dm.models.open_detector package¶
Submodules¶
dm.models.open_detector.adapted_models module¶
Creates models for window opening detection.
-
dm.models.open_detector.adapted_models.
filter_only_open
(values, attr_name='value')¶ It gets events when a window was open.
- Parameters
values – dictionary of values
attr_name – name of attribute
- Returns
list of events when a window was open
-
dm.models.open_detector.adapted_models.
prepare_adapted_data_co2
(devs, cls, start, end, lat, lon, weather)¶ It creates a model on the basis of CO2 concentration.
- Parameters
devs – list of devices
cls – list of clients
start – timestamp that denotes start of required time interval
end – timestamp that denotes end of required time interval
lat – latitude where data was gathered
lon – longitude where data was gathered
weather – object used to get information about weather
- Returns
model on the basis of CO2 concentration
-
dm.models.open_detector.adapted_models.
prepare_adapted_data_t_h
(devs, cls, start, end, lat, lon, weather)¶ It creates a model on the basis of temperature and humidity.
- Parameters
devs – list of devices
cls – list of clients
start – timestamp that denotes start of required time interval
end – timestamp that denotes end of required time interval
lat – latitude where data was gathered
lon – longitude where data was gathered
weather – object used to get information about weather
- Returns
model on the basis of temperature and humidity
dm.models.open_detector.create_attrs module¶
Creates attributes used in models for window opening detector and predictor of optimal ventilation length.
-
class
dm.models.open_detector.create_attrs.
ColumnMapper
¶ Bases:
object
-
NO_EVENTS_RECORDS_CO2
= [('2018/10/08 00:07:30', 'nothing'), ('2018/10/08 00:55:30', 'nothing'), ('2018/10/08 23:20:30', 'nothing'), ('2018/10/09 10:21:30', 'nothing'), ('2018/10/09 11:10:00', 'nothing'), ('2018/10/09 14:13:00', 'nothing'), ('2018/10/09 22:51:00', 'nothing'), ('2018/10/09 22:55:30', 'nothing'), ('2018/10/11 13:48:30', 'nothing'), ('2018/10/11 19:07:30', 'nothing'), ('2018/10/07 09:01:00', 'nothing'), ('2018/10/07 16:18:30', 'nothing'), ('2018/10/08 20:41:30', 'nothing'), ('2018/10/08 20:57:00', 'nothing'), ('2018/10/10 22:58:30', 'nothing'), ('2018/10/10 23:13:00', 'nothing'), ('2018/10/11 13:47:30', 'nothing'), ('2018/10/16 17:02:30', 'nothing'), ('2018/10/17 10:51:30', 'nothing'), ('2018/10/18 19:56:30', 'nothing'), ('2018/10/07 08:58:00', 'nothing'), ('2018/10/07 11:36:00', 'nothing'), ('2018/10/08 19:20:00', 'nothing'), ('2018/10/09 22:28:00', 'nothing'), ('2018/10/11 18:51:30', 'nothing'), ('2018/10/13 13:25:30', 'nothing'), ('2018/10/13 18:31:30', 'nothing'), ('2018/10/14 00:40:00', 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02:29:00', 'nothing'), ('2018/11/10 04:31:30', 'nothing'), ('2019/01/09 15:46:30', 'nothing'), ('2018/11/09 23:27:30', 'nothing')]¶
-
NO_EVENTS_RECORDS_T_H
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12:16:00', 'nothing'), ('2019/03/02 12:17:00', 'nothing'), ('2019/03/02 12:56:30', 'nothing'), ('2019/03/05 08:32:00', 'nothing'), ('2019/03/25 22:22:30', 'nothing'), ('2019/01/20 21:26:00', 'nothing'), ('2019/02/26 13:00:00', 'nothing'), ('2019/02/26 13:00:30', 'nothing'), ('2019/03/02 12:17:00', 'nothing'), ('2019/03/02 12:56:30', 'nothing'), ('2019/03/07 21:01:00', 'nothing'), ('2019/02/26 12:59:30', 'nothing'), ('2019/02/26 13:00:00', 'nothing'), ('2019/02/26 13:00:30', 'nothing'), ('2019/03/02 12:17:00', 'nothing'), ('2019/03/02 12:56:30', 'nothing'), ('2018/12/03 16:00:00', 'nothing'), ('2019/02/26 13:00:00', 'nothing'), ('2019/02/26 13:00:30', 'nothing'), ('2019/03/25 19:22:30', 'nothing'), ('2019/01/20 21:26:00', 'nothing'), ('2019/01/20 21:26:30', 'nothing'), ('2019/02/21 18:02:30', 'nothing'), ('2019/02/26 13:00:00', 'nothing'), ('2019/02/26 13:00:30', 'nothing'), ('2019/03/21 08:02:00', 'nothing'), ('2019/02/26 12:59:30', 'nothing'), ('2019/02/26 13:00:00', 'nothing'), ('2019/02/26 13:00:30', 'nothing'), ('2019/03/05 08:32:00', 'nothing'), ('2019/03/21 08:02:30', 'nothing'), ('2019/01/20 21:26:00', 'nothing'), ('2019/01/20 21:26:30', 'nothing'), ('2019/02/10 21:28:30', 'nothing'), ('2019/02/26 13:00:30', 'nothing'), ('2019/03/21 08:02:00', 'nothing'), ('2018/12/26 04:36:00', 'nothing'), ('2019/02/03 08:20:30', 'nothing'), ('2019/02/21 18:02:30', 'nothing'), ('2019/02/26 12:59:30', 'nothing'), ('2019/02/26 13:00:00', 'nothing'), ('2019/03/02 12:17:00', 'nothing'), ('2019/01/21 21:45:30', 'nothing'), ('2019/01/16 11:29:30', 'nothing'), ('2019/02/26 13:00:00', 'nothing'), ('2018/12/03 16:00:00', 'nothing')]¶
-
OPEN_CO2
= {'co2_in_ppm': 'co2_in'}¶
-
OPEN_T_H
= {'rh_in2_absolute_g_m3': 'rh_in_absolute', 'rh_in2_specific_g_kg': 'rh_in_specific', 'temperature_in2_celsius': 'temperature_in'}¶
-
PREDICTOR_CO2
= {'co2_in_ppm_diff': 'co2_in_diff', 'rh_in_absolute_g_m3_diff': 'rh_in_absolute_diff', 'rh_in_specific_g_kg_diff': 'rh_in_specific_diff', 'temperature_in_celsius_diff': 'temperature_in_diff'}¶
-
PREDICTOR_T_H
= {'rh_in2_absolute_g_m3_diff': 'rh_in_absolute_diff', 'rh_in2_specific_g_kg_diff': 'rh_in_specific_diff', 'temperature_in2_celsius_diff': 'temperature_diff'}¶
-
-
dm.models.open_detector.create_attrs.
func_co2
(timestamp, row_selector, interval_selector, columns_map, end=None)¶ - It calculates attributes used for detector creation on the basis of CO2 concentration that
detects window opening.
- Parameters
timestamp – timestamp when an event occurred
row_selector – selector of row
interval_selector – selector of interval
columns_map – mapping of column names
end – deprecated
- Returns
attributes used for detector creation on the basis of CO2 concentration
-
dm.models.open_detector.create_attrs.
func_predict_co2
(timestamp, row_selector, interval_selector, columns_map, end=None)¶ It calculates attributes used for predictor creation on the basis of CO2 concentration.
- Parameters
timestamp – timestamp when an event occurred
row_selector – selector of row
interval_selector – selector of interval
columns_map – mapping of column names
end – deprecated timestamp that denotes end of event
- Returns
attributes used for predictor creation on the basis of CO2 concentration
-
dm.models.open_detector.create_attrs.
func_predict_t_h
(timestamp, row_selector, interval_selector, columns_map, end=None)¶ It calculates attributes used for predictor creation on the basis of temperature and humidity.
- Parameters
timestamp – timestamp when an event occurred
row_selector – selector of row
interval_selector – selector of interval
columns_map – mapping of column names
end – timestamp that denotes end of event
- Returns
attributes used for predictor creation on the basis of temperature and humidity
-
dm.models.open_detector.create_attrs.
func_t_h
(timestamp, row_selector, interval_selector, columns_map, end=None)¶ - It calculates attributes used for detector creation on the basis of temperature and humidity that
detects window opening.
- Parameters
timestamp – timestamp when an event occurred
row_selector – selector of row
interval_selector – selector of interval
columns_map – mapping of column names
end – deprecated
- Returns
attributes used for detector creation on the basis of temperature and humidity
dm.models.open_detector.generic_training_file module¶
Creates adapted models for window opening detection.
-
dm.models.open_detector.generic_training_file.
add_func
(events, cls, devs, model_type, interval_extension, mapper, func, weather)¶ It adds events to training dataset.
The training dataset contains events when a window was closed.
- Parameters
events – list of events
cls – list of clients
devs – list of devices
model_type – type of model - based on CO2 concentration or temperature and humidity
interval_extension – time shift that is subtracted or added to start or end of an interval respectively
mapper – dictionary containing mapping of attribute name in database to the name used in dataset
func – object that determines function used for attribute calculation
weather – object used to get information about weather
- Returns
records containing information about given events
-
dm.models.open_detector.generic_training_file.
generic_training_file
(events_file, no_event_time_shift, model_type, columns_map, cls, devs, weather)¶ It creates generic training dataset.
The training dataset contains the same number of events when a window was open and when it was closed.
- Parameters
events_file – file containing events
no_event_time_shift – number of seconds subtracted from start of event to define event when a window was closed
model_type – type of model - based on CO2 concentration or temperature and humidity
columns_map – mapping of column names
cls – list of clients
devs – list of devices
weather – object used to get information about weather
- Returns
generic training dataset
-
dm.models.open_detector.generic_training_file.
training_data
(json_f, cls, devs, model_type, no_event_time_shift, interval_extension, mapper, func, weather)¶ It creates training dataset.
The training dataset contains events when a window was open.
- Parameters
json_f – dictionary that contains information about events
cls – list of clients
devs – list of devices
model_type – type of model - based on CO2 concentration or temperature and humidity
no_event_time_shift – number of seconds subtracted from start of event to define event when a window was closed
interval_extension – time shift that is subtracted or added to start or end of an interval respectively
mapper – dictionary containing mapping of attribute name in database to the name used in dataset
func – object that determines function used for attribute calculation
weather – object used to get information about weather
- Returns
training dataset
dm.models.open_detector.generic_training_file_from_local_db module¶
Detector for window opening based on CO2 decrease with iterations.
-
dm.models.open_detector.generic_training_file_from_local_db.
generic_testing
(directory, columns_map)¶ It creates several testing datasets.
- Parameters
directory – name of directory
columns_map – mapping of column names
- Returns
None
-
dm.models.open_detector.generic_training_file_from_local_db.
testing_month
(table_name, start, directory, columns_map)¶ It creates testing dataset month by month.
- Parameters
table_name – name of table
start – timestamp that denotes start of time interval
end – timestamp that denotes end of time interval
directory – name of directory
columns_map – mapping of column names
- Returns
None
-
dm.models.open_detector.generic_training_file_from_local_db.
testing_set
(table_name: str, start, end, filename, columns_map)¶ It creates testing dataset.
- Parameters
table_name – name of table
start – timestamp that denotes start of time interval
end – timestamp that denotes end of time interval
filename – name of output file
columns_map – mapping of column names
- Returns
None
-
dm.models.open_detector.generic_training_file_from_local_db.
training_set_co2
(events_file, no_event_time_shift, table_name, output_filename, columns_map)¶ - It creates balanced training dataset for detector creation based on CO2 concentration
and CSV file containing the dataset.
- Parameters
events_file – file containing events
no_event_time_shift – number of seconds subtracted from start of event to define event when a window was closed
table_name – name of table
output_filename – name of output file
columns_map – mapping of column names
- Returns
balanced training dataset for detector creation based on CO2 concentration
-
dm.models.open_detector.generic_training_file_from_local_db.
training_set_t_h
(events_file, no_event_time_shift, table_name, output_filename, columns_map)¶ - It creates balanced training dataset for detector creation based on temperature and humidity
and CSV file containing the dataset.
- Parameters
events_file – file containing events
no_event_time_shift – number of seconds subtracted from start of event to define event when a window was closed
table_name – name of table
output_filename – name of output file
columns_map – mapping of column names
- Returns
balanced training dataset for detector creation based on temperature and humidity