def updatePlot(self):
self.plot.clear()
if self.data is None:
return
if self.filtered is None:
self.filtered = self.filter.filterData(self.data)
data = self.filtered
if len(data) == 0:
return
colors = np.array([fn.mkBrush(*x) for x in self.colorMap.map(data)])
style = self.style.copy()
## Look up selected columns and units
sel = list([str(item.text()) for item in self.fieldList.selectedItems()])
units = list([item.opts.get('units', '') for item in self.fieldList.selectedItems()])
if len(sel) == 0:
self.plot.setTitle('')
return
if len(sel) == 1:
self.plot.setLabels(left=('N', ''), bottom=(sel[0], units[0]), title='')
if len(data) == 0:
return
x = data[sel[0]]
#if x.dtype.kind == 'f':
#mask = ~np.isnan(x)
#else:
#mask = np.ones(len(x), dtype=bool)
#x = x[mask]
#style['symbolBrush'] = colors[mask]
y = None
elif len(sel) == 2:
self.plot.setLabels(left=(sel[1],units[1]), bottom=(sel[0],units[0]))
if len(data) == 0:
return
xydata = []
for ax in [0,1]:
d = data[sel[ax]]
## scatter catecorical values just a bit so they show up better in the scatter plot.
#if sel[ax] in ['MorphologyBSMean', 'MorphologyTDMean', 'FIType']:
#d += np.random.normal(size=len(cells), scale=0.1)
xydata.append(d)
x,y = xydata
#mask = np.ones(len(x), dtype=bool)
#if x.dtype.kind == 'f':
#mask |= ~np.isnan(x)
#if y.dtype.kind == 'f':
#mask |= ~np.isnan(y)
#x = x[mask]
#y = y[mask]
#style['symbolBrush'] = colors[mask]
## convert enum-type fields to float, set axis labels
xy = [x,y]
for i in [0,1]:
axis = self.plot.getAxis(['bottom', 'left'][i])
if xy[i] is not None and xy[i].dtype.kind in ('S', 'O'):
vals = self.fields[sel[i]].get('values', list(set(xy[i])))
xy[i] = np.array([vals.index(x) if x in vals else None for x in xy[i]], dtype=float)
axis.setTicks([list(enumerate(vals))])
else:
axis.setTicks(None) # reset to automatic ticking
x,y = xy
## mask out any nan values
mask = np.ones(len(x), dtype=bool)
if x.dtype.kind == 'f':
mask &= ~np.isnan(x)
if y is not None and y.dtype.kind == 'f':
mask &= ~np.isnan(y)
x = x[mask]
style['symbolBrush'] = colors[mask]
## Scatter y-values for a histogram-like appearance
if y is None:
y = fn.pseudoScatter(x)
else:
y = y[mask]
self.plot.plot(x, y, **style)
def updatePlot(self):
self.plot.clear()
if self.data is None:
return
if self.filtered is None:
self.filtered = self.filter.filterData(self.data)
data = self.filtered
if len(data) == 0:
return
colors = np.array([fn.mkBrush(*x) for x in self.colorMap.map(data)])
style = self.style.copy()
## Look up selected columns and units
sel = list(
[str(item.text()) for item in self.fieldList.selectedItems()])
units = list([
item.opts.get('units', '')
for item in self.fieldList.selectedItems()
])
if len(sel) == 0:
self.plot.setTitle('')
return
if len(sel) == 1:
self.plot.setLabels(left=('N', ''),
bottom=(sel[0], units[0]),
title='')
if len(data) == 0:
return
x = data[sel[0]]
#if x.dtype.kind == 'f':
#mask = ~np.isnan(x)
#else:
#mask = np.ones(len(x), dtype=bool)
#x = x[mask]
#style['symbolBrush'] = colors[mask]
y = None
elif len(sel) == 2:
self.plot.setLabels(left=(sel[1], units[1]),
bottom=(sel[0], units[0]))
if len(data) == 0:
return
xydata = []
for ax in [0, 1]:
d = data[sel[ax]]
## scatter catecorical values just a bit so they show up better in the scatter plot.
#if sel[ax] in ['MorphologyBSMean', 'MorphologyTDMean', 'FIType']:
#d += np.random.normal(size=len(cells), scale=0.1)
xydata.append(d)
x, y = xydata
#mask = np.ones(len(x), dtype=bool)
#if x.dtype.kind == 'f':
#mask |= ~np.isnan(x)
#if y.dtype.kind == 'f':
#mask |= ~np.isnan(y)
#x = x[mask]
#y = y[mask]
#style['symbolBrush'] = colors[mask]
## convert enum-type fields to float, set axis labels
xy = [x, y]
for i in [0, 1]:
axis = self.plot.getAxis(['bottom', 'left'][i])
if xy[i] is not None and xy[i].dtype.kind in ('S', 'O'):
vals = self.fields[sel[i]].get('values', list(set(xy[i])))
xy[i] = np.array(
[vals.index(x) if x in vals else None for x in xy[i]],
dtype=float)
axis.setTicks([list(enumerate(vals))])
else:
axis.setTicks(None) # reset to automatic ticking
x, y = xy
## mask out any nan values
mask = np.ones(len(x), dtype=bool)
if x.dtype.kind == 'f':
mask &= ~np.isnan(x)
if y is not None and y.dtype.kind == 'f':
mask &= ~np.isnan(y)
x = x[mask]
style['symbolBrush'] = colors[mask]
## Scatter y-values for a histogram-like appearance
if y is None:
y = fn.pseudoScatter(x)
else:
y = y[mask]
self.plot.plot(x, y, **style)
def updatePlot(self):
self.plot.clear()
if self.data is None:
return
if self.filtered is None:
self.filtered = self.filter.filterData(self.data)
data = self.filtered
if len(data) == 0:
return
colors = np.array([fn.mkBrush(*x) for x in self.colorMap.map(data)])
style = self.style.copy()
## Look up selected columns and units
sel = list([str(item.text()) for item in self.fieldList.selectedItems()])
units = list([item.opts.get("units", "") for item in self.fieldList.selectedItems()])
if len(sel) == 0:
self.plot.setTitle("")
return
if len(sel) == 1:
self.plot.setLabels(left=("N", ""), bottom=(sel[0], units[0]), title="")
if len(data) == 0:
return
# x = data[sel[0]]
# y = None
xy = [data[sel[0]], None]
elif len(sel) == 2:
self.plot.setLabels(left=(sel[1], units[1]), bottom=(sel[0], units[0]))
if len(data) == 0:
return
xy = [data[sel[0]], data[sel[1]]]
# xydata = []
# for ax in [0,1]:
# d = data[sel[ax]]
### scatter catecorical values just a bit so they show up better in the scatter plot.
##if sel[ax] in ['MorphologyBSMean', 'MorphologyTDMean', 'FIType']:
##d += np.random.normal(size=len(cells), scale=0.1)
# xydata.append(d)
# x,y = xydata
## convert enum-type fields to float, set axis labels
enum = [False, False]
for i in [0, 1]:
axis = self.plot.getAxis(["bottom", "left"][i])
if xy[i] is not None and (
self.fields[sel[i]].get("mode", None) == "enum" or xy[i].dtype.kind in ("S", "O")
):
vals = self.fields[sel[i]].get("values", list(set(xy[i])))
xy[i] = np.array([vals.index(x) if x in vals else len(vals) for x in xy[i]], dtype=float)
axis.setTicks([list(enumerate(vals))])
enum[i] = True
else:
axis.setTicks(None) # reset to automatic ticking
## mask out any nan values
mask = np.ones(len(xy[0]), dtype=bool)
if xy[0].dtype.kind == "f":
mask &= ~np.isnan(xy[0])
if xy[1] is not None and xy[1].dtype.kind == "f":
mask &= ~np.isnan(xy[1])
xy[0] = xy[0][mask]
style["symbolBrush"] = colors[mask]
## Scatter y-values for a histogram-like appearance
if xy[1] is None:
## column scatter plot
xy[1] = fn.pseudoScatter(xy[0])
else:
## beeswarm plots
xy[1] = xy[1][mask]
for ax in [0, 1]:
if not enum[ax]:
continue
imax = int(xy[ax].max()) if len(xy[ax]) > 0 else 0
for i in range(imax + 1):
keymask = xy[ax] == i
scatter = pg.pseudoScatter(xy[1 - ax][keymask], bidir=True)
if len(scatter) == 0:
continue
smax = np.abs(scatter).max()
if smax != 0:
scatter *= 0.2 / smax
xy[ax][keymask] += scatter
if self.scatterPlot is not None:
try:
self.scatterPlot.sigPointsClicked.disconnect(self.plotClicked)
except:
pass
self.scatterPlot = self.plot.plot(xy[0], xy[1], data=data[mask], **style)
self.scatterPlot.sigPointsClicked.connect(self.plotClicked)
def updatePlot(self):
self.plot.clear()
if self.data is None:
return
if self.filtered is None:
self.filtered = self.filter.filterData(self.data)
data = self.filtered
if len(data) == 0:
return
colors = np.array([fn.mkBrush(*x) for x in self.colorMap.map(data)])
style = self.style.copy()
## Look up selected columns and units
sel = list([str(item.text()) for item in self.fieldList.selectedItems()])
units = list([item.opts.get('units', '') for item in self.fieldList.selectedItems()])
if len(sel) == 0:
self.plot.setTitle('')
return
if len(sel) == 1:
self.plot.setLabels(left=('N', ''), bottom=(sel[0], units[0]), title='')
if len(data) == 0:
return
#x = data[sel[0]]
#y = None
xy = [data[sel[0]], None]
elif len(sel) == 2:
self.plot.setLabels(left=(sel[1],units[1]), bottom=(sel[0],units[0]))
if len(data) == 0:
return
xy = [data[sel[0]], data[sel[1]]]
#xydata = []
#for ax in [0,1]:
#d = data[sel[ax]]
### scatter catecorical values just a bit so they show up better in the scatter plot.
##if sel[ax] in ['MorphologyBSMean', 'MorphologyTDMean', 'FIType']:
##d += np.random.normal(size=len(cells), scale=0.1)
#xydata.append(d)
#x,y = xydata
## convert enum-type fields to float, set axis labels
enum = [False, False]
for i in [0,1]:
axis = self.plot.getAxis(['bottom', 'left'][i])
if xy[i] is not None and (self.fields[sel[i]].get('mode', None) == 'enum' or xy[i].dtype.kind in ('S', 'O')):
vals = self.fields[sel[i]].get('values', list(set(xy[i])))
xy[i] = np.array([vals.index(x) if x in vals else len(vals) for x in xy[i]], dtype=float)
axis.setTicks([list(enumerate(vals))])
enum[i] = True
else:
axis.setTicks(None) # reset to automatic ticking
## mask out any nan values
mask = np.ones(len(xy[0]), dtype=bool)
if xy[0].dtype.kind == 'f':
mask &= ~np.isnan(xy[0])
if xy[1] is not None and xy[1].dtype.kind == 'f':
mask &= ~np.isnan(xy[1])
xy[0] = xy[0][mask]
style['symbolBrush'] = colors[mask]
## Scatter y-values for a histogram-like appearance
if xy[1] is None:
## column scatter plot
xy[1] = fn.pseudoScatter(xy[0])
else:
## beeswarm plots
xy[1] = xy[1][mask]
for ax in [0,1]:
if not enum[ax]:
continue
imax = int(xy[ax].max()) if len(xy[ax]) > 0 else 0
for i in range(imax+1):
keymask = xy[ax] == i
scatter = pg.pseudoScatter(xy[1-ax][keymask], bidir=True)
if len(scatter) == 0:
continue
smax = np.abs(scatter).max()
if smax != 0:
scatter *= 0.2 / smax
xy[ax][keymask] += scatter
if self.scatterPlot is not None:
try:
self.scatterPlot.sigPointsClicked.disconnect(self.plotClicked)
except:
pass
self.scatterPlot = self.plot.plot(xy[0], xy[1], data=data[mask], **style)
self.scatterPlot.sigPointsClicked.connect(self.plotClicked)
