def test_temperature_data_from_csv_gzipped(sample_metadata):
meter_item = sample_metadata["il-electricity-cdd-hdd-daily"]
temperature_filename = meter_item["temperature_filename"]
with resource_stream("eemeter.samples", temperature_filename) as f:
temperature_data = temperature_data_from_csv(f, gzipped=True)
assert temperature_data.shape == (19417, )
assert temperature_data.index.tz.zone == "UTC"
assert temperature_data.index.freq is None
def test_temperature_data_from_csv_custom_columns(sample_metadata):
with TemporaryFile() as f:
f.write(b"dt_custom,tempC\n" b"2017-01-01T00:00:00,10\n")
f.seek(0)
temperature_data = temperature_data_from_csv(f,
date_col="dt_custom",
temp_col="tempC")
assert temperature_data.shape == (1, )
assert temperature_data.index.tz.zone == "UTC"
assert temperature_data.index.freq is None
def test_temperature_data_from_csv(sample_metadata):
meter_item = sample_metadata["il-electricity-cdd-hdd-daily"]
temperature_filename = meter_item["temperature_filename"]
fname = resource_filename("eemeter.samples", temperature_filename)
with gzip.open(fname) as f:
temperature_data = temperature_data_from_csv(f)
assert temperature_data.shape == (19417, )
assert temperature_data.index.tz.zone == "UTC"
assert temperature_data.index.freq is None
def load_sample(sample):
""" Load meter data, temperature data, and metadata for associated with a
particular sample identifier. Note: samples are simulated, not real, data.
Parameters
----------
sample : :any:`str`
Identifier of sample. Complete list can be obtained with
:any:`eemeter.samples`.
Returns
-------
meter_data, temperature_data, metadata : :any:`tuple` of :any:`pandas.DataFrame`, :any:`pandas.Series`, and :any:`dict`
Meter data, temperature data, and metadata for this sample identifier.
"""
sample_metadata = _load_sample_metadata()
metadata = sample_metadata.get(sample)
if metadata is None:
raise ValueError("Sample not found: {}. Try one of these?\n{}".format(
sample,
"\n".join([
" - {}".format(key) for key in sorted(sample_metadata.keys())
]),
))
freq = metadata.get("freq")
if freq not in ("hourly", "daily"):
freq = None
meter_data_filename = metadata["meter_data_filename"]
with resource_stream("eemeter.samples", meter_data_filename) as f:
meter_data = meter_data_from_csv(f, gzipped=True, freq=freq)
temperature_filename = metadata["temperature_filename"]
with resource_stream("eemeter.samples", temperature_filename) as f:
temperature_data = temperature_data_from_csv(f,
gzipped=True,
freq="hourly")
metadata["blackout_start_date"] = pytz.UTC.localize(
parse_date(metadata["blackout_start_date"]))
metadata["blackout_end_date"] = pytz.UTC.localize(
parse_date(metadata["blackout_end_date"]))
return meter_data, temperature_data, metadata
def _get_data(
sample, meter_file, temperature_file, heating_balance_points, cooling_balance_points
):
if sample is not None:
with resource_stream("eemeter.samples", "metadata.json") as f:
metadata = json.loads(f.read().decode("utf-8"))
if sample in metadata:
click.echo("Loading sample: {}".format(sample))
meter_file = resource_stream(
"eemeter.samples", metadata[sample]["meter_data_filename"]
)
temperature_file = resource_stream(
"eemeter.samples", metadata[sample]["temperature_filename"]
)
else:
raise click.ClickException(
"Sample not found. Try one of these?\n{}".format(
"\n".join([" - {}".format(key) for key in sorted(metadata.keys())])
)
)
if meter_file is not None:
gzipped = meter_file.name.endswith(".gz")
meter_data = meter_data_from_csv(meter_file, gzipped=gzipped)
else:
raise click.ClickException("Meter data not specified.")
if temperature_file is not None:
gzipped = temperature_file.name.endswith(".gz")
temperature_data = temperature_data_from_csv(
temperature_file, gzipped=gzipped, freq="hourly"
)
else:
raise click.ClickException("Temperature data not specified.")
return merge_temperature_data(
meter_data,
temperature_data,
heating_balance_points=heating_balance_points,
cooling_balance_points=cooling_balance_points,
)
from eemeter_baseline import *
font = {'weight': 'normal', 'size': 20}
plt.rc('font', **font)
plt.rc('figure', figsize=(15, 10))
plt.rc('xtick.major', pad=8) # xticks too close to border!
plt.rc('ytick.major', pad=10) # xticks too close to border!
plt.rc('xtick', labelsize='small')
plt.rc('ytick', labelsize='small')
##############################################################################
#### LOAD & FORMAT DATA ######################################################
##############################################################################
# load 3 datasets
temperature = eemeter.temperature_data_from_csv("data/temperature.csv",
freq='hourly')
t3_ami = pd.read_csv('data/t3_measure_ami.csv')
t3_daily = pd.read_csv('data/t3_measure_daily.csv')
# split ami out by measure type
aev_ami = t3_ami[t3_ami['Type'] == 'AEV']
phev_ami = t3_ami[t3_ami['Type'] == 'PHEV']
cchp_ami = t3_ami[t3_ami['Type'] == 'CCHP']
ebike_ami = t3_ami[t3_ami['Type'] == 'eBike']
aev_ami[['start', 'value']].to_csv('data/aev_ami.csv')
phev_ami[['start', 'value']].to_csv('data/phev_ami.csv')
cchp_ami[['start', 'value']].to_csv('data/cchp_ami.csv')
ebike_ami[['start', 'value']].to_csv('data/ebike_ami.csv')
# split daily usage out by measure type