def spatial_grouping(f=None, user=False, summary='default', use_records=False):
if f is None:
return partial(spatial_grouping,
user=user,
summary=summary,
use_records=use_records)
if use_records is True:
map_records = lambda x: x
else:
map_records = _binning
def wrapper(_user,
groupby='week',
summary=summary,
day_type=None,
time_type=None,
datatype=None,
**kwargs):
m = [
f(map_records(g), _user, **kwargs) if user is True else f(
map_records(g), **kwargs)
for g in group_records(_user,
groupby=groupby,
interaction=None,
day_type=day_type,
time_type=time_type)
]
if groupby is None:
m = m[0] if len(m) != 0 else None
return statistics(m, summary=summary, datatype=datatype)
return advanced_wrap(f, wrapper)
def recharges_grouping(f=None, summary='default', user_kwd=False):
if f is None:
return partial(grouping, user_kwd=user_kwd, summary=summary)
def wrapper(user, groupby='week', summary=summary,
split_week=False, split_day=False, filter_empty=True,
datatype=None, **kwargs):
parameters = divide_parameters(split_week, split_day, None)
operations = {
'grouping': {
'using': 'recharges',
'binning': False,
'groupby': groupby,
'filter_empty': filter_empty,
'divide_by': parameters
}, 'apply': {
'user_kwd': user_kwd,
'summary': summary,
'kwargs': kwargs
}
}
return _generic_wrapper(f, user, operations, datatype)
return advanced_wrap(f, wrapper)
def recharges_grouping(f=None, summary='default', user_kwd=False):
if f is None:
return partial(grouping, user_kwd=user_kwd, summary=summary)
def wrapper(user,
groupby='week',
summary=summary,
split_week=False,
split_day=False,
filter_empty=True,
datatype=None,
**kwargs):
parameters = divide_parameters(split_week, split_day, None)
operations = {
'grouping': {
'using': 'recharges',
'binning': False,
'groupby': groupby,
'filter_empty': filter_empty,
'divide_by': parameters
},
'apply': {
'user_kwd': user_kwd,
'summary': summary,
'kwargs': kwargs
}
}
return _generic_wrapper(f, user, operations, datatype)
return advanced_wrap(f, wrapper)
def grouping(f=None, user_kwd=False, interaction=['call', 'text'], summary='default'):
"""
``grouping`` is a decorator for indicator functions, used to simplify the source code.
Parameters
----------
f : function
The function to decorate
user_kwd : boolean
If user_kwd is True, the user object will be passed to the decorated function
interaction : 'call', 'text', 'location', or a list
By default, all indicators use only 'call' and 'text' records, but the
interaction keywords filters the records passed to the function.
summary: 'default', 'extended', None
An indicator returns data statistics, ether *mean* and *std* by
default, more with 'extended', or the inner distribution with None.
See :meth:`~bandicoot.helper.group.statistics` for more details.
See :ref:`new-indicator-label` to learn how to write an indicator with this decorator.
"""
if f is None:
return partial(grouping, user_kwd=user_kwd, interaction=interaction, summary=summary)
def wrapper(user, groupby='week', interaction=interaction, summary=summary, day_type=None, time_type=None, datatype=None, **kwargs):
if interaction is None:
interaction = ['call', 'text']
elif isinstance(interaction, str):
interaction = [interaction]
else:
interaction = interaction[:] # copy the list for more safety
for i in interaction:
if i not in ['callandtext', 'call', 'text', 'location']:
raise ValueError("%s is not a valid interaction value. Only 'call', "
"'text', and 'location' are accepted." % i)
mapped = ((i, [f(g, user, **kwargs)
if user_kwd is True else f(g, **kwargs) for g in
group_records(user, groupby=groupby, interaction=i, day_type=day_type, time_type=time_type)])
for i in interaction)
returned = {}
for (k, m) in mapped:
if groupby is None:
m = m[0] if len(m) != 0 else None
returned[k] = statistics(m, summary=summary, datatype=datatype)
return returned
return advanced_wrap(f, wrapper)
def spatial_grouping(f=None, user_kwd=False, summary="default", use_records=False):
if f is None:
return partial(spatial_grouping, user_kwd=user_kwd, summary=summary, use_records=use_records)
if use_records is True:
map_records = lambda x: x
else:
map_records = _binning
def wrapper(user, groupby="week", summary=summary, split_week=False, split_day=False, datatype=None, **kwargs):
part_of_day = ["allday"]
if split_day:
part_of_day += ["day", "night"]
part_of_week = ["allweek"]
if split_week:
part_of_week += ["weekday", "weekend"]
def map_filters_spatial(part_of_week, part_of_day):
"""
Call the wrapped function for every combinations of interaction,
part_of_week, and part_of_day.
"""
for filter_week in part_of_week:
for filter_day in part_of_day:
if user_kwd is True:
result = [
f(map_records(g), user, **kwargs)
for g in group_records(user, None, groupby, filter_week, filter_day)
]
else:
result = [
f(map_records(g), **kwargs)
for g in group_records(user, None, groupby, filter_week, filter_day)
]
yield filter_week, filter_day, result
returned = AutoVivification() # nested dict structure
for (f_w, f_d, m) in map_filters_spatial(part_of_week, part_of_day):
if groupby is None:
m = m[0] if len(m) != 0 else None
returned[f_w][f_d] = statistics(m, summary=summary, datatype=datatype)
return returned
return advanced_wrap(f, wrapper)
def spatial_grouping(f=None, user=False, summary='default', use_records=False):
if f is None:
return partial(spatial_grouping, user=user, summary=summary,
use_records=use_records)
if use_records is True:
map_records = lambda x: x
else:
map_records = _binning
def wrapper(_user, groupby='week', summary=summary, day_type=None, time_type=None, datatype=None, **kwargs):
m = [f(map_records(g), _user, **kwargs) if user is True else f(map_records(g), **kwargs) for g in
group_records(_user, groupby=groupby, interaction=None, day_type=day_type, time_type=time_type)]
if groupby is None:
m = m[0] if len(m) != 0 else None
return statistics(m, summary=summary, datatype=datatype)
return advanced_wrap(f, wrapper)
def grouping(f=None,
user_kwd=False,
interaction=['call', 'text'],
summary='default'):
"""
``grouping`` is a decorator for indicator functions, used to simplify the source code.
Parameters
----------
f : function
The function to decorate
user_kwd : boolean
If user_kwd is True, the user object will be passed to the decorated function
interaction : 'call', 'text', 'location', or a list
By default, all indicators use only 'call' and 'text' records, but the
interaction keywords filters the records passed to the function.
summary: 'default', 'extended', None
An indicator returns data statistics, ether *mean* and *std* by
default, more with 'extended', or the inner distribution with None.
See :meth:`~bandicoot.helper.group.statistics` for more details.
See :ref:`new-indicator-label` to learn how to write an indicator with this decorator.
"""
if f is None:
return partial(grouping,
user_kwd=user_kwd,
interaction=interaction,
summary=summary)
def wrapper(user,
groupby='week',
interaction=interaction,
summary=summary,
day_type=None,
time_type=None,
datatype=None,
**kwargs):
if interaction is None:
interaction = ['call', 'text']
elif isinstance(interaction, str):
interaction = [interaction]
else:
interaction = interaction[:] # copy the list for more safety
for i in interaction:
if i not in ['callandtext', 'call', 'text', 'location']:
raise ValueError(
"%s is not a valid interaction value. Only 'call', "
"'text', and 'location' are accepted." % i)
mapped = ((i, [
f(g, user, **kwargs) if user_kwd is True else f(g, **kwargs)
for g in group_records(user,
groupby=groupby,
interaction=i,
day_type=day_type,
time_type=time_type)
]) for i in interaction)
returned = {}
for (k, m) in mapped:
if groupby is None:
m = m[0] if len(m) != 0 else None
returned[k] = statistics(m, summary=summary, datatype=datatype)
return returned
return advanced_wrap(f, wrapper)
def spatial_grouping(f=None,
user_kwd=False,
summary='default',
use_records=False):
if f is None:
return partial(spatial_grouping,
user_kwd=user_kwd,
summary=summary,
use_records=use_records)
if use_records is True:
map_records = lambda x: x
else:
map_records = _binning
def wrapper(user,
groupby='week',
summary=summary,
split_week=False,
split_day=False,
datatype=None,
**kwargs):
part_of_day = ['allday']
if split_day:
part_of_day += ['day', 'night']
part_of_week = ['allweek']
if split_week:
part_of_week += ['weekday', 'weekend']
def map_filters_spatial(part_of_week, part_of_day):
"""
Call the wrapped function for every combinations of interaction,
part_of_week, and part_of_day.
"""
for filter_week in part_of_week:
for filter_day in part_of_day:
if user_kwd is True:
result = [
f(map_records(g), user,
**kwargs) for g in group_records(
user, None, groupby, filter_week, filter_day)
]
else:
result = [
f(map_records(g), **kwargs) for g in group_records(
user, None, groupby, filter_week, filter_day)
]
yield filter_week, filter_day, result
returned = AutoVivification() # nested dict structure
for (f_w, f_d, m) in map_filters_spatial(part_of_week, part_of_day):
if groupby is None:
m = m[0] if len(m) != 0 else None
returned[f_w][f_d] = statistics(m,
summary=summary,
datatype=datatype)
return returned
return advanced_wrap(f, wrapper)
def grouping(f=None,
user_kwd=False,
interaction=['call', 'text'],
summary='default'):
"""
``grouping`` is a decorator for indicator functions, used to simplify the source code.
Parameters
----------
f : function
The function to decorate
user_kwd : boolean
If user_kwd is True, the user object will be passed to the decorated function
interaction : 'call', 'text', 'location', or a list
By default, all indicators use only 'call' and 'text' records, but the
interaction keywords filters the records passed to the function.
summary: 'default', 'extended', None
An indicator returns data statistics, ether *mean* and *std* by
default, more with 'extended', or the inner distribution with None.
See :meth:`~bandicoot.helper.group.statistics` for more details.
See :ref:`new-indicator-label` to learn how to write an indicator with this decorator.
"""
if f is None:
return partial(grouping,
user_kwd=user_kwd,
interaction=interaction,
summary=summary)
def wrapper(user,
groupby='week',
interaction=interaction,
summary=summary,
split_week=False,
split_day=False,
datatype=None,
**kwargs):
if interaction is None:
interaction = ['call', 'text']
elif isinstance(interaction, str):
interaction = [interaction]
else:
interaction = interaction[:] # copy the list for more safety
part_of_day = ['allday']
if split_day:
part_of_day += ['day', 'night']
part_of_week = ['allweek']
if split_week:
part_of_week += ['weekday', 'weekend']
for i in interaction:
if i not in ['callandtext', 'call', 'text', 'location']:
raise ValueError(
"%s is not a valid interaction value. Only 'call', "
"'text', and 'location' are accepted." % i)
def map_filters(interaction, part_of_week, part_of_day):
"""
Call the wrapped function for every combinations of interaction,
part_of_week, and part_of_day.
"""
for i in interaction:
for filter_week in part_of_week:
for filter_day in part_of_day:
if user_kwd is True:
result = [
f(g, user, **kwargs) for g in group_records(
user, i, groupby, filter_week, filter_day)
]
else:
result = [
f(g, **kwargs) for g in group_records(
user, i, groupby, filter_week, filter_day)
]
yield filter_week, filter_day, i, result
returned = AutoVivification() # nested dict structure
for (f_w, f_d, i, m) in map_filters(interaction, part_of_week,
part_of_day):
if groupby is None:
m = m[0] if len(m) != 0 else None
returned[f_w][f_d][i] = statistics(m,
summary=summary,
datatype=datatype)
return returned
return advanced_wrap(f, wrapper)
def grouping(f=None, user_kwd=False, interaction=['call', 'text'], summary='default'):
"""
``grouping`` is a decorator for indicator functions, used to simplify the source code.
Parameters
----------
f : function
The function to decorate
user_kwd : boolean
If user_kwd is True, the user object will be passed to the decorated function
interaction : 'call', 'text', 'location', or a list
By default, all indicators use only 'call' and 'text' records, but the
interaction keywords filters the records passed to the function.
summary: 'default', 'extended', None
An indicator returns data statistics, ether *mean* and *std* by
default, more with 'extended', or the inner distribution with None.
See :meth:`~bandicoot.helper.group.statistics` for more details.
See :ref:`new-indicator-label` to learn how to write an indicator with this decorator.
"""
if f is None:
return partial(grouping, user_kwd=user_kwd, interaction=interaction, summary=summary)
def wrapper(user, groupby='week', interaction=interaction, summary=summary, split_week=False, split_day=False, datatype=None, **kwargs):
if interaction is None:
interaction = ['call', 'text']
elif isinstance(interaction, str):
interaction = [interaction]
else:
interaction = interaction[:] # copy the list for more safety
part_of_day = ['allday']
if split_day:
part_of_day += ['day', 'night']
part_of_week = ['allweek']
if split_week:
part_of_week += ['weekday', 'weekend']
for i in interaction:
if i not in ['callandtext', 'call', 'text', 'location']:
raise ValueError("%s is not a valid interaction value. Only 'call', "
"'text', and 'location' are accepted." % i)
def map_filters(interaction, part_of_week, part_of_day):
"""
Call the wrapped function for every combinations of interaction,
part_of_week, and part_of_day.
"""
for i in interaction:
for filter_week in part_of_week:
for filter_day in part_of_day:
if user_kwd is True:
result = [f(g, user, **kwargs) for g in group_records(user, i, groupby, filter_week, filter_day)]
else:
result = [f(g, **kwargs) for g in group_records(user, i, groupby, filter_week, filter_day)]
yield filter_week, filter_day, i, result
returned = AutoVivification() # nested dict structure
for (f_w, f_d, i, m) in map_filters(interaction, part_of_week, part_of_day):
if groupby is None:
m = m[0] if len(m) != 0 else None
returned[f_w][f_d][i] = statistics(m, summary=summary, datatype=datatype)
return returned
return advanced_wrap(f, wrapper)
def grouping(f=None,
interaction=['call', 'text'],
summary='default',
user_kwd=False):
"""
``grouping`` is a decorator for indicator functions, used to simplify the
source code.
Parameters
----------
f : function
The function to decorate
user_kwd : boolean
If user_kwd is True, the user object will be passed to the decorated
function
interaction : 'call', 'text', 'location', or a list
By default, all indicators use only 'call' and 'text' records, but the
interaction keywords filters the records passed to the function.
summary: 'default', 'extended', None
An indicator returns data statistics, ether *mean* and *std* by
default, more with 'extended', or the inner distribution with None.
See :meth:`~bandicoot.helper.group.statistics` for more details.
See :ref:`new-indicator-label` to learn how to write an indicator with
this decorator.
"""
if f is None:
return partial(grouping,
user_kwd=user_kwd,
interaction=interaction,
summary=summary)
def wrapper(user,
groupby='week',
interaction=interaction,
summary=summary,
split_week=False,
split_day=False,
filter_empty=True,
datatype=None,
**kwargs):
if interaction is None:
interaction = ['call', 'text']
parameters = divide_parameters(split_week, split_day, interaction)
operations = {
'grouping': {
'using': 'records',
'binning': False,
'groupby': groupby,
'filter_empty': filter_empty,
'divide_by': parameters
},
'apply': {
'user_kwd': user_kwd,
'summary': summary,
'kwargs': kwargs
}
}
for i in parameters['interaction']:
if i not in ['callandtext', 'call', 'text', 'location']:
raise ValueError(
"%s is not a valid interaction value. Only "
"'call', 'text', and 'location' are accepted." % i)
return _generic_wrapper(f, user, operations, datatype)
return advanced_wrap(f, wrapper)
def grouping(f=None, interaction=['call', 'text'], summary='default',
user_kwd=False):
"""
``grouping`` is a decorator for indicator functions, used to simplify the
source code.
Parameters
----------
f : function
The function to decorate
user_kwd : boolean
If user_kwd is True, the user object will be passed to the decorated
function
interaction : 'call', 'text', 'location', or a list
By default, all indicators use only 'call' and 'text' records, but the
interaction keywords filters the records passed to the function.
summary: 'default', 'extended', None
An indicator returns data statistics, ether *mean* and *std* by
default, more with 'extended', or the inner distribution with None.
See :meth:`~bandicoot.helper.group.statistics` for more details.
See :ref:`new-indicator-label` to learn how to write an indicator with
this decorator.
"""
if f is None:
return partial(grouping, user_kwd=user_kwd, interaction=interaction,
summary=summary)
def wrapper(user, groupby='week', interaction=interaction, summary=summary,
split_week=False, split_day=False, filter_empty=True,
datatype=None, **kwargs):
if interaction is None:
interaction = ['call', 'text']
parameters = divide_parameters(split_week, split_day, interaction)
operations = {
'grouping': {
'using': 'records',
'binning': False,
'groupby': groupby,
'filter_empty': filter_empty,
'divide_by': parameters
}, 'apply': {
'user_kwd': user_kwd,
'summary': summary,
'kwargs': kwargs
}
}
for i in parameters['interaction']:
if i not in ['callandtext', 'call', 'text', 'location']:
raise ValueError("%s is not a valid interaction value. Only "
"'call', 'text', and 'location' are accepted."
% i)
return _generic_wrapper(f, user, operations, datatype)
return advanced_wrap(f, wrapper)
