Source code for

# coding=utf-8
"""Methods for resolving Air Temperature and Humidity for EnergyPlus output files."""
from __future__ import division

import os
import json

from ladybug.epw import EPW
from ladybug.sql import SQLiteResult
from ladybug.analysisperiod import AnalysisPeriod

[docs]def air_map(enclosure_info, sql, epw, analysis_period=None, humidity=False): """Get MRT data collections adjusted for shortwave using Radiance .ill files. Args: enclosure_info: Path to a JSON file containing information about the radiant enclosure that sensor points belong to. sql: Path to an SQLite file that was generated by EnergyPlus. This file must contain hourly or sub-hourly results for zone comfort variables. epw: An EPW object that will be used to specify data for any sensor outside of any enclosure. analysis_period: An optional AnalysisPeriod to be applied to all results. If None, all data collections will be for the entire run period of the sql. (Default: None). humidity: Boolean to note whether relative humidity values should be returned instead of air temperature. (Default: False) """ # load the enclosure information with open(enclosure_info) as json_file: enclosure_dict = json.load(json_file) zone_order = [zone_id.upper() for zone_id in enclosure_dict['mapper']] a_per = analysis_period if analysis_period is not None else AnalysisPeriod() # load the indoor values if they are needed air_data = [] if enclosure_dict['has_indoor']: assert os.path.isfile(sql) and os.stat(sql).st_size != 0, \ 'Indoor sensors were found but no EnergyPlus SQLite file was present.' sql_obj = SQLiteResult(sql) if humidity: in_avg_outp, id_key = 'Zone Air Relative Humidity', 'System' else: in_avg_outp, id_key = 'Zone Mean Air Temperature', 'Zone' in_avg_dict = {d.header.metadata[id_key]: d for d in sql_obj.data_collections_by_output_name(in_avg_outp)} air_data = [in_avg_dict[z] for z in zone_order] if air_data[0].header.analysis_period != a_per: air_data = [d.filter_by_analysis_period(a_per) for d in air_data] # load the EPW and outdoor values if they are needed if enclosure_dict['has_outdoor']: epw_obj = EPW(epw) out_avg = epw_obj.relative_humidity if humidity \ else epw_obj.dry_bulb_temperature if not a_per.is_annual: out_avg = out_avg.filter_by_analysis_period(a_per) air_data.append(out_avg) # create a base matrix with the same values across all rooms air_mtx = [] for sen_enc in enclosure_dict['sensor_indices']: air_mtx.append(air_data[sen_enc]._values) # go over the base values to and interpolate across any air boundaries for pt_1, int_facs in enclosure_dict['air_bound_proximity'].items(): zon_i1, zon_i2 = tuple(int_facs[0].keys()) z_fac1, z_fac2 = tuple(int_facs[0].values()) dat_1 = (v * z_fac1 for v in air_data[int(zon_i1)]) dat_2 = (v * z_fac2 for v in air_data[int(zon_i2)]) dat_comb = tuple(v1 + v2 for v1, v2 in zip(dat_1, dat_2)) if len(int_facs) > 1: for fac in int_facs[1:]: zon_i1, zon_i2 = tuple(fac.keys()) z_fac1, z_fac2 = tuple(fac.values()) dat_1 = (v * z_fac1 for v in air_data[int(zon_i1)]) dat_2 = (v * z_fac2 for v in air_data[int(zon_i2)]) dat_comb_i = (v1 + v2 for v1, v2 in zip(dat_1, dat_2)) dat_comb = tuple(d1 + d2 for d1, d2 in zip(dat_comb, dat_comb_i)) fac_len = len(int_facs) dat_comb = tuple(d1 / fac_len for d1 in dat_comb) air_mtx[int(pt_1)] = dat_comb return air_mtx