def cli(
cli_config,
data_folder,
logs_folder,
imgs_folder,
cache_folder,
use_cache,
log_file,
log_console,
log_level,
log_name,
log_filename,
trnsys_default_folder,
ep_version,
):
"""archetypal: Retrieve, construct, simulate, convert and analyse building
simulation templates
Visit archetypal.readthedocs.io for the online documentation.
"""
cli_config.data_folder = data_folder
cli_config.logs_folder = logs_folder
cli_config.imgs_folder = imgs_folder
cli_config.cache_folder = cache_folder
cli_config.use_cache = use_cache
cli_config.log_file = log_file
cli_config.log_console = log_console
cli_config.log_level = log_level
cli_config.log_name = log_name
cli_config.log_filename = log_filename
cli_config.trnsys_default_folder = trnsys_default_folder
cli_config.ep_version = ep_version
# apply new config params
config(**cli_config.__dict__)
def config():
ar.config(log_console=True,
log_file=True,
use_cache=True,
data_folder='.temp/data',
logs_folder='.temp/logs',
imgs_folder='.temp/imgs',
cache_folder='.temp/cache',
umitemplate='../data/BostonTemplateLibrary.json')
def schedules_idf():
config(cache_folder="tests/.temp/cache")
idf = load_idf(
idf_file,
include=[
get_eplus_dirs(settings.ep_version) / "DataSets" / "TDV" /
"TDV_2008_kBtu_CTZ06.csv"
],
)
return idf
def config():
ar.config(
data_folder="tests/.temp/data",
logs_folder="tests/.temp/logs",
imgs_folder="tests/.temp/imgs",
cache_folder="tests/.temp/cache",
use_cache=True,
log_file=True,
log_console=True,
umitemplate="tests/input_data/umi_samples/BostonTemplateLibrary_2.json",
)
import pandas as pd
from path import Path
from archetypal import config, run_eplus, parallel_process
config(cache_folder="../../tests/.temp/cache", use_cache=True, log_console=True)
def main():
# setup directories and input files
necb_basedir = Path("../../tests/input_data/trnsys")
files = necb_basedir.glob("Ref*.idf")
epw = Path("../../tests/input_data/CAN_PQ_Montreal.Intl.AP.716270_CWEC.epw")
idfs = pd.DataFrame({"file": files, "name": [file.basename() for file in files]})
# setup the runner. We'll use the DataFrame index as keys (k).
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()
}
def test_load_umi_template_fail():
with pytest.raises(ValueError):
pu.config(umitemplate='../data/noneexistingfile.json')
data_json = pu.settings.umitemplate
load_umi_template(data_json)