Added new features to the ndcube.add method #794

PCJY · 2024-12-11T15:32:25Z

PR Description

write down the scenarios.

(list, to see what has been covered)

Testing scenarios without mask

redrafting the tests.

One and only one of them has a unit. [Error]
Both NDCube and NDData have unit
Both have uncertainty.
NDCube has uncertainty.
NDData has uncertainty.
Neither has uncertainty.
Neither has a unit.
Both have uncertainty.
NDCube has uncertainty.
NDData has uncertainty.
Neither has uncertainty.

name them again:
test_cube_add_cube_unit_mask_nddata_unc_unit_mask

ndcube/ndcube.py

DanRyanIrish · 2024-12-11T16:38:16Z

ndcube/ndcube.py

+            # addition
+            new_data = self.data + value_data


The addition should be done as part of the masked array addition. You've already done this below, you just need to extract the added data from the results as well as the mask.

DanRyanIrish · 2024-12-12T11:52:40Z

ndcube/ndcube.py

+            return self._new_instance(
+                data=new_data, uncertainty=new_uncertainty, mask=new_mask
+            )


Instead of having a separate return here for the NDData case, I think we should build a dictionary of kwargs that we can give self._new_instance, here. So, you can create an empty kwargs dictionary at the start of the method, and add the new data, uncertainty, etc. in the relevant place, e.g.

kwargs["uncertainty"] = new_uncertainty

Then the final line of the method would become

return self._new_instance(**kwargs)

Let me know if this doesn't make sense

…thmetic

Co-authored-by: DanRyanIrish <[email protected]>

…o nddataArithmetic and further modify the _add_ method.

DanRyanIrish · 2024-12-19T09:58:08Z

ndcube/ndcube.py

            if self.uncertainty is not None and value.uncertainty is not None:
                new_uncertainty = self.uncertainty.propagate(
-                    np.add, value.uncertainty, correlation=0
+                    np.add, value.uncertainty, result_data = value.data, correlation=0


The result_data needs to be the result of the operation. So, assuming you moved the addition of the datas using the masked array to before the uncertainty propagation, you could do:

Suggested change

np.add, value.uncertainty, result_data = value.data, correlation=0

np.add, value.uncertainty, result_data = kwargs["data"], correlation=0

DanRyanIrish · 2024-12-19T10:01:20Z

ndcube/ndcube.py

            # combine mask
            self_ma = np.ma.MaskedArray(self.data, mask=self.mask)
            value_ma = np.ma.MaskedArray(value_data, mask=value.mask)
+
+            # addition
            result_ma = self_ma + value_ma
-            new_mask = result_ma.mask
+
+            # extract new mask and new data
+            kwargs["mask"] = result_ma.mask
+            kwargs["data"] = result_ma.data


As mentioned in above comment, I think it makes sense to do this before the uncertainty propagation so you can use the kwargs["data"] value in that propagation.

DanRyanIrish · 2024-12-19T10:10:57Z

ndcube/ndcube.py

+            kwargs["data"] = result_ma.data
+
+            # return the new NDCube instance
+            return self._new_instance(**kwargs)


Move this line to the end of the method and use the kwargs approach when handling the other cases, e.g. Quantity. So, for example, L1082 would become:

kwargs["data"] = self.data + value.to_value(cube_unit)

DanRyanIrish · 2024-12-19T10:13:21Z

A changelog file needs to be added.

And your branch needs to be updated with the latest version of main.

…thmetic

PCJY · 2025-01-18T22:01:37Z

Hi @DanRyanIrish, as we have discussed in our project meetings, below are the issues we encountered and may need further discussions with others in the community:

The issue is mainly around how NumPy handles masks when performing an addition for two NumPy.MaskedArray.
We think the expected outcome for an addition should be: the sum of any value that is not masked by its individual mask.
E.g. [1] ([T]) + [2] ([F]) = [2].

However, from experimentation, it can be seen that
NumPy returns in this way:
[1] ([T]) + [2] ([F]) = [1].

I find this confusing because even if it does combine the mask and then apply it on the result, it should be:
[1] ([T]) + [2] ([F]) = [-].

Please correct me if there is anything wrong in my understanding.

PCJY · 2025-01-18T22:27:46Z

@DanRyanIrish, Secondly, we also encountered some issues around the propagate method:
it ignores the mask of the objects that are passed in, and still takes into account the uncertainties of the masked elements when it should not have done so.
Following your guidance and suggestions, this issue is currently being worked on by setting the corresponding entries that should be masked of the uncertainty array to be 0, before passed in to the propagate method.
A clearer example of the issue was implemented as shown in the code below with the screenshot of its output attached.

from ndcube import NDCube
import numpy as np
from astropy.nddata import StdDevUncertainty
from astropy.wcs import WCS

data = np.array([[1, 2], [3, 4]])  
uncertainty = StdDevUncertainty(np.array([[0.1, 0.2], [0.3, 0.4]])) 
mask = np.array([[False, True], [False, False]])  
wcs1 = WCS(naxis=2) 
wcs1.wcs.ctype = ["HPLT-TAN", "HPLN-TAN"]

cube = NDCube(data, wcs=wcs1, uncertainty=uncertainty, mask=mask)
print(cube)

def add_operation(cube1, cube2):
    """
    Example function to add two data arrays with uncertainty propagation.
    """
    result_data = cube1.data + cube2.data 
    # Propagate the uncertainties using the NDCube objects
    propagated_uncertainty = cube1.uncertainty.propagate(
        np.add, cube2, result_data=result_data, correlation = 0
    )
    return result_data, propagated_uncertainty

# adding the cube to itself
result_data, propagated_uncertainty = add_operation(cube, cube)

print("Original Data:\n", cube.data)
print("Original Uncertainty:\n", cube.uncertainty.array)
print("Result Data (after addition):\n", result_data)
print("Propagated Uncertainty:\n", propagated_uncertainty.array)

DanRyanIrish · 2025-01-20T11:31:10Z

Hi @PCJY. I think the first thing we need to do is decide what behaviours we want to implement in the case where at least one of the NDCube and NDData have a mask. I think we need to get some feedback from other users on this decision. I propose the following scheme (@Cadair, thoughts on this?):

Firstly, if an object has no mask, that is equivalent to all pixels being unmasked.
Secondly, for a given pixel in both objects:

If both are unmasked, the resultant
i. data value is the sum of both pixels
ii. mask value is False
iii. uncertainty value is the propagation of the two uncertainties. If one or other object doesn't have uncertainty, the uncertainty of that component is assumed to be 0.
If it is masked in one object, but not the other, the resultant:
i. data value is equal to the unmasked value
ii. mask value is False
iii. uncertainty value is the same as the unmasked pixel
If both pixels are masked, this is where is gets ambiguous. I propose, in order to remain consistent with the above:
i. The operation is not performed and the data, mask and uncertainty values remain the same as the left-hand operand, i.e. the NDCube.

Alternatives for parts of the scheme could include:
2. If it is masked in one object, but not the other, the resultant:
ii. mask value is True.

If both pixels are masked:
i. The operation IS performed as normal but the mask value is True.

Once we agree on a scheme, the way forward on your uncertainty questions will become clear.

@Cadair what are your thoughts on this scheme. I also think we should bring this up at the sunpy weekly meeting to get other thoughts.

DanRyanIrish · 2025-01-20T11:46:55Z

I find this confusing because even if it does combine the mask and then apply it on the result, it should be: [1] ([T]) + [2] ([F]) = [-].

This is where I also find numpy masked arrays counter-intuitive. However, the logic is as follows:

If one pixel is masked, retain the data of the left-hand operand and set the mask value to True.
Notice that order of the operands matters. Because you did [1] ([T]) + [2] ([F]), the results is [1] ([T]), which is displayed as [--]. I would expect if you did the operation the other way around ([2] ([F]) + [1] ([T])), the result would be [2] ([T]).

Notice that this is not the same as the scheme I've proposed in my previous comment, in part because it's confusing, as you've found.

DanRyanIrish · 2025-01-20T11:52:08Z

@PCJY, until we agree a way forward with the mask, you should proceed by implementing in the case for which neither object has a mask. So no need for masked arrays.

…thmetic

Cadair · 2025-01-21T09:25:46Z

I propose the following scheme

I haven't thought too much each of these individual cases, but the fact there is a list is enough to make me think we probably need a way for the user to choose. This is obviously not possible with the __add__ operator, so we would need to have a NDCube.add method (and presumably subtract) which accepted kwargs.

Is this also not a problem for other operators as well?

DanRyanIrish · 2025-01-21T09:45:44Z

I think this is a good idea. As well as add and subtract, I think we would also need multiply and divide methods.

As far as I can see, this ambiguity only arises when there are masks involved. So we could still implement the dunder methods as wrappers around the above methods, but have the raise and error/return NotImplemented if the non-NDCube operand has a mask, and require users use the NDCube.add method instead.

…o nddataArithmetic

…array

ndcube/tests/test_ndcube.py

Co-authored-by: DanRyanIrish <[email protected]>

…thmetic

DanRyanIrish · 2025-02-11T10:26:42Z

This is looking good. The test_cube_add_uncertainty should also check the other attributes are as expected, i.e., the data and the unit. For this, it might be better to not initiate your fixture with np.random.rand, but a known set of values, e.g. np.ones.

ndcube/tests/test_ndcube.py

ndcube/ndcube.py

Co-authored-by: DanRyanIrish <[email protected]>

ndcube/conftest.py

…thmetic

…o nddataArithmetic

DanRyanIrish · 2025-02-18T10:27:34Z

ndcube/ndcube.py

@@ -987,7 +987,7 @@ def add(self, value, operation_ignores_mask=True, handle_mask=np.logical_and):

            if (self_unmasked and value_unmasked) or operation_ignores_mask is True:
                # addition
-                kwargs["data"] = self.data + value_data
+                kwargs["data"] = (self.data + value_data)*self.unit


Suggested change

kwargs["data"] = (self.data + value_data)*self.unit

kwargs["data"] = self.data + value_data

…thmetic

PCJY · 2025-02-20T19:42:38Z

Notes for the current stage of development:
1, The 'TypeError: None is not a valid Unit' occurs whenever an NDCube/NDData object with a unit enters the propagate method.
Their values become None after they enter the propagate method. Their values remain correct before entering the propagate method.
It might happen due to how the unit is handled or converted inside the propagate method.

The stack trace output of pdb w tells me:

the test function performs the addition, the dunder add method is called,
inside the dunder add method, the propagate method is called,
it then calls the _propagate_add method within the astropy nduncertainty file,
which then calls the _propagate_add_sub method,
which calls the method .to() for the result_unit_sq,
then, inside the .to() method, it tries to convert the unit to the unit of the argument, but finds that unit of result_unit_sq is None.
the __call__ method inside the core file is called and raises a TypeError.

result_unit_sq is not set correctly, in _propagate_add_sub() .

result_unit_sq is None, but other objects do have values:

> c:\users\junya\anaconda3\envs\ndcube-dev\lib\site-packages\astropy\nddata\nduncertainty.py(717)_propagate_add_sub()
-> .to(result_unit_sq)
(Pdb) locals()
{'self': StdDevUncertainty([[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2,
                    0.2],
                   [0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2,
                    0.2],
                   [0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2,
                    0.2],
                   [0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2,
                    0.2],
                   [0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2,
                    0.2],
                   [0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2,
                    0.2],
                   [0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2,
                    0.2],
                   [0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2,
                    0.2],
                   [0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2,
                    0.2],
                   [0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2,
                    0.2]]), 'other_uncert': StdDevUncertainty([[0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1,
                    0.1],
                   [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1,
                    0.1],
                   [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1,
                    0.1],
                   [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1,
                    0.1],
                   [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1,
                    0.1],
                   [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1,
                    0.1],
                   [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1,
                    0.1],
                   [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1,
                    0.1],
                   [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1,
                    0.1],
                   [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1,
                    0.1]]), 'result_data': array([[  1.,   2.,   3.,   4.,   5.,   6.,   7.,   8.,   9.,  10.,  11.,
         12.],
       [ 13.,  14.,  15.,  16.,  17.,  18.,  19.,  20.,  21.,  22.,  23.,
         24.],
       [ 25.,  26.,  27.,  28.,  29.,  30.,  31.,  32.,  33.,  34.,  35.,
         36.],
       [ 37.,  38.,  39.,  40.,  41.,  42.,  43.,  44.,  45.,  46.,  47.,
         48.],
       [ 49.,  50.,  51.,  52.,  53.,  54.,  55.,  56.,  57.,  58.,  59.,
         60.],
       [ 61.,  62.,  63.,  64.,  65.,  66.,  67.,  68.,  69.,  70.,  71.,
         72.],
       [ 73.,  74.,  75.,  76.,  77.,  78.,  79.,  80.,  81.,  82.,  83.,
         84.],
       [ 85.,  86.,  87.,  88.,  89.,  90.,  91.,  92.,  93.,  94.,  95.,
       [ 85.,  86.,  87.,  88.,  89.,  90.,  91.,  92.,  93.,  94.,  95.,
         96.],
       [ 97.,  98.,  99., 100., 101., 102., 103., 104., 105., 106., 107.,
       [ 97.,  98.,  99., 100., 101., 102., 103., 104., 105., 106., 107.,
        108.],
        108.],
       [109., 110., 111., 112., 113., 114., 115., 116., 117., 118., 119.,
        120.]]), 'correlation': 0, 'subtract': False, 'to_variance': <ufunc 'square'>, 'from_variance': <ufunc 'sqrt'>, 'correlation_sign': 1, 'result_unit_sq': No       [109., 110., 111., 112., 113., 114., 115., 116., 117., 118., 119.,
        120.]]), 'correlation': 0, 'subtract': False, 'to_variance': <ufunc 'square'>, 'from_variance': <ufunc 'sqrt'>, 'correlation_sign': 1, 'result_unit_sq': None}

Then, when checking how result_unit_sq is used, it shows this:

(Pdb) l
712                 ):
713                     # If the other uncertainty has a unit and this unit differs
714                     # from the unit of the result convert it to the results unit
715                     other = (
716                         to_variance(other_uncert.array << other_uncert.unit)
717  ->                     .to(result_unit_sq)
718                         .value
719                     )
720                 else:
721                     other = to_variance(other_uncert.array)
722             else:

This is not the same as expected, since it entered the conditional scenario when the two objects' units are different, but in the test case written, the two units are set the same.

TODO: 1) look more into the stack trace that pytest --pdb, ll, w, returns. 2) using pytest --pdb, u, check how result_unit_sq is assigned.

2, The test cases testing the case when neither of them has a unit, and that when only the NDData has a unit, have been implemented and passed. TODO: add the test case for when only NDCube has a unit.

DanRyanIrish · 2025-02-25T11:35:15Z

Here is an example of including fixtures in parameterisations: https://github.com/sunpy/ndcube/blob/main/ndcube/tests/test_ndcube.py#L48
The trick is to add this indirect kwarg at the end of the parameterisation given the name of the input variable(s) that has to be retrieved from conftext.py

Added new features to the ndcube._add_ method

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PCJY and others added 5 commits December 16, 2024 23:41

Merge branch 'main' of https://github.com/sunpy/ndcube into nddataAri…

1d5d2ab

…thmetic

Update ndcube/ndcube.py

aaa9ef0

Co-authored-by: DanRyanIrish <[email protected]>

Update ndcube/ndcube.py

ed4f61e

Co-authored-by: DanRyanIrish <[email protected]>

Modified the _add_ method further.

ea43a1d

Merge branch 'nddataArithmetic' of https://github.com/PCJY/ndcube int…

a891ff9

…o nddataArithmetic and further modify the _add_ method.

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PCJY added 8 commits December 23, 2024 14:10

Further modifies the _add_ method.

f575e2c

Added a changelog file for this new feature.

8951635

Merge branch 'main' of https://github.com/sunpy/ndcube into nddataAri…

58e4363

…thmetic

Added a new method test_cube_add_uncertainty_and_mask to test_ndcube.py.

bcf4fb9

Modified the test_cube_add_uncertainty_and_mask method in test_ndcube.py

c4d639a

Modified the test_cube_add_uncertainty_and_mask further.

bd317e3

Fixed how the masks are combined.

e0375ec

Set masked uncertainty entries to 0.

0158737

PCJY and others added 3 commits January 21, 2025 08:51

Moved uncertainty combination out of the mask-combining If Statements.

9074f45

Merge branch 'main' of https://github.com/sunpy/ndcube into nddataAri…

9e267d3

…thmetic

Merge branch 'main' into nddataArithmetic

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PCJY added 2 commits February 4, 2025 01:46

Merge branch 'nddataArithmetic' of https://github.com/PCJY/ndcube int…

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…o nddataArithmetic

Fix uncertainty propagation and ensure expected_uncertainty is numpy …

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…array

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Apply suggestions from code review

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Merge branch 'main' of https://github.com/sunpy/ndcube into nddataAri…

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…thmetic

PCJY commented Feb 11, 2025

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check value and unit of addition

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Update ndcube/ndcube.py

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Merge branch 'main' of https://github.com/sunpy/ndcube into nddataAri…

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…thmetic

change values for uncertainty in a fixture to fixed values.

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Merge branch 'nddataArithmetic' of https://github.com/PCJY/ndcube int…

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Merge branch 'main' into nddataArithmetic

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Merge branch 'nddataArithmetic' of https://github.com/PCJY/ndcube int…

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…o nddataArithmetic

added unit in ndcube for kwargs['data'], changed values for uncertainty.

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new test method for units of both objects being None.

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Merge branch 'main' of https://github.com/sunpy/ndcube into nddataAri…

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…thmetic

within a new ndcube-dev env, removed any unit involved for now.

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Added new test case for only one of them having a unit.

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PCJY and others added 3 commits February 21, 2025 15:57

Test case for both objects having the same unit, and causes TypeError.

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Fix test for adding nddata and ndcube uncertainties.

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Added more test functions for full coverage.

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DanRyanIrish changed the title ~~Added new features to the ndcube._add_ method~~ Added new features to the ndcube.__add__ method Feb 25, 2025

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Added new features to the ndcube.add method #794

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	np.add, value.uncertainty, result_data = value.data, correlation=0
	np.add, value.uncertainty, result_data = kwargs["data"], correlation=0

	kwargs["data"] = (self.data + value_data)*self.unit
	kwargs["data"] = self.data + value_data

Added new features to the ndcube.__add__ method #794

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PR Description

Testing scenarios without mask

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Added new features to the ndcube.add method #794

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