Source code for cytoflowgui.op_plugins.xform_stat
#!/usr/bin/env python3.8
# coding: latin-1
# (c) Massachusetts Institute of Technology 2015-2018
# (c) Brian Teague 2018-2022
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 2 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
'''
Transform statistic
-------------------
Apply a function to groups of a statistic, and add the result as a statistic
to the experiment.
First, the module groups the data by the unique values of the variables
in **By**, then applies **Function** to the values in each group. This is
repeated for each feature (column) in the statistic.
.. object:: Name
The operation name. Becomes the new statistic's name.
.. object:: Statistic
The statistic to apply the function to.
.. object:: Feature
The column of the statistic to apply the function to.
.. object:: Function
The function to compute on each group.
.. object:: Subset
Only apply the function to a subset of the input statistic.
.. object:: Ignore Incomplete Subsets
Should we ignore groups with an incomplete number of levels?
Defaults to ``True``.
.. object:: Group By
How should we group the statistic before applying the function?
'''
import numpy as np
import pandas as pd
from traits.api import provides, Property, List
from traitsui.api import (View, Item, TextEditor, VGroup, EnumEditor, CheckListEditor)
from pyface.api import ImageResource # @UnresolvedImport
from envisage.api import Plugin
from ..workflow.operations import TransformStatisticWorkflowOp
from ..workflow.operations.xform_stat import transform_functions
from ..editors import SubsetListEditor, InstanceHandlerEditor
from ..subset_controllers import subset_handler_factory
from .i_op_plugin import IOperationPlugin, OP_PLUGIN_EXT
from .op_plugin_base import OpHandler, PluginHelpMixin, shared_op_traits_view
[docs]
class TransformStatisticHandler(OpHandler):
# prev_statistics = Property(depends_on = "info.ui.context")
indices = Property(depends_on = "context.previous_wi.statistics, "
"model.statistic, "
"model.subset")
levels = Property(depends_on = "context.previous_wi.statistics, model.statistic")
features = Property(depends_on = "context.previous_wi.statistics, model.statistic")
# MAGIC: gets the value for the property indices
def _get_indices(self):
if not (self.context
and self.context.previous_wi
and self.context.previous_wi.statistics
and self.model
and self.model.statistic
and self.model.statistic in self.context.previous_wi.statistics):
return []
stat = self.context.previous_wi.statistics[self.model.statistic]
data = pd.DataFrame(index = stat.index)
if self.model.subset:
data = data.query(self.model.subset)
if len(data) == 0:
return []
names = list(data.index.names)
for name in names:
unique_values = data.index.get_level_values(name).unique()
if len(unique_values) == 1:
data.index = data.index.droplevel(name)
return list(data.index.names)
# MAGIC: gets the value for the property 'levels'
# returns a Dict(Str, pd.Series)
def _get_levels(self):
if not (self.context
and self.context.previous_wi
and self.context.previous_wi.statistics
and self.model
and self.model.statistic
and self.model.statistic in self.context.previous_wi.statistics):
return {}
stat = self.context.previous_wi.statistics[self.model.statistic]
index = stat.index
names = list(index.names)
for name in names:
unique_values = index.get_level_values(name).unique()
if len(unique_values) == 1:
index = index.droplevel(name)
names = list(index.names)
ret = {}
for name in names:
ret[name] = pd.Series(np.unique(index.get_level_values(name)))
return ret
# MAGIC: gets the value for the property "features"
def _get_features(self):
if not (self.context.previous_wi and self.context.previous_wi.statistics
and self.model.statistic in self.context.previous_wi.statistics):
return []
stat = self.context.previous_wi.statistics[self.model.statistic]
return stat.columns.to_list()
operation_traits_view = \
View(Item('name',
editor = TextEditor(auto_set = False,
placeholder = "None")),
Item('statistic',
editor=EnumEditor(name='context_handler.previous_statistics_names'),
label = "Statistic"),
Item('feature',
editor=EnumEditor(name='handler.features'),
label = "Feature"),
Item('function_name',
editor = EnumEditor(values = sorted(transform_functions.keys())),
label = "Function"),
Item('by',
editor = CheckListEditor(cols = 2,
name = 'handler.indices'),
label = 'Group\nBy',
style = 'custom'),
Item('ignore_incomplete_groups',
label = "Ignore\nIncomplete\nGroups"),
VGroup(Item('subset_list',
show_label = False,
editor = SubsetListEditor(conditions = "handler.levels",
editor = InstanceHandlerEditor(view = 'subset_view',
handler_factory = subset_handler_factory))),
label = "Subset",
show_border = False,
show_labels = False),
shared_op_traits_view)
[docs]
@provides(IOperationPlugin)
class TransformStatisticPlugin(Plugin, PluginHelpMixin):
id = 'cytoflowgui.op_plugins.transform_statistic'
operation_id = 'cytoflow.operations.transform_statistic'
view_id = None
name = "Transform Statistic"
short_name = "Xform\nStatistic"
menu_group = "Statistics"
[docs]
def get_handler(self, model, context):
return TransformStatisticHandler(model = model, context = context)
plugin = List(contributes_to = OP_PLUGIN_EXT)
def _plugin_default(self):
return [self]