#!/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/>.
"""
cytoflow.operations.channel_stat
--------------------------------
Creates a new statistic. `channel_stat` has one class:
`ChannelStatisticOp` -- applies a function to subsets of a data set,
and adds the resulting statistic to the `Experiment`
"""
from warnings import warn
import pandas as pd
import numpy as np
from traits.api import (HasStrictTraits, Str, List, Constant, provides,
Callable, Any)
import cytoflow.utility as util
from .i_operation import IOperation
[docs]@provides(IOperation)
class ChannelStatisticOp(HasStrictTraits):
"""
Apply a function to subsets of a data set, and add it as a statistic
to the experiment.
The `apply` function groups the data by the variables in `by`,
then applies the `function` callable to the `channel` series
in each subset. The callable should take a single `pandas.Series`
as an argument. The return type is arbitrary, but to be used with the rest
of `cytoflow` it should probably be a numeric type or an iterable of
numeric types.
Attributes
----------
name : Str
The operation name. Becomes the first element in the
`Experiment.statistics` key tuple.
channel : Str
The channel to apply the function to.
function : Callable
The function used to compute the statistic. `function` must take
a `pandas.Series` as its only parameter. The return type is
arbitrary, but to be used with the rest of `cytoflow` it should
probably be a numeric type or an iterable of numeric types. If
`statistic_name` is unset, the name of the function becomes the
second in element in the `Experiment.statistics` key tuple.
.. warning::
Be careful! Sometimes this function is called with an empty input!
If this is the case, poorly-behaved functions can return ``NaN`` or
throw an error. If this happens, it will be reported.
statistic_name : Str
The name of the function; if present, becomes the second element in
the `Experiment.statistics` key tuple. Particularly useful if
`function` is a lambda expression.
by : List(Str)
A list of metadata attributes to aggregate the data before applying the
function. For example, if the experiment has two pieces of metadata,
``Time`` and ``Dox``, setting ``by = ["Time", "Dox"]`` will apply
`function` separately to each subset of the data with a unique
combination of ``Time`` and ``Dox``.
subset : Str
A Python expression sent to `Experiment.query` to subset the
data before computing the statistic.
fill : Any (default = 0)
The value to use in the statistic if a slice of the data is empty.
Examples
--------
.. plot::
:context: close-figs
Make a little data set.
>>> import cytoflow as flow
>>> import_op = flow.ImportOp()
>>> import_op.tubes = [flow.Tube(file = "Plate01/RFP_Well_A3.fcs",
... conditions = {'Dox' : 10.0}),
... flow.Tube(file = "Plate01/CFP_Well_A4.fcs",
... conditions = {'Dox' : 1.0})]
>>> import_op.conditions = {'Dox' : 'float'}
>>> ex = import_op.apply()
Create and parameterize the operation.
.. plot::
:context: close-figs
>>> ch_op = flow.ChannelStatisticOp(name = 'MeanByDox',
... channel = 'Y2-A',
... function = flow.geom_mean,
... by = ['Dox'])
>>> ex2 = ch_op.apply(ex)
View the new operation
>>> print(ex2.statistics.keys())
dict_keys([('MeanByDox', 'geom_mean')])
>>> print(ex2.statistics[('MeanByDox', 'geom_mean')])
Dox
1.0 19.805601
10.0 446.981927
dtype: float64
"""
id = Constant('edu.mit.synbio.cytoflow.operations.channel_statistic')
friendly_id = Constant("Channel Statistics")
name = Str
channel = Str
function = Callable
statistic_name = Str
by = List(Str)
subset = Str
fill = Any(0)
[docs] def apply(self, experiment):
"""
Apply the operation to an `Experiment`.
Parameters
----------
experiment
The `Experiment` to apply this operation to.
Returns
-------
Experiment
A new `Experiment`, containing a new entry in
`Experiment.statistics`. The key of the new entry is a
tuple ``(name, function)`` (or ``(name, statistic_name)`` if
`statistic_name` is set.
"""
if experiment is None:
raise util.CytoflowOpError('experiment', "Must specify an experiment")
if not self.name:
raise util.CytoflowOpError('name', "Must specify a name")
if self.name != util.sanitize_identifier(self.name):
raise util.CytoflowOpError('name',
"Name can only contain letters, numbers and underscores."
.format(self.name))
if not self.channel:
raise util.CytoflowOpError('channel', "Must specify a channel")
if not self.function:
raise util.CytoflowOpError('function', "Must specify a function")
if self.channel not in experiment.data:
raise util.CytoflowOpError('channel',
"Channel {0} not found in the experiment"
.format(self.channel))
if not self.by:
raise util.CytoflowOpError('by',
"Must specify some grouping conditions "
"in 'by'")
stat_name = (self.name, self.statistic_name) \
if self.statistic_name \
else (self.name, self.function.__name__)
if stat_name in experiment.statistics:
raise util.CytoflowOpError('name',
"{} is already in the experiment's statistics"
.format(stat_name))
new_experiment = experiment.clone(deep = False)
if self.subset:
try:
experiment = experiment.query(self.subset)
except Exception as exc:
raise util.CytoflowOpError('subset',
"Subset string '{0}' isn't valid"
.format(self.subset)) from exc
if len(experiment) == 0:
raise util.CytoflowOpError('subset',
"Subset string '{0}' returned no events"
.format(self.subset))
for b in self.by:
if b not in experiment.conditions:
raise util.CytoflowOpError('by',
"Aggregation metadata {} not found, "
"must be one of {}"
.format(b, experiment.conditions))
unique = experiment.data[b].unique()
if len(unique) == 1:
warn("Only one category for {}".format(b), util.CytoflowOpWarning)
groupby = experiment.data.groupby(self.by)
for group, data_subset in groupby:
if len(data_subset) == 0:
warn("Group {} had no data"
.format(group),
util.CytoflowOpWarning)
# this shouldn't be necessary, but see pandas bug #38053
if len(self.by) == 1:
idx = pd.Index(experiment[self.by[0]].unique(), name = self.by[0])
else:
idx = pd.MultiIndex.from_product([experiment[x].unique() for x in self.by],
names = self.by)
stat = pd.Series(data = [self.fill] * len(idx),
index = idx,
name = "{} : {}".format(stat_name[0], stat_name[1]),
dtype = np.dtype(object)).sort_index()
for group, data_subset in groupby:
if len(data_subset) == 0:
continue
if not isinstance(group, tuple):
group = (group,)
try:
v = self.function(data_subset[self.channel])
stat.at[group] = v
except Exception as e:
raise util.CytoflowOpError(None,
"Your function threw an error in group {}"
.format(group)) from e
# check for, and warn about, NaNs.
if pd.Series(stat.loc[group]).isna().any():
warn("Found NaN in category {} returned {}"
.format(group, stat.loc[group]),
util.CytoflowOpWarning)
# try to convert to numeric, but if there are non-numeric bits ignore
stat = pd.to_numeric(stat, errors = 'ignore')
new_experiment.history.append(self.clone_traits(transient = lambda _: True))
new_experiment.statistics[stat_name] = stat
return new_experiment