Bring in the latest Develop

activitysim/core/configuration/top.py

@@ -3,6 +3,8 @@
 from pathlib import Path
 from typing import Any, Literal
 
+from pydantic import validator
+
 from activitysim.core.configuration.base import PydanticBase, Union
 
 
@@ -119,6 +121,11 @@ class OutputTables(PydanticBase):
     h5_store: bool = False
     """Write tables into a single HDF5 store instead of individual CSVs."""
 
+    file_type: Literal["csv", "parquet", "h5"] = "csv"
+    """
+    Specifies the file type for output tables. Options are limited to 'csv',
+    'h5' or 'parquet'. Only applied if h5_store is set to False."""
+
     action: str
     """Whether to 'include' or 'skip' the enumerated tables in `tables`."""
 

activitysim/core/steps/output.py

@@ -9,6 +9,7 @@
 import pandas as pd
 import pyarrow as pa
 import pyarrow.csv as csv
+import pyarrow.parquet as parquet
 
 from activitysim.core import configuration, workflow
 from activitysim.core.workflow.checkpoint import CHECKPOINT_NAME
@@ -226,8 +227,13 @@ def write_data_dictionary(state: workflow.State) -> None:
 @workflow.step
 def write_tables(state: workflow.State) -> None:
     """
-    Write pipeline tables as csv files (in output directory) as specified by output_tables list
-    in settings file.
+    Write pipeline tables as csv or parquet files (in output directory) as specified
+    by output_tables list in settings file. Output to parquet or a single h5 file is
+    also supported.
+
+    'h5_store' defaults to False, which means the output will be written out to csv.
+    'file_type' defaults to 'csv' but can also be used to specify 'parquet' or 'h5'.
+    When 'h5_store' is set to True, 'file_type' is ingored and the outputs are written to h5.
 
     'output_tables' can specify either a list of output tables to include or to skip
     if no output_tables list is specified, then all checkpointed tables will be written
@@ -261,6 +267,16 @@ def write_tables(state: workflow.State) -> None:
         tables:
            - households
 
+    To write tables to parquet files, use the file_type setting:
+
+    ::
+
+      output_tables:
+        file_type: parquet
+        action: include
+        tables:
+           - households
+
     Parameters
     ----------
     output_dir: str
@@ -277,6 +293,7 @@ def write_tables(state: workflow.State) -> None:
     tables = output_tables_settings.tables
     prefix = output_tables_settings.prefix
     h5_store = output_tables_settings.h5_store
+    file_type = output_tables_settings.file_type
     sort = output_tables_settings.sort
 
     registered_tables = state.registered_tables()
@@ -388,14 +405,20 @@ def map_func(x):
                 ):
                     dt = dt.drop([f"_original_{lookup_col}"])
 
-        if h5_store:
+        if h5_store or file_type == "h5":
             file_path = state.get_output_file_path("%soutput_tables.h5" % prefix)
             dt.to_pandas().to_hdf(
                 str(file_path), key=table_name, mode="a", format="fixed"
             )
+
         else:
-            file_name = f"{prefix}{table_name}.csv"
+            file_name = f"{prefix}{table_name}.{file_type}"
             file_path = state.get_output_file_path(file_name)
 
             # include the index if it has a name or is a MultiIndex
-            csv.write_csv(dt, file_path)
+            if file_type == "csv":
+                csv.write_csv(dt, file_path)
+            elif file_type == "parquet":
+                parquet.write_table(dt, file_path)
+            else:
+                raise ValueError(f"unknown file_type {file_type}")