class ScanModel(Model):
points_rewritten = QtCore.pyqtSignal(dict)
points_appended = QtCore.pyqtSignal(dict)
annotations_changed = QtCore.pyqtSignal(list)
def __init__(self, axes: List[Dict[str, Any]], context: Context):
super().__init__(context)
self.axes = axes
self._annotations = []
self._annotation_schemata = []
self._online_analyses = {}
def get_point_data(self) -> Dict[str, Any]:
raise NotImplementedError
def get_annotations(self) -> List[Annotation]:
return self._annotations
def _set_annotation_schemata(self, schemata: List[Dict[str, Any]]):
self._annotation_schemata = schemata
self._annotations = []
def data_source(spec):
kind = spec["kind"]
if kind == "fixed":
return FixedDataSource(spec["value"])
if kind == "analysis_result":
analysis = self._online_analyses.get(spec["analysis_name"], None)
if analysis is None:
return None
return OnlineAnalysisDataSource(analysis, spec["result_key"])
logger.info("Ignoring unsupported annotation data source type: '%s'", kind)
return None
def to_data_sources(specs):
return {k: data_source(v) for k, v in specs.items()}
for schema in schemata:
sources = [to_data_sources(schema.get(n)) for n in ("coordinates", "data")]
self._annotations.append(
Annotation(schema["kind"], schema.get("parameters", {}), *sources))
self.annotations_changed.emit(self._annotations)
def _set_online_analyses(self,
analysis_schemata: Dict[str, Dict[str, Any]]) -> None:
for a in self._online_analyses.values():
a.stop()
self._online_analyses = {}
for name, schema in analysis_schemata.items():
kind = schema["kind"]
if kind == "named_fit":
self._online_analyses[name] = OnlineNamedFitAnalysis(schema, self)
else:
logger.warning("Ignoring unsupported online analysis type: '%s'", kind)
# Rebind annotation schemata to new analysis data sources.
self._set_annotation_schemata(self._annotation_schemata)
class Context(QtCore.QObject):
"""Describes the environment in which a certain plot is displayed.
This is the moral equivalent of a container for global variables and should be used
only sparsely (i.e. for actual properties of the environment).
"""
source_id_changed = QtCore.pyqtSignal(str)
title_changed = QtCore.pyqtSignal(str)
def __init__(self, set_dataset: Callable[[str, Any], None] = None):
super().__init__()
self._set_dataset = set_dataset
self._title = ""
self._source_id = "<unknown>"
def get_title(self) -> str:
return self._title
def set_title(self, title: str) -> None:
if self._title != title:
self._title = title
self.title_changed.emit(title)
def get_source_id(self):
"""Return a short string that helps the user to identify the data source.
This is usually based on the run id, and shown in plots for data traceability
purposes.
"""
return self._source_id
def set_source_id(self, source_id):
if self._source_id != source_id:
self._source_id = source_id
self.source_id_changed.emit(source_id)
def is_online_master(self) -> bool:
"""Return whether the plot is run in an environment where there is a connection
to an ARTIQ master (as opposed to e.g. displaying an offline results file).
"""
return self._set_dataset is not None
def set_dataset(self, key: str, value: Any) -> None:
"""Sets dataset ``key`` to ``value`` on the connected master, if any.
See: :meth:`is_online_master`.
"""
self._set_dataset(key, value)
class Model(QtCore.QObject):
channel_schemata_changed = QtCore.pyqtSignal(dict)
def __init__(self, context: Context):
super().__init__()
self.context = context
def get_channel_schemata(self) -> Dict[str, Any]:
raise NotImplementedError
class Root(QtCore.QObject):
"""The root of a plot data tree, i.e. all the data making up a plot displayed in a
given window.
This is a reference to (at most) one :class:`Model`, but makes it possible to
represent situations where the model is not yet known (e.g. because we are still
waiting for the experiment to set the top-level metadata datasets), or might change
(because we are showing a subscan for a user-selected point).
"""
model_changed = QtCore.pyqtSignal(object)
def get_model(self) -> Union["Model", None]:
raise NotImplementedError
class Context(QtCore.QObject):
title_changed = QtCore.pyqtSignal(str)
def __init__(self, set_dataset: Callable[[str, Any], None] = None):
super().__init__()
self._set_dataset = set_dataset
self.title = ""
def set_title(self, title: str) -> None:
if title != self.title:
self.title = title
self.title_changed.emit(title)
def is_online_master(self) -> bool:
return self.set_dataset is not None
def set_dataset(self, key: str, value: Any) -> None:
self._set_dataset(key, value)
class AlternateMenuPlotWidget(ContextMenuPlotWidget):
"""PlotWidget with context menu for integration with the
.container.PlotContainerWidget alternate plot switching functionality."""
alternate_plot_requested = QtCore.pyqtSignal(str)
def __init__(self, get_alternate_plot_names):
super().__init__()
self._get_alternate_plot_names = get_alternate_plot_names
def build_context_menu(self, builder: ContextMenuBuilder) -> None:
alternate_plot_names = self._get_alternate_plot_names()
if len(alternate_plot_names) > 1:
for name in alternate_plot_names:
action = builder.append_action("Show " + name)
action.triggered.connect(lambda *args, name=name: self.
alternate_plot_requested.emit(name))
builder.ensure_separator()
class _XYSeries(QtCore.QObject):
def __init__(self,
plot,
data_name,
data_item,
error_bar_name,
error_bar_item,
plot_left_to_right,
fit_spec=None,
fit_item=None,
fit_pois=[]):
super().__init__(plot)
self.plot = plot
self.data_item = data_item
self.data_name = data_name
self.error_bar_item = error_bar_item
self.error_bar_name = error_bar_name
self.plot_left_to_right = plot_left_to_right
self.num_current_points = 0
self.fit_obj = None
if fit_spec:
self._install_fit(fit_spec, fit_item, fit_pois)
def update(self, x_data, data):
def channel(name):
return data.get("ndscan.points.channel_" + name, (False, []))[1]
y_data = channel(self.data_name)
num_to_show = min(len(x_data), len(y_data))
if self.error_bar_item:
y_err = channel(self.error_bar_name)
num_to_show = min(num_to_show, len(y_err))
if num_to_show == self.num_current_points:
return
if self.plot_left_to_right:
x_data = np.array(x_data)
order = np.argsort(x_data[:num_to_show])
y_data = np.array(y_data)
self.data_item.setData(x_data[order], y_data[order])
if self.num_current_points == 0:
self.plot.addItem(self.data_item)
if self.error_bar_item:
y_err = np.array(y_err)
self.error_bar_item.setData(x=x_data[order],
y=y_data[order],
height=y_err[order])
if self.num_current_points == 0:
self.plot.addItem(self.error_bar_item)
else:
self.data_item.setData(x_data[:num_to_show], y_data[:num_to_show])
if self.num_current_points == 0:
self.plot.addItem(self.data_item)
if self.error_bar_item:
self.error_bar_item.setData(
x=x_data[:num_to_show],
y=y_data[:num_to_show],
height=(2 * np.array(y_err[:num_to_show])))
if self.num_current_points == 0:
self.plot.addItem(self.error_bar_item)
self.num_current_points = num_to_show
if self.fit_obj and self.num_current_points >= len(
self.fit_obj.parameter_names):
self._trigger_recompute_fit.emit()
_trigger_recompute_fit = QtCore.pyqtSignal()
def _install_fit(self, spec, item, pois):
self.fit_type = spec["fit_type"]
self.fit_obj = FIT_OBJECTS[self.fit_type]
self.fit_item = item
self.fit_pois = pois
self.fit_item_added = False
self.last_fit_params = None
self.recompute_fit_limiter = pyqtgraph.SignalProxy(
self._trigger_recompute_fit,
slot=lambda: asyncio.ensure_future(self._recompute_fit()),
rateLimit=30)
self.recompute_in_progress = False
self.fit_executor = ProcessPoolExecutor(max_workers=1)
self.redraw_fit_limiter = pyqtgraph.SignalProxy(
self.plot.getPlotItem().getViewBox().sigXRangeChanged,
slot=self._redraw_fit,
rateLimit=30)
async def _recompute_fit(self):
if self.recompute_in_progress:
# Run at most one fit computation at a time. To make sure we don't
# leave a few final data points completely disregarded, just
# re-emit the signal – even for long fits, repeated checks aren't
# expensive, as long as the SignalProxy rate is slow enough.
self._trigger_recompute_fit.emit()
return
self.recompute_in_progress = True
xs, ys = self.data_item.getData()
y_errs = None
if self.error_bar_item:
y_errs = self.error_bar_item.opts['height'] / 2
loop = asyncio.get_event_loop()
self.last_fit_params, self.last_fit_errors = await loop.run_in_executor(
self.fit_executor, _run_fit, self.fit_type, xs, ys, y_errs)
self.redraw_fit_limiter.signalReceived()
self.recompute_in_progress = False
def _redraw_fit(self, *args):
if not self.last_fit_params:
return
if not self.fit_item_added:
self.plot.addItem(self.fit_item, ignoreBounds=True)
for f in self.fit_pois:
f.add_to_plot(self.plot)
self.fit_item_added = True
# Choose horizontal range based on currently visible area.
view_box = self.plot.getPlotItem().getViewBox()
x_range, _ = view_box.state["viewRange"]
ext = (x_range[1] - x_range[0]) / 10
x_lims = (x_range[0] - ext, x_range[1] + ext)
# Choose number of points based on width of plot on screen (in pixels).
fit_xs = np.linspace(*x_lims, view_box.width())
fit_ys = self.fit_obj.fitting_function(fit_xs, self.last_fit_params)
self.fit_item.setData(fit_xs, fit_ys)
for f in self.fit_pois:
f.update(self.last_fit_params, self.last_fit_errors)
class AnnotationDataSource(QtCore.QObject):
changed = QtCore.pyqtSignal()
def get(self) -> Any:
raise NotImplementedError
class Rolling1DPlotWidget(pyqtgraph.PlotWidget):
error = QtCore.pyqtSignal(str)
def __init__(self):
super().__init__()
self.series_initialised = False
self.series = []
self.point_phase = None
self.showGrid(x=True, y=True)
self._install_context_menu()
def data_changed(self, data, mods):
def d(name):
return data.get("ndscan." + name, (False, None))[1]
if not self.series_initialised:
channels_json = d("channels")
if not channels_json:
return
channels = json.loads(channels_json)
try:
data_names, error_bar_names = extract_scalar_channels(channels)
except ValueError as e:
self.error.emit(str(e))
for i, data_name in enumerate(data_names):
color = SERIES_COLORS[i % len(SERIES_COLORS)]
data_item = pyqtgraph.ScatterPlotItem(pen=None, brush=color)
error_bar_name = error_bar_names.get(data_name, None)
error_bar_item = pyqtgraph.ErrorBarItem(
pen=color) if error_bar_name else None
self.series.append(
_Series(self, data_name, data_item, error_bar_name,
error_bar_item, self.num_history_box.value()))
if len(data_names) == 1:
# If there is only one series, set label/scaling accordingly.
# TODO: Add multiple y axis for additional channels.
c = channels[data_names[0]]
label = c["description"]
if not label:
label = c["path"].split("/")[-1]
setup_axis_item(self.getAxis("left"), label, c["path"], c)
self.series_initialised = True
# FIXME: Phase check will miss points when using mod buffering - need to
# directly read all the data from mods.
phase = d("point_phase")
if phase is not None and phase != self.point_phase:
for s in self.series:
s.append(data)
self.point_phase = phase
def set_history_length(self, n):
for s in self.series:
s.set_history_length(n)
def _install_context_menu(self):
self.num_history_box = QtWidgets.QSpinBox()
self.num_history_box.setMinimum(1)
self.num_history_box.setMaximum(2**16)
self.num_history_box.setValue(100)
self.num_history_box.valueChanged.connect(self.set_history_length)
container = QtWidgets.QWidget()
layout = QtWidgets.QHBoxLayout()
container.setLayout(layout)
label = QtWidgets.QLabel("N: ")
layout.addWidget(label)
layout.addWidget(self.num_history_box)
action = QtWidgets.QWidgetAction(self)
action.setDefaultWidget(container)
separator = QtWidgets.QAction("", self)
separator.setSeparator(True)
entries = [action, separator]
self.plotItem.getContextMenus = lambda ev: entries
class SinglePointModel(Model):
point_changed = QtCore.pyqtSignal(object)
def get_point(self) -> Union[None, Dict[str, Any]]:
raise NotImplementedError
class Image2DPlotWidget(pyqtgraph.PlotWidget):
error = QtCore.pyqtSignal(str)
def __init__(self, x_schema, y_schema, set_dataset):
super().__init__()
self.x_schema = x_schema
self.y_schema = y_schema
self.set_dataset = set_dataset
self.plot = None
def setup_axis(schema, location):
path = schema["path"]
if not path:
path = "/"
identity_string = schema["param"]["fqn"] + "@" + path
return setup_axis_item(self.getAxis(location),
schema["param"]["description"],
identity_string, schema["param"]["spec"])
self.x_unit_suffix, self.x_data_to_display_scale = \
setup_axis(x_schema, "bottom")
self.y_unit_suffix, self.y_data_to_display_scale = \
setup_axis(y_schema, "left")
self.crosshair = LabeledCrosshairCursor(self, self, self.x_unit_suffix,
self.x_data_to_display_scale,
self.y_unit_suffix,
self.y_data_to_display_scale)
self.showGrid(x=True, y=True)
def data_changed(self, datasets, mods):
def d(name):
return datasets.get("ndscan." + name, (False, None))[1]
if not self.plot:
channels_json = d("channels")
if not channels_json:
return
channels = json.loads(channels_json)
try:
data_names, _ = extract_scalar_channels(channels)
except ValueError as e:
self.error.emit(str(e))
if not data_names:
self.error.emit("No scalar result channels to display")
hints_for_channels = {
name: channels[name].get("display_hints", {})
for name in data_names
}
self._install_context_menu(data_names)
def bounds(schema):
return (schema.get(n, None)
for n in ("min", "max", "increment"))
image_item = pyqtgraph.ImageItem()
self.addItem(image_item)
self.plot = _ImagePlot(image_item, data_names[0],
*bounds(self.x_schema),
*bounds(self.y_schema), hints_for_channels)
self.plot.data_changed(datasets)
def _install_context_menu(self, data_names):
entries = []
x_datasets = extract_linked_datasets(self.x_schema["param"])
y_datasets = extract_linked_datasets(self.y_schema["param"])
for d, axis in chain(zip(x_datasets, repeat("x")),
zip(y_datasets, repeat("y"))):
action = QtWidgets.QAction("Set '{}' from crosshair".format(d),
self)
action.triggered.connect(
lambda *a, d=d: self._set_dataset_from_crosshair(d, axis))
entries.append(action)
if len(x_datasets) == 1 and len(y_datasets) == 1:
action = QtWidgets.QAction("Set both from crosshair".format(d),
self)
def set_both():
self._set_dataset_from_crosshair(x_datasets[0], "x")
self._set_dataset_from_crosshair(y_datasets[0], "y")
action.triggered.connect(set_both)
entries.append(action)
def append_separator():
separator = QtWidgets.QAction("", self)
separator.setSeparator(True)
entries.append(separator)
if entries:
append_separator()
self.channel_menu_group = QtWidgets.QActionGroup(self)
first_action = None
for name in data_names:
action = QtWidgets.QAction(name, self)
if not first_action:
first_action = action
action.setCheckable(True)
action.setActionGroup(self.channel_menu_group)
action.triggered.connect(
lambda *a, name=name: self.plot.activate_channel(name))
entries.append(action)
if first_action:
first_action.setChecked(True)
append_separator()
self.plotItem.getContextMenus = lambda ev: entries
def _set_dataset_from_crosshair(self, dataset, axis):
if not self.crosshair:
logger.warning(
"Plot not initialised yet, ignoring set dataset request")
return
self.set_dataset(
dataset,
self.crosshair.last_x if axis == "x" else self.crosshair.last_y)
class XY1DPlotWidget(SubplotMenuPlotWidget):
error = QtCore.pyqtSignal(str)
ready = QtCore.pyqtSignal()
def __init__(self, model: ScanModel, get_alternate_plot_names):
super().__init__(model.context, get_alternate_plot_names)
self.model = model
self.model.channel_schemata_changed.connect(self._initialise_series)
self.model.points_appended.connect(self._update_points)
self.model.annotations_changed.connect(self._update_annotations)
# FIXME: Just re-set values instead of throwing away everything.
def rewritten(points):
self._initialise_series(self.model.get_channel_schemata())
self._update_points(points)
self.model.points_rewritten.connect(rewritten)
self.selected_point_model = SelectPointFromScanModel(self.model)
self.annotation_items = []
self.series = []
x_schema = self.model.axes[0]
self.x_param_spec = x_schema["param"]["spec"]
self.x_unit_suffix, self.x_data_to_display_scale = setup_axis_item(
self.getAxis("bottom"),
[(x_schema["param"]["description"],
format_param_identity(x_schema), None, self.x_param_spec)])
self.y_unit_suffix = None
self.y_data_to_display_scale = None
self.crosshair = None
self._highlighted_spot = None
self.showGrid(x=True, y=True)
view_box = self.getPlotItem().getViewBox()
self.source_label = add_source_id_label(view_box, self.model.context)
view_box.scene().sigMouseClicked.connect(self._handle_scene_click)
def _initialise_series(self, channels):
# Remove all currently shown items and any extra axes added.
for s in self.series:
s.remove_items()
self.series.clear()
self._clear_annotations()
self.reset_y_axes()
try:
data_names, error_bar_names = extract_scalar_channels(channels)
except ValueError as e:
self.error.emit(str(e))
return
series_idx = 0
axes = group_channels_into_axes(channels, data_names)
for names in axes:
axis, view_box = self.new_y_axis()
info = []
for name in names:
color = SERIES_COLORS[series_idx % len(SERIES_COLORS)]
data_item = pyqtgraph.ScatterPlotItem(pen=None,
brush=color,
size=6)
data_item.sigClicked.connect(self._point_clicked)
error_bar_item = None
error_bar_name = error_bar_names.get(name, None)
if error_bar_name:
error_bar_item = pyqtgraph.ErrorBarItem(pen=color)
self.series.append(
_XYSeries(view_box, name, data_item, error_bar_name,
error_bar_item, False))
channel = channels[name]
label = channel["description"]
if not label:
label = channel["path"].split("/")[-1]
info.append((label, channel["path"], color, channel))
series_idx += 1
suffix, scale = setup_axis_item(axis, info)
if self.y_unit_suffix is None:
# FIXME: Add multiple lines to the crosshair.
self.y_unit_suffix = suffix
self.y_data_to_display_scale = scale
if self.crosshair is None:
# FIXME: Reinitialise crosshair as necessary on schema changes.
self.crosshair = LabeledCrosshairCursor(
self, self.getPlotItem(), self.x_unit_suffix,
self.x_data_to_display_scale, self.y_unit_suffix,
self.y_data_to_display_scale)
self.subscan_roots = create_subscan_roots(self.selected_point_model)
# Make sure we put back annotations (if they haven't changed but the points
# have been rewritten, there might not be an annotations_changed event).
self._update_annotations()
self.ready.emit()
def _update_points(self, points):
x_data = points["axis_0"]
# Compare length to zero instead of using `not x_data` for NumPy array
# compatibility.
if len(x_data) == 0:
return
for s in self.series:
s.update(x_data, points)
def _clear_annotations(self):
for item in self.annotation_items:
item.remove()
self.annotation_items.clear()
def _update_annotations(self):
self._clear_annotations()
def channel_ref_to_series_idx(ref):
for i, s in enumerate(self.series):
if "channel_" + s.data_name == ref:
return i
return 0
def make_curve_item(series_idx):
color = FIT_COLORS[series_idx % len(FIT_COLORS)]
pen = pyqtgraph.mkPen(color, width=3)
return pyqtgraph.PlotCurveItem(pen=pen)
annotations = self.model.get_annotations()
for a in annotations:
if a.kind == "location":
if set(a.coordinates.keys()) == set(["axis_0"]):
associated_series_idx = max(
channel_ref_to_series_idx(chan) for chan in
a.parameters.get("associated_channels", [None]))
color = FIT_COLORS[associated_series_idx % len(FIT_COLORS)]
vb = self.series[associated_series_idx].view_box
line = VLineItem(a.coordinates["axis_0"],
a.data.get("axis_0_error", None), vb,
color, self.x_data_to_display_scale,
self.x_unit_suffix)
self.annotation_items.append(line)
continue
if a.kind == "curve":
associated_series_idx = None
for series_idx, series in enumerate(self.series):
match_coords = set(
["axis_0", "channel_" + series.data_name])
if set(a.coordinates.keys()) == match_coords:
associated_series_idx = series_idx
break
if associated_series_idx is not None:
curve = make_curve_item(associated_series_idx)
series = self.series[associated_series_idx]
vb = series.view_box
item = CurveItem(
a.coordinates["axis_0"],
a.coordinates["channel_" + series.data_name], vb,
curve)
self.annotation_items.append(item)
continue
if a.kind == "computed_curve":
function_name = a.parameters.get("function_name", None)
if ComputedCurveItem.is_function_supported(function_name):
associated_series_idx = max(
channel_ref_to_series_idx(chan) for chan in
a.parameters.get("associated_channels", [None]))
x_limits = [
self.x_param_spec.get(n, None) for n in ("min", "max")
]
curve = make_curve_item(associated_series_idx)
vb = self.series[associated_series_idx].view_box
item = ComputedCurveItem(function_name, a.data, vb, curve,
x_limits)
self.annotation_items.append(item)
continue
logger.info("Ignoring annotation of kind '%s' with coordinates %s",
a.kind, list(a.coordinates.keys()))
def build_context_menu(self, builder):
x_schema = self.model.axes[0]
if self.model.context.is_online_master():
for d in extract_linked_datasets(x_schema["param"]):
action = builder.append_action(
"Set '{}' from crosshair".format(d))
action.triggered.connect(
lambda: self._set_dataset_from_crosshair_x(d))
builder.ensure_separator()
super().build_context_menu(builder)
def _set_dataset_from_crosshair_x(self, dataset_key):
if not self.crosshair:
logger.warning(
"Plot not initialised yet, ignoring set dataset request")
return
self.model.context.set_dataset(dataset_key, self.crosshair.last_x)
def _highlight_spot(self, spot):
if self._highlighted_spot is not None:
self._highlighted_spot.resetPen()
self._highlighted_spot = None
if spot is not None:
spot.setPen("y", width=2)
self._highlighted_spot = spot
def _point_clicked(self, scatter_plot_item, spot_items):
if not spot_items:
# No points clicked – events don't seem to emitted in this case anyway.
self._background_clicked()
return
# Arbitrarily choose the first element in the list if multiple spots
# overlap; the user can always zoom in if that is undesired.
spot = spot_items[0]
self._highlight_spot(spot)
self.selected_point_model.set_source_index(spot.index())
def _background_clicked(self):
self._highlight_spot(None)
self.selected_point_model.set_source_index(None)
def _handle_scene_click(self, event):
if not event.isAccepted():
# Event not handled yet, so background/… was clicked instead of a point.
self._background_clicked()
class Image2DPlotWidget(AlternateMenuPlotWidget):
error = QtCore.pyqtSignal(str)
ready = QtCore.pyqtSignal()
def __init__(self, model: ScanModel, get_alternate_plot_names):
super().__init__(get_alternate_plot_names)
self.model = model
self.model.channel_schemata_changed.connect(self._initialise_series)
self.model.points_appended.connect(
lambda p: self._update_points(p, False))
self.model.points_rewritten.connect(
lambda p: self._update_points(p, True))
self.data_names = []
self.x_schema, self.y_schema = self.model.axes
self.plot = None
def setup_axis(schema, location):
param = schema["param"]
return setup_axis_item(
self.getAxis(location),
[(param["description"], format_param_identity(schema), None,
param["spec"])])
self.x_unit_suffix, self.x_data_to_display_scale = \
setup_axis(self.x_schema, "bottom")
self.y_unit_suffix, self.y_data_to_display_scale = \
setup_axis(self.y_schema, "left")
self.crosshair = LabeledCrosshairCursor(self, self.getPlotItem(),
self.x_unit_suffix,
self.x_data_to_display_scale,
self.y_unit_suffix,
self.y_data_to_display_scale)
self.showGrid(x=True, y=True)
self.source_label = add_source_id_label(
self.getPlotItem().getViewBox(), self.model.context)
def _initialise_series(self, channels):
if self.plot is not None:
self.removeItem(self.plot.image_item)
self.plot = None
try:
self.data_names, _ = extract_scalar_channels(channels)
except ValueError as e:
self.error.emit(str(e))
if not self.data_names:
self.error.emit("No scalar result channels to display")
hints_for_channels = {
name: channels[name].get("display_hints", {})
for name in self.data_names
}
def bounds(schema):
return (schema.get(n, None) for n in ("min", "max", "increment"))
image_item = pyqtgraph.ImageItem()
self.addItem(image_item)
self.plot = _ImagePlot(image_item, self.data_names[0],
*bounds(self.x_schema), *bounds(self.y_schema),
hints_for_channels)
self.ready.emit()
def _update_points(self, points, invalidate):
if self.plot:
self.plot.data_changed(points, invalidate_previous=invalidate)
def build_context_menu(self, builder):
if self.model.context.is_online_master():
x_datasets = extract_linked_datasets(self.x_schema["param"])
y_datasets = extract_linked_datasets(self.y_schema["param"])
for d, axis in chain(zip(x_datasets, repeat("x")),
zip(y_datasets, repeat("y"))):
action = builder.append_action(
"Set '{}' from crosshair".format(d))
action.triggered.connect(lambda *a, axis=axis, d=d: (
self._set_dataset_from_crosshair(d, axis)))
if len(x_datasets) == 1 and len(y_datasets) == 1:
action = builder.append_action("Set both from crosshair")
def set_both():
self._set_dataset_from_crosshair(x_datasets[0], "x")
self._set_dataset_from_crosshair(y_datasets[0], "y")
action.triggered.connect(set_both)
builder.ensure_separator()
self.channel_menu_group = QtWidgets.QActionGroup(self)
for name in self.data_names:
action = builder.append_action(name)
action.setCheckable(True)
action.setActionGroup(self.channel_menu_group)
action.setChecked(name == self.plot.active_channel_name)
action.triggered.connect(
lambda *a, name=name: self.plot.activate_channel(name))
builder.ensure_separator()
super().build_context_menu(builder)
def _set_dataset_from_crosshair(self, dataset, axis):
if not self.crosshair:
logger.warning(
"Plot not initialised yet, ignoring set dataset request")
return
self.model.context.set_dataset(
dataset,
self.crosshair.last_x if axis == "x" else self.crosshair.last_y)
class Rolling1DPlotWidget(AlternateMenuPlotWidget):
error = QtCore.pyqtSignal(str)
ready = QtCore.pyqtSignal()
def __init__(self, model: SinglePointModel, get_alternate_plot_names):
super().__init__(get_alternate_plot_names)
self.model = model
self.model.channel_schemata_changed.connect(self._initialise_series)
self.model.point_changed.connect(self._append_point)
self.series = []
self._history_length = 1024
self.showGrid(x=True, y=True)
self.source_label = add_source_id_label(
self.getPlotItem().getViewBox(), self.model.context)
def _initialise_series(self):
for s in self.series:
s.remove_items()
self.series.clear()
channels = self.model.get_channel_schemata()
try:
data_names, error_bar_names = extract_scalar_channels(channels)
except ValueError as e:
self.error.emit(str(e))
return
series_idx = 0
axes = group_channels_into_axes(channels, data_names)
for names in axes:
axis, view_box = self.new_y_axis()
info = []
for name in names:
color = SERIES_COLORS[series_idx % len(SERIES_COLORS)]
data_item = pyqtgraph.ScatterPlotItem(pen=None,
brush=color,
size=6)
error_bar_item = None
error_bar_name = error_bar_names.get(name, None)
if error_bar_name:
error_bar_item = pyqtgraph.ErrorBarItem(pen=color)
self.series.append(
_Series(view_box, name, data_item, error_bar_name,
error_bar_item, self._history_length))
channel = channels[name]
label = channel["description"]
if not label:
label = channel["path"].split("/")[-1]
info.append((label, channel["path"], color, channel))
series_idx += 1
setup_axis_item(axis, info)
self.ready.emit()
def _append_point(self, point):
for s in self.series:
s.append(point)
def set_history_length(self, n):
self._history_length = n
for s in self.series:
s.set_history_length(n)
def build_context_menu(self, builder):
if self.model.context.is_online_master():
# If no new data points are coming in, setting the history size wouldn't do
# anything.
# TODO: is_online_master() should really be something like
# SinglePointModel.ever_updates().
num_history_box = QtWidgets.QSpinBox()
num_history_box.setMinimum(1)
num_history_box.setMaximum(2**16)
num_history_box.setValue(self._history_length)
num_history_box.valueChanged.connect(self.set_history_length)
container = QtWidgets.QWidget()
layout = QtWidgets.QHBoxLayout()
container.setLayout(layout)
label = QtWidgets.QLabel("N: ")
layout.addWidget(label)
layout.addWidget(num_history_box)
action = builder.append_widget_action()
action.setDefaultWidget(container)
builder.ensure_separator()
super().build_context_menu(builder)
class ScanModel(Model):
points_rewritten = QtCore.pyqtSignal(dict)
points_appended = QtCore.pyqtSignal(dict)
annotations_changed = QtCore.pyqtSignal(list)
def __init__(self, axes: List[Dict[str, Any]], schema_revision: int,
context: Context):
super().__init__(schema_revision, context)
self.axes = axes
self._annotations = []
self._annotation_schemata = []
self._online_analyses = {}
def get_point_data(self) -> Dict[str, Any]:
raise NotImplementedError
def get_annotations(self) -> List[Annotation]:
return self._annotations
def get_analysis_result_source(
self, name: str) -> Optional[AnnotationDataSource]:
raise NotImplementedError
#
# TODO: Having these as elaborate implementation in the base class leaves a bit of a
# bad aftertaste, although it's slightly hard to qualify why it should be bad
# design.
#
def _set_annotation_schemata(self, schemata: List[Dict[str, Any]]):
"""Replace annotations with ones created according to the given schemata.
This will be called by concrete subclasses once/whenever they have received the
annotation metadata.
"""
self._annotation_schemata = schemata
self._annotations = []
def data_source(spec):
kind = spec["kind"]
if kind == "fixed":
return FixedDataSource(spec["value"])
# `online_result` was called `analysis_result` prior to revision 2, with
# identical semantics; analysis results proper didn't exit.
if kind == "online_result" or (self.schema_revision < 2
and kind == "analysis_result"):
analysis = self._online_analyses.get(spec["analysis_name"],
None)
if analysis is None:
return None
return OnlineAnalysisDataSource(analysis, spec["result_key"])
if kind == "analysis_result":
name = spec["name"]
source = self.get_analysis_result_source(name)
if source is None:
logger.info("Analysis result data source not found: %s",
name)
return source
logger.info(
"Ignoring unsupported annotation data source type: '%s'", kind)
return None
def to_data_sources(specs):
return {k: data_source(v) for k, v in specs.items()}
for schema in schemata:
sources = [
to_data_sources(schema.get(n)) for n in ("coordinates", "data")
]
if any(s is None for t in sources for s in t.values()):
logger.warning("Ignoring analysis, not all data found: %s",
schema)
continue
self._annotations.append(
Annotation(schema["kind"], schema.get("parameters", {}),
*sources))
self.annotations_changed.emit(self._annotations)
def _set_online_analyses(
self, analysis_schemata: Dict[str, Dict[str, Any]]) -> None:
"""Create and hook up online analyses from the given schema.
This will be called by concrete subclasses once/whenever they have received
the schema metadata.
"""
for a in self._online_analyses.values():
a.stop()
self._online_analyses = {}
for name, schema in analysis_schemata.items():
kind = schema["kind"]
if kind == "named_fit":
self._online_analyses[name] = OnlineNamedFitAnalysis(
schema, self)
else:
logger.warning(
"Ignoring unsupported online analysis type: '%s'", kind)
# Rebind annotation schemata to new analysis data sources.
self._set_annotation_schemata(self._annotation_schemata)
class XY1DPlotWidget(pyqtgraph.PlotWidget):
error = QtCore.pyqtSignal(str)
def __init__(self, x_schema, set_dataset):
super().__init__()
self.set_dataset = set_dataset
self.series_initialised = False
self.series = []
path = x_schema["path"]
if not path:
path = "/"
identity_string = x_schema["param"]["fqn"] + "@" + path
self.x_unit_suffix, self.x_data_to_display_scale = setup_axis_item(
self.getAxis("bottom"), x_schema["param"]["description"],
identity_string, x_schema["param"]["spec"])
self._install_context_menu(x_schema)
self.crosshair = None
self.showGrid(x=True, y=True)
def data_changed(self, data, mods):
def d(name):
return data.get("ndscan." + name, (False, None))[1]
if not self.series_initialised:
channels_json = d("channels")
if not channels_json:
return
channels = json.loads(channels_json)
try:
data_names, error_bar_names = extract_scalar_channels(channels)
except ValueError as e:
self.error.emit(str(e))
# KLUDGE: We rely on fit specs to be set before channels in order
# for them to be displayed at all.
fit_specs = json.loads(d("auto_fit") or "[]")
for i, name in enumerate(data_names):
color = SERIES_COLORS[i % len(SERIES_COLORS)]
data_item = pyqtgraph.ScatterPlotItem(pen=None,
brush=color,
size=5)
error_bar_name = error_bar_names.get(name, None)
error_bar_item = pyqtgraph.ErrorBarItem(
pen=color) if error_bar_name else None
# TODO: Multiple fit specs, error bars from other channels.
fit_spec = None
fit_item = None
fit_pois = []
for spec in fit_specs:
if spec["data"]["x"] != "axis_0":
continue
if spec["data"]["y"] != "channel_" + name:
continue
e = spec["data"].get("y_err", None)
if e and e != ("channel_" + error_bar_name):
continue
fit_spec = spec
fit_color = FIT_COLORS[i % len(FIT_COLORS)]
pen = pyqtgraph.mkPen(fit_color, width=3)
fit_item = pyqtgraph.PlotCurveItem(pen=pen)
for p in spec.get("pois", []):
# TODO: Support horizontal lines, points, ...
if p.get("x", None):
fit_pois.append(
_VLineFitPOI(p["x"], fit_color,
self.x_data_to_display_scale,
self.x_unit_suffix))
break
self.series.append(
_XYSeries(self, name, data_item, error_bar_name,
error_bar_item, False, fit_spec, fit_item,
fit_pois))
if len(data_names) == 1:
# If there is only one series, set label/scaling accordingly.
# TODO: Add multiple y axis for additional channels.
c = channels[data_names[0]]
label = c["description"]
if not label:
label = c["path"].split("/")[-1]
# TODO: Change result channel schema and move properties accessed here
# into "spec" field to match parameters?
self.y_unit_suffix, self.y_data_to_display_scale = setup_axis_item(
self.getAxis("left"), label, c["path"], c)
else:
self.y_unit_suffix = ""
self.y_data_to_display_scale = 1.0
self.crosshair = LabeledCrosshairCursor(
self, self, self.x_unit_suffix, self.x_data_to_display_scale,
self.y_unit_suffix, self.y_data_to_display_scale)
self.series_initialised = True
x_data = d("points.axis_0")
if not x_data:
return
for s in self.series:
s.update(x_data, data)
def _install_context_menu(self, x_schema):
entries = []
for d in extract_linked_datasets(x_schema["param"]):
action = QtWidgets.QAction("Set '{}' from crosshair".format(d),
self)
action.triggered.connect(
lambda: self._set_dataset_from_crosshair_x(d))
entries.append(action)
if entries:
separator = QtWidgets.QAction("", self)
separator.setSeparator(True)
entries.append(separator)
self.plotItem.getContextMenus = lambda ev: entries
def _set_dataset_from_crosshair_x(self, dataset):
if not self.crosshair:
logger.warning(
"Plot not initialised yet, ignoring set dataset request")
return
self.set_dataset(dataset, self.crosshair.last_x)
class Root(QtCore.QObject):
model_changed = QtCore.pyqtSignal(object)
def get_model(self) -> Union["Model", None]:
raise NotImplementedError
class XY1DPlotWidget(SubplotMenuPlotWidget):
error = QtCore.pyqtSignal(str)
ready = QtCore.pyqtSignal()
def __init__(self, model: ScanModel, get_alternate_plot_names):
super().__init__(model.context, get_alternate_plot_names)
self.model = model
self.model.channel_schemata_changed.connect(self._initialise_series)
self.model.points_appended.connect(self._update_points)
self.model.annotations_changed.connect(self._update_annotations)
# FIXME: Just re-set values instead of throwing away everything.
def rewritten(points):
self._initialise_series(self.model.get_channel_schemata())
self._update_points(points)
self.model.points_rewritten.connect(rewritten)
self.selected_point_model = SelectPointFromScanModel(self.model)
self.subscan_roots = {}
self.annotation_items = []
self.series = []
x_schema = self.model.axes[0]
path = x_schema["path"]
if not path:
path = "/"
identity_string = x_schema["param"]["fqn"] + "@" + path
self.x_unit_suffix, self.x_data_to_display_scale = setup_axis_item(
self.getAxis("bottom"), [(x_schema["param"]["description"], identity_string,
None, x_schema["param"]["spec"])])
self.crosshair = None
self._highlighted_spot = None
self.showGrid(x=True, y=True)
self.getPlotItem().getViewBox().scene().sigMouseClicked.connect(
self._handle_scene_click)
def _initialise_series(self, channels):
for s in self.series:
s.remove_items()
self.series.clear()
try:
data_names, error_bar_names = extract_scalar_channels(channels)
except ValueError as e:
self.error.emit(str(e))
return
colors = [SERIES_COLORS[i % len(SERIES_COLORS)] for i in range(len(data_names))]
for i, (name, color) in enumerate(zip(data_names, colors)):
data_item = pyqtgraph.ScatterPlotItem(pen=None, brush=color, size=6)
data_item.sigClicked.connect(self._point_clicked)
error_bar_name = error_bar_names.get(name, None)
error_bar_item = pyqtgraph.ErrorBarItem(
pen=color) if error_bar_name else None
self.series.append(
_XYSeries(self, name, data_item, error_bar_name, error_bar_item, False))
# If there is only one series, set unit/scale accordingly.
# TODO: Add multiple y axes for additional channels.
def axis_info(i):
c = channels[data_names[i]]
label = c["description"]
if not label:
label = c["path"].split("/")[-1]
return label, c["path"], colors[i], c
self.y_unit_suffix, self.y_data_to_display_scale = setup_axis_item(
self.getAxis("left"), [axis_info(i) for i in range(len(data_names))])
if self.crosshair is None:
# FIXME: Reinitialise crosshair as necessary on schema changes.
self.crosshair = LabeledCrosshairCursor(
self, self.getPlotItem(), self.x_unit_suffix,
self.x_data_to_display_scale, self.y_unit_suffix,
self.y_data_to_display_scale)
self.subscan_roots = create_subscan_roots(self.selected_point_model)
self.ready.emit()
def _update_points(self, points):
x_data = points["axis_0"]
# Compare length to zero instead of using `not x_data` for NumPy array
# compatibility.
if len(x_data) == 0:
return
for s in self.series:
s.update(x_data, points)
def _update_annotations(self):
for item in self.annotation_items:
item.remove()
self.annotation_items.clear()
def series_idx(ref):
for i, s in enumerate(self.series):
if "channel_" + s.data_name == ref:
return i
return 0
def make_curve_item(series_idx):
color = FIT_COLORS[series_idx % len(FIT_COLORS)]
pen = pyqtgraph.mkPen(color, width=3)
return pyqtgraph.PlotCurveItem(pen=pen)
annotations = self.model.get_annotations()
for a in annotations:
if a.kind == "location":
if set(a.coordinates.keys()) == set(["axis_0"]):
idx = max(
series_idx(chan)
for chan in a.parameters.get("associated_channels", [None]))
color = FIT_COLORS[idx % len(FIT_COLORS)]
line = VLineItem(a.coordinates["axis_0"],
a.data.get("axis_0_error",
None), self.getPlotItem(), color,
self.x_data_to_display_scale, self.x_unit_suffix)
self.annotation_items.append(line)
continue
if a.kind == "curve":
idx = None
for i, s in enumerate(self.series):
match_coords = set(["axis_0", "channel_" + s.data_name])
if set(a.coordinates.keys()) == match_coords:
idx = i
break
if idx is not None:
curve = make_curve_item(idx)
item = CurveItem(a.coordinates["axis_0"],
a.coordinates["channel_" + s.data_name],
self.getPlotItem(), curve)
self.annotation_items.append(item)
continue
if a.kind == "computed_curve":
function_name = a.parameters.get("function_name", None)
if ComputedCurveItem.is_function_supported(function_name):
idx = max(
series_idx(chan)
for chan in a.parameters.get("associated_channels", []))
curve = make_curve_item(idx)
item = ComputedCurveItem(function_name, a.data, self.getPlotItem(),
curve)
self.annotation_items.append(item)
continue
logger.info("Ignoring annotation of kind '%s' with coordinates %s", a.kind,
list(a.coordinates.keys()))
def build_context_menu(self, builder):
x_schema = self.model.axes[0]
if self.model.context.is_online_master():
for d in extract_linked_datasets(x_schema["param"]):
action = builder.append_action("Set '{}' from crosshair".format(d))
action.triggered.connect(lambda: self._set_dataset_from_crosshair_x(d))
builder.ensure_separator()
super().build_context_menu(builder)
def _set_dataset_from_crosshair_x(self, dataset_key):
if not self.crosshair:
logger.warning("Plot not initialised yet, ignoring set dataset request")
return
self.model.context.set_dataset(dataset_key, self.crosshair.last_x)
def _highlight_spot(self, spot):
if self._highlighted_spot is not None:
self._highlighted_spot.resetPen()
self._highlighted_spot = None
if spot is not None:
spot.setPen("y", width=2)
self._highlighted_spot = spot
def _point_clicked(self, scatter_plot_item, spot_items):
if not spot_items:
# No points clicked – events don't seem to emitted in this case anyway.
self._background_clicked()
return
# Arbitrarily choose the first element in the list if multiple spots
# overlap; the user can always zoom in if that is undesired.
spot = spot_items[0]
self._highlight_spot(spot)
self.selected_point_model.set_source_index(spot.index())
def _background_clicked(self):
self._highlight_spot(None)
self.selected_point_model.set_source_index(None)
def _handle_scene_click(self, event):
if not event.isAccepted():
# Event not handled yet, so background/… was clicked instead of a point.
self._background_clicked()
class OnlineAnalysis(QtCore.QObject):
updated = QtCore.pyqtSignal()
def stop(self):
pass
class OnlineNamedFitAnalysis(OnlineAnalysis):
_trigger_recompute_fit = QtCore.pyqtSignal()
def __init__(self, schema: Dict[str, Any], parent_model):
super().__init__()
self._schema = schema
self._model = parent_model
self._fit_type = self._schema["fit_type"]
self._fit_obj = FIT_OBJECTS[self._fit_type]
self._last_fit_params = None
self._last_fit_errors = None
self._recompute_fit_limiter = SignalProxy(
self._trigger_recompute_fit,
slot=lambda: asyncio.ensure_future(self._recompute_fit()),
rateLimit=30)
self._recompute_in_progress = False
self._fit_executor = ProcessPoolExecutor(max_workers=1)
self._model.points_rewritten.connect(self._update)
self._model.points_appended.connect(self._update)
self._update()
def stop(self):
self._model.points_rewritten.disconnect(self._update)
self._model.points_appended.disconnect(self._update)
self._fit_executor.shutdown(wait=False)
def get_data(self):
if self._last_fit_params is None:
return {}
result = self._last_fit_params.copy()
for key, value in self._last_fit_errors.items():
error_key = key + "_error"
if error_key in result:
raise ValueError(
"Fit error key name collides with result: ''".format(
error_key))
result[error_key] = value
return result
def _update(self):
data = self._model.get_point_data()
self._source_data = {}
for param_key, source_key in self._schema["data"].items():
self._source_data[param_key] = data.get(source_key, [])
num_points = min(len(v) for v in self._source_data.values())
if num_points < len(self._fit_obj.parameter_names):
return
for key, value in self._source_data.items():
self._source_data[key] = value[:num_points]
self._trigger_recompute_fit.emit()
async def _recompute_fit(self):
if self._recompute_in_progress:
# Run at most one fit computation at a time. To make sure we don't
# leave a few final data points completely disregarded, just
# re-emit the signal – even for long fits, repeated checks aren't
# expensive, as long as the SignalProxy rate is slow enough.
self._trigger_recompute_fit.emit()
return
self._recompute_in_progress = True
# oitg.fitting currently only supports 1D fits, but this could/should be
# changed.
xs = self._source_data["x"]
ys = self._source_data["y"]
y_errs = self._source_data.get("y_err", None)
loop = asyncio.get_event_loop()
self._last_fit_params, self._last_fit_errors = await loop.run_in_executor(
self._fit_executor, _run_fit, self._fit_type, xs, ys, y_errs)
self._recompute_in_progress = False
self.updated.emit()
class Rolling1DPlotWidget(AlternateMenuPlotWidget):
error = QtCore.pyqtSignal(str)
ready = QtCore.pyqtSignal()
alternate_plot_requested = QtCore.pyqtSignal(str)
def __init__(self, model: SinglePointModel, get_alternate_plot_names):
super().__init__(get_alternate_plot_names)
self.model = model
self.model.channel_schemata_changed.connect(self._initialise_series)
self.model.point_changed.connect(self._append_point)
self.series = []
self._history_length = 1024
self.showGrid(x=True, y=True)
def _initialise_series(self):
for s in self.series:
s.remove_items()
self.series.clear()
channels = self.model.get_channel_schemata()
try:
data_names, error_bar_names = extract_scalar_channels(channels)
except ValueError as e:
self.error.emit(str(e))
return
colors = [
SERIES_COLORS[i % len(SERIES_COLORS)]
for i in range(len(data_names))
]
for i, (data_name, color) in enumerate(zip(data_names, colors)):
data_item = pyqtgraph.ScatterPlotItem(pen=None, brush=color)
error_bar_name = error_bar_names.get(data_name, None)
error_bar_item = pyqtgraph.ErrorBarItem(
pen=color) if error_bar_name else None
self.series.append(
_Series(self, data_name, data_item, error_bar_name,
error_bar_item, self._history_length))
def axis_info(i):
# If there is only one series, set label/scaling accordingly.
# TODO: Add multiple y axis for additional channels.
c = channels[data_names[i]]
label = c["description"]
if not label:
label = c["path"].split("/")[-1]
return label, c["path"], colors[i], c
setup_axis_item(self.getAxis("left"),
[axis_info(i) for i in range(len(data_names))])
self.ready.emit()
def _append_point(self, point):
for s in self.series:
s.append(point)
def set_history_length(self, n):
self._history_length = n
for s in self.series:
s.set_history_length(n)
def build_context_menu(self, builder):
if self.model.context.is_online_master():
# If no new data points are coming in, setting the history size wouldn't do
# anything.
# TODO: is_online_master() should really be something like
# SinglePointModel.ever_updates().
num_history_box = QtWidgets.QSpinBox()
num_history_box.setMinimum(1)
num_history_box.setMaximum(2**16)
num_history_box.setValue(self._history_length)
num_history_box.valueChanged.connect(self.set_history_length)
container = QtWidgets.QWidget()
layout = QtWidgets.QHBoxLayout()
container.setLayout(layout)
label = QtWidgets.QLabel("N: ")
layout.addWidget(label)
layout.addWidget(num_history_box)
action = builder.append_widget_action()
action.setDefaultWidget(container)
builder.ensure_separator()
super().build_context_menu(builder)
class OnlineNamedFitAnalysis(OnlineAnalysis):
"""Implements :class:`ndscan.experiment.default_analysis.OnlineFit`, that is, a fit
of a well-known function that is executed repeatedly as new data is coming in.
:param schema: The ``ndscan.online_analyses`` schema to implement.
:param parent_model: The :class:`~ndscan.plots.model.ScanModel` to draw the data
from. The schema is notexpected not to change until :meth:`stop` is called.
"""
_trigger_recompute_fit = QtCore.pyqtSignal()
def __init__(self, schema: Dict[str, Any], parent_model):
super().__init__()
self._schema = schema
self._model = parent_model
self._fit_type = self._schema["fit_type"]
self._fit_obj = FIT_OBJECTS[self._fit_type]
self._constants = self._schema.get("constants", {})
self._initial_values = self._schema.get("initial_values", {})
self._last_fit_params = None
self._last_fit_errors = None
self._recompute_fit_limiter = SignalProxy(
self._trigger_recompute_fit,
slot=lambda: asyncio.ensure_future(self._recompute_fit()),
rateLimit=30)
self._recompute_in_progress = False
self._fit_executor = ProcessPoolExecutor(max_workers=1)
self._model.points_rewritten.connect(self._update)
self._model.points_appended.connect(self._update)
self._update()
def stop(self):
self._model.points_rewritten.disconnect(self._update)
self._model.points_appended.disconnect(self._update)
self._fit_executor.shutdown(wait=False)
def get_data(self):
if self._last_fit_params is None:
return {}
result = self._last_fit_params.copy()
for key, value in self._last_fit_errors.items():
error_key = key + "_error"
if error_key in result:
raise ValueError(
"Fit error key name collides with result: '{}'".format(
error_key))
result[error_key] = value
return result
def _update(self):
data = self._model.get_point_data()
self._source_data = {}
for param_key, source_key in self._schema["data"].items():
self._source_data[param_key] = data.get(source_key, [])
# Truncate the source data to a complete set of points.
num_points = min(len(v) for v in self._source_data.values())
if num_points < len(self._fit_obj.parameter_names):
# Not enough points yet for the given number of degrees of freedom.
return
for key, value in self._source_data.items():
self._source_data[key] = value[:num_points]
self._trigger_recompute_fit.emit()
async def _recompute_fit(self):
if self._recompute_in_progress:
# Run at most one fit computation at a time. To make sure we don't
# leave a few final data points completely disregarded, just
# re-emit the signal – even for long fits, repeated checks aren't
# expensive, as long as the SignalProxy rate is slow enough.
self._trigger_recompute_fit.emit()
return
self._recompute_in_progress = True
# oitg.fitting currently only supports 1D fits, but this could/should be
# changed.
xs = self._source_data["x"]
ys = self._source_data["y"]
y_errs = self._source_data.get("y_err", None)
loop = asyncio.get_event_loop()
self._last_fit_params, self._last_fit_errors = await loop.run_in_executor(
self._fit_executor, _run_fit, self._fit_type, xs, ys, y_errs,
self._constants, self._initial_values)
self._recompute_in_progress = False
self.updated.emit()