y = scale.inverse(np.linspace(scale(scale.clip(plt_min)),
scale(scale.clip(plt_max)), 500))
pdf_scale = patch_area * gmm.weights_[idx]
mean = gmm.means_[idx][0]
stdev = np.sqrt(gmm.covariances_[idx][0])
x = stats.norm.pdf(scale(y), mean, stdev) * pdf_scale
axes.plot(x, y, color = annotation_color)
else:
scale = kwargs['xscale']
patch_area = 0.0
for k in range(0, len(axes.patches)):
patch = axes.patches[k]
xy = patch.get_xy()
patch_area += poly_area([scale(p[0]) for p in xy], [p[1] for p in xy])
plt_min, plt_max = plt.gca().get_xlim()
x = scale.inverse(np.linspace(scale(scale.clip(plt_min)),
scale(scale.clip(plt_max)), 500))
pdf_scale = patch_area * gmm.weights_[idx]
mean = gmm.means_[idx][0]
stdev = np.sqrt(gmm.covariances_[idx][0])
y = stats.norm.pdf(scale(x), mean, stdev) * pdf_scale
axes.plot(x, y, color = annotation_color)
# from http://stackoverflow.com/questions/24467972/calculate-area-of-polygon-given-x-y-coordinates
def poly_area(x,y):
return 0.5*np.abs(np.dot(x,np.roll(y,1))-np.dot(y,np.roll(x,1)))
util.expand_class_attributes(GaussianMixture1DView)
util.expand_method_parameters(GaussianMixture1DView, GaussianMixture1DView.plot)
return {'xlim' : xlim, 'ylim' : ylim}
def _error_bars(x, y, yerr, ax = None, color = None, **kwargs):
if isinstance(yerr.iloc[0], tuple):
lo = [ye[0] for ye in yerr]
hi = [ye[1] for ye in yerr]
else:
lo = [y.iloc[i] - ye for i, ye in yerr.reset_index(drop = True).items()]
hi = [y.iloc[i] + ye for i, ye in yerr.reset_index(drop = True).items()]
plt.vlines(x, lo, hi, color = color, **kwargs)
util.expand_class_attributes(Stats1DView)
util.expand_method_parameters(Stats1DView, Stats1DView.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])
thresh = flow.ThresholdOp()
ax = plt.gca()
color = kwargs.pop("color")
ax.pcolormesh(X, Y, h.T, cmap=AlphaColormap("AlphaColor", color), **kwargs)
return ax
class AlphaColormap(Colormap):
def __init__(self, name, color, min_alpha=0.0, max_alpha=1.0, N=256):
Colormap.__init__(self, name, N)
self._color = color
self._min_alpha = min_alpha
self._max_alpha = max_alpha
def _init(self):
self._lut = np.ones((self.N + 3, 4), np.float)
self._lut[:-3, 0] = self._color[0]
self._lut[:-3, 1] = self._color[1]
self._lut[:-3, 2] = self._color[2]
self._lut[:-3, 3] = np.linspace(self._min_alpha,
self._max_alpha,
num=self.N,
dtype=np.float)
self._isinit = True
self._set_extremes()
util.expand_class_attributes(Histogram2DView)
util.expand_method_parameters(Histogram2DView, Histogram2DView.plot)
plt.draw()
@on_trait_change('interactive', post_init=True)
def _interactive(self):
if self._ax and self.interactive:
self._widget = util.PolygonSelector(self._ax,
self._onselect,
useblit=True)
elif self._widget:
self._widget = None
def _onselect(self, vertices):
self.op.vertices = vertices
util.expand_class_attributes(PolygonSelection)
util.expand_method_parameters(PolygonSelection, PolygonSelection.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])
p = PolygonOp(xchannel="V2-A", ychannel="Y2-A")
v = p.default_view(xscale="logicle", yscale="logicle")
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()
bins are shown in different colors.
Attributes
----------
"""
id = Constant('edu.mit.synbio.cytoflow.views.binning')
friendly_id = Constant('Binning Setup')
def plot(self, experiment, **kwargs):
"""
Plot the histogram.
Parameters
----------
"""
view, trait_name = self._strip_trait(self.op.name)
super(BinningView, view).plot(experiment,
annotation_facet=self.op.name,
annotation_trait=trait_name,
annotations={},
**kwargs)
util.expand_class_attributes(BinningView)
util.expand_method_parameters(BinningView, BinningView.plot)
color='blue',
useblit=True)
self._cursor.connect_event('button_press_event', self._onclick)
elif self._cursor:
self._cursor.disconnect_events()
self._cursor = None
def _onclick(self, event):
"""Update the threshold location"""
# sometimes the axes aren't set up and we don't get xdata (??)
if event.xdata:
self.op.threshold = event.xdata
util.expand_class_attributes(ThresholdSelection)
util.expand_method_parameters(ThresholdSelection, ThresholdSelection.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])
t = ThresholdOp(channel="Y2-A", scale="logicle")
v = t.default_view()
super(KMeans2DView, view).plot(experiment,
annotation_facet = self.op.name,
annotation_trait = trait_name,
annotations = self.op._kmeans,
xscale = xscale,
yscale = yscale,
**kwargs)
def _annotation_plot(self, axes, annotation, annotation_facet,
annotation_value, annotation_color, **kwargs):
# plot the cluster centers
km = annotation
xscale = kwargs['xscale']
yscale = kwargs['yscale']
ix = self.op.channels.index(self.xchannel)
iy = self.op.channels.index(self.ychannel)
for k in range(self.op.num_clusters):
x = xscale.inverse(km.cluster_centers_[k][ix])
y = yscale.inverse(km.cluster_centers_[k][iy])
axes.plot(x, y, '*', color = 'blue')
util.expand_class_attributes(KMeans1DView)
util.expand_method_parameters(KMeans1DView, KMeans1DView.plot)
util.expand_class_attributes(KMeans2DView)
util.expand_method_parameters(KMeans2DView, KMeans2DView.plot)
x = scale.inverse(support[:, 0])
y = np.exp(log_density)
# Check if a label was specified in the call
label = kwargs.pop("label", None)
color = kwargs.pop("color", None)
alpha = kwargs.pop("alpha", 0.25)
# Draw the KDE plot and, optionally, shade
if orientation == "vertical":
ax.plot(x, y, color=color, label=label, **kwargs)
if shade:
ax.fill_between(x, 1e-12, y, facecolor=color, alpha=alpha)
else:
ax.plot(y, x, color=color, label=label, **kwargs)
if shade:
ax.fill_between(y, 1e-12, x, facecolor=color, alpha=alpha)
return ax
def _kde_support(data, bw, gridsize, cut, clip):
"""Establish support for a kernel density estimate."""
support_min = max(data.min() - bw * cut, clip[0])
support_max = min(data.max() + bw * cut, clip[1])
return np.linspace(support_min, support_max, gridsize)
util.expand_class_attributes(Kde1DView)
util.expand_method_parameters(Kde1DView, Kde1DView.plot)
for k in range(len(km.cluster_centers_)):
x = self.op._scale[self.xchannel].inverse(
km.cluster_centers_[k][ix])
y = self.op._scale[self.ychannel].inverse(
km.cluster_centers_[k][iy])
plt.plot(x, y, '*', color='blue')
peak_idx = cluster_peak[k]
peak = peaks[peak_idx]
peak_x = xscale.inverse(peak[0])
peak_y = yscale.inverse(peak[1])
plt.plot([x, peak_x], [y, peak_y])
for peak in peaks:
x = self.op._scale[self.ychannel].inverse(peak[0])
y = self.op._scale[self.xchannel].inverse(peak[1])
plt.plot(x, y, 'o', color="magenta")
util.expand_class_attributes(FlowPeaks1DView)
util.expand_method_parameters(FlowPeaks1DView, FlowPeaks1DView.plot)
util.expand_class_attributes(FlowPeaks2DView)
util.expand_method_parameters(FlowPeaks2DView, FlowPeaks2DView.plot)
util.expand_class_attributes(FlowPeaks2DDensityView)
util.expand_method_parameters(FlowPeaks2DDensityView,
FlowPeaks2DDensityView.plot)
if orient and orient == 'h':
x = data_scale(x)
else:
y = data_scale(y)
plotter = _ViolinPlotter(x, y, hue, data, order, hue_order,
bw, cut, scale, scale_hue, gridsize,
width, inner, split, dodge, orient, linewidth,
color, palette, saturation)
for i in range(len(plotter.support)):
if plotter.hue_names is None:
if plotter.support[i].shape[0] > 0:
plotter.support[i] = data_scale.inverse(plotter.support[i])
else:
for j in range(len(plotter.support[i])):
if plotter.support[i][j].shape[0] > 0:
plotter.support[i][j] = data_scale.inverse(plotter.support[i][j])
for i in range(len(plotter.plot_data)):
plotter.plot_data[i] = data_scale.inverse(plotter.plot_data[i])
if ax is None:
ax = plt.gca()
plotter.plot(ax)
return ax
util.expand_class_attributes(ViolinPlotView)
util.expand_method_parameters(ViolinPlotView, ViolinPlotView.plot)
super().plot(experiment, **kwargs)
def _grid_plot(self, experiment, grid, xlim, ylim, xscale, yscale,
**kwargs):
kwargs.setdefault('alpha', 0.25)
kwargs.setdefault('s', 2)
kwargs.setdefault('marker', 'o')
kwargs.setdefault('antialiased', True)
grid.map(plt.scatter, self.xchannel, self.ychannel, **kwargs)
return {}
util.expand_class_attributes(ScatterplotView)
util.expand_method_parameters(ScatterplotView, ScatterplotView.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])
thresh = flow.ThresholdOp()
thresh.name = "Y2-A+"
thresh.channel = 'Y2-A'
ax.text(xy[0] + 0.025, xy[1] - 0.025, name,
ha='left', va='top', size='small')
elif xy[0] >= 0.0 and xy[1] >= 0.0:
ax.text(xy[0] + 0.025, xy[1] + 0.025, name,
ha='left', va='bottom', size='small')
ax.axis('scaled')
def _get_necklaces(L):
import itertools as it
B = it.combinations(L,2)
swaplist = [e for e in B]
unique_necklaces = []
unique_necklaces.append(L)
for pair in swaplist:
necklace = list(L)
e1 = pair[0]
e2 = pair[1]
indexe1 = np.where(L == e1)[0][0]
indexe2 = np.where(L == e2)[0][0]
#swap
necklace[indexe1],necklace[indexe2] = necklace[indexe2], necklace[indexe1]
unique_necklaces.append(necklace)
return unique_necklaces
util.expand_class_attributes(RadvizView)
util.expand_method_parameters(RadvizView, RadvizView.plot)
annotations[i] = (self.op._keep_xbins[i],
self.op._keep_ybins[i],
self.op._histogram[i])
super().plot(experiment,
annotations = annotations,
xscale = self.op._xscale,
yscale = self.op._yscale,
**kwargs)
def _annotation_plot(self,
axes,
annotation,
annotation_facet,
annotation_value,
annotation_color,
**kwargs):
# plot a countour around the bins that got kept
keep_x = annotation[0]
keep_y = annotation[1]
h = annotation[2]
xbins = self.op._xbins[0:-1]
ybins = self.op._ybins[0:-1]
last_level = h[keep_x[-1], keep_y[-1]]
axes.contour(xbins, ybins, h.T, [last_level], colors = 'w')
util.expand_class_attributes(DensityGateView)
util.expand_method_parameters(DensityGateView, DensityGateView.plot)
out = pd.Series()
for i in range(len(df.columns) - 1):
new_series = df.apply(lambda x: [(i, x[i]), (i + 1, x[i + 1])], axis=1)
out = out.append(new_series)
lc = matplotlib.collections.LineCollection(out.values,
colors=color,
antialiaseds=aa)
lc.set_label(label)
ax.add_collection(lc)
# have we already annotated these axes?
if ax in ax_annotations:
return
ax_annotations[ax] = True
x = np.arange(len(df.columns))
for i in x:
ax.axvline(i, **axvlines_kwds)
ax.set_xticks(x)
ax.set_xticklabels(df.columns)
ax.set_xlim(x[0], x[-1])
ax.grid()
util.expand_class_attributes(ParallelCoordinatesView)
util.expand_method_parameters(ParallelCoordinatesView,
ParallelCoordinatesView.plot)
kws.setdefault("lw", errwidth)
else:
kws.setdefault("lw", mpl.rcParams["lines.linewidth"] * 1.8)
if isinstance(confints.iloc[0], tuple):
ci_lo = [x[0] for x in confints]
ci_hi = [x[1] for x in confints]
else:
ci_lo = [stat.iloc[i] - x for i, x in confints.reset_index(drop = True).items()]
ci_hi = [stat.iloc[i] + x for i, x in confints.reset_index(drop = True).items()]
for at, lo, hi, color in zip(at_group,
ci_lo,
ci_hi,
colors):
if orient == "vertical":
if capsize is not None:
kws['marker'] = '_'
kws['markersize'] = capsize * 2
kws['markeredgewidth'] = kws['lw']
ax.plot([at, at], [lo, hi], color=color, **kws)
else:
if capsize is not None:
kws['marker'] = '|'
kws['markersize'] = capsize * 2
kws['markeredgewidth'] = kws['lw']
ax.plot([lo, hi], [at, at], color=color, **kws)
util.expand_class_attributes(BarChartView)
util.expand_method_parameters(BarChartView, BarChartView.plot)
horizOn=True,
vertOn=True,
color='blue',
useblit=True)
self._cursor.connect_event('button_press_event', self._onclick)
elif self._cursor:
self._cursor.disconnect_events()
self._cursor = None
def _onclick(self, event):
"""Update the threshold location"""
self.op.xthreshold = event.xdata
self.op.ythreshold = event.ydata
util.expand_class_attributes(QuadSelection)
util.expand_method_parameters(QuadSelection, QuadSelection.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.QuadOp(name="Quad", xchannel="V2-A", ychannel="Y2-A")
rv = r.default_view(xscale="logicle", yscale="logicle")