Source code for eemeter.io

#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""

   Copyright 2014-2019 OpenEEmeter contributors

   Licensed under the Apache License, Version 2.0 (the "License");
   you may not use this file except in compliance with the License.
   You may obtain a copy of the License at

       http://www.apache.org/licenses/LICENSE-2.0

   Unless required by applicable law or agreed to in writing, software
   distributed under the License is distributed on an "AS IS" BASIS,
   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   See the License for the specific language governing permissions and
   limitations under the License.

"""
import numpy as np
import pandas as pd

__all__ = (
    "meter_data_from_csv",
    "meter_data_from_json",
    "meter_data_to_csv",
    "temperature_data_from_csv",
    "temperature_data_from_json",
    "temperature_data_to_csv",
)


[docs]def meter_data_from_csv( filepath_or_buffer, tz=None, start_col="start", value_col="value", gzipped=False, freq=None, **kwargs ): """ Load meter data from a CSV file. Default format:: start,value 2017-01-01T00:00:00+00:00,0.31 2017-01-02T00:00:00+00:00,0.4 2017-01-03T00:00:00+00:00,0.58 Parameters ---------- filepath_or_buffer : :any:`str` or file-handle File path or object. tz : :any:`str`, optional E.g., ``'UTC'`` or ``'US/Pacific'`` start_col : :any:`str`, optional, default ``'start'`` Date period start column. value_col : :any:`str`, optional, default ``'value'`` Value column, can be in any unit. gzipped : :any:`bool`, optional Whether file is gzipped. freq : :any:`str`, optional If given, apply frequency to data using :any:`pandas.DataFrame.resample`. **kwargs Extra keyword arguments to pass to :any:`pandas.read_csv`, such as ``sep='|'``. """ read_csv_kwargs = { "usecols": [start_col, value_col], "dtype": {value_col: np.float64}, "parse_dates": [start_col], "index_col": start_col, } if gzipped: read_csv_kwargs.update({"compression": "gzip"}) # allow passing extra kwargs read_csv_kwargs.update(kwargs) df = pd.read_csv(filepath_or_buffer, **read_csv_kwargs) df.index = pd.to_datetime(df.index, utc=True) # for pandas<0.24, which doesn't localize even with utc=True if df.index.tz is None: df.index = df.index.tz_localize("UTC") if tz is not None: df = df.tz_convert(tz) if freq == "hourly": df = df.resample("H").sum(min_count=1) elif freq == "daily": df = df.resample("D").sum(min_count=1) return df
[docs]def temperature_data_from_csv( filepath_or_buffer, tz=None, date_col="dt", temp_col="tempF", gzipped=False, freq=None, **kwargs ): """ Load temperature data from a CSV file. Default format:: dt,tempF 2017-01-01T00:00:00+00:00,21 2017-01-01T01:00:00+00:00,22.5 2017-01-01T02:00:00+00:00,23.5 Parameters ---------- filepath_or_buffer : :any:`str` or file-handle File path or object. tz : :any:`str`, optional E.g., ``'UTC'`` or ``'US/Pacific'`` date_col : :any:`str`, optional, default ``'dt'`` Date period start column. temp_col : :any:`str`, optional, default ``'tempF'`` Temperature column. gzipped : :any:`bool`, optional Whether file is gzipped. freq : :any:`str`, optional If given, apply frequency to data using :any:`pandas.Series.resample`. **kwargs Extra keyword arguments to pass to :any:`pandas.read_csv`, such as ``sep='|'``. """ read_csv_kwargs = { "usecols": [date_col, temp_col], "dtype": {temp_col: np.float64}, "parse_dates": [date_col], "index_col": date_col, } if gzipped: read_csv_kwargs.update({"compression": "gzip"}) # allow passing extra kwargs read_csv_kwargs.update(kwargs) df = pd.read_csv(filepath_or_buffer, **read_csv_kwargs) df.index = pd.to_datetime(df.index, utc=True) # for pandas<0.24, which doesn't localize even with utc=True if df.index.tz is None: df.index = df.index.tz_localize("UTC") if tz is not None: df = df.tz_convert(tz) if freq == "hourly": df = df.resample("H").sum(min_count=1) return df[temp_col]
[docs]def meter_data_from_json(data, orient="list"): """ Load meter data from json. Default format:: [ ['2017-01-01T00:00:00+00:00', 3.5], ['2017-02-01T00:00:00+00:00', 0.4], ['2017-03-01T00:00:00+00:00', 0.46], ] Parameters ---------- data : :any:`list` List elements are each a rows of data. Returns ------- df : :any:`pandas.DataFrame` DataFrame with a single column (``'value'``) and a :any:`pandas.DatetimeIndex`. """ if orient == "list": df = pd.DataFrame(data, columns=["start", "value"]) df["start"] = pd.to_datetime(df.start, utc=True) df = df.set_index("start") return df else: raise ValueError("orientation not recognized.")
[docs]def temperature_data_from_json(data, orient="list"): """ Load temperature data from json. (Must be given in degrees Fahrenheit). Default format:: [ ['2017-01-01T00:00:00+00:00', 3.5], ['2017-01-01T01:00:00+00:00', 5.4], ['2017-01-01T02:00:00+00:00', 7.4], ] Parameters ---------- data : :any:`list` List elements are each a rows of data. Returns ------- series : :any:`pandas.Series` DataFrame with a single column (``'tempF'``) and a :any:`pandas.DatetimeIndex`. """ if orient == "list": df = pd.DataFrame(data, columns=["dt", "tempF"]) series = df.tempF series.index = pd.to_datetime(df.dt, utc=True) return series else: raise ValueError("orientation not recognized.")
[docs]def meter_data_to_csv(meter_data, path_or_buf): """ Write meter data to CSV. See also :any:`pandas.DataFrame.to_csv`. Parameters ---------- meter_data : :any:`pandas.DataFrame` Meter data DataFrame with ``'value'`` column and :any:`pandas.DatetimeIndex`. path_or_buf : :any:`str` or file handle, default None File path or object, if None is provided the result is returned as a string. """ if meter_data.index.name is None: meter_data.index.name = "start" return meter_data.to_csv(path_or_buf, index=True)
[docs]def temperature_data_to_csv(temperature_data, path_or_buf): """ Write temperature data to CSV. See also :any:`pandas.DataFrame.to_csv`. Parameters ---------- temperature_data : :any:`pandas.Series` Temperature data series with :any:`pandas.DatetimeIndex`. path_or_buf : :any:`str` or file handle, default None File path or object, if None is provided the result is returned as a string. """ if temperature_data.index.name is None: temperature_data.index.name = "dt" if temperature_data.name is None: temperature_data.name = "temperature" return temperature_data.to_frame().to_csv(path_or_buf, index=True)