def test_not_mocked():
ws = ISDWeatherSource('722880')
ws.add_year_range(2011, 2011)
def read_meter_data(trace_filename,
project_info_filename,
project_id=None,
weather=True,
merge_series=True):
"""Read meter data from a raw XML file source, obtain matching project
information from a separate CSV file. Fetches the corresponding weather
data, when requested, too.
Parameters
==========
trace_filename: str
Filename of XML meter trace.
project_info_filename: str
Filename of CSV file containing project info.
project_id: str
Manually provide the project ID used in `project_info_filename`.
If `None`, the first part of `trace_filename` before a `_` is used.
weather: bool
`True` will obtain weather (temperature) data.
merge_series: bool
`True` will return a `pandas.DataFrame` with merged consumption and
temperature data.
Returns
=======
A `DataCollection` object with the following fields:
project_info: `pandas.DataFrame`
Contains columns for project properties.
baseline_end: `pandas.Datetime`
End date of the baseline period.
consumption_data: `eemeter.consumption.ConsumptionData`
Consumption data object.
consumption_data_freq: `pandas.DataFrame`
Consumption data with normalized frequency.
If :samp:`weather=True`:
weather_source: `eemeter.ISDWeatherSource`
Weather source object.
weather_data: `pandas.DataFrame`
Temperature observations in, degF, with frequency matching
`consumption_data_freq`. Values are averaged if raw temperature
observations are lower frequency.
If :samp:`merge_series=True`:
cons_weather_data: `pandas.DataFrame`
Merged consumption and temperature data.
"""
from eemeter.meter import DataCollection, DataContainer
from eemeter.parsers import ESPIUsageParser
# TODO: New API.
#from eemeter.structures import (
# EnergyTrace,
# EnergyTraceSet,
# Intervention,
# ZIPCodeSite,
# Project
#)
#from eemeter.io.parsers import ESPIUsageParser
from eemeter.weather import ISDWeatherSource
import pandas as pd
import os
with open(trace_filename, 'r') as f:
parser = ESPIUsageParser(f.read())
consumption_datas = list(parser.get_consumption_data_objects())
cons_data_obj = consumption_datas[0]
all_projects_info = pd.read_csv(project_info_filename)
fuel_type_map = {'electricity': 'E', 'natural_gas': 'NG'}
if project_id is None:
project_id = os.path.basename(trace_filename).split("_")[0]
fuel_type = fuel_type_map[cons_data_obj.fuel_type]
project_info = all_projects_info.query('project_id == "{}" and\
fuel_type == "{}"'.format(
project_id, fuel_type))
baseline_end = pd.to_datetime(project_info.baseline_period_end.tolist()[0],
utc=True)
# Sometimes the data have differing observation frequencies,
# so choose the most common one (in the usage data) and align
# everything to that.
cons_index_diff = cons_data_obj.data.index.to_series().diff(periods=1)
new_freq = pd.value_counts(cons_index_diff).argmax()
cons_data = cons_data_obj.data.tz_convert("UTC")
cons_data = cons_data.resample(new_freq).mean()
res = DataCollection(project_info=project_info,
baseline_end=baseline_end,
consumption_data=cons_data_obj,
consumption_data_freq=cons_data)
if weather:
station = unicode(project_info.weather_station.tolist()[0])
ws = ISDWeatherSource(station)
res.add_data(DataContainer("weather_source", ws, None))
ws.add_year_range(cons_data.index.min().year,
cons_data.index.max().year)
weather_data = ws._unit_convert(ws.tempC, "degF").tz_localize("UTC")
weather_data = weather_data.resample(new_freq).mean()
res.add_data(DataContainer("weather_data", weather_data, None))
if weather_data.empty:
raise ValueError("No weather data")
if merge_series:
cons_weather_data = pd.concat([cons_data, weather_data],
axis=1,
join="inner")
cons_weather_data.columns = ['usage', 'temp']
res.add_data(
DataContainer("cons_weather_data", cons_weather_data, None))
return res
