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eventable/vobject | vobject/vcard.py | serializeFields | def serializeFields(obj, order=None):
"""
Turn an object's fields into a ';' and ',' seperated string.
If order is None, obj should be a list, backslash escape each field and
return a ';' separated string.
"""
fields = []
if order is None:
fields = [backslashEscape(val) for val in o... | python | def serializeFields(obj, order=None):
"""
Turn an object's fields into a ';' and ',' seperated string.
If order is None, obj should be a list, backslash escape each field and
return a ';' separated string.
"""
fields = []
if order is None:
fields = [backslashEscape(val) for val in o... | [
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eventable/vobject | vobject/vcard.py | Address.toString | def toString(val, join_char='\n'):
"""
Turn a string or array value into a string.
"""
if type(val) in (list, tuple):
return join_char.join(val)
return val | python | def toString(val, join_char='\n'):
"""
Turn a string or array value into a string.
"""
if type(val) in (list, tuple):
return join_char.join(val)
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eventable/vobject | vobject/vcard.py | VCardTextBehavior.encode | def encode(cls, line):
"""
Backslash escape line.value.
"""
if not line.encoded:
encoding = getattr(line, 'encoding_param', None)
if encoding and encoding.upper() == cls.base64string:
if isinstance(line.value, bytes):
line.value... | python | def encode(cls, line):
"""
Backslash escape line.value.
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eventable/vobject | vobject/vcard.py | VCard3_0.generateImplicitParameters | def generateImplicitParameters(cls, obj):
"""
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"""
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"""
Create PRODID, VERSION, and VTIMEZONEs if needed.
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"""
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eventable/vobject | vobject/vcard.py | Photo.serialize | def serialize(cls, obj, buf, lineLength, validate):
"""
Apple's Address Book is *really* weird with images, it expects
base64 data to have very specific whitespace. It seems Address Book
can handle PHOTO if it's not wrapped, so don't wrap it.
"""
if wacky_apple_photo_ser... | python | def serialize(cls, obj, buf, lineLength, validate):
"""
Apple's Address Book is *really* weird with images, it expects
base64 data to have very specific whitespace. It seems Address Book
can handle PHOTO if it's not wrapped, so don't wrap it.
"""
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eventable/vobject | vobject/vcard.py | NameBehavior.transformToNative | def transformToNative(obj):
"""
Turn obj.value into a Name.
"""
if obj.isNative:
return obj
obj.isNative = True
obj.value = Name(**dict(zip(NAME_ORDER, splitFields(obj.value))))
return obj | python | def transformToNative(obj):
"""
Turn obj.value into a Name.
"""
if obj.isNative:
return obj
obj.isNative = True
obj.value = Name(**dict(zip(NAME_ORDER, splitFields(obj.value))))
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eventable/vobject | vobject/vcard.py | NameBehavior.transformFromNative | def transformFromNative(obj):
"""
Replace the Name in obj.value with a string.
"""
obj.isNative = False
obj.value = serializeFields(obj.value, NAME_ORDER)
return obj | python | def transformFromNative(obj):
"""
Replace the Name in obj.value with a string.
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obj.isNative = False
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eventable/vobject | vobject/vcard.py | AddressBehavior.transformToNative | def transformToNative(obj):
"""
Turn obj.value into an Address.
"""
if obj.isNative:
return obj
obj.isNative = True
obj.value = Address(**dict(zip(ADDRESS_ORDER, splitFields(obj.value))))
return obj | python | def transformToNative(obj):
"""
Turn obj.value into an Address.
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eventable/vobject | vobject/vcard.py | OrgBehavior.transformToNative | def transformToNative(obj):
"""
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eventable/vobject | docs/build/lib/vobject/vcard.py | VCardTextBehavior.decode | def decode(cls, line):
"""
Remove backslash escaping from line.valueDecode line, either to remove
backslash espacing, or to decode base64 encoding. The content line should
contain a ENCODING=b for base64 encoding, but Apple Addressbook seems to
export a singleton parameter of 'BA... | python | def decode(cls, line):
"""
Remove backslash escaping from line.valueDecode line, either to remove
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eventable/vobject | vobject/behavior.py | Behavior.validate | def validate(cls, obj, raiseException=False, complainUnrecognized=False):
"""Check if the object satisfies this behavior's requirements.
@param obj:
The L{ContentLine<base.ContentLine>} or
L{Component<base.Component>} to be validated.
@param raiseException:
I... | python | def validate(cls, obj, raiseException=False, complainUnrecognized=False):
"""Check if the object satisfies this behavior's requirements.
@param obj:
The L{ContentLine<base.ContentLine>} or
L{Component<base.Component>} to be validated.
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eventable/vobject | vobject/behavior.py | Behavior.serialize | def serialize(cls, obj, buf, lineLength, validate=True):
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eventable/vobject | vobject/win32tz.py | list_timezones | def list_timezones():
"""Return a list of all time zones known to the system."""
l = []
for i in xrange(parentsize):
l.append(_winreg.EnumKey(tzparent, i))
return l | python | def list_timezones():
"""Return a list of all time zones known to the system."""
l = []
for i in xrange(parentsize):
l.append(_winreg.EnumKey(tzparent, i))
return l | [
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eventable/vobject | vobject/win32tz.py | pickNthWeekday | def pickNthWeekday(year, month, dayofweek, hour, minute, whichweek):
"""dayofweek == 0 means Sunday, whichweek > 4 means last instance"""
first = datetime.datetime(year=year, month=month, hour=hour, minute=minute,
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weekdayone = first.replace(day=((dayofweek - first.isowee... | python | def pickNthWeekday(year, month, dayofweek, hour, minute, whichweek):
"""dayofweek == 0 means Sunday, whichweek > 4 means last instance"""
first = datetime.datetime(year=year, month=month, hour=hour, minute=minute,
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weekdayone = first.replace(day=((dayofweek - first.isowee... | [
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eventable/vobject | vobject/win32tz.py | valuesToDict | def valuesToDict(key):
"""Convert a registry key's values to a dictionary."""
d = {}
size = _winreg.QueryInfoKey(key)[1]
for i in xrange(size):
d[_winreg.EnumValue(key, i)[0]] = _winreg.EnumValue(key, i)[1]
return d | python | def valuesToDict(key):
"""Convert a registry key's values to a dictionary."""
d = {}
size = _winreg.QueryInfoKey(key)[1]
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eventable/vobject | docs/build/lib/vobject/ics_diff.py | diff | def diff(left, right):
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Take two VCALENDAR components, compare VEVENTs and VTODOs in them,
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eventable/vobject | vobject/icalendar.py | stringToDateTime | def stringToDateTime(s, tzinfo=None):
"""
Returns datetime.datetime object.
"""
try:
year = int(s[0:4])
month = int(s[4:6])
day = int(s[6:8])
hour = int(s[9:11])
minute = int(s[11:13])
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SoftwareDefinedBuildings/XBOS | apps/hole_filling/pelican/backfill.py | fillPelicanHole | def fillPelicanHole(site, username, password, tstat_name, start_time, end_time):
"""Fill a hole in a Pelican thermostat's data stream.
Arguments:
site -- The thermostat's Pelican site name
username -- The Pelican username for the site
password -- The Pelican password for the site
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"""Fill a hole in a Pelican thermostat's data stream.
Arguments:
site -- The thermostat's Pelican site name
username -- The Pelican username for the site
password -- The Pelican password for the site
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SoftwareDefinedBuildings/XBOS | apps/data_analysis/XBOS_data_analytics/Preprocess_Data.py | Preprocess_Data.add_degree_days | def add_degree_days(self, col='OAT', hdh_cpoint=65, cdh_cpoint=65):
""" Adds Heating & Cooling Degree Hours.
Parameters
----------
col : str
Column name which contains the outdoor air temperature.
hdh_cpoint : int
Heating degree hours. Defaults t... | python | def add_degree_days(self, col='OAT', hdh_cpoint=65, cdh_cpoint=65):
""" Adds Heating & Cooling Degree Hours.
Parameters
----------
col : str
Column name which contains the outdoor air temperature.
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SoftwareDefinedBuildings/XBOS | apps/data_analysis/XBOS_data_analytics/Preprocess_Data.py | Preprocess_Data.add_col_features | def add_col_features(self, col=None, degree=None):
""" Exponentiate columns of dataframe.
Basically this function squares/cubes a column.
e.g. df[col^2] = pow(df[col], degree) where degree=2.
Parameters
----------
col : list(str)
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""" Exponentiate columns of dataframe.
Basically this function squares/cubes a column.
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SoftwareDefinedBuildings/XBOS | apps/data_analysis/XBOS_data_analytics/Preprocess_Data.py | Preprocess_Data.standardize | def standardize(self):
""" Standardize data. """
if self.preprocessed_data.empty:
data = self.original_data
else:
data = self.preprocessed_data
scaler = preprocessing.StandardScaler()
data = pd.DataFrame(scaler.fit_transform(data), columns=data.c... | python | def standardize(self):
""" Standardize data. """
if self.preprocessed_data.empty:
data = self.original_data
else:
data = self.preprocessed_data
scaler = preprocessing.StandardScaler()
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SoftwareDefinedBuildings/XBOS | apps/data_analysis/XBOS_data_analytics/Preprocess_Data.py | Preprocess_Data.normalize | def normalize(self):
""" Normalize data. """
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data = pd.DataFrame(preprocessing.normalize(data), columns=data.columns, index=data.index)
self.preprocessed_data ... | python | def normalize(self):
""" Normalize data. """
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SoftwareDefinedBuildings/XBOS | apps/Data_quality_analysis/Preprocess_Data.py | Preprocess_Data.add_time_features | def add_time_features(self, year=False, month=False, week=True, tod=True, dow=True):
""" Add time features to dataframe.
Parameters
----------
year : bool
Year.
month : bool
Month.
week : bool
Week.
tod : bool
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""" Add time features to dataframe.
Parameters
----------
year : bool
Year.
month : bool
Month.
week : bool
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SoftwareDefinedBuildings/XBOS | apps/Data_quality_analysis/Model_Data.py | Model_Data.split_data | def split_data(self):
""" Split data according to baseline and projection time period values. """
try:
# Extract data ranging in time_period1
time_period1 = (slice(self.baseline_period[0], self.baseline_period[1]))
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""" Split data according to baseline and projection time period values. """
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SoftwareDefinedBuildings/XBOS | apps/Data_quality_analysis/Model_Data.py | Model_Data.linear_regression | def linear_regression(self):
""" Linear Regression.
This function runs linear regression and stores the,
1. Model
2. Model name
3. Mean score of cross validation
4. Metrics
"""
model = LinearRegression()
scores = []
kfold = KFold(n_sp... | python | def linear_regression(self):
""" Linear Regression.
This function runs linear regression and stores the,
1. Model
2. Model name
3. Mean score of cross validation
4. Metrics
"""
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SoftwareDefinedBuildings/XBOS | apps/Data_quality_analysis/Model_Data.py | Model_Data.lasso_regression | def lasso_regression(self):
""" Lasso Regression.
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1. Model
2. Model name
3. Max score
4. Metrics
"""
score_list = []
max_score = float('-inf')
best_alpha = None
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""" Lasso Regression.
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1. Model
2. Model name
3. Max score
4. Metrics
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SoftwareDefinedBuildings/XBOS | apps/Data_quality_analysis/Model_Data.py | Model_Data.random_forest | def random_forest(self):
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1. Model
2. Model name
3. Max score
4. Metrics
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1. Model
2. Model name
3. Max score
4. Metrics
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SoftwareDefinedBuildings/XBOS | apps/Data_quality_analysis/Model_Data.py | Model_Data.run_models | def run_models(self):
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Returns
-------
model
Best model
dict
Metrics of the models
"""
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self.ridge_regression()
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""" Run all models.
Returns
-------
model
Best model
dict
Metrics of the models
"""
self.linear_regression()
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SoftwareDefinedBuildings/XBOS | apps/Data_quality_analysis/Model_Data.py | Model_Data.custom_model | def custom_model(self, func):
""" Run custom model provided by user.
To Do,
1. Define custom function's parameters, its data types, and return types
Parameters
----------
func : function
Custom function
Returns
-------
dict
... | python | def custom_model(self, func):
""" Run custom model provided by user.
To Do,
1. Define custom function's parameters, its data types, and return types
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----------
func : function
Custom function
Returns
-------
dict
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SoftwareDefinedBuildings/XBOS | apps/Data_quality_analysis/Model_Data.py | Model_Data.best_model_fit | def best_model_fit(self):
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dict
Best model's metrics
"""
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Best model's metrics
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SoftwareDefinedBuildings/XBOS | apps/Data_quality_analysis/Plot_Data.py | Plot_Data.correlation_plot | def correlation_plot(self, data):
""" Create heatmap of Pearson's correlation coefficient.
Parameters
----------
data : pd.DataFrame()
Data to display.
Returns
-------
matplotlib.figure
Heatmap.
"""
# CHECK: Add saved... | python | def correlation_plot(self, data):
""" Create heatmap of Pearson's correlation coefficient.
Parameters
----------
data : pd.DataFrame()
Data to display.
Returns
-------
matplotlib.figure
Heatmap.
"""
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SoftwareDefinedBuildings/XBOS | apps/Data_quality_analysis/Plot_Data.py | Plot_Data.baseline_projection_plot | def baseline_projection_plot(self, y_true, y_pred,
baseline_period, projection_period,
model_name, adj_r2,
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site):
""" Create baseline and projectio... | python | def baseline_projection_plot(self, y_true, y_pred,
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SoftwareDefinedBuildings/XBOS | apps/system_identification/rtu_energy.py | get_thermostat_meter_data | def get_thermostat_meter_data(zone):
"""
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It returns a handler to call when you want to stop subscribing data, which
returns a list of the data readins over that time period
"""
meter_uri = zone2meter.get(zone, "None")
data... | python | def get_thermostat_meter_data(zone):
"""
This method subscribes to the output of the meter for the given zone.
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returns a list of the data readins over that time period
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meter_uri = zone2meter.get(zone, "None")
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SoftwareDefinedBuildings/XBOS | apps/system_identification/rtu_energy.py | call_heat | def call_heat(tstat):
"""
Adjusts the temperature setpoints in order to call for heating. Returns
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"""
current_hsp, current_csp = tstat.heating_setpoint, tstat.cooling_setpoint
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"""
Adjusts the temperature setpoints in order to call for heating. Returns
a handler to call when you want to reset the thermostat
"""
current_hsp, current_csp = tstat.heating_setpoint, tstat.cooling_setpoint
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SoftwareDefinedBuildings/XBOS | apps/system_identification/rtu_energy.py | call_cool | def call_cool(tstat):
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Adjusts the temperature setpoints in order to call for cooling. Returns
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"""
current_hsp, current_csp = tstat.heating_setpoint, tstat.cooling_setpoint
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"""
Adjusts the temperature setpoints in order to call for cooling. Returns
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current_hsp, current_csp = tstat.heating_setpoint, tstat.cooling_setpoint
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SoftwareDefinedBuildings/XBOS | apps/system_identification/rtu_energy.py | call_fan | def call_fan(tstat):
"""
Toggles the fan
"""
old_fan = tstat.fan
tstat.write({
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def restore():
tstat.write({
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return restore | python | def call_fan(tstat):
"""
Toggles the fan
"""
old_fan = tstat.fan
tstat.write({
'fan': not old_fan,
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tstat.write({
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SoftwareDefinedBuildings/XBOS | apps/Data_quality_analysis/Import_Data.py | Import_Data.import_csv | def import_csv(self, file_name='*', folder_name='.', head_row=0, index_col=0, convert_col=True, concat_files=False):
""" Imports csv file(s) and stores the result in data.
Note
----
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SoftwareDefinedBuildings/XBOS | apps/Data_quality_analysis/Import_Data.py | Import_Data._load_csv | def _load_csv(self, file_name, folder_name, head_row, index_col, convert_col, concat_files):
""" Load single csv file.
Parameters
----------
file_name : str
CSV file to be imported. Defaults to '*' - all csv files in the folder.
folder_name : str
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file_name : str
CSV file to be imported. Defaults to '*' - all csv files in the folder.
folder_name : str
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SoftwareDefinedBuildings/XBOS | apps/Data_quality_analysis/Import_Data.py | Import_MDAL.convert_to_utc | def convert_to_utc(time):
""" Convert time to UTC
Parameters
----------
time : str
Time to convert. Has to be of the format '2016-01-01T00:00:00-08:00'.
Returns
-------
str
UTC timestamp.
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""" Convert time to UTC
Parameters
----------
time : str
Time to convert. Has to be of the format '2016-01-01T00:00:00-08:00'.
Returns
-------
str
UTC timestamp.
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SoftwareDefinedBuildings/XBOS | apps/Data_quality_analysis/Import_Data.py | Import_MDAL.get_meter | def get_meter(self, site, start, end, point_type='Green_Button_Meter',
var="meter", agg='MEAN', window='24h', aligned=True, return_names=True):
""" Get meter data from MDAL.
Parameters
----------
site : str
Building name.
start ... | python | def get_meter(self, site, start, end, point_type='Green_Button_Meter',
var="meter", agg='MEAN', window='24h', aligned=True, return_names=True):
""" Get meter data from MDAL.
Parameters
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site : str
Building name.
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SoftwareDefinedBuildings/XBOS | apps/Data_quality_analysis/Import_Data.py | Import_MDAL.get_tstat | def get_tstat(self, site, start, end, var="tstat_temp", agg='MEAN', window='24h', aligned=True, return_names=True):
""" Get thermostat data from MDAL.
Parameters
----------
site : str
Building name.
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SoftwareDefinedBuildings/XBOS | apps/Data_quality_analysis/Import_Data.py | Import_MDAL.compose_MDAL_dic | def compose_MDAL_dic(self, site, point_type,
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Parameters
----------
site : str
Building name.
start : str
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Parameters
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site : str
Building name.
start : str
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SoftwareDefinedBuildings/XBOS | apps/Data_quality_analysis/Import_Data.py | Import_MDAL.get_point_name | def get_point_name(self, context):
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SoftwareDefinedBuildings/XBOS | apps/Data_quality_analysis/Import_Data.py | Import_MDAL.replace_uuid_w_names | def replace_uuid_w_names(self, resp):
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Parameters
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resp : ???
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Returns
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???
???
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col_mapper = self.get_point_name(resp.context)["?point"].to_dict()
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SoftwareDefinedBuildings/XBOS | apps/data_analysis/XBOS_data_analytics/Import_Data.py | Import_XBOS.get_weather_power_tstat | def get_weather_power_tstat(self, site, start, end, data_type=['weather', 'power']):
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Parameters
----------
site : str
Site name.
start : str
Start date.
end : str
End date.
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site : str
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start : str
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SoftwareDefinedBuildings/XBOS | apps/Data_quality_analysis/Clean_Data.py | Clean_Data.drop_columns | def drop_columns(self, col):
""" Drop columns in dataframe.
Parameters
----------
col : str
Column to drop.
"""
try:
self.cleaned_data.drop(col, axis=1, inplace=True)
except Exception as e:
raise e | python | def drop_columns(self, col):
""" Drop columns in dataframe.
Parameters
----------
col : str
Column to drop.
"""
try:
self.cleaned_data.drop(col, axis=1, inplace=True)
except Exception as e:
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SoftwareDefinedBuildings/XBOS | apps/Data_quality_analysis/Clean_Data.py | Clean_Data.rename_columns | def rename_columns(self, col):
""" Rename columns of dataframe.
Parameters
----------
col : list(str)
List of columns to rename.
"""
try:
self.cleaned_data.columns = col
except Exception as e:
raise e | python | def rename_columns(self, col):
""" Rename columns of dataframe.
Parameters
----------
col : list(str)
List of columns to rename.
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SoftwareDefinedBuildings/XBOS | apps/Data_quality_analysis/Clean_Data.py | Clean_Data.resample_data | def resample_data(self, data, freq, resampler='mean'):
""" Resample dataframe.
Note
----
1. Figure out how to apply different functions to different columns .apply()
2. This theoretically work in upsampling too, check docs
http://pandas.pydata.org/pandas-docs/stable/... | python | def resample_data(self, data, freq, resampler='mean'):
""" Resample dataframe.
Note
----
1. Figure out how to apply different functions to different columns .apply()
2. This theoretically work in upsampling too, check docs
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SoftwareDefinedBuildings/XBOS | apps/Data_quality_analysis/Clean_Data.py | Clean_Data.interpolate_data | def interpolate_data(self, data, limit, method):
""" Interpolate dataframe.
Parameters
----------
data : pd.DataFrame()
Dataframe to interpolate
limit : int
Interpolation limit.
method : str
Interpolation method.
Returns... | python | def interpolate_data(self, data, limit, method):
""" Interpolate dataframe.
Parameters
----------
data : pd.DataFrame()
Dataframe to interpolate
limit : int
Interpolation limit.
method : str
Interpolation method.
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SoftwareDefinedBuildings/XBOS | apps/Data_quality_analysis/Clean_Data.py | Clean_Data.remove_na | def remove_na(self, data, remove_na_how):
""" Remove NAs from dataframe.
Parameters
----------
data : pd.DataFrame()
Dataframe to remove NAs from.
remove_na_how : str
Specificies how to remove NA i.e. all, any...
Returns
----... | python | def remove_na(self, data, remove_na_how):
""" Remove NAs from dataframe.
Parameters
----------
data : pd.DataFrame()
Dataframe to remove NAs from.
remove_na_how : str
Specificies how to remove NA i.e. all, any...
Returns
----... | [
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SoftwareDefinedBuildings/XBOS | apps/Data_quality_analysis/Clean_Data.py | Clean_Data.remove_outlier | def remove_outlier(self, data, sd_val):
""" Remove outliers from dataframe.
Note
----
1. This function excludes all lines with NA in all columns.
Parameters
----------
data : pd.DataFrame()
Dataframe to remove outliers from.
sd_val : int
... | python | def remove_outlier(self, data, sd_val):
""" Remove outliers from dataframe.
Note
----
1. This function excludes all lines with NA in all columns.
Parameters
----------
data : pd.DataFrame()
Dataframe to remove outliers from.
sd_val : int
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SoftwareDefinedBuildings/XBOS | apps/Data_quality_analysis/Clean_Data.py | Clean_Data.remove_out_of_bounds | def remove_out_of_bounds(self, data, low_bound, high_bound):
""" Remove out of bound datapoints from dataframe.
This function removes all points < low_bound and > high_bound.
To Do,
1. Add a different boundary for each column.
Parameters
----------
data ... | python | def remove_out_of_bounds(self, data, low_bound, high_bound):
""" Remove out of bound datapoints from dataframe.
This function removes all points < low_bound and > high_bound.
To Do,
1. Add a different boundary for each column.
Parameters
----------
data ... | [
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SoftwareDefinedBuildings/XBOS | apps/Data_quality_analysis/Clean_Data.py | Clean_Data._set_TS_index | def _set_TS_index(self, data):
""" Convert index to datetime and all other columns to numeric
Parameters
----------
data : pd.DataFrame()
Input dataframe.
Returns
-------
pd.DataFrame()
Modified dataframe.
"""
... | python | def _set_TS_index(self, data):
""" Convert index to datetime and all other columns to numeric
Parameters
----------
data : pd.DataFrame()
Input dataframe.
Returns
-------
pd.DataFrame()
Modified dataframe.
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SoftwareDefinedBuildings/XBOS | apps/Data_quality_analysis/Clean_Data.py | Clean_Data._utc_to_local | def _utc_to_local(self, data, local_zone="America/Los_Angeles"):
""" Adjust index of dataframe according to timezone that is requested by user.
Parameters
----------
data : pd.DataFrame()
Pandas dataframe of json timeseries response from server.
local_zone :... | python | def _utc_to_local(self, data, local_zone="America/Los_Angeles"):
""" Adjust index of dataframe according to timezone that is requested by user.
Parameters
----------
data : pd.DataFrame()
Pandas dataframe of json timeseries response from server.
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SoftwareDefinedBuildings/XBOS | apps/Data_quality_analysis/Clean_Data.py | Clean_Data._local_to_utc | def _local_to_utc(self, timestamp, local_zone="America/Los_Angeles"):
""" Convert local timestamp to UTC.
Parameters
----------
timestamp : pd.DataFrame()
Input Pandas dataframe whose index needs to be changed.
local_zone : str
Name of local zone. Defa... | python | def _local_to_utc(self, timestamp, local_zone="America/Los_Angeles"):
""" Convert local timestamp to UTC.
Parameters
----------
timestamp : pd.DataFrame()
Input Pandas dataframe whose index needs to be changed.
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SoftwareDefinedBuildings/XBOS | apps/Data_quality_analysis/Clean_Data.py | Clean_Data.remove_start_NaN | def remove_start_NaN(self, data, var=None):
""" Remove start NaN.
CHECK: Note issue with multi-column df.
Parameters
----------
data : pd.DataFrame()
Input dataframe.
var : list(str)
List that specifies specific columns of ... | python | def remove_start_NaN(self, data, var=None):
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CHECK: Note issue with multi-column df.
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----------
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Input dataframe.
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SoftwareDefinedBuildings/XBOS | apps/Data_quality_analysis/Clean_Data.py | Clean_Data.remove_end_NaN | def remove_end_NaN(self, data, var=None):
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CHECK: Note issue with multi-column df.
Parameters
----------
data : pd.DataFrame()
Input dataframe.
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CHECK: Note issue with multi-column df.
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Input dataframe.
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SoftwareDefinedBuildings/XBOS | apps/Data_quality_analysis/Clean_Data.py | Clean_Data._find_missing | def _find_missing(self, data, return_bool=False):
""" ???
Parameters
----------
data : pd.DataFrame()
Input dataframe.
return_bool : bool
???
Returns
-------
pd.DataFrame()
???
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# Th... | python | def _find_missing(self, data, return_bool=False):
""" ???
Parameters
----------
data : pd.DataFrame()
Input dataframe.
return_bool : bool
???
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SoftwareDefinedBuildings/XBOS | apps/Data_quality_analysis/Clean_Data.py | Clean_Data.display_missing | def display_missing(self, data, return_bool="any"):
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Parameters
----------
data : pd.DataFrame()
Input dataframe.
return_bool : bool
???
Returns
-------
pd.DataFrame()
???
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Parameters
----------
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Input dataframe.
return_bool : bool
???
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SoftwareDefinedBuildings/XBOS | apps/Data_quality_analysis/Clean_Data.py | Clean_Data.count_missing | def count_missing(self, data, output="number"):
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Parameters
----------
data : pd.DataFrame()
Input dataframe.
output : str
Sting indicating the output of function (number or percent)
Returns
-------
int/float
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Input dataframe.
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Sting indicating the output of function (number or percent)
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SoftwareDefinedBuildings/XBOS | apps/Data_quality_analysis/Clean_Data.py | Clean_Data.remove_missing | def remove_missing(self, data, return_bool="any"):
""" ???
Parameters
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Input dataframe.
return_bool : bool
???
Returns
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???
"""
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""" ???
Parameters
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data : pd.DataFrame()
Input dataframe.
return_bool : bool
???
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pd.DataFrame()
???
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SoftwareDefinedBuildings/XBOS | apps/Data_quality_analysis/Clean_Data.py | Clean_Data._find_outOfBound | def _find_outOfBound(self, data, lowBound, highBound):
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Input dataframe.
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Lower bound for dataframe.
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SoftwareDefinedBuildings/XBOS | apps/Data_quality_analysis/Clean_Data.py | Clean_Data.display_outOfBound | def display_outOfBound(self, data, lowBound, highBound):
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Input dataframe.
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Lower bound for dataframe.
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SoftwareDefinedBuildings/XBOS | apps/Data_quality_analysis/Clean_Data.py | Clean_Data.display_outliers | def display_outliers(self, data, method, coeff, window=10):
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Input dataframe.
method : str
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SoftwareDefinedBuildings/XBOS | apps/Data_quality_analysis/Clean_Data.py | Clean_Data.count_outliers | def count_outliers(self, data, method, coeff, output, window=10):
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SoftwareDefinedBuildings/XBOS | apps/Data_quality_analysis/Clean_Data.py | Clean_Data.count_if | def count_if(self, data, condition, val, output="number"):
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SoftwareDefinedBuildings/XBOS | apps/Data_quality_analysis/Clean_Data.py | Clean_Data.find_uuid | def find_uuid(self, obj, column_name):
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the object returned by the MDAL Query
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input point returned from MDAL Query
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input point returned from MDAL Query
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Dataframe to check for missing data.
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SoftwareDefinedBuildings/XBOS | apps/Data_quality_analysis/Clean_Data.py | Clean_Data.diff_boolean | def diff_boolean(self, df, column_name=None, uuid=None, duration=True, min_event_filter='3 hours'):
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SoftwareDefinedBuildings/XBOS | apps/Data_quality_analysis/Clean_Data.py | Clean_Data.analyze_quality_table | def analyze_quality_table(self, obj,low_bound=None, high_bound=None):
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-----
Need to make it specific for varying met... | python | def analyze_quality_table(self, obj,low_bound=None, high_bound=None):
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SoftwareDefinedBuildings/XBOS | apps/Data_quality_analysis/Clean_Data.py | Clean_Data.analyze_quality_graph | def analyze_quality_graph(self, obj):
""" Takes in an the object returned by the MDAL query, and analyzes the quality
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SoftwareDefinedBuildings/XBOS | apps/Data_quality_analysis/Clean_Data.py | Clean_Data.event_duration | def event_duration(self, obj, dictionary, low_bound=None, high_bound=None):
""" Takes in an object and returns a dictionary with the missing data events (start and end)
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To Do
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Need to make it specific for varying m... | python | def event_duration(self, obj, dictionary, low_bound=None, high_bound=None):
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SoftwareDefinedBuildings/XBOS | models/ciee/thermal_model.py | execute_schedule | def execute_schedule(day, sched, popt, initial_temperature):
"""
sched is a list of (hsp, csp) setpoints at 30m intervals
"""
output = []
actions = []
prev_temp = initial_temperature
weather = predict_weather(day)
print len(sched), len(weather)
for idx, epoch in enumerate(sched):
... | python | def execute_schedule(day, sched, popt, initial_temperature):
"""
sched is a list of (hsp, csp) setpoints at 30m intervals
"""
output = []
actions = []
prev_temp = initial_temperature
weather = predict_weather(day)
print len(sched), len(weather)
for idx, epoch in enumerate(sched):
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SoftwareDefinedBuildings/XBOS | apps/data_analysis/XBOS_data_analytics/Clean_Data.py | Clean_Data.clean_data | def clean_data(self, resample=True, freq='h', resampler='mean',
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SoftwareDefinedBuildings/XBOS | apps/Data_quality_analysis/Wrapper.py | Wrapper.get_global_count | def get_global_count(self):
""" Return global count (used for naming of .json and .png files)
Returns
-------
int
Global count
"""
# Check current number of json files in results directory and dump current json in new file
path_to_json = se... | python | def get_global_count(self):
""" Return global count (used for naming of .json and .png files)
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Global count
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SoftwareDefinedBuildings/XBOS | apps/Data_quality_analysis/Wrapper.py | Wrapper.write_json | def write_json(self):
""" Dump data into json file. """
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""" Dump data into json file. """
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SoftwareDefinedBuildings/XBOS | apps/Data_quality_analysis/Wrapper.py | Wrapper.site_analysis | def site_analysis(self, folder_name, site_install_mapping, end_date):
""" Summarize site data into a single table.
folder_name : str
Folder where all site data resides.
site_event_mapping : dic
Dictionary of site name to date of installation.
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Folder where all site data resides.
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Dictionary of site name to date of installation.
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SoftwareDefinedBuildings/XBOS | apps/Data_quality_analysis/Wrapper.py | Wrapper.read_json | def read_json(self, file_name=None, input_json=None, imported_data=pd.DataFrame()):
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-----
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Parameters
----------
file_name : str
Filename to be read.
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SoftwareDefinedBuildings/XBOS | apps/Data_quality_analysis/Wrapper.py | Wrapper.search | def search(self, file_name, imported_data=None):
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----
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file_name : str
Optional json file to read parameters.
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file_name : str
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SoftwareDefinedBuildings/XBOS | apps/Data_quality_analysis/Wrapper.py | Wrapper.import_data | def import_data(self, file_name='*', folder_name='.', head_row=0, index_col=0,
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SoftwareDefinedBuildings/XBOS | apps/Data_quality_analysis/Wrapper.py | Wrapper.clean_data | def clean_data(self, data, rename_col=None, drop_col=None,
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SoftwareDefinedBuildings/XBOS | apps/Data_quality_analysis/Wrapper.py | Wrapper.uncertainity_equation | def uncertainity_equation(self, model_data_obj, E_measured, E_predicted, confidence_level):
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An instance of Model_Data() which is a user defined class.
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SoftwareDefinedBuildings/XBOS | python/xbos/services/mdal.py | MDALClient.do_query | def do_query(self, query, timeout=DEFAULT_TIMEOUT, tz=pytz.timezone("US/Pacific")):
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SoftwareDefinedBuildings/XBOS | dashboards/sitedash/occupancy.py | get_occupancy | def get_occupancy(last, bucketsize):
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and aggregate it by zone. Take mean occupancy per zone (across all sensors).
If building does *not* have occupancy sensors, then we need to read the results from some occupancy file. | [
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SoftwareDefinedBuildings/XBOS | apps/prediction_service_framework/prediction_service.py | run_service | def run_service(prediction_fxn, namespace, prediction_type, block=False):
"""
Supported prediction_type:
- hvac
- occupancy
"""
subscribe = '{0}/s.predictions/+/i.{1}/slot/request'.format(namespace, prediction_type)
if block:
print 'Blocking is True! This will loop forever and progra... | python | def run_service(prediction_fxn, namespace, prediction_type, block=False):
"""
Supported prediction_type:
- hvac
- occupancy
"""
subscribe = '{0}/s.predictions/+/i.{1}/slot/request'.format(namespace, prediction_type)
if block:
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SoftwareDefinedBuildings/XBOS | dashboards/sitedash/app.py | prevmonday | def prevmonday(num):
"""
Return unix SECOND timestamp of "num" mondays ago
"""
today = get_today()
lastmonday = today - timedelta(days=today.weekday(), weeks=num)
return lastmonday | python | def prevmonday(num):
"""
Return unix SECOND timestamp of "num" mondays ago
"""
today = get_today()
lastmonday = today - timedelta(days=today.weekday(), weeks=num)
return lastmonday | [
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SoftwareDefinedBuildings/XBOS | apps/consumption/iec.py | med_filt | def med_filt(x, k=201):
"""Apply a length-k median filter to a 1D array x.
Boundaries are extended by repeating endpoints.
"""
if x.ndim > 1:
x = np.squeeze(x)
med = np.median(x)
assert k % 2 == 1, "Median filter length must be odd."
assert x.ndim == 1, "Input must be one-dimensional... | python | def med_filt(x, k=201):
"""Apply a length-k median filter to a 1D array x.
Boundaries are extended by repeating endpoints.
"""
if x.ndim > 1:
x = np.squeeze(x)
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SoftwareDefinedBuildings/XBOS | apps/occupancy/OccupancyThanos.py | find_similar_days | def find_similar_days(training_data, now, observation_length, k, method=hamming_distance):
min_time = training_data.index[0] + timedelta(minutes=observation_length)
# Find moments in our dataset that have the same hour/minute and is_weekend() == weekend.
selector = ((training_data.index.minute == now.minute) &
... | python | def find_similar_days(training_data, now, observation_length, k, method=hamming_distance):
min_time = training_data.index[0] + timedelta(minutes=observation_length)
# Find moments in our dataset that have the same hour/minute and is_weekend() == weekend.
selector = ((training_data.index.minute == now.minute) &
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SoftwareDefinedBuildings/XBOS | apps/data_analysis/XBOS_data_analytics/Wrapper.py | Wrapper.preprocess_data | def preprocess_data(self, data,
hdh_cpoint=65, cdh_cpoint=65, col_hdh_cdh=None,
col_degree=None, degree=None,
standardize=False, normalize=False,
year=False, month=False, week=False, tod=False, dow=False,
... | python | def preprocess_data(self, data,
hdh_cpoint=65, cdh_cpoint=65, col_hdh_cdh=None,
col_degree=None, degree=None,
standardize=False, normalize=False,
year=False, month=False, week=False, tod=False, dow=False,
... | [
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data : pd.DataFrame()
Dataframe to be preprocessed.
hdh_cpoint : int
Heating degree hours. Defaults to 65.
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SoftwareDefinedBuildings/XBOS | apps/data_analysis/XBOS_data_analytics/Wrapper.py | Wrapper.model | def model(self, data,
ind_col=None, dep_col=None,
project_ind_col=None,
baseline_period=[None, None], projection_period=None, exclude_time_period=None,
alphas=np.logspace(-4,1,30),
cv=3, plot=True, figsize=None,
custom_model_func=None):
"""... | python | def model(self, data,
ind_col=None, dep_col=None,
project_ind_col=None,
baseline_period=[None, None], projection_period=None, exclude_time_period=None,
alphas=np.logspace(-4,1,30),
cv=3, plot=True, figsize=None,
custom_model_func=None):
"""... | [
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data : pd.DataFrame()
Dataframe to model.
ind_col : list(str)
Independent column(s) of dataframe. Defaults to... | [
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SoftwareDefinedBuildings/XBOS | python/xbos/services/pundat.py | make_dataframe | def make_dataframe(result):
"""
Turns the results of one of the data API calls into a pandas dataframe
"""
import pandas as pd
ret = {}
if isinstance(result,dict):
if 'timeseries' in result:
result = result['timeseries']
for uuid, data in result.items():
df = pd.D... | python | def make_dataframe(result):
"""
Turns the results of one of the data API calls into a pandas dataframe
"""
import pandas as pd
ret = {}
if isinstance(result,dict):
if 'timeseries' in result:
result = result['timeseries']
for uuid, data in result.items():
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SoftwareDefinedBuildings/XBOS | python/xbos/services/pundat.py | merge_dfs | def merge_dfs(dfs, resample=None, do_mean=False, do_sum=False, do_min=False, do_max=False):
"""
dfs is a dictionary of key => dataframe
This method resamples each of the dataframes if a period is provided
(http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases)
"""
if len(dfs... | python | def merge_dfs(dfs, resample=None, do_mean=False, do_sum=False, do_min=False, do_max=False):
"""
dfs is a dictionary of key => dataframe
This method resamples each of the dataframes if a period is provided
(http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases)
"""
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SoftwareDefinedBuildings/XBOS | python/xbos/services/pundat.py | DataClient.query | def query(self, query, archiver="", timeout=DEFAULT_TIMEOUT):
"""
Runs the given pundat query and returns the results as a Python object.
Arguments:
[query]: the query string
[archiver]: if specified, this is the archiver to use. Else, it will run on the first archiver passed
... | python | def query(self, query, archiver="", timeout=DEFAULT_TIMEOUT):
"""
Runs the given pundat query and returns the results as a Python object.
Arguments:
[query]: the query string
[archiver]: if specified, this is the archiver to use. Else, it will run on the first archiver passed
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SoftwareDefinedBuildings/XBOS | python/xbos/services/pundat.py | DataClient.uuids | def uuids(self, where, archiver="", timeout=DEFAULT_TIMEOUT):
"""
Using the given where-clause, finds all UUIDs that match
Arguments:
[where]: the where clause (e.g. 'path like "keti"', 'SourceName = "TED Main"')
[archiver]: if specified, this is the archiver to use. Else, it wi... | python | def uuids(self, where, archiver="", timeout=DEFAULT_TIMEOUT):
"""
Using the given where-clause, finds all UUIDs that match
Arguments:
[where]: the where clause (e.g. 'path like "keti"', 'SourceName = "TED Main"')
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