def show_indexed_timeseries_plotly(timeseries: TimeSeries,
istart=None,
istop=None,
fig: go.FigureWidget = None,
col=None,
row=None,
zero_start=False,
xlabel='time (s)',
ylabel=None,
title=None,
neurodata_vis_spec=None,
**kwargs):
if ylabel is None and timeseries.unit:
ylabel = timeseries.unit
tt = get_timeseries_tt(timeseries, istart=istart, istop=istop)
if zero_start:
tt = tt - tt[0]
data, unit = get_timeseries_in_units(timeseries,
istart=istart,
istop=istop)
trace_kwargs = dict()
if col is not None or row is not None:
trace_kwargs.update(row=row, col=col)
fig.add_trace(x=tt, y=data, **trace_kwargs, **kwargs)
layout_kwargs = dict(xaxis_title=xlabel)
if ylabel is not None:
layout_kwargs.update(yaxis_title=ylabel)
if title is not None:
layout_kwargs.update(title=title)
fig.update_layout(**layout_kwargs)
def show_indexed_timeseries_plotly(
timeseries: TimeSeries,
istart: int = 0,
istop: int = None,
time_window: list = None,
trace_range: list = None,
offsets=None,
fig: go.FigureWidget = None,
col=None,
row=None,
zero_start=False,
scatter_kwargs: dict = None,
figure_kwargs: dict = None,
):
if istart != 0 or istop is not None:
if time_window is not None:
raise ValueError(
"input either time window or istart/stop but not both")
if not (0 <= istart < timeseries.data.shape[0] and
(istop is None or 0 < istop <= timeseries.data.shape[0])):
raise ValueError("enter correct istart/stop values")
t_istart = istart
t_istop = istop
elif time_window is not None:
t_istart = timeseries_time_to_ind(timeseries, time_window[0])
t_istop = timeseries_time_to_ind(timeseries, time_window[1])
else:
t_istart = istart
t_istop = istop
tt = get_timeseries_tt(timeseries, istart=t_istart, istop=t_istop)
data, unit = get_timeseries_in_units(timeseries,
istart=t_istart,
istop=t_istop)
if len(data.shape) == 1:
data = data[:, np.newaxis]
if trace_range is not None:
if not (0 <= trace_range[0] < data.shape[1]
and 0 < trace_range[1] <= data.shape[1]):
raise ValueError("enter correct trace range")
trace_istart = trace_range[0]
trace_istop = trace_range[1]
else:
trace_istart = 0
trace_istop = data.shape[1]
if offsets is None:
offsets = np.zeros(trace_istop - trace_istart)
if zero_start:
tt = tt - tt[0]
scatter_kwargs = dict() if scatter_kwargs is None else scatter_kwargs
if fig is None:
fig = go.FigureWidget(make_subplots(rows=1, cols=1))
row = 1 if row is None else row
col = 1 if col is None else col
for i, trace_id in enumerate(range(trace_istart, trace_istop)):
fig.add_trace(
go.Scattergl(x=tt,
y=data[:, trace_id] + offsets[i],
mode="lines",
**scatter_kwargs),
row=row,
col=col,
)
input_figure_kwargs = dict(
xaxis=dict(title_text="time (s)", range=[tt[0], tt[-1]]),
yaxis=dict(title_text=unit if unit is not None else None),
title=timeseries.name,
)
if figure_kwargs is None:
figure_kwargs = dict()
input_figure_kwargs.update(figure_kwargs)
fig.update_xaxes(input_figure_kwargs.pop("xaxis"), row=row, col=col)
fig.update_yaxes(input_figure_kwargs.pop("yaxis"), row=row, col=col)
fig.update_layout(**input_figure_kwargs)
return fig
class PapayaConfigWidget(VBox):
"""A widget that displays widgets to adjust NLPapayaViewer image parameters."""
lut_options = [
"Grayscale",
"Red Overlay",
"Green Overlay",
"Blue Overlay",
"Gold",
"Spectrum",
"Overlay (Positives)",
"Overlay (Negatives)",
]
def __init__(self, viewer, *args, **kwargs):
"""
Parameters
----------
viewer: NlPapayaViewer
associated viewer.
"""
super().__init__(*args, **kwargs)
self._viewer = viewer
self._init_widgets()
self.children = [
VBox([
VBox(
[self._hist],
layout=Layout(
height="auto",
margin="0px 0px 0px 0px",
padding="5px 5px 5px 5px",
),
),
VBox(
[
self._alpha,
self._lut,
self._nlut,
self._min,
self._minp,
self._max,
self._maxp,
self._sym,
],
layout=Layout(width="230px"),
),
])
]
def _init_widgets(self):
"""Initializes all configuration widgets. Possible image config parameters are:"""
layout = Layout(width="200px", max_width="200px")
self._alpha = FloatSlider(
value=1,
min=0,
max=1.0,
step=0.1,
description="alpha:",
description_tooltip="Overlay image alpha level (0 to 1).",
disabled=False,
continuous_update=True,
orientation="horizontal",
readout=True,
readout_format=".1f",
layout=layout,
)
self._lut = Dropdown(
options=PapayaConfigWidget.lut_options,
value="Red Overlay",
description="lut:",
description_tooltip="The color table name.",
layout=layout,
)
self._nlut = Dropdown(
options=PapayaConfigWidget.lut_options,
value="Red Overlay",
description="negative-lut:",
description_tooltip=
"The color table name used by the negative side of the parametric pair.",
layout=layout,
)
self._min = FloatText(
value=None,
description="min:",
description_tooltip="The display range minimum.",
step=0.01,
continuous_update=True,
disabled=False,
layout=layout,
)
self._minp = BoundedFloatText(
value=None,
min=0,
max=100,
step=1,
continuous_update=True,
description="min %:",
description_tooltip=
"The display range minimum as a percentage of image max.",
disabled=False,
layout=layout,
)
self._max = FloatText(
value=None,
description="max:",
description_tooltip="The display range maximum.",
step=0.01,
continuous_update=True,
disabled=False,
layout=layout,
)
self._maxp = BoundedFloatText(
value=None,
min=0,
max=100,
step=1,
continuous_update=True,
description="max %:",
description_tooltip=
"The display range minimum as a percentage of image max.",
disabled=False,
layout=layout,
)
self._sym = Checkbox(
value=False,
description="symmetric",
description_tooltip=
"When selected, sets the negative range of a parametric pair to the same size as the positive range.",
disabled=False,
layout=layout,
)
# figure to display histogram of image data
fig = Figure()
fig.update_layout(
height=300,
margin=dict(l=15, t=15, b=15, r=15, pad=4),
showlegend=True,
legend_orientation="h",
)
self._hist = FigureWidget(fig)
self._hist.add_trace(
Histogram(x=[], name="All image data", visible="legendonly"))
self._hist.add_trace(Histogram(x=[], name="Image data without 0s"))
self._handlers = defaultdict()
def _set_values(self, config, range, data):
"""Sets config values from the specified `config` and creates histogram for `data`.
Parameters
----------
config : dict
configuration parameters for the image. Possible keywords are:
alpha : int
the overlay image alpha level (0 to 1).
lut : str
the color table name.
negative_lut : str
the color table name used by the negative side of the parametric pair.
max : int
the display range maximum.
maxPercent : int
the display range maximum as a percentage of image max.
min : int
the display range minimum.
minPercent : int
the display range minimum as a percentage of image min.
symmetric : bool
if true, sets the negative range of a parametric pair to the same size as the positive range.
range: float
range of image values.
data: []
flattened image data.
"""
self._alpha.value = config.get("alpha", 1)
self._lut.value = config.get("lut", PapayaConfigWidget.lut_options[1])
self._nlut.value = config.get("negative_lut",
PapayaConfigWidget.lut_options[1])
self._min.value = config.get("min", 0)
self._minp.value = self._get_per_from_value(range,
config.get("min", 0))
self._max.value = config.get("max", 0.1)
self._maxp.value = self._get_per_from_value(range,
config.get("max", 0.1))
self._sym.value = config.get("symmetric", "false") == "true"
# set histogram data
self._hist.data[0].x = data
# leave out 0 values
self._hist.data[1].x = [] if (data == []
or data is None) else data[data != 0]
def _add_handlers(self, image):
"""Add config widget event handlers to change the config values for the specified `image`.
Parameters
----------
image: neurolang_ipywidgets.PapayaImage
image whose config values will be viewed/modified using this config widget.
"""
# Dropdown does not support resetting event handlers after Dropdown.unobserve_all is called
# So handlers are stored to be removed individually
# github issue https://github.com/jupyter-widgets/ipywidgets/issues/1868
self._handlers["alpha"] = partial(self._config_changed,
image=image,
name="alpha")
self._handlers["lut"] = partial(self._config_changed,
image=image,
name="lut")
self._handlers["nlut"] = partial(self._config_changed,
image=image,
name="negative_lut")
self._handlers["min"] = partial(self._config_changed,
image=image,
name="min")
self._handlers["minp"] = partial(self._set_min_max,
image=image,
name="minPercent")
self._handlers["max"] = partial(self._config_changed,
image=image,
name="max")
self._handlers["maxp"] = partial(self._set_min_max,
image=image,
name="maxPercent")
self._handlers["sym"] = partial(self._config_bool_changed,
image=image,
name="symmetric")
self._alpha.observe(self._handlers["alpha"], names="value")
self._lut.observe(self._handlers["lut"], names="value")
self._nlut.observe(self._handlers["nlut"], names="value")
self._min.observe(self._handlers["min"], names="value")
self._minp.observe(self._handlers["minp"], names="value")
self._max.observe(self._handlers["max"], names="value")
self._maxp.observe(self._handlers["maxp"], names="value")
self._sym.observe(self._handlers["sym"], names="value")
def _remove_handlers(self):
"""Removes all event handlers set for the config widgets."""
if len(self._handlers):
self._alpha.unobserve(self._handlers["alpha"], names="value")
self._lut.unobserve(self._handlers["lut"], names="value")
self._nlut.unobserve(self._handlers["nlut"], names="value")
self._min.unobserve(self._handlers["min"], names="value")
self._minp.unobserve(self._handlers["minp"], names="value")
self._max.unobserve(self._handlers["max"], names="value")
self._maxp.unobserve(self._handlers["maxp"], names="value")
self._sym.unobserve(self._handlers["sym"], names="value")
self._handlers = defaultdict()
@debounce(0.5)
def _config_changed(self, change, image, name):
if name == "min":
self._minp.unobserve(self._handlers["minp"], names="value")
self._minp.value = self._get_per_from_value(
image.range, change.new)
self._minp.observe(self._handlers["minp"], names="value")
elif name == "max":
self._maxp.unobserve(self._handlers["maxp"], names="value")
self._maxp.value = self._get_per_from_value(
image.range, change.new)
self._maxp.observe(self._handlers["maxp"], names="value")
self._set_config(image, name, change.new)
@debounce(0.5)
def _set_min_max(self, change, image, name):
if name == "minPercent":
self._min.unobserve(self._handlers["min"], names="value")
self._min.value = self._get_value_from_per(image.range, change.new)
self._set_config(image, "min", self._min.value)
self._min.observe(self._handlers["min"], names="value")
elif name == "maxPercent":
self._max.unobserve(self._handlers["max"], names="value")
self._max.value = self._get_value_from_per(image.range, change.new)
self._set_config(image, "max", self._max.value)
self._max.observe(self._handlers["max"], names="value")
def _config_bool_changed(self, change, image, name):
value = "false"
if change.new:
value = "true"
self._set_config(image, name, value)
def _set_config(self, image, key, value):
image.config[key] = value
self._viewer.set_images()
def _get_per_from_value(self, range, value):
return round(value * 100 / range, 0)
def _get_value_from_per(self, range, per):
return round(per * range / 100, 2)
def set_image(self, image):
"""Sets the image whose config values will be viewed/modified using this config widget.
If image is `None`, all config values are reset.
Parameters
----------
image: neurolang_ipywidgets.PapayaImage
image whose config values will be viewed/modified using this config widget.
"""
if image:
self._remove_handlers()
self._set_values(image.config, image.range,
image.image.get_fdata().flatten())
self._add_handlers(image)
else:
self.reset()
def reset(self):
"""Resets values for all config widgets."""
self._remove_handlers()
self._set_values({}, 100, [])
self.layout.visibility = "hidden"