def cumulative_radiation(average_irradiance, wea, timestep, output):
"""Postprocess average irradiance (W/m2) into cumulative radiation (kWh/m2).
\b
Args:
average_irradiance: A single-column matrix of average irradiance values.
This input matrix must be in ASCII format.
wea: The .wea file that was used in the irradiance simulation. This
will be used to determine the duration of the analysis for computing
cumulative radiation.
"""
try:
# parse the Wea and the average_irradiance matrix
conversion = Wea.count_timesteps(wea) / (timestep * 1000)
first_line, input_file = remove_header(average_irradiance)
# calculate the value for the first line
output.write('%s\n' % (float(first_line) * conversion))
# write rest of the lines
for line in input_file:
output.write('%s\n' % (float(line) * conversion))
except Exception:
_logger.exception('Failed to compute cumulative radiation.')
sys.exit(1)
else:
sys.exit(0)
finally:
input_file.close()
def test_equality():
"""Test the equality of files imported from the same source."""
wea_file = './tests/assets/wea/chicago.wea'
wea_1 = Wea.from_file(wea_file)
wea_2 = wea_1.duplicate()
assert wea_1 == wea_2
wea_2.direct_normal_irradiance[12] = 200
assert wea_1 != wea_2
assert Wea.count_timesteps(wea_file) == 8760
def annual_irradiance_to_folder(folder, wea, timestep=1, sub_folder='metrics'):
"""Compute irradiance metrics in a folder and write them in a subfolder.
This command generates 3 files for each input grid.
* average_irradiance/{grid-name}.res -- Average Irradiance (W/m2)
* peak_irradiance/{grid-name}.res -- Peak Irradiance (W/m2)
* cumulative_radiation/{grid-name}.res -- Cumulative Radiation (kWh/m2)
Args:
folder: Results folder from an annual irradiance recipe.
wea: The .wea file that was used in the annual irradiance simulation. This
will be used to determine the duration of the analysis for computing
cumulative radiation.
timestep: The timestep of the Wea file, which is used to ensure the summed
row of irradiance yields cumulative radiation over the time period
of the Wea. (Default: 1).
sub_folder: An optional relative path for subfolder to copy results
files. (Default: metrics).
Returns:
str -- Path to results folder.
"""
# get the time length of the Wea and the list of grids
wea_len = Wea.count_timesteps(wea) * timestep
grids = [
g.replace('.ill', '') for g in os.listdir(folder) if g.endswith('.ill')
]
grid_info = os.path.join(folder, 'grids_info.json')
# write a record of the timestep into the result folder for result processing
t_step_f = os.path.join(folder, 'timestep.txt')
with open(t_step_f, 'w') as t_f:
t_f.write(str(timestep))
# setup the folder into which the metrics will be written
metrics_folder = os.path.join(folder, sub_folder)
metrics_folders = []
for sub_f in ('average_irradiance', 'peak_irradiance',
'cumulative_radiation'):
m_path = os.path.join(metrics_folder, sub_f)
metrics_folders.append(m_path)
if not os.path.isdir(m_path):
os.makedirs(m_path)
grid_info_copy = os.path.join(m_path, 'grids_info.json')
shutil.copyfile(grid_info, grid_info_copy)
# loop through the grids and compute metrics
for grid in grids:
input_matrix = os.path.join(folder, '{}.ill'.format(grid))
first_line, input_file = remove_header(input_matrix)
avg = os.path.join(metrics_folders[0], '{}.res'.format(grid))
pk = os.path.join(metrics_folders[1], '{}.res'.format(grid))
cml = os.path.join(metrics_folders[2], '{}.res'.format(grid))
with open(avg, 'w') as avg_i, open(pk,
'w') as pk_i, open(cml,
'w') as cml_r:
# calculate the values for the first line
values = [float(v) for v in first_line.split()]
total_val = sum(values)
avg_i.write('{}\n'.format(total_val / wea_len))
pk_i.write('{}\n'.format(max(values)))
cml_r.write('{}\n'.format(total_val / (timestep * 1000)))
# write rest of the lines
for line in input_file:
try:
values = [float(v) for v in line.split()]
total_val = sum(values)
pk_i.write('{}\n'.format(max(values)))
avg_i.write('{}\n'.format(total_val / wea_len))
cml_r.write('{}\n'.format(total_val / (timestep * 1000)))
except ValueError:
pass # last line of the file
# create info for honeybee-vtk results visualization
config_file = os.path.join(metrics_folder, 'config.json')
cfg = _annual_irradiance_config()
with open(config_file, 'w') as outf:
json.dump(cfg, outf)
return metrics_folder
