Dev to Stage sync

src/calculators/qm_xn_lib_calculator.py

@@ -12,10 +12,11 @@
 import itertools
 import numpy as np
 import pandas as pd
+import os
 
 
 class QMXNLibCalculator(QMCalculator):
-    def __init__(self, edges_file_path:str, output_file_path:str, polygon_file_path:str=None):
+    def __init__(self, edges_file_path:str, output_file_path:str, polygon_file_path:str=None, partition_count:int = os.cpu_count()):
         """
         Initializes the QMXNLibCalculator class.
 
@@ -31,6 +32,7 @@ def __init__(self, edges_file_path:str, output_file_path:str, polygon_file_path:
         self.default_projection = 'epsg:26910'
         self.output_projection = 'epsg:4326'
         self.precision = 1e-5
+        self.partition_count = partition_count
 
     def add_edges_from_linestring(self, graph, linestring, edge_attrs):
         points = list(linestring.coords)
@@ -147,8 +149,8 @@ def calculate_quality_metric(self):
             gdf = gdf.to_crs(self.default_projection)
             tile_gdf = tile_gdf.to_crs(self.default_projection)
             tile_gdf = tile_gdf[['geometry']]
-
-            df_dask = dask_geopandas.from_geopandas(tile_gdf, npartitions=64)
+            no_of_cores = min(self.partition_count, os.cpu_count())
+            df_dask = dask_geopandas.from_geopandas(tile_gdf, npartitions=no_of_cores)
 
             output = df_dask.apply(self.qm_func,axis=1, meta=[
                 ('geometry', 'geometry'),

src/calculators/xn_qm_lib.py

@@ -170,7 +170,8 @@ def calculate_xn_qm(osw_edge_file_path: str, qm_file_path: str, xn_polygon_path:
         tile_gdf = tile_gdf[['geometry']]
 
         # Compute local stats using dask-geopandas
-        df_dask = dask_geopandas.from_geopandas(tile_gdf, npartitions=64)
+        no_of_cores = os.cpu_count()
+        df_dask = dask_geopandas.from_geopandas(tile_gdf, npartitions=no_of_cores)
 
         # print('computing stats...')
         output = df_dask.apply(qm_func, axis=1, meta=[

src/config.py

@@ -14,6 +14,7 @@ class Config(BaseSettings):
     storage_container_name: str = os.environ.get('CONTAINER_NAME', 'osw')
     algorithm_dictionary: dict = {"fixed":QMFixedCalculator,"ixn":QMXNLibCalculator}
     max_concurrent_messages: int = os.environ.get('MAX_CONCURRENT_MESSAGES', 1)
+    partition_count:int = os.environ.get('PARTITION_COUNT', 2)
 
     def get_download_folder(self) -> str:
         root_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))

src/services/osw_qm_calculator_service.py

@@ -27,6 +27,16 @@ class OswQmCalculator:
 
     """
 
+    def __init__(self, cores_to_use:int):
+        """
+        Initializes the OswQmCalculator class.
+
+        Args:
+            cores_to_use (int): The number of cores to use for calculating quality metrics.
+
+        """
+        self.cores_to_use = cores_to_use
+
     def calculate_quality_metric(self, input_file, algorithm_names, output_path, ixn_file=None):
         """
         Calculates quality metrics for input files using specified algorithms.
@@ -93,7 +103,7 @@ def get_osw_qm_calculator(self, algorithm_name:str, ixn_file:str=None, edges_fil
 
         """
         if algorithm_name == 'ixn':
-            return QMXNLibCalculator(edges_file, output_file, ixn_file)
+            return QMXNLibCalculator(edges_file, output_file, ixn_file, self.cores_to_use)
         else:
             return QMFixedCalculator(edges_file, output_file)
 
@@ -164,7 +174,6 @@ def parse_and_calculate_quality_metric(self, input_file, algorithm_names, ixn_fi
 
         if 'ixn' in algorithm_names:
             # get the edges file from the input
-
             ixn_calculator = QMXNLibCalculator()
             input_json = ixn_calculator.calculate_quality_metric(input_file, ixn_file)
 

src/services/servicebus_service.py

@@ -73,7 +73,8 @@ def process_message(self, msg: QueueMessage):
             output_folder = os.path.join(download_folder,'qm')
             os.makedirs(output_folder,exist_ok=True)
             output_file_local_path = os.path.join(output_folder,'qm-output.zip')
-            qm_calculator = OswQmCalculator()
+            cores_to_use = self.config.partition_count
+            qm_calculator = OswQmCalculator(cores_to_use=cores_to_use)
             algorithm_names = quality_request.data.algorithm.split(',')
             qm_calculator.calculate_quality_metric(download_path, algorithm_names,output_file_local_path,ixn_file_path)
             # Upload the file