Revert "clean up dependencies and remove depricated components"

This reverts commit 9f294cd.

alfalfa_client/alfalfa_client.py

@@ -26,6 +26,7 @@
 # OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 # ****************************************************************************************************
 
+import functools
 import json
 import os
 from collections import OrderedDict

alfalfa_client/historian.py

@@ -0,0 +1,151 @@
+# ****************************************************************************************************
+# :copyright (c) 2008-2021 URBANopt, Alliance for Sustainable Energy, LLC, and other contributors.
+
+# All rights reserved.
+
+# Redistribution and use in source and binary forms, with or without modification, are permitted
+# provided that the following conditions are met:
+
+# Redistributions of source code must retain the above copyright notice, this list of conditions
+# and the following disclaimer.
+
+# Redistributions in binary form must reproduce the above copyright notice, this list of conditions
+# and the following disclaimer in the documentation and/or other materials provided with the
+# distribution.
+
+# Neither the name of the copyright holder nor the names of its contributors may be used to endorse
+# or promote products derived from this software without specific prior written permission.
+
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
+# IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
+# FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR
+# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+# DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER
+# IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT
+# OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+# ****************************************************************************************************
+
+
+# Credit: Nicholas Long
+
+import os
+
+import pandas as pd
+
+
+class Historian(object):
+    def __init__(self, time_step=1):
+        """
+        :param time_step: int, time step in minutes
+        """
+        self.data = {}
+        self.name_map = {}
+        self.units = {}
+        self.conversion_map = {}
+        self.time_step = time_step
+
+    def add_point(self, name, units, point_name, f_conversion=None):
+        """
+        Add a point to store to the historian
+        :param name: string, name of the datapoint. Must be convertible into dict key and dataframe column
+        :param units: string, units in which the values are stored
+        :param point_name: string, name of the point to extract from the model output dictionary from Alfalfa
+        :param f_conversion: function pointer, function to call to convert the value
+        :return:
+        """
+        if name in self.data.keys():
+            raise Exception(f'Historian point already exists for {name}')
+
+        self.data[name] = []
+        self.conversion_map[name] = f_conversion
+        self.units[name] = units
+
+        if point_name is not None:
+            if point_name in self.name_map.keys():
+                raise Exception(f'Point name in name map already exists for {point_name}')
+
+            self.name_map[point_name] = name
+
+    def add_data(self, values):
+        """
+        Append the data in the fields into the mapped column names. Pulls data out of values and
+        into the historian.
+        :param values: dict
+        """
+
+        for point_name, value in values.items():
+            if point_name in self.name_map:
+                name = self.name_map[point_name]
+                # print(f"name {name} and point {point_name} with value {value}")
+                f = self.conversion_map[name] if self.conversion_map[name] is not None else None
+                if f:
+                    value = f(value)
+                self.data[name].append(value)
+            else:
+                # point_name is not registered in historian, skipping
+                pass
+
+    def add_datum(self, name, value):
+        f = self.conversion_map[name] if self.conversion_map[name] is not None else None
+
+        if f:
+            value = f(value)
+        self.data[name].append(value)
+
+    def rm_incorrect_length_vals_from_data(self):
+        num_timesteps = len(self.data['timestamp'])
+        to_rm = []
+        for point, data in self.data.items():
+            if len(data) != num_timesteps:
+                to_rm.append(point)
+        for p in to_rm:
+            self.data.pop(p)
+
+    def to_df(self):
+        # create the time index
+        f = '{}T'.format(self.time_step)
+        ind = pd.date_range(
+            start=self.data['timestamp'][0], end=self.data['timestamp'][-1], freq=f
+        )
+        return pd.DataFrame(self.data, index=ind)
+
+    def save_csv(self, filepath, filename):
+        os.makedirs(filepath, exist_ok=True)
+
+        self.to_df().to_csv(f'{filepath}/{filename}')
+
+    def save_pickle(self, filepath, filename):
+        os.makedirs(filepath, exist_ok=True)
+
+        self.to_df().to_pickle(f'{filepath}/{filename}')
+
+    def evaluate_performance(self):
+        """
+        Return the overall performance of the control
+        Assumptions:
+            * Timestep is 5 Minutes
+            * Occupied hours are between 0800 and 1800
+        :return: dict of performance indicators
+        """
+        df = self.to_df()
+        df_occ_hours = df.between_time('08:00', '18:00')
+
+        total_hours = len(df.index) / 12
+        total_hours_occupied = len(df_occ_hours.index) / 12
+        total_hvac_energy = df.sum(axis=0)['TotalHVACPower'] / 12 / 1000  # kwh
+        total_hvac_energy_occupied = df_occ_hours.sum(axis=0)['TotalHVACPower'] / 12 / 1000  # kwh
+        average_ppd = df.mean(axis=0)['PPD']
+        average_ppd_occupied = df_occ_hours.mean(axis=0)['PPD']
+        # average_ppd_occupied = df_occ_hours.mean(axis=0)['PPD']  # this is not valuable
+
+        return {
+            'start_time': df['timestamp'].iloc[0].strftime('%m/%d/%Y %H:%M:%S'),
+            'end_time': df['timestamp'].iloc[-1].strftime('%m/%d/%Y %H:%M:%S'),
+            'total_hours': total_hours,
+            'total_hours_occupied': total_hours_occupied,
+            'total_hvac_energy': total_hvac_energy,
+            'total_hvac_energy_occupied': total_hvac_energy_occupied,
+            'average_ppd': average_ppd,
+            'average_ppd_occupied': average_ppd_occupied,
+        }