Running multiple files ====================== Running multiple IDF files is easily achieved by using the :meth:`~translater.utils.parallel_process` method. .. hint:: The :meth:`~translater.utils.parallel_process` method works with any method. You can use it to parallelize other functions in your script. To create a parallel run, first import the usual pacakge methods and configure `translater` to use caching and to show logs in the console. .. code-block:: python >>> from path import Path >>> from translater import load_idf, config, run_eplus, settings, parallel_process >>> import pandas as pd >>> config(use_cache=True, log_console=True) Then, use .. code-block:: python >>> from translater import load_idf, config, run_eplus, settings >>> from translater import parallel_process >>> import pandas as pd >>> config(use_cache=True, log_console=True) Then, use `glob` to make a list of NECB idf files in the input_data directory (relative to this package). The weather file path is also created: .. code-block:: python >>> necb_basedir = Path("tests/input_data/necb") >>> files = necb_basedir.glob("*.idf") >>> epw = Path("data/CAN_PQ_Montreal.Intl.AP.716270_CWEC.epw") For good measure, load the files in a DataFrame, which we will use to create the rundict in the next step. .. code-block:: python >>> idfs = pd.DataFrame({"file": files, "name": [file.basename() for file in files]}) The rundict, is the list of tasks we wish to do in parallel. This dictionary is passed to :meth:`~translater.idfclass .parallel_process`. Here, we want to execute :meth:`~translater.idfclass.run_eplus` with the following parameters: .. code-block:: python >>> rundict = { k: dict( eplus_file=str(file), prep_outputs=True, weather_file=str(epw), expandobjects=True, verbose="v", design_day=True, output_report="sql_file", ) for k, file in idfs.file.to_dict().items() } Finally, execute :meth:`~translater.utils.parallel_process`. The resulting sql_file paths, which we defined as the type of output_report attribute for :meth:`~translater.idfclass.run_eplus` is returned as a dictionary with the same keys as the index of the DataFrame. .. code-block:: python >>> sql_files = parallel_process(rundict, run_eplus, use_kwargs=True, processors=-1) >>> sql_files {0: Path('cache/06e92da0247c71762d64aed4bcf3cdb2/output_data/06e92da0247c71762d64aed4bcf3cdb2out.sql'), 1: Path('cache/aee8caf562b3519942ef88f533800dd0/output_data/aee8caf562b3519942ef88f533800dd0out.sql'), 2: Path('cache/9d14a6aa6fda03a77ed5c5c48d28a73b/output_data/9d14a6aa6fda03a77ed5c5c48d28a73bout.sql'), 3: Path('cache/5ddfa8827d2a577aabb02d60195bf53a/output_data/5ddfa8827d2a577aabb02d60195bf53aout.sql'), 4: Path('cache/225c3428099e2abcc4051750db12731b/output_data/225c3428099e2abcc4051750db12731bout.sql'), 5: Path('cache/0991d42c5af387833b68adffc0d7b523/output_data/0991d42c5af387833b68adffc0d7b523out.sql'), 6: Path('cache/e10a4bf8bae93b0b0d2ad2638c807b61/output_data/e10a4bf8bae93b0b0d2ad2638c807b61out.sql'), 7: Path('cache/86439047af9e8ff4650d6bab460d5e70/output_data/86439047af9e8ff4650d6bab460d5e70out.sql'), 8: Path('cache/68da0886afa316f75bc63d7e576d0228/output_data/68da0886afa316f75bc63d7e576d0228out.sql'), 9: Path('cache/68a8be47fe4573a61d388a0101798958/output_data/68a8be47fe4573a61d388a0101798958out.sql'), 10: Path('cache/f6f8abae5272bf607a9f53d18c10a50d/output_data/f6f8abae5272bf607a9f53d18c10a50dout.sql'), 11: Path('cache/4cf8589df098bb0c3f2b9f8589ec6ed6/output_data/4cf8589df098bb0c3f2b9f8589ec6ed6out.sql'), 12: Path('cache/5dd643faf859ed1aed5adffcecd0d47c/output_data/5dd643faf859ed1aed5adffcecd0d47cout.sql'), 13: Path('cache/e7cf6ae2be8917a409c9a1acad3bc349/output_data/e7cf6ae2be8917a409c9a1acad3bc349out.sql'), 14: Path('cache/3f122e04f7d8d19195cb8818a0be390f/output_data/3f122e04f7d8d19195cb8818a0be390fout.sql'), 15: Path('cache/d263b5b5d3bc56f2fb3795c61ac89cfe/output_data/d263b5b5d3bc56f2fb3795c61ac89cfeout.sql')}