adding types to utilities

pyproject.toml

@@ -144,7 +144,7 @@ python_version = "3.12"
 mypy_path = "stubs"
 
 # Exclude specific directories from type checking will try to add them back gradually
-exclude = "(?x)(^temoa/utilities/|^stubs/)"
+exclude = "(?x)(^stubs/)"
 
 # Strict typing for our own code
 disallow_untyped_defs = true

temoa/core/config.py

@@ -70,6 +70,7 @@ def __init__(
         output_threshold_activity: float | None = None,
         output_threshold_emission: float | None = None,
         output_threshold_cost: float | None = None,
+        sqlite: dict[str, object] | None = None,
     ):
         if '-' in scenario:
             raise ValueError(
@@ -167,6 +168,8 @@ def __init__(
         self.cycle_count_limit = cycle_count_limit
         self.cycle_length_limit = cycle_length_limit
 
+        self.sqlite_settings = sqlite or {}
+
         # warn if output db != input db
         if self.input_database.suffix == self.output_database.suffix:  # they are both .db/.sqlite
             if self.input_database != self.output_database:  # they are not the same db

temoa/utilities/capacity_analyzer.py

@@ -4,11 +4,10 @@
 is populated will influence the utility of using this method
 """
 
+import argparse
 import itertools
-import os.path
 import sqlite3
 
-from definitions import PROJECT_ROOT
 from matplotlib import pyplot as plt
 
 # Written by:  J. F. Hyink
@@ -17,58 +16,75 @@
 
 # Created on:  7/18/23
 
-# filename of db to analyze...
-db = 'US_9R_8D_CT500.sqlite'
-
-source_db_file = os.path.join(PROJECT_ROOT, 'data_files', 'untracked_data', db)
-print(source_db_file)
-res = []
-try:
-    con = sqlite3.connect(source_db_file)
-    cur = con.cursor()
-    cur.execute('SELECT max_cap  FROM max_capacity')
-    for row in cur:
-        res.append(row)
-
-except sqlite3.Error as e:
-    print(e)
-
-finally:
-    con.close()
-
-# chain them together into a list
-caps = list(itertools.chain(*res))
-
-cutoff = 1  # GW : An arbitrary cutoff between big and small capacity systems.
-small_cap_sources = [c for c in caps if c <= cutoff]
-large_cap_sources = [c for c in caps if c > cutoff]
-
-aggregate_small_cap = sum(small_cap_sources)
-aggregate_large_cap = sum(large_cap_sources)
-
-print(f'{len(small_cap_sources)} small cap sources account for: {aggregate_small_cap: 0.1f} GW')
-print(f'{len(large_cap_sources)} large cap sources account for: {aggregate_large_cap: 0.1f} GW')
-
-plt.hist(caps, bins=100)
-plt.show()
-
-
-# make a cumulative contribution plot, and find a 5% cutoff
-cutoff_num_sources = 0
-caps.sort()
-total_cap = sum(caps)
-cumulative_caps = [
-    caps[0] / total_cap,
-]
-for i, cap in enumerate(caps[1:]):
-    cumulative_caps.append(cap / total_cap + cumulative_caps[i])
-    if cumulative_caps[-1] < 0.05:
-        cutoff_num_sources += 1
-
-plt.plot(range(len(cumulative_caps)), cumulative_caps)
-plt.axvline(x=cutoff_num_sources, color='red', ls='--')
-plt.xlabel('Aggregated Sources')
-plt.ylabel('Proportion of Total Capacity')
-plt.title('Aggregate Capacity vs. Number of Sources')
-
-plt.show()
+
+def analyze_capacity(db_path: str) -> None:
+    res = []
+    con = None
+    try:
+        con = sqlite3.connect(db_path)
+        cur = con.cursor()
+        cur.execute('SELECT max_cap  FROM max_capacity')
+        for row in cur:
+            res.append(row)
+
+    except sqlite3.Error as e:
+        print(f'Error connecting to database: {e}')
+        return
+
+    finally:
+        if con:
+            con.close()
+
+    if not res:
+        print('No data found in max_capacity table.')
+        return
+
+    # chain them together into a list
+    caps = list(itertools.chain(*res))
+
+    cutoff = 1  # GW : An arbitrary cutoff between big and small capacity systems.
+    small_cap_sources = [c for c in caps if c <= cutoff]
+    large_cap_sources = [c for c in caps if c > cutoff]
+
+    aggregate_small_cap = sum(small_cap_sources)
+    aggregate_large_cap = sum(large_cap_sources)
+
+    print(f'{len(small_cap_sources)} small cap sources account for: {aggregate_small_cap: 0.1f} GW')
+    print(f'{len(large_cap_sources)} large cap sources account for: {aggregate_large_cap: 0.1f} GW')
+
+    plt.hist(caps, bins=100)
+    plt.show()
+
+    # make a cumulative contribution plot, and find a 5% cutoff
+    cutoff_num_sources = 0
+    caps.sort()
+    total_cap = sum(caps)
+    cumulative_caps = [
+        caps[0] / total_cap,
+    ]
+    for i, cap in enumerate(caps[1:]):
+        cumulative_caps.append(cap / total_cap + cumulative_caps[i])
+        if cumulative_caps[-1] < 0.05:
+            cutoff_num_sources += 1
+
+    plt.plot(range(len(cumulative_caps)), cumulative_caps)
+    plt.axvline(x=cutoff_num_sources, color='red', ls='--')
+    plt.xlabel('Aggregated Sources')
+    plt.ylabel('Proportion of Total Capacity')
+    plt.title('Aggregate Capacity vs. Number of Sources')
+
+    plt.show()
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(
+        description='Analyze capacity distribution in a Temoa database.'
+    )
+    parser.add_argument('db_path', help='Path to the SQLite database file.')
+    args = parser.parse_args()
+
+    analyze_capacity(args.db_path)
+
+
+if __name__ == '__main__':
+    main()

temoa/utilities/graph_utils.py

@@ -40,7 +40,9 @@
     GraphType = TypeVar('GraphType', nx.Graph, nx.DiGraph, nx.MultiGraph, nx.MultiDiGraph)
 
 
-def convert_graph_to_json[GraphType: (nx.Graph, nx.DiGraph, nx.MultiGraph, nx.MultiDiGraph)](
+def convert_graph_to_json[
+    GraphType: (nx.Graph[Any], nx.DiGraph[Any], nx.MultiGraph[Any], nx.MultiDiGraph[Any])
+](
     nx_graph: GraphType,
     override_node_properties: dict[str, Any] | None,
     override_edge_properties: dict[str, Any] | None,

temoa/utilities/unit_cost_explorer.py

@@ -8,6 +8,14 @@
 from temoa.components.costs import total_cost_rule
 from temoa.components.storage import storage_energy_upper_bound_constraint
 from temoa.core.model import TemoaModel
+from temoa.types.core_types import (
+    Period,
+    Region,
+    Season,
+    Technology,
+    TimeOfDay,
+    Vintage,
+)
 
 # Written by:  J. F. Hyink
 # jeff@westernspark.us
@@ -29,8 +37,8 @@
 
 
 # indices
-rtv = ('A', 'battery', 2020)  # rtv
-rptv = ('A', 2020, 'battery', 2020)  # rptv
+rtv = (Region('A'), Technology('battery'), Vintage(2020))  # rtv
+rptv = (Region('A'), Period(2020), Technology('battery'), Vintage(2020))  # rptv
 model.time_future.construct([2020, 2025, 2030])  # needs to go 1 period beyond optimize horizon
 model.time_optimize.construct([2020, 2025])
 model.period_length.construct()
@@ -132,21 +140,23 @@
 
 # More VARS
 model.v_storage_level.construct()
-model.segment_fraction_per_season.construct(
-    data=seasonal_fractions
+model.segment_fraction_per_season.construct(data=seasonal_fractions)
+
+model.is_seasonal_storage[Technology('battery')] = False
+upper_limit = storage_energy_upper_bound_constraint(
+    model,
+    Region('A'),
+    Period(2020),
+    Season('winter'),
+    TimeOfDay('1'),
+    Technology('battery'),
+    Vintage(2020),
 )
-
-model.is_seasonal_storage['battery'] = False
-upper_limit = storage_energy_upper_bound_constraint(model, 'A', 2020, 'winter', 1, 'battery', 2020)
 print('The storage level constraint for the single period in the "super day":\n', upper_limit)
 
 # cross-check the multiplier...
 mulitplier = (
-    storage_dur
-    * model.segment_fraction_per_season['winter']
-    * model.days_per_period
-    * c2a
-    * c
+    storage_dur * model.segment_fraction_per_season['winter'] * model.days_per_period * c2a * c
 )
 print(f'The multiplier for the storage should be: {mulitplier}')