def add_to_plot(self, fig: go.Figure, bars: List[Bar], time):
super().add_to_plot(fig, bars, time)
lines = self.channel.get_number_of_lines()
styles = self.channel.get_line_styles()
names = self.channel.get_line_names()
offset = 1 # we take it with offset 1
self.logger.info("adding channel")
for idx in range(0, lines):
sub_data = list(map(lambda b: self.channel.get_data_for_plot(b)[idx], bars))
fig.add_scatter(x=time, y=sub_data[offset:], mode='lines', line=styles[idx],
name=self.channel.id + "_" + names[idx])
def add_to_plot(self, fig: go.Figure, bars: List[Bar], time):
super().add_to_plot(fig, bars, time)
styles = self.mean.get_line_styles()
names = self.mean.get_line_names()
offset = 0 # we take it with offset 1
sub_data = list(map(lambda b: self.mean.get_data_for_plot(b)[0], bars))
fig.add_scatter(x=time,
y=sub_data[offset:],
mode='lines',
line=styles[0],
name=self.mean.id + "_" + names[0])
sub_data = list(map(lambda b: self.mean.get_data_for_plot(b)[1], bars))
fig.add_scatter(x=time,
y=sub_data[offset:],
mode='lines',
line=styles[1],
name=self.mean.id + "_" + names[1])
sub_data = list(map(lambda b: self.mean.get_data_for_plot(b)[2], bars))
fig.add_scatter(x=time,
y=sub_data[offset:],
mode='lines',
line=styles[2],
name=self.mean.id + "_" + names[2])
def timeseries_comparison_plot(
series_1: pd.Series,
series_2: pd.Series,
traces_opts: dict = {},
layout_opts: dict = {
"xaxis_title": "Time",
"yaxis_title": "Values",
"autosize": True,
"height": 200,
},
line_opts: dict = {},
) -> Figure:
"""Create a single time series line plot Plotly figure
Returns the plotly figure object.
"""
fig = Figure()
# Only thing I figured is - I could do this
s1 = series_1.sort_index()
fig.add_scatter(
x=s1.index, y=s1, mode="lines", name=s1.name if s1.name else "series_1"
) # Not what is desired - need a line
s2 = series_2.sort_index()
fig.add_scatter(
x=s2.index, y=s2, mode="lines", name=s2.name if s2.name else "series_2"
) # Not what is desired - need a line
fig.update_layout(**layout_opts) # see https://plotly.com/python/figure-labels/
fig.update_traces(traces_opts) # set line color?
fig.update_layout(margin=dict(l=0, r=0, b=0, t=5, pad=0))
fig.update_yaxes(automargin=True)
fig.update_xaxes(automargin=True)
return fig
def multi_plotly_timeseries_plot(
dataframe: pd.DataFrame,
traces_opts: dict = {},
layout_opts: dict = {
"xaxis_title": "Time",
"yaxis_title": "Values",
"autosize": True,
"height": 200,
},
line_opts: dict = {},
) -> Figure:
"""Create a single time series line plot Plotly figure
Returns the plotly figure object.
"""
fig = Figure()
columns = list(dataframe.keys())
to_plot_df = dataframe.sort_index()
# Only thing I figured is - I could do this
for key in columns:
fig.add_scatter(x=to_plot_df.index,
y=to_plot_df[key],
mode="lines",
name=key) # Not what is desired - need a line
fig.update_layout(
**layout_opts) # see https://plotly.com/python/figure-labels/
fig.update_traces(traces_opts) # set line color?
fig.update_layout(margin=dict(l=0, r=0, b=0, t=5, pad=0))
fig.update_yaxes(automargin=True)
fig.update_xaxes(automargin=True)
return fig
def substituted_data_plot(
raw_values: pd.Series,
substitutions: pd.Series,
message_series: pd.Series = None,
traces_opts: dict = {},
layout_opts: dict = {
"xaxis_title": "Time",
"yaxis_title": "Values",
"autosize": True,
"height": 200,
},
line_opts: dict = {},
) -> Figure:
"""Create a single time series line plot Plotly figure
Returns the plotly figure object.
"""
fig = Figure()
s1 = raw_values.sort_index()
s1 = s1.loc[~s1.index.duplicated(keep="first")]
s2 = substitutions.sort_index()
s2 = s2.loc[~s2.index.duplicated(keep="first")]
completely_handled_series, replaced_originals, replacements, new_values, deleted = handle_substitutions(
s1, s2)
fig.add_scatter(
x=completely_handled_series.index,
y=completely_handled_series,
mode="markers+lines",
name=raw_values.name +
"_substituted" if raw_values.name else "raw_values_substituted",
line_color="blue",
opacity=0.6,
) # Not what is desired - need a line
fig.add_scatter(
x=replaced_originals.index,
y=replaced_originals,
mode="markers",
name="replaced raw values",
line_color="orange",
marker=dict(size=10, opacity=0.6),
) # Not what is desired - need a line
fig.add_scatter(
x=deleted.index,
y=deleted,
mode="markers",
name="ignored raw values",
line_color="red",
marker=dict(symbol="x", size=10, opacity=0.6),
) # Not what is desired - need a line
fig.add_scatter(
x=new_values.index,
y=new_values,
mode="markers",
name="added values",
line_color="green",
marker=dict(symbol="cross", size=10, opacity=0.6),
) # Not what is desired - need a line
fig.update_layout(
**layout_opts) # see https://plotly.com/python/figure-labels/
fig.update_traces(traces_opts) # set line color?
fig.update_layout(margin=dict(l=0, r=0, b=0, t=5, pad=0))
fig.update_yaxes(automargin=True)
fig.update_xaxes(automargin=True)
return fig
def _plot(data: SampledField,
fig: graph_objects.Figure,
size: tuple,
colormap: str or None,
show_color_bar: bool,
row: int = None, col: int = None,
):
subplot = fig.get_subplot(row, col)
vector = data.points.shape['vector']
if data.spatial_rank == 1 and isinstance(data, Grid):
x = data.points.vector[0].numpy().flatten()
channels = data.values.shape.channel
if channels.rank == 1 and channels.get_item_names(0) is not None:
for i, name in enumerate(channels.get_item_names(0)):
y = math.reshaped_native(real_values(data[{channels.name: i}]), [data.shape.spatial], to_numpy=True)
fig.add_trace(graph_objects.Scatter(x=x, y=y, mode='lines+markers', name=name), row=row, col=col)
fig.update_layout(showlegend=True)
else:
for ch_idx in channels.meshgrid():
y = math.reshaped_native(real_values(data[ch_idx]), [data.shape.spatial], to_numpy=True)
fig.add_trace(graph_objects.Scatter(x=x, y=y, mode='lines+markers', name='Multi-channel'), row=row, col=col)
fig.update_layout(showlegend=False)
elif data.spatial_rank == 2 and isinstance(data, Grid) and 'vector' not in data.shape: # heatmap
dims = spatial(data)
values = real_values(data).numpy(dims.reversed)
x = data.points.vector[dims[0].name].dimension(dims[1].name)[0].numpy()
y = data.points.vector[dims[1].name].dimension(dims[0].name)[0].numpy()
min_val, max_val = numpy.nanmin(values), numpy.nanmax(values)
min_val, max_val = min_val if numpy.isfinite(min_val) else 0, max_val if numpy.isfinite(max_val) else 0
color_scale = get_div_map(min_val, max_val, equal_scale=True, colormap=colormap)
# color_bar = graph_objects.heatmap.ColorBar(x=1.15) , colorbar=color_bar
fig.add_heatmap(row=row, col=col, x=x, y=y, z=values, zauto=False, zmin=min_val, zmax=max_val, colorscale=color_scale, showscale=show_color_bar)
subplot.xaxis.update(scaleanchor=f'y{subplot.yaxis.plotly_name[5:]}', scaleratio=1, constrain='domain', title=dims.names[0])
subplot.yaxis.update(constrain='domain', title=dims.names[1])
elif data.spatial_rank == 2 and isinstance(data, Grid): # vector field
if isinstance(data, StaggeredGrid):
data = data.at_centers()
x, y = math.reshaped_native(data.points.vector[spatial(data)], [vector, data.shape.without(vector)], to_numpy=True, force_expand=True)
extra_channels = data.shape.channel.without('vector')
data_x, data_y = math.reshaped_native(data.values, [vector, extra_channels, spatial(data)], to_numpy=True, force_expand=True)
lower_x, lower_y = [float(l) for l in data.bounds.lower.vector.unstack_spatial('x,y')]
upper_x, upper_y = [float(u) for u in data.bounds.upper.vector.unstack_spatial('x,y')]
x_range = [lower_x, upper_x]
y_range = [lower_y, upper_y]
for ch in range(data_x.shape[0]):
# quiver = figure_factory.create_quiver(x, y, data_x[ch], data_y[ch], scale=1.0) # 7 points per arrow
# fig.add_trace(quiver, row=row, col=col)
data_y_flat = data_y[ch].flatten()
data_x_flat = data_x[ch].flatten()
# lines_y = numpy.stack([y, y + data_y_flat, [None] * len(x)], -1).flatten() # 3 points per arrow
# lines_x = numpy.stack([x, x + data_x_flat, [None] * len(x)], -1).flatten()
lines_y = numpy.stack([y - data_y_flat / 2, y + data_y_flat / 2, [None] * len(x)], -1).flatten() # 3 points per arrow
lines_x = numpy.stack([x - data_x_flat / 2, x + data_x_flat / 2, [None] * len(x)], -1).flatten()
name = extra_channels.get_item_names(0)[ch] if extra_channels.rank == 1 and extra_channels.get_item_names(0) is not None else None
fig.add_scatter(x=lines_x, y=lines_y, mode='lines', row=row, col=col, name=name)
if data_x.shape[0] == 1:
fig.update_layout(showlegend=False)
subplot.xaxis.update(range=x_range)
subplot.yaxis.update(range=y_range)
subplot.xaxis.update(scaleanchor=f'y{subplot.yaxis.plotly_name[5:]}', scaleratio=1, constrain='domain')
subplot.yaxis.update(constrain='domain')
elif data.spatial_rank == 3 and isinstance(data, Grid) and data.shape.channel.volume == 1: # 3D heatmap
values = real_values(data).numpy('z,y,x')
x = data.points.vector['x'].numpy('z,y,x')
y = data.points.vector['y'].numpy('z,y,x')
z = data.points.vector['z'].numpy('z,y,x')
min_val, max_val = numpy.nanmin(values), numpy.nanmax(values)
min_val, max_val = min_val if numpy.isfinite(min_val) else 0, max_val if numpy.isfinite(max_val) else 0
color_scale = get_div_map(min_val, max_val, equal_scale=True, colormap=colormap)
fig.add_volume(x=x.flatten(), y=y.flatten(), z=z.flatten(), value=values.flatten(),
showscale=show_color_bar, colorscale=color_scale, cmin=min_val, cmax=max_val, cauto=False,
isomin=0.1, isomax=0.8,
opacity=0.1, # needs to be small to see through all surfaces
surface_count=17, # needs to be a large number for good volume rendering
row=row, col=col)
fig.update_layout(uirevision=True)
elif data.spatial_rank == 3 and isinstance(data, Grid): # 3D vector field
if isinstance(data, StaggeredGrid):
data = data.at_centers()
u = real_values(data).vector['x'].numpy('z,y,x')
v = real_values(data).vector['y'].numpy('z,y,x')
w = real_values(data).vector['z'].numpy('z,y,x')
x = data.points.vector['x'].numpy('z,y,x')
y = data.points.vector['y'].numpy('z,y,x')
z = data.points.vector['z'].numpy('z,y,x')
fig.add_cone(x=x.flatten(), y=y.flatten(), z=z.flatten(), u=u.flatten(), v=v.flatten(), w=w.flatten(),
colorscale='Blues',
sizemode="absolute", sizeref=1,
row=row, col=col)
elif isinstance(data, PointCloud) and data.spatial_rank == 2 and 'vector' in channel(data):
x, y = math.reshaped_native(data.points, [vector, data.shape.without('vector')], to_numpy=True, force_expand=True)
u, v = math.reshaped_native(data.values, [vector, data.shape.without('vector')], to_numpy=True, force_expand=True)
lower_x, lower_y = [float(d) for d in data.bounds.lower.vector]
upper_x, upper_y = [float(d) for d in data.bounds.upper.vector]
subplot.xaxis.update(range=[lower_x, upper_x])
subplot.yaxis.update(range=[lower_y, upper_y])
quiver = figure_factory.create_quiver(x, y, u, v, scale=1.0).data[0] # 7 points per arrow
if data.color.shape:
# color = data.color.numpy(data.shape.non_channel).reshape(-1)
warnings.warn("Multi-colored vector plots not yet supported")
else:
color = data.color.native()
quiver.line.update(color=color)
fig.add_trace(quiver, row=row, col=col)
if data.points.vector.item_names:
subplot.xaxis.update(title=data.points.vector.item_names[0])
subplot.yaxis.update(title=data.points.vector.item_names[1])
subplot.xaxis.update(scaleanchor=f'y{subplot.yaxis.plotly_name[5:]}', scaleratio=1, constrain='domain')
subplot.yaxis.update(constrain='domain')
elif isinstance(data, PointCloud) and data.spatial_rank == 2:
lower_x, lower_y = [float(d) for d in data.bounds.lower.vector]
upper_x, upper_y = [float(d) for d in data.bounds.upper.vector]
if data.points.shape.non_channel.rank > 1:
data_list = field.unstack(data, data.points.shape.non_channel[0].name)
for d in data_list:
_plot(d, fig, size, colormap, show_color_bar, row, col)
else:
x, y = [d.numpy() for d in data.points.vector.unstack_spatial('x,y')]
color = data.color.native()
subplot_height = (subplot.yaxis.domain[1] - subplot.yaxis.domain[0]) * size[1]
if isinstance(data.elements, Sphere):
symbol = 'circle'
marker_size = data.elements.bounding_radius().numpy() * 1.9
elif isinstance(data.elements, BaseBox):
symbol = 'square'
marker_size = math.mean(data.elements.bounding_half_extent(), 'vector').numpy() * 1
elif isinstance(data.elements, Point):
symbol = 'x'
marker_size = 12 / (subplot_height / (upper_y - lower_y))
else:
symbol = 'asterisk'
marker_size = data.elements.bounding_radius().numpy()
marker_size *= subplot_height / (upper_y - lower_y)
marker = graph_objects.scatter.Marker(size=marker_size, color=color, sizemode='diameter', symbol=symbol)
fig.add_scatter(mode='markers', x=x, y=y, marker=marker, row=row, col=col)
subplot.xaxis.update(range=[lower_x, upper_x])
subplot.yaxis.update(range=[lower_y, upper_y])
fig.update_layout(showlegend=False)
subplot.xaxis.update(scaleanchor=f'y{subplot.yaxis.plotly_name[5:]}', scaleratio=1, constrain='domain')
subplot.yaxis.update(constrain='domain')
elif isinstance(data, PointCloud) and data.spatial_rank == 3:
lower_x, lower_y, lower_z = [float(d) for d in data.bounds.lower.vector.unstack_spatial('x,y,z')]
upper_x, upper_y, upper_z = [float(d) for d in data.bounds.upper.vector.unstack_spatial('x,y,z')]
if data.points.shape.non_channel.rank > 1:
data_list = field.unstack(data, data.points.shape.non_channel[0].name)
for d in data_list:
_plot(d, fig, size, colormap, show_color_bar, row, col)
else:
x, y, z = [d.numpy() for d in data.points.vector.unstack_spatial('x,y,z')]
color = data.color.native()
# if data.color.shape.instance_rank == 0:
# color = str(data.color)
# else:
# color = [str(d) for d in math.unstack(data.color, instance)]
domain_y = fig.layout[subplot.plotly_name].domain.y
if isinstance(data.elements, Sphere):
symbol = 'circle'
marker_size = data.elements.bounding_radius().numpy() * 2
elif isinstance(data.elements, BaseBox):
symbol = 'square'
marker_size = math.mean(data.elements.bounding_half_extent(), 'vector').numpy() * 1
elif isinstance(data.elements, Point):
symbol = 'x'
marker_size = 4 / (size[1] * (domain_y[1] - domain_y[0]) / (upper_y - lower_y) * 0.5)
else:
symbol = 'asterisk'
marker_size = data.elements.bounding_radius().numpy()
marker_size *= size[1] * (domain_y[1] - domain_y[0]) / (upper_y - lower_y) * 0.5
marker = graph_objects.scatter3d.Marker(size=marker_size, color=color, sizemode='diameter', symbol=symbol)
fig.add_scatter3d(mode='markers', x=x, y=y, z=z, marker=marker, row=row, col=col)
subplot.xaxis.update(range=[lower_x, upper_x])
subplot.yaxis.update(range=[lower_y, upper_y])
subplot.zaxis.update(range=[lower_z, upper_z])
fig.update_layout(showlegend=False)
else:
raise NotImplementedError(f"No figure recipe for {data}")
def timeseries_comparison_plot(
series_1: pd.Series,
series_2: pd.Series,
limit_violation_timestamp: str,
limit: float,
traces_opts: dict = {},
layout_opts: dict = {
"xaxis_title": "Time",
"yaxis_title": "Values",
"autosize": True,
"height": 200,
},
line_opts: dict = {},
) -> Figure:
"""Create a single time series line plot Plotly figure
Returns the plotly figure object.
"""
fig = Figure()
# Only thing I figured is - I could do this
s1 = series_1.sort_index()
fig.add_scatter(
x=s1.index, y=s1, mode="lines", name=s1.name if s1.name else "series_1"
) # Not what is desired - need a line
s2 = series_2.sort_index()
fig.add_scatter(
x=s2.index, y=s2, mode="lines", name=s2.name if s2.name else "series_2"
) # Not what is desired - need a line
fig.update_layout(**layout_opts) # see https://plotly.com/python/figure-labels/
fig.update_traces(traces_opts) # set line color?
vline_y_min = np.min([series_1.min(), series_2.min(), limit])
vline_y_max = np.max([series_1.max(), series_2.max(), limit])
hline_x_min = np.min([series_1.index.min(), series_2.index.min()])
hline_x_max = np.max([series_1.index.max(), series_2.index.max()])
fig.update_layout(
shapes=(
[
dict(
type="line",
yref="y",
y0=vline_y_min,
y1=vline_y_max,
xref="x",
x0=limit_violation_timestamp,
x1=limit_violation_timestamp,
)
]
if limit_violation_timestamp is not None
else []
)
+ [
dict(
type="line",
yref="y",
y0=limit,
y1=limit,
xref="x",
x0=hline_x_min,
x1=hline_x_max,
line={"color": "red", "width": 1},
)
]
)
fig.update_layout(margin=dict(l=0, r=0, b=0, t=5, pad=0))
fig.update_yaxes(automargin=True)
fig.update_xaxes(automargin=True)
return fig
def timeseries_comparison_plot(
series_1: pd.Series,
series_2: pd.Series,
freq,
freq_factor,
max_shifts_past,
max_shifts_future,
traces_opts: dict = {},
layout_opts: dict = {
"xaxis_title": "Time",
"yaxis_title": "Values",
"autosize": True,
"height": 480,
},
line_opts: dict = {},
) -> Figure:
"""Create a single time series line plot Plotly figure
Returns the plotly figure object.
"""
if max_shifts_past < 0 or max_shifts_future < 0:
raise ValueError("Shift maxima values must be greater equal zero.")
fig = Figure()
s1 = series_1.sort_index()
fig.add_scatter(
x=s1.index,
y=s1,
mode="lines",
name=s1.name if (s1.name and s1.name != "measurement") else "series",
)
s2 = series_2.sort_index()
for step in np.arange(-max_shifts_past, max_shifts_future, 1):
shifted = s2.copy()
shifted.index = shifted.index.shift(periods=step * freq_factor,
freq=freq)
fig.add_scatter(
x=shifted.index,
y=shifted,
mode="lines",
name=shifted.name if
(shifted.name
and shifted.name != "measurement") else "series_shiftable",
visible=False,
)
fig.data[max_shifts_past + 1].visible = True # start somewhere
steps = []
for i in range(len(fig.data)):
step = dict(
method="update",
args=[
{
"visible": [False] * (len(fig.data))
},
{
"title":
f"Shifted by x times frequency {freq_factor}{freq}: " +
str(i - max_shifts_past - 1)
},
], # layout attribute
)
step["args"][0]["visible"][i] = True # Toggle i'th trace to "visible"
step["args"][0]["visible"][0] = True
step["label"] = str(i - max_shifts_past - 1)
steps.append(step)
sliders = [
dict(
active=max_shifts_past,
currentvalue={
"prefix": f"Shifted by x times frequency {freq_factor}{freq}: "
},
pad={
"t": 80,
"b": 10
},
steps=steps[1:],
)
]
fig.update_layout(
**layout_opts) # see https://plotly.com/python/figure-labels/
fig.update_traces(traces_opts) # set line color?
fig.update_layout(margin=dict(l=0, r=0, b=40, t=30, pad=10))
fig.update_yaxes(automargin=True)
fig.update_xaxes(automargin=True)
fig.update_layout(sliders=sliders)
return fig
