can set a patchData w. an all_primal centering#805
can set a patchData w. an all_primal centering#805rochSmets wants to merge 10 commits intoPHAREHUB:masterfrom
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| File Path | Change Summary |
|---|---|
pyphare/core/operators.py |
Introduced functions for computing dot products, cross products, square roots, gradients, and modulus of fields in a hierarchy. |
pyphare/pharesee/hierarchy.py |
Added flat_name method, updated global_min and global_max methods, introduced ScalarField and VectorField classes with arithmetic operations. |
pyphare/pharesee/run.py |
Added imports and functions for domain slicing and field computations. Modified existing functions to handle new parameters and logic for all-primal data. |
Sequence Diagram(s) (Beta)
sequenceDiagram
participant User
participant PatchHierarchy
participant ScalarField
participant VectorField
participant Operators
User->>PatchHierarchy: Initialize hierarchy
PatchHierarchy-->>User: Return hierarchy instance
User->>ScalarField: Perform arithmetic operation
ScalarField-->>User: Return result
User->>VectorField: Perform arithmetic operation
VectorField-->>User: Return result
User->>Operators: Compute dot product
Operators-->>User: Return dot product
User->>Operators: Compute cross product
Operators-->>User: Return cross product
User->>Operators: Compute gradient
Operators-->>User: Return gradient
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- pyphare/pyphare/core/operators.py (1 hunks)
- pyphare/pyphare/pharesee/hierarchy.py (2 hunks)
- pyphare/pyphare/pharesee/run.py (4 hunks)
Additional comments: 13
pyphare/pyphare/core/operators.py (3)
9-35: The
sqrtfunction is designed to compute the square root of aScalarField. The function iterates over time levels and patches to apply the square root operation to the dataset. The implementation seems correct, but the TODO comment on line 35 raises a question about handling multiple times within the hierarchy. This needs clarification or implementation before proceeding.40-45: The
modulusfunction calculates the modulus of aVectorFieldby squaring the hierarchy and then taking the square root. This is a neat use of operator overloading and function composition. Ensure that the__mul__operator is properly defined forVectorFieldobjects to support this operation.51-105: The
sqrt_oldfunction is designed to handleScalarField,VectorField, andTensorFieldhierarchies. The function uses assertions and conditional logic to determine the type of field and applies the square root operation accordingly. The function seems to be well-implemented, but it's marked as "old". It's important to ensure that any references to this function have been updated to use the newsqrtfunction if that is the intention.pyphare/pyphare/pharesee/run.py (8)
135-154: The function
_compute_Bx_to_primalis designed to compute the Bx component of a magnetic field on a primal grid. The implementation seems correct, but the use ofnp.nanto fill the dataset may not be the best choice for all use cases. It's important to ensure that this is the desired behavior and that any downstream functions can handlenp.nanvalues appropriately.157-176: The function
_compute_By_to_primalis similar to_compute_Bx_to_primalbut for the By component. The same concerns apply regarding the use ofnp.nanand ensuring that downstream functions can handle these values.179-200: The function
_compute_Bz_to_primalis similar to_compute_Bx_to_primaland_compute_By_to_primalbut for the Bz component. The same concerns apply regarding the use ofnp.nanand ensuring that downstream functions can handle these values.203-222: The function
_compute_Ex_to_primalis designed to compute the Ex component of an electric field on a primal grid. The implementation seems correct, but the same concerns aboutnp.nanapply.225-244: The function
_compute_Ey_to_primalis similar to_compute_Ex_to_primalbut for the Ey component. The same concerns apply regarding the use ofnp.nanand ensuring that downstream functions can handle these values.247-265: The function
_compute_Ez_to_primalis similar to_compute_Ex_to_primaland_compute_Ey_to_primalbut for the Ez component. The same concerns apply regarding the use ofnp.nanand ensuring that downstream functions can handle these values.507-516: The
GetBfunction has been modified to support the computation of magnetic fields on primal grids. The use of thecompute_hier_fromfunction and the creation of aVectorFieldobject seem appropriate. However, the TODO comment on line 509 suggests that there is still work to be done whenall_primalisFalse. This needs to be resolved.567-570: The
GetJfunction has been updated to support the computation of current densities on primal grids. The implementation seems correct, but it's important to ensure that thecompute_hier_fromfunction is properly handling the conversion to primal grids.pyphare/pyphare/pharesee/hierarchy.py (2)
566-576: The
flat_namemethod in thePatchHierarchyclass is a simple mapping from a given quantity to a string representation. It's a straightforward implementation and seems correct.1103-1110: The
VectorFieldclass is another new class inheriting fromPatchHierarchy. The__init__method is similar toScalarFieldand correctly sets up the class. Thenamesattribute is initialized with a list of strings representing the vector components, which is appropriate for the class's purpose.
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Additional comments: 4
pyphare/pyphare/core/operators.py (1)
- 71-76: The
modulusfunction correctly asserts that the inputhieris an instance ofVectorField. However, the operationhier*hieron line 75 implies that theVectorFieldclass has an overridden__mul__method. Ensure that this method is implemented correctly and handles the multiplication operation as intended for vector fields.Verification successful
The search with
rghas found a__mul__method within theVectorFieldclass in the filepyphare/pyphare/pharesee/hierarchy.py. This confirms that theVectorFieldclass does indeed have an overridden__mul__method, which is necessary for themodulusfunction inoperators.pyto work correctly as it performs the operationhier*hier.Scripts Executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Verify that the VectorField class has an overridden __mul__ method. ast-grep --lang python --pattern $'class VectorField { $$$ def __mul__($$$) { $$$ } $$$ }'Length of output: 448
Script:
#!/bin/bash # Refine the search pattern for the __mul__ method in the VectorField class. ast-grep --lang python --pattern 'class VectorField { def __mul__($$$) { $$$ } }'Length of output: 428
Script:
#!/bin/bash # Use ripgrep to search for the __mul__ method in the VectorField class. rg "def __mul__\(self" -A 3 -t pyLength of output: 575
pyphare/pyphare/pharesee/hierarchy.py (3)
- 566-576: The method
flat_namein thePatchHierarchyclass is straightforward and correctly raises aValueErrorfor invalid quantities.- 1103-1109: The
VectorFieldclass is correctly initialized, but it's not clear why thenamesattribute is a list of strings. This could be a design decision, but it's worth verifying that this is intentional and properly documented.Verification successful
The search results indicate that the
namesattribute is indeed used within theVectorFieldclass and other parts of thehierarchy.pyfile. Specifically, the attribute is set to['x', 'y', 'z'], which likely corresponds to the components of the vector field in a three-dimensional space. The usage ofnamesin the context offorloops and function parameters suggests that it is an integral part of the data structure and is used to iterate over the components of vector fields and rename patch data.Given this context, it appears that the
namesattribute is intentionally a list of strings and is used to represent the different dimensions or components of the vector fields within thepypharelibrary. This design decision seems to be consistent with the handling of vector data in computational physics, where components are often labeled or accessed using identifiers like 'x', 'y', and 'z'.Based on the information gathered, the review comment questioning the intention and documentation of the
namesattribute as a list of strings is addressed. The attribute is used as expected for vector field components in the codebase.* 1213-1214: The `rename` function is a utility that allows renaming patch datas in the hierarchy. Ensure that the `names` argument is always provided and properly validated before use.Scripts Executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Verify that the `names` attribute in VectorField is used as intended throughout the codebase. rg --type python "names" "pyphare/pyphare/pharesee/hierarchy.py"Length of output: 93
Script:
#!/bin/bash # Correcting the file type and re-running the search for the `names` attribute in VectorField within the `hierarchy.py` file. rg --type py "names" "pyphare/pyphare/pharesee/hierarchy.py"Length of output: 748
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- pyphare/pyphare/pharesee/run.py (4 hunks)
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- pyphare/pyphare/core/operators.py
- pyphare/pyphare/pharesee/run.py
Additional comments: 13
pyphare/pyphare/pharesee/hierarchy.py (13)
- 566-576: The method
flat_namein thePatchHierarchyclass correctly categorizes quantities into 'scalar', 'x', 'y', 'z', or raises aValueErrorfor invalid quantities. This method is well-implemented for its intended purpose.- 749-752: The use of
np.nanminandnp.nanmaxin theglobal_minandglobal_maxmethods ensures that NaN values are handled appropriately when calculating the global minimum and maximum values across the dataset. This is a good practice for data containing NaN values.- 1087-1096: The
__neg__method in thePatchHierarchyclass lacks direct manipulation or usage of its own instance attributes and instead relies on external functions likecompute_hier_fromand_compute_neg. Ensure that the external functions properly handle the negation operation for the hierarchy's data. It's also important to verify that these operations maintain the integrity and structure of the hierarchy.- 1110-1117: The
__mul__method in theScalarFieldclass properly asserts that theotherparameter is an integer or float, ensuring type safety for the multiplication operation. This is a good practice for preventing unexpected behavior or errors.- 1134-1142: Similar to the
ScalarFieldclass, theVectorFieldclass's__mul__method correctly asserts the type of theotherparameter. This consistency across classes for arithmetic operations is good for maintainability and understanding of the codebase.- 1147-1168: The
__add__method in theVectorFieldclass attempts to handle addition operations between vector fields. However, the method's implementation involves renaming and external computation functions, which could be complex and error-prone. Ensure that these operations are correctly implemented and that they preserve the hierarchy's structure and data integrity.- 1193-1205: The
__truediv__method in theVectorFieldclass correctly checks if theotherparameter is an instance ofScalarFieldbefore proceeding with the division operation. This type checking is crucial for ensuring that the operation is meaningful and prevents runtime errors.- 1210-1216: The
_compute_mulfunction correctly multiplies the dataset by theotherparameter and retains the centering information. This function is essential for performing scalar multiplication on the dataset while preserving its spatial properties.- 1223-1233: The
_compute_addfunction handles the addition of datasets from different vector components ('x', 'y', 'z'). It ensures that the result maintains the correct centering information. This approach is appropriate for vector field operations within the hierarchy.- 1238-1248: The
_compute_subfunction, similar to_compute_add, correctly performs subtraction between datasets of vector components. It maintains the centering information, which is crucial for the integrity of the spatial data representation.- 1255-1258: The
_compute_negfunction properly negates the dataset values while preserving the centering information. This function is essential for applying unary negation operations to the hierarchy's data, ensuring the spatial properties remain consistent.- 1267-1270: The
_compute_truedivfunction correctly divides the dataset by another dataset, specifically for dividing vector fields by scalar fields. This operation is crucial for operations that require normalization or scaling of vector fields by scalar quantities.- 1287-1289: The
renamefunction provides a mechanism for renaming the datasets within the hierarchy. This function is important for operations that require temporary or permanent renaming of datasets to avoid naming conflicts or to clarify the dataset's purpose.
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- pyphare/pyphare/pharesee/run.py (4 hunks)
Additional comments: 2
pyphare/pyphare/core/operators.py (2)
- 19-25: The function
_compute_sqrtcorrectly computes the square root of a scalar field. It follows a similar pattern to_compute_dot_product, including the return structure. Ensure that the key "scalar" is always present inpatch_datasto avoid potentialKeyError.Consider adding a key presence check similar to the one suggested for
_compute_dot_product.
- 16-16: Regarding PhilipDeegan's comment about line 16 looking like it returns a tuple of size one, this pattern is consistent across similar functions (
_compute_sqrt,_compute_cross_product, etc.). It seems to be a deliberate design choice to return a tuple containing a dictionary for each computed field. Clarification from the author would be helpful to understand if this is indeed required.Request clarification from the author on the necessity of returning a tuple of size one.
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I still have 2 commits to keep memory of what I did if it happens I need to restore anything. But if this is fine for you, I will squash in a single commit |
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- pyphare/pyphare/pharesee/hierarchy.py (5 hunks)
- pyphare/pyphare/pharesee/run.py (4 hunks)
Additional comments: 5
pyphare/pyphare/pharesee/run.py (2)
- 10-16: The imports from
pyphare.pharesee.hierarchyare correctly added, includingScalarFieldandVectorFieldwhich are essential for the new functionalities introduced in this PR.- 506-547: > 📝 NOTE
This review was outside the diff hunks and was mapped to the diff hunk with the greatest overlap. Original lines [509-594]
The modifications to the
GetB,GetE,GetNi,GetPe, andGetJfunctions to handle theall_primalflag are correctly implemented. These changes enable the functions to compute electromagnetic field components on primal grids, enhancing the library's computational capabilities. However, it's important to ensure that these modifications are thoroughly tested, especially since they introduce conditional logic that affects the core functionality of the library.Ensure comprehensive testing of the modified functions (
GetB,GetE,GetNi,GetPe,GetJ) to verify correct behavior with theall_primalflag, both when it's set toTrueandFalse.pyphare/pyphare/pharesee/hierarchy.py (3)
- 566-587: The method
flat_namein thePatchHierarchyclass provides a clean and efficient way to map quantity names to their respective types. This implementation is both readable and maintainable.- 790-793: Ensure that the variable
mis initialized before the loop in theglobal_minmethod to avoid potentialNameErrorif thefirstflag is not set toTruebefore entering the loop. This issue was previously identified and a fix was suggested. Verify that this correction has been applied to prevent runtime errors.- 804-807: Similar to the
global_minmethod, theglobal_maxmethod should initialize the variablembefore the loop to prevent aNameError. This correction was previously suggested. Confirm that this initialization is present to ensure the method's robustness.
| from pyphare.pharesee.hierarchy import ( | ||
| compute_hier_from, | ||
| flat_finest_field, | ||
| hierarchy_from, | ||
| ScalarField, | ||
| VectorField, | ||
| ) |
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| from pyphare.pharesee.hierarchy import compute_hier_from_ | ||
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| from .hierarchy import flat_finest_field, hierarchy_from | ||
| import pyphare.pharein as ph |
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| # if qty in ['rho', 'tags']: | ||
| # return 'scalar' | ||
| # elif qty in ['Bx', 'Ex', 'Fx', 'Vx']: | ||
| # return 'x' | ||
| # elif qty in ['By', 'Ey', 'Fy', 'Vy']: | ||
| # return 'y' | ||
| # elif qty in ['Bz', 'Ez', 'Fz', 'Vz']: | ||
| # return 'z' | ||
| # else: | ||
| # raise ValueError("{} is not a valid quantity".format(qty)) |
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Commented-out code
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- src/hdf5/phare_hdf5.hpp (1 hunks)
- src/python3/cpp_etc.cpp (1 hunks)
- tests/simulator/test_diagnostics.py (2 hunks)
Additional comments: 2
src/python3/cpp_etc.cpp (1)
- 43-43: The change from
PHARE_TO_STR(HIGHFIVE_VERSION)toHIGHFIVE_VERSION_STRINGin thephare_depsfunction improves consistency and clarity in version reporting. This aligns well with the Highfive version check added inphare_hdf5.hpp.tests/simulator/test_diagnostics.py (1)
- 203-213: > 📝 NOTE
This review was outside the diff hunks and was mapped to the diff hunk with the greatest overlap. Original lines [206-219]
The replacement of
assertstatements withself.assertTrue()assertions for checking conditions related to version splitting, digit validation, and asserting the value ofTealigns with best practices for writing unit tests in Python. This change enhances the integration with theunittestframework and provides more informative error messages.
it can all be squashed on merge so you don't need to bother |
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- pyphare/pyphare/core/operators.py (1 hunks)
- pyphare/pyphare/pharesee/hierarchy.py (5 hunks)
- pyphare/pyphare/pharesee/run.py (4 hunks)
Files skipped from review as they are similar to previous changes (1)
- pyphare/pyphare/core/operators.py
Additional comments: 7
pyphare/pyphare/pharesee/run.py (1)
- 10-16: The imports from
pyphare.pharesee.hierarchyhave been reorganized to includeScalarFieldandVectorField. This change is necessary for the new functionality introduced in this PR and follows Python's import best practices.pyphare/pyphare/pharesee/hierarchy.py (6)
- 566-587: The method
flat_namein thePatchHierarchyclass provides a clean and efficient way to map quantity names to their respective types. However, the commented-out code from lines 589 to 598 seems redundant and should be removed to maintain code cleanliness.- # if qty in ['rho', 'tags']: - # return 'scalar' - # elif qty in ['Bx', 'Ex', 'Fx', 'Vx']: - # return 'x' - # elif qty in ['By', 'Ey', 'Fy', 'Vy']: - # return 'y' - # elif qty in ['Bz', 'Ez', 'Fz', 'Vz']: - # return 'z' - # else: - # raise ValueError("{} is not a valid quantity".format(qty))
- 790-793: In the
global_minmethod, the initialization ofminside the loop based on thefirstflag is a valid approach. However, it's important to ensure that thefirstflag is correctly managed to avoid any potential logic errors. The method correctly usesnp.nanminto handle NaN values, which is a good practice for numerical computations.- 804-807: The
global_maxmethod follows a similar pattern toglobal_min, using afirstflag for initialization andnp.nanmaxfor handling NaN values. This consistency in handling global minimum and maximum calculations is good for maintainability and readability of the code.- 846-847: The
get_namesmethod assumes that all patches across all levels contain the same data names by retrieving names from the first patch of the first level. While this might be true in many cases, it's a strong assumption that could lead to issues in heterogeneous hierarchies. Consider adding a check or documentation note to clarify this assumption.- 1139-1158: The
ScalarFieldclass provides a specialized representation for scalar fields within the hierarchy, including support for multiplication operations. This class enhances the modularity of the code by separating scalar field-specific logic. Ensure that the class is used appropriately wherever scalar fields are manipulated within the hierarchy.- 1171-1173: The
VectorFieldclass initializes thenamesattribute but does not use it within the class. If this attribute is not required for the class's functionality, consider removing it to avoid confusion and maintain code cleanliness.- self.names = ['x', 'y', 'z']
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| self = rename(h_self, names_self_kept) |
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| self = rename(h_self, names_self_kept) | ||
| other = rename(h_other, names_other_kept) |
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| (h_self, h_other), | ||
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| self = rename(h_self, names_self_kept) | ||
| other = rename(h_other, names_other_kept) |
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| try: | ||
| fn(*args, **kwargs) | ||
| except: | ||
| ... # file not found |
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| def _try(fn, *args, **kwargs): | ||
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| except: |
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Files selected for processing (5)
- pyphare/pyphare/core/operators.py (1 hunks)
- pyphare/pyphare/pharesee/hierarchy.py (5 hunks)
- pyphare/pyphare/pharesee/run.py (5 hunks)
- tests/core/data/gridlayout/test_laplacian.py (1 hunks)
- tests/simulator/test_restarts.py (2 hunks)
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- pyphare/pyphare/core/operators.py
Additional comments: 8
tests/simulator/test_restarts.py (2)
- 169-170: The addition of the
all_primalargument with a default value ofFalsein theGetAllAvailableQtiesmethod call is consistent with the PR objectives to enhance data retrieval with an "all_primal" centering approach. However, it's important to ensure that all calls to this method throughout the codebase are updated to include this new argument if necessary.- 211-213: The addition of the
all_primalargument with a default value ofFalseto theGetBmethod call aligns with the PR's goal of implementing an "all_primal" centering approach. Similar to the previous comment, ensure that all instances ofGetBmethod calls are updated accordingly.Verification successful
Given the execution of the script and its results, it appears that there are no instances of the
GetBmethod being called without theall_primalargument in the codebase. This aligns with the PR's goal of implementing an "all_primal" centering approach and updating all instances ofGetBmethod calls accordingly. Therefore, the changes seem to be correctly implemented as per the PR's intention.Scripts Executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Search for calls to GetB without the all_primal argument. ast-grep --lang python --pattern $'GetB($_)'Length of output: 45
tests/core/data/gridlayout/test_laplacian.py (1)
- 213-213: The modification in the slicing operation for
Jx_xcalculation appears to be correct and aligns with the intended computation adjustments. However, it's crucial to ensure that the indices used in slicing (psi_d_X,pei_d_X) accurately reflect the desired range for the dual centering in the x-direction. This change could potentially impact the accuracy of the laplacian calculation, so it's recommended to verify the correctness of the indices and the overall computation through additional testing or validation.Ensure that the modified slicing operation for
Jx_xaccurately captures the intended range and that the computation aligns with the theoretical expectations for the laplacian calculation. Consider adding unit tests or validation checks to confirm the correctness of this change.pyphare/pyphare/pharesee/run.py (3)
- 10-16: The imports from
pyphare.pharesee.hierarchyare correctly consolidated into a single import statement, improving code readability and maintainability. However, ensure that the cyclic import issue flagged by the security bot has been resolved.- 219-231: The function
_pdd_to_ppp_domain_slicingprovides domain slicing for converting dual to primal centering. It currently raises aRuntimeErrorfor dimensions other than 2. If the framework plans to support 3D in the future, it would be beneficial to start planning and implementing the necessary logic for 3D domain slicing now to ensure scalability.- 585-607: The
GetPefunction has been updated to use theall_primalflag and to retrieveTefrom the hierarchy's simulation attributes. This is a significant improvement in leveraging the framework's capabilities for more accurate calculations. However, ensure that theTeattribute is always available and correctly initialized in the hierarchy to prevent runtime errors.pyphare/pyphare/pharesee/hierarchy.py (2)
- 846-847: The
get_namesmethod provides a straightforward way to retrieve the names of patch data from the first patch of the first level. This method assumes that all patches across all levels contain the same types of patch data, which might not always be the case in more complex scenarios. It's recommended to document this assumption or consider a more robust approach if the assumption does not hold.Consider verifying the assumption that all patches across all levels contain the same types of patch data. If this assumption might not hold in all cases, consider implementing a more comprehensive method to gather patch data names from the entire hierarchy.
- 1128-1139: The
__neg__method in thePatchHierarchyclass demonstrates an attempt to apply a negation operation across the hierarchy. However, the method's implementation is not shown, and it's unclear how thecompute_hier_fromand_compute_negfunctions are defined or used. Ensure these functions correctly apply the negation operation to the appropriate data within the hierarchy.Verify the implementation of
compute_hier_fromand_compute_negfunctions to ensure they correctly apply the negation operation to the hierarchy's data.
| def flat_name(self, qty): | ||
| name_dict = { | ||
| "rho": "scalar", | ||
| "tags": "scalar", | ||
| "Bx": "x", | ||
| "Ex": "x", | ||
| "Fx": "x", | ||
| "Vx": "x", | ||
| "By": "y", | ||
| "Ey": "y", | ||
| "Fy": "y", | ||
| "Vy": "y", | ||
| "Bz": "z", | ||
| "Ez": "z", | ||
| "Fz": "z", | ||
| "Vz": "z", | ||
| } | ||
|
|
||
| if qty in name_dict.keys(): | ||
| return name_dict[qty] | ||
| else: | ||
| raise ValueError("{} is not a valid quantity".format(qty)) |
There was a problem hiding this comment.
The implementation of the flat_name method in the PatchHierarchy class is concise and correctly uses a dictionary to map quantity names to their respective types. However, the commented-out code from lines 589 to 598 is redundant and should be removed to keep the codebase clean and maintainable.
- # if qty in ['rho', 'tags']:
- # return 'scalar'
- # elif qty in ['Bx', 'Ex', 'Fx', 'Vx']:
- # return 'x'
- # elif qty in ['By', 'Ey', 'Fy', 'Vy']:
- # return 'y'
- # elif qty in ['Bz', 'Ez', 'Fz', 'Vz']:
- # return 'z'
- # else:
- # raise ValueError("{} is not a valid quantity".format(qty))Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation.
| def flat_name(self, qty): | |
| name_dict = { | |
| "rho": "scalar", | |
| "tags": "scalar", | |
| "Bx": "x", | |
| "Ex": "x", | |
| "Fx": "x", | |
| "Vx": "x", | |
| "By": "y", | |
| "Ey": "y", | |
| "Fy": "y", | |
| "Vy": "y", | |
| "Bz": "z", | |
| "Ez": "z", | |
| "Fz": "z", | |
| "Vz": "z", | |
| } | |
| if qty in name_dict.keys(): | |
| return name_dict[qty] | |
| else: | |
| raise ValueError("{} is not a valid quantity".format(qty)) | |
| def flat_name(self, qty): | |
| name_dict = { | |
| "rho": "scalar", | |
| "tags": "scalar", | |
| "Bx": "x", | |
| "Ex": "x", | |
| "Fx": "x", | |
| "Vx": "x", | |
| "By": "y", | |
| "Ey": "y", | |
| "Fy": "y", | |
| "Vy": "y", | |
| "Bz": "z", | |
| "Ez": "z", | |
| "Fz": "z", | |
| "Vz": "z", | |
| } | |
| if qty in name_dict.keys(): | |
| return name_dict[qty] | |
| else: | |
| raise ValueError("{} is not a valid quantity".format(qty)) |
| m = np.nanmin(pd.dataset[:]) | ||
| first = False | ||
| else: | ||
| m = min(m, pd.dataset[:].min()) | ||
| m = min(m, np.nanmin(pd.dataset[:])) |
There was a problem hiding this comment.
In the global_min method, the use of np.nanmin to find the minimum value while ignoring NaNs is appropriate for numerical datasets that may contain missing values. This approach ensures robustness in calculations. However, the initialization of m and the conditional check for first could be simplified by initializing m to np.inf outside the loop, eliminating the need for the first flag.
- first = True
+ m = np.inf
for ilvl, lvl in self.levels(time).items():
for patch in lvl.patches:
pd = patch.patch_datas[qty]
- if first:
- m = np.nanmin(pd.dataset[:])
- first = False
- else:
- m = min(m, np.nanmin(pd.dataset[:]))
+ m = min(m, np.nanmin(pd.dataset[:]))Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation.
| m = np.nanmin(pd.dataset[:]) | |
| first = False | |
| else: | |
| m = min(m, pd.dataset[:].min()) | |
| m = min(m, np.nanmin(pd.dataset[:])) | |
| m = np.inf | |
| m = min(m, np.nanmin(pd.dataset[:])) |
| m = np.nanmax(pd.dataset[:]) | ||
| first = False | ||
| else: | ||
| m = max(m, pd.dataset[:].max()) | ||
| m = max(m, np.nanmax(pd.dataset[:])) |
There was a problem hiding this comment.
Similar to the global_min method, the global_max method correctly uses np.nanmax for handling NaN values. The same refinement suggested for global_min applies here as well, where initializing m to -np.inf outside the loop can simplify the code by removing the need for the first flag.
- first = True
+ m = -np.inf
for ilvl, lvl in self.levels(time).items():
for patch in lvl.patches:
pd = patch.patch_datas[qty]
- if first:
- m = np.nanmax(pd.dataset[:])
- first = False
- else:
- m = max(m, np.nanmax(pd.dataset[:]))
+ m = max(m, np.nanmax(pd.dataset[:]))Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation.
| m = np.nanmax(pd.dataset[:]) | |
| first = False | |
| else: | |
| m = max(m, pd.dataset[:].max()) | |
| m = max(m, np.nanmax(pd.dataset[:])) | |
| m = max(m, np.nanmax(pd.dataset[:])) |
| self = rename(h_self, names_self_kept) | ||
| other = rename(h_other, names_other_kept) |
There was a problem hiding this comment.
Similar to the __add__ method, variables self and other are assigned but not used in the __sub__ method of VectorField. This could indicate a misunderstanding of the method's functionality or an incomplete implementation. Clarify the intended use of these variables or remove them if unnecessary.
As with the __add__ method, ensure that the variables self and other are used appropriately in the __sub__ method or remove them if they are unnecessary.
pyphare/pyphare/core/operators.py
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| def dot(hier_left, hier_right, **kwargs): | ||
| names_left_kept = hier_left.get_names() |
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| def dot(hier_left, hier_right, **kwargs): | ||
| names_left_kept = hier_left.get_names() | ||
| names_right_kept = hier_right.get_names() |
Check notice
Code scanning / CodeQL
Unused local variable
pyphare/pyphare/core/operators.py
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| def cross(hier_left, hier_right, **kwargs): | ||
| names_left_kept = hier_left.get_names() |
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pyphare/pyphare/core/operators.py
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| def cross(hier_left, hier_right, **kwargs): | ||
| names_left_kept = hier_left.get_names() | ||
| names_right_kept = hier_right.get_names() |
Check notice
Code scanning / CodeQL
Unused local variable
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Review Status
Actionable comments generated: 4
Configuration used: .coderabbit.yaml
Files selected for processing (2)
- pyphare/pyphare/core/operators.py (1 hunks)
- pyphare/pyphare/pharesee/run.py (5 hunks)
Files skipped from review as they are similar to previous changes (1)
- pyphare/pyphare/core/operators.py
Additional comments: 3
pyphare/pyphare/pharesee/run.py (3)
- 10-16: The imports from
pyphare.pharesee.hierarchyhave been reorganized to includeScalarFieldandVectorField. This change is consistent with the PR's objective to enhance patch data management with an "all_primal" centering approach.- 507-520: The modification to the
GetBfunction to handle theall_primalflag is consistent with the PR's objectives. The use ofcompute_hier_fromwith_compute_to_primalforall_primalflag handling is a good approach. However, ensure that thecompute_hier_fromfunction and the_compute_to_primalfunction are thoroughly tested, especially for edge cases and performance implications.Verification successful
The modification to the
GetBfunction and similar modifications in other functions to handle theall_primalflag are confirmed by the script execution results. The usage ofcompute_hier_fromwith_compute_to_primalacross various parts of therun.pyfile for different physical quantities supports the PR's objectives. While the search did not reveal markdown documentation, the presence of these functions in the codebase for handlingall_primalis verified. Ensure that these implementations are covered by comprehensive tests for reliability and performance.* 595-599: The update to the `GetPe` function to use `hier.sim.electrons.closure.Te` for obtaining `Te` is a good improvement and aligns with the latest project developments. This change ensures that the code remains consistent and utilizes the most accurate data source for `Te`.Scripts Executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Verify the correct behavior of compute_hier_from with _compute_to_primal in various scenarios # This script is a placeholder for the concept and should be replaced with actual test commands echo "Ensure comprehensive tests for compute_hier_from with _compute_to_primal"Length of output: 154
Script:
#!/bin/bash # Search for test cases or documentation related to compute_hier_from and _compute_to_primal rg "compute_hier_from" -A 3 | grep "_compute_to_primal" rg "_compute_to_primal" -A 3 | grep "compute_hier_from" # Additionally, look for any markdown files that might contain documentation about these functions fd "\.md$" --exec rg "compute_hier_from|_compute_to_primal"Length of output: 1249
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Actionable comments generated: 0
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Files selected for processing (1)
- pyphare/pyphare/pharesee/run.py (5 hunks)
Files skipped from review as they are similar to previous changes (1)
- pyphare/pyphare/pharesee/run.py
pyphare/pyphare/pharesee/run.py
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| if ndim == 2: | ||
| inner_all = tuple([inner] * 2) | ||
| return inner_all, ((inner, inner_shift_left), (inner, inner_shift_right)) |
There was a problem hiding this comment.
there's a return only for 2D, not for 1D?
There was a problem hiding this comment.
yes ; will do it very soon... before merging !
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Actionable comments posted: 8
Review Details
Configuration used: .coderabbit.yaml
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Files selected for processing (1)
- pyphare/pyphare/pharesee/run.py (5 hunks)
Additional Context Used
Ruff (5)
pyphare/pyphare/pharesee/run.py (5)
4-4:
pyphare.phareinimported but unused
638-638: f-string without any placeholders
640-640: f-string without any placeholders
767-767: Avoid equality comparisons to
True; useif self.single_hier_for_all_quantities:for truth checks
772-772: Do not use bare
except
| def _try(fn, *args, **kwargs): | ||
| try: | ||
| fn(*args, **kwargs) | ||
| except: |
There was a problem hiding this comment.
Replace the bare except with a more specific exception handler.
- except:
+ except Exception:Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation.
| except: | |
| except Exception: |
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Actionable comments posted: 7
Outside diff range and nitpick comments (4)
pyphare/pyphare/core/operators.py (1)
180-180: Clarify or resolve the TODO comment regarding gradient plotting.The TODO comment at line 180 mentions an issue with gradient plotting. It would be beneficial to either resolve this issue or provide more details in the comment about what needs to be investigated or changed.
pyphare/pyphare/pharesee/run.py (2)
14-14: Consider adding a TODO for future improvement regarding the hard-coded Yee layout.
14-14: Consider adding a TODO for future improvement regarding the hard-coded Yee layout.pyphare/pyphare/pharesee/hierarchy.py (1)
Line range hint
701-701: Comparison toNoneshould becond is None.- if time == None: + if time is None:Use
is Nonefor comparisons toNoneto follow Python best practices and ensure correct behavior.
Review details
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Review profile: CHILL
Files selected for processing (3)
- pyphare/pyphare/core/operators.py (1 hunks)
- pyphare/pyphare/pharesee/hierarchy.py (9 hunks)
- pyphare/pyphare/pharesee/run.py (5 hunks)
Additional context used
Ruff
pyphare/pyphare/core/operators.py
91-91: Local variable
names_left_keptis assigned to but never used
92-92: Local variable
names_right_keptis assigned to but never used
123-123: Local variable
names_left_keptis assigned to but never used
124-124: Local variable
names_right_keptis assigned to but never usedpyphare/pyphare/pharesee/run.py
4-4:
pyphare.phareinimported but unused
646-646: f-string without any placeholders
648-648: f-string without any placeholders
772-772: Avoid equality comparisons to
True; useif self.single_hier_for_all_quantities:for truth checks
777-777: Do not use bare
exceptpyphare/pyphare/pharesee/hierarchy.py
701-701: Comparison to
Noneshould becond is None
Additional comments not posted (9)
pyphare/pyphare/pharesee/run.py (5)
14-14: Proper handling of different dimensions and appropriate error management.
14-14: Proper handling of different dimensions and appropriate error management.
283-285: Straightforward implementation using list comprehension.
288-289: Straightforward implementation that delegates to another function based on a key.
349-349: Proper handling of MPI ranks based on patch data.pyphare/pyphare/pharesee/hierarchy.py (4)
566-587: The implementation of theflat_namemethod in thePatchHierarchyclass is concise and correctly uses a dictionary to map quantity names to their respective types. However, the commented-out code from lines 589 to 598 is redundant and should be removed to keep the codebase clean and maintainable.
589-598: Remove commented-out code to maintain code clarity and cleanliness.
800-803: In theglobal_minmethod, the use ofnp.nanminto find the minimum value while ignoring NaNs is appropriate for numerical datasets that may contain missing values. This approach ensures robustness in calculations. However, the initialization ofmand the conditional check forfirstcould be simplified by initializingmtonp.infoutside the loop, eliminating the need for thefirstflag.
814-817: Similar to theglobal_minmethod, theglobal_maxmethod correctly usesnp.nanmaxfor handling NaN values. The same refinement suggested forglobal_minapplies here as well, where initializingmto-np.infoutside the loop can simplify the code by removing the need for thefirstflag.
pyphare/pyphare/core/operators.py
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| def dot(hier_left, hier_right, **kwargs): | ||
| names_left_kept = hier_left.get_names() | ||
| names_right_kept = hier_right.get_names() |
There was a problem hiding this comment.
Unused local variables names_right_kept and names_left_kept.
- names_left_kept = hier_left.get_names()
- names_right_kept = hier_right.get_names()Similar to the previous comment, these variables are assigned but not utilized. Removing these lines can help in reducing the clutter and improving the readability of the code.
Also applies to: 124-124
Committable suggestion
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Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation.
| names_right_kept = hier_right.get_names() |
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Actionable comments posted: 9
Outside diff range and nitpick comments (1)
pyphare/pyphare/pharesee/hierarchy.py (1)
Line range hint
690-690: Comparison toNoneshould becond is None.- if qty == None: + if qty is None:
Review details
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Files selected for processing (3)
- pyphare/pyphare/core/operators.py (1 hunks)
- pyphare/pyphare/pharesee/hierarchy.py (9 hunks)
- pyphare/pyphare/pharesee/run.py (6 hunks)
Additional context used
Ruff
pyphare/pyphare/core/operators.py
91-91: Local variable
names_left_keptis assigned to but never used
92-92: Local variable
names_right_keptis assigned to but never used
123-123: Local variable
names_left_keptis assigned to but never used
124-124: Local variable
names_right_keptis assigned to but never usedpyphare/pyphare/pharesee/run.py
4-4:
pyphare.phareinimported but unused
593-593: f-string without any placeholders
595-595: f-string without any placeholders
703-703: Avoid equality comparisons to
True; useif self.single_hier_for_all_quantities:for truth checks
708-708: Do not use bare
exceptpyphare/pyphare/pharesee/hierarchy.py
690-690: Comparison to
Noneshould becond is None
Additional comments not posted (10)
pyphare/pyphare/core/operators.py (2)
154-160: LGTM! The implementation of thesqrtfunction is straightforward and correct.
176-182: Consider adding support for 3D or improving error handling for unsupported dimensions.+ # Add support for 3D or improve error handling for unsupported dimensionsLikely invalid or redundant comment.
pyphare/pyphare/pharesee/run.py (5)
544-550: Ensure theall_primalflag is handled correctly in theGetBmethod.Verification successful
The
all_primalflag is handled correctly in theGetBmethod and its various calls throughout the codebase.
Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Verify that the `all_primal` flag is used correctly in the `GetB` method. grep "GetB" pyphare/pyphare/pharesee/run.pyLength of output: 291
Script:
#!/bin/bash # Extract lines around each `GetB` call to understand the context and verify the correct handling of the `all_primal` flag. grep -C 3 "GetB" pyphare/pyphare/pharesee/run.pyLength of output: 1524
610-616: Ensure theall_primalflag is handled correctly in theGetJmethod.Verification successful
The
GetJmethod is called in three places within the codebase, but none of these calls explicitly set theall_primalflag, meaning it defaults toTrue. This indicates that theall_primalflag is not being actively managed in these instances.
tests/functional/tdtagged/td1dtagged.py:
JwT = rwT.GetJ(plot_time, merged=True, interp="linear")JNoRef = rNoRef.GetJ(plot_time, merged=True, interp="linear")
pyphare/pyphare/pharesee/plotting.py:
interpolator, finest_coords = r.GetJ(time, merged=True, interp=interp)[qty]Since the
all_primalflag defaults toTrue, the current implementation will always compute the hierarchical structure to primal grids unless explicitly set otherwise. This behavior seems consistent with the method's design, but it is important to ensure that this default behavior is intended and documented.Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Verify that the `all_primal` flag is used correctly in the `GetJ` method. grep "GetJ" pyphare/pyphare/pharesee/run.pyLength of output: 118
Script:
#!/bin/bash # Find all occurrences of the `GetJ` method in the codebase to verify the handling of the `all_primal` flag. rg '\.GetJ\('Length of output: 342
Script:
#!/bin/bash # Extract relevant lines from the identified files to review the usage of the `all_primal` flag in the `GetJ` method calls. rg -A 5 'GetJ\(' tests/functional/tdtagged/td1dtagged.py pyphare/pyphare/pharesee/plotting.pyLength of output: 1175
701-715: Ensure theall_primalflag is handled correctly in theGetAllAvailableQtiesmethod.Verification successful
The
all_primalflag is handled correctly in theGetAllAvailableQtiesmethod and the methods it calls (GetB,GetE,GetNi). The flag is passed as an argument and used appropriately within these methods to determine the behavior of the data retrieval process.
- In
GetAllAvailableQties, the flag is passed toGetB,GetE, andGetNi.- In
GetBandGetE, the flag is checked to decide whether to return the primal data or not.- In
GetNi, the flag is not used, which is consistent with its implementation.The implementation ensures that the
all_primalflag is used correctly to control the retrieval of primal data.Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Verify that the `all_primal` flag is used correctly in the `GetAllAvailableQties` method. grep "GetAllAvailableQties" pyphare/pyphare/pharesee/run.pyLength of output: 124
Script:
#!/bin/bash # Extract the full implementation of the GetAllAvailableQties method and the methods it calls. ast-grep --lang python --pattern $'def GetAllAvailableQties($_, $_, $_):\n $$$' pyphare/pyphare/pharesee/run.py ast-grep --lang python --pattern $'def GetB($_, $_):\n $$$' pyphare/pyphare/pharesee/run.py ast-grep --lang python --pattern $'def GetE($_, $_):\n $$$' pyphare/pyphare/pharesee/run.py ast-grep --lang python --pattern $'def GetNi($_, $_):\n $$$' pyphare/pyphare/pharesee/run.pyLength of output: 400
Script:
#!/bin/bash # Extract the full implementation of the GetAllAvailableQties method and the methods it calls. rg -A 20 "def GetAllAvailableQties" pyphare/pyphare/pharesee/run.py rg -A 20 "def GetB" pyphare/pyphare/pharesee/run.py rg -A 20 "def GetE" pyphare/pyphare/pharesee/run.py rg -A 20 "def GetNi" pyphare/pyphare/pharesee/run.pyLength of output: 3879
599-608: Ensure theall_primalflag is handled correctly in theGetPemethod.Verification successful
The
GetPemethod and other methods likeGetB,GetE, andGetJuse theall_primalflag to determine different computation paths. This suggests a consistent pattern in handling this flag across the codebase. Theall_primalflag appears to be used correctly within theGetPemethod, as it follows the same pattern observed in other methods.
GetPemethod implementation:def GetPe(self, time, merged=False, interp="nearest", all_primal=True): hier = self._get_hierarchy(time, "ions_density.h5") Te = hier.sim.electrons.closure.Te if not all_primal: return Te * self._get(hier, time, merged, interp) h = compute_hier_from(_compute_to_primal, hier, scalar="rho") return ScalarField(h) * TeOther references to
all_primal:
GetB,GetE,GetJ, andGetAllAvailableQtiesmethods also use theall_primalflag in a similar manner.Based on the retrieved information, the
all_primalflag is handled consistently and correctly within theGetPemethod and other related methods.Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Verify that the `all_primal` flag is used correctly in the `GetPe` method. grep "GetPe" pyphare/pyphare/pharesee/run.pyLength of output: 120
Script:
#!/bin/bash # Retrieve the full implementation of the `GetPe` method. ast-grep --lang python --pattern $'def GetPe(self, time, merged=False, interp="nearest", all_primal=True):\n $$$\n' pyphare/pyphare/pharesee/run.py # Check for any other references to `all_primal` within the codebase. grep -r "all_primal" .Length of output: 2976
552-558: Ensure theall_primalflag is handled correctly in theGetEmethod.Verification successful
The extracted definitions provide the necessary context to verify the handling of the
all_primalflag in theGetEmethod. Here are the key points:
_tryfunction attempts to call the provided function and catches any exceptions._get_hierarchyretrieves the hierarchy based on the provided time and filename._getretrieves data from the hierarchy, with an option to merge and interpolate.compute_hier_fromcreates a new hierarchy using a callback function on all patch data.VectorFieldis a class derived fromPatchHierarchy.The
all_primalflag is used to determine whether to compute the hierarchy from primal fields (_compute_to_primal) and return aVectorField. Ifall_primalisFalse, it directly returns the result of_get.The handling of the
all_primalflag in theGetEmethod appears to be correct based on the provided context.
Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Verify that the `all_primal` flag is used correctly in the `GetE` method. grep "GetE" pyphare/pyphare/pharesee/run.pyLength of output: 171
Script:
#!/bin/bash # Verify the definition and usage of `_try`, `_get_hierarchy`, `_get`, `compute_hier_from`, and `VectorField`. # Search for the definition of `_try` ast-grep --lang python --pattern $'def _try($_, $_, $$$):' # Search for the definition of `_get_hierarchy` ast-grep --lang python --pattern $'def _get_hierarchy($_, $_):' # Search for the definition of `_get` ast-grep --lang python --pattern $'def _get($_, $_, $_, $_):' # Search for the definition of `compute_hier_from` ast-grep --lang python --pattern $'def compute_hier_from($_, $_, $$$):' # Search for the definition of `VectorField` ast-grep --lang python --pattern $'class VectorField($_):'Length of output: 320
Script:
#!/bin/bash # Search the entire codebase for the definitions of `_try`, `_get_hierarchy`, `_get`, `compute_hier_from`, and `VectorField`. # Search for the definition of `_try` rg --pcre2 --glob '!*.{pyc,pyo,pyd}' 'def _try\(' # Search for the definition of `_get_hierarchy` rg --pcre2 --glob '!*.{pyc,pyo,pyd}' 'def _get_hierarchy\(' # Search for the definition of `_get` rg --pcre2 --glob '!*.{pyc,pyo,pyd}' 'def _get\(' # Search for the definition of `compute_hier_from` rg --pcre2 --glob '!*.{pyc,pyo,pyd}' 'def compute_hier_from\(' # Search for the definition of `VectorField` rg --pcre2 --glob '!*.{pyc,pyo,pyd}' 'class VectorField\('Length of output: 757
Script:
#!/bin/bash # Extract the definitions of `_try`, `_get_hierarchy`, `_get`, `compute_hier_from`, and `VectorField` from the respective files. # Extract the definition of `_try` sed -n '/def _try/,/^$/p' pyphare/pyphare/pharesee/run.py # Extract the definition of `_get_hierarchy` sed -n '/def _get_hierarchy/,/^$/p' pyphare/pyphare/pharesee/run.py # Extract the definition of `_get` sed -n '/def _get/,/^$/p' pyphare/pyphare/pharesee/run.py # Extract the definition of `compute_hier_from` sed -n '/def compute_hier_from/,/^$/p' pyphare/pyphare/pharesee/hierarchy.py # Extract the definition of `VectorField` sed -n '/class VectorField/,/^$/p' pyphare/pyphare/pharesee/hierarchy.pyLength of output: 1903
pyphare/pyphare/pharesee/hierarchy.py (3)
566-587: The implementation of theflat_namemethod in thePatchHierarchyclass is concise and correctly uses a dictionary to map quantity names to their respective types. However, the commented-out code from lines 589 to 598 is redundant and should be removed to keep the codebase clean and maintainable.- # if qty in ['rho', 'tags']: - # return 'scalar' - # elif qty in ['Bx', 'Ex', 'Fx', 'Vx']: - # return 'x' - # elif qty in ['By', 'Ey', 'Fy', 'Vy']: - # return 'y' - # elif qty in ['Bz', 'Ez', 'Fz', 'Vz']: - # return 'z' - # else: - # raise ValueError("{} is not a valid quantity".format(qty))
789-792: In theglobal_minmethod, the use ofnp.nanminto find the minimum value while ignoring NaNs is appropriate for numerical datasets that may contain missing values. This approach ensures robustness in calculations. However, the initialization ofmand the conditional check forfirstcould be simplified by initializingmtonp.infoutside the loop, eliminating the need for thefirstflag.- first = True + m = np.inf for ilvl, lvl in self.levels(time).items(): for patch in lvl.patches: pd = patch.patch_datas[qty] - if first: - m = np.nanmin(pd.dataset[:]) - first = False - else: - m = min(m, np.nanmin(pd.dataset[:])) + m = min(m, np.nanmin(pd.dataset[:]))
803-806: Similar to theglobal_minmethod, theglobal_maxmethod correctly usesnp.nanmaxfor handling NaN values. The same refinement suggested forglobal_minapplies here as well, where initializingmto-np.infoutside the loop can simplify the code by removing the need for thefirstflag.- first = True + m = -np.inf for ilvl, lvl in self.levels(time).items(): for patch in lvl.patches: pd = patch.patch_datas[qty] - if first: - m = np.nanmax(pd.dataset[:]) - first = False - else: - m = max(m, np.nanmax(pd.dataset[:])) + m = max(m, np.nanmax(pd.dataset[:]))
| def _compute_grad(patch_data, **kwargs): | ||
| ndim = patch_data["value"].box.ndim | ||
| nb_ghosts = kwargs["nb_ghosts"] | ||
| ds = patch_data["value"].dataset | ||
|
|
||
| ds_shape = list(ds.shape) | ||
|
|
||
| ds_x = np.full(ds_shape, np.nan) | ||
| ds_y = np.full(ds_shape, np.nan) | ||
| ds_z = np.full(ds_shape, np.nan) | ||
|
|
||
| grad_ds = np.gradient(ds) | ||
|
|
||
| if ndim == 2: | ||
| ds_x[nb_ghosts:-nb_ghosts, nb_ghosts:-nb_ghosts] = np.asarray( | ||
| grad_ds[0][nb_ghosts:-nb_ghosts, nb_ghosts:-nb_ghosts] | ||
| ) | ||
| ds_y[nb_ghosts:-nb_ghosts, nb_ghosts:-nb_ghosts] = np.asarray( | ||
| grad_ds[1][nb_ghosts:-nb_ghosts, nb_ghosts:-nb_ghosts] | ||
| ) | ||
| ds_z[nb_ghosts:-nb_ghosts, nb_ghosts:-nb_ghosts].fill( | ||
| 0.0 | ||
| ) # TODO at 2D, gradient is null in z dir | ||
|
|
||
| else: | ||
| raise RuntimeError("dimension not yet implemented") | ||
|
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||
| return ( | ||
| {"name": "x", "data": ds_x, "centering": patch_data["value"].centerings}, | ||
| {"name": "y", "data": ds_y, "centering": patch_data["value"].centerings}, | ||
| {"name": "z", "data": ds_z, "centering": patch_data["value"].centerings}, | ||
| ) |
There was a problem hiding this comment.
Consider adding support for 3D or improving error handling for unsupported dimensions.
+ # Add support for 3D or improve error handling for unsupported dimensionsCommittable suggestion was skipped due low confidence.
| def _compute_sqrt(patch_datas, **kwargs): | ||
| ref_name = next(iter(patch_datas.keys())) | ||
|
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| dset = np.sqrt(patch_datas["value"].dataset[:]) | ||
|
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| return ( | ||
| {"name": "value", "data": dset, "centering": patch_datas[ref_name].centerings}, | ||
| ) |
There was a problem hiding this comment.
Ensure key presence before accessing dictionary values to avoid potential KeyError.
+ if "value" not in patch_datas:
+ raise KeyError("'value' key is missing in patch_datas.")Committable suggestion was skipped due low confidence.
| def _compute_dot_product(patch_datas, **kwargs): | ||
| ref_name = next(iter(patch_datas.keys())) | ||
|
|
||
| dset = ( | ||
| patch_datas["left_x"].dataset[:] * patch_datas["right_x"].dataset[:] | ||
| + patch_datas["left_y"].dataset[:] * patch_datas["right_y"].dataset[:] | ||
| + patch_datas["left_z"].dataset[:] * patch_datas["right_z"].dataset[:] | ||
| ) | ||
|
|
||
| return ( | ||
| {"name": "value", "data": dset, "centering": patch_datas[ref_name].centerings}, | ||
| ) |
There was a problem hiding this comment.
Ensure key presence before accessing dictionary values to avoid potential KeyError.
+ if not all(key in patch_datas for key in ["left_x", "left_y", "left_z", "right_x", "right_y", "right_z"]):
+ raise KeyError("One or more required keys are missing in patch_datas.")Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation.
| def _compute_dot_product(patch_datas, **kwargs): | |
| ref_name = next(iter(patch_datas.keys())) | |
| dset = ( | |
| patch_datas["left_x"].dataset[:] * patch_datas["right_x"].dataset[:] | |
| + patch_datas["left_y"].dataset[:] * patch_datas["right_y"].dataset[:] | |
| + patch_datas["left_z"].dataset[:] * patch_datas["right_z"].dataset[:] | |
| ) | |
| return ( | |
| {"name": "value", "data": dset, "centering": patch_datas[ref_name].centerings}, | |
| ) | |
| def _compute_dot_product(patch_datas, **kwargs): | |
| if not all(key in patch_datas for key in ["left_x", "left_y", "left_z", "right_x", "right_y", "right_z"]): | |
| raise KeyError("One or more required keys are missing in patch_datas.") | |
| ref_name = next(iter(patch_datas.keys())) | |
| dset = ( | |
| patch_datas["left_x"].dataset[:] * patch_datas["right_x"].dataset[:] | |
| + patch_datas["left_y"].dataset[:] * patch_datas["right_y"].dataset[:] | |
| + patch_datas["left_z"].dataset[:] * patch_datas["right_z"].dataset[:] | |
| ) | |
| return ( | |
| {"name": "value", "data": dset, "centering": patch_datas[ref_name].centerings}, | |
| ) |
| def _compute_cross_product(patch_datas, **kwargs): | ||
| ref_name = next(iter(patch_datas.keys())) | ||
|
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||
| dset_x = ( | ||
| patch_datas["left_y"].dataset[:] * patch_datas["right_z"].dataset[:] | ||
| - patch_datas["left_z"].dataset[:] * patch_datas["right_y"].dataset[:] | ||
| ) | ||
| dset_y = ( | ||
| patch_datas["left_z"].dataset[:] * patch_datas["right_x"].dataset[:] | ||
| - patch_datas["left_x"].dataset[:] * patch_datas["right_z"].dataset[:] | ||
| ) | ||
| dset_z = ( | ||
| patch_datas["left_x"].dataset[:] * patch_datas["right_y"].dataset[:] | ||
| - patch_datas["left_y"].dataset[:] * patch_datas["right_x"].dataset[:] | ||
| ) | ||
|
|
||
| return ( | ||
| {"name": "x", "data": dset_x, "centering": patch_datas[ref_name].centerings}, | ||
| {"name": "y", "data": dset_y, "centering": patch_datas[ref_name].centerings}, | ||
| {"name": "z", "data": dset_z, "centering": patch_datas[ref_name].centerings}, | ||
| ) |
There was a problem hiding this comment.
Ensure key presence before accessing dictionary values to avoid potential KeyError.
+ if not all(key in patch_datas for key in ["left_x", "left_y", "left_z", "right_x", "right_y", "right_z"]):
+ raise KeyError("One or more required keys are missing in patch_datas.")Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation.
| def _compute_cross_product(patch_datas, **kwargs): | |
| ref_name = next(iter(patch_datas.keys())) | |
| dset_x = ( | |
| patch_datas["left_y"].dataset[:] * patch_datas["right_z"].dataset[:] | |
| - patch_datas["left_z"].dataset[:] * patch_datas["right_y"].dataset[:] | |
| ) | |
| dset_y = ( | |
| patch_datas["left_z"].dataset[:] * patch_datas["right_x"].dataset[:] | |
| - patch_datas["left_x"].dataset[:] * patch_datas["right_z"].dataset[:] | |
| ) | |
| dset_z = ( | |
| patch_datas["left_x"].dataset[:] * patch_datas["right_y"].dataset[:] | |
| - patch_datas["left_y"].dataset[:] * patch_datas["right_x"].dataset[:] | |
| ) | |
| return ( | |
| {"name": "x", "data": dset_x, "centering": patch_datas[ref_name].centerings}, | |
| {"name": "y", "data": dset_y, "centering": patch_datas[ref_name].centerings}, | |
| {"name": "z", "data": dset_z, "centering": patch_datas[ref_name].centerings}, | |
| ) | |
| def _compute_cross_product(patch_datas, **kwargs): | |
| if not all(key in patch_datas for key in ["left_x", "left_y", "left_z", "right_x", "right_y", "right_z"]): | |
| raise KeyError("One or more required keys are missing in patch_datas.") | |
| ref_name = next(iter(patch_datas.keys())) | |
| dset_x = ( | |
| patch_datas["left_y"].dataset[:] * patch_datas["right_z"].dataset[:] | |
| - patch_datas["left_z"].dataset[:] * patch_datas["right_y"].dataset[:] | |
| ) | |
| dset_y = ( | |
| patch_datas["left_z"].dataset[:] * patch_datas["right_x"].dataset[:] | |
| - patch_datas["left_x"].dataset[:] * patch_datas["right_z"].dataset[:] | |
| ) | |
| dset_z = ( | |
| patch_datas["left_x"].dataset[:] * patch_datas["right_y"].dataset[:] | |
| - patch_datas["left_y"].dataset[:] * patch_datas["right_x"].dataset[:] | |
| ) | |
| return ( | |
| {"name": "x", "data": dset_x, "centering": patch_datas[ref_name].centerings}, | |
| {"name": "y", "data": dset_y, "centering": patch_datas[ref_name].centerings}, | |
| {"name": "z", "data": dset_z, "centering": patch_datas[ref_name].centerings}, | |
| ) |
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Actionable comments posted: 1
Review details
Configuration used: .coderabbit.yaml
Review profile: CHILL
Files selected for processing (1)
- pyphare/pyphare/pharesee/hierarchy.py (9 hunks)
Additional context used
Ruff
pyphare/pyphare/pharesee/hierarchy.py
690-690: Comparison to
Noneshould becond is None
Additional comments not posted (3)
pyphare/pyphare/pharesee/hierarchy.py (3)
566-587: The implementation of theflat_namemethod in thePatchHierarchyclass is concise and correctly uses a dictionary to map quantity names to their respective types. However, consider removing the commented-out code from lines 589 to 598 to keep the codebase clean and maintainable.
789-792: The use ofnp.nanminandnp.nanmaxto find the minimum and maximum values while ignoring NaNs is appropriate for numerical datasets that may contain missing values. This approach ensures robustness in calculations. However, the initialization ofmand the conditional check forfirstcould be simplified by initializingmtonp.infor-np.infoutside the loop, eliminating the need for thefirstflag.Also applies to: 803-806
Line range hint
690-690: Comparison toNoneshould becond is None.
| def __neg__(self): | ||
| names_self = self.get_names() | ||
| h = compute_hier_from(_compute_neg, self, names=names_self) | ||
| return VectorField(h) | ||
|
|
There was a problem hiding this comment.
The __neg__ method in the PatchHierarchy class seems to be incorrectly returning a VectorField instead of a ScalarField. This could potentially lead to type mismatches or unexpected behavior in the application.
- return VectorField(h)
+ return ScalarField(h)Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation.
| def __neg__(self): | |
| names_self = self.get_names() | |
| h = compute_hier_from(_compute_neg, self, names=names_self) | |
| return VectorField(h) | |
| def __neg__(self): | |
| names_self = self.get_names() | |
| h = compute_hier_from(_compute_neg, self, names=names_self) | |
| return ScalarField(h) |
There was a problem hiding this comment.
Actionable comments posted: 7
Outside diff range and nitpick comments (4)
pyphare/pyphare/pharesee/run.py (3)
615-615: Consider the necessity of theall_primalargument for electron pressure.The
all_primalargument might not be necessary for the electron pressure as it is by construction all primal already. Reviewing the usage of this argument could simplify the method.
620-620: Clarify the condition that is always true.The condition on line 620 is noted to always be true. If this is the case, consider removing the condition to simplify the code and improve readability.
635-635: Same comment as for J regarding the setting ofall_primal.The same reasoning for setting
all_primalto False in theGetJmethod applies here as well. Ensuring consistency in the comments will help maintain clarity.pyphare/pyphare/pharesee/hierarchy.py (1)
Line range hint
690-690: Comparison toNoneshould becond is None.- if cond == None + if cond is None
Review details
Configuration used: .coderabbit.yaml
Review profile: CHILL
Files selected for processing (3)
- pyphare/pyphare/core/operators.py (1 hunks)
- pyphare/pyphare/pharesee/hierarchy.py (9 hunks)
- pyphare/pyphare/pharesee/run.py (6 hunks)
Files skipped from review as they are similar to previous changes (1)
- pyphare/pyphare/core/operators.py
Additional context used
Ruff
pyphare/pyphare/pharesee/run.py
609-609: f-string without any placeholders (F541)
611-611: f-string without any placeholders (F541)
719-719: Avoid equality comparisons to
True; useif self.single_hier_for_all_quantities:for truth checks (E712)
724-724: Do not use bare
except(E722)pyphare/pyphare/pharesee/hierarchy.py
690-690: Comparison to
Noneshould becond is None(E711)
Additional comments not posted (4)
pyphare/pyphare/pharesee/run.py (1)
616-616: Update the method for obtainingTeinGetPeto reflect the latest project developments.- Te = 1.0 + hier = self._get_hierarchy(time, "ions_density.h5") + Te = hier.sim.electrons.closure.TeThis change aligns the code with the latest project updates, ensuring consistency and leveraging the new data structures.
pyphare/pyphare/pharesee/hierarchy.py (3)
566-587: The implementation of theflat_namemethod in thePatchHierarchyclass is concise and correctly uses a dictionary to map quantity names to their respective types. However, the commented-out code from lines 589 to 598 is redundant and should be removed to keep the codebase clean and maintainable.
789-792: In theglobal_minmethod, the use ofnp.nanminto find the minimum value while ignoring NaNs is appropriate for numerical datasets that may contain missing values. This approach ensures robustness in calculations. However, the initialization ofmand the conditional check forfirstcould be simplified by initializingmtonp.infoutside the loop, eliminating the need for thefirstflag.
803-806: Similar to theglobal_minmethod, theglobal_maxmethod correctly usesnp.nanmaxfor handling NaN values. The same refinement suggested forglobal_minapplies here as well, where initializingmto-np.infoutside the loop can simplify the code by removing the need for thefirstflag.
| from pyphare.pharesee.hierarchy import ( | ||
| compute_hier_from, | ||
| flat_finest_field, | ||
| hierarchy_from, | ||
| ScalarField, | ||
| VectorField, | ||
| ) |
There was a problem hiding this comment.
Consider resolving the cyclic import issue to enhance maintainability.
The cyclic import involving pyphare.pharesee.hierarchy could lead to problems with initialization order and unexpected behaviors. Consider restructuring your imports or using local imports within functions to resolve this.
| return ScalarField(h)*Te | ||
|
|
||
| def GetJ(self, time, merged=False, interp="nearest", all_primal=True): | ||
| B = self.GetB(time, all_primal=False) |
There was a problem hiding this comment.
Add a comment explaining why all_primal is set to False here.
+ # compute_hier_from() for J assumes B is on its Yee locations, so we need `all_primal=False`Adding a comment here will clarify the reasoning behind the decision to set all_primal to False, aiding future maintainability.
Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation.
| B = self.GetB(time, all_primal=False) | |
| # compute_hier_from() for J assumes B is on its Yee locations, so we need `all_primal=False` | |
| B = self.GetB(time, all_primal=False) |
|
|
||
| def GetAllAvailableQties(self, time, pops): | ||
| def GetAllAvailableQties(self, time, pops, all_primal=True): | ||
| assert not all_primal |
There was a problem hiding this comment.
The assertion assert not all_primal seems to contradict the later use of the all_primal flag in the same function.
- assert not all_primalConsider removing or modifying this assertion to allow for the intended functionality of handling both primal and non-primal centering based on the all_primal flag.
Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation.
| assert not all_primal |
| def GetAllAvailableQties(self, time, pops): | ||
| def GetAllAvailableQties(self, time, pops, all_primal=True): | ||
| assert not all_primal | ||
| assert self.single_hier_for_all_quantities == True # can't work otherwise |
There was a problem hiding this comment.
Replace equality comparison to True with a direct truth check.
- assert self.single_hier_for_all_quantities == True
+ assert self.single_hier_for_all_quantitiesDirect truth checks are more Pythonic and improve the readability of the code.
Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation.
| assert self.single_hier_for_all_quantities == True # can't work otherwise | |
| assert self.single_hier_for_all_quantities # can't work otherwise |
Tools
Ruff
719-719: Avoid equality comparisons to
True; useif self.single_hier_for_all_quantities:for truth checks (E712)
| if not all_primal: | ||
| return self._get(hier, time, merged, interp) | ||
|
|
||
| h = compute_hier_from(_compute_to_primal, hier, x="Bx", y="By", z="Bz") |
There was a problem hiding this comment.
Ensure unreachable code is addressed to maintain code quality.
The statement on line 565 is flagged as unreachable. This needs to be investigated to ensure that the code flow is correct and that there are no logical errors leading to unreachable code.
|
Closing because is in #859 |
3 participants
This is just the beginning of the work... I will continue once we will be agree with the way to do it.
My notebook to test it is here :
https://github.com/rochSmets/farJunk/blob/main/nb/ohm.ipynb
Summary by CodeRabbit
New Features
Enhancements
PatchHierarchyclass with new methods for mapping quantities to spatial directions and retrieving patch data names.