def __init__(self,
dataArray,
title=None,
usrAttribs=dict(),
root=None,
newWindow=True):
# Creating the default root within the __init__ call somehow
# causes the root variable to persist and only a single root to be
# created for multiple scenes. To avoid this we use the explicit
# None initialization
if root == None:
root = VisusGroup.construct()
#root = VisusSceneNode.construct()
# Create a root node
self._root = root
# self._root.drawBoundingBox(False)
# Create the attributes
self._attribs = dict()
# Create the extractor, data description and update attribs
self.setData(dataArray, usrAttribs)
# Attach the extractor to the root
self._root.attachSubTree(self._extractor, True)
self._extractor.propagateUpwards(VISUS_GLOBALTIME_TYPEID)
# Create the drawing window
if newWindow:
print "Creating new window of root ", root
createWindow(self._root)
# Create the node for the slice of data
self._orthoSlice = VisusOrthogonalSlice.construct()
# Connect the output of the extractor to the input of each slice node
if not self._orthoSlice.connectInput(self._extractor):
raise RuntimeError("unable to connect extractor1 as slice input")
# Attach the ortho slice to the root as a subtree
self._extractor.attachSubTree(self._orthoSlice)
# Inherit/Share the data request
# Now that we have positioned the slices where we want them we want
# to freeze their position. To do this we simply uninherit the data
self._orthoSlice.inherit(VisusSharedDataRequest.typeIndex(), True)
# This should be done autmoatically in the SliceNode
# Set the correct bounding box for the slice
# request = VisusDataRequest()
# self._orthoSlice.getValue(request)
# self._orthoSlice.setValue(request.queryRegion())
# Set the colormap
#self.setColorMap(ice())
self.setColorMap(bgry())
# Draw the bounding boxes of the slice
self._orthoSlice.drawBoundingBox(True)
# Set up the annotations for the colorbar etc
self.setAnnote()
# Add the world map
world_map = self.loadWorldMap()
# And parse it as a collection of polylines. A VisusIndexedData
# object stores an array of n-dimensional points (n being 2 in this
# case) and a set of elements defined as ordered lists of indices
# into this array. Therefore, each contour of the world map is
# parsed as a single element
outlines = VisusIndexedData(2)
for m in world_map:
outlines.constructElement(m)
# Once the data is constructed we create a display node designed to visualize it
self._map = VisusIndexedDisplay.construct()
# And load the data.
self._map.loadData(outlines, 0)
# Move the map to the top of the data set so that is it always above the slice
matrix = translationMatrix(0, 0, 0)
self._map.getValue(matrix)
matrix = matrix * translationMatrix(0, 0, 0.01)
# This is a really ugly hack to adjust for the [0,360] [-180,180] ambiguity
if self._bbox[0] > -90:
matrix = matrix * translationMatrix(180, 0, 0)
self._map.setValue(matrix)
# Attach the map as child of the extractor. Note, that from this moment
# on the map will inherit all transformations from the extractor and
# thus "stick" to the extractor if it is moved or rotated
self._orthoSlice.attachSubTree(self._map)
#self._extractor.attachSubTree(self._map)
# Add a title to the scene
if (title == None):
if "name" in self._attribs:
title = self._attribs["name"].replace("_", " ")
else:
title = "Title"
font = VisusFont()
font.fontSize(12)
self._title = VisusLabelNode.construct()
self._title.text(title)
self._title.setValue(font)
self._title.position(0, 0.92)
self._root.attachSubTree(self._title, True)
def cartesianSlice(data=None,**keywords):
"""
This function constructs a default ViSUS scene displaying a single
z-slice through a regular block of data. The user can either pass a
cdms2 array using the data attribute or enter the necessary
information into the vcdat gui. If no data attribute is present the
function will try to extract the necessary information from the gui
and if that fails an empty scene will be displayed.
The scene contains multiple \"nodes\" each of which can be
manipulated using the keyboard and mouse. Som basic commands are
Tab : Switch node
Middle Mouse button : Pan scene
Left Mouse button : Rotate scene
Right Mouse button : Zoom in and out
Usage: cartesianSlice(DataArray, keyword=value,...) where DataArray
can be a standard numpy array, a cdms type data set, or
\"None\". The function will attempt to extract a number of
attributes from the given data or the gui each of which can be
overridden by the user by passing it explicitly. Possible keywords
are:
name : Verbal description of the data set or variable (used in the title)
var_name : Short name of the variable
domain : Physical domain of the data set represented as
[[min_x,max_x],[min_y,max_y],[min_z,max_z]]. If
the data is only two- or one-dimensional the extra
dimensions will be assumed to be [0,0]
samples : The number of samples in each dimension represented
as [samples_x, samples_y, samples_z, samples_t]. For
lower dimensional data the additional dimensions will
be set to 1, e.g. a time-dependent 2D array would be
[dim_x,dim_y,1,dim_t]
units : VisusUnit identfier describing the natural coordinate
system of the data (e.g. cartesian length, polar
coordinates, etc.) All possible values can be found in
VisusUnits.h
range : Local function range of the data. Note, that percentages
and fractions will automatically get a range of [0,100]
and [0,1] respectively.
time : The cdtime.Comptime of the first time step in data
"""
# If the user did not pass in an array try to extract one from the
# gui
if data is None:
data = guiDefinedData()
attributes = keywords
try:
# Unless the user has explicitly specified different units we
# treat the domain as a length box
if "units" not in attributes:
attributes["units"] = VISUS_METERS
# Try to infer as many data attributes as you can and return a
# string describing/defining the data in the ViSUS internal format
data_description = constructDataDescriptor(data,attributes)
except:
print "Could not parse data. Unknown format"
raise
# Create a font to be used in all the nodes that render text
font = VisusFont()
font.fontSize(6)
# Construct a root node for the scene graph hierarchy. This node is
# mainly used for the global transformation (e.g. rotating the
# complete scene) and is a convinient way to collect independent
# nodes (nodes that should have no ancestor-descendend
# relationship. Note, the use of the construct function rather than
# a standard "constructor." ViSUS uses a smart pointer / reference
# counting scheme which requires this.
root = VisusGroup.construct()
# To have a convinient reference point we want to display the
# bounding box of the root node which will automatically adjust to
# include the bounding box of all child nodes.
root.drawBoundingBox(True)
# Create two points that will form the left lower and right upper
# corner of the data bounding box
left = VectorDouble(3)
right= VectorDouble(3)
# Create a ViSUS data source based on the data description created earlier
source = VisusDataSourceFactory.make(data_description)
if not source.isValid():
raise RuntimeError("Problem loading data set")
# Get the bounding box of the data whose value may or may not depend
# on data attributes such as units. In particular, if this
# information was available the bounding box will be given in
# pyhsical units rather than in index space
source.domainBoundingBox(left,right)
bbox = VisusBoundingBox()
bbox.set(left,right);
# The ViSUS system is based on data "requests" that describe a
# particular piece of data that should be displayed. The data
# requests can be manipulated through the user interface or
# directly. However, here we want to create a sensible default
# request to see a portion of our data.
# First, we create an empty request
request = VisusDataRequest()
# Second, we construct an array that describes the length in each
# dimension of the box of data we want to extract in whatever units
# the data source uses (typically physical space).
extent = VectorDouble(3)
extent[0] = (right[0] - left[0]) # We want to see all of the x-coordinate
extent[1] = (right[1] - left[1]) # and all of the y-coordinate
extent[2] = 0 # But we want to look at a plane so the z extend is 0
# And we pass the extent information into the request
request.extent(extent)
# Second, we need to position our extent box in the data
# set. Initially, the extent will be centered around the origin. To
# see the middle of the data we want translate our request to the
# center of the data's bounding box
# So we create the corresponding transformation matrix
matrix = translationMatrix((left[0]+right[0])/2,
(left[1]+right[1])/2,
(left[2]+right[2])/2,
)
# And pass this matrix to the request
request.transformation(matrix)
# Third, we want to indicate to the ViSUS system that we are
# really interested in a 2D slice of the data (rather than a
# degenerate volume) as slices are a special case and implement some
# special user interactions
request.requestType(VISUS_2D_REQUEST);
# Finally, we describe the resolution at which we want to see our
# data. The resolution is defined in terms ofo sub-sampling
# "strides" where a stride of 1 defines the full resolution data a
# stride of 2, the data sub-sampled by a factor of 2 etc.. Strides
# must be powers of two but can be independently set for each
# dimension. Furthermore, for large data it can be useful to
# progressively refine the data starting at some higher strides /
# coarser resolution, ending at some lower strides / finer
# resolution. This preserves interactivity while still providing
# high resolution data if the request remains unchanged long enough
# Since, for default scenes we expect the data to be small we only
# want to see the highest resolution
start = [ 1, 1, 1 ]
end = [ 1, 1, 1 ]
request.setStrides(start,end)
# Armed with the description of the data and the request we just
# constructed we create a scene graph node that will extract the
# data for us. This node, will continuously test its current request
# and update the data accordingly. Therefore, one could adapt the
# scene by simply passing different requests to this node (which is
# what the graphical user interface does on mouse movements or
# keyboard commands)
# Create a producer node specialized to extract and axis aligned
# bounding box from a data set. As before with the root node the
# node must be explicitly constructed.
extractor = VisusAxisAlignedExtractor.construct()
# Set the data to extract our data from
extractor.setValue(data_description)
extractor.setValue(request) # Our default request
extractor.setValue(bbox); # And the bounding box
extractor.drawBoundingBox(False);
# Now that we have a node that will extract the data we want to see
# we construct another node to visualize the slice
# We want to view an axis aligned slice of the data an the
# appropriate visualization node is one for orthogonal slices
slice = VisusOrthogonalSlice.construct()
# Now we connect the output of the extractor to the input of the
# visualization node
if not slice.connectInput(extractor):
raise RuntimeError("unable to connect extractor as slice input")
# For reasons internal to the underpinnings of the ViSUS system the
# visualization node must know about the current data request. The
# simplest way of achieving this is to attach the slice node as
# child to the extractor
extractor.attachSubTree(slice,True)
# and to inherit/share the request
slice.inherit(VisusSharedDataRequest.typeIndex(),True)
# In this configuration the slice node will naturally draw the data
# at the position of the data request which is the natural
# place. However, by manipulating the transformation matrix of the
# slice node we could draw the data anywhere in space / on the screen
# Instead of the default greyscale color map we want to use a
# typical red-to-blue map
color_map = banded()
slice.setValue(color_map)
# In order to see the slice even if it contains no data (if the
# request has been moved outside the data set, or the data is
# extremly large) we draw the bounding box of the slice
slice.drawBoundingBox(True);
# Now, we want to create labeled tickmarks to give the user a notion
# of the data range.
# Create a tickmark node that by default will create tickmarks along
# the x-axis of its bounding box
xTickMarks = VisusTickMarks.construct()
# ViSUS uses "axis" to control the labels of images, graphs, and
# also tickmarks. Here we get the current axis
axis = xTickMarks.axis()
axis.legendText("longitude") # Set the label
axis.labelFont(font) # and font
xTickMarks.axis(axis) # and update the axis
# Finally, we attach the tickmarks node as a child to the
# extractor. By default a tickmarks node inherits the bounding box
# of its parent and thus this node will now annotate the x-axis of
# our data set
extractor.attachSubTree(xTickMarks,True)
# Create tickmarks for the y-axis in an identical fashion
yTickMarks = VisusTickMarks.construct()
# Except that now we must name the axis explicitly
yTickMarks.direction(TM_Y_AXIS)
axis = yTickMarks.axis()
axis.legendText("latitude")
axis.labelFont(font)
yTickMarks.axis(axis)
extractor.attachSubTree(yTickMarks,True)
# Finally, we attach the whole subtree that renders the data plus
# annotation to the root node
root.attachSubTree(extractor,True)
# To further enhance the scene we want to add the continental
# outlines on top of the slice
# First, we load the cdat internal world map
world_map = loadWorldMap()
# And parse it as a collection of polylines. A VisusIndexedData
# object stores an array of n-dimensional points (n being 2 in this
# case) and a set of elements defined as ordered lists of indices
# into this array. Therefore, each contour of the world map is
# parsed as a single element
outlines = VisusIndexedData(2)
for m in world_map:
outlines.constructElement(m)
# Once the data is constructed we create a display node designed to visualize it
map = VisusIndexedDisplay.construct()
# And load the data.
map.loadData(outlines,0)
# The cdat world map is given within (-180,-90)x(180,90). However,
# in order to display the normal lat,long, elev data easily in
# cartesian space we told ViSUS the data units are meters (meaning
# general distance) which will draw (0,0)x(360,180). To adjust the
# map to the slice we therefore must shift it
#map.getValue(matrix);
#matrix = matrix * translationMatrix(180,90,0.01)
#map.setValue(matrix)
# Attach the map as child of the extractor. Note, that from this moment
# on the map will inherit all transformations from the extractor and
# thus "stick" to the extractor if it is moved or rotated
extractor.attachSubTree(map)
# Lastly, we attach more annotations to the scene
# A VisusLabelNode is a general one line text of arbitrary font and
# size. First, we create a title string using the variable name if
# we found one
label_node = VisusLabelNode.construct()
title = "Variable: " + attributes["name"].replace("_"," ")
label_node.text(title)
label_node.setValue(font)
# Positions of labels are given relative to the screen size
label_node.position(-1,+0.8)
root.attachSubTree(label_node,True)
# If our data set is time-dependent we attach a label that displays
# the current time step
if "time" in attributes:
time_node = VisusLabelNode.construct()
t = attributes["time"]
title = "Date: %02d/%02d/%04d" % (t.month,t.day,t.year)
time_node.text(title)
time_node.setValue(font)
time_node.position(0.6,-0.95)
root.attachSubTree(time_node,True)
# A VisusColorBar displays a bar with a color map on the inside and
# allows us to attach tickmarks and labels
color_bar = VisusColorBar.construct()
# As before fopr the tickmark node we modify the labels via the Axis construct
axis = color_bar.axis()
# If we found a variable name we use it as label
if "var_name" in attributes:
axis.legendText(attributes["var_name"])
# If we found a range we use it as well.
if "range" in attributes:
axis.minValue(float(attributes["range"][0]))
axis.maxValue(float(attributes["range"][1]))
font.fontSize(4)
axis.legendFont(font)
axis.labelFont(font)
color_bar.position(-1.2,-0.8)
color_bar.axis(axis)
# Finally, attach the color bar to the slice and make sure it
# inherits its colormap from there
slice.attachSubTree(color_bar,True)
#color_bar.inherit(VisusSharedColorMap.typeIndex(),True)
color_bar.inherit(8,True)
# Lastly we must make sure that we actually see the data on the
# screen which means we need to make sure that the data is scaled
# and translated appropriately. (Alternatively, once can think of
# this step as setting up the viewpoint of our virtual camera.
# Using the current bounding box of the scene we adapt a default box
# to the aspect ratios of of our scene and use it as our "world
# coordinate system"
world_box = constructWorldBox(bbox)
root.setValue(bbox)
# Now we make sure that the data is drawn in our standard coordinate
# system
root.mapToWorldBox(world_box)
# Finally, most climate data contains significantly more samples in
# x/y than in z. Since, for the cartesian slice we effecticely draw
# the data in index space since is inconvinient as the volume wille
# very "flat." To compensate for this we simply scale the z
# coordinate of our system
root.getValue(matrix)
matrix = matrix * scaleMatrix(1,1,10)
root.setValue(matrix)
# As the final step we create ourselfs a window to display the scene
# graph hanging under the root node
createWindow(root)
return root,data_description
def cartesianSlice(data=None, **keywords):
"""
This function constructs a default ViSUS scene displaying a single
z-slice through a regular block of data. The user can either pass a
cdms2 array using the data attribute or enter the necessary
information into the vcdat gui. If no data attribute is present the
function will try to extract the necessary information from the gui
and if that fails an empty scene will be displayed.
The scene contains multiple \"nodes\" each of which can be
manipulated using the keyboard and mouse. Som basic commands are
Tab : Switch node
Middle Mouse button : Pan scene
Left Mouse button : Rotate scene
Right Mouse button : Zoom in and out
Usage: cartesianSlice(DataArray, keyword=value,...) where DataArray
can be a standard numpy array, a cdms type data set, or
\"None\". The function will attempt to extract a number of
attributes from the given data or the gui each of which can be
overridden by the user by passing it explicitly. Possible keywords
are:
name : Verbal description of the data set or variable (used in the title)
var_name : Short name of the variable
domain : Physical domain of the data set represented as
[[min_x,max_x],[min_y,max_y],[min_z,max_z]]. If
the data is only two- or one-dimensional the extra
dimensions will be assumed to be [0,0]
samples : The number of samples in each dimension represented
as [samples_x, samples_y, samples_z, samples_t]. For
lower dimensional data the additional dimensions will
be set to 1, e.g. a time-dependent 2D array would be
[dim_x,dim_y,1,dim_t]
units : VisusUnit identfier describing the natural coordinate
system of the data (e.g. cartesian length, polar
coordinates, etc.) All possible values can be found in
VisusUnits.h
range : Local function range of the data. Note, that percentages
and fractions will automatically get a range of [0,100]
and [0,1] respectively.
time : The cdtime.Comptime of the first time step in data
"""
# If the user did not pass in an array try to extract one from the
# gui
if data is None:
data = guiDefinedData()
attributes = keywords
try:
# Unless the user has explicitly specified different units we
# treat the domain as a length box
if "units" not in attributes:
attributes["units"] = VISUS_METERS
# Try to infer as many data attributes as you can and return a
# string describing/defining the data in the ViSUS internal format
data_description = constructDataDescriptor(data, attributes)
except:
print "Could not parse data. Unknown format"
raise
# Create a font to be used in all the nodes that render text
font = VisusFont()
font.fontSize(6)
# Construct a root node for the scene graph hierarchy. This node is
# mainly used for the global transformation (e.g. rotating the
# complete scene) and is a convinient way to collect independent
# nodes (nodes that should have no ancestor-descendend
# relationship. Note, the use of the construct function rather than
# a standard "constructor." ViSUS uses a smart pointer / reference
# counting scheme which requires this.
root = VisusGroup.construct()
# To have a convinient reference point we want to display the
# bounding box of the root node which will automatically adjust to
# include the bounding box of all child nodes.
root.drawBoundingBox(True)
# Create two points that will form the left lower and right upper
# corner of the data bounding box
left = VectorDouble(3)
right = VectorDouble(3)
# Create a ViSUS data source based on the data description created earlier
source = VisusDataSourceFactory.make(data_description)
if not source.isValid():
raise RuntimeError("Problem loading data set")
# Get the bounding box of the data whose value may or may not depend
# on data attributes such as units. In particular, if this
# information was available the bounding box will be given in
# pyhsical units rather than in index space
source.domainBoundingBox(left, right)
bbox = VisusBoundingBox()
bbox.set(left, right)
# The ViSUS system is based on data "requests" that describe a
# particular piece of data that should be displayed. The data
# requests can be manipulated through the user interface or
# directly. However, here we want to create a sensible default
# request to see a portion of our data.
# First, we create an empty request
request = VisusDataRequest()
# Second, we construct an array that describes the length in each
# dimension of the box of data we want to extract in whatever units
# the data source uses (typically physical space).
extent = VectorDouble(3)
extent[0] = (right[0] - left[0]) # We want to see all of the x-coordinate
extent[1] = (right[1] - left[1]) # and all of the y-coordinate
extent[2] = 0 # But we want to look at a plane so the z extend is 0
# And we pass the extent information into the request
request.extent(extent)
# Second, we need to position our extent box in the data
# set. Initially, the extent will be centered around the origin. To
# see the middle of the data we want translate our request to the
# center of the data's bounding box
# So we create the corresponding transformation matrix
matrix = translationMatrix(
(left[0] + right[0]) / 2,
(left[1] + right[1]) / 2,
(left[2] + right[2]) / 2,
)
# And pass this matrix to the request
request.transformation(matrix)
# Third, we want to indicate to the ViSUS system that we are
# really interested in a 2D slice of the data (rather than a
# degenerate volume) as slices are a special case and implement some
# special user interactions
request.requestType(VISUS_2D_REQUEST)
# Finally, we describe the resolution at which we want to see our
# data. The resolution is defined in terms ofo sub-sampling
# "strides" where a stride of 1 defines the full resolution data a
# stride of 2, the data sub-sampled by a factor of 2 etc.. Strides
# must be powers of two but can be independently set for each
# dimension. Furthermore, for large data it can be useful to
# progressively refine the data starting at some higher strides /
# coarser resolution, ending at some lower strides / finer
# resolution. This preserves interactivity while still providing
# high resolution data if the request remains unchanged long enough
# Since, for default scenes we expect the data to be small we only
# want to see the highest resolution
start = [1, 1, 1]
end = [1, 1, 1]
request.setStrides(start, end)
# Armed with the description of the data and the request we just
# constructed we create a scene graph node that will extract the
# data for us. This node, will continuously test its current request
# and update the data accordingly. Therefore, one could adapt the
# scene by simply passing different requests to this node (which is
# what the graphical user interface does on mouse movements or
# keyboard commands)
# Create a producer node specialized to extract and axis aligned
# bounding box from a data set. As before with the root node the
# node must be explicitly constructed.
extractor = VisusAxisAlignedExtractor.construct()
# Set the data to extract our data from
extractor.setValue(data_description)
extractor.setValue(request) # Our default request
extractor.setValue(bbox)
# And the bounding box
extractor.drawBoundingBox(False)
# Now that we have a node that will extract the data we want to see
# we construct another node to visualize the slice
# We want to view an axis aligned slice of the data an the
# appropriate visualization node is one for orthogonal slices
slice = VisusOrthogonalSlice.construct()
# Now we connect the output of the extractor to the input of the
# visualization node
if not slice.connectInput(extractor):
raise RuntimeError("unable to connect extractor as slice input")
# For reasons internal to the underpinnings of the ViSUS system the
# visualization node must know about the current data request. The
# simplest way of achieving this is to attach the slice node as
# child to the extractor
extractor.attachSubTree(slice, True)
# and to inherit/share the request
slice.inherit(VisusSharedDataRequest.typeIndex(), True)
# In this configuration the slice node will naturally draw the data
# at the position of the data request which is the natural
# place. However, by manipulating the transformation matrix of the
# slice node we could draw the data anywhere in space / on the screen
# Instead of the default greyscale color map we want to use a
# typical red-to-blue map
color_map = banded()
slice.setValue(color_map)
# In order to see the slice even if it contains no data (if the
# request has been moved outside the data set, or the data is
# extremly large) we draw the bounding box of the slice
slice.drawBoundingBox(True)
# Now, we want to create labeled tickmarks to give the user a notion
# of the data range.
# Create a tickmark node that by default will create tickmarks along
# the x-axis of its bounding box
xTickMarks = VisusTickMarks.construct()
# ViSUS uses "axis" to control the labels of images, graphs, and
# also tickmarks. Here we get the current axis
axis = xTickMarks.axis()
axis.legendText("longitude") # Set the label
axis.labelFont(font) # and font
xTickMarks.axis(axis) # and update the axis
# Finally, we attach the tickmarks node as a child to the
# extractor. By default a tickmarks node inherits the bounding box
# of its parent and thus this node will now annotate the x-axis of
# our data set
extractor.attachSubTree(xTickMarks, True)
# Create tickmarks for the y-axis in an identical fashion
yTickMarks = VisusTickMarks.construct()
# Except that now we must name the axis explicitly
yTickMarks.direction(TM_Y_AXIS)
axis = yTickMarks.axis()
axis.legendText("latitude")
axis.labelFont(font)
yTickMarks.axis(axis)
extractor.attachSubTree(yTickMarks, True)
# Finally, we attach the whole subtree that renders the data plus
# annotation to the root node
root.attachSubTree(extractor, True)
# To further enhance the scene we want to add the continental
# outlines on top of the slice
# First, we load the cdat internal world map
world_map = loadWorldMap()
# And parse it as a collection of polylines. A VisusIndexedData
# object stores an array of n-dimensional points (n being 2 in this
# case) and a set of elements defined as ordered lists of indices
# into this array. Therefore, each contour of the world map is
# parsed as a single element
outlines = VisusIndexedData(2)
for m in world_map:
outlines.constructElement(m)
# Once the data is constructed we create a display node designed to visualize it
map = VisusIndexedDisplay.construct()
# And load the data.
map.loadData(outlines, 0)
# The cdat world map is given within (-180,-90)x(180,90). However,
# in order to display the normal lat,long, elev data easily in
# cartesian space we told ViSUS the data units are meters (meaning
# general distance) which will draw (0,0)x(360,180). To adjust the
# map to the slice we therefore must shift it
#map.getValue(matrix);
#matrix = matrix * translationMatrix(180,90,0.01)
#map.setValue(matrix)
# Attach the map as child of the extractor. Note, that from this moment
# on the map will inherit all transformations from the extractor and
# thus "stick" to the extractor if it is moved or rotated
extractor.attachSubTree(map)
# Lastly, we attach more annotations to the scene
# A VisusLabelNode is a general one line text of arbitrary font and
# size. First, we create a title string using the variable name if
# we found one
label_node = VisusLabelNode.construct()
title = "Variable: " + attributes["name"].replace("_", " ")
label_node.text(title)
label_node.setValue(font)
# Positions of labels are given relative to the screen size
label_node.position(-1, +0.8)
root.attachSubTree(label_node, True)
# If our data set is time-dependent we attach a label that displays
# the current time step
if "time" in attributes:
time_node = VisusLabelNode.construct()
t = attributes["time"]
title = "Date: %02d/%02d/%04d" % (t.month, t.day, t.year)
time_node.text(title)
time_node.setValue(font)
time_node.position(0.6, -0.95)
root.attachSubTree(time_node, True)
# A VisusColorBar displays a bar with a color map on the inside and
# allows us to attach tickmarks and labels
color_bar = VisusColorBar.construct()
# As before fopr the tickmark node we modify the labels via the Axis construct
axis = color_bar.axis()
# If we found a variable name we use it as label
if "var_name" in attributes:
axis.legendText(attributes["var_name"])
# If we found a range we use it as well.
if "range" in attributes:
axis.minValue(float(attributes["range"][0]))
axis.maxValue(float(attributes["range"][1]))
font.fontSize(4)
axis.legendFont(font)
axis.labelFont(font)
color_bar.position(-1.2, -0.8)
color_bar.axis(axis)
# Finally, attach the color bar to the slice and make sure it
# inherits its colormap from there
slice.attachSubTree(color_bar, True)
#color_bar.inherit(VisusSharedColorMap.typeIndex(),True)
color_bar.inherit(8, True)
# Lastly we must make sure that we actually see the data on the
# screen which means we need to make sure that the data is scaled
# and translated appropriately. (Alternatively, once can think of
# this step as setting up the viewpoint of our virtual camera.
# Using the current bounding box of the scene we adapt a default box
# to the aspect ratios of of our scene and use it as our "world
# coordinate system"
world_box = constructWorldBox(bbox)
root.setValue(bbox)
# Now we make sure that the data is drawn in our standard coordinate
# system
root.mapToWorldBox(world_box)
# Finally, most climate data contains significantly more samples in
# x/y than in z. Since, for the cartesian slice we effecticely draw
# the data in index space since is inconvinient as the volume wille
# very "flat." To compensate for this we simply scale the z
# coordinate of our system
root.getValue(matrix)
matrix = matrix * scaleMatrix(1, 1, 10)
root.setValue(matrix)
# As the final step we create ourselfs a window to display the scene
# graph hanging under the root node
createWindow(root)
return root, data_description
def __init__( self, dataArray, title=None, usrAttribs=dict(), root=None,
newWindow = True):
# Creating the default root within the __init__ call somehow
# causes the root variable to persist and only a single root to be
# created for multiple scenes. To avoid this we use the explicit
# None initialization
if root == None:
root = VisusGroup.construct()
#root = VisusSceneNode.construct()
# Create a root node
self._root = root
# self._root.drawBoundingBox(False)
# Create the attributes
self._attribs = dict()
# Create the extractor, data description and update attribs
self.setData(dataArray, usrAttribs)
# Attach the extractor to the root
self._root.attachSubTree(self._extractor, True)
self._extractor.propagateUpwards(VISUS_GLOBALTIME_TYPEID);
# Create the drawing window
if newWindow:
print "Creating new window of root ", root
createWindow(self._root)
# Create the node for the slice of data
self._orthoSlice = VisusOrthogonalSlice.construct()
# Connect the output of the extractor to the input of each slice node
if not self._orthoSlice.connectInput(self._extractor):
raise RuntimeError("unable to connect extractor1 as slice input")
# Attach the ortho slice to the root as a subtree
self._extractor.attachSubTree(self._orthoSlice)
# Inherit/Share the data request
# Now that we have positioned the slices where we want them we want
# to freeze their position. To do this we simply uninherit the data
self._orthoSlice.inherit(VisusSharedDataRequest.typeIndex(),True)
# This should be done autmoatically in the SliceNode
# Set the correct bounding box for the slice
# request = VisusDataRequest()
# self._orthoSlice.getValue(request)
# self._orthoSlice.setValue(request.queryRegion())
# Set the colormap
#self.setColorMap(ice())
self.setColorMap(bgry())
# Draw the bounding boxes of the slice
self._orthoSlice.drawBoundingBox(True)
# Set up the annotations for the colorbar etc
self.setAnnote()
# Add the world map
world_map = self.loadWorldMap()
# And parse it as a collection of polylines. A VisusIndexedData
# object stores an array of n-dimensional points (n being 2 in this
# case) and a set of elements defined as ordered lists of indices
# into this array. Therefore, each contour of the world map is
# parsed as a single element
outlines = VisusIndexedData(2)
for m in world_map:
outlines.constructElement(m)
# Once the data is constructed we create a display node designed to visualize it
self._map = VisusIndexedDisplay.construct()
# And load the data.
self._map.loadData(outlines,0)
# Move the map to the top of the data set so that is it always above the slice
matrix = translationMatrix(0,0,0)
self._map.getValue(matrix)
matrix = matrix * translationMatrix(0,0,0.01)
# This is a really ugly hack to adjust for the [0,360] [-180,180] ambiguity
if self._bbox[0] > -90:
matrix = matrix * translationMatrix(180,0,0)
self._map.setValue(matrix)
# Attach the map as child of the extractor. Note, that from this moment
# on the map will inherit all transformations from the extractor and
# thus "stick" to the extractor if it is moved or rotated
self._orthoSlice.attachSubTree(self._map)
#self._extractor.attachSubTree(self._map)
# Add a title to the scene
if(title==None):
if "name" in self._attribs:
title = self._attribs["name"].replace("_", " ")
else:
title = "Title"
font = VisusFont()
font.fontSize(12);
self._title = VisusLabelNode.construct()
self._title.text(title)
self._title.setValue(font)
self._title.position( 0, 0.92)
self._root.attachSubTree(self._title,True)