def get_chain(self,
name=None,
data_keys=None,
filters=None,
x=None,
y=None,
views=None,
post_process=True,
orient_on=None,
select=None):
"""
Construct a "chain" shaped subset of Links and their Views from the Stack.
A chain is a one-to-one or one-to-many relation with an orientation that
defines from which axis (x or y) it is build.
Parameters
----------
name : str, optional
If not provided the name of the chain is generated automatically.
data_keys, filters, x, y, views : str or list of str
Views will be added reflecting the order in ``views`` parameter. If
both ``x`` and ``y`` have multiple items, you must specify the
``orient_on`` parameter.
post_process : bool, default True
If file meta is found, views inside the chain will get their Values-
axes codes checked against the category lists and missing codes will
be added.
orient_on : {'x', 'y'}, optional
Must be specified if both ``x`` and ``y`` are lists of multiple
items.
select : tbc.
:TODO: document this!
Returns
-------
chain : Chain object instance
"""
#Make sure all the given keys are in lists
data_keys = self._force_key_as_list(data_keys)
# filters = self._force_key_as_list(filters)
views = self._force_key_as_list(views)
if orient_on:
if x is None:
x = self.describe()['x'].drop_duplicates().values.tolist()
if y is None:
y = self.describe()['y'].drop_duplicates().values.tolist()
if views is None:
views = self._Stack__view_keys
views = [v for v in views if '|default|' not in v]
return self.__get_chains(name=name,
data_keys=data_keys,
filters=filters,
x=x,
y=y,
views=views,
post_process=post_process,
orientation=orient_on,
select=select)
else:
chain = Chain(name)
found_views = []
missed_views = []
#Make sure all the given keys are in lists
x = self._force_key_as_list(x)
y = self._force_key_as_list(y)
if data_keys is None:
# Apply lazy data_keys if none given
data_keys = self.keys()
the_filter = "no_filter" if filters is None else filters
if self.__has_list(data_keys):
for key in data_keys:
# Use describe method to get x keys if not supplied.
if x is None:
x_keys = self.describe()['x'].drop_duplicates(
).values.tolist()
else:
x_keys = x
# Use describe method to get y keys if not supplied.
if y is None:
y_keys = self.describe()['y'].drop_duplicates(
).values.tolist()
else:
y_keys = y
# Use describe method to get view keys if not supplied.
if views is None:
v_keys = self.describe()['view'].drop_duplicates(
).values.tolist()
v_keys = [
v_key for v_key in v_keys
if '|default|' not in v_key
]
else:
v_keys = views
chain._validate_x_y_combination(x_keys, y_keys, orient_on)
chain._derive_attributes(key, the_filter, x_keys, y_keys,
views)
# Apply lazy name if none given
if name is None:
chain._lazy_name()
for x_key in x_keys:
for y_key in y_keys:
if views is None:
chain[key][the_filter][x_key][y_key] = self[
key][the_filter][x_key][y_key]
else:
for view in views:
try:
chain[key][the_filter][x_key][y_key][
view] = self[key][the_filter][
x_key][y_key][view]
if view not in found_views:
found_views.append(view)
except KeyError:
if view not in missed_views:
missed_views.append(view)
else:
raise ValueError(
'One or more of your data_keys ({data_keys}) is not in the stack ({stack_keys})'
.format(data_keys=data_keys, stack_keys=self.keys()))
if found_views:
chain.views = [
view for view in chain.views if view in found_views
]
for view in missed_views:
if view in found_views:
missed_views.remove(view)
if post_process:
chain._post_process_shapes(self[chain.data_key].meta)
if select is not None:
for view in chain[key][the_filter][x_key][y_key]:
df = chain[key][the_filter][x_key][y_key][view].dataframe
levels = df.index.levels
selection = {}
for var in select:
level = functions.find_variable_level(levels, var)
if level is not None:
selection[var] = level
#Don't do anything if the selection doesnt produce a result
if selection:
# selection = {var: functions.find_variable_level(levels, var) for var in select}
list_of_dfs = [
df.xs(var, level=selection[var])
for var in selection.keys()
]
new_df = pd.concat(list_of_dfs)
# Reconstruct the index
new_df.index = pd.MultiIndex.from_product(
[levels[0], selection.keys()], names=df.index.names)
chain[key][the_filter][x_key][y_key][
view].dataframe = new_df
return chain
def get_chain(self, name=None, data_keys=None, filters=None, x=None, y=None,
views=None, post_process=True, orient_on=None, select=None):
"""
Construct a "chain" shaped subset of Links and their Views from the Stack.
A chain is a one-to-one or one-to-many relation with an orientation that
defines from which axis (x or y) it is build.
Parameters
----------
name : str, optional
If not provided the name of the chain is generated automatically.
data_keys, filters, x, y, views : str or list of str
Views will be added reflecting the order in ``views`` parameter. If
both ``x`` and ``y`` have multiple items, you must specify the
``orient_on`` parameter.
post_process : bool, default True
If file meta is found, views inside the chain will get their Values-
axes codes checked against the category lists and missing codes will
be added.
orient_on : {'x', 'y'}, optional
Must be specified if both ``x`` and ``y`` are lists of multiple
items.
select : tbc.
:TODO: document this!
Returns
-------
chain : Chain object instance
"""
#Make sure all the given keys are in lists
data_keys = self._force_key_as_list(data_keys)
# filters = self._force_key_as_list(filters)
views = self._force_key_as_list(views)
if orient_on:
if x is None:
x = self.describe()['x'].drop_duplicates().values.tolist()
if y is None:
y = self.describe()['y'].drop_duplicates().values.tolist()
if views is None:
views = self._Stack__view_keys
views = [v for v in views if '|default|' not in v]
return self.__get_chains(name=name, data_keys=data_keys,
filters=filters, x=x, y=y, views=views,
post_process=post_process,
orientation=orient_on, select=select)
else:
chain = Chain(name)
found_views = []
missed_views = []
#Make sure all the given keys are in lists
x = self._force_key_as_list(x)
y = self._force_key_as_list(y)
if data_keys is None:
# Apply lazy data_keys if none given
data_keys = self.keys()
the_filter = "no_filter" if filters is None else filters
if self.__has_list(data_keys):
for key in data_keys:
# Use describe method to get x keys if not supplied.
if x is None:
x_keys = self.describe()['x'].drop_duplicates().values.tolist()
else:
x_keys = x
# Use describe method to get y keys if not supplied.
if y is None:
y_keys = self.describe()['y'].drop_duplicates().values.tolist()
else:
y_keys = y
# Use describe method to get view keys if not supplied.
if views is None:
v_keys = self.describe()['view'].drop_duplicates().values.tolist()
v_keys = [v_key for v_key in v_keys if '|default|'
not in v_key]
else:
v_keys = views
chain._validate_x_y_combination(x_keys, y_keys, orient_on)
chain._derive_attributes(key,the_filter,x_keys,y_keys,views)
# Apply lazy name if none given
if name is None:
chain._lazy_name()
for x_key in x_keys:
for y_key in y_keys:
if views is None:
chain[key][the_filter][x_key][y_key] = self[key][the_filter][x_key][y_key]
else:
for view in views:
try:
chain[key][the_filter][x_key][y_key][view] = self[key][the_filter][x_key][y_key][view]
if view not in found_views:
found_views.append(view)
except KeyError:
if view not in missed_views:
missed_views.append(view)
else:
raise ValueError('One or more of your data_keys ({data_keys}) is not in the stack ({stack_keys})'.format(data_keys=data_keys, stack_keys=self.keys()))
if found_views:
chain.views = [view for view in chain.views
if view in found_views]
for view in missed_views:
if view in found_views:
missed_views.remove(view)
if post_process:
chain._post_process_shapes(self[chain.data_key].meta)
if select is not None:
for view in chain[key][the_filter][x_key][y_key]:
df = chain[key][the_filter][x_key][y_key][view].dataframe
levels = df.index.levels
selection = {}
for var in select:
level = functions.find_variable_level(levels, var)
if level is not None:
selection[var] = level
#Don't do anything if the selection doesnt produce a result
if selection:
# selection = {var: functions.find_variable_level(levels, var) for var in select}
list_of_dfs = [df.xs(var, level=selection[var]) for var in selection.keys()]
new_df = pd.concat(list_of_dfs)
# Reconstruct the index
new_df.index= pd.MultiIndex.from_product([levels[0],selection.keys()], names=df.index.names)
chain[key][the_filter][x_key][y_key][view].dataframe = new_df
return chain
