def rca(functionNode):
logger = functionNode.get_logger()
logger.info("==>>>> in rca (root cause analysis " +
functionNode.get_browse_path())
progressNode = functionNode.get_child("control").get_child("progress")
progressNode.set_value(0.1)
variables = functionNode.get_child("selectedVariables").get_leaves()
tag = functionNode.get_child("selectedTags").get_value() #only one tag
annotations = functionNode.get_child("annotations").get_leaves()
feature = functionNode.get_child("selectedFeatures").get_value()
algo = functionNode.get_child("selectedAlgorithms").get_value()
target = functionNode.get_child("selectedTarget").get_target()
p = Progress(progressNode)
p.set_divisor(len(annotations) / 0.5)
p.set_offset(0.1)
#now create the data as x-y
results = {"x": [], "y": []}
var = variables[0]
#now iterate over all annotations of the matching type and create feature
for idx, anno in enumerate(annotations):
p.set_progress(idx)
if (anno.get_child("type").get_value()
== "time") and (tag in anno.get_child("tags").get_value()):
startTime = anno.get_child("startTime").get_value()
endTime = anno.get_child("endTime").get_value()
data = var.get_time_series(startTime, endTime)
#now create the feature
feat = calc_feature(data["values"], feature)
targetValue = get_target(
target, (date2secs(startTime) + date2secs(endTime)) / 2)
if feat and targetValue and numpy.isfinite(
feat) and numpy.isfinite(targetValue):
results["x"].append(feat)
results["y"].append(targetValue)
else:
logger.warning(
f"no result for {var.get_name} @ {startTime}, anno:{tag}, feat:{feat}, target: {target}"
)
#now we have all the x-y
progressNode.set_value(0.7)
fig = figure(title="x-y Correlation Plot " + var.get_name(),
tools=[PanTool(),
WheelZoomTool(),
ResetTool(),
SaveTool()],
plot_height=300,
x_axis_label=feature + "(" + var.get_name() + ") @ " + tag,
y_axis_label=target.get_name())
fig.toolbar.logo = None
curdoc().theme = Theme(json=themes.darkTheme)
fig.xaxis.major_label_text_color = themes.darkTickColor
fig.yaxis.major_label_text_color = themes.darkTickColor
fig.scatter(x=results["x"],
y=results["y"],
size=5,
fill_color="#d9b100",
marker="o")
fileName = functionNode.get_child("outputFileName").get_value()
filePath = os.path.join(myDir, './../web/customui/' + fileName)
progressNode.set_value(0.8)
output_file(
filePath, mode="inline"
) #inline: put the bokeh .js into this html, otherwise the default cdn will be taken, might cause CORS problems)
save(fig)
#print(results)
return True
def varstatistics(functionNode):
logger = functionNode.get_logger()
logger.info("==>>>> statistics " + functionNode.get_browse_path())
progressNode = functionNode.get_child("control").get_child("progress")
progressNode.set_value(0)
#functionNode.get_child("control.signal").set_value(None)
vars = functionNode.get_child("variable").get_targets()
widget = functionNode.get_child("widget").get_target()
bins = functionNode.get_child("bins").get_value()
tags = functionNode.get_child("annotations").get_value()
startTime = date2secs(widget.get_child("startTime").get_value())
endTime = date2secs(widget.get_child("endTime").get_value())
vars = {var.get_id(): {"node": var} for var in vars}
#first 30% progress:
prog = Progress(progressNode)
progressNode.set_value(0.1)
prog.set_offset(0.1)
#prog.set_divisor()
if tags:
allAnnoNodes = widget.get_child(
"hasAnnotation.annotations").get_leaves()
allAnnos = []
prog.set_divisor(len(allAnnoNodes) / 0.2)
for index, node in enumerate(allAnnoNodes):
prog.set_progress(index)
if node.get_child("type").get_value() == "time":
thisTags = node.get_child("tags").get_value()
if any(tag in tags for tag in thisTags):
anno = {}
for child in node.get_children():
anno[child.get_name()] = child.get_value()
if date2secs(anno["startTime"]) >= startTime and date2secs(
anno["endTime"]
) <= endTime: #take this anno only if it is inside the current start/end time
allAnnos.append(anno)
if allAnnos == []:
give_up(functionNode, "no matching annotations in selected time")
return False
else:
allAnnos = []
progressNode.set_value(0.3)
logger.debug(f"statistics annotations to look at: {len(allAnnos)}")
prog.set_offset(0.3)
totalAnnos = max(len(allAnnos), 1)
totalCount = len(vars) * totalAnnos
prog.set_divisor(totalCount / 0.3)
totalValids = 0
for varIndex, var in enumerate(vars):
info = vars[var]
if tags:
#iterate over all start and end times
values = numpy.asarray([], dtype=numpy.float64)
for annoIndex, anno in enumerate(allAnnos):
thisValues = info["node"].get_time_series(
anno["startTime"], anno["endTime"])["values"]
values = numpy.append(values, thisValues)
myCount = varIndex * totalAnnos + annoIndex
prog.set_progress(myCount)
else:
values = info["node"].get_time_series(startTime, endTime)["values"]
valids = numpy.count_nonzero(~numpy.isfinite(values))
totalValids += valids
hist, edges = numpy.histogram(values, bins=bins)
hist = hist / len(values) #normalize
info["hist"] = hist
info["edges"] = edges
#make a plot
if totalValids == 0:
give_up(
functionNode,
"all Variables are have no data in the time and annotations selected"
)
return False
progressNode.set_value(0.6)
hover1 = HoverTool(tooltips=[('x,y', '$x,$y')], mode='mouse')
hover1.point_policy = 'snap_to_data'
hover1.line_policy = "nearest"
tools = [
PanTool(),
WheelZoomTool(),
BoxZoomTool(),
ResetTool(),
SaveTool(), hover1
]
title = "Statistics of " + str(
[info["node"].get_name() for var, info in vars.items()])
if tags:
title = title + " in annotation: " + str(tags)
fig = figure(title=title, tools=tools, plot_height=300)
fig.toolbar.logo = None
curdoc().theme = Theme(json=themes.darkTheme)
fig.xaxis.major_label_text_color = themes.darkTickColor
fig.yaxis.major_label_text_color = themes.darkTickColor
for index, var in enumerate(vars):
info = vars[var]
col = themes.darkLineColors[index]
hist = info["hist"]
edges = info["edges"]
fig.quad(top=hist,
bottom=0,
left=edges[:-1],
right=edges[1:],
fill_color=col,
line_color=col,
alpha=0.8,
legend_label=info["node"].get_name())
fig.legend.location = "top_left"
fileName = functionNode.get_child("fileName").get_value()
filePath = os.path.join(myDir, './../web/customui/' + fileName)
# now make the trend box plot, but only for tags
# for each variable we create statistics for the annotations and prepare the data
# {"node":Node(), "boxLower":[], "boxUpper", "mean", "limitUpper", "limitLower"}
#
startTime = date2secs(widget.get_child("startTime").get_value(
)) #we only take tags that are inside the current zoom of the widgets
endTime = date2secs(widget.get_child("endTime").get_value())
boxPlots = []
allTimes = []
if tags:
for index, var in enumerate(vars):
info = {
"node": vars[var]["node"],
"boxLower": [],
"boxUpper": [],
"median": [],
"time": [],
"limitUpper": [],
"limitLower": [],
"mean": []
}
for anno in allAnnos:
data = info["node"].get_time_series(anno["startTime"],
anno["endTime"])
if len(data["values"]):
data["values"] = data["values"][numpy.isfinite(
data["values"])]
#remove the nan
if len(data["values"]):
#make the statistics
info["time"].append(numpy.median(data["__time"]) * 1000)
allTimes.append(numpy.median(data["__time"]) * 1000)
info["limitLower"].append(
numpy.quantile(data["values"], 0.01))
info["limitUpper"].append(
numpy.quantile(data["values"], 0.99))
info["boxLower"].append(
numpy.quantile(data["values"], 0.25))
info["boxUpper"].append(
numpy.quantile(data["values"], 0.75))
info["median"].append(numpy.median(data["values"]))
info["mean"].append(numpy.mean(data["values"]))
boxPlots.append(info)
format = "%Y-%m-%d-T%H:%M:%S"
custom = """var local = moment(value).tz('UTC'); return local.format();""" #%self.server.get_settings()["timeZone"]
hover = HoverTool(tooltips=[('date', '@x{%F}')],
formatters={'@x': CustomJSHover(code=custom)},
mode='mouse')
hover.point_policy = 'snap_to_data'
hover.line_policy = "nearest"
tools = [
PanTool(),
BoxZoomTool(),
WheelZoomTool(),
ResetTool(), hover,
SaveTool()
]
fig2 = figure(title="trends",
tools=tools,
plot_height=300,
x_axis_type='datetime')
fig2.xaxis.major_label_text_color = themes.darkTickColor
fig2.yaxis.major_label_text_color = themes.darkTickColor
progressNode.set_value(0.7)
fig2.xaxis.formatter = DatetimeTickFormatter(years=format,
days=format,
months=format,
hours=format,
hourmin=format,
minutes=format,
minsec=format,
seconds=format)
fig2.toolbar.logo = None
#fig2.line([1,2,3],[1,2,3])
#calc with of vbars
if len(allAnnos) > 1:
xTimesStart = min(allTimes)
xTimesEnd = max(allTimes)
width = (xTimesEnd - xTimesStart) / 2 / len(allAnnos)
else:
width = 1000000
for index, info in enumerate(boxPlots):
#each info is for one variable
col = themes.darkLineColors[index]
fig2.segment(info["time"],
info["limitUpper"],
info["time"],
info["boxUpper"],
line_color=col)
fig2.segment(info["time"],
info["limitLower"],
info["time"],
info["boxLower"],
line_color=col)
width = 20
#fig2.vbar(info["time"],width=width,bottom=info["median"],top=info["boxUpper"],fill_color=col,line_color="black",width_units='screen')
#fig2.vbar(info["time"],width=width,bottom=info["boxLower"],top=info["median"],fill_color=col,line_color="black",width_units='screen')
#upper box
sizUpper = numpy.asarray(info["boxUpper"]) - numpy.asarray(
info["median"])
medUpper = numpy.asarray(info["median"]) + sizUpper / 2
fig2.rect(x=info["time"],
y=medUpper,
width_units='screen',
width=20,
height=sizUpper,
fill_color=col,
line_color="black")
#lower box
sizLower = numpy.asarray(info["median"]) - numpy.asarray(
info["boxLower"])
medLower = numpy.asarray(info["median"]) - sizLower / 2
fig2.rect(x=info["time"],
y=medLower,
width_units='screen',
width=20,
height=sizLower,
fill_color=col,
line_color="black")
#sort data for line
x = numpy.asarray(info["time"])
y = numpy.asarray(info["mean"])
order = numpy.argsort(x)
x = x[order]
y = y[order]
fig2.line(x, y, line_color=col)
progressNode.set_value(0.8)
else:
#no fig2
pass
output_file(
filePath, mode="inline"
) #inline: put the bokeh .js into this html, otherwise the default cdn will be taken, might cause CORS problems
if tags:
save(layout([[fig], [fig2]]))
else:
save(fig)
return True
