Heating controller with neural thermal model written in Python
Jacek Kowalski
2018-06-24 425bf71fc0b24b547006686d83404c54b983de0b
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from collections import Iterable
from copy import deepcopy
 
from .DataReader import DataReader
 
 
class SlidingWindow:
    def __init__(self,
                 model_past_values=5, model_past_fields=None,
                 model_future_values=0, model_future_fields=None,
                 past_values=0, past_fields=None,
                 future_values=0, future_fields=None,
                 plot_fields=None, **kwargs):
 
        self.model_past_values = model_past_values
        if model_past_fields is None:
            model_past_fields = ['temp_in', 'temp_out', 'mode']
        self.model_past_fields = model_past_fields
 
        self.past_values = past_values
        if past_fields is None:
            past_fields = ['temp_in', 'temp_out']
        self.past_fields = past_fields
 
        self.max_past = max(self.model_past_values, self.past_values, 2)
 
        self.model_future_values = model_future_values
        if model_future_fields is None:
            model_future_fields = []
        self.model_future_fields = model_future_fields
 
        self.future_values = future_values
        if future_fields is None:
            future_fields = model_future_fields
        self.future_fields = future_fields
 
        self.max_future = max(self.model_future_values, self.future_values, 1)
 
        self.observations = []  # list of dict
 
        self.plot = None
        if plot_fields:
            self.plot = {}
            for field in plot_fields:
                self.plot[field] = []
 
    def add_observation(self, observation: dict) -> bool:
        self.observations = self.observations[-self.max_past - self.max_future:]
        self.observations.append(observation)
 
        if self.plot:
            for field in self.plot:
                if field.endswith('_calc'):
                    self.plot[field].append(observation[field[:-5]])
                else:
                    self.plot[field].append(observation[field])
 
        return len(self.observations) > self.max_past + self.max_future
 
    # SLIDING
 
    def next(self):
        self.max_past += 1
        self.max_future -= 1
 
    def copy(self, plot=False):
        result = SlidingWindow(model_past_values=self.model_past_values,
                               model_past_fields=self.model_past_fields,
                               model_future_values=self.model_future_values,
                               model_future_fields=self.model_future_fields,
                               past_values=self.past_values, past_fields=self.past_fields,
                               future_values=self.future_values, future_fields=self.future_fields)
        result.observations = deepcopy(self.observations)
        if plot:
            result.plot = deepcopy(self.plot)
        return result
 
    # GET VALUE
 
    def get_value(self, field, offset=-1):
        return self.observations[self.max_past + offset][field]
 
    def get_previous_value(self, field):
        return self.get_value(field, -2)
 
    def get_current_value(self, field):
        return self.get_value(field, -1)
 
    def get_current_orig_value(self, field):
        offset = -1
        return self.plot[field][-self.max_future - 1 + offset]
 
    def get_next_value(self, field):
        return self.get_value(field, 0)
 
    # SET VALUE
 
    def set_value(self, field, value, offset=-1):
        self.observations[self.max_past + offset][field] = value
        if self.plot and field + '_calc' in self.plot:
            self.plot[field + '_calc'][-self.max_future - 1 + offset] = value
 
    def set_current_value(self, field, value):
        self.set_value(field, value, -1)
 
    def set_next_value(self, field, value):
        self.set_value(field, value, 0)
 
    # PAST/FUTURE HELPERS
 
    def _get_past_values(self, num=None, fields=None, offset=0) -> list:
        if num is None:
            num = self.past_values
        if fields is None:
            fields = self.past_fields
        result = [
            self.observations[i + offset][j]
            for j in fields
            for i in range(self.max_past - num, self.max_past)
        ]
        return result
 
    def _get_future_values(self, num=None, fields=None, offset=0) -> list:
        if num is None:
            num = self.future_values
        if fields is None:
            fields = self.future_fields
        return [
            self.observations[i + offset][j]
            for j in fields
            for i in range(self.max_past, self.max_past + num)
        ]
 
    # MODEL
 
    def get_model_values(self) -> list:
        return self._get_past_values(num=self.model_past_values, fields=self.model_past_fields) \
               + self._get_future_values(num=self.model_future_values, fields=self.model_future_fields)
 
    def get_model_size(self) -> int:
        return self.model_past_values * len(self.model_past_fields) \
               + self.model_future_values * len(self.model_future_fields)
 
    def get_model_target(self):
        return self.get_next_value('temp_in')
 
    # WINDOW
 
    def get_window_values(self, offset=0):
        return self._get_past_values(num=self.past_values, fields=self.past_fields, offset=offset) \
               + self._get_future_values(num=self.future_values, fields=self.future_fields, offset=offset)
 
    def get_window_size(self):
        return self.past_values * len(self.past_fields) + self.future_values * len(self.future_fields)
 
    # PLOT
 
    def get_plot_data(self, field):
        return self.plot[field][:-self.max_future-1]
 
 
class SlidingWindowAdapter(Iterable):
    def __init__(self, reader: DataReader, **kwargs):
        self.reader = reader
        self.aggregate = SlidingWindow(**kwargs)
 
    def __iter__(self):
        return self
 
    def __next__(self) -> SlidingWindow:
        line = self.reader.pop()
        while not self.aggregate.add_observation(line):
            line = self.reader.pop()
        return self.aggregate