I'm trying to get the element density from the abaqus output database. I know you can request a field output for the volume using 'EVOL', is something similar possible for the density?
I'm afraid it's not because of this: Getting element mass in Abaqus postprocessor
What would be the most efficient way to get the density? Look for every element in which section set it is?
Found a solution, I don't know if it's the fastest but it works:
odb_file_path=r'your_path\file.odb'
odb = session.openOdb(name=odb_file_path)
instance = odb.rootAssembly.instances['MY_PART']
material_name = instance.elements[0].sectionCategory.name[8:-2]
density=odb.materials[material_name].density.table[0][0])
note: the 'name' attribute will give you a string like, 'solid MATERIALNAME'. So I just cut out the part of the string that gave me the real material name. So it's the sectionCategory attribute of an OdbElementObject that is the answer.
EDIT: This doesn't seem to work after all, it turns out that it gives all elements the same material name, being the name of the first material.
The properties are associated something like this:
sectionAssignment connects section to set
set is the container for element
section connects sectionAssignment to material
instance is connected to part (could be from a part from another model)
part is connected to model
model is connected to section
Use the .inp or .cae file if you can. The following gets it from an opened cae file. To thoroughly get elements from materials, you would do something like the following, assuming you're starting your search in rootAssembly.instances:
Find the parts which the instances were created from.
Find the models which contain these parts.
Look for all sections with material_name in these parts, and store all the sectionNames associated with this section
Look for all sectionAssignments which references these sectionNames
Under each of these sectionAssignments, there is an associated region object which has the name (as a string) of an elementSet and the name of a part. Get all the elements from this elementSet in this part.
Cleanup:
Use the Python set object to remove any multiple references to the same element.
Multiply the number of elements in this set by the number of identical part instances that refer to this material in rootAssembly.
E.g., for some cae model variable called model:
model_part_repeats = {}
model_part_elemLabels = {}
for instance in model.rootAssembly.instances.values():
p = instance.part.name
m = instance.part.modelName
try:
model_part_repeats[(m, p)] += 1
continue
except KeyError:
model_part_repeats[(m, p)] = 1
# Get all sections in model
sectionNames = []
for s in mdb.models[m].sections.values():
if s.material == material_name: # material_name is already known
# This is a valid section - search for section assignments
# in part for this section, and then the associated set
sectionNames.append(s.name)
if sectionNames:
labels = []
for sa in mdb.models[m].parts[p].sectionAssignments:
if sa.sectionName in sectionNames:
eset = sa.region[0]
labels = labels + [e.label for e in mdb.models[m].parts[p].sets[eset].elements]
labels = list(set(labels))
model_part_elemLabels[(m,p)] = labels
else:
model_part_elemLabels[(m,p)] = []
num_elements_with_material = sum([model_part_repeats[k]*len(model_part_elemLabels[k]) for k in model_part_repeats])
Finally, grab the material density associated with material_name then multiply it by num_elements_with_material.
Of course, this method will be extremely slow for larger models, and it is more advisable to use string techniques on the .inp file for faster performance.
Related
This is mostly a logical question, but the context is done in Django.
In our Database we have Vertex and Line Classes, these form a (neural)network, but it is unordered and I can't change it, it's a Legacy Database
class Vertex(models.Model)
code = models.AutoField(primary_key=True)
lines = models.ManyToManyField('Line', through='Vertex_Line')
class Line(models.Model)
code = models.AutoField(primary_key=True)
class Vertex_Line(models.Model)
line = models.ForeignKey(Line, on_delete=models.CASCADE)
vertex = models.ForeignKey(Vertex, on_delete=models.CASCADE)
Now, in the application, the user will be able to visually select TWO vertexes (the green circles below)
The javascript will then send the pk of these two Vertexes to Django, and it has to find the Line classes that satisfy a route between them, in this case, the following 4 red Lines :
Business Logic:
A Vertex can have 1-4 Lines related to it
A Line can have 1-2 Vertexes related to it
There will only be one possible route between two Vertexes
What I have so far:
I understand that the answer probably includes recursion
The path must be found by trying every path from one Vertex untill the other is find, it can't be directly found
Since there are four and three-way junctions, all the routes being tried must be saved throughout the recursion(unsure of this one)
I know the basic logic is looping through all the lines of each Vertex, and then get the other Vertex of these lines, and keep walking recursively, but I really don't know where to start on this one.
This is as far as I could get, but it probably does not help (views.py) :
def findRoute(request):
data = json.loads(request.body.decode("utf-8"))
v1 = Vertex.objects.get(pk=data.get('v1_pk'))
v2 = Vertex.objects.get(pk=data.get('v2_pk'))
lines = v1.lines.all()
routes = []
for line in lines:
starting_line = line
#Trying a new route
this_route_index = len(routes)
routes[this_route_index] = [starting_line.pk]
other_vertex = line.vertex__set.all().exclude(pk=v1.pk)
#There are cases with dead-ends
if other_vertex.length > 0:
#Mind block...
As you has pointed, this is not a Django/Python related question, but a logical/algorithmic matter.
To find paths between two vertexes in a graph you can use lot of algorithms: Dijkstra, A*, DFS, BFS, Floyd–Warshall etc.. You can choose depending on what you need: shortest/minimum path, all paths...
How to implement this in Django? I suggest to don't apply the algorithm over the models itself, since this could be expensive (in term of time, db queries, etc...) specially for large graphs; instead, I'd rather to map the graph in an in-memory data structure and execute the algorithm over it.
You can take a look to this Networkx, which is a very complete (data structure + algorithms) and well documented library; python-graph, which provides a suitable data structure and a whole set of important algorithms (including some of the mentioned above). More options at Python Graph Library
Good evening,
i have done a script for getting a model and to generate results. I've tried to write in the same script a way for getting to read the values of stresses but python says :
" File "C:/Users/TFG", line 250, in <module> RegionTen=odb.rootAssembly.noseSets['Set-1'] KeyError: Set-1 "
I understand like Set-1 doesnt exit but that's not true. I hope someones can help me.
I create Set-1 :
mdb.models['Model-1'].parts['Part-1'].Set(faces= mdb.models['Model-1'].parts['Part-1'].faces.getSequenceFromMask(('[#1 ]', ), ), name='Set-1')
And my code for getting to read the stresses is:
odb = openOdb( path='C:\Temp\Job-1.odb')
RegionTen = odb.rootAssembly.nodeSets['Set-1']
tamFrames = len(odb.steps['Step-1'].frames)
lastFrames = odb.steps['Step-1'].frames[tamFrames-1]
stress33 = lastFrame.fieldOutputs['S'].getSubset(position=ELEMENT_NODAL, region=RegionTen)
stress13 = lastFrame.fieldOutputs['CTSHR13'].getSubset(position=ELEMENT_NODAL, region=RegionTen)
stress23 = lastFrame.fieldOutputs['CTSHR23'].getSubset(position=ELEMENT_NODAL, region=RegionTen)
print(stress11, stress22, stress12)
You are now trying to get an assembly level node set. Yet, you are defining your node set inside a part. Inside the Odb, you need to access this node set through an instance.
Figure out the instance name from the part name. Most likely it's just 'PART-1-1'. After you do this, get the region like this:
regionTen = odb.rootAssembly.instances['instanceName'].nodeSets['Set-1']
You can see the difference between these set types in Abaqus. Instance level node sets have a prefix - 'InstanceName.'. Notice a dot after an instance name.
You can access the Node Sets defined in the Assembly using the following way:
odb.rootAssembly.nodeSets.keys()
And yes they seem to appear all in upper case.
I recently got introduced to the library configparser. I would like to be able to check if each section has at least one Boolean value set to 1. For example:
[Horizontal_Random_Readout_Size]
Small_Readout = 0
Medium_Readout = 0
Large_Readout = 0
The above would cause an error.
[Vertical_Random_Readout_Size]
Small_Readout = 0
Medium_Readout = 0
Large_Readout = 1
The above would pass. Below is some pseudo code of what I had in mind:
exit_test = False
for sections in config_file:
section_check = False
for name in parser.options(section):
if parser.getboolean(section, name):
section_check = True
if not section_check:
print "ERROR:Please specify a setting in {} section of the config file".format(section)
exit_test = True
if exit_test:
exit(1)
Questions:
1) How do I perform the first for loop and iterate over the sections of the config file?
2) Is this a good way of doing this or is there a better way? (If there isn't please answer question one.)
Using ConfigParser you have to parse your config.
After parsing you will get all sections using .sections() method.
You can iterate over each section and use .items() to get all key/value pairs of each section.
for each_section in conf.sections():
for (each_key, each_val) in conf.items(each_section):
print each_key
print each_val
Best bet is to load ALL the lines in the file into some kind of array (I'm going to ignore the issue of how much memory that might use and whether to page through it instead).
Then from there you know that lines denoting headings follow a certain format, so you can iterate over your array to create an array of objects containing the heading name; the line index (zero based reference to master array) and whether that heading has a value set.
From there you can iterate over these objects in cross-reference to the master array, and for each heading check the next "n" lines (in the master array) between the current heading and the next.
At this point you're down to the individual config values for that heading so you should easily be able to parse the line and detect a value, whereupon you can break from the loop if true, or for more robustness issue an exclusivity check on those heading's values in order to ensure ONLY one value is set.
Using this approach you have access to all the lines, with one object per heading, so your code remains flexible and functional. Optimise afterwards.
Hope that makes sense and is helpful.
To complete the answer by #Nilesh and comment from #PashMic, here is an example that really iterate over ALL sections, including DEFAULT:
all_section_names: list[str] = conf.sections()
all_section_names.append("DEFAULT")
for section_name in all_section_names:
for key, value in conf.items(section_name):
...
Note that even if there is no real "DEFAULT" section, this will still works. There will just be no item retreived by conf.items("DEFAULT").
I've this small snipped of code that load a file in an embedded Neo4j Database.
With this code I've two problems and I don't find documentation to solve it.
I'm following the samples of the documentation to create an index, but:
a) How can I detect if the index exists yet? The documentation explais that if the index already exists,
it's returned, but in my case it returns an error.
b) When I get the a node from the index I get an error
from neo4j import GraphDatabase, INCOMING, Evaluation
# Create a database
db = GraphDatabase("c:/temp/graph")
with db.transaction:
# Create an index for "users" nodes
# to look for them using some of the properties
# HERE I GET AN ERROR WHEN THE INDEX EXISTS PREVIOUSLY, BUT THE DOCUMENTATION EXPLAINS THE OPOSITE.
users_idx = db.node.indexes.create('users')
# Create the "users_reference" node to connect all "users" nodes to here
users_reference = db.node()
db.reference_node.USERS(users_reference, provider='lucene', type='fulltext')
'''
Content of the file
1,Marc
2,Didac
3,Sergi
4,David
'''
f = open("../files/users","r")
for line in f:
v = line.split(",")
i = v[0]
name = v[1]
# All write operations happen in a transaction
user = db.node(id=i, name=name)
user.INSTANCE_OF(users_reference)
users_idx['id'][i] = user
# I suppose that here, the transaction is closed
# I want get the node whose property "Id" has value "3"
# to print the property "name" of the node with id = 3
# HERE I GET AN ERROR WHEN THE THERE'RE MULTIPLE NODES WITH THE SAME VALUE FOR THE PROPERTY "ID"
c = users_idx['id']['3'].single
print c['name']
'''
If the file has a duplicated ID, the previouly code returns an error...
1,Marc
1,Marc_2
1,Marc_3
2,Didac
3,Sergi
4,David
'''
# Always shut down your database when your application exits
db.shutdown()
In your first example, the documentation is wrong. There is currently only one way to determine if an index exists, and it is to check for a ValueError when getting an index. Like this:
try:
idx = db.node.indexes.get('my_index')
except ValueError,e:
idx = db.node.indexes.create('my_index')
That should be changed to some more specific exception, since this pattern breaks if something else triggers a ValueError.. Will add an issue for that.
I've just pushed an update to the documentation, and I've added an "exists" method, to check if an index exists. It will be available on Pypi after the next neo4j milestone release.
if db.node.indexes.exists('my_index'):
db.node.indexes.get('my_index')
else:
db.node.indexes.create('my_index')
In your second example, I think this is correct behavior. The 'single' property assures that there is a single result. If you expect a single result, but get multiple, that is an error. If you want the first result, you should be able to do something like:
hits = iter(users_idx['id']['3'])
c = hits.next()
hits.close()
I am trying to work out a way to change between two sets of labels on a map. I have a map with zip codes that are labeled and I want to be able to output two maps: one with the zip code label (ZIP) and one with a value from a field I have joined to the data (called chrlabel). The goal is to have one map showing data for each zip code, and a second map giving the zip code as reference.
My initial attempt which I can't get working looks like this:
1) I added a second data frame to my map and add a new layer that contains two polygons with names "zip" and "chrlabel".
2) I use this frame to enable data driven pages and then I hide it behind the primary frame (I don't want to see those polygons, I just want to use them to control the data driven pages).
3) In the zip code labels I tried to write a VBScript expression like this pseudo-code:
test = "
If test = "zip" then
label = ZIP
else
label = CHRLABEL
endif
This does not work because the dynamic text does not resolve to the page name in the VBScript.
Is there some way to call the page name in VBScript so that I can make this work?
If not, is there another way to do this?
My other thought is to add another field to the layer that gets filled with a one or a zero. Then I could replace the if-then test condition with if NewField = 1.
Then I would just need to write a script that updates all the NewFields for the zipcode features when the data driven page advances to the second page. Is there a way to trigger a script (python or other) when a data driven page changes?
Thanks
8 months too late, but for posterity...
You're making things hard on yourself - it would be much easier to set up a duplicate layer and use different layers, then adjust layer visibility. I'm not familiar with VBScript for this sort of thing, but in Python (using ESRI's library) it would look something like so [python 2.6, ArcMap 10 - sample only, haven't debugged this but I do similar things quite often]:
from arcpy import mapping
## Load the map from disk
mxdFilePath = "C:\\GIS_Maps_Folder\\MyMap.mxd"
mapDoc = mapping.MapDocument(mxdFilePath)
## Load map elements
dataFrame = mapping.ListDataFrames(mapDoc)[0] #assumes you want the first dataframe; you can also search by name
mxdLayers = mapping.ListLayers(dataFrame)
## Adjust layers
for layer in mxdLayers:
if (layer.name == 'zip'):
zip_lyr = layer
elif(layer.name == 'sample_units'):
labels_lyr = layer
## Print zip code map
zip_lyr.visible = True
zip_lyr.showLabels = True
labels_lyr.visible = False
labels_lyr.showLabels = False
zip_path = "C:\\Output_Folder\\Zips.pdf"
mapping.ExportToPDF(mapDoc, zip_path, layers_attributes="NONE", resolution=150)
## Print labels map
zip_lyr.visible = False
zip_lyr.showLabels = False
labels_lyr.visible = True
labels_lyr.showLabels = True
labels_path = "C:\\Output_Folder\\Labels.pdf"
mapping.ExportToPDF(mapDoc, labels_path, layers_attributes="NONE", resolution=150)
## Combine files (if desired)
pdfDoc = mapping.PDFDocumentCreate("C:\\Output_Folder\\Output.pdf"")
pdfDoc.appendPages(zip_path)
pdfDoc.appendPages(labels_path)
pdfDoc.saveAndClose()
As far as the Data Driven Pages go, you can export them all at once or in a loop, and adjust whatever you want, although I'm not sure why you'd need to if you use something similar to the above. The ESRI documentation and examples are actually quite good on this. (You should be able to get to all the other Python documentation pretty easily from that page.)