def get_default_title(self):
"""
Get the default title, which for a plot is just a list of DataSet locations.
A custom title can be set when adding any trace (via either __init__ or add.
these kwargs all eventually end up in self.traces[i]['config']) and it looks
like we will take the first title we find from any trace... otherwise, if no
trace specifies a title, then we combine whatever dataset locations we find.
Note: (alexj): yeah, that's awkward, isn't it, and it looks like a weird
implementation, feel free to change it 👼
Returns:
string: the title of the figure
"""
title_parts = []
for trace in self.traces:
config = trace['config']
if 'title' in config: # can be passed using **kw
return config['title']
for part in self.data_keys:
data_array = config.get(part, '')
if hasattr(data_array, 'data_set'):
if data_array.data_set is not None:
location = data_array.data_set.location
if location and location not in title_parts:
title_parts.append(location)
return ', '.join(title_parts)
def _initialize_metadata(self, dataset: DataSet = None):
"""Initialize dataset metadata"""
if dataset is None:
dataset = self.dataset
config = qcodes_config.get('user', {}).get('silq_config', qcodes_config)
dataset.add_metadata({"config": config})
dataset.add_metadata({"measurement_type": "Measurement"})
# Add instrument information
if Station.default is not None:
dataset.add_metadata({"station": Station.default.snapshot()})
if using_ipython():
measurement_cell = get_last_input_cells(1)[0]
measurement_code = measurement_cell
# If the code is run from a measurement thread, there is some
# initial code that should be stripped
init_string = "get_ipython().run_cell_magic('new_job', '', "
if measurement_code.startswith(init_string):
measurement_code = measurement_code[len(init_string) + 1 : -4]
self._t_start = datetime.now()
dataset.add_metadata(
{
"measurement_cell": measurement_cell,
"measurement_code": measurement_code,
"last_input_cells": get_last_input_cells(20),
"t_start": self._t_start.strftime('%Y-%m-%d %H:%M:%S')
}
)
def update_plot(self):
"""
update the plot. The DataSets themselves have already been updated
in update, here we just push the changes to the plot.
"""
# matplotlib doesn't know how to autoscale to a pcolormesh after the
# first draw (relim ignores it...) so we have to do this ourselves
bboxes = dict(zip(self.subplots, [[] for p in self.subplots]))
for trace in self.traces:
config = trace['config']
plot_object = trace['plot_object']
if 'z' in config:
# pcolormesh doesn't seem to allow editing x and y data, only z
# so instead, we'll remove and re-add the data.
if plot_object:
plot_object.remove()
ax = self[config.get('subplot', 1) - 1]
kwargs = deepcopy(config)
# figsize may be passed in as part of config.
# pcolormesh will raise an error if this is passed to it
# so strip it here.
if 'figsize' in kwargs:
kwargs.pop('figsize')
plot_object = self._draw_pcolormesh(ax, **kwargs)
trace['plot_object'] = plot_object
if plot_object:
bboxes[plot_object.axes].append(
plot_object.get_datalim(plot_object.axes.transData))
else:
for axletter in 'xy':
setter = 'set_' + axletter + 'data'
if axletter in config:
getattr(plot_object, setter)(config[axletter])
for ax in self.subplots:
if ax.get_autoscale_on():
ax.relim()
if bboxes[ax]:
bbox = Bbox.union(bboxes[ax])
if np.all(np.isfinite(ax.dataLim)):
# should take care of the case of lines + heatmaps
# where there's already a finite dataLim from relim
ax.dataLim.set(Bbox.union(ax.dataLim, bbox))
else:
# when there's only a heatmap, relim gives inf bounds
# so just completely overwrite it
ax.dataLim = bbox
ax.autoscale()
self.fig.canvas.draw()
def _update_image(self, plot_object, config):
z = config['z']
img = plot_object['image']
hist = plot_object['hist']
scales = plot_object['scales']
# make sure z is a *new* numpy float array (pyqtgraph barfs on ints),
# and replace nan with minimum val bcs I can't figure out how to make
# pyqtgraph handle nans - though the source does hint at a way:
# http://www.pyqtgraph.org/documentation/_modules/pyqtgraph/widgets/ColorMapWidget.html
# see class RangeColorMapItem
z = np.asfarray(z).T
with warnings.catch_warnings():
warnings.simplefilter('error')
try:
z_range = (np.nanmin(z), np.nanmax(z))
except:
# we get a warning here when z is entirely NaN
# nothing to plot, so give up.
return
z[np.where(np.isnan(z))] = z_range[0]
hist_range = hist.getLevels()
if hist_range == plot_object['histlevels']:
plot_object['histlevels'] = z_range
hist.setLevels(*z_range)
hist_range = z_range
img.setImage(self._clean_array(z), levels=hist_range)
scales_changed = False
for axletter, axscale in scales.items():
if axscale.revisit:
axdata = config.get(axletter, None)
newscale = self._get_transform(axdata)
if (newscale.translate != axscale.translate
or newscale.scale != axscale.scale):
scales_changed = True
scales[axletter] = newscale
if scales_changed:
img.resetTransform()
img.translate(scales['x'].translate, scales['y'].translate)
img.scale(scales['x'].scale, scales['y'].scale)