Source code for lena.flow.group_plots
"""Group several plots into one.
Since data can be produced in different places,
several classes are needed to support this.
First, the plots of interest must be selected
(for example, one-dimensional histograms).
This is done by :class:`Selector`.
Selected plots must be grouped.
For example, we may want to plot data *x* versus Monte-Carlo *x*,
but not data *x* vs data *y*. Data is grouped by :class:`GroupBy`.
To preserve the group, we can't yield it to the following elements,
but have to transform the plots inside :class:`GroupPlots`.
We can also scale (normalize) all plots to one
using :class:`GroupScale`.
"""
from __future__ import print_function
import copy
import numbers
import lena.core
import lena.flow
[docs]class GroupPlots(object):
"""Group several plots."""
def __init__(self, group_by, select, transform=(), scale_to=None,
yield_selected=False):
"""Plots to be grouped are chosen by *select*,
which acts as a boolean function.
If *select* is not a :class:`Selector`, it is converted
to that class.
See :class:`Selector` for more options.
Plots are grouped by *group_by*, which returns
different keys for different groups.
If it is not an instance of :class:`GroupBy`,
it is converted to that class.
See :class:`GroupBy` for more options.
*scale_to* is a number or a string.
A number means the scale, to which plots must be normalized.
A string is a name of the plot to which other plots
must be normalized.
If *scale_to* is not an instance of :class:`GroupScale`,
it is converted to that class.
If a plot could not be rescaled,
:exc:`~lena.core.LenaValueError` is raised.
For more options, use :class:`GroupScale`.
*transform* is a sequence, which processes individual plots
before yielding.
For example, ``transform=(HistToCSV(), writer)``.
*transform* is called after *scale_to*.
*yield_selected* defines whether selected items should be
yielded during *run* like other items.
Use it if you want to have both single and combined plots.
By default, selected plots are not yielded.
"""
if isinstance(select, lena.flow.Selector):
self._selector = select
else:
self._selector = lena.flow.Selector(select)
if isinstance(group_by, lena.flow.group_by.GroupBy):
self._group_by = group_by
else:
self._group_by = lena.flow.group_by.GroupBy(group_by)
if (scale_to is None
or isinstance(scale_to, lena.flow.group_scale.GroupScale)):
self._scale_to = scale_to
else:
self._scale_to = lena.flow.group_scale.GroupScale(scale_to)
if isinstance(transform, lena.core.LenaSequence):
self._transform = transform
else:
self._transform = lena.core.Sequence(transform)
self._yield_selected = yield_selected
[docs] def run(self, flow):
"""Run the flow and yield final groups.
Each item of the flow is checked with the selector.
If it is selected, it is added to groups.
Otherwised it is yielded.
After the flow is finished, groups are yielded.
Groups are lists of items,
which have same keys from *group_by*.
Each group's context (including empty) is inserted
into a list in *context.group*.
The resulting context is updated with the intersection
of groups' contexts.
For uniformity, if *yield_selected* is True,
single values are also updated:
data is put into a list of one element, and context
is updated with *group* key. Its value is copy
(not deep copy) of context's values, so future updates
to subdictionaries which existed during this run
will be effective in *context.group*.
If *scale_to* was set, plots are normalized
to the given value or plot.
If that plot was not selected (is missing in the captured group)
or its norm could not be calculated,
:exc:`~lena.core.LenaValueError` is raised.
"""
def update_group_with_context(grp):
if isinstance(grp, list):
# get common context
contexts = [lena.flow.get_context(val) for val in grp]
context = lena.context.intersection(*contexts)
# add 'group' to context
context.update({"group": contexts})
# data list contains only data part
grp = [lena.flow.get_data(val) for val in grp]
return (grp, context)
else:
# single element
context = lena.flow.get_context(grp)
context.update({"group": [copy.copy(context)]})
# context.update({"group": [copy.deepcopy(context)]})
return (lena.flow.get_data(grp), context)
for val in flow:
# I can't understand why, but without deep copy
# histogram.bins (not context!) will be same
# if several histograms update group_by
val = copy.deepcopy(val)
if self._selector(val):
if self._yield_selected:
yield update_group_with_context(copy.deepcopy(val))
self._group_by.update(val)
else:
yield val
# flow finished
# yield groups of selected plots
groups = self._group_by.groups
for group_name in groups:
grp = groups[group_name]
if self._scale_to is not None:
grp = self._scale_to.scale(grp)
# transform group items
grp = lena.flow.functions.seq_map(self._transform, grp)
yield update_group_with_context(grp)