existing(
 
 
 def build_single(
-    location: str, years: Optional[str], output_dir: str, replace_keys: Tuple
+    location: str, years: str | None, output_dir: str, replace_keys: tuple
 ) -> None:
     path = Path(output_dir) / f"{sanitize_location(location)}.hdf"
     build_single_location_artifact(path, location, years, replace_keys)
 
 
 def build_artifacts(
     location: str,
-    years: Optional[str],
+    years: str | None,
     output_dir: str,
     append: bool,
-    replace_keys: Tuple,
+    replace_keys: tuple,
     verbose: int,
 ) -> None:
     """Main application function for building artifacts.
@@ -196,10 +195,10 @@ def build_all_artifacts(output_dir: Path, verbose: int) -> None:
 
 
 def build_single_location_artifact(
-    path: Union[str, Path],
+    path: str | Path,
     location: str,
-    years: Optional[str],
-    replace_keys: Tuple = (),
+    years: str | None,
+    replace_keys: tuple = (),
     log_to_file: bool = False,
 ) -> None:
     """Builds an artifact for a single location.

{{cookiecutter.package_name}}/src/{{cookiecutter.package_name}}/utilities.py

@@ -1,5 +1,4 @@
 from pathlib import Path
-from typing import List, Tuple, Union
 
 import click
 import numpy as np
@@ -11,7 +10,7 @@
 
 from {{cookiecutter.package_name}}.constants import metadata
 
-SeededDistribution = Tuple[str, stats.rv_continuous]
+SeededDistribution = tuple[str, stats.rv_continuous]
 
 
 def len_longest_location() -> int:
@@ -42,7 +41,7 @@ def sanitize_location(location: str):
     return location.replace(" ", "_").replace("'", "_").lower()
 
 
-def delete_if_exists(*paths: Union[Path, List[Path]], confirm=False):
+def delete_if_exists(*paths: Path | list[Path], confirm=False):
     paths = paths[0] if isinstance(paths[0], list) else paths
     existing_paths = [p for p in paths if p.exists()]
     if existing_paths:
@@ -86,7 +85,7 @@ def read_data_by_draw(artifact_path: str, key : str, draw: int) -> pd.DataFrame:
 def get_norm(
         mean: float,
         sd: float = None,
-        ninety_five_pct_confidence_interval: Tuple[float, float] = None
+        ninety_five_pct_confidence_interval: tuple[float, float] = None
 ) -> stats.norm:
     sd = _get_standard_deviation(mean, sd, ninety_five_pct_confidence_interval)
     return stats.norm(loc=mean, scale=sd)
@@ -95,7 +94,7 @@ def get_norm(
 def get_truncnorm(
         mean: float,
         sd: float = None,
-        ninety_five_pct_confidence_interval: Tuple[float, float] = None,
+        ninety_five_pct_confidence_interval: tuple[float, float] = None,
         lower_clip: float = 0.0,
         upper_clip: float = 1.0
 ) -> stats.norm:
@@ -106,7 +105,7 @@ def get_truncnorm(
 
 
 def _get_standard_deviation(
-        mean: float, sd: float, ninety_five_pct_confidence_interval: Tuple[float, float]
+        mean: float, sd: float, ninety_five_pct_confidence_interval: tuple[float, float]
 ) -> float:
     if sd is None and ninety_five_pct_confidence_interval is None:
         raise ValueError("Must provide either a standard deviation or a 95% confidence interval.")
@@ -127,7 +126,7 @@ def _get_standard_deviation(
 
 
 def get_lognorm_from_quantiles(median: float, lower: float, upper: float,
-                               quantiles: Tuple[float, float] = (0.025, 0.975)) -> stats.lognorm:
+                               quantiles: tuple[float, float] = (0.025, 0.975)) -> stats.lognorm:
     """Returns a frozen lognormal distribution with the specified median, such that
     (lower, upper) are approximately equal to the quantiles with ranks
     (quantile_ranks[0], quantile_ranks[1]).