The CGNS/Python mapping defines a tree structure composed of nodes implemented for the Python programming langage. A special links structure is also defined for a correct mapping of the management of files on the disk. The mapping presented here is NOT a library , it is the lowest possible correspondance between a CGNS/SIDS structure and a Python representation. This specification is public and could be used as the basis for Python based CGNS application interoperability. Python is an interpreted langage and it has a textual representation of its objects, this representation can be used for CGNS/Python trees as well.
The mapping of the SIDS into a CGNS/Python structure uses the node as atomic structure. Comparing to CGNS/ADF or CGNS/HDF5, the contents of a node is unchanged in CGNS/Python. The way we represent data is different but all nodes attributes found in the section 6 of the SIDS-to-ADF File Mapping Manual  are applicable to the CGNS/Python mapping.
The data type mapping is changed compared to CGNS/ADF or CGNS/HDF5, the actual representation of basic types such as integers, floats and strings are closely mapped to the Python data types. See the table Data types.
Other elements of the node description are like the CGNS/ADF or CGNS/HDF5 mappings, in particular the dimensions and the order of these dimensions. The CGNS/SIDS section 3.1 states that the dimensions order should be the so-called Fortran indexing convention which states the column index is the first. The CGNS/Python nodes should respect this requirements.
The Python arrays can be defined with either a C or a Fortran flag, this flag is used to set or to find the order used for the internal storage of an array. It has no effect on the dimensions of a numpy array, but on its internal memory layout. It’s up to the user to manage this flag and its impact on the use of an array, in particular for the read/write on the disks through the C API.
For example, section 220.127.116.11 describes the DimensionalUnits_t node with dimensions values (32,5). This should be understood as Fortran order values, and thus (32,5) should be found as this in the shape of the numpy array  whichever status the Fortran flag set has.
A numpy array with the C flag set should also have a shape of (32,5), again, the internal representation of this C array has to be taken into account during read/write operations.
See the C API and Examples and Tips sections about this requirement and its impact on numpy array use.
The structure of a CGNS data set is held in a so-called CGNS/Python tree. The tree is composed of nodes, each node may have children which are nodes too. The node structure is a python sequence (i.e. list or tuple), composed of four entries: the name, the value, the list of children and the type.
Attribute type Name string Value numpy array Children list of CGNS/Python nodes Type string
The CGNS/Python mapping requires that:
The name is a Python string, it should not be empty. The name should not have more than 32 chars and should not have / in it. The names . (a single dot) And .. (dot dot) are forbidden (but names with dot and something else are allowed, for example Zone.001 is ok).
The representation of values uses the numpy library array. It makes it possible to share an already existing memory zone with the Python object. The numpy mapping of the values is detailled hereafter. An empty value should be represented as None, any other value is forbidden.
The children list can be a list or a tuple. The use of a list is strongly recommended but not mandatory. A read-only tree can be declared as a tuple. It is the responsibility of the applications to parse sequences wether these are lists or tuples. A node without child has the empty list  as children list.
The type is the Python string representation of the CGNS/SIDS type  (i.e. it is the same for CGNS/ADF or CGNS/HDF5). A type string cannot be empty.
We have now a typical CGNS/Python node, which can be represented with the pattern :
node = [ <name:string>, <value:numpy.array>, [ <child:node>* ], <cgns-type:string> ]
We use there the textual representation of a Python object. All the Python types used in this CGNS/Python mapping have a full textual representation. This is detailled in the next section.
The order of the values is significant, for example node should always be the name of the node (Python has an index ordering starting with zero)
We see now that a CGNS/Python tree is a node. This node has children which have children and so on... Any node can be held as a subpart of a complete tree, we say each node is a sub-tree. Our CGNS/Python tree has a root node which is its first node. There is no clear definition of a root node in the CGNS/SIDS or in the SIDS mappings.
In the case of a CGNSBase_t level node, the CGNS/ADF or CGNS/HDF5 defines a sound node which can be mapped to CGNS/Python. However, the CGNS/SIDS states that several bases can be found in a CGNS tree. The father node of a base would have the pattern:
root = [ <CGNSLibraryVersion:node>, <CGNSBase:node>* ]
Which is not consistent with a normal node. We want to remove this exception, we define a CGNS/Python tree root, or first node, as a list with a compliant CGNS/Python node. which is not the node pattern. Then the applications have to have a specific way to manage this first node. This lack of root node is not that important when you use the CGNS/MLL because the function are hidding the actual node implementation. With CGNS/Python, the user can manage the nodes as true Python objects, and we have to provide him with a sound interface, or at least as sound as possible. For this consistency reason, the CGNS/Python mapping defines a new type for the root node, see the CGNSTree_t type section.
It is possible to declare a CGNS/Python node as a textual representation. There is a exemple of a zone connectivity sub-tree with the CGNS/Python in textual mode, a simple PointRange node with two 3D indices:
pr=['PointRange', numpy.array([[1,25],[1,9],[1,1]],dtype=numpy.int32,order='Fortran'), , 'IndexRange_t']
The PointRange node has no child, the children list is an empty list. The values of the array are initialized with a list, the order of the elements in the list matches the Fortran indexing: in that example the first point indices are [1,1,1] and the second point indicies are [25,9,1].
The evaluation of this string by the Python interpreter creates a CGNS/Python compliant node as a Python list. Please note the types of this pr node, there are only native Python types (list, string, integer) and numpy types or enumerates. You have to have a variable to hold the node or the CGNS sub-tree, if you have no reference to the actually created Python objects these will be unreachable and thus garbaged.
The textual representation can be import-ed as any Python textual file, with all possible Python use you can imagine.
The Python lists are objects. When you refer to a list you do not copy this list unless you ask for such a copy. This is important because if you modify an existing list you modify an object that could be used by others. In the CGNS/Python mapping the children of a node is a list of nodes. If you refer to such a list without a copy, any modification of this child list will impact nodes using this list. This is detailled in the section Examples and Tips .
A CGNS/Python node value is a numpy array, this python object contains the number of dimensions, the dimensions, the data type and the actual data array. Then this implicit information is not a part of the node structure. As we really want to have the most generic node as possible, we require that even single dimension values should be stored as numpy array. A single integer, float or a single string should be embedded into a numpy array.
As we mentionned before, an empty value has to be represented by None which is a native Python value, not a numpy value:
Here cx, cy, cz, are nodes, not arrays.
The numpy end-user interface makes it possible to define some of these required data as deduction of required parameters. The number of dimensions is the size of the so-called shape. The dimensions can be forced for empty values or can be deduced from the data itself:
The first declaration has dimension 1, number of dims 1, data type float64, all deduced from the data declaration, the second has dimensions (5,3,7), number of dimensions 3, data type set as int32.
A numpy array can be declared as C order or Fortran order. There is no requirements in this mapping wether the internal layout of the memory should be C or Fortran. However, an array should have a shape with the same order of dimensions as described in the SIDS-to-ADF File Mapping Manual ([CG2]).
If you use the Python C API, it is the responsability to the application to check the numpy ordering flag and to manage the arrays with respect to memory layout. See the C API section.
The way to get the node data information regarding the [CG2] datatypes and dimensions requirements is to access to the numpy object attributes:
pr=numpy.array([[1,2,3],[4,5,6]]) dims=pr.shape ndims=len(pr.shape) datatype=pr.dtype fortranorder=numpy.isfortran(pr) corder=not numpy.isfortran(pr)
A value is a numpy array, the contents of an array is homogeneous and has a data type. The data types of your CGNS/Python arrays depends on the data type as defined in [CG2].
The type of the data can be set at the creation time, the numpy type is associated to the ADF type required by the CGNS/SIDS. A bad data type, even if it silently looks like the result you want, would lead to an non-compliant CGNS tree. The required mapping for the end-user interface uses the types :
|ADF type||Numpy type(s)||Remarks|
All other ADF or numpy types are ignored. The string type is a bit special, see the remark (5) about the strings used in numpy arrays.
The 32bits precision has to be forced, the default integer size in python the int64 data type. To create an I4 array, you can use:numpy.array([1,2,3],'i',order='Fortran')
The 64bits precision is the default integer in python. To create an I8 array, you can use:numpy.array([1,2,3],order='Fortran')
The 32bits precision has to be forced, the default float size in python is float64. To create an R4 array, you can use:numpy.array([1.4],'f',order='Fortran')
The 64bits precision is the default float in python. To create an R8 array, you can use:numpy.array([1.4],order='Fortran')
The array has to be created as a char multi-dimensionnal array. An incorrect creation with a simple statement such as: numpy.array('GoverningEquations') produces a wrong zero dimension array. The correct creation for a single value could be: numpy.array(tuple('GoverningEquations'),'|S1') where the shape (i.e. the dimensions of the array) is (18,).
The tree structure of a CGNS data set is broken by the exception of the root node. We take the opportunity of this new CGNS/Python mapping to add a consistent root node for the CGNS tree .
The CGNSTree_t type is a node with the pattern:
root= [ <name:string>, None, [ <CGNSLibraryVersion:node>, <CGNSBase:node>* ], 'CGNSTree_t' ]
The children list is the CGNS/ADF-like root node. The CGNSTree node has a user-defined name, no value and a fixed CGNSTree_t type.
The CGNS/SIDS defines all CGNS types and has a rule to suffix them with _t. There are some exceptions where some CGNS/SIDS types have been translated into strings with a special syntax.
The CGNS/Python mapping allows the use of alternate types for these, the user can either use the legacy type or the alternate CGNS/Python type. The alternate types are:
|CGNS/SIDS type||CGNS/Python optional type|
Please note the ["] character which is part of the CGNS legacy type.
This CGNS/Python feature adds NON-SIDS type(s) and this should be added or removed by the user application during the read and the write to the disk with a CGNS/ADF or CGNS/HDF5 compliance. The CGNS.MAP module has an option to check and remove these alternate types. As long as your application has interoperability with another CGNS/Python application there should be no problem.
There is no requirement on the way you would create or manage a numpy array at the C API level. But you have to remember that the definition of the node contents is SIDS-to-ADF which states that data arrays and index ordering use the Fortran convention.
You can manage all your numpy arrays with the C order in memory, but you have to be sure that the storage on the disk, i.e. using ADF or HDF5, has the correct fortran orders. The storage also has to be contiguous in the memory. When you create or obtain a copy of a numpy array you can set a flag to force a C or Fortran ordering: one of the NPY_CCONTIGUOUS or NPY_FCONTIGUOUS flag can be set. In the case of a NPY_CCONTIGUOUS flag set, it is up to the application to set a Fortran memory layout and a Fortran index ordering while reading/writing data to/form a CGNS/ADF or CGNS/HDF5 file .
The numpy C API allows the share of memory zone. In other words you can have a Fortran or C array you can directly set as your numpy array without duplication. You can reduce the memory use when your application can handle this, you can also set the NPY_OWNDATA flag to indicate to numpy that it should not release the array memory when the numpy array object is garbaged.
Python comes from the C world, as well as the numpy library. This means that many behavior are assuming C-order in dimensions. The CGNS/Python mapping states that arrays should have a Fortran indexing for their actual data and that the dimension order of the data is those detailled in the [CG2] and [CG3] documents.
We give here some known issues and tips to handle this Fortran indexing in CGNS/Python. We use specific CGNS/SIDS structures to illustrate our examples.
The IndexRange_t is an integer array of dimensions (IndexDimensions,2) as detailled in . The node data, in the example here, is two points with three indices. The Python-ish way to define them is to have a list of two lists of integers, which leads to problems if you forget your fortran order. We want to set a node with the following Python code:
node=['PointRange', a, , 'IndexRange_t']
Now we see how to declare a correct a variable as a numpy array. If you do not specify an order to numpy, the default is the C-order:
>>> a=numpy.array([[1,2,3],[4,5,6]],dtype=numpy.int32) >>> numpy.isfortran(a) False >>> a array([1,2,3], dtype=int32) >>> a.shape (2,3)
This numpy array is correct but you would have to transpose dimensions are memory layout before a storage on disk. Or you can enter the list itself using an explicit Fortran-order:
>>> a=numpy.array([[1,4],[2,5],[3,6]],dtype=numpy.int32) >>> numpy.isfortran(a) False >>> a array([1,4], dtype=int32) >>> a.shape (3,2)
In that case, the shape is correct but the user has no mean to know wether your convention is C or Fortran. You can set the fortran flag for this. The possible creation of the array above is then:
>>> a=numpy.array([[1,4],[2,5],[3,6]],dtype=numpy.int32,order='Fortran') >>> numpy.isfortran(a) True >>> a array([1,4], dtype=int32) >>> a.shape (3,2)
Then an application can detect your array has Fortran order and should be stored as found without any transpose.
There is another example switching from one order to another, this is used to add a point in a list in an easier way
node=['PointList', a, , 'IndexArray_t']
The possible creation of the array a above is then:
>>> a=numpy.array([[1,4],[2,5],[3,6]],dtype=numpy.int32,order='Fortran') >>> a array([[1, 4], [2, 5], [3, 6]], dtype=int32) >>> a=numpy.array(a.T.tolist()+[[7,8,9]],dtype=numpy.int32,order='Fortran').T >>> a array([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=int32)
You see that the syntax is completely unreadable, we use the numpy transpose attribute T to switch from Fortran to C order and back.. If you start with the C order, the Python syntax is clear:
>>> a=numpy.array([[1,2,3],[4,5,6]],dtype=numpy.int32) >>> a array([[1,2,3], [4,5,6], dtype=int32) >>> a=numpy.array(a.tolist()+[[7,8,9]],dtype=numpy.int32) >>> a array([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=int32)
And the application in charge of the write to the disk that would detect the abscence of Fortran flag and then transpose the array and its dimension.
This node contains strings. The strings are an issue in CGNS/Python because we want to use the raw level for numpy (instead of numpy module proposed for string manipulation). We want to keep a common interface for all nodes and we do not want an exception with strings. The DimensionalUnits_t node can be defined as:
node=[`DimensionalUnits`, a, , `DimensionalUnits_t`]
Now we see how we can defined the numpy array in variable a. The DimensionalUnits_t states we need a (32,5) array of chars. In the case of a fixed size multi-dimensionnal string array, each entry should be split as a sequence with a fixed max size (usually 32 chars):
a=numpy.array([ tuple('%-32s'%'Kilogram'), tuple('%-32s'%'Meter'), tuple('%-32s'%'Second',) tuple('%-32s'%'Kelvin'), tuple('%-32s'%'Radian'), ],'|S32',order='Fortran').T
The shape of the resulting array is (32,5) again note the T at the end of the command which produces the transpose. You can use a S32, |S1 or c type directive. An important point in this string as an array is the trailing spaces you have to fill the array cell. You have to use a string.strip before any string operation unless your Python application is aware of this forced size.
There we have an interesting example with the use of a data of a node. The Zone_t node has the dimensions of the zone. These dimensions are a data and theses data values should be used as dimension attribute of the children nodes. In other words, the user takes the Zone_t dimensions and creates a numpy array with them:
The zonedims numpy array can b set as:
in the case of a 3D structured zone with (ni,nj,nk)=(3,5,7). If you want to create a solution array with these dimensions, you can to use the following syntax:
zonevertexsize=zonedims[:,0] zonecellsize=zonedims[:,1] zonevertexboundarysize=zonedims[:,2]
This numpy syntax allows the user to take the whole column as a so-called slice.
For example, the following snippet imports a truncated ReferenceState:
import numpy refvalues=[ ['Mach',numpy.array([0.2]),,'DataArray_t'] ['Reynolds',numpy.array([23300000.0]),,'DataArray_t'] ['LengthReference',numpy.array([0.5]),,'DataArray_t'] ['Density',numpy.array([1.22524863848]),,'DataArray_t'] ] data=['ReferenceState',None,refvalues,'ReferenceState_t']
Once import-ed, your Python code can insert this node in its structure (here our previous code snippet is in the file refstate.py:
import numpy import refstate tree=['CGNSTree',None,,'CGNSTree_t'] base=['Fuselage',numpy.array([3,3],dtype=numpy.int32),,'CGNSBase_t'] tree.append(base) base.append(refstate.data)