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How to to calculate unnested watersheds in GRASS GIS?

I am running into a few issues using the GRASS GIS module r.accumulate while running it in Python. I use the module to calculate sub watersheds for over 7000 measurement points. Unfortunately, the output of the algorithm is nested. So all sub watersheds are overlapping each other. Running the r.accumulate sub watershed module takes roughly 2 minutes for either one or multiple points, I assume the bottleneck is loading the direction raster.
I was wondering if there is an unnested variant in GRASS GIS available and if not, how to overcome the bottleneck of loading the direction raster every time you call the module accumulate. Below is a code snippet of what I have tried so far (resulting in a nested variant):
locations = VectorTopo('locations',mapset='PERMANENT')
locations.open('r')
points=[]
for i in range(len(locations)):
points.append(locations.read(i+1).coords())
for j in range(0,len(points),255):
output = "watershed_batch_{}#Watersheds".format(j)
gs.run_command("r.accumulate", direction='direction#PERMANENT', subwatershed=output,overwrite=True, flags = "r", coordinates = points[j:j+255])
gs.run_command('r.stats', flags="ac", input=output, output="stat_batch_{}.csv".format(j),overwrite=True)
Any thoughts or ideas are very welcome.

I already replied to your email, but now I see your Python code and better understand your "overlapping" issue. In this case, you don't want to feed individual outlet points one at a time. You can just run
r.accumulate direction=direction#PERMANENT subwatershed=output outlet=locations
r.accumulate's outlet option can handle multiple outlets and will generate non-overlapping subwatersheds.

The answer provided via email was very usefull. To share the answer I have provided the code below to do an unnested basin subwatershed calculation. A small remark: I had to feed the coordinates in batches as the list of coordinates exceeded the max length of characters windows could handle.
Thanks to #Huidae Cho, the call to R.accumulate to calculate subwatersheds and longest flow path can now be done in one call instead of two seperate calls.
The output are unnested basins. Where the largers subwatersheds are seperated from the smaller subbasins instead of being clipped up into the smaller basins. This had to with the fact that the output is the raster format, where each cell can only represent one basin.
gs.run_command('g.mapset',mapset='Watersheds')
gs.run_command('g.region', rast='direction#PERMANENT')
StationIds = list(gs.vector.vector_db_select('locations_snapped_new', columns = 'StationId')["values"].values())
XY = list(gs.vector.vector_db_select('locations_snapped_new', columns = 'x_real,y_real')["values"].values())
for j in range(0,len(XY),255):
output_ws = "watershed_batch_{}#Watersheds".format(j)
output_lfp = "lfp_batch_{}#Watersheds".format(j)
output_lfp_unique = "lfp_unique_batch_{}#Watersheds".format(j)
gs.run_command("r.accumulate", direction='direction#PERMANENT', subwatershed=output_ws, flags = "ar", coordinates = XY[j:j+255],lfp=output_lfp, id=StationIds[j:j+255], id_column="id",overwrite=True)
gs.run_command("r.to.vect", input=output_ws, output=output_ws, type="area", overwrite=True)
gs.run_command("v.extract", input=output_lfp, where="1 order by id", output=output_lfp_unique,overwrite=True)
To export the unique watersheds I used the following code. I had to transform the longest_flow_path to point as some of the longest_flow_paths intersected with the corner boundary of the watershed next to it. Some longest flow paths were thus not fully within the subwatershed. See image below where the red line (longest flow path) touches the subwatershed boundary:
enter image description here
gs.run_command('g.mapset',mapset='Watersheds')
lfps= gs.list_grouped('vect', pattern='lfp_unique_*')['Watersheds']
ws= gs.list_grouped('vect', pattern='watershed_batch*')['Watersheds']
files=np.stack((lfps,ws)).T
#print(files)
for file in files:
print(file)
ids = list(gs.vector.vector_db_select(file[0],columns="id")["values"].values())
for idx in ids:
idx=int(idx[0])
expr = f'id="{idx}"'
gs.run_command('v.extract',input=file[0], where=expr, output="tmp_lfp",overwrite=True)
gs.run_command("v.to.points", input="tmp_lfp", output="tmp_lfp_points", use="vertex", overwrite=True)
gs.run_command('v.select', ainput= file[1], binput = "tmp_lfp_points", output="tmp_subwatersheds", overwrite=True)
gs.run_command('v.db.update', map = "tmp_subwatersheds",col= "value", value=idx)
gs.run_command('g.mapset',mapset='vector_out')
gs.run_command('v.dissolve',input= "tmp_subwatersheds#Watersheds", output="subwatersheds_{}".format(idx),col="value",overwrite=True)
gs.run_command('g.mapset',mapset='Watersheds')
gs.run_command("g.remove", flags="f", type="vector",name="tmp_lfp,tmp_subwatersheds")
I ended up with a vector for each subwatershed

getting error while accessing the same code for get pixel value

I am trying to read RGB pixels from a numpy ndarray type image. I implemented it in 2 class files. To search for histograms and look for momen. The first process will go through a histogram search first (calling the class histogram), then the process continues by searching for the momen (calling the class momen). In both of these classes there is each process of reading the RGB pixels of the input image. I access RGB pixels using the following code:
def getbyte(self, gambar):
for i in range(0,gambar.shape[0]):
for j in range(0,gambar.shape[1]):
self.p.append(gambar[i,j])
self.flat = [i for sets in self.p for i in sets]
return self.flat
Then I run the program. If you see from the flow process, then the histogram will be processed before momen. When I run, when I look at the console (I use spyder IDE), the process has arrived at the momen search line, it indicates that the histogram search has succeeded, right?
Now then what I want to ask, when searching for moments is executed, I get an error in the reading of the RGB pixel of the image, as follows:
self.flat = [i for sets in self.pMoment for i in sets]
TypeError: 'numpy.uint8' object is not iterable
Strangely, when searching for a histogram with the exact pixel capture code, the error does not appear. But why when searching for the momen this code has a problem and an error appears? What error is that?
Please help

assuming the self.p attribute is a list (my assumption is based on theappend ()method used) then the error code is in the loop adding a value to the self.p attribute. Supposedly, before flattening, attribute self.p should be a list of list. However, in the code self.p is still a list of ints.
So what needs to be done is to temporarily store the pixel values ​​in the image in each row using the new variables. Then this variable will be added (append) to self.p.
The code snippet will look like this:
def getbyte (self, image):
for i in range (0, image.shape [0]):
rowPixel = []
for j in range (0, images.shape [1]):
rowPixel.append (image [i, j])
self.p.append (rowPixel)
self.flat = [i for sets in p for i in sets]
return self.flat
I have tried the code above using a 2-dimensional image variable of type np.ndarray. Because of my assumption you are doing 2 nested loops.

Extracting quantities of elements with IfcOpenShell in python

I am trying to read the deepest level of quantities for objects in an ifc file, using IfcOpenShell in Python. So far I have:
import ifcopenshell
path = r'D:\ifcos_1\slab.ifc'
ifc_file = ifcopenshell.open(path)
geometries = ifc_file.by_type("IfcProduct")
for geometry in geometries:
if geometry.is_a("IfcSlab"):
print geometry
test = geometry.IfcPhysicalQuantity()
print test
I've studied the definitions
No matter which type of function I try to place for the test = geometry.X(), I get an error:
File "C:\Python27\lib\site-packages\ifcopenshell\entity_instance.py", line 48, in __getattr__
"entity instance of type '%s' has no attribute '%s'" % (self.wrapped_data.is_a(), name))
AttributeError: entity instance of type 'IfcSlab' has no attribute 'IfcPhysicalQuantity'
Not sure how to solve this and would appreciate help.
EDIT:
Further work which gets the slab and further references:
for geometry in geometries:
if geometry.is_a("IfcSlab"):
print geometry
definedBy = geometry.IsDefinedBy
print definedBy[0]
for each in definedBy:
test = each.is_a()
print test
As of this moment the obstacle is the compatibility with IFC4, which I will try to recompile with instructions from this forum post.
EDIT 2:
Further work using for now the IFC 2x3 standard, with a file that has quantity information (verified via the raw data). The following is the relevant code:
for geometry in geometries:
if geometry.is_a("IfcSlab"):
definedBy = geometry.IsDefinedBy
for line in definedBy:
test = line.is_a()
# print test
if line.is_a() == 'IfcRelDefinesByProperties' or line.is_a() == 'IfcRelDefinesByType':
step1 = line.RelatingPropertyDefinition
step2 = step1.is_a()
print step2
There is an error no matter what I place after step1 = line., none of the following give a result:
line.IfcPropertySet
line.IfcElementQuantity
line.RelatingPropertyDefiniton
The output from this code is nevertheless:
IfcPropertySet
IfcPropertySet
IfcPropertySet
IfcPropertySet
IfcPropertySet
IfcPropertySet
IfcPropertySet
IfcPropertySet
IfcPropertySet
IfcPropertySet
IfcElementQuantity
IfcElementQuantity
Which means that I can access IfcElementQuantity but none of the Attributes work. I've looked at schema reference but can't find the correct one.

When implementing this you should take care which version of IFC your import library works on - the version distributed by the IfcOpenShell-Website works with IFC2X3. For IFC4 you will probably need to compile a version yourself. (you can check your IFC version with ifcopenshell.schema_identifier)
I am putting links up to the buildingSMART IFC 4 definition even if I am talking about IFC2X3. Differences to IFC2X3 are marked in red on the buildingSMART web page. And the IFC4 definition is nicer to read (IMO).
Quantities aren't directly attached like an attribute. They are written as property sets and then related to the element or element type. So first you should ensure your IFC file contains quantities - otherwise you will not find any. Usually you start with a specific product - ifc_file.by_type('IfcSlab'). You can reach the property sets via inverse attributes - those are usually set up by the IFC library, they don't appear directly as attribute in the file.
I took you example file and shortened it to about a third (so it's still a valid IFC2X3 file):
ISO-10303-21;
HEADER;FILE_DESCRIPTION(('ViewDefinition [Custom, QuantityTakeOffAddOnView, SpaceBoundary2ndLevelAddOnView]','Option [Drawing Scale: 100.000000]','Option [Global Unique Identifiers (GUID): Keep existing]','Option [Elements to export: Visible elements (on all stories)]','Option [Partial Structure Display: Entire Model]','Option [IFC Domain: All]','Option [Structural Function: All Elements]','Option [Convert Grid elements: On]','Option [Convert IFC Annotations and ARCHICAD 2D elements: Off]','Option [Convert 2D symbols of Doors and Windows: Off]','Option [Explode Composite and Complex Profile elements into parts: On]','Option [Export geometries that Participates in Collision Detection only: Off]','Option [Elements in Solid Element Operations: Extruded/revolved]','Option [Elements with junctions: Extruded/revolved without junctions]','Option [Slabs with slanted edge(s): Extruded]','Option [Use legacy geometric methods as in Coordination View 1.0: Off]','Option [IFC Site Geometry: As boundary representation (BRep)]','Option [IFC Site Location: At Project Origin]','Option [Curtain Wall export mode: Container Element]','Option [Railing export mode: Single Element]','Option [Stair export mode: Container Element]','Option [Properties To Export: All properties]','Option [Space containment: On]','Option [IFC Domain For Space Containment: All]','Option [Bounding Box: Off]','Option [Geometry to type objects: Off]','Option [Element Properties: All]','Option [Property Type Element Parameter: On]','Option [Quantity Type Element Parameter: On]','Option [IFC Base Quantities: On]','Option [Window Door Lining and Panel Parameters: On]','Option [IFC Space boundaries: On]','Option [ARCHICAD Zone Categories as IFC Space classification data: On]','Option [Element Classifications: On]'),'2;1');
FILE_NAME('D:\\Side Projects\\Paragraph3\\The database\\IFC Files\\Local tests\\ifcos_1\\slab.ifc','2018-06-13T18:28:40',('Architect'),('Building Designer Office'),'The EXPRESS Data Manager Version 5.02.0100.09 : 26 Sep 2013','IFC file generated by GRAPHISOFT ARCHICAD-64 21.0.0 INT FULL Windows version (IFC2x3 add-on version: 3005 INT FULL).','The authorising person');
FILE_SCHEMA(('IFC2X3'));
ENDSEC;
DATA;
#1= IFCPERSON($,'Undefined',$,$,$,$,$,$);
#7= IFCPERSONANDORGANIZATION(#1,#10,$);
#10= IFCORGANIZATION('GS','GRAPHISOFT','GRAPHISOFT',$,$);
#11= IFCAPPLICATION(#10,'21.0.0','ARCHICAD-64','IFC2x3 add-on version: 3005 INT FULL');
#12= IFCOWNERHISTORY(#7,#11,$,.ADDED.,$,$,$,1528907320);
#13= IFCSIUNIT(*,.LENGTHUNIT.,.MILLI.,.METRE.);
#14= IFCSIUNIT(*,.AREAUNIT.,$,.SQUARE_METRE.);
#16= IFCSIUNIT(*,.PLANEANGLEUNIT.,$,.RADIAN.);
#17= IFCMEASUREWITHUNIT(IFCPLANEANGLEMEASURE(0.0174532925199),#16);
#18= IFCDIMENSIONALEXPONENTS(0,0,0,0,0,0,0);
#19= IFCCONVERSIONBASEDUNIT(#18,.PLANEANGLEUNIT.,'DEGREE',#17);
#29= IFCUNITASSIGNMENT((#13,#14,#19));
#31= IFCDIRECTION((1.,0.,0.));
#35= IFCDIRECTION((0.,0.,1.));
#37= IFCCARTESIANPOINT((0.,0.,0.));
#39= IFCAXIS2PLACEMENT3D(#37,#35,#31);
#40= IFCDIRECTION((0.,1.));
#42= IFCGEOMETRICREPRESENTATIONCONTEXT($,'Model',3,1.00000000000E-5,#39,#40);
#45= IFCPROJECT('344O7vICcwH8qAEnwJDjSU',#12,'Project',$,$,$,$,(#42),#29);
#59= IFCLOCALPLACEMENT($,#39);
#62= IFCSITE('20FpTZCqJy2vhVJYtjuIce',#12,'Site',$,$,#59,$,$,.ELEMENT.,(47,33,34,948800),(19,3,17,204400),0.,$,$);
#68= IFCRELAGGREGATES('0Du7$nzQXCktKlPUTLFSAT',#12,$,$,#45,(#62));
#74= IFCQUANTITYLENGTH('GrossPerimeter',$,$,0.);
#76= IFCQUANTITYAREA('GrossArea',$,$,0.);
#77= IFCELEMENTQUANTITY('2GNZepdf73fvGc$0W6rozj',#12,'BaseQuantities',$,'ARCHICAD BIM Base Quantities',(#74,#76));
#82= IFCRELDEFINESBYPROPERTIES('2Hm9JvZjohDNSD2kdxZI3b',#12,$,$,(#62),#77);
#93= IFCLOCALPLACEMENT(#59,#39);
#95= IFCBUILDING('00tMo7QcxqWdIGvc4sMN2A',#12,'Building',$,$,#93,$,$,.ELEMENT.,$,$,$);
#97= IFCRELAGGREGATES('2b_h_mYcGArd6glJG2Fmbt',#12,$,$,#62,(#95));
#101= IFCQUANTITYAREA('GrossFloorArea',$,$,0.);
#102= IFCELEMENTQUANTITY('1kQMlmT0rD35a9E43iKTas',#12,'BaseQuantities',$,'ARCHICAD BIM Base Quantities',(#101));
#104= IFCRELDEFINESBYPROPERTIES('0L87OdSD3DqSTjSRlAciZL',#12,$,$,(#95),#102);
#115= IFCLOCALPLACEMENT(#93,#39);
#117= IFCBUILDINGSTOREY('1oZ0wPs_PE8ANCPg3bIs4j',#12,'Ground Floor',$,$,#115,$,$,.ELEMENT.,0.);
#119= IFCRELAGGREGATES('118jwqMnuwK1xuf97w7fU5',#12,$,$,#95,(#117));
#180= IFCSLAB('3W29Drc$H6CxK3FGIxjJNl',#12,'SLA - 001',$,$,$,$,'E0089375-9BF4-4633-B503-3D04BBB535EF',.FLOOR.);
#195= IFCRELCONTAINEDINSPATIALSTRUCTURE('04ldtj6cp2dME6CiP80Bzh',#12,$,$,(#180),#117);
#326= IFCPROPERTYSINGLEVALUE('Fragility rating',$,IFCLABEL('0'),$);
#327= IFCPROPERTYSINGLEVALUE('Tile dimensions',$,IFCLABEL('Undefined'),$);
#328= IFCPROPERTYSINGLEVALUE('Anti-static Surface',$,IFCBOOLEAN(.F.),$);
#329= IFCPROPERTYSINGLEVALUE('Non-skid Surface',$,IFCBOOLEAN(.F.),$);
#330= IFCPROPERTYSET('0LYX8AqOOS9ft8M4aJYEYa',#12,'FLOORINGS',$,(#326,#327,#328,#329));
#332= IFCRELDEFINESBYPROPERTIES('1G6WWCSQGg0PdTnW7hwMrM',#12,$,$,(#180),#330);
#335= IFCPROPERTYSINGLEVALUE('Renovation Status',$,IFCLABEL('Existing'),$);
#336= IFCPROPERTYSET('0cR6wsk2QWcLKPchA8mF3u',#12,'AC_Pset_RenovationAndPhasing',$,(#335));
#338= IFCRELDEFINESBYPROPERTIES('3nYD8KGPhoBw5okmj1JjsA',#12,$,$,(#180),#336);
#341= IFCQUANTITYLENGTH('Width',$,$,300.);
#342= IFCQUANTITYLENGTH('Perimeter',$,$,22000.);
#343= IFCQUANTITYAREA('GrossArea',$,$,28.);
#344= IFCQUANTITYAREA('NetArea',$,$,28.);
#345= IFCQUANTITYVOLUME('GrossVolume',$,$,8.4);
#346= IFCQUANTITYVOLUME('NetVolume',$,$,8.4);
#347= IFCELEMENTQUANTITY('1RfXJewSc7OCIaD$L2ZoXT',#12,'BaseQuantities',$,'ARCHICAD BIM Base Quantities',(#341,#342,#343,#344,#345,#346));
#349= IFCRELDEFINESBYPROPERTIES('085uLttAQRllG3nL_YikZ8',#12,$,$,(#180),#347);
#375= IFCQUANTITYVOLUME('Gross Volume of the Slab',$,$,8.4);
#376= IFCQUANTITYVOLUME('Gross Volume of the Slab with Holes',$,$,8.4);
#377= IFCQUANTITYLENGTH('Holes Perimeter',$,$,0.);
#378= IFCQUANTITYAREA('Holes Surface Area',$,$,0.);
#379= IFCQUANTITYLENGTH('Perimeter',$,$,22000.);
#381= IFCQUANTITYAREA('Top Surface Area',$,$,28.);
#382= IFCELEMENTQUANTITY('0DuZ12CVtssgcIQPaQ$1sp',#12,'ArchiCADQuantities',$,'ARCHICAD BIM Quantities',(#375,#376,#377,#378,#379,#381));
#384= IFCRELDEFINESBYPROPERTIES('0KgGv0Y8Fc2jg8BCPhxnM5',#12,$,$,(#180),#382);
#393= IFCSLABTYPE('0K1otpnkQcEpOBXPxnZ3dB',#12,'Timber - Floor 300',$,$,(#396),$,'14072DF3-C6E6-A63B-360B-859EF18C39CB',$,.FLOOR.);
#395= IFCRELDEFINESBYTYPE('353egCMRpZtJd$CDCoSsCb',#12,$,$,(#180),#393);
#352= IFCQUANTITYAREA('Area',$,$,28.);
#353= IFCQUANTITYLENGTH('Height',$,$,300.);
#354= IFCQUANTITYVOLUME('Net Volume',$,$,8.4);
#396= IFCELEMENTQUANTITY('1Zyxf4r7NogSp4V7ORMpET',#12,'ArchiCADQuantities',$,'ARCHICAD BIM Quantities',(#352,#353,#354));
ENDSEC;
END-ISO-10303-21;
This is a slab with an area measurement attached. It should have an inverse attribute IsDefinedBy. In IFC2X3 this points to a list of entities IfcRelDefinesByProperties and IfcRelDefinesByType. With IFC4 IfcRelDefinesByType will be put in the inverse attribute IsTypedBy.
Each IfcRelDefinesByProperties point to a property set in their attribute RelatingPropertyDefinition. There are various property set types, you want it to be of type IfcElementQuantity when searching for physical quantities. You will have to check at run time which type you currently hold while iterating the list of property relations.
The quantity set has a list of IfcPhysicalQuantities attached in the attribute Quantities. These can be simple quantities or complex quantities, which are composed of multiple simple quantities. For simple quantities there are specific subtypes for area, count or weight. Again you will have to check at runtime for the concrete type.
The area quantity itself has a name and description to give further context (not ours, but possibly in the real world). The value attribute is named after the quantity type, so IfcQuantityArea has an attribute AreaValue. Also of interest is attribute Unit which is a reference to the unit of the value. If it is not set (as in our example) you will need to look for assigned units at the IfcProject entity.
Unfortunately this might not be all. If the object (here our IfcSlab) has an assigned object type, that type can also have property sets attached (I modified the example file to be this case). Thy type entity does not have an inverse attribute, but a direct one, HasProperties. If it is set, you can discover attached properties through it.
To summarise, you will probably need multiple loops:
For each object, get property sets
For each property set, test if it is quantity set
For each quantity set, go through quantities
And potentially repeat this search if the object has a user defined type.
The following code should do exactly this (written on my machine with python 3.5.4 and ifcopenshell with IFC2X3 schema)):
Get all slabs from the file (there is only one)
Go through all entities in the inverse attribute IsDefinedBy - these can be IfcRelDefinesByProperties or IFcRelDefinesByType.
Get property sets from the entity
Check if the given property set is IfcElementQuantity and proceed to print quantitites if it is.
import ifcopenshell
def print_quantities(property_definition):
if 'IfcElementQuantity' == property_definition.is_a():
for quantity in property_definition.Quantities:
if 'IfcQuantityArea' == quantity.is_a():
print('Area value: ' + str(quantity.AreaValue))
if 'IfcQuantityVolume' == quantity.is_a():
print('Volume value: ' + str(quantity.VolumeValue))
if 'IfcQuantityLength' == quantity.is_a():
print('Length value: ' + str(quantity.LengthValue))
ifc_file = ifcopenshell.open('slab.ifc')
products = ifc_file.by_type('IfcSlab')
for product in products:
if product.IsDefinedBy:
definitions = product.IsDefinedBy
for definition in definitions:
#In IFC2X3 this could be property or type
#in IFC4 type is in inverse attribute IsTypedBy
if 'IfcRelDefinesByProperties' == definition.is_a():
property_definition = definition.RelatingPropertyDefinition
print_quantities(property_definition)
if 'IfcRelDefinesByType' == definition.is_a():
type = definition.RelatingType
if type.HasPropertySets:
for property_definition in type.HasPropertySets:
print_quantities(property_definition)
For the example this results in:
Length value: 300.0
Length value: 22000.0
Area value: 28.0
Area value: 28.0
Volume value: 8.4
Volume value: 8.4
Volume value: 8.4
Volume value: 8.4
Length value: 0.0
Area value: 0.0
Length value: 22000.0
Area value: 28.0
Area value: 28.0
Length value: 300.0
Volume value: 8.4

Quantities in IFC are stored in quantity sets, which are very similar in structure to property sets. IfcOpenShell offers utility functions to extract this data in a schema-agnostic manner:
for slab in ifc_file.by_type("IfcSlab"):
quantities = ifcopenshell.util.element.get_psets(slab, qtos_only=True)
print(quantities) # A dictionary of qtos and quantities
# For example print(quantities["Qto_SlabBaseQuantities"]["GrossVolume"])

I wrote a few functions once, to extract all properties.
https://github.com/johannesmichael/ifc-python/blob/master/modules/ifc_pset_utils.py
Might not be complete, but can give you an idea.
I extract all to excel for further analyzis.
BaseQuantities:
def get_related_quantities(ifc_instance):
"""
Returns a list of IfcElementQuantity for given IFC ID
argument: ifc_instance
return: list of property sets
"""
quantities_list =[]
for x in ifc_instance.IsDefinedBy:
if x.is_a("IfcRelDefinesByProperties"):
if x.RelatingPropertyDefinition.is_a("IfcElementQuantity"):
quantities_list.append(x.RelatingPropertyDefinition)
return quantities_list
def get_quantity_single_value(x):
"""
Returns a dict of dicts of IfcElementQuantity single values.
Returning a dictionary of dictionaries
is used, because it is easy to transform to pandas.DataFrame
argument: IFC Element as contained in list from get_related_property_sets()
return: dict of property single values like {"IfcName":"xx", "IfcGlobalId": "klkhlkh", ......}
"""
quantities_dicts = {}
for y in x.Quantities:
if y.is_a('IfcQuantityArea'):
quantities_dicts.update({y.Name:y.AreaValue})
if y.is_a('IfcQuantityLength'):
quantities_dicts.update({y.Name:y.LengthValue})
if y.is_a('IfcQuantityVolume'):
quantities_dicts.update({y.Name:y.VolumeValue})
if y.is_a('IfcQuantityCount'):
quantities_dicts.update({y.Name:y.CountValue})
if y.is_a('IfcQuantityWeight'):
quantities_dicts.update({y.Name:y.WeightValue})
return quantities_dicts
This was done for IFC 2x3 TC1
Hope that helps

astropy.fits: Manipulating image data from a fits Table? (e.g., 3072R x 2C)

I'm currently having a little issue with a fits file. The data is in table format, a format I haven't previously used. I'm a python user, and rely heavily on astropy.fits to manipulate fits images. A quick output of the info gives:
No. Name Type Cards Dimensions Format
0 PRIMARY PrimaryHDU 60 ()
1 BinTableHDU 29 3072R x 2C [1024E, 1024E]
The header for the BinTableHDU is as follows:
XTENSION= 'BINTABLE' /Written by IDL: Mon Jun 22 23:28:21 2015
BITPIX = 8 /
NAXIS = 2 /Binary table
NAXIS1 = 8192 /Number of bytes per row
NAXIS2 = 3072 /Number of rows
PCOUNT = 0 /Random parameter count
GCOUNT = 1 /Group count
TFIELDS = 2 /Number of columns
TFORM1 = '1024E ' /Real*4 (floating point)
TFORM2 = '1024E ' /Real*4 (floating point)
TTYPE1 = 'COUNT_RATE' /
TUNIT1 = '1e-6cts/s/arcmin^2' /
TTYPE2 = 'UNCERTAINTY' /
TUNIT2 = '1e-6cts/s/arcmin^2' /
HISTORY g000m90r1b120pm.fits created on 10/08/97. PI channel range: 8: 19
PIXTYPE = 'HEALPIX ' / HEALPIX pixelisation
ORDERING= 'NESTED ' / Pixel ordering scheme, either RING or NESTED
NSIDE = 512 / Healpix resolution parameter
NPIX = 3145728 / Total number of pixels
OBJECT = 'FULLSKY ' / Sky coverage, either FULLSKY or PARTIAL
FIRSTPIX= 0 / First pixel # (0 based)
LASTPIX = 3145727 / Last pixel # (zero based)
INDXSCHM= 'IMPLICIT' / indexing : IMPLICIT or EXPLICIT
GRAIN = 0 / GRAIN = 0: No index,
COMMENT GRAIN =1: 1 pixel index for each pixel,
COMMENT GRAIN >1: 1 pixel index for Grain consecutive pixels
BAD_DATA= -1.63750E+30 / Sentinel value given to bad pixels
COORDSYS= 'G ' / Pixelization coordinate system
COMMENT G = Galactic, E = ecliptic, C = celestial = equatorial
END
I'd like to access the fits image which is stored within the TTYPE labeled 'COUNT-RATE', and then have this in a format with which I can then add to other count-rate arrays with the same dimensions.
I started with my usual prodcedure for opening a fits file:
hdulist_RASS_SXRB_R1 = fits.open('/Users/.../RASS_SXRB_R1.fits')
hdulist_RASS_SXRB_R1.info()
image_XRAY_SKYVIEW_R1 = hdulist_RASS_SXRB_R1[1].data
image_XRAY_SKYVIEW_R1 = numpy.array(image_XRAY_SKYVIEW_R1)
image_XRAY_SKYVIEW_header_R1 = hdulist_RASS_SXRB_R1[1].header
But this is coming back with IndexError: too many indices for array. I've had a look at accessing table data in the astropy documentation here (Accessing data stored as a table in a multi-extension FITS (MEF) file)
If anyone has a tried and tested method for accessing such images from a fits table I'd be very grateful! Many thanks.

I can't be sure without seeing the full traceback but I think the exception you're getting is from this:
image_XRAY_SKYVIEW_R1 = numpy.array(image_XRAY_SKYVIEW_R1)
There's no reason to manually wrap numpy.array() around the array. It's already a Numpy array. But in this case it's a structured array (see http://docs.scipy.org/doc/numpy/user/basics.rec.html).
#Andromedae93's answer is right one. But also for general documentation on this see: http://docs.astropy.org/en/stable/io/fits/index.html#working-with-table-data
However, the way you're working (which is fine for images) of manually calling fits.open, accessing the .data attribute of the HDU, etc. is fairly low level, and Numpy structured arrays are good at representing tables, but not great for manipulating them.
You're better off generally using Astropy's higher-level Table interface. A FITS table can be read directly into an Astropy Table object with Table.read(): http://docs.astropy.org/en/stable/io/unified.html#fits
The only reason the same thing doesn't exist for FITS images is there's no a generic "Image" class yet.

I used astropy.io.fits during my internship in Astrophysics and this is my process to open file .fits and make some operations :
# Opening the .fits file which is named SMASH.fits
field = fits.open(SMASH.fits)
# Data fits reading
tbdata = field[1].data
Now, with this kind of method, tbdata is a numpy.array and you can make lots of things.
For example, if you have data like :
ID, Name, Object
1, HD 1527, Star
2, HD 7836, Star
3, NGC 6739, Galaxy
If you want to print data along one condition :
Data_name = tbdata['Name']
You will get :
HD 1527
HD 7836
NGC 6739
I don't know what do you want exactly with your data, but I can help you ;)

Python Image Library: How to combine 4 images into a 2 x 2 grid?

I have 4 directories with images for an animation. I would like to take the set of images and generate a single image with the 4 images arranged into a 2x2 grid for each frame of the animation.
My code so far is:
import Image
fluid64 = "Fluid64_half_size/00"
fluid128 = "Fluid128_half_size/00"
fluid512 = "Fluid512_half_size/00"
fluid1024 = "Fluid1024_half_size/00"
out_image = "Fluid_all/00"
for pic in range(1, 26):
blank_image = Image.open("blank.jpg")
if pic < 10:
image_num = "0"+str(pic)
else:
image_num = str(pic)
image64 = Image.open(fluid64+image_num+".jpg")
image128 = Image.open(fluid128+image_num+".jpg")
image512 = Image.open(fluid512+image_num+".jpg")
image1024 = Image.open(fluid1024+image_num+".jpg")
out = out_image + image_num + ".jpg"
blank_image.paste(image64, (0,0)).paste(fluid128, (400,0)).paste(fluid512, (0,300)).paste(fluid1024, (400,300)).save(out)
Not sure why it's not working. I'm getting the error:
Traceback (most recent call last):
File "C:\Users\Casey\Desktop\Image_composite.py", line 24, in <module>
blank_image.paste(image64, (0,0)).paste(fluid128, (400,0)).paste(fluid512, (
ste(fluid1024, (400,300)).save(out)
AttributeError: 'NoneType' object has no attribute 'paste'
shell returned 1
Any help would be awesome. Thanks!

The only problem there is that "paste" does not return an image object - it rather modifies the "blank" image inplace.
So, when the second paste is called (the one that uses the fuild128 image), it tries to be applied on "None" - which is the return value of the first image.
If that is the only problem you are having, just make one paste call per line, like this:
blank_image.paste(image64, (0,0))
blank_image.paste(fluid128, (400,0))
blank_image.paste(fluid512, (0,300))
blank_image.paste(fluid1024, (400,300))
blank_image.save(out)
Although it looks likely you'd need to scale each image so that their format match as well.
And your code for the "image_num" variable is unecessary. Python is really good with strings - just do something like this:
image64 = Image.open(fluid64 + "%02d.jpg" % pic)

You may want to be using something along the lines of :
blank_image = Image.new("RGB", (800, 600))
This will create a new area in memory in which you can generate your image. You should then be able to paste you images into that.
Then you'll need to save it out again later on with:
blank_image.save("blank.jpg")

Read the error message:
AttributeError: 'NoneType' object has no attribute 'paste'
This means you tried to call .paste on something that was of type NoneType, i.e. on the None object.
Image.paste returns None. You can't "chain" together calls like that except when the functions are specifically designed to support it, and Image.paste is not. (Support for this sort of thing is accomplished by having the function return self. You get an error that talks about NoneType because the function is written not to return anything, and everything in Python returns None by default if nothing else is returned explicitly.) This is considered Pythonic: methods either return a new value, or modify self and return None. Thus, so-called "fluent interfaces" are not used when the functions have side effects - Pythonistas consider that harmful. Returning None is a warning that the function has side effects. :)
Just do four separate .paste calls.

Tiling figures in a 2-by-2 grid would be easy to achieve with the append_images function defined in this reply
https://stackoverflow.com/a/46623632/8738113
For example:
img1 = append_images([image64, image128], direction='horizontal')
img2 = append_images([image512, image1024], direction='horizontal')
final = append_images([img1, img2], direction='vertical')
final.save("Fluid_all/00.jpg")

Unlike PIL APIs copy, crop, resize or rotate which return an Image object, paste returns None which prevents chained method calls. Not so convenient API design.