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2,000 | ampl/amplpy | amplpy/dataframe.py | DataFrame.setValues | def setValues(self, values):
"""
Set the values of a DataFrame from a dictionary.
Args:
values: Dictionary with the values to set.
"""
ncols = self.getNumCols()
nindices = self.getNumIndices()
for key, value in values.items():
key = Utils.convToList(key)
assert len(key) == nindices
value = Utils.convToList(value)
assert len(value) == ncols-nindices
self.addRow(key + value) | python | def setValues(self, values):
"""
Set the values of a DataFrame from a dictionary.
Args:
values: Dictionary with the values to set.
"""
ncols = self.getNumCols()
nindices = self.getNumIndices()
for key, value in values.items():
key = Utils.convToList(key)
assert len(key) == nindices
value = Utils.convToList(value)
assert len(value) == ncols-nindices
self.addRow(key + value) | [
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2,001 | ampl/amplpy | amplpy/dataframe.py | DataFrame.toDict | def toDict(self):
"""
Return a dictionary with the DataFrame data.
"""
d = {}
nindices = self.getNumIndices()
for i in range(self.getNumRows()):
row = list(self.getRowByIndex(i))
if nindices > 1:
key = tuple(row[:nindices])
elif nindices == 1:
key = row[0]
else:
key = None
if len(row) - nindices == 0:
d[key] = None
elif len(row) - nindices == 1:
d[key] = row[nindices]
else:
d[key] = tuple(row[nindices:])
return d | python | def toDict(self):
"""
Return a dictionary with the DataFrame data.
"""
d = {}
nindices = self.getNumIndices()
for i in range(self.getNumRows()):
row = list(self.getRowByIndex(i))
if nindices > 1:
key = tuple(row[:nindices])
elif nindices == 1:
key = row[0]
else:
key = None
if len(row) - nindices == 0:
d[key] = None
elif len(row) - nindices == 1:
d[key] = row[nindices]
else:
d[key] = tuple(row[nindices:])
return d | [
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2,002 | ampl/amplpy | amplpy/dataframe.py | DataFrame.toList | def toList(self):
"""
Return a list with the DataFrame data.
"""
if self.getNumCols() > 1:
return [
tuple(self.getRowByIndex(i))
for i in range(self.getNumRows())
]
else:
return [
self.getRowByIndex(i)[0]
for i in range(self.getNumRows())
] | python | def toList(self):
"""
Return a list with the DataFrame data.
"""
if self.getNumCols() > 1:
return [
tuple(self.getRowByIndex(i))
for i in range(self.getNumRows())
]
else:
return [
self.getRowByIndex(i)[0]
for i in range(self.getNumRows())
] | [
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2,003 | ampl/amplpy | amplpy/dataframe.py | DataFrame.toPandas | def toPandas(self):
"""
Return a pandas DataFrame with the DataFrame data.
"""
assert pd is not None
nindices = self.getNumIndices()
headers = self.getHeaders()
columns = {
header: list(self.getColumn(header))
for header in headers[nindices:]
}
index = zip(*[
list(self.getColumn(header))
for header in headers[:nindices]
])
index = [key if len(key) > 1 else key[0] for key in index]
if index == []:
return pd.DataFrame(columns, index=None)
else:
return pd.DataFrame(columns, index=index) | python | def toPandas(self):
"""
Return a pandas DataFrame with the DataFrame data.
"""
assert pd is not None
nindices = self.getNumIndices()
headers = self.getHeaders()
columns = {
header: list(self.getColumn(header))
for header in headers[nindices:]
}
index = zip(*[
list(self.getColumn(header))
for header in headers[:nindices]
])
index = [key if len(key) > 1 else key[0] for key in index]
if index == []:
return pd.DataFrame(columns, index=None)
else:
return pd.DataFrame(columns, index=index) | [
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2,004 | ampl/amplpy | amplpy/parameter.py | Parameter.set | def set(self, *args):
"""
Set the value of a single instance of this parameter.
Args:
args: value if the parameter is scalar, index and value
otherwise.
Raises:
RuntimeError: If the entity has been deleted in the underlying
AMPL.
TypeError: If the parameter is not scalar and the index is not
provided.
"""
assert len(args) in (1, 2)
if len(args) == 1:
value = args[0]
self._impl.set(value)
else:
index, value = args
if isinstance(value, Real):
self._impl.setTplDbl(Tuple(index)._impl, value)
elif isinstance(value, basestring):
self._impl.setTplStr(Tuple(index)._impl, value)
else:
raise TypeError | python | def set(self, *args):
"""
Set the value of a single instance of this parameter.
Args:
args: value if the parameter is scalar, index and value
otherwise.
Raises:
RuntimeError: If the entity has been deleted in the underlying
AMPL.
TypeError: If the parameter is not scalar and the index is not
provided.
"""
assert len(args) in (1, 2)
if len(args) == 1:
value = args[0]
self._impl.set(value)
else:
index, value = args
if isinstance(value, Real):
self._impl.setTplDbl(Tuple(index)._impl, value)
elif isinstance(value, basestring):
self._impl.setTplStr(Tuple(index)._impl, value)
else:
raise TypeError | [
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2,005 | ampl/amplpy | amplpy/set.py | Set.setValues | def setValues(self, values):
"""
Set the tuples in this set. Valid only for non-indexed sets.
Args:
values: A list of tuples or a :class:`~amplpy.DataFrame`.
In the case of a :class:`~amplpy.DataFrame`, the number of indexing
columns of the must be equal to the arity of the set. In the case of
a list of tuples, the arity of each tuple must be equal to the arity
of the set.
For example, considering the following AMPL entities and corresponding
Python objects:
.. code-block:: ampl
set A := 1..2;
param p{i in A} := i+10;
set AA;
The following is valid:
.. code-block:: python
A, AA = ampl.getSet('A'), ampl.getSet('AA')
AA.setValues(A.getValues()) # AA has now the members {1, 2}
"""
if isinstance(values, (list, set)):
if any(isinstance(value, basestring) for value in values):
values = list(map(str, values))
self._impl.setValuesStr(values, len(values))
elif all(isinstance(value, Real) for value in values):
values = list(map(float, values))
self._impl.setValuesDbl(values, len(values))
elif all(isinstance(value, tuple) for value in values):
self._impl.setValues(Utils.toTupleArray(values), len(values))
else:
raise TypeError
else:
if np is not None and isinstance(values, np.ndarray):
self.setValues(DataFrame.fromNumpy(values).toList())
return
Entity.setValues(self, values) | python | def setValues(self, values):
"""
Set the tuples in this set. Valid only for non-indexed sets.
Args:
values: A list of tuples or a :class:`~amplpy.DataFrame`.
In the case of a :class:`~amplpy.DataFrame`, the number of indexing
columns of the must be equal to the arity of the set. In the case of
a list of tuples, the arity of each tuple must be equal to the arity
of the set.
For example, considering the following AMPL entities and corresponding
Python objects:
.. code-block:: ampl
set A := 1..2;
param p{i in A} := i+10;
set AA;
The following is valid:
.. code-block:: python
A, AA = ampl.getSet('A'), ampl.getSet('AA')
AA.setValues(A.getValues()) # AA has now the members {1, 2}
"""
if isinstance(values, (list, set)):
if any(isinstance(value, basestring) for value in values):
values = list(map(str, values))
self._impl.setValuesStr(values, len(values))
elif all(isinstance(value, Real) for value in values):
values = list(map(float, values))
self._impl.setValuesDbl(values, len(values))
elif all(isinstance(value, tuple) for value in values):
self._impl.setValues(Utils.toTupleArray(values), len(values))
else:
raise TypeError
else:
if np is not None and isinstance(values, np.ndarray):
self.setValues(DataFrame.fromNumpy(values).toList())
return
Entity.setValues(self, values) | [
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.. code-block:: ampl
set A := 1..2;
param p{i in A} := i+10;
set AA;
The following is valid:
.. code-block:: python
A, AA = ampl.getSet('A'), ampl.getSet('AA')
AA.setValues(A.getValues()) # AA has now the members {1, 2} | [
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2,006 | ampl/amplpy | amplpy/errorhandler.py | ErrorHandler.error | def error(self, amplexception):
"""
Receives notification of an error.
"""
msg = '\t'+str(amplexception).replace('\n', '\n\t')
print('Error:\n{:s}'.format(msg))
raise amplexception | python | def error(self, amplexception):
"""
Receives notification of an error.
"""
msg = '\t'+str(amplexception).replace('\n', '\n\t')
print('Error:\n{:s}'.format(msg))
raise amplexception | [
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2,007 | ampl/amplpy | amplpy/errorhandler.py | ErrorHandler.warning | def warning(self, amplexception):
"""
Receives notification of a warning.
"""
msg = '\t'+str(amplexception).replace('\n', '\n\t')
print('Warning:\n{:s}'.format(msg)) | python | def warning(self, amplexception):
"""
Receives notification of a warning.
"""
msg = '\t'+str(amplexception).replace('\n', '\n\t')
print('Warning:\n{:s}'.format(msg)) | [
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2,008 | ampl/amplpy | amplpy/utils.py | register_magics | def register_magics(store_name='_ampl_cells', ampl_object=None):
"""
Register jupyter notebook magics ``%%ampl`` and ``%%ampl_eval``.
Args:
store_name: Name of the store where ``%%ampl cells`` will be stored.
ampl_object: Object used to evaluate ``%%ampl_eval`` cells.
"""
from IPython.core.magic import (
Magics, magics_class, cell_magic, line_magic
)
@magics_class
class StoreAMPL(Magics):
def __init__(self, shell=None, **kwargs):
Magics.__init__(self, shell=shell, **kwargs)
self._store = []
shell.user_ns[store_name] = self._store
@cell_magic
def ampl(self, line, cell):
"""Store the cell in the store"""
self._store.append(cell)
@cell_magic
def ampl_eval(self, line, cell):
"""Evaluate the cell"""
ampl_object.eval(cell)
@line_magic
def get_ampl(self, line):
"""Retrieve the store"""
return self._store
get_ipython().register_magics(StoreAMPL) | python | def register_magics(store_name='_ampl_cells', ampl_object=None):
"""
Register jupyter notebook magics ``%%ampl`` and ``%%ampl_eval``.
Args:
store_name: Name of the store where ``%%ampl cells`` will be stored.
ampl_object: Object used to evaluate ``%%ampl_eval`` cells.
"""
from IPython.core.magic import (
Magics, magics_class, cell_magic, line_magic
)
@magics_class
class StoreAMPL(Magics):
def __init__(self, shell=None, **kwargs):
Magics.__init__(self, shell=shell, **kwargs)
self._store = []
shell.user_ns[store_name] = self._store
@cell_magic
def ampl(self, line, cell):
"""Store the cell in the store"""
self._store.append(cell)
@cell_magic
def ampl_eval(self, line, cell):
"""Evaluate the cell"""
ampl_object.eval(cell)
@line_magic
def get_ampl(self, line):
"""Retrieve the store"""
return self._store
get_ipython().register_magics(StoreAMPL) | [
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Args:
store_name: Name of the store where ``%%ampl cells`` will be stored.
ampl_object: Object used to evaluate ``%%ampl_eval`` cells. | [
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] | 39df6954049a11a8f666aed26853259b4687099a | https://github.com/ampl/amplpy/blob/39df6954049a11a8f666aed26853259b4687099a/amplpy/utils.py#L11-L45 |
2,009 | ampl/amplpy | amplpy/variable.py | Variable.fix | def fix(self, value=None):
"""
Fix all instances of this variable to a value if provided or to
their current value otherwise.
Args:
value: value to be set.
"""
if value is None:
self._impl.fix()
else:
self._impl.fix(value) | python | def fix(self, value=None):
"""
Fix all instances of this variable to a value if provided or to
their current value otherwise.
Args:
value: value to be set.
"""
if value is None:
self._impl.fix()
else:
self._impl.fix(value) | [
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Args:
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2,010 | eventable/vobject | vobject/base.py | toVName | def toVName(name, stripNum=0, upper=False):
"""
Turn a Python name into an iCalendar style name,
optionally uppercase and with characters stripped off.
"""
if upper:
name = name.upper()
if stripNum != 0:
name = name[:-stripNum]
return name.replace('_', '-') | python | def toVName(name, stripNum=0, upper=False):
"""
Turn a Python name into an iCalendar style name,
optionally uppercase and with characters stripped off.
"""
if upper:
name = name.upper()
if stripNum != 0:
name = name[:-stripNum]
return name.replace('_', '-') | [
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2,011 | eventable/vobject | vobject/base.py | readComponents | def readComponents(streamOrString, validate=False, transform=True,
ignoreUnreadable=False, allowQP=False):
"""
Generate one Component at a time from a stream.
"""
if isinstance(streamOrString, basestring):
stream = six.StringIO(streamOrString)
else:
stream = streamOrString
try:
stack = Stack()
versionLine = None
n = 0
for line, n in getLogicalLines(stream, allowQP):
if ignoreUnreadable:
try:
vline = textLineToContentLine(line, n)
except VObjectError as e:
if e.lineNumber is not None:
msg = "Skipped line {lineNumber}, message: {msg}"
else:
msg = "Skipped a line, message: {msg}"
logger.error(msg.format(**{'lineNumber': e.lineNumber, 'msg': str(e)}))
continue
else:
vline = textLineToContentLine(line, n)
if vline.name == "VERSION":
versionLine = vline
stack.modifyTop(vline)
elif vline.name == "BEGIN":
stack.push(Component(vline.value, group=vline.group))
elif vline.name == "PROFILE":
if not stack.top():
stack.push(Component())
stack.top().setProfile(vline.value)
elif vline.name == "END":
if len(stack) == 0:
err = "Attempted to end the {0} component but it was never opened"
raise ParseError(err.format(vline.value), n)
if vline.value.upper() == stack.topName(): # START matches END
if len(stack) == 1:
component = stack.pop()
if versionLine is not None:
component.setBehaviorFromVersionLine(versionLine)
else:
behavior = getBehavior(component.name)
if behavior:
component.setBehavior(behavior)
if validate:
component.validate(raiseException=True)
if transform:
component.transformChildrenToNative()
yield component # EXIT POINT
else:
stack.modifyTop(stack.pop())
else:
err = "{0} component wasn't closed"
raise ParseError(err.format(stack.topName()), n)
else:
stack.modifyTop(vline) # not a START or END line
if stack.top():
if stack.topName() is None:
logger.warning("Top level component was never named")
elif stack.top().useBegin:
raise ParseError("Component {0!s} was never closed".format(
(stack.topName())), n)
yield stack.pop()
except ParseError as e:
e.input = streamOrString
raise | python | def readComponents(streamOrString, validate=False, transform=True,
ignoreUnreadable=False, allowQP=False):
"""
Generate one Component at a time from a stream.
"""
if isinstance(streamOrString, basestring):
stream = six.StringIO(streamOrString)
else:
stream = streamOrString
try:
stack = Stack()
versionLine = None
n = 0
for line, n in getLogicalLines(stream, allowQP):
if ignoreUnreadable:
try:
vline = textLineToContentLine(line, n)
except VObjectError as e:
if e.lineNumber is not None:
msg = "Skipped line {lineNumber}, message: {msg}"
else:
msg = "Skipped a line, message: {msg}"
logger.error(msg.format(**{'lineNumber': e.lineNumber, 'msg': str(e)}))
continue
else:
vline = textLineToContentLine(line, n)
if vline.name == "VERSION":
versionLine = vline
stack.modifyTop(vline)
elif vline.name == "BEGIN":
stack.push(Component(vline.value, group=vline.group))
elif vline.name == "PROFILE":
if not stack.top():
stack.push(Component())
stack.top().setProfile(vline.value)
elif vline.name == "END":
if len(stack) == 0:
err = "Attempted to end the {0} component but it was never opened"
raise ParseError(err.format(vline.value), n)
if vline.value.upper() == stack.topName(): # START matches END
if len(stack) == 1:
component = stack.pop()
if versionLine is not None:
component.setBehaviorFromVersionLine(versionLine)
else:
behavior = getBehavior(component.name)
if behavior:
component.setBehavior(behavior)
if validate:
component.validate(raiseException=True)
if transform:
component.transformChildrenToNative()
yield component # EXIT POINT
else:
stack.modifyTop(stack.pop())
else:
err = "{0} component wasn't closed"
raise ParseError(err.format(stack.topName()), n)
else:
stack.modifyTop(vline) # not a START or END line
if stack.top():
if stack.topName() is None:
logger.warning("Top level component was never named")
elif stack.top().useBegin:
raise ParseError("Component {0!s} was never closed".format(
(stack.topName())), n)
yield stack.pop()
except ParseError as e:
e.input = streamOrString
raise | [
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] | 498555a553155ea9b26aace93332ae79365ecb31 | https://github.com/eventable/vobject/blob/498555a553155ea9b26aace93332ae79365ecb31/vobject/base.py#L1075-L1147 |
2,012 | eventable/vobject | vobject/base.py | readOne | def readOne(stream, validate=False, transform=True, ignoreUnreadable=False,
allowQP=False):
"""
Return the first component from stream.
"""
return next(readComponents(stream, validate, transform, ignoreUnreadable,
allowQP)) | python | def readOne(stream, validate=False, transform=True, ignoreUnreadable=False,
allowQP=False):
"""
Return the first component from stream.
"""
return next(readComponents(stream, validate, transform, ignoreUnreadable,
allowQP)) | [
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2,013 | eventable/vobject | vobject/base.py | registerBehavior | def registerBehavior(behavior, name=None, default=False, id=None):
"""
Register the given behavior.
If default is True (or if this is the first version registered with this
name), the version will be the default if no id is given.
"""
if not name:
name = behavior.name.upper()
if id is None:
id = behavior.versionString
if name in __behaviorRegistry:
if default:
__behaviorRegistry[name].insert(0, (id, behavior))
else:
__behaviorRegistry[name].append((id, behavior))
else:
__behaviorRegistry[name] = [(id, behavior)] | python | def registerBehavior(behavior, name=None, default=False, id=None):
"""
Register the given behavior.
If default is True (or if this is the first version registered with this
name), the version will be the default if no id is given.
"""
if not name:
name = behavior.name.upper()
if id is None:
id = behavior.versionString
if name in __behaviorRegistry:
if default:
__behaviorRegistry[name].insert(0, (id, behavior))
else:
__behaviorRegistry[name].append((id, behavior))
else:
__behaviorRegistry[name] = [(id, behavior)] | [
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2,014 | eventable/vobject | vobject/base.py | getBehavior | def getBehavior(name, id=None):
"""
Return a matching behavior if it exists, or None.
If id is None, return the default for name.
"""
name = name.upper()
if name in __behaviorRegistry:
if id:
for n, behavior in __behaviorRegistry[name]:
if n == id:
return behavior
return __behaviorRegistry[name][0][1]
return None | python | def getBehavior(name, id=None):
"""
Return a matching behavior if it exists, or None.
If id is None, return the default for name.
"""
name = name.upper()
if name in __behaviorRegistry:
if id:
for n, behavior in __behaviorRegistry[name]:
if n == id:
return behavior
return __behaviorRegistry[name][0][1]
return None | [
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2,015 | eventable/vobject | vobject/base.py | VBase.validate | def validate(self, *args, **kwds):
"""
Call the behavior's validate method, or return True.
"""
if self.behavior:
return self.behavior.validate(self, *args, **kwds)
return True | python | def validate(self, *args, **kwds):
"""
Call the behavior's validate method, or return True.
"""
if self.behavior:
return self.behavior.validate(self, *args, **kwds)
return True | [
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2,016 | eventable/vobject | vobject/base.py | VBase.autoBehavior | def autoBehavior(self, cascade=False):
"""
Set behavior if name is in self.parentBehavior.knownChildren.
If cascade is True, unset behavior and parentBehavior for all
descendants, then recalculate behavior and parentBehavior.
"""
parentBehavior = self.parentBehavior
if parentBehavior is not None:
knownChildTup = parentBehavior.knownChildren.get(self.name, None)
if knownChildTup is not None:
behavior = getBehavior(self.name, knownChildTup[2])
if behavior is not None:
self.setBehavior(behavior, cascade)
if isinstance(self, ContentLine) and self.encoded:
self.behavior.decode(self)
elif isinstance(self, ContentLine):
self.behavior = parentBehavior.defaultBehavior
if self.encoded and self.behavior:
self.behavior.decode(self) | python | def autoBehavior(self, cascade=False):
"""
Set behavior if name is in self.parentBehavior.knownChildren.
If cascade is True, unset behavior and parentBehavior for all
descendants, then recalculate behavior and parentBehavior.
"""
parentBehavior = self.parentBehavior
if parentBehavior is not None:
knownChildTup = parentBehavior.knownChildren.get(self.name, None)
if knownChildTup is not None:
behavior = getBehavior(self.name, knownChildTup[2])
if behavior is not None:
self.setBehavior(behavior, cascade)
if isinstance(self, ContentLine) and self.encoded:
self.behavior.decode(self)
elif isinstance(self, ContentLine):
self.behavior = parentBehavior.defaultBehavior
if self.encoded and self.behavior:
self.behavior.decode(self) | [
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2,017 | eventable/vobject | vobject/base.py | VBase.setBehavior | def setBehavior(self, behavior, cascade=True):
"""
Set behavior. If cascade is True, autoBehavior all descendants.
"""
self.behavior = behavior
if cascade:
for obj in self.getChildren():
obj.parentBehavior = behavior
obj.autoBehavior(True) | python | def setBehavior(self, behavior, cascade=True):
"""
Set behavior. If cascade is True, autoBehavior all descendants.
"""
self.behavior = behavior
if cascade:
for obj in self.getChildren():
obj.parentBehavior = behavior
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2,018 | eventable/vobject | vobject/base.py | VBase.serialize | def serialize(self, buf=None, lineLength=75, validate=True, behavior=None):
"""
Serialize to buf if it exists, otherwise return a string.
Use self.behavior.serialize if behavior exists.
"""
if not behavior:
behavior = self.behavior
if behavior:
if DEBUG:
logger.debug("serializing {0!s} with behavior {1!s}".format(self.name, behavior))
return behavior.serialize(self, buf, lineLength, validate)
else:
if DEBUG:
logger.debug("serializing {0!s} without behavior".format(self.name))
return defaultSerialize(self, buf, lineLength) | python | def serialize(self, buf=None, lineLength=75, validate=True, behavior=None):
"""
Serialize to buf if it exists, otherwise return a string.
Use self.behavior.serialize if behavior exists.
"""
if not behavior:
behavior = self.behavior
if behavior:
if DEBUG:
logger.debug("serializing {0!s} with behavior {1!s}".format(self.name, behavior))
return behavior.serialize(self, buf, lineLength, validate)
else:
if DEBUG:
logger.debug("serializing {0!s} without behavior".format(self.name))
return defaultSerialize(self, buf, lineLength) | [
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2,019 | eventable/vobject | vobject/base.py | ContentLine.valueRepr | def valueRepr(self):
"""
Transform the representation of the value
according to the behavior, if any.
"""
v = self.value
if self.behavior:
v = self.behavior.valueRepr(self)
return v | python | def valueRepr(self):
"""
Transform the representation of the value
according to the behavior, if any.
"""
v = self.value
if self.behavior:
v = self.behavior.valueRepr(self)
return v | [
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2,020 | eventable/vobject | vobject/base.py | Component.setProfile | def setProfile(self, name):
"""
Assign a PROFILE to this unnamed component.
Used by vCard, not by vCalendar.
"""
if self.name or self.useBegin:
if self.name == name:
return
raise VObjectError("This component already has a PROFILE or "
"uses BEGIN.")
self.name = name.upper() | python | def setProfile(self, name):
"""
Assign a PROFILE to this unnamed component.
Used by vCard, not by vCalendar.
"""
if self.name or self.useBegin:
if self.name == name:
return
raise VObjectError("This component already has a PROFILE or "
"uses BEGIN.")
self.name = name.upper() | [
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2,021 | eventable/vobject | vobject/base.py | Component.add | def add(self, objOrName, group=None):
"""
Add objOrName to contents, set behavior if it can be inferred.
If objOrName is a string, create an empty component or line based on
behavior. If no behavior is found for the object, add a ContentLine.
group is an optional prefix to the name of the object (see RFC 2425).
"""
if isinstance(objOrName, VBase):
obj = objOrName
if self.behavior:
obj.parentBehavior = self.behavior
obj.autoBehavior(True)
else:
name = objOrName.upper()
try:
id = self.behavior.knownChildren[name][2]
behavior = getBehavior(name, id)
if behavior.isComponent:
obj = Component(name)
else:
obj = ContentLine(name, [], '', group)
obj.parentBehavior = self.behavior
obj.behavior = behavior
obj = obj.transformToNative()
except (KeyError, AttributeError):
obj = ContentLine(objOrName, [], '', group)
if obj.behavior is None and self.behavior is not None:
if isinstance(obj, ContentLine):
obj.behavior = self.behavior.defaultBehavior
self.contents.setdefault(obj.name.lower(), []).append(obj)
return obj | python | def add(self, objOrName, group=None):
"""
Add objOrName to contents, set behavior if it can be inferred.
If objOrName is a string, create an empty component or line based on
behavior. If no behavior is found for the object, add a ContentLine.
group is an optional prefix to the name of the object (see RFC 2425).
"""
if isinstance(objOrName, VBase):
obj = objOrName
if self.behavior:
obj.parentBehavior = self.behavior
obj.autoBehavior(True)
else:
name = objOrName.upper()
try:
id = self.behavior.knownChildren[name][2]
behavior = getBehavior(name, id)
if behavior.isComponent:
obj = Component(name)
else:
obj = ContentLine(name, [], '', group)
obj.parentBehavior = self.behavior
obj.behavior = behavior
obj = obj.transformToNative()
except (KeyError, AttributeError):
obj = ContentLine(objOrName, [], '', group)
if obj.behavior is None and self.behavior is not None:
if isinstance(obj, ContentLine):
obj.behavior = self.behavior.defaultBehavior
self.contents.setdefault(obj.name.lower(), []).append(obj)
return obj | [
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2,022 | eventable/vobject | vobject/base.py | Component.remove | def remove(self, obj):
"""
Remove obj from contents.
"""
named = self.contents.get(obj.name.lower())
if named:
try:
named.remove(obj)
if len(named) == 0:
del self.contents[obj.name.lower()]
except ValueError:
pass | python | def remove(self, obj):
"""
Remove obj from contents.
"""
named = self.contents.get(obj.name.lower())
if named:
try:
named.remove(obj)
if len(named) == 0:
del self.contents[obj.name.lower()]
except ValueError:
pass | [
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2,023 | eventable/vobject | vobject/base.py | Component.setBehaviorFromVersionLine | def setBehaviorFromVersionLine(self, versionLine):
"""
Set behavior if one matches name, versionLine.value.
"""
v = getBehavior(self.name, versionLine.value)
if v:
self.setBehavior(v) | python | def setBehaviorFromVersionLine(self, versionLine):
"""
Set behavior if one matches name, versionLine.value.
"""
v = getBehavior(self.name, versionLine.value)
if v:
self.setBehavior(v) | [
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2,024 | eventable/vobject | vobject/base.py | Component.transformChildrenToNative | def transformChildrenToNative(self):
"""
Recursively replace children with their native representation.
Sort to get dependency order right, like vtimezone before vevent.
"""
for childArray in (self.contents[k] for k in self.sortChildKeys()):
for child in childArray:
child = child.transformToNative()
child.transformChildrenToNative() | python | def transformChildrenToNative(self):
"""
Recursively replace children with their native representation.
Sort to get dependency order right, like vtimezone before vevent.
"""
for childArray in (self.contents[k] for k in self.sortChildKeys()):
for child in childArray:
child = child.transformToNative()
child.transformChildrenToNative() | [
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2,025 | eventable/vobject | vobject/base.py | Component.transformChildrenFromNative | def transformChildrenFromNative(self, clearBehavior=True):
"""
Recursively transform native children to vanilla representations.
"""
for childArray in self.contents.values():
for child in childArray:
child = child.transformFromNative()
child.transformChildrenFromNative(clearBehavior)
if clearBehavior:
child.behavior = None
child.parentBehavior = None | python | def transformChildrenFromNative(self, clearBehavior=True):
"""
Recursively transform native children to vanilla representations.
"""
for childArray in self.contents.values():
for child in childArray:
child = child.transformFromNative()
child.transformChildrenFromNative(clearBehavior)
if clearBehavior:
child.behavior = None
child.parentBehavior = None | [
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2,026 | eventable/vobject | docs/build/lib/vobject/change_tz.py | change_tz | def change_tz(cal, new_timezone, default, utc_only=False, utc_tz=icalendar.utc):
"""
Change the timezone of the specified component.
Args:
cal (Component): the component to change
new_timezone (tzinfo): the timezone to change to
default (tzinfo): a timezone to assume if the dtstart or dtend in cal
doesn't have an existing timezone
utc_only (bool): only convert dates that are in utc
utc_tz (tzinfo): the tzinfo to compare to for UTC when processing
utc_only=True
"""
for vevent in getattr(cal, 'vevent_list', []):
start = getattr(vevent, 'dtstart', None)
end = getattr(vevent, 'dtend', None)
for node in (start, end):
if node:
dt = node.value
if (isinstance(dt, datetime) and
(not utc_only or dt.tzinfo == utc_tz)):
if dt.tzinfo is None:
dt = dt.replace(tzinfo = default)
node.value = dt.astimezone(new_timezone) | python | def change_tz(cal, new_timezone, default, utc_only=False, utc_tz=icalendar.utc):
"""
Change the timezone of the specified component.
Args:
cal (Component): the component to change
new_timezone (tzinfo): the timezone to change to
default (tzinfo): a timezone to assume if the dtstart or dtend in cal
doesn't have an existing timezone
utc_only (bool): only convert dates that are in utc
utc_tz (tzinfo): the tzinfo to compare to for UTC when processing
utc_only=True
"""
for vevent in getattr(cal, 'vevent_list', []):
start = getattr(vevent, 'dtstart', None)
end = getattr(vevent, 'dtend', None)
for node in (start, end):
if node:
dt = node.value
if (isinstance(dt, datetime) and
(not utc_only or dt.tzinfo == utc_tz)):
if dt.tzinfo is None:
dt = dt.replace(tzinfo = default)
node.value = dt.astimezone(new_timezone) | [
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2,027 | eventable/vobject | docs/build/lib/vobject/base.py | defaultSerialize | def defaultSerialize(obj, buf, lineLength):
"""
Encode and fold obj and its children, write to buf or return a string.
"""
outbuf = buf or six.StringIO()
if isinstance(obj, Component):
if obj.group is None:
groupString = ''
else:
groupString = obj.group + '.'
if obj.useBegin:
foldOneLine(outbuf, "{0}BEGIN:{1}".format(groupString, obj.name),
lineLength)
for child in obj.getSortedChildren():
# validate is recursive, we only need to validate once
child.serialize(outbuf, lineLength, validate=False)
if obj.useBegin:
foldOneLine(outbuf, "{0}END:{1}".format(groupString, obj.name),
lineLength)
elif isinstance(obj, ContentLine):
startedEncoded = obj.encoded
if obj.behavior and not startedEncoded:
obj.behavior.encode(obj)
s = six.StringIO()
if obj.group is not None:
s.write(obj.group + '.')
s.write(obj.name.upper())
keys = sorted(obj.params.keys())
for key in keys:
paramstr = ','.join(dquoteEscape(p) for p in obj.params[key])
s.write(";{0}={1}".format(key, paramstr))
s.write(":{0}".format(str_(obj.value)))
if obj.behavior and not startedEncoded:
obj.behavior.decode(obj)
foldOneLine(outbuf, s.getvalue(), lineLength)
return buf or outbuf.getvalue() | python | def defaultSerialize(obj, buf, lineLength):
"""
Encode and fold obj and its children, write to buf or return a string.
"""
outbuf = buf or six.StringIO()
if isinstance(obj, Component):
if obj.group is None:
groupString = ''
else:
groupString = obj.group + '.'
if obj.useBegin:
foldOneLine(outbuf, "{0}BEGIN:{1}".format(groupString, obj.name),
lineLength)
for child in obj.getSortedChildren():
# validate is recursive, we only need to validate once
child.serialize(outbuf, lineLength, validate=False)
if obj.useBegin:
foldOneLine(outbuf, "{0}END:{1}".format(groupString, obj.name),
lineLength)
elif isinstance(obj, ContentLine):
startedEncoded = obj.encoded
if obj.behavior and not startedEncoded:
obj.behavior.encode(obj)
s = six.StringIO()
if obj.group is not None:
s.write(obj.group + '.')
s.write(obj.name.upper())
keys = sorted(obj.params.keys())
for key in keys:
paramstr = ','.join(dquoteEscape(p) for p in obj.params[key])
s.write(";{0}={1}".format(key, paramstr))
s.write(":{0}".format(str_(obj.value)))
if obj.behavior and not startedEncoded:
obj.behavior.decode(obj)
foldOneLine(outbuf, s.getvalue(), lineLength)
return buf or outbuf.getvalue() | [
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2,028 | eventable/vobject | docs/build/lib/vobject/icalendar.py | toUnicode | def toUnicode(s):
"""
Take a string or unicode, turn it into unicode, decoding as utf-8
"""
if isinstance(s, six.binary_type):
s = s.decode('utf-8')
return s | python | def toUnicode(s):
"""
Take a string or unicode, turn it into unicode, decoding as utf-8
"""
if isinstance(s, six.binary_type):
s = s.decode('utf-8')
return s | [
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2,029 | eventable/vobject | docs/build/lib/vobject/icalendar.py | numToDigits | def numToDigits(num, places):
"""
Helper, for converting numbers to textual digits.
"""
s = str(num)
if len(s) < places:
return ("0" * (places - len(s))) + s
elif len(s) > places:
return s[len(s)-places: ]
else:
return s | python | def numToDigits(num, places):
"""
Helper, for converting numbers to textual digits.
"""
s = str(num)
if len(s) < places:
return ("0" * (places - len(s))) + s
elif len(s) > places:
return s[len(s)-places: ]
else:
return s | [
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2,030 | eventable/vobject | docs/build/lib/vobject/icalendar.py | timedeltaToString | def timedeltaToString(delta):
"""
Convert timedelta to an ical DURATION.
"""
if delta.days == 0:
sign = 1
else:
sign = delta.days / abs(delta.days)
delta = abs(delta)
days = delta.days
hours = int(delta.seconds / 3600)
minutes = int((delta.seconds % 3600) / 60)
seconds = int(delta.seconds % 60)
output = ''
if sign == -1:
output += '-'
output += 'P'
if days:
output += '{}D'.format(days)
if hours or minutes or seconds:
output += 'T'
elif not days: # Deal with zero duration
output += 'T0S'
if hours:
output += '{}H'.format(hours)
if minutes:
output += '{}M'.format(minutes)
if seconds:
output += '{}S'.format(seconds)
return output | python | def timedeltaToString(delta):
"""
Convert timedelta to an ical DURATION.
"""
if delta.days == 0:
sign = 1
else:
sign = delta.days / abs(delta.days)
delta = abs(delta)
days = delta.days
hours = int(delta.seconds / 3600)
minutes = int((delta.seconds % 3600) / 60)
seconds = int(delta.seconds % 60)
output = ''
if sign == -1:
output += '-'
output += 'P'
if days:
output += '{}D'.format(days)
if hours or minutes or seconds:
output += 'T'
elif not days: # Deal with zero duration
output += 'T0S'
if hours:
output += '{}H'.format(hours)
if minutes:
output += '{}M'.format(minutes)
if seconds:
output += '{}S'.format(seconds)
return output | [
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2,031 | eventable/vobject | docs/build/lib/vobject/icalendar.py | stringToTextValues | def stringToTextValues(s, listSeparator=',', charList=None, strict=False):
"""
Returns list of strings.
"""
if charList is None:
charList = escapableCharList
def escapableChar (c):
return c in charList
def error(msg):
if strict:
raise ParseError(msg)
else:
logging.error(msg)
# vars which control state machine
charIterator = enumerate(s)
state = "read normal"
current = []
results = []
while True:
try:
charIndex, char = next(charIterator)
except:
char = "eof"
if state == "read normal":
if char == '\\':
state = "read escaped char"
elif char == listSeparator:
state = "read normal"
current = "".join(current)
results.append(current)
current = []
elif char == "eof":
state = "end"
else:
state = "read normal"
current.append(char)
elif state == "read escaped char":
if escapableChar(char):
state = "read normal"
if char in 'nN':
current.append('\n')
else:
current.append(char)
else:
state = "read normal"
# leave unrecognized escaped characters for later passes
current.append('\\' + char)
elif state == "end": # an end state
if len(current) or len(results) == 0:
current = "".join(current)
results.append(current)
return results
elif state == "error": # an end state
return results
else:
state = "error"
error("unknown state: '{0!s}' reached in {1!s}".format(state, s)) | python | def stringToTextValues(s, listSeparator=',', charList=None, strict=False):
"""
Returns list of strings.
"""
if charList is None:
charList = escapableCharList
def escapableChar (c):
return c in charList
def error(msg):
if strict:
raise ParseError(msg)
else:
logging.error(msg)
# vars which control state machine
charIterator = enumerate(s)
state = "read normal"
current = []
results = []
while True:
try:
charIndex, char = next(charIterator)
except:
char = "eof"
if state == "read normal":
if char == '\\':
state = "read escaped char"
elif char == listSeparator:
state = "read normal"
current = "".join(current)
results.append(current)
current = []
elif char == "eof":
state = "end"
else:
state = "read normal"
current.append(char)
elif state == "read escaped char":
if escapableChar(char):
state = "read normal"
if char in 'nN':
current.append('\n')
else:
current.append(char)
else:
state = "read normal"
# leave unrecognized escaped characters for later passes
current.append('\\' + char)
elif state == "end": # an end state
if len(current) or len(results) == 0:
current = "".join(current)
results.append(current)
return results
elif state == "error": # an end state
return results
else:
state = "error"
error("unknown state: '{0!s}' reached in {1!s}".format(state, s)) | [
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2,032 | eventable/vobject | docs/build/lib/vobject/icalendar.py | parseDtstart | def parseDtstart(contentline, allowSignatureMismatch=False):
"""
Convert a contentline's value into a date or date-time.
A variety of clients don't serialize dates with the appropriate VALUE
parameter, so rather than failing on these (technically invalid) lines,
if allowSignatureMismatch is True, try to parse both varieties.
"""
tzinfo = getTzid(getattr(contentline, 'tzid_param', None))
valueParam = getattr(contentline, 'value_param', 'DATE-TIME').upper()
if valueParam == "DATE":
return stringToDate(contentline.value)
elif valueParam == "DATE-TIME":
try:
return stringToDateTime(contentline.value, tzinfo)
except:
if allowSignatureMismatch:
return stringToDate(contentline.value)
else:
raise | python | def parseDtstart(contentline, allowSignatureMismatch=False):
"""
Convert a contentline's value into a date or date-time.
A variety of clients don't serialize dates with the appropriate VALUE
parameter, so rather than failing on these (technically invalid) lines,
if allowSignatureMismatch is True, try to parse both varieties.
"""
tzinfo = getTzid(getattr(contentline, 'tzid_param', None))
valueParam = getattr(contentline, 'value_param', 'DATE-TIME').upper()
if valueParam == "DATE":
return stringToDate(contentline.value)
elif valueParam == "DATE-TIME":
try:
return stringToDateTime(contentline.value, tzinfo)
except:
if allowSignatureMismatch:
return stringToDate(contentline.value)
else:
raise | [
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2,033 | eventable/vobject | docs/build/lib/vobject/icalendar.py | tzinfo_eq | def tzinfo_eq(tzinfo1, tzinfo2, startYear = 2000, endYear=2020):
"""
Compare offsets and DST transitions from startYear to endYear.
"""
if tzinfo1 == tzinfo2:
return True
elif tzinfo1 is None or tzinfo2 is None:
return False
def dt_test(dt):
if dt is None:
return True
return tzinfo1.utcoffset(dt) == tzinfo2.utcoffset(dt)
if not dt_test(datetime.datetime(startYear, 1, 1)):
return False
for year in range(startYear, endYear):
for transitionTo in 'daylight', 'standard':
t1=getTransition(transitionTo, year, tzinfo1)
t2=getTransition(transitionTo, year, tzinfo2)
if t1 != t2 or not dt_test(t1):
return False
return True | python | def tzinfo_eq(tzinfo1, tzinfo2, startYear = 2000, endYear=2020):
"""
Compare offsets and DST transitions from startYear to endYear.
"""
if tzinfo1 == tzinfo2:
return True
elif tzinfo1 is None or tzinfo2 is None:
return False
def dt_test(dt):
if dt is None:
return True
return tzinfo1.utcoffset(dt) == tzinfo2.utcoffset(dt)
if not dt_test(datetime.datetime(startYear, 1, 1)):
return False
for year in range(startYear, endYear):
for transitionTo in 'daylight', 'standard':
t1=getTransition(transitionTo, year, tzinfo1)
t2=getTransition(transitionTo, year, tzinfo2)
if t1 != t2 or not dt_test(t1):
return False
return True | [
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2,034 | eventable/vobject | docs/build/lib/vobject/icalendar.py | TimezoneComponent.registerTzinfo | def registerTzinfo(obj, tzinfo):
"""
Register tzinfo if it's not already registered, return its tzid.
"""
tzid = obj.pickTzid(tzinfo)
if tzid and not getTzid(tzid, False):
registerTzid(tzid, tzinfo)
return tzid | python | def registerTzinfo(obj, tzinfo):
"""
Register tzinfo if it's not already registered, return its tzid.
"""
tzid = obj.pickTzid(tzinfo)
if tzid and not getTzid(tzid, False):
registerTzid(tzid, tzinfo)
return tzid | [
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2,035 | eventable/vobject | docs/build/lib/vobject/icalendar.py | TimezoneComponent.pickTzid | def pickTzid(tzinfo, allowUTC=False):
"""
Given a tzinfo class, use known APIs to determine TZID, or use tzname.
"""
if tzinfo is None or (not allowUTC and tzinfo_eq(tzinfo, utc)):
# If tzinfo is UTC, we don't need a TZID
return None
# try PyICU's tzid key
if hasattr(tzinfo, 'tzid'):
return toUnicode(tzinfo.tzid)
# try pytz zone key
if hasattr(tzinfo, 'zone'):
return toUnicode(tzinfo.zone)
# try tzical's tzid key
elif hasattr(tzinfo, '_tzid'):
return toUnicode(tzinfo._tzid)
else:
# return tzname for standard (non-DST) time
notDST = datetime.timedelta(0)
for month in range(1, 13):
dt = datetime.datetime(2000, month, 1)
if tzinfo.dst(dt) == notDST:
return toUnicode(tzinfo.tzname(dt))
# there was no standard time in 2000!
raise VObjectError("Unable to guess TZID for tzinfo {0!s}"
.format(tzinfo)) | python | def pickTzid(tzinfo, allowUTC=False):
"""
Given a tzinfo class, use known APIs to determine TZID, or use tzname.
"""
if tzinfo is None or (not allowUTC and tzinfo_eq(tzinfo, utc)):
# If tzinfo is UTC, we don't need a TZID
return None
# try PyICU's tzid key
if hasattr(tzinfo, 'tzid'):
return toUnicode(tzinfo.tzid)
# try pytz zone key
if hasattr(tzinfo, 'zone'):
return toUnicode(tzinfo.zone)
# try tzical's tzid key
elif hasattr(tzinfo, '_tzid'):
return toUnicode(tzinfo._tzid)
else:
# return tzname for standard (non-DST) time
notDST = datetime.timedelta(0)
for month in range(1, 13):
dt = datetime.datetime(2000, month, 1)
if tzinfo.dst(dt) == notDST:
return toUnicode(tzinfo.tzname(dt))
# there was no standard time in 2000!
raise VObjectError("Unable to guess TZID for tzinfo {0!s}"
.format(tzinfo)) | [
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2,036 | eventable/vobject | docs/build/lib/vobject/icalendar.py | RecurringBehavior.transformToNative | def transformToNative(obj):
"""
Turn a recurring Component into a RecurringComponent.
"""
if not obj.isNative:
object.__setattr__(obj, '__class__', RecurringComponent)
obj.isNative = True
return obj | python | def transformToNative(obj):
"""
Turn a recurring Component into a RecurringComponent.
"""
if not obj.isNative:
object.__setattr__(obj, '__class__', RecurringComponent)
obj.isNative = True
return obj | [
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2,037 | eventable/vobject | docs/build/lib/vobject/icalendar.py | RecurringBehavior.generateImplicitParameters | def generateImplicitParameters(obj):
"""
Generate a UID if one does not exist.
This is just a dummy implementation, for now.
"""
if not hasattr(obj, 'uid'):
rand = int(random.random() * 100000)
now = datetime.datetime.now(utc)
now = dateTimeToString(now)
host = socket.gethostname()
obj.add(ContentLine('UID', [], "{0} - {1}@{2}".format(now, rand,
host))) | python | def generateImplicitParameters(obj):
"""
Generate a UID if one does not exist.
This is just a dummy implementation, for now.
"""
if not hasattr(obj, 'uid'):
rand = int(random.random() * 100000)
now = datetime.datetime.now(utc)
now = dateTimeToString(now)
host = socket.gethostname()
obj.add(ContentLine('UID', [], "{0} - {1}@{2}".format(now, rand,
host))) | [
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2,038 | eventable/vobject | docs/build/lib/vobject/icalendar.py | DateOrDateTimeBehavior.transformToNative | def transformToNative(obj):
"""
Turn obj.value into a date or datetime.
"""
if obj.isNative:
return obj
obj.isNative = True
if obj.value == '':
return obj
obj.value=obj.value
obj.value=parseDtstart(obj, allowSignatureMismatch=True)
if getattr(obj, 'value_param', 'DATE-TIME').upper() == 'DATE-TIME':
if hasattr(obj, 'tzid_param'):
# Keep a copy of the original TZID around
obj.params['X-VOBJ-ORIGINAL-TZID'] = [obj.tzid_param]
del obj.tzid_param
return obj | python | def transformToNative(obj):
"""
Turn obj.value into a date or datetime.
"""
if obj.isNative:
return obj
obj.isNative = True
if obj.value == '':
return obj
obj.value=obj.value
obj.value=parseDtstart(obj, allowSignatureMismatch=True)
if getattr(obj, 'value_param', 'DATE-TIME').upper() == 'DATE-TIME':
if hasattr(obj, 'tzid_param'):
# Keep a copy of the original TZID around
obj.params['X-VOBJ-ORIGINAL-TZID'] = [obj.tzid_param]
del obj.tzid_param
return obj | [
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2,039 | eventable/vobject | docs/build/lib/vobject/icalendar.py | DateOrDateTimeBehavior.transformFromNative | def transformFromNative(obj):
"""
Replace the date or datetime in obj.value with an ISO 8601 string.
"""
if type(obj.value) == datetime.date:
obj.isNative = False
obj.value_param = 'DATE'
obj.value = dateToString(obj.value)
return obj
else:
return DateTimeBehavior.transformFromNative(obj) | python | def transformFromNative(obj):
"""
Replace the date or datetime in obj.value with an ISO 8601 string.
"""
if type(obj.value) == datetime.date:
obj.isNative = False
obj.value_param = 'DATE'
obj.value = dateToString(obj.value)
return obj
else:
return DateTimeBehavior.transformFromNative(obj) | [
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2,040 | eventable/vobject | docs/build/lib/vobject/icalendar.py | MultiDateBehavior.transformFromNative | def transformFromNative(obj):
"""
Replace the date, datetime or period tuples in obj.value with
appropriate strings.
"""
if obj.value and type(obj.value[0]) == datetime.date:
obj.isNative = False
obj.value_param = 'DATE'
obj.value = ','.join([dateToString(val) for val in obj.value])
return obj
# Fixme: handle PERIOD case
else:
if obj.isNative:
obj.isNative = False
transformed = []
tzid = None
for val in obj.value:
if tzid is None and type(val) == datetime.datetime:
tzid = TimezoneComponent.registerTzinfo(val.tzinfo)
if tzid is not None:
obj.tzid_param = tzid
transformed.append(dateTimeToString(val))
obj.value = ','.join(transformed)
return obj | python | def transformFromNative(obj):
"""
Replace the date, datetime or period tuples in obj.value with
appropriate strings.
"""
if obj.value and type(obj.value[0]) == datetime.date:
obj.isNative = False
obj.value_param = 'DATE'
obj.value = ','.join([dateToString(val) for val in obj.value])
return obj
# Fixme: handle PERIOD case
else:
if obj.isNative:
obj.isNative = False
transformed = []
tzid = None
for val in obj.value:
if tzid is None and type(val) == datetime.datetime:
tzid = TimezoneComponent.registerTzinfo(val.tzinfo)
if tzid is not None:
obj.tzid_param = tzid
transformed.append(dateTimeToString(val))
obj.value = ','.join(transformed)
return obj | [
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2,041 | eventable/vobject | docs/build/lib/vobject/icalendar.py | MultiTextBehavior.decode | def decode(cls, line):
"""
Remove backslash escaping from line.value, then split on commas.
"""
if line.encoded:
line.value = stringToTextValues(line.value,
listSeparator=cls.listSeparator)
line.encoded=False | python | def decode(cls, line):
"""
Remove backslash escaping from line.value, then split on commas.
"""
if line.encoded:
line.value = stringToTextValues(line.value,
listSeparator=cls.listSeparator)
line.encoded=False | [
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2,042 | eventable/vobject | docs/build/lib/vobject/icalendar.py | VAlarm.generateImplicitParameters | def generateImplicitParameters(obj):
"""
Create default ACTION and TRIGGER if they're not set.
"""
try:
obj.action
except AttributeError:
obj.add('action').value = 'AUDIO'
try:
obj.trigger
except AttributeError:
obj.add('trigger').value = datetime.timedelta(0) | python | def generateImplicitParameters(obj):
"""
Create default ACTION and TRIGGER if they're not set.
"""
try:
obj.action
except AttributeError:
obj.add('action').value = 'AUDIO'
try:
obj.trigger
except AttributeError:
obj.add('trigger').value = datetime.timedelta(0) | [
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2,043 | eventable/vobject | docs/build/lib/vobject/icalendar.py | Duration.transformToNative | def transformToNative(obj):
"""
Turn obj.value into a datetime.timedelta.
"""
if obj.isNative:
return obj
obj.isNative = True
obj.value=obj.value
if obj.value == '':
return obj
else:
deltalist=stringToDurations(obj.value)
# When can DURATION have multiple durations? For now:
if len(deltalist) == 1:
obj.value = deltalist[0]
return obj
else:
raise ParseError("DURATION must have a single duration string.") | python | def transformToNative(obj):
"""
Turn obj.value into a datetime.timedelta.
"""
if obj.isNative:
return obj
obj.isNative = True
obj.value=obj.value
if obj.value == '':
return obj
else:
deltalist=stringToDurations(obj.value)
# When can DURATION have multiple durations? For now:
if len(deltalist) == 1:
obj.value = deltalist[0]
return obj
else:
raise ParseError("DURATION must have a single duration string.") | [
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2,044 | eventable/vobject | docs/build/lib/vobject/icalendar.py | Duration.transformFromNative | def transformFromNative(obj):
"""
Replace the datetime.timedelta in obj.value with an RFC2445 string.
"""
if not obj.isNative:
return obj
obj.isNative = False
obj.value = timedeltaToString(obj.value)
return obj | python | def transformFromNative(obj):
"""
Replace the datetime.timedelta in obj.value with an RFC2445 string.
"""
if not obj.isNative:
return obj
obj.isNative = False
obj.value = timedeltaToString(obj.value)
return obj | [
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2,045 | eventable/vobject | docs/build/lib/vobject/icalendar.py | Trigger.transformToNative | def transformToNative(obj):
"""
Turn obj.value into a timedelta or datetime.
"""
if obj.isNative:
return obj
value = getattr(obj, 'value_param', 'DURATION').upper()
if hasattr(obj, 'value_param'):
del obj.value_param
if obj.value == '':
obj.isNative = True
return obj
elif value == 'DURATION':
try:
return Duration.transformToNative(obj)
except ParseError:
logger.warning("TRIGGER not recognized as DURATION, trying "
"DATE-TIME, because iCal sometimes exports "
"DATE-TIMEs without setting VALUE=DATE-TIME")
try:
obj.isNative = False
dt = DateTimeBehavior.transformToNative(obj)
return dt
except:
msg = "TRIGGER with no VALUE not recognized as DURATION " \
"or as DATE-TIME"
raise ParseError(msg)
elif value == 'DATE-TIME':
# TRIGGERs with DATE-TIME values must be in UTC, we could validate
# that fact, for now we take it on faith.
return DateTimeBehavior.transformToNative(obj)
else:
raise ParseError("VALUE must be DURATION or DATE-TIME") | python | def transformToNative(obj):
"""
Turn obj.value into a timedelta or datetime.
"""
if obj.isNative:
return obj
value = getattr(obj, 'value_param', 'DURATION').upper()
if hasattr(obj, 'value_param'):
del obj.value_param
if obj.value == '':
obj.isNative = True
return obj
elif value == 'DURATION':
try:
return Duration.transformToNative(obj)
except ParseError:
logger.warning("TRIGGER not recognized as DURATION, trying "
"DATE-TIME, because iCal sometimes exports "
"DATE-TIMEs without setting VALUE=DATE-TIME")
try:
obj.isNative = False
dt = DateTimeBehavior.transformToNative(obj)
return dt
except:
msg = "TRIGGER with no VALUE not recognized as DURATION " \
"or as DATE-TIME"
raise ParseError(msg)
elif value == 'DATE-TIME':
# TRIGGERs with DATE-TIME values must be in UTC, we could validate
# that fact, for now we take it on faith.
return DateTimeBehavior.transformToNative(obj)
else:
raise ParseError("VALUE must be DURATION or DATE-TIME") | [
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2,046 | eventable/vobject | docs/build/lib/vobject/icalendar.py | PeriodBehavior.transformToNative | def transformToNative(obj):
"""
Convert comma separated periods into tuples.
"""
if obj.isNative:
return obj
obj.isNative = True
if obj.value == '':
obj.value = []
return obj
tzinfo = getTzid(getattr(obj, 'tzid_param', None))
obj.value = [stringToPeriod(x, tzinfo) for x in obj.value.split(",")]
return obj | python | def transformToNative(obj):
"""
Convert comma separated periods into tuples.
"""
if obj.isNative:
return obj
obj.isNative = True
if obj.value == '':
obj.value = []
return obj
tzinfo = getTzid(getattr(obj, 'tzid_param', None))
obj.value = [stringToPeriod(x, tzinfo) for x in obj.value.split(",")]
return obj | [
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2,047 | eventable/vobject | docs/build/lib/vobject/icalendar.py | PeriodBehavior.transformFromNative | def transformFromNative(cls, obj):
"""
Convert the list of tuples in obj.value to strings.
"""
if obj.isNative:
obj.isNative = False
transformed = []
for tup in obj.value:
transformed.append(periodToString(tup, cls.forceUTC))
if len(transformed) > 0:
tzid = TimezoneComponent.registerTzinfo(tup[0].tzinfo)
if not cls.forceUTC and tzid is not None:
obj.tzid_param = tzid
obj.value = ','.join(transformed)
return obj | python | def transformFromNative(cls, obj):
"""
Convert the list of tuples in obj.value to strings.
"""
if obj.isNative:
obj.isNative = False
transformed = []
for tup in obj.value:
transformed.append(periodToString(tup, cls.forceUTC))
if len(transformed) > 0:
tzid = TimezoneComponent.registerTzinfo(tup[0].tzinfo)
if not cls.forceUTC and tzid is not None:
obj.tzid_param = tzid
obj.value = ','.join(transformed)
return obj | [
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2,048 | 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 obj]
else:
for field in order:
escapedValueList = [backslashEscape(val) for val in
toList(getattr(obj, field))]
fields.append(','.join(escapedValueList))
return ';'.join(fields) | 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 obj]
else:
for field in order:
escapedValueList = [backslashEscape(val) for val in
toList(getattr(obj, field))]
fields.append(','.join(escapedValueList))
return ';'.join(fields) | [
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2,049 | 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)
return val | [
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2,050 | 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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2,051 | 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.
"""
obj.isNative = False
obj.value = serializeFields(obj.value, NAME_ORDER)
return obj | [
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2,052 | 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.
"""
if obj.isNative:
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obj.isNative = True
obj.value = Address(**dict(zip(ADDRESS_ORDER, splitFields(obj.value))))
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2,053 | eventable/vobject | vobject/vcard.py | OrgBehavior.transformToNative | def transformToNative(obj):
"""
Turn obj.value into a list.
"""
if obj.isNative:
return obj
obj.isNative = True
obj.value = splitFields(obj.value)
return obj | python | def transformToNative(obj):
"""
Turn obj.value into a list.
"""
if obj.isNative:
return obj
obj.isNative = True
obj.value = splitFields(obj.value)
return obj | [
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2,054 | 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 'BASE64', which does not match the 3.0
vCard spec. If we encouter that, then we transform the parameter to
ENCODING=b
"""
if line.encoded:
if 'BASE64' in line.singletonparams:
line.singletonparams.remove('BASE64')
line.encoding_param = cls.base64string
encoding = getattr(line, 'encoding_param', None)
if encoding:
line.value = codecs.decode(line.value.encode("utf-8"), "base64")
else:
line.value = stringToTextValues(line.value)[0]
line.encoded=False | python | 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 'BASE64', which does not match the 3.0
vCard spec. If we encouter that, then we transform the parameter to
ENCODING=b
"""
if line.encoded:
if 'BASE64' in line.singletonparams:
line.singletonparams.remove('BASE64')
line.encoding_param = cls.base64string
encoding = getattr(line, 'encoding_param', None)
if encoding:
line.value = codecs.decode(line.value.encode("utf-8"), "base64")
else:
line.value = stringToTextValues(line.value)[0]
line.encoded=False | [
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2,055 | 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:
If True, raise a L{base.ValidateError} on validation failure.
Otherwise return a boolean.
@param complainUnrecognized:
If True, fail to validate if an uncrecognized parameter or child is
found. Otherwise log the lack of recognition.
"""
if not cls.allowGroup and obj.group is not None:
err = "{0} has a group, but this object doesn't support groups".format(obj)
raise base.VObjectError(err)
if isinstance(obj, base.ContentLine):
return cls.lineValidate(obj, raiseException, complainUnrecognized)
elif isinstance(obj, base.Component):
count = {}
for child in obj.getChildren():
if not child.validate(raiseException, complainUnrecognized):
return False
name = child.name.upper()
count[name] = count.get(name, 0) + 1
for key, val in cls.knownChildren.items():
if count.get(key, 0) < val[0]:
if raiseException:
m = "{0} components must contain at least {1} {2}"
raise base.ValidateError(m .format(cls.name, val[0], key))
return False
if val[1] and count.get(key, 0) > val[1]:
if raiseException:
m = "{0} components cannot contain more than {1} {2}"
raise base.ValidateError(m.format(cls.name, val[1], key))
return False
return True
else:
err = "{0} is not a Component or Contentline".format(obj)
raise base.VObjectError(err) | 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.
@param raiseException:
If True, raise a L{base.ValidateError} on validation failure.
Otherwise return a boolean.
@param complainUnrecognized:
If True, fail to validate if an uncrecognized parameter or child is
found. Otherwise log the lack of recognition.
"""
if not cls.allowGroup and obj.group is not None:
err = "{0} has a group, but this object doesn't support groups".format(obj)
raise base.VObjectError(err)
if isinstance(obj, base.ContentLine):
return cls.lineValidate(obj, raiseException, complainUnrecognized)
elif isinstance(obj, base.Component):
count = {}
for child in obj.getChildren():
if not child.validate(raiseException, complainUnrecognized):
return False
name = child.name.upper()
count[name] = count.get(name, 0) + 1
for key, val in cls.knownChildren.items():
if count.get(key, 0) < val[0]:
if raiseException:
m = "{0} components must contain at least {1} {2}"
raise base.ValidateError(m .format(cls.name, val[0], key))
return False
if val[1] and count.get(key, 0) > val[1]:
if raiseException:
m = "{0} components cannot contain more than {1} {2}"
raise base.ValidateError(m.format(cls.name, val[1], key))
return False
return True
else:
err = "{0} is not a Component or Contentline".format(obj)
raise base.VObjectError(err) | [
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2,056 | 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,
day=1)
weekdayone = first.replace(day=((dayofweek - first.isoweekday()) % 7 + 1))
for n in xrange(whichweek - 1, -1, -1):
dt = weekdayone + n * WEEKS
if dt.month == month:
return dt | 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,
day=1)
weekdayone = first.replace(day=((dayofweek - first.isoweekday()) % 7 + 1))
for n in xrange(whichweek - 1, -1, -1):
dt = weekdayone + n * WEEKS
if dt.month == month:
return dt | [
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2,057 | eventable/vobject | vobject/ics_diff.py | deleteExtraneous | def deleteExtraneous(component, ignore_dtstamp=False):
"""
Recursively walk the component's children, deleting extraneous details like
X-VOBJ-ORIGINAL-TZID.
"""
for comp in component.components():
deleteExtraneous(comp, ignore_dtstamp)
for line in component.lines():
if 'X-VOBJ-ORIGINAL-TZID' in line.params:
del line.params['X-VOBJ-ORIGINAL-TZID']
if ignore_dtstamp and hasattr(component, 'dtstamp_list'):
del component.dtstamp_list | python | def deleteExtraneous(component, ignore_dtstamp=False):
"""
Recursively walk the component's children, deleting extraneous details like
X-VOBJ-ORIGINAL-TZID.
"""
for comp in component.components():
deleteExtraneous(comp, ignore_dtstamp)
for line in component.lines():
if 'X-VOBJ-ORIGINAL-TZID' in line.params:
del line.params['X-VOBJ-ORIGINAL-TZID']
if ignore_dtstamp and hasattr(component, 'dtstamp_list'):
del component.dtstamp_list | [
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2,058 | 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
tstat_name -- The name of the thermostat, as identified by Pelican
start_time -- The start of the data hole in UTC, e.g. "2018-01-29 15:00:00"
end_time -- The end of the data hole in UTC, e.g. "2018-01-29 16:00:00"
Returns:
A Pandas dataframe with historical Pelican data that falls between the
specified start and end times.
Note that this function assumes the Pelican thermostat's local time zone is
US/Pacific. It will properly handle PST vs. PDT.
"""
start = datetime.strptime(start_time, _INPUT_TIME_FORMAT).replace(tzinfo=pytz.utc).astimezone(_pelican_time)
end = datetime.strptime(end_time, _INPUT_TIME_FORMAT).replace(tzinfo=pytz.utc).astimezone(_pelican_time)
heat_needs_fan = _lookupHeatNeedsFan(site, username, password, tstat_name)
if heat_needs_fan is None:
return None
# Pelican's API only allows a query covering a time range of up to 1 month
# So we may need run multiple requests for historical data
history_blocks = []
while start < end:
block_start = start
block_end = min(start + timedelta(days=30), end)
blocks = _lookupHistoricalData(site, username, password, tstat_name, block_start, block_end)
if blocks is None:
return None
history_blocks.extend(blocks)
start += timedelta(days=30, minutes=1)
output_rows = []
for block in history_blocks:
runStatus = block.find("runStatus").text
if runStatus.startswith("Heat"):
fanState = (heatNeedsFan == "Yes")
else:
fanState = (runStatus != "Off")
api_time = datetime.strptime(block.find("timestamp").text, "%Y-%m-%dT%H:%M").replace(tzinfo=_pelican_time)
# Need to convert seconds to nanoseconds
timestamp = int(api_time.timestamp() * 10**9)
output_rows.append({
"temperature": float(block.find("temperature").text),
"relative_humidity": float(block.find("humidity").text),
"heating_setpoint": float(block.find("heatSetting").text),
"cooling_setpoint": float(block.find("coolSetting").text),
# Driver explicitly uses "Schedule" field, but we don't have this in history
"override": block.find("setBy").text != "Schedule",
"fan": fanState,
"mode": _mode_name_mappings[block.find("system").text],
"state": _state_mappings.get(runStatus, 0),
"time": timestamp,
})
df = pd.DataFrame(output_rows)
df.drop_duplicates(subset="time", keep="first", inplace=True)
return df | python | 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
tstat_name -- The name of the thermostat, as identified by Pelican
start_time -- The start of the data hole in UTC, e.g. "2018-01-29 15:00:00"
end_time -- The end of the data hole in UTC, e.g. "2018-01-29 16:00:00"
Returns:
A Pandas dataframe with historical Pelican data that falls between the
specified start and end times.
Note that this function assumes the Pelican thermostat's local time zone is
US/Pacific. It will properly handle PST vs. PDT.
"""
start = datetime.strptime(start_time, _INPUT_TIME_FORMAT).replace(tzinfo=pytz.utc).astimezone(_pelican_time)
end = datetime.strptime(end_time, _INPUT_TIME_FORMAT).replace(tzinfo=pytz.utc).astimezone(_pelican_time)
heat_needs_fan = _lookupHeatNeedsFan(site, username, password, tstat_name)
if heat_needs_fan is None:
return None
# Pelican's API only allows a query covering a time range of up to 1 month
# So we may need run multiple requests for historical data
history_blocks = []
while start < end:
block_start = start
block_end = min(start + timedelta(days=30), end)
blocks = _lookupHistoricalData(site, username, password, tstat_name, block_start, block_end)
if blocks is None:
return None
history_blocks.extend(blocks)
start += timedelta(days=30, minutes=1)
output_rows = []
for block in history_blocks:
runStatus = block.find("runStatus").text
if runStatus.startswith("Heat"):
fanState = (heatNeedsFan == "Yes")
else:
fanState = (runStatus != "Off")
api_time = datetime.strptime(block.find("timestamp").text, "%Y-%m-%dT%H:%M").replace(tzinfo=_pelican_time)
# Need to convert seconds to nanoseconds
timestamp = int(api_time.timestamp() * 10**9)
output_rows.append({
"temperature": float(block.find("temperature").text),
"relative_humidity": float(block.find("humidity").text),
"heating_setpoint": float(block.find("heatSetting").text),
"cooling_setpoint": float(block.find("coolSetting").text),
# Driver explicitly uses "Schedule" field, but we don't have this in history
"override": block.find("setBy").text != "Schedule",
"fan": fanState,
"mode": _mode_name_mappings[block.find("system").text],
"state": _state_mappings.get(runStatus, 0),
"time": timestamp,
})
df = pd.DataFrame(output_rows)
df.drop_duplicates(subset="time", keep="first", inplace=True)
return df | [
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Arguments:
site -- The thermostat's Pelican site name
username -- The Pelican username for the site
password -- The Pelican password for the site
tstat_name -- The name of the thermostat, as identified by Pelican
start_time -- The start of the data hole in UTC, e.g. "2018-01-29 15:00:00"
end_time -- The end of the data hole in UTC, e.g. "2018-01-29 16:00:00"
Returns:
A Pandas dataframe with historical Pelican data that falls between the
specified start and end times.
Note that this function assumes the Pelican thermostat's local time zone is
US/Pacific. It will properly handle PST vs. PDT. | [
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2,059 | 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 to 65.
cdh_cpoint : int
Cooling degree hours. Defaults to 65.
"""
if self.preprocessed_data.empty:
data = self.original_data
else:
data = self.preprocessed_data
# Calculate hdh
data['hdh'] = data[col]
over_hdh = data.loc[:, col] > hdh_cpoint
data.loc[over_hdh, 'hdh'] = 0
data.loc[~over_hdh, 'hdh'] = hdh_cpoint - data.loc[~over_hdh, col]
# Calculate cdh
data['cdh'] = data[col]
under_cdh = data.loc[:, col] < cdh_cpoint
data.loc[under_cdh, 'cdh'] = 0
data.loc[~under_cdh, 'cdh'] = data.loc[~under_cdh, col] - cdh_cpoint
self.preprocessed_data = data | 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.
hdh_cpoint : int
Heating degree hours. Defaults to 65.
cdh_cpoint : int
Cooling degree hours. Defaults to 65.
"""
if self.preprocessed_data.empty:
data = self.original_data
else:
data = self.preprocessed_data
# Calculate hdh
data['hdh'] = data[col]
over_hdh = data.loc[:, col] > hdh_cpoint
data.loc[over_hdh, 'hdh'] = 0
data.loc[~over_hdh, 'hdh'] = hdh_cpoint - data.loc[~over_hdh, col]
# Calculate cdh
data['cdh'] = data[col]
under_cdh = data.loc[:, col] < cdh_cpoint
data.loc[under_cdh, 'cdh'] = 0
data.loc[~under_cdh, 'cdh'] = data.loc[~under_cdh, col] - cdh_cpoint
self.preprocessed_data = data | [
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Parameters
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Column name which contains the outdoor air temperature.
hdh_cpoint : int
Heating degree hours. Defaults to 65.
cdh_cpoint : int
Cooling degree hours. Defaults to 65. | [
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2,060 | 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)
Column to exponentiate.
degree : list(str)
Exponentiation degree.
"""
if not col and not degree:
return
else:
if isinstance(col, list) and isinstance(degree, list):
if len(col) != len(degree):
print('col len: ', len(col))
print('degree len: ', len(degree))
raise ValueError('col and degree should have equal length.')
else:
if self.preprocessed_data.empty:
data = self.original_data
else:
data = self.preprocessed_data
for i in range(len(col)):
data.loc[:,col[i]+str(degree[i])] = pow(data.loc[:,col[i]],degree[i]) / pow(10,degree[i]-1)
self.preprocessed_data = data
else:
raise TypeError('col and degree should be lists.') | python | 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)
Column to exponentiate.
degree : list(str)
Exponentiation degree.
"""
if not col and not degree:
return
else:
if isinstance(col, list) and isinstance(degree, list):
if len(col) != len(degree):
print('col len: ', len(col))
print('degree len: ', len(degree))
raise ValueError('col and degree should have equal length.')
else:
if self.preprocessed_data.empty:
data = self.original_data
else:
data = self.preprocessed_data
for i in range(len(col)):
data.loc[:,col[i]+str(degree[i])] = pow(data.loc[:,col[i]],degree[i]) / pow(10,degree[i]-1)
self.preprocessed_data = data
else:
raise TypeError('col and degree should be lists.') | [
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Parameters
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col : list(str)
Column to exponentiate.
degree : list(str)
Exponentiation degree. | [
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2,061 | 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.columns, index=data.index)
self.preprocessed_data = data | python | 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.columns, index=data.index)
self.preprocessed_data = data | [
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2,062 | SoftwareDefinedBuildings/XBOS | apps/data_analysis/XBOS_data_analytics/Preprocess_Data.py | Preprocess_Data.normalize | def normalize(self):
""" Normalize data. """
if self.preprocessed_data.empty:
data = self.original_data
else:
data = self.preprocessed_data
data = pd.DataFrame(preprocessing.normalize(data), columns=data.columns, index=data.index)
self.preprocessed_data = data | python | def normalize(self):
""" Normalize data. """
if self.preprocessed_data.empty:
data = self.original_data
else:
data = self.preprocessed_data
data = pd.DataFrame(preprocessing.normalize(data), columns=data.columns, index=data.index)
self.preprocessed_data = data | [
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2,063 | 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
Time of Day.
dow : bool
Day of Week.
"""
var_to_expand = []
if self.preprocessed_data.empty:
data = self.original_data
else:
data = self.preprocessed_data
if year:
data["year"] = data.index.year
var_to_expand.append("year")
if month:
data["month"] = data.index.month
var_to_expand.append("month")
if week:
data["week"] = data.index.week
var_to_expand.append("week")
if tod:
data["tod"] = data.index.hour
var_to_expand.append("tod")
if dow:
data["dow"] = data.index.weekday
var_to_expand.append("dow")
# One-hot encode the time features
for var in var_to_expand:
add_var = pd.get_dummies(data[var], prefix=var, drop_first=True)
# Add all the columns to the model data
data = data.join(add_var)
# Drop the original column that was expanded
data.drop(columns=[var], inplace=True)
self.preprocessed_data = data | python | 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
Time of Day.
dow : bool
Day of Week.
"""
var_to_expand = []
if self.preprocessed_data.empty:
data = self.original_data
else:
data = self.preprocessed_data
if year:
data["year"] = data.index.year
var_to_expand.append("year")
if month:
data["month"] = data.index.month
var_to_expand.append("month")
if week:
data["week"] = data.index.week
var_to_expand.append("week")
if tod:
data["tod"] = data.index.hour
var_to_expand.append("tod")
if dow:
data["dow"] = data.index.weekday
var_to_expand.append("dow")
# One-hot encode the time features
for var in var_to_expand:
add_var = pd.get_dummies(data[var], prefix=var, drop_first=True)
# Add all the columns to the model data
data = data.join(add_var)
# Drop the original column that was expanded
data.drop(columns=[var], inplace=True)
self.preprocessed_data = data | [
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Parameters
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year : bool
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month : bool
Month.
week : bool
Week.
tod : bool
Time of Day.
dow : bool
Day of Week. | [
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2,064 | 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]))
self.baseline_in = self.original_data.loc[time_period1, self.input_col]
self.baseline_out = self.original_data.loc[time_period1, self.output_col]
if self.exclude_time_period:
for i in range(0, len(self.exclude_time_period), 2):
# Drop data ranging in exclude_time_period1
exclude_time_period1 = (slice(self.exclude_time_period[i], self.exclude_time_period[i+1]))
self.baseline_in.drop(self.baseline_in.loc[exclude_time_period1].index, axis=0, inplace=True)
self.baseline_out.drop(self.baseline_out.loc[exclude_time_period1].index, axis=0, inplace=True)
except Exception as e:
raise e
# CHECK: Can optimize this part
# Error checking to ensure time_period values are valid
if self.projection_period:
for i in range(0, len(self.projection_period), 2):
period = (slice(self.projection_period[i], self.projection_period[i+1]))
try:
self.original_data.loc[period, self.input_col]
self.original_data.loc[period, self.output_col]
except Exception as e:
raise e | python | 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]))
self.baseline_in = self.original_data.loc[time_period1, self.input_col]
self.baseline_out = self.original_data.loc[time_period1, self.output_col]
if self.exclude_time_period:
for i in range(0, len(self.exclude_time_period), 2):
# Drop data ranging in exclude_time_period1
exclude_time_period1 = (slice(self.exclude_time_period[i], self.exclude_time_period[i+1]))
self.baseline_in.drop(self.baseline_in.loc[exclude_time_period1].index, axis=0, inplace=True)
self.baseline_out.drop(self.baseline_out.loc[exclude_time_period1].index, axis=0, inplace=True)
except Exception as e:
raise e
# CHECK: Can optimize this part
# Error checking to ensure time_period values are valid
if self.projection_period:
for i in range(0, len(self.projection_period), 2):
period = (slice(self.projection_period[i], self.projection_period[i+1]))
try:
self.original_data.loc[period, self.input_col]
self.original_data.loc[period, self.output_col]
except Exception as e:
raise e | [
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2,065 | 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_splits=self.cv, shuffle=True, random_state=42)
for i, (train, test) in enumerate(kfold.split(self.baseline_in, self.baseline_out)):
model.fit(self.baseline_in.iloc[train], self.baseline_out.iloc[train])
scores.append(model.score(self.baseline_in.iloc[test], self.baseline_out.iloc[test]))
mean_score = sum(scores) / len(scores)
self.models.append(model)
self.model_names.append('Linear Regression')
self.max_scores.append(mean_score)
self.metrics['Linear Regression'] = {}
self.metrics['Linear Regression']['R2'] = mean_score
self.metrics['Linear Regression']['Adj R2'] = self.adj_r2(mean_score, self.baseline_in.shape[0], self.baseline_in.shape[1]) | 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
"""
model = LinearRegression()
scores = []
kfold = KFold(n_splits=self.cv, shuffle=True, random_state=42)
for i, (train, test) in enumerate(kfold.split(self.baseline_in, self.baseline_out)):
model.fit(self.baseline_in.iloc[train], self.baseline_out.iloc[train])
scores.append(model.score(self.baseline_in.iloc[test], self.baseline_out.iloc[test]))
mean_score = sum(scores) / len(scores)
self.models.append(model)
self.model_names.append('Linear Regression')
self.max_scores.append(mean_score)
self.metrics['Linear Regression'] = {}
self.metrics['Linear Regression']['R2'] = mean_score
self.metrics['Linear Regression']['Adj R2'] = self.adj_r2(mean_score, self.baseline_in.shape[0], self.baseline_in.shape[1]) | [
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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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] | c12d4fb14518ea3ae98c471c28e0710fdf74dd25 | https://github.com/SoftwareDefinedBuildings/XBOS/blob/c12d4fb14518ea3ae98c471c28e0710fdf74dd25/apps/Data_quality_analysis/Model_Data.py#L176-L203 |
2,066 | SoftwareDefinedBuildings/XBOS | apps/Data_quality_analysis/Model_Data.py | Model_Data.lasso_regression | def lasso_regression(self):
""" Lasso Regression.
This function runs lasso regression and stores the,
1. Model
2. Model name
3. Max score
4. Metrics
"""
score_list = []
max_score = float('-inf')
best_alpha = None
for alpha in self.alphas:
# model = Lasso(normalize=True, alpha=alpha, max_iter=5000)
model = Lasso(alpha=alpha, max_iter=5000)
model.fit(self.baseline_in, self.baseline_out.values.ravel())
scores = []
kfold = KFold(n_splits=self.cv, shuffle=True, random_state=42)
for i, (train, test) in enumerate(kfold.split(self.baseline_in, self.baseline_out)):
model.fit(self.baseline_in.iloc[train], self.baseline_out.iloc[train])
scores.append(model.score(self.baseline_in.iloc[test], self.baseline_out.iloc[test]))
mean_score = np.mean(scores)
score_list.append(mean_score)
if mean_score > max_score:
max_score = mean_score
best_alpha = alpha
# self.models.append(Lasso(normalize=True, alpha=best_alpha, max_iter=5000))
self.models.append(Lasso(alpha=best_alpha, max_iter=5000))
self.model_names.append('Lasso Regression')
self.max_scores.append(max_score)
self.metrics['Lasso Regression'] = {}
self.metrics['Lasso Regression']['R2'] = max_score
self.metrics['Lasso Regression']['Adj R2'] = self.adj_r2(max_score, self.baseline_in.shape[0], self.baseline_in.shape[1]) | python | def lasso_regression(self):
""" Lasso Regression.
This function runs lasso regression and stores the,
1. Model
2. Model name
3. Max score
4. Metrics
"""
score_list = []
max_score = float('-inf')
best_alpha = None
for alpha in self.alphas:
# model = Lasso(normalize=True, alpha=alpha, max_iter=5000)
model = Lasso(alpha=alpha, max_iter=5000)
model.fit(self.baseline_in, self.baseline_out.values.ravel())
scores = []
kfold = KFold(n_splits=self.cv, shuffle=True, random_state=42)
for i, (train, test) in enumerate(kfold.split(self.baseline_in, self.baseline_out)):
model.fit(self.baseline_in.iloc[train], self.baseline_out.iloc[train])
scores.append(model.score(self.baseline_in.iloc[test], self.baseline_out.iloc[test]))
mean_score = np.mean(scores)
score_list.append(mean_score)
if mean_score > max_score:
max_score = mean_score
best_alpha = alpha
# self.models.append(Lasso(normalize=True, alpha=best_alpha, max_iter=5000))
self.models.append(Lasso(alpha=best_alpha, max_iter=5000))
self.model_names.append('Lasso Regression')
self.max_scores.append(max_score)
self.metrics['Lasso Regression'] = {}
self.metrics['Lasso Regression']['R2'] = max_score
self.metrics['Lasso Regression']['Adj R2'] = self.adj_r2(max_score, self.baseline_in.shape[0], self.baseline_in.shape[1]) | [
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This function runs lasso regression and stores the,
1. Model
2. Model name
3. Max score
4. Metrics | [
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] | c12d4fb14518ea3ae98c471c28e0710fdf74dd25 | https://github.com/SoftwareDefinedBuildings/XBOS/blob/c12d4fb14518ea3ae98c471c28e0710fdf74dd25/apps/Data_quality_analysis/Model_Data.py#L206-L246 |
2,067 | SoftwareDefinedBuildings/XBOS | apps/Data_quality_analysis/Model_Data.py | Model_Data.random_forest | def random_forest(self):
""" Random Forest.
This function runs random forest and stores the,
1. Model
2. Model name
3. Max score
4. Metrics
"""
model = RandomForestRegressor(random_state=42)
scores = []
kfold = KFold(n_splits=self.cv, shuffle=True, random_state=42)
for i, (train, test) in enumerate(kfold.split(self.baseline_in, self.baseline_out)):
model.fit(self.baseline_in.iloc[train], self.baseline_out.iloc[train])
scores.append(model.score(self.baseline_in.iloc[test], self.baseline_out.iloc[test]))
mean_score = np.mean(scores)
self.models.append(model)
self.model_names.append('Random Forest Regressor')
self.max_scores.append(mean_score)
self.metrics['Random Forest Regressor'] = {}
self.metrics['Random Forest Regressor']['R2'] = mean_score
self.metrics['Random Forest Regressor']['Adj R2'] = self.adj_r2(mean_score, self.baseline_in.shape[0], self.baseline_in.shape[1]) | python | def random_forest(self):
""" Random Forest.
This function runs random forest and stores the,
1. Model
2. Model name
3. Max score
4. Metrics
"""
model = RandomForestRegressor(random_state=42)
scores = []
kfold = KFold(n_splits=self.cv, shuffle=True, random_state=42)
for i, (train, test) in enumerate(kfold.split(self.baseline_in, self.baseline_out)):
model.fit(self.baseline_in.iloc[train], self.baseline_out.iloc[train])
scores.append(model.score(self.baseline_in.iloc[test], self.baseline_out.iloc[test]))
mean_score = np.mean(scores)
self.models.append(model)
self.model_names.append('Random Forest Regressor')
self.max_scores.append(mean_score)
self.metrics['Random Forest Regressor'] = {}
self.metrics['Random Forest Regressor']['R2'] = mean_score
self.metrics['Random Forest Regressor']['Adj R2'] = self.adj_r2(mean_score, self.baseline_in.shape[0], self.baseline_in.shape[1]) | [
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This function runs random forest and stores the,
1. Model
2. Model name
3. Max score
4. Metrics | [
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] | c12d4fb14518ea3ae98c471c28e0710fdf74dd25 | https://github.com/SoftwareDefinedBuildings/XBOS/blob/c12d4fb14518ea3ae98c471c28e0710fdf74dd25/apps/Data_quality_analysis/Model_Data.py#L338-L364 |
2,068 | SoftwareDefinedBuildings/XBOS | apps/Data_quality_analysis/Model_Data.py | Model_Data.run_models | def run_models(self):
""" Run all models.
Returns
-------
model
Best model
dict
Metrics of the models
"""
self.linear_regression()
self.lasso_regression()
self.ridge_regression()
self.elastic_net_regression()
self.random_forest()
self.ann()
# Index of the model with max score
best_model_index = self.max_scores.index(max(self.max_scores))
# Store name of the optimal model
self.best_model_name = self.model_names[best_model_index]
# Store optimal model
self.best_model = self.models[best_model_index]
return self.metrics | python | def run_models(self):
""" Run all models.
Returns
-------
model
Best model
dict
Metrics of the models
"""
self.linear_regression()
self.lasso_regression()
self.ridge_regression()
self.elastic_net_regression()
self.random_forest()
self.ann()
# Index of the model with max score
best_model_index = self.max_scores.index(max(self.max_scores))
# Store name of the optimal model
self.best_model_name = self.model_names[best_model_index]
# Store optimal model
self.best_model = self.models[best_model_index]
return self.metrics | [
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Metrics of the models | [
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2,069 | 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
Custom function's metrics
"""
y_pred = func(self.baseline_in, self.baseline_out)
self.custom_metrics = {}
self.custom_metrics['r2'] = r2_score(self.baseline_out, y_pred)
self.custom_metrics['mse'] = mean_squared_error(self.baseline_out, y_pred)
self.custom_metrics['rmse'] = math.sqrt(self.custom_metrics['mse'])
self.custom_metrics['adj_r2'] = self.adj_r2(self.custom_metrics['r2'], self.baseline_in.shape[0], self.baseline_in.shape[1])
return self.custom_metrics | 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
Parameters
----------
func : function
Custom function
Returns
-------
dict
Custom function's metrics
"""
y_pred = func(self.baseline_in, self.baseline_out)
self.custom_metrics = {}
self.custom_metrics['r2'] = r2_score(self.baseline_out, y_pred)
self.custom_metrics['mse'] = mean_squared_error(self.baseline_out, y_pred)
self.custom_metrics['rmse'] = math.sqrt(self.custom_metrics['mse'])
self.custom_metrics['adj_r2'] = self.adj_r2(self.custom_metrics['r2'], self.baseline_in.shape[0], self.baseline_in.shape[1])
return self.custom_metrics | [
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2,070 | SoftwareDefinedBuildings/XBOS | apps/Data_quality_analysis/Model_Data.py | Model_Data.best_model_fit | def best_model_fit(self):
""" Fit data to optimal model and return its metrics.
Returns
-------
dict
Best model's metrics
"""
self.best_model.fit(self.baseline_in, self.baseline_out)
self.y_true = self.baseline_out # Pandas Series
self.y_pred = self.best_model.predict(self.baseline_in) # numpy.ndarray
# Set all negative values to zero since energy > 0
self.y_pred[self.y_pred < 0] = 0
# n and k values for adj r2 score
self.n_test = self.baseline_in.shape[0] # Number of points in data sample
self.k_test = self.baseline_in.shape[1] # Number of variables in model, excluding the constant
# Store best model's metrics
self.best_metrics['name'] = self.best_model_name
self.best_metrics['r2'] = r2_score(self.y_true, self.y_pred)
self.best_metrics['mse'] = mean_squared_error(self.y_true, self.y_pred)
self.best_metrics['rmse'] = math.sqrt(self.best_metrics['mse'])
self.best_metrics['adj_r2'] = self.adj_r2(self.best_metrics['r2'], self.n_test, self.k_test)
# Normalized Mean Bias Error
numerator = sum(self.y_true - self.y_pred)
denominator = (self.n_test - self.k_test) * (sum(self.y_true) / len(self.y_true))
self.best_metrics['nmbe'] = numerator / denominator
# MAPE can't have 0 values in baseline_out -> divide by zero error
self.baseline_out_copy = self.baseline_out[self.baseline_out != 0]
self.baseline_in_copy = self.baseline_in[self.baseline_in.index.isin(self.baseline_out_copy.index)]
self.y_true_copy = self.baseline_out_copy # Pandas Series
self.y_pred_copy = self.best_model.predict(self.baseline_in_copy) # numpy.ndarray
self.best_metrics['mape'] = np.mean(np.abs((self.y_true_copy - self.y_pred_copy) / self.y_true_copy)) * 100
return self.best_metrics | python | def best_model_fit(self):
""" Fit data to optimal model and return its metrics.
Returns
-------
dict
Best model's metrics
"""
self.best_model.fit(self.baseline_in, self.baseline_out)
self.y_true = self.baseline_out # Pandas Series
self.y_pred = self.best_model.predict(self.baseline_in) # numpy.ndarray
# Set all negative values to zero since energy > 0
self.y_pred[self.y_pred < 0] = 0
# n and k values for adj r2 score
self.n_test = self.baseline_in.shape[0] # Number of points in data sample
self.k_test = self.baseline_in.shape[1] # Number of variables in model, excluding the constant
# Store best model's metrics
self.best_metrics['name'] = self.best_model_name
self.best_metrics['r2'] = r2_score(self.y_true, self.y_pred)
self.best_metrics['mse'] = mean_squared_error(self.y_true, self.y_pred)
self.best_metrics['rmse'] = math.sqrt(self.best_metrics['mse'])
self.best_metrics['adj_r2'] = self.adj_r2(self.best_metrics['r2'], self.n_test, self.k_test)
# Normalized Mean Bias Error
numerator = sum(self.y_true - self.y_pred)
denominator = (self.n_test - self.k_test) * (sum(self.y_true) / len(self.y_true))
self.best_metrics['nmbe'] = numerator / denominator
# MAPE can't have 0 values in baseline_out -> divide by zero error
self.baseline_out_copy = self.baseline_out[self.baseline_out != 0]
self.baseline_in_copy = self.baseline_in[self.baseline_in.index.isin(self.baseline_out_copy.index)]
self.y_true_copy = self.baseline_out_copy # Pandas Series
self.y_pred_copy = self.best_model.predict(self.baseline_in_copy) # numpy.ndarray
self.best_metrics['mape'] = np.mean(np.abs((self.y_true_copy - self.y_pred_copy) / self.y_true_copy)) * 100
return self.best_metrics | [
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Returns
-------
dict
Best model's metrics | [
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2,071 | 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 filename in result.json
fig = plt.figure(Plot_Data.count)
corr = data.corr()
ax = sns.heatmap(corr)
Plot_Data.count += 1
return fig | python | 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 filename in result.json
fig = plt.figure(Plot_Data.count)
corr = data.corr()
ax = sns.heatmap(corr)
Plot_Data.count += 1
return fig | [
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Parameters
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data : pd.DataFrame()
Data to display.
Returns
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matplotlib.figure
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2,072 | 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,
data, input_col, output_col, model,
site):
""" Create baseline and projection plots.
Parameters
----------
y_true : pd.Series()
Actual y values.
y_pred : np.ndarray
Predicted y values.
baseline_period : list(str)
Baseline period.
projection_period : list(str)
Projection periods.
model_name : str
Optimal model's name.
adj_r2 : float
Adjusted R2 score of optimal model.
data : pd.Dataframe()
Data containing real values.
input_col : list(str)
Predictor column(s).
output_col : str
Target column.
model : func
Optimal model.
Returns
-------
matplotlib.figure
Baseline plot
"""
# Baseline and projection plots
fig = plt.figure(Plot_Data.count)
# Number of plots to display
if projection_period:
nrows = len(baseline_period) + len(projection_period) / 2
else:
nrows = len(baseline_period) / 2
# Plot 1 - Baseline
base_df = pd.DataFrame()
base_df['y_true'] = y_true
base_df['y_pred'] = y_pred
ax1 = fig.add_subplot(nrows, 1, 1)
base_df.plot(ax=ax1, figsize=self.figsize,
title='Baseline Period ({}-{}). \nBest Model: {}. \nBaseline Adj R2: {}. \nSite: {}.'.format(baseline_period[0], baseline_period[1],
model_name, adj_r2, site))
if projection_period:
# Display projection plots
num_plot = 2
for i in range(0, len(projection_period), 2):
ax = fig.add_subplot(nrows, 1, num_plot)
period = (slice(projection_period[i], projection_period[i+1]))
project_df = pd.DataFrame()
try:
project_df['y_true'] = data.loc[period, output_col]
project_df['y_pred'] = model.predict(data.loc[period, input_col])
# Set all negative values to zero since energy > 0
project_df['y_pred'][project_df['y_pred'] < 0] = 0
project_df.plot(ax=ax, figsize=self.figsize, title='Projection Period ({}-{})'.format(projection_period[i],
projection_period[i+1]))
num_plot += 1
fig.tight_layout()
Plot_Data.count += 1
return fig, project_df['y_true'], project_df['y_pred']
except:
raise TypeError("If projecting into the future, please specify project_ind_col that has data available \
in the future time period requested.")
return fig, None, None | python | def baseline_projection_plot(self, y_true, y_pred,
baseline_period, projection_period,
model_name, adj_r2,
data, input_col, output_col, model,
site):
""" Create baseline and projection plots.
Parameters
----------
y_true : pd.Series()
Actual y values.
y_pred : np.ndarray
Predicted y values.
baseline_period : list(str)
Baseline period.
projection_period : list(str)
Projection periods.
model_name : str
Optimal model's name.
adj_r2 : float
Adjusted R2 score of optimal model.
data : pd.Dataframe()
Data containing real values.
input_col : list(str)
Predictor column(s).
output_col : str
Target column.
model : func
Optimal model.
Returns
-------
matplotlib.figure
Baseline plot
"""
# Baseline and projection plots
fig = plt.figure(Plot_Data.count)
# Number of plots to display
if projection_period:
nrows = len(baseline_period) + len(projection_period) / 2
else:
nrows = len(baseline_period) / 2
# Plot 1 - Baseline
base_df = pd.DataFrame()
base_df['y_true'] = y_true
base_df['y_pred'] = y_pred
ax1 = fig.add_subplot(nrows, 1, 1)
base_df.plot(ax=ax1, figsize=self.figsize,
title='Baseline Period ({}-{}). \nBest Model: {}. \nBaseline Adj R2: {}. \nSite: {}.'.format(baseline_period[0], baseline_period[1],
model_name, adj_r2, site))
if projection_period:
# Display projection plots
num_plot = 2
for i in range(0, len(projection_period), 2):
ax = fig.add_subplot(nrows, 1, num_plot)
period = (slice(projection_period[i], projection_period[i+1]))
project_df = pd.DataFrame()
try:
project_df['y_true'] = data.loc[period, output_col]
project_df['y_pred'] = model.predict(data.loc[period, input_col])
# Set all negative values to zero since energy > 0
project_df['y_pred'][project_df['y_pred'] < 0] = 0
project_df.plot(ax=ax, figsize=self.figsize, title='Projection Period ({}-{})'.format(projection_period[i],
projection_period[i+1]))
num_plot += 1
fig.tight_layout()
Plot_Data.count += 1
return fig, project_df['y_true'], project_df['y_pred']
except:
raise TypeError("If projecting into the future, please specify project_ind_col that has data available \
in the future time period requested.")
return fig, None, None | [
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Actual y values.
y_pred : np.ndarray
Predicted y values.
baseline_period : list(str)
Baseline period.
projection_period : list(str)
Projection periods.
model_name : str
Optimal model's name.
adj_r2 : float
Adjusted R2 score of optimal model.
data : pd.Dataframe()
Data containing real values.
input_col : list(str)
Predictor column(s).
output_col : str
Target column.
model : func
Optimal model.
Returns
-------
matplotlib.figure
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2,073 | SoftwareDefinedBuildings/XBOS | apps/system_identification/rtu_energy.py | get_thermostat_meter_data | def get_thermostat_meter_data(zone):
"""
This method subscribes to the output of the meter for the given zone.
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 = []
def cb(msg):
for po in msg.payload_objects:
if po.type_dotted == (2,0,9,1):
m = msgpack.unpackb(po.content)
data.append(m['current_demand'])
handle = c.subscribe(meter_uri+"/signal/meter", cb)
def stop():
c.unsubscribe(handle)
return data
return stop | python | def get_thermostat_meter_data(zone):
"""
This method subscribes to the output of the meter for the given zone.
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 = []
def cb(msg):
for po in msg.payload_objects:
if po.type_dotted == (2,0,9,1):
m = msgpack.unpackb(po.content)
data.append(m['current_demand'])
handle = c.subscribe(meter_uri+"/signal/meter", cb)
def stop():
c.unsubscribe(handle)
return data
return stop | [
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2,074 | 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
a handler to call when you want to reset the thermostat
"""
current_hsp, current_csp = tstat.heating_setpoint, tstat.cooling_setpoint
current_temp = tstat.temperature
tstat.write({
'heating_setpoint': current_temp+10,
'cooling_setpoint': current_temp+20,
'mode': HEAT,
})
def restore():
tstat.write({
'heating_setpoint': current_hsp,
'cooling_setpoint': current_csp,
'mode': AUTO,
})
return restore | python | def call_heat(tstat):
"""
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
current_temp = tstat.temperature
tstat.write({
'heating_setpoint': current_temp+10,
'cooling_setpoint': current_temp+20,
'mode': HEAT,
})
def restore():
tstat.write({
'heating_setpoint': current_hsp,
'cooling_setpoint': current_csp,
'mode': AUTO,
})
return restore | [
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2,075 | SoftwareDefinedBuildings/XBOS | apps/system_identification/rtu_energy.py | call_cool | def call_cool(tstat):
"""
Adjusts the temperature setpoints in order to call for cooling. 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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tstat.write({
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})
def restore():
tstat.write({
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'cooling_setpoint': current_csp,
'mode': AUTO,
})
return restore | python | def call_cool(tstat):
"""
Adjusts the temperature setpoints in order to call for cooling. Returns
a handler to call when you want to reset the thermostat
"""
current_hsp, current_csp = tstat.heating_setpoint, tstat.cooling_setpoint
current_temp = tstat.temperature
tstat.write({
'heating_setpoint': current_temp-20,
'cooling_setpoint': current_temp-10,
'mode': COOL,
})
def restore():
tstat.write({
'heating_setpoint': current_hsp,
'cooling_setpoint': current_csp,
'mode': AUTO,
})
return restore | [
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2,076 | SoftwareDefinedBuildings/XBOS | apps/system_identification/rtu_energy.py | call_fan | def call_fan(tstat):
"""
Toggles the fan
"""
old_fan = tstat.fan
tstat.write({
'fan': not old_fan,
})
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,
})
def restore():
tstat.write({
'fan': old_fan,
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return restore | [
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2,077 | 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
Folder where file resides. Defaults to '.' - current directory.
head_row : int
Skips all rows from 0 to head_row-1
index_col : int
Skips all columns from 0 to index_col-1
convert_col : bool
Convert columns to numeric type
concat_files : bool
Appends data from files to result dataframe
Returns
-------
pd.DataFrame()
Dataframe containing csv data
"""
# Denotes all csv files
if file_name == "*":
if not os.path.isdir(folder_name):
raise OSError('Folder does not exist.')
else:
file_name_list = sorted(glob.glob(folder_name + '*.csv'))
if not file_name_list:
raise OSError('Either the folder does not contain any csv files or invalid folder provided.')
else:
# Call previous function again with parameters changed (file_name=file_name_list, folder_name=None)
# Done to reduce redundancy of code
self.import_csv(file_name=file_name_list, head_row=head_row, index_col=index_col,
convert_col=convert_col, concat_files=concat_files)
return self.data
else:
if not os.path.isdir(folder_name):
raise OSError('Folder does not exist.')
else:
path = os.path.join(folder_name, file_name)
if head_row > 0:
data = pd.read_csv(path, index_col=index_col, skiprows=[i for i in range(head_row-1)])
else:
data = pd.read_csv(path, index_col=index_col)
# Convert time into datetime format
try:
# Special case format 1/4/14 21:30
data.index = pd.to_datetime(data.index, format='%m/%d/%y %H:%M')
except:
data.index = pd.to_datetime(data.index, dayfirst=False, infer_datetime_format=True)
# Convert all columns to numeric type
if convert_col:
# Check columns in dataframe to see if they are numeric
for col in data.columns:
# If particular column is not numeric, then convert to numeric type
if data[col].dtype != np.number:
data[col] = pd.to_numeric(data[col], errors="coerce")
return data | python | 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
Folder where file resides. Defaults to '.' - current directory.
head_row : int
Skips all rows from 0 to head_row-1
index_col : int
Skips all columns from 0 to index_col-1
convert_col : bool
Convert columns to numeric type
concat_files : bool
Appends data from files to result dataframe
Returns
-------
pd.DataFrame()
Dataframe containing csv data
"""
# Denotes all csv files
if file_name == "*":
if not os.path.isdir(folder_name):
raise OSError('Folder does not exist.')
else:
file_name_list = sorted(glob.glob(folder_name + '*.csv'))
if not file_name_list:
raise OSError('Either the folder does not contain any csv files or invalid folder provided.')
else:
# Call previous function again with parameters changed (file_name=file_name_list, folder_name=None)
# Done to reduce redundancy of code
self.import_csv(file_name=file_name_list, head_row=head_row, index_col=index_col,
convert_col=convert_col, concat_files=concat_files)
return self.data
else:
if not os.path.isdir(folder_name):
raise OSError('Folder does not exist.')
else:
path = os.path.join(folder_name, file_name)
if head_row > 0:
data = pd.read_csv(path, index_col=index_col, skiprows=[i for i in range(head_row-1)])
else:
data = pd.read_csv(path, index_col=index_col)
# Convert time into datetime format
try:
# Special case format 1/4/14 21:30
data.index = pd.to_datetime(data.index, format='%m/%d/%y %H:%M')
except:
data.index = pd.to_datetime(data.index, dayfirst=False, infer_datetime_format=True)
# Convert all columns to numeric type
if convert_col:
# Check columns in dataframe to see if they are numeric
for col in data.columns:
# If particular column is not numeric, then convert to numeric type
if data[col].dtype != np.number:
data[col] = pd.to_numeric(data[col], errors="coerce")
return data | [
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Parameters
----------
file_name : str
CSV file to be imported. Defaults to '*' - all csv files in the folder.
folder_name : str
Folder where file resides. Defaults to '.' - current directory.
head_row : int
Skips all rows from 0 to head_row-1
index_col : int
Skips all columns from 0 to index_col-1
convert_col : bool
Convert columns to numeric type
concat_files : bool
Appends data from files to result dataframe
Returns
-------
pd.DataFrame()
Dataframe containing csv data | [
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] | c12d4fb14518ea3ae98c471c28e0710fdf74dd25 | https://github.com/SoftwareDefinedBuildings/XBOS/blob/c12d4fb14518ea3ae98c471c28e0710fdf74dd25/apps/Data_quality_analysis/Import_Data.py#L106-L174 |
2,078 | 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.
"""
# time is already in UTC
if 'Z' in time:
return time
else:
time_formatted = time[:-3] + time[-2:]
dt = datetime.strptime(time_formatted, '%Y-%m-%dT%H:%M:%S%z')
dt = dt.astimezone(timezone('UTC'))
return dt.strftime('%Y-%m-%dT%H:%M:%SZ') | python | 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.
"""
# time is already in UTC
if 'Z' in time:
return time
else:
time_formatted = time[:-3] + time[-2:]
dt = datetime.strptime(time_formatted, '%Y-%m-%dT%H:%M:%S%z')
dt = dt.astimezone(timezone('UTC'))
return dt.strftime('%Y-%m-%dT%H:%M:%SZ') | [
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Returns
-------
str
UTC timestamp. | [
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2,079 | 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 : str
Start date - 'YYYY-MM-DDTHH:MM:SSZ'
end : str
End date - 'YYYY-MM-DDTHH:MM:SSZ'
point_type : str
Type of data, i.e. Green_Button_Meter, Building_Electric_Meter...
var : str
Variable - "meter", "weather"...
agg : str
Aggregation - MEAN, SUM, RAW...
window : str
Size of the moving window.
aligned : bool
???
return_names : bool
???
Returns
-------
(df, mapping, context)
???
"""
# Convert time to UTC
start = self.convert_to_utc(start)
end = self.convert_to_utc(end)
request = self.compose_MDAL_dic(point_type=point_type, site=site, start=start, end=end,
var=var, agg=agg, window=window, aligned=aligned)
resp = self.m.query(request)
if return_names:
resp = self.replace_uuid_w_names(resp)
return resp | 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
----------
site : str
Building name.
start : str
Start date - 'YYYY-MM-DDTHH:MM:SSZ'
end : str
End date - 'YYYY-MM-DDTHH:MM:SSZ'
point_type : str
Type of data, i.e. Green_Button_Meter, Building_Electric_Meter...
var : str
Variable - "meter", "weather"...
agg : str
Aggregation - MEAN, SUM, RAW...
window : str
Size of the moving window.
aligned : bool
???
return_names : bool
???
Returns
-------
(df, mapping, context)
???
"""
# Convert time to UTC
start = self.convert_to_utc(start)
end = self.convert_to_utc(end)
request = self.compose_MDAL_dic(point_type=point_type, site=site, start=start, end=end,
var=var, agg=agg, window=window, aligned=aligned)
resp = self.m.query(request)
if return_names:
resp = self.replace_uuid_w_names(resp)
return resp | [
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Parameters
----------
site : str
Building name.
start : str
Start date - 'YYYY-MM-DDTHH:MM:SSZ'
end : str
End date - 'YYYY-MM-DDTHH:MM:SSZ'
point_type : str
Type of data, i.e. Green_Button_Meter, Building_Electric_Meter...
var : str
Variable - "meter", "weather"...
agg : str
Aggregation - MEAN, SUM, RAW...
window : str
Size of the moving window.
aligned : bool
???
return_names : bool
???
Returns
-------
(df, mapping, context)
??? | [
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2,080 | 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.
start : str
Start date - 'YYYY-MM-DDTHH:MM:SSZ'
end : str
End date - 'YYYY-MM-DDTHH:MM:SSZ'
var : str
Variable - "meter", "weather"...
agg : str
Aggregation - MEAN, SUM, RAW...
window : str
Size of the moving window.
aligned : bool
???
return_names : bool
???
Returns
-------
(df, mapping, context)
???
"""
# Convert time to UTC
start = self.convert_to_utc(start)
end = self.convert_to_utc(end)
point_map = {
"tstat_state" : "Thermostat_Status",
"tstat_hsp" : "Supply_Air_Temperature_Heating_Setpoint",
"tstat_csp" : "Supply_Air_Temperature_Cooling_Setpoint",
"tstat_temp": "Temperature_Sensor"
}
if isinstance(var,list):
point_type = [point_map[point_type] for point_type in var] # list of all the point names using BRICK classes
else:
point_type = point_map[var] # single value using BRICK classes
request = self.compose_MDAL_dic(point_type=point_type, site=site, start=start, end=end,
var=var, agg=agg, window=window, aligned=aligned)
resp = self.m.query(request)
if return_names:
resp = self.replace_uuid_w_names(resp)
return resp | python | 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.
start : str
Start date - 'YYYY-MM-DDTHH:MM:SSZ'
end : str
End date - 'YYYY-MM-DDTHH:MM:SSZ'
var : str
Variable - "meter", "weather"...
agg : str
Aggregation - MEAN, SUM, RAW...
window : str
Size of the moving window.
aligned : bool
???
return_names : bool
???
Returns
-------
(df, mapping, context)
???
"""
# Convert time to UTC
start = self.convert_to_utc(start)
end = self.convert_to_utc(end)
point_map = {
"tstat_state" : "Thermostat_Status",
"tstat_hsp" : "Supply_Air_Temperature_Heating_Setpoint",
"tstat_csp" : "Supply_Air_Temperature_Cooling_Setpoint",
"tstat_temp": "Temperature_Sensor"
}
if isinstance(var,list):
point_type = [point_map[point_type] for point_type in var] # list of all the point names using BRICK classes
else:
point_type = point_map[var] # single value using BRICK classes
request = self.compose_MDAL_dic(point_type=point_type, site=site, start=start, end=end,
var=var, agg=agg, window=window, aligned=aligned)
resp = self.m.query(request)
if return_names:
resp = self.replace_uuid_w_names(resp)
return resp | [
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Parameters
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site : str
Building name.
start : str
Start date - 'YYYY-MM-DDTHH:MM:SSZ'
end : str
End date - 'YYYY-MM-DDTHH:MM:SSZ'
var : str
Variable - "meter", "weather"...
agg : str
Aggregation - MEAN, SUM, RAW...
window : str
Size of the moving window.
aligned : bool
???
return_names : bool
???
Returns
-------
(df, mapping, context)
??? | [
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2,081 | SoftwareDefinedBuildings/XBOS | apps/Data_quality_analysis/Import_Data.py | Import_MDAL.compose_MDAL_dic | def compose_MDAL_dic(self, site, point_type,
start, end, var, agg, window, aligned, points=None, return_names=False):
""" Create dictionary for MDAL request.
Parameters
----------
site : str
Building name.
start : str
Start date - 'YYYY-MM-DDTHH:MM:SSZ'
end : str
End date - 'YYYY-MM-DDTHH:MM:SSZ'
point_type : str
Type of data, i.e. Green_Button_Meter, Building_Electric_Meter...
var : str
Variable - "meter", "weather"...
agg : str
Aggregation - MEAN, SUM, RAW...
window : str
Size of the moving window.
aligned : bool
???
return_names : bool
???
Returns
-------
(df, mapping, context)
???
"""
# Convert time to UTC
start = self.convert_to_utc(start)
end = self.convert_to_utc(end)
request = {}
# Add Time Details - single set for one or multiple series
request['Time'] = {
'Start': start,
'End': end,
'Window': window,
'Aligned': aligned
}
# Define Variables
request["Variables"] = {}
request['Composition'] = []
request['Aggregation'] = {}
if isinstance(point_type, str): # if point_type is a string -> single type of point requested
request["Variables"][var] = self.compose_BRICK_query(point_type=point_type,site=site) # pass one point type at the time
request['Composition'] = [var]
request['Aggregation'][var] = [agg]
elif isinstance(point_type, list): # loop through all the point_types and create one section of the brick query at the time
for idx, point in enumerate(point_type):
request["Variables"][var[idx]] = self.compose_BRICK_query(point_type=point,site=site) # pass one point type at the time
request['Composition'].append(var[idx])
if isinstance(agg, str): # if agg is a string -> single type of aggregation requested
request['Aggregation'][var[idx]] = [agg]
elif isinstance(agg, list): # if agg is a list -> expected one agg per point
request['Aggregation'][var[idx]] = [agg[idx]]
return request | python | def compose_MDAL_dic(self, site, point_type,
start, end, var, agg, window, aligned, points=None, return_names=False):
""" Create dictionary for MDAL request.
Parameters
----------
site : str
Building name.
start : str
Start date - 'YYYY-MM-DDTHH:MM:SSZ'
end : str
End date - 'YYYY-MM-DDTHH:MM:SSZ'
point_type : str
Type of data, i.e. Green_Button_Meter, Building_Electric_Meter...
var : str
Variable - "meter", "weather"...
agg : str
Aggregation - MEAN, SUM, RAW...
window : str
Size of the moving window.
aligned : bool
???
return_names : bool
???
Returns
-------
(df, mapping, context)
???
"""
# Convert time to UTC
start = self.convert_to_utc(start)
end = self.convert_to_utc(end)
request = {}
# Add Time Details - single set for one or multiple series
request['Time'] = {
'Start': start,
'End': end,
'Window': window,
'Aligned': aligned
}
# Define Variables
request["Variables"] = {}
request['Composition'] = []
request['Aggregation'] = {}
if isinstance(point_type, str): # if point_type is a string -> single type of point requested
request["Variables"][var] = self.compose_BRICK_query(point_type=point_type,site=site) # pass one point type at the time
request['Composition'] = [var]
request['Aggregation'][var] = [agg]
elif isinstance(point_type, list): # loop through all the point_types and create one section of the brick query at the time
for idx, point in enumerate(point_type):
request["Variables"][var[idx]] = self.compose_BRICK_query(point_type=point,site=site) # pass one point type at the time
request['Composition'].append(var[idx])
if isinstance(agg, str): # if agg is a string -> single type of aggregation requested
request['Aggregation'][var[idx]] = [agg]
elif isinstance(agg, list): # if agg is a list -> expected one agg per point
request['Aggregation'][var[idx]] = [agg[idx]]
return request | [
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Parameters
----------
site : str
Building name.
start : str
Start date - 'YYYY-MM-DDTHH:MM:SSZ'
end : str
End date - 'YYYY-MM-DDTHH:MM:SSZ'
point_type : str
Type of data, i.e. Green_Button_Meter, Building_Electric_Meter...
var : str
Variable - "meter", "weather"...
agg : str
Aggregation - MEAN, SUM, RAW...
window : str
Size of the moving window.
aligned : bool
???
return_names : bool
???
Returns
-------
(df, mapping, context)
??? | [
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2,082 | SoftwareDefinedBuildings/XBOS | apps/Data_quality_analysis/Import_Data.py | Import_MDAL.get_point_name | def get_point_name(self, context):
""" Get point name.
Parameters
----------
context : ???
???
Returns
-------
???
???
"""
metadata_table = self.parse_context(context)
return metadata_table.apply(self.strip_point_name, axis=1) | python | def get_point_name(self, context):
""" Get point name.
Parameters
----------
context : ???
???
Returns
-------
???
???
"""
metadata_table = self.parse_context(context)
return metadata_table.apply(self.strip_point_name, axis=1) | [
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2,083 | SoftwareDefinedBuildings/XBOS | apps/Data_quality_analysis/Import_Data.py | Import_MDAL.replace_uuid_w_names | def replace_uuid_w_names(self, resp):
""" Replace the uuid's with names.
Parameters
----------
resp : ???
???
Returns
-------
???
???
"""
col_mapper = self.get_point_name(resp.context)["?point"].to_dict()
resp.df.rename(columns=col_mapper, inplace=True)
return resp | python | def replace_uuid_w_names(self, resp):
""" Replace the uuid's with names.
Parameters
----------
resp : ???
???
Returns
-------
???
???
"""
col_mapper = self.get_point_name(resp.context)["?point"].to_dict()
resp.df.rename(columns=col_mapper, inplace=True)
return resp | [
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2,084 | 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/generated/pandas.DataFrame.resample.html
Parameters
----------
data : pd.DataFrame()
Dataframe to resample
freq : str
Resampling frequency i.e. d, h, 15T...
resampler : str
Resampling type i.e. mean, max.
Returns
-------
pd.DataFrame()
Dataframe containing resampled data
"""
if resampler == 'mean':
data = data.resample(freq).mean()
elif resampler == 'max':
data = data.resample(freq).max()
else:
raise ValueError('Resampler can be \'mean\' or \'max\' only.')
return data | 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
http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.resample.html
Parameters
----------
data : pd.DataFrame()
Dataframe to resample
freq : str
Resampling frequency i.e. d, h, 15T...
resampler : str
Resampling type i.e. mean, max.
Returns
-------
pd.DataFrame()
Dataframe containing resampled data
"""
if resampler == 'mean':
data = data.resample(freq).mean()
elif resampler == 'max':
data = data.resample(freq).max()
else:
raise ValueError('Resampler can be \'mean\' or \'max\' only.')
return data | [
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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/generated/pandas.DataFrame.resample.html
Parameters
----------
data : pd.DataFrame()
Dataframe to resample
freq : str
Resampling frequency i.e. d, h, 15T...
resampler : str
Resampling type i.e. mean, max.
Returns
-------
pd.DataFrame()
Dataframe containing resampled data | [
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2,085 | 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
-------
pd.DataFrame()
Dataframe containing interpolated data
"""
data = data.interpolate(how="index", limit=limit, method=method)
return data | 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.
Returns
-------
pd.DataFrame()
Dataframe containing interpolated data
"""
data = data.interpolate(how="index", limit=limit, method=method)
return data | [
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Interpolation limit.
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Interpolation method.
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Dataframe containing interpolated data | [
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2,086 | 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
-------
pd.DataFrame()
Dataframe with NAs removed.
"""
data = data.dropna(how=remove_na_how)
return data | 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
-------
pd.DataFrame()
Dataframe with NAs removed.
"""
data = data.dropna(how=remove_na_how)
return data | [
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Dataframe to remove NAs from.
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Specificies how to remove NA i.e. all, any...
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Dataframe with NAs removed. | [
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2,087 | 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
Standard Deviation Value (specifices how many SDs away is a point considered an outlier)
Returns
-------
pd.DataFrame()
Dataframe with outliers removed.
"""
data = data.dropna()
data = data[(np.abs(stats.zscore(data)) < float(sd_val)).all(axis=1)]
return data | 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
Standard Deviation Value (specifices how many SDs away is a point considered an outlier)
Returns
-------
pd.DataFrame()
Dataframe with outliers removed.
"""
data = data.dropna()
data = data[(np.abs(stats.zscore(data)) < float(sd_val)).all(axis=1)]
return data | [
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Dataframe to remove outliers from.
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Standard Deviation Value (specifices how many SDs away is a point considered an outlier)
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2,088 | 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 : pd.DataFrame()
Dataframe to remove bounds from.
low_bound : int
Low bound of the data.
high_bound : int
High bound of the data.
Returns
-------
pd.DataFrame()
Dataframe with out of bounds removed.
"""
data = data.dropna()
data = data[(data > low_bound).all(axis=1) & (data < high_bound).all(axis=1)]
return 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 : pd.DataFrame()
Dataframe to remove bounds from.
low_bound : int
Low bound of the data.
high_bound : int
High bound of the data.
Returns
-------
pd.DataFrame()
Dataframe with out of bounds removed.
"""
data = data.dropna()
data = data[(data > low_bound).all(axis=1) & (data < high_bound).all(axis=1)]
return data | [
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Dataframe to remove bounds from.
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Low bound of the data.
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High bound of the data.
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-------
pd.DataFrame()
Dataframe with out of bounds removed. | [
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2,089 | 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.
"""
# Set index
data.index = pd.to_datetime(data.index, error= "ignore")
# Format types to numeric
for col in data.columns:
data[col] = pd.to_numeric(data[col], errors="coerce")
return data | 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.
"""
# Set index
data.index = pd.to_datetime(data.index, error= "ignore")
# Format types to numeric
for col in data.columns:
data[col] = pd.to_numeric(data[col], errors="coerce")
return data | [
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2,090 | 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 : str
pytz.timezone string of specified local timezone to change index to.
Returns
-------
pd.DataFrame()
Pandas dataframe with timestamp index adjusted for local timezone.
"""
# Accounts for localtime shift
data.index = data.index.tz_localize(pytz.utc).tz_convert(local_zone)
# Gets rid of extra offset information so can compare with csv data
data.index = data.index.tz_localize(None)
return data | 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.
local_zone : str
pytz.timezone string of specified local timezone to change index to.
Returns
-------
pd.DataFrame()
Pandas dataframe with timestamp index adjusted for local timezone.
"""
# Accounts for localtime shift
data.index = data.index.tz_localize(pytz.utc).tz_convert(local_zone)
# Gets rid of extra offset information so can compare with csv data
data.index = data.index.tz_localize(None)
return data | [
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Pandas dataframe of json timeseries response from server.
local_zone : str
pytz.timezone string of specified local timezone to change index to.
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-------
pd.DataFrame()
Pandas dataframe with timestamp index adjusted for local timezone. | [
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2,091 | 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. Defaults to PST.
Returns
-------
pd.DataFrame()
Dataframe with UTC timestamps.
"""
timestamp_new = pd.to_datetime(timestamp, infer_datetime_format=True, errors='coerce')
timestamp_new = timestamp_new.tz_localize(local_zone).tz_convert(pytz.utc)
timestamp_new = timestamp_new.strftime('%Y-%m-%d %H:%M:%S')
return timestamp_new | 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.
local_zone : str
Name of local zone. Defaults to PST.
Returns
-------
pd.DataFrame()
Dataframe with UTC timestamps.
"""
timestamp_new = pd.to_datetime(timestamp, infer_datetime_format=True, errors='coerce')
timestamp_new = timestamp_new.tz_localize(local_zone).tz_convert(pytz.utc)
timestamp_new = timestamp_new.strftime('%Y-%m-%d %H:%M:%S')
return timestamp_new | [
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Input Pandas dataframe whose index needs to be changed.
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Name of local zone. Defaults to PST.
Returns
-------
pd.DataFrame()
Dataframe with UTC timestamps. | [
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2,092 | SoftwareDefinedBuildings/XBOS | apps/Data_quality_analysis/Clean_Data.py | Clean_Data.find_uuid | def find_uuid(self, obj, column_name):
""" Find uuid.
Parameters
----------
obj : ???
the object returned by the MDAL Query
column_name : str
input point returned from MDAL Query
Returns
-------
str
the uuid that correlates with the data
"""
keys = obj.context.keys()
for i in keys:
if column_name in obj.context[i]['?point']:
uuid = i
return i | python | def find_uuid(self, obj, column_name):
""" Find uuid.
Parameters
----------
obj : ???
the object returned by the MDAL Query
column_name : str
input point returned from MDAL Query
Returns
-------
str
the uuid that correlates with the data
"""
keys = obj.context.keys()
for i in keys:
if column_name in obj.context[i]['?point']:
uuid = i
return i | [
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Parameters
----------
obj : ???
the object returned by the MDAL Query
column_name : str
input point returned from MDAL Query
Returns
-------
str
the uuid that correlates with the data | [
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2,093 | SoftwareDefinedBuildings/XBOS | apps/Data_quality_analysis/Clean_Data.py | Clean_Data.identify_missing | def identify_missing(self, df, check_start=True):
""" Identify missing data.
Parameters
----------
df : pd.DataFrame()
Dataframe to check for missing data.
check_start : bool
turns 0 to 1 for the first observation, to display the start of the data
as the beginning of the missing data event
Returns
-------
pd.DataFrame(), str
dataframe where 1 indicates missing data and 0 indicates reported data,
returns the column name generated from the MDAL Query
"""
# Check start changes the first value of df to 1, when the data stream is initially missing
# This allows the diff function to acknowledge the missing data
data_missing = df.isnull() * 1
col_name = str(data_missing.columns[0])
# When there is no data stream at the beginning we change it to 1
if check_start & data_missing[col_name][0] == 1:
data_missing[col_name][0] = 0
return data_missing, col_name | python | def identify_missing(self, df, check_start=True):
""" Identify missing data.
Parameters
----------
df : pd.DataFrame()
Dataframe to check for missing data.
check_start : bool
turns 0 to 1 for the first observation, to display the start of the data
as the beginning of the missing data event
Returns
-------
pd.DataFrame(), str
dataframe where 1 indicates missing data and 0 indicates reported data,
returns the column name generated from the MDAL Query
"""
# Check start changes the first value of df to 1, when the data stream is initially missing
# This allows the diff function to acknowledge the missing data
data_missing = df.isnull() * 1
col_name = str(data_missing.columns[0])
# When there is no data stream at the beginning we change it to 1
if check_start & data_missing[col_name][0] == 1:
data_missing[col_name][0] = 0
return data_missing, col_name | [
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Parameters
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Dataframe to check for missing data.
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turns 0 to 1 for the first observation, to display the start of the data
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Returns
-------
pd.DataFrame(), str
dataframe where 1 indicates missing data and 0 indicates reported data,
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2,094 | 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'):
""" takes the dataframe of missing values, and returns a dataframe that indicates the
length of each event where data was continuously missing
Parameters
----------
df : pd.DataFrame()
Dataframe to check for missing data (must be in boolean format where 1 indicates missing data.
column_name : str
the original column name produced by MDAL Query
uuid : str
the uuid associated with the meter, if known
duration : bool
If True, the duration will be displayed in the results. If false the column will be dropped.
min_event_filter : str
Filters out the events that are less than the given time period
Returns
-------
pd.DataFrame()
dataframe with the start time of the event (as the index),
end time of the event (first time when data is reported)
"""
if uuid == None:
uuid = 'End'
data_gaps = df[(df.diff() == 1) | (df.diff() == -1)].dropna()
data_gaps["duration"] = abs(data_gaps.index.to_series().diff(periods=-1))
data_gaps[uuid] = data_gaps.index + (data_gaps["duration"])
data_gaps = data_gaps[data_gaps["duration"] > pd.Timedelta(min_event_filter)]
data_gaps = data_gaps[data_gaps[column_name] == 1]
data_gaps.pop(column_name)
if not duration:
data_gaps.pop('duration')
data_gaps.index = data_gaps.index.strftime(date_format="%Y-%m-%d %H:%M:%S")
data_gaps[uuid] = data_gaps[uuid].dt.strftime(date_format="%Y-%m-%d %H:%M:%S")
return data_gaps | python | def diff_boolean(self, df, column_name=None, uuid=None, duration=True, min_event_filter='3 hours'):
""" takes the dataframe of missing values, and returns a dataframe that indicates the
length of each event where data was continuously missing
Parameters
----------
df : pd.DataFrame()
Dataframe to check for missing data (must be in boolean format where 1 indicates missing data.
column_name : str
the original column name produced by MDAL Query
uuid : str
the uuid associated with the meter, if known
duration : bool
If True, the duration will be displayed in the results. If false the column will be dropped.
min_event_filter : str
Filters out the events that are less than the given time period
Returns
-------
pd.DataFrame()
dataframe with the start time of the event (as the index),
end time of the event (first time when data is reported)
"""
if uuid == None:
uuid = 'End'
data_gaps = df[(df.diff() == 1) | (df.diff() == -1)].dropna()
data_gaps["duration"] = abs(data_gaps.index.to_series().diff(periods=-1))
data_gaps[uuid] = data_gaps.index + (data_gaps["duration"])
data_gaps = data_gaps[data_gaps["duration"] > pd.Timedelta(min_event_filter)]
data_gaps = data_gaps[data_gaps[column_name] == 1]
data_gaps.pop(column_name)
if not duration:
data_gaps.pop('duration')
data_gaps.index = data_gaps.index.strftime(date_format="%Y-%m-%d %H:%M:%S")
data_gaps[uuid] = data_gaps[uuid].dt.strftime(date_format="%Y-%m-%d %H:%M:%S")
return data_gaps | [
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length of each event where data was continuously missing
Parameters
----------
df : pd.DataFrame()
Dataframe to check for missing data (must be in boolean format where 1 indicates missing data.
column_name : str
the original column name produced by MDAL Query
uuid : str
the uuid associated with the meter, if known
duration : bool
If True, the duration will be displayed in the results. If false the column will be dropped.
min_event_filter : str
Filters out the events that are less than the given time period
Returns
-------
pd.DataFrame()
dataframe with the start time of the event (as the index),
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2,095 | 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):
""" Takes in an the object returned by the MDAL query, and analyzes the quality
of the data for each column in the df. Returns a df of data quality metrics
To Do
-----
Need to make it specific for varying meters and label it for each type,
Either separate functions or make the function broader
Parameters
----------
obj : ???
the object returned by the MDAL Query
low_bound : float
all data equal to or below this value will be interpreted as missing data
high_bound : float
all data above this value will be interpreted as missing
Returns
-------
pd.DataFrame()
returns data frame with % missing data, average duration of missing data
event and standard deviation of that duration for each column of data
"""
data = obj.df
N_rows = 3
N_cols = data.shape[1]
d = pd.DataFrame(np.zeros((N_rows, N_cols)),
index=['% Missing', 'AVG Length Missing', 'Std dev. Missing'],
columns=[data.columns])
if low_bound:
data = data.where(data >= low_bound)
if high_bound:
data=data.where(data < high_bound)
for i in range(N_cols):
data_per_meter = data.iloc[:, [i]]
data_missing, meter = self.identify_missing(data_per_meter)
percentage = data_missing.sum() / (data.shape[0]) * 100
data_gaps = self.diff_boolean(data_missing, column_name=meter)
missing_mean = data_gaps.mean()
std_dev = data_gaps.std()
d.loc["% Missing", meter] = percentage[meter]
d.loc["AVG Length Missing", meter] = missing_mean['duration']
d.loc["Std dev. Missing", meter] = std_dev['duration']
return d | python | def analyze_quality_table(self, obj,low_bound=None, high_bound=None):
""" Takes in an the object returned by the MDAL query, and analyzes the quality
of the data for each column in the df. Returns a df of data quality metrics
To Do
-----
Need to make it specific for varying meters and label it for each type,
Either separate functions or make the function broader
Parameters
----------
obj : ???
the object returned by the MDAL Query
low_bound : float
all data equal to or below this value will be interpreted as missing data
high_bound : float
all data above this value will be interpreted as missing
Returns
-------
pd.DataFrame()
returns data frame with % missing data, average duration of missing data
event and standard deviation of that duration for each column of data
"""
data = obj.df
N_rows = 3
N_cols = data.shape[1]
d = pd.DataFrame(np.zeros((N_rows, N_cols)),
index=['% Missing', 'AVG Length Missing', 'Std dev. Missing'],
columns=[data.columns])
if low_bound:
data = data.where(data >= low_bound)
if high_bound:
data=data.where(data < high_bound)
for i in range(N_cols):
data_per_meter = data.iloc[:, [i]]
data_missing, meter = self.identify_missing(data_per_meter)
percentage = data_missing.sum() / (data.shape[0]) * 100
data_gaps = self.diff_boolean(data_missing, column_name=meter)
missing_mean = data_gaps.mean()
std_dev = data_gaps.std()
d.loc["% Missing", meter] = percentage[meter]
d.loc["AVG Length Missing", meter] = missing_mean['duration']
d.loc["Std dev. Missing", meter] = std_dev['duration']
return d | [
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] | Takes in an the object returned by the MDAL query, and analyzes the quality
of the data for each column in the df. Returns a df of data quality metrics
To Do
-----
Need to make it specific for varying meters and label it for each type,
Either separate functions or make the function broader
Parameters
----------
obj : ???
the object returned by the MDAL Query
low_bound : float
all data equal to or below this value will be interpreted as missing data
high_bound : float
all data above this value will be interpreted as missing
Returns
-------
pd.DataFrame()
returns data frame with % missing data, average duration of missing data
event and standard deviation of that duration for each column of data | [
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2,096 | 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
of the data for each column in the df in the form of graphs. The Graphs returned
show missing data events over time, and missing data frequency during each hour
of the day
To Do
-----
Need to make it specific for varying meters and label it for each type,
Either separate functions or make the function broader
Parameters
----------
obj : ???
the object returned by the MDAL Query
"""
data = obj.df
for i in range(data.shape[1]):
data_per_meter = data.iloc[:, [i]] # need to make this work or change the structure
data_missing, meter = self.identify_missing(data_per_meter)
percentage = data_missing.sum() / (data.shape[0]) * 100
print('Percentage Missing of ' + meter + ' data: ' + str(int(percentage)) + '%')
data_missing.plot(figsize=(18, 5), x_compat=True, title=meter + " Missing Data over the Time interval")
data_gaps = self.diff_boolean(data_missing, column_name=meter)
data_missing['Hour'] = data_missing.index.hour
ymax = int(data_missing.groupby('Hour').sum().max() + 10)
data_missing.groupby('Hour').sum().plot(ylim=(0, ymax), figsize=(18, 5),
title=meter + " Time of Day of Missing Data")
print(data_gaps) | python | def analyze_quality_graph(self, obj):
""" Takes in an the object returned by the MDAL query, and analyzes the quality
of the data for each column in the df in the form of graphs. The Graphs returned
show missing data events over time, and missing data frequency during each hour
of the day
To Do
-----
Need to make it specific for varying meters and label it for each type,
Either separate functions or make the function broader
Parameters
----------
obj : ???
the object returned by the MDAL Query
"""
data = obj.df
for i in range(data.shape[1]):
data_per_meter = data.iloc[:, [i]] # need to make this work or change the structure
data_missing, meter = self.identify_missing(data_per_meter)
percentage = data_missing.sum() / (data.shape[0]) * 100
print('Percentage Missing of ' + meter + ' data: ' + str(int(percentage)) + '%')
data_missing.plot(figsize=(18, 5), x_compat=True, title=meter + " Missing Data over the Time interval")
data_gaps = self.diff_boolean(data_missing, column_name=meter)
data_missing['Hour'] = data_missing.index.hour
ymax = int(data_missing.groupby('Hour').sum().max() + 10)
data_missing.groupby('Hour').sum().plot(ylim=(0, ymax), figsize=(18, 5),
title=meter + " Time of Day of Missing Data")
print(data_gaps) | [
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of the data for each column in the df in the form of graphs. The Graphs returned
show missing data events over time, and missing data frequency during each hour
of the day
To Do
-----
Need to make it specific for varying meters and label it for each type,
Either separate functions or make the function broader
Parameters
----------
obj : ???
the object returned by the MDAL Query | [
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2,097 | 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',
interpolate=True, limit=1, method='linear',
remove_na=True, remove_na_how='any',
remove_outliers=True, sd_val=3,
remove_out_of_bounds=True, low_bound=0, high_bound=9998):
""" Clean dataframe.
Parameters
----------
resample : bool
Indicates whether to resample data or not.
freq : str
Resampling frequency i.e. d, h, 15T...
resampler : str
Resampling type i.e. mean, max.
interpolate : bool
Indicates whether to interpolate data or not.
limit : int
Interpolation limit.
method : str
Interpolation method.
remove_na : bool
Indicates whether to remove NAs or not.
remove_na_how : str
Specificies how to remove NA i.e. all, any...
remove_outliers : bool
Indicates whether to remove outliers or not.
sd_val : int
Standard Deviation Value (specifices how many SDs away is a point considered an outlier)
remove_out_of_bounds : bool
Indicates whether to remove out of bounds datapoints or not.
low_bound : int
Low bound of the data.
high_bound : int
High bound of the data.
"""
# Store copy of the original data
data = self.original_data
if resample:
try:
data = self.resample_data(data, freq, resampler)
except Exception as e:
raise e
if interpolate:
try:
data = self.interpolate_data(data, limit=limit, method=method)
except Exception as e:
raise e
if remove_na:
try:
data = self.remove_na(data, remove_na_how)
except Exception as e:
raise e
if remove_outliers:
try:
data = self.remove_outliers(data, sd_val)
except Exception as e:
raise e
if remove_out_of_bounds:
try:
data = self.remove_out_of_bounds(data, low_bound, high_bound)
except Exception as e:
raise e
self.cleaned_data = data | python | def clean_data(self, resample=True, freq='h', resampler='mean',
interpolate=True, limit=1, method='linear',
remove_na=True, remove_na_how='any',
remove_outliers=True, sd_val=3,
remove_out_of_bounds=True, low_bound=0, high_bound=9998):
""" Clean dataframe.
Parameters
----------
resample : bool
Indicates whether to resample data or not.
freq : str
Resampling frequency i.e. d, h, 15T...
resampler : str
Resampling type i.e. mean, max.
interpolate : bool
Indicates whether to interpolate data or not.
limit : int
Interpolation limit.
method : str
Interpolation method.
remove_na : bool
Indicates whether to remove NAs or not.
remove_na_how : str
Specificies how to remove NA i.e. all, any...
remove_outliers : bool
Indicates whether to remove outliers or not.
sd_val : int
Standard Deviation Value (specifices how many SDs away is a point considered an outlier)
remove_out_of_bounds : bool
Indicates whether to remove out of bounds datapoints or not.
low_bound : int
Low bound of the data.
high_bound : int
High bound of the data.
"""
# Store copy of the original data
data = self.original_data
if resample:
try:
data = self.resample_data(data, freq, resampler)
except Exception as e:
raise e
if interpolate:
try:
data = self.interpolate_data(data, limit=limit, method=method)
except Exception as e:
raise e
if remove_na:
try:
data = self.remove_na(data, remove_na_how)
except Exception as e:
raise e
if remove_outliers:
try:
data = self.remove_outliers(data, sd_val)
except Exception as e:
raise e
if remove_out_of_bounds:
try:
data = self.remove_out_of_bounds(data, low_bound, high_bound)
except Exception as e:
raise e
self.cleaned_data = data | [
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Parameters
----------
resample : bool
Indicates whether to resample data or not.
freq : str
Resampling frequency i.e. d, h, 15T...
resampler : str
Resampling type i.e. mean, max.
interpolate : bool
Indicates whether to interpolate data or not.
limit : int
Interpolation limit.
method : str
Interpolation method.
remove_na : bool
Indicates whether to remove NAs or not.
remove_na_how : str
Specificies how to remove NA i.e. all, any...
remove_outliers : bool
Indicates whether to remove outliers or not.
sd_val : int
Standard Deviation Value (specifices how many SDs away is a point considered an outlier)
remove_out_of_bounds : bool
Indicates whether to remove out of bounds datapoints or not.
low_bound : int
Low bound of the data.
high_bound : int
High bound of the data. | [
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2,098 | SoftwareDefinedBuildings/XBOS | apps/Data_quality_analysis/Wrapper.py | Wrapper.write_json | def write_json(self):
""" Dump data into json file. """
with open(self.results_folder_name + '/results-' + str(self.get_global_count()) + '.json', 'a') as f:
json.dump(self.result, f) | python | def write_json(self):
""" Dump data into json file. """
with open(self.results_folder_name + '/results-' + str(self.get_global_count()) + '.json', 'a') as f:
json.dump(self.result, f) | [
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2,099 | 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.
end_date : str
End date of data collected.
"""
def count_number_of_days(site, end_date):
""" Counts the number of days between two dates.
Parameters
----------
site : str
Key to a dic containing site_name -> pelican installation date.
end_date : str
End date.
Returns
-------
int
Number of days
"""
start_date = site_install_mapping[site]
start_date = start_date.split('-')
start = date(int(start_date[0]), int(start_date[1]), int(start_date[2]))
end_date = end_date.split('-')
end = date(int(end_date[0]), int(end_date[1]), int(end_date[2]))
delta = end - start
return delta.days
if not folder_name or not isinstance(folder_name, str):
raise TypeError("folder_name should be type string")
else:
list_json_files = []
df = pd.DataFrame()
temp_df = pd.DataFrame()
json_files = [f for f in os.listdir(folder_name) if f.endswith('.json')]
for json_file in json_files:
with open(folder_name + json_file) as f:
js = json.load(f)
num_days = count_number_of_days(js['Site'], end_date)
e_abs_sav = round(js['Energy Savings (absolute)'] / 1000, 2) # Energy Absolute Savings
e_perc_sav = round(js['Energy Savings (%)'], 2) # Energy Percent Savings
ann_e_abs_sav = (e_abs_sav / num_days) * 365 # Annualized Energy Absolute Savings
d_abs_sav = round(js['User Comments']['Dollar Savings (absolute)'], 2) # Dollar Absolute Savings
d_perc_sav = round(js['User Comments']['Dollar Savings (%)'], 2) # Dollar Percent Savings
ann_d_abs_sav = (d_abs_sav / num_days) * 365 # Annualized Dollar Absolute Savings
temp_df = pd.DataFrame({
'Site': js['Site'],
'#Days since Pelican Installation': num_days,
'Energy Savings (%)': e_perc_sav,
'Energy Savings (kWh)': e_abs_sav,
'Annualized Energy Savings (kWh)': ann_e_abs_sav,
'Dollar Savings (%)': d_perc_sav,
'Dollar Savings ($)': d_abs_sav,
'Annualized Dollar Savings ($)': ann_d_abs_sav,
'Best Model': js['Model']['Optimal Model\'s Metrics']['name'],
'Adj R2': round(js['Model']['Optimal Model\'s Metrics']['adj_cross_val_score'], 2),
'RMSE': round(js['Model']['Optimal Model\'s Metrics']['rmse'], 2),
'MAPE': js['Model']['Optimal Model\'s Metrics']['mape'],
'Uncertainity': js['Uncertainity'],
}, index=[0])
df = df.append(temp_df)
df.set_index('Site', inplace=True)
return df | python | 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.
end_date : str
End date of data collected.
"""
def count_number_of_days(site, end_date):
""" Counts the number of days between two dates.
Parameters
----------
site : str
Key to a dic containing site_name -> pelican installation date.
end_date : str
End date.
Returns
-------
int
Number of days
"""
start_date = site_install_mapping[site]
start_date = start_date.split('-')
start = date(int(start_date[0]), int(start_date[1]), int(start_date[2]))
end_date = end_date.split('-')
end = date(int(end_date[0]), int(end_date[1]), int(end_date[2]))
delta = end - start
return delta.days
if not folder_name or not isinstance(folder_name, str):
raise TypeError("folder_name should be type string")
else:
list_json_files = []
df = pd.DataFrame()
temp_df = pd.DataFrame()
json_files = [f for f in os.listdir(folder_name) if f.endswith('.json')]
for json_file in json_files:
with open(folder_name + json_file) as f:
js = json.load(f)
num_days = count_number_of_days(js['Site'], end_date)
e_abs_sav = round(js['Energy Savings (absolute)'] / 1000, 2) # Energy Absolute Savings
e_perc_sav = round(js['Energy Savings (%)'], 2) # Energy Percent Savings
ann_e_abs_sav = (e_abs_sav / num_days) * 365 # Annualized Energy Absolute Savings
d_abs_sav = round(js['User Comments']['Dollar Savings (absolute)'], 2) # Dollar Absolute Savings
d_perc_sav = round(js['User Comments']['Dollar Savings (%)'], 2) # Dollar Percent Savings
ann_d_abs_sav = (d_abs_sav / num_days) * 365 # Annualized Dollar Absolute Savings
temp_df = pd.DataFrame({
'Site': js['Site'],
'#Days since Pelican Installation': num_days,
'Energy Savings (%)': e_perc_sav,
'Energy Savings (kWh)': e_abs_sav,
'Annualized Energy Savings (kWh)': ann_e_abs_sav,
'Dollar Savings (%)': d_perc_sav,
'Dollar Savings ($)': d_abs_sav,
'Annualized Dollar Savings ($)': ann_d_abs_sav,
'Best Model': js['Model']['Optimal Model\'s Metrics']['name'],
'Adj R2': round(js['Model']['Optimal Model\'s Metrics']['adj_cross_val_score'], 2),
'RMSE': round(js['Model']['Optimal Model\'s Metrics']['rmse'], 2),
'MAPE': js['Model']['Optimal Model\'s Metrics']['mape'],
'Uncertainity': js['Uncertainity'],
}, index=[0])
df = df.append(temp_df)
df.set_index('Site', inplace=True)
return df | [
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] | 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.
end_date : str
End date of data collected. | [
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] | c12d4fb14518ea3ae98c471c28e0710fdf74dd25 | https://github.com/SoftwareDefinedBuildings/XBOS/blob/c12d4fb14518ea3ae98c471c28e0710fdf74dd25/apps/Data_quality_analysis/Wrapper.py#L150-L235 |
Subsets and Splits