#!/usr/bin/env python3.4
# coding: latin-1
# (c) Massachusetts Institute of Technology 2015-2018
# (c) Brian Teague 2018-2019
#
# 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.range2d
---------------------------
'''
import pandas as pd
from traits.api import HasStrictTraits, Float, Str, Bool, Instance, \
provides, on_trait_change, Any, Constant
from matplotlib.widgets import RectangleSelector
import matplotlib.pyplot as plt
from matplotlib.patches import Rectangle
import cytoflow.utility as util
from cytoflow.views import ScatterplotView, ISelectionView
from .i_operation import IOperation
from .base_op_views import Op2DView
[docs]@provides(IOperation)
class Range2DOp(HasStrictTraits):
"""
Apply a 2D range gate to a cytometry experiment.
Attributes
----------
name : Str
The operation name. Used to name the new metadata field in the
experiment that's created by :meth:`apply`
xchannel : Str
The name of the first channel to apply the range gate.
xlow : Float
The lowest value in xchannel to include in this gate.
xhigh : Float
The highest value in xchannel to include in this gate.
ychannel : Str
The name of the secon channel to apply the range gate.
ylow : Float
The lowest value in ychannel to include in this gate.
yhigh : Float
The highest value in ychannel to include in this gate.
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
>>> r = flow.Range2DOp(name = "Range2D",
... xchannel = "V2-A",
... xlow = 10,
... xhigh = 1000,
... ychannel = "Y2-A",
... ylow = 1000,
... yhigh = 20000)
Show the default view.
.. plot::
:context: close-figs
>>> rv = r.default_view(huefacet = "Dox",
... xscale = 'log',
... yscale = 'log')
>>> rv.plot(ex)
.. note::
If you want to use the interactive default view in a Jupyter notebook,
make sure you say ``%matplotlib notebook`` in the first cell
(instead of ``%matplotlib inline`` or similar). Then call
``default_view()`` with ``interactive = True``::
rv = r.default_view(huefacet = "Dox",
xscale = 'log',
yscale = 'log',
interactive = True)
rv.plot(ex)
Apply the gate, and show the result
.. plot::
:context: close-figs
>>> ex2 = r.apply(ex)
>>> ex2.data.groupby('Range2D').size()
Range2D
False 16405
True 3595
dtype: int64
"""
# traits
id = Constant('edu.mit.synbio.cytoflow.operations.range2d')
friendly_id = Constant("2D Range")
name = Str
xchannel = Str
xlow = Float
xhigh = Float
ychannel = Str
ylow = Float
yhigh = Float
_selection_view = Instance('RangeSelection2D', transient = True)
[docs] def apply(self, experiment):
"""Applies the threshold to an experiment.
Parameters
----------
experiment : Experiment
the old_experiment to which this op is applied
Returns
-------
Experiment
a new :class:`~Experiment`, the same as the old experiment but with
a new column with a data type of ``bool`` and the same as the
operation :attr:`name`. The bool is ``True`` if the event's
measurement in :attr:`xchannel` is greater than :attr:`xlow` and
less than :attr:`high`, and the event's measurement in
:attr:`ychannel` is greater than :attr:`ylow` and less than
:attr:`yhigh`; it is ``False`` otherwise.
"""
if experiment is None:
raise util.CytoflowOpError('experiment',
"No experiment specified")
# make sure name got set!
if not self.name:
raise util.CytoflowOpError('name',
"You have to set the gate's name "
"before applying it!")
if self.name != util.sanitize_identifier(self.name):
raise util.CytoflowOpError('name',
"Name can only contain letters, numbers and underscores."
.format(self.name))
# make sure old_experiment doesn't already have a column named self.name
if(self.name in experiment.data.columns):
raise util.CytoflowOpError('name',
"Experiment already contains a column {0}"
.format(self.name))
if not self.xchannel or not self.ychannel:
raise util.CytoflowOpError('xchannel',
"Must specify xchannel")
if not self.xchannel in experiment.channels:
raise util.CytoflowOpError('xchannel',
"xchannel isn't in the experiment")
if not self.ychannel:
raise util.CytoflowOpError('ychannel',
"Must specify ychannel")
if not self.ychannel in experiment.channels:
raise util.CytoflowOpError('ychannel',
"ychannel isn't in the experiment")
if self.xhigh <= experiment[self.xchannel].min():
raise util.CytoflowOpError('xhigh',
"x channel range high must be > {0}"
.format(experiment[self.xchannel].min()))
if self.xlow >= experiment[self.xchannel].max():
raise util.CytoflowOpError('xlow',
"x channel range low must be < {0}"
.format(experiment[self.xchannel].max()))
if self.yhigh <= experiment[self.ychannel].min():
raise util.CytoflowOpError('yhigh',
"y channel range high must be > {0}"
.format(experiment[self.ychannel].min()))
if self.ylow >= experiment[self.ychannel].max():
raise util.CytoflowOpError('ylow',
"y channel range low must be < {0}"
.format(experiment[self.ychannel].max()))
x = experiment[self.xchannel].between(self.xlow, self.xhigh)
y = experiment[self.ychannel].between(self.ylow, self.yhigh)
gate = pd.Series(x & y)
new_experiment = experiment.clone()
new_experiment.add_condition(self.name, "bool", gate)
new_experiment.history.append(self.clone_traits(transient = lambda t: True))
return new_experiment
[docs] def default_view(self, **kwargs):
self._selection_view = RangeSelection2D(op = self)
self._selection_view.trait_set(**kwargs)
return self._selection_view
[docs]@provides(ISelectionView)
class RangeSelection2D(Op2DView, ScatterplotView):
"""
Plots, and lets the user interact with, a 2D selection.
Attributes
----------
interactive : Bool
is this view interactive? Ie, can the user set min and max
with a mouse drag?
Notes
-----
We inherit `xfacet` and `yfacet` from `cytoflow.views.ScatterplotView`, but
they must both be unset!
Examples
--------
In a Jupyter notebook with `%matplotlib notebook`
>>> r = flow.Range2DOp(name = "Range2D",
... xchannel = "V2-A",
... ychannel = "Y2-A"))
>>> rv = r.default_view()
>>> rv.interactive = True
>>> rv.plot(ex2)
"""
id = Constant('edu.mit.synbio.cytoflow.views.range2d')
friendly_id = Constant("2D Range Selection")
xfacet = Constant(None)
yfacet = Constant(None)
xscale = util.ScaleEnum
yscale = util.ScaleEnum
interactive = Bool(False, transient = True)
# internal state.
_ax = Any(transient = True)
_selector = Instance(RectangleSelector, transient = True)
_box = Instance(Rectangle, transient = True)
[docs] def plot(self, experiment, **kwargs):
"""
Plot the underlying scatterplot and then plot the selection on top of it.
Parameters
----------
"""
if experiment is None:
raise util.CytoflowViewError('experiment',
"No experiment specified")
super(RangeSelection2D, self).plot(experiment, **kwargs)
self._ax = plt.gca()
self._draw_rect()
self._interactive()
@on_trait_change('op.xlow, op.xhigh, op.ylow, op.yhigh', post_init = True)
def _draw_rect(self):
if not self._ax:
return
if self._box and self._box in self._ax.patches:
self._box.remove()
if self.op.xlow and self.op.xhigh and self.op.ylow and self.op.yhigh:
self._box = Rectangle((self.op.xlow, self.op.ylow),
(self.op.xhigh - self.op.xlow),
(self.op.yhigh - self.op.ylow),
facecolor="none",
edgecolor = 'blue',
linewidth = 2)
self._ax.add_patch(self._box)
plt.draw()
@on_trait_change('interactive', post_init = True)
def _interactive(self):
if self._ax and self.interactive:
self._selector = RectangleSelector(
self._ax,
onselect=self._onselect,
rectprops=dict(facecolor = 'none',
edgecolor = 'blue',
linewidth = 2),
useblit = True)
else:
self._selector = None
def _onselect(self, pos1, pos2):
"""Update selection traits"""
self.op.xlow = min(pos1.xdata, pos2.xdata)
self.op.xhigh = max(pos1.xdata, pos2.xdata)
self.op.ylow = min(pos1.ydata, pos2.ydata)
self.op.yhigh = max(pos1.ydata, pos2.ydata)
util.expand_class_attributes(RangeSelection2D)
util.expand_method_parameters(RangeSelection2D, RangeSelection2D.plot)
if __name__ == '__main__':
import cytoflow as flow
tube1 = flow.Tube(file = '../../cytoflow/tests/data/Plate01/RFP_Well_A3.fcs',
conditions = {"Dox" : 10.0})
tube2 = flow.Tube(file = '../../cytoflow/tests/data/Plate01/CFP_Well_A4.fcs',
conditions = {"Dox" : 1.0})
ex = flow.ImportOp(conditions = {"Dox" : "float"}, tubes = [tube1, tube2])
r = flow.Range2DOp(xchannel = "V2-A",
ychannel = "Y2-A")
rv = r.default_view()
plt.ioff()
rv.plot(ex)
rv.interactive = True
plt.show()
print("x:({0}, {1}) y:({2}, {3})".format(r.xlow, r.xhigh, r.ylow, r.yhigh))