Add Fortran ErrorV and associated wrapping code

.github/workflows/ci.yaml

@@ -113,6 +113,12 @@ jobs:
           # TODO: figure out whether we need/want to use other compilers too
           compiler: "gcc"
           version: "13"
+      - name: Set DLL directory environment variable
+        # Used when running the tests, see `tests/conftest.py` for details
+        if: ${{ startsWith(matrix.os, 'windows') }}
+        run: |
+          echo "PYTHON_ADD_DLL_DIRECTORY=C:\\mingw64\\bin" >> $GITHUB_ENV
+
       - uses: ./.github/actions/setup
         with:
           python-version: ${{ matrix.python-version }}

.github/workflows/release.yaml

@@ -28,7 +28,6 @@ jobs:
         pip install -r {project}/requirements-only-tests-min-locked.txt
       CIBW_TEST_COMMAND: >-
         pytest {project}/tests
-      MACOSX_DEPLOYMENT_TARGET: "13.0"
     strategy:
       fail-fast: false
       matrix:
@@ -57,6 +56,14 @@ jobs:
           # TODO: figure out whether we need/want to use other compilers too
           compiler: "gcc"
           version: "13"
+      - name: Set deployment target - MacOS 13.0
+        if: ${{ matrix.os == 'macos-13' }}
+        run: |
+          echo "MACOSX_DEPLOYMENT_TARGET=13.0" >> $GITHUB_ENV
+      - name: Set deployment target - MacOS 14.0
+        if: ${{ matrix.os == 'macos-14' }}
+        run: |
+          echo "MACOSX_DEPLOYMENT_TARGET=14.0" >> $GITHUB_ENV
       - name: Specify LLVM - windows-arm
         if: ${{ matrix.os == 'windows-11-arm' }}
         shell: pwsh

Makefile

@@ -49,8 +49,7 @@ test:  ## run the tests (re-installs the package every time so you might want to
 	# because it is looking for lines in `src` to be run,
 	# but they're not because lines in `.venv` are run instead.
 	# We don't have a solution to this yet.
-	#
-	# Coverage directory - needed to trick code cov to looking at the right place
+	uv run --no-sync python scripts/inject-srcs-into-meson-build.py
 	uv run --no-sync python -c 'from pathlib import Path; import example_fgen_basic' || ( echo "Run make virtual-environment first" && false )
 	COV_DIR=$$(uv run --no-sync python -c 'from pathlib import Path; import example_fgen_basic; print(Path(example_fgen_basic.__file__).parent)'); \
 		uv run --no-editable --reinstall-package example-fgen-basic pytest -r a -v tests src --doctest-modules --doctest-report ndiff --cov=$$COV_DIR

changelog/25.feature.md

@@ -0,0 +1 @@
+Added demonstration of wrapping Fortran derived types

docs/NAVIGATION.md

@@ -6,11 +6,11 @@ See https://oprypin.github.io/mkdocs-literate-nav/
 - [Home](index.md)
 - [Installation](installation.md)
 - [How-to guides](how-to-guides/index.md)
-    - [Do a basic calculation](how-to-guides/basic-calculation.md)
-    - [Run code in a notebook](how-to-guides/run-code-in-a-notebook.py)
+    - [Basic demo](how-to-guides/basic-demo.py)
 - [Tutorials](tutorials/index.md)
 - [Further background](further-background/index.md)
     - [Dependency pinning and testing](further-background/dependency-pinning-and-testing.md)
+    - [Wrapping Fortran derived types](further-background/wrapping-fortran-derived-types.md)
 - [Development](development.md)
 - [API reference](api/example_fgen_basic/)
 - [Fortran API](fortran-api/home.html)

docs/further-background/wrapping-fortran-derived-types.md

@@ -0,0 +1,281 @@
+# Wrapping derived types
+
+Here we describe our approach to wrapping Fortran derived types.
+
+## What is the goal?
+
+The goal is to be able to run MAGICC, a model written in Fortran, from Python.
+This means we need to be able to instantiate MAGICC's inputs in memory in Python,
+pass them to Fortran to solve the model and get them back as results in Python.
+
+Our data is not easily represented as primitive types (floats, ints, strings, arrays)
+because we want to have more robust data handling, e.g. attaching units to arrays.
+As a result, we need to pass objects to Fortran and return Fortran derived types to Python.
+It turns out that wrapping derived types is tricky.
+Notably, [f2py](https://numpy.org/doc/stable/f2py/)
+does not provide direct support for it.
+As a result, we need to come up with our own solution.
+
+## Our solution
+
+Our solution is based on a key simplifying assumption.
+Once we have passed data across the Python-Fortran interface,
+there is no way to modify it again from the other side of the interface.
+In other words, our wrappers are not views,
+instead they are independent instantiations of the same (or as similar as possible) data models.
+
+For example, if I have an object in Python
+and I pass this to a wrapped Fortran function which alters some attribute of this object,
+that modification will only happen on the Fortran side,
+the original Python object will remain unchanged
+(as a note, to see the result, we must return a new Python object from the Fortran wrapper).
+
+This assumption makes ownership and memory management clear.
+We do not need to keep instances around as views
+and therefore do not need to worry about consistency across the Python-Fortran interface.
+Instead, we simply pass data back and forth,
+and the normal rules of data consistency within each programming language apply.
+
+To actually pass derived types back and forth across the Python-Fortran interface,
+we introduce a 'manager' module for all derived types.
+
+The manager module has two key components:
+
+1. an allocatable array of instances of the derived type it manages,
+   call this `instance_array`.
+   The array of instances are instances which the manager owns.
+   In practice, they are essentially temporary variables.
+1. an allocatable array of logical (boolean) values,
+   call this `available_array`.
+   The convention is that, if `available_array(i)` is `.true.`,
+   where `i` is an integer,
+   then the instance at `instance_array(i)` is available for the manager to use.
+   Otherwise, the manager assumes that the instance is already being used for some purpose
+   and therefore cannot be used for whatever operation is currently being performed.
+
+This setup allows us to effectively pass derived types back and forth between Python and Fortran.
+
+Whenever we need to return a derived type (or derived types) to Python, we:
+
+1. get the derived type(s) from whatever Fortran function or subroutine created it,
+   call this `derived_type_original`
+1. find an index, `idx`, in `available_array` such that `available_array(idx)` is `.true.`
+1. set `instance_array(idx)` equal to `derived_type_original`
+1. we return `idx` to Python
+    - `idx` is an integer (or integers), so we can return this easily to Python using `f2py`
+1. we then create a Python object (or objects) with an API that mirrors `derived_type_original`
+   using the class method `from_instance_index`.
+   This class method is auto-generated via `pyfgen`
+   (TODO: implement auto-generation)
+   and handles retrieval of all the attribute values of `derived_type_original`
+   from Fortran and sets them on the Python object that is being instantiated
+    - we can do this as, if you dig down deep enough, all attributes eventually
+      become primitive types which can be passed back and forth using `f2py`,
+      it can just be that multiple levels of recursion are needed
+      if you have derived types that themselves have derived type attributes
+1. we then call the wrapper module's `finalise_instance` function to free the (temporary) instance(s)
+   that was used by the manager
+   (we cannot access the manager module directly as it cannot be wrapped by `f2py`)
+    - this instance is no longer needed because all the data has been transferred to Python
+1. we end up with a Python instance(s) that has the result
+   and no extra/leftover memory footprint in Fortran
+   (and leave Fortran to decide whether to clean up `derived_type_original` or not)
+
+Whenever we need to pass a derived type (or derived types) to Fortran, we:
+
+1. get an instance index we can use to communicate with Fortran
+    1. call the Python object's `build_fortran_instance` method,
+       which returns the instance index where the object was created on the Fortran side.
+       Under the hood, this calls the wrapper module's
+      `get_free_instance_index` and `build_instance` functions
+    1. on the Fortran side, there is now an instantiated derived type, ready for use
+1. call the wrapped Fortran function of interest,
+   except we pass the instance index instead of the actual Python object itself
+1. on the Fortran side, retrieve the instantiated index from the manager module
+   and use this to call the Fortran function/subroutine of interest
+1. return the result from Fortran back to Python
+1. call the wrapper module's `finalise_instance` function to free the (temporary) instance
+   that was used to pass the instance in the first place
+    - this instance is no longer needed because all the data has been transferred and used by Fortran
+1. we end up with the result of the Fortran callable back in Python
+   and no extra/leftover memory footprint in Fortran from the instance created by the manager module
+
+## Further background
+
+We initially started this project and took quite a different route.
+The reason was that we were actually solving a different problem.
+What we were trying to do was to provide views into underlying Fortran instances.
+For example, we wanted to enable the following:
+
+```python
+>>> from some_fortran_wrapper import SomeWrappedFortranDerivedType
+
+
+>>> inst = SomeWrappedFortranDerivedType(value1=2, value2="hi")
+>>> inst2 = inst
+>>> inst.value1 = 5
+>>> # Updating the view via `inst` also affects `inst2`
+>>> inst2.value1
+5
+```
+
+Supporting views like this introduces a whole bunch of headaches,
+mainly due to consistency and memory management.
+
+A first headache is consistency.
+Consider the following, which is a common gotcha with numpy
+
+```python
+>>> import numpy as np
+>>>
+>>> a = np.array([1.2, 2.2, 2.5])
+>>> b = a
+>>> a[2] = 0.0
+>>> # b has been updated too - many users don't expect this
+>>> b
+array([1.2, 2.2, 0. ])
+```
+
+The second is memory management.
+For example, in the example above, if I delete variable `a`,
+what should variable `b` become?
+
+With numpy, it turns out that the answer is that `b` is unaffected
+
+```python
+>>> del a
+>>> a
+Traceback (most recent call last):
+  File "<stdin>", line 1, in <module>
+NameError: name 'a' is not defined
+>>> b
+array([1.2, 2.2, 0. ])
+```
+
+However, we would argue that this is not the only possibility.
+It could also be that `b` should become undefined,
+as the underlying array it views has been deleted.
+Doing it like this must also be very complicated for numpy,
+as they need to keep track of how many references
+there are to the array underlying the Python variables
+to know whether to actually free the memory or not.
+
+We don't want to solve these headaches,
+which is why our solution does not support views,
+instead only supporting the passing of data across the Python-Fortran interface
+(which ensures that ownership is clear at all times
+and normal Python rules apply in Python
+(which doesn't mean there aren't gotchas, just that we won't introduce any new gotchas)).
+
+## Other solutions we rejected
+
+### Provide views rather than passing data
+
+Note: this section was never properly finished.
+Once we started trying to write it,
+we realised how hard it would be to avoid weird edge cases
+so we stopped and changed to [our current solution][our-solution].
+
+To pass derived types back and forth across the Python-Fortran interface,
+we introduce a 'manager' module for all derived types.
+This manager module is responsible for managing derived type instances
+that are passed across the Python-Fortran interface
+and is needed because we can't pass them directly using f2py.
+
+The manager module has two key components:
+
+1. an allocatable array of instances of the derived type it manages
+1. an allocatable array of logical (boolean) values
+
+The array of instances are instances which the manager owns.
+It holds onto these: can instantiate them, can make them have the same values
+as results from Fortran functions etc.
+(I think we need to decide whether this is an array of instances
+or an array of pointers to instances (although I don't think that's a thing https://fortran-lang.discourse.group/t/arrays-of-pointers/4851/6,
+so doing something like this might require yet another layer of abstraction).
+Array of instances means we have to do quite some data copying
+and be careful about changes made on the Fortran side propagating to the Python side,
+I think (although we have to test as I don't know enough about whether Fortran is pass by reference or pass by value by default),
+array of pointers would mean change propagation should be more automatic.
+We're going to have to define our requirements and tests quite carefully then see what works and what doesn't.
+I think this is also why I introduced the 'no setters' views,
+as some changes just can't be propagated back to Fortran in a 'permanent' way.
+We should probably read this and do some thinking: https://stackoverflow.com/questions/4730065/why-does-a-fortran-pointer-require-a-target).
+
+Whenever we need to return a derived type to Python,
+we follow a recipe like the below:
+
+1. we firstly ask the manager to give us an index (i.e. an integer) such that `logical_array(index)` is `.false.`.
+   The convention is that `logical_array(index)` is `.false.` means that `instance_array(index)` is available for use.
+1. We set `logical_array(index)` equal to `.true.`, making clear that we are now using `instance_array(index)`
+1. We set the value of `instance_array(index)` to match the the derived type that we want to return
+1. We return the index value (i.e. an integer) to Python
+1. The Python side just holds onto this integer
+1. When we want to get attributes (i.e. values) of the derived type,
+   we pass the index value (i.e. an integer) of interest from Python back to Fortran
+1. The manager gets the derived type at `instance_array(index)` and then can return the atribute of interest back to Python
+1. When we want to set attributes (i.e. values) of the derived type,
+   we pass the index value (i.e. an integer) of interest and the value to set from Python back to Fortran
+1. The manager gets the derived type at `instance_array(index)` and then sets the desired atribute of interest on the Fortran side
+1. When we finalise an instance from Python,
+   we pass the index value (i.e. an integer) of interest from Python back to Fortran
+   and then call any finalisation routines on `instance_array(index)` on the Fortran side,
+   while also setting `logical_array(index)` back to `.false.`, marking `instance_array(index)`
+   as being available for use for another purpose
+
+Doing it this means that ownership is easier to manage.
+Let's assume we have two Python instances backed by the same Fortran instance,
+call them `PythonObjA` and `PythonObjB`.
+If we finalise the Fortran instance via `PythonObjA`, then `logical_array(index)` will now be marked as `.false.`.
+Then, if we try and use this instance via `PythonObjB`,
+we will see that `logical_array(index)` is `.false.`,
+hence we know that the object has been finalised already hence the view that `PythonObjB` has is no longer valid.
+(I can see an edge case where, we finalise via `PythonObjA`,
+then initialise a new object that gets the (now free) instance index
+used by `PythonObjB`, so when we look again via `PythonObjB`,
+we see the new object, which could be very confusing.
+We should a) test this to see if we can re-create such an edge case
+then b) consider a fix (maybe we need an extra array which counts how many times
+this index has been initialised and finalised so we can tell if we're still
+looking at the same initialisation or a new one that has happened since we last looked).)
+
+This solution allows us to a) only pass integers across the Python-Fortran interface
+(so we can use f2py) and b) keep track of ownership.
+The tradeoff is that we use more memory (because we have arrays of instances and logicals),
+are slightly slower (as we have extra layers of lookup to do)
+and have slow reallocation calls sometimes (when we need to increase the number of available instances dynamically).
+There is no perfect solution, and we think this way strikes the right balance of
+'just works' for most users while also offering access to fine-grained memory control for 'power users'.
+
+### Pass pointers back and forth
+
+Example repository: https://github.com/Nicholaswogan/f2py-with-derived-types
+
+Another option is to pass pointers to objects back and forth.
+We tried this initially.
+Where this falls over is in ownership.
+Basically, the situation that doesn't work is this.
+
+From Python, I create an object which is backed by a Fortran derived type.
+Call this `PythonObjA`.
+From Python, I create another object which is backed by the same Fortran derived type instance i.e. I get a pointer to the same Fortran derived type instance.
+Call this `PythonObjB`.
+If I now finalise `PythonObjA` from Python, this causes the following to happen.
+The pointer that was used by `PythonObjA` is now pointing to `null`.
+This is fine.
+However, the pointer that is being used by `PythonObjB` is now in an undefined state
+(see e.g. community.intel.com/t5/Intel-Fortran-Compiler/DEALLOCATING-DATA-TYPE-POINTERS/m-p/982338#M100027
+or https://www.ibm.com/docs/en/xl-fortran-aix/16.1.0?topic=attributes-deallocate).
+As a result, whenever I try to do anything with `PythonObjB`,
+the result cannot be predicted and there is no way to check
+(see e.g. https://stackoverflow.com/questions/72140217/can-you-test-for-nullpointers-in-fortran),
+either from Python or Fortran, what the state of the pointer used by `PythonObjB` is
+(it is undefined).
+
+This unresolvable problem is why we don't use the purely pointer-based solution
+and instead go for a slightly more involved solution with a much clearer ownership model/logic.
+We could do something like add a reference counter or some other solution to make this work.
+This feels very complicated though.
+General advice also seems to be to avoid pointers where possible
+(community.intel.com/t5/Intel-Fortran-Compiler/how-to-test-if-pointer-array-is-allocated/m-p/1138643#M136486),
+prefering allocatable instead, which has also helped shape our current solution.